diff --git a/DL_Object Recognition System.pdf b/DL_Object Recognition System.pdf
new file mode 100644
index 0000000..11b31e2
Binary files /dev/null and b/DL_Object Recognition System.pdf differ
diff --git a/InfosysNew.ipynb b/InfosysNew.ipynb
new file mode 100644
index 0000000..165ccda
--- /dev/null
+++ b/InfosysNew.ipynb
@@ -0,0 +1,540 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyMRglrZ8LdRU05o+d7Qotcg",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/AabidMK/Object-Recognition-System__Infosys_Internship_Feb2025/blob/SAHAANA_SG/InfosysNew.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "R_BWs-lJIOME",
+        "outputId": "330ba608-b920-461e-f3d6-3f35565962f5"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /content/drive\n"
+          ]
+        }
+      ],
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Import the necessary libraries\n",
+        "import os\n",
+        "import json\n",
+        "import zipfile\n",
+        "import cv2\n",
+        "import numpy as np\n",
+        "import random\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "import pandas as pd\n",
+        "from sklearn.model_selection import train_test_split\n"
+      ],
+      "metadata": {
+        "id": "JumwhNIAIO5S"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Extract the dataset\n",
+        "dataset_path = \"/content/drive/My Drive/dataset/coco2017_subset.zip\"\n",
+        "extract_to = \"/content/\"\n",
+        "\n",
+        "with zipfile.ZipFile(dataset_path, 'r') as zip_ref:\n",
+        "    zip_ref.extractall(extract_to)\n",
+        "\n",
+        "print(\"Dataset extracted successfully.\")\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Sp1j_wOEIZ9D",
+        "outputId": "4231172e-9229-4b45-d5f5-e767106f91f6"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Dataset extracted successfully.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Load and explore the dataset\n",
+        "annotation_file = os.path.join(extract_to, \"coco2017_subset\", \"annotation_subset\", \"instances_train2017_subset.json\")\n",
+        "\n",
+        "with open(annotation_file, 'r') as f:\n",
+        "    coco_data = json.load(f)\n",
+        "\n",
+        "print(\"COCO annotation file loaded successfully.\")\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Wb9vSBM-Ieos",
+        "outputId": "13782428-ae2b-4e91-c918-6258790d9015"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "COCO annotation file loaded successfully.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Verifying the JSON keys\n",
+        "print(\"Available keys in COCO dataset:\", coco_data.keys())\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "IBiuJdojJWY3",
+        "outputId": "b103b958-8a18-4f93-8482-8c2b2bff88ac"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Available keys in COCO dataset: dict_keys(['info', 'licenses', 'images', 'annotations', 'categories'])\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Categories in dataset\n",
+        "categories = coco_data[\"categories\"]\n",
+        "print(\"Total categories:\", len(categories))\n",
+        "print(\"Sample categories:\", categories[:5])\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "kJMGCmJsJhRe",
+        "outputId": "3ca6977f-f471-4b63-d439-cb129405b9c1"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Total categories: 80\n",
+            "Sample categories: [{'supercategory': 'person', 'id': 1, 'name': 'person'}, {'supercategory': 'vehicle', 'id': 2, 'name': 'bicycle'}, {'supercategory': 'vehicle', 'id': 3, 'name': 'car'}, {'supercategory': 'vehicle', 'id': 4, 'name': 'motorcycle'}, {'supercategory': 'vehicle', 'id': 5, 'name': 'airplane'}]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Image Metadata\n",
+        "images = coco_data[\"images\"]\n",
+        "print(\"Total images in subset:\", len(images))\n",
+        "print(\"Sample image metadata:\", images[:3])\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "RdWzkbviJlGN",
+        "outputId": "3ea41587-a2bb-48df-edaf-ea903b7b1444"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Total images in subset: 29571\n",
+            "Sample image metadata: [{'license': 4, 'file_name': '000000522418.jpg', 'coco_url': 'http://images.cocodataset.org/train2017/000000522418.jpg', 'height': 480, 'width': 640, 'date_captured': '2013-11-14 11:38:44', 'flickr_url': 'http://farm1.staticflickr.com/1/127244861_ab0c0381e7_z.jpg', 'id': 522418}, {'license': 3, 'file_name': '000000554625.jpg', 'coco_url': 'http://images.cocodataset.org/train2017/000000554625.jpg', 'height': 640, 'width': 426, 'date_captured': '2013-11-14 16:03:19', 'flickr_url': 'http://farm5.staticflickr.com/4086/5094162993_8f59d8a473_z.jpg', 'id': 554625}, {'license': 2, 'file_name': '000000309022.jpg', 'coco_url': 'http://images.cocodataset.org/train2017/000000309022.jpg', 'height': 480, 'width': 640, 'date_captured': '2013-11-14 17:28:23', 'flickr_url': 'http://farm4.staticflickr.com/3790/10167396295_e63f2856d0_z.jpg', 'id': 309022}]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Number of Annotations\n",
+        "annotations = coco_data[\"annotations\"]\n",
+        "print(\"Total annotations in subset:\", len(annotations))\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "J8gnK1-JJpbh",
+        "outputId": "2f47c221-05a7-4e1f-e95d-769e42e85622"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Total annotations in subset: 212806\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#WORKING WITH IMAGES"
+      ],
+      "metadata": {
+        "id": "fAQeFj0LJuDV"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Images in Training Dataset\n",
+        "train_folder = os.path.join(extract_to, \"coco2017_subset\", \"train2017\")\n",
+        "print(\"Files in train2017:\", os.listdir(train_folder)[:10])  # Show first 10 files\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "2NEXAm0GJxf4",
+        "outputId": "c7884302-9302-45f1-d1e5-49424f56445f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Files in train2017: ['000000318087.jpg', '000000109005.jpg', '000000350534.jpg', '000000466491.jpg', '000000208976.jpg', '000000401433.jpg', '000000085048.jpg', '000000423172.jpg', '000000413999.jpg', '000000157416.jpg']\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Display a random image"
+      ],
+      "metadata": {
+        "id": "U5ydE4PjJ1mF"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "random_image = random.choice(images)\n",
+        "image_path = os.path.join(train_folder, random_image[\"file_name\"])\n",
+        "\n",
+        "if os.path.exists(image_path):\n",
+        "    image = cv2.imread(image_path)\n",
+        "    if image is None:\n",
+        "        print(\"OpenCV could not read the image. Check the file format.\")\n",
+        "    else:\n",
+        "        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
+        "        plt.imshow(image)\n",
+        "        plt.axis(\"off\")\n",
+        "        plt.title(f\"Sample Image: {random_image['file_name']}\")\n",
+        "        plt.show()\n",
+        "else:\n",
+        "    print(\"Error: Image file not found!\")\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 224
+        },
+        "id": "1CYXK9kDJ5an",
+        "outputId": "a74d60ca-bc4c-45e4-e201-072f3fbc3d3c"
+      },
+      "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": [
+        "#Annotations of the image"
+      ],
+      "metadata": {
+        "id": "MZqlxvrGJ51E"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "image_id = random_image[\"id\"]\n",
+        "image_annotations = [ann for ann in annotations if ann[\"image_id\"] == image_id]\n",
+        "\n",
+        "print(f\"Annotations for image {image_id}:\")\n",
+        "print(image_annotations[:5])  # Show first 5 annotations\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "0NxOJaD1J-Th",
+        "outputId": "ccd9075e-ebd5-4032-d019-8d07a5c4ff5b"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Annotations for image 381887:\n",
+            "[{'segmentation': [[379.93, 129.41, 386.89, 124.77, 402.55, 114.33, 417.05, 113.17, 425.17, 117.23, 436.19, 121.87, 437.94, 134.63, 425.17, 141.01, 408.35, 134.63, 392.69, 127.67, 381.09, 129.99, 271.46, 129.41, 258.7, 131.73, 244.2, 129.99, 238.4, 122.45, 245.94, 111.42, 260.44, 113.17, 269.72, 118.97, 272.04, 125.93, 283.06, 129.41, 287.12, 121.29, 284.22, 111.42, 281.9, 98.08, 287.12, 98.66, 277.26, 89.38, 277.84, 77.2, 275.52, 66.18, 281.9, 44.72, 288.86, 38.34, 290.6, 40.66, 301.62, 30.22, 314.97, 24.42, 327.15, 24.42, 341.65, 28.48, 353.83, 40.66, 361.95, 41.82, 367.17, 53.42, 365.43, 54.0, 370.65, 62.7, 376.45, 72.56, 377.03, 80.1, 372.97, 89.38, 368.91, 94.02, 370.65, 96.92, 373.55, 99.82, 367.75, 105.62, 366.01, 114.33, 371.23, 117.23, 376.45, 112.0, 375.29, 118.97, 379.35, 117.81, 378.19, 128.83]], 'area': 10203.7284, 'iscrowd': 0, 'image_id': 381887, 'bbox': [238.4, 24.42, 199.54, 116.59], 'category_id': 18, 'id': 6984}, {'segmentation': [[165.79, 63.86, 160.7, 76.95, 161.97, 87.68, 165.25, 98.23, 174.34, 103.69, 186.16, 107.32, 202.71, 106.23, 209.81, 102.05, 215.63, 93.32, 218.9, 83.86, 219.08, 73.13, 215.44, 63.86, 209.62, 58.58, 204.9, 52.94, 195.07, 51.85, 184.34, 50.4, 186.16, 48.58, 187.25, 48.76, 186.16, 46.94, 185.25, 43.49, 190.71, 42.94, 191.8, 40.03, 193.44, 36.39, 191.44, 30.21, 190.89, 28.03, 189.8, 26.21, 191.26, 25.48, 186.53, 22.93, 184.16, 20.57, 186.16, 18.75, 181.98, 18.39, 181.8, 17.48, 179.98, 16.75, 179.98, 18.75, 174.71, 17.3, 174.16, 20.21, 178.71, 20.93, 179.25, 22.75, 175.8, 24.94, 174.71, 23.66, 173.98, 24.03, 175.61, 33.48, 175.25, 34.76, 164.7, 42.21, 155.06, 48.4, 155.43, 52.4, 154.88, 68.4, 177.25, 44.4, 179.25, 48.03, 178.89, 51.31, 179.25, 52.76, 172.34, 55.85, 166.88, 61.13]], 'area': 3386.211200000002, 'iscrowd': 0, 'image_id': 381887, 'bbox': [154.88, 16.75, 64.2, 90.57], 'category_id': 2, 'id': 2168497}]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Boundary Boxes"
+      ],
+      "metadata": {
+        "id": "d04VYQQ_J_t3"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def draw_bboxes(image_path, image_annotations):\n",
+        "    image = cv2.imread(image_path)\n",
+        "    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
+        "\n",
+        "    for ann in image_annotations:\n",
+        "        bbox = ann[\"bbox\"]  # [x, y, width, height]\n",
+        "        x, y, w, h = map(int, bbox)\n",
+        "        category_id = ann[\"category_id\"]\n",
+        "\n",
+        "        # Draw bounding box\n",
+        "        cv2.rectangle(image, (x, y), (x + w, y + h), (255, 0, 0), 2)\n",
+        "\n",
+        "        # Add category ID\n",
+        "        cv2.putText(image, str(category_id), (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)\n",
+        "\n",
+        "    plt.figure(figsize=(8, 6))\n",
+        "    plt.imshow(image)\n",
+        "    plt.axis(\"off\")\n",
+        "    plt.title(\"Image with Bounding Boxes\")\n",
+        "    plt.show()\n",
+        "\n",
+        "# Call the function\n",
+        "draw_bboxes(image_path, image_annotations)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 266
+        },
+        "id": "Oe-xhY8TKEPr",
+        "outputId": "b7429c02-7abe-4efc-dc4b-2a7fd837839a"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x600 with 1 Axes>"
+            ],
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoAAAAD5CAYAAAC3f4QDAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzs/Xm4NVlZ341/1lo17eGc84w9Ak3TIMigKBEw0ICCIkIQUcGol4ADxiECMQZN3oSYN+JEoj/FCEjiQCBcKq8jThBBOg4IEhVkEOmmoednOsMealjD7497rdq1z3OepsFOMGHffZ1+zqldu2rVqjV87+89qRBCYCMb2chGNrKRjWxkI58xoj/dDdjIRjaykY1sZCMb2cj/XtkAwI1sZCMb2chGNrKRzzDZAMCNbGQjG9nIRjaykc8w2QDAjWxkIxvZyEY2spHPMNkAwI1sZCMb2chGNrKRzzDZAMCNbGQjG9nIRjaykc8w2QDAjWxkIxvZyEY2spHPMNkAwI1sZCMb2chGNrKRzzDZAMCNbGQjG9nIRjaykc8w2QDAjWxkI/ea/Nt/+29RSn1S5549e/Z/cav+fsrb3/52lFK8/e1v7489//nP5/73v/+nrU0b2chGPnNkAwA3spFPUn7+538epRTvfve7P91N+T9CXv7yl/Nrv/Zr9/p1n//856OU6n+yLOO+970vX/u1X8v73//+e/1+/7fLk570pLX+LIqCa6+9lhe+8IV8/OMf/3Q3byMb2ci9LNmnuwEb2chG/u+R/+f/+X/4vu/7vrVjL3/5y/nqr/5qnvWsZ93r9yvLkte+9rUAWGv5yEc+wqte9Sp+93d/l/e///1cddVV9/o9/1fKz/7sz+K9/7Td/z73uQ8/9EM/BEDbtrz//e/nVa96Fb/3e7/HBz7wAcbj8aetbRvZyEbuXdkAwI1sZCP3mmRZRpb971tWsizjG77hG9aOPfaxj+UZz3gGb37zm/nWb/3W/21tuTckz/NP6/13dnYu6s9rr72W7/qu7+KP/uiP+JIv+ZJPU8s2spGN3NuyMQFvZCP3gjz/+c9nOp3ysY99jGc84xlMp1OuvvpqfvqnfxqA9773vXzxF38xk8mEa665hje84Q1r3z9//jz//J//cx7xiEcwnU7Z3t7maU97Gn/5l3950b1uvvlmnvnMZzKZTLjssst4yUtewu/93u9d5E8G8M53vpMv+7IvY2dnh/F4zBOf+ET+6I/+6G6fJYTAqVOn+Gf/7J/1x7z3HDt2DGMMu7u7/fEf+ZEfIcsyZrMZcLEPoFKK+XzOL/zCL/Smxec///lr99vd3eX5z38+x44dY2dnhxe84AUsFou7bePdyRVXXAFwERC98cYb+Zqv+RpOnDjBeDzmsY99LG9+85vXzknm/Y9+9KNrx4/y13vSk57Ewx/+cN7//vfzRV/0RYzHY66++mp+9Ed/9KI23XLLLTzrWc9ae2dN01x03mEfwI9+9KMopXjFK17Ba17zGq677jrKsuQLvuALeNe73nXR93/5l3+Zhz70oVRVxcMf/nB+9Vd/9e/sV3ip/vyf//N/8rSnPY3t7W2m0ylPfvKT+dM//dP+8z/4gz9Aa82/+Tf/Zu17b3jDG1BK8TM/8zP9sVtvvZVv+qZv4vLLL6csSx72sIfxX/7Lf7moLT/1Uz/Fwx72MMbjMcePH+cf/IN/cNFc2shGNnLPZMMAbmQj95I453ja057GE57wBH70R3+U17/+9XzXd30Xk8mEf/Wv/hVf//Vfz7Of/Wxe9apX8Y3f+I184Rd+Iddeey0g4OTXfu3X+Jqv+RquvfZa7rzzTl796lfzxCc+cc2UOZ/P+eIv/mJuv/12XvSiF3HFFVfwhje8gbe97W0XtecP/uAPeNrTnsajHvUoXvayl6G15ud+7uf44i/+Ym644QYe/ehHH/kcSike97jH8Y53vKM/9ld/9Vfs7e2hteaP/uiPePrTnw7ADTfcwOd93ucxnU6PvNbrXvc6vuVbvoVHP/rRvPCFLwTguuuuWzvnOc95Dtdeey0/9EM/xHve8x5e+9rXctlll/EjP/Ij96jfUxCJc44bb7yRl770pZw8eZJnPOMZ/Tl33nkn//Af/kMWiwXf/d3fzcmTJ/mFX/gFnvnMZ/Irv/IrfOVXfuU9utdhuXDhAl/2ZV/Gs5/9bJ7znOfwK7/yK7z0pS/lEY94BE972tMAWC6XPPnJT+ZjH/sY3/3d381VV13F6173Ov7gD/7gHt/nDW94AwcHB3zbt30bSil+9Ed/lGc/+9nceOONPWv45je/mec+97k84hGP4Id+6Ie4cOEC3/zN38zVV199j+/jnOv7s+s6PvCBD/Cyl72MBz7wgTzucY/rz/vrv/5rrr/+era3t/kX/+JfkOc5r371q3nSk57EH/7hH/KYxzyGL/7iL+Y7vuM7+KEf+iGe9axn8fmf//ncfvvt/NN/+k95ylOewj/5J/8EkHfz2Mc+FqUU3/Vd38Xp06f5nd/5Hb75m7+Z/f19XvziFwNiHv/u7/5uvvqrv5oXvehF1HXNX/3VX/HOd76Tr/u6r7vHz7iRjWwkStjIRjbyScnP/dzPBSC8613v6o8973nPC0B4+ctf3h+7cOFCGI1GQSkV3vjGN/bHP/jBDwYgvOxlL+uP1XUdnHNr97nppptCWZbh3/27f9cf+w//4T8EIPzar/1af2y5XIaHPOQhAQhve9vbQggheO/Dgx70oPDUpz41eO/7cxeLRbj22mvDl3zJl9ztM/7Yj/1YMMaE/f39EEIIP/mTPxmuueaa8OhHPzq89KUvDSGE4JwLx44dCy95yUv6773sZS8Lh5eVyWQSnve85110j3TuN33TN60d/8qv/Mpw8uTJu21fCKs+P/xz9dVXhz//8z9fO/fFL35xAMINN9zQHzs4OAjXXnttuP/979/3fXq3N91009r33/a2t631bwghPPGJTwxA+MVf/MX+WNM04Yorrghf9VVf1R/7iZ/4iQCEX/qlX+qPzefz8MAHPvCiaz7vec8L11xzTf/3TTfdFIBw8uTJcP78+f74r//6rwcg/OZv/mZ/7BGPeES4z33uEw4ODvpjb3/72wOwds1LSXqewz+f/dmfHW688ca1c5/1rGeFoijCRz7ykf7YbbfdFra2tsITnvCEi57zYQ97WKjrOjz96U8P29vb4eabb+7P+eZv/uZw5ZVXhrNnz67d42u/9mvDzs5OWCwWIYQQvuIrviI87GEP+4TPsZGNbOSeycYEvJGN3IvyLd/yLf3vx44d48EPfjCTyYTnPOc5/fEHP/jBHDt2jBtvvLE/VpYlWst0dM5x7tw5ptMpD37wg3nPe97Tn/e7v/u7XH311Tzzmc/sj1VVdZGv21/8xV/w4Q9/mK/7uq/j3LlznD17lrNnzzKfz3nyk5/MO97xjrsNNrj++utxzvHHf/zHgDB9119/Pddffz033HADAO973/vY3d3l+uuv/1S6qpfEBA3vfe7cOfb39z/hd6uq4i1veQtvectb+L3f+z1e/epXM51O+fIv/3L+5m/+pj/vt3/7t3n0ox/N4x//+P7YdDrlhS98IR/96Ec/5ajh6XS65jNXFAWPfvSj197tb//2b3PllVfy1V/91f2x8XjcM6L3RJ773Ody/Pjx/u/U5+k+t912G+9973v5xm/8xjU29olPfCKPeMQj7vF97n//+/f9+Tu/8zv8xE/8BHt7ezztaU/jzJkzgIzP3//93+dZz3oWD3jAA/rvXnnllXzd130d/+N//I/+3Y3HY37+53+eD3zgAzzhCU/gzW9+Mz/+4z/O/e53P0DcDd70pjfxj/7RPyKE0I/Ts2fP8tSnPpW9vb1+/B87doxbbrnlSNP3RjaykU9eNgBwIxu5l6SqKk6fPr12bGdnh/vc5z4X5cbb2dnhwoUL/d/ee378x3+cBz3oQZRlyalTpzh9+nRvek1y8803c9111110vQc+8IFrf3/4wx8G4HnPex6nT59e+3nta19L0zRr1z0sn//5n894PO7BXgKAT3jCE3j3u99NXdf9Z0NQ9alIAgNJEtAZ9s+lxBjDU57yFJ7ylKfwpV/6pbzwhS/krW99K3t7e3z/939/f97NN9/Mgx/84Iu+/9mf/dn955+KHPVujx8/vtb2m2++mQc+8IEXnXdUey4ln6iPUvsPj4NLHbuUTCaTvj+/7Mu+jBe96EX8xm/8Bh/60If44R/+YQDOnDnDYrG4ZH9679fSxjzucY/j27/92/mzP/sznvrUp/JN3/RN/Wdnzpxhd3eX17zmNReN0xe84AUA3HXXXQC89KUvZTqd8uhHP5oHPehBfOd3fucn9GfdyEY2cmnZ+ABuZCP3khhjPqnjIYT+95e//OX863/9r/mmb/om/t//9//lxIkTaK158Ytf/CmlBUnf+bEf+zEe+chHHnnOpfz2QKJRH/OYx/COd7yDv/3bv+WOO+7g+uuv5/LLL6frOt75zndyww038JCHPOQi0PvJyj3pn09G7nOf+/DgBz94zYfxnsqlklg75448fm+3/VLyv+s+R8mjHvUodnZ2PqX+BGiapg+e+chHPsJisejTyaRx+g3f8A0873nPO/L7n/M5nwMIuPzQhz7Eb/3Wb/G7v/u7vOlNb+I//af/xL/5N/+GH/iBH/iU2raRjXwmywYAbmQjfw/kV37lV/iiL/oi/vN//s9rx3d3dzl16lT/9zXXXMP73/9+QghrYOVv//Zv176XAi22t7d5ylOe8im16frrr+dHfuRHeOtb38qpU6d4yEMeglKKhz3sYdxwww3ccMMNa4EWl5J7Whnk3hRrbR+ZDNJvH/rQhy4674Mf/GD/OayYtWGkM3zqDGG69vve976L3tlR7fm73AMuHgeXOvbJinOu78/Tp08zHo8v2Z9aa+573/v2x172spfxgQ98gFe84hW89KUv5fu+7/v4yZ/8yf5aW1tbOOfu0TidTCY897nP5bnPfS5t2/LsZz+bH/zBH+T7v//7qarq7/ycG9nIZ5JsTMAb2cjfAzHGXMTm/PIv/zK33nrr2rGnPvWp3HrrrfzGb/xGf6yua372Z3927bxHPepRXHfddbziFa9YA0JJkj/X3cn1119P0zT8xE/8BI9//ON78HL99dfzute9jttuu+0e+f9NJpOLANX/Svmbv/kbPvShD/G5n/u5/bEv//Iv58/+7M/4kz/5k/7YfD7nNa95Dfe///156EMfCqyA85Dtcs7xmte85lNuz5d/+Zdz22238Su/8iv9scVi8Xe65mG56qqrePjDH84v/uIvrr3vP/zDP+S9733v3+nab3vb25jNZn1/GmP40i/9Un791399LV3OnXfeyRve8AYe//jHs729DUgaole84hW8+MUv5nu+53v43u/9Xl75ylfyh3/4h/21vuqrvoo3velNvO9977vo3sNxeu7cubXPiqLgoQ99KCEEuq77Oz3jRjbymSgbBnAjG/l7IM94xjP4d//u3/GCF7yAf/gP/yHvfe97ef3rX7/mZA/wbd/2bbzyla/kH//jf8yLXvQirrzySl7/+tf37EcCaVprXvva1/K0pz2Nhz3sYbzgBS/g6quv5tZbb+Vtb3sb29vb/OZv/ubdtukLv/ALybKMD33oQ2sBC094whP6HG73BAA+6lGP4q1vfSv/8T/+R6666iquvfZaHvOYx3xS/XMpsdbyX//rfwXEnPjRj36UV73qVXjvednLXtaf933f9338t//233ja057Gd3/3d3PixAl+4Rd+gZtuuok3velNfQDOwx72MB772Mfy/d///Zw/f54TJ07wxje+EWvtp9zGb/3Wb+WVr3wl3/iN38if//mfc+WVV/K6173uXq+q8fKXv5yv+Iqv4HGPexwveMELuHDhAq985St5+MMffqQScJTs7e31/Wmt5UMf+hA/8zM/w2g0Wqvw8u///b/nLW95C49//OP5ju/4DrIs49WvfjVN0/R5EOu65nnPex4PetCD+MEf/EEAfuAHfoDf/M3f5AUveAHvfe97mUwm/PAP/zBve9vbeMxjHsO3fuu38tCHPpTz58/znve8h7e+9a2cP38egC/90i/liiuu4HGPexyXX345H/jAB3jlK1/J05/+dLa2tu7NrtzIRj4z5NMVfryRjfyfKpdKAzOZTC4694lPfOKRqSuuueaa8PSnP73/u67r8D3f8z3hyiuvDKPRKDzucY8Lf/InfxKe+MQnhic+8Ylr373xxhvD05/+9DAajcLp06fD93zP94Q3velNAQh/+qd/unbu//yf/zM8+9nPDidPngxlWYZrrrkmPOc5zwn//b//93v0rF/wBV8QgPDOd76zP3bLLbcEINz3vve96Pyj0sB88IMfDE94whPCaDQKQJ8SJp175syZtfMvlYrlsByVBmZ7ezs8+clPDm9961svOv8jH/lI+Oqv/upw7NixUFVVePSjHx1+67d+68jznvKUp4SyLMPll18e/uW//JfhLW95y5FpYI56t4dTuYQQws033xye+cxnhvF4HE6dOhVe9KIXhd/93d+9x2lgfuzHfuyi+3AolVAIIbzxjW8MD3nIQ0JZluHhD394+I3f+I3wVV/1VeEhD3nI0Z04kMNpYJRS4cSJE+GZz3zmRWl1QgjhPe95T3jqU58aptNpGI/H4Yu+6IvCH//xH/efv+QlLwnGmLWxE0II7373u0OWZeHbv/3b+2N33nln+M7v/M5w3/veN+R5Hq644orw5Cc/ObzmNa/pz3n1q18dnvCEJ/Rj+brrrgvf+73fG/b29j7hs21kIxu5WFQI/xu8iDeykY38L5Wf+Imf4CUveQm33HLLJ5X4dyP/98sjH/lITp8+zVve8pZPd1M2spGN/D2SjQ/gRjbyf5gsl8u1v+u65tWvfjUPetCDNuDvM1i6rrvIVP32t7+dv/zLv+RJT3rSp6dRG9nIRv7eysYHcCMb+T9Mnv3sZ3O/+92PRz7ykb3P1gc/+EFe//rXf7qbtpFPo9x666085SlP4Ru+4Ru46qqr+OAHP8irXvUqrrjiiouSbW9kIxvZyAYAbmQj/4fJU5/6VF772tfy+te/HuccD33oQ3njG9/Ic5/73E930zbyaZTjx4/zqEc9ite+9rWcOXOGyWTC05/+dH74h3+YkydPfrqbt5GNbOTvmWx8ADeykY1sZCMb2chGPsNk4wO4kY1sZCMb2chGNvIZJhsAuJGNbGQjG9nIRjbyGSYbALiRjWxkIxvZyEY28hkm9zgI5JZzdzCtRuTaoAP4EFh0DQBVUVLmOSpIBYLb9s5hraPKcnbGE0yW0TYNIUilAq9hv15g247gPJnWVFXFzs4Ou7u7zJYLUIosy1BeUWQ5o7KkKApQsKgXXNjdpSzLvph4lmU451BKoeJ3syyjdZama6UOZwgoF/AhkGUZIayqJiilCTEFasATVECjCM4TABSgFMpoDEpSpSr5QgirH+cDnvX6pyHWANVak2UZdV3TdHVf+ss5BwrG2YjxaExRlbS248LeLs57FFIyqchyMqXpgscHT9d11HXNdDrtn9973/+A7/tEa43SCqWyvk2p74zW5HneF5x3zuFc15/jvY/Pp2KfOnb3drnlllv4wkc/huVy2fc7KIw2GBPY35/hgbyqmC9mnD9/F5edOk1VjMBrTA628yzahrbt8M5TGM3O5Vu87R2/z0c//EE+/pGP8Ofv/GsunNtlXExQShGCJxDW+ncoq+eXd6u1lj4enG+MQSnVp81I78Y5RwiBzGRopbDO84jP/QJ+8qd+mute8WOUr3nVJedIAO54//vxp071bfLeS5vj+ErjJMlwnKTfU3/3xwbjrB+vQYbk8Hvp87V7aAOotc9Sm4b9EUf5Wr1ao3T/+6Xq+Q6vu3ZMKwgOg6wVTddxZn+fpes4NpmyVVaMspxMx3egVV+Nw4Y4LmN5vMPXHo5L7z1FUaydk959+lcpRZ7nfbkwGUNSPkwphTEGY0x//5Rmxxgja5DR1HXNsq6x3qHiWFkul4QQGFcVKkDdtlg8i8UCkDXJGLNqh9Y0TUNT14TYhr12ycjknN46xv2uvpq7zpzh1vNn6bzHec+iXmKt5fSpExQmwygt61IITKuSIs/Js4w8z5mOJzjn2D/Yx8V+KYqC87u7ZFmG1poQAtZaJpMJt956K2VZMp1OY59Ap+DC+fM0Tcvx48e4+sRpFssl3jlMllGNR+hg2R5PGOUlRVxnnfc0TYPSgdlsn7/+67/mX/3Ll3HTRz7K9sPvz6P/4aP4osc+hkd91mfxkY/cxLHjJ9nenqIVnD13npMnT3JwcEDbylqdFTmTyYRlXTM7OEAF2NneRilFURQYY+i6jqZpmEynHCznHMzn5CZnZ7LF1mTKLbfcgqkKHAFnLfiAbTuU1igTMEZT5iVFlrO7u8+x48coqopz5y9w5513cvqyywi2IzOarek2ZVlyMJtz9sI5lDEcO3aM/f197n+/+zHb3cNaS1YUjKfb3PKxj3NmtsfO9hYntrbYPXeO0ydPsLtw7C3m1LamqgquvewqvDHkTWA+X3DjwV1cceokD7n6Om659VbuunAeZ2AyqrjfqdPQWtquY9Z13Lp3gTO75/jcax/AicmUuq754Mc+zvETp+iaBT54UAof152lt2xPJ5yYbjHJK87uXmC+XDAqR1R5iQ+epp0xykdYC1aByw2zgz0u2z6O8rBoWvabmul0RGgt83qJC548z9kajfjYx27icx/+MO536nK28xHztu7npeyzEJC5Nx6Pqesa7wPj0Zj3vvN9/Nzrfo65nxMM2Naig6KzLd566nnD/u4By7ZjuaxRjQOjUZVhNC5ZZi3f+pLv4Eu+8Iv5rMuuk3msTNz/DFoblJL1Q2vdz/m0tllr19YVrTXWWjrn6Lyjc466bWUtaC3zuqaulyi35PSxkpv+/Cbe+a4/4y8+9JeEHCZlQcDSLQOz/YY7z53jQY+6gi976hM5fux+VOWU6chTNlu88j/+PH/7kY8wW+xB7gk+EPz6Gp3k8Np9eK3TWtM096w04j0GgAoBBpnSaJOhgEIFmrbFx8XKKFlAtdJkGjJjMNoQFDjA2k7OjQNAhYBWoOMCbFQEIo2RwetDfHG6X5zTBjV8eWlTSAtcAn/GGJR3/aKHD+i1TVgPruEiABQAJ7trSLhP/jfYaFUA7x0++H5DCgRcRIvDgZW+472P4MrJC2YFDAWAmP4Z0kKo4ktVCMByzuHjdj387nCjTH2V+iYBQ2MMJpNnVEqRZ5mA2kODyjlH07YoBc55QvAYk8kgUwqtFVVVcfnll7Ncyga12kADPmiMUigdAbTzbE23ePef/QnjoqQ6UWG0wnlH13V0XUtnLcF7MpUxn884mO3hQ0ee533/QUApzRDQpM8Og8DDgCf1x+HPh8cS8BIwuxr5WZZR/fRPYd71Z59wjmz9//5/zL/+6+ke/ODBtWRz7c87BKaGICu1db2N621N4+LwNYff7/8O9GB52E9DoHe4Lf0Pq3YdXoCOUnAO/+68R2mFR5QuICokcSqFIL2mYtsIsmG59feVrtmD6cG7u1Q7Dj/bEBQOP0vzdAi+vfe9QnSp+62/24BzHuscKtO9gpEAZNu2WOfQ8fpBKcaTMV3b4ZYzLErWOwR0eqCzorgum5qqKHHO07qOXBuqssTEDUyxvjmktcUY04Neay1lWfYKTzqWReCYZRneRyW2n5Ndv94Nx8Tw75D+S+tfHJepf5x1BKAsS/IsB++p6xqTZThnWS4WAsLKkizLaJqm7/uyKCjyAtdZRuUIhQDqxWJBCIE8z/t3GoRZkOfJcvIsJ3hPWZbM6yU6kz7LlaFWS1RmCHi8d7Rti+tsHJMKozWjquLUyZMUxtC4jtZa5osF2hh8kNmkpCP69+ucw4eA9466XkKca6mvp9MpRVHSXtjFWUuR5WxNt9Bac/b8eU6UU4o868eSD4G6a2ldR1mNyYqCeddSBAhKoeKetl1WWGdpfEte5Tzwftdw7sIuhdIUZYkxGQfLpYyrLhCco20aRiajbWuCt+RGMxqVABS5YpxXOA+zpuH8bJ+urjFbkOU5LkBuW7quxXcdznUopcmNYWd7i62tLUZlhVEa71eEzFCpdfF42pfSflrXNZ3tsN4KEeM9zq8AjvcOwmqO6vQekHPRq7EEAW0GxI6cdeS6cXhcHx7rRmsC4APkWYbPcqyH3GR0WhO8rJTW2p5AUEqUVx9WgFIpMJlBBVHgdQhoBOgG5yCuAcIUrC1fF63B95bcYwCYm6zfcZRSoBW5znsA6J1HayXMmdIoDSaCt8Y7usjEOWtXjJoX0JYZI2AkBLRSZMbgHBDogQ/ETUKtAOBwAU4AMCH3ISjqGSEvbQN5WQIu42YVbxhWsxsfQBN3q7Rx+iAbYwDrHM5beV/e44nEoFoBsMObSxoUsu/JZ1pptNFoHRk4a2mapr+GPM9ggxoAwKIo+vYPJ5uKQE0rhVdKJgjR5q90rwGhVu1KP9ZaOttFACHPbWTHlmsozWQyYWdnh+VsTtu2/UaitcYHizaaPMsIweI6y87ONovZAbP9fU7snGA6nnJQz+OmIe9cZYYs08wXMxbLOT54stxcPBiVumiCHAZ0w3Fx1GRP3zkMfIH4HCYCZTmneO3Pks3n8j1jqD/nc/vVx1y4QHHTTQBMX/1q6sc8hvazPuvI+x0lh9u+BtSEku4B6eoaRzNyh8UfwaAd9buKQCy1o+/HQ+zhsJ2Hn20NXMbvhfhvmoNpnK6xkv01Vo91FFN5uA2H5/hQ0lowBPWHr5HWicMswBDcrV2zBz6r500Mn3UW5x2lyS9SUhM4wMvGjlJUo5GsIdbilbDzxGey1rJsatqu7Zk87wLgMUrmdJHnonANwPqwz0x8rqRIpna2bdsDvDRnY6+gFHStAETvnKwd0QLRD8DVMkBIm5tarR9pw3XOicJPwGQZmZH51DRNBKPQtA1Ga8aTKSEEmrahLErKokAhz2izjFBUPUCz1gqDpzUqvsPE3AigzVBKWNyiKDi3d4G8KNgaT9gaTwnOY4qczrY0rY9tVCil6awls5bpeMJkNGZ2cEAdAtbJ+9CZwfnQ788+9utisYwbv6ftEgkg4qzDWsux6RZ5XuC8rDFFnjMZT5gvFswXc05VW2RFhu2E3arrmmXT0HQdJQI+zuztcnI8JVdGgJC1TIuKum0ouozjW9tctXWSC2fPkxc5o7IiMxltaynKki44gvO0TUOtDW3XxH7OqIqCQKAsCnZGEzrncPt7uPMNuNW4g6QkeFzbogFjFJlRVGXFZDqV+2rT7+9pPsj4DL1CmgBT2pObponMVwAd99J4rnfS9oGGIXuoUmiEFTfGUBQ52uh+nsk6EOLwTQTCxXIpAiHNJZSS/d17rFZkWpEZ2UOdBbyna1varsV6R45ZW/eGrCMBsUYSMAo660VZGir00O/xqT1HkRZ/V7nHAHAyqrBtK6jcOQgKkxlZiPpGq9ho0f6992hjaOYLZovFgM2CTClssJiohabFyocgYCA+tHWWzJhoJqJfgMWEu9rgkzZrre21C+dcP+GUUiit0TqLaL0lywx5nsVFpB10slDGxHsRwZ0ngj+l0Cic93Ru9eIu9bKGG0rbtf3Ckc43g0HpvOs3xMR+MTD3GWPwEXwS+2FoBgd6Tdpai1aaIivQOoHmFXOawKIbbKLOe5q26Tdrk2WYLMN6R1WUPVPjfaBtml5zT8/qvaexHdbCqBoRPMxmC4JzfN4jH8liPqdrGo5dvcP+fEZVlhTVCG0EjKtg2T+zix9qh4c1tvjvUbT4pcDeURv68F2l50+bttZKKHgV2Y2yJNQ1hIA7cYKbf/VX4yYU2Hrzm7ny27/9omsP78Fgk74Ivabz+udNTxlA3Q3gOwLUHA2YZExfCoyuzOkrMDG87Wo8y3WSknXJZ42SXCsg9ODDWzdQaFZALITQg6DDGvlR108AMI253p1hMK8Oz8M0VxLzkP5O10nsWFpbvPeo+JyJ/Q6BnvVP93Be5oPWmqIoLmpz27bkZdlvojquCV3TYLuOfDxhNBqtlC8nCqAPge3tbUajEfhAYQxlLmCqKAqCs6t+0LLmJVeGtAm21vYm8rquewYtneej6Vb6LqdtxVUnL3KKosA6h3WO0L9zFQGXIwzGgKyv8ruL7Q8h0HnHfLlAac3Ozg6TyQiMITMZTV0Dgel0Stu2KK2ZTKdMx2Pm8zlGZdjG4q2NY0P6ZzQeU5Zlbwbe3d3FKchLUYbbrqNrWoqiQOvVRjwajdjf35f3plbjXhlZNy9cuMB8vuCa+12D0Zq98xfEdAyYLGO+WKCUKBbBSn+OypK6rsE5tI77UFFgtKYqCvLMoAJkJutdfbI8I8tzsI5b77yDq66+imPbx6iXNa5pWSwWnLlwjjq6TXWdZdntcXCwz+R+I6rxCOMchc5QnWNvb5eiyChOlswu7FJkOfmoxCuo24YyF3N64zoBKJ1jrztAGSMmfGME2HjHZLzFuBDTbWYM06qiM4bRaETdtSyWc/b29ihHGTjPsWPHMFHZapcLglZkOsOYDKUCeNsrJWmeyh4mTHU/fuM8yfKMzGcEHQjOigXRryxmSQlTCWhoITqM1phMx/ct+CMASiPwcLAm3N1ecNRnJrrciDLrhagCMi1uWa3VBC9Ws66zQMCYXpPt8YHWiizX5Fken0PIoq6zNE2LdRYP6KBAyTUPr3vDdt4bjOA9BoAa0Vp0bxYRFqbM8siKQcBjdI5SirIS34rOdezPZ9RdQ5nl4sOiNZPxWHzHkBJGaYIOqWEQM7IPjkXTEuoQtW3X+6sdZtjatl37W+cZKkSQpRRBQV4UZHkm5udDi9hhVkSBgD8vvJtskDqCWY1QvcnfzECspR6CA1RPCSdzUNQd0TqXzTFS3AbRDoKif76ktffMofdiglcK5eNm0TWU5egiQOyiOXXo2yfXsBiTA0EGXifmiyzLKMpSaG3bYHxGHRpkqOueDczzHB8C1otmW5iM48eP94yLdeL7GVxD09RkOuPEyRMsFwuuvPJKDvb3yfKcvYMDjMooixLVv0ukX7xCYyhMTpEX/bvxQUwY8eX0zyR9r9f+vtQEGWpj6fzhNZRSshCZrB+LdV1z4Yb/wakf/3H8h/+GW37uv5BcBI6Uo8CYEmAXvE84Ko4w0QZXXFjSbNdNvBc9x4qwI7AezTVc7CKRSbpbelY/bHv6PGrVK7+HdD1p/91g14sWzUxpnDKgxVyllJh2k+lSG43yhxa1QPRTXV+s0+9AD3JA1o0h+582k7VrDpTDdXZ8HcAKY2XxBMoi7xXBdJ2VMjJg7pPPondooxnlZe9XmECtc448z6mqirauUVqT5wZlNBd2dxlFINNaS0jrlnVUZUVRFVTjMUQ/aQGPup/rzgVh5oOWH4bMqFgSvPeYPKNzltZ2uOApsrzvt+QXWZYVXdfhnO99CrMs75X2oFajreu61TBQsrF5l96VuK8s5nOC8yiT4QlsT6dcffkVjKqc/fkiutCIzcTG96iUouss1gWmk20KbUQJNYayqijKksWy6cHleDzu/cjmbc18PqfRmSiqzrNoaxzRMhTdchb1ksWBACutFEYpJpNJv2bWTc2tt97C5ZddLusxGmUU4/GY/f19mqbux+RyPu/NkCYyvV3TMNneJss03lvG4y0uO30ZBsV8PhOwmGdkJmOiS+pgCZ1jf7bHoq6p8pLOem4/eycZmlwpztx5F52W9u7P5zjn6ZqOrdGYO++8k7lbkFclVzQNlSnQRcHZ+QznHVlQjLOKrLNMt7Y4ONhndjCTfazQLJuW836PPJvjFPigKG3gpjtvZb+ec3x7h62tLapqzB0fP8vBYi7KirLkVUlZlNFiJ+b0Ljg0isLkmFzTRisPrBh3ANd5ynGF91E51YrlcklmMtmD6PA4DBld0+KtFWZwMLdDZNJ6ZjFTBC9ztYuuX8Z7FGbte5das4aK59CNKi2gBhkvuRG/aqU0WgWUzdGhpWlbrLdCZADeW7TO0MpDEKWpmlSgNC54rLcEXWA9tN4TtEIZcGGl0MpyrNaY93tTPikfQBI4C+LT45yLWq34+pks4+DggKZtGI9GOGs5t7tH27UCfkJAa8WoKJmMRlhre/q+HxiJ2TIGrTR11+JbOdazNUgn9VSqUuKf0dva1UVtN0rjFX27dUA02AAwMBXqaJJViujSRnBO/P3iYqKT1hE0QWU96yimQ/qFv79/WAEYbTQhLZYhbXyKyXRC61raSCOLL0y59hzee7zRve9gD36DFxN0fC9Duvkwm+J9Ml3HCRtZBBU888W8d6rvWkeZFygTzS1KkWsj7bPdCgwOwGliXedNy3RSYiJb5KwTNsF72s4SWFDGhdRaG83Lqjf/K6WYzeYsZ3Pquu0nwMpv8tLs3lGgb6gQwGohSmNleK3hxBdTv7QxaMO57/qn1Ad7OKXQkdU6/Yr/wM6v/n9r9wvytXXOLgyYtvjBGgTpQWP63zoIPMpEMQyE8Yi7gj5Cs00NCMEh4R56nTFU0dVh0Fer+60CbuJris3zayCg5//VatHyEdT1mjXCpKTrD0FoCOIaoQaW3MNm4KP+PszypcU7MQ3A2tqS5moCaOnczlqsd2tzJvVJsmYMGemLAlSUvIOD+RzrXM9QDc8d+jXN53MypcnHwvzNFws6J36wiSX01lHPFxACk3IkSkTsm8VixmQ0xUTriQK0zmgjey8mSlGIq8xEcBfXJC1KDtAriKPRiN3dffGdzqTfrLOoPItfkU0ahFVJ85S4+Q4d5733NG3LctlgioLJZEJmDE29ZLees7u/R5VXhOAx2tDWLSiNjn5TCsX2dEqRSWBLCtpL/dm2XTStW3yIDJ3SHD92TN5X0+EQnzWg94es61r8Uo2s+945lsuG7e3tvh8CsGhqFvUCbTRFkct8TvuGFpBhsoyiEB9FnKeL/pA6+jcmVwKtVO8+tTXdYjzZY2E78RvMW9rgmO3vMT6Zk+cFynuazqK159or7oPB4D7+cc4uZ1RVxWy+oGk7mUsOjp8+TdnNGVUjnAvi3+g8ddvilCdThm42g8ww3dkiz0vyvI3gw1CUQkR0PtA4y2xZkzWOpetQZcZoXHHXubNMxlMWtQDnssgJGKbTKc2ypvHi69tp6LwAQOccNipDQ0A1nMO96T6SHcvFgs52OOUIyvcKq1jyfG+h8t5HE7BaWVacIysmQuwMlUBFXJdWFpjD/vnDteOw8pnai/f9vmuMIuiMEIGckU0f21msdf38W+07q2euKvHLlCVDwO9iuYxWO/FFJQHloVtLUo7vxpLzqcg9B4BKUbcty3pJ07Y4At5GAIhopVVZksxIXWdpraOpG0ymVyYSI/4GBPErTIBOaY2NzF9RFORZjkJRRz8YlR42vSSt1hiExLQNN3VhHcSMKSBHAJK1llybno0IYQgaVXzJK/PSCniuM0WHRc67+HgfXZtMR4mdioyKMZoiz1m2S9pOfHRkg/IMWQiVFuGILvoAFu+jeVyjGGgPaj1IxntP11mCWgHp1JY8z9nf32d/f59jx45RFqPhy8foFYuYmUz6P25SqS/SAn3+9tsx5gTHpltkJsdZ6XMXPGVVojAyhuJiYaJTsGx8YnpSKDGtBR+fGfH/HMCqIct3mBb/RIDwMHCAQwAxop3+PSuFveJy2p0tQnyPJ1/502z9/u+R33qb3Au48OIX0zz4wQwIJLkfFweqwCdW7I56xks9V2rD6vdDbKhKLOC6ttubVIbfDYGA73t7Nd5XXCXh0HGZpGvXAIGKyc0g+RF574Rx1NmRc+mi5zp03ZVC4488LwGvdN5QKRv2oYnsRWLx15jjCMDT90JY+ScBa/7G8j1xuwiH7u+co1suyZWOFg1RlqZbW9TKY7tOzD/B92AuOAdKkZc5bV1L4IApUWhSoISYOA9FascNziVXHWQ8L5fLi6Ke03XyfMUI5nlG1+Xy4APFTimFCet93wP/tfcXzeTOCTjSMg6ttdi2JS+yXknNspyqLBgXI5a2IzNZXGeERdRI9LZDXIkg+miS1rIO3Uj/N3WNim4uaePsI4pNRpkX0X9Q9UATpZhOxIWlruuebfTes6xrmrpBxT1C3J4EiPq2w2jNZDQmOM9yPo/WIXm/afxUMfglxL7EC9spwQyBEIM9WitroTE5VVFyMJ+TZYagAkWWccWpU7CfU3cNs+WC0pVMRmOWzRKlNA4vyrXt0NVErEQh0HqLw1F4Td3WmKVYoba2ttid7dNaS5YV0VwaUFlGORqhWseyrem0tE9rzcHBjCzPKcqSwmTMmyWj0YhF24oblA10tqEtoiuPUkLSKNbmYBqnacz1fnGsXJeCFncQbTTKrca2uFu4wf6WXFZk+cnLQnxNEaInra3RE7kfq4etBIetAodJgbQW4Fd7sAkab0QxIu2vToiYhB9MdJfon1uL+Tcg5l9tZLy2rbCbIaR1FwgSNJJIomEzhvvB31U+KQCYnGHn9VI0TOuiM6PQop0dsbN9jEwFXGfxnWjbeZbReUeR5ZSFpC7wIZBnmbzQ+CAp8qwcTpzBJqoi4tdao/OMYF1clEUzGAI/PXihZS4+MJ13BGvlftqsWB5WQEkNBmxiCVLPq8iwrAZIoMd7/Xi5eKMXfycl0V1JO/e+R/OJIUjO2Sv2QgbtYaAiv2vSOA5OBouKjGav6Q8GSlownRPHbJSYw9uuo4jO4Ekrs9YyqlZ9AtFckvyG5Cn7CZzapmO0lIrmiizPyHSGd1ZYTSwmywleCQAMLSpocuJGqgxGB/GhiUxR6ieJnrwYAK1t2KyDpMN9dhhAHfZlu2hChUOLTFxOlHOM3/1uTvz8z5GdPSef5DnLz/88LnzLN6NOnMCkiXyoDcN2qYgwDj/TkWzfPZz0F5s41seOKAyf+PsJAB7e4I8aixd/l/6+omAQnecFAAY8PtArcCtQv+7/N/z3MMt3lAYPHMn8wTr7N2zvMEXQEFSmtSaBoP76IfRgufcZjXPVH2pjzzx6L/OsGgn4irpflufUXS1zLvqWpf7QyHwr85x2sZSxqrREJIZAWQ7AX2KXI9AwCQCmvggChobAMfmkpUCQ+XweNzeDMZrgV+zo2phISvdg3UvM4OrZ6dehYOKs8bI55rkQBSoo8jxjPJ6wNZnSzfZ7MAqIr2IMoFCZpA9Jfs0mNwTC4J3G1D1GUxUS7RuUgC0QtjIzBtt00pjY3iwzTCdT6rkEshHBiA+Bum1ZNEum4wlGS/oe23WUZYkzwlzmeU5RSeCGDwLYE1A1RjPKR2SZgEcFqCDR3USg6qwEQAQlUeCZgmo0IszmKBVY1gvKUcbp4ycJxvCxs3fQtl1kKgO17SS9RuYkatx2Yq4uSsZtTttIFHFlxqADTVuTjyeUVUG2NMyXNYUyOOclqHNUSnBSc4D1ltY7OivBQgcH+4ynUyFvnGdWL3tdL60zbdtiTd6TNCEqRetrQ4j9s54eyVqxSDG4ptaaYP1AIV9PkZVWDgGAgSzPRMGNSksfxKSS1exoYuBSrGC63+pBBetorSEotFer+Rd8ZP8GAWC9r3ACjuKnmJ4xWRKbplv1T/qfDyit++wAh+XeAH/wSQBAH4TeLqoKn5ymfYe3Dnwg04asyKiqCtvWBOfRmfhjWBXIVM6kHDHKC4w2kgKmLKnbRgI84qCpqpIilyixum3EZy9Pvii6X3hG4zG2kahi51xvpuzNsdFRWinF9mSKtZZZXbNMWmBa3CI9G7yP0WUr5/KE9rXWRGjTT3DRKuImEyPPVhvZ+kaQJLEBgUCIZvMUAV0vFz0lDkN/ifUUL97L97VS0LclBq1ohYq5gxSrNBwEAdfL5RKTa9DCSLVtx5kzZ7jffe6Lc46trS3G43EEXKtAlQSWqrKM/nv7LOuasiiZjsd9nydT7UMe8hCKzGAjy5dlhs5B07YxwEShVIY2juAchpijSUPnOpTRaCOL4lDjD3qlEBwGgHcHkA6zaIcnfzpnaP7z0VcvBNWPLaUUOgSy8+e533Oeu3rbxtBdcQUf/+VfFkbXe2SoDvxI1Lp5ViWT6SGAn5aBT2aCry1cA5bm0uemRdkTAhGU9R3bL1pDBvGo/jo8tvu+DeAltrLX4n1k6OMtZKFMqUfUahPp2ZJLyOEgj+QTOAzk6M2RbYuKClerdZ/6ZPAw6OUS1bQYJK0EIfTvWxsT8zfKXLBu3RUlKRAh3oOwmjNqAD4DyUIhf2utycqCC7fdSaslGpboP9u0DUZJKpKskHQmtnVMKgm4y/IcJR7pvb+zUZpc65XPX3S3cTEAZFkvI+OWMR6PmU6nLOeLngl0zrG/v894PMUGWRNTrkPlV8FlKFlTVg/m489qvPWuJxFY61z8fIsilzU5yzl16hS753ZRSlMWBePxmFm9ZFSNyQsJKlzMZzSzBXVXM5pOKEcjCHLtsiyEQdYC4rrgaeqanRPHmUzGmKBpm44uzyWTQwpSqGu6tsVmCpQmLwvGkwlZBBvLupbACwVdcNjg0dEcPts/YD6fSZqTUYXWmq7r2J5MGY9GYm63XT9GyrIEJYp5F5XssizleJ4xKkuWs5qqKCjKsgfJVVkBivl8QeYVI11y2fYp2q7jFiNtVlqxXCxwGiqdR4CsaFyHDY7Ljh+nM452t6NdtJSjgp1j23RNS9PUhBCzRyxmeOewEfwWWvJBzudzYZeNsHJoGYdCVEC9rOm6jrNnz6CdoywKyrLCestSxT0sEhIhMmDD9SwpXClo01qLazs6a3sXKy9ajQB+JI9qr5z180pUVLk+fSBU13V0WbcCdSqAxHSiYnTucN24O0kEkw+BMCSWQmLwor+fk2CqdF2lxOdZSJ5V1LoxegBapS8Ws0WvJBilUcH3xNJhguPelnsMAF3nGWcVxSinMQ1N26B0QTWtqKoReSZgq8NiD/YJ1pJnee+km7THznlcUNSuYT6bSf63ECMA8bRdzcFsvx80JjPyYi19hxZZxlY2ImQVs/mS2tYo43taOYGqTItviwf2F3PquiaLC6H3lmSuXdGuQZxXtQSMdHhaPF6Ln55BkccNISjVU8smmkJAQKqwc8J8ZFmGCuJD2JspvaQXyZQiNxrZLiXXXlGkjTQG2rCKhg0hSBLUsDJNS1JTjcehgmyiWkv+vizP8DFYo3MdJte9iSrLM8ZVxZWXX06mM1znY7LpLLKQMpHSJmGM4ZZbb+XYsWOooKjySlhaB5pMJmA0gSzrOXUECNZa2rYjzwtGxZRxuR3NShbrOvLKkGelsETO0wbL3u4ZYT06COQoHVAuIy9ztAn44HBWUvqID9DKxHBYw1tz5mWd+Tvsl5K00rSBgSwy1XSMV2KOHr/jHdz3m76ZIbw6+NIv5dZX/rT4u7kAweG1xyolQVIh9JHeIZkiSEDI90FUyayW3vVR2upRz5Yk5a+UmRT6jXkI4m2/yKbcVl4oW6LDq3QWiRsfytBvLt17aI7po3lBNFjnBfxZS2dbyKPp0XpcXCRNnvVARqvomxVZpuRn5kPMzXmoL9K9h6mQUt+FELjim7+VyQ3/g/oB1/LB3/z1PjEySOCGWyy5/yMfheounTR1//rH8+FX/TSA+OghCeJxnsJkKOcptKbzjmUrefsmpSSG9m5l3dieTmmBQhu28hyjDR+a7zPenqJNhkY29blrGE/GzJZLDhYLlNEEb6mKHJUpMJCZ6NsmDpZ9LlUI6MzQtA3LWjbpXGvO7e2zNZmyNZ0yLiuCk419NBnTdK0Ec0Vz8jjLCFncxDtHMBqTraLXvW1RBrSN7L/W+JRuyjp8KQFibdeick0eDEVm0GWGLwxKrJ9cefnldJ1jNl9y4/JGMlVw5enLsNayqBdkZcZyOeegnrF98jjj0YiD/QOqqiAvMto2AAa8oZ4v2NneYWtSCWPaOmb1gq2tLViIL18XHD5X7Bw/xmKxYDwaM51MMUGhiwptcjrr8U0TozJrxuOK6WgMHvaynKyqmDcN4/FINvLgqZcLITMC4AJd03FwMGOUFVy+tUXjLYuuxZiCU6Mtrto+zrJrcNaxMI5tPWIyHkf/6BqnAlmmWXYtk60cU5VkucYuF4TWctnll1FmBc3+jMVigaNFeQidw9cW61rGozHbTcWeHjEPHV2eUS86tMlobYN3julki2o8pXEOn2VkAdivuW1xK8vlgmo0YruqMBrOnT3HaDyijCm5dK7YySac3d9lPB5TTaZM84p521GdPQve4UNHcO2aG1uas8nUnhKu5yYn0xltcDTB4l0H3uM6iabVLqMLMVDIGFTj0Mkf3iicMZiwpMpAaYO10DRd9OsEr4LESCnds4XJkneUpLUs7RO9hUArcIHSZ+LzqRSl0WQeXCjAR6VfG1rbMFYF3iu860RxUo5cFyjdxf0lI3QCgJfdAS50UblzWCXRwIdX4WTavrfkHgPAum0oAW2kakdZSsRbVZQxEs3jPezN9tjb22M6nogpN8/pvMN2lmVbUzc1CpjVyz46L+WM0kb1STVXVLEW51ido7MMoyQ/3+58j8xk2GDBCAO2WNQxjceq+kHbtszrJXXb4gm9j0DaEGHdrCR+BjGwgUDXScRZhkLprPevScEkQz8buZZQzun+3tNHKqXzlFJ9jkRg7TrDQSmMxapCRaq4IekOog+JXzdfDe8j4GvVn4nlMlkmoe1aMa5G/fnJDH1YW0v9dNNNN/GABzyA8XgcmYOOtm3Is4LkcFG3nkC3Mt0GL75kKu/vDxrnNKUqREuOjKMCslyzu7sru4Qiap6JBfW9WVY0KR1Tclza5Dt03P9E5sUh6PLe4b2YLpMP1KmfeiXVf/kvqMGqdte/eCn7z342RO14pd7Fdxh8BDa+n8ypiT1oUkMG03MY8F1KjgqISNc8PB6GY/xSJnLoISDpUQ7L8H4XfXdgxggM2CC1MrOsmGyPO3T/IYu0NjcJR76jIehMn5dnznCfr/sGQoD8tttQ3lPe/DEe8o++go//9m9J6pDIOmitUM6tvc/DMv2zd/HZz/06PvDG18u6EnzP5CaWMD1B75c2GmPMam4bYyQBciGbuVHi+K2VitYTTa4zbN2yPZlyvjugaVvmywVFVbK9vUVVSYR+cmTP8wKCE7eMmFYlDNak1B9t2zIqK3a2tiSoy68UoBSclhlDGKSv6dcoYxBf9NWoCIE+ilvSa9CbhVPSfu8l15y1HYxyxqMRisBiMcOOc5ZtQ2E0IQhwNAibVi+WwjJay3K5YHt7WyKenWM+m9F2EvBxcHAg1pMy7+dUCIGmbhhVY7a3p9x+xx29b2DXtti2ZTwes729LcEb1jFfLHrwuz87I4mXrZXUPLGYQFJ2xqMRre36YBFtDJmOyb5HI+rFkqoQE+psuRDG23uaumHvYJ96vuDaY5extb2FWWY0MSK7rTuauqbKCrQy4MRM7skY5wV5kdN4x127F8i1gc6hlKcshXi58+ytdJ1jVFVMxpWkpskK6q4FDXlu2LuwS6cztremsjz5wHJ2ADEBNiEyWHRob5lORmLt0RJoUxjN9mQizKhzoIVxTsRAWZSMR2PMwa6s5YBTiqAMSq0Cg4YKWyrWkFyeuq4lOE+Z5bS+ESY6SJIMj7hvJZ9CWAGh5EalgiYvKryPyl3waLciMYZuGcqvfPfuDgSufQfwWhOcmPAzk0XM0+HjPBM/Xgd4rA10KJwLuJgjVOdGij2YTFLSKU2WyTzKi1LYZx+DmrQmJRZeW3MPtTEd/1TlHgPAxlqyIkcN/suzPEbupU0zMJvN0FqT55I/ThmNiRncOysZvlO+pFTSp3eyxvU5Afs8ftaiVYbKvJR9QaFVwMVwc2UUmTYER7/ZSO8AWlG3kjYgpXC5lAwZjd7EowSEqUhBBxWiCXcFLoY+Cf2Gq1LQCvK7j+aq6MCqIpWcUl4MAUrPohwyW6brW2slWCb4tQ3/KF+2ld/fKkl0HktHrUdB0p8/PHc4wLqu48orL6coc2HgOiuDUSmsayOjJ4telus1KhxUnBigolajo2ndHQIxXdtibYu1HSE4UOJcK89pQLlotgejh5PgYrPksC+GfXhPRFhc8fdyzjH6sR9j9Cd/Qnb77Wvnjf/kT8jOnh3ekb3nPIfmsx8i/ZPAtzZoxdr7PiypysCqDRdP8KNA6/CzoS/L4YXhkuAwtvuipNGAGrQoLeBHLTiHzTxxcCB500KfA9CpGIRikoGctfE+nIcrcLcOIA/Pl+E1tHMUf/uRtbbprqO68SayaLazzpHdfjuX/ex/Xo+0O0LMckl500dJpulkukkrcZrL4gS/mmupXNuaAoJstJYE3CQnaW4MVV4yripCJv6xne0IalWmUfJkig+gjlaIlLZHxbUEP1RuohLoPdPxmDIvyLReVVlhPaVOqnaQnmk4l4bvXTZbSQa/Cn4J0fk9urlYJ2XhlJjCM2P6eZSUgq5tiR5VUSGVnIQqJsTXynBwMBuAh4C1XSwPt1yx1IM2zucLiqLCTAqC88xmM8bjcU8EFEVBXYvPZZ4JEzybzSiqMjJwcWzEyGe8JPfVsZxYWZbyrr2sSQ7xoy6LAtt2gEIXmayDVsqY5Uby7O0fHDBbLKhy8b/sIpBYdhLsMvT7rPICpSRdjwqK2ra4XFNlFb6zLDuhUUejMWVVgQ9MJ+JLqQNS/tRJWqEiz8UZI0gKsHFVUZoC5QKTsWa/WbIMSwJC7GR5THBMwODJMsPJY8dw3rGI0dUmF3N+v5coJSZ5AidOnUIZHV0FLt7XhmtJb5mwntC4PminX5e0wdkOH8CF0FcFSVNQFgbf5zDMYoCFcykHrkK59VyhKel0UuIuZWkZsoAqvufVXNORwVN0ehWc1DmLQ8y3Koh1JVWH8d5hCtn3CMQ90KAx1Mu6D4BN95b5vU5mHBWod0/3s0vJJxEEolHaCLsVF8OUADKEgHVC+3dtS1VW5HkRK01EJq7r+hIwIYizbgKA1tpYFsz2ADAtdq6Tih1GS4i+87Y3vfoQkyYHhQmS1Nk5S4gpmwNSg9R60VpSpx2WdeZnUPtWx00pDZDEWgyulRZ5GTCp+gEoFaLvmsZ42QQTANRa94MJ4mBOiy4cWnxXEYpJY+rcqlrBMIrq8EA+iuFMWpuKC5ksooMNPE3WwfUSC3jZ5ZeJ71EElTqmzHE+Jhi1EsloY+TWqsZqRoq0Dj6sPTv9s4t5VACrjRVWPMMyaElblc348PhcnyyfCPDdnda0xpZFUFz+1/9KFmu8DmV6wzvghnesHXPb23hjaB/0IGH3WOWhPNzGEFkVwt373A2PX6rth8Ffz/QMGKvDP6v3vn6/BNwvxQQebsdF7UyAKY1j56S6hJF6nEZpjL74OYf/9sCDlYl+KIeBbHbnXVTvevclWiuSfEqL8xc4+Quv64/X97sv7dVXAwKGRu95D3q5qmOaACCpr44A0127qjWsiOxFdCLPskyew1nx2y9WrgF5jFKtqop5U7No6j71SZbla0pePw/kTjJujpgPOvrweWsZj8ZSmWH4bsP6+pCud5gtT30/7IeeAe3p7lVGAcUq6AUQQBdBsknlyZzDWSf7iZFrZiaTKOjE8Gc5e3u7TKcT2UN8wHlLVe2glSHPCwkWG7htOOeplw1GLVBKUdcNO9vboFSvCM9mM5xzVDuyR0ktcAVqpfyL76OnzEsIAjytc+RZFhMTe2yavyH0vmvGyPtqs5YQU4kVJqMsCnb39tid7TMZTSjKEusdy66hyHNUZJWNlpJqeZbhMVRZToZk3/CZIiMjdA4bgx9HpeRGzLWR8muRlZwvlyytWLyKvMCYHO8tWZ4xHY/ZHk3QTqFMhtq7AM7jjGc0HqG1RwWHjmNzVFYURcFd589LmhOlIM+lwosx6AT4fYMPnpPHj4nLlnfg/RqIGa43ifTxESimPILeh+jyEcS9IO6nPoiC7J3vx1ocoDGnpMQoBA+tddRKAkp9nAs67esqlXNYVfSBi9ev9f2pH/zphKjkrdZc7wSE+n59WH094EHF/spiXt1YvQSvxJRv5bua0O/HCpmngYvn970l9xgATidTyqIQ2jNqL0opskIiepu65sLeLkWei2Nr1BIIkuDRYtHZelqSPOacGi5gaQEq8gJTaoJ1FKMxSmuWTc1y2eCd+Kg1sYasVpqt0YStne1onkUGtY3JYlXMSeZXC+AwOXI6lgZmAnF4mQRZJj46CXxkcdFJOc6ShOBXSRyVjFEVPIq8X0iTRpHsZKsAB9UzMKsNO/TgDwQA2piZfjhAD6ejGNZYTIEwiUHpS9GwMrWldDFpUB/O75eYQ9tJcmutDDpL/lnSHrT4PuBhNjvok3qLyUo0/yKvVlpfNK1muSwiKRBAaxXTVdCDv7zIe+debZJ2qOIi4o8EVkfJpdjBw5+tAaKYVieMx4Suu1t/sSQnf+ZnMGfOcPu///f9Bh20mLyUks0m+IAbmDbjXn5Rey4Fsu5O+xuy04cZx0umOjg09lZtiKh7cOweAexBG00mbMGys+Rx04jdcMnngxVASfU0jwKvQ3A7/uM/5rLv/1dy7/EY1TSoQT7Otm1RVSXmw7zATyao+RwFnH/mP+KuFzwPgPF4zP2f9gyKj9689tzDNqT5pmWgRobK9qZVSENIPs+zTCLflaIwGmNyZrMFW9vbcZMWpXBeL6mbRkopFoWAgZhrTKX3hIx/HXT/3pJzfJrnmcox4qlOVZYE53pTrVKKlUOCtDFlEAAGa6NUCTrsapAqizjvIqsdLQoRCPbjL0ii6uAdmVaMRyOq8Rg/W5CrLIJimM1njEdjKZVmldRU1pqirATk5TnOrlxdjMmYTreYjCcE7+mclWTOWvymDw4OyPNcWEgUVTViPB7TNA2LxaKfA8LmeVzX4WIgmzGSf21UlJw+eRpQHMxmfdWjfLQKXEjPeOeZuzg+3WE8KsjznL2Z59TJk9imRcXsF6PRiAv7e1TliCxx7l760ja15BzMDaENRO8BxlVJVRacPzhgXtdM81H0i5UUYZXJWAZDUVY4F9jbO2Bn5xjn9vfZX9Z92hHtAqYq2JpOOLazzfHxlFLlWOupI7jtnJSLa5sDYfV8oMwLTmxLpY/lsqHzirntaJoaj49rsmXplnQhzi8tfeqdA+dJaYtSlolk3dNaKn+JS5LBagnm9ARsEJ/4FdUeIpGEZBeJqcGUlhKqxhisFxctaz3LukW7BT54Chd9+pEYhGAGxRTi3B0qkcP5nv4N4gch2TZk4KwWub5Wsestc7JHKowyeO2krUaY1SzPyWJlLkLAdoHlchnvv7IAGa0kwpuoaK0t1Xe/9n4y8kklgu7aRhoWBFiVo4Jlu2S5rFk2La13FOg+nF9rg/WOvdkBPtjep4IArm1oGsNIjyA4Mf9mJd5LJnp5UtjZ3sFkObXthCJXOibbBL90LGZzlvWSu2yHyTOm29vsbO8wKkcQkpkxxAU0wCCSb6jZrkX+Dha7PLKPiZ5PfouppNLhgZPSHcgiHRdbnfwZZHHOs4wiy8SUk/wb3Lo5eThR0oLcWSu5jooVQEvtTUAvmXFloa7WgO5QW0kgrOus+KlFit5ay3w+59SpU4faI0yej6YekyZeSuRtPFTS7mk16p3tU/m9VHYqaXlaS04qBdiuo0kJsJVnf+8Czg3YlDWgAkMu/Cjt6J6wgXfHAA6/o5U854Ub/gen/+N/YPyzP3vJ7yW55T/+OAdP+zIirSkJQ53v26mMJmjVLx7Ddg810kux1ZeSpBzAetb9odn08P1W9/TImqTWAHU8k2GU8MXM0Xrb+vsqYinHjOnWFvOmFT0BeXbHelWW4TWUUqBVLMG4au/hKOBhO/af9RUsPv/zecATnsTH3v1OLvsn38n4HSt2tm1bskqUkOZhD+Ujf/HnPPCzHw7WcuUr/xNX/vTPDB9m/dlwHCUB+jx+Av5iSboIBDNjsF7cXKx3ZEWOKQssSurLRuDnnI1VMBzKiI9VHlO1BGNWZdf8kK1NZlCDirVXvYc8l3W06SKj4n3v19wzMEEqJJiYe09Bn3902L9KgfU+Vj+QdoL4gqsgm3JwogyntaaulxzEkmu6LNAKbNuwt7vLx1vF5VdeTVsvWS4Xkm+va9FZhs4zrPPUjeSbPX36NLP9A5yNG2Rsl9Eab1cZApqmYTqdsr29he06DvZnjHaOsTWdsnPsGKBoGomuftCDHsRdd90lzKvSnDx+Ak9gZ7JFVUTTu/dMRmMKLf0+nUzQSnHb7bejdTSVakNrO85f2KWzYmGy3tEtLWVZUlUVZDlhPoelMJJdBAndbMZsuWBvcYApC5xzTCZjdrZ3WCwb9tqlBNAog29amvlM8u1qJJVLUXD6+Emuu+wq/vymD4My1E3H3u4eZy7s0ikJBqqbltl8wdZowmic47qOxWxO7qEYbROsZ3s8Ye/ggFldow0UWc7Zu86yszVlXI5wzlHkJQ+45lq49VaWZ+5iMZszqhTLxmKUwRlNiCl5di9c4PTWMcY6x+hc3umh9EsXBa+FQN21WBw6M2irxbc9CBnhQogpwjRFJoEszkn2Ea0N5AU6y8QnNLoV1a3FaIdEPLtotYtgLO5zyZfzqLVxuLb0iZ6dWAVNJHC882LVNJqu7ajbpVTW8uIWF5Sw1h7J04sRN4KAifuiBq/7pOxaGxS+T/1yFDA9qo1/F7nHALDMs+gnIsHXJjfM6wVnzp6RbGHRFl7kou2YmPC0sx22bWM5NUumDbkx8oKdVImw0d+vcxLyXSaH5LgAATR1HcGD+IUZpTl9+jJOnTqNcx22qzm/uye+HG2DQqLlymJMFzOYp9QGhzewZC5JGvywg5PmPTS39n4pgxe18qVRKLUy25oIID2hL9CewLGOzKT3ns6JD0m615DFGfrqKCWbp/g/6r6WsopmjlTsvY/0PLRJpnana9tYeziBhxRskvwxVkBCEp0mJ+hV34n5JQvJh8yTjyb9RqL1qpLIom76LPZZBlipyZymXp5lqFwmhLWWznYyfuJklShvu3IDUOIv5GMSzaNC+g8DqcOT6rDJKx0bap/pGc6+6MW0//jr5Dpa9b6JhxeP7vRp+qLj8TPrpZSgV2DUuoa5uufRcpSmd/i5jgK6l7r2YZZvvX8SSh2Ax9QTITCMZj/83aGvmB7MNa0146oSIGAtxmiypFVfgnlcWY7k+kNH7uFzrbUFsFddyU1v/+/Y8XigT6/EuZh8PDPrtWzlghedv/eoz+Nvf+BfCzBTKyXqcB8kZSm5sEjR+3WWU+pxy3xqncMT64mHTgIAjGZRN3TOkQEmJh+uihLlPdpIEBwKWuco0X2JNnn+wbsIQdbWmIDdxnRZKCn75pG0TDoCzTBQhld9rMSlJqwSeXduVWM801JdySGmOaLpysWcbqnm63g0YlKNyLQWH/E77mJ7e8J4NKasShpryeP621lL17U0TcPZM+c4trNDWQibt7u3S5EVnD51GbPZjPl8LiVHi0IiflF0nYzP8XiMNoad7R1m8xn7TUOI695yuUR70AGaZc3WzjaTrakAkaZmuVwwqcbkec7ZCxdY1jVFUXD5ZRKlnBdFb6ZsmgadGbIip6hKgg808469g31yNE3b0HUt3jnyqmQ8GYsrk4aDtpFcjTr6tgfIjKJZ1owmY8qyIFdi1h2bjGOTCXUplUlcsLQpVyIWo6AYCWPqjaJL1Uac7B+Z19RNTbtc0i6XjC6XBNx2MSc4R5nnnDx+glxn7F3Ykzy5eY4ZVSzrGpMX0r6gMR5s23Fie5vtreOMswLlLP4ARkVJHvO/mqAEvB1yv0r7Utq3BAO0LJbLfr13QYJdtAfvrOQbDgO3Bb1K2aa1RhmpwRwCWCcKrenkWpmOqZryPGYb0dB1FEr1e9NwDUq/r6wzKjKQwiLqmO0iFXZQRirtWNsRiJV8IlnVr1N+uE7K/qK0IlhNvVxGxSpE65C4zKRKRIfXyCFxlebrpyr3GADeedcdfZHxyPszn8+kA0PAaMWoGrFVTXpqNBDEOTaIoyyKPplpkUnOwNZK4fDOWlCpXq4AJ60kKjhoR9NJoWRlDLpPfxB91Yyg9JMnC7pB0s2U408rHcuriOlpCI6G5pz0+0Uby4CVGVYcGe4Xw81JB91vnJkx/UYYQugBZLqeOIpKDrDcrModDQdheuEpSeoQDGaxn2z0oUhRv1VVoXWMrh08R4q66r+fZX3uKhAz887Ozlr0b9rMU2qbkDxiWQcbAhOFXk9sr1jjJcqrsy0+iB+o8w6j1gGq0prAIFVJTAuite5Bq9TUlVQ7YpJfH6efisZ0lCm4Z0qSOZ2APXmSZjw+cowcef2w+kVM/avPLwVWj5KLQdrRTObdAb+7Yz8v1jSDFGAfvOP+eVlXeC51naHPno6mqDzPYtCPjsxXWOtHYXlAKX1RuoPDwSGHpZ+TWqMe8ICLPk/tkxQ/iuzW2zj+yp8GfzSzl6S89Tau+KU38bF//mLoc4Sum9FluZPnyIwwFSEC/r7PjMGQSs1p6uVCWGHpbryPZRTn81jyCQiSX7Wqqn6dFRMrktcsloYaPn8Pwr1fVcZYUw60pNZiVeIusF4lZTgn16qgxB/pg1iOE9mMSQBUCTvYA2EjCZ+lAEAsB9m2LOaKspKE73kmtcklcjrrn7FtWokgDoGFszTtkvl8wbHtY9iuk6ANK6ZLrTV13eC8Iy+EOU2maiITq40AyaapGRWVBCLmOVVZYtuOZVPTdi3aiPXFDKwWSimOHTvGfD7v+wOI+6GmbprISMmzN10HJosggd7dCaV6t6LgA175GNEdE1f7QJFn7IynaK1ZNg1123DlqdMc395mdzHDEhhVJZ1t2dnaAqUig9zQdQ2uDTRtg2sbMgPT6ZjpaMLu3i4+iNl+aaX8XlCBopBcgrnJMF5zfOcEJje01nHXubMcn27RWvHvxzuKzJBnipM7UjFKuUBnnZQOVKUE9RiDckIIDImIoeVqNS9BlA3Xj2EfPAQrAR2dI3hh/AgxLC3uAwFh2TBQVpWUhEWsp9ZLnEAzIESMc1KZjNDvKWlfHsphv1cQlwEbZL4kkBecmKq9tXhv0YqYgk6i2/vnVMQ90cT5EwjB4a2m69qVAhfnmDEK/LpSt+qvv7vpN8k9BoDnzp9FZdFkEEFg13VMxhMpkJzlbI8nlEWF62LyR+9xMR9gkQurl+q95loAzbJtBJQ4MY+kBUhrRaY1i7rGBUUXc2rJWJHoQueGprqcUVVRxpfhUrRsrJLRR/Co1QY2ZLiGbGBa6JIDt9CH0q5U2iU5r66/kMHGGL+WmYw8M7i4wPfsWZDcZsPIz6FGczgtzFqb0/lpo/eidXdxQVwBTUMIq9xoyY+OSGuna6bNK03M8XjcF4kfMkuiQcuk7oFP2izS9QbXVkr8Qay11E0jucGUAqXxrKc7UbEvFrHqweq+Eej7EE3HccFQKrIsoQeBh1mxT1bWNK2k/HnPF5w5w9Y7bmA0GWPars8wTz8Wh9foLXRrU1cR+jGYkomufDuEaVxbGMPKI2QdlBKB/XqFhtV56+0ZymGWbfjMw+v0x2Oaj9Q+ccUZAHal1gD4kDkNwUdlTuHuex/sdQ/oK97IOav+uOje6mLwmsb+YX+01I71MXO09AFQgD57jp3Xv6H/rLn2/rT3vW//9+Rd70Yvl1R33Mnlv/obfPxf/LN+HRi2KwHA/l1FV5HgV+AvzzJsVESzWIKyiRUddAqqQ7rEWktZFOigyFB9pY50ngBL0Crrx0Lqj6HSBlGJ1n5NkUtK2UA36d1WhmxIer6j+ls2SxWtMTH4bXi9wXqT5XkfBJLnOWWeU5qKECRFlXYWdA7KU5ai4ObkZJOChVqiUGJib9s+MXxmMkal1FBedjVZnlHXNW3boZBydiAm7ZSDtigkgXJSgMUvS6p+lEXJwXzGYj6nsx3VeEQXJJ3IUNnJooIuY833BQh0lok/ettKTeKTJ3BdK30SDikKzoGO0eBdhy4NRZ5Ff0SHQnFse4vt0RjvPXXX4hUcm24xLgoaJ4UVyqJguZhTFeJf3gap3JHGgG0aNIGiLMlyw3Q07tdYpyQ3nkOqZ+RFTrCIxc0FdqbbtKFjUS9YzmdMRhXGGgKOLNdsZSNGhWZajVFK6k8vlzWt7UTRibl7sReDPRBwlcgMpaKfuPfgvbh4aalZ7wO4aDZPpRIjno8WIAnlkBKKUI6qfk2SvRW8lYAkEwkgYwy5yfBKmDiv1v16j2rvaj5prPNrcyL5gXorQW5JkTFKEfqCXLImZnmOiSlg5LjDOUPXtX1fpDVDK4W/uAkXkVJD5fxTkXsMADvncF0rwR+xTm7KBTiN5XzG1Yg2muuS+dcHz3g0iRFJAgRzY2ibuUykPgmudH4CCYUPBA2N7Wg7MZeEkLRVxagoY8oFKSGmMDR1g84lpYDODVrLSwmRFQTVZ00fvuzDdG8/UbwX6nkAdBKISgM3pTORjVfF9oiZNkSGLqH74YvyYbUMr6KepQxT6os0UXr2w3mZXG6lKTii+Tw68/oInpXRfTqDrmtp225VfDvPYlkaiZxLG3MyN6eKCavoZp16r9fQeiYgDkJtpP1Ka4q8kIhw5yL714nm3dayoZkcZYT6d8l3Ck1nO86cuQsdk4TWTSO+kWGdaRPg52NEsUwuuJi6P8zSHTZFXEqE1lfkKE4E+LG/+EvMt37LPZ0qGzkke1//9cz+5ffHd6Dw3uLcauwMtWRRENZB4RDkDQMxhjI0LyVF67BUVYVVUpXEHWL+Lnz1V3H227+t//u6r3g25Qc/hO46IFDM57Sj0UVjJ4EBFZW/4FN1ENenSymLnHY+l8+UklymzjEZi6uEjmbAPMukWHxZgpeI2GQRGQLxxGpZZ8liJOwqoCuudDFC37eeLoHNCPC6WHM1ZSPwAx++Yb8mRWM9j5pae9bEpKS+t85SL2sODg5YLBZcVsmzpLJsV115JeNqysHeLrt759nfn4HOGU8qJqOKrrV4H5hMtpiOt5jt7UlFIWcZVSOqqsJ7yYOYdS12Ieboc+fOMRqPyWNgSVGWnDt3ltFoTFVWlEVJYxu0NuRFjjGy3uwd7HOfa+5H1gi4a21LXdeUZcn+wQFEhTjLMtqY2sYYg7JWXG0yw6iQtDPLeonvLCevuIzpqKSdie+fUTImm7SeWUe9XNLUNce3TlAYTZHlEglN4D5XXIVdtuwt5jg828d2CM5ycGGJ04GQaZqmplkusErcGVzX4m1HWUilreAkv2E1GlHXc8oTV7GzfYylqymqElMW5KYgKxXZfEZdO7x1GK8xZUldt9Rtw7xeSG1iY8jLnG29RZZnbJcFS+sxWuE6y2K5xGuoRpWkf0PMv8O5OZzLCTtkWYb1gXq5FEthWRJsKQnkvcFbCypgvVjJQiJk4j6nE7DTUBQlyTcPFXDOE6I7QlLTVCSgMm2wSgCbHlQYG0qvLMUAK5MZlHd9VaOkBCQf2z4NlBcA2nUp+4EQSXlMAcSh9C5NdE9QKuEgyb9IkOj3S+1Wl7Q8fRJyjwHgwx/+OWgtNRGTmVHFKKaqkmCDzjmcEbZwNjugaWpQga2d4xLhFbXXLC/RAYzKGZUa5w2dmwkICYH5fI5Wiq1yi63pDufOnYMgvmOSFyuj7WqZXNqAloS9KXrV+vhitUTSpe5J5NyQ7UsbjvUOMs2sXfZMmrVWollRLA8kVPvYzg5111LmuSSn1okloWfcbAyyyExGY2vapbBfXUwwKvmkTB85CFBiyE1MrBpp+nFVkcxNIQSsswSnxLG266SWZBA6e57MF1ozrkaUJhczXhBwuGylfrMxUifT+w7XOTE/KE3bddJPWpPFSRKcRNimfIXegXWORbNkvlywWMzpuobxWHx5TCybNDElVTWWzcFLJGFRSdm/pnN4NKXKIqNo6bynKAuyypCNMlRmCNrglfwkKj0EH1P/ROffNAFYB4GwMjWkY2s+lIN/0+d9xOlAO/0Hbcebzu0ftTZs5JOQ7de/nuyP/4hbfvmN4iMTc70pv1J+xD8tgr/IYA8VkPSODgeBDJWqT7QgKqV6Blzl2UWfpe9nWcaNv/7/ccXLfoCTr3s95mDG5z3mCfzVW3+P5uore79dkFGnI+vlksKmNL7Q+KiYhUac4J2K5boaz2y5i8llgZ+Ot5gWJc5atrfG+KhoBSdVPnzXoswo1vcV4Cqp4B1KCyuuNdHZPFC3DWgd/Q1B43Fth8mlEopHY1QQZdhAEKKWSV6KTxsxwjkG0Vnvor+WxjuLCeL3lzIe6BD6XHm26ehacYS3GLSSPnHW0SxrppOSj9/+cal+YBu8UZzc2eL05Zdz55m7WDQ1eZYxHhXMZjMuHJwni0GFIyXJiTsrCf+z3FDmBa3tGE8nzOdz9pczlq7l1M5J7KImLwtixr7IIHoa65nXtaSNaWuW7ZKDek4bnGzcdc2orPoayW3bSZ69Y1vMZjNaKybFSTUCo6Vvi5xiVBFyR9c0uDowKkpxr6mXZEEYHZUbyjxnW3smuxmhXrJ98iRaa8kNqDSTYsSsaRmXBZOqpBhVgOa2225DAVVZMR5VlNsG7zt25zNc8IymE05s7XB65xhnzohvflGWkcCwkohY5aigOTg44K8//Oc84MEPIviWygTGWoIe97smMtQFp05fARhm+0vapoVMQ5Zx9sI+VVURtCevDMcuP4HbO0fhNHnjUbkn5AqjZJ5preNep6U6lwetM7TOCN7SNQ7vLY1bin9sAK0cozwjc2B1hlOWzjhUE7NmxLKoShuwHT7TiOFVArC6rkEDbQDXQefBIQB+XOaAwyP5iBVS1lCrrFdwQCgarYT9tL6LZekyrHc0rqbzCzIczkLtxFRdRmeozigCBjqLdy12lGE6hybOl5BhO8dytsRZySAiOlkmcxsVS7le2kqT/v1UzcL3PArYgsehPZQ6w+QxKWaINT3xaBR7F85z4cKFPkP9ol5w2223cdVVV2HI6DpFkxlyA4u2oe068Q1zovE626Fj2TXlA2fOnKFpGspYAzGE0PtkANFUaTE6Q9IRqB40JRn6DB0OqgB5ya2V/HpNdNglxCokmYSRB+fpVNuzYzqynN6tUrAkB/n0kzSCznaSvy/W482Wyz76LjMyAAniYB3sKvVLnhd08X4mE+0QpHaoEodK2rbtayaXKUv9YDAsl0vm8zlN1zIajURzUpKEUiFMhMf3TuApwj2lldA6VjtQCudamSTRL6ksxcema1sWszmLGDxyYusY29vbVKORaIPRn288Gku90hD6wBxrGwKSQ7JpGz76kRs5cXwHA2AdxALbMsg1ShlSYTDxGVuBP1hR4kNTfv+ehybewflDE9pQhubcjXzqIv5s8tvQB3aY3qV/X269DiasFrqUZHnI6vaJlwfvu/f9OiTWOlQuDJE/xAAOWUaA+3/D86j+6q9W7UcUjRRBPxwY/QgcHHPRt04pSexcVBWzvRnBrWpql2VJ23a0WYszYgZ0zjGvJYgtNzlFVa2yIrCuuCQTe+rDsiyo22aVNDozffqULMtASTk7MSGnJLwOF/2ErXf9GjNUipIze2JgJXjN9/dJnVBVFfP5jOViIWU3s4yubclzqQaS5zkXLlxgOV9w9vw58qLg5ImTXH75Fdx6x+2cPX+OEALj0ZjZbMZsNpM1C1Fcq3KECwGbGMzIzs3PnWXn+DFh5GpRJvYPDqi0YTKZ4qxld/c8LjiyoqJZ1uRaLDVlVXHrrbcynk6ZTqd0bcuF8+f7ClVdUtDLEpB9Ym9/Hx8C48mEUydOcDCbcfbCeYzWbI0nLOcLDmYzrr766hj0mHH82DFqb+maBmL6lKKq2NnZZjydSgR40+BsR71cxn1RmNvMS+nMna3tvoJGZowoHkjZsIN6ifOOwmTgA1VZUcdgmizL8Dmo4PFtR2stdZFRmAJQlKMKazr2lwtCu6Dc3sLbjno5p62h3N6hacRMGTqY1Qtuu/MOHvGghzAuK1wIdLUEDxZXFJg8k9RrPvS5PodlGoc+6P0+3sr+6KVqgow1QKXsGCBBj0b2LXExi+uFcxI04kN0QTKEoLCdoygkd67F4Y0nM5quyHFZHLd65VsuJggx0UMiiIZeYClNmcWHlUUiZc6wMddx8EMfadlrM11QlkKWJcsHSE5drbNoURRJqWQOM6f/K+QeA0Bxzo/MmlmlmBhGxXVty/7eQR8l44MiMzmiBNUURSX2+ZhE2JiM1ra44DC5wSgBfpPxmMlojEaCCSQiNb4jVDRzHjLz4eg614MVOVNfxAQl35TDjuTJR0+jpGpDtNln0SxbFoWYhqOWH68Iqs9+hfcXR0X66FuTxY1nVfYtmotjgEXyg0ymYkmIKWXoPAHtJY+XjgE43ntcZ2napmcbU94woyTJZEotM4z6LeKi3DgB7Ds7O7Fsk4v+bGpldjK691dIrEfQYnKQyZiSdUtJwPGooWtblDaxLm00iwcgBAqT4ZXuKfPWynvI8pwQPMs6lmYKMB2P8a3F1XblI5Wsg2Hdh+kwSjvsI9GfeoT2dDiqc/X5yq8rye4zn8n8C7+wv21cFVb35WgZXiX5g0hAjaPIDda1fOhv3s/HP34zl11+DTvHdpiMKymj2HX84Q1v46M33YzROZ/7OZ/HaFxwxVVXc9mpy5mMpxJ0FJWw5FtHDJJZeWcNYEwQs4ew30czZkc9f/qgN8nH6w5NuMkHTAGX/dt/i65TMuV0X9O7TwzzTwJr1zlsyj/slD00d6Zjh33XDou1HSEz8ZnXP9v6nd+j+OjNfaqm6r3vw8zE4d9VFR996ffijh2LX1z1Xyr5teZOQjT9hFVksLWWg/kMV42iKUiAQZrPLnh828a62XnclA6Pm/V0VX2XD/rDR79rPwia8SmdFGCQiHTnPXnKRxYlbbRpA+7dO5KLC/T50xQKBlkQUpqj5CKTzM7VaCTBaXF9m8/naK37PKFd13H2/DnuuOtOUKp3ZSmKXHwEExPjHE0r+RGbuhFfybJkNB73a5uwghmmk7Q7LqjeN7rrLHmZkWdZzEIR/QmLnLptYbEQlhP6UqcqKsqOVfUnENN7ay0q02IBy3KKmCS6KoT/8aMxTV0LGCsKlNHU81kfuNK0kuzbh0DTthDEV7TMC/b39weZDhQu+XVnmrIsKSIpoZWirhcSYGU6CJLE2nZWKnHE9nZdx4FaSl6+aHWazeccO3YM27R0XlLvLBY1o6wk9yPyzFBoqSsdgMn2VJQE22Hnc1oCuwf7VCYXN4LOxuoXQdzEDllk0tjtx+TALzXtMUqpfl4lciKkwI9DbD9BQT8X5E8zqE5DkJKxIQS8CygkyrbtMtquockUWcjIVcrUkZzLdV+6VdaxdbKgd/kKKzJJ2P+0f8a5YnSMoF8pjDKNxB0s5ULtulQnOD5T7x8dXTgG+GXownT42Kcq9xgAHiwOeuYti4Nd8uLlaCMlhhb1ks46yiImfQSKrMCYlR+L945F2wAx4XDcZlJViaLK2ZpuCVBJ7FfvgxIHSnArLSD+JLNrTp5eV9yM1jWNtFgOfYmEEYjVNqKGkZyWE5gyWvdl6/qoJhVQ+BjdqePesIoG7l+UYpWCJs/7zS+PKVyMMVKuTlZcAoFlvezNSVhJ35DK2ASCBLlYi7MS7ZbnOUUsMp+crtuYs8uYVXS11lqcqjvbB2xY5/tI4JSwc6ilJPOWdY6gkTqYaRwoTYiVDJL/YGNX5vqeaYg+R9oYiP0YlPhKZoXUMa2qETs7x6jrc5SFOIwvjRpglyMAyWCTPMoEuObfeYQc5S8YP+Ew/lk86lFc+NqvvcT5l5Z14CmX1kahcGgT2N8/x9v1ko9PFScvvz/Hjx1nazqhLIVR+d2bP8DfLPcxuuD2667h+PEtrrjv1Zw+cTn3vfJ+PPCaB+Gtx4UOlI+pQRR41SeVPdyWIZA4Ciwld4yLJTFO8nu6Fqz6WtgZOP2DPwj1qpoGIUSTRrL2qt7Um2So4AzbMgRY/hDgGgLItTFwqPn9+zrilY3+6q8YRcbvor7Ic85+1Vdi8lwSS4d1xcFHX7iUHB+QoKUg7yLPc5azGZ21kRkUOJVnUlXCefGVdZ3FoxiVpSi97u6eMzHg66loErAhrT9x3uIcBavk76I0rnKEKiU+W30yeoim3ZW/X/JpHJ6vGATOEasYsGJ3iwiqjDGr3KBKMZ1M+ijk3b09UfyqEt8Fmq6lbhpUoC/hhoK6bVHRDUmidCOYjy86Bb4lIOGD5E5NLPGoGqG1gMDgvBQRyDJs20ixgq4TZi36fnkneeNMrAGfxnogBidEdjCPLF+e54yrEcE6jMmw3koxAS0KMz6wrGtC9I8HYaWdF2Vcco5mzBcL8iKLwRSroB2lZcxkmZTWS5W40jrudFTIXUxFYswqCK9rRAE3mZR1q5dcfvIy2tay7FrmTcOy7ahGI5SCMstwRSH1nBEyAKXACTjLRyXzeknbtX11FAbji4EVbqiYHVbOVaTYbKpuNZzzflUOLYR14Jj26BS4pGIFFaXXx4S1brXoerHGNZ3BaEUexE9f66gaaYMe+Or37YzMQ2DgS64SlohYxKe1JZ6XxlAPABVaR7IopL1KYa1buV9FUREMqsF6kvpr2J/3htxjAPixO26maVqkjm9OUVaMipKyqKLJBRbLJXlZRKAnGmRZlgJycgEWbRtNvgTm9ZzJZNL7m1nn2Tm2Ff1NgJhtPAGYYTi5MQbrOpIjZkLaxujI8vlorlF98em0aA9NfT1AU0oi74zkO0yh+oAsxH49ZUxacJ0/VIA9yIAa1jh2MWGp1pqyrChiHco8E9YOovlRSwSVD1Kj8tjODp4Qy8RIVGXTNFLs2snAK/KcqqoiWFQ9I5DMVemeZVX16VXyLJc8TVoWpSGD0G84sX/6TcVZOtugoznaGMlvSJykeVaQZ+Byx0itqiKAinUh5TrDjPB5VeJti3WSAPTY9nGuvfYBvPe9d+GsE0ZLrZgimZSegBeWVstk6+dDWHFWn8g/YgjQjwKMsmSsT7QhKL474HTUsSGwTDni8twwn+9y00038s53/gnTrREfu/2dWBvQGMp8xLlzZ0A7jh07BmTc/LGbWdan+NDNH6ZbWv7B53wBD/3Wz6ZznTDQAzCggu43idTm4b/D9h4OrrioLnA/73L6kod9H6d+Sxhd/DWPkl6rjRvecD4dbuPhd3MpP87hv0NA6acT/GiEahrcsWNiHYiRnxiDPbYjG8nBwVrFkKH4LMPubEs7VexfpXq2oQ+KimUVU5RxUiC0olfwirKQlCVK2I2iyDnY36c0hjLLaLu2z2vXxaAtl7mYrqTo32tikVNQSeq/no1wwlgpJQEnLggLmOc5JirW6Xsry0Au50VfORUV6PQ8Lr7cbOg6E8Gs1quAG2MyqXGsYsRtCEymU06cOMGJkycJzrFsZuI/hgC0pu04ffo0i0ZqohLfe1VWnD9/ntFoRJbn7O0f9DVetdYERQw0DJJSLOYudS4wmZSEVvYFpWQP2JpuSyUQpWisxXVQlTmttWTR9Jj6LpvP8G2HV2ImTr50WZYJ0I/kh7M2gmWoipKdrS1m+wfkJscFSTvTNi1GZagQhAW2DqVgOpVKJkUm5maJdvU0TUM1rihKUejTWiM618qk2tdXRvVEhYwJzWg8xnSS2sYYg9ewPZmSa4OdexZtQ5mVWBcEhFiPynOq6YQ8F8U+vQetFGfPX5DAqhi9PJ1u4TvPspFydihxISiLQuonR8dUY3QPlNM6LuPE9OPVWgkc6roOn8VIZifEikHqv4sFQXzGBQzr/npKK3SZxxrKUvghREa4bRpMchcP0LqOupXxWsZ1XqsYkIbC64AOcQ6EFUEkqxo9WSW4NFpDYum7EL/nguppKEhgFSmU5RKcl/8766SohF8dF6tZWFuTD5vOhxjk7yL3GADO6/2IcDMcMSHosubs+XNU1ZjJZEI5qpgfNJRGBm6ZZ0xGY8qqoCxHnD13Dtt2fbJniqIvbq5yQ/AK58ESCNYKo5gibg9pwVprlFe9T0QIEm2aNiGlJDIVIC9EawKFs35tw+h/9x6riWHipvd7QYmfXefEd22NBSQcudENNZXku5LukxlDkQ1C4LWmtWL2CbHqhMoyrrjsckajEXVdiyYat5XcZLRupW0VRUFRZP0i0vsagoBHJxtIVVV0TharMi/joJf0Ogno9htn3AyNEV+fzov5JYl1Ek1MgGwQpCK/EDcKg7Ci64AoKCW7YoBcaRofGI9kbNx55i6KSrN3cEDTtf1mMEx1kZgFjZQZEvNB6JW8NEmPksNA8DCIW9Ow1MqMcSk5zC4OQefh+ykVzfoxe724EhSU5Rilc26/4xzzG/djFHuJUQVGZeyev4C1nrLKGU9L2nLG1vY1XLjtLF/y5C/hix7/ZHaOb3PTh29isr2FCw7ftcIM5+Uqco511opBW9PPMB3S4edKfyc25XB/DUFuYowO9ZaY+42RFBggDHrcVIcM1rD/0nWHbT/s0xvivEzKXTr/jlf/DDs/9wvs/OLruOmtv9czi9575g98IO9/55+QFwXXPuOZVH/9/iPf8e6jv4D3/dRPMIlsmdJ6bVS4GHkPK4UhywzGmVhAXlHXdVTERhSFKMQ7Ozvs1nNm8zmlydiZTJlOJixd16eUSMBlNBphlI6J8AcuLV7SZfRTL4TeP5kQyKPiXMfcmyaulcYYxjHNSFM3EALFdIvlsun7/fDG1/tOp/cFA+Bp+/fuveTNa5qGcjxCDZIkK6O58/bb0VlB13WMxiMpP9btydhxnulovErXY8RUnOrNhhCYbk/7oKHOWmZnz3JwcCC+k4uFBC9og0KD8mxtbUlQinOUo4rZbNa78VSTCdVkzLmz58kUGCUMYm1bzu/ukqGYLxdobbBNy7X3uwbd1L3Cb4yiKHLmiwVFGcv2eU9VFFI3JkguQbqO3d1dXPBkZYXKBGCX5QiQTBNSC1mYuXxcMa8X6HwLpTX1fNH7/c3nB5RZyWQ0oSpLlk0NwdJ5R9e1zA5mXHXFVdRthw8CiMsiZzKdMGqhXizQKK666r5cdtllzOYL5k1LsDXZuGRWL5lkJaNyRJGVKGVYLBbcesttnDh1UrJvtI62m+NQtNMtls2S83sXqOua3GQCtJoWGzzHjhWkXIBJ+QXZN0IQ4OytYz5f9IGdDJhAIBI54t4yDIpMDJwPiiwrybO8Z90SSy/jU9hTj8d3fuUWow3KOIxxMXBEQ9YRcJigo8kWCNL2RG97HyLBudoj9vf3+3mQdiBjpEqKMhnGQFHE/TAygc4J+yf+leKHGEQ1lrUlKmK91eLeI/56uee1gMtxDzCauqUNEsqdkTPOS8Z5ScbKp63IMsZlydZkgtI6mnpXC360g0i9RqXI85LMlCzamto2tF1N1zaSsmSw0aTFSWtNWVSirWQG7wLz+UyiRAebRtoYZOEPEYFz0cZijMF4L7UT4wbjon9P77c3YBhSDqg8L/o+cnaVSiVdPy1m0c4sJbCGDCSBtteQApJMUjMZjWMZnoJ8JOxl17bi26dUbz6RTY0+v1UyRy3qZc9SpBQEymi0llyLUljbcjhgxjlHEwJ5bsh1IaYs56LpIjCZTERzQfpGF8XKHE+MWvKJVl8l7nVE0yDRr0IrskyjdUWW52zv7OCAZTvjiiuu4K//8i9YzOf4zveAXsUJCAlwEPtN9dpqb74mpt45NI6HYOdScph5Wr2r1bg56py7MzfLmABQBEQ73dvbp6pKHvrQR/LPXvJ9/MVfvIfPe+QX4r3ngx96H7/8K/+Vyy47gbOassjo/JLbbrmJg4MZ5y/s4htFt7RMx1OuvOYKLpzf65nXrBLfWedlnEppsgE7eIhpOyyXOj58/sMmnaHGejHruori1VqqHqSkqAnApWuEEAgxeEHH84dg/Ki+T0A+gaB0/4Pnfg0HT//yqG2vSuUlc43Wmlt+4efwMbCAyNi1bcuybamN7gMjemXhCAY1mVvXntgHPL736Vss5xJ8UElAxMc+cgtN3XB8ewdjJDtAPRdzUJkXqBzxH0PyA6Y5nkBcplbWDllnxKVm+GwJvAkDEgjOoYHOi8JMWFUvaZqmzzuYwLQxqn92KekVmUWtcf27F5N0Omc2n3H+/HnaGAR3fvcC5y9cYH5aykt2TcOJEyck3VMtzM9iNo9K8Co3W9u2zGYzKZ1ZlJRFwbgasaiXfSYKkPd1x113MZvNcF1HpsWPSzItQGc9Tdsxn89FkfeeY9s7HD95gjZ4Thw/zlZRUeQ5rXM0u+dZLpccm2wxGU8JQVi5pmu54447CM6RRXZpUS85c+E8Lo5V23WMypLaOrpa8uKNJmOWTc3+/i7jTOoYV1WFBhazObffdZayLCmrkrIsWTSSRifNiXPnznFse4tTJ06BD9RtTdN1TMqKoGC+XNI5K3WI9/a4z5X3wVpHXYvv5WQylv4KhunWFuXWBFdm7B3s4z3sTLewKO64cJ7z+x2XX3ccgKLIuWx8mnNnzmJURlVNmIzHtCy4Y+8sD3ngg1jOF5zbvcDebJ/Tl50iOMdoPMHlOY2z/TvqlTwn0e1eD5Mtq8iKxv1SaazWKDyuk9gD71f58VxU+FVU0oPQeJRGcmN6L1VDQpCAsM52vduHdY4aMJlHdzZihEx8/YNHeyUlKjXoNL5S4QHlVhYm1n0A27ZFG9O7u/XpYZz4+OksJy/NKvcnwv7VSwHpSVlU8XiqzZ0U2tQ3qRrK4TzBn6rcYwB4+ckrJTKpa8XJ1EnAxagcMZ1uSe63pkMFYalGRUmVS7oTlMLalqbr6IIjMxkaTWclCbQsXh6nPbb1ON/hfEfwDttZiiIWUFbRt4mBOUhrkpOoUiaaXVaLctpQZLApFCtQNASTSXptN6SBF8ulDRiO/l9WTqviPL6eNT+Zt8RpNEh6BwIqArdUoqmxUowcFc3CWUbdSD8NTTuCH1Svd6R6grJBtH3ya+cld142TBLrJRUCQUNkCDwQ/HryWOkP1fvCpGuliOf0fM47fCdRbUWeYXJhHjQakOTS0Q1tmPaoH7zOO5xSZLmmsZZl0+CB6XQLrcRMRRAHXQF3KUpZS5RWz2QN39zR4OswI3cpgDc89xIfHsn4Da99KRlqrgLSIzCOZvrP+qyHcZ/7XMtlJ+9LlsHVV53izjs/wrve+R5cC9c94LMZjQvO/+lZ9nZn4Aw3/u2NUnara3jE5zySBz/ooeQmx0czksLjCBF0E99/r3sdYiiP/v3u+uMwU5eODRnGQdf1yoDkyqQvi5aAYyqJtbqORx3yUzt8n5TDMs3nYSWdEAJ+NILRiDBYMA+znPbUqbWo3aA1bV1LGS/bDUtPr/VRz5h7CbTqWTEvnSxVfgJlVTFfLLBdFyP/DXfdIZWVsq1MansjwNXF9TBVFNFK9+zXUKEMCvLM9P5Ew+cyA5ORUbovYp9KoYGA4fQ+klJ72LeyVxSSH9ohYJ+6RQrbD6wq8X2rpPgFqUyxWCwkenY8wTkxfS2WSzTiE1iWJQezGcumYbolTF9ZlYwqKS06my84d+6cAEU/zLZgUbWwR5kxBKWoyooqK+k6Ac7OOc6fPy8WnrKQoI0gJtP7XnkVupNcjI3rmLc188UCF5Ny53lOlmec391lNp8zqso+x+qdd93FrF5SjUagJF8udY2pKlonJc1C8NStVBgpTU6ZibvO3sE+586eEzceb/EKdra2IQbU7O7uopTq3aiatqEsSjQa23n2ZzNMnpPKvfmYUzUpQVmW46LVxxCDGjNwTcOdZ86gJ1uUecV0PAFt2J8tOH9hl6ZuMEESXwfgxM4x7nvVVYRckj63XUNRSWUXVXqachT38UyUI63QmL7kZZqfSRKw7a06XnwiQ2T+okrRB32GMBhtYTVvUw5ApRQ6T5Y1FVk0el/EVDEkBGI+YYW1nlbb6NKWYaPlz2slTGBI9bIleEq4B1F0xP1giCui4hqnRvCeznpJRB3LlCqtyAuDzrKoREmuwDYlDAcial1b3y6yyoTV+jNcwz5VuccAcDI6RplbQqzg4J34tVVlRVVWAHStRQdxIK1i6Z8QYLlYUndNb0Z1iF9BgJVZIjMxy3aIAQkRQHhL28qQMCbv0XMS7zxBBayNpdlCpEt7utmT0rOA6lOkDBnAoQ+SgL+AjTnnkgnNBwFuadEUUYSQijrHpNR6tTkMk68m8GatFQ03gqvWSSF56x3EvH3Jn7F/yXbVto6Yb5D0nPSf2eRHElkfNWBA0zOv+SuyvnmnvhgGjfTnaY0Z3jCCwnq5pCtyCi/h/wpFpktp/8A3qY9SjCxy27bUWkoaWR9YNmLyrbJKWA6TE8wK+KaJBAIe0Bo/qATTU+V989b9+A6zdpeaOOvs1SEQc8T5dzcB11k0Kf+TvuO9k/co6iunTlxGcWWJt7BcnqfrZmit2N2d8fjHPo5HfM5DWDYzsndlhJBzxcnTqAyUhg/f9GFuvu0W6sby2Z/1UGEtrBQhD4hyIgUmY/qPZDKPksbqUYD2Uizg8N8hGPpE30sMIH6lxUMcykr3jtLSdwJa8Kp/v4ffodaa8Vveil8uUYpeAz+KIVwtqCt82l+zHyfyedm2jGJEqgKqvIS4tSQWTGtNVUuOz62uo/WWyWjMtKykpm10bK9GI5a33IIPjhM7x9maTGlvv40tPMpoJmXF1mjCdDRGXziPybLezy0z2WAuihJrjAGtKKJylNg+7xyd65g0UjUjBeoV9RKlxF+4yHKcl+S4KeI5z3OJTD6Yxd9lI7dd12+wqZawQjE+fYqiqjApOlUbQoCszJnu73HdX7yXJ1/Y44HWcfzMHvf/yC1c18Hpj95BsI7JdButFctlzaRtyYuCUVWRZTl7+3u0bctkKgDQth1b0ykAxd4+1lrGsc5zYj+35zOyGFyXnO4nk6kQEFrTtg07TU0gUJYl1lom5YiyrJgEx7HtbVzdikuMd5TzfbbOX2AUy8QVhQQyLtsavbvHZDzuWd1l27ATPKPRiHE1ZlxIjttiNCbM9uO48xSzAwIwrSZUVQVKkR3sY3Z3GY1GFFnGqKzYnm6R7e5yvKup6xqlIzguJA1NVYr7jrWOru3I8oItL3uIcw7tA9sfuYWxD4zrJW3XUFWSILmM1qpR3dCdP8Pxre3ow19yzDqyc+eYnL+T07eco8wKUiqhSVmRzw5YdpLlwbUdJ0LHFR+9E6zj5HLBbLkgrwqOX/ZxJoU8n4vWnmSVSDPdRECW1h23d8AD/+qvedLZXbJS9+u9sx7v4a1lzgLierAeEEZUGU0mALBfuiOSTCZnwU4yy5U2OC+Kg9HQZRk2zgvtB/7vOlbJksVJrp3uO8iioNTKRSHtOUKAmL4xSmlh01P5WqHksXYQYX4EuTDcj/rIfC72AfxUmcB7XgnEarSpqMqCssgxCkJwPQjpuk7MekpSphR5gTZitrzzzjvwWuGVOEym6LiirCgKcUwmQDer4+KWI/Vm5frLhRTNLjJPluW9H56AMQVIWTjXl19a1xaGDJcPAwA3WMgTKOqc5MJKZK+Pvgc+BHCOFhhFB+a0QSXzh4SIDxyikwMvvk+j0ratTP7MyH0iy6GDsHRNTAVRFgWdE/9HQippI/kSy3LUV/NwCVj5tCA6MQeUhUSWJQYS8Slw1lHXS3xMnD2qBLwnM6GYlyRtTdd1UstSIZHGKjE00YSqoekarLdYb9GtOOCOq63oLynR4V0rzzEajfDOi+lksSAY8Mrjg6KN0XDy/JbpZMzcOeqmjht2IEVGoQTc+gGQja+zlxVjuj6OD5stOfSd3i1gNYrWz4GL2K1kkk43U3HBWN1jBbiUgizTdJ0kPjWxvrZ1luVuw3x5hr967zv5oz96O2956w0ot80//+ffh3Vz/uDtv4+1jmM7J/ncz/l87n/dfam2Cu66cCf/7Y2/xF13XeBbnv9CHvbQh5GXJcvG9lHYzjm88mQpeCECwuFzH15ELgbLasDirZ+fAL6kdVA4d6iPFL3/mELmVUpqrJVo1AwWPa01OBsVPAnOGioSACoE8vMXuPJ7X4o5f/6It/X3Rx586O+HfFpa8b9eKuDL4g8AH7hZfj4D5X6f7gbcjTz0iGOHx+hQ7ns3n/1d5XLgQcCzjvgsAM968DXMfaAegL/1Mzw6S6RFdH+Iim/XSeCqsPEebURJlCIMnk4FbEy31aa9OzNk0JdpDQwVRLlnYgKN0pHJE/eMIJQ8ihDLz61S1UjwXPw+oILqzdshBNRAmbMurLJ+JOYv7SF6xbYPyau+Rz4JVvAeA8Dl4hw7OydQwdM1lhALGyslDFvTtswWC4oik0LSoWW+P+fM2TtYLPcZj8cUWYFCo7whd5pjJ3YoyhwbLHVTUxgJ1FDeABnaKJxpKYJEEjVdg3WtVNIIOgaISGF5rzwYjbcqpadCK8TcGsT3DQ0eS+s7TGHQhbAebdcRlMHrjDYEgrfoINn7jdJ4a1HR/JkrDc6LM7uWXOIpWahRGcHJy+rTBiiFjwyJVMKQ9lsn4fUm/j94yMtRX0JvaVvaEOv7xjqI6aUvlx1FV5CbvNfse1OHd2IC6iz5aCylZxDwZ5SmtUuCd7H0lEaFmNsvmpaM1sRURtHs68B5SpORFRXz+UIS6ipxdt/e3sY5S2s7bNtgO8t8vybPTc8ceO85deoUSgWCDhSVZOe/7fZbIT/NdFSSZwbrBHRdOH+WTHm08XRIgm6TSRQVUct3XQsCcUmzY8go6aS9XcIsfFgu8mOLibLXzsHjiKZExKwS8fkhTUyxAoXpy1aSo2JQwUrLi5ILywt88EPv45d+6Rd50uc+kYP6PO/6s3fz3r/4a4yqeOvvv5lrrrmGv/jL9zBbBMpyi9vvug2zlXPy6suZTsaoLOPF3/0Sfuu3f5v/781v4m8++jc88nM/n+PbO/jg+qdQSuHMWo/J6MskMGMYKX94EVkxe24N7KY+k6A/BYhbw+F+1yjGeU6pUvSfxqPxrVTm0CrOYcAghenrVvxWjV6ZgYeRq9nZc9z/Cx93GONvZCMb+b9EFPBrH7qZF54+xm/nOTrWZ67bRthWYgqWqqLJS7YcGIVggtChVViLlidI3IHWGq8VwSuyEDC2k9SpISP4HJUXieGBPJBhYlnXAMZDUHjr8a3DNx22FQVWSMGYvqbxdHNH4ztMadgelRgzJuiCXGuC71geWDoaMh0IGLwX17DMSIaL4b7iBwpyOASGPxl3pKHcYwC4e+EcW9MpWSGgw7oli4N92q5huZzTNBJBllVj7jp7RjJlB4/OSsbVcWb7exTGUZVjJuMRW9MpeZ7JxuEBAk27pKpKqSwSAt6JiWtUVuhRrOHpVj5rPbulxK6PF/+zTGuMBqUD1rZU4zIyiuCVjoXDxfkyhbu3nRNHzaxAmYwwCKzI85yiKPooRogJT4Pv/fjkBXkZJFphg0V5RQeR2ZJcUyJ+7YVpDVkulUSyLJY3ch3eWWzosK2ltZKJPTMZXgeC8/jMxiAU6ccQQ22yLJci7FkmphGt0N6zqGu8VpiyoMhzyqLAWYt2gI/mQhRGmb6tKagnhPD/Z+/PYm3b0vs+7Dea2a21dne629/qSIoUWUWRakhTlBwxtmNRkqEotpOX5CEvARwESPOQ5MlAAAMJ8pKnvOQlcAAnQJC8OrZlSbZDFSl2IqtYdauv299zzj67WWvNdnR5+Maca+19zy1eWnQCBJ4X55599t6rm3OOMb7x//5NVuk1hx2LVkBBURhCEvSubVvGIRe32furKArW6/WCshpjWK3WvP7q6wSjiT5RWCOqtmnixeX1Qg1QGX1chDfiKi2pMHGG41Xm590l4d8vZO6LFH4yD/AlCKH8cPlHbih8ZpF55xobncdEfpTzFHWDTond9S2//9u/x0U64xvvfIPLF9d85Ss/y//u3/vf8/jxY9577z02mw1/49d/nbKyvPO9d3jtjTc4PTvHTQMfffQRX/3a1/ibv/43+P73fsA/+Uf/mH/29d/lf/Pv/rv4cVrSILQSo/VjAvHC9eLIuFypOz8/nnCOOXb3uaN32vH3DAilPWsWNDelOG+n77zep3b4cxF/1P5ZkEnSf138/dfHf338//mhADdNuPz1PA/F3NnQhRZRzdHmdXbqWLqFKS7r1iEnXBGDYhrFmcTMgtAZVctzo4Y76VpKqdxtlPlycgPOTTn5y2OUprSGmGTtLFLItAqJqEtxRgBF8BHCbHMzW0kpiWH9vOfneO79M7aCP3cBWJSG2/0No5tygsfIzfaam5sXeO8wRrNardCj9O6LosJasbJQRnG2uaAqKuqqoakbmvUKrWF0A/3Q0fYt3o+07SRt3sV8U3AYjbT+IgfypcCfWd2bUbfCmtzakpNZlTWFLYRPkA5tqqnviSFQlZX4GHkvSQEqyWvNxHRjligzayQCLYYg4deZ5I0S5GpyEzH7CCqVlZcKvPN3islDMaMWTpRS4hVksipJaY2pG6qyZlVPC0nWO09IaTGmLvLjY8zPZQxFWaKNFFRyro79w+7ytFI+wymmTFgWX6b5ZibzsYqigJQo7MFoWxtJs5CbOZIMpAq0GpfifD7fxzuZ+YZtyobeO0orXCetC8rTFW+9/TY/+t4tICasM0n9UJilAwdDPom0GD8Fh3/aA/B+gfFZA+dltaE6huGXV3h5K/m4UAGIAVLmAKokXpreeZqq4Y1X3uCtN97mO9/5DjfXLb/+a3+L3/zbf5fX33ib55cvSCmgjeLi4pxf+ZVf4Ss/81OMSXhbbppYrTZ8/NEnrJo1f+2v/QqresXp+hQ3TJAOROL7hd3y3qIY0d4v/v604ye1jD9toaOWgn5BR9OBB3T8vpbnXdDUw7W9W8TfLTL3f/Wv8OK/999dXP1no9j5mC2l5ja1zyrAQ4ErfpcpF6luEgXfnEUr/mYi0NAqq+vdxODFnsVoRWkLMUWPER+lO7DvOpx3nG5OqKuaGBOfPH2Kj56qqahL+bNZrYjOE47uqblAL3JU3HJ+EnkRnBM7JHnAZ272zFtGK1LI4zPPnSnPpYKo5lSaGNh3I01dU5UiuHNOoh+VSng3ME0949ARXaDvO6ZpyPxq4QJDolmJL6w1muBmH1e55iF4aXmlyDBKFKQ24n263e5RSotNTllhihJTlrzx5lvyOQNMPoqPXozCwzOGmLnXt9c3nKxPJNfVyL1RFgW73Y71as3F+QUAu3a/8CVJUFQl+7bl8vKSx48ec3YmsWcuONq+5dHpBSfNGpI89uOrZ7gQOFlvePLwMcF73v3gfSakK9LYki++9QWurq+yMEN8bC8ePODHH31EInK62XC63lDZAj9MTNNEURboQsRvfddRljVD3wmHdCWqcY2mqYQj3XYtu6HlfHO6cOiVVgzDSMzhACEEphwnR4JV3UiW+zCw7/ZSHHlHWdUkpbnebrm6vuSLb38BkyTdw3nP7dDjU+S1R485W2/QKfHehx8zjj1vvvEWVVnRdi3Pb5/x029/kVpXMrYz1Wseu/Pca5TBuWmhc714fsXvff33+N6H38GUBkLi5272/FvvPz+aW3LKSKZ4mVmYhNzjc8LLfU774guaDtSlmZufSPiYGCcvHTGTJYwJsYXJc4j32YbNsLR1UxaVJvJ66US0OLd7j+fGeYypJKkudV2jlM5taJ/HoRGNwYFbduAb3ptvPw+P/fMen7sADES6scPlCdsHxxQmurHHe7mBK1VD9LRdR1UGmlramrao2Jw0ElljC4qyxBaWYehou45uaOmHjmkSh/eqaiiLSm4QpYmRjPDkYiVz76TwEpJtTAlipBsnUW4ZC4hNi1aapDUkiVqbL2DSOt8Uoh4LMUGaTWoP6uDZBFZrUTTHo5MvC0qGZ+Mc9Xa4qEVhmW88+XeRb5DDQiYDRCat+WYhzTnEkhc8F1DjKHzIY5J4kT0ATXa8tLkQ1FqL2i+rblOM+CxXj0oTVC7elFpMXec0j3lnAywDxhqD0jD7FOlM5hVxB2hlKGx5sKI4uhnnG31ZvFFYW1IpTVnkYlEbiqLmtdff4Aff/yYxCV/jLjJ0xJeYjzT/794AgeX9HR+f5pF8+jiqPe5+/z7utCCBh+d+2dfLk8kWFqUM3gequuLxwye89cYX+MPf+l1+9i9+jd/4jX+NX/u1X8eHSNf1NE1BSmKl8OjRYy4ePuTZzY4QIs4WnJ2es9vtsYXhzTfe5K033uTRxSO6bZsfZxb/upn4f0wsnv++Xyi+rA38eVoNC2fl3mOXlJn5+e+3yTnwWpTKPJ6jn81c3cPG4u7ru7feYvtv/L07G4/565l/q1TOttZKPEkXNEAKlNFNKCP0kiFbH9WrlURSjpOMNyObksk52qGnG3uJs9SapqhYNysm78Xja5rYDz27ds8rD5/Q1A2Tc/zwR+/ikmO1blg3DafrEx4/eEAYJnzmDB+f88oUy5ifN1TL33kj6ZzD+XGJGAsyYcIyfx5SCZKWlASttXQtgNv9npP1mroqUSnRdy3T1JPSxDTtGYcdQ7dj6j1dt8dNAyHIxtaPjqKwVHVFUcrmfRomnHMUhRSnbpoW4+ZhGHMmsSzIXdcRoiimy7JGG0vSho/efI26qbFlgylO2fYtUwjUZSWbXyWK14/f/4gH5xeCBBmJTDs7O+Pq8gUnmxPCw4eEEPnk+TNJu0jCITWFZbff8+GHH+Leehv3+Ald1zFMPZdXLxgfv8ajswdYpbm6veHZzTO27Z7TzSnmzbcx2vCjP/rnOCtm4JUuWP/FX+DDDz/I87ggTv7xY77z7W/j8Tx+8IBXHzzmtGpQTvwLTWHRZUFMke3tLatmQ9fu8cFTNWJEbZThfH2ar9WW9559xOuPnlDVtRSQWrPb7Rj6gdPTE3EDyPN9YQp2yhBSoJ8mbva3eDdRGst6fUJUiqeXl3z4yYfYn/5pam0I48Ru3/HJ9obd0PG1v/BzpCdPKJVm++HHfPzhB+gv/zRFUXJze817L97l9Od/kfPmDIMWpwgl89YhKAGslrnPZseL5z/+gN97es3vTE+xtYQLeLhTAM6NnpjSsmYpEGN2ctchzWrczzb/n9fheZ6ScRaYnMdmcZpsrjRFtOh0oJzMbh6HjWe6A5iQeX5K53SxRcyZgR0ttYAgjBB8lFzhmITepOZxOov1Pvv4LIrOn/X4/BzAqWdlN/goVi4oaFZrTi8uaNs9CrBFRaEMyTtc0pSmwDRrVs2KBxcXi9u11oYQAzfbbUb+xK9MxAmzyjhSFpHCVnKB56iVrIPxIQjHL38v4fHB8fHT55yfXXB2ekZTNiglhZ3KMS8xHLwIZ84TCQpjKAqFjzEDSofF8LjtNUzjYsg6I3cpKkgBncmk4zgyjj0kccGvK8n/TEmKnll6PhctM5oxB2cfFG53292z/9f8nmeYqixLyclUh4WzsFaC3n3IO2+PHyf6rqOuG6zSRKWwOTpuTpCYb2j5W3b38+Btmgqd5DySxBBaK3JRqwUiy9fkvk/RXFTO59xog1GGTVODnnllmugjJ2en2ecrLvyv+RzMxf+MBi5FScptYe6qf4/fw59e9B3//CVG0HlwouTvtHz7Jw3Ve0eepHwmKVemYrM65fVX3+CfuX/K3//7/4Bf+7W/wWZ9yscffkhT1wuBOYSED46yKjk/OaXrB9w4cbI5pa4a3v/gPV5cXVGXFQQ4bU6y1xsysd1DAO/yS3LqitZ/6nn6zMJQ/eRdaVmWC9I9127HyPTxe5P7+NB2X1Cto0L1U69xRFc4Lv6AO4h7QtwDjovReZIPIVBYs1iHaGNylm8Qo2A+/RjIt/5yQ8wZtBL9Vtc12/0OqxXJe4aulzFlJEHA53FurSVpQSO4T3U5Kozn1xbj+3QnLjPF442RRDgWxhIjpCQRVtookgqE5Ileo4ymNBV1YSl0gjDh3Ei3v2G3fYFze5zf412Hdz1DG3F+knEYBUk72azZbE5lngmBMadQ+MnjvGacJimo47xxz4hO9OjgaOqGbhyIPuBiR3Sermt59sEP2JyecPH4DR6/8TOYssZoxb7v0VrT1BWVTkzB002SkmSysnlVN7j1BqM1fd8zjhOXl5ecXjygKUuMgiGDDmVZZr53IDiPnxxd23Hb7KiKik1Vo1Ti0cUD4YwniRU7vTilsgWrVSk2aaOj7Traruf8/JSqKDHaME2ecZzo48h6GJjGiWRKVnWNDwGX052quqIua+q6xijFNA1M3uON2ITFFCmLkqZZMY2ecZqwRYGJJtupSOZ009QZHJAlvlk1XD6/ks5SWbDabLj85BMeP7lgvd4QE/gTh4oBjfjnBaVh3xFC4ObmBu8lKm9TNfzsT/8M+9sb3DTR9wPXNzcM40DXdZyUJ1g724iFZaMyr2cpBoqiEp/M/H3nnNit+Yh5SYDAPBsv83pC5qv8b4lUjPgkwro5seZu54gFPZ87Y3NXRsITokQeKiijlajDECiyW8OhA0G2ehLASPQJDrR0PpRWhAQhfz6UFHiFrbFm9uaU+8e7QAyJaOYc94RMxXO84mEsH7789Nf/lReArzx+XVzlx4lhnCTRwRiePHwDtxmZppEUPWfrUx586XFulViKosJksUJIXiY8N3CzvaXdiz2B0QXGWIrzIqNWEa3Fx4g0+0rNDDdISoOS5BDxhBNExRQFV9cv+MY3vsHjR0/4jd/4DQbf0fe9TJRGMhtntKxcWh1uKZz8NImyFrPw/uabaEbH7mQiG4kk0iZH1YWYTSwPmJRsug/RcoIElketp6xI8ofUk2PEYr7AIYhyV6V5J6PmNT1nQ+YWcFEK3Jwcu25H13dSCJBbY0YvCKEpLFrPpsEyACR7M7LeNIuJ9H6/Z9ftOD8/X5JEUkr4lLK9j158j0IulI8XsLkoLMlpKjFQ6RKrS1kQMqeisIa2G7je7gjDQIkUygekFFIyJI4jEDKKNU8P88m/dxwjiT9pwBxg/Lvfn0Ux6mh39jK07GUFZySbd6coXn25xWi9IN6/+LVf5v/+f/u/cru9pd3vOdmciB2Shsl5lE6idFOG4BOn6zVhCkQXCFPgr/3Kr/Le++9SlSU+eD765EMe/dxXl/t2uQ5H0UzHFkj6KFc1prucl/nzHU82n0VORql8m336/C62SEdI7cvaGWpGaDPiu9jYwB0E8GXXbUYJ76OVwJJF63Or9Nh8OkSxT5pbzKNzKDSlrSh0gUc2fHN292x67CZHCFLUyJwlr9EPA5NzMtZmu5+Y0CZvKK2Ut3W1Wtqt0zSJuCifp/k1vPdU1UGgM89HU/DSsopzOzdfT8XCl3bBUzcFyit0FK50Som+7wjB5c8TMcaT3I6nly9yodcxji37bodR0oHwzjONI34c6MaOSMiF1loQ0mFclPzjOBCitCNvt+Md/m8/DDk+NHtiKoXvnJxzJS24EIIgQdPE0/1Tnj695Ec/+iGvv/Vl6s05Z49e4fzigs16zfZ2y5MnT4SmEqKI1gpJG6lXDd6JCbS2kmL10bNPKK0Vi5dKWqWmLHAxsN3vuLq6wqeIC5527OnDwGlzgp0sH3z0MWM/kGLJdrfl4cUFq6qkXq2ZQmCX9rz/8UfctDvWZxvO1ytWVc3VzRarDY2pKbVEca5WG8ahx2W/TrLhtAZSFPoSSjH4bZ5/El3XMZkRHyPrphH3gKFfokKVEgPt3X7PZr2mqRvqoqReb7i52i4Fb9PUPDp7QFNU4CMuBJQxfPXnfpYPP/oI7zxow+bsnNdMIabcuz2Xl5fYBw9YnzzglSdP0Kbk2YsXXF5fUZ1WnJyeyJiNEWM1GP2pMWuKAoiU2WliGLpDBwsBf0K8y4GTqDy1+NGq2VA+O1asVmuaqqZWNQaz3FvLHJARw+MNIRl4UUoTEviQcCaivUf1Ayg43WxwMaC8hqShEBN7QLj1eZxKRGtEmUSUVgtJKVxIOOWIKWBttdhU6fzaPszzmngSirdwnhs/jT8stdBnHX/WQvBzF4CPTh+ijWKsHG3Xc7vbUxcNtap59OAhVVXkvj+HdingQySESNuP9EPHMA1MfiAGT93UKPTc7hbVb/Bs1hVlUaG0oR1aeR4XFxRJuCJyE8lKr9GmIATHX/6lv8pPf+WWmCK3u1uuri7Z7baUpWGzOuHi5CFVXeeUjznjEqxVS3C0UUZCpWG5kXzwDKMIOWIIrNbrTyEq8028XgnqORuI+pwCoVR1tHiqBUEUywyZOGdrjJTEXdxmTp+0g6UwCXFW54p1zG63WzgV88Tq3MS23zPkXTEZ2bO6wHqPKcRcG62I0aMM8vxJdutzziTIwnl6eorDyXndb4k+yOAzFpXPv844ttWWupTJKYZAjBLcXtYFZSETrpsmkpaoJG01PgamYaLtpKVmygLlPDqHN0Tksy7t3vTygXC3XXz3+Oz27Ke/N/Mm7//8/nO/DME6/vv+e1Mg0X/GoqzGeYe2hl/+5b9GWRf8wR/8Pj/1U3+BN996Q4q/aWByOS3HWsqyFjTHOzZNRb9ec3u9pa5r+qHn+YvnxBg4aTZiwps5MnPRehzafvyZ77dmQ8pc2HuffX7csnn5zPP3MhKzoFFTGmBuqeh7xuxH50q4X2GZ+Y5f/2XneEbFgMXX8Ph6qbzZ80emtCHFPG9l/0FzxF1Vh41Loc2S/jOP4TmDPMa7bv0pZyVbYyjqil3XLudq9hhV2rBuKhkLoyd5zzSMVFbm0RmBPCB+h4SM443VnFmutF7Mro8vipDghT5vcoHgpkRwUBYFhUrEccuL/RXPn77H2LcYFdEq4d1Et91lj1XhMGmlIBrGIVDWBVW1YrU5QStp+U5ulHGqRTk5RLdsllMuYPq+ZxyG5a3OnE1bFngfGXMHwpYF2jSUKaFUoEw73vveH7LaPGB//Rrxzbc5++JX+MKTRzx99hxb1WKMPozM5sL9MGRUt+Bks6GuV7TjDf04MgaHnoTTGXzA31xJ0aU1u3aP16BLiyosUQlfcNgLVQlgcBNt1+K942R1kvmWiQ9fPCMp6Iaetm1RPpJCoC5LJu+otCBzc6Tq5LLTgdYUqxVFXWOy6bIPnqIsWW3WFKqgUIaYXRcuLi4wWu4D5xx937PZbLi4uCDN6HTeAHb7lrKweOeYnMcYxyuPHjN0LS+2W7bjgLeG19IDmrICrRkmx3a/I0bYrE8Yp4nb3Y51XQMFpyenTAmKsqSua+qVpbIFFvGRdEGizuaN14xSD8NA9F7SbpQihMgwCEijP4N6Y60VazLv8xotFmgesbOqm5JEwEUv95I6zFMSXTi7RNzNH1e5OxKVtJ11iCjlRTlslCCxVhO0BCsAKCPUquADPvjcrZqky6JlnhIQKXMFTcodvHpBRLW2BB+5vbld5o0Z31RKwWyFnF/zEDf58uPz0JpednzuAlAbIwtDjPjoJXINUEaqcGNmQYZidBPWyG66Hwfc5Akp0vZ79v2ettsydnu+/OW/kFutB8KlreQtTZMjppEYp2yymMUgSuDgQs85sHLHBK+oi5r1pqa0Bft2T9/1eBdYNQ0pye51G295kHfxKgdoS1sHRifKYIFyD4ukD2Jz470X1C87s0usi/qUsfKy0zEmq3pydqWbjjhXgUQ2j4xygzvvFgRiblGDLNopepJWOBcycVVQV+ccXddK/qixFL6gzOKP/X7H5MRmoygkUH5VrairiqooKY1A5R5PmITw67xjHB2bzeYOWibE8Hk3JyafIcxt3SLHjIlZNxG0lfZyssKxNFYvpHwHOGDX7vAhYgpZ6IMLhJB49PgRtrKEQWHinLKSB+xLb3w+1X58GQJ0//hJPz/wQ46/mQuhmJZJ6v5zHCNad4qq/BiT2/chinVKSokUEmen5/z0z/wUH370IU+ffoz3jqoWK4KiLGRiTIlhaCnLmqZu2O1aUpSfT9PE22+/TUpC0FcR3DgtRdN8Pu63UF/WQjj+3fs/m/99/L2k7kax5S/uPQas1lhz4LHp7JN5XKAppSDO0WZZ6ZcORfb9wvVlx/0icf4zo6FKsbx2OmovywKhjjxG7xXJ85+jTYY2BhX8nddZzrE+cPO01gzTiImSrR2VeI6WJqcEHan+pmlaCpeqkk0jS/tJLZ8xThGM3EvmiKc086SWLkES83SVEip5rE7oNECIjGPP2O/Y714QQkeKgsQF52TeDYppcLhpko0iiWmMJAu2qLGF5LBPbi6G42JsrxL0kxDkQ+ZEdl0rAj8Om4qUi5hu36OMwVhJFgoI2pPtt4kYCq0JY8fN848Yui1Xzz7i9Vfe5PHrb2ELk5Eiz77vqDYrRueka+Mc0+R49OAhbnK46LGlpawr+r7HhYlxmohRwgkEzdG0fcftdsumrEmD4+L8AtO3uBTZdx3Xuy0JRdvuSUozOclBfvLqE85OT6Xj5GTd0EoLJ1wLhUnaniLisdos6wVaNomDmxjGgWEaUKVlXSqaqiQpJYWV1pRlpiv4wDROhEYKjGnmXOd7uNCGvuu53m7p3UQ5lfR+wCZw2TJ+t9tyeXPF+WpNRDFNW/Ztj4sBnxIWud/K0i4dtO3tFgVcnJ0RCke7b6lP6oUvV1dVbpkfEq3quiYag3eZ7gCZbpQOIoh7pc5CtzgAemSyFEopmrKk0GaJu7w/r4lQVFKR5vZvTFJlzVx2v8xTJboQ3nQ/DqiYIGWrqphotFmoDplvJHqFvFalGCGKt6+s5YaqLoXbmjtnsm533N5u8/2dFnZ5+qxC7jM6W/8ix+cuAMfgGaeBtpf4Hpc8U3IYr2FI9BMEL3mDITqpllNidBPeS4tkCiMuDIyup+t3tO2WqjyhsCVa20yGzK7zPkqBkXratscYQcGsLbFGSJpw6OkrrSitzUFkmRDqEnXZUDelRMv5hGFWHM6tqIOqTmV1nM6t1PlGCrk1lJK0YIuyzHAty85+QRvSIbIJxKA5Iu3cruty9JfCB5eFF3JTr1ZruWnvL4iwTP4xo4Ojd7RDxzB2GdFwJFOKO3xwjP7QOq6KUrKZsx1LU62oimJRMkYX6KaOYZSWlfOBGIS3pNVBDDOTtWdUQhuNz1wYYzTeJ7QWIcicQRx1JsAaUUPndYyQP5cLDrffUtZlJqIrUorYOd9ZZ9bHYt59dwQcioeZP3E3Kmz++09DA19WDH3mZiojkPMU9bJfm1/zU689FzFKL5nV0toUwc+Xv/Ilfv93vs1HH33E8+fPOD8/5/LyEoXE+M1Ptdtu8f6UEMAUhtVqxTAMnJ2doTW0uz2Xz54TvUfP8UhHaNj9Fu6f1hr/LKRVvnh5EfwS/FOM0fVBJadf8nrSPTky5NZ5N5yPP60APP48n14EcgGl7m4QZpN2crE852wro5Z27DGn9bgYnceGyhvT49eR341LZ8B5R8yvp2dlu5JYtpAXDmMtYZiWxXIpZu9tOEwWshy/f+/9shFXSTw+ldZoxLifFIhhxE09KW5x3uGGjrFrcV2HT174ws7jncRwdv3AdrvDu0kEH8FR2IL1g1OJtjKymY0uCI2DGWVJhGxy671bisDDPXZAUSMSwyZJDFKIz2b8wYdl1Edlpf0WYRo7Jj/Qdzv63Z5oDJsHjwCJgpu8Z9e1TG6UzVdMtMOet98+49HFA/qpJxJp6obkPF5NEGOOyDtwt7qupzIF7uyCQilOT04Yo2caO/ppZJgmyrqi63rpUuTrVlpLVZRUpsAkxRij0G4yB3WcRsapkqg2AY6k5e2cBCNoRT/0bHc7Jj+C0ZRnhljUMo/ERIievRvyfato6hrv/HLvz/dFVDLnDNPEtu/Yjj1Mio1rOC1qVrlNfHN7y+XtNRdP1pgEOilUjh2cpgHlJblq3pCFGOi7TqgsxhLiRMi811kooRDh4ozIHyPlMdtTxRgWPquYwqf7+8fl3j9I8O6ulfNmhzBv3g+bpZTHlmw2lh5Dvl9FNDKPs/me9EERosV7hdeWYA5c3aY+2ryQsmNGptmQUDHlNv7ctRBhptV2ARFikPE1jOMRZ32uJaJE6N7nsf8pKOB/meNzF4DboeN2e0Pb7XF+wtiS5Aw+OnZdxLuRod+DFl84ozNapyWr11YlpoCagpBqVGy4vn7B6YlivTrNvJqAcyHvaKQAdL7j5uaaqlyxWZ9Q2EIQxyNungIKq9Em0XUtXdsyjQ6rLZv1KWVdYkwmjAYZECFDyRotRZ+ahRkKnbkL3gm/Z8mh1XNMmsmDK5cB6nDTxVlZZ+1yE49uZN/t2d5u825BPuusGiIlQmxE8JCLI0E9j3gR8w5fKSYvNgUhK56KqsRk0YfzkxjoKsXF5oymbigrKZ61UhiVicEp4SfH2HVcba/oMzcEZHHpu46qqmTnmlh24ctCyAFRkT8OpTygaMoGYwpMjMKTMMJ/Ixc8Ku+M0Iq+64g6sqpXVEWJVpHb7hqjDV4psbXIghtJtcivro6L908rpu63e++gcZ9Cp+4WIVpnZFi93ItJ1F2QdEaPj57nU0XhXNTLA+U85qIh5Na8zsXxW2++xTf+8Hu8++Mf8o1v/DG//uu/zvXNFRAhCa/p5OSEH/zo+1xcPODho8fUdcMpmrbtAHjzjTf5+MOPeOeb79CPI2fNanGp1xxaozFzYI7bIfePOxMoB/Tr7vk6FOU/mZtyCISfobT5t1/WWj+IODSzYn45//fa7sfvd/Ya9H6+l/Wdx80FoLxmtnI4QhiNMZLnymG8G2NkEdOzLOWwiMnvzTZO8h7nMRJjJDh5Xmsk6jIpaSMXZSGIjZMWUjIGHwNlVaKGfmmXiQDFL12SuY2vlGS7Lh2FnAdrbbEIVWZ+k055DKdI9I52+xzvb5nGjjA6/OBIPjD6iWkQxCNFaU0+e/aMm+utbN5iwPuJN994yHpdUpQaRSLGjPK5iC0LeQ9JEyaPUYiRfhLOV12V9FkMEmMgeI+LYkNkrcUHRxhGfEoUVSUoEYqkLF5ZSg2rVSFzjUrE5Hn69ENa73n1C1/k4sET1s0pMcGzFy8obUFpLMTIvt3jnOOVx4+52d4INUgbNlVNnDzOB0JMBC9cTO9EzNIUJSkmVs1aCtHtNaPzRCVF6vpkzc31VnLYtdxD0zDimgmzqVmVNVPboou8dsRI2/eU2mIRBwbJks5+dSTqVU3X99zutqDAp1tOVxsmNwm67z3d2HN1fclqteLi/IKTzYbr66tcvAoa5YPH6IIUEj4G+uC4nXq6fuQhHt3A6XpDYwtuteV6v6M/HbBKAIKz01OCVuw/bBmdINP90FPbNUMvcXXD0BNTwJkRbczii4uC4BzjNErcXVYqey9IclGWBOdyNOgI5A1YmKV89+eibPpMXgdmSJ65RXrg/inuzvcxHp5RNpdinZYyMjiLExOCvqbosEZT5M5nzEBEyCKj487OMPR0XQdFOAhDlcZHSd7SUaJv9Tx2U5JCcBaSIAilytpOmasP4sc7k+g94OJP63L9acfnN4K+7XBuRKUefEe786jVY4rTC1GcqoGuv6auV0xB5Z2+9LpJcFrWGCxV1XC+eoQ7H/noow9wfk83OlyY0b0G76PkvGIwZs2jRys2mw2r1QpjLLvdnsk5KdiSIGyeCT9K63h0kvtY143ssAOUuuRktWLd1Hxw8xFX+xv86FmXay42p8IrsBYoQEeS9tK7V4roYi5opRHBFOTG0MLpkVxXGcjiiSjt22masLZk2/ds+x4HrOuadSPFns1Znd57yrJe+DMhhPxYi8pqKjk0zgdsVJxUDalshIAOVFW1FGMpJTlXSpC/+eb23hEUhCBtkWEYGKaJIXnG4EgxYbNVjXMDTVVJdFhGSMfeETyMoxOPMBLWWMZxksGeJvZjy6Mnb0JwVNqI6qmwWCNKvBQhoVHKEJVitVkzThP9JKRbawzJQWVXjGyZwoTRYowZdELiB1Nu9+RZIC/Md8qSIxToZQXG8e/MXx8HlqtcSBwfSSuiOSgt53bbsRJ05mXND53FLckYUn7sXAAbUwpHNoHRJY1t+Pkvv8IPf/BH/J9/+A7vf/BjfvXX/0b2VSuoqoa6btgO8ObZBYOTdl1d1QQ/UBnL44dPGMdAMJrf+cYf8ff+1r9G8oEJQZu897lovY+85Zb9jJwp0PaoWERoIOLHefex86R7OJdwf5+qlWbVNPRjJ6iCF5+8+bqYo8nN5MJmRmJfdp2OEb7j6zm31ZWac4fNgkbMebay5zugyzNKnZI8d1UUQsEwwg+Tal94RzqTtJ2KTGHCq0BMAZ1mJDoKWqJVzhyVDU9VVoQgC5NBUeXPm0IkKIgm0bqR825AozNZXN6fMQaf0kJVUSaRmKRrEsSFIBmLNYHgR2LSGFVgbMPoOwqV6LsbvNsRfItOPcO+Z+jFwsv7kWHc093sxGYqQN8NPH16SdePDMPIOI6UpeXR4wc8fOVV+ahTIpm4tNBXm4ZxHGl3HV3XLd6nzrmlkAYICbquz10D8f67vb0FxFFA56zWmAQVakfJoE0p0DQNWp0Kr8wpPJH1yYapu+bD79/SPn6Vx29+mfXmMdvtyPmmQFuh0HgFV/sbqsbSDj27YeTk7ISL81PQmq7tmcaJmKAqS57ebAlGzm1lSs5PzrjsOi63e273LU3TcHt7y6OTU548esKu63l68wJrLDe3O/ZtS/fkCa8/foVCK2hbzOaUIUy0oUMTeeXBQ9out/uLkrP1mue3N5ioIcoa1jSNZPCGxBQCKiYmPzEGR102jDFy3Xc4rSRlauhIVkkuQoSVLhjGPaqylFVFM4yYCFZpisKAEuT54uFD0nALq5rgEgWaN+sVr9QnGCK7qaMuCxFf2kR0E+dNTRgnXtxsKRtHHwONhdJIwMNEZHMqWc4+OJgCShnKdYNFM/Y90ziAzSkaSWchz/25WqOVJUURHtZKZfKWjF3daIx+jE47kgqkzBFMubhTc9SkEuRUG4VOipnpfLBVytFsRtP1A9aKcNToCVSiUhYXBlwU+lPyAT86vNFCfdKSOqaixoc9AUdhJU+6LBQoT1FE4mSYRk2iJ6UBjUEj6DDKE5OHrI/4rONftPiDP0MBaHSkqhvaMBFV4OH5ORcPn+AmKTqMtjx68DqTCzg35hxcQemMKdjvWjabk0UFU9iaBw8eMY49Qz8x4FitFLoWY+aU35m1K+qm5HRzSiLRjwNJJ4w1TNmAdRpHdre39H6i73vW9YrzkzPWq1WOOSs4XW8otGHft7y4uhIOXTdinMIVNWVhiElRWCXoX1KM2ceqymHgWklxBDPhXKxnZhsBpRSbzQalNNM05aJrYuoHkgusq5rz04M1wLyTmBdQH8SmwVorpNq6XlC2uR0kRYe0dmaUgJgWou18YxhtCEhBOnNQUCqTbWXSjioSVeRsc87FiXAcJCJOkJoiE9Jj3qECC/lVa8N6tZLdWH6dYRzxPlBn9MynRD/07G96tvs9TVmxaWoZVKXmTJ9LushaEMKQF+i6LklJsnJTFKh8IdgnRUr+TotXCrC7AgH4NK9t/v3jgmLZMd5pJcwmoYGXHXd4a7DwWO60GvNzoaXo8yH7RAExTlkAMqCUwdiGtt3zn/zD/4x3f/BDrq9vePj4VYrihF/82i/z3o9/yDQNBN8TfOTLX3qVqiq5vr4GYLVa8cknn/D2m1/AoFnXNT/zF36Gj599TEjixaVyOzUkLxhWmt99VtYqjQtijIw6tLKOuW3Akj29nM+XoH8xJuGs3jlnsri3/aHo47iYmwuilEhZ7JXSAW07fv6ZGzv/fbjvD5ZFVebBhiCdhFkhKVnjkFTCjW5R8R3v6GeurzZ6GX/z9ZXW1yFVoO97ovM5iF6eoygKnD/wgnXexA3DhEseaw1NI+rUqMQ7LpK4vLzkK6++Sb+dMHOrPrfxZ8QdlHB9C02aJpSJ+RzN7AQNsSJGDSpSl5r25hP67pZx3DFNe2mfDiND30vHpGvZ7/d07Z4QArtdS98NpARlWbHb7Sgry8OHF7z11hsMw5A7NgljQuZ7iQdd27aZJiJknL7vKQor4o6xy52VkI1zDSElbm9v8/WGEMbc4ZwLeEtRlFk85mhbKS6tlY1tURSkCFUtFKIXn1xxvXU8fnXLT33lZzFJSR55SpnT1/P+h58weeF/dfuWy25P1IbNySlPXllhUPzw/fdxme+srYHC8vHlM3a3NxRKUdsC7xwvrm/YlBUPTx9QFQU+TGyNqMhfXF+hlGFVr3nz8Sts6g0fXF0RFJhC0129wNYV5+sN3b5lihFd11zoC6ZhYnOyYXN2wmq95vKTp7R9R1kUrKuas9Mzwm1ijLDb3bLrOnb7HY82Zwx9h20qgo5MNlGcnvD8k/eFi5qgNHYp2qu6Zr0+YVOtqOo1/oVQUp7fXDFNjvOLcz64fs5ut8NUFqsMwzjy7Plz3nr8Ktvdnm5y2G5PUZDj0jJnz8dFbJlSghiJztH3I4+aStr0ux232y1Tplm5bFxti7tj27mJKUZiPLhKaCPtYoWg4SnNc5MmzTYqaeb9HtlLHY35eV6YHT4WH15EhDmMJaUWZbZSiqqwjJO0bsdxFLuktpX6QCe0kc3izCOOceYKKym0jXD1xSoqZlGIdMPmlKi5q6KOPsOyAf7/FQdQRZGJn64vKE4fc3Jywr5vGeMkgoaosXqFbRS+ONiZKJ0j1UIULy0tbYoYI3W1IkXQypFIaGXEa8jq7LgtF6uuGoZxoB8Hxmkkpch2t2Xf7rLRcSS6gE+S7LGqG6qyRKmU+QSecezpfGDb7vBJhAvlytLYipQizgUxH40BHz391LPfy4RoTUFZHHawIDsF7/2i2h3HkbIoFqIpHIirdW6llkVBXVby2Y6SLLTJRZvRqHS4SY85d3CMah36Z9JuPniAHRPWQwrZJkf4dl3Xsd+LA3xRSExc3VQQDl6H8pwcdutB4u5kQMu5LnObpygqTL5Zo6moioa4jlRW2ue7vmPb7bltRYyiT84w2TrC2pJ1vSFkFaz3nokRP42CkuSEAGXUnc8krcPDYjifiGPu13zc54DdLwKPf+9lvzuHcd//veO9qSy6n37s8e/MynUpcEImAs+EfVHBrVc1f+fv/j3+g//g32eMntXZhn23YxgcYLGmRgPT4Nk0J0tL33lPP41UVc2TR4/EjLYoef211xmDCHvmFrCo4aRo01l5uXwuJUKE43Mz74pfJoa4w6FMnz7/xhQv5UcqZTJnNGYS9t1ze3wtZAxIQarypJ1Z0vl7d19hRpvmtB3xsyRPzjpP8LJokA7iGH/ETZsLTzkPB16QzGX6cMul2b7JY9RsCXVINEkpSQsoo8Mzr9AaQeWVlYWUFAnI703jBEpsPJQ+2MdYbUDpfH0iKQkZXSXDTDpICpJS+CmhiZK+kRy7248Y9le4aWAcWvp+xzh0+HGi6wa22y37fYtzjv2+I8XEOEykBHVdM4zC8z07O+fBgzOMAeehqgpmRWVRlEyjGGfLtc9tzrYV9NIlET14L7y1fU9V1aicCx1jEnQre3+SQJlZbS3FfFmUWC3JQDEF4dzFhDGWcfTUdUldl9gqYBy8SJ5XHp7z+OHrOGUYx4E+TJTBYaI8d1VXPHr4kOcx0vtA14vx98XJqXCgrdx/MSVuuz39TubOL77xNvuh55Or5+y7HbYsqQpLqQ2nGRUUNbpicI7t0FM0a15/9BrPu54uTDKHGc1Hz59T65JpcoQU2A8dq6Ii4GlsISr5JF2D61ZSTTYrg7WWk5MTtLbU3hHGHu/lPnWTp2ykk9alkaA1vXPsdy2lKXh0fsH19pZdtxd2SZ7TSm0xUfHixRX7rqUbRl7stpxW4jBwYtaUK0tRyabg8sUl1paUVUXZ1IQ44f0kLW0t9ABjJCY0BC/cOC0m3eM4YqKMX6OzJVnm3x42O4djXg+SjwIQhoBNc9xowWq1RjH7ciaUYVlHj1Xzx+NzoZDd6f4olkVTIe4UzouiP9MxoDxsglNiHB2TG7AmZIsYUYPPn0Ab2UQs66vSS4xd8GGZx45b1J8F7t1ft/5Fj89dAMq2DMpqxaZZs1mtuN3tMhfTiHoLk+1EikP7RkNhyqW/Th7UMcrvlWWN1sKJkx1gRGLzVPbZUXgvPIJhGpmcqHFvd1vGUaKItNJUtkQpS1PX1KW4wccYSFozTD3TMDBN4tyvS41NirooqbIizQdPZYXTOHm3mD3LCT8Q8MUcVi2E0OMbKGiBbLWSxXUuBKqipLDzjvbIzDXlmzUpIpGjema5QY9bazNB9XBJspEmLCbb8+NjDEzJCacgt5T3+734TBkjWcB1zXq1gqgXw+WZGxUzB8hlFZ3s5GSXXhYlZSlegDbvUoyWNjtaY3RiyEKVfd/ST6MoQLVeBneUXrAgJ8aI7Yz2uDiLaOwiGNBmLkCYx2Umm8tAUeoQPXaf53e/sLt//CQexf0CYyYTK53l/Tq3eu+1KKV1x/wuSYg4hpSLHj3HmmnAoLXwWt/6wus8fuUhY5goa8Oz5x8z9j1EhcLmwitQlpLWEGJk37agNa8+egWrNIFAaS2rpmaaJnzIbvMpLdZGHLVxUxJ+i0oHo9P7SOpSFB7dm8fndCmW53ZtnsTvPodEIsaQFbIvuUb3LowUn2neTqXDeVVkjo9+ycMOqObsP3Y/MWQu3uDTdjXSsTB3xuqhAMxFYrqrED82lZ8Lx/sLjKD2cm94H1C5tTtOLv+uJDSkfA7nqMf5M8xZ5jKgEZ5UkDYYWjhHMV9LoxMpjrhxy9C9wE17hqFn7KXt27d7vHPs9y3tfk/fDYQghd8sXjNZjRu6HmMNq1VDVVXEzEs2xi6fTess3spiM6GXjDKvh8Q0SfHngsM5cS6wtiJmKopwqZWMlShrAFE2xDHCNHliSDJ/5MIiBDmP3kW8C/RFQVOXlNVAseoJvubpR+9TV2t02RAUTCnmVAxLTInCWs7Pzhn7Abdv6ceR2HoKlaPFyhJrZWxf7bb4YeDJ2QMenj+g7jqutjf4GHMKh8FoxbpusoWIoSgrXAhc73d0buLB2QVn6xNiv8MxJy/lzwU459nv9zRnxUI5CCnR92KQve07ejcxBQErqqKiMw60IWXz5LKuqaoarQyjcwyj49n1FWNG2E5WNU2zwrmJXb9d/CeDrShNgQpw2+1wSeyKdm0LJNbNinVVo1Ji37cM48g27dlsTqQLMM+BeY5M6sDjm1urBpVtv2RMGTXDB2npQsSc/XN/RlBH6ykcbFGU1mhrKYoacg1xZ055ScF02GTepQfFlHLGbyAljTJKHDe8w3pDlWL2Cp1FJbJOusnjnUOJGa9QppQImWaDfZ03hynf67LpCXf9Dpf3fO8z3Jvb/jxVIJ+7AIwx4ufc3ZiYstM9Kl9UJX3vmMSP7jDpJjarDXVd0/UdwzBm42WDMYqiKGWX6H22SxDUUM1Yp4rs293ShgwhsN1uabuWsqwwVng663JF1ErakplcKfFnSci/w0jwkaQ0TbNCxUBd1pRKQ/C4EEhajEclrkgGtmTUZpF2Eqd1qyBEB6R8seXmdN6j9aGdVGRUwBgtu5+5hZTmm+xw8wUS0YeZYi6LnFeLeikmQWhm02rxTxTkcphbxOagGk4p4XK2qbjw90y55RWioLE+BGxhKKkWh/6Yia4xm6eK1YxfuFH1ak1hS2xeAGavNqVEMaaMJsaRbhzoJzHHLIqSTVmzqVcUhRUuGjD0HZvVaiHFqoys2KLIKigpnmzOH5ZzhZwhqapAJeGNfMagOf7ey352bPR9t3h8ecG44I5R3vBss3AHeUQmtZSL2ZQLFrJGPWGW1rrJPoo3N9d89Mm7rE9WnJ2vmbznw4/epe/bfK0DCYU1JSlFmlXDMI1cXV8TI/ylX/hF+n0nam9rIQY+/vhDQogiPliKO7XEcCz3lSwfhxZnLjoO51wdfTKW68LRz46zOZNSnwozTykxZHWrERhXNktHxeadIj2fM71gXJI0k7IAR6lPm1WbvEs/ePQdt/nDIjQ7pjTMqODc9p8fU2SlPByKxLltnTjwIq0ReyVJwzmIL5YzlTey4ptpspnyhCktTdPQDh06b677cSCQqMtqQcDmczwLzubxHXyCoFB2FoeIFZZSGmMC47hld/uUMHX4MDL0LeMw4saJvhuYJkH/hl5oGyGwUFmKosiiBLHKqOt6+XwhROq6OupG5Ba5SqzXa5QSYVefepqm4frqVhAS73De4/2EUoahH5gmnyPBZI40uiRqRwiO4CQMwGhNCmLR1cfAalWDlgVV5/Zu23YMStO3hsJqqsYQwzkfvvdjTNFw9uhVdL2StaooKK0YEGsFTVWzahpuh4k4DEzjSHKO1ckpdVWhtdiAvbi9Zl2XVFXFqqzwzlFoEQMmpQgpSrRl3aC0prQFQSn2Q8/19pbLm0sen73Oo9MzXPRsh44Y4cHpKat6xTSOtH3HfrfjbLVZ+LHOO3ZDh3eBdhzZti2VrQhNZGNK+mmSpJAQMUZTr9YQIpObcN7TDiM/fP89VquCqio5Wa/YrDdM48DN7gXjOND1HeuiZlM1qKTohwlblZIwYg1T9HzhlbexKbFvd1zeXrOqV4SYGNxEP43S4eFg1xVDyPnykbKu5gGeEXiJBhQLIekQTk6EN1aZw2bnzvxxXLhp7uz9lELpArIHYEoH1D6lIznJ0Xp72MTO85CStmwUIY5JkljmfWDKAQUuBEot1AcBRlzm60sn0DJ3PbXQTJAaBHWgCeUaOc9RMYtM764dav49Mr1jnnvuFbf/X0UA9/2ewhiGfst+V3Lx8BEhhby4BSnarEH7hNJBOBxaU1c163VDSFGMoIdB2ro5rFppMRpumhXjMDG5QawIQiQl6Y+v12u8h6EXI2bvPQ8ePODs9Jw5RoaUKPLOPaXcNjWaru+5ub1FBTGhLcuKMDlJy8jKG6U1qtS4KJE8kSRtVbmkeQIO+ChtjjJWufUqvLsZlRvHcYlRiyGwWokZdHTzLjd7fPUtQzYT1cYc5WLJrrTIJHRUIqRwFC8nj1nXDUpZYpDEjW4cpDDUaqlbtBauDUpu7qqq2KylZRCzWMX5idvbW07qk4OZLDC6gb7v6fseyPB7oWnqms16lQdZwJjiYIWRH+tToO17tkOXi7+KdVFwWq04XYkie0ye3dDRtTtUclT1CkldKNCrE+zGopQBLMZWmOJIEZXufpGSlCPzoJo/O9xF9+60LO8VDvdb7PM1nRMzjn9vRoWMtfh0KByPH3uM2i6KcJ83T2jc6KjqgnVT0vc9P/zx9/lH//g/5j/+h/9P3GQ4PTlhfbKiH1o+/uhjXn/1TeHW+kEC45MDVXNzc8M4TXzhC18EZKGyUTJJow9cXT5bbHx8DATA5Jkz5XMyp0WQW5Mpyr2r8ilPuaWxFNhkc+lctB+3iFV+YoUi6bsqtplTWRRVPpea+0jjccsm5iIr5dexRhDkOSECwPDpx8/iq+PrMiP48rUixLvI37EFxHztZ07tjMIf6B+C3I9O+MG2sJnXrBd+Ukr+wNs10nYfvSP6iBsdCnj88ILtfs8UIiWKOAWeP79icIHNyQk+o++FsRBiniIOBfjMu054+TxJEVziZGMZ2hfsb56xv76isop23+OmSPCSOORdZLdraduOcZgIMWFtxdnFmcwjSaHTQVH54MEDmcvKkqIQ8VrfdtkRwZKUZlU3WGu5ubllv28Zh4G2G+m6jm3bMY5izlzXtRhMdx2zSnm/FxS0KApm3qZ3gb4bsyhERF4heYZpZLVu0Llt6J1QHNw0URUFhTUMo2bbDQRlGN0f8+T1Kx6/8UWazQXXux2THSi0xljLzXbH7b6nGybG0aOA1arh7GTDs5stXT/QRYfS8OqTL3N2coJOCaMUp5sT9PUlnzx9jgqa85NT8aFTWlJNjMEWBSF4nj79hL9w/oRVWVLbgrEoWK1rnjx8QNQwBb+Yf98OsulTXgpAHwLKGlyKfPDxR2yvb7g4Pee0qPjxi0tUWWAKgy1L+nGgKEputzu8E8HFvm+ZPDw6OUcCaDyvPn7AFIaM5g30biCqU8q6Im2FTmC0odIW7zxPHjzk9upFpicoTi/OeePhEz5+/oxdJ89R2twt8o6UPNFHMBoTpO3rnWPXtvT9gDaa01c3eOfpuwFQ1EXFEB3T5Jnuz7360HLVWtC5FEQsCuQuks/Cq4Mwb5kH8lwm89vskTgDCukwfyF+tkElcJEYR1Jh8jze0xQlkxOE2/sJVGTos7/vbAelVBYmQtLiAzgroGcFc4zZm/BeAUiaW/Jq+d35c3wmIvgZ//48x+cuABPiN0eKEB0+NtSNZt8P3F5vGcYeYzRnzRkpsbQJuq6jG4S4G8lydy87+dVqJd51OeUDslUEgXHoub294b33foxSii9/+StYW5ISXFxcUNU1RChMQWEKIbtnYcYqc+5cikz5Yhe2oDBWVL3ZdkBnlY1PQQrUcU8KCs0s0Eh3MkPnXUUIwmPLQAbWFIQhUFSyoLtplAvWisO5LSQCRnbUCecDwzAQckti8Rg0lngk5phtM+b4OVHilRSlROfFGClSoEwFSiuZLDKBvixLqqLCe0lGsNrQ1DVF0ZCiJIUMY0837LneXgvHppAicJpkcWuahtVqRdM0OQNyzTiNuAxdh+BQOqBQtIMU5/u+Y9+1aAxFUVGVFXVVcb5as6pWJAN+GNl3LbfbG54PIw8ePOTs5JymWmGMZnWyYdWsKYqSEb0Qag/392EgzKjqDMcfX6efVPQtz3S8+7qDdn3WQBAEtyhK3BSWlsX91vMxj04OiXtKKE7OThj9lj/+k9/n937vd/jd3/0DvvXN79Nub1mvH2AoyLUK3//Bd3njjTcplMGFyOgHHjy84A//6J8zDBOvv/o6b7z+Gp988ozT9YbVyYbu6pLLF8/5q7/0l5dCby72xpTEFkMjCSFokk+LV5cguXoxd1bp7vlUuR0/mw2/jD/5svOdElntLkk1KUaS95967NySnl9zbscuDQENSefC+/4GOLFsCI9FUTP/ceYIzm0XpdTyfibn8NnQ+WA6GxZe7Vz4K8URAuAzgnDXYHtWv5IXngMiiXQVtCE4IcCHGGhWJzg18eHTp3z0yVO+/OoruGlicp7Vak0IjsJqpngw99VJ4hxRCZ0iKUJRwNRecnP5IWO/Q6uJ3bbN3MDAfttyc3PN7c01u92OGMOilPTeY+waiBgzpzMMxBg5Pz+jLIt8/iJJx4UwL10cMQWWyEgRlIjv6SCfMYDCYoxCmwqbAnWtcM5LSIBPtF1Pit2dTdY0eWJsF56zLQ1labi5vs1G/GKuPDsdeOfopkAaI3Se0b3Lxfka33akceK1L/0sujmlx2GqBh8i7/zwB3TjRD86jNGs64qyFtXmypYSsYahsIbNao1BMHyr5XUl2m7knXd/zNn6hMdnF5xsznj24Xvo0lCUlnW9IrqJpzeX7KaW3vVolXj79TfRGp7evOB6v2UYR6w2+NtbvvzgsaDWtqSuV/jJ0eyvcf3Ibppw7Y4r1XI7dFSpYmVWGGvYtnviOKKMptAVKUSsj2jrODs7oUiKqe9wMfHqo8d8/ALaviO8eMGqWnF6doZ/+iFVYanLkjIpXApcXb6gKi3np2ek20jbD/zg/ffZtjvGLHgMkxSrUcmGzQBjDOzavThshIi1hidPHrPvusUgWiklmcZlEgUwdzq3MoazfZsIGNUynxS58G1WpxgCo4vEvGGd72tri2Usog4b9BQiSh+6QM7J79qcUOLcJNoAldBOYSbDeuoptM1iuig1QpT5PSWhhhEQFXDuuNjSYku7bDQNiuAF1PHBs3zg7GBwtN9+6Zpy//sv+9nnPT53AWhtyTR01GVJ06wFvoyi4Lq+uebm9obb7S0X6zO+9KUvUde1+Pg5zzAW6MIIfy/I5LjbBs78WRZDCA/q4OHlCMGTUqQoCsYcwVbXirKsaKqaFAVhVOLtfbRYWEpbQAI3TEQf2Kw3rMpK4re0Imgg5iQCxHyyNKX095PJNgxG7G1y63aeGI9bTChFRLJwUw6FFshXjB/L4pB3CWFBKE5PTqnqWtpRKWUfpGlpac1FHBwKiHmyqes6L25yl9hkqUEK4iRxacEHovcEj/gAFpayKkFZUpw9zCRrubAFMRNRZ97PLLypqoq6kmSVlJJYEXif21qJoe/Z7kYp8PueYRpx3kEynG9OOV2tWa03VGWF1WLsPLmJfhwXC5qUFyGfkSiVNw9N0ywtu5lDJZ85Heq/owJAMWcFHwqSYxTugAZx57weD67jr5VWS7FzfMw8lxiX5OHlMXPhcB951Fpji3zvKHj3/R/y2//sP+PZ0x/xrW9+i3e+9QOUqvif/s//17z1+tv8zj/7L/jdP/in1OuGb73zLX7lr/11ilLQlqoqePdH7xFipKpryrIiBbH9wWi+96MfcX3zAq01r77yRJASpSGFpZA2mWtlzEziLxjDyJgVeFplfzcg5LYrGWG1yh6KtbuX4HDuUuJl+ukZgZP7TxOM/vT5undOl0I6CDVBQebSqU9nZeYW7qzIPy4igSUTfEYTZAEpFrFJyKT0WV0aw8FQdh4XS+96fn+8fIKeFcox85uEDC8bSh99dgmwGFsQfMQNE2jNzW5HfOWJFEJzyyzpPB+GTC2QzoFzE2WROUc4Ymx5cfUBbtjjpxE3DUyqxY2G/f6GfmizEfSIG6UlW1bS5mvbPSgoyyIXucIbbpoGa8WLNATxRQ0L8poWyyqZ5w5+at7PNALFME6MOTPZ7PuMHEt7zU2SdUw6tO5EzKCXU+2dzGneGwqzxuQYrRRc7vhkpFbuLhSShNG1Cddd024Httcdzmne/os/R2EbjNFMMfH8xSVTzAb2iGBwta5442TN6WrF2Io5PikxtB1pdUYkMU4T+7aV7lXd0LU7brsdVikenT3gbH3CfuowSVPrEoPm2z/+AaZsQCkqUzB2PX10PL+6FsuX1Zo5xcM7nz3qQPmI1gaLQtc1xhq0tUzBU9e1WGeFQL/vSNPEOPaCtIaEC4lmvaIuagpjqfK8H/sBqywajQuJKfR89OKSJ+eP8N7lrOfcCjWGfho5OzuhKkvGrufd58+53W4pm5LKVpRa1l5tJAkMZTBR1qOk8/yKtHudu6WsGyDRti3b7VY2U/qQrvEpDjFzekcEZbPBuUTOSoeqzNxjcbuQdSLPE+lAx7k/Fx06SZlioY7HsfzcR+H+hSjATaXrJaPbZa9gWxiszU4LedPogiSgSRfuQKnR2uBcn8Eid+gALp/1/hd358P7oMX948/SGv7cBWBZ1hS6YN2saHJx14893kXKouHsVGNtxbosl4p63o2nFChUwTB29INkKRZYitJI5q8yKDxaW5qmXj5AVZW8+uqrDMO4EK3LosQoIWdKnFBWqGazWK21QMNR8nKtkQzBpqgorMmZf5EU9CELODuBG2PENFUZrJHWTmERA9+UHcONwWe4fgqOyU8M4ygLsFKUVYWG7PxtlgltPhYz6cIeeABloNc9Sh1IqTOKMPPsZn5T0zQYpSVfOAo3yppCOEPzDVAkgvN4L355RSGvFyIED8KxkBzfuqoxSmFsscTbeWUwylCXNYUpxJ8oRZzrSUSGYWKYcpt4EIR38oKgRBLrYsPpes26qYWPphRRwegndu2WtttLq1xJjqS2lpAkLcUovRTAcwKLtfZoQpiLiBl9OgyOGd39STumz/re/QJw2S7eP46K9J/0nMfPVZYlPo7c3lzy9PIpf/In3+K/+H//52wvn+HGyJe/+Bf5pV/+Ff7Wb/yrvPb4VT55+j5/+Me/S0qRT55+Qtd1XFRnKBQvLi/p9h3NShT0TV2jlFhw7LqWfuyByOnZGdE7SlvgipKQhE6QktiYyO7VLveWLoxsZABjYdYkzRm5JBZuzN3z8fIJ52BpcLg+prBZ2Tqbid89f/d//04hrw4CjTml5/4xf29WoR5f1zv0AHWUrfmS6zdbQ5Di4vk4F5G5I70UljH4w52pDgkrhbWEPI7HUZIcRLAmrbV5szBODkPEjw5bFNxut0sHJUZBuGNGFWfD6skFoXIEh0OjoidOHcHdMLbX+Ek4gj6AixNDHxmGPX2/o2v39J3wAUNW0eYueRbOyFgfRxEHPHhwQYyeyYm34izKk8LLEDPPWOYyQVxinn9TlFZX8IEQhALhvSNGyVsPXsQh3vkFQU0z14uZ66nzYp5IHtwY0Fri/DA6b3pT5gVqKU0dDCESkxHqxNgR/CVF/UPKTcmT116nwKB0Q+cdyRSIZj1K4Tj2VHXNqqrR/Z6YFczdbk/xmkRdjrmNbYwRK49sdj9MA9YYHl5cML6YUCmhM9Xoqm+plaLGUES4vb6mTcIhK41FKY3P3qaDn0BpfIhEN7Jan1DpQmgeylDakmAtU5pEJR4jzo1YoxiDJww9PiRiUlSrlXy6EElGNjNVVYtyuxtJSRTJ+2HgPBfz/TgSYqROBh8SldEM3rGyBWerE4aPPuTF7Q2NbzhbQ1WvMx1AxCMmgop5U0bejOVxMk0TtqwIPtyhNsXFI1V/SoA3i42EHy/z84IWay0Ct7mjlsiiyrSMSWXmluqRy0HuRNwXVh4rgoXaFJZc49Foxmla5tCYEtPkM1dfNr/5zS3z1rzBXLooSlwyxmEQwORe9+hoRmPeMN+f447Py7/I8bkLwKbcsDlfsaoatNLs2y1+aqnKhrOzBzTNSvzj4kjbtbisbpt5aYIGjnR9S991PDx7yOylNft3aRVoGokgs8ZiVmsePXoF70WxVhRCwo0h5sUpLhwmn6QVqYuSRG6VoKiKSkyXc/sXJYUj1uAGJxO4llxWETZotCrExkJng1o9k89zwy8jbf3Q0/X9slMWMrhdij+iuN0Po5PJ3JgFNTHGYnTC6IhSJRpRAs/2MrNR84xozAVgYW0ucFMmkGZ+RLaG0UphrAFTZk5ChpTzpn2OolKQOTw1TVGByQMLRYpBFN0I+TxJvg4uSlF6c3vDdr+TXXl0+OiXolZpxUVzxsXZGdaWuOQZ+0FsPJznZnvN5CZJMClKVlVNWVVAbp1pTde19EOLmwZIEuQ+c7mWm16R23O5JMz/W/h8sAys+fiswvA+Onj0gJeOhZkMj75LJl5EAjHOY1cSWog8v3rOd7//bf7oj/85337nO7z7o3cxQ+Lnf/4v8d/4b/5t/o2//w9wfk9VlKxPTjg9O2Xf77m5uebm9prNScMwDPz4x+/y2mtvEQvxZDRGWvYffvQhlzfXvPbGa5ydnqOIdDe3VEWR460Ck8/FX4z4FFjQOG2wRUnXdyQVsuqYnFZy5JHH3YLpUyRrdbxvvz9RCYqtkl5UyfN/h2sii4U6GmMLK5oDmjoX2Pcv2SLCyNfumNuXYlo8PJcElqOfh3BAb0XhCse2SouLwfG/U0JHnS1b5ucKh+LQe9w0MQwDRV0JraKSHGdJchjYdy3GVKiUJI5rewvI/BDD4Z51zucNYpQFM4E2E1MfiG4kTC1huiGMPc7ZHGWlGCfH0PdMU0+723Jzdc1uNzAOEa1LnBELriK3ybwPmW4yEINnsxHaBznysSgMwzDlSExJ+5G8Zs8wTPRdxzhOeCf2GN6H3K0RQcc4TUQf8UEsXwRF8YRsLzUjMzLfqmXhFGQZpn4gJi9WVGUWEXhPuSqxWFTuthAjPhpSsBAT++3AB++9RzIOFSb8o0DRXKBUwpQGE7WISlSi6weMlcQklXKknQ+0uxZbWNwwMU4j/dCjK8swjMJT1qLy995xcX7G5e6akC3QktE57SUSUPggNjlD7ippNG4K9OPIarNmO42oLBpy3US93rCpGik6o2KlLKYpeTHeyGbDOSbvF27kMI6EkFDa0o8DxoNrPFbbbEVUcX29ZbvbY5qaoqhwMdIOPUaLglhM0wsGP2FIXG9vsZszTtcnaCWRe/04UhcVNKuD5UrecIUAyZALf7/gxlobJucwSS28Rxk+eXy/ZD4WrrFB6yCgTZIuDblTYxC0HZ0FgvEwJ8zzRVLpzvw1t4hnhB5YbImAfM9mGxsvNk2T1oxmEjeH/BaHYcrPk1CqyEVqTuFR+sglQDovah5n4ySeusVREtPcAlYzUHM4XrbpfRnP/c9SFH7uAvBJ85jNyQptFaMfc1xNw8nZOVYXlLagrir6NGCKBufG3O4VtWwIomKsygb9yHBy9gorXeDHiagT2DnDEoyqJds2RvpB2oKFqTAYUoBgEhh5H4JC9QxuJE4jF6dn1JXc0NqWJC+cmWiBqIhOgYHJD4Qk1gS+dxilODs7Y1KOmAIKS10W4uo/+wmqmTTvaPuWoRfX/lcev4JRmpWtl2JFOEBiZFqti0PBFsD7eMd3T1onJS4GCltS10oSRLY3zH5m49BjlSIqDapEK402YrodXC4ojRWLEYFWULkpQoIUyBB5DqAHYpIdZlmUh4DyjMyFFGiH9s4N1WvPi8vn7Lc7wuQpbcmq3lDW1aIMLgrLWVbqRS3cshe31zx9+pSiKFivV4I6oTmrN5yfrSkqS0wR5ya8m7i66eiHVjySkkYpu6hKlZZP5UPAKINW4JFCwUR9J6niUF4cjvtk2vsCgPnnBpOVyIcjxIjLtkNmnlhyIaiMZXQOjUZHMdOumgpnAv/4n/4jvv71f8iPf/RjLp/e8tGPnvG1r/4i/5N/59/hV371V7h4/IgPP3nKbhwJ24+hGHj82jk8gw8//IDvfP+PuLz5gMJWnJw/4ud+4av857/zT7i9uYYAH33yCTFG/vIv/xKnZyd8/PHHfPvb3+PXfvVXGfpWigujMdHQZ1/HRC5UnaQAFIBNSlAZoGkaovO5uFbLuTqeUBdubJ5v1GzEqhVldoicD6UUpS0YR0kvcTEQlBID2Mz5m43RXUbV1Fzco0haLa1deT4kxeDoMFpTF6WgZSFi82IoVklS8AJiEquPiyvHNIr33WrVLItHSImkZGNojfD2QGEwGGXxbsJo8feUaKlZUSwvNU0Tu/0eP7ls6j5R2YZNXfP06oaPLp9TlQXnDx9Qa8uHzz7m+dRKizUGJj+QtGxmXfTEMBH9gJv2tNtLYuxx40hhDCfNGjcGdp0XYUgITOPA1Aain9jf7Lh+vuXqusVJCia29Lio0EFRFSV92KLSCe1OUKmzsxVlUdDtJKZrGhzbvmVSA48ePWEYA26KWJ0orMIPA1M/0LeOfeu4vb2VuQ5DihE3TYw59WK2AtIoghcT3649GPFqozBmNiQX/q/WmpA0wxBxMVLGgLFi6OwHjzEFKEMogEkRhkGsVIj0IYGOXJrnMHievHLNw8evsHlwSnNxAcWGiJWukIt0Xc9u6NjvO9zgWa/WjB4+unxOIrJzLTE5TLRgC4hONi1K8cn2llVck9CYqoKqZIqB2sPZeoWLgX0YmcxMXRHHAx8D3TQQJ4Nz45K/XhgriSOnZwx1wzBKQX+yKmmKAlMV9ESCk1QV7RM9CqcCLkWubq7wq4JXHz6k0BrlI7fdnsv9li5M2CES8ib+k2HHqq4pJsuUAlPeqO/2XebgG+pKgJjXn7xOPw1Yq5jiQKESaRqzQE7ThUgRE8oapmEQ2om2VNWG/bBHN0G6aG5i8q0o2o1Ysvng7oxt6fJpBLpwWFZCUbGaVFs2OtEHMClI8SUDfckfn11JojpwAEMSXqGagZko1IPZfzgEL8CJUhhdYlTBNASGyoEfid4xhYQPmspqjELmfyxuCthUouilcLUVSUWKokYFwCfZaGu9eOmqlCPn8vuYqS7H52DeBM8dhPtCunmu/bzH5y4An98+pWeNj55ubNm1O5pqzWZ1SmVLDCYTZlds6hpnsqkniWEYcMFxcvGK7DRRTPSg4MGDRzRlBSnx4bOPGPxEXdSUwWCDIhUWq7QQp0PAB5/VwzIoRHnrCMlxstlkqDtJDI4W3k/bbVk3TTZkLpiCz23GkNsTHqPtkvoRohgnj2Og6/bEFJbIr7mtrZTiZHNKUwtXbuyH5VxpYxZz42Ea6bp2SSOQuDux8tAzdK0iKkr4dsoeQ1VRcnp6zjB08l5zq8PFAGrKC3Gk7TtudzuqsqGqBIUsC8ljVvOtlVtRu+2OfhoXRNEYkxMThLCOTiQVafd7cX7PqKPOwpA+OtwwcXFyyrpZU1UNMSsmZ8FKyMReY2zOXM6B4c7x6MHD3JrPHJ+8CIsBrCR/aKUlKH0c0Zlo7abbo58bcX9XGfVEkKq5TfnndczF8J0jt3OKnNnoAUopiLp2ILiELRSnp2v6seOdH3yXr//e1/nxu+/y7T/6I65eXFE1G/7ev/n3+O//D/6HlKbgG9/5Fu3v79isGk5OT/jd3/4tvvvdb9NetQy7jgcXp3zrT/6Ev/2bf4evfvUX6TvHN7/5Tb75x3/Mk0eP+PIXvsJXvvRTrFYrrq+v+cYf/RFucvzUl7/Mer2m3/U0VZ1FSwoPbHsx6E1aAx6rhBtU1xWDEyJ33/e5LXUwSZ2FFPCTJxlpo3xaQV1VlSAY1qD8IQXk/vPNX5scw3bcwl1oHkffe9kxc/COd/zzxDlz047TTpYc8KMNwYxmLMKVJBwkHw/ve26dwiGZR2vNvu0WMYTWmouLC9KNCMW00bQ5dePi7LHMH13Pru8xbcWuv+asaaibgrKs6Lpe+FqlxiXZFFtKXlw/J0WPampiGbjdXhGCZ7ffM7lJeJM+cnNzy3a3x/lIYSogMDpZKMVvU9N1HTftDqMUbdsCAWgYB8d2u18KMKU062aDy3SNMps0ezfy0YfPud127PYTbTcxek/fj7mVLQgOuaU7Op9pLgmtRKwWkwhCBAmSgrqqqiMLGnGFqOsarWcEJ6G1oDBlKchLDIL4hpBj+vKmZBgmnj+/IimY3MSufc6j4TFn7hVOzl6lXp1RFCsKbXn+8RV960nGkkrLGD3KaP7wnXdoqoqqqnnw+FXGSexMhmGX1zuZK/fPWiY/UZTCny6LAuqaui4xIRBI6CzyKLQhTA6rNeenp9JC1bNJdsSFwNXVFW+/9Qa2tpSqJPRipzT0PWXmtJaVzMPNyYncu96RYgDv8JOAC9qWhOTFwUBHmqam64dsJl/B2PH2o9cw2tCOA5fbG1KSxJB9u6frWtkQWcvKWqECdR1hTFSnK2YDcGUKUcAnKaxWq7W0W7Oyeb6e4Sj1x2rLMHWyQb03toUaEJdrOm9EtVZHlmSyUVNJkLaYvRbVUacxyeCGJAbrVuvFucPOwqLZmDpTTVTu9IzTiI8euyoxzjH1Pdvtltvb2zwfzMilzDei1NfYwmDs3DUiOxVIqk3wHgwLt3khcqWXI3r3OYD/osfnLgC7MNDddFzfvODm9opIpCob/I++z8PzB1ycXVDXK1QyaE3m+ii8CzmdoEYjdgnWGCpVcs3Ed97/EWlwrEzB6fkJRWFw0yC7bCs7OqNkr6i1wioNSWTiSs2TQYOxisKWsntPOaM3BFIKDENP295SFSLAaFanUt0rQ1lWVGVBVVhSkCzhmJIYQE6etmuJWdgx37TjOFKvMpKljaBPuaCKKeLGnmEa2HedDIYUhQtX1RhlSNovrzNbaigtqFFGwuX5vLjsi+FtWrhLttASf7ffcX1zgw8JYyOh72i7lpQiZVHQrNaLx6JYuiS0VfjgmJzYMtihYF2WVOtG0LW8wLngGacBM1lsKWKR0E2UWtNUkmdcFAUhcybhcMMuCQuZ/+SdFzNsI/m/0rbWwkuco8CQdm7btVCQi8LMsyMjS0ekXRQLp2P+959nAah0RJtPT0IxCpdOa4XRCBk9yaBerUsijj/54Tf53vff4dvf/hYf/Pg9fvC9H7Gpa772F3+Jv/ALX+Uv/0v/EsM4shv3FFpxcrJh6Lf8X/6P/yeunl5y/eKatmsx1mKKge995zu88dqbtPuBZ89eUDdrKltwstpATLz343fphwHvPVVV8OCVC87PH7Df7dDKZp8/MdQuS0sTKhkfSRYXNY3oskRpgzExO/cHMHaZBBfxy1Hr9HBe7pKSX9ZSnwv9mWNz33txFkDdf64U8zR+VPjJz+5yd/JVW9qv81id38udCf2o7XwfET6g8vn9Hf3esQG0Ngbl/dL+nS1RZvsgl/0F16sV2uhlMSClnMySqKsK58Vv0w8TVd3gk4wlk7NF3eTwTjoU0Xnc0DP2e65fPOX69hmPLi4otGHse9w40bYt+3aHC5LQNAwDwxCIQRODxvlE308UtTgqjINjwhG8Z5wUMQx4H2hqy3q9loi4rpOkEy0dmKKsqVci7HKTo921XF1e8/77zxjGyDgFhtEzeC9WVIA2FqtVTnQR30JjC1RMxJwJrJG4N8lDT4DOf/ul+JR23YErKDwvnYV0YRGyQe6wpLSovnWwTGPg5mqP0YqmLhjbnr25IjhYnwysmg2xqPnoY+GJh7FFRY8pK4qyJkXFMHkm31GUFqsNu12H9wNKaWym0si9qfCTo4sBs15x8fAhmmwJlJFvTcwGyGJ8vVrV4nIUEm3fSdGREo5I5yZOmhWbzYbzkw0hRIqyxE/T4ihRFAW4wMwdr0qLV4naGpQysrkbBl7cXNP3Az7GhUPjg8cQMSlQ2YIQDCYJQm6rEq/EYqkdes43pwQ/oZXwXevSZBpUgXMS4am1rB3Cl3SZPiH1gfMTJsWcM30UupDmoIhPb+5S4k5PJwQRykj79rCp06jFU1ZMqTOfl7mjIDzDlJND5teSeeMQ/zgLxZRRi9uEIW8iEVGIiJhcFrNmoceRsl7puQVsIIk41HlP27a0bUuSrnX2Fc1zldFEP8fI3Z2X/ryPz28EjSBloxvY93t2u1uKoqJvBwlzJnISnLzhKCdYlKYlknSQSMqDShirsAGubq/5f/3H/xE2JH75536eqvkSY3QUysgOAoNBdhFKcVAd5gtjjBZ7l1KUbFoZiBI7ND9GKVmch7Gj7/a0+1teefUtQhBipjFiJG0LQ0iBEMW80R2hhAvhHyHSkiTdo7QFVmmkgy1KOeccwyj+Su3Qi3WL1pi8ow3RQxC3+3lBWfJLyxJrRIAxq6bmGCtgabGCyOa7vmcYHVW2vSHJjRSiwvmAGntZCGPEZfW1fJADdw4SfcjIREbxYhK5fshF57zTKoxd/sxRfWSCr/f+oMzOfKkpm2UqJQbdhSlQdjbLlGscoz+60WXwzwiN+D0dUJVlEKTDwFjUW/fu1+Ve+S95yCRx9xki0jaAREkhEXpJzK9NqbntrvjBj7/Hd77zbb77zjt8753v0t62/PSXfppf+Nov8oUvf5k33nyTN998m5vbLaqEorRMU8e33vkhH3zyAZfvP2XoHEpZNiclfbcjJsXtzRY3eR4+fEhRVWy3z8WoOAnCHrynris2JxsRJEFGtC0+BtmoKCksqrJkcBPBZ1ui4JmckMPnjOmZhyqqYBlIMUb0S3amLyugXlYAHsje84Sr7/5eOkzvS0selrE8P8/y594iMfMwj9G/Y2HI3J5G6oaXPhYOEXCCHB2K3ZhFGPNDfQgYfWTUmo+5wNVK+F0+BtzkmNNL5t83WjZyTknKS1lWDD7gfSBGhU6SnqJiIviJodvS7a/o9lc8f/YeUQeK4jHGKIa2YxpHtrc3jNOQ3QAcu/2O3S7StQN9O9C1A8MwclZXxCAZ1XMh7lwS2yoFxgjyNgwDs2djjIlpdKy9JcWYkf3AMEw8fXrNbjcyTIHJBcYpMPqczJBAZb6zpIbI2DpucQnvMhKjkXGXskdqAu8TSkWMma+NJyVptauM/GslC6vwytWyIIs9WMqFgtBy+sGx3fYSzdlssHaEdENyE67eUVU13e6KsqjxYVapT8RCEZL4zKEUIRqqssC5ST5PnrNTPFgCpRhImeuojcaPDjc5sexSiuC98Nbz3FkYQ0LSJ1JMuevjiSot87Ex4lAbJhHjTIPDpyAUoRiFtxhjVqAK/720BePkiDrSjQPt2EvLNWRleb6vz09POD3ZQJJgg6YscTmaUGkgKFLuzgbvsVpjbMWqskvxFFO2aMrinRiFg2SUya3VwDgOFFXN5JzYweTxKgbf8aXzR8pz72HlEnGNLUxuo8/8dinYjjeiiVwEqpkrDCS1iLIE4Z69L9NSQOdpSYr1JNdjHCe0KFxISfwSlJa0nhgT5E6hcCgVh3xh8Qn0Lsci5nSgmObPlJbi//7i9Vn8v5cdfxaE8PNHwSEmxev1mq5fcXn5TFI9Rk8/dnRji7aQVMT1E8MwUtiShw+fsNmcEsmDM2iUt+Dg5vqKP/z93+esXvFzX/gil9dXbLstTy4esqobudRFkSf6eQceiUQmN2KMoqoKqrICjqLD/Oz1lSTGqGjw3nPd7nlxeYkta05PHpEQV3LhH6YcOZaYYhDZd26Nzpy+udioyop1s6K0Iu9PuWURol+c1fuuw6cgyjStcwGbcspGzLsZst9aWCxyrLEUpqBOVX5esckxWmxpCqsZ3Ew6jVhTsF5vWDUrQGXrgExuxWX1oFjZDONIyIkIM4xv8m5j5lsJyhWpyhKKMhc8gFLUVUWhD5nIKYriy3mHGyeAzHWS5AXnJ+Ysz7ooqatGCkcjE8EYItMwEcmZs8yZj9L6DUWBUDqyAmzeBORFZfbgO2ztPl2QvOx42cD5lLqKQ4v56JvigZYiRSogSppNUoG23/Gt7/0x//A//Y94+v4nvHh6xe3VlouzB/x3/s1/m1/+1V/l5OQcNzqYAtVZRbUq2A87fvD8A/75N/45zWbNMI6U1Yazs4doG+le3FDme//s7Jyvfe1r3G5veffH38MHub+qsqJuGtabtexeY1x8/WLKGyJy5ibSMtHZ825GSNzkAY2yGmsLubZeMjB1IRpJf2zZ8pJWxE8iIad0pJxeSNaa+2ke5OeIKeYp8R4Kd8825v4FOuYpHueAWmuz6u+QyRLvvef5HstvhMXgPB3SA2YkNOXCWTI+kSi9/N4kH/xQtap5oc+Lsfj3yWf1zqGarIZXCu8mhnEmlIujgSHRD3va3Qt2u2e0++fc3n7Mk9feoqqETtK2O7p2x257ndufouS9udry9LJjmhxuyr57IbD2YUE6UkpCjRkDQzewWpVLOodsIiq0VkzBMTpP8JG+69m3bUZAFNfXLdOYBPmbHOPk8eHTav3Zekg8BPUyv4l1TOZgKQsKsajKbg5iSiRpF9IZEN6cFO7CU5vTTNzkxB4piSXHcau/1BaciEJIiqrZ0NTil+fHnsFaTCE5x1VVUdpabFPGApUcQdegJb4yRY0ioNA5/Sr7yvqALYT0H4ImhoOoL4xh4cEma/DDhCql5auzxRgpMozj8p5DjEQdSZlbGnwgOBEYkdfENKvKx5FCGcmW9g5iQBkBR/ZthzWZYhMDq9VGLMDme1cXvPL4MednZ7T7FqMVJ+sVfT9IQZmjzuRcR7zzWGOpSkNdWVKQzUtCrsmMgonqW5AwrQzTNDFNA7EoRUjoxH1gpmfIPHWP25akvS7XOwuDNBirM03JkGJGn7RafsfMfPcYiUktCP4y5pVZMt+XDs9s4p43wyELnZRK+Ojpu0RZzx0pCUWYLcPkXg8HZFGJZZpsVsSBIEQ5R1qLSGRKkUKrw+v7JEgyd9ez447JYbr8HN61P+H43AWgSWIM2VQV56dnvPLkVd5//32U0qxW0j7sx44QI8+ePueHP/wR3ge+9MUv8/M//1WqqqAfB/pedqqvnz/mrVde4//w7/1vsQnads8/+v2v8+zqOT/z9hd57dETzk/OSGvh/4mliSAUYz+w3d5wfnaCUlLopaTzLm+Os5LCp65L3BR59OhVVuuGujHs93tONg/wwcnONCp67ymNmMfaQuxffNsthOWqqqjKkrIoqTKnyjmxgZlvpuNsP1sUKCWcKQNoa5ZM2xkJkbZRIRNOStzud4RsXXN2ckbTNMtNopUw+qIXtXNdN2hteWQMZ2dnwME/UCmxuJniQNu2S6qHd8KpWa1WC6pYFyWlrcSWJi+2hZZWlrWGIheuMUQKbRYj6mQ03TTy7MXl4uCvkCD72HhpGcSIKSwnVUlpLKenpwsfI6bEGIT752PIGZ+K4BPresX5+Tm+3bLfddlXKd7bFMnriQckx1zZ41/5Mx130Kso7S7ol59X1lBZyzhOOAWrsw39cMO7732f3/lnX+ebf/yHfOuPv8HNJ7c8efwa/+pv/G3+F//L/xW6Kuiub7n55BkKjQ9QnNZcdy/4ra//E/6Lf/KfMt7uePqj93A+8Tf/5b/JV7/6l/itr/9jKAa0LnlxdckH77/HL37tFzAqsmt3PHAPqFcNr73yBl3XMWVfqrkNGkIguWnxcYw6ewAqqLMz/ZgjuhIpX3OxB9KFYsjtzBmBBST2SrZOUlSmT5+/+1+DoGl1XdL349G9GtjUazFkPX5cSuQ2QuZ6yc/mlu9crIlfzd3X19ouGzaljrlCeol/W5ABfzc95lDk+Yw2sBRJx68tVAyP1lCWhzZ58H7pUCiliVHQjegDzXpFVQmVYpr84klYFIaqrjFIalFRlfisjLQS20BKnq69YRy3uGnHNO24OF/zsz/9CwzjwIvLT3j6/EN2Ny+IbqJvfRZhjDx/est1uxMeXm5HFUVB3w13rtHkRrk2yVNVNVWls3Izb9bGgWGYSEKf4vL5C7p+wHsIXjMOgdttR9SGECVtZS78PmXNFAMYhSKKKKCumEZPCCICsXZGYmTDUJZF5mGF3GYXCGpGamKEoqiWe1542pIfHHwkLqkLkjB1cnJCAoZx4tnTZxQWLh6cUKeS5CP9vhceWkpURtJFdF3w8HWPKh+gixptCpLWWKVYVQ22ks3WOAlw0JSltHkzX9Q5x3bf0lhBFQcn6FlRlBCFHzZGB6rDJEXf93IdikJawWnKXRaxx3KDcKTbvgOlZAOhNbe3tzw4v1g2LsE5Cq0lmxmFsXrh0ddliWoa3DjinUMnUeqLr2sHCS7Ozokx8t7zZ3gla2NVlPTDwOQdZaEziiFgizIGlb38ovdMKXcPUmIcpKj1IWUkMOZiUNbq+ZwvSP29sb2MWyWiMKsNZSX2ZcEnJuWwVsoyrw4G7jMAoyKQizuxpZmRQr2M3+OW8DRNy8ZRFMdahCc+QNY8jKMke1HODgTz+5NaBKPRJrtzmAqFAE0pz2cxZQpAyu9HSxEb7xV2f568v+Pjz4AAgooSwWJ1yaba8FNf/hmc9wxjnzMeARRvfeFtHj95IvyPpHjnu3/CJ598wuZ0Q9u1vLh8zt/8K3+dr/zsz0IIedAofvEXf4nv//C7qAT9NKL6PT94713efuNNzs8fMIVE3w3cbm9YrVbEGNjvtlg9UDcPqFcV/dhy+eI511cvGIeer371F6mqBucjVbnh9dfe5uOPn9J1LWVZg5KIqmmaqEvPqqrxMS48HWMsq2bFqq6pq0ravlYSTryTndycJzofVVktUWGy+3cZYTMLVzAE2fkEJ6kihbHUq0YQOy87eoDVaoWx0u5IOgkvyIuqcLM5FVFEiotqauE/pURVNogSqqAqGzar1WLUeky01yh8Svjg0Zl7Q/KZ4C3WGeMwUJfSajaFxQXPvu94cX0lO+WiwGpL0FaSJrIIZl44ffKMY59NuoUP1vYdKQTGaRJH9ARNIcaqCkMIKdtq3ENqskmsylyPGSta7lN18Ht62cD503ZNB97Y3d8JE+ip5Kxe46vA73/3t/nmH/8z3vnGN/nON77Hh99+l4dPXud/9D/+n/Eb/+q/ws/+xZ/j5nZL/3xL0h5daKzSNEoz+S3/yX/4/+Cb3/4G1zfPGW5bPvj4Kb/2t/4VvvZX/jJf/MKX2Q23/MG//3VeefwE8Fir2Ww2fOtP/pg333qTV197lbKquLm5kc+l1DK5zOVyCIEJiMZQYInAqqoBoS0Yrdjlc++8I5AoCmkZNU2DmyZBGlMSgvW98yb78sN5ldfMQonjcw7LhGysRk0wC6Hmny/PrA4Fw6Hle7g283H/+hxfX/lbdgYzCnD8fCCoYIgHHtBxoStohFkKJnk9LSbOTiLO5km8yGbbMcc2+lxQa2Mo871UlxV1UZBQeD8xBS/P0VQIsmVIwbGpN6Q04GOH9pqh2+PHPSr1pDiSUqQu17z95heYhp7L50959snHPH/6nHZ/S3KRdjdJAbj1XF61RCVWGcHPpvMendtWIZHnuwAhUtclJ5sVTVPixoEYEtt+z3Z7i5s8Zblm6B373ciLmz1tN+HGyM3NFh80KCOL5OyvluZ2ulwvnVE7YwxlKdyoGCfWm4oTXS2m+MIjE95qTE7SFTA52UHmvOOxPW9UZi/TYZgg6YP7Qkooo8Rv0Y6sTUlKiu22RZtLtFGkdEJhC7yPhODZ91sgJ02cVKTrhvVZw9o24hFbWDbNKVaXlKXJwsFDS70si4UiEEKg6zp0JXYwpqyYnGNVrThbrSWObRzZdwNn6xO8UaJojYlSG/ohcfP8BRssvfNcvbjk1bfeQGmNCx6bBCToxp4T56iaGlsWeO/Zdy1DFmTaQgqXqCJj1/Lmk9f50htvc7Pf8c6Pfsj3fvQjLtan6KRYrdas12teXF+x2+1xOnF2esbZxQlT5Qi3ETf19MGhk2NdnS5jLEYv0ZMYkpY+2Yz46yQIubUW5zxdPxBiorGWaZIu1dwlWKYEpZdCUitFCA6tpIWqywKyajeRiNqgMXkTZXO62Oz5m2kd6cAv1uqADM4eoDOtaXYvIQknr8g51BAJzuFHSQPyMUJQC8ijtRa0tJQC1RYGowoM0lofBnEvubOxzBPhkh3/GTXf8Vr4L1oYfu4C8MXlcx4+fpgnQ4U1MnH51FMVNVVZ5j66tF1W5w2gJKOxtmw2a6q6pu1b1quGMDlurq/ZbM4wpeyyHpuai6/9EsPQsWv33N5u6fs9bd9S1GIYPU1u8RVSaLybmHAoY1F2xfXtDd0wMjrPbt/y4Ycf8sYbbwvRMimg5vTknHHsQAWBhb0kUYyTozAiNTdodFFRNQVNLW74SokB6OAmur6jG3sxhcwRV6UVde1sNqtSzibWVnhrOW2jaIrFw9DPRHal2KxWcuFjEnjdFhgji04/OvwkryvqsoqyqDC6oCxrqkqyg1MUZMaagmEYcwu2ZrVaE52nKA4+itJ21YxhYJwmRid/fJTPU1i7WHOY9RqbJPlkHEeGaWJyjqZZ50VV7AzquqZpGlK+D8ZpzG0N4aRtVmuMNQtS2vc92hqKosQow7ppcG4OpbeHAUsu+JRC5/idORRkLv+OB8TLBsefZcD4NDGl6c73yqbBrEqe31zyT//Tf8w3vvPP+e63vs0n732Eaz1/++/+ff7b/9a/zVtf/iKrTcOzy+dMQy8GrlGhrAKr6P3AP/xH/yHf+taf8MlHH7PfthS64jf/7j/gN/713+TtL3yJ05MTXn/9MS+u34fo8Q6urp/xzjvf4rd/57fw2nB+9oBXnxScrE7Y7XbS6lWHz2mUcLViApRG2bzjhUWhb41hXdWSCRsEvRAeERSFbBbmxTwlUTPe4f4pFt+sQ+GdFbFH5y5ldWAIwkcVa4+jNk86IvodHTEedsNzgSYFWzpAg/eOYzGSfCl2LjHEbJEk94w2ZhmT8R7CfNxyPhauhJkbnJKIDXKLDn0gis+q/WPhibS2xHbJZ6+xcXKcn53KeAie4CfWZSEiOiX5v0p5QhhwbmAceskSVgXTAN3+Gc8/+pinn3zCi+c3YnTv4XY3cLvt2LaOMQbhJ+fWZAieuiokTUgpMSmO4hc3hp66KrFGuGs+gp/Au8Q0BcbBkaJj3460fWQYYOhhHCOJAh89MXlywHoW4WWu1JzlyiyY00cb0VnhO7fzNNZ6umwZI8iKXvh1EoPJck+GIDzY4+ec55yUWLJhbb7f+r6nLBRVUaN0wdWLLSEGTs9bNicnrJo1pTVZORzpxp7BD6zOz1kTicGjFZysGy4uThk6QbMBqqpC6dxyRnjmZWFxSnjbzgtfLyx+kRrnxFhYGfn9fhpop5EhDhRZXf7K48e4oWcaRvHxVIpn+y1jzgomn7uyLBnGMXO0c9sVBdbiovA1IbJqGh4/eIhJiThOxIxI9pPjZCUq/mF7w37oiSGhrUaliJtGbm5u8HEWhCmsVuI+UZZ3Nv1zsldCul5GiwB0iRstJrzPFl5L0o5iToW5Mw+EIAbPUe4TcaeNYIQuVDUrUqhJTNIxyxGi8xiGmSUk4zQcvcAsUJv5w8e0j5nX6UPE+5GgDEWlCHb2C1SUpWWKOs8VIuaYTc8LCoyW+MDgA9RmAUCmyQnv2gaid6R5PdMaspL9ZUjovIn98xCGfO4CcPQj3dCy1mtBzsRIi0IHIgYxt4mSPOAdOgsZTCk35ma9IaEkW7ZqKL2IBFyQwGqFgpBYVavMpxP43gC2tPggyiLnAyo76RtTYJXYmDjfoZxabpCiKCnLmtubWx48ELTPaEOKhtVqjdbi3h5TysIPMCou78cgLVuJQitJxMzj6BeO1RRkMMfc8w/5tbWWdA6dEsaIAEbPC7NUhnnCMxg7R4plTkPmaXnjRCiBIDP9NODGiV23Zxh7rC3F61BZ6rpmtWqYQUilFFUFopyeJ1mNKUrmeBzhLijavmc/bvHZYmcWv1g9G+6S+ToGnERxBR+EvD6O1FUtA8cYqkIoAmVZMga/8MSc81Ik24Ju6MQr0cjz20IEPBJLV1IVZVazVuhshXDMy1pwoswDnAeocMEPqNHx35/3uDOoFJ+KGnuxv+Sb736Tb3/7G/zBP/2n/PCd79L3E08u3uCrf/0X+Nd/8+/w81/9OZLW4m01jXLfJIdSJVobpjDxjXe+wXe+9w4fvvcBL569QGH58s/9HH/77/4DvvClL1GUBSBCnIcPHxNcy/NnV3z8yUf8wR/+Pp88/ZjbbqSp11S24itf+orcd0eT3eyzNu9059aYMia3MKTeMkpTFkVWM0vOcYyCZM2bmRnpct6JEflxOy8dkLvPg6x67xYbFX1kpH2/cL+j+I0J9Eue+3PskEEtyuU7VjJZvDQXGDoXSTOV47Oec0YKjLU5lUTnolodyPRJ4p6OC1yVC++UA+CVOSAUWmui91ijaKoSYhIrq0xvSNHj3YhG+LRaGYbBcf3imqvnL7i52tLuB1lgrWUYHe0w0Q4TY1BYXYiZcRRUsq5ryc7NWcbayOKutWazWVNXVXY3UJJ3nVROFknokOi6kbYd6bvAMESmKeI8hJAISfhf5E5+Sod5L0ZZSMXiRdqec1E/TX7xR9U5GrQojtrqsv3DByliF/FX5nrJNTssjMYYsZRRh7nO+4ixskZE71CxxNqSXbsj4OmniX03sDkZOT9Zi/uBgzR5/Djixx5LxOqIETM3UJ6oAm4Sb9yE+NlKMaCXe3TpSOTNiDoMGmmTp4iP8gwxgUtS8BilKLSmKkp0iDgnbeWoFVftniqrxr33dH0vnNpSWrWzLbu1FqyVdcpL/m1VyVxtnKR+TMNIYSxdmJiy1VnwATtONFWDNpYyAwz77Q5HYr2qxa3DZHpBUUgE6ZHPI5laIsV4TstQcg2macr2P3IfzC4Bh7H70klEwJV8XeV154QhTTxaH+bib34vmeAjm2Iv4pfcXsLNgrc8Ryyq5HmeyRvUtLwNmT+cc/jg0EZxMESVXWbM0ZVay9ouvF6hM86bMebNyp3JJj//cUv8v6J28OfnABaKcRqoqyqrAjUYcZD3IU+MKqMOKu/OyLmausQWBZN3VGXDyfqMYb8jJnKxYbDK4CfJ0g1AU61pioZwfiEcpUQe/GIYC5HCFpTWEqNnN9yCE45ajAq1FuPZbrfl5uaGhw8fYsuChKWyDYWFyU9MPqKCKIAONhFiUFoYmxc8IdQO40Dft7KDT0g0XOYGHBAD4bNZI5Yu1hYUefcP0m72UdoVovoqWC5/9rnSSqNLmSV8lM8cvKAzSZGFJGNWC8LoRkKY8g5YIGrvpV1jsrLJOZfzIcN8FxFC4Prmhr3bMpdPsqOOTIgtDSUoXWAgx9/Ibsw7LwHlm5q6rrM6WIQq1hgGN+WJ2KJ1ZIqCpkmMTsDaQngoqya3boRfqZN4HYqqWRaAubCV8m9WrOWQIfWSSu34yNXhXCgeW8ncbRzfPZT6tBH0D9/7Lv/4twZ+++tfZ/vuJWmX+OJP/Qx/9V/6VX7zN/9b/NRPf4mrFy/o9sJ1MdaSjMV5R1kaRu95evkxv/3bv8VHH3zA8w+fET289vqb/NW/+tf5K7/y1+n2uxyZuOPF1VP2+x1VCdM4cnP1gt31nmJl2N5u+fEPf0RTNJysNjx6/Dhbu2TRRC581NKWDUyTg0LIzLbI5Gclm5OyKMkwESHHezkviLjJ/Dk3CmItyLHKEyjLzlWQ78yRi/c4m+ooTu3IAmbeZKhluUh3kL75Is6Lx59WZM5KwXnjEqNi/m/29ROEQIkwwomK3ugDwrS0g5W54+0XZi5bbjUCS14v6cg6ZkGt1YJ0z+M/JUHFzFGrSOXdhjGWqpCcW+c8JpkljcR7T1EWVFrSVK4ur7i9uuH6asf2pqftJrwfOD87zYuLOBpEtLTHnENpRVUXYuhvFNEForCSCDFSFobNekVdW4yB6MGHMUcyQoqSYLHvRtp2oB884+hxXjbmWQCZx5swipU6UCoSsvDNqNBcLIsvWiDGCWsKrC2w1lCVNU4LHzJltGVWIqNmhHUuKO6KhKy1kluOyoV4TpvKBXuMkeA9RSVFrk8icOn6iV074saJJ6+cUdoCHTUuTQy7PWEa0auAIuDdSDd2TE7mtZQSKmcca2WIRkOKy/VQWhPzfKXzOElRvOF8TEInSpFVWYIWk3uFzsPSU5UV0QV8kLF1u9/zZH2+qPaHYaAsC05WEtdKlG5NURbooqQbe9CKwmrqoqDUGpSAGcMwYG2BiiLicZm7GpTGaGmjKqNJzjMNHT2R1bpBWysIV17jXDwuUATJm7mds+DLBQ8p4Ny0JIFAYhwH5qLu00N85gDKHKO1Jiq5nsaaefCL2GIpNg8eooeMItk4hDDzdeW1YkqSJjK3hqNYt8mcEFBaUegCncBohdEiuByHSTxrVyqLT3JhmotljMr8dpOFNoY4pZxtncUiPmD03TlQK/XSPPU/7+NzF4AX67XcuCERJ4ctKyIBlCcwEQmiGFSlTH5zNq8tsChMkHSLZCTJQRvDzdUNbhxZVRWbkxXtvsdNkbJscrafQnnP3t8yJU/K+X4g8L2Rvo4UjQnCEDg9eUChHDSRqjZcX1/y0UdPKYsGfVYha5Ciqs5JoQXtKGsh0RZFgZsmpnEkhJFV0aBswfXuim5ocW4EIkPvWFcnYk4dxQcoGmkLdOOAcZbN6oSyrGVxsBafb7oY5hteC0qa/j+8/fmzpFl634d9zjnvmttd61ZVr9Mz0zPAAARBakiBpASSFgkSBMXFImmFpKAsR1ih8EJvoQj/L45QWJIlRci2IhQh0QpaNClhG2CAAWbDLD1Lb9Vdy91ye7ez+YfnvJl5q6qBHljyG327qu6SmTffszzn+3wXg0EmjfhbJaPkAwQk05pcSyExq4+Y1BOsk4iptm1RyhCViCkIEG3kcvuMPJuyWMwTmqbZNFKEjZD3ZiNmtZOp2ZHShTORM9gev7JMp1OUmiA4PWJsPbRYb8nznCozHE1m6XQ0muoaSZHwUOUVdTVjqVYMiWMUYxT+Q0IopR0Fzg24oSebaNpum/yVUjssy9N7IopApcwOIZGpIzP/sEAQn0BRXBNlsx79Bnctx3GxOlhwYozgDdoWQLP7/Pd/9+v8xne/w4/feR+VT/jf/O/+D/y1X/4l3nzjdZqm450ffsiknqBVKeM0qRKLvCaUiu9859v82j/9f+Ol5DQAAQAASURBVPHsvR/x/jd/xM31lr/013+Z/9m//W/zp//Ml3n8ww8FPaoMVnne+fEf8PTqA978zGep8gq/vuSDHz/mf/L3/1U+97mfwVuPGwa+993v8Nrrr9Pc3CTPPC2EZh8wWtz3VZRs2a53xCqk+QkQJfTdTDBBY13A+4GoNdaD0ZXQFgCnHZ0VnuyIoikUVtK1EetxOTyp5zg8RLGNmE4m2M0aQhCkB0HKlN4Xb6Of3ogUiNUCqXg6jHB77imitGjH/OgYI0YrMpVhk0AGPVrBRIJzEiiPwiQz8S4plY0xYpyrzU61F5RwK0Pc8wij84RsFJ4lzhEFXS8K+LGtabRGRfEljUrirHJT4F3AO4V3hq6J4B3Rdfghw3qN7RuZV3kmxrrO0qyX+GFN1y25uWl58qxnve1ALZlNp+RKM8kyqCAwxUVHbgxFrpnUOXluCSqic0Hu+sHR9Z7PvrKgyKOk3ATohw6nWpbrBuvlDvdNzza9Tz56ggrJcDcdm0deRgy7TVw2bhkEEUFbbMoTFiGc283bEKULYayiqjKKMhNk0UXsAHlWJHXl2A1IkzsVCFJwcGARFCHFeALYIVCWBdYbNn3EKYsdIspqTJ0Dmq5vebTcMmw77j84p55WxKHmyaMbMvM+vKHJ8xI1KdlsZD/AJHTIiuF/3/VikO0j3ZBUrii23hOS396kygk4tkOgTcppOSzmHOUzvLV4N7ANHR2Ws/kReSHrYNs4NpdrXjs638GJIQR6N9ANG94qTslVxipGtq4DMprgQAVKkzE3BbOs4MZ2rGzPxkq8X6kM2yateT5gnacbeopcuN0Kgy0iMXZ0fSCWGWWmKQrpEOkgwIlSGoImotEMBB8xqsA7xVW/pAzix+ls8tEzSACD7e8k/uzmdoh4NNEblPQcE6dPk+UlRitcsCnRVKXWsKi5x+7cOGeNEURud2JRilpaZilmLqZc44SgGgBDVCL8zNSAxoCPBOcJMSPYHpOlYi9qdIjokBGqgNc50ZboqsdnJ9AGhs7S2Q7nHYUxB2NUkaXD4nhg3u1J7OfTvtPwXGfjQIvwaa5PXQC++vZnuHx2xWazZb3pmE3n4qLtB1QIZIAJ4OiFCGlFEat1pJ5MZEEnQojE4CmM4ez0GHyg225o1itOT8+Z1hN653EhoqIjyxyzacYQoOm8mJqGyGKxwPYD3opZ80dPP4BYkr1eUpYFJtOAJ+Jo2iW3y5Ish6OjUxkEGZSTkpIyxaiZZPpYEExgiD2D67GtZ7m6lWxa2zP0PcfzY7IUqp3rjKLIabvtLg1EZ9IClOJG472lHzrW6xXNdst0NqUsSilyETRBoVIUmsMHaYUwkteNJitzquQ15b1O8vuKxeJ4NwD25HaDUh03t5d0/ZYsE0Vf33ccHx/vkLmizDmrFjTN5qBdJbypqhBVXQiBpmnEJV4r1us1TfKdy02ONgYfLCbLk5+S4np1w81qidKGoqzEnPjkiKbZiEeW0eRFSde0ZCaTzOOEzpR5Rhjg+voa6yxlVe6QldES5O6EiLsF8I9zjW2Zw0uK8UiW3/3eR+99zOr+Bb/yt/4O/+j/+H/i4auv0Gwanj69wrnAdDpDaUEyxOcRlM6Imea/+2f/Nd/9g2/w4Y/f58fvfMjjD274d/7RP+Kv/PJf43OffYv3vv0dqtkU5RxD21MXU/7u3/zXcU3Pjz/8Ma7r6b3le9//EX97esSjjx/zp37+T/GFt7/AYjrHe8/52ZkQ/Me27mBZR4u3AR9kvYtBoQdNsIJAZ9qQmZyoPXU9kZZEl1A6F+l8l+yCDFVd0w6Wzg5UuagO/SgLjVEOaElY4sPd86vQEkpBpvM8WTbpNMaSbUq6nyGZB+9yOZWgC8+3Pp4XgYzIz3gPh2FAcZfYDWLcERLSrJTC5PIzPiE1Wkh4B8+j6dp2V28cbk4uBHKtybT4Xw5WxFhaaWlbp8KkrCpsu5VSJb2WohS3geB7rLXkOcxnko0tOmtFWRasV89QCmzfiRH0dsvjDx/x+1//IT9+/4bbVccweJS23D+zlJMJKstRTYdXhuhrvO+pK8OkLogMLOZzNm2L3fRkRvHqg3sURRJseWnPXV3f0rSeYQj0rcMOnugjujQ4Kwpxec+e25wY7XLY3Ud10PYU5AVgj9LcRYQFlY5RMsLFtkpDzp6Yn1rpKLcbFzu6SwRZxw48I+P+rOd9EvlFxzBIvJ/WYhLtrCfLNXVd0LaSHDLrpsznc0xRcnN7SzF5SlAaGxX3X3mdqp7StuL5GmKUTotzEofppK2blyVYi3Nin2JyzdnZGdv1hmGIZEZ8Cl2iEQ3WEoOjyDPqcsJqs+Lp5TOm0xllWVPOJpwcHcv9SkkWRVkwKSas1yvCQ0VUisFZVps1Kgz0tiXLFcFkbPuObT8QlcLFQDt0NLaXrGWlqOoKUxQMfc/N7S0nF+esh47tek2zWlNNKnwPawbspKA6WXBSzVA67hC9sSPqRsupZNot+53dJWX1ybmgqsRd41CJO153Dl3jGEk+wEUutAhSB05sWOQgl+flnTVBHnekfoxxa/sW/e5IMVI+QiAb0xkIZMkyjgjWDmw3a5wb0MZiMumijNSSGCN1XVNVJUVuKE0mZu69PaCeyPsihdxP1t49FLT9ca9PXQBuVhvWqxVKaaoqx7qOPkQypYhKbpizjtxEVEztTm9ZbW/58OOW1197jejESDbPcgqlcMqwHjqeXj7jyeUziqLgzdde5+RIfADzwoBT9M5T5gWoDJOsWqwdMEak40We8/DiFUw2oSrKnSG0jyImWCzm+GBp2w1VJZV+32+TOkzaM5N6SvSRoR/oXY8NDtcGVpsNq/WKpt0QgmM+mVIWBcZosgQ/K5UTgqdttjuuVUjWJSEE2q6j7Vr6vmOzXROVx/oJeS7RPUMrEPw6/bxSirIQsY02miIvyPOCvCiESD4o8mqCyQWittbSdu3OG9A7z9HimPl8irU2wezsOF2SsiEcs7KssbaiLCZ7BXFaUMY2mnC3ZLJummbn54ZyqXWQUWmN855ts+Xp1WNCiMxmC+GNRE9RZIRQYe2wm9yCnqZiyYtxblXk9L0gm13X4we7g/N1SkRR+70lzYT056esA18Ug7zYDBYG1t04sz/3i3+J07/+1/iLf/EXqecTnj17jI6ZnPgSyiGCCYgodG7ISsOv/vo/5Vu//9t88O57PP3olqtHS/71f+/f45f+xl/n4vyc5fUNKMOI+WulUV7TrnrcEFiu1vjO0g0DbrD87m/8Dn/+r/xLLNc3fPNbv8/F2TmPHz9OKs1ijziFyOR4QV1PKMuavJDMZ5PVSeAjr906D6En09J+K0to2ybxdXQSjECZFLFd24r6OhmCK60wO55KCn43d9vnSiuKstyZnor5dzgoDA6KKu/2xs0HRcGuFZuoIOq5qL5xgR/H+RglN/5cURQpeYe0gQiHbzzojCfqvCx2f1dKCoZRKTi+JqXUjlYB7FqKIPMs4nb8I2PE6NmHiPNJwBISZqag71v6fuDoaEZeGPrBUZUFUSm6viOmdmN0jmAHtpsNy6sb3v3giqdXDeicerbg9vqKtnOURSUKSRR1lbFeDuS5pFQYE5nN5mIF0/VURUapNNOJ5mgxxacCxQ4eQiZ83yFgbcR7pNXa91g3rnNjEbffSF+2KY0F2J77NrbbBBlWBy1C4cEF2qbf2VEJp1zG1DgGRmR//PyhMEipsFvDXrxi4hnKfe+6gcxEiiIHbXA2sPGN7A8IT7meStdhGAbh0CrhOt7e3lDPFqL4LoqdijnGSNt3YvjrA8p7TJEzKw2uyNGEZDuSaA/p9w8p/choRe8dGk2eTVjM5iw3K9ZtQ+scdV1z7/wUl2ycQBS340q27TpUNUFnOW3To5QXIUJn2QwDy6g5OTpltdlys7zldr0mq0u8dwzOogpDaeRQU05qaXOiKE2OM7kEHNQlCrMTCo4ouLTCpR0rqR4pFSVYIgkBVRpvZd0fx4vb7TvxE9byPZIrl97x8uRnJURB+MIxuUjIoW80sx+FJiZZSI3Ah4A/5o6jwU4Ell5LiGltIfGXQxAfT6UwefIDTV6FIxVBwa4rAuwoHQpSipAhxn3ucYwRZz1Kx/STL16H+9fzc+0nFYd86gJwfX1Ns1pJfE9dixdcCGBGGxJRQmXGELzDE+mGjuvbaz5+/Jhu6Kizgnk94WRxRIwKbxS9H7Aq4E3k6eqazTtbHpxfcHp8wvHsiNnkmLKa0nYNzgXEm1m8zLRWmFz4IkUoCV74CkppomTBYHTBfLYQ0YYd2DQbnB24vV3ubkBmMmbVlElV40PARY9DWqrbZit5nkjQ9qSWRIsiz5JII53w8xJfuN0gHonJzjm6vqO3AyFGyqoEpejT4jC2qrq+xQaJhyOAax3OSpt1Uk9QShIcSHytMYZOJT7WmMQR0+m3rmuMmdI0W4gtMUhUnjE54JJZrCAUWZoMWSoKhnS6kdOMYkzE2JmTHsDMwqlh97u2XUM39CnzOZ2GrEz4IsvxqXUUI6JaTETaEMIu0xFGdCfuvNtk5Kd2UhjZEoctoLvXbgp8wmTZtYlT0TJ+aU+6fXER+umf/hJHv/DnOTo5Y92uGOxApqT40SrigxOMUmXCEcHx3e9+k2/9we/ywXvv8+TRM5qV40/9wp/jl375r3N+fipzxzqUNlgXEpKmyDTkRnN2doL7nmWbfP5mizk//v4POX1wwtALIp2ZjKIUb67MGJyzdF3Hxfk9Tk9m3Lu4EK+5LGe+WJCZgpPjc6aTBWU5oaxrSObnkJBBMxK6g3AwVWRwTiK9jPBZvErxZgdv1LjphucRQBRFUYgfpR8938wOxTts7Sr2/LgQwo4zCCOPcK8IPrzGDSiEsFOTcnDvxWrFCxKxE2uZVNwlIoEe0UbuWMIYY8S/7gABHFvNSqm7r1HpPVd1XAvcGPWk05yTNr1SWnK+o5gZb5stZ7O5jOwYGIZOPvqWDEFm7eBou4H11vKZz77Fyek5IUR+89euxEQ6ABiImr5v0CqjzHMmdS52K/Mpw+CoygqdkieMCUwmE25vl3TdQN85QlBYF3BWitaQijZvPSEh3PHgvf8018u+LUaJKvSMm5t83kWfNveA0QFjwkE6Q9ypz+U2x92Y2x8cDsbfiD6mGjGm+yeiClGU7ux7UuqR5LWKCHEyn4pCt++4vrmhmEyZLI7QeYXzjjKt49bu7bh2RQQS/2cUFEbJ4UuxU97LYUg8+kxmZD30QVKjlKF3Vlr4RSU0GSLWe/JksbMvCIR3prVhcJ6mH9h2PV3fk2VQlWLBpWKktQMfPP6Yru1pu54I9NZRVwX9VorK4AO266nqemzwU5QVxULzeHm5Q2aLdOAcFfshSmZ6IIhgZUTek1A0xkBA7zpe49f3CB0vLukHh4ydUMxosgSMjGk1JKqBUhGtRzEZu8ccaUpyfw77Dtx57HGMyV9U2keSb6hONjHJ+FoWBaGHqBhRcR8coUmc/lQk58bQ+XQwSLUCdw4qn654O9y/XlYEftrr02cBb7dst5vUEqw4PjmRhTKTRTZ4n9SpMkGtd7Ttls1mzXK7pvlxw/FkxsOze0zKEhcjMTPY4CjqguOzE/rgeP+DD9i2G5abU85Ozrl34jk/O2fbdgx22C3kRSl8jaIoUVqJ6rL3ieclRFulMowqWMyPWbPCRZH0N+sNjz76EDMaO2cFXbHl5PiErCz2sTtpUyrLktxMKIuC2WRCWYjS1SeDUYIs3vW4AaWiZTRmjkqqfaMVdSGIQdt2ROdwTqxeQgypqBa7ir7r6Pt+V0hKZJrEzZV5JafkEMm0IJhj+/XQ7DXPMmkpu4hS8jxZZkRhGAIEUebtTIMD6SQ6RuvcVSCJ3cA+sNooTZkCyL33DLZP8WFZgvnFDNSGSKY0WZFTmByLwkdJGxm6gRBcOs3Le25ys/Pz8gcZi2r8UHcLQNkH4p7oO9KD0vXHk8sfpkLIde/eBfbeBTc3t1hEwk+QjRojyRBGlWSmIATLcnnDV3/71/nRD77Hs4+e0a4tJ4t7/Kt/7+/xpS++zXJ1gx1c4i8aXBAxkqxhAe9bikIMcHsrC/90Mefpo8f85n/3a2SZoWtblssln/vC25LvHB3r9ZrVasXnPvc5zmYlDx4+IMszIpFXX38DVMb9i4ccH59zfHTK2dkFhZGMzhjSQp/ldM4lFFv8/nwcqCtRe49Fu7RIMkFMU/GKguD8828nsEfKQrLzOCymYGzj6d3YizGRqXd8wBHRM2T5cz36tECPyNuoJB75YOPjKZWMWZXaZXmrVISMKnzU3taFIDmrUUWU36MD43zbKdETp1Qd/sLpb8FLHN/ojlDkuZimp4JyLAI32610n2PADxbbtwx9y9A1ouRMsWLdYDFFyRd/+gvcv3+fjz9+SojC5/M+Ipmq0LmGaXlMVWlm84qj4ylFUeJ8Q1kWKKPw0WGMrHXWSk75MHi8CwyDS2IS2Tx9DDiXrIXi4e/5IvL3ss3pzu06+NJYzI1bsVIpEcsGYlBE43dj5BAVHsfOYdtw7FrcLQAPD3qjvY9kv4+Fvktrd1HkGJPh3EDX9uSFoW176rrAB896s6a6vWVxekZRz1NxLKbGTdMwn8+l7TgWoojHarAQnadOfqpyyGaHCEl2b7kTYESExLTpe2ZVLUK51LJ13uP6gXwXrTlGz8ka0tqBfhDzaUnwEV6bUQYVAz5GHj15gvJKDK2znDY66qqmbcRuxg2WZrvlM2++iUEOMdpk5BODWYvgsEh7zVjwivgr7A4LMe5pFUFKfLl3RnKkB+uSO8F4EHz5WIrjQX9cLyKpKC6o0uEWp0j5PjLHEy0jwaw7asIoJjl0jYjc3SfG55fWtWakRo3xbsEH4WAnIeb42BrZVyNJWJJSt0awRvZkKy4aKaVLja8hHULT8rOLtfukufS8OviPc33qAjCf1awftzx99pShHzg7Pef1V1/j9ORECNTWc3t5i0f63hJKvsUUOT/9M1/i/R+/S9u3bLst1lsGYzAIcXyWVxxVU87nx7hNgy4Ny37D9aMl3/7Ot/nSF7/EbDoXwrQPFGVBPyiy+RFVnpEphTeW6XwiGZRtRwTKqgClOT07Fw/CrqXpOpyL1LM5QYHJ8p2Leu96sqm0XqMVE+h79y4wSu82izyT0Ou+H/Yh41omg6RzyIQfhoHVaoX3nkk9oSgdwzBQliXDMGAS2mWMoa4rYidDFx0JyhOyjDwZcRZFgUmWKDFGyEfRw779YQdH3w+g2GUDOxswOqeuNLn3oIRTNJnMcFYyKRkHaZYRfVIb5jnT6RQfXNrTAk3XisVEs5XxkOdUVSW+V7siVRCRupyQGwNBJcWzp9CGqppg6lQsessQBmw/pMVj78GUl9lu4oTEP7t7PVfhjf8Oe47gOJH+uFeMpGih/bVc3XK9vBJ00wvCIlM+EHwPKLyPzKYlH3/0Eb/+a/8t7/7gHT768cdcf3jD21/6Of7GP/gH/Mov/TXe++hHydksSwcWTVHnTCclwTtWN9f81te/yT/+x/81NhP7jyFzNP2WYdNQXBsm05pMa6b1lJunl1R1hiIyLXIm56d0m1u27px3tx9hfU+Mlu1mw8npMY9+/H2KouTo6JgHFw946+2f483PfBZtSvphJPAL4mBTy5UARS8msyNy2w09Ez1GHEXhs4xemAeX957b21uZK8aItUVZyqYRw852Jstz0CplsILzUuCXRZGoC2PhpRL6eHCpQ5/AAxuHg4IhBC+m7EqTm4xMaUEhvHy+NNkYQgJxjHOCGEWhPxZz3gu95LAYFAuWfsd52o3ntEmqEOXrVuaYc47Be5SPbNqW69WS4/MLCqPw7Ya239J1G7ztJFrSZLjBsm07ltstn3v7TQI93/3eN/jG178jMWlB2pO2dzTbnvlJzelpTV4ojs+mnJ4e07YdRZnTND3WDugMJnXNer2WzGKdoY2m7zdpTTEEgog+vCMwGjGzg07utuY++RrvyV00fiSyH25waaNHoaJ8aEYVsRwFZW2KO0rK4WY4FpQhPM/wZX+oEIH07nl1UoRKC9endVfadl3ToDO4OL9HWU8oioLl7YqinlPMFuhhoNk2rNfrxPuqsMHvs2lTi3cYBnItmb8Sx1mgEg+OECjyjLZ3GG3EH5JIM/RopcmiYtRBuiAuDNYGikoSQ0QoOKCM5ur2Jq3ngbquCZkWn0EFCoPKDVVQ4t4GZNETLGRRYxuhJLVdKxSEN99EZYaua2nalmHoyascax2lkQ6U0QcemAS0EsqOD/u2biCNmRAhUwzdgLMSBSqHek0MYn4d/d27JvY5cd9ijSKUHL0PUSRO/f6gEmMkTYed/+lYgPq0H8aDsfj8n/vxJICSFJHjgVXhPYn/KpQWk6m0b4Td4bEqJUBCRCXp3nnPsAuQUOmgKoUk6YC649Ae0GCef40vK/7+R2sB6yxndnTM42eX3K43ZHVNdnPJx1fPUIhSta5q6mqKVprpdEE9mcsvkBne/sufIziH63qGVswsy7IiR07cxhgoFF/+k3+Kb37n26xXa1SmqeYVq+2Kk9MT8hz6fiBE2Sy6fo0btsIRqQt++M738AFm0zllWbFpFXmREVYDSgfy3FCpKZMHE17Vr+ONKIJyZTDOkRUZH99esVquCTby4OIBZZYzqaaM8Lq0KYUjtW07QoTpdIox0oZ1zu6KoRAjve2pzIT5ZE55IgTXYZeVeZBRme0LqCLPMceiYhoj57q+Y9sKz0/HBClrhQ0y6Nu+AWTATeqa4D15lk5OSlTXwtuxZMaQ55LB6ZLPltaGoirIs1xyGLWmysRjsU+nFdjH44xGo5vNJqm+pEA2ylPlEiSfJW6Vc47ODoT1iqqqMZkox/AQp1MWJtu14qdVybK7parK5J1kxRCa8aQWd4u6tKbTRv8JQMMe9fnJXNNVski58zkt1IPeWVAa7+VwGIHBifhmcVzzm7/x/+Ybv//b/PAH3+HjR5c8eeeaX/zrf5Vf+Tt/h7/4l/4yP3z6iMobVJFDIiKXhcGFhq/97m/wO7/1u3z3D96h225454ff4af+5M+ijMbZnqxS5POMflgxuDV5kTOdTjg/n3E0nZJnYkxaFDlNu6XrbEq80LRD5MN336VZnXF5dUtV1RwdnfDDH3zAN779fSbTBV/44pf44hd/hgevvIr3nuXaMrh9rqUEufc7jt0wDKISTPxCSVvylM9leY4iEGvl0BESf2Yb19JiMggKoNideg8RHjk07VHuPVK4v0YE4lD0MSLGIe4tQnZIczpchBAwB4UcCB3CjbYu8gvIqT9l6I6vZcxLlnrxAIlK7Z8xPtGk9ADS++O95/TsTCxC3ID3jrrMefutNzDWEnHkBnSRs+o7+qbh8c0NIUTmsyPe/Ozn8I8u+e4ffIthcNy/d4+rJ++KZ6AfaHtp/X323utMZoqTkxMWizlaG5rtLd3giTpST2vyhA5765nNFsSg2Ww7rm/Xsv6k6K6gAynFlDFhw3tBiH/S62Voy/OfH78m3GaJ9zJG30E+DpHAw4Sj/dfYQY0jChiCeJOOSMt4gNVa/EzHOMGRf0gEO1jcYFkul7xxdMyD+w84uXfB9bbbPd/5+RkPHj5gtVoBckj23uOdo6zELssaiQdt2hYdYTIRIZVNyTCj4K4sq2QZE9E6J8tL2vWGpmmwzqG0ZrFYoKNNoj+hIdRFKSlMtid6EbKp4Fn5HhBQQyuF9ZHjk1O2y40IsqKntQMfXz3DVAVFpikXEt5wu11TZ0Ixsd5BbphMSppGjI+1kpQPHxw+2AOfQzniEscccTE3toOFPKdrB2k1E1NxL2ig0S8eIIXypHE+khRCacCwCyfQDrSJOzQuxABR0siMNncOjLsEoihZ6Ca916PY5AV6ya4bNv6s/JxzXsYLkgamUh5xwBM1GJP2DUTxHJy/48t6OPb1AZIbokuc4xfnzh9VBP4k16cvAG3kzVdeozAZ708/4PGTJwzOpio7UBUl9y/uk5GltqyRzU1pQdCcKA+JGVmhqMZ7GNOyGEUNW5UTXn/4Ko+fPma5XpGRURcTSlOjcy25tYUmyxTddiuCgQ5cn3N5e8PV5RUnp6ccHZ1Sl7W0V/3A0DcU5YTj0wdkWlqM5OJInytNVSk6bxlcUi+jGPoBV1p85pKfHrRdh48Dy80Sm7hbxhq0U/R9z3a7oe/3DvYm00wGm9pSM4BE1C9wye/LuUGsNbT46OVG0hbyQhIKBmtTIHWg6zu6fhD380Lar13XCvIYD+wWoqRw9N2w8+hS2qB1uduQvLdcXT6jt72ogvOKqqyoyxpSoToOtJFPNZtMpQAcrTpQBBV2i2+W56I0C0KgVkZsOJwTtbaLnqqeUJSFxDsF4diYTApGEFHLdDqlyHPWPqDY82me2x6e+/PlJ6Lx+qOKwLu8Ck+Md0UGSqvkLZfyHMPo/SaxYfPjKb/1W/+c3/v6V/jxj37I08fXfPijj/nLf/Wv8Q/+rX+DNz/7GS6vnpD5wDYGFspQFIbBtbzzg3f47//Jf8OHjz6g7x1hsKyvr3j1/D520+C1wblANamZzSYszs/Jy4LJbMbZ+RmTuqZdraiqgkldUdWl8C2d+G15L2KgpunYbLecnp7Q9R2X108o64rV+grrFB99/BHf/oNvc3J2j1/8xV/k4v4DsqJgve0Yei8E8eilkC9ycq1w/YBCkRc5mdYE9eIJXitNVVX0vRD7R86wCpGyqnYo+lifjQXcKN44bBXvCNbPVR4xHTZGj76xtcOuiNjbyGglRIYQRzPybMdj3SPifqdmNkrvkAqj9a5tsxOFsC9A5bXIBrTbSILwtgZrGaxwv6y1TGcT/GDRIfDgZIHbLBnQlJkhBMdyuaRtW+zQ03etIIpFxvHpgunNmkk1pW9WrNolD+8f88qr59h+Q4ye45Mj8qRonc2m5HmFsx5rA5vNNr3fCW3PDToI327bNCyXm5T9qySWDUUImhj3SSzBx4PILvHae9ncexnq97Lve9n37ND/eNhtGVMX9oXfYSEIh8XhXgyyn9/s2vbexZ2RcYzgbBDj/mR+Hz3i22YjzkXabccH732Ac5FqOuOVV15N/nql7GUxJuushOxkucS55cK/y7KUzuOFCtENPTrPhFKUDldjUopRimgDXdtx1XYMfY9RgiibTKMzEX5YZ9EoqqIieo/Os+TO4HDe4YLHRFG5twnt7q2lt57LZ5cJlddYNxByjYkqAV6i5K2LkrPFMV1VY70jGEWMlqFvpZNkM0IQn1pB25MmQLI6iD4QRpFPOnwFNF2iOYm1yv5+i6n6i2NkRJnHg1+WZZRlSZWiLb0XH+EwWnKPQjBU4uXu1/dxDI2m77uCMIQ77eARdNjRzZG2dogBOwz0XY/3DhU93itM1JLklYRN6uBQEtWeG3r4OiQ9hNRpSMKZnwis+ONHwn36JJC2Y14uuDg/pygK5vM5y/WKoR9ERDGdce/kXKxBosHoPPGZtBBkEaKtqFsUvW2w1pOZPKnkxD8ny3LO790jalBZhusH6rwiUxl5VkAeUwJ9pCzBq1IW1hBYLE64Wa5p+4Gi71JWrsIOHTc3NxRFx3Rywmy+SNYXcnJXMZLpDDd0ZFnBbDan0jnTyQStFH3XJVK5wXlL021w3srJJSqGoWMkAFs/MLghDeyB6WxCnpVpYmuqUqLY8lzQNqMMITc7IqxJCQLSbhKibNf3dEOfIqcSFyHIZhyiT55aSWnovUD1TcOzp48p85K8KDF5gSDkg1jQJHVns20ZfIvWGS5zeCsfZVkJopMmM1G4hiPfYzxxqnSiHQvAkWDrPImzlfgMOhJ0lEST4DBdjrU9tu0ZrCPLCzJjyFTEujHsfZ/UsB/t6c9dkXBADn7JuD1s0f1RG9Gdn0sT/fBS6UATcbK5I2ag2gi/8fvvfIff+/pv8/Hjj7i9XnJzteW11z/P3/zbv8zrb76BNpp2vQUUk+mUQOCjxx/w3o+/z3e+9TXe+e4fsFm1O/R0WmWcnZ+yHTxb54lRk5c1pydTju49YHFyQlkLonp6dk48s4mTaXa8o9LIwSS4geAsQyfUhBgCTbth22yIePrWst22DE4EJ1fXV4Tg+amf+mkevv4m0+mCGHuGbosmQ7mBjFyMkFPsl7cKnSK0XniP0+IXEp9OJ8+r532s5B7JYmyt3WV5Ho6F/WM/j9DqO1nXI9p3yC88jK3LkuXE/v7K6xkLSGJMIe4HcW7GJGPkVIQ+ZyMxHnx2SGZ6rZkxkheaXn+fBFXicwmZitS5JvQbdH2U1hOxbmq22xSp6JP7gKIsc7y1otxWBb0d0CpydDRjsx4Qs5uCrm/4zMkD5rMFzklkmncRo0zqMGSirg+C7m+3HavlhvWqwQ6W3kUGK1YvyaZxh/zJffjJEIg/Co2XKbpPgxnf0wP610t/9kXk7+CD8TbcbTGnO5fGxGilJeNPXqdODgWIs0WIuMGxdmt09phyOqFYHOFTiICNYvydZRmTyeTO4WBX/CRemihDs916GWIgz7Mdf9DHgIkKEyKh6xliQOeGvCjEckiJy4YczAWttMOA0Zph6DGZkXmWRXIP1u9VqVobytJIERoD220LRDk8ZrX4Y+oMnRnqsiLXmujENSJqGMJ+TXbO4l2eCpewR9qBoLQUMulrajw4hUBA0HPrHSF6lMr2B6mwB/j29zTgU9sVhE83+khqsxd6qNRmHV/Hbu3XIpo8HCMyCuSwppO9y1h4jWNUXrdQDsQj0KfX4hPi6YCQbK3Urlsg3MGRBjJyhOV527YT/m96T2S9k7kUeZ4L+OL8+kn2sT/q+tQFYNs1VEPNdDJjNjvi7PQejx8/pm9bqqpiMVtwsjjGduItZJQkDcSQDMjYn7pc8Fyvl3TbjsVswbSekpscoljEZLOFEFPzgtXlNbO6llQNYzBZJuajwVNUJRRitNl1Def3HmJdoOtairxkMqlRETQyYPu+Y7NZc7Y4SghAWogiBC0qV2NyqrxkmudURYl3IhyxQbJ5nbe0fS+LOeDxRDv6TEXyPGfkGg1Dn3r8YnnSdC1KI0VzVuxUt3kuxVtgTx5VCFegaRrathUBDGO+4n4iuXEQpIXOe0fXedbrFT/4wfc5PT3n5OSM6WxOjmbwA7a0aRPq2DYtaItSQvoWLmHPbO7Ji0LaJc7iUnTOyE9QSEEoJ1dBwEbitdISS2a9TBad3PpNJobT22Yjm4h1BOsoupY8L6UlR4Ai0LVNmiTx7uK9mxwH/AwSX4K7m8Dh9Wn4Eof/jgfmsQePkiZxIPoxYUBUsP3Q8ltf/TW+9853aVcdm/WApuQv/it/hX/xz/+LbHpHs20JMZDlGZmJvP/h+3zv27/Pd77+u7z7ne+glSJXNYXOqaqMe2f3uXhwn0dPbrE3G6wf+ZKavJjyxmfeZjKb8ezqmrP7rzKt6pTM4yQz2lsWdYkbxGDb2x5vLYvjjr7b0vdbunbDZrtkYzZYZxmsZbO6pe8GPvjgQx4/ecyX/8wv8Lm3f4qimGB7vSO8j2pblWnCYBlsxMRAlkkM4ktWcSlitCZoaQ8Vhdg67TJfD1IbxiJLUjMi1vo0v9RLyw45oBQJ4RmTN6S9pAEd9ikmu2JupGuk+WwSsbzveyAlHCCxlTpltSqn2CmD2c+HcdPwB2pUQRtlIzBhP096K/fSKDHPjkSIQu/ItAhe7CCHyL7dYvuOiHhoamPI84gbBlSM5HlGXRfEMDCdlmg1pcstzmqMcZydnlOWFavVlqGXzNGqLKiqYmfX46xj27Tc3q5ZrxuaZqAfAu3gcB68kyJMBGupVZY2LclWff5u7CCT3Xv0sr8fXgLCKMZ5fGdu3iHsx8OHvnPAO0QC4/6HP+HaHwpH264YNUqlAiZ4vDPJ0zPskLuAY3l9w/v6R8zOTjh98CqYSg7L1tMPA9WklrjBmJSwPqnA9eg9J0X3aPgfEhI1nUzQxrBtGxEjxYgderxSVDPhkxml8N4JCKHyZCUiSSJlltPanul0Kj55CMLbtUJVMErGo8lzmm6grmsGO6RWpqY2Gb11YtVWldT1BD9I6IDKUrvaOWKQZJmxqBn3nxDDbv/SifMXEjc4JmQuIBiOWOSIzdc4oUfzfp5be3dCi2SjksVEGdFi5T7SwMdIxxj2WdEoUNlY8h8UT1GKPxGJ7VPAxr1llwrE/veMQToX0QfJJw6jbU/al6PaAQdjkapHEaOSvaJpGjmUBw9JFCaHnv1B5RDl/qQ278voEj9pS/hTF4DON8RwDMFgfE2ta37m8w/Jc4npNkrsRB4//ZigI5Lk5elsx/XtlSBohcZFz6bd8N7H73P77IZ7p+fcP7/g4vQ+mc6YFjNWqxUzPePswQnm/hsM1tIMHbjIpJiymEzAiw9XiBEL+D4yrab86S/9SdqtpEiYUmG95Wh+Spkrrm5uuLp9wunJEZN8QpUXFFkGRtPEnuWwpfceqyJhgNjmwltSjpVt8T1UpiCgWQ8WHQOFVhhTUE+F+5hnUsi6YUiDOOCiI6iI057rdsX15RXr9RqtNdPplJPZMRfzY3wpxsqZNmQ6Y7NZc3V7vTslWmsldicmwvJoVqvNQeEikyXPS263T7E0ODqUfsDp0YUMMsT4c7VZ0jEwy6epsJPINjtYupuOup6mVkIQH0PfUhYVBMh0zvH8iKPFMdb1+9OSUnS9xRQ5SgVCVEQPmdJEFdh6y3a7ZLNdE5ynyitcaFHGoExGkdfQNVw9eUzX9ChVEEIr/BHGdSFKlJUPQhyOHpVIx8+bYh9Oluc5F7vvU/vNe/f9B//ffS+pUEhEbKsik3LGenPN17/33/E73/hVVjeej374jGkx4c/+wp/h3/1f/c9ZbixDYyVUvcg4Ol/wzW/8Lv/Z//U/5Oqjx5TKYLzwKs/unzA/nnJ674i3Pvcmk8UCvvVdmu/9iL7dQFfw61/9Fn/pr53xc//an+DzP/VFfvDujzk6OoIQKYsCO1hW6zUYxbyYEmOkabes1kuaYQs60DRrbNfg2pbZakm3+DFnZwseP17z9MmKm6sVve3Zrm9594c/4Gd/5uf4W3/r73J+cs6z66udh6ZYG+ViF+IcJhgqJaj/HQJLjHhvRUTgLd5btBZBldbSDpJvS56izlKWeTJelU10HKMj/29cWPf3R+7ZGDPok5AkyzLwnt4KL8sojY6CMHZdt7NuEkFWvUeyMxEyWeckW9VoSE4EGuE9x/HwowUh8AeZrpnJKHNDXeQ7npTJMvGaI1KUJZWpGVxD27dUZc6suiBGxzC09MOWod8QQ49zrSg/o0FlFZqWWVFxaZ4xWVjuL46ZVxWLSUGVz2nKhhA9b7zxFpPpnGGw6fChKErNGKMm/FqJxdtsB9bNQGsjvdd0g6cfPIpxfZGkFZRK0Z9StCkFKo2Hw82Mg6Lt+ev5+Ta2/+8Q72XGp8JQEwm7DHFJc5FN+k67TgFGpZQHiOmwKAXdHuWT5x43zHEcyThzTqHN+BoNynqybktWRpQvUVHhQ0+zesrTR+9wdHJOMTvF5JLW1LSWddeloASLixbXeoypmCbE1UVH0wt40q8bvHVSNJVgCkWBhkzjdGTjOhGW6Iwy2RZFrUGV2Chc1rH7s3bSkiyspzAZBjlQECNZEl6ZXChG3nlOjxZMZ5PkojEIbcgOTKdTqqokUwoXIy56fCeqZQ08uVpyeu+YvNRUeUZwgqKNCFqIwpFTyW4sU1IIOiJOa6Z9TIdIh8Jj07wvtIg8XsizCDI+jJZCs7QWn0W80WhnUDHHE9DJNF4rWaelcBf0OmoSij5GUu5to7yXz5eHNkPeCxqa6d0eqwgo68SqzTl636OMIzcTQhCBmHMeN1h0boRGHsW71gSNGI6HVPBbfOiTUbVOdAqxsAlxVEz/4Ujf8+IQmUufPg3k06uAixmr9YaoDbO5FD0uDigvsW/O9bTNlhvX8+TyCevVknbb0DUdl4+f8We+/GUevPKAED23qxu225bjs1N8gOVqg9EF03rCtlkJxy/PUBo2Q8/jm6d8750fYAfHtJ5wcXaPo8lRkn8XKKOZKIVyFhVKzk5OxQvJDqy3LTpqThYzjhYPcbGjbwdscOiQIP4QWDdblusNVze36OiZFIaZqTg7O0NPKzQK6wea7RbbWzbNloJAVlUs5kcJVi9T61uj65phEH6D8nnKV5WTzfH8mK7tuLx+xnsfvEuRGU4nM6r5CdPZnOlkwqSqZQKnFXY82TZNT1FXGKNSJqHG9xZSUTi2vObzOT/3s1/me9/7Hj965z1WVw2/8jd+lu12izEZduiIEc7Pz6lMuUM8RlRkGAZRrCU/NY0UX30nLeQ8z/DBs9muUtSctD9620vo+OB3HmN2sORllXwcNVoXVNVMLHbygjw3iTtYkpmMp09vWC+Xkr5w4I6uIEHmd8mxIyn3ZZyj8SM8t1E8f73APdJZigDqD74HiBodMyKe49M533rnd/n2t7/GD7/7DVh53v/Wuzx89S1+6Zd/mX/r3/432PatWP5oxeJoilaB//I/+8/5f/4//nN8P1BkOcW04uGbrzCf1rz+2Ve4d/+C6WyBdTCb3+fttys+/uiG99//CBMHTK159OgRjx59xOnFvcQ93ZIbiaDyzqWMUU1QchAzmWG+WHBe32PwcvrcrJesb2+oJ0doXuH66gmLky2vvbnho48+5p3vv0fXblmvK7793e/w4ePH/O2/+/d467Nv07Sd5J8CoQnUZQVxIPqAtwNB7z34QLbykDbc1WaNMobFXIq/sQA75HWN92os2g8Le0Hf9I4zengPxwV9XOSD9/TOYr1nu92S5Zmof/UYQ5btlOxju7HrOnn+IAt3WZbozNAl/uJ40h5N1Y2WzF5rLUVR0K3XVGVJlYj/IQSyMiPafYvMWktVVORZxu1yyeXlE157+EV0psmUp4sDbdewXK6wrSejotveEnyLd55uuwE98PrrF8wWC87Ozhjaju1qic4Mk9mMvBALrBhVatsKXWIsusdC2llP3/UpTUHtwJcRJY1+TGVJfKls7/u2R9z2tkmfVhEs93ls/e8pAIcfO3RWsbvfO7SYlyPBxENfz3EduDtO7iIld+f++Hwu5ZBnGYQgHNyhaSgSmmtvBz58/xGf+dwWZzu0qZM4D4a2ZdghmcmT0kSatt2hxWUpaUuL6Yztdit7RRpXZVnS2YEiL3j91ddElZueOyvL3eHKJzHVOKZub285Pz8DrbDB049xh1qzvF2ijUZlBh8jtunRmfgMBiLTesLRbMbTJ0+wzrFtmp1P7Zhusl6vubq6orWWs4sTiuTDO9Ia+r6XwjwdEMrRcNkIp9V7MYMhCTrHVv9YyD9/BD+8J9FIApZQk6KIGYs8zd+RLyr8VIXMW4Cu63ZF3qjcR76TqCAr8jtpIXAw9lPXgii/A4ifqR5cam0LVUOpfQKIcJw9mIyqrsTQ3Vmqsk6ZzS1D4tXuOa7jmNx3oH5SesUf5/rUBSAqwwbLcn3LulljnRfCrNYEb+n7lvV2yUdXt+RFLshalPzb2XzKxcU5fduKVyCatmlp1w2vPXzI8fGCo6O5hDuj6fsW4+S0rI1ApuJQ3tMvW548e4pWiqOjY7QxTCc1r188ZDpb8PT6GWKaXDKbzpnVEyLJRV57gi7pC+HVbYetnHyciD+OF8fMp8eipMwU2nomkylPVktW7Zau7wnWUxcV08mEeZEzT4qtbdtjVUZIvnyywBp0DpmWzU/SRRxaKe7fu+Dk5Jimb9msV3TrLavNitVmnaw9Jrz+2hvoxJs63NQ2XYPRhiLPxXomywijGlGpXfrH6699jqpc0Lc907pmOp0CYrAaoscHS64KimSxMS7oEs4+7BZJrTWTyYTQIVFJRtoOOzPMVjwi267Fh0BuMvGqsn63mMWUmJDlBbMUvVeUGbYf0EYEAnlR0LYdq9Vqp6QcTVJ3wzBxhHhuqYjs1b6H6MKOlxX3yq5Pw51QymD08xuDJDmoEDg+nvLV3/sNvvbt3+bRh+/S3mz49u/9kHtnr/Nv/pv/kD/9C19m27VsmzWZLpkuah59+D6/+5Wv8Jv/7J9R+kAxnTE7mnNydszDVx/w+c9/lqzIyYsSbUqKouTegzep58dMfu/3idpQFCX1NGPbbBOPbE+Ud87tgu6HYcDEDF+KcnWw0jIyeYYNQSgSs2OKfEKZF9yue2I+wXbXTJsbFJYiy/j48ZLVuuXJ40cs17f8p/+3/5C/+bf+Lq+/+VnKakLXD9Li1FaUdFrh3ECza9qxe8+briVoxdnZOSHFGm63W6qq2m008j6HO2N+5JwetmpHXtHde7Zv7Y5jQBnhIXd9z2w2E4sW7vplHo6RXeE48gaVEOKHYaDrO9br9XN2TiNiID/vrIMYReiVBADKyGsZnLR0YxRfzGlVs1mLaKwqCx4+uE/Xb3Bxy3azpGs26CD85A8+eszNesvJeUFRGG6uW6pCMz0+Zn68oKprbK5RUeLERoHQ0dExGo1db3a+Y3kukVXDYHf2UW7wooxFJdU4WOMxThNNRCnJknZOTKDvtlw/mV/7PMXi8HP7rwnf7hC5GAv9HT80/f8Or2vHaXteBJIGyB9y3UH77+yzspqMhaME8knr0egMpS3dYFE2koXI8nrDs48/pKin1GLCxyhSGL0uhdPniEiLn7Txz2YzESGEuMtrjzEmm7Ee4+XgTQgynqoalBy0RxuhEdkcx65SSvaapLaPPojJtpdDYN8P2LYlr8SapLcDbd8RlcR/uhjIq5II9NZyu15hB4nk1Eo46UGx98hUd+/9odAmJpQ9BoUxid9GRKVD/eglGtP3jhSiQ8HH4W0Z7V2MzglR6FZlCkUIiF8k0UtClzG7sVMUxe49GvfGw88djonDsbXfL6T7Q1CUmQADbbuhaRucF1rKMAw4Byrlf2dZhleQlzl5kZGZjDwvkgjMJpqI0FvkPo6G+iNgAXqXd/1yCtMnjeefhBv4qQvAkaxp/UDXtXz89AnbTUueFwn98XRdQz8E5vOZQO+pp/76a68CBwNVG05PTrl8+pRhsLRdk8iScrOctzgPKp1M8izj4YP7DNbS9z3L5YrVZsXN5koiWLITyT3E8+T6KTc3N2ileXB+j/vnFygyirKUtmEc6PqBTbtlub6l7bpkxRB4cO8VTo7PKKucTIsnkndO+v0uSPCzD2TGUE5qZkVBlUvbNkQh3evUUlAotMmE9RCCQMBGIuoybZhMaqJSzIPnaH7C+nZJZ0UV1bcdt8tbZrM5R8dHIvBIiIqY8HZ45+mTX1JVltRVvTNfzrI8bZw5rzysZEPKMkS9aojJN0srI15MxiV7gERST27yigRpJxSkyCuKjF3UmE92BD44NtstTSvbvo6Koixw3pPlOVor+mGQ3nR6fWVZUhQ5OmZUZU5e5qAUTWxRSgs/JKS8xnGRGRcaOOBc7f+NerH4G/98cdNh91jjdWezCmEXgXbnezWoAn78/jt8+xtf4/LxE1aXW559dMVkccbf/nt/jz/x83+C+XzKttsQPNSLCT/8wff53d/+Cl/99V+nvb2lzDVHRxPO759y8doDHr7+CroyTOf3mNQzirIkM6UIOeqaejKjqGqJVKsLBm/puhZrB/Is36Ewkq4RGK0QfAgEEAVzlHFqVIaznugVJispqpqJqjDG0awytlpzHhRa11TVnGeXN1xe37JcXaFC5Dd//Vfpe8tbn/sC0/mC7XaLc5KeoLUmU5n4mh1cIUbaQTab1WoFCuq6pp6f3uHfxRiT35/fUR/GxXgUhYwbh3F3n2O8h+Piv7vnyJgNpJQCdWglc7ewNAco02F7xSTu4IgaFkUBETKTpc0ucQrdPo5PaSUG0FXFmDUugqpAbsSJYLlaC5+5yARiVwnRcZbgBryz3F7fcvnshnU3MJkfYbKMST0lzzXTaUlVixl+VVe4wRGVYjKbMl8ciQo8vZ/j7ydzJBCj3bXNfQgH3EV20YZZauvpIJ5n4tkYGBNcDlGL/T3Y/W33PXeLgxe/f2eoffCeH85jpe4W/IcF4OHneO7g8Uddz2/6d1+//ApKkw68UpgI4qPQWY5tHavrK85f21LGCSoWYsacacTgXviDCsVgHYXO77QdlVI7tfg4tnVmCL0UdHmei0E1kbZpRU3MvkBWSVU8vlejbVhM3LXR3FxoRSKaG+1NJ5MJoYkUQRCrdttIopEXY/7RyFiV8gOjgt9oQzCRtm0pykLQ/oN3fUSKo/wDpcxOXTty6VwqxJx3KTghGTZLpceLY+rg/iTlrEluFDsT7JH8l372UPw1xgeOc30sXsebPfIWnwcQdt2lRCUYQQGxsZK5FdMhQQ6Lsid574kG9sr4pJg3iZaQeIXj+7QfyQe2ZS+8C3fH7f8Q16fPAt6uyPIMkkpz22x4/8MPqaqaqp6gtaYfehb1nCorEhkyUhUlDx88lHBrYlLjZLzy4AFD29J3Hbe3t2lgKQpbJBd1CM7inKUqCmbTqbQYh4HFYsHl1TOarqEoMuazKUU1wRLY2o7L5TVD17LdLgmhh6h2Zs8ianBsh4bVdi2n3oDkAM86ijMjQfcGCpWzWq3QSidEsyIoR54ZqqLctUe9T6qgIeyjX1CQ/IxcFOKoS8KCwpRUeSUFolLMp0fMyjnbYUPbNTTrDddXV6zWK8paiLiZScVdbQhpIgOoGCmygtl0niKtApkRVZL3ikk9TUpKaYn7ZCsTgxRiQ2Ppe/H2U2OKi7XJFFUxSrK0UpRFLUTiBLVb6TvjbNhlnCotthFVVe7sDFAiOMl3+YtKEDalKbKCSVWhM5MOGQGT5WlDCmQHBaBi7Coe8vbSv3mx9fuyltI48eXrnzzeDy0CxktpIWA2/Zrf/9ZX+ei999isOprrgfXS8uVf+Av81V/5Zeq6YLCS9FIVJZvNit/5ylf47d/8dd7/0Q949f49pnXJ/QdnPHjtIRevPuD4/IxVs6WenDFfLKgqUWg+fXZNNamZzsQUvNtuqSY1TWNZb9e0bUOWS0KBFDaSHz3G9YW08EpkYEIyjKbtRLku73UkyzX5/ChFGRnqco61MKln1JOKsjT03QZne77/3W9TVlOyvOSnv/Sz4pmXNrMiy8iynN66F97P9bbBDwM3Nzf46JnP5hxPj4lwpyg7bN3D/r6NRV1MyPDz9+fw2i2QUVCHPM9pkj1TliVydnqucWMYx9ALi2uIsvYhBZ/WEssYUp52CEJvHx+vSK09UgF+qB4W6yQo8izdhxZUpCwy3NCSmfR6nMPbgWaz4fbmhvWqYdVZjtsepTXTyYKiLPZrEJGyqKhqj84yprMZdT3h9vaWrmsJ0e9ex07BvNtU5XWOIi9RJsvfs3y8HwrlxJR7GPbI/CjceR5N2SP1478/qUj8JP++w+Jv3Cxf8rl49/GFRfXJ3MM/7BrXhLE2kAeOqU5QabxJgaQwRKfwg5i2d+2aaTiiyEqcSwdo5UnRTmRZRtt5KPOdBdB2u5XuiD+gN0SJXLRefqbUWg5x3nPTig2QSar49EMJjWOHAvZ9jzNGXBV0hlbCDc5VohPplMRUlrggtjHDIGOt78bWeyQqSazJc0HLNm5N9F6Q0ELTti0TrfB5RgjZft6O5ttqf69DFCPnEbl31uKs3anJ1XjfU1H2/NosRXgq2BQ40pqWBJFKqeQwIqNgfH9Gh4pxjDx/ENHp0BnTOORg7dlTPYwUbVoBbof2e+9xVmJVNYoo+A4xptQspZPwZRTDHJjTx/0fO07rKIBB6Fyfdvy+dM36lNenLgA//PB9zs7vsTg64t69Bzx48JDr62uyPGd+NEPrHH+7ZjqbUlUVw+DI84LTk2OKutqRvJUyVOWEk0VNrjXvfO/7rNcDZZHTNFuurq54+PAhJm0qnr1vV5YXTMop98/u85lX32C9XFJVJXU9Q2c1V6sbXn3ldY4WC26unnH55CnXt9dst2uarsFa2dzyMsepwGQ6pa5qiixHBUVVFqjgUFFOLW3fMTiL0Zqj2UKK3LYjRiG3OufwEXrn2LYN1jrqvKMuxU8vzzOyIkcbTdd33G7WaC2ZjvnYRkURFMwmC0xuOJovUPci52dnPH78mPV6TYwwny+YVBNyk3Gsj1OMlpxqikx8zPqhl1auD7RNCyqjVynbNTPkRcZ2u2XbNAAUJifWE7pWbG4mk8kOGh/bXGWR705PdTUhM8ks1WjibLrjfVT1hKKqJN+3d5jc7KKVRn5hURSYVAR7N4AWLpa+U6yk4s5ooh5tXuIOAVSM5q5jyy8VhWkzuFs83N1cdhyTw5PXbpG628JS+sWs2arM8bHnm+/8Hr/3ja8yCxXPPrriZtXx1ltf5H/7v/9HZJlm26yJzlPlBcfHR/xH/9l/zK/+03/K+vaahw8uODqdc//inC996Wc4u3dBVtY0nWNx8gqL43OMUnR9n/wZNWWVc3Z2zPHJEe9ePeP4+ITl6iOubq65ub3l/OIiLWIJ8Yvx4D2VE3mInhg90XuCh2BFhKGJ2L7HKYuKGZPpKbPpGa5vePr0EoCLi3OO5lOmVclvfOX3eePNz/P973yL9XpNWVb83M//SZ4+fUocJIlG58VO3Tte3nuePL3k3tkJ86Mjbm5veHZ1RWFK7t+/j1H7/N/R+kU4PHvrlhEFVEqRZfqF+3N4/3YqYmTzGBfscSyNBeDoDwh3W+lKpSi49Lha651nmTbi76kQv7hDVGFsK0u0oiD/48LvnAUk8rEqCoL3bLoGlKKuSqJ3KO1xvURFNZs1Tx9/nAjqBdvthuVqg7OOLKuYTBcokyeTBUVRlBRHOVGJUrjveyKebSvWQyoJLaR1vecwbrfNrqAzGfgg75nJEsKZoq+GIeK9vPf74nssAveHsxeKqP3deeF+ydzbb3bPo3x/6DW2aQ8QKAUpyWXfTotxfG1/+DVmno9XGPPuotAovHNoo9BGpZi8gUIrbi6vuL58yvTomEm1oAsBrTPKQgE+dbZyNmux+bJIJ2sYhl2a0ljAKRSXl1eg4PT4mKwsd2NrLPbH+DPhk/UUufhkDlZCCJquETVxXsjBJQiHrzQZsW3Evze106tKhCnOe3JtKItc1p6+l0NIiMynM4qiJNNGuIrDwGy+4Hp1vXuvhPvmUjGj9yIOFbDOo8bI2DjSO8S+LCJdKZRK65YCo+7kzY9jYQw/iCFgkW4cB+PFpPjS58fPmBQzHiJH2scLz8HePuZOByGh3HvKw9hKFgN3ELEJ6fXJ40rm88izVWh0FLssaRdL1jL68MBz6HX4YifrDyvyPgld/6OuT10ATmc18/k88VpgMp3xV/7yX+F3vva7LG+W+CDtJI3ilYevUqW83LIs6ftWHPGzIiFZis6KBcxnPvMWV1eXXF5d4byn7wfmRwsmkylEhR96Li9vOFmcc3HxkOOjE5yPFEXFxZFkilo3sG1XLIqSqDQPXz3HP3yT61euJLmiMMmJXGOUoWk3bJo1REVZVsxnM45mR6jgiViWq2uGdMpr2obZdEFV1cJTmqWQ7nRDBmsZupbeO1brFdeDZVJVPLy4j8lmLDctwUW6YaCzHvA0fZfIxSkqyhgGPwgHw4s0/vT4jLIsdwVg33YUKmO6mFIV5e5m61LjgqfrOrpefNx89PS2p+/Wu+cAica7urqS58xyQsjw1qYQcslGHHlXs9mMybRGKfF66oaOdnnDZFIRiwITM3Sm6fuOruvQWpFnJXmWc3w6wyaTU+f3UUDjxguQmVEur8Tqwkp6gbee999/l77vqasJhSm4fnb5wuBPoPxLF/Y7Bd9BMfhJ3/cJX0wnvv31+PH7/M7v/Br/7Ff/CYtqxlf/+99DVxP+7L/8Z/mH//AfMqkNt9dLoo9ysChL/tk//2/5T/+T/4B79Yyz4zlFXfL6Z97kp37qS5wenYMqMHrKl/+FP8XNdsvy5glVWVDXtZjYhyhF//Gco6MJw9AyDFMRcWw2tG1DVRbJXV/tSMjj71eUGX3vwSNtfhTdZk2eUGGtFS54SjOh6wbqqaCJ3/3BD/ne99/jV/7GX+HqyUc8bT7k9Yev8eU/5fjq175JXs7kwHZ9xSuv3mc6m2AHhw+BwTomZfFcK02RFxXL9ZbMZNy/uC+ImYvcrpY8uLgPMWCHHtBJjTuapqodPWR/kHzxhCyFYXZnwVTJgHbbir+iNjL2D9GA5xGCw88rpQhG79s0CcUgIZCSIJFQca2YTCY7IZVSmjzPdjxGn+K+qiLn3skZV+s1gUDbbdnSYdwFx+czHl1dcnNzzXJ1y2w2YzWseHa1pO09yf0XgmY2P2YggM5TB0CjdaSqJ1gvhP1ms5U4P5s8OdFM53Our69p246+s1iv6AaHT+bcEDBGMSkLdC6FfPAhFc0GpWW9ETuesT27t3E5ROVfvMbP7wfHIbL7SVwmrdXOX/JOO5B98blDu9NhcCxMX/Z4z1+itUucr4Qj7mK6FGmjzzE6pyhgiAPWDhRlyc3lhj/42rdZ3W75zOe2zE8ucMOEuq4p8gKtRShQZiUkjmVd1zvhQQhCe3AhkBXS4s3zjNV2g2H/vpydnQF3aQpjXvyIPgPkJqPIRVRZFQVd27LZbHB5DlozmU4YvOOjxx/TNA3n9y9YLBZMqloMptMhbCzGt1sBN5y1KRxJePny+8taVWrFatXigicbkUbE6qW3DqVLFHHvn0pMY0gKW4zBWp9CIV5cs1UqwEblfzASbTfOUe9FP6DSnnKYEnS4zo9q30N6yDg2/GF7+HC8pD+CD4C0drfbDU0jcyDLMrwfsDaiEUWv3ENDVUjspTyPZrtas15u6Qcr1Jz0HCM3ebye5zf/Ydf/L+3gT10AnhydM6/E4TyGiLeOs+Nz/vwv/AU+fPSIp5eXOO95/dVXOTs6EphYXh4xRlarJRrNbDLj5PiMSTGjtz3z6YKLexes1iv6vmez2aCNoe1a2ralbzcsr2/JVcnJ0RkRhc4N0YifYDcI0X61vSG6wMX5fSqToYua4rzk8voaCJwdzQVRUIinXgjJj0+SS6ztITOs1rdsmi1N3zH0AyHA7WrNydEJJ8fHTCZTtNM7ZVAIURIQbMZgB9qtuPBHFYgG2qYjS95SaGmfP7t+SlNWAssXBWbMM/QGRSayfQKZzqmriXBzotqpPTfbtZw00uIgdjgem4QyEhu3Icacuqqp6oqiKFhvt6ntUDDGqEU8k3pGjBHnbDotyeamtLQ7ttsNV1dXOBdSioikttSTWiwtnCXPi+TDllHmOUVVEDYB7H6QjkjieDkcKoLvepwXw2lrB54+fkLftHgMmJA8llRq56X5uDsxcUin2F3PE8qfRwZ3PIs7bavDlpWIeA6v73zn23w9rDG94Stf+SpFWfM3/tVf4S/8y/8yR8fH3FwviVFT1RXWW977wXf4D/+D/wtHZc1sOuHk5JiL+/f5wud/inp6RO81F/cfcO/+q1zd3FCUFXVd4Z2laVom1QyJGdJM6ikXF/d58zNvcnO5ZjqfsW22LJdLisT7KapSTpYjgTpEgrWEdE+JKZIoRHLJXsOHdOrtHGVRoo0GDa+/+Qaffestch2YHUkc3PXlU958601sVHz08VParqNvV/zf//P/hH/nf/G/JGpDP1iGYMlCuFOci/AiQ4msG+8CQad7azSD7VEJParKAuccRZHt0IKQfL3KsqTrxEgVd7fNPN5X59yBgbTk1xZFwaZtdr534/eNvMO7nJ8RjUnAcoi4xOUaT/UhiOFv8F7iIZUC78izTHzSYrJ9SY8nkXGaPDOYAFEnT8JULMagODs54frqY66vLun7jiLPmR5NeO8Hj9isB2xUaJ1TVDld32GyjMIoirqink7I0EQratBts2WzWe/scEKIaC2+o33fs21bunagbQbadqBtW7yXaKsYk8dnnhOUpmu7XasuzzMyKwXKaAVEFH/AHYoXA/uJ+umv5ykc43V4T/aIHweFwgFtQB7oJ3/y8fnTWrI7GCiDeMQpht7i1Gh3Ix/OQhZzVs+2PDUfMylLZvOa6M/wLhKLHJNprPMiBsgzxj1x174PkTAMbNuG7ZUgwhcX9wjOoaLQqLIso0+io3H82eTS0LbtbjwezRe4IJFjKkoCSYbi8eUzrrZbKTKrkpOzU8kzXi55+uQJbdNw7+QMhaZre7IiT92gCAxcXV0xGptHokTVuUC7bWiNIS/loFAUBTpEnPcS83ZQyOzYO1phO8tmsxGkUQsKmGWZRKWl8Xp4RTFNFA/DLMfpQF1V1FWVUnxiKrvUnbn9PA985Ocejpfnk2UOx8OIykLaLb1FqXz3XnjvISG8OkJwCmvlsYpCDtnCH9aiA1DQ22HXpRm5tNHvGX8vgB2fcCh6/nX+ca5PXQBOyqmkVgSBepv1htlkxmwy5f7FPSaTEhcc945PMWYMcxGuwfX6hseXT1Bo6nLCsuv4/OufZ1LN8NGR5TlFUXJzey0ctsykiKYMF0uO751zdHyCKTMG1xMVuG6g7xq6rmXTbblul2yu17Rtz8nilMX8mOlszsnxMWWeyak/cWVykxI4SolIk0Fg6N2W1nasmw3bpsGmdtkmtLuWknN7lV/0Ee+8+P/lObP5lNxo6rIADV3f4qMjOPE1s66jtQODA2d7Mb3OC6rJhJqYQsIztM4J0e42iP0RF9q2pUs2HjHKybgsCvFvGloG22O9xQeL0oaow44/0bRbAoKYhiDGtpN6gsTICcnbe4dN0XOj94sficxK4tGsFdsfHyxlPUmQuhhcd21LHgIqKaN8CJjMUBZChO86yTMOIWC9lQQJZxNPTax0Htx/wNXH7xN7T8ST5bLpyoh6+QQQypG6w2+CuxPm05yq9oUiz3uR0jZbnj1+wtN3n1LpKb/0N3+ZL3/5F7h3+pCucwQlCtsyMzx+/Ihf+7Vf5dmTx7xyfsHp2SnnF+dc3L8vtkGmZHZ0SlFXDK4nxAHvA0VeiK+ldwxWNgPvA3U942hxQlHWRJYsjha0bcvt7Q0+oTMu5dTG9LuqCNE7tNoT40WYU4A2YpUWAzYG8rSnusTdm08XZMZg+5bj0wvKuiZqw7OPf8Abb76K84Fnz66x/ZYf/uD7fPObX+dzn/0iVTmhbTu8usvPi1FsIorc7O6ZDuDEUIvb5a2ImUox1L26uubi4kFCNkJqt+w9vLR+0QZmfJ7x/gs6IC1wnZSBO8J43HMKR57QiKIcKu5HJMkOe4uOw4/gPXmRQ2qp5VlGl7KSd4/pR/GbFm84IDPCe3TOMZlOODuZomKgbxv6oUXC4QOr9Yb1eksIhmGwNF1Hbzt8lHZkVpYicMs0BsPQO7Ho6YddEaZGRCTCMFhublb0vZM88hCTAb3YdBSFoFTERL9InROlYioQ3a4Ik7b6AUVjfP9JDxHv3pc0RV96z/ZzT9705zfuQw7g7mde1ttVh92Bg0PiJ9LpX/YaE9478r+iZuht4m5Kwe6DGONLUojGOEe3atiurvFDgw8DbdeDVigte4/zPaEXi5kxUUnez3297ENg8JbBWXRM3LK4t7IaowTzLKfvOsqqpqqqXaGS5zmuHVgtl0QvNJTciGjJ9T3NdkvrBurphMlkIj+rYBTbrdZr1uuV8P9Sq3m5WpKXBUaJV91ojizzUkCDkFJ1ok+3JRVUO3Q3SIyM8Ew1dhh2Pr6QuHoJNXxZKTNm9KpEaXLeibI2ofkjMHV4lw/FXSNSeJg8NL5nI7q4H4N3DxqRmFrDnmyc94kjHkMkLzK87wlpTR2NrWXb3sdHkt4PicEMu+G7oyaNQ/gnLOj+/4IAWu/Ztq10xWLEBpsUdZrFbM6kKnHRMc1qItJW6gfLut3w8bMnXK9u0SpDbxtutxuOJ8ecnZ2gMiGaagV9VyflU5EW5YxiOqHQGfN6hsk0/dAweMdye0vfbCF4Ottz26wERewst8s1J0cr7t27oJ7WFJlis2lxPoiBZlTURUmm994/WmsGZ+nsQDP0NMliww4WHRVNm1HkGSCCj+lsJrl/USZPVZacHJ8QpnOJ6tFibhsIqOjkVBak2AkOgvN0dsAMPTZKamKdK8hLskw8B50X3kkkefwpgYqboWUYemKQSDltNE3X0LQbXLBoYzCZxPGNpHUUWGd3vAfrPXgnRGDnGBMKQEQgLhVuwnkxlHWNjh4Uu4E/LrBlVRK8RNxsNhu0t6gsT0WdJqfYRfa4lIc62voMfZfaGJqqrJkc13zxC1/kx+98h21/S/CSJLLbOVIXbregqwP39hGO4NCTbL+J7BCCdO0m2nj4urNhhT2Slq62abh9prh5tuQX/qW/yF/+q7/E+dF98ErI/KWYvDZtw7vv/ojf/Z2vkmcZi8mM83unXDy44Oz8HtrkzOcnLI5PiVqzbdeURYFzHSarMCYjRs/gOowusVYKwPniOJGpxUKkaxtul7cMdiAzBm/93iA3bfz7ojedjFMMISSz1ijEfp0p8W+0Y/qGEMO1yZlUE6rJFOvh+uoRdaZ4cP+U4Cwff3zJzbrhq1/5Lc5O7/PKK3Ph6nl35w2NUdpcWhWSfmGSR6EXtKjvpPWb5wUMA1c311QTOVzoxMc7FGoYsz89P38dFnG7AiIqyqJMAesH9g67TX8vkNgVOGkcjK0srTUEKUaMEWVjZsTiQeaoKP9hrzxUBxu4RqO0Qakg0VNEqrLg9HTG6+cL3NClgkuGsx0Gnj27YbMV82VrHdutWNFMTUWWZxRVRVaIrUsMkoLUNo2ojYHUqJC1wzm6bmC93TAMnq6zu4zwvKyAQFlWid9mxeLJheRrJlyuoXf7Ju7I/Rt5u4wiq/HWH6Lr45jkpVXgy6gahwc5npu7hwVgPITkxiJyPMQdfv8fcu0KT3knGf38dOJ/eRexymP0yBGTx/MhEL0juEi7aVld3bJZLSkX2/Sa5NAalacdWvJMRBVZlu0EGyNiaYymKHIRlzhJnsqNIEhSJER6O4CFIpe9oSQK+qQlccIYzWa9puvFYLjrO1RZUZclBuisZdu1DMPAfDFhknzqqsTZXa9W9E7oOHmec6IU6/WG83vnFFkOQcbG0A+4we2K4ZAOLDGK7czh2631GAknli9aqR0lAWQdIrVwd4PlJTdovM06LfNZngkXPibRJezQusjh3L77eIcCGtkD9ntLZM8D3P1sZLffGZ2llnMSCSrh/drQIgbjOhWrpLU4JYQYWUu8l3noQ9iPVT45HvEFbvpLvu/wc4eHsU9zfeoC8N2PH6GU4nSx4GQ+x2QZgx+Y1hNM1AyDhmT9UM5L+nbD49vHfPedd2jagaPZUcoMdaxWt3zt61/h7S9+gVdff5UiE0L0op5Snd6nbXt66wjAZDIVDyXbEZNn3+VmyXs3z3B9z2k1JUMxbIWY2nY915cf8e6TD5m+X3F8dISK0GwbUJp6MkGZjOOjY2b1lMVsxmwywbmB5eaWzg7YEHFRo4JnaBuOFgumk5yy0tjQ8eTqkqqtOJoeMa1mGJ0zMzWLaoFKBVI/tHSuRxPQupT8XQoImiEOcoKMwrtxmwHnO2w10OUFCinCjJHotNHzL8syTGZQPhKUxyOS/FWz3PnCaW2YlsK/rIwogLWSk0mWlcQonk4osNZxs2nxTu282Ea+kvU+LXIKRcZ8cYJRkgiSebHyKcuSzBTUZY11LvmIRS6vJWA8y8XTLnclt8sbylI2YOcsbnDoEGnaNU3foqIhDoGjasrP/PzP81u/99u02xa3la+NCHlaj4lhbPF6IuK4LqctEOn96Kv0osrwZZ/TqVhS499VxKm7BeDTDy65PBl4/fOf5d/5X/+7TE6Osc2A76zEhQ0tJ+cnfOtrX+MPfu9rPHn0IQ8f3OP0bMHF/TMevPKA4+P7bNvIG5//IptmS9+3EMXUtB88vWvTPcgYnIWg6AfHbD7j9OwI53tQBdE6tn7Dcr2k7RtMNHKw0BEfBdFRCoIydxNQdlYE6VQdIrlO5qpB1OMRsFa84abTKT4aIobj01d46/M/z3e/9c+5fz6jVopnHzxjc3nL1377d3j78z/DYnbMdDblpulfPMmHiLUD5Lk4nkSNyFA0SuU4D21vQUW2Q8O7j35IJLKYH/HFz/00/uZWijpSC9e+3AZmLOL2v7eIBMq83FEQ0vqLUoYsEyurcTwc8oqc98nE3aGMJgxx187uupbFdAaRndoXLZup0WaX/7p/TXIw1BqUlrn/02+9xRvnR5zWmquPf0hZTyinU9rNLe2m4emTJZsmx3MtiTzbgatnN1x85g36kFGWNUppXO8YQs/N7WUqgAWdaocB5+W9aLuOpm2JKtK0PetVizYZZVUyndW42pCbjKurG5brtbTdAzRtT0wm0oeohdAkxshE4VsarQghuRSou/cF9hs03N2snuf2PV+4yeEtip/c+L0jR3M3vOIuR3YUxuzRyD8KJRnNqGXjzjIRxMToURGKsqRtG3yAqpIsdWsHlLZAZHAwXHVYPzA//xizOGMxC7gOlo2nDQ6n1pyXZ0nANLacFZGAUol3Oak5KSb03Zas0JSZHJiiUgzBy/lDKaLRoDKUijg3UNcTFvMFZZazvL5hcXSEyXNa77F9xyLLqY+OsTGiNllSpVsyrchMTmFyrPW4tmd+doxfCYJI8hZdrdeczBZkSuH7gcsnN6zbFWeLqXQRvN0JMtx4Bw8KcCmKxEtPGYVzkTwviVrjo8V4RdCIZyaR+Ly+a+dKAXhHlRdUk4osL8EajPSWSQRZILywxh+OAeHuSofS2UQFISZSlAhZfIyY8TSjwahMYmUJDIMXcYuORK3AV2gvnTNPZAiSb2+yOdEYvLZ4k2GtZhiSACQhhHI4fDGn+Ce9XkZr+qOuT10APru+YjKpqMqcIs/IjGHz4ftcnN7jaDJnPp0yDAO361t+/OgDVCGGxPN6RrQbUTa20kot8pzT81OaZsvt1TUKGWBPnj7l/sVD3v78T3GUFwy9pBoYo7Ha0G86Vqul+Af2Dbe3t8S64/75OT/39k/x9NklG9NRmqRkGjoePX3E7WpJPZmwmMzIeuHz3Fw/pS5qjhdH3Ds7Z3G0QGOosoo4NVRFQAXH7MErTKsyQc2GITiapsO6gAkZWTTMZmL5YEYVmVYJWRvwwRKjJteZpJic3+N2teTq6hlN2+KITCYTQLO+Xe4Kq6qqyE3GoHRqpXpOp6c45yjLGm0y+q6j2TQ455jNZkynEiQ+tgRyRbLuUQzOkmeKqpju2jeZMmQmk/c52Q6Mykg49EJLiEs0HE9PGMUcRSEEYOsdbrsVDsriCIUowa21WJfQT2dZNtsdfO6sk6IPxdHRKXVZU2c1eVbgvednv/QzhNWGRz/8EYXJd5M5Jg7ZiOIoUntB7aF5GE9FdyH98Xq+GHzZgUmjyJ4vGsucL/3pP8m//+//n8mrmtV6JZ/PFTrCpChpt2v+P//kn/CN3/sap8fHnJ2d8tZn3+L1N98iaM3V7Yp/8c/9RZbLFQFpaerMCCE6RRcpBdpoynLK0DtW62tOjhecHF/wxS/8Cb7Zf4MsQDtYmk3DkyeXvHr/NQgeEcIFcLIZHrY9hJeyd8If76P3njZleo/FioypOSDmv0qpRNYuOL73KpubZ6g858/82X+BR//VPyY3kd/8jX+O1p5f/pW/yc1meQdRVUpR1hXr9ZKQXofWkrFbVRXRe7quT8pE4fkVxZzl8obbmyW5Kvmpt7/AarXa27aEu21mncaAtVZ8/9LvMR6mDq1fgB0X6FAIMo6f0TR5/LDWigmtMSiEj5tlWfIAFdQ0yzJcek15nklhmOZQnuegJX7LRUs0iqHr+Nf+zt9h/ewj3vvht9B5Tjb02HbJ+nbDatliXUvbd/R9seMfFdkRDy7e4uNn13TbgaZtWK9XdF3q0CTOcFTS7cjzCq0d3kfsENDRU1U1N9druk2Du/I4N5AVOXU5IcsKJvWc5XJFN1ick80vBElJyfO9ndPz/KTdXPsj9qAXiPZ/xKWU8Ayff64XVP7qj37uT35N6bnSv43Ody4EpKK/bbvEL0sIbxTqTJpRbBrHt77xB0wfnHD6M/cpq5Kug3615eTimCwrQJkUkSnjxrpAN1jWmy3r1Yajk1Os7XctYUJkvV7T2YF6MmE2nzGZTBi6Hh88VTmhzEvwntvVhnc//IDF6Qn1bEZdVegQMGiabUN0kTKT7ldV1GziGu8DTduy3rSEquIz999gNdvwbHnNdbfh3iuvsL28xPa9cBqrApXWjb7rcG6K1tW+wzKuvSOvLgRUpgVpUyRD6aSEtgOefdxgiH7X7n3JKBCaT/pXnjj02gjNwsVwRzTzsrV/nOeC9h+k22gtNk0hpHutd2DAzpRaiS5w6AcRxaS1RVtLXmiCVahUNOY6Y1LWFCYBNoODQrKkpe3/vMfs3f3pj9sC/h8NAfyTn/8i1WxCnmcS/u0d3333hzLY6inzyYysyLl+doXHc3Z+xtH0iMnrM66eXRGjIjvJqeqS6WzCydERQ9cTQ2RwlqZr+cF779I5qaxfuXjIxdk96rLgWXvL9fKG5e0t6/Wadd/SD5aiKMUIMyoyMh5evErvrFTgdqBptvS+58NHj0SVai3O3ZKXJVmZMcSB6+Ul22bFveaC07NzpsWEKlcEJXqeo9lEcmyD+DMNTcNicYz1jqZpUNaTm4zpTDbWrMjIlMI64VB0vaMwBWVZUScj30lVsZhMGVKrtWkbAlFOR8hgsE44R0VRpM1S4wZLpjTWRwwZZV4TK4nUmVRT8ejLxDwUYFKWknfa9wz9wHw6RykthtYhEk2kyGBQe/Wk914MaxPUP2788/mceSktAznpS6EaVMRu1sl+JGDyjOP5aZpczc4dPdc5x2fHu81U7GMc03rKZDqhyEpyLe2+29tbyiJnNp1S15NdkkJI7YUxyeGw2Ht+0O/5RC+fMIdF0HP0IgBMku0fXn/1l/86n/nbfwNdZHRNgw4QsohP7Y8HJw/4T/7j/4gf/+AHBGuZn865uHfGw1dfBZ2jTM68nrBebemd21mZRBJpOZ3oY4wE6baTGWjcQLNpcYPi4vxVhv6/ByqMybGD5eNHj/nsm1+g7a+TYEZer2a/mIxcnF3b6fB9U2C02vG7gJ0KULhwwg3tOsvR6QXVJPKj73+bTbzhaFbwxhv3aPuBxx+9x/e+f8rbX3qbVx5+dp9Vnd5eDxR1JT5lMVCVFWVVpVa0WCtFB8pp3nrrczTNRlIRuoH33nuPk+NjjhdHwh8CTP6c1UwQf7WRKzgKQWDv6/j8ZvD8gjka6Y6f88FjB7GFyVOrVyEt6TKh9VmWoUleel7GlzFZygV1HB0dobWWVAU3UFcFje35zGtvMC9qNlEQkqIuWd0+ZX27ZbPsaVaebRNAh8TTE+RQ5y2r1YqPPvqIzWabvDOTRU6M9G2X0L6ethmIMSRFfoq0IqNtLdNqSp6VckhzjvW2Y728ITNZoiFovAM7OIYgKsUsyxl5TXfm0PMcKuKhQ8c48e605sbr02xYL3uO8b6+yN/ipf/+pMdN3y0HASMZycZkaG3w3qVxAyONQlBkWam9F+GScxblMlTMcY3j5qOP2L7xWYpyRlFXLNSU25sVvvLMpjPyvGCz2fD42dOdwKPtOnrraLYdppDeRp9EXIvjYybekec5VVlS6IzeNXR9R2akg2YDXF1dcbte0etI0IrMaOamYLlp0Lnh+mbJ5c01i8WcRT0lBrDR4XzEeouzhuW25dHjp3z87DGboaEwGdV0QlXXxOCx25ZXX3uF+XHJdJKoPU7mnAuj8TU7juzYUt3FmymxIrLWShFsDhT3PqZM3BevGGWOZRhJI0mc3gBCeYqpYBNAek/7eW4A7A+Bd8evURlB+934JY4HctEtB+/RuRSGwYn/n9J7RbbRmqAiIVicHwg4OYyF0UtXY5tmF9333C/HHcj8J7h+koLv+etTF4CvXdzHqshIsrVeMXjPcnVDN/Ss20ZujleiAA0RY3LmswWlKfBBTv0mMztRwnqzYkgoUdO2mDyj6Ts+evyYrutZr1YclSVhmtF0Hctmy3K7obcWR2TTNOACk7yiay1HJ3MmMyPCCa0gBgY78Mq9V9lsGrbdlqbbsN6uIQonrrOOpm1QOmNxcooes3y1oTBGClutsdYRklIuzwpcjPh+oPWBbbOhrGsInsENySi7wdqOfuipyykg8XRVURK85mh+hA8B6yxFlhMUrDbrZIYcGbxkBdZlmaTvETXC6Gnx0VqLEXdRU1f1ro0lJtyBoXe0XU/bdQzOEdi3p7LktSRqyykh+RruDJAjiauVVL25EN2VFqjcBQ92oO1aluslm6bBB09RlGiVCa8oC5I2oTJms6mghcl3zSfLHzLIMiOvOcjYur6+4umTJ2y3m3ToS2XxODHZL/7j118u/NhPjOc3rHFhUOyL7rGoHFsNKt6dWKfn59x78EByHNOk9t4TdaQwmu16zff/4DsMXct0UrGYzbh//z71bELIaurZMcfHJ2IBEANBK3QitIyG4vtqNCRGUhJ4WA9R8+DBA46PF2xuO5SCru344P0PiH8uudlHEX8IEJQ8Fw/8716G2GiliSogyQX7mLTg/Q45HH8+z0uq8j4n59fYfmC7vuXn/sRP89u/9XXs4Pn4ow/4/a9/jc+/9aU77T6IuODQmUF7ScgZ3LCLQjJqTOWQhIGqLCjKmjzPyLOOzWrD1c218J2UAiUNm8NrLHCzLLtT5B6ie89bvggCKhmlKnH7xqgolfgG4gsoI0/+rqmK0exZ3utR9jY+h9H7fNOyLFkul2itdwk4AXj99TepcrHLiTodvgZLsJ5227JetfS9fK3rex6+cs4Xv/gmFxdHfPTxI2IMOG/phz4tCUEU4U7QPoUgTV1nado+ef3J779tul0rd/8b5GgcwyCekVophkHi4fatKrV731928Dq8Fy+D4j4J2djx+Hj5NvhJhePz/KfxKQ+Lvz+sANyvCaC0lnx1c7d1OK6Je1WpIFwhCSK0FvaaipHoIr5xrJ5esbx6RlnPqScGExWaDGsdbduJoKdpRPRnzI6CU5YO76QFOaLWIXXNxrbx0PW020bGaV6wWi5ptw3TekI9nXJ+7x5OCadQ+HaW3ltMJtQWnWmhARlNOalFRew9ZVGwenzNV6+/xuAlGrTKMrquw8eMrCzItb6TgiMCy2xH+9zdo8Rtk/9G+sD+a6MXHuz5sqMq1r+kaBvX6BCF8qP1PiVoHDu7ITeu34fIcHyOYrAbGwdjJ4EBY8GqovANtWF3iJaAAieeoIPE7SktQrORA6sQtFrG0fj6FZmRpBfv9p21ccB+Uvn3MkTvxX3uj982/tQF4Hw+Zzt0uOBxwTE4J5uvVpjM4FXgZnXLLJ9Q1JXYeliHrjUnR0doLb5xIQb6oadptlzf3jB4x2Adw2ApqwofPKvtmr7vWS1vmeclJ6/cE2QvKdZ0XjApcmwKnnZeeupFUZKXe8QsJmXRw7OHrNcN62bFsrnl2dUzUeVaS991yRahY/Bi0polhWFZ5Pt8xSjWFYLKZOQxEIuASb/PYAfQsnk0bcNms8Y5yzD0DJUn+PGEaVCpGM5VShZQQprv3EC0ssnYXvhNtSplAqUEAdKiMJ7mi7zYbUZKyYlljKzrBlGitX0vkvy0gJdFsWtfidI4SsZnUk1578kzkcvneYpty3JiRDhFbiAEjzGa7XbLerOiO9iEbNcLPG8ydAEoxfHRiQxUNZBl0uKcTgOdFV83n8jmg7csb2958vHHrNdrfOKNxDhusiN59hDB++QT0IuLQPo8z/37Oa5IfAlvSCL2cqx1GPaO+Uorsjzng/fe56P3P0SFwNHxnJOTY87Pz2WhrafMFyfM5scsV2tBxLwDdGonmZQwQFqYpCCJUe5F9JrMwMnJMYvFMevlY1lI+47LZ5eyACXVqyxWkjEqSOx+w34eMdm9N4xoik4xYEq4t2P3Q8k81xjKYsrJyT22q1tWV0954/XX+ObXv8PQDNxcX/G9736Poe84LABiFCFFXhiyIhNjXe/pU+Z0ljbdECM+tV8nE+G3aZMznU1BRbqhE+N2IMQXlcYhBCaTCX3fpcPSHsUNwe82GkFvZAwfvkcgm9NoFhujHNLGnx9TcnRVJ1FI3BWBxL34ZGxjjQXpNlEk0GCyjEWW85mHr+6Uj9rolIxg8banb3vazuKDZnCOrFB88afe5Es/+znyPOejy8cUlWQbj+heVYga34Wkg1UZPii63tM08jtoLaa/XS8ijyytAdY6ggdjcpwLO36l8EL3G21MaKXMoZjMpV/coCLPb1yjNpcdEiiPQWoZxkTtULuN+M78i6MIYD+fDz8+CTX6SS6V9rPnbWbuGgOPBaCgf6OaM1Pj8heIA2wuV9xeXjI7PqPIZwQrPrgKLwK+RA+YTibolMAkHpqe5XK1P1ikA4xWYsAvhtxi25PnOZOqZrlcsvUNPkTOz8+4f3Gf681SPCgzI1y+QrJpszJnMpswnU3RRlOkLlHwAZUZ+rbh3acfcXRyxPFiwTSp8tuuoy4qirqmqmu2mx4fPUoVIsDYoXx7kCiORtqoOy3OGKVDN3YczEFhtjsIf8I1KnK1lm7XoXJfoBEBfl68t3eLv/3BNj1q3I/P8evye6WDdPoelyhN0ga2e26jGsdDGulG4gBJ7w1KwJORzhXHNWN8fdxdk1/6u7/ka5/2c590feoC0KkIRYbtHZu25fLyGcTI2ckZ985OIYo54u1qTT2b0XQ9Iazo24FXLu4xn4s5qQuBWMCjjz5guVmjMskNbZoGo4QSHlSktR3boeXSw60bOD5ZMCunTIoJQSuOT05w919hu1yigxhVV1WRDJ/FsuPJo4+4f+8BdT0lx3BULZhPprx671VcGEBD0zZcX13zwYeP8D6QIeRmk/r9Xd/Rdz2bpklt54IyesoqpzqW6KzNZiseTXlG73oGbwkKbAj0zmHXYk+zacSFfVrXEmadSTu9yHK6piUzmhANMXj66PEBWtvJ5HJS8M3nc2nDBAj4lCs82S1Ssicmy5ro8Dh8FMVtCI7oHOboSPhQwHQ6ZdusyDKD92L0KgbZJZOkwhw3MRcDV7dXtG1LDI4iRQh1XSOnNiJ2GCjJOTo6AqAqs9Tmc6w3W3orSuSyKJlUJVjIsxyjIr3vWTctm/WG5c3Nzt9qRFK0kaI+hID6hEJm/HP/tf0YPtyQXmYnc3i9TAUcfSA6UUCTCP/aG3JTkGcFv/2Vr/Ls8RPunR1xcnzEg/sXzOcLeus5mR9TlDVt11MUBb0dsLYneIXKC7IsJ8tGtCER3rVL1iIT2WFwKGvI8pmQ3IOXosH2iRsq/o52sImnlKVTdtipvA+RjEObhCiGgGgjBfWQLCxMZvbIR0RQQZcxnRxzcnzObfUxwUXeeO1VfvzhY1Zdx9PHz/jRj37ILxwUVTEVXFhJHzDGYAdL27acnJzs2jsctFWapqVLRe1kNuOVV16j75rE21Ho5zZ9awc2mxWTSbXjqe0W9yh8H6Ukc1xEEi4VgHuUdIzpAvDBYdO8UXr0mgsJbUgfWkOUDcIkDmKe5wx9T4xx14Iec19tcJigeOXsgvP5guV2iYsBbQxD63De02xXKXEkwxRw83HDF774Or/w53+WV1+/xzs/+C5d61hubsXPb+iIBKp6ys1yKXYwNrBZd9wu19wutyyXK5QS9MY5T55LhF3v7Y4r2XdjtvFeEZ3nGdZJmoUyBoVmGNLmCanVt78PuzkIHFD2BAGCtE4cXM8Vj4eIx8uQjsPn2X2MnzsEn154+JcfEsdD4IjDKDjY+Pft43G+HLYUD/mhKtOSE6s8WmesbzbcXD7j5MFD5ov7DANkdb6zb3HOUddi4VIUeUqWEOuvNhMgwXYD5HGndi+yApvswbIso55MmFY1VVGy7VqavmW92TCbTVlv12i0HFp1xkQburalqkqKzDCpK4zSGKUp8wIdBXjQpeLsZMHDhxfMZjM5DE0qtss1ruuJlRSBbWMJwSUgRSgkwXtiMusmBLGMSX61HL6PMdJ3wu8fb8s4P0bayPP3fAT2Rq6eSRZqJlGXnHMjLj2W6HfRvXSN93Cv5B4L+oQxhijIH4o8Ifwu2ZQZrfDWEUgxds6nzHhPViqcDwJSBXH1IAlaAuCjtPTbrmeMFHx+LO+dNQ7b1J98/XFRv8PrUxeA16tbfu9b32Sz2VBkGWenJ9RZwdAPvP/jD+iGjs4NkGmeXD5jUk0os5xMwXp1y5e+9NMordi2DZdXV7z36COBlI1hXlUcLY7YXC+FG+YcKjNSHALtsye06w1f+MxneeP110S2PqnJc03ftyKHL3Oub58yBI+/9vz4Rz/mt77yW/z9/+nf58H91whoxEiyIARLndcURc7Z4pjX7z/kc6+/xfWzayGRhiAFnFestw1Pnl2yXK4JQdzYq6piPptSFWJ1kpUVKMW2a3cB2nnywcvzAqImRmiahqeXT2XCbrcoYFLVzGazVAwGSp2Rl4boA6vVio+fPN6JLfKiwG2hMkPi6extLkIIdH2740AB3Gw+lna4AZ052m7Lai0ojNLgvGUYhmT4LAvL8dERx0fHu8pp5FGJZ1WPDT2dbcTAMzim1YTODrvvLYqcrl+x+vDyTovGBo/zUQxDk+9gaSqKumAxXaC1qF433ZbtZo3th7QA70m444arDgd+gtLH09l4yfPenSSHp8VP2lQO/aKejxqLIRK8/FwgpkitDB0V69WW/+Yf/2PqquDiwT3uPbxgcXJCjND7uLMHGYZB7mdZYJQsfMNgJSmgLNNzp/ZSdCglDvmogDGBvMp5++2f4/0PPuT28pqVX+Gd4/bmGdOjOWM+JUjRNaJZh7/LIfdtXDyds7vCzHuJ7zP5eN+FAye8QMdy2aB1YDo95uErb/Lu977Dl372Z1j3PZsPn7C+ueU3f/3X+AfOs2PpxfFkLX6AeZ5TlAW2H1KSjN5ZBakI4SBEXSuNC4F333+P+xcXbLdbnB2YtO3d+5NQldVqRV3XO6rBWOSOwqU9j4tdsTe+J9ZaylJyvmU+uTuFSF3X5HmxQwi10gy9+F0qo5lUtaDhUegFRVGkuZKK7RCpTM5nHr6G8oH1esNqectmvSL2PUVVslg8oJ5E4tU1y9WW3sK/9vf/FmdnE64uL2k2A6tVx9VVy2qzFs84A48+fMbJyRFVWdJsW66vlzStxXsIXtE0Lc5tpAUd5BA7bqA+GW3HdEDI8zyZiu8tMYSjoe7M693m/nx9pdSd09eITD9/xbgXcvyhBPaxxcdLOE/x7l/vFmkvf7jxucfXpvSeM3vntcSXiwnGnx8/euvRJpBlHouhax2XT25YnD+jqI8JasrR5BgdhP40RguCFBhjexGlODk95snjp2w3LdOp/Hu5XHK0WKTxC711Yv8TIovFgqgV62bL9fU1XdexaRvmiwUGTbCWj54+YzGfkWeGuiw5mk7p1lsyk9PZwHa5ZrlcMj2Z8cqDC8qqYgiOdui4mC0oHFJkftyS5zcczY5YLVdo31Mez1nMZ9j0O4xeuSml9+D9kjsUgqdvu7SmCGUrDLJmjdGJL4yVg3sFiEgyrWt3WsDpMV7WFXq+eB/X+Z3n58HnlVJkWvjye/R7TMnJBOkNYUcg8j7KIErgi9biODKZTJIHrrgcdO2w82v9pMbV/xCF3ae9PnUB+JXf+ipX6xtOjo45OzmV4s7k3Hv9DR599BEffPwRnXOoAJNJgQ0e5RV5VZIVOR98+D59ivwanKWY1rTbBqM1VTnhaDbnwdEZl5eX3G7WNH1H33d4H3lwes6inqZ8WcfJ8Sk60wxhwAbP5c0V7777I+ZHRwQFm+2GR48/ZN1t+NXf+jV+/uf/BbQuKKuao6Nj8enLcgbraTsRJHRdz+OnH4tYY1pj8pzLmyshwjbi8ZOZHLVc8kY9kRzdVJxUeUE3DDTrDVmRC5pjcjKdSYqAd7ikGkIpOjsQkp/Uqt8wRCvo3mJBnheycZiCs9N7KCWwscZwfnqPtm1xCblQ2uC8ZbORZJBhGARiHknw5NhBTkaZyfDaMZlPxHrGDVRVTd+3tO2GqpoIfy8TlePY/j3cGG+ur1je3OK9QytpZWV5RlVNEI6QwjnIMoUyGf3Q4azbqSSrSUU/NLt4OKsrJszAJdI8ApvPFxOKLGcL+BgoxvSGIMHnyghKKdFPUiCM6NbhJd2kl/Pe/jhXiCEhzCKC8jFQALZrefToAzbrJfeOphR1RlkXTKZT8rzi6OIC6zyxa9DaYHeI3WgnIfF/wzCg9ZgUIOKDvMwlVSbKopGpnM9/8Uv8F//Vf4GPUGSaod/y7T/4Or/wF35RqAYIWtr2a8qsAsag9oT2BRE3jAutUhJhNqIQAGWZ4+PeSmUsmgJQ1sLzLDNNoTTf/973sXFgflRxvz+mH3K+9a1v3LkfSkk0kndOVH9KobKc2WzGdrsFkOKqLFGIX9bufoVIbgq8Hbh8dkVZFGLD0nd37k+eZ0yn01TQ7lMIxmLeOUd7UDSOKNe4YI8Lv2Szpqzv1Kby3jPYYaeU9j5gVLZD+aJz0s72jrbvxO8tHZxCEMNcT6AqC06mc+qixIXA7fImcZAVWVUSbIbJahyw6jZc3i75y//Kl3n9tfs02yWr2xWr5YqPP7rh6mqNJ1JPa46PF5hMDq9Pn1yyWTd0nYWY0zQdw+DRKqPIRcQxpA03z6MUgWiU8thhj0QZY0DJwc47IfOPbclDTqnsZXctXYBdvvP4uZfNx+e5uYcF2N3P3wULd0Xg81P5ueJv/1Qvn/NKCcdL/hTu5phYFONoADxy/fYZ0uPrG8dYZhKXM4qRc1nkPH3vKUU5o5oe8drbb1OVGW5wtMNA13YEH5kvoFTFrg1rsoyutxRVjTEZRV2BySinU6p6wna75frmhuVqTTWZ0qmBiCR+TMsK2w/EGFmuV/gYKYuCQhlWqxXTSU1VyLjMdUaLYtv8f4n7s1/L0jS9D/t9w5r2dMaIyIwcq7K6qquruptsTpI1WhIkWfQAGzCsKwK6MewbX/jKvjDgf8KAIRsGryQRtmzAsGQDJimItkiKZDd7qu6uIeeIzBjOfPbea/gmX7zfWnufE5FZUU0RXkBkRpyzhzV8w/s+7/M+T4ctK6rZguunT4le886vvYU2hu72hm7TElqh9MwLw+16zfnLF3jnmS8WLGYlBp15hBFGUAIJwoRCsOfkEhNK5wQzKyBooxlUXo9iLh3fX6P3nrdCqE87r90sgE1+X9zRCe6jteOxzwXeH6Pjz3T+ucjFjCLOAYMiOEffDlNzmNhS9tIJrUcR8khU4tYyDA6tC1Klqat66o4eecOvjslvpjS97vhnCRjfXAjagG0airoW5w4j6NfhbMnyu7/GW4/e4quLl3z+9Alu8CQN89mMtx8/5vhgyfn5S3rXE3K5IzgnkiE+UiTFvKhZHq5ICqp5w+1mk10lFN//3kcMw0Akcb6+og+ey6tLhuTo/MD17Q0vzs54YLSQNIMnWcv8YIULnucvnpMwLGYLdEqsVgu2oaPrttxu11yvb3E+cHF1RTNfcMSKednQth3d4IhaScABdIM4NkQXhPyJbCDtdkvf9TjnKcpAUZTSBp7NvL13DM6RNKLFFUfV88QQJAh1JJaLFbOmIQDzZsb7s7mU54C6qVFDwKUh802kbf2276mqatflqJSghX6BMUlKlVpxsDimLitCyNpVQ8ft+lpEPX2kLKWzuiwqQt5AhUuZGIae9c0NXbvNdjyWkQNX1XWeUCJ8a5WQqYu+xQ19Jtn7rJofs2yLbBr90KGSorQVZNeT1WpFU9fcaIPHT4vu/Yxp7KCVJpnXdXypO5vGfmZ5315qPKZ/J6ZFYPodkJSgc4oonZvWcLO+5ZOPf473PbPFCQeHK2aZY2NtxXJ1lIPeOG0mu/ORcxQ/SblHYzOPtSXD4NE6E43ROAfHDx7w+PG7fPXZJ0Q3UFdzvvj8U37nL/81lLLTvfAxMDNG1sQoZWBpdEmTor+U+Yo7AdNYijGoSSZhWpS0Ep0sJZZMZT3n6OiErr/k8GDO0HmePr3m6vqGfakDpRSlsbTeT1wpKcEWkxSN9x5nDGVh7qBBCXEwIctHkBJ1WTJv5nefz3SdO6R3RACLopi+Y5+bB0y2UWOgKM8io4f5PKy1k7iybE47R4SxscKHQMqenvu6gmOJNSKUi9OjY9H47N00pmXzE7FpZSJtvybS89EPHvBv/bv/Mgq4ub7m8uKSy4s1Nze3GBsorGGxKFitGmIKtNsO70Sot88l5b4XhCkmQetGyadxv2T8g3RTxrArdRorUk3SWTnaoN0v093lUk3j5RXETtDtu++9u/He52ntAvNXqsUTvnRnzu9oht967M5ph/iMbhj75V4Z/7vz268gjGVErZV0r2ckMShH0hq3idy8uOTs6694/J13UEE80p0PuBCJIeF8oEQ4xEFHafZCERH5r9b1bLst86bm/PKCvu1FhsyJmPC264TLZwtUmcBHDo9nvLi5zNZlkaqZcXB4wHwxl4bG4Dm/vuLJl18Toqy3SkNzcEjAse56mqamMAWNLbm+uaZsGmxR0FQ1bjFDaSUNWoXF2L21NJdyZWzFbBaRcml3V1IfBglUR8T27lhIrwRGd8ZGHOfvjqOfYJqP+5H/NwV494Os/b1hrDRppTBK45OMAWuMxD3beGetnILMmMTSLfceFFZE4gtjMVrWNKFm5/uwh4pPM0S9bh/753e8cQCoigITsvG5FjPrwYka+OHhIcdHxxysDkgJzl+eY63l+Eh00LSGy9tbsS6ywjFRuSMOfM4U5LOXyyVlXQk61bbMkuat0wc8O3/J9fqWy/UNz85e8uLlS1ShiErR+4E+BDZtPy0WZdlweHyKKUputxtiVHjnqaymKITLdHF1wcXtDTfbDckoOteTvKXsC1RM0tVa1aisq2XQlMZgrSYGR0rgouiX3ayv2bRrYuZdzOoaM5tjZ3N0qUmpoo4RFwacH+iHmIn7ipAC225D7wIiTFtQFiVNVbOczYXwnrlJhbGE6IS4nsSOqO0ECZkGLuQyToNSwuEqC0szq9E60bUb2m5L123ZbteMtk/ODfR9J3I1StEPIgdBQjrP2q3A3tZkdwaNUYa6qrFZJ1EphTUVVWGlOWbocEMvHsXdlqSENzeagsfgicYTsNkvUbFaHLFcHXBxfknX94J2MpZomMq+TIu26J1pne6hgOnO318nG/M6nsj4+vtdpuPrxLZIvCeN1bTths9+8QtiCMyWC5bLJU1dg9aoUpT/UQnvUy4dBJTe81IlTRWzkZdmsxh0P/QoFVG5mSEGaOoZH33nI66eP2Pdd9jC8tXTJ9xc37BaHaOVwcVevuceqqIUxCio7H6A5rwXFCTPTemfUIwbpGzEmqQhJk/IgauylsOTE55++TVNM2O58KR4ztC3d26/UtKB7vwg63NM+CSdc6MEEUnEnY3OW3sOwhW7wC4mCbR8iBNXcDxCboho6nqPtB3yPTX573EaF9baCd0brxGY0EIJAAXBUGOXohJOk0p740uRCWgyJuJYEx3HaoKgpNS5mM05Xh2glKbzA60faIeebdtR6iAdlVbRzCvefnzKux+9z3e/8x4vnzzn4uKK68tbtrctisR8UWKtYTEvmc1K2q7P6Kc4eHifmz0m0vluYxVdUxF0V6isf8b072k+5GcgVlbszaHxwtMURN6fQRIcjSigYnTtUfk/Uwg2boJ5vE0vVzv0T7j9cVrj7mzfd6geu8DulweBu3PWOUkeOWgxpZyspgl1Io0NMGn6/JTGNSGAUhglupneiCbd5uaWi+dfc3t9TlMekoKoKxRG45II5yslzTdKi9uKMRYbE203MAw9rVYQDwhuyKhRoqwstjD4wVEwWiOKoPtsMWe5WExNGLawHBweYJUgm70P3G57vnr+HBcSh0PLwWrJ8mCFG3qu12tSSpRaKA1X21sWZYEtLEVZMJvPISpiGKY7nnI2EVPKftuCnJJStu4YuYAJlRTOO5mjUcbJCFyIEsSrjX0Kpj0nKC/2dVZPnPWU0VfGbuMkzSX30Wa4m/yP/7+buKjMbdVoo4mDnKuxoufrfMruJzLTSaCUgcCEYCqjKK0VoERrssU6zg05qYjTdaVxEN+bQfso+zcd+bb9uY83DgCjj8S2R9dz6qLk4PiQl2dnfPnsa27XG04Pjnh0cMTyR3+Rp0+/pCgNZV2D1Xzx9Vf8/PMveOvRI1aLOSp55k1D27UsmxlJKy7W13gSy9WKWd1QFTVxeUCjFN0wcHVzw/mVKNRf3VwTSRwcHFBWDXXRoJuEcpHZfM4weEpTc3ryiG7oiUqhDPS+5fn5c5SFwhScX1xxu93ilcrCywVNUUMMbN2Woqg4PTqibXtUUlS2YDmfsVrOJLBJgdYNnN9ccbu5Zt2tcVGI0s2NISznJCVSLWVZUhZi2VRXNbc3t7hhQBuNtoZtt8WogEoKjeI7771PUzbEIZAQP97eDyQrgcQwDBOy4YLDOk1ZVrIIIJpFpjAYDE1Zs5rPsUXJzfaKEGFwge12i/dOmkrw9EPHtg20nQQtKSXKssZoMRx3wVM12TFEG4wyLOdLaluirZWAR2kW80PKssKFgbZvJXBUkc1mjfeO3nUMfYfuPYXRmEKDgagSKhiODh7x4OG7PH/5kpv1Jdo2GVnJht9KiTPIiBLdWYjjN0wa8TOVH3/zjNkJTgdi8nd+l6LYPulcZjDGEJTI93z5i09QKrE8OKQuZlhlSUYRGs0wSNnRaC33SGtSkKB+DFQElSpIYciipmALhTUgUZdshBpFHDy//tH3+fhP/4Sbq0t8jJw//ZpnT5+xXBxRVgXdkCAEQkgUxa4BJCURWxXuVe72DSJLI3IwuwVRIzJA+wGzDZJ4hKiIWqMVLA4P2PzMUx8saZo580XJJgx3brNWimVdMwxbcfmJiajEYmte1ZS2wA0DfhBtsPl8jgoRk3J3sM3P1hrQmiFFrtbrO89n8I7b7S31rJJEQ1u0jjnoE60yKe9KEDgGevtcrv1O+BAjISVsRoRIYDEUGAwKgwQOXgWCilijmJUVZzfX+MJisRTIphsKmNuag9mcWTUjhMBVd8NXVy/56vyM9uqWB6uKYlZji4Zf+8Gv0TQz3nnvPa5fnPHs6+ecvbxmfdujYuT0wYpyVhKTCGdbWxB9z3bT0/Ve+FiMshlMkhRKgUpZNWDsvI4BN/QYJaR3nQPrlMTr3BjD6O+lFFNzlMq7l9xOdWf/Skl8ngttprkpbxI0O+XS4Dch8UYrqQjk7xk5Vzo3r02oT4pE0nRO4/z+5QiKRHU63xNrdbacRALm8ZKy+wkopp6mPceyMb4NykvXi9H4ACYIFaYdWi4vX/D86RcsVu/TmJKyKvFGsWkdVZHDJ8WEqmllqAy4VhGiBO3r9QZSYj6vaaoaYxWLVUW7tYTMQY3ANnjqFDk5PMJ1PSYlkorUdcnm7Fos/YD14Oh7x/nNBet+TQinfO/xu2y14tn5S5QPLGcLZrMFF9tbucdIg4RF4VKiX2/xlSUosUgdg+TR+zrEiIoKbdMU+Kmk0V7cS7zKaPlIMVUQkpL1Tt1N7sbkVOuCVvV4ItFootLgIdqY9YklqdZaHs7IXx6TmHEd29c83X2HujMetZbnOVrdGVugULhWJKxcdAISUQIKgyEgbi0pQl1YAgmfHDFqypho2w0hDhO4sGti2gsA06vzYXztm/Bbf5US8pvLwJQlj9//gMdvPeL0wQkYTdpe8wc/+X22txuqouSt0wf8y7/zV3n7wQNiCvgoVkTXL88pMZiYKJWhqgquh5b19Q3Hi5WUhtYbLjcvOFkckELKJ6aICp69eM627ek6aR0/XB1QWsPq8Eiy3LYj5NLh8fEx202L815M0jV8/vnnNE1D1HDtOtzZc957532KxZxSAc5hlWExm2EzGqmA1XxBVc84PtBYbajLisODJSF4zi96fB9JHlblnKYsqOqG4CAMkeR6tDFiWJ8EXfO5466qKg6ODrm9vWGzWaNSZLVa0beOrm+5uoKXs4YHhyfSTOId3dBnNIipU3GfyB4zx7DMEi8heMp6BiHgfEfbQ4WnbbdcXV+wadf0/TY3Tgi3ZRxQIoSr8/d4rCmoqoaFWlLXtZDkrXAcm6rebZZRyhK6u8IF4R9671ExYcuS1eIQNwTmMaJXisZC0okhiiC2c4lt6NDG8P777/P115/x8vmTiTOxKxlE2LPOiZI2vvExoj7wet6RlL725CCm9wlqphC+ZVXXrG9vefniJZcXl7z98BF1VTGbN9iyEo5p2zOUQvzVo4YW4KMD9NRgk5I0RxhrMiE80G+EJG4KcWEhIw3X11e8+8H7nDx6wJdffcHl7YaI4qc/+zM+/Oi7LJYNQy/eumVZSqNPLmNaa++4XIzlyXrW7DV7mKksu58lj+4bIYmmpFKCS85mM5aLRR7vmtV8jtIVWn2894wS190Wn5GBUbYjJCnLVs1MXheFH7RcLkXk1cfsE6ruiDoDd8jb47Mcf1Zogy1Lqixzsd8Mo7NY9j6/cZ8LOGqUGWtoioaqqoR7m0vF+64ixhj6YSdpMcrAjBtOYUWU3Q2O9x485nC5oht6/vTPfsI//JM/xNTik71YrojIOc4PG5bHc4qiZDOsef78JR9/+RkhROpVRbWQMXRwcCA6qs5xdX3D4ERGxNqS2aygqhIbK530NgeAKY5lRuGBKqWxVrNczWnXLcaO/Fq5z6Om4r6pfbzD7RTdvBFMviuvwTSmdnNtt9HuJxa/DJEfS3L7YzFmxGn/+/aqf7/8SFKy3S//jqjxRIlIb76ZjsGaNZYYPOgSHTXGGeLaUSfR9hxConeJIWpS5zBO6BnGGAqjWS7m3G4Si9UBRVPTdh3eB5aL+RR0q6TxnacqSi6vbyAlykoC+77rODk8ZLvZslmvCTFycnzMdR/4/MsnXNzccPTgIX/xt36Tr14+x0XPvK5F16+xLA8OaMqKWd1Q5zKrVioHcLLWjuM/RtHlM9YQnX/l+e24w5HkE4TcFOjcxBMcx02+iQKO3XuII4c5Jk8iUc0asUfNoENEyrQ6W0UC0xofQpgS7zER3q8Gjd+/PxZH7m7f91RFCTqREHQdRLYsJeEdl6WljwMheLkfSlGV9TSOBEVWeB9xzmfUTuVkav/6Rhj9m8fcfnCn1Kvz5Fc93jgA/Ff/4r+AKTRJJzyBbmj52c9/io+Bat5QFxUuwZ/+9M/40Y9+Ax89ZxfnPHn6FW3XUWVduMJY6qphCIHV22+TYuR6e0PXdayWS56ePZcuGQVlWXJ6ckLrBpz3GG2oi4oUA+++/ZjFfCGC0XbN2miOj49pqpqmFHJ9UZYs5jNWszk+eDb9lovbG/ww4Lqe+WxOVdX4GFgsFpTKYBX4occ7R3Kecm4piyoHVtK5dXNzlflfJZaSiKPFcPjwMSoodIxYHSnrAt/CttvSDwNJQbMQ3tJ8NkNrRVGIZuF2u6bfOkgiyLvZ3DKva+JWeB79MGBzcFfXDTFGttstt7eimahzkFnXNU0jotDRe+mqUjD4nm2/4dmLZ6zXtzg/oBQsFotMYJYSTUpS0h6GIZcdIzp7Di8XK6qqFrTU2gyz5yaRGOjdQNdv6be3mTgruoe2KEmDaDcVxkDmRJQl2MpSOM+26xiGFoXIbxijKMuCqipzqXckAe1W+kRGBhIo9J0O3v1jyuamY4c87G88+6XAfRmB8dhfLEYe1cXFGednL3C+56A+ZrmaTxvlfHXAanmIxqCUcPJcu911vObr2V+QfNaqM1rEkr33EEbETpA8HwZWR0s++vUf8PL8nF/89Gf85o9+zC8+/jm/8eVnaP2BZMFKGnnGEqtSagr+9i2Rqkqaf8ZAdLy+cZHaL3WlIKUu0eEqqArLrIDlwQHt7RUhOhbzmuvL8zsxufOOz75+SmE1JQadfXEpLW3bohJCTq/KSRR93sykSzDLQ2ml5fcZ+a1yF+V4tG3L82fPMMpQHBU7akBe0HdBrbx+5PmN3L/7CMDQD/iMlnZZL7TQliLzFsfvjCqXp1V2z0lKzsEWWFvSdR2zsuLxyUM0ik++/Jzf/ckfUdYNiURRN6jS0N68JIWSZnEgOqpZxPnrl2vWrcIYCfykWUgTr9vcdZ+4uLhkvd5C0uIakoWbZS2IVGWZN7ROqhfOE6M0cpVlQVFYjnO3qfMBlaks8hli6+iDJ2X7wle4s/fmyK5Eere8tmcO81qk4nU/G225XikZc5cDOJalf9mWKOM6TcGfBDMj6imI8ZQgfstmvH8Yo/P17pLT4D3dBm70hq8/+YrHj77g4fvfQWtD0oLEWWMIEVIM6KQwRUFwnuAlwB1rjMPQ0ZweUxcF5LXq+uIaU1UkBbYoMIWlLEvc4Ni4NUopFsslthHrt9VyxcnRET5Fhr6l7bY8ODnGFBYVE+ubNU8vzpgdrKhWNaYoWG+2VFXFZr1BNYnSig1sMpZytaQo9gK67D89li5fj0SlyQ5tFK7XaZeAaz1qvr76PhkLBoLKvMOMzqqYaQs7/chxPb9fAt6XV9lvEhsDwPH/4+uE9qFJSmTYKlvQ5+v13k/cQ/L3xrG6UBRTIi3jVwLA2/VWuMJJRNm1srtKU8ohsdqVf3/Z8Sof91c73jgATMkTohbPwO0tl5trhqEXIdGosclgkubs+pKnL58TU+Tq5pqL9Q3D4HEusOk6ilJ8BGdVTVVWvDh7yfnFBW3XosuSTb6xzjsUiq7vSEqzWCwkmx8GjEb8a6saoxVVWeCdTBxK8d3UKstJhMhiviAER1EKAnJ7fSN+fcYyn80oyhJrDIU2Yu9VNRnNkkHUp56ub7P3X8/5+RnaarQuqcsZJ6slOhlizMrhCM9q2zq6TWDTbkRmxRiGMFDXM1AKqwuaShbFQhdUtkYBZVkwn83ZdhvW6w1d3+FCQBnDanXA3AgqMaIVB6sVQ96g+l4cIsqyxJhsaaWEW9J2svgrpSisuLJU1SzfH+E0hBBEmw6Xu5kNRVFKd2Vikr9w3ksZFykhthmhHLzDxMyFgKxuX6NQVLVIaGilcxlRkN8Qg/hC9h1tu2U2E9QzxJBLUju9L5UJQSP3Qio5ryIJ39QBlvIb9yfOhG690TxIEwJijGG7WXN1ecF2u6Gu32K5XMqCai11PaewJUoZlE4QEskHQhrlMwTRS4lJ3zDdm/yysYhXa8yBrjEW5x1vvfUWjx+/w5/80U/EEixGvv76CYvFjMPDY5wLr6B4Y5f4iAaOC12/F0yNG7cQrdV0zyRIsvkcEaHpIKWdum7Y3lygjWJ1MOfTnz99NYsni2ZrK/IUSayuQhA9rRG9RkHbdfnvlXBptEjB9Jt2movVxQW/sff5RVEwn8+lESvrV46yJYNze3aHfgq6x+vcbwK5izanCRHcR4vHMSZo+5DHqSbmDnerDYW2aCToXs0WNLbkZn3Lxc01x28/YrNpSUSq2kKy3F6JkGzTHOJjou89LqxBz0hqw5CFh50XNC/GgI+K4EGbGqU9fT/QO493Ij4fI8xmNlMq8+ass68zaleGTVIurusarfMzIU7InzYKq3KCkDvw9+dZ2msQ2Q8C782eaWyNx/6G/E1Bw8gPexXa2yWE06/fcB8ck5v94F++awzixnN77WW8QrwS9CY3BGVkUinhO7su8vzJC/7k93+P8/MXHJ6cUM8XYAp0qgTpDlHGTqzEGKEfxLVDm5zwB2whSeEYpK7Xa2ZKOmKVEn9qrTTWlrS3t5RlyaxeoJTm/PycZVnTzGfMh46bzRrvB7RVFKoABcPgWN/ecvToAVprrq+v+fKzzyiaUqTKykLUJHI1wxiLIuaGQTfNoWnNyfdOONM7Luh+sq1ysrJbp3Sed/efl566iRUKlQPuGCNxFIDkbol0TPj2Jb3G773z3Hh17I3vs1aMGlJK+CC88LbrGQaXg0WR8dGFmag1KQ8aQa2NjAkUWpmp+vHagE2pbxy/d5KtvXMem5D+vMcbB4DPzp6JQPPQsm7Fkq2wllkNoY8YoLKWgcjz8zN8CLR9h0+R3ovq/LbvMG2BMpZlU6ONWMBttuJpuG3bzO8StfPoA4TI4dEJ8/mCsiwJVYXV8PLs5R0RSJKi77u8gAmnTKMI3lNYgzEyaFCKfitCnLaIubHDivhjFIKuMiYLWgaubkSV3QcR3e2Hjhcvn1PWFdbWLOaR+aLGKFA+5tJEgOTY9C3X6552uyYFj7ViCH9yIkGV0eKKYJRGVZrlfD6VVJRSnJ/fst6ucTkYGrynrErKwlJXNWVRTk4gXdWKPpp3UzbrfRCB5xhwXkzjIQfIWrS+mnpG08wZBulgTlFRVTOqSl5njXQylWWB68Tars8I6bZdS9CslRjWe+mQ1lHKRCGXw3wm4aNBqZibDCLeGW67rWweMRFcIASHC45u6Ag+MOqM3Ye+BbAcN49v2DTYBXi7P6++7s4iMCKF6lU0Yh8dG1G8drPh5vqarh0wxlDXWUrHWIwt0YjpODp/drZ+mzrBxkh2RNnI3Jn83WN2GlPMFkKyyPVtx+HBEY8ePMRqw/XlJacPTlivb1hvbnnw4CHOifyIlDezOLgPu6aLvSNmwvhYChvxFXEeFBRNMlkNKuHDaIUn2nZN0+TXISjdK5I80gRS1xW1KtCAD1FcEWLMVoUJGwPWFOI+0PcTyusVdEP2tc6d5fXmLgfQWstsNqcqq2kTGc97vJf7AeAYaO8jpJIwZVqBGu/Nzkd5yuj3n88gz8dqjc/j0WaUm/w8Z3VN8J7b21tuthsOT0/w7gXGaDCRru9JefNWgyA+N7ctZ2fXtK0DDFqkTBl5nChwLuFcQHTiNcMQGIYwNbykCKXXpORkTo6yFlqR0u46RN/S5+YxmQ/D4PJU2AVLggK/uhlNKsx7m9j9TVWNJLF7c+91m+ErP8uv2w/U9kbXbm6/KRAyriH3EJQY764HY/Lzypv3v0/taSIiya9UU5Q0Hw5we73l6eefE2NH9B1HDx9hZyu8G6jqGYMPhBimZDh4L0LCSgLS2WyG0ZJM7BDCuEPpY3ZvURrvPJttSwKazHVs25ZZUWLLgqou0Z24KomDlcYqI0CI1qQg7lZt29JuW5KBMmtjkqRKYZVw45Jh4jGP3a/7z5YIKsqcyu1TkwTVdL/2xoHWTGX9O3dcq3zbEwYjtJiM8IvUzN1xM52H2tENRk3Lsflr94xfRdv2kUDNyDmXJKfvhNITx/aNlCShJeyYfONeknOUlMfJSBPYP9d9lPlXwfF290i+5M8DAr5xAPjzL37O4HqSShIcaVgsF0Sf2N5sUVExm1ccLo94/uKFBHMIMtAPA4REP/SYzmIKw9BvKatKECNrKJIIpo46TIXSBC2+oPOFlK0EtTDE4On6Hn99JZ04IWUCJtRZa7AwlkUzkxLh2N2VJLj0MeWOXYUPgc1mIyVNH2jqSky4sxfm02dfkVIm52atvdu2ZWbAoondBnUeOJ4tWcxWDMETooPoRbKh7xk2a3Hg0IbCVpwenUrJy1hKW1IVIgCMzVpz3rHe3NK2HSEGtMmbbic2bK4bqAsRwjS5y3c+FwK+TCS5phAixpT46LlZbxjabS45W+k0LitmswUqc368k02jzuLUO/2jRPCettuy2Wwm3ljft9RFyXK5JMSAD4LcWkQiyOVgLhKka9z3oAVVcIPDD56t6yEmKltKSV3D4DraTrhLRom0zy7QS3nNzV2ppIl/dH9S30X+9hd28oSRz5xKf3vSHYp4Z2JCLhn6HfpnraXdbFhf35KCfIc2arLHUmgSii6j20mpcQeX84lZs0/p3H2oiDrJNeXrkODXTN3YzomVFNvEar7g4ckpxweHXF9e8O47jyB5gnPEOIpAq6lcIYuaOMfs+E4iPVJVdea4jRy5XC6OUUqwWvyapdwihOxIXigTzBdZzDsmrCYjTLtDK80io81KYBaK3AiiUFLuDjlosSKL0fUdzju2ShOcE/3MII1PZVlQZ97gdOSF/ODwgG3XUlpJkPYlX/Y3/F35b48OoITL5aMs7+PmUZblhJyO0hOjZqJz4mhgjCaQ0NaIDaISUrpQAgy361uubm9Ydy3V4ZK6qlnM57y8fMnl9TW2qmmaBS9eXPL06+d89fVznnz5HBcCRVUyX8wprMF5Qf6tKthsNqzXHbfrNcPgaLc93kuyMDZwtNsBhdhVKcAYyzAECTKQ7ldjZWzG6CbExHs/lYHT1OSRkficrIxI2BiIjPMTEqItuBcEKoC7Ir/fVOp6BcEHiBE1IUnTKyekiZT21olvP3bVBPnXfiVgtHcbx9T9Yzr/XLLLMVa+KYmYwFjxtI0xEpyiNIq2a4FICJLIVIUnbLfUiyXROVk7/cDt7YZZJUoM3dBxu77hvfffFUF05+kyJ3Ceq2J+cAKWAEprLi6vRFtTK2bDQF0WUhr2IQuWG6qyQhkZR857ZnXD0ekxD9a3XF9dMvQzSmN57733aH1H1dQkpXITnxMt0b6lsU2mBAjfTbHXhJdAI0lDiAGNRkeZL6Nof8rJ70grAE1K/hUXJpUh3hBlH7WVqGXoHIiqkJMxUhYj2z1nnZ9ZiDsXKanC3KUQ7GgvOSkaS7tKM4q4JyK+zxZwQsaU9WAcEnmsGpOrKyPiF9Nk9XefyjAl2+n15fNvKqnLz0fUev93b44IvnEA+ONf+x5t19H7ns71bLatcDVJFPNKgpsior3n5GDF8cEBIIRLHT3b7VaIk33HoBTFfMbx4eGOm7XZyKZnDEPImxVQl2J2PZ/PaZpGtHjKktOHD/jks09lcbQFs6LBVhW37ZbtZkN0PotVi21c0oqb9S0vXrwkRVgdHtC6ns7L5nhzc0MIjuVyCUoxuIG260hI125ZWqqqoqoqhuyFXBiDUYlNt8ENjqt2w9HpEWcvzvgv/vO/zU9+94/5n/4v/me8/8MfS7DQdoBi3szpewc+YgpF1TRCzvc9m+2aq+tLvn72jISgJCJwO+PBgwc8evBAsv7Bsd1uSHnyWCM+oFLeqVgsDNvtlm7oJy/Tom6IzsuCr60gLSGRyBwzI3B1YUvxE3V9fj6JTbfm6uqCvu+n8mFd1yyaGQcHB2z7Dn8rgWfZFEBCa+Fhjc9YJn0W5N1uccOWpBU6lzRD71hvt3gzwweX0YiSvm9zGUo4idJANcq0ZGsupPwy8jb2F/QxwBtLfvtSMaO+YIRJb1Hn7ta7kwrRMYyRwtopCPZOeJtVI/yswojLSkiRfnDYvs9m4X7KbEe5nHFRi0nso7Q1xOSwSkoHoBgG8dQtiwpVSsNN1w3cXF5T6YK3Hj3i3/jX/zX+T/+H/5A/1UHumzIoZTg5fYTWOjf1KObzefbJHrsr5Ry8WEUAkSjAXl6S1FReSIg+GUnkVlISAnOMCa0iylT4kHC9pyiFT7a/YGmlmFczonPiEKAUaMXBYk5hC1rXT9ZbkuyJk07f9yitqeuKWVnQNA1NXYsE0b1NwvnA1c0t1ze3PDx9gC8cNImqqu80gYwB4RjQOeekw1FrClPsGjpCyI0Saqo0jDZpzjnWuQtZlzYT/xN95zg4nGG16On54Cnqim27ZkOLV4mylsYUlaSrcugHvEs8fucxX372MZ9/8SXPvn7B+dkV2/WGq5sb+r5DG9mkttttRuzEiWkc4zJX+kkrUeV7qbXGWPGctlY2q5ubDc4JGlgUUg2oSjuNFa01dV0TvCThI/o5HkqpSbB3TFbu8ml3wds+sgqK13EIX3e8EhjeC/7SiJp/A7r/bcf40h3n89XPCSHsgrxfeuQZk8t93nlBAYkoFTGx4PqyZT0kTusl9cEpydT0MYES+0DnBs7Pz5gvljQz4eDF20C73bK5XTN78ICyLGm7jm3XcnRyLPtr5r8Ng2MzbNn0LR4RpF63WzAKoxRfPP0SZQzz+Zx33nlXxlESTmnbd1R1y/HJMedPvgAUxXzBarWiTg0pRLqhncba0HUQd+XUGEXjFeJURVDkNVtJs5eUUT1d10pjUQ7+lFZUhaD+MUBIfgqopucVx7J6wqqKoioEYU8SaJuEzKf8dFNiF2TmMTomdOOY1PnP+Nzvy0GN1ya8aQ8pTAixlK6FhlGUFV3rpuQhpogtRY+Vve/q+17WjHtzZbrGbxjA+8nQ/nntEOrX5ilvdLxxAPi3/ub/mQePHvHg4QOOT455sHpAPasJyKYzuJ5t17K+vmTdtoQQKMuK1WrJW8dHhMMVQ++ICbQxpBTZ3FxzuJhzfLDExcDt7S3JWHo3cHN7y3azgSwA+/XXX/PgwQMOVku2txtevHzOMEipsE0KXwXa4LBFVq33HiWtbJzfXtM7KU9aZPHv244UYLFcsjpYkVTkZrNmG3rRfwoRY/NGnTcDqw0PTh/SzOaCQA49fujou4E0L1n3G84/fckf/pPf5//6t/5v+FvP//Z/+b/mN//SX+Stx485PTnhb/yNv8HB0SHr7ZbbzZrNdsuL82dgDO1aLKHW6y2D85w8eMBiuWA5n1OVJcEFCImb6zO2bcvQO7wPHB4es1wuKUyZvVuFh1EWlqEVzldSciuNFVs5Y+wUIJWFJvU+y5LA4AeaUmRdXPB03ZazF89Zb9YTF3PkV9R1AwiZddYsKEuR0Rl5WBJs5M2TApK8tigqtFI4hMNQmoJSGUxl8TpwfnnBZnuLImL0ftlNkaYJkSb9yLQ32cbjl20wO8kX7nSByoLxeimJlITzMmSFd9c7ohdU6p23H2OsoECRJKLJQSzcYpCOS9G4k/EV9z4/BJ83j4hIJeeAKzfiOBdyMG2pa0NU4i15eX3B18+eUM8Ljg5XdG3L0ydPSEnx1luPBCnKAXtZltMCNy6OEoRGXLYCJHOMEsKRlZJpmOQVbG5E0LlshNIo5YWPawq0EY/O+/cuJVlIizJ3DyYyGTyjat6BijmoDBBhPpvTOaEmDCGwnM0prCVkd5vy3sasjMaWhbiNJJFOEn3D4Q4qLKiXvttFz5gA7YjgZVnig5+SnrGhZnzP2NVtsrtG73pcyOoDObhPgLaG4DydFhSzz8Lt3/3gI65vrmUTC5Hb9ZrNtkWrkrpaUpWO6NbgFL4LGCPjX4cS3w/E7BYTM8rYb4ddk0/WkhTHD6GVGCNcPgnkxqqGzIFhcFODj+wm4/hjr5Pa3UFS95Oq6Rnkzc0YTdxzc2H8NLV7zfg87gd6r0M/xg+4i+jvSrZjMJjf9cq8vf/5Y6vI7jru2gb++Y7xvlmUMiR8PqmISprDw2OCT7jBo5Vhvlrx9YsXSAlDBODLWpC5Tb/FBktRFXz44YcYpbm9vkEBTVVhC8vlzRWVKmlsiesGbm/XXG7XmHktUjlGGvVc19FtB5arlYihO/HgLnOX/GazoW1bXrx8ydHikGYxY7lcMatqovdopem6jsIYSWx0wg3ycLu2xatACjI2rCl23fWIjJCymTOay6FjNcJYoQUlUhZKT0SfJg3Oew8NrbMffABTmF1lAgMqiKYgmW84Dpn9kvT4lEZgQOspGBsTpTtPM489a6ysiV4xDD2bzRbnpBtaimQpIwgZlsiC/1LlCVn30eJQuGwIMJ7b6wK7+3//ZfvY/me9Dk3/tuONA8C/83f/AU0zo26ky3Q+q/no177D6mhJ1ZRU84rV4ZLVYsbB4UqytZz0Be8I3guhPD8ZHyIxDAxtfqgp0WhDTImmqrEhUITcKWMMzg1025ZZ5iKcHB4xm8+52ay5vV3Tth2zgyWdH6QFPwkvsSxLdCFkVWOg1AWz+Yx2s6WwlnlTs5ovMDFSGIPzjt4NeIR0OwyO0hYsZgtW8yVWaQ7mK2Z14La9ZrMOGBfwnePodMkf/O6f8os//AntdcusOOD64oY/+Cd/wM9mv6BpGj792Sf8i//Sv8jJwwcsjg6olw3roeXZ2UuU61BRYPCYB1RTNVRlg9EGF7e0my3ODbmM0HJ9dZ3LueVUSkxJuF/tdiPSMylMGlZ1XVM3DaQkAYwXRfa+b4WHlRImlNSzmpgig5MGmJDChCBVVZ1LyQqddC5zaQpboZTFAIUOFKVkaTFFnPEonU27U8zizpWIa9qCWVFQKo3utqSZoqqzHVcIFGW5W0yy8fy4+UwTAKaA6v7EGn/2uok+BntjOXD38/jKZiDNKLuStM4aZ955+k6EwbXWpGxxNMmpxEGQMqkT5o1Q78H3Esxqo8QhZg+NGL1rx2YRgBQVtixROpEUFJXlhz/8Pr/923+BP/6Tn/L8+XPKsubrr57y7vvfmTbtYRDh1rKU8uWIhKWUN2wh/GX+jJR7YCyFSOCUEllOQW66PIdcwolZQsN51je3d9LShJRVC8wkAaOVVM1UEnpHTH4Srx4GJ+tKWYktlRvEiq0oUUrgx7Hsde8hUVYVLkgYPab+TdPkErpjbGwYx8OYIKHUJJkzEqzHsVbX9TQuRgmmnS2cnzqKRwvFEZxUWomGagj0zuFTxJYFi9mc0hi+/PwLzi/O8cnTDw5tC7bblrOzM54/e8nFxSXX19eoHJh7H+h74epqo6R5IPM3R1qBVgpiyvdAkfI9D8QpUPIhTOWvHUF+V/pUatcEMdICUto1FY1lcJ35vInXzbd9VC3PTb177zcd+0He9HnsOjP3y7xaf7O39+uOV3mJY4CwCwzG6/9VjqSkszghYIHKzQwScGtZu5Li4dEBB/MKEx2+3TBvSgpNpp9Id2k3eMqyoNE1hRbUejlf8Pz5M8qiQBlZ+7wbsEVB23asr255eXbO2fqaRx++S1EIjz2EQNHUSO6t8c7RdR1t22OLgsODJXVVYbRmcAOBRFDQeyeBS+abRi/i60aL57W1ltD3AhronWDxmICMzyykXdf4GJSNgbaS2DjTeHa/nx7Iqw8vcwiZGhRFTUFLud1Fsc0EUPeoHWm09YvZ0zyv72l37vvjclwXjDHkHUIULXLlQK5HXiu2dhUx7ho/tIaiLLIodtarzGjr/U7p1+1V+3Sm8Xxe9zsJJvWdOXM/kP22440DwM+/+Fq+LJHLDZovvnjC4fEBzaJhvpxxfHrM6fGK49NTmtmMqiqpqoKmqUQPT2sCWQsnRJIO9NkHU0oRJSkEEVQtCvR8Rsgb0qAdJMfQb6nrmlnTMFvMaaqKyljO4jXLxRzTKRKldFUOXjiFxgpakKC0ltXBim7bYrQWPoE2LGdLyqJkcKK5N3iRWthsNjRlzWK2oK4aUoKqqKhKRVIygIqkaNsNw3bDZz/7hM9/8SVEYWyHBGcvL4ErtNb84mcf8/LFOW+98xanbz/gwdsPOTo9QpcGrSUIAI0KmVIShYwdovB8+q6FFDNPR2XunTSnSCAtpfN2vaFzHSEPPFAUtpDJbrR4nEbZdDocwyBZWUKIx8PQ5w1HoG1tDIUtRaR4cBRaED/XZ8s46fAgaTBodGEpbDEF9yprr00LuJbJ3FSNlJ+KkkIpAqBnBQerI8qiEkHrsrrDx9ttTrtJeH+52N949ifE/Ql3Z6LtQeuTQNPeoXLZMo5E3py9hhgJzkuXbi55j0LZGsUQAykgQd90Kunuf8cdJ29uI0IhBuQgK2XKG75spEpBU9e8/fZbbG6uaLc9t9fXbLc9N4dHdH2XM+98zWNHnBcnGaUgZktCpTXkACAb1SF9EGoq1ZA3ODWdZ5pKNeIbK0RrFzzt0N59Jgl88oQ4dsWNRTMgRQqtJTjOWXzwUm4RbUFLEQKu64lVkzXJvqFzWylsUeK8J4aUJT0iaJjVTQ7sZXIprYUTnIS8Pm4UdxbkRJYskrGYkpT3Ve4QHIYBF/zkG1wWFquNnF9GHWISCSAVZa2ZVzXL+Yyb7ZaLm0v6ocWWCh0811c3PHnyNU++/Iqzl+fc3GzYbluhHQRp7hA02GLMaHu6f75qQvHSfSeOvJ4IF2rnWZrS6zeaTPTLQtpMaHDMyc/omjGOkbsb0z30ZQwA7t9fdq+bZkPa/X3vseY/KtMUxkh19/vde19/7IK6PHb36Cl3uH9p96rxA/e7lceELZF2zQYjvJUD9ZwviXYeGQEzitl8RlNXaJXouy3aGPzQS/kxJeqq5OLqlroqs/uKjGGbO+FFpSGIXJQSh6cUPH3XcX5+xsvbKw7fPqEo5nlfNRS6wFQiWt/1vXBtY6LzA3VTUmgZSK7tGZwkK8F7BqRpwWglFYuxrKuzM47av5c5cQ1hB/Ui3FEd95t0hE8ua4qMMVny1MiE+wYAV9QGtDI466mLGquLad6qvb1gekYTmp3Xml3GPb1m7NaWPequw8xuPOeSdpS1IAbhcaa8DoQERd4LUpLm07IQxzBpXhEuuHeDGDvc23++jff3TYHg3b/vDdp7r/llxxsHgNYowlQVSAxD4Kc//QTIjlH53hoL7zx+zIOHD3j77Ud857sf8MMf/YBmWaKLnLkkB0FspVwSZMgUBvD43DhgjGU+q9CIZIhelHgfGIY1Yh9nmDUNh7OGxhgKXXI0n7NqKora4n3g8vKKzbZDeyn96iwZc7BccHV+CQghtes9hbY8PDmV84s9PnYM/cC6nmHNDokIIWGQgbCoFpTK0hUany747NMv+OzjL7k831DP5jjfCscNcQdRSuRT/uHf/yckAkWhOX14zL/z7/xb/Pf/B38dV3l676SLz8liMLie7bCl7Tsub69RWnNc12LqPptxcORRhaZzwlf0xmLRrK+uUDNBVIkKqzWLZial5OAJfiCEgc3mht4NE3w/ZkHr9c008LUusLZCJei2PW3YEmcDR/MlITcyBLKmE9IlZoyRUi0S9BRTK75CqR7neiDSFBXKGAmslKaqZxhTcHL0iNlsIQFJSLkl37AvTTFyK/KonwKL+xNgPxN8XSYlb2K32Btx7HjFC1hJcByV2H35FAgKkkSCQhkw0pmmApgoSFWIEZKgKFrvSu/7/ESFEtkOv6dFqMAN2/0zmBYt3QFlwcF8xQ++/0N+/qd/xv/zP/t/MbqRhBRZHR/RDf20mBSFILI+StOCj57gsi9s0JAyN4udjZfOQuY75CWXymV3IyJNSskaTGHFCYFIKO+uH4mECw4fxDtUkptIsuIWYJXoRZYaejfQ+kjwUkLRCuam5HrdMnSCXOjsSXz3ULJJaMO261EqMGhFO3Rc3V7x7jvvkHSS5NBHdGFxKeBSoNRZLzGK5p+2hhhFD7IsipzgIBuVVhSmJCCvDUq4TSE4lvVCPJQV2Tc6ZmeXSKMMi6ImWcusqvjTrz5HNZa5rijxFG7gj3//T/jD3/9TLi+vaNsuN+LA0PvdNWqD944QXw9TxbiPLkgSIaitjFNRTchBf55fKUVsoXdl1iTNVUbtyXpMny+NMnq3Zb+yeaWU0Oput+WIGt8Z968gICO6p+4EfROnTEuTwBiAfUO08C3H3Q135HMKKpVLyZGpAU4SyF25G8g2itnCLQu3E+Veptxsk2KQ5oMgHaSmVJSLimAtlBWmKNluOnyIXGtpqCqqkgcnJ9xcX7NqGqwuxMt5kMaPxWLBzc0N264jpMTh0SnLpgYbObeJdXfNentF6FpoKgrdCHc4GAorwWLb91mlQLHZrNl2W4qoGDYdly8u6GaWxWyGKcWpqO16Fs1MbM60Ilo9OWxUSsqbKEkaDYISpynQJwdhelq3pFtZkOkxG9Y5mSUmgpNKSuLe2E6SbBplCLMty3KJVXa65zqjbzFmxA1ZX/Y1/rQMRGl+SmkaaglwIUAIgirqnUaqMkbkj2IgOqGmxBCyx72jqETGRyVHip7RErCpC0nQALQloem2W7QfQ+O9xIjdfvRNwdtd9Hu8JTvOdL60fLw5jeGNA0DvNJpdZ1+ZoU2Ty6YgXJGu6/jy85d8+flzEn+IkELhR7/5Qz787oe8+957vPv+O3z0/SPKsiSS6PqB9aZlfb1BSlAFSmVXAqVRVoETNEBrmbB+6Li8OMNoRW1Ljs2ceL3FFApixKjEsqoolPDQhkGU/efzWe761ZSFIfqB2+tzQXBy00NIiZARoMPlIUUxEqCTdGAmz9nLS7p2oG1bbm4viKmlrkr+R//+f5f/zn+v59nTF3z5xRd89eRLLl/ecHu9YbPZolNkBHWci3z91Qv+5t/8j/hH/+h3+eC7H/Kbv/1b/Pg3f5N3H7/Fw8cP+fLLz1EpUtU1TQLvowhyK7GmOypLnp+fkdKa1eqAuqoJzuMLaJJiXjekrEI+zzIzMUZ8EOmJEMW7lwQpaUBDhHbopQnDliilsbpAJVGUjxnCf3l+RlFVeCXZViDhvMfGjEQqGYzj4A1xYHB9LmHLhrbZrkUWJnupGqWJa4UPPvPWxLN0t5mJKdGu7DOSdsWN4Js4FbDLql79fd7I9jJDo9UE349H8B43yhNB7u4aPXUVpRUks3PyuvVmQ9koTF1isZMW1si/e915ReQziTudOqWl/JyS8GOGYaAqazbbNSAB3F/+l/5lVsenuBA5ODnh/e98h8XBIRYzoaBjQ8i+7EmMka7rJ+RkX+ZEqAH99F4Swl8ygDEi39L2dP2aqshaeFma4a2H76D0p6Lsj6BgXTcwqxsGL57WpjBssv5gXYoGpraKRbNk8I5h6NFGmi6iEhH1zg1UWlHqgvv6V+M1bTab6Rq0EU5s57b87Oc/59HDRyyamZDuc0eqaFtqQVVQFHWVvUpHT23phB07gceASGtNPZ/JXFHS+Tybz0XtQMvzNii67Zb5fE5VNpOW6Ha7Zeh6jo+OUX7O9uqCn/3kJ/ztv/13ub3pBTnZy/qVGhsn4p1x+iacn7GcNfKqpuDF7CzfUuYt3UHpAWuzqHHY2ecpxZ0EZv8U7mxSavez3TN6/fndfY53/rX7oPyMhdIgjUthjwM4Bo1vhIDcK0dK0KtG2G962SQhMtE+dA4QdkmRNECIl/ae3B2yKkhibI3i8XunPHp0zLypMDoxqwtuNltevngOSlHPZiilOT44wA8922FNQlGWFWdnZ3gv3L3rmxvavuOd99/DYChnhpPTU957/wOWm2MePXwgXfRty+b6lq5ZcvjgiNAKBWTRzERCzQeKkd9mDMV8xtntBQ9PT1GAd27SjGxv1wxuoIw1TbHg6dOnvLUoKOYlhTVS7lZC5XAhI3woUpYCCyFlP3uxgZQ5vUs2RUZhfL53G47GQyRyWmIRKes6B3zCpTZK5YBvRKXHXsG9tTXGibtbFIXYasYo/QEpvSIHNa63qIiKgRQVwUfW2+2UmIQgEk0BSQy01iijCcELZ9ZIuXnTbjg/P39l7R+P1yN7bzCMf0Wqwv3jjQNAGEaeI7ArHeUmTFCw7dsdF0TpHf8kRX76p5/w859+ijaKoix48OiEtx495O333ubhWw948OCYo+MDQvDYwlNVJXVVUlUztt1GdOG6TiBWpamqmZRgupbLm2su2y/Zbrc0sxrvnKAGzZzj1RExCDHUG83QC/lVkxiGFu96ichj4OJKujqVKbFFna9jjvd6Uk4fhk46dS8vs+im1PmDFw2vlDzNwvDB9x7w7ocnGP0v4IaBruvYblu2m5YQEtdXN1xf33B7u8F76ZL++U9/ypeffcF/+Xf/LqpU/Kv/5r/Gj37zN3j65VP+4J/+AX/6h3/C//h/+D9heXTA6YMHHJ8eMz9dcrw45mYteoG+D7m8Y3C+J7YenTt+pWtYSmBdt2VwHWVlccPIZxNEwzlHXddSxquT8AZnNS44hhBzJ6Gi7zqiElkBWxSQRNfttutYLg5IBJSXERpCYLMR1xKloKpKVqsDtrdbQgojZZS+7SmaGqWCdCsbg/MBn+Ie0rDTL7u/+ex7Ab+OSwSv41i8GgCi9Cub68ghGb8jBM9iucic15S9lUUdfuxWL4qC2+0NVklAMH7mvjjpqC2YUprQUIWgC+O9m9TjjWW2qEkKZnUl6HuKnD56h4OTR4QUsUVJ1cwYOkfZlDuuTA4A9wMYcQKRAMi5nUDymDmPwt9ys4R7g4oTwb+sa+aLhrOXn+y0O2Pis0+/vFOi1Vozn8/zZwtaqLWivx1Yr9cYJdJEddVweHTIbDETuYvgcWHAJUkAvXdTTFDdW6Fkg3HTdQFZrkgakkrg088+JaXEYrHgvQ/e35VgdNYOS0wNISYJ77TrOrHYys0bxhjpAM9k9VkzE1HrvmMYPKv5TJomXI+PnsVima0X+wlNenl2RlUYqqrmD/7xn/L/+Tt/h7//X/592tYLfyztmoSUMq+Q4u+P7V+W7Lz6+rs/SwmUkk173Ai99xKs20LoGbkZaOy2n5A0rWAPlf+mQ363jyTGCXGbzk0x0T0EJdxHD+Mv4Te92capcrlxXC9EiPzuub3yyXvXq7WUvHcOELsELee1EjinzAkziqJWHJzWHBwsqCq5vwerA95++22++uoZLgS0McShF4UIJblTVTc0Vc2Nu+Xs7JzHjx9zdHLC+dUFn3z+Kd959A7rIdB7z+rkiNnRipPVIUkrnm1e8NWzr/is83wwfMDRwSFWi4917B02CcASraGel8yWB9jbhu1aEqiiLKnqim7omS3m9FciAVOEwMnJA+rUTRIrkr5Htm2LsllUXSlRzRgGeu8obJXBmEG4cLkhSSlJpmMIqKj2kvvdoXMjYFJgyoKiriaO6j4ViLQT2RcjiCCyXDmQHxM6kHGm9M6VyWR+9v73J6RfwcSESUqkYFKWlEmieSvAeubixUBSAWsLFJph6JlVM5q6YXVwgIvSlPW6efKrBH53781eif1XPN44APxv/7t/LXM/JHqu6oZmNpt4CZvthrOXZ6wvO1rfTUrykFAx4YcAGNzgaduO6+srFvOGzz5t+errJ6xWC97/8F3mc7EZGzeg9WbLe++/S900uXFglwnc3t7Sdy1aax4dHLItStAwaCPQed/z4uwFSo3SHkLQnM0a0THLmYhIY0CITja8OND3IvbYdddUZUG73bJe39B2LUVZSRdtDHjfo5OVDDmNgyc3RZQVREM1t5TzmvnBQizmEjwObxGDdCm2nZQCyiClFx8jQ3Rc3rzgJ3/i+PzTJ/zJn/wZH//8M/6T//g/pSwNVV2LOGdVMls0/Pbv/AU++rVf4+HxkUiQ9D0xWRHrjEwbmUwaGaxFUUnnWZWySnlEqFgaH32+voFhkAnocklTG8XI2CAmQVJjxA8DQysafuLqUE7oX9tuJwuropCOyhgjVVXSu4GYPD54hqETo+/o6Ac3QeW7gE+jVMrjY58AnvPtP+ck2j++CY6fLNLyOcWUMIXBFnYKnkcEYeLNIXWskANYrfSUee5/F+xKb5MQah63wssU1EFkSApBU/uetIdGVlVDSBLMGwy6ANLOEmm0fRsDl4kIzY6zNW7+MWfu+4RppUSPEjJfRhm00TJ3nJtkRlzbc3t7twkkhMD5+bloktmCrhCLv/V6zTB4lvOauq4oCsNmsxZ+lU7ghY8UUdLVj3AMU5couu7ec7sb+AkasCN327JkdXjIZrNm0265ublhsVhIuSokYtbJ2z0T2SBs3jzG+zYGOtJhjDx3JVp/xljhxGjRlSQpjDUM3iFFKHED8IPn+OiIJ59/zh/+0z/i9//JHxEdkIQLnNIuQLL22/lB09i5d7yOW7T/vrscyhHNGoMcPXX6j+NwepZx91ljMBUiFMUOOR4baMbX3T92iRqIuPk4XHZlXfm9RilZl1A5AWNXht19/v1O4FeP/TiBfO47BHD3um+8t7lrIabXBYs7JFKpfH4RVIooa6nmFcvVjJPjQwpT0/eObtMSfKIpLXNbY8sSYy3OBdquxYWI1RD8IOtslkYKvaD2prCEIG46bd/R9cIDV0BwntJaDg6PpDErdznrBComUggU1uL6AbRBaUvIDRKLZibmBJkKs1mvcTFmyTURjJ6VM+zmgpQ6Ytbg8zn5UlPFJ+Sk0E+lVu8DbduKOkFTTc8j5qY7nfS3PENJNkyZmzWUoOTj07gvL6Sz8UNih9aOOwXj6/IcCTGicnyjUFl2Rjh+JscOKiR81hkOY8NcrkoO/UDKpW4F2MJSlOXu8zKa2PXtnbHzJgj+P8/jjQPA3/mrP8YHycBsUVA3DXVdT7ZU282Gi4tL2psen4Lwi1IkRRFedL1onXkXJRBJgZOTY7pBxF5lQ3c4L103KYh12fnZJQcHKxSKoszdjE46Sc9enrG+XVNXFd9ZLFnNZlmIWOO1iBKLmKlBK9EQ0kT6dpuDSCccNi0SCWMHnzUGa4RLJDY2KhOwPd55jBHEJCLdxlMJxY9ReObSKOmQ1WrMNES4WTZNPTl+bNoWawuaQhbcYRjwKXJxdY1zPVVlefvxQ1SE5MUb+OL6gn4YxBrPgHcdKTjeff89bClCzKawWQKkENseLZtPHDc3jJTtdMr/zyLARrNtt6gowX3fDyLAjSB/UYs4idZ24sulFLLI9sgX2kHpYxepMSLkLfdZdJJs5qMMThA1lXlnbbuZghAfQl4oRoQgH2m38MP482/eAfZLAa87pncqpkXjzu/jjv+WoqBDxlpsJfe723bZAi8KZyR3bVpjst7iTuVecZebuAs6Y35NnK5IoSdpg1FeJDhH9NJMJYHFXqksgTRWCHdFSMhRVPFzpmu02ZO9SDjnp01/PBe7l4hBLr+nIOemkbHgAykGXN/luEeCXWko2rt3eQF0AhbhxLqClGCWtf3KskQhelnGGsYuqJQimXUopZ5xc3H3yil5jNynAYzXo7WmnjXS5NT3kyRGigltc7NZ1gvcjbfc8AC52zffkxygj4GEyc46hS2kKSjuAmyfS5Vmck0QodjlYsHm9pYXXz/n5fMz5vWMXdPP+Dy/mRf0OoTvm47XIYSv0CVy7CXB291OaTLSp5LeoX3sqj/3aUd5f87oYMrPOt2Znjl8m+bvK/Mt5WQjI5PKSAPLOI92jhFjqfxbb8F0XuP1TnNuClZ3wcT4/7FczhRo7EWZ+YUjgpX2Pl/eJ2ujKTSLgzmHR4ccLJbMmhXBJ27WG3oXoBaJFEGhE/WswehIP3hQhsIorFI5YBOOvB+8NE3GmLuwszSTERFyN2QzhMWctnfTXB7PTxsJ5Nq2I5FdMoK4xZSzUhyxtDTzDX3Pdr3J7jgS9DRFhVvn56nyc8gOKHHkjCaykLzcJKEi+FwF2iUm+88k5Y72+w9zQoeVRls9NR6ovM8EoihgjM1NqCn/jnt6lUmxU3FITLzAMaHfpzSklGRlVWQvchGinprryOj1mKTvScGgVQacZNzEID7hMb1GuQBenYtvcLzKn/3V3g+/QgD47ruPGbybOkpRSrIcJQKyzazhu4eHNKWhns9IaHzeJIMXbo3JfB5jNAbZ3Puhp+t62rafgo2xxFK0IjAdvGfbbrHOAoK0DMPAxfk552cXFEXBcrXk+OREgogYKVDSuFFUlKU0N2ijCQourq5pu5b1est6u2XTCpIgul+BpipYzWccHKyw2rBttyzmc5arFYvFgr4XflJMcWrzd84RfBTuT252SCGPU8/eYhWx2uJ7j8+3MvQeXWja3DAQAlRVw3uPDyBGPnzvffidxO3tmugC55dbri6vubq64vrinM8+/5w/+ekfsh1ueOfzdzg4PGR5sOLg4CHHxyccHFbM5wusKdms14IyJo/3wpnQUcSIR3cQUxSQtZ+kZCivU8rmcnkS+6CyRJdWuFRKGgEoDGWqqar6juVOVTV5cEp5RIJSTRgiTSMNNjEGqDX1fEG7XUsHW1Hi04jmkBeZ++vDLgIUDs5oyzRuDDtU43WlsR3cP37Ot2y8MU1cMeccRVVSzWdgYH27FlsuFVEmiN/x0FE0Fhf9ThsqiVfsPt9k/DN4x6jZo5TEQFJmy8tdAj84hqGTxd5WWQ0/4UfwJAdOglpJs5HKKbgbdvp1ydrJlF0SpZ038Bh8jwjrhLTGrHEYIzFKdl+YSNeuGevCIUaqsmB/GdJa/LxT8Dmxkg1iPl9wsFhO4yrFiNUKazVDRtik5DaKy+4F8vcej3iKiojzqHm4/4zHoHY2m7FYLCitZegHsYEs73IktdppIpJLnvI7WUt8DFMAX1cV1ehjjMybvu/xwRFSIAwGlS0Txw2rbmpsIbziEY2Qbv8E+Gm8jty9nWDxrhS5j4B902awP/7H147z5f5cgFzejBGf+VTjGNinAexXYV9Hxdj9ctqGp8BpJ64+VhF25/i64642J5NU0e6Nr33baw+JQ+S9YmqocoPWHY2B6Zy1Eru0MbG407SS439yM4xLickLWLRNsIVFGUVZW05ODjk+fUjTLHlw+oj5fMHgHM9enHF1dcHN7Q1duyUpODosmFVlHuuWWVMRfWS7NkQfiF4sTK3WED1aky0oJTiqi5J2s5WEzlr62zWHyxllUcj8TSLRVc8a0Iahd+LOkT2Ih6IT9QVrmS2XzGYztpvNNHZiDKiM7tUlE2JWWEs/tNkZZ0TjNVVZ0mU1iZArc1VVEfbuuslB1NiME79hPKiMrhtrc3AlurAxRILK3cDIwjn6d4esbzmihmNyJoL7eof6R7EkTYo764wkZQGjRSKn67aT1h8ukozCmCo7hEQB/qPQJchVsxQSfe+kAW7qin598PZtydz9399F9+X//1xkYK4vNyKYmnbZxrhgjryb+XyOKmdcX74UX9gsbqmVotAFmdtOjIkQspmyFuFXWWTkgqqqwlrL8ZF4na63ggb1vbgiSADR8P3vfx/9AyGghiSl1BjjZHivxh20U5gkG0QMotxdlxX16YxTLfI0LvPwwuiKEALX12tOjg45PjikrmtKW+C9Y3Bt1vWRIE88M7NKeda1k7EtaEnwaSpXApMGG4gAbJGt4FKSKWG1wSTD7fmttJIrGZEaxfzggPnJAd8170lpKm8o282GrmtpW7Fr67pLbm6u+OQTaf54/Pgx77z7HgBFaSlMIBrRARy8YrUUXcWyqlBK8+jBIzYbeeZ933N9c0MyI5I5Iotii7XZ3lLVNVXdYIsSXJgQH8n6dtyssTQ8ujK4rmXwgWYmjiJDO+CBoT/isqon3t+Urau80Kaxe9aM8dLdiaN2G84+ifv+sdtY0p2foWKmMLzm9fk9bduyOjjkwaO3sGXNV89e0K432KZEV0CKhDCgnDTd7OvKKZMlAhJ3Ntgx4AMmNC4EL0HhGJ8m4QYlUl5oCxSKbruZ7BLFqF0R+53AtTR8dFNZd9zQjNYcHBzgs1ftZrMBYLPZTM94fG0/9GirGHzADY7oHIeLkouLM1S2RRoGx8HBAUo955UdOiZsYSiairKqWDSVSEckKKsin5unbVuwYIzChsC2E6FylwNTnS317j4cpvs7kq3H8x7L22NApYByscBYw/XVNbebNbOZuO1025aQx6ut5Povzs9Fp5E46Qa22dGgLkvmTcN8vmSxWLFer/FeEtkmN70sZjNiFsCNCI3lxfPnPHr0gMOjg0lzM+kRh7p32/a4rffH+utKveOxT2e4u3nsubzs/V6CLTWV5GL0OWmz08ZSluVUDt/nFI66afcD0um8tJqQwjGp2EcAv+kYS6ujD/Y3Nf+wp3P46n1g1yAwwpN79y7tBaf7t1HtNXzAnnNIFOeL8edmB6DuJSjikmMLy9HJAWW1ZDZbsFysWC5XdP1AYSsenBzx4uVLLq+u6IeOm8tLuqGjmc2o5wWlhccPT9Ah8ezFS1zXYo2mthajE0VlcxVHqgE3NzdcXF6gygJTlSQfsnyX6JGGFMFoKiXX572n73qil+u5ub7hi88/58XLlwQSv/HjH3F8fCzzxweuL6/YuMjDWYlOQ64y5HWW3IU+6rUqRT8M9EOPtRXeZyebjIyruKMbKCUB/qiruX/sB9/KaGaNWNCRpIs9EAlDoDSSXIacoGlrJ8mo8b0jF3BsyksxSmOP1jmxzaoTxmY0LzccBYcfxMEkIg5WwifU+Libo9Zoyqqcxpk2orLQd3lde7W/5ZXxvD/23+S4m+C9+fHGAWBpxElASi+O7XY7ZdSrpWjoAQxDm+Fyg8ZiKCi0Yeg6EmKPo7SitAbvZVLGqKZgoSzLKYjr+x43uEkncJLGcEIivb1dU9jsiWsQK6SunRZ/s+dH2nkHMWBVNtROYpw91ipSSjRlwWJxhEJKt0YpunZD2w90vTSLGGMoG/FNFU0t6IMDlSirir5r2YYNY/moLIULWJQ2I2iKrhsIGQpOaq/TMBakGAk+0q4zj64YH2giRsV2u0Ub6PeQg3FRKgpLWR6IK0hRMPqQhhhxfs3HH/9EULmxnUcJNG1MzfXlS7SxVGXN0dExN0VBSlA3NbOqQK0atiEyaxoxulea0lqauoHDw8kxQQ+Ba387Laxj8DciMuPPpcyWcvAqWWNdlUTn6XxgcAPWauq6pO03Uu3NPL+7Y1zlefuaFvm9v+///9smybf9LvhAipGyKHHDwNB1lE1Js1iyOj7h8qunXFxc8vCdh8Toub66YLU6FZsiW0zh5NRE4sOOV5jviSks5J+NVmWysAp6rDL6rpTJ3LjMj0LskJqizEGUZLJNM3aeumnhG3mA+89i8O20qYvXbjkp9u8HUzFCGALWauazmnl9iHJbbq+umc8a+iHw9dfnfPzxZ3dK7WNiU8xkwQTpMrz1A3VZYo3N5PAekOTBk5GApGnKii43UYxIkE+vNkbs2zmNQcYY/I3eveNmss1uCMponj1/znqz4Z3rx7z79mOZP2nHlRyt0MqywuRAchwrZxdnfD0MlEXFD3/wo9ychtSHUiD0DmMKMDsxb5Tm4vaMg1nNb/32j3n25Cn/8O//I2lEDbtxel+0/Fc97r9nF5gJUnJ/zgyDmxpVhGvq7pzLmEjsI5J3EEJ2Adn+Jj6W1kbdwl1Q98uvScpzQEh3gjiVqQ1vctyf1iNPcfr+qQNYAkiVHR7UCAftfxZSikSPXcT75ykggNAUHHVlOH0456Pvvctbj97LdoU3JDTz+RKlZD429ZyHpy3r9ZoQAm234ezinNvbW24vr3DeU1UrHp2sOGWJj4HOdQztDbN6TmMNVjcENH7bEXyk69fQGk4Ojthut6xvblkuliyWS7TWrNdrzs8u0MpQmBJrNNEnZkXJb//mb9H1HedXlyStJ3WJ3sm617U9Lz7+gvfeecB8MQOlcX4QFQMFChkXQ4ikECibGpLwBGMUNH12uCAhuoDe+zw4cpn3HoolpXoRHC+NwZSl5PhBBKW1HbntipEjPkr5qL0EyWQbTOc9MYmj0DhOR77epC1IrtwFhOriAqAnFy0g07hAACCDVtJ0EpPYISrBEvA+sNlsMUVJzLZyryLwv/z4Zejgr3q8cQDYDY4h7hbEpBXWFDRNLYbhiMq1D71Y0Gidb6A4a6ByBpuTwNF3ddRemsyT9zJDrYX3J4uSuC7IhiRq43U9w9oqD5aYv0fJJiq3S841l6K1AmW0cBCMBZ2Fj1Mu9cTI5vaaGKXzZxSlViqzrJRCKcem7RlLBbt7nuj8diogSFktkGJNCgGt5ZqsNeigWW/WpBSlLB0CdVMzkRa0oqhkAxpV/mOQa6nKZppcIkiccKPg67goJnBeYOix06kuaoIe2+79LhjSCsWAdx0MCtev2W6upmcw8fWUojAL2owISTlx4OWLM45PT1gsFswWc2aLObYpJeh0js1mw7bdglbinJA7uWIS0m3ft4SYiJ1kqEZlTUE11lhSZhzcXaBJkRAj2shGdn8D2h13Lav2M6X7aMjdcuyrUPr4sSmTpdu+w6aCZj7nnXff5cVnn/PkyROWJ0tsVRPDwHzW0Pkesv9viunOGN8/J0GmMg0gI+JCCRD7IJWSIA0pYpNFaSvodwhE58EFtI8yAlNEWwM5wdgJ3e6Q2fFnMUoJf+S/zefzqXw/druOm3xRVCgr82HsANysb6gyf69re26ub5k3K0CCufF+G1NkgWSxaUMpbC45VqOTTcZRYoqymaQkXBpdMKiBIXdZK/3qYiclYAmax/LP/rPev9djgrDebNDGsDo6pGhqOifJXrIFMYYpUZskU0h3/m2LgmY+o6pLrC7YdlsWswWHh4eM7h3BiF3h1cW1UCcKcSpxznE1dLz/wXv8K//qvyKyUS+fidOBsdN3jA4uv+x4XbA3/nx/bKeJO/vqZqIzt2ocKyZLcoyJwIim73daT/p4e4HgfmI63nsZa/uI3htdVr6411xruhvEvulnTvNPjXzH1yAu+0lj2pWPx3MfS4RjWVQxOl/tmmnK2rBYNpycLjk6XlLXDTNboo3OPDKYz+ekpKnrGU0zY7U84PmzF3jTc7haMW8afHDc3q5FOsiJoHRRFRwfLihtRWErnIsMbsgOUJ2ANS7hssi5zwllP/QUnXD4R7ODlJIEdr0n6UBjS2KKlGXJW2+9RTLSuDYGUMZq6qoiXBXE6AnBYzKlxztHUjksj1ICVYjerkLn8/CTcxBqt76naa1/9ZmNP9uBB+McjkIPQbiJKcQpUJT9MQBJGjlyQKiNIelshrA3TpVSU0l4HNPaGmmaSSKGLV7snWCAE4IeiYzNRBGlzNRPkJJIKAUXcYN/ZXy+Fim/9/tf9fhVEsU3DgBb19P1wpWz1jCbzVCMxt6ith+jFw6WSnl3QCDVEDD5IYuAbkRF8mODTE8iErOR+ZhhKuEbaHmQIaSsRB8prBWOBUkCmrE0k8huGqPivZzbWC4jCfsjhsB9PmYk0A5D7jDLC2ex877MRQbRFDQGoxVGK1S2YokjTyR3H4VR98jvOgeNMWh0FrxMpKTph2EqByolA1nljVGAxHGAiL2QaI0qqahMc0jnYDpASmJEkpIEvYAycq5yf2RzT2OAlXzWs0ukzO1wIQh83+Xys9JUppPtOYH3js16zdOnT6k+k9JvM1twfHrK8viI1WpFVVdYNJUVcnyZEwVSFj0OvTiN5ADcpSQZlZXOv0jILg1iN4WyU1Ao2Z502qHGDWcsJ90NAMfnd/+4UzbLizfjc9cjn2fvSLvNQ+sxIIDZYs57H77H7/29xPMvn/O9H3wEKymRaqvE3SJ60jjQRbBQ8MyxHJWUjM8cAE0bSZAmi1xbIjGq0Ys3sJrcSGShizGgY25CihGtsk/yXpAsnq65rKvHIJSpi7co7BQo7G/yMfPzJBkTxxkpGV3RVDrzXgIuGmzTkPdGuU8pl82SlKBSXvh9UsQgc6sYk0CEg5b2nk0mOxBjyNiVemW3DzHRDZ5ai0ahNeaVIGT8O0iA77yn0GL1VpalIBQp4oKf+ITW2qlKsL9YGyVi9vNmJgGRtvk8glglquzxbJSUi92Q6R5yr7zzDK5ndXTA93/9u3z3ow94dvYSUyaOj0Un9fz8kmHYBSnfxBF605/dP+6Xm3YB3Pj+u7/fR1nH9++X2af7k/+zi6d2JdYxiPpVjnHdkdwm7f6dv+3bPnN/6ksiKZFiBpyY0NCpiHvvi8fEL8+TUUA7ZTpUSkyajVLV0ECgmmmOTuY8eHDCrFlijaFpZhg0KYpsUJl9pIWjbqjKMiNqfkKtBz9Q2pLCrun6Aec9KQVUCBgLJiVsdqC5uL7lut/S+4FuGBhCwGDo+kAMHq0GjJUOYoXob7bbns3NlqvLG1KZWH2wIERxtlFa0cwWlLZkHTfZXUaUNIbVCmN05u6myRkphZS5eruGN9FOTUQ/Sq0ZQhKFkDvI68ihfN1DVIqkElVZg5L3a3K5OVOD8hlAXmdAFCxIEg9ABpuUJowVqb1BkqaxIONM5/1E1iIlfMnBj7gEpL0OY5UpAUpR2BKr7QgHZd/uIXMMXx/w/XkQwd3t+fO971cIADuc6yiMpbQly9lMumidcHPIwr+2KEhKk63mpfyiIioLBiaERE+EyoiVS0Qe1igDUmoFaEKEwXXMmhk+OvrBZ85cQVGVEiC4XtrMGTemhBBphOwZgmTxktkaQhT5k227ZuzwNcZMreuDExSmtEI0BXkwPlsgkTuBEg5SQYqaFLP/oTHihqEMxlqxtHI9Lu66p1KSziKjtHTQ5o3dZ06JVgod4ySWO3K0rDGYomBwPSruZzRSqlYqy0bkQM4qK/4jKkPewyAbuLUoc9eLMyF8h4mbUECVs/wwdj97z7a/wRPph0H4hps1zq/56ulnvHh5QdcFZvMD3n/vPb770Ue89/67LA+WFNZS24LSltJJnsWmBdlIJO/wUTq7exep64oUHD4EXEyYVEit3eaATEUgSwskk1eOtJely59xExqPiZszNVnsoP77AYJCCP37hwRkMQcioJQhRWhmMz746AO0NZx/eY7bDpA8zm0Y/BZjClzXEiMYnZsjYhJnkdwtRtIYW5Bil11Ex866kO3WdqR5UxT0XbYyVLmjV0OyiqAiSklwGILDFCWj3RcojLFs2g3oiqoqJ6kPpRNFWaNQk/2fLEh6mscpJVzXkhDhblMYCqu5uLhg3mg2a+HQKjvPIn17ESAJH5xcy+gfhyATFsUQI6oUMeWoBCE0OgevKRFxUurWZKFs0fjaP3yIbHtHMobK7tNGFN+EAGutp07FylhWs/nU5eucLPRFWRJcLi8lNYIW0sgDVGWTbQ8tSUHf9aSYsohyImk4X1+ii5KyqjFGoZNIY6WoKErDycMlv/UXfp1/+A/+gLpJPH7nlKpsuLy4mEpMOZVjH/V6JWD5hmMfmbv/nv17sj83GNeU+/cN7pTatZKy2bhejEdMO528sdFiFx7+eSJASEntlVzv8wdf/cwJzd/lElMidTdojNNacWcv3Q3VnHRKE2NK4FwYww1U0lPVQmlLYRWrw5KHbx3y6O23KM0Kg6Y0QncIIdBut2y3a2l4GnZ0jOPjFUVp2WzWrNcbBudZro6YL5YMQ896veHq6ophvUUNCW/EJeRovmDYrjmPHbfXV9xuO1yIuXO4kKYkn4EQrWjqhqZs2Fx23Jzd8vL5Gb2JfPjBBwQi6+0tm5s1R4sT3n70DkPr6aKjqC0axepwhc6l2+ASqRRqUfLSGIGWgE2GgaCB0flJeiYk0CHtBUUqu0e9ekizRyIqqKsGpQo80jVdmBy+h5hZXaNHdm6eUuROfJGysgXi3Tx29wssnQNFCUhNjlWCD2gt/0YboW+5iE4St4z7AVHJvmTIyWAhlqhKkoKUAsPQSSnZpnvzcW+4/TMEgePxz6UJ5Opqw3K+oKwbojJ89fxSbNG0oio0pdEYJUK+1pSCiuWMvygMRdXkGx1BW3q3xVqdTeAFeTJFQalLCHHavItKpBGCazMHp6aqRD8oBI8LEe8iyTlMYamqSlC/GBmclFlC9NR1jS4jQQ9s2l4IukaDtmAMpjBsuy3BeAoMo/L+qC0UonR1WmMnY3jvPMFLQ0cii04mkcHoh4H2dkNTiYWa0moKKHvnJjQpkUhaQ9eSEpNobyLR5rExBnsmq6yHYRBErSynP2NJYnJAyBub29/MjRGSbkYfxwVn5EaNum/AxAcbfzYMA8l4CoSYX1WW05MDmqrAKNGuWm86Xp5f8vWTL/nH/+i/4L/+R8KLXB0e8N77H/JX/+q/wOmDh5RVhQ+JPixw0dH7DOOrSNdvgZ4wdKQQsVo8LOu6FrPvRF5oZfHWereZ70pQu83mTlB3D8W5///xmCbgK5CCdNC5zhG8p6gqhm3AWsPDR484fvdtzj//imcvLlieHtLUM86eP+fg8BF9J9eYby+JSFlbMV5JHpK4n6TgJpeU8VkIui5UBumcFy5KSpooXnSIUKrZI+HnjUwNaCMZaAgBbRJVbYnB0/dper7BJ8BPY2WnAzjdSYyVgLW/GYCI8YkBx8XVS37jO+/wxWd/zJPPv6LfdnzwvQ9Rf/LJ1NE83tcUReon5qqB0RqXklizGSNl6+zCEiekRjhD2hjmM3HacIPD3SuNJiCkNDml+BhEhy3tApmqqu4gVpPQdb7mrpPyWVVVbMKa6+sr3AvH6ekphwcH0+ZitXRb1rMZMUs3bbpbqqoRlYKupywsZVViTMGsrpnNFigSXddOQcZ8VtP3W0od+Pf++r/Nf/wf/d+5uLjmJ3/08dRQI2vRTgppf1i+brPYH89vQijfRzV3QVHCmDFQGuddXiP2HERijBPd587PcgC5/x0h/OrI3+467p7r7jxfv5G+7hpfh57ef9u3bb33KwJKjU14gYRwv7SCQM8H77/NR99/zFvvnLBcLJkvSqqyke5wLaV0k6kK3ie8F47uYrHITVeJ1cGSsirwYeDFi2eANEyWZcn777+Hc46zszM+/vjnXFxckIDf+PGPeP/dB6wOZnz9/CVnZ+fE/ho/RG7XLdvrmuuzGSjLbLng3Xcfs+7X6Hng1377XZ4/uSYMDmMVy8WCuqj4/Bdf8nu/9/tgFQ8en7I8/pCbmxvK4LApZfBNbCz7vgcMJkuWoaAwBSjRzeyHgd45bFFBqUmD2zU4IXPXe08M8c69lrVALE2bphGpNpVdaZBnawtLCH5qKgV2SclYfctOYvc1A8fXjmtBCjFTnRIKqWCiFYPriSnsmWJI4Foai2fY+xxZL0ex6qkaOCb03xLk3aeqyFh7tZL1q5R6v+l44wDwxdkFV9e3jKXNruuYzeb88PvfZ95UWJHdJsbAs+cvWd9uRFusMNiy5Pb2Gtc76rLi5PCQ08entH3H9qrHBdH5iilxuFyJWGWWNgvBs92umc8amrqhrGqssbTdBh8MZVFSFQodnZi0C0YvHCUjkg8m3zTnHW0rEg2z2XKya9JaZaQroK34paq9hcU5MXwfO0XHRhVrrGhT5ax3WkBDmoJHbUR5feQXjOc4Qs8pI1Dt0IvQZH5PiOKooHJ2bbRGjYM2hKkDLeXNMe0NjDEAnOVAddItY1fKG5XMXfAYpadGARhFdHdd3gAhReZVRT84jLWYZKQEHWBwLQmoa8t77zzkow8fMQwO53NJ0Htuty3/6B/8PWbzFYeHRxwdn6DqOav5kvrkMKM9EIKj628xHOG2HTeXVwx0ODegsYzyLlpZovISDJK4G9jl0vDeM9zngI2B1f7v9u+d3DODvleJCFE8IN3gmM1moCLJCf9n3iz4K/+tv8rf+fo/48/+7GOKuuTHv/UDzl58zeNH7+JCJCDq+uPz94OTzyDlRKm8o8MncilhsjYrs7Co917EpNO+HZdsuDs9P6EEhNDfLc0pRWEL+iB8oRHdHV1LxnEwfq7z/aRBmIA+RCpbUJmE8xsubp6xXNa8eHHFZ5885YvPn+CU5S//tb+K/X//PdizPkpp1AeTRVNrDUZDiPiUwAspW1s1LdAK4cOVVUmbXWQKa1/ZIHZHwoWBroO6rKiy//EoQj42Eo1jI8Y4Cc+Px5Cde5ICWxW8vDrn088/47sffoeDxQHL+YKD5Sp7Vac8XlQm1t9QWelwLguLMZreO1bLFSOVQim4WV9xcnrMoinpbs/oN2t0dPwH/8G/z//xP/xP2GxaYtRSYWAM+Mexvn+937yRvG7T+LbjmwPEb3/f2NlM3BNnRpCVHXf7rjj0r3KMCc245b5OuPnb3jtt1fsbK3++DXR/Luk9+o8xmhAjPibKBfyFv/x9fvDrP2S+qClKTdNUBC9gyDhfrbVsh0Ecm3LFZb3d8PTrr7DWsFwuWSwWfP/7v8ZsVvH06VNJhGPk/PyldKDXM374wx9K4JMi9azh4uICFToeHS14+/RAPITXPV989oTnLy94/vQ5X339El0UfPLWQ77znQ947/13OTk+Zrj8hJ/8099n0244fXDKb//2b3N7vGHbtiQLVVVQaCXqHC9bUt9LKV0LMl7YkpREucA5L5p9Me9BIQqfLsbMv0P2epXpTtOzfrWjXSp1AoIoLRaL1histrmCFiajihh3gMC4vmuts9TYjsYw4dETgBAmFHCUXjLG7MZ1jJOINTFmZC/bJyJI5kgdiklMHsqimByKBj9QFJagwmvH0/8/jjcOAOv5jOAGQnCkGKhKDdHx7NlTgcaTcG680Vxe3jC4QW7OoIm3wlc4mM05OTjm9OSEjsDHn35K1zlxOcik0BfmOfO6ZrlasFjMgEjTzGm7jptNK3V5pQkhm5GnSGkNBwfzSd5inNyjTEFI0mkk2j+G48OjHPhl/qIPpOBpykYGl1JkGuFEmLdZIHm0lBFe2m6QmVyileBqT7srZdq4GvmDIlEzNsHoPKitLVDZ6khbi8FImTh/xrjiRcR+a+xUShnx8CFM5zb+GaUsxiOllPmFYylVsh0fdq8byd4mB6Ehl76ttbRtRyRzMWISX9Zc+gqZl2KNxTvh+1hToJWgqQdzKalqW9C2Le2TL0l4lvMVq9URdT3HFBXKQN1Ynj55ws3tVUZcZ7JAZoReHFzUxMODvBir0Tpt5x18n5s03oe7SMIuONwtPLty2Hh0bctms6EsCtQI/6ssDZI83/v+R/yXjeWrJ19zenzED3/wESH0rLe3lFWNj4m+X6OUlB5G0/GUM1iilAvHQxp/QCuLNXZC1qVRZMhF4Uw+V8JBS0kJg1CNPEGbnUoEDW63A1VdTNIuo7hz3/d79wHRq8rOC0nl7ks1pkWOsihwQ8/lxXPeevst/t5//vd4+fKKejbn3Xff5Qc/+MGd5yOk7DgJ1+YWD0gWIxMWHxwxeXSQLJ+M8sQU8OROZiM3SDaDu3oKWiuKwuJ9z7ZtpzlTFePv83yM4m4xUj8Uu8RoPISLq5jP57zzzjs8OHnAcr5Ao+j6lmHoOT19iLa5JBhg8AM+eA4PDqnLGhBkx3vPJ198QlVV1FVJCJ4vv/ycxeERqTIEP7BdX3PVbfidv/Qj/uLv/Ig//qM/4+z8Mp/L6K098jV3AdDrkpf9zfN+yXuqpe4d+5vkLtjaBU535FO+4RirGcAk/MveOb4OZX/TjW/3vbvu4fvf/cs+O41XlL69xDa+7pU1IzIxQsZ1RecSpwoIZ1orDg5n/MZf/JD3Pnyb0wenFEWJ0pHSlpCkiUKp3To7Juvjv0MIRIQfuN1uODvTVFU1dfGPiVlZlgzDwM3NLSDJoi0sfe94cPIAENWCoR/o2pa6sHzvux/wne+8x+AcV9drnjx5ztfPXvDkp7/g+adfMp8t2WxbumGLT55NVXPx4hKN4ejwmIvrc24ubzirzzh6b8Fms6UeA54Y6bddbhbJTXcI1SuZXdAlUnKyn3jCTuJKj3MwZKm5V8dojNKYid7RL0JGm402+MHnAHMfQVM7TjN7Cgw5AJT7L25gKSUZuzFBpsaMiWgMkZiTY3E5G4O9seN917hqrMZYnTWCrdyPIO4tPgbUr2DA+8r4/G84WHzjU1m3LToGSAGrYT5rAM2m3dC2PT4EyrIiWkU39NkTNWGNYT5rWDUNB7M5i2aO0opnZ+ecXUnHrTZW+E8hEJJY46js3GE0hKS5uLpl23dibRYSKlqsUsybAnswR1szEczHbHmcMGPwJwMom0dbIxydmKZsxNhCSrUZQUzsAoNRBiKpMR7LixFM7hW70iFCih0fVkYlE2QegsIUxY7Hp4T7oIxMZG1Gc+1dKZe0K3FKB9Wug9M5h8uDdT/gi0om47SoK4Xas7IaFx/vPWUunY8BryxKSRpoUpReKrlYua8p4VyAqCirQuyRYiKqmAV6R/K9nENdywIYAR8Cw9CTXMt119Kt12hdkrRBWcXpoxUvXz7h+uqabtPTtV42+xz8KQShkwBln5C+V+qZMNzxEez+NWpZkjet3daVXnnt/iGdX+JjHDOnRCuND+Bd5O133uKtx2/x1adPuHh5wcvnZxy/fcjl1Tmnj95CEem6NVXRyDn6XKxWo8yLSNqM2aSAYiHzj4STptTYnJKm8Ukui5uxiz6Jcr18Ts62c8A6DD3W6omPOpVKFHlDGjfA3Kk5Nm6M90VrykLhhg3b7Y2gmCFxc32LC4lmueThO28zX8y5cxuTNA6FbF1HzppDDBTZHg+tMnoQpFQrD4uYYAgR5wd0NFNs8jqpiBDFUpAYGZyoB4zSDjFvNLm9LCMFcXrvfhIwldG1ZlbPaKqGpqwErd+2XFxdMATPYrVkMZ9D3hBMUWAKIyWxjLQrLbJS3jvaFBj6jtv1LRiDTR7fbXFDy/XVGYvVCb/zl35IP2wJfzpwdXmbl5F95EnnDef1x90ALuX3jq9/fXl4vAd3A8a7c+JOMvVKwgRMqyZ5kVRTg8DruE5vctz9/PuB3Z+DK5UDgSkrfw3jTO5YvtZXApFMQWEkmow84kRRGQ5P5nz/B9/hwYNTFsslVVmjNVgtSPTIJYzZMamuawRQkrXX5ybHmAWTQwis15vpXngfctKW97cg9A5jEijo3ZAF4oucFOqsLZowFkpTsFw2PDw94q3TE558eczlxQ031xtur69YtxtckMrd9nbN5598Bmgiis31BmUStSlYHxzRt1uKqpK9m6xGAChkbSavJSFJU1sI2T87RaGbp70qmNqV9aemkruPbULhTCEyZCplmRYt9PU0rvp5jRSQQE+KBqP+59h8uv8Vk+PR3hzzd6oXcfp9SvnaMhA0xhny492+CkmeeVJ4l+1ZERHv3ee+Oje+7div5NwvE/95jjcOAM/OLmgKQ10aqnnNarnE+8jVessQIl5iQ1Lc1ddVStRlyXuPHvHw9ERQo67j2eVLPvv8S3xIFFkU2FjJxhdlwenxIc28EXsmAtdXG16cXYrenkYifWdoCstyfizevragb9vc2MH04IdhEFV7pRi85/r6BucGZos5hZWSok5QlgWN0Xu6T+OimSYoeJRDCFFQQ1KauH2joGYIIXcYhymTxGTrmgQoJdIRZYXN98l7D2Enrs0eGjcNkLQTah1t6cbfhxw4j4Nh2thNLmXn0pO2Ozh7P7D1UfhXY/lvutbpGnQuOWftwjG2TUqkd7Q0tYyvQ5mpuUAamSUTGs8ZEkVh0aph267ZrLe0nacbHEkHbreHdNsNt9fXXF9uuL2JNHVD3w74TLo1RuU2/P1z2iuHplfLPNJhOlUg76AUO+BPTUjq/Q3GWktdl5R1yXazyQG5JnlFcPD2oxO+94PvsT6/4frqmp///BP+vR//23z51TMOjw6JKPzQoxE/7aHtJ5vAIQTKGFF693xShME76rrB+UgMwjGp6maH3uVFSamIsTpzTML0OcH3OJcm9C5EhwsiND4mEIJel5krk3KSM46nOEk/xRgpdMn8eMbz519zfX1JXcx4+sVXkoWXBfOjQ04fPxZR4717N1Ep8vgaEx8/DChjsz7XrmNXOhAlQUtRmkJC8IQc8Ct2xP7xEJeBFp8bqHwI9MNAjNDUIsdEtmcCEaMX2ZhymuN3Ao4EhETwkagCqqqpywrvPJvthpcXZ5w8eMCDk1OapsEUlqI0DM7h8NNmoa3h6OQUomfoW5wfUEZxc3ON9h02bEl+YLu94cmXn/LDH32A8xtS6vnjP/qYzXrIqEoOTZQC7mrv7cb4fjI03vtXg8X7730daiiai3cDxhzyTJSS+wj7Tm4IRm/U+yXbb/re1yF59179+g/5lmNEtF8Jdhnvz32EVH6Wxvs8+nuxfz8l0Bg1KY2W5qXV0YyHbx3x+J23ODo6YdY0zGZLyrLMdpoWoUEEEhFjdNbW9Sgn8lg+j3FSynpz0Pc9fd8zn8/FiGAYiDFgjKYqC3wc94tA13fCYx2pTON6GCPrzSYbIRScnJzw0Ucf8OH777NZt7x8ec4nH3/KZ1894+z8DKUV/WbLp+e/IEZFWc/o+i3WGkpluDg+Q7keb2TdT9pM9yXlhs+xSiWizIJIeu8znz43XmQ9xVHP8ZuCmuk6tKJoanlquat8lOzXSlQNVJRnbu3u+se9cgz+zNhtvPd7MYFIE/AyjYvx4Yc4yeGo0ZkoN/FNwSvS0DKBEShSAO+EJ5x2p/2NNITXzevXJVBvSu34tuONA8DjwxPOXnxNZRccHZ7w+J33+elPf87NzYakLbYQ2zajxIP04PCY1WLBvK5IfsBvW26GlifPn/HJ559TVUsKZSiURuVs9nCx5Mc/+B5KwRAG1ts1L86e8ezZDQ4j3asaysqglOY3f/zrHB00oALXN2vG1E6Z3SBq25aiKrld3/L85RlfPz+jqua4ry4IXhBBozWP337Ab/3Wj3Cuy+3iQsqXIEkWXMni5UG4wQvEm/2CfQgMfQ+ZyzE2Z2it6dwwtaDrsWylVeaUOUZNKpVXJWneEGHlGOPUBWytnbTX9gdBURQUOZCAXeDo9jIYrTUmiazM6NUoZS4h3W9yQDN2941+jVVVSWdmjKQoIrGCpAr3Tb7LTsGlcyLJ45x09iot5NzOtYAscFPwCgQdiYWiKmpq5qClw7NQFaHTnD274Wcff8qH77xHcFe4IRAieB8pSjWhITt+1JhFvhrA7ZepvumYgmcF6Z5OUEyJtpfyolF60nrshkH047Y3HD08ZnW45MlnX/J7v/vH/PC3f8jR0YL11TVVI7ZnP/3FL3j06G1832VhVAnAFyqx3l5l6z0ZQ1Uzy40YGu/FKnG97qnK2UR41kYWW2s1g+vkZ4OadM77rs8SEzUxBa6uNiJTkD2ipbNym50y4h1+jIisyxjfbrfMcagTy+3tBdE73n/7ff53/+n/novLK8xswQ8+/JB/7d/4N3G3t3fAFaWYBKb3E5vKlnkx36GRMUoTFbbAZGTQmILKKLZtlzfHXTPC3tODKHSNERn3PuCsoON1aSfLQe/CpNVVFDaXq3dNQjLeJdipypoQHNfXN3SlNI0cn5xw8ekvePLVl1xeXXJ4cMjJyQnHB0d8/sXnnB6dUNfiZNP3bd4sjKC9KWXOIDR1RdispUt8aNnGK9r+mnfePWS1+hc5OT3mH//Xf8xm3eO98JSd8xhjp/G+XyId77X8/9VS6D7C92p5eDcHlMpICndddMYSpnT/6qmsus9FHT9vTBTjN4OVd7732873dcf+6/eRkddtqvv3aVdG/vYNNKURPc2oX3YiUQmUGe9hoigK3v3wLd778BHvfecRzXzG4fEDNJaiKGnqBVp5/DAQtJ8Ck+12w/n5BahRwiphlJQkB595Y7kpwlpLl52u7jQxVQm82I6lmKgqKQ1vu62gxEnmQ1nPefed9wWF7rZstluuPv6ZCL+XBasHB/z1v/Bv0VRH/O2//bf59NPPOTu75CZ4vnr6gtlsIbxn5zn/+jlnpxUPD2dScXOOoGK2o/RoTKaQSONH0hqdZE9p21aMASCbNUmmHolT0Bb9Tmh8/1lro9FVwWy5yGh+IikturHeC9/eaEaK3QhE9X2fy8I58R6GnOyZCRwZhgHyPjKCR+O6x+SBvnMsu7+NjIj5aPkaEPpUiorgAkPv8C5rs77BHvQmQeB/E8ebcwCt4q/8pd9hsVgwuIF/8I9/l6dPvqIoS5qqojQGrxVvvX3C6dEJi2Ym/DCtoCkZUsRtIioZFrMDXEgT9ynGKBvhbM6z5y8JBDbdluvtmu3GgS0olXTzqKA4Ojjg3ffeZrGoaF3H0HtcMlgUB6sl3bYVj08FZd3w2Zef89Wzr3EhUc8OiVFRz+copSmNZTWf8/2PvkPXtYToIYTcuSslxhBEf0wWQiW8DrXruA1BTH+LoshZjQSGKSXW2+2dBzh28yayWHQSyReAyiIaUewGwdgAsi9nMSIy7L0upjR1944/t0UxbWghRtEhytxEaVWXQqExlrZtd58VI2VRSLc14PMi1Pc9RWEpCiN8MxS6LNHG4Fy2BMqlx2FwGKNFhy4J/H2nQzmfZ0g+WybJJm+tJQwtV+drnj+94uq85eTkhKo2FKXGOY0bsi9rMW5Gu2DvlxPEX529r980XoXmm7rm4OCAmJugus2Gze2Wvh9w0XN1fs6mXVOt5syPDrm8XvN/+Vv/D/43/6v/OS8ub3Des1gd8vDhQ/7oJ3/I8eogZ5Ka09NTBhdoWyd2gkWJtRVGWa6v1zsKQtYM67o2B+tQGXHhSYqpbB+jBIRt3+ZgB7RytK14by8XBxMq27VrbGFYLBaMbjtjiXHkwYitWc133n6bP/zj/4rD1YJCWf6rv/cPePnijF4r/tJv/phf/43foDSWbi+IlPG4a5YKewmIKcROyTsRu465vNL24kdqjaG0BWVl8T4yn9XZNSeSrq/vPB+jFWWROyvjKFYsS9x6uwEadIBCaQpt7sjEjMf+2Bzd5nwQPnORecqJRFlU/PjHv8W2a3nx7BlnZy/pux6rDKcnp8yb2R4nORGKkBtRDHVds1ws6LuOylq2IeL7njg4gusoKNh0N7TbgYODmvc+OOb87Jbbm5btticlk4Or120IKQcsrw+mfhXUQNCc3WcJvSBmtH8Pac+85/3AZBSr//NuWr9sA/w25PCXHb/s5feD4f3vm6oHjHZniqo0vPPeA97/4BEnp4d5vYzMm0KaGNs1s2ZJSmIJOQxhqr6UZYnzw7SmhyyFBjvHlaqqpsBppwwQ8pq6S/jHvUErRZ2VIUbqiE6Wj3/2MV23RZnE8mBBPwwcn5wQU+LJkyf83u/+HieHx7z/3gc8OFpxdXnD2dklP198xpdffsXF+RU+eMqqoPdvoXRNDI6oIkkZyM5OpJQlmgRAuO5aQnL0Q0+IkSJbvfrOM+4Xo7euc37S+t0/gvMEpTGqoFktRNzaeQYCg4UaQ2EEfVfs5vFoVTtWjfYPpdRU7dh1AN8dWzGKdiHeTw2pIURGFYqYNYZTkmpgYS1VXVKWsq5ZW9C3kb73eL/XJLVXcZrWyV8yLl+H0v+zHm9uBVfC5dUZz18+E1eQlFgcHTKvK47mDYu6pLRQHa4giqZfSpEhOL46e8n1egNBoZJmVc659GIk37khL1hw220ZnvXYpqSPntZFfGogbYluS2kKVoslbz04ZTE3PH/5hMurW9qth1TSFBUvypdosu1UVdC5jourK2xVYRDuXAge7zpKYzk4PuTB6Yyhu0IhKJfRFlsU2MLQ993E10i72S/SKyNfi5TFrjVGS4U/jfwfpaSMlYQHoZVsPM47Yu6SRUFRlViVO5D2Fs77Iqv3yerja3all10Gk0QmUMZV7lgDyDWR3aI+iVKP5yk2XU5rVNZ5SilhdE2KCT9AUCLIWzQlKWkUdipNoTVlufMPTSRU0RCSdB6LjpsiDYFFPccNTho3gqeezbi56Tg/e8719QVFofnhD3+DT37+Z/isXF8UakL9pOwrQdR4L1JKoybodE27jT5NP5dbcXej3N3rVzeWtu+4vrkhZPmFfhjwrWfoHa3vMGVBNIaH77wFWLb+KevrLf/4//v3+fFf+UsMSvPs+VM++N73eX72kvXNhrqsWS4OCNGyvu4IQRY5P0DXblGqRWnpHjZ53NRVzXzRyLWjMFbkQoKPpKjRqqQoShE3Lpb0/YB3jr71eKdYLg5pmmYaN7NFncPiKA1JyrBZuzvd4FVVcXJ8wsXVC3wYGAbP1fkNX376OWVdMTs84Hvf/wEPHz5ifXU1OWjs7qswG/pekp3x2Q0+b2q5Gj+W3nzwJJewXrLztL1luVxKt75zpBCpwv3vEP7uppfys7jOyLiPIbDVkVlTi9A4O/tJGTeGkS97f1yIs4jLziTCzRX6BlRFxfHJMYW1HB4es5wtKHQhItGZtK7ZcZFCELTRWEtRWNww4Ie8FjhPspH17RaVFQpOTg559/1HmQ82Ivqe7drtNtx8pFwBmRArdsHO6HSzf9xvetkf//KPHafp/rGvF3g/SBL3mpiRv18tONsPNvfPSa7ll3/W69G/6W9vfB773KwdHSTfU3b7gNYyPm5uzrm8ssxWBWVdcXNzRWkrdGkyot1KMqLMFOwJp3fHbxs1V0mJwfUMY3WInRvM+N6pUS8h/GtGyTLZb6LP4EUUdLDdvkDryGLeSIK1bnnx4oxh3dPMZmhjOFoeooh88dkv0Gi0spyeLGma73NwuGBwnnbb0vYts5k0Ior9gSImjY+OsigZY7eU2NkvGg0xyb7idvaCKaZpXn3TuptfPIEnJrvkyL4j0jvCMfa5vKvujKNRoH0MCHeuPhLAxSDVgOjDHiacTRNGvq0PhOxjHEIgTVTkSAx7ziEZGR9BF3lW4zULIBLTzoZzRNqJ316dGq/nFSrDPyMq+MYB4HbbTRo+KM3h4SEHsxmrecNqVlEXBlIgFqIp1kdPvx04u7ziq+cvaLuBylTURUlpC7SKLOZz6qrE2lFHKnF7fcuw9bgUpdNXVaQE1mgOVgtOjg4pCo0bBm5u16zXW/peOo7arqNv20kjrygsLjh8UBhbktC4kPB+oLKaR6fHPDg55HDZTM4c0i4uMho7DbzMHTJZFDc/ZBh5BUw/07lsMrqjGDPR8QUd1JrggxDz86ApslOGSrsJPqGF+Y9iFLb2E0kZBEVUMbJf8BQOg8KNfKmkJgKryZyNUbx1DBxlMu8FTJnDlzIHayxhiTyPZHkhRFLX59J0ksXQSMdqtDkrzdkdirwh5k7QLACqsHjfoZSgN0YrXj4/5/LihmFwlNWM1cGcfnD4ETXWKiu059KAVq/ykvJ02m/vGOM/tbeYK3lDfmbjzxSjsPT+cXNzzfPnzyAlzs/Pcd6R+ogfPL3vqZoGpzyV1cxXMw6PFlw+2/JPf//PePDeB6xOjygMrG+u+Oi73+Pjjz+l63s2/ZZk/n/s/cezZVmW5of9tjjqiqf8uYeHyIjIjIwUVZlV1ZVV1V0tALINRIMAQVobzGicYUASE/4DNOOUA3LAAQeccALjgJgQMANhBLoLhe6q7uounZW6UkSGcg/X7k9ddcQWHKy9z73v+fNIj8ysBgY4aZ7h/t4VR2yx1re+9X2WxaZFh4BzAWP6cdGISvTrTOK1DYOUTKR0qWEYWC5X1GUlZSIvIsrGiExSQErmzjkCEVsGwnozoqdFWVBaI88tCid1kzr+jauYVBWzqqKIPe999EOstjz45Cl3P77Po2fPcDHyG7/667z26uuUhWWzXlLVzeWbpwQ5H5wkRjLWBD33LidYuy+XLn2UElTTSUe0G9zo7Xm1BGyUprIGq5QQvEMY57UgEkbcEJopVZ3QvBiIzsmCbtLTz+M7IZIiueMujSWUzEeUoioa6qphPt1nUomlY3Rb3mREeMkGi1Gim2lSIhh8x+Bb4YEpnbokFWixiazrituv3eb8dCHoUYjYENEbNwatkLl3uWT5fCl4uzq8KJjamTtKpeBv50PYPp+MdoQo9d0cEMkSk20rt0HSZz3y/M2nfbUV4yoS8qJN8LOgJC/cSMdbJ9ekES3Msccv6b2t1ysuzi84uDhgf34DHSIGsKlBEEiJ/facvXdjWTk3hri0L7jd8bNzbs/9bAz6Iz6h/wrZR3zY7iU+egmeUoKslaYuK0EH204AD62J0bNcLiltSVlWWK2Snd0e4yPVirqpsMrL8w+MyajaBUqC+NBbFYXZ4yPOZYqOT+vz9iYrlddmdU2snjJIq1NlpCBotaWI+LTHpfq8fMc2sMzPOHOeBaoIKYCNyW1r6yytgxex58zdT0h312/S/AdxokrXmrtDFYmCJj/bVQrJ37m9zm3S9jPHIc+P5xwQ/iJB4EsHgKv1wOAGrNFMmoYb8xnzyYS92YTCCpLQ9h3e95iiZr3ueXJ6zsd37rNedWhlUUXAu56NHqhLzc3b+xwdH1CVFh8di8WS1eJCxDKdlFvLqoUIe3t73H7lmP29OcvVkmEJeEtTzSjLiEexXq252KyIO4iQtZppM5Xu3wAxCEn0+HDOFz//BnvTGd4FuqiEs2ZMIubLzZYSknDfLsmnaBnQMWStsi3hNG9mIQYsGmW3wqlKiTxLbrAwyf6HyOgBG+K2QcPqRLINkRhTE0VSco9RHEoiUFo7Qv4KmQfByaTPG6CGkfC+e645gA0xJlHugC0Kse3xLvEQDZEOoyxK2cQN8nSbNaGQUn9hzajNFJR4U7owCEKarmu3cQGj6J2n7TsmTUPVlGw2LXc+fMhm2UMUGN2HDucjPhrEcUYskGLMtj4S0Mk5XSnZwLiIh3RvJNhNneJ5KqosNsooeaKvlAxOT064d+8uVVmyWa3p+p7YOVG+j5FuGNj0LWHSYGzkxvGUzUXBTz96yCvf+RFf+40v88prN/nkzsf81t/5h7Sbng/vfsijk0ccEOj7gI2afvBE8n2v6PqWopTMNwQorOd8MVA3tWjiOc9isWA6qbm4uKBrW6IXFGE+nTKZzNGmwEcFRtP2G5zrsUZTJdHj+bRmMjUM3rNqW1btCuc7KjtjUlY0Cp4++JD7d37I7dtf4b0ff8xP3vuAi/USUxb89jf+Nvuzfdr1mtVmwVExubyASKxAhJHyoJVCRynl7BRCRmkWk8q00rlb4AaP73usTUj7FcF7pSJWQ20NvZcd2vnUFUwktpp2s6FAMSulbJ7R+qi8kLeToHaWpHAxd/55cbCJsknEGEa7xOAVeA1OUU6rJPWUqBrRjxuMCdJ8o5RoTBqtcaHDxRYXHcZa/LrHmgoXPC5GrNEc3Tjk1Vs3Wbc9GyedlqH1FGX2a5akIKMVIWwlLuS+6Gs3idxNPv5bJyeF8ZElG8YYLs2rqARtci5JdsSI0UUK0gPbYP6zbWa7qIgZfVafR+d333P1s677WXo3u1d29TyuKy9LIBzH9whfTUEUEXYfRalC2ST023l8H6lMw6yZUtpCPJ1tAcqy7jbST6vks3MwF7Jj1VgGvqzvubvR71aARteVtGZnTc8s4bUbKOqiBMR5J4ZAUZbcunVzVOzo+nbn/gpyPnhP16+xlUWbiC00+wf73Lx5zOZsxfn5eUL6RXC90FrkzSTqEuQ9aKyOED3egffyHERdgPQ6UlVGwIqdyPDS84sKooHS1Bgs6IgFTEyiwTHz8zKPN5LB2pzMxHSvlDHjd0iVTkAKn9/vZe/ufczhKZHAEPrkPpCCxxhkT8zlNiU9CKIjm+bXuOeBi45Kb/dqmauXx9zLBnRXObCfleIBnyEA/PIX3yaqSFkUNFVF01T0bYd3TmxgYrJ4Q2Qyni5OOT87IwTP/GDG4B22EFRCK5i0BUWv6U471mrDql1z58F9Vl1HUU3wKLxzlOtn7O/PeecLn6ewhq5tMVFR2Iq3br+FNqKfF/yS5WqDe/M17j16wtOzczrnmE2n+NZJppA6dI+Pjvi1r/0qhdVs+o5u00PU2EKPZUsF44SyibM3LgUJrt+VZ4lRUBrS+3IThUoDPJeAxi7bK5Bu27b0qWkjI49lWRJ3On4lKLT4GMdOytxtm82+4875KKW3BFhI5tTb3+dSwm4HpFaiMQWiOagT/0trjQtB3AFissVL1lw+DngH/RBZrlbM6gaMSJsoI00g4lJhGbqeoDzRRIy1XFycU9cT6npC1zq++ZffYrXcCJHYGJEs0Vls1aSSXuJjJdjfI9dqdhAhleQPxoV7fHaMz2O37A1XNoMUJO4e6+WS86dPmc9muE3HMPS4TkSZy7KmXa/ohx6iqPXvz+YMt475ZNPxZ3/6LTbthn/rH/4ON44rvvVX/5Jv/PY/QBt4/KePeP/HP+aVV97k4JUZ0WnWS89mtaGpIvSOzWpBN2zou5a9+auEKtI/SdzRqqKwGlOKwHC7aUe+4Afv3+HWrdsoXbDedJLElUYoEqZAKy3OFGVkf++Yvb1DiqLg5OwJy0XHu//oNbQ+496De3z7z/+M49eO+Itv/QXvvfeAk9MlZV3xu3/v32Y6P2Cxann46D4PHn3C8asXY+PIYvAsAAEAAElEQVQTQNe1fP9736JuSplTWoLPppmJTp9SggpE0GXJxdkZMQTme3vMplOqugQGlutTvBuIMVCfXVx6Ppt2w+OTB4So8C6gizJpfBWoGNmsW5HdcIEni1VCVQ2lSkIXKblobCMJgFIU6NTpJwFoiJHgYeg8Q0ie4DLrcX5IHZ+i9+VSCU4cPXoKXRK0ow89/TBQGklbpHPQo3WgmRi65Qlt2zEoKP0E3AHT6ZRXb9xgagratmc52TA5bDg5OWW5XBO84vDwmLt3H7BpgZhdD7ysjyF3w24DKZUzHxn1CIoXtlihfnFAZZPMiBukKWXwA7ls9rLHZyntfsZK8kt970v9Lo5Rwhi0jfZ3WhOjNAo2h3OO336dt371y3zhi1+hLCdENMEYbHKbqYPYsWVtyLzmOjeMa3dez+Wrt2t0pivk1+VzHWk8Ifve+0vvz//tk3h8DGFsAiyMHZ2n8vdkEfi+7xMoEYldxJYF2giVotu0TCYTnj17trMvmfG94z7ofap6yW2MGSFL99haM/IeQwgEH5OKRhirObuH1ip5lQvXPuTzS8mZtXbkZZKqXiMPN6HaMQQcEfwW0R+DsZ3Sfua/Ou+wMaR/XUbI5QN1SoIkAc0c97y2GGPou57lYilrgC1GDeCr4/5q48tVqtffxPHSAeCNo+lOyWbg4nxDGBxVci/QWqOjQaHZLFa8cvOYV165RTf0rLuOqKWdvR8GBufwquCvn9zDXGSEPUKpef3ma6wXK0KMVPOGG0evc/uV2zw+eca9u3d58vgJoLlxeJNJPaUsS6xRFDhmBwc8fnbK2fkS7yNNPaNtewjQdgNlYbl9fMjXv/4VytJwfn5GcB6rRSk2BtEt9N7TJg2mPCltIXY2uSsLMhq+1Qjc1Q0Svo8fjeR9kEaITOQtrd1y5HYQP9jy/rI4rxn5QzK5TepQyguSnMzW1ip3PxkjLifBb/WLdo3cYdspJR+x/fmWuyFweQwiMxLZCpaOfIdURlRK4Z3n5OSEDz/+iF/9ta9xfPMmgxcLoBidlLRLkStphzV7hwdEF3j86Cn37t7n4mRNUdTifqEUUFPYIi00ZkQcsDmDE72nfC/GTS2SkJrttW2FgF/sxZgP5/2IVOXj4uyMB3c9i6Yh9PIcRQza0FnprLOlxfUtfWqime1Nmc1ntJuWD356h/VqzT/4n/4G6IG//va/5t0v/zrf+E/+9/yT3/99vvXt7zA1r9GUNYUbiJslbbvkzbc+x5MnHetlh1aGi+VDCl+NyJlxFbausXVJaNfEvqOaTmnqhuXTSPADVoM1juXiDE1NFzxOGawqsMowPzxg6M85eXqGVpammvIf/fv/IXW15oOffI97dz9CG8UPvn+P8/PAej2gFbx6+5ijw5pv/sUfMpvPKUrL8Y0JXXe63TwRiYrVxVPOn/ZMmob9vT2sVdy78x7L1YpXbt1iuVzS9wNvv/02bduyXC7YLKc8CpGHDx5w48Y+hVVsVks26xXlyfml5/Pk8T3+5F/9t8SoeOX26zgnKIOxBXt7+ywWoqc2aWrmsyl1XVNVJdNmSlkKPWU+nWKckM5V2nCstVycnrJpe/YPDmlmU2KEthdLSe83rNZPOTl9TFXUHOwf0rVt0kIV4VptLL3v2QwbuqHFG8Ba4WuWNVU1QQ0et9xwvtygVGRSV8yrmr1qn3/6T/8J+9M5e9MJptLo/RpjDd2q5ebRMb/xt36NDz66w/n5grJyI9piDZhikO78HWK9dMnvHkrOdfynImufCVUgZXupkJXnk9J5M4McRP6sgO5F3D75zJ2q85X3/AKVrl/gEGw6hDhK7IQgCXfooaw0xzeP+NVf/Qqfe+sN9uf7rDct3kFZ1FL27IatJEqu4iD3dLNZp4rXVtYrNy7sooJX/wBjsLibxO6u57vJPolrx87vuqG/tGdl2lGVmjRiFJOBxWIhriNNjbMF69WGs/XpeM45CLTWjtqFoh5gCSHS9x14Rddt6Dpx4hJ1CVEE8Akdy0CE05e5vYCMTWVHVQIZi4LCSdVKY0wJaY/zLtvQFoLiJ2pH2C23pkY1k7R3TUxkrQgYqQ1ZQA3yWt9LI0dwnhCHVAgUpFMb0XU11lCVldy7QXibIQaxrmw7Yq24Znj/93a8dADo+2GLNAWx+CmKQiroITsibMtyJC08GyOHzYTB9+yXWwcLay3u1k188PRDT9/1+BApi5Jhf4ZLyurzvTltJw4Mtqw4Or7JMHjaoWe1XhN80ghTCvvgMaUVYcqyqqUMG6QrqJ5NOD7Y583XbmG15uLiQjZvLRm+1QUhOrp2g/Mit5Cj/ewlKIKufhzwAKRr0VqLfVTcZgH5v7k1fAz09FbvLP8pyxJ1pbt3W04JKQiLWFOI7uBOCUFO43IZJMbI0PXbhpAYsXmCZb3CnYVkdAKJWz/GjBgK+pkWkSC8Bx0ZETarrfw9RPzguH//Pq+9/jrTyWzkQTjnpayjGI2667qk61qePT7jwSePePLohOiFh2KtFa/oImeJwqkRW7I4lnZHEu14+dLFGvNgDNIQkrtbr5aaXnSMNkA7x2w65fDgAB0ifeewaEJqpBj6jiqNA2OEmBydwwWYH0wxBoZ24MG9J/zB7/0Jv/K1tzk6hvfct3ny8D5//3d+i6ODPb75Z3/CvcVK3D90yd7ejOXilK5dQ4zYwhJix9QahiB8vdi1DN5x2rVsLs4lm9eKJ6cnTOup2CYphakK2sJQ2rQ5uUC3aVluOr70jXeZ1paqqJg3+7x6/Car1VM+/PhjTp6ecH6y5v33Pub+/QUhGOZ7U1595y2++OXPUZoWUyusatFeYaLH9Fv9tDQoif3As8ePOTOWzf4qdcIumZYlrl1TacVsPmVSGNYXG5rCEPtWumcZUKHncP+Qg72Kvpuhy8vL13zS8PlXb3KxWLLfKEK0LNctF8sLXL9iNpvjvGN1ccrqrGc2m1GVBetmhtUGnZq42rajsiU3XrlNNZlycXHBxfkZxzdvsrh4zGK55vz8gmrSEBHUUJCOiqFr+fu/+w+p60bm0tCKS4Ff4p1iGDzd4OidF16lndDZBhUtrosMvUKVU0wMlKbEhpp//s/+iFu3bvLq7VtoIvfu32P/1hF33/+Qr3/9a7z++ueo6pr7d/+cdtOzWov2odYG5wJN6pYfy6AxkfcjqbSbxdOzkG5MCEtI63mSQkmBkFKKvpfNP/M38wT8WcHfbuJ6tQllDAxf8N6r8/UqMf5nffd1Zd7rS+Pb322DqRwKChIlTjQq3ZsInYPWoXuP6ge0LSHJYCmlx9IrKo7lWe+3ovv5nuSgapcLvsv7u4r4CYJ4ORDb3UPGErBOzVRhCwbslhCvnkM+L2vFTtWnjvzgPe1mQ3D+UhUlAwt5H5HAFBSpQtX5FOhe7mTOiLPSikzAUzyfBMRIkhQzo6Xb+DsY+fQh5r1BjRQrlYPksPNh+dnnSlb6sZSmZdznhk1JeVJzpktWkrm6hDgVBZ/pFJFRozMm7cPU2ayNZvA9akykrh+fvwin77MeLx0AusGPPC4Q8UNjbBJnTXc2acRZJXZcxMS5MgbjI1aBVSJObBWUhSFEQ6kNg7FIlC5NE3lwFIB3jnnTMK0aIgrnA6vlis1qRd/3DMHTJS9CWySP2ujwzst3WsvxjUNuHu4zbSq8k2hcGymTqMw1CpohtaoLPWGrayWBT7hUZrU7gVOeNLuLWllKuStzEKzZWoDlRebqZM0T0bkslbGFp8W+ziafYJ77vly6DGlgXypxxgipozMfecJe+tmVc8qH1gafELFcQlXJ11QhyN8wDGw2GxbLNa+/KU4xzicUxgh/Txay5KMIPH70mMcPTjg/W+KdBLjOSfCQz2+8BpXt09L1xC1hP6aIcLyvO3bdaTmX6xo71J6Xedk9pEnmMiRflgWTpiY6j04LnksIoE0SANJJa2QMO0+IUFYlpbGsl2uWF0se3HtKaUtuvbJh7+iM48UFe3sz3nrjddrV17lz5xMePXzEydljmpnm/Kxl6KTMoYPoQopLhiekZzKfTlMnuhopBiqm7FaJvSHKMGlqMZ4v5Jlt2g2btsV1Hbc/9zr7sz1KXeHblqePPuTJwwfc++QRn3zyiHt3T1mvHVVVsr835fjGPpPKMHQrUCWbZS+8MB8oq/JS/Oed48mjR6xWK0HYVxvOm5oQe/b29lgnxLcoCgiCggjvUa6lqasxcWrqmtmkobpSop9OJnzu9ddpuw22rEBrmsUKiPS9w2rPbNoQXUFwvegSFpbZpBH8y3sIjrJQ1LVB4QhDi4o9wW9ww5LNumW1XLBaLbhYiqC5zGMpD1+cP+HW8SFvvfkVppMp7kJcb5TyKLUNxIYhYG2JSYhD3/Wslku0EV3C0kig9uTJGSsXePPVmzTTgq5tqWcNe0cTXtu8wuuv3UKryAfvf8Bm3eFcxKcmjBhlk+377TqRc6XsibpdPqI0eGUEJM0epbZBWya0Aztd+Jebd37WcTW4Gjd/JMDcDfLyOiY37cXf8zKI41Wqx27g+Gmfd5WTtS2hS3VFa0XfdZw9OxOnmCDNSUVVUFYVSoMLDu9j2ic9MXH+csk07y1Sns06qbvInx8Dnt1zyeXo3YBQ+KspZd+55kxZilf2i6uBYP6cTA26VGWKAvZk9YpdK7urn5WbkozODS+ZyiTuJ9JcKdzcHAt5L3JCL3qeuWFNayNc+TSXVBKvz/xFsVRNWwRx5BhCJAvoK6XH8Q5SSpYydBglaJxWFFHhXM/QtXSbjr5LihlZuDrmzuUsRy3Xo0eUW8rIQ0IDtRZh6N3F8WUR85f5/aeN6+uOl0cAXVLNJmuvZdP6pEMkp5JkPnLwJywqsYAKqOAISm7cppcMSCsjXsAIJwCSojeKgCe0A4VWHO3toY0FpYlKs16vhXPV9wzR403qQPLi2dpuOnQIVHVFXVbcvnHI3nRCDI7ehTw6yEGnUgLVOpd06bKbSRrgg3MMbgvfhxBQOyXcXDLdTgS5nrbdkPXIitSokZGlq4TeQKRIvCXvB0DKrvLBYKwEya53lybbVdifHYQrLy4KCAnB3F0IczZ2KVDcOcf8OuEAygQifXb+3hgiQz/Qti3r1RqdoP/BOSnVKuEfDn0WtlYoZVhcrPjk40+4OFsTnMIam4JcTT8ErI0J5WMM9JQRL9zgtwitXE+ESwFBRjbSgp03eDI5+XkJjEsLPWqcxPkwxlBWBSp1zQ7DAL108lVlhbEa54eRRhCcOH2YwjKZTanLkhgGFmeRD37yiJOTM159/RDnpAT0m7/9d/mt3/nb3HjlNX7wve/ykx+doezA4sJBsCgMMQ4UVSHId5aNAGazGe1mI7zRIKWPyWTC0HtMXpA0NM2Ex48fA4ahd4QAs9keysNsssekmrFZLPngJ+/x7OmHPHx4wvs//YR7905oW81sXnJweMDBwQxrlRDBQ4/Sns2mp2sHQojcvHkwcmxALO0ePXpEWdW0XctmveHiQgOetu2kahDlvj95/JS9vT2qukwOG+ISJOUk8SGuy5LZdH7p+diiYH5wwKG9Qdt1KGOwtoSoOTu/AO/Zn06pqwYVNcPQM51NONzfE1H2QbRDJxMpD/tBNrrZpETFHuKA1p7JxKLNjIuLC6ZNifPQ9i19u+S8O+Wvvv2vOD66yeHha5RFxfL8QlyN9FaOxbuIViXEAdc72s2aTbtkur9P5aEsNH3reHK24PYX3+HWrT1Oz5/SDhsOjw7YO5xwPP0ifev58MMP+d73f0QIReKyC78vJ1tdl0tsavz+HADmxCn/f9xBNmTMb9cIF/2oD/n8nnX9xnM1gLqKcsis3a5H+RO2VRMS/+rlyPHXBXvPnekLNsndc92WqcfLA7YNYvlEtdZ0bcfTx08J3tH3PdWkIhixBSxVTYxSBfIuEEJPdtfp+35E8CRx8oQgFKvd6o80iVwvASKBOJee4+XqUdzuTzuVq90GkauvzQFb3iuyD7ExhuT1NvIEs9nBLhhxKTGIeR/R45gRhQ7hskrioZL9XeIn5uaNK4esv1KeDuPenfjcOu15yesxpA7d7OZFjCkR1skqUxGNlSJyCLgcHO8kHbkp0LmermvZtN14zpdPUFQC8gXKWahxfHgnSSCE0WHr6lC+OiY/azPHz3u8dAA4DB5bildujJLRZ45VTAtNCIzlSTlk6g6DaN31CjJR0g0OrVTSypPAQpT7t5lHRssi0G5aQhp4m64VDkFlsaVGR6h1IZyeSUPnBtq+ox96rNYUQGkUsd/QyQpJWVZJr8pBlGBuuVyO3Dar1KVAbSzn7pRcM3q2O5lEZkVd4meEnPlds/DEGLdNHmZrVSaE3I6qFpmcHGw5J0FM5qNc54ea/2uMwaf7mcvAOVDNr8mE4d1JbBIS4+MWkczcw4wQ5msJztMlknLwguB98d13qJsmEeZ9KpME2rZl1tTUydz8hz/4CQ8ePKYuJhhl0wLaipdqiAQUMcmB2GyFFbYZozwDQwgvIMym1G9EGJQa5+11i/3ljep5B4OyKpntzamKEt8L2ul7lxb2KCbgpRGboShlfReiBM4GZvtz6qbirn/A4rzj7HzNYrnk0aMTfvN3Jnznm3/K3q1XefOtt/mP/vF/SPT/c77znW/xJ3/yl+IJGyJNU1E0BX4IGF1QGHmm3abn5NkZ6+VKxiWasrTEwoNKMkZaEognT07Ym3v29/d590tv87u/+3f41a++w4fvf8hff/d7nJ09YT433L93wR/8wXeoqgnT6THRtEyPCn7l17+Kc45F27NoI+t1S1X5cUMCuFgPl9ZIbTTT/X20UdiqEFmUlLF3w4CINMkzaS9WPD05Zav7pbGl5eaNfQ4OvkjX9qxXa1Zni0vP53y55kcf3sMWpJJVgVEGhWU+3efi9JSTxyeUlUapSN/13Lz5qxR1ifEKW0yZzeYc7B9RljWEQWRahhYULJcXaGUxpqCuAu+8/Q6TyYTeeZwfiNERQ8d3v/8D/uo7/4KvfPk3ePvtL7PelCxWnqqK+DDQdi1KazbrjlINDH0PKnBwY8raXzCtKs4WS5yq+JXf+R2qo1ss7rzH09ZThoI3br7B3tGMH/7gLn/xZ9/i8eMTtK7oBs+mDSPyEZP80u6YV8pcWq/GqaKQJsadhEnpLT1EFA9+viaP3bk2dq7ufO/ua198SBD4yz6uoie7f5f7kE8wJ9V6XLNBMTElSmlWmw3lqmKyabnzyQPe/+gTXnnlFd794hd584236DY9F6sFQxjIhYVR028YGIZeqmk7DRyXqxQi7r57fhKEDZeuYbccvFtKzqDASPUJYdxvd0GBvJ/trvu74uj5ezO4kPeTq2VraeArkkQMyXGnRCtD7/od5FACXzc4kUZC4+LzKGCujtW1SLzpqMdy/LaEG8fqrI+JupTQ1Pwks/WminHkA+b3aaNTc6YhaBkbBkVRWHxRpPMlyZwl9NJvHUBMIcoEIbqR/x+8eLDHdE8HJ+ui3tlzfhnB3qclPJ92vLwTSCORvg/CuSMK6oMPosNjLU1Z4dROi3rcTlplTcoq0gBRGoyWAAWNzZt0jMmzU0mrdikevzFB2j75vZZlKYiYEvcw2xkeP3pGJFBPG4q6lJKcFxLzcrmUtv3UtNL3G+q6vmRzlR/aJU5ckOYNlfScMmomJc1tQJUDWJuaO8StoR0HTZ7MZVmO2dXuhGmaBhekLNAlz1fJDgdCkIXHO6jr6RZ1vfLQ83nn7zJKoRNKuRtg2xSk707mXV5g/oxiJwB2fb91MElPNSDciD4hWJFIM6159513WLdrXBAJDB+kMWRvNkMry4fvf8x3v/NdPrr7kBsHNyRJ1CKpEOKAHwZ8FKmCqmpwLjucJO9NJX7Ku4KjO/vcpXuy+29jDDFk3tNnnyzei3WfiYrgPfP5HNd5NpuW5XrFeiOyK7YUTS25R4qL5QqiI2KoJw2/+bd/nYcP7/Hxx3e5OGtZ3lvy9P/3R/zG33qHd750Qn9+n/vTQyaT1/mVL/09fvVrv8WDR3e4f/8THj9+yvn5OcvzDa4fgEhZV1RNjS0L6tlUNnGr6bwTl4EYoJPO4Fu3bvMf/Af/C7765S9z8+YNikLz7Nlj/uif/T7L9ZrF8oLzs1O+/70H/MUff0DXRVabBXta8ZWvvcurr9+k6zpQiqKsUUrTTPZpJlXKcoWnqEyxA59IYnjr9qv0Q8fQ9agopeyiLBkGR1PXKGXwPqTO9dz1KLpoWhmePjmhaT7h9ddeZX9/j9mV5aupG167/RqTqXRFF7YkuMhm1fLw3mOMsZydnhP8gDEwm0/5/ne/S1UabGHGjsj9/X2892w2Ee+1+D9XkkS9+trr7O0dEIOmLKXTb7U6o+9ahiFwcnJKqSd899vf5Kfv/YjPf+HL/O7f+0cMveLR4484XZ6xbDcMg2O+9xpFNKzn+2zmNxj6Gu96Hj+94I13fpvPvf1FJrM5f/xH/ww1OH7t63+bQlfc/+Qhv/df/AHf/P4d+l48k02xtX6UJFLWAGslcdJJF/Jq4iOC0c979Sqlx3BrGKSCI9SM5+fYdYnUi/h6V9+r1M8OAj9L0Hn1+Kxz/Oq1bDmSjEGP0lLdKUuLtQXD4Jju1+wdH/LaW5/n5iuvcuP4Nk8eP+av/uqv+ee//0fcONjnK19+l8PbxwJotMJtFZeKrVhwLqvudtDv3ter+8ZuFSRXBHZBgd1ALqOOl6pYOyXcPD6ygsXu/ch7Rf6c3T0jN0Hu8v+2527S9xRj8Dw2NxqbPlPKrlbnvXfbmLLzZKQEnOwbbbCC2uXAXAsXLwZB7+wudUjeLqjeDldwSDJGWmlItq7EkOhDCYV2AZ2ef/CRrhtGjiGX5pGgkUVhqaparjkgf9JrnPdEG0dd36tj7r+P4zMggNJQMAYJiLK3NTaVFiQg2rQbKRlIWIyKolHnh0GQtiQs3A2SWWRHjb7vpfVc7XDSfECn1vTMFzHG0EymFEkpXylNMJGuHnBrTwzQ+Z7ooNAVxpa0boCyoUjkXa0MutL44Oi6dvv58qTGFSlnejGKOX3cad0PIRB25Fm2E2Y7SbLki0rXVJbliMLtTuSiKFKDjWNr4yWWayDoqVx/Rv3UpWxwF4XcHfTOi07ZLt9kVEG/shjn3+dr2Z3EY5k6kcSlDJts6aJIBAiE74kx8PDRfYw1aCuluzEjNAUfvPch9+7eY73o2Z8fgYfoA23Xj2NMW+F4GCNemkO/FQoO3uMR27hsHyQOK4IW7tqMXVd+CulzdhfJq+UPlcqlV3ceNzjcMKCrmtAPtKtWqAHeU1QlZVNvEZQkTh19YDaZEULigBhLtI4br86x1Ts8fnDBs0fn9O2a737rAx4/esobb97ijTdf5fYbHT/88VOKao/ZfM47b3+Rd7/wLm6InK3XLFcr1us1XdtiC0NvBGEvUgdyWRTsVVMmkwnNpKFpGuq6ESHyoeXunZ+y2Zyz2Vzw5P5j7tx9yP2HTzk/WxF8wabvODg64vXPvcprn3uF49uH9BtPsNLFvS2vKPpuM25CQ9czmz6vA+gDBK8oijoJxgYGN1DWFVEluV+l0NbQt70IXRv5LkLk4MYNYow8efqE05MT5sv1pa+wStEAxTAwqxrKqqTDsfEe78T/s6pKBidTvCwalI5oLYjq0Ipsy+mTh+KeEsXRqO9bDo/2mUyn3Lv/kBi1lHC1gagxxmOsxhqxqyNorK5ZLTp+/OOfcnra8Xd+999msVhycnrGolviveNHP96wPymh9+wfvEpdTvjXf/lt7rWWV/dfx+69yqMHH7BfBGj2CFrzyYNH/Nkf/xmfvP+IwfdEE0GFpGFKgj+k5CvzOFcDts0LMQqP1hj5b0wOPVplPuO2yrGbKF6Hylx3vIiTdB3qLn+/9mNS4nz5Ndeh9Z/1eJmgcFxDshAwV8vKIgadyf2qNJhJRTmfMju6yeTgmK+/9Q5f+XrL6bNHfPLx+3zvp3+N+qlmf39ffKOPjhiGgYvz0zGYUiopTVxRa8iNcFeDP/I6nNZ47/24z+w2j+Sg7kVIXl6j898z8pf3grxH5SC1rmvqur4kT5OrVFulCY1KDRVd39G1HYPzZJ3NmJA+kXex0jDh3KUxs/vM8h9jjCCAUaSckz3DtgQdtkYKeW/Mn+e9RxdWQIMo1UOjND7tsTEFo0GLLm8/iNECUXi1cn4qVZVjioVMkrYyKCOJYlVVMi6UqHBIYJ+oVe6XF/BdF6h/lrnx8gFgP0j5N3NHUqeYC6Cj8O/wjr7rxVJFbXtdnBJ+Xdb2kU0+iYwSIbixFdzYLfdMPlJcM3L5QkWVRBflxoomXcCFgaoqR+uzkDtWcbjgUSpz10Q3KHopT/rU1Zw1UQMBvJQfdydQPneT2KUjdB62OkjAaNi+LReQ5Fj0CJlfRe8yOudD7pzSxOAFcdQ2cUPkfsukSTZzIF2hIbXSK5AFwafvDlJGJSbFcsbzhVxUiTvq7duJtjuYxnJvjIn7mbNRkbnQRrNKvrhGWfq+4+bNm1RWFqLVes1queHk8RkPPnnAYrHG2JJaKdarJTFttM7JM7SqQuEwhaFsbKIdBBRWMMAUPMREuM0QvsolX7W9uu1Y2rlGrZBOxy1quHvd5PvCZcQjBulsN1qjq5Jh8BSmoqyVCHOHmDxRFRmtVEo6zL1LC1Gy5LOm5uBgilYNVTXh/PSMxw8f8+DeOeuV4/RkxenpKa+8epv5wQ3icECYTrC2wJqSSVVRlyVhbx/vHcoobhweQiI658X4sN7HGLCFwhYQ3QXdYsH5+Tlnp6ecnp5ycnrKJ+8/5unJGWcXS7reU1czbr5yzFtvv8XN28fM9wV5Fjsy0aOUxCgnFxkht6Ai4coapBSUSu6oTj+IaDwSOImHsZDEjZFkqSyTbmVSFIhR0XeBGDwKB4vNpe84PT3ne9/7IVoHZvMJtihxDjabnvVafL4H16buS+g6jyls4glmOosnDIM0vamAj8LLWm8GumGRNuGQSt0KpS11pbGFNA35EKnKGluWqMSdu3/vDh99+EMW6w3DsCEMHdZour7lPA4YwKiSwRtO155V1Oiyoapq8IH9+SF1vcfDT57x8fsf8fHduyz6YTQfkOEvWphk0rtS4+9k3VKYJLFiraKuqxHFcU6404rd0ifp81Kw/xzGfn0wtg0yomyUV36fx0L+LJXWrBh3GaOXS81X47WX3eBejCi+2EHhapAkM1mBsgSy/FcOOSKBjv0br/K5tz7Pa2+8xf7hMZPplLKsKYqaST1jOp0zme5xdOs2J4+esDg/48MP7nD3zl3me1Omk5qyMBgF7aYDHyGGpLiRn7HQbYKXByVcbNGk9LvOK0kDM+8Ru3vNdYH8rhqETwl8DuK2nL5t1WRsLkpUJO/DKKullZFnF2S/DNoTowPliD6dl49oU8jPgtjIRSVAR0Sakq57vJFINBpVVmincEGST2mGyw8PyGNOxa0V6/i7iE9gQRibNuSzdUhbQgzpD0Qvsi8mZM5mNzafiAqGzBOJDXQaGUJZCkoRM/LqXHomwgP28XnljheN05/1uufn1t9QCbjv+nHiaK3x+GtelYzonZc2+RwBu2EMyDIfTZcGo4z4XwaxkbHJqkbgb5HS8GwFHbXRqNSMoAL4MBCiS4FcoCxKVKHFUDpEhr7P4yERQxUBLaCai6P7RVSgjKA1MXpcQKzK4m73XILKQ8A7QYJ2eWdZMiVzMLLRd85E8mKbib+whee11iMsLv8W7TasRpO073x60EEC2lGXKpULfPApuIhJpmdncKQSaW5myTwIcnYbtg0V+Y/W+pJGlEqQt00t+D4MOO9QJtJ1PWen55w9vcCogqIWvpnRlq7rWZyvefbohE/u3pMNXguZt1v3DK6X5ICYJg1oLEppTKGxtU7E9ay5RArit44pGbLXSQJmHI3XZENKkQLKOC4K8uLttccYE+x/OQC8uel552LFgS6xRtP3YUeTUbJsH8Ol71UojOnxQ+KCxEDdVXjvMKYgakO/N+XZ0HOvqdicrQgXzzAPT3EPn3L0a5EbNzcYc5+IwkWFKWum0xtjs0JZlpSVJUQ7JjQhODabNVM7MAxrBr9hCGtct4TFknIz0Cxa2tMV6sEThvceMyeypyOm0FRsePP263z+xhFWaYaTCzabzdguYJMVS9/39IMTC8Ck0aWURm9a9G5gAJRaCY83+9QSQWU7s0yCD1hTUqZOwRAC0QSi99JgUoBWJdoo3BUtu2cnZ3zzOz8kREczKWQN0QWFEV9kpSMhDCNyeb5YY8oSbaSUZ62hsAqjIrYSTVOjSyGtmyL5sWZ0V6aOKTS6tEQlTjCD82hboAtLZcS95eTklJ/85PtM9g/FYSA4mrICpWgHcR9SGlzbQl1yUDVM6hKDuAVN9m4yN1N+cP9HfPzTD7lYruhsRXQ6dXWmRjZyd+suchZHa0iJcgNlYZnOGunGbhOqHhk1NjNyopSCoBBjEZWe4nUI3pXkafz+/LPrArYd+Zb8J+akbZu8/azgL8/XF22Eu8HPp33OddeRXojYoxQENWCUzGmtNGVpmB9Y3v7SF/jq136DV26/xnw2p6knlMbSrjbEqqFp5nz+Czf50pd/jQd37/Kjv/4eP/nR9/nggw+ZzSvefON15vMpWmkBWhCgIgTHMHgGL2iV74WvboxGlVaa75wa7ci0MgSdeeK5XJwCJXO5SpT3rt01Xv4uTyOMe7AmBmkCzaCG0WrU2cud2kpJcKiVkmA1yJwNKLT1qJD216DQhZbgMAAJJAl4sUB8AcIbYwCj0U2D8QYXPFrFcQkPUcrBmgT0IEFqRv/G605uISGh3rI3KkyUsD6itqCC9wTvUFGoP5t1iwe88uI4FzVaRQZAKSOe3ykQHGIkKMayvPe5fpbnxnVJ06ePzxcd14E3L3t8Bi/gNev1eqzBS1BmRFl/RzeurCbEKJIHWhu0VknUdYn3KQAsCsqmFlg1eRdaY7AW+mFAPIAlgNSGJMaqKQr5TMkWB0EjlCdq4SVebFYcHhwQnaNbb+jalqaRDdJoRVCRjhQoRIUKfgyeSkrKqk4PyyXu4OWgKCN4ESiLYrzpRVFgraVt29HFAxih8HEx3XlYuyXIXfh8t1yyOzAu2beFmAyvNd0m+ejaYvx8nUqPAeEbZOV6BZcadxTi++STU8hu+TdLAeRzstZSGDGwFx6JJABWl7z3wQdsli1lWTKbztibH0LUPHn8jIuLBednF9z5+GO8jyO/arVc4YhMpg0+KpEKshpLlEaPECGId7CPMZl1JyKvzvdGysQxWSc9n5RcnkRjiXvnZ7slrnyIYLQfUdN8/McfP+E//vjJy06ZX+zoPdw9h7t/8W/m+/LhkRJiu4az9+A77/1SPna3DFUWFp/GF2ksEpO7jRKkw06qka9qrBHEQYm8TogewvNaWlUz4fiVV1E6jGhiWVRUleiYDa6jmkwoi0hhoShreucYnMcWlumkYX9/D7yjLOyYxBhjcJ2U8F0I9MNAN0hJOcRIWUyEehJbCisk8WHo6QeP8j37hwcMw0B7cjJqabbrNe3QU00n7B0eMZ1PCVHzjW98nTff/AKFLTh5+pj3737MFz7/Bg+fnPH+nXs8fPyMsmzovCYE4VwKBYcxUQpBsVtea+oybURyj+taaAHr9Tq9Vycnh6sNGnpEci8lNddsMlepFnI835h13f50ebPbDf6eD+JedoO7eq4vQvyue9+lCkhKMKPrMFZRWEWhFc2k5ObtQ77xD36DX/31b/DK7TfZm+8Lx9tFjCnFAg5J7lerBdZaDm+9yr/z1hf5d/7R/4pP7nzMf/b//n/x+3/wTZqJ5ZVbh7zx+k3msxo3iM+ujZE4eAYX6FNVKCgle9Qg+1dVVeP+hPfE1HAoUUru5L1e//Rq85wkY2obxI/VHn/5vVGcnAor+1ZI5dvn4DulAIMPLrlVOXQwkDrS8/gyxuD7QQAgng+AIrIul1UlwEovFRqp5iSwwgdUsgZVKJQVLuCumPZu6Zs8JpQmKrCmkJK1U3jXi1OSjvhBOp7bdpNKyDq5FsWU4BucknMJbHmasFU2kaBcpG70lXF99fisQdwvcrx0APjBT+5QlkVC8CTwkwaJx2PGGInMDw8k21YDohiiiXiCV2hlKWxBYYtta3kiRGaCahZV7t3AsHFEH5MNFHgHMUr5pq4r3BDGbF6jqEzBZrnGGMN8NmNvPme5lE7BbBvjo8dHRV1UKAyeAe9DKts6hnag78Wcvaoq+n6gqqpRHb3ve/FIHAYKaykSJzATeseuuTTIiqJgvV5f6sqKMV5qNtn9nXD/inHBzU0mIwIZI01VjZPSeS8uJXlDhTFAH0WYo7vU0qp2mjtQSGH1ShZxldBbWhGgjkmmgBAoTcH5+YqTp2fsz/e5eeMG+/M9+sFz9+49QggsLi549PAR3ntu3LjBYrGibVtBbJsJk3qGG9yYSfq+Z7lccvOVm9w8PmY2ndK3GzAaFZQ0tmixY2TIGZ7BGE2WtoC8YV0ew1eFnbc3JC1G4fkN6388fjlHjJHODYIsd35rMZgSFmuFY5SD+MxJAunoC6SHHuNY2sqLaj60DpjCU1UFNtEPYlQ417JcLQnRYYsp1k5o6hpjCqwJDEWSN4qG1bJlNqlYLNZoIoVJ8kTa4mOga3u6fqB3jmhkjhjlKYxIWzg30La9UGSUUGN0zIkXIg4+DHTBc3Jxij63PHj0gIDCB7h5/ArdySkheBbrBWenT4hvvsq3v/s97j98CEpzsH/A6f3HowhxpsHUlQR1IKCVUVCUlqapWa83Y0DYdR177FGVDetVR9cNYzd9tl6U+S+yO1eToxdtXD8rwBqrcepyAry79uTy8W5w/zLB24teczWp/rTPuS7AHAXOtKBFhSmoSk1UHScXT3nw5Blv+56+FzFgayZYneW3xE+8KDTGVBijOFuccnZxRmFLbrzyOv/H/9P/meXygm9968/58z/7V/zzP/xXnDy+z9e/+iU+98Yb7O/v0zQilB57TzsMxE6KN9NqgveOrouXqk/bRsDsCw1a20sdwruVqXztItGVTAoSuh6DYtevNr8nAqt2Iw0gWo8d/D7JqMlnprnbtxgx2d0mADEyDI7cwhtSB3ShkyzZleRdG40utg2WXoneaAaKsq5gjJ6IuUTt2W2MzJz4EVgxGmVEqmVIQV1IerXTqkEFWA+C+pdlKd8TpSktUWhpnSMEhwsKZaCsLKXVdH3HsjdsNh0+CI+ypx2pdP9DOF46AHz/vbsoJdltJpnLHzGTL6uSsqxYLZa07TpFuVuCsdGGsqyIIdD3HeuuQ0URhSwKaSRx/UAb1phE0oxAVdRYIx2fPgikPQwuoVAOY40YbifF9eAEWs4ZsTGWqm6krOyH1GEsEzt6cQmpqgqx5WkptKUoS5ST7reyLEYCbNf3EqQl9C8HcFJKaS8FGDmQ2yXZ5oV0t5EkZws5C88IXEY/8pEDPq3lGvNEzsheDLKRjhWUlACCEoNtJZ1Su44fEZkAhPicOLUgNSXWWDIvpO86QVaS2G/ret770fuoKBp1m01L328ISsRs203LarlBKU3TTFguV5cabqL39F2XbKpk8a2KAu9XHB3dwBYl680GnSalVjbdRyRoS6RlY0zi5VxGLXMWeynzvaZkpBKB8NLirxQ/KCz/+GDGf3G2vCI5/D8en+X41le+wD/5O7/BZNqk5KcQZCEhz9clPkopqiQX1HcdfggUtkgZv5Q2i+LyU6lLy82DCUpHJpOJNJgp0dxrj/YoS0tVFxhVCM3A6FT6zHzhOMrTTKcTysSbEwFkqRyEQpD1qFOnplJYHVCqx7meQKQsK3o30A/pT3+OAvb3phSVwVaaRmvKWYUpLYNzdF3PZtPRL895Ek4Y+oHgHPPC0j15xvmjx1TWMp9P6P0AcSBEP64x+b5llYGQmqj29/fwg6MsC0Z3huQRngn9kJ1vtqL2Mlf0c0nR9Ujfpx+y5Mh83KW/ID+WJD5e5t6R2KL52q4L3q4GdtdxEq8eP/s1z5eTjRVJJR20dK7Xmr0bU45eOSZiefTkGcd7rwk31DkR+DY2lRhDQmQlGSktgp4ph+sXnG4uUErzK1/5FT7/1lv8z/6df4/3f/ITfvj9v+SHP/mIfui4efOIV165yXw2IYRI1/es247FZoUmJpHhtP8F4YKKo4UipmYJuGwpl/eTDMTk+5I7vXeVEpwLYwPR1cDcJHODLHUSQuL6p7WWlKCHnaArRjBoCh3xUaU9fRBVAxMSr/C5B4fKLIb8OWpb4xmdm2K+1wjqYy7TmoT/l+k5cRT8lxJ0FGRSWQolQIrfSE+DH1xysJKGK5G7kwgwz5eiKlFG4wZH33bcmN1AOZ00Rgf6oceUO1nQNePwZZHqXxZK+NIB4GohApb5RhuzSSXfYpQ+scYQtEObPKAkEsl8OGvLhPAphtihgLqqJchKUKn3AzaVVG1hcaqD1CSwFS+NQCWZSZpk3hh870fELPvnahmdUs93Trp5oyIYNaqijyWQvkcXiq3mk6IsK5QS6DbsaCJlTlwO4HL7/W52sdt2v9tpBdsga/dh7iqvX7XW2YWuhYgPmfQqrxV+pHA/0r0i81VUkrHZlrIl00kTIQr/bvd7SO+V+5Ds2GIcCb/ee05Pznn86CmHh0cAqYvZoayc4yo5tWhlUvCYMk8MaJKqfO68iiMa6pxjfrDPdL/BxQ39SjxcGc9NuGM6leBIwezzXKCtIO545NekRUqeo2ScOetUSkOEhVL8WWH5vx9Oeeut18eSn5ThMzeUlIEKOu4Gt23YYUd3a4dzUhYlCnEQ2KzXtO2aGFMjEpJUWGOpSpvelzrEo5QZjDEoXYzODDl4yQ1a2SUmxojzQ2ZOpumYr1mkbIZhoOs6Jvtz9vf3mM5mo4ONJCEi5yLE7i0p3KSAwRg9Lox5bjjvUdqMQd7Hr7/CnZvHGJeoFVonkCclMGPZSWz08txxzsm8z51dJK/NqqSwmj17eRG0xjBtakLwlNpQGuEkxkJRmAJrk/p/iBAH4fCgqKsq3R7pKC9Ki1JQKI1OC/zQe4oq0Uk2LW65wiMuR7K5DviQuVZaJCnY3UwVnRtQ2lJaKYHVtkQbTWktdVHQFCW+c7RR5pIO0va0Pl+yuViigvCX2m4tAbCxFLZM65MXBpKS7NcmKkpd1az9krIq0/iQjS2rLmSEPyeXef2KO2jNpZLoNSjZpx0SB+ht8Kd2f662SI8CFbNA9e4nbIO/HFOMb+D64O/q+V1XDpZA7/J5Pv93hTXi7GGNQhsrCM+kYP9oj9c+9zqvfO4d9g4OcU44tyKMLNxPk5AyyctT8xdJugwIrhcnIW1EJ3M+o5lM2N874PiVm7z+4U95cP8Tzk+f8uDeI+pCM5vPKOtakqPo0cEnly4JMGUNlZ+NXfpKjUF22hGQBhLh+e2CrhlV371nucqSl82MLqodekCWfQtBmle0MWhr5fmh6PrU5ay3cybrSxJFLSSEQOf6a8ZAOr1UfcyVK6PNls+dqBAqCm0hEsaxlq9jpHKlMWKUUNTyy6RJT+amQhOIo/lDfr7OuVHHMe8aWkFUiRucAlWT9Gu9l9J3SDSXfK+vC/JeNvi77n0/7/HSAWDwOnXpZBFMB3RpsuxkbmnCs7N4lGVJWRSoFGgVhUXbIS1+BbYQbllVloDov9V1TVWXDEOL0cWoM6eVDAKsET4N6THEQNe1qTtJut4KbzFKs9EpIEtlo4jCkrgLQTp5ZSNz9GRtLKnfa61HjiMIOuC8p7BWgo/MC9wJnHa7qjabzTb7SH/yIrv7s93sN2/q12n25Y7fLRQvGVNGQ6Sbckt+Fe9cna7p8kIuZYow8v+uk3rIJflIbuJREKDvHc+entB1/aVz1FYyzvVqLeLdPqCiol236Oz0oWQ5dEHK+bkrNAfUznum8zn7hzM2g2F9fpE6xrNDQJBycCHaUttM+9Mnz2WEgRwWiQRJCiJVmsgEWRic1vzfjub8/d/8ClVdkZtvhqHHmIKi0BTWCCocNa7Peooy2aWTFYYwSFdaVFRFiesGlhcXnJ2dsFhcoPAp8bCURUnd1MymDT4GAo4+BYDaamxRUtgmURfcmPTIIpw68tM4aIdWgrckIREToV9HcTBYLVcsFnDj9iG3X7vN8c1j5vN5Qs688JgSUduHbLweKIsioeM2dbkJiudD0s20xZgoaKWwPkIiaMcxQAVlSGhJDgRUIpInuaQgtk1lmRHqSF1XTKc1+8Xlkr7WhsKUMq68bC4qBeEKhRtE5kVFj1JJKD14SqvEbUQmAvvzCf3Qo0Oq3WpN0IGqlGvq++18z2eQy0ZKIWKviNhr7mgGIc47HUQuJkTKVHqyxlKUFZUt6U0vqIStcG2H8gPrVYvvBYFwXnzQUVCVlYjtpk1F5ojomRmjqaqKuqro+w2FLSW8TW4+y+VyRMtBNn5jCyJbhCSv6591Yxr3I8XYgLJtnAnj7zIjPpemc6CkISFZ133nDnKvnsflRy7czr/Hd15CEmWdv27vzGuAcKvlXoowtiBK5aRiMp+xf3TEF77wRcppg3aaEARgCEXAmLQXktEsaREgSnMjSCOd1gqPp+3axJE37B/t85uv/wO+9Ku/zid3PuR73/5Lfvy9b3Fy8oS2HZjMp9SzSSqJpn2sd6PPrnDVXUrWE78uPXeFRjQusrLCFmCIYcv12w32d5O7rQRNHK3lxr0vvcd70fE1AFE6fUUpwKONwlgjlKtxGKixAtUnNPNqCZh0HWVZyrxTIjGmxiqPR+fYI6RUbsfqbXyuO2in2VlrdJQKo47SvYtKFm5eml+dc6ySVrAuhJKWE32jNc5Hua9KEtGqKNER2n6QptSYu/T91St74fEylIXd11792cscLx0ARtxIvt+eQDJNTg8idw5tO2cl2W7dhpatZEP+CGvNJfSrmVSps7GiLHvhoxQD+/uHlIXo6kQFzkcGlYKgbI/muiSZMB9RRggsFwvu3nssGmhljdUlVVMxne0RY6RtN6zWy7ShWzabVTo3izENZ2dntK00WmSksygKIat6P/IqMhF3RF52uoF3A748oXY7snYdQ3a5MSOaabb36Tr/xazftNu6v73Zkvj5ILqF4+dHaSQJqYy1+94ilbjz95eliGcv2zWlKfB9YHGx5tnTU24d32Q6naKTm0tZWk5Ozzl5eiobX4h0bUeMKi06WUogSfQo4ViZhAZkFLCZTJgfHGBaxTN1P5HUt51ocUQ9VFJNSJnkc+PzMooq4+/yqp8D4N1GG9K/rdlyUqTBR16z2ayZz/fZm08wWrFarTg7O2OWxpU0QkkAOAwDOop4tVGa85Nz7nz8MYuLc2L0FNZQlCKfUxaashCOSYgFWluqZo/9usGWFcpIsF1XlXQBDoOgrMmasSoLikSE7vseZwbazYblxQWLi3NcP7Batxgsgx8IMWJNRb/ZcPfDD3n04AGHhwd8/u23uXF0zNnFQsqcRSnBhHMMfYvWjCVDY4U2kQYoRSy3toHpcJnjqsXdQ+QRQiLMS5OPHhMJTzOdUKTuOa0Us6ZisViwWCyYTCqUaqiq6tJzDCHSO5E+0krjPPgkKN33fUJEoNBQWoVVmvVmTfBn6fmr5DhyxLOnT3H9gE7zL3hYrTa03UA3DAQn1YfoPbaosLoimDAG4VVZpm79DqOhair6zhOjJcYCrSw+xoT+9qkT2aJNRRE1VCWubFBxYPnwKeV0Slz1hKApioo+bjhqmp2EUjo/y7IUPU2jqJuS/YM5zrdYW+Jc4jB23bim7aItVzfI7X29LFvx/Dy6ftOxdouI5JfGHXT6anAmybAkZJdAhcxpuXTE9DyvR/t+1r9z88vVnyul0GbLFzbaYoAYenrnqMpSnGNKEYB++vQZ7974EjePbkry4eJIafDe4Zz4YxtdUFVCSfI5alEWKc46MSiwgvY/W5zhTldM65ovfemr/PY3vsHjpw/40z/5I/7VH/0RP/zpT1hfXPDOm2/z1rtv0NQNWnViueqHFGzFhAhLVWhwYXSD2uWc7wZ2sreY8fns3pcxsEt/LwvZc0SMPq+hNjWgpCpbFNkopVJjho6gAjHtpcNmA1FRGINVJb11rOkw6rKaA2xt4JrJRJ6bv7qOp3GbUM2MhO6O3RGkIDmmJHBEQlQEwUxyUyEGcs5qtAQyQy8cf2OtNNvEpEGrpSErJn/owljqUgJA1wtdbXAeldDkzDW8erwIFbz6u19W+Rc+QwCYjy2BlHRi20AwoyOZu5bLdbuuGhnSD0ExDLsTO9C2on+2LR/nEq0ha+iInJ8M5slkwnQ6Y9LUTCaGqqro9h37B/s001o6gvSE6Z4skqu1J3ZLTNHxyb0nDENPURiapsYaWQxd6kAees/i4inDMDCbTbCllQDGB8qmICb0ZXdSZMQjN4Tk7uA88KSTaBtM7uoCTiaTEfXIaOMu8rcbLF5dqDN3Kn/WiA7GpLKeCL1Sjs6BjN023CRpmvwdwzCIF7ISvaWQytjCldN88uAe9+8+YBg8r766D0pQ2+A9jx4+4+z8gtIW4lkcIkSNd33K3rbownYTSBsRgSJ1Qx/s7zNtGtp+gU42PEqJF/DIG0qHVpqYSsq7C9buxrZ7fBqfaHyW6b9d11GXNbPZHk1Ty/22JZvNSjZSqyXostXoLOO9jF2ltgvPpK6IzrNcLLh/72Pa9SItohBVlM74Sc3x4QGTyYS6qaknM1RpExpiiVEkjvpuQI8IdiSXZ0SAtMFogxsGrC1ppg1NXXF4MAde4eL8jGcnJygqbHVB2/dsNuc0TYXWEILn9MkTTh4+5M033+SLX/oyRV3jYqQbHKXVGCUOGFnWCMAUVgJFa6mamlqZUZg2P+cc3Pso3ZVoQ1QBU5UUZTmOP+H66e38AbzSTPcPsHUjSKxSdO6KY4BCOh886MKmpUVQkGZSUpdVQkEEdajrmr2DY5RSdF1L27eEEHj65Iyjw1u06wXLxQXr9Zq6mggiVJUUVUVAAuyyqhiclN9MKTQLY0wGO4lxNpbTXVmMCJCxmqIoUaaiG0T4N8aEWvYtffBgFE1jKRtJLtERl8Tr33j1NZqq4PT0hK7rEhm/5PBoj/V6TVkWzOcz2nbNjRuHWFtxdnbB4mLFMIgE0bYxRBQW1E7z2vPdoZc3ouuCJ/l5Lv3E59ap3WM3yNj9rLz2787R3bn8ouOzIh/XrQlJKnY8n3ENTcWT2azk5qs3+Mq7X+Lz736R19/8AvODYybNnGHwBNcDBmukUjD0vSDOCmxhiDHgQpcoHJoQRBPOFjWDczjfExxMJjNC5wm+5/S04/T8jKIu+Lf+3f+Qf/jv/S85e/qMH33ve/x///P/nP/mv/5Dbt26ySu3b3Lz1g2m8ynnpyfSeJjoH1ZZ3I72XN5HdoPALajw/H26GvzJX3xqnJI1KLiA6zeJGxvFDi0EtHIUpsGWJZFI17VoZZlUdTJyQKoCyoznoJVGXxl/ZVVRN83I692ey2XaT4jCc415HKZEQWXTiJ3PdV5sMnVSH7DWQt5fUBQhoIyi7Xv6tiU6h0aJe5lS4x6RlUxKY0XP0SjC4DC1IvPn090Ut6RRUunljp+nLPyyx0sHgFuET46Y+AAgDaZXeSJbqPgynJxf82nQ7Baizz6vOUsBJQI8ibu04OJ8nTJHgVglQjeJ76aZTEom0wnWWkprqQqLLi31bCrK91FJlB49k+mUqq4luEu2bpJRD3RdP0b7FxcXgm6lUoHWIoQ8mUzoui6hDXK9OeDLDSNtK84jk8lkDA7zsYso7uoK5nuUBaN3G0byJM4yMnmySoBn0kTfmk9be9m2JweMuw0paE2TgsMQk+BzjGhrePLkGc+enjL0nuPjYwAKq+m7lrbt6LqeqpoKOdl1wvFIz9QaQzRIEDOIkG1ViQtDhHEBKsuS/fkcBXSbTdpIc6a1NU0vKpES0lqlJqHL9kg5uNzdSK4baz5sx+suQooile/YKXVEiI79/X1AY6weR/Le3gFd1433nlQ239/f59H9Tzh5+pTFxQJiYDKpBTVToIxm72CfvcMDmrJKCKcmmpK2zeLgQQIHbZhWmqAF5VJRizi1saCVlAdTKccaTX+hIVqUtmhd0tQVt28fUdc101mStFE91hqaRji6KorXd7dZ88mdj9k/Pmayt0dZF4QhUE+mZKFZYwymLEZkOqOnMtbsOEZjQkNzwKhVcnyJDlsWo7C5Ti3efd+j0mdoYwnBii1iNAwuEOkpNpeFoBUKozS2MJS6wDvhQSmNOAel34ueKHRDh9YkaadIWVmqSjaY3rVJ1Hs714wR5wCURmlDU9eEEGkmEvzGGMeSPzn4RzYa5xwhSils6DriELn1yhHOOzZp3mQSvS8QTcwYiK6lrgvmTclFZSlKje01xwd7PHn2COd6tBYLqr29GSG4FMg7VqsFg+sx2lKWDe2mxzlZy/KaLqU/8VMYXH8pKLscgO0GeVu3ppz8A4Kc5SrRlf3tatK1Tc6eT75izOXTy/vMi47PVp6+PiCV4E9tkcqMlEEalwGjIAwD3WZDu+7ou4HNpsOWA6a22NJS2DoBFp6iLCnIdmuG4LMTS0iC8uLV7pPkS0FElQblPFENKCtmB0PvUMCyuxARYWX40q//Jv+Hr3yVxz/5iH/+z3+fH//kr/nJjz9kutfw+muvMJtOsJXsb37w9MPmkgZt3iN2aQAZRbv6vHd57UIrksbCfI8UGqsNQ0wSbon/p7QierGrXK/XDEkvVFvLer1GeeGH51gtn0u8phHn8piLwt+TGvPYzGK0NKmK3MwgPExjReg8OXQ5J04pVSU6nPl+DH1PGDwKCfqUImmPyn7t0nvTTZAm15hE4ZOOblGWVGVFaezY9NJ1/WWXrcy5vGbc/qxEKb9mdx+7+trPerx8Cfg5KH3793weu5Mnn+zV9143oa9u0Omn6bWXg8W48wE+ePyIEl79LvnvoiqoqmWyIjMUVtq+y7qGRBw1KepvJhMpfyrhlFlrqZsqfV8cs8MQxDZKm7TZVLJxDm0ni0PbSvffpk2BTTFuIl3XEmKg74bkEGIoipKuHdBJjt8YI6Vtdbnw4bx0Komwth8DOfELzkR5QSNQkag0CgMxTeKgsGU5lvLHrC+V6/K9Nkl3URpfUgekMqigOXl6Qt92lKWlrKRRJ8bIerOhbTs5BR/o3ZAM5LfBf2T7PHNjSkTjokNFKVkMPZiioJ5N6NmIv7KWlvvsQZ0DhRikuzxE/9yYuzr2rk6aqyUouGqELnwS5UH5KDIFzpH1uKuiErK9TjwSBS54sJmTmBdKOHnyhLNnT1lenLLZtKMESlFYmumE2WzGweEBtrD0jhSQCgcFFNEnrpKRyCKkcrpWkoETPNZqnPMitBwC3nm0KgghoWQjH0ijVcF8vk+MirYbCEoxLWxCaz1ET/CRoYtcLJb0ITDtNuwfHVFXDSHJjgRykLP1uxaKaHJMSXOO1OClUFDIAq21lkQtFpcQM9n5o/DuYsQl6aY8pRWCkjliQlp3DiU8LaOSxrnRKWmUIFDunkZFAyHgfYfziqqaIOLikeAVMUgWb4sa7xWb3uGV1INCohtkpxhtNUX24NYiU4SPsvmRiPbOYZBEqTAlvVacn52z3KyFO5p0/NAKGxVBGwLgQmTwClVWlJMKqxWVMezP5wxIotjUFVqLEoKKmj6h98SI6x06GparJQf7pTTuaOE6b9dWEr/XI/m9urSeC2F+d0WFiCYGRWZgC5grPLmse7bljTGOv7yxS3z3fPl350Gmdf7TS18x7p7Vpx8KEq9vfOPlt8ZcdZLIcwQ7dr1mQ6TvHCfPzpjOHzCZNbjoqeqGpqzJ/FtFTNQKMS+IRNbtBhMNthA+qMwdQwzSVKBT0JxWRxLrUBIlY/DZ2SNKo1GhDLP5nOmX3+UbmyVHr77Cw8cPePb4MQ/vPsSoZzRNxXQ2Yb4/p7R23CdCTNamidMdk+ZpiAEVUnOXRp5vjKBDcsaS5+FCRCdh5hSmi3afVeKdaxQ2qXL0XsSpvRdeXspO8F6s00TuNci8igoV5PuucgCd9kQDpa0wac/Kd0uRmhONHtUtrmYgstflOCFLwqQO5uhFHg6PJStKKNCFIJxOqnI+qu2an57haEaQGvSiktK3VPk0Qx/oe48Pg2QZO+d1bSJyBfm++rurx6eVjV/m+Mwl4Bd/yfUnvbsxv+hkr0a5V9/7okm+ew5Xs8i8iHTtQNcO175/twnjUlnUgDWKqq6ZTGsKm/2Os7sJKbADW1qaScVsOqPrW4ahF9Kn8/QxeQebtKlHIeEboxm6ni4tMmUpYtqFSqbthaWZDOjSiktAKot4l2zGMuoVI0VZkYUwVf6fkrb8QO56InV8wbazd/vHRemYknuhKEuLsXok9qoonVvrszWnT08BEoJpUEaxXm3YbMTgvrAWP0T6rh8XdZ86P3dL2Tk48EG0okx6bsPg0GVJMSnp2w3eu0TWTbw6JDAjXb8yEN11CPM4GsaxsKt7tcu3VGwR1tEDOZPWB48KBTG41ICSSlRotC1kuUiLd9cOYE1ymYvSLLNueXDnDuvVOf3Qp+kfsEVJM2k4ODjg4OCA2XTKarVmGDw+lcmluadA7Sxp0uQTsGiUkbGggjyj6DwxLY7BpyBUR9xoMQgETYzSeNBMZuwd3MBhuDkzXJwu6DYrBtcSLQyDNHRsupb1ZkWMgePbr6KiTx3dyQNceUKSj4haoQKjBnBhU+NPev6FlQ5blbiAtTZj801Mi7dPqF3wXgzfYyAqT5nU9jONIF4V51EZyUnbqE5dv0Si8qA0RhuRc/EBrQM+Wup6Qgyevm8ZuoA1tZDStcYFCGhSbDTq+YmxMdSlQeexZMXnU4WQgoCAdwOu61HGYLShqWqqsuZiseLp2QV7e3toouhcps5Kq2xq/gkoKwFi0VRYo6mNpZpMeNpvkt+zUFNc7+nbgc47JtMKFQLeBUpTcdqewp6Q1a1REMJIYM+HdHgbtgBuCvy2MdHOlEqNImonqTCS7JicSCa0I6/dY7d+tizj6np9eb0frSvTaY7JwaXj+Q3y+j0moXhjIBvTde1IzAShJSgFWsWd920T5QD0feDifMmzZ0+YHdQUTYUbXh3lhGLwRJMcjbRo5Lng2bQdpdKUtSU3hMiqJy5CksuleatUcoBCEppyZy1GArO2XTMsHdO64d2v/QpvvPsFFosLfvrD9/jTf/YHnDx6SLu+YLVe4/DUZa42qRRYC/9WWUErM1IencjvaCMuF1GJNWNEGh2GZHXqEigQkdIqaQyTSt6KmIT8NQ4vAV6yfo1eHE2UFr1aHzzOyzNJdannnq1TKQAshMbhSee8szZKtSElGGqblO6CAsbI2iANkHI/A9JgGs2WBhCjGRHCGJLSR8wrf3qNjCqUkgpWTAmTi+IyprVi6KUhR5D5rfRRvryfFQTuvuZFgd1ujPUihPtFx2coAX/6l/+sn1393eWM7+U+99NeM27cV46rQeLuYpPJsM8fGpmkC1C7vr1SQi2KQkpuSrqaqqpkMm0oi4K9/T0x+z7cYzYT/k9RRiIuScXIebStQPJdJwrj56en9J2h6wY6J11+e3szmtkE6SQTePzoxgGTyYF0IQeFj57JZMJyeYFWySEkwhADxH4s/0rmo2m7DUWxFaGOMQpvI5lOyiJpiC4SBi/oktZYHfnrH/6Qs/MLjo+P2duTc1iuFjx6+JCqKCi0YWh7Ytg2vmwH5ZZsnBdUY4xYZymFNRqjNH3XUVS1iE5D4leIjV5MHaEqWeBdLWFcn0jI4pTL8HmsjJB8+rlJ/JBtWT6NjVFaIf1OW0HJ8hhkO/kyr7MpGlSEbrXm3gcfc/74mSjFF5qyLijrmoMb+9x+5TXquiEC58s152cLVBLczuN2V6ctB9DS+RuTHJMZdSp3y3YZcdbW4Du5h9oYFOLSMwyevpeSzWy6x/GtA5R6xMWFJq4U3Qb6/gLvRKDY9T3tZoU2Bcc3jhk2Qm63ZQWFIB7GGBEeD2Es6w6bDUZrEZFXcp8mVU3O8fXOM8mBQxZcz2XliGLwnkDAqmRrdiWbBkH3DNJoQwwj185YTdutQRnq2hIDOBdpmoagLcEJN7fvOwpTsXfQYMpIP7SE2GEN2LShFAaxjUQRMdhoUiVCOuBLUxIGT9cPgoRGcSBRySu5LOY0kwnOB+7de4CvB+pJg7U1rusY+g5tShGQVh6tPQZFVU+o6oayHfBa49uO28c3CSGyuBBv5xgtvR9o6kICb0sqg0fWqxUo6WAW2yzGdUEpJQ1WJDmpNF9lQ/XkeGm7jkqzgjUGW4iUhs/II9vE2rnryO5X1v2UmeR1OCOEV4+XQfuuVgF2fvPca58HDPIv5P/yfRFv1xw4pMTfaqJS9M6jMldaRbyBYOTntixAiyxU125wbU8wUPYlxhQopHJirQGd7fti0sBzglbmIIE4Bta7dCprLZu+AwWz6ZSjw0O+/rWv8+/++/+Ib3/zm/zFn/4x3/nmX/Ltv/oB01nNG2+8wd58SlloARYoWa1WhCBc+PlszrodWG3WFIWhqkvqusQUYr9qg5yL61tQwnV2PqC1FT5rquqEKFqFwQeiF2k3mc9Zh1JdeiqRiA8dSnuslT1t7BZPh9VWUG617UMIya2LuF3bFUIH20UQ8zqfn/MopzYkDr/yYAuauhZJtLBtzFT5CURZo7quk2755DFrrWVwfQIX9Jj4CJghElv5ulEqCVHvUumuH7OfJYj7RY6fGwH8eY+XvbAXZXO/6HFJx+8F30vU4wNWyiLEn9wg4Xcsk/JnrNH6ghD8thRNtsopsWWkLDVNU3HjxjGz+YS6rmmahv29A27drPjKl2Y0+5KNDMNADIqh96zXHe2mY7NuWS5XXDxd8didiO1MDLR9PwYBVVkyrRv25hJ8mlKhlEjslGWJGySoMbqACH2XNI4YMNokP9TUKeahqRuqsqRdr/nOd7/LR3c+5p0vfpGDvX0ikadPn3Lv7l329/fFq3LwBCcacDlgkkBfxErHDT0HKZl0HnNOLPp689mMup6wai/QxjCZJG26vk/K9HEs++5yJHNjQn42KqnE72o17gZI2w7tMMo+5OuPjLADwQdOT88o6glNM5PNygh3LAenq80atGI+n4MLnJ2ecvejj/nkzl2sMZjSsNfMOT6+QTWpuXX7Fm0/8PTsjL7zVNWEcrKH926H76LSJpE6keNlb+qryVTmmuTxrbVmvVzhg1xvodS4/y6XKyE3p67g08owmUtw0m16Tp+dMww9fggYVQDS1PHTv/4R9dd/jRs3jkEZLhbLERYbXC+LnE1fopV0USa5Dula7nGpm85aSzSXkwIUDKnRSZw1Uje9yo1ljNqBahRhkcNqS1NNBMGJA3t7DVFFNu0Gouf9937Axx9/wGu33+BL73yZcNHzwUcf8vbnv8re/JDD/RtUdcGf/fmf8O6Xv0RdKawK7M0mGCxuGFhvFoToqesGZScyzjDyrJynjxsKlbjByoAylAFsaSAYnj09oXePsGXJdDKVDdKFlJCJz/bQtSNXrG9byrqk2Z9gphbdSad3s4Zu7bm4OGezaSEaurYVX2zvhP+lLOerJaJLaHC9Y73uSMJPGKNSk5h4h4eEDMmwkoTx6hIs5d5A3ZRpHkny5s0OgsU2kNtNyuJYZn3xsd0Q8zqdf/6pb/vUQ1C8z/4Bxhg0FqJYgdmiTtqXBqUNSlvm+weYoqAiQLthMyyJ2tLcOMISaLs1i/NnnFycUNU11hrKUlxofPRJvDjfp+29G1G1nfu1O/8hyQxpS7de4wn4dsP52RkYzd/67d/hb//u32WzWPCj736X/+6f/B4//skPuTg/Z29WU1clp88es1wuKQpDXZesLta4vmcYemazKd6VrJdJNDpmU4Wao71DPD0Xi5WgvNEzDB0og+sHokkqFyFgdFbp6AERgRealdmqXFhLpTVtv0FbjY4eEx2wrdwVpaWqS6qylGDLqjRgkTVHi0sVmVyws72PVKdUBdp149LaSKKXlTrSW7UWKsfgZL3ySVYnm2A4txXVHoYhUaWyGLYfm0Hbtk/7zrYy+T+k428kALxairuOrPiL1q5f5n1Xv+fTAsrLWYJosgGZVpf/j+y3Cfn1uWPusqZUDHEcOLrTbLTi4qzn5MkGVOIeajPydWxRUjSaelIwmTRMJg1KSYdvXZXUZcnN4wOq27fQqTEhxIjzTrKMwbNarRj6gdPTM+7dvUvXJdRHg9aR2XzGfG8iqIhRaTEqUAq83spaGKVo24H1Ys16veHifMG9e4949ZXXOJgfEJxnsVxydnZGDOLWkgVAnQ8Ywogw6oQuZtJxvsdZ3kOLNJKUpZLx0t7BPlVVjSjWGOSlWlRiwI3i27m05ONlce0cIOaS86XFc0SgJbvLBPzngy1Rcg85+07jwDnHEAMuiH1SPZuyWC1Zdy0Xp2c8efiIB/fvoypDFwNv3LrF4cEBk+mM2d6c1WZg07YS0NiCIXiMSXpyXnQOlVKpqzTb3G35rkVRjl3oXdeNjhC7c0F0JA06SkIypEW4KIokBdLjXKCuJxRFTddtpPSmSo5u3ERrx9Mn9xnageAG/BAhRH78g7/m3S9/mVdffY1bN45YrTeCdiQNRzd4OremKsSDe4gDvQ/CwS2K8Vn0fU9UjIG4+HIH4RqxFUIXjq7w9pSOW6rGlS7gkcsWA/NZwdnZiUiiaMvh/g3+8MN/yeAc2mg2myV/8Vd/wm//1m/x2q1jmsk+EcVqc87h0ZR/+Ud/wM3DY167/Sr7+/voAr7/g29R1YbJfEobZsz2hCPYKIsi0PseYyRYlfKYjK2i0NIV6RxKiX6ftiWDFgehGFOZKDoIEV0V+MHhBkmYeueYHewz3d+Tzl3nmBYl65XH94roFGGIDH1gttewNz/AaFgtFqJJGBVDL5aXwtdMZbqEpspYh1zz3aJtfhxv1pq0bhgUjqZp8H7r6qO1EReJhKDkDW+rLbrLAb5c1rq0fEd5nVZXy2DXr9svd1wtMe+WnRmvUfisVw4lPDlrRAHA+4EYPIXRNFVNu1xxsOfolwvOVmvW6443P/9FbFSsTs548uQhD54+5KJbcePGEbPJPlobfAgUJImh4K5+6/gsduPlkf8uNwgQdJ5UwTBGU6Ru8rOTc0Canr7y67/FO69/if/0P/1/cPfu+3TrFevzBWdnC+p6QvCwWUuzwv58irWicxqCo24qQgicnpxTljXWbjjVF8z3K24cHBCBrne0fS9jyUjilpO9vl1SVjXTaY1zW1muxXKJDz4hm4q+dfRdwKQyMfHyk8gAh81uW3GrKEIq5Eb8qIW5TRy2erv5GDuaFSidbChJ0jCjYLawAYmyJnvvccOAirDZtGlsb+MLsYrV4ziScb9FtoV7LWX0v+njswBnP3cTyGc5Rp7Fz/iMT+MI5r9f97rr6uUv+q4XQv/P/fuqTtX256lSxVaK5PmFZXcAhiwoi4i57raG58GilBaz8VLELosUmNnCUqasyVpDWZToQga0tYaiEkHeqiyZVBN0oyispZtOhbsUpaO5H3qIwokMwY0SHXVdM51Uo79zXdegFE1V8ej+E+7fe8RisWFv75gbx/vgA6vFksXFgr7rmc3m9Al5Cslqyrlhy5WMIWlBXYbhc0ZmjaAeco9kos3mcyFPh4Af3Khr9VwxKRmKZzmw3fJRvv85S8vlKZQaHQdImXYWud7tqM7lDBJvcDKZiEVgWnBCDMn5RjPEII05WtP1Aycnp1ycn6M1lFVJPZtwfHzE3nwPW9aAIJ9lUY85fozSGIPZekNLWUP4VuIIErfjMi1eREGbfMo8xV8zlYyHHqUi2lgy7yd3i0tgLAtugFQOSbIyqQFqtndI2y1ZsaBd9/gQMVoamR4+uI/Rms+9+RZ1XTE4z0i00goVnfChSN3KVpCOEAJFmQSj+5bVZiVoms5dIBI0uR0nHCkdh1FuQfQHA7tSQNsJLu/d9I7zixOM0Rwe3Bi7PG8c32A6m9J3Hc+ePBVtw27D6ek5i+UatOf4+JCmqXjy5JTTpyvm8yk3jmYAPHt2Qtu1fP6LN6jLkkgD3Zq2XbFyLdNpIyUgIsnkIPmFm8RDVMklJaZyWaAfnAAZWro+lRGtU++jjFWlMeWE/cNjVucbnnzyAOsLgt8QY0dkIDAAgcJYaVgaAm3bE5wExdJ8RhLWZuT4ZtJ8bnLYnTso4UKXpaUoLUWRPLe9xVqFMQW5uzL6OHJhJYpKjQFxN6Dc7gV57buyfDIGPerl9hx15XXXvkVdj7vkeOoyG0mNl5BC1hTgyphzTmy+1os1i9ML3PHA+eMn3FmcEVDM9g/pwsD52Sn3P/iAh4/vc75ZUO1NmDUTQhDR58yLha380XgvgnCbx5NnRzJnLJ/K2QWlUEUBOX3W0uQk5lfCR6ybKR/d+REnT5+xWa3xbmCzWqMI+GHABRl7pijo+ijWqzicF93Iqqo4ONjD2lLGMRGrpdMVxC3HpkRdpJYMGGl6mtSGopCGy65zUo3Qhhs3Dum6Fjc44cwnQIJr1nlIWsOpmhSSrJLRWdxagQqMd0apBAzucDzj802A+X4WVni0IsxtkjxNgfJCZo5emuoyEBHCliqxi1KrhPznwRXjttlmO+bitWPxFzl+kXLx31gJ+NODq7+542ehfJ/27+s+Yxu0XJUxuAzPK7VdfLYD4tKnjn9yU8cWFk6B0gDBebp1n4KC3fNUKfBURCXiwWUljQSHB3vM5zMmk4amqTC1yBGUE5tKnyVuqFivNwQX8B42q57lYklRtvSzCUpD09RMpw1FYZlMZiwWS07PF/Rd4LXXD2iqimfPnrG4uBDvY62ZTCasslVbvsLgL8mg7KrL5+vJyJ1MXpG8CEEQpNlsBgTcIOWIbAOY7sZ4/00KKvKPt3I2W05NjFvLu+0+pMbuZAFgwyWx7vHh7Uzouq5FxiZ9pkJtg6ne0/UdRWFZnJ5zfn5G224oC8ukqrhx44j9/T3quiGgWG96tCmxZSprxijbZRAi/u59ksVsizyKK4ssQjloQKUgMUR8Nn83gsjZHS/MfH27JWKjgzQoJWN2kQQRYnbTzNg7OEqI7YJ+cJgkrn12+gytFfO9PfYPD3EhoKN0yEn50jC4YXTuMdrQ990omZApDF3fClm+LLCmSGXQsA3qka1NRZ8ccBQxGqIIP16at0p2atCai+UZi9WCg/0502nNxfkCYyz7B4fU9YRh09NuejbrlidPfirjvB84unlEPWm4deMWH57f596jRxA8+3sTPve52zx7fM75yZLbtz5HbVuKwnJ69pjNsEaVFcFPGHwADS7Z/2klxPUtqCFBmTZqDAJ9CBSmwAWITq41BkFco5Jtdu/gBquDNQ/uPGK97sX5KAxE5YlKui9R0LUd3g20bYf3Uo0YgstsVRm3kKgUMVfRtqhgSi5M0iGsqiIFfypx+xTBO5TRCYjZeinHMYC6jOzlIDD/9/LmfOkpkgOe3Z/vrq+7P9v97+XXPw84XF2frz9iIqPks4njOYFQVYZ+YHm24GnxhL3pEY/CE877FfvHR0yPDjlfnfPg8Qnv//AHnC/P0bXltaMZdVHQd63w5pJcWVlWu1+dTvLFFbJ8XyNJFUFrCcoCBJ/msAJbFBRWQQycPH7Cf/f7v88nd+/gXSdNiL1nOivxQUsgl7KA1WqgKERiRStpgCgKmM/nhLQHESUxWS5W4zqilJJu97LEuUEaLDTUlfhvbzbJtz3I+JNGqMAmeLyLWBuhEC9iTbwSlIO2dsvhdgNu8KhiW5FTO3Mr36vd/17lT477d5KOA0UcfOqAluacGEJqmJIgMNug5uAy7+P5eY35T6pSSQDoE+hzeTD/TRWCP43edt3xczeB/DwB3YtQwJ+F2v08n/lpr/u0G3Q1yLvus682sIRUttr97MsL2LbhIMPP1y1oRuntUqMUga3/quxt6f0R3BBwQ8tq0XHy5AKy1+x4wTCZ1lRVyXxvztHhIaggotnTiXSoth0+OC5OehaLc5zvsaXi8HCftuvYtC1GG2azmrPTUzbLwOPHj1Fa0zQN0+l0vB8xBSrBudTpudWJyvdjt/Ei/XD0kMyvGYaB+f4cN/S06xV9144cMgka08YVAnVR07abNDH1pTLvGOQk/96xA3nHMcQmR4q+HxjckHw8U7OFEoQp6xjmErd0z/XURZ30prwET4OjLCrufvQx68U5wQ9oZamairoR8eA+lc4Hp9nbL+l7J2WbZGkWdN5sLLlsvR1zWSolE4yzZ6a8pyqFn1SmklouwWe5mXz/hqRvWdc1tB1dP4g1oxGnUq3D6J5hioqDg2OEj1TgwoLgxP7Re8/Z2TO+891v87f/zu+Kw4ETKaLc6WvS9+eg00fRPkMxCrnX00m6zkSiTt7WmaMpz0w+V5OkddKCr6/OYy2WbkVV8ujRHULwY9no/ffvcHHecnAU6aeK6DRdBz/+8V3+6I+/zc2bR3zlV96lLA/4/d/7Y37967/CrZvHhBB4eO8J3/zLH9OtDI8eXtC1S86frPna179CU1d8/OB95sdH/Po3/i4xNPTLXhBHHVBBdDRdtCnYCpAkV5RSo7VVfj5aa4LrqVBgDEMYcMHROk+zN+PWm69zcnHOH/7hH6H7EltY0BWoAm0cfT/QWY0bxO0jYMRVJo0hpeV5LNerPAXTXNSUhThx5DFZVpaDvSm2yFIhqUTvYgpQU3lLa2xpWa+lNCaZqixkUuHYXQu36+On7QcvRxF6PgnP37H7WjW+9urnxDHolfPJKNE2sIiKxKuMWGWYTmaYqNgsW+63D/j4/U8YBsdv/L2/xf7kVcrQ8eDDn/Ctf/3HnJ48ZrI/4/bbb9DMCk7OnuKDEdWD5FNflAVlUaMS5Bej+OX6uEXCdvchtXtNMWKtIgx+5F6ipAqxP52w30zpFgv+6//yv+S/+W/+Kw4OLGVZEHygrCvm+1NMUbNctaJDGSOr9Yb1asH+wZzZbIKyltWmw8XAerURBQJjqMqSzWaNMlA3jXDOjWa9WnBxvqRtO0JwTKYFR0fHBCJlkZorFSwXZ5w8fZasWyNoccowKmJMQQmw7MdnVlY1Vd1QjBUj0a68/EyTdiY6MwGvDQBHDVyfnm0hygBGGVSMgviFgdCJbmbmZBptUgKryS40u8NUvlsSIW10MouQAJdAsqbTY5B4HSXhs6J518VOfyMl4M96Qn8Tx9Xo9irf65d5XL2M3Y9/kUq+kKbzorQ7VXdfv32tDIIw/jzkDjySSjnik5ozjBw8asNo7J61BL1LylFq27W6Xg2sVwOnJ0vufPgA0edI36azXExER4P4GAo38f69Z5SlpevE01ZrRVUX3DjeG4O/LJCdy4kZ5bMpU9vtys7E21zWzMGYUSJiDCTUQYMK3LhxyOpiyfLinKHdgK1H6Hzr/5gkEUIOMgPeq/H7nHOgVCpbbEm/bpBNUtwPihH5ywtDbiTJUhEZgRMkR0ESdLXW0nY9MXgaazmaHfHw3n0WJydoq5nszZnvzWhmU+Z7B7igpbnBVEzqktV6Rd002CLfL9CqZAhbMd7dMb0rWZTHeybs50BC7vlW0sdaA0onD2xpprAmf59IgSikUzRUhUjuJO/aEDzn5+fCQa0PODwqCcCw0ayWG7ousNm0XFyc89P3fsKXv/JVNNIB7IZI1HJPq7Le8hONzB3hvcn9nkwnFEXJZtPS906cMJQiu5vItatxvuTPIgo6dmXWEqPMhV/5ytfQ6pyHDz7iz//sv+Pdd3+V41f/FmeLUz688xMe3D1ldnADT8H/+n/zj2mmDZ988oj/y//1/4mJ8OzRGXtHFUNwPDtdcO/BGX74gIODmmoyY7GSruLf+71/xmtv3ebwVs3Z2QWTRrF3MCOoSCAheR60tujkBkEK7r2LtG3LarUZBeQPDo7ArURnLUSc1WxiZDabEIHZ3jGvvf0/4ebnb/LBN3/Chx99xMn5Bf0QUVjoSlbLRYrBGrQqQV9gCyt6csAiEf9zk1Zev2azEquyS4k0BszmE/wgPOPBS1NQu3Ej+V0plfhPanxWUYkUUAiXlRniWNodf/JS+4W85OX2lcuVmLQvqBfvEc/TfBJymXNUL0Zh2socXS+zhV4YaTSHR/ucPHjAcHbKD1vH6dkZnd/QzBuKooHYg/eoSYXqHVYpjIK+X+OHHlU2wl2LCUPNFaJ0j8arz1XiHBCGCO2A1RofFd7DdDrhczcP+ZM//Jd88KMfcveD9/n+t75FMw1oW1DWDc2kpqkLfvLee0xnJa+99hqvv/YKq1WLYcF0UtJ1HavlmvnenNlsTtd1VOWcEEQa6vRszXK14OatQyaTibhlFZrFcs1ms7VMrUqbgI1IURZJKQHWyxVNVdHsT2QNKA39EFkuV7gQac5W8Gy9fU5GZKOMKYleONCpiCTc793nzeVmuV11BGAc88YmnroSl6MR/VYib7XZBDabNcvlQoSsMxddZy3NHRH0ncY9BRS2EC/hQfYSrQ27PtD/Qzl+aRzAl5nIvwyE7+rfn+OTvOB7diPsn51dSnPH9kG9LGp5naRM5ggknluG8GPWOtr+e0tAhcxbyYvo7rVmKBqyfAaCLOTSQBgR/REhSvQJsXgbqYvJnQOXTl0TovTjBmcwSpwHtFEoAlErmtmUYke6I5/brkr7qOi+c29y12d+zeWFOYxdk6YsmM9mrDdL2s2a6D2qUOPrRgmA1EQgkzYVnUapFFivNyilmc23TQdb9FWxWCzGwGkymYyl0+19VmODQtt3IuqqhWC8dYYwmMairWbTtXx49yPKSUNZV9STimoy5dVXX6UyBX0oMKagbkp89DRTsV5zXkrcVhcEL0r20cTxGnPntFi+RVCBqipTKVRQs8IWeJ+C67jtig4hiAelkvEco8J5KXvngFwAm8jgRbPSRy+dfEH0v4gGrS1NrXnllcjpk0SKXyvhhxUFdz76kKOjIw4Ob0iZyzk2Qy8oY/LvjsGNjUA58PbejdaH0lmfy+oKaUBIZOog+o+mKNCFHfP+q7MyhoBzHZu155Ubb/DXP/gx3brnxv4XuPdhx3/2//mvee1zb/D6mzf47b/7G/zOb3+Dk2fnPHryiCfP7vPk/CFvfeE29z58zHJ1TtQz1hvH3TuPsKXFzgKvfuGI/YOG5eKcf/qHf8ydD8/pw4T5YctrbysePPqAj1YrPv/Ol6ibPaRKZ9MY9ONZxxhxfhAaRdNgtOZiIRtN1ShC1GgjFlilMfhhAB+Jg8f7nl/54hf59S+8wZPTJzx++oRnT88wpmK9WHJ2csJm3YrchCqZ1K9wcnrKputwPjCdT6VJJaOp6Uba5HtqrMJaUMrTtxvW65ahd6IRHiUp7fv1pXG2Wm24lBdfux88/7NL69pzCT47lZV46f27FJz8lt1y8nXrfA4Od8vP29/tlBKVuvKZimGQxhmf9S5ThaesCtp+4ON7jyki2Faa6NTcYKOmava4ffw5Pnf78zx5tmZ1cY5O1o+rzQpra5SyVOVEEFqlsYWIne/y1GIUvc9LyWEEiHRuoKhrZlXN4tkFP/jzb/NP/6v/ivd/+h7L9QWzvZL9yQQ/WBbrNat2yc3jfd56+/PUtaIuDavFKXc+fsLFYqCuSykDWzg/W9B1HTEGisKKUkShOb9w7B8c0kymhBhZrBbUdUVVVSKnogxFWeLbFlDMJhP6YWCxWNC20rR4fHhEDLBebHh6ckGI4qilreHCXU4evB/EuzxEYkrAidvmvMKIbNi41seQmuC21Y/d8ZAT4hgZlRGKskT55G2cPJGDl/XQZZvVS5Say/t/7tz2yeu3azu6rktcySuD8NI45tK//yZBtKvHL4wA/jJO9tMCw58VNF6FTi8/kOt4IC/3XddlnS9Tlrjudlx+W17ItsHl7u/V1b/sLGj5u3a7j+Tf12sgErMYq3ygUmpEDi+fT4a0IyT1dxe30hyFFQ2zumy2i3IMY2lHyOCyIEv5xKRyL2mShPG+bBc1TQgKUySNpSASMFZLJ/RqI2UEFcVrl4QsxJ1rD8mP0sek7ZS6dkPweCdwfN+Ja0la+UkgHkpB23ZYWwiXUMEw9NvgPAXgPgwEAloVMsFVQFvARHQQXcgYPe1yie9bigImtWbSFEybkqKs6FpPNEHKb8pAIHWrMTajRJWFi2Xh8UGaHkJIJPW04ATvMSpZ9iE8rRgloOuHQXZzpcZmI63NeN/zfRuClLYHHwkqq+frUQsyIzPGmnHMaQx1MaOZtyIwHhUqatywJnjF48dPaGZzZlVJoTX7diYodvASaKCxtmA6ndI0DcMwsFqv2PRd4stAHCV5NDZnLJDM40XPLxlm7JDod+aOAqMjtV6zWT3k2dMHaDR1MeeP//hbPHx8jotw8/aE2azm44/u8+Mf/pjDG/sYVTBtZhwdz7k4P6cbInNd0zRSzvHBsVw6Pr7zjMPFlP2Dhne/8i6vvfEm1gb62PHRRx8xqWsefPKUstjjtTcMR0dHYiNpRKZm1JdTisIo3OBwRoJE5wpUdBS6FqQgKozWwvOLSaxWBZQOzMoStGXPNWh1xP6soW8H3F7F0V7FcrFkvWlRyqKpiKGn3IjuZkThnSgIjAmAc3ifeLlK8kHnI0M7MPQDMe4uSDu+sUrjPPTDFmVXSURdx6vrb5QE9wpKl4PI7RjdFarfkuyvirfvfvKl0qhCuHvjYpr41mREn3EjjmzlqOSVW83S8RzZcmezBJhKFQtJeD1+sWbQmhJpTCuCiCeXtqYqKlZnFzz+5CHt+gRrI2VdUk3m9Ddfo/MDJjiRDxoTBMaxDyQaRBwdO4YYCEYxVQ1FjAybjnsf3eNf/Lf/LZ988BF37n7Iar0g4HHRwpC4fuk6+35gNmuIIbBaCxc2RBhcwK876tpQRLFS67qALRRd7+l7aVgsKsXh4ZymqejajpOnC4ZBBO7btqVuSmbThjg42pMFVb2hGzq6foM2gaqs6IYO75OeotW4jVRmYohjoJ2PZjKhaaZEZQjKE5V01eablelF+YntVsJA1gvUDicdhIqBF+BByboia5V0T6skpu+GiBsCxIAxNnHKtzBxiJ6oZe/K4tguAhQEHC56pHHGJIH+6xtdxtF6JYZ5mdf9vMcvLAT9WV//Wfl6v+zS7tXvuf68XowgvszxMkHrtQHrbsB5zUd85nux8/KYAqCr93VbbssZdcyJpQQBQZxErBZpixAy9J2bWBRFYdPPtvqIWyHZQEZT8wKvVELrXM6alOgoOeFB2tJwcb5J2VNIMiA7DRGXsv98H/MGoZJLhfx76AeqqmB8USIYV3XJ0Av/z8aYpHLUeG15I8nIqkIMw6NOIK0OqQEDXNezPD1DhUhRappSM60KZrX4TfY+YrVYh22fsvBUtIrjNanMAUyLkCI3oSRhWpToQw7DGNwJNUDQY+cGdGETciwLnzV5EYwj98T7gPMBFxJpXyebpxDQiQagU3OJ0rmRBqwqqadzhr5H+YgfPBpFWVScnJxz49aSqilFs6upWa/XEMMo4G2MpapqJpMpfSeOOd3ghBCvTSJaZ5U6La4wSPnHWvmjFEk+KJ3jpeEuvZH7E8Wj+z/Fhw1lPccHxycP7nLz9iExblhvLnj67ISfPrnPD3/wA/7ev/V3qOsJVdFCdFSNph3EhaSwltm8EV3KtefRozNQms+9+Tpf+crbxOBYtEtpmDo5wzX7XJx33Pn4HpPJhNdffUUCOIQbaXRy20jP26tkBWY0RVHinXCQwqid6HH9RpwP0nVqAr4f6EPH0A9YpZk3DYt+wBSG2IgTiJh+RNbrXpyNCotKgX6MHpX5rTHiBkeMiqowaXMMY2KllZaxT5IiinItIYqgdu/86KetTcQqcWBR/mqDR16UdsO1qyVYeY0aeb+76+R2/djOo3jp0+KVf+f1TOy50pzOn7GFBGVtzHORnY5o4sg7vcTFU/rSz1TvidbgywKnIjpEYhDf84vzC9bL93l4/zHBbygrmMwaMAaMwqvEjw0ak9egdAGazKXO66fM68JaTFXiLzzriwsefvIJP/zed/mD3/snbJZLdKnRhTSauMERhp0EXSm6dqAs2jGoHQbxLrY24H2PjwqbwIG2dUxthUvNFxHF/lHJbDZhaAeW5xtOnixYrsXuLISB6bSim3XYqFksVwS1xHlpWJrOSoiWEDqIYqE4mdfjvXY+YtTlAFCE0KdoXRC1CJFrpHs3P9NtWiDPaJeKlBtoRpmt9OxVovoYJXtYZhBqFCQUUpKlANmxJYMoMSXlETDJhEHJKu99RAeb7Pv8mHx8WuT3i1LZfp73//ciBP08NP/iMu8v+7uvOz6tZPyi4xeFan+e7/y042XQzhd953VyGjl76kYirGR2siBLQCGaTlmgdytAnPX5chkzJHugzL3I5Rb5fkhedUQf0MqijGXwnk3fsu42zBPXSI0OEGkxDJqgUlCltrwcN+Tvk02rKIrnmkP29vZZLVvW6w3aKMlYZ1PRpotJaDoIV0vHvE8YtNEQ0gTXBu88y8WSp0+fEkKgmswoypqyrKibCVn3Ki9SI49T65GDJYvwMHKq8n0qS4vxKkkeSNnDGlitVhSlSUFaJESHUlFQMr0tT8j1hrSwAongL920oEMUdw1j0ncj9nJK4WMUOR9rBVREgRfpmqqsCb08Y6sNvQ90mw3PHj/BWsON4xtsNptU6pWMO0RBb/zgWC9Xo7NLWZYSZE2m44LdbVqWyyUhSPnbaPHnzLqS2wDw8ph1g6PbdLz+2iF/9K//BW+/8y4H+wdsVmt+5euvcfO1t/n44485Oz/nT//823zu9ptMZhP29vbpB8f52TkP7j1g6EXS6PHjB9R1yd5BzVe/8jX++E//gropeeed2/yt3/wqxB7vFMevfJGud3xy9z5//d0fUlcln3zyCfO9GV9454sUZUM/ePAQnQTrg3OsNh1DsmwcvGbdGbre0bnVKExuCNjYE8hOGxHfD6w7KZVJwhLo2w7nHBdn5yyXy9F9YLXacHa2oW0dLjmgrFbdiCBrLdu8OMP0lHtzIdk7caSoaxEtDsFLKV5pQnCUVUPfO3yQMtfW9tFjrMYay4AI44/WfWPRI0djcLUCImvZc8vRc2vWGOSp69f1S2vdOBd2/vEpn7lLFxHxF8VWE1E411m1aExoE2d1GEQOKVQirfT44UPOTk/FqjFoysowmRYoC7V3FEVBUZSSwPkBY8UlxyswUaEly5YApBBP4EpbDpsZh82Uf/FX/4o///M/53vf/y7vf/AeIXimhzPazYYweLQTFyCfAkltckDZ4f2QupBVoipoJpMaHxRllYTag+b09JSiPMC5gb53EBVVNUdR8PFHdzg9vSBGKKqSvm9xPrJcruk2LSZalNWsNy0xKowtUKGh38CNm5aiiuKuYwvqouDxg2dYKw4zl44grixFKv1GJxzb7X6yTRR26UXbPYcR1MiHSfMpVzyC9/jgAEPUYZwjeY/M7iQZmc7KCzF6tC7kuQfP4Jyg650AEPK9esek4GcfW5738+P1l9n78G88APwfj1/+8YuX4Z9vaskLW84++75P3ZmWXDbKJUjIAWAY37MbAHofsFZfGazSFNC2rZTulKYoDVGVHB29wmpYg9Ws+zWbtdjmRSelROHg7QaU24AUFHWdHSQ8ZWnH88muJDGKEbj3nrYdKKusVr/VKxSB0kDfSTDZd+KaYTGghaMTkM28bR2r1QZtCogaayuKYiLisdpSVML/GsvfO4Fw3jhz0KziVo1eyrB2zCxJqFFZmUSCTrpYualGHNzT3R1vswRxMHJRjNFMipK261iuV2zWGybNDGv16HsdgqdzAy43zNgiPftIUU3o246irJjtKx4/OcHqgpOnT7HWMJ9PcWkBtsZizfa9i4uLbVnGGKbzmSjsG1mK+sGxWC4IwWHTZuV9Tz8EtGoo6pKi0JSFYcaOhAYQfM9q9YSfvPcxs/mEz7/zJYyx3PnwPW7ebvj7f/83OT1dcHrecXx4zP/uP/nf8i//8A/56MM7VFXF0cEhX/3yV3n66JTVqqXtOs5Ol8To+bj5kKODqeihGfjRD3/E6bMT5vMj9o9OaSYin/TWm2/ywQcfcOfj+yyXHd5bfufv/l0ePHqCToG81hofIpu2x6EJSetPacty3RLjmrqumDY1k9Ki44COEZ1sKaNyRAWb9TIlBZZyOqEuC45vHPH0yTNWq1Wyt+tpqoZ2s2DoevreMXQued8Kv1cbS1NPmFS1WH/lDSZmbm8uxcqmV1Rl+uwhSXhI139uBJNELAXvxgDbsb/LE7yM2uwGfj97PYu8OPj71Pftxp5sFR0kwdytKMg5+ZjnYkb8cjVDjWfqnEMlpwxbGrSCvnWszArvBqq6oiwtVVVRV/OxYtJ2a1aLCybNFGPLlHgFgvPgAKUZUlJ7eHhIU1X0qw3nT57xnW/9gD/+5/+Cf/1Xf8JqvaZzA0MMFLXl2fKc0HtKbaltgY6KdpAgvVSlrAc6MPSOslDJt1buTaQXZNpL8H90eIxzjsePzqnrEptcflbnG+4sHnF21tL3pCbBfYyd07U9YQCCNKe0bcvh0WGiBnmWqwtciDx5fMFkWjOZNzSzQhKQqqAf/GVFC6CuS5qmprQWFRRDGLYDIcZLutF5l8nP0jlHTHqBeV/L+4FowEpgJjQIRzSR0og+o/iRSzXAh8DgHeIOrokalM9UqDgqHuTxs9lsRhcQlZxFtqDzi6qPL/7Z38TxSwsAX7Zu/bPe+2/i+GV936d9zs96wD/rPZ/l86/+7LNe35ZLtc3Ut6WOLWdOCPq7wV7+TsmiLvMwrj8yj0cpCSyLohSC79CjUNw4vo0tKvYP9jk4OmB5ukczaVBK/GBLaymsxfvAMHRjMJHFk0OQRoqqqkSJvu8pk4BzGFXYNVFF+r5jPq8pygJjNG3bUlWifK9J7hMpODY2NbEo8VO1thC/303Larmm63ohQdcN9WSKsobVZoOJOnVS57BuKzrt3FZ0enxmOyVtQVMMKtnZZakHnYLTvAmFIGidIBNqRCkkyNwuSNqmUn1qLpEgWQjpRSnq/c57VFCgQZtCFlalRNTXO1QIFFVN2UxwzjN4JwGfh/WmY7Vc0a47qr0Jfd9DJNkmtWgksM4BcAiBp48fo/X/v71/67EsS9LEsM/WZe9zjrtHRFZmXbq6ZoacW2s4nIEECBAEvRB8IEVIoAgBEqC/QUBvgt4ECdKLAAH6C3rRizQCBILkaGYoakBCQ7ZmON09fanurq6uzqq8RYS7n7P3XhfTg5mttc5xj8zIa2V1+SpkhfvxffZl7XX5zOyzzzyyloUy4H51dSXhoJSQtC6w44q8nuA5InPAcjqdja3DfsYPvvsCH/3iNX7zh38FwU9Y1wTmgL/xN/4O0lLxZ3/8c5RK+P53f4DXt5/gb/6tfw0ff/QhfvKnf4bb2yMO++fwbo95YuQMrGvF8bTiT/74Z3jnnXfwwS9e4qMPPoHzGqKu7+Pv/Xf/Nn7jN78LB4dXrz5GzhXH+4JpzjgtFXGSDb6QQ1ZuZykFFCNc5VYNYdlOmCfglCO2QnBrQXSEyQP7aUKpWTagmrHbBzDE0NnWhFoKTvdH3N0q8EsZKWektGEXJ+znCSVXlMzY7TzWJaNCuX+bhO6m6LFuJwQnhkDQhLG+UQpvMLNoWsh6YCK5RTTclAcVQsBWtrbZ9iSmyxXhoZfDPGv9dzr7146xXy/XvRHkPUgUf0NjZpAmwI3nAo96hqRzl3U9lIgEEYmEiDP+oMj/iPGYGt9siuI5dJ7gY8B+f8D19TXmacYUoq45hBiiiIM7j2k/4/DsGj//s5/in/2//yn+5A//CD/76Z/j57/4BW6P93AkpRNlzQKwVUyIUi+6Vhy3hDnO7ZnMoCeI0LgoIsi6nnNFzhviJIZlzgnrtuD585tW03ZZBCytS9BwbQE5QpwDdruILZ1wfXWF492CV5/cYisFIUy4u7vHvAu4udnh+z/8PoCEw+E5trXg7n7F+3/+GtEHnO6SVhbpZeAAleFKGWlNAEy8ufObrdQnAAG03rXv2aDwbU20RD6nYd8+jrz3kvAHMWhS2nBaTtjS2ryKRBIF4lpRSxLqj1fqBMyBIGXxGn2iiiG+pfPnGsfvL6M9eQDf0D4PGHusfRHQ9zZJJp/nupcu4rcLgUtIxkLA5CTR4XDYi6VVROcILJpmMsH6An2p+ycW1xvc1QzUrMkiwkbDzfUz7HYzbtw1bm5usN8dUHRjqXVrIqMm32Iuegv/iuwLwQcHRoDwnqskXwBN94+Zsd/vZMNSEGYLYesTlsWEWna2EI+9Cz38uW1YTgtYQd7+sJdsMidyB8QCanppvC7EzFylfJbx/khCuBUAWMSY81YG3qMmz3BFLQA5Vl4LY9sKYiTAstdMBkH5RMaaqppJXbhgyxmVGeQ9cilCfFadOJDo70m5JJMn0nrNVAQElowtbdhdMdZTwlREsmhdFrz4wXsigcJA0aQcr4kvnC20K9paMcZWx1ikgjTcC8nSjjEANcPru3AaDt/Pu7PhlFLG/e2CWme8973vASAs64JUCr734hn+8Pf/FV7fvsRufw1ixt3ta8xTxDvvPMNPf0p49eolXn58wkcfnUCuSLH7LOWlJKFKMuNLTViWE+5uj+AS8PO/eB/XNzPeffEePvzwQ3zy8WuACbev7/AHv/+H+Nt/528jzB5rTihmsDCDOEvVAa1rTVQQnAMVMXYIjG0CZjBqMK6ohAqdedsrN97kFCOur6/gHGFSMelaC5bbgjkGrMFjc8L1I2KxbSqrN1Dq+DKAAgZpiJ7UH11qFU9IkWomXEUiqlfUIX1vWgVl5F7RKNY78ICHNWEEc+McPA+F9WNlPD6yTvejhk/QvvdYGxNQhJICmEuJWd8VaX/rOUnYFwJByA0nHxJZIELZJVcUX0EkfDyXCZ5nuCCcWD9I8VTlL09xh1oKltsj7l5+gv/n//3/ht/75/8Ct69vcX864ricUIngGNjS1nhtngkOUoUGIDAIa5UEIqri5WIo55kIW8ogEuH1XExoHRqaDqg1t8iIlHJjTNGrOgMQJ9cS1VLKyKkgOJZiAlc75Pt7WX8csK5JpFHyhnkXkPOdeE0DYbeTxK8pXmPbEq7uTsDadQB9ZPhgvPN6to7aOzwfBef7mjk1JAlL9TihHL7KAElJT6jeHxhSiGBbxSvIutYqfYYU2Dmyd9drAdvwS7krS4z/vQlbvO1eP86Lx87zedoXAoBfxtt3+Z03Pfzn6ZQ3xcq/jKfvs3iKnyf+/lnP8Gmeuzdd4237/U33+Vn3PlrdJhxciw5++QNAVsbuodbS+Ts552fYQkvQ0lRaSJ6cw9XVDeYp4HatmMKEKcy4fX0v2Ve1iiUGAXBdx6xfQ65jJcMIRCY9I/yMZdmwLCsAxjvvPIdl/AJo4e3Ra9aWdVvUSTlAKjydU0ZOCTEEBSsTnJci46RAJSvQkmRe8X4aWDZBXWoLmmwszNxq41ofEgFM8rmHaK0pQU8+092PWEBBZRZvnoU9FCwyoJu5JHc43eBD8M2LCIH3jaZvAJB0g/fTjFgyXLxHZAFKcw0gMJblKPIlSerP1pQBEmkG7zxKVkFw5zDFiBBj05KUbDnJKmcwQpQQsiOtA6qLsXMOMZ+P4ZIr1rVgnp9hv7/Bui5YliMYDEcOP/7DP8C8A0Jk3L58iY8++Bjf/9672O9nzLPwRD/5+CU++viIaedlCycSSQxyWJfUZCVyrtjWDTkl/Pz9D0QHDTNevbrD3d0J5ByW04Kf/Mmf4J//9j/D3/37v4VaHFKW9xKieHkmL+F55tp4msEJz67UglIDimNsqbREIDCQUoJzsYMhR5hVhsM5Qs4ikVHB4O0WuTC2lLFuWeYcANOD6p4r1rEtW2gpVZMSWBOHFKg6OpvLBKM19I0pW6blxZoyevce8PSoz7NHj2nLDvVfHrQO3N6mjetjX7Oo3YPYgR3kGv/LjDbxAOqiSKJH2p6RRWw4O0ZQUJlzBpJDZMBPM0KYWgEAsCSOOBB204y/eP+n+PM//VP85I/+EP/0H/0TfPLJx/AxqGgqIXiP9bQipaoJXJK4UZs+qEgwpZzh1YNbs/xd9D6jAHqWyEnOBSFKKFsoJl4SP4wLp+LlIQYBPg4IUerbMgtfnEtFchnBexyuD9hqRslAKcC6igfxdFxxdTXjuNtwdb3HvJuwm8VLHKcJcfLYlXNlCx8DwiTrfZdw4QbARmA1Gg22zvYEH5Vb02cRrKjRKy+hcKcevpwS0ro1qTWQORugyXt9DLXxY84RFk5yc1SYUX4xZN8GQ3xert/nwWRf2gP4GKL9oud528+/7PW+aKj0so3x/rE9Bl4/Dch9XSHwL3Jeux+z4L3zCgx6+NZZnV8AJu58LlD8uMVz9rlmhjpHqNhkcjiH65sbgBP+4mc/wenuCKoB77//C3gnHLJt20S8enDnS0JFBEDyd1ebtReCLCzeO9zfH3E6rUipYJ4Dtm2T8lmAhMC0KoPx1SR5QYWitW+ceih6lrNwBK+uDnj27LqFCXyIcCEizhJ+KQO/EAN3spSq9VklE7cUk+MRPUegVzipllFGQHRCYCbZfVvf1yreSgOXhA5kSbPWhLPlW4ZvrhVz0FqbzI0S6vV3qoYspfh80OzCUhLIe3hf4D2heKDkFfe3t3DMOBwkTBxCxuYTjqcTwjS14qtj4kDWRBFL9CmlAJb97T28B6YQUTbxYhZmnC7CKSEEXF1dY391jcqEl5/8Aqd1wRQ9jnf3+PAX7+Nv/q0f4ZOP7/Gnf/xjfPe957g6HLCsK4JzuNofcBeBKSbEKSJtm46HGcuy4dXLD7RKhlN9zIh02vCLn79CTn+CX7z/SsJ+CHDKS727u8U/+Uf/Kd57d8L3v/dXkbcNt8cTXrzzroCBwsP8kXF42BH82snsII81ZczRw7kJMUrYlnUjJ8h8CkHA9TRN8F48gC+ePwOtDB8WFK5Yc8aWTgLyi4A/8cw61JKk5rjOb1sDrMSgAW/1RasnRDbEXMe5DVgk4U3GeW+XWbwdBD44bviozaULo5z4UmLDzvqwPbaPjNWKxnu3Qw0Am2i8rBVV10KG1du2SEOoHkAAdg7bJrp0rjJAHrurG1knvBfPehFpqf28By0Jv/1P/0v843/4n+IPfudfiibkbgK8gMi8JkwhiOh6JYBFlmfTueS9qBagMjgVVK9dVXrCh3Nek7T6f0QOz24OEkGpBad1xW63V2NbPHJSXWhGhYT1mcWwiEtEdIS8ZbidQ5wCnj27wbJsON6vIrfjIkoGbl9vSFtW+kpGiAEpVSy1gHxAvjDugt8jhkMzqEspECaCb1WD2jsDt72pv9cCcAFRUA1SWbMcuRZFqSkDnsBBvN5p27Aui0QnNPIgY2agPegcCSHoviheYskAT7If1SprZymSuFU/a058c+3XJgT8y4yzf1r7qgD019FCcJjmiGma2jpL5vZWkemKkdfXs7EuF9E6kHEsBOnYYd0y9lcC3mKY8Lf++t/Ef/wf/QP8y9/9r6XUVdlhXQpSyoghYr6ZUUvF7e0t1lVCFbYgAEOlCABm2QFAzsLb2+895knkKipLiNX5gBBcE7C2EmzN66WbYQwBYZpwdzxit5+QyibSBqiIk8fhag8fPPwUVDuugktGmCcBl7rwWMZtmKJYthDwJ5tP57Mw95Jh3eIlHA4HOIiFWRXkBefhyfiGhClOAIBVSxmBWUJWFCDZaiLvEcOkoqoauq8VBSq/wVJ3k4gQfUCcAljckwgxYLc7qDzKCQ6AJ2DdVrz85CO8evkKVzfX8CD4ecY87yRUrcLPbdH2Duu2iVg0yfXT6YhZOZebZkYzhH5AWrbUeYcX63o+XqMHBeAf/pN/jH//f/TvIX2Q4YiwPxzwp3/wB/i7f/fvYbp5D4w/xwe/+AP8o//Xf4H91Q2ubwKurm9wdXWDm0PAJ58ccbo/gqtwedY1YV0SchbP37ox6KjeGppwf5tR8x3SRmAk3N2vEtoOE2KION29xu/+9r/CD/+t72HHFbfrEbP/Dr7zg+8JCGQJZ1b1RnsU3LPDqp7S3fUNJjC4JpS0gLGCIbWQ74/3KLnAAViXBVyplTTkWnG8u8NWTwAVDbUFnE6iRxkLI+WKLVdspZyF1bxxqJJw/sbNlIiw283CP9w2TfhQigVL1ikBTS6j0UTI4du21I3h6Mf+dtnMCDSuLSCKAOQFgBkP0uZsygw4YEsJd/f3KLXgEB1uXMTu+plk26eCQITJT7i6usarD1/if/O/+l/jxz/+IxyXe2AiLNsKvxQEL1pyNRUsKu0ia58AU6v9TCQghZklo5aMkiPPZVWcLEvb1heAcDqt2O1meC/jYLfb4TRlOLcgZ+XkzgFryiiFUTOhpILkEjg4UCXkWjDNAc+eH/Ds2Q0++eQOH3/0Cuu24nA94f6+4rRUpMQ4HhPmPWFbM073BZWBDy66Pk4zvArI23vwUQVbqNdlL6VYyOLMs1trUcA6fF4r4J0qWWi1pFKUjOSwnZZmBFpEBsr/FLlcNYVs3gye45STyJhlq8pwNrC+6HD9ytvXAgC/zaBmbG/rCXzT4vBVofiv0hp40zN9Hn7B2KwahXjSREaiMg3WELUw5WNh/D4J5TObLFzFThNuSdYMMcZv//Z/jdf0M/hY8Is//xgvf35CuPZ48eI50rIq146w3+9RyjnXyKRUpknAj/dOSw8xwA7btoJZRHenyWFLx+Y1swxGZm4K8saD3B32+PGPf4xpv8OP/upfwfd+4wfYlnucTvfYNpFTiERwgTDNkyZsJMCFVkzcVfFEEMm9m+ZU1fBCrWJF5rKJxpUnHKbDWfa1V0/Btm7Y7SapV6wZabt5RpwmWbCY4Vi4SyUnzNMsBGiWfiqF4WJEUO9fYSB48fygWjjJY7+bsIfw7AhCXpcCHcIB8+TwnefPcf/xR3j+7Blub+/k/fiA+9s7fPe738WyrFhTQqpdjieEoNmrHnldkGrBPM+yabL0iYuT8MyKWPOpMmpKOOyu4MmhcMH9cg4A53mPZ8/fw93dhn/1+3+Ew37C1WECWFT5g5/x8ctblAocrq7xO3/8p/j//Of/X/y3/s4PsS6rhFa3JJ4TcUWhlILTKTdgDUC5eIDXig2osnm9vr3F4WqC94z1tCH6Cbv5gJvZ4c//+CP8J6f/CHEfcPPee/itv/d3sJUNk3fIaUPJCcF5hDkigOAOO8TC2ErB/XEBzZNU4qgE8hNCmOGdjK1tXVGy9GHJ3DzlpchntWSkfITw2ID9fodlzUinDSlvyKWKFEkaNrohqSu0GtsmEB5xf3/fPOHzPCNtYoR58kJdyFW4YkMUwELAX6y92ZP3RQ6z+7oUEz+PWADmZKxVZfuCa+NgLHdJWu3FeWBNMi6JxLuaS8Hd3R0OB6n48mLe4fk77+L5i3cQwwQHL++eAk53J/wf/rf/O/zz3/kXSHmV+t/MQAC2nCQJSD35JRdUEu+jIzoXtDZPpvOAJjSoIwpEUgJ0q1tbK2GgZ9tE+DgXHA4T9ocZQD0b+6Vk3N4fRQcysZRvhJPShVuCg9QtF3pLBvkJ97e3WHX93tYiUlJmIBQCOOLF8xegeoslbaBcgNJfZJyDRh2GPleQXXNBjLF9NgLA9n753IAxypGdoxmkqIjkwBlastIcFqYRKMZxZYYRTonEIM1Z6EAue2waOjZ+eVXPcFJqhKlYfh0RwM9zvi8FANtc5v7z532Uryoc+0Xb2173i9zf+J03hYO/6uf/PKH0fg/tJ3T+zcNFWz8Fa91JhoSbgnkDBzK1PVvzCOoi1K0fWYmEmyYLRioJf/yTP8KKW8BVHF9tIHL40Q+/L4tdLeqRc5qwUYf7IwV7dZj459wQ76VKSNXF0LvYxyuTrhFCrK9KeC5ZxIjv717jj/7o9/Hq9hN87wc/wLObK2xrAVNF2HvM+z3cbkLcTUr+LRKSqysAgrL2wLViG4jVxq+qtSpASo10HELE6XRSi97BtAPTtuH2NLwUAOu6QRZq18Ihy7Jg2l3B6sQL2bmtW7JRF6nU4dmL3Ix6GecpopYsfav1a50jVEeoEG4YcUVaVyxpw5YSXr9+heP9PSoz9u/9EO9+f5EMum1tRoKFZ5xyp7YtqzfFSwgaABfGtibl49g4cRIaQlFemniUxuaIMMeIH37vXdy+eokXN7+Ju9t7/Nmf/RlSJpTK4LzBcYL3FcF5/OTHP8M7zyfsdgTnE1xI0jtVNkSQAwvyQrfxte+r/uTEe7ctCbsY4apH9DLe7+5vNbEn4A/+5CN87ze+g+/+6BpX+yssn9wpkdwDgQGuyFtFFfVrBAfkVJC2jOxFzgPsQRzg/QHMR+FrTUDwFU4BbEpJvRsSWvZO9OmWU0b0GZsmgMjGLV7O4D3Cbsa6LmBHYFFG1znmFVcJ2DitSXhfbQBKyUGQhDEJgNdIAaMgqIyAjQDvfQPRMv/RjEFbLWzu9pDsuF4NHjv9jtEoAOPoQbN1dexQBwxynIJZzZaFhnZJ+a8CEABiyRYFATGqKoBFQ6yqi1OUyJomVcUb7xxJxZwKqRBRJCQL7uHqXDJAHle7K5xe3eO/+Mf/Gf7Fv/j/aWSBgSq9HmOAC2IkW9KOCwA4yPmc8Ss1VDnQLEIIoJVQoMoBLH3sg5R6rNwjCbUATAk5E1L2mAuwrSKPYjUDczZJIKiouKwZRa+XawUyoQZ1HngZx6Vw4xrudkFoRWzeygSuBTF6pERKO+nNk7yHXIXD552XChuscly2yJFy+CC0hQot96Zjo1aCc1Erq5iEiyYekYjSlwKsxxNO6xEg8QqWVMGZwQ4ozEB1YHZSPSWtsob5AAcHXxgBwo2EEw8wy6tUqa/+XJcUqfFfG+uf1i7pZp/XMfWlawG3xRDUPZ1fwMr7Mh7Dr8Pb+GXv59MB15vP/VUDws9nYZhsiCzqlulEw0ZrIFDuEWAWPpsRc9txIxCEbAZFxZXbmZxxSHTRpYJluwc8IZ0qnA+4en7Ae+++g5/82U+Fa8OuVatgPudgGh+wAVdQ2wTABgArrNqHiVj3kICGCCqUSC2LdS0Z+/0OzFIcPG0r5vldPH/mQaGgIuH6+hpTnLHb7zUkJpzClLI8vwsw3iAYwj90rmXlWhiDIDWOjTvynXe/A9OZssVcLM0icge6qR2PR2zbinmeRKKGCHd3d9g/ewGviQvGCTReYMmleT6JpRIHkfTTbo666Wv4GMq7DA6v715jt99jniYRhy0iQxIOe+zu71AqY96LoK0Poj0GSJ9WFl4pyAC3A6Fit5vhvJWBEjkH7/vGTc6DKIBRhT/JhHm6WL5IPDTPX1yL9xOMl7ev8dO/+Bmur24AzmLAICFGh91uxqtP7vHhz1/h3e8eEAIhTlLKEEx9nKP/3DZ/Ih3LSjBnBgojraWVrWMniRMFQCaP01rxvADsPbZtkSxrBb4eDrWIfh4IQlEAwCUj14IteExOMjwFQEQp5ccAqAtkUykoG6PY+K2QvnNOPOLeg0iND6pmh4GIEcmhkEfNjMSle+3ZQrlS9mrdpPKHzB3zGqqH1CpoqJdJJEc02xIWZnTtfbV/uduGI1jr8TNqfW9rj70QSaAa/9BjbtRGPLXPBKhpFQid98xoyVcY/7VvGoAopmqga1yV9a+SALNQhDsn9FmpLoKCxmf1oeB4vMX961e4f/USzw/v4vr6Gcpa8Gc//gn+4X/8n+Cjjz7ENM1yh7ZeOagxcmFkD+OSWZ9F9UC791LAqT1/NYcNOwVlwl2EHc/iAcxaIYS5IOfUDO1SbN3u/1UAqQrYr/mc07yt6QEFqNYCkAF0UYOQ6E1UY/a8BRcw+SgSYBpFkKQx2afMHBHuoyRxwEM9gYr/WMZn9AHwYpbLnqEyYSSREDiH7LWm/OmIbUuizahhXtbs91plbegRMAF3lOWdb0nLNzrWkLEJUr95P/6mHWFfPgkEXwjvfWva2wK9rzK0+ti5Ls/zRQHolwXD58/XCc9y8g7sDIwYeBk/t8V7rHoB9ELdBsJ8AEAVOa8AEaY54N33nmO/n6WSAQhxmlAr4aMPP0b0EVwYmbOCt6Khjdruu1tU6kEogEk4EJGGdv3Zs172Wa3q7SEJy27rhh/96Ef4/g9/AzfPbrA77PHOO+/g+uoZ4tW/ielqh/1uh2VZUFU2RPQKJ6R1lY3ZEeIUsd/tMc+zCFtbmA2Ew9UVShEL9P7+iNevX+H17S1+62//lnDkahcwZWZQ7bwsAiHlhNPpdOYBPJ1OeO0TQgyiv2cGqVYYqKmgGu8lTAI6mZtO2Wld5D04gvNSym1PAX/4h3+I9977Lt599zuIcxSvEQGvX79EUX7f3Se3CEHCNt4HSK3bjJwYV1fXIOdQckJab/Hy5Uu89+57qq2XsK4nlSBR7p+aHdMUNRFGwk6HfJ4Ews4hR48cPd777ru43Vbclw3hao/X2xE5i4aYJ4dpnrDb7/HJxws+/PAe+6s9nr+4xm6XsNslHI+rVAXQ3YO8AizzPjkAthGzlOgjYmx5g59nMCqurne4ujlgSSu4VhwwI155vDx+jD//+U9wc/1dqUMNyx5l5FyRioTRKoCUJQy1bhU0eQQCACktt5YsmblaX9QBWFLCksULaOAe7LCUCg4eYT8jlIo5eKzYwEk8Gs4x3DHjOsw4phWndUOujDBHFJLknALClqsAdSunRWIkxGlGqflsP0gy+RuQAtBLG1rmrAIXyz9qGUgEkBd9PTQATm1M6PIkhwavGnp6pHqBxNvF7bsCrJXXi6oGAcMFj3EXYxIjhdlKhWn+KBeQbwxHoaBURq0C7ErNSBmYJqAgIxd1UhOwVSDQinWpoJceu/cP+Is/ew+/8eIH+M3f+Ov4V//iX+G//M//M/yz3/6vNHFMkhPIm3i9gDVWOSLSzGG+KIc4hUnM9yHeu+UMOMtQVS98Ff1Aztw7UjueWbiMKWWsa1LecgFBNB7TEMEY18zOxVY5mSrh3U9ev2zvvECS5tZ1AwOYokfUEoXblvHiOzdYSgLlJJ2mLTCwdwE380FCy2IKKvDu634MomdaUBDV8KlWR1vrKEeuYhRrJavd7koSnZjgXYQPE/bPrjD7Pd7/2ce4vz/Kuyf0LOSSJbLCXeeyaiJNpoxlWbEsi0a91GC098KjUTH0/BfYu7/sfv8lAeCvMvT78u2rAI/flmygthpjtNANdCjnAWLljBm/ALoA5xCaGbPpnHMtHX6sAiHlk0oT7cwp4cMPPlCrTjgZlRmv725xc7gSj5EufJdckEYAtqYu9waSyJJS8AAsju9xzOy2EFGuBb/7O7+Hly9f4+r6Cnd3d5jnWeRxasDOT4i7GaVW3J3ukWoGHMHFABeEG7SeTvDe43A44ObmRsossYTDQgiIMcKFgJv9vnkXiAif/PQDONeBdgiSvXx9feiZwbUizpOWXuugOMYZV7poMREyADgCxYAwRYCk8kCuBZRP2E0zgvIMwYyrcIVpP8ksr7K4rdOEv/dv/Lc1Q7iC14x3p+c43d3iZn4Xu2czDvsDbr+bmqi2JbXUSpp0MkuFkOjgQ25l4zqNoCJvW3uvy7rg1e0rXF3tsW0r8iab0bPDuaegFIfgXuDf/rf+fZxOwtH8qz/6Lfx3/n7G7e0tfud3fge///u/j9cvP8Z6SkjK0Xn/F58gzAFh2uN73/8R/uXv/hmK0g1CjNjtZzx79xm++8Pv4MX3XuDqxQHTIWKtC47rEfl9xvGDE169fIl1XfH69StsdcO/8Vv/Jv6df+/fxe6ww7Ic4WmHkhekckQMHsHt4BFFaN0pt3WOcKFrUi7LIuOxZPW6VNScAK549s4zgCXDfFtXlC1hPjhcDUlRu90OFKQ+tnl01mQlqQipiph3rgVl2nB1dYWaK9bTirtXd3AAJvK6aQm4Op1WkRPyHqe04fXxDm4/Y9rvJGwMqRgyOY8pSJKWhKa3tlZ4SIi8ZMDhCrd3r3B9fQ1ACftVklKW9YRnz26wP1yBKTYjcl3X5ikf51CcHOJOxpklVnEV0BSiVPxRcUuUXHF7+0rqMM9RQtzMmvwgfD6HKB7RtOHZsxtMc8D9/S3W9YjKGVdXe8TJw/tnIAjnFyQ84t1u19YJE8U+LJD+RkFaVrz/sz/Fy1cf4Wd/8T7CbwD/i//wf4bXfMLOsXr/pLdKJtzfbSiZUYt44dZ1xc3zSbznqjF6OBxwd3eHDz78BSqLp2xZFqT373A6nbAcF6zLhrxKJvHt69coWXQAa+1gsGTx3NnauC6pexPh4P1lljRBEoHEmyoamgyuHsuS4WOET7Vp49kanZJEhebgUeCQfcYP/tr38Ju3C/D7f94O/fHv/jf4r7Y7XO1fwHjaTsPCXJUqkcTAk/UmYdlW3NzciIexyhqUt4R5N+F0f1QvNqmcEiEEoZY4ivA84Sd/9Id4/fIOqXSN0lKoAWJmp953NM+z+Z1zys0IYwXjpLQXrvytQU5vDQDfCHb43H74y96+COJ+DGhcAr83eaM+7Zxve1+P/e38+8YfaX/V8EaFTWhwBZM7S45IqW/yl/IJ5gV8mBBklrwDs8e2SnaieIvUqq1JalYWOf7mcAXnPNK6qcaeaxbnCDKBDvhUAU2t3g5K5RnP79P6w7KZex9JgsQ0TTje3uH3fud3UWoBqmUKy3mklHHRElq1e+sUQG7rhkoBzgfJrnQaVmSLxQh3a7ffYfKuZQsLAFUBaVWaF2Kx0zqUJg0jYZAwBYQQW2g554zrwzPJGtSFaAS2FsYspSBjxX6/1/CxQ81SeYSceiJs/FbGpmXrAAnrFpKQTggB026H3X4PX+T86yCkWmvFPO+UXK3gVzOkT8eTfOacKOavi3LFdPGuktzjlVfFBXi+bPifDCPrd/6b38P/8X//f5bKL06qtFjFmuPxiLRtWJYFy7oh54oQPaaDlDP7+JNPcDwtmOcdiAjTLsJF8VLHWEFISHdHHCuhfrIihElBVcJxPeF4OmHLG9Z1RalAWhm/9y9/jNu7f4Dn77wQQAMPUAU4gXNCdAfEKM8EqpohzaJ3qFQBlIo4BRHtrlYiSzTLPElIl1nI9yVnzFGkjCwstd/vkVjqkXItMn4BAba7HZggEjya/MMQ7ik5J6LFXgS31yQJI8yMuJtRK7BuC4gc9vN7OPg91lcn8bypl2P/4gan1yfkQoj+gEN8DsSKu7s7ubcYEXYBNRe8eGfuBqI3w8hjm64kuevlEeykwoUjwkQOs5tBh1krZ2woa4EvjFgimDd4CtjFSTZdBtIpIyfpO1ABlw1hOYlm4jzj6uoa87wDmHH38ggfJ9RyApcCz8Dd7UdwziOECRPvkVLC/ccVu90E+FvR1lMg+my3x67ugAUonDHBIU4TggtInFDqhi0BP/nxz3DKCY4JkSYcnHjKAu8lTAqLsFTc7AvmecI0RYm+cEJOFdM0o1ap5ezg8J3n38UP9s/anD0tC5Z3Vvz85z/HcjwhrRuO90fUVHDz7BqvPnmNu9sj6lB9w9ZJk7RhrjD7uicAAoAm9XHFtmYADOchovQg3N4tykEeozSmGwkYB7oQwL4grQtqmXGJKuqacPvhh/hofR8AsCzrQCWR8b8uAtR8BKa9JCd9dHwfBKnaU0pFDBN+cXeP4/09snqsoYlM5MRIyolQs0NaF8Bv4FzF25vNkJVMZfE8VsBJIkrJYiSLwlIR/rLttWxUp9o8gsBDJ9AYTbPfv8725TmAX/MNft72TXXcZ7XHiJ1vCv1+O7yA5gFE/1dDqWJBqYUzDFBbmBxpiKYN9HPQx8adYOsHee5kJdWoTxIpNi/cF++VlE1idZkIL7PIHvRi3tDwsz3H8FQaouqhRHpwzBmXZcxoJuEQllLgnMi1SKhJ659CeFQVFdU7iDZa6O5+1ozKXUSAE69PDKovWJWsDAU+Ad4JuZtNaV411zIXUd4nkWXIKjYLfV5ik6AobXG113i/nDRjrVunXCuokgaZ1JtaGfd5gfdJxZ6FMwgSQrmJ/yLXdm7B1R04p1RwWla8fvVab410E5PjmBl0P9Rcdg78WjKCs8q9eN0YROiYlMPDSvJX0WFIeKlclItalhU//+AD/U2pBipxgSpE+m2rIEgWuA8V+6sZ5hWqDJyWBfurfYtLCg/WIS0VL9MdXn1ybLwy41JVUsmeLGHrGCZ4H1FywZ//5Gf4+c9+LmF1J+Fe5gwqBc6JRqTw94DMFSWb/iJsh5FKKTkrqZ0bn69o5QYa5p4jFfuunRNFWoNVEllYQZ5wUsVbp3MlFzmnl4ow5kXvSRtiQPmgIuhZwlveS9JD1oQp0Ukm+N2MbV1VQ02zLB1hW5ch87LFftt6YlQSAx/Qeyy1G9JdN1HnhEkiEYZQfa+wwYw2HiT7oA5AhJFr0eoX4g063t+DLH6r/WtrRAhBxm61fmYwaYUaLxJQMUSQMz1OkU1iMGZ2yFyRUeGCB7FDygmOPTyJdt/GBaTrpFP6RS0CVkIIWnEFYBTkTfpG+kTWgFK4VdMBGMuywsWAu7tbSYArkrxFIKRUkEtWqRio8SkVoHxwTWaL1furw0wT13DWv51LDZi0jOmY2li1cXu2H4KQCkAZSEvBqw9f4fViXmppf/6n7+P3P/wEXCp8kApMVtoNjLNSmeQBHwgxTlrysmqFFRLDLWdJKkTVKJfHGhO2dNIkGwJXmftpy43LKv8pdxFa1YhlkpRccTotKHUTuaUgCT7G7QYkAcXActXxbu6QsT86//WzG5/Nnc+PI/5S6AB+OwDUZ7fH7vOL3PubzvNlQe9j1sh4SqtWMfL9eNT9YjSexZkwJwtaOM/QlUSIeZ6Eo8EVtXoE8k3DjJlb9QqwbHwWUmQGesWOwVMJ20/GPjp3U4/PeDnxGrAdvl0rIwTJPIZXsreBUSrtWqWy1qH0YBIAlbPUc/WVEYLHFANqldC2a53LYJ30LsT2DG6ouGAhPeFdKh+nZTrLwg3b+PSszkkxec+kBd/FiyheJOW0AaDiQEVCsM4rMJMHV96UENhrKUDwvb+sz2ClAyV0V0pB9awcpjZ62rsMsSfESKm+LPIaZALjDBd0g1dPhHO+e7AsKek8CRiVhT8nXl+xtGutqEXqgFZhfsF5h6DnDyHCkVegL/08TcYR9QBLX0i4Mml5NfG62nM7x9qXhOAiphDAjqSU37pgPZZWTcT6yBPgnGgiQjd6OJFa8qrxZ0aXhT1LldJ8TkV+5bv6Hp0D8ajn1pt3E6zgvbrYhYOpGzEMUFWVJYJsSv2YegamSA0g2xS7Qdj9+3qgnE/fI5GAGYbUmQVZNrh8w6k3s9ufMl68eb+rVKQADQY+C0+5ZYGyjh8tz2YizQbg2jyvaCF+5wgprTpHxMO4ros+c1BvfV9jnDPQLscan7c9j3rImseIGWGS+TXp/VUCwhzBLPdObFnnQCoMcIZ3pBxaqcghdYnFAJUFULiwKQl32EoqplQka5hMky5j3glHU7tPhell/SAScf9AWgZQMa9UUuqqEIwO9MwbeLl+2me1WK3zQaAe5wk0vQm/1FegbAV5TVi2cwD46uUdPrw7wkG812KU09k1oe+9v4tNAWAvGyphY3l3AgAB5xhpy/LOdWGR5MYgIWvuUSS9TPMh2FaTs0QDZI90oLKpDqP1ywDu+td7D1zs3Y/9/ti+f+kx/Lx44i8FAHysfRWA6Ku4h8famzyBX8f13ua8j/WVWJ/ubHJ3i9zAIYE1y3FM/jAAOJ7fwpqWxCBK+Qv2h4iSNJuLK5ybME1BM2mFeF2LeG9kMmedVL55Ji2Nn03M+ALgyaQ9f74xecXaefh3tKwUC1FfAHKr86icRt2EmBg+6ELNBGQBHCIaXZBLbSK7qYomlHhxWMFi1O9K8XOn3jcAQ8JLQc5o/CKGeDCcc3BsWnqW1aeypl6yUiub/AZQdWFqm1TwgHNwJBZzrVXs3KrjtVZ4F0R42DkpnUROwWtufRaCh/eSNWzk6FoZPgSVd9BN3xGcN51JlcmBWNcSepaNSjykHs4H5JKad2PKl8tXBahgjhPIEbLyiyiah4+Vo5Yla5U8SnYoDXMwvBfPilcOHqsXTkCPByho1q9KakCyXbma97uqnmOGJ0bcBTgKKuBr2fJSuSYErYHcshEl5G0czHNPu2xqBlJqreBpUvAku7qAXa39TMMcrQAQFDihZbvWwSPvfRDaxQAibT6YQHpbGHBuDNrcMc+c0Udcw2T9WXzwcN4jpa2DRvOS2LPo9lhyn3SSjHYZfbCfefCGegC+g0CHNn/sOcSxzAqy5Vln9dra/TsfsNvNItRcjFNpVBMGsZUJjPDrqt3CzSggEl1Sp8LnznvM0wQHQuYCR4wpRnBKKE7Gt1R1IQkJ11mqtHipTSvzwbU1iLVqDhFjqgIpiMQzfDh07xQzY6YZxAQg2uqG5Iqun/Jdy2x1nlQ1gNVAy+3ntpZeNBsrtleIEVhVjoiQy9bW/Mr88Huu5WNrEp0Yi2Pz5CXxjxlgr3V7x/3TzgA0j34FAI95Duo4MH3LIteskiRYnAFE1rVXnBhZJbLAGnlwmjXNRerDo4/XkiRx0PuAGDy4sGhy1j7+q4bCz/Zas5oGw+ezwNybwsffuAfwlw2wftXbJQ/tl9kuQ9QGAMV1bWZPP042FtkcW4k4y1CFEWUlROdcaBPIEkK2bW0cNltgACG9Oy37Vmu33ErZJFFCwxwSfkG7Pws5nFtEtpmcT4yxuy8rHLTn0/CjAdl1XbXElkfhgnVbmyVJJHpQpNnFuQhIs1ZKAWESa3yrKE49hqUiePEK5lIQ4wzKgATUCVyEQxdjkM2tysYfwwQK3Ejw0QdMKrIMzQglFs7Kfj6o/AqDswg/9+fTviCCcwzWrOqs7zSEgAqp7iAdBDgoMFBL1uqOllIsf1OyjIvojjkVuCVP2LasG0gFSAAkB8CHoALGcgbvPTRGouEY4RVJ2FKqDNQymOF2XfKYKAJZwpwOnRdKLIkHArIIjIJ1LXAUAWIwF1TbNKpweHoYDToGyxnokXkgwBWOxEpg8W4571DBSCWBIJUgPDQJgQJKlSobMqdsM5JKD6mm5vm08do4trWKnlsVKRbztI98W/m9g9HKZaiUIH2ccxYRYPWSckkiVzLMi1qr1Kklk7yRLFomIOl8gAIddYs17qrT8QGSLOP2P2YgCwC3ichZvNRJaQAjpzeEKGO31HYPbaNTl5TNWweCI/PzSnm6vOZ2PwYztpxVVkhCc07BxLZtes2AED2W9ahYQvpSQJSscVkN2inN+o76mMg5tTVxjgF7FYdHLXA+gshLn2yA54AEBXdJx0QQD2/JCSUNlYlUSgpKuzCvZ9CoQbEEC5Z5SerxkoQJah7CnHOTzklbPivlaRIrxvE2kX7vvYSPG+g637suQ5bkSEGtRy4WzXBtnpFK2phX0HvdT5BQ1Bt/3kT/UxIHdd7YmHQWqmedK2KUee8BrjgeT0183pKLZAxZKF08n6obI7OOpdQbfIBrwKxHPuxxbZ9clg1x2XC42mG/P2A9agLIEJkSMe1OWzprbwn+vur2pQDgeRy/N7o45rH2bQA9X7Z9lsdtROiXnLjHjrk87ptqj11TBrYBQpmoVmWjHa8bgz1DS8AYfrbRwAOokBJSsvly9ahVvX0VSNsGEZDooFM2tALmk/KI3CDsPm5+AJq3QRZlK3JvXDTzODBz05KyezUPZwihWW62KQjHLbV3ZYupiUoYP0/3CDBXyST1EkYvRSQUvPfI2yabs/Oi8bYuIAJy2gB4xRGs38tIOUMsfA1v5QymrmmYOWGhpYXKzBMFAEc+NpBti6RT/pIjakr1WxbBavE2yHtNaZOH0fddakVGHwNSW1jKHhE5tcq131gX2VwseAQfPMgDWUERsRf9wVzE++AkTJq2pa0tznn4AHBZpJIAueZFyBfzT4jqpY3PMRlmrPTC1TQkA5Z8jxCMU+bANWroCig4T24SvpFtENrP4LYpOgVepQqHS0ByAFp4T4W1ISA2eI8K1RdroSUCUAH1ijCLR7VuWTTGQDquJXQFEs+UqKpwuyfWuZNLEcAxZMgXZtBQ7tBA5Lasmv3q4UPvZxDJ8+gQIubGlSOSsoNc1curSUO6v6oWpuhEVu6aglFDtGBGZiCAECZJBEHzSjowKhzJ01TztwyZ+rUyPIk4uYP1WQfpZjQAAnoJ0Dq2VYn6ru9dCgy2lOCLZIFXe36Smr+5Ag5KV9BShmDWjFLztgeEYN5ehgMrD7MiL4sYls6hEKOqNikqowCoTvuQPIoCSdJyZQxqfE04Qi2yZqWc1IgztQU5pLIYChJW3mRdIgYctfFghoHpuEq42QwTMyI0ZE6k62unp7RVuHmlOoAptcApvcdAJCChcQP5xgsk8ihIIPLiheQCDBxfgsrKqJFq4888i8zyXLIOAcEzcpL+m+eoz5jamGnnldIeMlfVCLV5ZCF0A9TSB051b4uCQJnzy1KA2ztc3cwgOrRolfMSIWE16Jj7+jSOU/3grD8vnRP9sIe0pS/a/tKGgL8t7ZsEdJ/nWuPAadBAXd1iMQ/jkRxUyfLsHLLB6mJSOzHbgZC0sLhzXsKjNUFcDNzCKoewQyAPJg8mCfOEfSe1G5eIS5Uam14Fevlh0gaglR6YEbyERUUANbcFnggyiYtmKaOTe/s5gW2TRAiw8GtiDGCVZTAieVo22au1ggiz8PhgwHkAow4QCRb1RIi7UDcLQXXIuWKeQttIoKBSgMv5u62VEb1v3jeTqmHq/D8DIEZEl01MTu20ZjCgPCanIt1sXi1q92qhbhsXovF16p4nUc3uHiv1KDbva0NLCl48SZUDFsAUfNByeAoA3VDOShdhEIFhYWSvYWzGkc+9DjVXbPeL1BpWbTdmqFabajuaPpkKwpaNQYU0Z1ySc6zeakt4afcOBWq2iYh0SxFkJv1hRPQWNtdxAKCS8ZbEO8ukz03iAaq1goIASgNw4pURIdrG7yQ0w4s0s9BK1Y1zwsANcWkhdZaOFU9VyXo+as/knHiS7Pree+GLMfp8hAoSu9rGW9Vs66pj2xKutjXBkkcIAlokTM2N80ks1/TBq/ECSOYmNyF6o5F477ugtI6DDAX/9p6q1sTWsdJWOCb1UkLng3nuSzOc7D+GzAemqM9rT0TqEVcmFyvdArZuka5FVnVDvK5EDrCkJh0YMuYJzgAJ6/gqLNqLLMLCZWO4KHOpWiIPQwzoUmAVfsASFSmaDNPwe3BAlkSZBjpYjAObZ/av8B5974taUTcFlczivyKchXKtWXjcDJeUKnJaG8CzczpnhqjMBdt3BEw59UqfnztXqatsFJZS1xZRsnWKoVxfPX/WcLLJ+5gKhHjwhE9re03RzHX7XWgASgOptsbrXGbxBvYlWQw8qoy6VWynIhI6TsfeYCyaATjuYXaGS0D9aeBupEKM33lTmP5N7cvLwNjfP+N735RLc7zmL6N9llfwbX7/5ltf3MTal89sJBERGO7Bu7RFUZI0mn3evBkiVmoJImJZWRKHLVKOZMHaHWZcP79G2EXx9lUWxQwWD1NOwhOzdHw7T8q5g48qVrdXz4/UG4ZOPjmk1ZckEjV4gnBp1MPWNiUn/CFXgwiVUudayQLFIAcpD1SEoG9cSCMnW395EGRdE26J1OSVG/LK7Vm3hBgcArgtrt47rMsmWZl2/wByqq0+q1m+rgLkxyoouqigh/30pQkwV4AtFq1H1dCzbZcMlusq2LA9tIBRSepiGvCNXr2WtTSAabOAB2u71goP37wUsknVFr53LrfvjiAQgFjRAHxJuglULBdzrdSCZRPit6/G4UO/F+7UBcn2U23K0u+BAWCaJOGAx3sXfTjX3r9ysArk3ernxnP1jgdtNQE0cN0LV4tw0SQZqCdMSZJL7QBQvbWXJdHMaPNO+G611ObxBIsxxQbEWAEp9znr1VvUQXpvNncATcyodcTxAKQMo05ywUEmV2ITxP4xT4V+X/rEPPbuDAT46tsG79TDDTKPImv4zsMCdrZGNQAE1tJrTpJ9dB1pBiBsTrJKMQHQjHoCnVX/MW+7zoT2vM6JpqIAWELJwi1ta4hep2bJpDVAC9L5aRu/vhevQt42Sgycs3eytrCWaKubjmE0IOF9QM06H9QAIfQxZ6ek4sG5tsQ6AGoAiEF+Po24lVKDZjlXAz79TZ7tAXY+vpgvZgAbD9VKXtrnXUJGvJSlVqkBDJyvWRADIVVq99q9itD3oMcr8VRumSDmfzk7Vx3HjPLyapHfTZnBACgzd99H45gP3ds8nrLWlVSRt9IqxgyTpn/fjh9PZP10saZdAjy7p68qVPy1ewC/CeD3ywdQn799mdD4V/68fYVsH9hC0/5OaFwUW8DbJmDfsoE5TB47VkqyYcjKAjhKyOO4HvHO91/gb/zWv46r59dSaL5WoMiiv+RV0/gFzGRN+d+2TfS/soDFUB3WdT0L1V6GCdSoFTmN4MC1ex8tG26eZ8A7UPQipKugKs4T4mRitHrOnFFzVYkOCzmZBSeALPoAF7TUVxZSddVMYk9OrfEs2YBRvXn697u7WxWNlj71wSNtCRECZCSxpACOsN/vB6vYSViplHbuqpsJAwiug8XgPe6PR93AWTk44m1xQJO7qMo/8z40YjwBmOadvOMsZe+sKL31+1itZZqmBkLM0jbC/LgAiwyM6zxRhoSJSoarDK4OoRJw28Wg2RPq3sOTLGvEDFJQ56knH6GyZBzWhGmOCvxUzqdW2ZDNVaCVcHKW9+styzMQyHhRZuSDkVlq8jIAJgHL1s+eoJnGXcanaljLjItUuJHiZf+wsLVa+4rXnPciZaLPJ0BaQJ1IejCy8lC9E95qN4oITFJLterNh+AQ4NT7Z5sUY80JMUpySmG5XiDLPu9eDYZM+8coL20NGbwdsPWlApYvlrMXyQ31JgrfU46RZwYkmXnwvrBQR5rHBBUVWTLEa/fYyngQD2SM4iEsEAkrTwYw0Tw9DWjXAgK3iAA5oKo+KTtCsrUuGOhlsQaK3C/sIxbQGbR2r62Vzo97gfWdQ41AzQwPQggey7ac8c7IAa4k8CYVkzT1ByGIhie3MWnD+DxpR8Lbo2Eo/1RUkI1HfZ8tFN7S7h/uXQ/CmUOzULhQNMZjauvnQqkbkQDyxak4S+b0WU/VvuYYAJdoutxtjFEoPMy6Lkri4Jo2NVTVq9o8qPKIBJJkuvIYqNT+p/MxXiujZCjdpDajzsrwmXOF2Yy78/lhn7xpb38TVewxT+DX4gF8al+s/eqBU4ZlVznTWcKnWRy9/q61ClGpN50v+xORl7qKtSBOAbv9DqkUhCngcLXHzc0N9lcHvH79UurvzjvM0x67dQ9A5BaWZUFiyZycdhPcQbwGOSWk4xGHw9w8P294OlRihEnq007zjDiJdhczY55nxBhxdXON3dUBRFKm7ebmBnGeRIoGKgPjhGQN7sXos2ZAG9H4/v4eV1dXyGltC1GtFcuy4PogIrfbtuFwOGC32+F0Okkf1orT6YTdbmpaXIBUdhjfRSkFa9qwlYx5jvrexKu33B8xuR1KTQ2ECeeLZClvG1GF32kpOGhoURd7x2jhXpSKNQtRv7SwrWvnNZHwBuhsPJjXV71j9h8AHJdT4x2Z188kdpr8SS4CtKpQATyJCPKz+xX4P/1f2nX+2l//a/if/rv/A6zrinVdEbSMVikZ26almVIWb0SquH31GsejSEvY+9pyFsL4FJVTRK0/7Z6893BakUUSbzCA+9qel1mI6PNBRH3LloR3eTFfpI+61M80TU082/reMlhH0v0+yjtLynWKmpRh/W9j6cWLF80osueR+diJ/DFGBBCWZdHsbBnDUF6nCY8zM4LrnEI5Rr83nNvGRlb+qs0JwBwhtoFJmHCeNZlCwW+rGKJhOLtfaWI0pZSxbQkxRh03EYULUl1bZRlu4r0VOxWtF1DdvaxcJbGsU1a6F8hDMnatzw1sivcpNG5pMa+49o8+XJurWZPiAnrCha2NxsOT7FmVP9FnspyLHpLsc39dVtEXjVPjunpLWmK05K8QAvJa2zXaPeWMorzZZrCpYLIBZzPWm3YJi2fws5oBcuuzrgfawbWzMqMEsBew69VLO5UKDFqA02HCYYogT+1+xwSoMWJAqiMLXzCFvhbbWJ9jaM4AQJwHkjBiUSsMfwckq7yDLVl3pS8cQWt/iztxXRLubu/bfZtHGeoVlzFMw7kf77v23bdsfVy89VcAPAHAr6VdLu7fZPj7i7YziwISfpRF+9zSeLh5KWenLf6Era7ItcCr5yprzdbD4QBfGb5Itmo6JcQpYHYzPvjpL/Dqw0/go3grSs7wRPAuANW3Tc0U3aGbl2QGqz5glcok2fhSwzNJHU2pshBDgPMeFYzD4YA4LCxbSri/v0dNBZ48DtdXuDocAAf84Ie/gffee1e8LwD2hwNOi5TF2u0FwLoYsLu6AjkRur5xB+zKhFK7APO82+H9j9/H61cvcXW4wjv7Z3j5wUu8Tp/g+c0zARku4jvXN3DeY10X7HbP4B3h/v4eAIGi1Nl1s0e4Cph3O0y7GR999JHoK8YZz//Kj3C3bggxKM8Moi3mHU4nyYybpgklSa3Q1l+ayOAgkgbmUXRE2Bw3+ZJccrNmc966VqH3OHg5RqxqggsejkLTqmvEfM6N0C1ae5Y5x+BKwxiUkmUAsOUNpVYcXt+ejeH/3u/9Cf7+H/95W9jH9sBDwQI++mdko7mH+sf5YR8QhuMHDzkuPObjd4n6387OeXG4ugEI56e4/P3s87NnfTgvAYDch4+f4Owe5fvdg/fwLserXF6RH3zwhgMv2+hW+byN+2UuT0k0HjN4Hoejzm6x3SddPMLlOLLDHnpw8Oh19EqP9P9D5wBf9GMPn9u45OFY+8WoHm/VzqbAcDZ+9ICLHy+IeZ96nc+wwgFQNi6ltqE7dhdf/Y2//kP87e+/g0kdBzlJHWznSCvXmFGqSgU494qZ90/Ks1VEKwsIoSFkFYc29YptXQGI+kPdGDUbQC+oqRsVtUJ5mBXH44p13Vp0BxAHgXGWrSpR8wRq/3iti33OxfxsDuB5x42j9u3xxjcOAH/1PGKfv126ZL9t7dN4is0qIsIotAmGFJ03QVgL+1QLwQDMmnTA1geS3i+ZrRVZgVvPVqtoLEEW3kyTcsmMzIyMBKLcrVRANknnUHNFyqmVNSPyUut0XWF1FwGxdJskDSTUVWuFnyPuXt3L8zgJtVRIRhwXkZVIW8Grj1+hMuPnP/ugZRWWwnjnO89h3lEr17bb7SSU6VzzbMUYWmjJxsayLFjXFbt5xrzb4f7uDsf7I26e3WCaxOsnZfYYpWbM0wQQcHd3JwsPM6Y4YbfbYX84YN7v4YMX8DpUNTDPlf2eU5LMwZR6WBFA2MVWU9Ws4VorphC7EG8pKMRaao1a6EW8Lf3dmd6fiRx751XQNuh5JcRERJLwwMZHFAtckgFEKNy0zUzpwceg4fYKf7uejeGpFEzHc77PU8MlBeqpPbVvZ/sU3HJ9c4133nkhgC8GxJvYnBXkpCTibr+XfaVksOqSWrTB1m2rzwt0jGqfmxex1iqlFUsRgztplnZVRYG11/ktWmazloJpmluImxTQlZYgh3ZNyzLWEi5NOLzTpeoDL/2nO5E+HSx+WvtKAeBn3cA3DYo+Dej8Mq79pvZtAosycNtvHRCie1HISD8YSL72HTW/jZBsQGjdNqxauL0ZLFTlP64NQFmFBSmbpRYvCcmdvGVBquCs3aVXvh33fh8tqHEC1VqwlYKUNkxQPS3J78cUoyZSCJitUA0zluc/HY8CDlV8+nR/J0kBkMSNnDP2+30LX1nolhxDNLA694lZZHUMHNVasRxPiPMEryAsxtjqs456hFWTGAzUhRjhhxrNQPfUxhBaUfgpRKybFKU34WKw6PBRkBq8YQjV1SLfs7BS0SL2IQRMcWr1iUP0bZzYeywmgeFIxIs1vKaxKXk/ri96EuJyCD60MbJtGwgqhutVSzL4Nr4O24b/67zD/3hdsPuKxv9Te2pP7dvV/sFuxo+XhLtP7psP1CgDo7/r6nCAJZXUmrXkpMhTkesJbSKUX5SXLnQHo23ISWW/KaVgPa2qUqDgrBSkrSAnqQpk4XCG0AJMmqupU7D4kfu+2rOwPy3UO0ZJHmvjd78MfPhGPYCPkRi/iWt9Xe3TnuHbGPZ9u3uSgINlxMkAszq7D0HgZfUMwX+y6ZsnLISALSesp0U8ZHI0ECoKEpiENyNioFITdZqCCPmyEJMDSxizNjAn1S2EQ+Ja5jIRYZpMo0zcRhImUB5IkUytxE40sYxkQ4zCCZ6D4iLJaPSQxUZqls46uQuCU3V5lRawWThal9u2AUQoKTXpCeekTmhOohFVuKCkgmmaMB92suisJ806NW262vq0iZg6h60knNYFy7I2fl2MUThbtcI7L0LEFtbiXjOTtG5p9CKQum4rvA9tkWs6YcH39wVgUw7RFKOIHdeKUrPeo2uAz2RLpI6skKENoHuVbgCAnLcmPHvJT2tyJs4j+mng/gQE/e//8fw5/s4nGd+xyifW+UMor4/Pi6jkcAAP/0ekmZ3Uctjb9y/OiIszts+5RTXHxb+3cUN4cFY+P+7TWjsFYSg+38/7kBz+KWvAxWNYf8lN8IOnfPN5z0Ot7bMhsn4ZcuxhzMce+PLcfNYxD8K47UyX9/HYfT3WPu24R677mef77PbY+Dg/AGfvpR0zrD09weMNd03n41M+ehPB4LE20CAeTIZLQsMDgsPw+RvCmrh4++o8+19eX+Hl+5+A3v9EMvdhEkIiSs6Vdf3yjYfMqMrR1WxtrUIS9e+5FDgI9y+oIb2ua+PukgI0ApBKQuHa+ipn4esSdweHcw4lW/JYl7qRWsMW5epzauQlGo/U5uqbvHlvAoyfOa8/pRG/JVIZN/rP234Znrg3PdYvw9v2bfEGftp9jAMQEAmDm5vrVrZMQrl0RpIW79y5m5qZseQMTgm7XUT0Aqhe392jMLCbJ9Scsa0rrp8L4JmCctjmCZmliob3XiQTCmvWXQc/rawPAzFO6orPIgarni8DqeY5E/HpTYuln3NPRqL9fr8/m4DjuBduXudAbtsG8lMTebVKBlOMzWt2Op2adllR8LRXEJxLbkRo+75Hz4QtuWu6jSFtIkLx3DyJgMl69Gcxz1ocargyV/VYnm+OlUWENoQ4EOArGKWR6U3YNLqeCW73ZiT8ouK5zjmRRtHVzurnEhEcfCOYS4imj5+xmoUlghgQjXECoPVvNStURKs9HEM0zjxQI0kCEnlE77usQylSZspJDdDCVZ4NJuAsY6rkgnmecX9/j/1+j2mapAYyxPsqY0KMC/H0ukZN6MT3jKLJJOapqNW02ZSLqiLkxiWysWpZhCIz45TjqvVizbOuQLrC6uPKtQ86dsdwVpxie9NVQ1hGoB8940SEmvu57J077zHNs8gc1YJcK/aWYMG9Ak/eLPO+l4Qj35M5zBDsFUnkPqQCSsU8z9g0QuB8N/QskUQEiYdqQ6LgCwBNXL4UlqQGEs+MZNlL39VSYYlN0h+qYGBlFms3bIPeA1Wg1ozKRWVxnM6H/q6zViipqnE6GjGWAHO2d3Ia5rMaQRZKrB2YTVNEF5aReeC1YpAksjDmaZbTEaFAykECkiQ1Jv2EEEBwcjxX5KJhUAKmGIFhHWnApHcUQFq/1pKiiEAs8lu1LaMP9y0inPGxLwFM34+46ZAGpzXWK7fELw+v1U5IRMw78m0/26lM9L1yRQzx7LnsPZjCQdCa2paTtCUVqIdl7Ytwc3WkJeGg0Z8xKdLWDhGPF3/EYBZcePqcCni3UTiso+d78NiPoxHHF//2/j+/r7fDHN8IAHzshp4A4OPtlw0A5RiGD5CkBl0gvSd4H88GcxPTHAEghHJ0ur/H9WFGDB4pZdzenTDt9pIEUBK4VlzfPEPeVsxzxH6/wzRFyTJ11g9V53lXpzeAU3LBukpVCqsPHEJsm5uovveJ1e53CDkCshhM09TOI4v4CG4dYgwiDk0QXTbVr4oxggu3sAJ0w4ICHMv2nWbZNJzzwjNkkZNwJIKxtXRl/H6v4wJHzXKUxBpGLuug66XeSlXalxcEpJzhY1TL02QYpEA7oYonTsOyqeAMYPog3rwK3fxIJW64S7wQRI6kpnw2fkopiLu5ARuRlanN2eqd07JdBDipthJCaO/I+qHVoHUEF5xuWBLSLhq6F2s+CShjBy7aZ8yaBYym02aLrQh2u8aFbBmqFiKqlskt3jvz9Lqq59bjbKOZQq+eIgXgVQhdy2HlKokzTt8htXvSxVpD+gwdUyRVWMQz4ISaoOdHZfVcBGw1a6UP+d1cJrWKvIlTIOCiCrRT71fxxllATTiYm9apdhB9w5KL1mBWzihrhjIU7A0qUMxW+7R7q6sz2RkdK0q4tzKJAiY8oo9Yt1U9uMYdFm/Otm1qSDgQicD7tpm4sHgN5ZzqoakMUkOoEkudZvUkSaUfBaRkpeAgkk8E2exVr9STPEuuTsJ7JF4lFdGxFVXWBCLkTcBeCF7kgYbxaZqGzIzgpiazVLlKCcdmPEt9EnIOOxVIb3W0YxD1Q1b/JgPReTQdOpLXkUoeyvfJuBWHnXBzuZoclOgvypwVvRzvPDgJK/scz9maifa8pSaIVGbPlBcD7TzbfDRG7Z03nnc7TmDuuqVBRgtgNZpJHQ9MJncj88+H0NZZk6wqJoEzFgyoXadRtCWVYqRzgBy1coXm6xb+c+jC7u3/u9FjfdK9eKMBbiXnei/K2utQenJzmzuf1h4DzuffsT3i8wPAb4QDeA4uvprzfdHWwpefcv43XeOX4cn8Jtpj78Ym4nBU67vLY0d3NgDkLbUoQS6MXBgEh+g9TqeTcstUGV7LE1UuKCxk3soVDqLVVVmUvVIRwIImsOnhvRXYloXQ9L8YDOcZ4GHDZsCd8fDkBn31srgArQg6K5iDgipJctHQa5HQMwjgwpDsDin9ReRl4stuC4KDC15lGlR0FbKpQ/Zy2RRV6JmImueMSRcuSOYu6WIjZfOkQHy1igysmasXHp0phrapQrcvOLSwBJMkYYChos+koNK8EZ2gTJDn7eB8+Fc5e21xjeFsXgTvRMQ7a3iFxJsAELiKDJA17z281uyUii66vKlMjwBtglMQPvkJU4iifQYGB9tkZFNs90mA8xE+AOPAphgUBOuA9Q5Z9RKJxdvmIAksFEgXfPEgUiB4Fn0ysYT69SeEFh4Ktej5XPPoUO0aaKAhOcY5UJV6z9HLjuvMgxLsXahUBgMRo6SFemGdllkjBxfl3AZEGAzvxGNrG55t+J4LnM0ZB5A374ls0jImikgfETehadvsQxCg38akTDIEH/R9S9KY8yKxIx514ZF6Eh5rqUKol7FJcM3bKPMihACvdA4MRpd5gmoFWG0wMbJUgxRirOSa1TupXj59I3I+B2/Ip4ruiVFMrMqFDJPaPEHBKwgLKpvkvBprhOBUQF4NR+iY8qQ6jtByfEN2KFHQa7k2T8gMURNJhxqdRAje6tpK0tocYzcsucA5efYQu9i4gReHbvS1dcNTf06dEwwZS4SeIU+I8IQ2P21/cN51b7iuSaMH0MaLCZG3Z2RqkYQ+L6x8XJ/Hjgc1Cpb7d95eOMHsoKrGGBFU8F9NL1JaSq3DUzo4qq28qM2JWsSTzDCbXutj++4Ms2iAOQ2kzyyZhFsJPTN2Sq5AJUPSiiklPA01tOWVOPX6t0upE+F8/+0/fj5sZe0b4QCOm9LbgMDPOmY835va2wK4L3Mfb9vedJ9f1fm/yH08du0RHF++K/FInRNTbULbzy10tFlmrhZO1zBAmyHUFx7TGmTImivrLGl4U7OMAfViUS/ZM4jR9nuU65hMlc15s9xkvmlJJrWkTfS+P/vFu9JzOSsbpZ4VsyzbAdRT+5l8sySdbsajl3RUj4culBbOrUxDcBpt47Sbkcc1vp1ueDy+l94fLcwH9TgNfUItc5q6h8YRRp1W2cR7WEJI02N/97DkeOXzZ+0eggIJ5bECkebxsbAehkXRUQvfnr0OPU5Ag9dkIXmmylV5nYTzr9qYdrByhVAr37uuEyZVOWycdq4pqwcRngZwbO+9e01lnFDbJHtVGHu+DgBtIzybT+1dG+A/3zztOtafVYGlzBMBLFYnGOqnEmNDri2Am3UjcW33aOcEN7Bp04BqP8Y8Qd5JJRB7hvEcMi7UCwmgsoRUod4bQhVPlI1B9cxY4hMp8LN77LW8q3qHCc7FFj6mYc2SFyD7q3G/4Bi2X5MjcLJlyMCr4sgBDNt7h74v+0t7TkYHv1L4WznS3fssY6Fr8oGtj4bj0Kv+dGAlx5qwswEv7pMLBtHkvqn9DNicsXtQ0EbnYejWX4MeY3tuGj8zQNO+qXdli8jIO9XnGMaVeCoHzvjYGgCidt5RQ1KWgof7Zvf6nj+PiT8Dtp7x0NcdABJLUkjR9bA9rT5jew6gVUCyPc7G4OV9tcewd8K9D/s6aNGdYa+ErMujlxytbx86zd4MF/o69XnbVwYAvyy4+iIewl/l9qvyrONGPobnALTFbgQCzKK1tD/sGn9HirA7UUh3anU7AXSOzFsk7vLc6q8OoVqGWHvMLRFg5BrJfT6cnGY5E6GVhCISr5t8yRbec17H6CEen9l4kGO/yPHiwcuaiGH9YJaqWLo2ScfF4qHLnrlXgWh9WntpLjvWQqafx+tuYdsOhMaFTAGMgqE3UT5sLJwJAQ9/s39bgXZ7mxZ+5QsODQk/r52HVYNwuL9xDLbNwYm3pp2L1KM5eNakD2RDHPvCPCaPhVQu+6t5tXOGa2XsBs6UN51EA/JOQVYFt5KH6rUbZZUu3hFzz/QGjeO8h5Uux2g1ywi2flJL5Dl7DvDZ9x/z5Ev5uvM55ZwDSj37jlwLj/bpuC6MyTy22bXreC/JU1WSzaLzEjIu4j33LqAya0UZeVfVEcrQdxayx8Ch0s7Tvw+g2VGrCTtyXKvKULVzKrgbPoC5kW3sNsmp1ufdULh8t+M4Gv6As7EHgNSIYaBl/Y+GqZ2jcLk4L1oCgfxetcpMBxo2AkahZDNeBTM91Ey9vP/L93w2HgwgXSxDzUC+WJ8acNX/2lp5sbeM37q8t8u1yz5voWa2+7G1ScHfG96P9eX5M/YxPT6z8bXHcQTYuuMGw7H1fHs34HNjkXWtQ1UutDPTVpwGzkM96vZOcNEMoj8Eym/bfiWFoD/N8ze28UW87XeeWue1WLucbJcTcvy31grnSUnuVYWbC6YYpJyWLXCQTTUE30AakYhtAjaxGhS6ABUPJ6Xs6V2O5oybYgCzZe2mtuh13tzjY8WuayGZ8bg+vqw/0AAHLNTRSO8VVg6PrFTZMCTtmrl20GCJEK0kEWzjr40fZ+9jtJ7t2caNsS/yEvpxF+KjI//wsXd82ezYcfN4DDSOiR09xGPX6VwgydwTj4glUIzXcK4T21tJQkLzANl9Gx9IrmPv4vxdGrfPQOz5JjokDyldoFVJuAC19uZLztBSou1e3bCxmacrpQ3M54R0e09WKQVQv83lnLqgWVipPOG52hwZDTE2dAG489rRY5+OPz+WGALnwOVcSHD0Sl6OAXvnl2OASMJ+MC6mEvHJOSTdJAvXoRa31nFtDiWCq0OClyKJZvQ1IGIJItB3m0BMYN24LRHF+tuSaJxzYBLuYq/X+/CZG2Ckh2Bo5Lo9+J4BhjJ6j8WD19bb4bvemRE5zMOLvjY9TADyjPUcwHX4B+Q0rG8ww7ePr0uDcXy2x9YAVuOjzbuhfN94ITNcx3PkWnUvGABt5bN5IRGC83VlvNfH+hjQsWeGvd0rC8Dqa3n3CI7PCvO0siSuibSL8PnA/f7GqkUCunF2bevzfn17FJ27xm0GWhKSGXMy9iuEMdTD0nLP/Xnt3xFMj/3xtu1XEgA+ta+3jRPqMav+bFGw+Vt7Cax5nhFCEL5frZimCdCNr+hO6YNHjA4gAUZi6fg2SY1E60g4NSUNKup6n2cLFEs2YdfCk2Or616tPmkBm2zM5cHCZZuqWYFyT6mBJOmLoMR9Od7I79s2AjNu1+6bNJRT2A1V8wUIJpNySFwZnAvYnS++9hxZq2yQI+Uuni+yBhItwaFv+Ap29SbOt7nHPRfjGBjHwfidSw+Q3cPY93Y+A2jSv1189RET9+y6433ZOQt6FrUBSbsfKXsFmPXf3z+15CbgIXBtQFHL/LVNUXURybnmpbTvF80y7n3U+VZUGazyl7WBUKc6Zf0+SimasXm+oRlYGYE9M8N5QlZGOZEkdzgXBg9IB/aZexh17MM+vstZv9p3nT6/ay6p834aAap4uQeaANMZkOcq3F4Blr6BlDqMsxGM2DOP3mFmBqkobxsbFjxjoVFVtizn0rito+fGjCt7b2ZUnCUtlAIQiVQpD+G5Rt94fN6ceX/UyJFOkzG/abWKbnwavea8We1cWzdsfJxFYRjC2dP1jAdvkI0q+7YlqQFmF1zId/H5c11+dgn0a7l8TuPKXRhJA4XAAEvrM3TPOXz34tk9Xvb1uB6NwEhCwr08pfrb23dkbWFImUYzEmUtlLlmxpg9j3YVW4/KOnlZ/tKOLaVinieRgRkdBMqjtuMALSE31lRm2Q/naZY9AVKXXPY05RfqviHzW+ujN4NAevJNhvpntS8NAN/mwp/lTbg87tPO/UW8f5e/v21nfZ1ew9Ha+ja0fj+Phy7OJt9o+fq+aNpGZRIX40aSk2afkuuJBMyoRcLDmSscSaWRlCtqYRAVcY1TB06WiccM3cQV0OgG0xcusXAdqmbZDc8zeKDGxXd83Y6655GAbl07s5zHkKYsQhIixtk9GOAaF1Hb2Ew+xC5MVZYGVk9mKVI8fXxOWcC4i15XqMRF30itPWYt89AXLSwNTaABtT4435QGrl91Ktlw7h2whck2NgcCqoCwytw4Op3L1SUZQoiqoK9WeIXKashjl1b2ScKUbJu/bvYj8KsGfpRX07uE2vdKBSp1r29t4976RsKTzknCjo1jAW7QTOgCVPGiGvhhVPXwVNQq3rlSiiQz1NK4j8yMDBbpGrDK2RBqzmCnnDMNj0rf2dixjVSqo5gciAFGR+q9GsbbWHJqBOCNryo7HVIqCM6ph0s/r7ad1QaYhdheGzBuArlmADS7kNA4dO1exoEoZHjJbJbx671H1SQSIgfHDHBt2dLF5iuPiR/C/W2eJuVPgS0JQPmgVbLQay59bIGat4i4A+sRFKrNOayFl3uCvJdaSwMXBjhHwNMB3cNmYULouyOSzGPLZG2eH3RA05JSBr1hAz0jL6/ddVs/tZ9q6ceMYK9K9roBFps3TRdTDdeadT9waOuXRFi6h41w7pkax6FxkgvLOcdMaaL+Dkc9TiIFY6UbKXJ/QHXa94B4ctvcH8LCIIhXnwF2IMsS014ppZ+zGXLkBn6h3HvOIisEnY+1ANuazsbGY57TcS3Sgad7IRCiQ/CyN7oAhCnidL+2ZCNAjgEAiRqP5+7JgHr3eNv2hQDg5wUub3v8m6yqX1b7POD2i7RvEgS+/XV6CGX06jx2HgNHAM4An4Gc0R1vC2ALTTQgQiAVfC5VvGBcLdxlWYO5/W6bmwAUrSyh9y33YwsanW1Iekk9UtsF92/0CtWLaVQry8Ki6vGVqlpil333MBQkYe2HwHpcoEHCU6rDTdrCcxkCAaAaVYymHcayQdqibQt1fy/n7/H8vcpGMob22gLLlrSmf2PXpRiGvjMtNgBg38VX2QAJuocHFaiuA0JHgwq/nrfkDl6ZpWrKNE0w5Q7rOMs8PvNOX4RLH+135pYVa+GwsdVS9TkGQFUrcso6niuKblzFDxSF2j18zUNGJJ400s0RhKR1R5kZ7CX0WWoBeIO3JCMDV8O9N3CB0HQlHUl2ZAe93TNHZAlW9ez70t/WHzpGS8+mbcNQn7t562pFyUPyxABOL5vJrLRxTj2cLqBdPTWVmxeoVgsnygzMJYv30UlVni4hgvZ9k+5pTRMR0pZUDmYYK60vdW2wca2GZbUqDmywoK87g4Ot/WDPnnM3CB05wHewJX0xXreDLTmH3bv2pfKjR0BEdr/MykxQaK4yKQ2vScfY6YZ5MOw1bE823sPA84bJlwzzpUUM+roDqGON+txuepWO1NDuNI+zxix9r5exsWxj1A4xCSqQ9fm5l7o50TRxUNF4u0/YkxqIHgbqOA5b/7er6HOWDCL17BHkuUpXTbCkqTrMEXs3Iwi2dyAh5d5n3qsMjgOcJ8ToEXce++sdpjBh23rpOgKpVmkPIcvY6QBQuvVrBoBfR/s2ecOALw5a39Te1gv6VV7Trvt5jj+3UgDzrgA9pGefM7o1akDDODbG1+qzl5SgDF0DBaQ5ChK2gnlOeniu6gRm9eINdwmx7IzDZJ+Onj45v1h/fYGTfVI9CG4gDgPorvlhzdF2zh2xvuohAlKZDK9VLcbzjqG+0Vv1YMEjat68dn7q7/DMQvZhCHvaxtk9bY6k/JHJHTTvkXlIzpBxt0QtSafrhzWfgTwpuV5DmNB+JiYkFQMuTkJvRaVZrH/Mw1O4dNDFAFXV/BvAmMjd6AYPAVWZMzgY4FWwYxvE0Ma+7qGnEZCbnIM/85KO4UbLMpWsYvEOEIDEeRhfrHI83EBx76r+jAQB9gJ2hQ/YxgSLILjdg1AdLsH6OQBkEsC+ldT4nOKB7TdwBoC9JlPo/phRmnkjAFEAd1aJoe7p0bBx7uHuWioqp4uwuW2054CjSSehh6ItUgDSBBoimLB3C0vqmAveY9uSDl7IXNDx68iy+s/nhp4IRGgaj+C+lrT1qzYMoutXN5oaIJMyEwDXtrGKV7aPMxtjOZchw1qF6m2qoXvWG4hBB1/NjQfrc7n0GAZuygP6HAbASsk9e7ytc65lsxugtX4bh9YZkIKBuD7exnEukYj+t8odlIzG2LmD4GEUYpxjpJuBecptnFwaamNfF/V6n7UBEEN71zxnzQMMHUaOJKHQoesvNiOg908bU5XhQw99hxhAkbS8Zd8l1nXr75Ylwcg3EXpW72IvYWpjz3t3Jn/jg8M8TzgcZuzjAff3J2xb0uMd8paQXXd85JwgUYr+rn7pAPCXHd78qq/9pg79toHWr6rRIwuFtW6BK7CqotM31r+95GRJ9RADiE5LuElWHjmCVy4gZ0bJSURz4RoKczSSdx0IfTG064IknAAAVDrwNOt13JTOdZzco++xhRuZz8DC5bF+AMQAWnLGZTjpMf7ko+Onni96zYMzuPidc1jyeobh+tf7d0cv1BmXaniv9q7tdY8b9fjO+8LSTOn2d9sAx+oDI/iyc65100W1h4oBYF3WB/dz+Qw5F2xLOts8bF7auxnfz3gPTrNJbTM2n6ToSI5JIwQavcKcG1C6HAOtbx20AgPO7u18I9R7AkQfUqQjzzYtAyfMjILzsnhnsjOyq2OtWwNRQAHzubrsCIJ9xdmYbMB0eIfN8KDSJILad0rt/VJqS+Sw818aMvYODYBJRMAK3fe+KikjM7cqHGMf2s958HaMYwn88Dt2L2OvWtJFK2048O7qkEUt/U9n/aava3gmMfJKKbqJj/O069XlLAbqgz5RqZh2ydb9j2zaPHqOxs+pAT19wgupKDt1976ecTqHvKiHY1RR9gioMbxjDdeaaPmb9nkB+XbN8+x164uiFB/72Kg+Z+NqOGfvcyCEzi0d35c9qyO0MWdrvR3rfUQJYtDMmoBoRknlLmgvhrD0oXcMKJCe5xm73UGfk/UaHtcVWNMq3uuRXsEkxlXKWNeEWjOmuEeuACMDxAhRKhb1MnVybCTf1lxR05A1X5ICud0Dc279+HkxybfGA/jUvj3tEqDYv9M0tWO618q+I5M9xoi7u7ueMdksVplA0xQUJAK5nDDNOxyeR0zzDEeEddu0nq0ubEwoCSjZVOGhPCUBENu26eQFyPm+4ClXSEJWuXsvyYOZGl/x0mJqoKua1XZuJY99Mm48lnBhHrFgGY4pIcbYnt/aSGw3S50E7Z6RvZ168cxD0oBHqWf8ywa+hoQPSwS5tLptwZN35tQz1/vIjjExcFnMAFt0Wpjb7h9dc9D6r6g+ofFyzEsllrYXaxuywG1pg2UB90xWAKBm2crirVnRw4Ymz2gJEk43qKLeB6sSgeHZxGvhnXiG8pbBvoO+BjDUO/kA8LWFVu4hpa2904JeP/myZJT3HrkWJM79vQ5gWY7TDPihH2UsXugt8ghSO0BtGzJRk5QopYBT/1tlCW/60DN+2zMGr0kldDYnbIyygWw6B7tnY2LYgJxzrZLH6Ik9u1f0JBoialVxGGiZwnLfrN4p25zbcASrPIp3mtlO3MBnj0Rc9BucgjYCUKVGeLEszP7olax/CxwF7V+bY1b5ot/D6D0zg6LNm9rpLgDQ9ce7576NawNkFsps1Sy6B5tZEoFoBNAKGiyk+nCdMk8RZI29HFO1G112L+0eq9E2CpqYPFvURUDcKFl06V2088kcK60G+EPPYZ8zrbYvM9gxXHAIMSCGAJCE25137VgAykXMSFtSZ0No9xBjbKXgnK7RVT2Qdm9GDUGx582A8VIhfzseT8haXtA7j1wKfCRE1+8XpTbPbU4V7n7B8XhCRcE0CfgsVUK8u90eRMC2JWzHFXE/g2tuzpOUEl6/usUcZwWeUqJwNHQv++9t2tcCAD8vCn2sfV6v26Xl9lW2caH+Muf4ZbS3u+7DY2win7nEB6uPoN4HAtKmGalavWPbxAqTMKvqbxEPPBWJcVANmCaPd9/7DuI8o1TguQPyuikfUCRL6laxrCu2TTc3kpDa3ekoZcUmWRCii1jXBVCBYCKgcNGU/qoZZw6oDrUm2fy5h+Uqy6LEpUqVDTJru4davBMPYi4JwXvkjZFTkSSF4M/6kCvDR+HHlSpeAtkohRPImkwB9bJ471CY4IYFPYbQPIBgTRIBkGMnN/uimzCj1VIFSO45zuLtca4BphADPETTzGn1Ek9+8Ehplh2hhbWgIWAj4lOQ0ktgYFs2eB9hoTIGEIOGJbzcsdyrJEIYBaDkBKpSWUVkL0g9M0XGk+MG2rj54s5BuICEAAcR0JbwvlSPCCE0b4d5nOXpelZkkz6peu9cm+fXglxOw96l2iYr98ylwsOpqHEfl1K2agRpShUwwjuoAeeREwcowJGHk3B5KW0TIbJMVdHULKhn1QKcelacYgzWrEd2gwewiiyNhQXH0CmjSjKIjiF546RnYsBpRRTuVRTEMBGvaowBEs7LuqHOOKNVDCCwASQFOc0Q0fsxgAqdbw6u8W9TLZosIv0q44VUFohAleARZbPXJCSAEdpzQwFlVjK/rmfOq3xPD5tqvQwQpE9SybKO6PsOFAUYBF1rUpYnIvE1d28dKceT2xgsuRvOJllUa0Ulhmhn8+Ca14gJhDfmg94r0MKKUv2igpyXGshVDUk4kBcPcS2aREaaBasgmNT5V5GgCt56XRm/TrlqYMC5ABcn843bigEAml2rYVBUeO2rvoQQyDs4muRf5xQUqyeOq9IMutHjtaa3j4Qwyzj3PmCapu41rxW7/V7Pb+/HoeSs81dC+c51RQnyAaQJWMxSecg74eIupwXEOt/CXkZ7YaQlgSpwffBIeUOuGftDwJYror+G032HIDW5SxKDatoT9jdXuL+71zkrupfHY8W2buByq+tzQcoZ211G3hK2bdP/ErgAS9makSFdOvxLukY+IqD/pvat9AA+Bv6+KgD1yw5P/7La2zx363Y6D8Vdft+8DCBgWzfk3An8OWXkVBDjkAlXB/6TelZQGVyA/X6Hd955AQoRy5qkoPbhgFIYpWbx5uwAXjxwWpFTVjc7I20r4n6Pw25GnCYNs7DwNCzERZAM47MqC3ITSYvPEzmghZktK1NjJQz46JFT1pCbQwgOKSfEEJC2ipQqUi1wPkh2ZqmqT4cmclzZYi+Eovw2sSw1kUYrrTiaUaoAG+ae7ZdzQS09HJarAASumlRRhNfV64oCHgHeEda0wQWP3W6H3WEnpdqg1jiJPt1uF7FtUlPXazkr8d4ouVo3sjIACcUNKKkgTpO+ZwG1cRJeS9MyVH1DqWUsi3ypBVxqpw2UoTA6upeVHLU6qFU3fwAtSzOG2DbBXE0Soj4Yw60OrIHholI5CgyqJnE49WZZ5Rfvu6eieSOdljJk2ZZZvbgpJQHc6MT9RgoPKntB6B5UdEDUAaOOU5UFks1dqBAVAqQlNslSO1ZhVGWWDZsG7zJB0hvUMybDkFGI4cCwel4GeoLWgAZZprlslkJUV61AsvCmeWk8ihpPIfp2L5e6dAY0xjBvowKYUVCFO2fgnRTwkm5uwXusKSGnpPxWUsFtA89aJpCA3WGHXLuXfwqh1aoVL5HMs6zeb4sxdlBNqJl7ZjczXBUZIAujBx8w73YIew82KozOP6fk/DEZzvRAK3dx98677N5n73Vd0PFCCmLIGbhRLEmmFyq83ZyySkjVxmGTao89SaDPh/pADsaF2MYhKTAHd663cYQxOXj1uhoPs2QxxogkqaPUguA67w1tTRYAa+NJqvH4trbbfcu6odVfNLEGgeHJIcRJQ6YydkqtiNMEHyNC8AjRI4YJ27pqzWDNLgcDVlFKIWwpBTEKEJZEr4QcA1AVbE5R1pRUUVJBcB5TnBBKQMpSxxyeEP2kjgdqxgnre2/GPzNYazxvWzc6APHqMUud4rRmHO9XrOsm3r5WdYrbfdu/bV/nAQS+ZftcAPCbAk7jovG21/x1BHVfXTNUpr81r1//eXwXYzjqeDwNum4Seml6Zmrhm/chxu5VFGJ/xdX1FZ69eC6bFh1BVHF1dUCtwLqt2FJCDR60TPC7E5bjSUrNgTDHgKvDHjfXVwgxINWMMAfsdjuULJaXDx4VyhOz+/dSXDznjHmeJexaK3IpSNuG3X6PSCIhAwbmwx61FBTW8Jd3yKUgBo/KhFwYqWSQD4ghomwbiIEp9hAqlGuSi4RPnBPrVICYZImmlDBPVwJMUm4bCteClDbknJoXoWzCLam6UFsY9LQs2HICgxCnCXvPuD/dI0wRz995ge9+/7ug4NtCDK5IacP+6grH+3uEEDBPE5iB+/t7WJ1ceYfcKhXknFuI3OtCzJVb5ZdpmhC0DrIBnlJEE9IEeO1fryE9I707J1U+LCwDElFgW5zHzSnEgMlr2AVa1F03I+MpyZhVMW4dmyFE5JyxLAv28w5cK1JOAuDYAIyG80NA1bA8qzeJhs2jJkLJMvaXZTmbWX1dUo+rhmZTTuIh9kLotlB2ZeHCknqlQRay5ybe3fiIemqpkmOA2cMI/6yTuAwhotHbCHRPf0oJjit2u10TTAe69ljwAT5IneYS5Z3HGDHFiGkK2IoA6f1+3zwz63rSjbBL1QBACL6NK3KEOMm8WbdVtjdNGBJPqoBQBgDnEGPAum7I29beD5eKnCtiCK1SB7zD1eHQfdpE8s7Tqu9R1j1mxrIsSCkhZfHYlJwR4wSAkVaJFOScsCxr0wtkDQ1M84znz58j7ANI+WZcBFyax73kAtSKaZp1HmeUXDT5xzcPsXeaEDCEkgHzeGmN9MGoyTmDgkeMsa3PJRVsy4q0rM2LV1NFzh6l1ObJq1WeiSFJFVUB0m5/I2sn2b4ABOewLCtCEA+w84TqVkwhyPxmh5Kr8NbiDOcCcslIKSF48/qiGeUMRghR34kYED46pHXrewzEU55L1kxjQqoVSxUP9TTN6o10Wo1H5v5uv8cUAyZde7YQEWKEacITAc5HmeeVUXUN2O12KKWIN64Cu7lTZSRT17UIi4eTClbew/kAOCDQpFEvnbOlyn352ozBoiFh8oRUk7xb5zApRcgAYPAB25awritSMvDXeZKXDrJzya7Ph4PeGgD+MgDWrwuoe1O4+5tuZ5N06HuzSpvXyBZZADmXxtOw38XqLRD1WzFVnXO6MXSNqVQybl68wPWzG5D3CPMEoAovYiuYdxN2hx0KALq+Uv8ZZIPKBeWvi0cnpYSUNrgii+Fut5OJU2RDdMGrcCjBB9esvXm3swcEk4A67z2E2ZFBVSbfbreDC0ELrNe2wQAqJUJSBq4yg1jAmAMwqYeqcMVuH1Fqxpo2TNMOBN9AUS216f+VnDTMLCGwLa04nU6Ny2TE+G2T0KBkUTLWdYVzAff39yAiHA4HCdX4jGm3w/5wwOH6CrvDHmvaBjK88Etyqbh68QzB+wYK3j173070tkpqZegqC1BwWlmBNfZFACgXTNOEdZUED+PoNA+fkb6ZsK4rmBlTiMKVSwklJez3e/UOEaxCIIGVGyo8yJHDRs7BBd8WbkAAmoHBGCNOp5N4klzXIdzvdk2g2YyX4D0KM7KeO69by3BnZl2cE5bjCWmtGlo753mWkpHS1j5vHCO99ras7WfjccYYkXPnbsncEoAm1+8h9VJT96xVKI1A3kVtISHZXFu/67w9nU5YlgVBgRwRYacC7na/BgwN9HgdG9lVfP/739cxpoZNLfBNI9PuX7iwmxqF9ky26QXz4DAwxQkpp7N7tPclnikPF4XTt20J0IiC9x5cKrZtE5kg5XQxM66urgYPtMy1lDon1p7JnCpmvNgYSCmZ7CdqrTidTrKGeaFL2Bo5TROWbYXzHvM8I+eKdV0xx0moCerV2u12bcyyjjdSDzwgfTPPs3rmYu8zNUKYS+MH27ULZ/GgK28ta4h7W1fxlKtHOS+jGgEjpRUhOtSasaUNOSXUytjvbgaun8wjm8e2zjtHYJcxhanPsSR9t9vt4FxoBrV5gbetJ4DVWrGfdw3YuuBAAVhX3zLOY4yY51mMSwU9uVQs2wauFduam3Fic9L5gMPVQSgQNSPnDfNe7t073yINuSa4AEQ3wdMO+/0e67phnncI6gGNIYIAfPzxxxKpqMKvjJHFCZFPbU8LU0QqGSEQSs7IKSP4gBg8CFLZyAzI++Mt8ibat7VwM0IP+z0sa35de1KHbk9n8+ISF1l064vgCOK3RFnfFpDyZdsXDQF/naHjz9u3X/X9n3v3gGmWhWy8Lecc4IbyZFWEbG9v77Df786I+VINQxYOC9MxM+Z5hpH8AcLxdMK//T/87+NH/9pvgrzDum5gyMT1bpKMSWZQ0Q2yFvjgZFHwDkvaWvkocAVSlkQQWJafeAhinLAuK4KPwhVUD5gstqWFnYRDAqxLEr6TblK3t3cILmC/22GKESbNIAu0ZKeRl/6pSbTjLt9oraYST4ADPvjgQ+z3e8yz6D1575C2hBIkq9oSOI73J2xJQEoIEeu6YVkWzLFiXU6yWXkvdWpzwm63V3J6QQwzbt55jhfvvID3ATnpBlKKhm/Um1EqTiUjhNg2cAtjkSMFS7JYRa8JHBoSlFCkhJJsg4kxIG9bAzQ+yHdSSsK3aQkSAFFomy0ArdvqkPOG4LSuLghwATll4Tey8rZIeWF6TgtVMoB1WRRcGa+w4vr6Gvf392AuUF44HDms63oGUEKUcVLhUDWMO2YnZvX0mUC3lZYzrpdzWgmk1hZeTFtCnCIsWSLnIvwk37MQ05bAYDg3qZeMGnWihcZU61FoBUVDaCLgzFW808Kh4hai3TZJVCEIdzFME1JKQiEYPAaHwwHLaWlGTvCyIccYhTfqjPO7IWpFFNaNnpwYHRIFEN7SPO8Q1eMr3DWvdYBLiwxwZaDIhj+Gwc3zuq6rEPlJxmlOSRLGdL7Z3ScdexWaLFZlvSE2b52sc9uWka3WtAJrux5svGvIP+eEig5kSxFuapvJ1BNrfHVdj9DLGE4D8DUgD1aJJgsT8nkymiWuOD/10Lh63i1LmcFtDhk3GCRh/lSyrrMqwl6Fm1nzovw0uW7OAhy5AlvOQs/wHihZoia1fx9ELWTNOo62E+C98KZNKgdUhAeqwkgAEPyhFQZIW8K2rdg2MaYmpW6ACaVIZKaFqc2YdLLey15QgFoQQxCP+5BgZCHXlBIqMSg4zLsZqlaD4B2mEHRMJZHrMk9sLk1w34zb0/Ek89M5lFpxfzzi5Sef4O72FtH7HpLVe2QCnh2uzwSttyVp5SDSZ6y4u7vD/etX4phw8hx3d3eyRxRJwpH90SGEnvBj+/Rje3cXsLbPoML6n93+0gDAt+UNftFsmW+KO/g2/fxVA0Cz4gFZAHe7gHmeHoR8jbQrm2DG6bQg54Ldbm4eFwOI67JhmvomEWNoi7xtzK9vb/Ef/M//Hfzor/0IcIRlXUTypUrt3VKylNyCbHJVvQo0AJKqpH1HDpUsXf5clNo5h23ZlKMU4UOU7DlQI30zGLt5h9N2EqOZPJyP2M8z1lWI337gV0zTLEkWprdHpJsvd24YK+eNgLwqXw9SPeX29Sscrg8KkEXA2oeAvEno2hFp3WKxnm3xljBFQoylE9yJEKaoYQfxgjnvUTLj+WEv4RJIWDT4ALCAE0fC4ymlIAcJtY7Cpi1bU8NdACEAaFI62ncAsNvNSDlr9iFQNOYyx6jh1dyMAMDWdwH4gIn8yjG28fVSdh7MAjB9cFqZQDmI+v5lYgh/PcQgQKYWfSannowdSi3IRcjmRCQ8zpTa5isARULF8rI7FymX0os4qWcGOuZsbMr86Jw6e55ahRdamYVqYJ41BUS1CL9r3TagQjmWVT3QM6yklI3nnDNK3SQkN5C+2zzXDTGEIOeqWhEHXTZnzPb03iOQx7ouAxfJYdWQdvDCG81FKAkjmKjKv4pTbOePMcD50JI1BKiGBnZiC3tJxGDe7ZqHCCA4H7o4OHc5n21dEaephbgZDNbMeadAJWfhB1pFGq+bbQgeJQvJPtdeFsx02IS/OkrwKNGuKlAl8SJqRBIWQt5KRjTNN9Wl9N4jDZzWyaIkGqqvyoO1RLDGs7U12Ufl8/Ya4s4AMvq6zAo0bR2XpAdqgBbMCI6AMNAAdPxyBWqlBvSUTDKMC2oGSq5FvacQTmySM1nYnZlxWo4yloJTQ06S7tyYGVyKrtF1cA5AgBpRy7yVB9Z9R9d+EAOOEdR6S1tqa5SEgqV/MwqYKnbzXjOv1UhVgJ+Tjf0CsMgexTBjSxvWRRIuck7YzROWdYWkg8k725YN67pgXTas26bODunTgCiC1PacqY99561ePYOLaAbWCiyrZJHnbRNjX72DzATvz2WtHmu2Vl96DN8WAH4rk0A+b/s6k0a+qvZVAsjPA8bf9poaqVUvyrnEw/n5Kkxg2ZI/HrikbfFR70m30izT0kRcgZKLJhuIHpyUU9MkCF3qMopa4g6AcDFKraACeJaFBk7Csg6yIAVvEieSpj+HSUIAIYJdQP+KhhOZJYNN+XnC3HCYY0B0vpU4AjQsGgmoHoECHNTrCBUNhYXBKuAcKgF+YtRKjeuzv94jzpN6r6qUZvIOZJIFzmGKDkCFDxr2A8MVRpgmRB9adl3lijAJZ4mUE0NOJDhisClO8FG4b6gSlm6SB8Uhhl7nGCT3ZBtwX4QcLM+ZlLcnY0G5jUAHQCTcHgCN1zX5KMLJtTR+WnAi/RGcR4VvIt0hhpa5TOTAheFiRIjGSTSAamCJG6hkBqb9PCSUECIgSToc4EttpeWISDlEPcRpoWDJ1u7WddCQnZXasu+bxp31n5HYbaP23iuXU0LmsVbsd3vpQ+WfcmWE4LGVDFclbJTVWyt8KaNdqEdyqsh1aqR82whHjpB4niRE2isqDNVmAFhZtxADAnmEQTrFOw+nhlibI86DNFvSB6MRVIQQ2+/Oexl3ziHV1EJ6wQfkJCAtajJQdpIdG2MP1TKb59JC2sK9pRDgHTQ7XxYr44gad9QyrU1Oxusx3kLvki8B0ndRK4kxSATPtkZJCNDeY+UKJkYME5zLmiCkYIxk3TCOo+QGqeYkSUa2hPLVCDAvoPAlUBtA6aFzSbSBrp39/kpKsIxh209IgbnxXQ2M6eormMm4aejZ744IhSqCJ1TXQYR3u752Qb3AjoGSWmJGcB5uhlJmhJsGdC+uUUj0oVBSEr4cOTgfEMhhSydYmfPKEvWQhCcRUJZXXJXGI31UHUNXDplLqI3bV1DAVQsRmCc2JUjmCElHEAB22O0nkfxhEg9xZaBUBHhslUGFESnAk8ccJ+S6CVc1BPC8x2mZkfZZJFu2DZvuYXUT0XSuatB7IG8FFVKtw6IDYYoACDlXUBLA7mhCShmlsCa5mQRWDwFb695jHuY7n/39bdtfCgD4bQN7j7W3vcdf5rMI+OuLxUPro6onQv5jBkK4SDk3FAmArLIoW5mh8sCT8erjl4hTQCmM00mIxo6AkkUKwgWHXCscCyfIFttSMlC5q60ToThCDBFMwDRFuKAhOnKIfsIUIkABWUvN+eDguSeloFbs/A4uqBu/SFjZJBWYAPLKqaIKigTPco7KjKpZcBYqY9aqFwwgmhdNFoZd2CkPz0KUHoUdXDT5C1koStkkwxaapaqAYHYHOOoAC+qd9bYhVpEocQoOnJfQpleOlNN3wM6BOID4vL6q3IPTEGQHFR7nJGRioOSMVT1a3nnZsFgENCzr0juPKQYlxadWacQp58yxAMzgAJCDg0fwPYOci23w6Js+SchmWZbmCYWTkO5+t2/l8pih2ccGykVyh9RjYkBTNhpG8FKXM2egVAF8zjlwuJibRPDeYeKe7DT+Z+OcQFjrKqEpKoCX4u+lZWYqUHQee0fwyhsyTpqVNezhOwVGPMFC9q02KTTDcpx3kGt6MwyygDKutfHEvPON4+h9GLx2KndhoNALuV5AXRBAacDY+wYUbVwafzB4Cb9555DyBpCFsoHKMke9giRL7jDeKJFsoRw8phglK7LIuBQ9OAI0SYYcwN4jU25g3f7HGgaWTE0H52rzxJkBQIxGq9g0W7PJ/6jER6aimce1JZiRZo8TdQ4iq9yTAB3uWeia4OM1kmF7N6vH2TuvtdGpgVwiUsqAgHSTEfE+IpAkiCjNuY15tbaHSAerSLHIkHjl4ToWLyoAxDCLR1ujFaLfKQCDNAQs6wzUe1ma8WEGnyMP84IDLOH5UuBDlCxhyPqkkx4Ao7o6AHfNLC7idfSq88fQgn0eSCW1akfkZA0SJ62s49AkNUdR7t2yp30AUUTOCZ4BFMa6LDidVkxxxrKuyBoRyGuCDw5rSmACygR4cnCI2E8TdpFR9gVr2nA6HZF9EhCXCzwkMuBaiTqlkFQGkweoS0WVkiWpahKv93IqEP1I6Z7HHTH235fDC7/yIeDP61n7toeAv672VvdPorK+389n3xOwpi471URLybKnDiilu+ItpLkuwkWRcmtFw1hF+Qqy4ZZU8Z339ri6uUbKFctpA3NRnp3wS5gLHHns93vEGLEsq2aZSojEwjcWitrv92An/L6gG2GIAQ7ieWOI1p73Hp4c5klAkfOE3X6G1/OVnJGUrzLPk+qgCSjbXx3gJ49pt0OoSsBXILfbzZLMoGCFHaNwRfZFQCMRdtMezk+IYRbLVifz6bhijq5xyCz84ifxmpVigKAguoPUpSXCbrfDklbhpqjXiKuK6IYuxyK1dFU3q4h2oYVCkUqbG+axSeqZjZMQok/Lgt00CZldz5FSQrZwmzPR2IqSlyaz0kn/WhFEE0MksUQ2waqcrd1uhxhnnE6nRsi3jaRo+MjuMYSAw+GA+/t74R96Dx8n9UiFBiBC8CAHHI+iGwmuINN8fANoE0+mZBbb+BoTNkbOawtGDvPLhLYJ8swpJSSop5QZ+/1eOJLOd+BdWQFSRuckildk3bYe6tJnr5mbB3hMDrANu90ncwO5ALAkoUPs4tQ1D0sBgkdJSZJmmFUTrSLlFSVLiHW322Oe9yJ3pF5i434KgX/wjOrY4qKZkE68WcvphOV0ap4OFya5n92u3aMtSGQ1ChXE7Q8HNSLkmkHFwYUm4dqYrCjNGzuG4okAzkWyxYcrNeoBs3CQlTuZcoVShAEikEYIxJNakVnGGlJpx2xpE1oIa9Z8tZJkjG3N7f3JGKuDp4fa2HEutDWNmTEpb9Pu1T6LzkEcWEUSlhTogKjNH+ccgnrIewh5rFQk/EJJJplUWN+S+boH3ICh3ZeMxd5/1v8tU5qhWawKsIMYx1yFRzpNU5dFCqL6cLq/b9WQzBNv4DzGCfO0w26/w3Ja0LLLTSYGEhlyXDVcThLaVS29olqtf/HzD3A8nhDhQYVxf3+PX/z8QxwO12CWbP7T6aTJhCsyS/Rgt9vhcDhgmiLi5DDPEbvdhDh5THMAVYeSGTknrKcFH330CYJTihBXbGlD0nlaWQwfgFqiE6poFJ5OG2o2A+kcr1x6+C1cf7m351zxNu0JAH5N1/m2tS8DAI0wLSGNgG3dtEh1xfPnL3A8HlGKWE3TNKGWjHXR7DQN6QqnUCw6QDwCrnpUdwR5j1oItRCCN9Fbh05492o9k0XAdGGgZgU5IkTnUGxRgxDifZCsUgKJtIqGl6Z5wnJaVIRY5C2mecJWZdFEIbgioSYfzEOh5eEg2XY3NzcIBw923LOhQZqhSJhixLvvfgfMjE8+OYrIbgx49uxGRaelAgej4vmz55jmgMPhGtAQd9XF7epwI3ILIKRUhGt1usOaVuSSUbhiLUk3epIFKkbcH+8xRdVHBKBoQTiZIWCa5ybZ4eao71kW2908Y384CHDZNiH6A5iDZOVaSD7ECFawcVwWkJNN2s9ePaK+gfQ//uM/xvNnz/D9739fElyOR/h50nqbHsSEkotKsxxwfX3dNslSJZFBPGC97KDJNxCJ/pucI2OaZwTvsSwLbm9vsW6rkt6FIE4KRi3D0XsvG6py08RbQq2CiSW02D0Y+CmlgLxqPqoWnZHrJeQjFSdyztioNF4TZwHqkwuq5VgRnBfOkbMQmnoHvMd51QYtu1edhtV82wh8MANCns/6qoUsIeH/ZVkQzWNLJiGSBzFmAQX2nqZZQrioFak6zV4XTljUZCkD9rUU5E0y6Kdp0soo4vld1xX397eaYStew+wY8yz6lM+ePcN+txe+lQrMl1zhmbBDxAcffIiPP/5YwJsjVGKs29b0RHeHPZz3WLcNTGiZ0xZGJAKIGblKMoitb8e7e9Xok3CeJwvvM8AET2JAkHqdyUnCCRMw73fwXsL0FcKP9pOAIZiUkI4howawegVDiI2DR0q/kazVub2/ZVkwz3scDoeWJLJtG3bzjJubA7ZNpG0s8pJSwhRnlccRoFwoY9vWZhTJe7JVvusDJk3QsIxZA0SWaWt7gnneW6UWBdqWQV2VpxuiR/CTcGp1DeJagSIasnevXuP29Wsc707Y7SVp6HA4IM4TqhPRbyYNE68V5Vb643i8x+vbW9zf32NbN+z3ItbsnEPcz5iv9jjsdrh7fYf72yNy2jT0HfDJy1fIqaDWBEes1UGkWodwXPtzVq6NOmFefujeEoNHiMI/ffe730HwE5Zlwf3tvQBUVsN7npq8T4weFLIypATU2hqc1hWn44Lj/YLbV0d9HuAS8NnPl7+P7dcGAH5T7VcBAH4aF/Kx+x85S9a8J+wPc3M9jxYH+QpHO6zrJrIrvuDq6hq3r1dITUTJIE6L6JlNk2Z5gZXLc57eDiaE2MPJFjaQ8FTPcJOJ2HktjD4BRr7L+LyX1n0nx8vBwQe9lko6KEeqqCcT3M+pJ2kf2EbqXYALIi0TgmyCOUkYwMp3xQaQZCMhkhCdxmga2LBNxTxGzkv2svHSmk6cviuRbRDvhPB/WHX3IuIUG6cqp01Cv96yStVjo/zIEAP2+z2O9yvmeW5E/DjN8OTw8uWrJs8RfIDzwOFwpYKvMnZOy4JpFtA5TRMOVzu8eOcGh8MB8zQh6gJ3d3uLZV0xaSJI5QoXIk7HoxCxlddoSRlj6UGpBqBZevaCNWRk74NzRdky0rahQjwW67riuIg38er6Gi9evMA8R5ATgDFF8W5WrXgSnIO3BITqQZB+y2rgiBaeGAM5ZeWLDhne1d5r92BY3xVHIOd18xBviKcuQg1Axb0zTotI/zT+GNBCu03mpajH3ThSWaRKkhpnfdybh8s1b+i6LpjnGYfDvmWNfvThSyynFYtK3NRqWeN8pn+35hUgYDfPOOz3uDocQORwWk4i4ZOFj+WDa1IstYrOYikZ8IZVKrzzuDpcYb+/wv3xBOMO51yQSsGLFy/EC1KAslb8yZ/8CX76058K58oAbRFj83A4YJo94izXff7iBaYYRfjcALLyS53zrcpIUVqFUR0M8C/rKjQQGJ9YKSX6TqIaDM47hHkWUKjrlmSBYwiJd13Jtj7p+4ETXVJZByvWlAATC04Jy2lVpQPz9spakHMGVC/OMpQBIGXJPp3ihHmeMe9mpLoiZ4muTGoI1VqxpdxoNDZejSYi82dDrZJA1TzgpeL27lbWyMFDBZYkoRBE19GHgJwZaUs4Ho+iVQqZ16fjgpcvX2FRiSsLw4cYsNvNcLrmSXaySHSVVIRTN4DNsWwmoHuW0/Jw6pGsQwUWoHN2RRgagzJBO6Q9T9cSvdxb+/HMjGkOAGwu90oyIIl0SNUWe98yF2MMmHeziEpPQQXopUJSKRV3r6VvbN03A2Hcx208GYfT9u23BYCfmwP4VQGhL+qJ+7raGffpkfZtuMfParbgj+1tgPtnvdMRZIm1J78L52MEYmJhFyuvRNQAYOOljOdFBXMfgkR9nhqPBrDN+XzQX7q9DRS+6d7b9zDIJ8id9d/VOjV1fL48xwXnIucCKoySHYqvSC61RcmOW1XcdAxtjWPtwXPojyZJYAD88rwdMHeuiAi1jv+J9yyEoIkhGm7R7FghRgtI3U4bpkn4P6aVhgrc3d31TDs957zbNR4VM6usg4Z4dGE7HOa2uHktul5yxrqtsiB6AVreB9HmU2+NZbgKSFXjRC1pAjDNs1T+0Iw/0xAkp4LAW8ayLhqaLBoak37b7fd49uwZQvRgVmkWzaI1cV4xOCTxgeBUfFnlLiClrnwQr1vOGSEGmxnyPiyjsoXvJNt6ipMQ2HURD8pntTJzbQwQULYsIatatbwatzA7iFoSDQFa5UEGhIPD6bRgW7YWrvPew6votY0rmaMJ8yzh/DhF1FLw6tU9tm1r/S+brHmmvPImCYkTphAxzROmGEUzjaQKUNqSfreoosBOZFU0UajWCh89pl1EjB67ecLzqw2g1/jww48lslBrK0X4/PkLBfoVec346KOP8fLlS2xbglWdkfnl8TregZyUkDwcrnB19VEf+2pEEpRLHL2COJHm2R8ObY4bILCqDWChf/jgu6yLD5jnGXGKCiBFH9ANGzLs/bDw4LZtaxqfQSVJyFkN7J5pvuWEuiWhoaSEZd2AyipXIuOrVDFg6pabwWljohTNbFWDbJ5nMElSkXdOE7FYjcEyAMBe99pkqLJm7/dEJDm/zFlnQZm2rrWQc5C5LUA1aUULAUUi5i2gsKs19KofVtmkh+7V41x7NZMxNDr29+Pr/vm6e7YGA62ykH3e9kPl1I7rdf8Zbc4DQNounBvo85K54OzuOg4e2QAADrdJREFUdDzXUlEzY3GbOhFCo29Y5OR8j3i8vvKXaZ8bAH5VQOjbBqi+bffzTbUHz928XH3iWBOZAcuOZBXNHIGIkqBrBblzz+Ob+neUazm3ZkZQ9HDgXw7+N53/sUk/Ht8Xlc6vuDQG3nQO0aqXY2yDvby/80WDHz1m/Hfsq1EfbJRlEa7lQ9c/c7fi7ThZRApgYshBVPGttFVl4V4SS9ig8fqyJK+M+nelFHCMyHf3uhkYr4xaGMpCP2Y9j8XhR4+BhZk8eZQsIRE3bDBbkpB2aRw2AIy2oQUl/6/rArBknlqfrevaQ05DOJScwwfTR3JcNQHuvvlZcoHIY1Q4x3Cuc8XauxczG1JCKsLEjA3wsWqnkX4neI8QI0qVygu2qdCQdCO3Kp+VTdhNUnYwKiiTTMoWloKUhitZyukxBNhw4fb+mAUAzvPcOFBNHkI9xgZCS5WNWm6nj9daBTAKEd/BOckK3c8zTvdL88SAJOVLrpEVqHQhccDq70rm5v6ww+Fqj6vDHvf7E5Zlw4cffIRlTS0T3DuPX+w+RC6WACWokNkqi7g2Nkg9OyVJVZDT8RYffvAaPXvaDK6q4EuiD0G9ePv9/szIapzZYQNmkrBlUY9TVM96SgkeInNjRptU8tHqLWok3d/fN6rBpFxaUg+kcSmzZn/nbdNa0FnBbqcdCBYgpJwBOg/NEizTV16jhdnDRArmdK2oVhrxPAuZLurIjtGTx5wLl2vnw99zD9vA1jmGLecWmSDlqIwUDxurI09SaD/cPF2X99PfM96qXT7Xpzmmzvq5HTfuEZfXPQehZ/fLkJBzWiX872SOOU3mCyqbZE4U52qLUjy8h36Ny2f4rPYUAn5De5vn/VUAjZ82oB/7mx84gCY8asf5CKQNWBYpSH3zTBbNu9sNtTJCdNjvI+5vj2AWontQuYfuuRq9Iw89YyP46YDMnYHCN4G/y3/f1B+PAbLL7z42wR47t7nkH7PMHgO3j93/5eeXoPESGD4MARA0hRBdplaabVbdU9J/7os0Wukv70kzrSWD2xIlLNRCLgxgV96NyJyo2DDZQhSaN8D6wq5nvCTvHFC4eQ7seSzUSDCZDzTgZMT0LhfiYJ7nsQ8vwZU9g952M3R6qLTXnHUwQdiMyuOzEkT2xjzOaLIx9k6cI3jy7bqWTWkUCdC519eGzTgkvIsgzSSXe8x6TE+yMSDEzQIzvpt/ML5qzTA5nf69vqEYkC/1dPY9exeEsRxgbRVx7DPpDDue2pgMKqUyblwANEFjAJUQw6CWHoqvVbQamaUKA2Hc/Lt3XPrHvLNePWryuVEJ7FlsHpk3mTXELbxknN2j7ucIk2b+ZrlnMQ4evrMHdvRjRh/QwdbZLD3/zqMnBAA1VqwCSuXBmHh0rdK+BwNqyLR1nlx79+N99hCjGfR25/Z7fy7zFI7fBex92GcSMfK+z8cxieqxdgkmL3lutpa8bXu4Xj78/LP+3oFXP+4hQHzzPRgAlLXufB97bB/q37nwSr6hjX8TCkN547Fj+0shA/PUvlx708C6tI5kYdbQrmlTFUlZdw5KJn84iS6VzMcN0Lwzl8d1Mjrjcq6/acJ82rO86Zmsvcma/Kw2ZoNe3sOlh++xe7lcnD7P7+155K921PBzhfex9WHbQMEISt4WLwUw7eIZsLP3cQa8B56NbRKlpLPMT3lWy/4lQDf7EAgiucjw07CgK1+HKp31Gxe5t0JF70eLjpCDi0EkXjRcfwaE0EHyCCoveh6k3snge01akNb/zEVqO1MnfhNBwJ+cUEK3eg1vHLzmLS1n49vuXzzG4uW0ahujp7xWAe/kGMxbCw23Tbp24OwcISeWvtP3IZ63dOZ1LaXAeUIZqq84R3BkoeuKWrOAmmGMMGtlAscA5V7lAoBzQdkDKq2hoN6yNW3sbFuvxmJZp845RMs8Thl5A6bQE2/aGPBOQpNEmjgjP9o7NRAwGjPMyp+qkmBFVDUEJ6PCni1tSUuUUcsuBXMj/xsIElqAzTc1EFxP1nBOqpOUUoDJGT5r44+ZVUZSwZNvKbMwKHgJ3vrzPPTEyRhgVCpgJ9I/oY33c2BKw3Jg8E0lCHW8Q+dPn1vmCR7nyQhTmaHJTVAwPFTu0MeUrNz+mQhNy7x8zDC2drk39P+UstKO6/3xRZxS58b1+TnHv1+uu0Sfvbc83KfOAaNd15JwLh0HgFNNQDk2lc3O9AAsflUOuV85APhZSPirar8K3r23aZ9lNYwDU9baN3vXqErWKkGybrlCpCiqanAxNERcGyCwZnwkuQ41yQD7eQw1mgfBPH+jh8W+/3mek4gA7qCjZL0G9cxAwEi6gGUZXC4Mj137DLRcuBAuvXj02H2O32MGwel92T3ldtx4zkaSajdiv9sDcDv+wXsevAa28Rr4k756gzeVGaXmoW9IifFVf+fGf3PqSbTEH3v2Umrj7zCAggqnAtnk6GH/qj/Hu55Z6IlEQ4sYYJGscO1Z5cRNbLr1imzAZC94CCtVZqAAgIFYG6cGKgUsoG0C3UMiYM9WeVbiNwk1gAggu4tqw0rPV3STZ/VWmteJpJqMzQH9/1qH98sOJVdASwYOW7Rk3ZMeS4DzpstmvFw5upTceYSaaViyICXzFBkgGseBzNUyeGU6NcEMggZiHPT5a+vHyqVVxIGBurbB63cgrrbKHTiwKiM/5qEZPYEMyd6uWfqQq00dZ5c8MypLycM55f0ZV9AHFfjRIWMgQP6t3SBwBK4FVNtrA1h1AdWwYBY+IUPvB+othQIi6195IOX0dS9cAyb6gkgVEgCTyWGldgyoA3Y/pGuLHMfMMsYgYVZiG1YG3LpHvQ8uHdMwrzF1wCefymvWjlJTsZXSG9/X2D5tLTeP8pkRxD256cu2EZy9jdH+2dcc1/zx/GifAeKR108GEOqGtUY+k7X1/Lq9C8/X9C/afuUA4FP7+poslDaoHi7+trECuhzousUsmzFDFhbz4H3a4Hxo6fXr2cb8GAh5zFP3Jqut/WxmMC6+iz5Zx+cd7rJ957Mm2qd5Ilk3gwf3dbmZEamKfDfhuxzORT+8+U7OcaGZ7Djv51KtWgFg3g838ArNs/iY92y8d0sEufQ4yDl7iaJz8NMfwAFajYLEDaGbK1iBkX2nigyNA8vPw6bsBLEqVKSGk9xZ/8o1DHQNtzMAjP772RMzK7gb+9G8nsNDsz2n9p/uz61jGB0QEoaKHOPY4Ycf9TtrHSdi0Or9tHeiPdTuA2JQMEvFifZsBLCBsgb4rR/6bbzJOyV9UM/WCTMA7Bqk4Fc8s2ieUzmMet8PD1vtDTZEUtHH8sVa0Iyk4d60D6h5uqmBqPG7zSByLDItrl+CDeTYM1n/yOp29p4sCUXA8BA6ZUiVieHWaXhD5oWH4XSzJ+xemdt3BfBZv6EbrCwgi1vnWreMPTr2sb3bPg7kfXfKyDgHHmK1/sbamD9bj+js0FatRZvJ3Ixj6rM9aufrs31//Pvl3vG2wO3ToiuPzf+3dQg9dtijfXm+xV7+cvY9GWNmtD+ytwyZwZ8XFP6lAIDflFfwm2hvOzm+qtYHDoABeDx+Tw/5FzI5rMSbnMOyUMfjxlDvOKEeTi46u8abwgSP3d/jrW/ubzpsBJCf16L6tPf1me56s8yHSVu4gniUNXBtgT4/JzAC1GGHGG4Ag7emc7/Mw9DqKtP5vY/ensvPL99He6d6QQJJBZHCPeGg3SO1kK0BDE/Usj7bfqsbolOEKqESLd3GBl7keJFXkHurightoRw3DOP6GYVB7rYnE1zsV+eLLABo9QRbgGsdF+XuB7FwVwO5Vp4Bj40TboNynBcNuDQsKOfsG4CF5aVUYON/Eg3gahx7FwDXwBXsb2/eSN7kHRmf480Z7uPzjucbOpv0PegLaGDWsPwILNU717pNH4nreaLO5T3bOUxKw1CXdte5h4rfkEBgY5yoGyXtPdgYMBBlfLvxBD3SQe0Ypczo38fjyWmCC58/rI00O2eVwtFD//a+H24eZolwR7bDNdBgr1EeeHg/DcFTB9iCX/t76WB5BOoj+Lb308fIZWi4PdUwhvq77P3cX8m4LqI9+3iOy58fa5dr+OU697A/H9lLLgDdY5ik3Zvr3+9hez47qQHz/lxyTud6ibivov1KAsDPQvl/WcDgN9lkXWXgPGH9rS2K8RDLoLOsSAADoOmTzTYc+8zU4y8BSCPvf+Y9PLTc+k78uCV3aQW2c9DlwvS4kfEoyLsAdm1ze4tx6ehh8kIHKmMY1xar4ZryCWwlkrVehIilaQmoQdRVPtV/h0QNIf8b2BmI3r7Xfz1bKPl8Qfc+PiB6y7m5h/5JNhLvpPbpGaBkHkALwTvrfwJBy54N48YWSRM8tuQXu+45mO1A49x7dtGnZ13bwY+MU6nWAGDwhLdvtI1u9JRKMse4SzwELHJPAlRoBCZ0ft9R60Sf39/5eBVtOWmmYfdpHhObZyN947HkK5u3Y4KPJfx0CRERaj/n+NpGej6/mXsSh433OniPTfC9qB7h2C8yBt3gMatDiO28Dxul0J3fTzmTAUH3rDJDhho1+koTmddn7RVY5B23cWYGCfo7lqB9501Kf0qZw0vgzUTiDx1eKTOL7kB7fay8WP3ZdbA0Ai1pXULFcFnLiTofNu0z6au+Jvd3N5qkgNXZHUG5AWmDq871taJcyK5cVrO4HG+meACcy72cra9jP1+oSHye9iYnQNsWHgBLhlkEXAeg/si8bs9fjbKiwA8Y1mi9D0Crmzx8jsu99G3u/03trQHgE6h6ak/tqT21p/bUntpT+8vR3Gcf8tSe2lN7ak/tqT21p/bU/jK1JwD41J7aU3tqT+2pPbWn9mvWngDgU3tqT+2pPbWn9tSe2q9ZewKAT+2pPbWn9tSe2lN7ar9m7QkAPrWn9tSe2lN7ak/tqf2atScA+NSe2lN7ak/tqT21p/Zr1p4A4FN7ak/tqT21p/bUntqvWXsCgE/tqT21p/bUntpTe2q/Zu0JAD61p/bUntpTe2pP7an9mrX/PwLoTHHLjy6tAAAAAElFTkSuQmCC\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Data Analysis"
+      ],
+      "metadata": {
+        "id": "SkhS0nnSKEnh"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Count objects per category\n",
+        "category_counts = {}\n",
+        "for ann in annotations:\n",
+        "    category_id = ann[\"category_id\"]\n",
+        "    category_counts[category_id] = category_counts.get(category_id, 0) + 1\n",
+        "\n",
+        "df = pd.DataFrame(list(category_counts.items()), columns=[\"Category ID\", \"Count\"])\n",
+        "df = df.sort_values(by=\"Count\", ascending=False)\n",
+        "\n",
+        "print(df.head(10))  # Show top 10 categories\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "xSZJ-zbzKHvr",
+        "outputId": "ef73d74d-15bc-4776-a0f5-32036b78628b"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "    Category ID  Count\n",
+            "19            1  65165\n",
+            "13            3  10945\n",
+            "10           62   9703\n",
+            "64           84   6185\n",
+            "8            44   5780\n",
+            "44           47   4868\n",
+            "11           67   3759\n",
+            "48           51   3745\n",
+            "69           31   3019\n",
+            "30           10   2982\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Plotting the category distribution"
+      ],
+      "metadata": {
+        "id": "dhWca0n_KLGY"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.figure(figsize=(12, 6))\n",
+        "sns.barplot(x=df[\"Category ID\"], y=df[\"Count\"], palette=\"viridis\")\n",
+        "plt.xlabel(\"Category ID\")\n",
+        "plt.ylabel(\"Number of Objects\")\n",
+        "plt.title(\"Distribution of Object Categories\")\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 651
+        },
+        "id": "jYiJpr9gKQJt",
+        "outputId": "4ab4f659-1368-49da-9efc-ac5e1a12c94f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "<ipython-input-22-787d6d2df665>:2: FutureWarning: \n",
+            "\n",
+            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+            "\n",
+            "  sns.barplot(x=df[\"Category ID\"], y=df[\"Count\"], palette=\"viridis\")\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x600 with 1 Axes>"
+            ],
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAABAIAAAIjCAYAAACZALkcAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAhL9JREFUeJzs3Xd4FFX7//HPpmwSQhIIpBC6Ir0JKIQiipEAQQVRUREBERQDUhQBCyB2LCDSrKDYAEVpEkTqA0SaIMgDCIiiQICHklATSM7vD3+7391kN7vBJKD7fl1XLmXm3rP3zpmZPXPvFIsxxggAAAAAAPgEv8udAAAAAAAAKD4UAgAAAAAA8CEUAgAAAAAA8CEUAgAAAAAA8CEUAgAAAAAA8CEUAgAAAAAA8CEUAgAAAAAA8CEUAgAAAAAA8CEUAgAAAAAA8CEUAgAAV7TRo0fLYrEUy3vdeOONuvHGG+3/XrFihSwWi7788stief+ePXuqSpUqxfJel+r06dN66KGHFBsbK4vFokGDBhVq+xaLRf379/cYN336dFksFv3222+F+v74e3JvQwCAKxOFAABAsbEdvNn+goODFRcXp8TERE2YMEGnTp0qlPc5ePCgRo8erS1bthRKe4XpSs7NGy+99JKmT5+ufv36acaMGerevXu+8RcuXNCECRN03XXXKSwsTCVLltR1112nCRMm6MKFC8WUdcGtXbtWo0eP1smTJwv0uhUrVuiOO+5QbGysrFaroqOjdeutt2rOnDkFzuHs2bMaPXq0VqxYUeDXAgCQH4sxxlzuJAAAvmH69Onq1auXxowZo6pVq+rChQtKS0vTihUrtGTJElWqVEnz5s1T/fr17a+5ePGiLl68qODgYK/fZ+PGjbruuus0bdo09ezZ0+vXZWVlSZKsVqukvw7qbrrpJs2ePVt33nmn1+1cam4XLlxQTk6OgoKCCuW9ikKzZs0UEBCg1atXe4w9c+aMkpKStHLlSnXs2FHt2rWTn5+fUlJSNG/ePLVu3VoLFy5UaGio/TUWi0XJycmaOHFivm1nZ2frwoULCgoKKpIzRl5//XUNHTpU+/bt8/osjVGjRmnMmDG65pprdO+996py5co6duyYvv32W61YsUKffvqp7rvvPq9z+N///qeoqCiNGjVKo0ePvrQPUsxyb0MAgCtTwOVOAADge9q3b68mTZrY/z1ixAgtW7ZMHTt21G233aYdO3YoJCREkhQQEKCAgKL9ujp79qxKlChx2Q9eAgMDL+v7e+PIkSOqXbu2V7FDhgzRypUr9fbbbzud7t+vXz9NmjRJ/fv31xNPPKEpU6YUOA9/f3/5+/sX+HVF5csvv9SYMWN055136rPPPnPqy6FDh2rx4sVX9BkQf9eVsg0BALzDpQEAgCtCmzZt9Oyzz+r333/XJ598Yp/u6h4BS5YsUcuWLVWqVCmVLFlSNWrU0FNPPSXpr1/xr7vuOklSr1697JchTJ8+XdJf1zDXrVtXmzZt0g033KASJUrYX+vu+ubs7Gw99dRTio2NVWhoqG677Tb98ccfTjFVqlRxefaBY5uecnN1j4AzZ87o8ccfV8WKFRUUFKQaNWro9ddfV+4T+mzX1n/zzTeqW7eugoKCVKdOHaWkpLhe4LkcOXJEvXv3VkxMjIKDg9WgQQN99NFH9vm2+yXs27dPCxcutOfu7hr9P//8Ux988IHatGnj8pr/5ORk3XTTTXr//ff1559/5pn/6aefqkaNGgoODlbjxo21atUqp/nu7hGwaNEitWrVSqGhoQoLC1NSUpK2b9+ep/2dO3fq7rvvVlRUlEJCQlSjRg09/fTTkv5a54YOHSpJqlq1qsfPKknPPvusIiMj9eGHH7os6CQmJqpjx46S/vrVfOTIkWrcuLEiIiIUGhqqVq1aafny5fb43377TVFRUZKk5557zp6D45kBO3fu1J133qnIyEgFBwerSZMmmjdvXp733rp1q1q3bq2QkBBVqFBBL7zwgqZNm+byM02ePFl16tRRUFCQ4uLilJycnOfyiIJuQ5mZmRo1apSqVaumoKAgVaxYUU8++aQyMzOd4vLbrgEAhYszAgAAV4zu3bvrqaee0nfffac+ffq4jNm+fbs6duyo+vXra8yYMQoKCtKePXu0Zs0aSVKtWrU0ZswYjRw5Un379lWrVq0kSc2bN7e3cezYMbVv31733HOP7r//fsXExOSb14svviiLxaJhw4bpyJEjGj9+vBISErRlyxb7mQve8CY3R8YY3XbbbVq+fLl69+6thg0bavHixRo6dKgOHDigcePGOcWvXr1ac+bM0aOPPqqwsDBNmDBBXbp00f79+1WmTBm3eZ07d0433nij9uzZo/79+6tq1aqaPXu2evbsqZMnT2rgwIGqVauWZsyYocGDB6tChQp6/PHHJcl+sJrbokWLlJ2drQceeMDt+z7wwANavny5UlJS9NBDD9mnr1y5UjNnztRjjz2moKAgTZ48We3atdP69etVt25dt+3NmDFDPXr0UGJiol599VWdPXtWU6ZMUcuWLbV582Z7kWXr1q1q1aqVAgMD1bdvX1WpUkV79+7V/Pnz9eKLL+qOO+7QL7/8os8//1zjxo1T2bJl8/2su3fv1s6dO/Xggw8qLCzMbX42GRkZev/993XvvfeqT58+OnXqlD744AMlJiZq/fr1atiwoaKiojRlyhT169dPnTt31h133CFJ9stmtm/frhYtWqh8+fIaPny4QkNDNWvWLHXq1ElfffWVOnfuLEk6cOCAbrrpJlksFo0YMUKhoaF6//33XV5+Mnr0aD333HNKSEhQv379tGvXLk2ZMkUbNmzQmjVrnAoc3m5DOTk5uu2227R69Wr17dtXtWrV0rZt2zRu3Dj98ssv+uabb+yfJ7/tGgBQyAwAAMVk2rRpRpLZsGGD25iIiAhz7bXX2v89atQo4/h1NW7cOCPJHD161G0bGzZsMJLMtGnT8sxr3bq1kWSmTp3qcl7r1q3t/16+fLmRZMqXL28yMjLs02fNmmUkmbfeess+rXLlyqZHjx4e28wvtx49epjKlSvb//3NN98YSeaFF15wirvzzjuNxWIxe/bssU+TZKxWq9O0n376yUgyb7/9dp73cjR+/HgjyXzyySf2aVlZWSY+Pt6ULFnS6bNXrlzZJCUl5dueMcYMGjTISDKbN292G/Pjjz8aSWbIkCFOn0OS2bhxo33a77//boKDg03nzp3t02zr0r59+4wxxpw6dcqUKlXK9OnTx+k90tLSTEREhNP0G264wYSFhZnff//dKTYnJ8f+/6+99ppT+/mZO3eukWTGjRvnMdYYYy5evGgyMzOdpp04ccLExMSYBx980D7t6NGjRpIZNWpUnjZuvvlmU69ePXP+/Hmn/Js3b26uueYa+7QBAwYYi8Xi1A/Hjh0zkZGRTp/vyJEjxmq1mrZt25rs7Gx77MSJE40k8+GHH9qnFWQbmjFjhvHz8zP/+c9/nOKmTp1qJJk1a9YYY7zbrgEAhYdLAwAAV5SSJUvm+/SAUqVKSZLmzp2rnJycS3qPoKAg9erVy+v4Bx54wOmX3jvvvFPlypXTt99+e0nv761vv/1W/v7+euyxx5ymP/744zLGaNGiRU7TExISdPXVV9v/Xb9+fYWHh+vXX3/1+D6xsbG699577dMCAwP12GOP6fTp01q5cmWBc7f1YX6/kNvmZWRkOE2Pj49X48aN7f+uVKmSbr/9di1evFjZ2dku21qyZIlOnjype++9V//73//sf/7+/mratKn9tPujR49q1apVevDBB1WpUiWnNi71poO2/L05G0D66/4Gtmvpc3JydPz4cV28eFFNmjTRjz/+6PH1x48f17Jly3T33Xfr1KlT9s967NgxJSYmavfu3Tpw4IAkKSUlRfHx8WrYsKH99ZGRkerWrZtTm99//72ysrI0aNAg+fn93/CwT58+Cg8P18KFC53ivd2GZs+erVq1aqlmzZpO/dKmTRtJsvdLYWzXAADvUQgAAFxRTp8+ne8BVdeuXdWiRQs99NBDiomJ0T333KNZs2YV6OChfPnyBbqp2TXXXOP0b4vFomrVqhX5M+x///13xcXF5VketWrVss93lPvAVpJKly6tEydOeHyfa665xukAML/38YYt5/yKOu6KBbmXtyRVr15dZ8+e1dGjR122tXv3bkl/3WsiKirK6e+7777TkSNHJMleFMnvEoOCCg8Pl5T/Z83to48+Uv369RUcHKwyZcooKipKCxcuVHp6usfX7tmzR8YYPfvss3k+66hRoyTJ/nl///13VatWLU8buafZ+rhGjRpO061Wq6666qo864C329Du3bu1ffv2PHlWr17dKc/C2K4BAN7jHgEAgCvGn3/+qfT0dJcHLjYhISFatWqVli9froULFyolJUUzZ85UmzZt9N1333l1J/mCXNfvLXe/JmdnZxfb3e3dvY+5DE8KthURtm7d6vRrtKOtW7dKktdPIciP7YBxxowZio2NzTO/KJ88UbNmTUnStm3bvIr/5JNP1LNnT3Xq1ElDhw5VdHS0/P399fLLL2vv3r0eX2/7rE888YQSExNdxuS3DRUGb7ehnJwc1atXT2+++abL+RUrVrS393e3awCA9ygEAACuGDNmzJAktwc3Nn5+frr55pt18803680339RLL72kp59+WsuXL1dCQkKhP1fe9muzjTFGe/bssd+4Tfrrl/fcd1eX/vql9aqrrrL/uyC5Va5cWd9//71OnTrl9Kv5zp077fMLQ+XKlbV161bl5OQ4nRXwd96nffv28vf314wZM9zeMPDjjz9WQECA2rVr5zQ99/KWpF9++UUlSpRwe8M+2yUR0dHRSkhIcJuXrS9+/vnnfPMvSD9Vr15dNWrU0Ny5c/XWW2+pZMmS+cZ/+eWXuuqqqzRnzhyn97H9mu8pB9tnCAwMzPezSn/13Z49e/JMzz3N1se7du1yWl+zsrK0b98+j+/jztVXX62ffvpJN998s8dl6mm7BgAUHi4NAABcEZYtW6bnn39eVatWzXP9sqPjx4/nmWb7xdn2OLLQ0FBJcnlgfik+/vhjp9O+v/zySx06dEjt27e3T7v66qv1ww8/KCsryz5twYIFeR4zWJDcOnTooOzsbE2cONFp+rhx42SxWJze/+/o0KGD0tLSNHPmTPu0ixcv6u2331bJkiXVunXrArdZsWJF9erVS99//72mTJmSZ/7UqVO1bNky9e7dWxUqVHCal5qa6nSt/B9//KG5c+eqbdu2bn8ZTkxMVHh4uF566SVduHAhz3zbJQVRUVG64YYb9OGHH2r//v1OMY5nThR0HXruued07NgxPfTQQ7p48WKe+d99950WLFgg6f/O3HB8v3Xr1ik1NdXpNSVKlHCZQ3R0tG688Ua98847OnToUJ73crx8IjExUampqdqyZYt92vHjx/Xpp586vSYhIUFWq1UTJkxwyuuDDz5Qenq6kpKS8vv4bt199906cOCA3nvvvTzzzp07pzNnzthzyi33dg0AKDycEQAAKHaLFi3Szp07dfHiRR0+fFjLli3TkiVLVLlyZc2bN0/BwcFuXztmzBitWrVKSUlJqly5so4cOaLJkyerQoUKatmypaS/DspLlSqlqVOnKiwsTKGhoWratKmqVq16SflGRkaqZcuW6tWrlw4fPqzx48erWrVqTo84fOihh/Tll1+qXbt2uvvuu7V371598sknTjfvK2hut956q2666SY9/fTT+u2339SgQQN99913mjt3rgYNGpSn7UvVt29fvfPOO+rZs6c2bdqkKlWq6Msvv9SaNWs0fvx4r2+Cl9u4ceO0c+dOPfroo0pJSbH/8r948WLNnTtXrVu31htvvJHndXXr1lViYqLT4wOlvw623QkPD9eUKVPUvXt3NWrUSPfcc4+ioqK0f/9+LVy4UC1atLAXVCZMmKCWLVuqUaNG6tu3r6pWrarffvtNCxcutB8w225W+PTTT+uee+5RYGCgbr31VnuBILeuXbtq27ZtevHFF7V582bde++9qly5so4dO6aUlBQtXbpUn332mSSpY8eOmjNnjjp37qykpCTt27dPU6dOVe3atXX69Gl7myEhIapdu7Zmzpyp6tWrKzIyUnXr1lXdunU1adIktWzZUvXq1VOfPn101VVX6fDhw0pNTdWff/6pn376SZL05JNP6pNPPtEtt9yiAQMG2B8fWKlSJR0/ftz+K31UVJRGjBih5557Tu3atdNtt92mXbt2afLkybruuut0//33e93vjrp3765Zs2bpkUce0fLly9WiRQtlZ2dr586dmjVrlhYvXqwmTZp4tV0DAArRZXxiAQDAx9ge+Wb7s1qtJjY21txyyy3mrbfecnpMnU3uxwcuXbrU3H777SYuLs5YrVYTFxdn7r33XvPLL784vW7u3Lmmdu3aJiAgwOlxfa1btzZ16tRxmZ+7xwd+/vnnZsSIESY6OtqEhISYpKSkPI+eM8aYN954w5QvX94EBQWZFi1amI0bN+ZpM7/ccj8+0Ji/Hos3ePBgExcXZwIDA80111xjXnvtNadH3Rnz12P3kpOT8+Tk7rGGuR0+fNj06tXLlC1b1litVlOvXj2Xjzj09vGBNpmZmWbcuHGmcePGJjQ01JQoUcI0atTIjB8/3mRlZeWJt32OTz75xFxzzTUmKCjIXHvttWb58uVOcbkfH2izfPlyk5iYaCIiIkxwcLC5+uqrTc+ePZ0eR2iMMT///LPp3LmzKVWqlAkODjY1atQwzz77rFPM888/b8qXL2/8/Py8fpSgbf2Mjo42AQEBJioqytx6661m7ty59picnBzz0ksvmcqVK9s/34IFC1z2/9q1a03jxo2N1WrN8yjBvXv3mgceeMDExsaawMBAU758edOxY0fz5ZdfOrWxefNm06pVKxMUFGQqVKhgXn75ZTNhwgQjyaSlpTnFTpw40dSsWdMEBgaamJgY069fP3PixAmnmIJsQ8b89SjKV1991dSpU8cEBQWZ0qVLm8aNG5vnnnvOpKenOy03T9s1AKBwWIy5DHcQAgAA+Bs++OADPfTQQ/rjjz/yXFoAzwYNGqR33nlHp0+f5kZ8AOCDuEcAAAD4xzl06JAsFosiIyMvdypXvHPnzjn9+9ixY5oxY4ZatmxJEQAAfBT3CAAAAP8Yhw8f1pdffqmpU6cqPj7efkM9uBcfH68bb7xRtWrV0uHDh/XBBx8oIyNDzz777OVODQBwmVAIAAAA/xg7duzQ0KFDdf3117u8Ez3y6tChg7788ku9++67slgsatSokT744APdcMMNlzs1AMBlwj0CAAAAAADwIdwjAAAAAAAAH0IhAAAAAAAAH8I9AgpJTk6ODh48qLCwMFkslsudDgAAAADgX84Yo1OnTikuLk5+ft7/zk8hoJAcPHhQFStWvNxpAAAAAAB8zB9//KEKFSp4HU8hoJCEhYVJ+qsDwsPDL3M2AAAAAIB/u4yMDFWsWNF+POotCgGFxHY5QHh4OIUAAAAAAECxKejl6dwsEAAAAAAAH0IhAAAAAAAAH0IhAAAAAAAAH0IhAAAAAAAAH0IhAAAAAAAAH0IhAAAAAAAAH0IhAAAAAAAAH0IhAAAAAAAAH0IhAAAAAAAAH0IhAAAAAAAAH0IhAAAAAAAAH0IhAAAAAAAAH0IhAAAAAAAAH0IhAAAAAAAAH0IhAAAAAAAAH0IhAAAAAAAAH0IhAAAAAAAAH0IhAAAAAAAAH0IhAAAAAAAAHxJwuRP4t0mqdI8CLIEe45afmFsM2QAAAAAA4IwzAgAAAAAA8CEUAgAAAAAA8CEUAgAAAAAA8CEUAgAAAAAA8CEUAgAAAAAA8CEUAgAAAAAA8CEUAgAAAAAA8CEUAgAAAAAA8CEUAgAAAAAA8CEUAgAAAAAA8CEUAgAAAAAA8CEUAgAAAAAA8CEUAgAAAAAA8CEUAgAAAAAA8CEUAgAAAAAA8CEUAgAAAAAA8CEUAgAAAAAA8CEUAgAAAAAA8CEUAgAAAAAA8CEUAgAAAAAA8CEUAgAAAAAA8CEUAgAAAAAA8CEUAgAAAAAA8CEUAgAAAAAA8CEUAgAAAAAA8CEUAgAAAAAA8CEUAgAAAAAA8CEUAgAAAAAA8CEUAgAAAAAA8CEUAgAAAAAA8CGXvRBw4MAB3X///SpTpoxCQkJUr149bdy40T7fGKORI0eqXLlyCgkJUUJCgnbv3u3UxvHjx9WtWzeFh4erVKlS6t27t06fPu0Us3XrVrVq1UrBwcGqWLGixo4dmyeX2bNnq2bNmgoODla9evX07bffFs2HBgAAAADgMrmshYATJ06oRYsWCgwM1KJFi/Tf//5Xb7zxhkqXLm2PGTt2rCZMmKCpU6dq3bp1Cg0NVWJios6fP2+P6datm7Zv364lS5ZowYIFWrVqlfr27Wufn5GRobZt26py5cratGmTXnvtNY0ePVrvvvuuPWbt2rW699571bt3b23evFmdOnVSp06d9PPPPxfPwgAAAAAAoBhYjDHmcr358OHDtWbNGv3nP/9xOd8Yo7i4OD3++ON64oknJEnp6emKiYnR9OnTdc8992jHjh2qXbu2NmzYoCZNmkiSUlJS1KFDB/3555+Ki4vTlClT9PTTTystLU1Wq9X+3t9884127twpSeratavOnDmjBQsW2N+/WbNmatiwoaZOnerxs2RkZCgiIkItI9orwBLoMX75ibkeYwAAAAAAcMd2HJqenq7w8HCvX3dZzwiYN2+emjRporvuukvR0dG69tpr9d5779nn79u3T2lpaUpISLBPi4iIUNOmTZWamipJSk1NValSpexFAElKSEiQn5+f1q1bZ4+54YYb7EUASUpMTNSuXbt04sQJe4zj+9hibO+TW2ZmpjIyMpz+AAAAAAC40l3WQsCvv/6qKVOm6JprrtHixYvVr18/PfbYY/roo48kSWlpaZKkmJgYp9fFxMTY56WlpSk6OtppfkBAgCIjI51iXLXh+B7uYmzzc3v55ZcVERFh/6tYsWKBPz8AAAAAAMXtshYCcnJy1KhRI7300ku69tpr1bdvX/Xp08erU/EvtxEjRig9Pd3+98cff1zulAAAAAAA8OiyFgLKlSun2rVrO02rVauW9u/fL0mKjY2VJB0+fNgp5vDhw/Z5sbGxOnLkiNP8ixcv6vjx404xrtpwfA93Mbb5uQUFBSk8PNzpDwAAAACAK91lLQS0aNFCu3btcpr2yy+/qHLlypKkqlWrKjY2VkuXLrXPz8jI0Lp16xQfHy9Jio+P18mTJ7Vp0yZ7zLJly5STk6OmTZvaY1atWqULFy7YY5YsWaIaNWrYn1AQHx/v9D62GNv7AAAAAADwb3BZCwGDBw/WDz/8oJdeekl79uzRZ599pnfffVfJycmSJIvFokGDBumFF17QvHnztG3bNj3wwAOKi4tTp06dJP11BkG7du3Up08frV+/XmvWrFH//v11zz33KC4uTpJ03333yWq1qnfv3tq+fbtmzpypt956S0OGDLHnMnDgQKWkpOiNN97Qzp07NXr0aG3cuFH9+/cv9uUCAAAAAEBRuayPD5SkBQsWaMSIEdq9e7eqVq2qIUOGqE+fPvb5xhiNGjVK7777rk6ePKmWLVtq8uTJql69uj3m+PHj6t+/v+bPny8/Pz916dJFEyZMUMmSJe0xW7duVXJysjZs2KCyZctqwIABGjZsmFMus2fP1jPPPKPffvtN11xzjcaOHasOHTp49Tl4fCAAAAAAoDhd6uMDL3sh4N+CQgAAAAAAoDhdaiHgsl4aAAAAAAAAiheFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfMhlLQSMHj1aFovF6a9mzZr2+efPn1dycrLKlCmjkiVLqkuXLjp8+LBTG/v371dSUpJKlCih6OhoDR06VBcvXnSKWbFihRo1aqSgoCBVq1ZN06dPz5PLpEmTVKVKFQUHB6tp06Zav359kXxmAAAAAAAup8t+RkCdOnV06NAh+9/q1avt8wYPHqz58+dr9uzZWrlypQ4ePKg77rjDPj87O1tJSUnKysrS2rVr9dFHH2n69OkaOXKkPWbfvn1KSkrSTTfdpC1btmjQoEF66KGHtHjxYnvMzJkzNWTIEI0aNUo//vijGjRooMTERB05cqR4FgIAAAAAAMXEYowxl+vNR48erW+++UZbtmzJMy89PV1RUVH67LPPdOedd0qSdu7cqVq1aik1NVXNmjXTokWL1LFjRx08eFAxMTGSpKlTp2rYsGE6evSorFarhg0bpoULF+rnn3+2t33PPffo5MmTSklJkSQ1bdpU1113nSZOnChJysnJUcWKFTVgwAANHz7cq8+SkZGhiIgItYxorwBLoMf45SfmetUuAAAAAACu2I5D09PTFR4e7vXrLvsZAbt371ZcXJyuuuoqdevWTfv375ckbdq0SRcuXFBCQoI9tmbNmqpUqZJSU1MlSampqapXr569CCBJiYmJysjI0Pbt2+0xjm3YYmxtZGVladOmTU4xfn5+SkhIsMe4kpmZqYyMDKc/AAAAAACudJe1ENC0aVNNnz5dKSkpmjJlivbt26dWrVrp1KlTSktLk9VqValSpZxeExMTo7S0NElSWlqaUxHANt82L7+YjIwMnTt3Tv/73/+UnZ3tMsbWhisvv/yyIiIi7H8VK1a8pGUAAAAAAEBxCricb96+fXv7/9evX19NmzZV5cqVNWvWLIWEhFzGzDwbMWKEhgwZYv93RkYGxQAAAAAAwBXvsl8a4KhUqVKqXr269uzZo9jYWGVlZenkyZNOMYcPH1ZsbKwkKTY2Ns9TBGz/9hQTHh6ukJAQlS1bVv7+/i5jbG24EhQUpPDwcKc/AAAAAACudFdUIeD06dPau3evypUrp8aNGyswMFBLly61z9+1a5f279+v+Ph4SVJ8fLy2bdvmdHf/JUuWKDw8XLVr17bHOLZhi7G1YbVa1bhxY6eYnJwcLV261B4DAAAAAMC/xWUtBDzxxBNauXKlfvvtN61du1adO3eWv7+/7r33XkVERKh3794aMmSIli9frk2bNqlXr16Kj49Xs2bNJElt27ZV7dq11b17d/30009avHixnnnmGSUnJysoKEiS9Mgjj+jXX3/Vk08+qZ07d2ry5MmaNWuWBg8ebM9jyJAheu+99/TRRx9px44d6tevn86cOaNevXpdluUCAAAAAEBRuaz3CPjzzz9177336tixY4qKilLLli31ww8/KCoqSpI0btw4+fn5qUuXLsrMzFRiYqImT55sf72/v78WLFigfv36KT4+XqGhoerRo4fGjBljj6lataoWLlyowYMH66233lKFChX0/vvvKzEx0R7TtWtXHT16VCNHjlRaWpoaNmyolJSUPDcQBAAAAADgn85ijDGXO4l/A9vzG1tGtFeAJdBj/PITc4shKwAAAADAv5XtODQ9Pb1A9627ou4RAAAAAAAAihaFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfEiBCwEpKSlavXq1/d+TJk1Sw4YNdd999+nEiROFmhwAAAAAAChcBS4EDB06VBkZGZKkbdu26fHHH1eHDh20b98+DRkypNATBAAAAAAAhSegoC/Yt2+fateuLUn66quv1LFjR7300kv68ccf1aFDh0JPEAAAAAAAFJ4CnxFgtVp19uxZSdL333+vtm3bSpIiIyPtZwoAAAAAAIArU4HPCGjZsqWGDBmiFi1aaP369Zo5c6Yk6ZdfflGFChUKPUEAAAAAAFB4CnxGwMSJExUQEKAvv/xSU6ZMUfny5SVJixYtUrt27Qo9QQAAAAAAUHgsxhhzuZP4N8jIyFBERIRaRrRXgCXQY/zyE3OLISsAAAAAwL+V7Tg0PT1d4eHhXr+uwGcE+Pv768iRI3mmHzt2TP7+/gVtDgAAAAAAFKMCFwLcnUCQmZkpq9X6txMCAAAAAABFx+ubBU6YMEGSZLFY9P7776tkyZL2ednZ2Vq1apVq1qxZ+BkCAAAAAIBC43UhYNy4cZL+OiNg6tSpTpcBWK1WValSRVOnTi38DAEAAAAAQKHxuhCwb98+SdJNN92kOXPmqHTp0kWWFAAAAAAAKBpeFwJsli9fXhR5AAAAAACAYlDgmwV26dJFr776ap7pY8eO1V133VUoSQEAAAAAgKJR4ELAqlWr1KFDhzzT27dvr1WrVhVKUgAAAAAAoGgUuBBw+vRpl48JDAwMVEZGRqEkBQAAAAAAikaBCwH16tXTzJkz80z/4osvVLt27UJJCgAAAAAAFI0C3yzw2Wef1R133KG9e/eqTZs2kqSlS5fq888/1+zZsws9QQAAAAAAUHgKXAi49dZb9c033+ill17Sl19+qZCQENWvX1/ff/+9WrduXRQ5AgAAAACAQlLgQoAkJSUlKSkpqbBzAQAAAAAARazA9wiQpJMnT+r999/XU089pePHj0uSfvzxRx04cOCSE3nllVdksVg0aNAg+7Tz588rOTlZZcqUUcmSJdWlSxcdPnzY6XX79+9XUlKSSpQooejoaA0dOlQXL150ilmxYoUaNWqkoKAgVatWTdOnT8/z/pMmTVKVKlUUHByspk2bav369Zf8WQAAAAAAuFIVuBCwdetWVa9eXa+++qpee+01nTx5UpI0Z84cjRgx4pKS2LBhg9555x3Vr1/fafrgwYM1f/58zZ49WytXrtTBgwd1xx132OdnZ2crKSlJWVlZWrt2rT766CNNnz5dI0eOtMfs27dPSUlJuummm7RlyxYNGjRIDz30kBYvXmyPmTlzpoYMGaJRo0bpxx9/VIMGDZSYmKgjR45c0ucBAAAAAOBKVeBCwJAhQ9SzZ0/t3r1bwcHB9ukdOnTQqlWrCpzA6dOn1a1bN7333nsqXbq0fXp6ero++OADvfnmm2rTpo0aN26sadOmae3atfrhhx8kSd99953++9//6pNPPlHDhg3Vvn17Pf/885o0aZKysrIkSVOnTlXVqlX1xhtvqFatWurfv7/uvPNOjRs3zv5eb775pvr06aNevXqpdu3amjp1qkqUKKEPP/ywwJ8HAAAAAIArWYELARs2bNDDDz+cZ3r58uWVlpZW4ASSk5OVlJSkhIQEp+mbNm3ShQsXnKbXrFlTlSpVUmpqqiQpNTVV9erVU0xMjD0mMTFRGRkZ2r59uz0md9uJiYn2NrKysrRp0yanGD8/PyUkJNhjXMnMzFRGRobTHwAAAAAAV7oC3ywwKCjI5UHvL7/8oqioqAK19cUXX+jHH3/Uhg0b8sxLS0uT1WpVqVKlnKbHxMTYCw5paWlORQDbfNu8/GIyMjJ07tw5nThxQtnZ2S5jdu7c6Tb3l19+Wc8995x3HxQAAAAAgCtEgc8IuO222zRmzBhduHBBkmSxWLR//34NGzZMXbp08bqdP/74QwMHDtSnn37qdInBP8WIESOUnp5u//vjjz8ud0oAAAAAAHhU4ELAG2+8odOnTys6Olrnzp1T69atVa1aNYWFhenFF1/0up1NmzbpyJEjatSokQICAhQQEKCVK1dqwoQJCggIUExMjLKysuw3I7Q5fPiwYmNjJUmxsbF5niJg+7enmPDwcIWEhKhs2bLy9/d3GWNrw5WgoCCFh4c7/QEAAAAAcKUrcCEgIiJCS5Ys0fz58zVhwgT1799f3377rVauXKnQ0FCv27n55pu1bds2bdmyxf7XpEkTdevWzf7/gYGBWrp0qf01u3bt0v79+xUfHy9Jio+P17Zt25zu7r9kyRKFh4erdu3a9hjHNmwxtjasVqsaN27sFJOTk6OlS5faYwAAAAAA+Lco8D0CbFq2bKmWLVte8huHhYWpbt26TtNCQ0NVpkwZ+/TevXtryJAhioyMVHh4uAYMGKD4+Hg1a9ZMktS2bVvVrl1b3bt319ixY5WWlqZnnnlGycnJCgoKkiQ98sgjmjhxop588kk9+OCDWrZsmWbNmqWFCxfa33fIkCHq0aOHmjRpouuvv17jx4/XmTNn1KtXr0v+fAAAAAAAXIm8KgRMmDBBffv2VXBwsCZMmJBvbMmSJVWnTh01bdr0byc3btw4+fn5qUuXLsrMzFRiYqImT55sn+/v768FCxaoX79+io+PV2hoqHr06KExY8bYY6pWraqFCxdq8ODBeuutt1ShQgW9//77SkxMtMd07dpVR48e1ciRI5WWlqaGDRsqJSUlzw0EAQAAAAD4p7MYY4ynoKpVq2rjxo0qU6aMqlatmm9sZmamjhw5osGDB+u1114rtESvdBkZGYqIiFDLiPYKsAR6jF9+Ym4xZAUAAAAA+LeyHYemp6cX6L51Xp0RsG/fPpf/786SJUt03333+VQhAAAAAACAf4IC3yzQGy1bttQzzzxTFE0DAAAAAIC/4ZIKAUuXLlXHjh119dVX6+qrr1bHjh31/fff2+eHhIRo4MCBhZYkAAAAAAAoHAUuBEyePFnt2rVTWFiYBg4cqIEDByo8PFwdOnTQpEmTiiJHAAAAAABQSLy6WaCjChUqaPjw4erfv7/T9EmTJumll17SgQMHCjXBfwpuFggAAAAAKE6XerPAAp8RcPLkSbVr1y7P9LZt2yo9Pb2gzQEAAAAAgGJU4ELAbbfdpq+//jrP9Llz56pjx46FkhQAAAAAACgaXj0+cMKECfb/r127tl588UWtWLFC8fHxkqQffvhBa9as0eOPP140WQIAAAAAgELh1T0Cqlat6l1jFot+/fXXv53UPxH3CAAAAAAAFKdLvUeAV2cE7Nu375ITAwAAAAAAV44C3yPA5n//+5/+97//FWYuAAAAAACgiBWoEHDy5EklJyerbNmyiomJUUxMjMqWLav+/fvr5MmTRZQiAAAAAAAoLF5dGiBJx48fV3x8vA4cOKBu3bqpVq1akqT//ve/mj59upYuXaq1a9eqdOnSRZYsAAAAAAD4e7wuBIwZM0ZWq1V79+5VTExMnnlt27bVmDFjNG7cuEJPEgAAAAAAFA6vLw345ptv9Prrr+cpAkhSbGysxo4dq6+//rpQkwMAAAAAAIXL60LAoUOHVKdOHbfz69atq7S0tEJJCgAAAAAAFA2vCwFly5bVb7/95nb+vn37FBkZWRg5AQAAAACAIuJ1ISAxMVFPP/20srKy8szLzMzUs88+q3bt2hVqcgAAAAAAoHAV6GaBTZo00TXXXKPk5GTVrFlTxhjt2LFDkydPVmZmpmbMmFGUuQIAAAAAgL/J60JAhQoVlJqaqkcffVQjRoyQMUaSZLFYdMstt2jixImqWLFikSUKAAAAAAD+Pq8LAZJUtWpVLVq0SCdOnNDu3bslSdWqVePeAAAAAAAA/EMUqBBgU7p0aV1//fWFnQsAAAAAAChiXt8sEAAAAAAA/PNRCAAAAAAAwIdQCAAAAAAAwId4VQho1KiRTpw4IemvxwiePXu2SJMCAAAAAABFw6tCwI4dO3TmzBlJ0nPPPafTp08XaVIAAAAAAKBoePXUgIYNG6pXr15q2bKljDF6/fXXVbJkSZexI0eOLNQEAQAAAABA4fGqEDB9+nSNGjVKCxYskMVi0aJFixQQkPelFouFQgAAAAAAAFcwrwoBNWrU0BdffCFJ8vPz09KlSxUdHV2kiQEAAAAAgMLnVSHAUU5OTlHkAQAAAAAAikGBCwGStHfvXo0fP147duyQJNWuXVsDBw7U1VdfXajJAQAAAACAwuXVUwMcLV68WLVr19b69etVv3591a9fX+vWrVOdOnW0ZMmSosgRAAAAAAAUkgKfETB8+HANHjxYr7zySp7pw4YN0y233FJoyQEAAAAAgMJV4DMCduzYod69e+eZ/uCDD+q///1voSQFAAAAAACKRoELAVFRUdqyZUue6Vu2bOFJAgAAAAAAXOEKfGlAnz591LdvX/36669q3ry5JGnNmjV69dVXNWTIkEJPEAAAAAAAFJ4CFwKeffZZhYWF6Y033tCIESMkSXFxcRo9erQee+yxQk8QAAAAAAAUHosxxlzqi0+dOiVJCgsLK7SE/qkyMjIUERGhlhHtFWAJ9Bi//MTcYsgKAAAAAPBvZTsOTU9PV3h4uNevK/AZAY4oAAAAAAAA8M9S4JsFAgAAAACAfy4KAQAAAAAA+BAKAQAAAAAA+JACFQIuXLigm2++Wbt37y6qfAAAAAAAQBEqUCEgMDBQW7duLapcAAAAAABAESvwpQH333+/Pvjgg6LIBQAAAAAAFLECPz7w4sWL+vDDD/X999+rcePGCg0NdZr/5ptvFlpyAAAAAACgcBW4EPDzzz+rUaNGkqRffvnFaZ7FYimcrAAAAAAAQJEocCFg+fLlRZEHAAAAAAAoBpf8+MA9e/Zo8eLFOnfunCTJGFNoSQEAAAAAgKJR4ELAsWPHdPPNN6t69erq0KGDDh06JEnq3bu3Hn/88UJPEAAAAAAAFJ4CFwIGDx6swMBA7d+/XyVKlLBP79q1q1JSUgo1OQAAAAAAULgKfI+A7777TosXL1aFChWcpl9zzTX6/fffCy0xAAAAAABQ+Ap8RsCZM2eczgSwOX78uIKCggolKQAAAAAAUDQKXAho1aqVPv74Y/u/LRaLcnJyNHbsWN10002FmhwAAAAAAChcBb40YOzYsbr55pu1ceNGZWVl6cknn9T27dt1/PhxrVmzpihyBAAAAAAAhaTAZwTUrVtXv/zyi1q2bKnbb79dZ86c0R133KHNmzfr6quvLlBbU6ZMUf369RUeHq7w8HDFx8dr0aJF9vnnz59XcnKyypQpo5IlS6pLly46fPiwUxv79+9XUlKSSpQooejoaA0dOlQXL150ilmxYoUaNWqkoKAgVatWTdOnT8+Ty6RJk1SlShUFBweradOmWr9+fYE+CwAAAAAA/wQFPiNAkiIiIvT000//7TevUKGCXnnlFV1zzTUyxuijjz7S7bffrs2bN6tOnToaPHiwFi5cqNmzZysiIkL9+/fXHXfcYT/zIDs7W0lJSYqNjdXatWt16NAhPfDAAwoMDNRLL70kSdq3b5+SkpL0yCOP6NNPP9XSpUv10EMPqVy5ckpMTJQkzZw5U0OGDNHUqVPVtGlTjR8/XomJidq1a5eio6P/9ucEAAAAAOBKYTHGmIK+6MSJE/rggw+0Y8cOSVLt2rXVq1cvRUZG/u2EIiMj9dprr+nOO+9UVFSUPvvsM915552SpJ07d6pWrVpKTU1Vs2bNtGjRInXs2FEHDx5UTEyMJGnq1KkaNmyYjh49KqvVqmHDhmnhwoX6+eef7e9xzz336OTJk/bHHTZt2lTXXXedJk6cKEnKyclRxYoVNWDAAA0fPtyrvDMyMhQREaGWEe0VYAn0GL/8xNwCLRcAAAAAABzZjkPT09MVHh7u9esKfGnAqlWrVKVKFU2YMEEnTpzQiRMnNGHCBFWtWlWrVq0qaHN22dnZ+uKLL3TmzBnFx8dr06ZNunDhghISEuwxNWvWVKVKlZSamipJSk1NVb169exFAElKTExURkaGtm/fbo9xbMMWY2sjKytLmzZtcorx8/NTQkKCPcaVzMxMZWRkOP0BAAAAAHClK/ClAcnJyerataumTJkif39/SX8dxD/66KNKTk7Wtm3bCtTetm3bFB8fr/Pnz6tkyZL6+uuvVbt2bW3ZskVWq1WlSpVyio+JiVFaWpokKS0tzakIYJtvm5dfTEZGhs6dO6cTJ04oOzvbZczOnTvd5v3yyy/rueeeK9BnBQAAAADgcivwGQF79uzR448/bi8CSJK/v7+GDBmiPXv2FDiBGjVqaMuWLVq3bp369eunHj166L///W+B2yluI0aMUHp6uv3vjz/+uNwpAQAAAADgUYHPCGjUqJF27NihGjVqOE3fsWOHGjRoUOAErFarqlWrJklq3LixNmzYoLfeektdu3ZVVlaWTp486XRWwOHDhxUbGytJio2NzXN3f9tTBRxjcj9p4PDhwwoPD1dISIj8/f3l7+/vMsbWhitBQUEKCgoq8OcFAAAAAOBy8uqMgK1bt9r/HnvsMQ0cOFCvv/66Vq9erdWrV+v111/X4MGDNXjw4L+dUE5OjjIzM9W4cWMFBgZq6dKl9nm7du3S/v37FR8fL0mKj4/Xtm3bdOTIEXvMkiVLFB4ertq1a9tjHNuwxdjasFqtaty4sVNMTk6Oli5dao8BAAAAAODfwqunBvj5+clischTqMViUXZ2ttdvPmLECLVv316VKlXSqVOn9Nlnn+nVV1/V4sWLdcstt6hfv3769ttvNX36dIWHh2vAgAGSpLVr10r6694EDRs2VFxcnMaOHau0tDR1795dDz30kNPjA+vWravk5GQ9+OCDWrZsmR577DEtXLjQ6fGBPXr00DvvvKPrr79e48eP16xZs7Rz58489w5wh6cGAAAAAACK06U+NcCrSwP27dt3yYnl58iRI3rggQd06NAhRUREqH79+vYigCSNGzdOfn5+6tKlizIzM5WYmKjJkyfbX+/v768FCxaoX79+io+PV2hoqHr06KExY8bYY6pWraqFCxdq8ODBeuutt1ShQgW9//779iKAJHXt2lVHjx7VyJEjlZaWpoYNGyolJcXrIgAAAAAAAP8UXp0RAM84IwAAAAAAUJyK9IyA3A4ePKjVq1fryJEjysnJcZr32GOPXUqTAAAAAACgGBS4EDB9+nQ9/PDDslqtKlOmjCwWi32exWKhEAAAAAAAwBWswIWAZ599ViNHjtSIESPk5+fVQwcAAAAAAMAVosBH8mfPntU999xDEQAAAAAAgH+gAh/N9+7dW7Nnzy6KXAAAAAAAQBEr8KUBL7/8sjp27KiUlBTVq1dPgYHOd8h/8803Cy05AAAAAABQuC6pELB48WLVqFFDkvLcLBAAAAAAAFy5ClwIeOONN/Thhx+qZ8+eRZAOAAAAAAAoSgW+R0BQUJBatGhRFLkAAAAAAIAiVuBCwMCBA/X2228XRS4AAAAAAKCIFfjSgPXr12vZsmVasGCB6tSpk+dmgXPmzCm05AAAAAAAQOEqcCGgVKlSuuOOO4oiFwAAAAAAUMQKXAiYNm1aUeQBAAAAAACKQYHvEQAAAAAAAP65CnxGQNWqVWWxWNzO//XXX/9WQgAAAAAAoOgUuBAwaNAgp39fuHBBmzdvVkpKioYOHVpYeQEAAAAAgCJQ4ELAwIEDXU6fNGmSNm7c+LcTAgAAAAAARafQ7hHQvn17ffXVV4XVHAAAAAAAKAKFVgj48ssvFRkZWVjNAQAAAACAIlDgSwOuvfZap5sFGmOUlpamo0ePavLkyYWaHAAAAAAAKFwFLgR06tTJ6d9+fn6KiorSjTfeqJo1axZWXgAAAAAAoAgUuBAwatSoosgDAAAAAAAUg0K7RwAAAAAAALjyeX1GgJ+fn9O9AVyxWCy6ePHi304KAAAAAAAUDa8LAV9//bXbeampqZowYYJycnIKJSkAAAAAAFA0vC4E3H777Xmm7dq1S8OHD9f8+fPVrVs3jRkzplCTAwAAAAAAheuS7hFw8OBB9enTR/Xq1dPFixe1ZcsWffTRR6pcuXJh5wcAAAAAAApRgQoB6enpGjZsmKpVq6bt27dr6dKlmj9/vurWrVtU+QEAAAAAgELk9aUBY8eO1auvvqrY2Fh9/vnnLi8VAAAAAAAAVzaLMcZ4E+jn56eQkBAlJCTI39/fbdycOXMKLbl/koyMDEVERKhlRHsFWAI9xi8/MbcYsgIAAAAA/FvZjkPT09MVHh7u9eu8PiPggQce8Pj4QAAAAAAAcGXzuhAwffr0IkwDAAAAAAAUh0t6agAAAAAAAPhnohAAAAAAAIAPoRAAAAAAAIAPoRAAAAAAAIAPoRAAAAAAAIAPoRAAAAAAAIAPoRAAAAAAAIAPoRAAAAAAAIAPoRAAAAAAAIAPoRAAAAAAAIAPoRAAAAAAAIAPoRAAAAAAAIAPoRAAAAAAAIAPoRAAAAAAAIAPoRAAAAAAAIAPoRAAAAAAAIAPoRAAAAAAAIAPoRAAAAAAAIAPoRAAAAAAAIAPoRAAAAAAAIAPoRAAAAAAAIAPoRAAAAAAAIAPoRAAAAAAAIAPoRAAAAAAAIAPoRAAAAAAAIAPuayFgJdfflnXXXedwsLCFB0drU6dOmnXrl1OMefPn1dycrLKlCmjkiVLqkuXLjp8+LBTzP79+5WUlKQSJUooOjpaQ4cO1cWLF51iVqxYoUaNGikoKEjVqlXT9OnT8+QzadIkValSRcHBwWratKnWr19f6J8ZAAAAAIDL6bIWAlauXKnk5GT98MMPWrJkiS5cuKC2bdvqzJkz9pjBgwdr/vz5mj17tlauXKmDBw/qjjvusM/Pzs5WUlKSsrKytHbtWn300UeaPn26Ro4caY/Zt2+fkpKSdNNNN2nLli0aNGiQHnroIS1evNgeM3PmTA0ZMkSjRo3Sjz/+qAYNGigxMVFHjhwpnoUBAAAAAEAxsBhjzOVOwubo0aOKjo7WypUrdcMNNyg9PV1RUVH67LPPdOedd0qSdu7cqVq1aik1NVXNmjXTokWL1LFjRx08eFAxMTGSpKlTp2rYsGE6evSorFarhg0bpoULF+rnn3+2v9c999yjkydPKiUlRZLUtGlTXXfddZo4caIkKScnRxUrVtSAAQM0fPhwj7lnZGQoIiJCLSPaK8AS6DF++Ym5BV4+AAAAAADY2I5D09PTFR4e7vXrrqh7BKSnp0uSIiMjJUmbNm3ShQsXlJCQYI+pWbOmKlWqpNTUVElSamqq6tWrZy8CSFJiYqIyMjK0fft2e4xjG7YYWxtZWVnatGmTU4yfn58SEhLsMbllZmYqIyPD6Q8AAAAAgCvdFVMIyMnJ0aBBg9SiRQvVrVtXkpSWliar1apSpUo5xcbExCgtLc0e41gEsM23zcsvJiMjQ+fOndP//vc/ZWdnu4yxtZHbyy+/rIiICPtfxYoVL+2DAwAAAABQjK6YQkBycrJ+/vlnffHFF5c7Fa+MGDFC6enp9r8//vjjcqcEAAAAAIBHAZc7AUnq37+/FixYoFWrVqlChQr26bGxscrKytLJkyedzgo4fPiwYmNj7TG57+5ve6qAY0zuJw0cPnxY4eHhCgkJkb+/v/z9/V3G2NrILSgoSEFBQZf2gQEAAAAAuEwu6xkBxhj1799fX3/9tZYtW6aqVas6zW/cuLECAwO1dOlS+7Rdu3Zp//79io+PlyTFx8dr27ZtTnf3X7JkicLDw1W7dm17jGMbthhbG1arVY0bN3aKycnJ0dKlS+0xAAAAAAD8G1zWMwKSk5P12Wefae7cuQoLC7Nfjx8REaGQkBBFRESod+/eGjJkiCIjIxUeHq4BAwYoPj5ezZo1kyS1bdtWtWvXVvfu3TV27FilpaXpmWeeUXJysv0X+0ceeUQTJ07Uk08+qQcffFDLli3TrFmztHDhQnsuQ4YMUY8ePdSkSRNdf/31Gj9+vM6cOaNevXoV/4IBAAAAAKCIXNZCwJQpUyRJN954o9P0adOmqWfPnpKkcePGyc/PT126dFFmZqYSExM1efJke6y/v78WLFigfv36KT4+XqGhoerRo4fGjBljj6lataoWLlyowYMH66233lKFChX0/vvvKzEx0R7TtWtXHT16VCNHjlRaWpoaNmyolJSUPDcQBAAAAADgn8xijDGXO4l/A9vzG1tGtFeAJdBj/PITc4shKwAAAADAv5XtODQ9PV3h4eFev+6KeWoAAAAAAAAoehQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIRQCAAAAAADwIQGXOwEUTELcPR5jvj/4RTFkAgAAAAD4J+KMAAAAAAAAfMhlLQSsWrVKt956q+Li4mSxWPTNN984zTfGaOTIkSpXrpxCQkKUkJCg3bt3O8UcP35c3bp1U3h4uEqVKqXevXvr9OnTTjFbt25Vq1atFBwcrIoVK2rs2LF5cpk9e7Zq1qyp4OBg1atXT99++22hf14AAAAAAC63y1oIOHPmjBo0aKBJkya5nD927FhNmDBBU6dO1bp16xQaGqrExESdP3/eHtOtWzdt375dS5Ys0YIFC7Rq1Sr17dvXPj8jI0Nt27ZV5cqVtWnTJr322msaPXq03n33XXvM2rVrde+996p3797avHmzOnXqpE6dOunnn38uug8PAAAAAMBlYDHGmMudhCRZLBZ9/fXX6tSpk6S/zgaIi4vT448/rieeeEKSlJ6erpiYGE2fPl333HOPduzYodq1a2vDhg1q0qSJJCklJUUdOnTQn3/+qbi4OE2ZMkVPP/200tLSZLVaJUnDhw/XN998o507d0qSunbtqjNnzmjBggX2fJo1a6aGDRtq6tSpXuWfkZGhiIgItYxorwBLoMf45Sfmer1sHHGPAAAAAACA9H/Hoenp6QoPD/f6dVfsPQL27duntLQ0JSQk2KdFRESoadOmSk1NlSSlpqaqVKlS9iKAJCUkJMjPz0/r1q2zx9xwww32IoAkJSYmateuXTpx4oQ9xvF9bDG293ElMzNTGRkZTn8AAAAAAFzprthCQFpamiQpJibGaXpMTIx9XlpamqKjo53mBwQEKDIy0inGVRuO7+EuxjbflZdfflkRERH2v4oVKxb0IwIAAAAAUOyu2ELAlW7EiBFKT0+3//3xxx+XOyUAAAAAADwKuNwJuBMbGytJOnz4sMqVK2effvjwYTVs2NAec+TIEafXXbx4UcePH7e/PjY2VocPH3aKsf3bU4xtvitBQUEKCgq6hE8GAAAAX3X3vKFexc267bUizgSAL7tizwioWrWqYmNjtXTpUvu0jIwMrVu3TvHx8ZKk+Ph4nTx5Ups2bbLHLFu2TDk5OWratKk9ZtWqVbpw4YI9ZsmSJapRo4ZKly5tj3F8H1uM7X0AAAAAAPi3uKyFgNOnT2vLli3asmWLpL9uELhlyxbt379fFotFgwYN0gsvvKB58+Zp27ZteuCBBxQXF2d/skCtWrXUrl079enTR+vXr9eaNWvUv39/3XPPPYqLi5Mk3XfffbJarerdu7e2b9+umTNn6q233tKQIUPseQwcOFApKSl64403tHPnTo0ePVobN25U//79i3uRAAAAAABQpC7rpQEbN27UTTfdZP+37eC8R48emj59up588kmdOXNGffv21cmTJ9WyZUulpKQoODjY/ppPP/1U/fv318033yw/Pz916dJFEyZMsM+PiIjQd999p+TkZDVu3Fhly5bVyJEj1bdvX3tM8+bN9dlnn+mZZ57RU089pWuuuUbffPON6tatWwxLAQAAAACA4mMxxpjLncS/ge35jS0j2ivAEugxfvmJuZf0Pglx93iM+f7gF5fUNgAAAIoW9wgAUJhsx6Hp6ekKDw/3+nVX7D0CAAAAAABA4aMQAAAAAACAD6EQAAAAAACAD6EQAAAAAACAD6EQAAAAAACAD6EQAAAAAACAD6EQAAAAAACAD6EQAAAAAACAD6EQAAAAAACAD6EQAAAAAACAD6EQAAAAAACAD6EQAAAAAACAD6EQAAAAAACAD6EQAAAAAACAD6EQAAAAAACAD6EQAAAAAACAD6EQAAAAAACAD6EQAAAAAACAD6EQAAAAAACADwm43AkAAAAAAHzXwnXNPcYkNV1bDJn4Ds4IAAAAAADAh1AIAAAAAADAh3BpAAAAKDINxzznMWbLyFHFkAkAALDhjAAAAAAAAHwIhQAAAAAAAHwIhQAAAAAAAHwIhQAAAAAAAHwIhQAAAAAAAHwITw0AAAD/SA1eHu0x5qcRnmMAAPA1nBEAAAAAAIAPoRAAAAAAAIAPoRAAAAAAAIAPoRAAAAAAAIAPoRAAAAAAAIAPoRAAAAAAAIAP4fGBAAAAxajhW6O8itsy8LkizgQomAFLB3qMefvmt4ohEwB/F2cEAAAAAADgQygEAAAAAADgQygEAAAAAADgQygEAAAAAADgQ7hZIABArR5+3mPMf955thgyAQAAQFGjEACfk3T9EI8xC9e/WQyZAAAAAEDxoxCAf7ykxp4fZSNJCzf51uNsktqP9BizcNGYYsgEl8MNfTz/wr/qPX7hBwAA8EUUAnBF6tCgv8eYb3+aWAyZAAAAAMC/C4UAAACAK9i1Ez2f4bW5P2d4AQC8RyEA+IfoeMszXsUtWPJCEWcCAAAA4J+MQgCAK07inc95jFn85ahiyOTKceODnq/5l6QVH3Ld/+Vy/VDPv8iuf83zL7sAAABFjULAv1jbSvd7jPlu/yfFkAkAAACKQ69Fnp+ONK09T0cCfB2FAAD/aG27ev4V9ruZ/AoL/BM0eGG0V3E/PeNd3N/R4HXPZx399ITns5d8XYvpni9rW9Pzr0vabvj4aY+xqx548W/n5I2k2cM9xiy865ViyAQAigaFgMusTdSdHmOWHf2yGDIBik77zp4Hy4u+9q1T/VE44gd6vmQi9S0ulwAAwFetWt/EY8wN128shkyuLBQCAAAF0rKf54Pv1VP++QffTR/37r4M6974539W/H0NxnlxBsFgziAAfN2EtXd5jHms+exiyOSf7fv113uMSbh+fTFk8s9FIQB2iVf19Biz+NfpkqR21R/yqs2UX97/GxnhStXhttFexX07z7s4/Lu1SPZ8QL1mEgfThanx054vmdn04l+XzFw70ruD081jOGvnn6LxZM+XQ2169NIeN9jsA8+n+v/Q+8p7ek3bL0Z4FffdPS9fUvu3zxnmMWbuHa9eUtvdFjzhMebTjq9fUttXkuErHvEY88qNU4shE+mV1d09xgxvOaMYMgGKDoUAAJKkpI6eB/kLF/zzf8265V7Pg98ln1/aPQXadPd8wLtsxv8d8N7U03P88ukcIOPK0nC05/3AltFXZtGgwaujPcb8NMxzDIDL79lVfTzGPH/De8WQie+Y9UMbjzF3N1tWDJmgMFAIQLFoX8dzlXfR9uKp8gIAAEB6+LvBHmPeaTuuGDIB/pl+3Oj5/gOS1KjJlXcPAgoBAAAAPuq6d7w762jDw97dMwOwGbK8v1dxb940sYgzAeAKhQAAAC6D64Z5vkxlw6t/XabSZITn2I0v85hMAPBl01KTvIrrFb+wiDPBPwGFAADAFaP5AO9+dVz79qXdO6HZYM/t/zCO+zIAALz3xpp7PcY83uLzYsjkyvH1uhu8iuvcdFURZwJ3KATkMmnSJL322mtKS0tTgwYN9Pbbb+v66z0/ngL/Xh2be75b74K1f92tt2Mrz3cNXvCfS7trMAAA+D9tPn3KY8yybi8VQya40o35Ty+v4ka2mlbEmQBXDgoBDmbOnKkhQ4Zo6tSpatq0qcaPH6/ExETt2rVL0dHRlzs9AAAAAD7kndROHmMejv+myPPwJT9saOwxptl1m4ohE2nPlhYeY6KvWnRJbVMIcPDmm2+qT58+6tXrr6rh1KlTtXDhQn344YcaPnz4Zc4O/0Yd23j+NWPBMn7NuFwSunl3mvr3n3IqOQAAAP45KAT8f1lZWdq0aZNGjBhhn+bn56eEhASlpqbmic/MzFRmZqb93+np6ZKki+aCV++XkZHxV3yO53hbbEHjC952lvdtZ3uOLWi8Yy4XChB/ITvTQ2TB451yueh9fEFii6Ltv5XLhQLk4kWsY/zFC+cLlEtB4i93207xWQXMpQDx3sReSbkU2+fMLGAuBYjP9iK2oPGOuRQk/nK3/bdyOV+AXM4XbN/iTbxzLt7HX+62neLPFTCXAsR7E+sYf7GAuRQkvsBtny1A217EOsZfKEDbBY33Jvbv5JJVgPisMwVsuwDxmWcKNlb0Jt4xl4LEny9gLt7EO+Zy/oz3Y+6CxErSuQLEexPrGD9zQ2ePsV2v+9r+/2fPXPS6bW9iCxrvuFzOnM72Or4gsQWNP+1FrGP8ji1tPMbWarjM/v+nTnteLsH/v21jjFe52FhMQV/xL3Xw4EGVL19ea9euVXx8vH36k08+qZUrV2rdunVO8aNHj9Zzzz1X3GkCAAAAAODkjz/+UIUKFbyO54yASzRixAgNGTLE/u+cnBwdP35cZcqUkcVisU/PyMhQxYoV9ccffyg8PNxjuwWJ/6e2TS7F3za5FH/b5FL8bZNL8bdNLsXfNrkUf9vkUvxtk0vxt00uxd92YeVijNGpU6cUFxfn8f0cUQj4/8qWLSt/f38dPnzYafrhw4cVGxubJz4oKEhBQUFO00qVKuW2/fDwcK9WhkuJ/6e2TS7F3za5FH/b5FL8bZNL8bdNLsXfNrkUf9vkUvxtk0vxt00uxd92YeQSERHh9XvZ+BX4Ff9SVqtVjRs31tKlS+3TcnJytHTpUqdLBQAAAAAA+CfjjAAHQ4YMUY8ePdSkSRNdf/31Gj9+vM6cOWN/igAAAAAAAP90FAIcdO3aVUePHtXIkSOVlpamhg0bKiUlRTExMZfcZlBQkEaNGpXnMoLCiP+ntk0uxd82uRR/2+RS/G2TS/G3TS7F3za5FH/b5FL8bZNL8bdNLsXfdlHn4glPDQAAAAAAwIdwjwAAAAAAAHwIhQAAAAAAAHwIhQAAAAAAAHwIhQAAAAAAAHwIhYAismrVKt16662Ki4uTxWLRN9984zb25Zdf1nXXXaewsDBFR0erU6dO2rVrl9v4KVOmqH79+goPD1d4eLji4+O1aNEir/J65ZVXZLFYNGjQIJfzR48eLYvF4vRXs2bNfNs8cOCA7r//fpUpU0YhISGqV6+eNm7cmCeuSpUqedq2WCxKTk522W52draeffZZVa1aVSEhIbr66qv1/PPPy939LU+dOqVBgwapcuXKCgkJUfPmzbVhwwZJnvvDGKORI0eqXLlyCgkJUePGjdWmTRu38XPmzFHbtm1VpkwZWSwWffDBB/m2P3r0aNWsWVOhoaEKCwtTVFSUoqKiPK4bjzzyiCwWi+rUqeO27Z49e+ZZpsHBwW7b3rFjh2677TZFREQoODhYpUqVUkxMjMt4V/1lsVgUHh7uMv706dPq37+/KlSooKCgIIWFhalUqVIuYw8fPqyePXsqLi5OgYGBioiIUMmSJd1uA+fPn1dycrLKlCkjq9Wq0qVL5xv/7rvv6sYbb7Tn2qhRI7fb2PHjxzVgwADVqFFDgYGBslqtslqtioqKctn2ww8/rKuvvlohISEKDQ1V6dKlFRoa6nH7NcaoevXqslgsCgkJcRt/44035lnmISEheWJ/++03t30UERHhsu20tDR1795d4eHh8vPzk7+/v0qUKJFnP5J7+b3xxhtu9zuOyy8kJESVKlXSTTfdpDp16rjdTzkuw5IlSyo8PNz+X3f7NGOM2rdvL4vFoipVqrht29XyK1OmTL77zNTUVLVp00ZWq1X+/v4KCAjIE5vf8i5RooTLtm3LOzY2VlarVSEhIW5j9+7dq86dOysqKkrh4eG6++67dfjwYft8V/tvx+2iZMmS6tKliw4fPuwyNnefnjx50m3brvr0scceU3p6uttcHPs0KipKt99+u3bu3Jnv945jn9r2Ee7ibX0UGhqq8PBw3XDDDTp37lye+Pz6qVu3bnnaduyj0NBQNWrUSF999ZXbXBz7KSgoKN/vzNz7rfxic/fP8OHD3ca76p+mTZvm275j/5QoUcKr73rH/skv3tU2l1/bjn2Z3zLMry/dte/Yn4GBgfnGutrmtmzZku+YxnG8EBwcrHLlyql06dIuY3OPFb777ju3bV+4cEHDhg1TvXr1FBoaqri4ON15553q0qWL21wcxxYREREqV66cIiIi8h2LSf83tmjSpInbtl2NLeLi4tzGe+oj21gv9z4rNDTUbbyr9dy2fbhr33E99/f3zzfWm/XcMd7Teu6qfdu67ufn5zbW2/XcsW3H9Ty/tl2t53fddZeGDBmS7/jatp7HxsYqICBAISEhCg4Odhmbez3v06eP27Zzr+flypVT/fr1ValSJY9j/ezsbDVu3FgWi0VWq9VlrKv1tkSJEvm2bRsXh4eHKzAwUEFBQW4/q7t+KVOmTJ5YxzFxcHCwypYtq7Jly7rNxXFcHBISosqVK6t8+fJ5jmkc+8d23JKQkKDdu3fnWWaeUAgoImfOnFGDBg00adIkj7ErV65UcnKyfvjhBy1ZskQXLlxQ27ZtdebMGZfxFSpU0CuvvKJNmzZp48aNatOmjW6//XZt37493/fZsGGD3nnnHdWvXz/fuDp16ujQoUP2v9WrV7uNPXHihFq0aKHAwEAtWrRI//3vf/XGG2+odOnSLt/fsd0lS5ZIku666y6Xbb/66quaMmWKJk6cqB07dujVV1/V2LFj9fbbb7uMf+ihh7RkyRLNmDFD27ZtU9u2bZWQkKADBw547I+xY8dqwoQJmjp1qtatW6fAwED9+OOPGjdunMv4M2fOqGXLlnr11VclSefOncu3/erVq2vixInatm2bXnvtNZUvX95t/9p8/fXX+uGHHxQZGany5cvnuy61a9dOhw4d0ieffKKBAwfqvffecxm3d+9etWzZUjVr1tSKFSs0efJktW3b1v45cnPsr0OHDtkHw+7ihwwZopSUFH3yySd655131KpVK506dSpPnDFGnTp10q+//qq5c+eqWbNmuvbaaxUWFqZ58+a53AYGDx6s+fPna/bs2WrSpIkiIyN1zTXXuN1mzp49q3bt2umpp56S9Nf64W4bO3jwoA4ePKjXX39d8fHxGjRokMqVK6cGDRq4bLtx48aaNm2aduzYoQYNGtgPSlNSUvLdfsePH68TJ07Yl2F+23ufPn104403avz48VqxYoW+//77PLEVK1Z06p8bb7xRnTp1UkhIiNtcHnjgAe3atUujRo3Se++9p8cee0znz59X7dq1nfYjuZdfXFyc2/2O4/L7+eefNX36dO3atUulSpVyu59yXIYvvPCCatWqpbCwMK1bt87tPm38+PGyWCySpO7du+e7D+zTp499uXz00Ud655133ManpqaqXbt2atu2rcaNG6cpU6bojTfe0Nq1a51icy/vQ4cO6b777lNwcLDWrl3rsm3b8p43b54mTZqku+66S+fPn9fHH3/sFHvmzBm1bdtWFotFy5Yt05o1a5SVlaVbb71VOTk5bvffjtvFypUrdfDgQd1yyy0uY3P3qY2rtl31aUpKinr37u02F8c+Xbx4sYwxat26db7fO4596i6X3H20fv16bdiwQf3799ePP/6YJ95VPz333HMKCQnRmjVr8rTt2Efbtm3THXfcobvvvtu+H3OMz91PDz74oMLCwtSwYUMdOHAgz3emY//07NlTISEhatKkicvvV1f94+772FX/7NmzR+Hh4W6/vx37p3v37goLC1NcXJz+/PNPt9/1jv2Te7nmjrdtc48//rhq1KihX375xWVs7r7s06ePKlSooN9++y1PvKu+vPHGG+Xn56c9e/a4bN+xPx999FFFR0fbD8IdY11tc6dPn1bTpk0VEBDgdkxjGy+8/vrrKlOmjAIDA1WiRAlt3rw5T2zusULPnj3djpfOnj2rH3/8Uc8++6x+/PFHTZ8+XfPnz9fKlSvd5mIbW6xevVolS5ZUWFiYsrOz9Z///MftWOzrr7/W6tWr5e/vL39//3zHbraxxY4dO1ShQgXdeOONbuNtfbFt2zb99NNP9vHTxx9/LOn/xnq591m1atVy2iYcx4au1vOyZcsqKSnJ7VjScT3/7rvvdMstt9jXc3fjTsf1/MMPP8x3nOr43bJt2zan9Tx3vOO6vnLlSv3nP//RO++8o99++80p1tV6/uSTT6pEiRL29Tx3247r+bp16/Tkk0/a13PHWFfr+fbt2/X2229rwoQJbsfXtvU8ISFBYWFhatCggaKiovT888/nic29nn/55Zdux+651/Pbb79dO3bsUFBQkMex/gMPPKCffvpJkZGRGj58uNtY23o7fPhwlS5d2r4+uIp3HBc/8MADCgsL07Bhw7R69WqX8bb+sbU9cOBASdKYMWPyxDqOiQcMGKDz58/rxIkTmjBhQp62c4+Lb7rpJp06dUoXL17UunXrnI5pHPvHdtwSGhqqxMREnT9/Ps9yy5dBkZNkvv76a6/jjxw5YiSZlStXev2a0qVLm/fff9/t/FOnTplrrrnGLFmyxLRu3doMHDjQZdyoUaNMgwYNvH7fYcOGmZYtW3od72jgwIHm6quvNjk5OS7nJyUlmQcffNBp2h133GG6deuWJ/bs2bPG39/fLFiwwGl6o0aNzNNPP+00LXd/5OTkmNjYWPPaa6/Zp508edIEBQWZzz//PN/+27dvn5FkNm/e7LZ9V9LT040kt7F//vmnKV++vPn5559N5cqVzbhx49y23aNHD3P77bfnacNVbNeuXc3999/vMidv8r799ttNmzZt3MbXqVPHjBkzxmlao0aN8sTu2rXLSDI///yzfVp2draJiooy7733Xp5t4OTJkyYwMNDMnj3bHr9jxw4jyaSmpua7zSxfvtxIMidOnLBP82YbmzVrlrFarebgwYMeY3/66ScjyezZs8dt25s3bzbly5c3hw4dcloeruLdbaPe5N2wYUP7duMqPjQ01Hz88cdOr4mMjDTvvfeey/2Iq+Vnk99+x7b8Lly44FW84zJ0Fetu+blqO799nKv4pk2bmmeeecar2Nwcl7er+PyWt2Ps4sWLjZ+fn0lPT7fHnTx50lgsFjNv3jyX+29X28XGjRuNJPPWW2+5XQ6Ofertd4Mxf/VpYGCg1/GpqalGkvnoo49cxubu088++8xt2676qCC516tXz4SHh7uMddVHpUuXNtHR0Xnic/fTqFGjTN26dY3FYjFLlixxaiN3/4waNcrUrFnTvt9yx9Y/w4YNK9D38Z133mksFovTNufOqFGjTI0aNZy2udxy90+VKlXctue4jDyNI3L3ZUHHHTExMSYyMtLtfMf+tLXtuM3ZuNrmBg0aZCTl6Usbx/GCbfzjOF5wxzZWuPbaa73+nLb+l2R+//13j7EtW7a0jy2+//57l3G2sUXv3r1NUFCQfWzhiuPY4lLGerbxguNYz9N3uTGex4a5v1s8xTt+t7iKze+7JXe8p/1M7vj8vls85Z37uyV3fH7fLY6xrtbzxMTEPOu54/jacT23jcUd13N3Y3Hbep57TOou3pi/xvq33nqr03ruKv7PP/80QUFBpnPnzk7j4tyxjuutN8cRjuPighx32GIdx8W5Yx3HxLZ4x+MSx3jHcbHtmGbevHn2cbEx/3dM4+m4pSA4I+AKZDv1MjIy0mNsdna2vvjiC505c0bx8fFu45KTk5WUlKSEhASPbe7evVtxcXG66qqr1K1bN+3fv99t7Lx589SkSRPdddddio6O1rXXXuv212hHWVlZ+uSTT/Tggw86/RrkqHnz5lq6dKl++eUXSdJPP/2k1atXq3379nliL168qOzsbAUHBztNDwkJyfeMBknat2+f0tLSnJZNRESEmjZtqtTUVI+fpaCysrL07rvvKiIiwuX8nJwcde/eXUOHDlWdOnW8anPFihWKjo5WjRo11K9fPx07dsxluwsXLlT16tWVmJio6OhoNW3aNN9LExwdPnxYCxcuVO/evd3GNG/eXPPmzdOBAwdkjNHy5cvt/ecoMzNTkpz6y8/PT0FBQVq9enWebWDTpk26cOGCUx/VrFlTlSpVUmpqaoG2Gcm7bSw9PV3h4eH2X9PdxZ45c0bTpk1T1apVVbFiRZdtnz17Vvfdd58mTZqk2NhYr3L59NNPVbZsWdWtW1cjRozQ2bNnPea9adMmbdmyxd5HruKbN2+umTNn6vjx48rJydEXX3yhc+fO6fTp0x73Izbe7Hdsyy8gIMBjvOMyjIuLyxOb3/Jz17ar5ecq/siRI1q3bp2io6PVvHlzxcTEqHXr1lq9erXHvHMvb1fxrpb3+fPn1apVK6fYzMxMWSwWBQUF2dsPDg6Wn5+fnn32WZf7b1fbxYQJE+y/CnqjIN8N6enp8vPz8yr+zJkzevDBBxUeHq577rknz3xXffruu++6bNtdH911111e5bJp0yZt27ZN7dq1cxnrqo8yMjLUoUOHPPGu+unXX3+VMUZ33XWX03emq/7Zv3+//P39lZSU5PH7VSrY93FmZqaMMapUqZLH+KysLO3du1f+/v5KSEjIE+uqfw4dOpRvLrZtbvLkyfrvf/+rcuXK5Yl11ZfTpk3Trl27vPqcmzZt0uHDh3X27Fm38Y79aYzRzp07deLECT333HNOsa76MiUlRRaLRQMHDnQ5pnEcL9jGPw899JBycnI0YMAAj+Of2rVrez1emjdvnq6++mpJf/3KnV/8vHnzdO2116pVq1ayWP66lCV3rOPYYu3atbJarZo+fXq+udjGFuPGjVN6erpuv/12r3K3jRd69OjhNNbz9F3uzdjQ8bvFU7zjd0tMTEye2Py+W9y17e67JXd8ft8tnvLO/d3iKt7dd0vz5s2dYl2t582bN5ckzZ07V1Le8bXjem4bix8+fFhNmzbVvHnz3I7FbdavX+/V2N2Wy/r16yVJpUqVchlvW3cTEhL0448/6sKFC/m2bVtvf/jhB82aNUvr1q1zGZ97XLx8+XJ98skn9jNw88u9efPm+u6777RgwQL17t3bZazjmDg+Pl4LFy7Uzp071bZt2zzxjuNi2zFNiRIl7ONi6f+OaQr1uKVAZQNcEhXgjIDs7GyTlJRkWrRokW/c1q1bTWhoqPH39zcRERFm4cKFbmM///xzU7duXXPu3DljTP4VzW+//dbMmjXL/PTTTyYlJcXEx8ebSpUqmYyMDJfxQUFBJigoyIwYMcL8+OOP5p133jHBwcFm+vTp+eY/c+ZM4+/vbw4cOOA2Jjs72wwbNsxYLBYTEBBgLBaLeemll9zGx8fHm9atW5sDBw6YixcvmhkzZhg/Pz9TvXp1p7jc/bFmzRojyRw8eNAp7q677jJ33313oZ0RMH/+fBMaGmosFouJi4sz69evdxn70ksvmVtuucVe9fV0RsDnn39u5s6da7Zu3Wq+/vprU6tWLXPdddflibVVu0uUKGHefPNNs3nzZvPyyy8bi8ViVqxY4XE9ffXVV03p0qXt65Gr+PPnz5sHHnjASDIBAQHGarWajz76KE9sVlaWqVSpkrnrrrvM8ePHTWZmpnnllVeMJHPLLbfk2QY+/fRTY7Va8+R03XXXmaFDh+a7zeT+Rdubbezo0aOmUqVKZsSIEW5jJ02aZEJDQ40kU6NGDbNnzx63bfft29f07t3b/m/b8nAX/84775iUlBSzdetW88knn5jy5cubTp06ecy7X79+platWvl+zhMnTpi2bdsaScbf399IMn5+fm73I47Lz9v9jm35PfTQQ/nGOy7DKlWqmBIlSriMdbX8xo8f77ZtV8uvTZs2LuNtv1pHRkaaDz/80Pz444/m/vvvty+f/D6nbXnnt1wcl3dAQIAJDQ01wcHBeWKPHDliwsPDzcCBA82ZM2fM6dOnTf/+/Y0kp+3Ocf+de7uw7esbN25snnzySY9nBLz//vtefzccPXrUlClTxkRFReUb79inVqvVbN++3WWsqz6tVKmSy7Zd9VH79u2NxWIx27Zt85h7QkKCCQoKcpt37j4KCQkxVapUcRmfu5/mzJlj2rVrZySZ9u3bO31n5u4f2/drnTp1zF133eX2+9XWP7NmzfL6+/jo0aMmKirKdO7cOd94x/6Ji4szCxYscBnrqn+eeOIJt207bnNDhw41kZGRpk2bNnliXfXl7bffbgICAsy8efM8fs5+/fqZihUr5rtcHPvTz8/PhISEmClTpuSJdbXN2faJDRs2dDmmcRwvOI5/EhISTOPGjd2Of2xjBavV6vV4KSgoyFgsFlO7du184+fPn28/w7BkyZJmxowZLmMdxxZBQUFGkklISHDbtuPYIjAw0FgsFlOuXDmzYcMGj7nbxgszZsxwGuvl913+5JNPehwb2r5bnnrqKWOM+7Gkq+9nV7Huvpvdte3qu6Vz584u412t64MGDTJWq9WMHz8+38/p+F3uLpfc+63w8HCzePHiPLGu1vPk5GT7OuNqfO24njuOxS0Wi5HkdixuW8979uzp9dj9zJkzJiYmxm0uxvzfunvx4kUzbNgw+7btKtZxvf3qq69MmTJl3Lade1y8adMm07p1a/v3f365Z2dnmxtvvDHfvB3HxP7+/vacXcXnHhc3a9bMVK1a1T4udjym8XTcUhAUAopBQQoBjzzyiKlcubL5448/8o3LzMw0u3fvNhs3bjTDhw83ZcuWtQ+4HO3fv99ER0ebn376yT7Nm9NmbU6cOGHCw8PdnhYbGBho4uPjnaYNGDDANGvWLN9227Ztazp27JhvzOeff24qVKhgPv/8c7N161bz8ccfm8jISLdfPHv27DE33HCDfYO77rrrTLdu3UzNmjWd4i5XIeD06dNm9+7dJjU11Tz44IOmSpUqeWI3btxoYmJinHb2ngoBue3du9flZQcHDhwwksy9997rFH/rrbeae+65x2PbNWrUMP3798/3c7722mumevXqZt68eeann34yb7/9tilZsqTL2I0bN9pPe/T39zeJiYmmffv2pmLFinm2gfwGDw0bNsx3m8ldCPC0jaWnp5vrr7/etGvXzvTt29dt7MmTJ80vv/xiVq5caW699VbTqFEj06dPnzzxc+fONdWqVTOnTp3Ks+y83d6XLl1qJJny5cu7jT179qyJiIgwr7/+er6fs3///ub6668333//vdmwYYMZMGCAKVmypOndu7fL/Yjj8vNmv+O4/GzrvLt4x2WYlJRkateubdasWeMU6275zZo1y6t9oOPy+/777/PE27b/ESNG2OMzMzNN9erVTc+ePd227bi881sujst7y5Yt5plnnjElS5Y0X3zxRZ7YxYsXm6uuuspYLBbj7+9vOnfubAICAsxdd91lf193hQDHfb1tUO2pEBAVFeXVd0N6erpp0KCBsVqtZuPGjfnGnzx50qxcudKULl3atG7d2jRq1MicO3fOKTZ3n+7fv99IcjpN2TE+dx/ZPmu1atXM8OHD8839l19+MRaLxQwZMsRt3o59lJKSYkJDQ03JkiXN1q1bXcbn7qf777/fNGrUyDzyyCNO35meDnrcfb+6uxzHXbzjNpeVlZVvvKv91rlz55xi89tnecrFxrbN7dmzxynW1fZmzF+Xbtj60l3bufdx7nLJvc2NHj3aREREmK1bt+aJzd2Xfn5+JjQ01DzyyCP29h3HNI7jBcfxj2284G78Yxsr1K9f32m6u/isrCxjsVhMaGio0yndruJPnz5tAgMDTd26de1ji8OHDzvF5h5bBAYG5rk0IL+xW2BgoLn22mudLjvIL942Xsg91vO0TeQ3NnS1nruLd7WeJyQkOMV6Ws+9Gac6rue54/Nb16tWreq2bVfruatc3K3nzZs3zxObez1v2bKlCQwMNAkJCS7H147rueNYvG3btub66693Oxa3recxMTFejd2zsrJMo0aNTGBgoPnggw9cxjuuu7ZcypYta4YOHerxuODzzz83sbGxRpJ5991388TnHhfb2m/UqJFp165dvu1//vnnJiAgwLRt29bt53QcE7/yyiumVKlSJigoyGUuts9qGxf7+fmZ0qVLG0nGYrE4HdNQCPiH8bYQkJycbCpUqGB+/fXXAr/HzTffbPr27Ztn+tdff20/0LL92VYqf39/c/HiRY9tN2nSxP4FnVulSpWcqqnGGDN58mQTFxfntr3ffvvN+Pn5mW+++Sbf961QoYKZOHGi07Tnn3/e1KhRI9/XnT592r5x3H333aZDhw5O83P3h+3A2fFg3hhjbrjhBvPYY48VWiEgt2rVquWJHTdunL1vHPvLz8/PVK5c2eu2y5Ytmyc2MzPTBAQEmOeff94p9sknnzTNmzfPt+1Vq1YZSWbLli1uP+fZs2dNYGBgnvs09O7dO9+2T548aY4cOWKMMSY6OtqEhobm2QZsX7a5B8YlS5Y0pUqVynebcRxUe9rGMjIyTHx8vLn55pvNww8/7PX2aFu2kZGReeIHDhzosk8lmaCgIK/a79u3r5GU75k2H3/8sQkMDDRHjhxx+zn37NljvwbNke3zutqP5HePgNzxjsvP9mtqfvGOMjMzTYkSJcxnn33mFOtu+fn5+ZnWrVt71fbp06eNJJOSkpIn/tdffzWSzIwZM5xec/fdd5v77rvPbduOy9vd5/S0vN21ffToUXPixAn7/tvPz8/l/vv777+3943jvt72Gnf7elufevPdYOvT+vXre/1d4imX/v37O/Wpbb5jTo7xtuVo6yNb+7ZfqPLLZeDAgfnmnbuPCtK2rZ+M+eu69bFjxxpj/u87091+q1KlSubNN990inWU3zaXO97TNpff93fubc4W6+02l1/bubc5W6w325u7tvPb5mzx3mxzrtq29WWlSpVMcHCwvS+NcR7TOI4XHMc/tvGCu/GPbazQqVMnp+mu4rOyskynTp1MYGBgnmuT3bXvmEu1atXMSy+95BSbe2xh29ZsY4v82nZsv2zZsmbq1Kn5xtvGC99++22esV5+28Qzzzzjdmzoaj33diyZmZlpgoODjcVicYrNbz1v2rSpV23b1vOPPvooT7y7dT0pKSlPLo5yr+euPqe79bx58+b5tm1bzytUqGDCwsKc1nPH8bXjeu44Fret5+7G4rb1PPf25SrecT1/9dVX3cY7rru2fbHjupvfcYEtd8f11jE+97jYFm8bF7vL3RhjoqKi8oyLHWNzj4ltbffu3dskJibm27bjuLhx48amR48expj/O6bxdNxSENwj4ApgjFH//v319ddfa9myZapatWqB28jJybFfX+Lo5ptv1rZt27Rlyxb7X5MmTdStWzdt2bJF/v7++bZ7+vRp7d27V+XKlXM5v0WLFnkeT/bLL7+ocuXKbtucNm2aoqOjlZSUlO97nz17Vn5+zquov7+/cnJy8n2d7XEkJ06c0OLFi3X77bfnG1+1alXFxsZq6dKl9mkZGRlat26dV9dLXypXn6N79+7aunWrU3/FxcVp6NChWrx4sVft/vnnny7vEWC1WnXdddcVuL8k6YMPPlDjxo3VoEEDtzEXLlzQhQsXXPZZfiIiIlS2bFndf//9OnLkiN5+++0820Djxo0VGBho7yNjjLp166bTp0/r3Xff9WqbGTp0aL7bWEZGhtq2bavAwEBVq1ZN8+fP92p7NMZo4MCBunjxooYOHZonfvjw4U59unnzZvvn/vbbb/Nt37ZvmDNnjiTp2muvdRtre4Tlc8895/Zz2q5ldLdduduPuOMYb1t+VqtV8+bNy3O/jtzxrj6rMcY+3xabe/lt2bJFkjRu3DhNmzbNq7Ztr3Hcj9niq1Spori4uHy3C1dtf/DBB7rtttsUFRXl9nN6Wt7u2i5btqxKlSqlgIAASdI333zjcv/dpEkT+3Zh29d//fXXkv66U7enff2aNWvy/W5w7NPvvvvO6+8SWy7r169XUFCQnnvuOafYp59+2qlP165dK0kaNmyYFi1alKftq666yqmPbO1Xr15dvXv3zjeXjRs3KiEhwW3eufvI1nazZs10xx135Nu2rZ+WLVumI0eO6LbbbnP6zsy935KkXbt2af/+/YqPj/f4/Zpb7nhP25yn9h23OcdYb7Y5T207bnOOsd5sb+7adrfNOcZ72ubctW3ry2rVqun8+fO67bbbXObmOF6wjX8cxwuevk9///13t59b+ut79O6779bu3bt16623eoy3cRyL2fYpjrG5xxbt27eX1Wp1Glvkl3uLFi20detWHTt2zL7s3MXbxgu26+Mdx3r5bROHDh1yOTZ0t557O5Y0xujixYsKDw93is1vPW/atKlXbdtek5qamife3bq+ceNGlShRwm3buddzV5/T3Xqelpam4OBgt23b1vP09HSdOnXKaT13/F5yXM9tY3HH9dzTWDz3fQ9yxzuu57anXbiLd1x3IyIi9PTTTzuNi/PLxXZvJcf11jE+97jY9lkd12137WdkZKhSpUpO42LH2NxjYlvbjjHu2o6IiFBUVJR2796tzZs367777nM6pinU45YClQ3gtVOnTpnNmzebzZs3G0n2a7Jd3fm1X79+JiIiwqxYscIcOnTI/nf27FmXbQ8fPtysXLnS7Nu3z2zdutUMHz7cWCwW891333mVW36XBjz++ONmxYoVZt++fWbNmjUmISHBlC1b1mUF3hhj1q9fbwICAsyLL75odu/ebT799FNTokQJ88knn7iMz87ONpUqVTLDhg3zmGePHj1M+fLlzYIFC8y+ffvMnDlzTNmyZc2TTz7pMj4lJcUsWrTI/Prrr+a7774zDRo0ME2bNjVZWVke+8N2yo7tuqKkpCQTFxdnfvjhB5fxx44dM5s3bzYLFy60/1L7xRdfmCVLluSJP336tBkxYoRJTU01v/32m1m1apW57bbbTGBgoMd1wxhjKlasaJ544gmXuZ86dco88cQTJjU11ezbt8/Mnz/f1KxZ01SqVMll23PmzDGBgYHm3XffNbt37zavv/668fPzMx9++KHbXNLT002JEiXMlClTPC7H1q1bmzp16pjly5ebbdu2meeee85YrVaXsbNmzTLLly83e/fuNe3atTMWi8W0atXK7TbwyCOPmEqVKplly5aZO++80/j7+5vatWu7jT906JDZvHmzee+994z017WT7733nvnvf/+bJz49Pd00bdrU1KtXz9x3330mPDzcfPXVV+ann34yf/75p1Ps3r17zUsvvWQ2btxofv/9d/vp22FhYWbbtm0et99+/foZSeb55593mfuePXvMmDFjzMaNG839999vQkNDTbly5UyzZs3ctr17925jsVhMUlJSvvuSrKwsU61aNdOqVSvzwAMPmM8++8w89dRTRpK56667nPYjuZdft27dzPvvv282b96cZ7/juPz27NljDh06ZPr372/mzJlj9uzZkyc+9zK8//77TfPmzU1ERIRZvny5x32aJPPCCy+43Ac6Lr99+/aZuXPnmlKlSpn69eu73WeOGzfOhIeHm9mzZ5uHH37YdO/e3VitVrNw4UKXudiW96JFi/LdHzsu73Xr1pmHH37Y3v+TJk3K0/aHH35oUlNTzZ49e8yMGTNMZGSk0ynttm3Mcf/tuF1s3LjRxMfH209Xzh2bu09XrVplNm/ebI4dO5Yn3lWf2v5sv4o7xufu0zVr1phbb73VREZGmsOHD3u8JE25zhrKHe/YR7t37zbPPPOMCQ4Ott/x3lX7jv3kbhnm7qM9e/aY119/3VgsFvv9G3K37dhP7du3N2FhYaZ3794uvzMd++f+++83tWvXNtdee63L2Nz907VrV/Pee++ZzZs354l31T+PPPKI+eqrr8yePXvyxOfun3vvvdfEx8ebiIgIs2DBAo/f9bZ9lquxQe5trlOnTqZcuXLm+uuvd/k5c/dls2bNjNVqNStWrHA77nDsy/zGKbn7s3fv3vZt7rXXXsvTdu5tLjw83Pj5+eU7prGNF15//XUTEBBgatWqZSpUqGCmTZuWJzb3WMF2RszatWvztJ2VlWVuu+02U6FCBbNlyxazaNEiExAQYIYPH262b9+eJ95xbDF37lzj7+9vGjVqZKxWq3n11VfzHYvZ7lHUoUMHl58z99hi4sSJRpIpU6aMy1xsbOOFSZMmuR3rudpnNWvWzGW8u/3QgQMHXMa72g917NjR+Pn5OV3a6I4k89VXX7ls29V3y1VXXWVatWrl9rPmXteffvppI8nt2Wu591nuxsyu9ltjx441ksydd96Zp93c67nVajUlS5bMd3xtW8/btGljoqOjTdOmTU2FChXMF198kSc293oeGRlpJkyYYNavX5+n7dzr+d13323KlStnZsyYYXbt2pXvWN92XBAVFWWeffbZPLG519u2bduawMBAExcXZ3bu3OmybcdxcefOnU2pUqWMn5+fmTVrlttc0tPTjb+/vylVqlS+y9BxTNylSxdTunRpExgYaJ5//nmX8Y7j4lGjRpno6GiTmJiY55jGsX9sxy233367qVq1qsszw/JDIaCIOJ566fhnO73Dkas4SWbatGku237wwQdN5cqVjdVqNVFRUebmm2/2ughgTP6FgK5du5py5coZq9Vqypcvb7p27er20UI28+fPN3Xr1jVBQUGmZs2a5t1333Ubu3jxYiPJ7Nq1y2OeGRkZZuDAgfZT9a666irz9NNPm8zMTJfxM2fONFdddZWxWq0mNjbWJCcnm5MnTxpjPPdHTk6OefbZZ01MTIwJCgqyP/LOXfy0adPc9lvu+HPnzpnOnTubuLg4Y7VaTWRkpNfrhjHGfhMVV/Fnz541bdu2NVFRUSYwMDDfWJsPPvjAVKtWzQQHB5urr77aY/w777xjQkJCzMmTJz0ux0OHDpmePXuauLg4e6HDXexbb71lKlSo4DYu9zZw7tw58+ijj9qvmfIUP2rUKI/9Y4t397lcxR44cMC0b9/eREdHe527I0/x+/fvNzfccIPb9cRV2yNGjDAVK1b0Kv6XX34xd9xxh/1USYvFYsLCwvLsR9wtv4CAgDz7nfyWn6v9VO5lWKJECRMaGmoCAwO92qdJf13f7qptx+UXFBRkqlWrZurVq2cqVaqU7z7z5ZdfNhUqVDD+/v4mKCgo31xsyzs7O9vj/ti2vKOjo42/v78JDAx0uQyN+esRXTExMfZH9L3xxht5Hi2Ve//tuF2UKFHCdO7c2Rw6dMhlrLs+ta0fjvH59em+ffvyxOfu0woVKpj77rvP7Ny502Uurvo0v0KAYx+VKFHCxMfHm//85z/5xjv2U37L0LGPSpQoYerXr+/0WK7c8Y79VLJkSRMeHu72O9Oxf/z9/U1wcLDb2Py2udzxnra53PG5+yckJMSEhISYwMBAr77rpb9uWumq7dzbXMmSJU1oaGi+4wjHvixTpowpU6ZMvvGOfelpnJJ7mwsICHC5DHP3pW2bmzdvXr5jGsfxgu0GoFar1WWsu7GCv79/nnjbadWu/gIDA/PE5x5blC5d2oSFhbnNJbeoqChTrlw5l58z99iicuXKpm3btqZmzZr5jvVs44U5c+YYyfVYz9U+y/ao5tzxnr6bc8e72g/ddNNNbnPJTZJ9O8wd7+q7ZejQofl+VmOc1/VatWrlG5t7n5XfmDn3fst2YzlXsbnX8xdffNE89thj+Y6vbet5VFSUfd8VFBTkMtbdeh4QEJAnPr/13Gq15jvWtx0X2L5Lc8fmXm8rVqxo6tata8qXL5/vcYRtXBwUFGTKli1roqKi8o233SzTVtRyF+s4Jg4KCjKlS5c2pUuXdhvvOC4uU6aMKVWqlMtjGsf+sR233HzzzV6t47lZjDFGAAAAAADAJ3CPAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAAAAAfAiFAAAAfERaWpoGDBigq666SkFBQapYsaJuvfVWLV261Os2pk+frlKlShVdksVk9OjRatiwodO/LRaLLBaLAgICVLZsWd1www0aP368MjMzL1+iAAAUgYDLnQAAACh6v/32m1q0aKFSpUrptddeU7169XThwgUtXrxYycnJ2rlz5+VO8ZJcuHBBgYGBhdJWnTp19P333ysnJ0fHjh3TihUr9MILL2jGjBlasWKFwsLCCuV9AAC43DgjAAAAH/Doo4/KYrFo/fr16tKli6pXr646depoyJAh+uGHH+xxb775purVq6fQ0FBVrFhRjz76qE6fPi1JWrFihXr16qX09HT7r+ejR4+WJGVmZuqJJ55Q+fLlFRoaqqZNm2rFihVOObz33nuqWLGiSpQooc6dO+vNN9/Mc3bBlClTdPXVV8tqtapGjRqaMWOG03yLxaIpU6botttuU2hoqF544QVVq1ZNr7/+ulPcli1bZLFYtGfPHq+XUUBAgGJjYxUXF6d69eppwIABWrlypX7++We9+uqrXrcDAMCVjkIAAAD/csePH1dKSoqSk5MVGhqaZ77jwbifn58mTJig7du366OPPtKyZcv05JNPSpKaN2+u8ePHKzw8XIcOHdKhQ4f0xBNPSJL69++v1NRUffHFF9q6davuuusutWvXTrt375YkrVmzRo888ogGDhyoLVu26JZbbtGLL77olMfXX3+tgQMH6vHHH9fPP/+shx9+WL169dLy5cud4kaPHq3OnTtr27Zt6t27tx588EFNmzbNKWbatGm64YYbVK1atb+17GrWrKn27dtrzpw5f6sdAACuJBQCAAD4l9uzZ4+MMapZs6bH2EGDBummm25SlSpV1KZNG73wwguaNWuWJMlqtSoiIkIWi0WxsbGKjY1VyZIltX//fk2bNk2zZ89Wq1atdPXVV+uJJ55Qy5Yt7Qfob7/9ttq3b68nnnhC1atX16OPPqr27ds7vffrr7+unj176tFHH1X16tU1ZMgQ3XHHHXl+7b/vvvvUq1cvXXXVVapUqZJ69uypXbt2af369ZL+ulzgs88+04MPPlgYi081a9bUb7/9VihtAQBwJaAQAADAv5wxxuvY77//XjfffLPKly+vsLAwde/eXceOHdPZs2fdvmbbtm3Kzs5W9erVVbJkSfvfypUrtXfvXknSrl27dP311zu9Lve/d+zYoRYtWjhNa9GihXbs2OE0rUmTJk7/jouLU1JSkj788ENJ0vz585WZmam77rrL68+dH2OMLBZLobQFAMCVgJsFAgDwL3fNNdfIYrF4vCHgb7/9po4dO6pfv3568cUXFRkZqdWrV6t3797KyspSiRIlXL7u9OnT8vf316ZNm+Tv7+80r2TJkoX2OWxcXd7w0EMPqXv37ho3bpymTZumrl27us23oHbs2KGqVasWSlsAAFwJOCMAAIB/ucjISCUmJmrSpEk6c+ZMnvknT56UJG3atEk5OTl644031KxZM1WvXl0HDx50irVarcrOznaadu211yo7O1tHjhxRtWrVnP5iY2MlSTVq1NCGDRucXpf737Vq1dKaNf+vnfsHaSMOwzj+FBchs4hLFPEPaCROgi5G5BangIiDEBQUXBwkZMjmFpRwYILeZVIREQSzaW7JdGSJ4CJi4EAHQQQdHEQQlXaQBjK01pra0vt+xhseeG98+L1vqeZbqVRST0/PmzOOjY0pEAjIsiw5jlO3tYBKpSLHcTQ+Pl6XPAAA/gUUAQAA+MDa2ppeXl40MDCg/f19eZ6ns7MzZTIZDQ4OSpI6Ojr09PSkbDar8/NzbW9vy7btmpy2tjbd39+rWCzq9vZWDw8P6urq0tTUlGKxmPL5vC4uLlQul5VKpXRwcCBJWlhY0OHhoUzTlOd5yuVyKhQKNU/uE4mENjc3ZVmWPM+TaZrK5/PVg4Q/09DQoOnpaSWTSXV2dlZneo/n52ddX1/r6upKJycnymazGh4eVn9/vxKJxLvzAAD4V1EEAADgA+3t7To+PtbIyIji8bhCoZAMw1CxWJRlWZKkcDgs0zS1vLysUCiknZ0dpVKpmpyhoSHNz89rcnJSTU1NWllZkfR6pT8Wiykej6u7u1vRaFRHR0cKBoOSXnf9bduWaZoKh8NyHEeLi4tqbGysZkejUa2uriqdTqu3t1e5XE4bGxuKRCK/NOP3FYaZmZnf+kenp6dqaWlRMBhUJBLR3t6eksmkXNf9IysOAAD8LV++vueCEAAAQJ3Mzc2pUqnIdd265Lmuq9HRUV1eXqq5ubkumQAA/I84FggAAD5FOp2WYRgKBAIqFAra2trS+vr6h3MfHx91c3OjpaUlTUxMUAIAAPAGVgMAAMCnKJfLMgxDfX19sm1bmUxGs7OzH87d3d1Va2ur7u7uqqsKAADgx1gNAAAAAADAR3gRAAAAAACAj1AEAAAAAADgIxQBAAAAAAD4CEUAAAAAAAA+QhEAAAAAAICPUAQAAAAAAOAjFAEAAAAAAPgIRQAAAAAAAD7yDaXaGh+JdqyDAAAAAElFTkSuQmCC\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "KJ120FANKQek"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Infosys_Final.ipynb b/Infosys_Final.ipynb
new file mode 100644
index 0000000..0b2077d
--- /dev/null
+++ b/Infosys_Final.ipynb
@@ -0,0 +1,1098 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "gpuType": "T4"
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "0P1eVHMHhLHc",
+        "outputId": "16145e53-09df-423a-b504-e349f46e0278"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Hello\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"Hello\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "PpB1Og1PhVax",
+        "outputId": "e7df9474-ab72-4525-d10e-03e75bace6e3"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /content/drive\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "import json\n",
+        "import os\n",
+        "\n",
+        "# Path to COCO annotation file and output folder for YOLO labels\n",
+        "coco_ann_file_train = r\"/content/extracted_files/coco2017_subset/annotation_subset/instances_train2017_subset.json\"\n",
+        "output_dir_train = \"/content/extracted_files/coco2017_subset/train2017\"\n",
+        "coco_ann_file_val = r\"/content/extracted_files/coco2017_subset/annotation_subset/instances_val2017_subset.json\"\n",
+        "output_dir_val = \"/content/extracted_files/coco2017_subset/val2017\"\n",
+        "# os.makedirs(output_dir, exist_ok=True)"
+      ],
+      "metadata": {
+        "id": "GaDkjdWblTbL"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def annotation_convertion(coco_ann_file,output_dir):\n",
+        "\n",
+        "  # Load COCO annotations\n",
+        "  with open(coco_ann_file, 'r') as f:\n",
+        "      coco_data = json.load(f)\n",
+        "\n",
+        "  # Create mapping: COCO category id -> new id (0-based contiguous indices)\n",
+        "  # For example, if valid categories are 1, 2, 3, 5, 6, ... then you might have:\n",
+        "  coco_categories = sorted([cat['id'] for cat in coco_data['categories']])\n",
+        "  id_mapping = {orig_id: new_id for new_id, orig_id in enumerate(coco_categories)}\n",
+        "\n",
+        "  # Build a mapping from image_id to image info\n",
+        "  img_info = {img['id']: img for img in coco_data['images']}\n",
+        "\n",
+        "  # Process annotations per image\n",
+        "  annotations_by_image = {}\n",
+        "  for ann in coco_data['annotations']:\n",
+        "      img_id = ann['image_id']\n",
+        "      img = img_info[img_id]\n",
+        "      img_width, img_height = img['width'], img['height']\n",
+        "\n",
+        "      x, y, w, h = ann['bbox']\n",
+        "      # Convert COCO bbox [x, y, w, h] to YOLO format [x_center, y_center, width, height] normalized\n",
+        "      x_center = (x + w / 2) / img_width\n",
+        "      y_center = (y + h / 2) / img_height\n",
+        "      norm_w = w / img_width\n",
+        "      norm_h = h / img_height\n",
+        "\n",
+        "      # Remap the category id\n",
+        "      orig_cat = ann['category_id']\n",
+        "      if orig_cat not in id_mapping:\n",
+        "          continue  # skip if the category is not in the mapping\n",
+        "      new_cat = id_mapping[orig_cat]\n",
+        "\n",
+        "      if img_id not in annotations_by_image:\n",
+        "          annotations_by_image[img_id] = []\n",
+        "      annotations_by_image[img_id].append(f\"{new_cat} {x_center:.6f} {y_center:.6f} {norm_w:.6f} {norm_h:.6f}\")\n",
+        "\n",
+        "      # Write YOLO label files for each image\n",
+        "  for img_id, ann_list in annotations_by_image.items():\n",
+        "      file_name = img_info[img_id]['file_name']\n",
+        "      base_name = os.path.splitext(file_name)[0]\n",
+        "      # Save the label file in the same directory as your images, or wherever you prefer\n",
+        "      out_file = os.path.join(output_dir, base_name + '.txt')\n",
+        "      with open(out_file, 'w') as f:\n",
+        "          for line in ann_list:\n",
+        "              f.write(line + '\\n')"
+      ],
+      "metadata": {
+        "id": "pINRP5lalYsM"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "coco_ann_file_train = \"/content/extracted_files/coco2017_subset/annotation_subset/instances_train2017_subset.json\"\n",
+        "annotation_convertion(coco_ann_file_train, output_dir_train)\n"
+      ],
+      "metadata": {
+        "id": "zc04PswRojRe"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "QMtNrcpU6kvU"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "annotation_convertion(coco_ann_file_train,output_dir_train)"
+      ],
+      "metadata": {
+        "id": "cNFtNmFYonxw"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import os\n",
+        "import yaml\n",
+        "\n",
+        "# Define target directory for COCO dataset\n",
+        "output_dir = '/content/extracted_files/coco2017_subset/'\n",
+        "# Ensure the directory exists\n",
+        "os.makedirs(output_dir, exist_ok=True)\n",
+        "\n",
+        "# COCO 2017 dataset configuration\n",
+        "data = {\n",
+        "    'path': output_dir,  # Base path to the dataset\n",
+        "    'train': '/content/extracted_files/coco2017_subset/train2017',  # Relative path to training images\n",
+        "    'val': '/content/extracted_files/coco2017_subset/val2017',  # Relative path to validation images\n",
+        "    'test': '/content/extracted_files/coco2017_subset/test2017',  # Relative path to test images (optional)\n",
+        "\n",
+        "    'nc': 80,  # Number of classes in COCO dataset\n",
+        "\n",
+        "    # Official COCO 2017 class names in order\n",
+        "    'names': [\n",
+        "        'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train',\n",
+        "        'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign',\n",
+        "        'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',\n",
+        "        'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag',\n",
+        "        'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite',\n",
+        "        'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket',\n",
+        "        'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana',\n",
+        "        'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',\n",
+        "        'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table',\n",
+        "        'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',\n",
+        "        'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock',\n",
+        "        'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush'\n",
+        "    ]\n",
+        "}\n",
+        "\n",
+        "# Path to save data.yaml\n",
+        "yaml_path = os.path.join(output_dir, 'data.yaml')\n",
+        "\n",
+        "# Write YAML file\n",
+        "with open(yaml_path, 'w') as file:\n",
+        "    yaml.dump(data, file, default_flow_style=False)\n",
+        "\n",
+        "print(f\"✅ data.yaml saved at: {yaml_path}\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Mf6_ajoCpFxE",
+        "outputId": "63dc678b-2518-4d2c-d7ef-e583931e6566"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "✅ data.yaml saved at: /content/extracted_files/coco2017_subset/data.yaml\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install ultralytics"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "collapsed": true,
+        "id": "BHV_huPYpL_L",
+        "outputId": "12a09ca2-e685-4022-cfdc-ff63071729f7"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting ultralytics\n",
+            "  Downloading ultralytics-8.3.89-py3-none-any.whl.metadata (35 kB)\n",
+            "Requirement already satisfied: numpy<=2.1.1,>=1.23.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (1.26.4)\n",
+            "Requirement already satisfied: matplotlib>=3.3.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (3.10.0)\n",
+            "Requirement already satisfied: opencv-python>=4.6.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (4.11.0.86)\n",
+            "Requirement already satisfied: pillow>=7.1.2 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (11.1.0)\n",
+            "Requirement already satisfied: pyyaml>=5.3.1 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (6.0.2)\n",
+            "Requirement already satisfied: requests>=2.23.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (2.32.3)\n",
+            "Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (1.14.1)\n",
+            "Requirement already satisfied: torch>=1.8.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (2.5.1+cu124)\n",
+            "Requirement already satisfied: torchvision>=0.9.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (0.20.1+cu124)\n",
+            "Requirement already satisfied: tqdm>=4.64.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (4.67.1)\n",
+            "Requirement already satisfied: psutil in /usr/local/lib/python3.11/dist-packages (from ultralytics) (5.9.5)\n",
+            "Requirement already satisfied: py-cpuinfo in /usr/local/lib/python3.11/dist-packages (from ultralytics) (9.0.0)\n",
+            "Requirement already satisfied: pandas>=1.1.4 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (2.2.2)\n",
+            "Requirement already satisfied: seaborn>=0.11.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (0.13.2)\n",
+            "Collecting ultralytics-thop>=2.0.0 (from ultralytics)\n",
+            "  Downloading ultralytics_thop-2.0.14-py3-none-any.whl.metadata (9.4 kB)\n",
+            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.3.1)\n",
+            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (0.12.1)\n",
+            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (4.56.0)\n",
+            "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.4.8)\n",
+            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (24.2)\n",
+            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (3.2.1)\n",
+            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (2.8.2)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.1.4->ultralytics) (2025.1)\n",
+            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.1.4->ultralytics) (2025.1)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests>=2.23.0->ultralytics) (3.4.1)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests>=2.23.0->ultralytics) (3.10)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests>=2.23.0->ultralytics) (2.3.0)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests>=2.23.0->ultralytics) (2025.1.31)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (3.17.0)\n",
+            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (4.12.2)\n",
+            "Requirement already satisfied: networkx in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (3.4.2)\n",
+            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (3.1.6)\n",
+            "Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (2024.10.0)\n",
+            "Collecting nvidia-cuda-nvrtc-cu12==12.4.127 (from torch>=1.8.0->ultralytics)\n",
+            "  Downloading nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
+            "Collecting nvidia-cuda-runtime-cu12==12.4.127 (from torch>=1.8.0->ultralytics)\n",
+            "  Downloading nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
+            "Collecting nvidia-cuda-cupti-cu12==12.4.127 (from torch>=1.8.0->ultralytics)\n",
+            "  Downloading nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
+            "Collecting nvidia-cudnn-cu12==9.1.0.70 (from torch>=1.8.0->ultralytics)\n",
+            "  Downloading nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
+            "Collecting nvidia-cublas-cu12==12.4.5.8 (from torch>=1.8.0->ultralytics)\n",
+            "  Downloading nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
+            "Collecting nvidia-cufft-cu12==11.2.1.3 (from torch>=1.8.0->ultralytics)\n",
+            "  Downloading nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
+            "Collecting nvidia-curand-cu12==10.3.5.147 (from torch>=1.8.0->ultralytics)\n",
+            "  Downloading nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
+            "Collecting nvidia-cusolver-cu12==11.6.1.9 (from torch>=1.8.0->ultralytics)\n",
+            "  Downloading nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
+            "Collecting nvidia-cusparse-cu12==12.3.1.170 (from torch>=1.8.0->ultralytics)\n",
+            "  Downloading nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
+            "Requirement already satisfied: nvidia-nccl-cu12==2.21.5 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (2.21.5)\n",
+            "Requirement already satisfied: nvidia-nvtx-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (12.4.127)\n",
+            "Collecting nvidia-nvjitlink-cu12==12.4.127 (from torch>=1.8.0->ultralytics)\n",
+            "  Downloading nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
+            "Requirement already satisfied: triton==3.1.0 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (3.1.0)\n",
+            "Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (1.13.1)\n",
+            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from sympy==1.13.1->torch>=1.8.0->ultralytics) (1.3.0)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.7->matplotlib>=3.3.0->ultralytics) (1.17.0)\n",
+            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.11/dist-packages (from jinja2->torch>=1.8.0->ultralytics) (3.0.2)\n",
+            "Downloading ultralytics-8.3.89-py3-none-any.whl (932 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m932.6/932.6 kB\u001b[0m \u001b[31m20.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_x86_64.whl (363.4 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m363.4/363.4 MB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (13.8 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.8/13.8 MB\u001b[0m \u001b[31m113.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (24.6 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m24.6/24.6 MB\u001b[0m \u001b[31m89.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (883 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m883.7/883.7 kB\u001b[0m \u001b[31m58.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl (664.8 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m664.8/664.8 MB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_x86_64.whl (211.5 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m211.5/211.5 MB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_x86_64.whl (56.3 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.3/56.3 MB\u001b[0m \u001b[31m14.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl (127.9 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m127.9/127.9 MB\u001b[0m \u001b[31m7.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl (207.5 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m207.5/207.5 MB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m67.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading ultralytics_thop-2.0.14-py3-none-any.whl (26 kB)\n",
+            "Installing collected packages: nvidia-nvjitlink-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, ultralytics-thop, ultralytics\n",
+            "  Attempting uninstall: nvidia-nvjitlink-cu12\n",
+            "    Found existing installation: nvidia-nvjitlink-cu12 12.5.82\n",
+            "    Uninstalling nvidia-nvjitlink-cu12-12.5.82:\n",
+            "      Successfully uninstalled nvidia-nvjitlink-cu12-12.5.82\n",
+            "  Attempting uninstall: nvidia-curand-cu12\n",
+            "    Found existing installation: nvidia-curand-cu12 10.3.6.82\n",
+            "    Uninstalling nvidia-curand-cu12-10.3.6.82:\n",
+            "      Successfully uninstalled nvidia-curand-cu12-10.3.6.82\n",
+            "  Attempting uninstall: nvidia-cufft-cu12\n",
+            "    Found existing installation: nvidia-cufft-cu12 11.2.3.61\n",
+            "    Uninstalling nvidia-cufft-cu12-11.2.3.61:\n",
+            "      Successfully uninstalled nvidia-cufft-cu12-11.2.3.61\n",
+            "  Attempting uninstall: nvidia-cuda-runtime-cu12\n",
+            "    Found existing installation: nvidia-cuda-runtime-cu12 12.5.82\n",
+            "    Uninstalling nvidia-cuda-runtime-cu12-12.5.82:\n",
+            "      Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82\n",
+            "  Attempting uninstall: nvidia-cuda-nvrtc-cu12\n",
+            "    Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82\n",
+            "    Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82:\n",
+            "      Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82\n",
+            "  Attempting uninstall: nvidia-cuda-cupti-cu12\n",
+            "    Found existing installation: nvidia-cuda-cupti-cu12 12.5.82\n",
+            "    Uninstalling nvidia-cuda-cupti-cu12-12.5.82:\n",
+            "      Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82\n",
+            "  Attempting uninstall: nvidia-cublas-cu12\n",
+            "    Found existing installation: nvidia-cublas-cu12 12.5.3.2\n",
+            "    Uninstalling nvidia-cublas-cu12-12.5.3.2:\n",
+            "      Successfully uninstalled nvidia-cublas-cu12-12.5.3.2\n",
+            "  Attempting uninstall: nvidia-cusparse-cu12\n",
+            "    Found existing installation: nvidia-cusparse-cu12 12.5.1.3\n",
+            "    Uninstalling nvidia-cusparse-cu12-12.5.1.3:\n",
+            "      Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3\n",
+            "  Attempting uninstall: nvidia-cudnn-cu12\n",
+            "    Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n",
+            "    Uninstalling nvidia-cudnn-cu12-9.3.0.75:\n",
+            "      Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n",
+            "  Attempting uninstall: nvidia-cusolver-cu12\n",
+            "    Found existing installation: nvidia-cusolver-cu12 11.6.3.83\n",
+            "    Uninstalling nvidia-cusolver-cu12-11.6.3.83:\n",
+            "      Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83\n",
+            "Successfully installed nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127 ultralytics-8.3.89 ultralytics-thop-2.0.14\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from ultralytics import YOLO\n",
+        "import torch\n",
+        "\n",
+        "print(torch.cuda.is_available())\n",
+        "print(torch.version.cuda)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "knsl_QyDpO6q",
+        "outputId": "ff389a95-60c3-4577-f0a9-00479856131e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Creating new Ultralytics Settings v0.0.6 file ✅ \n",
+            "View Ultralytics Settings with 'yolo settings' or at '/root/.config/Ultralytics/settings.json'\n",
+            "Update Settings with 'yolo settings key=value', i.e. 'yolo settings runs_dir=path/to/dir'. For help see https://docs.ultralytics.com/quickstart/#ultralytics-settings.\n",
+            "True\n",
+            "12.4\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model = YOLO(\"yolo11n.pt\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "OP1-6I39pxXP",
+        "outputId": "47bb135c-f689-436e-e1d1-d5ecba39eaba"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt to 'yolo11n.pt'...\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 5.35M/5.35M [00:00<00:00, 87.4MB/s]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data_yaml='/content/extracted_files/coco2017_subset/data.yaml'\n",
+        "epochs=2\n",
+        "img_size=640\n",
+        "batch_size=2"
+      ],
+      "metadata": {
+        "id": "u7k4fz7ap8EE"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Starting Yolo Training\")\n",
+        "# Change device to 'cpu' if cuda is unavailable\n",
+        "res = model.train(data=data_yaml, epochs=epochs, imgsz=img_size, batch=batch_size, device=0)\n",
+        "print(\"Training completed\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "k54qAEtdp-ei",
+        "outputId": "33cd707b-f5f0-4c17-d4e1-8386584f1e03"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Starting Yolo Training\n",
+            "Ultralytics 8.3.89 🚀 Python-3.11.11 torch-2.5.1+cu124 CUDA:0 (Tesla T4, 15095MiB)\n",
+            "\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=detect, mode=train, model=yolo11n.pt, data=/content/extracted_files/coco2017_subset/data.yaml, epochs=2, time=None, patience=100, batch=2, imgsz=640, save=True, save_period=-1, cache=False, device=0, workers=8, project=None, name=train, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=None, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train\n",
+            "Downloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf'...\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 755k/755k [00:00<00:00, 20.2MB/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "                   from  n    params  module                                       arguments                     \n",
+            "  0                  -1  1       464  ultralytics.nn.modules.conv.Conv             [3, 16, 3, 2]                 \n",
+            "  1                  -1  1      4672  ultralytics.nn.modules.conv.Conv             [16, 32, 3, 2]                \n",
+            "  2                  -1  1      6640  ultralytics.nn.modules.block.C3k2            [32, 64, 1, False, 0.25]      \n",
+            "  3                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                \n",
+            "  4                  -1  1     26080  ultralytics.nn.modules.block.C3k2            [64, 128, 1, False, 0.25]     \n",
+            "  5                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              \n",
+            "  6                  -1  1     87040  ultralytics.nn.modules.block.C3k2            [128, 128, 1, True]           \n",
+            "  7                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              \n",
+            "  8                  -1  1    346112  ultralytics.nn.modules.block.C3k2            [256, 256, 1, True]           \n",
+            "  9                  -1  1    164608  ultralytics.nn.modules.block.SPPF            [256, 256, 5]                 \n",
+            " 10                  -1  1    249728  ultralytics.nn.modules.block.C2PSA           [256, 256, 1]                 \n",
+            " 11                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
+            " 12             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
+            " 13                  -1  1    111296  ultralytics.nn.modules.block.C3k2            [384, 128, 1, False]          \n",
+            " 14                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
+            " 15             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
+            " 16                  -1  1     32096  ultralytics.nn.modules.block.C3k2            [256, 64, 1, False]           \n",
+            " 17                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                \n",
+            " 18            [-1, 13]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
+            " 19                  -1  1     86720  ultralytics.nn.modules.block.C3k2            [192, 128, 1, False]          \n",
+            " 20                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              \n",
+            " 21            [-1, 10]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
+            " 22                  -1  1    378880  ultralytics.nn.modules.block.C3k2            [384, 256, 1, True]           \n",
+            " 23        [16, 19, 22]  1    464912  ultralytics.nn.modules.head.Detect           [80, [64, 128, 256]]          \n",
+            "YOLO11n summary: 181 layers, 2,624,080 parameters, 2,624,064 gradients, 6.6 GFLOPs\n",
+            "\n",
+            "Transferred 499/499 items from pretrained weights\n",
+            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/detect/train', view at http://localhost:6006/\n",
+            "Freezing layer 'model.23.dfl.conv.weight'\n",
+            "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks...\n",
+            "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/extracted_files/coco2017_subset/train2017... 29315 images, 256 backgrounds, 0 corrupt: 100%|██████████| 29571/29571 [01:21<00:00, 364.15it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/extracted_files/coco2017_subset/train2017.cache\n",
+            "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, num_output_channels=3, method='weighted_average'), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/extracted_files/coco2017_subset/val2017... 0 images, 1250 backgrounds, 0 corrupt: 100%|██████████| 1250/1250 [00:02<00:00, 547.33it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[34m\u001b[1mval: \u001b[0mWARNING ⚠️ No labels found in /content/extracted_files/coco2017_subset/val2017.cache. See https://docs.ultralytics.com/datasets for dataset formatting guidance.\n",
+            "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/extracted_files/coco2017_subset/val2017.cache\n",
+            "WARNING ⚠️ No labels found in /content/extracted_files/coco2017_subset/val2017.cache, training may not work correctly. See https://docs.ultralytics.com/datasets for dataset formatting guidance.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Plotting labels to runs/detect/train/labels.jpg... \n",
+            "\u001b[34m\u001b[1moptimizer:\u001b[0m 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... \n",
+            "\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.000119, momentum=0.9) with parameter groups 81 weight(decay=0.0), 88 weight(decay=0.0005), 87 bias(decay=0.0)\n",
+            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mmodel graph visualization added ✅\n",
+            "Image sizes 640 train, 640 val\n",
+            "Using 2 dataloader workers\n",
+            "Logging results to \u001b[1mruns/detect/train\u001b[0m\n",
+            "Starting training for 2 epochs...\n",
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "        1/2      0.57G      1.281      1.852      1.332         17        640: 100%|██████████| 14786/14786 [28:30<00:00,  8.64it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 313/313 [00:16<00:00, 18.50it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all       1250          0          0          0          0          0\n",
+            "WARNING ⚠️ no labels found in detect set, can not compute metrics without labels\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.11/dist-packages/ultralytics/utils/metrics.py:619: RuntimeWarning: Mean of empty slice.\n",
+            "  i = smooth(f1_curve.mean(0), 0.1).argmax()  # max F1 index\n",
+            "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:121: RuntimeWarning: invalid value encountered in divide\n",
+            "  ret = um.true_divide(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "        2/2     0.611G      1.241      1.727      1.305          3        640: 100%|██████████| 14786/14786 [27:37<00:00,  8.92it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 313/313 [00:14<00:00, 21.42it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all       1250          0          0          0          0          0\n",
+            "WARNING ⚠️ no labels found in detect set, can not compute metrics without labels\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.11/dist-packages/ultralytics/utils/metrics.py:619: RuntimeWarning: Mean of empty slice.\n",
+            "  i = smooth(f1_curve.mean(0), 0.1).argmax()  # max F1 index\n",
+            "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:121: RuntimeWarning: invalid value encountered in divide\n",
+            "  ret = um.true_divide(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "2 epochs completed in 0.946 hours.\n",
+            "Optimizer stripped from runs/detect/train/weights/last.pt, 5.5MB\n",
+            "Optimizer stripped from runs/detect/train/weights/best.pt, 5.5MB\n",
+            "\n",
+            "Validating runs/detect/train/weights/best.pt...\n",
+            "Ultralytics 8.3.89 🚀 Python-3.11.11 torch-2.5.1+cu124 CUDA:0 (Tesla T4, 15095MiB)\n",
+            "YOLO11n summary (fused): 100 layers, 2,616,248 parameters, 0 gradients, 6.5 GFLOPs\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 313/313 [00:12<00:00, 24.83it/s]\n",
+            "/usr/local/lib/python3.11/dist-packages/ultralytics/utils/metrics.py:467: RuntimeWarning: Mean of empty slice.\n",
+            "  ax.plot(px, py.mean(1), linewidth=3, color=\"blue\", label=f\"all classes {ap[:, 0].mean():.3f} mAP@0.5\")\n",
+            "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+            "  ret = ret.dtype.type(ret / rcount)\n",
+            "/usr/local/lib/python3.11/dist-packages/ultralytics/utils/metrics.py:491: RuntimeWarning: Mean of empty slice.\n",
+            "  y = smooth(py.mean(0), 0.05)\n",
+            "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:121: RuntimeWarning: invalid value encountered in divide\n",
+            "  ret = um.true_divide(\n",
+            "/usr/local/lib/python3.11/dist-packages/ultralytics/utils/metrics.py:491: RuntimeWarning: Mean of empty slice.\n",
+            "  y = smooth(py.mean(0), 0.05)\n",
+            "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:121: RuntimeWarning: invalid value encountered in divide\n",
+            "  ret = um.true_divide(\n",
+            "/usr/local/lib/python3.11/dist-packages/ultralytics/utils/metrics.py:491: RuntimeWarning: Mean of empty slice.\n",
+            "  y = smooth(py.mean(0), 0.05)\n",
+            "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:121: RuntimeWarning: invalid value encountered in divide\n",
+            "  ret = um.true_divide(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all       1250          0          0          0          0          0\n",
+            "WARNING ⚠️ no labels found in detect set, can not compute metrics without labels\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.11/dist-packages/ultralytics/utils/metrics.py:619: RuntimeWarning: Mean of empty slice.\n",
+            "  i = smooth(f1_curve.mean(0), 0.1).argmax()  # max F1 index\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Speed: 0.3ms preprocess, 5.7ms inference, 0.0ms loss, 1.7ms postprocess per image\n",
+            "Results saved to \u001b[1mruns/detect/train\u001b[0m\n",
+            "Training completed\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "MtffG6hv9uBe",
+        "outputId": "d75d9036-48bc-412d-d21b-5d6c5e53bb3d"
+      },
+      "execution_count": null,
+      "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"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import files\n",
+        "files.download('best_yolo.pt')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "SmoUDTn_9wnp",
+        "outputId": "d30ad812-5d32-4a6a-a587-3c7ba6e7cef4"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "\n",
+              "    async function download(id, filename, size) {\n",
+              "      if (!google.colab.kernel.accessAllowed) {\n",
+              "        return;\n",
+              "      }\n",
+              "      const div = document.createElement('div');\n",
+              "      const label = document.createElement('label');\n",
+              "      label.textContent = `Downloading \"${filename}\": `;\n",
+              "      div.appendChild(label);\n",
+              "      const progress = document.createElement('progress');\n",
+              "      progress.max = size;\n",
+              "      div.appendChild(progress);\n",
+              "      document.body.appendChild(div);\n",
+              "\n",
+              "      const buffers = [];\n",
+              "      let downloaded = 0;\n",
+              "\n",
+              "      const channel = await google.colab.kernel.comms.open(id);\n",
+              "      // Send a message to notify the kernel that we're ready.\n",
+              "      channel.send({})\n",
+              "\n",
+              "      for await (const message of channel.messages) {\n",
+              "        // Send a message to notify the kernel that we're ready.\n",
+              "        channel.send({})\n",
+              "        if (message.buffers) {\n",
+              "          for (const buffer of message.buffers) {\n",
+              "            buffers.push(buffer);\n",
+              "            downloaded += buffer.byteLength;\n",
+              "            progress.value = downloaded;\n",
+              "          }\n",
+              "        }\n",
+              "      }\n",
+              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
+              "      const a = document.createElement('a');\n",
+              "      a.href = window.URL.createObjectURL(blob);\n",
+              "      a.download = filename;\n",
+              "      div.appendChild(a);\n",
+              "      a.click();\n",
+              "      div.remove();\n",
+              "    }\n",
+              "  "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "download(\"download_db2f30b9-385b-4439-9213-9823680dfcb1\", \"best_yolo.pt\", 5534714)"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pip install torch"
+      ],
+      "metadata": {
+        "id": "mkIv0jqp9zuz",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "73d5d8d3-7a59-4cf9-b22d-06353dd7cd69"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: torch in /usr/local/lib/python3.11/dist-packages (2.5.1+cu124)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from torch) (3.17.0)\n",
+            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.11/dist-packages (from torch) (4.12.2)\n",
+            "Requirement already satisfied: networkx in /usr/local/lib/python3.11/dist-packages (from torch) (3.4.2)\n",
+            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.11/dist-packages (from torch) (3.1.6)\n",
+            "Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from torch) (2024.10.0)\n",
+            "Collecting nvidia-cuda-nvrtc-cu12==12.4.127 (from torch)\n",
+            "  Downloading nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
+            "Collecting nvidia-cuda-runtime-cu12==12.4.127 (from torch)\n",
+            "  Downloading nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
+            "Collecting nvidia-cuda-cupti-cu12==12.4.127 (from torch)\n",
+            "  Downloading nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
+            "Collecting nvidia-cudnn-cu12==9.1.0.70 (from torch)\n",
+            "  Downloading nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
+            "Collecting nvidia-cublas-cu12==12.4.5.8 (from torch)\n",
+            "  Downloading nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
+            "Collecting nvidia-cufft-cu12==11.2.1.3 (from torch)\n",
+            "  Downloading nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
+            "Collecting nvidia-curand-cu12==10.3.5.147 (from torch)\n",
+            "  Downloading nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
+            "Collecting nvidia-cusolver-cu12==11.6.1.9 (from torch)\n",
+            "  Downloading nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
+            "Collecting nvidia-cusparse-cu12==12.3.1.170 (from torch)\n",
+            "  Downloading nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
+            "Requirement already satisfied: nvidia-nccl-cu12==2.21.5 in /usr/local/lib/python3.11/dist-packages (from torch) (2.21.5)\n",
+            "Requirement already satisfied: nvidia-nvtx-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch) (12.4.127)\n",
+            "Collecting nvidia-nvjitlink-cu12==12.4.127 (from torch)\n",
+            "  Downloading nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
+            "Requirement already satisfied: triton==3.1.0 in /usr/local/lib/python3.11/dist-packages (from torch) (3.1.0)\n",
+            "Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.11/dist-packages (from torch) (1.13.1)\n",
+            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from sympy==1.13.1->torch) (1.3.0)\n",
+            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.11/dist-packages (from jinja2->torch) (3.0.2)\n",
+            "Downloading nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_x86_64.whl (363.4 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m363.4/363.4 MB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (13.8 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.8/13.8 MB\u001b[0m \u001b[31m43.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (24.6 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m24.6/24.6 MB\u001b[0m \u001b[31m28.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (883 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m883.7/883.7 kB\u001b[0m \u001b[31m23.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl (664.8 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m664.8/664.8 MB\u001b[0m \u001b[31m1.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_x86_64.whl (211.5 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m211.5/211.5 MB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_x86_64.whl (56.3 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.3/56.3 MB\u001b[0m \u001b[31m9.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl (127.9 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m127.9/127.9 MB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl (207.5 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m207.5/207.5 MB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m52.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hInstalling collected packages: nvidia-nvjitlink-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12\n",
+            "  Attempting uninstall: nvidia-nvjitlink-cu12\n",
+            "    Found existing installation: nvidia-nvjitlink-cu12 12.5.82\n",
+            "    Uninstalling nvidia-nvjitlink-cu12-12.5.82:\n",
+            "      Successfully uninstalled nvidia-nvjitlink-cu12-12.5.82\n",
+            "  Attempting uninstall: nvidia-curand-cu12\n",
+            "    Found existing installation: nvidia-curand-cu12 10.3.6.82\n",
+            "    Uninstalling nvidia-curand-cu12-10.3.6.82:\n",
+            "      Successfully uninstalled nvidia-curand-cu12-10.3.6.82\n",
+            "  Attempting uninstall: nvidia-cufft-cu12\n",
+            "    Found existing installation: nvidia-cufft-cu12 11.2.3.61\n",
+            "    Uninstalling nvidia-cufft-cu12-11.2.3.61:\n",
+            "      Successfully uninstalled nvidia-cufft-cu12-11.2.3.61\n",
+            "  Attempting uninstall: nvidia-cuda-runtime-cu12\n",
+            "    Found existing installation: nvidia-cuda-runtime-cu12 12.5.82\n",
+            "    Uninstalling nvidia-cuda-runtime-cu12-12.5.82:\n",
+            "      Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82\n",
+            "  Attempting uninstall: nvidia-cuda-nvrtc-cu12\n",
+            "    Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82\n",
+            "    Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82:\n",
+            "      Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82\n",
+            "  Attempting uninstall: nvidia-cuda-cupti-cu12\n",
+            "    Found existing installation: nvidia-cuda-cupti-cu12 12.5.82\n",
+            "    Uninstalling nvidia-cuda-cupti-cu12-12.5.82:\n",
+            "      Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82\n",
+            "  Attempting uninstall: nvidia-cublas-cu12\n",
+            "    Found existing installation: nvidia-cublas-cu12 12.5.3.2\n",
+            "    Uninstalling nvidia-cublas-cu12-12.5.3.2:\n",
+            "      Successfully uninstalled nvidia-cublas-cu12-12.5.3.2\n",
+            "  Attempting uninstall: nvidia-cusparse-cu12\n",
+            "    Found existing installation: nvidia-cusparse-cu12 12.5.1.3\n",
+            "    Uninstalling nvidia-cusparse-cu12-12.5.1.3:\n",
+            "      Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3\n",
+            "  Attempting uninstall: nvidia-cudnn-cu12\n",
+            "    Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n",
+            "    Uninstalling nvidia-cudnn-cu12-9.3.0.75:\n",
+            "      Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n",
+            "  Attempting uninstall: nvidia-cusolver-cu12\n",
+            "    Found existing installation: nvidia-cusolver-cu12 11.6.3.83\n",
+            "    Uninstalling nvidia-cusolver-cu12-11.6.3.83:\n",
+            "      Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83\n",
+            "Successfully installed nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pip install ultralytics"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Kz7nfBYUKV2G",
+        "outputId": "ffb1e6a6-6b14-48b9-b5b6-023daa41383e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting ultralytics\n",
+            "  Downloading ultralytics-8.3.89-py3-none-any.whl.metadata (35 kB)\n",
+            "Requirement already satisfied: numpy<=2.1.1,>=1.23.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (1.26.4)\n",
+            "Requirement already satisfied: matplotlib>=3.3.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (3.10.0)\n",
+            "Requirement already satisfied: opencv-python>=4.6.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (4.11.0.86)\n",
+            "Requirement already satisfied: pillow>=7.1.2 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (11.1.0)\n",
+            "Requirement already satisfied: pyyaml>=5.3.1 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (6.0.2)\n",
+            "Requirement already satisfied: requests>=2.23.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (2.32.3)\n",
+            "Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (1.14.1)\n",
+            "Requirement already satisfied: torch>=1.8.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (2.5.1+cu124)\n",
+            "Requirement already satisfied: torchvision>=0.9.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (0.20.1+cu124)\n",
+            "Requirement already satisfied: tqdm>=4.64.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (4.67.1)\n",
+            "Requirement already satisfied: psutil in /usr/local/lib/python3.11/dist-packages (from ultralytics) (5.9.5)\n",
+            "Requirement already satisfied: py-cpuinfo in /usr/local/lib/python3.11/dist-packages (from ultralytics) (9.0.0)\n",
+            "Requirement already satisfied: pandas>=1.1.4 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (2.2.2)\n",
+            "Requirement already satisfied: seaborn>=0.11.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (0.13.2)\n",
+            "Collecting ultralytics-thop>=2.0.0 (from ultralytics)\n",
+            "  Downloading ultralytics_thop-2.0.14-py3-none-any.whl.metadata (9.4 kB)\n",
+            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.3.1)\n",
+            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (0.12.1)\n",
+            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (4.56.0)\n",
+            "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.4.8)\n",
+            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (24.2)\n",
+            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (3.2.1)\n",
+            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (2.8.2)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.1.4->ultralytics) (2025.1)\n",
+            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.1.4->ultralytics) (2025.1)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests>=2.23.0->ultralytics) (3.4.1)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests>=2.23.0->ultralytics) (3.10)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests>=2.23.0->ultralytics) (2.3.0)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests>=2.23.0->ultralytics) (2025.1.31)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (3.17.0)\n",
+            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (4.12.2)\n",
+            "Requirement already satisfied: networkx in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (3.4.2)\n",
+            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (3.1.6)\n",
+            "Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (2024.10.0)\n",
+            "Collecting nvidia-cuda-nvrtc-cu12==12.4.127 (from torch>=1.8.0->ultralytics)\n",
+            "  Downloading nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
+            "Collecting nvidia-cuda-runtime-cu12==12.4.127 (from torch>=1.8.0->ultralytics)\n",
+            "  Downloading nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
+            "Collecting nvidia-cuda-cupti-cu12==12.4.127 (from torch>=1.8.0->ultralytics)\n",
+            "  Downloading nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
+            "Collecting nvidia-cudnn-cu12==9.1.0.70 (from torch>=1.8.0->ultralytics)\n",
+            "  Downloading nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
+            "Collecting nvidia-cublas-cu12==12.4.5.8 (from torch>=1.8.0->ultralytics)\n",
+            "  Downloading nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
+            "Collecting nvidia-cufft-cu12==11.2.1.3 (from torch>=1.8.0->ultralytics)\n",
+            "  Downloading nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
+            "Collecting nvidia-curand-cu12==10.3.5.147 (from torch>=1.8.0->ultralytics)\n",
+            "  Downloading nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
+            "Collecting nvidia-cusolver-cu12==11.6.1.9 (from torch>=1.8.0->ultralytics)\n",
+            "  Downloading nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
+            "Collecting nvidia-cusparse-cu12==12.3.1.170 (from torch>=1.8.0->ultralytics)\n",
+            "  Downloading nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
+            "Requirement already satisfied: nvidia-nccl-cu12==2.21.5 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (2.21.5)\n",
+            "Requirement already satisfied: nvidia-nvtx-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (12.4.127)\n",
+            "Collecting nvidia-nvjitlink-cu12==12.4.127 (from torch>=1.8.0->ultralytics)\n",
+            "  Downloading nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
+            "Requirement already satisfied: triton==3.1.0 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (3.1.0)\n",
+            "Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (1.13.1)\n",
+            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from sympy==1.13.1->torch>=1.8.0->ultralytics) (1.3.0)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.7->matplotlib>=3.3.0->ultralytics) (1.17.0)\n",
+            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.11/dist-packages (from jinja2->torch>=1.8.0->ultralytics) (3.0.2)\n",
+            "Downloading ultralytics-8.3.89-py3-none-any.whl (932 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m932.6/932.6 kB\u001b[0m \u001b[31m26.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_x86_64.whl (363.4 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m363.4/363.4 MB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (13.8 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.8/13.8 MB\u001b[0m \u001b[31m114.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (24.6 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m24.6/24.6 MB\u001b[0m \u001b[31m88.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (883 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m883.7/883.7 kB\u001b[0m \u001b[31m58.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl (664.8 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m664.8/664.8 MB\u001b[0m \u001b[31m2.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_x86_64.whl (211.5 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m211.5/211.5 MB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_x86_64.whl (56.3 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.3/56.3 MB\u001b[0m \u001b[31m12.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl (127.9 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m127.9/127.9 MB\u001b[0m \u001b[31m10.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl (207.5 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m207.5/207.5 MB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m95.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading ultralytics_thop-2.0.14-py3-none-any.whl (26 kB)\n",
+            "Installing collected packages: nvidia-nvjitlink-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, ultralytics-thop, ultralytics\n",
+            "  Attempting uninstall: nvidia-nvjitlink-cu12\n",
+            "    Found existing installation: nvidia-nvjitlink-cu12 12.5.82\n",
+            "    Uninstalling nvidia-nvjitlink-cu12-12.5.82:\n",
+            "      Successfully uninstalled nvidia-nvjitlink-cu12-12.5.82\n",
+            "  Attempting uninstall: nvidia-curand-cu12\n",
+            "    Found existing installation: nvidia-curand-cu12 10.3.6.82\n",
+            "    Uninstalling nvidia-curand-cu12-10.3.6.82:\n",
+            "      Successfully uninstalled nvidia-curand-cu12-10.3.6.82\n",
+            "  Attempting uninstall: nvidia-cufft-cu12\n",
+            "    Found existing installation: nvidia-cufft-cu12 11.2.3.61\n",
+            "    Uninstalling nvidia-cufft-cu12-11.2.3.61:\n",
+            "      Successfully uninstalled nvidia-cufft-cu12-11.2.3.61\n",
+            "  Attempting uninstall: nvidia-cuda-runtime-cu12\n",
+            "    Found existing installation: nvidia-cuda-runtime-cu12 12.5.82\n",
+            "    Uninstalling nvidia-cuda-runtime-cu12-12.5.82:\n",
+            "      Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82\n",
+            "  Attempting uninstall: nvidia-cuda-nvrtc-cu12\n",
+            "    Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82\n",
+            "    Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82:\n",
+            "      Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82\n",
+            "  Attempting uninstall: nvidia-cuda-cupti-cu12\n",
+            "    Found existing installation: nvidia-cuda-cupti-cu12 12.5.82\n",
+            "    Uninstalling nvidia-cuda-cupti-cu12-12.5.82:\n",
+            "      Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82\n",
+            "  Attempting uninstall: nvidia-cublas-cu12\n",
+            "    Found existing installation: nvidia-cublas-cu12 12.5.3.2\n",
+            "    Uninstalling nvidia-cublas-cu12-12.5.3.2:\n",
+            "      Successfully uninstalled nvidia-cublas-cu12-12.5.3.2\n",
+            "  Attempting uninstall: nvidia-cusparse-cu12\n",
+            "    Found existing installation: nvidia-cusparse-cu12 12.5.1.3\n",
+            "    Uninstalling nvidia-cusparse-cu12-12.5.1.3:\n",
+            "      Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3\n",
+            "  Attempting uninstall: nvidia-cudnn-cu12\n",
+            "    Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n",
+            "    Uninstalling nvidia-cudnn-cu12-9.3.0.75:\n",
+            "      Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n",
+            "  Attempting uninstall: nvidia-cusolver-cu12\n",
+            "    Found existing installation: nvidia-cusolver-cu12 11.6.3.83\n",
+            "    Uninstalling nvidia-cusolver-cu12-11.6.3.83:\n",
+            "      Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83\n",
+            "Successfully installed nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127 ultralytics-8.3.89 ultralytics-thop-2.0.14\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "fiHknmKWxlbt"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/app1.py b/app1.py
new file mode 100644
index 0000000..4210fc0
--- /dev/null
+++ b/app1.py
@@ -0,0 +1,81 @@
+import streamlit as st
+import cv2
+import numpy as np
+import tempfile
+import time
+from ultralytics import YOLO
+
+# Load the YOLO model (adjust the path as needed)
+model = YOLO("best_yolo1.pt")
+
+# Sidebar configuration for confidence threshold and mode selection
+confidence_threshold = st.sidebar.slider("Confidence Threshold", 0.0, 1.0, 0.5, 0.05)
+mode = st.sidebar.selectbox("Select Mode", ["Image", "Video", "Webcam"])
+
+st.title("YOLO Object Detection with Streamlit")
+
+if mode == "Image":
+    st.header("Image Detection")
+    uploaded_image = st.file_uploader("Upload an Image", type=["jpg", "jpeg", "png"])
+    if uploaded_image is not None:
+        # Convert the uploaded image to a NumPy array
+        file_bytes = np.asarray(bytearray(uploaded_image.read()), dtype=np.uint8)
+        image = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
+        st.image(image, channels="BGR", caption="Original Image")
+        # Run YOLO detection on the image
+        results = model.predict(source=image, conf=confidence_threshold, show=False)
+        annotated_image = results[0].plot()  # Annotated image with detections
+        st.image(annotated_image, channels="BGR", caption="Detection Result")
+
+elif mode == "Video":
+    st.header("Video Detection")
+    uploaded_video = st.file_uploader("Upload a Video", type=["mp4", "mov", "avi"])
+    if uploaded_video is not None:
+        # Save the uploaded video to a temporary file
+        tfile = tempfile.NamedTemporaryFile(delete=False)
+        tfile.write(uploaded_video.read())
+        cap = cv2.VideoCapture(tfile.name)
+        stframe = st.empty()  # Placeholder to update video frames
+        while cap.isOpened():
+            ret, frame = cap.read()
+            if not ret:
+                break
+            # Run YOLO detection on the current frame
+            results = model.predict(source=frame, conf=confidence_threshold, show=False)
+            annotated_frame = results[0].plot()
+            stframe.image(annotated_frame, channels="BGR")
+            time.sleep(0.03)  # Small delay to control frame rate
+        cap.release()
+
+elif mode == "Webcam":
+    st.header("Webcam Live Detection (OpenCV Method)")
+    
+    # Initialize webcam run state in session state
+    if "run_webcam" not in st.session_state:
+        st.session_state.run_webcam = False
+
+    # Buttons to start and stop webcam capture
+    start_button = st.button("Start Webcam")
+    stop_button = st.button("Stop Webcam")
+    if start_button:
+        st.session_state.run_webcam = True
+    if stop_button:
+        st.session_state.run_webcam = False
+
+    webcam_placeholder = st.empty()  # Placeholder for webcam frames
+
+    if st.session_state.run_webcam:
+        cap = cv2.VideoCapture(0)  # Open default webcam
+        st.write("Starting webcam...")
+        while st.session_state.run_webcam and cap.isOpened():
+            ret, frame = cap.read()
+            if not ret:
+                st.write("Failed to grab frame")
+                break
+            # Run YOLO detection on the current webcam frame
+            results = model.predict(source=frame, conf=confidence_threshold, show=False)
+            annotated_frame = results[0].plot()
+            webcam_placeholder.image(annotated_frame, channels="BGR")
+            time.sleep(0.03)  # Small delay to control frame rate
+        cap.release()
+        st.write("Webcam stopped.")
diff --git a/best_yolo1.pt b/best_yolo1.pt
new file mode 100644
index 0000000..690cfec
Binary files /dev/null and b/best_yolo1.pt differ