From 2e8f65952125a238e99d605d52ef4f55e0173761 Mon Sep 17 00:00:00 2001
From: Akansha-Mulchandani
 <123954924+Akansha-Mulchandani@users.noreply.github.com>
Date: Mon, 10 Jun 2024 16:10:17 +0530
Subject: [PATCH 1/2] Add files via upload

---
 .../Model/Gssoc'24_FineTune.ipynb             | 1716 +++++++++++++
 .../Model/Gssoc'24_ResNet50.ipynb             | 2217 +++++++++++++++++
 .../Model/Gssoc'24_Xception.ipynb             | 2025 +++++++++++++++
 3 files changed, 5958 insertions(+)
 create mode 100644 Indian Truck Detection/Model/Gssoc'24_FineTune.ipynb
 create mode 100644 Indian Truck Detection/Model/Gssoc'24_ResNet50.ipynb
 create mode 100644 Indian Truck Detection/Model/Gssoc'24_Xception.ipynb

diff --git a/Indian Truck Detection/Model/Gssoc'24_FineTune.ipynb b/Indian Truck Detection/Model/Gssoc'24_FineTune.ipynb
new file mode 100644
index 000000000..862f2384d
--- /dev/null
+++ b/Indian Truck Detection/Model/Gssoc'24_FineTune.ipynb	
@@ -0,0 +1,1716 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7G8sp8ET4qaB",
+        "outputId": "82a08e60-7d90-43fc-f623-f22ff8a1017c"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
+          ]
+        }
+      ],
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "F9KS-nfZRxqL"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import os\n",
+        "\n",
+        "# Define the path to the image folder in Google Drive\n",
+        "image_folder_path = \"/content/drive/MyDrive/Dataset_truck\"  # Update this path accordingly\n",
+        "\n",
+        "# Function to recursively print the contents of a directory\n",
+        "def print_directory_contents(folder_path):\n",
+        "    for root, dirs, files in os.walk(folder_path):\n",
+        "        print(f\"Directory: {root}\")\n",
+        "        print(\"Files:\")\n",
+        "        for file in files:\n",
+        "            print(f\"\\t{file}\")\n",
+        "\n",
+        "# Print the contents of the image folder\n",
+        "print_directory_contents(image_folder_path)\n"
+      ],
+      "metadata": {
+        "id": "hwg7e0zq5KuT",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "eb887abd-f2e1-41c2-fc6d-0740e9e294bd"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Directory: /content/drive/MyDrive/Dataset_truck\n",
+            "Files:\n",
+            "Directory: /content/drive/MyDrive/Dataset_truck/test_data\n",
+            "Files:\n",
+            "Directory: /content/drive/MyDrive/Dataset_truck/test_data/truck\n",
+            "Files:\n",
+            "\t00000194.JPG\n",
+            "\t00000176.jpg\n",
+            "\t00000175.jpg\n",
+            "\t00000191.jpg\n",
+            "\t00000180.jpg\n",
+            "\t00000196.jpg\n",
+            "\t00000189.jpg\n",
+            "\t00000180 (2).jpg\n",
+            "\t00000192.jpg\n",
+            "\t00000181.jpg\n",
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+            "\t00000214.jpg\n",
+            "\t00000208.jpg\n",
+            "\t00000205.jpg\n",
+            "\t00000235.jpg\n",
+            "\t00000218 (2).jpg\n",
+            "\t00000212 (2).jpg\n",
+            "\t00000229.jpg\n",
+            "\t00000236.jpg\n",
+            "\t00000225.jpg\n",
+            "\t00000232.jpg\n",
+            "\t00000207.jpg\n",
+            "\t00000211.jpg\n",
+            "\t00000230 (2).jpg\n",
+            "\t00000206.jpg\n",
+            "\t00000209.jpg\n",
+            "\t00000217.jpg\n",
+            "\t00000238.jpg\n",
+            "\t00000262.jpg\n",
+            "\t00000251.jpg\n",
+            "\t00000254.jpg\n",
+            "\t00000253.jpg\n",
+            "\t00000242.jpg\n",
+            "\t00000247 (2).jpg\n",
+            "\t00000244.jpg\n",
+            "\t00000252.jpg\n",
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+            "\t00000246.jpg\n",
+            "\t00000241 (2).jpg\n",
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+            "\t00000282 (2).jpg\n",
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+            "\t00000304.jpg\n",
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+            "\t00000299.jpg\n",
+            "\t00000321.jpg\n",
+            "\t00000302.jpg\n",
+            "\t00000327.jpg\n",
+            "\t00000332.jpg\n",
+            "\t00000313.jpg\n",
+            "\t00000310.jpg\n",
+            "\t00000331.png\n",
+            "\t00000328.jpg\n",
+            "\t00000329.jpg\n",
+            "\t00000307.jpeg\n",
+            "\t00000303.jpg\n",
+            "\t00000317.jpg\n",
+            "\t00000319.jpg\n",
+            "\t00000311.jpg\n",
+            "\t00000314.jpg\n",
+            "\t00000320.png\n",
+            "\t00000318.jpg\n",
+            "\t00000323.jpg\n",
+            "\t00000324.jpg\n",
+            "\t00000338.jpg\n",
+            "\t00000360.jpg\n",
+            "\t00000358.jpg\n",
+            "\t00000341.jpg\n",
+            "\t00000347.jpg\n",
+            "\t00000364.jpg\n",
+            "\t00000337.jpg\n",
+            "\t00000351.jpg\n",
+            "\t00000343.jpg\n",
+            "\t00000359.jpg\n",
+            "\t00000361.jpg\n",
+            "\t00000346.JPG\n",
+            "\t00000350.jpg\n",
+            "\t00000344.jpg\n",
+            "\t00000366.jpg\n",
+            "\t00000342.jpg\n",
+            "\t00000335.jpg\n",
+            "\t00000353.jpg\n",
+            "\t00000368.jpg\n",
+            "\t00000362.jpg\n",
+            "\t00000365.jpg\n",
+            "\t00000334.jpg\n",
+            "\t00000356.jpg\n",
+            "\t00000340.jpg\n",
+            "\t00000363.jpg\n",
+            "\t00000357.jpg\n",
+            "\t00000348.jpg\n",
+            "\t00000349.jpg\n",
+            "\t00000352.jpg\n",
+            "\t00000354.jpg\n",
+            "\t00000355.jpg\n",
+            "\t00000345.jpg\n",
+            "\t00000380.jpg\n",
+            "\t00000377.jpg\n",
+            "\t00000395.jpg\n",
+            "\t00000370.jpg\n",
+            "\t00000376.jpg\n",
+            "\t00000378.JPG\n",
+            "\t00000392.jpg\n",
+            "\t00000388.jpg\n",
+            "\t00000382.jpg\n",
+            "\t00000379.jpg\n",
+            "\t00000390.jpg\n",
+            "\t00000386.jpg\n",
+            "\t00000381.jpg\n",
+            "\t00000383.jpg\n",
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+            "\t00000393.jpg\n",
+            "\t00000384.jpg\n",
+            "\t00000369.jpg\n",
+            "\t00000389.jpg\n",
+            "Directory: /content/drive/MyDrive/Dataset_truck/train_data\n",
+            "Files:\n",
+            "Directory: /content/drive/MyDrive/Dataset_truck/train_data/truck\n",
+            "Files:\n",
+            "\t00000011 (2).jpg\n",
+            "\t00000010.jpg\n",
+            "\t00000008.jpg\n",
+            "\t00000000 (2).jpg\n",
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+            "\t00000005.jpg\n",
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+            "\t00000006.jpg\n",
+            "\t00000012 (2).jpg\n",
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+            "\t00000014.jpg\n",
+            "\t00000006.jpeg\n",
+            "\t00000009.jpg\n",
+            "\t00000000.jpg\n",
+            "\t00000020.jpg\n",
+            "\t00000022 (2).jpg\n",
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+            "\t00000016.jpg\n",
+            "\t00000016 (2).jpg\n",
+            "\t00000029.jpg\n",
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+            "\t00000036 (2).jpg\n",
+            "\t00000034.jpg\n",
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+            "\t00000019 (2).jpg\n",
+            "\t00000031.jpg\n",
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+            "\t00000021 (2).jpg\n",
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+            "\t00000017.jpg\n",
+            "\t00000035.jpg\n",
+            "\t00000030.jpg\n",
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+            "\t00000025.jpg\n",
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+            "\t00000024 (2).jpg\n",
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+            "\t00000038 (2).jpg\n",
+            "\t00000040 (2).jpg\n",
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+            "\t00000039 (2).jpg\n",
+            "\t00000045 (2).jpg\n",
+            "\t00000059.jpg\n",
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+            "\t00000057 (2).jpg\n",
+            "\t00000045.jpg\n",
+            "\t00000058.jpg\n",
+            "\t00000041.jpg\n",
+            "\t00000055.jpg\n",
+            "\t00000053 (2).jpg\n",
+            "\t00000057.jpg\n",
+            "\t00000050.jpg\n",
+            "\t00000058 (2).jpg\n",
+            "\t00000040.jpg\n",
+            "\t00000048.jpg\n",
+            "\t00000072 (2).jpg\n",
+            "\t00000074.jpg\n",
+            "\t00000075.jpg\n",
+            "\t00000061 (2).jpg\n",
+            "\t00000068.jpg\n",
+            "\t00000073.jpg\n",
+            "\t00000063.jpg\n",
+            "\t00000080.jpg\n",
+            "\t00000070 (2).jpg\n",
+            "\t00000067.jpg\n",
+            "\t00000078.jpg\n",
+            "\t00000065.jpg\n",
+            "\t00000076.jpg\n",
+            "\t00000066.jpg\n",
+            "\t00000078 (2).jpg\n",
+            "\t00000064 (2).jpg\n",
+            "\t00000072.jpg\n",
+            "\t00000066 (2).jpg\n",
+            "\t00000062.jpg\n",
+            "\t00000081 (2).jpg\n",
+            "\t00000071.jpg\n",
+            "\t00000061.jpg\n",
+            "\t00000071 (2).jpg\n",
+            "\t00000079.jpg\n",
+            "\t00000064.jpg\n",
+            "\t00000077.jpg\n",
+            "\t00000062 (2).jpg\n",
+            "\t00000067 (2).jpg\n",
+            "\t00000060.jpg\n",
+            "\t00000070.jpg\n",
+            "\t00000069 (2).jpg\n",
+            "\t00000073 (2).jpg\n",
+            "\t00000069.jpg\n",
+            "\t00000098.jpg\n",
+            "\t00000093.jpg\n",
+            "\t00000099 (2).jpg\n",
+            "\t00000092.jpg\n",
+            "\t00000097.jpg\n",
+            "\t00000088 (2).jpg\n",
+            "\t00000090.jpg\n",
+            "\t00000081.jpg\n",
+            "\t00000095 (2).jpg\n",
+            "\t00000089.jpg\n",
+            "\t00000084.jpg\n",
+            "\t00000094.jpg\n",
+            "\t00000083.jpg\n",
+            "\t00000087.jpg\n",
+            "\t00000082.jpg\n",
+            "\t00000098 (2).jpg\n",
+            "\t00000096 (2).jpg\n",
+            "\t00000086.jpg\n",
+            "\t00000087 (2).jpg\n",
+            "\t00000095.jpg\n",
+            "\t00000088.jpg\n",
+            "\t00000096.jpg\n",
+            "\t00000091.jpg\n",
+            "Directory: /content/drive/MyDrive/Dataset_truck/validation_data\n",
+            "Files:\n",
+            "Directory: /content/drive/MyDrive/Dataset_truck/validation_data/truck\n",
+            "Files:\n",
+            "\t00000102.jpg\n",
+            "\t00000101.jpg\n",
+            "\t00000101 (2).jpg\n",
+            "\t00000099.jpg\n",
+            "\t00000100.jpg\n",
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+            "\t00000111.jpg\n",
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+            "\t00000114.jpg\n",
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+            "\t00000109.jpg\n",
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+            "\t00000116.jpg\n",
+            "\t00000105 (2).jpg\n",
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+            "\t00000131.jpg\n",
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+            "\t00000130.jpg\n",
+            "\t00000121 (2).jpg\n",
+            "\t00000122.jpg\n",
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+            "\t00000115.jpg\n",
+            "\t00000126.jpg\n",
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+            "\t00000107.jpg\n",
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+            "\t00000132 (2).jpg\n",
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+            "\t00000144.jpg\n",
+            "\t00000141 (2).jpg\n",
+            "\t00000142.jpg\n",
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+            "\t00000139.jpg\n",
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+            "\t00000143.jpg\n",
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+            "\t00000137.jpg\n",
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+            "\t00000138.jpg\n",
+            "\t00000140.jpg\n",
+            "\t00000133.jpg\n",
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+            "\t00000146 (2).jpg\n",
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+            "\t00000150 (2).jpg\n",
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+            "\t00000168.jpg\n",
+            "\t00000165.jpg\n",
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+            "\t00000170.jpg\n",
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+            "\t00000172.jpg\n",
+            "\t00000157.jpg\n",
+            "\t00000153.jpg\n",
+            "\t00000161.jpg\n",
+            "\t00000154.jpg\n",
+            "\t00000147.jpg\n",
+            "\t00000151 (2).jpg\n",
+            "\t00000153 (2).jpg\n",
+            "\t00000158.jpg\n",
+            "\t00000148.jpg\n",
+            "\t00000159.jpg\n",
+            "\t00000167.jpg\n",
+            "\t00000155.jpg\n",
+            "\t00000150.jpg\n",
+            "\t00000148 (2).jpg\n",
+            "\t00000149.jpg\n",
+            "\t00000166.jpg\n",
+            "\t00000156.jpg\n",
+            "\t00000174.jpg\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import os\n",
+        "import numpy as np\n",
+        "import tensorflow as tf\n",
+        "from tensorflow.keras.preprocessing.image import load_img, img_to_array\n",
+        "\n",
+        "# Define the path to the image folder in Google Drive\n",
+        "image_folder_path = \"/content/drive/MyDrive/Dataset_truck/test_data\"  # Update this path accordingly\n",
+        "\n",
+        "# Get a list of all files in the directory\n",
+        "all_files = os.listdir(image_folder_path)\n",
+        "\n",
+        "# Filter only the image files\n",
+        "image_files = [file for file in all_files if file.lower().endswith(('.png', '.jpg', '.jpeg'))]\n",
+        "\n",
+        "# Load images and convert them to arrays\n",
+        "images = []\n",
+        "for image_file in image_files:\n",
+        "    image_path = os.path.join(image_folder_path, image_file)\n",
+        "    try:\n",
+        "        img = load_img(image_path, target_size=(150, 150))  # Adjust target_size as needed\n",
+        "        img_array = img_to_array(img) / 255.0  # Rescale pixel values to [0, 1]\n",
+        "        images.append(img_array)\n",
+        "    except Exception as e:\n",
+        "        print(f\"Error loading image {image_path}: {e}\")\n",
+        "\n",
+        "# Convert the list of images to a NumPy array\n",
+        "images = np.array(images)\n",
+        "\n",
+        "# Print the number of loaded images\n",
+        "print(f\"Number of images loaded: {len(images)}\")\n",
+        "\n",
+        "# Example: Accessing one image from the array\n",
+        "if len(images) > 0:\n",
+        "    example_image = images[0]\n",
+        "    print(f\"Shape of the example image: {example_image.shape}\")\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Bcxu56CZ8tWn",
+        "outputId": "727908a3-565b-4a82-afa0-a9a0793417fd"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Number of images loaded: 0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import os\n",
+        "import shutil\n",
+        "from tensorflow import keras\n",
+        "import cv2"
+      ],
+      "metadata": {
+        "id": "4eUpapu4HiWE"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from imutils import paths\n",
+        "from pathlib import Path"
+      ],
+      "metadata": {
+        "id": "uxQEx--HI3-r"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "images_path = Path(r\"/content/drive/MyDrive/Dataset_truck\")\n",
+        "trucks_data = list(paths.list_images(images_path))"
+      ],
+      "metadata": {
+        "id": "F-cjwkZgJB6h"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "trucks_data[0:6]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "slqxxXIqJLWq",
+        "outputId": "95ba7106-42d4-4907-f89a-7cfbbb339639"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "['/content/drive/MyDrive/Dataset_truck/test_data/truck/00000194.JPG',\n",
+              " '/content/drive/MyDrive/Dataset_truck/test_data/truck/00000176.jpg',\n",
+              " '/content/drive/MyDrive/Dataset_truck/test_data/truck/00000175.jpg',\n",
+              " '/content/drive/MyDrive/Dataset_truck/test_data/truck/00000191.jpg',\n",
+              " '/content/drive/MyDrive/Dataset_truck/test_data/truck/00000180.jpg',\n",
+              " '/content/drive/MyDrive/Dataset_truck/test_data/truck/00000196.jpg']"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "truck_image_data = pd.Series(trucks_data, name=\"JPG\").astype(str)"
+      ],
+      "metadata": {
+        "id": "5z6aVQZHJWB_"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "truck_image_data.head()"
+      ],
+      "metadata": {
+        "id": "lhom1o9dJaJr",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "6072f824-9cb9-4c53-a381-040a56f6dea0"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0    /content/drive/MyDrive/Dataset_truck/test_data...\n",
+              "1    /content/drive/MyDrive/Dataset_truck/test_data...\n",
+              "2    /content/drive/MyDrive/Dataset_truck/test_data...\n",
+              "3    /content/drive/MyDrive/Dataset_truck/test_data...\n",
+              "4    /content/drive/MyDrive/Dataset_truck/test_data...\n",
+              "Name: JPG, dtype: object"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt"
+      ],
+      "metadata": {
+        "id": "l1QrjOQ7JdwJ"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import cv2\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Read the image in grayscale\n",
+        "image_path = \"/content/drive/MyDrive/Dataset_truck/test_data/00000176.jpg\"\n",
+        "truck_image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)\n",
+        "\n",
+        "# Check if the image was loaded correctly\n",
+        "if truck_image is None:\n",
+        "    print(f\"Error: Could not load image from {image_path}\")\n",
+        "else:\n",
+        "    # Display the image shape and dtype for verification\n",
+        "    print(f\"Image shape: {truck_image.shape}, dtype: {truck_image.dtype}\")\n",
+        "\n",
+        "    # Convert image to float32 if needed\n",
+        "    if truck_image.dtype == 'object':\n",
+        "        truck_image = truck_image.astype('float32')\n",
+        "\n",
+        "    # Display the image using matplotlib\n",
+        "    plt.imshow(truck_image, cmap='gray')\n",
+        "    plt.axis('off')  # Hide axis\n",
+        "    plt.show()"
+      ],
+      "metadata": {
+        "id": "2thUsTv6JjOp",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "4518a52c-887d-4893-81ba-a8f03c56ede5"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Error: Could not load image from /content/drive/MyDrive/Dataset_truck/test_data/00000176.jpg\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from skimage.morphology import skeletonize"
+      ],
+      "metadata": {
+        "id": "phHMwLkWJ3uK"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def simple_vision(img_path):\n",
+        "    Picking_Img = cv2.cvtColor(cv2.imread(img_path),cv2.COLOR_BGR2RGB)\n",
+        "    return Picking_Img\n",
+        "\n",
+        "def skeleton_morph_vision(img_path):\n",
+        "    Picking_Img = simple_vision(img_path)\n",
+        "    Gray_Img = cv2.cvtColor(Picking_Img,cv2.COLOR_RGB2GRAY)\n",
+        "    _,Threshold_Img = cv2.threshold(Gray_Img,90,255,cv2.THRESH_BINARY_INV)\n",
+        "\n",
+        "    Array_Img = np.array(Gray_Img > Threshold_Img).astype(int)\n",
+        "    Skeleton_Img = skeletonize(Array_Img)\n",
+        "\n",
+        "    return Skeleton_Img\n",
+        "\n",
+        "def threshold_vision(img_path):\n",
+        "    Picking_Img = simple_vision(img_path)\n",
+        "    Gray_Img = cv2.cvtColor(Picking_Img,cv2.COLOR_RGB2GRAY)\n",
+        "    _,threshold_Img = cv2.threshold(Gray_Img,90,255,cv2.THRESH_BINARY_INV)\n",
+        "\n",
+        "    return threshold_Img\n",
+        "\n",
+        "def canny_vision(img_path):\n",
+        "    Threshold_Img = threshold_vision(img_path)\n",
+        "    Canny_Img = cv2.Canny(Threshold_Img,10,100)\n",
+        "\n",
+        "    return Canny_Img"
+      ],
+      "metadata": {
+        "id": "RyTaSHDOLFZE"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import random\n",
+        "number = random.randint(0, 466)\n",
+        "global truck_label\n",
+        "truck_label = \"Indian Truck\""
+      ],
+      "metadata": {
+        "id": "7AACMN4JLGqf"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "figure,axis = plt.subplots(nrows=1,ncols=2,figsize=(10,10))\n",
+        "\n",
+        "Skel_Img = skeleton_morph_vision(truck_image_data[number])\n",
+        "Simple_Img = simple_vision(truck_image_data[number])\n",
+        "\n",
+        "axis[0].imshow(Skel_Img)\n",
+        "axis[0].set_xlabel(Skel_Img.shape)\n",
+        "axis[0].set_ylabel(Skel_Img.size)\n",
+        "axis[0].set_title(truck_label)\n",
+        "axis[1].imshow(Simple_Img)\n",
+        "axis[1].set_xlabel(Simple_Img.shape)\n",
+        "axis[1].set_ylabel(Simple_Img.size)\n",
+        "axis[1].set_title(truck_label)"
+      ],
+      "metadata": {
+        "id": "lR4AuaK1LKAT",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 305
+        },
+        "outputId": "d065f9fc-eca2-4b51-d42e-88d660a8a069"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0.5, 1.0, 'Indian Truck')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 17
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x1000 with 2 Axes>"
+            ],
+            "image/png": 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EWZPefsMFEtcBgUjzb1dwsMWYR+vU+pljgOaeKf/OXPh4dV+bpFAoFH1OU/hddcO/qK5/ElPWdXB8lHBsDZHYRgLhRRRm/h8O25BmgkcSjW/pgog6iGFVUVZ7H7qWgdd5PACB8NsEQm/Q3ZsxSwapCDyE2zEJu62wxT7TChCOqSQTvYkSUmlAmzKO4PAMin73CViHLyBMN+XfOpEBf1me1jUxtkEDqZwzhOzHPkpbnwD6+NHUTc7F99/0xgdX3HAiRX9fiRUKpbXfvbedSPWULHL352NWVqa1b0X6afJMRU6bROiq2eT8bzVWUCXqUCgUxyYJEdVAee0D1IZeorWkDW2eS5yGyDvEzf0MzL4Tp300QggsGaS87jddFlFNmFYN5bUPMCTvd2iam+qGfyOJdavNJmLGTupCr5DrvxohEjP2Ukpixm7ilgrr6036gX/k6EabMo7Q0AzcLy7rFyKqz5B9lD7Akn3Xdx9gC0PuSxupOWsken5+X5ujSANWJILj9U/Je3s3FVdOQZs6HjS9r81SKBSKtCOJUxn4S8oiqjnR+Gb21/wUwyxDSkkg9BbB6Kc9Yl/U2EZN8GlC0ZVE41u73I6UTQ+BtDSkqVFX/zamFWyWjp3EWi4Z7QnTFW2gPFK9iOb1UjE7m7y/LDm2RZQirZh1AbJfXk/NhRPIfsPCPFDV1yYp0oCxdx/5fysneOFxiCEluF4+xidvFArFMYWUkobw+9QEn6GrIqqJSHw9lfV/oyDjempDz3W7vYPaRlIXepVofA+WNIDGZRaWBpaOtHQwbAf/Ne3IuB0ZdyBjzsQj7mz5d9yBjNtpsNzYp2xh4KxxaDYXMWM3dcFXUHlwexclpHoROWEE/j2GuplRpB0zECDrxXXUzJtI9puaCvM7RpCGgfu5JQlP+AVKTCkUimMHS9ZzoP4xpAz3QGuSQOh1HPqgwzxHUgJSgNQSAqjRK4TUwNSRRkLYJP5NPIg5sOJOiDkJx13UxX2Y0c8mBRGGHWnqyTalFIk+hETY4gh7HOGINj4iiYcrjJ5ZjXCGEa4Iwh6lzvUKxoFCXM6hhGKriZm7e+BaKNpDCaneRk0EKPoIq76e7JfWU3PBBLLfVJ6pYwlr9UY8KDGlUCiODaSUBKPLiMY3t7Kv6Q/R7N9GT5DUkIYNadoSYsawH/w77mC/sQwjOgkZdSBjrmaeoEaRlBRN9kYxZGsUWY19aCZCNxA2A2zxRlEURbjCaM4wwl/bKITCCEcUzREBeywhmOzRxHmaidAsEBZokuZZrJJptCQgJAhBxCwjElrVa9da0RIlpBSKoxgzECDrpUbP1BsoMXUMocSUQqE4VpCmSc2+1cSqBxwMfTssHM7RbF/Tv3YwG0PpLB1M7eDfEtBNhGYm/tUN0A2EI4ZwRNCcYTRvPcIZSYghZwSt0VMkHJHEdls8cU7yfBM0i7Zm2cVhf7SDBbbddjwfetHrNGIjo4RPDGH5ZOfOV/QISkj1IqZTx3SpfB6KvqWlZ0qJqWOJpJg6vwTXK0pMKRSKo5NYIELZU+OI1Q9v3NKoJIQEJGgWQlgJQWSPJbw9jiiapyHxtzNy0EuU9BBFGo+NNXqSYgmvkmY1en8a2xbyYI9dETAStHoN51onIqQRHxkjPjTefjo4Ca5P3OQ+kI9e2Xgrr0N4RpCqmyuxclTdqHShhFQvoRcWcGCiG0sXZAwfirFjV1+bpDiGMQPNE1AoMXUsYa3eiEeMV54phUJx9GKLYR++Ds2qToggZ+M6ImciXC65vsgeA808GC6nNXmIoEUt20MdRp0RSBJERGDfYUfEBPHhcSy/1f65EvQyG7n35eNa4wYTzByT2q9VETy7oU0xpQU0sv+Wg15pQzR1YIJ7iRf/C1HqrqpVebnThBJSvUTV3JEUvbwbs7yS2oumkwVKTCn6FDMQSHimLpygElAcY1irNuCRyjOlUCiOTjRPCO8pr2JY1S22iy56iPRyHfdSD0iIzAhjDDA6FFOiQSPnd7l43vchTEF0XISq71diDI63fa6EjKcyca10JwWRXq2T9UgOkalhzKLWf6vte2zY9jkOiqgmG6TAtcxN3RW1SkiliT69zO+99x7z5s2juLgYIQTPPfdci/1SSu644w4GDBiA2+3mrLPOYsuWLS2Oqa6u5oorriAjI4OsrCy+8pWv0NDQkMZX0TrhPIGxdx8yHsP/3ApqZxRhGz60r81SHOMkxdTZI9HzcvvaHEUasVZvxLOnnsj5JV28uzh2OJrHJoXiqEVIhODgg4Qg8r3kw/uaD61W6zgBWKOHKP/HReQ8kE/Ob/LJ/2ERtr22Ds/1LPbgfduPFtUQhsC51oX/mYz2TzLAudHZQhAJBHqVDVupvW0z27h7l6AEVJrp08sdDAaZOnUqf/jDH1rdf9999/G73/2Ohx9+mE8++QSv18vcuXOJRCLJY6644grWrVvHm2++yUsvvcR7773H1772tXS9hE6hxJSiP5EUU+eMVmLqGMNatQHP3gbC82f2tSn9mmNlbFIojma0Go28uwvJub+A3PsKyPl1PiLc8SSS5wMPji1OhBQIKbDvcOBd5OvwPNt+OzRbmiQQ2Pc5Wmw7DD0RyidbqDSJ5bKwMtqOHIgPjhMfET3kPECThE8IqXizNNKnl/q8887jvPPOa3WflJLf/OY3/PCHP2TBggUA/OMf/6CwsJDnnnuOyy+/nA0bNvDaa6+xdOlSZsyYAcDvf/97zj//fH71q19RXFycttfSETIew//CSmrnT1Nhfoo+JymmVAKKYw65YRuBU0pw97Uh/ZhjaWxSKI5WnJucODe4EI1pz93L3dj22omPibV7nogd7rkSsY4FWHR8GOnMQkQTx0pNEpkcbt9loUH9Z+pwbnChVesIBFKH0Jx64kPibZ4mfZKar1eRc38+9j0JsSYdktCpDTRcWK+y9qWRfusA3LFjB2VlZZx11lnJbZmZmRx//PEsXrwYgMWLF5OVlZUcqADOOussNE3jk08+abPtaDRKIBBo8UgHMhpVnilFvyGZgOKc0ei5OX1tjkJxRNBbY1NfjUsKxdGK6beQjoQikkgsl0S6O85mFz4+iFlgIBv/M3NMQieGOjwvMj1C3ZeqiQ+KYRTGabgwQMOCDr7HInFe5Y/LaLgwQPDUeqpvqKTm2ur2XR0CopOjVNxbStV3K6j9WhWVd5ZRfdMBLJ/K2JdO+q3zr6ysDIDCwsIW2wsLC5P7ysrKKCgoaLHfZrORk5OTPKY17rnnHu68884etrhzJMP8VAIKRT+gRQKKN7Yoz5RC0QG9NTb15bikUByNxMZECXyuBt8rGWCDustqMIqN9k8SEB8Zp/InZXjf8oMFoTMbiI2LduzlsUPg0joa5taDCVamBTodn6clRFF0UjRpQ4t/27HVLDQJntfQYpsivfRbj1Rvctttt1FXV5d87NmzJ639J8P8Zg5Iu2dKOJ0EB0q0wvy09msW5xIsFqDpae03OiiThoF6WhfXC7ujT66x5nIRHCixFeSldJ4ZCJD9ygblmVIo+pC+HpcUiqMOB9RdVUvZQ/so/cNeGubXJ4RNRwiIjYtRc30VNd+qSgicztwtC0ADK8vCyrUSrorO3no0novW+Hcq5zV/KNJOv/VIFRUVAVBeXs6AAQOS28vLy5k2bVrymIqKihbnGYZBdXV18vzWcDqdOJ3Onjc6BWQ0iv/Z5dR+5ri0eqbqLp7O8BdCVJ84kBzDxNi3v9f7tA0exIFxPgYsjhBJYy0bfcxIAgV28tZEic2dgeP1T0F2lLKn+9RdchzDXgxRfcIAcuIGxt59vd4nQPXnpjPimRDVZw4ne6HELK/o+KRGzNq6g56p1zdjVlV3fJJCcQzSW2NTfxiXFIqjDh3MvC7cb7QnTGR7CfxkOye2cXiLfyXSEok1T1ZjX43/Jrc1/isbt2EKpCUT2ywBpkQaAhkHaYA0wYonlJrAgXmWADVn2mP0WyE1fPhwioqKWLhwYXJwCgQCfPLJJ1x33XUAzJ49m9raWpYtW0ZJSQkAb7/9NpZlcfzxx/eV6Z1GxmP4n19B7YL0hPnZhg5GaiA+WkXWSg8186eQ/T69LqYqzxxM/sLdGHv3YZ+Svlo2FacVUPDsZswDVYiZk4meU4LjjWW9KqZsgwZi2UXLa/wBvS6m9DEjsUUkYvEqstb6qblwItlvyZRqRakwP4WiY46FsUmhOOKQgCUSwkE2Pm8mUlpsa3zIJkFi0kyoNAqQuMQyE1n7pCmRpkhsM0CajULHpPFYsIzG802JjGvIGFhxiWUIMEkKGyuW2Cabjo83CZ7GY63GvsxGu8yDdpLc3nhMk6gyOSiurENeOyR0nQVIgWYXjJkAPiWkeow+FVINDQ1s3bo1+XzHjh2sXLmSnJwchgwZwne+8x1++tOfMnr0aIYPH86PfvQjiouLueiiiwAYP3485557Ltdeey0PP/ww8XicG264gcsvv/yIyYoko9GDYkpKjJ27e68vjwtbJPHtskIhMl9YTfWCKeS8Y2GUtr2mrLvE/QKr8abcWr0RD+MIXVCC66WlvSpq4l6BWV0LgFy6BsesycTOKUl4pnqJw67xcyupuWga2e/LXhWslt+FPZgQplZ9PVkvraNm3sRERr5UxdQrG6g5fzzZr0vlmVIck6ixSaE4sjBqYfcDdiJV9oRYiZMQEyaNIgisWOP8bZNoMhOCJnnsISIrIUZEwkvUdM4hAu1QwZZ0RmnN6lkJCQKEJhG6QNNAaKBpEjQLTU/s0zQa90mETSa2OyS6DkKXaDaJZpfoNtBsEt0mETYr+VyzgW6XaDar8Xni2APb3Gx+PZG+va36U4qu06dC6tNPP+WMM85IPr/pppsAuPrqq3n00Ue5+eabCQaDfO1rX6O2tpaTTz6Z1157DZfLlTznX//6FzfccANz5sxB0zQuvvhifve736X9tXSHFmIKelVMNccKhch6fnWjZ0pLS5gfNBdTM9PimUqyZA3OmZOJzp3R656pJqxI5KCY+kCkLczPqq8n68V1Cc/U26LrYX7KM6U4BlFjk0JxZGFFILTMIlpjIZqEiZ4QLLouDwoRh0SzicS2pOBILN/W9IPiJLGtUZzYJJq92bFN2w4RLM2FjNBl4nxdoOkSoVsIrVEQNQopocnkvy23JbbTrLhwU7HhVukgknDbuzIppBQ9T58KqdNPPx3Zzs2sEIK77rqLu+66q81jcnJyeOKJJ3rDvLTSl2Iq84VGMfVu73qmWvS7eiMeMT4R5vdy73qmmtPCM5VOMdV0jXvZM9Wi3/r6RHrzCyZ0LczvlQ1UXzCenJcNzNq6XrRUoehfqLFJoTiy8OQZnPezfQiroplAaRQimjwoRESj7mj6WwAc3AfNclMdIlDSmLNKcQShnHz9iGQCilnF2IYNSVu/yTC/04dhG9B2ko4e73fVBjx76olcODO9v1BL1uCoiRI7pyRt/SbD/E4Zgm1g+kJ7zEAgEeZ39kj0/NSyCJq1dbiqTazR6fssKnofzefFVDkNFArFUYSmgzc/hjfPwJNj4s4ycWWYOP0mDq+FwyOxuyV2l8TmkticEptDotslup1mXilairDmXiGFohWUkOpnJBNQ9IGYynp+NTWn9YGY2l1P5IKZ6U2N3hdiqskzlWYxlQzzOyt1MaU4utCzMqk+dwxCgub397U5CoVCoVAc0fTbrH3HMn0d5pdIQEH6wvxWbcBD+sP8WJII84ufXYL9jd5LQNGcFqGUaciYmOy3Kczvwglkv2F1b92TpiO01MSnNnYksQJvu8dITSCszr/3qRxv2TVqxjiwOvOLJyFvTRT7e6uQRgfFG48g9IwMas4fT87rWxDnjEbLy8Gqr+9rsxRHKFLKxnX2h38HNQSilyaopJRYrfQpEIlorT7oV+utPmm6zum/xsfSe6tQdAclpPop/SIBxbt9IKb6IAGFfdbkRJ2pdK2ZapHNL31iygwEEp6peRPJflNLac1UE7aiQsoWjMBwpTag+feauCtibe6XumDXuQ6GvxDpdJu7z3cyaFEcPdzxZ8UWjVP017VYsXiHxwpdxzx+AsF5JXhfXHZUiCk9I6NF8pDsVXkEphfhSVP9OkX/or31X6nw0r4NrA+Ut9iW5XBz1bDjcOv2HunjUCKmwT93LacqGmqxfVxGAfMGjkfrpZ/w0nA9T+xagSGt5DaB4KyiURyXPbB3OgWWV+/jrfItLbbZNZ0rhk6n0NU7CQQk8OyetWxpONBie57Ty5XDjsPZS9EjITPOP3Ysoy7echyYkjWAcweMbXd8Fu1UdlIoehMlpPoxh6VG35WeSvdWKETmi2uonj85/Z4pMYHwecfhfCU9xXOBhGfqhCkJz9SbfZGAIs2eqZcaE1C82bFnyru+jKqTB5KzNRvh9VB9ymAKnljb854MIfBOn432/opOn+KdfCLODzdgBYOdOt7q+BAApGWifbAS//jR1B8ipjSXi/AZk3G+lsbPZzfRMzIS73fzDIxSItXk7jFLyLT45dqd1MW6N0kQtPYSpQZIiDMjEMThzWRjdQaC3rnZlpgErFJMoonnlkW8toFtOTHe3a+TUjHUFDAJUWeV05Tr2oobmKEIO+vtuETnfoO6QlRWEpQHs64awTCaZmNXIAMdd6/122DtI0YiyZCUEqOuAac/h7VVfkQvrQqRGNRZZVgkJt2a3tvtOQZv7m3/fR1kK+NGt8R5ZPwsp5dmP/ZSgmWkaaL6GEEJqX5OCzElRPo8U8EgWc/3QZjfyvW4mZDwTKUzzO/j1dhPmNLrdaaa06eeqZc655mSdQHCeYMp/cJ4LDsUfVR/zISDmRu24OegmELXqVswjeyP9mIcSSJKpbFXHELUtHhyRzll4ba9xB0jOS4/zOBmS+1Cu0sJFtt4pb6MmNU7N9t2zeKUYgO/o9EKyyK4bQ8VtlyWHyhF9pKQynLEOblYoje+LDMYJrS3gg3xAewIdOzp7irDM0JMyTv4PFpejXQ4ea+qkrpY73j9QDKrMMIAb/IpoV2lNAxx8WJtKUYvFSNyaBanDjTwNr4saZgEt+2lVM9nZdV+2hPJUxx1fMMzDN1qrdKsaOXU1D8nos1zDt3eD2apJFhoBGM5VO3PArE5MYFmmtRu3kHe1PEqVLKHUELqCKC5mMp0O6GiKi2FUq1QiKwX1lAzbzJZyzxwoAazpqb3+125Hk+jmPIt241ZeSA94VUfr8ZxwhRi587Es3I3RsWBXg8xbO6ZylrmSt81buaZyvnEi6yuaTXFefiEMRQtrocla0DTiVxQgmfKOKzVG3vdxv5Ak5gKLCjBsguyP9iTtlpg3UWJKEW6yZg0iqBhQ6ZxrkXoOlkzJhAKpjd3li3TR0amD3r1q3X4hI1neDGmFJCeebcEAjKmjKY+boPaNHZrt5E1YzyB+o5v+KVpwywrxjIy02BZ/0VKMCwnteEBVDSMJBApItoQwubejxGqBymp+HQ1Iy8+F6GnMcHXUYwSUr2APmYkDcNM9IljMddt6pE2m8RU3fxpRDPyyF9ak5abWSsYJPPFNdTOm0zMW0jB+xWYm7f1fr8r1+OR46k4dziO4DD8zy5Hxrsze9pJPl6NY9ZkKs4bgatuGN4Xen+NTFMCitp5U4j5Cin4oBJz09Ze7RMaa0W9vJ7qCycQdw+g8O39GIesmZEChGwczi0T18vLCF1Qgoc+FlNCEM0hUQa+lzE3bCFy8mw8leYRI6I0vz8hot7cpkSUIi301ex2X/bbF45pIURr+qr3++wDkv2m2L2UYNXbiK/PQMa7MkZI9OII9uFBhNbYXp2d2AY/GK20JyS2gRFsw4II0Xh8jZ3YxgxIzsWm8CKExDYkjG1wqFNJhROfQ0HY8FMVHEplwwjCsUwkiaq+ukvHmTMgIaSAqtUbMMIRHL72E0ApOocSUj2MPmYk9ZPyGHvrGhrOmYSPnhVTvqc+wafpiYXw1jistekRU/4nP0bYbAQWlJAB6RFTqzaQswpsw4dSe9F0/M+tSI+YWrKGnCWgt7JGprewQiH8/0lc4/r5JfghbWIq44mPEXYHtRc1JjZpLwFBo5hK5+evNfTxo7GFIDhnPO4Xejc5iTxpGp5KE9Mu0I4Ab5zyRCnSh8QyTISupfdmW4I0TdDS26+UEmlJRBomcFr02wevFRKhdfTRe9tdb0lsbSahV4u6LDr1oigZX9mBcCfGlujybMKL8ttsTx8YJvPLO8GZWI0b/TSH+vdy2U41o8lNOdjPNjxExtU7wdb2C5ASLGmjPppHRcNIakKDiJtN6+aa9Sg03IXDCO7dAkga9pbRsKeUnPGjUrRK0RpKSPUgTSLK+8IyLMPA/cIyGuaX9KiYAsAycb/YzDOQpptZaRj4nl+WVjEFiRv7LEivmOLwNTLpCC+UhoH3hWVpFVPQWL/shZUHs0R2IKaSnz/bBOSGbchotFv9C13HdICw2Tq+zkJQPzabgW/XImIG+284noH/2wmWRemC4XjLLfzrq3rk2skTp2I6ddzPLwWh9Q9vXDu0mljiEITNxoFZuQSLBRlZma2GdCoUnUGaFjVL1pI9YwLC0VvrdQ7HaAhRs20L2TMnpq1PgNDuMjTLwDdycPo6lZLalZvxjhiEIzt9td9k3KB65RqyZ01C2NN3q2gEGqjduZOsku6t4XGMCyDDOjLehTYE2AaGEc6DE3TOSXVggTRaaU+AfXAI7AkRZVoW5vhqFu3Yyzqjku8Nn4YtlZIhAuzDg6C3LqKkhLjppjo8iIr6kTRE87Ckjba8XkIIXAVD0Ox2rHiMeEOQqrWbyR43Uq2T6gGUkOoh9DEjCUzOw/d8sxtuyzwopuQYzPWbe67D5p6BvhJTUmJu2Z6WfpWY6n1kNIr/2eXUfuY4sgBzbymRHB3v6ioOe9XNPn/1J5Z0v7S3TIQQln99VscREBJcNRZywzasaJTC9ZsxAM3rpeiJdchhxQQm5xI8u6BbJsUyoOiTKLaFyxr77Uehja2QDOfrQEQF55eQ91EFeVU11Jw/nuyX1mMGAmm2VnE0IHQtccObxhttAJvPjXfa2LT2CeAeVIguOpv/s4cQgszJoxC29K5nEbbE2rN092vL8OKeMrpb6RqEAC07jvvMio4P7qCdZHv5UdxzOmpPIiW8tekAz68u5+2yAxhSkjkuzBdnDiLf50hJuDQ/VEqQCEKxLCqDI6gKDiMS95EYMDtu0+bLwZ6RS7SqFKSkcvkaRl1yXqdtUbSNElI9QKsiqonmYoqeF1PuF/tYTAmRXs+UEEpM9Wa/8VgysUl4bjFFC8vbziZombifX4LHZkM4HN3r2LKwIhGE3dHxTVnjsYfO1SXToK8O4F0j8Lu7nho4Nns8pkM7KKKSnTQKyPklWGOOx//6+n6RxbCznqjg/BL866qS39ns1zcnxNcrG5RnStEFBLqzm9/9LnUr0NLoAWtC0zW0NIf1AX3yWhF9+N7aU3+9EcNi6Z4QrlgDjox8bM62a2zJ5L/tC5BoyEwpatyyYqzZvoV/L9nOij0Hf0/vfWMrH+62+NbZ03HanW2eLxw6miMx/mXoccY5KxGAYTkIRAqpaBhFbXgApuUg1YVjQrfjLhqGNIMUn3o8oy49P6XzFW2jhFQ3aQrna1VENdEopuounkHV5bNxVvesK1XqsPesTAo+OQHv/rbTsNYOdFAx2yRj0Ik90q9lg30XZjHkmVnYgiZSg5oxTqxDfnsDE+NEbjkORw/dp4ULJTVjZ1C4NIYWbzt+ODDOQH7neHpqAjE4yMI5bhZFnyTC2ML5doIDWg6qcS+ER0XJKO7Ba3xGLoUf5eEpb/u9rRvhoHqKhX90D/XrgEiuJL8THkdpGD0mLmU81jMCWUqsUKjj41pBOJ1Uj3VS+JdPWw+Ht0zczy1BHz+ayksmUfDqdoyy8taOTAua358QUW+1nViihYhqJsrNqmqy39iiPFMKheKI5kDQ5LZX9xCvDyAmzYIBU7vXoCWR++uQkc4pKWkEqdn+LHUVazGMlmN1JG7xzqb9LNkvyB4+H7tvUOuN5LghO5EAYqa/hl8VLaY+XExlw0iCsSyk7HqdNLvPyajPz6dw6rVkjxuJZrepsL4eQgmpbtCuJ+pQLBPf/z5FWDPwb6nDWrul/eNTREwdR/1oP1qs7S+9HpcMeE/D/99PeqRP27DBVJUV4ahJVCEXlqTosY1Y4ZZVyQuPn0QsB5yvLu+RfjW3i9oFk9GjFsJoWyU5Kh0U//7TxELdbiLsNoIXTMMWjCWvcdaKWvxP7Tzs2P03HU/R73rmGgMwayLBQe5231stDoMWStwv9ky/+tgR7LgsHzS911PA9zfiJ08ib3WoQ0FnbthC3u79lF05hby/9H6q/NbQ/H5q501MZOdrox5YWyKqCfNAFdmvS+WZUigURyyJdUOSmGFhmVpiFrI7WBJhaZ2aiJVmhFDVRqrLViGt1u8F47EQDfVl+IUTrFbCJaXEsjSwNBCCulgOa0rnIltLHpECjiwXuVMGUFQyCG++L1FSSwmoHkUJqS6i5+ZQeVIBuY8v7fxsvGXiffZTgvNL8BkjeyzMT5syjtBAL76nl7Z7M5c5ZRy1E7N65IbPNnQwNTOLyHpqRYtEA63+5mgisaMH+tVcLuounEzOex3X83Gc2Oid6W6/mk7ovKn4N9W2SBrSaquisfBfD91UixmTiGQ78f5vSbvFibONKYQGuHukX33UcAITcxj6cj3h80vSWxg5BfTcHEKzRuJetAYxuJiGiYmqlf6VZRi79nTJZs3lomaMg4K/LO1UsicrGMS/20CUTEAuXZNyf91B8/upvbB7IqoJs6o6EeZ33niyX1ViSqFQKDqLtAzq938ASFy5xbjyBhPavxW7PwfvwHGY0XpCZTuIVpcRa9iP7irsUMyYlp2Y6aBLQZ0CnDke8qcXUzR9IK5sjxJQvYgSUl2k7szR5H9UiZlqSFNjmF/9ghn4e2DNlDZlHKGhGbhebj8FtJ6RQcXx2cS9gpwRwzC27+xyn7ahg6k5YSAZz63oMFubnpdL2RQ39gZwdSYjWztoLhd1F03rVFFU67TpAIQumNa99NiaTnh+Cb4tdZ3KvBg/uwQAffSIbifiECUTiRS4cb76abuiQM/NoWyaF8shyBg6OCEiuog+ajiBaQX4nk+sQZMXzUKbPLZfJVawFRUSnjKYrefaEBZ4xxyH4YVoroW7TKNu6CCK/1p1cM1UCsROnEjumnBKn1P3e+spv2oK+cvS573rCU/UoZhV1WS/qcL8FKkgCW7dg7MoF5vPk75eTYv69dvxjhmKlsZkCGY4SnhfGb4xQ9PWJ0Ck9AAIgasoN639NmzehWtQITaPK219SsOkfusOfGOGHgEFYyXSMjAiBxA2GHL+N/APm0ystpJQ2VaEpmPzZWNzevENGkf18veQVpTEKq0ORI3ViWMORYCrwEdRyUDypxbjzEi8b0pA9S5KSHWRhkE6vqe6mADAMvE8/2m3xVSnRVRWJjXnj6fgmU1YgQbqFkwnE7okplIVUTVnj6boX+ugMI/AgpLOhUG2QldEVPF9H6FPHEvDvBLcL3ZBTGk64XmdFFFCEJ9zHPZAjIG/WU1gwfREVsOtO1Lrs6m5GZOI5HdCROXlUnPO6ES2uliMmgXTyLYkxp69Kfd5qIgC0KIS6eyDhc7tIHOzqJroYNwvt2MMzkfbsBOawjfHDGPfmVldKtQr7A5qxjoo+PvylEqPWMEgeatCHLh2FvlLAoitu3s1AUUysURHImpe50VUEyrMT5Equs+T9sxuCIEt05f2G0Sha9j86ROMTejuthMU9CY2vxdNT29yDaEJdL+PTlWi7Q9Ig1DlMnSXHd3hIrjxQ8Z7LTx5LkyHC9Ptpt5bQGDQKMwa0G3j6JRA6uTLl1IihcQ3MJMBM4eQO6kIh9fRePmOkGt4hKOEVBeR3f18dlVMCYGw2RHjR3TaE1Vz/vhENq+qagB8z684WHy1k2JK2Gzog4qpOb648yLqnNEHZ7YDATIgIaZe6HzGPWF3IFxO6s6f2DkRdcp0pBDo7yTWY5nrNuEV4wimIqaEQHM6CZ47pfOeqDnHYQsa8PFqJOB7bhmBixrrbXVWTGl6YgZuyuiEiHqtY09UzTmjEze8jd6DjOdWUHPRdLI/otNiStgdaMMGEZia30JE9VekEBQuCYHTQfVEH8G5k7E3gG+vSahQZ9DrVZhdEDLhc6dhC9Glmljio1UUrs0geOo46o+fhH+viWfRui55xdojmVji7e095ok6FJWAQtF50u8lgcTNtntg98obdAXNYcc+ID/t/dqz0lc/qjmuAXnp71TTcA9K/3vbIVICEqwILRYxmGEEBrrdQXzj+9gD+5h/3gwGZoMkiiXqiWgVbCePR60oMaspCZIGmovGmLvD+xOiXRkkpcSwDHbW7mZZxWq++tnrGDBziPI+9QFKSPUlKYopzeWi+tLpRLMFGbuNDkPW9IyMxMzy65uTIgoaU1w/13kxpU0aR+kZObhqrMPWRLXa76EiqhFzy/aEmJo/vVOeKTFjEmWzMxAmFL+wq9OeqCYRldy+diNeOiemhNNJ7aXTiWRrFL9d1TlP1Nkl2OrjiMWrkpsTKeJXJDxTdCymNK+XqkunEMsQZG2L43ylEyJq7pjDrrGMRlMSU2L6RMpOzsRTaZHxv+X9XkQBsGsfobkToGgAoQGCYX/eivC4KT23mOzNMQLjsvCuS61Jbep44h4NR9BCzJzc6nonPSMjIdDaeF/MQADXS0twCYE4bgLlX5yCLQz57+1vv8BxZ23shXC+tmjhmVJiSqFQHMtICWYdWv0niGgph66QznbGsMVq+cKEAkKxEeR4nOyorGNglg+nXcdrNDA8Wos/vI3aSNP9iYZ0FGL5jwdbTqt9tpaeXUpJ3IyzuWob7+/4iF0N+6hrqCP+J8n9E36Dz9d22ndF76CEVF/TXEzJ0ZgbWs/m1xTalrdod4eCAgAhqLh0IgXPtxRRTXRWTGmTxhEcmUHhHz4By+ww5KkpnK+tm6+kmOogzK8ptK3wwcUg5eEFYQ/BOiUhorR3V7S+vxNiSnO5qFswjdx392Ls2dt6MokWRibC+Q4VUU3IeKxTYkrzeqmdP5n81zqXRjspopp5olr0G42S+fzKRJhfO2JKTJ9IZIAneY37XzqJ1rHq68lcVkbF6cXY68Esr0AfPQJXtcT+1gqq/u94vCm2WT86g6zVVYiGMAdOH0zW0sOPqfjcRPL/s7bjsD0pkcvWUbDKhl5USNVpgzFPLyZ7Uwht6YYuidVOJ5boQjhfWzR5pqrnTyD72TU97l1THH3IPkpK0yf9yi6sYemRbvuy3/QjE5Vo+xYZRatZiIjtbf3KS0lcCrxOB1OGFGLTNey6lnybth8IsP1APaFIAGE282aFA+hGHWbufNAOCRmVTU0ffPERI8qGyi28v+NDtlbtIG7GOeHE2ZSVlvLJxx/z7P+e4YovXtkntc6OZdTV7g9YJt4Xl1E/Lgd9/OjDdqeyPqiJ2Dkl5K4LtllXBpqJqZJCbCOGHd5vo4jqbEhc0hP1cvsz2OaW7WSsrqRhQQnCdriWTyZZ6CC0rQnrlOlIXbQpopLHrd2Ivd7EOnHy4X06ndQtmEb2R3s7HRLXFM7XmohqIimmphWgjxp+2P4mEZWzaEfnRFR2NtXnjulw/YoViSQ8UycOwjb48JoVYvpEIsWedtdgCbuDSI6Ovr/tz1BfYezYRfbGEN4yE4QgPDKXwDCNqi/Povid2i63a5aWEc1uGWphKyok8IUTqJ5mYUwd2em2pGFg7N1H5r8+puC1HRg+OxVfLcE84zj03FZmINtA83oTnqiOwvnmleDfWN2jxZvNA1Vkbg4SPnVCj7WpOHqJlB4gsr/1z2hvIaWkYdNOjGA4rf1apklg7Vak2UOFCjuJ2RCiYXP3PdypEt5bTrT88EnZ3kRKSf2GHZiR1MOtexIR3Y+I7WtTvgohGJibRa7fjV3X0IRgULYPR+P6soFZXoIxg/ghnxUBEK9ERHa2bFBKNM1A18IYYgNbGpbziaOch7c+zROr/83m6p0UFBVwxpwzmTptGnPPOw+bzcYjf/sbWzZv6TPRe6yiPFL9BGkYCTE1rwSfcwJaZW1ih6ZRc9KgzouoxlAzR00UlnScjrm5ZyrT7UTUJmbbzaJsgoO8qYuoToYBmVu2kyEEgQUlZH6yN3kzHx+STzTL0WGShSaaRNSh4XxtYXtnJeH5JXhKJqKX1SQ2aho1Jw7qvIhq8kR1IKKaaO6Z8rucaDWN18emU33iQLLf3o5RXtFhO02eqJyXO5cEoLlnKmu1GxFIeBSMwXlEcpwdhg8G500nZ2UNxr79HfbVF2ifbiB03Axqb51NziaTvNVxPB9vxayp6XKb0jDI2G2gTRmH2FdBbPJQagc5yfuwlKznKoicOhFnG6F/7WGUlmEvLaPA6cQ8YQIVnxmLs84i8+0trXqMk68xFU/Uxuo2PdrdQduwk8AVk8gXol+mwW/OkiVLWLx4MWVlZQAUFRUxe/ZsZs2a1ceWHRvYfG5scRv0Xq6VVnHkZKK1MinXmwih4cjLSntSBOGwY8/JhDRHYdv9XnTLCWnOP+PIzUprZsZWMetozy0mgBmDc8lwOQ5ua/a5kBK2V7X+pRBIhBlAaJKJjjKGiHq2aQM4y/kJ+Rl7KRpWSOmyPSzJczH7XItTaibxfqiYiMPL5k1beOO115k3fx7nnn8+zz/7LH988EHuue8XeDzpT4pyrKKEVD+iSUyFzjuO8NTMxEYBWlxidsJbAYn02/ZArFMiKtlvo5iqnzedqD8xU254oOi9aszOiKhWkh50BnPzNjKkpHLOkOQ2wwNFb5Zh9oKISpyUSD8fPr+E8KSMxDYBmiExS8s61YRxZudFVBNNYqrhwulEM7IBCA0QFH0cweyMiMrOToTzvboppUxqViRC5vMrqZs3lbgnsXDY8EDRwor2r7GmExis43m2Z2qd9ThCEJw3ncwdcZyvLU+K/c7mZdTHjCQyJCtR4wwIFulYei5ZldV431jLvm9Mw96QRc6GCNn/XY7RuC7Q9c4aqi6fTt7W7C4JNhmNor27gtz3BPqYkVR8dixxjyBrm4F7XxDWbkEa8cSMpNebEFGd9UT1goiCRCil6Uwkuemva+gqKiq4+OKL+fDDDxkyZAiFhYUAlJeXc+ONN3LSSSfxv//9j4KCfriI/SjCnuHDFtfTKqSEEDgLctAa0ptdVOgarqI8RFV6hZTudODMc0Ca57fsWX5sMT2tQkoIgaswB1Hf17eq7d+PZLkdjMz1EYnFcDSKviYhJaVk+d4q9taG2mlBcsLUCD+Ys4vMhmrCxi40LQAeJ96iGCdlD+GsnHrsQS9xewEuVya/eWARyz9NTPw++e9/c/uPfsTyZct49513eOXll/nsxRerEL800defTsUhSMPA9eISmldt0MeOon5+Cd4X2knQ0OiJsgdi8PHq1PuNx/A88wlNcxjCZqN+fgl+q+11W5C6J+pQzC3byW5Wb0nYHdRdNJ0s02p3gX6XRFTyZDORFKDZJn3MyE6lZ4+fVZKyiGpCxmN4nj14jbM1ndCCGfgntJ9opMWaqC6ko7YiEXxPfZJ8LuyOxLoty2o/CYag33ogNI+HhiKdgj9+0vHBrSDiBrawSeU0D7aQpOi9aqTTTvXc0fj3RsnafjCZS/MrYEUi5H1UQd3ZY/H99+OuvwApMTdtJXfT1kR469SxhAd6qTtlBnpUIizAgoKXtrUptNMhoo4UvvnNb2KaJhs2bGDs2LEt9m3atIkvf/nLXH/99Tz11FN9ZKFCoTga0YDpfj8eh07MMAmEwnhdTmyNNbBqQjHe3VKK1cFYWtegUZmbT3WtTv5IO65Bg0BIgppG5mDAsrBMCDUIVr2/ka3bamkcpKkor+D5Z5/jmi9/iXt/fg9/fvhPlMyYwfDhhy8nUPQ8KQupWCzGc889d1j4xIknnsiCBQtwOBwdtKBIFXPTVvzAvutnIQ/xcOtR8JWaGE5Bxo5Ql0RUa0jDwPtCIoV34LwT2zzOEZAUPN1zWb2avGPVlx5H8DMD2zyuYHmELomoNjA3byOD1q9xE0JC4SfBLomoVmlMNNIwvwRrwvGIZu4UqUP9IB2pgaNeUvD0xh6r6SPjMfwvrWL/tcfh39v6DL3UIN43GXfbRc/IALeLmjNHYPgSIhPTxAw0pFQnzNixC7FjF8W7B3Hg9MHJgsM5mzLY9v2JDL9zKbKN9szN22Bafo8UXYbEd41l63ABrqbXaLchwxHMUBuzmJquRFQzXn/9dd57773DRBTA2LFj+d3vfsfpp5+efsP6MRoH0ysLq2vrfHTg0CTNmhDoQmB21lEjgRT617DQLHmYzZoEXQi6vWLJslp1QGjSQlgS0WynkKBZFlpb9msHEw50DYEuDn9/NEugyXb6bbUpkfTAd9yrQBOi9fdWSqwufl6addBqrT9dJN7DQ/vVSXymDn1bpCWT64GETO9aNQBN0yjwZ3KuzKa2MQdIzDCJN4SoLI9QGouyoTpARUOkw7bWbXVyw4NDOS4vgwWbt2BmRimvqSarcBiFegNazGBPOId3akax7N11BKuCoOlgJSZ+33/vPU44cTZz5szh1Vde4U8PPcRP7roLlyt9xZSPVVISUlu3bmXu3Lns37+f448/Phk+sWLFCh5++GEGDRrEq6++yqhRo3rF2GMZc9NWilpZRK55PDBiCFow3CMplpsjDQPv0590mAEtxTK3Hfcbj5HxxMdk9HC7HWFu3kbR5m3p7bQx1FAfNxLZbGARpon3me0ph6x1uttIhOIntyAL264BY2/wYZ5+HD0pWLuK5nIROX0ydcNs6DHI/6CCnI8Nyi4ei9RAj0HBwr0Yu/ak1K6xZy/e0kLk7KmIj1dj1tfjrBFIs/0rnvnmJg4sGEfunv1YkY4HyVTocFKiyZOpRFQSp9NJoJ3rVl9fj9PZN0VN+yMeXePWiUMJGSaEQuh/+St0YTJswJwScuafDc3WJxmW4HNDHJidLLYo43EiTz2NsbpzNQs0ISnyRLFpLW+rI/kDCX3uy4isrE7bf7gxkui77xN7a+HBIt+NODSLQk/0MF10btROfby1WymBbWYJ7vkXtrg+qeKTIdzP/xtj/YZkhIAEzgo5iVmdD93SR43E/fnLEJ1cO5PvjuG2tRQnMUMyf+P7xN79ICXxeyjC58P9xSvQhwxuaaOQFHpj2ETL9zYY16mO2A8TUitWrOD5554jHo/jNOKYkfQlHPF4PBRnZHFSzE1WvSAWhpAnYWHRfknhdkFuXFBnSnYLQUx0EN0hoT6i835oCA2mg8ujaxkuXLCvnFqcLI2PY40xgqh04B0+h1DFFqS0aPJKxeNx/vHoY9x2++2sXLGCN15/g1NOOZXzL7xA1ZbqZVL6dl933XVMnjyZFStWkJHR8jY3EAhw1VVXcf311/P666/3qJGKtrFCIVi7sfuzcIq+wzI7X5C5BzErK6GNtTcAjrWJEEpjTgn2D9Z2qVBtT9CUtdK/PUj+a4m1f023OHl/TkweCLuD6EmTaDh5IDkvpuYhdbyzioprZ2KeMBuAhuEdy1azpobs9Q1ETp+M47VWcqX3Fk0ialONElHNuOyyy7j66qt54IEHmDNnTnJ8CgQCLFy4kJtuuonPf/7zfWxl/8Gpa3xx9AAAZG0dwS2rkZ1ch9scsXUtbpsbzzVfRHRRqEopMb9yOQ0/+gnxDxZ3KZxYAkJbiyMYx3fH7WhFhV26eZRSIgdcSLBsJ5EnnoR4POU2Wti0aRUev47ny9eAy9Vlm8zPXUPDD39M/OMlXQ+3XrsMlxbDe/N30bIyu5QkQ0qJvCKPhv3bif7veeigFmR72OrK8d9zN/q4sV2+Lp8bnMuo8p386aGHiEQiPT7h2Bq6rpOXncNxmp+zqzQ+yYT/5lhcvtmkeqiOv17i2mXy10II2JyMayjgypogC40adhOjo/kFSwo2xvKodHkYao9TL928FpvFHrMoISSFwJU3CnfhJEL7V7TwSu3ds4fXX3uVL331K/zy3l/w0B//yLTp0ygeOFCJqV4kpZVoH374IT/96U8PE1EAGRkZ3H333bz//vs9ZpxCoehbtPdXoEdMyq8tofIbszFPP67VlPW91n9jba/sD/a0m0BFxmPo7ywn+7k11M8ZT+TCWZ27URCC0IXHUfh+DUUPfETRAx/h397JDFFL1hAqsGEb1HYIao/SXESlUXgLu4NYFp0OC+oL7r//fs477zwuv/xysrOzcbvduN1usrOzufzyyznvvPP41a9+1ddm9huEEC0WwzcuyUv5YQaDBH/7e+p/+yBmQwOWZaX8kFKiDRyI9757sJ13DpampWxHIjrQIvLmQuq+dwvGjp0d922ahz2kZYHXi+e738b1jWuRLleXr40ErHCY4IMPE7j/N927PkMGJ67P2XOwGsMXU34YBqGnnyHwwx8Tr6josi1kZuK97RacX7oSy2Hv8vWJrVlL7be/S2zpssT174ItDoeDr37tWr53yy34/f5eXdMrAIfDwcDCQuYbmVxVpvNBlmRZhmSHC16ymxSuMvBtM/lHvkmZA0I6LM8QrBjoY05GMeeQibMDJeXWDC72rmeIrY64JXg3NpXdZlFjcd7EuUKzkTHqTITddVg9szdeex2/z8/Jp5zCzh07+Ouf/0I8Hlcp0XuRlO6IsrKy2LlzJ5MmTWp1/86dO8lKwa1+zz338Mwzz7Bx40bcbjcnnngiv/jFL1rEuUciEb773e/y5JNPEo1GmTt3Ln/84x+TYYUAu3fv5rrrrmPRokX4fD6uvvpq7rnnHmxpToeqUByNiA9XUvCRQPf7scYM6fiEHqIr9dOsYBDPs58k6mSdN6PDNPr6hDFITWCt3ZjclrHTRJs4tsW2tsh5ehWlV02l4K/l7SYp6TZ9FM4n7A7qL5pOwTKD8NlTcb3SuXII6cbpdPLQQw/xi1/8gmXLlrVYv1tSUtLq5F9bHGvj0ktvvcXSYD3YuiiUzTg89hj2jevRmr3+riClSczrgnA3QrQ+XYp+w7ewTZnc9mSKtDpcFybjBrFML5bRldDdZv1accS/nsC+ZTNa8YDUmjnkp0tqkpjbgYx1ITpANv7vrbewl+5HHzu66+u3ZCIDaSzTh+xGuQl2bUf/7vewzz4ekWKKc9O0MEwj+XdGZma74b3dxWazUZxfwGfqXZxUI/jXAIvVPpLXcI1Xst1toUmo1w9uR0DABu/n64x353F1hYun5QGqxeG/ozYsznVvYZZzHwLJhsoImxxZ4Gz5RgkhcGYPxVM8neCuj0CzJb1SkUiEv/31L3z3+99n3dq1vPjii5x86imcOWdOr12bY52UftG/+tWvctVVV/GjH/2IOXPmtEgxu3DhQn7605/yrW99q9Ptvfvuu1x//fXMnDkTwzD4wQ9+wDnnnMP69evxehMrc2688UZefvllnnrqKTIzM7nhhhv47Gc/y4cffgiAaZpccMEFFBUV8dFHH1FaWspVV12F3W7n5z//eSovT6FQtIWUiXC5T9empbvmnqjOiqjmyBXrcGntiynN5aLs1BwK/rK0xf2KPWhi+p2dusewQiEKF9cSvOA43M8vSdnOTqHphOeXpD2cT9gdNCyYTtaycoztO9Gmjid0fgmul5f222yOGRkZnHHGGcnnUsqUQ1qOtXFp+erVPFvbzUKrUeDtt3vEnh5h/brEoz/xzjt9bcFBln+aePQH9uxKPPoxAsjMzGSC6WROleDxAZI1PtlSqAtBUG9+RssGLGCdD+ptPq4q1fmPVUkp8RaCa6qrnFPdu9EExE2LxVtLMcbJVm/UhaaTMeJ0wqVrsOJBEBo0JtzYtnUbH7z/AVddczUP/Pp+/vjgH5g0eTIFBQUqxK8XEDJFf98vfvELfvvb31JWVtYiNKCoqIjvfOc73HzzzV02prKykoKCAt59911OPfVU6urqyM/P54knnuCSSy4BYOPGjYwfP57Fixdzwgkn8Oqrr3LhhReyf//+pLB7+OGHueWWW6isrGw1i2A0GiXabL1HIBBg8ODBnM4CbKJztShKbzqRAfd/1OXXqlAo2iYybxaePQ1YK9d3qx0xYxKRAjfOVw5fx6SPH01gYg7ep1umUNdzcyi/eCx5f17cuU40nYZLZpK5oqJHsvgd2nZfhfM1LJhO5vKEiEpunz6R8CAvrhd7TjQaMs47PE9dXV1KnqPO4HA4WLVqFePHj+9yG309LpWXl/f4dWlCSslP7vgxT//3v73SvkKRfgTW5MuheHr3mrEkYn8NImKhB1cyxL+dK6udFEfh94MlZldLNEkYEYaz98d50F7ObiOK5Z9JbsF0rhu3mXxnwgO6NxDlwXc3kX3S97G5s1tvyjKpXvsM9dveBmEDeTAqwufzcffPf8bT//0viz9azJVXXcV3v/+9fucR788EAgEKCws7HJtSvqK33HILt9xyCzt27GgRPtET+err6hLpnXNyEkVhly1bRjwe56yzzkoeM27cOIYMGZIcsBYvXszkyZNbhFTMnTuX6667jnXr1jF9+uFfpnvuuYc777yz2/YqFIrewXAJRLj7xV/l8g3UXXc8hU5ni2QZwmbjwPF55L20+bAFyjIYwnKkMGtnmWS+vYWKBWPI3bqj57w1zT1RfSCiMlZWtBBRAHLVRswRM9DHjsJsJYtoX3HTTTe1ut00Te69915ycxPZKe+///6U2z7ax6XzFsxje5GDBqN/FltOGSnBTGP6JU20msq7S/Sk7Ue5XYGNOylb1Mw7LkSvecp1XUfXdHLisM9J51P7t0GuISgekcmlg/38a8V2KjXJuYP2k++MJF/GhnAmkY6ixYWGf/iphPavwAzXgtBBJka0hoYGHvnb37ju+uvZuGEjzzz9NKecegqzTzxReaV6mC5L0+HDh/dosS/LsvjOd77DSSedlFyDVVZWhsPhOGzdVWFhYVLElZWVtRismvY37WuN2267rcXA2zTzp1AojjIsE3tQoudkY5Q2+z2YPh5PhYF5oOrwUyIRhCHR/H6s+vpOdWMeqCJrWxTr1Glo767ovt2NIsq3ubbPRFSrxZotE9/zyyj76kwK9+xPZA3tB/zmN79h6tSph40VUko2bNiA1+vt0s3D0T4uCSGYNHUKBXoZWrR/vJcKRWeIxWLwDsl1ZIkk4L0jEIQQiRpRGnjNpr5SQ8pEzauiuMZ5pp3aCW4GOQWXlYxgZUWE6dnVyUjBGDprYgUdvh4hBHZfPr4hs6nb9Oph+9euWcuqlav4/BVX8McHH+TB3/+esePGkZOTo8RUD5LytMCDDz7IVVddxZNPPgnA448/zoQJExg3bhw/+MEPMLq44Pr6669n7dq1yXZ7E6fTSUZGRouHQqHoP3j3RogMzeqRtnJX1lFz2rBEmlhAOJ1UTfLhWdy2R8VXZsLw1LLx2T5cS2CIC72w9SLHnaa/iqhGpGEw4MVdVF8ytUsplHuDn//859TV1fGjH/2IRYsWJR+6rvPoo4+yaNEi3u7CGh41LikUiibKHTAkIrB1wfFlGAbVB6o4sU5ijHVjOBOZM4fl+Jg31oNdO+jx2xXPZK/R2e+/wDfsJGy+/MQaKXEwaYdlWTz5738zcdJEjispYc2q1fzr8X92v6CyogUpCamf/vSn/OAHPyAUCnHjjTfyi1/8ghtvvJErrriCq6++mr/+9a/cfffdKRtxww038NJLL7Fo0SIGDRqU3F5UVEQsFqO2trbF8eXl5RQVFSWPKS8vP2x/0z6FQnHkYdu8h8DQzq1X7Ahr9SacNQZl3zqe+stPoPKa48hdU4/ZTrYp7/YAwRGp3cjKeIy89/dRc+aIpGjrCpHzS/DsCvZLEdWEsW8//t1RzNO6uQ6hh7j11lv5z3/+w3XXXcf3vvc94t2o/9OEGpcUCgUc9HTtdEvyY1CYYhSslImCuYGGej4wqyjNPOjREkJg07TknJQhBR9GBxOTnRtDhBDY3Nn4h53afGvyr9qaGv7x6GN8+atfwef38+S//83KlStVOvQeJCUh9eijj/Loo4/y9NNP89prr3H77bfz29/+lttvv53bbruNP/3pTzzxxBOdbk9KyQ033MCzzz7L22+/fVioYElJCXa7nYULFya3bdq0id27dzN7dqJ45uzZs1mzZg0VFRXJY958800yMjKYMGFCKi9PoVD0E8zqhMjRs1tfZJsSlonj9U8p+u1HZH1aTuG/1yE7yD4oSqsI56Quhoydu3HVmDCji789QlA3woZc1XHq9Z4iVRHVhP7uCgJDndgG9A9hMHPmTJYtW0ZlZSUzZsxg7dq1XS70qcYlhULRhKllYFmw25WoDTW5IfX1WNFoFCklS0J1PLVmJw3Rw2s7SQmb47msiaVWRkAIgW/ILOwZxYk1UqLlrf2SJUvYuWMnl11+GfWBAL//zW97NVX8sUZKQmr//v3MmDEDgKlTp6JpGtOmTUvuP+6449i/f3+n27v++uv55z//yRNPPIHf76esrIyysjLCjTUkMjMz+cpXvsJNN93EokWLWLZsGV/60peYPXs2J5xwAgDnnHMOEyZM4Itf/CKrVq3i9ddf54c//CHXX389zi5WWlcoFH2MlOSsDRGZObJHmzW37kikce/ouMpKolldC1tzvP4plcf50Pz+lM/VJo8lY7eRtlpNXRVRAEhJ/uvbqTi359bKdhefz8djjz3GbbfdxllnnYVppn4d1bikUCiaY9oKiYtsGnRY5ZdMqQdHig6dWCzhxpLA6n3V/HPpNiobIkkxJSVUWy6eD44lIlNPX6A5fGSMPKMxtK9lkV7TMHj0739n9oknMWnyZJYvW8ZT//mvCvHrIVISUkVFRaxfn0hHvGXLFkzTTD4HWLduHQUFnV8f8NBDD1FXV8fpp5/OgAEDko///Oc/yWMeeOABLrzwQi6++GJOPfVUioqKeOaZZ5L7dV3npZdeQtd1Zs+ezZVXXslVV13FXXfdlcpLUygU/Qxt+UZqR9oR9sNTRacDZ43EVtSFAqNSJmpLnZl6ym3T58TWcASIqEaMsnL0mMQ2fGgPW9c9Lr/8cpYuXcozzzzDkCGpFZFW45JCoWiBsBMhFwv4JFMyICoYGhboEqRlJ2p5OvHwIW05SFsOli2HjTUO/r7Gwfq6TGKWYF/Ywz8rxrHHzKQrlZKFEHiKp+PIHtq4Vqrl7X1FRQVP/vsJrv3613C5XDz+2GOsX79ehfj1ACnJ3iuuuIKrrrqKBQsWsHDhQm6++Wa+973vUVVVhRCCn/3sZ8m6Gp2hM2+gy+XiD3/4A3/4wx/aPGbo0KG88sorne5XoVD0f2Q0irNWog8uPiwNdzpwBCUy0w9l5R0ffAjWqg3EJp+Ad8o4rNXpC9PrLMJm67aIaiL37Z1UnDucnB39q6jm4MGDGTx4cMo3CmpcUiiOYIRAEwLR3Qx+QiKtKDSWBQhLN9m6YLdLst8JXyjVCNhhsbeIx92Dmy16aqUtCdIHmiYwrYMH7rXsPLLRotAdpirqpCHTi56rJV9Hqmh2N5mj5lC9Lohw56I5/QibG6HbAclHmyOcErVz8aWX8vg//sGDv/0dv3rgfnw+X8p9KQ6SkpC68847cbvdLF68mGuvvZZbb72VqVOncvPNNxMKhZg3b16Xkk0oFApFa2S/vJ6q+RPIqalrNzlEc/SsTMInjMHx2uFFeFsgBELXkW1kGs1cW01gUi7eLtRL0rxeACpnZpOdPR372l2YVdUpt9Mb9IQnqjlGeSWGewTikFpd6SQajXL77bezZMkSLrjgAm655RZ++tOfcu+99wIwf/58Hn74YZUJT6E4Bsi2O/C4PN1rxDI4UL2IaFWiyHrEJsgtLOSksJ08oeHKtaEX2xmaUYP7k08JRttPcOO06VwwdRhvrNtNQ+TgsRGgaQoqY9y5+F2nARAL2Ul1xBBC4C6aTGbGcKTmbPRKNRdkksc/inFK8WCyBoxl2YZSnnn5Xc4483RsuoZNF+haQohqmsCmN4pSwWH/NvWnSFFIaZrGD37wgxbbLr/8ci6//PIeNUqhUChsRYXExhbjqLewRg6ETzsnpMxAAzVj7BS+obe71sg6dRp6IAYr1rV+QGkl4ZNz8aZot+bxUDt/MrkLd2KUlqFnZVJz/niyX93UaTHYWyQ9Uasqe0REAWCZ2MISPTsLowveu57gtttu4z//+Q+f//zneeyxx9i9ezcvvfQSf/rTn9A0jTvuuIMf/vCH/O53v+sT+xQKRfrQhcDezaK/UgqEGQIjsabWMGBmuZ/TBhVSPtNF1K8jNcixJCOyYc3e9tfeCqEzOkew3BmjoaH1+oS6jGMTibToZhdFitBsCLu3jUJXgsqAwb+WfoCMWuTP/ApPr3DwyqblSfGkiYRAkkJi0xLb3E4dl0PH7dTxuex43Tb8HhuZXgdZPgfZPgcZXjtepw2Py4bLoaMdQ2KrywV5FQqFojcxyitgdDH7T9UY9e8UTrRMhAWaw44VaVtIBYa4yHunkrYq35k1NZgugbA7kPHO5bvVvF5q508mZ9GOpKgwa+vIfmVDQky9vrnPPFMJEVWSEFFbtvdo27lr6gkeNwTnK30jpJ5++mkee+wxzjrrLL75zW8yevRonnnmGRYsWABAXl4e1157rRJSCoWiy7zuq2fY9GE47Foy9E7XYFxRNmv2Hl7cvTlep50Ml4PPHjeS7QcCvLtpHzWhnvfgdyaQWdjdxOpLQVqY2KgPHT4KNg9xlhIsJJYlsaRMPG+2XxNgt2t4XQlxlZvpZEiBl+FFPoYVeRmU78XvtqFr4qgUVikLqVWrVvHiiy+Sk5PD5z73OfLy8pL7AoEA3/nOd/j73//eo0YqFIpjECnR3l/B2H3DqJlZhH95+x6m5uQvCxI9dRL2Nz7tlgm2oETLysSsrOzwWM3joW7eZHLe2XmYZ8asrSP71U3UnDeW7De2YB5ofdCNZzhwVEe6ZXNrJMP5ekFEAWBKpNZ3A+SBAwcYM2YMACNGjEDXdUaNGpXcP3r0aCo78R4qFApFW+yobWB7VYCxRVnJgDkhBMPyMhia6yfb6yTD5cC0JDHDJGKYROImAjht7ED8bgeZHifD8zOIGSavrOmldaUdqClpSaQVTySlaIPmgkcI0BCgHSKwSIgsKSWWKalriFMTiLF9fwNL1h8AAU67Tm6Gg/FDMzlxYgHHjc4h2+9AcPR4q1ISUm+88Qbz5s1j9OjR1NfXc8cdd/DUU09xxhlnABAOh3nssceOCSElzMQMb1vrKxQKRc9gbN+JPrUQbdLoVhM32AYUIXMyW2yL64Ld59gYtajz3qTWyN4UJjp5CLa3278J1zweahdMSYio0rJWjzFrash+ZQMVF08gb1X9YbWsxMzJSAEsbb/GVaoIm42G+b0oogCxaQeBE6fh6pXWO2bIkCEsXryYIUOGsHTpUoQQLFmyhIkTJwLwySefMHDgwD6yTqFQHA3EDJM31u1maK4ft+Pg7fPgbB/fPmsqNl07LNdEk9hoCplrYlCOD02IFp6dHqETzQktUQdLdiH9eQuB1fg/iUBvpSaWKSWmZVFWFWHfgTBvLy+jOM/DebOKueCEQeT4HUeFmEpJSP3kJz/he9/7Hj/72c+QUvLLX/6S+fPn89RTT3Huuef2lo39koGvVVA/vwTvC8uUmFIoehn/wg2UXTEReeqJ2IKSaPbBH19XlcRZ33JAsAcMxvx0A2Y3RBSAvSZM/disdn8oWwvnawuzto7cv39MdO4MnDMnI5euARIiKprrwvnapykXemwPYXckRNTq3hNRAFYohNmH5ZG+8Y1vcM011/DXv/6VZcuW8atf/Yof/OAHbNy4EU3TeOihh/jud7/bdwYqFIqjgq0Vtby7aR9nTxyM3rgOS9MEDq2NAu5SJsSGhCaVI4HqYLTvUo8LPSGkZM+U2kgIqqYXKUFaCKEnElZoOtjAtCRx02JfZYi/vrKVRSvL+eoFozh+XN4RH/KXkpBat24djz/+OJBQpTfffDODBg3ikksu4cknn2TmzJm9YmR/xNy0Fa/Pyb7vzELvm0RVCkVK5K8Mo72/oq/N6BJmIED+Q4vRXC60rMxOJTXoiSHC3LCV+jOPx9uG97kpsURr4XxtIiWO1z8lcu4MXDMmARDJ6wUR1eSJ6mUR1R/4zne+Q0FBAYsXL+bLX/4yn//855k8eTJ33HEHoVCIG2+8kdtvv72vzVQoFEc4loSFG/ZSkOFm2pD8w0LUkgV2SXiwKurDLN9ZQTBmkO9zI5HUR+Os2n2gU+uZegUtUbRX9nThd2ngr3wFe2Q3Ue84grlnIERCZuga6JqOZdOIxk227qvn7sfXcMWc4Vx62lCcdu2IFVMpCSmn00ltbW2LbV/4whfQNI3LLruMX//61z1pW79Gz8qkZqyfwY9vxaqt62tzFIoOMU6ciHXyNLQPVva1KV3GikSwynp+DVHbHZoUv15OYEEJvudbep87E87XJlLifO1TKr5xAp4KC+8zS3peRC0oOSZEVBNf+MIX+MIXvpB8fvrpp/Pee+/1oUUKheJoJBw3+O/SrTRE40wfko9D17GkJGaaVDVE2F3dwM7KAKV1QaqCESLx9BRZ7wyieY2tLoT2tYctWo6v6g2EFcER2kbEPwXTWZjsF0AD3A4bcdMiFDb4+6tbqQ5EuPbCMbgd+hEpplISUtOmTWPRokWUlJS02H755ZcjpeTqq6/uUeP6M4E548heUYVZXtHXpigUnUJftBzzjOPSKqb0jAxiJaPQFy1PS3+9gbl5GxnQQkxpHg9187soopqQEnsQMpfsw+gNEbXmQFpFlKNOoufndyoxh0KhUBzJNETj/G/ZNt7ZtI9sv52sPIkvz8SdYSIzLPz5knCZRWSvJFbd6TxJ6UFoQM+F9jVhi5UjrGjCS2dF0czQYZEhTULJrmtoQhCJGzz34V58HjtXnzMSm36UC6nrrruuzRm+z3/+80gp+ctf/tIjhvV3LLuAuFobpTiySLeYssYOxXDptBE9fsTQXExlvLmBmgsmdE9E9RJ96YmyhyXC74U+EFJLliyhpKQEXU980l566SV++ctfsnXrVgYMGMD//d//cdVVV6XdLoVCcfTi9komnhjnpDmSISN0PD6RDGUDME2oq5FsWmPyzutxNq6xMNqv29t9OqtDJO1m7UsVCehGHQezXQikaLuWlxACXUt4p8Ixk/8u2sX4IZnMnpB/xHmlUhJSn/nMZ/jMZz7T5v5DQysUCkX/oy88Uz2Fnp1N+PhRxPwawQE6lg30GHjLTJw1Bo4P12FFeif0r0lMbbtlIqP+uKv/iqg0e6L6A7Nnz6a0tJSCggJefPFFLrroIq688kouu+wyVqxYwVe+8hX8fn+745dCoVB0BqHBhCkan/uSk+FjNHS99VTeNhvk5gtmnyE47gQbSz4w+N8/YlSW9/LqKEH72fuaBE4Pe6Q0q1nCAM0GwtHu8UKIZKhfJG7w2OvbmTgsi0xv++f1N3qkIG88Hsdut/dEUwqFIg3oi5ZjnFkCR4iY0jweqi+ZiuGCvJUNuHeE8T+3GxmPoblciKGDMPJ8lH9pOvkrgvDx6l6xw9y8DVdFYb8L6U1m51tzAHPztr42J+00z3513333cfPNN3PPPfcktw0fPpz77rtPCSmFQtEtNA1Om2vjsi878GeKVhNNNEeIxDEuD5xyto0hIzT++kCU7Zt7dn1Ssr9OHaU1pmXvWRtkM/VmCSeW7unwnKSYstvYuq+ej9ZVcu7M4iPKK9W2360V/vvf/xKLHUwn/OCDDzJ06FBcLhd5eXncddddPW6gQqHoHWxvL0PaNayTp/W1Ke2ijx3FgcumkrdwF3l/XgxL1mBu2pqsD2VFIpibtiI+XEn+n5YQy3QQ+MIJCGcv5OMWArOviiW1Q8OCfiCi+iwFVUs2b97MJZdc0mLbxRdfzMaNh9cgUygUis4iBJx0po0rv+5sIaIsS1JZbvHxOwmP0xN/jvHSf+OsWWbSUC+TAksIwdCRGt/4vpNBQ3tPKHTUsmj0SKUqpBLJzROvp+lhNT6ktLAaPVASMJwFmLq7cd/BR+v2JB52XeetZWVE470jMnuLlDxSn//855PhE4888gjf//73ufnmmzn++ONZsWIF99xzD8XFxXz1q1/tLXsVCkUPoi9aTuTCWXimTcBaub6vzTkMfewoApNyyf/fOoxAoOMTLBPH65/iGT6U2vnT8L+wEhntufoE+piRxH0Sa9ZExMdr+80KYu/Tn/RIuvdu0ccTiOvXr6esrAy3243VSjYqQ9X7UygU3WD4aI3LvuLA5UmIIikl9XWSN56P8+7rBjXVEss8GFlnd8CgoRoXXGpn5kk27I6E+Bo4VOOKrzv5/c8ihIK9YWkHsX1aU2hf4ncyUeLq4PFSJgSTJRNetqb9FhA1DeKWQdwyMSwTQ1qYloklLfIikiwEEo0t+ljKaw4kPE5CQxeJmlIOTceh23HabDg0HZumJ4cOTcDu8iAVNRGGFHp7/Kr0FikJqeZq8uGHH+auu+7i+9//PgDnn38+OTk5/PGPf1RCSqE4gvC8vTaRxrussPO1kNKA5nJReVI++U+vw2xFRAmnE2GzYQUPH4mMHbvI8LsJnjcV93NLesQe4XRSdmY+w3+6nNgpkwhfPpPM/63oUaF2RNPHHqk5c+Ykx6gPP/ywRV3DFStWMGTIkL4yTaFQHOHYHXDRFxxk54qkiDpQIfnr/VHWrjCTOiTb7mVW9gjeqFhDPAY7tlj8+VdR9uywuOgKB05n4vxJ03VOmmPjzRd6foJHdLRGSrMligRbVsKjZEkMS2I1Pk8IqTbalgIHdhyaHUtr9EohsaSFzgjC4eHUucdS6y5BSkHMNDFkDEtaWNJKtqsJgU3oOHQbbpsDj92Bz+4EIdm6v57BBZ4jJrwv5TVSTS9s+/btnHPOOS32nXPOOdxyyy09Y5lCoUgLVihE1kvrqL5wItlvy36z/id85mRyVwVaFVEAYvxIwsVenK8sbXW/tWYTwRNOwJeVidkDtd7iJ00id00YGY1if2sZ7mFDqF0wHf/zSkwBfeqR2rFjR4vnPp+vxfNYLKbGJoVC0WXGTNCZdNzBOkfhEPzjD1HWLG8ZC3BW/kQ+P2g2awJ72B+pBSAWg5efjuPLEJz3WTuaJtB0OPN8Ox+/Y1DfiWCLVOhIf2jODByF0zAdOYSicawUJsGaixsd0fJ33zWI3cXfRgobuY07msIBE94rk5hpEDHjRMwYMcugIR6hIR6BcEJcuW0Onl3qZPwwP0VZR4aYSllIvfbaa2RmZuJyuQiFQi32RSKRI+JFKxSKllj19WS9sIaaBZPJXkifiyk9O5v6QTacr6xr8xgpBFJr5/dGSgo+rqHh9LHd9krpuTlUD3WQ+89lyRk1Y+dusqDLYkrzeqkdDc66Ytx79nbLvmOdoUOHtrtfpT5XKBRdRQiYdYqOs3F9rJSST96Ns3JJSxGVZffw2QEzGOTK5rzCqfx917sHx4s4vPxUnGmzdIoHJwRZ8WCN0RN0ln/cs4HZooPQPlvWcGzZI5CIlERUxx0LZOM6qaaRWTT+X9c1nNjx2prCBSVxyyRkRKmLhwgbMSwpCcaj/PujjWzcX8UDXzqZiUNy+r2uSFlINS+6+/bbbzN79uzk848//piRI0f2jGX9GGGzUT9EI2u1ylSoOHqwgkGynl9DzfzJZL/dt2JKeNzosWbPbTb0okKM/WXtrkvSszLBZsM8UJXYsHkn9Scfh1vTu7WeqWbuGAreLcWIx1ps76qY0vx+audNZNQfthMfUURs7gw8mxLXWzYED9p/JNEPkk2YppmsJQXwySefEI1GmT17tsos2w6OaBxnJFWvqkgkdLF1s0qcJZGRSPdr2thsCIeze55RCTIaAbOHFrs7HQhbN5MjJ69PN79gdjvC0c3vgCRhSytrEFNC0xAuV7feK7fTidPpJB6LY5m9u0LU6YKxkw56oyJheOd1A2kKCpx+8p1+ipyZzMgawQhvAUIIPjOghHojzJ5wNeWROipi9dRWR1i8yODiqzRAYLPD+Ck9L6Q6Qmh9W9lRCIFA4NQ1HJqNTIeHoBHlQCRA2IwhJSzfUcn3//ER//rO2eT4+2GGp2ak9A1vbQFvcwoLC1uknD1aabiohOJ36qmZlkN2ZT5mHxSgVCh6AysYJOulddRcOLFPxVRg1mByP62h6RdH83ioPnUwofyh+Pea2IMWeiSxqlebOp5wsY+GgTakBjkbImjvJ4SIFYmABprDjhXp2mCl5+cTdwuM7Ttb3d+mmBICze0+7HjhdlFzzhiyF27HKK9AlJXjmDWZqpOKE+25BXG/QAoQErQoFD+7HasuAJbVa3Wyuk0fThqWlpZy6aWX8vHHH3PSSSfx3HPP8cUvfpFXXnkFgNGjR/POO+8wYMCAvjOyn+IKR7nmoeeIVab6XRc4TjkR99evRfN2fWG4jMcJP/5Poq+8nqhg2kW0oiK8378R24gRXW4DKYkueofw3x9DHhJx0xVsx8/E981vIPz+rpsUixF69B/E3ljYrckgfeQIvN+9Eb2osOu2WBbh114n+vgT0I1wZuHz4rn+OhyzZnYch9YGxoiTqTppPu+/+x5//fOfu2xLZ8jIEmRmH7TzQLnFvt0WDt3OV4eexjn5k3FpdnShkYh2ExQ6MvneyAswpElVrIFfbnuF9w5sZMNqk3gMHM6EoBg0VEPTuq9Nm2N1V3SnkSZR5bO5cHsdVEbqqIkl1j0v217BC5/u5OrTx/Zrr1SP1JFq4sILL+zJ5volmsdDsEDH+7+1ZG3wUTNvItlvosSU4qihKcyv6uIpxD0JD3PB0gByWdthdj1ugw1E/OAiXDMQIOOJj8kQAn3CGEy/k71n+ojkWwx624+7LIxn6f5e8eTEJg0me3P74qVJTJVdfRyyqaiEAKkf/uOvxSSFz25t+ZuxZA2ZbUQfan4/lRdPIu4TIEFYiZXA+SuDsGRdv8kc2JfccsstSCl59tln+de//sWFF16Iruvs2bMH0zT5whe+wM9+9jMefPDBvja136GZFsV7ypFdSTSz5zmcIRPfbTcjsrK6dLMjpUR++RsE66JE/v1fiMdTtwNgZxn6D+/B//O7sE2e1GVbuOAiIkGT4K8eQHYmU2h77H4ZZ0Tiu+0WRG7XQpSklMhrryNYGyby1DPQ1eyTO8uw1cfw/+wu9GFDu359PvM5QkGD0G8fhGDXxab24/vw3fkjHGeejtC74CHJAIqHMLB4IC+/+CKlpaW9Npnj9gicroONVx+QxKMQN+M8vONtMmwezswb3+KaCiFASiJWnD/vWsQHBzYBUFUhiccTQgrAlynQ9Z4RUlJKTCvxaGVv8i+XI4bXGcHjiOBzh3HaY9h0EySYlkbctBGNOwjHnERiDiKNf5tW84pJPXuxhRDoaBS6E78j1dEGTEvyzMfbuPykUbgcPSpXepQesezMM8/kkUce6TBO/ahANC6ukxKrvp7sl9ZTc8EEst9SYkpx9GAFg2T/exmaO+FSD546jrpvnYi33CJraSnGjl19Y5iUmOs2IZxOHCUlSF3D+8EmzJqaXkn/LewOqsc5Kfj78g4j14yduyn454FkKI80zVYzCgIp2WrV15P96OJGgwS63w+aIHzCGKr/73jsDZKC9yuRu/b2X29VL/PWW2/xzDPPcMIJJ3DSSSeRl5fHm2++ycCBAwG46667uPbaa/vYyv6JJS1imobUUiormUBKYi+8RLQhiO/2W9HycrtmhNOB/ds3EPd4CD/6eGJ1flfYvIXYTTfju/MO7MdNa9vb0cGMvVhwIXaXk+A9v0TW1qZmQ/M+pST20qtEAvX4f3w7Wn5+am012el0Yr/x24nr849/dU1MCUHs0+XEbvw+vp/+BNvoUam30Yh+2aXYXU5Cv/oNsr6ha41UVBD9wR14b/0ervPPPZiSO0UKiwr58V138pM7fpwQU73AYZ+iZlntquNB7t3yIh7dweycUY3rkxLEpcnvt73Oy2UracpXJxPZF9puu4tImci8F4kf+tmQuOwxBueVM3HIDiYO3sGgvApy/XX4XGF0zUIIiWiyj8SEnSUFptQIR53Uh71U1Wew50AhG/cNZcv+weyvziMSd/TgKzjoncp3ZRK3TOrjYTbsrWFvdZBRRZk91k9Pk5KQeuGFF1rd/t577/HSSy8xePBgAObPn999y44QzECA7JfXU3PhBLLfsI7MtQ0KRSvIeAyzcU2Q66UluJ1OtKGDkFU16TGgnUQStZdMp/ilPciGINXnjyXnxfVtZvfrDrJkHJk7jE6vfbJ6IByofYNk8nU6XltK0Wug5+VSd+Zowmfmk70phv39NclixWmlD6NJampqkqIpJycHj8fTYmJv1KhRvXaTdaSzdvsOHoxHsLqzDuGTj9C+8Q20zIxu2SLjBqbPBfGu3VQDcKAccfPNaMUD2hZSltWxC8CSmJqFdKbiLREIu+3wfpctRbvum2iZqdwMSmTcaCn6YjFMp47UU/2yNbNr13a0m25CK8inazfBjXaZJqZdpHh9DiFUj/arX6H/7+kuCymAUDhEpGkdWS+EgEUiknhM4nIn2s7MEdjtiQQSAHXxEPsih4+LhjTZEqrAavbjmJWTWBvVRLBBdjmitancgyUlMcMinlzblxBGBVk1nDx+FadPWsHwwv14nZGUL4/XGSUvI8DwwlJKRm5CAsGIm50VA/hwwxQ+2DCF/dX5WFLQU6JKQ1DgyiRqxqkNRtlaWnf0CKmLLroomT//UL71rW8BCUVp9vLCv/6GGQiQ9WLjupK3NOWZUhyVyGgUc/O2tPSVuaKCmpmFZGzYcvjOE6bgPmBg7NoDQM6KaurPGo/n2SWHzTQLuwMskF1ZPC4EVRO9FDy/ue+L3baDeaAK33+r8NtsmLMnU/nlEgoXVaTtvUrShyHsBQUFlJaWJifzbrjhBnJycpL7a2pq8HZjHc/RTE2gjk8qyjtcA90hVf1oErG2Bvb1s0yY1f3o+tRUw+bNfW1FgmA9lO7vayvapa5GEqiV+Bvv5fOLNAqLNXZuTXxnNKExzJMHEurNCDXxIMXOLFyanYGubNYGDn4Wx0zSsScS2yGlZP8eq0thfbKx3lPMNIkbB6WaQFKUXcWFMz5kztSlFGTUJjxOLR2lWBJiUYjGIBqVxGKCuNFYSFgHh0PicAicTnA6QdcOBmT53WEmDdnOxCHb+dxJC/lgw1ReWHoy28uKsWQikUZ3EELg0GzkuTIoDVWzeX8t507vv3UAUxJSc+fORdd1/v73v1NQUJDcbrfbWbVqFRMmTOhxA48UrPp65ZlSKHqKiipiGUWJX+7m4kgIQsVufK+sSiaiMNdvxpp4PPrIYZhbW9YTsmaOJ3tztEseGj0vL9H+EfJdloaB9v4KCpZ5qJ03Be+ADLT3Vx8Ta6imTZvG4sWLmTVrFgD33ntvi/0ffPABU6ZM6QvT+iVSSsym2WwkwqYjzN5Wwo3xUI39HrpGR3JwX/KOLfGka32llR4L0Oqhdpo4eu2S0qJnc3e3TSQC2zZaFA/REELg8cKJZ9jYtS2RYc6rO/HbXLxdtZ5/7/2YPeEqzsgbz6XFxzPEfTDc1euD2afb0BqjLUwTNq1N/fe5KYwvGjebJZaQeJ0Rzp62hEtPfJsBOVVo4mA4oWlCXR1s3QFr10s2boK9++FAlaS+HmJxibQkIBAC7HbweiU52YLiATBmFEyZBGNHC3KyQdcTQSM5/nrmzfyAkyes4o2Vs3h28emU12U32tT191kIQYbdTb3Nzc6KAFLKfptwIiUh9eqrr/LAAw8wY8YM/vjHPx4TySVSwQwEEmumLpxA9htHzg2YQtHfMAMBnLUSfdRwzC3bD+6QEs8zn3DoBJ7/uRXgPTxDXu0YD/mvbacry7ODxw8nb2WgP2T1TgkrFML/n4+xTptO7RWzyPnfqt4POexjnn/++Xb3z5w5k9NOOy1N1vR/IqbB37YvoT4epc4VYNzNV7caadJtTAsRjEJNA6KmAREII0MRMCw0TUvOkkuZuDGWUiIcNnA5kB4X+F3ILC/S7waXo91w31bpzEtqv+ROx8f0XERTU/XSrtlw6L502dWRDR3Y1dWPXWDNVkpf+QBpWo2f3c68kV1DWrDkA4MTzrDhcCRu8k89x8ayxQab1lpYWNy39WU2BvYTkwlh9PT+pSw6sIEiV8KNpWkw50I7Q0cmQhillFSWSTau6bw7quk7GjMsosZBASaQjBqwl6+e/QIlIzeia1bj8dAQhOWr4I2FkiVLJfvLIBKxsCwLwzQxTRNpWQlB1ihWhBBomoau6+i6jqZpaJrAZhMUFkhmzRBcMBdmTBd4PIl5j2xvPZedtJATx67lX++ew6K1JcQMG90SUwhyXX4qA1FMS2JrJXlTfyDlZBM33ngjZ5xxBldccQUvvvgiDzzwQG/YdcSSFFMXTEhk8+stMaXp6DlZIDSsIQXUj+xaelXLBqFCDWGAp8KicQKDzLXVUFkDlolZXdP9GhYKRYpkLdxC9dzRZJdVYtXXt3usjMcwa1t6nUTJRDyVBkZXspEBgSE2PIt2HnFCqgnt3RXkDSiiZv4UMl9YfdSLqfZo8lQpEphYbG2ooiYWBg3cowf3TMNNMUOBENruSsTeKkRNA8QMsCQCHTydC7GUIQPCDXAglBBWOT6sQbnIgbngdydEVT+doVb0PuGKqkQ4ajOPTG+yfpXJlnUmE6Yl6kn5MwVf+paTP/0qyo4tUVbX7TnsnKpYA1WxBnQdTjnLxrzLHMmlYFLCe2/EqavpnN2JIrYQjZst1kLZdYOzpy7lmjNfIT+zJhnEUVsHb74t+e8zsHGTRShsEY/HicZixONxLMvq1OSJEAJd17Hb7TgdDvbstbNvv+CV1wVTJ8NVnxecejK4nImv4+C8cm5a8CRThm3lb2/No7ohg66KKSEEbt2BjOnEDAub3o21k71Il7L2TZs2jU8//ZQbb7yRadOm9c5M1hFMiwQUb2qYVdXdbtNWkEdsdDHhAgf1g3SEBbZw4rr7Sg0yV3ZtXZaIG2Ts3I1wONCGDkpuD0zJI5KVCyJR10aLSXI2RrHVR2HVpkYXsOJoRNhtiNHDsdZu7FM7zANV5LylU3PhRLJeWNNmBrzW0LOzOTA5g9wnVxzmveoMtoHF2IMypT77I0ZpGVmr/ATOn4z36U96tS/Zx/e0VVVVrF69mqlTp5KTk8OBAwf429/+RjQa5dJLL2X8+PF9a+DRTKOAEpV1aBv3IfYegEj8sNunVO8VBCJRHDcYQQQjiD0HwO1ADszFGluMLMhSguoYRfSYu61zRMLw3BMxho504/UnPDeDh2v83w9dPPdEjKUfGIQOGS6EgLwCwdnz7cy50I7bk7BZSsn2TRbvvBrv1Bx1k4iKxAwM66Bw9LnCXHX6q8yf9T4uR6KtWAze/QD+9HeLteslkahJJBwmGo1idmExlpQSwzAwDINwOIzNZsPldGKaLj5ZqrFqjeCs0wXfuk4wYljiNTvtcc4rWUxRdhX3P/959lV3NbFJAt2yY5r9956zy+nP3W43Dz/8MC+88AILFy4kr3E9QSo89NBDPPTQQ+zcuROAiRMncscdd3DeeecBEIlE+O53v8uTTz5JNBpl7ty5/PGPf6Sw8GBBud27d3PdddexaNEifD4fV199Nffccw+27lYT7yZNnqmKSycS93f/Cx8ukgx+I0bGhhp8L+wEaSGbpUDt7iqIQxMJeDdvo2neUNhsaB4PseNGES72sudLJfi39m1lbEXvISzIXx6iP8z9mOUVZL8NNQsmk7NoJ0ZpWbvHC5sNa9ZEKiZ4KHh6PWYX04GHJg8ke0MX0/r2M8wNW2DC8Yn6W+t7Z4G5bdBA6odq+Hdno7VRuLg3WbJkCeeccw6BQICsrCzefPNNLr30Umw2G5Zlce+99/LBBx9w3HHHddjW0Twu9QpSQk0D2ppdaDsrIW60uGWSUhKPxwmHwwTq62loaCAUChGNRIgbBlZjcipN07DZ7bicTjweD16fjwy/H4/Hg91ub0yNDIRjsLUUfWcFclAu5pShkJfRraxvCkVn2LDa4rl/Rfncl53JgroFA+Ar33ZyzgI7a5eb7NtlEYtKfBmCkeN0Jk7TyckXLcJYqyokjz8Upa62c/22JqLyMmr51vlPc/KEVWgikXiitEzyhz/DC69IGhoMQuEwkUik1QmMw0L3mk1GWFJiNYb9mabZosCvYRg0GIm2PW43luXi5dc11q4XfP9GjTmng61x/dRxIzbzo889ys+fvprdBwroipgSQhCJmW3UxuofdPtXff78+VxyySV84xvfaJGAojMMGjSIe++9l9GjRyOl5LHHHmPBggWsWLGCiRMncuONN/Lyyy/z1FNPkZmZyQ033MBnP/tZPvzwQwBM0+SCCy6gqKiIjz76iNLSUq666irsdjs///nPu/vSuo0ZCJD7t8U90lbpTSdie3tZn2QPk4aBGQigv7Mcm82Gf+Qsin7zUR9YojgWMcsryF4IleeNRDOGkbWuHrGpZVIJ4XYRmjWSwDAbWVti5P7t4+Ri+pQRgrrhNgrf29Ylb1Z/xP/qGiqumEreNmenU7l3FtuggdScPJihf9rIgQvHkrc+p0e88Klw++23c+mll3L//ffzpz/9iYsuuohzzz2Xv/zlLwB8+ctf5u677+bZZ5/tsK2jfVzqMaSEuInYtA99zS4IRZO3SVJKotEYVVUH2F9aStWBAzQEgxiG0WmvlBACm82G1+slLy+PgcXF5OXlJUUVhgk7K7CV1mCNKcaaNAQ8TuWdOgaQ0Cfvs2XBGy8aOF2CeZc7cLoaP6d2GD5aZ9goLbHez2rU9aJlUpWmdVF//22ELRs6N7pImQjnay6iinMO8L0FTzBtxGY0kbBr5Rq4+17J2vWSUDhCKBg8zAOl6zpOhwOHw4HNZkNrNvnQZGfzpC+JzIAyGRIYi8WS2T0ty6IhGCQSjeLzetm528EPfmzxresEn79ENK4lg7EDd3HzZ/7JT5+6mrLaXLoipsLR5uGM/Y+UhNRNN93U6nbTNLn33nvJzU1kJ7n//vs71d68efNaPP/Zz37GQw89xMcff8ygQYP429/+xhNPPMGZZ54JwCOPPML48eP5+OOPOeGEE3jjjTdYv349b731FoWFhUybNo27776bW265hZ/85Cc4HI5W+41Go0Sb3UwEeqH+TE9iKyrE2ck42t5GmiZ6VKJ5vUd82JPiyMEsryD70QpsQwfTMHkAgROntdivxSV5KxsoXLSl20VpNacTqYujak2RFQpR8F4ldfOn4Xuq50L8mkRU5nMrMSMR8j4qp+7M0fj+92laswUuW7aM3/3ud/j9fr797W9zyy23tCjAe8MNN3S6vqEalzqBlFAXQl+yGbGnqilbBBKor29g166d7N6zh/r6+i6H/jfdwNXW1lJbW8v27dvJzMxkxPARDBkyGKfTmbj5i8bR1uxC7KvGmjUaObAx7b0SVIpewIjDC/+JU15qcfFVTooS5euSSRqEgEPDOaRM1IrasMrkib9E2bWt89+JuGliiINrogblVnLbxY8xYfBOICGi3n5Xcuc9kn37TRoaGogcMllmt9vxuN04HI6kYGorA55I7GxxjKZpOJ1OLMsiGo0SDocxGj3JhmFQFwjgcbuR0sOvfqtRVQXfvFbgciWamjhkO9+e91/uefoqAmEvqYqpuGERjR8lQuo3v/kNU6dOJSsrq8V2KSUbNmzA6/V2OT2haZo89dRTBINBZs+ezbJly4jH45x11lnJY8aNG8eQIUNYvHgxJ5xwAosXL2by5MktQirmzp3Lddddx7p165g+fXqrfd1zzz3ceeedXbKzL6g+czgF7+zvUuaxHkdKfPtNGDUEVm3oa2sU/RytNkh8uBfnoWnMu4ixaw+uXXtoq3RoT/zUmtPHkrWlDwra9jLmpq3ET8jHNrAYY1/367bYBg2k5qSEiGoSr+aW7WiT89HHj8Jct6nbfXSWWCyG253I2mi32/F4PC3CzfPy8qjqQp2jo3VckoZF3eZd1IZSD18VQuCTOs61eyAQSuRKk5JIJML27dvZvmMH4XC4x+tJWpZFXV0dq1avYvfuXUyYMIGioqLkDSzV9eiL1hCfNIR6l2gRjqQ4+gjtq0gkTLAOptPX0iSeTRM+WmSyeV2Y0+baOP5UG/kDNOz2xsz9jfVWpZVYW7Vzm8k7rxks+8ggnML8nObKJi7tNPmK8jNqufkz/2TC4J2IRk/U6wslP/mZpLwyTn2gnnizJR+6ruP1enE2Cqiu3p83nafrOm63G6fTSSQSIRQOJ5NWBEMhDNNESh9/e0wnGoPvfPNgVr9Zo9dz9Zmv8PBrnyFu2jvosSWmJYnG+m8Zj5SE1M9//nP+/Oc/8+tf/zo5GweJgevRRx/tUh2pNWvWMHv2bCKRCD6fj2effZYJEyawcuVKHA7HYaKtsLCQsrLEOomysrIWg1XT/qZ9bXHbbbe18K4FAoFkIcf+hrA7iGRpmHtL+9qUJL53N1H+uQnkreprSxT9HXPLdny5PmLnlOB4/dO+NqdT1I71kP/6jv4xcdHD5L+zl6rTh5D5r+4JqeaeqEM9gBnL9lN1ykAy13Wri5QYPHgw27dvZ9iwYQA8+eSTDBgwILm/tLQ0pXW8R/u4ZESibPzDf6isqEjpPKFpFBcXkzV0KGg6SIklJZWVlezcuZP6+npsjesu2hJSzdMrJ9c9kViXIaVsN5uYEAK3y4WUkk2bNlFfX8/w4cMPhvtF49g+3Upgzx727Nnd/SLDin6LtCSy2WdMEwKHbk+rI/JAheR/j8d5/fk4g4dpDBmukZ2XEFThMFSUWuzaZlG2zyKaaqCEZsc1Yg7YEtOGbkeUb5z7LJOGbktm5vvgI8ld90jKK+LUBQItvnMulwuf15v8nvUUTVn8PB4PDoeDhmCQWCwx8RiNRpGWBRkZPP5vHbcLrv+awG4HXZNcMOMjNu8bwusrjycVr5RlSSJHi5C69dZbmTNnDldeeSXz5s3jnnvuwW5PTVkeytixY1m5ciV1dXU8/fTTXH311bz77rvdarMjnE4nTqezV/voETSd8LnTKPqgGqsLBUV7C7O2jpz1YYwzS7C9vayvzVH0dz5ejWPWZGJzZ+B4Y1n/TqWv6UQzRYdJLY5UjF17yByQTe0XZ5P7/DrMLoSPteaJOrSPyILBZNlsLRLi9CaXX345Fc1EwQUXXNBi/wsvvJBSCvSjfVwSEoibWPHOvz+apjFk0GCGDBmCJjRoDL3buXMn+/fvJx6PEwqHCYfDhwmhpvTJDrs9uTZDayU5RJOQMk2TeDxOLB5vsa7KNE3qGxqIxmL4vF727dtHKBhizNgxeDyepDAbMnAgVjzO7t27VVbhY4S+fJcbAolEFBtWNwr3bpa0klKieQuwO/JACISwuHDGh5w2cSVao4havxF+co+ktNxoIaKEEPi83qSHvreK2DatYczMyCAYDBIKhwGIxeMEAgEyMjL4++M6RYVw2cWJkEenLc41Z77Cpn1D2VlZRGfFlCUhfLQIKUgUNly2bBnXX389M2bM4F//+le33iiHw8GoUaMAKCkpYenSpfz2t7/lsssuIxaLUVtb22L2r7y8nKKiIgCKiopYsmRJi/bKy8uT+45k9Lxcyj87hoJParFW920a6tbQPlhJ/RUnkDtsCMbO3X1tjqK/s2QNjhOmED+7BPub/UNMuWpNzLxM2LM3uU2bMpas7UejL6oZH68mb98gSr8wEW+FhR5NoSCkEMR9Wpsiqgn/XhMxbhQyTSn0f/zjH7e7//bbb0fXO59pVI1LLdF1neHDhzNo0CA0TUuG8m3etJmq6ipisRgNDQ0twooEYHc4cLtc2B2OZNhVe/cLTZ4qm82Gw+HA05h6OdK4fqzJwxSLxag1DLweD1JK1q5dy7hx48jIyEjOmA8bOpR4LMb+0v4TzaE4RuiB4S0utcYC1JIRhfv53EkL0TUzkfWvGn52n8XOXSb1h4ioDL//4PrBXqbJu+zz+dA0jYbGdfOxeJyGhgaE8PO7hzQmjIMpkxJiqii7iitPf437nr2SmNE5R4yUkki0/47LXcoX6vP5eOyxx7jttts466yzejQWumkxW0lJCXa7nYULFyb3bdq0id27dzN79mwAZs+ezZo1a1rMRL755ptkZGR0Kcywv6B5PFTOH0PBv9di9eN1SNn/W0nNCcXYBg3sa1MURwIfr8YeiBE7p6SvLQHA/epygsN96GNGJsKUZk2mYYQf96vL+9q0Xscsq+D/27vv+Diqc/H/nzOzfbWrajXLcsfduOECOBAwEGMSQxrFECeQRkwClxtuAil8k1wCF1J+JAGSEOKQC4QbCAkdAw4Ggw02cpd7r7KtvtL2mfP7Y7UrrVVXlrSyfN6vl8CamZ09O9LonGfOOc9xVkucx0M4Kvxd/nIeaew0iAIQpoR+tAp9VVUVt956a7dffzbXS5qmMWzYsKQgKhAIUF5eTmVVJYFAgLr6+qQgymq1kpmZSVZmJna7HT0+jC+Fxl08qLJarXgyMsjOysLpdCbOYZomvoYGfD4fPp+P8vJyamtrEz1Qmq4zYuRIsrOze/aCKEovM0yZSPdt0Q2uvXA5ed46hIBoFP64VLJ2nUx6eCGEwNPNICqena/lVyqEELhcLjLczYttB0Mh/IEAJyslv/4dNDTGj4W54zcyc/RWuhpxyoHWI9XSddddx4UXXkhZWRlDhw5N+fV333038+fPp7S0FJ/PxzPPPMOKFStYtmwZmZmZ3HLLLdx5553k5OTg9Xr59re/zZw5c5g9ezYAl19+OePHj+emm27iwQcfpKKigh/+8IcsWbLkzBi6147ARRPI+7gG0+dLd1E6ZAaDZP5rA5VfmErG0UKs72/p8dTKygDz4aZYz9TlM9LeMyWjUdwvlXHktpnooXwy98W+76vhaOkirDZ8V08la+0xovsOpPzwtCvHezadoHp2Id5+Mo+yurqaJ598kj//+c+dHqvqpWRFRUWteqK2bdtGXV0d/kCAxsbG5uBFCFxuN06H47Qmt7fUcqK7JyMDu91OQ0MD0ab7NBAMJuZXbdu2jQnjx+PNzEQIgdVqpaSkhJqamtMuh6L0leZU35Jxgw9wwdhNiXlRq9fC31+IpThvmZ3P7XbjSCGIigdM8aG0pmkiifUka03zHPWm4bddOWc8mDIMg0DTgza/34/NauWjj228/iZ84ZpYr5TNEuHaC99mw77RNASddDbEz5QSf9BAStknPW2pOu11pEpKSigpKenWa0+cOMGXvvQljh07RmZmJpMnT2bZsmVcdtllAPz6179G0zQ+97nPJS18GKfrOq+88gq33norc+bMwe12s3jxYn7605+e7sdKK9cHO7o1dyEdzGCQrP9djbxgClrpYIxde9NdJKW/+3AT1vgwvzfTm4BCThtH9q4IJydbsb2xNq3j7PtK3eenkfNRBdF9B3rtPWRlNaHMos4P7CEvvfRSh/v37u363yVVLyVLZCBrGma3c+dOamtr8QcCNDQ0Z/2zWCx4MjKaEz/0QjkAbFYrWZmZsTVsmhpsoVAImhpZW7dtY+TIkWRlZaHremIifEccdsn8yzV2BfKImgJNSMZ7KnltmYk/0LsNt9LBksnnOdhdnwmA2xolL1TJm+9qPfacafJYk7zRXo42ugDIcwYxK2pZVdb7ixhfOU9yXM+lMRJrbo7JquPjVUGOVPTGde3Jv+ACi3sQ1nDfLfOi2T1ICdGmIay6ZnLljA9w2WO/5w2N8Ps/SerqojS2WH7G4XDgatFb256Wcw2DoRAyEiFLt5Bns5Nlc2AVgqA0qQ6HqQr68ZkmFpsVu93e5aQV7owMok1zHKWUNDQ2YrFY+N+/aVx6sSA3J9YrNa7kAHPGbOGtjed1ek4pwReMdHpcuqR1mfUnnniiw/0Oh4NHHnmERx55pN1jhg4dymuvvdbTRUurMyWIakl8sCEtiwUrZ6imYCo0/zzcO06mpQjhIdlEHTquHZV4vQWdv2AgmDkJR41BdO/+dJekR1199dWJBn97utq4V/VSsiNHjuDz+cjJzsYfCFBVVUUwGExqyFmtVrweD7qu9/oT4/iQP0/TvAx/03pvoXAYX1NgV15ejsPhQNd1Ak2T4BOvB6y6hi4E8TVOs9ySaxdKXqnLI2joWDTJFwqqWL1Kxwj37ucZNcTgms/beeNoPhIocAYYV3uSlastGNF4am9iyT1ME6OTWMEiBBZNgxYrTsyYaDL2SjdrKgcBMC6rFmNdDWUbuj5vsDuEgKsvj7DBmcOJgAMBfHqIn6oDISpPNr+3JsAwTSKm7D8PszSd3HOvQ/bhsuxC6ERMs+nnJinOOcms0VsTvVFvvyNZv9FMpB2H2IObjC4sPRTvfQoEAjgMkwsyc5iRlUuJw41H2LFKKxo6hogSIUqtGWBfoIGPqivZ4qtD2qw4OulpFkKgARluN7V1dYm14IKhELv2OHnr3zKReELXDD593vu8v20ygXB7C5o08/n770iRtAZSytktfMUMHO9v6/OFfTW3m+CF43o1HbeelUlo+igsy/s+q6E2cSzCMDC27erz907Jh5twzJhI9az0TMB3HwvHsgiOPwdLqN9U371GHz2CumHuHl2Qt78oKiri0UcfZeHChW3u37BhA9On94+5eWcaKSV1dXXU1dUBsUQPDS2G81mtVjK93h5Ps9yRxCR3txsBNDYFU8FgMLZ2jsvVKoCC2KRwh8WCjC80mvi/icBIrAEkkLEhTqLt7II9SdOaM70JYlkHhYi9t2z51kJi13QiRpSI2fbfK5uuYdH0pmBEJFKBayIaO79o3h4LSHs7kJJogqbPdOp1bfneEl3XsGgmgajRM8HUaXbnCQBNR9C716glCUTN5oBh5uhtZGXEpng0+uH/nodA0Ej0xAK4Xa5Of0fjAU3YH2CmN5tP5RdTasmmUM8mP9OG/fAQrIYdIQXSFkG/eC1V24oZY0aZOuQku4M1vFxxiB0+H063u8MHJvEhtU6nM/GQIxAI4LDbeeElnas+BRkZsd/Fc4oPMq5kP+v2jiF5eF/rn53PH0HK/rnOtgqklPQQgurxNgqX9W0QBSCGDibi0bH14nuYo0sxbFpabrCTs7PJf3F3Gt45dfLjLXjTvLyU6bKhBwb4ejNC4Js0iMzNVRj9IGNiT5s+fTplZWXtBlKd9VYpnYsP72toaEg8DbfoOl6Pp8tBVMufQfwJeVy8p6nl950RQuB2uzGbEmBAbF5GPNX6qeewW/SmIKoftsY6JZBIrLoFU0Za9UzFeqKag6gzS9OSs0LDrkuCxmn8PZaSwIntmOEUVr7tJ4TFgcybDJoFqx5l9jlbEunON2ySbNkqCbZYXsBqtXaaXEJKSTgcRgRDLCoeygWZhQwLD6WkYQjeqQfJ+PxKau+/Hlkfy6AnrBG85x7DfrSEvEPDGa41UOjYT8lQN29UHmZZZQX2jAwsnfQ+u5xOgsEgpmkSjUYJhUJs3eZkw2bBBbNjAZHdGuG6uW8zdFAFoagVaeogJFY9gs0SRdNMTFMjGLExKn8w0nQjxdB+N09KBVKKopzVKqdkkP/CjgE9NDW44Dzc+xr6fy9lN911111JQ81ONWrUKN55550+LNHA5Pf7W2UJ68pwvnjDLxqNEg6HMQwDXdex2e1YdB3TNAmHw0SiUQSx9PPxuVZdaTRluN0Y0SjhpnkZjQ0NWLOykl5rEQKExpkXZLTUFExpGsYpwYZV187QIKqlWC+Zbpin8fdY0njoIxoPftiD5eob1rwxZORNRgB53jpGFh0BwDRh2XLwB43YfMAmzk7mRcV7orRgiC+XjGC2u4RxjePIieYiEETLh1F/MgvZ4Gx+TaODuv/vsxDVEQjcZgbjA+PJcXtwDrLh1Cz868QRhCcjkY3zVPGHIg6HI9ErFQyFCIYcvLZMMmemQNdjwdR5o7YxY+T2pD4okfhPU/oLCaBBZBfo/0N/C136V2mUHiXsduTUMfiLndQPae6etgQlBS/sxqypia0Mrp7UKmcxqQE9uIRDW4TFApPGUDPRSzCnueLJ3h3BvfEo0SNHe+0+tBQW4B+k43ilvFfO3x/MnTu3w/1ut5uLLrqoj0oz8MQTTbQcLud2ubqUWCLx2mAQh93OyBEjGFJSQnZ2NlarHdG0uK9hRPA1+DhWUcH+/Qeoqa3BZrMlMh12NJQIYsuy1NbVYZomkab3azkBv789xT4tQoMWc3dE4r8D4zNqQnSz57zp80tJepfo7R5L1ggQOiAZWXgEjzMWhNTVw5q1kkg4gtFibpTdZuvw99o0TcL+ADcUlTLbW8R55zhx7rZiVsX2y6Ad4+Cp84MFRKwtvhNYSqoY+8U12J6eipkrqYuGWV5bicfj6fDzOOz2xALdkUiEqGHw4RpBVbUgf1DT+UVsCGj7ZFMsZSCx081Vm3qVCqQGIL0gn5pLRlAzViNztyR3XTXul/Yk9mseDzt+PAYtJPAcgEFlDYh12wZ82mdFaUXTkRrIYO+l7dcmj+XYRTlk7ouSt+IQxrGK5p3njqHyklIi7lIKPqjplcW36+cMI/+dI6i7Wzkd/kAAs8WQos6ehkMsiPIHAmiaxpTJkzln9DlYbdlIHNQ25hAMOTGlhiYkQ/MO4/Xk4M0axfhxEzl69DDrN22ktrY2MQ+ko2DKYrHgdDoTPZPxeRnxhZhj04oGQqDRPNdIJrbEt575TicEkkiQAoRM+cFU2gNtTceSPSzxGzq66BCWpgV49+yFoxWxhCpxnQVR8fXeZnqzuTCrmPHmKPLnvUvAzCBc5U2paLLBSWTXYPJ8JYyN2lmQH2ZPo48jwWAiAcWp4vek1WolHA4nhhhWnLCwfpPk8kua5/BJCVEDwiEIRyAUgppaOHESausk0Si43YLBpVkMG1mPt2lOZn+hAqmBRAiCC87Dn6+Tv+IYOR/Bzm8UkfdWbVKQZNTUMOb3lXD8JDIcIfiJCQSunUHu8n1EK46n8QMoSt8SVgumRXS6wGx3aZPH4h/qpeCRj8A0WgczZeVklcUW4fZfOhGXGNfji3C7X9tAVK3vpnSTlJKokTykyOVyddqIk1LS6PeTk53NBbPnkJE5mJrGImxo3LngKf7y7kK2H4k9Ddc1gyunraHOn8FfVizEbmkkrzCXTxUUsmHjerbv2IHL5ep0krvT4SAYDGIYRizFczCYKKtFA1c7eQOcltaT2AXg1Nt/TU+xa63fWxex922vnzxqQsu/WAJw6O1PxLe20ea0it7/bEIItDbKZO/kuvoMUo6mpJSEjRZ/Ybv4ek0IrHrHF0KzWhPX1ohEe2f0gGZFc+SCEGjCZFh+RSJbX/l2SSBgEok0pwC3dRBISSkxDAOnIflU/mCGh0vJDhRT/+vPIUPWNl/TEbMmg8BL5wNQzGCqLJUsKKjnsYO7kJ3M0bLZbIklCCKRCOGwZPkKwbyLY4sLb94qee8D2LkLTlZK6uqhsRECAYhEYgEWEjRdYrf9hZIh73DllVdy3XXXkZeXl/4AGBVIDSih+TNwVvhxvLKFKKB7vYx81o3Z2HrSpbGjORmB7Y21uIaUcPi6kQz+c+CMTL+uKN2hZbjReyljnzZ5LI3DvThfLgOz46GDpt+P49UyTt4yk/wDmRi1dT1WDrVIttKKANFWC7cdoWAwMc/JZrV22IiLa2xspKioiLkXXEiEIRyuHkLEsGG3hnmnfCYn6nISxxqmxtJ3rsYwNQxTxx/2EqjJIMuVxYzpDtxuN2Xr1nUaTGmahtPpTKxvFQyFEj1ncwfBf45r+3W6S8PhMKDFbZdtg0ema0Qbereh5hqlceCUt5ieLXhytqC9zNurTgr+e0vz91Yd/t8kwWhP22XNGq7x/inbFgyGWXN6uREqIC8TylpEfbqAH4wXBFxtv3fYFPxgo2BLbep/l7uTUKadBIhJhCZ6PWGN0CyIpoDOohsUZsfG30kJ27aDYZiJ5CyapmGxdNx8D4ZCXJCZzVBrNoPrhwAC6W87zbiUEhNJFAOrsCDkqT10zf/W0BgaGsZ490mG2V0cDofbTXgRz+AXF41GkVLy3geS91YJXn5VsnyFxN86wWYrZhSi0SA7duxg586dvPfee9x///2MHDky7cGUCqQGAiHQJo2haryV4jfWJTYb9fWwvrxrD2YiERqGmRz+2kRKntyBUVnVa8VVlP4iOHU4uVt6PruTnpvDifOyyV26ptMgKsE0yP+givpLx+L+x8BLUa70H1arlby8PCoqKjo9VkqZ1BvlcDo7HCAXH1KUk5PD3AsuxOXKpdKXQ8SwAYJQxMayjeef8ipBTWNm0habbnDZpI2UHy5h3NhziUQibNq8mYyMjA57pRx2O36/P5EtLBKJYLfbyLDA8Ix2Su6UVJ/Sa6MLKHTHytarHHDwlE0uHYZlgDBbv7eUsPuUZ50CKHK2/fkkoLWRojbTBp72rkcPkUiibXT2FDoFtPPeIQMcel82jPvLgMjmOW52azgxPyoahQOHYj1M8UBO1/UOh7ZJKSESZXpWLvnhfBzS0eZvsZQSP2GWhXawLLQD02NltKeIWTW5nG8ZjkVoTQn5m2nuIDn5AfKqspiRncu+E0cS8xjbomuxJQRM00x8VVVp/Mf3TLq78o2UkrKyMn76k5/wyKOPdjpXq7f1n0GGSreFr5hB3bgsit/zdfsc0YrjnPOjckpePEbdJaOhl9eXUJR+QRO9Uo8KhwMtSnIQJUTivvJ/dhby/HNbvc7YupNAroaeldlqn6L0FKfTybx587r0JDcajRJtSsaiaVqbacXj4oklEILzZ8/B1AYzb/IWrr/gDbTEhHJBy0Zjeyx6lJGFh9E1C5UNw5g08VwKCwsJtOgda4umadhszZFDbH5Gpx9TUdJLmkgZ63Fy2kLYLLHhcI1+qK4Go0VCJL2ToYimaZKlWyhxuCnMFzgv2QCW1g/0AkT4a/YOXpvsY0V0HyOvuZBp37uO7wZeZ2lwTZsJP2zn7iHz5jcZZPUw2u1FN2Wn92M86Gu55EFPLB/64UcfsXr16rQvbaECqTOcZegQgtk63n98DGs2d/3pdxtMnw9j9z4sARNx7tgeLKWi9E+O9QeoHu/q8fP6ZpSQu6E2aZs2eSz1154HgOtfHyNWbwJiQ3Bb0kMg3O4eL5OixGmaxqJFi/jkxRd3+mQ73HJehtXa6STvQDDIhLHj8GQO5nhdKc+tvpznPrwMU6bWy9AYcvLgi19m08FzqA/k4Y8UMmPatFbrT7XF3jKQikT6TZ+DorRHmhEwYveaVY9i0WNtOX8AfI0ksvUBnd6DhmEwyGbHIxx4BoWxnbsXYU2eoSul5LXwNiqmerlh8ZdwOBy89sqr/PY3DxPSTF7NOUy52brHOrzuHOp/ezWe+nyyrTYympYv6EjL8nZ2bCqi0Sjvvvtu2gMpNbTvDHfy4hIGvbkv9hSwhzheLePYt2dRuEGkPqkyvlbHaQR0itJXjMpKpGUUmseD6et+j+6pwhkanrrGpCkO5sZteDfGv2m+PxouGYvzX2sS32fvaMQ/aTC2I0d7rDyKcqqcnBwefOgh/vGPf/DOO+9QV1fX6u+9YZps2rw58b3V1vEy5oZhYLfbOWf0aGoai4gYdo7XtT0vA1rPaTl1XoYhY0/eJVDdWERJdjWlJSUcOnIkKbX5qeewWq1oQmBKiTRNhpaWUlwkgcoOy99a+ieyt8Xj9TJu3KDEj8uuSRyOCuDMnw8pNMHQoaU0Dmo9XCwUDrN///4ebYzH3rTrP+eW2RJ7lDQxg9Xo7nyEkIkihYKSYDD5XtE6Ka9pmnjtDhxYMdePoX7jOIgkN/cjmLwZ2sHqF4/w+lvL8Pv9+P1+jlUc45OXXMKtt36Ljd99ksmNRUn3mQzakEEbNq0BO1YydAtVnbQTk17fw0HPnj17CDXNhUwXFUidwfS8XEwrRI91Ps49FcJqIeIBbcIYzC2ppWPWc3PwfWIU7pfKVDp1pf+TksI3j0JeTo8GUlGHQDZ2YQYtJAVRAJaKWupGFdNxk1VRTo8QguzsbL761a/ypS99KZFZq6Xa2loWXHUVdXV1CMBqsXQ4rC8UDjN82DBsjhx8Vdm0F4hIKQmHaqitKicUOEmGNxebcyjujCFA6zTnAonDahCMZjN61Cj2HTjQ8WfTNDRdx4xG0XSdO++8k3nZAfjHT7tyaVqWtN3PkDYCzps1k79dd39zABAOYP3TrXC0ZzN+poPVauXeH9+LHD611b5Dhw6xaNGiWNDfTUKIpGBI0zQGlwxJSorQkmmanDh5AsM00DSNgpISrJbUM991oWSYoppGwJQaZtP8uGBYEI2eks69C8sOWDUNTWoIQwejdVPfxCTs0rlg2lxWr16VtG9dWRkP3H8/51SZmI5x6G3cAxoCXWjoXUjC0fLVPR2EVlVV9WhHQneoQCqdNJ3oxVOwfbQdsxsDRo0RxdSOk+SPGo6xZ3+nvUd6VibBGaOwvl3W4XENV01hyJs+Kmdmk7Olw0Nbl6myCs/mTOoXTifjRRVMKf1fdF/HjbLusAQlIsMFJ5u3+T87i4xlWzq916OFWThPqvtG6X3xgMVut7c5Ybyqqorq6urYsS3mOrQnGo1SWlJCxMxgTPFB9h4vIRix07IpJaUk0LiPaOMqjuxdx5gxY7jtO9fz3z+7nzrbBIqGXIIQyQ3VwTknuWvhUp5YvgArg3A6HBiG0W7mMgFYdJ1oNIphGJw4cQJH0aB204m3r58FUTR9NosFhycjtpgxIEM6UV0bEEMYBeByOdHaSCBQUFBAdnZ2twMpIQQupyuRsTK+tpHHm0lmVlabDwmikQhV1VUYpoEQgry8fJyunh8ODmBQSWX0INGQA3/QSpYbfL6m9eJT6NURQhAyTEwR+2rrF0NHY/aIScz65g3s27ePvXtja43quk5hYSHbtpZzkT4DrdU9IMEWxTRMotIgapqxBec70PLte/qOikQiaR/ap+ZIpYsQBK+cjsUf6XZ64vpRboa9EqZx3CBEO0Mu9EGDqPz6HDS3G6O2DvtHOxPv3x7PG1vQtuwh6hDdSjph7NqLd9NJGhZO7/QGU5SByNZgYnqTK1vXCx9hBoJELp8BxBbOrvraHLRTKuWaMW5cW9SwPiX9KisrEz1VmhCdrh2l6zqZmVm47FG+Pf9vjMg/0uq4cKgWi7Ge+39+DyNGjKCy8iTvr3yP2tqT1FS8R11VeauGUVVDJs+vvpxDVSVYrS68Xm9SFrO2aPGFeE2T48ePd2Ptn/4XRJ3tsrKyuOyyyzpNttCe2JposYdUoun32Wq1cvxEBaZp9oMGeYRBwTIyTqzhwV84+J9fwcOPQjQqkh5idFZOTdepi4QJECbqbETLr44tUtyCBY3R+zTu/NZtHDlymPnz5zN48GAKi4p48KFfcMmUOcyzndPqntcKasn87nNEhxwmJKP4jGinD1jkKfO7RCd/S1Jht9vTvjivauWmg6YTvHI6rkM+zI3bur+KtwD7vkr0/QfbPYdx8iR5f6rGbJqTER++FL1kGvZNBzFOnmz1mvgTc2kBoevIbsx3MnbtxQuqZ0ppk6VkMNJpR0QNovsP9s4Ch2nk+fgwJ+cNJXvjKTukiRYxEVYbxvET5D5Rlbg34wwHyJ5IaaQop6mhoSGRLUxorYfctWSaJlaLBYfdydHqQn7y3Dep9GW3Oq6uehvH9q/mJz+pY//+fYRCIZ763/8lPz+fn/33fTzzfyvwGyZCNDeWA2E7K7dPRRMGWS4rnowMqmtq2i1LbCHY5rLW1tV1o5Es6ZdD+85imqZx25IlFBUV8e6771JZ2frvZ2eEENTX18eGq4pYgFJfX0dFxVEmTz4Xh8ORVB2FwyF27twOkdhr8/MH4fG2zqoaP9fpfj6Px82QISWEQrMxDIOS4XDlVbBq1SqeevppAEyj48+saxonggHqzQCRmevxfvIgdQ9ei2xsnkckhGCuNoxr/Ed5wbWbz3/hi0QiUVaseIdf3f1Tvlg5jFH6oFbnlg0OQmWjqPGHORkJ0miaOLswZwtiwza/9rWvMXz4cILBIH/5y19iDzlOQ25ubqdravU2FUj1taaeqHgQdToCeRrRg62f+LXSxh8a63ubMaKRNg7uOcauvXiFaD+YSmPbueYcHY/Lhenv+TWEADDNtPX3GnZBYNpQbMtaB8n9RWBsIcFcK6YFAlcPRgtD8Qt7W833My+aStjbG+PRW5QlRyeYK9DDkL/GF8t+eZrMqmqirqEIiyX5915K9HfWNf/qn3Jv6hPG4Dpp9tiCvIGrZ+J8ce2AC1SVvhEOh1Oa2K/rOkLTiJpWKmpz2jwmFDzJoPx8du7YkbQ+VWNjI+++u4JDB7aRXTwFTWs5ebxpKBYCKfXYfJbOfqdbNO5iKdDVPXCmE0Lg8XpZvHgx119/fWJuTKo/27KyMn70ox8lgimAxoYGiosLueqqT1NYWJBonNfW1rJ+fRmBYACbzcZXv3YLo0aNard8p0vX9cR7tzxfUVERf3v2WQzDSCxH0NE5GqTJ/kAjR7YWkr3/cqS/9dBdq9C51XkBs6JDWXv3/+JpPMk3bDO54ugYxuiD2kxqIRsdNCybxgnPx2z31WHqnT9gMZt+Pl6vl0WLFjF69GgMw+DgwYP87W9/69J1ac/IkSOTljtIBxVI9SUhCC44r0eCKABnlYllSDHRA4fafjuLhZobziPv3cOtjpGR1hOLe4Oxcw9eKVsHU1KSvz6AcfE09BXrOjxHT/MPzyRvcxQZ6b1eMlG+h/DoKbhLBhM93IVgt4doHg9aROI8VN+N+QB9x/p2GfHwKBPQXC6iE0fCKYGU9Vg9lrr2F/vrCRnraokeOYrmcBC8eBKRz8/C/fzpLYhrBoMUrKyhccE0nC+XdSmLpT5oEDXnZpP1cjk9lZOqbpgFp9BA9uffBqW/0nU9pcahaZogJaKD3+Cs7CLuuesrPPf3v/Piv/6VtO/5557D5iwht+TUpkmsISYAIcykNXW6wqLrKWVmU/o3IUS78/q6Yu7cudx555384he/wOfzIYTA1+DjySef5NVXX2XGjBnMmjWLsWPHJvV2xN/X4Wg/E2V3tQwG4/9uua20tBSv10tNTU1iYduOhjgKq5U1tZVMdecztNKLS566tC4IBFahM9NSykyjFFp8rHaTygA1lmpOihrK6qo7DWIMw0g8jCkoKKCgoACI9b59+ctfZtWqVRzoJHlMeywWCxdccEGPDRPsLhVI9ZVThvP1yCmjIK0WhN2ODIdbPaGT0Sg5z28kGgxhKSpERg3Mmhr8V03D8+EBohVtd6lqbjdaTjZIkClWWG1pr2dKe3c95typfRdMCUHksunYTwZhzeZe7RAzg0Ey/7WBmoVTyP6APgmmdK+XmgXjKfjn7jaHbPZnpt/fZk+QsXNP7793/P/BILY31mK323vkd8Pcsh03Y6leNJO891o/zIgTVht6SRHVs4tiQVQPZg9UlNPhdrsTjTUpYwtvttdoEZpGNBwmHAlh0cM4bQGCETtSJnfNW2yD+cVDv+HA/p2UlJQgpeTkyZP8+N7/x6ZNm3hvdR1CJDdNrHqUa89/gy2HRuLzR/H7/Z2uf9VyXkZGhgfN5sQQGsgeTp2dBsLmJGnIoaaDtXcfOPUZTQdL7/YwCCG46qqrcLlc/PrXv+bo0dic1Pjv4uuvv86yZcuw2Wx4PJ5EwpVQKMSjjzyC1+tN9LLEnRoINX8rWwdJLb+n9dpKp/awSSkJBoOYRuw40zSJRKMdBlIOu53Nvjr2hGoodBxgrH/cKYFU83edByISHBGEPUyk3s5+5342+Ko4Eg6R4fV0mMmz5Tp0kyZNwtU0J1gIwahRo3jooYe499572b59e8o9i7NmzWLOnDkqkDor9OBwvpasDSb1U/IJXFJIwd+3tjkcKDF0zW4jOGEQ1uWVeMqOYnYwdEiOHcbR2U1jgHtoPShj554250xpK9djXjQV86KpaO+u75H3ak/ksulY68M9MnSrK8xgkMwXN1Bz9RSy3+/dYErzeKhZMJ7st/eccUFUf9Pd5C9tMbdsJ6+mmKpPlhK4egiWgCRrdxghJVII6kbaMOwCR7VJ9itbMXo4iHIfM9FHDsXYtbdHz6ucHbJzchJpoU3T7PABgyYEkWiUhgYfJcVH+fInl/P4259jV0UpLRttDlchx6qKaGzcxO23304kGuXB/3mAZcuW4Q9lM6hoThtnl7gdAdz2Bup9Eep8vk57y+KNUyEEgwblIQpGgtMD/q4OmxVJ5e4/BKJ0UvJnt9gQJeORe/t2dEevyMxH5Azu1bcQQmCxWJg3bx7Dhg3jj3/8IytXriQYDCaOMU2TYDCYtM0wDN5ZsaJXy9aeWJDRfAeGQyHsNlu7a6lpmkbUovPK8cMMHuIm25bNkOFRLMMrCL45HczUEnY4P7kBy8Q9bPjDFPaZx3n1+BHsTkenCWjiyWp0XWfuhRcmBX9CCKZPn84f//hHnn/+ed5++22OHDlCIBBI9DpLUyJbfG5N08jOzmbu3Ll85zvfwXvKgvbpoAKpFJl+P8jYUKQuza/phZ6oOPd729n73QmMePpkp3MqovsPYj1wCKQkeuhwh8fKsnIKzAkEBrt7srjtB1PvxoKpXuuZauqJstaG+iyIikv0TPViMKV5PNR+egLZb6kgqj+KHjlK5lNHydR09OxMIuOHArG1cfJf2IFRUwem0StDMZ2VESIFXrRdvXByZcDLyc7G6/VS15SsQZomdNATpGkaR48do6hwCMs3n8fJ+tbJJhAahSUX43AN4u8vrCboP4HDPZRdB9wMKpqOxeJu1TiLGFb++NbnyfUcgdBGGhsacLs7rp/iDTFd1xlSWoooGIZ27uWYq58nrRN0T5MoGoU2+dJW27VZ12BufBPqTqShVD1Es6DP/jxktD2/rsffTtMYPXo09913H+vWrePFF1+krKyM6urqtK9N1BaXy0VdfT3QPH+xvV4pIQROp5Nyn4+3qo7gyLORkWelqNgHmiTV8ePBtaM5dijKDu0A/zhygJPSIMPW9qLY0JQhMRpNXMfs7GwuvPDC1mvECUFxcTHf+c53uOWWW6isrKS+vj7WA2eaRCIRgsEgkUgkEUQNLi6msLAI3ZLa0OPeogKpVEmJMCXCZoXO4ighCM2fhutwQ48HUQBGfT2OSoG592DXXtDVblMhODnTS/4zW3psrkZcZ8FUj/dMxYOoPuyJOpUZDJL50iZqPjOZ7JWS6JGeS22teTzUXqWCqDOCaWBUVaOtrE5s6u2ZS44dFVReUkrW+738RsqAlJGRwbBhwzh06FCiYdRRT5DdZuPAwYNMnDCJ97dPojHk4dReHYFAaDrZuZOQcjyurCg5gzU0zdpBo0igaVEy7NVs3rEfOkmfbEqZmJBvt9sZNXIkQreiX3kb0l+PLH8Hop3NE+5nWfuEhigahf6FH0NmQfIuIaB4DJbP3kP0Xw9C7bEzL8GMzYU2+7NoF16H6MN01vF5T7Nnz2bGjBmcPHmSnTt3snPnTg4fPkxdXV2/WKsIYg8H3l6+nOPHj2OYJqFQCKez/WBGCIHD7ea1k8dw6RYoH8zY9RMoiAo0ZKzP1REGTTYlo2g6j2YgPAFkvRspISqiHGyoYschH/+sOEBZQy3uNtb5OlUwGExctwvOP5+hQ4e2W06I/b3JyMhI+bqkmwqkuiFvk5/gzNFY3/y4/YPiiSUON2Bu2NprZSleXoXv01NxvXB6k+NbElPGk3HE6LW5GvFgyveZ6bhfSg6mjIun9WgwlQiiPtzUI+frLtPvb9EzJXqkZ0r1RCmdiR4+ArIUSx8nPVEGBrvdzqSJE1m5ciUQW/yyvQn+8TV56urqOHz4IIWD8wiEMzBlyyfmyfMyhLCA1lEzRGKzRAhHrWQ6TxINV7J7zx4cHSQZkFJiRKOJoX0lJSUUFxfHkk148rAsug9z+yrkrg+hoSmFujWIsK9MKh82B9qkT0DI2fpNelLuMdC2N7+3AJFVgDblfER8fpnNiRgyAW3SJbGhb20N59I0OPcyrMXnYG5ajqzYBdEIEok2ZBeIqhafTyDyR6BNndqrH00iwbsSGlv+3DXE6JmIvIL4BsjMRxs3FzFiOlh6N0tre+K/v0VFRRQVFXHRRRcBrecvpZOUkgcffJCf339/bGHrYBCHo/3hdUKIWKIVt4vnKg5SH41wRV6Eoe5aSoPD8BgeMhauQs9qxPfHK2Nr6gCW0UfwLH6T6l9+lqo6k/3OfeyTFfzzyEE+8tXgzsjocC0o2fQgI9g0TN5ut3P99dcnhgkPNCqQ6gZ9816qbphInhBtP/XR9OaeqF4MogCM8h0EZ83BM2gQxsmT6KOGI4+dSKwFlQpt8li0qnqOT/cy6G+berw3qiVj5x48QrQKpvQV63qmZ6plT1Sag6i4pJ6p0xzmp3qilK7KXXOS2jklZDynAiklNUIILrzwQv7wxz9iGAbhpifzHfUG2ex2NmzaxPyiwWS5MglGspkybAfr948lEE4t29nIgkN8+eKX+MNbV2PXKyhbt4VQKERGU0OuPfF5GQBTp0whMzMz8Xmwu2JD4yZd0iLxRBhP4wd8Ic+OIUWsbe9pRB8/F0TvJnGQ5glG12/ihqLYUEW7HkXPsmOZdh7NwZWI9Uh1MoxJCAH5w9AuvTn22eLZ36J7Oa9uP6OGx4JCjzUEWYPQz5/Qa58rTgZXs6AwSsiINTeLXT70KdMQWl7zQZoG9Nwirafj1DJ0d/Hf3iCl5Nprr+XJv/6Vw4cPE41GCQSDuDrplbJYLIiMDF6vqmBXYz0L8n2MyThBnpFDYZkHj56HJgJoQsPAIHxc48DLQzgqt3Hc5WNTQxWvHj/CcTOK2+PpdHFuAL/fn+iNmj59Op/4xCd6/Hr0FyqQ6gbT5yPjqIF27rjWgVLTcD7nkcZeD6LiBr26h+rLR+I6XkpNiY28F6q6dZ5gUQaHb8jmnMcOE+2DBUGNHbvxSNl+z9TcqWgruxdMRS6d1q+CqLiknqluDvPT3O5YEPXvvSqIUjpl7NpH8OJBeN3ubj1gUc5eQgimTZ9OYWEhR44cScx5sFrbHoYnhMBus1Hv87F+wzpmnufA6ajiugte53B1Pgcri1N6/9pGD5sPDsfrOMq+A/vYvnMnTmfHPURSSkItJrhfdtllrTL8CSGa0qHHt1tweS9jYhrmrQu9mPzcYvJze/CcQkCLBY2Ffg6l+edQ2nNv0WU291zG9ux067OWEIJhw4Zx00038cADDyClxO/3Y7fZOhxyK4RA13XcHg/7QiF+d2Ano10ZzMjKYcRRDzkWJw7PcQQCiYnfjFC5Icj2hqN8XFvFwXAQq8NBhi0jcb72xBNMhFr0Rn3r1lvxer39IlDuDSqQ6ibn6+vwLZyOt3FEc0YsIQhdOaNPgygA4/gJcpYZHL1hDIPW+TGaJiOmQtjthLIsnPPIoU6TUfQkY+cePLQe5tftnql+2BN1quQEFKkN81PD+ZSUmQaFL+6l+jOTyHppswqmlJQUFRZy0UUX8cwzzwAQCAY7HaLjdrnYsWsXbpebSRPP5Zcvf5HDVfnE5xwJJBY9SsSw0DzcTGLVo0QNHYkGSBpDVtbsGkJt9V7eX7UKm9XaYYMxnm45PsF98ODBXHjhhT1yHRSlP9A0jVtuvpmXX36ZLVu2YJomDQ0NnQYq8aF4TocDabezOxRi27GD2CR4dAsZFgsWoRE2DXxGlAbDIKrF5o+5PZ4Oh/LFSSkT5Yn3Rl1++eV86lOfGrBBFDQ/jlFSJKNRMl4so37yIPTRI9IWRMUZlVUU/O4jQrk2IpfPQIsvGKfpBK6e2eZr9DGjEBYL+jkjOfGVaWR/eKRPg6g4Y+cePOVVNH5mOqLF4nfxAMqc28Vx3EIQuXQaFl+k3wZRcYlhfhcOwTK4a09pE8P5lqueKCU10Yrj5Lyzj9rPTELrJNuZorSk6zpf/OIXEz1BoVCIaDTa7uT7eOplt8vF+o0bKFu/Fk0eoyDzADZLAJCMK9nDXZ95kgxHc8amDEeA//z0X5lYuhtdi5DjrmBw1naOH9vGv1esQAK2dtI9txQIBBL/vnL+fAoLCwd0I045uwghKCws5J67704kZgiFwzS2GErX2es1TcPhcODxerF5Pfiddo7rgsOa5KRFI+x04PB68Hg82Gw2NK3zYaUQC6R8DQ2JRC8lJSXcc/fdvbKAcX+iAqnTEA+maqflc/j7c3Ac9acliEowDZwvl2HxRTj+lamEP3UeDZ+bgTz1BhCC0Pzz2PFDD4fumknt1EHkP1HW7oKhfcHYsbvdYErqokvBVOSy6Vgao4jVG3uzqD0mMcxvbmmnwZTmdsd6ov69F+P4GZzeVkmbpGCqaVFERemMEILz58xh5szYAzkpJf4WwUp7r9F1HZfbzZatW1n+ztsEfeUMySmnKGsvhhll74kCTBOEMNBEFClNDlXlYNHqKc3dikvfyrr1q3jnvfeA2AKjnQ0pCoVCiflRWVlZ3Hjjjf1qjoui9JT58+dzy803J4at+v1+/IFAYuHszsR7mDRNw2qxYLPZsNts2Gw2LBZLInjqagBlmiYNjY2JIX0Oh4N77rmHiRMnDvgHGf0mkHrggQcQQnDHHXcktgWDQZYsWUJubi4ZGRl87nOf4/jx40mvO3jwIAsWLMDlcpGfn89dd93Vp/n/ZTRK1rJtWBtAri/vs/dtl2kgVm8k//G12KqDWP0mDUU6x+48P/FVccccrP4o53xjB6W/Xofn/z5ERjpLBdv72gum9BXrOg6mhCAyLzac70wJouISPVMdBFOax0PtZybFhvOpIEo5DYlgauHk3u2ZslnR+t8yLN1yptZNPcnlcnHrN7+Z1CsVDoc7bLAJIdA1DU9GBlVVVbzx5jJWrnybusp1BAMVrN2ZT0HmLobmljM0r5wC73ZWb8vj0NEKNm1cycuvvcq27dtxOhyd9kTFG3KNLdZ2vObqq8+KRpxy9olnGPzud7/L/BbD5hobGzt9yNHT4oFbQ2NjojfYYrHwjW98g+uuvbbV/MSBqF/MkVq7di1/+MMfmDx5ctL2//iP/+DVV1/lueeeIzMzk9tuu43PfvazfPDBB0Asp/6CBQsoLCxk1apVHDt2jC996UtYrVZ+/vOf91n5pWEientBmBTJaBTWbMYO5LdzTP9J6tnM2LG73TlT7SWgiFw6DUtjtN8P52uP6fe3WGeKpAQUKrGE0tNiwRSxOVMvburawuIp8s8cQd6GhjN42dOYM71u6ilCCC699FKuuOIK/vWvfyUaTlktnly397r4wqCmaXL46FH2HzyI0+EgMzMTt9uNzWrFMAwCgQB19fU0NM3hc9jtiaFLXQmGGv3+RKBaUlLCkiVLsFj6RRNHUXqcEILs7Gx+8YtfUFtbywerVsXuy4YGTNPE7XJ1uUepu+IPMHwNDYmeKE3T+OIXv8j3v/c9bDZbr713f5L2ULGhoYFFixbx+OOPk53dvAp6XV0dTzzxBL/61a+45JJLmD59OkuXLmXVqlV8+OGHALz55pts3bqVp556iilTpjB//nx+9rOf8cgjjySlP1XOLF3umWrqiTqThvO1p61hfprbTe3CSWo4n9LjertnKurS0BpDPX7evqTqpmROp5Pvf//7DB48GIBoNJo0qbwj8SFELqcTr8eDpmnU1tZy8NAhdu3Zw779+zlx8iTRaBS3y0WG2x1L2dzFCe7BYDDxNNxqtXLH7bczZswY1RulDGhCCIYMGcJjjz3G3AsvTPy++/1+6urrO5zLeLri2flq6+oSQZSu69xw/fX8zwMP4GlKUHE2SHsgtWTJEhYsWMC8efOStpeVlRGJRJK2jx07ltLSUlavXg3A6tWrmTRpEgUFzat8X3HFFdTX11Ne3v4wu1AoRH19fdKX0r90GkxdNDXRE3WmB1FxLYf56eeMjA3nW66CKKV39FoCCiHwDdYxtu/puXOmQV/XTf29XhJCMGH8eL7/ve8lJo8HQyEaGxtTmpMRX9fGbrfjcjpxu1y4XC4cDgdWqzXluRnhcBhfQ0PiPa666ipuvPHGs2JIkaIIIRgxYgSPP/44V111VWJOYDzIafT7MQyjRwKq+DC+aDSKr6EhEaxB7EHLrbfeyoMPPkh2dvZZE0RBmgOpZ599lnXr1nH//fe32ldRUYHNZiMrKytpe0FBARUVFYljWlZU8f3xfe25//77yczMTHwNGTLkND+J0hs6CqZC2VZ8pbYBE0TFxXum9l1fQPba4yqIUnpVtOI4OSv292gCCm3CGLwHDTD72XjnFKSjbjoT6iUhBDfccANf+cpXmie5BwI0NDZidnGSe0+JB1H1Pl/ifc8991z++7//OzEkUFHOBkIISkpK+P1jj/Gfd96Jx+MBiM0bbGykprYWf9PQ164mo4iLHx8PoBoaGqiprSXQlNgCYkskPPjgg/zk//2/Ab1eVHvSFkgdOnSI22+/naeffrrPUyPefffd1NXVJb4OHUpftjqlY8aO3Xi2VdP46eRgKmPLSaz+M30GRtvMYBDPAYkInNlDo5QzQ/RYRY8N87MMK6V+fBbu1zb0TOHSIF1105lQLwkRW1fmBz/4Addcc3VSxjCfz9cnwVS8URcMBqmrr8c0Y7N9R40axW9+8xuGlpaedQ05RRFCkJWVxT333MNfn3ySGTNmJHqnDMOgobGR6poa6urqCASDRCIRTNNMCpRO/TJMk0gkgj8QoLa2NhaQBQKJe85utzN//nyef/55vrx4MQ6H46y899I2E7OsrIwTJ04wbdq0xDbDMHjvvff43e9+x7Jly2Jdk7W1SU/+jh8/TmFhIQCFhYWsWbMm6bzxzEnxY9pit9ux2+09+GmU3mRs24WH0a0SUCiK0jNiPVOCmk9PIvOl7iWgsAwrpXZmMZ4X1yNDZ+5DgHTVTWdKvSSEICszk1/+4pdEowYvv/wypmkSDAYxDANPRkYiyUNPN6qklJhS0tgiQxjAiBEjeOzRR5k2depZ2ZBTlDir1cpll13G1KlT+dvf/sYTf/4ze/fuTQRNoXCYUDicmLcY/xJCIIgtmR1PImEYRiJoOvU9Jk+eHBv+fOWVuN3us/q+S1uP1KWXXsrmzZvZsGFD4mvGjBksWrQo8W+r1cry5csTr9mxYwcHDx5kzpw5AMyZM4fNmzdz4kTz8Ke33noLr9fL+PHj+/wzKb3H2LarzZ4pRVF6RvRYBdnv7o/1TKU4zG+gBFGg6qauEEKQl5fHb3/zG750001YrVYAIpEItbW1NPr9iYZbT4g/IQ+FQtQ2DSuKmzJlCn9ZupQ5c+ac1Y05RYmL35/f/va3ef211/jFQw8xe/bspCGvUkoMwyASiRAKhWIJW4JBgsEgoVAo0WPV8pzZ2dlcccUV/Onxx3npxRf54he+QEZGxll/36WtRerxeJg4cWLSNrfbTW5ubmL7Lbfcwp133klOTg5er5dvf/vbzJkzh9mzZwNw+eWXM378eG666SYefPBBKioq+OEPf8iSJUvOiCd7SmoSPVOfno5na1W6i6MoA070WAU5K6DmM5PJfHkzZlMq6o4MpCAKVN3UVUIIcnJy+J//eZARI0bw/z38MNXV1Ykeo2AwiNPpxGG3J4YAptrgigdQ4UiEQCCQlPHQarVy1YIF/OxnP2PYsGFnfWNOUVqK3w/FxcV8/etf54ZFiyjfsoUV777LqlWr2LVrF1VVVYTD4VbJKDRNQ9c07A4HBQUFjBs7josu+gRz585l1KhR2DtZHPts068f7f/6179G0zQ+97nPEQqFuOKKK3j00UcT+3Vd55VXXuHWW29lzpw5uN1uFi9ezE9/+tM+LacMxhoPwmrrFwvbDmTGtl24XRPYd20+Ea9JIO/8dBepV9SPMgkMGoYlMCxpu61ekvP8xl5Z+0dRIN4z1bV1pgZaENVVZ0rd1NuEEGRkuLnjjjuYPn06P/vZz1j78ccYhhGbl9HQgN/vx2azYbfZEln5OhMfVhRuGoZ06kLGgwcP5vbvfIfFixef9cOKFKUzQgg8GRnMmjWLWbNmEYlEqKmpoaKigsOHD1NZWYnP5yNqGNisVrxeL/n5+ZSUlJCfn09WVlZivpW611oTsi/T7PRT9fX1ZGZmcjELsQhrt85xYsn5FP5lY5ee4Crdp3u91Fw1npx/78OsrUt3cfqcNiiPYwuGULC6FnYfVL9vSq+xFBVSffGwdoMpy9Ah1M4afNpBVFRGWMGL1NXV4fV6T6fIA0q8Xjp+/PgZcV2klFRXV/P000/zxBNPsHffvlbzKzQh0HQdi66j6TpaU5rzxLyMpgDMMAyMNuZmZGdn85nPfIZv33ZbYp0o1bBTlNPTVhig7qvY3+CCgoJO66Z+3SOlKC1pHg81C8aT/dYeoidPprs4aWEeOkz+nypoXDCNhvMnY9gFSMjeHUELtW54dEYzJLay3Rg+H6hnKkoLScP8TklA0VNBlDJwCCHIzc3ltttu45prruEfL7zAc3//O9u2b08s2GlKiRmNtuph6oiu6xQVFTF//nxuXLSIyZMnY7VaVUNPUXqIupdOjwqkeogelginA1QPQa/QPB5qPz2B7Lf2YJylQVScjEZxvrgGpxAgNIQmYMpYTHvqt7PUBBU3TEBIyN4Zwrbl0Fl/fZVmiWF+CycneqZUEKV0RNM0SkpKuP073+HLixezbt06lr35JqtWrWL//v346uuJdBBIaZqG2+2moKCAadOmcdm8ecydO5fi4uJEdjFFUZT+QgVSPSR3YwOBGSOwvaGSIPQ0PS+XmstGqyDqVFKCNJAm8PEWutO8EMCglbEhk5FzR3LiM6MIZ47GUS1xnYziWrMfImFkOKLmZZ2l4j1TJ647F0etiWEVeP+lgiilffFgJysri09+8pNcfPHF+P1+jh07xr59+zhw4ADHjh2jpraWcCiMbtHJyMigoKCAoaWlDB02jKGlpXg8HiwWiwqeFEXpt1Qg1UOEYSL1dJdi4NEmjuXErGzyX9qtgqheZNTXo61cT+7K2PeWokKM4lxOLByFaQEESL3/NmbcxwwyXlRrjPUGPS+Xmk8Mw15n4tlRi7l7vwqilC6Lz2PKyMhg9OjRjBo1qsuvUxRF6e9UINVD9OoGQmM8DIzEtumneTz4Lh+PMCF36RoM00h3kc4q0WMVcKyC3LKmDZqOZuteIpa+IEYOpeJr55G7JYi+Zqtq6PcAPTubyISh1I5wkvvhCYxdezHUPDrlNKkASVGUgUQFUj0kuu8AjdcMpv/nVurnNB3z/EmcnOik8L0qjK07010iBcA0MIP9OJgt30HBHgfh8ydQdfM0vPujuNbswaiqTnfJzjh6bg51l55DKFOQuSdMzt/XYwSD6S6WoiiKovQ7KpDqQY5qiaWwgGjF8XQX5cwjBGLKeE7M8pK9K8ygP3yonn4rKTGDQSz/LiN/pQ05dQwnrhkDwKAPazC3bE9z6fo3zeVCjhlO5fTYo6D8905g7NwDQOq5IBVFURTl7KACqR6U+9wmTn5hMvb6YXje2x17Gq6CgQ5pbjfBueOpH2oh46hBwdNbMH2+dBdLOYPJSBjWbCZ3rUDPzcH3iVHUX3o+niMGmR8dwThWoeZSEbv35PgR1IzLIOwReA9EyX9xN0ZlpXqIoSiKoihdoAKpHmQ2NpL9l9XoE8Zw4upzAMjb2AAbdsQad0qCZVgpDZMK8Q3WGbShkYJVOzDq69XTb6XnSIlRWYXrhSpcmo4+diSVFw8h7C0l45iB+2AjbNx59tybQqDnDyI6oojqCS5MK3j3R8n790Gih48A0I8HbyqKoihKv6MCqV5glO8gtzw218A/cyT1X52Os0qS9dERooeOwtmYOEEILIOLqZ9ZQkORjr3WJLu8HserO8A0VANO6V2mgbF1J1lbQVhtaOcMxz/MS+2tM9DCEnu9JHvtCUSDPzY0dwD0yGguF1pONpGhg6gb6SSUJbA2SDKORBI9T0iJ6ptTFEVRlO5RgVQvMqqqsb9eTb7djja0hKq5gwnmDMHqk+Rsa0Ss34EMhwdEo60twmqDyaMJFLupG27BVifJ+7iajDcOYfr9qvdJSQsZCWOU78BeDgXE1tASeTnUnFdI1CkIZY4ADTwHDWw+Ay1sYl27I3k4oGGkdXigsFhAb15vQS/MJzCmAICwR8dXqqOFwNYgyTgSJu+dQ0SPHEs8xFEPLhRFURTl9KlAqg/IUAhj5x4yd+4hUwgsBfn4zx1Czdeno0UktgZJzvoaRGOA6P6D6S5ut2luN2LoYMIFGdSMtmPaBN79UZyHG3G8ul31PCn9klFfD/X1ePbuj21oSs+sjxtNNMuJtGpULD43aZ04YYAWTd8DENMiksrjqDHx7g3E/n3Eh/uFpmyXTQ9pVK+ToiiKovQ8FUj1NSmJVhzHVnGcgmWx9ZK0nCzqpxcTcWbj/2wJFr/EfcJEC0vca/dDOAKA6fendX0cYbWhZbhj3+ga/vNGYDg1onZBY5GGHgT3CQPn8TAFz23HqKsH02Bg9rcpA1ZT8GFs3YkABJD/bvIhwm5vvhfSwGxobPdvgXpYoSiKoih9QwVSaWb6fJg+H64DhwDIJDbUiCFFmDYLJ64aiWz6KZl68lPovibM5qfwwoS8DQ1o/jAiEMIbf5rfRDXmlIFMhkIYatFfRVEURTmrqUCqHzLq66G8HoDc9c3bhcUSmxuRJtIwkzKcSVTApCiKoiiKopydVCB1BpHRqFr/RlEURVEURVH6AS3dBVAURVEURVEURTnTqEBKURRFURRFURQlRSqQUhRFURRFURRFSZEKpBRFURRFURRFUVKkAilFURRFURRFUZQUqUBKURRFURRFURQlRSqQUhRFURRFURRFSZEKpBRFURRFURRFUVKkFuQFpJQARImATHNhFEVRziJRIkDz32ElJn49fD5fmkuiKIpy9on/7e2sblKBFFBVVQXA+7yW5pIoiqKcnXw+H5mZmekuRr8Rr8RHjRqV5pIoiqKcvTqrm4RUjwGpra0lOzubgwcPqoq8C+rr6xkyZAiHDh3C6/WmuzhnBHXNUqeuWWrO1OslpcTn81FcXIymqdHmcaZpsmPHDsaPH3/G/UzT5Uy9B9JJXbPUqOuVujP1mnW1blI9UpC4QJmZmWfUDzndvF6vul4pUtcsdeqapeZMvF7qAVZrmqYxePBg4Mz8maaTul6pU9csNep6pe5MvGZdqZvU4z9FURRFURRFUZQUqUBKURRFURRFURQlRSqQAux2O/feey92uz3dRTkjqOuVOnXNUqeuWWrU9Rp41M80Nep6pU5ds9So65W6gX7NVLIJRVEURVEURVGUFKkeKUVRFEVRFEVRlBSpQEpRFEVRFEVRFCVFKpBSFEVRFEVRFEVJkQqkFEVRFEVRFEVRUnTWB1KPPPIIw4YNw+FwMGvWLNasWZPuIqXF/fffz3nnnYfH4yE/P5+rr76aHTt2JB0TDAZZsmQJubm5ZGRk8LnPfY7jx48nHXPw4EEWLFiAy+UiPz+fu+66i2g02pcfJW0eeOABhBDccccdiW3qmiU7cuQIN954I7m5uTidTiZNmsTHH3+c2C+l5Mc//jFFRUU4nU7mzZvHrl27ks5RXV3NokWL8Hq9ZGVlccstt9DQ0NDXH6VPGIbBj370I4YPH47T6WTkyJH87Gc/o2WOIHXNBiZVN8Wouun0qHqpa1TdlBpVN7Ugz2LPPvustNls8s9//rMsLy+XX/va12RWVpY8fvx4uovW56644gq5dOlSuWXLFrlhwwZ55ZVXytLSUtnQ0JA45pvf/KYcMmSIXL58ufz444/l7Nmz5fnnn5/YH41G5cSJE+W8efPk+vXr5WuvvSbz8vLk3XffnY6P1KfWrFkjhw0bJidPnixvv/32xHZ1zZpVV1fLoUOHyi9/+cvyo48+knv37pXLli2Tu3fvThzzwAMPyMzMTPmvf/1Lbty4UX7mM5+Rw4cPl4FAIHHMpz71KXnuuefKDz/8UK5cuVKOGjVKXn/99en4SL3uvvvuk7m5ufKVV16R+/btk88995zMyMiQDz/8cOIYdc0GHlU3NVN1U/epeqlrVN2UOlU3NTurA6mZM2fKJUuWJL43DEMWFxfL+++/P42l6h9OnDghAfnuu+9KKaWsra2VVqtVPvfcc4ljtm3bJgG5evVqKaWUr732mtQ0TVZUVCSOeeyxx6TX65WhUKhvP0Af8vl8cvTo0fKtt96SF110UaLCUtcs2fe+9z154YUXtrvfNE1ZWFgoH3roocS22tpaabfb5d/+9jcppZRbt26VgFy7dm3imNdff10KIeSRI0d6r/BpsmDBAnnzzTcnbfvsZz8rFy1aJKVU12ygUnVT+1Td1DWqXuo6VTelTtVNzc7aoX3hcJiysjLmzZuX2KZpGvPmzWP16tVpLFn/UFdXB0BOTg4AZWVlRCKRpOs1duxYSktLE9dr9erVTJo0iYKCgsQxV1xxBfX19ZSXl/dh6fvWkiVLWLBgQdK1AXXNTvXSSy8xY8YMvvCFL5Cfn8/UqVN5/PHHE/v37dtHRUVF0vXKzMxk1qxZSdcrKyuLGTNmJI6ZN28emqbx0Ucf9d2H6SPnn38+y5cvZ+fOnQBs3LiR999/n/nz5wPqmg1Eqm7qmKqbukbVS12n6qbUqbqpmSXdBUiXyspKDMNI+kMBUFBQwPbt29NUqv7BNE3uuOMOLrjgAiZOnAhARUUFNpuNrKyspGMLCgqoqKhIHNPW9YzvG4ieffZZ1q1bx9q1a1vtU9cs2d69e3nssce48847ueeee1i7di3f+c53sNlsLF68OPF527oeLa9Xfn5+0n6LxUJOTs6Au14A3//+96mvr2fs2LHouo5hGNx3330sWrQIQF2zAUjVTe1TdVPXqHopNapuSp2qm5qdtYGU0r4lS5awZcsW3n///XQXpV87dOgQt99+O2+99RYOhyPdxen3TNNkxowZ/PznPwdg6tSpbNmyhd///vcsXrw4zaXrn/7+97/z9NNP88wzzzBhwgQ2bNjAHXfcQXFxsbpmyllH1U2dU/VS6lTdlDpVNzU7a4f25eXloet6q0w1x48fp7CwME2lSr/bbruNV155hXfeeYeSkpLE9sLCQsLhMLW1tUnHt7xehYWFbV7P+L6BpqysjBMnTjBt2jQsFgsWi4V3332X3/zmN1gsFgoKCtQ1a6GoqIjx48cnbRs3bhwHDx4Emj9vR/dkYWEhJ06cSNofjUaprq4ecNcL4K677uL73/8+1113HZMmTeKmm27iP/7jP7j//vsBdc0GIlU3tU3VTV2j6qXUqbopdapuanbWBlI2m43p06ezfPnyxDbTNFm+fDlz5sxJY8nSQ0rJbbfdxj//+U/+/e9/M3z48KT906dPx2q1Jl2vHTt2cPDgwcT1mjNnDps3b066Md566y28Xm+rP1IDwaWXXsrmzZvZsGFD4mvGjBksWrQo8W91zZpdcMEFrdIW79y5k6FDhwIwfPhwCgsLk65XfX09H330UdL1qq2tpaysLHHMv//9b0zTZNasWX3wKfqW3+9H05L/TOu6jmmagLpmA5Gqm5Kpuik1ql5KnaqbUqfqphbSne0inZ599llpt9vlX/7yF7l161b59a9/XWZlZSVlqjlb3HrrrTIzM1OuWLFCHjt2LPHl9/sTx3zzm9+UpaWl8t///rf8+OOP5Zw5c+ScOXMS++MpUy+//HK5YcMG+cYbb8hBgwYN2JSpbWmZHUlKdc1aWrNmjbRYLPK+++6Tu3btkk8//bR0uVzyqaeeShzzwAMPyKysLPniiy/KTZs2yYULF7aZLnXq1Knyo48+ku+//74cPXr0GZcutasWL14sBw8enEgx+8ILL8i8vDz5X//1X4lj1DUbeFTd1EzVTadP1UsdU3VT6lTd1OysDqSklPK3v/2tLC0tlTabTc6cOVN++OGH6S5SWgBtfi1dujRxTCAQkN/61rdkdna2dLlc8pprrpHHjh1LOs/+/fvl/PnzpdPplHl5efI///M/ZSQS6eNPkz6nVljqmiV7+eWX5cSJE6Xdbpdjx46Vf/zjH5P2m6Ypf/SjH8mCggJpt9vlpZdeKnfs2JF0TFVVlbz++utlRkaG9Hq98itf+Yr0+Xx9+TH6TH19vbz99ttlaWmpdDgccsSIEfIHP/hBUgpidc0GJlU3xai66fSpeqlzqm5KjaqbmgkpWyxDrCiKoiiKoiiKonTqrJ0jpSiKoiiKoiiK0l0qkFIURVEURVEURUmRCqQURVEURVEURVFSpAIpRVEURVEURVGUFKlASlEURVEURVEUJUUqkFIURVEURVEURUmRCqQURVEURVEURVFSpAIpRVEURVEURVGUFKlASlFOQ1VVFfn5+ezfvz/dRUmr6667jl/+8pfpLoaiKMqAp+qdnrN161ZKSkpobGxMd1GUM5QKpBTlNNx3330sXLiQYcOGtdpXVVVFSUkJQghqa2uT9j399NOce+65uFwuioqKuPnmm6mqqmrzPZ599lmEEFx99dUpl2/dunVcdtllZGVlkZuby9e//nUaGhoS+zdu3Mj111/PkCFDcDqdjBs3jocffrjVeVasWMG0adOw2+2MGjWKv/zlL0n7f/jDH3LfffdRV1eXchkVRVGUrlP1Ttd94xvfYOTIkTidTgYNGsTChQvZvn17Yv/48eOZPXs2v/rVr1I+t6KACqQUpdv8fj9PPPEEt9xyS5v7b7nlFiZPntxq+wcffMCXvvQlbrnlFsrLy3nuuedYs2YNX/va11odu3//fr773e8yd+7clMt39OhR5s2bx6hRo/joo4944403KC8v58tf/nLimLKyMvLz83nqqacoLy/nBz/4AXfffTe/+93vEsfs27ePBQsW8MlPfpINGzZwxx138NWvfpVly5Yljpk4cSIjR47kqaeeSrmciqIoSteoemdZG+/avunTp7N06VK2bdvGsmXLkFJy+eWXYxhG4pivfOUrPPbYY0Sj0ZQ/r6IgFUXplueee04OGjSozX2PPvqovOiii+Ty5cslIGtqahL7HnroITlixIik43/zm9/IwYMHJ22LRqPy/PPPl3/605/k4sWL5cKFC1Mq3x/+8AeZn58vDcNIbNu0aZME5K5du9p93be+9S35yU9+MvH9f/3Xf8kJEyYkHXPttdfKK664ImnbT37yE3nhhRemVEZFURSl61S9c8WpL03Jxo0bJSB3796d2BYKhaTdbpdvv/32aZ1bOTupHilF6aaVK1cyffr0Vtu3bt3KT3/6U/7617+iaa1vsTlz5nDo0CFee+01pJQcP36c559/niuvvDLpuJ/+9Kfk5+e3++SxM6FQCJvNllQGp9MJwPvvv9/u6+rq6sjJyUl8v3r1aubNm5d0zBVXXMHq1auTts2cOZM1a9YQCoW6VV5FURSlY6reWX3qS7ussbGRpUuXMnz4cIYMGZLYbrPZmDJlCitXruz2uZWzlwqkFKWbDhw4QHFxcdK2UCjE9ddfz0MPPURpaWmbr7vgggt4+umnufbaa7HZbBQWFpKZmckjjzySOOb999/niSee4PHHH+92+S655BIqKip46KGHCIfD1NTU8P3vfx+AY8eOtfmaVatW8X//9398/etfT2yrqKigoKAg6biCggLq6+sJBAKJbcXFxYTDYSoqKrpdZkVRFKV9qt5Jrne64tFHHyUjI4OMjAxef/113nrrLWw2W9IxxcXFHDhwIKXzKgqoQEpRui0QCOBwOJK23X333YwbN44bb7yx3ddt3bqV22+/nR//+MeUlZXxxhtvsH//fr75zW8C4PP5uOmmm3j88cfJy8vrdvkmTJjAk08+yS9/+UtcLheFhYUMHz6cgoKCNp9YbtmyhYULF3Lvvfdy+eWXp/x+8aeOfr+/22VWFEVR2qfqndQtWrSI9evX8+6773LOOefwxS9+kWAwmHSM0+lUdZfSPekeW6goZ6obbrhBXn/99Unbzj33XKlpmtR1Xeq6LjVNk4DUdV3++Mc/llJKeeONN8rPf/7zSa9buXKlBOTRo0fl+vXrE6+JfwkhpBBC6rqeNLa7qyoqKqTP55MNDQ1S0zT597//PWl/eXm5zM/Pl/fcc0+r186dO1fefvvtSdv+/Oc/S6/Xm7Ttww8/lIA8efJkyuVTFEVROqfqHW+rY1MRCoWky+WSzzzzTNL2T33qU3LJkiWndW7l7GRJYwynKGe0qVOntspS949//CNp2MHatWu5+eabWblyJSNHjgRiPTYWS/Ktp+s6AFJKxo4dy+bNm5P2//CHP8Tn8/Hwww8nje3uqvgQiT//+c84HA4uu+yyxL7y8nIuueQSFi9ezH333dfqtXPmzOG1115L2vbWW28xZ86cpG1btmyhpKTktJ5mKoqiKO1T9c6cVsemQkqJlLLVXN4tW7bw+c9//rTOrZyl0hvHKcqZa9OmTdJiscjq6up2j3nnnXdaZU9aunSptFgs8tFHH5V79uyR77//vpwxY4acOXNmu+fpTvYkKaX87W9/K8vKyuSOHTvk7373O+l0OuXDDz+c2L9582Y5aNAgeeONN8pjx44lvk6cOJE4Zu/evdLlcsm77rpLbtu2TT7yyCNS13X5xhtvtCrjzTffnHIZFUVRlK5R9c4bbb1lm/bs2SN//vOfy48//lgeOHBAfvDBB/LTn/60zMnJkcePH08ct2/fPimEkPv370/5syqKCqQU5TTMnDlT/v73v293f1sVmpSxtLPjx4+XTqdTFhUVyUWLFsnDhw+3e562KrR7771XDh06tMPy3XTTTTInJ0fabDY5efJk+de//rXVOYBWX6ee95133pFTpkyRNptNjhgxQi5dujRpfyAQkJmZmXL16tUdlkdRFEU5PareiVm6dKnsqD/gyJEjcv78+TI/P19arVZZUlIib7jhBrl9+/ak437+85+fdlp15ewlpJQyDR1hijIgvPrqq9x1111s2bKlzYm0vWnx4sUIIbq12ntPe+yxx/jnP//Jm2++me6iKIqiDGiq3om59957effdd1mxYkW3zxEOhxk9ejTPPPMMF1xwQc8VTjlrqDlSinIaFixYwK5duzhy5Ei3xpB3l5SSFStWdLguR1+yWq389re/TXcxFEVRBjxV78S8/vrr/O53vzutcxw8eJB77rlHBVFKt6keKUVRFEVRFEVRlBSpdaQURVEURVEURVFSpAIpRVEURVEURVGUFKlASlEURVEURVEUJUUqkFIURVEURVEURUmRCqQURVEURVEURVFSpAIpRVEURVEURVGUFKlASlEURVEURVEUJUUqkFIURVEURVEURUmRCqQURVEURVEURVFS9P8DRJmKtSFY/pQAAAAASUVORK5CYII=\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from keras.preprocessing.image import ImageDataGenerator\n",
+        "from keras.utils import img_to_array, array_to_img"
+      ],
+      "metadata": {
+        "id": "N3yuyFLdLNmS"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cv2.imread(truck_image_data[number])"
+      ],
+      "metadata": {
+        "id": "AhrZ3qBPLQi-",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 543
+        },
+        "outputId": "7a32bae8-dd5d-46b2-b28d-bf2c8ea2f301"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[[247, 247, 247],\n",
+              "        [247, 247, 247],\n",
+              "        [247, 247, 247],\n",
+              "        ...,\n",
+              "        [247, 247, 247],\n",
+              "        [247, 247, 247],\n",
+              "        [247, 247, 247]],\n",
+              "\n",
+              "       [[247, 247, 247],\n",
+              "        [247, 247, 247],\n",
+              "        [247, 247, 247],\n",
+              "        ...,\n",
+              "        [247, 247, 247],\n",
+              "        [247, 247, 247],\n",
+              "        [247, 247, 247]],\n",
+              "\n",
+              "       [[247, 247, 247],\n",
+              "        [247, 247, 247],\n",
+              "        [247, 247, 247],\n",
+              "        ...,\n",
+              "        [247, 247, 247],\n",
+              "        [247, 247, 247],\n",
+              "        [247, 247, 247]],\n",
+              "\n",
+              "       ...,\n",
+              "\n",
+              "       [[247, 247, 247],\n",
+              "        [247, 247, 247],\n",
+              "        [247, 247, 247],\n",
+              "        ...,\n",
+              "        [247, 247, 247],\n",
+              "        [247, 247, 247],\n",
+              "        [247, 247, 247]],\n",
+              "\n",
+              "       [[247, 247, 247],\n",
+              "        [247, 247, 247],\n",
+              "        [247, 247, 247],\n",
+              "        ...,\n",
+              "        [247, 247, 247],\n",
+              "        [247, 247, 247],\n",
+              "        [247, 247, 247]],\n",
+              "\n",
+              "       [[247, 247, 247],\n",
+              "        [247, 247, 247],\n",
+              "        [247, 247, 247],\n",
+              "        ...,\n",
+              "        [247, 247, 247],\n",
+              "        [247, 247, 247],\n",
+              "        [247, 247, 247]]], dtype=uint8)"
+            ],
+            "text/html": [
+              "<style>\n",
+              "      .ndarray_repr .ndarray_raw_data {\n",
+              "        display: none;\n",
+              "      }\n",
+              "      .ndarray_repr.show_array .ndarray_raw_data {\n",
+              "        display: block;\n",
+              "      }\n",
+              "      .ndarray_repr.show_array .ndarray_image_preview {\n",
+              "        display: none;\n",
+              "      }\n",
+              "      </style>\n",
+              "      <div id=\"id-5b5afb25-e4cf-4d8a-af3f-a5afbbbf7acc\" class=\"ndarray_repr\"><pre>ndarray (484, 920, 3) <button style=\"padding: 0 2px;\">show data</button></pre><img 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\" class=\"ndarray_image_preview\" /><pre class=\"ndarray_raw_data\">array([[[247, 247, 247],\n",
+              "        [247, 247, 247],\n",
+              "        [247, 247, 247],\n",
+              "        ...,\n",
+              "        [247, 247, 247],\n",
+              "        [247, 247, 247],\n",
+              "        [247, 247, 247]],\n",
+              "\n",
+              "       [[247, 247, 247],\n",
+              "        [247, 247, 247],\n",
+              "        [247, 247, 247],\n",
+              "        ...,\n",
+              "        [247, 247, 247],\n",
+              "        [247, 247, 247],\n",
+              "        [247, 247, 247]],\n",
+              "\n",
+              "       [[247, 247, 247],\n",
+              "        [247, 247, 247],\n",
+              "        [247, 247, 247],\n",
+              "        ...,\n",
+              "        [247, 247, 247],\n",
+              "        [247, 247, 247],\n",
+              "        [247, 247, 247]],\n",
+              "\n",
+              "       ...,\n",
+              "\n",
+              "       [[247, 247, 247],\n",
+              "        [247, 247, 247],\n",
+              "        [247, 247, 247],\n",
+              "        ...,\n",
+              "        [247, 247, 247],\n",
+              "        [247, 247, 247],\n",
+              "        [247, 247, 247]],\n",
+              "\n",
+              "       [[247, 247, 247],\n",
+              "        [247, 247, 247],\n",
+              "        [247, 247, 247],\n",
+              "        ...,\n",
+              "        [247, 247, 247],\n",
+              "        [247, 247, 247],\n",
+              "        [247, 247, 247]],\n",
+              "\n",
+              "       [[247, 247, 247],\n",
+              "        [247, 247, 247],\n",
+              "        [247, 247, 247],\n",
+              "        ...,\n",
+              "        [247, 247, 247],\n",
+              "        [247, 247, 247],\n",
+              "        [247, 247, 247]]], dtype=uint8)</pre></div><script>\n",
+              "      (() => {\n",
+              "      const titles = ['show data', 'hide data'];\n",
+              "      let index = 0\n",
+              "      document.querySelector('#id-5b5afb25-e4cf-4d8a-af3f-a5afbbbf7acc button').onclick = (e) => {\n",
+              "        document.querySelector('#id-5b5afb25-e4cf-4d8a-af3f-a5afbbbf7acc').classList.toggle('show_array');\n",
+              "        index = (++index) % 2;\n",
+              "        document.querySelector('#id-5b5afb25-e4cf-4d8a-af3f-a5afbbbf7acc button').textContent = titles[index];\n",
+              "        e.preventDefault();\n",
+              "        e.stopPropagation();\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 19
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sample_image = cv2.imread(truck_image_data[number])\n",
+        "sample_image.shape"
+      ],
+      "metadata": {
+        "id": "dO3DfUVjLTKv",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "4be11ee9-34b2-47fa-a681-de85273afc79"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(484, 920, 3)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 20
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "trainImageData = ImageDataGenerator(rescale = 1/255)\n",
+        "validImageData = ImageDataGenerator(rescale = 1/255)"
+      ],
+      "metadata": {
+        "id": "Vjmj2YQXLWJI"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_dataset = trainImageData.flow_from_directory(\n",
+        "    '/content/drive/MyDrive/Dataset_truck/train_data',\n",
+        "    target_size = (200, 200),\n",
+        "    batch_size = 3,\n",
+        "    class_mode = 'binary'\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "ePwrukRaLZRD",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "0eb7c4da-648b-430b-8736-72cea6b395c5"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Found 150 images belonging to 1 classes.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "validation_dataset = validImageData.flow_from_directory(\n",
+        "    '/content/drive/MyDrive/Dataset_truck/validation_data',\n",
+        "    target_size = (200, 200),\n",
+        "    batch_size = 3,\n",
+        "    class_mode = 'binary'\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "GmmLoguILfKM",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "c955d4b6-9a3d-454f-db9d-a4e5c10d7b06"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Found 101 images belonging to 1 classes.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_dataset.class_indices"
+      ],
+      "metadata": {
+        "id": "__ewNOOhLgIR",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "7b0d920b-e22d-4988-9391-c4333505ee8e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'truck': 0}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 24
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from keras import layers\n",
+        "from keras.layers import Flatten, Dense, MaxPooling2D, Conv2D\n",
+        "from keras.models import Sequential"
+      ],
+      "metadata": {
+        "id": "9fVpfXfLLi-i"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from tensorflow.keras.models import Sequential\n",
+        "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout, BatchNormalization\n",
+        "\n",
+        "truck_model = Sequential([\n",
+        "    Conv2D(16, (3, 3), activation='relu', input_shape=(200, 200, 3)),\n",
+        "    BatchNormalization(),\n",
+        "    MaxPooling2D((2, 2)),\n",
+        "    Dropout(0.2),\n",
+        "\n",
+        "    Conv2D(32, (3, 3), activation='relu'),\n",
+        "    BatchNormalization(),\n",
+        "    MaxPooling2D((2, 2)),\n",
+        "    Dropout(0.3),\n",
+        "\n",
+        "    Conv2D(64, (3, 3), activation='relu'),\n",
+        "    BatchNormalization(),\n",
+        "    MaxPooling2D((2, 2)),\n",
+        "    Dropout(0.4),\n",
+        "\n",
+        "    Flatten(),\n",
+        "\n",
+        "    Dense(512, activation='relu'),\n",
+        "    BatchNormalization(),\n",
+        "    Dropout(0.5),\n",
+        "    Dense(1, activation='sigmoid'),\n",
+        "])"
+      ],
+      "metadata": {
+        "id": "5V9Lk7A3Lm7s"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "truck_model.compile(\n",
+        "    loss = 'binary_crossentropy',\n",
+        "    optimizer = 'adam',\n",
+        "    metrics = ['accuracy']\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "lUx5JbjqLqDi"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "truck_model.summary()"
+      ],
+      "metadata": {
+        "id": "RaTU0LTALseV",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "133f5e9a-241a-4f3b-bb60-47e1ce6f1efc"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Model: \"sequential\"\n",
+            "_________________________________________________________________\n",
+            " Layer (type)                Output Shape              Param #   \n",
+            "=================================================================\n",
+            " conv2d (Conv2D)             (None, 198, 198, 16)      448       \n",
+            "                                                                 \n",
+            " batch_normalization (Batch  (None, 198, 198, 16)      64        \n",
+            " Normalization)                                                  \n",
+            "                                                                 \n",
+            " max_pooling2d (MaxPooling2  (None, 99, 99, 16)        0         \n",
+            " D)                                                              \n",
+            "                                                                 \n",
+            " dropout (Dropout)           (None, 99, 99, 16)        0         \n",
+            "                                                                 \n",
+            " conv2d_1 (Conv2D)           (None, 97, 97, 32)        4640      \n",
+            "                                                                 \n",
+            " batch_normalization_1 (Bat  (None, 97, 97, 32)        128       \n",
+            " chNormalization)                                                \n",
+            "                                                                 \n",
+            " max_pooling2d_1 (MaxPoolin  (None, 48, 48, 32)        0         \n",
+            " g2D)                                                            \n",
+            "                                                                 \n",
+            " dropout_1 (Dropout)         (None, 48, 48, 32)        0         \n",
+            "                                                                 \n",
+            " conv2d_2 (Conv2D)           (None, 46, 46, 64)        18496     \n",
+            "                                                                 \n",
+            " batch_normalization_2 (Bat  (None, 46, 46, 64)        256       \n",
+            " chNormalization)                                                \n",
+            "                                                                 \n",
+            " max_pooling2d_2 (MaxPoolin  (None, 23, 23, 64)        0         \n",
+            " g2D)                                                            \n",
+            "                                                                 \n",
+            " dropout_2 (Dropout)         (None, 23, 23, 64)        0         \n",
+            "                                                                 \n",
+            " flatten (Flatten)           (None, 33856)             0         \n",
+            "                                                                 \n",
+            " dense (Dense)               (None, 512)               17334784  \n",
+            "                                                                 \n",
+            " batch_normalization_3 (Bat  (None, 512)               2048      \n",
+            " chNormalization)                                                \n",
+            "                                                                 \n",
+            " dropout_3 (Dropout)         (None, 512)               0         \n",
+            "                                                                 \n",
+            " dense_1 (Dense)             (None, 1)                 513       \n",
+            "                                                                 \n",
+            "=================================================================\n",
+            "Total params: 17361377 (66.23 MB)\n",
+            "Trainable params: 17360129 (66.22 MB)\n",
+            "Non-trainable params: 1248 (4.88 KB)\n",
+            "_________________________________________________________________\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "truck_model.fit(\n",
+        "    train_dataset,\n",
+        "    steps_per_epoch = len(train_dataset),\n",
+        "    epochs = 3,\n",
+        "    validation_data = validation_dataset\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "9aEj5aB-LvNe",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "e811e91a-5f5f-45b2-a2e9-a44ade0acc5c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/3\n",
+            "50/50 [==============================] - 71s 1s/step - loss: 0.6999 - accuracy: 0.6467 - val_loss: 0.0898 - val_accuracy: 1.0000\n",
+            "Epoch 2/3\n",
+            "50/50 [==============================] - 44s 878ms/step - loss: 0.3576 - accuracy: 0.8667 - val_loss: 5.9229e-08 - val_accuracy: 1.0000\n",
+            "Epoch 3/3\n",
+            "50/50 [==============================] - 45s 889ms/step - loss: 0.1411 - accuracy: 0.9600 - val_loss: 0.0068 - val_accuracy: 1.0000\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<keras.src.callbacks.History at 0x7a29758e4e80>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 29
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "testingImageData = ImageDataGenerator(rescale = 1/255)\n",
+        "testing_dataset = validImageData.flow_from_directory(\n",
+        "    '/content/drive/MyDrive/Dataset_truck/test_data',\n",
+        "    target_size = (200, 200),\n",
+        "    batch_size = 3,\n",
+        "    class_mode = 'binary'\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "DK7gLarALyog",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "1dd6df50-e51c-4385-bc72-616d1598fe91"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Found 215 images belonging to 1 classes.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "loss, accuracy = truck_model.evaluate(testing_dataset)"
+      ],
+      "metadata": {
+        "id": "AAouLoXXL16c",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "7f8c284e-3897-4aef-807c-29f809f649cf"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "72/72 [==============================] - 10s 102ms/step - loss: 0.0409 - accuracy: 0.9860\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "from tensorflow.keras.preprocessing.image import load_img, img_to_array\n",
+        "\n",
+        "# Define the path to a single image (example)\n",
+        "image_path = '/content/drive/MyDrive/Dataset_truck/train_data/truck/00000000 (2).jpg'  # Update with your image path\n",
+        "\n",
+        "# Load and preprocess the image\n",
+        "img = load_img(image_path, target_size=(200, 200))  # Ensure the image is resized to (200, 200)\n",
+        "img_array = img_to_array(img) / 255.0  # Rescale pixel values to [0, 1]\n",
+        "img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension\n",
+        "\n",
+        "# Ensure the model is already defined and loaded as 'truck_model'\n",
+        "# Example: truck_model = tf.keras.models.load_model('path_to_model')\n",
+        "\n",
+        "# Make prediction\n",
+        "prediction = truck_model.predict(img_array)\n",
+        "if prediction[0] < 0.5:  # Adjust threshold based on your model's output\n",
+        "    print(\"Truck\")\n",
+        "else:\n",
+        "    print(\"Not a Truck\")\n"
+      ],
+      "metadata": {
+        "id": "i1O_2myohySV",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "97b2f3b3-6694-4c5d-db46-d87fe2031339"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "1/1 [==============================] - 0s 396ms/step\n",
+            "Truck\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from keras.utils import load_img"
+      ],
+      "metadata": {
+        "id": "U8TrkZk1L4f8"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import os\n",
+        "from keras.preprocessing.image import load_img, img_to_array\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "test_path = \"/content/drive/MyDrive/Dataset_truck/test_data\"\n",
+        "count = 0\n",
+        "\n",
+        "for i in os.listdir(test_path):\n",
+        "    file_path = os.path.join(test_path, i)\n",
+        "\n",
+        "    if os.path.isfile(file_path):\n",
+        "        img = load_img(file_path)\n",
+        "        plt.imshow(img)\n",
+        "        plt.show()\n",
+        "\n",
+        "        X = img_to_array(img)\n",
+        "        x_imag = np.expand_dims(X, axis=0)\n",
+        "        images = np.vstack([x_imag])\n",
+        "\n",
+        "        prediction = truck_model.predict(images)\n",
+        "        if prediction == 0:\n",
+        "            print(\"Truck\")\n",
+        "        else:\n",
+        "            print(\"No truck\")\n",
+        "\n",
+        "        count += 1\n",
+        "        if count == 5:\n",
+        "            break"
+      ],
+      "metadata": {
+        "id": "qziIOnHyL7iz"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "QuKdA8rwRZXt"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from keras import models"
+      ],
+      "metadata": {
+        "id": "v9xYeLmcL_Rc"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "truck_model_ann = models.Sequential([\n",
+        "    layers.Flatten(input_shape=(200, 200, 3)),\n",
+        "\n",
+        "    layers.Dense(3000, activation='relu'),\n",
+        "    layers.BatchNormalization(),\n",
+        "    layers.Dropout(0.5),\n",
+        "\n",
+        "    layers.Dense(1000, activation='relu'),\n",
+        "    layers.BatchNormalization(),\n",
+        "    layers.Dropout(0.5),\n",
+        "\n",
+        "    layers.Dense(1, activation='sigmoid')\n",
+        "])"
+      ],
+      "metadata": {
+        "id": "jk8g92UOMCt0"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "truck_model_ann.summary()"
+      ],
+      "metadata": {
+        "id": "dICWnparMFdx",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "fd034714-f7b4-4438-961f-9c78c1609d21"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Model: \"sequential_3\"\n",
+            "_________________________________________________________________\n",
+            " Layer (type)                Output Shape              Param #   \n",
+            "=================================================================\n",
+            " flatten_3 (Flatten)         (None, 120000)            0         \n",
+            "                                                                 \n",
+            " dense_8 (Dense)             (None, 3000)              360003000 \n",
+            "                                                                 \n",
+            " batch_normalization_6 (Bat  (None, 3000)              12000     \n",
+            " chNormalization)                                                \n",
+            "                                                                 \n",
+            " dropout_6 (Dropout)         (None, 3000)              0         \n",
+            "                                                                 \n",
+            " dense_9 (Dense)             (None, 1000)              3001000   \n",
+            "                                                                 \n",
+            " batch_normalization_7 (Bat  (None, 1000)              4000      \n",
+            " chNormalization)                                                \n",
+            "                                                                 \n",
+            " dropout_7 (Dropout)         (None, 1000)              0         \n",
+            "                                                                 \n",
+            " dense_10 (Dense)            (None, 1)                 1001      \n",
+            "                                                                 \n",
+            "=================================================================\n",
+            "Total params: 363021001 (1.35 GB)\n",
+            "Trainable params: 363013001 (1.35 GB)\n",
+            "Non-trainable params: 8000 (31.25 KB)\n",
+            "_________________________________________________________________\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "truck_model_ann.compile(\n",
+        "    optimizer = 'sgd',\n",
+        "    loss = 'binary_crossentropy',\n",
+        "    metrics = ['accuracy']\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "hpPOleyQMIWe"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "history = truck_model_ann.fit(train_dataset, validation_data = validation_dataset, epochs = 2)"
+      ],
+      "metadata": {
+        "id": "n7G2-SaaMLbV",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "a627ba86-5cd0-4533-ecb5-fc50bde18396"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/2\n",
+            "50/50 [==============================] - 129s 3s/step - loss: 0.9580 - accuracy: 0.5733 - val_loss: 0.3147 - val_accuracy: 0.8911\n",
+            "Epoch 2/2\n",
+            "50/50 [==============================] - 124s 2s/step - loss: 0.6753 - accuracy: 0.6867 - val_loss: 0.3783 - val_accuracy: 0.8713\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.plot(history.history['accuracy'])\n",
+        "plt.plot(history.history['val_accuracy'])\n",
+        "plt.xlabel('epoch')\n",
+        "plt.ylabel('accuracy')\n",
+        "plt.title(\"Model accuracy\")\n",
+        "plt.legend(['Training', \"Validation\"], loc = 'upper left')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "4C5Ft_yQMOKU",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "outputId": "f4962dd4-7fdc-477c-96fa-999330e5fdf5"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.plot(history.history['loss'])\n",
+        "plt.plot(history.history['val_loss'])\n",
+        "plt.title('model loss')\n",
+        "plt.ylabel('loss')\n",
+        "plt.xlabel('epoch')\n",
+        "plt.legend(['Train', 'Validation'], loc='upper left')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "XU5zooGkMQ2G",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "outputId": "b29bb66e-ccc1-4717-cc71-9152d0cbd6ac"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "7IyAzqHVMUbS"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Indian Truck Detection/Model/Gssoc'24_ResNet50.ipynb b/Indian Truck Detection/Model/Gssoc'24_ResNet50.ipynb
new file mode 100644
index 000000000..8cce4d378
--- /dev/null
+++ b/Indian Truck Detection/Model/Gssoc'24_ResNet50.ipynb	
@@ -0,0 +1,2217 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7G8sp8ET4qaB",
+        "outputId": "67f794e6-2db3-4e2b-f55e-94c7951e45a4"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
+          ]
+        }
+      ],
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "F9KS-nfZRxqL"
+      },
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import os\n",
+        "\n",
+        "# Define the path to the image folder in Google Drive\n",
+        "image_folder_path = \"/content/drive/MyDrive/Dataset_truck\"  # Update this path accordingly\n",
+        "\n",
+        "# Function to recursively print the contents of a directory\n",
+        "def print_directory_contents(folder_path):\n",
+        "    for root, dirs, files in os.walk(folder_path):\n",
+        "        print(f\"Directory: {root}\")\n",
+        "        print(\"Files:\")\n",
+        "        for file in files:\n",
+        "            print(f\"\\t{file}\")\n",
+        "\n",
+        "# Print the contents of the image folder\n",
+        "print_directory_contents(image_folder_path)\n"
+      ],
+      "metadata": {
+        "id": "hwg7e0zq5KuT",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "c5aaccb2-96ce-427b-cb4a-6fa90c728ea9"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Directory: /content/drive/MyDrive/Dataset_truck\n",
+            "Files:\n",
+            "Directory: /content/drive/MyDrive/Dataset_truck/test_data\n",
+            "Files:\n",
+            "Directory: /content/drive/MyDrive/Dataset_truck/test_data/truck\n",
+            "Files:\n",
+            "\t00000194.JPG\n",
+            "\t00000176.jpg\n",
+            "\t00000175.jpg\n",
+            "\t00000191.jpg\n",
+            "\t00000180.jpg\n",
+            "\t00000196.jpg\n",
+            "\t00000189.jpg\n",
+            "\t00000180 (2).jpg\n",
+            "\t00000192.jpg\n",
+            "\t00000181.jpg\n",
+            "\t00000183.jpg\n",
+            "\t00000200.jpg\n",
+            "\t00000188.jpg\n",
+            "\t00000198.jpg\n",
+            "\t00000190.jpg\n",
+            "\t00000195.jpg\n",
+            "\t00000187.jpg\n",
+            "\t00000197.jpg\n",
+            "\t00000193.jpg\n",
+            "\t00000186.jpg\n",
+            "\t00000178.jpg\n",
+            "\t00000182.jpg\n",
+            "\t00000179.png\n",
+            "\t00000185.jpg\n",
+            "\t00000202.jpg\n",
+            "\t00000184.jpg\n",
+            "\t00000201.jpg\n",
+            "\t00000203.jpg\n",
+            "\t00000184 (2).jpg\n",
+            "\t00000231.jpg\n",
+            "\t00000233.jpg\n",
+            "\t00000217 (2).jpg\n",
+            "\t00000227.jpg\n",
+            "\t00000215.jpg\n",
+            "\t00000219.jpg\n",
+            "\t00000223.jpg\n",
+            "\t00000212.jpg\n",
+            "\t00000210.JPG\n",
+            "\t00000221.png\n",
+            "\t00000221.jpg\n",
+            "\t00000216.jpg\n",
+            "\t00000224.jpg\n",
+            "\t00000222.jpg\n",
+            "\t00000228.jpg\n",
+            "\t00000230.jpg\n",
+            "\t00000218.jpg\n",
+            "\t00000214.jpg\n",
+            "\t00000208.jpg\n",
+            "\t00000205.jpg\n",
+            "\t00000235.jpg\n",
+            "\t00000218 (2).jpg\n",
+            "\t00000212 (2).jpg\n",
+            "\t00000229.jpg\n",
+            "\t00000236.jpg\n",
+            "\t00000225.jpg\n",
+            "\t00000232.jpg\n",
+            "\t00000207.jpg\n",
+            "\t00000211.jpg\n",
+            "\t00000230 (2).jpg\n",
+            "\t00000206.jpg\n",
+            "\t00000209.jpg\n",
+            "\t00000217.jpg\n",
+            "\t00000238.jpg\n",
+            "\t00000262.jpg\n",
+            "\t00000251.jpg\n",
+            "\t00000254.jpg\n",
+            "\t00000253.jpg\n",
+            "\t00000242.jpg\n",
+            "\t00000247 (2).jpg\n",
+            "\t00000244.jpg\n",
+            "\t00000252.jpg\n",
+            "\t00000247.jpg\n",
+            "\t00000246.jpg\n",
+            "\t00000241 (2).jpg\n",
+            "\t00000263.jpg\n",
+            "\t00000240.jpg\n",
+            "\t00000250.jpg\n",
+            "\t00000259.jpg\n",
+            "\t00000265.jpg\n",
+            "\t00000243.jpg\n",
+            "\t00000237.jpg\n",
+            "\t00000238 (2).jpg\n",
+            "\t00000264.jpg\n",
+            "\t00000253 (2).jpg\n",
+            "\t00000249.jpg\n",
+            "\t00000239.jpg\n",
+            "\t00000257.jpg\n",
+            "\t00000260.jpg\n",
+            "\t00000258.jpg\n",
+            "\t00000248.jpg\n",
+            "\t00000261.jpg\n",
+            "\t00000256.jpg\n",
+            "\t00000246 (2).jpg\n",
+            "\t00000255.jpg\n",
+            "\t00000241.jpg\n",
+            "\t00000266.jpg\n",
+            "\t00000271.jpg\n",
+            "\t00000279.jpg\n",
+            "\t00000289.jpg\n",
+            "\t00000285.jpg\n",
+            "\t00000275.jpg\n",
+            "\t00000291.jpg\n",
+            "\t00000277.jpg\n",
+            "\t00000267.jpg\n",
+            "\t00000272.jpg\n",
+            "\t00000284.jpg\n",
+            "\t00000270.jpg\n",
+            "\t00000282.jpg\n",
+            "\t00000278.jpg\n",
+            "\t00000268.jpg\n",
+            "\t00000276.jpg\n",
+            "\t00000287.jpg\n",
+            "\t00000288 (2).jpg\n",
+            "\t00000273.jpg\n",
+            "\t00000295.jpg\n",
+            "\t00000281.jpg\n",
+            "\t00000294.jpg\n",
+            "\t00000290.jpg\n",
+            "\t00000296.jpg\n",
+            "\t00000293.jpg\n",
+            "\t00000298.jpg\n",
+            "\t00000292.jpg\n",
+            "\t00000269.jpg\n",
+            "\t00000282 (2).jpg\n",
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+            "\t00000389.jpg\n",
+            "Directory: /content/drive/MyDrive/Dataset_truck/train_data\n",
+            "Files:\n",
+            "Directory: /content/drive/MyDrive/Dataset_truck/train_data/truck\n",
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+            "\t00000062 (2).jpg\n",
+            "\t00000067 (2).jpg\n",
+            "\t00000060.jpg\n",
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+            "\t00000073 (2).jpg\n",
+            "\t00000069.jpg\n",
+            "\t00000098.jpg\n",
+            "\t00000093.jpg\n",
+            "\t00000099 (2).jpg\n",
+            "\t00000092.jpg\n",
+            "\t00000097.jpg\n",
+            "\t00000088 (2).jpg\n",
+            "\t00000090.jpg\n",
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+            "\t00000095 (2).jpg\n",
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+            "\t00000084.jpg\n",
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+            "\t00000095.jpg\n",
+            "\t00000088.jpg\n",
+            "\t00000096.jpg\n",
+            "\t00000091.jpg\n",
+            "Directory: /content/drive/MyDrive/Dataset_truck/validation_data\n",
+            "Files:\n",
+            "Directory: /content/drive/MyDrive/Dataset_truck/validation_data/truck\n",
+            "Files:\n",
+            "\t00000102.jpg\n",
+            "\t00000101.jpg\n",
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+            "\t00000130 (2).jpg\n",
+            "\t00000130.jpg\n",
+            "\t00000121 (2).jpg\n",
+            "\t00000122.jpg\n",
+            "\t00000125.jpg\n",
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+            "\t00000126.jpg\n",
+            "\t00000118.jpg\n",
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+            "\t00000142.jpg\n",
+            "\t00000144 (2).jpg\n",
+            "\t00000141.jpg\n",
+            "\t00000139.jpg\n",
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+            "\t00000140 (2).jpg\n",
+            "\t00000137 (2).jpg\n",
+            "\t00000134.jpg\n",
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+            "\t00000140.jpg\n",
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+            "\t00000142 (2).jpg\n",
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+            "\t00000146 (2).jpg\n",
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+            "\t00000166 (2).jpg\n",
+            "\t00000162.jpg\n",
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+            "\t00000170.jpg\n",
+            "\t00000160.jpg\n",
+            "\t00000172.jpg\n",
+            "\t00000157.jpg\n",
+            "\t00000153.jpg\n",
+            "\t00000161.jpg\n",
+            "\t00000154.jpg\n",
+            "\t00000147.jpg\n",
+            "\t00000151 (2).jpg\n",
+            "\t00000153 (2).jpg\n",
+            "\t00000158.jpg\n",
+            "\t00000148.jpg\n",
+            "\t00000159.jpg\n",
+            "\t00000167.jpg\n",
+            "\t00000155.jpg\n",
+            "\t00000150.jpg\n",
+            "\t00000148 (2).jpg\n",
+            "\t00000149.jpg\n",
+            "\t00000166.jpg\n",
+            "\t00000156.jpg\n",
+            "\t00000174.jpg\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import os\n",
+        "import numpy as np\n",
+        "import tensorflow as tf\n",
+        "from tensorflow.keras.preprocessing.image import load_img, img_to_array\n",
+        "\n",
+        "# Define the path to the image folder in Google Drive\n",
+        "image_folder_path = \"/content/drive/MyDrive/Dataset_truck/test_data\"  # Update this path accordingly\n",
+        "\n",
+        "# Get a list of all files in the directory\n",
+        "all_files = os.listdir(image_folder_path)\n",
+        "\n",
+        "# Filter only the image files\n",
+        "image_files = [file for file in all_files if file.lower().endswith(('.png', '.jpg', '.jpeg'))]\n",
+        "\n",
+        "# Load images and convert them to arrays\n",
+        "images = []\n",
+        "for image_file in image_files:\n",
+        "    image_path = os.path.join(image_folder_path, image_file)\n",
+        "    try:\n",
+        "        img = load_img(image_path, target_size=(150, 150))  # Adjust target_size as needed\n",
+        "        img_array = img_to_array(img) / 255.0  # Rescale pixel values to [0, 1]\n",
+        "        images.append(img_array)\n",
+        "    except Exception as e:\n",
+        "        print(f\"Error loading image {image_path}: {e}\")\n",
+        "\n",
+        "# Convert the list of images to a NumPy array\n",
+        "images = np.array(images)\n",
+        "\n",
+        "# Print the number of loaded images\n",
+        "print(f\"Number of images loaded: {len(images)}\")\n",
+        "\n",
+        "# Example: Accessing one image from the array\n",
+        "if len(images) > 0:\n",
+        "    example_image = images[0]\n",
+        "    print(f\"Shape of the example image: {example_image.shape}\")\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Bcxu56CZ8tWn",
+        "outputId": "1a7e066a-6971-4af6-f165-690f47911340"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Number of images loaded: 0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import os\n",
+        "import shutil\n",
+        "from tensorflow import keras\n",
+        "import cv2"
+      ],
+      "metadata": {
+        "id": "4eUpapu4HiWE"
+      },
+      "execution_count": 7,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from imutils import paths\n",
+        "from pathlib import Path"
+      ],
+      "metadata": {
+        "id": "uxQEx--HI3-r"
+      },
+      "execution_count": 8,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "images_path = Path(r\"/content/drive/MyDrive/Dataset_truck\")\n",
+        "trucks_data = list(paths.list_images(images_path))"
+      ],
+      "metadata": {
+        "id": "F-cjwkZgJB6h"
+      },
+      "execution_count": 9,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "trucks_data[0:6]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "slqxxXIqJLWq",
+        "outputId": "447bbfd4-c8d0-4492-b7e3-daf1f2a7e192"
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "['/content/drive/MyDrive/Dataset_truck/test_data/truck/00000194.JPG',\n",
+              " '/content/drive/MyDrive/Dataset_truck/test_data/truck/00000176.jpg',\n",
+              " '/content/drive/MyDrive/Dataset_truck/test_data/truck/00000175.jpg',\n",
+              " '/content/drive/MyDrive/Dataset_truck/test_data/truck/00000191.jpg',\n",
+              " '/content/drive/MyDrive/Dataset_truck/test_data/truck/00000180.jpg',\n",
+              " '/content/drive/MyDrive/Dataset_truck/test_data/truck/00000196.jpg']"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 10
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "truck_image_data = pd.Series(trucks_data, name=\"JPG\").astype(str)"
+      ],
+      "metadata": {
+        "id": "5z6aVQZHJWB_"
+      },
+      "execution_count": 11,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "truck_image_data.head()"
+      ],
+      "metadata": {
+        "id": "lhom1o9dJaJr",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "ad1116cf-64de-4650-aaf1-1cc6d0d6ea3f"
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0    /content/drive/MyDrive/Dataset_truck/test_data...\n",
+              "1    /content/drive/MyDrive/Dataset_truck/test_data...\n",
+              "2    /content/drive/MyDrive/Dataset_truck/test_data...\n",
+              "3    /content/drive/MyDrive/Dataset_truck/test_data...\n",
+              "4    /content/drive/MyDrive/Dataset_truck/test_data...\n",
+              "Name: JPG, dtype: object"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 12
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt"
+      ],
+      "metadata": {
+        "id": "l1QrjOQ7JdwJ"
+      },
+      "execution_count": 13,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import cv2\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Read the image in grayscale\n",
+        "image_path = \"/content/drive/MyDrive/Dataset_truck/test_data/00000176.jpg\"\n",
+        "truck_image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)\n",
+        "\n",
+        "# Check if the image was loaded correctly\n",
+        "if truck_image is None:\n",
+        "    print(f\"Error: Could not load image from {image_path}\")\n",
+        "else:\n",
+        "    # Display the image shape and dtype for verification\n",
+        "    print(f\"Image shape: {truck_image.shape}, dtype: {truck_image.dtype}\")\n",
+        "\n",
+        "    # Convert image to float32 if needed\n",
+        "    if truck_image.dtype == 'object':\n",
+        "        truck_image = truck_image.astype('float32')\n",
+        "\n",
+        "    # Display the image using matplotlib\n",
+        "    plt.imshow(truck_image, cmap='gray')\n",
+        "    plt.axis('off')  # Hide axis\n",
+        "    plt.show()"
+      ],
+      "metadata": {
+        "id": "2thUsTv6JjOp",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "a93a9ff7-ec06-4027-bfe6-1ebc08a963cd"
+      },
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Error: Could not load image from /content/drive/MyDrive/Dataset_truck/test_data/00000176.jpg\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from skimage.morphology import skeletonize"
+      ],
+      "metadata": {
+        "id": "phHMwLkWJ3uK"
+      },
+      "execution_count": 15,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def simple_vision(img_path):\n",
+        "    Picking_Img = cv2.cvtColor(cv2.imread(img_path),cv2.COLOR_BGR2RGB)\n",
+        "    return Picking_Img\n",
+        "\n",
+        "def skeleton_morph_vision(img_path):\n",
+        "    Picking_Img = simple_vision(img_path)\n",
+        "    Gray_Img = cv2.cvtColor(Picking_Img,cv2.COLOR_RGB2GRAY)\n",
+        "    _,Threshold_Img = cv2.threshold(Gray_Img,90,255,cv2.THRESH_BINARY_INV)\n",
+        "\n",
+        "    Array_Img = np.array(Gray_Img > Threshold_Img).astype(int)\n",
+        "    Skeleton_Img = skeletonize(Array_Img)\n",
+        "\n",
+        "    return Skeleton_Img\n",
+        "\n",
+        "def threshold_vision(img_path):\n",
+        "    Picking_Img = simple_vision(img_path)\n",
+        "    Gray_Img = cv2.cvtColor(Picking_Img,cv2.COLOR_RGB2GRAY)\n",
+        "    _,threshold_Img = cv2.threshold(Gray_Img,90,255,cv2.THRESH_BINARY_INV)\n",
+        "\n",
+        "    return threshold_Img\n",
+        "\n",
+        "def canny_vision(img_path):\n",
+        "    Threshold_Img = threshold_vision(img_path)\n",
+        "    Canny_Img = cv2.Canny(Threshold_Img,10,100)\n",
+        "\n",
+        "    return Canny_Img"
+      ],
+      "metadata": {
+        "id": "RyTaSHDOLFZE"
+      },
+      "execution_count": 16,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import random\n",
+        "number = random.randint(0, 466)\n",
+        "global truck_label\n",
+        "truck_label = \"Indian Truck\""
+      ],
+      "metadata": {
+        "id": "7AACMN4JLGqf"
+      },
+      "execution_count": 17,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "figure,axis = plt.subplots(nrows=1,ncols=2,figsize=(10,10))\n",
+        "\n",
+        "Skel_Img = skeleton_morph_vision(truck_image_data[number])\n",
+        "Simple_Img = simple_vision(truck_image_data[number])\n",
+        "\n",
+        "axis[0].imshow(Skel_Img)\n",
+        "axis[0].set_xlabel(Skel_Img.shape)\n",
+        "axis[0].set_ylabel(Skel_Img.size)\n",
+        "axis[0].set_title(truck_label)\n",
+        "axis[1].imshow(Simple_Img)\n",
+        "axis[1].set_xlabel(Simple_Img.shape)\n",
+        "axis[1].set_ylabel(Simple_Img.size)\n",
+        "axis[1].set_title(truck_label)"
+      ],
+      "metadata": {
+        "id": "lR4AuaK1LKAT",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 355
+        },
+        "outputId": "a9f417f2-7848-49b3-ea69-6836dba7a2bf"
+      },
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0.5, 1.0, 'Indian Truck')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 18
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x1000 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from keras.preprocessing.image import ImageDataGenerator\n",
+        "from keras.utils import img_to_array, array_to_img"
+      ],
+      "metadata": {
+        "id": "N3yuyFLdLNmS"
+      },
+      "execution_count": 19,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cv2.imread(truck_image_data[number])"
+      ],
+      "metadata": {
+        "id": "AhrZ3qBPLQi-",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 459
+        },
+        "outputId": "fad8e127-4688-400b-86ec-d93dbc770cc0"
+      },
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[[223, 185, 150],\n",
+              "        [222, 184, 149],\n",
+              "        [220, 182, 147],\n",
+              "        ...,\n",
+              "        [229, 191, 156],\n",
+              "        [229, 191, 156],\n",
+              "        [229, 191, 156]],\n",
+              "\n",
+              "       [[224, 186, 151],\n",
+              "        [223, 185, 150],\n",
+              "        [222, 184, 149],\n",
+              "        ...,\n",
+              "        [231, 193, 158],\n",
+              "        [231, 193, 158],\n",
+              "        [230, 192, 157]],\n",
+              "\n",
+              "       [[225, 187, 152],\n",
+              "        [225, 187, 152],\n",
+              "        [224, 186, 151],\n",
+              "        ...,\n",
+              "        [232, 194, 159],\n",
+              "        [232, 194, 159],\n",
+              "        [232, 194, 159]],\n",
+              "\n",
+              "       ...,\n",
+              "\n",
+              "       [[162, 171, 184],\n",
+              "        [173, 182, 195],\n",
+              "        [184, 193, 206],\n",
+              "        ...,\n",
+              "        [ 47,  32,  46],\n",
+              "        [ 47,  32,  46],\n",
+              "        [ 47,  32,  46]],\n",
+              "\n",
+              "       [[163, 172, 185],\n",
+              "        [171, 180, 193],\n",
+              "        [180, 189, 202],\n",
+              "        ...,\n",
+              "        [ 47,  32,  46],\n",
+              "        [ 47,  32,  46],\n",
+              "        [ 47,  32,  46]],\n",
+              "\n",
+              "       [[165, 174, 187],\n",
+              "        [171, 180, 193],\n",
+              "        [177, 186, 199],\n",
+              "        ...,\n",
+              "        [ 47,  32,  46],\n",
+              "        [ 47,  32,  46],\n",
+              "        [ 47,  32,  46]]], dtype=uint8)"
+            ],
+            "text/html": [
+              "<style>\n",
+              "      .ndarray_repr .ndarray_raw_data {\n",
+              "        display: none;\n",
+              "      }\n",
+              "      .ndarray_repr.show_array .ndarray_raw_data {\n",
+              "        display: block;\n",
+              "      }\n",
+              "      .ndarray_repr.show_array .ndarray_image_preview {\n",
+              "        display: none;\n",
+              "      }\n",
+              "      </style>\n",
+              "      <div id=\"id-c0708b4e-86ed-4f2f-b837-fc3cf6b14d8b\" class=\"ndarray_repr\"><pre>ndarray (420, 630, 3) <button style=\"padding: 0 2px;\">show data</button></pre><img 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\" class=\"ndarray_image_preview\" /><pre class=\"ndarray_raw_data\">array([[[223, 185, 150],\n",
+              "        [222, 184, 149],\n",
+              "        [220, 182, 147],\n",
+              "        ...,\n",
+              "        [229, 191, 156],\n",
+              "        [229, 191, 156],\n",
+              "        [229, 191, 156]],\n",
+              "\n",
+              "       [[224, 186, 151],\n",
+              "        [223, 185, 150],\n",
+              "        [222, 184, 149],\n",
+              "        ...,\n",
+              "        [231, 193, 158],\n",
+              "        [231, 193, 158],\n",
+              "        [230, 192, 157]],\n",
+              "\n",
+              "       [[225, 187, 152],\n",
+              "        [225, 187, 152],\n",
+              "        [224, 186, 151],\n",
+              "        ...,\n",
+              "        [232, 194, 159],\n",
+              "        [232, 194, 159],\n",
+              "        [232, 194, 159]],\n",
+              "\n",
+              "       ...,\n",
+              "\n",
+              "       [[162, 171, 184],\n",
+              "        [173, 182, 195],\n",
+              "        [184, 193, 206],\n",
+              "        ...,\n",
+              "        [ 47,  32,  46],\n",
+              "        [ 47,  32,  46],\n",
+              "        [ 47,  32,  46]],\n",
+              "\n",
+              "       [[163, 172, 185],\n",
+              "        [171, 180, 193],\n",
+              "        [180, 189, 202],\n",
+              "        ...,\n",
+              "        [ 47,  32,  46],\n",
+              "        [ 47,  32,  46],\n",
+              "        [ 47,  32,  46]],\n",
+              "\n",
+              "       [[165, 174, 187],\n",
+              "        [171, 180, 193],\n",
+              "        [177, 186, 199],\n",
+              "        ...,\n",
+              "        [ 47,  32,  46],\n",
+              "        [ 47,  32,  46],\n",
+              "        [ 47,  32,  46]]], dtype=uint8)</pre></div><script>\n",
+              "      (() => {\n",
+              "      const titles = ['show data', 'hide data'];\n",
+              "      let index = 0\n",
+              "      document.querySelector('#id-c0708b4e-86ed-4f2f-b837-fc3cf6b14d8b button').onclick = (e) => {\n",
+              "        document.querySelector('#id-c0708b4e-86ed-4f2f-b837-fc3cf6b14d8b').classList.toggle('show_array');\n",
+              "        index = (++index) % 2;\n",
+              "        document.querySelector('#id-c0708b4e-86ed-4f2f-b837-fc3cf6b14d8b button').textContent = titles[index];\n",
+              "        e.preventDefault();\n",
+              "        e.stopPropagation();\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 20
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sample_image = cv2.imread(truck_image_data[number])\n",
+        "sample_image.shape"
+      ],
+      "metadata": {
+        "id": "dO3DfUVjLTKv",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "6ddbd3e8-f216-4f18-b677-63d006e0b284"
+      },
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(420, 630, 3)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 21
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "trainImageData = ImageDataGenerator(rescale = 1/255)\n",
+        "validImageData = ImageDataGenerator(rescale = 1/255)"
+      ],
+      "metadata": {
+        "id": "Vjmj2YQXLWJI"
+      },
+      "execution_count": 22,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_dataset = trainImageData.flow_from_directory(\n",
+        "    '/content/drive/MyDrive/Dataset_truck/train_data',\n",
+        "    target_size = (200, 200),\n",
+        "    batch_size = 3,\n",
+        "    class_mode = 'binary'\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "ePwrukRaLZRD",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "e532d4a4-a605-4020-99b5-70bb00f2c008"
+      },
+      "execution_count": 23,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Found 150 images belonging to 1 classes.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "validation_dataset = validImageData.flow_from_directory(\n",
+        "    '/content/drive/MyDrive/Dataset_truck/validation_data',\n",
+        "    target_size = (200, 200),\n",
+        "    batch_size = 3,\n",
+        "    class_mode = 'binary'\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "GmmLoguILfKM",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "e4a99483-3d7c-41e0-d950-6f24a22e762b"
+      },
+      "execution_count": 24,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Found 101 images belonging to 1 classes.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_dataset.class_indices"
+      ],
+      "metadata": {
+        "id": "__ewNOOhLgIR",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "9d181584-727e-4529-f513-f226e13be591"
+      },
+      "execution_count": 25,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'truck': 0}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 25
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from keras import layers\n",
+        "from keras.layers import Flatten, Dense, MaxPooling2D, Conv2D\n",
+        "from keras.models import Sequential"
+      ],
+      "metadata": {
+        "id": "9fVpfXfLLi-i"
+      },
+      "execution_count": 26,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from tensorflow.keras.applications import ResNet50\n",
+        "from tensorflow.keras.models import Model\n",
+        "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D, Dropout, BatchNormalization\n",
+        "from tensorflow.keras.optimizers import Adam\n",
+        "\n",
+        "# Load the ResNet50 model, excluding the top layer\n",
+        "base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(200, 200, 3))\n",
+        "\n",
+        "# Add custom layers on top of ResNet50\n",
+        "x = base_model.output\n",
+        "x = GlobalAveragePooling2D()(x)\n",
+        "x = Dense(512, activation='relu')(x)\n",
+        "x = BatchNormalization()(x)\n",
+        "x = Dropout(0.5)(x)\n",
+        "predictions = Dense(1, activation='sigmoid')(x)\n",
+        "\n",
+        "# Create the full model\n",
+        "model = Model(inputs=base_model.input, outputs=predictions)\n",
+        "\n",
+        "# Freeze the layers of ResNet50 to prevent them from being updated during the initial training\n",
+        "for layer in base_model.layers:\n",
+        "    layer.trainable = False\n",
+        "\n",
+        "# Compile the model\n",
+        "model.compile(optimizer=Adam(learning_rate=0.001), loss='binary_crossentropy', metrics=['accuracy'])\n",
+        "\n",
+        "# Train the model\n",
+        "# model.fit(...)\n",
+        "\n",
+        "# After initial training, unfreeze some layers of ResNet50 for fine-tuning\n",
+        "for layer in base_model.layers[-10:]:  # Unfreeze the last 10 layers\n",
+        "    layer.trainable = True\n",
+        "\n",
+        "# Recompile the model with a lower learning rate for fine-tuning\n",
+        "model.compile(optimizer=Adam(learning_rate=0.0001), loss='binary_crossentropy', metrics=['accuracy'])\n",
+        "\n",
+        "# Fine-tune the model\n",
+        "# model.fit(...)"
+      ],
+      "metadata": {
+        "id": "5V9Lk7A3Lm7s",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "d3e2e3b0-33b0-49ea-e1b1-325c2e00c6f2"
+      },
+      "execution_count": 27,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
+            "94765736/94765736 [==============================] - 4s 0us/step\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.summary()"
+      ],
+      "metadata": {
+        "id": "RaTU0LTALseV",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "5c625a0b-1bd9-4411-fb93-de382a95dd8c"
+      },
+      "execution_count": 30,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Model: \"model\"\n",
+            "__________________________________________________________________________________________________\n",
+            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
+            "==================================================================================================\n",
+            " input_1 (InputLayer)        [(None, 200, 200, 3)]        0         []                            \n",
+            "                                                                                                  \n",
+            " conv1_pad (ZeroPadding2D)   (None, 206, 206, 3)          0         ['input_1[0][0]']             \n",
+            "                                                                                                  \n",
+            " conv1_conv (Conv2D)         (None, 100, 100, 64)         9472      ['conv1_pad[0][0]']           \n",
+            "                                                                                                  \n",
+            " conv1_bn (BatchNormalizati  (None, 100, 100, 64)         256       ['conv1_conv[0][0]']          \n",
+            " on)                                                                                              \n",
+            "                                                                                                  \n",
+            " conv1_relu (Activation)     (None, 100, 100, 64)         0         ['conv1_bn[0][0]']            \n",
+            "                                                                                                  \n",
+            " pool1_pad (ZeroPadding2D)   (None, 102, 102, 64)         0         ['conv1_relu[0][0]']          \n",
+            "                                                                                                  \n",
+            " pool1_pool (MaxPooling2D)   (None, 50, 50, 64)           0         ['pool1_pad[0][0]']           \n",
+            "                                                                                                  \n",
+            " conv2_block1_1_conv (Conv2  (None, 50, 50, 64)           4160      ['pool1_pool[0][0]']          \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv2_block1_1_bn (BatchNo  (None, 50, 50, 64)           256       ['conv2_block1_1_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv2_block1_1_relu (Activ  (None, 50, 50, 64)           0         ['conv2_block1_1_bn[0][0]']   \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " conv2_block1_2_conv (Conv2  (None, 50, 50, 64)           36928     ['conv2_block1_1_relu[0][0]'] \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv2_block1_2_bn (BatchNo  (None, 50, 50, 64)           256       ['conv2_block1_2_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv2_block1_2_relu (Activ  (None, 50, 50, 64)           0         ['conv2_block1_2_bn[0][0]']   \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " conv2_block1_0_conv (Conv2  (None, 50, 50, 256)          16640     ['pool1_pool[0][0]']          \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv2_block1_3_conv (Conv2  (None, 50, 50, 256)          16640     ['conv2_block1_2_relu[0][0]'] \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv2_block1_0_bn (BatchNo  (None, 50, 50, 256)          1024      ['conv2_block1_0_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv2_block1_3_bn (BatchNo  (None, 50, 50, 256)          1024      ['conv2_block1_3_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv2_block1_add (Add)      (None, 50, 50, 256)          0         ['conv2_block1_0_bn[0][0]',   \n",
+            "                                                                     'conv2_block1_3_bn[0][0]']   \n",
+            "                                                                                                  \n",
+            " conv2_block1_out (Activati  (None, 50, 50, 256)          0         ['conv2_block1_add[0][0]']    \n",
+            " on)                                                                                              \n",
+            "                                                                                                  \n",
+            " conv2_block2_1_conv (Conv2  (None, 50, 50, 64)           16448     ['conv2_block1_out[0][0]']    \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv2_block2_1_bn (BatchNo  (None, 50, 50, 64)           256       ['conv2_block2_1_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv2_block2_1_relu (Activ  (None, 50, 50, 64)           0         ['conv2_block2_1_bn[0][0]']   \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " conv2_block2_2_conv (Conv2  (None, 50, 50, 64)           36928     ['conv2_block2_1_relu[0][0]'] \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv2_block2_2_bn (BatchNo  (None, 50, 50, 64)           256       ['conv2_block2_2_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv2_block2_2_relu (Activ  (None, 50, 50, 64)           0         ['conv2_block2_2_bn[0][0]']   \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " conv2_block2_3_conv (Conv2  (None, 50, 50, 256)          16640     ['conv2_block2_2_relu[0][0]'] \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv2_block2_3_bn (BatchNo  (None, 50, 50, 256)          1024      ['conv2_block2_3_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv2_block2_add (Add)      (None, 50, 50, 256)          0         ['conv2_block1_out[0][0]',    \n",
+            "                                                                     'conv2_block2_3_bn[0][0]']   \n",
+            "                                                                                                  \n",
+            " conv2_block2_out (Activati  (None, 50, 50, 256)          0         ['conv2_block2_add[0][0]']    \n",
+            " on)                                                                                              \n",
+            "                                                                                                  \n",
+            " conv2_block3_1_conv (Conv2  (None, 50, 50, 64)           16448     ['conv2_block2_out[0][0]']    \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv2_block3_1_bn (BatchNo  (None, 50, 50, 64)           256       ['conv2_block3_1_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv2_block3_1_relu (Activ  (None, 50, 50, 64)           0         ['conv2_block3_1_bn[0][0]']   \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " conv2_block3_2_conv (Conv2  (None, 50, 50, 64)           36928     ['conv2_block3_1_relu[0][0]'] \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv2_block3_2_bn (BatchNo  (None, 50, 50, 64)           256       ['conv2_block3_2_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv2_block3_2_relu (Activ  (None, 50, 50, 64)           0         ['conv2_block3_2_bn[0][0]']   \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " conv2_block3_3_conv (Conv2  (None, 50, 50, 256)          16640     ['conv2_block3_2_relu[0][0]'] \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv2_block3_3_bn (BatchNo  (None, 50, 50, 256)          1024      ['conv2_block3_3_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv2_block3_add (Add)      (None, 50, 50, 256)          0         ['conv2_block2_out[0][0]',    \n",
+            "                                                                     'conv2_block3_3_bn[0][0]']   \n",
+            "                                                                                                  \n",
+            " conv2_block3_out (Activati  (None, 50, 50, 256)          0         ['conv2_block3_add[0][0]']    \n",
+            " on)                                                                                              \n",
+            "                                                                                                  \n",
+            " conv3_block1_1_conv (Conv2  (None, 25, 25, 128)          32896     ['conv2_block3_out[0][0]']    \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv3_block1_1_bn (BatchNo  (None, 25, 25, 128)          512       ['conv3_block1_1_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv3_block1_1_relu (Activ  (None, 25, 25, 128)          0         ['conv3_block1_1_bn[0][0]']   \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " conv3_block1_2_conv (Conv2  (None, 25, 25, 128)          147584    ['conv3_block1_1_relu[0][0]'] \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv3_block1_2_bn (BatchNo  (None, 25, 25, 128)          512       ['conv3_block1_2_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv3_block1_2_relu (Activ  (None, 25, 25, 128)          0         ['conv3_block1_2_bn[0][0]']   \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " conv3_block1_0_conv (Conv2  (None, 25, 25, 512)          131584    ['conv2_block3_out[0][0]']    \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv3_block1_3_conv (Conv2  (None, 25, 25, 512)          66048     ['conv3_block1_2_relu[0][0]'] \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv3_block1_0_bn (BatchNo  (None, 25, 25, 512)          2048      ['conv3_block1_0_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv3_block1_3_bn (BatchNo  (None, 25, 25, 512)          2048      ['conv3_block1_3_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv3_block1_add (Add)      (None, 25, 25, 512)          0         ['conv3_block1_0_bn[0][0]',   \n",
+            "                                                                     'conv3_block1_3_bn[0][0]']   \n",
+            "                                                                                                  \n",
+            " conv3_block1_out (Activati  (None, 25, 25, 512)          0         ['conv3_block1_add[0][0]']    \n",
+            " on)                                                                                              \n",
+            "                                                                                                  \n",
+            " conv3_block2_1_conv (Conv2  (None, 25, 25, 128)          65664     ['conv3_block1_out[0][0]']    \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv3_block2_1_bn (BatchNo  (None, 25, 25, 128)          512       ['conv3_block2_1_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv3_block2_1_relu (Activ  (None, 25, 25, 128)          0         ['conv3_block2_1_bn[0][0]']   \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " conv3_block2_2_conv (Conv2  (None, 25, 25, 128)          147584    ['conv3_block2_1_relu[0][0]'] \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv3_block2_2_bn (BatchNo  (None, 25, 25, 128)          512       ['conv3_block2_2_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv3_block2_2_relu (Activ  (None, 25, 25, 128)          0         ['conv3_block2_2_bn[0][0]']   \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " conv3_block2_3_conv (Conv2  (None, 25, 25, 512)          66048     ['conv3_block2_2_relu[0][0]'] \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv3_block2_3_bn (BatchNo  (None, 25, 25, 512)          2048      ['conv3_block2_3_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv3_block2_add (Add)      (None, 25, 25, 512)          0         ['conv3_block1_out[0][0]',    \n",
+            "                                                                     'conv3_block2_3_bn[0][0]']   \n",
+            "                                                                                                  \n",
+            " conv3_block2_out (Activati  (None, 25, 25, 512)          0         ['conv3_block2_add[0][0]']    \n",
+            " on)                                                                                              \n",
+            "                                                                                                  \n",
+            " conv3_block3_1_conv (Conv2  (None, 25, 25, 128)          65664     ['conv3_block2_out[0][0]']    \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv3_block3_1_bn (BatchNo  (None, 25, 25, 128)          512       ['conv3_block3_1_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv3_block3_1_relu (Activ  (None, 25, 25, 128)          0         ['conv3_block3_1_bn[0][0]']   \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " conv3_block3_2_conv (Conv2  (None, 25, 25, 128)          147584    ['conv3_block3_1_relu[0][0]'] \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv3_block3_2_bn (BatchNo  (None, 25, 25, 128)          512       ['conv3_block3_2_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv3_block3_2_relu (Activ  (None, 25, 25, 128)          0         ['conv3_block3_2_bn[0][0]']   \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " conv3_block3_3_conv (Conv2  (None, 25, 25, 512)          66048     ['conv3_block3_2_relu[0][0]'] \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv3_block3_3_bn (BatchNo  (None, 25, 25, 512)          2048      ['conv3_block3_3_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv3_block3_add (Add)      (None, 25, 25, 512)          0         ['conv3_block2_out[0][0]',    \n",
+            "                                                                     'conv3_block3_3_bn[0][0]']   \n",
+            "                                                                                                  \n",
+            " conv3_block3_out (Activati  (None, 25, 25, 512)          0         ['conv3_block3_add[0][0]']    \n",
+            " on)                                                                                              \n",
+            "                                                                                                  \n",
+            " conv3_block4_1_conv (Conv2  (None, 25, 25, 128)          65664     ['conv3_block3_out[0][0]']    \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv3_block4_1_bn (BatchNo  (None, 25, 25, 128)          512       ['conv3_block4_1_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv3_block4_1_relu (Activ  (None, 25, 25, 128)          0         ['conv3_block4_1_bn[0][0]']   \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " conv3_block4_2_conv (Conv2  (None, 25, 25, 128)          147584    ['conv3_block4_1_relu[0][0]'] \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv3_block4_2_bn (BatchNo  (None, 25, 25, 128)          512       ['conv3_block4_2_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv3_block4_2_relu (Activ  (None, 25, 25, 128)          0         ['conv3_block4_2_bn[0][0]']   \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " conv3_block4_3_conv (Conv2  (None, 25, 25, 512)          66048     ['conv3_block4_2_relu[0][0]'] \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv3_block4_3_bn (BatchNo  (None, 25, 25, 512)          2048      ['conv3_block4_3_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv3_block4_add (Add)      (None, 25, 25, 512)          0         ['conv3_block3_out[0][0]',    \n",
+            "                                                                     'conv3_block4_3_bn[0][0]']   \n",
+            "                                                                                                  \n",
+            " conv3_block4_out (Activati  (None, 25, 25, 512)          0         ['conv3_block4_add[0][0]']    \n",
+            " on)                                                                                              \n",
+            "                                                                                                  \n",
+            " conv4_block1_1_conv (Conv2  (None, 13, 13, 256)          131328    ['conv3_block4_out[0][0]']    \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv4_block1_1_bn (BatchNo  (None, 13, 13, 256)          1024      ['conv4_block1_1_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv4_block1_1_relu (Activ  (None, 13, 13, 256)          0         ['conv4_block1_1_bn[0][0]']   \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " conv4_block1_2_conv (Conv2  (None, 13, 13, 256)          590080    ['conv4_block1_1_relu[0][0]'] \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv4_block1_2_bn (BatchNo  (None, 13, 13, 256)          1024      ['conv4_block1_2_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv4_block1_2_relu (Activ  (None, 13, 13, 256)          0         ['conv4_block1_2_bn[0][0]']   \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " conv4_block1_0_conv (Conv2  (None, 13, 13, 1024)         525312    ['conv3_block4_out[0][0]']    \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv4_block1_3_conv (Conv2  (None, 13, 13, 1024)         263168    ['conv4_block1_2_relu[0][0]'] \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv4_block1_0_bn (BatchNo  (None, 13, 13, 1024)         4096      ['conv4_block1_0_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv4_block1_3_bn (BatchNo  (None, 13, 13, 1024)         4096      ['conv4_block1_3_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv4_block1_add (Add)      (None, 13, 13, 1024)         0         ['conv4_block1_0_bn[0][0]',   \n",
+            "                                                                     'conv4_block1_3_bn[0][0]']   \n",
+            "                                                                                                  \n",
+            " conv4_block1_out (Activati  (None, 13, 13, 1024)         0         ['conv4_block1_add[0][0]']    \n",
+            " on)                                                                                              \n",
+            "                                                                                                  \n",
+            " conv4_block2_1_conv (Conv2  (None, 13, 13, 256)          262400    ['conv4_block1_out[0][0]']    \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv4_block2_1_bn (BatchNo  (None, 13, 13, 256)          1024      ['conv4_block2_1_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv4_block2_1_relu (Activ  (None, 13, 13, 256)          0         ['conv4_block2_1_bn[0][0]']   \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " conv4_block2_2_conv (Conv2  (None, 13, 13, 256)          590080    ['conv4_block2_1_relu[0][0]'] \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv4_block2_2_bn (BatchNo  (None, 13, 13, 256)          1024      ['conv4_block2_2_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv4_block2_2_relu (Activ  (None, 13, 13, 256)          0         ['conv4_block2_2_bn[0][0]']   \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " conv4_block2_3_conv (Conv2  (None, 13, 13, 1024)         263168    ['conv4_block2_2_relu[0][0]'] \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv4_block2_3_bn (BatchNo  (None, 13, 13, 1024)         4096      ['conv4_block2_3_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv4_block2_add (Add)      (None, 13, 13, 1024)         0         ['conv4_block1_out[0][0]',    \n",
+            "                                                                     'conv4_block2_3_bn[0][0]']   \n",
+            "                                                                                                  \n",
+            " conv4_block2_out (Activati  (None, 13, 13, 1024)         0         ['conv4_block2_add[0][0]']    \n",
+            " on)                                                                                              \n",
+            "                                                                                                  \n",
+            " conv4_block3_1_conv (Conv2  (None, 13, 13, 256)          262400    ['conv4_block2_out[0][0]']    \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv4_block3_1_bn (BatchNo  (None, 13, 13, 256)          1024      ['conv4_block3_1_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv4_block3_1_relu (Activ  (None, 13, 13, 256)          0         ['conv4_block3_1_bn[0][0]']   \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " conv4_block3_2_conv (Conv2  (None, 13, 13, 256)          590080    ['conv4_block3_1_relu[0][0]'] \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv4_block3_2_bn (BatchNo  (None, 13, 13, 256)          1024      ['conv4_block3_2_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv4_block3_2_relu (Activ  (None, 13, 13, 256)          0         ['conv4_block3_2_bn[0][0]']   \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " conv4_block3_3_conv (Conv2  (None, 13, 13, 1024)         263168    ['conv4_block3_2_relu[0][0]'] \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv4_block3_3_bn (BatchNo  (None, 13, 13, 1024)         4096      ['conv4_block3_3_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv4_block3_add (Add)      (None, 13, 13, 1024)         0         ['conv4_block2_out[0][0]',    \n",
+            "                                                                     'conv4_block3_3_bn[0][0]']   \n",
+            "                                                                                                  \n",
+            " conv4_block3_out (Activati  (None, 13, 13, 1024)         0         ['conv4_block3_add[0][0]']    \n",
+            " on)                                                                                              \n",
+            "                                                                                                  \n",
+            " conv4_block4_1_conv (Conv2  (None, 13, 13, 256)          262400    ['conv4_block3_out[0][0]']    \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv4_block4_1_bn (BatchNo  (None, 13, 13, 256)          1024      ['conv4_block4_1_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv4_block4_1_relu (Activ  (None, 13, 13, 256)          0         ['conv4_block4_1_bn[0][0]']   \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " conv4_block4_2_conv (Conv2  (None, 13, 13, 256)          590080    ['conv4_block4_1_relu[0][0]'] \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv4_block4_2_bn (BatchNo  (None, 13, 13, 256)          1024      ['conv4_block4_2_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv4_block4_2_relu (Activ  (None, 13, 13, 256)          0         ['conv4_block4_2_bn[0][0]']   \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " conv4_block4_3_conv (Conv2  (None, 13, 13, 1024)         263168    ['conv4_block4_2_relu[0][0]'] \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv4_block4_3_bn (BatchNo  (None, 13, 13, 1024)         4096      ['conv4_block4_3_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv4_block4_add (Add)      (None, 13, 13, 1024)         0         ['conv4_block3_out[0][0]',    \n",
+            "                                                                     'conv4_block4_3_bn[0][0]']   \n",
+            "                                                                                                  \n",
+            " conv4_block4_out (Activati  (None, 13, 13, 1024)         0         ['conv4_block4_add[0][0]']    \n",
+            " on)                                                                                              \n",
+            "                                                                                                  \n",
+            " conv4_block5_1_conv (Conv2  (None, 13, 13, 256)          262400    ['conv4_block4_out[0][0]']    \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv4_block5_1_bn (BatchNo  (None, 13, 13, 256)          1024      ['conv4_block5_1_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv4_block5_1_relu (Activ  (None, 13, 13, 256)          0         ['conv4_block5_1_bn[0][0]']   \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " conv4_block5_2_conv (Conv2  (None, 13, 13, 256)          590080    ['conv4_block5_1_relu[0][0]'] \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv4_block5_2_bn (BatchNo  (None, 13, 13, 256)          1024      ['conv4_block5_2_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv4_block5_2_relu (Activ  (None, 13, 13, 256)          0         ['conv4_block5_2_bn[0][0]']   \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " conv4_block5_3_conv (Conv2  (None, 13, 13, 1024)         263168    ['conv4_block5_2_relu[0][0]'] \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv4_block5_3_bn (BatchNo  (None, 13, 13, 1024)         4096      ['conv4_block5_3_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv4_block5_add (Add)      (None, 13, 13, 1024)         0         ['conv4_block4_out[0][0]',    \n",
+            "                                                                     'conv4_block5_3_bn[0][0]']   \n",
+            "                                                                                                  \n",
+            " conv4_block5_out (Activati  (None, 13, 13, 1024)         0         ['conv4_block5_add[0][0]']    \n",
+            " on)                                                                                              \n",
+            "                                                                                                  \n",
+            " conv4_block6_1_conv (Conv2  (None, 13, 13, 256)          262400    ['conv4_block5_out[0][0]']    \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv4_block6_1_bn (BatchNo  (None, 13, 13, 256)          1024      ['conv4_block6_1_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv4_block6_1_relu (Activ  (None, 13, 13, 256)          0         ['conv4_block6_1_bn[0][0]']   \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " conv4_block6_2_conv (Conv2  (None, 13, 13, 256)          590080    ['conv4_block6_1_relu[0][0]'] \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv4_block6_2_bn (BatchNo  (None, 13, 13, 256)          1024      ['conv4_block6_2_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv4_block6_2_relu (Activ  (None, 13, 13, 256)          0         ['conv4_block6_2_bn[0][0]']   \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " conv4_block6_3_conv (Conv2  (None, 13, 13, 1024)         263168    ['conv4_block6_2_relu[0][0]'] \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv4_block6_3_bn (BatchNo  (None, 13, 13, 1024)         4096      ['conv4_block6_3_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv4_block6_add (Add)      (None, 13, 13, 1024)         0         ['conv4_block5_out[0][0]',    \n",
+            "                                                                     'conv4_block6_3_bn[0][0]']   \n",
+            "                                                                                                  \n",
+            " conv4_block6_out (Activati  (None, 13, 13, 1024)         0         ['conv4_block6_add[0][0]']    \n",
+            " on)                                                                                              \n",
+            "                                                                                                  \n",
+            " conv5_block1_1_conv (Conv2  (None, 7, 7, 512)            524800    ['conv4_block6_out[0][0]']    \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv5_block1_1_bn (BatchNo  (None, 7, 7, 512)            2048      ['conv5_block1_1_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv5_block1_1_relu (Activ  (None, 7, 7, 512)            0         ['conv5_block1_1_bn[0][0]']   \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " conv5_block1_2_conv (Conv2  (None, 7, 7, 512)            2359808   ['conv5_block1_1_relu[0][0]'] \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv5_block1_2_bn (BatchNo  (None, 7, 7, 512)            2048      ['conv5_block1_2_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv5_block1_2_relu (Activ  (None, 7, 7, 512)            0         ['conv5_block1_2_bn[0][0]']   \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " conv5_block1_0_conv (Conv2  (None, 7, 7, 2048)           2099200   ['conv4_block6_out[0][0]']    \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv5_block1_3_conv (Conv2  (None, 7, 7, 2048)           1050624   ['conv5_block1_2_relu[0][0]'] \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv5_block1_0_bn (BatchNo  (None, 7, 7, 2048)           8192      ['conv5_block1_0_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv5_block1_3_bn (BatchNo  (None, 7, 7, 2048)           8192      ['conv5_block1_3_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv5_block1_add (Add)      (None, 7, 7, 2048)           0         ['conv5_block1_0_bn[0][0]',   \n",
+            "                                                                     'conv5_block1_3_bn[0][0]']   \n",
+            "                                                                                                  \n",
+            " conv5_block1_out (Activati  (None, 7, 7, 2048)           0         ['conv5_block1_add[0][0]']    \n",
+            " on)                                                                                              \n",
+            "                                                                                                  \n",
+            " conv5_block2_1_conv (Conv2  (None, 7, 7, 512)            1049088   ['conv5_block1_out[0][0]']    \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv5_block2_1_bn (BatchNo  (None, 7, 7, 512)            2048      ['conv5_block2_1_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv5_block2_1_relu (Activ  (None, 7, 7, 512)            0         ['conv5_block2_1_bn[0][0]']   \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " conv5_block2_2_conv (Conv2  (None, 7, 7, 512)            2359808   ['conv5_block2_1_relu[0][0]'] \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv5_block2_2_bn (BatchNo  (None, 7, 7, 512)            2048      ['conv5_block2_2_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv5_block2_2_relu (Activ  (None, 7, 7, 512)            0         ['conv5_block2_2_bn[0][0]']   \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " conv5_block2_3_conv (Conv2  (None, 7, 7, 2048)           1050624   ['conv5_block2_2_relu[0][0]'] \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv5_block2_3_bn (BatchNo  (None, 7, 7, 2048)           8192      ['conv5_block2_3_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv5_block2_add (Add)      (None, 7, 7, 2048)           0         ['conv5_block1_out[0][0]',    \n",
+            "                                                                     'conv5_block2_3_bn[0][0]']   \n",
+            "                                                                                                  \n",
+            " conv5_block2_out (Activati  (None, 7, 7, 2048)           0         ['conv5_block2_add[0][0]']    \n",
+            " on)                                                                                              \n",
+            "                                                                                                  \n",
+            " conv5_block3_1_conv (Conv2  (None, 7, 7, 512)            1049088   ['conv5_block2_out[0][0]']    \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv5_block3_1_bn (BatchNo  (None, 7, 7, 512)            2048      ['conv5_block3_1_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv5_block3_1_relu (Activ  (None, 7, 7, 512)            0         ['conv5_block3_1_bn[0][0]']   \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " conv5_block3_2_conv (Conv2  (None, 7, 7, 512)            2359808   ['conv5_block3_1_relu[0][0]'] \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv5_block3_2_bn (BatchNo  (None, 7, 7, 512)            2048      ['conv5_block3_2_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv5_block3_2_relu (Activ  (None, 7, 7, 512)            0         ['conv5_block3_2_bn[0][0]']   \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " conv5_block3_3_conv (Conv2  (None, 7, 7, 2048)           1050624   ['conv5_block3_2_relu[0][0]'] \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " conv5_block3_3_bn (BatchNo  (None, 7, 7, 2048)           8192      ['conv5_block3_3_conv[0][0]'] \n",
+            " rmalization)                                                                                     \n",
+            "                                                                                                  \n",
+            " conv5_block3_add (Add)      (None, 7, 7, 2048)           0         ['conv5_block2_out[0][0]',    \n",
+            "                                                                     'conv5_block3_3_bn[0][0]']   \n",
+            "                                                                                                  \n",
+            " conv5_block3_out (Activati  (None, 7, 7, 2048)           0         ['conv5_block3_add[0][0]']    \n",
+            " on)                                                                                              \n",
+            "                                                                                                  \n",
+            " global_average_pooling2d (  (None, 2048)                 0         ['conv5_block3_out[0][0]']    \n",
+            " GlobalAveragePooling2D)                                                                          \n",
+            "                                                                                                  \n",
+            " dense (Dense)               (None, 512)                  1049088   ['global_average_pooling2d[0][\n",
+            "                                                                    0]']                          \n",
+            "                                                                                                  \n",
+            " batch_normalization (Batch  (None, 512)                  2048      ['dense[0][0]']               \n",
+            " Normalization)                                                                                   \n",
+            "                                                                                                  \n",
+            " dropout (Dropout)           (None, 512)                  0         ['batch_normalization[0][0]'] \n",
+            "                                                                                                  \n",
+            " dense_1 (Dense)             (None, 1)                    513       ['dropout[0][0]']             \n",
+            "                                                                                                  \n",
+            "==================================================================================================\n",
+            "Total params: 24639361 (93.99 MB)\n",
+            "Trainable params: 5516289 (21.04 MB)\n",
+            "Non-trainable params: 19123072 (72.95 MB)\n",
+            "__________________________________________________________________________________________________\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.fit(\n",
+        "    train_dataset,\n",
+        "    steps_per_epoch = len(train_dataset),\n",
+        "    epochs = 3,\n",
+        "    validation_data = validation_dataset\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "9aEj5aB-LvNe",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "6cc53e74-decf-46da-dd05-0b9548c00e2f"
+      },
+      "execution_count": 31,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/3\n",
+            "50/50 [==============================] - 109s 2s/step - loss: 0.7366 - accuracy: 0.5333 - val_loss: 0.5821 - val_accuracy: 1.0000\n",
+            "Epoch 2/3\n",
+            "50/50 [==============================] - 57s 1s/step - loss: 0.7246 - accuracy: 0.5600 - val_loss: 0.5834 - val_accuracy: 1.0000\n",
+            "Epoch 3/3\n",
+            "50/50 [==============================] - 55s 1s/step - loss: 0.6973 - accuracy: 0.6000 - val_loss: 0.5725 - val_accuracy: 0.9802\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<keras.src.callbacks.History at 0x784175e98400>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 31
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "testingImageData = ImageDataGenerator(rescale = 1/255)\n",
+        "testing_dataset = validImageData.flow_from_directory(\n",
+        "    '/content/drive/MyDrive/Dataset_truck/test_data',\n",
+        "    target_size = (200, 200),\n",
+        "    batch_size = 3,\n",
+        "    class_mode = 'binary'\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "DK7gLarALyog",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "649087e3-bad5-4151-944c-07906a592fca"
+      },
+      "execution_count": 32,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Found 215 images belonging to 1 classes.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "loss, accuracy = model.evaluate(testing_dataset)"
+      ],
+      "metadata": {
+        "id": "AAouLoXXL16c",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "48100514-737f-4f1e-e0f7-63fe194a8cd8"
+      },
+      "execution_count": 34,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "72/72 [==============================] - 43s 583ms/step - loss: 0.5795 - accuracy: 0.9860\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "from tensorflow.keras.preprocessing.image import load_img, img_to_array\n",
+        "\n",
+        "# Define the path to a single image (example)\n",
+        "image_path = '/content/drive/MyDrive/Dataset_truck/train_data/truck/00000000 (2).jpg'  # Update with your image path\n",
+        "\n",
+        "# Load and preprocess the image\n",
+        "img = load_img(image_path, target_size=(200, 200))  # Ensure the image is resized to (200, 200)\n",
+        "img_array = img_to_array(img) / 255.0  # Rescale pixel values to [0, 1]\n",
+        "img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension\n",
+        "\n",
+        "# Ensure the model is already defined and loaded as 'truck_model'\n",
+        "# Example: truck_model = tf.keras.models.load_model('path_to_model')\n",
+        "\n",
+        "# Make prediction\n",
+        "prediction = model.predict(img_array)\n",
+        "if prediction[0] < 0.5:  # Adjust threshold based on your model's output\n",
+        "    print(\"Truck\")\n",
+        "else:\n",
+        "    print(\"Not a Truck\")\n"
+      ],
+      "metadata": {
+        "id": "i1O_2myohySV",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "03180d5c-0fa2-442b-ad9c-5ca35b20695a"
+      },
+      "execution_count": 35,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "1/1 [==============================] - 3s 3s/step\n",
+            "Truck\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from keras.utils import load_img"
+      ],
+      "metadata": {
+        "id": "U8TrkZk1L4f8"
+      },
+      "execution_count": 36,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import os\n",
+        "from keras.preprocessing.image import load_img, img_to_array\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "test_path = \"/content/drive/MyDrive/Dataset_truck/test_data\"\n",
+        "count = 0\n",
+        "\n",
+        "for i in os.listdir(test_path):\n",
+        "    file_path = os.path.join(test_path, i)\n",
+        "\n",
+        "    if os.path.isfile(file_path):\n",
+        "        img = load_img(file_path)\n",
+        "        plt.imshow(img)\n",
+        "        plt.show()\n",
+        "\n",
+        "        X = img_to_array(img)\n",
+        "        x_imag = np.expand_dims(X, axis=0)\n",
+        "        images = np.vstack([x_imag])\n",
+        "\n",
+        "        prediction = truck_model.predict(images)\n",
+        "        if prediction == 0:\n",
+        "            print(\"Truck\")\n",
+        "        else:\n",
+        "            print(\"No truck\")\n",
+        "\n",
+        "        count += 1\n",
+        "        if count == 5:\n",
+        "            break"
+      ],
+      "metadata": {
+        "id": "qziIOnHyL7iz"
+      },
+      "execution_count": 37,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "QuKdA8rwRZXt"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from keras import models"
+      ],
+      "metadata": {
+        "id": "v9xYeLmcL_Rc"
+      },
+      "execution_count": 38,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "truck_model_ann = models.Sequential([\n",
+        "    layers.Flatten(input_shape=(200, 200, 3)),\n",
+        "\n",
+        "    layers.Dense(3000, activation='relu'),\n",
+        "    layers.BatchNormalization(),\n",
+        "    layers.Dropout(0.5),\n",
+        "\n",
+        "    layers.Dense(1000, activation='relu'),\n",
+        "    layers.BatchNormalization(),\n",
+        "    layers.Dropout(0.5),\n",
+        "\n",
+        "    layers.Dense(1, activation='sigmoid')\n",
+        "])"
+      ],
+      "metadata": {
+        "id": "jk8g92UOMCt0"
+      },
+      "execution_count": 39,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "truck_model_ann.summary()"
+      ],
+      "metadata": {
+        "id": "dICWnparMFdx",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "27557968-b94b-4f63-ccba-7da38c27c595"
+      },
+      "execution_count": 40,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Model: \"sequential\"\n",
+            "_________________________________________________________________\n",
+            " Layer (type)                Output Shape              Param #   \n",
+            "=================================================================\n",
+            " flatten (Flatten)           (None, 120000)            0         \n",
+            "                                                                 \n",
+            " dense_2 (Dense)             (None, 3000)              360003000 \n",
+            "                                                                 \n",
+            " batch_normalization_1 (Bat  (None, 3000)              12000     \n",
+            " chNormalization)                                                \n",
+            "                                                                 \n",
+            " dropout_1 (Dropout)         (None, 3000)              0         \n",
+            "                                                                 \n",
+            " dense_3 (Dense)             (None, 1000)              3001000   \n",
+            "                                                                 \n",
+            " batch_normalization_2 (Bat  (None, 1000)              4000      \n",
+            " chNormalization)                                                \n",
+            "                                                                 \n",
+            " dropout_2 (Dropout)         (None, 1000)              0         \n",
+            "                                                                 \n",
+            " dense_4 (Dense)             (None, 1)                 1001      \n",
+            "                                                                 \n",
+            "=================================================================\n",
+            "Total params: 363021001 (1.35 GB)\n",
+            "Trainable params: 363013001 (1.35 GB)\n",
+            "Non-trainable params: 8000 (31.25 KB)\n",
+            "_________________________________________________________________\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "truck_model_ann.compile(\n",
+        "    optimizer = 'sgd',\n",
+        "    loss = 'binary_crossentropy',\n",
+        "    metrics = ['accuracy']\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "hpPOleyQMIWe"
+      },
+      "execution_count": 41,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "history = truck_model_ann.fit(train_dataset, validation_data = validation_dataset, epochs = 2)"
+      ],
+      "metadata": {
+        "id": "n7G2-SaaMLbV",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "f3ae2dbe-13dc-4909-b16c-8107dca6f15a"
+      },
+      "execution_count": 42,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/2\n",
+            "50/50 [==============================] - 138s 3s/step - loss: 0.8899 - accuracy: 0.5667 - val_loss: 33.7531 - val_accuracy: 0.0000e+00\n",
+            "Epoch 2/2\n",
+            "50/50 [==============================] - 133s 3s/step - loss: 0.7199 - accuracy: 0.6333 - val_loss: 0.2613 - val_accuracy: 0.8614\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.plot(history.history['accuracy'])\n",
+        "plt.plot(history.history['val_accuracy'])\n",
+        "plt.xlabel('epoch')\n",
+        "plt.ylabel('accuracy')\n",
+        "plt.title(\"Model accuracy\")\n",
+        "plt.legend(['Training', \"Validation\"], loc = 'upper left')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "4C5Ft_yQMOKU",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "outputId": "66a3922c-b4a9-42e4-d3e2-1efc1d62864e"
+      },
+      "execution_count": 43,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.plot(history.history['loss'])\n",
+        "plt.plot(history.history['val_loss'])\n",
+        "plt.title('model loss')\n",
+        "plt.ylabel('loss')\n",
+        "plt.xlabel('epoch')\n",
+        "plt.legend(['Train', 'Validation'], loc='upper left')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "XU5zooGkMQ2G",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "outputId": "54b96467-3974-44d9-d284-b056de23f86c"
+      },
+      "execution_count": 44,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "7IyAzqHVMUbS"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Indian Truck Detection/Model/Gssoc'24_Xception.ipynb b/Indian Truck Detection/Model/Gssoc'24_Xception.ipynb
new file mode 100644
index 000000000..b05e19543
--- /dev/null
+++ b/Indian Truck Detection/Model/Gssoc'24_Xception.ipynb	
@@ -0,0 +1,2025 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7G8sp8ET4qaB",
+        "outputId": "68400e38-c88d-4eb8-af08-9d129935013b"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /content/drive\n"
+          ]
+        }
+      ],
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "F9KS-nfZRxqL"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import os\n",
+        "\n",
+        "# Define the path to the image folder in Google Drive\n",
+        "image_folder_path = \"/content/drive/MyDrive/Dataset_truck\"  # Update this path accordingly\n",
+        "\n",
+        "# Function to recursively print the contents of a directory\n",
+        "def print_directory_contents(folder_path):\n",
+        "    for root, dirs, files in os.walk(folder_path):\n",
+        "        print(f\"Directory: {root}\")\n",
+        "        print(\"Files:\")\n",
+        "        for file in files:\n",
+        "            print(f\"\\t{file}\")\n",
+        "\n",
+        "# Print the contents of the image folder\n",
+        "print_directory_contents(image_folder_path)\n"
+      ],
+      "metadata": {
+        "id": "hwg7e0zq5KuT",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "1179be91-a2b6-4abd-ed0f-e095d8dbf972"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Directory: /content/drive/MyDrive/Dataset_truck\n",
+            "Files:\n",
+            "Directory: /content/drive/MyDrive/Dataset_truck/test_data\n",
+            "Files:\n",
+            "Directory: /content/drive/MyDrive/Dataset_truck/test_data/truck\n",
+            "Files:\n",
+            "\t00000194.JPG\n",
+            "\t00000176.jpg\n",
+            "\t00000175.jpg\n",
+            "\t00000191.jpg\n",
+            "\t00000180.jpg\n",
+            "\t00000196.jpg\n",
+            "\t00000189.jpg\n",
+            "\t00000180 (2).jpg\n",
+            "\t00000192.jpg\n",
+            "\t00000181.jpg\n",
+            "\t00000183.jpg\n",
+            "\t00000200.jpg\n",
+            "\t00000188.jpg\n",
+            "\t00000198.jpg\n",
+            "\t00000190.jpg\n",
+            "\t00000195.jpg\n",
+            "\t00000187.jpg\n",
+            "\t00000197.jpg\n",
+            "\t00000193.jpg\n",
+            "\t00000186.jpg\n",
+            "\t00000178.jpg\n",
+            "\t00000182.jpg\n",
+            "\t00000179.png\n",
+            "\t00000185.jpg\n",
+            "\t00000202.jpg\n",
+            "\t00000184.jpg\n",
+            "\t00000201.jpg\n",
+            "\t00000203.jpg\n",
+            "\t00000184 (2).jpg\n",
+            "\t00000231.jpg\n",
+            "\t00000233.jpg\n",
+            "\t00000217 (2).jpg\n",
+            "\t00000227.jpg\n",
+            "\t00000215.jpg\n",
+            "\t00000219.jpg\n",
+            "\t00000223.jpg\n",
+            "\t00000212.jpg\n",
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+            "Directory: /content/drive/MyDrive/Dataset_truck/train_data\n",
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+            "\t00000095.jpg\n",
+            "\t00000088.jpg\n",
+            "\t00000096.jpg\n",
+            "\t00000091.jpg\n",
+            "Directory: /content/drive/MyDrive/Dataset_truck/validation_data\n",
+            "Files:\n",
+            "Directory: /content/drive/MyDrive/Dataset_truck/validation_data/truck\n",
+            "Files:\n",
+            "\t00000102.jpg\n",
+            "\t00000101.jpg\n",
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+            "\t00000154.jpg\n",
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+            "\t00000151 (2).jpg\n",
+            "\t00000153 (2).jpg\n",
+            "\t00000158.jpg\n",
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+            "\t00000159.jpg\n",
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+            "\t00000155.jpg\n",
+            "\t00000150.jpg\n",
+            "\t00000148 (2).jpg\n",
+            "\t00000149.jpg\n",
+            "\t00000166.jpg\n",
+            "\t00000156.jpg\n",
+            "\t00000174.jpg\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import os\n",
+        "import numpy as np\n",
+        "import tensorflow as tf\n",
+        "from tensorflow.keras.preprocessing.image import load_img, img_to_array\n",
+        "\n",
+        "# Define the path to the image folder in Google Drive\n",
+        "image_folder_path = \"/content/drive/MyDrive/Dataset_truck/test_data\"  # Update this path accordingly\n",
+        "\n",
+        "# Get a list of all files in the directory\n",
+        "all_files = os.listdir(image_folder_path)\n",
+        "\n",
+        "# Filter only the image files\n",
+        "image_files = [file for file in all_files if file.lower().endswith(('.png', '.jpg', '.jpeg'))]\n",
+        "\n",
+        "# Load images and convert them to arrays\n",
+        "images = []\n",
+        "for image_file in image_files:\n",
+        "    image_path = os.path.join(image_folder_path, image_file)\n",
+        "    try:\n",
+        "        img = load_img(image_path, target_size=(150, 150))  # Adjust target_size as needed\n",
+        "        img_array = img_to_array(img) / 255.0  # Rescale pixel values to [0, 1]\n",
+        "        images.append(img_array)\n",
+        "    except Exception as e:\n",
+        "        print(f\"Error loading image {image_path}: {e}\")\n",
+        "\n",
+        "# Convert the list of images to a NumPy array\n",
+        "images = np.array(images)\n",
+        "\n",
+        "# Print the number of loaded images\n",
+        "print(f\"Number of images loaded: {len(images)}\")\n",
+        "\n",
+        "# Example: Accessing one image from the array\n",
+        "if len(images) > 0:\n",
+        "    example_image = images[0]\n",
+        "    print(f\"Shape of the example image: {example_image.shape}\")\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Bcxu56CZ8tWn",
+        "outputId": "70e5c59f-2840-41d3-806f-a63a0e1e8cfa"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Number of images loaded: 0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import os\n",
+        "import shutil\n",
+        "from tensorflow import keras\n",
+        "import cv2"
+      ],
+      "metadata": {
+        "id": "4eUpapu4HiWE"
+      },
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from imutils import paths\n",
+        "from pathlib import Path"
+      ],
+      "metadata": {
+        "id": "uxQEx--HI3-r"
+      },
+      "execution_count": 5,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "images_path = Path(r\"/content/drive/MyDrive/Dataset_truck\")\n",
+        "trucks_data = list(paths.list_images(images_path))"
+      ],
+      "metadata": {
+        "id": "F-cjwkZgJB6h"
+      },
+      "execution_count": 6,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "trucks_data[0:6]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "slqxxXIqJLWq",
+        "outputId": "7a16a9a3-d852-407f-8bb2-c7d0728ed424"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "['/content/drive/MyDrive/Dataset_truck/test_data/truck/00000194.JPG',\n",
+              " '/content/drive/MyDrive/Dataset_truck/test_data/truck/00000176.jpg',\n",
+              " '/content/drive/MyDrive/Dataset_truck/test_data/truck/00000175.jpg',\n",
+              " '/content/drive/MyDrive/Dataset_truck/test_data/truck/00000191.jpg',\n",
+              " '/content/drive/MyDrive/Dataset_truck/test_data/truck/00000180.jpg',\n",
+              " '/content/drive/MyDrive/Dataset_truck/test_data/truck/00000196.jpg']"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "truck_image_data = pd.Series(trucks_data, name=\"JPG\").astype(str)"
+      ],
+      "metadata": {
+        "id": "5z6aVQZHJWB_"
+      },
+      "execution_count": 8,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "truck_image_data.head()"
+      ],
+      "metadata": {
+        "id": "lhom1o9dJaJr",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "d6666b1a-b0c0-415f-a195-fed7e5de409b"
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0    /content/drive/MyDrive/Dataset_truck/test_data...\n",
+              "1    /content/drive/MyDrive/Dataset_truck/test_data...\n",
+              "2    /content/drive/MyDrive/Dataset_truck/test_data...\n",
+              "3    /content/drive/MyDrive/Dataset_truck/test_data...\n",
+              "4    /content/drive/MyDrive/Dataset_truck/test_data...\n",
+              "Name: JPG, dtype: object"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt"
+      ],
+      "metadata": {
+        "id": "l1QrjOQ7JdwJ"
+      },
+      "execution_count": 10,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import cv2\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Read the image in grayscale\n",
+        "image_path = \"/content/drive/MyDrive/Dataset_truck/test_data/truck/00000175.jpg\"\n",
+        "truck_image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)\n",
+        "\n",
+        "# Check if the image was loaded correctly\n",
+        "if truck_image is None:\n",
+        "    print(f\"Error: Could not load image from {image_path}\")\n",
+        "else:\n",
+        "    # Display the image shape and dtype for verification\n",
+        "    print(f\"Image shape: {truck_image.shape}, dtype: {truck_image.dtype}\")\n",
+        "\n",
+        "    # Convert image to float32 if needed\n",
+        "    if truck_image.dtype == 'object':\n",
+        "        truck_image = truck_image.astype('float32')\n",
+        "\n",
+        "    # Display the image using matplotlib\n",
+        "    plt.imshow(truck_image, cmap='gray')\n",
+        "    plt.axis('off')  # Hide axis\n",
+        "    plt.show()"
+      ],
+      "metadata": {
+        "id": "2thUsTv6JjOp",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "outputId": "97547244-b15f-4f77-e2c6-bf550f7852fe"
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Image shape: (599, 756), dtype: uint8\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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ytOObN19/fDR3EJAGtaP99I3BV1Qx4j72tR+0/r450ed9/FLjQ7phgQbC2vpsfw7atzof+j/3ggV2W/9hhPcPzMfMv5Uxq0ZqpV7trA8kem1X2zzoM5aDzAmqKdg6gza7vmuLZoUBbmdWCiJO29duzFYZo10DJRarXbIfvvHYIyvaTqu1n2N5d3fX1aNE7mMubCsWi3Vs4lAodAeoax2qGRGMtc+cR9KXmgqj0ShisRjq9brrSywW65hTLYfZoL7SC1P3lSCt8qC2KHTo+vj8tpzvvb09l/FMfV92DZhL2+5V315QECd9qlZgwVn7FgTQVmj0jY2fKc2pdqL5v4OEzKB59dEFf6xGHqSM6Pj1fe2Pz2oA3DaB2ue1XTv+g8ag31na1/74QNFXr36ue1Hnyc7ZQe/r53bclhZU8PLhhtKsCj1B47JzpDwoCLOAzkxz5KtKa6q0BNF7UPnAj0vppAYRiJ3Ew4CyvsfSiym7l+juv6ii47PjtdKgJYRuEa4s1o+q2oK94o2FTJggxroJdEGCCS8+sP7tICal7wJw+a19RbVbG+SjzLDZbHbUz/mx/mWCcjgcRq1Wc6ZyH73pOhyGDj/I4puXIGHMrlGQ1mSFHP2fc7G7u9t1PXWe1TxrfcqWAdkgLa3Ptw+0LdbfTUtW5mnBT8dqwSJIKNO/OV9BIGK/Cyo+mtL9qe4gfY59pQBm62DpZe/ZufJpc70IpHYuuglbQT++Z3vRkG1bat3yAbgKmwqQ3frr64fWq23qHea2TyzdMPCg8oEDs0+Ss6CoA/cxgMOWIND9q2a0LEoc1jcL3J4PXzCX3cTWr0vmp2Cpkbc+JsVCYmJd1ISViamGuru7677vxSJhJVdG7drAIdVuCPyq+apG1G7vX9nYbrfRaDRQqVRQr9dRr9fd3MTjccTjcfd3NBrF7u4uYrEYdnd3O0CJQh3b6OYH6hU4P8jiAxIe/eJ8RKNRNBoN7/tW+LDmNhWAuO6WNhWQfUKQXWfVnHWPW1DwCRI65iDt1SfQ6vfWF27fv9viA/CDQNH2z46j2zMqcFtBxo7FJ5gBd54b9gkyQeOz82xdPfqMHYvGbPC3D5h1Dg+yCFgwt0KUtWjYdpU2tU67Hr65tHxBlUHlyT5w9s3vQeWvNPMXi06Iz0ykhNGr5vtXqSEHFd+YSAQKECwWlPkdNUbdFArMOl9kouoD0o1AoqJ2Yi/6UHMpQZPfadFNYwUPfq6ChGpuer5YATIWiznA6evrc32Yn5/H6uoqlpeXkcvl3Pj4Xl9fH/r6+pBKpTA6OoqJiQlks1mk02mUy+U7GIBvbYLG9xddgsBKy7vvvotms4mHHnqowyfrq4vCkGXIrNtn/g4CG5+WZBm/rq3Sp/Wn2nZUUPDVbfuo4/ExVGtJOMhX6gNL2087P0H8qBvY2bZ8gGyBwV7LGbSWQW3adqzgHNR3X2SxbwxBAlTQPgqiITsPvv7bdbQut6B+6Ds+oUTn0ycM+ebZR5++cjfC+6GA2SfldOuMz8TQbSFZlOCtNGWJyjKX77VwYXx+aMuYgoDW9x7BMxwOu2AWZVzd5kqZHcFa79TVd/mZNfeq/5Xji8ViHZuPfSUgW3OpfU59zL7+B32nc2TNlXyGQgKjtbe3t7GwsIDZ2Vlsb2+7MVqBhJYJANjZ2cHGxgZu3ryJTCaD8fFxnDlzBkNDQ04Q4HsqUPjGETSmoO9tCdJour3nE2zC4f0kGrVarUOwss/rO+oi0PgBXzv8X60ZpDnViFm/ZZiqdfN/CwxBc6EaiGWeFujtfFlw1vFyPJbGuhULHj669oGM9sXH2LW/QcKrfV4tAPq+BTU7b900cu2nr3/WQuIDZT7nK3wm6PugMeg8+EBa+2fr4nz6hA8di20raA6CaNTWc5Af2af9H1QOfe2jTpolcjuZfMYy/4NAPUhC1neDLrnX0o0obHtWarN9tGYSZTg+f4dPUlTQ85Ughq7MyAo2dnOrVm0jku142u12h8/XasT82/q41GxjGSaZA7VgXXvd5PyeddHErPXHYjG022289957uHz5MvL5vOujBp8xOpzv8md3dxf1eh21Wg2VSgU7OztYXV3FmTNncP/997tgKF1/Chw+2rECm4/RB62hlm5CnJ1PzhXnkcKUtkPhzNIKsO9+sGuuwYj6fBAz1v/tO76oZFtn0LzoO+qqoJCk+0utAjrnSlt2bdRtYscYNO86V92Yuc6NBWIVFA9i9rbPPrrTcdu9r2ui63dQzE0QiPja9o3DJ+D4ntP1txY9SyMWVw4CM9KLroHVoC0tEY8OGnvQeH1uwqA5CBKOeinfsylbpbQgE6EFIhKOSh/KiLQuZSaHMWUfpvj6bYs9X8l+deuT3Qwc8+7urgMt6+/0FZ1L4E4pUIUVazK0fbNaqTJEfTZIkOJaKNO0DMO2ZwUt3Zg+Ezv7VCwW8c477+DmzZtotVoolUru+VqtBgAOvNVcT62ZpnAFos3NTVQqFayuruLhhx/G5OSkG1MkEkG9XkcikegqPPoA0DdXWnSdgkzP3d63bo52u+3mQNdfaUU1CLueVtjjPrRnMfV/7UM3TcmOoxsDU1CxPMP3ns6d8hC+r0Bss5z5BCk7LrsXfUxW++4DTd9tST4mHQRuQUX3jrWSWBDrxcyuxTdeO/ZeNE6fFcD2X4Vg22d9R7+3vM/2tVvf7d0BGrWvdSv+2Lmxa2h5n68O249eFUXgA/Ixa2dsJ7VYpmwnSxeuV/v9X2axplybgrBbUUmWQEJGopKuLT7mFEQQ2patI0jLUbO3D2x9jEeFKety8DE9/dsClNbHn2g0ip2dHbz11ltYW1tDo9FArVZzZnqts9FodDBqpTH1n9dqNbcho9Eo1tbWUK/Xcd999+H+++93zwblILbrEcTkfaVXSTkIlAkwnIdGo4F2u41qtdrRXwswaqq3wOcTBn1alY8xHqTJ9Do29sm6dVTw0z5aQVgtN/o3v1NNW/eeLaqpBwnplukH8TZtV+v0CWMKQEHCms/iwL/1Gk2th89046HWQugDN2sN8AFlUD1B47RzpX1Toaob3/fNm21bn7FCRTehm8U3Lt/7PouJnUv7dy/lA0nJqYwQCGbSOiDV0qwfiiUcDnekUGQbymC+V+C2k23N2rZoXw6abJ0Pax1QZsTN7KvPxxyYqF+PBKjWpNoj3+NzOt9WulPm2E3D0s98/fXVbWnEN0/APl0UCgW88cYbuHXrFgA4UKZZttVqOT89N6bSHJm79a22Wi0kEglUKhW0Wi2Uy2XMzc0hFArh/PnzqNfrXqbrW4ug4mNOdq6C6MbHuDk+jl9977wpSnOFA3dGpVqm5Fs7Kxz5jpf4mJAtpB0fo/eNl3TCNWMd/J4mfD6nmfC0XywUMn3XOnYDSBv0qHsmSBixYM12bf9Yj54+sL5+e6GKtqGf2bkJclcdBJJB/be/tY9WCPDVbWnE164CpV1HVR6UL+lvKzxYoaxbsbRtBS1Vluye1bX08U4+ZwXguymHBmafpGi/s+Bl3yFjUG2NjFY3jwUt1bgsc7GStC0HAS6fCSrK2KxmosVKqco4fVqupom0EjDrs4tukyfopRL6HhmO1ZZVIGK9CpzWT+Prs50X/d8Sra2D/fOZ25rNJt5++22srKyg3W47jZiMjWCkmrKlL4IXjxFxo7VaLVSrVUSj0Q4rwcLCApLJJE6dOoV6vd7hErACoQWwbvPAErRn7Mb17RP+1nErEOszWrfVJuyxN6UL604Jh8PumJr2WWlO92TQ2JXp6pl5jQnQPWG1Vh2bTyPmuPQZHufTaH/W2Y0H0K1Uq9XQbDbR19fn3CTqp7fj1P1uU5QqjagVUPvAyz+CrGZ2b9q2fUBoaVOFNis06/NBQoEFTduGFdZ8VidL5762u5nBbRtKE0H9DyrdrGJBe9snvGi/WKzVTp/vRbBn6RmYrRnXdtB2QgnWgibNkSr9+jRWS6zW9OUDsV79vdpXqyHyc+2f9llBxvbVzhmfJ+haRsp2fCZFHzEoQfqyXmnbOjaVOn3ar47d5/v3bUq7DtqWClD6N8fHOVHtNxwO4/Lly5ifn8fe3h7q9bpjWiq0kNHzO73VRfvD+lWgUybdaDSc5ry4uIhkMomRkZE71p5j82madu194Grny84j67WCEteY/Ww2m9ja2uq42tDm//bRE+tksFvQuc9uDMiOl0BGAYh9tWvMfqVSKQBAtVpFIpG4I8OY9l/BVoMI2+22y+bmMzXyewpjut+0XlsIjqurq1haWsLe3h76+vowOjqKsbExpFKpjniMRqPhztLrfKoAqTRB+uV86fnzvb09xOPxDr5n3VxKR5a2+LzlkT7wVRrz+Vj5vrWWKV3YfujntljBxMfLgoRW39/6v0+Q8YE694jOja8o7fkEHh//7SYI+ASPg97RcihgttKsb4L5Gc+fcqHVJKfRvywqbfJZ/cxKnmqOtRq2FSIsA7QSj0/osCBng1Es8Svz8D2v71hGrBHKnItoNOoYniV8BWVqhwQ6Mkq7LnZcdk60+CRnfdc3Ll8J0ub0e02Wsb6+jitXrmB3d9fRj2alIoMDgFQq5c4m53I5bGxsuLkkA+RcUJOKxWIdG4Tg3NfX58zaIyMj7hmaUe36+CwmlolwXgmS7XbbZSLTY3NqEVJa5tgVvAqFggNlBS3bBwvU9jueCec8U8CxWqh9TwUoMjDOqWqV3IP9/f1otVrI5/O4cuUK/vRP/xSbm5s4c+YMnn32WUxPT6NarXZYMSx4sd1qterGnUgkOo4DKhPVObTM3I5J+7u2toYrV66gr68P4XAY9XodGxsbWFtbw/T0NJLJJCYnJxGNRp3lRgUU0ou1NHC9Y7GYGx/nrlwuI5VKOYHCavWWqSvPsfs2SNv08WiutfJKtqO0aHku69H5U3ea7lNLl759YoWHoDXy/e0TcHU/+Proa8vOtU+A0PZ8Qo+v7yqYdms/qBzquJQukP1OB8DjLGS8yqCAO68/42esiwxRwV3t/vouNzQ3M5/Vdqz2o0UJ1PpmdUw6B7rxlch9x4pIMEHalo7bmij5vxKM1meFExafn9m3TgdJfEEA7CNY33P827alY1ZrxJUrV1z2MZqUdV5pYjx27JhjaPF4HP39/YhGo878rZdrWM2Fvlp+V61W0dfXh3q97jTno0ePdmii7Kv1ZwfNjRVC2ScFLwqRodC+JksNTC0zpP92u41isYhisYhEItFxDl7HpjTJovuB4OFj1tovC2xWYNTv2A91SUWjUeRyObz55ptYWlpCLpfDO++8g2q16lwVs7OzePbZZ/Hxj3/cmdap0et8bm9vY3d3F8ePH3eBb5wzS4+cU+VTvTBCJq0h3TAyv1KpuDbX1taQyWQwPT2NM2fOIJlMOrAlH6LmTf7Ffup6zM7O4ubNmwiHw1hdXcWzzz6LkZGRO2IitO++9VI6tFZJ+5xPEdF1tpH4vnd9/eLnluZUmOhWLFj5BItuf1sBwGdpJE1o0ffsc7Z+26+gvvu+t/UdFFiqpWdg9h2p8UlEVhsmiNgzscCdSQm0XpWASOj6vH1fGTHraLfbd5zjtG0Dd2rNPq1af1tiUGAP2hA+yVeLMjma+YD940CxWCzQRK/ArNqdbafbeHx1qsZviU/f841ZN4P2SYEyFLqdo7mvr68jcUilUumYD6Wz6elpZDKZO0yh09PT2N7eRqVScdootcG+vj4HyAwm5Jzt7e2hVqshmUyi0WhgfX0dExMTAO4MstM5IR0rE7Y0ohYNdTtoHaRRRlrbTF3hcBiVSgWlUgmhUAiJRALA7bPLliFSI7PaC9tT4RaAE5z1R/2lfNdK/bomKgjt7u7i0qVLePPNN9HX14darYb3338f29vbzoIRCoVQLpfx27/927h8+TJ+5md+BqlUyq27DWp67733UC6XMT4+jsHBQdRqtTsYv85/NBp1ax4kXGqp1WrujDwVikKh4CxPxWIRmUwGlUoFV65cwdzcHEZHR3HffffhyJEjiMfjqFarjpb7+vqcJWhlZQU3b97EtWvXcOPGDeRyOTeO3d1dvPjii/gbf+NvoNVqOeFM1866SazQS7qkUBDEW7QoiKvZVhUKK/SxHh/Q6zNKR9Za6euLgnoQMNv/rcCi4/IVH05YWuZ3KphYS4yvj0HgbIHZruVB5VAaswUHn0+M5jd+b5mQZVzsNAdiJXb9X49/2KKSsmrUNrpP+wbcOeF28oLAjZoc+2RNOfquBS4yM5/v124YNc+Q2K0PTjVkBQkfY2I9QYSu/Qkiom7Ch31Gn1MBLRQKOY1jd3cXS0tLqFQqzj9IoLKC4NjYGMrlsjMDh0IhxONxB1q1Ws0F8eg80gJDIOJZcmrs1WoV8Xgc5XIZ6+vrmJqa6lhf1qUaB+dYx855Y720HqmAaumJmrWdPwJdoVDwMmi24xPadD9YIVSZhEZ5K+0pYw2ylHEMpLtYLIZr167h9ddfRzQaRaVSwcWLF5HL5ZwrodlsIhaLoVwuIx6P4/XXX8etW7fw7LPP4sknn0SlUnH0HA6HMTw8jHA47ISuqakpjI6O3kHXPj+vCneWDvXvZDKJcrmMSCSCXC7n3iW9hMNhFwxGOtra2sLS0hJSqRROnTqFEydOYGxsDLlcDleuXMHs7Cxu3LiB1dVVlEolxONxNBoN3H///YjH46jVaohGo1hfX3dm+ng87ujBzr8FXOUZFgAtH1E6tTxE69Pn+bcFVn3W94wFJH3HfsY1tjzEx198PJTFCge+fWTn0wKz7gndXz5eaL8PwiMWa7nrpfQMzFZz0Q4CtyNhNZm+EhhwO0NTtw76gjZ0c1nzhY/AtH4FZpriNCjIHpOgxmHrtBtDiarbIllQssBnpTmdVzJr9XGGQiEHKjrvlqn6pGv2x0qo2qegYsHEalmWNixY+OomIGxvb2NnZ6eDhtRPr9IstRh1XZTLZWSzWXdWmYyHtKfzpyCkc6W0sL29jeHh4Tu0YRWk7FpZwSMcDjtN2AqBdn58NMD3qcklEgnXLk2+Oj82Stt33En7yPlRU7ua05PJpHufwVoMimo0Gg6k2u22++ytt97CW2+9hb6+PuTzeVy9ehXFYhHtdtsJT5lMBltbWx3++62tLfz2b/82Ll26hJ/8yZ/E8PAwqtUqdnd3MTAwgEQi4SwGi4uLqNVqOHr0qHNB0LSvhbTB8Snw6F7Z29tDMpnE6Ogo5ubmHPD29/e7vUctmtpwNpvF66+/jqmpKRQKBbz55pt46623kEqlUCgUcO3aNezt7Tmte3d3Fzs7O3j44YeRSqVQKpWcJewnfuInkM1mHT9S0LR7StdRhUR+rsK5jcS2Juqgom32Aq66D7rxQNsuAdOCL9eEawh05mH39d/n7uNzuud8pm5fv3QcvjaDeJ5vzHfjXwYOAcy6mUkMCnDqT7OSh2oJPg3O5wtV5sf3fNKTHk3waVgWPGlK06heH3O1bbE+7V/Q9z5NGejUqoE7zeC6eXR8LCq0UICwi94NlH3FrotvXOy77YNvvoOEAf6vxEnAWV9fd2eLrWSs1oBYLIZ8Pt9BY7u7u4jH49jc3ESxWEQ8Hkez2ewIwuF68tpHzrWCjZrFq9WqM28HuSk4FzaIUS0hKjD55svOLf/m+pZKJWeaJ4BwHqiF09zerQ0VaqwQpvuDJR6PY3d3F4lEwml3tVoNQ0NDADqzrZFmv/Od7+Dy5csuGO/KlSvI5XLOfM36stksIpEINjY2nJupXC4jkUjglVdewdzcHP723/7buO+++1Aul9Fut5HNZl3g2+7urotMV7eGZvniHCkgWYGKz3H9W60W6vW6s1JUKhUXvEZaYH+r1SpSqRQSiYSz7gBwQsjZs2dRKBQQCoWQz+extbWFD3/4w5iYmEAul3PulHA4jJmZGbe+7LPV5ny8xIKP/m/55mGLBREV9qzlJEh79AFXENDbsVjrkApVaulRi5CdM33Hx6ODTNbaR9/+1vmwY2HR/vi0+F7Koc4xqy+pXC47SZKd1cANJSg7+T4iUo3FEkaQGU0LGUVQexQcrFmOi8xxKHgGSY9apw8EdU6slmQFDP0JOi/JokSiBEiB6aA+WCFICVPHa/utz1ggVsZgmYhth3+zH6FQCPV6HVtbW46eVMBS+qGP+NatWzh27Bj6+/uduXtjYwNLS0uuT6opqSDB+evr63Ogxr9ZP58hyOt82PHpbyuIKtDrvPkEVMuYYrEYarWauwmLYyBoaD8tsHLulGYVQFVYBW5fIcnv4/E4YrEYKpUKFhcXcfXqVbz00kuoVCo4fvw4PvvZz+LYsWMd/X7llVdw/fp1xGIxzM/P49atW+7Wr3g87oT606dPY3Z2FkeOHHHm4Fgs5qxYoVAI6+vr+JVf+RV86lOfwic/+UkkEgkcO3YMN2/eRKVSQTKZdG6Kvb09HD16FIODg4EuBx/j10JhZWFhwWnmkUgElUoFkUjEpWclPe7u7rrUsDdv3nT0RIGfz7RaLWSzWSSTSZw7dw7ZbBbb29uOpprNJnK5HL785S/j537u5+4AJC3WPcc15Vr69mRQ8e1PLb65smZe3zvKQ3TefWDcrXAefPUr4Nl73YOA2fJG+zzHZ+tisdYsnyJn58D2527KoW+XIpGqL9k2bgHETrZPG7NAErSZfEyefyuxsk0fEKmZ1JqE+L1v0RRE2TcLSDp2HwPXd6mxHbSAShycK8tsg+bJChn6vQ8kgspBz7C+buZeLdRUqtWq055oPqX51/fezs4OqtWq8zMWi0UUCgUHaBQWFYy5OQn8esczn7Gm4VKphJGRkTs00m5zYwUtn3Zsn9fCNW02myiXyy5wDegM0qJAYgUjbdMyKY7V5o+mJl2v15FOp7G4uIjLly87C8R7773nBJdLly5haWkJzz77LB5//HEkEgl8/etfxzvvvIN6vY719XWUy2Xs7OwAgBMkYrEYJicnce3aNYyNjWF+fh5Hjx5FJBLBzs6O00gJzuFwGF/96ldx+fJl/PzP/zwmJibcuHgOulwuo9VqYWlpCe12G1NTU45urA9V96/dz+FwGKVSCeHwfjR2X18fKpWKO6JFYK5UKu4CEfqLGSFeq9Wctm1vfYvH45iamsLW1tYdgXbRaBS3bt3C1atXcd9997k6fPRigYS/re9Y97lPWA/iNT5g4jvqGrI0azXDoO+V7wW1yzGpW0YFSW0jyFXjU6bsvPFvy8dtf9iOzq0Csz5/0JwethwKmCnZ8mIAdl4Zkc8sEAQOyny5CEEaGYsSiyUUn1an79nfutCsT4PU1D9pma4uFvvtM8lbU6bOjc6Zj0AVhH3+FivEBAkOQZvSgrdvjeyz2lcLnr7++Da8gmO5XEYul0M2m0V/f7/zodZqNWxvb98BNMA+c75x4wYikYg7jqIBQ3YOlSb1LC7QeftSPB7H4OAgWq39DGEAOgQnS8c+Rs9ykF/ZNz+sm0fFYrGYe4/rXygUXL90zu28W8GN86HCSqt1O7lLNpvFG2+8gZdeesn5+efn5x3oMLBub28P/+k//Sc89dRT6Ovrw3e/+123JpVKBcViEZVKBX19fchkMmi32+69TCaDXC6HmZkZzM/PY2BgwDE7TaVJulpYWMC//tf/Gp/5zGcwMTGB9fV1Z1pPJBLY3d11mjkAjI+Pd+wnZd4qbOteIh3kcjk3F2TG9Xod29vbGBoa6kjqQh87sG+G57o1Gg2nLbdaLaRSKZw7dw7Ly8uIx+OIx+MoFotuLaiZf+UrX8EXvvAFJ4Sp20jdaj4TaRA9WV7lAzF9j5/rb503q4TY57RNn1ITBMg+s7UKJz5rgY7BzoX+fxBw2znz9dP2WYVg3ztBc3PYcuiobPpiLKhaIrB2f3ZaJzlIo2DhmcCDfCaWIbN0kyAtaJGZBdXvC7DqxmiDpDc+p/VZHzyBhm3bNlSIsQKFrosyIwV4n+TrG7ev+N7zEau24TO3AvsgWS6XUavVMDo66vrb19fnIqvz+bx7l3MWCoU6zMycXz1Lazc+55XzSc1RaTSZTGJgYAAbGxsdx5tUGGLh94za1c/Yhk9QC2KCuu7VarVDWGQCFGph0WjUmXN9zLEbE6Q/ln7YVquFZDKJ1157Dd/85jedlruwsIB8Pu/2397enjPH9vf349VXX3XAQvOurhHBk+u5srKC48ePA9i3eoRC+2ZrzhP7SI1TA/T+1//6Xzh69CiOHTvmzqDTr0vrxq1bt5BIJJDJZFAsFh2oKa2RJhlT0NfXh93dXczPz7vPtA+h0P6xrkwm46wxuqZ9fX2uLQ2io+A5PT2N9fX1DiFU+RQFh+3tbbz22mv4gR/4Aa/Vz9Kp8k8VQKzVT+lBeZbuS/7vMx9rH61vnu9bOg8CYn1e61HezbF0A1rW67P8qLnZ8lytrxcgPghYDxI4euWnQeXQPuZ6ve7Oh9pOWBDmb19nu0lbWo/PLA7cmaTEp81YIcEWNV9qsRGNCnS2DQu+PvOtCgEkRh+QkqiUYC2Y2eL7/KB18QFzr8W+GyR8aPsW2DhWmgs1CQM1iUgkgvHxcZf4g9/pfGnUtJqBaZKkuVzXxDevXG8Cejgc7tB8FNx1TLqOwJ1HAn2lG61SS1NmSQtVq9XC5uYmZmZm0Gg07mBedq1969JqtZymycjudDqNb3zjG3jzzTcdeCwsLKBYLDoBIJFIuJu46BtNJpPY2dlx2czUzUJQLBQKSKfTbmyzs7MYGxtDPp93Y9AkOhTG6vU6UqmUA/hGo4Fbt27h5MmTd8ShKJBduXIF586dc+urR8DUulIsFnH9+nW89tpruHz5MgqFAqamplCpVNw5cY6LR5pmZmactULBgWZ1atTMJHf06FFnOWD/6vU6ksmkiyTns61WCy+99BLOnz+PkZERd6RMI859fManLVqaUKFMaZ/0pc/Z/Ws1T61L/9d5Jj3bd5QH2v4qf7Zmc9KFj7b1XQpDlucfxD8PWw4C7MM+F1R6BuZ6ve4CTyhZsviYhAUy+9NN87Igx+/1OfusJUTtl/V7kono51psdiQ1ZyojtqZCNZHxHU0OwrpUG7PAT2ZjgV3/ttKwzouv6Jr4nrlbDVrnLeh9H2Bw3qiF0I9nN3w8HsfY2Jg7KqP0RDqkyddK/sViEbFYzNXNuVJgVZMnNVHVqm1RbdjSuH5vLS9WS+azyrSolapJNxTaN2u3221kMhmkUil3zpvvKd30wgzUjxsOh/H888/j4sWLSCaT2NjYwMLCgkv0QZ/nzMwMdnZ2cOnSJZw8eRKNRgO5XM4J6Fq3zjMFJPpmQ6EQNjY2Oo4t8jtqyRQCcrmcy+hGgWFpaQljY2MA4GIJCLa1Wg3tdhvXrl3DzMwMRkZGOkzSa2truHz5Mt555x1cuXLFCXuMNmewXavVcufZdW0Jsnr8DQCSySSKxSL29vaQy+UQi8XcGXjSUiQSwebmJkZGRhzgK21QWHjuuefwi7/4iwiFQh0CC03cLL51tlqv0qCuSTeFyQJ7kBbta9sHurY+PmsB19eGCuq+OvV7H2AH9fmDAui/6NIzMNM3ohKq+rlYfD5mW4JMxko8/JvtaJ3Wj6iaptbF3xZklcGyDn7PzWxBH7jN1BSYLRO3wV/KvAnYNjWj/s02VBuwz+nnWqzwon8ftCa+/tjPrdnJt8ltHZYh6Bja7du3RLEOa57v7+9HJpNxx0/4TiwWc0dvqOXs7Oy4IzYsukbWn0X6YlsaPKQgS2HK3r9tNWQCk8/0zTFrsWtsg5fq9Trq9TqGh4cRi8WQTqcDjzzZtbLMWPcNGf5LL72Ey5cvu7mbnZ11rqpIJIJ0Oo14PI75+XlMTEygWCxiYWEB4+PjAIC1tTWnJXIOqQGShpkRK5VKOTCmpUTNltzn4fB+oo1cLucC8PjZ/Pw8xsbGOtw8rVbLAXyrdTuNbyaTwfz8PC5duoQrV65gaWmpI/KfPvFGo+Giuqmlq7mcZ7rX1tYwOTnpLCmc2729PQwMDGB1dRWVSgUXLlxw2jlppFgsotlsYnR0FMvLyx10SC0a2E/X+Z3vfAcf+tCHOjTabv7kIDqw+1EFfs6dCslWw7Uap48PWXpTmtP6lK9aS6ctqu2yHz7eZc9A6/vaVzsu377stfQi+H5Q5VCXWOjkA3cyXRIi/7ZMPAhofAMOMuXyWdWebN36jPbBajQ+gtd32G43SVA/UwDgeyohK3PvNnbOHftqJc9u2pFvw/JzKxAFtW3X2QoXdn6sz8nW5QNtMjWekc1mswA63Qhk1Ol0Gvl83rUfjUYxNTXlztUqg6zValhaWsL29rbzGyo4a0CV1dJXVlYQCoU6/JH6rP5Wa4xq0fq3zrFPiNF1sgIb/bbxeNwxbrXIaF3WVWP3AdeGfY5Go/iTP/kT3Lp1C/l8HuVy2QVtUSgIh/cTt0xNTaFWqzlA3trawvLysstdTT8xGaUGS9JU3W63nfAUjUZRKpU6MmnxXVrkeN55YWEBu7u7TjDZ3NxEPp/HyMiIE2Jo/qbwlEwmkc/ncfHiRfzO7/wO1tfXOwRvuuJUmOf8t9ttVKtVd8NYtVp1mnW73UY+n3cR2ZxbCnTDw8OYnp52LhQ9gpdOp5HJZBCPx52fvtVqYXJy0rkD2Jevfe1rOHv2LAYGBhyt232stKNrrmCqvFNpgM9ad4otOmeWVn0ask/4tn2ztOkbi+UhaoLvpv368Mb24bDlLxOIbTn4gPD/v1jGZEHOamn2Ha3HV3yArW3Z/20btj9W2+HZT31OE6T4gMiCCMdoTc3aX9tntqNpGS1Q+eZP27Pf+Rivb27tnAZpVt3mvxetjCWILuzn6l8Ph/eT+es4OWesP51OO7BotVoYHx9HJpPpWGv2Ix6P49ixY8hmsx1jI7PS87/aF35G07HGH/B/XRubeMDSg6UV9sMKNWQ4mudYz9MypzKFFO27zxzu+9EAtWQyiRdffBHf+ta3cOPGDSwuLmJjYwPb29uo1Wou0Qvp99q1a5ienkY4HMbW1hamp6cRjUaxurrakeWPAg1/a6Ce7oFareZAORy+fTuVrnez2UQqlcLw8LDLX761tYVwOIzFxUV3HSgAt17MssaLJ3Z3d/HJT34S+XwetVoNxWIRpVIJtVoNrVbLPcMxMKK6WCy6wLp4PO60+3a77VKL8nmubyKRQCKRcKBMgUPTkDYaDSwvLzvw5pG4VqvVcetZtVrFn/3Zn3VYAbTouhLQ9H+uhf74aIL7KeinW3s+oUB/6KrQH/2ObevfQe3yHds/7cNBfFD3YxD+2PJBg/JBCpUtPQOzz2SrQKMaKYuPiVmtIugn6HvLkMhcNeNSs9l0P/zO+nZ1XArMqu2ybgUAfY/j07b4Gc1q6pcP8i3bMSuI8TOdDzWtanS3FgVaXSNr6vHVzed9GrstvRK6LWTW8XgcKysrTmtQZkINsb+/3wUrtVotDA8P3yEEso+RSAR9fX2Ynp52TJeMgW2SHslAlClouzrHQYKYFUC6CWwsBEvOM2mWY2cCC5pxE4kE8vk8KpVKx0UHFGx8TJPzwVuPNKDm29/+tjtqtL297cAoFNo38e7t7d+FzQxoN27cwJEjR1CpVFw6Tb6jWidBi/3SG5cKhUKH/5yWC46dfVXrxtDQEFKpFMrlMra2trC4uIjFxUUXc0AtW/cDwXZ9fR1DQ0N47LHHsLa2hnK5jHK5jEaj4ea3WCwilUphamqqg38QmBOJhDtfzfmg/x2Aa4/gQxqiAJJIJJzbJRQKOdfE4OAgEomEi+jWs/etVgvvvfcebt68eYew7FvjoD1thWbf+0F0E1S3dQV1EwB6qZN06fvR/llt2dav+///lnKolJwq0VimYBmlZfjWFN4LQ7eLr++oMMD/NeAC6Lz5x2ozfIdjYV18Vvvp09D5vdWcWJ8Cu6/4CMkn+Wk/9HsLuH8VxScQHLaQMZNJaeCdrjEZdDqd7jBvtdu3o29VWEqlUhgcHOx4lkAdj8fdO4yM1bXWvvno5yB61/53ozsAzprDdvv7+3Hr1i2MjIw4beytt97C1atX8alPfcq9r8CsQiWLakVqDo3FYujr68PW1lbHHiawhcNhFykP3M5QxXSRa2trju4JSGqZYkQ2512PIOnf1EZ5mQXPSrMwypn5p4H9s8b1eh0DAwN48sknnb+Y7bNNXqBRq9Xw5JNPYnZ2Fpubm05w4FG7TCaDz3zmM1heXgYAp0XT1N5ut11Oa140sbGxgaNHj7q55A/nm4ICr+hkwBjna3Z2FqOjoxgcHHRnp/ku56fRaOC5557DsWPHkEwm7+CVPv5peUM3DU155EHFmqD5tw+gD1OPT2jwtaNjsm37nvt+KEH9OUw/e9aY1XymNn+fyS7o/27aok8zPuh/n5ncMlZ919cHFp/mraZX255PUyUj4kb0gXIvY2exEnOQ9utbcEvIvjasxEkpls+TaXcTonzaYC9F34tGo9ja2nLz5gMZpnYkKKiwwnrC4dvHnOhvpElRtRwycT2ja2nVCoRBc3AQo9Fi6YkaH60FTIXJM7bNZhOvv/466vU6fuInfgLJZPKOEwN2/XTurJbBdy9cuOBMqNQQScsEcfqHCRZra2tOg+b8EeiojduAHOUVBCjWSX822+FvPsP/+/v70d/f70zse3u3r1Lk/FG4YzYuarKc26eeeqpDy2Zmrx/90R/F9evXsb29jXA47DTlcrnseAEj+5VvbG5uurElEgnHCykMtFotFz3O+VEhMhwO4+TJkx0+dtUCm80mlpeX8fLLL3esre6doGI1TDUZW22U33f78Wm5tIQojfnasW1azbqbtms/0/2qz/yfojEftn89A7MFGSVUNRNb8POBgg+EWHwgbLUS/u3TVvg3GbI1Z2sfrR/X9t/3t4KzmshZtz2frP0KMnXqHAcJMNZkav+37QYJLlqCAN4KLGpWt8UyDZ95Neg9NZPncjkHSmS2apkg861UKnfMCcGDgEJNOJfLdQgh0WgU6XTaMXS1tihDU/OsHZtPeNL3fUdAtOh4CB7av/X1dSdQ1Go1TE5O4iMf+QhisRgajQY2NzedFkUBhe/rb58fjmB6/vx597ced+KxHIIM+0vAUssDTcN8Xl0EWngjk4Juu70fhMUjSEr3GhzZbDZdus7x8XGMjIxgaGgI9XodFy9edP5lRnjrPlc30vDwMD760Y+6z/r6+vC5z30Oy8vLeP/99xEO7995zYxrenaeZvdIJOIi/kulEvr7+zvyaNfrdWeup6AC3LY00pwNwPn0R0dHAaAjhoAR8dFoFC+++CKWl5c7BGUfHVoN+iCTsj5nwbKbL9fSuwJvUFvdgF7rU3eSfT/oHe2T/v5eSjee1e35bkrS3ZSegTmoWM1RJzjIr2tB1QJZUOkGogqM1i/oA25bpwU61ZxJ+BaEtT1lTNbk72s3iIh9xNhNYg4SKIKEIEs49n0rvHQruta29EKgmt2JuZl5VEVvT+K8MqiIZ2HZd3VZkJGWSqUOTZnt2LHZ+ABd8yAJn/Pmsxb4Nqf+r9oTI4vVt9psNjEwMOBMuxMTE86Puby8jEKh4M4y66Usyix5MQTXRoXkZrOJI0eOIJ1Ou6sN+RxN/QqwNMFyvmq1mrvpqVardQRUMSCK80rfPiPvm839e69pISD4MwMXQZHAyHd4dIuCA/t3/fp1ty85l+wn9yl95g8++CCmp6cxMTGBn/mZn8Hc3BzeeustZ6VhCtZoNIpYLIZisYilpSUsLy+j3d6/4SqbzSIej7s83wq+odB+5rjNzc0OayLXiX3mHltbW8P09LTLD8+ANj7faDRQr9fx3HPPOeFNs6IFCWS2BClCH4S2eRAYdQNXXx1BQoDS8UFj/H4th+3joYBZNTP+3+1Z+7/VUGyn+bc+o2DJ731arP1RTVLNyipdkhkqUw4CJJ/kZzVWXyR1L8Tkk35Z5wcpkdk6dB6Usfdqjta50WKtBd3eIRDX63UsLi46LUn7RhBrNBqoVqtYX1/H+vq68+3Ztc/lcrhx40bH8SMCt66t0pINZrQClQVrpU/Orf62RcGRAKRxG+12G8vLyw60mFAkEomgv78fkcj+7UyTk5POl8sxcf002lWD2nS9+e7p06edxgp0mrD5LAGXa6ICK4+vEVwZ6GQF9b29PedrpVZLwN3b2+sIpmL0MwO0KHgA6DjmRP/0zZs33XEoFczZT85xtVpFs9nEY489ho997GNYXl7GxYsXEYvFnBCkghujuCuVCkqlEjY2NgAAg4ODzvfOIEOl5Xg8jlQqha2tLUdzaoIlffJazdnZWUxMTCAc3vfrc505f7FYDDdu3MCbb77pNG6bBYvt689Bxadt9lJ89O97v5fvgp77oDXP/1NLz8FfVksLmjgLajbQgMRqizK5oDotM+xWVFPSd21ffP3yaUcWjH3mFT5rNXQLhEHzp/X/ZUmAtt92U9j103KQRt2t/2SeiUTCHTFZWVnB5OQkJicn3VwwOxiPljDxw+rqKvL5PCYnJ5FKpRAKhdxNVfw5duyY6z8BnkBGRqd0orTi67/VVvQZtaz45ljfI+jTDEhA2djYQK1Ww/z8PFZXV52mmEwmsbe3hxMnTmBubg6nTp0KnN9uTLfdbru7kS9cuIDXX3/d5Ry21ha+29/f74Ct1Wq5JBmh0O3sa7y3mMeVmLFLSzwed2DLeUwkEqhWq87FsLKy4kz2ANz57XK57NJTRqNRpFIpl7rz0qVLeOyxx5BKpZyZXMGZAgOtJ/l8HlevXkUikXBpMXd2du6IQ2C6Tb30JJFIYGBgAIlEAoODgy7pEtc9HA5jbGwMy8vLqFarzhIA3LaU0JdNoaNYLOLIkSMolUooFAodbgryo+eeew73338/BgYG7lhr5Us+ugwqlk6CaEn520FCthaLF93aPAiEv5814b+ocihgtmYFJYZuQG2Jxpq+9XO7YJYgfBIW+6PF9znr1+98/eJvBWjVuq2GZedJ58f3fa+E1qsQ0mvxWSX0O/UP63NB5SDQVlD0vcv5JSOMRCK4fv06ms2mM/O1WvvHWujfIwOlP3prawuJRMJpYMw0Bdw+JqXHn/i3Clv8X88S2/lRQUx9fbpGQWvLtsLh2wFOZLxst9lsYnh4GKFQCFNTU7jvvvvQarVcFHW7ve9Lfeutt7C3t4ehoSHXnub4tm3aQpqdmZlxGdV0vThW9SfzWshGo4F4PI7x8XEsLS25tSDYMkgrHA6jv7/fWXy4vvF4vAPQabqt1+sIhfYvJuG6ptNpB/Z7e3vu7DPzd1Njr1aruH79Oh566KE7XE3U+kulEiqVCpaWljqOePFolZ6/Bm4fk2q320gmk442+TePO9EFowJbs9nE0NAQdnZ2MDU15dwvAJxQ2Wg0XB7xra0tnD9/HsPDw25e2D9aMarVKl588UX81E/91B3rGaQcdCu9gmKv1qCgNrQeXx0+zdgnTP6/WA5ls1Rzr2oIarrU57ppfsrcWIJMG/q31Uj52/74TMvadxZf4JrPFE5GwoASNa1r3XYsasKywow+a5noYc05B5mXgoQb7adGWxK0fMDrM/kroNu/gwoZFbAPogMDA2i39/MdX7t2DUtLS7h16xaWl5cdMIdCt4+okWmXSiV3+xI1HAaQ0XxqXRqWRq2wpj5LNRXr+Hu13KhGowFf1OpIf8PDw5icnHTzl06nnRbKoKUPfehDzsdMwYDtaHv2c9XSw+EwZmZmOrRG9SFns1k3twzY4lGmpaUljIyMOJAMhULuqBuBpFKpOJM1BQdaP9LpNAA4cz7b1AskqBmr37W/v98Jagz64vyTRhg8xfXn752dHayvr6NUKnVEbdPMzp90Oo1sNuvOT7M/tVoNuVzOZYbjuDKZTMeZepZkMumOgPE7jmdjY8OZstkP+pv1spZQKOTuJ4hEInjttddcylQVnH3KkqU9+7d+H/S3pSMfH/d9382Ko799cRt/VaUb3/yrauNQ1z4Cd2qZPpDU5+3fPg3Sp1X5TCH6W02TvfRb//aZLFmXT+oMAt9u49eiDFTf82ms2tduJqaDin03SAvuph3qmgRJ4zqXh5VuKQxQU+H54mq1ipWVFeTzeactMgEHcPtecApgrGd3d7fjuJEKFgQltsX5JtCz7zoe9b2rIBlkFQlaF37PyF/6RLUf9EVqXbFYDLOzs9je3sZHP/pR7O7u4pFHHsHGxoY7PgTceQWlTd3JdqiNUROcnp52Ocj5fDqdxo/8yI8gmUziueeew+Liolurvr4+1Ot1rKys4OjRo7h586YbvyYMYfAWtUUKEfzNtJYEXQoH4XDYpaIkcKoWSyGFWie/i0QiuHHjhjNzc02bzSYWFxeRSqWchSGXy6Gvrw+JRMKdQVYBmvOpR8iA2/d2b25uYmJi4o61UwDmOObn53H27FkAt/Oo89hYf38/bt68ibNnz2J1dRXZbBZHjx7F0tKS42/qb240Gvjyl7+ML3zhCy5KXBUENX0fBAA+gNXvfNYWu/+D+HtQvfq//g7iR/+vl541Zt+kWqbs0yJ8AKcErYunGqoG5eg7+r8tvs+1TSUuqyHzR7Vj1ZJ955t9muNhi4/hH2SSstYB32d2Pe4GNH0/BFINNPIFf1nfvq8wiQS1qkgkgkwmg/HxcWfK5NlZrkW73XY+Q64VNUoALs8ybxci81egJRMOmi8NFOTc9WJiC5pjDSpSOuJc6XsMKgqH948zPfjgg5iamsLc3Jx7XrN46ZEpmn6pafn6owLOQw89hK2tLRf9TjPtwMAAJiYmMDIy4kCsr6/PBUrxvuqxsTG3Z4Db1yAqb+CtTXqMqa+vzwlX7P/u7i6q1aoL7OLNVKyz3W6jUCigWq2i0WggkUggHo+7gKtGo4G5uTlHL1tbW7h16xZKpRKq1Sry+TwGBwcxMTHhIqwZbMWgwlwud0c2NHvaIp/Po1AouGcrlQomJyedtabdbrtUnCMjI1hfX3frxTnb29tDJpPB7u4utra2kMlkcOPGDQwODqK/v98BOI+y8bja9vY2vv3tb7t9wH3H89ZKs9+rBmj50UFCZ9AzPu24m3b9f2s57JgPHfxFJuXTaPV72xmfKq+aiDJOOxj93wby+EAoSFLzgZT+bTUMbdMeqPdpuLa+g4pPCtU56BXwD2s5OEyxmnvQ5rPlIDMV62UAlE3UQI2GN/No9iQAzu/I99PptNPOQqFQR0SvrgmZpvqadQwcKwGU2q2eT+5VQ2BptVodwWsKjmyfQo612kSjUTz66KMolUquX1aI0L5Z+uFc0GWgVwheuHDBWRb43PHjx7G5uemyYU1NTQHYB4ePfvSjGBkZwTvvvIOXXnoJg4ODSCaTDniBfcGIx6mo9VM40ij5vr4+d4xKYwC43gRM9TUDcHnTGRDHMVNA2NzcRDabdYlQxsfHkUgknCUmFos50zn7o/PIuaPWrLkQGCm/traGVCqFdruN0dFRtFotDA4Odqxxu912t07l83n09/djYmICkUgEw8PDmJubw/Hjx3Hjxg1kMhnnrz516hQuX77slBOCGt0e3/jGN/DAAw84/7VVkLrR4fdSgviar80gfm/r+34wY3+/lkPdLgXc6cj3AS8ZgmXqB2lvNqvRQaWbKdFKctpX3zM+846vDz4zuO2vBdSg4KcgrdfOq2/ctq++YoOBDlOC2rBrbPvvAzBf29SU+JsSP4COM6sEDmqBaqplPRqZynZpUqSJm0errIBJxqdRs5ZuVSu1Y+vF10wAAm5bIhRU2A8KDAquzeZ+ulLOOU2/VrtXS44GShHcmf5ya2sLhUIBOzs7WFlZwZEjRzro8L333sOf//mfuysQATizca1WQ7VaxeOPP44f/uEfxuzsLF5++WXMzs4in88jlUq5Hx6j4lowKK/dbrv1JbiWy2WnlXPdM5mM87lSI+S1l1wTjlc1db4Ti8XQ39/vcoLn83knEFG75fpxrguFggNAnsmmZsoIagqBq6urTlPWgC1GYpOmxsfHsb6+7k4fxONxbG9vO2DneBuNhgtoS6fTKJVKLs6AqTtpJfrSl77kUo1OTU3hmWeecVHrd1N8e93HS3upwwrDBylL/y+VbsqNLYfyMVumxr8VmDUwTDVNXyct8FhmZdumhqV98Gk8wJ0M0we0Pu2/l7Hr5/oZgeagurRtnTN+bsH/oLpsv3p9p5fnfFYOwH9Pqm8s3QQjPs9gGTJbahwKWOpX5ne8ICAWiznTJxkpNThqUgQzmsy1X2Qm1EDsWVwdi/0saO4ISDTlsqg7hD5EmmUJTHTX1Ot11Go153untqcmYP7N7zc2NlAoFLCxsYGdnR3kcjkUCgWXwIVR0/T/ZrNZNJv7l0wQNNTUT6GGa9RsNrG0tIS1tTUMDw/jp3/6p9FqtXDx4kW8++67WFxcdMIDg6R4LIqCB4OqGJhH4SEUCrkocTXnc6645gRiBUPWyblYW1tzYEZLA4919fX1ubuZacZutVrukhAKdaRZmpU18Q1N48Vi0a3hyMiIu+SDa01wzefzaDQamJqawsTEBFZWVrC0tISpqakOf/L29jbOnj2L2dlZl8wFgHMB7O7u4tVXX8XFixcxPj6O999/H6dPn8b4+LjbT91OS/iKj6fZ4nM92j3hwwTf3ghq+//mcthx3pXGzN9Bk6yAo5pVkCRmwcoGgykjC1psrd9nGvZJK900Oh+Ia1vdtH5fXSoE6G9fe1bw8ZW7kTi7vRM0Pz6ADtLi7frq55wDH8NQ0zIZMotK4Ey4wasD4/G4Y7CMYCa9afBXUOAbx8J+WQFCPwc6tatu9MR2bUYxasr8u9lsuvuWFXgYWUzhgpoQA8ei0SiSyaRj1jTrv/LKK/gv/+W/uDkiuDPqmO/xGBRvqqIwwxzSfIf1NptNDA4OussfaN3Y2dnBxsYG+vr6cOrUKTzxxBMol8t477338MILLziQ393dRTwex9DQkBM+GCGtmvLAwACWl5fdRSXqvuBZ93g83kEvXAuufbVaxfb2NnK5HFKplBP4GFy2sbHRESQI3L5lDLhtHbGxFTR7VyoV14/19XWn+bMttY4w+npgYMBFhfNoWKlUQiaTcUf9NNakVCrh+PHjuHbtWoeyUq1WXRKWwcFBVCoVHDt2zAWwaTBjN97c7bMg/uhTYIL4WDeM+L+tHCTUWL7ea7nrBCPaEbtolkH5IqiVAfpMvRac2W4v4GJNUOzHYUrQmLpp2YfpnwUAbUOf/cssCqwHCQRBknMvErFqZGSK1pSsdTE1pZ43pZnw9ddfdzmx9Y5fXmZvI8vb7dvmX5/pzydcaL9tXXa+rFBI8ytvRuLFBe12G/F4HOl02iVRISArM9YEJJpko1qtuosSKIRMTEw47ZsmVkY8U1NmwhJq2AS6vr4+FzVeLBY7xhqJRDA0NOSCxLi3qOlS09vc3EQsFsMTTzyBlZUVfOMb30A6nUYmk3EgyD7R5E0fdF9fHwqFAhKJBDKZDBqNBjKZTIcwHo/HO/zJFF7oB+aaaMKSs2fPolAooFAo4ObNm64PmsGLghwTmmhqUoId6bPZbLqIc7oGJiYmXBaz0dFRLC4uOmGCvGdwcBCLi4sOXJmKE+jkddFoFNvb2xgYGEAmk3H3cjOdaa1Wc0e0MpkMPve5z2FsbMwJf6xLTx/0UoLAmf9bRcu3/7u19f+KZvxBlUOZspWQfIBitVa7WEGSlF3sIICzWo1+pv20fdL2ewEOnyQY9J013/hKN5OOtTL8RRNwtzFZcDlsnT6g0uA+MnR9RqO8gds3FfHd3d1djI6O4uzZs3ccmQqHw9ja2urwI9O/p8yEbfkERO2jtYpYDdqO1c6B0rACSCQScRoOfa8DAwNOw8rlcoGR7UzAwYCnZDKJwcFBrK+vIxqNYnx8HPl8HvV6HWfPnsXY2JjzSevVjQToXC7nNOBQaP8Mcn9/v5tTatVq1o9EIkilUm6cPkGac9Js7t+MlM/nMTEx4YK16Gqgu4FaIwUEBniNj4+740Y0HVMQ4blnao3WZExhpNXav8t6cXERKysrLtpZg7NIK6QpzdCl9EFtlDTKMdZqNWQyGeRyOWQyGQBwVoaxsTGXxhOAGzMTj4yOjrrYCLpXGA8A7FtGcrkcBgYGUCqVXCBatVrFyMgI0um0iz3Y2tpCs9nExYsX3bWgdPv4AjB75VE+ZaFbOezz90r3cuj7mNX0F+RzsKZnNeXaZ/m31mcZpBZrEqWWxef1N3BnIJaPofpKLxqztRgcBGgHmT18df1FE7oViKygo0XX0QKX9lWfo8ansQf6rGrMtg6Cy5kzZ5z5klon03LyInuCC5m5XtUHwDFA/q+XPFiw0f53W3f7HOlXzdUasKVaWTQadUdyWK8GwPEZnb+1tTXk83kMDAzg0qVLSKfT+Pa3v41nn33WHcO5cOECvvzlLyObzTohQM+90rS+ubmJH/zBH8TJkyfx6quvYn5+HvV63R0v0nGxj2oi1RSWfE7jAKglUnMjuBLo+vr6MDo66njE3t7+HdoDAwPuOBwTlSiI6prRVcBEJDw6l8vlsLe3h+npaSwvL6Ovr88lTWFaUE1Aw7ra7bazJrANTenJNaULhf7yzc1NHD161JnHT5w4gXw+77KM0SKQTqextrbW4SrQ44a6n5rNJtbW1hCPx1GtVlGpVJwwR+tKuVzGb/3Wb2F4eBhbW1uYnp7GhQsXnMBLGu2l+Nw0KkiTHuw+CbIc+XhIr7z3XrmL4K9eNUMlNp9/OuhdZQh6aF4XX8FWzTcWKIPq9/XXCg36edBz2tZB4KwaWy8A7WvfAufdgrgKTwRPqx36AFfHEtR/33xZwUzHQMZEk6bWTVCbnZ11tw4xYEeTauh8MKCIdEGzrg0G5P/KaFR4sBq3PZLUTYDh93xX6Zg+RfrGCb40E/OYEJN5XL9+HQDwzW9+E3/rb/0tPPTQQ4jFYrh16xZOnjyJUqmEX/u1X8M/+Af/ANlsFh/96Efx4osvuvOwTC3J+SENHjt2DM8++yxOnTqFXC6HN998EwCQz+fRarUceAC3fcq+dedvFUY0CxsBsK+vD5lMxh1xY53hcNgBWDgcdmeDfW6sdvt2QBzzd5NPKC3RbA7ARVDfd999TgNlghMCIP3Y7Xa74wpGCoBqxQH2o+xjsRgKhYILJCuXyxgZGXGAOjU1haWlJRdHsLu722HtOHnypMvZ4BtrOBx2VgfmH6emzHgAAO67cDiMa9eu4cMf/rAz8VtXjY8nHmQJ8ilVvjq7CbLd2rNt99KnIMG4Vz6ozx40vsOUD6oe4C6vfbSSE0u73Xk1YlB0q/5ttWO7CMrg+D8QfOWgatP28yANmO+o4NELgPtAyQaNqFlVtSr7mR2PtQToe3qkxteG7YuPWOwmAm5rr0HzetCc2nYBdIA/P9f+6+fRaNRFWVPLa7f3b13a2tpywTN6JIiMSjUPRuySHrm2NjlNkKAZi8Uck1YwY1Ea5DlqO2/WR8v3yuWy0+g4Th07NcfJyUmsr6/jwQcfRDgcxj/+x/8Ya2trSKfTeO+99zA4OIhMJoMf//Efx8/93M/hN3/zN5HJZDA6OoqPfexjyOVyzvQdi8VcAFgmk8HU1BTGx8dx7do11Go1fOhDH3LPUKhIp9MuGjybzd5BR7q+ut/oXmD09MDAAKanp5HJZJBOp935dIIir+e8detWx9Ej1r27u4uNjQ3Mzc3h6tWruHr1KhYWFpDP51GpVDqOobF/qVQKuVwOoVAIg4ODaLfb7haqycnJjvVggBvpgzm9+/r6nODHW6r0dAC1YLa9sbHhXBIUumjxYdQ3A8ESiQTW19cB3A4QI71R4Gg0GhgcHMTNmzfRbDaRyWScS4Ma9MbGhgsCjMViuHjxYocJXQUUtRxxn9rvdE9YAdv3jOXfPp5q6YN8XrPtKf+xReuwyad0HN0UIq1DrXc+7OmmXAWVIJ5y2Hq0HDr4y6eV6u8gpm5NHvpeUFsslki0H0HvWQ1Qi/Up2vF067fVlIKkOfuZT5qyWof1tfueJxDoBvNZJSyg+Or0BVrpe0H9sHPkK1b4sH3jbwKSrVM3q25ubmodm938e3v7l1nQfBsK3Y7S1nlk+2SKZMY0W+ocBCXyCJL8+QyLMoRkMtnRHz7Hc64aGHfz5k18+MMfRiQSwdjYGG7cuIG+vj4MDQ2hWq1iZ2cH58+fx+zsLF555RU8/PDD+NEf/VG8/fbbTgPXJC78Afajqpm5KpVKoVwuu8snBgcHEYlEUCgUuppFuRa6pyqVirvOMBQKOcsAtVMCj57Hjkaj2NnZweDgIJrNJlZXVx3AKYCyHqa1tEebuA7Uvvv7+3Hs2DHMzc05X3OlUnG3WvFZRm4T6Cn0ce2YSjMcDrtMZNRcGbnOSPBwOIz19XW35rauoaEhzM/Pu4h6ujcsr2J9IyMjLnaCcQr9/f2o1WpYW1vDyZMn3bz/u3/37/B3/s7fwczMTEcQIdtWOu21dOP9LNqGj040wM2CvI7dCsB0O5AGuGc1YNRnobP9/T+t3JXGDNxplrYSSLfnu0kT+pxP4uL3lJ587/t+uvVBFzDox/eeTxDwlW512rZ9GrdPO7aasmqj1m/VS31Bc3JQ8Y0JuK1NWq1d+xuJRFxUsDJ4nW8VVjQ9IvMlMzCG5kQmzlheXnYX3S8vL2NtbQ3r6+vY3Nx0x2noT6WWRgZJDUdNgWoiZ7/0fx9ds9+aVSoWi7loZF0rmnfpg11dXcXu7i7GxsZcJPXDDz+Mubk5VCoVjIyMAICLlv6hH/ohXLp0Ca1WCwMDA3j66acB7F/bSDBmCsv+/n4HQouLi5iYmMCpU6dcNHIoFHK+7ImJCWeC1j1g/fdKQ3RBFItF5wNm8BaPcWm+7L29PYyMjHScYeZ5bWqpBE8CHQUYNTuTaedyOYyOjrpgsWg0ipGRESwsLLhLOlKpVMe5cZ7TJuC227fPLxMINDsdQQOAyyJWKBRcEhLOI+unAEg6mJiYwObmZofpne20Wi0UCgU3L+oSoZuDF2hUKhV3/WQymUSlUsGXvvSljgs0fILr96LNWQFIBdAgXkeBmj8+EKYbgZaDYrGIQqHgBClaCrjXy+VyR5pb7Zv9+27G+EFpv3dTDn2JBXCnBGU7HsSkgjRm+50FUKuF09ym/7MEmb21v/bzIE1Z/w7S+nopvvmx71vt3Gr8dt66rQcZi4KaAqPP1M/vbVSqTwg57LgtMHPew+Gw04JoLvSZm3RuCB6t1u3c0+122zF8Xu6gY6YJmWMjI2fiDJpXecvPiRMnOubSrpXOkWrWlt75m1HHZNR8nuukghJTVG5ubuL69et46qmn8M4772B9fR3NZhNzc3PY29tzKRmXl5dx7tw53HfffS5tZ7lcxg/8wA/g9ddfdz7OeDzuAo44fgDY2NjAjRs3UKlUMDQ05IQRao568YUdF+dHgZnJSvgewbXVanUIUnreN5VKYXBwEIODgy6j1okTJzA7O+uYOH3vzWbTnX2mQMO6KezUajUHhPx7bGwMlUrFBWql02ns7Ox0aJFKa6RRpS+lH4KvmouB/du3aBGhxYLzRwE0FAo5uisWixgdHQUAl4gmlUohn8/j2LFjHf5tunGy2SyAfTN4JpPBwsICzp8/78ayuLiIP/3TP8VnPvMZ735ksXzRPmtpWefH8hTf87YQdFV75m1r3AcUtqyQR75FgYjxBSo82XZ9rjzWad1f32/l0AlGrGakG1YXyH7OzzSYxmdqsJNrwdQSh35nixK1RpVr37uBkO87BfOgzezrv2pPlIpJbHrmUdvTerUtrZfP2GNGKvlr/zkvvnHadbCBZrYErbHvc+0j6yZTpf9Pr+LTXMZ6gQXPkFICJ/AAt9eYTJt94XPU1DjnDEQCbid2uHLlChYWFjA4OIhPfOITbqxqnVG6sf5nK8AoaFmTq4+OqTlvbm5ibGwM3/rWt7C+vo52u+2imunvpJYxNzfnfJebm5sYHh5GPB7HU089hRdeeAGZTMZpnswbzTYLhQJ+7/d+z2lvu7u76O/vd4AzODjoGKIVVnRP8W/GAXDdSqUSADjrhNJSNBp1bfHYFn2no6OjGBkZcfcdU5hQ4S0cDjsNUueVdMX9yMsxpqen3cUWPFtdKBRckBeZNc3GzPalpwZUU9ZsdUwcEg6H3dqp+Vv5F+l/eHgY6+vrbn36+vrc/dJnzpzpEDiZ+3tgYMDNNY/Qlctll4mN7piXX34ZTzzxBKanp+8IMLOmbZ9rzFessmAFUp+Aqlr77u6uG4danGzdtk0fH+J+pFCmvF3nh2Pj/rPxID43jY9/aV98+BCEi/bvXsuhkqtaScnXYDft07fgPs1Rf3fTooP64dMKLaD6+tdtHEH99X0fpFWTYGjatNrIQQt+kCDjk2b5uYK8bkg7Bz7Q6bZZuxVtVxkbAAeuIyMjCIf3I1CpCVifMs8oA3D3+bKoqZHzSemZ2qqanAloPH9K87WaL3d2dpz5l0zB+ut0rVTzsBYOn8XD/q3MS7N8jY2NoVAoOLOwHgsqlUqOEddqNTQaDQwPDyOXy7kz0xcuXMB7773n5oaBZqFQ6I6bvZidizcu0SybyWS8/kE908v9GA6HXTpQJirRdaRgoEeoaOqmD5nnj3lLGDOEsegRTM1RHQqFHGCqRUb9vMwuxihtgjPPCmumM2ptpCdq5ArYFPgAOKEjm8064Yh7XfeD3UfZbBZra2s4deoUKpUKdnZ2cOLECRcb0W633YmEsbEx569nPTyGRZrl+pw9exbj4+N3COF3U3wCfK9KDeeIAEqrhwb56fNB7VvFR/cMFT7VspXnsH66O2jJsNjkUwr1Ges6tfvcWmLt2A6zBj0Ds8/s4QNhX/GZVK2UpXX7NEOWIO3Mvq/v2YlSyc4npQU9p2Ox8+FbPAU4JRx9VwN9qDUHCT92rJapU+PSZ+xcKJP1ERLH1k3A0NKLtmzBXudqeHgY//yf/3N85zvfwXvvvYfNzU2XGYkaxOjoKPr7+1GtVh2jJsPkplcNWUGZ89rf3+9M1zMzM047VgGpv78fw8PDOHv2LB577DGnbeum9lkgfIJhEBAfVMj06/U6+vv7sb6+jp2dHTd3ZHAaELW1tYVarYbBwUGXrpSa5JNPPolXX30VAwMDKJfLzppAkGk0GpicnMTIyAji8ThKpZJLJkLfpSbu8I2RoBiPx3Ht2jXMz887ACMIp1KpjoAdAp8G8jGga2RkBOVyGdlsFuPj47h16xbC4XDHRQ18j/9bOmBfKewQQMfHx1Eul7G5uYnR0VFnneGc0GJBYYAMnHRLaxe1sVqt5gCx2WyiXq8jHo9jYWEBMzMzHYFflkaazaaLqp6bm0Or1cL4+HiHkMmjZ9SU7Xn/cDjsEtVw7P39/fjMZz7jBC7rSvKVbnyQxRc8ZveB5ZWMKyBA65FI8kpffbafqiT42tPxqWvIFtX2GUTI+tX0TXrgWlNA4rOWzwbxUt9YeimHvo7Et3h307D17QRNpK8tXXj2x+cH80mLunm79T9Ik7f/+8w5FpysllGr1fDOO+/g1KlT7oYafV5/lJCC5sPOi47V12frg2UhUdp+K+gF9cHWZzes1bharZY7KhIOh/H000/j6aefRrlcdlpXLpfD4uIi5ufnnYatgWI0z9rAFvaZjJsBZtTY+vv7kc1mMTg4iNHRUeffHBgYcJmq6PvSSyV0DEFMotv66Ph9/1OIoKmNoEHzJAOweDFFPB7H8PCw878y8QWFtEajgXPnzuHixYtOOLGCBes9ceKEu+qRfln68KlNa9HxEmhDoRA2Nzc7LAzcbypQ9Pf3O3ohwDGb1t7eHkZHR5HL5VAsFjEwMOAyZlGo0IhqmpYp3HKP0YRJawo1Nebs3tjYcGeDG40Gtre3EQqF3JgBuHnVuScd0AqjDLrV2s/VTV/+5uamy2TG+bGWlnZ7371SqVQwPj7uzp2rJSGdTrvnKfDoGu/t7TnTOb+7evUqxsbG7uBNQZphr6WbQMp+U2ikhkyXlMaOKA+yYM4+qfDhoz2fcme1ajtmfZf9BW4nHNI4AhXuaFFhf30KGOuxQvndWCvuKle2fma1q17rsf5eoPN6Pcv49H2rgVtGaSUy20fVGn1js+PRxQ3S1u1z+jdNaQxcuXHjBr75zW+i1WphZmbGXU7gs0ro+Pi/9t8SpE/T5udWk1STsfrSyIx17nzrpX2ygKNzYOeC7QFwUimZYSwWw9jYGKamppBMJnH58mUsLy87fySPFKm0TUBjfyORiNMaAbho276+PqRSKVfP008/jampKQcI3HAUFshg2V+foOMTjOwc+GgqqFDQSKVSHRHobIvWAgLwyZMnnTUhk8lge3vbrSXH8fjjj+Pll192Vzlyzqg18NgRM6lFIhGMjIxgaWkJOzs7HdqE1Ub1f15VmM1mXfYwHosio1O3gt7PrPu6WCxicnISS0tL6O/vx/j4ONrtNnZ2dtBu799WxT1D/zJpl2Z9ChLsM4W0er2OdDqNWq2G5eVlHD9+HNls1iWuSSQSLtMZcDvDGd0pbJfjIm2RPvb29nNmM/NaqVTqAFYW7ide9zg+Pu5uv6LLolgsYmhoyPEOatlKh0xckkwmnSBSq9Xwta99DefOnXMZ4XT9fCWIb9hnrIDt05iZmYxCDusmPXOfqfbv47f6P+lNi/J9yyP5vH7H/Pn6HOeG66wBYbT4aIIixS9riaDQpOlQ+aP8vZdyV5m/FAzsBFptSxmyD+AsQQQBoi6EXQCftmzfZ5+sbzpI4PBJlkFjtIRk61fTXblcxiuvvIJMJoOrV6/iySef7JhfHzhb/3iQ9hUk1FAgoDlpc3PTSbT1eh2rq6sAgPX1dZw5cwYPPfQQ0um0Y6IkYCaG0KKbVTcC4E8YYD9TExAFGAaBUWPQAA793xaCMhlsf3+/yxamN/BQc6J51LZB4FetTvtuaTBoPSw92LXiOilNaiQv5yOfzyMajaJSqSASiaBcLuP48eNYXV1FqVRymtrg4GCHdsJ2zp49i8uXL7uoXjXLM7XjzMyMuxCEZ5cTiYQzbROc1JSsWgOZW39/v+sTAbPRaLgz0jTdUgChYKbmbGDfdzoxMYH19XVMT09jaGgIhULB7SUKkBrIRmuDnhnm57SCVKtVDA0NOXDe2trC6OioO8fNax9DoZBL6MG1ZVpQWl80xav60uluGRwcxPb2tgNc60Li30NDQy4okSBQLBbdBSgcjwZLNZtNd1afaWlVAanVavjKV76CX/iFX+gAB9t+L9ZDliAFiP3j/DLQi5YMCl3WWqB1ahs+BcDHY4MwQ4vFHfJYazXkfuc6kc5UeGS79jnOo35GIY1jtUcvDyqHihW3UoKvw9ZUwMmyQKffWSZ3kHbRDTStH0zb6EZ03YolLtuOfdb+rcx4d3cXW1tb7txjuVx2gKLzRm2WgK6by8fog+aGQBMO7ydcuH79OqLRKN566y3cuHEDAwMDOHHiBH78x38cP/3TP412u43Lly8jl8t1RDWSCVkJMEgYYF+s5Gh/7Bwzz3UsFsPq6qpj3AQTwJ+sgMCQz+edJqL5hdWkCsBdFcmrJNlf9SvZteOY9fyxgjeTpRAsCExal76v9Vi/GPtKJks/KPs4OjqKqakpvP766wDgzuJybMBtBhCNRvHAAw+4/vB5G8wSiUTw3nvv4datW8jn826umWWNjIqah9JoKLRvxh4aGnI5sOPxuBsTg/doemXb1DA4PzQ5M+1lNLp/21I0un9hhwpV6l9mgBRdD9RSSR8E7VAo5NwDo6Oj2NraQqFQQDqdxsDAQAfTJX3p+VtG87PQnMxjQASkQqGAVqvl/NkMdFOaBfatORR8aBlhpDjXU+mBgFEqlRCNRjsuxLAg9/777+Pdd9/t2Ddan+9oZJCyosXy+EajgWKxiGKxiGq12nE+Xc3AKrz46glqV/mu5cP64xsLi08hDGqXa05BiWPRQDb+zfXWwDarbPBZ0mgvpWdgtsBgwaEbWOpz/Mw3wRyMT0PWyfe15dPkfN/rggT1Wd/RjW3P11nhI2ih1SdEST+Xy6Farbr+qJSvwMy6+b/PB+NbG2sq3tvbw/LyMp588knMzs7ip37qp/ATP/ETeOGFF3DixAl3OcSTTz7pglfIiAlsOrdsw1or9KfbWtp1bLdv3zRVq9Xwh3/4h/iVX/kV/N7v/Z5L/E9mrSZpJokgKE5MTLjkGzQB8gq9arXq1rBer+Odd97BV77ylQ7TpwKlT6iw4OwDVwvSWp99V8EYuH1xBa9h5PgYta4JKEhLoVDI+YVVgOGc1ut1PPDAA+6cs90jlUoFjUYDDz/8MCKRiItGJwjTBK4pUwkGqpFsbGwgn8+7tWEkM4VDMnEFO6XtcDjsNFYyvdHRUXfxRDabdaZmu68J/tRyVRhQwYjWEII4E300Gg0MDAx07DcFLwoLTHJBRktzJ4PpSAMMytvd3cXm5qazdlgQpTBKIOA7w8PDbozK4GkqB+A0ZVoldD6AfQHtT/7kT5zbQxUKnX/LR3pRYLiGtLrpXeiqUFht2So0ytfUB610YZUdW6ftl61L6d0qfeyrHZeCcRCvV8GUAK7ATEGwF2HHlkMDc9DiBX3v05L1ef3bArSaXnyasNarTNMWu+D6nm/C2BefVmEX6KB6tF/hcBg7Ozsd5jBqH7ZtFp+VoptQpL/1zN7c3BzOnTuH2dlZnDx50iXfP3/+PDY2NpBOp50mEIlEcOLECczNzSGVSnWYgg9adyu4BWmJPmagvsf3338f09PTOHbsmIsWJrOmhqISaygUcmd0GfRChjoyMuJM5MzLTBDJ5/P43d/9XaeVWxClsMB5tACrkcY6dq4vx2lBPEioIqDEYjF3Zvvo0aMu+Qe1VzLz06dPO4ZfqVScNknGqP3LZrMdDJLgS4Cfnp52KSVDoZADr0wm4zRCugZ0P3CeQ6GQO2utvk0CpLo6uNYUQnwMnYxudHQUa2trCIVCLrEKz6CzLhVsy+UyVldX3dE6jp99ZNQ7NWtmC4tGoy7Lmpo6Q6FQB6BSc+a8AXDATV+l0ube3h7W19c75o3jV7Mp7+weGBjoOEpJgV77zZu7aIGwQjDL1tYWXnzxRQfuapq3wr/d00GFc6BrxM8tQPl4r1UybPFZJi3Q2t/2Pdtfq7nbZ3zCgU8zV+uJ/uh7qk0HCRAHlZ6B2ZoOfUBhQUgZlU8r9jFyNQMoyAZpZdoHy/Rt0bp7kQq1buuf8W2CIIDmYsXjcVy/fr1jvnipetDYgoSVIJDkc/Sr8fhGpVJBJpNBuVzGkSNHsL29jbfffhvRaBS3bt3CzZs3sbW1hXg8juPHj7t0dwQrlay1X1YoCjJx2x/LCBQICa4AcObMmY46+T3rVbADOq8j5LEX3tRErY8bKRwO48SJE5iYmACAjksJgNtgSqAkQOtFG/yfmi2zbGmUp4K6zqEFeL1xiXS8t7d/qcX999+PgYEB1Go1lEol3Lp1y4EIA764XgQVMmOaRc+ePduxRjSj8p3+/n6cPHkSiUQCw8PDSKfTzhd748YNbG9vY3p62lkiNICnWCyi2Wx23FE8PT3d4aflnGruYzV3kpkR2LhGqVQKw8PDzgfMu6RJC6HQvhmYZ4nL5XIHcOh8UMOk1SQej2N8fNylaOXFHcpL6G8Hbls0dA0JwLQ2MChMM63x7mS6hnQfkKc0Gg2MjIy4aHzdz+32foKZcrnsQFnBSfmeglpfXx9effVVzM3NOTM/x8F62X9fkFO3wnVTnuhTjHz87KD6yTNVmNO15DP2HR/Y+4DZ95y1SNr6fThg21Q61qNYhy13nZLTB9A+KcY+R+ZkJ0aZvL7jM5XyWRIj3wuSwNgui5W+bN8UCHUMarqzY7TEp3Xp99vb224zUINWsPXNW5BGbb/TNhXIyHSZVej69evI5/PuiEgoFMK7776L4eFhTE1NuWxBU1NT2NnZ6ThmY/upn3Ecdjx2XewGtutA8ycFC0YYA7cDTTQSm8/yXKhuHtVEaYpktqlGo4HR0VFcvnz5jr7b/qpWrP3VtbUWFAVHMlJuZJ0j1fxUOKlUKh2uiHq9js3NTZTLZUxPTyMSiSCXy2Fzc7MjQEuFnFarhXw+j0uXLuHNN99EsVh0ZtKNjY2O/MSt1n66x4ceeqjDhAvABYmdPn0a09PTzh2zvb2NcrnccRypWq0in89jaGgIuVzOXZ1IEEskEu6sOiOYa7UaUqlUh1lUA5qSySQ2Nzexu7uLoaEhJ6DocSidQ/a/Xq+7I0hcC2qO1K6j0Siy2SzW19cxMzODwcFBbG1t3XHmlpoyg9cAOM1dj/PZvUrhsFAoIJPJuHPitkxPT6PdbjuriCbHYT74gYEBd3yO7Vm6UgGY59q/9rWvuasmaUmhxUT3m86Pj9eo6ZdgqX8TmJSXBykz3RQk5RkWIDUwU79nexp0pZ/r37pHtQ79TPumAoKPr6lrh7+5t/X7XhXCuwJmCzh2crq9YwfK73zSVpBEZJ89aLBWytHPbAmS5HTDK7F1MzOzaLQopSlqbgQcErvOhWX2LHp212cWVcKj1ByLxdylAgySajQaHecmy+Wy8y/mcjmcOnUKKysrOHHihOtjNyEhqFiC5mdKwDpXSlsE4Gg06hJkKGNg+zxGZN0PZCbWhBwOh52Zlm3ohQMsPiFL14QbzrfprJDk0xT4roI1NSyaLVkvA0d4MQW1RIIZmbdqsolEAu+99x5+93d/15mp8/l8h8ZLZkpgVz8mx0gG/2/+zb/BAw88gFQqhXa7jSNHjmBmZganTp3CmTNncOnSJQD7JtRwOIyxsTF301IoFEJ/f3+H8NBq3U6pSJ8tLQ9sl3MyMjKCtbU1DA0NuSQk1IBpDqcpmNqznsem1kutnPOWzWZdfbQKJBIJ55vlXPH8Na0jDFLkeHRNQ6FQx0UddGvk8/mObHNqxSH909XVbrfdvNdqNbfujCdQIVGvQGVRs/WtW7fw2muv4YknnnDuBgruKnjqXuG7pGXtH9dNg6Gs2danfGmxgO0DRH1PhQ6fEqZgbf/WeqwCaPsWpITYzy1P843LCgi9lkOn5LR/+zTdoE7o91ay4ztBE+TrC9vuZirwfXfQRB3Urq9uW5cCTjKZRLVaRTqdxtGjR3Hp0iXHcHk9m4KBzoll+L7jRj7TsYIBGXClUsHe3p674L1YLDq/ZCgUQi6Xw9LSEo4ePYrl5WWXiN/2ydKBbVv7rmY7LQpCPo2anw0NDQGAYwIcm2+TkImSabDQD6fWDkr6asFhPRS+fGO0Y1AaVualn1mtw2e90TE1m01cu3YN2WwW5XLZnQ8mwFC4493O+Xwe6+vrqNfrSCQS2N3ddelN+/v7XUDc6uqq02boK9boaIK0an5cH5p4q9Uq3nrrrY717uvrww/+4A/iIx/5CCYnJ/Hv//2/RzqdxubmJmZmZlCr1bC5uYn+/n6Uy2U339Vq1dFhf38/ALhAQybq4PE3+ppTqRRWV1dx5MgRjI+PO62f4MV+UStU3zOD6YDbx9IYxJVIJDAxMYGVlRU3f+12G8Vi0WWgo4ALdJ7757pbC54WrjEtUBrcRVovlUrOTK2aaTi8f6aewhDfobld188Ki0przz33HO677z4X6Eg3laVF0jDpm9YatWawHaVZu49VeeC62KI8nG2xKB/R53U+fXihGq3dg/ZZ/T9IIPBp2T4hXIUGyy+7WeR85dCXWNjO2N9WA/JNiharefvq12L9b0Hg6psAlSD190Ftsj4fKAcJK/ocg2gajQamp6fds+l0GqdOnerQllh67Rvf002sYEO/WjwedxoYtStqC5T6KdETjDXoq5tFxDdu7Z/doPo3gZB/K0BSA45EIi5LUy6Xc3XYeASOV68KJJ3oeukY1HRnv7d0Yt9VS0wQHXf7X83NOneVSgUrKyvY3Nx0AEoNLxQKYWtrC/V6HQsLCy7pxtbWFsbGxhAOh3Hs2DEcOXIEOzs7KBQK7rgRL6pQq4L1VXJOufYUBKjVqumSPlUA+MpXvoLNzU2k02mn0UajUWxtbXX4xvU2IN4qxDXSI3mkU/Xp1mo1jI6OYmFhwdVbr9c7jnYRbBhfQHMt+6xjAOCupBwZGUEqlcLAwABWVlYwNTWFwcFBFItFRKNRd9EE028yyI5zqC4WziN5FQUA0vbGxkbHvdgA3MkCWoVIl6RjgjJ99rq30+m0u76U/aLfmHXz/+effx5//a//9Q7w8gEc+QetFjzRoEKwtVyp39VXny3dtFufYOMrypu4vtZCpfvTx2etcMDP+VvbPgiUrUCvQYM+Ph9UDp35Kwi4fJNrTcgWzKxZ0EojQZoKJTUfo9f+aL/5dxCQ2oU/qL4grdHXFsGt0Wjg5s2bTjtOp9PI5/OYmppy0dk+ArV/67lP32LzefXNRqNRl/uYzI7HWqj10HwJoON6RRs97ptDX1GAtWOym1f9Wko/9LMVi8UObdZnJlM/rtbpixC1QYIaka6f2/W2a2HNY745sGPW9qnB8r7kdruNt99+GxsbGy6/tRa6JtieMnfGDPyH//AfOjQftk96oCkUgMuxTH+2+mHJ7Bk81263O8zQoVDIJZRIp9O4ePEinnjiCZe0ZmxsDCdOnHCCwfz8vOsXgbNWqyGdTrt2CMY2IQN9wdVqFcPDw9jZ2XFCAIVMWguYjIVaNHNJcxw0ewNwiXSAfZAeHBzE8vIyNjY2MDk5ieHhYQd+nD8N6ANua4ukNT0+w0jtUqmE3d1dTE9PY319HYVCAR/60IecAES3Ui6Xc8KLJrpRyxcBv9ncz9p26tQpzM3NuTucSV8M3KM2HQ6H8c477+DBBx/EuXPn3NyqUK80T1ph4KTuCdWaLX1qHfZvLcrvVSi3+0c1824groqa1XCVL6ggos+xLv3bWiSDwNXub7ah+13n56ByKGDm4nISVWXnj2o5asrUBbP+tG7MPUgLYVusV7Uen/aixM0SBOy6uAdpQPYztqPERibz3e9+F6+88or7LpfL4Ytf/CJ+/ud/HqOjo3ekwfSBPRk514HSM9uw8xsKhTo0Lpp0GVSkV9eFw7fPkW5tbbn2uAkt0etYff3VDW/nSM1mZHTsF02SKvWTjtR/yne1PjK1UCjkzKCpVAq5XM4xNNbHDa9anJWOSWOcc9VudePRT6jmRTUl8hlGTzcaDaf5VqtV7O7uOoZ9/fr1jkhyMm6e0w2FQi5qnn5xzht9qaQT+loZgUyzMI+OURhg8BfHzPFQW1Y/vN5MxTPQzWYTg4ODeO+99/DEE0/gj//4j10msPvvvx/vv/8+RkdHsbKy0jHfXGcKYPV63aUjJfjx3DDpIpFIIJPJuIsoksmkMzlTe+UcMDiL/vVwOOxSstIioPMyMDCA6elpLC0tdVxQAdxO0UgAIPiSr/CKyrGxMSwtLaFcLrsUp9xf29vb7rKRra0tDA8Pd1iJ6B+naV8tOUr33FvFYhFzc3OYnp6+4y7ysbExzMzMYGVlBYuLi04Ie+GFF3Dq1Ck3T9yfFCq4l3R83GO0clhN1GqLPr7IOqyAzfc5T9wvCnY+HmN5uVpSLeizDxYDrODtA2gNJiPdW4HbpyhYxaNXbRm4y+CvoGI1YJ10ZVAHad++en3PBgUXdJNqrI/WV6zGFKQpBkmCCljsD7VmzYBEIUZ9p0Eg5+vfQUKC+jrpd+W5UTI6BhQNDQ2h0WhgaGjIzR9D/oMkWf72CRC+vvk2sK4TfXk0/7Pf6jsjw+U8Mi9vPB53jDOfz3cAL310wO2c1wQYFQ74nZo++Tn7Tn+89p/zxbmqVCqo1WrY2dnB9vY2tre3sbKygkqlgkKh4LKSUVBSv6sKXkxAw+dp1VA/sQoBnA8CK+eRf9dqtTuOf4VCIXc0h58DcHcjUxjg3HO+OJ9MysH/q9UqUqmUA5+9vT1cu3YNjz76KL71rW8hm826412kHQIo6Yz5qDkeCgTtdtsdaWIQGa+/bDabTqsmbfNiEp/vkwKrJmXR9KCpVArz8/M4d+5cRwAaE92USiUUCgW0221MT09je3vbBVRmMhmXL1zHSTqiBr62tob+/v6OBD5cH407sVqp9QvTxz42Nuay5bXbbWchO3bsGKampnD16lVsb29jbW0NL7/8Mj7ykY84C4AGAWpQlwUqBTbL73Wf26Jgrmug/MAKxb46gr7zWWWDeGi3/gXVr8XyvoPqs33spXwgt0v10jErwfjMEUFAp0UZIeA/m3wQ+FrTRhDoBPXTPucbh8+SQMKk5Kqart18anYJKir0BI2FUalkRMlkEqHQ/jEKAkF/f7/TUph8n5mbVEr0jdvXH37v89mw6Hd8hwyY5kxGGdOHWKlUHJiqxkXTLAE3kUigWCw6pn7jxg13wxQZZzKZxMjIiAOGcDjsooEBOM2WGgTNurlcDjs7O9jZ2UGxWEShUMDW1hYajQZ2dnZQrVY7skPRCqFjo2mW86oZpJhnmn/X63VkMhkAcPNAk64mMNGUnaQzggGvkEwmk+jv78fAwAAGBwcxPDyMiYkJDA8P48UXX8SNGzdQr9fxS7/0S3j22Wfxy7/8y1hfX8fW1paL/qbAo8IRwYECQaFQwNTUFNbX19FoNLC5uYl8Po9jx45hcXHR+YVJEzQTMy83tdJ2u+2yslHIbLVaWFtbw+joKNLpNFZWVpBIJDA4OOiOdGWzWRc7QbpQ07P6s+mn5bO00DBd5+rqKlKplBO0aJVgvQSwY8eOodVqYWNjA+VyGaOjo2g29y+i0P3TarWcuZpjOX78uBsbACc4c63VJ85CkKdVZHNzE2NjY9jc3AQAt1YMrms2m9ja2nK88/nnn8e5c+fcETXSEundF2WtvIx9Vb7ts1TachBYWj5meXFQGz7N9SBrbFAJwiHto8UxW3zWgoPmRsuhNeYgANDPVVO0IOirs9fOEnDVD3g3C6LSm/Up6jMHaaYHAbsSMZ/XMdi+qilR37fFSl7dhB3VDBj0w2sPFxYWnD+vv78fo6Ojjhn29/c7iVuDdILmxGcd8T3jizPQ+SZQLi8vo1qt4j//5//ccVMNfbH0gTOhA7+j35wgTEDJ5/PO9EvwXlhYwNWrVxGNRvHCCy8gHA67RBNMmcoLCXgMSS+ZoOZKkx/7QLClYEDLCAUigiVpgGZojYputVoOSOmKoBZNjVKDqEhLLNVqFWfOnMHp06extLSEX/mVX0E6nUYikXC3EQHA5uYm/vAP/9Dlc/57f+/v4bOf/SwikQj+wT/4B/iH//AfuqNZbI9JQlhIw9Q4l5aWMD4+jrm5OSSTSezu7mJubg7nz5935uexsTEcPXrUCUkPP/ww3n33Xbz66qtYX193c8X1pbba39/vXAFMtUpBcmhoCM1mE8PDwzhy5AiKxWLHTWp6hIzCBOlfBT5qhalUygXg0arByGxauXg+OZfLuStEaSpPp9OuTjWvtlotd21noVBw11vq+vVi2VMrz/r6OsbGxjA+Po6VlRXXR56hTiQSSKfT2NnZcXvva1/7Gj772c86GgZuH4din61w7TPbckxBfJ7PWOWsm/nXhyc+IcFXtB6NVWAJ0lx70Zitqb3b8xajelE8WQ6lMfsQ3zamEx8kIQRpnUHP2QWxgQgA7pDifMVq2d1AWYlIHfkHacz6vzV16KJSWgdwB/O2wk23EqSR8jtKvqFQyOVXphbcbDaxsbGBVCqFRCLhLqYnU6nVao5J2SNEdp7Ynmpr6mO241Dtju+SWWYyGXzqU5/CF7/4Recj1MAemv/IQFg3TcetVstpUQToer2O8fFxFyTE/t+4cQMAcO3aNfcZhQPOP4PfqAHrNYxsn4FGPhpTbZJt0LdLMylpgNphJLJ/a1AikcDx48cxPDyM9fV1LCws4JFHHsEXv/hFhEIhDAwMYHNzsyNwC4CbH2DfTTEzM+MSeuzu7uLixYt488038dprr2F2dhYnTpzAF77wBTzxxBNu3Y8fP45f+IVfwO///u87wYEATKGH9MHEIaHQ/mUWk5OTSCaTGBwcRKFQwM7ODmKxmBs3halHH30U9XodGxsbOHfuHM6fP4/5+XlcunQJV69edUDBTFrRaNRZd2q1mgOiWq2GgYEBl3WLcQvsL4EUgAOsRCKBzc1NNJtNl+Ob0eB0WfCGraGhIXdzFk3YXDeOme3yFiymjuWzFGxIy/RBb2xsIJlMdqxhNBp1biYFad0vetqgXq9jeXkZ4+PjKJfLLukL/fYMEnvzzTfRbO4n4rl06RLOnTuHc+fOOTeMasxKx9zrPvDSv4N4vvIFjdWwANuNx9o50GeC8MZnbTjI13sYzfageoBOs/0HrjHbxoA7Qdj3vS6kNb0qkHbTcn3aug/sfHX7+k7NzTdRVlJTM5QG8wT5XQkw6lOnZGsJ0C6YCgDaRw1mskBo59D6n2gWSyaTTkujhsDPSby1Ws1lJ9LgPPZFN6dqSLZPXB81N2s0qbUSaIALE558+tOfxpEjR/DlL38Zr7zyimMUq6urzixNUAiFQshmswiF9o97EZSomfBoSqlUcmeCCeqcC6ZP5BzpMQcNhNJxUPOl+ZbvagCYJgWJRvdzMVOzYhYoAufU1BQymQwymQwGBgacZsc5fOGFF/Brv/ZrDuTa7bYDKw12opm22Wzi+vXrGB8fx6/92q9hb2/PHbGi5jQ1NYUvfOEL+Mmf/EmkUikXQMS5/Rt/42/grbfeQq1Wc5oqYyYmJyfRbt/OPjU0NOTM8nrPNgPZCoWCu4qTFgiatMPh/TO+wL5p+4d+6IfwiU98AvPz87hy5Qo2NzddNHUoFHLm/d3dXQwMDLggNL35idpkuVx2STUSiQSuX7+OVmv/LutWq4XV1dWO43UEVK4FwZvzTGDW+Id2u+00fa4ZQQ7oDCDifqpUKk64WVlZcZm5+DzpUAOlaEK3/thYLOausOSlJ6FQCN/5zneQyWTw6KOPIpFI4MyZM7h8+bITIp9//nl3ZJOCMQUZy28P4vHKd6w2qe/qSQE+r3yRfEd5pLanbSq/s7xb++UDbuU7Pn+6Khc+JU7rtPil31ls6qUcGpht8YGl75kgaUE779PIgDvTeOpiqKkY6J7KLahtLUpIusCcWD7j87vqHBCQ2R+9VYfv09SpZ0n1b61HwVo1I/u/SvLax4WFBWeuC4fDTqqmlM47menzooZCn7T6m1UA4Vho4iTz4DPUGHg0pl6vo1KpoFgsOv9xpVJxv9V0yOMaDEpqNBpIp9MuYpUaLC/gIGDm83l3fjcUCjmmTUGFDFMvTld/pAoQ1KA0aIVjjcVi7nx1OBx2LoLR0VFMTk5iZGQEo6OjGBoawuDgoLNM6BEnrhnbVI2lVCq5cUajUTzyyCP4R//oH+FXf/VXHRD39/fj/PnzTtPmZR9k+OVyGSsrK9jY2HBBcKOjo/jYxz6Gj33sY7j//vtdylUG3jEginvy85//PObn550/kgyNAVl6XIhH7sjUNP80L4qgcEQrBMetQujGxgbi8Tjuu+8+nD17FoVCAbOzs1hYWECpVOrQ4AmYuVwOIyMjrg+5XM5FIxNop6enMTw8jLW1NWxubuLEiRMuyI5jAuDSig4MDGBoaAhLS0tuDXmxC3+4z2j9UDcVrR4UDigU8rhTLpfD2NgYtra2MDIy4jR1zSimfIo077NSNRoNJ3To6YbV1VV39ItJVHjHdz6fxze/+U089dRTLokKhUmfdmz/9wG2BeWgYt8LwhIdv8USKwTo59xXvnYVdH3m+iAQPmgs3cbdKygD3yMw+0wKvmdUg+RndvDdgNk+6wNoX7H9sUFjSvQHmVMOIk79WwM2QqGQ8zuqVMbn6IeixkawsAEqOm4dP+tUiU4ZPaOW/+iP/sjdPUyNgFGkbIdtUsscGRnBww8/jJGRkY6sU7x7lFoUgTSXy7ljPAyUIRO1iU24+Rh9zTknONLkGgqFMDo6CgAdgoIGpSnQalpHPdepwUocB4AOQO/v78fg4CAGBweRTCaRTqed9ppKpZBOpzEyMoKxsbEOjZcAtbe357KrtVotF4BDgSWXyzngUXpXOrR/6zEy+sW3t7cxMjKCkZER/K2/9bfwYz/2Y2i1Wh05pIvFojs7S8bDc7/0gVK4ooZULpextLSE9fV1bGxs4NatW1hfX3f+fnUbAHBR+0wTyVuRKCSQnmid0YtE1ErDsTGAjRYHmstZ10MPPYTz5887kz6TsFCIKpfLzt88MzPjhDnSQ6lUwsLCAk6ePOmC7+jiyOfzziTPQEAG9w0MDLho8vHxcWSzWRfUxcDKSCTiLgWh0KYaGfewRkFz7SuVCqLRKK5fv45HHnnE0asmBlFBGMAd+53BhVzPU6dOYXFxEYVCAdFoFLOzsy6r2KlTp/DGG2+4/fX666/j7NmzGB0ddefmGRlOvuXji/zMp0nb0gso6b7w4YPlb3xOLY+2Ptu+D1PU8qHPq9UzCJi1z7YPytd6nQOW7xmYg0wW2jnV3OwAuklV1ixrn7WRzAdJaBbI1Qxsf4IAW9uyz3GDsu5IJIJSqYTXX38dMzMzWFtbQyQSwfDwMKanp7G7u4u1tTUsLy8739SZM2ec0ECfKtslA7KfEcjD4fAdafYAON9iKLSfdtMGvTBamH5SZtcqFAq4evUqvvzlLyMUCjlwVhBUotY0mCx61ISmYrbLPlGj0OAnBtvwSAz7S5MkmZFGTRN4qf3y9iXeJ8xjNKrtjoyM4MiRI8hms8hkMh3aIteSEeyVSgWVSgU3b950CTfU1EUNSs+tttu3k3LQXKgbneum2jKBlOPjulCjfPDBBx3tfvWrX8XXvvY1AHC3LrGOVCqFY8eOIZ1OO82vVCq5s6tzc3O4fPkyVlZWsLOz4wQH9pfglU6n73CBqBWk2Ww6QYppNFut26cPCJCMeOdatVotZ2r9wR/8QZdQhfdGazQ2aTcSiSCTyeCRRx7Bo48+iuXlZVy6dAk3btxwe5FR3qQ7aqft9v4Ros3NTXcMinuWR61I0wTDfD6PdDqNwcFBrK+vI5fLuSAzHiHjGvIWLGrstFBx36ZSKRdQCaDD3M2fhYUFnDhxwgmPpBs9PkW6VLBQ+gmFQiiVShgfH3dR/vl8HnNzc3jggQcwOjqKo0ePYmlpCe12G6lUCs899xx+4Rd+wdXlM2WrhYw8rlsMzEFaZhC/tny5W93WQsr+2T5rmz6s8h1Js3X7BBPts7at/QmyPHQrH4gpuxdA1Em0kkRQPWoy5f/AbTMF32Mb+oyv+EzBykAPqkPbDPKJs75YLIZKpYJXX30Vt27dwp/92Z91bEBeiD4yMoIvf/nLyGazLrXek08+6Z7TSxvIrAluBDD6qlRbIsMsFAqOGQNwEbkEFqbqpPkT2L82j2NlWzSnqt+Y5m0yYZqw9R31+2kSC5rxaXIlGKo1gAIE/ZBkoqHQvi+6r68PqVTK+Rs5RiaTIJiwX9QoFPgo6ORyOeRyuQ6fOf+mFmI3GM2NNG2rFqoCqW5Qfq5ts14KFvyf37VaLXfWVW8W0kITvu6RnZ0dzM3NOaAlcNJCoyZ6mldJI6olMaCO60nhhTSXTqc7+lytVjE5OYmf+ImfwLe//W33DIOQ2EcCeiwWw8WLF9FoNHDy5ElMTEy4SHie39a50ij0mZkZ3H///djd3cW1a9fw/vvvY2VlxR01U0sMwXZ7e9tpxTs7Ox2Ztmhq5hq2Wi2srKzg+PHjGB0dxfLysrsOkwIGrQHUNknPHKPSSCqVQqFQcPtbzeLhcNi5YXROqUGzDtIggZv0TP5FE/nx48eRzWad2Xp1ddUdzVpbW3O8pdFoYH19HW+//TbOnj3bEYxqNWKlO+uX9fFJ/f4g87BPu+zGi3WvWsC1dfvqsZYrPmMtlIpfFnhtn+w4fN/3Uu4q+CtokoOKSiOqKXcDWk4aiUS1QJVCtM6DijJkFvXf+kwl6ofQ/ilhKOBzPNwo8/PzDviYBJ6Rxslk0pkC2a+33nrL5ateWFhwplv6qCjRczOTiXBjEyBYN32CTLpBMGHSiHa77TSbZrOJVCrl0h2qyZdnoFUDUoleGbz6cWnS5TElarMaacpx6HEemtIIsmSuNAPqfGs/7Oa2tMF2lQao1Ss9Kb2phqLPaGCOXgjRbW8EMQN+Rm2TQgi1ZuB2itV2u91xDIR9tZo6AHfFoAYVka4puLTbbSc86bEZXo85MTHhzLwbGxsuiIvR+0rTFByazSaOHj2Kf/bP/hlWV1dx69Ytd70mQZDAmUgkcPPmTcRiMdy4cQPZbBbHjh3DqVOnMDQ05JKxcG/p2EulkouVOH78OB5//HFsbm5iY2MD6+vruHjxohuf+k6Xl5ed1h6LxdylEoyBIDCFQvtpR3d2djAyMuLytY+NjSGZTHZYc9rttjtfrpddKO1Sg1YLBN1BPAa3vr6O/v7+Dr7C/QCgQ0hUcGYymGg0ip2dHWQyGRw/fhzvv/++cwlcvny5IypdLW1f//rXcebMGWc5UHpTmrLaJefUB1iWn/to3rdX1CLpwxbOa1BdQcBuwVaVLBXGuKeUD+iRNzsen6JmnztMOdQ55l6046B39W9bhy66Are2S2ai2rMy514Gr6aJbu/6NBJ9N4hg+D+ZQC6XQzabxY0bNxAKhVzSiUqlgtHRUafFqNmvXq9jdnbW1UUzFhNThEK3A7GA2/5SBtPQLE3iYqANTbEkRGpMZKTUXJjXWM/hMjMSzXc8UkUQ1aQVDG6i35BaiN1o1GgosNBfqWtMHxjXXhmDHhVRf7xlBFZDVs2bAKI0yHdYP+eWmpGl2W5MyKcxE1isqczWwXFpJDg1MfaP72sUrY5NQZ2gz/6pFqAaB83NBD/WS1/w8ePHAewHFDGRSjwedyZTCmWlUglra2uYnZ3FkSNHMDExgXfffdeNnYIBLS3UCimkvvvuu1hcXEQsFsPRo0dx+vRp90yxWOywhnBMPP4XCoVw5MgRPPDAA5iamsLv/M7vOEsBgzDHx8edFp7NZp1GTYCkS4J0kMvlnEl7bW3NXZGayWScALqzs9ORyKbdbnecEOCcU4gBbifNaTabKJVKSKfT7oIOZuHjmlraVEsf+6C0ValUsLOzg+HhYWxsbKDd3r/sRF00jGGgFeKFF17Aj/zIj3Tkzuczlg9y3pW2fYqbBbEgDVyfP6go3eoe89VlBQbrn7d9USuttsfC5xSMFcAV8O+23HWCEf2sl2I14iAThzKxbuaHw2jtvnp8/hFlzFYCsn4Pa7bQRaevdXx8HM888wxu3LiBT3/60/gP/+E/IJPJIBQKuehZMgBqJ9PT04jFYnj//ffvMDcCnWYWAgV9pgz2oemW5q9qteoAMpvNupSGyWQSAwMDuHbtmjNvabIFBZfd3V186lOfwvvvv49f//VfRyaTcZHBzPu7sbHhzOg7OzvY2NhwWj5BQhMXsO8cF59RjdX6ZKlx6EYis9INo2uszMvSifVBWXeLasRKk8oY7NpYE6D6Ga1W342u7B5QAUX3j35GpsIftst5t+1qW6oJMV8zz40fO3YMOzs7Ljr77Nmz7ujXpUuXsLS0hFwu5+phUpFYLIZ33nkHx48fd1aTUKgzHSawDxK8wYlXPjKj1c7ODq5fv46RkRGcOXMGu7u7GB8fx9bWVocvWQGCdDczM4Mnn3wSr7/+utPQR0ZGMD09jVKp1BHXwLiEkZERbGxsdETpc/76+vqQzWZdBHgikXDmZz7L6GbuQaUN/k3huN3edxUQsFOpFMrlMpaXlzE8PNxxsoNrxxznqtVZuuLxqXA47O6aLhQK7nsVZFl/NBrF3Nwc5ubmcObMGSck0Rev9KO04yu6Fnb8BxXLU7s9E/Q/++DrL/eu3WO6N3rpb5BWfxCG9VLuKld2kJnAV3yT62M+VrLxDVifITP2AftB/VfGrQzbJ/3YOoL6xbbV/MTsRL/4i7+IeDyOoaEhRCIRLC8vO/Cu1+uYmJjAv/pX/wqbm5sYHBzEb/zGb7hoUzKuVCrl/KrhcNhdms7jGLOzszh37hzOnDmDsbExB5w843np0iXUajV87nOfc2a1UCiEVCqFf/Ev/gWSySTeeOMNVKtVHD161PkhmR2M2vCjjz6KP/3TP0Uul3MmRmpVCpqA/+wfmbFqfGTmGjinEjpN9HaeuR6qmVppVRm29kWPnai0z/XUeAMCTdBZRt/f1HJ0fAqAVkDVttSSw7Gxv6oZs//Uqq3gQdohIGkKTcswQ6HQHeZxRs/HYjHk83kMDw+jWCxia2sLAPDEE08gm83igQceQCgUws2bN/GlL33JCYY3b97E4OAgAODixYvIZrOYmJhAvV53pxFoNVKfJl0Y+Xwe/f39Loc6zc+FQgEnTpxwgoO6dGjd4ZhqtRo+/elP4/jx4/j93/99tFr70fHxeBzpdNqNV4/FxWIxpFIpZwXY29tDPB7HxsYGhoaGXPId3qBFMOXepGmax/r07LGCKP3omut9bm4O2WwWrVYLN27cwMmTJx29cl6p4fpcLFxX+s0Zn8F83taEy9MKFJRKpRK+/e1vu2xmAwMDeOyxx+5w9wTRvAVkn8Ya9L7W49Owfc/4hOyDAFuBV5/3maOtm9JihLWk8retq5uQ4SuH8jHfDfr7wIyf+Zz3vjasdqqfK3O1z9s++0yHFhy0WKYeNBbbV/pZ+dzIyIgLMlldXXUmZWZCohltZmYGv/zLv4x3330X9913H372Z38Wk5OTLmiEySPIAEiI6XQav/M7v4OxsTF86lOf6sg2xOvkJicnnSZSr9c7Et3/9E//NHZ2dvDII49gbm4OZ8+exeLiIiqVCsbHx93YOD/Xrl1z76uPi/OkR8U0mtTnm7J1K03o+up3um7qBlGGoODFdaDQQBBTbTloTcnwyVyVFvi+T7vXMXGtOFdafFqyj5atcKHCiRWI+Dy/Z9/5ma4X6yHTZ93qk6YZu1QqYXBwELu7u8jlcnj77bfxzDPPOGHiwQcfxPPPP+/MvIODgx13KtOHSnM5aSObzeL48eMolUrY3t52VhiuP908BO6tra2O28/C4bA7S85of9J4PB5HsVjEU089hTfffBPXr19HLBbD5uamS87CKORSqeRovr+/H4VCwdF5vV53Wb9mZmaQTqddwhoeq6tUKu42Kd65TM2UQg4FUwpuGvzHMTJCfH19HcPDwy7aXiP4lfYs8HFdqeVHo/t552dmZjA/P+/qoJWA7iq6IhYWFjA/P49sNgsAmJycxNDQUKBWqsUCkKXnoBKkYR6kNQd9ZufkIKHBKmeW9+izlmf48MvixWFLz8AcFCTlk058kosPcDUAyBY1QdrBHTRoH7OzzKhbv4KYp0+jsiZffm8Do3ixAo+g8Dv6rSKRCL7xjW/g6tWrmJiYwD/5J//EpZC0gMRbazg39Xodk5OTuHHjhrvZiKaugYEBRCIR3Lp1C7/6q7+Kv/N3/g6azaa79YjMiEdRYrEYlpeXO/zCHJOOlRK2jlHBgcxdpXs9WqS0w9/WAqKbVYGYc6tmb9UU+LmuPX+TuetZUhbSN4FKfcoAOsCLQKO0ZLVd/k+mqrRltV4VHnS8fNbWafutGhSADjeHzo+6adg+GbX6LnWtOWfhcBiDg4MuMHBpaQnvv/8+zp496+qOx+PY3d3F2NgY/vbf/tv4wz/8QywsLKDV2r8OsdVquXmrVCouWpu5ohlhv7m56ehQAVr/1v4zOPHdd9/FQw89hOHhYRfnAewnznnwwQexurrqcq9vbm7i+PHj7pw9NVwCViqVcgBKdwwjmI8cOYLBwUFsb2+7oMZQKOQS4zApDtdqeHgY29vbLpqbPl3mpFehr9W6nUt7bm4OFy5c6BDAuJYq6Fori1qgGN1OKwHnUfcLf5iHYGBgwCWGIW2qpSdISTtIO9bPLY8JqpcWIeVBat3xYQM/173F51QwZ71B+9JXr23DjtVXX9CcBJVDa8y6+YOAmUUZghafyYDvq5nBJ0EBfvPBQZKVSjb6vP3c1s/++urkd9pvK6GFQiG3UWna5vlPRlyn02l3lrDZbOJzn/scJiYm3DlfEo9vHOzr5OQkrly54gJiKpUK1tfXXcKIXC6HU6dO4aWXXuq4mYj18fo+bsCgedX10WMlPuFJf1tfLv8mjfA36Us1cQ3aoumNmoBqdgCcIENXAU2TGiWu5mA+Y0FUN7XWr31msQlaVOO05kY1i/F/9kMzpimD4DOqLWndKqRZOrHMVOeXDI4Mz1oEtJDGa7Wau9Kw2Wzi6tWrmJycdGbhgYEB52uemJjAF77wBXzzm9/EH/7hH7o6isWiA6PBwUHkcjlcv37dgRVvvjp79ixCoZC7WIQXivAYYLvddr5cnnK4evUqCoUCAOC+++7D0NAQhoaGXOpOCqKZTAa1Wg0rKysYGxtzJxkITgxipKmZ2ifnjn3lUTu6jjTLHI8lAnDBVBSAeLFEs7l/6QbznRMgKTzs7e2nUZ2ZmbmDF6gGba0zSl97e3vY2trCzMwMTpw4gUuXLiEUun0FLde7VquhWCy62JNQKIRPfOITGBsbQ6lU6ogMVxq+Gz+y7omDPrO0aDGHv300awV0q9nqXrF163h8fN32ye5TFfSDsCyo9AzMQSYCn6nADqqbZhxULMMOkqoUDG3xadRan5XegO7mbluPD+h9ixmPx7G6utoxDg33Z2DGzZs3kU6n8fjjjzvwtBIXwYsaMQOt5ubmUK/X8cUvfhHb29vufHC73Zmogb4ialj07+k4uZnt3KjWrt/xe19wSJDUyXr0SBYAxwAZgMYsZWyfCVFoNeDRD86nnrf2AahK31wHzis1H46FDI/CjvoK9Wo+NQWzXfoMlSHYTdputztStVo618hx3WdM28h66NoIhULOigHcPtdNRkHNkvXQaqDR2xy37g21Omgu7P7+fpf28r777kO1WsXQ0JBLmAMAxWIRn/rUp/DGG28gFAq5m6WoWROAdnZ23Pxub2/j2rVrGB4exvj4OI4cOYKZmRnnVmFqUKXLSCSClZUVJJNJR+Nf//rXnWabTqdx9uxZ/P2///fxW7/1W+7SCp5aoOaupmcKdewrryFttfZvc2Lillwuh+3tbWfG5qkMBrIBwPLyMvb29lAoFNzlHgRe9Y9TSGQehEwmg4WFBQwPDyOdTnfc6uULDNP9SSDlc+vr6xgZGemgET7LTIDh8H6wmAqqnGu11gQB2GHKYTRIfd7y7l6UMgVn5XNqsbJaMeC/59mHKUHC8d2O93sK/lKmp5JckEbcS/36ru+sWtC7Qe2RIdqJtmNisQtni33fas4+qS0cDrsNPTQ05PIA06/2T//pP8XW1haazaZLsMDUjupTpu9tdXUVS0tLWF5eRi6Xc0yk1dpPyE8iAeCkb0pyZKqapYtMgUzaJ+HZseo8WLOrSvRk5syeRdBl33gem6BmJXJuHg3q0SQXTIHI+oBOsKU5Tk24BABrGeD7eqTHJ2xwvgiqnFP+DdzOPkZBinPGNVXwsxYiNbnp/HJdlDnzmBnXn0DOc6oUGlgYp5BMJt3cEOhp3dG4AF1/aorMrEXz79zcnMs5TQ2WyTcoeA4MDKBWq+Hs2bP4yEc+gvn5eVy/fh35fB4rKyuOTpaXl929wRsbG9jY2MB7773nAH1oaMgl5QiHw+5IT7u9H8vBUq1WMTg4iHA4jKWlJcTjcVy+fBmPPfYYLly4gBdffNFpjdvb2y6tqN5D3tfXh8nJyY6MW1tbW6hUKk5jZzwJ95ieCyf9M0e65i9gtHu9Xsf29rYzo+ve5XEyBnc+9NBDjl7IG7lfVWvWPUCwb7VaLjc96Zv0RtDe3d3FiRMnOhIIffOb38TP/uzPdtCmglcQT/aZdw96JqgEabcWnLthjvIk/h+EGz6hw7p/tA/qDmLhXve5pHopPQOz1UxV+rCdtoPzlW6gaMHYSjHsi/bHZ37uRji2qHnQ+kC1bf2toM+2bP+pmVAyP3LkCD784Q/j4sWLmJmZwZEjR9yNNWyfPmkyuKWlJSwuLmJxcRE7OzsdG1Db5dxrUn6CMY9xhcO3o22pfapZTNdT545am0YXk/iYyIRaLn8IXGRgliGQuTDCnJ9RyleLgYImGR/NodScaZpV/ypvS+Iaq/+Z9XHsypA5VtbNtQ5yszDVIsGQsQRcA84t61INWjV4FXq0bY2k5zucI8ssgNv+buYmJ3gzv7nOJSP7GWCogYvsE4UUXo4wODjoMl1tbW1hc3MT09PT7sgT15ygPzQ0hNXVVUcTk5OTOH78uDsStby8jL6+PvT397tkJDdv3kShUHABXfpcIpHA5ORkx1l5Ci60wjDhBmk9k8ng4sWLTtCjhkjrRr1ex+DgoFvn4eFhVydwOwteOBx2oM/xMFkH15T3XcdiMaTTaad5EpRbrRZKpRJ2dnYwPj6OSCSCdDqNtbU1FAoFl4aVgnkul8P6+jqmpqYcLfssXLQeqItNQZV0opay9fV1tFotTE5OOiDnHNVqNXz3u9/Fk08+6ZKpkLZYv5a70Z6tNfYw77B0A1jFC2vSDsIHgqwFY/1bBQVVJll8ikav5dCZvywzsRNi/7dmUD7jM0UosOv7PjO6laKCzOVk9tpu0GRZQPVpv/Z7vYXH1qd9480yyWQSn//853Hs2DE89NBDHSBWLpddPt3Z2VnMz89jc3PTJcwnIwbujABWUxNN1AA6NiwJyuaCZn0EXQIb/Yjt9n5GI/ZTwVctJaolJhIJ9Pf3uwAYnU9qUlZDZFYz/s/blVgntXyCNxkXP1PTsNISTc/0T+r/qp3oGWvtn246anahUMjd38v5pUVDTeb2GJNaLvg5LSL6DJmjmua5ZlYL5nrRZaGmbZpiOQZqw3o7UqPRQKlUQi6Xc8DMyzts/VwbgjRPDLz//vs4duyYa4sBUzzix0xWFDLYbjgcxtGjRzEzM+M0uvvuuw/33XcfKpUKtre38d3vfhfz8/NO8KOPuVgsOu2fSW5Onz6Nvb09LC4uuvmjVqslk8lgb2//2kmeVOCxK5qYC4UChoeH3TwwNSktNlyTbDaLSCTi+sRgtmh0/27oarXqznwzSrzZbGJ1ddWdhR4aGkIymUS5XEaxWOxwLdD8vbCwgGw267RrBWQFYwVMPRZIK4/ysK2tLeTzeUxMTCCVSnUItXR3vfvuuzh79qxbQxWebVHhVYvl/+y/FquR635QXqeCq9atOKF16XMU8oMULfZLFQjlJfqZr78UYEmrGmx6mNIzMJPRq1/K+qJ0oFqspuHTKoPetd9bM4b97euHBbAg6cXXD1uP9sOnVQcRFzUVnt8k0yIQNRoNPP300xgdHcV/+2//rSMSltI4GYFvrGrBUK2XG5efqXmFmhQAp5VoliIF3729/bOcmlZTMy9xw5NBaEILzjdNrAQe3qrDZBCsSzN+EegInAQq1seNoMFsOu+qUeu80G8I3PaNk3mzj9S6NAUi/dm7u7vOPeEzS7Nfao72aTIKcHyPddGPbWmM0fNaqL0RNIDbiWf4vwI7n+vv70d/f7/Lxc0AICaLYQ53rhlpm7RcqVSQSqWwurrq7i3mRRGTk5MuwPHo0aMuIpoCqrWuRKNRl1Wu1Wq5JCdHjhzBe++9h69+9asoFotu7MwN0NfXh5s3b2JlZQUTExOYnJzE+fPn3RGo7e1tZzkA4ECYpuVwOOyErc3NTUcjPALFkw6NRsMFeZH+SEs7OzsolUouF/3g4KBzG3Be2D5z2vOGMGZUY47sTCbjzhyTJhiItrS0hNOnTyMcvn3zmK6L0hGtCisrK85ipjEUPKvM+8FJX0qHrPvP//zP8ZM/+ZNOaCHNW7dWNwCydEwcUGzwabMWmC1YWqulAqe2axUDLToen/larVpsW+eavIhgbINJD1sOpTGrH0PP3+mgfUUniotoJw8ItsFboFMtuts7QfUEmSP5WZA5Xuu0Zne+55OyIpGIM/utrq7iV37lV7C9vY0zZ87g1q1bmJubQ61Ww9DQkGNQSrTKAFSStiBkgZnfa5CN3glL7ZdaDCV8ms+ATqFK15/ryKMXBD/WTxM3AKeFUBq3AV+sj9nPmKCBmqFqsGQKujk0LaNmglKNlIAaj8fdHOsd09Qs1FysfSPIUROm74/zrhme2Fc15XMs1Kx1vVgno+lpRrRRt9z4PFdsNQfNU21NnUqbBAwNnOM54Gg06qKUy+Uytra2UK1Wkc1mnZle6S+ZTDoAXVpacildr1y5AgAYGxvruFKQN0epIKfR6MrQRkZGMDg4iGq1iomJCZw9exZf+tKXcP36ddTrdVSrVaytrWFiYgL9/f3Y3t5GvV7HwsIC3nnnHYyOjuL06dM4deoUALi7qWkJ4rj7+/uxsbGBbDbrfOEE4NnZWTSbTWSzWZTLZTQaDXdbGQUb7iteG8oxUIigqZ1ulVAohLGxMWdVYoKSSCSCycnJjmtV9T505kFIpVI4evRoR1CW1fhIo7u7uxgeHsbOzo5LJEIXB/s8OjrqNSez7kgk4mICTp061SEMq6Cs7/uUhyAtVTV9+53yY2vBsq4ibdfiiypjKjzYcdPCZ7FN37fBc5wP7nPVlHU8QVjlKz0DczKZvOPsqjUzd9N47YRbcwBwp2nDt1gKmj5gte3aetQX59Nsfe93K7ZvVmLj57u7u06zmJmZwdGjR/HlL3/ZMWo9AlSr1VzwCAGDZrYg0z6DvGgiZKCOXtVIcKf5L51O35GsRBk6CcweFaGZErgN+hyHavYECa1fN7PeRc2jI7u7u85/R/+fMmulE711SNvgHKlUy8L5IZOiBsH3uX78jnSmVgS7ZjTxqnmaRTcwx846qK2piZ7rqtqkmsJ10+sxHo6dLgfVfKzZUfcu+8RxUMPu6+vDwMAA9vb275hmjIReuakBXnt7exgaGsLi4qI7r7y6uort7W1ks1m8/fbbaLfbuHr1KoaGhlwSD95kpsKV1k/Tejgcxvnz5/HYY4/hxo0beO2119yVlZubm4hGoy7LXSi0788tFApYW1vD4OAgUqkUzp07hyeeeAIvvviiOxLFMdIywrPL0WjUxQ3wiBiPELGfPHKVz+fdGeFms+k06nQ63eHKSaVSzm9LYXFjY8MFsyWTSQwPD6NcLqPZbGJtbc2tIYWZ3d1dLC4uYnh42LVjFR4ti4uLOHfuHDY3Nzs0t52dHUQiERw9erRDsNQ91GrdjhTv6+vDt7/9bZw9exbtdtv5wCmsWdDsBZh9wkDQM0H1cB9ReFV8ULyw7il9X7+33wVhAvelWu30c66bHU+vpWdg/ta3voUHHngAk5OTyOfzSCaTHfZ/BVnbCTvxQVJEt/f0f2vWYLGfKSO32qVtN6jOoL74tGpqAFpYL5nNQw89hEQi4Rgdb39S/65GDfNdEo6ez6VUTclYNxcAdxcxgcvna2HQiwIPiYwRumxLb1BSjVclaL1eUv2NmuWMYErzabvdduk/qa1QaLDuEoKMmuY5ZvaJGpj2VWlN6ZaMSI/fcH0VuLnp+ZvrQX8p6YDmZ2WCCqxqpua6UovXPmv6TEtzWq9q4XxfhQTrA1fgKxQKLvqf1gOCGK+ITCQS7ihRLpdz+Zu5j2n9GBkZwcWLF7GxsYEHHnjAre/TTz+Nd999F1euXMHw8DC2trbw1a9+FVNTU/iBH/gBp2FyPbneOh6lt0ajgenpafz0T/809vb2cO3aNReLwSQaOzs7WFxcxMbGBsLh/RMR5XLZ+d9pcua6RaNRnD592llryOjVT85z2uFw2GnJtBJFo1GMjIx0WLRII+ouIZDRakDz8vr6OiYmJtzaDAwMuAtCcrkcwuHbAYnMe3/r1i2cP3/+jvu/ATjBMhwO4/HHH8fS0hLGx8eRy+VQq9Wwvr6OZrPpAsn0hAbpWoVz0n29Xsef//mf45lnnum45lV5ntVUWYKsk/qevq/F7kuLM2pVsgBr61AlzfJ9XxCmT3nTID/Lp1Xw1nFxvXstPQPz5cuXMTs7iwceeAAf+chHOiQ0Srw2+1CQ1KPf6SSqpO/Taq1UBtyZvN7Wz/esScRnztC/rcDBHx23mvNVQrPPx2IxlMtlnDlzxqX6A+A2j2VErEOBlFoLzXDUVDVNJ7Mm6bwQNC3xkLjU90UTGgFYA0Wo0VBj0lzDsVjMRfaqWUvfsRoztVYCAgULmjbVf6MmWc6bNePq9yp4KDipz5zrQmGH9an/mhYHgoz6/fXsNJmXRlwTbFkvI4SVebP/9G/qHbycf66XnhRQM736znU+rElfo9Z3dnYA7CcDSafT2N7eBrDve83n86jX624tS6US4vE4MpkMqtUqisWiuxlN3SrVahWvvPIKBgYG0G63nW+4VCrh6NGj7gpC5ny/cOFChyldzZHJZNJpo6Q/vTGNawgAZ86cwYMPPoitrS0Ui0UsLCxgaGgIoVAIKysrHRaaL37xi3jllVcQDofd0SpeP0nAVC2oVCqhv7/fBVzRVcA6Cc40R9M8rM+odYbrw/XkWq6trblobWr+VugmPUciERdNPjY2huHhYTe+YrHogtrC4TAefvhhxONxbG5uujVjlPv09LSLXSH/UuuO7if9/+rVq3jggQcwPT3thFkfT/YVBUPuU37OYnmzVXzUcqYgagUC2x8fv7DP8XurOPI39xDnRHkrP7MWLuXDfyGmbBLs66+/jrm5OTz77LMdUXwEGZ9W20s5zLM+qcwCp36m/2vfgkzhQaBOsOxmgtGFZf0E0BMnTrhAEGXACsIkAgbhVCoVB8YELN5ww/Y0WpjrRG2GwMOgDUrd3OQajcq6eW0jwZ4mOGq+/FtNxiph6tyr+VWfsf4pHuHhXFDoUW1KaUppTetm4Xujo6O4//77sbCwgIGBAezu7uLatWuIRCLuDOzJkyext7eH1dVVZ4rkhfI0xc/OzqKvrw9HjhzBtWvXkMlk0Gg0MDIy4rSZUGg/ypVnYmkC5BpEo1EUCgWMjo52CFGJRAKRSMSZFzn+YrGIkZERrK2tObMq4D8aGMTkVOq3mgfXmBpgqVRyEcO06GQyGQcMPNbHlK88DlWpVPBLv/RL+PznP4/Z2Vk8//zzqFQq+O53v4tXXnkFkUgE4+Pjbm5brRYuXbqEU6dOYXR0FJVKBRsbG2g2m86PSwDhFYtbW1suToMm2AsXLuDRRx9FLpdzSUTGxsYQDu9fd8qYDUadj42NAYBzF5H24vE4CoVCh8WHZ48TiYRTOBgEBsBZuiigMBqbViiuqQpIfI+fMR4jEom4CyO4DykIMTsZ3Yh0G2UyGVy/fh2PPPIIYrEYisUiXn75ZTz66KOYmJhwNPL66687uqQWTqsIaVL3T5C2qzT3rW99Cz//8z/vXDi6160l0afQ+Pg229G58vVHeY2tQ3mvfkea4W8VDrR+CuwUDhRkaRnielNwD7LE2t+qIPRSegZmSgYk4j/6oz/CZz/7WWcS0kk4qBwGhIOKzx+tJQiU9flehAedWF1UZXranvomCQ7tdhtHjhzB8vKyIyAuEombm75QKDjgJHjG43EHAOwLNSWN1OZ9xkwLSOIhwFhpLhaLuSAX+p0ptTP6OplMunoo7atW32rdzoVtpVXrc1UpUxMfUAPlhqCJzGq7alFQLdG3oVkGBgZw7tw5fPOb30QoFMLP/dzPIZ/PY21tzWk7Fy5cQLlcxtWrV9HX14fz58/jwoULeO6555wwVKvV8PGPfxzHjh3DrVu3XJs/8iM/ghs3brgjLcViET/6oz+KxcVFvPTSSx1HsthWsVjE9PQ0tra28PLLL2Nqagqf+tSn8D/+x/9wzL7VarmMcTMzM0gkEvjud7/b4adWmlSrE2lTtVACAAPr9AIH3p2dSCSQzWaxsbHhos4bjQaGh4dd3bx/uNFouGM7e3t7mJubQ6PRwOTkJKamppBKpfDII4/g6tWrePnll7G9vY3NzU2srq6iUCigv78fr7/+uhM+KSTxDD6PODEGYXBwEKdPn3ZZ34rFIv77f//vuHnzJj796U93BNSFQiGnOVLIHRwcxDPPPINXXnnFmeRVw0mlUgDg+sEYCtK+zjktKEziwhMNnGuCO4VhBuvx8hoeoapWqxgZGcHQ0BDy+TzW19dx7Ngxx2dp0i4Wi1hbW3NCHvdcuVzG6uoqTp8+jW984xsoFAp49913MTY2hkgkgu985zvu9EEkEnEWgP7+/o60vAqA1kKpPA7YB6+1tTVcunQJjz32mBu3BWblib4ffcYWCpqqEet3KpRr8X3G/aT/+95h3zkXGv9B2lKepDkbWCye+BTBXsuhLrEgU6CU/Ad/8Ad45plncN999zlCDhr0B12sBObTGHx9sIEKPkD3TS7rUELVyfZp7NSGVQPgOzT9b21tuRSalUrFRWoODAw4HyvrYSAXJWngth+YJi/gtjTPd+lrTqVS7mgMsH+W03cuWj+jhE7tldqy9a8AnVHbBAx7xSPQaVoi0PKC+kQi0bExuZHshlfJXLV34LYZt1Qq4cSJE7hy5Qp2d3dx/PhxB3Z8//z583jllVfwzDPPOHNkNpvF9evXceXKFXd288KFC7hx4wbOnz/vciIPDw+jr68PL7/8sjtTOzw87C4fUCZ05MgRnDt3Dt/4xjdQKpXw5ptv4md+5mdw4sQJtw7PPPMMvva1r7k5LhaLSCQSeOutt/Dggw/iiSeewLe//W0nzKkpnTRCAFbLjlo1yFRIBxR2CNCkER6XajabLsezmhlJA4xSvnLlirsIhRpwqVTC9PQ0BgYGsLi4iEuXLuGhhx5CJBLB5cuXnWZJl8jo6CieeeYZnD9/3vUzHN5Pl8n2KJzWajW8+eab+J//838im83ik5/8JIrFIkKhkAvsotUlm82i3W7j4x//OB599FG88847uHLlirvSNJPJuHvEda+r8Gijj0k/3JOcYwbkcW40bkNdMVwn9rXVamFra8udZyY4Dw8Pu/2+t3f7og2m9Hz//fcxNjaG8fFxrK+vI5/PY25uDqdPn3b1EkyTySRGRkbcWCgAWb6n5lvlhaSfdruNl19+GWfPnnWCCWndZ2rmu1Y79j3HOtSt6ePt6uaz/de2SPdWeNf2gM6724HbiqjmbbBaMN+3Jmyf1cGC+EHlrjRmDRb46le/igsXLji/s0YO+yaeA7Nm5IPAW6U5axqxAKnv9NKGnWifiZugHPS9vk8myWCTa9euueCTUCjkshhtbW1hb2//7tlMJoOpqamOs4lqIms0Gu68L3A7GIZgSQ2D6RIZIZrJZJyplcFWqt0rwKmGqoJMOBzuiN7lhrbmI2vWUYnXSuHUPPgcfY2aftQKOj5iZ/2W8MPhsNO0yuUyPve5z2F2dhb/9b/+V3cs6cd+7Mfw7rvvdgSu8chKvV7Hj/zIj6DZbGJzcxOtVguzs7PY3t7GzMwM3nnnHQwPDyMajeKhhx5CNBp1GZSSySRWVlYcY4zFYrhw4QL++3//70in07jvvvswPDyMjY0NfOQjH3Fa7MmTJ/Hxj3/cpVnlFXypVAqzs7P4uZ/7OXzrW99yIKlaMX3/3ear3b6dPEYtATRla0zAxMSE0xYoMGpMhQYkFQoFR0e8sYy/r127hmg0ihMnTuAHf/AH8aM/+qNYW1tDo9HA/Pw8XnvtNZfRbnV1FS+88AJef/11fPzjH8epU6dckhqaD/V2to9+9KNYXV3FSy+9hKeeesoJaBSmlLZJI5lMBk8//TSeeOIJrK2t4Td/8zexs7PjaJzvRSK3k/Rwv2nWtFAohGQy6eIkmBSEe4jH8SjoqqCq61atVpHP55HNZlGpVHDz5k2cPn3aAXAmk3EBcqVSCaHQfpxGNpvF1NQUrl69infeeQdPPvkklpeXUa/Xce3aNXcnO8E/mUy62+pUgNaLKSzIkZfrPqYAks/n8eKLL+LHf/zHOwIig/zLPnpU/qyxA1wvnwZqeYmvDatV96K40aTPQquE8jENqDxIW9b6D6stA4c8x0ypTTM8tdttvPHGG8jlcnj66afvuHaPkq7trAXQbgMLAvigosBvJTS7qLZNa47Rz4P+1/7zXQovNPPxvOBrr72Gzc1NtNttd9cqhR4yQV69pn5hmozYRjabdZoW01kyAQglYm4sO4/8nlq7Bij4zERKhPxbQZnaNBkaCVf/tkU3ovpreBGABqypCcwXBKUmcRYVeL761a/ix3/8xzE/Pw9g36Lw2GOPYWdnB6Ojo26zHT9+HDdv3sTQ0BD+4A/+wF2998M//MPY3NzEJz7xCYRC+8ky3nzzTRw9ehTr6+u4evWqu5/3kUcecXnPydhHR0fdZQakjXw+j5MnT7rc07wOkMeUmCGK+4faPIWrIMlf51c/pyDGY14aDKbaNvd1JBLB8PBwRwAVNUJdA0Yt/7W/9tfwe7/3e5icnESxWMTY2BhOnDiB1dVVlEolDAwMoFgs4rnnnnOWhoGBAXz60592Pvm33noLly9fxtzcnPPjHzt2DMePH8cDDzzgUtjm83kXUf7EE0/gO9/5jku8wbPDqtmrFYe0EY/HcezYMfzdv/t38R//439Eo9FAPB532eao8VLZIPBy3HQDMZuXHosiA6cfXs2ptE6Qlnlsiib7UqmEzc1NzMzMuHXg8/F43GUqW15exuOPP+7WaG1tDR/+8Ifx/PPPI5FI4OrVq+4ESDwe78iHrrzLBi5xnsi7GYcSj8dRLpddgGI4HMbFixdx/vx5nDlzxuUz0L2qPN1qrAp4GrCo2q3SdpAG7Cv6vN0b3XBHLUoUcjXAUQO9VHFRnqhjt3yvm9Biy6GCv5jgXX2c9EfevHkTN2/exNNPP43HHnvMnU9UcLCT082MbE3ElqDUBGGLz6ZPYupFgvF952tbTT78nHPDTXfz5k0X7bqysuIAgMcPGE1KczfHxGCPSCTiokLpZ2YUaKvVcj45n/mERGzPP1PzUQavAWnW4qFjpSZLBq5zo9q1grXPoqHEy8AKapdqFVGwZx/I3JSurBVmb28PU1NTzqz6xhtv4DOf+Qz+v//v/8O5c+fQ39+Pl156yfV7YmICx44dw/LystP4Wq0WfuiHfgj/+3//bxetvr6+jmeeeQbRaBRHjhzB22+/7awawH4g2fXr190GTyQSWFtbw4c+9CGXUGZ7exuf+MQnsLCwgLfeegu/+Iu/iGKxiD/7sz9zmacopDQaDSSTSTz11FP4yle+csf6qrCl68Q5VoGJ+zEajbp0oqQNMn/OM3M8M/GI0osyIWDfd66JQxKJBIaHhzEzM+MCvAqFAmZnZ/Huu+86oD5x4gSmp6fRbu9fzPHEE0/g2WefRavVwquvvor5+Xm89957uHjxIv73//7fGBsbw7lz5/Doo4/i/vvvx+TkJG7evNmhSZPJ06+qdEcBg0IQABw9ehQ/+7M/i3/7b/+to2/6mdV0y7q5twG43NHcj7Rs6VzRGqRHi6jQMH5EeUur1cLa2hoymQyy2ayjIfadAl+r1cLly5edYHXlyhX8tb/213D69Gmsra1he3sbKysrmJmZcWukx/rUVMz/lZ/x/729PWdJeuihh1wcBsvzzz+PM2fOOOuJLZYv+bDApzDps1b7DcIPX9yRmrx9mKJ4oJZKdWEof7PPco6sEGB984cth7pdipt6cHDQ3bCiBBeLxfDCCy9gdnYWn/zkJzuOCfiKMlOV8K3GGyQB6QT5+qvtaHtB2jJLkGRjgdiCgWrN1C7W1tbcLVCjo6MolUou0pTHM+jLZcAVo6AHBwedBqlMhptbb7RRkFSfqwVfvq9zbhmtDXbQebFzb+fK+oYsA/BtqnK53HFESm/DInPku5wvG8ShwgTrjsViuHXrFpLJJG7cuIFTp07hySefxNjYGF555RWXaazRaGBtbQ33338/Hn30Uezs7KDZbOLjH/843n33XWcWZwrKZDKJkydPIp1OY2pqyl1kf+XKFWSzWQwNDeGxxx5DX18fVlZWUCwWXXKG0dFRTE9P48qVK5ifn8eZM2eQy+XwR3/0R26s1LQajQZOnz6N4eFhvPzyy6jVak5zVj89BWQrqZMW9Vy1Hvnhc5oBje/RMsboXfpO+Z7ds9vb2/j85z+Pc+fO4dd//dexubmJX/qlX8Le3p4TSC9cuICnnnoKjUYD169fx+bmJr72ta8hmUxienoaExMTmJ2dRalUwuTkJD772c/ih3/4h/HGG2/gxRdfRLVaxdtvv425uTkkEgkcPXoUJ0+edEFRU1NTWF9fd1ozAzEp2FEIYTQ0tf/HH38cP/zDP4wXXnihw4JEmuQ4GRWuAg6DqPijGpRalKylR/3S7XbbRXSHQvtJYpaXl10qVB6fKpfL2N3ddek6NzY2XDu7u7u4ePEiHn30UfzJn/wJdnd3MT8/j6GhIZfwhUf2dC8rILNvls82m82Oaz2Zu7/dbmNrawuvvfYaPvaxj91h+j0IkPUzq1RYfm8VBfs818XihdVkfW1asCU4k2bYf+WpCrzaVwVv7ddhy6HuYyZBMQmEXuXHQUSjUczOziIajeKnfuqnAh3ePh+C1Uq7mSDs87av9n8FUduuXeQgyU+/6yYd6oJdvHjRMfPl5WUsLS258ZNwBgYGnGlaTWIslMKpjTL5BBmBPapG4lPpnP+rJgt0Jr84yJqgkqGawHQ+qHX55kyf1zlTZkFTq7bJ51TA0KAz3YScl1AohLfeestpf9FoFM8//7wDJeB2+r10Oo0rV644fyjp+Nq1a1hfX3fCUbPZRD6fx+/93u9hd3cXv/u7v+vcCezfl770Jedj7O/vd9p0u93Gq6++iuHhYeRyOReRG41G8eKLL6K/vx+h0H52qMHBQWSzWTSbTaysrOD999935meOm5YLjl8v72DRID7OG7NO6blanWfSDIUE9eOTBpXZlctlHDt2DK1WC2fOnMHw8DCmpqawvLyMbDaL6elpPPjgg6jValhaWnKCCs318/PzWFpawrVr1xx9JRIJB3ga8MR+5PN5tNttXLlyBdevX0coFML777+PZrOJJ598Eg8++CA2NzextLR0x/3aCowM4guHw/j85z+Pl19+ucNcyTljYhwGTRLoOVecS7VE6ZyFQiG3ftynpFeuo1pYeKxuc3MT4+Pj6Ovrw/DwsDshUSqVnGZPTTUWi2FjYwPVahUPP/wwXnvtNRQKBdy6dQtDQ0OO1tk3C0oKyip07+7uOkFxY2PDHcnk+7FYDN/+9rfxyCOPoK+v744YJB2v3a8+Dbfb/yw+oNbvghQ6Wy95IzFM95d+p9bGg7AiCBcOC86H0pi5iNzcDBoB4FK0AftHgK5du4Y//uM/xic/+UnHDKkVWgnFN3k+rdVqwT7ppNvz/N0rAWgdlHp9gGDbikajWF1dxY0bN7C1tYVIJIL19XW0220MDg466ZoRmGSOKrFpoA03hvbVBir4fDcahAbcZrqUshXM7TOW8FVztURqhSvr07ObUsfKtvf29jAwMODaV2lds2CFw513MxOkNAEImR2PwOhlHRolrlq3+l0pABWLRaTT6Q7JGUDHmWLeHUwfHt0SkUgEGxsbTgPmOjKBR7VaRTKZxM2bN12WMwYMra+v49q1a65O9cdXq1V3QkL98GqOtvRMWqJWlkwm73BXKKC0Wi23T9UtYcGE68m5zmazLtGMvWM7EongxIkTGB8fx+OPP4433ngD//Jf/kt3EQgjkUlrPL5FcKAGzD4yDoN0HA7vnzX/+te/joGBAVy4cAFnzpzBzMwMIpH9c8KFQsFZmrjeg4OD7vcv/MIv4Dd+4zfccSbOPemEmrn2i7TBNQmHw+450hn3u+4z3S+Wh3AP3Lx5E/F43F0wkU6nXX/n5+ed9YMJjNLpNL773e/iox/9KEZGRpy/emFhASdPnnQAbhUVrqvtm9JsPp93OQzS6TSWl5fdHADAn/7pn+Jv/s2/2SFkqzmfn2mbOgcs1v2g31ugtfTrA2LliwrISuvqSiOtc+1IWyqw2D5o20EA7hMiupVDATM7znzDNKFEIpGOw/jt9r5/7P3338fk5CR+4Ad+ALlcriPqUYlAB6OLZANYdCFUorF16P/afyXIbs+qqU7NUNqeRiLaflOTePvttxGNRt0misViyGazGBwc7HjHmnupnalvUAHbFj37SgZKxkCGwbY0uloZYVAcgK590P8EBu2PzrmOkfPI8dFMCsAl+yej03c1eMauuz7PceslEIyEVaBhXRyL0iX9+8DtSEzVLBWsOH+aelEtFnxOg5G4Z+i3JgiodE7mr/OmVhauD/tpLTmcIwKsRtRTw9f+Kh2ruVqf0Sh+Cqk8z0we0NfX15F4iH2h1YB1fOtb33KBTARLpW3mlObaka9wjTRordVq4eTJk/j0pz+Nmzdv4t1338U777yDr3/968hkMi4ZyYMPPujcEdvb2y6iem9v/8IO+vEXFxf/f8T9R3CkWZamB78uoB2Au0MjgJCZERkZKSpVqa6urulpdrM4ijTjcEEbMy6aNMpNc8fVGBdckcYdpXHDHWdIa2sx02Ksq2e6q7qrMktkZUZkiIzIENDKAYc7HNrduUA/B6/f9MiMnJ9j/2cGA+Di++4998j3nHtuRO0uRzggbnRYX6/jwLDCH8icr4tHjY5o+fnOe3t7Wl5e1vDwcKxLuVzW8fGxJiYmtLCwEM4cp3UdHh5qcXFRv/IrvxKQ9qeffqqZmZlYJw8uGJPrd/iA6vxqtarNzc2gJYgHx4SCWDx+/FgzMzMdvM6Pw9zd0pbIUTddnxq1NIpNP893uumtdA1cZtJAz1OIbsx93dwZcNnrFlk/L8rvdn2lYx/dg2y32x0MkclkwoOTFCX6P/nJT/TkyRP96q/+arTrc8PoSjy93OP0qJLXU2+F+6QESg3yl11u5BzSS/sm+329IOno6EgXLlxQrVbT8fGx9vf3NTU1FcVdaaV6t/GhLKGtK600+ndnxSsbXQH7Z9PtNn51Sz349xym87H45cLncChKDqEnAqQ6PF0rn79f6dYG5oPB9Epch9wxUOlap80pHLblMxgI5uMGLl0H7uGFj2k04ttn/B5el9Fqne+FdWXn/OkOTOpgYYwZb+qopWPiOa6guWc2m439vszRC0BJF3j7yFarpWq1qkajEU7XxsaGxsbGIk8M+uYOk6TYvkVUDD2gJfxL2qHdPtvlMDQ0pJs3b+rv/t2/q62tLT1+/Fjr6+v63d/9Xf0//8//o/n5eb3xxht66623NDExocPDQ62srESR0/e//3397//7/95xdjHrQ3U2PQHa7ba2trZiDR2qBnXxACI1BJ6zdOcL52VoaEjValVra2uanp4O5GBwcFClUkmVSkV7e3vB77z/6aefanJyUi+//LLu3bungYEB/fKXv9Q777wTxWae4kplttU6K/SdnZ2NAzNarZbq9XocPFKtVjU4OBi91gcHB/XP//k/13/8H//HHZE/zhj8zrNczt248Z7/7Tz9RUFU+rkv0vduM1I00O2SjxPj7Par25p2M8rdEOAvur7SPmb3FKXzM5ohCp4jnjqMt7Gxod/7vd/T66+/rnfeeacrcfw1j4hSeMKJky5gapTT3+lrX3S54c1mswH/AUtCk97e3ugZC5OMjIzo448/1vHxsba2tlQsFtXf36+dnZ3I7/nC+9x5zYUkZQDvOuMGwZkz9SbdQLnBcZp3u/zefr80N+nvuVPTzeHySIxCHI8QU/grZX74jq15fAfDIJ1vCfNndlMMTh+PYojSgJPd+ezGS3zPx+oRderwdHs+EY+voUeyfN6dQIxUt0jD3wedcEfBaYIRSR0ZR1bo++zbfdrtdtAImSdP2mg0tLu7G9unOLlpfX1dOzs7YXyJvOFfKo+p/vXaBtCkdLcHHbLoXpfLnXV1m5yc1CeffKI333xTrVZLt2/f1vvvv6+/+Iu/UG9vr65fv66vfe1rGhsbU7vd1t/5O39Hn376qT766KNo/kH/a1CvdrsdhXi0LAUed/4CaXFDiNOSRsvQHD7hsIpMJqONjY1IFZCKOD091fT0tBYWFjq6kqFb7ty5o29+85va2trq6MwI0uly6H8zvoGBAd28eVOrq6s6PT3VrVu39Fd/9Vfa2tqK4kxOFyONuba2ph/96Ef6zd/8zUBI0vVKUdBuNsBlBLqkxju9UsOXBmx+Oe3TYMgNbDdUNw0s+fE19Ug9ddhf9Hphw0yehL7NrVYrPF5gLCKLWq32OcV4dHSkjz76SMViUbdu3epo8O+G1S8I4HtCuxHKCQZRnHhf1SgjSMy50Wjo/fff19OnT6MLEjRh7l7lWiwWtbu7G01FaNYgKZSTj9mfmxrCVIhdSbl31y2Kd3q6ccCQQSfPtbpD4P+nV7fI+nm0dKfKYV1JHXlP3wPvBtcvn687cL6tQTr31lMhcp5yiBoac1/fbZA6fM636d9853m5eqefP8eLiXxLmq+bP89pmM4xdfpyuVy01ySPj9w630id/Ob3yGbPcqceMUuK4wpdEbE9iFabNCoBAVhbW4v0hZ/L7PPB4YX2e3t70eoSOWDPviStrKzod3/3d9Xb26tr167p4sWLGh0dDQNSrVZ17do1/eqv/qp+4zd+Q+vr61paWtKdO3f0/vvvq6enRzdu3NDbb7+tf/yP/7HW1tb0v/1v/5v+xb/4Fx1OP9F/JpOJIya9mQgwu9PWA5Z0jdI0AvUS0nnfiNPTU1UqlejWxh7q/f19TU5OamVlpUPuOWf50aNHevfdd/Xo0SO9/vrrwQvulHk06PI1PDys09NTvfbaaxoYGNDg4KD6+/v1+PHj6BPProZM5ry47Re/+IWuX7+uy5cvh47HmUmf6TLdTWf51U2ff5GhS535bvdxBIy1gBap45TuAkmfn+pt1z8vYnPS6ysZZqojDw4OQiDp51qtViP/0t/fH8UZ5Fc4V/UnP/mJtre39c1vfjMWzRVwGkX4xPnpBsP45QuXGr70c90idvfO/+zP/kx//Md/rKmpKU1PT6tYLEbRR6PRCCgWI+wGvVarqVQqaWZmRo8fPw4l7NAVgszzUNLMPY303FAh1GlO0GmSeo3OQGnVdjc6uvf/PHp3+45Hxe5J+rYSlNXh4aEKhUJU+aeHlbuTgqFy45MaWL6T5tL4PHNHCaFYuY8LbHrCljsXbij9eblcriM3yt+ssfO1dxNCwXEPd54czpbOW7HiBHh0nqIYIBNAzg5Bw0PAp2l07/Olgtmr5qXz6vZWqxWdrKRzpwuDw/iAhNvts1wqhtujDSJpIkGQJt/K541PcrmzgzLy+bzu3r2rDz74IM5bnpyc1NTUlC5cuKC1tTVNTEyoUCjo+PhYf//v/33Nzs7q9u3b+uSTT/R//p//p46Pj3X16tXgJ/Y9e9SMbnN+RVZYc/KvjihiaJ2fXYl7YSN5d1ICbBEDTeA4TpC5XC4XiE69XtfKyoomJib06quvdsgQ/JTyl+teHJDbt2/rlVde6Vhz3mOezKu3t1fValV/+Id/qL/1t/5W8DtpDIIYzz2TAnG0KXWiPXL2v9Pv+eX6x//nQm/A3x7k8GyvW+Ee3QxtikKmAZLXnbzo9ZU6fzGAvr6+KFTAOEtnBpvIGaJAQDe0t2/fVrlc1uXLlzsKOdxjSaEGN8ypx+SeaLp4z7t8oVID3W6fnYn6h3/4h/qTP/kT/fZv/7Zu3boVleWMzfOO7vlubW3pv//v/3vl83lNT0+rUChE1aQbARwWXoO5gX/cMDqDpXBQClOjCKG7M52jCt1okdLI6fpFdHweQuHG36MySTHnZrMZPMVPOi43yJ5r9AIN3kP5O3+kXrDPy6NuPyjA6Q0fMwbmSRTnitdp5+/5/bxwSVJHD/LUEeJ76Tzc63dHi3lAB9aHCL3ZbHYoWo/e+Azz9c5V8L9HiY6kbW9vdxxI4XOm6rrVasWWG294wRhZU0dOWEPndxS6pBjLxx9/HIc/zMzMRIOWpaUlPXr0SLdv347GKvn82RnK5XJZzWZT3/ve9/Stb31LOzs7+s//8/88Diah4xUOHEeB+rwymUzQBb3oDp93knKnPA0cXH58LXGmcCo4qpIGJCcnJ9rd3Y1T5Lh3b2+vPvvsM33ta1+L17zxSopY+hhqtZo+/PBDnZ6e6sGDB7p+/XpHCs6r7uFF+HB9fV2PHz+OAzmQc/i+Wx0Mr+Hk+FGxnj5x/ce94GUP7lzfMFd39N0gp3Uarm8dBUtTDx60uI5NaYocpgHlF10vbJhhRqIbFD+b9GlEQM5wYGAgqiphMs91/NVf/ZV+9rOf6fvf/35ATqlnlHoeTnTuk77mC9ENBk2jx9Qr4+/NzU392Z/9mX77t39bN27cCAgPBXV6enaYRDqWgYEB3blzR/v7+yoUCpqbm9POzo6Ojo5UKpU6DDpzPDk5CU94Z2cnNvBDOxbdGZYcDnNKC7NgJITRozyHiVO6Ow1ceNNI6nlQkjsSCFyqfBFIIip35pivQ/YOwcNDaaTsAuRRgY8/FTrGSgESShXFz3c84nXD7nzH89O8thtbN5SpoLtT6fN3xwBjSVSN8+Heeje+h26M2xuF8L7zAvP2HDKfcScFPu3r69P6+rrW1tYknXXEQnH7mePMleYpPva0KI/5+LzSaMYdhqGhIf2n/+l/qpWVldjfzLGcnCdNdPdnf/ZnunHjhr773e8ql8vFoSaZTEbj4+Mdhjd1frngM+bhegpDDS84/6fGkAibz0BzHCIMPum8Z8+e6dq1a8H/6N2RkZGYB47u0dGRNjc3tbm5qfHx8Q4kxp/hRXvIFrsFZmdn1Ww29ctf/rIjzcXYnYeRgVarpQ8++ECVSkXj4+PKZDJhpHk+z+Ze5KEdYXBHnrV33UZ6hd0A7tx3SwOla+QBjuvS5xlPd4KdVim6x/zy+Xwcn+r1MC9yfaWImXZ7WH82uzcajXgwjOoHJgCvsIGebQgHBwf68MMPowcxE3PD9UWQgHufKI4UhvMrjTRTo89rVDb29/fr6tWrobhdeQwODnZ8z73CJ0+eKJvNqlQqaXR0NHo000bz+vXr2t7e1sbGRizWjRs34rzYVuusAnJ7ezuqWvf29uIEKejnhUl4j+5lwuwYQpSJR2VpDhU6Em13i1q/6PJI2f9PP4MRxIljbKkC5PM8t1uk0c2xcEdF6iz44H/oxr5V5u0wtUcYPm4MpFeTc18Xfkkd0Xs6B+br/J06J4yHC0eLNUEZe0TGc7lYV6LI1MPnvv6aG2UvMHL4m+fQ/zubzYbTiPOayWS0ubmpUqkU48ToOC+mzhN0YF1YT3f2UPDkYy9evKjr16/H5+7du6d79+5pY2NDOzs70Qnr6dOn+tVf/VW122d96Hn+4eGhyuWy1tfXw6kYHh5WNpuNYxvz+fNjH5kLTjRGjugVfcZYfc1Tpy11+KA7RVbZbFZbW1sqlUqBUuZyZ0VuVJhzwhey39vbq3v37umb3/xmV2OUGinG09PTo42NjXidrmEeEbrj4noUOv74xz9WoVBQf3+/vva1r6lYLGp4eFgDAwOS1MGzrL3rd+dx/veUCDRi/MgqKA5yQ1oBw00wgPFmPhh8xsV34WWXrTQK5x58l7Gvra1paGgoHKMXvb5S5y8uTr7BGOAhOhH5zuHhYRh0L6xh8ZeWlvTgwQPdvHlTzeZ5yT/fdyZNDbAvjvR8Q5zOI41y0++2Wmf9allIhwePj4/15MkTVavVgI+uXr2qmzdvRsRL04hWq6Vnz57p8PAwGOHk5ETXrl2LwhFnRhY0k8lEa0d3SpgrUTVG++TkRHt7e9rf3+84Us6j7larFRCb5yRTZnEh6EYnrm6v+XupIfL3WD+cOFe6/jmP/vx3apTc0CAc6fgQLpSgQ7nwXJqacIjLDST0pN+yQ2PSOSztSsQ/5zTxaNU/6xGJR+HkWRmfPweapYUqKAu6iwGxeiTrUbIbC6clytxPe2JObC/inihnaESeu91uR07ZaYIy9dc9Wu0W3aPguSfnDDOH3t5eff3rX9e3vvUtbW9va21tTXfu3NE/+Sf/RK+99lqcKezb7w4PDzU3N9cha9lsNuBsL/wiXeBGjvtAY3ee0+gqjdBarZb29/eVz+cjpeLbUZnr8vKy+vv7Y6fIwMBAdIpDB0AneP7x48e6detWx73Qwxg0R4VweHCQHGVx+e5mmIHd6W0BvJ7LnR0/SXtb5gkvefGnI348B15JHWH+9/PqsUc+Xx+rR9QuS9QQpGNwFM71mTvDrseh4eHhoUZGRjrqLV7k+koRM4bTL7ZQuDJjQrTtdG+D7l/ZbDb24P31X/+1dnd39d5773UYBlcwDulCIIiQRnZOtDTC5nIFmF4IHkSXFAe0/+AHP9DCwkIc6CFJ//Jf/kv9t//tf6uhoSEtLi6Gt8qRg41GIwSnWCxqaGgoDDNz87NTYYRUKfJ7bGxMc3NzHVEmTtDR0ZFqtVp0OmLLSup94mHyLF535cjzukXKDku5h+mCwncRaoekgAv5nAucG4V07VIvtRuEzLPcWDMHb7ThQkclrI8R4YTO6UlcvvUEyJE55HK5jhRCSl9HhKADY3TUod0+b86RFgkyXp7v/ONj9S2M3lIRurtySteAIk/eB+Ynt8nYUez+fQwE6w28S6Ek68ePR9M+/1RuQTKgJ+iRpMi7YkQ2NzdVLpfjIJjr16/r0aNHunv3ri5duqS33367I1A4OjoK+bp48aL+8i//Uj/+8Y/1j/7RP4oWo63W2bar+/fv68GDB2q1WtF5jig5jca83sP5GnqBqvh2PG9asr+/r8HBwdjbvby8rCtXrgSdQPC2t7e1vr4ezz44OFChUNCzZ880NTWlsbGxDrlznmc8rKWvBX+7PnUjxf1OTk6iwxonrr322mu6ceOG2u12FNJVq1Vls2dpkKGhoajV8DnDO24HXA+m0Ws3HYTMpOOE3q5vDw8PIz3j8+Q5aWSfBla85ogbvAoPf1FA49cLG2aE0ZUU8AreDgKBUAIheHs/mBY4CAI+fPhQuVxO7777bgeU5gR1ZkijsC8yzlweKb4IJIsnjwF+/Pixtra2NDo6GoqOYpdarabh4WHdv39f+/v7Gh4e1o0bN/To0SO1Wq1ADNguRXSB0KJY0rn5vN0pAXrlMzB5f3+/RkdHdfHixfBW8dz+/M//PKAuaOz5Zl53Bnfl7golze2mkQOC7MoaXvHmDY6gpBGvF4A4DdKr23MwFn4/vOrUi4Yv+Bth9upZv4dDloyL7/C3G3U35h7x+d5O32PsjqcrGVfsKbxNhJVG3qxRNpuNaIAKbV8nHCHmlyIVOBS8TwU9su3GEpqlTi/8QxQNfXFGPNr0nKM75PAO/Adkyd/IE0aCQjT2KR8eHurXfu3X9Itf/EJLS0uxX1g6QyPq9Xo8q1AoaHZ2Vu+++64uX74sSRFM/Nv/9r+t/+A/+A90+/Zt/a//6/8aB0tQZe7OKlug3Dl23pHO2+66AUdPYqSJAiWpWq2qWq1qZGQkHOve3l6NjY1FMAANaC374MEDff3rX4/17YY6Ok+lRvh5F3Mlp1qtVjU9Pa2BgQG1221NTU0pm82G8zo4OBjtnA8PD7W5ualMJqNisRgOhhvoVEd1i5ilzh0hqT1IA5w0hcR7LoP+fYysOwxum/hx+WNNms1mx4llL3K9cA23VxamF4xBpSRQgJ9B614H3jN75VBgDx8+1L1792IrhRfApFBzN+OcXqlicMgo/b5/xhfYP7+xsRGKDcFiGwURxJMnT9Tb26uBgQENDQ3F8ZcsEOf/8j2nWTej7MyQKuxutPW8DWfIZrNZjYyM6Hvf+16H0+NVraxjNwaG9mnBDZAtOTY3zn6x5h6dA8nyfGdqN4Bf5GEiqG5UEAh3aBA8z5PyWebuwuZzdkfDfzM20h1DQ0MdnjJ5125Igzs+Do/7ejJv9+x93G6s3TFKeYJIgM+kuWh30vy7jpLglHmOEcQjm83GNiqPvlGuTjPnU6enN4zx+ft3obmvvTtD/O7v74/9vpK0v78fR6s2m03VajVNTExE1O5FSGzj6u/vj2evr6+rp6dHe3t72tvbC3llbi+//LL+6//6v47oHthWOt8qhO7kvHSiQ+TBURSMOvqE/1P0pNVqaWVlJaJ1dE6xWNTU1FTIJuuXy+VUr9f19OnTDj6EdmnL0/R6XtDj6UkCFPLK8MAvf/nLjkIzZJbDOaanpzU0NKR6va719fXokOgOstOEQjj/P5XX9O9u/M0c+L7rE5ctfvv2LLcR7tADzzOewcFBtVqt2FrLCWVfdr2wYeaEFTxZmNrzEwiDC40bEyaDMKLQUYanp6e6d++ePvroI9VqtY4+v9y3G9OkV7fF4T6+iFInNOqOB163R0qbm5txH3KUvgdza2tL1WpVp6enmpiYULPZjNwUhmtkZKRDCLiXw6cpXJN6sd3gnPRzUue+6NPTUw0PD4dHymc9okP5IQTu+fMdz2sSSSIcDu2kaQi8Z9aQQpo00nPF7rnj1PCwVg5/utFMYVBfY+k8LeNzc75JDSivEQGBDrkx90g/LSJDufq4HLVIDS3/Q+cvMsLQzefnPEwkBo38UJDnoVMYWk8hOQ/yud7eXtVqNQ0ODsZ9eQ4yDo95xAMvQStSAxgn1xW+zjwfGhIQOG2YK7RjjQ8PD7W9vd2xXXFzczMKJfmdrkm1WpV0pgP39/d1cHCgyclJjY6O6vDwUJOTk/qd3/kdZTJnOWF4HTgdZx45zWbP8sK+5QsDzUXNA/rK000gB/V6XZubm7ElC2M3MjISR0VCl0qlomw2q6dPn4aDAVLjNT2uD90pSp2k1OidnJxobW1NuVxO5XI5dNvp6am2trb04YcfRt2NR5WMb3h4WKVSScPDw2q1WlpbW9PKykpsT+3v7+/YM41Oc3vkOsFlxsfqOgoa+xpgXxw1Sr/vv/m7m/5lPOiENBXwRddXKv7y8n/39t34QjxJHcl8lIlPDKOFl0WU/fTpUz158kTf+973NDExEd93YfTFTb2lNKpwhkoVrQs0ChaFwr37+/u1sLCgjY2NOAkIhccz+vr69ODBg1AKMzMzqlQq2t/fj0Mr+vv7I4pmHqenpx3G0n+c8VMj0U0xuyfonyHfydqkESrK6OWXX9av/Mqv6PDwUI1GQ/V6Xbu7u9rc3IzOUXt7e8pkztsveiES4/A8p9PWDT/zp+cwHicVz26cXSH73FKBwVPl3gga8/UIy4XGPWDnU+jjfOU5XeZIGgdlD2TsitYjC+4D/aCZQ7lc3mwFGjn0lkYEGGEcMk7WIoJJ18OvVK4YCzl41pq0Fv+zrQZnJTWiKWqQOh2kBby2AUPJe45s+Dr7Z6Ava49jKZ0XzR0dHQWsmM/nValUNDk5GXwnnUU529vb4Rjs7u5qYmJCtVot2mNub29H+89araY333xT/+V/+V/qf/6f/+eYoxc9gRp5AxkvjHM0w50pR0uYvzsqW1tbGhgY0OTkZKTK2u12VGnXajUNDQ3p4OBAR0dHGhgY0GeffRZ9Gdy5cQcgRTm4fC096iRo4cQ8HO/Lly9rf39fn3zySZxh7pE6MkBEihPvuWh0Z6FQiPSnB1jIqsPTqQF8nl5lfl6Amqae0rl3M65OC5xwAlnXVd0Q527XV8ox+2DT4heao0Nc4Ck8G1rJ8bdDAyMjI2q1zrv94FH++Mc/1re//W3NzMx8rujMt6D44sK43QjoMPAXQRwp5JfP53Xnzp2OPBBjkM4jkwcPHujk5ETFYlG9vb168uSJ2u12tK4Dbtre3u4wUoVCQVInjM7/fqUL220OaWTYjTYYQDfc7XY7qlwzmbOq8HK5HIoHY0P+ant7W8+ePVO73Y7iMioPWT+e70LearU6Ct/YhubK1qMX3+bFXHyuTivWl886vdwRQmg8Os7lcjEWPvNFhk86317E/flByRO1cQ/onBbb+DqkEQufwVHkfqnC9ANWkC0vkOJkIT9QgPt6dMHnSTe4vHdzhlFi3aJ4Lp+v13h4xESjDt5zhQltPGrMZs8qpZEt35EArOpOObxJhEgumLOdyUe322dHs25vb8ezJycnoxfB6OioJicnY6sjle6VSkXf/va3NTExof/xf/wfA471NF8aCHjlNpGhK3jfw8yaoYcbjUYUq21ubsbRkAQSw8PDmpiYiIYo5H9HR0e1sbGhUqmkS5cudeyuSQ2dXy6/rAvO1cHBgQ4ODuIEq0ajEbSr1+vK5/Pq7+/XBx98oN/8zd/sgJ/dyXQnTTo75nJkZCRqZFZWVpTJnO01JzDAqUXmuiFH0C/l2zRN4rTn6uagO5rCZ+BRR3W9ZiMNrL7semHD7JGMK0CiBhpwUMbPoGBIhJyCMIiCN47yaDabcaZtNpvVT3/6U/3Kr/xKR49pqfOEISckrz8vqkgjhW4REwtMlLmxsaHV1dVYTN4nZ8AcFxYWAnZZWlpSrVaL+wNvs+XKFTl5iG7eVBohp5Hel3mCvAfDdFOkRKbexQqhw9PnmXiuY2NjUWzCnIGtHj58qGq1Gls/oBlGgdwQ83WjA495fhJjR7TB5dG5dO4oMbc0OpfOHQEuR3RS2Ivx0gCFz/JM+k3zGRd6VzxEdhRNwWu+W6EbTOuGymmBDKY84zkyvkMkiUJ/nizwnjs1Ph7WyQ219xvwSLZb1Oy0Zh0dxnZnDF1B20yHFOELonaqjA8ODnTnzh19+9vflqSQMZ5Jm9DT09NwGk9OTrS9vR01Ldybfe0Yuenpae3t7WljY0P5fF4DAwMqlUqRN0Q3Pn36VKOjo/qN3/gN/dN/+k87HHjXS6wf+Uhy9843bmS8ut/7RXDP3d1draysaHZ2Npw/2nWyQ4P12N7e1tjYmB49ehRdxKAtfOC85U41ULlHhfV6PQw+0frs7Gw0O2m1Wpqbm9PW1paWl5f19OlTXb9+PQIDN45eXJgGJ96OtFqthpNEu094x3kFOvtrfqVOsOsPD+JS+riecFn11BaymEbiL3q9sGFOB01E0NPTo/39/fDK8FrdeKDwfWL9/f2q1WrxeTyt4eHhyDPAkH/6p3+q7373u7p8+XJUG3IxWbwmFjVVtM+Lkp3o/PZq2Uwmo4cPH+rg4EBDQ0Oh4I6OjmLrSX9/v7a2trS7u6vR0dFo9s/2EE5fAW7a3d3tiABwSP51L+bo8H1qpLPZbBjY5+VPaCbBd3jfvVCUPfmr/f19DQ0NdTzn9ddfV19fn/75P//nHdEjwsy8C4VCR4EKQsnFZ90YunPRbQ3dK3XB80iL9xAehIoL5euFGi7krBV87bUSOBx4/SkkiOJEhniv2/p3UyjOl07zFB1wpc5zmZuPA/pCY3dKeL8bzZkXSJk7VlzuOLvz606vpwAymbNUFgc0eCMVdjX4egNV47T9s3/2z/T1r389jDn1H3yWtQO9k86qrOlYBqrAFp5Wq6VisRjrure3p2KxqEqlooODA5XL5XASoO/x8bGmpqaClz3VAz1dXp0vXX5ZW1f2KT/hHOVyZ93LisVipAT7+vqiqcXp6al2dnZCP1Pd/fjxY928eTOeS5CFY+D8x9rj8LB9dH9/X319fSoWi+H0oP+hd7vdjnOdf/azn+nSpUsdKabnwbvObziykjQ+Pi5J2t3djSYoVHS7XMMbqbF2+eiGuDnvplFuKnseFCAzbpjdDr1otCx9heKv3d1d7e3tRZS4t7cX8FA+n4+tOlLnnjcEn9OD2L5zdHSkoaEhjYyMaHBwUIVCIfKvabP8gYEB/eIXv9DHH3/ckej3IhYI5YrGvZ5uno4bJf/fjVgmk9Fnn30WBhkP6Pj4uMPT/eSTT2JrmHTeKcdharZZseVCOt/D7FHcl12+4GmhgxujlDbelpHLoyG8YTeO/hmnk6SoROb7+fx5b3DyQZJCQBCuo6OjgK9Tg+TRtMOn5FrTYiWPSt3AehGH/582waCoBJp6wRa/Ga8LPT+sNxfrAUqUKh0vtvNnSedC79Ey69VNqFMl4XSB9z1X6UcrOmrizosXavlauSMB/U5OTlQqlTqiLMbPvYmG01QCcuHzQLHv7++Ho0NExFwc0ZIUW26AT8n5Y7Qwyg6bwqvsua3VapGDzWbPD/T467/+66AB25Og1dHRkSqVSsf+deDVSqXSMV5on64xaBTfc/pL5/lKeIlKf99q1Wg0Qg8/e/aso1NdPp/XyMiIxsfH44ChTOYMdm+321pdXdX6+nqHgYQf0ijPC2dBfra2ttRqtVQqlWL9gbU5BAT+ccf8gw8+CFTVdXO3xlK87hEo6zgyMqLLly+rXC5rf39fi4uLqlQqgUqhj3lGWqzputERAt6DDl4F7ohRmuZBH8CzjhRzr+c5Ien1whHz8PCwms1mHHtIv2z2RDocBWMjxMBCDu8xYDwdWveBxwMRe7T74MEDHRwc6O233w5ipTAZz9ne3tbU1FQHjNktak6VHu8DjdAac2RkJMZHpIyiazQaWlhYiNzVxMREwC1TU1NR4EVkSQckjAh9mp8X0Xe73MD6ortDksIvRBh4w24AJEW+LL3S13wNUSi0ZeV9og2iFgQTZqbIg6jClRuMnEKi3WjgjoXUiXZInZ4vwuZRCsrCq8wRMDfiDpPxXZ7PeHk+f3vBko/X6cn4UpjLlWQ3JMSjnHStUGIcYu9RrEfV3CeNtIGbU9icdXLHjTUiX5pGtayNyx78R7TlNQZuzJrN861e/jqfw3l22kMTDLzzBXMkBeHICoYP9OOjjz7SycmJJicn4xAa6SxnivHPZrOB+s3MzETeenNzM+4Lj6EbnabwuK+x8zp/Y6y9WEs6PzITvtjf3w+9x/woOB0dHY0CrXw+H5D2wsKCisViR41QiiDCU86HW1tbajabmp2djXkztv39fY2NjWlyclILCwt6+vSpTk9PVS6Xtbe3pwcPHujq1auamprqcF6fp/+6QdTIEcgLh5FwmEo+n1exWIx0ifOf1344UuRohCM8TmNfU5c/X2eXzW5B4ItcX6nzl7ct7Kbcm81mQBmuQNxTAGLCAG9sbMTWAZQ9z+Lz3PPo6EiPHz/W6emp3njjDWUy56e6wLTAyuwf9oIyV37dCOawBAv69OnTDkFqt8/3KrrHtbe3p2bz7HSo+fn5yLFgaEZGRgKO865X0NQh0m5XGrWmME8a+ft4YRQcCi9O8Oinv7+/Qzk4Y7oi889QyCedFy9RMwCj8xxJYSx8DKmy5ztp9MjaeJSeQkUerXUzDA7RuTC5l4whppWqVw/71hQEGicTD525+RYlX18XWOfJbpGyIxjQxOnjqQGXM9bZHcCUxozFoyKiEqcRn0tRHdY4zdnzu91udxhG+JznuwF2JxvESlLQn4iTsTEnDMLR0VGgH9ARVMNpg0PthX4+Dy+uGhsbU7PZjMgY5Ic6CTobbm9vq9ls6sqVKxFAsH+5G2SL/kxTO2nE6kGJ1LnXGJ1HgAJNl5aWoqoZ+tGjnIgf40HR5tOnT3Xr1q24v69lWrPQbJ7XAJXL5UAGvWCw1Tqr0p6bm4udKePj49re3tbMzIzq9bp+8pOf6Pvf/37QhKLgboie6wFHCuEVD2pAYff397W1tRWoS6lU6rg/DV+4L/R04+t0cCOMTHVzin3MqUx2C3qed72wYWbgwFEQJYU8dnZ2YvtPNnuWP0XQKKKh+hHD5Yqd4jAUIHsAEXAO6x4YGND169cjF+RMT54K5ieS89L1bpcbabZorK6udhSr0ZweSJp9g3zv6tWrAZENDAwESjA+Ph6MgDJw78oVUzqmbmuRRsw+L2ei1Ki58kuFAAb3+/rzoEGq4F35c1+PsFiXXC4XeygdtuqmnDBqXLznSqKbo+WeKt9z5dgtCnBjz3jhV/4mimarDHPiGfx2o0uP3K2trY4Uh6+XKx2Hz9IqUjc23XgCReXb2KgaR+7guZQvnB4YM5dJX1ee5dXd6fic/h71IafAfY1GI+QDw8vhEFzkRDHQ3tgkl8tpY2MjzkgndZLP5zvamGKEgarZLuWyjuPB2IvFog4ODjQ1NaVM5uwYRAzLyMiIenp6ojqYs8TRW0STTjenZ4rOkErxHCW09OgrRY7Qj9Ac53t1dVVXrlyJ1wcGBtTf36+xsTFtbm4G7Vqts6LElZUVlcvl2P2C0XEZhi9JNZbLZQ0PD8frrOH4+HggloODg7py5YoeP34c3cByuZzm5ua0tLSkx48f65VXXtHBwUE4tMwX3kkjZC7/33UGzlBfX5+mp6d1enqqRqOh1dVVtdtn6cTR0dEOY+tIiwcJqZ7rpm/dmfY0m8tYqk9f5HrhHLMLPAYGmJJiIGesNOeMgjo8PIwiBZQFW3GAyiVFMwwi5uHh4aj6Gxoa0ieffKIf/ehH2t/f7+iOw9aJwcFBSdLKyop+/vOfR2QNgdyrcZjGiby3t6fNzc1QqkRHzAnGQfCLxaJGR0cjZ4VQSAqohX2lXP39/dE96kUvj46lL+965grfFb1/3w3hl8EuHu25wk4dAod43eAxb/+cpzo8Kkhzsigbz/O618wYHALHGHkxEpGyRwkoSSI7+I3n9/T0RMEakYF73NwTvsIgQl+iMb7n0SP097XAcZUUfxPdebTsyh0aOq3dCeJKESR3YHk/XVMgZ0cKqC/hHo62OL25FwaBYlE+l9YCsB44bvl8/nPRsrfn7Ovr05tvvvm5iBz0Jp0zCBz0hCdYx0KhoP39fT19+lQff/yxdnd3NTg4qN7eXpVKJU1PT2t7eztqZnp7ezU8PKwf/ehHqtfrwQfoRUdy+E0ERyfEbrUink/GoeEYSxqJDA0NdWx13NnZ0fr6etCWGh70kzvBIFhPnz6NE7qcVzwvn8lkYvuTB0SOCo2OjoYTtb+/r3q9rpGREe3t7cXOlnK5rEwmo1/+8pfhHHC5Y+1oTrdgwXVNqpP4bLt9trd6bm5O5XJZh4eHevLkiXZ2dsIhc/SX76CrXZ55Xmor0qubnDkPv8j1whEzShAFymEUQEksFhPB0Nbr9ahyRAlSqXt0dNRRlV0qlbS7u6vDw8OonMznz859RfhQDIODg6rX67p9+7befffdqPKu1+uq1Wra399XtVrV5cuXNTMzE2NNDaALDIrDPdi9vb2OaMtzVaenp+FgHB0daXZ2VsViUc+ePVOz2YzqUm8TSFMVPGYqsr2JwotcboT4/8s+73vB04gzhazcWHn06UpM6vQKU2PvETZKSuqEreCpbkyevu7w0PO8Zx8j92eeDgXzG6Pg9IOuCCbrwvF7jNmRFF8LjBxdpthl4PNy2qfKyOcCjJtCy8w7daK6pUO8AMajcF87lJPfy9fdx01VL9GGFy/52rux5zXky2WN+6SwM89mXI5IuNPS39+vXC4XfaOZK85QyiM9PT0aHh7W2tpax/58d/Da7bY+++yzoMGzZ8/U19en+/fvx6lJ4+PjKhQKmpyc1NDQkEqlUsDY8DoIiY+FcTuU6k5WJtOJoLHHG92VOnUYGO5/cHCgjY0Nlcvl0HuDg4MaGRmJYjlSVzgntVpNi4uLevnllzvG5YWDlUpFPT09KhaLHXzIukxPT+vJkye6fPmyLly4oJWVFY2NjUVx1uHhoRYXFzUyMqKLFy9qYWFBP/3pT/WNb3wjnAVPi7D2LjsuI6lxTOkCWoRjNDg4GMee7u3taWFhQblcTsViUcVisaMmxA2xBzXIkG8DdVq4bvr/JWL+SvuY8RhhHGd69ywwvhhQvFA+u7e3d/bwv9mWgtHf3d0Nb5qFIhr1845zubPexMPDw1pcXIyj2SqVijKZjC5cuKB6va433nhDKysrGh0djec5NCJ137OWy+X07NkzDQ8PR1cnvFmqDIHhmF9vb6+mpqYilwMSwByB1TlXmXnTXOWrRMzQkTGjrJ73GRcu1sgVeLc8pt+bz3h0iFFzxeL5Hy/OQ0goAvR1xcghFKnB7TZfF1YvJmMtUVTc29Mg7lm7QXYD6+Mj6mVeNI4oFArPrSL1iNLHAb/59jFe8+f7eNxBYr6OHHj07fd0VMYVmysfj0j5LmuL45l23fL7eD7ZFRL8lm6xBP7G6UaWiBopBEsvcsEuqyhEcrmTk5NBJ2op0jFJ51X/PMu3eXr/BU6pIiXG8/f393V6enYOPQdXZLNnTZJmZmY68tS+9owberJeXuwILfgs76e7FzyS9LVn7TAaT58+1UsvvSRJkWeF5mtra/G8RqOhUqmkp0+famJiQkNDQx35b2QLFNRllXEPDg7GXn6eD6IB0rS/vx/7j7EBd+7c0fXr1wOJSp1THLxUZ3dz5Pmeo24u/8gwzgUtVff397WzsxNFcul50Sm6xH18/qnxdZvCnL6Kjn9hwwx0DURHkYWXslPZCNPDzCxsWhginVcWoqw83Pej7BzCojEAioOoNpfLRQXlN77xDVUqlShCcOHwyMYVPcrj937v98JbBg5kPhgXqkXJgVNkwYZ+FFCz2YxOYJLiWDiUH9FUt0jG/04X/nmQDu85Y/IecD7C5srf84VS5x5ad7ik8zyZRzo8g897To/vc5Qmxs6jX2jgxsodjjS/xnh4nht3dxa4PCLsJrhOKwyu5+QZX3qmNc/2tXBEgft4tatHrz5P1iDNeRFtOWLj6+LpCWQSw4oRZN4+Xh+P/4+CdAQEeXH0od1ud+wJ9ojY14jPY7CZk68zqSeHv92hcXSKHGIul4utVa7UuTy6SYMHV76eEuEqFAqRI0WOgeyHhoY0ODio8fHxKDSqVqvRgIR5pjLlRjutfsa5x1j29PTEMY/5/NlRjxR0ecMR53FQOHiiUqmoXC5rcnIyZJHdNfV6XTs7O+HYkOd9+PCh3nzzzQ5nAJqPjo6G7EvnuziOj4+1v7+v1dVVXbt2TSsrK7GuR0dH2tjY0MWLF6Mhy9LSksbGxvS1r31Nn3zyiT744AP9O//OvxPRu/Og82j69/MiUec71y2pPoR/qYmq1+taWFhQPp/XxMSERkdHY+44lY4E+fjQJ65j0jGnOuyLrhc2zI7D1+v1gGApcHKYFMg6jRyAFKiKdION0T89PY2qQmBy4AgvmpqdnY1CB6CsTCajoaEhvfnmm9F9anJyskMR+MK4ksnlctEe79GjR5E7Oj09a3Q/NDQUi0GeGDiWM1xLpZJWV1fD+261Wh3efCaTicI3xkM+JlUszzPEUueRi+li+/ecuXO5XDgF/jnulx4m0e1KmYvIw+FH7uHOF+t6dHTUcXB8t8pG+AFF3o2Z3at2w4wAovyYm0cj8ILnj9iPzrOI9BmH1y/w43T2aM4VcgrBeZTPurhDkioYdyap7XDad9vawj2RF+TU6e1OCt5/6py54yCpw+D4kaWNRkOzs7MdVddOB3c2vHiUyMsdER+zO0IoUP4nDeY5Z9aIFp1saWJ7U1pYNDAwoHq9HnzjeWBSTFevXtXY2FisOZAyypltRhMTE3r33Xc1MzOj/+l/+p+0uLgYzqfn/N3IYnzb7XYcz+rV4dDOG34QZUoKBBFY/eTkJBxfX0d2vRAF07lvYmIi0DsCkHb7bMvV0tKSLl++HDLoehL6IA9jY2N69uxZFMARefO8np6ecAAmJia0uLioer2u4eFhPXnyRJlMRuvr67p3757eeeedOKeZToSkSlIjnabS/O9Uj3mQkQYc8D9OS7FYVKPR0ObmplZXV6PIzcfAuvj9fBeK25bnOQ9fdr14jzApmK2/v7/DsLonQncsb+KPUnbs3j2Pnp6eOJUF75cii/Hx8ciFcJB1Pp/Xzs6Otra2grkRpqGhIX300Ufa2trShQsXNDAwEK0xU/iTH48APbdCBE07UHc+2IPJorBnbm9vL7x76cwwsF0BD9+dFYpTEKb0x6/UcLtBSq9uzElBGvfy316Akb4PfblvLpeL4ysd7mK+2ez52asYCtabyAOecSOFEmPsruh9HukYXRDSOeDtumGDDkDujBG+YOwIYCaTCUctFT6E06H8NNJk3I4eOB86PzoMC0+mSAD38TVmLvzt24tSBwGa8T/393Xw6Jxnt9vnx5c6kkAkCx1cmUNHipY4mpAtkD4O4FLm3mq1Qj/g1PrBEA71UoTmuof1d0PF2GiK4bzN9x3VwWix/tCF55OyajQa2tnZ0czMTAf8i74keOjv79fFixc1MTGhYrGo8fFxDQ8P69/79/49vfbaax28QAX00dFRHFtJR0H4cn9/Pw7FcUSAeR4eHmp9fb2jiIvdBTQdkRSH1nCIEHvAc7mcqtVqBCfNZjNqiUgLcAiIJD148ECDg4Pa29vTlStXNDk5GTTs7e2NKHR1dVXFYlEvv/yyWq2WfvKTn0TfcuaAU+Y/XKnxe96P87p/11Er/sYO9Pb26sKFC5qZmdHx8bFWVla0tLQUToPrJWSLIj/Wz514dE86hy+6vlLEXCwW1d/fr6dPnwZDet6IhcKDRZFgPCEk3gdXqnwpNJAUHiWfwQtjAR2OrtVqoUCKxaIuXLig+/fvq7e3V1evXv1cXtUVFkSGeR49eqTx8fEOWM8PXyACINqi0nBnZyciAumsutyhPU6Ykc67fnnklzIVStPn6RDuF0W4KZzilel4wyhfjxi53KvkM97xDPjNDwxgjBQIEbnQU7i39/w8Wh+n9Pn8Ma/5+qTj87+dLtwXZwDBgz94Pd0/T1TDZymYcUWbKotUCaTePfTGoDAfh8MxCH7xHRQBESbKxNfPIUCPEAcGBjr6IXv6IL0PRppUlNMEerqzzfOgmRssd674LZ3nOt14I0fQJy0AckXH2Lif155gNHF2STE5SsXau3MIbxBwnJ6eRrVzoVDQ9vZ28Cx8neo0+GV4eDj01OHhYeRY2X5HfnVgYCAMLDSfmprqcKTcmUfXQnt3ao6Pj8Nwu2PEelWr1TinmVTMwMCAZmZmlMvlVKlUgudrtZpGR0f18OFDfeMb39D29rbu3Lmj09NTTU1NhRMCPxLZMmeKfcvlsjY2NjQ3N6dSqaRKpaKBgQGNj49Ht7LNzU3t7u7qxo0bevjwoX74wx/qu9/9bkfTKtY0RXJS+Xcdmsqe/+1ImyNW6DZH4Hp6ejp6sXPSHlvPRkZGOvRMKut+pQjal10vHDFzdBiL4gVZx8fH0c3KoxPPZeExo+DYrwzh/Xg/hBzlWa/Xo9gKj9pL+2FGYKxyuaxXX31VGxsbWlpair11Hp1IndWmXMfHx/qv/qv/SuPj4xoYGIge2BR98EMu7fDwUENDQxofH1elUlG1Wo1tXsfHxyqVSuH1UzSG8OHQOMOkzOROi0dn3Rg1vTyn6Pki7uVGz7fKuGHhtyMMPJf7YmDc+0y3p3Xrwcuz+fGqdDdgroScpxwqd683vT8Gje9yb5QRNAU6y2azHU35eS7Gw9eL/K9D6b6OOAKuCBwZgI8YCxAvW2M8AmQ/ak9PT+xQYLudo1jIRzabjYJNeJDokh/PeWLcpM87gp6HxyH2dEzKgxho36pDvnN6eloTExMdzgQOMPdBLoiQ/XhQRwZ6eno62k3Cy45mSOcnFeFMem6Wz0JrjPjo6GhEcYwFGJ8jG523+KnVatrb2+to/NHf36/5+fmQAfSZdI4oOkw6MDCgqakpXbhwQbOzs5HP/v73v6+hoaFAD6AfhWxsUSL6BUlZW1uLqD+bPetTzxnIHuig2xcXF7W0tKStra1o2ERLZPjzjTfekHQWbY+OjkZA0m63NT8/H8c2kq9n///ExESkRMfHx4OXnz17psXFxQjyoJ0ja44UejTqTrj/pHoiRZvgDyJleCetOxkcHNT09LQuXryooaEhra2t6cGDB9rc3PxcIXQ39POrXi8cMcNI5IBcgIigJIUC5n8iQoiZRlbuoROxuleD8UJwiFxRKmkEXSwWdenSJX3yyScBe2cyGW1sbGhycjIiVjfyUude31qtpuvXr2t5eVnDw8OSFNEeXrjv3SwWixoaGtLGxkZA3wgYbUHz+XzsGeS5KMNuC+eK3Bc4hVG7Xf5Z6Vzx4TSlMCzKSups5OEeKOvn93aHBsMMf3DaGOMhJ0i043R3pUpEgjeeFoFwP+bFPRAGp1lKCwwK33Wjw/pTSQqUR/TJmOBxL15yZ8Gjc77nTSCgOXTwrm+Mww2W5yiBL7lSB9Oh+2azGWffegTGmFBaPJvnUYzJ+x4JIHMoTRwJ35mAA8F86EfAD+9PTEyov79fe3t74WygYPmbZ2K4vNoXRcr/Dms6vIjjTBOSnZ2dDgOfzWajN//g4GAUR+Gkcf54Kof5fF7r6+taW1vT6OiohoeHoxEKVd5uVI6Pj/XZZ59pdHQ00lyOMrTbbb399tuxxXJ/f1/r6+uq1WoR4R4eHuprX/ua7t+/r8XFxY7jZNGRnmppNs/6QhQKBe3u7mppaUkXL14M+ngL4bW1teB3tlI9ePBA7733noaGhmIf8oULFyK9uLu7q2q1GoYURDCbzWpnZ0fz8/NxUMbg4GDsr/ZzqTc3N9XT06NLly7p008/1S9/+cs45MJ5HD6Hh9Po09NHbgyxTakjlqJcrk/5LHKM3eO1kZERFYvF6J++u7sbFd3oduTd662ep+u7XS9smPf29tRqtWJjOczL6ygfPHeUD1AnhHYYEYgL4zk4ONiR6/R2e75tyns6E2lIihNfPvzww2CuYrGo3d1d/ehHP9Kv/dqv6eWXXw4mcsjEFyyXy+ny5ctaXFwM4UHwEVicEzzrTOasEAbvidwS56RmMudFM4w77U2dRh2pE+MwKvfkQrj9coUN3XnNn+UwH/dyhe0QKEzOvdw4OFTv3d1YG5wbp7V7we12O1AUj0BTmrhTB5/4+zhrOACeH/cKYodjeQ3lhMLotja87sLt6ItHm8wjTVVI5yc/YWjSNUphOb8P9OM1nkWERDSdRvrucEmf7y3uvO70dsjPt/wMDAxocnIyDEx/f7/K5bKOj481MDCg+fn5iERQVhQCEbUzLv8bmcfJA84FiXLIGhqndRA49eiVbPbsFCKKkdA96ArPu/peY4f8oS/bvXDkiFahFdEpzhTOKUWkzm/ow/X1dVWr1TjYZ2ZmRuVyWTs7O7EttFqtRrDj2/Hy+Xygmowb2UDvbG1taXR0VKVSqeOYVww4xWXI/snJiR4/fqy33npLH3zwgU5PT3Xv3j299957Oj09jX39zvOs4crKSmyBWl1d1dTUVBjyQqGgl156SXfv3tXm5qZu3bql5eVlSWedIx89eqQrV64EL2LYQOHcwLkO9KJER9Dga0dk0+AlDX7QC+hG5wfeL5fLQct6vR72ggZaqUPgzsOXXS9smKnu7Ovr6+j1XCgUVKlUQnD5wZtlsZ2QHqV4P1nuiYcC47VaZ202gcBTmMqLLVZWVoLwNCx59uyZ+vv79aMf/UjValWXLl1SqVQKYrky3NjYiCiX6N8Ji9CxUCzE8fGxNjY21Gq1oiKbCkiEhKpJfohongd3eITlDJPmNZ25UgPAe94dzWEhfvt5wtAUxZFG9hh6j2YxcsB9XqTjZ9Z6+1KpE17yXCEC4dGxQ6jtdjsMssOvnl/zMaeOAPTisz4fDLg7RY4QEDU6FOZrlq6bX8zNjVw3Tx/HwdMWzoPQJl1HHGI+043W7jh4sRFr3c0hZG38B4XFrgOg8osXLwaMzdGIXleSz+c1NTUlSbEVyWse3GhUq1VJ6jiznMp+0B/o6WvLWg0NDcUhC0SeW1tbGhwcDJk4PDxUuVwOQ4+eIa9LFERE3NfXF9u0JEU078U/jpo4T9El0Z1BeBfYH1k5Pj7W9vZ2FMLiYJHKcGQj1SEUzDI+UgELCwuREkFH9fT0aGJiIngR3dtut7W2tqarV6+qWCxqc3NTlUpFT5480c2bNzuQImSHXHQul4vqdDqbzc7OanFxMeZKnn15ebkDmfnxj3+sq1evduzxdhvheo3/pfPGLf56mvaD5k6rVP4creyGFkJ3ZCKXywVqQi6auoTh4eGo5k+Rvy+6XjjHPDIyEoUNKGE8fnrHwtSNRkO1Wi0UBJGve1bSuZec5vooMKJP7t7eXuRUPIrj8vviybIPbWtrKw477+vr0+3bt/Wv/tW/0t27dzsWQ1LHNhs3DszLq7BxPMhd0U2GXLpD2lycUINS8Y5Qz4M4PGeawse+0M+LpHnteTlen7sredbHI0E+I3UegHF8fBz7t2u1WrReZfypsuL7rBk/XtzkSErqAadFSSldUiFwxKCbQKPY3RFIo2GiPuBacrZ4xf7jzmOqFBwNgBa0Y3TYOc2VuxdPrQaf72aYnQ7uwPq6eqGWO8JEhc4H8ADvo9SBqtnbS2qK6Nm79QGFt9tn5/NiYFx5Im/tdjuQLf7nMziYzgPdECNqP7yVqe+ld0fVx8dnMY4nJyfRt4C18nlLiqNwcaaAl/v7+zUwMKByuRzV2ayR7xQYGBjQwsKCfv7zn8ehGMyBLanVajW6dkEX9Cvrx9pBD49oQbK2trY6iulKpVJsUXVHsd0+2wr6ySef6J133glH89GjR+EwOW3RI9vb25HeAxqvVCpxwMT29rYePXqkixcvqq+vL+blcvn+++93pLN4FvPCaKYG1GXMd1x4tJpG1c8LitBJHpikRprP4iQWCgXNzc3pwoULymazWl9fj4pu59svu144YuYg7Hw+r1KpFOeXSuro6kIVokNoCCjGFuK6ciG6hnmazWZEya5oUSiu1FEcMMGlS5c0Njam+/fvq16va3p6OoquJicntbOzo3v37mlnZ0dvvPFGBzxPHtaNPTkojBGKot1ua2xsTP39/dre3laj0Qh4utVqRbQgnR83x0KSf4ORnudNpVGeM40LkP92Yw/NUAKpV5gaYY9cU6eAqsVWqxV7QIlwMF5EHSsrK7G+u7u7wTtuQLhSwyqdQ09uFJx2fEZS5CTdsXCvjtSYsQABAABJREFUuJuBxAh7rpL5Y1hTA+dwFt9JK2SJwDm2FF5l/gi7r5UXmqBMnM98banHcIjVHShk0Mfia+v/u7FnrUjBkIt1iJnPshbkZNkKBM3dgWG9fCzNZjPkBoPn65zP5+P1TCYTcuMFYYzFc9PdHEB3XDBcAwMDsZ5EzD6/tKjN9RXGYmJiQtlsNva4ory3t7fjc8hCb29vR8oPxA2EAGh8Z2dHP/vZz/Txxx+rXC6rUChEBXCxWIz+EY4KwY++lRN4HXoB/xN1rq+vRwtRegtwjG+tVgungFTf+vq6pqam9Nprr+mDDz5QoVDQvXv39Oabbz43mmw0GhofH9f6+nps99re3tbExES0Tp6amtLw8HDk8XEUjo6O9PHHH0c+u16vq1QqRf7ajSSXj8PlHNlxVKFbAOOf8//TQDJ17Hmup71Yl3K5rImJiZg79RQvcr2wYV5YWIiwPJ/Pa3h4uGu0CdPjQQAfsdh4cygAP5VFUuzJg5Cu0Nh6g3LzvBg5RSJmIuLe3t5gDBQNxRebm5v62c9+ptnZWV2/fl3S+X40lLIrRjyenp6e6HldLpcDrj05OQnIP5s9a9bucI8bZiJw5gcDvIiBdiZL844+Zr88J8xvjILDrv43zgQCgWfu+yjb7XbsT/WcF84V887n85F3YawOLfFch7J5j7F4npN7Q1+fE8ofHuwG64YAWFWtN+3w7xPlpZ6455B87VCAQJcO9aVz90IhaIdy88+nz5PO0QiubDYbqBLbpLzIyWF7DBn8gtPhMDGfp27AZY7owA9QcHoxt3Tu/KYYDJp7kZ3zJbSGlkSp3Ae9wZj5vqcf/LOOaPT29gbMyjjpbZ8ichhUIO79/X1NTk4GckLkjBPKWmQyZ61oDw4ONDk5qVdeeUUPHz7sQOTQOzg3BCX5fD6KtkAlU4eTaBU6seYESMDs5Giz2bP8PU1AcCJoEDIxMRFHNTK2ZrOphYUFvf3227pw4YJ2dna0vb2txcVFvfTSSxGNO1+ur69Hbweeu7OzEweA0MBjbm4uDpRAxxCl/sVf/EXk8f+tf+vf0oULF+JzKRydXt2ccV7v9nd6IXceaXt6J9W7qQPtaB4pioODgw4n8IuuF4ayZ2dnozk7e+ZarVZA1gMDA7E1o6+vL5oIFAqF6AstKSIN4CEExAs00gYm0rm37FFUtwVqtVpx7qh07uGznYsqOgoJKpWK7ty5ow8++ECSoj0bCp8ilDRyZWtAoVBQu90OgSSn3NPTE4VfGBMKM5iD5/Oel6tkcT2y9dxuGtH4d1utVtCRoypTpuLzeHI4JYeHh9rd3VWlUomoolqtBgyIg+YX9z04OOgo3CD6TSNIh6MwpkR8rmS9qIdoFCPAa+7wpWvlhj+Nnnif3ykk7n9jNB1SSy9fKx8PRhX6e3T3PAXjcLPD0RgRlwP/7RXNfN/H5U6Gd+NDcbjywHF0Z4L7eYMK5BgacX+HOZ1GPT09IfM08PHIE7oAd8M/jJM8tK89xtujf2SMSmfu444G/CqdBQa+C8SRJoyeO5fka7e2tmJurBsOLEaWTlvOUy7/yBP8itEfHR2Nv90xSvnDHdgUykUnOQrBaU8YeE6r4mwBZLLdbqtWq+nu3bt6991343lra2sdOxRwKJlDpVIJp1w6q7HZ2NgIh2xvby/6dIO6uUNWrVZDH92/fz/o9jyj7Dz6RZdH9+4o+49/1r/jEXvq+Pl33aDz+uDgYND2y64Xjpip8qTZAh4i7eCks3wIhhuIhCILhImFRjlms9k4GCCtsiV3i4D39PQE/Msi8jdedkwsqbZuNptRFYzAIdCnp6daWVnRP/tn/0wDAwP69V//9Zgrjc692AtF43mZSqXSEcH4lhEEmS04ODJ48dDQBZ7LoTiiMV9s6fONQFL4H2iJaIjLv8cakZJInR2cE+bjUCqCxLhxAogWUBSuePzeKdzK/NyQOGqCJ86FwiLXjZLz9ffnuePFmgIrEhH53HzMrhx9/6nPw9etm+F1JQodfV2lzrNgXWY8JYDxcKOLLOLIwleupH393VkDEmUuXuDmUSjf4+ACj4ihJw4U90WZszYzMzO6cOGCdnd3tbu7G/LmCi11yHp6ejp2UuDAMb9Lly5FPpbXcDrIDXNfdAFyyUUjkL29vahncB0nKbZEORKD8cQAArETzDCuo6OjQBXdQcWQOmrltS2OOKaolssLc0fWPaDBQceBqlQqcT4xhoMgicIsnMCenh5tbm5qfn5eL730ku7cuaOTkxPdvn1br7/+ekTezCN1yICpyTXPzs5qc3NTCwsLsT94dXU1eIl8PbVN3/rWt3RwcNDh+PnVLZJ1uriu7HalMvq8aBs6OjqZBlapvnF0Mg3Anne9sGFeW1vT4OBg7Fvb2dmRdGaAYMjd3V1Jna07XWENDw9HTs+PRctms6FgOP0ERe/VmBQ4eE6sm2Hwi+8AyUFIFhKYBaW8tLSkBw8edMDZ/Ob+jJ2zR/f29nR8fKzR0dHw2ovFYnjNkiIa5D49PT1xslQKY/qCOrzrguhRYAoVugJ2iC71BFEORLlbW1sdEQURCw6UG06HQd1jdgcsn89Hfh5lxnhcYDBCnr90urvhabVaGhsbi0PaXdBqtVrkyKAN9+U+HmmkEWTqQWOsHRZ1SJx8owufKwE3LBhf/vbdDV7Q4mvoysQjU/jVoUx3riSFTDrUndIrjRZwUFi/o6OjyD0SfWNI4MXUIXQIHkcPaBU0LZM5S3n9rb/1t3T37t04J9flzcfp9QCugEFvms2zAxbGxsZCeTs/NhqNoCFzw/GAtzGa7OOWznsweOc0d5zoT43hpO7Gd5NQwIpTubq6Ggd2pPNMn4FxZm2YA6ijF0c6/Z1n074QzBX+W15eVrlcjmeTkqJzF7Smf8OjR4/05ptv6vHjxx1Oqcswc0v3o6O7q9Wq5ubmtL29HffFoe7p6dHW1pZOTk5CxsfGxlQoFIK2aedIxs6ap2jW8wIYv1LDnOpTrm6ImstrKrvwK7r2RSJ66SsYZvpY04bMjz/jvGUGXq1WO6o8fbKen9vb24vF4LcLIe0sEQ4MBZCXHz4As0IMIhHpTMDo2UqUSgGPGxuasD958kT1el17e3uRh4GpYex8/uwEklwuFwemj4+PhwBy9jJec71eD8FBqP/8z/88YGG6EoEMwODeUcb7CyNwXgjkzNNut2ObCLSSznsqo4Slc7jSG8e4woem/HiUBe1hSPJqGGZgQvLL3MsjMtAIUBQMjhtGxs5vh8EdZUCZAY25g+KpD76bRmfk/REkpy3Gzw0vY3EacRFlDQ4OanZ29nOf29ra6jjb2Gnjr6XvQ1sivxTGJJJFXlxRuyy6U+SOIK/BH45IcQ+KdNwJdGOdzWYjTURlNvs+2Sb43e9+VyMjI/on/+Sf6MGDB5Grdj6luJAcL04/OVGcm+vXr2twcDCK7jjhjYpfxl4oFKIIhwMqMO4YaWgzPDwc6BB1KVxEkevr69G6l3oWr2pnffhdr9c7DmdI0xrugEJzxgii4A16oH8aRbuDnvK6H69Jrnh+fl7tdlvDw8OR5tnb21O1Wu04iWttbU3j4+P6+te/rkePHumll16K52J44R0PMjytUK1WY0tlJpPRkydPdHBwoNHRUdXrdVWrVU1PT4deWFxcjAMl6GfRLQjzQAb6dbte1DimOsZ5PNWLrIW/zuWvPS9iT68XNsxHR0efaxsH8+EBEgmxuAgOUQUTJIcDAVlQijtyuZx2dnY0PDwcHaS8yGJsbEybm5uSzk/dwYC7gkHxHBwcRPEXhvH09FS7u7uRW+EUGrY07OzsdDRGkc6ZDQNOl55ardZxnmwmk4lcwsLCQjQeQWEh/IeHh+HNOxPBdKnHyXyp9jw5OdF7770XleDMnwgFxyCTyQSa4YrXIcaRkZGA5RFMGMkbg/i6uXLI5/Ph9fr+yW6Gk3s7HO1RYSoMDqW68HUTUN5jnhgyRxo8gkgvYE0+53l0okXPFTPe9J5OL1dSkjqibD6D/KQK1udDNArPu3PFs3GocDqdvqw5it1fcxiQMeIY8Zv+0lzINrRxFIX1r1QqYZgZPzUnrVZLb7/9tm7cuKFHjx5paWlJmcxZ3cfPfvYzPXz4MKJinoHuGB4e1tTUlH79139df/iHfxi7OZaWljqiby+Ey+fPmnDghJZKpUD+vNiNaDQNFDxyPT09jQJQDvEAIWKvPoYE9IG9z+TMPYICgmbtPALFGOEIMh7Gx9/S+bZIAh3XCeg91oj1Wlxc1PDwsEZGRjrSlOPj46EDMaTDw8NaWlpSuVzWSy+91HEUJXunGe/zLqruoSVR8OHhoXZ2djo6aMHf77//vv7df/ff7ThKlvWFTum8kAu/UiPpTk23z3tqyhE2R9H8vj62bgjV/+cRs+c28Ao9ssNzhVE8n+zbH8hVE+2yAESZMChM0tfXp1qtFsYf4zw0NKT19XXNz88rmz2rgNza2gplRbHFxsZGQLPS+VmmwKvMjQjDIXIUOjkhik845nFoaCgEDmE/ODiIJgYbGxsB5XrE8rzcpy+cGxOHQTKZTEBuCB9GgLERIUhnnXTILfm+W2hZLBYDjvMcOYzkzMhvaMN6MxavmHRaSYoCH75PxOfGzhVIGj24EEGX1ID55Q5AWnvgwuPr4PflN/dhbJ679vyuOxXuUMDz3Dufz3c4Y2kqw/dtOt197jwLY8KcSAORNnBkBEWJU+FV7ShA5ga/dSsq9PwtzTKePn3a8bnNzc2OPs08Y2trS+12O07tgT8lRVMKTlz61re+padPn8Z9+vr69D/8D/9DNAj6B//gH6hYLGpyclJ//Md/rL29PW1vb3f0T2i1WmEkOUCClBNRMHt6Dw8PValUIo8tKXoS0CWM+2Kkqbbt7++PfDpOAQ4ABphomZOZUifGZQre4fsELhh5N+KtVqujAYo7a0T1Xjvgzh/POTo60sLCQuxMoTCPbUqbm5vhXCMLT58+1auvvtoRUIAQOczezRD655GpdrsdpwWCrvnnt7e3de/ePd28eTPWx4OMbk5ot6iW8fF6+r0UrUoDC/S0zykNKBhPCm2/qFGWvoJhdk9a0ueElkGjAHwbCkYc48T+5LS6FcHP5/MdlYEY6d3dXc3NzQWDTk5OhtCwTxnvSzrPA1EkAmEcoiMCHhsb0+joaEcBGk6BK7d2ux0FCdIZc9VqtYDauSc9bh1SIvJ0he/wj1fASp2HuKOYXSDb7XacqEUuD6ieivdGo6GpqakOeNaFgyYAeK6eI2RNWFv4gCja4VLGAZ2azWa0UXS4G2PGWLk/80oNLGuIovKuc2lE7cY7FR6enz7Ho29JEdG4551GmOkYHd5i/VMYGmPmThnjgt88ikqjZEcBuHCGyL2xtr7DwXPCzkc+B1fWXslN1MyzPBLifhsbG8pkMpHy6e3tjWpa+NSRjb29PT19+lQbGxtRMDk5Oan+/n4tLi5qZWVFkjr6JPAbA3tychLPoIcC8gKPeD6z3T7bNfHSSy9pYmJCx8fHWl1djSp0+PjZs2eanp6O6m30E6kYunIdHR1FGqJWq2l9fV35fD46P6EvcFAlRTXy2NhYR2tJaIQDlzqJ8KzXubjRIyXn6Arrx1rCZ643HFE8OTlRvV7X7u6uJicnw4DTN5yGUZJUr9dDZ05NTWl2djby+tCfYMyblXi076kv5rq7u6uDgwPNzMx8DklDp3z44YfRlMS3yaXoV7dI1X97dNwt+OB1l2t/jc+kesY/7/bm36hhps2kb1OA2Cgb4AlvvkA05e3uXAlCYJqQsMAUsrAliUhxa2srChRQTCMjI1HtB3xOpMozMFwwLp4miofDxKUzw728vKz9/f3IuyJku7u7sR0MA3V8fKxisdgRibtTkjICkatD7v4bBc73vSjKu14dHx/rwYMH0dCB+dCE4PDwMLaJ8R2Hslgj7u1GgcsjfeiAd0+e0WHKZ8+eBe1ZBxweBJfcYRrNOnN79OANRGZnZzta76UwlP/NeqfHXeK8cU9ymIwRReMFinzXjYyjHO5pp965Q/AuzJI6DB28wdihAY4X46XoiK04wMMUE4Io5XJnx/gRYbgi9ArRTOb8rHEahqyurkanOoxM6kS7QwmNUbLQi8/SnObw8FDb29uRq0en0C0vhfkdEiVSdAXLdr79/f2OzljIDfLH6UhXrlzRycmJFhcXw3lFlxCx8UwMJTSenZ0No8d2HwpZaW1JSgsn9eTkJFJKnEXtDTWYlzvoLnPQjnXwdURXeFc/dCjXyclJIIbInEPyyEe73dbS0pIKhUI4zxT+TUxMdOyu4GCQJ0+eaGRkpGOXgKcKU+fZZQJ+wrFsNBodOWTm7nx0eHion/70p/r1X//1DoPp9PLvQav0PT7vBrNbsOAy63T3ezAX19/dIu9/Y4aZYi+EGE/GDQyCQL6AyUrn+5dRjJlMpqNSFyiGCkcq9diz6hE6/Val89wCAoDS96IpFALMC1RCpAHjAhuh3IaHh/X6669rY2MjvOeTkxOVSiWNjIyo2WxGgwKgWun8FB4vuvE5OGzl48QwoCRhdI9ieQYGdWdn53O5Me+6JKmjxZ/DLg4fYzC/iIGYizP2wMCAKpWK7t69G80tcGJAMVBCKEFo4AzscC58xNr4Oqdz8Oi3W56JdU1bhOIc+TrBn05351nu7wVXnqZI14Z5Mido19vbG9XjGCKiFr7P3MjjHhwcfE6pkBdkW6FHuECrGEBfV68fgDdJd+AcwMcYZT4/MjISBX0YY+SAZxM14kRAd+SNi/FQOc2zPcpHGTJGnCgUOfUbNBECjXP0xCHWx48fd6xHWhiHkYbP6JfNObwXL16MseLc0MuAFsAYl3a7He2E3fnGmfUoE/nL5c7PaMeZQR9CBzfAOPHoUsYE/b0+hDl5cyB4MpfLqdFo6NNPP9WtW7diHdjmNTU1pbW1tXgdpPDZs2d67bXXgnfgr2azGUEN5yU4PO1O4f7+frR0ZU3Qhy5b+Xxejx8/1quvvhqFe84n7ig7EufFdSmSxZp4qjBNMbnMdYuqPXBy/cTlzuSLXi9smFEiGA+PNohMmQBKkI3kRG5eZOKwJJ4pxCMfI513zKI6EGJRcQwhvFAK4nl/256enmBuqjCLxWIUmJFnxQslanrvvffU29urSqWin//852q325qYmAia1Ov1ECgWTOrMV7rHhRJGoefz+YgwpM8f7Qdd/HKBYi1c+RAJSGfK9dNPP+3Yix2L/zdGvFarBXOjiBz+9PkgVDDy2tqaHj9+HMqH9SN94EVDblA9ynTI36Fb0gNu9HZ3d+M9R2scNuJ1Ipc04mduNEnxugPmzOvp2bde4MdaefW459zpIndycnbcIA0VUHiVSiXOqCWydVTDZQ8FOTY2FrxcLpeVyZzlOldXVzsc4aOjo0BEmJMrK/iS6EhSxz5h8pXwbF9fn4aHh/Xaa6/pL//yLzvqR+ALd1goDk0dL4+Gfd08peBIAbLiss19gaLdgLkCdgfHHQIcD98fDJ84T8E/HLkI7O2onkPN9Xo9uljBt1Rfs5eZ9QWxcf7HQHraABSM2gF3OJgLepjnMR8MDrKIw+zIAxEujUJoPHLx4sV45sHBgUZGRqJKG12bz5+165yeno7zmFM0yIMeX0PmgO4rl8sdiFSKfrls/+QnP9Hf+Tt/p4O/PUWXGly/h9/H9QK8maaiXKaYs7/nToPree7zr3t9peIvjyiZiEcbTMJhSIeFmAhMxKQwgni3DrcAz7midki62WxGMRr7DdM8Yk9Pj0qlkorFYggExp1IHm/eC9CkM+FcW1vT7u6uarVanPRCBWalUlGhUPgchOS5HM+T+ut41BRrEcHh/BDNpp6epwNobeiN7Kk2x5EiOuS50OX09DQK1/DiUahegOXK06Oj09NTPX78OLafoPxwZFgLj3ykc8SA8fCaC4p7nShrBI4xwmcpGpEKhq8L0bI7ks5LbMfBeWy1WhGNoLjoeEU0ChTYbDZj3tB2b28v0iQYbvKjHHzgBh3ZYBcC0RGHPoyNjXU4HuRfoT08nUJ1PkfoxOUygbOJks9kzrfm4WB5XtZhekcX0BOMjXG48YSffN3TdURenFdYF5xQN/ppZONGTOp03NxZcSPOe3wenbWzs6Nf/vKXmp6eVrlcjqKxWq2mYrGoRqMRDjAprcnJSZ2cnOijjz7qSOdAJy9GdJ0KfZBL5uKOlxsYnGC2Nnn6xvU1iCZG0OUFfbG1taVyuRzthSlYm5qaCmfe96Q/evRIb7zxRkdw5DLuTj50lhT0mp6eDkQqRaK4QHQ49/7TTz/VtWvX1G63g0auq1g3ns2YGIfzVMprfB99AW+mfMnnUiid+3fj4Re9XtgwO9wCczls4sYC5eYb9n3RngczQyQqjfH62+2zrQfFYrEDDsKblc4iKY+68GyazWZEGWnji76+Pl24cEEff/yxLl26pIWFhXif8RBR7u7uam9vT2NjYyqVSiFYHJJBMRj5PBwW6JLmdrk/BtuNshsMV2JuoBHW3t7eOLUFD5XK92q1qp2dncjDSYo0BIxOAc7GxoZmZmaCCXFSgNRgfNb79PRUd+/eDa/evWUcEcbr1fu+7t1gYPeU/XUuaOfOn9/Tv+NFMwgSY0mfQZ6TIz/r9XoofXKY7NlfXl5WJpOJ406hK7n0J0+ehHNzenqq6elp7e3txcEQvb290bJ2cnJSjUYjDijAeBYKhTgkwrfYdDuggjEQ9TMvf415OK1SWA8D5HlLjDHRMYYV9AlZ4cJBwPhhANxBcwPokKA7Aw4fuuPlDYGIzP1+BBApyuR8gu7o7+/X/v6+isViOHs4RhjBnp4eLS8vBx+B5FGNTRpLUjjY6DpSZn19fZqdnY3PeQDi/Ozw/8HBQThEOAo4i57ThW7pzhM+646PyxaoILIMfekDvrS0pCtXrgS03mg0Io++u7sbxvrChQt68uSJNjY2NDc3F3NiXD521juXy4V8jYyMqK+vL4IzN3iuA3Go6WX+4Ycf6tKlS+FodNvNkKI1bnRTw91N5ziK4mNx/mJ90nRjGnU7+vgi1wsbZiJYqdN78HNBgQD98+yNhem9wIGohUiEwbPvEKiJggoK0IBkgJSXlpY6CJlG8ggMigxFcXBwoJ/+9Ke6deuW6vV6R0EEY+R4M7ZhcHybdObxcVi4jz+FTdwwc1/mAaw1OjoaRSIspCsnd1xgFsYJFIuSPDw81Pr6eni7FA55xO4M12639fHHH+uzzz7T+Pi4xsfHNTAw0LGPmwiw1TqrVt3e3g5nyPMxICBek5BWWUqdUWwaUbtRcS/X1zWlpQuiR4ko2LRwCR6Cb3p6eiJPSROdtbW1DmeO1AzIwuzsbHTH2t3d1draWvA9ThbKNZvNamxsLCLeVqsVnaow1KyXV4YzbwxSGhngAPNc2uAyd/LBTg+PItw5ovjKt9akUQyRDffGCfO149445hhPj9p8/bo5aM6j8J9D3URejJV1grYpQuPj4zl0k8pkMmHQHUo9ODjQ4OBgh4xOTU3p8ePHWl1d1fz8fOT+c7mzhknXrl2LCmZSSkNDQyoWix1Gy6FPlxPWHN0oncPVfJY5ehcz183wC3qP+bGerqeYv+fmh4aGApYfHx9XNpuNAIhtVKR2lpeXdXx8rIcPH2psbKyjK5c729AdOTs4OFBfX59GRkaiMDfVjamDzRzQcR9++KG+/e1vR9olNYjokJQHUp2T6tNuY+8WTaefTXUQfM37aTDxRdcLG2YvfYc4VJDu7+93nGYCY/FZhBNvH0/clS5em+/Zox2nH3yRy51t6L927Zo2Njb05MmTyN04XJB6Jy7gKBtglNPT0zg83ZvdNxoNLS4uqtVqRYX2xMREGMn9/f14HpAkkXO6YP6/jw8lf3R0pOHh4WB4j6pdmTlDZTKZyLFR8MXcOIEnLfBKYRUEgChpeXlZy8vLunTpki5duhRbr/L5fFShkmtybxyGJOKjopeOZm64gTBRSAiVw/yurF9EENIIyY15CjMhgA61wbMrKyt6+PChcrlcRye37e1t3b9/P9YEpYdj1mq1oop1amrqc5XjKKWRkZFwVIGdvREETp/nzFzppA4ZtES2CoVCxznf3XJvKHdX0hgknGGUOHyIo8w8WBvkm/VlfN6r+4vQDenzdRjd1hzZZ/1S5+x5V4oOeLTKfJw/nZfa7fNOYXRTq1arymTOurbRZYzOh4VCQY1GI4pF6fDVaDRUrVY7dIPzqes2jCX6k3Wh8pv5IrOex0cnMyZSfI6GgNjQyRFkzDsE0jNibW1NQ0NDsWOGdUBPoNPJfT948ECvv/560M6RS3i83W7HFlQaUXmwwTzgJTfqzguDg4PhDHAksTuQjuY6zRhbGtmmEbEb1VRnuvF3neVj9YCKZ36V64UNM0wFdECXJ0nR2UfqbNYhnR9+4YKbLkS73Y48KRddciAqCmF8fFyXL1+OPY8oEiJTjDjP4v7exg5DB2S5ubnZcVgFUQxjJjLOZrMaHR2NKKFWq8X/vtgsFGMKYv9NBOWKOpc7259NYwIgS8YJsgBtGTfCikFB0FDueJW+fY3Lo0W2mO3u7nZE+8+ePdP6+rreeOMNNZtNbWxsRJ50bW0t8qQ4ANCZcXoEx3vuXHgE5miAMzhGBMPpSsifxz39b6IfT584DItSItKhCxupgYmJiTgQvlardSjwbPbsSE/ml8lkoihoeHg4Iu9cLhfefF9fX+Sq/eAH1oG1ROHibHklqztX0M4LtEj/0M8eKJM14XJD6BGow8UOv0Fz7nF6ehodn1CIyDXfZ94eeRPlppEN82HtvOCQz+O0pwaJZ7u8uqJ059GNL/d3KNx3XvAdcsfSWeMUEBDa9ZZKpXBQLly4EM06XnnlFV25ckUPHjzQ4uKipLMImoNvoKU7We+9955+53d+Rzs7O9F5cH9/PwoGicA9osawSuf1E/z2SA/ekM5zoMiFoxHQGedzcXFRr776aqR6cC6Pj4+1vr4u6UxXDwwMaG1tTeVyWRcuXAgd404hxWIHBwcRiVPk5vuScQSdvxxqRrZzuVyceOU7hdyQujF14wyfeYrQebLb6/4d12Oud+Dd1HCnOurLrhc2zE6cfD4f+RL3hj1Ck84Lf1ywXIA9qvZqRRQERGBy4+PjunjxojY2NjpOIgFW8/ul0Cifo8iGOTUaDc3NzWlzczMEGu+OObMPen5+vqP5xNHRUVQjekEaF3NHgaIA/bzXbDYbilo6i7wRLO7J9xmTfy9NA5Cjpkl9u322P/Hg4CAaBtCvt9lsqlaraW9vL46t5H5ECv/yX/5LSYpDOUqlkhYXF9Vun+X9vYc54wI1kT5fcY/gpfClMztrFkz6N7Tz/cQOdXqEzb3a7XbsP/UtP4VCQaOjo3FYe6VSCZodHx/HFpBGo6GRkRFNTk5qbGxMlUolon+KxCTF7gAatbBNiZQM+4KBtt2LdrlivVkzlwM3Ls7TICREQtyH/J/vekAmULqei/ViRzd6FJj5Ptrt7W2dnp4dyVculzuaf6SQHTUPOLXMV+o0xp6zc/nxFIUrZm8ghBz6nNIIOaU3POJ84flwvkP/e3RdLpfTlStXtLGxoVwup4mJCQ0ODmp7e1sDAwP62c9+Fm0rkSMcPa69vb2OvtvwSrPZDCdwdHRU09PTHTyOkcRQsf7IFrLjTjc61+UI9JJctcuSfwZnp1qtamtrS2NjYzo+Po6xj46Oxr5xePjw8FALCwuanJwMg49eZ47oDYKF4+NjTU9P6/HjxxHt53I5ra6uamJiIuYED3gNDnD94uKirl27Ftuy0BVp2ssDJ2QBHkmdNv8sn3H+4XIb54baDTn/f5XrK0HZ3Ypm3PBiPBxChLAeNVDBXKlUIjoFFm63z4rBgJUxdhRbVatVra+vh+fc19eniYkJbW5udhSakWeRFIabLScODw8PD2t3d1eNRkNjY2MaGBiIz8LYlPRTfEYkzD5d725FxOvCgofrRRkULBDBobSbzWbk0oHzgY84PWlvby/mhiDRzIPj2gqFgjY3Nzuqdq9cuRLdi9gjvrGxEQ4VdJfOogOePTg4qG9961v69re/rbt376rdbkcuFCXpxhfnAtq7cLiycEHDMMJbXA6ZSufKmIgDw+y5RY/2cAR9a9P+/r4ePnyora2t4D+cQuksj1YulwOdyWTOCr0qlUoYOYfDc7lcwJp+shrPx/jBFy7MXgXP+Hmfz7vRJcqAl9iOCDRL/p+o3WFzP2sXWcV4eNGXIy0YPBywtbW1qD2Yn5/XxsaGxsbGwri5Q4DiZe1S5wL4NFV23eA/7u0oiCNS3ZzxVBkiXyhchzldAaMDoNPg4KAqlUqkmkBQ2GpJDQzQ7czMjNrtthYWFsJRAiamQ56k6PcAL5EKbLVa4SgTOXr9C7ovpRNOGp/B8fDiJRxI7wnPGvA/90WXffbZZ1HjA73JD6Nz6fJXq9X08OFDvfzyyx0oCXMAdj49PVWxWIyWqHNzc1paWgqD67qC6D9FeuDre/fuRWptYmJCly5d+lxTnZSvUgPqdo3fX8ZP8GWaRuO+/t30tS+7vlLxl3S+T5acqm8vkRTeMUpJUuRniDaJNvA8UQg8AwZA2Fn8RqOhra2tOCv1woULGh8f18rKSkAtLnz8XygUIsrnJCiUDjARuWuvPB0cHNQbb7yhu3fvdkS1ksI4YsgQzLQKWOpsX5rJZGI+eK8oCoz0ycmJBgYGVK/Xtb29rfX19YDBisWiLl++HNFLtVpVq9XS6OhonKP65MkTDQ4ORkcd6LC+vq6dnZ2gVX9/v6anpzvyP+5RYqjGxsb08OFDlctlbW1taXx8/HOM5kyPgfUtZPCIRyQON6UGHN7x+gNoy9/dnu8FY9yTeR0eHur+/fva3NzUzMyMpqenQ6lmMhmVSqWO/6Wzav+JiQnNzc3F5yVFoR77OovFYpxryzwcPvY19vwtp45lMueFOETAdHCDTzDKnlOGTvDY5uam9vb2NDIyosuXL2t2djaiN/ZSU8CGI0m/6mazGcVM0AwHIN2PyrbB9fX12IPKOFhzDlfBYfN0RqroUuTNX3enzIui4CWMcjdlmkZD/hnuhdOMAS2VSuE0gEIdHh7qypUr0R2McblDUygUwlFcX19Xf3+/pqamAr3C6U5z/KwdtRvFYjEaFmFcT09PtbOzo8uXL3+u5sZlNpfLRc6bbmre48AP3iFlgYPj38fZpyJ7dXVVL730UqQmc7mcyuVyBEvwRi6Xi8h/bGysw0lmWxOyyAlh6+vrunTpUjwL/qzX63EkqOsQeArH5vDwUPfu3dPk5KQ2NjZUKpU6eiA4D3gU7LKZXq5DkFuPiLmnI3/d4POUn1/0emHD7PkXFEgmc3aSFIR2Bcgiw9S+1xaiec7IjUJahecw+sHBgYaGhvTKK68on8/HkWDuzW1ubmp3dzeqXBGgiYmJ+H9vby+ehYcGfNjf3x9V2pnM2clMxWIxIls8TubnECtRfrqIkjoiej/uz71VOgXt7u4GdFQqlYJpq9WqFhcXdXJyEpCyQ73k2p48eRInZXH6Dc+mkG10dDSU+MnJ+fmnRA3QD8jo7t27HYVKKEyMYTabjUJAHJvUG2edHab1yNDphiCm0bTnrSUFNMd3/AIt4UCTkZERfec734kUgUODeOCen8tms6EgUNQ9PT2RS/ZIGEXH91PUwKN9noMDiZPoyhoUA950hdxsNkOmMCLI5ltvvaVvfOMb4QRjXKvVarSurFarajQaKhQKGhsb09raWlRwE8Xh/LCORFmMqb+/XysrK7pw4ULMCb5mi6JDpfAgxv15uTrW1tE23kcfAD07hJ0WI7rT5/d3WfBtRhRFjY2NqV6vh/JvtVrRjEiSSqWSGo2GRkdHtbS0FDzE+m9ubkZNxiuvvBIOEbqLeXCwDLTb2NjQH/3RHwW6QevfwcHB2F0hnfeUQI8C5TuawhY8aEylvvc2QJ+60+i8i4NI/rhUKmlycjKiepwzzmBnTplMRgsLC+Ho4FymRrFarWp2dlbPnj3T0tKSpqamdPv2bQ0NDam3t1eLi4saHByMQjZHR1hjdDHpG5xd0L4USu7Ga65L0vegH85fql9czp1vuz2nm1PwvOuFDTNEQPBQ5sBeVO8dHBxoc3MzmI49yplMRmNjYyFYLLp7cywix8UBDSIc+/v7mp6eDpjk3r172t3djchuf39fjx49iujy4sWLYZTJfzSbzWgIgufnhUUUThFlMg6iURQrsHFa4OBddqgqxzv2dAD/o0CazaZ2d3e1s7MTB4kfHp6dmHX//v2IZngevZExSDzPW/NBt/7+flUqlWCqcrkc709MTHRUTrpCRlHNzc3p/v37gXQE8xi8RsTB9jJyqxhwj4hBQjyals73ZlPxjOJNDTXvOXyJYsExwZk4ODjQzs6OarWaZmZm9K1vfUs9PT0de7sx+ozT83LsVV9cXIyKZ5wQDJSjHt70g73zjjaxdtDJoUePDFkHvue1Fp6/Z514/7d+67c0Pz8fcsd3Ud7s3e/r69Pq6mqcfTs7O6sf//jHHfx0cnISBgInj4paZGJ+fl4/+clP4vxc1vL69etaWVnR1atXO9YIxe/FovCSoy4pvDs6OhqODI4gOwVw1KBBiq4ggxh65kfk7zs6qEEAtj44OFChUND09HQcfMF5wjzLoWJ2pzhKAL3o/Y/u8wvHGllsNBo6Pj6OmoV8Ph+9CoaHh3Xjxg391m/9lm7fvq16va4nT57EFkXy7oODg2Eocdwo1jw+Pg5EgzQMzo6kDvQRR/Dx48cqFoth1EdHR9VqtTQ+Pq719fWgCemUzc1Nzc3NdSBFoF/krwuFgsbHx7W8vKxc7uxUQb5XKpW0trYWKIE7YC6bOBTw7O3bt8PWPA+Cft6VOnDd3vdI3NfZ3/fvdoO6v+x6YcPM/jWMMPkH8liSolhkcnIyFonfTBQlRqTqxrq/vz8g2MuXL6tWq2lzczOi0KmpqTDKCwsLcQ4yxyw+efJEkvTee+9FGX21WtXg4KA2Nze1uroaVav5fF63bt0KA/X06VM9fPhQ165di0MyGo2GVlZWNDQ0FDnn4+PjaBZBZxwWRDpXKp73dGhQOm9a71XBq6ur2tnZ0fz8vK5fv67h4WFtbGxoeHi4o1gEJAGvEo+bgi6cCuk85yQpIjQqKz3vB7MTsbkBpTsV0SWFT61WqyMNQIEKkJ6kqBugKQM8gCAh9LlcLo6fBDVAkTrkBy3pD+7pkjRPWa/Xtbe3F0VeMzMzGh0d1d27dztqD5gX8BmG1I0jz6lUKmH02QojncPIKGWP7nCmvD0oaBFK2CtPqS51hIq5Onyfz591aCKvfHJyor/9t/927Iff39+PqP7k5CRSTsytUCiEM720tKR3331XW1tbevz4cdAUeeQZGEJJ0dv4zTff1JMnT/Tpp58qk8lEyqrZbKpUKumXv/ylbt68qYGBAY2OjurZs2fRDe3ChQvq7+/v6GPPunikjGMxOTkZzpArQ6p6WStPIWBsUsi33W4HWjQwMBDIwtjYWDi41GpQ30EF9O7urk5OTrS2ttaRt2XNmU+9Xtfy8nIgUfA2tHRE5fDwUOVyOY5SpA0saTecbegyODiov/zLv4wK+enpafX394eRKxaL+vDDD4NfGo2GisWipPOqe9BLol3XCX5ADfrm8PBQT58+1a1bt9RoNEIOyBtvbW2FczI0NKTFxUVNTEx0tBJ1GDqfz2tnZ0dzc3NxjC/BGFGwp1f8arfboS843xvZpecEgZR/x3W1X9DWr/R//363orLnfddToC96vbBhxpuBybPZbBRJIQzkUYBNMMJsTnfFTm9lCpxQWLS+XFlZ6VBewDa9vb1aXl7Wzs6ODg4OND09rVarpV/84heSpK997WsqFAqxyZ9ip6mpKTWbTX322WcBrb///vu6detWdLIZHR3Vw4cPQ5Ei8DMzM7Hwvb29WlhYiI5MLAhG2D1zVwbMH+VBJLC6uqq1tTXl83m98sorGhoa0vHxcRRlUSCF547DgGF3L5L3PJLleYwT4wHagcLGk/VCO+hO3QA08UI1EBO8fSJA75rlka9HNQ5x1mo1LS0txb0c3vZohKjdIw5SCsD2h4dnJxhRQZzP51Wr1QL9gAePjo5CeMnrp/RyHjw+Po6CH28j69A8Ss4PZKnX6xoeHu7oZ4whxjlwmNphNy8gg8a+BvPz8zo5OdHVq1c1OTkZtQ+ctgbtyDsC25PK2NnZCeP8D//hP9R/99/9d/GcmZmZMFiDg4NaW1sLBYYCXV5e1n/2n/1nUUzINjpg9OXlZS0tLWl7e1tra2va3NxUPn/WeOjv/b2/p62trWjoMjg4GM6m1wZIZ47I1772Nc3MzOjx48caGBiItBm8hcH3HQ1+D+jpPP31r389HEC6rYEUbG5uBmKAkfbUDE7K6emp9vb2gl9BfI6Pj/X48WO98sorHWvJ1iVH6jKZsxqHW7duaWVlJU4Po2gW3YFO4tn5fD5a8NKZcGxsTNPT0zo6OtJHH32kZrMZDkerdbbfHocRg0lU7Ckh5Bl9TXHX2tpapPZ8uxZOvHRWm1EoFPTgwQO9/fbbHYYKFAdEdXNzU4VCQevr61EFf/v2bV26dCly7Z6OQIbYX46OR54ODw91584dvfXWWx0G93npEt7zWilo4GNOrzQN6d9xJ5zXv0qu+YUNc71eDwOL8iDvAZFhNm/e7wYKI0wk8/LLL0s6PwyiXq9rbW0tPDoMBs9tNBra3t4Oo33t2jXNzs7qD/7gD3R8fKxbt25FIQoKrNFoaGBgQMfHx7p06VJ0cwIyX19fVzabjSPdiCzpAHV6ehrl/UQiVLwCU+Hhek6K9xBUEAFyhQcHB6GoKB4iWiEKdgWOU+ANRdLCAxiLe2AYu3mKfIeTcsghIpBAnzgBpCkc5vVoBwF3uBKnBGOVGmMfEwJNrpa6A6+wRzjhtXa7rWKxGO0RW62WNjY2woDg6UudjRocVqctIYbX4V+UJt8FVcBwOeTuY4d+zAGHNc1/uuPG1i74xvN4RM9eBOOtIAcHB3X16lWtrKwEdMlzca5Atrxn9/DwsNbW1nT9+nXdvn1b4+PjunnzpjY2NjQ5ORlrg9NKcw0iJZzFe/fuBdRcKpV0//59HR0dBT99+OGH2tra6oCYC4WCqtWq3n333XB0aHtKpEgQwHpwkAYGFJ0ALQ8ODrS7uxtr6LSDx0kvtNvtqLx/+vSpLl26FHoGGWa/7fj4eKwnTsTw8LAqlUo0naGgEl7nwB3qRUDh3PjRFpiIamNjQ3fu3NHg4GCH4XQ5AW1hHzVVyzi7z54904ULF7SwsKBXX31V09PTunfvnprNpvb29uK4Wu9tMD09rZWVldB9OKuDg4O6du2abt++rdPT06h0fvbsWfBeoVBQq3VWRU4vCFCe4+NjbW9va2FhIeoQvHCPnRD7+/uamppSsVhUrVbTpUuXNDc3F3l8eB5HmFqe/v7+2Ee+sbERe6xzuZw++eQTHR8f65vf/ObnUmJcbljRX37Bd90iZL7D5/w76dXt3l92faWI2dvPoYAwyhhehyzII7HY/f39oYDxdpeXl1WtVnV0dKS9vb2IaKjaY0Hw3ra2tiRJr7zyiiYnJ/XLX/5Sjx8/1vz8vKanpwOS4XuZTCaUHlAPxyC2Wi1tb29renpa1Wo1oEOgl5mZGZ2ensa2h/39fa2trX3u8AGUIF48ygcFWiwWO47nW1xcjJzMwMCAent749Qk38ZCbg8l7b3DMRowDkzvWy6I6tLcLJ2F8Nw9b4LBIeLDMPFMojUYzRECHLHUEDuc5HlEN7REB8C17sHyWd4HYhsZGQlFs7m5qUql0tGwBGFyx0JSdGgi34dShS4odqcvws1YGAdRtisdDAp8TAEZOT3kx+mGsaeGASMtdTb84Fk9PT2ampqSJM3NzX1uzMhst+06yCY56lKpFOmhl156qSNq97QMZ/Ji5NAB1BaA7KytrUV6ZX9/Xzs7O8EzrCO9Aer1elQrU2SWz+cj8pUUfEp64sqVKzo+PjsKkzUAERgfHw/HFl6DZuxSoHCTNSiXy5qcnAx95Skd1hxHwU8DGxkZ0fe+9z09fPhQn376aRSSgcDgWG5tbWlubq6j+I0UE44CCCN92NFjGHPSQ+gtEETgaEe9dnZ2dO3aNX3wwQf6R//oH+ny5cv6kz/5E01PT6tUKgWCg2NNw6b9/X1tbW1peHg4mqrgAIBijIyMBNKGk358fKxqtRqykcrJwsJCnITGnOFNXtve3laxWFSlUtHm5qamp6e1tbXV4eyjT7EhIyMjkbsfGBiIgjtsDWsF3dKI2FOM3X57hItMeUScRsXp5bUlX/X6Sg1G8IQ8j5rL5TrgU7z6UqkUk+jr6+vImdBXGELCKCjeoaGhjgbrfBdI5u2331az2dTy8rJ+/vOfK5/PBwTCAgJzsPj7+/tRfOGFMLVaTdvb25qYmNDo6KjGx8cjD+u5wlqtpp2dnYgE6NTl+RPG6MprdnZWMzMzOjk50dLSUsBU7AvEefDcWApzppAeDIMRJnrgdT/Qwu+Ht46x9DSDMyGGBWXK3mjPaaPYGBv5Ra/CdQfNPX/Pl/Ec6bz4izEHk/7N++R1fftUtVoNtKW/v18zMzMB2RE18R0USm9vr8bGxgL+9ufh3HB/FI1D2j5G3nch5Te8gNJ0g8l70rmQ45Rg0N1AE83jxPCMVqulcrkcxWwgKTjNXswDr+KUwAs7OzvR75smKr5mrLXfN5PJRJtJEBrWCBpQ2UsdBYe/SAqYd25uLvomM3d4t90+Lw6FvzAm5PqfPXum8fFxFYvFcCid3t6tanR0VCMjI0E7ToZ79uxZpDd8zaBFJpPR+vp68AuoFbR/66231Gqd7fdFbzgPovO44EV4E6Tx5OTseNBc7qyDHI4cPIIcuyynuxGQKQrBHjx4oPHxcc3Pz8fzisViODrw0He/+109efJEf+/v/T3l8/mo4dna2tLh4WEUejWbZ3uF9/b2ojETwZVvnWXtpbPtr0tLS7px40bYB9cToHW9vb2amprS1taWrl27pqOjI+3s7HT0hsAZw16AVJJi2t7e1sWLF3VycqLLly9HSod6IHSa6xdHvaBHGg17ygHeZN3Sym/XiVxfBcaWvoJh5sZedIPA1ev1jj2vo6OjoTwkBRNUKhVtbGwEDOEMBuzm27FgXqKOk5MT3bx5U6enp9rY2NDCwkIoi4GBgQ54FGiTfqx0s4GxiaJOTs4OI3jzzTejyImK02KxGFHn3t7e5xpgUOiCMKAEeG90dFTtdluLi4tRqT4xMaFbt251eHBANCw4HjCM4YaOe3se1CNY1se7m3nEyv+M0yNVhIV7AZ9yPzckRAXckyvNn+J0sC6MFwPNXN2wOY/hGKCwqVat1WqRh89kMrp8+XI4VTgOjM0NphcYZbPZiBoZqwtUani75aAcAWCObjS9JiMVTs+beWEXSoDoX+o8fg5Hg7w1xgInLZvNRuU3TnEul9Pg4KDGx8e1t7cX0RZbnpDP/v7+jnPPeSaR9vHxsY6OjjQ9Pa233norDDWpq+PjY925cyeK+qAvvMbaska+PmlFMIbV6QbEe3p6qh/84Afa39/Xb/7mbwbticZAosrlsoaHhzuKHsnT4liSH5+YmOgohJqcnNTKykoYNAzhxMRE9BbY2NjQ+++/r+985ztaXV1VrVbT1NRUpNsoKNzY2FA2m9X09HQEHfANPOfnB9DWFDlnnXFoBwcHNTY2FkVptVotAhOv68jlcvrrv/7rSCW4vmVP/NjYWGwxffr0qe7evav5+flwVBz+l86i23K5rJWVFWWzWW1sbASc7YYP+Tg4ONDq6qqKxWL0n0c/wdvomtnZWe3t7Wl5eVnT09PRzAUnFafSa00w2mNjY1peXo7jee/evavPPvtMY2NjmpmZCXn0TnjwN8Vo6DJ0FPqItffAy3chuE5BB+Bscz/W/EWuF/4khAQWZoHxLMvlcgyMRd/e3g5DfHR0FIVHRLWtVku1Wk2lUknVajWMK8aJwgIIyjFqjUZDlUpFFy5c0LNnzzQ8PBxwcbN5vne5p6dHGxsbHVV9eK4wnSQ9ePBAx8fHeu2116KgKpPJxPnNPT1n5+BiFFBAKDxy0+wrJQpZX19Xo9FQrVbTwMCAXnvttShWSw0EDMJvDJznlmBAGNINkEd4MI7D+TzTGc0v9/y4P2vEtql03T2idMiZ+3Efxs/c3Gh6dOWGWeo8xpC9yDRh2N/fj/QArS897REMnj9v4cf9EUpX+K4g3UjCexgOn6Pnp3yO/O/zcufEkRg+g0L1wi6noY83jdqZ3/DwcBjtmZmZgPy8sx6OGUViwKpA0+zXzmazEf2Sl261Wrp06ZIKhYLq9XpUuGMQoTvb+NrtdjgKbOthDBScvfLKK2GEJEULSKqsyVmyLmwhun//vqrVqn7t135NMzMzgULhlJRKpdj/70bw4OAglD1riwNO9ArNZmZmIhdPk5DR0VFVKpUwHDRuWV5e1s2bN3Xnzp2g4b1794JX0GMXLlwIo+7yzlowTuTVc+wgCAQvv/Zrvxa7WLa2trSwsKDNzc2QRfTT3Nyctra2otMiXRXhwYsXL+rhw4fa2dlRvV7XysqKrl27pv39fS0sLEQ0jF45ODjQxMSEGo2GZmdnVS6X1W63tba2FuuLEwR/Hx4eamlpSWNjY3EfPsvvk5MTra6uSjrbMum7OVqtVvCz1zoxB4KxiYmJ4Kfj42PNzc1F7QI5dGwZzhmOAYVmyBiOG2kTUgg4nKBvXmDGmrk8IKPPg7y7XS9smMnPodhow8dJNgguBmthYSEgbrYlsJ8YrxXvKg378fgoMuHepVIpDP3ly5dDyEZGRpTNnh/qQDQ2NTUV+SJgZqAZIgk8oPX1dV2/fl2jo6OxUETO5JYcki+Xy2GcWFw6ou3s7MSpVBMTE3r55ZejWIK8nG9+d4PIgnqej4vFdRjIjQNM7tEIxpOIF08PY00RCI6AKwdQAMaH8WAswJ3ploQU0uHzHhXjCDAPIniMD0aZNWOtiDwuX74c+S4/EYn74wA6FJo6Cx6ZQxMvzvFxu2fsRp75u/A5jI+Rc7is28X4cEz8GWmOyh0ISVFFDI8PDQ1pb29Ply5d0vT0dDSbkM6iHWoapqamtLq6quPjY9Xr9fh7amqqw6CBbCBnpVJJf/VXf6Xr16+rXC5/rjgOpdpqtSLFcOPGDc3Pz+vChQv6/d//fRWLRf2Df/AP9Kd/+qd68uRJwL0uS0S5QNcovWw2q/n5ef0X/8V/Ec4EET1GwQ2vV8wjv6yLb/mpVCo6Pj4OxyKbzQZMns+fdQQrFotaX1+P9R0aGlKtVtPy8rLeeOMNbW9v69NPP9Xo6Gj0mydYoVnP6elpGFRHzlxW4DOcQo/OvNqfa2RkJLog8nkcq2984xv65je/2dE4BoRkcXFRmUwm0MLV1dVALhuNhtbX11UsFjtSMjgprEuxWIytShTYZbNn6UQ3VCsrKyoWi7py5UoHVOwOihdnUreAYWRtkA838PymboTA7fT07Dz0qamp0L3UArkeIJcP74BQkJo4PT2NoA1di77xYC5N7zla9FXg7K/UK3toaEiTk5ORj0HR0sO5Uqlob28vIDKHS2AKj6rxONjb55EGFY7AXwjWyclJGNyVlZUwUlQNNhqN6Hu9vr7esZ1rc3MzCgroGCMpPOyHDx/GGKgYlDrzDkDPEJp7HR8fa3d3VxsbG9rc3JSkOALN78dC+V5izxt71Ju+l+Yr+Rul7jlaLsadz+ej13e1Wo3xUOyEUUEQDg/PDzFvt9vRRQjhSZ/hF/Adfai9jV83A+m5dYducYgQcpT95cuXNTo6quPjY5VKpfguvz0NgAD5GN3hOTw8jDaIaaMKh+T8fqlQozzd2+Y3f0Mzf4/veZ9l5u3OmisgRzJ4nT2+RKInJyeqVCpRF+DNIujDjjLmTG0cMVAVPl8qlQIWJr30/vvv65vf/KZ6enr06quvdvB4u92OGpBGo6HHjx/rhz/8YTzjtdde0x/8wR+o1Wrp93//9/XOO+9oc3NTAwMDmp+fj4iXSvChoSGNjIx0VFiD+PT09ERKg/7U0vn+/eXl5XgNR62bY4tipqCQrTjkkjmDnQLC6enp0IM40tvb21FF/K1vfSvWAafowYMHHQ16SN1J5zJO1IcBJ2r2VBVGg90q7rixhuS1MRroOXf4R0dHVSqVdPXq1eBH6EHQdHR0pNdeey3G8fOf/1zVarXDSe3v79fq6mqkR6CjO+0gHQMDA1pdXY32waQIuLhvJpOJfuXw3N7eXhSsEYD5uHF8T07OzjYYHh5Wb2+vCoWCbty4EbqN+gWnGzwHvSTFOdzoT3QYTh7PJ0XaarU61rfdPs9BoyM8d/1l1wsb5itXroRSYlDLy8s6ODiIalgYa3t7W4VCIRiPfYN+tCKQm9S5lYUtALyH50IOdHx8XMfHx7Gtanx8PKA5P+ybBWCxKfKg4AGvGeX07NmzYKjT09OoCOf73IvqUYdZDw8PValU4hQnmjKQyykWixoYGOjYXgOjsIjMF+NKdIWi9vyQw1owp1freo6Z7/f19enrX/+6Wq2W/vzP/1yVSkVXrlzpoD/P3Nvb05MnT3Tr1i1JivwR48MrTLfxkPvDu93e3tbOzo5GR0c1NzeniYmJWEeMoBtM/5sqYKBrHIPR0dEo4Etha4fBoWu3iJNnwXPPnj2L3Gqa+3RIMS3Y4tk0byB1wlGgPgYfF3TGyZPOc1o+di7Gwhw9Qs9ms7FVZWhoSK1WK4qd9vb29OGHH0bTn2w2G1sciWCIbtmWwnPocuZ7d4nGUcT0aS+VSpqfn4/2jOvr63r06JHW1tZUqVTiSNHDw0NdvHhRb731lh49eqSLFy/qH//jfxzOJUbHi83Yz4uCd3k5PT3V5OSktre3oxjJa1VOT0/jgBqqv6lZILrCWaQTm1fn835/f39UHWMU4RMcW4w5hXT0tCcweeedd/Tzn/88kCGXW2jurS0lxRwIglD8NDuqVCpRIMZxkn19fZqdnY3dJleuXIk6HXfuMChpige+hFbIWC6X03e+8x3duXNHjx8/DgM8NDSk3/7t39bjx4+1ubmphw8fduzz90gaRGdpaSnqbFKECTkBUaSDH+kqd4jdkEvn+8l7enqiWO3q1auxXRbnks+TOnV5JPpvt9uxxY0xoPO80x61Gz4mAkhv6uNy/yLXCxvmnp6e6Payvr4ekSdMWS6Xoz0m8BMGkOgGYwGEKp17cd3yihypRtMI6cwZ2N/fD6Z8+eWXdfv27dgzOTIyEhBaNpvVxMSEpqamtLi4qAcPHkhSwH4I8M7OTkcOlyYDOAQIBJBwNntelALTsWA0oMejHR8fj3vBfCgZb9+I1+yePJG5e10eTXnk5sYVBnHjw/jpLY6CYfwwLFHVSy+9FONaXV3V4uJiKBUanbgXipctnZ/QQ8N8hJLqW+jsBtMNjwsq+bALFy7EPmp4amRkJAw0NHse/M893dBiqN58880YV/p85wvo7nlj0CNJ0Se8Wz4YpQPSwjM8rZBG9W6c/W8UmqSAoTmmkufQWtIrmRuNhnZ2dlQqldRqteKkKNIVvr6jo6PhDErnebzT01O9/PLLevr0qUqlkj766CP19fXpxz/+sSRpfX09YOXR0VH9h//hf6g/+IM/0Nraml555RUdHx/rv/lv/pvgOYwRWxGXl5fV19enwcFBTUxMRCMfIn32OxM1F4tFzczM6C//8i/je4eHh9GrW1IUBHq+8ODgIKBgPzsbWaWPPND417/+df3FX/xF0MO3Q9EqmHXk1DLy6qB2wNhEpT42xueoDDzqCIkXaa6trcXODPioXC6H3I6MjETFOYECjnu6E4LxwD+e+0WvUCNEVTSQ8eLiorLZrN58800tLy9Hugn5c3nI5XLRrnNiYqIjpcd6MUeQMvS6O6Uuz+hhnCd2BF29elXT09PRtpXGKuhc6Mg4CQZYF+m8YyLBgNdDYOsctmZM/h5Ozr+R4q8nT55EfhYYutlsdhx9Jim8cTaz7+zsdMAVQKUoawQ0hQK8CpLP7ezs6LPPPtOlS5dUr9cDVsFpIEdMu0q2LGxtben3fu/3IhJmcRHQVqsVh5f7xnv3pIhEMPp4qUBOw8PDunbtmmq1WkT2eMsofT5P5zE3xDwrNVCeb0IZez7KYUjo6PlK4LatrS393//3/x3FNOz59g5EDqd5t7Zs9qwqlwp3EAF3WFL4l0INnBu8SHcC/HJvnnmzPYUzcMl/jo2NhaPj0WwKk/tzXACZl3vOPv9uETb8gELwPat4zNL5+dPMiWd7Fb2kSD043Xw9fe3Ti/VyGJaKaiA48qHMlZaYT5480ebmpm7fvh17933bHmvmuXPmkslkQkmNjY2pWq1Gtzbkd2RkRF//+td148aN6IP8n/wn/4mkM2VHqouot1KpaH9/X+vr61F3gC7Z2dmJKIX6jtXV1Y6I9dmzZyoUCrHTA0NJvhgIlO1Hvb29Gh4ejoLV4eHh6CQ4OTmpVuusX8A777wTxUzs3Pj44491/fp13bt3TysrK/rOd76j+/fvd+zvls4bMPn+ckna2trSvXv3NDc31+HQehETvIwRlTqPx8Qx9xQXusMjU5BH5gDEjXzy/LRamHoCcq38j5ywxoVCIRyN9fX12IaF84Oe98gWXj85OdFnn30WuXvok9oJUm44ZwRwbgDRyRTmlkolDQ0NqVwu68aNGx3fIehgt4MHQB7QUN/EeuAg4bw6UulRvu9Nxyi73v4q1wsb5rW1tVCsEI5CCjxpNnv73lDPn2GQGo1GEAvmwPi6kZLOzywdGxuLzj5Ua/b29mptbU3f+973dPv2bS0tLXUcrrGxsaFHjx7pL/7iLyJyQNA49oyIhypfGAdPlt+uJIF6mBdGvaenJ/IgGESHkMjdHh8fa3R0tAPCY6GhG8+ROk8wcQPm3mKqQD2fQeEKEWs+n49tGylsi2Hz6mAaeYAIYMh4BnTwiLNQKHRA9NQTeKTJ3NIrm82Gx8m4xsbGVCqVAq6Tznuzu5F1AePq9gynm0cL/p5/1yNfaETXM2oMKC7xiNqdKcbFc1EqTguHFl2g/W+e4bAfUcurr76qN998U61WKyL4XC4Xhy9sbm7GoSjwl9c7cFgLypvxMmbQnnK5rLW1NV27di2a1eDQHR0d6f79++EQcCQmUfr29nbHvlLuSyFZu92OKmlykzSTQH+w1uTyJAWcWywWAwZeWFiIqBx+Ainzoh06Eo6NjUWhKtXomUwm2lBmMmdbfcbHxwMRcgQLB5Htaxifo6Mj3bx5M7r7kT91A8w96KaFYSSP7frRv4ce9Ugb+fROhOhi52N3HEAMWGuXG+ffdvu84rvRaKhUKml5eVk//OEPA8KncCx1UukvwRaq+fn5oJs75HyW5jFpgakHLIeHh7HzhUYs7LBBH4EqUH2NU+HpEX4c+UNOMMxeh+F1IIyftXKH+4uc7OddL2yYUULkEcHspXMjTTSKUgeG2NvbCzgaIcQTo+sSBIIBEVCYDAiL0n1J0W9bkt5++239+Mc/Dkgrl8upUqlobW2tI7qr1+s6OjqK1nr5fL7jkHtyxGmhD0UH0vk5wzApStJzDShcoJ/+/v7YyuDeNfODlqmhdUHy+3aDS2ECh2KJ2snPIqi+TcA9SsbAfNlb+eabb4bgumfrY+YiEqcCUuo8bcoVEc98njNCv2dXftAUqD81qKwd9IEeKX15HYWHkk4NojtGrAEIwPDwcCA3Dhm6wPtcU8grNcapA+gwWUozhz6bzaYePXqkzc3NOBgA/t7e3tazZ8/07NkzbW5uxrPIT0LLZrMZDpEXgTkd+Ont7Y19pu5ooUzZb8oWt+Xl5TBQmUwmjCs6gMgfxdlutzu2QQJn9/aeHThD/pIoBqcN40shKM9otVpRJUxUhfPCXEHaOGeYFADr8sYbb+jhw4fa29uLSnBog2OK/sNhg2fYwnnx4sXImbZarai/8aIkh3Jx3hylhIboWTdc6BJkG10BT3pqwvUXPIEecmNE2ks6P7q2Xq93RI/UAzAm7g+/4hjgtAwODmppaUmlUim2+bluZP3RGdDKO/vBu6wzRvmNN96I9Wi321FIBt24pxtm9IrrDNejDuv73HgG48M+uqxD568SOX+l85hrtdrntlE4/IuxRsBOT087zsV0+ISCDohCotwhECYFVDU9PR370pgs8Mnp6am+/e1v65NPPtFnn30WDISQ12q1jpNdyuWyBgYGorcu/asRBJgNyMz7FANDenTG4vl3HWYmimQLgHufbGvgu90MA397jpnxpcYIxnFjxOf5jXMknbf5w0ChYPicdA7PeBSMx+3eN98nukgdnJReOC9813OZ7nmiPKm87MbkjMNzR9LnvX3GznM8Gvbv+DryGs8gqiSNAnrUrUqcNfBnQ3dfH/g9NYKpcUT2fIx00Nvc3NQPf/jDgLUzmbPTuEBq+vr6whii8HZ3dzU/P99R55Dmw1J0BtiQohjknbmlNRjtdjvOFfZ6k56eHpXLZfX09Gh6ejr4i+gbWkO3crkcXfzQOc1mM4o1JcWBGaBwvg6np6eRlunv749gAiNMUVVPT08UK7KVCMVPQSq7D6AjkbAXNZF2qVar6unp0dzcnO7evRs09HRGoVDQb/zGb8S+c+7LsaUuBy638Db8ie5zWXYaOK9hoBizG1Pk140m/IpTzPnx3KNSqcS6+qE3yBF6k7V4/Pixbt68GePDoeGQFVKFOCQOETebZ7t99vf3NTExoUwmo4sXL2piYuJz6ShP1xEcONoCkgp6lBZqoXe8FsPHlOaTuxnibsjd864XNswukChhh1bwtGBWPAcWhggK5YCRJAJzyBjPirxsJpPRrVu3NDY2FhWoNBvxArF8Pq933nlHr776qh48eKDFxUUtLi5GxM1C4KVKCkgchmQerlC9QA0Iyb0gV5wYmUwmEwzLgpOfd3jJPWSPjNzb4rPQxyFkBBNGSRVoauwRMs/VuAD7d1MDF0xjCjz1CLknCpkx+dy84EM6N4I+LtbAhdi3PaFwPW8NTTxnJJ038kgjfa9+xeFizXk+zg80AoKt1+sBqVOFTUvA8fHxjnVK19TTDC68jN95Crp45JwKvCvU3d1djY+Pa3V1tSONhDIlckB+gRZ9bL4W8BVOVYoA4FBjkOhVjOOCYz00NBTRkW8tbDab0R4VQ+zrRSRJhNtsNjU7OxsOFVsgGU+9XtfGxkbwmBsYNxZbW1sRYeOsoJQ3NzdjPnS1arfbcegMKR967jv6ku5TJvcpKeB+ZILomrXv7e2NIxCJOl1HPHv2LI6dzOfzunDhQvBvWkBL4MMaOMzN+0S7Hh26vvDXkAHXO7VaLRwIb8frDrkbLNYSvcA4NjY2NDs72xHIYC88isVhy2az0UBkf38/qtSLxaKuXbsWNHVHBGPqRasUzCEP0Btehzbd9K0X//mauu5y2qay/mXXCxtmZ0D2DzIRop7UM/OFJGIGxgAOBiZhcnithUJB5XJZ09PTmpiYUD6f1/LyclS90uADrxNjjlPw3nvvaXh4WLu7uwFhjY2NRTk9BVE4BalBdiK6Qeas1hRy9PlxsY8Rg+pVmRRIoDxd6blRhqYIDJFeGtkgdHw2Newe1XrUzdri1bJubiQdKnYGdsSDsXleNTXq7u27ADjf8Dzuk+Z7uA/fRfD5PoqYcSHo0NjH789DSXkEwuVedrVajRxppVJRJpOJ9pc4fjifPgZHC9zQpVcavaf0cmPvCA3pJYwjzXegn+fURkdHQxl6FJamNVKUAfr4ulGgx5qMjo7q1Vdf7aj+RQnyHOpG6LjlTR1wgoEsieipy2DrC7Cm70Zgr7t0ZgTZAoVSBspkTltbW2H0oTVpLHiEnQDDw8Oanp7Ww4cPwwAzVnRfs9mMObRaLVWrVS0vL8c2ycHBQe3u7nakcghWMpkzeH91dTV0ocPb2WxWOzs7KpfLIccXL14MtA16ocdIOzFGLzJjfPz41kpqJdgmhEOcyWQ6uqORokDuqd/xHKzzrsPArAlysLa2FgepeNEq6wT9PKCCvrlcLk42A8J2mWde8LY3oXJYGh533eey5k67r6fr1G6ynMrvi14vbJgxYJzkgaGBSG5sYABKzMnd4i0RLWcymTjJBBiQnsd4z6VSKRrVz8/Pxx7KWq2miYkJ7e/vq1QqdexRI8pptVp64403otE6jLSzsxPGmuKCXC4XzRZYOBYDhgSKpwBNOi/EAQrx7QeFQkETExOSFIUSt2/fVqFQCKXgeROUQQrDePSS/k+EjoJgPFwOr8CE6fsO5/rlEXe393jfx+P/e/SbfpcLAXKIKnVI3FmCRuxzxWnywkR3KqCLG7YUpmVdGYtDvakxZz9+JnN+aMfAwID29/fDYEudufOUPk6/FPXwCDuF4F2wUweS51Ab4FH3xMRE8Dzbi3Ai6ffO/YAGaaXpzqKnN3idwi+c45mZGZXL5bino2soTTey9CPACKBToCFwIQ67K16ePzQ0pLGxMX322Wean59Xu90OJ56xOxowMDCg2dlZVSqVKByCfvQXHxsb0/DwcKAP9C8Aki6VStG4g3kSAUJbikyJvuE7ik3RhTQjkRQBBnUxrAuFo/AzRpgIMEWKqBWQzo+x5MeNjTvb7jCw9qwBjt7y8rLu378f53jzHYpDHbVzQ+uGzaNv0NSnT5/q+vXrkj7fJQu6UfQ7MDCger3eUQn+yiuvxBGQHIjhSAY84Ll2UAq22KV62B3q1MlwXoavPA3K9bwg5cuur2SY8XQpm3dj5QuPQcYzYyJe9j84OKharaZyuayLFy9qamoqurXkcjnNzs7q93//95XP5zU5OanLly9rZmZGr732mhqNRkDYq6urmp6eDoUKYwLbUf1IDq7RaERnLiAjvDeawEsKz8//JrJ2AXBj4sLv1em81myebaOamJjoiAh96wv384gJOiMknkLoZszd+0uFjXHA/N6NzA22Q7/dojagNmdyBBKauhHCq/To3a80F+aFWJ7bwnP3yACnEGOA8qJamqYxjMO3xDEXBIz6BYfnGVM2e9axjgPcMYDuoHDUYboenoP3qALaeXEb65c6ZP6eO0asVbVaDaOLwW02zxo60AWJgiwgUZQpERUGAEPr8Bx87uuKQcVBAZViaxK6o9FoxBqvr6+HI0vdCh2qiNY8cmd8nufz3ODBwYFKpVKcsEXU7JC4O0jVajU6X+VyuciTchUKBY2MjGh/f19DQ0NRnEQBJ1XbaR0GNPEoem9vTxsbGyFLnHSUIlf5/Nn2oNu3b0dlO4dduDGYmZkJ2YKn0igTmsG/qSPsELUjXC7v0ByZ94h7aGgoKpvRGzgffj+XDZ6LAwCyiRxUq9UoqvPWoe12O6Lxw8PD+C5ngZ+cnGhmZkZXr179HGrAc113oAtABxgPrzts3S2wwNlw+now559Dd3nq6kWvF9/x/DcT5aAICOdRCsaa/DJl/5nM+Zm7kiIf8Morr2hsbCyiY/YXjoyM6ODgQNvb28pkzvLL4+Pjks73ZF6/fl19fX26ceOGent7I/eDp7S6uqqlpaUotmq329GsAqXrXbr8ZBfPOUuK4yOB54A1UfAOF8OQHIeHkkFgC4VCKMg0qnLFxxgpsnDhQeF6UZ13DEojKy/WkhQ1AR45IoCed+U7DmsyFhSPR6Y8D37w/30czsjuVEDDNOp3Jd1unxX8uXDg7GH4naYYXpTG8xCAdOyshys3FFShUAh6swbt9tn53cPDwx3QvtMeGqTpiJRe3NtzpL6mKESP0kB8UPDUM0jnvQXo2ra/v69isaj5+fmObUuHh2dHTdbrdeXzeV25ciX29nJ5qqDZbEbkByR69+5drays6PLly9HQYX9/P05w8vFyH/b0Y+R9zVHOvrau9FDYRFDQ0ve9eoRPZPTpp5/GNsDx8fHYNUKES1EbBoqmKUDb7O7wyJJ1Oj4+DuPrp7NBY3RDKgunp6daXl6OPcLUxUjnndiYA93LkGHfV59GfciV8zPPThEpR2ccMXG0DZ2D80zUSw9t/3xq2FgL6nRYR/Y2A0tjW1qtVjgofHZ9fT0q6AuFQhy7yRwcFfBnuK6Ar5hDNpuNAkC+68hbiiqiI5ArRxdcr7qhT433F11fqSobTwZPUlIHAYFmSMYTkeHdsKH/2rVr8RkOS3/11VdVqVT0/vvvR6Xb0dGR5ufn1Ww29cEHH6hWq2lwcFClUkmffvqpxsbGovFEoVDQ9PS0fvrTn6qnp6djexaLAEQJLFYul6OKlog7jVLSQha/0nyFR47pZ/GQqUDFO+PZMJUXKKXFSgiJb35vtVqRO/PP8Ex/zfM2aZTtDoYrQf9utwgYheSG0L/nQuIM+rycjCvhbsrE7+nGM72Pj5VneUSQ3hfD3y3H65G78wHPx9Cwx5r1QGmlRtjvz+UOkB+FiRPkc/Ye7iA5mUwmimFIyTg9MTCcMoXRoGYCh5R5nZyc6O7du3HAPT/Q8uTkJBApdj74MYoPHjwIfoB27pixHu12W3t7ex25Pj7nuVtXvCg/N+LtdjtgZqIq5AMD7YhFs9mMYq6FhYXPKdVW66zwlC1ROLPIOxGzGyqv6PW5uXzAOy4DoIsgAayDHyBDioT5DA0NxTqnDrZHaL526ByvsQBddCOapr4c1oUf+c28gd1xQny3Tjf9yPr6uqytrWlpaUlzc3PxLB9TNpuN4ytBhC5cuBDRbipTyGa3ubnuhSfSaNhlyJ0VdxBdv0AT7pGiZl8UFKTXCxtmDJxva0KIiByk83wdjDY4OKiRkRFNTExobGws9nkeHx/r008/VbVa1cbGRuRdyL0eHR2pVCppbm4uIGiKOSgGoyerL3CzeVblWa/XozLRPVpv6A5h8XzobIXwUOzlxRIwH5dHcq7oYWw+S+GN0wq68uPGulv0m17O8P5ZVwBu4PgOlxtRf457y65U+H4KbaewtH8+vb/n41Lh6zZf5pEKh48rjQL8t88jNfA82yNp/35KE57tY+P+Pk43nP6eKylfk3RtnJfS4hSndTo+4GJXxLlcLhpl8AwcQ1fWvm7wLp2z/MQ1jwopvvG0QyaTCTjaecabPfiYnd8Zb+oMpkZGOjfqGHwgec+5egTozh4FpKR5vADNo+zd3d3o3+A85xE4BiPNoSPvbBvjx4ssqb8Baue9FMp3mJVn0sebtXLj4DzisLZ/33VWys/oUV7Dserr69P09LT6+vpUrVZjGx7OGL3TcYpoDgPUjRNJaoOiQFDQvr4+raysqFQqRQe7drsdNEKGSqVSOAWMMXV6U53n6ByOFP/DF7lcLmp+QIKcbvyN7WB93N7Ba76mbn9e9Hphw+zKmtwSBouH5vP5KBohlzoxMRHFWQhHo9GICJwFv3v3rubm5iJf3Gq1IspeXl6WdCa8HPvIPkI/VcZhHYyyMyyOBLnibDYbzUXIJbnBcOL7lRoeFgE6Oc0w+syTezs044oRAw0NuDeLC/27jYmFT6N15s9nUgWJIKcRdGqU03nzPp9PDY6PzSPAL/IcuxnA9PJnpQUXrgT5rOfx04jY550qqTT6cCfKc8JEsYwFI5Y6V6lRTgXVaePeOfzAd/2+fJ4cIAqMVAXGAKMFBIrCcwPqCh5eo57DI0mPqLhvGv2kKIFH76kTxfM9ZZMqw5Q3iNpcrpvNZhgKdz4c+mYs5BiRQ2oustlsIG3b29uRb6cehQIw6NXNUXUj0Gw2O/QRxhQnxOWbQ3hIVaXIANu7GLsX+vkWTr+ch1M5SXPR7gi6jLjxoh/50NCQFhcX9ezZM7VarajMd/QOnsVIu+4D/sWJgU9PT89afNIRDL5AnrAl8GNaKOby5Y6vo16uz5xGrFV66pWjic633JcoGTlM9Sh89UU6Lb2+kmEmEqCoCegXb3FgYEDlclnz8/NxWg1eOY08Wq1W7O+l/y1t9jiuCyJms+cndUiKIoxnz57Fc7hogdfTc9Yrl24weDFs2ud5zAEIm885LORGh8sVDPfnde5HFAkjNJudm+NdgGCK1KB5dP48Y8b4XKmka8bYUqOZGl2/0ijWx5SO2Z/1vHsCv6X3fB6jutFKaZ8KXfqs1PFwujzPY/UoM71Xt7w0awpNvYLbv8e6Ob0cGkwNlDtWqQPhgp1+X1JEJK7IPZrlXt5BihPDfA+q1yFks2cnV01OTkaxDodmEBFCtzQySA0BlzuxKb+kNONzaUQOP/l6c3m06fNxuvnzUkWNo4LxQ08hvwMDA9E9DXqjMzzKZGxUwrtjCu0waJI60ncgADhRzIFUBWvsUDFzcVlnDNzDIXb42B3+1ID4erjDx35sOqg9fvw41tXz5Tg87GH3NaP+iDy902V9fV3lclnlcjm2R0Ff2h/7qVWMGZ71QMbRRH8vdZw9LYcD5jl+l0t/jtPanwENuedXiZalr2CYWZzT07PN83hsw8PDKpfLGhsb0+TkZMAMEJ1KOrr3eGEVJ7JQjdlunx+1xf1ZDAjc39+vS5cuRUMAKjC5D4rSt8mwGCwmBPPciI/LFQqvuQLxiI0FceZ3oXCh8W0pacSLosWT5vs8L/V2Hcbysfi4U8/NDaErwHTO/hzm4wyWRps+d5jW55Qay25RO/d0Rcq8UfqpwWVtu92L+bmS6HaPbvdKFWx6Oa0wVj5Gdx7cYeF3eg8fl3/f/06NsvNiPp+P3QcYJqKulEeRQ+nsJCjWb2xsrGO+OKpEoG4AHMrlcxgVXvfWiaBlbsBZJ6JIxud1GzyTWhSiMj+Cz+83NDQUjofTyBWyr6sbPW/I4TsNcG64F81YMHJOA4fKMWCtVkuzs7Nx5CE6iLaQ0vn+fu7Jj2+XokDVDQv38RQENGc86Ad+PAfqqQIPhtzpSg22B2K9vb362te+punpaX300UfhLJAuJPJ04+8y5N23oB3bX58+fRrBncvI6elp0NLPo/Z5s8bucPscuVK9llbvg3R0062po+xoTPpMDyReNGr+SjlmjkLL5/OanZ3V8PCwxsbGNDU1Ff1xFxYWQhilTsUlKTwg4InLly8Hs5CzQlghDNsgIOTk5KQKhYKePHmibDYb3YS8u1gmk4kqVF4jlwTs496uQxH+eqpMneGl883rCDWMS37OmbFUKoUweEEPFwoAQXUjzeXG2g1nernix8P2jlsecXkUw3v+LDcwTgP/TreoP436nvc+dITWLlTpM573nsPxzxMCH7cLaboO6b08cnMliHKjuBGe6gZ5OR27RbzpOH086d9OI4eByefB9yh3xu5Hj0qKClrfouTrQTGnrzUKC948OjoKR106b/4CmsY683o3hyNVZp4PJrKhwA2HGmef40WfPn2qYrGolZWVuAfr6P2ufScDjoDzCoakt7dXk5OTcbwt9OBIwdThh3+hY7vdjtzpzZs3dXh4qMXFxY6uiDwXmrhOcGfbI0DG6LsgvLkTaIIbVuZNlO/OIpc7s/4/9Hd+dV0pSXNzc7pw4YI+++wzbW9vB8ROt0Pyyeg07wbGM8j3I1PHx8daWlrS/Px8x9hBVtnn7QVYyLfzKzbEURLveOZr6YgCPOZoAZc7kl4T4bTx15yeL3p9JcOcyWR0/fp1zc7OxhaD09Oz/ZMfffRRRxEFA3EBYSF84bylIgZrdHQ0qj2BktwoHB4exsKcnp5GXgOmJAdUq9XU29sbm/hrtVrAIDAIBQpUrMLMaTTpTAs93LtiQR0GymbPc/Ecw8Y92fKSRtcItCtnmM2FyXPnqePgRW0IJWNmbTyXlDJeykAurP7/84q+PDryqMCVrs/Z+cvn744Bv10Y/D5pxM+zHG7yKMw/5wbhefNPHSiMMFECUZpDc93omTonz7vcOXIFmkJ2yA7P9TH6/Ij8MDrZbFZTU1ORt/O8Jd/J58+7RuFo53K5OLC+1WppZWVFudxZP2u2W7nCS9fQq19TyJHvubJj3RgLqSdknSMOqT/p6+tTqVSKnRR+X78n30d2WWOXTwKM4+Nj1Wq1MOoTExNaW1sLQ8wOE3em0Xunp6fa3t6WpIjmHYofGhrqiFw9QmWcyJHfu9U6O5SDIMRTa9zDD95JadwtMEijO2Sl2zYs50106cnJSRhLjCHFvsg1fRM8UvVAqN0+q2sYHh7W4uKiyuVyR7HV7u5ufId5pc4q40vTOj5mp7PLljvVNIBivdLgjb9Tnes65asYY79e2DDfvHlTFy9eVKFQUCZz1j7uX/yLf6F6vR5bpzACLNbQ0JAmJibCQDG5/f39iDSmp6cD9u7p6VGxWAxGgDG9ZSYN8JeWltTTc9ZofmtrKxSKpOjkQ5cyoG2v7ONi6xStOllgYHQ31Gl0mnporVYrjDz3gTZsY0EgXKmmEal0XgjzRZGsR+huWHiPC4GCUfH+PVLxKIXLvT+YLy1gcbjQDZwzPfN1CC0tMnFnzmmOcnXIMTV8jhxAM//thj2lp0fXLqz+HR9TrVbTzs5ORG6tVqtjL3AK+X9R1PxlQpvSMHUuoFFPT0+c5Oa5QCI5Xx/ep5MV64zyw+iTuwM6BjYkepXUoTBfeeUV7e7uxnbGdDcDY2DdMc7ValWVSiXyeTyT3Ry5XE6PHj3S6OiohoeHIz2Gkj86OtLo6KhmZ2fjhLu5ubkoNoJPMVBOC5cX5EI6P5ygUqkEMkil7uzsrK5cuaI//uM/jgMbnFfRcScnJxobG4scq+9vz2azsXXT0z++rSuXy3X0SXDEAqOOzLJlifcxJC7PFFux/swfuXY5hR4uf6nMuCHk89wnm82GnfDvkXpw44Xz4g6Hd3FcXl7Wyy+/rFarFV0fScd0c8676Z9Ut6aOOPR2p8YdHeTcAwmXZ+TAx9TN+f43EjG/9dZbYSyWl5e1trYWjeU9QY9RogirVqupp6dHL7/8chRrOSPA8I1GI7ZN4QVL500MyNvgCLTbbd27d08zMzPhdTqUyP8whDdl8NwUii2Xy3U0/WARvArSo0CHdKXzxhDeDYrxMxaKSLj4LgyJwvL3UEAoXYccYWoicZRku92O12B+FJQXf/j40oIGxuCQG2NwJQEju5PhkS+wHVXLvM6cWSunCXRG8F3oPRrnfrzuht4Fy40ZY+bZ3KMbxI+Au3eey+U0MjISvHFyctLRMMIjMHdkuLevbTcD4bT3NXJolzmioHE6KY6UFDlAV+REJo54ANnxHC9+lBTbR9xp9TFhBOlpT0MNcoHOg8zLFVcmc9aMB5l2maBDX71e1+7urgqFQhTlkL5qNBqqVCqhSxYWFvT6669HG11kyvnYHXWHu/3Z8EC5XNb6+nqkwlZXVzU3NxdGk1y+o1roL+7HKVY4RugI0ELmzVnStChOjbLTx7dduaPG5UWLGNduSBEXcuhFYkDOVIsjS+lY4A8idAoR+RzBCp/FaLqT7Q2LHFFYW1vT5OSkstmsFhcXo1ubG3eXGzfI8B3y5Xz8RUEP+oLgEbnxxjipQ5/m0P3614maX9gw/+AHP9C7774rSdHZ5unTp+rv79fQ0FB4q/QePTo60trammq1WpzQQlTa19cXXiR9blF4q6urkhRbObwo4+DgIKARFM/6+rqmpqZULBbPJmTKxw2Rw82cD91snheWubJA6CA2CqJUKkXU6Qzhyt8VOe8jkCjrNLJj8Rx+5m+UJuNxr5bxpVCp/w/sAjPhFaIUHP7mOc6wKWzsUbobAcbjF8LnaIdDoj7fdG6MJZM5P1Pa6eEwGvPzsTld07n7Vgv3klGuKSzuQs98uS85XCISdzZarfM9nW64mT/NPhztwXFxh8odLeaE48s9PRpy/ieKd/4jKmENcayJoHmdoqtaraYnT558bi6sx8HBgX7wgx9obGwsEC/fIsTaEMWlBgbniPHRztOdKaDlzz77TP/X//V/RZTfaDR08+ZN/cN/+A/VaDSCDqwz60FUg1PozTFYP1e4/j1ow2lRGxsbcZ65R/+S9PLLL4fc0X5zb29P8/Pzeu+998JQDQ0NaXR0VNvb2/rFL34R0TPbNx0qdbjXoz/o5fLgqEcqu85/vs4OxcLXnmpIkQ/4kedy3//oP/qPdPny5YD9QUKd36vVahjkkZERtVpn22f/5E/+JGTm8uXL+s53vqONjQ396Z/+qba3t+MoR8blPx4McDFG7AV854EOgaajhg6vuzwzV+jlzV9cNzhN0ut5hrvb9cKGuVKp6Pj4ONrC4SW89957+ulPf6pKpRK9d7PZrEqlkqampjQ3Nxe5XnrGZjLnZ4Zubm6qv79fMzMzGhwc1MTERMcJIuRoDw4OVCwWQ9HQl/f4+DggLxaICNmVJFWSVIlDPJjTF5oFgMmr1Woc1u7MyyLgvaaRQRpx+XNcsNIFdiOI8XAYsJug5XI5VSqVjs9QFUlTd2BXN2bcwyNcaOnFaCl05dFC2mkplzsrCqLC3gvpUhiO+xLBPI+WeNQprVKj64KEwQdJYV0dHejmDDjE7dG2R9IeuftZ3X4/j/L9f8buThfGiZaRyJHDjdyf10CXuC/92dmH2263o5Yjl8uFYwsvEtESFefz+Y5OV1Qg4/Q6bR0FoKVusVjU1atXY50YN2P2/tI8A0eNeznN+X6z2Yy8djabVb1e74jCdnd3Y435DM6LK10cdU/r+Jq5kfOaE/ouYGx++MMfxhpx/4ODA21uboYBnZyc1OTkpJaWlrS8vKytra04HnRgYECjo6PRt+Hu3bshq8gg0Sfz8ogXXmDbqesC9LLriNTxhH/4LAGQ8z9/u5w62uUBwenp2WESf//v/321Wq3oIw8PgVKOjo7GaXuul9fX11Wv10OW+/r69K1vfUtLS0v60z/9U2Wz2aA/6+gOt8sla+/zhQepSXLZxRY5neEBR+N4DZlMddH/19cLG+aZmZlg3tdff119fX1677331Gg09N3vfjcY5fj4WBsbG1pfX48Wa+12W7du3dL09HQoMY7MkxSnVh0dHUUeaXh4WMvLy9rY2NDAwIDGx8cDyi4Wi0Gky5cvd1Qdpxvt+TvNaWLc8eiAzj1HgPKcm5vrgI28GCKNLlkwxhGENo/XmYXxcHm7TrwyNzoIhysSGHV9fT3adVI0h5HM5c46QF29ejXosrOzEz3GT07O+nhfuXIllPTx8bEWFxc7aJGOV5KuXLminp6zIxGr1WrHQQk4VkDZnMnrxRRuuOr1ekCq7XY7lBkOggsEBtOdhTTidWeL+7px4fPu+HCv1EB3M/zch/d9XTx6ceUBP3gujC5T5FI9f5g6a4wrdRRwxjijtt1uxzYjbxLChWKm6Mu/J50XQDlU64o7jcjX19fV398f21wcYs3lzjuJeQTokYpH0e5s+Xwp8Mrlzk5r6+/v76g294gZOUoNDI6Koy6sA/PDYNVqtXA+0RVAu+gC4PHV1dUwTOgxIsSBgQHt7u6qv78/uhh6bh2597QZBs51CIb48PBQT58+DXlAJ6RRo/MuNHQD5JAw83a6oFc9WnYYvdFoaHt7O055khRbWdnfjxGllztrztr4aWjUE2HAeRYy4PLQzQlhvi4boCU4rNAMeBq6OlTviKbT1NMi0ONfB6r+suuFDXOxWIxtEVQ1k/fBewUKm5ubC6XEnrPNzc1o7zc0NKRbt26p0Wjo1Vdfje46x8fHWltbi1OmlpeXNTQ0pJdfflnZbFaPHj3S1NSUDg4OAvIaHBzU5ORkjPP4+FjPnj0Lj9a9HhbT4Y/U0+Z1N4be0pDLPS7e47crQF84hNkVNwLjRsGrFL0CkOcSCaSGDYFjTvl8Powyxtcjir29vWjjKJ0VzXlHN4wz48CpQoniNOAEnJ6eRtTuDVWOjo7i8JNqtar5+fnIuTEu6cwQrKysRHW/dOYQ+iHqHl0yRqfp89IDXJ7X88gszRu5IU5TD8+Dv3wtuI9Dgj4m/310dKTd3d3IlU5MTISi9aKkNE8LJJjNZjvOqkURAeVlMmf7Q/2I0XR7FHPFyIKGIMfeuclTOfAu642cOYLFd1KIOYVO/TXo6kVM2Ww2ThaDL3l2s9mMBkIuC4yx27pivEEdvICKtST1hh5zJC015Hwe+nHeO3LIdjYMAM5KPp8PhxSe9L3xrLHzL/PyYCMNQlIn1S/Xb9yfCBb6O/2cX6AD+uPk5ERbW1t69OhR8Bw0cp75+OOPNTw8rFdeeSU6RO7v7wdtiV5JMXgBGnN0WXU57GZYGQO6GvpnMpkw0n6wEd9Dxrg8NcTz3HH//6th9sOqHSKEmB4tAmMwcAx3Pp+PrVAHBwdaW1tTsViMhaHggZx1b2+vZmdn1dvbq8XFxaiOw7unLSeV1dJZ5AtjoNjSyBYmJlLwFnhOZIcq/B58DiGAkaXnt618npC4cHAfPHqiZt/aAJM5JIOQXbt2LQoWaJVHesAdEcbC+dSnp6dxzN3u7q4uXLjQ0fAF5Ver1cLgs5WNiAX646ywzcGhYNaXo/PSfKU7BkCu29vbKpfLUQuA8kwvRyBS+nrhD8bDFZ4bXzeg0IvPYCxTL9o9aOcB1siVFGNFmTjCAi2dJ5iDK0ufDwWLDsXxPIwk38V4wsOuxCkec9ifiMePcMTZcIcXnpTOYGVPLXEh364w0/vgaPmce3p6tL6+Hm0gr1y5ounpaUnS6OioCoWCfvrTn2pra0tra2taWVkJnoEnmL873/yN4YcvPO/IudFHR0fa2toKxxYkiGiMLZdEiaTxent7tbq6GnAuawof40BlMplwmhwpYMso4/eKapwftoo6suDoQYo4uFMILyJ/qfPIViFP/ZycnMQJZV5DsL+/r3/6T/9pONXu/CI3AwMDKhQK+uSTT+I1trM6b6H3UoSQvz0V+UVGEToBqW9uburOnTtqNpt65513OhA9nCt3WEj/rKysaHh4WNPT0x31H0TcX2Scoak7hy9yvbBh9r2LkiJ3BfYPQyMAeI3t9vnezsPDQx0cHOjBgwfKZrOamJjQ9va2stlswEREeMA+xWIxqiILhYJmZ2cjV+BQJAxGvsCjT+9CJJ03Y0B5stjuNbkn7DlWlBjE5tlf5qE+7/Kx+gIjoH5PV6IoNxd2IL3R0dE4gD6bzWpsbCy8cNapUqkEzTnGkEIelNbIyIhu3rwZR9B9+umnajbP9pFeunQpoh+UthfezczMxHgY89OnTyWdV8DiVBF1b29vB9/gOWezZz2Lqdr1iNgVu/925eMRtiMXLkjugUvn1deurKRO2M8VA8aEtUphRTdG0Ad6YyjHx8c1OjracXY0EaY7YMgc73H2M5GkQ4TeTMc9fEeFUpQFOXIjyvoyHxQZ64/yPTw81M7OjorFYhx8Ab2BK3E0uZgHP+70Yow8cqMSG8RnYmIiIF+2UD59+lQjIyPxbL98rTE08BJ5ZRACirwajUY4NUSIjAnacfX29mpnZ0c//OEPNT4+rtPTswMbRkZGOhACuhviDCC3v/Vbv9VxXnizeX6+NIVhkjqamrjO8EgXnmTerp9xCJzWac6UAICeETj9jnJJZ4VxJycnunDhQugS+OLk5CSO0dzZ2YngjqCK87iJ1HHOkKs0fea84MGfr6//jf54+PChXnnlFb399tu6c+eO/uzP/kx/9+/+3RgLCCtIE3oEh/KTTz7Rq6++qrffflu7u7vxOrrFx+B8xuvu/L/I9cKGmTwrXhpntsKYMDYKwA+kZn+ye/ODg4N66aWXYlvD/v6+tre3Q6E/evRI165d089+9jMdHx9rbGxM9+7dUy6X07Vr1z5HBDwZWtd5txg3fozXC6BST8m/k3qiqafpxtEXIlXeHknz9xdBpg6xw4z+vtSpKP3+Hgk5QxOB5PN5PXz4MMZy/fp1ra2txda1nZ2dgFOZJwKCUnJvP5jpb8bheRhvQwrdGJ8rN7bMQYvx8fEovqGRgnv3TgOPRuA/zyFCW+cXvut0w7v2CNXX3cfsTtmLXBg5X1/GDe/CQ857rvjdCXUD40rN0wvdovvUuWDuvn3I84yMvZvjw3vOd3t7e6pWq3FmNpChjxtl3Ww2oxHI4eFhR9okm80G+oVjcHh4qN3dXe3t7QV/YTRx1vP5vLa3t7WysqKLFy+GU4Aj5OtLSo6x12q1gGcPDg5i608aybM+OLZpx65msxnFsoVCIYKD9LOsFcp9aGhIr732WkedDDQ5PDzU2tqa8vl8dF/EqYH+nrf3H3QBOoX7U2DofOMGxXlKOkNDNjY2OpBFnAjQs3z+7EjG2dnZSEHl83mtrKzod3/3dyVJ3//+9/XWW2+Fg37v3j39L//L/xJ8LJ3XN6DToR06hrk7n3ZDRTOZjP7oj/5IzeZZxfv3vvc9Xb16VZubm/r5z3+u+fn5jram7rgw9qtXr2pkZEQff/yxpqentby8rPHxcU1NTX3OMevmJLi8pQb8edcLG+YnT57EwdQcUr27u9vh1btn74Ya6EM625eG5/nnf/7nkddAIRSLRR0cHKhUKqlSqUTl4ubmpm7cuBEMQoQIFH16ehptOek+A1O7cWNsrsiBZygeg4AImaTPGUeHkNPFSL2idDH82dI5hOOfRTBhMBjAFaMbYBwm7usCyP14D6UDxEYx1meffabe3l5tb29rbGws4DTo5pAfTItiIbrJZM5ypsvLy1FzgPeLsUPQPG+1tbUV9BwcHNT8/Lw++eSTmPPBwYFGR0c/J4xuVNPoKDWijNnTMIzP14/vOmyFcfcKWRwnv9yY+f+soTslXpzkxtWfxX3SvK7D1CgUj44czmTtMYQOmbK2yA+OFJE9cuE0dR53qBM6b2xsKJs9a+GLUQL6JUIEveHHjTv3Gxoa0uzsrCYmJvTpp59GXpM2oUTpQKQgPoVCQXfv3lU2e97dzB1T5pHmqSuVSjgI5NSbzbOKcD/b3YvepPOcPvd1NIGzrklP8TwPBKD9yclJdAmDN4DHM5lMx86UoaGhOKseZJL7pzrIZcX53tfMHdHU+QcBdfTOHV7qi548eSLp/EAOdsKQw+WQo3q9rs8++ywKejHQ1Bo56khK04MQD6q6OYj8j95gh87W1pb+1b/6V5GaKBQKWl5eDkjb91Uzr9PTU21tbalYLKparerZs2cqFAqanJyMAlbogJyl0XI3PfNl11cyzPl8XsPDw7F3kypSX2igAJSNM97Q0JAGBgaCWVEQp6enYehZcPcUHz9+HHlm366Fh8ieTFrzDQwMxLM97+YQmkc6eO1pFJHC0qlCcuXsSjKFO13pcB+/tzO7R0J8xxeevB/OjBtonxdCBwPjvEiK3uMw1uLiYihj6awIDNgsjdL4jM+BsbqhRomheAuFQtxjdnZW0nkf9dPT0zg/O5/Pa2pqSn19fZHq6Onp0c7OjgqFQszRaZQKSDch5bOpAu3mxbrzxZo4rO33exEPGIWYFvG5k+VOJPd3Je/OlkOV0BGEypWAoznZ7HmPa9IZniLphn5ICijT6xrgr5SHocXBwYGWl5ejQp9nsdaem8Sw+wXvcCJdvV7X9va2jo+PValU9OjRo44tLpcuXdLIyEhHpX9/f78+/vhjjY+PR3/roaGh4CGULj3FHSJ3Z9yNuteT8BkvloKWTpfDw8OOQ3g8GnWn2w0N8g0tPGIFou/t7VWxWAyj7Q6c6wx3IJy+6Zp54MHl6QUQlRQq5/6cfMVFIxDm6EWs9XpdjUYjtrsiz94wCLTg9PQ0quAZAwFF6li4HpUUXdf+/X//39dHH32kvr4+NRoNLS8vq1QqRbplZGREk5OTMd/NzU29//77Ghwc1HvvvadarabDw0Ndv35dxWJRV65cCbSkm+5I/3Zd9aLXCxvml19+WXt7eyHEFE41Gg0Vi0WVSqWOfGezeda8w7uCdTz4byoViVhzuZzGxsYCxiLx7o0b3FOFGbyqmHNTJYUB81y0Kzn+53NcDo0+TxlLnUfRSeeGmsuNlnufqeHldanT2/a8dXpPohm/D8KM8WY/bOrNtVot7e7uhjPSarWitSD3abfb2tra0uTkZDwLZZ6mLfwUnXa7HblJCrao1CbqKhQKEflKZ0Lk59s2m2eV2+T6GPf+/n5EQ6lT4LR0Z8XXyOFpSR3dsDwVwdq7onLIm8/yfYedudI1xiHyvJ6PkWI3xkuVLo4Vz3DkJOUPbxsIDzm07/yVogWeX6Toz51Y8plOW4/+4WGn8cnJiXZ3d7W7u9sRyTldHDVgfs7nkuIQBI/U4XMgXXKEPg4M1u7urmq1WsyJYkY+6woWB9ShWniaaDyXO9uvTTTrCIdDxr7mDhe7vKdzbTQaWllZidQOuWXudXh4qMuXLwcfuOHlGc4T3ZxO1wfwbzpOvo/D4KmW/v7+6IvOqYAgl7/zO78TBnp4eDhqHIDf6/W6/o//4//QlStXOnogpIVioBWOyg0ODnboDE8tMFb0giMD7XZbk5OTunnzpu7fv6+3335bw8PD0Vb39ddfj/HW63Xlcjn90R/9URy7+eTJE73xxhu6c+eOfvrTn+rGjRu6cOGCWq1WIBVpIMXVDcL+/xzKPjg4UL1eV09PT0APVATSR5dFdAaHgDCAl7Wn234kdXjnHE6OIQa+dgbc2tqKzeme+0Z5+YK7EQO2kzo7UqEsnflRQO32+baBFBLiu1+kpHmfz7qS7KaU0og9VaqpseC5RK0U7jh0X6/XoxFFLnfWhpSCO/Iw7LmkI5vf1x0Q9+ZZcz6Ps8Z7pDCOjo7CqSNioc8zvEWkBBTO2gM/+TrmcrnPFV9hVF0Zshbu7Xu+2yFoHDq/oM/zcsrdFCA08qgbfmdO8CrP5nMYRs9rc183qim640gJ/zu9UChEPzw/VSDIKeNwR8f50nO07sR6ugAFnH6ez6YGK0UNWMeTkxPNzMzo7bffjvk0m03t7e3FKVCpwfH1A5lxPuU9ngtNUrryeqPRiD22buhxVDAqzNVRC+4HTzAn//nBD34Qr/sebY7YdbrhGPhae1AB33gVc7c02PMud2zz+XxU5w8NDWlkZETr6+vRkIZ7+1kK0Blj/uDBgzgDnM5wDonz/XK5rJ2dHd27dy+KPqEl/I2dkRSV8dzD5cyDhR/96Ef64IMPwpm7ceOG7ty5o8HBQVWr1YDW6W+RzWa1tbWl6elp3bhxI3a6rKys6NKlSx1V8qn8pCio8/eLXF/JMDcajdjuRH5HOo8+8D5hYhcwiDQwMBCeJ8zj+QNeYz8ckBUM7pEuwkbOGUbAIDisk82eH0WJwoHhWOj/l7Y/e47sys470C8HjImcMQNVhRrI4tCkyG6r5ZYst0KSFW15ksP2m8Nv/iP89/hBT3qwQlJIVoRCUg9qN6Umm80mWawRBRTGnIHEmMN9SP8WvjwEu8F7fU9ERVUBmefss/cavvWttde+zol75IHgOy3F/ZK0kSMpFMgVwQ0s75yMgty4JQ0fFz/3P5Li8A4EmXscHR2F4ymVSlpZWYlUQq/X09bWlo6Pj3V6ehoVtrwj+SKPFt0Ie8GR7/2rVCpqt9uRu6vX69Gwhu5FrHGxWIztdbAlyAvV4Wwh8TVKOmB3wkSlLj+uMMw1TBAgIUkBesToz0oyK8yN077+TNY76bQx9i4L1yk1z8M4OYPi0azLB+9E1b1HI+jedRdrmXzP5PwlZd71UdIYzeyf9Xfzn7mTQYclRS6YnCwgO8lYfRVQ4p3cqDqz8FX38LQb8z47Oxvb23w9oGQ9HeDbCXk3CtKoeMa5zM3N6fz8PLYMeeGln3rl88uFfcFeJiM45IV3dR3m+/zOaXmnpXFsyG+r1dLExISePn061vwJP4Ct/fM///OxugzmHXZsdXVVl5eX+uCDD/T48ePQV/74eBi3gypfS5w+etTtdqPnButApzaaIRWLRRWLRc3OzkaBV7FY1OPHj2M/e6fTiYLAJAOUtMEue0nZ+lXXjR0zL0ZOmWjz7OwsDlmnHaBTTt7rmgIQKE5+jxCyCR+ECC2LYXBkiqFGORFEDtqm9aQbUh+bCw4CjnFkIXlHBIHJ5/Jo0WnGJKXohtgdtyNnPueKIF01FHDq0xXSoyyn4ZaXlyNC9ef6cZ3kqzAW09PT0TCeTlFOO1YqFc3Ozo6hfvKWjB8j40CGdnz1el0TExM6OjqKNaIz0uXlpQqFgl577bUxQDMcDvX06dP4zM7Oju7evfulvKjnjJkXgJgDHd7XjZT/nrVzMOlrgRw4lZZUTh8Dl7Ma3NPXPemkPPLxgiXGijNy+U3S7w78HHgSrXNv38vpRtkNDTqTND7IQVLmk7riF/qQpPg8AuJZznrw+2azOaY//CFP71RukpFjbn2e3SlJX05TMV+ZTCZa85Iyo1iLe3mbVWdzHIT5PFKwii6x7twLNs/tDykA7B/y5HPo42ccrFO/f1Vwy1hdZl1GmQ8AEDaBc7BLpZKePXumer2uP/7jP4518Qpt1oYcdKfT0dHRkc7OzrS8vByHEL148SLob04QS9o5j56d1XJGytlOZPzJkycxx7zTzMyMut2uarWacrmcbt++HXaDcZ2dnenx48daW1vT4eFh1Mowh65XziY5c+Og+abX14qYZ2ZmdHBwoOfPn+vhw4fq9Xo6PDwMh8vEnJyc6P79+3r27JkymYx+4zd+Q8+ePVOj0VChUIim5OxjI2rLZrM6ODgIAXTkSfs9JoLotFwua3V1NQQ8l8up3W6HwCYXNZVKBd1EhSrjdofixkMab3uYpIDcQMIiuGFMp9Nf6tjjaNrpmWQk4s4HtJqkuJ1iRjCdAnLjx749LjfU0mhP4vr6epyqQpSVzWY1Pz8vSXEetkcJ09PTqlarKhaLAboY53A4VLVajcI93kWSlpaWVK1Wx0BTkmJdWlqKSIUo4jqDy/u4YXHny+/cSSZ/7kbb7zkYjBczsu5OQydRsRt9NzDXjRV5d8Pq7IortjMW0hXgQH68kAhDORxepWF8WxT/pmVqMgJBrq8z+Fy+zq4fPn5/Hp9N6ieynmSrfA6TdQNud/xyQPzL1sWj5yTtnwQk0Lm0nKWCG932w0jQKdaDeyZtgINctlfxf9aGvgusE0GD76FNUtLcg9+5bfBtSS7DLis+5x5lJ5memZkZra6u6sWLF/r4448DmDvw4Lurq6s6Pz/X+fm5Njc3I3iq1+vRzc3lBr/iKRQHjB5Nu+1wZot3cnDiAAPfwr95Ji1GV1ZW9OMf/zgOa0o6ff6dtAOu61/XKUtfs8EIAtntdpXNZtVut9VoNJTP5zUYjKonp6am1Gq1dH5+rq2trXDMZ2dn2tzc1O3bt/Xo0SNJozzD9va27ty5o1arpaWlJX344YeanZ3VwcFBoBPacy4tLSmfz+vWrVtx0hPVupxuxVYg/u0GhgiKBcRhcrnQUU2aRPRJ6pB7ucFJUkOcIuQ0tRstFMypUxbVKVj+7wKHA/KoBQeC4CQrCHEy5P4cKDAPjBGDmU6noxmI03LSVeEIc+zzimGBCmLcbJnBEFGs5kAFo5vP54P6w6gyH966z68kA+G1D46smRPWz9MRKLgbWZ8PX8+vQssOBJJRWHIcLhMeDfhnk2vP2B2ZMw7e350198JIAYidYuSeADOMmUenPs9cSVDpFwDRI91kRO4RUdKpO8PFeySdu+umpyXc+QwGgyg6xSnzfJcp7+jEM32HCBXD/KHam+clZcHZO59/tzkcvgNTl6SCiZD9IB7Gxxwk6yV8fRzgOzPnNo7P8I7uMCcmJqK3AONjXIuLi/qjP/qjeFefa1KT6P3JyYmOj4+1v7+vzz//XMPhUN/+9rd1fHysvb29YOUKhYLy+bzm5+c1Ozurd955J/Lu0pVtbDQaYXeSTlEadYPkO75XXroqqGOcdHGj6LDVaumP/uiPopHP3t5eAAyANHJ/HVuUlNGbXjd2zNVqVScnJ1FBykKVSiUVCoVw1pTxE0ERnYI4/HSbwWDUSKLT6UT0enJyEselXVxcqFKpRLEZRn4wGAQt6lEGaI08QjLqwAkx9uFwOFb05QYsm82GAjjCxhF40Y4bRenLhxfgmNxIu/HkHXBcjAfjw7OS7d+YQ4yGt8nzvJYbh0KhMJYL43OOoCUFkuTfSQrX+zIPh8Og9FAclJE5k66iCHdCbvx4R9YHo+kRPZEfW1CSc+L/xnC4o2Wu/X6pVCocEIjbIw0+64idnOl10aOvscugr63TdIzB91H6+nodBBfzwnpAq+VyubhPUpaIHHhPB2qwEa4HvMt1xt11hnvwHs5EXEfjJanT5H0ZY1JOvurf7liTzESSYvTn+/iYdwe7ybHzzFarNfZM/nYgRkDhcsO/HZS4E/d/k9YDKCbBcyaTiSga2XDGw/UsCcoA18ixF/r1+1fdvTw1AJBGBnke7+gAQ7qyk9hoTiq7uLhQqVRSOp0eY1qdnWCsAAPsyNLS0pcAdSqV0s7OThQMD4fDsP9UeKdSKT158iRsHbIuSe+8845qtZok6eDgQI1GI5pdwcQeHx+HTLTbbT179kxvvfXWmA1wPfHxeVR9Hdv0VdeNHfNbb72ler2u2dlZ/f7v/37kC3K5XBhwKILLy0sdHx/r3//7f692u63z83O98cYbWlpakiS99957Y40Fdnd3o2Xd7/7u74bzR0D6/X4Y+4mJCdVqtWjl5g6Wva+VSkWbm5tRxMQEMWG+7corVJnUJO3siM8LnbwoxifcHbYbdl8cPuNK4tS5o1sW2UGCK9pgMNCzZ88i0vA8DXkqABLRM0o2HA6jAAVBlq46djEfbF1z+p9xsn+crWvkPJNzSFc29o6y/cn3bPK8fr8/ls+mH/rc3FywM8yjz1uSMkpSSyh5ch2YGwdKHmldF5H6v5Exfw+PwpNjc5CA0vu6OrhAjrxC2juh+T2RE941CbgoqsQx+Lt6BJCcV/Qs6dD8cgDkkRv3dyDilKPfk6jP18/nMRl9JNc96bw9uvfPM67rokU+lzSivha+hjyLwIFdB9L4eeisre/X5VncY319XaVSSd1uV4eHh2NFrr5XHcfPnFENjUzicHu93thRi4PBYKzLGjLA3Dn7hjxgQ3G0fHYwGIxtZaM6mvs6y0NwhRwyR16X45E+rIUzDb4mDugZG7aSZ/X7ff393/99tPyE1eB38/PzOj4+Dlax0Wjo6dOnccoadoZzGZ49e6bp6WnNz8+PBXrSVXFjkglK2pGbXjd2zD/4wQ90fHw8VojAIRNESSCuycnJKJJYXFyM74GUBoOrIqFsNqtKpTK2rxSlpfo2uWfUI2WcqHcD4rNEWqA3z1U4FcFz3eCSU0d5cShQThg9EBh/I1jXXUkjL40X+Xik7QqCo6KRijtFr7QFZLhgIiAoN1XZCGq73Y5TeXC8w+EwGn5AKZVKJR0eHuro6CgKJ4rFojqdTlRlsi2C8XrOPpUatdZst9sxPxQwERVy8bNarRaIn4iBPdqzs7OqVCpjkf91c59kMwBhHkV6BbY7gSQdmaSpXNncqLgzdPllvf0eHg0RuTBGZxqSeVvuwbidzkzmBzFi3IfxQkO6Qwc0uXx5lJKcX490kmkWnuFgw41s8j787RFtMrL3z/qVjIqTY+BnzIlHlP4Ofn9nUtCFfD4fkVQmM9oBsrW1FbKO/nyVjrvz9JoS9gO/9tprymZH52Lv7e2NHWrizol0IUVg2DQcMMEEf5AnoknSUl405mDJQQb3RR/7/b5WVlY0NzcXYHswGOgXv/iFfvazn0Uxl6foqGHg3pw457rh6UbsM3KfBKF+FrZ/BnuTy+V0fHys1dXVSJN5sDM3N6dut6u9vT1JioJYr5Lf39/X6emp5ufn9ejRI1WrVS0sLIzJiTducplNzuXXvW7smDudTijf2tqaZmdng8qgH6yk4OJBLkRl7HdLp9MRvR0cHMQpME7JkGtJUmX8e2pqStVqNcYGDQw94nuhkzkUFhsDQncYV3qnlKWr/FA6nY5j9Byxe2SMAIKSUWq/khEzhhRj4dGNF98wPxgc5pF14f/khdxx0+6O+/X7/Tg0guMz6XPM98jd07g/lUpFUcTJyYkODw8DQKGcGIpUKhXr4I7K55ye5hiRbHZ0+thgMIgucHyPCslWqxXNN05OTvTgwQP1er2oCfC5dSDm9LLTn1xUqdJMAwPB892pOXjid+4Y/TMYYo+GPSfM9VXUJjrDc7gnhvM6BsULUlxWk6CAiA/D5kyIfxeHwrw6A8RnfZzIqesv8+DOxSMLZzZ8PnGAnhpIjsHvl2SvfJ3deSfTCB7t+P3daaNb/HxiYkIbGxs6OTnR9vb2GG0M6HT9x/Ek5YVtU4PBKF/K7hbkxqPaycnJ2Lq6u7sbdDBbC2HEnGkgkGE+XS7p/SAp1r/T6ejw8DAKWSXpzp072t3djTagrVYrAgCi5oODgzF5xPnzb04RxAZR6IvNZ96dgXAm03/maTpff2wBR3XevXtX9Xpde3t7XzocpFKpqFQqaWdnRxsbG2o0Grp9+3ak5ZrNZjSxIa2QTqejD4QHewA45BkZ+bqRMteNHfP3vve9eHH4f4zAzs5O9MVlA3ny8iKTo6MjHR8fj5X7u0FxEADlyWLw0rQDla6iBSJaSRHZYmi5t6MqX2CcCse38a5JQOCKjMK68cF4coEc3Th4kxb/rIMIFzQEAjaBZ3jeG4eH4iPAnU5HmUwmCvTOz8/DSdOSrtfrqdlsKpVKqVqtxviazaak0fF6IEMqGGdnZwN9o+DeNAAqN4nG6bN+dHQUDrZQKETBRaPRiPGVSiX1er3YXjEYDFSpVLS2thZKQcTrURWREVfSqaCcnm9OUrdeF+ARMsyGr5uDwOsQMmP0CBAw6mvO+JN5Q+bYo90kPezRNsbKnZSvics/QJR7OMj0SNNpb36WvBxo8Jkk+GA93OknHbYDLX+OGzzu7SA7OSZ3bP5MB3zJy9faG2Ag4ziXTOaqOc/s7GzYEuyiR+yMEX3l/kkZubi4iL21XmznzBxrCKB1IHV5eanZ2dmIcAmO2P0yGAyiDuX4+DjAMGACR04vaQp7l5eXNT09He+Jk/X89+XlZdQZYYd8FwCXpzk84nVmCQbN89bIP+vnhX3O4sI+kLLB5vl9mcfJyUlVKhU1Gg0Nh8NYT1qdtlqt6KmA38AOMd/OKDk4TtafuC7d5LqxYz44OBhTIkljqNZRj+/3dArr4uIijCxK6cKVNFK+h8+dEBeLToTmjjH5OYSbBUqlUoEyWfQkZYbRS0Yajrr890mq0ZXGF4658+pTfk+E4MrodC25W9/fDJXlXYiSqPj09DRYBSqhvaXe/Px8RKSTk5NxQgy5l8Fg1FABdoQDTIgCoIMBEAAdxl4oFMLxkh9bWlrSYDBQs9lUq9XS1NRUNLpPniL04MGDaOvHYQNsXfGqd5wZjs1pREmx5t7CkXnk325UAVa+/c7XFwWloMQdPZ/xiB2D4L9z2tz1ABDn4NXl0CnoZKGfOy/+j9yjk8mmH8ko3o0mc+aOLXn5d6/7TDKqTQIc3hmZcqDgOnJdiuE6g+ff9fVKzqMbVNYDkDMYDALc12q1mDuc3GeffTYWWSJDAF7PMSdlEv32uWi326HrOCDaUbqzwkESuHDMa7vd1tnZmcrlsi4vL1Wr1fTw4UO9evUq2Kipqakv5aT9ojd1u91WqVTSrVu3gs3ywjDmDxvsFeU4LWcxWXc/BwEqnIADoID9IChx2+5BFuvkdRPSVUqUeXb5BGT2ej3t7e1pfn5euVwu7Mjx8bGazWa8qzQKynK5nN5//33du3cvGA1fdw8KnH35/+a6sWPmoX7hgP3cZYyUh/FUONPlydE/k8rEe/XicHjV9YlIGiNVqVTCAUOZSFf5ODpf0Qc1uagIPgqEQXD6JFnheB3lxeURuqNzd8Z+Py6ezT1c2JNVlh4BOb2NMCe3fg0Gg0CvHB5CS0FyKcViUe12OyoTC4WC5ubmdHl5GUpOLuny8jL6DqOovo+TqADFpnIaYAAVDmvy6tUrDYej9p3ValWnp6dqt9uq1+vKZDK6d+9eUF61Wk3Hx8cqFAoql8tBtZEL9yImTrNBjpKye52x53LFYv0cIHFfZ14wti5fDmIdsPE9aZwK5vfOMjB+X1MAJ3Lh29KSTtX/+HMAO/3+VVGl/565ggJNUnNJ9A/48poCfu7z6I7AAXsyMvYqdIy2RyVJHUo+x+nnJGXu65wE5ciKR0FcUMheyDUYDCIHzPGU2CcHfzAADtB9TYmA6QdPx0PueX5+rnw+H/J1cnKii4sLffLJJzo9PdXJyUkc21osFpVOpzU/Px/3YYupM5bOrnjRFuObnZ0NlqxWq4UNpzFIu90eW3/0pFwux5x72gZZhQlBZvEb7i9Yv6mpqTF/QPCH/cG2edU592EdkTVfd37+5MkT5fN5tVotffTRR2q325HfHwxG52PjwFdXV/Wd73xH1WpVx8fHmp2dDTbE6yBY46Quft3rxo6Z/WsIrNPHUJqOhHGEvd7oqDaiMybRIz7piorzyJVoRLqq4qtUKlFljBCTO8ApMykc4M3lEToCiICCeun7nZxQj5oxum4kPOF/HW3Kd5LG3cFI8pnMAZQU7wMa5PNOBeVyuXD2xWJxrME/Ck7LUvrZzs/Pa3p6OrYGtNttlctllcvlOAvZm9UPh6P8LjQZDh9l4H2o5iSfzJpR4b2wsBAFRzs7O+r1erFn8ezsTE+fPg1Fe/3113VxcaHNzc14NrKCkkLlOdhx48C8uWNBnpP5fadJWTPWy/9gkDBu5Oy9CNEpcf/DPZMRoMuIU+MuW95u1SPqZNTq4FPSWO2Dp1hcR9yZ44B+WZR8HU3M85MAIen8r6P7iMCcheNK0tLu1PldktlK6rM7aP+d67SviRt59NDlhNwsaSSYCM/fs37IC/dFfkkRbmxsxD1hS0jzkDt9/fXXdefOHaVSqXDM/X5fe3t7SqfTevDggdbX19VsNrW6uhp6SLWxy4DLGLJEqnFubk5zc3N6+vRptGGmbqRQKMROGg8+0ul02AUYAyrUYYAcuEoak09YAgATgCGXy0UEzjo4EOr1eiqXy+r1emo0GhGJU8DG5TR7u93W8+fPY4cRzOtwONTCwoJyuZzW19d1//59ra6uamZmRicnJwFY8vl8+EUCqf9fI2WuGzvmH/7wh2NbbDKZTBggIkVQFU7bCx2gXohKk23oPC/iBsMpHOmK3naBzmQyMbk8m31zOF4QHP9ncZzyAJX5lgCieegVV9ikEXDKHEeJU8AQuwF1h+xGnnthwBl3EvX7WIrF4lgva+hmelRDX9frdZ2fn4dDBakzTs7bbrVa2tvbiyh3fn5e5+fnUQiWzWZVrVbHDqFAEXHiNHmhqGw4HEaT+k6no/39fQ0GV5WubOpnj3qxWIxqyefPnwf6n5+fV61WizV3tgOjCO2ajLScmkWGuDyyZM5x+G7IMA6skVcjo6R+P887OXh1B+j5TMbJPRyoIicOzAAGyc/znaShQJbQIwCxg1vk1+m/ZO7cQbi/fzJ6SEatviZJGjUJmjwaSQIGXxOPlvz7fs8kCEiCEtcx5tdTEcfHx5K+XL3OtibYJU9ZQMdyYQ+Za/QFlop8sKSIBtPpUYvck5OT2IlA0dfExES0xux2u0G7c5iP7xYBPPb7/cirwmTSAxonhn1Ipa4OpMDBZjKjrZHQ1zgkT+nRbMrnjznDppK2m5mZCWaE+7NGMFUwDr3eqBfDzs6Otra24nnU0fzrf/2v9cEHH2h6elp7e3t68uRJzKGzUnt7exHYzc/Pa2FhQXfu3NH9+/dVLBajstz9itfuTE9PB5vrOuPg/pcBwl913dgx/8Ef/EFMsFcNnp+f69WrVxGtIHzeyYnJIOIiD0FS3nNqCFs2e3UsJKiThfFFTBZHeb7u6dOnQQmhtC6oHuFiKHz/LoYyaTxQNKc9vbiN7/hRZXzPqcrkQrkB82eC/DxycsM5MTGhlZWVQLtnZ2exDSabzUZ1IZ102NLkFZ1zc3NjNQAo5mAwUKvVUq1WC4WCenv16lXMBe+VSqVivyAUGKeEXVxcxOlWuVxOlUpFqdRoGxVbTorFolZWVnR6eqr9/X0dHh5G9L+wsKC9vT3t7u4GXY58MD8eRSeprSQl6kUp0pWCu/NzgISTZg28GMrlpN/vh1GiSMeBAZ93oOagQLqiqH0rlzuSZGEa9wYck1vDAfFv34LlOddkqsbHmrzcwV4XtfIeSZDgzjLpqP3nrKN/LnklHasbw+Sf5LOvG891RtQdP+sPi4f8kOYBNCFnzlYwPmdcMpnMGFWbz+dj50GS7gcwp1IpbW1taWlpaSzqBQjTPOjy8lLb29vRw4BeEtT2rK+v62c/+1nIuztuGAoocXpeI0/8PpvNBnOFY8a+OmtycnISabNk0yACMnK5+AhnY5FNL/B6+PChbt++rT/7sz+LeZqcnNSzZ8/0p3/6p8rn8zo8PIzoOZvNBttaKpVUKpX0ne98J7b60lnQwQPBBuDRawXQGz/Rytmu62TPZf0m140d86//+q/HgviRicfHx/roo4/CQfBw3xweD7PIApoGA0c0ziKD1AaDQTwLhO8GlwlwyhLHtb+/H9u3eLZTygAABMENqAMKokSKq2AKfBF4J89HUtHnRs+fgUCxeCixNw5gwUHfoNZ+vx+0uzv0Uqk0lo9mLTAqIGbpKqJxkOWFFOToU6nU2AElRA4834udqM7kwBPOlcU5zM3Nxfxxvjf9u0HfsCmLi4uhLNDsFIghTy5bRCOuJMnojbVBfjC0jqqZHwywF5DhcFmvrzLofN4dO7/n8nXjShYaMnb/jssLjtTlCEN6HVJ3MHIdY8Bn0UuP4vlusmjLKVH+79Gn3zfpyJNRsM8B9/K58zX1CNkjNrcDSRnwKP2rjKQzWujg5eWl1tfXNRwO1W63gxF68OCBLi4utLu7O1br4YVHrofMqXRlbzjm8N69e+p0OpqdnVUulwun3+uNdiZ897vf1c7OTjRYIoqntiedTuub3/xm2E1s0fPnz3VwcBBge2pqSoVCIXLm0vi+4OHwqoYHWhl7PTs7GzszcKrn5+daW1uLd8cRt9ttHR4eRi2LO118Bc652WxqbW0tZNrZJbYmoYuTk5M6OztTpVKJ3SU0uSJtSg+Nd955R/Pz83FSFHaUtBpjxgZip/xyG4dN8K2qyc8im/z9dZ2y9DUcM2XiTObR0ZHS6XTQIG5kkmjhOgqYP1DR9OCm0pZIAcWATrnu/lxu5LyykUn1XFnSYKBUjnYBF7Ozs0GXU/QAOwBKPD091eHhoaTxs2z97GPG7NR88rAHoj+PaByxklv1IiN/N97J5whB7/V6cbgFcwkaZ+54np84hMPjueR3PHcjXfWhRbjJ3fu7D4fDsU5CfIfP8zcggZyYO0V631KA4dsWuIfnYJMXa5xEtF/lzFgfjJNXjzI/PAs6P7kG3BOZRC6vo778+V6DgMwwXuaV75NPYyz+u6Rx8PtzIIJTkRhBf4ekA3bwl4y8k+9z3ZV00g4w3FAmI29/fnKNJEWaKpku4nPX3cuv5Dswjxh/B/TeYxmnwf1J7yVTZ+SjkX9SfouLi/ov/+W/RM0Hqaa/+7u/009/+lOdnJxodXVVL1++1MXFRRRC9vv9aMXKszmrgK2ng8FVi83Nzc3YzeB2hHWEHcjlcmNtOU9OTuId0QNvakRvi+FwGIVi2Jl2ux3MAoDQe3Cju/l8Ppw9NpYeA8zdF198oYmJCT148EDf+c53okraGYvJycmwUego27ywc9660/2Xy50D01Tq6sQ6t5/unJP25joAepPrxo652Wx+qTlHKjWqlCaSSjrgpBPk/9nsVWN2kKakaLfoe6T7/b7y+XzQ3u7YEZgk1YwBJNLG+LEAHjF6BzGnuumPyr5borlCoaCDgwOVSqXY18eiUw3M+yDsCLEbbIST9niOxvz7Tu0nI3T/m+/gLBAGf0+crXQVEaF4DhRQkmShmUeTIUDGgnAUHso+OTkZ+6c9iiWH43tC3YG4rEgKZweo8GjUIxQofxC+03RJMMI4UWIHfQ4SeD7Po6kOxTrJyNEjuF/moPx37hD9ZxgU5px59fXFiCJPyLPn0/xK7vl0BgoZ5b2Tn2MOmU8HvUkgk3Tev+pizZOsRtJQJg0cYNHZJGl0vCn2KSmzyStJg/PuRLu865MnT4ItwKlRLMmcAAaSfe+5/1elucrlss7OzvTw4UPV63XVajW1Wi01m00dHBxoampKL168iAiQeaKuhHOqKR5Dr9fX1+PIQ2kkDzs7O5HbdbvDu56dnWl3d1e3b98O/Qe8AdCTgQKOEMfnTZbQMebW5ZlxLi8vj+WsYeNarVa0wNzf348xU3iWy+XUaDTiOYD58/PzMXvsOsWaYI+RX+QvyXhhD9GT6xgfl1e3A0lgeV2gcN11Y8fcaDRi4mg4QfTjxVIsXKvVUrfbVS6Xi6YdLvRuKJkkV2bfqM8CMlH+vaTQ8/KgOwyNR/ugumSRGc6b3AFbBihworgDp+Lt8tLptEqlUuzp9UWSFPdx53pdURdKggC4U3QEjtP2fBeV5Th7nAyCyX0cIIUgZMe3ivleWyIZjB8Mh1PHpAaYc36HQrjgMscYeS98YkwoOM5GUmzNwkh4hM5cwZR4YVOSLUFOvGAKA8WYkoaEe+EckX/PK0uKvPxX5Zp8ThmXOz6u65y6R8Ie4TsI8Hl2OtDf3Wlf9MvBrUcJyXF5jt2f6XnwmxihpKP3eybHfB2LwZy74QToZzKZqOJNzqPPdTIqTspHEsT5WmWz2ahQzmQyEVRwX4ICB/voCrKaBPFs22m1WpEbZkdEKjWqD2i327H18Sc/+Ylu3bqlSqWiVqsVbYmhrB88eBAsn+shOkGxWTqdDnuGDtEtkHoR3hGGb2ZmZiy16ECB+aMAFx1G96Xx08skjTU7Oj4+jvPaOX1wbm5OR0dH0UfB6xBgHIbD4ZheOoOYZHNcF10+XH+QKw820X9nk9AJB5df5bxven2t7VKdTieq1KARpdGpH57ToaNKt9tVs9kco3Y92uA6OjoKxytJ77//vv7pn/4pFo4cCttsnN9nAqG+cVQoEpNM2zunIhEUNzK8Uz6fj2gMBaIycGpqSp1OJyJdijj8nTzaZYH9/h7ZoPDJ/BOfY2xe/ejOLJVKqdlsxh7jdDod/+Z7Hnl5D3IfJ5/D8DBelKDf70ehiVNyAAan66SrvdgePXiO6ezsLNqh4vSTDIi37eRzPIP7+xyjSLwXzh654J08ReAOgqK5ZJTnKJq5cMXDOHBfd+a8t7+brz2ywPsk6xKSjpF1gkVKFj06/Y0RTo7H3zuZU+N31xktN2w4Q3fkXiDj3006bu7l90RHflWUDUvg96TYtFaraWNjQ91uV2dnZyoUCtHVjtwnMuB/+1h5BvPuc+1UNe/roBU75UyNgzPXGZ5/cXGhbrerWq2mH/zgB2OFURRc3rp1S5nMqDc3aZ7Nzc04GCiTyahSqcTRvDi8Dz74YGzOfPsZdhKHxueQmU6no0KhED9PBgJEyn45c8dJUhSKOWDDHhHc8Z39/X0tLCzo7t27yuVy+vjjjzUxMRFdAkktoofIUDKnnwSXHrkmWRhsJrrmfoN5cv8Gw+pyypy5Q/Z35d83vb5WxExE0el0ogAIGpBoyfOR2exow/nu7q5arZYGg0HsaeUzt27dUrvd1mAwiGIBjCvIKZPJxFYAR7lMBoYtk8moUCiMIcKDg4OYVI/KHVVJV8YMxaEZ/cXFhebn51UoFCK3k8vlxoopqEx0Y+oOjcVifnzcOChQK5EagobTcWSWzGt4XpaIEtqXQyegowaDUS9cQI8jQsARP0egcKxE5MwdTow5AzAwz/4zF1jADAwCuSfP2WYymQAAyXwhskMTGWm8WxtrzP0YI3NGtO59zz2ScUflhU4ou++N988lETIy6NQiis2aueIyp85WuOwAmJAtByNcyIbPFXKMIcUoApqYe4w57+C64SyOU9fJaPO63/s9/Tt+MQ/+N59NRrfD4TD23XsXKe5D68zp6ekoKpKkdrsdxUsux27EPWp2IIc+ACaZczrUAfzRU+wAzky6Asj0ZMhmR0er0rzk448/jt0G2Ww2thVOTEwERe2y43I+MzOjfD6ver0e25z29vbC6fgWL5ww0aw7TJ9neminUiNK2Z0pwQjjcdDnOWGekUpdHZjCfQBMFH6urKyMtVVmnNvb22P9xKGreQbv6KDP5S35f3fK7oz98jRNUka50Bd38Dh1H08SnN3kurFj9igAwWP/62AwOlfZoywUB4p3OByq2+3q6OgoDiiYmJjQ2tqa5ubmlMlktLy8rPX1daXTab3//vuxaJeXl/qt3/qtsYpYtv94/2qomOFwdCbns2fP9Omnn471SsWgoRzc340WIIEiNCrGcRhOXfCefvpU0qh5ZARq9a1iXhWMYjPO5POYfwSFZ2Bo2ffLnkTyNSjZcDjKBafT6WgOQ7SR7MDmjsiVACMIsOB5RCXelo8cFLLR7/ejyYjTW164hFP3whIKSXCoKDuInvV15WH8MCxuePr9/liHsCRlhQEAKLlyeR6WvflQgg4uOG7uugIk/jB33lBjMBjENg4cO4Yik8kEQ4EMeRTA+N1QIIOZTCYiF96LCBy5YgzIE/dzGh9ZTBqbJBXsa8J4fCxJY+hO2Y2bG1Q+4/uImRsOUyDvyjg7nY7K5bJWVlZi+5074+R4GTPP9Krd5eVl9fv92IYD/UoEmex7AAj11Ibfe3Z2VkdHR3HIwsXFhcrlsrrdbuyTHQ5HxVPb29va2trSyspKHA9JR7B0Oh0nxA2HQ33yyScql8t68OCB2u22Xr58GY173Ca53Lv8EPG+fPlyrOqbNWCO6JEAkHYQyncIOJwF4F3ZvtntdiNlxnefPXumo6MjdTodzczMqFwuR+76OqYjGeV+FQD03/s9HKh5esjv7TKJbf6qZ36dCDl53dgxv/POO1GBSNn8F198ob29PRUKBVWr1WiZyaLRvIIN5NlsVqVSKXLVkqI3M9Wqg8HomME333wzaNNnz56FE8bQzM3NxVaBmZmZMWSKgcdRI4g4KoyMU8GSAmWDZF1Yzs7O1Ol0Yj+h0ycevXiRlhvgJG0nXRkUzwETFWUymaB4pPEGJlwYN4wZOdh0elQUMTs7q3a7HQruTpgICQcJ2id6l8b3eTO/3BuHgkOmeppGC5ICFEgKeg82gntiUEH9RCfQksViUeVyOcZONNTr9VSv1/XWW2+pUCh8SbHc8CeZEXcSGDI38iiXN8p3B+6fdcDC75ibk5MTzc7OxhGoHvU7w4EM0GTi/Pw8coZQgUknhm5h+HnPZCoAXWR9fU+/OwoHk1xOdSKbHn1c9x0HP34f7uHgx+XZjdh1Bs7/AE7QZ+QBJwkjhAxyGtODBw9i7pwdSI7fn+MXe40B7nNzc7p3754Gg4G2t7fH5ETSGGCTrrZHOri5uLiIRj6Li4tKpUZ5ctaKlFomk9F7772nnZ0dvXr1Srdv39Ybb7wRjS663W44xkajoSdPnmhxcVHz8/NqtVqRJvGIHtDtc+9y0e+PDijy9I23wET2kO98Pq9erxe0czabjX+zZtSHfOMb39DPfvYzlUqlse2UnkbxHSTYZc/1+3ohW0kwl7ySwCgps/gtZ8r8u8gEdsoBO3NynVN3sHeT68aOWVJEAFDKBwcHOjw81MrKit566y1JV4ar1WpFboEDD+hGQzVjJpOJY85wogsLCzo9PQ3lQeCYDC9YSOZcvX1bKjU6KWlxcTHO3MTIUriBcXaUzH5l3sErk53mBUiAlJP5ZQy2G0kMsDu8y8vLoHJYfJCsdLXlwqMfaXw7GAVnCDJKNRhcnQ97dnY2RpMBJBA+HIuP1wUznU5HtaN3FSKqozIUg8K78dnrIm7AEwwCe6yJui8vLyOXz/sNBuPt+ABDXpTBurohcqcCQJDGDzj3iJifESX4vks/VYvqUdYGQFAqlcLpJqNDHIhTmpOTk5qdnY1mCxwoAhhE3l0eHWAkHRzGyylzLj7L3F1nMPz9rzNy7pR9btGRZISBXP4yA5Wk/JJMgAMJ5NijdOaGg+1JO/HzRqPxpfXgHZN0pzMEmUwmTmM6OTkJp88+XsbioIt7sc7YAqdHWbuZmZk4QY/UHVR1r9eLs4NLpZKWl5dDD9bW1nRxMToFKZ/PRzTMHB0cHMQhNLwz64aus86M2WsEPD3nDhO5cScN+J+dndX8/LxOT09VKBS0vLw81mWsXq/r8vJSu7u7GgxGp8XNz89HZz/kZWpqSqVSKQAt9QFHR0exjQpd4jt81tmJJJBMfsdlzdNZSbnkPthwdNGL/K77PPYjCax/1XVjx+xVaBRCHR0d6fDwUNVqNeglJjGVSkVf5tu3b4fTm5iY0OPHjzUxMaHXXntN7XY7zthttVp64403dHBwEMIPxQz9hpHFCaAs0M0IGNEECoUQ+hnRGFyElEiNSBVh9gYdmUxGh4eHsT3ACxA8N+kRuS+aL7r/G2rZnymN59iSAMDvC5WOEYP2hQr2ykhXMgRL0thzyIfxWafQAUREJ8PhMBgRugHhxKjqZA6haT3/hkLSC5z1ZUw0A+C7w+EwtrOxVY/5cKPI930tUVrG5xGlb8lLFlDh6J32S8oP800k4cWGSecM7eggDsq92+1qOByqXC5re3t7DAi4o8Tpup5gWNww4cg99wvwQYeSlfTIGUDHnaYXXV0n4y5fvhbuBJ3eTl7JCMPrBvg9ES/6LF0xPAA9qFPWx1NOrkPu3N1Jk8bK5XLRPW91dTUa77RarbBd2DzkCjn2tAfzz/N4r9nZWa2vr0cTHfQffV1dXdXKyorm5uYiaPC58E6MrCey4Wk4noussTbIsLN6TlvzTvwcMEHABLikrScpqJOTEy0uLmptbS22lD18+FAvXryQJL399tt68eJF1Oign0nAB+tRrVaDume9kkA8Ce58nlwW0Yuk/HlU7PYC+XKHzveTwCUJjtGZZAT+y66vFTF7ZOBHBELBMTA3Rp6fgSpNpVJaWlrS0tJS0DdEx0Sq0FF0mWk2m3GikDsoXt5zsq6ITAYK6PuWKSyiWtz7saKcTk3ncrnYt80ev2TVNM6MsXmuzoXD6ROex/c9OnJH7LSyR/oIy+XlZSBnnDQUKc6M++AcARwIGk43ifqcauI7OEkcrOfNMQwUr8CKQN96VTegxPOpXryEAnS7XbVarWgtiuFhDX28OB3uzXp4VEO+yovq+H2SpmW++Qzv7Ef+Oc3lFDC64GxIJpOJfd/kJSWN3ZM2i+12W4uLi2PtZRkj8oUcMg9JWhDd8DaC/I7IB8fsNJ1T48mo4JddSSeMc0p+LxkNs05JEJKkHf1dXeY4+YcITlIcV8o7sw7+HGQBcOSUbyqV0sbGRhzukM2O+sRns9lIfxEUwOj1+/0x0MucOOUJML59+7b+03/6T/rRj36kn/zkJ2PBQaFQiCJWZ3Lc0PMuHu1ic1utVswveodTBoD7eyJTbp+wC8gwQA67lsvlgtmkuFcasX0HBwc6Pj6OTmInJydaWVnR9PS0FhcXVa/X1Wq1ou4FIJNOj/r6I8e9Xi/qfjxwgeF05+gyc52T9XdkbtypJmWEIIfxecU93/dnIE98hnsnAeYvu27smKFVcBbD4VDNZjPOz8Uxc4oQDpKfgz4pQCDidtRC5R8Gi3ylU5jSFRWJgBJJu4BlMhlVq1XdvXtXzWZTkiIf6pQ2Cysp+kXjcHwbBK09oWgQWIQDFgHD6i0Sr1tEp5T4HePxAhgXIsbtdBsCQ46WrRelUmksws9kRtspiDK9ejqVSoXRT6VSUUuAQpLH8u1h0tVeY8Y5OzsbYIr3yufzgXh9i4WDJBd0p9mJ0nHYUNzOYrBfFYPqTgQn5EbSnSvzCSOTRLpcHmn675I0r4NAH4eDSNYedJ+MUB14np6eqlwua3NzMyJxojB3cC4n0vjWJIwO9DmRO2vHWjht79932fuqaMSvpDNlvt3wMf9f5dy/KrK4Dix6xDs5Oanj4+PQ52q1qrm5uQAuznz5ON3REQWys4FocGFhQb/5m7+pdrutH/zgB2q1WioWizo4ONDJyUkcL0ubR5cTZ6J8DtD33d1d/c//+T9Vq9Wi1z0OpdvtamdnR7du3Qrmy2lznwuvuPb3JMUFQ8OakzpCtpzudoYJOwAjQ43J5eVl7JxhntPpUfMP9MmPlb17926Md25uTs+fP1elUglaG9tL4dvTp0/DthJEUOMDQ9tsNr80H66ryDFBkstXktVM6vJ1zIFH9B68MWf8jv872/h1ouYbO2ZO/HEj9b3vfU+SgtplAAjqYDCIHI/nA2ndhoNmUnFs5F25XyqVGjtpBePBy3OoBTRqKnV15OE3v/lNvf766/r+97+vzz//PFCiNH6KCZSPRwgetRGdlcvlEGyPygeDQaBGp7hZZL/ndcY8FsToEZSH9+W5boTdudy6dSv6VJNTpuBOuirAcuHhDzl+jDcRFMpAHpvImHkkn+yFYK68UI1+T+mqkpz78F13PE4XSVf1C9zLc9EIvaNuNzR83xUOis/3QTt95rQUF+P2NfR59LV2ACWNGwrApjvziYmJiPaQewASRY44c+SCe2OIPZLh37wzz8O4Y1xdTpl/xvNVzjPJKCR/hyFMyqunEjy6kcargplrf0ef66RBBYhz0tODBw80MzMzFvU4Jcu96L1eKBSigJHgAWqZwGIwGBWm/uZv/qb++I//WMPhME5ee/ToUegX70UXK58TB+jIA+eNu4Nh3c7Pz3VwcKCHDx/GLhG+7w4DORsOr3o2YBfz+bzu3Lmj1dXV0FcKcAkgfvGLX2h3d3csDcUYnXlkznDEl5eXajabyufzmpubU6PR0O7urhYWFsK2s0Wt2WxqaWkpwMfi4qKePXumXq+nUqmk9957T4eHh5qdndV7772nbrer58+fh8wTiDAGZNXf3dNFyFuykCvpuL3mxz/j6SwPsEiPoCPJ6NvXx4Gx+85fdX2tBiNeSOIUGNugpKs+tXt7e1peXg4HyyCPjo5UKpU0NTUV3aNwjCykKxOKx/mavGSygAqjxv385BaKzXAWUJsUFHEPgALtNaWrAhmnIN2YUUhG9EklN8rseWcMChETc0kUeO0CWXUp98Mwe+TR6/W0uLg4RqeT+2JbWyaTUbvdDifPvmVymswZXX+g5IhEyOvT9Yc8G+PjdBtycCDjVCoVfWrT6asiMlgV1syNLAbIG8+4koCYnQZ2x+sORhqvzPYIHYoOZI4TcQDIHCeVjot1wGk4bea0Ps/HcDpIIY0CO0F0xDMZkwMTT5VAi7Jjgq17SQcGA0Rk1ev1QgfQ02Tk71Scy3JyHphn1w13GnzPAYqvjwOeZGTuz/M/AI1MJqOlpaWoYodmBsDB3FAln8/nw+4cHR2F46BOwtedtBo07osXL3T79u3YD53P5wPUIo/IL98jyEBXvA6ANZAUKSjmyKvp+Z6DRnfMLltvvvmm8vm8ut1ugAt0tlKp6NWrV9HN8bXXXtOv/dqv6e2331Y2m9XOzo5++tOfRn7dC12hkpHZxcVFNRqNYOEAc1SWU4A7OTmpdrutTGZ0/rv7jPn5+Whx/Oabb8Ya5PP5L+kdQBX9dp110JX8HmvA2AnyXNb5nsuj57C5eL6nGpNMDvdyUAx4vsl1Y8e8vLw8xuEjECicN1A4OjpSuVwOxEZuCzRKZIpQ0sgdg5LJZCJvAM2EcceYSlcUMo4a58gC8H8O4CiXyxHFY5yh1GkfSrGSCwKGjUIyFgBamK0bbvwcgfF9/mBIEBAWEZTMBfhhjokqEUCvpPR5QAnpU+3OBxoeQ++5yGTU6SzH6upqOBzQdrVajQIbmA4YDBw/zhgAA01ONEwU7Gvq7wZbAv3q3+WEH+TQi9vS6XRQb9dVJuN0eHev1Hbn4YU7yDIy4cDPKWt37ElUDy3PPDI2V1zyi0ml9xxzEsiB3mEocBLogMsFFCbvynoTmSSdresUsupG0i/PsyNTOJckDYgNcJlzffHn+4W8MJedTieATaFQ0OTkpObn51Uul1UoFIJxIM2zubmp3d1dSSO6G0fNOeSvXr0Kp0vESeMi5q5areqTTz7RrVu3YlwYYt7ZgSQy6owN7+rBCPvMPWpFv/m8R+XMj+uzM02Li4taWFiIyu/t7W3Nzc3pjTfeiF7c8/Pz6vf74WCr1ar++3//7/rBD36gp0+fxrPPz8+1tLQUzp8gRhrZppmZmdgRQ61Lu93WxMTobPX5+fl4L2j//f19ZbNZffLJJ/rwww/DfqRSqehpgT1Ivm8yeHO5db1z+wtgdRB4nazxt8s8+k+LaX6OTHptEt9FJvj+VwVgyevGjploiSiK0z+gO6WrHCkHdzNwpzHZXuCRKFQqhhWj4Y0ovPUjQuzP9QVgrG6MiDKhA1k0nGgqlQoqCpRNbtIRFjkX/3/yb4+oJY3R3VBDUF2uYG6QMDqACJ7pdKRXSjK/oGnQqIMAF0jWiqjTqSA+Q+s7N97Qee6UmGPewefWQRzMhCNNxktUj6Izv3R8S6fTsX2C505PT4eccfm8O4XMHHieFZDj/0b5HIT6exEdXxdFu7HFEfk88T3WCtYImeK4PtgagNrp6amWl5fH7pMEcOiar4eDEhgaB18OMngH1gUWh3oRB3A8y9/V59/TQf5ZH7M7I58bjHCvd9V6ETny+gvebW5uTrdu3Yo+B41GQ9VqVTMzM3r06JF2d3dVq9U0OTkZTnh1dVV3796N4qRWq6UXL14EK8j8pFKpL9W3uCOgOQZ71nO5XMwfwMjTUnzXmQwHLxSsJZ0NoAs95/Ioz0H/0dGRfvazn6larWpjYyPaI+fzea2srOhnP/uZ3njjDaVSKZXLZT158kTf/va3o+L8888/1w9+8AP9+q//uhYXF/WTn/wk2JhWqxW1JDA7bG1ytuD09DTAzunpqSqVigaDQQCoyclJraysqFQqxZZZ3hGAyqmFpDbdBmYymWBjkWPsFxGyb9vD5sMoORP0yy4HsICdZrOpubm5aKwijZ8Y6PdFfpN68quuGzvmBw8e6PDwUJ1OR9LV+cCgAVcgXhqnQNTEJDv9jBHh3+6YvHUeKNEF9TrKgGiOaA+hKRaLkUMhsgR1c3mHG1ccjIUbFpwFDh9Dw9ikK8Xx/X4YQgylR4ZcOFAAAfOHoeTd+BnOBkcPRYzBZ46cJsYA+TilK0fO+rlxcHDhldWeD+V+nr/1bTxEv1NTU3HQSCo1qglIGnAazLhD9fQFNQkwLtflch2xEmWznm7g3eGwrkT3XvXsqQynz5hvr8MgPeNOm/GxNt5Wkm1/3JcWix6FoQPIJvPBOPx3LtvuAImkmWt37MgAF0bOHaJHuR61cDnY4TN8j7lOzh3v5TRwOp2OVpNsvQRQSiPg3mw21W63tbOzo3q9Ho4C4Hbv3j195zvfCeBTq9X0/PlzffTRR6GbVFLD5vm7k4LhgBrmEiCwubmpiYkJlUqlsWMReWfsnEdNLkdE5KyZAyhngZhf1tipfpdx9JIx3bp1KwD+cDhUo9HQ3Nyc9vf3VSqVYpwffvihLi4udO/ePb355puanJzUhx9+qLt37+rtt9+OI22R11Qqpbm5OZVKJe3v78ccEkljA6DoYSQJeLBti4uLsd8avUL+1tfXA5iTYkun07ELaGpqSuVyOd7ZWQanj/1n2Cy3tdL41lVff+bX7TPgh+ZZ2DgPGlwnkmzdTa4bO+YXL17EPmWKkBBiBjQxcXXghIf4HGqd3ACPsaDogst5fSbMDYoXtWC0veCKXArOSlIIEIuLMLvRYDGSTph39YX16NbH7kiJBU06CuaF+3kUw+9xBoPBYIxeTwoJiz4xMaFaraZarTaWvwasMFcYF57vThgnTwcuBJ058gIMd/rIBM9jvM5qJCNZ32bk6RHmFhTMHkfPqyF3ODR3fv4c5tXnnbV2GfWtIL4O3JcxOiXrTtINgT9/ZmYmjD3Ghbnu9XqxfS2dTkf3I2nkLHK5XOzxX1hYGKOEHeSyls6G4EgZEyAap9frXe0/z+VykWNmnnBqzCFr7iDSx5GMBFzeXcZ8Oxy/x5GxL7ZcLgeQ9pqQer2unZ2dsEGvXr3Shx9+qG984xt66623dHJyEu0ycbJnZ2fa39+PHtSsucsx6+9biMh/np2dqdlsampqSnfu3InKaHaelEolXVxc6O7du6pWq/qbv/mbcC4OmIfDqzw1dgz5+dnPfjZGc1KRzXoCTt0+JVkLD1iIgu/duxedGAGIg8FAS0tL6vV6cZSiNDqE6PT0VM+fP9cvfvELra2t6fbt2zo5OQmnOTs7q06no0qlEsEPcrO8vBwHEU1NTen09HQMZLAWbP1Cd2dmZnT37l198cUXX+ohkEqltLOzo7Ozs2vTHun0qNXn8fGxFhcXv2RnfP7dGSd1/DqHzHVdsIT8wA54sISD9oBG+vIBNje5vtYhFhRnER1AG3sxEnQBlDdRLwo1OzsbC4jQets6JsSNjaQQbgTN6VvQF9/jYmyZTCbyTa9evYr8NRPOv6nMxighWG7cfEzS1dYt3//IQnhU6gvM9zwqdwp5bIES0Zz/3B064yeSBNkCWBAolAY0jvPE4REl4nz7/asjN0HiOAUE1B04qQy+K11VomMccEw49mRaIJ2+2rbi88bcIDseufJ7p/cAcJ7rc8fAPa5LSbhiObMABQ0IZX34m/V0lgGQBIjkd7lcTvV6PeaK+WRd0Dnmw8fuIC4JLtEh5or18XSC6w1OwbuhJSNj5svnEQDlFKLLKfKI3ORyOWWzWeVyOVUqFVWr1aAwofCbzaY+//zzcIDS1YlONJo5OjrSs2fPYufB7u5u2Aeq19mh4fQ7Do75Hg5He/uhZGkWglPm2cxFpVJRPp/XycmJUqlR4dj8/Hw4Oe4LI+Xg1WlPeibkcjkdHx+rWCyOHdXqkRey7YVNyKbbBQfPg8FA1WpV0mg3TafT0WuvvRbz/4tf/EKFQkHlcjlkgnqNV69e6eXLl8rlclpYWNCzZ89UqVSi7oGdHuytXl5eVjY76gK2ubmpTCajUqkUbTqJkIvFYjgxZJL5KxQKUeeDQyfw8oIz9qM7pe3pmSQ1nWQVWB9PVbiuJi/m3YtFsYfYb8bJ91nz64rLrnvGV103dsxs0MfYOvo8PT39Eg0EWvHzjB3NI6ReiY0S0IweGsIpXTeYOAyMjSMYNv0zWey3bjQaY+jLKVeUgy5W/NypCArQeEccN0bXt5gwBywMqMrnCSSJkCEwFJrgAPg5Do258PsTyXW73djjm8lkxvKY0HygcadtU6lUFOAxb5KiIxc1Ah7pHx8fBzjBCDUajTHqmXeRrpw0ggtdyGkxXkyGk3KAJI0UDpYFR+kVj8k8nBtILpyQdNUi04GB5+V9LrhPMuJ3ajd5ARL4rufESqVS1FEw71S4zszMqFgsjoFILyRDxqibSKev+sHj1Dx1AnCkApZoBj0kcvT1TTpep6/RHX7nOwUw/EtLS7EViTOLO52O6vW6Njc3Va/XdXR0NLZtqVgsSpI6nU5Qhn6ATa836pPutPHk5KTu3bun6elpbW1tBch1WWYvPl28jo+Po2EQ78I8c6EX5KFfe+21+D3NXy4vL7W9vR36g7yxbsi2p/r487u/+7uamprSRx99FA01cAAYfr+Xy5c7/7Ozs+jvj727c+dOyHmtVtP6+rqGw6Hefvtt/eIXv9Ddu3ej8A1bvbq6Glu01tbWVC6XlcvlYg1qtVrIE/JHlL+0tBRjBxjdv38/dqo4aGYXTCaT0d27d/XTn/5U6XQ6IlHsEofZ5HK5CO4AmhSfJYOl6xgc7unOm+8ldZvPJ1km9CKfz0fPfgIy7odTBoz6dkV09ibXjR1zs9mMaNfRAHu6aCrhNAwK7Hw/xs8NJXQldATREltuQFdMjhdNOeLxoiLPxTEx1Wo1WsBxMAXoj7GD8pPRwXA4DAeCUvkWG/74cz0K8+1BRFoolRedOECBiUgiZAyg04PevYk8zHA4DISLYHj1LUbN+3TjEH0u+A6GEKFN/oyx8DOvzCaX57Qv7Ar5P8AU84YxoKKWdYDxwHF5YZfLgjtr1gMwBUDxvZyATS/o8JQALMOXlMj0wZ/nUQ5ynUT1GBhAIDInKbYIMh7AzXA4DCPlbI/rFWsH3YgBYx65L1Tp8fHxGLDgmUkdc5nG+bFFaTgc6o033ohK6MFgoMPDQ3344YdxxF86PaqcpkPU8vKyisViHNpBJyjWF9nE0NP7udFo6O7du7EdENZtcnJy7KjTbrcbHQMbjYZarVY4r69iBJhj3i+dHhUh1ut1bWxsxPqWy2Xt7OzEISywf4DhYrEYbIBHUfw9NTWle/fuhY49fvw45BibdX5+rlKpFE2O/JSmbrer/f39yJ1zBvXt27c1MzMTVPvy8nLcG+AgSXt7e9rc3Ixi3UKhoH5/dF4B5wuQlqpUKnr58qUmJydVKBR0eHgY0SvjYS45eIM2y7Ozs2POy9+RBiUejLDXGhCKo3ff0uv1tL+/r1wuF2dVfxUtzftel+f1VIvv1+Y72Az0/OLiQsViUTMzM8HeYmv4P0ETTp38MzJwk+vGjhkEDlogH4KiEtWisEmaFYOYpPsw5rw8xhuUCQJPp9Pxs9nZWaVSqbHTpphY5/M97zIcjrpQVatV7e/vx2dxgBhnFzAMN3SKOzYiet+P6iDEi2t8G4sbeUdmTnX6UZREWkRUHnk5hUn6wJGd5zi8RaFHsU4V8XMKXhwkAIZYV2hB3ypGpyQOuiDq8zU4PT0d631OxOa0E85qYmIitqix/sw3DfGbzabefffdMTlgDZwCZI6YA4Bjshgkma9lLd0hSeMV0NI4ewNr45GX09CsE9EYxhqZ5PetVivAgI/FQSxzCOXN/PlJXPwMoIGj4dkXFxex5ahQKEQFrJ8Ch7EE9M3NzWlpaWmMctze3taPfvSjkLlsdlQ1PT09rYcPHyqbzarVaqler6tWq8XBDS6TrAu6AHg7Pj6OM90BGfPz82Oyn06noxDp+PhYX3zxhba3t6NPMxcGM5lCchlM5iK73a7a7ba63e6YHN66dUuzs7NaWlrS559/rm63q/n5+QBhzkZ51CaNApKdnR1lMpnY49vtdsfAH7pyfHwcIGN3d1f1ej1O6uN+2LBbt24FwE6lUvriiy80MTGhZrMZqR0aJXW7Xe3u7mp/f1/ValVvvfVWNFJKHgYyPz8/lpfHdmCTsCno1dLSko6Pj8d23HjUypwA0tBPAiyiauaa4IhdIdhfZ3p8jbmYHw+e3I6yLrC5bl/dYXt9DTac4kTqSHzrKDrO+v//xTFTul4qleLMX2m8qs2RPYPAqZL7ZFF8by2OC4PjFddMFErOwvb7fT169Ejf+9739L/+1//SzMyM7t27F2jUI3ZJIagbGxuq1Wox8T5uDGQyv0lkmXS4TjvhEIi8nerxbV1OYzsFn6wG9+gPysS/w3tyvyRVyjthxEBubEuCIoJG9/Z8ksaKmwBifrqYj4dGAN7qE3qV9SNHhUIzdqeMpqamgjYnnUEBFSCFjnLZ7Kij2dnZ2dgJTM5USF8ulkPWGDv38qpSFJfPI4coLnOcRNfIEsY4yZr43+7smQ9kASYK/XHDARr33u7cg2Ip5AZ5QGd4PoCK98W4LCwsxBm/5PM82gHcTU1NRWEVaZPhcLRnlqMVu92uTk5OwpmgI14X4MDcGSrmGMfc6XTi3xSQIhdObzebTR0dHalWq+kf/uEf4lQjB+usGWP2uUmyXs7uYAMBpjAcbKdySrTT6UTemPVzJobL6xMAu8m1J6f5wQcfjOnh+vp6yABR2uHh4VhkxoEPgLy1tbWxoxRp+UrefGdnRysrK2HzXB5hqvr9vnZ3dzU9PR3pBPTEz2Oml4UfOsF8egqE77BLw+uP/uN//I/KZDL6q7/6K52enmplZUULCwuSRum1hYWFaAeNw/R1czvrtgDd9c+6v8HJwnagbwBm9JV78n/sI2vm4BKb6fb/l103dsz5fF7Hx8d6+fKlPvzwQ333u9/V0tLSWA6KFnQInOdxvaKUn+EEcWRUXWOEEVLn7pmwg4MDPXnyRPV6PbaULC0tBdVM1O5U38XFRSB4HJajKRwnFDrPRFAYs1NnjBNHJV1FXQ4ivFgBI4xTSOY3EVyiSjcmfi4pguFFICg8wkouEaNGhDUcjjp94dSgMwFHSXCAUPLudP8iumYOQbCgRgzP3NxcIEdkBgOPc8CYT0xMRLHM0dHRWJ5wZmYmGhmAqH1OPQL2XB7r51v3cKQopoM5j4R5Ny5PI7D2yDwgjDUDFCGTyZ8DFGAJMHTkY5E51pkUhaSoJ3CHA7PAWrAGGBccq6dPcOCc3d3v99Vut8fSB7Ozs+HIyevD5BC5UcHraSbmFZnCVnjfeqchfW0o9iTy97oFl1laPtKcgxO5FhYWoj2wr4nrvNOb/D8JqHDCrE0yz4tuoufsaQbUMk+eK/b35v0ajcYYg+RHgk5NTYUjSqfTUaFOX+n5+XlJo5Rjv98Ph41ddqaFNaPyv9cbtcQcDoc6PDzU4uJi6IUDTOw1hWuDwahfAgcRYSdh287Pz4PWxm5fXl4G0JqcnFSr1dLi4mL01GZ9h8NRn+2VlRX97d/+rS4uLrSysqJbt27p9PRUi4uLWlpaCuBEajFpE69bW9YgKQeMCYaCNfLe4jRNwQ6hZwByZ3mdHXb29SbXjR0zVc13797Vt771LXU6nZhEHAqOlUVw5wQFRXTkHW4uLy+jIQahPxGWpLGGHPSBJr9A9SbN3qHonPIiMh8OR9tDyuWyjo6OwnGjOKC9/f39MDyec3OKiYXwiB6j4rQiRsYNjuehh8PhWG6V511XZEQkJl1Fe2w74Pdzc3NjtDUC6Hl7+mkzH+l0OtYDY+EGBMOWSl1thSIyhu6mWQbGgPwmkfRgMIhqbS9mI0I+Pj4eAw2Xl5ehAOxxZk8jOWKqSqGPcL6M2yNij8pA7dJ4P2Y3xPzfc+dJqpPLKWSAlRdpuSFnPbgHz8ZJufP2dU4+2w2JR/Kgdn6HE2QOSElhYAGw0ihKKpfLmp6eDrrW0wC0qmVtuDDIDoio7vcUDTqTnBcHFsh0u92O7WSAPJz5xMSE1tfXx9JlABhOoZuenlar1dLbb7+tVCqlWq2m7e3tkEXm0J/rUbuvFfM+MTERLYU9pYGN80gbucNGOp3tETy/7/V6sV/b7QBnMPN8otaVlZUA9dSXUMQ1OTmpv/u7v1OxWFQ+n9fi4qKmp6e1u7urt956K+R+YmIiHGI6ndbi4qL29/fHctxeAOj1K71eT5VKRZ1OR7VaTbOzs9ECeDgcqlAoqNlsBnvGNlWcJ3lj3oH0HcGIb+sE3Hsq1NOV6I5vqUR3GHcy5emsIvfAwfb7/ehg5s4eWcFBo1fcywu+HIRxbw8Ef9V1Y8f8Z3/2ZxGxsF0AqgNDhDCBwjGioEheigmBLuGevCCUndM/TF6j0YgXhCZm7xwL6QbZq28xChgdHy+R0WAwiH7STO5wOIxIIZ1OR44bA8g4eWdoH8bBM4g4PEpD6BinKy2d1aSrnCgGDoHxaDWVSkVV5dHRUcwRzotxojyc7oUh8Tw2F0ibk7ecBuYPYAGjgGxgjIrF4liuhfd3kOJ518FgEM0LTk5OVKvVxiLMVGpUJUvhD1EG4+Z9kBmYAhTFnSYgBMDohjUJUJKshxt1d+pOPXvdgYMFLlfUpAPnXZKfu+6z/B9w5AafIh0YBQeKyBw6hLxAA/u7UdORZIuICr3S1gs9WTtkGOfrxlG6OvKSPcfZbDbAgI+Dfd58hvUcDkd1JO+//34A0JcvX2piYkKrq6t688039dlnn+nVq1fBdhDN+xw6A+VGnn3NOEVpPM2FTCUpUpxxEtS5vM7MzETRFs8D8MJ0IP+SAjxh9BmDt2Sl2Gs4HGppaUmXl5d6+vSp3nrrrbH90WzbmpmZ0dLSUvSpdlbR54h1YYvVy5cvYx0BBwRAFO1xjjQMW9LGENxgs7y3hUfsDuLcN2DbHHyj18y9pyUcWDtzwr2Z59nZ2TjTAbl3dopAhHG4DWNM2H0f+6+6buyYt7a2xjoGeZ6ZqJXkv1fjxoOyV3tW+b3nV0FLnuQHIWaz2YjQiRARLCaWc1KJWtyII9Qs/vT0tJrN5hj1Jl0pmSu6KxUL3Wg0xnLTIF83oh5d+7+5D4ae6I/WdYzDIyjek/fzuaxUKiHQHm0TUZCzIYJhTQA+gCK2fUBTg2ihxFqt1pigIYCOEHu9XuQcJycnQ0aosoVVYU7T6XQYYeaJasfhcKh6va7p6WkVi8VgVbylH4cO8M5ecIWyAmzcsOOQkTWcVPL3XLx3kiL36M8jW+lKwd1o81lkwI3IV10uUw7mkg6etWbuYJY8Agc0AgZ5L4Ab4MRP+nKqkG13ScOEM/H6EOY0OWZPb3BRdc9+YtYGBw8YpVgpl8upUChExMIFHby8vKy1tTXdu3dP+/v7+sUvfhH7nr/3ve/p8ePH+pu/+ZuxXKGDOb+crej3+2o2m3H0qwMDdzTJ9BLRqcugO3LmHR2dm5sb28OdzWaD4fvmN78ZNgpHjN0APF1eXuq1117T48ePlc/nVSwWdXJyolKppM8//1xPnz4NOZ+cnNTy8nLYVqhn+h3w3p6SmJmZ0d7eXjTFIe3kerC/v69isTi2fezi4iK2dDFOIkk/btNTCUT1HtAxt9hD5N/l0vXtOj1yB53UP4Iu7BIsIczHxcVFtBh1lsRZEwcEsD0+jl913dgxM2kIc6fTCSQHqmJLEJMKJTEYDKLwi5fmb482WQgUm56sFBJRYJDJZHRwcKB/+qd/CiM0GAy0vLys9957b0xIEGDG1Ww21e12tb6+rr29vaDeAQkYToQKJ+R5W6Iw5sTzWJzkAp3vxR9EVAgqwnl8fBxMAILtxVzMoTMKCAlAhwVHEF1oAQYgPhAtrEClUtHCwsKYk3Ihw0GfnZ3FVpRUKhVOttFoxF7FcrmsiYmJ6HOeTqfDsWJ4ibJ4HzfsFJAVCgVVq1Wdnp4GpekROcab7Qtu7DzSZS7cifE7z9Nf93NX+jGlsfslFd0dZjLi4h35HcaEdb4u/+SgIIny/VkU9yEf7Nn1ghVSQo70cZIYZrYQOijlHQBg5Muy2WxsseE9PKftzALMi4+di2Iheh0ABj2FIF1FtByGgAHkfru7u9rc3NSTJ0+0tramt99+WxsbG8rlcvrss8/U6/X0ox/9SO+++67efvttbW5ujr2zsywOhHg2OsdWKC4HITAODgy5L7Qzc8C89no9HR0d6dWrV8pms3r//ff19OlTdTqdsap3olkPHFg/ns+cnZyc6M6dO3r06FHkZQeDQWyhYnsr7MXKykr8fHJyMuw7eoadGQ6HY0WgzI0XsGUyo8IygDv2C52iNsDZQSqrkSOiTGSYOWNMzppiW702xC8HiNfZAf+MywDjZR0BJthqOs15fQT21QuCWafrGLGvum7smP/zf/7Poy/8X0WlByoLA1XhuWXPD3iul5cALeKgoG7IY3mVH7Ql31lfX9fu7m58f3Z2Nmgbou1kno1oEKf4k5/8JIybFz0R7UmKPKkjXRgCCpYymYwWFhbUarXGcjPVajUEyOlRLzpyp5DJZMb2WGJIfQsC9/GqSb4PqAHE8M44ec/BoGQ819Eu68s4AFWSVCwW4144jbW1tTAW2Wx2bMsH+WRyexThMS7v5pROp8NwQKfiHGZmZsJYU3SGI/JiuBDs/4vQvcLeDSb3TUadyCZy7REPlJ5HxeRP0QtAgT/Ho2Nof2c5PFJLOiE3NO64fa24D+/sPZClq+1ZADPPuWNAMNQc3QkY8OgFQ8j40S3Pnbkj90IYqNVk1TtHh0It+xpBizNOorW1tTWdn59rYWFB6XQ6qoOhhHu9njY3N5VOjwrh3nzzzXDEp6enevz4sR4+fBiFRx999NFYKu46mpN5YLzkfZMOHJuHHCTXl7UDDDF/FG15USNAJ8nIOWODnfMIkx7iHm1D/xPhlsvliP4ajYbK5XLYbHTMGSfkHtuGPvIMryEgZ+wBBezY/v5+zCt6SzEh8oPtQE4BBvgVZ9x4bxyoO8CvYqWSYM/lFSCVZArYrnZ2dqZnz57pxYsX2tjYiM5lDrQBaNDfjOOmNLb0NRzzrVu3ItKBAgURs4WDhWdiMASObB0ts5hEm/1+Pzbrp1KpyGUjGJlMJtq5HR8f67vf/a7q9br6/b7m5+eDvvMtOaBA0BZFL2tra1pdXdWzZ89C6REqmh4wJt+UD7VMlLO+vh60cLvdDsEoFApR2OQRO4aLhWThmBfmy5kISu/pwPONb3xDn3/+eVDUkoImJpcLGAEISVctRxkPgsl5shihTCYT0Qv3ZvwYVBRXUuR4YUaS70OaAxbFqX4QtUdw/JyIm7wUTUEcWSfz88wtNBkUGArmuVWP7NwIuMOTrlp3sl6u2En5ROacDeK+3BP63520gwKnzNEnp8Xc2DsIYByAINIC3tDGjQ9zWSgUQid5jhtN3zaCDjAvyLHnVrk3oJJIG2cDWGJvsssUeUgK+vz5VBPjZEqlkqrValTuV6tVLS8vq1arxc+Oj49VKBS0tLSk27dvq9Fo6NWrV1pZWQnd/fa3v60PP/ww7A0y4RS82zwHEsiA/9uBEu/uOofRdrksFAp677339Omnn2pnZ2fMJjm4RqecXUNueM7MzIyWl5e1t7cXrCPjAExms9lgGicnJ1Wv16OyG8fsKUbmBbtHMESbT9JkbiNSqVTYhidPnujg4ECrq6va2NiIE6uQaeyEA06a0jhzwnuTXnMnyjy4A0fP+Rv9c/n2QJL78Q4EE69evYoudrVaTblcTsvLyzo4OND+/r5u3boVWzbxN9QAIM9eT/Grrhs7Zhc8L5rBUQ0G4yd5IJgYJj+b2R1Tr9eLLTIzMzNBl1NcwOQwgb7o0pXRpEHCda3bHNEwnrOzM/3e7/2ePv/881Aa8mWcX8qzEUZHaIwDBe52u9EZbXZ2No6e83fx7/rCe2SG0/F8HHPihtUVwg2Jo1aoX0lR4JHNZsdOcnGqGsqY+3i7VAACeTA+MxgMgjXwdEAy9+OOmL/duSA3yMni4mIANow58pDP53V0dBR0vOd/nTIFoCUpP4yHU7W+nv59cpzMlW+Nwuk7lYmcAAoc8OBkWEOnsT2FkQSwLifIo/dy5vdJPT0+Po6Wn3wWHYWW5TmDwSBSAsio53mZNz7v2yIx9knHzDwmuzkBtGjniryit6wZz3K7UqlUoksVtSe8W7FY1NHRkSqVStShdDodvXr1SgsLC0qlRt3/Tk5OtLu7qwcPHujZs2fqdrt67bXX9Pnnn8c4fD0dfBE8nJ+fB43vgA6bxJowP64P10VxCwsLqlaruri40Keffhq1FP7ZZF0Az/D0E5/FOUxMTKharUaU68yGp8uoNYGF8mAK9gIgjKzwfIq1pqenIzhh7bjX3t5e2IqNjQ199tlnYbfob97v98cqnZEhgL2DP0nBLvqc+7xweSTteofPQm94ptPszC0tT2nhenx8HPlwSfroo4+UyWQCPALOnU3Cnt/kurFjTuY1KARB+Yi82JOXy+WCtgSpgcQYLEYPChtnAqJzI4nDImqZnp6OAgUOOMfJ4wQdRYFyQdmdTkfValWlUik6+kxOTmp9fT2oQDfYTCxjBM1Kip67t2/f1u3bt0NQ+D49nz3q8yvpnJlj3h+h6vV6arfbevToUQgsxTJsffEI2is4y+VyOFaPBDCk7AUlypmentbs7Gw40JOTkzA+jPW6MXM5LelOxvOXY4L4f5WeU2tarVbkxbyIL5PJ6NatW2q1WvEcOgednJxobm5OxWJR9Xp9jHngGR5lSOPFeN72MElFs07SeCMRLyDyd3EldKOJ3PNzjL1TlUn5cFlKpogwnv4+DkoBdBgZ5ChZM4Es8F68O9Eacujr6RSq2wYAo4MMrwg/PT1Vq9UKdguZlTTWt9p1j9wi6SoAZbPZHANldJ6bnp6OLY9soQKgLy8v6+XLlzo/P9fGxoZ+/vOf6/79+3r69GmwC16n4H/D8LVardjaxLg9eoMJwmZ57QxzBcgitQbg5lnMCwARnbrOfuBImGu2n9K7odPpjJ0XTQEsNnNubi4YFsaMbMA+IZvIiv8NCGY3ihc7YVMo3vv0009VKBSingS7nASic3NzUZHvoA3ZoGId+5NkEJjn5P89mvZUCfaRrVs4awIGwOi3v/3tMVt4dnYW0XK9Xpc0qoNwFpC19ULgX3bd2DEjcFAl0No4UX5P3gIKBcPIxPK3F3x5AYdT4RQRkLem+Kvf7+vo6Ggs/1AqlfTTn/5UkgLRXlxcRLN6nkeh1q1bt/Rf/+t/1Xe+853oHDY7O6vl5eUwWNAYTuFgIF0JWIDt7W3dv38/6GSUzou4pPEzj91JOBDxCAnHyXcpoPJFB41iEHhnIifoIG+E4gpOisLHwzi8a41HSNKXHbCj+SSV5NEQv+cPczYcDsOouCPiWRMTE3r8+PGXaFKP1nGwzKUrvCsm64oh9iK55DoBfpALz20jx1wANy9a4fduONyBgvgBoDzDnQLGHQckXUVxvl4YFeaT+gyiIeYBWeF+PA/ZdRnxvedQowAMZ9B4Pt/3nCvjpXKfgkJ3rFxe0MS6ciZzu90e211AodHCwkIAVt6RHOjh4WFEjhzW0O12tbi4qPX1dbVaLb3++uv68MMPx2ySMwrIzuXlZRwKAWDBOQA0iMo8CPGfOeUNmOH//I4I19MXyUjX0x4OHGdnZ1Uul/X48WN1u92IcL1nArIMWwb1CqBz3eW51K0A1vP5fKwHh5HQTZB3JehptVrRWYz7eSqP57F+1B4xH8ldNg6MPIBJsghu05hf5AKHTG6dAJP1Qp6y2WykQf7yL/9SMzMzYack6f79+yoWi5qentbh4WH0IOdd/Vk3uW7smDFcnveURvlFjIAXj7hDpdUagubRIKhkampK9Xo9Iuzt7e2gMpOV2vyfSS6XyyoWi9H/1bsPcezk0dGRms1mnD3a6XT09OlTlUolra6u6vT0VGtra7GoGEwoFoyNF2zwc3KnnnvCEHuOESPoTtmjR49iEFwujJ9TulxQ45y1KinaZAIsvEkIzwFYcQ8UknlDuHH4Tlci9EknnEw1fNXlzp05cso3+X9/HnPLeyM/qVQq8uqDwSh3BuL250pfRs5Jh+1UrUebyB5z4c5OGi+E5OdejcwYGIejfDfaTu/7nLrM8I5+Twd2RGF8HsbHWZt0etQwIkmx+zxRVOYyjY6wjjgjnuUpGcZAlyqPRkkh8V1kkndHXnu9UUMLIr1sNhutWjudTmzfSafTevPNN4NSZQsQuXfeoVAoRIvN+fl5bW9v6+7du2q323r69OmXWBDmgr+bzebYlk1fI58TcsRee5FcL+QLefE0FjbHOxpycT9+5jYFB3z//n1tbm7q9ddf19HRUexplxRs4czMjNrtduRBkXt6ITD/6CLAoF6v6+XLl2GXafTE+vHO7XZbpVIpAi0HLMgAskS0TTtPZAed5vkANepQPPLlD7Lj4IUgA2CCL3K5I7rFLp2engbr+PLlSz18+FCzs7MqlUrRf9wDCJ5L73m66GGLb3Ld2DHzgr1eL7YYgGzITWazWbXbbXU6nUBGRD3JXJ4XNNGpCqH55JNP9OjRo3COGBk34E5tSqMo5/DwMJ5Fzpp9ecViUaurq1GQ1Wq1tLm5qX6/r29961uRd3UE5hE5tBT5Yrb/IDi8lxsa6ar3KgbWi2+cMeByY+zOGZSXpIY9SvSrVCqF4yJH4t11+N3m5mbQSQAHzyshSAAmfk/qIUmjJv9IV07YjY2jWQyLK5LLiwMBp/Q8WvdcLQWCONKkoZM0tnY4YoyGOw6PcD168CpVp8gzmUwUpQDY+D6onv+nUqMCEXcY0I0uH553c5aK8fkhJA78cN5OTWazoz2q7rT9pBwvikHmXP8xgl5UyMEOyAxzjtwhX+Qp+YxvzSIl43OJnJyfn2t2djYOC/DCJN6R+ajVatrd3dXCwkI4ammUw63Varpz587YrgrkaX5+Xv1+XxsbG9rZ2Qng6hSps2bsmQbMw2gR2c3OzoZdS9ZAuFxhH5l75gmKFpYPx8zcYx8cLCBXXl/CDpMvvvhCpVJJW1tbmp6eVqfT0cLCQqSF5ubmtLW1pZWVldCpiYnRITI4bE+Z0ZGPwIcIFD11G00g5DLqDASADJ1FfukzgazxPYKEnZ2dMUDNVs5KpRJ1LzAY2H0AJDs7+LePhzG7PadhDbUNvV4vajjoUe+BDTo3OTmparUaY06mIb7qurFjxoAykUQUnjNmQVZXV2OCEUoUHoOOcp2fn0dnKF6GieBFMB4sjisI6D2pPFyOxjzS9gINGlcwfhTCezTzDN6J9zo+Po68GAUQCKh01XoONgHl4fnu0N0JMW6cjNPnGMnkHHO5IeadvA/zcDiMegD+z/oS8fnPMB4exSX/8HyPoN1oMG6PsjzyYg4YP/OO02QukxXCjAtjgYx6nrjXG3W/wgiTZqnX69GK1OWFcTpFyUXBh38GpQSReyTurSkljbFOyLK/g8szf5gzR/8AK88/JtfJqVX+zd84Z9/mCPB2mUpGaHTNg4KGNcCxsk7IJ/vZ+R0GCifkYBjnxNx6mqxYLKpQKARAp8al3+8rn88H7Tk/P6/Dw0OVSqVoBUkAQL92wA5OjjUBKKyururly5dfkuFkzrzdbsdWQU74cvDt8+a1NsnUB88YDEZ51YcPH0a//qmpKd2+fVvr6+vKZDJB4bu95fJiR6qsG41G0K6Ar1QqFTUle3t7yuVysY3Ut8C1Wq0xW4t846zI8dJClQMrkB/ktdvtBkianp5WqVRSvV7/EtUMGESG2YsvaYx6Rlb8oBue6Ts8oMlnZ2ejMtrfh0DE037ug5ztAVAuLCxoamoqcubYS6epk/Ui2KSkLfll140dM1QuBUc4CpLx7tA8R8FEgiqcIqMzFN/HwCedUK/XC4H0PKlH6EyI55f4PjSnGyePNJlUp9cwZn60pEe2IHkHA/1+P1AYAppKpaJLDHMDXe0XqNkvDAC9f8vlsiRFkZbnc/0ebtw8h8WcISQHBwchwLyb5xwxYE6RSvoS+MD5+N5HjzS5GK/T0awVyoiMsFYcJUnukvy5y5YrNp/zSJp3JGo/Pz/X8vJyNAqQFMUdjN8dHLLhwIafs6bIl7M4rujIM0DLnQ5z48rrlB3viH6gC4DHTqcT75FcdweszlKhA96PwClLz9P5Wl5nZDCi0PrMPyks3hEQQbEnxZs4aM/zu00gCMjn87p7926cqew0PwBjdnZWnU5HBwcH2tjYiOgSStv30PuWP9ZtZmZGDx48CKd1Hf3c718VsDl4np2djYDCC+uSgYGvjzt/ZPfs7Cx2eHz7299WpVKJucK2IiNuKwBYZ2dn2tvbGwNrHDThTWguLy+jN8Tk5GTk6EkRtNttvXz5MhppsH44Iih65JBIfzAYRLcudCqfz6ter+v27duqVCp69uxZ6BI22ms2kEVATrIg01MZzuAQ8PlWJd8qRtOTZArM04ewUOgO9p934QCf5Bw64HK7y3q7LP2q62udx8xCMyicIErkeUH6PINEKQxwKmI4HEb1NoN36syV2h3bcDjKHe3s7AR9AdXlY7i4uFCz2RwrUnG6ljwC90fIUAocFGPyKlQQHIVG0gjFgQQRJhAyC8S93FmCqvz/bujn5+fHhMudsue0eWcUkLn3CAbg0+l0VK/XgyKbmpqKnBDKy2EiCBvOg61hGBro12S06hE27+xAwqN8DKzvMaTvrm+bc8PhKQJ3xIyT+/MuIOmzszNtbW1JUhyQQQTHOJlXV1pH7M5WsN6eXnCa3J2Lr4WDVPY2s+7umJKUrkcW6AXRGrlPN1jO+FCDgaPDmDOHDkD4t+fAXSfRWwekRPIUxhCdwwYQsZK/5Dm8o7M8/nOqXllDZyX4DseAUhFLVIzeImceNbEmPJOIfHV1Vbu7uwHsnKnhebSyrVarGgwGkbfmOYD+JN2cBAPMPc6NSvN8Pq+dnZ0Alcgc64UcMk8EGDB3vPP09HScE01EyXNhMLPZbOxnz+VyajQaun37tlqtVlR0ox9Ey6lUSvl8PnQQFiOVSkVhGMW25GQHg0F0OGPOKbxyUMp42BZJVOzbLD3aRoccRDrI54IN9FQjTtcjZQfe1E9RVU0xGwAsycYCBqTxA17cRvyq68aOGWVlUdwJMBlOMfLS5I5rtVrktVAAjyzposWEupFw5I9yHx4exjFhCwsL0WCExgKXl5fRg5ptFRgN6cp4ek6L0046nU7kjBAs8pSMHeoHQwnVAWXijROy2Ww0RMBRI9g8G4qV98WBOsqCPnRn7EDHDZkLGMrInEL50RCG+zgjgBH0feEYOCo8eRdkwA0t64/hZ749ugWoJIUVoa5UKvFOThMDoNxJ0ATGu6RJV0VtRIqZTCbWOZPJRJN6WgC6EuOESQOcn5+H7PNvHHo6nQ6QQ8TIGmEIyV25gfECNv5mTQADNLBBZpgz/jAf6XQ6zk1HHqBpAQPIQL/fj5oODDMGH71NGkrkg2IZxuUGyaM55hLj6FEuRo3P+XMwdE7dkzOmo5W/E1EsERv6VavVIr/HZ3lHjDbgBVuDrM7Pz8caeEqLdJw0KihttVoqlUqamJjQ4uKiNjc3x57hzjzpJDzdxN9sEeTZRG7Osvk7YxM9ekb2sNHogu/ZRc+QW5zS3NxcUNjD4VBra2v6+OOPQz+YS+SlUCjEuLwXAuMYDAZaXV2N3C/nVDsIZS4c2LJXGKeJc3NnDPvC+6ND/p4wvDhS5BNfhP54QIhssh69Xi86HvJ7P5tcukpFYg89PYUuOpv2q66v1StbUpys5MjTX9CVySMJvoeA8n0MG0KMk6Ydn0coGIFWq6V2u62pqSmtr69rY2Mj9k6jaES8dP7JZK6KcjCyGJR0etTjGRqs0+nEOa80LfFc8PHxcbQBXFhYCCEtFosReU5MTGhzczOe1+12wwEg5BhZIlbeCeVHuZKRJ/OOgqCgSSMiXZ3mxed4D7839yQy9eIdDKorAMrJMYzMeTabHauC9jUDZHjVOfJxenoa+1PZS7iysjJ2oAWfd7SbRP/IIErKtj0HGF6kQroCx5uMIHDgfn43z6Vi9PLyUnNzc2NAA+fiEa3TXDgWxkVuk/WinoJ150xtZ40csPCH2gyenSzo42c4ZYwV8iBd0fTOVOGwGCdMSjY72iPM83DU6DxpA+YPYALw8uItnDLP44KWzGZHNQHFYjGMHTS8F6Ihy6xrEkxBdboT8EYRGOhSqaS5ubnogOc2DZs3GAzUaDS0sbGhXq+ncrkc94bG9WjcI32en4zqPFWQbJTE5UyQswauY5VKJewT840jOT8/jyDIgQLzT949lUqF7eTnnp5xOr3ZbEZTpaOjo6CSX716JUlaW1tTs9lUuVzWwsKCPvzwwwhw0D/eHQCOPGED3B94ag5ZhB1yZhU5wMZic7At2Ww2UhKsF9ttCbzoe+GO2cFONpsN2842TWo7mC+3wTe5vlaDEacKHQ248SWXk8z7OTIHDWYymaiQdGGTrnLEnn+ZnJxUrVaLQ6zX19c1Ozurer2uBw8eRMeVvb09HR4eShpt9N7b2wu6CcRNJH3r1i3Nz88HalxaWtJgMNDz588DQUqKwpF6va5arSZp5MDYmI8RQLiy2azW1tZUr9djvETxg8FgrPsWToB7+Dw52kJ4PPIFFHAfd7ZufBBij06d7pU0FpF6b2uiU8+X8P1CoTAWdft+a9bNUx38AUCAjv3ivZxqxDAk2xMiVzgvoi3eB0PLmDCODixRWMaDc3WHR0TudRIoKo7LnXPSufE8TuJhzoiIktEvsuHPcUrWmRb0ptFojKWaMNbMP+ObmJhQuVweo0H9/Z1O96gDNoK2uci1U4ue7nF95r4OwP1zPDNJYadSKa2vr4+9O9+v1WpaWFgIsIscAWyRY+7voIdgAFvD/ANep6entbq6qkePHoXt4bnUghAkYMSxBRh+l113gM6C8d5JhgxHhFzxXt5Qw3XXdQz2YH19fWwekTn0hs96qo/eD1tbW5qYmNCbb74Z6TovCk0CD9IoAF7ysGxJK5fL4QAPDw+1tLSkzc3NyKd7yoW5Xl5e1tHR0RjdT54b9oMUlcs87+h2krVF9/f391UoFLS1taVut6tbt25FTQSpQ2SNs8cp4iVYkRR74xk3eoveMf/8Lmnrvur6WhEzUQ2TghFyCpOID6TMINmqg9BBT/BvFs2VJKms6XRa7XY79hyjjP/8n/9zLS0txURWKhXt7e3ps88+08nJiZaWllSr1bSysqLB4Or4uvv372t1dXUsv8d+s9XVVXU6nVBwKI1OpxORFU0xyJNTXQ6dPTs7q/v372t3dzfeh4XBmaO4gBpH5DgMjBLz6tEPyuH/9qiH6JH18S1TrIHnY1Dqzc1NVSqV2AOaNCSOmKGrfX0dXaP0TjF7BTV7HGk6gYL638fHxzo9PQ3H5gyGpxOkqyIlegE7IEE+HSj6uIjonU70Dj7IJs9xis2duYMjn38ABywSBsVBiHSVh/Rcs7MWyLrfm6MBfazMsef3vde7gz0uDDwyhNxCqyepaNbBc26ejrq4uAiw5TrteULmi8iWwlCivrm5uTHm4ujoSAcHB2o0Grp37158HodLgZsXLZJq8HdFr3By5AR7vdFBOTSzYYzoiTvpVqulpaUlZTKj9r+Hh4fxPv4c7sEzmQfWx+lnPoszJjfv8+oX+gMb52NCx7vdbrw7TtO7JLImOFmiytnZWbXb7bAvTkOTIqT63lsZI3+Xl5fBus3MzETLSw88eGf+APw8YENemCvYreFwOBbguc9Axz01Uy6XNTU1FcdrYl+mpqZUKpViDrAT+DiYMY79pZFKMqVAEEMNB1Q6zvsm140dM06Em3tltKQv7S+l8Yjn89ygpdPpscIijCiC5BGXdNX/FaRWKBR0fHys9fV1zc3N6eDgQMViUanUKK+2srKiVqulJ0+ehPM8PT0NSnN+fl7ValWVSkWLi4sRGUuj3NEPfvCD+DfoCKWHbkmlUlHYQt6EsWNEer2eVlZWotcqhTnkdtgr7IbJ83lcoESoPRQ3qdR+IYiec+EdPAI5OjrS6empFhcXwwC3222dnJxEJbhvJ0MA3ZG4wwbps36ueKw3Rp2tB7yv58KJFKGjKABxZcPxMa7kWEDMruw0PHDjD2ghD8rYJyauWivys2T06vOObDNHyfSDU8a8r6SIdD3q5zkevXqRiYMeaiFwdC4P6CvfQzd9LO4kpKtOSoBR9sEjrwBEpz8lfYn94h7IKJGD51U9PeEy0+/3tbKyomq1OpaLxzHn83k1m01tb2/rjTfeiGIhZJzPOrMEPYqcME5n/HAai4uLqlarOjg4CPny9WeMgP5er6dqtRoRFgGJ2z6XE9d5LubV890OJgAb7oDcBqAbHOaxvLw85tBJdVA1DTtDlEq0+/DhQ83Nzen09DSKuFgXZ0gbjYYGg9E2L9bSWc+JiYmxBi8O2mEiXI5ZC/wJskR6DV1iDmAnAAzYPU8bMHf0gmd7F4WxrjMAIdaZeaVuIJUabd2jzzspR+qd+A4OmoZH7h9ucn2t7VIoZCYzfsCBpMjjYAR5MegP74frToV8L0pKtOnGmQmAFllcXIzF2NjYCCqpXq+POeD5+Xm9evVKvV4vTgUplUrqdrvRDH9lZUXpdHrsmdCzjUYjct5ET0nqjc4v5JUxoPR6dgOLgYAyJgfOliCMCWgOxzEcDkOQHC0jwCiy76Nz2hZa2sEQAkI0c35+rv39/UC2VFCi6DhWFAPjKl3l5VBMnJA7JRy2I33mgH+7sUHm/PkItrMG3Bd58xwTl/+btnn9/lWLTZysK38qlYp9mci0R6u8O3INxUhOkzVx1sKjSxww7+e5Xt6XueM7XiPgVHU6ndbx8fFYBy13Oh4toJNe2evyxLvxrMHgqigSKg+Dh57DODnV6OvrVO1gMIiol4iHZzPHHll7T2Uoy4uLi2CxqtWqjo+Ptbm5qQcPHkR0Q6RHO1oAYDKi8mgROeJ95+bm9Nprr6lWq42BI69Gz2azUXA6MTERjX0Ann5sLO/GmmDzSAkyP7wjOnZ+fq4XL14ok8no3r17YxS8MzZuUyuViur1ejQNaTabkkZ07NnZmdbW1kI+kfsXL15oc3NTS0tLmpiYUKfT0f7+fkSRvC9gy4EiETtrTcrj7OwsOnRRZwR4wZ7iLLFR7px5R7cpzpYR2bImvAtzil5iA5rNZtQ6kKKC2Z2YmIh5Z61dNtFl5NJTDvPz8/E55ki6ahjElQyevur6Wr2yiYSlq0jWK5UpcPJTV3yPLIuLYoJwiVZAiRQOoIAgT4RhYWFBx8fHmp6eHtvUzkuDYvL5vNbW1vT8+fN4Pk6aTmDeJpOxOfVGPmJmZmbsWEuvqmWBisVidNJyOhQE7VQzjoZ3ZUEvL0ddyarVqprNpk5PT5VKpQLgIByMwSsBHa1i2FESj4RYD6JjfoYjxAjSrtRPU/ECH56FAnkuDAWlOINohfG68yCSwojDCLCuPl5QMc7BwSHr6MDHUbZXqCaNAYyMd+rC2PBvT3c4qva9864T/Nsroz2S9twujARzSt7UTzrid8mI3fO9fM6jFvbAZrPZ2MGQSqWi8MzTJcgXqRsvvmP+nbYHSPh7exTnh6Y4hcsaYEwZAway1+tpcXFRCwsLEclyHR0djTX1KZfLOj09VaPRiBOapFFHQuYUA56s32BMADWef3JyolwuFyfNeXTF3DJnzWZTR0dHWlhYUKVS0TvvvKNCoRApLN7Tc44O2nzN+JmntthqenR0FDYCGXX77PQ6tT77+/sxHzBP1OVQ31AqlcaAM/N4enqqt99+W3t7e7EH3nPegFbmjfkEjBB4VCqVqOvBxiIz2CRv9MP9cZTpdDoicgcrSWrY7Zfnk9GRZGQ7NTWlcrkclLkHMlRqQ9Mja8iS1wOg065/7gPQcb5zk+tr9cpGmBFOlGNyclLtdjuKZpgIn0gWEcObSo236+v1elFg44YHRSKS9g3tpVJJpVIphIPnI+STk5NaXFzUo0eP1Gq1QuFv3bqlmZkZzc3NhdNj0oj833//fWWz2chV41zK5XIoTio16ipD9F2v19XpdMbACA37USiiLxpaJB0MisyJV6B+hJzctgMlF5RkPjFJYzrAwVnRds+d2OXlpZrNZvR7nZycjJ6xTvt4RDc9PT1WGANdyn0BTMgTwosgYzyZX34HMHOH69XPOAna6NE8HqaFZznl7xQhVCypCX7v4AEFI7qlkAhDI12BBfLGzD8OlDE7Xc86sS67u7tKpVIhmxg9H7PnxpELABxGAhnjbPF+v6+FhQXNzs7GIfe+J9qduSQtLy9raWlJf/zHfxx1FcltWoBDChYxqKwrgAZHgbHm8oJC1z3Gc+vWrQB25I2lKwBULBYj1UADC2hVADbG05kllz+3M8ixdLX/emZmJtJmLgtcyA/7hEulUlQ9r6ys6PDwMIIGT8P4PLC+DlrcUUujc98///zzcI4uQ1zIX6/XU61WU6832uZDMa6fnIU8Sgpwtra2FswhcsvZAjTj8Qp4bCf66uDcGRuYTG/3mZQH5oWdMZ7iGQ5HpxlWq9WwO9gRvgdjCfhwh8gzO52O5ubmlMvlIiB0ih4b4jYUO4ZNdNsPyCLQRLe9PbKvKe98k+trUdlO97mh4t+9Xi/yElCykqLsH6PV6/WiKQdGVNLY/VFEjExScTB+jtC8tL3RaMTnMPyMDSoHatQNGc9B6cm3Pnv2TL1eT/Pz82PG0QsLarVavCcl/BSIeWTmFZHJPIjTJd71im1BOJP5+XmtrKwolUrp8PBQjUZjrLEHguXGE4CTFFg3NC7QkkLBKfxjnTEuKI4bYqduHIkCUHg268fY3PAxL6wN9CL3Qx4xVHym2WyOHUZCJyacnNP6oH+PfnFA/g4+FgCDM0YeNXoNgcsIMsFzuS8yTWS9uLgY0TOGxgtuTk5OIv3BfXq90QEmbqykK4amUqmM1VCwRgAr2KWVlZWIzI6Pj/Wzn/0s0ipEETg7IjoiSe4JtQ8I9qjYi2SYQ+YaZ4+cA5y9IBKanI5TOAV0kLOW6Y+PE0EfWCfGzjw5QONZFClNTU1pbW0tOmm5rjitSm2CR2vs2sBewAS5XElXxpv1xEb4M3Z2dsZ06quuXu/qvGvWGkBLZJrM1UL793q9KBD7jd/4DZ2enurk5CTSAOVyOcAN9oreDES8zoLRsvTo6EgXFxcRpKRSqaD+WRe207G+6B/ADMfMO2G7nTlKslHIU7/fV7vdHksZAngJPrCdgAPXD2yDy587bc6ylhTFtV5zxboCPG9y3dgxdzqdaCzhhR6Xl5dhNMkj8CJMSr/fj4o2BozSeU5SUuRVMYAouueqEHzQ9vz8fLScy+Vy2tvbi+pKCn3I0Xr0gmHgYruJ03QINecZ9/v9iDqGw2FsHUEhHPl6Ps2BCcLoVDg5N9AqDvTg4OBLedapqSl95zvf0dTUlPb39yVJBwcHAVq8Ixtr4UbJ89tEM3459TIYDOKITdabOYRK4v9OUblRdAPkkRa0sOdtiRIw3tDGrqjp9NWRoxhV5m9hYSE+5+1RQedu6N25XFxcaHFxMcbt1bxOfXq+kLXHiPMsFBpajXfyufAcMnLGe2KkHEB57jWfz8f8IfN0xuIZbiC5v0cXa2tryuVyKhQKuri4UKPR0McffxyHDSAvfkAG8w1QdMPFH9aOIi/eB6PmNDhgB2OK7BL1ujx6npGIxMEq+l4sFtVsNiPXyxwxj27EcdieigHge9poeXl57PesP/dIbi1ER2D4cPAeHToNzdw62PR5ZO04Mc71xK9kKoMWpA7AmAcfN2Pd29sLR9vpdFSr1bSxsRF2xVNi6CP0rrc2Rq4Hg4E6nU7sUDk/P4/uYvV6PWTMo04/YOXo6OhLRV9cp6enOjo60uTkZPTC9h1COMdGo6GlpSWVSqXY9oVjR+e63W4AKLcN/X4/UpjYSRw7wIILu0K3Qnzb1NRU+Efm6ibXjR0zkS0DcPrCK6UxFKBnIlYvFvCclwsXDRIwCNyfyBX63DfJg8z39/eDxshms1pcXNRgMIjuMUQkCBgL5wq4u7sbTdYnJibUbDb1wQcfjIEEojsvTEqn04HoeB+QKe+BUjoVLY07Iz7nrIRHqSByAAn5re3t7XAAKI0XmjBmp31xVp4X5HI6jfEltzJJ46dGOR3PHwwExtcjJae8PPJLRvM+b06joRi8J84e5+Hv6sbSnQzPoRm/O3OP9JFDHC1ROHJBBEdOmHfFyfi+Zt7L82NOkV03D64nsAMgdy53XtCVHj1Uq1U9ePBAlUolqM5Xr17p008/jblxp84ccS+ezxwh+84G8WzAnFcYu2Nz1sWZCdiLiYkJLSwshGw4oHNwSzTHnDE3k5OTYwfTIM+sIXrs2wVZG1IUjI/8Lnlm7sNYYOJ8zRg3+uUyS9SGc+d7Jycn2tvbCyqXOo9+vx8AivdzgOeXR9Ln5+fK5/NxHCNRKv28eV+Cg3Q6rc3NTZXL5ehHnkql4pQs9r3zfqzrxMSE6vV6AC/OdWYft4/NGTHWFBmmrodINpPJBPuB3CDTrIG36p2bmwvWgoDp4uJCd+/ejVSId91zPwWjiB1iHJeXl8F8ev7baWxsADaEsXNQTjab/VLXsZtcX+t0KQyMbzvwbkqee8RAelQ8MzOjTqczVkCAcBNxQm+6EeXv6enRWayOcPb29jQ9PR0b0dm2wBmYe3t7arVaMaZ0Oh2UinRV5DM9Pa3l5eVw7P1+X9VqVd/85jf1l3/5l7ENA+GC1oNegcrw/INvJsdwMT8Yao9uEDYUB+oVocT5SNIPf/hDVSoV1Wo1nZ2dBfXnc+cImYsoGKXwHIjnYLkwhFNTU2M9xP07LqgAB97LjZVT0EQ5ScOMsXKqiN8xt8xF8nte3IUBBqx44VgyN47R5yAGPyQCubm4uAiKl3dBL5CfTCYzlqN2A8ZzkDfuz/uw59HXhjF6cxOvgPetdkSM7jwwKADn73//+zo6OorT38g9JilAAALv4YWQvEOSVQJAu8EnGmG+WE/YDZ6RjEar1arm5+ejixIsEhQk92efLeP3NITbEeacdAfG2AsS0UfGjFEdDocR6SRzj17k40wbTBC5fAeibEdywAOlurOzE2CG8V2X+kCnuCdjQx4vLy9VKpU0OzsbESoyx8E1yAkpCtaLcw0uLi60sLCgvb09ZbOj7Ve7u7sxVzB3JycnOj8/jwAIRlFS2ApkhEDDa5MczKMLMGI4U7dHznjgmHG4sAt8n9/3+/2xNBbNnOj3z5zhnGEQHGjCeLh+uvz6oRfOeDAP6IXbu1923dgxg4L8wSBcIlcQIoYSx82/W61WUNksiuefpqenw8B4dEvUxoIfHByoUqkEBbewsBCFZJKiWQldWbrdbhQe4TBpNo9BmJqa0v3792MB2b/83nvv6fHjx/rkk0+USqXinE9ypjzz/Pw8qJfBYBB5imSRh0emTuXwjtIVFY3BRXBwjkSAbHRHiZP5zaQiO+2LISmXy2o0GpH/ZO1YZ+bdGzh4/iv5PHIxyITLAu/J5VGGVzESyWDwnErD0STpTT/dqNFoxP5rz6snaTin9Z2CJnfJn+Fw1K0IxcSAQ50CJNjvjqFhjX3nggMCpz6JUHBcPp9E/XwnGVGje8668Bzu12g0Ajzyc+aMZxFR4viYEwofHUwj1x414Ej5OUYSgO4sijsXfkakUiqVVKlUIo1FoVqr1ZKkOFCBd/cUhec/kTePhn37Iu/DWqEbUKsYW0lx9rADLn8eER9ygZNw1sUbxPB8nOFnn30WDWKohaHYDqfgcuHsC2N3p7GwsBC7YxxoraysxDYmUibpdFqfffaZJicno6qd7wAa8/m89vb2xlJx3W43dpzwHT88Q1K04UylUqrX6zo6Ogo2EVvBWLzpEHLvQMjtKL9DN1yGsfcrKyvBlPp2T2QWfQCUoLe0YgUUOWvl4wLwJFMlbhNpqgITl0w/fNV1Y8fM1gNHLNJVW0peECFFMM/Pz1WpVDQxMaFGoxHGJ6kcUCwYAQQD1IoxmpwcHftH95hOp6Pd3d2gayiS2NjY0J07d/Tpp58qnR7ltKHJa7WaTk5O1Ol0VKlUxp7hleJUsyJAPnYWK5fLhfEhyqHC2SMRvsscTk9PR/6Jbjk4dXJn/Oz4+DjyRVAjnn93ChMF8VyGsxgeibJelUplrFbA6VYOOgBhY9g8MufiXXGsXhGOQvAHg0IkyPcYu9PYGDzeg0IVvs/zpKuzpvk5z/Fm8xheUC9zhzw6TYicEMlMTk5qfX1dy8vLsRYUTJ2dnanRaKjZbKrZbMZcJ3Nz/N/BbalUGjMOrCMG5+zsLKIf5hIE7ikVHDgAmfeD7cBRJx2x3w/5uI7V8nfBqQAMAd2saRIA+Tp5fQBj9si11+vF9jyiy729PWUyGS0tLalYLEZE6tGxFwMlnRbrD5VNdCyrWdUAAMjBSURBVM3vGDNGmHm4vLzU0tJS2JbkvHmdBrLM/aHUMerMHbYG+e50OkGh+/o6g+UsDe/C//13HCThbCP3g7ECePA7emfPzMxEsRc6eXl5GXUYdLsiD03RKe/kDp0+2/3+qFFMNpvV5uZmMI3MIwf+MF4ugJo7NH7vfa899cS7Ly4u6vT0NJy02xbG5PpJ/pggCHuAHLi8endL3hf/4OtAxTfz7KDxV103dszkfobDYWzOnpycjIO4aQQAqmcbA87t8vIy+lVLGtuHhlNIClIygiPC293d1cHBgdbX12ObVrVajYjl/v37evDggV68eKGPP/44In0EklxIp9NRuVweizD6/VEF32AwULlc1rNnz0IoccSOVH2LEO+DgXPan99DASEcOB0W1ZEjAlIoFCKf5oULFFSAzqSrogveCeVGWFyQnKqErut0OoH4nL7DgTia9sIvlAMnIl0ZOgT7unyz5zQZv6N/BxjcJwkOUDLP/fG+jI21la6oP+4hjTczYZ5gQZjjXC6n119/XdVqNQwbhnkwGKhUKqlcLuvo6Eibm5uq1+tjjWuYR4z9cDhqD8gJSDg2igBpP4uxoIWi1y0kAQ7/9kpc3gnd4X3Rs6Qj839fR8Hxvr4NijVw54iD9SjCIz2XM+YllUpFxMK7TExMaHd3N1i13d3dKNZjrZ2290jHo5d+vx/RGakJvovzRw7IMcIEVKvVL9Um+DsmAQFGnPu7o2f+HSTzbKf8vU4gKc9O07uzz2azUWmMQ8CR7OzsxBxiK6mPYM8zDXiYO2wRQNt1EdvllLOnO6anp3X79m1NTk7q7t27yuVy+uijj+IUPfT++Ph4jFXj/u4LklEp8+Ftn7EFBEgwOOTr8/l8RMJuA3mep4qS9sF12Ld2Yrs9mOF+dBpzvbzp9bWOfbwO+TL4er2uk5OTyHN6volJpTAGpwIdQj9qCji8uMQpxsFglBc7OjqKCsL5+Xm1221VKhVtbGxocXFRs7Oz+ulPf6r//b//d+x3pgHA1taWzs/PtbW1pXw+r0KhEHlijsw7PT3V0tKSjo+P9fOf/zy6BxWLxTCqklQoFALl+oKg5ESySSTMQtPQwqNDooeZmRn91m/9lp49e6anT59qY2MjuvB0u13NzMxEl6nkiVKey3QqHafO85hjipYmJkatTInY6X/LqV3J/AjvzfcxAuRyQKjuOBkfPyfixNA4/Y2yeI7QKyExTNDBRCpUUWLoMSIYSMZMtAnb4UoO1XtxMTrmdGpqSm+++aaKxaJmZ2f12muvKZ/Pxzp0u11tb29H/cODBw/i6EGq2v390+nRFiX2Zk5NTUW1NWtULBbV6XTUarWiuKXRaATL41sLAa5cnIjmznJzczPmnO0tzJtThu44oYmRH8+j+Y4JHA1sE4bW9zS703Fq0GX34uIixu6GF4eN3p2enmp3d1fz8/PBECB7gBF3fDQ/Qu587F5ABkDwf19eXqpQKIw5NAeN7kRhBnu9qzOpkUkud9wOQpEPz107MPUIj+85yOX9V1dX9fHHH4d8vHjxIvSL9CI2imdPT09HkELRGd0W3Sk72+OXsxysmcsbctbr9SJAY1273W7YCM+nIx8OGB3cENVjw9Cz+fn5MWCBHAJKmHfmCzvlrBpzg0+C2UGnWAvmEZ32VK6P13XzJtfXajDCQqN8VC9jvCTFRm8UCwPpVGQ+nw8aAKfFfslut6u1tbWYBM9VU/mKgjx58kQXFxe6deuWDg4O1G631Wg0dHx8rKdPn8Z4pFFVeavVCkqw1xtVptI5bGFhISJWjsT78Y9/rFqtpk6nM0YrQRmn0+mxgwN4byhPpwUx4J5DmZqaCiOEM3SDtL29rffee0/n5+d6/fXXtbW1pd/+7d/Wy5cvA/26o4FyQjFA4BhFz9t6ji+VSo05eu/C5qiQXC2OFDQKBe5bpKC/2RbndJsbNBwK97quStyjsOQ9pCv6EGWE/nQw4BELoARj42kGN+Ts6RwMBrp165aKxaIWFha0trY2VhFKrpkD5l++fKmpqSndu3cv5ti3UyX3LDPX9+/fj0M6QPmtVkuvXr0aK645PDyMVrLIIfIJW8Fa8r5nZ2dqtVq6c+dOzCO65SkWLubCozeif77r65M8ZMGZDI8aMIisTZLlKJfLIXcAQZyUg0pksFarRbrFI0iXe6LkSqUSW5dmZmbUbDYDoHj6jL8pukOfAfjS9dQlBrnb7QaF7c0mHJSjR/wM8EKKBXYIZ+bfc4DjTot5mp6e1vr6etRFoIcECtwDm9zr9eIMekA1n2u1WlpeXo7UIXqLzcMeU1OTz+fDxq6urqrdbmt7e1uNRkN3796N9p6+bxgwxfz5ViOnk9El7CepDOpZAGScKe3pAFq4up4DsnK5XMwp+/IB4wQEnj8HBLo9Yq0IUpwdcv26adR8Y8fsoT20HtS179EDlaFIRC8M3ulR9l4Oh8NonECbtD/8wz/UX/3VX4VAks/KZkdtBYfDUSeXZ8+eqdFoROFWq9WKXDaRNpRrrVYLZ4gDx+h1Oh2VSqVQ2P/zf/6P+v3RXjr2cDtdhlHxPC+G0iuDEXKiCkkhUDgzog0MxPT0tA4ODvT666+r0WjotddeC4Nwdnamhw8fxnF0OMVkrgOUSETtDpl5Ae0hdL4/lKKFi4uLoKmSgorR+IM/+ANNTEzo5z//uX77t39bn376qT744AP91m/9lj744INgPpxZcNrHkS8/82ifn/mFUiNv3B/mBXlz+cWgJCNXFAZKX7o64i2dHlUJLy0taXFxMRrTIAOe38xkMkFLf/bZZ8pms1paWoqudTxzMBiMOfder6d/+2//bYyZuoOpqanYg/n48WMdHBwol8tpamoqzr9lLXCI7sRYx3R6tA//93//9yMaSObzkrqepF6TuWiXcRy2gyovtmE9pfEGOlDgOCiiZZwbn+fENpwL0afvj4YW5b1g9jjcY3V1Vfv7+2q1WlGEur6+HiDMIyneH+qbYwnZDpqkVfnZ2dlZnFxUq9XCwboz9Dl1kPKtb31LMzMz2t/f18uXL8PmoRMOXjz1g/x7zvTg4CAc6enpaTAsrF21Wo19xMh6o9EYayKDrbsuRUGNSz6f1/z8fNi1ZrMZ21mHw6GWl5eVz+c1OTmpVqul58+f686dO2P1J9JVWtNpa0AENtF1GTYPP+MV/ufn59rd3dXk5OQYzS5d7bmenBx1qvStbhTb4Viz2dE5zcwbTC72HCDmdgsd8YptTwWyXje5buyYcUg4AwoBXMhwtI4MaBXoL0Nl8/n5uU5OTlQqlSRdNRdotVpaXV3VH/3RH+nHP/6xut1uGF2Eb2JiQpVKJQ5eePTokRYXFzU5ORmTjTNvt9s6PDyM8njf+N/r9cIxY5SdviOfValUJF11U0oiTumqLJ9FIIp95513tL+/r/39/bES/qmpKR0fH0fRF3t1j4+Ptbq6qvfff19/+7d/q3Q6rd/4jd/Q8+fPtb29rYWFhahsJIrwOcfAICCuCETyzmS4EYXGSqJHL5oCnXoksbW1FcYC9P3P/tk/0+HhoZ4/fx5olvnlszgVdxiABX8v5MyNFIYHY4fjRRmShUZuZKSr3s5Oo7OWVFHmcjmtra0pn89HEaPTWlyeZ5yZmdHdu3f16NEj5XK56DuMMWAbC9Hcv/t3/26sIxFrQEvIjY0N5XI5ffDBBzo4OAgA2m63x3YyEG1gwH3dMHAABAdG6JQ7zyRrAZjDYEoamzfYMd93zHwnIztP83jUOTs7G6ceIZPoBM8AADkbgIx7LhaHOxiMjoGFii6Xy3r+/LkODg709OlT3b17Vzs7OzGXAFbuw7vgdAGzzBPvf35+PrZn2YEM85wEir3eqKPXzs6O3nvvPaVSqaCTHz16FPf00+Qc0Lij9veGnmVtubLZbFRkw9zg/HkHwDiOEnn3in5PUV5cXEQ9APYUWZCkTz/9VJL0zjvv6OXLl+r3+8GiETXzbsgObGKyGBTddzmCsWS74cbGRhQJ+jYq0q8UcXLKIPekyIx5I6hB7vB/nBZVrVbH0pDMx/HxcdhE8tweOPh6/LLraxV/YexxPigKwuwFORgKL2jI5XJjWyqcMoGOopJbktbW1vQv/sW/0I9+9KPI/5IXnZ2dVS6Xi5NLms2mdnd3NRwOgyovlUrR5xejc3R0NHbgN7kgUKIjqE6no6Ojo8gDonhevOXFAsncFMbn4OBgrAqU6Pr8/DyEhwXGILZaLf3Jn/yJqtWqvvjiC3U6ndji9emnn4YCJalFR52Mh+cRCTCHACuUk6iHeUG53Kl5ASDUjje5//73v6/f/M3f1Oeff66XL19qe3s75IDvIpwIPAafsVME5c4C44aM+Z53p92ZxxDw7FULUN9jLV0BBHdYrB/vfOvWLd26dSu6a3nkgtLieLwSulqtRj0ABX9cdCejNWypVBrbCtTtdkMucOTLy8v69V//df3N3/yNzs7OtLCwoJcvX+r111+P7yJvzJ0XWHIvUlLIHOvgTtjXCLnh3sgORWnoA8YcOfDiHgcAHj1gsJClUqkU+4yRbZgbPu8XjIY7d9JtjGV2dja6gDHm+/fva2ZmRk+ePIlalXq9HvPkUarrpW8F8vSB6whjSEZ5zJvLcyo16hT1/vvvRwoGfZ6ZmYk0W/JK5neRcwem2Ww2omanW7PZbDCKBAkO2gDf3oENB+x9230cpApwym4LNzY2YgxTU1NxhCdzh1Nn7OgU+WEoaX8eNgOfwpqx/9wBFWNyXyRd7fRwe4PMEHzOzc1pb29PkgJcT0xMRNMVbLrrFZE1aVJ6aNBC97r1vO76WlQ2i+LRGQLHwpIsRwBxwkTKOF+qtofDYQiQI2HQzeLiov7Df/gP+uu//mvt7+8HLZ3NZqOTjXTVyrPf76vVao0JOkILpUGuFxTE74hS3JHQuIOLijz+DVJiLjAmKLWkUIRklMVCM79UpvO5Vqulo6MjDYfDaNBP9I+xQgkxZAguVBxC6dQzP4O+4j5ugDGu1ADAMBCpe+rixYsXunXrlprNpn7nd35Hn3766Zhisx7IAWtNXh0j59XtrAEy4lEBY+F9vZBlYuLq8AGUUxqPKnBKGHS+SwRN5X42m9WrV6+0uLiojY2NsZw0n4Uy5kKeZmZmtLy8rGfPno3JD7KKAXj48KFqtZqy2axevHihpaWlaB+bSo1y/+12W7Ozs8rn87p3754+/fTToBIPDw+j6tZlztfU9dUjPoAJxtSjEafjWDfkB9BGNEs+nKIy31pGBMdY3Omjh/l8Xrdu3QqqGuqd/Dt6zX28IOfk5CQKCBmDpDGWjb4FXiy3vLysk5MTPX36NLb1eL5+eno63oOoFdvnjofxeU2J5595n6RDxzbSfYrT8pxp4L4ekfn9krSo6yQ6Vq1Ww3mhF7SyxCa1Wi0VCgVls6Ptp+gfzoYGIrBenutvNptxXjfz7YwV/dd3d3eVy+XiuegMds0pX+7v9Dxzh+wSCJJycvBBCoN0EHaAgAU7izwDomBy3Va5bHhhKzaaZiXul3jHbrcbkTzA/f95xOxGCBTu3VUQWi9IQjndAHgxF1QsbddwHr4fEoH9nd/5Hf30pz+N3Go2mw1qm2iEAgQ3hKBqz8Uh1PzMkSp7zxDyXC4XiNsXn3FSZNPvX2138PwJi++CwOL5/jandpwu5x6SYi5RFoQfZ+fjc6oQY8i9aRYzHA4janbg1Ww2dXl5GWeX4iBobkIxHIrz2WefaW9vT91uV3/+538exv+v//qvI8r0gjnpqiEBAu1UNd9PVjKylhRCYfxAuh4xJGnDZFTImDAwHkl7pE4xDe+L4jM2HAZj80if82dTqdEZ32zDA3QQFTWbzbG5h/WhKKXX60VhI+0ZoV7JC2LMkDuMoEcNzvoQsXh0y7y4AyU1RJtIHCA6d3R0FGfaFgoFzc/PR2FOoVAIYyZJh4eHkkYNgMg7YsRwHk6jYuRZX4yd6zS/o3mLgzr0kkAgSc2vrq7q+fPnajQasT0Kh5aMSgGEzgo6pezbCAkqPJpCdylcA1jT8tHrTYgafT1c3jxf6cxdOp3WwcGBpFGEV61Wx3pZA4Q4Upb3rNVqajQaWl1dlaSwT7A1rJsDd/bse0W1zxPv70EPTg5fQrqBXhDS1UlXtBLFxvGHQMvtonQVMNF+kxaiDigB39Q0eI0CNgA7DAXuNTXMN9Q4tpT0I4wKtgY74FQ4duVXXV+rhtsVEwfhUYaksRwRRRooXC6XC3ri4uJiLFoDfbuTxDhgqL/5zW9qeno6CmuIOpzKZAJZVK8kZFK5PE/j7+h/40C9YhgDMD09HZRyv99XqVSK+/nJNwgmjsCjFwyPU1EAExwXIMCjPM/JOLXEXKG8/M32tHQ6HRQZwIB5o2Ma9NJwOIzokYic70JnE/EjD/ybsdBHNwQumx1bV5gPxkBeibnw6BSlBtRBrwF8MG5EIZ6/8hy2O10cuUdKno7xfDpz75Q1LWH9HVkPIkBockmhzM1mU0tLS/FzWsLiHDCoVJLyHcDUwcGBJiYm4vg6HBrFls7M+Jj88jn1nLJHhXyX39PqljUpFot6++23gzImLYKMUzw1GAz0+uuvh+FGJo+OjlQqlbS7uzt2stYXX3yhmZkZNRqNGGO5XFYul4uoDmeNfLqsDIfDAEZcHrlCL66ururFixfRE8GP8kQmMaiMA1khasJheArG59N1c25uTtPT05qbm1O5XNbJyUnotueIPbrieV4M6aB2enpam5ubsRV0dnY21kG6qhuhoxgdu7BXxWJRjUYjuh3Ozc0F49Xv9+MoUsAFOt5sNsNJwW55kMRRmM4m+DqkUqNWpP1+f2y7JZ/BluGcPcAgt4zusZ7IKkWx7MDxrojoVCaTGTtti/lut9shPzTmwW55R0ePuP0kRFKd0N3IG8zPTa6vVfyVRIbQGuzzBQGBhqCQCPXr9Xpsx2HiMczJqjho5cnJyahwnJiY0De/+U2VSiX96Ec/imYcTGzSsEtfroJL5is8eiYKd4ochfOckTtHd7TODHgRCYuWzWbj3zgeR78osxcggTy9yIC14GfkfmAg2DfJvk1Oy0mn03HogFNE/JvoBqdPOgCB4+g0N+YeMThF3O/3g07yQzSS+Sne29EpsuX7s5ENIjQAD3OVdOg8wx2Uj/s6mXDF8c8xz0mqnbGhpGyxABxOTk6GYtPUAiclXR0RBwDytIbn0jGAHC8He+Fyi0MH2F7nVP1n7qh/2ZzAEAC28/m8lpeX9e6774bj84Y4GFHqUPiMOxLWgZ0AxWJRd+/e1cnJiQ4ODsZ6lUNVn56eqtVqRW2JN3dB5nFI/q6+/jhc6OfT01OtrKzo5cuXIR/+eeTVdR1n7NEy9gDdwi5iTwDB7myohIbVYsyNRiN6JiBHzsa4flDYur29rWfPnoXNohcBQNPtSbvdHtu6l2Sc0P3Dw0PNz8/H8bnS1W4N5IMirV5vdNxvoVAINhQGo9/vB0PHuuIgLy4uog7D0xOMBZ8Cg0BkC6AmCPRdI2dnZ1EJjsNnvDC9nufH3hUKhdjRgD0BjGEvKcYDQHighFzxHG+rzHdgwG5yfa19zKBZ9todHR0pl8uFIE5PTwcCxFBxmo3nE3u9XlTFccwX+SKibz+dA+qHCVhdXdW/+Tf/Rn/xF38RY+HlEXIMFQvgVCaGyYWc6JtJxRkxXgwlRhlaivENh8NAm9JV44Jk9Oo5Io++MSSMT7rqrIUxIL8BRQQSxgA6aCKfDs3IzzFoPMcbb0ijimqYDnIu0lVxC2uePKwBBUJx2DfoNI5Tp7Ao3Dvp2D0v43PKM9z5eOoAJ4XT8zlFiXC0jCOZRoFmxNien5/HXKMDPOu1116L6BYgBZjiHkRA5MBxZqwp78HeZihg1hOQwhh5NqDJIytkBjbBDYHXIDhyZ42TVC9jun//vrLZrDY2NrS2thb5Rm9ww5aifD4/RscSZRUKhehrTyRCP+ytrS3t7u7GFinkhyIkah0AIgAAmg35/nmKb3xt+d3JyYk+++wzpdNpFYtFra+vxx5cP5oRw4s98bkgR4vD5DPMOWvkqT1PIcDsIK/IQ71e17Nnz/Ts2bP4OSDH2SNpVP387NmzcGjNZjOOvn369KlOTk4iRUVdz+7u7tiWPa+IZm6bzWbYM9bx5cuXY9u2OIADsDg/Px/Am5QO+l6tVrW5uamlpaWwEW5r7t69q+9+97va2dnRwcGBHj9+rFqtpn6/H7VG7gixexRgIR+zs7ORqya65buAKXboIAswb/TUB6gROKI72HFsitP52CzeBx8CKACEYqM8GPpV19eiso+OjqL5RjqdVqVSiaO5MDIoKM7LjYbTi4eHh7FIbHnBMNOkHmeYyYzats3NzUU0PTk5qT/8wz/Uhx9+qJ2dnSj6IbIGwaOcGHLumYwWAQEok+cCuAeTTR6Ke3Iv6GIW1HOkKCu5Ye7r4IBno5TusIl8nWYk/+eoV1K0NISpwKFDewE23NGfnZ2p2+3GekLDkFcj4vHqZdaY92BeuT+RluflmDf+D4LFGfNvpw8d2OEYMQbQmL4OksIRYmzdkXudgTReHS6N0hAo18nJidrttprNZmy3Yz0kxZYKvo/y47AwDhhg5BdnCwUIeCJnVygUVCgUdOfOHQ2Hw6itePPNN/Xs2TM9f/481h3d8fwsDtpZhCRb4ZdH0cwT9F2lUtHCwkIU3ZXLZR0eHqpUKuny8lLlcllnZ2dRuUr0AYiiov3evXvRWrZWq+nZs2chA7xLLpeLPfvSKB9NgQ3FPOQ3K5VKADAiVBwpEQvzSRvVdrs95sxmZ2c1Pz+vV69eBVCA1XKA6MwMv3M7gBw5+MZmEKS4nfFDgX784x9rc3Mz9koDuIjY0adOp6Pt7W0dHx9rd3c35rdarQaDdPfu3dhCyh56gAQHgaDv6PZwOAyKHfD4zjvvRNcvfu653V6vp2KxGDT2xMREHJtLQJJKpfSNb3xDg8Eg9gQja9iCk5MTLSwsqFwu69d+7dc0MTE6bvfi4kJLS0v60z/902AFCQJLpZLee++9kFO3U9gq7zUhXTlNbIqzF9gt5DufzwczRdBJ+ob0FkWO5XI57u05f3QdkI98/D93zAgKFW1OGdL4g6ILp3aZPHe8ztUzsSDUVqs1thFcUkTS7CGTrozHu+++qzt37qher+vTTz/Vb//2b+vs7Cz2WlPKjwGnGARFAz15oQgL5pEdBgZKz5Ef90kiNqf8+QxGiHdgQd14ez4UwWL++R73d+PrgonB5v1Bg97Hms95kUUqlYqjDSkScWHHgGJ0PLKF/gGlE2HyHeYCB4wTSToN/zwXjpAxAyCSFG2r1VKxWBz7DobanbNveXPaFgfJ/F5eXurw8DCMHHl51snTNuSyAK7SKLoZDAZRXc+9adDT7/djS9/l5agfNlsHmYsXL15EdHL//n09fvw45pA5SbI7Tsl60ZKvM+/suTvkBqBHkdby8nLQhO12W9VqNYqDhsNhOJl8Ph9rubi4qJWVFdVqNaVSKe3t7en4+DjSXV7k53KM82MXAPuRkTtAKeAAWXLdIvXy6aefqtvtqlqtRucp0l/I5sLCgp49exZMAuvE+HguOeJWqzUG5nx7EXoMg+j7hJEL7AG1Et/61rf08OHDqGr/0Y9+FGDk/Pxcz58/19bW1pi96fV6Wlpaiu2lc3Nz4UDefPNNPXnyRFtbW2MFpQ6Sk/leUl/D4TD24R4cHMTneQdk0ntCZDKZyPnyN8wFMsR9CLjIszN/2Pdut6utrS3VajU9ffo09p3v7OyoUqlodXV1rINbtVqNU/aYU4JDLgI5Ahuv95Gutk4NBqM977BVROWSIpXmRavSqKkKDhofeHZ2FiCC9AknD3qw9suur3UesyMeR5FJI40AemSEM8GI48ARdpxmsViMhWLfrqSgZ7LZbJy9zEQQhRJt03PY8xq9Xi/QOnlxokScLScJTU1dnRbEBnFXfnfOjAeUzSKi4PzMUTWK7sZIUlBwfNdbu+Go3WgmQYDnJRkbJ3ZR0OXgA1DC+dTMJ7mZcrkcuRtfL4yx52RwDpKCejo5OQlQhNNzR5hkKLwYKxllY0AAcdJ4vYAbUadvvZDOo+JkIU2v1wvnAigkKt/d3dXy8rJyuVzk2FlLzz1D3dNc4KOPPoq8GBF/r9dTo9GI/B1KS66UeaYTHvM0MzOj6enRueO7u7uRwmHsDjKYPyIjrxpP5pM955sssEGP2YqI8yF1xPq7g1peXg7HNxgM9OTJkyjqxOkis8w/75xOj04s2tzcjMIo5sVzjIBAdCafz4+tJeuOU0+n02o2m7p3755KpVK0SnUgzbuypqxFMpXDHMCAuT0kSgOoEXzA7CCrpItIQRENSiMwMzU1pYODAz1//jwo/uFw1EkL/V1ZWYnjWLe2tsL2DQYDbW9vR/SHHcAmYEsc+GL7SqWS2u22Op1OgCIAJBfg2/tZ7O/vj3XSchsIUKVOAF9RKBRiXXGcBwcH+ru/+7s4zQ25Wl9fVy6XU7lc1szMjD788MMY37e//W29++67YzUvrsOAJVKlpJC8UAvQjg8iFUvvDNaNlBY5ae8HQOqQWhDvTwFYpQbhJteNHTOcv+c/XVA9J4uAe8EPxhmjgYL6tiIEGEoYJHN6ehrVwOTOyIEQ7WKAyZEgBCxWMgfNVpWVlZVYOC84Q/kACcfHx2o0GkGFcXABDswjcOmq2xT0MZdXCEIJYfg8P4ZCec4VA4zDxtAiaLw/1LJHfRhTonY3KF5EJSmc5O7ubtwDZ8waufAj1Bhv6hDILdI1iDw8CgG4w3gxf556YM1xOBhOhN0dUq/XU6VSifFwT0fJ/p4ARxyqAwPSJgCdV69eKZvNBugDcCHXOA4Unx7X2WxW+/v7MZ5eb9SjfWNjQ3Nzczo4ONDi4mJsf0L29vb2Ihe6tLQUTQ0++eSTiGw4Wc2ZB3ewOEMYKIwCxhFH6XLrjpk1IIWF3CGTtKicmJhQsVjU5eWlut2uWq3Wl9IGACeofWQIuSa66na7evz4sb797W9HZEZ+3XUcYAJjwfOI1vjs2tpaNIl49eqVJicn9fd///d69913o2qeKmTyw86YoQ+eN4buJV3hjIl0lf/HprD22KDPP/9ct2/fDifGdx3kEJkiF5nM6OS3wWCg5eXlyMFnMhktLCxE0dzk5KQ6nU5EqexdZi3QGaJ6WE72IqdSo61rBwcHIdukdviDnLRarYgYGW8qlYqIlX7zPoe87/Ly8lgXulqtpn/4h3+IyBi9hbGp1+tqtVpaWVlRPp/X1tZWdMN78eKF/tW/+lcxfkCJp8sATdg69MUjZwImr89AVwCb1KFQ9Q24khT1VdhZbIT7OU+R/rLrxo7ZKRCnLtPpdCwMTtoRMMbAv8d3EXSnuxEWBMqjcv8MykoUgqGB10cpfEuBJ+zJH+KEku/EQs3NzQUVRMcmkPZf/MVfqN1u67/9t/8Wi07XJqIgkB3OBsfJOyHo/AxjQYENBsL7KtO2kfnwHCPGhf/7vLnSe8QDEAAZsy2OuT8/P491QeBQRISQ98GZJY0Ba8JYktEz32ctPI/Hcxgr6B8j49E7VbxuQJMR1szMTOTnBoNBHM+GokJNlstl7e3tKZVKaX9/X7lcLs7krVarYzIEQs5mRx2X/uEf/iF6PB8cHISc89xms6mVlRU1Go3ow47RKJVKQfUtLi5qaWlJ+XxetVpNr169it0NR0dHcQi9NN4MhAgZliEZJTlt7ayOrxGfBwRdXFzEPmXoPNIbOzs7Y9vX0CN3UrwjMnV8fBy97ZHxs7Oz2INKT4Hr9iB7KomtmE7PSqNagcXFRW1vb8fWvrW1tZChfr+vg4MDra2taWlpSXt7e5H7JxDxvCS1DzzT6yIw6gACHKNvW0KHyI3zf08P4sRnZmZUrVajzeXR0VGMqdVq6fz8XMViMYKM8/Nz7e/va2VlJdaSCI8gyJ2jByk8G91A792RM6fIeq1WCzYM8A2dDnhD7nzrF6CAwA6d5FhP2ioDGEhbYfPJVS8vL+utt97Se++9pz/5kz8Z6wnBmJkXl3eodewC4MO3aUmKtCl1DQ7AK5VKyJAXZDqrKl0d98kJis6y/Krrax37yFYEHKFTWRhPPsOFAUfBiAShMfylHeXjDDD8FP1gdH3RQC7+O1C9IyDGyzNmZ2eD7kJJpKu8G+MnwpUUFEsqNdpX+erVq8gfUM3okbvngUGPbC+jwOrk5EQ7Ozs6Pj7W0tJSVCYyR07zDgaD6MfqEaSzFbwza+F0MZ/F+Tl9LV1tH4MaR3k9P+mRC0LPnHBfABmgxql5xs4a8wwYFsbltBTFF0SHKARg0VMmGF3qCtyI8m7uzHEcGCkcDvlUnFy9XtfS0pIeP36sRqOhtbW1qOJEKXd2dvT06dMw4hTiQFUy37VaTfPz88rn89rb29Pq6mqcKZ5Op7W+vq5qtaq1tbWIwD/66CP94he/UKPR0OLiYqyx13WwhpLGest7xMzaobceISPzrsOdTkfVajXyijjmRqMRUaOkMbADdQvYYz2IqD///HM1m80AoPfu3RsDougJBjUZcc3NzY0VW3p0i8wRbc3Pz8cugY8//lipVEqPHz/W+vq69vb29OrVK7322msxn4AIgAL3ZQ59+xiyx6lJe3t7YdDRKeQHHdjY2Ai5B4AeHx+HzB0fH4edqVQqX6oDoNaBqmzAT7lcVqPR0HA46kWwuLiodHrURc2d597e3hiIAEAgG1RAY2+9eYs02ufbbreVTqdVKBQCfEDPe8QO6PDcPfU8ksaYzfX1de3v78e6I2f0lWcM2ezoIKOpqSk1Gg39j//xP/SDH/xAFxcXkQLBdnoKQrrqLwFT4/uSsTEcFMN8+N+SIvpOpUatpPE32B58EmAKmfRUy6+6buyYaQwOLeYo0unMJF3KgrhTwPi6Q0dwUUKoO/ImFBR5xOh0LhOHofZqXBzI5eVlOAd+TyRFXoBOPBREYHRQBiIPEvyZTEZ7e3u6d+9eOBAvy8dw8n7MIQJNfufly5eanp7Wv/yX/zK2onW7XT179mysEQsomP175HCIgjFSODZpZKA7nc5Y3oO5coRHsUmSyZCu9vJ6tOmRluc2MSCeH4byceAgXTEF7hh4NsbdDYfnjgBY7mSYJ2n81CoHG8gqvx8OhwHceDbjq1QqIRetVks//vGPtbq6Guckz8zMhBNOMjcvXrwIOcHp8C69Xk/b29taX1/X5OSkXr16peXlZd2/fz9yiFNTU/GcL774Qk+fPtX+/r5mZma0vb0dJ/sw/4APCo8wOF6j4NGLzzny6k6Z33mhoDM1vqWHucPI8rnLy0vV63XVarUo6kHG3KmenZ3p8PAwzjiHdSHi554AVmTB6WfWk/FvbW1pfn4+jOPk5KTu37+vZrMZW3OInjza9pQA8shcuR2Trg7e4QzgbrcbW5MmJiaiqh47UKlU1Gg0VK/XwwbyO0AtgAogkEqltLKyoqOjo2AX6N/Pn4ODg2iWMT09raWlJfX7/WjqdHFxEft+SQswdhpzOPPA9iAHzOg4wBd5LpVKwUR2u93QKZc3j8Kp2ucz7D8fDAZaWVkZqwuq1+uqVquq1Wpj9P3c3Jzu3Lmjly9f6h//8R/DPhAgnZ6exmex+7wTAR81DNQouB0geHRQ7dsg2dGCbs3NzUUggU3AL2InaYV6k+vGjhl6CedI9Iehc2oJh5hKpeLlcaQeXUhXtJsbcyJvFAZDJikKlSgU8eYdRLegMN+G4XkE8qXQrkdHR2Pnsk5NTUWXJe/ahMFF0fP5vKanp4NaIheeyWSCCaCU36l95omiDopa6MSDEaxUKrGQIDqQ2NbWVrAPzWZzjHaH5qdDEtvRPJLlwqB6ZIxRd1DFfCLcXswDVexbnNz4u1y4DGEAkvlIj6QxCPzxyNudgAMAFBID7tWkDjZop8fcuIH0Qh6KuWhVurm5qcPDQy0sLIThA5QNBqOtIbVaLRTd83roxNTUlFqtliYmJrS6uhrbTTqdju7duxdGuN/vR8tTHBcRLFW8bC+8LmrG0EtfbqAhfbmRis8z+p1KXbUvdJbEIxJ+xnweHx9re3s7Wl6yBcqZL/SWPc0AzEKhoFqtJukKXLG+gARnP7xSHid6eXmpJ0+ejOn+rVu3opBofn5ez58/1zvvvBN1H0lK3wMJQB2RFJQt9gtZw6A3m83oLIZcdbtdLSws6PLyUltbW2Pb/HAG9Xo95pgtogBL5gJgi0yRo4dZLBaLUZuDDGQymYh0cXzoLlEfh4gsLi6OFYth+3Byl5ejBihTU1MqFosRfR8dHcX+ZuwyNC6RKsVR6DtsJIHP4uJi2NypqdGZ5sPhUHfv3o0toGxz+8d//EednZ2pXq8H+wToQL+Rh6SjxBeRj6aQjvU/PT1VsVgMWcAvUIc0HA7j/0TnDlyQE8AdqR+vFv9l140dM8UZbuQQIIw4+Q4vjqEKGhRB0Q/oEsSHYkFD8AL1el3Ly8uhpFRUP3/+XIVCQcfHx2NbJlBioka2NiDEPJexgga73a46nU4gX0lxeABjwymhfLdv39bPf/5zHR4eam5uLu6HYaAHOHNFFMEzd3Z2xtDd/fv3w3iSk6BPN2OcmZmJynGc4/7+fuxnho4jSvYyf5RKujLcXgSB0fYo1oGEpJgDjDKfxWBQyZzs2sWVNP7u+Hk2aQWnv3iGKxZzxLswT6x9Op2OLkeAGO4FmIRWR9lQIMAXn5+YmAgHeHZ2FlX92WxWKysrKpVKsVXDi/M86oE6x5EMBgO9evVKg8EgnHy329Vnn32mR48ehUE9PDwMoHB0dKRCoRB6B8L3dAAGlHfwtUzmuAAqyZ8n0xcYGk83eXrg7OxMrVZLh4eH2t3d1ebmZoAm1hMWBx0E0BwdHWlnZydAzvr6egBa5gzj74Vr6FlSHgES6XRajx8/1htvvBG5YArx7ty5E7UFtDV15w/gAHwCAnDEyBK/x0bmcrkADjgGxsrnWEtki7oFWDwCmU6no35/dMgHoNzPlS6VSlFrcHJyovn5+QDWqVQq+qXncrnY/0zAwznVUM6pVCoKX6Gur0tvpNOjrX40gJqenlaz2Yy9x7A1vCOpDuSddwFQSQqbMRgMggV0xo0Wmul0Wrdv39arV6/0ySefqFQqqVqthkyyo4eLlCFg0G0N63t+fh4OHRvJ9jM/0ZCOg8maKHSDbWv5fD52+sCosjPCbeuvum7smL0xAugBYcdBO4plHx6L7nkZDA5GmMXAsOAIyGlBO9DNZmZmRuVyOZQb4+v5VJ84zgdGad15QAlL0vr6uvr9fiAo6Wr/mkc9FKawfebw8DCiNBwHiuf7oqECeWfOKcYx073LUZhfCMDh4WHkuqQR3YphQlnIe3H6Sq/X0507d2JsbszJwRPBOdDyiCNJ6XlUwbPJ5xABZDKZkB0UPkmZ4hS5PEXhQBAZY4w4O9924QifOfMqW2RDUnzXaUSn30HZvDfOl3wl6Hh/fz+KmQBaGCbmCScE6+IKenBwoHa7rfn5+bGCSgyLA1ioUKIOjKszFF605+Aq6ZiTACn5O+aI9WO++TdH2knSP/3TP+n58+cxlwCyZO7XKVKv5ajX62q323r33XeVzWajypu96hTS+S4QaghYu1Rq1Hv55cuXmp2d1be+9S1tbW1pe3tbr7/+elTN5vN5nZ2daWVlRYeHh0FZumwja7BcpHyI8AFz2BRkAeBLNIZt5EIWT09Po9KdC7DoFdODwagZU7lcjiLBcrkcbCEGf2lpSfPz86rX69rd3Y3tVIAe3scdHXnhqakp3b17V/fv39fJyUnYIfQMXWD9iPSnpqZitwosEAdSsFZeFwBljO3CPgCivTqd9BHBTrfb1fvvv69araZOp6NCoRBFicg3uz9YM59DnsX7YU8mJycjPcDuGAA9n/Ptv7wPzK5vJXUgenx8HAHUYDBQtVoda5zzq64bO2ZyDzgDjLfnjEGTbGdyqglU6lEdEYe3OYTahMKhypn8p9OOIGOuJEUOp7+0tBRggdyut5SkIQWOGgdM1aPTbhhIEOO9e/ciQnj33XdjsYiOMU5+aDeCwt5UnNiLFy+0uLgo6cow4kyY40ePHkXRB4pCo/ZmsxlomHQDwkpFrOeReWeAQb/fH2t9yDwi7KBHKt+hwA4PD4M+Rw4QUBC0U+muKKwTAIbcDVGl7xcfDodjhzTgkCi2SQJEL2jDgHMf2BPmlXdkGx7ryDwRhTIuKDlacOI8GZdXuXtuFsc2OzsbRgCHcXx8rJmZmSgkopId1uTs7Ezb29sR6aMrpAXcgHoOEIrd2QIcpYM/T/cgg6QOAAAXFxfa29uLKG1ubk63b9/W8fFxGHzuBRjj+c5QuMNj3LOzs+GQSRn5Xm1SKIwzWSdwenoaDr7RaIzdu1AoaHd3N1o49nq9YCvQUWTB5Qr9x0klnbUXhXktC3KOHrtsUjhHYRQ2lX4IXunLeniqbn5+Ps6L59045xiqnCNjKX67uLiIQifej+Cq3+9rY2Mj8tg0GHIWgYu0G+vNoSZzc3NxdjOtfAk6MpmMCoWClpeXx1JP+AP08/z8PLbmLSwsqN1uRxT/5ptvqtVqBZ2NvMMkkh5hrtA3gDbAnAAR++NV66wHgQ3y7uNFlr2Gw1Mg+BwABhdMMEHgr7pu7JjdCDj17AU3IKR0erT30VEhggGdDcdPVaZvzwENk9PhBRFunuuVhCwAC0y+xaut/WQkImEvSCLawRG4oWORnULLZrO6e/eufvjDH+r58+d67733AtmzbQSB4BnD4ajrGQdCMEcUUTh1xrs6DYShoLrVO+hgQBEejBvOAPqVMWGYacQCgHDDTQSEYcAhT01NhQOp1+vBDiArOIKLi4toCQg6Jirw1ANUGgIOKoc9QBG8aQPADhTP5TQkkQ3bLTyX7jluZAfwwbikq5oJ6arKmO8iz7A5gIZsNhvrAk0HewAgZM2YK4zd4eGhUqlUGFT+EAXMzc2NMUTIKtEy7y4p6gva7XbkBd1h8K7831kSUirT09Oq1+va3NyMIiNo5/n5+Xhvzz9jC4ggHUDwGacr0UV0hZSBpKBjPU2ULLLxyAzQ1Wq11O12tba2plqtFlHOYDBQp9OJ6BU7wfgp8mHtPGWHrEpX/azRMYAedsztFWvCM3kXcqL8nKibOZAUESx6uL29rYmJCVUqFaXTo90CtEyl0pgtmnNzc1G8hAOhnmV+fj72eJ+cnOiLL774UiU/zgz7wHrQKnU4HKparWphYUHdbvdLlC1Av1KpjKVykFNnYKhAp8EJEf+DBw+iWBK555RCai2YZ/QffXX2kWf53GJb8WvIDywtTAnAcGFhIRoncS/y0YwZJo68vrMO/88dMxSeKwFK5IaQ4hiEiEETLbniT01NBX2AkcVwkNP21mZOmzu1yTFd/J7G5H459cvCgdQ8D+eNS7xyEIfp+VJahd6+fVvNZlMvXrxQpVIZM/wgM6J78op7e3saDEZbnzCad+7cCWECxV9cXESeHsWamJiIvrfQ+BRFoPydTifa+LkRToIqHDjz4+N2UOFUDXkmIk+MjCNH6Qpd+7OSVcE4T49aATD82x3T0dGRpqenValUIgJjPd3RY8BxUKyfb6PwCAmgIGnMEPu9mTuntCSNOSCXcc9lcT+PnBg/SkwfYz/jlrGyn9LzXOTohsPhGKMEG0VXI9aCZiZEBHzX349xOhjb2trSkydPonqWrVr0wCYKdLbJ7+MgkHlhPTxVMj09HW0QmZeDg4Og0pFRQBp9311+lpeXVS6X9ejRo2CQSD1cXFwEmMQZevSI00gaT6+j8Dy561QSmPp3nMXAEfv6AoixRzyTphbYGSLDQqGgpaWlcCpzc3Pa2dlRPp9XNptVu92OZ5PDl65sOD2u6Qx2enoah5MwBuaWe6DjL1++jI6M6fRouxQpHkApawJgoeAO3efenmJkPmAAkE2ai7BeDswBWMgQ8oTekz5Cp/ke0THy5KkwcuKwboAvmD2icWzo0dGRZmZmYs7p1uc9LzzQ+n+eY+aUKCadKAKaF2GbmprSgwcPxgqAiFZYABAS1EwqlYoowLeVgDxQAF9AJhhlcdTpUa7Tciw2ERZGjLylO2RACM6J7xKRc18aqn//+9/X559/rqmpKf3ar/1aUK44NQQM2rPRaOj8/Dx6I//zf/7PgzrBmEpXJ0xhLCg6GQ5Hexz39vZCkdzog/RQLgCCCxo/BzG6MEqKrV/keTHiXoyF0cCI+PzjlFkL1pz19VoAN9DIGHOXdG7kXmmGkGRyeGcqVkHt0hWVDGOCQQUIJfPr3JudAjhqCsOQQX7OO/IdBxr8HkfoWy6IeqWrlqYYZ9ghz2WxhkQ11E2wnoybbRzD4XDsYBPmkrnyaMOj8Hq9rp/+9KdhXPwkLbbsDIfDsR7SHjF6sY1HLrBmpJV4po+j0+lEdzNylx5J+3Y51p1tjzh3QAFOhzXxtBSgCseCvhP1M8fD4TACBpxEsrOW20VkGflwnfMuasihg0qAF3IAk8UY2MtN9Ntut7W/vx81FRyGQY/8paWloJXn5uaignp+fj4cPuyhF8wivzhw9q5TGQ2AIrAaDAbBPOI31tfXI6XG3A4Gg2ACOCca5iWfz0c3s6WlpUiZoJNE26QDcZhJGwJb5GwDldSehsDOkkpi/tE5no9s4CNgLdBL7MLJyUn0KUe+6J7mgP6XXV/rdCmEy/f9eS4CocH4gggZGPlUp734LM4FdEx0ymK7kmOAEGYm1Y2Ao3/P0SZzph7teMW4d4xhDzXGENRHlLO8vKzZ2Vnt7++r0Whoa2tLv/d7vxfURaPRiKhve3s73nV2dlYPHz7UN77xjaDlmEvyNCiygxEEiry5pPg+RhGayWkd1gcBwsB7Lon5bjabYaRxFORjhsNRO8hCoRCHXOCccTZc7vxxxr4OHmm703EDzec8egfVXufMPQWBvBCVJot1kEmcLM/kvZF1Bxw+Pt4PYwHw4z5ENQ5CeL6nK8iRYhwljRXoYRQAbjzDQRaV+qSNqDJlfqjZoH4CQ+YMjwPYZrOpjz/+OMYhKQqlyLNJVz3CuQA+yK10tROBy6No6kyIngC/HKKAcUX2Ac7ch7nCCJLyAFRKCp1jHh1Eco9bt25FGsHX2J255yydTXHQ6u+JHPh6c2+cmef+WeuJiQm1220tLi6qWCyqVCqp0WiE7APsJUV3MOYO+5FKpbS8vBw7Ckj/kEf2OgeiaQfAziINBoPY+kTLzIuLi6CvmQdv3fvOO+/EVkPqJHhX5MZrYi4vL3Xr1i2l02ltbW2p1WoFS0Shr0er6XQ6nDJROPUhgCy2QPl9CACTTC/zx+VMDE714uJirPCSz/V6o3bAtKP15lLFYjFkxt//l103dsxe3o4g+JFW0pXykadwWoYXcQFnAXEyLuy8sNM+TiElC3RYCASG5yYpJ6Ij3+PKAfQ8m8iISMUpEowNf0BCCwsLcRRdtVrVj370oxAYnBWOZGVlRW+++aZu374dysz8OqDBaDAvp6enWl5eVq1WCyoKGqlWq2ltbS2cEPtoMSoINHPjFCPOzJ20R7lO8QJazs7OAkQxRgTSoyYcG8/FsbMeGEF3DDyfHKd0RStL43Qxhoz14Nmsn6RgZRzYsZ7UPXjOm3Um8vL9kO5QPDWCXLujJ7Lhvsi0zymXg1UcBuN3o44RwEEk1wyn6FtyMMB+7i6GCsfN3DO24+NjffTRR+p2u6EP3tiFIkq25QHI0Wdndpwmd+AEUENuYEDIJQJ+YeRcF0gDFQqFMVkdDkcFOJywRIcsZ8ZwzOgBgDWfz+vg4CDkwfsgeESW3D6VTl8VICHXjAlA4JESxj6bHVWfUwuCg2L+KKwCBC0uLurw8DDWjO1B2A1SW5OTkyoWi9GsBXmZnBw1LnJbyzpwHwId3tsDmGazGTlg+lhIij3AnAl9dnamO3fuRKCF7QFQEm36Vj/sZrfb1YsXLwKwUePgVDXMDwWfnM6GPzo6OooK9mazqUKhEIWwOFjvgw/74SAN2wO4w3c1m01lMpmoV3LwcnFxESxmKjUqKj49PY1KcNb9JteNHTMG0Js3YIAcMfJS0vhJSgg2uSE36kw6QoizRRm5v1MP0pWRl0ZV45T85/N57e/vj0WMTDSIrtvtBs2HUXRH5kUoKBHRj9NSLPTt27djPrywi7xhtVrV0tKS7t27FwLplBXvmKRkoNxxuDMzM9rY2NAPf/hDSQqKipy1K34mk1EulwuFlzSWfgAMMD+MJ5VKBeVJkYzT/17AxJ8k0OLfnkf0HDkOm+/CfiQRpQuzU+HOyuBM3Hn72ByAuPGEIpQ0ZnS98tYpPS9eYY5wQER2GDOKowaDwRiyxlET+flaJ/d7Mwb+5r1ZNxwu32POYZCc+iMi4bm0VAQU+1y122399Kc/jegCAMMYGDd0tFdds5bJaBwb4PUNyAJMEFEujTWkq4576XQ6tuQwl+5ouf/x8bG2trYiaoaOdKYNOSkUChH1DQYDHRwcBPOUpNVZI6f/eb5/1u2Ip5bcEeDoe71e7F0nGoU+Bxw0m82omSHXWiwW43hTwAtOPJPJaHFxMahfHM/FxUU0rHHWgAiQVpLkn3HmOHFsf6FQCLp8eno6xpbP52N7K89GL5B7nDM0M/fg+MqXL1/G53CybIf1+UTGKdJjVwrRMzbn4uJi7Gxw5j2VSgU17akfAj32eHOgBvqOL6AxEX4MpsdbDlOrwDiRUbbh/qrra53HjOJ695tkNHsdykQ4k/lCkCr5BihOj7K8WhLji6LRNB3nivIMBgOtra1FO0OcOttbyCuy7ceNB89h7B4V+eK4Mg6Hw2i99/Lly6iMzuVyevjwoW7fvh2ODmqf57jT8oiFohSnTAFDMzMzWlpaiqiNYp4XL15odXU1DMH6+roGg0EILArKHkD2XyedG8anWCyObbdyRwLokcYbkiQ7pfnWEZyNrylrliyW8QiX7/H5ZM4bwOQRZ5JO9+d4xO2O3Mfj7E3S6bB+ni5IplqSqQH+7Y7EoxEAj1PAyAc5XRyGMyBEyJ7X7ff70WnJnbtHar6NhTENh6Nez48ePYqctedcnZb2P/SbdyDsY/W6EG9U4vtcoeIBzIAm7AbvhZF1eQKkMS/D4WinAPStb7nDfp2cnKher4eMTk9Pjzl+ly1/BnOAfPNe6AYNOnCIfAYggTxQnEeffaJer+il9zXbhiYmJuI0PGyRF89SFLm9va3l5WUtLCwok8moXq/HwSeemmFeeU9SH54qQ1empqb01ltvjQVG7NWmIHNubi7GgU1D9rwAim1JRMk///nPI8jyYCuZsup2u1pcXByrqUAX5+bmQgZpcOKOHDsuKWy/pAC3gEHmf25uLt6JAy0IGnkHttP6uzIv6B3V8bRgTbLMX3V9rYiZYiZvu+Z7fKHQnEt3ZwpSoeUi1AZG17c2QRdgsNyxUBQxGFxVMWLIQT1nZ2cqlUrK5/NR2k8BBVszQIapVGpsXzMLj8CyWJ5jcvCAQd3Y2NCdO3dUq9UCDboiXFdsk6ThvsrpARwQPC9MI294eXkZikxERZWgO9TLy8t4X4+yHQCxbYNcCfkdd2SeE0b5fPvDdZG4Rw0uW8x7kh7y6Iqxg8Y9IuSz/O00uSsOa9fv90M5eRbr49GeGy2MmDMEDjaRFc9TSVcgzoGApCjs8blCLtEHjLlHwzgT5gDlJ4rv96+a9sPqQMVBmyLfw+Ewcob1el2ffPJJUP/JQiYMNq0YC4VC6DZdrxwEsWbIiXTFpjGvvKvXjdBogiidn/Huvt4OAtBDgClbpbi/A25smdOlVA571OW6x5w4KMCpt9vt2O6YZIIw+FDUAGXAd7fbjRPFYKgcbMJUwfDlcjndvn1bL168iHfN5XJaXl7W5OSkVldXdXFxoVevXmlqakr1el3D4TAaksDSYX9dNn1bK+9COsfPIscWDgaDCJCGw+HY4R28A3MFayApqOZ/+qd/isNiFhYWIsg6Pz9XoVCIrV6AfsAUDZkYN0DH/Q4sKbpONy/SmQSF2OqLi1E/8W63q729Pc3NzYXMDQaDaJACk+R7+5lX0rzcv1QqBXuFzt7kurFjJqpFmTDiOGI3aBgZHAkTQcTI91wAkzQ1Qo+hpzAGegqjRI63Wq3qs88+U6lUUr/fV7VajWesrq6GgHz22WfRqhIjk0qlQgAckTuNmizQ8AIf6DJAA9Wq7gycRkyiLBTWqXLmwyMQV27oJwwtyr62tqZSqaTnz59HhxxSA2yPcDDFPHoaAcUjqkKAidA8P+jRCsrpY3b5kb4cmfq/eXfPs2HgcGDcwxEwRsSBjVPE/gyf+2TqgFQHURcFb8gv/+e9PRpKPjOpC4AOnJ1TX3wGvWD+MCpetOZgwSNQdAsdcqfOeJKsEpECrNGLFy/CCXs1PtGCdHUuO+MFwAEoGJuvDfLibAXRG+OmfoWTeWAOoOHdoZJXdlaB+2KjiL48r/n/ae/NmhvLruz+hYkzSGIgCA7JTOZUVVKVBssOdTvc7Qi3ox1+84fxgz+A3/yt7AjJ7m63utWuKtWQyeRMzCDBmRj+D/BvY+EqVcr8RztCD7wRFVkkgXvPPWefPay99j58Fn2CI9Hv94PE5I4g8ujzi4Pta4Ai9vvjEDFXjI/vkvtNkqncCRoMBtHhEIVPG05yyRjYXq8XBzVIY8PXarWUSqXCCPKuBD2DwfjY0nw+H4YEB86jatYL3c+coCMIQJhj54ZIUrfbDb1NyqJcLuvt27fhkPMenjPnwBYMLmPMZDJT/AZJqlQqajabsZabm5vRUZK9R/RNK2dpfAYE8pFKpXRycqLl5WU9efJEqVQqWOj+bGQrCV9nMuPSNAhvcDg4ZwLn9UOujyJ/udLhRVxRI8goTu+SxCLj1Tq0AyOQ3BReIJuRJiQIL8xUIglJqtVqmpub097ens7Pz0MJEL1ns1mVy2VdXl5GbgYh9mJxjJQ0fZwhf2NByBG78DJ2Iix3TPAEeS6K1j1xz/VxTy/YRzj5LEJxfn6uZrMZwrS4uKjV1VVdXFyEl4enXSgUovEFDE08WcbkOVbmDgcJZ8Xzbp5Lc4PFlfzZDTbzzOeSTouPw2Fj7vE+o83Y/PMOobNGyTEOBoMgtWxtbQU05yQi5IFxugz6szzK8jVlL3hUgQF0pe4ODzKc/B4y4vKJUeQwFRyKZATBHN7f38cxey9fvgyyzuHhYbQq5LO873A4VKvV0u9+97up1pnUNyMHniYh6kV5o4iZeyA+9j37BqNA1MPz2VueKmG+NjY2Ih9LZzZ3Kt0RR2Z6vV7wXzz9Jv3+IShutFhr3oU1Jl/s6Avrw9wkYeThcFKOhlxTTsThJRwIks1m9eTJk5A3ys6ksfN0eHgYXcRotVsul6PP9WAwLjskR5xcZ0emGKfrPp5DwAVaw3xQxgQjHKep3+9rZWVF33zzTZTAIVfX19fROIQAC2eJPDJjoyyO4yyZR4I0ZBJ5Gw7H5Fk6UDIWDiui4xhnDrBHMKrsHfYaY8B2eQDqqa7r62utr6+r1+tNtWz9Y9cHG2YmnQFjmFgUfscG8Lycw71MEs3++b3DChhAlEk6ndbp6WlsYDxlool8Pq/r62vt7Ozob//2b3VycqLhcBjF73hHHPMG1AekyYZBoeP9APnS6cjnQJo+OQmB4X4QfzxHhXHwHCOKie87VEnJA3OLJwm5ZTQaRTvH7e3tyDelUik9e/ZMtVpNX3/9dUD3QC5JyMkNmUN0CB7Gw6N9hyd5f6/bRuG58vPrfYba1yLpuLgj4PB40lC6I4WCkCaGzDkCjk74uqbTaTUajShzS6fTURLkzoMbA48skw6Lb2bWbTic9PJ1o+zkQ59n3s3v6bC4O4HSpDJBmkR7/X5f5XI5aoOJoP/tv/23UwdwrKys6OLiIkrmqNdkvhgHDgPEm5mZGa2uroZhwtDhaB0dHUVebnFxMaIdnNVWqxWQJNGN73N3yJyzwlpzUMPq6qqePXumX/3qV7+X42WugJGRF8bsKAMIgDvs3v4RPYazDIqVdBw9bTMajaLZCQgAMgRyAvpGlDczM6N8Pq/l5WWlUqlAvi4uLkL/Af23Wq0YI6175+bmVK1W44AhapghqCYPXvGgSZoQu9D5HIeLvnLHFI4QOhcjiIEtFos6PDyMfPHDw4M6nY4WFhZ0cXERMuGljA8PDwEHo78J1hqNhqrV6lQag6YjnNbHeo1GI62vr4e+YA1xHnAqQV3cHvCOrBF6GjIxRps9i4EGzXA5+ZDro6DspNJl8ESCbJQk6YvNhvFhwh2y9QgHBcpmQEnQ+jGVSuni4mKqB3OpVNJvfvMbtVqt8D4PDg7CO+Wot+fPn2thYSHgOzecrnjJB2Sz2SDIEG2Rg0OIYzKzk6Yn/F1S5NR5pneWwfCy8Chq7sWcea7JGyzADPX8HvDd2tqabm9vY05oBDAcDgMydFKMrzPesiMjjgwgA0kD4fCzG/sPvTz6c4PE3yRNbRKXGwyAf9cdSAwesuekJzaMk554Hj/7GB3V4HdJZ8WRAjfYLnMOq/+h90w6OIwZ+eD93zeH7Ceen06ntb6+rlqtpsFgEO1VGSPwOP3j0+kxCXBtbS1YwkSIjiDRBpLa09XV1TAQGHtaSEqTMiK+12w2p3oVYyzYJ46cSAp9A7R6e3sb5UKQkTBYSblORrvp9LiRiqQoj8TQus7DmWHfe3tb1tTXiJ/dAWAt2W8QUXHcnOzkRuj6+lqlUinSZL1eT/V6Xdvb20EMw+mG4c2hKKwVqF2r1QoDvbCwEPoCFLLX68XPflay7xUMm1eY+N5yVBTmdbPZjBaga2trury8jO5xg8EgIGDmheAp6eymUqk4JAlnhTmgrwJr7wQwHMl0elLaiAwDcaP7nTXu78O63N/fB4yPc8rnkKn5+XkVi0X1er1APP/ZI2bf/ES6HOTAgjkEzCZHCFk4BJ1kPAsNgcXLD3hB6vE2Nzd1fn4eJ6vgodEt5vDwUIVCISYUctry8rIKhUJErHg8RK7+XCdDOIHGozpJU0qdeXEGt5eSsQlRCIybuXGoku+DTDhMy/1XV1eD2NHpdDQ/P69araZSqRS5KDxMDDJNUkgFuFPj5RGudPH+vc47OR/8y7r6Z9yAJq9kBJ38vcPZye8nn+lz7AoEwhqGlYjU+QyuNH39HCbGUcLhcwXM5xmvjzGJrPBMj+iTSBJ7xo2yKyWUos/J+9ALlIRH9rzX06dPo4FHoVAII8TpWKPRSNVqVfv7+7q6ugqDmkqlVKlUgi3t/bDv7u707NmzqRQRip2yQc/XA6murKzo6OhIw+FQhULhvSVobgCJ8kFxnMiXyUw6pX3//ffxzsCTzLujLRh3cs70IvDPufONLEgTiJuo0I22O+6O/Dm3gXliPZ245E6VNG7GcXR0pMXFxcixk4aoVqtheE5OTsLZxnBg+EhZIGej0UiVSiWaGcE3wLCnUikVCoUoZXX5JRWAHnQODjI9Go20ubmpbHbc3xvCVi6X0+HhoYrF4lRHQ6JqeEO+l0EvCHIgZvV6vdj/sNb5Pak8nJ52ux1nVdOxkHuCPoLW4BAQFJHXBwViHprNZtQvu44gd48OZY7+2XPMbiw8RyVNOuNgzIbDYZxJ6V6qL8JgMIizK2ERswh4YzyXjQhRwheMzSNJP/rRj+K7eJ2uIB1ix6DzN3ciEDCUbTK/4PWbSeWcjB59Y3q9JxArG9vzx06y+ENRKZEF55t6tEjfVtCKVCoVHiT3wiHheR5R+LP5G3PjRoh18egDg+HKhXXw632/c6WWnMv3Qd9J+STaWV5eVjY77jz09ddfR+92lxuHtN2wQ0DBYWJDsW4ejbuSTRLjHBrn88nomvnj/XjfpBFxGUsqeuT7fWiC8xa4D9FXtVqNnB6OGTKIAS0Wi9GHHmWPvABnUmXheXv2NhekmHQ6Hc0+IIzBe5AUChmDwn5gHtzxh6TJ/WdmZlQoFDQzMxMnSx0cHCiXy0VU6M1vXMbRVwQasH0xvNyf9rl0GcRguF5Djj24SDr2yJWvkZOJMGqeOiI1AKrAoSmS1G63g9BE2gFdNxgMdH5+HlwSDChIIL/j+fTVpnyId3DiLvPu7+I6vVgsanl5WXNzczo7O1On0wkdRAXJ1tZWOFpE3KQ9/CAd9JjrE+QduBvnm7nCAHNvUhTFYjGiehw6ZNqbu4CoOnLQbrcjvQfqyF7zNp/YCZBkgtF6va58Pj9FWv2h66PqmKVJr2EnRJBnxsCgnFCE/D9QCUXtwNReQoWiScImUPg9ugTucrjDc9wOM+EksLFRxh7NesRDxy42KNEN33MyEhsxCW3y96RyTEbEKIbV1dUwGIwLj02aKGzgpfv7e5VKpcjNEYlAGDk4OAglXi6X1e12Y74YE7lm1s+JeswLCsJz43j8jgC4UWYOXCm5kfFIlcujBObuD0XW3IN5SafHZxXncrlgrhYKBf3FX/yFarVaKFCHkpAnN/rIBFEsa+CK0Z1Bhy3dy8dRcXn03DGlWjhoHsXxbxIC5W/INc6CR1qeJycHhuxi3CjjIEUDNOjyn06ntbW1pVqtpouLi4iseQ9kJp1OB/fh6upKpVIpomgiWtI4o9EoDlpYWlqKZgukeFzPzMzM6OTkJJAe9hz5YU8TsA6rq6vqdrvRBY/DBSAcZbPjBiSlUim+I2nKgWHePEjwy4lpoC8+RtaV9XEHF0POPeFw4HAkiYM4OegWDPTd3Z3Oz88jikSucUKkMaqG7L19+zbk4PLyUpubm1paWooe2zgBpVJp6mhMcrSu30EgfB8Bi5PuaDQaGgwGqtfrymQy0U8aGLjX600dVAFJDV2Lg0Gkyzz43IEWootw+iTFcZjMe7PZjJwz9wFFubi4CIeCplesP3v+4eFBq6urYWjZd84JQhZBnYDDec9qtRrB4IdcH9Ur2717jLArCfegXMCTnj4eM4tCdIzHIk2UexIGlsYbyk/I4WWTOQ6MjHu/3DuZL2fsLKg7AO4weLTjXqQ0DWkmc1qMhXGyGdi4eItECeSR0+l0OASpVCqMLt/Bg6b+kVNnyC3zrhcXFyqXy1OlAbw7ip25RsARNq8V5/ee3+RyONtzbEmj5JEhn8WDdUPpcG4Sqk0asHQ6HekNZ5Kvra3pP/2n/6T/8T/+R8gXDhdy6BEja7y6uqrFxcVg9/IdRwZ4p6RTgyF344yzigJyg+nt+ly+GJdDoMnIHIOdhPz4HXPhyqNer6vb7ardbodTzL7FOLGeP/7xj/Xdd98FOoQhJqf38PCgzc1NPTw8RKQmTWq00+kxkY7j72ZnZ7W1tRX3Yw8QzWaz2amIzGXKHWD0hV/M/c3NTZxKRYqKPsnZbDbKk5D9TqcTvZxhs/tzmH+Yvul0OvKj1EBTWohcJ/cLexcolPvjtED2xFgzhzhDoGKUJmGMqfcl10nkenR0FMbLiWrValX5fD6iWQ9y3rx5o7m58dGGa2trGg4n56IToeLcoH+Y408++UQPDw96+/atbm9vp/qpg6iCxoE2ptPj88+B2D24obTKI1wv4QNZ8JwuAQy5Z+Y7n89HFA4szXnY2BFSddRqA+vDmaBsChY3lS10VuQ7wN84NqwH8vHPHjF7hOBwo8N7CAfGyHMkDid7YT3HB7rR4lkYR8/1OMToB7MjPA4/InQ+3nQ6HVGiQ8u8A58Fvubv/OtjS8JSzI973ozN85ugBTwfASaqkSY1jdDx7+7u4n3z+bzK5XJ082m1WtEzG+WH5+Zs90wmo42NDbXb7SnCyOzsbJQWYLzcucBx8lrs5PthgFC0rB0GhMiJdYABiQFJwsRu8JOXe8OsF2vFvZhDoL3/+B//o7788kt9//33kX9DAXhNbTab1dnZWWyixcXFOE/ay9zcOKAU3EC6Uk8iFMjX7u6uFhYWtLGxEVFrJpPR/v6+Tk5OgqAH1EhZGw4u64RycqfE+RLu1Pb7/ahv5UIZMm/8DSdwcXFRR0dHev78ubLZyVGVHE+ZTqe1u7urVCoV9cA43kTovV5PW1tbajQa0b8Yx8ihf94VYqNXbHipIBEPip6Lue90OlMOEmegg7yxv4+Pj9VsNuOcXWTddREyJikM++rqajwPw0sJD/LgqAtOhK+PV4wgf+6QwYNxtIP+7dLE+aFlJdUX2eyYsY1+JveKE3h/f6+zs7NwAHDY5+bm9LOf/SwcXJ9P9BYXeqlarer29lZv3rwJfQvMTvqO2nN0IM7jcDiM8+iJXHFScrlcrBfGm/dF50oKJAamOrLDkZLwE+hSRpc6jDpG1uWVvYbzcH19rWKxGCequUNIrpq9jly6k0SO3g8J+WPXBxtmIhqE1PMpHrG6Rw8E4rlmh4iJ/ICsPZflBhPPB+XIBiEKcNjHYUaMAV43C+G5VZ7hOWuPiFAYCIM/y40XE86i8VyHZPGWYKtKEy+f5yFY3uOVv5G3u76+1tbWlm5vb3V4eKjZ2Vltb2+rVqvp/PxcGxsbyufzKhQKAW0Dn97e3saZvMwrm//k5CSMD+/FGP09PIL1DcznWFPmwRERabrphDtuzKfDuHw+eSWNHwxRDBReMIeaz8zM6PXr13rx4kV0ZiuVSmFQnaX5P//n/9Te3t5U/p3okGd79IwcuZLmnjMzMyoWi/r000+Vy42b4X/33XfK5/P6sz/7szBcuVwuypTa7fbUM7ysD4PraBTeOwzcQqGgRqOhs7OzyOtxuVODg4cicXnm/jSceHh40MnJiTY3N9XtdrW1taXd3V2trq4Go5l/yVeCCFDS8urVKy0vL+v777/Xn//5nyubzU7tBWSdaB6ZcWKR/4xThFxiEClRcWQCB4wDGHCu6IbFGiBLKGhp4pBiNJE7jAPzjh7EMeVyPcL8VioV9fv9cBQ8ugbVcZ2Bk+2HUJBj7ff7EUF7WgP9y5wSaeOcbm1taXFxUdvb2xHZYngcGUKmcSJAOOArYFRBP0lX4igQ9aO3vbYYlALOAgdUIOukjkAP2Ac4URjTZP6c9ODMzEzA9eg99AJ7J51Oh7ytrKyEIffDMm5vb+NEPSfdUi4FqtlsNrWxsRHIjZe5Js/5/qHrgw0zERf52iSM5LAU+SqHGtg8GGmHIBFuBItFdHIHXiTECybNezNzcU/qAfG0HHrH+8Z7ZvPyLgg7bMEkTIWS4POOGniOm3+9sQQC7yVbGDX+c1KENIlegI7u7u60vr4eXXWkcQkC9bf5fD4ic+YSzxNYhz6w7XY7YBgnuTmBzpWj55vceLLOHhl7ZO1oAnPptb9JRfBDV9JRwFHjX06cefHiRUR4RNWrq6vxDDYO6zkcDvWLX/xCxWJRf/d3fxfNHDxlw7u4rLBHkFfe8bPPPtPr169j3YvFojY3NwMO5X5EPBcXF3r9+rVmZ2e1t7cnaVx/T6vAbrcbMj4cDlUqlfTLX/5yig8B3OyNF9gDi4uLKpVKAecS1WB0/CQ0nI1UalKLSnew9fV13d7eBpv5/PxchUJBf/u3f6vd3V0Nh0M1Gg3VajXt7OxobW0tctU7OzthQBkz+7jb7Ybx7ff74ZyyBsyv6x13inFwbm5upngcCwsL2t3dVaPR0N7ennK5nJ48eaLl5eVopIIhxKh4+sUDB/SR55eZf2TSnXzfK3w/n8+r1+tNNQBKpjp4p3R6TCpaXl6OtMHc3Jw6nU7ITr8/OV1pYWEhyto4S5i88dzcnP78z/882N0wjKUJrwZZ471AG7a2tlQsFvU3f/M3arfbkXdlbEDMpCloXbm6uhrwtqN0vHe32w0SYiqVin4TKysrWlpaivpxom4QA6JxZJRmNxhJjCaVAff395HPptIEFGFtbU3dbneqCQj/Ml7y1ZlMJmBqImbaks7MzET0jmN/eXn5e+nTP3Z99HnMGBqPkLwOkggCohfKzD1CV/be4QsPjwuimTSOEqm74/Oeu8C4AVGTc2SC2CSMn4liLGwSjDAbHoXlm5Z3cs/d58ihQxYDTw/PD++QHBUGDrgSA+wXkZ/D3CgsHItCoRDRXrPZDKG7ubkJYer1ejo9PQ2G6dXVVfSpBULkwugB/wIBMm/A157P92gaJw4nzQ2+Oylcyfzy+65kuiCTGZ+uQ+6Ldf4P/+E/TOUoWU8MjjThJbj8plIpPX36VKVSSf/7f//vINFhbJNQu6d0cPaq1aq++OILFQqF2KjpdDoMH8/m+yi1crkc77eysqLRaFLG0ev19I//+I9REgdS4sepAg3jABwfHyuTyahcLgcsjdLnszhkrKM7ocg90CWKms9D5Eyn03GwyunpqVZWVnR2dqZnz55pbW1tKtpFoWGEKf/DSWB+vAc4Y2e/YYxQfo7UOZkM2FsaE6IKhYJ+9atf6cc//nHs8aurq4Bz0Vn+/ZmZmWBtuyPq3BMcIfY0e4b3dv0kjVEzcr2gg/RHIK+LY+19ENgzRG5EwUT/ED1BAG5ubiJvzTGJzBMNkygfwsjVarUoGcvlcgFZ393d6fvvv5ekKDsiwr67u4tGJETQHLdIBzDmlmZNIBHk3OmOxfnFOO3offLpvV4vDKW36MQAgjC1Wq0oF0NPULY3Pz8f74+ePD8/19LS0lTARG/tbrerQqGgfD4/pfvZt/QrlxSoCjqBhi5Pnz79569jdkYmwul5LElTxgdDiMfH99j0QA/els/vwQsj3FDYgZhubm7ieEXIMxh2TkRCiIlkmEiHJHkHFo+6TfdsMCREn0TWnt/ifZ0ohaFCyBwi9iYrHn0hNJC0JMVGpdsOm9zbKSIoeMmVSiXyIldXV8GqJd/U6/Widena2poWFxdVq9ViLRy6dwgGZMMRhiSMjfJhDh1lcIfHI2Te/49Fyj4Oz/Xy//w7GAz061//Wm/fvlWlUtHs7GzUOCa7YnW7XRWLxZANz2P+1V/9lc7OzrS3t6e3b98G6gKMhqEZDsc8gY2NDb169SrIMziq5LSBnJFRnxeXOdIOyG6/31epVIq2srOzs3r69Kk2NjamYFc+OxwOValU4mAFuBzUYuJAoKBZA3cikXsOachms8FF4ExwIqaZmRnVajX9u3/37/Tll18Gf8Tzw3Agstls9Ip+8+ZNOIjO4QBeJmLFwWZspA8wepTMEN28fPky0KFsNhtpnaurK/3lX/5lGDN6TiNDrKffnzwhjOgkioZxBVr2IMSdPWnsDHFmsSNH0uRcY9aPCHh2djY6peE44aRLijVGT2AokXtyt+xtHBmiZfKwGDvSD9vb29E74vDwMPK+pVJJx8fHev78ubrdrhYXF8NZpCSIoIozoz0dgx3gWETqpkm7eUB0dHQU7zccDnV+fh7jZM5cZ3jKEvjfeUfUuVNShVNKUx10MKmxer2udDqtnZ2dgMBxNOmfgR2Di4Jeo6fG9vZ2yNcPBRx+fbBhdgPsECIRkAsYwoBBclgT5efwIwaNv3t+E8PsRC/YcCTaIUf4phoMBlP5CcZABIdXA+wCQ29paWmKdMMGwBmAsML7OtzlRoL3QLEmoyR/L29NiuJjszO3MCeZe897SZNDQSDWFAoFjUajiI795Bs6JLEZPT/j48TA4vkxDr6HIsMR8znxzYIxdkPt6YyPuTwFAqQI65bOR8xtOj1u5Xp2dqbRaBQR0/b2tsrlckQjyApjI2rjgJDl5WX9/Oc/1xdffKH7+3vV63Wdn5/HqTts4lKpFBAZhhhZYbwgEg5zSpMze5l7FAbyAkL06tWrgPJo3oDjhNHDuPqaOgSeRK8ceqNrlhspGKk8Z2ZmRsfHx5FTpFPX1dWVms2mPv/882ihy8EyOADX19dTRg4EbG1tTa1WK+Z/ZmZmqtbf/00GCeiSu7s79Xq9yC2CONze3ur4+Fg/+clPNDc3F13PiNLQQ8w1usuJe0SayCvsaOYRw3hychLEMPSj72Np3CykVCqF8+EoBTqTdpluUEk/0O8f1AKeCqxxRzGBkXkn5AXdAynKU2eDwUAbGxsqlUo6ODiInDywLc2b7u/vgxdBZI1hJLebrEbBWNNrgYoH5gteEBEsJwR6IOAGngDOOUw0mwG5xSkENSA1wB4D6fDUR6fTUbPZVKlU0mg0CtgfPbG6uqparTZFIoVYV6lUQsZyuZxqtZpWVlZ+Ty/+0PXBhhlPg2iChwDN4uGg1JID4HeDweD3DrLw6JiomPuh0J1MlSQZYQjd00rmhIlc2GxsKoy00/hdIUJQI5LCSCMgfnFvzzMhmBgvFJJ/lo3tMJM7ObwbToXnsiBaYDyBnVgHmrKcnZ1pMBi3YISVSxce5n84HAZbm3H4RuByUgmKCkdBmiZrueH1yDmZa/lQgWWtuS8bjHynEwZ9fNlsVo1GQxcXFzo4ONCPfvQjffbZZ+p2u0FyQkGi9DH0bDDmoVgsqlwuh9Pl6Q8IMu4kovRYL0dXiPhIE5CnwriizIiys9msXrx4EfMF/OcoB147aRc+yzthiJhHL83zdXCCGUqZVp25XE5v3ryZig5oP1iv16ON593dndrtdijkq6srPXv2TDc3N9Gv+e7uLghqPNNTEIyJseMQeCc2/v/m5kZXV1eB6tzf3we8iqJHhkknkZtEwSZTDMytzyHRFXoFJAT0AcPAmvN9IF6iWwwczoSnCchbQgSDoIhDivxfXl7GPdFVcFIYAzA37+lGsNfrRee3paUl/fSnP9XDw4O+++47SQqHDKfw/Pw8DDUn2EFYpI4dh7fT6UTkCvPdu4jhNKAfQAZoY5mUf9YAPQhrH2ePUiVy5M5TYp0zmYwODw81NzcXqaNyuax2ux06lOgf2wOJlL/VarUpkjIpHuQW/gTOOxE7zXT+2PXBhrnRaEROA0FEeNmokqKsx42OK38mk0knr+S5VocePFcjTRLybgAdkuG+eJBEVChpjzLxclG4eFpsIIfgWSAUpjQh+yQjZTwvz4NjYIEdnZQFWoCDQxE+Rt2Nm+fMZ2dno6kChhvIEmWSSqVUKpW0srKir7/+Ok5R2d3dVbvdnuqvCyGH+7PO/M49W4ehk+iAG20fezLy8f//0MjZHTHWkByo5715/9vb22Amk0sjD/rs2bPIMSJrtIlFBog8WSfeibwf8oHyYlwYZJchnFruMRxOyG943ihPovhsdtyhiSiHseLIuUzxfY8s2QNOwkRmUSK+l8n3MUacOfLYLhfX19c6Pj4OQ8iZwtlsNrgL/+pf/SsdHx9HFLyxsaGFhQX93d/9nZaXl1UsFtVoNAJ5wTg6YuV7DT1BHhCEhKABp5E5ZS2IFO/v73V6ehpdoPgO+53PO8/EESJkgL3barVCZsg1Iy+ua5J6AuSE9ybC5/kOTTPnGEOIdKwnUS88AMbA8y4uLjQ7Oxss4lQqFeQ4YOhcblwa+Mknn+jbb78NmSYNkMlk1Gg0gkFN9JpOpyOHjOG6ubkJhBPZJCU2HA6jtpoyJY9EMabA7/1+X41GIw5Hubq6inQjgVwqNe52CFqxurqqSqWiwWAQpY+UcNXr9UC3FhYWQs96TTPvLWmqXp80I2vGu1D7DfpJGoJ0D+VnOBAfcn2wYcb7QiF7rgeFQN4CY5T0OPF8kpvGlQSbj886pAbkAiTtxhnYwr1bj2jx0oCiUWwoHKIah9rY4IyL7/Dz/f19QGHLy8tTMC4Gls3vUC/KGCWWjIyANPF+fS49HzscDqMZvLez6/f7U/kaFIA0OWmI9+GZm5ubsXn39/djE3m+kedyoRSdeOSXIxrJi/dIGuTkZ90QO7qAsQGewoHBcEH4WV9f1+bmpgaDgb7++muNRqMg3QDHOozoPXRTqVREcl5jjsyzjv7uSecNeND3CdEKdcvAdJQLIc9uZHAMcY4ZR9KgSNNd67xuGiPkeXG+gzxgQKUxQYlSrOfPn0envtFoFDlnGO7tdlt3d3cqFouan58PNm232w2IHwIVKAWsXqB0ejrjjEsKJIT3Yi/g5JMDnpubC1LQ/Py8Li4uwhA5GjU7O6vvvvtOT548CULcaDQKyNgjY+7vUTukISBRDACGtNlsxvyj89xBhZ/gznsqNT7WEIPMOpGKQ17gwHBEJagjeswdRohOi4uL0Z7y9PRUlUpFpVIpTpW6urrS3NycVlZWtLKyou+++y4MbLlcjiAHecGoElDRS5vc+XA4JqZhsDxqB/FweB3jDEqJbeCwDhjz1CuTWgLpIY3Fe3733XdKp8dVGZVKReVyWc1mU41GI/aXyy2OAPuFroo0BEGW2BPD4TAMOg4crPJMZsy2B9oHpaAHOzL6IddHsbK9JWUSikT48HZRWHgR2ezk8PNkRIfAIqQIIgYNo43R5F54MQ6vY9SAEVkMNlwyouBfN1YYTzZV8rvAaEBEKBEnZqFAkh2jJP2eMkRp9Pv9qTyepDC4QHUebUNy8NxOv99Xu92O06Z4HqgETgAK+MWLF5ImuVVJIVDkPV0x+bq7cWXzEQk6KvGHjPOHXEk43clQ+XxeKysryuXGzS4gaqDYc7lxe06MbaFQiHe8vr5Wo9EIB4l1c4QGefe1xyj6nDiCgkEFnuZ5zAOGm/XiPqwXUCNy60Q1nuOpAGAzJ3R5OonvQIpyJxG5B+5FeaJwmAsQn1KppK2tLb158yaMBGdWQyRsNptaX19XtVqNVAkR1PLycjRsSaVSYQyRccblzW5g8LJnMMSemsJwSJqaPxwr3gMyGlF3u92OOlvPGfMve4R9hwzy/1ykqdB/7F3f58gySACGl2jZL6JkDAfyI42bgTBeJ8ZhQCDCIl84jJAHaQqDAVldXdXu7q4ODw/129/+dqprFVGeO1p0E0Q/YDir1ao6nU7IkiOmQPSwqgmCsA1+2AxEQ9jl2Ww2zlKA40MbZww060y7UWSs1WqpXC5H+RxtSk9OTnRycqKFhQVdXl4GUZPe8aT5IKXhRFcqlSixIggkhUB5Jshcr9dTqVRSt9vVycmJJAWp90Ouj2owgtFEILxzlStyPoeicTaclw5wL37GCLuBdmXE5+mA4yQNJ6ehBIH6vGMTm0KaeD8YYZQhl0dnXkqCAscAe1RNvgbyEErBc4b8h/J3ZwFv17tssZiMMZOZ9OQl2n379m1EHIyjUqkEbEXfYHKgEHYYN44QuSkgIxQ00dT7jK+TOxziTsLOf+j6Q3/nHk6wI7p5eHgIRjL5LNoqOpKBInIEA88YL9e5EV76x7wQnSBTrInnEL1OXdJUNA2MjZw4lI1seDMZHDoYzayTR+feScjvSykUsocSx1Fhrt1AODEPEhVKkr3oxBnaOl5eXsbc53K5aJqxt7en0WgU8COdnUajcenX/v6+MplxhzNyc6yN93CmQYM3znCZg+yEfiEXCxLhDj57LJvN6ic/+YlyuZz29vbCCXDUzZEZIE7XA66vgGIxyF4+5nrwffIO4Y014H34fyBjdJ4zuTHYriMgGDGPEJ5I58AvYT8/f/5cmUxGe3t76na7mpubU6VSUSYzLj9EziDvNRoNjUbjRh2SVCwWQyeglzCcOA43NzdqNptRqtVsNmMOkEf+5dz7L774Qu12O0qVisWiVlZWwjCC5sFgx+m7u7uL+aQLG7IN05we7bnc+LAbSKM4ehcXFzGXnuqAcS0pzvzmBCt4CysrK1F2Ozs7G2gbaAc650OujzrEgs3sDGpenIg1SQpbXFwMI4wAIXiek3xf3tIhQ48i8ARR1nj0GH2MBx4rgsh7eG6NMTsE7TlSxudeMcqLzYnxIi/C5xwOJ6JhzOQbPJ/HvbzEBKOMwQAaRUGjlCqVSrQ79ChmY2MjGt4jILe3t1paWgqFDSTMM9nwRAo+b4yfd3eCjs8X7+2KyK8kJP6+37tSI02AEa1UKgGDecOBYrGoy8vLqZIHIspisRiHoOdyufDUUdjIC1EqDpAbUN7ZeRaMjTF7dO3OqqcGnGjI53zOMCaO+PBOLsMOieOQvI8ljyxhdJEnnovSh4yFIcRhYB5hESMzGEMiKyBCadwsJJUak+VgCx8fH8ceKxQKETHirCabjpCOYV8zR6wHKSoOgEFu2aM4KjjI5GEbjUYYXD++1u/LfvVok6YZjFMaoxGMA/nCQWOtCRJ8bwHBe2oC2eRf1zEYIubH2dCeasEhxSmji5U0DlpevXqlXq+nt2/f6vr6Og74kMY5VUhUdMECOgee5XIyFOvPMwk6cLoeHh4iDQBSMTMzE9Ek8jEajUJuVlZWIkKn1hj9dHFxoXfv3gXCUywWI40ijQMTDCZrdXFxET2w2Rs0XyEd+PbtW6VSKbVaLWUy41JSIG32NmtSLpd1dXWlu7s7XV9fBwsdlMf3AQdoeBe+H7r+f5VLeXSLskKw+QzKEkHh80BpeJUefRH5wkiUJqf3YNgwoJ5/899LkygHhYtD4VCS53X5mUX0XCJesUe5Hk0xH16//D641x0Gj1QYJ8Ya4wB7GyXjkCTwNQoO4k2329XKykqUWjSbzegCBjR0cXGh9fV1pVIpvXnzRs+ePYtcVaFQUK/X09nZmW5uboKV6HPlcDx/Y+14X8+V/aGLz3O/PwR383t3oHK5nP7sz/4sZM4JOowFxwnHENnieXwH2WTePZ/mOXie7eVPOKYezXt+GQPHvTxvmUqlpjgTvIfLCZCrN91wh4n34bmUa/h8ObSNY4Uz4waOaM/hU/giRLqQWDBgKysrofSdZ+D7czQaqdFoRBlWp9NRqVQKgwLK4Y4IjgTwqadMkBmPhKhHpXQHJ531I0/O2FCQq6urIee8E2NGt2FsWQfuhfxidPw7nqJATyAH2Ww24F7Klfgue9lRMYh06Ewg4lwuF2fMY0jpl88cgHjhlM7Ojnvin56eqtFoxByQH8fJJqIEFcVoMj50KP9534Xl5eXIySbZ4RAaQXqAnrvdbgRsFxcXwd4HAcIBxF6MRqNY87m5Of32t7/VT3/600C2QJNqtVrUJ+dyuThhj/1xeHio5eXlOGVtMBhoc3Nzag2RI8q3gNlBLL1rIDYPxjYyUy6XVavVpvbWH7s+2DDjORIFutFKKhw3uK6wXTn47z3acGOJYCc/gyF0eAmlzPd8I3DxXYQMpeZQJQvhhsJhSAwpih0l7M4FhpSfR6NRkJXcmeH7DlM6lOnKnveXpvtK+2Z2Ys38/LxWVlZ0fX09tWEKhULkZl68eKGbmxu9efMm6p4PDg7CgXGF7hGyow8+n8wxf0siEH65QZY09X5crrDccfvlL385VW/uToA7TkCwkqZSAk66Q375PpEsisrlGpmnDWW9Xg/SH5/xOeI/NqQ7oX4gBYYZo+vyRZTLONgbnmsnavGcKHLMZ/0dmZdMJhN5NXKxfFdSkLJmZmaiptMd2KWlpTgWcm5uTnt7e/rkk09CXngf8mw4Duvr69ELnrak7kB7CsrlyOWFOWO89/fjgxKIyHlPnAqfRwhPqVQqulUhG47SoBPS6bTOz8+nDCoRLexqkC7XN0mH0Pc5eoI0Bo6D6ywMJvvb9ZGkiIoxVPf394HCDIeTPuBPnjwJGJne+qAjMzMzarVa0dWq3W6HnsfAO7qJviU3D7LBmrdaLd3c3KhQKASELikcwaWlJZ2enobBpxMXe2V2djaIhexDUnT1el2vX7+Odbq6uop+3+gZyrdosIKDCMkQ56zf72t5eVkbGxtqtVqxt3A+CRyvr6+jGQnnfS8uLqrT6YSuXVlZiTw8zgIoEojN8+fPtb+/P4U4/ND10cc+eg0aHqUrTgTbja/nIz2idqXrkYY0qc9EIJ1IgWAnYXQ3CozXlTT/ubHAkXDD6+/Ee3kk652wUKheauUG3Mfj0C+fdxjeIW2eyb34GThOmhDVeAYwD8JHnun4+Fh3d3fRz5jyMo5DS6fTOjo6miqtIers9/sB9VGE7951ct7xNj2CcAThffm2ZPTsitgjl42NDZXL5egX7OVbGB43sA6xO8zpMgk06+uF8UXhYLxxToHDyElBcGItnQzmz8fYOpEPpy3Z891TBOQteT77kHdmL7CvSJGwnkRs5P+Wl5fjcxwXyroBuzszGmRhb29PlUolCEa1Wk39fl/Pnz+PYxaRLd7XDVOn04nc7r/4F/9CjUYjkAPWmB4HktTpdKZkC+XusKKXqRUKhSlDDVTteopUDtEyqBeG0uWZ8kM4GsPhMOYvl8vFISGkRc7OzuL9kQWcffbKcDjmMcCNoIpifX09kDDex7k4yCjjheg1Pz8fJEa+7wHSq1evdHFxoZOTk8iTkgN1p63b7QarGmNE/v3y8jL4HBBDb25u1O12o9Ya+SfyxGG8vb2dOlkqnU6rVCqp2WxGbhidQgc+9imVCjMzM8FXoCY+lxufqYxuY6wgLv1+X2dnZ1pbW4s8+dLSUtTYM37gdS/rwumA0Q9jHser0+nE53gWRng4HE7JC8gQ8D0lcX/s+mDDjIIZDAZByYf4gCAThbghRslglPw+bHju70oQsgO5OoePUBzcyxUI9/Zne+SSjKiIThhjMipkU3veCs/ejSIGwUkZbvxd8UuTNn7etQbhYB6BttnQKGU3eK7YYSlzHKQ0UdSVSkXLy8s6OTnR8fFxHDXokVUmM2nT52xielB7jtodHcbDvPs6OfrA/Eqacj482nbjyPstLi4ql8vpL//yLzUcDoPV6QbJnR6/lzsFzKmkqN30Zh58hnWntpgNhrzOzs5G7uv6+jrO6OXy6N3fw3OM3Of8/Dy+6/A4MitpyoAzVld8kgLyIwJzuBsDQcRH5AJBh1yhj5Pv0/ABNAby0MHBgWZnZ7WzsxMlZ3t7e1NtKdkzNzc36vV6+uKLLzQ7O6s3b97oq6++0vPnz2PvLSwsRJOXdrsdcGAulwuo0x0S1hhYkcietptevfHy5UsVCgUtLCyo1WqFYXWnmLaMvk78LZVKBTJyfn4ebGp6H1NfC/LgTUYcGXE56vV6Wl9fj77SNC1BR8ID6ff7YUg8vYcMgFZcXFxEFF8ul5XP53V3d6evvvoqoOAnT55EyZukqASAA9Dr9eI4TnTA9fV1lIA9efJE5+fnIYPklDc2NsLBAKpmX83Pz6tWqwWrutVqqVQqqVqtRqc30iEYRHLAOC84CsViMXLffgoVe8NJqrSj7XQ6U1HvaDSK5iLZbDZIs7lcTs+ePQuoWlI43Z5KglXO/IKi4US4w+IlrKQHfii959dH5ZhRTHgsLCBGyiNGBMgVjhtFcgEoKv8sG4qicBQcz0/Ccu7l+s+MyyEgj9y5l5ewOJzNxcJDspEmCtxhTs85ugPCe3vU72VUrsghdHmUjkeGUfa5dGN2e3urra0tpVKpOI+WGkXGCYllf39f2Ww2GLBEIeQoISkABTE+hJCfuRyacyfH58Z/73PLv2683aACSw2Hw4BXkwgLc4dj5PAha+wMbubOYTiHFT2idY4E6+D5dj6DkQCx8HQNY+VvRKRsXrxx5xOw9uwVZwMzRqJC/uYRO5G2N+ngfig8jB/KkX2D0qP+kvkdjUba399XPp9XtVoNxbS8vKydnR3t7++rWq2qUCjo7u4uoO6f//zn4fS9evVKh4eHOjo6UrFYDLIiSq3b7UZej0Yl3myHdyViITrGMO/s7EiSnjx5EmQ0oF7eyxGuZJqJC0VKW8ZUalzWhjHGGPF7OAEeRDDv6K7hcFyCBBwKZMoak45gz9FmlBTixcVFQNwgFJy4VygUlEqNy9BOTk7iKEP6UKNvcGxBFkDW0CUYaHLUL1++jJ+RAXqMEwljkOkChgMtTfKwlCWdn5+r2+1qZmZyFroT1igzpNRxYWFBW1tb4UCBLnEMYy6XC5iaLnIc7rK4uKhCoaButxuOH44M5LCzszMVCgX9/d//vXK58aljpJsWFhZULBbDIGNTCIRwyICxU6lUMMPr9boWFxe1tbWlZrMZ6aIPuT4qYkbBenN1J9SgUD169BxuEqJM5k15BgqO/0fJYbAZj+dtUMCM0Z0EBM7zcigaV6C8gzsJ3MPrAT0fCNSOQeM/L/HwnJtvOGA8ohrmkfcgQuA59/f30aQBRcIm4Vm5XC4K+DOZ8bF0KBAvE+Bc3FqtpuXl5TiBhrHSYvH+/j6aR7jn7xBtMiXAhnwfOuHpBEdSGLsbdtZsOBxqfX1dzWYzDCQEEsqKWCeP0KVJ+gWj3O/3Q2ZZUwwW68v6ZLPZKeYphgW5ZGxAosiV5z7dWePvwJG9Xi/ygOwBd1BQ6B7Ne4rg9vY2CHtuaBg3BliaOASZTCZK+pzIQ2kK8uf10+xHoiScBBxFIsOtrS0dHx/rH/7hH/TLX/5S5+fnevv2rT755JM4ZQvD9ezZMx0dHWl/f1+ff/65RqOR3rx5E5D9aDSK/t5JZExS9ETA6YL1+9lnn+l3v/tdOJxzc3Pa2toKBG5mZibGgYwmUQ3mCV2Vz+fV6XRCwVM+hHHqdruqVqtxuAjRk7f2dTQH5juHHDjULin2d7vdnkJt+H/qwmdmxv2oO52OXr58qZWVFdVqNb19+zYcaPY27OFcLqednZ2p/QUKQVCCPuMYRDp+pdPpaGH55MkT1ev14Hog7xwnKY0DBfRDq9VSPp8Ph6ZcLgekTYvPUqkU6TI+B+ICeZX9hd5tNBrBx7m7u9P29rZ2dnaiucpoNJo6EQoYnz3HGCXp008/VavVUrvdVjo97k3+zTffKJfL6dNPP9Xm5makDdhjnU4n0hvYhsXFRV1fX+vzzz/X4eFhpL5IY3zI9VGnS8WX/i+M7HA0XiwG16FEh3rdeHrkwybgHt7Eg+fTQMOJYQ7vcjnUhQLhZ4/kcBqYaGmabey5Xb7PBmPjOgzJ5VGhQx3cN51OTzXYZ87c2Pp7unEjwmaRESo2M1HB6upqFN1fXl7q9vY2BJv6RnI75LC4gMzIT2LgO53O76EAScPL+3O5wvH5STpzvLdD9cxptVqNKAe0ptPpRITpzpfXWjvMjYGWJv21mWMMi0eGLu+8TzJah1VLSY9HzZ6vZBzuvPCzO3TIURJaliaK3ecOhjVyw3u7IXMSJEqB6Au2Pp8j30fUjdwOBmNWL8QeeBYcXeiKsdvtRg4bxMbHj1ylUil99tlnKpVK0bDE54e0EXsMpQrxClmnmQSHQhQKhYjMfvKTnwRigBNCSYvDiq4jiIpYd8qwQELYc5QhSZo63/v//J//E1FgtVqNIIba2ydPnmhrayu+y3Nc3+A0kkMmJ41yp0ZXGjtc//Jf/ktdXl5GPTJRMPlVD5bS6XSsG92r6HmAPqBPBDl71o/gqN/vB1zbarUiqry8vIyfr6+vI4L3QztAeJgTkAbSFTQBymQycfzk9fW1+v2+Op1O3JsgLZ/Pa3t7O8h59Xo9yK7s03a7reXl5UgVDAYDlcvlCFA2Nzcjml5fXw8HaGlpSZ9//rn29/cDOaLMDjnJ5XJRZeB9v9FR1ElDxP1nh7Kpz3Vj7KQrJzjggRExu/JE8XF5FOURGQLrf3eiSyo1yT0TLWD8khEbho+IyZ0DDALPZMwOvXoO2o2POxzSdPcyNjufcUPDZ/Gq3chh9Ck3cONBlMD7+Zg9t4jXRiTR7XZ1fn6ufD4fxwZytmyz2VSr1YoexuRb6LLGJuFsXGnS3hQBdGPFvHpqwSNB1sIjW59Tj6aHwzFRZX5+Pvrxkmvk/ZkL5oUOQsihEwUxcu70EL0y3ygSNhrRFYqU7yYVlqdJMJaO5HgEzb++ltwLeBuPntwchoS5RoZhNXt6gTwm+4y/OaTtPAWXeVAI8s90TfO9zc8PDw9qtVqqVCq6ubnR2dmZJOnHP/6xstlxm9psNqtvv/1WP//5z2OukdPr62tdXV3p9vY2DAP5OOQI3gXRD0RE34OOIvR6Pb18+VK/+93vdHJyotnZ2UAegIkZh6MvyDUIAw4i6Yy5ubmp9E7Scbu+vlapVNL6+noc/uBtIHd3d7W7uxuH4tCkwts7sgboqFwup3a7rfPzcxWLRXU6neCMrK+v6/LyUq9fv9bJyUn8jY5d7XY7Ol8Bv9NTH71CRy7ejb1MigBUKpvNxpnIlMal05PSQeSKgOLk5CTy0cvLy1HjCxcB2c1ms9Hz21OUzA3flRQQOUbx4eFBR0dHSqfTwVUA0WHeIHc55NztdsOhB34fjUYRrUuTtBxQ+Pb2thqNRjhL7jQSKWMfqDwYDAbROcwdQk9T/dD10REzdYUoVMhgCGySAISiQNhGo1EM3L1kLo+uvLYSZeBlFRhMSgT8vv58FK575BhGPocRxcij/D1iccPChTECNWBjeSTvUTXCh+L36BAFyZiTEBd/Yx7ZGJ538zlkI0A0effunTKZTETExWIxomo8axjVGA5qEd15IvLCQHuKwh0pxpaErv1zfrlDxtxx3vbt7W20wkMm6EYlaSp3nCQI+b2lCV/CNwn/n81mo/d2Op2ONn7JnDX3JP+PEiMPhkftToKkqRxZ0onB2DFXwGW+tu6YALG7E+hoBfLB37ycCqeE8fs7MZ9Etuw5oMherxdQc6FQ0NHRUSjsjY2NqXFXKhXlcjm9e/dOS0tLqlarMTZgyGq1OlUPXSgUgtREhJVOpwMaZm5JS0gKJYqeev36tbrd7lR6ARgdecT4OfuZPdjv98NJJZXkES374Msvv9TJyckU56RQKAQJDCSsUCjo8PBQW1tbGg7HDHVyoazDycmJtra2Qp8Ax4IMOHt5bm58cMJXX30VsDaySr4ZXXZ/P26lub6+rsPDw+hRTl6XfQ9kD0KWSqWiLJB9eXt7q/X19ejNDVzu+4OOWsga65ZOTw68IIVTKBTiMB1JQWAl9TM3N+4/Tq6Whij39/dB6EMXgiIgAysrKxFhj0ajaICzvLysWq0W5yQwJzi5RMD9fj86CII+8M7oItC3VqulXG7cGrhSqejh4SG6iEG4gzP1IVdq5Dv5B67/9t/+WxhVaQJne2TpcDYeIMblfUrQlXcySuYeXnbhitzhT6j6KDUv9fAolGfjxaKgUG5sUr6TfF5MWuK92dD+ee7PvaVJCQ+bgr85PMd43KB5zotnOpzvSlSaNFhB+ZydnQUMyWkqeMTHx8fqdrv6/PPP4zPch8iAshjGASTDezBnDh+7Y+Tz8b4ombVMpSYnhYEOQBAZDofhUHi5SyqVCniR8WF03Wi5zCaNLAbS2wny3STBjGd4NOpOABuXNWTPcE/31h3p8MoCSVNGnUYb/F6aPjGJuXK4cjgcxjM9mgfapePUaDSKxvrwHZJ8A0lRvzszMz5m7/T0VJIicru/v9eLFy+i5tSNWDY7LrVDGX7++eeq1+vRvtD3mHMlaPvpuWWiGhQ3zjU1pnNzc6GAgdjdUUMG+Y85wNHnv+FwfEAMOoYIyfd5Op3W3/zN34RRIKIEXdre3g7j8vr164hSOUIQCJwSNPY+jl0qlYpa4Kurq3A8tra2dHt7q3fv3un6+lpPnjyZ6ieBfMLUJw0kKQ75oFOX58nT6Unv5/39/WjxiUPHOmBwMeykGDzdlE6nlc/nIyoFHby+vo4yOIwmSCaoCWdNp1Jj8iEtXVkX71PN2l5cXASUDAkQ3Qj8z37inWCnF4vFKHHCCSEdAaIJ9I/s0d4Yu3F9fa2trS11Op0I/EqlUqTI5ubmgvPwn//zf9Yfuz4qYk7mkD1qQ/khsEmYFUPm8GHS2DgU6ReKHIFjY2B82FweOThEx6YkapA0ZcjIv+FdJo0HY0DZMxbmwiNtRxJcuTvEDSQrjZsEUL/nkC9zxLwx/9I0xI9Hzs84Mp5XLRQKkfuknOrs7Ezb29tBQKIcwyO6k5OTcFbYZBDNko4SitIdmmRE7GmDpOPD5QYZRYUxIKrHObi+vg5GL+uLTAEJM1aeT0oDGXBEhPVyRAYZAp4GxSHKIIJFYROB+n5wYp/npsjzUrNPtyvugVx6aoZoFDgN+fX5cwPgCBMRD5EmRhAyDE4rcooD4WkVIjWMLXWlg8FA3333narVahxWgQxSrpdOp/Xtt9/qq6++UqVSCaYwzomnrEql0lTZEQxjh6+dMNrr9VQul8OxoKaVKJPvetVDUr+4bGJ8KIcbDoeRB6VHcjqdjvOFm81msJvJ0ZOPJNJzhJDOXLOzs2o0Gnr9+nXkWlkjTr7iXe7u7rS4uKiDgwMdHR2pWq0GSYzcLGxjojqP+EkNsOZA2JlMJno/S4r2kY1GQ9VqNVr6zs/Pq9VqqVgsxvtTIoRDAZSeSqV0fHwc0W+r1Qr2Np24iEa97he9A5O82WyGM0btMk4VDVAgLrJ3MMS0EkWf4kS0Wi3Nz8/r+vpa5XI5OBOk+HBo4NgMBoPoXAfUjqPKGNPpdPTiJkgkNXh1dRUlZ//sDUYwQA7/QohwBedeNpvOIzrfGF6AL2kKZn1fIO/wNTBrJpOJzj1eaoKxRNGjZPHQ3XgMh8PYDJ53c8PBu5CHYYxJaJM54jsodgy+pClS28zMjKrVaswJz8bQYzDcIXL2std3xqJaZIgTsLGxoWKxqIODg6jlnJub02effabz8/PI9TBfREfSOFqCEU1eivuSI3Qngbng8kiaOffog+iO/B9r60ccMm8eEQOHYURw0qRJ3otNhkEjlcB9PAdNlOrrPRwOw7NmM0sTZi2Q12AwiC4/OEdcKFzugWGFeYqXj6LHAUCefN7YIzh26XR6quMU7+qpl+T7MkbGkERfkNPBYBDKDiOPw5HP5yOSOzo6iuim3x+zZ2mmwOHzEOyWlpaiRIj7zs3NRYRULpdjnCAoTppBXojiWDOYwcgWOUUcOfYKMoED4LrCkTB0hqdHBoOBfvOb36jT6Whra0ubm5vKZDLB1KWJhHMCtre3VSqVQm6Sjun9/fg4xr//+7/XT3/6Uz19+jRkEnIh322327q6utLTp0/16aefht5hjknBNJtNvXz5MuBwYHzmE+gYiJczi6lHfvv27VSuGrLZaDQKaBs9AbMaqJeSu/n5eW1tbWkwmLTZ5MQm5mZjY0O1Wi3qmu/v73V+fq6dnZ3IrRNV39/fxzGUOGKeI/ZUKecEkAJD9jHARNq5XC7uD4GQtqbD4ZgFT304cDWGdTgchsP1+vVr1Wq1yN+vrKxE2ZYjoYPBuDPah1wfbJg99+l5RDYritq9T88FuUDiUXi0QbTphpWNlISIgZZ4YZQewseGo9bv9vY24A8fh/+XdDqYfAyMs4VRvBhIj2CJ2DyfTOSDAsS4erkWz0kSnICm3CHAC8S5IcLCy+b9vV85kcfOzo7y+bzq9brq9XpEBBwbh5AyVp41Ozsb5R1Ex0ReKA4nxPh88HufJyfzYABYO2DlZI6TuU/WUWOkHQHx6ASD6vl8hzNZS9aV7zH3pVJpyqF02U+n06EAOp1OdCDiOUSvGB5J4dwAATv64DCpQ/y+L/gdxtudWy7QA9bJUw7Of+Cdk4iXt+Pkd05o4344UZTdlUolbW9vh2PppX2NRiOMJQgB5Bz2rpPbcN7YT8PhMKInLifiMTcYbZQve5l6ZUehPI0GWofRpFzn+vpaBwcHurq6UrVajZJDSWo2m2o2m1OoXyaTCUIbOXWi6L29vShXW1paiuqM2dlZ/epXv9LJyYn+zb/5N4ESpFLjBiDn5+fa3t6Ocq9ms6lKpRKHamCQPDfKnpibmwujgi4kwu33+1pbW4u5XFxcnGocQvTpUOza2ppOT09jnTgEolgsRppjc3MzOCKLi4uq1Wqxf0G/QADo782zIAE6C79YLMYxkM1mM3pK+HGiBAhUNFxdXYUMIOPpdDoIYMgIaYAvv/xSr169ChmhAQxO/fLychxuISlK0BYXF7W+vh52iHnEhsHiT3b3+6Hro+qYEX5XsMk8FlGaGxMnK2EEPbLmvsn7E4lj6B2W8OjG7+efx1jxfBbc85soYHcO/B7ukKCIPI/jkLfDs/4OCAXv6MbcSSeUZeAc8EyHW1l4Nw4YJpS8RweeQ+V3ECzm5+f17t27IOH48XscuZfJZFQul1WpVKJm2CNgxo/h96jFkQf+zjwTtUCIIOpMGmWiXNiOGGmiGSJtZFCa5MAcynZGuztKbuhxJFA4Hj3DY4DQg7xjCNwJcTlytIY1YCy+d5JIg+fCkDnmO9lshLEmHQ8cHY/Q3KgSCcLAJjrEaAGBY+jgcFDWgqKpVCpRSseJZp5/RCmhpHgnejgzHuYV/SGN23Le3NxM7eVUapzL9NwxStbJgcyls+pdNnFovMSOvQuMyTGVJycnsbZeB0suHPgch4R15r329vamTp5rt9u6vLzU2dmZVldXtbW1pd3d3YgqZ2Zm9Mknn0RtMnXE7EFSTnTEuru7i9ptnExIXoPBQMvLy1paWprqjMa69nq9KRRmaWkpnPOZmRmdnp5qY2ND/f7k/GVIV8gj+dl0elz/e3p6Gnlgz/cyjkajof39/WCmM6a1tTXd3NyoWCzq66+/VrFYjO82Go24F2St2dnZKKtyfhPQ9tbWlg4PD6OJEsjIcDiMFAWM+mKxqMPDQ5VKJS0vL+vs7EypVCrgadYT7kG32w1di6wtLS1NlVFC8sVxcCTxh64PNsxeU+j/0uUEL4XfoxBQEO+LTl1ZSZMOOV5uhaeLUueeGO3RaHJSlTOIUTxEwR4lsJk9/8zn3VnwiFmaHNrgOXZpkjv1MiLmAoXGO6JMMSIOOXJ2MK3pUPB8TpqG4KVpMhCMao+yyR0SATA+cj2ffvqpLi4u1Gg0ogmDU/7xpim6RyFdXFxMkSEcTkaBkoeE6UxpyPr6euSNiWgxwh4lYTT8Pfksc888YMhxdjyyh7HL54B7MUZOMHSCCzLMGFkHf7bnpHEG3XnAYXQkhXExl/AbnASWdAx8nVlDL5tzYovLJPuKsTp8z/eJGNwQY8xIRXFvunlls9noBtVqtXR/fx+s37OzM+3u7sYceX0nyimTyUSpiTsRvg/v7u7U6XQip++OKe/tUTztQjGSRK5Eh+4kYpxZf8ZAYEFKgflDZkajUewjnHwQEY5PBKWjlSb13aTS0FeeGuPkp62tLVWrVS0sLGhvby/OsU6lxscggojRaerp06fq9XqRBpmbm9PGxoa63a7q9bparZZWV1eVy+UiL+zwqssdThepLWS5Wq2q0WhElEpul97gGNvV1VXV6/WAiNPpdDhw6KaLi4uYv+fPn8eal0qlaPeJYwB5DmPNWcwQqkAeyBPTBIauXMPhMAiHHMXo/Row1ktLSzo7O9OTJ0/iAIx2ux377+rqKsoGieIZAzoN5AdZ56AgdD4tVh2Z+qHro3LMGCQUGU3RCdtRQJ53xdD6qVQYSxSFR5nSRMkikGxCjFoyf+bKyHOGTvpAUfrYuK9HEx718gyPhtwpYZJdUfuYEHoEnpIaDC5RojsR5Ex4H58DxsI4MF68G+/J392w3N/fR0s4vH6ijpmZmWgeX6/Xg7HJuIlAIP9gcFjHbDY7Bev3+/2omX779q1KpZIkRX7RWxwiTxAjWF/mjd7OwKEodSfXeU7QIVCPWhzxcaTFo6MkKYg5d4TC14/1Bc5irh2+lybkLJcdxgijnAjVYWt/tkPofD/JxvYx80z2nZPZkFXK3tyZoqSInCKGg8ieKP/du3fa2toKI/306dNIS5yfn0dUx3hAq/r9vorFovr9vvb29vTq1aspOZQU+/bs7CxydR7BI0ue4kBmaKMoKcaAw4TuQQ8k02zMN+fsshc3NjY0Nzen77//fqpkD/n3HCIyB4Hq4OAg9hf7A6cHg8heef78uTKZjLrdrmq1mrrdrsrlshqNRuQ+gdez2XFtLJGx55NxetLptF68eKHhcBiNX8id1ut1ra2tRXTNWDDenEJHTf1oNNKzZ8/Ubre1tram29tb7ezsaGlpSc1mM0hx9JAGRidwYu3YX+xddB6/Ax0hxYYzwL6H2f3w8BAVAHxemnTTY00Jloj+3717p3w+r1KpFE0/QHRgizsRDWQPfZ9Op4O5DXkR20FTla+++ipIeE4y9rLEP3Z9VI6ZDY1Hura2NgVPuzeP8gJSBHb1KNUXCoOIB8xm5v4O27oBdkcA6NijcI8cXBFJ032LeTcWhH8dYuZ3kHMwFGxuFEsyX8e9Gbu/L8KDh4nBIbJwp4Pn8R8LzVzjqWMwHMolp4cicUXszg5ENCJmIk5XiMxLJpMJIhmfw+CiGF6/fh35qXQ6HefvkgZJygFQphsin0fWD4PrV1JBurPF/CEDyTQEz0YWeEdkxdcTo8qcuXw7V8INsss9f8PpcOPiSBFjcEcPI8tYkjlp5ADozI2rM4bZI4zRHVongyG7GAOeReS8uLioYrEYjtn29nYoaZQ6+6LT6WhnZ0fPnj1TNpvVmzdv9ObNG62trQXqdnl5qePj46ivxYANBoMwlg5R41Sgf8jzI+POU0nm6ZEBdAZNNDif2Z3OxcVFffHFF5FPPjk5iVpVjs5k7yXRMKL3xcVFVavVqLctFAoqlUpR3vO//tf/0rNnz8I5uL8ft8V0suf6+rpOTk50cHCgfD4fBtrzzeyFfD6vs7OzWAPy3YzDne6NjY2IbFkvnJpyuaxms6nl5WXV6/XQL5KCeUxgwd4fjcbNi7rdrkqlUhjwdDodh1CQzjk5OVG5XA7nGHQN483BEsPhMJjfmUwm2rwCy5MCcALWYDCI8wEeHh4i5UeveEiM9/f3sf6+TzD4OASMjX2Cg4BzAtTt+xzInzn6kOujjn10FjaeJ0oDWNMbCyDcDkN7KYnngSVFQh7PEq+dZxIdIqhO/EAonLzjDoPnn9mgwOEeoQB18lwIFUSYCKXnMD2HyDvyOzYUUKaPGYXNPfgsytRzhmx4BPp9sByQJAKNM8EmdUfHoVFPP7gyQlFTSI+SwWgPBoNQIOSFB4NBRNsYHxi9vDOpCeAgNrm3xUSBsGasIQoXWeB+7xu7N0sBenTnjoY4fMaPccPoOSzOZz1dgdzyHb7v9c3ImBNOkDfu4zliLu6XNNzOL3CIHAXs7++cgGStezJVw7z6OBi38xPy+XwoS/YO8zMYDFStVtXtdrW3txdw5JdffqlPPvlExWIxxrq+vq6Hhwft7+9rYWEhaqLpEpVKpaLFJIYNw+wEH2QNg+KnJLlOYA+xVjD/M5lJZ7tCoRBzT+kV36HHcyYzZjCT7yX91O12lc/ndXR0FJUi19fX2tzcjHXhWQ63Ig/Pnz+P5iHAptyP6PLq6ioMcKPRmCorZPw4D3Qqu729jWZClEl5KRrtMtED5ESPj48jDQA7fG5uLt5vMBgEg5ta6Gw2G7YAVjLGlSMPqU+nBSxyR/SMHMPn8L7XpFna7Xb0JQd5cvSs2WzG32mWgt6jkgTjz/Gd6EIqUtCjEMSA2FOplNbW1lSv14NvgB4cDseHdDCnIBBPnjwJctiHXB9smPHymByMDYqOSWTjeUKcDc73uZgsh3OdgIGyxDiiHDBufMdLLjBSnpNFcSWVjue0eZYrGknh4bDp8cIQPD47Go3ibw6Z8TOGib8xNnJDSbY7G87zhhhevFqH9gaDQeQ4mD+UKr/jPgigpwccsvdcezab1eLiYkRcHr1Jk+MrfVNg0JETnk0dIsbC0QSiQT8kQVIoCaJZxoXh4x5+qhLj9giUMVDik8y/cn+UPPdmfvgs0aSnDri3ozNcyAZr6sgHv/faYXd2WZMkauJwODLG/ZiDJNsa45pEmFwukpwPjAaevjOt7+/vI+93ezs+UpD8J888OzvTyspKGBvvBgVaAyHp+vo6PlcqlSI/nM1mo8YW4wyUTK4StAZljqL3vcaecJnkX5qCIGdO2nn69Gl0cAL+f3h4CCIS5ZegBeiMbDarQqGg09PTqaCEtB5zWC6Xtbq6qm+//TYcSYwfdbKlUinOfcbAdzqdYPdTdoehGwwGwV6nkUmj0dCrV68kSfV6PVKQfOb29lbtdlvb29uhJ2lvib6hrST9Jsh7u46bmZlRp9PR7u5uVHpgnDy3zslRvV5PGxsbETTgkA2Hw+jpz5xCwPTyzOXl5ZBDSXGoC+kSDCqoJufR45x3Op0gvrH3nAw6HA4jBXB9fa3l5WU1Gg212+1wbOjGNhwOtbKyoqdPnwYKUCqVVK/XA8lxHfVD10dFzCh0cmKSYtLJgZDXASZgQ/CiRHvkEFkwDJF75wgRXqekEF43Jp7UR8G5gU8qV37P7+hHTBLfiUREdm40EVYUnUPM0rQC9YgQAXbnADRgalEsd+bKl7HiBHBPPoNn6M90Q8NmQgA9p4nhdCPi0TvRKoYYLz+TycSG4wKORpGTK3aYjfUjoieC8zUcDofRN5mIz6NT3sFbXOKMYGQdCcBDZo7x5kFHnCDoEbjLEw4A93ej7mMCxuQ9kB2H1ZEVj07dkXCugTsAzLHvST7PHmJeHaXiPR3VkSYG2CNPmigQpWUymUhD3N/fa3t7WxcXF7q8vFSv11OtVosxwM59/fq10um01tbWNBgM9M0332hra0tPnjwJNAzDCozKPoJwxprSmtWdPtYM2SB9k06n1Ww2o7wGPYEsOM+FnObMzIxKpdIUDE2UyfiIuObm5lQoFJROp1WpVIIJjYOKUdzb24u1l8bHXdbr9SiRWllZ0fn5ue7u7vSLX/xCBwcHgQzSuvP777+PvY0BpkEHawkTGEdmZWUl0kvkpdfW1iLKxEjd39+HYX14eNAnn3wSjidBDmks12+QvWjswelVHunu7e3pk08+iaCjXq8H2sB35ubmtL6+rnq9HrlpadxjnCgduXVkjHWHeEVdOffmYBOifdJooH5OfCsWi1GV4g4IUT2OXKFQiK5pw+Ew+g+0Wq2Imsnzk/qAO5HP5wOJSDruf+j6YMMMmYGNzwLSicWVIz2MUZooFjYUm58I0A2bY/FE4OQ9ndgkTWBzJ3sQtSUhancOUGDAv9Kk7IeJJpfqYyH/QWTsOSuUmt8fRch9eIZ78yhqL43ynAT5PDYF7+8GnXfhuyhSHAEvssex8LF6VMX93RHx+UExuvFzg+JRGvd0xem/I9/D74hKPHon4gdSZD14f6B25tjngXfDeLFOzrBGZp3Qw/ugDKRJ32n+TmTmxpN7OtzMvPH/jAfnCKQgmUv23KjLHw6NO7fSBLkg4kKhjEajgPm87zTfZQ8k+59j0Lx5CXMBJEnuEPjx9PQ08uYvX75UJjNm8JK3I/r88ssvtb6+HlFTp9OZkgEUK/JAFMS8Mc+eUmGtLy8vY+/SHhHHnV4GXO6QkI4BMiUlAmGS4wJzuVxEjnTboqnI6elplARxeYtQ2r0SjXuziWazGYaEJh5zc3N69eqVBoNBwLkYYyDt9fV1pdNjshaGiXOGG41GrDuETAwKRo/0pPN/OOyBE+qQTcZ3f38fZy6TbsjlxsfNAtem02l99913ev36tb777rsph3dnZyecgaOjo9DZoAjwR9BTtAvFYczlcsFzwqb89re/1Y9//OMICigVkxTG9PT0NOqqcVpWVlbiBCtknFPX0A3ZbDYY+OTQb29vVSqVoqkKn52bG59ARVUCx+7iBDl344euDzbMKHsEm02KEsaQsHAYXjcOzgB8n3KXJoYH79QVpUOsmcx0b2n/PcqRCwHCI8cgsbGz2WzAHXhVPNMNLLAzAuxKziM+lK+3UkSZonjxoJys5NEx8+Lv7M4J88H8I/QOlTAGRxhQfu7AMGfJ9SSawUjynCShARKXpCljhIfr48GJ4P8dUnX+AGNwqNcjes+Nvw+G9/lh/ZCXJPHJ0zI4DTgurK/LAuPgebwnaIVHFx7FEw2RNqHXrqcYmB/m0iF1ojXIKYwLciI8CAwac8j3XH788jnn/YmqkStyo8gUMl4oFOL7GMLhcBiwNMxZ8n2lUkndbletVkvn5+chS4w1nR73PUa/eBtDWLoHBweRp2VP0U+c7k5AonRtw/Hl+EXfc5L0k5/8RJ1OR9VqVc+ePdPi4qK+++47HR0dhSw8ffpUNzc3evbsmU5OTlSr1YJE9fbtW11fX8chCdTvdrtdnZ6eamZm3Ie9UqnEYR3r6+va399XoVCYClIokWKPcSgCF0jUzs5OnGfs7Gm6bD19+lTff/+98vm8arWabm5utLq6GsYDHcpZyalUKtIKoBblcjlKIx3lkcY9po+PjwMRo0Uusnl8fKyvvvpKxWJR6+vr0TDlzZs3Gg7HdcRA8aBoOBacHLW6uhqGFRskTcome72ePvnkE7VaLR0dHU0x1CXFARvp9LjZyerqajic5JsJHGiUwv6enZ1Vu90OaJ8GI6VSSYPBYIpzhdP28DDusri3t6dKpaJCoaDz8/PYN16u9UPXBxvmZDTIhmUD41F5iRIJdmlirFyh4Omi5ICagA+l6dOB3GihFFEyGASUuTepwBh7rocNiTcPlEF0ipL0C0VA1MvlOUMUDXODEDMulA8XChFhcIYpsCHz7cQ0N5YYGcbMnGKM/Ds8n/lGqXqE7Pllf0fWNUlq83ck2mMMzl7HA768vIxn8978HVlBkUqTjm5udB3m5OK5OFeex2UOPZp344XxYJ55d+bQI2OXNe6DfKFceBciWP7fnVNPF3gUTnTA/Pp8Mi8Qu3BkiQBwAJBjImEMMs6HEwSTDib/+sEJoBaDwSBKUpx4JSkij3a7rZOTE62ursbviOJZM28I4o4EkRNlW/welu3Z2VmUacJJYK2TKRaO6PNyv5mZmTjSdDAYqNPpBKOZTlEcmLC7uxsOx8rKSszFV199FUjCxcWFdnd3tby8rK+//lpLS0u6vr7WwsKCzs/PgxBFJNrr9QKRe3h40ObmpnK5nN6+fatisRhnG2NYrq6udHBwENFkKpUKh47zknFa8/m8Dg8P9U//9E96+fJlRI71ej0gVdcVNPYgVywpHBlJcY57o9EIMh0Oz2Awbv0JonV5eRncgIODA21sbGhra0vZbFatVktzc3PRDazX6+ns7Cz2PWgI++T6+jrSH+g00hQ8m/ani4uL2t/fD1Y57GqalJydnSmXy6lQKAR/hZI6jD1IEjnsQqGgTqejubm56GbGPkyn02G80R2gMV7atbCwoIuLi4jK4Re4/vmh64MNMwre84ej0SjOHEVwna3NxseD9bwv/2KweEk8Nc8JoxjwTHACpImiJHJirGxI7oGH4yxqFLhDtChKaboLkzSd+2Tz8x7u9eNoeCSUNGiM2yMVnuGGm5/9O9wT5c0zPb/OuyRzm/zr0Sz3TDoMvgbSBM1wR8HX0OFUd+KSCISTL5hPvudRrrPwXW4wgswDBtLRDt7HDbnndzyidvSD+UF+SF+k0+lQ8D5XIA8+Xxh2z+li+N3B9Zy0f9fznL4vWEdnv2O0cXKA5LgXjid7ASXjrRk9lUF0wO+Imtk/KBnKZ2q1mjY2NqJEKZWadOWamZmJmlfuiULEqWataPRA5OHtC5Gb09PTgBSBOpE5dyDcAYdvQbqN6GwwmPRw3traivIs3x93d3dhJOG6NBoNjUYj/cVf/IW+/fZbvX37NpqJbG1taW5uToeHh0GQIjoFCic/iZJfW1vT2dlZNGuRFAYbyLzb7YbMO3cCwhFyDEx8fHwcUXW/359KNzAvVGqwx6iJpvTp9vY2OoJtbGzo5OQk0iO0z5UUhotGHi9evNCTJ0/UarUCLSHiZf7b7XbYg06nE1EtiAYw++3tbeTvU6mU6vW6lpaWVCwW4/ugmDQnwXlcWFjQ7OysNjc3Q2YWFxd1dHQUOgS+CHPF2lHihWwCiyNLIFTsmVQqFTXMsPefPHmi09NTpVKpQEZZvw+5PrpXtudn0+l0kLJ4WY4VZBN6/sJxeo/g3MBBOri8vAyl6mUvHmE5IzkJuRIFs8mkCSPaFbsrW2n6SDM8eTayNMkhInQ836NclKhHkpLCo0IhuTF1WA1FzHjwGIfD4RQU4vdJGm4MDfOCYvRnOUkpaUwdasZQJlEMN9weUbshTEb3eJismTs/GFkcJ+TIoz2H03ye/T0oS3HH7H2XR6RulH3uHe529AWD/r41wbA7JM4cYljdMeC5bH4+hzFx+I45vLy8DLY80S6eP8oDBjyKGFgNR8rRGsZDZOyla75farVa1K4iT99++62q1WpEZDSSOT8/V61W0/r6eswTpJharabz8/OoS+XwCmQCeUNH3NzcqNVqxT6jAgGj7PLFOhDNSYr8p6NZ8/Pz+tGPfqRWq6XPP/9clUol4OsXL14on8/rzZs3Oj09jSADourFxYXW19cjauVd+v2+qtWq0um0Tk5OVCwW9fr1a52enoYR3NnZUavV0tXVlYrF4lR9LdEpMCosaRrYAJcvLS2pUCioVqvp/v4+Tn7b3d2NXPLt7a22trZi7igJgpjbbrejpSfnIqODB4OBnjx5EjDvz3/+c93f30d3NU6GYr/COD45OYmxjkbjAzI2Nzd1dXWli4sLra2tBYQNwgqi8vz5c+3t7QWx6+rqSicnJ9rZ2VE2mw3yIXnyYrEYv8Mho4afOcFOXFxcqFwux9wuLS2p0+lodXVVtVotAh0cKJAkCHf9fj/qs6mTJvrmHTh9DGcBmSaCxrn7kOujyqU8CsT7QymhGFCGKA+ULQaOjYEywPg5RAzs4xEXkDiGKAkpO4kGg8AY8cY8GuEz7kliAFwpEhFi4N07PT8/n1JaRIN4lknj6+0aGQPv4cQe3tPTBYzd7+nRkl9unEEa3vd5N0JJ2N6hf2nikHjk6dF7MkLmHTwVgZL1vDDz4NA6z5EmTT6c7cx33elLIgTuIHgu3UlV7nhwP4hSXE7QY8583BhGf3fge5c1R1oYqzs96XQ6oDxkwnPs7rGzbswJazAajWLfMG4gc8hzRLJErrwrRhlZ9FQOBon3Zy9gxFHYzWZTn376aRz1x95ifoDapYnhZF8fHR1pd3c3cr+gH9fX13FOOA6xp6GI8NADXpvu6ZxutxstG3GYgOz//b//9/r1r3+t//7f/3s07fj222+1tramy8tLPX36NIwFufMkAoQjyGlEL1680Pn5eRjfra0tpdNjQhSNLFhHdCcnOh0dHandbkf3r3a7Hb2c6/X6VBnTwsKC5ufndXJyEic0FYtF3d/f6/DwUDc3N9H7GeZ0vV6P/CdcB2kc/b579y7OQ56ZmdHx8bE2NzfDqFC+yrqTjmAuiCTRWX5IzdLSUjgkkmLt1tbWdHJyoqOjozCirE2hUFAqldLV1VUwzdvttlZWVtRoNNRoNEKmSCOw35eWllSr1ZROpyNIRAfANufwDQhavtf5eXl5WZeXl0GEA/bmuMqZmRnt7Ozo4eEhTgC7vLwMRIK6fhCeD7k++hALj1jd8wZGQ6k4PInx9t+R58Eg4k1iyL1kCa/MWbg83yMMNxpsUCALxkQE6RPvG4T7AnnxeYwDi+teIl48AoIBwaMCFsNIJPMMjIu5Jc+HcsQh8hKJ5PMkTSkk5o57ShPY1x0ANxAOAbtx5HnMw/ugeIyjv5MjEf5Z3gcHxyF/jCvj4MKZcEPoSIo7GR49oYQxko5+cHkuk+e6s8C7JokbvBOy6U4Oc++Qva+pO1w8zx06ZNbXwdEd7kvU4jkv9hRjosa31+tNoUeMl9SQo0vsVd6Zo/cwutT9np+fxzF65+fnarVaevLkiUajUbCWaUZCdOmMYiKYVqulvb29cMqJ+rrdbihNr4PGKWPdO51O7AmMezY7OU+ahhPAzru7u/rRj36kubk5/frXv1a9Xo8GJih22LWMyaOmbrerzc1NpVKpqNeemZmJ8purq6uA/YHN0+m0nj9/HshAtVrV2dlZdGkDwj4+PtbDw0Oc1Y1iR1ZZS5jR7pjQKrVYLOrly5exdzudTrSJ/MlPfhJELGTo9vZW1WpVq6urur291dramq6urlQul4PISz724uIiOvtJCuIhei6Xy4VR5F0Jfl6/fh217Xd3d3r79q2Wl5fjRDJy4UTl6fTkVDJajpLnBRFy/UzAxBnI5H63trYi+CE/TctR+mKTjuHwFaB2dD3oC/l+9hYOOg7K2tpanBePbHuDmQ+5UiPXnj9w/df/+l/DACVJSMPhMCBoNjoKjQnj/2FkkxNy5eWetbNIgflQJkQOCASXQ4BupP3/vcTFozsMGkobcoobOrxs7umwNPdz6Bkj6vfjwgDxHuRFeGccE2nSWCSdToeB5z2TBiXpMPHOHhnyvtzbv+v39eidn11c+HzS0CVhRZ9vv7fnnj0y5zs+Bl/XP3axjsl0SVIukBf+3xGTJALgKQfWgvdzI+dr4O/lZVd+IAxKDxgOFIl9RoRLbtjX2+UJB8xTMCg3nD1v1sKFEcMBzWbHh1NAAiI3mEqlpkqRqOmEY0JFA4davHjxItI99/f3+vrrr/XFF18olUpFVPb8+fOpCg7mtNfrBdKDE+CkJRxDHGcaZjjJjgiWteLYz8XFRf31X/+1PvvsM719+1b7+/vRN1lSKHuY04uLi6FUqQMGSpfG+7rRaATZq1AoxHnFsHapW6bsh/ItDDY6kdakGDIQgnQ6HaU+GBoczp2dnYBtXR+iY5H/brcbiB35V6Ld7e1t/cM//IM+/fRTtVottdvtKBny7z958iTG2+/3owYaozQcDgMiJ5pnTESR8/Pz8WzgaDptnZ+fT5V4LS0tqd1uR9oExIf0BU4j+51om5Om3GhydKs05k3QMMsbOQE5EySCCridwJF5eHgI8iU/e6dC9hJ7GJQgl8vpv/yX//JHddhH5ZhdwXHBcPY8IsIlTSBRFtCjRwQeL0vS1P15HhEASsKfiZJIGgk8Rc+LMgbPh/Isvp+EUfk9YyPakTSlxDGEKBiMLYsOK8+Z4swV0Jj3s/Z8mzs1/MsiE+myWVgnb1HnAvyHDJ/0+/24fV1xsJgH5gwlyHpxJQ07909G3T4mH4dHzvwu6YgkP896ejTva+kGiXFwQdjyHDjPY4wu1y4TrIG/B2Pwv7N23Atol/XkQHpk1KF2TwM5dAq5BuPtEXqSWOZtZR0GJ7pnnSn5kKSTk5Ng1gLh8dmDg4MgxtGQpFwua25uTvV6Xb1eT91uV4VCQV988UUgTLu7u7q5udHbt2+1vr4e+4B5dUSGPGEmM931i/Kok5OTUKjMP9ENc0XKKpVK6a//+q+1u7urb775RvV6XdlsNhi4HFfp69/pdJTL5bS1taV8Pq+Li4swPsD/OGxA3UDSdJkjvbC5uanvv/8+GNmUVt3e3kZulJObgHBB6egjAWsbiBwG+fHxcZDnPL2RzWZVr9e1uroazTYwtOVyWYuLi+p0OnFwRyaTid7Y5XJZl5eXuri4UL/fV6PR0Pr6eoxrbm5Op6enarVa0dTk8vJS/X4/jkzkuc7Ch2iWy+XUbDanUmK0/yVo4yAInFXeDSiafYEBpI/2aDSKqDeVSk3tK9IIThjk96QCaC6CQ4gecDQGvbe8vBzIDsgXDhjB1uLiYjRO+ZDrgw0zUZdDoQ5x8hkWDG/LoWL/PPCb14h6h61kvhKKOsqFXC0TSK2jez8od4/GUFRuxJMwbPLCicD7d8Xh7+7NGVC+KGecEZ7nCpP3xTkBMiECx6hgeIEogcA9Ysfwe47Vx5iMmDF4Ho3yu/cZJ77vEWwykvbP83Pybw6Z+lx6FJg0wn+IfMaY3RFyB8rJaz7nyCLOozSJPP1vXE6k87UkJ/q+OWaNmCvPodMQn43vDqykUMSeh2TvjEajKCECnUm+g6SAbzGe6XQ60C34EDgHzhchDUN9v6TIHxLNOD+CTlAoRvKzd3d3ury8jGYVREM43ShYxkAVRz6fj0MKqD/FIcDRbbfb6vV6UUvNOCSp2+0Gx+XFixeqVqvRoeo3v/mNfvazn2ljY0P/+I//qPn5eZ2enqpQKGhpaSmIcoeHhwHdn56ehlxkMpmAViG6dbvdgDJZE/YmYyT1RnSPc4VTSFOV2dnZINJxoX/u7u6CrU39Muc1s3btdjv2BA4CupX1Yw9w3CHQby43bn2Jg+CEKYhLvANs8fX1dfX7fX3//ffa3t7W9va2yuWyTk5OohwOo3x1daVCoRDyQIcudCyOHXIATE/LZYwp/bixH/Ai2IfeblQaIyHk2AnsWK9MJqOlpaUpoqU72e7EgvBS1YDc+t5mH+OYra6u6uTkJNboQ64PNsyexyI3xr9sCjwFFKQrRLwgabr8hklH6SOofA8lhJFjwi4uLjQ/Px8lI9J0FOhRn5f58DkEMxkd4iz43yFueYToxpCIxslxzIHnNn1cGCTyoChmZ/XyrkBdHuGwDkmDjtJ3o+xko/dFqe4IJaFpnuWICBfv60bax+ZRn89x0uB5dO3//z5j62uYjHqTUTqIQZL0lxwb/zrhzqNHjybfB7t7m0M2pTR9UAQQtb+n16ZSX+tENPYIsDFOH7Cyw2vIDzA5BgTlRtTGPuL/cSApS+EaDodRuuJ7P5Wa1Lw6lI9C7Ha72tjYiBwmUeCbN29UqVTCuUCpIfOUsRDVMN8chsO8skchEdFhEEicrlT9fl+lUkmffvppvP/JyYlKpZJSqZTa7baq1apev36tTCajXq+ny8vLYFIjazjUq6urGo1G0RQFvYWznsvlouwJdvzV1ZX29/dVLBY1Ozsb+UpQh+3t7an6cE89cAQhqBnrTj9s6rcvLi6UzWZVqVSizAd5R5fc3NyEg0fv7JOTk2hEwjo6fLuxsRFOBk76+vp6nBiFzka3SdLLly91fHwcepn9tra2FvXFRODOl/F8MjKEfK2trcU+pC8GqRqgcpxcbzBDND0YjGvVcQoo6cURSKKzd3d3qtfrQUqE+EizGK/U4Xv0DPAeAF4bDcxNG9IPuT7qPGaUBANC2aB8khsb6NXzRQ5J8pJ4VGx8jDEeJxA4Coc8GAYSBXx7ezvVsByFlRyXNMkLJsfNu3rO0A0byoFFdwgVI8sFHCMpamLd2eCd3GCirJJOgBuTZHTLeBEWN7A+P8wt3h1/RxE68zs5BzgoKAoUZ9LYMj6PTh0lYeyu1BmHO0we9XkE77KIs+BOhkP1rNX7nCl+z8YmpTIcDqeMhjsbrqggAzE3jJe/u3wQ4TEOoGcQHxQ90ev7ImhnvwLdurx62sbnF+WDbDEuFCsOI5E0tcrSpGvS0tKSut2u5ufnA0alTtVz5/l8Xt1uVysrK3p4eIjcb7Va1XA4jNwxpCgiI2SZzljkQR3mR276/XG3JqBf1jmdHpNuFhcX9a//9b9Wo9FQr9fT2tpawOXcEwNLQxHOhIaBXa/XlcvlVKlUAhK/uLiYkpnhcHwQw+Lior755hutrKxoY2MjCHDkTl++fBnvyRpimKjqgMgF9Imj0+v1dH9/H2xkUA/0Cev48PCgk5MTFQqF0JsYIVqCkje/vb3Vu3fv4uhNWqJy5jPOA8gnpVO1Wk2zs7NqNptaXFzUyspKNJiB4Fcul/X06VMNh8OIjGu1WsxVtVoNohu6cXFxMeBs5xvBlkZHSIpTs7zckjQC6AHNZ9gPmUwmznHw3tuXl5eBTABZI0/IB13ncDLX19fjmE+vkshmsyoWi5GS7Xa74Qitra3FevthG3/s+qgGIygaj7BQSAiER2pEJ0AE74Ow3SigVDEO74uAuI8bBJ6P4fAyGhbWS4XciDoUz/hcEbvB8I5hnsfhQnG4Y4CgOYOUz/Js4E4MDZ9zY4cHxnh43z80X/7uHjkzHuaPeWJ+uK/DtYzBIZ1kPtWfyRx7zpvP8VzPSztszPu5Y5KEk5Nkq+Q9eA/PYzMXDkezyZM5JOSa+7GZPRrGecI5JT+MgfcuP/6+jprwPbgF5PSISnFYPXeMRy6NSxgLhcIUOx+FTSRHcwRPRTlCwzssLCyoUqmo0WgER4HcsqQ4EQkDVa1WAyrG2AI7A8lysAAGYWlpSZeXl+FEA6eDnC0vL0djh/Pz88glPzyMzz1uNBoBA+NkYFwKhULksL/55hs1Gg09e/YsGMJE0f1+P+aEulXKh7LZbByNeHBwoF/84heq1+t6+fJlGGdKZm5ubtRutyMHTU/kfD6vp0+fqtvtRnSMzOfzeaXTY2Y2XBmcGxAT5JauVu5wIyeUIUHEIo/JgRuw4XGGyKNms1k9f/5ctVpNzWYz5ovmLpDThsNhnBLY6XQCtm02m9rd3Q04nWj7+vpaqVRKh4eHqlarkT7BCQV5oQZ6NBrp4uIi3g+SGu/CyV3z8/NRFkVDG1IfGFt3itPp9JSsQ/Di99Qko6O8vI39RQ0/ETgoZLPZDGeQtZIU3AscTeaFUjDnBTGOD7k+6nQpFAOK0SMjZ4WiCPmM59HYHCisGMj/VRIYbpQgCl7SlPHn/kQYflAD9wYmc7IYET5Ghk1GFOS5W4/I3OHgHYACyd8QKfk7sSj87IaFsfKeEAocYvXPIDxcySg96bj4/3s+PQnn+t8YmztOfCaZ50xC2b7mrAHfdy/WYW13QPyzjId/+bvfg79xD58bdzr8SiIlGG5n8Pr7MyeQ+ZIwoTsINzc3kaP0/Dbj7vf7UYOK0R0MBgFPenc75JmDHhiTpCArYcRQRsCUjk4RVUEwwuEgYoZpTZTie4+oC6PJ/HpqCsdiMJiUGGaz4yMPUchEYjDDR6NRwPbetWlmZnxkIIQyolpOEBoOh3FMJLICBD8ajcubNjc39ezZs9ANmUwmyqxqtVp81wMFaXxEJXW6sHhrtZpyuVwc3djtdrW+vh4OBWs+Pz8frG+iwXK5PKXnMD7sRUrXgMsxtg6ToyNweDhiczQaRb6Vhi3cCzIY8kPkje6p1Wra2trSV199FTJJyVG1Wg1DBb+A5hkY/+PjYw2Hw4DBMWLcC0cNkt7CwkLsXxAI5pw9SGDWbDYD2idXzv7e3d3V6elpyBt5fHd+R6NRvAOtTz04yGTGBEKMM+kA5sp1B3XflLA1Go3Y96CGtCltNpvhMBeLRQ2Hw4DjYcDjQLrN+6Hrgw2zRx8oJDaj56sgL3jHIV46+V2MkaQpAguKi9o+78BEWzeUh0cw5Cq81Sa5MursUCx8XpqUnQCl8AwEjdwVwg8LHNr9aDSK4nZv3whUj0Pg0CylWBgHjIUbSY+0eHeibxAGojuPcPm9zyuKGmPlqMb7fvYLoweU6wbSjTAXmwnl54xof0fm06Na7sXncLYcTXAOgDRds+7j8J8ZE/8lkQ82VpJjwJiRTYeG+Qy5XG9iAPkEyM1RBzxxOhA5e9qJMtRCAknyHsgGUZA7jw7b43TyHBAduipxlCIOF2UryAGsVkdQXFZXVlY0HA6jwxHwrx8NSolWoVDQ+vp6wICsm+9z5sCjCrpXQQzr9Xq6u7vTj370I3377bcqFAp68uSJcrmcdnZ2lMlk1G63tbGxoU6nE214OQ+XfHOj0QjGtreqBBIn0oM0NBwO9fTpU2Wz2WCjEz2hT5AVomoMD5Gsn51MVMV+okStUqlEe0tOI8JYkRfFMHBvcrq0EGXOgU/L5fJUynB/fz/kASIgR0c6p4B1eP36dRzwACmw3W5PlRw5Wx+nhTkg7w9Jt91uazAYqFwuR2kVjWOWlpbCCQS6Jrc7GAyCYIi8ss/YG/V6PQhfOBUekXPQBk4wRpt5oLc5a4ne2NraCodgdXVVZ2dnQfhbWFiI7mEwsNEZ3pcdB/lDro8ql+JCCbqC88gFRePeUiaTCaKLe/MoE4y7Ry9sJLwUz7/e3d0FO9u9S4wBubu5ubmI8pxe3+/3f6/pBwpJ0lQu3Y0jEZLnIqmzZOKd1OL1mSg5hFeaEIT8OW5Yucgv+ly4g8O8+n08EnW4GcOKY4NCdCOIE+PROMfO8bPXQLtT4DnppNH1v/NdT3v4HLFBkA93bLg3Rs9TE3zHYXAfg0fWKEx3Xny93bnwHH7y3kliIw6EIyhsfiBsFCzGmzmAWTwajUL5w2iGDU206uQTFILPPcbPndt0Oj2VH2PcGGVJkQsEAUCpujxgLDOZjOr1euRCaShye3ur3/72tyqXyxoOh9rf39ft7W3USDv0iDJnX7GXOPc4kxkzvSuVimZnZ/Xq1atgEwORo5MWFhaC3DQajXRwcKD19fVoUAEJich9NBrp6dOnOjo60sbGhur1eqAV9/f3gSRQCkTuu9FoREtNxkjdMvJLmgDInvt2Op1oX0wEfnNzE2cHj0bjPCeGB+4CuqHdbkdvZnQATHCXceaVFMvy8nJEnYz79vZWZ2dnkhTkNXQH7HdkBDg3l8sFYSsZKEA49Hw+68oJVwRt5HEZ98PDQ+TR0dcrKyvKZrMRhRP0ZbPjUjfSAegI9gVjBIGAhOdrJY2jWpjr5PhpH/3w8BA15HwWJ8RTMvRJf3h4iPz63d2dOp1OEA7Zvx9yfVTnL/ewgDuAcdlgrqwwGK5gibKT+UCiBJScRxj8jEeWNEzAjK4wnHTlEZg0Ia7xfP7l/VCqKA7KRPg3CREjFDgRRF4sBt2HiPJdCTusysXGdPKSw9GudPFkpUnPY+YomTJwg++Qf9JwuqH0tefvzA8GgrXytWcc7rihqIDrMIZEfKAuHv2yoRmPzwljYc6SDhT39ryyR5V810mNPBelwrzzPWAvnAIuxo4MJN95NBqp1+tFNIAxYsweObGnkDPkGfmhLhTEhfdjrnu9XqRlMMxEHI4WDQaDcH7dmfT5b7VakQ+FkQwr150RECUcBgz9xsaGNjY2onsTNbrk1Kklpkym1Wopl8upWq1O8TPQC3Nzc+p2u5HDX15e1nA4jOP+GBNzOzc3F0o3n8+HgQI+ZV82m83IK2LwMNywbCUF0kCum32AvsB4oFeAqofDoS4uLqLHNuVCo9EoWm+Wy+UgLzEuctUYHLpSsb7ugFL+5mhZPp+PsjXnxXAQBXrbyY/n5+dBUPT87e3tbRACCUS8+QvPIDpstVra2toKiJ0OWBhj0AugduQD9jVjwzEkAOM9IHChBwjo/HCK5eXlQDXb7XbA10DulKU1Go04kQpHGH4CDVyYI2zY3NxcdJxjj7fb7TDsjI35dY7SH7s+ipWN4snn81EkziZAsbm3zs/ASW5sEViH8GiKgULmXig54AEMNMYZBYWSAL5mo7Fo3uQfpeslYC7UPNONdkyaKQrujbAQVTp07c0+UPQobMbHhsFII5AOi/l43TB41I2Tg0Jn/EC0jN2NKcoe4otHoJ5D9jVhDTxyZG4wABA12OTc1yFRlKcrDeSCMTNelymiezYlMocicm8YBejd6aivJIrBGLOR3IkioibF4akN3pVx4Nlz316vF0oTA8bzQW084uD9YKw6yuQOqjRxMDljFplizCA25KGZX/r83t7eqtVqKZPJ6OLiQvV6XZIiGmHMKPIXL16o3++H8spkMnr+/Lnq9Xo8n3QOOcByuRw6wlMF/D/NHFjfUqkU+4LIjr2J8aU5C/l6IE2UJ0p9fn4+ynPQI/Pz8zo+Ptb29nakQID0r66uglUujY0wBhJYcmNjIw7DAPmABIeeoJVmJpNRsViMQxuur6+jd3On0wlj+/r16yjxwTFCBmDN8wxyn+xPiIa0OYX57DqBPuOs69LSkhqNRswHzk0qlYpaW5wy18neCQvZg6WfTqcDmmcvz8zMaH9/Xz/72c/05s0bDQbj4xpXV1dDZ9DXm6Ymjsrh7NKfm3t7+g7dRGctT1E59wfjz9xjC9CBs7OzwfUgp876U/oFyuW2ixwyTivsbMaA4Ue3/rND2URmGD0EBwPF7zB4bmDdq5EmdblOUnFIO2kciT4xhBgdlD6ELoQIMgSREIw5BI5FdRjXF9X/9SiMTSIpCBOSwrgiSAgmUCVU/STzzy+PmhF0LyVJGkAUFu/DOhAJe0TFRvf5TEbEEJFQnDhavBtKBwPGhudeeOtAVGwuOvmMRqNQIhhA7kcvZDYIhsgjOJ6PswIzE4PJBnRIWlKgCWwQ4LQkIc83KUaf9IrLMNETipgcFj8nEQLklnsk0QjWnhaGkqIfM54/8u2ygAPGOCn9oTkE+ThO7sFJ4kQq5Ilj9La2tiJiWVtbCzn2HLqkqXOYyY/7MYC8D3W/kgL+ZC8SpQ8G4wYqyCh5P48uMGoQ2ziqkTlwx/3h4UGFQiH6UuNwplKpqXWEaMTfHJEgkuMd+R4yQttNnKxMJhOOoBtpyqG++uorlUqlMAbLy8thJJgbTq9iDuG19Pv9eFfeaX9/X+vr60qnJ6xjoNPZ2Vk1Go3f06W0tOz1ejo+PtZoNCZJwcBHhtPpcetS4Gb2LHXd7Bt3JGF/c64z+oE67HQ6rS+//FK7u7txdOTt7W04OaRgms2m+v2+ms1mtHJlTZ1ciF4nBcHaslcYo6fxYJ+z79zhZW9KivlEZ49Go7Br6FoMviOa6HeaouDAOAJISstTWz90fbBhpi0bBoANgdLwDea5Z48SHaJ1coBvMCaASeU+EA9op4YAOzTqXqRDqwi6KzJIOA5nA4PzbGcP8rsknCopSCQoQbxT6P+MkQgM8ofnTbyOG4XoUbpHctKkGw1rwqLj3bsR4fMe7TIWhOfy8jKgOWlyYIJ30MHoSorIiPHTCxdeAcKOYfH1QLilCSqRRCHY9P4ZLqJsEBSPKGu1mjY3N0PhePqDzcN6ecrCUxNOnoJA5YbcuzD5psexeHh4mKrxRc4fHh6ipIZnemckhxPJxQIVUp5BVIdRYC8RwQJzp9Np7ezshBIul8s6OzsLA+/OEnlregzj0KZSqamKhmQ+O5vNRqSCgULe7+/vozUmESkOBI4YMCMK12HHcrkcuUkQuf39fe3s7AQCh5NXqVRibEdHR8Emzmaz0YAD2PPm5kavX7/W+fl5QNoczAAiUi6Xwyh4E4vZ2VmVSqV4B+pvOYbRu7BxYEcmkwlHBEVPLpPoEKfs4uIinDNQD0k6PDyMGmeY+Og+DmOAPfzixQu1Wq1wQklrsO99/ESCzA+Es+FwqLOzM+VyuTDQELyQVRwYSVpbW5tiVOMI4BAeHR3pn/7pn/T06dNADjB+jA/0wIm1ngbAcbq5uQk4n/0Fac85M+huZBOHDHnLZDJB9AJlAnljf+LUs3+pYPA0htsK5BEZxrFCzpLj+qHrgw0zXjZehXuswJQoF4TQiUp4cElYEoXr+Uk3ghjsTCYTsAGR3WAwiDaDKF82ELg+3jCGwMcmTQwHxtAJYdKEnOSLAITrxpQxooDT6XQUrBO9Agex6WEISor38ndhfIwDL8zn3RUUY3FDBQkOVMJTEhgjlC9ROobL/5ZKpbS2thYGCucEj9mZkETlTgjhucgKhtVhdQwNF2NiTnCK0ul0lDJQwE+NI32KcSCGw+EUYQpniGNMHY73MeIUplKpUFYoDyc3ohyQHWSQzUqUBZEK5ImmBSAfNN9w/kKxWIwj/yRFdPrkyZMpRAQjy75wuaF8h2oF4ExPSzgqgRJE7lHMfB/lwvdBqfwoQxQbCqnX60V7Qo4apKWipJi3+fl5lcvliPzX19ejMUY+n49SJRw71hiEbH19PVAc9FSv1wsWL/sElAU9QL4QR+Ly8lIbGxvBg+j1eqH0UdY7OzvRyrPb7QYiQf9qUgw47BjR+fl5tVqtqf7ml5eXAccTHbbbbV1fX+vZs2fa3t4OGVpbW1O3241Wmhg4HL5vvvkm5t/TfgQ1GDpkllxxOp2O3C+6FSIWKQOMHmkWollaXaLvgXubzaYWFhb06tWrQHLy+bw2NzcjjdNoNMJBxbnKZDKBhKbT6SALStOcIfYuBC+vzkB+IRASqWPLQGN8v8Jyx+Fm/yD3Nzc3UZO/vLwcaCW2BTQ0m82G3mQf8i4fen2wYebGeF8oZRSYkz+k6ZpjIjqEAaWfNGwoQifVIEQYWyBsIkXuTyTgsCdRIvAmi5mE21FyzqJ1uBBD7ZCiK3ScAMYLsYBxe37byRZ4Xg7xujFDiBy29tIuNrtDcbAJmYckwcjJcShPSVFSwHhZa+YcQgYX40SRY/wwbkRdGEQcCPc0+RvGnQhVmrBKPTJkPhgnzhpMSqIy5ghIjHXJ5XJT8utr7Llmh7fYtGxylyWichwTIEei/bm5uan0D8qBvBrEl62trZhbSo88J82+Qq4xjChPJ8WgEHCkYbxiiCqVylQZh6dkUDTuOONQlEqlICJRqwkvgdyn7wuej2wBDQ8GA7VarXDO2HfO6PYmETCEz8/PVSwWw1nEuUB+KS9jnshD4wgiw+wTSJ0oYM4gZi7r9XrcU1IYd6Jy2MB0wKLrFxExY8IYjkajqEcmh03nr9XV1YDraVCxs7MThpDvnZ+fq1QqaWNjQ99//33M32AwCOcN9jhIGKVBOG8cuMA8PDyM64Vx8EBA0Bt+0IOkiDJxTEEbqNs+Pj7W+fm5Xr9+rXw+r7Ozs8jjg8YQ8TYajchvg9g1m82pM7NxZmlsg74ul8vBGEdWSCd4KgSHwgnC6Bhp+uAd9trDw0M4Vx4cgm7hNGA3HEkFqWCu0Q/oX7eFP3R9sGEGCnH4MAkBOgztDGKHM5Nwq0duTvjxfCeKinymNN0m0qMuSUEY4m/SdJ2tk6bwsNjQKGwULd753Nzc79VkO+RO5OEELko+8LaJ1Lw+lOd4asCFhDkHekIh4r27oRsOh7/XJAUINxmVJpEI1sBzvKyb9yOGCOTwMkoPRcDnmGt3hDxd4PwCLm8RKU0UBAgBcCGec61Wi3nHqYLswv18zpBNIDWUBdAVDh3zTYkMawCHwR05ynAoeSGvt7KyEgcY0FyhWq2G/GcymYjyveaXzlnMke8RaUKww8tPp9NaWVmJNoLA7cCqlHhks9k4ppG5IqVC1OkoBU4QcCMHUyDrNAlxWBRnDn2Asue0pHQ6rePjY62trcW7QZjBONN8g0i13++H4ZmZmYna17W1Nb17927qZClPtSwvL0cHNSL04XAY+eV8Ph+GmNrcSqWiZrOpy8tLffbZZ7FW/X5fh4eHWl9fV7VajfdeWFhQu91WrVZTtVrVyspKzBP7g+5lsILZ4+hS5wBIk7QdTsPd3Z1evHihh4cHdbtdVSoVVatVdTqdMDLUvEN+496kF/iZPcpzVlZWVCqVAjmggiSdTqvVasXJU6Sxtre3lcvloiOcR4uQ3R4eHnRwcBCyRU6/UCiEU0irUGSZQGJnZ0fFYlF7e3tR9UPwBYGq3+/HARrcB8ePozmRPfQBzUBoE4t+RM/Rxez+/j4cek+rYWzhqhBMuU0gvYNj2Wq1gogInO6cpR+6PsowM0H8P4LEJmYjO2TJy6MogVb4nOej8cxYZFcSKHf+5mUiDhO4l+0wqhsbIjSMnzQhKUEgQDhRWM6a9YgeZS1NyhOAmDudTrBFeVcgNcYKMSaZ18QpQXjx3omsoPNDWIFQgfBzD9YOwonnOoj+JUW+Mcka9g3N/wPHA+niNEFycKPqpCqH53m256NxtNioGEhJwYZ2RiUbBs8Vh8OjYu7VaDSicxJGCmXt+SFyhSsrK6rX67FJafKBUiMy4iB53gUUgKje5R8YlP2Bl027wVwup8PDwziIgKiQNUT+gSUxvKzL9fW1isViQKfMgaQwnPw+Sc4j4ubCiUQGkA/Y3pQgQcKhzAsFT+0vkRaO+mAwiDkEmkeGa7WayuWycrlcGCXWGYf24eEhEAe6fnFKF9EckWi73Y5xsYdx8qUJLErOdTgcN7N4+vTpFIkulUoF1Ly/v6+NjQ1lMpmI7vP5fJQCOYcFI4xs+glb/X5fm5ubOj09jfytJG1vb2s4HJdWoaeA6Nlr5+fnWl1djeizWq3q+Pg48ujValW5XC6MzPX1td69e6dsNqvt7e0Y0+XlZZS+4agAi7Pn3Ig50oAjTKTK70DxCBJgpxPJszfb7XYQASE7YsxwWgka0AXSGNnAKXSiK/Xs1MxTfoveJ59NHhxZJ5KnmQx2jfO1sT93d+M2pBsbG2FzWO90Oh2pHvS72zuCUQ8O/9j1UadLsaAYV2m6ZtUJPMBiTuxy2IBN4rlnlCgvxWKgoNj0nr/G45UUuRTG4gofA8092PT8nYXyHAWRMhvJ7+c1fggbSoQNIinyyA4hAwsDE7NgnudLp9NTbd0ckmROKNTH48OrJKfiz2AjAfMxFxgSyBFO/AHWRLD5OxEaET/rwJphXImiUezJ9eedkRfeizlweBkvlA5IzKOTPjC6dIeiyxSQ0sbGRnQZQpnjdVOXyzgxqqlUKpAJolNKPjBW5IKRI3cCkR9QFNaFHs1EyP1+P5QtssPGv7q6CkM8Ozur1dVVdbvdQF4g7gBTs4d8XR0RYJ6JPsvlcsg/eU+cFX6HscAoJlMX8CmQAfYHiAEIC/JG84X7+/s4ZAKnUpqcsQ6ygWPKaVXoIDp0IVO+DzAQyDIXsCbzvLCwoGazGTJwcHAQ+7pYLIZ8A6VjSB2JKpVKmp2dDficdeBnvoeTfHt7G1AuDVnIV2KEichqtVrcn5OPcIJarZYqlYrW1ta0s7Mz1ZubSLfZbKpSqSidTqter6vf7+vly5dTJyaRhmHN0Y/pdDoic9JOOCm1Wm2K7Le4uBhkyE6nE0ESPbKz2Ww4jZVKRdIYGueIzuXlZa2vr4d+dfIn70vHMSJgT6Xc39//XnMSRwJwsNFn6NfV1dU4zAKjix2AuAehs9frRZR+fX0d+4gSORrXgJp5etTn9o9dqdGHgt6P1+P1eD1ej9fj9Xj9P78+nCb2eD1ej9fj9Xg9Xo/X//Pr0TA/Xo/X4/V4PV6P15/Q9WiYH6/H6/F6vB6vx+tP6Ho0zI/X4/V4PV6P1+P1J3Q9GubH6/F6vB6vx+vx+hO6Hg3z4/V4PV6P1+P1eP0JXY+G+fF6vB6vx+vxerz+hK5Hw/x4PV6P1+P1eD1ef0LXo2F+vB6vx+vxerwerz+h6/8DD0PPeF2eTUYAAAAASUVORK5CYII=\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from skimage.morphology import skeletonize"
+      ],
+      "metadata": {
+        "id": "phHMwLkWJ3uK"
+      },
+      "execution_count": 13,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def simple_vision(img_path):\n",
+        "    Picking_Img = cv2.cvtColor(cv2.imread(img_path),cv2.COLOR_BGR2RGB)\n",
+        "    return Picking_Img\n",
+        "\n",
+        "def skeleton_morph_vision(img_path):\n",
+        "    Picking_Img = simple_vision(img_path)\n",
+        "    Gray_Img = cv2.cvtColor(Picking_Img,cv2.COLOR_RGB2GRAY)\n",
+        "    _,Threshold_Img = cv2.threshold(Gray_Img,90,255,cv2.THRESH_BINARY_INV)\n",
+        "\n",
+        "    Array_Img = np.array(Gray_Img > Threshold_Img).astype(int)\n",
+        "    Skeleton_Img = skeletonize(Array_Img)\n",
+        "\n",
+        "    return Skeleton_Img\n",
+        "\n",
+        "def threshold_vision(img_path):\n",
+        "    Picking_Img = simple_vision(img_path)\n",
+        "    Gray_Img = cv2.cvtColor(Picking_Img,cv2.COLOR_RGB2GRAY)\n",
+        "    _,threshold_Img = cv2.threshold(Gray_Img,90,255,cv2.THRESH_BINARY_INV)\n",
+        "\n",
+        "    return threshold_Img\n",
+        "\n",
+        "def canny_vision(img_path):\n",
+        "    Threshold_Img = threshold_vision(img_path)\n",
+        "    Canny_Img = cv2.Canny(Threshold_Img,10,100)\n",
+        "\n",
+        "    return Canny_Img"
+      ],
+      "metadata": {
+        "id": "RyTaSHDOLFZE"
+      },
+      "execution_count": 14,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import random\n",
+        "number = random.randint(0, 466)\n",
+        "global truck_label\n",
+        "truck_label = \"Indian Truck\""
+      ],
+      "metadata": {
+        "id": "7AACMN4JLGqf"
+      },
+      "execution_count": 15,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "figure,axis = plt.subplots(nrows=1,ncols=2,figsize=(10,10))\n",
+        "\n",
+        "Skel_Img = skeleton_morph_vision(truck_image_data[number])\n",
+        "Simple_Img = simple_vision(truck_image_data[number])\n",
+        "\n",
+        "axis[0].imshow(Skel_Img)\n",
+        "axis[0].set_xlabel(Skel_Img.shape)\n",
+        "axis[0].set_ylabel(Skel_Img.size)\n",
+        "axis[0].set_title(truck_label)\n",
+        "axis[1].imshow(Simple_Img)\n",
+        "axis[1].set_xlabel(Simple_Img.shape)\n",
+        "axis[1].set_ylabel(Simple_Img.size)\n",
+        "axis[1].set_title(truck_label)"
+      ],
+      "metadata": {
+        "id": "lR4AuaK1LKAT",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 355
+        },
+        "outputId": "aa1c9297-e417-4282-d6a8-b75c1537a9b1"
+      },
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0.5, 1.0, 'Indian Truck')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 16
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x1000 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from keras.preprocessing.image import ImageDataGenerator\n",
+        "from keras.utils import img_to_array, array_to_img"
+      ],
+      "metadata": {
+        "id": "N3yuyFLdLNmS"
+      },
+      "execution_count": 17,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cv2.imread(truck_image_data[number])"
+      ],
+      "metadata": {
+        "id": "AhrZ3qBPLQi-",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "1865085c-6025-442c-e413-124c462dbb51"
+      },
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[[195, 188, 185],\n",
+              "        [196, 189, 186],\n",
+              "        [196, 189, 186],\n",
+              "        ...,\n",
+              "        [195, 189, 182],\n",
+              "        [195, 189, 182],\n",
+              "        [195, 189, 182]],\n",
+              "\n",
+              "       [[197, 190, 187],\n",
+              "        [197, 190, 187],\n",
+              "        [198, 191, 188],\n",
+              "        ...,\n",
+              "        [195, 189, 182],\n",
+              "        [195, 189, 182],\n",
+              "        [195, 189, 182]],\n",
+              "\n",
+              "       [[200, 193, 190],\n",
+              "        [200, 193, 190],\n",
+              "        [200, 193, 190],\n",
+              "        ...,\n",
+              "        [195, 189, 182],\n",
+              "        [195, 189, 182],\n",
+              "        [195, 189, 182]],\n",
+              "\n",
+              "       ...,\n",
+              "\n",
+              "       [[115, 119, 130],\n",
+              "        [115, 119, 130],\n",
+              "        [115, 119, 130],\n",
+              "        ...,\n",
+              "        [124, 148, 166],\n",
+              "        [124, 148, 166],\n",
+              "        [122, 146, 164]],\n",
+              "\n",
+              "       [[115, 119, 130],\n",
+              "        [115, 119, 130],\n",
+              "        [115, 119, 130],\n",
+              "        ...,\n",
+              "        [123, 147, 165],\n",
+              "        [126, 150, 168],\n",
+              "        [126, 150, 168]],\n",
+              "\n",
+              "       [[115, 119, 130],\n",
+              "        [115, 119, 130],\n",
+              "        [115, 119, 130],\n",
+              "        ...,\n",
+              "        [122, 146, 164],\n",
+              "        [125, 149, 167],\n",
+              "        [126, 150, 168]]], dtype=uint8)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 18
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sample_image = cv2.imread(truck_image_data[number])\n",
+        "sample_image.shape"
+      ],
+      "metadata": {
+        "id": "dO3DfUVjLTKv",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "2e1eee48-c36e-4389-f856-8cc065630825"
+      },
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(1333, 2000, 3)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 19
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "trainImageData = ImageDataGenerator(rescale = 1/255)\n",
+        "validImageData = ImageDataGenerator(rescale = 1/255)"
+      ],
+      "metadata": {
+        "id": "Vjmj2YQXLWJI"
+      },
+      "execution_count": 20,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_dataset = trainImageData.flow_from_directory(\n",
+        "    '/content/drive/MyDrive/Dataset_truck/train_data',\n",
+        "    target_size = (200, 200),\n",
+        "    batch_size = 3,\n",
+        "    class_mode = 'binary'\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "ePwrukRaLZRD",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "36f0ab07-2e05-4b68-e381-220771981fc5"
+      },
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Found 150 images belonging to 1 classes.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "validation_dataset = validImageData.flow_from_directory(\n",
+        "    '/content/drive/MyDrive/Dataset_truck/validation_data',\n",
+        "    target_size = (200, 200),\n",
+        "    batch_size = 3,\n",
+        "    class_mode = 'binary'\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "GmmLoguILfKM",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "dcd4068a-a330-4cc6-e557-515d245e310f"
+      },
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Found 101 images belonging to 1 classes.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_dataset.class_indices"
+      ],
+      "metadata": {
+        "id": "__ewNOOhLgIR",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "b69bf5c8-55c8-42f7-93bd-3ced58f43236"
+      },
+      "execution_count": 23,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'truck': 0}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 23
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from keras import layers\n",
+        "from keras.layers import Flatten, Dense, MaxPooling2D, Conv2D\n",
+        "from keras.models import Sequential"
+      ],
+      "metadata": {
+        "id": "9fVpfXfLLi-i"
+      },
+      "execution_count": 24,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from tensorflow.keras.applications import Xception\n",
+        "from tensorflow.keras.models import Model\n",
+        "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D, Dropout, BatchNormalization\n",
+        "from tensorflow.keras.optimizers import Adam\n",
+        "\n",
+        "# Load the Xception model, excluding the top layer\n",
+        "base_model = Xception(weights='imagenet', include_top=False, input_shape=(200, 200, 3))\n",
+        "\n",
+        "# Add custom layers on top of ResNet50\n",
+        "x = base_model.output\n",
+        "x = GlobalAveragePooling2D()(x)\n",
+        "x = Dense(512, activation='relu')(x)\n",
+        "x = BatchNormalization()(x)\n",
+        "x = Dropout(0.5)(x)\n",
+        "predictions = Dense(1, activation='sigmoid')(x)\n",
+        "\n",
+        "# Create the full model\n",
+        "model = Model(inputs=base_model.input, outputs=predictions)\n",
+        "\n",
+        "# Freeze the layers of Xception to prevent them from being updated during the initial training\n",
+        "for layer in base_model.layers:\n",
+        "    layer.trainable = False\n",
+        "\n",
+        "# Compile the model\n",
+        "model.compile(optimizer=Adam(learning_rate=0.001), loss='binary_crossentropy', metrics=['accuracy'])\n",
+        "\n",
+        "# Train the model\n",
+        "# model.fit(...)\n",
+        "\n",
+        "# After initial training, unfreeze some layers of ResNet50 for fine-tuning\n",
+        "for layer in base_model.layers[-10:]:  # Unfreeze the last 10 layers\n",
+        "    layer.trainable = True\n",
+        "\n",
+        "# Recompile the model with a lower learning rate for fine-tuning\n",
+        "model.compile(optimizer=Adam(learning_rate=0.0001), loss='binary_crossentropy', metrics=['accuracy'])\n",
+        "\n",
+        "# Fine-tune the model\n",
+        "# model.fit(...)"
+      ],
+      "metadata": {
+        "id": "5V9Lk7A3Lm7s",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "7727f6a7-7337-4f89-b9bf-fd8aa6dac2ef"
+      },
+      "execution_count": 46,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/xception/xception_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
+            "83683744/83683744 [==============================] - 1s 0us/step\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.summary()"
+      ],
+      "metadata": {
+        "id": "RaTU0LTALseV",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "b9a2d29e-85fb-423b-e309-2a662880e8c0"
+      },
+      "execution_count": 47,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Model: \"model_4\"\n",
+            "__________________________________________________________________________________________________\n",
+            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
+            "==================================================================================================\n",
+            " input_5 (InputLayer)        [(None, 200, 200, 3)]        0         []                            \n",
+            "                                                                                                  \n",
+            " block1_conv1 (Conv2D)       (None, 99, 99, 32)           864       ['input_5[0][0]']             \n",
+            "                                                                                                  \n",
+            " block1_conv1_bn (BatchNorm  (None, 99, 99, 32)           128       ['block1_conv1[0][0]']        \n",
+            " alization)                                                                                       \n",
+            "                                                                                                  \n",
+            " block1_conv1_act (Activati  (None, 99, 99, 32)           0         ['block1_conv1_bn[0][0]']     \n",
+            " on)                                                                                              \n",
+            "                                                                                                  \n",
+            " block1_conv2 (Conv2D)       (None, 97, 97, 64)           18432     ['block1_conv1_act[0][0]']    \n",
+            "                                                                                                  \n",
+            " block1_conv2_bn (BatchNorm  (None, 97, 97, 64)           256       ['block1_conv2[0][0]']        \n",
+            " alization)                                                                                       \n",
+            "                                                                                                  \n",
+            " block1_conv2_act (Activati  (None, 97, 97, 64)           0         ['block1_conv2_bn[0][0]']     \n",
+            " on)                                                                                              \n",
+            "                                                                                                  \n",
+            " block2_sepconv1 (Separable  (None, 97, 97, 128)          8768      ['block1_conv2_act[0][0]']    \n",
+            " Conv2D)                                                                                          \n",
+            "                                                                                                  \n",
+            " block2_sepconv1_bn (BatchN  (None, 97, 97, 128)          512       ['block2_sepconv1[0][0]']     \n",
+            " ormalization)                                                                                    \n",
+            "                                                                                                  \n",
+            " block2_sepconv2_act (Activ  (None, 97, 97, 128)          0         ['block2_sepconv1_bn[0][0]']  \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " block2_sepconv2 (Separable  (None, 97, 97, 128)          17536     ['block2_sepconv2_act[0][0]'] \n",
+            " Conv2D)                                                                                          \n",
+            "                                                                                                  \n",
+            " block2_sepconv2_bn (BatchN  (None, 97, 97, 128)          512       ['block2_sepconv2[0][0]']     \n",
+            " ormalization)                                                                                    \n",
+            "                                                                                                  \n",
+            " conv2d_94 (Conv2D)          (None, 49, 49, 128)          8192      ['block1_conv2_act[0][0]']    \n",
+            "                                                                                                  \n",
+            " block2_pool (MaxPooling2D)  (None, 49, 49, 128)          0         ['block2_sepconv2_bn[0][0]']  \n",
+            "                                                                                                  \n",
+            " batch_normalization_98 (Ba  (None, 49, 49, 128)          512       ['conv2d_94[0][0]']           \n",
+            " tchNormalization)                                                                                \n",
+            "                                                                                                  \n",
+            " add (Add)                   (None, 49, 49, 128)          0         ['block2_pool[0][0]',         \n",
+            "                                                                     'batch_normalization_98[0][0]\n",
+            "                                                                    ']                            \n",
+            "                                                                                                  \n",
+            " block3_sepconv1_act (Activ  (None, 49, 49, 128)          0         ['add[0][0]']                 \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " block3_sepconv1 (Separable  (None, 49, 49, 256)          33920     ['block3_sepconv1_act[0][0]'] \n",
+            " Conv2D)                                                                                          \n",
+            "                                                                                                  \n",
+            " block3_sepconv1_bn (BatchN  (None, 49, 49, 256)          1024      ['block3_sepconv1[0][0]']     \n",
+            " ormalization)                                                                                    \n",
+            "                                                                                                  \n",
+            " block3_sepconv2_act (Activ  (None, 49, 49, 256)          0         ['block3_sepconv1_bn[0][0]']  \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " block3_sepconv2 (Separable  (None, 49, 49, 256)          67840     ['block3_sepconv2_act[0][0]'] \n",
+            " Conv2D)                                                                                          \n",
+            "                                                                                                  \n",
+            " block3_sepconv2_bn (BatchN  (None, 49, 49, 256)          1024      ['block3_sepconv2[0][0]']     \n",
+            " ormalization)                                                                                    \n",
+            "                                                                                                  \n",
+            " conv2d_95 (Conv2D)          (None, 25, 25, 256)          32768     ['add[0][0]']                 \n",
+            "                                                                                                  \n",
+            " block3_pool (MaxPooling2D)  (None, 25, 25, 256)          0         ['block3_sepconv2_bn[0][0]']  \n",
+            "                                                                                                  \n",
+            " batch_normalization_99 (Ba  (None, 25, 25, 256)          1024      ['conv2d_95[0][0]']           \n",
+            " tchNormalization)                                                                                \n",
+            "                                                                                                  \n",
+            " add_1 (Add)                 (None, 25, 25, 256)          0         ['block3_pool[0][0]',         \n",
+            "                                                                     'batch_normalization_99[0][0]\n",
+            "                                                                    ']                            \n",
+            "                                                                                                  \n",
+            " block4_sepconv1_act (Activ  (None, 25, 25, 256)          0         ['add_1[0][0]']               \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " block4_sepconv1 (Separable  (None, 25, 25, 728)          188672    ['block4_sepconv1_act[0][0]'] \n",
+            " Conv2D)                                                                                          \n",
+            "                                                                                                  \n",
+            " block4_sepconv1_bn (BatchN  (None, 25, 25, 728)          2912      ['block4_sepconv1[0][0]']     \n",
+            " ormalization)                                                                                    \n",
+            "                                                                                                  \n",
+            " block4_sepconv2_act (Activ  (None, 25, 25, 728)          0         ['block4_sepconv1_bn[0][0]']  \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " block4_sepconv2 (Separable  (None, 25, 25, 728)          536536    ['block4_sepconv2_act[0][0]'] \n",
+            " Conv2D)                                                                                          \n",
+            "                                                                                                  \n",
+            " block4_sepconv2_bn (BatchN  (None, 25, 25, 728)          2912      ['block4_sepconv2[0][0]']     \n",
+            " ormalization)                                                                                    \n",
+            "                                                                                                  \n",
+            " conv2d_96 (Conv2D)          (None, 13, 13, 728)          186368    ['add_1[0][0]']               \n",
+            "                                                                                                  \n",
+            " block4_pool (MaxPooling2D)  (None, 13, 13, 728)          0         ['block4_sepconv2_bn[0][0]']  \n",
+            "                                                                                                  \n",
+            " batch_normalization_100 (B  (None, 13, 13, 728)          2912      ['conv2d_96[0][0]']           \n",
+            " atchNormalization)                                                                               \n",
+            "                                                                                                  \n",
+            " add_2 (Add)                 (None, 13, 13, 728)          0         ['block4_pool[0][0]',         \n",
+            "                                                                     'batch_normalization_100[0][0\n",
+            "                                                                    ]']                           \n",
+            "                                                                                                  \n",
+            " block5_sepconv1_act (Activ  (None, 13, 13, 728)          0         ['add_2[0][0]']               \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " block5_sepconv1 (Separable  (None, 13, 13, 728)          536536    ['block5_sepconv1_act[0][0]'] \n",
+            " Conv2D)                                                                                          \n",
+            "                                                                                                  \n",
+            " block5_sepconv1_bn (BatchN  (None, 13, 13, 728)          2912      ['block5_sepconv1[0][0]']     \n",
+            " ormalization)                                                                                    \n",
+            "                                                                                                  \n",
+            " block5_sepconv2_act (Activ  (None, 13, 13, 728)          0         ['block5_sepconv1_bn[0][0]']  \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " block5_sepconv2 (Separable  (None, 13, 13, 728)          536536    ['block5_sepconv2_act[0][0]'] \n",
+            " Conv2D)                                                                                          \n",
+            "                                                                                                  \n",
+            " block5_sepconv2_bn (BatchN  (None, 13, 13, 728)          2912      ['block5_sepconv2[0][0]']     \n",
+            " ormalization)                                                                                    \n",
+            "                                                                                                  \n",
+            " block5_sepconv3_act (Activ  (None, 13, 13, 728)          0         ['block5_sepconv2_bn[0][0]']  \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " block5_sepconv3 (Separable  (None, 13, 13, 728)          536536    ['block5_sepconv3_act[0][0]'] \n",
+            " Conv2D)                                                                                          \n",
+            "                                                                                                  \n",
+            " block5_sepconv3_bn (BatchN  (None, 13, 13, 728)          2912      ['block5_sepconv3[0][0]']     \n",
+            " ormalization)                                                                                    \n",
+            "                                                                                                  \n",
+            " add_3 (Add)                 (None, 13, 13, 728)          0         ['block5_sepconv3_bn[0][0]',  \n",
+            "                                                                     'add_2[0][0]']               \n",
+            "                                                                                                  \n",
+            " block6_sepconv1_act (Activ  (None, 13, 13, 728)          0         ['add_3[0][0]']               \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " block6_sepconv1 (Separable  (None, 13, 13, 728)          536536    ['block6_sepconv1_act[0][0]'] \n",
+            " Conv2D)                                                                                          \n",
+            "                                                                                                  \n",
+            " block6_sepconv1_bn (BatchN  (None, 13, 13, 728)          2912      ['block6_sepconv1[0][0]']     \n",
+            " ormalization)                                                                                    \n",
+            "                                                                                                  \n",
+            " block6_sepconv2_act (Activ  (None, 13, 13, 728)          0         ['block6_sepconv1_bn[0][0]']  \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " block6_sepconv2 (Separable  (None, 13, 13, 728)          536536    ['block6_sepconv2_act[0][0]'] \n",
+            " Conv2D)                                                                                          \n",
+            "                                                                                                  \n",
+            " block6_sepconv2_bn (BatchN  (None, 13, 13, 728)          2912      ['block6_sepconv2[0][0]']     \n",
+            " ormalization)                                                                                    \n",
+            "                                                                                                  \n",
+            " block6_sepconv3_act (Activ  (None, 13, 13, 728)          0         ['block6_sepconv2_bn[0][0]']  \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " block6_sepconv3 (Separable  (None, 13, 13, 728)          536536    ['block6_sepconv3_act[0][0]'] \n",
+            " Conv2D)                                                                                          \n",
+            "                                                                                                  \n",
+            " block6_sepconv3_bn (BatchN  (None, 13, 13, 728)          2912      ['block6_sepconv3[0][0]']     \n",
+            " ormalization)                                                                                    \n",
+            "                                                                                                  \n",
+            " add_4 (Add)                 (None, 13, 13, 728)          0         ['block6_sepconv3_bn[0][0]',  \n",
+            "                                                                     'add_3[0][0]']               \n",
+            "                                                                                                  \n",
+            " block7_sepconv1_act (Activ  (None, 13, 13, 728)          0         ['add_4[0][0]']               \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " block7_sepconv1 (Separable  (None, 13, 13, 728)          536536    ['block7_sepconv1_act[0][0]'] \n",
+            " Conv2D)                                                                                          \n",
+            "                                                                                                  \n",
+            " block7_sepconv1_bn (BatchN  (None, 13, 13, 728)          2912      ['block7_sepconv1[0][0]']     \n",
+            " ormalization)                                                                                    \n",
+            "                                                                                                  \n",
+            " block7_sepconv2_act (Activ  (None, 13, 13, 728)          0         ['block7_sepconv1_bn[0][0]']  \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " block7_sepconv2 (Separable  (None, 13, 13, 728)          536536    ['block7_sepconv2_act[0][0]'] \n",
+            " Conv2D)                                                                                          \n",
+            "                                                                                                  \n",
+            " block7_sepconv2_bn (BatchN  (None, 13, 13, 728)          2912      ['block7_sepconv2[0][0]']     \n",
+            " ormalization)                                                                                    \n",
+            "                                                                                                  \n",
+            " block7_sepconv3_act (Activ  (None, 13, 13, 728)          0         ['block7_sepconv2_bn[0][0]']  \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " block7_sepconv3 (Separable  (None, 13, 13, 728)          536536    ['block7_sepconv3_act[0][0]'] \n",
+            " Conv2D)                                                                                          \n",
+            "                                                                                                  \n",
+            " block7_sepconv3_bn (BatchN  (None, 13, 13, 728)          2912      ['block7_sepconv3[0][0]']     \n",
+            " ormalization)                                                                                    \n",
+            "                                                                                                  \n",
+            " add_5 (Add)                 (None, 13, 13, 728)          0         ['block7_sepconv3_bn[0][0]',  \n",
+            "                                                                     'add_4[0][0]']               \n",
+            "                                                                                                  \n",
+            " block8_sepconv1_act (Activ  (None, 13, 13, 728)          0         ['add_5[0][0]']               \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " block8_sepconv1 (Separable  (None, 13, 13, 728)          536536    ['block8_sepconv1_act[0][0]'] \n",
+            " Conv2D)                                                                                          \n",
+            "                                                                                                  \n",
+            " block8_sepconv1_bn (BatchN  (None, 13, 13, 728)          2912      ['block8_sepconv1[0][0]']     \n",
+            " ormalization)                                                                                    \n",
+            "                                                                                                  \n",
+            " block8_sepconv2_act (Activ  (None, 13, 13, 728)          0         ['block8_sepconv1_bn[0][0]']  \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " block8_sepconv2 (Separable  (None, 13, 13, 728)          536536    ['block8_sepconv2_act[0][0]'] \n",
+            " Conv2D)                                                                                          \n",
+            "                                                                                                  \n",
+            " block8_sepconv2_bn (BatchN  (None, 13, 13, 728)          2912      ['block8_sepconv2[0][0]']     \n",
+            " ormalization)                                                                                    \n",
+            "                                                                                                  \n",
+            " block8_sepconv3_act (Activ  (None, 13, 13, 728)          0         ['block8_sepconv2_bn[0][0]']  \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " block8_sepconv3 (Separable  (None, 13, 13, 728)          536536    ['block8_sepconv3_act[0][0]'] \n",
+            " Conv2D)                                                                                          \n",
+            "                                                                                                  \n",
+            " block8_sepconv3_bn (BatchN  (None, 13, 13, 728)          2912      ['block8_sepconv3[0][0]']     \n",
+            " ormalization)                                                                                    \n",
+            "                                                                                                  \n",
+            " add_6 (Add)                 (None, 13, 13, 728)          0         ['block8_sepconv3_bn[0][0]',  \n",
+            "                                                                     'add_5[0][0]']               \n",
+            "                                                                                                  \n",
+            " block9_sepconv1_act (Activ  (None, 13, 13, 728)          0         ['add_6[0][0]']               \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " block9_sepconv1 (Separable  (None, 13, 13, 728)          536536    ['block9_sepconv1_act[0][0]'] \n",
+            " Conv2D)                                                                                          \n",
+            "                                                                                                  \n",
+            " block9_sepconv1_bn (BatchN  (None, 13, 13, 728)          2912      ['block9_sepconv1[0][0]']     \n",
+            " ormalization)                                                                                    \n",
+            "                                                                                                  \n",
+            " block9_sepconv2_act (Activ  (None, 13, 13, 728)          0         ['block9_sepconv1_bn[0][0]']  \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " block9_sepconv2 (Separable  (None, 13, 13, 728)          536536    ['block9_sepconv2_act[0][0]'] \n",
+            " Conv2D)                                                                                          \n",
+            "                                                                                                  \n",
+            " block9_sepconv2_bn (BatchN  (None, 13, 13, 728)          2912      ['block9_sepconv2[0][0]']     \n",
+            " ormalization)                                                                                    \n",
+            "                                                                                                  \n",
+            " block9_sepconv3_act (Activ  (None, 13, 13, 728)          0         ['block9_sepconv2_bn[0][0]']  \n",
+            " ation)                                                                                           \n",
+            "                                                                                                  \n",
+            " block9_sepconv3 (Separable  (None, 13, 13, 728)          536536    ['block9_sepconv3_act[0][0]'] \n",
+            " Conv2D)                                                                                          \n",
+            "                                                                                                  \n",
+            " block9_sepconv3_bn (BatchN  (None, 13, 13, 728)          2912      ['block9_sepconv3[0][0]']     \n",
+            " ormalization)                                                                                    \n",
+            "                                                                                                  \n",
+            " add_7 (Add)                 (None, 13, 13, 728)          0         ['block9_sepconv3_bn[0][0]',  \n",
+            "                                                                     'add_6[0][0]']               \n",
+            "                                                                                                  \n",
+            " block10_sepconv1_act (Acti  (None, 13, 13, 728)          0         ['add_7[0][0]']               \n",
+            " vation)                                                                                          \n",
+            "                                                                                                  \n",
+            " block10_sepconv1 (Separabl  (None, 13, 13, 728)          536536    ['block10_sepconv1_act[0][0]']\n",
+            " eConv2D)                                                                                         \n",
+            "                                                                                                  \n",
+            " block10_sepconv1_bn (Batch  (None, 13, 13, 728)          2912      ['block10_sepconv1[0][0]']    \n",
+            " Normalization)                                                                                   \n",
+            "                                                                                                  \n",
+            " block10_sepconv2_act (Acti  (None, 13, 13, 728)          0         ['block10_sepconv1_bn[0][0]'] \n",
+            " vation)                                                                                          \n",
+            "                                                                                                  \n",
+            " block10_sepconv2 (Separabl  (None, 13, 13, 728)          536536    ['block10_sepconv2_act[0][0]']\n",
+            " eConv2D)                                                                                         \n",
+            "                                                                                                  \n",
+            " block10_sepconv2_bn (Batch  (None, 13, 13, 728)          2912      ['block10_sepconv2[0][0]']    \n",
+            " Normalization)                                                                                   \n",
+            "                                                                                                  \n",
+            " block10_sepconv3_act (Acti  (None, 13, 13, 728)          0         ['block10_sepconv2_bn[0][0]'] \n",
+            " vation)                                                                                          \n",
+            "                                                                                                  \n",
+            " block10_sepconv3 (Separabl  (None, 13, 13, 728)          536536    ['block10_sepconv3_act[0][0]']\n",
+            " eConv2D)                                                                                         \n",
+            "                                                                                                  \n",
+            " block10_sepconv3_bn (Batch  (None, 13, 13, 728)          2912      ['block10_sepconv3[0][0]']    \n",
+            " Normalization)                                                                                   \n",
+            "                                                                                                  \n",
+            " add_8 (Add)                 (None, 13, 13, 728)          0         ['block10_sepconv3_bn[0][0]', \n",
+            "                                                                     'add_7[0][0]']               \n",
+            "                                                                                                  \n",
+            " block11_sepconv1_act (Acti  (None, 13, 13, 728)          0         ['add_8[0][0]']               \n",
+            " vation)                                                                                          \n",
+            "                                                                                                  \n",
+            " block11_sepconv1 (Separabl  (None, 13, 13, 728)          536536    ['block11_sepconv1_act[0][0]']\n",
+            " eConv2D)                                                                                         \n",
+            "                                                                                                  \n",
+            " block11_sepconv1_bn (Batch  (None, 13, 13, 728)          2912      ['block11_sepconv1[0][0]']    \n",
+            " Normalization)                                                                                   \n",
+            "                                                                                                  \n",
+            " block11_sepconv2_act (Acti  (None, 13, 13, 728)          0         ['block11_sepconv1_bn[0][0]'] \n",
+            " vation)                                                                                          \n",
+            "                                                                                                  \n",
+            " block11_sepconv2 (Separabl  (None, 13, 13, 728)          536536    ['block11_sepconv2_act[0][0]']\n",
+            " eConv2D)                                                                                         \n",
+            "                                                                                                  \n",
+            " block11_sepconv2_bn (Batch  (None, 13, 13, 728)          2912      ['block11_sepconv2[0][0]']    \n",
+            " Normalization)                                                                                   \n",
+            "                                                                                                  \n",
+            " block11_sepconv3_act (Acti  (None, 13, 13, 728)          0         ['block11_sepconv2_bn[0][0]'] \n",
+            " vation)                                                                                          \n",
+            "                                                                                                  \n",
+            " block11_sepconv3 (Separabl  (None, 13, 13, 728)          536536    ['block11_sepconv3_act[0][0]']\n",
+            " eConv2D)                                                                                         \n",
+            "                                                                                                  \n",
+            " block11_sepconv3_bn (Batch  (None, 13, 13, 728)          2912      ['block11_sepconv3[0][0]']    \n",
+            " Normalization)                                                                                   \n",
+            "                                                                                                  \n",
+            " add_9 (Add)                 (None, 13, 13, 728)          0         ['block11_sepconv3_bn[0][0]', \n",
+            "                                                                     'add_8[0][0]']               \n",
+            "                                                                                                  \n",
+            " block12_sepconv1_act (Acti  (None, 13, 13, 728)          0         ['add_9[0][0]']               \n",
+            " vation)                                                                                          \n",
+            "                                                                                                  \n",
+            " block12_sepconv1 (Separabl  (None, 13, 13, 728)          536536    ['block12_sepconv1_act[0][0]']\n",
+            " eConv2D)                                                                                         \n",
+            "                                                                                                  \n",
+            " block12_sepconv1_bn (Batch  (None, 13, 13, 728)          2912      ['block12_sepconv1[0][0]']    \n",
+            " Normalization)                                                                                   \n",
+            "                                                                                                  \n",
+            " block12_sepconv2_act (Acti  (None, 13, 13, 728)          0         ['block12_sepconv1_bn[0][0]'] \n",
+            " vation)                                                                                          \n",
+            "                                                                                                  \n",
+            " block12_sepconv2 (Separabl  (None, 13, 13, 728)          536536    ['block12_sepconv2_act[0][0]']\n",
+            " eConv2D)                                                                                         \n",
+            "                                                                                                  \n",
+            " block12_sepconv2_bn (Batch  (None, 13, 13, 728)          2912      ['block12_sepconv2[0][0]']    \n",
+            " Normalization)                                                                                   \n",
+            "                                                                                                  \n",
+            " block12_sepconv3_act (Acti  (None, 13, 13, 728)          0         ['block12_sepconv2_bn[0][0]'] \n",
+            " vation)                                                                                          \n",
+            "                                                                                                  \n",
+            " block12_sepconv3 (Separabl  (None, 13, 13, 728)          536536    ['block12_sepconv3_act[0][0]']\n",
+            " eConv2D)                                                                                         \n",
+            "                                                                                                  \n",
+            " block12_sepconv3_bn (Batch  (None, 13, 13, 728)          2912      ['block12_sepconv3[0][0]']    \n",
+            " Normalization)                                                                                   \n",
+            "                                                                                                  \n",
+            " add_10 (Add)                (None, 13, 13, 728)          0         ['block12_sepconv3_bn[0][0]', \n",
+            "                                                                     'add_9[0][0]']               \n",
+            "                                                                                                  \n",
+            " block13_sepconv1_act (Acti  (None, 13, 13, 728)          0         ['add_10[0][0]']              \n",
+            " vation)                                                                                          \n",
+            "                                                                                                  \n",
+            " block13_sepconv1 (Separabl  (None, 13, 13, 728)          536536    ['block13_sepconv1_act[0][0]']\n",
+            " eConv2D)                                                                                         \n",
+            "                                                                                                  \n",
+            " block13_sepconv1_bn (Batch  (None, 13, 13, 728)          2912      ['block13_sepconv1[0][0]']    \n",
+            " Normalization)                                                                                   \n",
+            "                                                                                                  \n",
+            " block13_sepconv2_act (Acti  (None, 13, 13, 728)          0         ['block13_sepconv1_bn[0][0]'] \n",
+            " vation)                                                                                          \n",
+            "                                                                                                  \n",
+            " block13_sepconv2 (Separabl  (None, 13, 13, 1024)         752024    ['block13_sepconv2_act[0][0]']\n",
+            " eConv2D)                                                                                         \n",
+            "                                                                                                  \n",
+            " block13_sepconv2_bn (Batch  (None, 13, 13, 1024)         4096      ['block13_sepconv2[0][0]']    \n",
+            " Normalization)                                                                                   \n",
+            "                                                                                                  \n",
+            " conv2d_97 (Conv2D)          (None, 7, 7, 1024)           745472    ['add_10[0][0]']              \n",
+            "                                                                                                  \n",
+            " block13_pool (MaxPooling2D  (None, 7, 7, 1024)           0         ['block13_sepconv2_bn[0][0]'] \n",
+            " )                                                                                                \n",
+            "                                                                                                  \n",
+            " batch_normalization_101 (B  (None, 7, 7, 1024)           4096      ['conv2d_97[0][0]']           \n",
+            " atchNormalization)                                                                               \n",
+            "                                                                                                  \n",
+            " add_11 (Add)                (None, 7, 7, 1024)           0         ['block13_pool[0][0]',        \n",
+            "                                                                     'batch_normalization_101[0][0\n",
+            "                                                                    ]']                           \n",
+            "                                                                                                  \n",
+            " block14_sepconv1 (Separabl  (None, 7, 7, 1536)           1582080   ['add_11[0][0]']              \n",
+            " eConv2D)                                                                                         \n",
+            "                                                                                                  \n",
+            " block14_sepconv1_bn (Batch  (None, 7, 7, 1536)           6144      ['block14_sepconv1[0][0]']    \n",
+            " Normalization)                                                                                   \n",
+            "                                                                                                  \n",
+            " block14_sepconv1_act (Acti  (None, 7, 7, 1536)           0         ['block14_sepconv1_bn[0][0]'] \n",
+            " vation)                                                                                          \n",
+            "                                                                                                  \n",
+            " block14_sepconv2 (Separabl  (None, 7, 7, 2048)           3159552   ['block14_sepconv1_act[0][0]']\n",
+            " eConv2D)                                                                                         \n",
+            "                                                                                                  \n",
+            " block14_sepconv2_bn (Batch  (None, 7, 7, 2048)           8192      ['block14_sepconv2[0][0]']    \n",
+            " Normalization)                                                                                   \n",
+            "                                                                                                  \n",
+            " block14_sepconv2_act (Acti  (None, 7, 7, 2048)           0         ['block14_sepconv2_bn[0][0]'] \n",
+            " vation)                                                                                          \n",
+            "                                                                                                  \n",
+            " global_average_pooling2d_4  (None, 2048)                 0         ['block14_sepconv2_act[0][0]']\n",
+            "  (GlobalAveragePooling2D)                                                                        \n",
+            "                                                                                                  \n",
+            " dense_8 (Dense)             (None, 512)                  1049088   ['global_average_pooling2d_4[0\n",
+            "                                                                    ][0]']                        \n",
+            "                                                                                                  \n",
+            " batch_normalization_102 (B  (None, 512)                  2048      ['dense_8[0][0]']             \n",
+            " atchNormalization)                                                                               \n",
+            "                                                                                                  \n",
+            " dropout_4 (Dropout)         (None, 512)                  0         ['batch_normalization_102[0][0\n",
+            "                                                                    ]']                           \n",
+            "                                                                                                  \n",
+            " dense_9 (Dense)             (None, 1)                    513       ['dropout_4[0][0]']           \n",
+            "                                                                                                  \n",
+            "==================================================================================================\n",
+            "Total params: 21913129 (83.59 MB)\n",
+            "Trainable params: 6546945 (24.97 MB)\n",
+            "Non-trainable params: 15366184 (58.62 MB)\n",
+            "__________________________________________________________________________________________________\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.fit(\n",
+        "    train_dataset,\n",
+        "    steps_per_epoch = len(train_dataset),\n",
+        "    epochs = 3,\n",
+        "    validation_data = validation_dataset\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "9aEj5aB-LvNe",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "da9a4181-caeb-4796-9bd5-f8f4d0ddbe2e"
+      },
+      "execution_count": 48,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/3\n",
+            "50/50 [==============================] - 76s 1s/step - loss: 0.9114 - accuracy: 0.5200 - val_loss: 0.6953 - val_accuracy: 0.5941\n",
+            "Epoch 2/3\n",
+            "50/50 [==============================] - 65s 1s/step - loss: 0.7911 - accuracy: 0.5000 - val_loss: 0.3552 - val_accuracy: 0.9901\n",
+            "Epoch 3/3\n",
+            "50/50 [==============================] - 64s 1s/step - loss: 0.7304 - accuracy: 0.5933 - val_loss: 0.5206 - val_accuracy: 0.8911\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<keras.src.callbacks.History at 0x7cdadbe8f220>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 48
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "testingImageData = ImageDataGenerator(rescale = 1/255)\n",
+        "testing_dataset = validImageData.flow_from_directory(\n",
+        "    '/content/drive/MyDrive/Dataset_truck/test_data',\n",
+        "    target_size = (200, 200),\n",
+        "    batch_size = 3,\n",
+        "    class_mode = 'binary'\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "DK7gLarALyog",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "82aa783f-08a4-4ce1-b9f9-9cfbb78e47de"
+      },
+      "execution_count": 49,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Found 215 images belonging to 1 classes.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "loss, accuracy = model.evaluate(testing_dataset)"
+      ],
+      "metadata": {
+        "id": "AAouLoXXL16c",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "15a4510f-77cc-492f-b70a-bf6ef65228d7"
+      },
+      "execution_count": 50,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "72/72 [==============================] - 47s 634ms/step - loss: 0.5176 - accuracy: 0.9070\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "from tensorflow.keras.preprocessing.image import load_img, img_to_array\n",
+        "\n",
+        "# Define the path to a single image (example)\n",
+        "image_path = '/content/drive/MyDrive/Dataset_truck/train_data/truck/00000000 (2).jpg'  # Update with your image path\n",
+        "\n",
+        "# Load and preprocess the image\n",
+        "img = load_img(image_path, target_size=(200, 200))  # Ensure the image is resized to (200, 200)\n",
+        "img_array = img_to_array(img) / 255.0  # Rescale pixel values to [0, 1]\n",
+        "img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension\n",
+        "\n",
+        "# Ensure the model is already defined and loaded as 'truck_model'\n",
+        "# Example: truck_model = tf.keras.models.load_model('path_to_model')\n",
+        "\n",
+        "# Make prediction\n",
+        "prediction = model.predict(img_array)\n",
+        "if prediction[0] < 0.5:  # Adjust threshold based on your model's output\n",
+        "    print(\"Truck\")\n",
+        "else:\n",
+        "    print(\"Not a Truck\")\n"
+      ],
+      "metadata": {
+        "id": "i1O_2myohySV",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "97477352-818f-4011-dda0-ba9bcfb2e8c2"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "1/1 [==============================] - 3s 3s/step\n",
+            "Truck\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from keras.utils import load_img"
+      ],
+      "metadata": {
+        "id": "U8TrkZk1L4f8"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import os\n",
+        "from keras.preprocessing.image import load_img, img_to_array\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "test_path = \"/content/drive/MyDrive/Dataset_truck/test_data\"\n",
+        "count = 0\n",
+        "\n",
+        "for i in os.listdir(test_path):\n",
+        "    file_path = os.path.join(test_path, i)\n",
+        "\n",
+        "    if os.path.isfile(file_path):\n",
+        "        img = load_img(file_path)\n",
+        "        plt.imshow(img)\n",
+        "        plt.show()\n",
+        "\n",
+        "        X = img_to_array(img)\n",
+        "        x_imag = np.expand_dims(X, axis=0)\n",
+        "        images = np.vstack([x_imag])\n",
+        "\n",
+        "        prediction = truck_model.predict(images)\n",
+        "        if prediction == 0:\n",
+        "            print(\"Truck\")\n",
+        "        else:\n",
+        "            print(\"No truck\")\n",
+        "\n",
+        "        count += 1\n",
+        "        if count == 5:\n",
+        "            break"
+      ],
+      "metadata": {
+        "id": "qziIOnHyL7iz"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "QuKdA8rwRZXt"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from keras import models"
+      ],
+      "metadata": {
+        "id": "v9xYeLmcL_Rc"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "truck_model_ann = models.Sequential([\n",
+        "    layers.Flatten(input_shape=(200, 200, 3)),\n",
+        "\n",
+        "    layers.Dense(3000, activation='relu'),\n",
+        "    layers.BatchNormalization(),\n",
+        "    layers.Dropout(0.5),\n",
+        "\n",
+        "    layers.Dense(1000, activation='relu'),\n",
+        "    layers.BatchNormalization(),\n",
+        "    layers.Dropout(0.5),\n",
+        "\n",
+        "    layers.Dense(1, activation='sigmoid')\n",
+        "])"
+      ],
+      "metadata": {
+        "id": "jk8g92UOMCt0"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "truck_model_ann.summary()"
+      ],
+      "metadata": {
+        "id": "dICWnparMFdx",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "9c3ba437-2203-4c87-932b-b1ad93dc3fda"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Model: \"sequential_1\"\n",
+            "_________________________________________________________________\n",
+            " Layer (type)                Output Shape              Param #   \n",
+            "=================================================================\n",
+            " flatten_1 (Flatten)         (None, 120000)            0         \n",
+            "                                                                 \n",
+            " dense_9 (Dense)             (None, 3000)              360003000 \n",
+            "                                                                 \n",
+            " batch_normalization_5 (Bat  (None, 3000)              12000     \n",
+            " chNormalization)                                                \n",
+            "                                                                 \n",
+            " dropout_5 (Dropout)         (None, 3000)              0         \n",
+            "                                                                 \n",
+            " dense_10 (Dense)            (None, 1000)              3001000   \n",
+            "                                                                 \n",
+            " batch_normalization_6 (Bat  (None, 1000)              4000      \n",
+            " chNormalization)                                                \n",
+            "                                                                 \n",
+            " dropout_6 (Dropout)         (None, 1000)              0         \n",
+            "                                                                 \n",
+            " dense_11 (Dense)            (None, 1)                 1001      \n",
+            "                                                                 \n",
+            "=================================================================\n",
+            "Total params: 363021001 (1.35 GB)\n",
+            "Trainable params: 363013001 (1.35 GB)\n",
+            "Non-trainable params: 8000 (31.25 KB)\n",
+            "_________________________________________________________________\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "truck_model_ann.compile(\n",
+        "    optimizer = 'sgd',\n",
+        "    loss = 'binary_crossentropy',\n",
+        "    metrics = ['accuracy']\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "hpPOleyQMIWe"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "history = truck_model_ann.fit(train_dataset, validation_data = validation_dataset, epochs = 2)"
+      ],
+      "metadata": {
+        "id": "n7G2-SaaMLbV",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "701d4dac-bafd-4a58-c59d-3d9e29db7989"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/2\n",
+            "50/50 [==============================] - 148s 3s/step - loss: 0.9125 - accuracy: 0.5933 - val_loss: 0.4900 - val_accuracy: 0.7624\n",
+            "Epoch 2/2\n",
+            "50/50 [==============================] - 145s 3s/step - loss: 0.7963 - accuracy: 0.6267 - val_loss: 0.0499 - val_accuracy: 0.9802\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.plot(history.history['accuracy'])\n",
+        "plt.plot(history.history['val_accuracy'])\n",
+        "plt.xlabel('epoch')\n",
+        "plt.ylabel('accuracy')\n",
+        "plt.title(\"Model accuracy\")\n",
+        "plt.legend(['Training', \"Validation\"], loc = 'upper left')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "4C5Ft_yQMOKU",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "outputId": "c72eee7e-38bb-4f77-d18e-fe9b656714e8"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.plot(history.history['loss'])\n",
+        "plt.plot(history.history['val_loss'])\n",
+        "plt.title('model loss')\n",
+        "plt.ylabel('loss')\n",
+        "plt.xlabel('epoch')\n",
+        "plt.legend(['Train', 'Validation'], loc='upper left')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "XU5zooGkMQ2G",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "outputId": "0f1e4f94-ea6e-44ec-a3f7-32c2827f6b10"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "7IyAzqHVMUbS"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file

From c1fd99e0e0a1a23e90a475a8e1211b9f88ef3cde Mon Sep 17 00:00:00 2001
From: Akansha-Mulchandani
 <123954924+Akansha-Mulchandani@users.noreply.github.com>
Date: Wed, 12 Jun 2024 15:09:25 +0530
Subject: [PATCH 2/2] Update Readme.md

---
 Indian Truck Detection/Readme.md | 24 ++++++++++++++++++++----
 1 file changed, 20 insertions(+), 4 deletions(-)

diff --git a/Indian Truck Detection/Readme.md b/Indian Truck Detection/Readme.md
index 252fdd757..64970e539 100644
--- a/Indian Truck Detection/Readme.md	
+++ b/Indian Truck Detection/Readme.md	
@@ -34,6 +34,8 @@
 <ul>
     <li>Convolution Neural Network using MaxPooling layers</li>
     <li>Artificial Neural Networks</li>
+    <li>Xception</li>
+    <li>ResNet50</li>
 </ul>
 
 ### The project also contains a Python script. It performs two functions:
@@ -43,7 +45,7 @@
 ### Details:
 > Convolutional Neural Network
 <ul>
-    <li>Accuracy: 100%</li>
+    <li>Accuracy: 90.85%</li>
     <li>Activation Functions: Rectified Linear Unit, Sigmoid</li>
     <li>Input Size of images: (200, 200)</li>
     <li>MaxPooling layers used: 3</li>
@@ -52,15 +54,29 @@
 
 > Artificial Neural Network
 <ul>
-    <li>Accuracy: 100%</li>
+    <li>Accuracy: 98.02%</li>
     <li>Activation Functions: Rectified Linear Unit, Softmax</li>
     <li>Input Size of images: (200, 200)</li>
     <li>Dense layers used: 2</li>
 </ul>
 
+>ResNet50
+<ul>
+    <li>Accuracy: 98.60%</li>
+    <li>Activation Functions: Rectified Linear Unit, Sigmoid</li>
+    <li>Input Size of images: (200, 200)</li>
+    <li>MaxPooling layers used: 3</li>
+    <li>Filters applied: 3</li>
+</ul>
 
-
-
+>Xception
+<ul>
+    <li>Accuracy: 98.70%</li>
+    <li>Activation Functions: Rectified Linear Unit, Sigmoid</li>
+    <li>Input Size of images: (200, 200)</li>
+    <li>MaxPooling layers used: 3</li>
+    <li>Filters applied: 3</li>
+</ul>