{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yexuy6iHJw_8"
      },
      "source": [
        "**IMPORT LIBRARIES**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WlGCOdL2HNmd"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.feature_selection import SelectKBest, mutual_info_classif\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "\n",
        "from sklearn.metrics import (\n",
        "    accuracy_score, precision_score, recall_score, f1_score,\n",
        "    confusion_matrix, classification_report, roc_auc_score, roc_curve\n",
        ")\n",
        "\n",
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K_Xrha8dJ1Bt"
      },
      "source": [
        "**LOAD DATA**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4wZxD5uPKLSs",
        "outputId": "bd9618a0-f3a5-47a9-e152-16c4d107d973"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataset shape: (11598, 471)\n",
            "   ACCESS_PERSONAL_INFO___  ALTER_PHONE_STATE___  ANTI_DEBUG_____  \\\n",
            "0                        1                     0                0   \n",
            "1                        3                     0                0   \n",
            "2                        2                     0                0   \n",
            "3                        1                     0                0   \n",
            "4                        3                     0                0   \n",
            "\n",
            "   CREATE_FOLDER_____  CREATE_PROCESS`_____  CREATE_THREAD_____  \\\n",
            "0                   3                     0                  14   \n",
            "1                   6                     0                  42   \n",
            "2                   4                     0                  23   \n",
            "3                   4                     0                  27   \n",
            "4                  11                     0                  18   \n",
            "\n",
            "   DEVICE_ACCESS_____  EXECUTE_____  FS_ACCESS____  FS_ACCESS()____  ...  \\\n",
            "0                   2             0              3                0  ...   \n",
            "1                  91             0             32                0  ...   \n",
            "2                   3             0             17                2  ...   \n",
            "3                   9             0             36                0  ...   \n",
            "4                   3             0             16                0  ...   \n",
            "\n",
            "   utimes  vfork  vibrate  vibratePattern  wait4  watchRotation  \\\n",
            "0       0      0        0               0      0              0   \n",
            "1       0      0        0               0      0              0   \n",
            "2       0      0        0               0      0              0   \n",
            "3       0      0        0               0      0              0   \n",
            "4       0      0        0               0      0              0   \n",
            "\n",
            "   windowGainedFocus  write  writev  Class  \n",
            "0                  0     37      10      1  \n",
            "1                  2   2838      46      1  \n",
            "2                  1    111      20      1  \n",
            "3                  7    987     197      1  \n",
            "4                  1     98      25      1  \n",
            "\n",
            "[5 rows x 471 columns]\n"
          ]
        }
      ],
      "source": [
        "df = pd.read_csv(\"/content/feature_vectors_syscallsbinders_frequency_5_Cat.csv\")\n",
        "feature_info = pd.read_csv(\"/content/dataset-features-categories.csv\")\n",
        "\n",
        "print(\"Dataset shape:\", df.shape)\n",
        "print(df.head())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ps_Ta-E6KM6V"
      },
      "source": [
        "**DATA CLEANING**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Rwxa56z_KO-3"
      },
      "outputs": [],
      "source": [
        "df = df.drop_duplicates()\n",
        "df = df.fillna(0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qUNQV8P1KRJb"
      },
      "source": [
        "**DEFINE FEATURES & LABEL**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jnXgs-hNKVRe",
        "outputId": "9e10f43b-9d15-4be1-fec9-55c0e162ea08"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Class distribution:\n",
            "Class\n",
            "3    3903\n",
            "4    2534\n",
            "2    2044\n",
            "5    1792\n",
            "1    1253\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "X = df.drop(\"Class\", axis=1)\n",
        "y = df[\"Class\"]\n",
        "\n",
        "print(\"\\nClass distribution:\")\n",
        "print(y.value_counts())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PYTyUMjsKgUO"
      },
      "source": [
        "**TRAIN-TEST SPLIT**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QoQI3XAKKZwu"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X, y, test_size=0.3, random_state=42, stratify=y\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JyUny3dUKbsa"
      },
      "source": [
        "**FEATURE SCALING**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uf5JjPySKi7m"
      },
      "outputs": [],
      "source": [
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rv1eg5kCKkwF"
      },
      "source": [
        "**FEATURE SELECTION**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2UfhEfbbKm4A",
        "outputId": "8822894a-4a2a-4c75-e009-f4dc670eb35f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Selected features: 100\n"
          ]
        }
      ],
      "source": [
        "selector = SelectKBest(score_func=mutual_info_classif, k=100)\n",
        "\n",
        "selector.fit(X_train_scaled, y_train)\n",
        "\n",
        "X_train_sel = selector.transform(X_train_scaled)\n",
        "X_test_sel = selector.transform(X_test_scaled)\n",
        "\n",
        "selected_features = X.columns[selector.get_support()]\n",
        "print(\"\\nSelected features:\", len(selected_features))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tnuVjkiOKolp"
      },
      "source": [
        "**HANDLE CLASS IMBALANCE (SMOTE)**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xgm2sOdEKqXb"
      },
      "outputs": [],
      "source": [
        "sm = SMOTE(random_state=42)\n",
        "X_train_res, y_train_res = sm.fit_resample(X_train_sel, y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tX3xL-JXKsTp"
      },
      "source": [
        "**MODEL DEFINITIONS**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9_JbKMvcKuZA"
      },
      "outputs": [],
      "source": [
        "models = {\n",
        "    \"Logistic Regression\": LogisticRegression(max_iter=1000),\n",
        "    \"Decision Tree\": DecisionTreeClassifier(),\n",
        "    \"Random Forest\": RandomForestClassifier(),\n",
        "    \"SVM\": SVC(kernel='linear', probability=True),\n",
        "    \"Neural Network\": MLPClassifier(hidden_layer_sizes=(64, 32), max_iter=200, early_stopping=True)\n",
        "}\n",
        "\n",
        "results = {}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mfz3nWpFKwg7"
      },
      "source": [
        "**TRAIN & EVALUATE MODELS**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XcZuYcRQKyjq",
        "outputId": "cf236179-76f0-40f4-c403-906c1921618f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "🔹 Training: Logistic Regression\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.71      0.67      0.69       376\n",
            "           2       0.68      0.73      0.70       613\n",
            "           3       0.82      0.93      0.87      1171\n",
            "           4       0.81      0.70      0.75       760\n",
            "           5       0.91      0.80      0.85       538\n",
            "\n",
            "    accuracy                           0.79      3458\n",
            "   macro avg       0.79      0.77      0.77      3458\n",
            "weighted avg       0.80      0.79      0.79      3458\n",
            "\n",
            "\n",
            "🔹 Training: Decision Tree\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.81      0.80      0.81       376\n",
            "           2       0.89      0.90      0.89       613\n",
            "           3       0.97      0.98      0.98      1171\n",
            "           4       0.89      0.91      0.90       760\n",
            "           5       0.88      0.84      0.86       538\n",
            "\n",
            "    accuracy                           0.91      3458\n",
            "   macro avg       0.89      0.88      0.89      3458\n",
            "weighted avg       0.91      0.91      0.91      3458\n",
            "\n",
            "\n",
            "🔹 Training: Random Forest\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.85      0.92      0.88       376\n",
            "           2       0.97      0.92      0.94       613\n",
            "           3       0.98      0.99      0.99      1171\n",
            "           4       0.95      0.92      0.94       760\n",
            "           5       0.91      0.94      0.93       538\n",
            "\n",
            "    accuracy                           0.95      3458\n",
            "   macro avg       0.93      0.94      0.93      3458\n",
            "weighted avg       0.95      0.95      0.95      3458\n",
            "\n",
            "\n",
            "🔹 Training: SVM\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.67      0.74      0.70       376\n",
            "           2       0.69      0.81      0.75       613\n",
            "           3       0.90      0.92      0.91      1171\n",
            "           4       0.85      0.74      0.79       760\n",
            "           5       0.92      0.80      0.86       538\n",
            "\n",
            "    accuracy                           0.82      3458\n",
            "   macro avg       0.81      0.80      0.80      3458\n",
            "weighted avg       0.83      0.82      0.82      3458\n",
            "\n",
            "\n",
            "🔹 Training: Neural Network\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.83      0.84      0.83       376\n",
            "           2       0.89      0.89      0.89       613\n",
            "           3       0.95      0.98      0.96      1171\n",
            "           4       0.90      0.90      0.90       760\n",
            "           5       0.93      0.85      0.89       538\n",
            "\n",
            "    accuracy                           0.91      3458\n",
            "   macro avg       0.90      0.89      0.89      3458\n",
            "weighted avg       0.91      0.91      0.91      3458\n",
            "\n"
          ]
        }
      ],
      "source": [
        "for name, model in models.items():\n",
        "    print(f\"\\n🔹 Training: {name}\")\n",
        "\n",
        "    model.fit(X_train_res, y_train_res)\n",
        "\n",
        "    y_pred = model.predict(X_test_sel)\n",
        "    # For multiclass ROC AUC, predict_proba returns probabilities for each class\n",
        "    y_prob = model.predict_proba(X_test_sel)\n",
        "\n",
        "    acc = accuracy_score(y_test, y_pred)\n",
        "    # Specify average='weighted' for multiclass classification metrics\n",
        "    prec = precision_score(y_test, y_pred, average='weighted')\n",
        "    rec = recall_score(y_test, y_pred, average='weighted')\n",
        "    f1 = f1_score(y_test, y_pred, average='weighted')\n",
        "    # For multiclass ROC AUC, specify multi_class and average parameters\n",
        "    auc = roc_auc_score(y_test, y_prob, multi_class='ovr', average='weighted')\n",
        "\n",
        "    results[name] = [acc, prec, rec, f1, auc]\n",
        "\n",
        "    print(classification_report(y_test, y_pred))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sWMHID3bK0S2"
      },
      "source": [
        "**RESULTS TABLE**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RFQ013xZK2gh",
        "outputId": "5b03b96d-7af1-46ea-e1f4-7d9094cba76b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Model Comparison:\n",
            "                     Accuracy  Precision    Recall        F1       AUC\n",
            "Logistic Regression  0.794679   0.797158  0.794679  0.793058  0.939652\n",
            "Decision Tree        0.908907   0.908445  0.908907  0.908525  0.943680\n",
            "Random Forest        0.946501   0.947744  0.946501  0.946705  0.995744\n",
            "SVM                  0.823308   0.831315  0.823308  0.824815  0.953823\n",
            "Neural Network       0.910064   0.909939  0.910064  0.909634  0.975191\n"
          ]
        }
      ],
      "source": [
        "results_df = pd.DataFrame(results, index=[\"Accuracy\", \"Precision\", \"Recall\", \"F1\", \"AUC\"]).T\n",
        "\n",
        "print(\"\\nModel Comparison:\")\n",
        "print(results_df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tfGKE21HK5B9"
      },
      "source": [
        "**BEST MODEL**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nHYZfeigK65N",
        "outputId": "317a2411-89cd-43bf-da8d-15a8b52c8966"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Best Model: Random Forest\n"
          ]
        }
      ],
      "source": [
        "best_model_name = results_df[\"F1\"].idxmax()\n",
        "best_model = models[best_model_name]    \n",
        "\n",
        "print(\"\\nBest Model:\", best_model_name)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-pdWAdZqK96b"
      },
      "source": [
        "**CONFUSION MATRIX**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "do3_33T-K_3-",
        "outputId": "978d2aa0-976f-430a-8581-f18ed6bdb012"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 600x400 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "False Positives: 3\n",
            "False Negatives: 17\n"
          ]
        }
      ],
      "source": [
        "y_pred = best_model.predict(X_test_sel)\n",
        "\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "plt.figure(figsize=(6,4))\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
        "plt.title(f\"Confusion Matrix - {best_model_name}\")\n",
        "plt.xlabel(\"Predicted\")\n",
        "plt.ylabel(\"Actual\")\n",
        "plt.show()\n",
        "\n",
        "# False positive / negative analysis\n",
        "fp = cm[0][1]\n",
        "fn = cm[1][0]\n",
        "\n",
        "print(\"False Positives:\", fp)\n",
        "print(\"False Negatives:\", fn)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SNSxboheLBhK"
      },
      "source": [
        "**ROC CURVE**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "8hraUrqwLDiG",
        "outputId": "39828757-244a-49d9-9cd9-cec8acbdef57"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "y_prob_full = best_model.predict_proba(X_test_sel)\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "\n",
        "classes = np.unique(y_test)\n",
        "\n",
        "for i, class_label in enumerate(classes):\n",
        "    y_test_binarized = (y_test == class_label).astype(int)\n",
        "    class_probabilities = y_prob_full[:, i]\n",
        "\n",
        "    fpr, tpr, _ = roc_curve(y_test_binarized, class_probabilities)\n",
        "    auc_score = roc_auc_score(y_test_binarized, class_probabilities)\n",
        "\n",
        "    plt.plot(fpr, tpr, label=f\"Class {class_label} (AUC={auc_score:.2f})\")\n",
        "\n",
        "plt.plot([0,1],[0,1],'k--')\n",
        "plt.xlabel(\"False Positive Rate\")\n",
        "plt.ylabel(\"True Positive Rate\")\n",
        "plt.title(f\"ROC Curve - {best_model_name} (One-vs-Rest)\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NLmJ4AGLLGgc"
      },
      "source": [
        "**FEATURE IMPORTANCE + CTI INTERPRETATION**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "le1a0uibLIq_",
        "outputId": "33fcb312-2be0-4622-eb8c-b350455f32e3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Top Threat Indicators (IoCs):\n",
            "                    feature  importance\n",
            "53  getInstallerPackageName    0.034617\n",
            "51              getDeviceId    0.032933\n",
            "52           getDisplayInfo    0.031981\n",
            "50       getApplicationInfo    0.029702\n",
            "0   ACCESS_PERSONAL_INFO___    0.026868\n",
            "56          getSubscriberId    0.026137\n",
            "55           getPackageInfo    0.023839\n",
            "80                     read    0.022456\n",
            "49       getActivePhoneType    0.020593\n",
            "23               _newselect    0.019459\n",
            "92              sigprocmask    0.019135\n",
            "71                 mprotect    0.018917\n",
            "78                  pread64    0.018600\n",
            "69                    mkdir    0.018196\n",
            "54           getLine1Number    0.017542\n"
          ]
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Feature: CTI Category Mapping:\n",
            "                    feature  importance transact API call signature\n",
            "0   getInstallerPackageName    0.034617      NaN                NaN\n",
            "1               getDeviceId    0.032933      NaN                NaN\n",
            "2            getDisplayInfo    0.031981      NaN                NaN\n",
            "3        getApplicationInfo    0.029702      NaN                NaN\n",
            "4   ACCESS_PERSONAL_INFO___    0.026868      NaN                NaN\n",
            "5           getSubscriberId    0.026137      NaN                NaN\n",
            "6            getPackageInfo    0.023839      NaN                NaN\n",
            "7                      read    0.022456      NaN                NaN\n",
            "8        getActivePhoneType    0.020593      NaN                NaN\n",
            "9                _newselect    0.019459      NaN                NaN\n",
            "10              sigprocmask    0.019135      NaN                NaN\n",
            "11                 mprotect    0.018917      NaN                NaN\n",
            "12                  pread64    0.018600      NaN                NaN\n",
            "13                    mkdir    0.018196      NaN                NaN\n",
            "14           getLine1Number    0.017542      NaN                NaN\n"
          ]
        }
      ],
      "source": [
        "if best_model_name == \"Random Forest\":\n",
        "    importances = best_model.feature_importances_\n",
        "\n",
        "    feat_imp = pd.DataFrame({\n",
        "        \"feature\": selected_features,\n",
        "        \"importance\": importances\n",
        "    })\n",
        "\n",
        "    top_features = feat_imp.sort_values(\"importance\", ascending=False).head(15)\n",
        "\n",
        "    print(\"\\nTop Threat Indicators (IoCs):\")\n",
        "    print(top_features)\n",
        "\n",
        "    top_features.set_index(\"feature\").plot(kind='barh')\n",
        "    plt.title(\"Top Features (CTI Indicators)\")\n",
        "    plt.show()\n",
        "\n",
        "    merged = top_features.merge(feature_info, left_on=\"feature\", right_on=\"transact\", how=\"left\")\n",
        "    print(\"\\nFeature: CTI Category Mapping:\")\n",
        "    print(merged)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sYlqVeGqLKtL"
      },
      "source": [
        "**CROSS-VALIDATION**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xo3hLzHQLMYw",
        "outputId": "b8182317-92a5-4c4f-c57a-1d055b22008b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Cross-validation Accuracy: 0.9684480234260615\n"
          ]
        }
      ],
      "source": [
        "cv_scores = cross_val_score(best_model, X_train_res, y_train_res, cv=10)\n",
        "\n",
        "print(\"\\nCross-validation Accuracy:\", cv_scores.mean())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lhn20kepLOcQ"
      },
      "source": [
        "**MODEL COMPARISON PLOT**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 635
        },
        "id": "7xZB3d5ALRGC",
        "outputId": "b59dc8a8-6494-4bfb-f055-63a49164fe09"
      },
      "outputs": [
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzoAAAJqCAYAAADjSDX3AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAg2tJREFUeJzs3Xd8Tuf/x/H3nW1FrCCqYlStlNoUNUtt1Zq19yqiRmwtonbV3lutqFlKCGrXaFFVitoJNRIJWff5/dFf7q9UtGLdcbyej8f9aO9zX+ecz7nv48553+c617EYhmEIAAAAAEzEwd4FAAAAAMDzRtABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABgH9hsVg0dOjQRM934cIFWSwWzZ8//7nX9CwWLVqkPHnyyNnZWR4eHvYuB6+4pLqfA4BE0AHwCpg/f74sFossFot+/PHHR143DENZs2aVxWJRzZo17VDh0wsKCrJtm8VikbOzs3LkyKHmzZvr3Llzz3Vdv/32m1q2bKmcOXNq1qxZmjlz5nNd/uvq2LFj+vTTT5U1a1a5uroqbdq0qly5subNm6fY2Fh7lwcAry0nexcAAE/Kzc1NS5cuVZkyZeJN37lzpy5fvixXV1c7VfbsPvvsMxUrVkzR0dE6cuSIZs6cqY0bN+r48ePy8vJ6LusICgqS1WrV119/rVy5cj2XZb7uZs+erY4dOypjxoxq1qyZ3nrrLYWFhSkwMFBt2rTRtWvX1L9/f3uX+cJky5ZN9+/fl7Ozs71LAYBHEHQAvDKqV6+ulStXatKkSXJy+t/X19KlS1WkSBHdvHnTjtU9m7Jly+rjjz+WJLVq1Uq5c+fWZ599pgULFsjPz++Zlh0eHq4UKVIoJCREkp5rl7WIiAglT578uS3vVbJ//3517NhRpUqV0qZNm5QqVSrbaz169NBPP/2kEydO2LHCFycmJkZWq1UuLi5yc3OzdzkAkCC6rgF4ZTRu3Fh//fWXtm7dapsWFRWlVatWqUmTJgnOEx4erl69etm6Fb399tsaO3asDMOI1y4yMlI9e/ZUhgwZlCpVKtWuXVuXL19OcJlXrlxR69atlTFjRrm6uip//vyaO3fu89tQSRUrVpQknT9/3jbt+++/V9myZZUiRQqlSpVKNWrU0MmTJ+PN17JlS6VMmVJ//PGHqlevrlSpUqlp06by9vbWkCFDJEkZMmR45NqjqVOnKn/+/HJ1dZWXl5e6dOmiO3fuxFt2+fLlVaBAAR0+fFjlypVT8uTJ1b9/f9t1GmPHjtWUKVOUI0cOJU+eXB988IEuXbokwzD05Zdf6o033lCyZMlUp04d3bp1K96y165dqxo1asjLy0uurq7KmTOnvvzyy0e6fsXV8Ouvv6pChQpKnjy5smTJotGjRz/yHj548EBDhw5V7ty55ebmpsyZM+ujjz7SH3/8YWtjtVo1ceJE5c+fX25ubsqYMaM6dOig27dv/+dnNGzYMFksFi1ZsiReyIlTtGhRtWzZ0vb8SfdFi8Wirl27auXKlcqXL5+SJUumUqVK6fjx45KkGTNmKFeuXHJzc1P58uV14cKFx35OpUuXVrJkyZQ9e3ZNnz49XruoqCgNHjxYRYoUUerUqZUiRQqVLVtWO3bsiNfu4c934sSJypkzp1xdXfXrr78meI3O9evX1apVK73xxhtydXVV5syZVadOnUfqTMw+9ySfNwD8E2d0ALwyvL29VapUKS1btkwffvihpL8P/u/evatGjRpp0qRJ8dobhqHatWtrx44datOmjQoVKqQtW7aod+/eunLliiZMmGBr27ZtWy1evFhNmjRR6dKltX37dtWoUeORGoKDg1WyZEnbwWiGDBn0/fffq02bNgoNDVWPHj2ey7bGHYynS5dO0t+DCLRo0UJVq1bVV199pYiICE2bNk1lypTR0aNH5e3tbZs3JiZGVatWVZkyZTR27FglT55cLVu21MKFC7VmzRpNmzZNKVOm1DvvvCNJGjp0qIYNG6bKlSurU6dOOn36tKZNm6ZDhw5pz5498bol/fXXX/rwww/VqFEjffrpp8qYMaPttSVLligqKkrdunXTrVu3NHr0aDVo0EAVK1ZUUFCQ+vbtq7Nnz+qbb77R559/Hi8czp8/XylTppSvr69Spkyp7du3a/DgwQoNDdWYMWPivTe3b99WtWrV9NFHH6lBgwZatWqV+vbtKx8fH9t+ERsbq5o1ayowMFCNGjVS9+7dFRYWpq1bt+rEiRPKmTOnJKlDhw6aP3++WrVqpc8++0znz5/X5MmTdfTo0Ue2/WEREREKDAxUuXLl9Oabb/7n55mYfVGSdu/erXXr1qlLly6SJH9/f9WsWVN9+vTR1KlT1blzZ92+fVujR49W69attX379kfeo+rVq6tBgwZq3LixVqxYoU6dOsnFxUWtW7eWJIWGhmr27Nlq3Lix2rVrp7CwMM2ZM0dVq1bVwYMHVahQoXjLnDdvnh48eKD27dvbrkWyWq2PbGv9+vV18uRJdevWTd7e3goJCdHWrVt18eJF236amH3uST5vAEiQAQBJ3Lx58wxJxqFDh4zJkycbqVKlMiIiIgzDMIxPPvnEqFChgmEYhpEtWzajRo0atvm+++47Q5IxfPjweMv7+OOPDYvFYpw9e9YwDMM4duyYIcno3LlzvHZNmjQxJBlDhgyxTWvTpo2ROXNm4+bNm/HaNmrUyEidOrWtrvPnzxuSjHnz5v3rtu3YscOQZMydO9e4ceOGcfXqVWPjxo2Gt7e3YbFYjEOHDhlhYWGGh4eH0a5du3jzXr9+3UidOnW86S1atDAkGf369XtkXUOGDDEkGTdu3LBNCwkJMVxcXIwPPvjAiI2NtU2fPHmyra4477//viHJmD59erzlxm1rhgwZjDt37tim+/n5GZKMggULGtHR0bbpjRs3NlxcXIwHDx7YpsW9bw/r0KGDkTx58njt4mpYuHChbVpkZKSRKVMmo379+rZpc+fONSQZ48ePf2S5VqvVMAzD2L17tyHJWLJkSbzXN2/enOD0h/3888+GJKN79+6PbfOwJ90XDcMwJBmurq7G+fPnbdNmzJhhSDIyZcpkhIaG2qbHvccPt417j8aNG2ebFhkZaRQqVMjw9PQ0oqKiDMMwjJiYGCMyMjJePbdv3zYyZsxotG7d2jYt7vN1d3c3QkJC4rX/535++/ZtQ5IxZsyYx74XT7PP/dfnDQAJoesagFdKgwYNdP/+fW3YsEFhYWHasGHDY7utbdq0SY6Ojvrss8/iTe/Vq5cMw9D3339vayfpkXb/PDtjGIZWr16tWrVqyTAM3bx50/aoWrWq7t69qyNHjjzVdrVu3VoZMmSQl5eXatSoofDwcC1YsEBFixbV1q1bdefOHTVu3DjeOh0dHVWiRIlHuhpJUqdOnZ5ovdu2bVNUVJR69OghB4f//Ulo166d3N3dtXHjxnjtXV1d1apVqwSX9cknnyh16tS25yVKlJAkffrpp/GuqSpRooSioqJ05coV27RkyZLZ/j8sLEw3b95U2bJlFRERod9++y3eelKmTKlPP/3U9tzFxUXFixePN0rd6tWrlT59enXr1u2ROi0WiyRp5cqVSp06tapUqRLvfS1SpIhSpkyZ4PsaJzQ0VJIS7LKWkCfdF+NUqlQp3lm6uPeyfv368dYZN/2fI/Q5OTmpQ4cOtucuLi7q0KGDQkJCdPjwYUmSo6OjXFxcJP3dhe/WrVuKiYlR0aJFE9yP69evrwwZMvzrdiZLlkwuLi4KCgp6bPe/xO5zT/J5A0BC6LoG4JWSIUMGVa5cWUuXLlVERIRiY2NtF/H/059//ikvL69HDkbz5s1rez3uvw4ODrbuTHHefvvteM9v3LihO3fuaObMmY8dmjnugv/EGjx4sMqWLStHR0elT59eefPmtYWDM2fOSPrfdTv/5O7uHu+5k5OT3njjjSdab9x78M9tdXFxUY4cOWyvx8mSJYvt4Pif/tmFKy70ZM2aNcHpDx8Inzx5UgMHDtT27dttISLO3bt34z1/4403bGElTpo0afTLL7/Ynv/xxx96++234wWsfzpz5ozu3r0rT0/PBF//t88y7j0PCwt7bJuHPem+GOdZ3ktJ8vLyUooUKeJNy507t6S/r7kpWbKkJGnBggUaN26cfvvtN0VHR9vaZs+e/ZFtSGjaP7m6uuqrr75Sr169lDFjRpUsWVI1a9ZU8+bNlSlTpnjb+qT73JN83gCQEIIOgFdOkyZN1K5dO12/fl0ffvjhS7vxZdz1CJ9++qlatGiRYJu4614Sy8fHR5UrV/7X9S5atMh2sPiwfx7Mu7q6xvul/Hl6+MzLPzk6OiZquvH/F+HfuXNH77//vtzd3fXFF18oZ86ccnNz05EjR9S3b99HrgP5r+U9KavVKk9PTy1ZsiTB1//t7EWuXLnk5ORkGyDgeXva9zIxFi9erJYtW6pu3brq3bu3PD095ejoKH9//3gDNsT5t8/+YT169FCtWrX03XffacuWLRo0aJD8/f21fft2vfvuu4mu83luM4DXC0EHwCunXr166tChg/bv36/ly5c/tl22bNm0bds2hYWFxfslPa4rVLZs2Wz/tVqttrMAcU6fPh1veXEjssXGxj42lLwIcWeaPD09n/t6496D06dPK0eOHLbpUVFROn/+/EvZzqCgIP31118KCAhQuXLlbNMfHnEusXLmzKkDBw4oOjr6sQMK5MyZU9u2bdN77733xAfxcZInT66KFStq+/btunTp0iNnWv7pSffF5+Xq1au2YcXj/P7775Jk6xK3atUq5ciRQwEBAfHOmMSNzvcscubMqV69eqlXr146c+aMChUqpHHjxmnx4sVJYp8D8HrgGh0Ar5yUKVNq2rRpGjp0qGrVqvXYdtWrV1dsbKwmT54cb/qECRNksVhsIzbF/fefo7ZNnDgx3nNHR0fVr19fq1evTvD+KDdu3HiazflPVatWlbu7u0aOHBmve9HzWG/lypXl4uKiSZMmxfuFfM6cObp7926CI889b3G/2D+8/qioKE2dOvWpl1m/fn3dvHnzkc/+4fU0aNBAsbGx+vLLLx9pExMT88hQx/80ZMgQGYahZs2a6d69e4+8fvjwYS1YsEDSk++Lz0tMTIxmzJhhex4VFaUZM2YoQ4YMKlKkiKSE3/cDBw5o3759T73eiIgIPXjwIN60nDlzKlWqVIqMjJSUNPY5AK8HzugAeCU9ruvYw2rVqqUKFSpowIABunDhggoWLKgffvhBa9euVY8ePWxnSgoVKqTGjRtr6tSpunv3rkqXLq3AwECdPXv2kWWOGjVKO3bsUIkSJdSuXTvly5dPt27d0pEjR7Rt27ZH7g/zPLi7u2vatGlq1qyZChcurEaNGilDhgy6ePGiNm7cqPfeey/BA/onkSFDBvn5+WnYsGGqVq2aateurdOnT2vq1KkqVqxYvIvAX5TSpUsrTZo0atGihT777DNZLBYtWrTombomNW/eXAsXLpSvr68OHjyosmXLKjw8XNu2bVPnzp1Vp04dvf/+++rQoYP8/f117NgxffDBB3J2dtaZM2e0cuVKff3114+9/iuu7ilTpqhz587KkyePmjVrprfeekthYWEKCgrSunXrNHz4cElPvi8+L15eXvrqq6904cIF5c6dW8uXL9exY8c0c+ZM2xmumjVrKiAgQPXq1VONGjV0/vx5TZ8+Xfny5UswuD2J33//XZUqVVKDBg2UL18+OTk5ac2aNQoODlajRo0kJY19DsDrgaADwLQcHBy0bt06DR48WMuXL9e8efPk7e2tMWPGqFevXvHazp07VxkyZNCSJUv03XffqWLFitq4ceMjXZIyZsyogwcP6osvvlBAQICmTp2qdOnSKX/+/Prqq69e2LY0adJEXl5eGjVqlMaMGaPIyEhlyZJFZcuWfewoaE9q6NChypAhgyZPnqyePXsqbdq0at++vUaOHPnYbl/PU7p06bRhwwb16tVLAwcOVJo0afTpp5+qUqVKqlq16lMt09HRUZs2bdKIESO0dOlSrV69WunSpVOZMmXk4+Njazd9+nQVKVJEM2bMUP/+/eXk5CRvb299+umneu+99/5zPR06dFCxYsU0btw4LVy4UDdu3FDKlClVuHBhzZs3z3bQnph98XlIkyaNFixYoG7dumnWrFnKmDGjJk+erHbt2tnatGzZUtevX9eMGTO0ZcsW5cuXT4sXL9bKlSsVFBT0VOvNmjWrGjdurMDAQC1atEhOTk7KkyePVqxYofr169va2XufA/B6sBhczQcAgGmUL19eN2/eTLB7JQC8TrhGBwAAAIDpEHQAAAAAmA5BBwAAAIDpcI0OAAAAANPhjA4AAAAA0yHoAAAAADCdV+I+OlarVVevXlWqVKlksVjsXQ4AAAAAOzEMQ2FhYfLy8pKDw+PP27wSQefq1auP3LQPAAAAwOvr0qVLeuONNx77+isRdFKlSiXp741xd3e3czUAAAAA7CU0NFRZs2a1ZYTHeSWCTlx3NXd3d4IOAAAAgP+8pIXBCAAAAACYDkEHAAAAgOkQdAAAAACYzitxjQ4AAACQWLGxsYqOjrZ3GUgkZ2dnOTo6PvNyCDoAAAAwFcMwdP36dd25c8fepeApeXh4KFOmTM90D02CDgAAAEwlLuR4enoqefLk3HD+FWIYhiIiIhQSEiJJypw581Mvi6ADAAAA04iNjbWFnHTp0tm7HDyFZMmSSZJCQkLk6en51N3YGIwAAAAAphF3TU7y5MntXAmeRdzn9yzXWBF0AAAAYDp0V3u1PY/Pj6ADAAAAwHQIOgAAAABMh8EIAAAA8Frw7rfxpa7vwqgaTzXfvn37VKZMGVWrVk0bN77cms2EMzoAAABAEjJnzhx169ZNu3bt0tWrV+1WR1RUlN3W/TwkOujs2rVLtWrVkpeXlywWi7777rv/nCcoKEiFCxeWq6urcuXKpfnz5z9FqQAAAIC53bt3T8uXL1enTp1Uo0aNR46b169fr2LFisnNzU3p06dXvXr1bK9FRkaqb9++ypo1q+24e86cOZKk+fPny8PDI96yvvvuu3gX/Q8dOlSFChXS7NmzlT17drm5uUmSNm/erDJlysjDw0Pp0qVTzZo19ccff8Rb1uXLl9W4cWOlTZtWKVKkUNGiRXXgwAFduHBBDg4O+umnn+K1nzhxorJlyyar1fqsb9ljJTrohIeHq2DBgpoyZcoTtT9//rxq1KihChUq6NixY+rRo4fatm2rLVu2JLpYAAAAwMxWrFihPHny6O2339ann36quXPnyjAMSdLGjRtVr149Va9eXUePHlVgYKCKFy9um7d58+ZatmyZJk2apFOnTmnGjBlKmTJlotZ/9uxZrV69WgEBATp27Jikv4//fX199dNPPykwMFAODg6qV6+eLaTcu3dP77//vq5cuaJ169bp559/Vp8+fWS1WuXt7a3KlStr3rx58dYzb948tWzZUg4OL66DWaKv0fnwww/14YcfPnH76dOnK3v27Bo3bpwkKW/evPrxxx81YcIEVa1aNbGrBwAAAExrzpw5+vTTTyVJ1apV0927d7Vz506VL19eI0aMUKNGjTRs2DBb+4IFC0qSfv/9d61YsUJbt25V5cqVJUk5cuRI9PqjoqK0cOFCZciQwTatfv368drMnTtXGTJk0K+//qoCBQpo6dKlunHjhg4dOqS0adNKknLlymVr37ZtW3Xs2FHjx4+Xq6urjhw5ouPHj2vt2rWJri8xXvg1Ovv27bO92XGqVq2qffv2PXaeyMhIhYaGxnsAAAAAZnb69GkdPHhQjRs3liQ5OTmpYcOGtu5nx44dU6VKlRKc99ixY3J0dNT777//TDVky5YtXsiRpDNnzqhx48bKkSOH3N3d5e3tLUm6ePGibd3vvvuuLeT8U926deXo6Kg1a9ZI+rsbXYUKFWzLeVFeeNC5fv26MmbMGG9axowZFRoaqvv37yc4j7+/v1KnTm17ZM2a9UWXCQAAANjVnDlzFBMTIy8vLzk5OcnJyUnTpk3T6tWrdffuXSVLluyx8/7ba5Lk4OBg6wIXJzo6+pF2KVKkeGRarVq1dOvWLc2aNUsHDhzQgQMHJP1vsIL/WreLi4uaN2+uefPmKSoqSkuXLlXr1q3/dZ7nIUmOuubn56e7d+/aHpcuXbJ3SQAAAMALExMTo4ULF2rcuHE6duyY7fHzzz/Ly8tLy5Yt0zvvvKPAwMAE5/fx8ZHVatXOnTsTfD1DhgwKCwtTeHi4bVrcNTj/5q+//tLp06c1cOBAVapUSXnz5tXt27fjtXnnnXd07Ngx3bp167HLadu2rbZt26apU6cqJiZGH3300X+u+1m98PvoZMqUScHBwfGmBQcHy93d/bHpz9XVVa6uri+6NAAAbKZ03P5M83eZXvE5VQLgdbRhwwbdvn1bbdq0UerUqeO9Vr9+fc2ZM0djxoxRpUqVlDNnTjVq1EgxMTHatGmT+vbtK29vb7Vo0UKtW7fWpEmTVLBgQf35558KCQlRgwYNVKJECSVPnlz9+/fXZ599pgMHDjzRSMhp0qRRunTpNHPmTGXOnFkXL15Uv3794rVp3LixRo4cqbp168rf31+ZM2fW0aNH5eXlpVKlSkn6+zr9kiVLqm/fvmrduvV/ngV6Hl74GZ1SpUo9kjy3bt1q22gAAADgdTdnzhxVrlz5kZAj/R10fvrpJ6VNm1YrV67UunXrVKhQIVWsWFEHDx60tZs2bZo+/vhjde7cWXny5FG7du1sZ3DSpk2rxYsXa9OmTfLx8dGyZcs0dOjQ/6zLwcFB3377rQ4fPqwCBQqoZ8+eGjNmTLw2Li4u+uGHH+Tp6anq1avLx8dHo0aNkqOjY7x2bdq0UVRU1EvptiZJFuOfnfX+w71793T27FlJ0rvvvqvx48erQoUKSps2rd588035+fnpypUrWrhwoaS/h5cuUKCAunTpotatW2v79u367LPPtHHjxicedS00NFSpU6fW3bt35e7unshNBADgv3FGBzCHBw8e6Pz58/HuA4Ok4csvv9TKlSv1yy+//Gfbf/scnzQbJPqMzk8//aR3331X7777riTJ19dX7777rgYPHixJunbtmm0EBknKnj27Nm7cqK1bt6pgwYIaN26cZs+ezdDSAAAAwGvg3r17OnHihCZPnqxu3bq9tPUm+hqd8uXLPzJiw8MS6utXvnx5HT16NLGrAgAAAPCK69q1q5YtW6a6deu+tG5r0ksYjAAAAADA62v+/PlPNPDB85Ykh5cGAAAAgGdB0AEAAABgOgQdAAAAAKbDNToAAACACYX8GfpM83tme7Vv68IZHQAAAACmQ9ABAAAAYDoEHQAAAOA1ldE7tTZt2fBEbS0Wi7777rsXW9BzxDU6AAAAeD0MTf2S13c3Uc1btmypBQsWSJKcnZ315ptvqnnz5urfv7+cnF7MYfvxg78rdWqPJ2p77do1pUmT5oXU8SIQdAAAAIAkolq1apo3b54iIyO1adMmdenSRc7OzvLz84vXLioqSi4uLs+8Pk/PjE/cNlOmTM+8vpeJrmsAAABAEuHq6qpMmTIpW7Zs6tSpkypXrqx169apZcuWqlu3rkaMGCEvLy+9/fbbkqRLly6pQYMG8vDwUNq0aVWnTh1duHAh3jKXrlikclVKKGvuDPIpllt+gz+3vfZw17WoqCj5Df5cPsVy683cnsqWLZv8/f1tbf/Zde348eOqWLGikiVLpnTp0ql9+/a6d++e7fW4mseOHavMmTMrXbp06tKli6Kjo1/AO/cozugAAAAASVSyZMn0119/SZICAwPl7u6urVu3SpKio6NVtWpVlSpVSrt375aTk5OGDx+uatWq6ZdffpEkzV80W0OGD9CAvkNVqXwVhYbd1cGfDiS4rtnzp2vLtu81a8p8ZfF6Q/eNO7p06VKCbcPDw23rPnTokEJCQtS2bVt17dpV8+fPt7XbsWOHMmfOrB07dujs2bNq2LChChUqpHbt2j3HdylhBB0AAAAgiTEMQ4GBgdqyZYu6deumGzduKEWKFJo9e7aty9rixYtltVo1e/ZsWSwWSdK8efPk4eGhoKAgFXq7pCZMHquO7bqqfetOtmW/W7BIguu8fPWysnvnUIlipWSxWOSZrcBj61u6dKkePHighQsXKkWKFJKkyZMnq1atWvrqq6+UMePfXeLSpEmjyZMny9HRUXny5FGNGjUUGBj4UoIOXdcAAACAJGLDhg1KmTKl3Nzc9OGHH6phw4YaOnSoJMnHxyfedTk///yzzp49q1SpUillypRKmTKl0qZNqwcPHuiPP/7QjZs3dD34msqWfv+J1t3o4yY6+etxla5YRP2H9tEPP/zw2LanTp1SwYIFbSFHkt577z1ZrVadPn3aNi1//vxydHS0Pc+cObNCQkKe9O14JpzRAQAAeImmdNz+TPN3mV7xOVWCpKhChQqaNm2aXFxc5OXlFW+0tYdDhSTdu3dPRYoU0ZIlSx5ZToYMGfTX5XuPTP837xQopEO7f1Fg0Fbt2hOkBg0aqHLlylq1atXTbYz+Hj3uYRaLRVar9amXlxgEHQAAACCJSJEihXLlyvVEbQsXLqzly5fL09NT7u7uj7weeceirG+8qd17d6pM6XJPtMxUqdxVt1Z91a1VX81aNlG1atV069YtpU2bNl67vHnzav78+QoPD7cFsD179sjBwcE2UIK90XUNAAAAeAU1bdpU6dOnV506dbR7926dP39eQUFB+uyzz3T58mVJUu8efpo+a7JmzZuuc+f/0C8njmn2/BkJLm/67MkKWLtKZ87+rj/OndXKlSuVKVMmeXh4JLhuNzc3tWjRQidOnNCOHTvUrVs3NWvWzHZ9jr1xRgcAEI93v43PNP8FtyZPPa9P9jefad0r/GOefubyU55p3QDwsiVPnly7du1S37599dFHHyksLExZsmRRpUqV5O7urge3pYYfN1Fk5APNmDNVw0YOVNo06VSzep0El5ciRUpNmTFR5y6ck6Ojo4oXL6ZNmzbJweHRcyPJkyfXli1b1L17dxUrVkzJkydX/fr1NX78+Be92U/MYhiGYe8i/ktoaKhSp06tu3fvJnhaDoB50Zf95Xtdg872Zww67Gt4UnyvvVgPHjzQ+fPnlT17drm5udm7HLsK+TP0meb3zGa/4+5/+xyfNBvQdQ0AAACA6dB1DU+FX6MAAACQlHFGBwAAAIDpEHQAAAAAmA5BBwAAAIDpEHQAAAAAmA5BBwAAAIDpEHQAAAAAmA5BBwAAAIDpEHQAAAAASJIyeqfWpi0bJEkXLlyQxWLRsWPH7FvUU+KGoQAAAHgt+CzweanrO97ieKLat2zZUgsWLJAkOTk56Y033tAnn3yiL774Qm5ubi+iRFMj6AAAAABJRLVq1TRv3jxFR0fr8OHDatGihSwWi7766it7l/bKoesaAAAAkES4uroqU6ZMypo1q+rWravKlStr69atkiSr1Sp/f39lz55dyZIlU8GCBbVq1ap48588eVI1a9aUu7u7cuTPotqfVNOFP89Jko7+fFiffFpHed/Nrlw+WVW3QXX9cuLYy97El4agAwAAACRBJ06c0N69e+Xi4iJJ8vf318KFCzV9+nSdPHlSPXv21KeffqqdO3dKkq5cuaJy5crJ1dVV27dv19b1O9X4k08VExMrSboXfk8N6zfRupVbtGnNNmXPnkNNWn6ie/fC7LaNLxJd1wAAAGBqv1y+Y58VXz2qk/8fUp7EnQd3tGHDBiVPkVyxsbGKioySg4ODxvfpoztHjmjkiBHaOHOmSmTJIkVEqGHRogqqUUNTv/pKxdOl08Svv5Z7smSaN3CgnJ2dFZbhTeXMkcu2/LKl34+3vnH+k/TWO29q74E9+qBStee22UkFQQd4RXj32/hM819wa/LU8/pkf/OZ1r3CP+bpZy4/5ZnWDQDAq6RYmWIaPHqwIiIitGj6InlEO6pulSr69exZRdy/r5rt28drHxUdrYJ580qSfvntN71XpIicnZ0TXHbIjRCNGvel9u7/UTf/uqnY2Fjdvx+hK1cvvfDtsgeCDgAAAJBEJE+eXG/m+PsHxi8nfanG732k+QEBypfr7zMzAVOmyCtjxnjzuP7/WaNk/zEy22e9Our2nVsaPuQrvZElq1xdXFTjoyqKiop+AVtifwQdAAAAIAlycHBQ73bt1HfMGP2yYYNcXVx06fp1lS1WLMH2BXLn1pK1axUdHZ3gWZ2Dhw/oqy/HqXKFDyRJV65e1l+3/nqh22BPDEYAAAAAJFEfffCBHB0cNGflSnVv0UJ9R4/W4rVrde7SJR399VdNW7JEi9eulSR1bNxYYeHhat6njw6fPKlz5//QyoBvdfaPM5KkHN45tHLNt/r97GkdPvqTOvdop2Ruyey5eS8UZ3QAAACAJMrJyUkdGzfWhHnz9Ov33ytD2rQaO3u2zl++LA93dxXMm1d92raVJKXz8NCm2bM1YPx4VW3VSg6OTiqQz0fFipaQJE34arI+799dVWqUk5dXFvXvPVjDRgyy5+a9UAQdAAAAvBaWVNr9VPO943D+OVeSsBGTRyQ4/fO2bfX5/4eZLp9+qi6ffvrYZfi8/bbWzZghSQpLFX8wIZ8CBbVlXVC8abWq1433PPjCXdv/e3t7yzCMJy0/yaHrGgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB0nexcAAAAAvAzOlUs91XynnnJ9ebcvTVT7AV0HaO3ytY9MP75xo66FhGjC/Pk6+uuvun7jhr6dOFG1K1V6yspeDwQdAAAAIIkoU7GMhk8abnv+5g1DGdKk0dk//5RP7txqXq+eGvfoYb8CXyEEHQAAACCJcHF1UfqM6W3PMxmGJKlq2bKqWrasvcp6JXGNDgAAAADT4YwOAAAAkETs/GGnimUrZnte7b0yWjJ+vB0renURdAAAAIAkoliZYho8erDt+dvhbnas5tVG0AEAAACSiOTJk+vNHG/anme+btixmlcb1+gAAAAAMB3O6AAAAABJ3L2ICP1x8aLt+Z9Xrujn335T2tSplTVzZjtWlnQRdAAAAIAk7sjJk6rWurXted8xYyRJn9aurZkjRtirrCSNoAMAAIDXQvS2fU813zsO559zJQkbMfnxgaVcsWKKOH78pdRhFlyjAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0uI8OAAB4NQ1N/Qzz3n1+dQBIkjijAwAAAMB0CDoAAAAATIeuawAAAIl0Kk/ep5+5/JTnVwgSZffwI08331Our8vgNE8137FDx9S8ZnO9V/E9bZkw1TZ916FDqta6ta7u2SMPd/d48+SpWlVdP/1UXZs1s037ce8uTZ05SUeO/aQHDx4o6xtvqmL5KurYtosyZ/J6uo16hRB0AACAXXj32/hM819we/p5fRb4PNO6VzzT3MC/C1gSoCZtmyhgSYCuhoTIy9Mz0cuYvWKFeowYoQb1m2jOtEXK+sabunL1slYELNO0WZP1xaCRL6DypIWgAwAAACQREfcitPm7zVq+bbluhtzU4rVr1addu0Qt4/L16/p81Ci1bdlRXw72t01/M2s2lSrxnu7evfOcq06auEYHAAAASCI2r92s7G9lV/Zc2VXzk5pauGaNDMNI1DLW/PCDoqKj1bVj9wRfT53a4zlUmvQRdAAAAIAkImBJgGp+XFOSVKZiGYXeu6fdP/2UqGWcvXhR7ilTKqNnphdR4iuDoAMAAAAkAefPnteJoydU/aPqkiQnJyfVr1pVCwICErUcwzBksVheRImvlKcKOlOmTJG3t7fc3NxUokQJHTx48F/bT5w4UW+//baSJUumrFmzqmfPnnrw4MFTFQwAAACYUcCSAMXExKiiT0UVzFRQBTMV1KwVK/Tdtm26GxamVClSSJJC7917ZN67YWFyT5lSkvRWtmy6Gxam4JDrL7X+pCbRQWf58uXy9fXVkCFDdOTIERUsWFBVq1ZVSEhIgu2XLl2qfv36aciQITp16pTmzJmj5cuXq3///s9cPAAAAGAGMTExWrd8nXp/0VurdqyyPQ6sWqXMGTJoxfffK1e2bHJwcNCRX3+NN+/5S5d0NyxMb3l7S5LqffCBXJydNXn61wmu63UZjCDRo66NHz9e7dq1U6tWrSRJ06dP18aNGzV37lz169fvkfZ79+7Ve++9pyZNmkiSvL291bhxYx04cOAZSwcAAADMYecPOxV6N1QfNf1IqdxT2abnuG6obuXKWhAQoHYNGqjlRx/Jb+xYOTk6qsBbb+ny9esaOGGCir/zjkoWKiRJeiNTJn3Vp498R45U2L1QNfiosbK+8aauXb+qFauXKUWKlBo2cISdtvTlSdQZnaioKB0+fFiVK1f+3wIcHFS5cmXt27cvwXlKly6tw4cP27q3nTt3Tps2bVL16tWfoWwAAADAPAKWBKhkuZLxQk6cOlWq6MjJkzp++rTG9uunprVra9CECSpSr57aDxyoArlza9XkyfGuy+nQqJGWL1qj69evqVWHpipTqZh8+3ZTqlSp1Ll9t5e5aXaTqDM6N2/eVGxsrDJmzBhvesaMGfXbb78lOE+TJk108+ZNlSlTRoZhKCYmRh07dvzXrmuRkZGKjIy0PQ8NDU1MmQAAAMAjyg4s/FTzveNw/jlX8qgpS6Y89rViPj6KOH7c9nxg584a2Lnzfy7z/TIV9H6ZCs+lvlfRCx91LSgoSCNHjtTUqVN15MgRBQQEaOPGjfryyy8fO4+/v79Sp05te2TNmvVFlwkAAADARBJ1Rid9+vRydHRUcHBwvOnBwcHKlCnhcboHDRqkZs2aqW3btpIkHx8fhYeHq3379howYIAcHB7NWn5+fvL19bU9Dw0NNW/YGZr6Gea9+/zqAAAAAEwkUUHHxcVFRYoUUWBgoOrWrStJslqtCgwMVNeuXROcJyIi4pEw4+joKEmPvcurq6urXF1dE1Oa3Xj32/hM819we/p5fRb4PNO6j7c4/t+NAAAAgFdQokdd8/X1VYsWLVS0aFEVL15cEydOVHh4uG0UtubNmytLlizy9/eXJNWqVUvjx4/Xu+++qxIlSujs2bMaNGiQatWqZQs8sI9TefI+/czlH9+PFAAAALC3RAedhg0b6saNGxo8eLCuX7+uQoUKafPmzbYBCi5evBjvDM7AgQNlsVg0cOBAXblyRRkyZFCtWrU0YoT5h7QDAACAfTyu5xBeDc/j80t00JGkrl27PrarWlBQUPwVODlpyJAhGjJkyNOsCgAAAHhizs7Okv6+fCJZsmR2rgZPKyIiQtL/Ps+n8VRBBwAAAEiKHB0d5eHhoZCQEElS8uTJZcREPdMyHzg8/dkFq8X6TOuOtD79uqOfdbsfPHim+Z+GYRiKiIhQSEiIPDw8nulSF4IOAAAATCVuNOC4sBNy+/4zLc/FcuOp5w1xerbDbeMZbif5IDT2mdYdGvUMo2Y9Iw8Pj8eO6vykCDoAAAAwFYvFosyZM8vT01PR0dFqGxD0TMsLdP38qeftnsXrmdY9YWbMU897vPjgZ1p302HPMHDVM3B2dn4ug5YRdAAAAGBKjo6OcnR01JWwZzuz4RZ96annvRZleaZ1O1x7+qDz4O6zdZtzc7PfGZ3n4dG7dQIAAADAK46gAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0niroTJkyRd7e3nJzc1OJEiV08ODBf21/584ddenSRZkzZ5arq6ty586tTZs2PVXBAAAAAPBfnBI7w/Lly+Xr66vp06erRIkSmjhxoqpWrarTp0/L09PzkfZRUVGqUqWKPD09tWrVKmXJkkV//vmnPDw8nkf9AAAAAPCIRAed8ePHq127dmrVqpUkafr06dq4caPmzp2rfv36PdJ+7ty5unXrlvbu3StnZ2dJkre397NVDQAAAAD/IlFd16KionT48GFVrlz5fwtwcFDlypW1b9++BOdZt26dSpUqpS5duihjxowqUKCARo4cqdjY2MeuJzIyUqGhofEeAAAAAPCkEhV0bt68qdjYWGXMmDHe9IwZM+r69esJznPu3DmtWrVKsbGx2rRpkwYNGqRx48Zp+PDhj12Pv7+/UqdObXtkzZo1MWUCAAAAeM298FHXrFarPD09NXPmTBUpUkQNGzbUgAEDNH369MfO4+fnp7t379oely5detFlAgAAADCRRF2jkz59ejk6Oio4ODje9ODgYGXKlCnBeTJnzixnZ2c5OjrapuXNm1fXr19XVFSUXFxcHpnH1dVVrq6uiSkNAAAAAGwSdUbHxcVFRYoUUWBgoG2a1WpVYGCgSpUqleA87733ns6ePSur1Wqb9vvvvytz5swJhhwAAAAAeFaJ7rrm6+urWbNmacGCBTp16pQ6deqk8PBw2yhszZs3l5+fn619p06ddOvWLXXv3l2///67Nm7cqJEjR6pLly7PbysAAAAA4CGJHl66YcOGunHjhgYPHqzr16+rUKFC2rx5s22AgosXL8rB4X/5KWvWrNqyZYt69uypd955R1myZFH37t3Vt2/f57cVAAAAAPCQRAcdSeratau6du2a4GtBQUGPTCtVqpT279//NKsCAAAAgER74aOuAQAAAMDLRtABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACm81RBZ8qUKfL29pabm5tKlCihgwcPPtF83377rSwWi+rWrfs0qwUAAACAJ5LooLN8+XL5+vpqyJAhOnLkiAoWLKiqVasqJCTkX+e7cOGCPv/8c5UtW/apiwUAAACAJ5HooDN+/Hi1a9dOrVq1Ur58+TR9+nQlT55cc+fOfew8sbGxatq0qYYNG6YcOXI8U8EAAAAA8F8SFXSioqJ0+PBhVa5c+X8LcHBQ5cqVtW/fvsfO98UXX8jT01Nt2rR5+koBAAAA4Ak5JabxzZs3FRsbq4wZM8abnjFjRv32228JzvPjjz9qzpw5Onbs2BOvJzIyUpGRkbbnoaGhiSkTAAAAwGvuhY66FhYWpmbNmmnWrFlKnz79E8/n7++v1KlT2x5Zs2Z9gVUCAAAAMJtEndFJnz69HB0dFRwcHG96cHCwMmXK9Ej7P/74QxcuXFCtWrVs06xW698rdnLS6dOnlTNnzkfm8/Pzk6+vr+15aGgoYQcAAADAE0tU0HFxcVGRIkUUGBhoGyLaarUqMDBQXbt2faR9njx5dPz48XjTBg4cqLCwMH399dePDS+urq5ydXVNTGkAAAAAYJOooCNJvr6+atGihYoWLarixYtr4sSJCg8PV6tWrSRJzZs3V5YsWeTv7y83NzcVKFAg3vweHh6S9Mh0AAAAAHheEh10GjZsqBs3bmjw4MG6fv26ChUqpM2bN9sGKLh48aIcHF7opT8AAAAA8K8SHXQkqWvXrgl2VZOkoKCgf513/vz5T7NKAAAAAHhinHoBAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDpPFXSmTJkib29vubm5qUSJEjp48OBj286aNUtly5ZVmjRplCZNGlWuXPlf2wMAAADAs0p00Fm+fLl8fX01ZMgQHTlyRAULFlTVqlUVEhKSYPugoCA1btxYO3bs0L59+5Q1a1Z98MEHunLlyjMXDwAAAAAJSXTQGT9+vNq1a6dWrVopX758mj59upInT665c+cm2H7JkiXq3LmzChUqpDx58mj27NmyWq0KDAx85uIBAAAAICGJCjpRUVE6fPiwKleu/L8FODiocuXK2rdv3xMtIyIiQtHR0UqbNm3iKgUAAACAJ+SUmMY3b95UbGysMmbMGG96xowZ9dtvvz3RMvr27SsvL694YemfIiMjFRkZaXseGhqamDIBAAAAvOZe6qhro0aN0rfffqs1a9bIzc3tse38/f2VOnVq2yNr1qwvsUoAAAAAr7pEBZ306dPL0dFRwcHB8aYHBwcrU6ZM/zrv2LFjNWrUKP3www965513/rWtn5+f7t69a3tcunQpMWUCAAAAeM0lKui4uLioSJEi8QYSiBtYoFSpUo+db/To0fryyy+1efNmFS1a9D/X4+rqKnd393gPAAAAAHhSibpGR5J8fX3VokULFS1aVMWLF9fEiRMVHh6uVq1aSZKaN2+uLFmyyN/fX5L01VdfafDgwVq6dKm8vb11/fp1SVLKlCmVMmXK57gpAAAAAPC3RAedhg0b6saNGxo8eLCuX7+uQoUKafPmzbYBCi5evCgHh/+dKJo2bZqioqL08ccfx1vOkCFDNHTo0GerHgAAAAASkOigI0ldu3ZV165dE3wtKCgo3vMLFy48zSoAAAAA4Km91FHXAAAAAOBlIOgAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMJ2nCjpTpkyRt7e33NzcVKJECR08ePBf269cuVJ58uSRm5ubfHx8tGnTpqcqFgAAAACeRKKDzvLly+Xr66shQ4boyJEjKliwoKpWraqQkJAE2+/du1eNGzdWmzZtdPToUdWtW1d169bViRMnnrl4AAAAAEhIooPO+PHj1a5dO7Vq1Ur58uXT9OnTlTx5cs2dOzfB9l9//bWqVaum3r17K2/evPryyy9VuHBhTZ48+ZmLBwAAAICEOCWmcVRUlA4fPiw/Pz/bNAcHB1WuXFn79u1LcJ59+/bJ19c33rSqVavqu+++e+x6IiMjFRkZaXt+9+5dSVJoaGhiyn0prJERzzR/qMV46nlj78c+07rvxT79/Pejwp9p3Unxs0zq2NeeDvta4rGvPR32tcRjX3s67GuJx772dJLqvhZXl2H8x+diJMKVK1cMScbevXvjTe/du7dRvHjxBOdxdnY2li5dGm/alClTDE9Pz8euZ8iQIYYkHjx48ODBgwcPHjx48EjwcenSpX/NLok6o/Oy+Pn5xTsLZLVadevWLaVLl04Wi8WOlb06QkNDlTVrVl26dEnu7u72Lgcmxr6Gl4V9DS8L+xpeFva1p2MYhsLCwuTl5fWv7RIVdNKnTy9HR0cFBwfHmx4cHKxMmTIlOE+mTJkS1V6SXF1d5erqGm+ah4dHYkrF/3N3d+cfDl4K9jW8LOxreFnY1/CysK8lXurUqf+zTaIGI3BxcVGRIkUUGBhom2a1WhUYGKhSpUolOE+pUqXitZekrVu3PrY9AAAAADyrRHdd8/X1VYsWLVS0aFEVL15cEydOVHh4uFq1aiVJat68ubJkySJ/f39JUvfu3fX+++9r3LhxqlGjhr799lv99NNPmjlz5vPdEgAAAAD4f4kOOg0bNtSNGzc0ePBgXb9+XYUKFdLmzZuVMWNGSdLFixfl4PC/E0WlS5fW0qVLNXDgQPXv319vvfWWvvvuOxUoUOD5bQUe4erqqiFDhjzSBRB43tjX8LKwr+FlYV/Dy8K+9mJZDOO/xmUDAAAAgFdLom8YCgAAAABJHUEHAAAAgOkQdAAAAACYDkEHAAAAgOkQdAAAAJ6Q1Wq1dwkAnhBBB0kCg/8BSIoSOqgNCwuzQyWwtz///FMXLlyQg4MDYQevvISOuw4fPqzz58/boZoXh6ADu/jnHwmLxWKnSvCqiPtSjo2N1YMHD+xcDV4XDg4O+vPPPzVx4kRJ0sqVK9W8eXPdvXvXvoXhpbp48aKyZ8+u999/X7///jthB6+8GzduSPr7eMxqters2bOqVauWwsPD7VzZ80XQwUtnGIbtprKzZs1Sjx49NHbsWP322292rgxJlWEYslgs2rRpk1q0aKGiRYtq4MCBWr9+vb1Lg8nFxMRo2rRpmjdvnlq0aKGGDRuqTp06Sp06tb1Lw0t05swZpU2bVu7u7qpbt65OnDhB2MEra+XKlSpdurSOHj0qBwcHOTg4KHXq1PLw8FD69OntXd5zRdDBS2W1Wm1nb/z8/NS/f38dP35cixcvVuPGjbV//347V4ikyGKxaN26dfrkk0/k7e0tX19f7dq1S3369NGxY8fsXR5MzMnJSUOGDFG2bNm0aNEiNWjQQC1btpT099lFvB4KFCigN954Q/nz51fp0qXVoEED/frrr4QdvJJSpEih3Llzq3Pnzjp69Kgk6fbt24qNjZWbm5udq3u+CDp4qeLO5Jw5c0ahoaHasmWLAgMDNWXKFOXOnVuffvopYQePuHnzpsaOHauRI0dq+PDhatKkiU6dOqXq1aurUKFC9i4PJhXXXdLFxUUeHh6qUqWKLl++LH9/f0mSo6MjYcfkrFarDMNQxowZ1b9/f/3xxx8qW7as3nrrLX3yySeEHbySqlevrl69esnT01Pt27fX0aNH5ejoqNDQUMXExNi7vOeKoIOXbuXKlapSpYoOHTqkN954Q5L03nvvqXfv3ipcuLCaNWtG2EE8bm5uioiIUI0aNXT+/HnlypVL9erV07hx4yRJ27ZtM90FlLCvuO6Shw8f1pUrV7RgwQItX75c7777rtauXRsv7Eh/h3GYx8WLF20hJq4XQoECBeTp6aksWbJo+PDhypo1a7ywQ+jFqyAuyBQoUEDt27eXl5eXOnfurN27dyt//vxauHCh1qxZo9WrV2v58uVasGCB9u7da+eqnx5BBy+dg4OD3n77bf3222+6c+eObXrRokXVp08fFS1aVFWqVNHJkyftVyTsLu7XdMMwdPfuXd2/f1979uzRBx98oA8//FDTpk2TJJ07d05z587VmTNn7FkuTCQu5KxZs0bVq1fXN998o7/++kseHh4aMGCAihUrpnXr1mnkyJGSpMGDB6tTp06KjIy0c+V4Hv7880/lypVLhQoVkr+/vxYsWCBJypcvnwoUKKD+/fvLx8dHX3zxhby9vdW4cWMdP37cFnqBpMzJyUnLli3T+++/r7x586pz587KmDGjPv/8c23fvl07duxQv379NGDAAI0YMUJjx45VunTp7F320zOAFyg2NjbB6Vu2bDFKlSplFCtWzDh16lS81/bs2WMMGjTIiImJeRklIomxWq2GYRjG/fv3DcMwbPvBgAEDDIvFYtSuXTte+/79+xs+Pj7GxYsXX26hMLVNmzYZyZIlM+bMmWPcuHEj3mvBwcHG559/buTMmdPImzevkTZtWmP//v12qhTP27Zt24x8+fIZLi4uRo8ePYzSpUsbFSpUMAICAoxjx44ZDRo0MLZt22YYhmH8+OOPRtmyZY2SJUsakZGRtu8vIKl5+G9rkyZNjHHjxtle++GHH4yGDRsaPj4+xsmTJw3D+N/xW1hY2Msv9jmyGAY3MMGLYbVabdfk7Ny5U5GRkYqJiVH16tUl/d3daMyYMQoLC9O8efP09ttvP7KM2NhYfiV7jRj//0v6Dz/8oDlz5igsLEzJkiXTlClT5OTkpD59+mjJkiUaO3asoqOj9ccff2jRokXavXu3ChYsaO/yYRJRUVFq3769PD09NXr0aIWHh+vixYtavHixsmfPrho1aihVqlTat2+fTp8+rWrVqilXrlz2LhvP6Pfff9fq1avl5+enTZs2adiwYXJzc1NAQIDGjRunEydO6ODBgwoNDVWrVq00ZcoUSdKBAwfk5eWlrFmz2nkLgH+3c+dO9enTR+nTp9fo0aOVP39+22s//PCDJk2apFu3bmns2LEqXbq0pP/9XX5VOdm7AJhXXMjp3bu3li5dKjc3N127dk3lypWTv7+/KleuLKvVqvHjx6tt27aaPn16vH90kgg5rxmLxaK1a9eqSZMm6tmzp3LlyqXJkyeraNGiOnDggL744gu9+eabmjZtmlKlSiVvb2/t3btXBQoUsHfpMBGLxaILFy4oIiJCwcHBGjRokM6cOaOrV6/q7t27OnHihCZOnKgqVaqoSpUq9i4Xz4HValVAQIAmT56sFi1aqFKlSoqNjVWvXr3UoUMHrVq1SpI0depULVmyRMWLF7fNW6JECXuVDSSKi4uLbt26paNHj9qu1YmOjpazs7M++OADOTk5adiwYRo0aJA2bdokV1fXVzrkSKLrGl6smTNnGhkyZDB++ukn4+LFi8apU6eMPHnyGGXLljXOnj1rGIZhbNiwwShatKjRoUMHO1cLe7t9+7ZRtmxZY/To0YZhGMbly5cNb29vo23btvHaBQcHG4bxv+5twLNIqLvRhg0bDA8PDyNlypTGRx99ZCxdutQwDMPw9/c3SpQowb5nQgcOHDBSpUplLFiwwDCMv79f1q9fb+TKlcuoUqWKrd3NmzftVSLwTKKiooz9+/cbOXPmNMqUKWNER0cbhmHY/msYhhEUFGSqruB0XcNzs27dOlWqVEkpUqSwTevevbuCg4P17bff2rqhhYSEqGjRoqpQoYLtIs/9+/erePHitrNAeD3Eff1YLBbFxsbqwYMHKlCggPbs2SMnJycVLlxYNWrU0IwZMyRJS5cu1ccffywXFxfb/K/8r02wq7h9aM+ePdq9e7du3LihypUr68MPP9TVq1d17tw5lSlTxtaue/fuunbtmhYuXGi6+01A6tq1q4KCgrR161ZlzpxZUVFR2rp1q3r16qUsWbIoMDBQ0t8jVzk50SkGSVfcd9bFixcVHh6uVKlS2Ua6PXjwoD7++GO99dZb2rZtmywWi+3MjtlwVInnwt/fX7NmzVLy5Mlt06xWq65evarQ0FBJf3dDe/Dgga3fe2BgoC5duiRJKlmyJPcieE388zOOuxnoF198IWdnZ+XOnVuLFy9WsWLFVKtWLU2ePFmSdP36da1YsUKbNm2KNy/wLCwWiwICAlSnTh3t3btXd+7cUY0aNeTn56d06dKpTJkykqTjx4+rf//+WrBggQYOHEjIMZGHv5OqV6+uBw8e6Oeff5b0d1efDz74QOPGjVNISIitmxohB0lZXMgJCAhQ+fLlVatWLb311ltq27at9uzZo+LFi2vVqlU6c+aMqlWrJqvVasqQIxF08Jz4+flpzZo1slgsOnr0qO7cuSMHBwc1a9ZMQUFBWrhwoSTZDg4Mw1CGDBnk7u4ebzmc0TG3uAEqjh8/rk2bNslisejYsWPq0KGDsmfPrtjYWOXKlUvDhw9XgQIFNG3aNNuX79dff61z586paNGidt4KmMnp06fl6+urkSNHat26dZo0aZLtINbV1VWS9PPPP2vcuHFav369du7cqXfeeceeJeM5uH79uo4dOyYp/t+d6tWrK2vWrPrqq69s0+KuXxg2bJgMw9DFixdfdrlAolgsFv34449q3ry5evbsqbVr12r27Nk6c+aMRo0apX379tnCzt69e1WvXj17l/zC0HUNz+zhkdHWr1+vli1basSIEWratKkcHR01YMAArV27Vn5+fmratKnu3r2rdu3a2X7J51f510NcyPn5559VtGhRTZs2TeXKldO6det09epVjR8/XpIUHBysBg0aKDIyUhUrVtRbb72lvXv3auXKldq5cyejq+G5OnjwoHr37q2dO3fqjz/+0Pvvvx+vu+SlS5eUNWtW/fTTT8qcObOyZMli54rxrEJDQ1W4cGE5ODioRIkS8vPzU9asWZUqVSpJ0pYtW9S5c2dNmTLF9mu3g4ODoqOjFRUVFa97NpDUxJ3NGThwoI4cORKvF0RgYKAGDRqkwoULa/LkyYqNjdXRo0fl4eFh2pEjCTp4Jg8PIR2nefPmOnTokHx9fdWyZUsFBwdr6tSpGj9+vDJkyKBkyZIpVapU2r9/v5ydnRNcBszl4TM5JUqUUM+ePTV8+HDlyZNHZ86cUd26dRUQEGBrf+XKFY0aNUr79++X1WpV9uzZNXToUEZXwzMzHhrC3MPDQ7GxsWratKmWLVumxo0bq0qVKpo6daocHR21c+dOjRkzRjNmzCDgmMSFCxf0888/69q1a3J0dNTYsWMVGxurt956SwMGDFChQoXk5OSkkiVLqly5cpo0aZIkrgdE0he3j8b9vR04cKB27typ7du3y8nJybb/zpo1Sz179tS5c+fk6elp56pfgpc9+gHM4+Gbga5YscLYvHmz7XmbNm2MnDlzGrNmzTIePHhgGIZhnDp1yli2bJmxceNG200gHx7pA+YUt5+cOnXKSJcundGwYUPba8ePHzeKFCli5MqVy9iyZUu8+WJiYozo6GgjIiLCiIyMfKk1w9x2795tpEiRwli4cKFx48YNo2bNmkby5MmNxo0bG4bxv1HY+vXrZ1SoUOGRG4bi1fTLL78YuXLlMurUqWMEBgYahvH398zkyZON2rVrG05OTka1atWMZcuWGQsWLDBSp05tHDt2zM5VA09u165dxr59+wzDMIzZs2cbLi4uxt69ex9pkzdvXuPy5cv2KPGlI+jgqTw8HGufPn2MnDlzGmPGjDGuX79um96yZUsjZ86cxsyZM41bt249soy4sAPzigs5R48eNZIlS2akTJnSyJ07txEUFGQbnvfXX3818uXLZ9SoUcP48ccfH5kXeJ4uXLhg+Pn5GSNGjLBNmzFjhpEvXz6jRYsWxokTJ4xDhw4ZvXv3Njw8PIxffvnFjtXieTl16pSRJk0ao1+/fsaVK1cSbLNq1Sqjffv2RvLkyQ1vb2/DYrEY48aN47sIr4wqVaoY+fLlsz1v0KCB4enpaezevdsIDQ01DMMwevXqZbzzzjsJHpeZEUEHz8Tf399Inz69sX///gRfb9u2rfH2228bEyZMMMLDw19ydUgKfv75Z8PR0dEYPny4YRiG8d577xne3t5GUFCQ7UzN8ePHjbx58xo1a9Y09uzZY89yYWKnTp0ySpUqZWTLls2YOnVqvNfGjh1rlC9f3nBwcDAKFixoFC5c2Dh69Kh9CsVzdf/+feOTTz4xunTpEm96VFSU7f5uccLDw41z584ZnTt3NkqXLm2cPn36ZZcLPLF/3gPs4sWLRr58+YxBgwYZhmEYd+7cMRo3bmy4ubkZBQsWNMqUKWOkSZPmtfpu4xodPBXDMHT79m01atRITZo0UcuWLXXhwgX9+uuvmj9/vm3UGicnJ9WrV0+urq5atmwZfZxfMxEREWratKl8fHz0xRdf2KaXKVNGV65c0fz581WqVCm5uLjoxIkTatq0qVKnTq0xY8Zwt3G8ED169NDChQtVrlw5LViwQKlTp7a9FhYWpl9//VWZM2dWihQplC5dOjtWiuclJiZGFStWVIMGDdS1a1dJfw84sHnzZs2dO1fp0qWTt7e3AgMDbX+joqOjFR0dHe+WCUBSFBgYqPDwcJUqVUoZMmTQqFGjtGPHDo0ZM8Y2QuSqVat06dIlGYah2rVrm3bggYQQdPDEEho0oGLFikqVKpU6dOigadOm6fbt2/Ly8tLmzZvVsGFDzZo1K968Bhd0vnYuXryoN998U5Li3ZAsobBz7NgxdezYUStXrlTWrFntWTZM4HHfN3379tWGDRvUsGFDffbZZ/Lw8Hj5xeGlCQ0NVYkSJVS2bFn16tVLAQEBWrBggQoUKKBy5copZcqU8vf3V+3atTVu3DgGyMEr48aNGypRooQuXbqkTp06qXLlyqpSpYqKFSum6tWra/To0fYu0e4IOngiD3/xr1+/Xu7u7nr//fe1aNEizZw5U0eOHFGPHj1UrVo1lS1bVl988YVOnjyphQsX2u5FwR+P18vjDjIfvqN4XNhZuHChihcvLldXV0VFRcnFxeVllwuTidv/Dhw4oD179sjFxUXZs2dXjRo1JEm9evVSUFCQ6tatq27dusnDw4MfYkxs+/btqlq1qrJkyaJbt25pzJgxqlSpknLlyqXo6GjVrFlTmTNn1vz58+1dKpAoAwYM0NSpU/Xll19q+vTpatKkiby8vNS6dWv98MMPqly5sq3t6/gdR9DBf3r4H0bfvn21Zs0a9ejRQy1btpSjo6NiYmIUHBysHDly2OapUKGC3nnnHX399df2KhtJ2MNhp0KFCjpy5Ii+//57lS5d+rX8IsbzFbcPrV69Wq1atVKhQoV0584dnTp1St26dbPds6lnz57at2+fypcvLz8/v3jd2GA+ly5dUkhIiLJly6b06dPbplutVjVq1Ehvv/22rYst30FIyn799Vfdvn1b7733niSpWLFiKlWqlHx9ffXJJ58oR44c2rx5swoXLqxFixbpjTfesHPF9sPP6/hPcV/4/v7+mjdvnubNm6eOHTsqefLkcnV1VYoUKZQjRw6FhYVp165dqlatmm7duqVx48bZuXIkVU5OToqJiZEk7dixQ6VLl7aN588BBhLLarXGe26xWHT27Fl169ZNX331lXbt2qWdO3dq8eLFmjFjhj7//HNJ0oQJE1SwYEEdOHBA0dHR9igdL1HWrFlVpEiReCEnKipKQ4YM0Z49e9S8eXNZLBa+g5BkPPzdFvf/ly9fVs+ePdW5c2fbjzajR49WcHCwwsLCtGPHDlWoUEFvvfWWfvnlF1uvmtcVZ3TwRG7evKmPPvpIbdu2VfPmzXXx4kWdPn1ay5Ytk5eXl4YPH67AwEAtWLBAt2/fVkBAgJydneP9co/X07+doWH/wLN6+Ga0V69eVdWqVSVJBw4cUPPmzRUYGBjv18ylS5eqbdu22rBhgypWrChJCgkJeT1unId4Fi9erEOHDmn58uX6/vvv9e6779q7JOARly9f1uXLl1WyZEmtWrVK9+/fV86cOXXkyBF9/vnnql+/vipWrKh9+/bp7bffVu/evRUTE6O7d+8qMjJSXl5e9t4Eu+IIA08kderUcnZ21vbt25UmTRrNnTtXISEhSpMmjVauXKn79+9r3Lhx8vT0VP78+eXg4MBB7GsmLtCcOXNGsbGxcnFxUY4cOeLdqfmf2D/wLOL2q19++UWFChXSsGHDbEEnefLk+uOPP/T777/rjTfesO2f5cuXV+bMmXXt2jXbcgg5r5/Tp09rzpw5SpMmjXbs2KG8efPauyTgEeHh4fL19dXNmzdVuXJlDRw4UAsWLFDp0qVVunRpVa5cWd26ddPGjRv1888/a/Xq1SpSpIgqVqzIqJH/j65reMQ/u4FIkrOzs2rXrq2zZ8/qk08+UZ48eeTv768NGzaoQ4cOunnzpiTJx8dHDg4OslqtHMS+ZiwWi1atWqWKFSuqQoUKatq0qSZNmiRJtn0CeF7iQs6xY8dUsmRJ9e/fX4MGDbK9nidPHn344YeaMmWKjhw5YjurmD59eqVNm5auaq+5t99+W8uXL9e8efMIOUiyUqRIoU6dOun27dsaOHCgBg4cqGbNmskwDEVHRytPnjz69ttvVbduXRUuXFh3797V6NGjFRUVZe/Skwy6riGeh395nz9/vo4dO6bY2FiVLVtWDRo00L1793T9+vV4Y7CXL19eRYoU4Zqc11TcL+XXr19X+fLl1adPH3l6emrXrl1asWKF2rZtq4EDB0pi5D08X6dPn1bBggU1ePBg9e/f3zZ9w4YNKl++vAIDAzV+/HilTp1a7du3V/bs2bVw4ULNmzdPBw8elLe3t/2KB4B/8fDf1po1a+r+/fvKkSOH+vbtqzJlysgwDMXGxsrJyUlWq1WxsbH64osv1KRJE8L7Qwg6SFCfPn20aNEiNWrUSA8ePNC3336r5s2b20ZRCw8P18mTJzVo0CBdv35dhw8f5gzOa2zfvn0KCAhQeHi4Jk2aJCcnJ127dk1z587V9OnT1aFDB8IOnqsHDx6oVatW2rp1q1auXKkKFSpIkkaMGKHp06dr69atypMnj9asWaNly5YpICBAuXPnVkxMjJYvX871GABeCdHR0bp7966OHTumMWPGyGKxaODAgSpTpoytzf3795UsWTI7Vpl0cWSKR2zbtk2rVq3SmjVrVLJkSa1YsUILFy603WFX+vtOvIsXL5aTk5N++uknOTk5KTY2Vo6OjnasHPYQERGhpUuXasmSJfLx8bEF3syZM6t169aSpDlz5igiIkIjR44k5OC5cHNzU/v27RUVFaUvv/xSKVOm1P79+zV+/HgtWbJEefLkkSTVq1dPNWvW1IULFxQbG6t06dIpQ4YMdq4eABIWdybn6NGjtiHRmzRpYrsfzpgxY+Tv7y8/Pz+VKVNGw4cPV5o0adSxY0eOwRLAGR088gv74sWLNW3aNO3Zs0cBAQFq2bKlxowZow4dOujevXs6fvy4SpYsqSNHjujdd99l4IHX1MOjqR0/flyzZ8/WjBkzNGnSJLVv397W7vr16/rmm2+0du1aBQUFKV26dAzfiudm165dGj9+vE6dOqU///xTQUFBKlmypOL+tFksFu7NBOCVsnr1anXr1k05c+ZUcHCwYmNjNXz4cDVu3FgbN27U9OnT9fvvvytPnjxav369fvrpJxUuXNjeZSdJHJki3jU5hQsXlru7u7y9vbV8+XK1bdtWY8eOVYcOHSRJP/74ozZs2KBcuXKpSJEiksTAA6+ZuIPG+/fvy9nZWc7OzvLx8VGPHj0UExOj8ePHy9HRUW3atJEkZcqUSZ999pl8fX0ZBQbPTdx+WK5cOTk4OGjUqFFKkSKFwsPDJf0v4MT9PwC8Cg4dOqSOHTtq7NixatGihW7cuKGMGTPq+vXrkqQaNWrIw8NDu3bt0u+//64TJ04oX758dq466eKMzmvs4TM5Y8aM0fDhw3Xo0CHdu3dPFStWVGhoqL755ht16dJF0t99QD/66CNlzpxZc+bM4eDhNRR3cLlx40Z9/fXXCgsLU4oUKTRs2DC99957+vPPPzVmzBht27ZNffv2VatWrexdMkzs4TM1u3fv1rhx4xQaGqrevXvrww8/fKQNACQVj7tedfny5Vq0aJE2bNig3377TdWrV1elSpU0a9YsSX9fI50iRQpJ4pKBJ0Bn+ddY3D+wkydP6v79+5o7d65y586twoULa8GCBZKkCxcuaP369QoMDFTt2rV19epVzZw5M96vpXh9xIWcevXqqUiRIqpXr56cnJxUv359zZkzR9myZdNnn32matWqqW/fvlq8eLG9S4aJPfw9VLZsWfn6+srd3V0TJkzQ2rVrbW0AIKlxcHDQmTNntH79esXGxtqmnz59WrGxsYqMjFTVqlVVpUoVzZgxQ5K0YsUKjRkzRjExMZJEyHkCBJ3X3I8//igfHx/5+/vH+4dWp04dLV68WOvXr1fbtm01YMAAJU+ePN7AAxxAmN+NGzfiPb9//74mTZqk7t27y9/fX3369NHmzZv10UcfqX///vrpp5+UO3dutW7dWq1atVKpUqXsVDnM7OEfWR4OO+XKldPnn3+umJgYzZ0719aNDQCSolmzZqlOnTpau3at7Rjsk08+0fnz5+Xh4aEaNWpoxowZtuOtvXv36vjx47p//749y36lcGHFa+afp0rLlCmjcePGqVevXjp27Jjq1asnZ2dnSVKTJk1UpUoVhYeHy9XVVZkyZZLFYmHggdfEkCFDFBERoREjRsjFxUXS379A3b59W5kyZZIkRUZGytXVVVOnTtVvv/2mL7/8UmvXrtU777yjvHnz2vYl4GnFdT07f/68bt26pXfeeeeR/erhAQfKlCmjESNGKGvWrLbuHQCQFI0ePVoPHjxQs2bNtGDBAtWrV09eXl6qU6eOVq9erRw5ckiSLl26pBkzZmjx4sXatWuXUqVKZefKXx0crb5GDMOwhZxFixbJx8dHhQoVUs+ePfXgwQMNHDhQ2bJlsw08IEkZMmSINxQrAw+8PvLnz698+fLJxcVFERERSp48uVxdXZU2bVpt2LBBvXr1kqurqy3sFC1aVMePH7fNT8jB82CxWBQQEKDOnTvLwcFBqVOnlr+/v6pUqRIvyDwcdjiTCCCp+ecPzXE/Gk+aNEmxsbFq3ry55s+frwYNGqhjx46KiIjQ2LFjNXbsWGXOnFn37t3T1q1bGXggkRiM4DXx8D+wuBE8ateureHDh6tAgQKSpOHDh2vo0KGaNm2a2rVrZ89ykYRs375dAQEB6tSpk/Lnz6+dO3eqbdu2qlChgmbOnGlr17JlS92/f992fyW6NuJZGYaha9euqVatWmrVqpXKlSunYcOG6cSJE+rTp48aNmyolClT2rtMAPhXcT/C/PHHH1q5cqUaNGggd3d3pU+f3tamY8eOmj9/vhYsWKCGDRsqLCxMwcHB2r59u95++23lypVLWbJkseNWvJr4af41ERdy/Pz8dP/+feXNm1fff/+9wsLC9M033yhfvny2O9d37dpV9+7dU8+ePe1ZMpKIy5cva+HChXJyclL37t1VpkwZ9enTR1999ZXee+89lStXTpcvX9aaNWu0f/9+zuTgmcUdFBiGoTRp0qhs2bJq1aqVUqRIodWrV6tly5YaPXq0JBF2ACR5FotFt2/fVtWqVXXu3DktW7ZMTk5OqlKliooVK6b69etr+vTpypQpk1q0aCFHR0fVqlVLuXLlUq5cuexd/iuNwQheI19//bVmzpypRo0aafny5QoMDNTJkyfVuXNnnTx5UpI0cOBA9ejRQwEBAYyq9pqK+9wvXbokwzDUvHlzzZgxQ6tWrdK4ceN07do1tWnTRosXL1amTJl09OhRRUdHa//+/bazg8CziBvdr2HDhipfvryOHj1qG2VI+vueXyVLltSECRO0YMECBh0AkOTFxsaqZcuW8vb2loeHhwYMGKAdO3aoR48eypUrl5o2bapy5cqpRIkS6t27t9auXauoqCh7l/3Ko+vaa6RVq1ayWq22oaMl6fz58ypRooSKFCmi0aNHy8fHR9L/urpxD4rXS9znvX79eo0ZM0bNmjWzdWNcunSp+vTpo7p168rX19d2kaQkBqjAc7V//36VKVNGrVu31okTJ3Tq1Cl17txZn3/+udKkSWNr99FHH+ny5cvaunWrUqdObceKAeC/BQcHa9GiRRoxYoTGjBmjtm3b6s6dO5o2bZpOnTqlbdu2KVOmTDp27Jjy58+vffv2ccb6GXFk8hqwWq2yWCy6efNmvOmRkZHKnj27Bg0apO7du8vZ2VlTpkxR1qxZJXGjvddJ3GdtsVi0Zs0aNWnSRKNGjVLZsmVtbZo0aaLY2Fj169dPTk5Oatu2re0MDiEHz8vp06e1Y8cOjR49Wr6+vpIkX19fbd26VcmSJVO3bt1soSYgIEBXr14l5AB4JWTMmFEtWrRQTEyMfH19FRISov79+8vPz0/S399/N27c0JIlS9S1a1dCznNA1zUTslqt8Z47ODjIYrGoTZs22r59u+bNmydJcnV1lSR5eHioTZs22r9/vwYPHhxvHpjbiRMn4t0T6fLlyxo2bJjGjx+v7t27K1euXLp//742btyov/76S82aNdOYMWNsw1xGR0fbeQtgJufOnVOHDh00adIk2/eTJI0fP15lypTRd999pylTpuj27du217y8vOxRKgA8Vlx32oQ6TWXIkMF2f8LRo0dr5MiRttdy5cqlMmXKaNq0acqfP/9Lq9fM+BnWZB4eXW39+vU6f/68nJ2dVb58edWtW1ft27fXl19+qejoaLVu3Vq3bt3S8uXLVbduXdWsWVNNmzZVz5499c4779h5S/CiTZ48WatXr9batWvl7u4u6e+zfHfv3lX+/PlltVo1evRobdy4USdOnFDKlCm1c+dONWnSRM7OzipUqBADD+C5evPNN1WxYkX9+eefWrt2rVq2bGkbQnr8+PHq3bu35syZIxcXF/Xq1YsfYwAkOXPnztWxY8fk5+enzJkzJ9g7Jn369GrTpo0kaeTIkXJyclKfPn3k6OgoiR41zxPX6JhUnz59tGrVKmXLlk0eHh5at26d9u3bp0yZMmnWrFkaM2aMMmXKJMMwlDp1ah09elQ7d+5U+/bttWvXLn4lfQ3cu3dP169fV65cuRQSEqK0adMqOjpajRo10m+//aawsDAVL15cJUuWVLt27VSqVCnVqFFDEyZMsHfpMImE/pjHxMRowoQJWrZsmUqXLq2RI0fagrgkDRgwQG3btlX27NlfdrkA8J/69OmjzZs3q1atWurWrZvtWCuh4HLz5k3NmzdPffv21fjx49WjR4+XX7DJcUbHhJYuXapFixZp7dq1Kl68uBYuXKi1a9fq7NmzKl68uIYOHarGjRtr//79Sp06terUqSNHR0dt2rRJnp6ecnNzs/cm4AWLjY1VypQplStXLh04cEBdu3aVn5+fPvroI40cOVI7d+5UbGysGjdurHTp0slisShfvnzy9va2d+kwibg//Hv37lVQUJBiYmLk4+OjevXqydfXV1arVWvWrJGfn5/8/f1tYWfEiBF2rhwAHm/06NFKlSqV1qxZI6vVqu7duz827KRPn14tWrSQi4uLqlWrZqeKzY0zOiYS121t2LBh+uuvvzRp0iQFBASoRYsWGj9+vNq1a6ewsDDduXPHNuCA9PfFb19//bWWLl2qXbt20W3tNXP37l1VqlRJLi4uGjBggKpVq2Y7fR73+rhx4zR9+nT9+OOPyp07tx2rhZnE3ROnWLFiun//vg4cOKAOHTpo3LhxcnV11VdffaXvv/9eOXLk0OTJk5UqVSp7lwwAj/Xw5QPDhg3T2rVrVbVq1X8NO/+cD88XZ3RecVarVYZhyNHR0faPJDo6WrGxsVqzZo1atGihMWPG2IYIXrNmjc6cOSM/Pz8lT55cUVFROnr0qMLCwrR7927b8NIwr7gv2p9++kmGYahYsWLasWOHateurS+++EIxMTGqWbOmHB0dtWHDBq1cuVLbt2/Xli1bCDl4bs6fPy9fX1+NGTNGHTt2lNVq1Q8//KD69evLwcFBU6ZMUe/evW0BKDw8nKADIEl7+LYcQ4YMkdVq1fr16yXpX8MOIefF4Z19ha1fv15t2rRR9erV41038dZbbykwMFDNmjWTv7+/OnbsKOnvX+a//fZbxcTEKHny5JIkFxcXffTRR5o+fToh5zUQ9wUbEBCgOnXqaPr06bp69apSpUqldevWKXny5PL399fGjRslSZkyZVKhQoW0Y8cOvfvuu3auHq+qWbNmad++ffFGIAoPD5ezs7Pef/9927Rq1appxYoVmj59ur7//ns5Oztr6NChWrZsmTJlymSP0gHgP8V9tz148EAPHjywTR82bJhq1qypLVu26Ouvv9b169dlsVi4IftLxBmdV9TMmTPVr18/1a1b1zYC0f3799W/f3+1aNFCW7du1ZUrV5Q+fXqdPXtWkZGR+vzzzxUSEqIvv/xS0v8Oel1cXOTi4mLnLcLLYLFYtGPHDjVr1kxTpkxRrVq1lC5dOlmtVlvYqV27tr766ivFxsaqbt26evfdd+N1ZQMSwzAMDRs2TKlSpdKiRYtUpEgR2z2bzp07p0uXLilv3rwyDEOGYah8+fLKly+fzp07J0lydHRUunTp7LwVAJCwuGOpTZs2af78+Tpx4oQ++eQTlStXTpUqVdIXX3whSdqwYYMcHR3VuXNnBnx6ibhG5xU0e/Zsde3aVcuWLVO9evUUHBysGjVq6M6dO/FGTKtVq5bOnz+v33//XUWKFJGrq6u2bt0qZ2dnxcbGcvD6mvLz81NwcLDmzp1r2w9iY2Nt904KCwtT2bJllT59en333XfcsAxPLe4AICoqSiVKlFBMTIzmzJmjwoULy8nJSU2bNtWFCxc0YcIEFS9eXNLf3XFLlSqlli1bqlOnTnbeAgD4b2vXrlWTJk3UvXt3pUuXThs3blRsbKx69OihevXqSfr77M78+fPVqlUrDRgwgGOwl4Sg84r59ddf5ePjo1atWmn27Nm26YUKFVJwcLB2796t6Oho5c2bV5J08eJF/frrr3rjjTeUL18+OTg4KCYmhjvZv8aqV68uR0dHW7/hh/sL//nnn8qWLZvCwsJ069YtZcuWzZ6lwgQiIyPl6uqqe/fuqVChQnrzzTfl7++vEiVKaMeOHRo3bpxCQkI0YMAAeXp6au3atZo9e7YOHjyoHDly2Lt8APhXv/32m+rVq6eePXuqffv2ioiIULZs2ZQ2bVqlT59effr0UZ06dSRJ/v7+atSoEcPjv0Rco/OKSZEihXx9fbVmzRotXrxYklS/fn1duXJFpUuXVu/evVWtWjXVr19fkydPVkREhKpVq6YCBQrIwcFBVquVkPMas1qtKlq0qEJDQ3XmzBlJf3dns1qtunr1qvr166ejR48qVapUhBw8M8Mw5OrqqhUrVqh3797KmjWrgoKC1KlTJx09elQVKlRQ7969lT9/fn388cdq3bq11q9fr61btxJyACRZcecI7t27p2TJkqlmzZpq2LChLl26JB8fHzVo0EDz5s3TpUuX5O/vr6VLl0r6u0cFIefl4ozOK+jq1auaNGmSpk6dqjfffFPJkyfXkiVL9NZbb+nWrVv6888/NW7cOO3Zs0d58uTR999/b++SYQdxZ2quXbumqKgoJUuWTJ6enjp27JjKli2rZs2aqVu3bsqbN6+io6M1cuRILV68WIGBgXrzzTftXT5MYvfu3apataq++eYbFShQQNHR0Wrbtq0cHR21ePFi2yAX586dk5OTk1KkSME1OQCSvICAAP3444/q16+fDMNQxowZ1aZNG0VFRWnatGlKmTKl6tevrwMHDqho0aJauHChUqVKleDw0nhx+Gn/FeTl5aXPPvtMLi4uGj9+vAYMGKC33npLkpQqVSq9++67mj9/viIiIri+4jUVF3K+++47DRgwQBaLRbdv31azZs3k5+en7777Ts2aNdPx48dlGIbSpk2r3bt3a/v27YQcPFeHDh1SwYIF1bx5czk7O0uSDh48qGLFiqlt27aaOnWqihQpwhkcAEle3N/W8+fPq3Pnzho+fLg8PT0l/d1j4rffflOFChVsx15p06ZVr1691LhxY9tNj/Fy0XXtFeXl5aV27drps88+k7+/v+bMmSNJcnZ2tl2D4+7uLgcHB8XGxtq5WrxsFovFNsR4hw4d9NNPP6lTp04aPXq0Nm/erEqVKmn9+vVq0qSJcuTIoZIlS2r//v0MIY3nJq6zwN27d3Xnzh1byLl//77c3d01adIkHT16VO3bt9cvv/xiz1IB4IlYLBYFBQVp+/bt+uSTT9S6dWtJf4eciIgIZc2aVb/++qsWLVokPz8/bd68WY0aNWJ4fDui61oS9rg76D7s6tWrmjx5sqZMmaIJEybY/tHh9RW333Tp0kVWq1XTpk3T5cuXVaFCBVWqVEnTp0+3d4l4jZw8eVKlSpWSn5+f/Pz8bNN37NihiRMn6tq1a/r22285owPgldCkSRN9++23KlSokIKCguKdqdm4caMmTJigc+fOydXVVUuWLFHhwoXtWC3oupZEWa1W251y79+/r2TJkiUYfLy8vNS1a1dZLBa1bdtWnp6eqlmzpj1Khp3E7SsP7zOSdOPGDdWpU0f3799XiRIlVLNmTU2bNk2StGLFCmXIkEEVKlSwV9kwmbjvp2PHjunkyZPKkyePvL29lT9/fvXt21ezZ8+W1WrVgAEDdO/ePW3btk3Zs2fX6tWrGSAFQJIV990WHh6uFClSaMmSJcqQIYOmTZum7du3q27dura2NWrUUOHChWUYhpydnZUhQwb7FQ5JnNFJkh4+YB09erR+/vlnTZo06V8v0L106ZI2bdqkNm3acNDwmojbT+K+hO/evavUqVPbXv/ss8+0detWhYeHq27duho3bpycnZ0VHR2t5s2bK3fu3Bo0aBD7C56bgIAAtWrVShkyZNDt27fVpEkT9ezZU56enpo8ebJGjhypdOnSKWXKlLp8+bK2b99Od0kASd6OHTs0Y8YMDRw4UAUKFJAkffrpp9qwYYMCAgJUsWJFW9sn6Y2Dl4egk4T17dtXixYt0oABA1S1alXlypXriebjPjnmFxdyLly4oMWLF2vLli26dOmS3nvvPVWvXl1NmzbVn3/+qcaNG+vSpUs6ffq0kidPrtjYWA0ePFiLFi1SYGCgbRAL4GnF/VG/dOmSunTpolq1aqlp06aaP3++Fi9erBw5cmjYsGHKmTOn/vjjD61bt06pU6dWuXLlnvg7DQDsac+ePapYsaIaNGig/v372+5V2LRpU23atEkBAQH0kEiiCDpJyMNncrZv366WLVtq8eLFKleunJ0rQ1ISt58cP35c9evXV9GiRZUqVSq9+eabmjNnjiIjI9WmTRt98cUXWr16tYYOHap79+6pWLFiioiI0MGDB7VlyxZ+Scdzc+jQIS1cuFBXrlzRzJkzlT59eknSwoULNX36dGXPnl19+/bVO++8Y+dKAeDf/fOMTNzzffv2qXbt2qpSpYoGDRpkCzvNmzfX4sWLFRQUxPFaEsTP/klAv379NGrUqHjXV/z5559Knz69SpQoYZv2z398/7wmA+YX95n//PPPKlOmjDp37iw/Pz95eHhIkj755BMNHz5c06dPV7p06dS9e3f5+Pho7ty5+uuvv1SoUCFNnDiRX9LxXG3dulXLly+Xk5OT7ty5Yws6zZs3lyTNnTtXAwcO1KhRo5QvXz57lgoA/yruOOu3335T2rRp5enpKcMwVKpUKa1du1a1atVSbGyshg0bpjx58mjhwoVydXVVxowZ7Vw5EmTAroKCgowPP/zQiI6Ojjd9/vz5RrZs2Yzz58/bplmtViMmJsZYtGiRcf369ZdcKZKKM2fOGG5ubsbAgQMNwzCMmJgYwzAM2z509uxZo1q1akb+/PmN33//3W514vUyefJkI0eOHEanTp2MCxcuxHtt5syZRrVq1YwrV67YqToAeDJWq9UICQkxLBaL0bZtWyM4ONg23TAMY8+ePYaTk5PRtm1b4+jRo3asFE+C0wF2VqpUKW3cuFFOTk5auXKlbXq2bNkUGRmpb7/9Vn/99Zekv39liImJ0axZszR//nw7VQx7slqtmjt3rlKlSmUbzcXR0VGxsbFycnKSYRjKmTOn+vfvr1OnTunEiRPx5jfoqYpnFLcPRURE6N69e7bpXbp0Ufv27bV//359/fXXunjxou21du3a6dtvv5WXl9dLrxcAnpTx/z1nMmTIoOXLl2vhwoX68ssvFRISYjvTU7p0aZUoUUJz5szRtGnTFBUVZeeq8W/oumZHsbGxcnFxkST9/vvvatmypRYsWKANGzaofPnyat++vUaOHKnbt2+rTJkycnd314gRIxQWFqZevXrZuXrYg4ODg7p27aqIiAgtXbpUERER6tevnxwdHWW1Wm1fxEWKFFG6dOl07dq1ePMzEgyeRdxBwMaNGzV79mydOHFCH330kd5//31Vr15dffv2ldVq1cqVK+Xk5KTOnTvL29tbkuKNCAgASUncd9v9+/fl5uamyMhIffLJJ3JyclL9+vUlSQMHDrR1TytVqpS6deumd99913Ych6SJoGMnN2/etPVj3759uypWrKiFCxfKz89PtWrV0vr16zVs2DC5u7vru+++09dff618+fIpXbp0OnDggJycnBQbGytHR0c7bwleNi8vL/Xr108jRozQd999J4vFor59+8rBwcG2Txw9elReXl4qWbKkvcuFiVgsFq1bt06NGzeWr6+vqlWrplWrVmnXrl26c+eOmjRpIj8/Pzk6OmratGlycXHR0KFDGQUSQJIVF3I2b96smTNn6u7du3J2dtaECRNUr149rV27VvXr11dYWJiKFSumy5cva9myZRowYIDt+lgkXYy6ZgcbN27UnDlzNG7cOH399deaNGmSbt26JVdXV33//ff6/PPPlT9/fq1fv16SFBISYvuHly1bNlsXNg4eXm/Xr1/XiBEjdOjQIdWrV099+/a1vebr66uTJ09q2bJlSps2rR2rhJmcPn1aH3/8sbp27aoOHTro/v37ypYtm9KmTSsPDw/17NlTDRs2lCRNmDBBdevWVfbs2e1cNQD8u3Xr1qlRo0bq16+f3n77bc2YMUOHDh3SkSNH9NZbb2nr1q0aPHiwQkND5eDgoIULFzJy6SuCoGMH+/bt0yeffCJ3d3cFBwdr586dthtQPXjwQJs2bdLnn38uHx8frV279pH5GW0NcRIKO8OHD9f48eO1a9cu234FJIbxmBveXbx4UVOnTlWfPn0UERGh999/X9WqVVObNm308ccfy8PDQ126dFGbNm3sUDUA/Ld/fr/du3dPdevWVZUqVdS3b19dvnxZ5cqVU5UqVTRjxgxb++DgYLm4uMgwDH5AfIUQdF4iwzBkGIYcHBzUoUMHzZkzR5UrV9aECRNs47FLUmRkpDZu3Ki+ffsqc+bM2rVrlx2rRlIXF3Z+/vlnRUZG6pdfftGePXtUuHBhe5eGV1DcDyl//fWXgoODFRsbKx8fH0l/X1d469YtZciQQR06dNC9e/c0ffp0pUqVSk2aNNHu3btVuHBhLVy4UO7u7lwTBiDJiAss4eHhSpEihe277ubNmypevLi2bNmiNGnSqFChQqpRo4ZmzJgh6e/7gdWtW1fu7u523gI8DU4LvCRxF4rHnYn54IMPtGDBAv3xxx8aOnSofvrpJ1tbV1dXVa9eXV988YXSpUsnq9Vqr7LxCsiUKZMGDBigXLly6datW9q3bx8hB08l7g//iRMn9OGHH6pGjRqqVauW2rdvL+nvEf7iRvs7ffq0MmfOrFSpUkmSUqVKpV69emnmzJlKnTo1IQdAkmKxWBQSEiJvb2+tWLFCDg4OMgxD6dOnl4+Pj5YtW6YiRYqoVq1amjx5siTpxo0b+u677/T999/buXo8LS7yeAke7mr2zTff6M6dO+rZs6dSpkwpb29vNW/eXGPGjFHfvn1tB6hbtmxR48aN1bhx40eWAfxTpkyZ9NVXX8lqtXLTMjyVh29G+95776ljx46qWbOmVq1apVmzZundd99Vp06dFBsbq8jISL355ps6ffq0Zs6cqT/++EPr16/X4MGD2f8AJFkODg6qXbu2mjVrJldXV9WpU0fR0dHKmTOnxo0bp9KlS2vatGm29uPHj9fvv/+u0qVL27FqPAu6rr1gD/cF7d27t5YuXapBgwbpgw8+UI4cOSRJu3fvVuvWreXj46PatWtr9erV2rt3r27cuEG4AfDSnD17Vj4+Pvr888/15ZdfSpLOnz+vPHnyqFu3bho7dqyt7Q8//KAJEybozJkzcnNz06JFi7g4F0CSktD1hiEhIRoxYoS++eYbrV69WvXq1dPt27fVuHFj3bhxQ2XKlFHu3Ll1+PBhBQQEKCgoSIUKFbLPBuCZcUbnBXnw4IHc3Nxs/8DmzZunxYsXa926dSpWrJikv/8BhoWFqWzZslqyZIk+//xzTZkyRe7u7rp+/brttCpdQAC8aA/fjDZdunS26d9++62io6N15swZTZw4UWnTplWDBg30wQcfqEKFCrp165YcHR1tw+UDQFIQd5Y6PDxcsbGxtmtsPD091a9fP1mtVtWvX18rV65U/fr1tWTJEo0aNUqHDh3S3r17lTt3bv34448M6vOK44zOC9C4cWM1atRIderUsQWVHj166Pbt21qwYIF+/fVX7d692zZe+6hRo/Txxx8rJCREUVFR8vLykoODA0NIA3iprl69qtGjR2v//v1q0aKFwsLCNGrUKHXp0kWFChXSkiVLdOnSJV27dk1v/1979xtaZf3Gcfy9sx33p6UyaTlJSPP/M5lOSBAsUZTNbHFaGaJORCQ1ycSpE5FqZA+EQhoViWZNTdGciVgUrpjLmU5xuKjsgeLc1NkxZKD7c3oQOy1/1c/R6pzO3q8ng3PDuG4OG/eH6/5e1+jRrFy5koKCgliXLUl/6Pvvv+fpp58mMzOTxYsXM3jwYKZPnw78Ovhp1apVvPXWW+zZs4dQKER7ezuBQIC2tjaSk5N9BksAfoP/gGHDhjFz5kwA2tra6NevH0OHDmXXrl289NJLfPHFFwwbNoz8/Hyam5tZtGgRU6dOJTs7O/o7Ojs7/QOT9K/qvoz2jTfe4MKFCxw9epTHHnsMgCeeeIKUlBS2bt3K6dOneeSRR2JcsST9sc7OTrZv387Zs2dJS0sjHA7T2tpKVlYWeXl5FBcXs3DhQgYNGkRRURH9+/dnxowZRCIRUlNTY12+eolP0r2oq01aVlYGQHl5OZFIhOLiYgoLCwmHw1RWVrJo0SKmT5/OmDFj+PLLL2loaPifyWqezZEUC4MHD6a0tJRAIMCxY8eoq6uLBp2u/1PLli2z4ywprgUCAZYvX05raysXLlxgxIgRPP/883z44Yd89dVXzJo1i6ysLIYPH052djYzZ87k2LFjTJkyJdalqxf56lov6npNretnfn4+DQ0NbNy4kWeeeYZ+/fpx69YtMjMzAWhvb6egoICUlBQqKys9iyMpbvzRMlrAgCPpP6WxsZGysjJqa2tZuHAhS5cuBeDEiRM0NjbyzjvvcPnyZerr66mvr2fcuHExrli9yaDTS7oPDbh06RJDhw4FYN68edTW1lJSUkIoFCIzM5Nbt27x6aefsnXrVm7cuMHJkycJBoOOkJYUV7rCTl1dHY8//jibNm2KdUmS1GNXrlyhrKyMEydOMGfOHNatWxe91tbWRmdnJzdv3vzdEQIlBp+qe0HXMlCAiooKli1bRnV1NQA7d+4kNzeXzZs3s3fvXlpbW2lpaeHcuXOMHDmSb775hmAwGD0AJ0nxomsZ7ciRIzl+/DgtLS2xLkmSeiwnJ4f169czadIkKisree2116LXkpKSSE1NNeQkKDs6f1P3Lkx1dTVvv/02hw8fZtq0aaxatYq8vDwA5s6dy5kzZygpKeHZZ5/lzp07ZGRkkJSUREdHB8nJybG8DUn6U83NzQAuA5X0n2aXuu+xhfA3dYWcF198kfnz5/PAAw8wa9Ysjhw5wpYtW6KdnYqKCiZMmMCKFSv47LPPuO+++6LneQw5kuLZgw8+aMiR9J9nl7rvsaPTC6qrqyksLOTAgQM8+uijAOzdu5dXXnmFUaNGsXr16mhnZ9OmTZSWlhpuJEmSYsAudd/h6JxekJKSQiAQ+N3c9VAoREdHB8899xzJycksX76cyZMns3HjRgBfV5MkSYoBA07f4atrPdTVALu7Edbe3s7ly5eBXyd4ABQVFTFmzBjq6+t5//33o9cBQ44kSZL0DzLo9ED36Wrt7e3RzydNmsTs2bNZsGABdXV1BINBAFpaWpgwYQILFixgz549nDp1KiZ1S5IkSX2NZ3TuUffpam+++SZVVVVEIhEefvhhtmzZwp07d5g7dy5Hjhxh7dq19O/fn8rKStra2qiqqiI3N5e8vDzKy8tjfCeSJElS4rOjc4+6Qs7atWt5+eWXGTVqFFlZWezbt4+JEycSDofZt28fL7zwAocPH+a9994jIyODo0ePApCamsro0aNjeQuSJElSn2FHpwfOnz9Pfn4+5eXlzJgxA4Aff/yRwsJC0tPTqampASAcDpOWlkZaWhoAGzZsYNu2bVRVVTFixIiY1S9JkiT1FXZ0eiAcDnPz5k3Gjh0L/DqQYPjw4ezYsYOLFy9SUVEBwP33309aWhrfffcdS5Ys4d133+WTTz4x5EiSJEn/EoNOD4wdO5b09HT2798PEB1M8NBDD5Gens7PP/8M/DZRLTs7m1AoxPHjxxk/fnxsipYkSZL6IPfo/IXuAwgikQipqakUFBRw6NAhcnJyKCoqAiAjI4OBAwdGp61FIhGSkpIYOHAg06ZNi1n9kiRJUl/lGZ27fP7559TU1FBaWgr8PuwANDQ0sH79ei5evMj48ePJzc3lo48+4vr169TV1bkfR5IkSYoDBp1ubt++zYoVK6ipqWHevHmsXr0a+C3sdHVqfvjhBz7++GM++OADBgwYQE5ODjt37iQYDNLR0WHYkSRJkmLMoHOXxsZGXn/9db7++muefPJJ1qxZA/y2LLT7wtCuQNP9s5QU3waUJEmSYs1hBHcZMmQIJSUlTJw4kQMHDrB582aAaEcHoLm5mfnz57N79+5oyIlEIoYcSZIkKU7Y0fkTTU1NvPrqq5w8eZI5c+ZQUlICwJUrVwiFQly9epXz588bbiRJkqQ4ZND5C93DzlNPPUVxcTGhUIjm5mbOnDnjmRxJkiQpThl0/o+mpibKysqora3l22+/ZciQIZw9e5ZgMOiZHEmSJClOGXTuQVNTE2vWrOHatWscPHjQkCNJkiTFOYPOPfrpp58YMGAAgUDAkCNJkiTFOYNOD929QFSSJElS/DHoSJIkSUo4tiYkSZIkJRyDjiRJkqSEY9CRJEmSlHAMOpIkSZISjkFHkiRJUsIx6EiSJElKOAYdSZIkSQnHoCNJkiQp4Rh0JEmSJCUcg44kSZKkhPMLolxXSj/aCqoAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "results_df.plot(kind='bar', figsize=(10,6))\n",
        "plt.title(\"Model Performance Comparison\")\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}