diff --git a/notebooks/analyze_fit_running_economy.ipynb b/notebooks/analyze_fit_running_economy.ipynb
new file mode 100644
index 000000000..584a046c3
--- /dev/null
+++ b/notebooks/analyze_fit_running_economy.ipynb
@@ -0,0 +1,756 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyMDq7gzt5sSZntnNoHpn6i/",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/jvinodraj/coreui-free-react-admin-template/blob/main/notebooks/analyze_fit_running_economy.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 📌 STEP 1: Install required packages\n",
+        "!pip install fitparse pandas --quiet\n",
+        "\n",
+        "# 📌 STEP 2: Upload your .fit file\n",
+        "from google.colab import files\n",
+        "uploaded = files.upload()\n",
+        "\n",
+        "import io\n",
+        "from fitparse import FitFile\n",
+        "import pandas as pd\n",
+        "\n",
+        "# 📌 STEP 3: Parse the FIT file\n",
+        "filename = list(uploaded.keys())[0]\n",
+        "fitfile = FitFile(io.BytesIO(uploaded[filename]))\n",
+        "\n",
+        "records = []\n",
+        "for record in fitfile.get_messages('record'):\n",
+        "    record_data = {}\n",
+        "    for record_field in record:\n",
+        "        record_data[record_field.name] = record_field.value\n",
+        "    records.append(record_data)\n",
+        "\n",
+        "df = pd.DataFrame(records)\n",
+        "\n",
+        "# 📌 STEP 4: Ensure essential fields\n",
+        "required_fields = ['timestamp', 'heart_rate', 'distance', 'power']\n",
+        "for field in required_fields:\n",
+        "    if field not in df.columns:\n",
+        "        df[field] = None\n",
+        "\n",
+        "# Drop rows without required fields\n",
+        "df = df.dropna(subset=['timestamp', 'distance'])\n",
+        "\n",
+        "# Convert distance to kilometers\n",
+        "df['distance_km'] = df['distance'] / 1000\n",
+        "\n",
+        "# 📌 STEP 5: Compute speed if not present\n",
+        "df = df.sort_values('timestamp')\n",
+        "df['elapsed_time_sec'] = (df['timestamp'] - df['timestamp'].min()).dt.total_seconds()\n",
+        "df['delta_time'] = df['elapsed_time_sec'].diff()\n",
+        "df['delta_dist'] = df['distance_km'].diff()\n",
+        "df['speed_kmph'] = (df['delta_dist'] / df['delta_time']) * 3600  # km/h\n",
+        "\n",
+        "# Smooth speed using rolling average\n",
+        "df['speed_kmph'] = df['speed_kmph'].fillna(method='bfill').rolling(window=5, min_periods=1).mean()\n",
+        "\n",
+        "# 📌 STEP 6: Split into km segments\n",
+        "df['km_split'] = df['distance_km'].astype(int)\n",
+        "\n",
+        "# 📌 STEP 7: Aggregate by km\n",
+        "grouped = df.groupby('km_split').agg({\n",
+        "    'timestamp': ['min', 'max'],\n",
+        "    'heart_rate': 'mean',\n",
+        "    'power': 'mean',\n",
+        "    'speed_kmph': 'mean'\n",
+        "})\n",
+        "\n",
+        "grouped.columns = ['start_time', 'end_time', 'avg_hr', 'avg_power', 'avg_speed_kmph']\n",
+        "grouped = grouped.reset_index()\n",
+        "\n",
+        "# 📌 STEP 8: Calculate RE using VO2max = 50\n",
+        "VO2max = 50\n",
+        "grouped['RE_ml_kg_km'] = (VO2max * 60) / grouped['avg_speed_kmph']\n",
+        "\n",
+        "# Calculate pace and duration\n",
+        "grouped['duration_sec'] = (grouped['end_time'] - grouped['start_time']).dt.total_seconds()\n",
+        "grouped['pace_min_per_km'] = grouped['duration_sec'] / 60\n",
+        "\n",
+        "# 📌 Final Output Table\n",
+        "final_df = grouped[['km_split', 'avg_hr', 'avg_power', 'pace_min_per_km', 'avg_speed_kmph', 'RE_ml_kg_km']]\n",
+        "final_df.columns = ['KM', 'Avg HR', 'Avg Power (W)', 'Pace (min/km)', 'Speed (km/h)', 'RE (mL/kg/km)']\n",
+        "final_df = final_df.round(2)\n",
+        "\n",
+        "final_df\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 317
+        },
+        "id": "FAhBHu-R-5cx",
+        "outputId": "600bd0f6-c68b-457c-a899-eadc7d036d80"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-88eea782-b898-456f-be81-08e9f8727f83\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-88eea782-b898-456f-be81-08e9f8727f83\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving C25k_Training.fit to C25k_Training (2).fit\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "<ipython-input-3-19baaa21c579>:45: FutureWarning: Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+            "  df['speed_kmph'] = df['speed_kmph'].fillna(method='bfill').rolling(window=5, min_periods=1).mean()\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   KM  Avg HR  Avg Power (W)  Pace (min/km)  Speed (km/h)  RE (mL/kg/km)\n",
+              "0   0   87.16         127.61          12.75          5.20         576.89\n",
+              "1   1  109.61         146.60          11.32          5.37         558.14\n",
+              "2   2  114.95         154.56          11.67          5.36         559.43\n",
+              "3   3  117.37         167.55          11.60          6.63         452.57\n",
+              "4   4   89.31          19.83          16.70          1.37        2183.64"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-0203cd21-ce5c-403d-9edd-34774eee067d\" 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>KM</th>\n",
+              "      <th>Avg HR</th>\n",
+              "      <th>Avg Power (W)</th>\n",
+              "      <th>Pace (min/km)</th>\n",
+              "      <th>Speed (km/h)</th>\n",
+              "      <th>RE (mL/kg/km)</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0</td>\n",
+              "      <td>87.16</td>\n",
+              "      <td>127.61</td>\n",
+              "      <td>12.75</td>\n",
+              "      <td>5.20</td>\n",
+              "      <td>576.89</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1</td>\n",
+              "      <td>109.61</td>\n",
+              "      <td>146.60</td>\n",
+              "      <td>11.32</td>\n",
+              "      <td>5.37</td>\n",
+              "      <td>558.14</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2</td>\n",
+              "      <td>114.95</td>\n",
+              "      <td>154.56</td>\n",
+              "      <td>11.67</td>\n",
+              "      <td>5.36</td>\n",
+              "      <td>559.43</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>3</td>\n",
+              "      <td>117.37</td>\n",
+              "      <td>167.55</td>\n",
+              "      <td>11.60</td>\n",
+              "      <td>6.63</td>\n",
+              "      <td>452.57</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>4</td>\n",
+              "      <td>89.31</td>\n",
+              "      <td>19.83</td>\n",
+              "      <td>16.70</td>\n",
+              "      <td>1.37</td>\n",
+              "      <td>2183.64</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-0203cd21-ce5c-403d-9edd-34774eee067d')\"\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",
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+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
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+              "</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-ffa8c478-bc38-462c-88b9-d8d5fd8b034a button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_45d7c66e-1107-467a-b372-6698d6fc526e\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('final_df')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_45d7c66e-1107-467a-b372-6698d6fc526e button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('final_df');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "final_df",
+              "summary": "{\n  \"name\": \"final_df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"KM\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1,\n          4,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Avg HR\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.396190468314876,\n        \"min\": 87.16,\n        \"max\": 117.37,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          109.61,\n          89.31,\n          114.95\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Avg Power (W)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 59.587609030737255,\n        \"min\": 19.83,\n        \"max\": 167.55,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          146.6,\n          19.83,\n          154.56\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pace (min/km)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.2427371669457834,\n        \"min\": 11.32,\n        \"max\": 16.7,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          11.32,\n          16.7,\n          11.67\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Speed (km/h)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.9944497988167063,\n        \"min\": 1.37,\n        \"max\": 6.63,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          5.37,\n          1.37,\n          5.36\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RE (mL/kg/km)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 738.1475091944699,\n        \"min\": 452.57,\n        \"max\": 2183.64,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          558.14,\n          2183.64,\n          559.43\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Set the figure size and style\n",
+        "# plt.style.use('seaborn-darkgrid')\n",
+        "plt.style.use('ggplot')  # or 'default'\n",
+        "\n",
+        "plt.figure(figsize=(14, 10))\n",
+        "\n",
+        "# Plot 1: Heart Rate\n",
+        "plt.subplot(2, 2, 1)\n",
+        "plt.plot(final_df['KM'], final_df['Avg HR'], marker='o', color='red')\n",
+        "plt.title('Heart Rate vs KM')\n",
+        "plt.xlabel('KM')\n",
+        "plt.ylabel('Avg Heart Rate (bpm)')\n",
+        "\n",
+        "# Plot 2: Power\n",
+        "plt.subplot(2, 2, 2)\n",
+        "plt.plot(final_df['KM'], final_df['Avg Power (W)'], marker='o', color='blue')\n",
+        "plt.title('Power vs KM')\n",
+        "plt.xlabel('KM')\n",
+        "plt.ylabel('Avg Power (W)')\n",
+        "\n",
+        "# Plot 3: Pace\n",
+        "plt.subplot(2, 2, 3)\n",
+        "plt.plot(final_df['KM'], final_df['Pace (min/km)'], marker='o', color='green')\n",
+        "plt.title('Pace vs KM')\n",
+        "plt.xlabel('KM')\n",
+        "plt.ylabel('Pace (min/km)')\n",
+        "\n",
+        "# Plot 4: Running Economy\n",
+        "plt.subplot(2, 2, 4)\n",
+        "plt.plot(final_df['KM'], final_df['RE (mL/kg/km)'], marker='o', color='purple')\n",
+        "plt.title('Running Economy (RE) vs KM')\n",
+        "plt.xlabel('KM')\n",
+        "plt.ylabel('RE (mL/kg/km)')\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 835
+        },
+        "id": "lpsXbjCM_Ulm",
+        "outputId": "13fda87e-84f8-4d21-998f-40e2a6a95c57"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1400x1000 with 4 Axes>"
+            ],
+            "image/png": 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44403ivzeR48eNSQZDRo0cPslv2EYxk033VSoxm1ef8aNG+c6fsGCBYYk4/bbb7/ktefMmWNIMu644w63fRkZGa4G8MUN2oLW9XLuMWPGDLdz7r77bkOS8Z///Mdt3+TJkw1JxoYNG3Jsb9OmjeHn52ecOHHCtS0zM9OoWbOmERISYpw/f97tWp5cd911hiTj2LFjHvdnN26HDx9uTJo0yXjqqaeMkSNHGpUrVzYkGdHR0R7fE9mN27z+nD171u287LH2wIED85X/66+/NiQZ/fv3z7H9xIkThq+vr9GqVSvXtsTEREOSERUVZTidznxd35Psxm32n3vuueeS51zcuDUMwxgzZkyOZ5Q8/fTTOX5BQOMWpY1fbjNxAaC8yV7P1WKxKCwsTNdff73uueceDRkyxHXMmTNn9L///U8rV67UoUOHlJycnOMax44dc/19+/btcjqd6tSpk4KCgvK89/bt25WVlZXrGm7Z6zTlti7a3w0fPlwTJ07UvHnzNHbsWEnS77//rtWrV6tVq1Zq3ry569jBgwdr0aJFat++vQYOHKgbbrhBHTp0UM2aNfN1r4tVrVpVNWvW1Hfffadhw4YpLi7ukh9pyta+fXs1a9ZM77//vp588knNmDFDTqeTZRIAACiD/r6OvsVi0cyZM3X33XcX2T08fZy9Vq1aki4sZ1VU51zOdXbt2iVJuu666+Tj476SYceOHQu1Pujw4cPztS7uN998I0n6xz/+ccljv/32W0lS165d3fb5+fmpU6dOOnLkiL777jvVrl07x/781qOo71G9enVJ0jXXXOO2r0aNGpIurO97sbFjx2rEiBF6//33NWHCBEnSypUr9dtvv+m+++5TcHCw27U8OXPmjKQLSzDk5cMPP3Tbdvfdd+v999/P87zDhw+7rbGcl0qVKklSrsuE/F1UVJQaNWqk5cuX6+zZs66v4+OPP1ZWVpbuuusu17GhoaG67bbbtHz5crVs2VL9+vXT9ddfr/bt28tut+c7Y7ZOnTpp27ZtmjVrlrp06ZLj57FLGTVqlN59913NnDlTMTExmjlzppo3b6527doVOAfgDVjjFgD+ZFz4FIKcTqf++OMPffHFFzkGCQkJCWrbtq2mTJkim82mYcOGaeLEiZo0aZLGjRsn6cIashcfL/01KMxL9sBu+/btiomJcfvz/PPPS5KSkpLy9bUMGzZMPj4+OQaCH3/8sTIzM93W4u3bt69WrFihVq1a6f3339egQYNUq1YttWnTRmvXrs3X/bLZ7XZ9/vnnuvbaa7Vs2TL16tUrx0MfLmXUqFH6v//7P61atUoffPCBrrnmGrVq1apAGQAAgPfLHnclJSVp7dq1qlWrlsaMGaPPP/+8yO4RFhbmti17Pc6srKwiO+dyrpP9cK5q1ap5vE5u24tKQcar2VkjIyM97s/enn3NixW0HoW5h6eHVmXfI699f3+Q1aBBg1SxYkW99957rocPT58+XZJ07733eszlSfYDyVJTU/M87osvvpBhGEpPT9fu3bvVtWtXffDBB3r22Wfzfa/8yB6TF+RBacOHD1daWprmzZvn2vbhhx/K399fd955Z45jY2NjNWnSJKWkpGjSpEnq2rWrKleurKFDh7o9kPlSbrjhBq1YsUKBgYEaPny423Mw8tK6dWu1bt1aH3zwgVatWqX/+7//YyIISjUatwCQTzNmzNDhw4c1adIkbd26VW+//baeffZZTZ48WQMHDnQ7PnuAevEs3NxkDyb/+c9/un6Q8fTniy++yFfWmjVrqmvXrtq2bZv2798vKfdBliT16NFDn3/+uc6ePav169frn//8p3744Qf17NlT+/bty9c9s4WFhWnt2rXq1KmTVq9ere7du+e74Tx06FDZbDaNGTNGx44d0+jRowt0bwAAULoEBQXppptu0vLly5WVlaXhw4fL4XC49mfPQvX0gCTJcwOvNAkNDZWkXBtbBW14FVRhxqvx8fEe9584cSLHcYVREve4FJvNprvuuktHjhzRmjVrXA8la9++vVq0aJHv64SHh0v6a4LGpfj7+6t58+Zavny56tSpo0mTJum7774r1NfgSXaO7Fz5MXTo0ByTQb777jvt2bNH3bt3V5UqVXIca7PZNHnyZB04cEBHjx7VnDlz1LFjR82ZM0f9+/cvcN4bb7xRn332mYKDgzV69Gi9+eab+T539OjROnbsmMaMGSObzVagGbuAt6FxCwD5dPDgQUlSv3793PZ9+eWXbtvatWsnHx8fffXVV25LKuR27MaNG4smrOT6+NKHH36oXbt26fvvv9c//vEPVa1aNddzgoKC1LVrV02dOlUTJkxQenq6Vq1aVeB7BwcHa9WqVbr55pu1YcMG3XLLLa4ZFHkJCwtT//799dtvvykoKIgnvwIAUE40b95co0aN0m+//aZXXnnFtT3749m//vqr2zkHDx7M1/jCm7Vs2VKStGXLFtfszott2rSpWO9/7bXXSlK+xnvZn4LasGGD277MzEzXOLZ169aFzlMS98iP++67TxaLRdOmTdPMmTOVlZVVoNm2klxLk2VPosgvu92uKVOmyOl06l//+leBzs1Ldo7s91x+1KpVS127dtXWrVv1008/uRq4f/8En6fzBg8erNWrV6tBgwbatGlTvhvYF8teKiQsLEwPPvigXnzxxXydd+eddyooKEi//fabBgwY4HHGN1Ba0LgFgHzKXkPq7wPJ7777Tv/973/djq9ataoGDRqkEydOaPz48W6D8aSkJNcPG+Hh4Ro8eLB27NihZ555xuNH8X755RcdPnw433n79u2r0NBQzZkzx7XG2cVrUWX76quvPM5iyZ7hUZh1qbLPW758uW677TZt2bJFN954o/74449Lnvfss89q8eLFWr16tUJCQgp1bwAAUPo8+eSTCggI0EsvveRa97RJkyYKDQ3V0qVLdfLkSdexKSkpeuihh8yKWmRq166tLl266ODBg5o2bVqOfZ999lmh1rctiNtuu01169bVsmXLNHfuXLf9F6//2rt3b1WqVElz5851rY2b7dVXX9Xhw4d10003ua09WxAlcY/8aNiwoW688UatWLFC7777rsLCwjRo0KACXaNLly6S5PZ15Ed0dLSaNWumdevWeWxiF0Z2jhtuuKFA52X//DBz5kzNnTtXVapUUc+ePXMcc+rUKe3Zs8ft3OTkZCUlJcnPz09Wq7VQudu2basvvvhCVatW1b///W+39bE9CQkJ0WeffabFixcX+ZITQEnj4WQAkE/Dhg3T//73Pz388MP64osv1LBhQ/38889asWKF+vbtq9jYWLdz3nzzTe3du1fvvvuuNmzYoG7duslqterw4cNavXq1li1b5hrUvfnmm/r555/11FNPafbs2erYsaOqVaum48eP68cff9T27ds1d+5c1atXL195bTabBgwYoJkzZ+rtt99W5cqV1aNHD7fjHnroIR07dkwdOnRQ3bp1ZbVatXPnTn3++eeqU6dOgQepFwsICNDChQt15513asGCBbrhhhu0du3aPD+iVbt27WIfjAMAAO9To0YNjRkzRq+99ppefPFF/fe//5W/v7/GjRunZ555Rq1atVKfPn2UmZmptWvXqnr16q6HT5Vmb731ljp06KCxY8dq5cqVat68uQ4dOqSFCxeqV69eWrp0qccHl+Vl165dHh94my17n9Vq1fz583XLLbfozjvv1LRp03TttdcqNTVVP/74o9avX+/6BX9wcLDef/99DRgwQJ07d9aAAQNUu3Zt7dy5U2vWrFFERIRb87mgSuIe+TV27FitW7dOv//+ux588MECrQ0rXXjAWlhYmFavXl3g5qHFYtHTTz+tPn36aMKECdq8ebPbMa+++mquM0m7dOni+hlDkpxOp9atW6fGjRuradOmBcrSp08fhYaG6tVXX1VGRoYefPBB+fv75zjm2LFjatWqlZo1a6bmzZurVq1aOnfunFasWKH4+Hg99NBDlzUho0WLFvryyy914403avLkyUpJSdELL7yQ5zkdO3Ys9P0Ab0LjFgDyqXr16tq4caMef/xxbdq0SatXr1aTJk309ttv66abbvLYuK1YsaI2b96sV199VbGxsZo+fbp8fX1Vq1YtjRgxQldddZXr2NDQUH355ZeaPn26PvnkEy1cuFCpqamqVq2aGjZsqFdeeUU333xzgTLfddddmjlzpjIyMnTHHXd4/E33hAkTtHjxYu3YsUPr1q2Tj4+PateurQkTJujhhx++5JNwL8Xf31/z5s3T3XffrdmzZ6tz585av359mfhBCwAAFK0nnnhC7733nl5//XU9/PDDqlatmmJiYmS32/Xee+9p+vTpioiI0KBBgzR58uQcY6nS6qqrrtKWLVs0YcIEff755/r888/VvHlzLV68WD/++KOWLl3qWgs3v3bv3q3du3fnuv/ipm6bNm20a9cuvfDCC1q1apU2b96skJAQNWjQQE8//XSO83r16qWvv/5azz//vFavXq3ExERFRERozJgx+s9//lMk47uSuEd+3H777apSpYpOnz5d4GUSpAufPrvrrrv06quv6scff9SVV15ZoPN79+6ta665Rlu2bHF9iu1ir732Wp7nX9y4XbdunY4fP55jGZL8stvtrskgkudlEurWrauYmBht2LBBX3zxhU6fPq1KlSqpcePGeuGFFy5rIki2K6+8Ul999ZVuvPFGTZkyRSkpKXr11Vcv+7qAt7MYhmGYHQIAAAAAAOQ0ePBgffLJJ9q/f78aN25sdpxy5dChQ2rQoIE6dOhQ6OdQHD58WE2aNHHNJDdLv3799OWXX+qXX34p1ge7ASh6rHELAAAAAIBJnE6n4uPj3bavX79esbGxuuqqq2jamuCll16SYRh64IEHCn2NevXqady4cZo+fbqOHTtWhOny77vvvtPixYs1efJkmrZAKcRSCQAAAAAAmCQ9PV21atXSDTfcoCZNmsjPz08//PCD1q5dK6vVqrfeesvsiOXG0aNH9cknn+jnn3/WBx98oBYtWmjAgAGXdc0nn3xSQUFBOnLkiGrUqFFESfMvPj5ezzzzjMaMGVPi9wZw+VgqAQAAAAAAk2RlZenhhx/W559/rt9++00Oh0NVqlRRp06d9Pjjj6tVq1ZmRyw3NmzYoBtuuEF2u10dO3bUO++8o/r165sdC0A5RuMWAAAAAAAAALwMa9wCAAAAAAAAgJehcQsAAAAAAAAAXobGLQAAAAAAAAB4GT+zAwAAAACl1eLFi7Vt2zYdO3ZMVqtVjRo10pAhQ1S9enVJUlJSkuLi4rR7926dPn1aoaGhatu2rQYNGiS73e66TnR0tNu1x40bpw4dOrhe//DDD/roo4/066+/qnLlyurXr5+6dOlS7F8jAAAAzEHjNg9nz55VZmZmidyratWqOnXqVIncq7SgJp5RF8+oiztq4hl18Yy6eEZd3JVkTfz8/FSxYsUSuVdh7du3T926ddMVV1yhrKwszZ07V88++6ymTp2qwMBA/fHHH/rjjz80dOhQ1axZU6dPn9Z7772ns2fP6tFHH81xrbFjx6ply5au1xc3dk+ePKkXXnhBN998sx588EHt3btX7777rsLCwnKckx8lNcblvx/PqItn1MUz6uKOmnhGXTyjLu6oiWfeOsalcZuHzMxMZWRkFPt9LBaL636GYRT7/UoDauIZdfGMurijJp5RF8+oi2fUxR01cTdx4sQcr++//36NHDlShw4d0lVXXaXatWtr/Pjxrv0REREaNGiQ3njjDWVlZcnX19e1z263KywszON91qxZo/DwcA0bNkySVLNmTe3fv1+ffvppgRu3JTHG5b3iGXXxjLp4Rl3cURPPqItn1MUdNfHMm+tC4xYAAAAoIg6HQ5IUHByc5zE2my1H01aSZs6cqWnTpik8PFw333yzbrjhBtcPEj///LOaNWuW4/gWLVpo1qxZud4nIyMjR4PWYrHIZrO5/l6csq9f3PcpbaiLZ9TFM+rijpp4Rl08oy7uqIln3lwXGrcAAABAEXA6nZo1a5YaN26s2rVrezzm3LlzWrhwoW666aYc26Ojo9W0aVMFBARo9+7dmjlzplJTU9W9e3dJUkJCgipUqJDjnAoVKiglJUXp6emyWq1u91q8eLEWLFjgel2vXj1NmTJFVatWvdwvNd8iIiJK7F6lCXXxjLp4Rl3cURPPqItn1MUdNfHMG+tC4xYAAAAoAjNnztSvv/6qp59+2uN+h8OhF154QTVr1tSAAQNy7Ovfv7/r7/Xq1VNaWpqWL1/uatwWRp8+fdSzZ0/X6+xZJKdOnSr2NW4tFosiIiIUHx/vdR85NBN18Yy6eEZd3FETz6iLZ9TFHTXxrKTr4ufnl+9fpNO4BQAAAC7TzJkz9e233yomJkaVK1d225+SkqLnn39eNptN48ePl59f3sPwhg0bauHChcrIyJC/v7/CwsKUmJiY45jExETZbDaPs20lyd/fX/7+/h73ldQPa4Zh8IOhB9TFM+riGXVxR008oy6eURd31MQzb6yLj9kBAAAAgNLKMAzNnDlT27Zt01NPPaXw8HC3YxwOh5599ln5+fnpX//6V66N1osdOXJEQUFBrsZrw4YNtWfPnhzHfP/992rUqFHRfCEAAADwOjRuAQAAgEKaOXOmNm7cqHHjxslmsykhIUEJCQlKT0+XdKFp+9xzzyktLU1jxoxRSkqK6xin0ylJ2rFjh9avX6+jR48qPj5ea9as0eLFi/WPf/zDdZ9bbrlFJ0+e1Jw5c3Ts2DGtXr1aW7ZsUY8ePUz5ugEAAFD8WCoBAAAAKKQ1a9ZIkiZPnpxj+9ixY9WlSxcdPnxYP//8syTpoYceynHMm2++qfDwcPn5+Wn16tX68MMPZRiGIiIiNGzYMN14442uY8PDw/X444/rww8/1MqVK1W5cmWNGTNGLVu2LNavDwAAAOahcQsAAAAUUlxcXJ77r7766kse07Jly3w1YK+++mq9+OKLBYkHAACAUoylEgAAAAAAAADAy9C4BQAAAAAAAAAvw1IJAAAAuKQsZ5a2/b5N6afTZU23ql21dvL18TU7FgAAAFBoziynft/2u06nn1a6NV3V2lWTj6/3zHOlcQsAAIA8rTy8Uk9teUonkk+4tkUGRerp655W93rdTUwGAAAAFM7hlYe15aktSj6R7NoWFBmk656+TvW61zMx2V+8p4UMAAAAr7Py8EqNXjc6R9NWkuKT4zV63WitPLzSpGQAAABA4RxeeVjrRq/L0bSVpOT4ZK0bvU6HVx42KVlONG4BAADgUZYzS09teUqGDLd92dsmbZmkLGdWSUcDAAAACsWZ5dSWp7bIwxDXtW3LpC1yZjlLNJcnNG4BAADg0db4rW4zbS9myNDx5OPaGr+1BFMBAAAAhRe/Nd5tpm0OhpR8PFnxW+NLLlQuaNwCAADAo5OOk0V6HAAAAGA2x0lHkR5XnGjcAgAAwKNwe3iRHgcAAACYzR5uL9LjihONWwAAAHjUPqK9IoMiZZHF436LLKoeVF3tI9qXcDIAAACgcCLaRygoMki5DHElixRUPUgR7Sw/4DAAAKBMSURBVCNKNJcnNG4BAADgka+Pr56+7mmP+7KbuTHXxcjXx7ckYwEAAACF5uPro+uevs7zw8n+bOZeF3OdfHzNb5uanwAAAABeq3u97up1RS+37ZFBkZp+03R1r9fdhFQAAABA4dXrXk+1utZy2x4UGaSbpt+ket3rmZDKnZ/ZAQAAAOC9nIZT3578VpL0QIsHFHVFlKzpVrWr1o6ZtgAAACiVstKydPLbCw/YbTexnWpeXVPp1nRVa1fNK2baZqNxCwAAgFxtOrZJR88fVQVrBf3zmn+qfq36OnHihAzD02fLAAAAAO93dN1RpSWkyR5hV/MxzVWjZg2vHON6TwsZAAAAXmfO/jmSpH4N+8nmZzM5DQAAAHD5DsQdkCQ17N/Qq2bY/p33JgMAAICpTjlOafWR1ZKkO5vcaXIaAAAA4PI5Tjr06xe/SpIaDWhkcpq80bgFAACAR3EH4pRpZKp1eGtdWelKs+MAAAAAl+3gooMysgyFXxOusAZhZsfJE41bAAAAuHEaTn3y0yeSpCFNhpicBgAAALh8hmHop9ifJEmNor17tq1E4xYAAAAebD6+WUfOHVGIf4huq3+b2XEAAACAy3Z692klHEiQb6Cvrrj9CrPjXBKNWwAAALj5eP/HkqQ+DfrI7m83OQ0AAABw+bJn29b9R11ZQ60mp7k0GrcAAADI4UzKGa06skqSNPjKwSanAQAAAC5fZmqmfln6iySpcXRjk9PkD41bAAAA5DD/5/nKcGaoZdWWalq5qdlxAAAAgMv2f6v/T+mJ6QqqHqTIDpFmx8kXGrcAAABwMQzDtUzC4CbMtgUAAEDZcGD+AUlSowGN5ONbOlqipSMlAAAASsQ38d/oUOIhBfkHqdcVvcyOAwAAAFy25BPJOvblMUlSwwENTU6TfzRuAQAA4PLxjxdm2/a+oreC/INMTgMAAABcvp8X/izDaSiifYQq1Ktgdpx8o3ELAAAASdIfqX/o08OfSpKGNBlichr8P3t3Hh5lee9//DOTyb4QQgghbEnIAgJCXEBARRYRAUVAiAKt1WpPjxRtqz+PHqkK1SrW1lq1VhF7Stl3UBAQxa24shQVSEIW9pCELJCELJOZ3x8xIzET1pk8M5P367q8yLPM83zmy6gz39xz3wAAALh0drtdmUu/nyZhcorBaS4MjVsAAABIklZkrVCNrUZ9ovvo8vaXGx0HAAAAuGQF2wtUllMmS7BFCWMTjI5zQWjcAgAAQHa7XYv2LZLEomQAAADwHZnL6kfbJoxJUEBYgMFpLgyNWwAAAOir418pqzRLwZZg3db9NqPjAAAAAJfMetqq7HXZkrxvmgSJxi0AAAAkLdi7QFL9omThAeEGpwEAAAAuXd67eao9VauwLmHqOLCj0XEuGI1bAACAVq60utSxKBnTJAAAAMBXNEyTkDIpRSazyeA0F47GLQAAQCu3KmuVquqqdFnUZerXvp/RcQAAAIBLVn6kXEc+PSJJSp6UbHCai0PjFgAAoBWz2+1auG+hJGlqz6kymbxvJAIAAADwY5nLMyW71HFgR0V0jTA6zkWhcQsAANCKbS/Yrn0l+xTkF6QJSROMjgMAAABcMrvdrqzlWZKklHTvW5SsAY1bAACAVqxhtO2t3W9VRIB3jkQAAAAAzpT/Zb5O5p2Uf6i/EsYkGB3notG4BQAAaKXKqsu0LnudJBYlAwAAgO/IXFq/KFnCLQnyD/E3OM3Fo3ELAADQSq3OXq2quir1aNtDV8ZcaXQcAAAA4JLVVtQq5+0cSVLKZO+dJkGicQsAANAq2e12Ldi7QFL9aFsWJQMAAIAvyF2fK2ulVRHxEYrtH2t0nEtC4xYAAKAV2lW4S3uL99YvSpbMomQAAADwDZnL6qdJSJ6U7PWDE2jcAgAAtEINi5KNSRijyMBIY8MAAAAALnDy4Ekd++yYZJJSJnn3NAkSjVsAAIBW51TNKa3JXiNJmtZzmrFhAAAAABfJWp4lSep0bSeFdQozOM2lo3ELAADQyqzJXqPT1tNKjkzW1R2uNjoOAAAAcMnsNrsyl9dPk5CS7v2jbSXJYnQAAAAAtKyGaRKm9Jji9fN+GW316tX68ssvdeTIEQUEBCglJUXTpk1TXFyc45yamhrNnz9f27ZtU21trfr27at7771XkZGRjnOKioo0d+5cfffddwoKCtKQIUM0ZcoU+fn5Oc757rvvNH/+fB06dEjt2rXTxIkTdcMNN7TgswUAAPBcxz47pvJD5fIP91f8qHij47gEI24BAABakd2Fu/VN0TcKMAfo9uTbjY7j9fbs2aObbrpJzzzzjGbOnKm6ujo9/fTTqqqqcpzzz3/+U9u3b9dvf/tbzZo1SyUlJfrTn/7kOG6z2fTss8/KarXq6aef1vTp0/Xhhx9q6dKljnMKCgr03HPPqVevXnr++ec1ZswY/f3vf9euXbta8ukCAAB4rIZFybrf2l2WYN8Yq0rjFgAAoBVZsG+BpPpFyaKCogxO4/0ef/xx3XDDDerSpYvi4+M1ffp0FRUVKScnR5JUWVmpDz74QHfddZd69+6txMRE3X///crIyFBmZv2Hi//85z86fPiwZsyYofj4eKWlpSk9PV2bNm2S1WqVJG3evFkxMTH66U9/qs6dO2vUqFG65pprtH79esOeOwAAgKeoKa9R7vpcSVLKZN+YJkGicQsAANBqVNRWOBYlm9pzqrFhfFRlZaUkKSysfjGMnJwc1dXVqU+fPo5zOnXqpOjoaEfjNjMzU127dm00dUK/fv10+vRpHTp0SJKUlZXV6BqS1LdvX8c1AAAAWrPcd3JlPW1Vm+5tFHNljNFxXMY3xg0DAADgnNZmr1VFbYUS2yTqmthrjI7jc2w2m/7v//5Pqamp6tq1qySptLRUFotFoaGhjc5t06aNSktLHeec2bRtON5wrOHPhn1nnnP69GnV1NQoICCgSZ7a2lrV1tY6tk0mk4KDgx0/u1PD9ZlDuTHq4hx1cY66NEVNnKMuzlGXpny5JplL63+ZnTo5VWbzhY1T9eS60LgFAABoJRoWJZvaY6pHvjH1dvPmzdOhQ4c0e/Zso6NIql84bcWKFY7thIQEzZkzR+3bt2+xDLGxsS12L29CXZyjLs5Rl6aoiXPUxTnq0pSv1aR4f7Hyv8yXyWzS4OmDFdEx4qKu44l1oXELAADQCnxb9K12Fe6Sv9lfk5InGR3H58ybN087duzQrFmz1K5dO8f+yMhIWa1WVVRUNBp1W1ZW5hhlGxkZqf379ze6XllZmeNYw58N+848Jzg42OloW0kaP368xo4d69huaNYXFhY65s51F5PJpNjYWOXn58tut7v1Xt6EujhHXZyjLk1RE+eoi3PUpSlfrclXr3wlSeo8pLMqzBWqOFZxQY9v6bpYLJbz/kU6jVsAAIBWoGG07c3xN6tdcLtznI3zZbfb9dZbb+nLL7/UU089pZiYxnOqJSYmys/PT998842uuaZ+eoqjR4+qqKhIKSn1C2ekpKRo1apVKisrc0yHsHv3bgUHB6tz586SpOTkZO3cubPRtXfv3u24hjP+/v7y9/dvNndLsNvtPvXB0FWoi3PUxTnq0hQ1cY66OEddmvKlmtjqbMpcVj9NQvLk5Et6Xp5YFxYnAwAA8HGVtZVavX+1pPppEuA68+bN0yeffKIHH3xQwcHBKi0tVWlpqWpqaiRJISEhGjZsmObPn69vv/1WOTk5+tvf/qaUlBRH07Vv377q3LmzXnnlFeXl5WnXrl1asmSJbrrpJkfjdeTIkSooKNCCBQt05MgRbdq0SZ999pnGjBlj2HMHAAAw2tF/H1XFsQoFtAlQt5HdjI7jcl434nbPnj1at26dcnNzVVJSoocfflj9+/dvdM7hw4e1cOFC7dmzRzabTZ07d9ZDDz2k6Ohog1IDAAAY5+2ct3Wq9pTiI+I1KG6Q0XF8yubNmyVJTz31VKP9999/v2644QZJ0l133SWTyaQ//elPslqt6tu3r+69917HuWazWY8++qjefPNNzZw5U4GBgRoyZIjS09Md58TExOjRRx/VP//5T23YsEHt2rXTL3/5S/Xr18/dTxEAAMBjNYy2TbotSZYgr2tznpPXPaPq6mrFx8dr2LBheuGFF5ocz8/P1xNPPKFhw4Zp8uTJCg4O1uHDh5v9mhgAAICvW7BvgSRpSuoUmU184cqVli1bds5zAgICdO+99zZq1v5Y+/bt9dhjj531Or169dLzzz9/wRkBAAB8UXVZtfLezZMkpUxufvoob+Z1jdu0tDSlpaU1e3zJkiVKS0vTtGnTHPs8cVU4AACAlrC3eK92FOyQxWTR5JTJRscBAAAAXCLn7RzVVdWpbWpbRff1zW/Ze13j9mxsNpt27NihW2+9Vc8884xyc3MVExOj2267rcl0Cmeqra1VbW2tY9tkMik4ONjxs7s13KMl7uUtqIlz1MU56tIUNXGOujhHXZzzlbos2rdIkjQqfpRiQmPOcfbZ+UpNAAAA4P0yl9ZPk5AyOcVn35/6VOP25MmTqqqq0tq1a5Wenq6pU6dq165d+tOf/qQnn3xSl112mdPHrV69WitWrHBsJyQkaM6cOWrfvn1LRZfEyGBnqIlz1MU56tIUNXGOujhHXZzz5rpU1lZqVfYqSdIDgx9Qx44dXXJdb64JAAAAvF/p/lIV7CiQyc+kpAlJRsdxG59q3NpsNknSVVddpbFjx0qS4uPjlZGRoc2bNzfbuB0/frzjfOmHUSSFhYWyWq1uTl1/v9jYWOXn58tut7v9ft6AmjhHXZyjLk1RE+eoi3PUxTlfqMvyzOUqrSpV1/Cuuiz4Mh07duySrtfSNbFYLC3+i3QAAAB4voZFyboM7aKQmBCD07iPTzVuIyIi5Ofnp86dOzfa36lTJ2VkZDT7OH9//2YXL2vJD2p2u91rPxi6CzVxjro4R12aoibOURfnqItz3lyXBXvrFyW7M/VOmWRy2fPw5poAAADAu9nqbMpamSVJSkn3zUXJGvjUssIWi0Xdu3fX0aNHG+0/duyYoqN9c5JiAAAAZzKKM/TV8a/kZ/JTemq60XEAAAAAlzjy0RFV5lcqsG2guo7oanQct/K6xm1VVZXy8vKUl5cnSSooKFBeXp6KiookSbfeequ2bdumLVu2KD8/Xxs3btT27dt10003GZgaAACgZS3KqF+UbGS3keoQ0sHgNAAAAIBrNEyTkDQhSX4BfgancS+vmyohOztbs2bNcmzPnz9fkjRkyBBNnz5d/fv313333ac1a9boH//4h+Li4vTQQw+pR48eRkUGAABoUVXWKq3Iql94dUqPKQanAQAAAFyjqqRKeZvyJEkpk317mgTJCxu3vXr10rJly856zrBhwzRs2LAWSgQAAOBZNuRtUGl1qTqFddKQTkOMjgMAAAC4RM7aHNlqbIq6LErRvX1/WlSvmyoBAAAAZ7dw70JJ9YuS+Zl9++tjAAAAaD0ylmVIah2jbSUatwAAAD5lf+l+fZ7/ucwms+5IvcPoOAAAAIBLFO8rVtF/imSymJQ0IcnoOC2Cxi0AAIAPWbSvflGyEV1HqGNoR4PTAAAAAK7RsChZ1xFdFdwu2OA0LYPGLQAAgI+orqvWssz6tQCmpLIoGQAAAHyDrdam/av2S2o90yRING4BAAB8xsa8jSqpLlHH0I4a2mWo0XEAAAAAlzi09ZBOF55WULsgdR3W1eg4LYbGLQAAgI9YsHeBpPpFySxmi8FpAAAAANfIXF4/TULShCSZ/VtPO7P1PFMAAAAfllOWo23HtrEoGQAAAHxKVXGVDr53UFLrmiZBonELAADgExbvWyxJuqHzDeoU1sngNAAAAIBr7F+9X7Zam6L7RKvdZe2MjtOiaNwCAAB4uZq6Gi3NXCpJmtZjmsFpAAAAANfJXFo/TUJKeusabSvRuAUAAPB6mw5s0omqE+oQ0kHDuw43Og4AAADgEie+PaET352QOcCs7uO6Gx2nxdG4BQAA8HIL9y2UJN2RegeLkgEAAMBnZC6rH23b7cZuCooKMjhNy6NxCwAA4MUOnDygT458IpNMujP1TqPjAAAAAC5RV1On/av3S2p9i5I1oHELAADgxRZlLJIkDek8RF3CuxicBgAAAHCNQx8cUlVxlYJjgtX5hs5GxzEEjVsAAAAvVWur1dKM+kXJpvaYanAaAAAAwHUaFiVLnpgss6V1tjBb57MGAADwAe8deE+FpwvVPri9bux2o9FxAAAAAJeoLKzUwfcPSmq90yRING4BAAC81qJ99dMkpKemy9/sb3AaAAAAwDX2r9ove51d7dPaq21KW6PjGIbGLQAAgBc6dOqQPjz8oSSxKBkAAAB8ht1uV+ay+mkSWvNoW4nGLQAAgFdanLFYdtl1XafrFB8Rb3QcAAAAwCWKvilSyb4S+QX6qfut3Y2OYygatwAAAF7GarNqScYSSSxKBgAAAN/SMNo2flS8AiMDDU5jLBq3AAAAXub9g+/reOVxtQtqp5u63WR0HAAAAMAl6qrrlL06W5KUPDnZ4DTGo3ELAADgZRbuWyhJSk9JV4BfgMFpAAAAANc48N4BVZdWKzQ2VJ2u62R0HMPRuAUAAPAiR8qPaOvhrZKkO3uwKBkAAAB8R+bS+mkSkm9PltmPtiUVAAAA8CJLMpbIZrdpUMdBSmyTaHQcAAAAwCUqj1fq8IeHJTFNQgMatwAAAF7CarNqccZiSdK0ntMMTgMAAAC4TtbKLNltdnW4qoMiu0caHccj0LgFAADwElsPbdWximNqG9hWo+JHGR0HAAAAcAm73a7MZfXTJKSkpxicxnPQuAUAAPASizIWSZImpUxSoF+gwWkAAAAA1yjcWajSrFL5Bfkp8RamA2tA4xYAAMALHKs4pi0Ht0iSpvaYanAaAAAAwHUaRtsmjE5QQHiAwWk8B41bAAAAL9CwKNk1sdcoKTLJ6DgAAACAS1hPW5W9NluSlDKZaRLOROMWAADAw9XZ6hyLkk3tyWhbAAAA+I4Dmw+o5mSNwjqFKW5wnNFxPAqNWwAAAA/38ZGPdaT8iCIDIzU6frTRcQAAAACXyViaIUlKnpQsk9lkcBrPQuMWAADAwy3ct1CSNDF5ooIsQQanAQAAAFyj/Gi5jnx8RJKUMolpEn6Mxi0AAIAHO155XJsPbJYkTU1lmgQAAAD4jqwVWZJdir0mVhHxEUbH8Tg0bgEAADzY0oylqrPX6eoOVys1KtXoOAAAAIBL2O12ZS7LlCSlTuZ9rjMWowMAAADAOZvd9sOiZD0YbeuJ9uzZo3Xr1ik3N1clJSV6+OGH1b9/f8fxyZMnO33ctGnTdOutt0qSpk+frsLCwkbHp0yZottuu82xfeDAAc2bN0/Z2dmKiIjQqFGjNG7cONc/IQAAgBZy/OvjOpl7UpYQixLGJhgdxyPRuAUAAPBQnx75VAdPHVREQITGJo41Og6cqK6uVnx8vIYNG6YXXnihyfE33nij0fbOnTv197//XQMGDGi0f/LkyRoxYoRjOyjoh7mMKysr9fTTT6tPnz667777dPDgQb322msKDQ1t9BgAAABv0jDaNnFsovxD/Q1O45lo3AIAAHioBfsWSJImJk1UsCXY4DRwJi0tTWlpac0ej4yMbLT91VdfqVevXurQoUOj/cHBwU3ObfDpp5/KarXq/vvvl8ViUZcuXZSXl6d33nmHxi0AAPBKtZW1ylmXI0lKmcyiZM1hjlsAAAAPVFhZqE15myRJU3syTYIvKC0t1c6dOzVs2LAmx9asWaN77rlHjzzyiNatW6e6ujrHsczMTPXs2VMWyw9jLvr27aujR4+qvLy8RbIDAAC4Ut67eaotr1V4t3DFDog1Oo7HYsQtAACAB1qWuUxWu1VXxFyhnlE9jY4DF/joo48UFBTUaA5cSbr55puVkJCgsLAwZWRkaPHixSopKdFdd90lqb7hGxMT0+gxDaNzS0tLFRYW5vR+tbW1qq2tdWybTCYFBwc7fnanhuu7+z7ehro4R12coy5NURPnqItz1KUpT6pJwzQJKZNSZPYzdlypJ9Xlx2jcAgAAeBib3aZFGYskSdN6TDM4DVxl69atuu666xQQENBo/9ixP8xf3K1bN1ksFs2dO1dTpkyRv//Fz/e2evVqrVixwrGdkJCgOXPmqH379hd9zQsVG8sIGmeoi3PUxTnq0hQ1cY66OEddmjK6JqUHSnX006OSpGunX6vIjpGG5mlgdF2coXELAADgYbYd3aa8k3kK9w/XLYm3GB0HLrB3714dPXpUv/71r895bnJysurq6lRYWKi4uDhFRkaqtLS00TkN283NiytJ48ePb9QUbhhFUlhYKKvVeqFP4YKYTCbFxsYqPz9fdrvdrffyJtTFOeriHHVpipo4R12coy5NeUpNtr+yXZIUd22cTgee1uljpw3LIrV8XSwWy3n/Ip3GLQAAgIdZuG+hJGl80niF+IcYnAau8MEHHygxMVHx8fHnPDcvL08mk0kRERGSpJSUFC1evFhWq9Uxz+3u3bsVFxfX7DQJkuTv79/siN2W+rBmt9v5sOwEdXGOujhHXZqiJs5RF+eoS1NG1sRuszeaJsGT/m488bXC4mQAAAAe5MTpE3o3711JLErmDaqqqpSXl6e8vDxJUkFBgfLy8lRUVOQ4p7KyUp9//rnTRckyMzO1fv165eXl6fjx4/rkk0/0z3/+U9ddd52jKXvttdfKYrHo73//uw4dOqRt27bp3XffbTSaFgAAwBvkf5GvUwdPyT/MX/Gj442O4/EYcQsAAOBBlmctV62tVv3a91Pvdr2NjoNzyM7O1qxZsxzb8+fPlyQNGTJE06dPlyRt27ZNdrtd1157bZPHWywWbdu2TcuXL1dtba1iYmI0ZsyYRk3ZkJAQzZw5U/PmzdOjjz6q8PBwTZw4USNGjHDzswMAAHCthtG2ibckyj/k4ufyby1o3AIAAHgIu93umCZhag9G23qDXr16admyZWc9Z8SIEc02WRMTE/XMM8+c8z7dunXT7NmzLyojAACAJ6itqFXOOzmSpJT0FIPTeAemSgAAAPAQn+d/rpyyHIX6h2pc93FGxwEAAABcJvedXFkrrYpIiFCHqzoYHccr0LgFAADwEAv31o+2va37bQr1DzU4DQAAAOA6mcu/X5RscopMJpPBabwDjVsAAAAPUFxVrPW56yVJ03pMMzgNAAAA4DonD5zUsc+OSSYpeWKy0XG8Bo1bAAAAD7Aia4VqbDXqE91Hl7e/3Og4AAAAgMtkLc+SJHW6vpPCOoUZnMZ70LgFAAAwmN1u16J9iyRJU1KnGJwGAAAAcB27zd5omgScPxq3AAAABvvq+FfKKs1SsCVY45PGGx0HAAAAcJmj246q/HC5AiICFH9TvNFxvAqNWwAAAIMt2LtAUv2iZOEB4QanAQAAAFwnc2n9aNvEWxNlCbYYnMa70LgFAAAwUGl1qWNRsqk9phqcBgAAAHCdmlM1yt2QK0lKTU81OI33oXELAABgoFVZq1RVV6XLoi5Tv/b9jI4DAAAAuEzO2zmqq6pTZFKk2qe1NzqO16FxCwAAYBC73a6F+xZKqh9tazKZDE4EAAAAuE7msu8XJUtP4b3uRaBxCwAAYJDtBdu1r2SfgvyCWJQMAAAAPqU0u1THvzouk9mkpAlJRsfxSjRuAQAADNIw2vbW7reqTWAbg9MAAAAArpO1PEuS1PmGzgqNDTU4jXeicQsAAGCAsuoyrcteJ4lFyQAAAOBbbHU2R+M2ZXKKwWm8F41bAAAAA6zOXq2quiqltk3VlTFXGh0HAAAAcJmjnx5VRX6FAiMD1W1kN6PjeC2L0QEu1J49e7Ru3Trl5uaqpKREDz/8sPr37+84/uqrr+qjjz5q9Ji+ffvq8ccfb+moAAAATtntdi3Yu0ASi5IBAADA92QurV+UrPtt3eUX6GdwGu/ldY3b6upqxcfHa9iwYXrhhRecntOvXz/df//9jm2LxeueJgAA8GG7Cndpb/FeBfkFaULSBKPjAAAAAC5TXVatvI15kqSUdKZJuBRe19FMS0tTWlraWc+xWCyKjIxsmUAAAAAXqGFRsjEJY9Q2qK3BaQAAAADXyV6brbrqOrXt0VbRfaKNjuPVvK5xez727Nmje++9V6Ghoerdu7fuuOMOhYeHN3t+bW2tamtrHdsmk0nBwcGOn92t4R58TfIH1MQ56uIcdWmKmjhHXZyjLs65qy6nak5pbfZaSdK0y6Z5Vd15rQAAAOBcspb9sCgZ7xsvjc81bvv166cBAwYoJiZG+fn5Wrx4sf7whz/omWeekdnsfC221atXa8WKFY7thIQEzZkzR+3bt2+p2JKk2NjYFr2fN6AmzlEX56hLU9TEOeriHHVxztV1Wff1OlVaK9UzuqfG9RvnlW9mea0AAADAmZKsEhXsLJDJz6SkCUlGx/F6Pte4HTx4sOPnrl27qlu3bpoxY4a+++479enTx+ljxo8fr7Fjxzq2Gz5AFRYWymq1ujfw9/eLjY1Vfn6+7Ha72+/nDaiJc9TFOerSFDVxjro4R12cc1ddXv3iVUlSelK68vPzXXbdltDSrxWLxdLiv0gHAADAxctcVr8oWdfhXRXSPsTgNN7P5xq3P9ahQweFh4crPz+/2catv7+//P39nR5ryQ+wdrudD8w/Qk2coy7OUZemqIlz1MU56uKcK+uyu3C3vin6RgHmAE1Mnui19ea1AgAAgB+zWW3KWlE/TULy5GSD0/gG53MH+JATJ06ovLxcbduy8AcAADDWgn0LJNUvShYVFGVwGgAAAMB1Dn90WKcLTisoKkhdh3c1Oo5P8LoRt1VVVY2+VlhQUKC8vDyFhYUpLCxMy5cv14ABAxQZGanjx49rwYIFio2NVd++fQ1MDQAAWruK2gqtyV4jSZrSY4qxYQAAAAAXy1xaP01C0vgk+QX4GZzGN3hd4zY7O1uzZs1ybM+fP1+SNGTIEN133306ePCgPvroI1VUVCgqKkqXX3650tPTm50KAQAAoCWszV6ritoKJUQkaGDHgUbHAQAAAFymqrhKB947IElKSU8xOI3v8LrGba9evbRs2bJmjz/++OMtmAYAAOD8LNy3UJI0rec0x0KoAAAAgC/IXpstW41N7Xq1U7te7YyO4zN8fo5bAAAAo31b9K12Fe6Sv9lfk5InGR0HAAAAcKnMZfXTJDDa1rVo3AIAALhZw2jbm+NvVrtgRiAAAADAdxTvLVbR7iKZ/c1KGp9kdByf4nVTJQAAAHiTytpKrd6/WhKLkhkpLy9PGRkZOnz4sE6dOiVJioiIUKdOnZSamqr4+HhjAwIAAHiphtG2XW/sqqCoIIPT+Ba3NW4rKioavTk2mUwKDw9Xp06dlJKSorCwMHfdGgAAwGO8nfO2TtWeUnxEvAbHDTY6TqtSVlamTZs26eOPP1ZhYaEkyWKxKDQ0VHa7XZWVlbJarZKk6OhoDRkyRCNHjlRkZKSBqQEAALyHrdam/av2S5JSJjFNgqu5tHFrtVr16aef6sMPP9S+fftkt9udnmcymZSamqqhQ4dq8ODB8vf3d2UMAAAAj7Fg3wJJ0pTUKTKbmKWqpSxYsECbNm1SSEiIBgwYoMsvv1yJiYmKiopqdF5xcbFycnK0e/duvf/++3r77bd18803a8oURkcDAACcy6Gth3S66LSC2wery9AuRsfxOS5r3G7evFmrVq3SqVOndPnll+uuu+5SYmKiOnTo4BjVUFFRoYKCAmVnZ+ubb77R3LlztWTJEk2cOFE33nijq6IAAAB4hL3Fe7WjYIcsJosmp0w2Ok6rsnfvXs2YMUNXX321TCZTs+dFRUUpKipKV111le6++2599dVXWrduXQsmBQAA8F4ZSzMkSUkTkmT2Z5CCq7mscbt69WrdcsstGjp0qEJCQpyeExAQoLZt2yo1NVWjR49WZWWltm7dqjVr1tC4BQAAPmfRvkWSpJHxI9U+pL3BaVqXZ5555oIfYzKZ1L9/f/Xv398NiQAAAHzL6ROndXDLQUlSymSmSXAHlzVuX3nlFfn5+V3QY0JCQjRmzBiNGjXKVTEAAAA8wmnraa3MWilJmtZjmsFpAAAAANfav2q/7Fa7ovtGK6pH1LkfgAvmssbthTZtXfVYAAAAT/ROzjsqqylTl7Auuq7TdUbHwfesVquKi4tVUVHhdD2GxMREA1IBAAB4n8xlmZIYbetOLl2crDk2m02VlZVOj4WFhbVEBAAAgBa1cN9CSdKUHixK5gkqKir0r3/9S5988omsVmuz5y1durQFUwEAAHinE9+eUPGeYpkDzOo+rrvRcXyW2xq3VqtVa9eu1datW3XixAnZbDan5/HmGAAA+JqM4gx9dfwr+Zn8lJ6abnQcSHr11Ve1fft2DR48WElJSc2uyQAAAIBzy1hWvyhZt5HdFNQ2yOA0vsttjds33nhDH330kVJSUnT11Vfz5hgAALQaizLqFyW7seuN6hDSweA0kKTdu3fr5ptv1s9+9jOjowAAAHi1upo67V+1X5KUmp5qcBrf5rbG7eeff67rr79e06dPd9ctAAAAPE6VtUorslZIkqb2nGpwGjQIDw9XbGys0TEAAAC83sEtB1VdUq2QDiHqdH0no+P4NLdNuBYYGKjk5GR3XR4AAMAjbcjboNLqUnUK66QhnYYYHQffGz58uLZt29bs9F0AAAA4Pw2LkiXfniyzhbUc3MltI24HDx6sHTt2aOTIke66BQAAgMdZuLd+UbI7U++Un9nP4DRocPvtt8tqteqxxx7Tddddp3bt2slsbvpBY8CAAQakAwAA8A6VBZU69MEhSVLK5BSD0/g+tzVup02bpr/97W967rnnNHTo0GbfHCcmJrorAgAAQIvaX7pfn+d/LrPJrPQUFiXzJMXFxfr222+Vl5envLy8Zs9j4VwAAIDm7V+1X/Y6u2KuiFFkUqTRcXye2xq3tbW1stvt2rlzp3bu3Nnsebw5BgAAvmLRvvpFyYZ3Ga64sDiD0+BMr732mnJzc3XbbbcpOTmZhXMBAAAukN1ud0yTwGjbluG2xu1rr72mL7/8UoMHD1ZSUhJvjgEAgE+rrqvWssxlkqSpPViUzNPs27dP48aN0+TJk42OAgAA4JWKdhepJKNEfkF+SryVb9C3BLc1bv/zn/9o1KhR+tnPfuauWwAAAHiMjXkbVVJdoo6hHTW0y1Cj4+BHIiMjFRYW5vLr7tmzR+vWrVNubq5KSkr08MMPq3///o7jr776qj766KNGj+nbt68ef/xxx3Z5ebneeustbd++XSaTSQMGDNDdd9+toKAgxzkHDhzQvHnzlJ2drYiICI0aNUrjxo1z+fMBAABoTubS+tG28aPiFdgm0OA0rYPbGrfBwcGKjY111+UBAAA8yoK9CyTVL0pmMbvtLRYu0tixY7V582YNGzasUUP0UlVXVys+Pl7Dhg3TCy+84PScfv366f7773dsWyyNXx9//etfVVJSopkzZ6qurk5/+9vf9Prrr+vBBx+UJFVWVurpp59Wnz59dN999+ngwYN67bXXFBoaqhEjRrjsuQAAADTHWmVV9tpsSVJKOtMktBS3faoYPny4/v3vf2vkyJFOFyUDAADwFTllOdp2bJtMMumO1DuMjgMnamtrZbFYNGPGDA0cOFDR0dFO36OOHTv2gq6blpamtLS0s55jsVgUGRnp9Njhw4e1a9cuPfvss+revbsk6Z577tGzzz6rn/zkJ4qKitKnn34qq9Wq+++/XxaLRV26dFFeXp7eeecdGrcAAKBFHNh8QNWl1QrtGKq4wazl0FLc1rjt3Lmzvv76a/3P//yPhgwZonbt2jl9czxgwAB3RQAAAGgRi/ctliQN7TJUncI6GZwGzvzrX/9y/Lxp06Zmz7vQxu352LNnj+69916Fhoaqd+/euuOOOxQeHi5JyszMVGhoqKNpK0l9+vSRyWTS/v371b9/f2VmZqpnz56NRur27dtXa9euVXl5ebNTQNTW1qq2ttaxbTKZFBwc7PjZnRqu7+77eBvq4hx1cY66NEVNnKMuzlGXpi6lJlnLsyTVL0rmZ/FzaS6jefJrxW2N27/85S+On898o/xjS5cudVcEAAAAt6upq9HSzPr3M9N6TDM4DZrzyiuvGHLffv36acCAAYqJiVF+fr4WL16sP/zhD3rmmWdkNptVWlqqiIiIRo/x8/NTWFiYSktLJUmlpaWKiYlpdE7DCN7S0tJmG7erV6/WihUrHNsJCQmaM2eO2rdv77oneA5MneYcdXGOujhHXZqiJs5RF+eoS1MXWpOTR07q8IeHJUmDpw9Wu47t3BHLcJ74WnFb4/bJJ59016UBAAA8xqYDm3Si6oQ6hHTQ8K7DjY6DZpxPs/LM0amuMnjwYMfPXbt2Vbdu3TRjxgx999136tOnj8vvd6bx48c3GkHcMIqksLBQVqvVrfc2mUyKjY1Vfn6+7Ha7W+/lTaiLc9TFOerSFDVxjro4R12autia7PrbLtltdsX2j1VNWI2OHTvmxpQtr6VfKxaL5bx/ke62xu1ll13mrksDAAB4jIX7FkqS0lPSWZTMgy1YsEDTpjU/Ivr06dN6/vnn3T74oEOHDgoPD1d+fr769OmjyMhInTx5stE5dXV1Ki8vd4yqjYyMdIy+bdCw3dzcuZLk7+8vf39/p8da6gOs3W7nw7IT1MU56uIcdWmKmjhHXZyjLk1dSE3sdrsylmZIkpInJ/t0LT3xteK2VcM2btx41uN1dXV66aWX3HV7AAAAtztw8oA+OfKJTDLpztQ7jY6Ds1i/fr2WLVvm9Fh5eblmz56t3Nxct+c4ceKEysvL1bZtW0lSSkqKKioqlJOT4zjn22+/ld1uV1JSkuOcvXv3Nholu3v3bsXFxTU7TQIAAIArFOwoUFl2mSzBFiXekmh0nFbHbY3bf/zjH/rggw+cHqutrdUf//hHffHFF+66PQAAgNstylgkSRrSeYi6RnQ1OA3O5r/+67+0cuVKrV69utH+0tJSPfnkkyooKNDMmTMv+LpVVVXKy8tTXl6eJKmgoEB5eXkqKipSVVWV/vWvfykzM1MFBQX65ptv9Pzzzys2NlZ9+/aVVL+gb79+/fT6669r//792rdvn9566y0NGjRIUVFRkqRrr71WFotFf//733Xo0CFt27ZN7777rlsWUgMAADhT5tJMSVL86HgFhAUYnKb1cdv3+SZPnqw33nhDFotF119/vWN/VVWV5syZo8zMTP32t7911+0BAADcqtZWq6UZ9YuSTe0x1eA0OJcbbrhBVqtVc+fOlb+/v8aOHauCggL9/ve/V01NjZ588kl17Xrhzffs7GzNmjXLsT1//nxJ0pAhQ3Tffffp4MGD+uijj1RRUaGoqChdfvnlSk9PbzSFwQMPPKB58+Zp9uzZMplMGjBggO655x7H8ZCQEM2cOVPz5s3To48+qvDwcE2cOFEjRoy4hIoAAACcnfW0VdnrsiVJqempBqdpndzWuJ04caJqa2v12muvyWKxaNCgQSovL9ezzz6rw4cP67HHHlPv3r3ddXsAAAC3eu/Aeyo8Xaj2we11Y7cbjY6D8zBixAjV1NTon//8p06ePKlPPvlEZrNZs2bNuuhVhHv16tXsFAyS9Pjjj5/zGmFhYXrwwQfPek63bt00e/bsC84HAABwsfI25qn2VK3CuoSp48CORsdpldy6gsYdd9yhmpoavfLKK6qsrNTGjRtVXFysmTNnKjk52Z23BgAAcKtF++qnSUhPSZe/2fkCUPA8o0ePltVq1cKFCxUXF6ff/e53jikJAAAA8IPMZfXTJKRMSpHJbDI4Tevk9qWPf/rTn6q2tlZz585VZGSknnrqqYv6GhoAAICnOHTqkD48/KEk6c4eLErmiebMmXPW40FBQQoNDdXcuXMd+0wmkx555BF3RwMAAPB45UfKdeSTI5Kk5EkMvjSKyxq3b731VrPHTCaTgoKCFB8fry1btjTaf/fdd7sqAgAAQItYnLFYdtl1XafrFB8Rb3QcOHHw4MGzHg8LC1NJSYlKSkoc+0wmRpIAAABIUtaKLMkudRzYURFdI4yO02q5rHG7adOmc56za9euJvto3AIAAG9itVm1JGOJJBYl82Svvvqq0REAAAC8kt1u/2GahMkpBqdp3VzWuF26dKmrLgUAAOCx3j/4vo5XHle7oHa6qdtNRsfBWaxbt05XXnmlOnXqZHQUAAAAr3H8q+M6mXdS/qH+ShibYHScVs2lc9x+/vnn6tu3r4KDg115WQAAAI+xcN9CSdLklMkK8AswOA3OZt26dVq4cKHat2+vK664Qmlpaerdu7f8/VlMDgAAoDkZSzMkSQljE+QfwvsmI7m0cfvSSy/JZDIpNTXV8ea4c+fOrrwFAACAYY6UH9HWw1slSXemsiiZp5s7d66ysrK0a9cu7dixQ5s2bVJAQIB69eqlK6+8UmlpaYqOjjY6JgAAgMeoraxV7tu5kqSUdKZJMJpLG7dvvvmmdu3apZ07d2rdunVasGCBY4TDFVdcoV69ejHCAQAAeK0lGUtks9s0qOMgdY/sbnQcnIPJZFJKSopSUlI0efJklZaWaseOHdq1a5cWLlyoN998U507d9YVV1yhK6+8UikpKTKbzUbHBgAAMEzu+lzVVtQqIj5Csf1jjY7T6rm0cRsaGqrBgwdr8ODBstvt2r9/v3bu3KmdO3cywgEAAHi1OludFmcsliRN6znN4DS4GJGRkRo2bJiGDRumuro67d27Vzt37tT27du1bt06hYSEqG/fvhozZoySk5ONjgsAANDiGhYlS56ULJPJZHAauLRxeyaTyaTk5GQlJyc7Rjg0NHHPHOFw5ZVX6oYbblBcXJy7ogAAAFyyrYe36ljFMbUNbKtR8aOMjoNL5Ofnp969e6t37976yU9+ooKCAu3YsUM7d+7U3r17adwCAIBW5+TBkzq27ZhkklImMU2CJ3Bb4/bHIiMjNXToUA0dOrTRCIevv/5aAQEBuv3221sqCgAAwAVrWJRsUsokBfoFGpwGrhYTE6NRo0Zp1Cia8gAAoHXKWpElSep0bSeFdQozOA2kFmzcnunHIxysVqsRMQAAAM7LsYpj2nJwiyRpao+pBqfBxVixYsU5zwkICFC7du3Us2dPRUVFtUAqAAAAz2C32R3TJKRMZrStp3Br49Zms2nXrl0qKChQeXm503Nuv/12WSyG9I8BAADOS8OiZNfEXqOkyCSj4+AiLF++/LzPNZvNGj58uO655x4WKwMAAK3Csc+PqfxQufzD/RV/c7zRcfA9t3VMs7Oz9ac//UknTpw463lMkQAAADzZmYuSTe3JaFtv9dprr+m5555TfHy8br75ZsXG1q+SfOzYMW3cuFEHDhzQb37zG1VVVWn9+vV677331LZtW02cONHg5AAAAO7XMNq2+63dZQlmgKWncNvfxJtvvqmamhr9v//3/9SzZ0+Fhoa661YAAABu8/GRj3Wk/IgiAyM1On600XFwkd58803FxcXp/vvvb7Q/MTFR999/v/7yl79o4cKFeuihhzR9+nSdPHlSH3/8MY1bAADg82rKa5T7Tq4kFiXzNG777tfBgwc1btw4XXXVVTRtAQCA11qwd4EkaWLyRAVZggxOg4v13Xff6bLLLmv2+GWXXabdu3c7ttPS0lRUVNQS0QAAAAyVuz5X1tNWtUlso5irYoyOgzO4rXEbFRUlu93urssDAAC43bFTx7T5wGZJ0tRUpknwZhaLRfv372/2eGZmZqN1F+rq6hQURKMeAAD4vsylPyxKZjKZDE6DM7mtcTtu3Di9//77qqysdNctAAAA3Oofu/6hOnudru5wtVKjUo2Og0swePBgffTRR5o/f77y8/Nls9lks9mUn5+v+fPn65NPPtHgwYMd53/33Xfq3LmzgYkBAADcryy3TPlf5MtkNin59mSj4+BH3DbHbVVVlYKCgvTAAw9o0KBBio6Odroq79ixY90VAQAA4KLZ7Da9ueNNSdKUHlMMToNLNW3aNJWVlWn9+vVav369432pzWaTJA0YMEDTpk2TJNXU1CgxMVEpKczxBgAAfFvW8ixJUqfrOym0I1Odehq3NW7/9a9/OX7etGlTs+fRuAUAAJ7okyOfKLc0VxEBEbol8Raj4+ASBQQE6De/+Y1yc3O1a9cuFRYWSpLat2+vvn37KjExsdG5t99+u1FRAQAAWoTdZlfm8h+mSYDncVvj9pVXXnHXpQEAANxu4d6FkuoXJQu2BBucBpfKZrPJbDYrISFBCQkJTs8pLy9XWFhYCycDAAAwxtFPj6riaIUC2gSo203djI4DJ9w2x2379u3P6x8AAABPU1hZqI15GyVJ03pOMzgNXOGVV14568K5xcXFevLJJ1swEQAAgLEaRtt2H9ddliC3je3EJXBb4xYAAMBbLctcJqvdqms6X6OeUT2NjgMX+Oqrr/Taa685PVZQUKAnnnhCFRUVLZwKAADAGDUna5S7IVcS0yR4Mpe106dPny6z2awXX3xRFotF06dPl8lkOutjTCaTXn75ZVdFAAAAuGQ2u02LMhZJku674j6D08BVHn74YT3//PPy9/fXfff98Pd6+PBh/f73v5e/v79mzZplYEIAAICWk/N2juqq6hSZEqn2/fhGvKdyWeP2sssuk8lkcqzQ27ANAADgTbYd3aa8k3kK8w9Teq90nTxx0uhIcIG+ffvqt7/9rf70pz/J399fP/vZz5STk6NnnnlGbdq00cyZMxUVFWV0TAAAgBaRsTRDUv1oW/p3nsulI27Ptg0AAOANFu77YVGy0IBQnRSNW19x5ZVX6sEHH9Rf/vIXlZWVaefOnYqNjdXjjz+u8PBwo+MBAAC0iNL9pSrYXiCTn0nJE5KNjoOzYI5bAACA7504fULv5r0rSZraY6rBaeAOAwYM0K9+9St99tln6tatm5588kmatgAAoFXJXFa/KFnnGzorpEOIwWlwNm5fMq6oqEjHjx9XRUWF05V8BwwY4O4IAAAA52V51nLV2mrVN7qvekf3NjoOLsHDDz981uP+/v4qLS3V7373O8c+k8mkP/7xj+6OBgAAYBhbnU2ZK+obt6npqQanwbm4rXFbVFSk1157Td9+++1Zz1u6dOkFXXfPnj1at26dcnNzVVJSoocfflj9+/d3eu4bb7yhLVu26K677tKYMWMu6D4AAKB1sdvtjmkSpvZktK23CwsLO+t8bYyyBQAArVHOezmqzK9UYGSguo7oanQcnIPbGrevvvqqMjMzddtttyk5OVkhIa4Zel1dXa34+HgNGzZML7zwQrPnffnll8rKylLbtm1dcl8AAODbPs//XDllOQr1D9W4xHFGx8Eleuqpp4yOAAAA4HF2/d8uSVLShCT5BfoZGwbn5LbGbWZmpsaNG6fJkye79LppaWlKS0s76znFxcV666239Pjjj+u5555z6f0BAIBvWri3frTtbd1vU1hAmMFp4Arz5s3TlVdeqV69esnf39/oOAAAAIaqLq3WvjX7JEkpk1MMToPz4bbGbbt27RQaGuquyzfLZrPp5Zdf1q233qouXbq0+P0BAID3Ka4q1vrc9ZKkaT2mGZwGrpKZmanNmzcrICBAvXr10hVXXKErrrhC0dHRRkcDAABocdlrs1VXXaeoy6LUrnc7o+PgPLitcXvLLbdo48aNGjFihAIDA911mybWrl0rPz8/3Xzzzef9mNraWtXW1jq2TSaTgoODHT+7W8M9WuJe3oKaOEddnKMuTVET56iLc9RFWrV/lWpsNerdrrf6xvSVRF2c8baazJkzR6WlpdqxY4d27typRYsWad68eercubOjiZuamiqz2Wx0VAAAALfLWJohqX60rbe8n2vt3Na4vfHGG2Wz2fTAAw/ommuuUbt27Zy+KR47dqzL7pmTk6MNGzZozpw5F/QCXL16tVasWOHYTkhI0Jw5c9S+fXuXZTsfsbGxLXo/b0BNnKMuzlGXpqiJc9TFudZaF7vdriWrlkiSpl8zXR07dmx0vLXW5Wy8qSaRkZEaNmyYhg0bprq6Ou3du1c7d+7U119/rXXr1ikkJER9+/bVFVdcoX79+ikiIsLoyAAAAC5XnFGswl2FMlvMSp6QbHQcnCe3NW4PHjyodevWqbS0VBs3bmz2PFc2bvfu3auTJ0/q/vvvd+yz2WyaP3++NmzYoFdffdXp48aPH98oR0PTt7CwUFar1WX5mmMymRQbG6v8/HzZ7Xa3388bUBPnqItz1KUpauIcdXGutdfli/wvtLdor4ItwRrWfpiOHTsmibo409I1sVgsLv1Fup+fn3r37q3evXvrJz/5iQoKChyjcd944w1ZrVZ1795dkyZNUr9+/Vx2XwAAAKNlLcuSJCWPSVZwdDDvb72E2xq3b7zxhiorK3XfffcpOTlZISEh7rqVw/XXX68+ffo02vfMM8/o+uuv19ChQ5t9nL+/f7MLVrTkC9lut/Mvzo9QE+eoi3PUpSlq4hx1ca611mXBngWSvl+UzD+sSQ1aa13OxldqEhMTo1GjRmnUqFGqqanRt99+qx07dujEiRNGRwMAAHAZm9WmrJX1jdt+d/czNgwuiNsat3l5eZo8ebJGjBjh0utWVVUpPz/fsV1QUKC8vDyFhYUpOjpa4eHhjc63WCyKjIxUXFycS3MAAADvV1pd6liUbEqPKQangZHWr1+vZcuWafHixUZHAQAAcKlDWw/pdOFpBbULUvLoZBUUFRgdCefJbY3bmJgYt1w3Oztbs2bNcmzPnz9fkjRkyBBNnz7dLfcEAAC+aVXWKlXVValnVE+ltU8zOg4MZLfbZbPZjI4BAADgco5pEiYmy8/fz+A0uBBua9xOnjxZ//rXvzRo0CBFR0e77Lq9evXSsmXLzvv85ua1BQAArZvdbtfCfQslSdN6TGNlXQAAAPicquIqHXjvgCQpZXKKwWlwodzWuN2zZ49CQkL061//Wn369FG7du1kNpsbnWMymXT33Xe7KwIAAECzthds176SfQryC9L4pPFGxwEAAABcbv+a/bLV2hTdJ1rtLmtndBxcILc1bjdt2uT4eceOHc2eR+MWAAAYoWG07a3db1WbwDYGpwEAAABcL3NppiQpeXKywUlwMdzWuF26dKm7Lg0AAHBJyqrLtC57nSQWJfNl5eXl531udXW1G5MAAAC0vBPfndCJb0/I7G9W0m1JRsfBRXBb4xYAAMBTrc5eraq6KqW2TdVVMVcZHQdu8vOf/9zt99izZ4/WrVun3NxclZSU6OGHH1b//v0lSVarVUuWLNHOnTtVUFCgkJAQ9enTR1OmTFFUVJTjGtOnT1dhYWGj606ZMkW33XabY/vAgQOaN2+esrOzFRERoVGjRmncuHFuf34AAMB7ZS6rH23b7cZuCooKMjgNLobLGrfV1dUKDAxs8ccCAABcCLvdrgV7F0iSpvaYyqJkPmzixIlu//utrq5WfHy8hg0bphdeeKHRsZqaGuXm5mrixImKj49XeXm5/u///k/PP/+8nnvuuUbnTp48WSNGjHBsBwX98OGqsrJSTz/9tPr06aP77rtPBw8e1GuvvabQ0NBGjwEAAGhQV1On/av2S5JS0lmUzFu5rHH73//93xo9erSGDx+utm3bntdjiouL9d5772nz5s2aN2+eq6IAAAA0a1fhLu0t3qsgvyBNSJpgdBy40eTJk91+j7S0NKWlpTk9FhISot/97neN9t1zzz363//9XxUVFSk6OtqxPzg4WJGRkU6v8+mnn8pqter++++XxWJRly5dlJeXp3feeYfGLQAAcOrQB4dUVVyl4Jhgdb6hs9FxcJFc1ri99957tXz5cq1YsUKpqanq06ePEhMTFRMTo9DQUNntdlVUVKigoEDZ2dn65ptvlJWVpY4dO7bI19gAAACkHxYlG50wWm2Dzu+XzYCrVFZWymQyKSQkpNH+NWvWaOXKlYqOjta1116rMWPGyM/PT5KUmZmpnj17ymL54a173759tXbtWpWXlyssLKxFnwMAAPB8DdMkJE9MltliNjgNLpbLGreDBg3SNddco6+//loffvihVq9eLavV6vymFosuv/xy/fa3v9VVV10ls5kXEAAAcL9TNae0NnutJGlaj2kGp4ER9uzZo4KCgmYXLhs7dqzb7l1TU6OFCxdq8ODBjRq3N998sxISEhQWFqaMjAwtXrxYJSUluuuuuyRJpaWliomJaXSthtG5paWlzTZua2trVVtb69g2mUwKDg52/OxODddnKpLGqItz1MU56tIUNXGOujjXmutyuui0Dr5/UJKUmp7apBatsSZn48l1ceniZGazWf3791f//v1VW1urnJwcHTlyxPHGOCwsTJ06dVJiYqL8/f1deWsAAIBzWpO9RpXWSiVFJql/bH+j46AF5eXl6cUXX1R+fv5Zz3NX49ZqterFF1+UVP9Ntebu2a1bN1ksFs2dO1dTpky5pPfMq1ev1ooVKxzbCQkJmjNnjtq3b3/R17xQsbGxLXYvb0JdnKMuzlGXpqiJc9TFudZYl8+WfCa71a5O/TvpsiGXNTneGmtyPjyxLi5t3J7J399fqampSk1NddctAAAALkjDNAksStb6/P3vf9fJkyd13333KTk5uclUBe7U0LQtKirSE088cc57Jycnq66uToWFhYqLi1NkZKRKS0sbndOw3dy8uJI0fvz4Rk3hhtd8YWFhs9+McxWTyaTY2Fjl5+fLbre79V7ehLo4R12coy5NURPnqItzrbUudrtdX8/9WpKUMD5Bx44dcxxrrTU5l5aui8ViOe9fpLutcQsAAOBJdhfu1jdF3yjAHKDbk283Og5a2KFDh5Sent7ii3k1NG3z8/P15JNPKjw8/JyPycvLk8lkUkREhCQpJSVFixcvltVqdcxzu3v3bsXFxZ11flt/f/9mR+y21Ic1u93OB0MnqItz1MU56tIUNXGOujjX2upS9E2RivcWyy/QT4m3Jjp97q2tJufLE+vC5LIAAKBVWLBvgaT6RcmigqIMToOW1rFjR7dct6qqSnl5ecrLy5MkFRQUKC8vT0VFRbJarfrzn/+snJwczZgxQzabTaWlpSotLXWMeM3MzNT69euVl5en48eP65NPPtE///lPXXfddY6m7LXXXiuLxaK///3vOnTokLZt26Z3333XrfPxAgAA79SwKFm3m7opMDLQ4DS4VIy4BQAAPq+itkJrstdIqp8mAa3PpEmTNH/+fF177bWKinJd4z47O1uzZs1ybM+fP1+SNGTIEE2aNElff13/VcVHHnmk0eOefPJJ9erVSxaLRdu2bdPy5ctVW1urmJgYjRkzplFTNiQkRDNnztS8efP06KOPKjw8XBMnTmzx0cMAAMCz1VXXaf+q/ZKklMkpBqeBK9C4BQAAPm9t9lpV1FYoISJBAzsONDoODDBgwADV1tbqwQcfVJ8+fRQVFSWzufGXz0wmk+6+++4Lum6vXr20bNmyZo+f7ZgkJSYm6plnnjnnfbp166bZs2dfUDYAANC6HNxyUNWl1QqJDVGn6zsZHQcuQOMWAAD4vIZFyab1nMaiZK3Unj17NHfuXNXU1Gj79u3NnnehjVsAAABPkbE0Q5KUfHuyzH7MjuoLaNwCAACf9u2Jb7WrcJf8zf6alDzJ6DgwyFtvvaWQkBA99NBDSkpKUkhIiNGRAAAAXKbyeKUOf3hYkpQyiWkSfIVb2+9FRUV644039OCDD+ruu+/Wnj17JEknT57UW2+9pdzcXHfeHgAAQIv2LZIkjYofpXbB7QxOA6Pk5+frlltu0eWXX07TFgAA+JysVVmy19kVc2WMIpMijY4DF3Fb4/bw4cN65JFH9NlnnykmJkaVlZWy2WySpIiICGVkZGjjxo3uuj0AAIAqayu1KmuVJBYla+26dOmiyspKo2MAAAC4nN1uV+ayTElSanqqwWngSm5r3C5YsEChoaF66aWXNGPGjCbH09LStG/fPnfdHgAAQG/nvK1TtacUHxGvwXGDjY4DA/3kJz/Rli1btH//fqOjAAAAuFThrkKVZpbKL8hPibckGh0HLuS2OW737t2riRMnKiIiQqdOnWpyPDo6WsXFxe66PQAAgBbsWyBJmpI6RWYTCzS0Zm+//baCg4P1+OOPq3PnzoqOjpbZ3Pg1YTKZ9MgjjxiUEAAA4OI0jLZNGJ2ggIgAg9PAldzWuLXZbAoMDGz2+MmTJ2WxsDYaAABwj73Fe7WjYIcsJosmp0w2Og4MdvDgQUn1gweqqqp0+PDhJueYTKaWjgUAAHBJrFVWZa/NlsSiZL7IbZ3TxMRE7dixQzfddFOTY3V1ddq2bZtSUnhBAQAA92hYlGxk/Ei1D2lvcBoY7dVXXzU6AgAAgMsd2HRANWU1Co0LVdy1cUbHgYu57TuDt912m3bt2qW5c+fq0KFDkqTS0lLt3r1bTz/9tI4cOaJx48a56/YAAKAVO209rZVZKyVJ03pMMzgNAAAA4B4N0ySkTEqRycy3h3yN20bcpqWlafr06frHP/6hLVu2SJJefvllSVJwcLCmT5+uyy67zF23BwAArdg7Oe+orKZMXcK66LpO1xkdBwYoKipSdHR0iz8WAACgpVQcq9CRj49IklIm8612X+TWSWavv/569e/fX7t371Z+fr5sNptiY2PVt29fBQcHu/PWAACgFVu4b6Ek6c4ed7IoWSv1wAMP6Nprr9XIkSOVlJR0Xo/JyMjQe++9p88++0wLFy50c0IAAIBLk7UiS3abXbEDYhURH2F0HLiB21cHCwoKUv/+/d19GwAAAElSRnGGvjr+lfxMfkpPSTc6Dgwye/ZsLVmyRI8//riio6PVu3dvJSQkKCYmRmFhYbLb7aqoqFBBQYGys7P17bffqri4WL1799asWbOMjg8AAHBWdrv9h2kS0hlt66vc1rjdvXu3vv32W02ZMsXp8cWLF6tPnz7q3bu3uyIAAIBWaFFG/aJkN3a9UbGhsQangVGSkpI0c+ZM5eXlaevWrfr666/14YcfOj23Xbt2uvrqqzVs2DDFx8e3aE4AAICLUfB1gcpyymQJtihhTILRceAmbmvcrly58qxzgxUXF2vlypU0bgEAgMtUWau0ImuFJGlqz6kGp4EniI+P19133627775bxcXFOnr0qE6dOiVJCg8PV1xcnKKiogxOCQAAcGEyl9ePtk0Ym6CAsACD08Bd3Na4PXjwoAYOHNjs8e7du2vHjh3uuj0AAGiFNuRtUGl1qTqFddKQTkOMjgMPExUVRZMWAAB4Petpq7LXZktiUTJf57bVOqxWq6xW61mPV1dXu+v2AACgFVq49/tFyVLvlJ/Zz+A0AAAAgOvlvZun2vJahXcNV8drOhodB27ktsZtly5d9OWXXzo9Zrfb9cUXX6hz587uuj0AAGhl9pfu1+f5n8tsMrMoGQAAAHxWxtIMSVLypGSZzCaD08Cd3Na4HTVqlDIyMvTnP/9ZBw8eVF1dnerq6nTgwAH9+c9/VmZmpkaNGuWu2wMAgFZm0b76RcmGdxmuuLA4g9MAAAAArnfq8Ckd/fdRSVLKJKZJ8HVum+P2+uuv1/Hjx7Vy5Up98cUXMpvre8Q2m00mk0kTJ07UDTfc4K7bAwCAVqS6rlrLMpdJkqb2YFEyAAAA+Kas5VmSXeo4qKPCu4QbHQdu5rbGrSRNmjRJ1113nb788ksVFBRIkjp06KCrr75asbGx7rw1AABoRTbmbVRJdYliQ2M1tMtQo+MAAAAALme325W5PFOSlJqeanAatAS3Nm4lKTY2Vrfeequ7bwMAAFqxBXsXSKpflMxidvvbGwAAAKDF5X+Rr1MHTsk/zF/xo+ONjoMW4LY5bgEAAFpCTlmOth3bJpNMujP1TqPjwIP85je/0Y4dOxzb1dXVevPNN3X06NEm537yySdKT2dROwAA4Lkyl9WPtk28JVH+If4Gp0FLcOuQlJ07d+qdd95Rbm6uKisrZbfbm5yzdOlSd0YAAAA+bvG+xZKkoV2GqlNYJ4PTwJMcPXpUlZWVju2amhq99957uuaaaxQXxwJ2AADAe9RW1Crn7RxJUspkFiVrLdw24vbzzz/Xc889p7KyMg0aNEh2u12DBw/W4MGDFRAQoG7duun222931+0BAEArUFNXo6WZ9b8EntZjmsFpAAAAAPfIXZ8ra6VVEfER6nB1B6PjoIW4bcTtmjVrlJSUpN///vcqLy/Xe++9p2HDhql3794qKCjQ448/rpiYGHfdHgAAtAKbDmzSiaoT6hDSQcO7Djc6DgAAAOAWDdMkpExOkclkMjgNWorbRtwePnxYgwcPltlslp+fnyTJarVKkmJiYnTTTTdp7dq17ro9AABoBRbtWyRJSk9JZ1EyAAAA+KSTB07q2GfHJJOUfHuy0XHQgtzWuA0MDJTFUv8BKjQ0VBaLRaWlpY7jbdq0UUFBgbtuDwAAfNyBkwf08ZGPWZQMAAAAPi1reZYkqdN1nRTWKczgNGhJbhuaEhcXp8OHDzu24+Pj9fHHH+u6665TXV2dPv30U0VHR7vr9gAAwMctyqgfbTuk8xB1jehqcBp4qkWLFmnNmjWSJJvNJkl6/fXXFRgY2Oi8MxcxAwAA8BR2m12Zy7+fJiGdRclaG7c1bq+++mq9++67+slPfiJ/f39NmDBBzz//vH72s5/JZDKpurpa//3f/+2u2wMAAB9Wa6vV0oz6Rcmm9phqcBp4qp49ezaZA65NmzZOzw0PD1eHDiz0AQAAPMuxz46p/HC5AiICFH9TvNFx0MLc1ri99dZbdeuttzq2r7zySj311FP64osvZDabdcUVV6h3797uuj0AAPBh7x14T4WnC9U+uL1u7Haj0XHgoZ566imjIwAAAFySjKUZkqTEWxNlCWZNh9bG5X/jNTU1+vrrr1VQUKDw8HBdccUVatu2raT6UQ89e/Z09S0BAEArc+aiZP5mf4PTwFfY7XZWaQYAAB6j5lSNctfnSpJSJjNNQmvk0sZtWVmZZs6c2WjRsYCAAP2///f/dPnll7vyVgAAoJU6dOqQPjz8oSTpzh4sSoZLZ7Va9eGHH+rtt9/WSy+9ZHQcAAAASVLOOzmqq6pTZFKkYq6IMToODODSxu3KlStVWFioMWPGqHfv3srPz9fKlSs1d+5cvfzyy668FQAAaKUWZyyWXXZd1+k6xUfEGx0HHs5qterrr79Wfn6+wsLCdMUVVygqKkqSVF1drY0bN2rDhg0qLS1ljlsAAOBRMpfVL0qWPDmZbwW1Ui5t3P7nP//R9ddfr5/+9KeOfZGRkXrppZd09OhRxcXFufJ2AACglbHarFqSsUSSNCV1isFp4OmKi4s1a9Ys5efnO/YFBATokUcekcVi0V//+lcVFxcrKSlJd999twYMGGBgWgAAgB+U5ZTp+JfHZTKblDwx2eg4MIhLG7dFRUUaN25co309evSQJJWWltK4BQAAl+T9g+/reOVxtQtqp1Hxo4yOAw+3ZMkSFRQUaNy4cerRo4cKCgq0cuVKvfHGGzp58qS6dOmiGTNm6LLLLjM6KgAAQCOZy+tH23a+obNCY0MNTgOjuLRxa7VaFRAQ0Gifv3/9giE2m82VtwIAAK3Qwn0LJUmTUyYrwC/gHGejtdu9e7duuOEGTZnyw+jsyMhIvfjii0pLS9Mjjzwis9lsYEIAAICmbHU2ZS3PkiQlT2K0bWvm0satJBUUFCgnJ8exXVlZKUk6duyYQkJCmpyfmJjo6ggAAMAHHSk/oq2Ht0qS7kxlUTKcW1lZmZKTG3/YSUmpX5F52LBhNG0BAIBHOvrvo6o4VqHAyEB1G9nN6DgwkMsbt0uXLtXSpUub7H/zzTebPR8AAOBclmQskc1u06COg9Q9srvRceAFbDZbs98GczagAAAAwBNkLq2fJqH7uO6yBLm8dQcv4tK//f/+7/925eUAAAAkSXW2Oi3OWCxJmtpjqsFp4E34NhgAAPAm1WXVytuYJ0lKSU8xNgwM59LG7Q033ODKywEAAEiSth7eqmMVx9Q2sC2LkuGC8G0wAADgTXLW5aiuqk5tU9sq+vJoo+PAYIy3BgAAHq9hUbJJKZMUZAkyOA28Bd8GAwAA3iZzWf00CSmTU2QymQxOA6PRuAUAAB7tWMUxbTm4RRLTJODC8G0wAADgTUqySlSwo0AmP5OSJiQZHQcegKV0AQCAR2tYlGxA7AAlRfIGFgAAAL4pa3mWJKnLsC4KiWEhVTDiFgAAeDAWJYOn27Nnj9atW6fc3FyVlJTo4YcfVv/+/R3H7Xa7li1bpvfff18VFRXq0aOH7r33XnXs2NFxTnl5ud566y1t375dJpNJAwYM0N13362goB+mBTlw4IDmzZun7OxsRUREaNSoURo3blyLPlcAAOA+NqtNWSvqG7cpk1mUDPW8rnF7rjfHy5Yt07Zt23TixAlZLBYlJibqjjvuUHJysoGpAQDAxfj4yMc6Un5EkYGRGp0w2ug4QBPV1dWKj4/XsGHD9MILLzQ5vnbtWr377ruaPn26YmJitHTpUj3zzDP685//rICAAEnSX//6V5WUlGjmzJmqq6vT3/72N73++ut68MEHJUmVlZV6+umn1adPH9133306ePCgXnvtNYWGhmrEiBEt+nwBAIB7HPn4iCqPVyqwbaC6juhqdBx4CK+bKqHhzfHPf/5zp8fj4uJ0zz336IUXXtDs2bPVvn17Pf300zp58mQLJwUAAJeqYVGyickTFWwJNjgN0FRaWpruuOOORgMJGtjtdm3YsEETJkzQ1VdfrW7duulXv/qVSkpK9NVXX0mSDh8+rF27dumXv/ylkpOT1aNHD91zzz3atm2biouLJUmffvqprFar7r//fnXp0kWDBw/WzTffrHfeeadFnysAAHCfjKUZkqSkCUnyC/AzOA08hdeNuE1LS1NaWlqzx6+99tpG2z/96U/1wQcf6MCBA+rTp4+74wEAABc5Xnlcmw9sliRNTWWaBHifgoIClZaW6vLLL3fsCwkJUVJSkjIzMzV48GBlZmYqNDRU3bt3d5zTp08fmUwm7d+/X/3791dmZqZ69uwpi+WHt+59+/bV2rVrVV5errCwMKf3r62tVW1trWPbZDIpODjY8bM7NVyf1bAboy7OURfnqEtT1MQ56uKcN9WlqqRKBzYfkCSlpqe6LbM31aQleXJdvK5xeyGsVqu2bNmikJAQdevWzeg4AADgAizNWKo6e52u6nCVUqNSjY4DXLDS0lJJUps2bRrtb9OmjeNYaWmpIiIiGh338/NTWFhYo3NiYmIanRMZGek41lzjdvXq1VqxYoVjOyEhQXPmzFH79u0v8hlduNjY2Ba7lzehLs5RF+eoS1PUxDnq4pw31OXLVV/KVmNTh74d1OdG9w869IaaGMET6+KTjdvt27frL3/5i2pqahQZGamZM2c2eUN8JiNHI5x5D0/s7BuFmjhHXZyjLk1RE+eoi3OeWBeb3eZYlGxaz2mGZPPEuhiNmniX8ePHa+zYsY7thr+3wsJCWa1Wt97bZDIpNjZW+fn5stvtbr2XN6EuzlEX56hLU9TEOerinDfV5eu5X0uSEicm6tixY267jzfVpCW1dF0sFst5/yLdJxu3vXr10h//+EedPHlS77//vl588UX94Q9/aDLaoYEnjEaQPLOzbzRq4hx1cY66NEVNnKMuznlSXd7Lfk8HTx1Um8A2um/QfQrxDzEsiyfVxVNQk/PTMCq2rKxMbdu2dewvKytTfHy845wfr8VQV1en8vJyx+MjIyMdo28bNGw3nOOMv7+//P39nR5rqQ9rdrudD4ZOUBfnqItz1KUpauIcdXHO0+tSvK9Yhf8plMliUvfburdIVk+viVE8sS4+2bgNCgpSbGysYmNjlZKSogceeEAffPCBxo8f7/R8I0cjNNyP33g0Rk2coy7OUZemqIlz1MU5T6zLX//9V0nShKQJKisqU5nKWjyDJ9bFaJ48GsETxcTEKDIyUt98842jUVtZWan9+/dr5MiRkqSUlBRVVFQoJydHiYmJkqRvv/1WdrtdSUlJjnMWL14sq9XqmOd29+7diouLa3aaBAAA4B0yl2VKkrrd2E3B7ViMF435ZOP2x+x2e6OpEH7ME0YjNNyLD4aNURPnqItz1KUpauIcdXHOU+pSWFmojXkbJUlTUqcYnslT6uJJqMkPqqqqlJ+f79guKChQXl6ewsLCFB0drdGjR2vVqlXq2LGjYmJitGTJErVt21ZXX321JKlz587q16+fXn/9dd13332yWq166623NGjQIEVFRUmqX3x3+fLl+vvf/65x48bp0KFDevfdd3XXXXcZ8pwBAIBr2Gpt2r9yvyQpZXKKwWngibyucXu2N8dhYWFatWqVrrrqKrVt21anTp3Sxo0bVVxcrIEDBxqYGgAAnK9lmctktVuVFpOmy9pdZnQc4Kyys7M1a9Ysx/b8+fMlSUOGDNH06dM1btw4VVdX6/XXX1dlZaV69Oih//3f/1VAQIDjMQ888IDmzZun2bNny2QyacCAAbrnnnscx0NCQjRz5kzNmzdPjz76qMLDwzVx4kSNGDGi5Z4oAABwuUNbD+l00WkFRwery9AuRseBB/K6xu3Z3hzfd999Onr0qP70pz/p1KlTCg8PV/fu3TVr1ix16cK/AAAAeDqb3aZFGYskSdN6TDM4DXBuvXr10rJly5o9bjKZlJ6ervT09GbPCQsL04MPPnjW+3Tr1k2zZ8++6JwAAMDzNEyTkDQhSWZ/s8Fp4Im8rnF7rjfHDz/8cAumAQAArrTt6DblncxTmH+Ybk281eg4AAAAgFucPnFaB947IIlpEtA82vkAAMBjLNy3UFL9omQh/iEGpwEAAADcI3t1tuxWu6Ivj1ZUzyij48BD0bgFAAAe4cTpE3o3711J0tQeUw1OAwAAALhPwzQJKemMtkXzaNwCAACPsDxruWptteob3Ve9o3sbHQcAAABwixPfntCJ707IHGBW93HdjY4DD0bjFgAAGM5utzumSZjak9G2AAAA8F0No227jeymoLZBBqeBJ6NxCwAADPd5/ufKKctRqH+oxiWOMzoOAAAA4BZ1NXXav3q/JBYlw7nRuAUAAIZbuLd+tO1t3W9TWECYwWkAAAAA9zj4/kFVFVcppEOIOg/pbHQceDiL0QFauzpbnb48/qVqimoUUBOg/h36y8/sZ3QsAABaTHFVsdbnrpfEomQAAADwbVnLsiRJSROTZLYwnhJnR+PWQBtyN+iJz57QsYpjjn0dQztq9sDZGp0w2sBkAAC0nJVZK1Vjq1Hvdr11efTlRscBAAAA3KKysFIH3z8oiWkScH5o7RtkQ+4G/WLLLxo1bSUpvyJfv9jyC23I3WBQMgAAWk6jRcl6TJXJZDI4EQAAAOAe+1ftl73Orpi0GLVNbmt0HHgBGrcGqLPV6YnPnpBd9ibHGvY9+dmTqrPVtXQ0AABa1FfHv1JWaZaCLcEanzTe6DgAAACAW9jtdmUuy5QkpaQz2hbnh8atAb7I/6LJSNsz2WXX0Yqj+iL/ixZMBQBAy1uwd4EkaVziOIUHhBucBgAAAHCPom+KVLKvRH5Bfkq8NdHoOPASNG4NUFBZ4NLzAADwRqXVpT8sStaTRckAAADguzKX1o+2jR8Vr8A2gQangbegcWuAmJAYl54HAIA3WpW1SlV1VeoZ1VNp7dOMjgMAAAC4RV11nbLXZEtiUTJcGBq3BhgQO0AdQzvKpOYXYDGbzLLarC2YCgCAlnPmomTTekxjUTIAAAD4rAObD6i6tFqhHUMVd22c0XHgRWjcGsDP7KfZA2dLUrPNW5vdpjvfvVOzP5+t6rrqlowHAIDbbS/Yrn0l+xTkF8SiZAAAAPBpDYuSJd+eLLMfrTicP14tBhmdMFpvjHhDsaGxjfbHhcbp5aEva2qP+rn+Xv/mdY1ZM0YZxRlGxAQAwC0aRtvekniL2gS2MTgNAAAA4B4V+RU6/OFhSVLypGSD08DbWIwO0JqNThitm7rdpC+Pf6magBoF1ASof4f+8jP7aULSBA3vMlwPf/Kw9hbv1c1rbtbj/R/X3b3ultlEvx0A4L3Kqsu0LnudJBYlAwAAgG/bv2q/7Da7OlzdQZHdI42OAy9DB9BgfmY/DYobpDv73KlBcYPkZ/ZzHLsp/ia9P/F9De08VNV11Xrisyf0k40/0fHK4wYmBgDg0qzOXq2quiqltk3VVTFXGR0HAAAAcAu73a7MpfXTJLAoGS4GjVsPFxMSo3+N+peeHvS0gvyC9OHhDzV8xXC9m/uu0dEAALhgdrtdC/YukCRN7TGVRckAAADgswp3Fqp0f6n8gvyUeEui0XHghWjcegGTyaS7e92td8e/q17teqmkukT3brlXD3/8sCpqK4yOBwDAedtVuEt7i/cq0C9QE5ImGB0HAAAAcJuMpfXrFSWMTlBAeIDBaeCNaNx6kZS2KXp73Nu6//L7ZZJJizMWa+SqkdpRsMPoaAAAnJeGRcnGJIxR26C2BqcBAAAA3MN62qqcdTmSpJR0pknAxaFx62UC/QL1+IDHtXTMUnUM7ai8k3m6bd1tenHHi7LarEbHAwCgWadqTmlt9lpJ0rQe0wxOAwAAALhP3qY81ZysUVjnMMUNijM6DrwUjVsvNThusLZM3KJx3cepzl6nF7a/oAlvT9CBkweMjgYAgFNrsteo0lqppMgk9Y/tb3QcAAAAwG0yl32/KNmkFJnMrOuAi0Pj1otFBkbq1aGv6uWhLyvcP1zbC7brxlU3amnmUtntdqPjAQDQSMM0CVNSp7AoGQAAAHxW+ZFyHfn4iCQpeVKywWngzWjcejmTyaQJSRP03sT3NCB2gCpqK/Tbj36r/3r/v1RcVWx0PAAAJEm7C3frm6JvFGAO0KSUSUbHAQAAANwma2WWZJc6DuyoiG4RRseBF6Nx6yO6hHfR8jHL9ejVj8pismh97nrduPJGfXzkY6OjAQCgBfsWSJJGJ4xWVFCUwWkAAAAA97Db7cpc+sM0CcCloHHrQ/zMfprRb4bWjVunxDaJyq/M150b7tRTnz2lKmuV0fEAAK1URW2F1mSvkSRN7THV2DAAAACAGx3/+rhO5p2UJcSihLEJRseBl6Nx64P6tu+rTeM36Sc9fyJJmvvtXI1dO1Z7i/canAwA0BqtzV6ritoKJUQkaGDHgUbHAQAAANymYbRt4thE+Yf6G5wG3o7GrY8K8Q/Rc9c+p/8b+X9qF9ROe4v3asyaMZr7zVzZ7Daj4wEAWpGGRcmm9pjKomQAAADwWbWVtcp5O0eSlJLONAm4dDRufdyN3W7U+xPf1/Auw1VdV62nPn9KU9+dqvyKfKOjAQBagW9PfKtdhbvkb/ZnUTIAAAD4tLwNeaotr1V4t3DFDog1Og58AI3bVqB9SHv986Z/6tnBzyrIL0gfH/lYw1cO1/rc9UZHAwD4uEX7FkmSRsWPUnRwtMFpAAAAAPfJXPbDomR80wyuQOO2lTCZTPrpZT/Vpgmb1Ce6j0qrS/WLLb/Qbz/6rcpryo2OBwDwQZW1lVqVtUoSi5IBAADAt506dEpH/31UMknJk5KNjgMfQeO2lUmKTNK6W9fpV/1+JZNMWpq5VCNXjdTXx782OhoAwMe8nfO2TtWeUrfwbhocN9joOAAAAIDbZK3IkiTFDY5TeOdwg9PAV9C4bYUC/AL02NWPaeXYleoc1lkHTh3QhLcn6E/b/ySrzWp0PACAj1iwb4EkaUqPKTKbeMsBAAAA32S32X+YJmEyi5LBdfgU1YoN6DhA7018TxOSJqjOXqc/7/izbnv7NuWW5RodDQDg5fYW79WOgh2ymCyanDLZ6DgAAACA2+R/ka9TB0/JP8xfCaMTjI4DH0LjtpWLCIjQy0Nf1t+G/U0RARHaWbBTI1eN1OJ9i2W3242OBwDwUg2Lko2MH6mYkBiD0wAAAADuk7E0Q5KUeGuiLMEWg9PAl9C4hSRpXPdx2jJxiwZ2HKhKa6Ue/uRh3bflPhVXFRsdDQDgZU5bT2tl1kpJ0rQe0wxOAwAAALhPbUWtctfXf3M5dXKqwWnga2jcwqFTWCctHb1Uj/d/XP5mf72b965GrByhjw5/ZHQ0AIAXeSfnHZXVlKlLWBdd1+k6o+MAAAAAbpPzTo6slVa1SWyjmKv4phlci8YtGvEz++n+vvfrnXHvKDkyWccrj2vKu1P0xLYndNp62uh4AAAvsHDfQknSnT3uZFEyAAAA+LQzFyUzmUwGp4Gv4dMUnOod3Vvvjn9Xd192tyRp3nfzNGbNGH134juDkwEAPFlGcYa+Ov6V/Ex+Sk9JNzoOAAAA4DYn804q//N8mcwmJU1MMjoOfBCNWzQr2BKspwc/rX+N+pfaB7dXRkmGxq4Zq7/v/rtsdpvR8QAAHmhRRv2iZDd2vVGxobEGpwEAAADcJ3N5/WjbTtd3UlhcmMFp4ItY6g7nNKzLML0/8X09/MnD2nxgs37/xe/1waEP9Jchf1FcWJzR8QAAHqLKWqUVWSskSVN7TjU4DeA5pk+frsLCwib7R44cqXvvvVdPPfWU9uzZ0+jYiBEj9Itf/MKxXVRUpLlz5+q7775TUFCQhgwZoilTpsjPz8/t+QEAQFN2m11Zy7Mk1U+TALgDjVucl3bB7fTWjW9pUcYiPfnZk/r30X/rxlU36rlrn9MtibcYHQ8A4AE25G1QaXWpOoV10pBOQ4yOA3iMZ599VjbbD99WOnjwoJ5++mkNHDjQsW/48OFKT/9hepGAgADHzzabTc8++6wiIyP19NNPq6SkRK+88or8/Pw0ZcqUlnkSAACgkaP/PqryI+UKiAhQt5u6GR0HPoqpEnDeTCaTpvaYqk3jN6lf+34qrS7VL9//pR788EGdqjlldDwAgMEW7v1+UbLUO+VnZhQg0CAiIkKRkZGOf3bs2KEOHTrosssuc5wTGBjY6JyQkBDHsf/85z86fPiwZsyYofj4eKWlpSk9PV2bNm2S1Wo14ikBANDqNSxK1n1cd1mCGBcJ96BxiwvWPbK71ty6Rg+mPSizyawVWSt048ob9VX+V0ZHAwAYZH/pfn2e/7nMJjOLkgFnYbVa9cknn2jo0KGNVp7+5JNP9POf/1wPPfSQFi1apOrqasexzMxMde3aVZGRkY59/fr10+nTp3Xo0KGWjA8AACTVnKxR7oZcSVJKOtMkwH34lQAuir/ZX49c9YiGdh6qGVtn6FD5IU14Z4Jm9Juh31zxG/mb/Y2OCABoQYv21S9KNrzLcOY/B87iyy+/VEVFhW644QbHvmuvvVbR0dGKiorSgQMHtHDhQh09elQPP/ywJKm0tLRR01aS2rRp4zjWnNraWtXW1jq2TSaTgoODHT+7U8P13X0fb0NdnKMuzlGXpqiJc9TFOXfWJfedXNVV1SkyOVIxaTFeU3teK855cl1o3OKSXB17td6b+J5mbpupFVkr9NLOl/TR4Y/01xv+qu6R3Y2OBwBoAdV11VqWuUySNKUH820CZ7N161b169dPUVFRjn0jRoxw/Ny1a1e1bdtWs2fPVn5+vmJjYy/6XqtXr9aKFSsc2wkJCZozZ47at29/0de8UJeS35dRF+eoi3PUpSlq4hx1cc4ddXl39buSpKvuu0pxcd43aIHXinOeWBcat7hk4QHheumGlzS8y3A99u/HtKtwl25afZOeuuYpTe0x1SN/YwEAcJ2NeRtVUl2i2NBYDesyzOg4gMcqLCzU7t27HSNpm5OUlCRJjsZtZGSk9u/f3+icsrIySWoyEvdM48eP19ixYx3bDe/JCgsL3T43rslkUmxsrPLz82W32916L29CXZyjLs5Rl6aoiXPUxTl31aU0u1SHth2Syc+k2BtjdezYMZdd2914rTjX0nWxWCzn/Yt0GrdwmVu736qrOlylX3/0a/376L/1P5/+j94/9L5euO4FtQtuZ3Q8AICbLNi7QFL9omQWM28tgOZs3bpVbdq00RVXXHHW8/Ly8iRJbdu2lSSlpKRo1apVKisrc0yRsHv3bgUHB6tz587NXsff31/+/s6nr2qpD2t2u50Phk5QF+eoi3PUpSlq4hx1cc7VdclcWr8oWecbOis4Jtgra85rxTlPrAuLk8Gl4sLitGT0Ev1uwO8UYA7Q5gObNXzlcH1w6AOjowEA3CCnLEfbjm2TSSbdmXqn0XEAj2Wz2fThhx9qyJAh8vPzc+zPz8/XihUrlJOTo4KCAn399dd69dVX1bNnT3Xr1k2S1LdvX3Xu3FmvvPKK8vLytGvXLi1ZskQ33XRTs41ZAADgerY6m7JWZEmSUiazKBncj2ExcDmzyaxfXv5LXdfpOs3YOkMZJRn6ycaf6O7L7tbjAx5XsCXY6IgAABdZvG+xJGlol6HqFNbJ4DSA5/rmm29UVFSkoUOHNtpvsVj0zTffaMOGDaqurla7du00YMAATZgwwXGO2WzWo48+qjfffFMzZ85UYGCghgwZovT09JZ+GgAAtGpHPjmiivwKBUYGqtuN3YyOg1aAxi3cple7Xlp/23o9+9WzmvftPP1jzz/06dFP9crQV9Q7urfR8QAAl6imrkZLM5dKkqb2mGpwGsCz9e3bV8uWLWuyPzo6WrNmzTrn49u3b6/HHnvMHdEAAMB5ylpWP9q2+/ju8gv0O8fZwKVjqgS4VbAlWLMHztbCUQsVExyjrNIsjV07Vn/7z99UZ6szOh4A4BJsOrBJJ6pOqENIBw3vOtzoOAAAAIDbVJdWK29jniSmSUDLoXGLFnFDlxv0/u3v6+b4m1Vrq9UzXz6j9A3pOlJ+xOhoAICLtGjfIklSekq6/M3MswkAAADflb0uW3XVdYrqGaXoPtFGx0ErQeMWLSYqKEpzR8zVC9e9oBBLiD479plGrByhtdlrjY4GALhAB04e0MdHPmZRMgAAALQKmcsyJUnJk5JlMpkMToPWgsYtWpTJZNKdPe7U5gmblRaTppM1J3X/B/drxtYZOllz0uh4AIDztCijfrTt9Z2uV9eIrganAQAAANynJLNEhTsLZbKYlDwx2eg4aEVo3MIQCW0StPqW1frtFb+V2WTWqv2rNGLlCH1+7HOjowEAzqHWVqulGd8vStaTRckAAADg2xpG23Yd1lXB0cEGp0FrQuMWhvE3++uhKx/S6ltWq1t4Nx0pP6Lb37ldz371rGrqaoyOBwBoxnsH3lPh6UK1D26vkd1GGh0HAAAAcBub1aaslVmSpJR0FiVDy7IYHeBC7dmzR+vWrVNubq5KSkr08MMPq3///pIkq9WqJUuWaOfOnSooKFBISIj69OmjKVOmKCoqyuDkaM5VHa7S5gmb9cRnT2hp5lK9susVfXz4Yy1NX6o2amN0PADAj7AoGQAAAFqLwx8e1umC0wqKClKXYV2MjoNWxutG3FZXVys+Pl4///nPmxyrqalRbm6uJk6cqDlz5uihhx7S0aNH9fzzzxuQFBciLCBMfx7yZ70x4g1FBkZqd9FuXfH6FZq/Z77sdrvR8QAA3zt06pA+PPyhJOnOHixKBgAAAN/WME1C0oQk+QX4GZwGrY3XNW7T0tJ0xx13OEbZnikkJES/+93vNGjQIMXFxSklJUX33HOPcnJyVFRUZEBaXKgxCWO0ZeIWXdfpOp22ntajnz6qn23+mYpO8/cHAJ5gccZi2WXXtXHXKj4i3ug4AAAAgNtUFVfpwHsHJEkpk5kmAS3P66ZKuFCVlZUymUwKCQlp9pza2lrV1tY6tk0mk4KDgx0/u1vDPVriXt4gLixOS8Ys0bIDy/Tolke15eAWDV85XH+6/k+6sduNRsczFK8V56hLU9TEOeri3PnWxWqzaknGEknStJ7TfL6OvF6aoiYAAKA1yV6bLVuNTe16t1O7Xu2MjoNWyKcbtzU1NVq4cKEGDx581sbt6tWrtWLFCsd2QkKC5syZo/bt27dETIfY2NgWvZ+n+3XHX2t4wnBNXTVV3xR8o7s23aX/vuq/9cLIFxTi3/zfZ2vAa8U56tIUNXGOujh3rrqs3bdWxyuPq31Ie9098G4F+AW0UDJj8XppipoAAIDWIHNp/TQJjLaFUXy2cWu1WvXiiy9Kku69996znjt+/HiNHTvWsd0wiqSwsFBWq9V9Ic+4X2xsrPLz85nP9XsNNWlvb6+1Y9fqua+e0xvfvKHXvn5Nm7M269Vhr+ry9pcbHbPF8Vpxjro0RU2coy7OnW9dXvnsFUnS7Um360TBiZaKZxheL021dE0sFkuL/yIdAABAkk7sOaGib4pk9jcraXyS0XHQSvlk47ahaVtUVKQnnnjirKNtJcnf31/+/s5XxW7JD2p2u50Phj9it9sV6BeoJ695UkO7DNVvPvyNssuyNXbNWD181cO6//L75WdufZOD81pxjro0RU2coy7Ona0uR8qP6INDH0iS7ky9s1XVj9dLU9QEAAD4uoZFybre2FVBUUEGp0Fr5XWLk51LQ9M2Pz9fv/vd7xQeHm50JLjI9Z2u13sT39PohNGy2q167qvnNGn9JB0+ddjoaADg85ZkLJHNbtPAjgPVPbK70XEAAAAAt7HV2rR/1X5JTJMAY3ld47aqqkp5eXnKy8uTJBUUFCgvL09FRUWyWq3685//rJycHM2YMUM2m02lpaUqLS1tkSkP4H5RQVF6Y/gb+vOQPyvUP1Rf5H+hEStHaNX+VUZHAwCfVWer0+KMxZKkaT2mGZwGAAAAcK+DHxxU1YkqBbcPVpehXYyOg1bM66ZKyM7O1qxZsxzb8+fPlyQNGTJEkyZN0tdffy1JeuSRRxo97sknn1SvXr1aLijcxmQyKT0lXdfEXqMZW2doe8F2zdg6Q+8ffF9/GPwHtQlsY3REAPApWw9v1bGKY2ob2Faj4kcZHQcAAABwq4ZpEpInJsts8boxj/AhXte47dWrl5YtW9bs8bMdg2/pFtFNq25ZpZd3vawXd7yoNdlr9GX+l3rphpc0KG6Q0fEAwGcs3LdQkjQpZZKCLMzvBQAAAN91uui0Dm45KElKnpxscBq0dvzaAF7NYrboN1f8RmtuXaP4iHgdrTiqyesn65kvnlFNXY3R8QDA6x2rOKYtB7dIkqakTjE4DQAAAOBe+1fvl91qV/t+7RWVGmV0HLRyNG7hE66IuUKbJ2zWlNQpssuuv+3+m8auHauskiyjowGAV2tYlGxA7AAlt2XEAQAAAHyX3W5X5tL6aRJSJrEoGYxH4xY+I9Q/VH+8/o+ad+M8tQ1sq+9OfKdRq0fp/777P9ntdqPjAYDXOXNRsqk9phqcBgAAAHCvE9+dUPHeYpkDzOp+W3ej4wA0buF7RsWP0vu3v68bOt+gqroqPb7tcf10009VUFlgdDQA8CofH/lYR8qPKDIwUqMTRhsdBwAAAHCrhkXJ4m+KV2BkoMFpABq38FEdQjroX6P+pd8P/L0C/QL1waEPNHzlcG0+sNnoaADgNRoWJZuYNFHBlmCD0wAAAADuU1dTp/2r9kuSUtKZJgGegcYtfJbZZNY9ve/Ru7e9q8uiLlNxVbHu3ny3HvnkEVXWVhodDwA82vHK445fdk3pwaJkAAAA8G0HtxxUdUm1QmJD1On6TkbHASTRuEUrkBqVqndue0e/vPyXMsmkhfsWauSqkdpVuMvoaADgsZZmLFWdvU5XdbhKPaJ6GB0HAAAAcKuGRcmSb0+W2Y92GTwDr0S0CoF+gfrdgN9pyegl6hjaUbknczVu7Ti9tPMl1dnqjI4HAB7FZrexKBkAAABajcqCSh3aekiSlDKJaRLgOWjcolW5ttO12jJxi25JvEVWu1XPf/28Jr4zUQdPHjQ6GgB4jE+PfKqDpw4qIiBCtyTeYnQcAAAAwK32r9ove51dMVfGKDIp0ug4gAONW7Q6kYGRem3Ya3rphpcU5h+mr45/pRtX3ajlmctlt9uNjgcAhluwb4EkaULSBBYlA3DBbHU2Hd12VN8s/kZHtx2Vrc5mdCQAAJplt9uVuax+moSUyYy2hWexGB0AMILJZNLtybdrQOwAPbD1AX15/Ev9+qNf6/1D7+vZwc+qbVBboyMCgCEKKwu1KW+TJBYlA3Dhcjfk6rMnPlPFsQrHvtCOoRo4e6ASRicYmAwAAOeK/lOkkowS+QX5qfut3Y2OAzTCiFu0al3Cu2jF2BX6n6v+RxaTRW/nvK0Rq0bo0yOfGh0NuGh1tjptO7pNi79ZrG1HtzGPMy7IssxlstqtSotJU692vYyOA8CL5G7I1ZZfbGnUtJWkivwKbfnFFuVuyDUoGQAAzWsYbRt/c7wCIgIMTgM0xohbtHp+Zj89kPaAru98vWZsnaGcshylb0jXf/X5L/3P1f+jQL9AoyMC521D7gY98dkTOlZxzLGvY2hHzR44W6MTRhuYDN7AZrdpUcYiSdK0HtMMTgPAm9jqbPrsic8kZ7NOfb/v00c/VWBkoCzBFvkF+Mkv8Pt/zvjZHGBmJW8ATtnqbDr+5XEV1RSpJqBGHfp34L8XuGTWKqv2r9kvSUqdnGpwGqApGrfA9/q176dN4zdp1ueztGDfAr3+zev6+MjHenXoq0qN4j/g8HwbcjfoF1t+IfuPPjXnV+TrF1t+oTdGvEHzFme17eg25Z3MU5h/mG5NvNXoOAC8SP4X+U1G2v5Y1YkqrZ+0/pzXMllMzhu7Ad//E1Tf4LUEWmQOMDv2OY6f0QRuOMcSaJE50Nz4Wmdc3xxgliXI0vi6gX4ymUyuKhGaQTMO54NpWOAuBzYfUE1ZjULjQtVxcEej4wBN0LgFzhDiH6I5183R8K7D9dDHD2lv8V7dvOZmPd7/cd3d626ZTbyJhGeqs9Xpic+eaNK0lSS77DLJpCc/e1I3dbtJfmY/AxLCGyzYW78o2fik8QrxDzE4DQBvUllQeV7nBccEyy/AT3U1daqr/uGfM//3ZbfaZbVaZa20uint+XM0hptpCDttLv+oIezYDvSTJdCi4zHHVX66/IdrN5wbaHZ6Db9AP5n9zT7ZRKYZh/PRMA3Lj9/mNkzDMuKNEbxecNEci5JNSuGXRvBING4BJ0Z2G6n3J76vhz5+SB8c+kBPfPaEPjj0gf485M/qENLB6HhAEx8f/rjR9Ag/ZpddRyuOaty6ceoY2lFBliAF+QUp0C+w/ufvt8/8M9Av8Id9liAF+wU73e9v9m/BZwp3Kawo1Lt570pimgQAFy4k5vx+2TPs1WGKGxTXaJ/dbpfdam/czK350Z/f/2yrsclaZZWtxub0+JnbzZ5T7eS8M7bPZKuxyVZjU61qXVari9VsQ7iZpnFDk7nRvjPPcTLauNmRzmfsM1lMLmki04zD+TjnNCwm6bMnP1O3m7rRdMMFqzhWoSMfHZEkJU9KNjgN4ByNW6AZMSExmn/TfP1z7z/1+89/rw8Pf6jhK4brj9f9UTcn3Gx0PLRSNXU1yinL0b7ifdpXsk8ZJRnKKM7QgVMHzuvxOwt3amfhTpdm8jP5NW3onuefjsZxM/sbmsU/3k+z2HXqbHX68viXWrVtlWpttbq83eXqHd3b6FgAvEzsgFiFdgxVRX6F8waLqX4kZeyA2KaHTCaZ/E0y+5vlH2rsf9/tdvsPjdyaOtVVNW4I26ptzTaVz9UQbvjZ3+SvipMVjnOs1Wc0os9oQNtqbI2yOWssG8FkNp1/Q7iZKSrM/mZ9O+/bs86J/Mn/+0RVJVUymU0/nPf9n3a7/azbjv06z/POdT2dx3mXeC2n55uksNAwlZeXy2770XVd9dzOdj131OkCr1dZWHn2aVjsUsXRCuV/kd/kl0LAuWStzJLdZlfsgFi1SWhjdBzAKRq3wFmYTCb97LKfaXDHwfrV1l/p2xPf6t4t9+rO1Ds1a+AshfqHGh0RPspmt+ngqYPKKM7Q3uK99Q3akgxll2bLar/4r47ef/n96hzeWVXWKlXVVTX6s7qu+od9PzpeXVfdZF+DOnudKmorVFF79rkNXamhWXxmw7dJE7jh5x81hWPaxqjmdI0Czc6bxs39aTH73v8ynS1md+DUAW3I3cB8yAAuiNnPrIGzB9aPoDSpcSPn+8GZA2cN9PgRcSaTydF8dNf1O3bsqGPHjv3QpGqG3WZv1BBuNNq4maaxrfqHRnBzDeGGZnSjc37UoP7x9e1We+NcVfXXcafq0mp9+sinbr0HfMeHv/5QsVfHKjI5Um1T2ioyOVJt4tvI7O/Z/82Bcex2+w/TJExOMTgN0Dzf+xQKuEFy22S9Pe5tvbD9Bf3tP3/T4ozF+uzYZ3p56Mu6IuYKo+PBi9ntdh2vPK6MkjMatMUZyizN1GnraaePCfcPV2pUqnq07aEeUT2U2jZVSW2SNHrtaOVX5Dud59YkkzqGdtSjVz/qkjlu7XZ7sw1dRxP4jO3Tdaed7m/ufKfNZIObxRaTRYEW56OKfzzlRLAl+OzNZCdTTjQ87sz97mwWN7eY3cmakyxmB+CiJIxO0Ig3Rjifs3QWc5ZeKJPZJEuQRZYg4z+y2epsTZvAlzBFRfG+Yh395Og579uudzuFdqwfKOGYnqFhloaGzR/tN+k8zzvX9dTMfmfb5zrnQq71o/2hoaGqrKx0rFngkud2llq5tU7nc96PrnfywEnt/edenUvFkQplH8lutM/sb1ZEQoTaJrd1NHPbprRVm8Q2bvvlDLxHwfYClWWXyRJsUcJY/v8Ez2X8uwDASwT4Beh/+/+vhnYZqge2PqC8k3m6bd1t+s0Vv9GMfjN8cjQeXKu0ulQZxRnaV7JP+4r3OUbRllaXOj0/0C9QyZHJSm2bqp5RPZUalarUtqmKC41zOrfc7IGz9Ystv5BJpkbNuIY35bMGznLZwmQmk0nBlmAFW4KlQJdc8pxsdpujwfvjhm7DKOEz9zc0i88cNVxdVy1TgEnFJ4vPu2ncwGq3ylprbfFmcUNjt7mpKBqayQ3N4vOZisJituixfz/GYnYAXC5hdIK63dRNx788roCaANUE1KhD/w4eP9IWZ2f2M8scbJYl2DXvd49uO3pejdtrnrymVX/9/UJGaPsqW51NBzcfPOs0LMHtg3Xtc9eqLLtMJZklKs0qVUlmiayVVpVmlqo0s1S563N/eIjZpIj4CEWmRP7Q1E2JVGT3SJe9xuH5GkbbJoxJUEBYgMFpgObxXyXgAg3sOFBbJm7R//77f7Ume41e2P6Cth7aqr8O/aviI+KNjgcPcNp6WpklmfVz0BZnOJq0+ZX5Ts/3M/kpoU3CDw3atvUN2viI+Atqmo1OGK03RrzR5KvvHUM7atbAWV4/etJsMv/QLL5IF/oB6Mxm8bmmlKiuq27SLD7fJvOPp6VoYLVbVV5brvLa8ot+zhejYTG7L/K/0KC4QS16bwDez+xnVtyguFbfcELzLmVOZLQu5zMNy+BnBiv+pvhGj7Pb7Ko4VqGSzJJGzdzSrFLVnKxRWU6ZynLKdGDjgUbXC+8arrbJ34/OTf6+oZscSWPPx1hPW5W9rn6ENtMkwNPRuAUuQpvANnp12Ksa0XWEHvv0MW0v2K6Rq0bq9wN/r8kpk12y0i48X62tVjmlOY0WCdtXsk8HTh5wOpJRkjqHdVZq21T1iPphmoPubboryBLkkkyjE0brpm436cvjX6omoEYBNQHq36E/oyYvkiuaxRfKWbP4XKODm/uzuSZzcVWxiqqKzpmloLKgBZ4xAKC18ZU5kdEyLmYaFpPZpLBOYQrrFKYuQ7s49tvtdlUer1RpZqlK9pfU/5lVopKMElWXVOvUgVM6deCUDm452Oh6oXGhjaZbaGjuBka20FfP4FJ5G/NUe6pWYV3C1HFgR6PjAGdF4xa4BOOTxuvqDlfrwQ8f1Of5n+u3H/9WWw5t0Zxr5ygqKMroeHARm92mQ6cOKaPkh9Gz+4r3KbssW7W2WqePiQ6Orm/Qtu3hmI82pW2KwgPC3Z7Xz+ynQXGDGOnkpVqiWbzt6DZNWj/pnOfFhMS4LQMAoHVjTmRcCFdNw2IymRQaG6rQ2FB1ur5To2OnT5yub+Rmlqgk64em7umC06o4WqGKoxU6/OHhRo8J6RDSaEG0hj+D27XcL/1x4TKXfr8o2aQUmcwMuoJno3ELXKLO4Z21bMwyvbb7Nf3x6z9qQ+4G7Ti+Qy8OeVHXd77e6Hi4AHa7XQWnCxwjZzOKMxzz0FZaK50+Jsw/zDGCtmGKgx5RPRQdHN3C6YHzNyB2gDqGdjznYnYDYgcYkA4A0FowJzIuhLunYQluF6zggcFNRmBWlVSpdH+po6lbur/+z4qjFao8XqnK45U6+mnjOZuDooIcc+ieOZducEww3840WPmRch359IgkKXlSssFpgHOjcQu4gJ/ZT7/q9ysN6TxE0z+YruyybN357p26t/e9euzqx1z2NXi4Tll1WaMRtA0/l1SXOD0/0C9QSZFJjUbR9ozq2exCYYAn8zP7tehidgAANIc5keHpgtoGKfbqWMVe3XjO5ZpTNT80dLN+mEv31MFTqiquUv7n+cr/vPEaFwFtAho1cxv+DI0L5TNFC8lcninZpY4DOyqia4TRcYBzonELuFCf6D7aNGGTfv/F7/XPPf/Um9++qX8f/bdeHvqyekb1NDpeq3Taelr7S/drb/HeRvPQnrl415nMJrMSIhIc0xs0jKCNj4iXxcx/MuE7fH0xOwAAAHcKCA9QTFqMYtIaTy1VW1mrsuyy+kbuGU3dUwdOqaasRse/Pq7jXx9v9Bj/UP9GzdzIpPppF8K7hPNVfhey2+3KWp4lSUpJZ1EyeAe6EICLBVuC9YfBf9CwLsP00McPaW/xXo1ePVqP9X9M9/a+V2YTX/1yh9q6WmWVZGnPiT2NRtDmncxrdqGwuNC4+kXCzpiHtntk9xZdiAowEovZAS1j2bJlWrFiRaN9cXFx+stf/iJJqqmp0fz587Vt2zbV1taqb9++uvfeexUZGek4v6ioSHPnztV3332noKAgDRkyRFOmTJGfH/++AoAn8Q/xV3SfaEX3aTx1mrXKqpO5Jx0jcxvm0i3LKVNtRa0KdxaqcGdho8f4Bfk5mrhnzqEb0S1CZgufKy9U/pf5Opl3Uv6h/koYwzza8A40bgE3GdF1hN6f+P/bu/foKOt73+OfmcyEXCCZJFySEMiFMAQPCEEKQrGAUvFETrfdtaCgrW0VK26l3qoU9Ri5WFFRKnYpi7iQpWg5Hu3WwkZESzdoKp7NLggIIZBwDyFmJoFcyGXm/BEzMM4TSCiZPJN5v9ZiyTz5PZnvfP3l4ZvvPPP7faKH/vMhbTq8Sfl/z9enRz7VixNfVEosO1deKo/Xo2Nnjmlv5V6/dWiLq4rV0NxgeE5CjwTlJOZoaOLQlnVoE1vWoo2L5KMxAJvZAcExYMAAPfHEE77HVuu5X7jfeOMNbd++XQ8++KBiYmJUUFCgF154QQsWLJAkeTwePfPMM3I4HFq4cKFcLpeWL1+uiIgIzZw5M+ivBQDQcbYomxKHJipxqP8m1p5Gj6pKq86tofttU7fqYJWa65v1za5v9M2ub/zOsUZa5Rjk8DVzE5wJsn3fpubYZlntNHTbUrS2ZVOyzP+VKXuMvYujAdqHxi3QiXpH99aq61fpzb1v6qnCp7Tl2BZN+b9TtOSaJbox88auDs/UvF6vKuoqWpqz3949u9e1V0WuItU01hieE2uPlTPBqaEJQ33N2ZyElo3CWDMKANCVrFar3x20rWpra/Xpp59q7ty5GjZsmCRpzpw5euCBB1RUVCSn06kdO3bo6NGjeuKJJ+RwOJSRkaEZM2borbfe0vTp02WzUdIDQKiy2q0tm5cNTvC7C9TT5NHpw6d9m6G1NnXd+91qqmtS5deVqvy60jd+kzbJYrMoPiM+YA3d+EHxskWF978VjbWNKvmwRJLknM4yCQgd4f2TCwSBxWLR7UNv17iUcbrvr/dpZ8VOzd40W9Od07Vg3AL1jOzZ1SF2ueqGat/6s/tc+3zr0VbWVxqOt1vtynZkKychp2Wpg8Qc/SDnB7LX2n0bKwEAYCZlZWW6++67Zbfb5XQ6NXPmTPXu3VsHDx5Uc3Ozhg8f7hvbv39/9e7d29e4LSoq0sCBA/0avyNHjtTKlSt15MgRZWYaf9yzsbFRjY2NvscWi0XR0dG+v3em1u/PG6f+yIsx8mKMvAQKp5xE2CNa7qod5FDG1Azfca/HqzPHzviauec3dH0bphW7VapS3zkWq0W90nu1NIid3zZ0nQlyZDu69Z2n58+X0vWlaqxpVFxGnFLGpoTFHDISTj9DHWHmvNC4BYIk25Gtf//Rv2vp9qVa/o/lWlu0Vl+c+ELLJi/T9/p9r6vDC4r6pnoVu4t9Sxzsde3V3sq9Ol5z3HC8RRZlxGUoJ/HcJmE5CTnKiM+Q3XquwLBYLEpxpOhEHR/zBgCYz+DBgzVnzhylpqbK5XLp3Xff1ZNPPqkXXnhBbrdbNptNsbGxfufEx8fL7XZLktxud8DduvHx8b6vteX999/3W1s3MzNTzz77rPr06XNZXld7JCcnX3xQGCIvxsiLMfISKOxz0l/SGP9DXq9Xp4+d1qk9p1r+fH1KFXsqVL67XPWuelWXVKu6pFqHNh7yO8+R4VCfK/qo9xW91eeKPuoztI96D+2tqPio4L2eTpacnKyP3v9IknTVr65SampqF0fU9cL+Z6gNZswLjVsgiCIjIvXY9x7T5LTJun/z/Tp0+pD+9cN/1dzcuZqbO9evGRnKmjxNKq0u1d7Kb5c5+LZRW1JdIo/XY3hOSmyKb5OwIQlDNDRxqLId2WwUBgAIebm5ub6/p6en+xq5hYWFioyM7LTn/fGPf6xp06b5HrfeRXLq1Ck1NTV12vO2PldycrLKysp4U/U85MUYeTFGXgKRE2Oteam11SpmeIzSh6crXemSWhq6dRV1vjV0WzdFcxe5W46XuuUudWv/+v1+3zM2JdZvDd3WpReiEkKnodual6JtRSrdXCpZpJSpLfs6hCt+howFOy82m63db6TTuAW6wNiUsfr4Jx9r/mfz9V7xe3px+4vafHSzXp70sjLjQ2d3S6/Xq+M1x1uWNvj2Dtp9rn0qdhfrbPNZw3McPRznNgn7tkHrTHAqvkd8kKMHAKBrxMbGKjU1VWVlZbryyivV1NSkmpoav7tuq6qqfHfZOhwOFRcX+32Pqqoq39faYrfbZbcbvykcrF/WvF4vvxgaIC/GyIsx8hKInBhrKy/RvaMV3TtaKeP9N8mur6z3NXHP/29tWa1qTtSo5kSNjv3nMf/v1Sf6XEN38LllF6KSokz5MXNJ2venfZKk/hP6K7Z/LHNH/Ay1xYx5oXELdJG4yDi9PPllTRk4RfO2ztN/l/+3rn/veuWPy9etQ2413T9639R941t7tnWzsH2ufTrTeMZwfIwtxtecHZI4xLcebZ/oPqZ7bQAABFN9fb3Kysp0zTXXKCsrSxEREfrqq6909dVXS5KOHz+uiooKOZ0tm6c4nU699957qqqq8i2RsHPnTkVHRystLa3LXgcAILRFJUYpZWyKUsb6N3QbqhtaGrn73b41dF1FLp05ekZ1p+pUd6pOJz73v2u1R0I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+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file