{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#Perform a practical on Random Forest algorithm"
      ],
      "metadata": {
        "id": "l3JGIwdwz1ik"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4fea8ec7",
        "outputId": "2609916f-efcd-4d0c-de20-d70441fa9233"
      },
      "source": [
        "# Import necessary libraries\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# Load a sample dataset (e.g., Iris dataset)\n",
        "# You can replace this with your own dataset\n",
        "from sklearn.datasets import load_iris\n",
        "iris = load_iris()\n",
        "X, y = iris.data, iris.target\n",
        "\n",
        "# Split the dataset into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Initialize the Random Forest Classifier\n",
        "# You can adjust parameters like n_estimators, max_depth, etc.\n",
        "rf_model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "\n",
        "# Train the model\n",
        "rf_model.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_pred = rf_model.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(f\"Accuracy: {accuracy:.2f}\")\n",
        "\n",
        "# You can further explore other metrics like precision, recall, F1-score, etc.\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "print(classification_report(y_test, y_pred))\n",
        "print(confusion_matrix(y_test, y_pred))"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 1.00\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        10\n",
            "           1       1.00      1.00      1.00         9\n",
            "           2       1.00      1.00      1.00        11\n",
            "\n",
            "    accuracy                           1.00        30\n",
            "   macro avg       1.00      1.00      1.00        30\n",
            "weighted avg       1.00      1.00      1.00        30\n",
            "\n",
            "[[10  0  0]\n",
            " [ 0  9  0]\n",
            " [ 0  0 11]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "a82552c2",
        "outputId": "ab3eeac9-441d-477b-c615-dcc4eb8454cd"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Generate the confusion matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "# Plot the confusion matrix\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=iris.target_names, yticklabels=iris.target_names)\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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3x89xcXGKjY1VtmzZlDdvXnXr1k3vvfeeChcurPz586t///4KDw9X48aNU30OEkAAAIAM9Cq4X375RTVq1HD8fHv+YOvWrTV9+nT17t1bV65cUceOHXX+/Hk9+eSTWrx4sby8vFJ9DpthGEa6R24y73qjzQ4BSObcd93NDgEAMjQvE8tS3g0/dlnf177r5LK+H1TGSXcBAADwSDAEDAAAkM4Pa2R0VAABAAAshgogAABABnoI5FGw1tUCAACACiAAAABzAAEAAJCpUQEEAACw2BzADJUAXr9+XQkJCU5t/v7+JkUDAAAsgyHgR+vq1avq0qWLQkJC5OPjo6CgIKcNAAAA6cv0BLBXr15asWKFJkyYILvdrqlTp2rQoEEKDw/XzJkzzQ4PAABYgM1mc9mWEZk+BPzdd99p5syZql69utq2bauqVauqUKFCioiI0OzZs9WqVSuzQwQAAMhUTK8Anj17VgUKFJB0a77f2bNnJUlPPvmkVq9ebWZoAADAIqxWATQ9ASxQoIDi4uIkSUWKFNGXX34p6VZlMDAw0MTIAAAAMifTE8C2bdtq+/btkqS+fftq/Pjx8vLyUvfu3dWrVy+TowMAAJZgc+GWAZk+B7B79+6Ov9eqVUt79+7V1q1bVahQIZUqVcrEyAAAADIn0xPAO0VERCggIIDhXwAA8Mhk1Ll6rmL6EPCIESP0xRdfOH5u1qyZgoODlStXLsfQMAAAgCvxEMgjNnHiROXJk0eStHTpUi1dulQ//vij6tWrxxxAAAAAFzB9CPjkyZOOBHDRokVq1qyZnnnmGeXLl09PPPGEydEBAAAryKiVOlcxvQIYFBSkY8eOSZIWL16sWrVqSZIMw1BiYqKZoQEAAGRKplcAmzRpopYtW6pw4cI6c+aM6tWrJ0natm2bChUqZHJ0AADACqgAPmKjR49Wly5dVKxYMS1dulS+vr6SpBMnTqhTp04mR2cNUSVy6evoRjr0WQdd+7G7GlYumOyY/i9X1qHZHXV24Rv6fth/VDA88NEHCsv7fM5s1av9tCqWLalWLZpq544dZocEi+OexL+V6Qmgh4eH3nrrLX344YcqW7aso7179+5q3769iZFZh4+Xh3YeOq1uH69IcX/PphXU6bky6jp2mZ7qNldXrt/Qd+81kd3D/RFHCitb/OMPen9kjF7r1Fmff7VAkZFF9Pprr+rMmTNmhwaL4p7MZCy2ELTpCaAkHTx4UG+88YZq1aqlWrVqqWvXrjp06JDZYVnGkl8Oa9DM9fp2/cEU93duXE4jPt+sRRsPadfhv9X+/cUKC/bRc1WSVwoBV5k1Y5qavNBMjZ//jwoWKqR3Bw6Sl5eXFs6fZ3ZosCjuSfybmZ4A/vTTTypWrJg2b96sUqVKqVSpUtq0aZNjSBjmypczQGHZfLRi21FH28WrCdqy76SeKBJuYmSwkhsJCdrz225VqlzF0ebm5qZKlapox/ZtJkYGq+KezHystg6g6Q+B9O3bV927d9fw4cOTtffp00e1a9c2KTJIUs6grJKkU+euOrWfOndVof+3D3C1c+fPKTExUcHBwU7twcHBiotjtACPHvck/u1MTwD37NmjL7/8Mll7u3btNGbMmPt+Pj4+XvHx8U5tRtJN2dxMvzQAAPAvkVErda5i+hBwjhw5FBsbm6w9NjZWISEh9/18TEyMAgICnLabB5e5IFJrOvl/lb+QO6p9IUFZ9dcdVUHAVYICg+Tu7p5scv2ZM2eUPXt2k6KClXFPZj5WGwI2PQHs0KGDOnbsqBEjRmjNmjVas2aNhg8frtdee00dOnS47+f79eunCxcuOG1ZCtZ6BJFbw+GTF3Ti7BXVKJPH0eaX1VMVI3Nq094/TYwMVuLh6amixYpr08YNjrakpCRt2rRBpUqXvccnAdfgnsS/nenjpP3795efn58++OAD9evXT5IUHh6u6Ohode3a9b6ft9vtstvtTm0M/6aNj5eH07p++UL9VapADp27dF3HTl/S+IW/qk+LJ/T7H+d1+K8LGvhyFZ04c+WuTw0DrvBy67bq/3YfFS9eQiVKltJns2bo2rVravx8E7NDg0VxT2YuGbVS5yqmZ0o2m03du3dX9+7ddenSJUmSn5+fyVFZS7nCoVoysqnj55GvVZckzVq6Wx1HLdEHX/2irF4eGte1lgJ97Vq/+08913++4m/wqj48OnXrPatzZ8/q43Ef6e+/TyuySFF9PGmqghlug0m4J/FvZjMMwzAzgKefflrz589XYGCgU/vFixfVuHFjrViR8uLE9+Jdb3Q6RQekn3PfdTc7BADI0LxMLEsFt57rsr7PzHjRZX0/KNPnAK5cuVIJCQnJ2q9fv641a9aYEBEAAEDmZlquveMf70v87bffdPLkScfPiYmJWrx4sXLlymVGaAAAwGKYA/iIlClTxvF49NNPP51sv7e3t8aOHWtCZAAAAJmbaQlgXFycDMNQgQIFtHnzZuXIkcOxz9PTUyEhIXJ3dzcrPAAAYCFUAB+RiIgISbfWTQIAADCT1RJA0x8CkaRZs2YpKipK4eHhOnLkiCRp9OjR+uabb0yODAAAIPMxPQGcMGGCevTooWeffVbnz59XYuKtteWCgoJS9S5gAACAh2Zz4ZYBmZ4Ajh07VlOmTNE777zjNOevQoUK2rlzp4mRAQAAZE6mvwkkLi5OZcsmf2+i3W7XlStXTIgIAABYDXMAH7H8+fMrNjY2WfvixYtVtGjRRx8QAABAJmd6BbBHjx7q3Lmzrl+/LsMwtHnzZs2dO1cxMTGaOnWq2eEBAAALsFoF0PQEsH379vL29ta7776rq1evqmXLlsqVK5c+/PBDtWjRwuzwAAAAMh3TE8Br167p+eefV6tWrXT16lXt2rVL69atU+7cuc0ODQAAWITVKoCmzwFs1KiRZs6cKUlKSEjQc889p1GjRqlx48aaMGGCydEBAAAruP16WldsGZHpCeCvv/6qqlWrSpK+/vprhYaG6siRI5o5c6Y++ugjk6MDAADIfEwfAr569ar8/PwkSUuWLFGTJk3k5uamSpUqOd4KAgAA4FIZs1DnMqZXAAsVKqSFCxfq2LFj+umnn/TMM89Ikk6dOiV/f3+TowMAAMh8TE8ABwwYoLfeekv58uXTE088ocqVK0u6VQ1MaYFoAACA9Ga1OYCmDwG/8MILevLJJ3XixAmVLl3a0V6zZk09//zzJkYGAACQOZmeAEpSzpw5lTNnTqe2xx9/3KRoAACA1WTUSp2rmD4EDAAAgEcrQ1QAAQAAzGS1CiAJIAAAgLXyP4aAAQAArIYKIAAAsDyrDQFTAQQAALAYKoAAAMDyqAACAAAgU6MCCAAALI8KIAAAADI1KoAAAMDyrFYBJAEEAACwVv7HEDAAAIDVUAEEAACWZ7UhYCqAAAAAFkMFEAAAWB4VQAAAAGRqVAABAIDlWawASAUQAADAaqgAAgAAy2MOIAAAgMXYbK7b0iIxMVH9+/dX/vz55e3trYIFC2rIkCEyDCNdr5cKIAAAQAYxYsQITZgwQTNmzFDx4sX1yy+/qG3btgoICFDXrl3T7TwkgAAAwPIyyhDw+vXr1ahRI9WvX1+SlC9fPs2dO1ebN29O1/MwBAwAAOBC8fHxunjxotMWHx+f4rFVqlTR8uXLtX//fknS9u3btXbtWtWrVy9dYyIBBAAAlufKOYAxMTEKCAhw2mJiYlKMo2/fvmrRooWKFCkiDw8PlS1bVt26dVOrVq3S9XoZAgYAAHChfv36qUePHk5tdrs9xWO//PJLzZ49W3PmzFHx4sUVGxurbt26KTw8XK1bt063mEgAAQCA5bm5uW4OoN1uv2vCd6devXo5qoCSVLJkSR05ckQxMTHpmgAyBAwAAJBBXL16VW5uzumZu7u7kpKS0vU8VAABAIDlZZCHgNWwYUMNHTpUefPmVfHixbVt2zaNGjVK7dq1S9fzkAACAADLyyjLwIwdO1b9+/dXp06ddOrUKYWHh+u1117TgAED0vU8JIAAAAAZhJ+fn8aMGaMxY8a49DwkgAAAwPIySAHwkeEhEAAAAIuhAggAACwvo8wBfFSoAAIAAFgMFUAAAGB5VAABAACQqVEBBAAAlmexAiAJIAAAAEPAAAAAyNSoAAIAAMuzWAGQCiAAAIDVUAEEAACWxxxAAAAAZGpUAAEAgOVZrABIBRAAAMBqqAACAADLYw4gAAAAMjUqgAAAwPIsVgAkAQQAAGAIGAAAAJkaFUAAAGB5FisAZs4E8Nx33c0OAUgmd/vPzQ4BcHJ8aguzQwBgkkyZAAIAAKQFcwABAACQqVEBBAAAlmexAiAVQAAAAKuhAggAACzPanMASQABAIDlWSz/YwgYAADAaqgAAgAAy7PaEDAVQAAAAIuhAggAACyPCiAAAAAyNSqAAADA8ixWAKQCCAAAYDVUAAEAgOVZbQ4gCSAAALA8i+V/DAEDAABYDRVAAABgeVYbAqYCCAAAYDFUAAEAgOVZrABIBRAAAMBqqAACAADLc7NYCZAKIAAAgMVQAQQAAJZnsQIgCSAAAADLwAAAACBTowIIAAAsz81aBUAqgAAAAFZDBRAAAFgecwABAACQqVEBBAAAlmexAiAVQAAAAKuhAggAACzPJmuVAEkAAQCA5bEMDAAAADI1KoAAAMDyWAYGAAAAmRoVQAAAYHkWKwBSAQQAALAaKoAAAMDy3CxWAqQCCAAAYDFUAAEAgOVZrABIAggAAMAyMAAAAMjUqAACAADLs1gB0NwK4I0bN1SzZk0dOHDAzDAAAAAsxdQKoIeHh3bs2GFmCAAAACwD86i99NJL+uSTT8wOAwAAwDJMnwN48+ZNffrpp1q2bJnKly8vHx8fp/2jRo0yKTIAAGAV1qr/ZYAEcNeuXSpXrpwkaf/+/U77rPZINgAAwKNgegL4888/mx0CAACwOKsVnUxPAP/p+PHjkqTcuXObHAkAALASN2vlf+Y/BJKUlKTBgwcrICBAERERioiIUGBgoIYMGaKkpCSzwwMAAHik/vjjD7300ksKDg6Wt7e3SpYsqV9++SVdz2F6BfCdd97RJ598ouHDhysqKkqStHbtWkVHR+v69esaOnSoyRECAIDMLqMMAZ87d05RUVGqUaOGfvzxR+XIkUMHDhxQUFBQup7H9ARwxowZmjp1qp577jlHW6lSpZQrVy516tSJBBAAAFjGiBEjlCdPHk2bNs3Rlj9//nQ/j+lDwGfPnlWRIkWStRcpUkRnz541ISIAAGA1Npvrtvj4eF28eNFpi4+PTzGOb7/9VhUqVFDTpk0VEhKismXLasqUKel+vaYngKVLl9a4ceOStY8bN06lS5c2ISIAAID0ExMTo4CAAKctJiYmxWMPHTqkCRMmqHDhwvrpp5/0+uuvq2vXrpoxY0a6xmQzDMNI1x7TaNWqVapfv77y5s2rypUrS5I2bNigY8eO6YcfflDVqlXT3Of1m+kdJfDwcrf/3OwQACfHp7YwOwTAiZeJE9NemeO6V9NO+U9ksoqf3W6X3W5Pdqynp6cqVKig9evXO9q6du2qLVu2aMOGDekWU6q+6m+//TbVHf5zLl9qVKtWTfv379f48eO1d+9eSVKTJk3UqVMnhYeHp6kvAACAjOZuyV5KwsLCVKxYMae2okWLat68eekaU6oSwMaNG6eqM5vNpsTExDQHER4ezsMeAADANBllHcCoqCjt27fPqW3//v2KiIhI1/OkKgFM7/X4duxIfZm1VKlS6XpuAACAO2WUZWC6d++uKlWqaNiwYWrWrJk2b96syZMna/Lkyel6HlNG28uUKSObzab7TT980IoiAADAv1HFihW1YMEC9evXT4MHD1b+/Pk1ZswYtWrVKl3P80AJ4JUrV7Rq1SodPXpUCQkJTvu6du1638/HxcU9yGkBAABcImPU/25p0KCBGjRo4NJzpDkB3LZtm5599lldvXpVV65cUbZs2fT3338ra9asCgkJSVUCmN7j2AAAAEi9NK8D2L17dzVs2FDnzp2Tt7e3Nm7cqCNHjqh8+fJ6//33HyiIgwcP6o033lCtWrVUq1Ytde3aVQcPHnygvgAAANLKzWZz2ZYRpTkBjI2NVc+ePeXm5iZ3d3fFx8crT548GjlypN5+++00B/DTTz+pWLFi2rx5s0qVKqVSpUpp06ZNKl68uJYuXZrm/gAAAHBvaR4C9vDwkJvbrbwxJCRER48eVdGiRRUQEKBjx46lOYC+ffuqe/fuGj58eLL2Pn36qHbt2mnuEwAAIC0yaKHOZdJcASxbtqy2bNki6dYizgMGDNDs2bPVrVs3lShRIs0B7NmzR6+++mqy9nbt2um3335Lc38AAAC4tzQngMOGDVNYWJgkaejQoQoKCtLrr7+u06dPP9AaNTly5FBsbGyy9tjYWIWEhKS5PwAAgLSy2Wwu2zKiNA8BV6hQwfH3kJAQLV68+KEC6NChgzp27KhDhw6pSpUqkqR169ZpxIgR6tGjx0P1DQAAgORMfO3yLf3795efn58++OAD9evXT9KtV8NFR0enakkZAACAh5VBC3Uuk+YEMH/+/PcsZx46dChN/dlsNnXv3l3du3fXpUuXJEl+fn5pDQvp7PM5szVj2if6++/TeiyyiPq+3V8leS0fTOTrlUV9m5RU/XK5ld3frp1HzuudOb9qW9xZs0ODhfG7MvPIqMu1uEqaE8Bu3bo5/Xzjxg1t27ZNixcvVq9evdIcQFxcnG7evKnChQs7JX4HDhyQh4eH8uXLl+Y+8XAW//iD3h8Zo3cHDlLJkqU1e9YMvf7aq/pm0WIFBwebHR4sakzbx1Ukd4A6Td6ok+evqWmVfJrXq7qqvP2jTp6/ZnZ4sCB+V+LfLM0J4Jtvvpli+/jx4/XLL7+kOYA2bdqoXbt2Kly4sFP7pk2bNHXqVK1cuTLNfeLhzJoxTU1eaKbGz/9HkvTuwEFavXqlFs6fp1c7dDQ5OliRl4e7GlTIrZc/WqMN+09LkkYu3KU6ZcLV9ulCipm/0+QIYUX8rsxcLFYATPtTwHdTr149zZs3L82f27Ztm6KiopK1V6pUKcWng+FaNxIStOe33apUuYqjzc3NTZUqVdGO7dtMjAxWlsXdpizubrqekOTUfi0hUZUey2FSVLAyflfi3y7dEsCvv/5a2bJlS/PnbDabY+7fP124cEGJiYnpERrS4Nz5c0pMTEw2fBEcHKy///7bpKhgdZev39TmA3/rrUbFlTPQS242m5pWjlDFQsEKDfAyOzxYEL8rMx+WgbmPsmXLOl2MYRg6efKkTp8+rY8//jjNATz11FOKiYnR3Llz5e7uLklKTExUTEyMnnzyyft+Pj4+XvHx8U5thrtddrs9zbEAyLg6Td6oj159XLvGNNbNxCTtOHJO8zceVel8QWaHBgD/OmlOABs1auSUALq5uSlHjhyqXr26ihQpkuYARowYoaeeekqRkZGqWrWqJGnNmjW6ePGiVqxYcd/Px8TEaNCgQU5t7/QfqHcHRKc5FkhBgUFyd3fXmTNnnNrPnDmj7NmzmxQVIB0+fVnPDV+hrJ7u8vP20F8Xrmvq61V05PQVs0ODBfG7MvNJtyHRf4k0J4DR0dHpGkCxYsW0Y8cOjRs3Ttu3b5e3t7deeeUVdenSJVVDyv369Uu2YLThTvXvQXl4eqposeLatHGDnq5ZS5KUlJSkTZs2qMWLL5kcHSBdTUjU1YREBWT1UI2SOTXoi+1mhwQL4ncl/u3SnAC6u7vrxIkTyV7TdubMGYWEhDzQvL3w8HANGzYszZ+TJLs9+XDv9ZsP1BX+z8ut26r/231UvHgJlShZSp/NmqFr166p8fNNzA4NFlajRE7ZbNLvJy4pf6ivopuX0YETFzVnbdrWHgXSC78rM5eMOlfPVdKcABqGkWJ7fHy8PD09U9XHjh07VKJECbm5uWnHjh33PLYUC2o+cnXrPatzZ8/q43Ef6e+/TyuySFF9PGmqghnWgIn8vT30btPSCg/y1vkrCfrul2MaOm+nbiam/DsJcDV+V2YubtbK/2Qz7pbR3eGjjz6SJHXv3l1DhgyRr6+vY19iYqJWr16tw4cPa9u2+z/+7ubmppMnTyokJERubm6y2WwpJpY2m+2BKopUAJER5W7/udkhAE6OT21hdgiAEy8TX1Db7Zu9Lut7TKO0PyPhaqn+qkePHi3pVgVw4sSJjid2JcnT01P58uXTxIkTU9VXXFyccuTI4fg7AACAmaxWAUx1Ang7UatRo4bmz5+voKAHX3ohIiIixb8DAADA9dL81PPPP//8UMnfnWbMmKHvv//e8XPv3r0VGBioKlWq6MiRI+l2HgAAgLux2kLQaU4A//Of/2jEiBHJ2keOHKmmTZumOYBhw4bJ29tbkrRhwwaNGzdOI0eOVPbs2dW9e/c09wcAAIB7S3MCuHr1aj377LPJ2uvVq6fVq1enOYBjx46pUKFCkqSFCxfqhRdeUMeOHRUTE6M1a9akuT8AAIC0crO5bsuI0pwAXr58OcXlXjw8PHTx4sU0B+Dr6+tYSX3JkiWqXbu2JMnLy0vXrl1Lc38AAAC4tzQngCVLltQXX3yRrP3zzz9XsWLF0hxA7dq11b59e7Vv31779+93VBd3796tfPnypbk/AACAtLLZXLdlRGlecad///5q0qSJDh48qKefflqStHz5cs2ZM0dff/11mgMYP368+vfvr6NHj2revHkKDg6WJG3dulUvvvhimvsDAABIK7eMmqm5SJoTwIYNG2rhwoUaNmyYvv76a3l7e6t06dJasWJFqt7d+083b97URx99pD59+ih37txO+wYNGpTW0AAAAJAKaR4ClqT69etr3bp1unLlig4dOqRmzZrprbfeUunSpdPUT5YsWTRy5EjdvMmrOwAAgHncXLhlRA8c1+rVq9W6dWuFh4frgw8+0NNPP62NGzemuZ+aNWtq1apVDxoGAAAA0ihNQ8AnT57U9OnT9cknn+jixYtq1qyZ4uPjtXDhwgd6AES6tXxM3759tXPnTpUvX14+Pj5O+5977rkH6hcAACC1LDYFMPUJYMOGDbV69WrVr19fY8aMUd26deXu7p7q9//eTadOnSRJo0aNSrbPZrMpMTHxofoHAACAs1QngD/++KO6du2q119/XYULF063AJKSktKtLwAAgAdhtaeAUz0HcO3atbp06ZLKly+vJ554QuPGjdPff/+drsFcv349XfsDAABAcqlOACtVqqQpU6boxIkTeu211/T5558rPDxcSUlJWrp0qS5duvRAASQmJmrIkCHKlSuXfH19dejQIUm31hv85JNPHqhPAACAtLDaQtBpfgrYx8dH7dq109q1a7Vz50717NlTw4cPV0hIyAM9sDF06FBNnz5dI0eOdHrFXIkSJTR16tQ09wcAAJBWvAs4DSIjIzVy5EgdP35cc+fOfaA+Zs6cqcmTJ6tVq1Zyd3d3tJcuXVp79+59mPAAAACQgjS/CSQl7u7uaty4sRo3bpzmz/7xxx8qVKhQsvakpCTduHEjHaIDAAC4Nx4CecSKFSumNWvWJGv/+uuvVbZsWRMiAgAAyNzSpQL4MAYMGKDWrVvrjz/+UFJSkubPn699+/Zp5syZWrRokdnhAQAAC7BYAdD8CmCjRo303XffadmyZfLx8dGAAQO0Z88efffdd6pdu7bZ4QEAAGQ6plcA27dvr5deeklLly41OxQAAGBRGfVpXVcxvQJ4+vRp1a1bV3ny5FHv3r21fft2s0MCAADI1ExPAL/55hudOHFC/fv31+bNm1WuXDkVL15cw4YN0+HDh80ODwAAWIDNhX8yItMTQEkKCgpSx44dtXLlSh05ckRt2rTRrFmzUlweBgAAIL2xELSJbty4oV9++UWbNm3S4cOHFRoaanZIAAAAmU6GSAB//vlndejQQaGhoWrTpo38/f21aNEiHT9+3OzQAACABVitAmj6U8C5cuXS2bNnVbduXU2ePFkNGzaU3W43OywAAIBMy/QEMDo6Wk2bNlVgYKDZoQAAAIuyWWwlaNMTwA4dOpgdAgAAgKWYngACAACYLaPO1XOVDPEQCAAAAB4dKoAAAMDyLDYFkAQQAADAzWIZIEPAAAAAFkMFEAAAWB4PgQAAACBTowIIAAAsz2JTAKkAAgAAWA0VQAAAYHluslYJkAogAACAxVABBAAAlme1OYAkgAAAwPJYBgYAAACZGhVAAABgebwKDgAAAJkaFUAAAGB5FisAUgEEAACwGiqAAADA8pgDCAAAgEyNCiAAALA8ixUASQABAACsNiRqtesFAACwPBJAAABgeTabzWXbwxg+fLhsNpu6deuWPhf6f0gAAQAAMqAtW7Zo0qRJKlWqVLr3TQIIAAAsz+bC7UFcvnxZrVq10pQpUxQUFPSAvdwdCSAAAIALxcfH6+LFi05bfHz8PT/TuXNn1a9fX7Vq1XJJTCSAAADA8txsNpdtMTExCggIcNpiYmLuGsvnn3+uX3/99Z7HPCyWgQEAAHChfv36qUePHk5tdrs9xWOPHTumN998U0uXLpWXl5fLYiIBBAAAlufKdaDtdvtdE747bd26VadOnVK5cuUcbYmJiVq9erXGjRun+Ph4ubu7P3RMJIAAAMDyMsqbQGrWrKmdO3c6tbVt21ZFihRRnz590iX5k0gAAQAAMgw/Pz+VKFHCqc3Hx0fBwcHJ2h8GCSAAALC8h12w+d+GBBAAACADW7lyZbr3SQIIAAAsz2rr4lntegEAACyPCiAAALA8q80BpAIIAABgMVQAAQCA5Vmr/kcFEAAAwHKoAAIAAMuz2hxAEkDgETk+tYXZIQBOgip2MTsEwMm1beNMO7fVhkStdr0AAACWRwUQAABYntWGgKkAAgAAWAwVQAAAYHnWqv9RAQQAALAcKoAAAMDyLDYFkAogAACA1VABBAAAludmsVmAJIAAAMDyGAIGAABApkYFEAAAWJ7NYkPAVAABAAAshgogAACwPOYAAgAAIFOjAggAACzPasvAUAEEAACwGCqAAADA8qw2B5AEEAAAWJ7VEkCGgAEAACyGCiAAALA8FoIGAABApkYFEAAAWJ6btQqAVAABAACshgogAACwPOYAAgAAIFOjAggAACzPausAkgACAADLYwgYAAAAmRoVQAAAYHksAwMAAIBMjQogAACwPOYAAgAAIFOjAggAACzPasvAUAEEAACwGCqAAADA8ixWACQBBAAAcLPYGDBDwAAAABZDBRAAAFietep/VAABAAAshwogAACAxUqAVAABAAAshgogAACwPF4FBwAAgEyNCiAAALA8iy0DSAIIAABgsfyPIWAAAACroQIIAABgsRIgFUAAAACLoQIIAAAsj2VgAAAAkKmZXgFMTEzU6NGj9eWXX+ro0aNKSEhw2n/27FmTIgMAAFZhtWVgTK8ADho0SKNGjVLz5s114cIF9ejRQ02aNJGbm5uio6PNDg8AACDTMT0BnD17tqZMmaKePXsqS5YsevHFFzV16lQNGDBAGzduNDs8AABgATYXbhmR6QngyZMnVbJkSUmSr6+vLly4IElq0KCBvv/+ezNDAwAAVmGxDND0BDB37tw6ceKEJKlgwYJasmSJJGnLli2y2+1mhgYAAJApmZ4APv/881q+fLkk6Y033lD//v1VuHBhvfLKK2rXrp3J0QEAACuwufBPRmQzDMMwO4h/2rhxo9avX6/ChQurYcOGD9TH9ZvpHBQAZEJBFbuYHQLg5Nq2caade9uRSy7ru2yEn8v6flCmLwNzp0qVKqlSpUpmhwEAACyEZWAesZiYGH366afJ2j/99FONGDHChIgAAAAyN9MTwEmTJqlIkSLJ2osXL66JEyeaEBEAALAaiz0EbH4CePLkSYWFhSVrz5Ejh+PpYAAAAKQf0xPAPHnyaN26dcna161bp/DwcBMiAgAAlmOxEqDpD4F06NBB3bp1040bN/T0009LkpYvX67evXurZ8+eJkcHAACsIKMu1+IqpieAvXr10pkzZ9SpUyclJCRIkry8vNSnTx/169fP5OgAAAAynwyzDuDly5e1Z88eeXt7q3Dhwg/1FhDWAQSA+2MdQGQ0Zq4DuPP4ZZf1XTK3r8v6flCmzwG8zdfXVxUrVlSJEiV4BRwAALCkmJgYVaxYUX5+fgoJCVHjxo21b9++dD+PKUPATZo00fTp0+Xv768mTZrc89j58+c/oqgAAIBVZZQZgKtWrVLnzp1VsWJF3bx5U2+//baeeeYZ/fbbb/Lx8Um385iSAAYEBMj2f0tuBwQEmBECAABAhrN48WKnn6dPn66QkBBt3bpVTz31VLqdx5QEcNq0aSn+HQAAwBQuLAHGx8crPj7eqc1ut6dqytuFCxckSdmyZUvXmDLMHEAAAIDMKCYmRgEBAU5bTEzMfT+XlJSkbt26KSoqSiVKlEjXmExPAP/66y+9/PLLCg8PV5YsWeTu7u60wRyfz5mterWfVsWyJdWqRVPt3LHD7JAA7kuYJqpcQX095jUdWjJU17aNU8PqpZz2N3q6tL77uLOO/zxC17aNU6nHcpkUKR6UzYV/+vXrpwsXLjhtqVnqrnPnztq1a5c+//zzdL9e09cBbNOmjY4ePar+/fsrLCzMMTcQ5ln84w96f2SM3h04SCVLltbsWTP0+muv6ptFixUcHGx2eLAo7kuYycfbrp37/9DMbzboi1Edk+3P6u2p9bEHNW/pr5owoJUJESIjS+1w7z916dJFixYt0urVq5U7d+50j8n0BHDt2rVas2aNypQpY3Yo+D+zZkxTkxeaqfHz/5EkvTtwkFavXqmF8+fp1Q7Jf/EBjwL3Jcy0ZN1vWrLut7vun/v9FklS3rD0naeFRyej1J8Mw9Abb7yhBQsWaOXKlcqfP79LzmP6EHCePHmUQdaihqQbCQna89tuVapcxdHm5uamSpWqaMf2bSZGBivjvgTgahnlVcCdO3fWZ599pjlz5sjPz08nT57UyZMnde3atYe8QmemJ4BjxoxR3759dfjwYbNDgaRz588pMTEx2ZBacHCw/v77b5OigtVxXwKwigkTJujChQuqXr26wsLCHNsXX3yRrucxfQi4efPmunr1qgoWLKisWbPKw8PDaf/Zs2fv+fmUHq023NM+1g4AACwsAw0BPwqmJ4Bjxox5qM/HxMRo0KBBTm3v9B+odwdEP1S/VhUUGCR3d3edOXPGqf3MmTPKnj27SVHB6rgvASB9mZ4Atm7d+qE+369fP/Xo0cOpzXCn+vegPDw9VbRYcW3auEFP16wl6dY6RJs2bVCLF18yOTpYFfclAFezZZQS4CNiSgJ48eJF+fv7O/5+L7ePu5uUHq2+fvPh4rO6l1u3Vf+3+6h48RIqUbKUPps1Q9euXVPj5+/93mbAlbgvYSYfb08VzJPD8XO+XMEq9Vgunbt4VcdOnlOQf1blyRmksJBbrzd9LF+oJOmvMxf115lLpsQM3IspCWBQUJBOnDihkJAQBQYGprj2n2EYstlsSkxMNCFCa6tb71mdO3tWH4/7SH//fVqRRYrq40lTFcxQG0zEfQkzlSsWoSVT33T8PPKtW8sRzfp2ozoO/Ez1q5XUlMEvO/bPGtFOkvTexB80dNIPjzZYPJCMsgzMo2IzTFiDZdWqVYqKilKWLFm0atWqex5brVq1NPdPBRAA7i+oYhezQwCcXNs2zrRz7zt51WV9R+bM6rK+H5QpFcB/JnUPkuABAACkJ4sVAM1/CGTHXd7labPZ5OXlpbx587KkCwAAcC2LZYCmJ4BlypS55/t/PTw81Lx5c02aNEleXl6PMDIAAIDMyfQ3gSxYsECFCxfW5MmTFRsbq9jYWE2ePFmRkZGaM2eOPvnkE61YsULvvvuu2aECAIBMyubCPxmR6RXAoUOH6sMPP1SdOnUcbSVLllTu3LnVv39/bd68WT4+PurZs6fef/99EyMFAADIHExPAHfu3KmIiIhk7REREdq5c6ekW8PEJ06ceNShAQAAi7DaMjCmDwEXKVJEw4cPV0JCgqPtxo0bGj58uIoUKSJJ+uOPPxQaGmpWiAAAAJmK6RXA8ePH67nnnlPu3LlVqlQpSbeqgomJiVq0aJEk6dChQ+rUqZOZYQIAgEzMYgVAcxaCvtOlS5c0e/Zs7d+/X5IUGRmpli1bys/P74H6YyFoALg/FoJGRmPmQtAHT11zWd8FQ7xd1veDMrUCeOPGDRUpUkSLFi3Sf//7XzNDAQAAVmaxEqCpCaCHh4euX79uZggAAAAZdrkWVzH9IZDOnTtrxIgRunmTcVsAAIBHwfSHQLZs2aLly5dryZIlKlmypHx8fJz2z58/36TIAACAVVhtGRjTE8DAwED95z//MTsMAAAAyzA9AZw2bZrZIQAAAIuzWAHQ/DmAAAAAeLRMqQCWK1dOy5cvV1BQkMqWLSvbPQbef/3110cYGQAAsCSLlQBNSQAbNWoku90uSWrcuLEZIQAAAFiWKQngwIEDHX8/duyYWrVqpRo1apgRCgAAAOsAPmqnT59WvXr1lCdPHvXu3Vvbt283OyQAAGAxNpvrtozI9ATwm2++0YkTJ9S/f39t3rxZ5cqVU/HixTVs2DAdPnzY7PAAAAAyHZthGIbZQfzT8ePHNXfuXH366ac6cODAA70h5DovFQGA+wqq2MXsEAAn17aNM+3cx87Gu6zvPNnsLuv7QZleAfynGzdu6JdfftGmTZt0+PBhhYaGmh0SAABAppMhEsCff/5ZHTp0UGhoqNq0aSN/f38tWrRIx48fNzs0AABgAVabA2j6m0By5cqls2fPqm7dupo8ebIaNmzoWCIGAAAA6c/0BDA6OlpNmzZVYGCg2aEAAADLyqClOhcxPQHs0KGD2SEAAABYiukJIAAAgNky6lw9VyEBBAAAlmex/C9jPAUMAACAR4cKIAAAsDyrDQFTAQQAALAYKoAAAMDybBabBUgFEAAAwGKoAAIAAFirAEgFEAAAwGqoAAIAAMuzWAGQBBAAAIBlYAAAAJCpUQEEAACWxzIwAAAAyNSoAAIAAFirAEgFEAAAwGqoAAIAAMuzWAGQCiAAAIDVUAEEAACWZ7V1AEkAAQCA5bEMDAAAADI1KoAAAMDyrDYETAUQAADAYkgAAQAALIYEEAAAwGKYAwgAACyPOYAAAADI1KgAAgAAy7PaOoAkgAAAwPIYAgYAAECmRgUQAABYnsUKgFQAAQAArIYKIAAAgMVKgFQAAQAALIYKIAAAsDyrLQNDBRAAAMBiqAACAADLYx1AAAAAZGpUAAEAgOVZrABIAggAAGC1DJAhYAAAAIshAQQAAJZnc+GfBzF+/Hjly5dPXl5eeuKJJ7R58+Z0vV4SQAAAgAzkiy++UI8ePTRw4ED9+uuvKl26tOrUqaNTp06l2zlIAAEAgOXZbK7b0mrUqFHq0KGD2rZtq2LFimnixInKmjWrPv3003S7XhJAAAAAF4qPj9fFixedtvj4+BSPTUhI0NatW1WrVi1Hm5ubm2rVqqUNGzakW0yZ8ilgr0x5VY9efHy8YmJi1K9fP9ntdrPDAbgn09m1bePMDiFT4L7MHFyZO0S/F6NBgwY5tQ0cOFDR0dHJjv3777+VmJio0NBQp/bQ0FDt3bs33WKyGYZhpFtvyFQuXryogIAAXbhwQf7+/maHA3BPIkPivsT9xMfHJ6v42e32FP+H4c8//1SuXLm0fv16Va5c2dHeu3dvrVq1Sps2bUqXmKiVAQAAuNDdkr2UZM+eXe7u7vrrr7+c2v/66y/lzJkz3WJiDiAAAEAG4enpqfLly2v58uWOtqSkJC1fvtypIviwqAACAABkID169FDr1q1VoUIFPf744xozZoyuXLmitm3bpts5SABxV3a7XQMHDmRSMzIM7klkRNyXSG/NmzfX6dOnNWDAAJ08eVJlypTR4sWLkz0Y8jB4CAQAAMBimAMIAABgMSSAAAAAFkMCCAAAYDEkgAAytMOHD8tmsyk2NjZD9od/l+joaJUpU+ah+1m5cqVsNpvOnz+f6s+0adNGjRs3fuhzA+mBh0Cgw4cPK3/+/Nq2bVu6/GIE0lNiYqJOnz6t7NmzK0uWh1+4gPvd2i5fvqz4+HgFBwc/VD8JCQk6e/asQkNDZbPZUvWZCxcuyDAMBQYGPtS5gfTAMjAATHXjxg15eHjcdb+7u3u6rn6fHhISEuTp6Wl2GHgAvr6+8vX1vev+1P639fT0TPN9GRAQkKbjAVdiCDgT+frrr1WyZEl5e3srODhYtWrV0pUrVyRJU6dOVdGiReXl5aUiRYro448/dnwuf/78kqSyZcvKZrOpevXqkm6tPD548GDlzp1bdrvdsQ7RbQkJCerSpYvCwsLk5eWliIgIxcTEOPaPGjVKJUuWlI+Pj/LkyaNOnTrp8uXLj+CbgKtMnjxZ4eHhSkpKcmpv1KiR2rVrJ0n65ptvVK5cOXl5ealAgQIaNGiQbt686TjWZrNpwoQJeu655+Tj46OhQ4fq3LlzatWqlXLkyCFvb28VLlxY06ZNk5TykO3u3bvVoEED+fv7y8/PT1WrVtXBgwcl3f++TcmqVav0+OOPy263KywsTH379nWKuXr16urSpYu6deum7Nmzq06dOg/1PcJ17neP3jkEfHtYdujQoQoPD1dkZKQkaf369SpTpoy8vLxUoUIFLVy40Ok+vHMIePr06QoMDNRPP/2kokWLytfXV3Xr1tWJEyeSneu2pKQkjRw5UoUKFZLdblfevHk1dOhQx/4+ffroscceU9asWVWgQAH1799fN27cSN8vDNZlIFP4888/jSxZshijRo0y4uLijB07dhjjx483Ll26ZHz22WdGWFiYMW/ePOPQoUPGvHnzjGzZshnTp083DMMwNm/ebEgyli1bZpw4ccI4c+aMYRiGMWrUKMPf39+YO3eusXfvXqN3796Gh4eHsX//fsMwDON///ufkSdPHmP16tXG4cOHjTVr1hhz5sxxxDR69GhjxYoVRlxcnLF8+XIjMjLSeP311x/9l4N0c/bsWcPT09NYtmyZo+3MmTOOttWrVxv+/v7G9OnTjYMHDxpLliwx8uXLZ0RHRzuOl2SEhIQYn376qXHw4EHjyJEjRufOnY0yZcoYW7ZsMeLi4oylS5ca3377rWEYhhEXF2dIMrZt22YYhmEcP37cyJYtm9GkSRNjy5Ytxr59+4xPP/3U2Lt3r2EY979vU+ova9asRqdOnYw9e/YYCxYsMLJnz24MHDjQEXO1atUMX19fo1evXsbevXsd50LGc797dODAgUbp0qUd+1q3bm34+voaL7/8srFr1y5j165dxoULF4xs2bIZL730krF7927jhx9+MB577DGn++bnn382JBnnzp0zDMMwpk2bZnh4eBi1atUytmzZYmzdutUoWrSo0bJlS6dzNWrUyPFz7969jaCgIGP69OnG77//bqxZs8aYMmWKY/+QIUOMdevWGXFxcca3335rhIaGGiNGjHDJ9wbrIQHMJLZu3WpIMg4fPpxsX8GCBZ0SM8O49YulcuXKhmEk/wfxtvDwcGPo0KFObRUrVjQ6depkGIZhvPHGG8bTTz9tJCUlpSrGr776yggODk7tJSGDatSokdGuXTvHz5MmTTLCw8ONxMREo2bNmsawYcOcjp81a5YRFhbm+FmS0a1bN6djGjZsaLRt2zbF8915f/br18/Inz+/kZCQkOLx97tv7+zv7bffNiIjI53u4/Hjxxu+vr5GYmKiYRi3EsCyZcve7StBBnOvezSlBDA0NNSIj493tE2YMMEIDg42rl275mibMmXKfRNAScbvv//u+Mz48eON0NBQp3PdTgAvXrxo2O12p4Tvfv73v/8Z5cuXT/XxwL0wBJxJlC5dWjVr1lTJkiXVtGlTTZkyRefOndOVK1d08OBBvfrqq465L76+vnrvvfccQ2YpuXjxov78809FRUU5tUdFRWnPnj2Sbg1nxMbGKjIyUl27dtWSJUucjl22bJlq1qypXLlyyc/PTy+//LLOnDmjq1evpv8XgEemVatWmjdvnuLj4yVJs2fPVosWLeTm5qbt27dr8ODBTvdahw4ddOLECaf/7hUqVHDq8/XXX9fnn3+uMmXKqHfv3lq/fv1dzx8bG6uqVaumOG8wNfftnfbs2aPKlSs7TeSPiorS5cuXdfz4cUdb+fLl7/GtICO51z2akpIlSzrN+9u3b59KlSolLy8vR9vjjz9+3/NmzZpVBQsWdPwcFhamU6dOpXjsnj17FB8fr5o1a961vy+++EJRUVHKmTOnfH199e677+ro0aP3jQNIDRLATMLd3V1Lly7Vjz/+qGLFimns2LGKjIzUrl27JElTpkxRbGysY9u1a5c2btz4UOcsV66c4uLiNGTIEF27dk3NmjXTCy+8IOnWvK0GDRqoVKlSmjdvnrZu3arx48dLujV3EP9eDRs2lGEY+v7773Xs2DGtWbNGrVq1knTrCctBgwY53Ws7d+7UgQMHnP4x9fHxceqzXr16OnLkiLp3764///xTNWvW1FtvvZXi+b29vV13cfdwZ8zIuO51j6Ykvf7b3vk/JTabTcZdFtq43328YcMGtWrVSs8++6wWLVqkbdu26Z133uH3J9INCWAmYrPZFBUVpUGDBmnbtm3y9PTUunXrFB4erkOHDqlQoUJO2+2HP27/n29iYqKjL39/f4WHh2vdunVO51i3bp2KFSvmdFzz5s01ZcoUffHFF5o3b57Onj2rrVu3KikpSR988IEqVaqkxx57TH/++ecj+Bbgal5eXmrSpIlmz56tuXPnKjIyUuXKlZN0638K9u3bl+xeK1So0F2rL7flyJFDrVu31meffaYxY8Zo8uTJKR5XqlQprVmzJsXJ8Km9b/+paNGi2rBhg9M/1OvWrZOfn59y5859z5iRMd3rHk2NyMhI7dy501FBlKQtW7aka4yFCxeWt7e3li9fnuL+9evXKyIiQu+8844qVKigwoUL68iRI+kaA6yNZWAyiU2bNmn58uV65plnFBISok2bNun06dMqWrSoBg0apK5duyogIEB169ZVfHy8fvnlF507d049evRQSEiIvL29tXjxYuXOnVteXl4KCAhQr169NHDgQBUsWFBlypTRtGnTFBsbq9mzZ0u69ZRvWFiYypYtKzc3N3311VfKmTOnAgMDVahQId24cUNjx45Vw4YNtW7dOk2cONHkbwnppVWrVmrQoIF2796tl156ydE+YMAANWjQQHnz5tULL7zgGBbetWuX3nvvvbv2N2DAAJUvX17FixdXfHy8Fi1apKJFi6Z4bJcuXTR27Fi1aNFC/fr1U0BAgDZu3KjHH39ckZGR971v79SpUyeNGTNGb7zxhrp06aJ9+/Zp4MCB6tGjx32TVmRcd7tHU6Nly5Z655131LFjR/Xt21dHjx7V+++/L0mpXvPvfry8vNSnTx/17t1bnp6eioqK0unTp7V79269+uqrKly4sI4eParPP/9cFStW1Pfff68FCxaky7kBSTwFnFn89ttvRp06dYwcOXIYdrvdeOyxx4yxY8c69s+ePdsoU6aM4enpaQQFBRlPPfWUMX/+fMf+KVOmGHny5DHc3NyMatWqGYZhGImJiUZ0dLSRK1cuw8PDwyhdurTx448/Oj4zefJko0yZMoaPj4/h7+9v1KxZ0/j1118d+0eNGmWEhYUZ3t7eRp06dYyZM2c6TZrGv1diYqIRFhZmSDIOHjzotG/x4sVGlSpVDG9vb8Pf3994/PHHjcmTJzv2SzIWLFjg9JkhQ4YYRYsWNby9vY1s2bIZjRo1Mg4dOmQYRsoPKW3fvt145plnjKxZsxp+fn5G1apVHXHc775Nqb+VK1caFStWNDw9PY2cOXMaffr0MW7cuOHYX61aNePNN998yG8Nj9Ld7tGUHgL555O5t61bt84oVaqU4enpaZQvX96YM2eOIcnxBHhKD4EEBAQ49bFgwQLjn//M3nmuxMRE47333jMiIiIMDw8PI2/evE4PUfXq1csIDg42fH19jebNmxujR49Odg7gQfEmEAAA7mP27Nlq27atLly4YNo8VCA9MQQMAMAdZs6cqQIFCihXrlzavn27+vTpo2bNmpH8IdMgAQQA4A4nT57UgAEDdPLkSYWFhalp06ZOb+kA/u0YAgYAALAYHnEDAACwGBJAAAAAiyEBBAAAsBgSQAAAAIshAQQAALAYEkAAGVabNm3UuHFjx8/Vq1dXt27dHnkcK1eulM1m0/nz5x/5uQHAFUgAAaRZmzZtZLPZZLPZ5OnpqUKFCmnw4MG6efOmS887f/58DRkyJFXHkrQBwN2xEDSAB1K3bl1NmzZN8fHx+uGHH9S5c2d5eHioX79+TsclJCTI09MzXc6ZLVu2dOkHAKyOCiCAB2K325UzZ05FRETo9ddfV61atfTtt986hm2HDh2q8PBwRUZGSpKOHTumZs2aKTAwUNmyZVOjRo10+PBhR3+JiYnq0aOHAgMDFRwcrN69e+vOdervHAKOj49Xnz59lCdPHtntdhUqVEiffPKJDh8+rBo1akiSgoKCZLPZ1KZNG0lSUlKSYmJilD9/fnl7e6t06dL6+uuvnc7zww8/6LHHHpO3t7dq1KjhFCcAZAYkgADShbe3txISEiRJy5cv1759+7R06VItWrRIN27cUJ06deTn56c1a9Zo3bp18vX1Vd26dR2f+eCDDzR9+nR9+umnWrt2rc6ePasFCxbc85yvvPKK5s6dq48++kh79uzRpEmT5Ovrqzx58mjevHmSpH379unEiRP68MMPJUkxMTGaOXOmJk6cqN27d6t79+566aWXtGrVKkm3EtUmTZqoYcOGio2NVfv27dW3b19XfW0AYAqGgAE8FMMwtHz5cv3000964403dPr0afn4+Gjq1KmOod/PPvtMSUlJmjp1qmw2myRp2rRpCgwM1MqVK/XMM89ozJgx6tevn5o0aSJJmjhxon766ae7nnf//v368ssvtXTpUtWqVUuSVKBAAcf+28PFISEhCgwMlHSrYjhs2DAtW7ZMlStXdnxm7dq1mjRpkqpVq6YJEyaoYMGC+uCDDyRJkZGR2rlzp0aMGJGO3xoAmIsEEMADWbRokXx9fXXjxg0lJSWpZcuWio6OVufOnVWyZEmneX/bt2/X77//Lj8/P6c+rl+/roMHD+rChQs6ceKEnnjiCce+LFmyqEKFCsmGgW+LjY2Vu7u7qlWrluqYf//9d129elW1a9d2ak9ISFDZsmUlSXv27HGKQ5IjWQSAzIIEEMADqVGjhiZMmCBPT0+Fh4crS5b//+vEx8fH6djLly+rfPnymj17drJ+cuTI8UDn9/b2TvNnLl++LEn6/vvvlStXLqd9drv9geIAgH8jEkAAD8THx0eFChVK1bHlypXTF198oZCQEPn7+6d4TFhYmDZt2qSnnnpKknTz5k1t3bpV5cqVS/H4kiVLKikpSatWrXIMAf/T7QpkYmKio61YsWKy2+06evToXSuHRYsW1bfffuvUtnHjxvtfJAD8i/AQCACXa9WqlbJnz65GjRppzZo1iouL08qVK9W1a1cdP35ckvTmm29q+PDhWrhwofbu3atOnTrdcw2/fPnyqXXr1mrXrp0WLlzo6PPLL7+UJEVERMhms2nRokU6ffq0Ll++LD8/P7311lvq3r27ZsyYoYMHD+rXX3/V2LFjNWPGDEnSf//7Xx04cEC9evXSvn37NGfOHE2fPt3VXxEAPFIkgABcLmvWrFq9erXy5s2rJk2aqGjRonr11Vd1/fp1R0WwZ8+eevnll9W6dWtVrlxZfn5+ev755+/Z74QJE/TCCy+oU6dOKlKkiDp06KArV65IknLlyqVBgwapb9++Cg0NVZcuXSRJQ4YMUf/+/RUTE6OiRYuqbt26+v7775U/f35JUt68eTVv3jwtXLhQpUuX1sSJEzVs2DAXfjsA8OjZjLvNsAYAAECmRAUQAADAYkgAAQAALIYEEAAAwGJIAAEAACyGBBAAAMBiSAABAAAshgQQAADAYkgAAQAALIYEEAAAwGJIAAEAACyGBBAAAMBi/h/3qcwAPtoQNQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}