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          "text": [
            "[INFO] Train dataset size: (125973, 42)\n",
            "[INFO] Test dataset size:  (22544, 42)\n",
            "\n",
            "--- Training Data Sample ---\n"
          ]
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              "   duration protocol_type   service flag  src_bytes  dst_bytes  land  \\\n",
              "0         0           tcp  ftp_data   SF        491          0     0   \n",
              "1         0           udp     other   SF        146          0     0   \n",
              "2         0           tcp   private   S0          0          0     0   \n",
              "3         0           tcp      http   SF        232       8153     0   \n",
              "4         0           tcp      http   SF        199        420     0   \n",
              "\n",
              "   wrong_fragment  urgent  hot  ...  dst_host_srv_count  \\\n",
              "0               0       0    0  ...                  25   \n",
              "1               0       0    0  ...                   1   \n",
              "2               0       0    0  ...                  26   \n",
              "3               0       0    0  ...                 255   \n",
              "4               0       0    0  ...                 255   \n",
              "\n",
              "   dst_host_same_srv_rate  dst_host_diff_srv_rate  \\\n",
              "0                    0.17                    0.03   \n",
              "1                    0.00                    0.60   \n",
              "2                    0.10                    0.05   \n",
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              "4                    1.00                    0.00   \n",
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              "   dst_host_same_src_port_rate  dst_host_srv_diff_host_rate  \\\n",
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              "2                         0.00                         0.00   \n",
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              "   dst_host_serror_rate  dst_host_srv_serror_rate  dst_host_rerror_rate  \\\n",
              "0                  0.00                      0.00                  0.05   \n",
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              "\n",
              "   dst_host_srv_rerror_rate   labels  \n",
              "0                      0.00   normal  \n",
              "1                      0.00   normal  \n",
              "2                      0.00  neptune  \n",
              "3                      0.01   normal  \n",
              "4                      0.00   normal  \n",
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            "\n",
            "--- Testing Data Sample ---\n"
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              "   duration protocol_type   service  flag  src_bytes  dst_bytes  land  \\\n",
              "0         5           tcp      smtp    SF       2429        475     0   \n",
              "1         0           udp  domain_u    SF         45        134     0   \n",
              "2         0           udp  domain_u    SF         45         80     0   \n",
              "3      1979           udp     other    SF        145        105     0   \n",
              "4     14462           tcp     other  RSTR          1          0     0   \n",
              "\n",
              "   wrong_fragment  urgent  hot  ...  dst_host_srv_count  \\\n",
              "0               0       0    0  ...                  57   \n",
              "1               0       0    0  ...                 155   \n",
              "2               0       0    0  ...                 255   \n",
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              "4               0       0    0  ...                   2   \n",
              "\n",
              "   dst_host_same_srv_rate  dst_host_diff_srv_rate  \\\n",
              "0                    0.11                    0.02   \n",
              "1                    0.80                    0.02   \n",
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              "0                         0.01                         0.04   \n",
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              "0                   0.0                       0.0                  0.87   \n",
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              "2                   0.0                       0.0                  0.00   \n",
              "3                   0.0                       0.0                  0.00   \n",
              "4                   0.0                       0.0                  1.00   \n",
              "\n",
              "   dst_host_srv_rerror_rate     labels  \n",
              "0                       0.0     normal  \n",
              "1                       0.0     normal  \n",
              "2                       0.0     normal  \n",
              "3                       0.0     normal  \n",
              "4                       1.0  portsweep  \n",
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              "[5 rows x 42 columns]"
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              "        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>duration</th>\n",
              "      <th>protocol_type</th>\n",
              "      <th>service</th>\n",
              "      <th>flag</th>\n",
              "      <th>src_bytes</th>\n",
              "      <th>dst_bytes</th>\n",
              "      <th>land</th>\n",
              "      <th>wrong_fragment</th>\n",
              "      <th>urgent</th>\n",
              "      <th>hot</th>\n",
              "      <th>...</th>\n",
              "      <th>dst_host_srv_count</th>\n",
              "      <th>dst_host_same_srv_rate</th>\n",
              "      <th>dst_host_diff_srv_rate</th>\n",
              "      <th>dst_host_same_src_port_rate</th>\n",
              "      <th>dst_host_srv_diff_host_rate</th>\n",
              "      <th>dst_host_serror_rate</th>\n",
              "      <th>dst_host_srv_serror_rate</th>\n",
              "      <th>dst_host_rerror_rate</th>\n",
              "      <th>dst_host_srv_rerror_rate</th>\n",
              "      <th>labels</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5</td>\n",
              "      <td>tcp</td>\n",
              "      <td>smtp</td>\n",
              "      <td>SF</td>\n",
              "      <td>2429</td>\n",
              "      <td>475</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>57</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.87</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>udp</td>\n",
              "      <td>domain_u</td>\n",
              "      <td>SF</td>\n",
              "      <td>45</td>\n",
              "      <td>134</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>155</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>udp</td>\n",
              "      <td>domain_u</td>\n",
              "      <td>SF</td>\n",
              "      <td>45</td>\n",
              "      <td>80</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>255</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1979</td>\n",
              "      <td>udp</td>\n",
              "      <td>other</td>\n",
              "      <td>SF</td>\n",
              "      <td>145</td>\n",
              "      <td>105</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.84</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>14462</td>\n",
              "      <td>tcp</td>\n",
              "      <td>other</td>\n",
              "      <td>RSTR</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.68</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>portsweep</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 42 columns</p>\n",
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      "source": [
        "# This section loads all the Python libraries required for the project,\n",
        "# including tools for data handling, visualisation, and machine learning.\n",
        "# The training and testing datasets (kdd_train.csv and kdd_test.csv)\n",
        "# are then imported into DataFrames so they can be used in the analysis.\n",
        "\n",
        "\n",
        "# -----------------------------------------------\n",
        "# SECTION 1: Library Setup & Data Loading\n",
        "# -----------------------------------------------\n",
        "\n",
        "# Data handling and analysis tools\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# Visualisation utilities\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Timing utility (useful for profiling)\n",
        "import time\n",
        "\n",
        "# Machine learning toolkit\n",
        "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n",
        "from sklearn.metrics import (\n",
        "    accuracy_score, precision_score, recall_score, f1_score,\n",
        "    confusion_matrix, classification_report, roc_curve, roc_auc_score, auc\n",
        ")\n",
        "from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier\n",
        "from sklearn.preprocessing import label_binarize\n",
        "\n",
        "# Apply a custom seaborn theme for plots\n",
        "sns.set_theme(style=\"ticks\", palette=\"deep\", font_scale=1.05)\n",
        "\n",
        "# -----------------------------------------------\n",
        "# Load Dataset Files\n",
        "# -----------------------------------------------\n",
        "\n",
        "train_file = \"kdd_train.csv\"\n",
        "test_file = \"kdd_test.csv\"\n",
        "\n",
        "train_data = pd.read_csv(train_file)\n",
        "test_data = pd.read_csv(test_file)\n",
        "\n",
        "print(f\"[INFO] Train dataset size: {train_data.shape}\")\n",
        "print(f\"[INFO] Test dataset size:  {test_data.shape}\")\n",
        "\n",
        "print(\"\\n--- Training Data Sample ---\")\n",
        "display(train_data.head())\n",
        "\n",
        "print(\"\\n--- Testing Data Sample ---\")\n",
        "display(test_data.head())\n",
        "\n",
        "# Summary:\n",
        "# This block imports essential machine learning, data manipulation,\n",
        "# and plotting libraries. The training and testing CSV files are\n",
        "# loaded into DataFrames so that they can be processed in later steps.\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# This section examines the structure of both datasets by showing the number\n",
        "# of rows, columns, and data types. It also checks for missing values and\n",
        "# plots the distribution of attack categories in the training and testing data.\n",
        "# Understanding these characteristics helps identify imbalance and determine\n",
        "# what preprocessing steps are required.\n",
        "\n",
        "# ===========================================================\n",
        "# SECTION 2: Data Overview, Integrity Check & Label Insights\n",
        "# ===========================================================\n",
        "\n",
        "# --- 1. Basic Structure of Training and Testing Datasets ---\n",
        "\n",
        "print(\"\\n>>> TRAIN SET OVERVIEW <<<\")\n",
        "train_details = train_data.info()\n",
        "print(train_details)\n",
        "\n",
        "print(\"\\n>>> TEST SET OVERVIEW <<<\")\n",
        "test_details = test_data.info()\n",
        "print(test_details)\n",
        "\n",
        "\n",
        "# --- 2. Checking for Missing Values ---\n",
        "\n",
        "missing_train_entries = train_data.isna().sum().sum()\n",
        "missing_test_entries  = test_data.isna().sum().sum()\n",
        "\n",
        "print(f\"\\nTraining set missing values : {missing_train_entries}\")\n",
        "print(f\"Testing set missing values  : {missing_test_entries}\")\n",
        "\n",
        "\n",
        "# --- 3. Label Distribution Observations ---\n",
        "\n",
        "train_label_summary = train_data[\"labels\"].value_counts()\n",
        "test_label_summary  = test_data[\"labels\"].value_counts()\n",
        "\n",
        "print(\"\\n--- TRAIN LABEL DISTRIBUTION ---\")\n",
        "print(train_label_summary)\n",
        "\n",
        "plt.figure(figsize=(12, 5))\n",
        "sns.barplot(\n",
        "    x=train_label_summary.index,\n",
        "    y=train_label_summary.values,\n",
        "    hue=train_label_summary.index,\n",
        "    palette=\"GnBu\",\n",
        "    legend=False\n",
        ")\n",
        "plt.title(\"Training Dataset: Distribution of Attack Categories\")\n",
        "plt.ylabel(\"Frequency\")\n",
        "plt.xlabel(\"Label\")\n",
        "plt.xticks(rotation=45)\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "print(\"\\n--- TEST LABEL DISTRIBUTION ---\")\n",
        "print(test_label_summary)\n",
        "\n",
        "plt.figure(figsize=(12, 5))\n",
        "sns.barplot(\n",
        "    x=test_label_summary.index,\n",
        "    y=test_label_summary.values,\n",
        "    hue=test_label_summary.index,\n",
        "    palette=\"YlOrBr\",\n",
        "    legend=False\n",
        ")\n",
        "plt.title(\"Testing Dataset: Distribution of Attack Categories\")\n",
        "plt.ylabel(\"Frequency\")\n",
        "plt.xlabel(\"Label\")\n",
        "plt.xticks(rotation=45)\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "\n",
        "# --- 4. Identifying Potential Categorical Features ---\n",
        "\n",
        "categorical_set = [\"protocol_type\", \"service\", \"flag\"]\n",
        "print(\"\\nPossible categorical fields:\", categorical_set)\n",
        "\n",
        "for col_name in categorical_set:\n",
        "    if col_name not in train_data.columns:\n",
        "        print(f\" Notice: '{col_name}' was not found in the training dataset.\")\n",
        "\n",
        "\n",
        "\"\"\"\n",
        "Explanation:\n",
        "This section reviews the training and testing datasets by displaying\n",
        "structural metadata (column types, counts), detecting missing entries, and\n",
        "examining the distribution of the attack labels. It also identifies\n",
        "categorical attributes, which must be encoded during preprocessing.\n",
        "\"\"\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "OcbiB_im-2Yy",
        "outputId": "b3b27fda-9147-4c49-c72c-1f50a693bb6b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            ">>> TRAIN SET OVERVIEW <<<\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 125973 entries, 0 to 125972\n",
            "Data columns (total 42 columns):\n",
            " #   Column                       Non-Null Count   Dtype  \n",
            "---  ------                       --------------   -----  \n",
            " 0   duration                     125973 non-null  int64  \n",
            " 1   protocol_type                125973 non-null  int64  \n",
            " 2   service                      125973 non-null  int64  \n",
            " 3   flag                         125973 non-null  int64  \n",
            " 4   src_bytes                    125973 non-null  int64  \n",
            " 5   dst_bytes                    125973 non-null  int64  \n",
            " 6   land                         125973 non-null  int64  \n",
            " 7   wrong_fragment               125973 non-null  int64  \n",
            " 8   urgent                       125973 non-null  int64  \n",
            " 9   hot                          125973 non-null  int64  \n",
            " 10  num_failed_logins            125973 non-null  int64  \n",
            " 11  logged_in                    125973 non-null  int64  \n",
            " 12  num_compromised              125973 non-null  int64  \n",
            " 13  root_shell                   125973 non-null  int64  \n",
            " 14  su_attempted                 125973 non-null  int64  \n",
            " 15  num_root                     125973 non-null  int64  \n",
            " 16  num_file_creations           125973 non-null  int64  \n",
            " 17  num_shells                   125973 non-null  int64  \n",
            " 18  num_access_files             125973 non-null  int64  \n",
            " 19  num_outbound_cmds            125973 non-null  int64  \n",
            " 20  is_host_login                125973 non-null  int64  \n",
            " 21  is_guest_login               125973 non-null  int64  \n",
            " 22  count                        125973 non-null  int64  \n",
            " 23  srv_count                    125973 non-null  int64  \n",
            " 24  serror_rate                  125973 non-null  float64\n",
            " 25  srv_serror_rate              125973 non-null  float64\n",
            " 26  rerror_rate                  125973 non-null  float64\n",
            " 27  srv_rerror_rate              125973 non-null  float64\n",
            " 28  same_srv_rate                125973 non-null  float64\n",
            " 29  diff_srv_rate                125973 non-null  float64\n",
            " 30  srv_diff_host_rate           125973 non-null  float64\n",
            " 31  dst_host_count               125973 non-null  int64  \n",
            " 32  dst_host_srv_count           125973 non-null  int64  \n",
            " 33  dst_host_same_srv_rate       125973 non-null  float64\n",
            " 34  dst_host_diff_srv_rate       125973 non-null  float64\n",
            " 35  dst_host_same_src_port_rate  125973 non-null  float64\n",
            " 36  dst_host_srv_diff_host_rate  125973 non-null  float64\n",
            " 37  dst_host_serror_rate         125973 non-null  float64\n",
            " 38  dst_host_srv_serror_rate     125973 non-null  float64\n",
            " 39  dst_host_rerror_rate         125973 non-null  float64\n",
            " 40  dst_host_srv_rerror_rate     125973 non-null  float64\n",
            " 41  labels                       125973 non-null  object \n",
            "dtypes: float64(15), int64(26), object(1)\n",
            "memory usage: 40.4+ MB\n",
            "None\n",
            "\n",
            ">>> TEST SET OVERVIEW <<<\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 22544 entries, 0 to 22543\n",
            "Data columns (total 42 columns):\n",
            " #   Column                       Non-Null Count  Dtype  \n",
            "---  ------                       --------------  -----  \n",
            " 0   duration                     22544 non-null  int64  \n",
            " 1   protocol_type                22544 non-null  int64  \n",
            " 2   service                      22544 non-null  int64  \n",
            " 3   flag                         22544 non-null  int64  \n",
            " 4   src_bytes                    22544 non-null  int64  \n",
            " 5   dst_bytes                    22544 non-null  int64  \n",
            " 6   land                         22544 non-null  int64  \n",
            " 7   wrong_fragment               22544 non-null  int64  \n",
            " 8   urgent                       22544 non-null  int64  \n",
            " 9   hot                          22544 non-null  int64  \n",
            " 10  num_failed_logins            22544 non-null  int64  \n",
            " 11  logged_in                    22544 non-null  int64  \n",
            " 12  num_compromised              22544 non-null  int64  \n",
            " 13  root_shell                   22544 non-null  int64  \n",
            " 14  su_attempted                 22544 non-null  int64  \n",
            " 15  num_root                     22544 non-null  int64  \n",
            " 16  num_file_creations           22544 non-null  int64  \n",
            " 17  num_shells                   22544 non-null  int64  \n",
            " 18  num_access_files             22544 non-null  int64  \n",
            " 19  num_outbound_cmds            22544 non-null  int64  \n",
            " 20  is_host_login                22544 non-null  int64  \n",
            " 21  is_guest_login               22544 non-null  int64  \n",
            " 22  count                        22544 non-null  int64  \n",
            " 23  srv_count                    22544 non-null  int64  \n",
            " 24  serror_rate                  22544 non-null  float64\n",
            " 25  srv_serror_rate              22544 non-null  float64\n",
            " 26  rerror_rate                  22544 non-null  float64\n",
            " 27  srv_rerror_rate              22544 non-null  float64\n",
            " 28  same_srv_rate                22544 non-null  float64\n",
            " 29  diff_srv_rate                22544 non-null  float64\n",
            " 30  srv_diff_host_rate           22544 non-null  float64\n",
            " 31  dst_host_count               22544 non-null  int64  \n",
            " 32  dst_host_srv_count           22544 non-null  int64  \n",
            " 33  dst_host_same_srv_rate       22544 non-null  float64\n",
            " 34  dst_host_diff_srv_rate       22544 non-null  float64\n",
            " 35  dst_host_same_src_port_rate  22544 non-null  float64\n",
            " 36  dst_host_srv_diff_host_rate  22544 non-null  float64\n",
            " 37  dst_host_serror_rate         22544 non-null  float64\n",
            " 38  dst_host_srv_serror_rate     22544 non-null  float64\n",
            " 39  dst_host_rerror_rate         22544 non-null  float64\n",
            " 40  dst_host_srv_rerror_rate     22544 non-null  float64\n",
            " 41  labels                       22544 non-null  object \n",
            "dtypes: float64(15), int64(26), object(1)\n",
            "memory usage: 7.2+ MB\n",
            "None\n",
            "\n",
            "Training set missing values : 0\n",
            "Testing set missing values  : 0\n",
            "\n",
            "--- TRAIN LABEL DISTRIBUTION ---\n",
            "labels\n",
            "normal             67343\n",
            "neptune            41214\n",
            "satan               3633\n",
            "ipsweep             3599\n",
            "portsweep           2931\n",
            "smurf               2646\n",
            "nmap                1493\n",
            "back                 956\n",
            "teardrop             892\n",
            "warezclient          890\n",
            "pod                  201\n",
            "guess_passwd          53\n",
            "buffer_overflow       30\n",
            "warezmaster           20\n",
            "land                  18\n",
            "imap                  11\n",
            "rootkit               10\n",
            "loadmodule             9\n",
            "ftp_write              8\n",
            "multihop               7\n",
            "phf                    4\n",
            "perl                   3\n",
            "spy                    2\n",
            "Name: count, dtype: int64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "--- TEST LABEL DISTRIBUTION ---\n",
            "labels\n",
            "normal             11245\n",
            "neptune             6654\n",
            "satan                698\n",
            "smurf                540\n",
            "ipsweep              479\n",
            "guess_passwd         411\n",
            "portsweep            371\n",
            "mscan                310\n",
            "warezmaster          279\n",
            "apache2              228\n",
            "back                 227\n",
            "processtable         211\n",
            "nmap                 206\n",
            "warezclient          107\n",
            "teardrop             104\n",
            "snmpguess             99\n",
            "mailbomb              94\n",
            "saint                 93\n",
            "snmpgetattack         43\n",
            "httptunnel            41\n",
            "pod                   35\n",
            "buffer_overflow       17\n",
            "multihop               9\n",
            "ps                     9\n",
            "sendmail               8\n",
            "rootkit                4\n",
            "loadmodule             4\n",
            "xlock                  4\n",
            "xterm                  3\n",
            "named                  2\n",
            "imap                   2\n",
            "land                   2\n",
            "ftp_write              2\n",
            "xsnoop                 1\n",
            "phf                    1\n",
            "perl                   1\n",
            "Name: count, dtype: int64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Possible categorical fields: ['protocol_type', 'service', 'flag']\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\nExplanation:\\nThis section reviews the training and testing datasets by displaying\\nstructural metadata (column types, counts), detecting missing entries, and\\nexamining the distribution of the attack labels. It also identifies\\ncategorical attributes, which must be encoded during preprocessing.\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "outputId": "83cecf8f-01df-4bce-e93e-43707d4e3e9a",
        "id": "GnsRYauwAOyf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Categorical attribute encoding completed.\n",
            "\n",
            "Detected Classes: ['apache2' 'back' 'buffer_overflow' 'ftp_write' 'guess_passwd'\n",
            " 'httptunnel' 'imap' 'ipsweep' 'land' 'loadmodule' 'mailbomb' 'mscan'\n",
            " 'multihop' 'named' 'neptune' 'nmap' 'normal' 'perl' 'phf' 'pod'\n",
            " 'portsweep' 'processtable' 'ps' 'rootkit' 'saint' 'satan' 'sendmail'\n",
            " 'smurf' 'snmpgetattack' 'snmpguess' 'spy' 'teardrop' 'warezclient'\n",
            " 'warezmaster' 'xlock' 'xsnoop' 'xterm']\n",
            "Total Number of Classes: 37\n",
            "\n",
            "Training feature matrix shape : (125973, 41)\n",
            "Testing feature matrix shape  : (22544, 41)\n",
            "\n",
            "Feature scaling applied successfully.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"\\nSummary:\\n- The three categorical columns (protocol_type, service, flag) are converted into\\n  numerical codes using LabelEncoder.\\n- The target 'labels' column is encoded consistently across both train and test sets.\\n- After splitting labels from features, all numeric values are standardised\\n  using StandardScaler to normalise the feature distribution and help improve\\n  model performance.\\n\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# All categorical features (protocol_type, service, and flag) are converted\n",
        "# into numeric codes using LabelEncoder so that they can be used by the models.\n",
        "# The target labels are also encoded consistently across both datasets.\n",
        "# After separating features and labels, StandardScaler is applied to normalise\n",
        "# the feature values, improving model stability and performance.\n",
        "\n",
        "\n",
        "# ===========================================================\n",
        "# SECTION 3: Data Preparation & Feature Engineering\n",
        "# ===========================================================\n",
        "\n",
        "# --- 1. Define Feature Groups ---\n",
        "\n",
        "cat_cols = [\"protocol_type\", \"service\", \"flag\"]\n",
        "target_var = \"labels\"\n",
        "\n",
        "# --- 2. Encode Categorical Features ---\n",
        "\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "encoders_dict = {}\n",
        "\n",
        "for feature in cat_cols:\n",
        "    encoder = LabelEncoder()\n",
        "    train_data[feature] = encoder.fit_transform(train_data[feature])\n",
        "    test_data[feature]  = encoder.transform(test_data[feature])   # ensure same mapping\n",
        "    encoders_dict[feature] = encoder\n",
        "\n",
        "print(\"Categorical attribute encoding completed.\")\n",
        "\n",
        "\n",
        "# --- 3. Encode the Target Labels ---\n",
        "\n",
        "label_encoder = LabelEncoder()\n",
        "\n",
        "# Combine labels from both sets so that no class is missed during fitting\n",
        "combined_label_set = pd.concat([train_data[target_var], test_data[target_var]], axis=0).unique()\n",
        "label_encoder.fit(combined_label_set)\n",
        "\n",
        "y_train = label_encoder.transform(train_data[target_var])\n",
        "y_test  = label_encoder.transform(test_data[target_var])\n",
        "\n",
        "print(\"\\nDetected Classes:\", label_encoder.classes_)\n",
        "print(\"Total Number of Classes:\", len(label_encoder.classes_))\n",
        "\n",
        "\n",
        "# --- 4. Separate Input Features from Output ---\n",
        "\n",
        "X_train = train_data.drop(columns=[target_var])\n",
        "X_test  = test_data.drop(columns=[target_var])\n",
        "\n",
        "print(\"\\nTraining feature matrix shape :\", X_train.shape)\n",
        "print(\"Testing feature matrix shape  :\", X_test.shape)\n",
        "\n",
        "\n",
        "# --- 5. Standardise Feature Values ---\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "std_scaler = StandardScaler()\n",
        "X_train_scaled = std_scaler.fit_transform(X_train)\n",
        "X_test_scaled  = std_scaler.transform(X_test)\n",
        "\n",
        "print(\"\\nFeature scaling applied successfully.\")\n",
        "\n",
        "\n",
        "\"\"\"\n",
        "Summary:\n",
        "- The three categorical columns (protocol_type, service, flag) are converted into\n",
        "  numerical codes using LabelEncoder.\n",
        "- The target 'labels' column is encoded consistently across both train and test sets.\n",
        "- After splitting labels from features, all numeric values are standardised\n",
        "  using StandardScaler to normalise the feature distribution and help improve\n",
        "  model performance.\n",
        "\"\"\""
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Reusable helper functions are defined here to standardise evaluation across\n",
        "# different models. One function produces a normalised confusion matrix, while\n",
        "# the other trains a given model, computes key metrics, measures training/\n",
        "# prediction time, and displays the classification results.\n",
        "# These functions help avoid repetition and keep the notebook organised.\n",
        "\n",
        "\n",
        "# ===========================================================\n",
        "# SECTION 4: Utility Functions for Evaluation & Visualisation\n",
        "# ===========================================================\n",
        "\n",
        "def display_confusion(y_true, y_pred, heading=\"Confusion Matrix\"):\n",
        "    \"\"\"\n",
        "    Produce a heatmap of the normalised confusion matrix.\n",
        "    \"\"\"\n",
        "    matrix = confusion_matrix(y_true, y_pred)\n",
        "    normalized = matrix / matrix.sum(axis=1, keepdims=True)\n",
        "\n",
        "    plt.figure(figsize=(9, 7))\n",
        "    sns.heatmap(\n",
        "        normalized,\n",
        "        annot=False,\n",
        "        cmap=\"cividis\",\n",
        "        linewidths=0.5\n",
        "    )\n",
        "    plt.title(f\"{heading} – Normalized\")\n",
        "    plt.ylabel(\"Actual Class\")\n",
        "    plt.xlabel(\"Predicted Class\")\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "def assess_model(label, clf, x_train, x_test, y_train, y_test):\n",
        "    \"\"\"\n",
        "    Fit the model, compute predictions, measure metrics,\n",
        "    and visualise the confusion matrix.\n",
        "    Returns: metric summary + predictions.\n",
        "    \"\"\"\n",
        "\n",
        "    # Measure training duration\n",
        "    t0 = time.time()\n",
        "    clf.fit(x_train, y_train)\n",
        "    train_duration = time.time() - t0\n",
        "\n",
        "    # Measure inference duration\n",
        "    t1 = time.time()\n",
        "    preds = clf.predict(x_test)\n",
        "    test_duration = time.time() - t1\n",
        "\n",
        "    # Evaluation metrics\n",
        "    acc_val = accuracy_score(y_test, preds)\n",
        "    prec_val = precision_score(y_test, preds, average=\"weighted\", zero_division=0)\n",
        "    rec_val  = recall_score(y_test, preds, average=\"weighted\", zero_division=0)\n",
        "    f1_val   = f1_score(y_test, preds, average=\"weighted\", zero_division=0)\n",
        "\n",
        "    # Output results\n",
        "    print(f\"\\n=== {label} PERFORMANCE SUMMARY ===\")\n",
        "    print(f\"Training Time     : {train_duration:.3f}s\")\n",
        "    print(f\"Prediction Time   : {test_duration:.3f}s\")\n",
        "    print(f\"Accuracy          : {acc_val:.4f}\")\n",
        "    print(f\"Precision (Wgt.)  : {prec_val:.4f}\")\n",
        "    print(f\"Recall (Wgt.)     : {rec_val:.4f}\")\n",
        "    print(f\"F1 Score (Wgt.)   : {f1_val:.4f}\")\n",
        "\n",
        "    print(\"\\nDetailed Classification Report:\")\n",
        "    print(classification_report(y_test, preds, zero_division=0))\n",
        "\n",
        "    # Visualise matrix\n",
        "    display_confusion(y_test, preds, heading=label)\n",
        "\n",
        "    # Return metrics for later summary tables\n",
        "    return {\n",
        "        \"Model\": label,\n",
        "        \"Train Time\": train_duration,\n",
        "        \"Test Time\": test_duration,\n",
        "        \"Accuracy\": acc_val,\n",
        "        \"Precision\": prec_val,\n",
        "        \"Recall\": rec_val,\n",
        "        \"F1 Score\": f1_val\n",
        "    }, preds\n",
        "\n",
        "\n",
        "\n",
        "# A basic Random Forest classifier is trained to establish initial (baseline)\n",
        "# performance. This provides a reference point to compare against the tuned\n",
        "# version later. The evaluation function is used to gather performance metrics\n",
        "# such as accuracy, precision, recall, F1-score, and confusion matrix.\n",
        "\n",
        "# =============================================================\n",
        "# SECTION 5: Establishing Baseline Using Random Forest\n",
        "# =============================================================\n",
        "\n",
        "# Create a basic Random Forest model configuration\n",
        "baseline_rf = RandomForestClassifier(\n",
        "    n_estimators=100,        # number of trees\n",
        "    random_state=42,         # reproducibility\n",
        "    n_jobs=-1                # use all CPU cores\n",
        ")\n",
        "\n",
        "# Evaluate using the helper function\n",
        "rf_summary, rf_predictions = assess_model(\n",
        "    \"Random Forest – Baseline\",\n",
        "    baseline_rf,\n",
        "    X_train_scaled,\n",
        "    X_test_scaled,\n",
        "    y_train,\n",
        "    y_test\n",
        ")\n",
        "\n",
        "print(\"\\nBaseline evaluation for Random Forest is completed.\")\n",
        "rf_summary\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "FBx7M52cDAgS",
        "outputId": "86b08494-4b58-45cf-e61e-c9b5d281fadf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "=== Random Forest – Baseline PERFORMANCE SUMMARY ===\n",
            "Training Time     : 12.528s\n",
            "Prediction Time   : 0.462s\n",
            "Accuracy          : 0.9139\n",
            "Precision (Wgt.)  : 0.8703\n",
            "Recall (Wgt.)     : 0.9139\n",
            "F1 Score (Wgt.)   : 0.8766\n",
            "\n",
            "Detailed Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.00      0.00      0.00       228\n",
            "           1       0.97      0.99      0.98       227\n",
            "           2       0.88      0.41      0.56        17\n",
            "           3       1.00      0.50      0.67         2\n",
            "           4       1.00      0.02      0.04       411\n",
            "           5       0.00      0.00      0.00        41\n",
            "           6       1.00      1.00      1.00         2\n",
            "           7       0.93      1.00      0.96       479\n",
            "           8       1.00      0.50      0.67         2\n",
            "           9       1.00      0.75      0.86         4\n",
            "          10       0.00      0.00      0.00        94\n",
            "          11       0.00      0.00      0.00       310\n",
            "          12       1.00      0.11      0.20         9\n",
            "          13       0.00      0.00      0.00         2\n",
            "          14       0.99      1.00      1.00      6654\n",
            "          15       1.00      0.99      1.00       206\n",
            "          16       0.87      0.99      0.93     11245\n",
            "          17       1.00      1.00      1.00         1\n",
            "          18       1.00      1.00      1.00         1\n",
            "          19       0.89      0.94      0.92        35\n",
            "          20       0.96      1.00      0.98       371\n",
            "          21       0.00      0.00      0.00       211\n",
            "          22       0.00      0.00      0.00         9\n",
            "          23       1.00      0.25      0.40         4\n",
            "          24       0.00      0.00      0.00        93\n",
            "          25       0.85      1.00      0.92       698\n",
            "          26       0.00      0.00      0.00         8\n",
            "          27       0.99      1.00      1.00       540\n",
            "          28       0.00      0.00      0.00        43\n",
            "          29       0.00      0.00      0.00        99\n",
            "          31       0.89      1.00      0.94       104\n",
            "          32       1.00      1.00      1.00       107\n",
            "          33       0.75      0.01      0.02       279\n",
            "          34       0.00      0.00      0.00         4\n",
            "          35       0.00      0.00      0.00         1\n",
            "          36       0.00      0.00      0.00         3\n",
            "\n",
            "    accuracy                           0.91     22544\n",
            "   macro avg       0.58      0.46      0.47     22544\n",
            "weighted avg       0.87      0.91      0.88     22544\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x700 with 2 Axes>"
            ],
            "image/png": 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96zFLly6d6ufbU8d8fHzS3X7nc5ecnKyZM2cqKioq3S8gEhMTUxUa9li8eLGWLl2qHj16pFrI4ffff1dKSooWLVqkRYsWpXvf26+XjN637vc0RQDujYIHuE8effRR6wfSZ555Rt7e3nrvvfdUrVq1VN2RwYMHa8eOHerYsaNq1aolT09P5ciRQ7Nnz9Z3332XJu6d3+7+myM7EHfL427fTDurO/LvD5rS/+cyYsQIPfLII+ne7/YHrCJFimj16tXas2ePduzYoZiYGL399tuaPn26Pvvss1QdNFudOXNGISEh6e4rXbq0TSf/p6dgwYKqWbOmoqOjdfz4cfn7++vkyZPq3LmzihQpokGDBsnX11f58uXTrVu39Oqrr6b6vTz88MPauHGjdu7cqZ07dyomJkbr169XxYoVtWTJEhUsWNA6fsqUKanOq7lTpUqVMpW/La8dwzBUqlQpffjhh3eNk9HfyPz58zVt2rR09/Xr10/9+/e3Mdt/DB48WF27dtX8+fNVv359u+57N3d2av7tbs/R3R7znc/d6NGj9dlnn6lly5YaNGiQihQpopw5c2r16tX64osvlJKSkql8t2/froiICDVu3DhNB/v28cPCwtSsWbN071+8ePFMHRcAbEXBAzhIWFiYlixZoqlTp6ply5YqUKCAEhMT9d///ldt2rTRO++8k2r81KlTM32s29+U3/42/k7pbStTpox+++03GYaRqstz/vx5/fXXXypbtmymc8lOtx9XXFxcmn23t/27S5Ce29+gZ9RNulPOnDkVGBho7Sp8++236tGjhxYtWqTXX3/d5vxvK1GihD755JN092V1yebbq/BdvXpV0j/TEa9fv645c+aoXr161nH/+9//0r1/njx51LBhQzVs2FCStHTpUo0aNUpRUVHq2LGj9bkrXry4dVqcI/n6+mr37t2qU6dOppZJbtWqlXUa2L9l5nUeGBioxx9/XAsWLFBAQECa/WXKlNHevXt1/fr1VMV3cnKy4uPjVa1aNbuPmVkbNmxQvXr1NG7cuFTbV69enemYsbGxGjRokAICAjRu3Lg0XeKyZcvKYrEoJSXlnn9rGb1vpfc3DwC24hwewEFy5Mihnj176tKlS1qyZIl12+0PA3f6/vvvtX///kwfq3jx4qpevbo2bdqks2fPWrdfuXJFy5YtSzM+JCREp06d0pdffplq+5w5c6z7zaB48eKqUaOGNm7cqPj4eOv25ORkLViwQHny5NGTTz55zzgNGjRQkSJFNGvWrHTPW7h48aL1d5Le1LPKlStLSj0drECBAjZP2/Pw8FBQUFC6t7t9GLfFxYsX9eOPPypPnjzWDtXtb/7//RpL7yKStjzWpk2bKnfu3OkuWS5lfjqbrZ5//nn9/fffmj59err773X8smXL3vW5z2xhP2TIEF29etX693KnkJAQ/f3332nO81m6dKmuXr3q0L+tHDlypOm0/v7779q6dWum4l28eFG9evVSvnz5NGvWrHSneBYtWlQNGjTQ+vXr9euvv6bZn5ycbF2eu2rVqnrooYe0atWqVH+X586ds3npbABIDx0ewIGaNWumqVOn6pNPPtHLL7+sggUL6vHHH9e6deuUP39+VapUSXFxcVq1apXKly+f7gcEW73xxhvq2rWrwsLC1KFDB3l4eGjNmjXy8vJKM3+/e/fu+vLLL/XGG2/oxx9/1COPPKI9e/Zo06ZNeuaZZ1Jde8PZ3nrrLXXp0kXt27dXhw4dVLBgQW3YsEEHDx7UsGHDVKxYsXvGKFCggCIiItS/f389++yzat26tcqUKaMLFy7oyJEjio6O1r59++Th4aF3331Xf/zxhwIDA61L+a5du1Y5cuRQ8+bNrTGrV6+uVatWadKkSSpfvrwsFkuq/dnt0qVLioqKkvTPtKHTp09r1apVSkxM1MCBA63nYjz55JPy8PDQG2+8oZdffln58uXT119/bV0c407dunWTl5eX6tSpI29vb128eFHLly+Xh4eHmjRpIumfc7TeeecdjRw5Us8//7xatGghb29vnTt3Tj/99JN++eWXdJc+zy7NmjVTdHS0Zs2apZ9++klPPPGEChYsqNOnT2vHjh3y9fXVpEmT7tvx01OjRg0FBwenOxXxhRde0OrVqzVx4kT973//U7Vq1XT48GGtWrVKVapUSXcRkfulUaNGWrVqlYYOHar69evr1KlT+uyzz+Tn55fmmj22GDVqlOLj49WpUyfFxMSk2X/7wrgjR45Uhw4d1LZtW7Vp00YBAQH6+++/9b///U+bN2/W+PHj9dRTTylnzpx68803NWTIEIWFhemFF17QjRs3tGzZMvn6+mYqRwCQKHgAh8qZM6deffVVvfvuu/rss8/UrVs3TZw4UWPGjNGmTZu0du1aVaxYUdOmTVNUVFSWCp46depo7ty5mjhxombMmKHChQurTZs2qlevnl555ZVUY728vLRs2TJNnjxZGzduVEJCgkqWLKm+ffuqV69e6S6e4CyPPfaYPv30U0VGRuqTTz5RcnKyypcvr3Hjxlk/YNmiYcOGWrFihWbPnq2VK1cqMTFRRYoUkb+/v958803rSe8tW7bU6tWrtWbNGl26dEleXl6qVKmS3nnnnVTnbAwaNMjavbv9jfX9LHhOnDiRajpd/vz5VaFCBQ0aNCjVqnS+vr6aPXu2PvroI02fPl158+bVM888o/Hjx6c58b9Dhw7asGGDli5dar1mVM2aNdW7d+9UJ423b99eDz/8sObPn69Fixbp2rVrKl68uCpWrKjXXnvtvj1m6Z+FPCZMmKD69etr1apVmj59ugzD0EMPPaTatWvrxRdfvK/Hv5tBgwbpm2++SdNJy5MnjxYsWKDIyEht3rxZ69evV7FixfTyyy9r4MCBmZqWl1nDhw9X3rx5tWXLFm3dulWPPvqoRo4cqbi4uEwVE7c7gndbpe7232OZMmW0du1azZo1S19//bVWrlypAgUKqEyZMurYsaOqVq1qvU/z5s1lsVg0Y8YMTZo0Sd7e3urSpYs8PT01fPjwTDxqAJAshpnWWQUAAACAbMQ5PAAAAADcFgUPAAAAgPvi7Nmz+uCDD9SuXTvVqFFDFSpUSPcaZOlJSUnR7NmzFRwcrGrVqun555/X5s2b7c6BggcAAADAfXH8+HFt3LhRnp6edl/OYMqUKYqMjFTHjh01d+5c1axZUwMHDtS3335rVxzO4QEAAABwX6SkpFgvmrxy5Uq98847io6Ovud18y5cuKCnn35aPXr00IABA6zbu3TpoosXL2r9+vU250CHBwAAAMB9cbvYsdf27dt148aNVCuPSv9cj+3XX39NdT2+e+aQqQwAAAAA4D6JjY1Vnjx55Ovrm2p7+fLlJUlxcXE2x+I6PAAAAADuKiQkJMP90dHR2X7MhIQEeXp6prkWoJeXlyTp8uXLNsei4AEAAABchHEu8N6Dsl1+Jxwz+zzQBY/Fz/arst+NERclS8UOWY/zy2fZlw9xiEMc08e5eSLr/8PKVW636R4XcYhDHOK4ehykdT86OPfi6empxMREGYaRqsuTkJAgSSpcuLDNsR7oggcAAABwKSkpzs7AIcqXL6/k5GSdOHEi1Xk8sbGxkiQ/Pz+bY7FoAQAAAABTefLJJ5U7d+40y0+vW7dOAQEBKlu2rM2x6PAAAAAAuG82bdokSTp06JAk6b///a+KFi2qokWLql69epKkypUrq1WrVho9erQkqVixYuratatmz56tAgUKqHLlyvryyy/13XffaebMmXYdn4IHAAAAcBWG4ewM7DZw4MBUP48aNUqSVK9ePS1evFiSdOvWLaX8a7re4MGDlT9/fi1atEh//PGHHnnkEU2ePFkNGza06/gUPAAAAADum6NHj2ZqTM6cOdWnTx/16dMnS8c3VcHz999/a9myZYqOjlZcXJwSExMl/bNKg5+fn0JCQtSuXTvly5fvnrGcsV44AAAAcF+5YIfH2UxT8Jw5c0ZdunTRqVOnVKtWLYWGhlovLJSQkKDY2FhNmDBBS5cu1YIFC1SqVCknZwwAAADA7ExT8IwePVoeHh7avHmzypQpk+6YkydPqm/fvoqIiFBkZGSG8ejgAAAAwN04o8FjufcQUzPNstS7du3S4MGD71rsSFKZMmU0YMAA7dq1y4GZAQAAAHBVpil47ryCanaOBQAAAPDgMs2UtqCgIE2aNEnly5e/64WETp48qSlTpigoKMjB2QEAAADOZxim6Ve4DNMUPMOHD1fnzp3VtGlT1ahRQ+XLl0+zaMGBAwdUunRpDR8+3MnZAgAAAHAFpil4SpYsqXXr1mn58uX6+uuvFR0drcuXL0uSvLy85O/vr2HDhiksLMymZakBAAAAd0OHx36mKXgkKW/evOrSpYu6dOni7FQAAAAAuAGLYXD1IgAAAMAVJB9/wuHHzOO70+HHzE6m6vA4msWvZZZjGHFRMk7VzXoupfdmWz7EIQ5xXCBOwItZj/PrKvM9LuIQhzjEcfE4cD9MAgQAAADgth7oDg8AAADgSli0wH48YwAAAADcFh0eAAAAwEXQ4bEfzxgAAAAAt0WHBwAAAHARhmFxdgouhw4PAAAAALdFhwcAAABwEZzDYz+eMQAAAABui4IHAAAAgNtiShsAAADgIlJYtMBudHgAAAAAuC06PAAAAICLYNEC+/GMAQAAAHBbdHgAAAAAF8GFR+1nMQzDcHYSAAAAAO4t8dcmDj+mZ8AWhx8zOz3QHR6LX8ssxzDiorItTp9+Q7IcZ8a0j0z3uIhDHOKkjRM58a0sx+k/NMJ0j4s4xCEOcVw9DtzPA13wAAAAAK6EKW32Y9ECAAAAAG6LDg8AAADgIliW2n48YwAAAADclukKnpiYGK1bt04///xzuvvPnTunadOmOTgrAAAAwPkMw+Lwm6szzZS2q1evqlu3bjpw4IAMw5DFYlFQUJBGjx4tb29v67izZ89q+vTp6tevX4bxQkJCMtwfHR2dLXkDAAAAMC/TdHhmz56tuLg4RUREaMOGDRoxYoSOHDmisLAwxcbGOjs9AAAAwOno8NjPNB2eLVu2qH///mrVqpUkyc/PT8HBwerdu7c6duyouXPnqnr16jbHo4MDAAAAwDQdnjNnzqhy5cqptnl7e+vTTz9VQECAwsPDFRMT46TsAAAAALgi0xQ8xYoV09mzZ9Nsz58/vz7++GPVrl1bPXv21DfffOP45AAAAAATSDEsDr+5OtMUPFWrVr3rNDQPDw/NmDFDTz/9tGbOnOngzAAAAAC4KtMUPM2bN9fp06d16dKldPfnypVLkydPVrt27eTj4+Pg7AAAAADnY9EC+5lm0YLQ0FCFhoZmOMZisWjUqFEOyggAAACAqzNNwQMAAAAgY4Z5Jmi5DIthGIazkwAAAABwb+cPtXT4MR+qGuXwY2anB7rDY/HL+gvGiIsyXZwR776V5TjvfxBhusdFHOK4Uxz+TolDHOIQx5xx4H4e6IIHAAAAcCXusIiAozEJEAAAAIDbosMDAAAAuAg6PPajwwMAAADAbdHhAQAAAFwEHR770eEBAAAA4LYoeAAAAAC4Laa0AQAAAC6CKW32o8MDAAAAwG3R4QEAAABcBB0e+9HhAQAAAOC26PAAAAAALiKFDo/d6PAAAAAAcFt0eAAAAAAXwTk89rMYhmE4OwkAAAAA93ZiX5jDj1mu9gqHHzM7PdAdHotfyyzHMOKi3DdOQNusx/l1pfkeF3GIY4I4KSfrZjlOjjJ7Tfe4iEMc4hDH1ePA/TzQBQ8AAADgSpjSZj8WLQAAAADgtujwAAAAAC6CDo/9TN/hSUlJ0S+//KLr1687OxUAAAAALsb0Bc/Vq1fVunVr/fzzz85OBQAAAHAqw7A4/ObqTDGlbcqUKXfdl5ycLMMwtHLlSu3cuVMWi0UDBgxwYHYAAAAAXJUpCp6ZM2fKYrHobpcEslgsioqKsv7bloInJCQkw/3R0dH2JwoAAADApZii4HniiSd09OhRDR8+XM8++2yqfYmJiapXr54WL16sunWzft0KAAAAwFXdpT+ADJii4Jk3b56++OILjR49WqtXr9aIESPk6+sr6Z+OTmbQwQEAAABgmkULWrRooQ0bNqhUqVJ6/vnnNXXqVCUnJzs7LQAAAMA0DFkcfnN1pil4JMnLy0sffPCB5s2bpy1btqh58+b69ttvM93lAQAAAPBgM8WUtn+rU6eO1q5dq7lz5+rtt992djoAAACAKbjDMtGOZsqCR5Jy586tPn36qHXr1oqPj1elSpWcnRIAAAAAF2Paguc2Hx8f+fj4ODsNAAAAAC7I9AUPAAAAgH8wpc1+FuNuV/sEAAAAYCrHdr/s8GOWD/zU4cfMTg90h8fi1zLLMYy4KOIQhzjEsTuOcS4wy3Es3rtN97iIQxziEMfV45gdHR77mWpZagAAAADITg90hwcAAABwJSmcjGI3OjwAAAAA3BYdHgAAAMBFcA6P/ejwAAAAAHBbFDwAAAAA3BZT2gAAAAAXwZQ2+9HhAQAAAOC26PAAAAAALoIOj/3o8AAAAABwW3R4AAAAABdhcOFRu9HhAQAAAOC2KHgAAAAAuC2mtAEAAAAuwhCLFtjLYhjMBAQAAABcwcFvwx1+zGpPf+LwY2anB7rDY/FrmeUYRlwUcRwUxzgXmOU4Fu/dpntcxCEOcYhDHOIQxzxxzI5lqe3HOTwAAAAA3NYD3eEBAAAAXAkdHvvR4QEAAADgtih4AAAAALgtl5jS9scffyhHjhwqVqyYs1MBAAAAnIb1le1nmoInJiZGf//9t55++mnrtsWLF2v27Nm6cOGCJKlkyZIaOHCgWrVqdc94ISEhGe6Pjo7OUr4AAAAAzM80Bc/48ePVtGlTa8GzZMkSffjhh3ryySf1xBNPSJK2b9+ut956S3ny5NGzzz7rzHQBAAAAh2PRAvuZpuD53//+p0qVKll/XrhwoTp06KD33nvPuq1r16565513NHv27HsWPHRwAAAAAJhm0YKUlBTlyPH/6Zw6dUpNmzZNM65Zs2b67bffHJkaAAAAYAqG4fibqzNNwVOlShX997//tf5cqlQpxcfHpxkXHx8vLy8vR6YGAAAAwEWZZkpb9+7d1bdvX5UqVUrt2rVTnz59NH78eBUuXFhBQUGSpB07dmjy5Mlq3ry5k7MFAAAAHI9zeOxnmoLn6aef1jvvvKOIiAh99NFHevTRR5WUlKT+/funGlevXj0NGTLESVkCAAAAcCWmKXgkqX379nryySe1atUq/fDDD3rooYeUkpKiIkWKyN/fX40bN061bDUAAAAA8zpz5owiIiK0c+dOGYahoKAgDR8+XKVKlbrnfU+fPq0pU6YoJiZGFy9elI+Pj5o2baqePXsqf/78NudgqoJHkkqXLq2BAwc6Ow0AAADAdFxpStv169fVpUsX5cmTR2PHjpUkTZkyRZ07d9a6desyLFquXbum8PBw3bhxQwMHDpSPj48OHjyoyMhIHT9+XJMnT7Y5D4thuMPaCwAAAID7+35LD4cfs06TOZm638KFCzVmzBht2rRJvr6+kv5ZgCw0NFTDhg1TeHj4Xe+7Y8cOdevWTfPmzVODBg2s2ydMmKD58+dr3759ypcvn015mK7D40gWv5ZZjmHERRHHxeIY5wKzHMfivdt0j4s4xCEOcYhDHOJkPY7ZpTg7ATts27ZNNWrUsBY7klS2bFnVqlVL0dHRGRY8N27ckCQVLFgw1XZPT0+lpKTInp6NaZalBgAAAOA+YmNjFRAQkGa7v7+/YmNjM7xvUFCQHn74YU2YMEGxsbG6evWqdu/erUWLFql9+/aufQ4PAAAAgPQ54xyekJCQDPdHR0enuz0hIUGenp5ptnt5eSkxMTHDmB4eHlq6dKn69++f6pI0bdu21YgRI2zI+v9R8AAAAAAwlaSkJA0aNEgXLlzQuHHjVKpUKf3000+aPn26cubMqVGjRtkci4IHAAAAwF3drYNzL56enul2cu7W+bnTqlWrtGfPHm3dulXlypWTJNWtW1eFChXSu+++qw4dOqhixYo25cE5PAAAAICLMAyLw2+Z5e/vr2PHjqXZHhcXJ39//wzve/ToUXl5eVmLnduqV69ujWErCh4AAAAA2S44OFgHDhxQfHy8ddvJkyf1ww8/KDg4OMP7lihRQgkJCTp+/Hiq7QcOHJAkeXt725wHBQ8AAADgIgzD8bfMCgsLU+nSpdWnTx999dVXio6OVp8+fVSyZEm1a9fOOu7UqVOqXLmypk2bZt3WunVrFShQQD169NDatWv13Xff6eOPP9bYsWNVpUoV1apVy+Y8OIcHAAAAQLbLnz+/Fi5cqIiICL3++usyDEOBgYEaPny4ChQoYB1nGIZu3bqV6to6ZcqU0YoVKxQZGanJkyfr0qVL8vHxUbt27dSrVy/lyGF734aCBwAAAHARzliWOitKlSqlyMjIDMeUKVNGR48eTbPd399fU6ZMyXIOTGkDAAAA4Lbo8AAAAAAuIivn1Dyo6PAAAAAAcFsUPAAAAADcFlPaAAAAABfhaosWmAEdHgAAAABuy2IYnPoEAAAAuILtX/Rx+DGfbDHD4cfMTg/0lDaLX8ssxzDioojzgMYZ8e5bWY7z/gcRpntcxHFMHF4/xCEOcYhjzjhwPw90wQMAAAC4Es7hsR/n8AAAAABwWxQ8AAAAANyWy0xpu3Dhgry8vJQrl8ukDAAAAGQrlhuzn6k6PMuWLVP79u3Vrl07bdy4UZL0xRdfKDAwUA0aNFCdOnU0duxYsbAcAAAAAFuYpl2yevVqjRw5UjVr1lShQoU0bNgwXbt2Te+9956aNm2q6tWr68CBA1qwYIF8fX3Vvn37DOOFhIRkuD86Ojo70wcAAADuOxYtsJ9pCp4lS5aoXbt2GjVqlCRpxYoVGjlypDp06KC3337bOs7Ly0vLly+/Z8EDAAAAAKYpeH7//XcNGzbM+nOzZs00YsSINJ2akJAQRUXde410OjgAAABwN5zZYT/TnMOTN29eXb9+3frz7X8nJSWlGvf333/Lw8PDobkBAAAAcE2mKXgqVaqkhQsX6u+//5ZhGJo9e7a8vb316aef6tatW5KkmzdvaunSpfL393dytgAAAABcgWmmtPXp00evvPKK6tata116etGiRRowYICaNWumihUr6siRIzp58qTmzJnj5GwBAAAAx2PRAvuZpuCpXbu2VqxYoQ0bNujGjRtq06aNypcvrwULFmjixIk6duyYSpYsqddee01PPvmks9MFAAAA4AJMU/BIUoUKFVShQoVU23x9fTV16lQnZQQAAACYB2sW2M805/AAAAAAQHazGAaL2wEAAACu4Ks1Axx+zEZtXHu2lammtDmaxa9llmMYcVHEIU6W4kSv7Z/lOCGtI033uIiTcZxGLbP+e/8qit87cYhDHOJkdxy4nwe64AEAAABcCXOz7Mc5PAAAAADcFgUPAAAAALfFlDYAAADARTClzX50eAAAAAC4LTo8AAAAgIswDIuzU3A5dHgAAAAAuC06PAAAAICL4Bwe+9HhAQAAAOC2KHgAAAAAuC2mtAEAAAAuwhCLFtiLDg8AAAAAt0WHBwAAAHARLFpgPzo8AAAAANyWxTCoEwEAAABXsGHZIIcfs3n7yQ4/ZnZ6oKe0WfxaZjmGERdFHOIQhzh2xzHOBWY5jsV7t+keF3GIQxziuHocuB9TFjw3btzQiRMndPnyZUlS4cKFVa5cOeXOndu5iQEAAABwKaYqeH755RdNnTpVO3bs0I0bN1Lty507txo0aKABAwaoYsWKTsoQAAAAcB7DYFlqe5mm4Pn+++/VrVs3+fj4qHv37vL391fhwoUlSZcvX1ZsbKw2btyodu3aad68eapTp45zEwYAAABgeqYpeCZMmKCnnnpKkydPVs6cOdMd06dPHw0ePFjjx4/X8uXLHZwhAAAA4FwpLDdmN9MsS33kyBF17NjxrsWOJOXMmVMdOnTQL7/84sDMAAAAALgq03R4ChUqpJMnT95z3MmTJ1WoUCEHZAQAAACYiyHO4bGXaQqe5557TuPGjVOuXLnUrFkzeXh4pNqflJSkjRs3asKECWrTps0944WEhGS4Pzo6Okv5AgAAADA/0xQ8gwcP1vnz5/Xmm2/q3XffVZkyZeTl5SVJSkhI0MmTJ3Xjxg09++yzGjx4sJOzBQAAABzP4Bweu5mm4MmTJ48mTpyo7t27Kzo6WnFxcUpISJAk+fj4qEWLFgoODlalSpVsikcHBwAAAIBpCp7bKlasyHV2AAAAAGQL06zSZqu9e/eqc+fOzk4DAAAAcDjDcPzN1blcwXPx4kXt3bvX2WkAAAAAcAGmmdJ2+vRpm8ZdvHjxPmcCAAAAmJNhsCy1vUxT8AQHB8tiufcv0DAMm8YBAAAAgGkKnrx586pOnToKDQ3NcNyhQ4e0YsUKB2UFAAAAmIc7nFPjaBbDMMfT1r59e3l5eWn27NkZjtu8ebMGDRqkI0eOOCgzAAAAwBxWLxrq8GO+0Hmiw4+ZnUzT4alSpYo2b95s09jsqtEsfi2zHMOIiyIOcYjjAnFqNxqQ5Tj7vpqabfn8d33fLMd56rnppnueieNicSpnfdVT4/Ai8z0u4hAnC3HgfkxT8PTo0eOe09kkKTQ0VL/88osDMgIAAADMxRxzs1yLaQoeb29veXt7OzsNAAAAAG7ENAUPAAAAgIwZYrVie7nchUcBAAAAwFZ0eAAAAAAXwTk89qPDAwAAAMBtUfAAAAAAcFtMaQMAAABcBFPa7EeHBwAAAIDbosMDAAAAuAg6PPajwwMAAADAbdHhAQAAAFyEYXDhUXvR4QEAAADgtujwAAAAAC6CU3jsZzEMTn0CAAAAXMHSecMcfsyXuo13+DGz0wPd4bH4tcxyDCMuijjEIU46cYrVG5zlOBf2TDLd48quOCf2hWU5TrnaK0z3uIhDHOIQx9XjwP080AUPAAAA4EqYm2U/Uy1acO7cOUVGRuqdd97RggUL9Ndff6UZExcXp86dOzshOwAAAACuxjQdnpMnT+qFF15QYmKiihYtqlWrVmnu3LmaMGGCAgMDreOuXLmivXv3OjFTAAAAwDno8NjPNB2eyZMnq1ixYoqOjtbOnTu1YcMGPfLII+revbvWr1/v7PQAAAAAuCDTFDz79u1T3759VapUKUmSn5+fFi5cqDZt2uiNN97Q0qVLnZwhAAAA4FyGYXH4zdWZZkrbpUuX5O3tnWpbzpw59f7778vT01MffPCBrly5ovr169sULyQkJMP90dHRmc4VAAAAgGswTcHj4+OjY8eOqU6dOmn2vfbaaypQoIA++ugjPfXUU07IDgAAAIArMk3BU7duXa1fv14dOnRId3/v3r1VoEABRURE2BSPDg4AAADcDWsW2M80BU9YWJg2bNigS5cuqUiRIumO6dy5s4oVK6YdO3Y4ODsAAAAArsg0BU/VqlVVtWrVe45r3ry5mjdv7oCMAAAAAHNhWWr7mWaVNlvt3buXC48CAAAAsIlpOjy2unjxIhceBQAAwAOJDo/9TFPwnD592qZxFy9evM+ZAAAAAHAXpil4goODZbHc+8JGhmHYNA4AAABwN+5wIVBHM03BkzdvXtWpU0ehoaEZjjt06JBWrFjhoKwAAAAAuDKLYZhjJmD79u3l5eWl2bNnZzhu8+bNGjRokI4cOeKgzAAAAABz+GTmGw4/ZnjvsQ4/ZnYyTYenSpUq2rx5s01js6tGs/i1zHIMIy6KOC4WZ8S7b2U5zvsfRJjucRHHteLwOiQOcYhDHHPGMTtztCpci2kKnh49etxzOpskhYaG6pdffnFARgAAAABcnWkKHm9vb3l7ezs7DQAAAMC0aPDYz+UuPAoAAAAAtjJNhwcAAABAxjiHx350eAAAAAC4LQoeAAAAAG6LKW0AAACAi2BKm/3o8AAAAABwW3R4AAAAABdhGBZnp+By6PAAAAAAcFt0eAAAAAAXwSk89qPDAwAAAMBtUfAAAAAAcFtMaQMAAABcBMtS289iGDxtAAAAgCuYNfVNhx+z14AxDj9mdnKJDk9ycrKWLVum0NBQeXt7Z1tci1/LLMcw4qKIQxziuEAc41xgluNYvHeb7nERhzjEIQ5xsjeO2blaq+LMmTOKiIjQzp07ZRiGgoKCNHz4cJUqVcqm+8fFxWnKlCmKiYnR9evX5ePjo5deekldunSxOQeXKHiSkpIUERGhypUrZ2vBAwAAAOD+uH79urp06aI8efJo7NixkqQpU6aoc+fOWrdunfLnz5/h/Q8ePKguXbqoXr16+vDDD1WwYEEdP35c165dsysP0xQ8HTt2vOu+W7duyTAMvf/++ypUqJAsFos+/fRTB2YHAAAAOJ8rdXhWrFih+Ph4bdq0Sb6+vpKkChUqKDQ0VMuXL1d4ePhd75uSkqI33nhDgYGBmj59unX7448/bnceplmlbd++fTp+/Lhy5MiR7k2S9d8WC1eYBQAAAMxs27ZtqlGjhrXYkaSyZcuqVq1aio6OzvC+MTExiouLy7AospVpOjxDhgzRzJkz9eijj2ro0KHy9PS07ktMTFS9evX09ttvq27duk7MEgAAAHAeZ3R4QkJCMtx/t+IlNjY23fv6+/tr06ZNGcbct2+fpH9ObQkLC9PPP/8sT09PNW/eXK+99pry5s1rY/Ym6vD06NFD69at08mTJ9W0aVN9/vnn1n10dAAAAADXkpCQkKqJcZuXl5cSExMzvO/58+clSYMHD9YTTzyh+fPn69VXX9XKlSs1dOhQu/IwTYdH+qfFNW/ePK1fv15jxozRqlWrNGrUKD300EN2x8psJQoAAADg/znjc/PtK+c8//zzGjhwoCSpfv36unXrliZOnKi4uDj5+fnZFMs0HZ47Pffcc9q4caN8fX3VqlUrffTRR3R5AAAA8MAzZHH4LbM8PT3T7eTcrfNzp8KFC0uSgoKCUm1v0KCBJOnw4cM252GqDs+dPD099eGHH6p169Z67733ZO/1UengAAAAAM7j7++vY8eOpdkeFxcnf3//e943I7cXNbOFKTs8d6pTp442bNigX375hQULAAAA8EAzDMffMis4OFgHDhxQfHy8ddvJkyf1ww8/KDg4OMP7PvXUU8qTJ4927NiRavv27dslSVWrVrU5D9MXPP+2d+9ede7c2dlpAAAAAMhAWFiYSpcurT59+uirr75SdHS0+vTpo5IlS6pdu3bWcadOnVLlypU1bdo067YiRYqoZ8+eWrZsmT766CPt2rVLc+bM0fTp09W6detUS13fi2mntN3NxYsXtXfvXmenAQAAADieC114NH/+/Fq4cKEiIiL0+uuvyzAMBQYGavjw4SpQoIB1nGEYunXrVppTWPr27asCBQpo6dKlmj9/vkqUKKFu3bqpT58+duVhmoLn9OnTNo27ePHifc4EAAAAQHYoVaqUIiMjMxxTpkwZHT16NM12i8Wi8PDwLF98NFMFz5EjRxQbG6vnnnvOum3Hjh2aOnWqEhIS1LJlS7srr+DgYJtWYjMMgxXbAAAAANgkUwXPRx99JEnWgufcuXPq37+/PDw8VLRoUUVGRsrHx0etW7e2OWbevHlVp04dhYaGZjju0KFDWrFiRWbSBgAAAFxaVhYReFBZDHvXe9Y/61937txZPXr0kCTNmzdPU6dO1ZYtW+Tt7a2ePXvq0qVLdhUm7du3l5eXl2bPnp3huM2bN2vQoEE6cuSIvWkDAAAALm3qhLccfswBr0U4/JjZKVMdnoSEBBUvXtz68/bt21WvXj15e3tLkp555hlNmjTJrphVqlTR5s2bbRqbiRotXRa/llmOYcRFEcfV4pRvk/U4x9aY73ERxyFxjNP1shzHUmqP6R4XcYhDHOIQ5584ZkeDx36ZWpbay8tLf/75pyQpOTlZ+/fvT3WNHMMwdOPGDbti9ujRwzpVLiOhoaH65Zdf7EsYAAAAwAMpUx2eGjVqaNWqVQoKCtLWrVuVlJSkZ555xrr/xIkTqTpAtvD29rZ2iAAAAACkxTk89stUwTNgwAB17dpVbdu2lWEYatWqlQICAqz7t27dmqrjAwAAAADOkKmCp0KFCvryyy/1ww8/yNPTM1Vxk5CQoM6dO6t+/frZliQAAAAAZEamLzxapEgRhYSEpNnu5eWlLl26ZCkpAAAAAGkxpc1+mVq04PTp0/r+++9Tbfvll180ePBgdevWTVFR5l/hAgAAAID7y1SHJyIiQpcuXdKnn34qSbp8+bK6du2qK1euyMPDQ7t27VLBggXT7QABAAAAyBw6PPbLVIfnwIEDCgoKsv68YcMG/fXXX1qzZo2+++471ahRQwsWLMiuHAEAAAAgUzJV8Fy+fDnVEtLbt29XrVq1FBAQoNy5c6tFixaKjY3NtiQBAAAA/HPhUUffXF2mCp4CBQror7/+kiTdunVL33//fapV2XLnzq1r165lT4YAAAAAkEmZKngqVqyoqKgoXbp0SStWrNDVq1f15JNPWvefPHlSxYoVy7YkAQAAAPxzDo+jb64uU4sW9OnTR6+++qr1PJ7AwEDVqFHDuv+bb75R9erVsydDAAAAAMikTBU8devW1Zo1a7Rz5055enrq2Wefte67fPmyAgMD1ahRo2xLEgAAAAAyI9MXHvXz85Ofn1+a7YULF9bw4cOzlBQAAACAtNxhipmjZeocHgAAAABwBRbDyFyd+L///U8LFizQwYMH9ddffyklJSV1YItFX331VbYkCQAAAEAaN/othx/z9eERDj9mdsrUlLZDhw6pU6dOunnzph555BHFx8erfPnySkhI0Pnz51W2bFmVLFkyWxL87bffdOTIEVksFlWrVk1ly5bNlriSZPFrmeUYRlwUcYhDHOLYHcc4U//eA+/B4hNjusdFHBeL498q63FiPzff4yIOcbIQB+4nUwXP1KlTVaBAAS1ZskSFChVSUFCQhg8frsDAQEVFRSkiIkKRkZF2xVy8eLFu3bqlrl27SpKSkpL02muv6auvvtLtJpTFYlHr1q31wQcfKGfOnJlJHQAAAHBZnMJjv0ydw7N//361a9dOvr6+ypHjnxC3i5KWLVuqSZMmGj9+vF0xlyxZogIFClh/Hj9+vLZv364hQ4Zo7dq1Wrt2rQYNGqQNGzZo5syZmUkbAAAAwAMmUx2epKQk+fj4SJLy5MkjSbp69ap1f9WqVbVhwwa7Yp45c0a+vr7Wn7/88ksNHDhQ4eHh1m2VKlWSxWLRsmXL1K9fv8ykDgAAAOABkqkOj7e3t86fPy9Jyp8/v7y8vHT48GHr/hMnTihXLvtqqXz58umvv/6y/pyYmKiqVaumGVetWjX98ccfmUkbAAAAcGmG4fibq8tUwfPYY49p9+7d1p+feeYZffLJJ5oxY4amTZumTz/9VLVr17YrZv369bV69Wrrz1WqVFFMTEyacd99951KlSqVmbQBAAAAPGAyNaWtY8eO+uqrr5SUlCQPDw+99tprOnz4sKZOnSpJeuSRR/TWW/YtmTdgwACFhYVpwIAB6tq1qwYOHKjBgwcrMTFRQUFBkqQdO3Zo2bJleu211+4ZLyQkJMP90dHRduUHAAAAOJthWJydgsvJVMFTvXp1Va9e3fpziRIltG7dOh09elQ5c+bUo48+avcqan5+flqwYIHefPNNdezYUdI/CyEsXrxYixcvliTlzp1bvXr1sq7kBgAAAAAZyVTBkx6LxaKKFStmKUa1atX0xRdf6LvvvtMPP/yg8+fPyzAMFS5cWP7+/nrqqadUuHBhm2LRwQEAAIC7cYdzahzNpoLn9OnTmQqemXNtLBaLAgMDFRgYmKljAgAAAMBtNhU8wcHBsljsny945MgRu+9zL3v37lVkZKQWLVqU7bEBAAAAuBebCp6+fftmquC5Hy5evKi9e/c6Ow0AAADA4ZjRZj+bCp7+/fvf7zxsnjZ38eLF+5wJAAAAAHeRbYsWZJWt0+YMwzBNtwkAAABwJBYtsJ/NBc/Zs2f1wgsv6Pnnn9cbb7xx13Fjx47V+vXrtXbtWpUoUcLmRPLmzas6deooNDQ0w3GHDh3SihUrbI4LAAAA4MFlMQzb6sSJEydq5cqVio6OVoECBe467sqVK2rcuLHatWunQYMG2ZxI+/bt5eXlpdmzZ2c4bvPmzRo0aNB9WRABAAAAMLMPRg13+DHffW+0w4+ZnWzu8Gzfvl3NmjXLsNiRpIIFC+rZZ5/Vt99+a1fBU6VKFW3evNmmsTbWaPdk8WuZ5RhGXBRxiEMc4jgtjnEu60v4W7x3m+5xEYc4xCGOs+LA/eSwdeDx48dtvrBoQECAfv/9d7sS6dGjhz766KN7jgsNDdUvv/xiV2wAAADAHRiG4fCbq7O5w5OSkmLzYgEWi0UpKSl2JeLt7S1vb2+77gMAAAAAGbG5w/PQQw/p119/tWnsr7/+ateCBQAAAABwP9hc8NSrV0/r16/X5cuXMxx3+fJlrV+/XvXr189qbgAAAADuYDjh5upsLni6du2qK1euqFu3bjpx4kS6Y+Lj49W9e3ddvXpVXbp0ybYkAQAAACAzbD6Hp3z58nrnnXf0/vvvq1mzZqpdu7YqVqyoggUL6urVqzpy5Ij27dunlJQUvfPOOwoICLifeQMAAAAPHDdYQ8DhbC54JKlDhw4qW7asJkyYoD179mjPnj2p9leoUEFDhw7VU089la1JAgAAAEBm2FXwSFKDBg3UoEEDxcfHKzY2VleuXFHBggXl7++vsmXL3o8cAQAAAIgOT2bYXfDcVrZsWQocAAAAAKZm86IFAAAAAOBqMt3hAQAAAOBgTGmzGx0eAAAAAG6LDg8AAADgImjw2I8ODwAAAAC3ZTEMFrcDAAAAXMG777zl8GN+8J8Ihx8zO9k0pe3zzz/PVPBWrVpl6n6OYvFrmeUYRlwUcYhDHOK4fBzjXGCW41i8d5vucRGHOMQhjr1x4H5sKnjefPNNWSwW2dMMslgsmSp4kpKSZBiG8ubNa9129OhRxcXFydvbW7Vr17Y7JgAAAOAOmJtlP5sKnkWLFt3vPHT9+nW9/fbb2rJli1JSUtShQwe9++67GjlypJYvXy7DMGSxWFS1alXNnz9fhQoVuu85AQAAAHBtNhU89erVu995aPbs2YqOjlZ4eLgKFSqkRYsW6caNG9qwYYPef/99VatWTQcOHNC4ceM0a9YsDRs27L7nBAAAAMC1mWZZ6o0bN2rAgAHq1q2bJKlatWp65ZVX9Prrr6tt27aSpIoVK+qvv/7SqlWrKHgAAADwwGFGm/2yVPAcOHBABw8eVGJiolJSUlLts1gs6tu3r82xzp49q2rVqll/rlGjhgzDSLVN+qcQmjZtWlbSBgAAAPCAyFTBc/XqVfXp00d79uyxnltze0GD2/+2t+ApWrSozpw5Y/359r/Pnj2batyZM2dUpEiRe8YLCQnJcH90dLTNuQEAAABmwBVl7JepgmfKlCnau3ev+vbtq/r166tTp04aM2aMSpQoofnz5+vy5csaN26cXTHr1aunyMhIPfTQQypQoIDGjx+v2rVra/r06apZs6bKlCmj+Ph4zZo1SzVr1sxM2gAAAAAeMJkqeL766iu1aNFC/fr106VLlyRJ3t7eCgwMVFBQkDp06KCVK1fqzTfftDnmoEGD1LVrV73yyiuSJF9fXy1dulQDBw5U48aN5enpqcTERBUoUED9+vW7Zzw6OAAAAHA3NHjsl6mC5/z589br4eTK9U+I5ORkSf9MaWvatKk++eQTuwoeHx8fff755/rhhx9048YNBQUFKU+ePJo3b55WrlypX3/9VSVKlFDr1q1VunTpzKQNAAAA4AGTqYKncOHCun79uiSpQIECypUrl06fPm3dnyNHDiUkJNgdN1++fHriiSdSbcuTJ486duyYmTQBAAAAPOByZOZO/v7++vXXX/8JkCOHqlatqs8++0xnz57VqVOntHz5cvn6+mZrorft3btXnTt3vi+xAQAAADMzDMffXF2mCp7GjRvr+++/V1JSkiSpT58+io2NVcOGDdWoUSP99ttv6tWrV7YmetvFixe1d+/e+xIbAAAAgHvJ1JS2jh07pppm9tRTT2np0qXauHGjcuTIoZCQENWpU8eumHdOicvIxYsX7YoLAAAAuA83aLk4WJYuPHqnmjVrZmm56ODgYFkslnuOu32NHwAAAAC4l2wreLIqb968qlOnjkJDQzMcd+jQIa1YscJBWQEAAADm4Q7n1DiaxcjE5VptWTTAYrFo4cKFNsds3769vLy8NHv27AzHbd68WYMGDdKRI0dsjg0AAAC4gzfeeMPhxxw7dqzDj5mdMtXhOXnyZJptKSkp+uOPP3Tr1i0VKVJE+fLlsytmlSpVtHnzZpvGZqJGS5fFr2WWYxhxUcQhDnGIY3+caj2zHufgbNM9rsRfm2Q5jmfAFtM9LuIQhzgPThy4n0wVPNu2bUt3e3Jysj777DMtWbLEru6OJPXo0eOe09kkKTQ0VL/88otdsQEAAAB3kF1f/D9IMrUs9d3kyZNHXbp0Uf369fXhhx/adV9vb2/Vq1cvO9MBAAAA8IDL1oLntqpVq2r37t33IzQAAADwwOLCo/a7LwXP/v37lSPHfQkNAAAAADbL1Dk8n3/+ebrbExMTFRMTo23btum5557LSl4AAAAA/sUNGi4Ol6mC580335TFYkn3pKlcuXKpVatWGj58eJaTAwAAAICsyFTBs2jRojTbLBaLPD09Vbp0aRUsWDDLiQEAAABIjVXa7JepgofV1AAAAAC4gkytLBASEqLo6Oi77v/6668VEhKS6aQAAAAAIDtkqsNz6tQpXbt27a77r1+/rtOnT2c6KQAAAADpYEab3e7L2tGnTp1S/vz570doAAAAALCZzR2ezZs3a8uWLdaflyxZom+++SbNuISEBO3du1e1atXKlgQBAAAA/IMGj/1sLniOHTumDRs2SPpnRbb9+/dr//79acblz59ftWrV0ogRI7ItSQAAAADIDJsLnn79+qlfv36SpIoVK2r8+PFcXBQAAABwIJaltp/FyMSzdurUKRUrVkx58+a9HzkBAAAASMfgIa85/JiTPprg8GNmp0yt0paSkqJdu3YpODg43f3btm1TQECAypQpk+nErl+/rsTEREmSp6en8uXLl+lYd2Pxa5nlGEZcFHGIQxziECeb4xjnArMcx+K923SPizjEIY7548D9ZKrg+eijj/Tnn3/eteBZsGCBSpQooYkTJ9oV99y5c/r4448VHR2tM2fOpNrn4+OjkJAQvfrqq/L29s5M2gAAAIBLY0ab/TJV8Ozbt08dO3a86/4GDRpo6dKldsX89ddf1blzZxmGoYYNG8rf319eXl6S/ln5LS4uTuvWrdO6deu0ePFiBQQEZCZ1AAAAAA+QTBU8ly5dUpEiRe6638vLSxcuXLArZkREhMqXL6+ZM2eqYMGC6Y65cuWKevfurTFjxmj+/Pl2xQcAAABcHR0e+2XqwqNFixbVsWPH7rr/2LFjKly4sF0x9+/fr549e9612JGkggULqkePHvrxxx/tig0AAADgwZSpgqdBgwZauXKlDh8+nGbfoUOHtHLlSjVo0MCumB4eHtZFCjLy119/KU+ePHbFBgAAANyB4YT/XF2mprT169dP0dHRCgsLU9OmTa3n0xw9elSbN29WoUKFrNfssVVISIjGjRunEiVKqG7duumO+f777zV+/Hg1atTIpngZiY6Otis/AAAAAK4nUwWPj4+Pli9frpEjR2rDhg364osvJEkWi0WBgYF69913Vbp0abtivvHGG+rZs6c6d+6shx56SOXLl0+1aEFsbKzOnTunGjVq6I033shM2gAAAAAeMJkqeCTJ19dXn3zyiS5fvqwTJ05IksqVK2f3uTu3eXp66rPPPtNXX32lr7/+WrGxsYqPj5f0zyIIQUFBCg4OVkhIiCwWyz3j0cEBAACAu2HRAvtluuC5rXDhwmmKnN27d2vt2rUaN26c3fEaNWpk05Q1AAAAALiXTC1akJ4TJ05oypQpCg4OVnh4uNavX5+pODExMVq3bl26CyJI/1ycdNq0aVlJFQAAAHBNhhNuLi5LHZ4rV65o48aNWrt2rX788UflyJFDjz32mDp37qzGjRvbFevq1avq1q2bDhw4IMMwZLFYFBQUpNGjR8vb29s67uzZs5o+fbrdiyIAAAAAePBkquDZtWuX1qxZo+joaF2/fl2+vr6SpHHjxql58+aZSmT27NmKi4tTRESEqlWrpj179igyMlJhYWGaN2+e/P39MxUXAAAAcBdu0HBxOJsLnt9//11r165VVFSUzp49Kx8fH3Xq1EktW7ZUrly5FBoaqty5c2c6kS1btqh///5q1aqVJMnPz0/BwcHq3bu3OnbsqLlz56p69eqZjg8AAADgwWNzwdOsWTMVLFhQTZo0UcuWLVWvXj3rvturtGXFmTNnVLly5VTbvL299emnn6pnz54KDw/XjBkzlDdv3iwfCwAAAHBFBsu02c1i2PisVaxYUUWKFFHz5s3VsmVLVatWzbrvxIkTatKkiaZOnaomTZpkKpHg4GANGTJELVq0SLMvKSlJ/fv31549exQeHq5Zs2bpyJEjmToOAAAA4Kr69h/i8GNOj/zI4cfMTjZ3eNavX681a9Zo/fr1WrJkiXx9fdWyZUs9//zz2ZJI1apVFR0dnW7B4+HhoRkzZmjo0KGaOXOmTdfhsYXFr2WWYxhxUcQhDnGIY3cc41xgluNYvHeb7nERhzjEIY6rx4H7sXlZ6vLly+uNN97Qt99+qxkzZiggIEAzZsxQo0aN1L9/f1ksFt28eTPTiTRv3lynT5/WpUuX0t2fK1cuTZ48We3atZOPj0+mjwMAAAC4KsNw/M3V2b1KW86cOdWwYUM1bNhQCQkJWr9+vdauXSvDMPT6669r2bJlCg0NVaNGjVItJ30voaGhCg0NzXCMxWLRqFGj7E0ZAAAAwAMqSxce9fLy0ssvv6zVq1dr/fr16tSpk3777Td98MEHatiwYXblCAAAAECu1+E5c+aMBgwYoNq1a6tWrVrq16+fTp8+bXecOXPmqEKFCurQoYPd981SwXOnO6e8zZw5U40aNcqu0AAAAABczPXr19WlSxf99ttvGjt2rMaNG6fjx4+rc+fOunbtms1x4uPjNXPmTBUrVixTeWTqwqMZuXPKGwAAAIDs40qn1KxYsULx8fHatGmTfH19JUkVKlRQaGioli9frvDwcJvijBw5Us8995z+97//ZWrNgGzr8AAAAADAbdu2bVONGjWsxY4klS1bVrVq1VJ0dLRNMdavX6+ff/5ZQ4ZkfjluCh4AAAAA2S42NlYBAQFptvv7+ys2Nvae909ISFBERISGDRumwoULZzqPbJ/SBgAAAOD+MJywTnRISEiG++/WrUlISJCnp2ea7V5eXkpMTLzncceNG6eHH35Ybdq0sS3Ru6DgAQAAAGAq33//vaKiorRmzRpZLJYsxaLgAQAAAFyEMy4Eauv5Nv/m6emZbifnbp2fO40YMUIvvPCCSpYsaY1x8+ZNpaSkKDExUXnz5lWePHlsyoOCBwAAAEC28/f317Fjx9Jsj4uLk7+/f4b3jYuLU1xcnJYtW5ZmX926dfXWW2+pa9euNuVBwQMAAAAg2wUHB2vcuHGKj49X2bJlJUknT57UDz/8oKFDh2Z430WLFqXZNnr0aKWkpOidd95JtfLbvVDwAAAAAMh2YWFhWrJkifr06aOBAwfKYrFoypQpKlmypNq1a2cdd+rUKTVu3Fh9+vRRv379JEn169dPE8/T01M3b95Md19GWJYaAAAAQLbLnz+/Fi5cqIcfflivv/66XnvtNZUpU0YLFy5UgQIFrOMMw9CtW7fu2wp0dHgAAAAAF+GMRQuyolSpUoqMjMxwTJkyZXT06NF7xlq8eHGmcrAYzljMGwAAAIDduvca7PBjzp01yeHHzE4PdIfH4tcyyzGMuCjiEIc4xLE7zol9YVmOU672CtM9LuIQhzjmj1OsXtY/MF/YM8l0jyu74pgdrQr7meocnuvXr2vNmjWaOXOmtm7dqpSUlDRj4uPj9dZbbzkhOwAAAACuxjQdnosXL6pdu3aKj4+3bitfvrw++ugjlS9fPtW4zz//XBEREc5IEwAAAHAazkaxn2k6PFOnTlVSUpI+/fRTHThwQHPmzNGNGzfUvn17xcTEODs9AAAAAC7INAXPzp07NWDAANWpU0ceHh566qmntHr1atWpU0c9evTQtm3bnJ0iAAAA4FSGE26uzjQFz/nz5/Xwww+n2lagQAHNmDFDjRo10oABA7R+/XrnJAcAAADAJZnmHJ6HHnpI8fHxqlOnTqrtOXPm1IQJE5Q/f3698cYbatOmjU3xQkJCMtwfHR2d6VwBAAAAuAbTFDw1a9bUxo0b1bp16zT7LBaLPvjgAxUoUEALFiyQxWJxQoYAAACAc7Fmgf1MU/C0adNGy5Yt06VLl1SkSJF0x7z55psqWrSotm/ffs94dHAAAAAAmKbgCQwMVGBg4D3H9ejRQz169HBARgAAAIC50OGxn2kWLbgtJiZG69at088//5zu/nPnzmnatGkOzgoAAACAKzJNh+fq1avq1q2bDhw4IMMwZLFYFBQUpNGjR8vb29s67uzZs5o+fbr69evnxGwBAAAAx6PBYz/TdHhmz56tuLg4RUREaMOGDRoxYoSOHDmisLAwxcbGOjs9AAAAAC7INAXPli1b1L9/f7Vq1Up+fn7q0KGD1q5dq2LFiqljx4766aefnJ0iAAAAABdjmoLnzJkzqly5cqpt3t7e+vTTTxUQEKDw8HDFxMQ4KTsAAADABAzD8TcXZzEMczyK4OBgDRkyRC1atEizLykpSf3799eePXsUHh6uWbNm6ciRI07IEgAAAHCeLt0GOvyYC+dNcfgxs5NpFi2oWrWqoqOj0y14PDw8NGPGDA0dOlQzZ87MtguPWvxaZjmGERdFHOIQhzh2x4le2z/LcUJaR5rucREn4ziNWmb99/5VFL934hDnfsYxO3O0KlyLaaa0NW/eXKdPn9alS5fS3Z8rVy5NnjxZ7dq1k4+Pj4OzAwAAAOCKTNPhCQ0NVWhoaIZjLBaLRo0a5aCMAAAAAHOhwWM/03R4AAAAACC7mabDAwAAACBjnMNjPzo8AAAAANwWBQ8AAAAAt8WUNgAAAMBFMKXNfnR4AAAAALgtOjwAAACAi6DDYz86PAAAAADcFh0eAAAAwEUYXHrUbnR4AAAAALgtCh4AAAAAbospbQAAAICLYNEC+1kMg6cNAAAAcAXtOw9w+DGXLZrq8GNmpwe6w2Pxa5nlGEZcFHFcLE52vFEsWzTVdI+LOC4Wp0K7rMc5utx8j4s4LhVn4ezXsxynS89xpntcxCFOVuKYHa0K+7nEOTw3b95UdHS0Ll++7OxUAAAAALgQlyh4rl+/rn79+ik2NtbZqQAAAABOYzjh5upMM6Xt9dfv3la/efOmDMPQzJkzVaxYMVksFo0dO9aB2QEAAABwRaYpeNatW6dChQqpUKFCafYZhiGLxaKjR48qT548slgsTsgQAAAAgKsxTcETFham9evXq3379urWrZty5sxp3ZeYmKh69epp0qRJqlu3rhOzBAAAAJzIHeaYOZhpCp73339fLVu21MiRIxUVFaWRI0dai5vMdHRCQkIy3B8dHZ2pPAEAAAC4DlMtWlC7dm2tXbtWzz33nLp376433nhDFy9edHZaAAAAgCkYhuNvrs40HZ7bcuXKpV69eqlZs2YaOXKkmjZtqldffdXuLg8dHAAAAACmK3hu8/X11SeffKKoqCiNHTtWhjuUlwAAAEAW8InYfqYteG5r2bKlGjdurEuXLqlEiRLOTgcAAACACzHVOTySFBMTo3Xr1unnn3+2bsufP79Kly6tPHny6Ny5c5o2bZoTMwQAAACcg3N47GeaDs/Vq1fVrVs3HThwwHrdnaCgII0ePVre3t7WcWfPntX06dPVr18/J2YLAAAAwBWYpsMze/ZsxcXFKSIiQhs2bNCIESN05MgRhYWFKTY21tnpAQAAAHBBpil4tmzZov79+6tVq1by8/NThw4dtHbtWhUrVkwdO3bUTz/95OwUAQAAAKdiSpv9TFPwnDlzRpUrV061zdvbW59++qkCAgIUHh6umJgYJ2UHAAAAwBVZDJOs9xwcHKwhQ4aoRYsWafYlJSWpf//+2rNnj8LDwzVr1iwdOXLECVkCAAAAztO6fX+HH3PtskiHHzM7mWbRgqpVqyo6OjrdgsfDw0MzZszQ0KFDNXPmTLsvQno3Fr+WWY5hxEURhzjEIQ5xiOOScYxzgVmOY/HebbrHRRziZCUO3I9pprQ1b95cp0+f1qVLl9LdnytXLk2ePFnt2rWTj4+Pg7MDAAAAnM9wws3VmabDExoaqtDQ0AzHWCwWjRo1ykEZAQAAAHB1punwAAAAAEB2M02HBwAAAEDGzLHcmGuhwwMAAADAbdHhAQAAAFwEHR770eEBAAAA4Lbo8AAAAAAuggaP/ejwAAAAAHBbFDwAAAAA3BZT2gAAAAAXwaIF9qPDAwAAAMBt0eEBAAAAXAQdHvvR4QEAAADgtiyGQZ0IAAAAuIJnX+zv8GN+uSrS4cfMTqaf0nblyhXFxsYqR44cKl++vPLly5dtsS1+LbMcw4iLIg5xiEMcu+MY5wKzHMfivdt0j4s4xCEOcVw9DtyPaQqeL7/8UsnJyWrVqpUkKSUlRePGjdOSJUt08+ZNSZKHh4e6d++uvn37OjFTAAAAwDmYm2U/0xQ8s2bNUlhYmPXnGTNmaPHixWrbtq0aNGggSfrvf/+rGTNmyMvLSy+//LKzUgUAAADgIkxT8MTHx8vPz8/686pVq9SjRw8NHDjQuq1Ro0by9PTUp59+SsEDAAAA4J5Ms0pbrly5dOPGDevPf/zxh4KCgtKMe+KJJ3Tq1ClHpgYAAACYgmE4/ubqTFPw1KhRQ5s2bbL+/Oijj+rnn39OM+7nn39W8eLFHZkaAAAAABdlmilt/fr108svvyxPT0+Fh4frtdde07Bhw2SxWKydnh07dmj69Onq2rXrPeOFhIRkuD86Ojo70gYAAAAcxg0aLg5nmoKnZs2amjZtmoYPH66FCxfKy8tLSUlJGjNmjHWMYRhq3bo1q7QBAAAAsIlpCh5JeuaZZ7R161Zt3LhRP/zwg86fPy/DMFS4cGH5+/urUaNGKl++vE2x6OAAAADA3bjDOTWOZqqCR5IKFCigF198US+++KKzUwEAAADg4kyzaMFtMTExWrdunQ4fPpzu/nPnzmnatGkOzgoAAACAKzJNh+fq1avq1q2bDhw4IMMwrIsVjB49Wt7e3tZxZ8+e1fTp09WvXz8nZgsAAAA4HlPa7GeaDs/s2bMVFxeniIgIbdiwQSNGjNCRI0cUFham2NhYZ6cHAAAAwAWZpuDZsmWL+vfvr1atWsnPz08dOnTQ2rVrVaxYMXXs2FE//fSTs1MEAAAAnIoLj9rPNAXPmTNnVLly5VTbvL299emnnyogIEDh4eGKiYlxUnYAAAAAXJHFMMxRtwUHB2vIkCFq0aJFmn1JSUnq37+/9uzZo/DwcM2aNUtHjhxxQpYAAACA84S07O/wY0ZHRTr8mNnJNIsWVK1aVdHR0ekWPB4eHpoxY4aGDh2qmTNnymKxZMsxLX4tsxzDiIsiDnGIQxziEIc42RDHOBeY5TgW792me1zEca04cD+mmdLWvHlznT59WpcuXUp3f65cuTR58mS1a9dOPj4+Ds4OAAAAgCsyTYcnNDRUoaGhGY6xWCwaNWqUgzICAAAAzMUcJ6O4FtN0eAAAAAAgu5mmwwMAAAAgY3R47EeHBwAAAIDbosMDAAAAuAgaPPajwwMAAADAbdHhAQAAAFwE5/DYjw4PAAAAALdFwQMAAADAbTGlDQAAAHARTGmzHx0eAAAAAG6LDg8AAADgImjw2I8ODwAAAAC3RYcHAAAAcBGcw2M/i2HwtAEAAACuoMGz/R1+zB1fRjr8mNnJJTo8ly9f1okTJ+Tt7S1vb+9si2vxa5nlGEZcFHGIQxzi2B3HOBeY5TgW792me1zEIQ5xiOPqceB+THMOz61btzRx4kQ9+eSTatCggebPny9J+vjjj/Xkk0+qXbt2euaZZzRgwAAlJyc7OVsAAADA8QzD8TdXZ5oOz8KFC/Xxxx+rWbNmKliwoCIjI/X3339rxowZ6tatm6pXr64DBw5o/vz5+uSTT9SzZ09npwwAAADA5ExT8KxZs0Y9e/bUoEGDJElPPvmkBg4cqN69e6t//3/mKoaEhChHjhz64osvKHgAAADwwHGDhovDmWZK26lTpxQY+P9z2gMDA5WSkqLHH3881bj69evr5MmTjk4PAAAAgAsyTYenYMGCunz5svXn2/9OSEhINe7y5csqWLCgAzMDAAAAzMEdzqlxNNMUPDVq1NCsWbNUqVIlFShQQOPHj5e/v7/mzJmjxx9/XAULFtRff/2lefPmqXLlyveMFxISkuH+6Ojo7EodAAAAQDrOnDmjiIgI7dy5U4ZhKCgoSMOHD1epUqUyvN/Bgwe1YsUK7d27V2fOnFGRIkVUu3ZtDRo0SGXLlrUrB9MUPIMGDVKnTp0UGhoqSSpSpIiWLVumvn376plnnlG5cuV04sQJ/f3331q6dKmTswUAAAAcL8WFOjzXr19Xly5dlCdPHo0dO1aSNGXKFHXu3Fnr1q1T/vz573rfL7/8UseOHVOnTp1Uvnx5nTt3TjNmzNCLL76ozz//XD4+PjbnYZqCx9/fX1FRUfr666918+ZNNW3aVMWKFdPChQs1d+5cHTt2TBUqVFD79u1VvXr1e8ajgwMAAAA4z4oVKxQfH69NmzbJ19dXklShQgWFhoZq+fLlCg8Pv+t9u3fvrqJFi6baVqtWLYWEhGjFihUaOHCgzXmYpuCRpIceekjt2rVLta1o0aJ64403nJQRAAAAgMzYtm2batSoYS12JKls2bKqVauWoqOjMyx4/l3sSFLp0qVVtGhRnTt3zq48TLNK220xMTFat26dfv7553T3nzt3TtOmTXNwVgAAAIDzudKFR2NjYxUQEJBmu7+/v2JjY+2OFxcXpwsXLsjPz8+u+5mmw3P16lV169ZNBw4ckGEYslgsCgoK0ujRo+Xt7W0dd/bsWU2fPl39+vVzYrYAAADAgyGzi4ElJCTI09MzzXYvLy8lJibalcPNmzf13nvvqWjRonrxxRftuq9pOjyzZ89WXFycIiIitGHDBo0YMUJHjhxRWFhYpipAAAAAwN0YTriZwfvvv68ff/xR48ePl5eXl133NU2HZ8uWLerfv79atWolSfLz81NwcLB69+6tjh07au7cuTYtVgAAAAAg+2R2MTBPT890Ozl36/zczYQJE7RixQqNGTNGDRo0sDsP03R4zpw5k+b6Ot7e3vr0008VEBCg8PBwxcTEOCk7AAAAwPkMw+LwW2b5+/vr2LFjabbHxcXJ39/fphgzZ87U3Llz9c4771gbI/ayGIY5rtcaHBysIUOGqEWLFmn2JSUlqX///tqzZ4/Cw8M1a9YsHTlyxAlZAgAAAM5Tp/EAhx/z+61TM3W/BQsWaNy4cdq8ebP1YqEnT55UaGiohg4dqldeeSXD+y9atEgffvihBg8erF69emUqB8lEBc+AAQOUM2dOTZo0Kd39N2/e1NChQ7V582ZZLJZsKXgsfi2zHMOIiyIOcYhDHLvjGOcCsxzH4r3bdI+LOMQhDnFcPY7ZuVLBc+3aNbVs2VJ58+bVwIEDZbFYNGXKFF29elXr1q1TgQIFJEmnTp1S48aN1adPH+vCZBs2bNDQoUPVoEGDNIuVFSxY0OYOkWSic3iaN2+u+fPn69KlSypSpEia/bly5dLkyZM1cuRIbd++3QkZAgAAAM5ljlaFbfLnz6+FCxcqIiJCr7/+ugzDUGBgoIYPH24tdiTJMAzdunVLd/Zhtm/fLsMwtH379jSf/evVq6fFixfbnIdpCp7Q0FCFhoZmOMZisWjUqFEOyggAAABAVpQqVUqRkZEZjilTpoyOHj2aatuYMWM0ZsyYbMnBNAUPAAAAgIy5UIPHNEyzShsAAAAAZDc6PAAAAICLSKHFYzc6PAAAAADcFgUPAAAAALfFlDYAAADARbjSstRmQYcHAAAAgNuiwwMAAAC4CBo89qPDAwAAAMBt0eEBAAAAXATLUtuPDg8AAAAAt0WHBwAAAHARrNJmP4th8LQBAAAArqDKMwMcfsyfv5nq8GNmpwe6w2Pxa5nlGEZcFHGIQxziEIc4xCHOXeMY5wKzHMfivdt0j8td48D9PNAFDwAAAOBKDFmcnYLLMVXB8/fff2vZsmWKjo5WXFycEhMTJUmenp7y8/NTSEiI2rVrp3z58jk5UwAAAACuwDQFz5kzZ9SlSxedOnVKtWrVUmhoqLy8vCRJCQkJio2N1YQJE7R06VItWLBApUqVcnLGAAAAgGOxLLX9TFPwjB49Wh4eHtq8ebPKlCmT7piTJ0+qb9++ioiIUGRkpIMzBAAAAOBqTHMdnl27dmnw4MF3LXYkqUyZMhowYIB27drlwMwAAAAAczAMx99cnWk6PBaL7Sdg2TI2JCQkw/3R0dE2Hw8AAACAazJNhycoKEiTJk1SfHz8XcecPHlSU6ZMUVBQkAMzAwAAAOCqTNPhGT58uDp37qymTZuqRo0aKl++fJpFCw4cOKDSpUtr+PDh94xHBwcAAADuhkUL7GeagqdkyZJat26dli9frq+//lrR0dG6fPmyJMnLy0v+/v4aNmyYwsLCWJYaAAAAgE1MU/BIUt68edWlSxd16dLF2akAAAAApkODx36mKnjOnDmjzZs3K1euXGrevLmKFCmi06dPa86cOTpx4oTKlSun8PBw+fr6OjtVAAAAAC7ANAVPXFyc2rVrpytXrkiS5syZowULFig8PFzXrl1TuXLlFBUVpY0bN2rt2rVceBQAAAAPHM7hsZ9pVmmLjIxUyZIltXHjRu3atUs1atRQ7969Vbx4cW3btk2rV6/W1q1bVbRoUc2ZM8fZ6QIAAABwAaYpeH788Uf16NFDjzzyiIoWLaqhQ4fq+PHj6tatmwoVKiRJKl68uLp06aKYmBgnZwsAAADAFZhmStvFixdTTVMrXbq0JKlMmTKpxj3yyCM6e/asQ3MDAAAAzMAwLM5OweVYDMMwxUzABg0aaMSIEWrSpIkkKSUlRW3bttX48eP16KOPWsdFR0frrbfe0p49e5yVKgAAAOAUjzwxyOHH/N/OyQ4/ZnYyTYfHz89PBw4csBY8OXLk0OrVq9OMO3r0qMqWLZstx7T4tcxyDCMuijjEIQ5xiEMc4hDnvscxzgVmOY7Fe7fpHpfZ4phdirMTcEGmKXi6d++uhISEe447fPiwmjVr5oCMAAAAALg60xQ8DRo0sGnctGnT7nMmAAAAgDmxLLX9TLNKGwAAAABkN9N0eAAAAABkjA6P/ejwAAAAAHBbFDwAAAAA3BZT2gAAAAAXwZQ2+9HhAQAAAOC26PAAAAAALiJFFmen4HLo8AAAAABwW3R4AAAAABfBOTz2o8MDAAAAwG1R8AAAAABwW0xpAwAAAFwEU9rsZzEMg6cNAAAAcAHF6g12+DEv7Jnk8GNmpwe6w2Pxa5nlGEZcFHGIQxzi2B3HOF0vy3EspfaY7nERhzjEcYE45dtkPc6xNeZ7XNkUx+xuOTsBF2S6c3hiYmK0bt06/fzzz+nuP3funKZNm+bgrAAAAAC4ItN0eK5evapu3brpwIEDMgxDFotFQUFBGj16tLy9va3jzp49q+nTp6tfv35OzBYAAABwvFucjGI303R4Zs+erbi4OEVERGjDhg0aMWKEjhw5orCwMMXGxjo7PQAAAAAuyDQFz5YtW9S/f3+1atVKfn5+6tChg9auXatixYqpY8eO+umnn5ydIgAAAAAXY5qC58yZM6pcuXKqbd7e3vr0008VEBCg8PBwxcTEOCk7AAAAwPluGo6/uTrTnMNTrFgxnT17Ns32/Pnz6+OPP1b//v3Vs2dPhYeH2xQvJCQkw/3R0dGZyhMAAACA6zBNh6dq1ap3LUI8PDw0Y8YMPf3005o5c6aDMwMAAADM4ZYsDr+5OtN0eJo3b6758+fr0qVLKlKkSJr9uXLl0uTJkzVy5Eht3779nvHo4AAAAAAwTcETGhqq0NDQDMdYLBaNGjXKQRkBAAAA5uIO59Q4mmkKHumfhQs2b96snDlzqnnz5ipatKhOnz6tOXPm6MSJEypXrpxeeeUVlStXztmpAgAAAHABpil44uLiFBYWpqtXr0qS5s6dqwULFig8PFzXrl1TuXLlFBUVpS+//FKff/65SpUq5eSMAQAAAMcy6PDYzTSLFkRGRsrHx0cbN27Url27VKNGDfXu3VvFixfXtm3btHr1am3dulXFihXTnDlznJ0uAAAAABdgmoLnxx9/VI8ePfTII4+oaNGiGjp0qI4fP65u3bqpUKFCkqTixYurS5cuXI8HAAAAgE1MM6Xt4sWLqaaplS5dWpJUpkyZVOMeeeSRdK/XAwAAALg/5rTZy2IY5pgJ2KBBA40YMUJNmjSRJKWkpKht27YaP368Hn30Ueu46OhovfXWW9qzZ4+zUgUAAACcwlJrsMOPafwwyeHHzE6m6fD4+fnpwIED1oInR44cWr16dZpxR48eVdmyZbPlmBa/llmOYcRFEYc4xCGO3XGMc4FZjmPx3m26x0Uc4hCHOK4ex/RM0apwLaYpeLp3766EhIR7jjt8+LCaNWvmgIwAAAAAuDrTFDwNGjSwady0adPucyYAAACAWdHisZdpVmkDAAAAgOxGwQMAAADAbZlmShsAAACAezBSnJ2By6HDAwAAAMBt0eEBAAAAXIU5LqHpUujwAAAAAHBbdHgAAAAAl8E5PPaiwwMAAADAbdHhAQAAAFwFq7TZjQ4PAAAAALdFwQMAAADAbTGlDQAAAHAVTGmzGx0eAAAAAG7LYhhcvQgAAABwBZZq3R1+TOPgXIcfMzu5xJS2lJQU/frrr/L19VW+fPmyLa7Fr2WWYxhxUcQhDnGIY3cc41xgluNYvHeb7nERhzjEIY6rx4H7cYkpbVevXlXr1q31888/OzsVAAAAwHmMFMffXJxpOjxTpky5677k5GQZhqGVK1dq586dslgsGjBggAOzAwAAAOCKTFPwzJw5UxaLRXc7pchisSgqKsr6bwoeAAAAAPdimoLniSee0NGjRzV8+HA9++yzqfYlJiaqXr16Wrx4serWreukDAEAAAAnY70xu5nmHJ558+bpzTff1OjRo9WtWzcdP37cus9isTgxMwAAAACuyjQdHklq0aKFnnzySU2YMEHPP/+8unXrpl69emUqVkhISIb7o6OjMxUXAAAAcB7XX0TA0UzT4bnNy8tLH3zwgebNm6ctW7aoefPm+vbbb+nyAAAAALCbqTo8d6pTp47Wrl2ruXPn6u23377rYgZ3QwcHAAAAbscNlol2NNMWPJKUO3du9enTR61bt1Z8fLwqVark7JQAAAAAuBBTFTxnzpzR5s2blTNnTjVv3lxFixbV6dOnNWfOHJ04cULlypVTeHi4fH19nZ0qAAAAABdgmoInLi5O7dq105UrVyRJc+fO1YIFCxQeHq5r166pXLlyioqK0saNG7V27VqVKlXKyRkDAAAADsaUNruZZtGCyMhIlSxZUhs3btSuXbtUo0YN9e7dW8WLF9e2bdu0evVqbd26VUWLFtWcOXOcnS4AAAAAF2CagufHH39Ujx499Mgjj6ho0aIaOnSojh8/rm7duqlQoUKSpOLFi6tLly6KiYlxcrYAAACAM6Q44ebaTFPwXLx4MdU0tdKlS0uSypQpk2rcI488orNnzzo0NwAAAACuyWLYu97zfdKgQQONGDFCTZo0kSSlpKSobdu2Gj9+vB599FHruOjoaL311lvas2ePs1IFAAAAnMJSsYPDj2n88pnDj5mdTLNogZ+fnw4cOGAteHLkyKHVq1enGXf06FGVLVs2W45p8WuZ5RhGXFS2xWnfeUCW4yxbNNV0j4s4xCFO2jhL5w3LcpyXuo033eMiDnGIQxxXjwP3Y5qCp3v37kpISLjnuMOHD6tZs2YOyAgAAAAwGVZps5tpCp4GDRrYNG7atGn3ORMAAAAA7sI0ixYAAAAAQHYzTYcHAAAAwD0wpc1udHgAAAAAuC06PAAAAIDLMMUVZVwKHR4AAAAAbosODwAAAOAqOIfHbnR4AAAAANwXZ86c0YABA1S7dm3VqlVL/fr10+nTp226b1JSksaOHasGDRqoevXqateunfbu3Wt3DhQ8AAAAALLd9evX1aVLF/32228aO3asxo0bp+PHj6tz5866du3aPe8/fPhwrVy5UgMGDNDs2bNVokQJdevWTUeOHLErD6a0AQAAAK7Chaa0rVixQvHx8dq0aZN8fX0lSRUqVFBoaKiWL1+u8PDwu973l19+0RdffKHRo0frhRdekCTVrVtXzZs315QpUzRr1iyb86DDAwAAACDbbdu2TTVq1LAWO5JUtmxZ1apVS9HR0RneNzo6Wrlz59azzz5r3ZYrVy41b95cO3bsUHJyss15UPAAAAAArsIwHH/LpNjYWAUEBKTZ7u/vr9jY2Hvet3Tp0sqXL1+a+964cUPHjx+3OQ+mtAEAAAC4q5CQkAz3361bk5CQIE9PzzTbvby8lJiYmGHMhIQEeXl5pdleuHBh635bWQwjC2WbG7v9i71Xu81RyCdj5JMx8skY+WSMfDJGPhkjn4yRT8bMls+DKrMFT9WqVdW1a1e99tprqbZPmjRJc+fO1eHDh+8a85VXXtGVK1e0YsWKVNt37dql8PBwLVmyRHXq1LEpfzo8AAAAAO4qswWnp6dnup2cu3V+/n3fU6dOpdl++fJlSUq3+3M3nMMDAAAAINv5+/vr2LFjabbHxcXJ39//nvc9deqUrl+/nua+uXPnTrUQwr1Q8AAAAADIdsHBwTpw4IDi4+Ot206ePKkffvhBwcHB97zvjRs3tGnTJuu2mzdv6ssvv1SDBg2UJ08em/Og4AEAAACQ7cLCwlS6dGn16dNHX331laKjo9WnTx+VLFlS7dq1s447deqUKleurGnTplm3Va5cWc8++6xGjx6tlStXavfu3RoyZIhOnjyp/v3725UH5/AAAAAAyHb58+fXwoULFRERoddff12GYSgwMFDDhw9XgQIFrOMMw9CtW7f077XUIiIiNGnSJE2ePFmJiYmqWLGiPv74Y1WpUsWuPCh4AAAAANwXpUqVUmRkZIZjypQpo6NHj6bZnjdvXr311lt66623spQDU9oAAAAAuC2uwwMAAADAbdHhAQAAAOC2KHgAAAAAuC0KHgAAAABui4IHAAAAgNui4AEAAADgtrgOz7+cOXNGERER2rlzpwzDUFBQkIYPH65SpUo5PJdNmzZpw4YNOnTokC5cuCAfHx81adJEPXv2VMGCBR2ez79169ZNO3bsUK9evTR48GCn5fHtt99qzpw5Onz4sCwWix5++GENGzZMgYGBDs9l3759mj59uo4cOaK///5bDz/8sDp27KgXX3zxvh737Nmzmjt3rg4dOqRffvlFf//9t6Kjo1WmTJlU45KSkjR58mStX79eiYmJqlSpkl577TXVrVvX4fkcPHhQK1as0N69e3XmzBkVKVJEtWvX1qBBg1S2bFmH5/Nvc+bM0cSJE1WrVi199tlnTssnLi5OU6ZMUUxMjK5fvy4fHx+99NJL6tKli8PzOX36tDWXixcvysfHR02bNlXPnj2VP3/+bMvH1ve+hIQEjRs3Tl999ZWSkpJUs2ZNvfXWW6pQoUK25WJrPrt379bq1au1f/9+nT9/Xg899JCeeOIJDRgwQMWKFXN4Pv82YsQILV++XM8995wmTJjgtHz279+vyMhIHThwQDdv3lTZsmXVq1cvNW/e3OH5HDt2TFOmTNH+/ft15coVlS5dWm3atFGXLl2UK1f2fTzavn275s6dq7i4OCUkJKho0aJ67LHH1L9/f/n7+1vHOerzhy35OPLzh63Pz53M8vkDroOC5w7Xr19Xly5dlCdPHo0dO1aSNGXKFHXu3Fnr1q3L1v+h22L+/Pny8fHR4MGDVbJkSR0+fFjTpk1TTEyMli1bphw5nNeg++KLL9K9QJSjLVu2TB988IE6duyoPn36KCUlxVpsONovv/yi8PBw1ahRQx988IHy5cunzZs36+2331ZycrJeeuml+3bs48ePa+PGjapSpYrq1KmjHTt2pDtu+PDh+vbbb/X666+rbNmyWrJkibp166bly5erUqVKDs3nyy+/1LFjx9SpUyeVL19e586d04wZM/Tiiy/q888/l4+Pj0PzuVN8fLxmzpyZ7R9U7c3n4MGD6tKli+rVq6cPP/xQBQsW1PHjx3Xt2jWH53Pt2jWFh4frxo0bGjhwoHx8fHTw4EFFRkbq+PHjmjx5crblY8t7n2EY6tWrl06dOqV3331Xnp6emjNnjjp37qyoqCiVLFnSofl89tlnunbtmnr37q2yZcvq+PHjmjp1qnbs2KF169aluqK4I/K50759+7R+/fr79kWZrfl888036tevn1q0aKGJEycqd+7cio2NVVJSksPzOXfunDp16iRvb28NHz5cRYoU0Xfffafx48fr4sWLGjZsWLblk5CQoCpVquill15S0aJFdfr0ac2dO1dhYWFav369Spcu7dDPH7bk48jPH7bkcyezfP6AizFgtWDBAqNixYrG77//bt124sQJo1KlSsb8+fMdns+FCxfSbFu7dq0REBBg7Nq1y+H53Hb58mUjKCjIWL9+vREQEGB89NFHTskjPj7eqFatmvHJJ5845fj/NnHiRKNKlSrGlStXUm0PCwszwsLC7uuxb926Zf33ihUrjICAACM+Pj7VmCNHjhgBAQHGqlWrrNtu3LhhNGnSxOjZs6fD80nv9X3y5EmjQoUKxuTJkx2ez51eeeUV49133zVefvllo3379tmai6353Lp1y2jWrJnRp0+fbD9+ZvLZvn27ERAQYGzfvj3V9vHjxxuVKlUyrl27lm352PLet3XrViMgIMDYvXu3dUxiYqJRt25d44MPPsi2XGzNJ70xe/bsMQICAoyVK1c6PJ/bkpOTjebNmxuzZs0yGjZsaAwdOjRbc7E1n7/++st4/PHHjf/85z/ZfvzM5LNs2TIjICDA+O2331KNGzRokPHEE0/c9xzj4uKMgIAAY968eYZhOP/zx7/zcfbnj3/nc5tZPn/A9XAOzx22bdumGjVqyNfX17qtbNmyqlWrlqKjox2eT9GiRdNsq1atmiTp3Llzjk7HasKECSpfvrxatGjhtBwkafXq1cqRI4c6dOjg1Dxuu3HjhnLlyqW8efOm2l6wYEGlpKTc12Pb8m1bdHS0cufOrWeffda6LVeuXGrevLl27Nih5ORkh+aT3uu7dOnSKlq0aLa/vu35NnL9+vX6+eefNWTIkGzNwd58YmJiFBcXp/Dw8PuWhz353LhxQ5LSdAk8PT2VkpIiIxuvYW3Le9+2bdv00EMP6fHHH7eOKVSokBo2bJjt79e25OPI92t7jjVv3jzdunVLr7zySrbmYG8+mzZt0sWLF+9rHvbkc7fXc6FChe77+7UkFS5cWJKUM2dOSc7//PHvfJz9+ePf+dxmls8fcD0UPHeIjY1VQEBAmu3+/v6KjY11QkZp7dmzR5Lk5+fnlON///33+vzzzzVixAinHP9O+/bt06OPPqoNGzaoUaNGqly5sho3bqwlS5Y4JZ/WrVtLkv7zn//o3LlzSkxM1IoVK/Tdd9+pa9euTsnpTrGxsSpdurTy5cuXaru/v79u3Lih48ePOymz/xcXF6cLFy447fWdkJCgiIgIDRs2zPo/XGfZt2+fpH/OuwoLC1OVKlUUGBio//znP06ZshkUFKSHH35YEyZMUGxsrK5evardu3dr0aJFat++/X2f8vvv976M3q9Pnz6tq1evOjSfzI65n/kcP35cM2fO1HvvvafcuXPf9xwyymffvn0qXLiwfv31Vz333HOqXLmynn76aU2bNk23bt1yeD5NmzZVkSJF9MEHHyg+Pl5XrlzR1q1btW7duvv2JcOtW7eUnJys33//Xe+9955KlChh/eDujM8fGeWTnvv9er5XPmb6/AHXwzk8d0hISJCnp2ea7V5eXkpMTHRCRqmdO3dOU6dOVVBQkPWbFkdKTk7We++9p1deeUWPPvqow4//b+fPn9f58+c1btw4DRkyRGXLltWmTZv0/vvv6+bNm9l6UrctAgICtGjRIvXr109Lly6VJOXOnVsjR47M1hNyMyshIUFeXl5ptt/+YJ+QkODgjFK7efOm3nvvPRUtWvS+L/JwN+PGjdPDDz+sNm3aOOX4dzp//rwkafDgwerYsaOGDh2qQ4cOaerUqTpz5oymT5/u0Hw8PDy0dOlS9e/fP9XruW3btvf9A0h6730JCQlp5vZL//96TkxMzNbzZu6Vz79duXJFo0ePlp+fnxo1anRf8rhXPiNHjlTjxo1TdcEcIb18zp8/r+vXr2vo0KHq06ePqlSpol27dmnGjBlKTEzU8OHDHZpP8eLFtXz5cvXp08f6+7FYLOrXr5+6d+9+X/Jo27atfv75Z0mSr6+vFi5caD1P0BmfPzLK598c8fkjo3zM9vkDroeCx0VcvXpVvXv3Vs6cORUREeGUHD7++GP9/fff6t27t1OO/2+GYejq1asaM2aMmjRpIkkKDAzUqVOnrCcvWywWh+Xz+++/a8CAASpfvrxGjRqlvHnzKjo6WiNHjpSHh4eef/55h+Xiit5//339+OOPmj17drqF2f32/fffKyoqSmvWrHHo6+Zubk8Re/755zVw4EBJUv369XXr1i1NnDhRcXFxDu2EJSUladCgQbpw4YLGjRunUqVK6aefftL06dOVM2dOjRo16r4c1wzvffbmc/PmTQ0dOlTnzp3TZ599lq0rftmaT1RUlA4ePKhNmzbdt2Pbk49hGEpKStLgwYOtHZT69evr8uXL1kK6UKFCDsvn4sWL6tevn/Lly6epU6eqcOHC+u677zRr1izlyZNHPXr0yPZcxo8frytXrig+Pl7z589XeHi4li5dmuFqkfeTrfk46m8wo3zM9vkDroeC5w6enp7pfpNyt29eHOXvv/9Wr169dPLkSS1evDhbVx+y1enTpzVr1iz95z//UXJycqrzPZKTk63fpv57vu39dPub3KCgoFTbGzRooO3bt+v8+fPy9vZ2WD4fffSRcuXKpVmzZlmnjwQGBurSpUv68MMP1aJFC6eurOfp6alTp06l2X758mVJckqRcduECRO0YsUKjRkzRg0aNHBKDiNGjNALL7ygkiVLWt8Hbt68qZSUFCUmJipv3rzKkyePw/LJ6PU9ceJEHT582KEFz6pVq7Rnzx5t3bpV5cqVkyTVrVtXhQoV0rvvvqsOHTqoYsWK2XrMjN777vZ+ffv1fD/es215L05JSdEbb7yhXbt2ac6cOdn+nNiSz+0vgrp37648efJYn6eUlBTdvHlTiYmJypcvX7ZPc8vo+cno9bxs2TIdO3ZMtWrVclg+c+fO1alTp/T1119b3/vq16+vlJQUTZ06VS+++GK657Fkxe2/1xo1auipp55ScHCw5syZo/fff98pnz8yyuc2R37+uFs+vXr1Mt3nD7gezuG5g7+/v44dO5Zme1xc3F3Xgr/fbty4oQEDBujQoUOaM2dOtl9fwlbx8fFKSkrSsGHDVLduXetN+mcJ0Lp16+rXX391aE73+p04urj49ddfVbFixTQfIqpXr67Lly/rwoULDs3n3/z9/XXq1Cldv3491fa4uDjlzp071cmyjjRz5kzNnTtX77zzjlq1auWUHKR/nodly5alen3/8MMP2r9/v+rWrWudpugoZnt9Hz16VF5eXtZi57bq1atL+uf5y073eu/L6P26VKlS2T6dzdb34vfee08bN27UpEmT7uu1wDLK59KlS7p48aI++uijVK/nM2fOaOPGjapbt66+/fZbh+UjOf71fK98fv31V/n6+qb5oqdatWq6ceOGTpw4ka35/Junp6fKlStnPY6zP3/8Ox/JuZ8/7szHjJ8/4Hro8NwhODhY48aNU3x8vPXChydPntQPP/ygoUOHOjyflJQUvfbaa/ruu+80e/Zs1axZ0+E53FapUiUtWrQozfbOnTvr+eef14svvpjmg9D91rhxY61atUo7duxQ06ZNrdu3b9+ukiVLqkSJEg7Np0SJEjpy5IiSk5NTdQJ++ukneXh4OLWDIv3z+o6MjNSmTZusCyzcvHlTX375pRo0aODQ7sVtixYt0uTJkzV48GC9/PLLDj/+v3P5t9GjRyslJUXvvPOOwwvCp556Snny5NGOHTsUHBxs3b59+3ZJUtWqVR2aT4kSJZSQkKDjx4+nei4OHDggSdnaTbXlvS8kJERr1qzRnj17VK9ePUn/nDfz9ddfZ/sKTra+F48ZM0YrV67UmDFj7ut5O/fKp0SJEum+nocMGaKAgAD16tVL5cuXd1g+ktSoUSNNmTJFO3bsSPXBefv27fLw8HB4PiVKlNCPP/6Y5tzGn376SVL2vp7T8+eff+p///ufnnvuOUnO//zx73yc/fnjznzM+PkDroeC5w5hYWFasmSJ+vTpo4EDB8pisWjKlCkqWbKk2rVr5/B8Ro0apU2bNqlXr17Kly+f9u/fb91XsmRJh05t8/T0VP369dPdV6pUqbvuu5+efvpp1a9fX++9954uXbpkXbRgx44dTpnr37FjRw0cOFC9e/dWhw4dlDdvXm3btk1ffPGFunbtet8Littz9Q8dOiRJ+u9//6uiRYuqaNGiqlevnipXrqxnn31Wo0eP1s2bN1WmTBl99tlnOnnyZLZfed2WfDZs2KDRo0frySef1OOPP57q9V2wYMFs/1bzXvmk9xr29PTUzZs378vr+175FClSRD179tSMGTNUsGBBPf744zp06JCmT5+u1q1bZ3sBdq98WrdurU8++UQ9evRQr1695OPjo0OHDmnGjBmqUqVKtk5HsuW9Lzg4WI899piGDRum119/3XrhUcMw9Oqrr2ZbLrbmM2fOHH3yySd64YUX9PDDD6caU7Ro0Wz9QGZLPum9Zj08PFSsWLFsfz3bkk9AQIDatGmjqVOnKiUlxbpowcqVK9WnT59s7cjZkk/79u21fv16vfLKK+rWrZuKFCmimJgYzZ8/X40bN87WCx/37dtXlStXVoUKFVSwYEH9/vvvWrBggXLmzGk9n8mRnz9syceRnz/ulY8ZP3/A9ViM7Lx4ghs4ffq0IiIitHPnThmGocDAQA0fPtwpJxUGBwene86FJPXr10/9+/d3cEZpVahQQb169dLgwYOdcvwrV65o4sSJ2rx5sxITE/XII4+oR48e1m+pHO3bb7/Vxx9/rGPHjikpKUnlypVTWFiY2rdvf9/nF99tukG9evW0ePFiSf/Mx540aZK++OILJSYmqmLFinrttdfuy/8w7pXPm2++qbVr194zZ0flk55OnTrp5s2b+uyzz7I1F1vzMQxDCxYs0NKlS3XmzBmVKFFCrVq1Up8+fbL9/Atb8omNjVVkZKT279+vS5cuycfHR8HBwerVq1e2djBtfe+7fPmyxo4dq+joaCUlJalmzZp66623sv28GVvy6dSpk3XZ3n9r3bq1xowZ49B87na/WrVqZfsXHLbmk5ycrOnTp+vzzz/XhQsXVLp0ab300kvZvqKmrfns379f06dP15EjR3TlyhWVLl1azZs31yuvvJLmempZMWfOHG3atEknTpzQjRs3rAVpjx49Un22cNTnD1vyceTnD1ufn39z9ucPuBYKHgAAAABui0ULAAAAALgtCh4AAAAAbouCBwAAAIDbouABAAAA4LYoeAAAAAC4LQoeAAAAAG6LggcAAACA26LgAQAT6NSpk4KDg1NtCw4OVqdOnZyU0d3FxMSoQoUKWrNmTbbHjoyMVIUKFXTy5Mlsjw3g/9q7+5gqyz+O429QwPQYhCwfQOQhBMkH3NDIJmaFqSikNoH5MJVZ5LSUatrmH5pKimKByEr0BFpTdJoucSoo+ZCmlmZaIJmFpgkqR52AinB+fzDuPEro5Pf7mcfPa2Nwrvu6r/M9N3/AZ9d1X7fI46n5wy5ARORhOXDgAGPHjjVeOzg4YDKZCAoKYuzYsQwYMOAhVvfgCgsLyc/PJyoqik6dOj3scgAoLi4mOzubAwcOUFZWRrNmzfD29qZv376MGjWK9u3bP+wSRUTETinwiMhjb/jw4YSFhVFbW8vp06dZvXo1U6ZMYf78+QwbNuyh1bV169YHOq+wsJD09HR69Ojxrwg8q1evZs6cOZhMJoYOHUpAQABWq5WioiLWrVvH+vXr2b9//8MuU0RE7JQCj4g89rp37050dLTxOiIigmHDhrFs2bJGA09lZSUtW7b8n9Xl7Oz8Pxv7/2Xv3r3Mnj2bLl26sGLFCtzd3W2Ov//++yxZsuQhVSciIo8DBR4RkTsEBwfj5ubGmTNnjLbAwECGDRvG4MGDSU9P58SJEwwaNIj58+cDUFBQgNls5vjx49TW1hIYGEhCQsJd9+VUVVXx8ccfs3nzZioqKujatSvTp09vsI6XXnoJT09PVq1aZdP+zTffYDab+fnnn6mpqcHT05OIiAimTp3KkiVLSE9PB2DixInGOZMnT2bKlCkAWCwWMjIy2LFjB2VlZbi5ufHKK68wbdo0XF1dbd7rwIEDLFq0iKKiItzc3Bg+fDi9e/e+72u5aNEiHBwcWLx48V1hB8BkMvHBBx80OsbRo0f58ssvOXLkCKWlpbi4uBASEsLbb79Nt27dbPoeOnSIjIwMCgsLqaiowM3NjR49ejBjxgy8vLwAKCoqYsmSJRw9epTLly/j6upKly5dmDZtGs8+++x9fzYREXk0KPCIiNzBYrFw9epV2rRpY9N+7Ngx8vLyiImJ4fXXX8dkMgGwatUq5s6dywsvvMA777yDg4MDubm5TJo0ieTkZKKioowxpk2bRkFBAYMGDaJXr16cOHGC+Ph43Nzc7qu2rKwsPvroI/z9/Rk/fjweHh78/vvv5OfnM3XqVCIiIrhw4QI5OTnEx8cTGBgIYHy/cuUKsbGxWCwWRo4cSceOHTl16hSrV6/m8OHDrF27lhYtWgBw+PBh4uPjcXd356233sLFxYWvvvqK3bt331etZ86cobCwkN69e+Pr63tf5zQkPz+fc+fOER0dTdu2bTl37hw5OTmMHj2aDRs24O/vD8Bvv/1GfHw8vr6+vPHGG7Ru3ZrS0lL27dvHX3/9hZeXF+Xl5YwbNw6TycSYMWNo06YNFy9e5NChQ5w8eVKBR0TEDinwiMhjr6qqivLycqxWKyUlJSxevJja2lqGDBli0+/kyZN88cUX9OrVy2grLS1lwYIFxMXFMWvWLKN99OjRxMbGkpycTGRkJM2aNWP37t0UFBQwZswYZs6cafQNCAhg7ty5eHp6Nlrn2bNnWbhwISEhIWRnZxvBBMBqtQIQFBRESEgIOTk5hIWFER4ebjPGJ598woULF9i4cSPe3t5Ge1hYGAkJCaxfv55Ro0YBMH/+fJycnFi7di3t2rUDICYmxibANebXX38F/g5bD2rSpEk88cQTNm1RUVEMHTqU7OxsPvzwQ6Bu+dyNGzcwm802YXXy5MnGz4cPH8ZisbBs2TK6d+9utCckJDSpRhER+ffSttQi8thbsGABzz//PH369CEuLo4ff/yRmJgYEhMTbfoFBwfbhB2Abdu2UV1dzWuvvUZ5ebnxdeXKFfr168eFCxeMf/x37NgBwIQJE2zGiImJMWaLGrN9+3Zu3brF5MmTbcIO1O0wdy9Wq5UtW7bQp08fTCaTTb09evSgZcuW7Nu3D4CLFy9y9OhRBg4caIQdqFuCFhsbe8/3Arh27ZpxTlPcHnYqKiqwWCy4urri6+vLsWPHjGNPPvkkUPc7uXXrVoNj1ffZsWMHN27caFJdIiLyaNAMj4g89saOHUv//v1xcHCgVatW+Pv706pVq7v63T4jUu/UqVNAXWj5J5cuXQLgzz//pEWLFnTo0MHmuLOzMx07duTq1auN1vnHH38ADz5jUl5ezuXLl8nLyyMvL++etQL4+fnd1aehtobUB52KiooHKddQVlZGSkoKBQUFXLlyxeZY/X05AJGRkWzatInZs2eTkpJCaGgo4eHhDB48mKeeegqA3r17Ex0dzaeffkpWVhYhISH07duXIUOG2AQ7ERGxHwo8IvLY8/Pzo0+fPvfsd+eyKvh7KVlqaqoxe3CnLl26NK3A/5L6Wvv372/z/KHbNXU25nbPPPMMULdJwIOqra0lPj6ekpISxo8fT3BwMK1atcLR0ZGkpCQqKyuNvs7OzmRlZXHkyBH27NnDwYMHmTdvHmlpaXz++ecEBwcDkJyczIQJE9i1axcHDx4kNTWVpUuXkpqaetcSQBERefQp8IiINEH9c248PDwIDQ1ttK+Xlxd79+7l3LlzNrM8N2/e5MyZM3ftkHYnHx8foC5APP300//Y75+Wt7m7u9O6dWuuX79+z4BXP3NSP4N1u4baGuLt7U1gYCDff/89JSUlD/RMoOLiYoqLi5kyZYrNvThQtwGDk5PTXef07NmTnj17AnXXasSIEXz22WekpqYafYKCgggKCuLNN9/k/PnzREdHk5aWpsAjImKHdA+PiEgTDBw4ECcnJ9LS0rh58+Zdx+uXiAHGFtVms9mmT05OjnG/S2MGDBhA8+bNWbp0KdevX7c5Vj97AxjPBrpziZyjoyORkZF89913fPvtt3eNX1NTg8ViAeoCXPfu3dm6dSvnz583+ly7do01a9bcs9Z67777LlarlcTERGPs2127ds3Y2rshjo51f6Zqa2tt2jdt2kRZWZlNW0Pj+/v74+LiYlyLy5cv21wrgHbt2uHu7n7PJYUiIvJo0gyPiEgTdOjQgZkzZzJr1iyioqIYMmQIbdu2pbS0lJ9++omioiJjG+d+/foRHh7OqlWruHTpEqGhoRQXF7Nlyxa8vb2pqalp9L08PT1JTEwkOTmZ4cOHExkZiYeHByUlJezZs4evv/4agK5du+Lo6Mjy5cupqqqiZcuWBAQE0LlzZxITE/nhhx+YOHEiUVFRdOvWjZqaGk6fPk1eXh4JCQnExcUBMH36dMaNG8fIkSOJi4vDxcWFDRs24OrqytmzZ+/r+vTr14+ZM2eSlJTEwIEDGTp0KAEBAVitVuOzOzo6MmPGjAbP9/Pzw9fXl+XLl3Pjxg28vb05fvw427dvp2PHjjZBKCMjg/379/Piiy/i5eVFdXU1ubm5VFRUGDvLbdy4kZUrVxIREYG3tzcODg7s2rWLU6dOGc8pEhER+6LAIyLSRLGxsfj4+GA2m1m5ciWVlZV4eHgQFBTEe++9Z9M3LS2NlJQUcnNz2blzJ127dmXFihUsXLjwvkJEfHw8nTp1wmw2s3z5cqAuCL366qtGH09PT+bMmUNmZiazZs0ydnbr3Lkzrq6urFmzhszMTLZt20Zubi4tWrSgffv2DB48mL59+xrjhIaGkpmZSUpKChkZGTYPHr1zp7nGjB49mtDQULKzs9m5cyc5OTk0a9aMTp06MXLkSGMb7IY0b96cZcuWkZSUxLp166iuriYkJISsrCySkpJsrtnLL79MaWkpmzdv5tKlS0bQS09PJyIiAoDnnnuOX375hfz8fC5evIiTkxM+Pj7MmzePESNG3PdnEhGRR4eD9c65fRERERERETuhe3hERERERMRuKfCIiIiIiIjdUuARERERERG7pcAjIiIiIiJ2S4FHRERERETslgKPiIiIiIjYLQUeERERERGxWwo8IiIiIiJitxR4RERERETEbinwiIiIiIiI3VLgERERERERu6XAIyIiIiIidkuBR0RERERE7NZ/ALqr6cgsRKSvAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Baseline evaluation for Random Forest is completed.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Model': 'Random Forest – Baseline',\n",
              " 'Train Time': 12.527853965759277,\n",
              " 'Test Time': 0.4616570472717285,\n",
              " 'Accuracy': 0.9139460610361959,\n",
              " 'Precision': 0.8703360538278941,\n",
              " 'Recall': 0.9139460610361959,\n",
              " 'F1 Score': 0.8765503040021617}"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# A lightweight Gradient Boosting model is trained using simplified parameters\n",
        "# to create a baseline comparison with Random Forest. This helps illustrate the\n",
        "# behaviour of Gradient Boosting before any tuning is applied and highlights\n",
        "# differences between the two ensemble methods.\n",
        "\n",
        "# =============================================================\n",
        "# SECTION 6: Baseline Gradient Boosting Evaluation\n",
        "# =============================================================\n",
        "\n",
        "# Configure a compact Gradient Boosting model\n",
        "gb_basic = GradientBoostingClassifier(\n",
        "    n_estimators=10,       # intentionally small to reduce runtime\n",
        "    learning_rate=0.3,     # faster updates\n",
        "    max_depth=2,           # shallow trees for lightweight training\n",
        "    subsample=0.5,         # use half of samples for each stage\n",
        "    random_state=42\n",
        ")\n",
        "\n",
        "# Evaluate via the shared assessment function\n",
        "gb_summary, gb_predictions = assess_model(\n",
        "    \"Gradient Boosting – Baseline\",\n",
        "    gb_basic,\n",
        "    X_train_scaled,\n",
        "    X_test_scaled,\n",
        "    y_train,\n",
        "    y_test\n",
        ")\n",
        "\n",
        "print(\"\\nGradient Boosting baseline evaluation finished.\")\n",
        "gb_summary\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "G_d0cSM5DLQY",
        "outputId": "a23c501a-3f12-4a18-ed9b-deeb75560d49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "=== Gradient Boosting – Baseline PERFORMANCE SUMMARY ===\n",
            "Training Time     : 27.232s\n",
            "Prediction Time   : 0.063s\n",
            "Accuracy          : 0.9065\n",
            "Precision (Wgt.)  : 0.8619\n",
            "Recall (Wgt.)     : 0.9065\n",
            "F1 Score (Wgt.)   : 0.8756\n",
            "\n",
            "Detailed Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.00      0.00      0.00       228\n",
            "           1       1.00      0.96      0.98       227\n",
            "           2       0.00      0.00      0.00        17\n",
            "           3       0.00      0.00      0.00         2\n",
            "           4       1.00      0.18      0.31       411\n",
            "           5       0.00      0.00      0.00        41\n",
            "           6       1.00      1.00      1.00         2\n",
            "           7       0.95      0.97      0.96       479\n",
            "           8       1.00      1.00      1.00         2\n",
            "           9       0.00      0.00      0.00         4\n",
            "          10       0.00      0.00      0.00        94\n",
            "          11       0.00      0.00      0.00       310\n",
            "          12       0.02      0.11      0.03         9\n",
            "          13       0.00      0.00      0.00         2\n",
            "          14       0.98      1.00      0.99      6654\n",
            "          15       0.81      0.69      0.75       206\n",
            "          16       0.87      0.98      0.93     11245\n",
            "          17       0.00      0.00      0.00         1\n",
            "          18       1.00      1.00      1.00         1\n",
            "          19       0.87      0.97      0.92        35\n",
            "          20       0.94      0.96      0.95       371\n",
            "          21       0.00      0.00      0.00       211\n",
            "          22       0.00      0.00      0.00         9\n",
            "          23       0.00      0.00      0.00         4\n",
            "          24       0.00      0.00      0.00        93\n",
            "          25       0.86      0.99      0.92       698\n",
            "          26       0.00      0.00      0.00         8\n",
            "          27       0.98      1.00      0.99       540\n",
            "          28       0.00      0.00      0.00        43\n",
            "          29       0.00      0.00      0.00        99\n",
            "          31       0.89      1.00      0.94       104\n",
            "          32       0.99      0.84      0.91       107\n",
            "          33       0.27      0.01      0.02       279\n",
            "          34       0.00      0.00      0.00         4\n",
            "          35       0.00      0.00      0.00         1\n",
            "          36       0.00      0.00      0.00         3\n",
            "\n",
            "    accuracy                           0.91     22544\n",
            "   macro avg       0.40      0.38      0.38     22544\n",
            "weighted avg       0.86      0.91      0.88     22544\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x700 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Gradient Boosting baseline evaluation finished.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Model': 'Gradient Boosting – Baseline',\n",
              " 'Train Time': 27.23175549507141,\n",
              " 'Test Time': 0.06290602684020996,\n",
              " 'Accuracy': 0.9064939673527325,\n",
              " 'Precision': 0.861866767182059,\n",
              " 'Recall': 0.9064939673527325,\n",
              " 'F1 Score': 0.8755915052789309}"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# GridSearchCV is used to tune both Random Forest and Gradient Boosting models.\n",
        "# The search spaces are kept intentionally small to reduce computation time.\n",
        "# This step identifies the best-performing hyperparameters based on weighted\n",
        "# F1-score, allowing the optimised models to be compared against the baseline.\n",
        "\n",
        "# ================================================================\n",
        "# SECTION 7: Hyperparameter Optimisation for RF and GB Models\n",
        "# ================================================================\n",
        "\n",
        "print(\"\\n================= RANDOM FOREST TUNING =================\")\n",
        "\n",
        "# --- Parameter search space for Random Forest ---\n",
        "rf_search_space = {\n",
        "    \"n_estimators\": [50, 100],\n",
        "    \"max_depth\": [10, None],\n",
        "    \"max_features\": [\"sqrt\"]\n",
        "}\n",
        "\n",
        "rf_grid_search = GridSearchCV(\n",
        "    estimator=RandomForestClassifier(random_state=42, n_jobs=-1),\n",
        "    param_grid=rf_search_space,\n",
        "    scoring=\"f1_weighted\",\n",
        "    cv=2,               # quick cross-validation\n",
        "    verbose=1,\n",
        "    n_jobs=-1\n",
        ")\n",
        "\n",
        "print(\"\\nStarting optimisation for Random Forest...\")\n",
        "rf_grid_search.fit(X_train_scaled, y_train)\n",
        "\n",
        "print(\"\\nTop-performing RF parameters:\")\n",
        "print(rf_grid_search.best_params_)\n",
        "print(\"Best Weighted F1 (RF):\", rf_grid_search.best_score_)\n",
        "\n",
        "\n",
        "\n",
        "print(\"\\n================= GRADIENT BOOSTING TUNING =================\")\n",
        "\n",
        "# --- Parameter search space for Gradient Boosting ---\n",
        "gb_search_space = {\n",
        "    \"n_estimators\": [30],\n",
        "    \"learning_rate\": [0.1, 0.2],\n",
        "    \"max_depth\": [2]\n",
        "}\n",
        "\n",
        "gb_grid_search = GridSearchCV(\n",
        "    estimator=GradientBoostingClassifier(random_state=42),\n",
        "    param_grid=gb_search_space,\n",
        "    scoring=\"f1_weighted\",\n",
        "    cv=2,\n",
        "    verbose=1,\n",
        "    n_jobs=-1\n",
        ")\n",
        "\n",
        "print(\"\\nStarting optimisation for Gradient Boosting...\")\n",
        "gb_grid_search.fit(X_train_scaled, y_train)\n",
        "\n",
        "print(\"\\nTop-performing GB parameters:\")\n",
        "print(gb_grid_search.best_params_)\n",
        "print(\"Best Weighted F1 (GB):\", gb_grid_search.best_score_)\n",
        "\n",
        "\n",
        "print(\"\\nHyperparameter optimisation successfully completed for both models.\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F1gcHa_wDVcV",
        "outputId": "6da28b62-da3d-4477-bded-3306ec640098"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "================= RANDOM FOREST TUNING =================\n",
            "\n",
            "Starting optimisation for Random Forest...\n",
            "Fitting 2 folds for each of 4 candidates, totalling 8 fits\n",
            "\n",
            "Top-performing RF parameters:\n",
            "{'max_depth': None, 'max_features': 'sqrt', 'n_estimators': 100}\n",
            "Best Weighted F1 (RF): 0.9980662680065071\n",
            "\n",
            "================= GRADIENT BOOSTING TUNING =================\n",
            "\n",
            "Starting optimisation for Gradient Boosting...\n",
            "Fitting 2 folds for each of 2 candidates, totalling 4 fits\n",
            "\n",
            "Top-performing GB parameters:\n",
            "{'learning_rate': 0.1, 'max_depth': 2, 'n_estimators': 30}\n",
            "Best Weighted F1 (GB): 0.9907667846310075\n",
            "\n",
            "Hyperparameter optimisation successfully completed for both models.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# The tuned Random Forest and Gradient Boosting models are used to extract\n",
        "# feature importance scores. Bar charts visualise the top contributing features\n",
        "# for each method, helping interpret which network traffic attributes have the\n",
        "# strongest influence on intrusion detection. This supports deeper analysis\n",
        "# and explains model decisions.\n",
        "\n",
        "\n",
        "# ================================================================\n",
        "# SECTION 8: Feature Importance Analysis for RF and GB Models\n",
        "# ================================================================\n",
        "\n",
        "# --- 1. Train Random Forest Using Best GridSearch Parameters ---\n",
        "\n",
        "optimized_rf = RandomForestClassifier(\n",
        "    n_estimators=rf_grid_search.best_params_[\"n_estimators\"],\n",
        "    max_depth=rf_grid_search.best_params_[\"max_depth\"],\n",
        "    max_features=rf_grid_search.best_params_[\"max_features\"],\n",
        "    random_state=42,\n",
        "    n_jobs=-1\n",
        ")\n",
        "\n",
        "optimized_rf.fit(X_train_scaled, y_train)\n",
        "\n",
        "\n",
        "# --- 2. Random Forest: Extracting Top Features ---\n",
        "\n",
        "rf_importances = optimized_rf.feature_importances_\n",
        "rf_sorted_idx = np.argsort(rf_importances)[::-1]\n",
        "\n",
        "rf_feature_labels = X_train.columns\n",
        "\n",
        "plt.figure(figsize=(13, 6))\n",
        "plt.bar(\n",
        "    x=range(20),\n",
        "    height=rf_importances[rf_sorted_idx][:20],\n",
        "    color=\"steelblue\"\n",
        ")\n",
        "plt.xticks(\n",
        "    ticks=range(20),\n",
        "    labels=rf_feature_labels[rf_sorted_idx][:20],\n",
        "    rotation=75,\n",
        "    ha=\"right\"\n",
        ")\n",
        "plt.title(\"Random Forest — Top 20 Most Relevant Features\")\n",
        "plt.ylabel(\"Importance Score\")\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "\n",
        "# --- 3. Gradient Boosting: Extracting Top Features ---\n",
        "\n",
        "gb_importances = gb_basic.feature_importances_\n",
        "gb_sorted_idx = np.argsort(gb_importances)[::-1]\n",
        "\n",
        "plt.figure(figsize=(13, 6))\n",
        "plt.bar(\n",
        "    x=range(20),\n",
        "    height=gb_importances[gb_sorted_idx][:20],\n",
        "    color=\"coral\"\n",
        ")\n",
        "plt.xticks(\n",
        "    ticks=range(20),\n",
        "    labels=rf_feature_labels[gb_sorted_idx][:20],\n",
        "    rotation=75,\n",
        "    ha=\"right\"\n",
        ")\n",
        "plt.title(\"Gradient Boosting — Top 20 Most Influential Features\")\n",
        "plt.ylabel(\"Importance Score\")\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "\n",
        "print(\"Feature importance visualisation completed successfully.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 803
        },
        "id": "zcLW6UMODpna",
        "outputId": "dfa5117c-c7d0-4cf8-daea-df2e5c24e817"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1300x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1300x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Feature importance visualisation completed successfully.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# This section calculates multiclass ROC-AUC scores using the one-vs-rest\n",
        "# approach. The true labels are binarised, and both tuned models generate\n",
        "# probability outputs. ROC curves and AUC values are computed for several\n",
        "# representative classes, allowing performance to be assessed beyond standard\n",
        "# accuracy metrics. This provides a more detailed view of model behaviour.\n",
        "\n",
        "# ===================================================================\n",
        "# SECTION 9: Multiclass ROC-AUC Evaluation (One-vs-Rest Strategy)\n",
        "# ===================================================================\n",
        "\n",
        "from sklearn.preprocessing import label_binarize\n",
        "from sklearn.metrics import roc_curve, auc, roc_auc_score\n",
        "\n",
        "# -----------------------------------------------------------\n",
        "# 1. Convert True Labels into One-Hot Encoded Format\n",
        "# -----------------------------------------------------------\n",
        "\n",
        "class_names = label_encoder.classes_\n",
        "num_classes = len(class_names)\n",
        "\n",
        "y_test_onehot = label_binarize(y_test, classes=range(num_classes))\n",
        "\n",
        "print(\"Total number of unique classes:\", num_classes)\n",
        "\n",
        "\n",
        "# -----------------------------------------------------------\n",
        "# 2. Generate Predicted Probabilities for Both Tuned Models\n",
        "# -----------------------------------------------------------\n",
        "\n",
        "# --- Random Forest probabilities ---\n",
        "rf_best = RandomForestClassifier(\n",
        "    n_estimators=rf_grid_search.best_params_[\"n_estimators\"],\n",
        "    max_depth=rf_grid_search.best_params_[\"max_depth\"],\n",
        "    max_features=rf_grid_search.best_params_[\"max_features\"],\n",
        "    random_state=42,\n",
        "    n_jobs=-1\n",
        ")\n",
        "\n",
        "rf_best.fit(X_train_scaled, y_train)\n",
        "rf_prob_raw = rf_best.predict_proba(X_test_scaled)\n",
        "\n",
        "# Align probabilities to the full class range\n",
        "rf_prob_full = np.zeros((rf_prob_raw.shape[0], num_classes))\n",
        "for idx, cls_val in enumerate(rf_best.classes_):\n",
        "    rf_prob_full[:, cls_val] = rf_prob_raw[:, idx]\n",
        "\n",
        "\n",
        "# --- Gradient Boosting probabilities ---\n",
        "gb_best = GradientBoostingClassifier(\n",
        "    n_estimators=gb_grid_search.best_params_[\"n_estimators\"],\n",
        "    learning_rate=gb_grid_search.best_params_[\"learning_rate\"],\n",
        "    max_depth=gb_grid_search.best_params_[\"max_depth\"],\n",
        "    random_state=42\n",
        ")\n",
        "\n",
        "gb_best.fit(X_train_scaled, y_train)\n",
        "gb_prob_raw = gb_best.predict_proba(X_test_scaled)\n",
        "\n",
        "gb_prob_full = np.zeros((gb_prob_raw.shape[0], num_classes))\n",
        "for idx, cls_val in enumerate(gb_best.classes_):\n",
        "    gb_prob_full[:, cls_val] = gb_prob_raw[:, idx]\n",
        "\n",
        "\n",
        "# -----------------------------------------------------------\n",
        "# 3. ROC Curve + AUC Calculation for Random Forest\n",
        "# -----------------------------------------------------------\n",
        "\n",
        "rf_fpr, rf_tpr, rf_auc = {}, {}, {}\n",
        "\n",
        "for c in range(num_classes):\n",
        "    rf_fpr[c], rf_tpr[c], _ = roc_curve(y_test_onehot[:, c], rf_prob_full[:, c])\n",
        "    rf_auc[c] = auc(rf_fpr[c], rf_tpr[c])\n",
        "\n",
        "macro_rf_auc = roc_auc_score(y_test_onehot, rf_prob_full, multi_class=\"ovr\", average=\"macro\")\n",
        "micro_rf_auc = roc_auc_score(y_test_onehot, rf_prob_full, multi_class=\"ovr\", average=\"micro\")\n",
        "\n",
        "print(\"\\n=== Random Forest ROC-AUC Scores ===\")\n",
        "print(\"Macro AUC :\", macro_rf_auc)\n",
        "print(\"Micro AUC :\", micro_rf_auc)\n",
        "\n",
        "\n",
        "# -----------------------------------------------------------\n",
        "# 4. Plot Example ROC Curves (Random Forest)\n",
        "# -----------------------------------------------------------\n",
        "\n",
        "sample_classes = [0, 1, 2]  # first three for readability\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "for c in sample_classes:\n",
        "    plt.plot(\n",
        "        rf_fpr[c],\n",
        "        rf_tpr[c],\n",
        "        label=f\"{class_names[c]} (AUC={rf_auc[c]:.2f})\"\n",
        "    )\n",
        "\n",
        "plt.plot([0, 1], [0, 1], linestyle=\"--\", color=\"black\")\n",
        "plt.title(\"ROC Curves — Random Forest (Sample Classes)\")\n",
        "plt.xlabel(\"False Positive Rate\")\n",
        "plt.ylabel(\"True Positive Rate\")\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "\n",
        "# -----------------------------------------------------------\n",
        "# 5. ROC Curve + AUC Calculation for Gradient Boosting\n",
        "# -----------------------------------------------------------\n",
        "\n",
        "gb_fpr, gb_tpr, gb_auc = {}, {}, {}\n",
        "\n",
        "for c in range(num_classes):\n",
        "    gb_fpr[c], gb_tpr[c], _ = roc_curve(y_test_onehot[:, c], gb_prob_full[:, c])\n",
        "    gb_auc[c] = auc(gb_fpr[c], gb_tpr[c])\n",
        "\n",
        "macro_gb_auc = roc_auc_score(y_test_onehot, gb_prob_full, multi_class=\"ovr\", average=\"macro\")\n",
        "micro_gb_auc = roc_auc_score(y_test_onehot, gb_prob_full, multi_class=\"ovr\", average=\"micro\")\n",
        "\n",
        "print(\"\\n=== Gradient Boosting ROC-AUC Scores ===\")\n",
        "print(\"Macro AUC :\", macro_gb_auc)\n",
        "print(\"Micro AUC :\", micro_gb_auc)\n",
        "\n",
        "\n",
        "# -----------------------------------------------------------\n",
        "# 6. Plot Example ROC Curves (Gradient Boosting)\n",
        "# -----------------------------------------------------------\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "for c in sample_classes:\n",
        "    plt.plot(\n",
        "        gb_fpr[c],\n",
        "        gb_tpr[c],\n",
        "        label=f\"{class_names[c]} (AUC={gb_auc[c]:.2f})\"\n",
        "    )\n",
        "\n",
        "plt.plot([0, 1], [0, 1], linestyle=\"--\", color=\"black\")\n",
        "plt.title(\"ROC Curves — Gradient Boosting (Sample Classes)\")\n",
        "plt.xlabel(\"False Positive Rate\")\n",
        "plt.ylabel(\"True Positive Rate\")\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "print(\"\\nROC-AUC analysis successfully completed.\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "DAA7Eo8aErBI",
        "outputId": "158e8c17-78cc-4db9-8893-b3c7aa322f21"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total number of unique classes: 37\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_ranking.py:1188: UndefinedMetricWarning: No positive samples in y_true, true positive value should be meaningless\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_ranking.py:379: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "=== Random Forest ROC-AUC Scores ===\n",
            "Macro AUC : nan\n",
            "Micro AUC : 0.964887816850131\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_ranking.py:1188: UndefinedMetricWarning: No positive samples in y_true, true positive value should be meaningless\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_ranking.py:379: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "=== Gradient Boosting ROC-AUC Scores ===\n",
            "Macro AUC : nan\n",
            "Micro AUC : 0.9458056834573524\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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ZjcZIxwUFBZnee+89U+3atc1tsXmvBQQEmDw8PEzTpk17aY7p06ebChQoYDp8+HCk9tOnT5sKFChgGjly5GtdL8LkyZNjfJ+L/JuGKom8xKhRoyhfvjwVKlSgefPm7N+/n3bt2pnvrEV4+vQpwCsnc0Y8HnF8bM97ladPn+Lo6PhG13iVggULUrp06SjtPj4+nD9/nrNnz5rbtm/fztOnTyPdzduwYQMZMmSgXLlyPHz40PyfnZ0dxYsXZ9++feZj06RJw/Hjx+M83CUhPHv2DCDa13nevHmUL18+0n//FdPr9+87wc+ePePRo0e4uLiQO3fuSMOV9uzZQ1BQEO3atYt0RzB//vxR7uRG59y5c1y4cAFvb2+ePn0a6d+gSpUqAOzduzfSOa6urtSsWTNSW+nSpXnw4IH5vfsm3n//fWbPns3s2bOZOnUqgwYN4sGDB3Ts2JEbN26Yj4sYftK9e/dI59epU4dcuXKxbdu2SMdmzZoVb2/vSMdG9IhFHJsmTRrgxRCd+Hgu/2Zra0urVq0itZUpU4bw8HDz87p37x4nT56kXr165jlPAA4ODjRv3jzW3+vhw4cxTmidOHEiQ4YMIVu2bPzxxx+MHTuW9u3bU6NGDf78889Ix0a8D00mk/n94e7ujrOzc7TD5ry8vMifP7/563z58pEmTRqyZMkSabhkunTpyJ07N35+flGukTdv3kjHpkiRgnbt2hEaGvrSXpfffvuN0NBQGjZsGOl97O/vT9WqVbl3716U4Y//FvGz/Ka/d1OlSmVe7S44OJhHjx7x7NkzypQpw5UrV8zvq9i811KmTIm9vT379+/nwYMHMX7PDRs2UKBAAXLmzBnpuWfOnJmcOXOaf4/G9noR0qZNC8CjR49i/fwledJQJZGXaN++PdWqVSMkJIRjx44xffp0nj17FmVS5H8LgphEPB7x4TPivGfPnr3RahZOTk7mP4YJJUeOHNG2e3t7M3bsWNavX2/+ILF+/XqcnJwiLbl56dIlHjx4EO2H6gihoaHY2dkxdOhQBg0aRI0aNciXLx/lypWjdu3aUVak+q+goKBox5HHVpo0aUiVKlW0j0X8m0X3Or///vsULlwYgF9++YUDBw5EOSam1+/u3buMGTOGHTt24O/vH+mxiBWSAHMRlSdPnijXyJs37yuHt1y6dAl4UQyPGjUq2mMihg1FiG4IlIuLC/BiSNCbfvByc3OLVPS8++67lC5dmubNmzNmzBh++ukn4MVzT5UqlXn4zb/lzZuXHTt2YDKZMBgMXL9+nbJly0ZZvjhz5sykSZPG/DpmzZqVHj16MHXqVNavX0+RIkWoWLEi77//Prlz536j55UxY8Yowz0ifr4j/o0jCohcuXJFOT+6tpcxxbA4or29PR07dqRjx448ffqU06dPs2XLFpYsWULfvn1Zu3at+XtdvnzZPL/i+fPnka4TEBAQ5dpubm5R2pydnaNdctrZ2TlSIRghuvdyRNvLbhpEvJdbtGgR4zEv+7D8sp/l1xESEsIvv/zC2rVro31+AQEBODk5xeq9Zm9vz5AhQxg+fDiVKlWiQIECVKhQgbp160aaT3Xp0iWCgoJi/D2aKVOm17pehP/OcRKJiQoHkZfIkyeP+YPNu+++S+bMmfnyyy8pUqQIrVu3Nh/n7u7O1q1bOXPmTJSxxhGCgoK4fPkybm5u5g9cefPm5dSpU5w5cybGSYyxkTdvXg4dOsT169cjfdiMycv2hAgPD4+2PaYx0lmzZqVkyZJs2LCBjz/+GH9/f3bu3Im3t3ekZSpNJhNubm58//33MX5vW1tbAGrWrMn27dv5448/2L9/P5s3b2b+/Pk0adKE4cOHx3j+xo0bGTJkSIyPv8rLllp0cnLC1dWVy5cvRxkHnDVrVvOH7OXLl0d7fnSvn9FopHPnzly9epVOnTpRsGBBHB0dsbGxYfjw4VE+wMWHDz/8MMYC7L+FQsS/R3Ri+rD6pooVK0aaNGki9UAllAEDBtC4cWN27NjBgQMHmDlzJr/88gtff/31G80XepuvW/r06WPVM+fk5ESZMmUoU6YMHh4efP7552zYsIFevXrx9OlT2rZtS1hYGN26dcPd3R0HBwcMBgMDBgyINnNMz/Flzz2+ROQZN25cjDdcChQoEOP5Tk5OZMmShUuXLhEcHBzn5XSHDx/O4sWLadCgAf379yddunTY2tqycuVKfv3110gfxmPzXmvdujU1atRg+/btHDhwgOXLlzNjxgx69+5tnvNhMpkoWrQoAwYMiDbTv38vxeZ6ESKKw4i5QiIxUeEg8hqaN2/OwoULGT9+PA0aNDDfuapZsya//PILq1atinGC9Pr16wkNDY009KN27dqsW7eOlStXvlHhULt2bQ4dOsTKlSvp16/fK4+P+GP73zuJISEh3Lt3L8a74zHx8fHhiy++4ODBg1y6dInQ0NAokw4jutFLlSoVq8l36dKlo1GjRjRq1Ijw8HA++eQTVq5cyQcffBDjSicxreASWy9bQQWgVq1azJ8/n40bN0ZZjjcuzp8/z/nz5+nTp0+UP+T+/v6RerYiCsJLly5F2ZTK19f3ld8r4t/U3t4+VkObLCk8PDzSCkjZsmVj9+7d+Pn5Rel18PX1JWvWrOZiOFu2bFy6dMncAxHh7t27PHnyJMr5OXPmNN+VDwgIoFmzZowZM8b8YS42Gy/GRUSRdvny5SiPRbeSUUzy5MnD4cOHCQwMfOUE6AjFihUDMK9I9ddff3H//n1GjhxpntALL252RNfbEF8ieg6ia3vZDZCcOXMC8M4771CqVKk4fe+aNWsyf/581q9fH+ciccOGDZQpU4bRo0dHal+5cmW0x7/qvQYvesZatWpFq1atCA4OpnPnzkyaNIlOnTrh6OhIzpw5efz4cax/hl91vQjXrl2LsVdP5N80x0HkNdjY2NC9e3cePXoUaZnRiCUf//77b6ZNmxblvAsXLvDjjz+SNm1aunTpYm6vUaMGJUqUYN26dTEuvbhv3z42btz40lzNmjUjR44czJgxI8YhKxs2bDDfxc2WLRspUqRg//79kY5ZvHhxjD0OL/Pee+9hb2/P+vXrWbduHa6urlEKIR8fH4KCgqLsbhshYmhBeHh4lA8rtra25mUv/zuc598yZcpEhQoV4vxfRDd/TLp06YKLiwsjRozg9OnT0R7zOneUbWxe/Ar+7zCBtWvXcvfu3UhtFStWJGXKlMyfPz/Sh+qzZ89GmZsQnUKFCpE3b17mz58f5drwYhhdUFBQrLP/m6OjI0+ePImX4Q4RQ2X+3XMXMeRt+vTpkY7dunUrV65ciTQkrkaNGty4cSPKz0zEz2XEsU+ePCE0NDTSMc7OzmTLlo0nT56Y/x0j9tqI7w/QGTNmpFChQmzatCnSkrLPnz9n2bJlsb5OqVKlCA8P59SpU5Har1y5wrVr16I9J2LOSMSKXRG9BP/995s1a1aCDmHx9fWNNNciLCyM+fPnY2dnR6VKlWI877333sPOzi7aJXbh5cOUInTt2pW0adPyww8/RHnt4MVrMX/+fC5evBjjNWxsbKL8vF+5ciXKRnyxea8FBgYSGBgY6ZiUKVOSJ08ejEajeZirt7c3165di7FnM2K4YWyvF+H48eMULVpUKyrJK6nHQeQ11a1bl/HjxzN79mzatm1r/mDx/fffc//+fcaMGcOuXbuoUaMGjo6OnD59mlWrVmFnZ8fkyZMjTYS0sbFh/PjxdOvWjSFDhrB69epIO0fv2rWLAwcORLuB0785OjoyZcoUunbtSpcuXXj33XcpV64cadKk4fbt22zbto1Tp06ZPzw5OTnh7e3N6tWrGTJkCMWKFePkyZP89ddfceqqdnFxoWrVqvz6668EBgbywQcfmD8U//t127ZtG1OmTOH48eNUrFgRJycnbt68ye7du8mZMyc///wzz549o3LlytSoUYP8+fOTLl06Ll++zKJFi8iVK1e0a66/La6urkyaNInevXvTrFmzKDtHHzhwgN27d8d6edQ8efKQO3duZsyYQXBwMDly5ODkyZNs2bKF7NmzR/rQ5uLiQu/evRkzZgxt27bl/fffx9/fnwULFuDp6cmZM2de+r0MBgOjRo2iY8eO1K9fnyZNmpA7d278/f3x9fXlt99+Y8WKFVF2R46NokWLsmPHDr7++mtKliyJra0t1apVe+UGdxcvXmTt2rXAi94uX19fli5dSooUKejfv7/5uCpVqlCjRg2WLl3Kw4cPKV++PNeuXWPhwoW4ubnRo0cP87Fdu3Zl48aNfPrppxw5coTcuXNz4MABNm/ezLvvvkv16tWBF3uZfPHFF9SuXZvcuXOTKlUq/v77b3bv3k3jxo3NPQ0RvTtjx46lYcOG2NvbU7Ro0Xi5M/vJJ5/QuXNnWrRoQYsWLUiVKhWrV682D2WMTW9H1apVSZkyJbt27Yp09/3s2bMMGDCAsmXLUqZMGTJnzszTp085ePAg27ZtI3fu3ObeUS8vL9KnT8+oUaO4desWGTJk4MCBAxw9etQ8aTYheHh4MGDAAFq3bk3mzJnZvHkzhw8fplevXrzzzjsxnufm5sawYcP46quv8PHxoX79+mTOnJk7d+5w/Phxzp49+8pNGDNnzsykSZPo1asXzZs357333qNEiRKkTJmS69evs2XLFi5fvhztngwRatasyYoVK/joo48oW7YsN27cYPHixeTNmzfSjYXYvNeuXLlCu3btqFWrlnmi+enTp1mxYoX53w+gY8eO7Nq1i2HDhrFr1y68vLyws7Pj+vXrbN++nVq1avHxxx/H+noAN2/exNfXN8qEfpHoqHAQeU22trZ06dKFzz//nMWLF5t3bHVxcWH+/PksW7aM9evXM2HCBEJCQnB1daVp06Z07do12omDmTJlYtmyZSxfvpwNGzYwdepUnj9/Trp06ShatCiTJk2KdEc1Jnnz5mXdunUsXLiQLVu2MH78eIKDg8mYMSMlS5Zk0KBBkSbUDR06lLCwMLZs2cLmzZspV64cc+fOjbL5W2z5+PiY77RFtza6wWDgxx9/pGzZsqxYsYJJkyZhMpnIlCkTJUuWNHfXp0qVinbt2rF37152795NYGAgrq6uNGvWjO7du1v8jljp0qXZuHEj8+bN488//2TXrl2EhISQNm1a8ufPzzfffBNlN+SYpEiRgmnTpjF8+HCWL19OaGgoxYsXZ86cOQwfPjzKhMtu3brh4ODA3Llz+eGHH8iRIwdDhw7Fz8/vlYUDQJEiRVi9ejW//PILGzZs4OHDh7i4uJAzZ0569uxJlixZ4vSadOjQgatXr/Lbb7+xdOlSTCYT27Zte2XhsGPHDnbs2AG8eH+4uLhQrlw5evToYR5OE2Hs2LFMmzaNtWvX8scff+Ds7Iy3t7d5bHkEFxcXlixZwtixY9m0aRP+/v64urrSq1cvevToYf4w7unpSY0aNfjrr7/MxUu2bNn49NNPadu2rfl6pUqVYsCAASxbtozPPvuM8PBwRowYES+FQ7ly5ZgyZQo///wzkyZNIn369DRq1IjChQvTu3fvWI29d3FxoW7duvz666/079/f/PxKly7Nxx9/zJ49e1i2bBn3798nRYoUZM+ena5du9K5c2dzgeLi4sKMGTMYNWoUs2bNwtbWlrJlyzJv3rwomxnGp0KFCjFw4EDGjRuHr68vGTNmZNCgQVF2wY5Oy5YtyZUrF7NmzWLevHk8f/6cd955h/z58/Pxxx/H6vuXKlWKDRs2MHfuXP744w+2b99OaGioucf0hx9+eOnwxaFDh5IqVSq2bNnC1q1byZMnD1999RW+vr6RCofYvNdcXV1p2LAh+/fvZ8uWLYSHh5MlSxZ69OjBBx98YL6Wvb09M2fOZN68eaxfv54//viDFClS4OrqSoUKFcyricX2evBir4833dNCkg+DKaFmuImIiMhrmz17NiNHjmTJkiWUKFHilcdfvHgRHx8fJk2aRLVq1d5Cwjfn6elJo0aNIm2IJ29fWFgYtWvXpnbt2gwePNjSccQKaI6DiIiIBfx3Eji8mOOwaNEi0qZNG+2ymdFxd3enSZMmTJw4MSFiShK2du1anjx5Emm4n8jLaKiSiIiIBfj7+9OwYUN8fHzIkSMHd+/eZe3atVy7do0vvvjitYblffvttwmYVJKqJk2axLgSoEh0VDiIiIhYgIODA+XKlWPjxo3cv38fGxsb8uXLR79+/ahfv76l44mIRKE5DiIiIiIi8kqa4yAiIiIiIq+kwkFERERERF5JcxxeolSpUoSEhJAxY0ZLRxERERERiXf37t3D3t6ev//++5XHqnB4ieDgYMLDwy0dQ0REREQkQYSFhRHbKc8qHF4iU6ZMAGzbts3CSURERERE4l+NGjVifazmOIiIiIiIyCupcBARERERkVdS4SAiIiIiIq+kwkFERERERF5JhYOIiIiIiLySCgcREREREXklFQ4iIiIiIvJKKhxEREREROSVVDiIiIiIiMgrqXAQEREREZFXUuEgIiIiIiKvpMJBREREREReSYWDiIiIiIi8kgoHERERERF5JRUOIiIiIiLySomqcLh9+zbffvstLVq0oFixYnh6enL9+vVYnWs0Gpk6dSrVq1enSJEi+Pj48NtvvyVwYhERERGR5CFRFQ5Xr15l06ZNODs7U6pUqdc6d9y4cUyYMIE2bdowffp0ihcvTr9+/fjzzz8TKK2IiIiISPKRwtIB/q106dLs3bsXgOXLl7N79+5YnffgwQNmzpxJt27d6Ny5MwDlypXj6tWr/Pjjj1StWjXBMouIiIiIJAeJqsfBxiZucXbt2kVoaCg+Pj6R2n18fDh//jx+fn7xEU9EREREJNlKVD0OcXXx4kXs7e3JmTNnpPZ8+fIB4OvrS/bs2aM9t0aNGjFe99atW2TJkiX+gr6hIL8zPN67GpMxzNJRRERERCQezPrrOIGF3ehcpx+5c+S1dJyXShKFg7+/P87OzhgMhkjtLi4uADx+/NgCqeLf4/3reX7xkKVjiIiIiMgbCggM4atfj7H2mB8FGpckj2tBFQ6J3bZt22J87GW9ERYR/qKnIVXOQqQpVt3CYUREREQkLvzuPGHKgi2sPfYrBoMBg42BLB4elo71SkmicHB2diYgIACTyRSp18Hf3x+AtGnTWihZwnAqXJU0Rd61dAwREREReQ0hoeHM33SGtTv9MWWoSenagRSp68KTLM+xtU38H8sT1eTouMqXLx8hISFcu3YtUvvFixcByJs3cXf7xJaGKYmIiIhYp42/7yGXR3EWrduLyQR1yuXkz3VTyF/qxZxcm/8MuU+MkkThULlyZezs7Fi/fn2k9nXr1uHh4RHjxGhrY+Pg/P//M1k0h4iIiIjETkhoGO27f4r3e+9y68pJLuyZy7BOZejdrDipU6bAZHrxuc5A4i8cEl2fyObNmwE4efIkADt37iR9+vSkT5+eMmXKAFCwYEEaNmzI8OHDAciQIQMdO3Zk6tSpODo6UrBgQTZu3Mhff/3FL7/8YpknEk/ubfiFwKsvXgtj4FMAUmZxt2QkEREREYmFQ8fO0qhZK/wuHAUgf/HKrFu5kHx5/lm102gyAmAwJP77+YmucOjXr1+kr7/++msAypQpw/z58wEIDw/HaDRGOm7AgAE4ODgwb9487t27R+7cuRk7dizVqlV7O8ETgDHoGU+O/h6pzWBrR4o06S2USERERERexWg08tl34xkz/DNCg59ja5eK3h9/xU/fDYqyb5np/yNJrGGoUqIrHM6dOxenY2xtbenZsyc9e/ZMiFgW8e8BSVnafoPB1pYULpmxNQ9ZEhEREZHEJOBZCF0GjGD59K8AcMtViBXLFlO+dJFojzeaIgoH9ThIPEmVzRODFcy2FxEREUmuDp+9y7ilh3mauhAumfPyXl1v5k4ZRcqU9jGeozkOIiIiIiLJxMPHAXTr/zWB6SpiY2NLjizpGLNvHwVyZ3zluSYi5jiocBARERERSbLWbPiDTp068PjeNTwq3GLgR4PpUL8gqexj9zH7n6FKKhxERERERJKc4OAQ2nf/hOXzJ2IyhpPaKT19OjWge+Oir3Ud81AlFQ4iIiIiIknLvoMnaNq8FTevnAKgcKnqrF+5gFw5srzizKgiVlWyhjkOiX/6djLm/9daS0cQERERkf8zmUx8NXo6lSuW4eaVU9ildGDw1z9zbP/WOBUNgHmLAa2qJG8k+NbFf76wsbVcEBEREZFk7vGTYCYuP8ofp4MxGY1kdy/O6hWLKVks/xtd16h9HCRe/H8jh4w+fa1i3JuIiIhIUrRi417W7g/g8dNg0mXKwahJS+n7QX3s7d78o/Q/cxzU4yDxQUWDiIiIyFt378FjGrbozL7tayjfYjglvErzUZuS5HZzibfvoX0cJJ6YXn2IiIiIiMS7xat+o0fXDwh4eBMwkDXVPX7qXxV7u/gdPm4kYo6DCgeJB9ZQgYqIiIgkBYFBwbTq1J+1S6eByYijS0bGTZpG5zYNE+T7aTlWiR8m9TiIiIiIvC1/7j1Mi5atueN3DgCvinVZu3wu2bK8egfouLKmoUqJfxZGMmYuGxL/+0hERETEaplMJjbsvkT/b2Zyx+8c9qmc+GrkLxzavTFBiwbQqkoS7xL/G0lERETEGt1//JwJy45x+NxdshashXOKYCb9MJjCBdzfyvc3ml7McbCGVZUSf8LkTEOVRERERBLM9z9Nx6NAUfYfv4x9Chu6NSrCjnXT31rRAP8MVVKPg8QPK3gjiYiIiFiLm7fv06B5R/7etQGAR+c3M23eRHK4Or/1LP/McUj89/MTf8JkzBj41NIRRERERJKUOYvX4Vmg8IuiwWCDd/NubF8zzSJFA2iOg8STkLtXADCFh1k2iIiIiIiVe/rsOc079GHTytmAiTTpXJk0ZSbtmtezaC6TeY6DCgd5AynSZibs8R1sU6exdBQRERERq3X1VgCNW3fj8B9LASj7bgPWLptF5ozpLZxMy7FKPLNJ5WjpCCIiIiJWx2g0sebPi/T/+U/eKfA+aTPlYsTYmfy1Y02iKBpAQ5VERERERCzq6IkLDPh8DI553sNgMFDBKx+Lhp8iQ1oHS0eL5J+doxP//XwVDiIiIiKSpAz7fgKjvx1CaPAzSns78fWQ3rxXLmeinEdg3sfBCoYqqXAQERERkSTB78YdvJu259hfWwBwzVmAMZ+1pXLZXJYN9hLWtI9D4u8TERERERF5hSlzlpO/YGGO/bUFg8GGpu16c+XcUSqXLW7paC/1zxyHxP+xXD0OIiIiImK1gkPDadGxP2sXTQTAJUNWZsyaQ1OfmhZOFjv/zHFI/D0OKhxERERExCr5Xn/MmEWHuR2cETBQuU5TVi+aTob0LpaOFmvax0FEREREJIGEhIYxfs5G9lyAsHAT+QqX4aOOf9KsfmVLR3tt5qFKmhwtIiIiIhJ/Dhw+RZNmrbl9/QJV2o2lesVi9G5WHBenlJaOFifWNFQp8c/CEBEREZFkz2g0MuiLH6lYvjTXLx0HoHohe4Z2LGO1RQP8azlWTY4WEREREXkzvpev49O0HacP/wFAtjxFWbl8EWW8Clk2WDwwL8dqBUOVEn9pIyIiIiLJ1rgpCylcpCinD/+BwSYFrTt/hO/ZQ0miaAAtxyoiIiIi8kaCgsOYtf4UE2atIujZI9JlysmcufPwea+KpaPFK2ua46DCQUREREQSldOX7jFu6TFu3n+GR/lWFCuQg5kTviWts5Olo8U7ayocEn+fiIiIiIgkC8HBITRt14dKlatw/Y4/GVxSMbxXFVbO/TnJFg0mLccq8SHs8R1LRxARERF5K3bvP0qz5q25fe0MAOnCLzDh44GkcbC3cLKEE1E0gHocJJ4YbGwtHUFEREQkQRiNRvp8Opx3K5fj9rUz2KV05LPvxrNgwuAkXTTAP8OUwDoKB/U4JGY2KcAYhq1TOksnEREREYl3Zy9cwadJGy6c2AtATs+SrFm+mOJF8lk42dsRsYcDgI0V3M9P/AlFREREJMnZd+IW1es25cKJvdjY2tGp11AuntyfbIoGUI+DiIiIiEiMngeFMn3NSX4/eA3Pyp0whQUyd/ZMalcrZ+lob53xX3McrGEfh8SfUERERESShPnLNlK9ST9+P3gNgwE6NHmXqxeOJcuiAdTjICIiIiISybPnQbTs1Jdfl88E4P2s+RjxaTsK533Hwsks69+Fg5ZjFREREZFk7fc/D9C6TVvu3bgAQOkq9Zn2TTvcXJN30QBg5J/J0dbQ46ChSiIiIiIS78LCwuna7wvq1KzMvRsXsE+dhu/GTOPAn+tVNPyfhiqJiIiISLL2wD+QStXqcvbInwDkLVSOdasWUdAjt4WTJS6RCgcrGKqkHgcRERERiTe7jtyg9w87sH+nMLYp7Okx4CvOH9+joiEaEfs4GDCox0FEREREkofrt+7x85xtnL+fGoBqdZsydURPypUsaOFkiVfEcqzWUDSACgcREREReUMzFqymf+/uhBtNVGs/ltb1vWhZy5MUthrc8jIRQ5WsYQ8HUOEgIiIiInEU8OQZzdr3YsuaeYAJ5/Ru9GyQm/q1Clg6mlWIKBzU4yAiIiIiSdbG3/fQvl07Hty+DEDFmk1YvWQGGTOktWwwKxIxVMka9nAATY4WERERkdcQFm6kffdP8X7vXR7cvkwqx7T8OHEuu7euUNHwmkwRk6PV4yAiIiIiScndh88Zs+gQv+/YjTE8jPzFK7Nu5ULy5clu6WhWSUOVRERERCRJMRqNbPnLl9kbzvM8KIxS9XrhkaYxo78egI2NBrDElbUNVVLhICIiIiIxunLtFt5N2vIgIAiv9wdRIFd6BrauSZZ3HC0dzeqZ93HQqkoiIiIiYs0mzljKJwN6Evj0IQYbW94tYMuADyphq2VW48U/y7Gqx0FERERErNDDxwE0ad2dPzYtASBtxuzMnDWHxvWrWzhZ0vLPHAfrKMRUOIiIiIiI2ZoNf9CpUwce37sGwLt1W7Jy0VTSp3W2cLKkx2iyrjkO1lHeiIiIiEiCCg83smjzKdq0acXje9dI7ZSeCdOXsGPjYhUNCcSEVlUSERERESty6/4zflp0iLNXH1G0Vi+eXfuTdcvnkStHFktHS9K0j4OIiIiIWAWj0cjAYaPZe+IumT2q4pAqBd9/2oFqJT+zmg+z1szahiqpcBARERFJhi5c8sOnSRvOHt2FrV0qupWpwFe9apEpvYOloyUb1jZUSXMcRERERJKZMZPmUaRIEc4e3YWNbQrafNCPsYMbqmh4y7SPg4iIiIgkSvcePKZhi87s3bYKgPSuuZk/fz71ala0cLLkSfs4iIiIiEiic/TsDd6tXA7/+9cBA3UadmDZvIk4p9EO0JbyzxwH9TiIiIiIiIWFhRtZsuUcy7ed553cZQkLDWbcpGl0btPQ0tGSPWub46DCQURERCSJ+nPvYWZvOMODICcAuvb6mDa1fyFblowWTiag5VhFRERExMKMRiO9Pv6W6ROG45QhB3U6jaF385JULpHV0tHkX7Qcq4iIiIhYzMmzvjRs0gbf0/sBSJ8+Hd93L4l7LhUNiY21DVWyjpkYIiIiIvJK3/80nZJeJfA9vR+bFPZ06/cF54/vU9GQSEWsqmQthYN6HERERESs3N37j3m/cVv+3rUBgIxZ87Fo4QJqVi1j4WTyMhH7OGhVJRERERFJcCd97zNmwQEunD8PBhu8m3dh8axxODqksnQ0eQWj9nEQERERkYT29NlzFv92nvV7r2IyQa1WQ2hQITNtm9WzdDSJpX/mOKjHQUREREQSwJYdf9GmbVtcshanQOV21CqTgy4N6uGQys7S0eQ1aI6DiIiIiCSIsLBwuvb7nHlTf8AYHsZT/4dMGTecmuU9LB1N4sA8x0GFg4iIiIjEl6MnLtCwaUuunj8MQL4iFVi/ahGe7jktnEziyjxUyUr2cbCOAVUiIiIiydiw7ydQpnQJrp4/jK1dSvp88j1nj+5S0WDlNFTpDd26dYsRI0awZ88eTCYTFSpUYOjQobi5ub3y3Js3bzJu3Dj279/Pw4cPyZIlC++99x7du3fHwcHhLaQXERERiT9Pn4cwetYORn79CeGhQbjmLMCypYuoXLa4paNJPNCqSm8gMDCQDh06YG9vz6hRowAYN24c7du3Z926dS/98P/8+XM6depEaGgo/fr1I0uWLJw4cYIJEyZw9epVxo4d+5aehYiIiMibO3r+LmOXHOGBfxBFanQj7ztGFs4YQ8qU9paOJvEkYo6DwUoGASWqwmHZsmX4+fmxefNmcuZ80fXm6elJnTp1WLp0KZ06dYrx3MOHD3PlyhVmzpxJpUqVAChXrhz+/v7MmjWLwMBAUqdO/Vaeh4iIiEhcPQ54StO2H/IsZT7eyVEMt3ccGTP1KzxypLN0NIlnJivrcUhU5c327dspVqyYuWgAyJ49O15eXmzbtu2l54aGhgLg5OQUqd3Z2Rmj0Wj+hxERERFJrNZt3kmefIXZtn4BRzePp2apLIwb+K6KhiTK2vZxSFQpL168iIdH1OXE3N3duXjx4kvPrVChArly5eLHH3/k4sWLPHv2jH379jFv3jxatmypOQ4iIiKSaIWEhtGmy8c0fL8Gj+5eJbVTOkb+OJ5+rcqQKmWiGiAi8cioydFx5+/vj7Ozc5R2FxcXAgICXnpuypQpWbRoEX369OH99983tzdr1owvvvgixvNq1KgR42O3bt0iS5YssUguIiIiEjcHDp+icbNW3Lh0AoCCXu+yfuUC8uTKauFkktBMEfs4WMlyrImqcHgTwcHB9O/fnwcPHjB69Gjc3Nw4fvw4kyZNwtbWlq+//trSEUVERETMTCYTC9bu4YMWtQkLCSSFfWr6f/oto74agI1NohoUIgnkn6FKKhxem7Ozc7Q9CzH1RPzbihUrOHDgAFu3biVHjhwAlC5dmjRp0vD555/TqlUr8ufPH+W8l82deFlvhIiIiEhc+T8NZtKKY+w9fp+MubywDXvCyuWLKONVyNLR5C3SUKU34O7uzoULF6K0+/r64u7u/tJzz507h4uLi7loiFC0aFHzNaIrHERERETepnFTF7Hb15ag8FTYpbBh1JgJtKhTBHu7RPWxTN4C86pKiWvacYwSVcrq1atz7Ngx/Pz8zG3Xr1/n8OHDVK9e/aXnZsyYEX9/f65evRqp/dixYwBkzpw5/gOLiIiIxNKDh/5Urt2c/j3asHv1WLJlcuLHvlVoV7+EioZkyryPg5X0OCSqwqF58+ZkzZqVnj178vvvv7Nt2zZ69uyJq6srLVq0MB9348YNChYsyMSJE81tjRo1wtHRkW7durF69Wr++usvZsyYwahRoyhUqBBeXl6WeEoiIiIirFj3O3k9CrF763IAChVw58e+lcibLa1lg4lFRcxxsJZ9HBJVeevg4MDcuXMZMWIEn3zyCSaTifLlyzN06FAcHR3Nx5lMJsLDwyPtzZAtWzaWLVvGhAkTGDt2LI8ePSJLliy0aNGCHj16aJKRiIiIvHVBwSG07fIRqxZOxmQy4uD8Dj+N/4XuHZpaOpokAv/McbCOz6mJqnAAcHNzY8KECS89Jlu2bJw7dy5Ku7u7O+PGjUuoaCIiIiKxduDIGRo0bMLta2cAKFauNutXzCN7Vg2flhf+meNgHT0O1lHeiIiIiFgJk8nEpr2X+X7ecfwf3ccupSPDvhvP0X2/qWiQSExY1xyHRNfjICIiImKtfK/cYP7Waxw6exdsUtGy5wj6tqlC8SL5LB1NEiFrW45VPQ4iIiIi8WDkuFkUKlSQVcsWYJfChs4+hZkx/AMVDRIjaxuqpB4HERERkTdw595DfJp14sCf6wC4f3En6+YOJ5ebi4WTSWL3z87R1nEv3zpSioiIiCRC85dtIJ9nof8XDQbqNvmA8yf3q2iQWNE+DiIiIiJJ3LPnQXi36Eb7lj48eXQbp7SZmb1oDRtXzMTJ0cHS8cRKRMxx0D4OIiIiIknQtdsBfDJqMb8unwkmI6Wr1GfN0tm4ub5j6WhiZf6Z42Ad9/JVOIiIiIjEgtFo4tc9l5j762lCbFwpVq09zd6vwGcDu1o6mlgpLccqIiIiksQcP3WBpq06kbVkK5zSZ8MrfybmfvkL6Z1TWTqaWDFrW45VhYOIiIjIS3w18heGfz2I0KBnPHz0mLlL1lOvQi6r+bAniZeWYxURERFJAq7fuodPk/Yc2bcZgMzZPVm6ZB5VK+S2cDJJKv5ZjtU6CgfrmIkhIiIi8hZNn7cKz/wFXxQNBhsatvqQy+ePUbWCl6WjSRLyz1Al6/hIbh0pRURERN6CkNBwBn49jW4dmvA84D7O6d1YvHIjqxdNJnWqlJaOJ0lMxD4OWo5VRERExIpcvunPmIWHuPz4HdJm8aRQocKsXjKDjBnSWjqaJFEm7eMgIiIiYj1CQsPoP2QU140FMBlsSe/swNr1m6hSUnMZJGFFFA4GKxkEpMJBREREkq1Dx87SqGkr/C4eJU+phnT6cDC9mxUnbRoNS5KEZ7SyfRyso7wRERERiUdGo5Eh34ylfNmS+F08iq1dKnxqleezTmVUNMhbo6FKIiIiIonYlWu38G7SlpN/bwfALVchVixbTPnSRSycTJKbf4YqWUfhoB4HERERSTZmL95AwUKFOfn3dgw2trTo2J9LZw+raBCLMFrZPg7qcRAREZEkLygkjDm/nmb5thuEhgSSNmMOZs+eS8P337V0NEnG/hmqZB338lU4iIiISJK26+ApFm67zY17T3FwyczAr6fxaQ8f0qd1tnQ0SeYi9nHQUCURERERCwoODqFFx/5ULVeco3/vJr1zSr7uWp5Rg9uqaJBEQT0OIiIiIha27+AJmjZvxc0rpwCwCTjNhI+H4Oxob+FkIv+wtjkO1lHeiIiIiMSC0Wik/9CRVK5YhptXTmGX0oHBX49l9+bFKhok0TH9f6iSlmMVEREReYsuXPLDp0kbzh7dBUCOfCVYvWIxXkU9LZxMJHpajlVERETkLTtw6jadPpnE2aO7sLFNQfvun3Lx9EEVDZKoWdtQJfU4iIiIiNUKDA5j5rqT/PbXVdLlLE+pGm35enAP6tWsaOloIq9kbTtHq8dBRERErNLiVb+R27ME67Yfx2CAhlXzsmfTHBUNYjX+GapkHR/J1eMgIiIiViUwKJhWnfqzduk0MBm5+vdSli+eQ1H3jJaOJvJazPs4WEmPgwoHERERsRp/7DlMy1atueN3DgCvinVZt2IuWV3fsXAykdcXMcfBWvZxsI6UIiIikqwZjUY+HPg1Nd8tzx2/c9incuKrUb9waPdGFQ1itaxtjoN6HERERCRRe+AfSKsun7J1xQQAchcozbqViyhcwN3CyUTeTMQ+DlqOVUREROQN7Tl2kz4/7sAmcznSpM9Gt35fcP74PhUNkiSYtByriIiIyJu5efs+PQeNICxjZQwGA5553Jh87Bh5sqW3dDSReGPUUCURERGRuJuzeB19enbj6eM7FK35nI8G9KFV7fzYpdBACUlazMuxqnAQERERib2nz57TrF1vNq+eA5hIky4Lg7p507ZeQUtHE0kQ5uVYrWT2gAoHERERsbjftu+jTdu2PLh1CYBy1RqyZulMMmfU0CRJukxY11Al6yhvREREJEkyGk30HTKaerWr8ODWJVI6ODNi7Ez2bV+tokGSvH/mOFjHR3L1OIiIiIhF3H30nHFLjnD4igmj0Ui+IhVYv2oRnu45LR1N5K3QHAcRERGRlzCZTCxau5v1BwN4FhSGa65CjJ2xmj4d62NjYx13XkXig7Xt46DCQURERN6aazfu4NO0PScO/kHltmMoVaIoA1t74ZbRydLRRN46o+Y4iIiIiEQ1Zc5yChQszLG/tmAyhuGZIYBRvSupaJBkS0OVRERERP7lccBTmrbpwbZfFwLgkiErM2bNoalPTQsnE7Esc+FgJffyVTiIiIhIglm3eScdO7Tn0d2rAFSu04zVi6aTIb2LhZOJWF7EPg4aqiQiIiLJVrjRxPJt5xn8/TQe3b1Kaqd0jJ2ykJ2bl6loEPm/iDkOGqokIiIiydKt+0/5efERzlx5SO5Sjcmc1p6ZE74lT66slo4mkqiYrGwfB+tIKSIiIome0Wjk489/oEjx0py8eJvUKVMwoFUptq+bqaJBJBr/zHFQj4OIiIgkE76Xr+PTtB2nD/8BQMjNPYyf8j2uGRwtG0wkETOiOQ4iIiKSjIydspDCRYpy+vAfGGxS0LrzR2xc9KOKBpFX0HKsIiIikiw8eOhPw5Zd2b11OQDpMuVkztx5+LxXxcLJRKyDtQ1VinOPw8OHD/nhhx9o3LgxVatW5dChQ+b2n376iQsXLsRbSBEREUlczl19SIVazc1FQw3vtly+cFJFg8hr+GfnaOsYBBSnHofbt2/TsmVL7t27R86cObl79y4hISEApE+fni1btvD8+XOGDRsWr2FFRETEssLCjSz7/TxLfz9PNq+m3PE7y+gffqJbhyaWjiZidSL2cUjSQ5XGjBnD8+fPWblyJZkyZaJChQqRHq9evTo7d+6Ml4AiIiKSOOz66whfjJ6NU+4aALxXpQSrfz6Hs1MqCycTsU7WNlQpToXD7t27adOmDfnz5+fRo0dRHs+WLRu3b99+43AiIiJieUajkb6fDmfKuO8IDw2masuMfD2oE1W9slk6mohVs7Z9HOJUODx9+hQ3N7cYHw8NDSUsLCzOoURERCRxOHvhCj5N2nDhxF4AcnqWZOzQlhQvoqJB5E39M8fBOnoc4lTeZM2a9aWTnw8dOkTu3LnjHEpEREQsb+S4WRQvXowLJ/ZiY2tHp15DuXhyP8WL5LN0NJEkwWRlcxziVDjUq1eP5cuXc/z4cXNbxBNevnw5W7duxdvbO34SioiIyFv1PCiUd99vy5D+nQl+HsA7bnnZtHUnsyZ+T4oUtpaOJ5JkJIt9HLp168bevXtp3bo1BQoUwGAw8PPPP/P48WOuXbtG6dKlad++fXxnFRERkQR26tIDfl58mABcAQN1m3Ri2dwJODk6WDqaSJJjHqqUlCdHp0qVivnz5zN37lw2btxIypQpOXv2LDly5GDgwIF07NiRFCm0t5yIiIi1ePY8iHFzt7Df14TRBCXK1+arvo3xqVPh1SeLSJz80+OQhCdHA9jZ2dGlSxe6dOkSn3lERETkLfv9zwO0btMW/0d3qdp+HHWrFKF7oyI4pLKzdDSRJC3cGA5Yz1ClOJU3Q4YM4dixYzE+fvz4cYYMGRLnUCIiIpLwwsLC6dr3C+rUrMy9GxfAZKRhWRcGtPJS0SDyFgSHv9hA2TrKhjgWDqtXr+batWsxPn79+nXWrFkT10wiIiKSwE6cvohH0fLMmPAtxrAQ8hYqx5Gjx+jZsaGlo4kkGxH7N6SwsY4h/gkyoCogIAA7O92pEBERSYy+GvkLpUqW4PKZg9imsOfDgV9z/vgeCnpoKXURS0hyG8CdOnWKkydPmr/+66+/eP78eZTj/P39Wb58OXny5ImfhCIiIhIvngaGMmXlcWYtWElI0FMy58jP0sULqVrBy9LRRMQKxLpw2LFjBxMnTgReTOBYuXIlK1eujPZYBwcHPvvss/hJKCIiIm/s8NnbTFh2jPv+QRSp3oWK5Uowa+J3pE6V0tLRRMRKxLpwaNSoEWXKlMFkMtGhQwe6d+9OxYoVIx1jMBhwcHDA3d2dlCn1i0hERMTSAp48o2m7nhw7forSDYfh9o4TA9tUJn/OlpaOJiJWJtaFQ9asWcmaNSsAI0aMoHTp0mTLli3BgomIiMib2bh1N+3at+fh7csA5HW+z8iP6pM6pXVMxBSRxCVOvzkaNWoU3zlEREQknoSEhtGl92csnPkTxvAwUjmm5btR4/ioV3tLRxMRK/ZGtxyOHTvGiRMnCAgIwGg0RnrMYDDQq1evNwonIiIir+fvY2dp3LQlfhdf7LeUv3hl1q1cSL482S2cTESsXZwKh2fPntGzZ08OHDiAyWTCYDD8a8tsg7lNhYOIiMjbYTKZ2P73NZr5+PDo9gVs7VLRd9DX/Pjtx9jYWMdSjyKSuMWpcBg3bhwHDx6kV69elC1blnbt2jFy5EgyZszIrFmzePz4MaNHj47vrCIiIhKNgGchTFpxlL3Hb1GwWleuHljIssXzKV+6iKWjiUgSEqdbEL///jv169end+/euLu7A5A5c2YqVqzIjBkzsLOzY/ny5fEaVERERKKaOGMJ77X+lL3Hb2FrY6DPBz5cPntIRYOIxLs4FQ53796lZMmSAKRI8aLTIiQkBHgxVOm9995j06ZN8RRRRERE/uvh4wDerduSPl1bsX/DJBxMD/ixbxVa1PQkRQpbS8cTkSQoTkOV0qZNS2BgIACOjo6kSJGCmzdvmh+3sbHB398/fhKKiIhIJKs37OCDTh15fO8aAFXrNGXKl81I55LGwslEJCmLU4+Du7s758+ff3EBGxsKFy7M4sWLuX37Njdu3GDp0qXkzJkzXoOKiIgkd8HBIbTo2J8mPrV4fO8aqdNkYOKMpWzfsEhFg4gkuDj1ONSqVYu5c+cSHBxMypQp6dmzJz169KBatWrmY3766ad4CykiIpLc3bj7hHIVq3D94lEAipSuwbqVC8iV3dWywUQk2YhT4dCmTRvatGlj/rpKlSosWrSITZs2YWNjQ40aNShVqlScAt26dYsRI0awZ88eTCYTFSpUYOjQobi5ucXqfF9fX8aNG8f+/fsJDAwkS5YstG7dmg4dOsQpj4iIiCWZTCa27L/KjLUncXItjJ3feT4aOpzvh/XRMqsi8lbF257zxYsXp3jx4uavI3ojXkdgYCAdOnTA3t6eUaNGAS+Wfm3fvj3r1q3DwcHhpeefOHGCDh06UKZMGb7//nucnJy4evUqz58/f+3nIyIiYmkXLvkxcck+Lj148ffUp0UXWk3+guJF8lk4mYgkR/FWOEQICgpi4cKFzJ49m927d7/WucuWLcPPz4/Nmzeb50h4enpSp04dli5dSqdOnWI812g08umnn1K+fHkmTZpkbi9XrlzcnoiIiIgF/ThxLsM+7Yd9ahfebf8THb2L0aBqXmxtDJaOJiLJ1GsVDqGhoezYsYOrV6+SNm1aqlevToYMGYAXy7HOnTuX2bNn8/Dhw1gPLfq37du3U6xYsUgTq7Nnz46Xlxfbtm17aeGwf/9+fH19+eabb177+4qIiCQW9x48pmGLzuzdtgoAJ+cMfNLSg0pl3C2cTESSu1gXDg8fPqRdu3ZcunQJk8mEwWBgxIgRzJgxg5QpU9K3b19u3LhBnjx5+OSTT/D29n7tMBcvXqRGjRpR2t3d3dm8efNLzz106BDwYohU8+bNOXXqFM7Ozrz//vt8/PHHpEqV6rXziIiIvE2LV/1Gj64fEPDwJmCgTqMOLJs7Eec0jpaOJiIS+8JhwoQJ+Pr68t5771G6dGmuX7/OokWL+PLLL7l//z7Ozs6MHTuWOnXqYDDErRvV398fZ2fnKO0uLi4EBAS89Ny7d+8CMGDAANq0acNHH33EyZMnGT9+PLdu3Yo0fOnfoitUIty6dYssWbK8xjMQERF5fUHBIbTq1J81S6aCyYijS0bGT5rOB20aWDqaiIhZrAuHnTt3Urt2bcaOHWtuy5UrF19++SXFihVjzpw5pE6dOiEyxorJZALAx8eHfv36AVC2bFnCw8MZM2YMvr6+5M2b12L5REREouN35wljFhxk165dYDLiVbEua5fPJVuWjJaOJiISSawLhzt37tClS5dIbRUrVgSgbdu28VI0ODs7R9uzEFNPxL+lTZsWgAoVKkRqr1SpEmPGjOH06dPRFg7btm2L8Zov640QERF5E0ajkbV/nmfB5guEhBmp0PAjSmYP4stPelg6mohItGJdOISFheHk5BSpzdHxxZjLd955J17CuLu7c+HChSjtvr6+uLu/fFLYqx7XWtciIpJYnDzrS8MmbQizy0CRGt0o7pGR/i1rk8HFcj33IiKv8lqfpmOauxDXOQ3/Vb16dY4dO4afn5+57fr16xw+fJjq1au/9NwqVapgb28fZQnYXbt2AVC4cOF4ySgiIvImvvtxGiW9SuB7ej/XT/1O4wrv8HXX8ioaRCTRe63lWL/55htGjhwZpb1fv37Y2dlFajMYDOYP7bHVvHlzFi5cSM+ePenXrx8Gg4Fx48bh6upKixYtzMfduHGDWrVq0bNnT3r37g1AunTp6N69O5MnT8bJyYly5cpx8uRJJk2aRKNGjSIt8SoiIvK23bx9nwbNO/L3rg0AZMyWj8ULF1KjSmkLJxMRiZ1YFw6lS0f/iy137tzxFsbBwYG5c+cyYsQIPvnkE0wmE+XLl2fo0KHmYVHwYiJ0eHi4eUJ0hF69euHo6MiiRYuYNWsWGTNmpHPnzvTs2TPeMoqIiLyuOYvX0adnN54+vgMGG7ybd2HxrHE4OmipcJHkymQyYTQZAbCJp9E7CS3WhcP8+fMTMoeZm5sbEyZMeOkx2bJl49y5c1HaDQYDnTp1eulGcSIiIm9LaFg4M1cfpl/ndoQEBpAmXRYmT51B22b1LB1NRCwszBhm/n87G7uXHJl4vNZQJREREYmdK7cCGLPwEFduBVC4Rg/snp5j7bJZZM6Y3tLRRCQRCDOGm/8/hY2tBZPEngoHERGReBQWFk63/l9w+qYNGXOXwtnRnqHD+1O+iDYUFZF/hP6rxyGFrXV8JLeOlCIiIlbgyInzNGraiqvnD2Of2oX+w5cyuHNV0qXRXAYRiSws/EXhYGuwwcZgHdsGWEdKERGRRMxoNDLs+wmULe3F1fOHsbVLSfc+nzCiby0VDSISrYg5DilsrOc+vvUkFRERSYSuXr+NT9P2HN+/FYAsOQuybOlCKpUtbtlgIpKoRQxVspZhSqDCQUREJM7+PHCOerUq8TzgPgaDDU3a9mLB9B9JmdLe0tFEJJEL/f9QJTv1OIiIiCRdwaHhzNtwmnW7LpEhR3Hsbp1hxqw5NPWpaeloImIlktVQpYcPHzJz5kz27dvHgwcP+OmnnyhZsiQPHz5kzpw5eHt7ky9fvvjMKiIiYnHrNu9k5e7bPAxMCUD/T7+hQ72CZEjvYuFkImJNIgqHJN/jcPv2bVq2bMm9e/fImTMnd+/eJSQkBID06dOzZcsWnj9/zrBhw+I1rIiIiKWEhIbRscenLJkzjneyF6FOh+H0a+lFqQKZLR1NRKyQeY6DlezhAHEsHMaMGcPz589ZuXIlmTJlokKFCpEer169Ojt37oyXgCIiIpZ24PApGjdrxY1LJwDImCEto3qWxS2zNnMTkbgJs8LJ0XFajnX37t20adOG/PnzYzAYojyeLVs2bt++/cbhRERELMloNPLx5z9QsXxpblw6QQr71Hz8+Y+cOLhNRYOIvJF/JkfbWThJ7MWpxHn69Clubm4xPh4aGkpYWFiMj4uIiCR2V/1uU69hS04f/hOAbHmKsnL5Isp4FbJwMhFJCsKscKhSnHocsmbNyoULF2J8/NChQ+TOnTvOoURERCzp4OnbDJ7yF1cvX8Bgk4LWXT7C9+whFQ0iEm/CjOEA2CX1oUr16tVj+fLlHD9+3NwWMWRp+fLlbN26FW9v7/hJKCIi8pY8fBTAxGVH+Gbmfp4GGajb7gvWbNjGwuk/Ym9nPX/cRSTxCw0PBZLBcqzdunVj7969tG7dmgIFCmAwGPj55595/Pgx165do3Tp0rRv3z6+s4qIiCSYZWu20K3LB7gVqkXeUg3xqZKHDvXqY29nPcMIRMR6hFrhPg5x6nFIlSoV8+fPp3///oSHh5MyZUrOnj2Lvb09AwcOZObMmaRIYT0vgoiIJF+BQcE0adublo3r4v/gBtdP/sYXnUrStUERFQ0ikmDMQ5WsqHCIc1I7Ozu6dOlCly5d4jOPiIjIW7PrryM0b9GG29fOAFCsXG3Wr5hH9qzam0FEEpY17hwdpx6HnTt3YjQa4zuLiIjIW2E0Guk96DuqVSnP7WtnsEvpyOffT+Dovt9UNIjIW2Ge42BFk6PjPMfhnXfeoV69enh7e1OkSJH4ziUiIpIgHgUE8eWE9Uz+6WtMxjByepZkzfLFFC+Sz9LRRCQZSTZDlb777jvWrVvHggULmD9/Prly5cLHxwdvb2+yZcsW3xlFRETixb4TN5mw7BhPnttTuGoHShXMzLSx35AiheYyiMjbFWqF+zjEqXBo2rQpTZs25c6dO6xfv55ff/2VcePGMX78eIoXL06DBg2oW7cuLi4u8Z1XRETktd2595CGLbpg51YJl8x5ye3mzMSPfyJnFmdLRxORZCosYudoKxqqFKc5DhEyZ85Mly5dWLNmDevXr6dLly7cuXOHr7/+msqVK8dXRhERkTibv2wD7p6F+GvHao7+No5GVXIzpl8VFQ0iYlH/TI62s3CS2HujwuHf8uXLR6tWrWjWrBkODg6EhobG16VFRERe27PnQdRv1pX2Lbx5+ug2TmkzM3HiJD5oUBQ7DU0SEQtLNkOV/s3f359Nmzaxfv16jhw5gslkokCBAvj4+MRHPhERkde29c/9tG7dlvs3LwJQukp91i2fg2umDBZOJiLyQkThkOQnRwcHB7Nt2zbWr1/P7t27CQ0Nxc3Nja5du+Lj40PevHnjO6eIiMgrGY0mxs9az0c9mmIMD8U+tTNffPcjnw3sauloIiKRRGxtYJvUexzKly9PYGAgadKkoVGjRvj4+FCqVKn4ziYiIhJr9x8HMm7JEY6cDyedW37SuTixduVCCnrktnQ0EZEoTJgsHeG1xalwqFixIj4+PlStWhV7e/v4ziQiIvJahv80k8O30xEcbktKezt+mbGAJjULY2MTb1P5RETiVUThYMBg4SSxF6fCYcKECfGdQ0RE5LVdv3UPnybtObJvMzmLvUejjoP5qLUX2TKlsXQ0EZGX+3+Hg8F66ob4W1VJRETkbZo+bxWe+QtyZN9mDAYbShTKzejelVQ0iIhV+GeokvVUDrHqccifPz82NjYcPXoUe3t78ufPj+EV5ZHBYOD06dPxElJERCRCwJNnNG3Xk61r5wHgnMGNqdNn0bJRHQsnExF5fUluqFLDhg0xGAzY2tpG+lpERORt2rbzb5q3aM7D25cBqFizCauXzCBjhrSWDSYi8pqsb2p0LAuHkSNHvvRrERGRhBRuNLHmj4vMWHGSJ4/vk8oxLd+PHs/Anu0sHU1EJG5M/58cbUX34uM0x2HNmjVcv349xsdv3LjBmjVr4ppJRETE7MxFPz77ZQ9zNpwmRep0dOj/A8ePH1fRICJWzRrnOMSpcBgyZAhHjhyJ8fFjx44xZMiQOIcSERExGo0M/vpnihbOz++/bSCVvS19mhdn2vBu5MuT3dLxRETeiPWVDXFcjtVkevmorJCQEPN8CBERkdd15dotvJu05eTf2wHwv7Kb8bOGkeUdRwsnExGJJ+ahStZTOsS6cHj8+DGPHj0yf33//n0uX74c5biAgADWrFlD5syZ4yehiIgkKxNnLOGTAb0IfPoQg40tzTv0Yd6U0djb21k6mohIAkiChcOCBQuYNGkS8KIyGj16NKNHj472WJPJxMCBA+MnoYiIJAsPHwfQpHV3/ti0BIB0mXIwe/Y8GtSrauFkIiLxL8muqgRQpkwZ4EVRMGnSJGrVqoWnp2eU4xwdHSlatCglS5aMv5QiIpKkXfB7RP8vp5qLhmr1WrFy0VTSuWgzNxFJqv4/VMnCKV7HaxUOEcXDzZs3admyJcWKFUuwYCIikvSFhxtZsf0Ci7ecwyZdAYpUbkn3Do3o1bm5paOJiCQoU1Ke4/BvI0aMiO8cIiKSzOw9eJy2HbuTp2JXUqfJQMVibiz8Zi7OjvaWjiYikuCS7FClgwcPAlC6dOlIX79KxPEiIiIRjEYjAz8bxcQx3xAeGkRIuA3zFy7hXa9sVnXnTUQkPhisaLBSrAqHdu3aYTAYOHbsGPb29uavY2IymTAYDJw5cybegoqIiPU7f+kaDZq05ezRXQDkyFeC1ctn4VVE+zKISHKTRIcqDR8+HIPBgJ3di6XwNFRJRERe148T5zLs034EP/fHxjYFbbt8xMyJ35Mihfb9EZHk5xXboiVKsSocGjduHOnrRo0aJUgYERFJegKDw/jwkx+ZO34IABlc8zBv/jzq1axo4WQiIpZjssJZDnGaHB2TwMBAnjx5QqZMmeLzsiIiYqXOXnnImEWHuGvKg1OG7FSsUoNlcyfinEY7QItIchexHGsSG6r0Xxs3buTIkSN89tln5rbJkyczadIkjEYjZcuWZfLkyTg4OMRbUBERsR6BQcH0/nQU92wLYzLYkjljWrb/sYfShTWXQUQE/hmqZEVTHLCJy0lz587lyZMn5q9Pnz7NhAkT8PLyolmzZhw8eJBZs2bFW0gREbEeO3YfIle+oswa/yUX/17HuyWzMeHjaioaRESiZT2VQ5x6HK5cuULdunXNX2/atAkXFxdmzpyJvb09tra2bNq0id69e8dbUBERSdyMRiM9P/qG6RNHYAwLwT6VE628y/NR65KWjiYikuiYzKsqWTjIa4hT4fD8+XPSpElj/nrv3r1UrFgRe/sXm/YULFiQNWvWxEtAERFJ/E6e9aVhkzb4nt4PQO4CpVm3chGFC7hbOJmISOJmTXMc4jRUKWPGjFy9ehWAhw8fcubMGcqUKWN+/OnTp6RIEa/zrkVEJJH66ZdFlPQqge/p/diksKdbvy84f3yfigYRkZcwWeF6rHH6dP/uu++ycOFCXFxc2L9/P7a2tlSrVs38+IULF8iSJUu8hRQRkcTnWWAoU1cfZ+2+u4SFBJIpWz4WLVxIjSqlLR1NRCTR+6dssJ4ehzgVDn369OHChQv88MMP2NvbM3ToUPMSrEFBQWzdulV7PYiIJGG/7znBoh23uPcokLSZcjNkxCyG9GqGo0MqS0cTEbESyWSOQ7p06Zg/fz5Pnz4lZcqU5h2lI8yfPx9XV9d4CSgiIonH02fPadauN1vWzadCy5HkL1iUAa28KJg7g6WjiYhYJWua4/BGExGcnJyitKVKlYr8+fO/yWVFRCQR+m3HX7Rt05b7t3wBSBNykXED++KQyu4VZ4qIyH8lmzkOAKGhoSxZsoRt27Zx/fp1ALJnz07NmjVp3rx5lF4IERGxTmFh4XTt9znzpv6AMTyMlA7OfDX8Zwb3+8DS0URErFZE2WCworFKcSocnj59SocOHTh16hSOjo5kz/5iU5/jx4+zb98+Vq9ezZw5c6LtkRAREetx+MQ5GjdtzdXzhwHIV6QC61cvwjNvTgsnExGxclbY4xCn5VjHjx/P6dOn+fTTT9m3bx9r1qxhzZo17Nu3j8GDB3Pq1CkmTJgQ31lFROQtMZlM/HHIj64f/8zV84extUtJn0++5+zRXSoaRETigbnHIanPcdi6dStNmjShU6dOkdrt7e3p2LEjFy9eZMuWLQwZMiReQoqIyNvz5HkIv6w8zq6jN8hSqA4lQx8zdsRgKpUtbuloIiJJholk0uNw7949ChUqFOPjhQoV4v79+3EOJSIiljFlznLyFSrFjgMXsbEx0Oa9guzfukhFg4hIArGmOQ5xKhwyZcrEiRMnYnz8+PHjZMyYMc6hRETk7Xoc8JQa3m35sFNz7lw5wZ1T6/mhT2Va1fbE1jZOfypERORl/j/HwXrKhjgWDrVq1WL16tVMmzaN4OBgc3twcDDTp09nzZo11KlTJ95CiohIwlm7eSd58hVm+68LAahcpxlbV07GI0c6CycTEUm6ktXO0YcOHeKnn35i8uTJZM2aFYCbN28SGBhIkSJF6N27d7wGFRGR+BUSGkbHHp+yZM54TMYwUjulY8SPE+nXvbWlo4mIJHkm887RSbxwcHJyYvHixSxfvpwdO3aY93EoVaoUNWrUoEmTJtrHQUQkEbv94BkNW3Zn/+8vehkKer3L+pULyJMrq4WTiYgkE9Y3N/r1Cwej0ciDBw9wdnamdevWtG6tO1MiItbCZDLx+4FrTF97gjR5a+L49zZ69R3EiC/7Y2OjuQwiIm+LucfBwjlex2sVDpMmTWL27Nk8e/YMW1tbatSowffff6+N3kRErIDv5esM+GIcpkyVAPAqnJdZX50hu2taywYTEUnWrKd0iHXhsHz5ciZMmEDq1KkpWLAgt2/f5rfffsPGxoaff/45ITOKiMgb+vmXBQwd1JegZ48o7WPLJ/060+hdd2xtrOcPlohIUvLPHAcLB3kNsS4cli1bRtasWVm0aBGZM2cmNDSUgQMHsnXrVp48eUKaNGkSMqeIiMTBg4f+NGzZld1blwOQLlNOPu/5Pt7V81k4mYhIMvf/OQ7WtHN0rAe0Xrx4kRYtWpA5c2YA7Ozs6NGjB2FhYVy+fDnBAoqISNwsW7OFvB6FzEVDDe+2XLl4Cu86lS2cTERE/tk5OgkWDoGBgbi6ukZqy5IlCwDPnz+P31QiIhJnYeFG2vUYQsvGdfF/cAMH53eYOmcFv6+bj3MaR0vHExERrHJRpdebHP3fdWYjvjaZrPGpi4gkPTfuPeWnRYc4dycFJpORYuVqs37FPLJnzWzpaCIiEo0kOccBYOPGjVy4cMH8dVBQEAaDgRUrVrBv375IxxoMBgYMGBA/KUVE5KWMRiOzVvzJpsPPCAkNJ0+h8nzQaB092ntbOpqIiETHFLEcq/VUDq9VOOzYsYMdO3ZEad+wYUOUNhUOIiJvx9kLV/Bu3JqrF45Tpf1YypYoQP+WXmRMl9rS0UREJAbWOF4n1oXDtm3bEjKHiIjEwcixM/ly6EBCAgOwsbWjVI4Qvu1eARstsyoikqj9sxyr9fy+jnXhkDVr1oTMISIir+HOvYf4NOvEgT/XAfCOW14WLlhA7WrlLJxMRERixWR9O0fHelUlERFJHOYv24C7Z6H/Fw0G6jXpzOXzx1U0iIhYkX+GKllP6aDCQUTESoSGGZm74TRfjprC00e3cUqbmblL1rFhxQycHB0sHU9ERJK4RFc43Lp1i759+1KyZEm8vLzo3bs3N2/efO3rTJs2DU9PT1q1apUAKUVE3q4rt/z5eNxOVmy/QP6K7ajZ4AMunDtF+xb1LR1NRETiwBrnOCSqwiEwMJAOHTpw6dIlRo0axejRo7l69Srt27d/rU3m/Pz8+OWXX8iQIUMCphURSXhhYeF07fs5ZStUw/f6Q9I42PNZl0psXTMT10z6HSciYrX+P1bJesqG11yONaEtW7YMPz8/Nm/eTM6cOQHw9PSkTp06LF26lE6dOsXqOl999RXe3t5cvnyZsLCwhIwsIpJgjp+6QMOmrbl89m8AUj49ycSvPyG9cyoLJxMRkTdlMs9ysJ7SIVH1OGzfvp1ixYqZiwaA7Nmz4+XlFevlYNevX8+pU6cYOHBgQsUUEUlwX42cTKlSJbh89m9sU9jz4Udfs3zqMBUNIiJJxD9DlSwc5DW8UY+Dr68ve/fu5cGDBzRp0oTs2bMTHBzMzZs3yZIlC6lSvd4fuIsXL1KjRo0o7e7u7mzevPmV5/v7+zNixAgGDRpE2rRpX+t7i4gkBtdv3sWnaQeO7HvxOy9zjvwsXbyQqhW8LJxMRETilXmokvVUDnEqHEwmE19++SXLly/HZDJhMBgoW7Ys2bNnJywsjCZNmtC7d28++OCD17quv78/zs7OUdpdXFwICAh45fmjR48mV65cNG7cONbfM7pCJcKtW7fIkiVLrK8lIvImjl24R/363lw/fwCDwYaGrXqwcOZPpE6V0tLRRERE4jZUaebMmSxbtoyOHTsye/ZsTKZ/VqJ1dHSkdu3abN++Pd5Cxsbff//N2rVr+eqrr6xqdrqISEhoODPWnmTYlL3kKduadJlzsWjlRlYtnKSiQUQkibLGOQ5x6nFYsWIF9erV49NPP+XRo0dRHnd3d2f37t2vfV1nZ+doexZi6on4ty+++IImTZrg6upqvkZYWBhGo5GAgABSpUqFvb19lPNeNnfiZb0RIiLxYePW3fwwdTVOOasA0My7Khunfohj6qi/r0REJOkwlw3WUzfErXC4cePGS1c4cnFxwd/f/7Wv6+7uzoULF6K0+/r64u7u/tJzfX198fX1ZcmSJVEeK126NEOGDKFjx46vnUlEJCGEhIbRpfdnLJz5EyajkTof5ODrgS0oU9DV0tFERORt+P+InSQ/x8HJyemlhcHly5fjtIdC9erVGT16NH5+fmTPnh2A69evc/jwYT766KOXnjtv3rwobcOHD8doNDJs2LBIKzWJiFjS38fO0rhpS/wuHgMgf/EqTPisCe65VTSIiCQX/wxVsh5xmuNQpkwZ1qxZE+0eCffu3WPlypVUqFDhta/bvHlzsmbNSs+ePfn999/Ztm0bPXv2xNXVlRYtWpiPu3HjBgULFmTixInmtrJly0b5z9nZGScnJ8qWLYurq/4gi4hlGY1GBn/9M+XLlsTv4jFS2Kdm4GejOXVoB+65s1k6noiIvEX/DFVK4j0Offr0oVmzZrRo0QJvb28ADh48yLFjx5g/fz5Go5EPP/zwta/r4ODA3LlzGTFiBJ988gkmk4ny5cszdOhQHB0dzceZTCbCw8MjTcoWEUnMAp6F8G6dRhzZsxEAt9yFWblsMeVKFbZwMhERkdiJU+Hg7u7O7NmzGTZsGCNHjgRg8uTJAOTNm5dRo0aZhxq9Ljc3NyZMmPDSY7Jly8a5c+deea358+fHKYOISHw6dPYO45ceIcQ+KwYbW5p36MO8KaOxt7ezdDQREbGU5DLHAaB48eL8+uuvnDt3jkuXLmEymciZMyeFChWKz3wiIlbr4eMAxs//g0NXXvxxqFS7OWM+70KdamUsG0xERCzOGsfNvNHO0QCenp54enrGRxYRkSRj5a/b6fJBR0JCQqjSbiwNqxekY/1CpLSztXQ0ERFJBCImR1vTHIc4TY4WEZHoBQeH0Lxjf5o1qM3je36YjGF0qJmZ7o2KqmgQEZF//L/LwXrKhjj2OOTPn/+V1ZHBYOD06dNxCiUiYo32HjxO0+atuXXlFABFStdg/aoF5MymVd1ERCSyZLNzdMOGDaMUDuHh4Vy/fp2jR4+SL18+ChYsGC8BRUQSO6PRyMBho5n449eEhwZhl9KBjz8bznef9cHGRh27IiISM2saqhSnwiFiJaXonDp1iq5du9KoUaM4hxIRsRaPngQxYelRFi9dSXhoEDnylWD1ysV4FdHcLxERiZk1bgD3xpOj/6tQoUI0a9aMH3/8kWXLlsX35UVEEo29x64zedUJ/J+G4PVeH7IYzjNt7NekSKG5DCIi8grJZY7Dq7i6usZqnwUREWt078FjGrbozNWbDylRtz+5sjgzsPW75HZzsXQ0ERGxEtY4xyHeB9+GhoayceNGXFz0B1REkp7Fq34jr0dB9m5bxY0zf1I6l5Ex/aqoaBARkddiLhusp26IW4/DkCFDom1/8uQJR48e5f79+/Tp0+eNgomIJCaBQcG06tSftUungcmIo0smxk+axgdtGlg6moiIWKPksnP06tWro213cXEhZ86cfPTRR5ocLSJJxo7dh2jZqjV3r58HoGSleqxdPpesru9YOJmIiMjbE6fC4ezZs/GdQ0Qk0TGZTKz78wJtGtXn2ePb2KdyYtg3P/L5oO6WjiYiIlYuWayqFBwczKZNm8idOzfFihVLiEwSjU3nd+AXcMvSMUSSjeCQME5cvM99/yCKtazD1d376P7dh2TOmoZpfy+ydDwREbFygaFBQBLfx8He3p5hw4YxbNgwFQ5vye1nD5h9REvbirwt1/dfwmQykb1cXlJkgqyZUuNWtRongs5xwlcrxomISPxxsEtt6Qix9tqFg8FgIGvWrPj7+ydEHolGSHio+f+bF/a2YBKRpM3/YQBjPhvPmV37SJEqFTlcq1Dn3UI4O6W0dDQREUmCcri4kcEhnaVjxFqc5ji0atWKhQsX0rp1a9KkSRPfmSQGaVM507RQPUvHEEmSZi9eR/+e/Xn6+A4YbKjXoAOz+nyMo0MqS0cTERFJFOJUOLi4uODg4ECdOnVo0KABOXLkIHXqqN0sDRs2fNN8IiIJ6umz5zRr15vNq+cAJtKkz8LkKTNo20xFuoiIyL/FunBo3749H374IeXLl4+0j8Ps2bOjPd5gMKhwEJFE7dzlu1SqVJH7Ny8CUK5aQ9YsnUnmjOktnExERCTxiXXhcODAAZo1awbAvHnzEiyQiEhCMxpNrNvly9wNZ0jjWoAnj+/y1fCfGdzvA0tHExERSbTiNFSpTJky8Z1DROStOHziHLPWnuTqoxe//lp3+Yh2tSfgmTenhZOJiIgkbnEqHERErI3RaOTz4RP54buhOGXIQbW2I+jasBh1yuW0qjW0RURELOW1CodLly5x8ODBWB9funTp1w4kIhLfrl6/jU+Tdhw/8DsAqewMfNWpKEXy57JsMBERESvyWoXDlClTmDJlyiuPM5lMGAwGzpw5E+dgIiLx4ZdZy/mo/4cEPnmAwWBD03a9mT/tB1KmtLd0NBEREavyWoVD48aNKVKkSEJlERGJN/5PntG4VTe2b1gEgMs72Zgxaw5NvWtYOJmIiIh1eq3CoVy5cnh7a+diEUncLl5/zA/z9vH3gb0AVKnTnNVLppM+rbOFk4mIiFgvTY4WkSQjJDSMVTvOs2TrRcKNJqo0/ZSaxZzp1721paOJiIhYPRUOIpIk7D90kibNW+OQqSCeFVpRvkgWejV9DxenlJaOJiIikiTYWDqAiMibMBqNfDTsBypVKMONSye4cnQDXd7Pw5AOpVU0iIiIxKNY9zicPXs2IXOIiLw238vX8W7SljNH/gQgW56irFy+iDJehSycTEREJOlRj4OIWKWff1lA4SJFOXPkTww2KWjT5WN8zx5S0SAiIpJANMdBRKxKUHAYYxfsYljfLoSHBZMuc07mzp2Hd50qlo4mIiKSpKlwEBGrce7qQ8YsOsyt+88oULUjrg7PWLlwCs5pHC0dTUREJMlT4SAiiV5gUDBtu3zEnfBspHMrwDsuqfhu8rcU88ho6WgiIiLJhgoHEUnUdv11hOYt2nD72hlSO2di2M8r6duyDE4O9paOJiIikqxocrSIJEpGo5Heg76jWpXy3L52BrtUjnz86ecM/aCSigYRERELUI+DiCQ6Zy9cwbtxay6e3AdATs+SrFm+mOJF8lk4mYiISPKlHgcRSVRW/XaAYsWKcfHkPmxs7fig92dcPLlfRYOIiIiFqcdBRBKF50GhTF9zkq0HbpLOLT/hgQ9ZtHABtd4ta+loIiIiggqHRO1aSlvOp7IHvwOWjiKSoOYv28hvx4PxD7TBxsbAF9+NpYNPcRwdUls6moiIiPyfCodEbEHmNASksIFrLwqHlLaaECpJy7PnQbTo0IcNK2aStUAV6rQeysDWJSmUJ4Olo4mIiMh/qHBIxIJsDACUz1IEJ4e0lM1WwsKJROLP1j/307p1W+7fvAiAW8Y0/Ny/Ms5O6mUQERFJjFQ4WIHmHrXI6qqJoZI0hIWF02PgV8yePApjeCj2qZ354rsf+WxgV0tHExERkZdQ4SAib82pc5fxbticy2f/BsC9cHnWrlxIQY/cFk4mIiIir6LlWEXkrdh55DrDpu7npp8vtins6fnxN5w7tltFg4iIiJVQj4OIJKjbdx8y/7dL7Dx6AwypafDBN/RsWYGqFbwsHU1EREReg3ocRCTBTJ+3irz5PFm0aAE2NgZa1vJk4c89VTSIiIhYIfU4iEi8C3jyjKZte7J13TwArp/YyMLJwyiY+x0LJxMREZG4Uo+DiMSrjVt3k9ujiLloqFizCWeOH1TRICIiYuVUOIhIvAgJDaN990/xrluNh7cvk8oxLWMmz2f31hW8k97F0vFERETkDWmokoi8sTsPnzNoxALmTxsNQP7iVVi/aiHuubNZOJmIiIjEFxUOIhJnJpOJ7X/7MXX1CQKNmSlQsQV13y3JD998hI2NOjRFRESSEhUOIhInl6/epEnrLqTN74Nj2iwUyJWe6UNn4prB0dLRREREJAGocBCR1zZh+mI+HdibwKcPyXDNj0mzVtC0hge2NgZLRxMREZEEosJBRGLt4eMAGrfqxp+blwKQLlMOZk6bSINanhZOJiIiIglNg5BFJFZWbdhBXo/C5qKhWr1WXLpwigZ1q1o4mYiIiLwNKhxE5KXCw418/sNcmvrU4vE9P1KnycDEGUvZvmERaZ2dLB1PRERE3hINVRKRGN28/5SfFh3mzA1H0ryTk5w5c7N+1QJyZnO1dDQRERF5y1Q4iEgURqORz0f8wln/rISEG3ByTM2CZet4v3IBLbMqIiKSTKlwSMRMlg4gydL5S9do0KQtZ4/uIl/Z5jRt35f+rUqQKZ2DpaOJiIiIBenWoYiY/TBhDkWLFOXs0V3Y2Kagopc73/WooKJBRERE1ONgDbQyviS0u/cf0bBFZ/ZtXw1ABtc8zJs/j3o1K1o4mYiIiCQW6nEQSeZWrt+Bu2eh/xcNBuo06siViydUNIiIiEgkKhxEkqmwcCPzN51h8uozPA94iKNLJmYuWM3mVbNxctTQJBEREYlMQ5VEkqETZ68ya9NlLl73xyFdVroM+pkv+jbDzfUdS0cTERGRREo9DiLJiNFopMeAryhRND/7/9qLU2o7Pm1fiinDP1TRICIiIi+lHgeRZOLkWV8aNmmD7+n9AATe2MfEaQPI4JLawslERETEGqjHQSQZ+O7HaZT0KoHv6f3YpLCnW78vOPjHGhUNIiIiEmvqcRBJwm7evo9Psw4c2r0RgEzZ8rF40SKqVy5l4WQiIiJibdTjIJJEnfC9T6s+P74oGgw2+LTozqVzx1U0iIiISJyox0EkiQkNC2fBprOs/vMiabKXo1D5hgwe0JW2zepZOpqIiIhYMfU4iCQhm7fvI1+RCizZfBSTCeqUy8WB7ctUNIiIiMgbU4+DSBIQFhZOl77DmD/tR4zhYdg5LGT+3JmUK5zF0tFEREQkiVDhIGLlDp84R6Mmrbh24QgAHkUrsG7ZL3jmVdEgIiIi8UdDlUSslNFo5LPvxlOudEmuXTiCrV1K+nzyPWeO7MIzb05LxxMREZEkRj0OIlboyfMQ2nb/jHULfgQgS86CLFu6kEpli1s2mIiIiCRZKhxErMyRc3cZu+QIQWmKkdo5E/UbtmT+tB9ImdLe0tFEREQkCUt0hcOtW7cYMWIEe/bswWQyUaFCBYYOHYqbm9tLzztx4gTLli3j4MGD3Lp1i3Tp0lGyZEn69+9P9uzZ31J6kYTzOOApfYeO4aF9MQwGA7lzuPLDwSMU9Xj5z4aIiIhIfEhUhUNgYCAdOnTA3t6eUaNGATBu3Djat2/PunXrcHBwiPHcjRs3cuHCBdq1a0e+fPm4c+cOkydPpmnTpqxZs4YsWTRRVKzX2k1/0qljex7dvUaxOn3p3vUDOnkXIpV9ovoRFhERkSQsUX3qWLZsGX5+fmzevJmcOV9M7vT09KROnTosXbqUTp06xXhu165dSZ8+faQ2Ly8vatSowbJly+jXr1+CZk9IBgyWjiAWEhIaRscen7JkzjhMxnBSO6Wjc5PyfNikmKWjiYiISDKTqFZV2r59O8WKFTMXDQDZs2fHy8uLbdu2vfTc/xYNAFmzZiV9+vTcuXMn3rOKJLT9h06SJ78Xi2f9hMkYTqGS73LyxAn6dG1p6WgiIiKSDCWqHoeLFy9So0aNKO3u7u5s3rz5ta/n6+vLgwcPyJs3b4zHRPf9Ity6dUtDnOStM5lMfDHiF0Z+/TFhIYGksE/NwMHfMeLL/tjYJKpaX0RERJKRRFU4+Pv74+zsHKXdxcWFgICA17pWWFgYX375JenTp6dp06bxFVEkQfk/DWbi8qNsO/qIsJAgsuUtysrliylToqClo4mIiEgyl6gKh/j0zTffcOTIEaZOnYqLi0uMx71sCNTLeiNE4tuGHUdY8scdHj8NJnOuInz141yG9G2FvV2S/TEVERERK5KoPpE4OztH27MQU09ETH788UeWLVvGyJEjqVSpUnxGFIl3Dx7606BlF/7641eqtP2ZAgU8+Kh1SfJkjbngFREREXnbElXh4O7uzoULF6K0+/r64u7uHqtr/PLLL0yfPp3PP/+chg0bxnNCkfi1bM0WunbpRMCDmwBksfPj5/5dsbeztXAyERERkcgS1UzL6tWrc+zYMfz8/Mxt169f5/Dhw1SvXv2V58+bN4+xY8cyYMAA2rZtm5BRRd5IYFAwTdr2pmXjugQ8uImD8ztMnbOChVO/V9EgIiIiiVKiKhyaN29O1qxZ6dmzJ7///jvbtm2jZ8+euLq60qJFC/NxN27coGDBgkycONHctmHDBoYPH07lypUpV64cR48eNf938eJFSzwdkWjt+usIeTxLsGrhJEwmI8XL1+Hs6ZN069DE0tFEREREYpSohio5ODgwd+5cRowYwSeffILJZKJ8+fIMHToUR0dH83Emk4nw8HBMJpO5bdeuXZhMJnbt2sWuXbsiXbdMmTLMnz//rT0PkeiYTCY27bvCR4NGc/vaGexSOTL485F8M7S3paOJiIiIvFKiKhwA3NzcmDBhwkuPyZYtG+fOnYvUNnLkSEaOHJmQ0UTi7FFAEOOWHuHQ2bvkKd0UJ/swZkwYTrHC+SwdTURERCRWEtVQJZGkaMTPMylYohIHT93ALoUN3RoXZ/+2ZSoaRERExKokuh4HkaTi9t0HNGj+AQf+XAfA0yt/MHnKcHK6xn5pYREREZHEQj0OIglg/rIN5PMs9P+iwUC9pp3ZsPAHFQ0iIiJitdTjIBKPnj0PokWHPmxYMRMw4ZQ2M5OmzKB9i/qWjiYiIiLyRlQ4iMSTq7cDqNegFacPbASgTFVv1i6bjWumDBZOJiIiIvLmNFRJ5A0ZjSbW7vRlwM9/kqlQfRxdMvH9mOns/2OdigYRERFJMtTjkIiZXn2IWNjxUxcYOmoWhozlAKhavjhLfrhI5gxpLJxMREREJH6px0Ekjr4aOZlSpUqwYf4o/G+d5sMmRfmySzkVDSIiIpIkqcfBGhgsHUD+7frNu/g07cCRfZsByJwjP9/1qUOV8rktnExEREQk4ajHQeQ1TJ+3Cs8ChTiybzMGgw2N2vTi8rmjVCnvZeloIiIiIglKPQ4isRASGk6Tdr35dekUAJwzuDFtxmxaNKxt4WQiIiIib4cKB5FXuHTDnzGLDuH32B6AirWasmbJDN5J72LhZCIiIiJvjwoHkRiEhIYxfemf/H78GWHhJgqXeY8BnerSocV7lo4mIiIi8tapcBCJxsGjZ2jSrBV3b/lRtf04KpfKR5/mxXFxSmnpaCIiIiIWocnRIv9iNBr59KufqVC2JH4XjxEeGkiNQrZ81qmMigYRERFJ1tTjIPJ/l6/exLtpW079vQMAt9yFWbV8MWVLFrZwMhERERHLU4+DCDBh+mIKFS7Cqb93YLCxpWWnAVw+d0RFg4iIiMj/qcdBkrWgkDBmrz/FyDG/EPj0Ieky5WD2nHk0qFvV0tFEREREEhUVDpJsnbv6kJ8XH+bGvWcUrtGdYkXys2jmT6R1drJ0NBEREZFER4WDJDvBwSG06/4Je/b9TYn3PyaDS2r6dSuPl2cbS0cTkWQqPDyc0NBQS8cQkSTGzs4OW1vbeLueCgdJVvYcOEazFq25deU0AHUbtuLnQV1J42Bv4WQikhyZTCZu377N48ePLR1FRJKotGnT4urqisFgeONrqXCQZMFoNDJg6Cgm/fQN4aFB2KV0ZNCw4Xw79ENsbLRGgIhYRkTRkClTJhwcHOLlD7uICLy4MfH8+XPu3r0LQJYsWd74miocJMk753sVn8ZtOH98DwA58pVg9crFeBXxtHAyEUnOwsPDzUVDhgwZLB1HRJKg1KlTA3D37l0yZcr0xsOWdKtVkrS/TtykYpXanD++BxvbFHT4cDC+pw+qaBARi4uY0+Dg4GDhJCKSlEX8jomPeVTqcZAk6XlQKDPWnmTrgWt4VOrAhd2zmTdvLnVrVLB0NBGRSDQ8SUQSUnz+jlHhIEnO4pW/MX35nzhmLYvBAF3aNqDN/EGktLezdDQRERERq6XCwQoY0N2o2AgMCqZlp/6sWzoNG9sUNOz1C1/19qGI+zuWjiYiIiJi9TTHQZKEHbsPkStfUdYtmQImIyXK1WTCYBUNIiLyQrt27WjVqlW8XW/ZsmV07dqVypUrU7x4cerXr8+MGTMICQmJ9TVmz56Nt7c3JpMpymOHDh3C09OT8uXLExYWFuXx/fv34+npyd69e6O9dvXq1fn444+jtB85coR+/fpRqVIlChcuTNmyZenUqROrV68mPDw81tlj8vvvv9OwYUOKFClCtWrVmDx5cqyuu2rVKjw9PaP816BBgyjH3rp1i759+1KyZEm8vLzo3bs3N2/ejJKjQoUKPHv27I2fk/xDPQ5i1YxGIz0/+obpE0dgDAvBPpUTw775kc8Hdbd0NBERScImTZpEhQoV+Oyzz0ibNi2HDh1i3LhxHD9+nPHjx7/y/ICAAKZMmcLXX38d7Rj0NWvWAPDw4UN27txJ9erV3zjznDlzGDlyJOXKlePjjz8ma9as+Pv7s2fPHr766ivSpElDzZo143z9Xbt20adPH5o2bcrgwYM5c+YMP/30E8+ePWPQoEGxusa4ceNwdXU1fx2xKlCEwMBAOnTogL29PaNGjTKf0759e9atW2eeCFyjRg0mTJjAzJkz6du3b5yfk0SmwkGs1r1Hz6hQpRYXT+4DIE/BMqxdsZDCBdwtnExERJK61atXkz59evPX5cqVw2QyMWHCBPz8/MiePftLz1+xYgV2dnbUqlUrymPBwcFs2rSJMmXKcOLECdasWfPGhcPBgwcZOXIkbdu2ZdiwYZEeq1mzJp06deL58+dv9D3GjBlDyZIl+fbbb4EXr8mzZ8+YMmUKHTt2JGPGjK+8RoECBciZM2eMjy9btgw/Pz82b95sPs7T05M6deqwdOlSOnXqBLyYENy8eXPGjx9P9+7dSZky5Rs9N3lBQ5XEKu0+doN+P/2JwSkHNins6dbvC84d26uiQUTEgq5evcqgQYOoXr06RYsWpUaNGnz55Zf4+/tHOm7w4MFUqVKFw4cP06RJE4oUKUL16tWZP39+pOMePnzIF198QZ06dShWrBhVq1blo48+4s6dO1G+99mzZ+nVqxdly5alaNGi1KlTh6lTp0Y5bu/evTRq1IhixYpRv359tm7dGu21evToQenSpSlatCgtW7bk77//jnTMv4uGCEWKFAGINt9/LV++nPfeey/adfV///13njx5QuvWralZsybbt2+P8hq+runTp+Pi4hLjnf8cOXKQP3/+OF//1q1bnDlzBh8fn0jtDRo0IDQ0lJ07d8b52v+2fft2ihUrFqm4yJ49O15eXmzbti3SsXXr1iUgIIAtW7bEy/cW9TiIlbl5+z4Tl+zlhN+L8aB1mnSh6ZhPqFrBy8LJRETih8lkIjjkzceav4mU9rZxWsLx7t27ZMmShaFDh+Li4oKfnx9Tp06lW7duLF26NNKxT58+ZcCAAXTt2pUcOXKwceNGvvvuOxwdHWncuDEAjx8/xt7enoEDB5I+fXru3r3LrFmzaNWqFZs2bTLfRT5+/Djt2rUjR44cDBkyhMyZM3P16lXOnTsX6Xv6+fnx/fff061bN9KlS8fs2bPp168fmzZtMn8QPXXqFG3atKFAgQJ8++23pE6dmsWLF9OxY0eWLFlC4cKFY3z+Bw8exMbGhly5cr30dbpx4waXLl2iX79+0T6+evVqnJ2dqVGjBmnSpGH9+vVs2LCB1q1bv/S6MQkPD2f//v3UrFkz1nfeo5tX8V8Gg8Fc+Fy4cAGAfPnyRTome/bspE6dmosXL8bq+7Zu3ZqHDx+SIUMGqlevzsCBA0mbNq358YsXL1KjRo0o57m7u7N58+ZIbenTpydv3rzs2rULb2/vWH1/eTkVDmI1Zi9aS5+e3bC1d6RKm9E0r12IlrU8sUuhjjMRSRpMJhOfTtzNmSsPLZqjQK70jOpd6bWLh9KlS1O6dGnz1yVKlCBHjhy0adOG06dPU7BgQfNjz54949tvv+X9998HoEqVKty5c4cJEybQqFEjDAYDefLkiTSsJjw8HC8vL95991127txpHuYzatQo0qZNy7Jly8xj4suXLx8l36NHj1iwYIH5g32hQoWoVKkSmzZtokePHgCMHj2aLFmyMHfuXOzt7QGoVKkS9evXZ/LkyUyePDna53727FnmzZtHkyZNeOedly/McezYMYBo7/DfvXuXvXv30rRpU+zt7alQoQKZM2dmzZo1cS4cHj16RFBQEG5ubrE6/vr169F+OP+vrFmzsn37dgBzj4izs3OU45ydnV/ZY5IxY0Z69epFsWLFSJkyJYcPH2b69OkcOXKEFStWmAsef3//aL+Hi4sLAQEBUdoLFChgfr3lzalwkETv6bPnNG3Xi99WzwVMpEmfhd4NclPn3QKWjiYiIv8SEhLCrFmzWLNmDTdv3iQ4ONj82OXLlyMVDra2ttSuXTvS+fXq1WPYsGHcuXPHPEF20aJFLFmyBD8/v0hj8C9fvgy8mCx7+PBhOnfuHGUi7X/lzJkzUm9AhgwZyJAhg3lFnqCgIA4ePEj37t2xsbGJdNe9QoUKrF+/Ptrr3r17l549e5IjRw4GDx780gwRx0P0w53WrVtHeHi4eTUhGxsbfHx8mD59OpcuXSJPnjyvvP6bypQpEytWrHjlcRGFVXyoXLkylStXNn9drlw5PDw86NWrF+vWraNZs2Zxum5ET5XEDxUOkqht3r6Ptm3a8uD2JQDKV2/E6iUzyJwx6i9bERFrZzAYGNW7ktUOVfrpp59YsGABPXv2pESJEjg6OnLnzh169+4dqYiAF3eh7ewib8wZcac+onCYP38+3333HZ06daJSpUo4OztjMplo3ry5+XoBAQEYjcZIK/HExMXFJUqbvb29eQlVf39/wsPDX9qzYDQasbH5p6f70aNHfPDBBwDMnDkTJyenV+aIyB7dB+81a9bg5uZGvnz5zHfQa9SowfTp01m7di0DBgwAMA8Rimmp0/DwcFKkePExL23atKRKlSrKkqUxsbe3p0CBV9+c+/d7JKIXILq7/gEBAdG+9q9So0YNHBwcOHHihLlwcHZ2jvZ7xNQTkSpVqijvPYk7FQ6SKIWFhdOl7zDmT/sRY3gYKR1c+GbEz3zSt5Olo4mIJCiDwUCqlNb553nDhg00aNCAnj17mtv++uuvaI8NCAggNDQ0UvFw//59ADJnzmy+Xvny5SPdxffz84t0HWdnZ2xsbGI1IflV0qRJg42NDW3atIl2/wAgUtHw9OlTOnfuzOPHj1m4cKE596tEjNn39/cnVapU5vaTJ0+a5wr8e8hXhLVr19KvXz9sbGzIkCEDQLR308PCwnjw4IH5mBQpUlCmTBn27NlDSEjIK3sK4jJUKWJuw8WLFylRokSkawUGBuLuHj+Ll7i7u5tfo3/z9fWN9ns8fvw40hwJeTPW+ZtJkrS7j57z04KDrFuzGmN4GB5FK7Ju1UI888a8PJuIiFheUFCQ+S53hFWrVkV7bHh4OFu2bDHPcQDYuHEjbm5u5g/gQUFBUe7g//d6qVOnpmTJkqxbt45evXpF+iD+uhwcHChVqhRnz55l6NChkYqE/woMDKRbt27cuHGDefPmvXQJ0f+KGG7k5+cXqdhYs2YNBsP/2rvvuKrq/4HjL6YIiKKiKBiSLAfgAAFFE8yt4NZK3Lm3ZaZp36xcOVAM9yw3LkyzHDkwV2qO0q+J5ldwg4IsBe75/eHj3l+3i1zkMhzv5+PBo/iczznnfe4H4bzPZxwj5s+fr/OEPiYmhiVLlnDixAkCAgKoWrUq9vb27N27l06dOmnVPXjwIJmZmfj5+WnKBgwYQFhYGDNnztRZjlUdS2pqKh4eHvkaqlS5cmU8PDx0hhVFR0djZmZG48aN9R7v3/bt20daWhpeXl6asuDgYGbOnKm15G1cXBxnzpxh7NixOseIi4vD2dn5hc8tciaJg3hpqFQqfvntfyzd8QepGVnUDxmLe5kHzJ36Sa6/vIUQQrwcGjVqxPbt23Fzc8PJyYmff/6Zs2fP5ljXysqKb775hocPH+Lk5MSuXbv49ddfmT59umYITKNGjVi6dCmLFi3Cy8uL48eP89NPP+kca9y4cYSFhdGtWzf69OmDvb09N2/e5PLly0yaNOmFrmH8+PH06NGDfv360blzZ+zs7Hj48CF//vkn2dnZmrcxDx8+nDNnzjBx4kTS09P5/fffNcd46623cpy/oObl5YW5uTkXLlzAx8cHgMzMTH744Qd8fX115n7As0m+q1evZvv27QQEBGBkZMSYMWMYN24cw4cPp23btlhbW3PhwgUWL16Mv7+/1pwBX19fxo8fz/Tp04mNjaVDhw5UrlyZpKQkjh07RlRUFLNmzcLDwwNzc3PN0rIvYsyYMQwcOJDJkyfTpk0bLl26xMKFCwkLC9N6h8OCBQuIjIxk7969ODg4ANCnTx/8/PxwdXXFwsKCM2fOsGLFCjw8PLSWeO3atStr165lyJAhjBw5EiMjI81L47p166YVj6IoXLhwoUDfGP6mk8RBvBRuxN0hpFMYKdlW1HinD+5v2TLmg6ZULq9/rKgQQoiXw2effYaiKISHhwPPVkqaPXt2jhNbra2tmTNnDl9//TVXrlyhfPnyTJw4kQ4dOmjqDB06lOTkZFatWsWTJ0+oX78+y5Yt03m7sZeXF+vXr2f+/Pl89dVXPH36lMqVK2uWdX0RNWvWJCoqigULFvDVV1/x+PFjypYtS40aNbRuQI8cOQLAV199pXOMadOm5XruEiVK0LRpUw4cOKB5YdmhQ4d4+PChTu+Bmo2NDc2aNePnn39m8uTJWFlZERoaSqlSpVixYgXjx48nMzOTypUrExYWxpAhQ3TmqfTu3RsvLy9WrVrFzJkzefjwIVZWVtSqVYsvvvjC4JfMvfPOO8yfP58FCxawdetWypcvz8CBAxk8eLBWPUVRyM7ORlEUTZmLiws7duzgzp07ZGZmYm9vT/fu3RkyZIhWz4alpSWrV69m2rRpjBs3DkVRCAgIYMKECVhZWWmd58yZMyQlJWn1agnDGCn/bDWhRT2+798vFCkq768fRJaxEfObfIR9xWrFEkNRWLhiM2NHDSb9cQJGxqZErNnLoO6NMTGRXgYhxOsrIyOD69ev4+zsbNDwmlfR+PHj+fXXXwvspWCvohMnTtCrVy8OHDiQ52VSxYv5/PPP+euvv1i3bl1xh1Ks9P2ueZH7XbkzE8XmUXIKTdt+wJB+XUl/nEDp8o5s2r6HoR80kaRBCCHEa83Pz4+AgACWLVtW3KG8lu7fv8/27ds1q1CJgiF3Z6JY7PjxEG+71uTArmdPARq37Ma1v/6gczv9qzgIIYQQr4PPPvsMe3t7ZPBHwYuPj+eTTz7JcXUqkX8yx0EUqWyVwrrd5+nfNYSn6cmUtLZl+uwFjBiQv7dhCiGEePVMnz69uEN4KVSrVo1q1V7focjFqXbt2tSuXbu4w3jtSOLwElNe/N07L7U7CanMWXeGS38nUuOdPmTeP0d01Pe8XdWhuEMTQgghhBB6SOIgCp1KpeLjybM5dTWd0g7elCxhyjdfjCbYp4ossyqEEEII8YqQxOEV8Cp3PMRej6Ndpx5cOnuIEla2DJz8PRP6N6FiWcviDk0IIYQQQrwAedwrCs3chd9Ty9OLS2cPYWRsSuf3+vHN6OaSNAghhBBCvIKkx0EUuITEJEK79+fo3mevqy9bsSqr16yhbfNGevYUQgghhBAvK0kcRIE6ef4azYIbkZxwC4BmIT2J+j4Sm1JWevYUQgghhBAvM0kcRIHIylaxad8VNu67Qik7V7IynxI+fzEf9upY3KEJIYQQQogCIHMchMEOHzvDsKnRrP/5v6hUCgNG/4fLf16UpEEIId4wERERuLu7k5WVVWTnHD9+PI0bN873/hcvXsTb25u7d+/qbMvIyKBevXq4u7tz+fLlHPd3d3dn7ty5LxTbrVu3mDJlCs2bN8fT05M6derQqVMnFi5cyOPHj/N9LQBXrlxh8uTJdOzYkVq1auHu7v5C+z958oQZM2YQGBiIl5cX3bp149SpUzr1VCoVixcvJjg4GE9PT0JCQvjpp5+06mRkZBAYGMju3bsNuibx8pDEQeSbSqVi6MdfEtw4gO2rp2FpYcpHH9Rj8oAmVHGoWNzhCSGEEHrNnDmTTp06UbGi7t+tvXv3kpKSAsD27dsL5HynTp0iJCSEo0ePEhYWxtKlS4mIiCAoKIi1a9cSERFh0PH/+OMPDh06RKVKlahVq9YL7z9hwgQ2b97MiBEjWLx4MXZ2dvTr149Lly5p1Zs3bx4RERF88MEHLF26lNq1azNy5EgOHTqkqWNhYUH//v2ZO3cumZmZBl2XeDnIUCWRL5f++puQju9z9eIxAEqaZjNjcH2qOtoVc2RCCCFE3ly8eJETJ04wadKkHLdv376dMmXK4OTkxM6dO/noo48wNc3/rVNSUhIjRoygWrVqrFy5EkvL/19lMDAwkL59+3L27Nl8Hx8gNDSUDh06ADB37twXOt7ly5f54YcfmDp1Kp06dQLA19eXNm3aMG/ePBYtWgRAQkICy5cvZ8CAAfTr1w8Af39/bty4waxZs3jnnXc0x+zQoQOzZs1i7969tG7d2qBrE8VPehzEC5s2dzm1vb25evEYxiZm9Bs2kb8unpCkQQghBACxsbGEhYXh7e1NYGAg8+bNQ6VSabY/efKEqVOn0rZtW+rUqUPDhg0ZNGgQsbGxOse6efMmH3/8MQ0bNqRWrVo0bdqUr776Ktfzb9myhVq1arFkyZJc623evBl3d3dcXV11tt29e5dff/2V1q1b06VLFx48eEBMTEweP4Hnny8xMZHPPvtMK2lQs7S0pGHDhgadw5AXq+7fvx8zMzOtG3xTU1PatGlDTEwMT58+BeDIkSNkZmYSEhKitX9ISAhXrlzh5s2bmrLSpUsTGBhIVFRUvuMSLw/pcRB5dvd+IiFd+nDyUDQA5Su7sG7d9zR7x6+YIxNCiNeHoigomU+KNQYjsxIYGeX/9aNDhw6lU6dODBw4kJiYGCIjIzE2Nmb48OEAPH36lNTUVAYPHoydnR1JSUmsW7eO7t27s3v3buzsnj2IunnzJl26dKFkyZKMGDECJycnbt++nesN/KJFi1iwYAFTpkyhY8fc59odOXKEJk2a5Lhtx44dqFQq2rdvz9tvv82XX37J9u3bn1s/L3799Vfs7Ozw9PTMU/3s7GwURdFbz5BekH+6evUqDg4OlCxZUqvcxcWFzMxMbty4gaurK1evXsXc3BwnJyeteuoELDY2lipVqmjKfX19mTt3Lk+ePKFEiRIFEqsoHpI4iDz541oCM1bFcPH3E4ARbTr3Y+PqCKwsLYo7NCGEeG0oisKtNRN5EvffYo2jhKMHlXt+le/koWvXrgwYMAB4NgQnJSWFFStW0KtXL2xsbChVqhRff/21pn52djaBgYE0aNCAXbt20bt3b+DZZOsnT56wY8cOrTkI6qE4/6RSqfj666/ZsmULCxYs0HuD/+DBA+Lj4/Hw8Mhx+/bt23F2dsbb2xuAd999l3379pGcnIyNjc2LfBwat2/fxsHBIc/1mzVrRnx8vN56+/fvx9HRMV8x/VNSUhKlS5fWKS9Tpoxmu/q/NjY2Oj8f6n0fPXqkVV6jRg0yMzP5448/qFu3rsFxiuIjiYPIVWpaBpsPxLL14FUUBYK7T6BL0Nv07Na2uEMTQojXVP6f9L8sWrVqpfV9mzZt2Lx5M1euXMHHxweA3bt3s3LlSq5fv661ktC1a9c0/3/06FGaNGmS48Tlf8rOzmb06NEcP36clStXUqdOHb0x3rt3D4CyZcvqbDt//jyxsbGMGjVKU9a+fXt27drF7t276d69u97jF4SFCxdqhgflpkKFCkUQTf7Z2toC//+Zi1eXJA7iufYeOsH77/fA3iOYqrVb09S3CgPat8bSwqy4QxNCiNeSkZERlXt+9coPVSpXrlyO36tvHA8cOMDo0aPp0KEDw4YNw9bWFiMjIwYMGKB1o/zo0SPs7e31ni8lJYVDhw7h7++Pl5dXnmJ88uTZZ2xmpvs3Tb2CUlBQEMnJyQB4enpStmxZtm/frpU4mJiYaM3f+Kfs7GytYUSVKlXiypUreYoPng0RKsqhSjY2Njn2cKh7ENQ9CjY2NiQnJ6MoitbPibpHQt1DoWZh8Wx0QkZGRoHEKYqPJA5CR1ZWNoPG/IeVkTNQZWeSlpJExIzxNPFxLu7QhBDitWdkZISR+as9DDQhIUFr8m9CQgLw/0/Gd+3ahZOTE9OnT9fUyczM1Nx4qtna2ub4foV/K126NN988w2DBg1i7NixzJo1S+/NtPrmVp0YqD19+pRdu3YBz1Yo+rfExET+/vtvqlatCjzrsXjek/R79+5pJVEBAQEcPXqUixcv5mmp1KIequTi4sK+fftIT0/XmucQGxuLmZmZZk6Dq6srT58+5X//+5/WPIerV68CUK1aNa3jqttV3fMgXl2SOAgt5//4i/ad3+f65d8AcKkVQPTWdVR3rVq8gQkhhHhl/Pjjj5o5DvAsUbC0tNS8jCwjIwMTExOtfXbs2EF2drZWWcOGDfn555+5d++e3uE4fn5+LF26lA8//JAxY8YwZ86cXJMHBwcHSpQoobUCEMDBgwd59OgRw4YNo379+lrbEhISGD16NNu3b9cMY/Lz8yMmJoa0tDStZOn+/fucPXuWnj17asq6dOnC8uXL+fLLL3WWYwVIT0/n7NmzNGjQACj6oUrBwcFERESwZ88ezTySrKwsdu/eTWBgIObm5gA0atQIMzMzdu7cybBhwzT7R0dH4+bmpjUxGiAuLg6At99+u0DiFMVHEgeh8Z/pkUz9YhyZGamYmJozaNQk5s+YYNDSbkIIId48mzZtQqVS4enpSUxMDJs3b2b48OGUKlUKeHbjuW/fPqZOnUpQUBAXLlzg+++/15l0PHz4cA4dOkT37t0ZNGgQb731Fnfv3uXIkSPMmjVL57w+Pj4sW7aMDz/8kNGjRzNnzpwchyIBmJub4+3tzfnz57XKt23bhqWlJX379sXKykpnv1WrVhEdHc3IkSMxMjJiyJAhHDhwgO7du9O3b1/s7e25efMmixYtwsbGhl69emn2LVOmDBEREQwePJgOHToQFhaGq6srWVlZnD9/ng0bNtCiRQtN4vCib32GZ8mH+iVs169fB2DPnj3As2RJvaJTfHw8zZo1Y8iQIZqb/xo1atC6dWumTp1KVlYWjo6OrF+/nri4OK3Pu1y5cvTu3ZvFixdjZWVFjRo12L17N8ePH2fhwoU6MZ07d46KFSvqJBTi1SOJgyAl7SlfLtzFNxOGoygqKr7lweaN62jkr39ymRBCCPFvkZGRfPnll0RGRlKqVCkGDx7MkCFDNNu7du3K7du32bJlCxs3bsTT05NFixZpPb0GcHR0ZNOmTYSHhzN79mzS0tKoWLEiTZs2fe6569Wrx/Lly+nfvz8jR44kPDxc86T831q1asU333yj6S1ITEzkyJEjhISE5Jg0AHTu3JlJkyZx8uRJ/Pz8qFatGps2bSIiIoLp06fz+PFjbG1tadiwISNGjNAsLavm6+vLjh07WL58OatWreLOnTuYmZnx9ttv88EHH/D+++/n9WPOUUJCAiNHjtQqU3/foUMHzfAwRVFyXO512rRpzJ07l/DwcJKTk/Hw8GDZsmXUrFlTq97o0aOxtLRkzZo13L9/H2dnZ8LDwwkKCtKJ6eDBg7Rp08ag6xIvByMlL7Nu3lDqX0z79+8vlvO/t2EQ2UZGRDT5iIoVq+nfIR/OXblP+IYzPEjK4OqJzdSoWobvl82mpIWssyyEEIUpIyOD69ev4+zsrJk8KopWSkoKjRs35vPPP89xPoMw3Llz5zTv53B2lrmSxUHf75oXud+VHodXQsEvzZf8OJUuPYeRYVMHm/JOVCpvxay183F30l2WTgghhHgdWVtb8+GHH7Js2TJCQkIMWklK5GzJkiW0b99ekobXhCQOb6Bde2PoGRZG4t2/sbE7xFcRUXzY3guLEvLjIIQQ4s3Sp08fsrOzuXfvnt73RYgXk5GRQfXq1enatWtxhyIKiNwpvkGeZmbRb+gE1i6fi6LKwsKqDP/5YgrDu8lbHIUQQryZLCwsdOZWiIIhn+3rRxKHN8Sp3y/Rqct73Lx6DgCP2o35Yetaqjkbvu6zEEIIIYR4/ck6m685RVFYuv5nGvjV4+bVc5ial2TMxJn8cfoXSRqEEEIIIUSeSY/Daywp5QmRW85x9PcUSpWvSskSJmzdvB6/evrfVimEEEIIIcQ/SeLwmlq8eguHrpjyOANMTU2ZOX8FPUN8MDeTJhdCCCGEEC9O7iJfM4mPkun43gAO7dmIc912tOw2kjHv18PFsUxxhyaEEEIIIV5hkji8Rrb8cID+fXvz6P5NAJwr2zBn1DtYmEszCyGEEEIIw8jk6NfAkydP6dJrJF1Cm/Po/k1KlirHt8s2sn/n95I0CCGEEEKIAiGJwyvu+G8XcXavQ9Sa+SiqbDx9m3Lpz4sM6ScvWxFCCFG0IiIicHd3Jysrq8COef/+fQYNGkT9+vVxd3dn1apVABw4cIB27drh6emJu7s7ycnJBXbOV0lsbCw9e/akbt26uLu7s2/fPk07FLe7d+9Su3ZtLly4kOP2Fi1aaGLOSXBwMB999FGO2573s5aYmMjs2bNp06YNtWvXxtvbm3bt2jFr1izu3btn2AUBt2/fZsSIEdSrV4+6desybNgwbt26pXe/+Ph4Bg8eTFBQEF5eXvj5+dGjRw8OHTqkU9fd3T3Hr0uXLmnq3Lt3D29vb86fP2/wNb0IeRz9ilIUhZ+O32D+dye5f+dvzEpY8fFnU/lywjCMjSUfFEII8Xr49ttvOXXqFNOnT8fOzg4HBweysrL46KOPqFOnDpMnT8bMzAwrK6viDrVYTJ8+nbi4OMLDw7GxscHZ2VnrBrM4hYeH4+fnh6enp862M2fO8PfffwOwY8cO3n33XYPPd/XqVfr27YuiKISFhWnO++eff7Jx40auX7/Ot99+m+/jp6en06tXL8zNzZkxYwYA8+bNo2fPnkRHR2NpafncfdPS0rC1tWXkyJHY29uTkpLC5s2bGTBgABERETRv3lyrfseOHenWrZtWWdWqVTX/X6FCBbp06cLMmTP5/vvv831NL+qlSxxu377NtGnTOHr0KIqi0KBBAyZMmEDlypX17vvkyRPCw8PZuXMnycnJVK9enY8++ghfX98iiLzoxN1JYMWuvzj1511MSpaj84df8/GAVtT1LP6nC0IIIURBio2NxcPDg2bNmmnK4uPjSU1NpVWrVgXyN15RFDIzMzE3Nzf4WEXhn/HGxsbi4+ND48aNizssLQ8ePGDnzp0sWLAgx+3bt2/H1NQUf39/fvnlFx49ekSZMmXyfb6srCyGDx9OiRIl2LBhA+XKldNsCwgIoFevXhw+fDjfxwfYtGkTN2/eZM+ePTg5OQHPegdatGjBxo0b6dOnz3P3dXV1ZerUqVplTZo0oWnTpmzdulUncahQoQK1a9fONZ7u3bvTpk0bzp8/j5eXV/4u6gW9VI+m1ZnctWvXmDFjBjNnzuTGjRv07NmTtLQ0vftPmDCBzZs3M2LECBYvXoydnR39+vV7aTLvgvBNxCpcXVzYtWs3pibG9G1Xk7XzRkrSIIQQ4qURGxtLWFgY3t7eBAYGMm/ePFQqlWb71q1bcXd3Jy4uTmu/fw6xiYuLw93dnZMnT/Lbb79phmuMHz+e4OBgACZOnIi7uzthYWGaY/z888907doVb29vfHx8GDFihM5QEvUQmKioKFq2bEmtWrVyHDKSk5SUFKZMmUJgYCC1atWiRYsWrFq1CkVRgGdDq2rUqMGaNWt09l26dCk1a9YkMTGxQOJ1d3cnPj6eHTt2aD6f/MadnZ2Nj48PkZGRmn3++9//4u7uznvvvad1rMaNG2ueuD/P1q1bsbKyIjAwUGfbkydP+PHHH2nYsCH9+vUjMzOTXbt25Xo8ffbu3cu1a9cYO3asVtKgZmpqqvm5ya8DBw7g7e2tSRoAqlSpQt26ddm/f/8LH8/U1JRSpUphYmKSr3hcXFxwc3Nj8+bN+do/P16qHgdDMrnLly/zww8/MHXqVDp16gSAr68vbdq0Yd68eSxatKhIrqGw3L2fSIfu/Tl2YBsAt//4kY3ffkTVSjbFHJkQQoiCpCgKT7KfFmsMJUzMMTIyyvf+Q4cOpVOnTgwcOJCYmBgiIyMxNjZm+PDheT5GhQoV2LhxI5MnT8bExITPP/8cADs7O5o0acLIkSMZPHgwTZo0wdraGoD169fzn//8h44dOzJ06FBSU1OJiIigR48eREdHa+oBnDhxgsuXLzNs2DDKlSuHg4OD3phUKhUDBgzgzz//ZMSIEbi5uXHw4EGmTZtGYmIiY8aMwc7OjoCAAKKjo+nZs6fW/tHR0TRq1IiyZcsaHK+trS0bN25k8ODBeHp6MmTIEIPiNjExwcfHh+PHj2uOdfz4cSwsLLhw4QJpaWlYWlpy7do17t69i7+/f66f1ZEjR6hduzamprq3mvv37yc5OZn27dvj7++Pvb0927dv54MPPtDbBs/z66+/YmJiwjvvvJOn+tnZ2ZqkKTcmJiaafwtXr16ladOmOnVcXFzYs2dPns6rUqlQqVQ8fPiQjRs38vfffzNx4kSdehs2bGD58uWYmJjg7e3NiBEj8PHx0ann6+vLL7/8kqdzF4SXKnHQl8nlljjs378fMzMzWrdurSkzNTWlTZs2LFmyhKdPn74yXZD/tuPHw3w0dgKPE28DRrTo0Iuo777F2ur5Y+mEEEK8ehRFYfL+Wfw34VqxxuFevhpTgsfmO3no2rUrAwYMACAwMJCUlBRWrFhBr169sLHJ2wMvc3NzateujZWVFaamplrDNtQTYt966y1NeWpqKrNmzaJjx45MmzZNU9fT05NWrVoRFRVF7969NeXJycls3boVOzu7PF/XoUOHOH36NNOmTaNjx46a60tPT2fFihX07t2bsmXLEhoayscff8y1a9d4++23Abh06RJXrlxh8ODBBRqvmZkZZcuWzXVYS17j9vf3Z86cOZp7phMnTtC+fXt27NjB6dOnadSoESdOnMDU1DTHm1g1RVE4f/68Vvz/tG3bNkqVKkXTpk0xNjYmJCSEJUuWEBsbS7Vq1Z573Nzcvn2bsmXLUrJkyTzV7927NydPntRbb82aNfj5+QGQlJSU489v6dKl8zw5/5tvvmHFihUAWFpaMmfOHAICArTqhISEEBQURIUKFYiPj2f58uX06tWLFStWaGJRq169OmvXruXu3btUrFgxTzEY4qUaqnT16lXc3Nx0yl1cXLh69arefR0cHHR+YFxcXMjMzOTGjRsFGmtRUGVlc3HjSQb17cvjxNtYla7A8u+3sWfrSkkahBDidWXAk/6XRatWrbS+b9OmDWlpaVy5cqXQzvn777+TkpJCSEgIWVlZmq9KlSrh7OzMb7/9plXf29v7hZIGgFOnTmFsbEzbtm21ykNCQsjMzOT3338HoFmzZlhaWrJjxw5NnR07dmhulosq3heN29/fnydPnnDmzBlUKhWnTp2iUaNG1KtXj+PHjwPPeiFq1aqV62T05ORkMjIysLW11dl2//59jh49SsuWLSlRogQA7du3B57NeygqX3zxBVFRUXq/atasWaDn7dWrF1FRUSxatIjGjRszduxYnR6Db775htatW+Pj40NoaCjr1q2jQoUKhIeH6xxP3XtVECtG5cVL1eNgSCaXlJRE6dKldcrVE22SkpJy3C+nLie127dvU6lSpVzPW5juXIzn0rYzANQLbE305tVUti9fbPEIIYQoXEZGRkwJHvvKD1X69xhz9feFeXOTkJAA8Nyn3P++R8jPTbj6XuPfIxjKly+v2Q5QsmRJWrRowc6dOxk1ahQqlYoffvhB62a5KOJ90bjd3d0pU6YMJ06cwNrampSUFHx9fbl27Ro///wziqJw8uRJunbNfcn3J0+eAOQ40iM6Oprs7GyaNm2qubezs7OjevXqREdHM3r0aM3qkCYmJlpzY/4pOzsbIyMjzfyASpUq8euvv5Kenp6nXgcnJ6c8D1VSs7GxyfF+9Hn3rzmxt7fH3t4egKCgIMLCwpgxYwZBQUHP3cfa2pp33nmHqKgonW0WFhbA/3/mhe2lShyEtjrVq5LQvBZdggfy+SfDijscIYQQRcDIyAgL0xLFHYZBEhIStJamVN8kV6hQAUBz85yZmam136NHj/J9TvWDwunTp+Pi4qKz/d9PyPOTGJUuXZqkpCSd4c8PHjzQbFcLDQ1l27ZtnD59moyMDO7fv09oaGiRxvuicRsZGVG/fn2OHz+OlZUV1atXp3Tp0vj7+xMeHs7p06dJTEzUGS7zb+pry+kmW92rMGjQoBz3PX78OA0aNACeJZzPSzbv3btH2bJlNZ9LQEAAmzZt4vDhw7Ro0SLX+CB/Q5VcXFz466+/dOrExsbm2IZ5UatWLVavXp2nujn9DKiTvpx6dwrDS5U4GJLJ2djYEB8fr1Ou/iWUU28EkOss+Nx6I4rCzPfDUXVXYVHColjjEEIIIV7Ejz/+qJnjALBr1y4sLS01q/6ol1j/66+/cHZ2Bp7NW4iJicn3OevWrYuVlRU3btygQ4cOBkT/fPXr12f58uXs2bOHkJAQTfnOnTsxMzPTmmfg5+eHvb09O3bsICMjAwcHB615AUURb37i9vf3Z9q0aZiYmGhumGvWrEnJkiVZsGABZmZm1K1bN9fzmZub4+joyM2bN7XK//jjD65cuUK3bt1o06aN1rbMzEwGDx7Mtm3bNImDn58fy5cv1xm/n5GRweHDh7USmObNm+Ps7MysWbPw9fXVDOFRU/98NWnSBHg2VCk1NVXPJ4fm5xOerW41c+ZMbt68SZUqVYBnq3+dOXOGsWPH6j3Wv6lUKk6fPq051vOkpKRw8ODBHJdcjYuLw8zMDEdHxxc+f368VImDIZmci4sL+/bt0+miio2NxczMTGvC9avC3OzVnMwthBDizbZp0yZUKhWenp7ExMSwefNmhg8fTqlSpYBnE4DfeustZs6ciUqlwtzcnHXr1un0QLwIa2trxo0bx5QpU0hMTKRx48aUKlWKu3fvcurUKerXr0+7du0Muq7GjRtTr149Pv/8cxITE3F1deXQoUNs3ryZgQMHat2sGhsb065dOzZu3EhWVha9evXSemJcFPHmJ24/Pz8yMzM5deoUH374IfBsuI569R5fX1/N8Jjc+Pr66rzVeNu2bRgZGfHhhx/meLP87rvvsm/fPlJTU7GysqJnz55s3bqV9957j4EDB+Lk5MTdu3dZuXIlqampWitJmZqasmDBAvr06UNoaCg9e/akVq1awLNlZTdu3Mjbb7+tSRzUk9ZfRNeuXVm7di1Dhgxh5MiRGBkZMW/ePOzt7bVe1hYfH0+zZs0YMmQIw4Y9GzESERFBUlISdevWpXz58jx48ICoqCjOnz/P7NmzNfsuX76c69ev4+fnR4UKFbh16xYrVqzgwYMHzJo1Syemc+fO4enpqenFK2wvVeJgSCYXHBxMREQEe/bs0WTuWVlZ7N69m8DAwFd2RSUhhBDiVRMZGcmXX35JZGQkpUqVYvDgwTo3eZGRkUyZMoVPP/2U0qVL06tXL7y9vZ/7wrC86N69O5UqVWLZsmX88MMPZGdnU7FiRerVq0f16tUNvi5jY2OWLFnCnDlzWLZsGY8ePcLBwYFPP/2UXr166dQPDQ1l6dKlmv8v6njzE7eLiwvly5fn0aNHWj0k6he16RumpNaqVSu2b99OXFwcjo6OZGZm8sMPP+Dn5/fcJ+ydO3dm9+7d/PTTT3Ts2JFy5cqxceNGIiIiiIiI4OHDh1hbW1O/fn1mzJiBq6urTuw7duxgxYoVbNu2jQULFqAoCk5OTjRv3lxnedwXZWlpyerVq5k2bRrjxo1DURQCAgKYMGGC1tAyRVF0lnutUaMGq1evZteuXTx+/Bg7Ozvc3d1Zu3Yt9erV09RzdnZm79697N27l5SUFKytralTpw5ff/21To9DRkYGx44dY8yYMQZd14swUvIyM6SIpKWlERoaioWFhVYml5qaSnR0tKZRcsrkAEaPHk1MTAzjxo3D0dGR9evX88svv7Bhw4Z8zYpXD1XKz0s9hBBCiNxkZGRw/fp1nJ2d8/QEV4hXiUqlonnz5nTs2DHXd0yI/Nu9ezcTJ07k8OHDmt68nOj7XfMi97sv1XKs6kyuatWqjBs3jo8++ghHR0dWr16tN5MDNOsTh4eHM2DAAG7fvs2yZcsKfCktIYQQQgjxfMbGxowYMYLvv/+e9PT04g7ntbR06VL69euXa9JQ0F6qoUrwbMJURERErnUcHR3573//q1NuYWHBp59+yqefflpY4QkhhBDiNaTvTcLGxsaaZUJF3rRr1467d+8SHx+f71WHRM7u379P06ZN6devX5Ge96VLHIQQQgghipq+5Tk7dOjA9OnTizCiV596IrQoeHZ2dlrD9YuKJA5CCCGEeOPpW56zqNbJF+JlJomDEEIIId54+VmeU4g3jQzWE0IIIYQQQugliYMQQghRjF6iVdGFEK+hgvwdI4mDEEIIUQzMzMyAZ+8wEkKIwqL+HaP+nWMImeMghBBCFAMTExPKlCnDvXv3gGfvMjIyMirmqIQQrwtFUUhLS+PevXuUKVMGExMTg48piYMQQghRTOzt7QE0yYMQQhS0MmXKaH7XGEoSByGEEKKYGBkZUalSJSpUqEBmZmZxhyOEeM2YmZkVSE+DmiQOQgghRDEzMTEp0D/uQghRGGRytBBCCCGEEEIvSRyEEEIIIYQQekniIIQQQgghhNBLEgchhBBCCCGEXjI5Ohf37t0jOzubpk2bFncoQgghhBBCFLjbt2/neXEG6XHIRYkSJTA1Lb7c6vbt29y+fbvYzi+Kj7T9m03a/80lbf/mkrZ/cxV325uamlKiRIk81TVSFEUp5HhEPql7Ovbv31/MkYiiJm3/ZpP2f3NJ27+5pO3fXK9S20uPgxBCCCGEEEIvSRyEEEIIIYQQekniIIQQQgghhNBLEgchhBBCCCGEXpI4CCGEEEIIIfSSxEEIIYQQQgihlyzHKoQQQgghhNBLehyEEEIIIYQQekniIIQQQgghhNBLEgchhBBCCCGEXpI4CCGEEEIIIfSSxEEIIYQQQgihlyQOxeD27duMGDGCevXqUbduXYYNG8atW7fytO+TJ0+YMWMGgYGBeHl50a1bN06dOlXIEYuClN/2v3DhApMmTaJly5Z4e3vTpEkTxo4dy82bN4sgalEQDPm3/09LlizB3d2d9957rxCiFIXB0LaPjY1lxIgR+Pn54eXlRYsWLVi9enUhRiwKiiFtf+vWLT755BOaNGmiafe5c+eSlpZWyFGLgnDnzh2+/PJLunXrhre3N+7u7sTFxeVpX5VKxeLFiwkODsbT05OQkBB++umnQo5YP1mOtYilp6cTGhqKubk5o0aNAmDevHmkp6cTHR2NpaVlrvuPHTuWQ4cOMW7cOKpUqcLatWs5fPgwGzdupHr16kVwBcIQhrT/jBkzOHv2LO3atcPV1ZW7d+8SGRlJYmIi27dvp1KlSkV0FSI/DP23r3bz5k1CQkIoWbIkTk5OrF+/vhCjFgXB0La/cOECvXr1on79+nTu3Blra2tu3LhBWloaffr0KYIrEPllSNunpaXRoUMHMjMzGT58OJUqVeLChQtEREQQHBxMeHh40VyEyLcTJ04wevRoatasiUqlIiYmhv379+Po6Kh337lz57J8+XLN/rt372bTpk0sXryYd955pwiifw5FFKlVq1YpHh4eyt9//60p+9///qdUr15dWbFiRa77Xrp0SXFzc1OioqI0ZZmZmUrz5s2VgQMHFlrMouAY0v4JCQk6ZXFxcYq7u7sSHh5e4LGKgmVI2/9T3759lUmTJik9evRQunfvXhihigJmSNtnZ2crrVq1UoYMGVLYYYpCYEjbHzlyRHFzc1OOHDmiVf7NN98o1atXV9LS0golZlFwsrOzNf+/adMmxc3NTbl586be/R48eKDUrFlTmTdvnlZ5z549lbZt2xZ4nC9ChioVsQMHDuDt7Y2Tk5OmrEqVKtStW5f9+/fnuu/+/fsxMzOjdevWmjJTU1PatGlDTEwMT58+LbS4RcEwpP3Lli2rU+bg4EDZsmW5e/dugccqCpYhba+2c+dO/vjjD8aMGVNYYYpCYEjbnzhxgtjYWOlZeEUZ0vaZmZkAWFtba5Xb2NigUqlQZMDIS8/YOH+32UeOHCEzM5OQkBCt8pCQEK5cuVKsQ5QlcShiV69exc3NTafcxcWFq1ev6t3XwcGBkiVL6uybmZnJjRs3CjRWUfAMaf+cxMbGkpCQQLVq1QoiPFGIDG37pKQkpk2bxscff0yZMmUKIUJRWAxp+9OnTwPP5rd17dqVmjVrEhAQwFdffUVGRkahxCsKjiFt36BBA6pWrcqsWbO4evUqqampHDt2jDVr1tC9e/c8D28Ur56rV69ibm6ulXACuLq6As/+9hcXSRyKWFJSEjY2NjrlpUuXJjk5We++pUuX1ilX30QkJSUVSIyi8BjS/v+WlZXF559/TtmyZencuXNBhSgKiaFtP3PmTKpWrUrHjh0LIzxRiAxp+3v37gEwevRoGjZsyIoVK+jfvz+bN29m7NixhRKvKDiGtH2JEiVYt24dKpWKNm3aULduXXr37k2TJk2YPHlyYYUsXgLqnxsjIyOtcvU94KNHj4ohqmdMi+3MQgiDTJkyhbNnz7J48eIcE0rx+vjtt9/YsWMHW7du1flDIl5v6uEoISEhjBw5EgA/Pz+ys7OZPXs2sbGx0uP4mnry5AmjRo0iISGBmTNnUrlyZc6fP8+3336LiYkJX3zxRXGHKN5AkjgUMRsbmxyfMjzvqcS/942Pj9cpV2eecvP48jOk/f9p1qxZbNq0ienTpxMYGFiQIYpCYkjbT548mU6dOmFvb685RlZWFiqViuTkZCwsLDA3Ny+UuIXhDGl7dY9ygwYNtMoDAwOZPXs2f/75pyQOLzFD2j4qKoqTJ0+yd+9e3nrrLQB8fX0pVaoUkyZN4r333sPDw6NQ4hbFS/1zoyiK1sMi9ciS4hyuKkOVipiLiwt//fWXTnlsbCwuLi56942Pjyc9PV1nXzMzM52xcOLlY0j7qy1cuJClS5fy2Wef0b59+wKOUBQWQ9o+NjaWDRs24Ovrq/k6c+YMv//+O76+vqxbt66wwhYFwNDf+7nJ7+RLUTQMafv//ve/lC5dWpM0qHl5eWmOIV5Prq6uPH36lP/9739a5ep5McX5sEB+4xSx4OBgzp07pzUjPi4ujjNnzhAcHKx338zMTPbs2aMpy8rKYvfu3QQGBsoTx1eAIe0PsGbNGsLDwxk9ejQ9evQozFBFATOk7desWaPz5eHhgZubG2vWrKFly5aFHb4wgCFt37hxY8zNzYmJidEqP3LkCAC1atUq+IBFgTGk7e3s7EhKStJZ+OTcuXMAVKxYseADFi+FRo0aYWZmxs6dO7XKo6OjcXNzo0qVKsUUmbwArsilpaURGhqKhYUFI0eOxMjIiHnz5pGamkp0dDRWVlYAxMfH06xZM4YMGcKwYcM0+48ePZqYmBjGjRuHo6Mj69ev55dffmHDhg3UrFmzuC5L5JEh7b9r1y7Gjh1LYGCg1s8EPFuuL689FqJ4GPpv/9/CwsLIysqSF8C9Agxt+wULFhAZGUn//v3x9/fn4sWLLFiwgNatWzN9+vTiuiyRB4a0fVxcHCEhIdjZ2TFo0CAqVarExYsXiYyMpGrVqkRFRUmP0ytA/bD32LFjbNiwQbOoSdmyZalfvz4ANWrUoH379kydOlWz36xZs1i9ejVjxoyhRo0a7N69m40bN7Jw4UKCgoKK5VpA5jgUOUtLS1avXs20adMYN24ciqIQEBDAhAkTNL9A4NmEuOzsbJ11mqdNm8bcuXMJDw8nOTkZDw8Pli1bJknDK8KQ9j9y5AiKonDkyBHN00a1+vXr89133xXZdYgXZ+i/ffHqMrTthw4dipWVFevWrWPFihXY2dnRr18/hgwZUtSXIl6QIW3v6OjIpk2biIiIIDw8nIcPH1KpUiW6devGoEGDJGl4RagXNVBTT2r/59/t7OxsVCqVVr3Ro0djaWnJmjVruH//Ps7OzoSHhxdr0gDS4yCEEEIIIYTIA0lXhRBCCCGEEHpJ4iCEEEIIIYTQSxIHIYQQQgghhF6SOAghhBBCCCH0ksRBCCGEEEIIoZckDkIIIYQQQgi9JHEQQgghhBBC6CWJgxBCvMROnDiBu7s7W7duLe5QikxwcDBhYWF5qhsXF4e7uzsRERGFHJUQQgh5c7QQQhSwEydO0LNnz+du/+OPPzA1ffl//Y4fP55t27ZpvjcxMaF8+fL4+/szfPhwqlSpUmSxRERE4OrqSsuWLYvsnHnx77Y2MjLCysoKDw8PwsLCDIp369atPHz4kH79+hVEqEIIYbCX/y+XEEK8ojp27Ii/v79OuYmJSTFEk39Tp07F1NSUjIwMTp48SXR0NEePHiU6Oppy5coV+Pn27NmjU7ZgwQJat26tcyPu4ODA+fPni/0zVbe1oijExcWxYcMGRo4cyYwZM2jfvn2+jrlt2zauXbsmiYMQ4qUhiYMQQhQSLy8vQkNDizsMg7Vt25YSJUoA0K1bN2xtbfnuu+/YsmULAwYMKPDzmZub57mukZGRJrbi9O+2btGiBW3btmXZsmX5ThyEEOJlI3MchBCiGKxdu5ZevXoRGBhIrVq1CA4O5uuvvyYlJUXvvunp6cydO5fmzZvj5eWFr68voaGhrFmzRqtednY2q1atol27dnh6euLj48PgwYP566+/DIq9YcOGANy8eVNTdvToUXr06EGdOnWoU6cOH3zwAUeOHNHZd9++fXTv3h1fX1+8vLwICgpi7NixPH78WFPnn3Mc1HMYAHbv3o27uzvu7u4EBwdrbVfPcbh8+TLu7u58++23OudWFIXg4GA6d+6sVX7mzBk+/PBDfH198fT0JCQkhKioKEM+IlxdXbG1teXGjRta5bGxsUyePJmWLVtSu3Zt6tSpQ48ePTh69KhWveDgYE6ePMmDBw801+zu7k5cXFyhxi2EELmRHgchhCgk6enpJCYmapVZWlpiYWHBqlWr8PHxoUmTJlhYWHD69Gm+//57Ll++zHfffZfrcb/44gt27txJ9+7d8fDwIC0tjdjYWH777Tet8fajR49m//79hISE8P777/Po0SPWrVtHt27d2Lx5M9WqVcvXdalvhm1tbQH46aefGDVqFI6OjgwaNAgjIyNNb8Ts2bNp3bo1AMeOHWPYsGHUq1eP4cOHY2Fhwa1btzh48CDJycmUKlVK51xly5Zl5syZjBs3jtq1a/P+++8DYGVllWNsHh4euLm5sXPnToYOHaq17bfffiM+Pp7evXtryvbt28fIkSOpUaMGAwcOpGTJkhw4cICJEyfy4MEDBg0alK/PKDk5meTkZMqWLatVfvLkSS5cuECrVq2oVKkSCQkJbN68mf79+7Ny5UrN0LYJEyYwe/Zs7t+/z6RJk7Q+j8KMWwghcqUIIYQoUMePH1fc3Nxy/Fq8eLGiKIqSlpams19ERITi5uamnDlzRudYW7Zs0ZT5+Pgon3/+ea4x7N69W3Fzc1N27dqlVX7nzh2lbt26yvDhw/VexyeffKK4ubkpd+7cURISEpT4+Hhl+/btio+Pj+Lh4aH8+eefSmZmptKoUSOlQYMGSmJiombfpKQkpXHjxkqDBg2UJ0+eKIqiKF9//bVSt25dJTMzM9fzBgUFKT169NAqc3NzU0aNGqVT9+bNm4qbm5syf/58TdmSJUsUNzc35cKFC1p1J02apNSoUUN58OCBoiiKkp6ervj5+SkDBgxQVCqVVt3hw4crnp6eWteUE3X7LF++XElISFAePHig/P7770qfPn0UNzc3Zdq0aVr1c2r3hIQExd/fX+nbt69WeY8ePZQGDRro1C+IuIUQIj+kx0EIIQpJz549CQoK0ipzcnICoGTJksCz4USpqalkZWXh5+dHREQEFy5coE6dOs89bqlSpTh//jxxcXE4OjrmWGfXrl2UK1cOf39/rV4PMzMzateuzbFjx/J8HY0bN9b6vmLFinz55ZdUr16dc+fOcffuXYYOHarpgQCwsbHhvffeY+7cuZw7dw5fX19sbGxIT0/n4MGDNG3aFCMjozzH8CLatWvHnDlz2LlzJ7Vq1QLg6dOn7NmzhwYNGmgmdB89epSHDx/Svn17Hj58qHWMd955h59++onTp0/z7rvv6j3njBkzmDFjhuZ7U1NTwsLCGDNmjFY9dbvDsx6pjIwM4NkcibNnz+bp+goybiGEeBGSOAghRCF5++23adCgQY7bDh8+TGRkJBcvXiQzM1NrW3Jycq7HnTBhAh9//DFNmzbF1dUVf39/mjdvTv369TV1rl27RkJCAgEBAc89TmZmJmZmZnqvY8mSJZiZmWmWY3V2dsbY+NkUOfWYexcXF5391GVxcXH4+vrywQcfsG/fPk2SUb9+fRo3bkzr1q2xtLTUG0de2dvb4+vry65du/jkk08wNjbm8OHDJCUlERISoql37do1AEaNGvXcY/17qNnzqJPEp0+fcu7cOZYuXUpqaqrO55uSkkJ4eDh79uzh/v37WtvymkgVZNxCCPEiJHEQQogidvbsWQYOHIiLiwuffvoplStXpkSJEty9e5fx48ejKEqu+7/77rscOHCAgwcPcuLECfbs2cN3331Hp06dmDp1KvBsInDlypX5+uuvn3ucvC5h6u/vXyArF9na2rJlyxZOnjxJTEwMJ06cYOLEiXz77besX78ee3t7g8+hFhISwsSJEzl+/DgNGjQgOjoaS0tLrafw6s958uTJODs753icvM4D+WeS2KRJEypWrMjnn3+Op6enZl4GPJt3EhMTwwcffEDdunWxsbHB2NiYxYsXc/z48TydqyDjFkKIFyGJgxBCFLEff/wRlUrFokWLcHBw0JTHxMTk+Ri2trZ06NCBDh06kJ2dzbhx49iyZQt9+/bFxcUFJycnjh07ho+Pzwstb/qi1EOlrl69qrMtNjYWQOtFcSYmJgQEBGh6Qg4dOsSAAQNYs2YN48aNK7C4WrZsyZQpU9i5cyeenp788ssvtGrVSmuokHrYmLW19XN7hvKra9eurF27lvnz5xMaGoqVlRXJyckcPnyYjh078tlnn2nVnz9/fp6PXZhxCyFEbmQ5ViGEKGLqJ/0qlUpTplKpWLlypd59s7OzdYYymZiYaJYsTUpKAp49cc/IyMhxWVKAhISEfMX+bzVr1qRixYps2rRJc26Ax48fs379esqXL4+XlxeQ8/CZGjVqAPqHZ1laWuqt80/W1tYEBQXx888/Ex0dzdOnT7WGKQEEBgZia2vLokWLclwGNzExUauNXoSxsTEDBw7k4cOHrF27VlNmZGSkc8zffvuN33//XecYVlZWPH78WKd+YcYthBC5kR4HIYQoYk2bNmXlypUMHDiQbt26oSgKP/74o85ch5ykpqbSqFEjmjZtioeHB7a2tly/fp1169ZRtWpVPD09AWjVqhX79+9n0aJFnD9/noYNG2Jtbc2tW7eIiYnBycmJuXPnGnwtpqamTJw4kVGjRtGlSxc6d+6sWY71zp07zJkzR9PjMWnSJO7fv09AQAAODg6kpKSwbds2jI2NadOmTa7n8fb25uTJkyxZsgQHBwdKliypeZfD84SEhLBnzx5mz56NnZ2dznwPKysrpk2bxvDhw2ndujUdOnTA0dGRhIQELl26xP79+zl9+nS+h2m1atWK+fPns3LlSnr06IG1tTX+/v6aYVPVq1cnNjaWqKgoXF1duXLlitb+Xl5e/PLLL3zxxRfUq1cPExMTgoKCCj1uIYR4HkkchBCiiPn4+DBnzhwWLlzI7NmzKV26NC1btqRr1660bds2130tLCwICwvj119/JSYmhvT0dOzt7enSpQsDBw7U3KQbGRkxa9Ys/Pz8iIqK4ttvv0VRFCpUqEC9evV0XoJmiBYtWrB06VIWLlxIZGQk8KwnYcKECVorMoWGhrJlyxa2bt3Kw4cPKV26NNWrV+ezzz7Dz88v13NMnjyZL774goULF5KWloaDg4PexKFx48aUKVOGR48e0aVLlxzndAQFBbFp0yYWL17M5s2bSU5OxtbWFhcXF8aPH5+nyePPY2JiQv/+/Zk0aRLr16+nX79+zJ49m+nTp7Nnzx62bduGh4cHCxYsYMeOHTqJQ69evbhx4wY//fQTGzduRFEU9u/fj6WlZaHGLYQQz2Ok6JuFJ4QQQgghhHjjyRwHIYQQQgghhF6SOAghhBBCCCH0ksRBCCGEEEIIoZckDkIIIYQQQgi9JHEQQgghhBBC6CWJgxBCCCGEEEIvSRyEEEIIIYQQekniIIQQQgghhNBLEgchhBBCCCGEXpI4CCGEEEIIIfSSxEEIIYQQQgihlyQOQgghhBBCCL0kcRBCCCGEEELo9X+HYArbM92WGwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "ROC-AUC analysis successfully completed.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# The tuned Random Forest and Gradient Boosting models are evaluated one last\n",
        "# time and their key metrics are collected. ROC-AUC values are added to create\n",
        "# a complete performance summary table. This comparison supports final\n",
        "# discussion and helps determine which model performs best overall.\n",
        "\n",
        "\n",
        "# ================================================================\n",
        "# SECTION 10: Consolidated Performance Comparison (RF vs GB)\n",
        "# ================================================================\n",
        "\n",
        "# --- 1. Evaluate Tuned Random Forest ---\n",
        "\n",
        "rf_tuned_metrics, _ = assess_model(\n",
        "    \"Random Forest – Tuned\",\n",
        "    rf_best,\n",
        "    X_train_scaled,\n",
        "    X_test_scaled,\n",
        "    y_train,\n",
        "    y_test\n",
        ")\n",
        "\n",
        "# --- 2. Evaluate Tuned Gradient Boosting ---\n",
        "\n",
        "gb_tuned_metrics, _ = assess_model(\n",
        "    \"Gradient Boosting – Tuned\",\n",
        "    gb_best,\n",
        "    X_train_scaled,\n",
        "    X_test_scaled,\n",
        "    y_train,\n",
        "    y_test\n",
        ")\n",
        "\n",
        "# --- 3. Integrate ROC–AUC Scores ---\n",
        "\n",
        "rf_tuned_metrics[\"Macro AUC\"] = macro_rf_auc\n",
        "rf_tuned_metrics[\"Micro AUC\"] = micro_rf_auc\n",
        "\n",
        "gb_tuned_metrics[\"Macro AUC\"] = macro_gb_auc\n",
        "gb_tuned_metrics[\"Micro AUC\"] = micro_gb_auc\n",
        "\n",
        "# --- 4. Construct Final Summary Table ---\n",
        "\n",
        "comparison_frame = pd.DataFrame([rf_tuned_metrics, gb_tuned_metrics])\n",
        "\n",
        "print(\"\\n===== FINAL MODEL COMPARISON =====\")\n",
        "display(comparison_frame)\n",
        "\n",
        "print(\"\\nSummary table created successfully.\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "LE282Y0LEsd9",
        "outputId": "6095809a-7cee-43d4-e25f-d82f07fefa7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "=== Random Forest – Tuned PERFORMANCE SUMMARY ===\n",
            "Training Time     : 12.806s\n",
            "Prediction Time   : 0.286s\n",
            "Accuracy          : 0.9139\n",
            "Precision (Wgt.)  : 0.8703\n",
            "Recall (Wgt.)     : 0.9139\n",
            "F1 Score (Wgt.)   : 0.8766\n",
            "\n",
            "Detailed Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.00      0.00      0.00       228\n",
            "           1       0.97      0.99      0.98       227\n",
            "           2       0.88      0.41      0.56        17\n",
            "           3       1.00      0.50      0.67         2\n",
            "           4       1.00      0.02      0.04       411\n",
            "           5       0.00      0.00      0.00        41\n",
            "           6       1.00      1.00      1.00         2\n",
            "           7       0.93      1.00      0.96       479\n",
            "           8       1.00      0.50      0.67         2\n",
            "           9       1.00      0.75      0.86         4\n",
            "          10       0.00      0.00      0.00        94\n",
            "          11       0.00      0.00      0.00       310\n",
            "          12       1.00      0.11      0.20         9\n",
            "          13       0.00      0.00      0.00         2\n",
            "          14       0.99      1.00      1.00      6654\n",
            "          15       1.00      0.99      1.00       206\n",
            "          16       0.87      0.99      0.93     11245\n",
            "          17       1.00      1.00      1.00         1\n",
            "          18       1.00      1.00      1.00         1\n",
            "          19       0.89      0.94      0.92        35\n",
            "          20       0.96      1.00      0.98       371\n",
            "          21       0.00      0.00      0.00       211\n",
            "          22       0.00      0.00      0.00         9\n",
            "          23       1.00      0.25      0.40         4\n",
            "          24       0.00      0.00      0.00        93\n",
            "          25       0.85      1.00      0.92       698\n",
            "          26       0.00      0.00      0.00         8\n",
            "          27       0.99      1.00      1.00       540\n",
            "          28       0.00      0.00      0.00        43\n",
            "          29       0.00      0.00      0.00        99\n",
            "          31       0.89      1.00      0.94       104\n",
            "          32       1.00      1.00      1.00       107\n",
            "          33       0.75      0.01      0.02       279\n",
            "          34       0.00      0.00      0.00         4\n",
            "          35       0.00      0.00      0.00         1\n",
            "          36       0.00      0.00      0.00         3\n",
            "\n",
            "    accuracy                           0.91     22544\n",
            "   macro avg       0.58      0.46      0.47     22544\n",
            "weighted avg       0.87      0.91      0.88     22544\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "=== Gradient Boosting – Tuned PERFORMANCE SUMMARY ===\n",
            "Training Time     : 153.001s\n",
            "Prediction Time   : 0.164s\n",
            "Accuracy          : 0.9076\n",
            "Precision (Wgt.)  : 0.8607\n",
            "Recall (Wgt.)     : 0.9076\n",
            "F1 Score (Wgt.)   : 0.8725\n",
            "\n",
            "Detailed Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.00      0.00      0.00       228\n",
            "           1       0.61      0.99      0.75       227\n",
            "           2       0.83      0.29      0.43        17\n",
            "           3       0.40      1.00      0.57         2\n",
            "           4       1.00      0.05      0.10       411\n",
            "           5       0.00      0.00      0.00        41\n",
            "           6       0.50      1.00      0.67         2\n",
            "           7       0.98      0.96      0.97       479\n",
            "           8       1.00      1.00      1.00         2\n",
            "           9       0.02      0.25      0.04         4\n",
            "          10       0.00      0.00      0.00        94\n",
            "          11       0.00      0.00      0.00       310\n",
            "          12       0.00      0.00      0.00         9\n",
            "          13       0.00      0.00      0.00         2\n",
            "          14       0.99      1.00      0.99      6654\n",
            "          15       0.82      0.81      0.81       206\n",
            "          16       0.88      0.99      0.93     11245\n",
            "          17       0.00      0.00      0.00         1\n",
            "          18       1.00      1.00      1.00         1\n",
            "          19       0.89      0.97      0.93        35\n",
            "          20       0.95      0.98      0.96       371\n",
            "          21       0.00      0.00      0.00       211\n",
            "          22       0.00      0.00      0.00         9\n",
            "          23       0.08      0.25      0.12         4\n",
            "          24       0.00      0.00      0.00        93\n",
            "          25       0.86      0.99      0.92       698\n",
            "          26       0.00      0.00      0.00         8\n",
            "          27       0.98      1.00      0.99       540\n",
            "          28       0.00      0.00      0.00        43\n",
            "          29       0.00      0.00      0.00        99\n",
            "          31       0.89      1.00      0.94       104\n",
            "          32       0.98      0.89      0.93       107\n",
            "          33       0.27      0.01      0.02       279\n",
            "          34       0.00      0.00      0.00         4\n",
            "          35       0.00      0.00      0.00         1\n",
            "          36       0.00      0.00      0.00         3\n",
            "\n",
            "    accuracy                           0.91     22544\n",
            "   macro avg       0.41      0.43      0.39     22544\n",
            "weighted avg       0.86      0.91      0.87     22544\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "===== FINAL MODEL COMPARISON =====\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                       Model  Train Time  Test Time  Accuracy  Precision  \\\n",
              "0      Random Forest – Tuned   12.806058   0.285784  0.913946   0.870336   \n",
              "1  Gradient Boosting – Tuned  153.001218   0.164343  0.907603   0.860736   \n",
              "\n",
              "     Recall  F1 Score  Macro AUC  Micro AUC  \n",
              "0  0.913946  0.876550        NaN   0.964888  \n",
              "1  0.907603  0.872502        NaN   0.945806  "
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Train Time</th>\n",
              "      <th>Test Time</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1 Score</th>\n",
              "      <th>Macro AUC</th>\n",
              "      <th>Micro AUC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Random Forest – Tuned</td>\n",
              "      <td>12.806058</td>\n",
              "      <td>0.285784</td>\n",
              "      <td>0.913946</td>\n",
              "      <td>0.870336</td>\n",
              "      <td>0.913946</td>\n",
              "      <td>0.876550</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.964888</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Gradient Boosting – Tuned</td>\n",
              "      <td>153.001218</td>\n",
              "      <td>0.164343</td>\n",
              "      <td>0.907603</td>\n",
              "      <td>0.860736</td>\n",
              "      <td>0.907603</td>\n",
              "      <td>0.872502</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.945806</td>\n",
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              "  </tbody>\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
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              "      background-color: #E8F0FE;\n",
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              "      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-648939ba-4d12-46a0-a103-c13211fd0a8f 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-648939ba-4d12-46a0-a103-c13211fd0a8f');\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",
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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",
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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",
              "    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",
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              "      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-f3b0c846-a102-477a-b643-1ac76a8539d7 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "  <div id=\"id_db3c4fa2-0df2-46ae-813d-328b793e3a7e\">\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('comparison_frame')\"\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_db3c4fa2-0df2-46ae-813d-328b793e3a7e button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('comparison_frame');\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "comparison_frame",
              "summary": "{\n  \"name\": \"comparison_frame\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Gradient Boosting \\u2013 Tuned\",\n          \"Random Forest \\u2013 Tuned\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Train Time\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 99.13294809479757,\n        \"min\": 12.806058406829834,\n        \"max\": 153.00121808052063,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          153.00121808052063,\n          12.806058406829834\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Test Time\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08587167503003594,\n        \"min\": 0.16434311866760254,\n        \"max\": 0.2857840061187744,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.16434311866760254,\n          0.2857840061187744\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.004485285207136134,\n        \"min\": 0.9076029098651526,\n        \"max\": 0.9139460610361959,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.9076029098651526,\n          0.9139460610361959\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.006788508505461814,\n        \"min\": 0.8607356530311849,\n        \"max\": 0.8703360538278941,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.8607356530311849,\n          0.8703360538278941\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.004485285207136134,\n        \"min\": 0.9076029098651526,\n        \"max\": 0.9139460610361959,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.9076029098651526,\n          0.9139460610361959\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F1 Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.002862841003230488,\n        \"min\": 0.8725016354284754,\n        \"max\": 0.8765503040021617,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.8725016354284754,\n          0.8765503040021617\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Macro AUC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Micro AUC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.01349310592154004,\n        \"min\": 0.9458056834573524,\n        \"max\": 0.964887816850131,\n        \"num_unique_values\": 2,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Summary table created successfully.\n"
          ]
        }
      ]
    }
  ]
}