diff --git a/Animals.ipynb b/Animals.ipynb
new file mode 100644
index 00000000..c20e6715
--- /dev/null
+++ b/Animals.ipynb
@@ -0,0 +1,831 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "machine_shape": "hm",
+      "gpuType": "A100"
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "jIO05x7M7SmE",
+        "outputId": "1fec9124-0605-40c9-a9f1-72131285ed89"
+      },
+      "execution_count": 1,
+      "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": [
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "import torch.optim as optim\n",
+        "from torch.utils.data import DataLoader, random_split\n",
+        "from torchvision import datasets, transforms\n",
+        "from torchvision.models import resnet18, ResNet18_Weights\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "from sklearn.metrics import confusion_matrix, classification_report\n",
+        "import seaborn as sns\n",
+        "from PIL import Image\n",
+        "import os\n",
+        "from tqdm import tqdm\n",
+        "from multiprocessing import Pool, cpu_count"
+      ],
+      "metadata": {
+        "id": "g5rztu8b0SMC"
+      },
+      "execution_count": 16,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "torch.backends.cuda.matmul.allow_tf32 = True\n",
+        "torch.backends.cudnn.allow_tf32 = True\n",
+        "\n",
+        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+        "\n",
+        "\n",
+        "def is_valid_image(file_path):\n",
+        "    \"\"\"Check if a file is a valid image.\"\"\"\n",
+        "    try:\n",
+        "        with Image.open(file_path) as img:\n",
+        "            img.verify()\n",
+        "        return True\n",
+        "    except Exception:\n",
+        "        return False\n",
+        "\n",
+        "\n",
+        "def filter_valid_images(file_paths):\n",
+        "    \"\"\"Filter valid images using multiprocessing.\"\"\"\n",
+        "    with Pool(cpu_count()) as pool:\n",
+        "        results = pool.map(is_valid_image, file_paths)\n",
+        "    return [file_path for file_path, is_valid in zip(file_paths, results) if is_valid]"
+      ],
+      "metadata": {
+        "id": "H8TBGswEOgQ-"
+      },
+      "execution_count": 17,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def load_dataset(data_path, batch_size=64, num_workers=4):\n",
+        "\n",
+        "    train_transforms = transforms.Compose([\n",
+        "        transforms.Resize((224, 224)),\n",
+        "        transforms.RandomHorizontalFlip(),\n",
+        "        transforms.RandomRotation(10),\n",
+        "        transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2),\n",
+        "        transforms.ToTensor(),\n",
+        "        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
+        "    ])\n",
+        "\n",
+        "    val_transforms = transforms.Compose([\n",
+        "        transforms.Resize((224, 224)),\n",
+        "        transforms.ToTensor(),\n",
+        "        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
+        "    ])\n",
+        "\n",
+        "    full_dataset = datasets.ImageFolder(root=data_path, transform=train_transforms)\n",
+        "\n",
+        "    train_size = int(0.8 * len(full_dataset))\n",
+        "    val_size = len(full_dataset) - train_size\n",
+        "    train_dataset, val_dataset = random_split(full_dataset, [train_size, val_size])\n",
+        "\n",
+        "    val_dataset.dataset.transform = val_transforms\n",
+        "\n",
+        "    train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers, pin_memory=True)\n",
+        "    val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers, pin_memory=True)\n",
+        "\n",
+        "    return train_loader, val_loader"
+      ],
+      "metadata": {
+        "id": "Fq5Bp7ePU0rc"
+      },
+      "execution_count": 18,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def visualize_images(dataloader, class_names, num_images=5):\n",
+        "    images, labels = next(iter(dataloader))\n",
+        "    images = images.cpu().numpy()\n",
+        "    labels = labels.cpu().numpy()\n",
+        "\n",
+        "    plt.figure(figsize=(10, 10))\n",
+        "    for i in range(num_images):\n",
+        "        plt.subplot(1, num_images, i + 1)\n",
+        "        plt.imshow(np.transpose(images[i], (1, 2, 0)))\n",
+        "        plt.title(class_names[labels[i]])\n",
+        "        plt.axis(\"off\")\n",
+        "    plt.show()"
+      ],
+      "metadata": {
+        "id": "j20RO3pWU2Iu"
+      },
+      "execution_count": 19,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data_path = \"/content/drive/My Drive/raw-img\"\n",
+        "train_loader, val_loader = load_dataset(data_path)\n",
+        "\n",
+        "class_names = train_loader.dataset.dataset.classes\n",
+        "\n",
+        "visualize_images(train_loader, class_names)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 279
+        },
+        "id": "I8YByPN9U6c4",
+        "outputId": "af9dce68-fc9a-4da1-e726-eef731dfaf27"
+      },
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-2.0665298..2.64].\n",
+            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-2.0665298..2.64].\n",
+            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-2.117904..2.4308496].\n",
+            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-2.0665298..2.64].\n",
+            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-2.0182073..2.6051416].\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x1000 with 5 Axes>"
+            ],
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxsAAACvCAYAAACVbcM3AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzsnXecHVX5/98zc/u92ze76b0HCC2hhm4D9AsqiPoFC8VeUH8odsGGfhXFih2xAioIItUgvYQ00nvdJJvtu7fPzPn98Zyzd3aTQAKkAPN5vW72Zu6UMzNnzjyf83ye57GUUooQIUKECBEiRIgQIUKEeJlhH+wGhAgRIkSIECFChAgR4tWJkGyECBEiRIgQIUKECBFivyAkGyFChAgRIkSIECFChNgvCMlGiBAhQoQIESJEiBAh9gtCshEiRIgQIUKECBEiRIj9gpBshAgRIkSIECFChAgRYr8gJBshQoQIESJEiBAhQoTYLwjJRogQIUKECBEiRIgQIfYLQrIRIkSIECFChAgRIkSI/YJXNdn46le/imVZtLW1HeymhHgNIux/IUKECBEiRIjXOl7VZCNEiBAhQhxYtLS08NWvfpWFCxce7KaECBEixAFFOP7tHiHZCBEiRIgQLxtaWlr42te+Fr5sQ4QI8ZpDOP7tHiHZeAlQSpHP5w92M0K8RhH2vxAhQoQIESLEoY7XBNno6urive99L7W1tdTU1PC+972PXC7X/7vrulx77bVMmDCBeDzO2LFj+fznP0+xWBywn7Fjx3Luuedy7733cuyxx5JMJrnxxhsBuP/++zn55JOpra0lk8kwZcoUPv/5zw/Yvlgs8pWvfIWJEycSj8cZNWoUV1111S7HCfHqQtj/QrxSsHXrVi699FKGDx9OPB5n3LhxfOhDH6JUKtHR0cFnPvMZDj/8cDKZDNXV1bzpTW9i0aJF/ds/9NBDzJo1C4D3ve99WJaFZVn87ne/O0hnFOJQxvP1N4B169ZxwQUXUF9fTyqV4vjjj+df//pX//ZKKRobG/nUpz7Vv8z3fWpra3Ech66urv7l1113HZFIhL6+vgN2fiFeWQjHv/2HyMFuwIHAhRdeyLhx4/jWt77F/Pnz+dWvfkVTUxPXXXcdAJdddhk33XQTb3/72/n0pz/NU089xbe+9S2WL1/OP/7xjwH7WrlyJe985zv5wAc+wOWXX86UKVNYunQp5557LkcccQTXXHMN8XicNWvW8Nhjj/Vv5/s+b3nLW3j00Ue54oormDZtGs899xzXX389q1at4vbbbz+QlyTEAUTY/0K8EtDS0sLs2bPp6uriiiuuYOrUqWzdupXbbruNXC7HunXruP3227ngggsYN24cO3bs4MYbb+TUU09l2bJlDB8+nGnTpnHNNdfw5S9/mSuuuII5c+YAcOKJJx7kswtxqOGF+ltnZycnnngiuVyOj3/84zQ0NHDTTTfxlre8hdtuu43zzz8fy7I46aSTePjhh/v3u3jxYrq7u7Ftm8cee4xzzjkHgEceeYSjjjqKTCZzsE45xCGMcPzbz1CvYnzlK19RgHr/+98/YPn555+vGhoalFJKLVy4UAHqsssuG7DOZz7zGQWo//znP/3LxowZowB1zz33DFj3+uuvV4DauXPnHtty8803K9u21SOPPDJg+c9//nMFqMcee+xFnWOIQxdh/wvxSsIll1yibNtWzzzzzC6/+b6vCoWC8jxvwPL169ereDyurrnmmv5lzzzzjALUb3/72/3d5BCvYLxQf/vkJz+pgAFjVm9vrxo3bpwaO3Zsf1/87ne/qxzHUT09PUoppW644QY1ZswYNXv2bPXZz35WKaWU53mqtrZWXXnllQfgzEK8EhGOf/sXrwkZ1Qc/+MEB/58zZw7t7e309PRw9913AwxwwwJ8+tOfBhjgsgUYN24cb3jDGwYsq62tBeCOO+7A9/3dtuHWW29l2rRpTJ06lba2tv7PGWecAcDcuXNf3MmFOOQR9r8Qhzp83+f222/nzW9+M8cee+wuv1uWRTwex7blleF5Hu3t7f2Svfnz5x/oJod4BWNv+tvdd9/N7NmzOfnkk/uXZzIZrrjiCjZs2MCyZcsAGU89z+Pxxx8HxIMxZ84c5syZwyOPPALAkiVL6Orq6p9pDhEiiHD82/94TZCN0aNHD/h/XV0dAJ2dnWzcuBHbtpk4ceKAdYYOHUptbS0bN24csHzcuHG77P8d73gHJ510EpdddhnNzc1cdNFF3HLLLQMMv9WrV7N06VKGDBky4DN58mQAWltbX5ZzDXHoIex/IQ517Ny5k56eHg477LA9ruP7Ptdffz2TJk0iHo/T2NjIkCFD+mUrIULsLfamv23cuJEpU6bssnzatGn9vwMcffTRpFKpfmJhyMYpp5zCvHnzKBQK/b8FiUuIEAbh+Lf/8ZqI2XAcZ7fLlVL93y3L2qt9JZPJ3S57+OGHmTt3Lv/617+45557+Otf/8oZZ5zBfffdh+M4+L7P4Ycfzve///3d7nfUqFF7dfwQrzyE/S/EqwHf/OY3+dKXvsT73/9+rr32Wurr67Ftm09+8pN79KiFCLG/EY1GOe6443j44YdZs2YN27dvZ86cOTQ3N1Mul3nqqad45JFHmDp1KkOGDDnYzQ3xCkU4/r00vCbIxvNhzJgx+L7P6tWr+2dMAHbs2EFXVxdjxozZq/3Yts2ZZ57JmWeeyfe//32++c1v8oUvfIG5c+dy1llnMWHCBBYtWsSZZ56514ZliFc/wv4X4lDAkCFDqK6uZsmSJXtc57bbbuP000/n17/+9YDlXV1dNDY29v8/7F8hXgh709/GjBnDypUrd1m+YsWK/t8N5syZw3XXXccDDzxAY2MjU6dOxbIsZsyYwSOPPMIjjzzCueee+/KfSIhXBcLxb//jNSGjej6cffbZAPzgBz8YsNzMAJtMFs+Hjo6OXZYdeeSRAP1pRS+88EK2bt3KL3/5y13WzefzZLPZfWl2iFcJwv4X4lCAbducd9553HnnncybN2+X35VSOI4zwBsHEgu0devWAcvS6TTAgLSjIUIEsTf97eyzz+bpp5/miSee6F+ezWb5xS9+wdixY5k+fXr/8jlz5lAsFvnBD37AySef3G/wzZkzh5tvvpmWlpYwXiPEHhGOf/sfr3nPxsyZM3nPe97DL37xC7q6ujj11FN5+umnuemmmzjvvPM4/fTTX3Af11xzDQ8//DDnnHMOY8aMobW1lZ/+9KeMHDmyXyN68cUXc8stt/DBD36QuXPnctJJJ+F5HitWrOCWW27pr50Q4rWFsP+FOFTwzW9+k/vuu49TTz21Pz3ytm3buPXWW3n00Uc599xzueaaa3jf+97HiSeeyHPPPccf//hHxo8fP2A/EyZMoLa2lp///OdUVVWRTqc57rjjdhtvFOK1ixfqb5/73Of485//zJve9CY+/vGPU19fz0033cT69ev529/+1h+sC3DCCScQiURYuXIlV1xxRf/yU045hZ/97GcAIdkI8bwIx7/9jIOYCWu/w6QeHZwS9Le//a0C1Pr165VSSpXLZfW1r31NjRs3TkWjUTVq1Ch19dVXq0KhMGC7MWPGqHPOOWeX4zz44IPqf/7nf9Tw4cNVLBZTw4cPV+985zvVqlWrBqxXKpXUddddp2bMmKHi8biqq6tTxxxzjPra176muru7X96TD3HQEfa/EK80bNy4UV1yySVqyJAhKh6Pq/Hjx6uPfOQjqlgsqkKhoD796U+rYcOGqWQyqU466ST1xBNPqFNPPVWdeuqpA/Zzxx13qOnTp6tIJBKmgQyxRzxff1NKqbVr16q3v/3tqra2ViUSCTV79mx111137XZfs2bNUoB66qmn+pdt2bJFAWrUqFEH5HxCvLIRjn/7D5ZSg/xCIUKECBEiRIgQIUKECPEy4DUfsxEiRIgQIUKECBEiRIj9g5BshAgRIkSIECFChAgRYr8gJBshQoQIESJEiBAhQoTYLwjJRogQIUKECBEiRIgQIfYLQrIRIkSIECFChAgRIkSI/YKQbIQIESJEiBAhQoQIEWK/4DVf1C8I1f/PS4QFYcH6ECFeXTBViV8taIzCnFEwqQFGVsOc4ydy2Ps/Q2Tc+8GKPs+WCuUt56ufPZdrvrf+gLX3UMWByh7/YvufXX0Zo+75Ka2HRcgr/XKKAD7gAs6gDVzA07/7gfUUEKMyRRnV6/Xp70kgq3/z5BjxavDL4OZBdejjxgPHKuiP2aYXKOvjoY+lkDa7QEn/Ne2vAnJ63eBvcb1dXn88/Zuv9+Xr9c0lLQfO2cDX25rba+vzd/T6vv5uBdb3Atcwpq9JVK9vUAq01dfttfWyLFDU51TQ25u2e4F2unq9OKi5HBDssf/FgDRyL6qAWqAayEBVFdSloLYW6hugeRgMbYahQ6GmCRqqoTkCdchligApoB5ImOMOOpwPdAOdHrR3Qcs2WLsRVqyH9Ruhqw36ctCZhWIBrBhYCrrL4Jl+ZPoPVO5PGbmmPfqTo3Ldo3I+JJB7ktafaOD8bb0PS3/Xz1k0ChkFngtFD3wFyge3pI/Rpz+mHysq/cPsL6qPkUH6nOk7fmBdS7evStqYqoP6Wqiug1gtxDMwpApSVRCpAjsCThScEmR7oLMdujsg2wVt7dDdBYVuUDuBdqBLtzH4jLD3499rlmwo5JqVFfgeeL48210udOWhNwedvdBVgFyvdBLHgaoUVKegLg0xCywLChFwI9KhUzaMiULKgagDjr6hEasypry6TJYQIUK80hADZo2zOPHwCKNrLBJln1JvlnznFqrGmbfrHqA8Nqx5kH/+a8eBam6IlwC/51Y2XTgM67or4eQ6WRhDDKsSA60AY8iWkS4Q18s85OXlUnmJ9erljv67g4pR5Mr/i52IcdaFvGCjgX2hl5UCx0b/ZgeOqwJ/iww0tiN6337gN4eKpWoMRkXFcISBxMBnINkwtpNiIEkw5MKcr03lMQkaiFAhG2UqhMnAnJc7qE1lKmTDXJciA8meFdiuyMszOfpSoZBrYvqL/tgxiMYhloRoQox+FYN4DaTqIZWBtCPdJUXFPoohp2lu5e4QB5I2ZDKQqYKaaqivg94sWL60xXMgF9HdS4Ht60sdZSCBNNfVGO7m3pQYeH2N0Rgkwuaco3ofDpW+bYHtQEyTHcuSn5Unv1me/l7WxzLkoYzcf2PUO/riGKJrUSHspr8ao9I8Ny6Uy+CVwS9BuQBOAoouOH5lHVUC8pDPQqkEbhkKBfleKoEqyu/95HcQ0dgXvGbJBuiJAgVlD0ou7PBhczts2ALb26CtF9o6YGcruAqqq6ChARproDoGqSjEIkI2lC33OQVsroYhDTC0BtIJSEWgyqqMoSFChAhxMDGhGs6fM5xZMxpoSkSI5srkust42zegvJ1YkVF73NbzOrjnjttZuS63x3VCHEroRm25FvXlXmK//w6lYVExIArIDHRPYNUIFcPFGGDBmfui/r/xcnRr296HYgS8blDGeAKZETWGWEp/LwT2awiPeTEGPQVmqjvogUnQP6NPETHOlV4vSoU8GcvVeDeMURYkGQT+H/wb/C1o0JtzNusaS9i0N7i9MRqNARqcYTTExnhwzLUwRmaJgW0KejQIbFfg0CAbxlNmPDkJ+UQTYDlCMEiCk4RkWshBKgPpKFRbQjZiVLqJuRQ2uycbNnL70xaUorKvZAaqaqA2S79XDUe8Cj0FMbIth0ofMATJGPiD770dWGbISZD82pVj9JNv44EIeDUiEbAteSYcC5SqdEtfgRckuIbsBJ8JQ26NB8Xs3zwvpcCFgwohd8ST4vlQLoFXAJWHfBzsgnh4bAtKnizP9UKxJNepqImGV0Cery7kGTIk60XiNUs2LOTexXTPVXFxdZGCHhs6tKurnAU3DpE4JBohMwSqa4VspGOQiAvRsDxh1Ekb6quhsQ4a0xC1pJMleQ1f7BAhQhwyqLPhzIkJTjxsHJPGVhF1HHCq8HuK9Ba3o/LzsaqGs9tXvXLJdj3OP++cR760688hDmGs+xWl/5uGdfVlqFpbrDwjgYLKrLkxfszsmTHIjBGkZ9lTSRhdD29tgHOr4LE+WNYJc9thQwdEUuA0igFDWu8zF9i/MfhigeNHqRjqxktiyIvpb0EvRQ9QQ4UYGc+L2aeDWKZGpmQ8OsYTYY5vfjMeBPT+gwa9MS6N7CYolzESFmOQmZnn3ZENc40JrGMH/hpDNhL43fwtB67doQIz8x5HDJ2ULHOiEIuCY4uyI5WETBpSaVket+SSxfSmZjeDHQQGwVO2ka6RsyCiiUwiA3V1sqJng6WN/aKCYjkgk7cCn6BRVqZyr0zfNA0zXi5bn6MhH6bPGZKr7Uk7qrmCU+ERTkmuhaXvve2Ab8hYFDHsg5Io09YoFflWkoGE1vRH00cKVLwW2kthW2Ln2lEoJkQ+VSqBbctku5uFYh7KRfmbz0G+D/Fc9iBjhJE6vgSE9q+GBSQtqHEg1QBWD5S65eb4abBS4GegnICClkjF4+AlwFZg2RCx5cGJRDXB9KSzJwg9GiFChDg0cHjK5rSpjTTYimjEwsKDRBInUUUs347a8TBkjgVrOLtYSf5Clj15C08s7DtYzQ/xYqH64I4voQoe1jXvh2FxMXSCVlyUgZIdGCjd0IaynYeTG+D/RsEUSzabXQOlGri7Hu6IwyUTYbMDX3kWNvv0G6EDYjagMmNvjG0tMxlghBuLLY28ULdDbDjE66F3LRXPRlCyFJyFDlqyhqyYl7L53RhvwesRYVcr1/w1RqKBMVYZtI0/aL3dGYimvcaQDBoMZpkV2M6c5/OFVh0oGBIWqXy3I/KxdN9RlthIti1yItuSbmCUQUaRZIiG6ZY+u16KYNeIWKAiYp8l0pDOg+uKWsVV4HoQ6xOjPxKX31SQTNuBnUYCO4cK0TCxGGpQIwzJMPvwKw2LRjTfdUS2pHywE/r/ebBdkZWV4iJ3KsepWONGomcuTkp/kuwqL4wEjh+UBZZAuZArihcjE4V0lRAcEI+HBxSL4BalDaUClLJCUPwSlRiWl4nchmQjgP6xNQ4kwI+BF4VyUmZpVBr8BPTactPM+JS2xV2XSkJ1VKRTlh7AIpq9h3EaIUKEONiIAYc1RxjfkCbu9mCVHD3F1QtWimhMUdy5hNSoZyB2GlgZZPTKglpHYcc9PHj7Y2RzL0G8G+LgwW+Ff19FbPwwnI+cR84YzIZIGAMmhxgxZmbfeDjiYOXgsDRcNwSmWxX72kHsobdVw9sO04cDps2Eh3rgez3QZgwl4wVIMFAi5ADbqXgjFDLDqmU6iREyI1504PTJ8KFauLEa/rMVCmOhugQ9OTFo/Y363BxEBmKMdWM8BY8dJBH912rQ3+CMOAz0PhhyZs6jOGh5cCZ88KNjDMggMQouN54Osw9jGAdjRg4WTNti9MuorAQkUpBMCtnwIvIp22L4+n5FChV05gS5Zp6KfWW4DFTUdOa0fSBqi82VTUAhAW4ayq4QjVIJEgmIlSARhVJeDO3+e2IHdmRgUZGDGWmT2cY0ZLCkyjQqKn3P8sSzgiXDayQGcSMLtMAp6O4XhXwM+nxwM1Q8dkY+FaPi1QhKCsuD2hOMawoQateFiKd/1iTQcUTOVXIhn5e4jFIRclkdu6ElV/1xGhF9fBMr9CKJx6uCbOzu3F+Mce+jJZO+3CTbES9FIiEPiPLEm1Gf1H3LkSDxRAoySUglJOApZot8KoUkDwiDwkOECHEooMaCyc0W1akS6ZQNXh4iaZl+K+fAzZHdvp14zUM44yKQaAYKKH8rXudiFt59J397cDNuyDVeweij+MfvETvtJDh2iFh9RvsdRwyZBCLrMBagMXCQuMVPNsHMxAu/12zg+CTMTEJfHn7cDd0xxPIwwddFKhImgDpwVkFjPbSuBeVAzXRocOCkGjipCrrq4fwkTLLhrKPhr1Ph/7bCETPgIRdoh209IuPyllAhUOZ8DNkxxCNouBkEZ7Jh99qewRIp87sx/MwyI/Myx/MD/3cCf2OBdYLHDBKVoNV2sA2LGGLkZICUeBjS1SKXisVkRt/S8xkg99J4NYJND3oxTNgBgXXMbTOXynAvBcRtMeTjMZnwLZcgk5Jg576CyKwiBXDKYmx75rqb+x+8r2UqjTMG/OBYBbPM9Jlg7I4mGr4v5+zouJFYRIz+SERsSsuGkg2WEsO/rAm9a5IcGE+KIZRBSZ0K/BYMKjefoBzRB98CPyrXvlyWdiklXo98DqyS9myUIZeDUi+VDFlGNmZIVx+vXbJhvEfBCQrj/YK9fxYVUFCQV0AWkllIliCjXauuC2kPqmyodUTfFrOFcEQdYc3JqMRsGPdgFZXMCiFChAhxsJGKQ12tR1UdRBsSUB+Rt1Fc3oqq02L7mk24PQ/QuKUHZ8gQ3Px23MI2lm1Yww9+u5Glmw4lwXiIF4WuJyn94t840y7G83VQYYZKBh5j9fVReZk5YLkwtg9OGy0zuHuLJPDZMTA0DTf0QXsauiwJku2XQOmsVVVZOO91cHUNzO+EhUXYDJzQBMfZ8imlK2qspA0XZ6B6EvzLguMVPJeBScNg5DCYu5qK3t5FNhwsndkdgrPEsCuxgIEyr6BnY7D0JPj/oASGQccw193MnAeNy8GSrmDw/sFCnIq0LQWRhMSxJlMSn+B58td2xOCOITGsULHd8lQm7Y0db2w6Y88bjmUcKUEVnLIgEZNjlkoyY68U5F2I9GqZVQxiLkQcKJvrFgscwNyTRKABwRgg48UKElCTsMD0IR2P4TiVWA1LS6qiUWlnRA+1SmcwdRASlIyBlYScK0SlPyA72B/Md9N2Q5iC6ZDLg7ZxK3KyUl6yc5VL+vqURTaliiKn6s0K2ehP/RskXiapw0sY+g8I2dhT+8yztC8z/8F9FYAOhCAUkWsTQ3I0Z6yKxC0Ii13HgKISorGzBC3t0NEGhTaIFSDpaXme3l+6JLEZqgRxJX3NKYs+Dl86lBPQIx7ssSBEiAMJpcSAcMIgpUMSmQw0D3VIZpRo+P2iFhBXQTyBVbZp31SidfNamrbkiTUkyat2drS38cg6lyeXied3X2ASBz0fgu8BM1E0OPNkiJcTLtYzPyG27a0Ux2VQBVDrkZuQQV6sRjI0mX4Z0pBm+FYExryIF1uVBR8cAmcNgVUKflESojBiBPxhJfRFoGzBpTPgkjqYCkxrgHciyqoysFPva3DYhwO80YZlPnwoAttqYNoxsKwbHo9J7CUOlTS5hmjAngMqg4QAdjW2BseUmP/DQM+GMQqDsRbGYnYC2wblU8HkC4ZsDPa8BC3ugwHjIdKycxIiF0okJAAc9GRsVPPJKMQjYj9hVQiFTp7UTyaCTpzgLTB2dDSwjbGzVVSyX8VSYre5LsRdkcRbJjOVpS9XUG7kBA6gqAxWMSoNJLCeE/iuZVNmmRXRsRoxvXtHB8c7WtanXTOOBbYvJAAFjgsR7drxfSEBCn0BTL81xnIw9iiYlSyYRMH0La1F89NQKIKTlexTkUjlPV12JRi8mAU3SDR6A+caTM9sB77vI/Y72TDPWzCVcAQdnEIleYG598+3H9PZzL7aFLQq6C1DtiAXMh2DnghUxSStWhWQsAa+wMykQVFBVkG2JC7eHV3Q0gGdXdDVB1ndKR3A0VH8hbK47SIIwVARYe8FHyzdMR3N4I3uMLS7QrxqYR5w/fB6nsdd/36U/zn3FFn8KiuE90pHdRqahidJVtsouw/LSkq1p6QN8SS+q9i5Hlp7CqyqWY+qB6oVa1vhwSWwtWffjpe2RWpafJ4XVBwYDwwHmhIyhnfmYXEZ1vGi320hXgCqawH5K/8P6/1XQzYCSz3o7JMiUR0uJKphaAQuRoIzkjAlAsfEX/w7LYKQiMkWvGFE5Z1/8eFwWxZuzsOba2FKYBsb6RsKGMme7YQ2JPh2WAyOjsp6qWp416nwp4UQHwbVDmxdrDXpwbSme4PB6VEHE4CgIRZcN0g6gnEChsyZGXOXgel+gzEBxsoOHu+FjKb9jWAQc1SyTyVjovhw0PIhbVzHrEroQRTxALhOJZOyOT1j5wUdRGYi39jb0cD6xg62bS1RciCekpl8W8cdRaPyGxHtoPDBLVC5/kGiGCQT5vxKgYOb+JSU/mss6CjE4iLliugT8X0x6j1XroGjjUHbFrIRS+pbr+R6eIH1i6ZtTqCNwexTg1MiB1Poeohk0DC1AhRjYqdafeJlUrpvup5OcZtHZJOGbBQQ49lcl3hg/2bf+4j9TjaM9ydHhWAEGaq5Rs83eCkqXgyTgS8CtCupEtmVg9Yu6CsJEah2oCYNdTVQn4KMI50cS3unFJSUEIRsUapM5guwLQvtZej1IOfodGkR6aiWZvAFIOGCHZN9lJV0Zi8pD0/JrsSimT4Zko0Qr1a4RY/8jiJVo1Jgw9//8RR33Pp7Vs8v0jDK4f3vP/NgNzFEAOlYjERVErs2hVUVg4aUkI14AqpqiGwbQjxq0dUBPV2KnRuhNwkdZVi3jX2O1ThiLDyzfs+/28AxFrwhA2OaYeJUaBgCW7bAvc/A37tgPaGHY/+gDEu+h/pWFqrPgqZJouNo75UXnNcBz6ZhbRO8xcY61mL0YRaxIS/9yDYS6G1wjAMTq/TEoILUbozo3YVVBJEGLk9KXJKNGG6dHlzVBGefAqMS8j6+NA/zVoDKsXtplAp8jNFnjJRgJDMMSAucyUDTMGl/NgctqxiYYonANkHZVZmBGZ2s3RxnsEfFXMTm57kg+xvG8DVB4TFI6JjVuA2JiMS4YkEyAcm4eDYcfY8dJQpOk9ApWH7E2NbGXg7G6CsGZkh2EZvOUzIhHI3KXxM3EdFkw48I0cACS3s8LE0AvGAxP0MijUfBGNrG6K5GOpuZNdeZpqKOlkhZYtD7vng0lPFa6L7kW2BrMhzzhWhQlvWU0twhIt4ZCBzHpIM2fdLcA/M7VKRfQdlQl0yMl81FC/Ypc44lBhKNIEyfNH2wyIuqubHfyIZpVx65gAUq7DZOpWLk3s78m2c1i1zzagVeDvIdkOsRF1ChBHkPCjEJfil5kCsJ04054qoqe8IgXR3EUypKqq9sDnp6IZsXL0nZFQ+FgR2RwdE2F70sATeOI2w+GdP5oq0BRTRDGVWIVx1yuSKJuIgE1z6+nSf/8Rjv+PpbyLslbvzBt1i7cgkP3f9fPv65jwEh2TiQSAB1wLY9/N5bsvESQ7BGjcFqiEDKFbFzWxskUliHT6K2+T9MsMs8vdylvQPKaSnGFdtHi39YlcXObnDVnjccBbx9CJwyFZqGwpCJkBgGE8bAEAe6Hoa7cyKjCWPS9wf6YOcPobAY6v8fxJog1wW9JXB3iMaj+3XwAxeVyDHu2yNJXlC3X1pSY8GliYEkZF/QALsYE0N9CUsanxLboQb4+SS4byR8+2HoLlKRNkFlat1YudVIYcIOKlPvQ/W6JuA8J7KhN06Ca08VO2BxAa64DXZuQ2Y6LST+xSIwHc9AI9IYdaZWgrGqzfVI6nW1vCZ2BBxzwYu7Vi8LjJETk7mKZFwkRPG4ZKKqTutYBRsaqqEuCamYkBDjoTD2r7H1TcH0YNhKnMrtCPIvoxQqIpMgrl+JTyiWdSC4J+QmGoNSUmzAqCcqlFhUp4K1IOcJWQF2jdMxkimbShraYBaoCDi6WroT0fxTCcFxLDn/EiK395BChBZiU9pxOa5VkgDyuKfP24E+j0qa3mzgeMHhNBq4eEZGFSQURm7VQ6X/GpJoZvxNZqRuRD5ltguS3xhCsAp6uz72uYL9y042zLXQVdD73WQFdMwD+x68bTrmEORl2gPgizYv4UKkCElfblpfEYq+ZCIoliCdk3dpNAK2gnyv3MCoTlHmuhIck8uJd8N1JUNA1NGuN93ISFynL9PuMU+7tCJRSEYqxWkS+hxTVLL3hQjxSoVSsG55D25JMXlmDaD4z12LmHr4kYwcEeU3f/0bq5Yv5E3tZ7Bk5Rr+++Q9eL6Z9ngZKgGF2GuYSZwqhGzUI++RrsA6Vs7DL9owcoLk6bYssHdA80iw+rAyMWb9z3g2LtiA77hkF4Mbhe4eiLoy1qUj0PYCt9a2YGRtkhVtpkrVrnCAGcBJk2HGYRAfAYwBaiFSA1NdOHcHdC2Ax9gzgQrxUlGG3gdhgQ3+2ZBoQtwXKYh2QdqFjk5o38mjj0LPW2qpfbGM4AUw8mVM5WpZ0KCL+0WB5cAJwFG1MLMGtp0EP7odVNA7YGQT5vR0sO6EM+DwUfDfp6Azp39LAz0w+RR45+EwJyLyLwV0J2DkkbBzg96XMVCNVAcq0/eD5VEE1jXkwkho0tIehkHiZJg07qVcoZcAE9uTkODrREKSTyTjImWKRsXTk9AFkRuroTojJCTiVOx5E5pi7OQyYqgb4uEhJDRYI9E4hcwkfplKWt2yJ0UkS758N94oS3sbHKdy/LjOpuYrsQ293c18m+BwEzthguENaXSAmJyvYwvh8Tw5lmWJ98TTREYBlidtsHVsh6OJl++L3YnS7YtJelzXpJs1pNNctEB2uH6PjPkMlvAFiYchGobcxvX6JgOVIS5mG72eldReGgdU0GuS33MXGYyXjWwMJhl9CBlLA7WWzCjEePHGtyFX9ci+sGFoHUytgexIyVPcloUNrbAtL0SjkJU8wnZUNrYV2Fmdei0mN91XUMgL4bAUJDXRULb8bumDO04ggFG7vuIxcRc6ejCw7cr9SxHW1wjxysTO7Tuw/BKNw0cBigWPLaE7286QptexZeM6fvKd7zLhpLfwjWvfyRe//UG8sks8FeGGj36vn2ikI1VMrJp+cE/kNYYq4Ghb3OWrinBMwmZIrcPftpdl7ExCfb5Maf0m2SA6Qs/K5CE1HYpguatIvu5CYmtvoLkuy/FTYOEKkQZ8+Ow4Owrw5Poibc8jjQIYWefgOB69+T372zPA9BqYcAQkjgDGOTA6DmmF1VEgnlIc3QGrNkN7G3QS0tf9Bw/cucCRUpY5VQX+TqhrEr1LdQxr9nE8tamFR9dlOXta5hU3kdaMTN72ASMtOLcBfjYUykFyYQw7o9V3IX0cXH00nBuFy2fCnQ9TIQgNMH0svC8u+7QQI3kLUFNNJZWwmZo3AeF+4LuxpI1XxcQGQEVjZJDWbcuJ7bKlE3mQDjTMrGoaUhmoTkm62UxCyEUyKQSkJiWTvTUZ8XTEYlqW7khcglGSlTTBcJUk/HGVVpc5YksVqHg4zGS9zcCETUUfcmVJ41osCeko+5KdquRqEmDrAG4lHpiII+qWWESMaNeQCxPQr6ikfjUyKk1gg0HTlrYllR+QTyHfzXLjqYkqsGz5RKIVOZgyiYYiEMmLxEop8IL33/QR0z7jyTBJD1Tgohii6gS2M16apF5e1J+s/hhPikmHHQO7WoLr8SSBgzLt2MeA5JeFbBhPiiFbJSrkoAbpky/XwNT/HFo6GMiR/TcBY5MwpQ5ai7C5D9bugPY+CepWUemEUU8zSKTj+Z4OnLF1UhZ0II+qsNNYVLu7PCgXdIdQ4hKMeFDohURScj0nkUrkhpiECPFKw3/veYB7bvk9H77meo48aiqP3ruG5ctWceyU05l4/AS+/b1vMnn2RJJJC8/z2Nnawfev+jkPPzq3fx8lVeT2ex/hiNcdTfPQJqoy0X1KlRli39EFdFpwbBTmFaV681XHxlh2f5nzp9q88cNjyezYxsjeHPRsg4aT5K1mNwMnQ7QWnB3g2DTOeJDEuCJDn1tBMtpLNO1w1lfPY0OLz93vuFUS0UR1gKGvxzwLenwYXQMfO62KL/2zu18SU9yNu70amD4cqo4BptgwqRHq0yLdqW8DO0fTVpgyEtZ1wnJP5FQh9heKwF3AO6HcLS+/qAutG6EAmakTifYm+dRX5pK/ejZvPbIJ5xXyUFvABOAODzbm4aMZmYVPTITyFiova53ClSqgAHYjnDAdZkRgI9CrA4QbJ8GRY2DRRoj2QFNjxTkRBxqRjJb96VSNlWykLMGYkKDm3jwnQYmLISlWYH9ZkeWMGfayX6oXRgQx7KohWgNVVZBOS1xGOgmJDFRXy6cqKSSkJi2/GbJh25VLUFQif3c9SVdrap1ZFlSlJMbBZLPz9KUwHhEHLcPyxb4rFEVOn83rOhJlTTpcHcagFS4OesI5IvMtlpLj9cflxALfDQwRgcq91LEdrg9Rv7IfpaRuhR8RA92xxavhKRkr0d4LSzuXlQsqoffhiS3te0KA8qZPGFZiYIiFIRV2oI1BomFYWdAjYeSDMFB+Zc7NfI+Josf2hAxZxlNi+uLBklEZD6Ehfrqg5AEzum0kOLw6JcRjXDWs6YaNbRIoBkIgvJKOyFe6o2nPBb7WyOkAHNuWThKNaGZaFi9GVQLqqqSuRswWNUKt9nBkHJloGJyaL0SIQxWlYpnWrR2MGNeMBaxt2czGnVliCR/Ltvjun96N7ysiEQfLhiNOmYhSiqWPbeTBJ/7F1V+5ikK+gG9SXABlr8Qf7/wet977G46b82G++42PMGtWU5idaj8gCswZDofPTPPAM0XubHM50oGWnI/n2RwbtZk4pp5Zl54D3Y/Conas6igwAqxa7ZodrdOk1AJQddbXqMpHqZv+B2qOWEDLqjaqpnwJd+sNeFl456xazj25jmef3cJDz5U5bAgs64SGRILzj/Jp2ZTlyLSiUUF7EdaVIevLrLJBwoaxoyB6ZArGZqB2CFgZcRunPBhdIDreZ9RwGLYKhuZCsrH/sQSsB8B+NwwfDfECdG2Fnjz5NePIdW4hgs9Xr3+W0V8/jdmjU6+ISTVjE60Hft4LIxKwxIaSw0C9cxQJeqoGOiSIfOkmuCoJ9Q2wuSjG6qWj4bIhcGcDvAWx/4PH2umJ3Cp+pKQVZRsDDcLBcioTpGAQlFXFqRi2vfpgtRKvumATksrtQMFGiFgtWNWQSgvZSFbpuI0k1FRBRpOQWk3qqlJi3EdjMnlrPBqWEkO9XISegiTnIQolS5Ki+UoM9LLuZGaC3kYr0FQl7MXEbGTL0JuXDKO9eSEhCkk9azJEmereSukYXi29H0ACzT0KGu1GvmMMXe0JcC0hOdGAMV4ui7zLj4qczIrSX/8jrm1NS+mQCUuIie+A78oQaFva0HeFeAxIoWy8ccYTYS6OkSHqUgy7kFgYmFd4T4TBnLsS8mMpXTNFx6D4UAmw2Uu8LGTDkByHSpX3IEE/kIOROVbUgrEJGBqHUVWwOQ9ZF3qLkO2S+AzP03EZSIMd7e7q35clv0cdccM5vg58ikJ9NUwZBg3aFVe2ZdsGXgWVEkO8ppDL5vn9T//JGy6cxTGzjuTSKy7j8ksvo6axgc72IlvWraKcVXzlO3+jva2Xd519GmvW/oe/3/MPOnp2UioXdjvmKOVTLLbz8APf5Pxlc7npj9/n5BNnkYjtZuUQLwpVdowZI9N846vHc9xbv8z8H1/LuV+6myc9eGsMott7Ob0GTjxFgb0Dq34GnBCR6FJrCDBW72mgaNxKngpJn2T9YQwd9yhDTrGxolMYHi1x09encNQJ0+jcvIHcto2sXAQXnJSmWKxi9usnseXxZ6nxfU4/OsOtt3SyugiTYrCwNLDtDhAfYmGNbYY6nebFqkHecFmo6sIal6V2CFTHYUiefZpJC/EioeZjuRN425VfZu4v76e9cx1EMqTdXmrGj2FE7TiUk+XW1WWah8EIXUdha0n09VUvY9zFy4V1ZdjkwJM+rOyFS20x6opdyOygMeRMwKWWUKkCbGuB9mEws05meEc0wHnVUqBtjiO2ftDGKSnYXoI3ToUnPFibhfZORF/eSYXcmEHTzDoHYgD6YWbZTVrLsv50QmkRLN4AfPllv1y7h4N4NJqAesmMnEmLTCqRFO9GOg2JlEiUUmmoqq4k0InHJTg8gRjnHnJJciXxQnRltR1viXTIt8Qj4cYqsfSGpERU5ZqXXQkIz3sio+opiJ3XV4B8EQqujsWNBtLR+lLczna018MDZWb4g/fDwGigjEFvsjjpv54uCuR79JdD8M3EdZUmOpbYkUSllEIiooPStdfFt4Vk5QvSPhNobhm5kInFMITGkCPTThOTARXPxu7Gy8HEdjACRIOykGVLgVUEuwy+SZNbfp597AYvm11s7eH7wYSFuKwmp2B0Sgr3bfFgUxq27YBsn2aMemUnmGONisfDtiWbVTwujL05DZObYWJmYBYx5YQB4SEOffg+zLv/IUY3DaPpiMkUOnLceOvP6LEaOXrWkdTXNTDvmQV85yMfoW17jlVL5hNHsaFbQnSfmvcDxlApmvnCWfA8Wloe5e1vfxs//svfefeZx4QejpcJJUvx0Y/+L0eddz5WzTEc8YYz+enND/KB1UVWWBApweyJ0NhXwMpGIDMUErORUNlm9vwKMG+uGiKZc4hkAKUYdurVDDu1FbX1btoXz2d4Y4b3vLuRMz/4JvzWTfzu/57i7HdO45hZY+lespoNCzpZ/xx4KejrqKhBPKApCiOmp6UYgpWkkvIEoArsGqwqF+UXaaiBpixYpZBv7H90YzGfw0ZEeHzYcKx1G4jNmMERM2oYOW44cQ+q622iVQ53tHrUey7LF2W59+F1fPDCcVx6bD32Pjzf5n7uzxFhk4IfleHZHNAL3Yb4NjFQjmICf0Fm8HX1dFUPm3ugsAn+3+EwIyk/j93NscpAwocrG+GYInxsK2KglqmkVA3KUIIzzbCrdMdFMl+ZbY2cplMbfgcKJmC2GtJaKpXJSLbPREIIRzSmU8DGIJUSwpFOQNKBlK0lROjTVpIxNJuXumYlV0iGWxLDPOqJcqSoyZcKEgwFjieTvCVXShjkS1pGVdRB4q54EfJFUaV4ruxXaamT64Ff1gHcJri6SKVOg5EkmT5hIUzJEMVgnE9RcwHtTVDm3uq4iKJJqWuJh9fPQ5UNBVvOA1/akC9Lu31Pe3XMduaeB+VOhoBAZbguBb7vIxkYANMffaBP5Fwqr/tbjn0KDDd4TUzC28jEQNKC6ggMqYcVCjZ60O0HPE2WvskEPDKOkIhYXAhHfS2MbIQxyYFjgsWhQ7JChHg+uCVY9UALv3zyd3zoh9dx1JHNrFz5BNs3t/GD635MX1+euQ8+xdwn/7bHfZiELH17XGNXdLdv5osf/DLHzb2LiSOtMIbjZUDRK/P3Bxfzzk99H3CITH0rp19wL1/55UN8eUeJq1fBNS4Mb7VgawKmDEfmYkewzyOWZUFkEjARa/Roxr1jImNjfeCORbWv5PEf/ZFv3t/FzDkTGXnWNArb5nHKe6aQunUlP3lGdnEYkkW0DRjhQN2weCX/JAqx7iyEdDRCrJeoUySTlPE5xIHCDkZWucw65VgWWjD9mJmMnjBU7oOCRBpWPNfHbX/ZwpaFSyht3o4zchLfULWcPq2eCWn26vkuK1hWUAyJwzDb2m/v0FFR2LwFtqyDWLXIdlQTlVLU5gMVQzNBv1Sk3AY72+DyY+AT9RVKvDtkgLekpDefEINZw+CBhYi1NYZKATVjEA6W7AQvQoyBFZzNb6a9B5J514vjMVUH9TXi1aiqEa9FOqZlUlGIJiGehFhCZ6mK6kydlthhhluhxNgvF8XzUFZCCsqeSHUivkiwPF+MclM3Q/kiMzIGuJeHbBa6+0Q6VSyIJMr3Zf+lAnhFcKJiy3leJfFPqSjJgfwCEixSoJKVyXiZHCpeJ5PNKXhPAmxZGckVVO6n8YDo++zZQrCKvkxmRy3JpurnJM6joLRxX9Z/DXExgeGGHGtvSn//CQaJm+O9GBhdmumnUEmPm2WfU94avKrIhu/BhrW9RBI2o0fvOhwYqdtQxAXotkLEFp2gGwNVowOUyuIijUSk03tKHppkVFh8c0JqhoQI8UpELAHzW1eRnjqVEcPrsGyLbZtauOAdF7Jg4Xz8FxBiKmDnHn4zMspOYDISVFkEhum/m9Y/yDe/fQM//d7HSCacPewlxL7goSef5bH//I05r7sAKzOcmoveyjsWP42z0OULm3w+vBp+NaOKkxOHY3EBkkT8xUJPq1gjsYb8LxYlChvu56Frf8Df7uvi/zXBr34xn5Ezmhh12TdZ/4/f4ForWVeE2igcn4F/dsok6UgHklURiZTsr8CkqCS9jKMKNl4f5HphZzn0ahwo+F4LP7j2Bk5911doOONEZhyWYWeHhY9i09YiaodNb6HAOWeNom92M+29PgUnw8Ll3Vz+2y5uuLCGw5uf38/vA8tz8Ll7i8TcPD96Sy2jEvuHcIwBfjMc5tZDhwU/fxZaO0DVMsAQ7J9lNJ8SMpufkIJ0J9dXZuf3BMuSTRSSsGFIDJxGXanZeCpMnQNjHJpjD5a3xBmY0tQYkr7+LcEBg9UM6VpoqJI0tqlqyUSViOvkOAnxZqQz8nsiKTZU3KqUAjBlIXzkOpmsTL4vmaM8X2IfLFuXMEgL+XCU9hro2IGokpgGtwSFPujthZ4s5HNSN61c0gl9tGfD0jUulKuDt3XMRD6n70sRMaZ7EYNaZ2KSE6dCQM0nWP/E/DXeADNfEowhCG5rCZEo5yUTmqtjU2zjfaEir1Im/V6JSkVsFfgLAxMMoJcHM6y9GLgI6TJpf825BI+7j3jFko18weP++57g5pt/Tcw5jOFDprO19VkWLXiYSNLmzLPezaRRh9O5rYXz3jmDaYePxnLk6luWxF2MGQpeO9hFYc5uXNyAKSWdwUMelpjOLd+QgmEJ0aWGXCPEKwVKKf790DNMHt/IhNHjsCyLj1z5cUZNqeLpZxbz8Y/9hGUrnmHJkiV7v8/A96qqDNGIQ2dntylYCvqvi8QxKeADI5P8uRP+/Osb+PiHzueow8a8PCf4GkdHdx8fv+or3POvY2kePg5r8kwa3zyJ9zXNJ/64w7dWelz33w5mLd9EYlQU7MFBM8ZfDgOTsj8fREyuykVW/uEGvn1bC9OK8OwOeMAvcPzcrVx+kkd2yUpqmx08PN42KkVVMkJrZw8TgIwOFKWsg+es2IB9o3rxd3i0r4dNO2DjPgQjhnipUCx54hekmyfyzvdczpA6m2xHGSIRkukIsbRNRjUwdIiFGhaha14n65esJ7uzl3ktZa6MNfObi8czKmWLCkXB5pyiNgq1MYvekuIf64r8/J8dLFrUQay5jl8OjfOFk5IknJf/7Rq14HAHDk9Dp5JUyz/pgC1V7CplilIp9JBCgsVT0k0f9GG2DeOsPU8eB2OMcx5sz4PVgLjzDLEIxIr2eyicwMYGwdlsGEiKjNF7gFDdJOl86zKQSgrZiCVE9ZGIQ0rX2qiNi32VsnShYyoOgiCPM2EROVfiXIpFHVStZLLX12TPjYo3wLZ1TLQ2/BXiCcjnINtbIRqujtEomu8lndxHCYnxEM+H52mPRp4K0ehFjGwz1vhUSIdPxaVv6lQYeIO2MffJZB+zA8scBtx/NyaB4XigikKIBhj1wSIkuUH7D3rCTNYyE1/yUh4jE9fRhTwDhlwFz3MfcUiSDaUrzu5O162UYseOHJ//ws389S+fJ5frBGwsy0apfgcdzz33ALZlo5Tix79r5soPfZv3vP9tNI5MYFlQUhZVKajzoJTTCVkciGl3vV/WjFuB5Uog+JQMNFo653CIEK8U+LD9yUXc9qdHedc738dZZ5xG04Q4X73qm9z4+xvo6Ol40buOANdc+UlGTx/JhRd9sN/rCtCq/7brv09WD+VDb34Tn/v5z/jEx77Krbf8muYhYZTTy4HFi1byux9/iau+cSNWJAUXnk98RIGLa5ZTPSTBHxaX6dvZQcLvBbtp4MaqF9RKnaKlHlRMRyXaQB1YqYCxExj8vDzFtQ/w7z89wxurI8yqifGlNTmagV//YgGzqm8gPn0GX/zJFsZ7cEZDiT+1uZUMjB7QnoMd7TDUE3ezFQMyEq2pCng7ymzbBpvKe/amhdhPcHtY+fC3SX/mEjpycZrHRejqg+LWHKRTtHZ08NyP57P8uXVk8yWqR41iyrFHkq6tZsn6nXz9vp189Iwmuvtc7rxvC/euy/CO8xq59AjFl36zipv+sIX6Y8cw/awxrHi0i+t/uhbLHc8XT00R2w+Ew6DWgo/XwA5LEv322tBlQTFChXgEKzGbmWQf7u+VNK7/m4BqSzy2CXY1pEx5jLl5WLJRyBbxwIomPamRwhivRtBTYSNGsDHyzKy5ztiEu5sD70c0NemUtgkhFom0tCMSF7vJcSRzp+NI3IbhQ0FVj7GZi0jK2948ZAtCGoo63gJb22BRHWuhY10cnanJV9r29SGXFa9Grg8KOchpr4ZbkkLNfb1SR833JKjcdbTUKacbkdeNMqQjS6XSu08l21EwE5U4XQfWrzCyGUMATPaqcuC78ThEqGRy0l4q3/SHArt6snxzwfQ6RpZlGJw36BhBKdVLidtA7zsbaPeegs73Aock2XhibjtPP76K937gGGob4/0Mzfd9fvrTm/nOd77L5s1rqZR48jXRqEApD0+Jb2lb61Y+97XL+fGvrmXSlDdzxYfO5dTTTmRIQwwvCk5KgnZiMQlatIGsEjdexJLcx8Mikt425BkhXglQCsq9ok/GhjXb1zO8ahSjho1n7tyH+clPfsXtf/8znnrh8O7ngwfcdeNP2ehEnjfBBcCjy9Zz9ddO4BPZPD/9y9+5/fbXc+llFxEJgzdeMnwFd931IJd9YD4NY2ZD9dvgTAen4T7ePGYdp79jLLUnHCMVmvqho2TbHofWZyRB/oiJsKMNOjrEAzLuBMhMgN5WYLikTIlbkIyjdsznR9f+mJtXZPn2SZNI54uMYxP/AYblXX57zyZqH1zPc71QCzyxzca1nf6ZzXIZvB0lIpt7gDI0F3WudB/IQy6Lt1OxOQtbCQv6HQx0d7Ryz51/5I3vupj2NsWIyXGeXtLGA797jO4Fj1Hs7qBqxmnMPOMsmofWUFdbQ1fBpWt1Kze39PDMqhitG3aQyNQw/cga/vGoy+Mrczz473YaTj+c1NBaVjy0kexTa8BR/PCbSzh76ts4fvgLp6wzk6z7OrlvIZO1H8vAUcBTwMPdsLYM5WpwC0AS4mko5fVMc0EyDnUX4YE+mDwaGiyYi6S+bWagqsbYqJPSMGQ47NxBJR9+kopaECoGqZHqmJMzUhiLSl2EoPyKF3HyLwF1zVAdk9ICqYRk4fQtUHGIJCUmwtGkAEvmLkpWxYNhilWXkNoR3UXoyUkGqXwZSiUpxmdHwdHXylNyDEsJYSgWhSw42pAv6cLNRU/kUJ42yG1XSEchKxPHOFq25Ytqpd9zldOfYHC4MdCNlykodwt6HYwK2BBGU9EZhAgYw998N/ssUOm8RqJk2tCfxzdwLEOEzDrBrETBWJ+g98u04eUYNA35fgkSKjhEyUZVdZTv/eQTPPHo27j+px9j2Lg0hYLLzTffx1VXfZp8vv2FdzIIPgU2bV3Fpq3f4z8P/Zh3vfdqfvC9zzGsNk5VWq5lkkqGnTLi2hr8bIcI8UrAhkXdLPnbEo69ZBLNE4bwzeu+SbaU5yPv+Qp/vvsnlEq5l9ynI8Bw4D87OvZqDCoDTu9qrv76Nex0Y3ztW79hyLhzOP+s6vD5ehnw1HOtLHzod5xxyRFY1gSInYV1RIxYah71o46Hqpn9dTTkrbFDphC3zoc124EYeAn8p56gfc12OguK4SeVSZ+YQLXlKS3cRDyRwTpiFKohTufKDWx4bi2fOft4jho9jvvvfYj/KqmFUQOsXdiK48nsbwEoUeLZTfJuzCDBj4UdLvGdLkR7IVKA+iyo9aKd2NBL52KPZ3tgJS99ki7EvkO5Of7546+QaRrKqW84g462IsuXLqdc8JhwytlMnTEMr2o8JdKMGmmzdXMfj9wxl+K2ncQmT8KaHiVm2bh+kZZtvRw2s4YnHu6hZngTOzbsINalKJQtmD4GVqygb8027ltSel6yUVCKWxf2MndRltrGBFNGO1w0PUN1xO630fcG420hCQCn1EGLD/+x4JEo5C0o9lDRAVngl6CYlBSqVZbEgZQZWGDa2KTGzmy2YHQ9rGjTs9c+FR28R6VWgzEuzXeTgtW4B4LB4OYAJvj4AKE6LRXBq1PgxGQitqxA6diMiE6kY1ta8mRJGtvBkXn9drSrpVNa6lQuiUMTR2JiI47EydhKO3dcCejGk1hbr6RlUkr+glzOshJCosxyR+egUGCZxhgRjGE/JmYjOMgY49pIlsz1NzIlU2k+mLY4QUV65SCs1ty7oMcsmK7WEJ9y4K9ph/nNeF/McYOSLEM+TAcMZqQyJPblwEsMmDskyEYup0gkRDZlWTBtZoZosopb7v0qW9+1ke9edx233vEnfvSjj+O6L/2Vo/wif/rtNWRqynz2U19l7AinXx4QJIUhQrxSUR1J8bM/XIP77GT++Y/rmf+Ex9P/vZc/3fVDyq7MaL8cwbZG4lx6oRU1nnvwYU5/71f45W9/wkMLStixynge4qWhDCz61wOccfE6sI4CpkC8HmZMBGJgHU7/q1/5wHZwU+DmYMtWVLoB9/5H2LJkCfc9tJLIkCHMSo5j2rjZtG3pYOu85UyZfBiZ6Hiymzax+rH5nH/c4UwbN4Grvn8T/27volO3ZQ2QKsMxCagpwEhb8t63UXk3ln3o3eJRvaUXC6WT3hfkRZx1UIt72PycT4eqqAtCHHiUu1v4wxcvp33DNbz+gnfytgtOw3JhxsQksYTF7f/q4c475vFQyzYKmxeg+nIQG4YzUjFjQpoRp0+ivaPMww+v5rF711HocqmqacLNluhr2wyrN4kmJ9eJ78TYsGoT6vXTB4wJRVfxx2dLxBIWz65q5Xc/eYKu1k6s+lpsP8fQP7yNcj7JglVtfOTMRoZX751pkwLeqb87Nrwe+EAWnk0gjDhoZFZL94xWwSoL7kcMqAkI4RiGVA63EKl7FNisYGOvDm42sRrG+NQkRi6y/s3IcoLB4ME4jqDhSuDvAUBNvQSAR+Ni7Cd0ELef0ClvI0I0LJ0xCiqOmWDse3+dC1dX2Xa1VKokXoiYzl4V0QOFDaAka1UxF6jDoWMyPJ2xKW5L+YKy9kCYgsxxXQ3csvTlMo2CynUuInKhwYa5q5cHYy5goLfC5LMwXc54LEyguCGYwUAV0w5DLFwqsSPBuhpGbmV+M/uHSr0P07ZgULiR3A0OHj+IOCTIxiOPbuDv//gjl1x8MUccPopUyuawSeewfv1cHnvqV7zlrffQ09v6shANA6V8fnnDd3n4v1v4yQ8+wUnHH0XsECxGFCLEi4FX63D8/76HSy97PX/+wwNce83X2Lp5BWW1t7TgheECW9g30vL7Z5fy8cLTJJPH88bjkkBINF5O3Lc4yydLrVgJgBRYo5GSyEWkMoC52nkoPCuu+fZ23PYOSlvbaOvO8++5K3lsQ4mjktBbsti2bDVr17fSPGw4m9t30rRkOTu2LEPlXQ6fMpNv/PiX3B8gGqClqIhEdbTOG39vm/xm1Ah5F3ashWGjPRyFWCCuB76N2m6Rne+zcDlsggH7DnHg4fVu5183XMkjf/oZR57/cS665HzG1lts22nx9D2P0fHfP0JZJ/q3xkGxE3p68Ys+Ucth8sQoDXWTKWVL2CWHDSsLLO7YSccTT8GWNTB0IjQOJUaK2sPG8N+tMK1JZM09LvzysV6++ol/4JcLFDp7Ubkc9W95E/Xjx5KpTvLFX+bZcPtcsvl2Hj+riT//3+sYWvvCU4YWA5VIU4EvVsHH8rDGyBwMbAngXe3DTQr6tKKwy5JyHROBE4HDkTi1DcB9Cja2UplRMQaiCYkyy4IGpPF2GEPYBOsODm87wLM09TUSmxHRQc9KSQYlxxYvhKMrYJeRiQTQ8ierEsdsJu+zntTFMGoiU+/MiUj6XDsq+/V9+gvxlUryiTmi8PSoeDQilj5+VEiKZUHUFk+LFRGJl4cuvmc8RsYNZTwVe5rNqIQCD8wKYGI4gq9Uc18NaSgjeZLNdsa7FYyS1yl8+8mOx0DSYQioud+D2xMN7N+QDDPIxqnI9Q4yDgmycewxjVz1/+Zxy19u5ZhjpnHBBR8g0u/vcmnr2LBfjut7JZbPv4m3v/0/fOAj1/PFz7yFVCpkHCFemVg1bzvb1nQx8ZQmhg2v48vXvout61v4yEcvIF/Yl4oYe4999Y5kWzvoe3QuVWcdhWXFX3iDEHsNC+gtF6CrE4YGs0qZOI125O1TBaoNOrfC9g7UkqV0btzKlg1tuM2j+O9ml40uNLUV2LBpJxt7nyKeaWDk9Ols2dRC76qVbFm9mKlDarjlb3fT09pFrWOz06tMs/pIYpeVWaiPwf05+T9U3u9ZBSs2wrAV0GxLVkArJzkwvbWw6CF4eCesprJtiIMH5WXp3rGI//7606x55Hbe9q6rae+Oki1CasQY8js2YkUnoBpPRm18DLdlIRuXn0y206a+WZGp92lMRdi2KcuOVdtpX7aIceNGkzzmWDpdl7Ztbaj2Lu7/8wr+2zSaN5/fyGHDfP75rx388+YFZJPVJKrSJCZOIlPfQPPUieSLLmtXbie/vAPXGgrpGA/f/yx3PVDNpW+bs8+1fBzgdAcuTsO3fZFTVS4AkNFJi3Q6VTsFdxehJgoT47DOghOUZFdaCCz0ZBu69T6Cs+PG+IRKitsIOm6JilbfeENgoEFs4jgOEDJ1Io+y9THdksQ/WJoMeI7EXvgFKYCsXJGcKatiLxtFUJ8uwFcqy/yCa4GKitciEgWlyYDvV7JUuSYrl6c9I554USKODiiPgIqIxCuZhKoidCcgrsTzUrbBL2q5lol3MLP/xgvxQjDB2IPlS4M9TSbewkbuofFSpakQDEM4fCr1K4LHCZIgU/djsE4vWPvDbBeMBTCB7IeAd2O/kA3FQEIOA2cPBt/T+voMX/vGl7nk3Wfz4IOLmTv3NtTLofHYS3S0bub737ycZKnA5758EdFYmP8/xCsPQ0c3ceWnv0L5L2P551+vYu3Czfzft75OqXQgy8w+P1IdHk9d931OGwLO9CuwovWE1f1eHtQAn54zBquumYHTnhbiwtiGVCkdimp/ksKjTxLzo7QtW8naFT10dJV5dtMaHiv52EBPSbFlWzubF6+jcfgY4ql6quvr6Ghvo5wv8Nz8jfx94RIaCmW2DWqLiefZGrEoRKE3N3BAN5N3a/ugdjnMAOo6IbFTAnQ3LoU7V8DDHvTstysWYt/hQ7mDrUvv4EdfupvGUR/iuHd9msIpJ7FpJ7RmFfmlT1Ns2cnUWW9gbHOSpQueYEuiDz+RZ/yIoYyrG8abThvO/7z5MB5/tpU1q7bS/sQDlFo6SY0cRX2kl/qRCdrb4DdP5unqSuPaUZyyy3GXnMqOBc+wdW0Ly7MR/O4e1NrVWFY9ltOEatuM7xV4YEk3F5+viL+IrFYx4D3APcATJSpSFDMrrYNxlQ1eToKSvQys96HHhseVZEcqebCzC3LdVIoHGomUMRRtxDjspZIj1kSYdzFQo5rV65aRjElx5KE/QIgk9GS8J39dC/yYvhw6EMOx5HfX1ellExWS4SIF6wraS+G6FRKhlBCHWEzS6dq27KdYFMLhedqDYkumK09JrIbvifoy4uh2xMBJAh7kPciUdOxIBFwd45Dvg6JJP2uKf5h7sTdGeTBmxhAW43lyGCiTMoHV6PVMtjFDHsx3E8sRlMUFpV6mz+zuE6ESx2FIjJHpmUxSr1ayAZWYG+PNMSWbjCwxOARYlsXZbzyKyz76M374nffju137q1l7RLHYyfU/+RRnnj2ZE0+cdcCPHyLES0WyweKEcybzzosv5+n/ruUjH3snS1bNP9jNGoBlwAcfamPOG67hk5fP44hrb8HDYeFzm5g0YxQ1dkg8Xix6gA2dRfA6qVQOM1gJrAU3C+UyO2+9ic3/mk88FWHF4l56uxSuCws6PSwlvpByvsjC+YvJuz6Wq2gZM5RMZjKFtk3Eezu4b948IuUyW9i1knwCca7syCuqsZiSsOgtKNqoqBg8JJ3tgu3Q68LwDoiuge4ueG4zPFBiFxIT4tCB8sv09T7E8gVHo4YfRaxuGO6m7RS3bWbq6y7g3R8+jbLnsW1DmmlHTGLYyCoS0TizJ2WwSvDIQpeWDR2se/Jxil29MHQUxZ3r6XNOYGgqz8P/XEVnbx9dyxZy9NvPY+2zHTz59WvxY9uwqt+G17JcSkcXckQmTcMrw+hTzyXbuoF7/nQ/d73haN52wvB9Pi8LGGXByTolbg/Q5kiq1pSWTuWBsiUz7KSg3APbC9BhS/yBKcbmGiPUGIsmTiMYd+GCUyuz7qo78N3EEjiITisL7ADGgDUV1CpEGXmAYNtCIMpaFunpj2XLdbB0/J1j6aJ6uhq2kVKZwPBsCfIFuTaeXyEckYiQAidSKe7nBSRbyoGENpxLrqzTX12bCllROmI/UYaaGjmO4+i4GQ+KVdBeFLLUX7wvKG/bG5iZdBOPYbxPJsbHGP0mzsLU5TAkw3gb8lSCy4OEwHheTJpcGBj/Eew/wcAY4/0wXjJT+NHI8g5gjM9g7Bey4SHzaAUq8sMgOTf3NhjPEo1YXP6xs7nv/hNZ+szd+6NZL4jOrlYuuugS/vjXv3Dy8Ufsts5HiBCHKqKOxWev/CRPPrued73vbWxt2fsifQcKRWCtB25rgUvXbKTY18OvfnszN/zlab73y9/w5hlhaoYXCx/4w+Ob+PiCvxM58Y2iYQBA4Xc/hL/+HiK10/AWr2HhX5fz0IIcWQ/qI9BahpUlWK8kwmMI0NWbo6gUJcsiv24lREtEyu3EOrrYvnwFPR2djIrDH3K7tqUJqInA0MZGbrjkDG658wFuX9pBm953LRWpVZcnqUEbO2XmsqcM6z1o4ZCYkAvxPMh3LGbtfZ9h2Kn/j2lHXkrjkKE8vr7EYUeNorkxQqbawfJm0rJ9O35OkaxJ8OTTPj6ws3U7y//zINlNW8Q90LcUr6aR+b/9FUuePhawKBVKpC0Pt9hJIepz+Hs+iNM4jFXP7qS06DmxUNs3Uu7uwj7yeLr7esh19lLKRrj/3uWcd9xwSZO6j7CA2RasANaiJ52V2C5uGZwSRGKSCbq7D4plMagLxoAtUJkBN7p5U4fBGKY6VsP2YOIR0LIEhlXDZTPh4VZ4aB5kbR38XAcjJsMJNmwowmYXOvqgb8dLvIH7AC+uwwiUeG18DyxX5EzEJJNULCKkxMRXxItQjonESgFFVxJFlLyK7au04Ry3IKKzQylfPBe2pQO9lVYtaS9HuSzZ7Bxf1i1p0hPTHqNiXFIXp41XJKKJkSPtzeVEyqUC2cb2abAxcipDKqJUCviZ/ZiYDUcfww4sC8ZrBEkEVMipyVo12JMRJDpmexgorzL7iVJJlxZM22sIyQHEfiEbfuATTBdsznFP6en6dmZp335wneabN6/g4nd/lrvu/iszplSHhCPEKwZ+yee6b9/A93/wEzo71x7s5jwvEkDD2cdw05c/wR8fW8b3r/8Db5oWxku9VHT2eGx98mnGHFeASEYvzdPxxDzW/+UZjv3QODoefI7b5rWzOif1K6qA9VSCsDPoOEOlGA5sUYpcsUjj+tV0p3IkO3N0rm3jJAduLMj2BSrFdUGy/KwogZvLkXFg3Ig4YzbAjqzsv1Ov06SP1Y28/KMIAckhhKSDQya+McSe4Lex7aEv8Wj3KuLDT2TKEUM57bThNNVD2bMY1RzHz9cwekgVhQIsXraTDctX8tSd36Hc3Q7pOZCzIdIIPYsh00XNuDcwevIUnnv8MfIb1rN+4WImzzmPSDTCUzc9Srm7BxIJSFcDVZCuwhqSoWvZUkglqJp5Ig+tjdPS6TGqYe9l0UGx35uBwyz4t4I/aMM350PKkeRZjgNWSfHMcrBGQMG2KrOoiopWP8XAFKmDZqHTaRifhvQ4+H8N8PYovH8c/CoGP7kXkg1w9Fh423B4gwWbPdhQhr9WwV1Pvgz3by/hI21WNniWjrPwpFK3UloNa2kC4goBixeFBKC9EPmSeCx8X66nueCxiGShsnTWKeXLdo4jy4ys3rOExKDk2MqT31EStxGxwY4L6Yj7kNASrVhMvCe2I2SpKivtLhWoSKn2hZQG61tEqKS4dQPLjLzHhCYG5VDB5AAmG5WRQ5kZ+lxgXbPMChyjoD+G5ATbFgwgN/FAhhCZtgSJ0YtAFKkYv7fYL2QjiryADKHKU7nGKQbGSPVDwYoVC9i++an90aR9wqYND/Lui6/kt7/+JkcfMfRgNydEiL3CH/78B675+mcplyu+4MB4fkhhDXDWp/5Iuax45L+3MeXISYR5qV46lAOxptESJdmP7ex8agEL5vVxTI/Hju4kj+WkHoaNSMOD2Z6M/NXEVUQRb4Tq82hbtJmqsqLBk+DX5QhhGPzOsoHNQKw7xzbf4o3vOJ0Fq/5Oe7bADuTdMBSop/KeDU4AmoQqJaQSvckuGXo6DlGoEt0LboIV88jMeRNlNZtSpArfgqomG7UlyvK1Oerra1i9YinP/PUGSMDII84nWj8Jz46RxSFqjWDiiaOI1o0hFnN494cupFy2wIrxyJ0r2PLYerzerEyfd66BnAsRRe1Jx5BLRvGSccjniAwfwbotrSxYm2VUQ/ULt38QjOJpInC2BXUW3OFDrgRHJ6DPhUcWKVb/eR25p/pIfu8I7OGSDak/wBvEkEwBUbCy4BQlS1IkptPDlqHGhSMA1QzHWJJZqQF47zAY8TqYNgQmJiBtyXMyPQLTIpAYCeNOe8l3bq+hfPFm+OiAbOTyl1z9nrGEDChbyIanvRvoquIghr9jaVvbqhQBdKISGG7qbRh5lmOLgzYSiAUpFvU2uk1KiefCjkIiBpYn8ivPqpCgaExXEvchWoR4QjJVlWMyZg4Iwn8hmNgLUwMlTcVrYTwOg2tfDM4mFtMfQz6hEsdjoulN+tugVyM4i29qg5hjQ6XGB4F9GwLsM5BsgBCaF2EgOBbUp6FxH2KG9gvZMA9qnMp1N1I1I2kznqTg/W3PB6uZHDwo5bJ43k186as1/OrGbzNsSJg1J8ShDbfosfTRdQOIBhyaRANkrGtpz+LYFrff+CM+8+0qnOqTwmDxl4iT6qM0z34DWCm9REF+G9vWdLFpIxRbPJYskEiIbmAsuxaZrQXGIwQki8RvDEeCMvN5xSjkxfGUXm8G8ChSb2A9IsM60oalvmyzcP16phw5mYZUhOPrYGGnrNOAvCPMxG9U/z+DvAVqkfdDu26Hi9TpaGfv67qEOIBQLuQWse7e5/jHiBjnfe6LLHhkAc/+/vfsLGSomTGT42YfRfOURmZc8CHedO5k6oaNoJhz6O3uZtu2NsYfcTxtXWXWru3CI8W8fz3D9nm34pbq6doeQ9XOAj8L7c9g2atxquYQmzKLWCpDbmcnrFsFxRyduXZIRfj6jb0cO/utDGPvpjIGr+MAY1xFrydS7yiKDUuy3HF3O+tW91L867egGMH+91eJXzaOvEk96lEJBi5DKiJpfE9ohNlRGBuBTS50FmF5j8SITLBhZODYzQ787+g9t/XkOBw9Yq/vzktGUUkNi5Irlb5zWTH8XVeM+JIkkiMarcjOTHC3rY1upQlGPCLSp0hEvA6OkrHCR2IsigXxQth6lj4akX0V8iKBSiTkYwLDo1GIJqSyueWDW4SMToHrKr29DkyPOxCPiiclb2m723gqXggmoN/MiiSQAcwUAiow0OA3hMSQEENsgsHfRvZjApyh4r0w2xkPiNm/8WyYWRlDSkxCA0NkzPFMAHqwPTYV0rKPSNlQm4LhB5tsBGEh90bXVeknd8GgeTGIFDsPoP7wheFz9+0/5P1+nOu/+3mmTtr32ZEQIQ4EfN/n1lv/zA9v/vbBbso+w/MVf/nLv3nX5acx6qiTDnZzXvF43cnNWOPPoFK8rw92/JcNS3t4qKz4UKfNxnKSHfTgARvZ9SVgxuZmJG4ihbxTLcQTkUIC/afqdXYARyOSLBu4Ajg8Bn8qyDtt/caNJGecx2U/Gsa2R59GffthonlFHRIvUqdfnsqSYzQpMUZ6PBhlS0xHDnkv7kRS4S5BPOYhDkX4rHnyb/z52i7m/fFXKF/0IDuftlj7uzhWbDRHnv1mph12PBu3lLFKWYZWw6rHn+OGn/2N3s0rgSSpSW8nt+LPKHcBYiXMgEga/CoYPoPGSZcSq6miZd0yco89A9tXgncPOGMgOwTKHWxSR/HsjrfwpqYIjvX8hEMp6C4pFre4uLlu5kxrYGOnx4OLdvLc6laOGDOEe5f1cu/P/kq2vSQWbHY5KJvs7X8i+eariI6NUjZSDlMjwYIhGXhdXKRZJ1ryHKkoFCKwMC0JFmZQ4ScvBIsKQT9QcMsSK1EsQbZPYvOLWvLko4mBgnIc7Bq5nr6W8/i+eB8iEYh74q0olyBnSaxGXEFBB30b6T2eBNpbJUlpi4JCnxT2i2qD3bLkuJEIJONCXPAgJc4wSUZRlvViCfFqRG1wPLBLUp3cMzPgz9c5TKxNHPFkmNn0FAOzPRlyYP5vvBEmDW6wvoepFh6UVhniYfYRjPsYnF7XxAYF2xjcX1BKFTxH4w0Ixm3sA+GwkGtdH4cRhxrZMNdxsKzMZCWxEdb55OOHWJJDpbj3n9/jFxPiXPftLxGNHhJlSUKE6EepW3Hzb2/iE1/8GMXi7keMCBWP6qHo6VjSBZv7RjLqYDfkVQA/GgFnFJU3ZzfsnE+cAks9uOuBJdywspV2sz67JijpRTLwJJB3l5k8q0LerZ36+xC9fhNSOfkxZGZ2PPDrQsVH3bqxG7+UoumMD1Mb/zFv+McjsE0xMikzmnEk374T1bOPWsfl2dDniNFQ8sUY2dIBw/NydvN20/YQhwZali2lZdnSwBIR3iuVRxVWMv/vq/jko39m+us+yyUf+xBRv8zyp+6nZ80tiMBuOtklvwLWIZR2ClCA9gewJ/4vw2a/kWxnH+1PzUOlHNi5ArwdgAVeF/jjwEqzY8li7rx3KW+8eOYLtrmrqPjBv3ewZmuWxx5+gndfcDLLF6xiw6Zt9OUi3Jm32briObzNd4PXh/jnNgFNsOgvlH7wOpJfnA1DdNB0GvAljqDegdcBp1kDJYJJC45DbL29LfVg4CPeybp92OaloFAET6e0LSkh+zlXPAcOEiNRyEsaYN8VEmJ7EvQd0aaThXg27LLInSwFUR0EbgF+WculPDmObwmxMAHm+T753RT6s22RSNm2yLB8SyQ+8ZgQF8eR9UFnutIXWPmyf2VmVkyGqN3B6P+Tep2Y/p6hEg9hDFwjfXKpeA+M8W9IiXkJG+PfkIFgsHOwirwhHsFtzfbFwPZBqZUhLWZ2P4HcMKNRNesbZldir40DB0jFoCYNdfswB39ArefBD5IVWOYBw0ZWI1fi0HGSK+Xyoxu+R13tCD772UuJhTU4Qhwi2LG5lR985hf86I7vkC1mX3D9IOl/aUaaRW3tEOrq0kw/+jyeePx+Ora9+MxXLvDM32/hhJMvxLJCQv9iEAMm1jiMHV41aKB1IetRn1b0uj7Xz13ANr/yVglKWU3/KCGV4dNUZk5N9s0GGwp+RQrbCEwGfoOQlKOAjAVbVOXd1dJZZutj/2XshMlEpzRxyv8MoeueHVRZIoOwnMD71xODw/fAiUsaSyutFQYF2LQF0ptkZnUt9JOmEK80KLpbt7Lgrh9z6htmE01MZNzs82ntiZMedxi9K5fQt+EZUHmgANZYUC04tSOIjJvN1gfvlQCJni5o74JyL5Up2xpQCbC7iaWb2LQmQperGBLdsynfVVR85U/rmbekhUQyRTSe5i9/eoTcli3s3LQVL1IL3esgew9ircyiYgVuAhUnsWUrkSc8ms+y2VxrUUoIma6zYJyjpYiIV7BOWkkCbbzt05WTz3YF93XCe+v35bq/eOSLOhOUjsco52VZ0dZZoRwJ9I4oIRJRbfhbdqUQYMQXguEj6+BqI1Qb3r4S70nZFTmUrXQRPkfIRTEnsRnouA9HS6WMXEp5OulXBCw922HrdTxvoLQLBb4J4obAzPegEzeZw0zwtoOQjWAsRDDY2hj7RrZkiEYwjsOsrwLrmEHQCvzftKUc2GcQQWmVkYIFj2kkVaZ4oQmQU4FzMm3by4C4qAXJCGQSUJPcu23gIFcQNy85HyhbFmeeluGPP45zKJENANfN8p3vfIPqmpF87KNvwg5rAbyqECT0u7uzSil8z8f3PVk3DzueaqXku4yaNhzqwYpbOLaD5VgHJIPZ5o2buOT8/+XhBY/hvwB1MJ5SkyQjiWjgX6yX442nXcxnv/L/OPnkqViWzdy5/+Wii97NmGGnsGbVPHrL61Fq3+jMj297nIsufZrmw07AL+exoskwE9xeoinq8MG3v5EPvO9Uhp1wKjITDOCi6EFFGmndGqXGKrI6Vxpw3z0qcqSKpFUkUachk3cTgcPikIiKvCmf1++2EuSUeDpW6O2iQK+qSJYBlhY87vj5v7lscjPpKU24rXmqkxAboQ2OKGJ1ad2zMi/luHg1jMs/VoCJaYi60LUWnvAkW9Wh6K0LsXcodK/it1//HKec93nO+p9TOOrcs2geBY8+uIN/fGUHdN0G5EG1Q6QK5bfj9bVD41DYsBIKTwMzwB4F/hrEcuyGZJLqCUdy7FnnsH7pOu5fWeZdh+05rfY9Szq556HFFHN5ultbaRg5kqOPP5FnH3iKWGeE/PadUDZPwxaE6maAPNGaIxk/+39593fP4p+P5elZFKPmlBjtFlRbEvPkKZEsYklChs3AqS/ievn6DHOIfFEdwEzhZe1tyOWgr1dLqcpQ1KTCiYiO39bB3rYjgfBRR8YOC/FQuugaF1oK5VtyfTxfPCee9jpEPW2X62xUrieEIqa9GLYtqYfNdSmXxRD2YzJOOaoSmA560t+TY5j9+SajE1SM7t2RDWP4m3SywZgAQw4MGTHeDaiQCcXAquNmv8aTEfSKmJobhpAYz8nuXqlBz4dBkNREZQy1IuAbV7WZKTIxHYaI7C3ZsCFhQdqG6n1gEAd9GrGf2FnQkDa+ouDdPTTQ17eJb3zjU0yaOpo3ve6wMI71VQQjrzXxXgYqp9iyYB1PrlrMnb+7gyfW/YcCMi1T7i3jo4gnYxCBKjvD+SdcwKwLj2Zy7VQmHzeJSMbZ5wRLL0R8ULB+zXoufue7eGzBvuc9VMiMWgR56e0rRgyZyDXXXs2xJ03pJwNnnnkaixfPJx6rJpvt5Qc3/JP5S+/kiUfmU8pt3av9bm7Zyd1/v423JuDma37AR677BQyrDRNU7QWKluLoU2Yy/KxPEKytAQ7e+tXc+Lm/8+i2MlceMYofLdtGa3nPCdbNiJtHMk1NRmz9qT60e+A6EoTZVAfjyrB0GyzyKx760UBjBHrcSl/ermD1lgLlZx4l+2g3FHpIzARrDJWcuaYWgSueDhxEq1VLv9vfahdPyIhumLodDu+RWeJDa2oqxL5ix6qH+Nv35/PUI+8h2nQE2dYtdC1/GHrTkDwW8kvA2gbxI/F7/ou/9B445jzoiUFfKzAa/P8gET0Odu0YRhx3DqMnNrPowSfpXPs4C0+3eddhR+z2+L0Fxf3/3cD2rS1kW1rwO7ZTaG2lY94T9G1rw6meglNThZdzodQMbIFMA02jJjJ67IVMPPp0zn7zkUyYlMbv7ua6H8yjfuIcmsc6xMvQkJAZ9HxcnolGhJy/mHiLHoS0KCS968jMC2zwMiKflxiNfA46ctCTFw+EciTY2rF0OltHiIcVEbIRT0q6YIV4HMq2fE/7UnPDc4U0WCWJpVBFsPVYoGz5+Mh6TlRkU7GIjAXKErml54oUUzlCRCxLpFvRqPztr3ruVgLavWB6u2A8w2AEvQ7Ge1Cm4iEwRmwwMDkYkGxIQzCuIxgkbgLDy4H9QkXi9HymsHFNB+MVTBtj8jpIxcBKaKJaFDLXf+x9zcSFlqzZcrrxfTDRDzrZCJ5zXicsduIfxivlQP30ILZsV+xsXcnXv349p538M1KpsPjYgUTQCDdJHQYvNwiOF3sz62kRkFe6Pts3bmP+zfdz09w/sWrpZpa1r8ILPO1mEqMEoh3R215367V4t8L49ATGHzmOwxrHcf7rLmL4KaOJJyPUZBrINGSwIy8QqbgHlPNl5t72NFd966MsWr5w33dAZWbsxYoBj5o1k2NPnDqAbFuWxfDhkiK6oTHF9793OV2F9/Ps00t44PGl3HrTT2nZtJR8rmuP+y0qePDPNxNb+hi/vXspE+p+yeuu+yROUl7JIefYM0quT1/LPPAK8nanDCxDdSzhX9+5nm8+0cE2H6au3skbRjRx64aWAXGFzclG2godeIO8UW2Ien4HUvQvX4beAlTFIRMXg2J8FXR2y3zvQsSI6nPFM2LgAzVVEB/VQGpyM3Rl4bkOeVvVMvBFal76MUTHZWQADpARCUaiDJN3wBEr4X5XUviGeGXDd3vY9MSPMOWOregYRp3+/4hVj2Td3T9EFXZCbgGoTdB9BzyVg/JmoAWii4lXTSJZdwz2iFlE6mbQ2dvH5vuehY2LwW2lsLV1QEZSA6UUjzy1lr/dfBdePILfvgU611M/YSpHnHASvRtyLJn3KMXN98ixLZc57/0Bs950MrWNdXRv7qLajmC7Fq2bFQseWI332D20/2UcjeeNZMpom+zmIpNHJliqg6e3WVBrCc82SpfB7YKKDWrszS6EXK9WkNVxDe0KXre3UeUvEV1ZKcpXLEPehWxBvIyRiKSmjSclQ1RVHJIxSMfEo5FyIK5PMA64USEJqRLUJKCvT+RPlMHPg5+T746vpVQ6ExW+7DcVBccVmZSljXwXHZCOToNry/8dLb8yBrtS4nGxLB2vYS68ySY12GAIEoJgdW6zbTD+IUgsgnEZwdoqASIwoJCg8WoEZ06CGaSCZMcQFdMOGOhtMQRCk614EuIpWbccg1xRsnApsw8TgL4XsBCS59hy7b19cCsfVLIx+D5maiaTrD2R0bPex5Z1PWTX/pVDTZX73MK/sWHzd5k2pT40gA4wzFgQjHuCgbbKniYnng+2gpTnsWX5Bn7zi99w82//wobsetQeqEo9EgQ7n8pYkUSCY/uADdm1rHtsLQ8AP/znr7Asi2arlmMnn8Kk06cQq5YWDo1NZcSISZxwxpE0jnIqMxgBfaHbBYseb2Hu/NvYsnEdf/3zX+n0O/fxDCtQiPTkxTz4lmVz8onvkQH+BVCbcDhjzkxOn3MEV3/8Ij7y4R/yh5uuYtcotwr+tbKNltVtnDKmjmfXrudMXcnJjNODSWT4/AlmTcpw4tFJFL6+JpuAf+H/6Uambc1x49vezP/7251sKJZ4/9BG7t/QIhIMoCqZ4bTZZ3L743fglQcmwc0ipMFDOMEoJPVlsgSJCKRTYgys7oXxPiwAnmDXRAQxJOuUM3E8TI7Bw0/J9O5IKqmvzIyeebGmEM+GyQBjUjTGhXDUt8HhO2BkW0g2Xl2QPhiJddNUt43YyCNpuPRa1s27i7Zn/iDT3FaOWP02Rs44i4bGy5l60niaJ0xkxsg4q1e73HjdnfRt2AydvVDuAJXl4WUbaelRjKi2dhk35q71yCSKbH3m98AwrNQ4ti19ju3z7kO53YAFqp1ochwnXHgxZ73nf8g0JuhpLfHkk6uYMjxCZ2eMZO1Eph1zBIuO3sqG7/yewvzDUR86k0Wf/wNTr34rjW9uYoNl9duZfwdGIHFOQ9j13dUJLFFCMDoUbC7J2N1bhE0t0NUFsRR8d+b+vB8V5EpCNIolcH3xYsSiEsCdiAkRqNKEI5WGTAYyUUja2v7VcRYlBXkHkgnIpMVT4nsig8rqYnuWzk6HJaTDccRrYdti6Pqe1ORwoqBcKNlatuVUjGETGG4h66CEiMTjep0o0rDBQd1BmHedkXMGgx6DHocgcdmdHMnEfZj4CRMrYYwaI5MKBmr7DKyL4SBcPKO/9yGDtJmQMZJUHdNiRcQLlEhBKqXVWa6M3Z1IcL+KIW7pYJD688Cy5H5Ho/oa7sOs5UH3bBg4wKTpjRx77lUkRk6mepzLM5vOgPKte7V9LDUEIs2UejbSP928H5Dt6+Hqz36Cn//qpwxrrNpvxwmxKwITFP1xCPD8Rudgs9Y8S0EDdufyDfzw+uv4/T/+xtb2NnxUv3cyKKE06AIWD1qWRWoMjEWkJ0bDrpRCKcU2Orhz+e2iTdGIWgmi0QSZ6pRMSO8OHuR6y/TkOvBfppJmZkzbV2SSI7nwomP3fgMLFBaZlMXrzz2Pf937IIdNHkdJbWbeEw/iuX0DVu8BqIL/fduZjDzrvUTjUXLsWtw1GFsQEg4499RxZJw+SQEDyNNRg33Jh3GZz7OPbGecD6McmD1xGJ8YMpp/PzqfJ7q2M2vGsZR8j1J510xmClGntyDvteMQ46geeclUpcS4ONWClg3wuCf8oE+3oFXvpxaoL4H791uIZ3yI5SXPZ5NeIditzWxdFVKIo0Y3JKc/BWlAZAiMroHh7YRBG69ClLOtPHvr13Dq72TiGVeSbJgKVSdD351EY0m+cOONHD6rkVjEZue2ElbWYWzaplQdZebhTTyVa6E0dSLl1UXY/ByL/303n/r5sfz0qpk0IOO1A2zpdNnZ0ka5qwPSKezMNKae/kbaViyhdf7ToEpgSSe0a0bgxxp49sEn2LRhMeseXE/jjKM5fMocho1p4tll23j6zifY9PBDMHocnf/5Bo92tpJb/Bd+/fltnDz8a0ydDWdHhJg/hXTz+cAwhF+P0d+3AwsU3FOANb3Qa0ldCrcE2S4o9UkihegB8moAlHMSt2HZumi2Lfr9VBzScUimJCVqIgrJJESSEr9hWxXnpI0U23PQKW8tCRr3dZarfF5IBwhhUFZFZu8beaUvXgnXAy8mpKKk9HH0sZKeeD76lNT+8JWWnkUrSSn6JzeMan93r1fjvTBSI3MiZhIEKgHhRXb/YrWopMqtplKx3MRsWIjBbwybYPaqoCzLeIKrGejhMC/EOBVSpOuOVCXEw5RO6vonrlR1V47EvxQNoQnKul4I+mVsWUIK9xaHBNkw16u22uKKL5/Ehu3QsjPCyvvfRc/6v/FCdMuyHS76yPc47Y1zyLa18J+7F/HovT9m5/ZlL3tblVL8845bsKN1/PZX36KmJh0aPAcIhkQbyZPB4BmhwbaHmQUf7BXxe3M8fc9DfPlzn+XRdUsoI4P/MCpkpl4fdwGVOLKg/DIIU0+nFnl+TcrtPaGsCpRLBXJtXc+z1qGDiy64kOHDm553nd2RAAu46K1jmTjuj0wZn6CsIlx/w0/5zrWf0zn45Z40RWDMUHhs6Ure9a4MlmP1e66D+w7uNwTcN38777n8RKxYHHnr1QGTsarTxKcP48SWpzn8k9P5yU9+yZiTJxHvSNHd0cb8Z9o4asJUHpr/5B69eOYdvBzxcgwD3uzDzHYYmgQnBROOgbc6UF4lHGI5UgfjTmQi7owEjBwOxZYsqWFgTUBIhJmhG0w20L9V6R0oS6yaZBz8InR5WI1QXQUjLMnR/yLqUoU45OHjdcxj5W2XQ2QKDa//IrklSbyO+0m7BeK9Fls3trN2VQfN0QhuPEVqSA3nnTeb5OhmHnj4YaxUDGv4KThOLU/eNY//fGomox14skexc3mRJ/69hJVPLGTI4cfz+g9/mh2bt7KttYueDSuxouOZctxZ9OahddXjlNpW8+gvLwUcrKp6hp7yNU4+53R2bt/I4/f+g9Xz7yFfniIvgXUP45W6yT3yfyRnfRhvxKnkOopsthLchZCJErDOhRW2GNh1UXH4ZRB5VIeCZdqbUdIvHd+Q8QSgK2AfKJSQwOuYCwUFbkJiJ1JxSCQlNiAeFy9HKiH/T1PJtGVS/gbHba8s5+S7QhqUjqVQCrwI2DGwkkIOIo4YzCCeFQXgQ86TgPOILQTI1USkHJA0mVS5oPmF9pL0E4HdZWMKxkMYgqG9BrsU5itSIRuDh9KIvgjV9I9pTkQ8MSZgvZ94GCPFkBFjuESANFi1EK2SthctfShTITtGf7+Iae9SPAWJuHib0mm5ttGCEL6cLsjo6W36jZbng9KSN1/IhrMXKofgZTjoMPcmY8HM8VA3HOwVUFV7DD2keSFPRTRazSXvPYtp04aRYSxvPu8E1i69gA+/50JWPjd3P7S4xO233Ui5lOXqr36TWYc14jgOtkUYOL6fUKLiMTTE3yA4FthU7BWlt0shz9FO/f9GIJkv8uzXvsMXfvQdFpckH88w4EhgOjIupIBjgIeA5/aijRm9/mL9twfxcBw6aQ5eCiKMHD+GeMIZQCj2NKlslptJo4htcfwxNf2/v+vi9/LnP/+BTaueBqCuBqw8LGyBDfkSb7EaaUTuqZn0MXWIzLGjhIQDYPPqnbTuWEWTWg3WkUC16MtpZvxp72Zk4xR+8+WvcOIxo+ld8QS//dV8qocNxfI9vv/Xn+/xfo5AiPMq5H3Wg3gtHgWO2QGJAtRXQ7oHalwYh8R4jEW+dwFjHfjUyRZT/nckttsrS0chCbNq9EFLVGb0zE1NUAnAtGyIpCBSA4290NENTVBVDSNsSPgh2Xh1I0ckVs3sM2bhnjSFJbda/PjbP+PtF1/BXX+8njmnnsvW9g7qquoYPX0yNaPHMmZoNU11SZKTTkJFhhL346y491n+tqibiUdXc/8zWdb9exnH1Kc5843nYFsOxx4/HOe0CaxY1YZVVOSUjevZ5LZu5fiLP8rm555h45N/Z9jk4Uw56xJyjOZfv7mB9qX/ROXXIB16GRUrcTvYM/G2b6e0aS6lOXF63zidhVQSKpR1TEEB2KlgTVG6fk8RulxZz4vrrEkmk1ASeWb6OKAvl3REp+pV8jgqpT0bSfFqVKWEYGSSOrYCycZlbFhLN9dVA1PQum6lYKBtCxFwXSEP8Rgk0rJ+Kl0J8vY1oSj6EkdSKktcSAnoK0HJqWS2spHrpjThsLT8xzGeEuMlCLrMCXw3pMRIrYJWs8kEZaQSg+EgjKseqAWnWlc/j4msrFSW9pajVO6laY8xaPS+nWpI1QmBMHZmAeSkS0AS7JQQvrSO00ik5PolkxBPyDWIONoblBdZXM7Iu/aGOFg6NsZcz314AR8SZMPAAdKOpE+LpsGPx5C30vOTjVPOuZiZYxqkYyuIRS0yMxt53xVX86VPP0O51Pe82784lPjXP3/Ho488wOGz3s0ZZx7FxLE2CSsNZMCJEU0mSSXiVGeqqKutJZlKYFke0aiDwqdcKJHrztHZ2UVvby+9uU7qa4YwefIEho5I4hxSd+fgIhj3tLvxNSiLCnpATDB0DHkVbNH/X/Dne/nGT/+PzlKeNGJYDQVeD8xGHuJuhID0MbBQ555wLjIYH4PocB/S++jTf1/JGNI0lAvfcQlKBzbGqUjazBhsZq3MvQrGWRjvk8n0N2N8Fee96Vxu0GSjJ2cxYuhwnLpm3vCWSxl1mJSMMxLZPUlpQ8BpR9lMalwP3iaIHI08JeOAYeAVmfutr/HP+//DL77yRtJrH+e0aRH+uHw7OXegP+PM6dOxLJ9yvo8xTTXMXLeBTa1ZhlHxbPiI7OMvLrS3w/G90NAq+t+JCNHYihDvtyOjt6Ms7HF1WCNGQGELxLNQ1QfRciV3fInKDJ2ikpklA6R80WMonX5GdyLHl5/DYXLf4SSi+EWFUnvOTLZvqEGe0peSVPt54C8CfwMNVSne8N4PsfyZZ+jrzoE/hDFjJvLmDw6lpdemrc2jpt7iaGcIzVXns3HTFubNf45cqpnUtGZa569lZONM1j26inFDihRbVmFVTaG6fhg715dxqxIUY/WMf8sc2hYvZsGDc8mtXsqCbcuxk2kaJxzP6994Ivm+bu74x/dIpaKkG99F3+YHkGmmNt1gPfLF45R2dEBxM9s3H0OiAD2ZSs02X3dnNwJ9nja2LfB1wUuyouQyA6tVAhWst3AAZ1tqolraFBdvouWJ4ZrWxmx1SgK+U0mRKyWo1MEzCiEzee8pPauu09l6ntTusNDBxzrrVCQuAeGpWptTp7yemshIlm55kgUtSyj6YqwXSlJwkBgUPIiXJIbDeDVsbSAr6E+jq/TMvGVJGNCAVLYG5toGA8CD1928zGzkhVhg4P2wkYugPRqRNKQzEquSioh3o1zWNUsy+mK5gf2ZOI+IbFtVDzX1cm18JWRLIfvwc7JePK3jZiJSTT2mvRqJhBBAEI+RqZeS86GQBd94Nl4AvhKCYlIVq3141A+Jcdrcx+AMte8AY+Lw5AhgzR63zVQ3cfnl7yediolny9Ls24L3XnwKP/3+DDatf2o/tdynu3MTj973LR69z5xJUj5WFCcRJxGLkUmmqKquIh6PgeUTjdgoFG6pTKGvQG9PL9lcjlyhh+pMHaNHj6SxKUZjwwyu+OB7GD9+KCOHNxOLR16zU7nG4AwmcwgimNHDXKLgWAAQ8yGyrYMHHn+Ez199BXY+SwYpttSEpO0cjzznm/R2SUSzvqdXsvZucgQwhwrZWKPbcTKwAXiGV7aHY/LkM5k4Id0vITPe4mChVKjYiTaVSWozYW0yBpaVzHp98dormXD48dx1+zLwOrnk4pM5+cRpNDc14znWgKKpRtZqYFNJVvRahW3DqUfAzKNriE49ExwTT+OAdSJ+z1bW/Pt6fvrfp7GSEYb2bmLl2ix3bFA8llUDCFzGibCxtZUj66q5cOYQWrb0cn/eowxsQ96X26jwgn8jtQJKJZhVgqa43FOl192EkI+SB60tPmO3bSd+zGyI1oBqgfI2yHeKhVVGbm6BgXnlk4girFlBygO/D3r7hLl3g98rM5yv0SHxJaF+2Agcv4qObdsolToZaEm9GOTYn3fCLfax4T93MO2s80llEpzw+tOocpI0jBtJV9tCipwHtkNTs2LrVpd81ibq+qSxGFGdIjFiCA8uf5xFf6/CbRjL7KnDOXakz/2/eIJN2yy6C8shYZGNQKGvjR2L/kmxZRl4WazYMJJTjie/s4ea4WNYs76LxY/fz8SZZ9G6rYV8691I3Y0uKpG6+unKzUPCcYvE1HEUC6fipaBkVWS2UegfQD3EoOufnbGQdNBK9PEOIhnalxnllwuJlHgDLKRwn63k/0kdgJyJQSYFqYwOItZtNA4Dc1WK6MJ9ZSELJV8K91EWYuA4cq7xFP2ZpmaMHs9hpU/gd1YxbfyVxO1PM3fdPZSVFBYsF6RSeT4qHo5oRK6jo6StJtGeUvq4RfGmDCjkt7trasYjk6BisDvdGKyaGAY9EWT0pwYwQfNVQgKiEU10omDnkMHTpDEOGMJWVK5Dugqqa8V7REw8OmVfiJIqSH+yHIgkwInLdYzE5FhxnR0sERMncbkMKR2vUVOEviqpm9Ifj/ICUirX17VQXCjtw1zFIfWuNnr3LBBNgj0kAlb9HidKLMvmLe94L+e9/nCpVDloX+lMjAsv+xj/94WnOTBRhIFIRgVeHrJ5yHbDjr1MmdLd181zyzaIN5b/cNvff0xdzSTe+j9v4n/OeRdnnXMU8WT0oMm1lFKUXI9oxME+gI0wsRZmXCgHfjOtMONA8E4bmaXv+fzudw/wve9+mxWrHiKpFOOpFC0bi3g6qxEjai0iqdpOJdh7dzgMuASYoLcbp9tWBRwOTAJu4ZVuEFl8+KNvw3Hsfg+PMUuC3gyDoMzNH/T/EhIQiA3pdIYPvPcsRjYfTjmbZ/apY2geamMhvkwjRjD7D97zl2oaveKhXy7dfXDOe2ZhVY1GRk8NbwXZv3+Qe369gv9s7+arrzuNp1truXLuIp7xd9kVQ+NJDq9Ks611Gx+/fR07VeWaGyVA8LkqIfO4/9T/bywKSa9zoNWHzUpXILdgdD3EXCX5jVMxsZT68pD1ZbjMIu6/HiqZYcysYFn/dUrCbNpcaAO/HTp6YWsYr/Gi0NfaTjyZZOLUE2nv2MHObZvwPRPSv6+JKMzIvB/vhHJZfs/PqR4zndTMY/CUy+oNz1FmO7//5e9wY/WMn3oM7e05EokYlqXo7s2Sropz5JGH01nw8dc9iTd0HIdPPI/jxjpsm7+Jd77tfHZu8rn5j39n47N/A3c50uO1AD95JtaIN0DNBBqHxVC93axf8SzKTrHkwZ+Ct5yKJrCdXSOEi8jL3GL73C4mf9ajveSArrmBkolRpS+jZ6b+dwPbljSzSulJGCO1OUCojkvMhGXp8d6X9sTiIndKpUXamEhW3gfGnjfEqr+chC8z5Nky5PIyHBQ9WW47MutvZE4e0JCPctWnLmfFY9u44ffX8z+vu45NXQt4cvMOcgUdLG+Jh8PVhMjXLMd8V77OYlWUY5bLOv4AZGIjjYxDBsHZNBNXYdY3s2nm+vv6d6PZdpAuUQVUQzIDqSrxAsXjQoJsT+ZYIlEhFcoEfpuXXkTS1lZlZPt0DaSqwYqDykNcx6VYZaSmRkTIh6vkWli23K9UQshgIlHx5qQcKLiSMawmD/lq8LqRMfgFHn/feKV04P3e4pAhG8E4GAV0dELvCg/UniVQh8+6kKu/9GU8y95lphsgZVucePRsoumJlLOr90/D9zOUUnR0reJXN63iz7fcxAkzz+VzX7iSM8456qBUWM7lCrz/A1/mvLe+gYvOP/OAtaEXeTBN2sDBHj8za2I8j6ZVMaCtR3HjL3/GN77wJYrFTiwkbiOKjA2jgBMQslFC6gpkkeJJz/B8fjUhKsORmV4fmf2diRheUxBJ1gL9/9yLPPeDjbHjjuWIw47Htyx85DyS+rfgS8TAEJDBy1zkmiutc3MsiCmL884Z2k9KWvW6RpRhZLVmHwZ7CtJ/zUBBfQ1ceUWUKq+F7ON3kjnuHRCdIj/2PEu03MmF58zghwufoap5FB++7V4WB4iGiafxgTW5Xpw2n6vGJamtivD9p3tpK4sopBt5/ga/V1xgNRLAOhKpHZCpF4dF0pXf6yPQWKt1IQU9/de5BdZnpSPlkYlf7a0Y4BYzNp8HdOelU20DNkGxBVZ2wSI10D4IsXfIZ3tx8UhlktQ2T6SvbwfZLh8hrDn2jTgYf9f+Rpbl//4dx5x2CjtaNtHZuZ4lyx+hpinKXf/+K+fEquntzfHUE49hWw6ve+MbSCRSbN+wmaqaZk59zxVkJg5lKA7ZTRZHjh6Jk1e4hSyTp06lbeMMst0NkF8N9EKkmdiYEyFdxc7lS2jrXIDKrQd/kz7nHmS6ykyPmKlpM39fKdzgDH09seQbWT/Po+EcRyZTrIC33kh9rOeZRLE04dADohsRA/NAoSqjj22JsWy5InUyxftSKTGmI3rWPliOwth3JvSkDBS0BCpXlLodnpYGucCwRvFQlIsyDDz973XcdV+JERHFmqcf5MhjT+HsCVfy9Povksu74IunxXYgb1diOuJ6pqpc1sHoJSgUJOuV64o6048iXb5Hn6iZ0Qwigkx6RPVfHVTdL8Myj45NJZWsJhvxaqjNiNcnnRYCYPtgFeWaOHEqQeQe/ZmvohGRXVVlxHuUrhFCpyJCMlygbMl+TKB7CaAEkSqw43JPkmkhK4mEjleJihwvra9JQRO+rl5QZsLneUiE6wupK+oij3uLl72rup5ibWsfE5ozOLaF8n1WbFpJfU2KoXVj9rhdUEZVArasgeyaInvKoh6JZvjiV69i3Mj0gKrPQVgWTD9sLKPHNrN26SuTbASRzXfxwJN/YP7FD3HFFR/gyk99jKbmmgPaBtctce9dv2Lpmqd5/Xln0HCAyEa3KwNSIqZlVIP0lcGYqhgVSceatZ186LM38sid1+KWxNyfjIwLQxGPxAxgGvK62E5lrMgBi3h+klDW2y1EpFh9wElI1s5RyHgzE/GY5Dkw/rWXGzOmT2Da9CH9mUeh4rEwnyCxCBJB0zvM7yaJRxEdjxFwtfdSke33MDA7oDmegbFJk7z20Fgrg31VCtatLPOHp5dy8pENHHlMXhcQt6BqPInJjRQe2Mz7pjcx5fjxLL65dcB+GpwIfZ5HAcWoRJz1vVl+vjzLV05IcUrKZku3z0qEC7RQkbEZWQTIWF1LJXVnwRPJR4NeJxKFeMbCSqblTZnvgdasMJRu5IFrA3pB9VKpdBYDK4as14bMDqSAbeBugpZNsK63Qk5D7Dv8cjddvW2UO7uYdPRMVi5yyXe0autpX3Fg7kLP1qe58/ovcOZ7P8zmnhYaaqOMmT6Z7RvyLF0yj1wuT119ktraGpJJRS63E9/vpSnZxIw3HkE+ZrN2WYHq+gjOcEVPe454MsLoCeN4bsQEst2dEGuE+JFERh4jOhSvDNv/iyo/h0xLHY+w3juRt0MMIRpGbFhA6HcP0oFB1Z2Ct/2f9P7do/FNJxB1KpIo0MRfkw/zSu3Xw2tL3ULIhm9rY9US4/pAoSatsw/peA10tqiIkfokxJBW2hAuUOkVwfeAj8QL5Es62FsN1P57Jehqh6Y6GTISDixZWKRcgEyVRUN9HTa9zKi/hMOafklL51p8S8iK5UtgeFFpiaUjcTFeGQrae1Ioy+x/PC7eFT/44nECDbbAqYHhJzQxbHI96SpYt207PW4Xvb3S7ig65iShTzZKJVA0BbEa8fakdSxLJgFxnYVK2VKzxLaEcLjGkLV1OEAMMtWQyEiQd6wKoikx9o20y7Uq71bf07EqWmvuJORjRYRgxNNgR7R3rCBpjD1PpGzlAng56OmFAS/63aCM1FzJFSTeY2+xT2QjOJzszsRUSrFm8yY++rlv8PtfXE91Ko7jlvjGtV8jPqyNH3/1NlKR2v5AHRft7gns30M6RE8LeF1mumtXDB83h9lHTyS1B1vXLK6ti5CpOoBP5AFAR9cWrvvul1m+ooXvXf9VxoweguNYu0irlL6gCnnoXiovKJfLdJeK+Mqnt9DF9nwfDenql7bTvUTOFRatEPeqFwgS8Hw94wMkLPmbBSK+4gtf/j/m/u3bmKmKOGIYFZEYjTlIMHc1EluxCnhEdss0RBr1fLLEFUhu9G3IODML+rMolRDHehMit+rjwM3Cmtkk45UPPmP7Asuyef3rPoqyKslQjLwpiD0NJIPlToYUulS8H+a5txB70simzLZGWRMkG4PJx2sBtgUNGahLwLoe2LgFNm2Ak06Lc+TlZ0I8kJY44kC8h7qjEry9to7o8SNotC3atN+7zo4xKpFiWb4X3/fYWJApqmfLcN3DOUYiBMNBzCYHUQSkENt/ORU5o4/MIKYcyHWLPCCNPAdDq8BJK7Bd6N4JO1fpnIuguoH1UNoiOfxLvcJFHEeMl1gSklVgdSAPUTXQCl1rYO0GWF+uhOOGeCGYcbqnf4lXamXTpkXEYzDjuJmk685gydPz6d68fPe7OBTgFdn89C38YdXj2Ok0U044lsYRw+htb2HLtlVsXLWKT3/uKkaNH8vOHe3EkjFqE000V8dQfZ1s3NyO50F7NsKGrhijxgyhLhZn/OSRjB47nHRsFnbDCNYtWkCkayv2kPG4yqPmsPPpXj4GrHFQXAn+fCojlIlGM3pAo9GowpANf/m3KZGld0MRu+cE/DrxpJvUN/1eeUdkQDYic3FsbYiWtYF/EJl1VVJm24H+B9+LCNlImJgAq+LVyFPxd8XRk1IKCr7OGKUDxG0tJQsO+q4LXd0wNAOTRsJTi2X7WFbR17EZnyK2FeWt0z7MY5s+Q66oiGgj3VNCWAyB8ZV4SArliqEcjWrvgE9F+hQw1HGg4agkV37kIi466+2MUD52YjyrivP50m8/xgOLusQJoT0qJUdIoIrRH3MWq4LqaqitlvTAsbgEhmdiOgFfGfr09rEo+DojlONLP6hKC8mI6m3jSflezMl1dD0drB2UeiWlHY4t46hvVc7HjgjBsiyIeuLpMLEzbgGKDZDrBbeXSvXy3UAh8rdsUeJl9hZ7TTbMY1WmMoM8GOu2r+cXt/yQhQ/fxqIVR/LMY5v4+KWfpLY6wp23LGDxRYs5bsYpZF2XNZ0FmoakqcIizUBdXy4HbZvRT2JqN0eCWbPG01AtVVWez4ZevbKTrZt7n2eNVyaUUtx51y+Z9+yzXHjheznqqBpmzTqFKVNG9EubCllh9L2euOCciJZCW89/zfaET1z9Ge767z30ZftIFfP09nbCASIbti0aRNsRoys491bWU62+NoiNIbts4TYe+P/s/XW4ZflZ5o1/vku263Etty5rd4mHCAECJDiDDDa/gRl0eIF5cWaYGXSQ/AIkJIEQEpKQpAnpeLtLuZ9Tx327Ln3/eNY6+1R1VXdVS3V3cp6+ztWnzt577aXfx+77fu75Emufmhhyn40iCcAbNQmSsIWncQ+iWBV2JxpcWII7tDGkvlVEFtRrgV2I2ykh7iaKkMangRO8/P5CR/b/BuA+BAY2ghzHNKH7uzTTtAxvf8foOWoi56fu5ycfa2HH50Os1iqThGtKmDhkkfpgmHyE8NHw82vP2zdbsqGAkT7w2jC2IA51OAdODbI9EUgqlCoioD6ANOzeReYaRfxzD1Oe/xI7B2Isz0p/ou1ZHK9b55DuoaM21Qv0KTjry/2TpzPpeB5RnCoh1+je4LUNATE0WoelJoxmoDsv1UbsNszMw9SKdLNK0B6DxeMwfRaadXFcC0VxvukYZOIwlIF0HrJDoCVhZRFOj8GBYgfCtW6XYhcqc/gkUzptr8bXPv8ZuodGyYz043smlYXj4IQCrTFebSBQpzTLd//M/yBqVzl7Zgm/vMLNN72eb33rzTzy+H1Mz43xrW+7HT3exdihk5Tq01SWxvFtnaH+DTSXV7DjGY4cXSE9up12u8o1N11HtWajdIM33b6LlcUKn/7bj+O2juBo82BlgBPgH0TOZxQB0m5GVta1KIxxzvVSNUDHmpnCXPYYzctra7vdPuLbIEB4KKlChz7PcyWR18NOgPHiC4iXY9mU7I8XYGJdJwDbaZIgWYYE3dqa+CJsGICsWZYPdUeEI1q2oBXsAALUrkln1NEgostQwJYLbt1gYtpFx0fF4cThCd7musSJsrf/vbxuy4d5ePIAmi5BufLluzQl3Y1mG9p1CdCbgWJULAqFJh14VBq5GA35963vGOSvf/v3UM8c5mPf/9M8+EyBa3b08KP/6z9x4/ZNPDb2DI02xIJOk+MLyd1uda5NKlCfiiUlEYtGA4WoAAsectKVgrgJWlxqMrom708nhW8RDeaYxOMCQXb1jg+1vIB3sqZqZ0Ql0TCCRDASKHuZZlDIiQr9LeJAzAmkh9uSuNSyUMiCX0eCmItYM4BS2ZcRzFxWZ6OO3DzR4PqsrZ62XZu/+ce/5s9/6/+SjOj804f/gC//6zJxVaFcn2F5qcT49ALX7PY5MXWCz371AD/xI99DQddpI0HGog8nW9DwoVEH3ByS/59rummy7/pt6IY8sGsrt2vN92Hi9BMsz528nMN8zZjnOczMPMaf/InIh+7cdTO/+mu/y7d+2+tpuSaVmjywye5gAilyY3XroLuim30+sR6CIDDoGITVgpnCWb56z78zdVjgaI5j0bqcHtqLND0gPDnBxfb8zqKmB5UgTcm+t31I4fPlBx6ivPL4OdtRdIRBb0RIWyoK9UVxFeOI++hGIBphnepiZiPKOwoJfEIFOwuhDZrB7xuRBGCal6+7oejMOdiHdGbywBEkKFwO9i9Ukzo/0LyQ3XT1d9OV65eBTsHfzi+wuZxL3l5tOvnBvbTmBgu7I6HqSthxVkocUY0gAVHnJiOKc133xcRDvlEtGQMcmCuLMxrpgu2jcMubkuz/jlGUt4xMOwap0nwSUlGU1yRy22aO/9nnObrQueLnh45hB2MCWeefAa5D7uUacu/MIc/FLmRVLiHX4XHgiz6kKjBoCKhkNAb5hCjBrGaFPpDSoexSXoaFGTg9B4cmAgywD2NewNmxIVOFoWVZp3qnxGkvlGC2DEcdua+vBFPgG8MuXKY0Iw757hxjT8wwUyhhplNEE0MBhi2ccNMgYFvx3H3eK2ken//rP+ZdP/qfmT9xnMrUSd7yre/iHa+7nn/5+/dx7z8c4gsfHOLqb3kXVtuiN2aitCibd+/n0QfvpjCzyI3X34Sl6ezfuJlsLsHZs1PkUjluuXUfe3bAx/75JH1bB5l95h58lpAUexgpi5hIYp9EWH5FzgX1nd+L1YEojI9hzy2Q2TZITT27kBUW1g3kOTcjawan2QLBcTQpvvm+ELOvlKWSQQXegXZLhspprgT2dpA8RDx5/tcWpzQ63em2v0bNSIk/95GEytdk3VcqIB8HMJ8TTzjoVXhjr8HosMHAYD9Lp06R7BrBiAzyg1f/J5btn2Gq6hExIeIH8KTgHNmOdIl8HyotER9aLAdKYEn5Ds8BEqBnFO/87mv43Z/4UdqPPMhf/dyHmFxw8Tw4sFznY7//AXb9ci+JmOxvJi4wJdsXhSurJfvtqSDRiEvwH0tCKiodYE0L4F5+0NlRQUIQlXMW0YKp7FlJMHQFely4JXbAaWkriZOtsKsRKHkoJbCzeDA40AjI9oYRQPB8SVDCjocZgWQC3LbsfzINtQS0E3TUAS9gDkLBa11Gxe+ykg0bqTopzpUUdoCVVoOv3/sVBkZ9yksOn/zIDO0a/Nn/eh9OymdgKM9IXxe23+T3/+hXMNQINe+9q1laBSi24d/uh+tul4NGi4G3CXj0nKN2bZuPf/zviaSz/Jcf/mHiEX1V2cH3A9KOL9X/k2dXRDLxm8BOHH+UH/uRb+e7f+T3+Zlf+TmaLnSlNBYaQuAyTIVvwGLFZ2YSdm9UDCUhq0mm6tqS9TqetBrbQbYcNSx+73/+b8bOjK1+l+d7uN6VczxRA9rB4tH05eE2lPxoQQLi05G1rjrwoff973O2oZAgaQhB3e5Rkmh4ZeFmfA0JqlrI4lihM1vgucylk1QcAW5GXE+CDvwxdQnbebGWAl5PhzMxjQwyrNFJipaC17uD/Srx3B2CO14/RKYrQptOslHn2UHehboZLlJlMtdkBSE5kOD9ti9YZYOO+mloYQfF4NmdjMia/flmMNOEZBT27oDrd4kzHhmE19/poDXmoGcDqHzwbguah2DyXokQhn6F0bcOk/u/H6N4kYu9O5sgbjlMNOXKzgLdviTJtyFwwTHk/lmic23Cuvcx4Mse7LMk2R1IyIsqiTygvgV6Cj8Zwa9bNDWYb8PxZXiwKfORlpHOn0MHstXjQY8F0XmZDtzw5D4eR97/cj9T3+hWWi5j23LNfcfFKpaxiheCLoc1apNzSwuvnDVKC/zzn/x3IMLodXdwdOwYn/3Xf2L2xBMoNDKbRjly4iALJ4+STWh4nsbuxTFKDY9UvJfjM+NMzyziRbN0d/dz+x3XoXyTWsnl8DMap4/OMHvks3QAhVFk9fOQlbOGeJs2Fy8hhTgQH2iBfZCTh8+w9c4BfNQF5weFyBdDSTKhEwTOHkQ9VgfSKSX++kqZGVTIMQXCgyvBb9OTNVz3IKE6c+/CLnhYkHaQAN1HIEReAI32HOlkRCNSpfcc6d7U22A2Fa0xA79hc+0dQ2zdOUymt4f5yXEiCcXQ/jewIfMd3LXpw3z25IPEo52B4CDxghsVEvVSUTowNVu+N5mAeqUTHkb7Fd/zAzfwpz/6Z8zd/zH+/Nf+nrE5b5UDmvRh7Mgit3oDZBM6GC6RZABLQobmufGANK0ECuoFiWHUkIRCqQC+FXR5fCWdDt2WZEIDIipQr0qKeJ+mhMjuK6g5UPUFVu5YAYQqdLYJ6SzHg+RCD9AgblDAaVuSZFg+eAb4QcKk+5IMJVrCL6lmwK6BV+eiDU2XgPtyGdWeS082giSj7kgwarehUoOUIQqG8ytlzjwzheuK1KvvycNy8839PHRwgZ/8b5vp7dZZqR3gmS8/zeY9Hi3LJ6Kket7yRP7LduHQQ9I+wjShfR0ys3aKMJyJZDVuvLWb0tJBpmamGewewbJBx2d6qsnwQIzFZQ9fhy985oFLPxvfAOZ7TT71oV+jbiWYrPTxa//nHZgJn4989ATf+xO7SRkmT391Ei2aJtffhWMIjMh1wG3JDabrwUAhH6ptWCpU+Pg/fx672VkadaUwryA7rexCrRF0MdYockBQDYHVYUEtX+7B1nnDHDWk0r8fSTbMHiC4l8eQzkYp+FmkQ1i+VLOAh4A3IEFTjM6g1wxSDxsI/n2JSsiXZaEcaRsJ3B06nQKDjiNrIe4zjvBWVliL5O5Y0shy84bXYekq7C6j6CQaa4sN50OlQnsuuJOPrBEE7/HPg8ed/961Flb/vhksHoe7bhcp7XwW6lXI52CoB2wvjt6bllKZWkH6E1nQ90HqSbCWgDhe937q1scuuH0FDBsmJ9zOWXaQsOoJOvP2wldX6AQRXYji2i6kk5ciEJMypGpIH5AOcAu+gz3fZH4Jjk3C+AIcKMFhX56PCToMvQKdoCvk8cS9Tpi33tF4acyqOljVC8OMVSyGjyeSQQDnTNGBV0+qZzH19NcoLI9z9a23selHfopt23dw/PRJjj7xBG61yHKxCg2HpeEebrzrHTTbcb73e76Tf/zYl5lfmsZ1bWy7jXIT1EpthnsGufN1V/PgV+9k6VSFzpCFCp3Ewg5+H0JkEpZYHeW8em48ZOX0kbvbxfn0DNGf6Yxu0Olw1sIzG3Z7jfB3Tb4+4nd4EUqJ2tCVMsfuoAh8RKbWCsjYKIHh2E5QCKTjk1d9swpixwCC5boCczK0zht1DfSI8Ak8Bbrtc/qwje3B9Pgyt7zuViaXykxWz3J20eYN+RsY2rqdm/t+inJ5hZq3SM2pUlY2liXnp1STEQSJJPg1WLEhmoNmSdZU4tC13+A3f/x7+fHb/xsPffwjfPT//P95+IzHBsRvh4XMPq9B3I6SjGnUPFf4EUbnOtkmuGagrBUclx4kaZoR+ENPSNlucIvomiQYjh/8bkA8KucgqgsUytUD6JItMXjbkd+9sH0U7EDEkKnupt7p7LiudFvatvChHVPuJ9+QYqBuSqKUSAYDGpPQTEArwXOq2mguL99Qv9klQPksjBfRGx7NlTbX7Rvk7CmbJw4+xL4dm7j368urA1Q0A+64YxvR7BL5RITf+c3f5hd/871oEYtCeZKY56M8qNehVHXR0ho37FE0FRx4HIlE2mGYNr161KYHjalD7Ny1k7njT9JMVDl1YoZNm0c5eOAAfT39eHqWE5NFjhz49OUc4jeEuW6Lz//Db6APvImzs29hqV7kC//wD7zp3b+Bl0zw5a+P87M/dzOxhHQ86nWwgplMZkBS8oBCEFXYeoK9t9zG/ZNnV79D13Wi5pXr4drBja0FlYJzjpegFesHxDQX0mnJVddaWNG/GugLVvhmWbDfU3SC7st4flZNQxrqDQTD/gDScE8j5zIb/P4G4BFenmQjVHR/rv0PEwSfDoFvK3CYZydWhm7Qle4mGmw7dCAhHXIVMBBUakLTVeeLfAIS3kX2xVSBWkiAiwoxy/DsJGUtbMoK9in5HMf6ajYNwftqBE6bC1+3rm644zZ4851w5BloVyGXhr274c7bYTBfoXKkRubGBIrPIiH/MER+AQYj4HwUd+b3+PpvrtD0Oo7//H2ZXSkzueZvDtKtuNi9FF6b8JnahwzEzCOwp0watAyoBBJ7RXyI6NQbipkCTCyJM5uKSGLf5NlSICFoJ0yEL1RvX7eXz5Ru0LdtK5WpaZqFFVAa2f4B2qUqJLtorSzBsybvvELm+9Qnx3locpzR/TewZdteFiamcMtl3vnDP8ST930d23XYfvU1uI7FxPgYjz3Yzy//xDv4p0/ex0MPf5Xu3mHiyR48L4qlXAaTI9zxrd/DfffkaTRbeLZPe+EU2MIDNBLbyGz/frTsdkorc+i1k7SXHkTXNDTl4zSL+M4y55dy1MxDZHkvLeS+V3TmqWl0pouHyUdYWFG6FHE1Q2oLfgCpulJmu8IpAAlam7YQhR3VKf6FvsD2ZV0P998PILOuJ59tK6mqm5FAmSmARduW/Ntuy3EOKJgdk+LDU5MNhh88xg/9yv9L1VbomkEk3sXK9Clmj9vcMfLrGFlYbh1jtnKKZCyLb/uYw23GF48wW5vl0WPL9G/2OT3nMVmDfK9i52Afv/Y9P8dbd3w/4w9/nI//1fv53ME2eVhVOg25rqbroLeipBMaC7YkASoI3jUgroNpB0lFcFxmAiKJjlKWZQXzQNxO4dQ0JXg3POl0xBJSZEoYsu22FpDcPYlvmm3R2ViFOgWJQzj4UYXn3JIko9WSgX+OK77GjLA6twVTVMXsqHxnOgOtMlipoOtzEWUqKwrNy3DAl5Vs6JbPynKJ5VNP4y1Wqc2U2Zt/C4UTM9z3qX8gkpwjExNJdN+H0WGYmZrjoft8Wo2jHHkmwT/949/QqNW4+c430CpqKENwbHqtguWnMKoG3WnojSvwbATIspZmBK26xyNfXuHbb5iizUO0knM8ec+/kX7TnSyffpRmZQTb62Zy1sVxvlld1DJUH6dVrfH1z30G353EMDS++JmHuXbAIRGLkYqAcqSyoEUk6PGUVB7CwUGuDa7n0rttK8mrBqkfmwPAtT2s1pWFUfnB8CAv6LKFkVAonxo2+dOGLIxryXMGHR7DNkSD2i3BMU+Sg88hSlQvJNEAqW0NIw/6E8Ahzp0ZMRT8vg340gv8jkuxi+3/mvh/9T0G8nQVkOrx+U9KvV3n4MQB7uBqonSmtoeCG+HVXwvJX/33mh15rnPqE0gfXuAY1nJDNDqdGehUBV+LpgeQgVRcHITVBsOWalWYtGlKZBJ783D9flieBzwYyMOurbBlC2SyUFzyGNzu0Zo9Q3zkM8CNoIaBLtDeAPoh6v/6WRbvs85Rv1lrLpJsnh8uXsqz0Ebun7D7kdSgKw16Inj+HKRv37IgO0p8JI++tEzPBh/DhNQYOIV1ValXo3n1GuXxCVRQvjR1k950Cj0Wo61pTBQsfN8Q0HqsD1oz4L/yEKupwwf49098msVjD6PsFke+5qPFE+geTJ2ewbXHmDxxmjMHn+BTH/hLvu+H/xNXbR9gbqlAtWlz7NhZNutNvKzGvt1DTB/vZmF6iVrhELZ2jFzPLvZe/4OcLfZSLX6Z+sxherZdT2RoJ82NfWzbfRUJfYDidJnJA59i+ew/IE9ZBqhh8zQ2HQEkHVlTk8gzV6MjTELw/yiybjiadAh8J1A/On/g1MtolgueJWtUvSFIg6YjSYEZLB6uBS0DvFgQVxAUkxDeRMif8DxJnEI/rhGAzYJgXPPA0yXxGDsTyKCn0+ipYQb23MrmVBfHn7if3/9//qtE0kqjp2eUVD4Fhk29neDavbewb9923FqJ7bl342+2eOu2R5mpnuQrh59gYYPie295N7fkd9M663Di6//I/Z/7HJWCzQ5TYdo+NTp+TwPsukutZJOIgd4M+BhBFyJmBgXOINFyXblGkSCodwPIWNuRpCpMJmMBlCqUPjYM6chEo3IOvRB6pSRZaDZFdtavcy6fwZV4zfY6XSPlCzTNduTRdJDv9wMiv6mkM+NrAZk8GsgYZ2T2RquMLPLnOQNfAzsH9WdTqi9ql5Fs+Hj1OlOHHybemMNvVCjOLzB7sI9t3eDXD/LIwRkiSRjOwPQ8jE/Cn/7FGVwXRgeivOl1r+ev/scnyAy59OYz5KKwuGBhNX3m5ufZdn0fhyYNJosppg8raN4DfAIBuaypc3pg1Twe+bf72Dh8kq7sCE/f/ygJ7yRnpueIDaSYK2k01Aa8K8greLWZ15zga5/+AJbVZrDP5Gt3H+Topz/LH//pb7GlR1G15OYydCEntfyAwBXIqWUzMDkHbcejb+teurbuXJNsuLQbV86xpDWRAAwXLw9Z9EK8ZARpRcYAPMF3G2uSjRHg+xDS6xBAQ4LrrwF3A0/y4tSNVpAKb0K+Ppw9RgsZ7mcgkrtzCJ3wlbK1AWX4ZJzPlei8brHiTUu8COcMzgwTjhCX66rOemQFJ/J5g1Vf3qvCBS9oJYf7GEIJ1rx9FQ6mc+VnbMSRdffF1nCVIfxbLyLOxIxIVUuzO/LOyZQkGroO5YJgaE0dtmyS+70rDY0S9A1AqwHxrA/tI6A/AeY7g2+6FtQoqc0ebxyEsbJM/L7Q/fdCj6kIPIzc921gWAezG1QWwVQlkItmO9DdSzSf5uqhKa5q21QKVZ5ZnONwDc7OvsAdWDcu3K96aaxVKQGgqQiDA8PUSyV2bNlMbqibZHcv48eOokV76Np2K1NHDhKPQ2PpKK8okdyzmXz4bolUgbEDzxDv7cXFx6sUsB2HytQEXUNDlJtVHrrvC+zZfyP3fuVBcoOb+e7v+y76N27HbmikMUj3dHPkqXuIaHVuuOsnuP2N7+HxB7/EzOkn0bUoo/tvp1hZpHz8MPnBHvLJKItLVbxUjuzgDSyf/WcEZtUFtHC9ArG2zFXoQfoeGh00TJSOpLRPsNapzggHlEBgDCB+BTsb7XaQaFhBIOpK8LsKgwohUk2wdSkQhpOsISCFOwHxO1j77RbQElSC6Ulg3vKDGRw+zEwEJG/gzW9/B+/5Dz/A2ImDPPb4IyxNTvHxz36NerVJPqaTSaYgAqWSRTpi8PiGZ3j3u99GOpNDj0TZtW8T1+//Ma7rafAtO6NAnuUTh/ndH/lplmYW2bxjC335Qa4e7ad0ZoxQkMlFljLLF45Hc8lhdKtPMy8CM33dotQlZH4NU9dQJriuj65cPBcaNZEFL1dgZUm4Z626cFwiMQn0PVc4JYYu6lRRxC9aniQbzbBLUQe3imSkFqutDF+T91qO7HOIAlFecK6VbKNpQ8QWH6MiQcIaCTgjpqBd9BQYzeDAqzzrcfZTYPdBKXfp989ldTaOPHgviwf+jcUzBykvNMhr3VgjfSzMzDHU47Oloji55GO35QZ0PMGQuUBpxeKhhx7EsV3ufDOMjpSZOHqE5bk2yajJ8SNfYbGQp9jaw9nju3nqS8vAPyHAkHPNA1Yc+PKjFW4bqWC7ZzlRsil//QxeDGpjDVoeFJxlbPubN9nwPZeHPv1+zGSaxOatfO6Pf57v+fFf4t8feJq3+tdw8540uiZVhKATh3KkKtm0PMrFBu1WDUyXvlyUfXuuYururwOg6TrG+Till9HCoHO1Qr+mGh5KQnvIguz68hCvXYffDHwX0BMEtBaSbCwgqeyLxYBXEZ5GNvh9mc7+hX+/CYGmXIoK1EttPs8ORy7UTXi2KRzkmML2bCPoejW9DibXXtOGcJxLDH38TitZd8EOFkdNSZAdUee+fS2sYG2n5kpZTxLKDREocHnhAbpjd85POiOKJa4nziWD6J5Ho/I8buiV1nq1Iu+tlGDfjQJ9rC/LbIquvEZ2vw41DTJrQdxJUHeibf1brrq9yntnYKoqnbUXk1hDpxIZQMlZQp4nS0du9m4EN5gPDioOxLKo2EaiXX2YC4uUpp5idEhnw4i7mmy8fGHzur0oUz7lSoWI4bFjxw6S2QjF5SL24BBmtpfunhgrWhnfHyHRdSON4kHwG7xiV9NzMRIZlKZh1yu0ikVyw6OsnJ3E812MSBRlOXQN9hJRLgtz45SmjzB7+AEahRlueft3M7JhNzMLDne+5VuIZvvx2z4OET736U8yc/xpujbuo3D2OIuzEyTSNoX5L2Am3szYqUm6hvZz5r6vUz17D/JAbAZOgNaFveRRfrhN8g2x1Q4HdIRGonRglSEXIB68ZgZrowFCQr6Ci2DbkaC1UZegthXwLvQAQuX54gcMDZQt3AGXQKpX61TbPU+kVm0rgE0FlfMQJo0SaFIXMPa0/P2akWEGN27BT8T5l098jHpxgb27R7F8qLo+jbrDslXj1v37+eVf+Fbuuftf+fx9h3liapLveMOdDA50c+rgE1z18Eau2ruB7l1bSfRHmDrwJPceXMFq2XQNNNm7s4t4u8ERfYy224FRxYJr4GiQyg1xzVVdjJgrdOcjDCR7iGtpdBVDw8BQOkp5eHgoHDxqFNuzLJdrrBR9VnKwMg/FFTDa4LfkfFieyB2HMrVa0BlyEMhUqy2oIauGBBFNOvjmICv1VZCcWeKHUZL4+RpgQaUp4j+6LVC2pCOcXS0g7mMKZyYUIVidmr42jI6BPgL0Q/vlglEdufczbFx6hMUTZ4knBzm+MM+bmrso1Up89v5pCUCQ7Cl8BqxgJ0+PlVFamWwcHv836Krfjz/5R6wsJtiwNcG/ffYjKDPNzqu+i7xaxFr8CAJGebb5BG25CDQjcO9xiyVgttTB+Yp98yYaobnFCXynG8/ZjFU7zMHlJzj7L4u8/Y1/jmdJ9uq44Jo+S3WLRw+Os21rlpmFaWbmTzO8IUOlPMcXP/s+4mtuLKUrlHkFyyrPYWEQGgYqyQvs1juAEQ2cOJysS6B4ACFUL74E+xC2v48hXY61LjbUKsnz6of+rA32TKL0GRtXp8E2fGnLNh1p6dZbQatWO5co5l5isuH78l7DEOxruMBG9UBp7AL7FjrfVwIh3p0QVZOYL6IYbSVdCl9xzhCp5zNDQSYJyXggTegHqiWmiEdZjlT6Uh4MpUDVZQiTVYC+nTKL4tQpGRbVtxty2yL4iznARGU3rvkmBeyAka2kbjjAnqd83nxQEruH6cDnMlw+h6gfIYUnEfjgTYi6W29Iscsj5PBQB9PwQaVBbQQ/hYqY9MUn+Pa7ennk6Cke1Sz6u6G7F2YWYHHlMnfom9peroC+A7z0PJtarcCWjZsYGh5m957NdKUinJ2YYX7FQtkVtm+7mhPHz9DWomjd1+GVpsGZ4znHEb9spogMbCM3upnZx76CplzspkUs04VVK5PJZHj3D/0IR48cors7z+EjB0imEtQWZxh75N/RdZ2HGx9Cy+7nHe/9afo3DXPgqQOcPXyC4uGHQXXRPn0vnl2jeuokVfdJUJupnF2gMvWRAD54ODj2DDALKgfdb4Wle7CeepybXn8nJ5QUqIO4cHV9C2s3Uc4dNRDGlUqTIsQV5IdTtQO+gSVBccuSboQXrP+rkNcg+QiLgq4rxRTXlR/HEolYqx10QkJdXL3D31AObOwxmLchpvu88VveztW33UU8rjM7fpwHnzjCxJkBdg/18XRrFjyX192wg9/7nZ/mqpuu4gtf/Dgl18N0bQ4++QBnogam43P/PT479+S58aarufn229m2dw9vuvVGvnLfQyhdoyup89T9TxOzZG0r04GIukjVP5Z02LF5JxvMMn1qiH4GiKgEGlEUCh8PDwsXBw+FS5lirIuMMUFOX6ZbWSw7UNJlmGm7KZ2iehvqbjCRPRi+p3Qp2rfaUG1Jh8QPukGrHYdQ+isi8HDfC+BujhSufBv0oCtVaUDFlu3WbMhEIZcSn+b4kjB6AWbZDStJoQp2eFGzoDKgwkTkEu2ykg3VqLJw7DR2RaPRLDFdKnHw6Yd4aKmwOoArtFAKNLQQ3m9ZsFKBh76+xFPxT6LwSZ9UTCw3KBaXmS18hJ7cQ1jW4zxXsuAD0zX4zIlX27ihV595zTrW4ceh2eTA+/6cb/vJX+Pehw8Rb+0nVm+SzsQ4PTPHTGmOT/zL37J1xyCl8iT11hKZLp9jB84wOTmL2d8J8XRDJxK7giLfYWk+yNzxJSDT9KDiwxo51PA9AfBHIRKengePNUT1xgE+iZC1XypXaNLpbK41hbibFSTBeTXb2rAlEY2zZ/iqVWJ4A6lYNS1o60IODKWm127At5+j63AeLMp1ZIEzCDCjhrTSI5xLmg47GmuTyiudcAzkZR8aHpiWJBuOJslGS4n2/POaIWTBRFqkF5UKOEfBeXF8cWiplMTshiGTdHUTRjfDwFb4ylfl/AwPiXJIuZhg5ZEFtn/PdUTYd+73qRTEtqJGj9G9r80ty/COFfjZBnwFOZ4YF7cQ3nHOJukILWxEFNaui8JQFhJ5adsDHVC6DbgGAma8C2iiMuNEbx4m4tW4cecMzpstdu+Lslhqc+AwPF4HIyUOb+WlqAas2wuwEMQjnt31PMYmznLPF79If9c7uf36vezfsYUHHjrMzHyDdkLH37KRqZUiFTS8RD+04uCXwKmAfzF52Jdn3xvjz5BLxtG1CHtuuJHiwhyF4gqDG7YxdfIgD3zpHqbnFvGBkU0bKZZLGNEETqvO6QfvxvcVaA/zzzOPo3L7SXRtpnfbKMWZSWiW8Zrz0LUfqgVwb4JIL/HBIeylB3Csw3RWL4mO4sPvQs8OUlvJcvbQMW5q30lvTKCIYac0rOeFwW1szU/YRfYCuLCuBOVypazRlESh7UPbA6sZYP1jUj23/ICfEAEzFoh9BJAezxO4Z7sJtarwDuoN+cEB3QHlSsfWsyQ5yXgprhtJsTO5keHRNH//kT/i7LFjPH5knkbbJRHr4T3vejvfFo/x1ENf4+ipI/ztX/9v3nDwNmYmmvzH974HU7OYPfgIsxNLJCI6E0WLqUIR3dXo7RomO+yTNhxSmiLqRLjnn7/ImTmLDEGXmY7U+wLBzJNkinR0kDxD9DNMmr5gNHU0uEJtxFu6uNg4pPCVhq0rfNPE0WZoRx1IdJQjPU+uvwpwyZkc9PUZtJVDcVK62I12gAxsItXLkBweQjsCKVufAKoWEMN15Jy2AzWrmit+xmxDOxqc60CRtOFJ574ZXGOgQ5gEWdOT8n+FwKcv1S4r2WgUVhifaTJuwzR1loH3P3yACdd9Vlv+YoFA+L5jUz4uDdFWPhu0fIDK6QVOs3BJtRqHc1Vw1u0i5rSgKrVLIxWFEcVXvvS3LJ/cSH1qgq3bRnnoqcfp39LFY1+5mycehHjWoTDdEKnTBkR7wFvD4oxEoqSSV04LyLXBa0nQ5dqykCVisuiG1SCDDuZVbDfwDH3Ig/EU8JAvQdx48O/zSdEvxkxkYTr/ltSDn0eRacyvGfMUWIpYwIFpIucfVxYpO8LqhG8nqFxhQcJao+KhddrsAH5QfVFIVSssjERMIRraAfytTccBKzoVPg1Zxpt05ISvlGWSoghieNLmb7qyONsEuOQ1baEwAQtb02HpUo8G6iQp+cHvKHGBEA2VJq9FzUDC2ILRQdh0NTzwNBTa0JuWy1PRNTJmFZUGIzUMPI3UOzcFe90PajNoGmYXXLMX3FSC7CekRFPl2aTxEIIdklYLFzkfPjAKbNVhQz/EsnQevrUPgYtcNC8H7JGymnE15Nrgn+Db3j3HbbedQfkNnj50hvmxJjftgtveGuHgjMcnP+bIlNx1ewXMBaUTMaNomgLf4cSpkzz9eB9pbyd6LMFgf55Tp6cYHd7Gxs05clMzHBk/Q7lWwY1qeO20ZKFuAVrLXLE5Hb7H7KGHQWmMHTtCPJXDNBNkB7ezNL/M8WeeBBQnj8XZsGc38/NzoOuY0Si9o6MsLZTJ7bmZ6uknaZ58EmUmMOI5SOwVrFA7CeVxcOaBOEpLkegZpKluxVtM49UfIYyEzPgQvZv3MXf6LCTfQOHuIwzV2hRjUaKwOgIwTUe2XKdTSDPpwEdDPxdTkuhfKbMsITjbrnS1W5YUTKKKVSleXwU76kuF3Uf8QtuBWhMaVWgESUazJQG04UmHpFkTiJbvCZfUnYtz6mSNnVfF+csP/hNnJjr912Qywff/yPdx13VXUa/Mc/IJE5M8D9w7wVe+PsnNN97Kt9+5i8VSjS9MnSZaapM2XFZKNssVl9TBSUqtz5PuyuErg7gOZ4+Oc3TJouELw6Y/uA4NxM/MAdl0kuRAHA2XOAni9KLoRpGmkxI2g98b6LTRsImRIk6aJkl0TGIRBzcq6l6uIxwK15eES0Ug3634wVv+F56u8dTWR/nHB++hcqCA20AW7BqdalxYEQq4MF7QcXc8kdjFl8TQdjuJYsOWa2UnBa3gRaXj3bRlhkfNFaL56vZD5xv4OV8PEqTLqNReVrIxXyowaQvsZBEJrI44z/YCCZ6/2xB+qn0BsPx6/vBCzeD5oGOe1eKL7/9zmuPLHOtKYuoWPU8nmJypkThiUCsJQ7Uyd+7n2udVFx27TfMKThD3dQlU/bBaqkStwqFT5Q5jtqAjiyzdEjTdj1QmFhB0R4mXXkqzyYWTbAdJMpq8urpwz4ePV4ZJtGsQ5csxlFXgRyLywWhwHVxfJPl8V95nGsGiF6iNrCXqq6hgQjWkK+UEyiQoqdRpqtMVXls0CYpkQAdOsCaHuSKWjEI7Jhwny5HF3PClWmSvPZEaq9mWChxueKJdBZYJXlxQRcDqgek6ZLoDvK2Slrppwo79cN0NMDENcxUw86KVHuuB3KBiatxjZIcP9iM026eIpf8MpTYFG4+A9i2w9wGYfYxEt0fzqlvIfeKraEjCVjrvOHvlU6vPVSh9HJqOJCERoMuEwR6IdtHBM66VDQuB55WWMCQTik74lEKpG+i98Q/p9ceh8gT9Q/cwnHmQ7g0Z9Kxi+RNLosKznmxceTOjmIludMOgK5WkO5UgGdfZPDrE6GgP0WSWRx59gieeOk6hZjC/4uFpKXbsv4qtu4Y5OzfPxNlpxg8/hWcH5KzYBgHpO/NBmfbl9vYie1RbPEst8GGluUmMRPjw+axMTbCytISWyjC6dyvTTz5KGx/XcBga7mLnd/wm//a+P6W+XMQuj0F5DFQc9G1gZlDmRvzmUfxmm8KxDFoiib7/e/GOxqDyAKgkdrPM9LGn8ew21B3c1gxHHi3zA2/v46gmj8swMjxzBelYKALhEzrFmzD+61Pw7cD2K8jZsIJEo21BLYBSmYb4YV8FwacfrBWBsp6v5Nmt12XWRa0F1YasmVVLuuOGHQhMWNBqijLTjmyKxYc0tmzaQ6W5xNhkJ9EwDJO3vuE2fvD734thV7nvS8d453f9ID/6X1L8zZ/8Ph/64hgn7vk6//a1B3jjbdfww//hhzj8xOMce/IJ9to6z8yXOLLQpFo+xa13XMP27ZvY1H2aB04tcTa4HRcRaGk6ON8W0oHalk3iDiocX0dXcSCBWu2DhGljmBpqqGBCh+4n0Pw4uooTj8Swo00hhDsilZtIij9xfCnCzc8rEryLnsRWNm2Cm4a/xgc3/gl/8uEvUJ5z8C06yUaolGIHPjiAQFmOdDbc4Bq4iJ+qe5Ioer68P2pIomEicK1aS3gdwjKnU8kNM2ILVEN8U/wyHt/LSjbmECmwIs8NPRlBLlTlcja+bi/e9A3gjj3nW9yGRfO0tCiqS5IsFBfK+D60yu4lr/2arhOJXEGCuCaBqodk/mE72UKqDqFOeaA8F3xoMyATWj6AYMxBFvImL3197WLdPB8ZovdqYxCFsLMqF77s8YzB9tvyLPmdCriuIKXLOQ/9nOtJxcTzg85EVBbMdisoYgfXKyzLhb/7eqAs5onDCq9f2LFdC9Iz1vw9Smee75W0XFIqe8kAn6yaUpnyEFiZ67A6gOScSnwYU/nyuuMJGd42paMTCd6rGaAlBIJgGpCNwXAX5Ifh7BKcmoT8BoEhxHPQtxmMtMtcWWL5fmeRvk0usfTaM6NA3QwjP4Z6twFHnkbTe4gj1/DtwMfo3Lsh5HAQOIt0NXYYcCi4eTVknsbNwDUxGO6GZBcyvDw8/vNjyBZQtKC4CD0VRIMnxI5FUWoU1DBkryW/P8cdw1G0SJGZA0dI2DZZE1bCotTzZcjr9tKYGcXI9qJcE6tRYrGySNeO3Wzbvo27br+FbFbHKiwxPrvIqckFIskBonqCSt1idmEFz5knlctwx53XoXSPRtOlVpyjujSBr0VQg9fgz6+AO8UVXxndOk51TaEsmkG1axjJNFNPPobnOJQWF+ka6OfgZz7KiS99mnYjIKgBid4h+oc2Mn5wlkhvP6aZoT6TBPc0tPPo5gbsE58nvuudmO7rcP06/sIzmNEk5bMPgVfE8yz+9f/5Hd625Q/58V1J5pUMWN2FCDi06YgvRAjIycijtQmZ13Sdem4I5EttYaW8bUv3QdcEyuwrsALehvIDGJQRdICVBLzVmgSyrbZ0L9rtgKPQEj5By+kUmbZuinBz/500yyNUSzp/8md/u8qH6xsc5pd+8Vf54fe8nb7RjXjNMhtHptlz1xtZmnyGp8/UVgvZS5bDJ+97kv6+Xn7gu95Ab86g9/gCzfse42ixzuk6bJ8pEWOCNgmm19yGPlKMrCC+ZgXYqMH2m7dh59oozxUlHSL4xFGkCORpOJd5I4uijY6m4uhaFMOIYJoK3fCJmqJKhh4M+lPiO+JxH18fQ7ENFIxG3sgv3X4dN2z5AB/4zD/yub97CiuCdDjiSCumLV9nIUmh3RYxEtuR5E4FyoeWK4phICIvBUMKaKYvXaeGHSCNPDqPpk9HArcivltvQvQyJCEvK9kIISLhTX8hCx+MKx0IrBvgTr6gj60SWy/TifuXMz7yRVpUdVrL4b0XfnuYNISV7kjwt+FrruXoEXlfCF/qRXRBKlx4tsTLZa+8+vyzTSFB4yQy2PD8ZOntb3g3Ta+BthIhkYXhqChEpYMOh+WL/KHvSdvW1WSRayrRWg8J4BqsAo41ZBsa0sXQdEk0Ysj1iNHhFEfoFMvDTUCn6u6GIOYrZP0ZIXPbwc5EI1BqQyxwvhUDnIYkUL4niZYZ6Ku7fkCcjAWDrDSpDGpGoEeviXqLZQtp3Ai0z+suTBeCCbjdULHAj8OG7eBGYa4M9QgsNSAXhXRvhnBScQd4EQe1F/Jvgt0LqAmfekzRaEnxaC0vJoZAo+I6pF3Z0lAKDpXkPUngdgW35mFbSgYMqqA7pWXpVKEUchHt4KflQnkB3JOCJSPNuRdPA5VCRd6E3j+FX76XVMRkIAJdMTlf+U1Q12HhEC9eTmvdLm4B7tEpFtBieWKZHKaTYHR0A339A1QrNRLxFH48xcCmq9jj51lYaeNHk9RKZZ45eJRm8TDZfIpNO65iy+ZBFstNNmzfwmJpF9MnDmLNPw0qizmwD3vhCFKqfYWsviTV64WO/3QrVZYrVYhCq1Jl27W7sTUPP5qiOFtg1+2vx+cpzh74EhY6Mr1yBN+xsUqPAgWap1Mkbno9rWcm8NUQ8Q3b6M4kWDnyb2BNUjv8z/zq71zH8Z//Dt42EmGoN8mQqRhEirUhciXsbPjI89eLBG6LyCO26QqdpnA2hucKx0IzJbnQ/GAGhwY0g+p8RNYxzRaeRrkMtZp0LhoN4X80AyiW4cry0G5Ddx5GB6MMJq9lPNrm5MRp6rY87LlsN7/w4+/hW68ZoKd/CKUM9HiO4dEujj76VX7zV/9fDo2dC8Ho3XwbH//iM7z1+l1ce9Umsvlh6vUC7YcPMd/wGB+fI+o2+PqppQumvDrQFTeItR1uvHqIX/jVd7HUOohmJvA0DV+FndoQdBpGKQpZ+Bx8LHxMlK+hoVb/03QfPyJJgGYH/d6oJALVhs/TM3/KW7bdjKYELJzQcrx9+Oe55ce+h7/f/j95/NTDzJ716R4wuO6GUR5/7BQPHT5GpWLLNzvBhPeAmG8Y4pNcm1XWu+9BTcl1NIBmHdo1JImp09F6D3HNAFXZRqMMzmUoFFxWshEmGs8FnjGRqnHxcja8bi+RXbkKkes62PaVcxAGksSGmFUf0eOOAiklgWr4iAPoKP7Dd8b40j8KoNFGMJcVZM6GxWUJKXxDWgs5p29BpHrPFwD618/+A08eOEUkM0rPRhkil8DAIE8qezvZ3DA7dg4wOhpjaBAyWYWLTlwzsZSBH1UYUUU0JhUuQzt33vDaZTlMLKAT9EYBfPAcH9tyaFtNTs97fOWe04wd/zR1b4mPvf/9L+9JWmNDeWk5a3FJEmINiFWFKK7b0vlwLVncfRcicZlV064Eg/vi4MYEQuVGBC6l+cGQvzgkY9I58QPHq9JgxmF6QpxQTpPJspoBg5vg2FHoGoB5R9rjKg26WUOpg8Bb6aTfQR9IxSGzFTfxLpqxT9FuuXw2eIdC8N/9wfkvuoIJd3xYKXXOQRzo0mGwG1QbanVpp0fLQbKRhwA5IFZHLq5rw4kj0Ps45GqQ2g1av5BfVq+8QoYRbkflPOLpRTbnFnnbtTB0FZy14VP/znqi8XJbIgqRJFQtlALPc6lWSpw4dRKwWZqbZWi4FxcdP5JhaFOeOgs89cRh2rUa6BHipotrtzj49DMMb96GnkgSNWNs2byZVH+SEw8/RHtmBntZg9Qm9IiFuzLBq65t1QZMMLOKxZlF7rrrWh5feRy/Okt2eBtq7CR+dRz8Mp1yTSBYas1RnDqNZ2qoSpFMNouey9Owt9I89kFwjzD94Nf5vWKJv0ileO9d+/m2d1+PO2QwoqtVVcOwi+si4esK8BDi89JcuWRD9zv8w1C1yDXkFOmWVNDtQDTEcWRdMx1oVKBclLWiWpNOR6MNtTa4rWBbAXlZ02Ek28NX/uV+Pvfxw6LuFBQ1S+UVfvX3/pT3/eWH+aP/9Xvcdd1GbNvjox/+JH/1oc8xXy8+KwL68R94K48fLfBbf/Qh+jM2/+f//CYDA9+F5/mcPj5JtVjj7FKZFce/YNP0pk2j/OJPfS/v/8u/5Ad+6I3ERxXFxQrRaBorKtcjgk0H3GsgkYXIqvjYONTw0PE8DcfxsG0b2/NFicsXWLHygjlihnSOGg04PvkMN48eJRu9BRWQABWKrtgwP//mPxc9/zXWvrPA3af+mKdO30ehMsOjR2aZmG2TTSoUGs2mixGTDkbLhOZ8kDw6UCnLuXfbdOS3JFeSf689sR54QcLoXcZafFnJhs3zzyRoI7j4dX/wjW2e5+E4Vy65ydOBg3vB/12CAqoDyysdHsBywWN+cRnf6yaW2k6rdmIVf15C4p84L362xjeCPc6z15LQCo0ihWNfkH88uvYVDaUiKM3ANDR0Q0lLXYui6ZuJZ/czMLSVDZu66O1V9PXppNNp8rE8qdwomXQ/3T1pens1cl0i94rq1OLLFTh+1uLU8ePMFQ5z6FSNR+87yuzpT+E4FVotB9cRhsyVTDZycUkOiEI9JsRtPyYQgkgqgAU05ThSKSFPplOgsoHkYxTsGNSiQvIuNcWBp6KQCZINxwr06FvgNKHoBFVDByqe4Jl7umGpDE8dg+461B2RL5xcgdFqgWz6fpT6UYLxlXJySQC2YMwzoxgjI1CaWH11HwKfKgJZE2oepCOQbnbuDQE9waIL7Qbk43B2HKKjkOtDLp5Dp7UdqqT4iLzJ4hQ8/mXIz8C2IgzuBXOb4CNXE44k8CZgK2bmALdcN8fV1/lMlitMfNElG4VSQiQj1+1lsnoQSbg+rlXBDUSkZpeX0aMx2BajvdLCjeQoF9ucOHECy4ZEPodlt4nke3DLFSptDdtyGTtxltzAKFYzxsJ8kQ27B7jhjrs48Ojj1GYLdA90k8jmmXcd3GYTr31+2eMVNhuO33+U4V3bOfLQM5SnZvny+EdxPUV2+400JkpYjVIA8ZtBWIFxqD6NN1VH2/oWPLfFxH0fxS+fJjb08xD7LqifgeUTMKZTUkk+8Ogp/vnDj3Pjz7ybH/2hEWIRydktAulxgrydziDWywrgXqQlDeGoeZYU+jw3gEy1BUrr+6IoqOtC+NaUJB31unQ0KjVZt5otISK3W1LFTyqZH+ID3QnFoDnCh790iOmZEgBDQyM060WKZSlzny0W+Klf+GXetnMj+/ffRslNE81sYEc2w1hhnkqrucpe/aM/+iu+/e3v5Tc+/FF+6qd/nN/+4w/xiz/7E/zoT/xHPveJz3H0yac5vVRZReSE5xXA1DW+91tfz43v+Bbe9+H3U414TFXGaDTrNBsu7aSHpXsk8FEhfpYYHWkTSUI8NFx02o5Hs+XStC0atk/bldkaHoGKVFTOTV1BQ4fx4jx3f/XXuXPD7zKy8wZ0s4MXUudIQIrF6Obd23+Lb99m4XouZ95xD0+cuoe+3i5i0TRzcyd4cvYREgmThDfAp+9/kImSTcOCWjHgw9p0CLBrk40L1AB8Xzpcl2qX3dl4PvE6B6kev8rqE+v2EpthGESjV0769ud+7m4OPfk4WiRCLJagWjqD48yjMPC8Eo3GiqDANWg0fKr1OpGIwm5Ja9xGEg0PgYbkeGWG673abB6plF1e4uXh+y2pwLucNzxkkWLhUWbH4akHw7+FYWqcSCxLNJoikYiSTCoSCUU00o+uhsllNlCrz1GqP0OxWGd5aQm7vcCrBYTWKAUDCwN9+bgCz4B4RGZw2A7UGlKpy+Uh1wXxOEQb0q1o6VAzoaBByoGsLe3r7jR0ZSEWkYm6bQtaNai2hagXNaUrpAdOOarD0iIs16BVApLSJXn0MMTaLndef4hI5gCdZAOk2pYFric+tJnf/sAfcva7f5rDkyU26PCuCCw2hZiezMH0kmjht+hIbsaRMGrJh0IFtmahEhH8LlnkEjsIKSqHZPWt4MMloL0C7jFwsqClJascWYbcFtCG6DBz+oEU2uA76Usl8GMu6pGHuGbbNJleuP8wPHMImkXWq1ovh3lAPIrSTPx6oFWmx2i1GoydPoKejNOn0iyWFpiZmaO2OAeNKvmtV5HZnKNZq2KmRnBsi0wmTqNURCNOcaVMLG5SWyixadMIWzZsYlYzce0SC6fHcVoQy12N1ayh2Wewm6+epMN3febH5zD0KKM9I8STUU4tTIO1gOu06d6yhx/80V/k0GMP8+CXP0+7mQHmoXwY72ST7LXvIlGzmDswS3PqT5GnqQ1+CSJxNC2GW7YoH3qKe3+/yO3X/Bfu2pnh5EKNSVfx1FmXO16XoTuqaMBqLX3hCp6DdEQ6Fa4uikaqLR1XLehqeFqQiABuwJFWTiBzW4N6GVpVKNeCAkrAvPaCGNowoa8rTm0uzokTAnDOZrr5z/+/7+av/+ojq8mGBiRSWejbwru+53WcPrVIubDIbddu4e6vfJVj01O02x5+TOe2O2/nu960kT5jip//ge/g1//07zj0S/+Dv/7tX+GHf+w/cN/oCNVSnY/c/e8cWamuujINePN11/D9v/yzFKcOsHdkgKFrNvDUiX9H6W2S0T7i8TqxVBNDq5BUUbQgxVGB/K2PhYz2A9d3aNsODbtNw3WpWdBwAxpEUGhrIdBaIlLAOr7sk048wtJnf4U3vuNnufpb39MZYnIR05WJrkxMDfZk3sOe6787eEXh97V4594pwCSup3jzVf/Cg7Nf5L6jX+P+Z0qsLLGqarUqqXuxSmRgl4N+fFkS4/VE4xvfYppP7grKAb3//77rRW8jfGZWkCpulFdLGPvKWai08fJ/i0wislpFrBZUrxRZ5iW0SkHa3m5ABtc90ZVXOqQDPGxeEyxzXy/0DQhO1qhLZ6IBlDSp5HVHoWGCsqE7BQP9YOoiUVgLYAfLTag4IofrEgx3qkLZgIVZqYotVoNuXhXKVdiZBO16CxlXeSfSKQhThX1I++E0W69/Fx/79a/xvl/6G7Zt8CjVYHYi6P65Ig3d8CXs70P+Xw+2liGYeNuCfAbqJXDKYG5EbqZQ5iqKVKc85OCLPmSLUJ6FsgPNwzByEt7wFuiKg0qyOr5MJSD2DogmUByjJ3+MW/fMMNr20WJySKdnYfkU67K4L4c12/jamtXRbYm0JopTR45y5thJ7KYDKgZOAWhTPFNDz27GUzqa52LVK4Aimeuir2+AeCLB0MggZqzN6ZPH8Dyfm2+9ldmZGWandErLRZrLp4kkUqR6riWW1Zg/fh+e80oMBTzXVCzLwJZrmT32GGeqRbL5HEYsQ1/vAKXxcdqlZebOPMbrvuUNdA8M8cT99zJz1sVqzUDtBO50htv/4//m7pkKrUIRnBnAB3sR/DzRfIbm/CHYmMe2ZvnLP/gX/iETYfzhM3iujea2SX7ol7jzzgESyGNWoSNEdCUspcsaZ/kSFHsBrl6zZT30DYjowWqvgaULTKdZEfhUuSDdDLsZyH0bgC2eIZKDgR7YPpSictzHcUEpg+9419uwCxOsLHeA+dt78/ztX/wht7/7+7FrE8SjDzF+KkqjucCT4+Ocml1EA3pSEf7pHz/OP3zk42zpibKnJ0/Edzg1Mccf/t+/5bd+4SfoH8xyw3X7yORT/OEHP87JspTTrx3u4w9+/SfIpj0+9rFPcfOb30E1Veap+4+Q7TKwtQhtx6aZbVJM1eiKl0lpeeIqR0TF0HCw/BoNitTsZar2MkuNFYpOkYrbouZBzQ+aBp7IzLY8SeI8AhUvByq0WJ47wI2Tp2mXl4jk+i/Y1bi4dd6rVIyssW31bzcM/BTXDfwo79zxOX5b/VeOThUYm2pRtXy8sI4cqlJdwFwCUZRLtCvZhVu3byCLBqpEr0ULERhhLLRu63YpdmYM0AKcbgocQxyFqYHWhpgp0KJ4FjZshKHBYDp4CXwbqkq6Grk41KNBu96HgS4YGZQ0wG5JUlOIQ3dTunCxhOCdi1Vx0lEdlotCPq+3xNnjgBYTomVlpUjOfj9kNDTzbcBWpPVwLTLS8hOgjTD8Y+/hF2a+xsrRk3z5cVGeOunDUiEY4AiMarDHACyYQob5bQAiljQwEt0wPSaxk9kn+7Eqb5akg/mwwHeAsyVU4yC+m4aSg1o8Cxkf9hWgdyvow6AG5EQzBOo28FeIpHrYtGU7ObdIw1nG0H16x+ELs2Ctyx6+DKYEJ2MkUJqO7wWlaCOF227iuuGos877fbeFi8KMR7GWCvjWMm2rQrtk06gs0ze8ha6uHGgaIyPbmF+Yo7BSZ8PGnWzavIupiWkmTs5QbboU5h9HL6cxs5toN5ehsfwKnQc5tuzGm5mdOInna8Sz/Vh2C89ro/tJ0rkevu8//iSf/MgH+PRHP8Dua27gP/3XX2Z8dp6//ZPfwKouUzv7DF94/x9C72YojQEzsmm3BGe+QLN+O/Eh8BonaGdyLM89wvK4JwpuK8fx9DwH/2aYt13/X2gmNLqAnA9NfLis4POFW8xH1j8lQbGNPNMh70K5oMfAs6VzUbNFZapdgVJRSOJtG5yAx6UCRauELiRxrw1+Lc74kRnw4err9uLax/mrvzlAY3XCHCSUR8KtQWMJ1ahx6OkDzE8t8q/3PsxgOs44sm8Ltc79eWK5zeLKPLt6M/SYFl8+cJwb7v4Shekxrrv+Wr77vd9JuivDB//9AZamprnr6gGOH/kq8WSbvTe/iU037uZ3vvZfuO+YQ3feYWL5MMM9k4z0bWa4bxsDXSN0ZYbpSUOWPAYaLTyKnkWxXaFUW6FQX6BcX6TQ8Ki0RPwjRJn6uiQcTV/mN7UcOX9nKz4D3T6PPfYUfvIfuON7f0Ycwgu+5muTD4VOlG2p7+Rvv+eNzLkP8/4n/oCnjj7FU0+2WKmDv8Rzdg8up7GwnmysW2BrZ5SGs7AvXjLUDePKThB/CW0Jwabng/9fTLJ23dZtrT2yIN0HU4dkTaRplSO8DKsbUBCNQn8ehjIwEJfEwEXwrSVHnrJ8GioK2gETfqAXRvshZQmGueDDjAVpMyCRRyUpLijRps9nwa5DOiZoJR+pisUcmF2BrzzmQmWMG+74bTbt/DhK+06ETXg9Ilz7JvC+gDr09+SrZ0U5sSoE8aNIaz+FhPt7cpB1oMuS52SIziBF04eMAYPJQGxhng4hqsW5I99dJHNq+WAVwC/hFkFvlVE0obAMN1wPfVshtQW0ruCbUsDtGNszZJwxkouPc0v5aYaMInHfono1LFdgbgmW51hvq79QU4EUse/Bau3cAy+Cr8eDATsNsMsBduL8YEd8h99qYpWmA23NgF3nN7CbZRzHoVAoosowMT5OvrubaFeEybFZ0pkcsUiWTTtjjI+PYdV03MqMeCAzGFmMxkUB5C+r+ZROfi34NYrVamIYKdrlWVamF0inc5w4fizoPjocOvgIf/6Hv0x2aCPJ7h6cegHPs6lPf42+N/8KrWkNrBFEssSB9iEoduMlHHp2DbFYcLFXZkWZwQLMNJhJznzqg3x0z4384k/ezmyhyoHZMl/60nF++nfeckXOQiJQD3QMcHQpfLiuJA1tJYqEKpi/UQs4GlUbGgGEyg4IEVFDijXptBRj/Lao8JkOmHWXxx4u4OOTjlf5ytcnWaicWz4/tFjmd//7/+Y363UGNiW5asdGUqluGvh86p57Lxi1mLrGLbsHeeNNO7n/0aM8c3iej99zP9tTBrmuk5wZO4Lut3nP667h1Jkuzo4/zcc+eop/+sRj7L3+Fn75W1/Pk4/PMX4MpvuhUPaw7DJGdJFMdpQuFSUa7SXNRlIMomFiqjKelsNOKmyzScNYoehO0aq3Vjl7TsBziSaCBrAra7kVyMk7uiI/GGHykSkWPvwxhjdczebXvxGlv3SVXqU0YmYXG8238mu37+f4/s/z9M338vvOPzE5zUWHg4WCUZdq68nGugW2EaGf5ZEQ5ks8J6tB+Sj12vTsRWR6+F4kBhp/ZXdn3V4j9mhZuhhpDbqA/gikPOSRyUo1L+KK8yDQMtci4qB9JXKyjoJYPAjO40AauruhJwWRljjslA5RD6omWDXwTGibUE1DsSWQA2UFeuh+MDRLiZMvN+DRk/Ik72zWwX4CzzmNluwFbkCC95uBJ2HpNDQt9CrcMCDVtJWKBA4JX5xJbxrcshyiQUclzAdKK9DfB70jSOfiFFjLENkMqhuJDVN0iOIekoQsg+ry0KJAsYavxlGZFKTjUKrAliLkNoA2CiRAbQO9B/Rh9L4II1o3A5tn6d/wDLu2lDk+6fHAU3BfWzo7bo1X31CbV7uFI6BdWNX+U7aUrtvLBGLTdKY/OHRUAAI/4DehPkaHZRqaD75HrVrh9NECvq+Ip7MkYikUJo16hcLyJNVqhd7eDNfdsJuZ4RxHH7sfv1UBuwYoMPpFT7U1xxUvEfkhpMzBbWt0j+6lXY5Qr3n4bpHHvnYPkVQa33HxHZgaP0W1XiXVN0gtEsVrNfHdFouPfoLE7t00Hv48nZvURpkLOCsN1MZR+rs3s7g8hrU8CTQgkoD2Ir5V5f7f+2mOf/n1LIyP4c9PQ/0U/M6VYR8mnYCg7stgODNQ3dM1SEVkLVKOJBXtuswDqgXzNNpVec30A5GNhpDF7RbggW5CXwoatSYrhQa+DyePTLBQfPaD7ACfPzFG/b/9Lt/7vbfSuyHP6FXX8Lt/8dts+6sP8Su/99fPio9v2JTiN37uu6g1SnzpvicBOF6osFiEWFLjzOlx/v2e+3nk6CRX79lBsmsDC+PjHDo2wY7d16Nr3ezY3MeBp2rC24tANK6Tyw6Qz26gv3srg9Ft5NiNRgaACAP0qH5SKkk6qtAjDezIClWtSsRzZeZSWx47R0niZgUzLrxA/WY4Y5JPd/PA+BHiXoKVUpXNvofn2vheFU15KD2JPLPRF9Xl0jBJq1FuzPwUm3fcyFPf+hgfPnuG5sNcMBR8HjrHs2w92Vi3wDKIwv4g4lT2Ak9ysUXd91w8xz538tprxHyEWPdmxF0usQ6nWrfnt5YvwWzVFangJVvG07UMGLYFZhBpCXE7mQDdELiBoQfTWnWRN1RRiOkyLyPRBb09Ek8oA6iCmRDFqZgna7weF6WQugdZD/xkoE3flAqjUuDFpA2/vALTMzKJvCsriA1Na5LdObjmSCKg3gY3fAVVuQfz6x4jJuyLwthJKEUhVpM2f8SUrn2rJCtEqABXB56ognkWBmIik6vFwUwFX+EiupwJpDJmyfH5TqASmgE8KJ6F+EqDmH4M5dswsiAHZwYZjzYAqh9ZlyyIODC0E8OfYsuGHP2bTzF68iyNdg0nCrMFOHYMvDA+XrdLM2/tOi9cC4ys/N+2QU8Dvmg7r05IDjsOFqsasTSRVTUmvysdVBofndryEvhVDNMkle+mWq2i6waxWIyurh6Wl+Zp1paZnXDoGxmgun8fs2OncYoBhMpZAC8Pej+487wyF1iSrqWxR8GP4PsNQh/ZrFXPeWdpaYHS0sKaQVbgl47ReOwM5zPl/MoUKhEnGxugWc0TVz1YURNaE7KIBObWfOa/fJRX4tjjSoa/JXzpQhiurGmGJ4IZtiNrEHWBNjYrskZZDjTLdG4Tl446rAFGEvq6oS8GpakGrbokdisXSDRCs4CvLJY5/lf3sHNDhDtvP8N3aj437OsjGYPGGppPBNg9HCUdqTN5psrEotDAfaR7cGK6yNzcEg3H5VTZpnl0gnfddRPJRI5EtE4qEWF++X6a1CAlqtDpNGRzcfK5YQZ7NjAS3USOrag184MUGoo8cbYzoJooVcBKL1HxJ1lpFPGrkrhFogEf25EhfO2WFJBu3GZyqzHMkS+3ODTdZCiiM3VmjNSDd2N5ZbBqDA0O0j2yAatdJdq3D7QcShmr+/BCrTu2j5+8+f+wMv7r/OvUUZxTz36Pw3MP9z7fLivZ2BAXJYRu5PE6gwyWWbfXuinkiq4gYUQZQXCbnCc11DHff00zMy0kYEyzDqNat8s3C1n7yohKqCpC3hdiZGReVNE8IJuUBMPzJWlQKWg7MgBRDya6xhLCswUgmPQa80C1pJMSz8iIjJYnkKam1snxNUOqgrYmDmsxKXyqXAqWF2Wy+PYbh5BRlqHpoEYg93bY+Ahqd5FYFLpsyC9BKgvOMpQXBX8d16QJk0BCyVCP/pgPJxbgFmCrAaNZSGQCNxdBUFAZebM/EZwsHTkxk1LNs5vgtX3M2SrGcFG+vFqG2VlINKDXh6grJ44EqKuCk9SDinmkBvsZKsLG3sO0FRgTQq6frYG3Ljf3IswXyFRongNGN/iF4MRqSAdj7dQtE4hDchDNdfAVmNEYvutht5oSSZHCserMjR0DXMx4D4lEki279rNx03ZSkS04dhPbg60bdjM8so3DTz1BdXJcSuJekc4dGAl+wsEAVy4A971QqmftH/3n/ndo7gUkOewiTqXMiS/9Ha6t4VtN5Gm7kI99ZbJoTwVE8LZANuO+rE9GQO/BknE6fgvcBtAUPpkWlsBdVpHaypd6QiwHibisHYNJ6PLyxD2JKp+vau4BU46PM2czOjXFFz/9UdpLda5OwdNtWAlOkwV88rFlzPiDfP+730CqJwVlCZNzuiLuKe5/4FHGZisyj6tU4+iRoywWq2zpzbL3hh2cLT/Ig19dgbrchrEopDMZsule0qlB0moDOr10RtAiB4pCkcNgB3mWyKkJ4rEEnl7EtiUZ0wK4qdOW+Uq2K12OyWmXkVaNg0/VmbZhY1Zx6tiD3P/AR/H8Gl1dGW686XauvuUNJHNptImn0DIp9K59KK2T9LwQU8rk2qFv569+Zjd7Nv8OH/7nuxn/VPkcRR2fyxPYuaxkoy+VYVYFNxmC8a3yykiIhsvNur0U5iPAIhAIVRIJxS+SaAC+7+C6r7xKyAs1FzgG7Gb9Plq3F2Y+UtmZdiCxIjLAJlKIbLag3IRsGlREKmjxNGQ8ICpyjwkNolWZpxGJIh/OI4+dBkYDDBuMBOhdkpikkQC9OxIM/IqBEYNKHSoNIAnmiHy+UoDBHtBjF8L36qBtgoFhGK2gGi49K7C9IWWGlZpo6tttcSg1OnmCi4R4/cj0+RnAcCBShM0RBDqlB280gn8jtQlNl/97DTDz0DcMVh1UVIkHN3SoVqTMl6iDnYB8XZIQYwhUDqmaZwWrFT1Kd2+Na3cukh9YIdfrkk7APcty/ldt3WG8AFtzwvwWUIfYiGBi3AD/ggKSQQcD8Boo10ePxXEtC6tWFlybr8vN6mug5fHdBlDAqtVw7Q0cPfgMpmYzPLgBAw3bt9myfze9+RzV7XC05KJnIrgLZ/DaYYLjgQI9PowXSeNXFwTzd1n11leR+R5O/dUj93u+tduSJ2k2RDyBe3rB0tLyBVbl2PK64ULcEE6GcsAywdEkZ0WBmYF8F/TnYLgfYppw4U7PLDJXeb4041xbaHocP7XItgGDysIKqRh0aTKYNCwkFls+//7AJHt2z3D7rl7OTC5TtKHs+Zi2gza3TLkuS1Xe1EgYJrlElKuv3cu+m/fwZOMZGnMuWMExahA1UyQSOdJGF1F6ODfRWGsail5iDJJSeQziOE7Aa6mLulfEkGTDsWRt9Q1o6h52xiAaSSKMlDbv/9d/o920GUlCd7eGriWIZTZwza0307IttMUJEroPuZtR6sWNLVYoeuM7+I13/x3bN/0RP/nob9E420kvQv93qXZZyUYk2c1gRKG3fYpc+aEyoa37jZfDfORqtujM125zsTPt+x7+a1zkPuzMvUZd07q9SswGFmvSYcCWoH+5BHMNyOalwFn1IN0FQxYkchCNQcqVAVi9aYgH8CoMiev8lkzUdXVJVvxYAENSYNQgaQpsiRiQgt4EJIvCC+lNw1INIj4MjYLSQrLuWksCW2D4dmGqjx8mnvHYtFHkZBuVQDKiLsWkYvCpHB0Kxvbgxwe26TAcIG0En4BkLcHoY2WCGgxeKwfVPENiQ6VAszyolKC7CxYWBIPWjcihNNrQ50JWQaQuXRnySL9FJzrY5tq7EmwqPM7A+DHmTrTIBzznREa6SHUX6kt0nMd6O/MyzQe72Ol2aH1BhyMir/kuqLS8s21ht5aBmJSwfQVGAjOewnU9vJZPNLeFdGoPtdI0rVoR17Jo+Q1OlSz2bNvH0PAomhunXVcMD22jvNNh+tDX8B2XTibrgW/jNiagGWbqoa1HCS+1uY5U9SMamCYktWDitdaR5vZckQSPaeCYcvldU5T4rBAwAVgW1FzY0CU8N9uGM8vgOA5agstyyhENNEMxPb5CYaaBXpd9CUT0Vm2m1aJS8chkYoTlF02D/lyUXCZNs13G9B02jvaQSqXJDw5x4xtfR6wXPvjnj9KeApLS2dE8MCMGiUSGjMqgkXievTTQGCRJFxE/h9/WaDQ8Wq1ABjguuguOK91kx4ZeHfLJAUq1UxgaPLFsrRb2q23YYHkMTJ6i59hjxNMGffkcht4iEi9hpmtgZIALFZouzwyifOeen+Wh//QAH/yf99AsdBbPl62zsbDSpmX7bEQPKGDuasJxJfl460vIy2UOl3oldV3DNF9c5vxKm0MniFq3dXsxVrECnkMrGLxXh9NFMCICcbIi0N8PIzXIZKGnD4Y0SGVgcUUGZiV6RT6SgFDpRAI5Ww9iITe3Bo2qTG6NGaCySLynQ8KDWBJqQRsiFoFomkCXPfwJTQEbQfsp6LWhZwqjbpFTDsm4tDPyEYgosBrSUSkhQcUS4sRTwDb5Koaz8l3owf4o5OHSkawlVKmKyQgNHEmotBAJ4/v404uomAk9jgSoDR/0HLSbkohEJmHrTsgashGVB0ZRuoaey9KdSjGyXGYkPcZNOyGRloRspQGzFVjuBT0J08egtS6X+wItCDS8FUCBlpCIzbMgZYtGs1cHYmBEpePhaOCUsWtLCLYui91sEesbRI8nSTRrNOplWpUKmqYolFeouzZqfp6uoSFiqST9Xb2Y11/PzKmnaC/OBbvhIau4JxEg82v2cz1KeKnNDQJ4zYRIXJYjzwD8IPg25SfiCZnca0vnwzMhnRSOvWUGErkm9A/LNueWpPOrgEi3QKsahefel5DyoZQkFg9NODw94aCAQa0zwwIE/mlqUHY9vvjQAV53zSh2EObETJPdV+1ibmaOkeE8mXwX27fvIpZIYsRz3P6md2ImfUqTFUmATOnO+D5ouo5pRIiQRV1CKK3IoZNE800ZmFqVWUW+CU4VUklZKu02clsrOLsyxompGkvnFUcqwHgNogcWaFtfwXFabNu+l0wmSjSWpHdwDt2IIVt88dLIsUiK3/m5v2Zy8ju4+y+fkfoCz4V9ebZdVrIxUS0y70GGJGkS5FhmDudiylivSVN0mmFrL9GqHvIV36NXpxmGTjS6ri+wbusGosRSt2RWnUsg5BO+GAj4FCxYcaTbMdSWieJ6RNRGIzpsVuJEW02YrYu/MSIQ9SFVlzkeblMUXiJuoHsfkCzRpTOixwLnGkw09yyQ3kSJjrR1aAlgQKQ1t25D6XXyFMmPzZPQfIyotPd7MzDagpONQEULyR2OB7+PIJ0LRpEMJGwXWkhmogUf0IKvVIAjQYdKBsPDY0DCgEQiSKoa4BVg5oxkTXoyGFesoL8OuW5IRAMuRwbURpTZYGjzMm+5pcHekSpGTEdL6jgxj4NjVc7MepyahqkjL9VV/2a2oKbpBZrIhg7thkSUpgF0Bc0HK8DORYIORwYcB6+9wMxYA6NnkFQmRVdvD5Vijvr8JDNLx1ClXiKJOLMLM3T19+NrPtE+j55dmymmozSXlvDLtdc0b/C1Zi1Page6KXK3viuXVCFJhjKEa4bRUVZSMeniKg3woBBAhfLD0KpL0BwzZd0KuRyR1HPthdiWJGzuk2SjWIATpU7h8PR5gbmBkNtrwGyxzKGnLTbnTA4WbRoo8r1DlEtl9u7dx7bt29GNCNlsnr7hTYxs382XDr6PiZNW0L6RvNaxwXV9NKXQMFAXhVCFpoAoOgk835fhhvVgJklbkherLET5rgyUClCqwdlWmUgP51KjwusBtFo+c1MrHM0ep294G5FUipV6jWylTTym0fFCLy7hUCi6oxv56R/4Gb72Lz9FfV62fDmx/2VFiw4NJoEVKvRh4ePLzceV62yETaGXa4kZQLr3GlKIC+4vSgh9unrRT35zmUJHU69BKapvYAsx9RdLiBMIX3eRdRTJS20eULc765J//osWNOowtQKLTeF0GBEhj+NJNwQfslFoWDBdF+WriAmpNBgWZKICGfAI5CPDCliYQwSJh7JFelJT8iPJxoWw4EEEEFGw42qIFolXThHRFojpPlEDRnsh70F9VhoQUeQ+0pDxgBayNg6VYTQk8tl0dBGrQANRXlkGFqQQroIh4cQIVKuUMObjMcFmNNpgl8CqilRXNA/xlGANyjUY3QAbIjJJUClkznkMsyfCtW/p55pmA3QXTBvbWiaTfRTfnOfRxyQmXreX2BxXfkCyXLUiWTTtYNQ0CAvWBj0rnSy3jN1OUlyuUpquQyxBom+IdCLByswsrdKiZKSmhuu7+OU66aEsXUPbcHo2UpmZojE/L/rQay2U8fXWV7mX0mp+MG4lHJ8Cwr+IyqPbaktdACUwqViAt6zV5VLomsAanYC7HDFFJlyLS5c2ogs0a+Z54nYD0BXk8/KdQ12gmnCqLcnL+fT7CnJrusD8SoWl5QquG3DeXI3FlRL7r7+egZEhNm7dRiqbwjTi9G/ehmYojp29n8Xx4N5ug9+AVgOsloPjuHirzPfnM4XCwHM93JaPa0kubrVlnY4qKLugMjKrqd4COwLdm2Bm4tlbc4GZBvQpaNRrzC8u0ze6hXKtxfjpY+zKJ/GbDbTUNpRKvihZ3NCu2nQLAxuSnJmX7Odl42yE1gYmaa2iIl/O2oKig9B8ub8rFXxfBfGXIajIpqPYtm5r7PxC6bq9ovZciQbIpcoiweE6T+WlNZ9nO7lnmQVuHRoalCyoWTBfFaiUoQs8oTsta82CA5WmEDJzTXHGPSnIpqS74LaE8xHPSeDug1zUmGjXW5awMrwK+BkXZVxs72wwBiG/GcxJ/INT2LZPMiEERs0TvnahKvtVQhIOAwGtaAhRvGGDPx3AuvqRMqNDp2LTEPnLWlH4JoksGDlk0U1rkEqIbqbTkG+wbGg0wGwJHsppixrSsgWugmigQjRUkWRE7wUVQ2n7UfkByFckUPUmqJ16CLduUppXTJ321zPtK2H+eaVYE0i40KxBNGj1OYC2jGo6+I0maL1okRSRdIS+HaM0qzWKU2M0aouoSAS/UKRQahMfHKKvZ4Ceq7YwxjEaM0fArbC6+vkvEIOg6YGs0rpdyFqBv9cMVketmErQcoaSbkbcl8DZiUnnw/REntsqy3u7s1BpSwMzmgQ0yMQgEpE1sNuE0atg5sDFxbw0hCP39CGJn7NKvj+rZB8vpEsWch0q55EMNN0nYkYYGRpmdHiYnu4cPb39ZPuH0PuGmCk+wMf/5UEJCiGoKglUtVZv0mjWsPNtfPxLCIV8fBx828av+2hNgcPGvQBCFZFzlDKgXILefkjmwemVc+pd4HzUfFhpQ1ejRbvl0DUwRKNeoFouUhg/gldbIZo+S2r4arT4aCCL+8Ktr7uPvTfu4czjj8FlLqUv6JtfSBfjQhfi+QIjjU5n4UqYRUedUSFFuXW/dGHzfR//YqvBur0i9nxXow6c5RKC4nV7Qfacj0MEqeIDOEL+bgELZQniVeAhq7bE0gsVSUYaNVhqyLTyah0G7SDYLwMuDMdktoetoNkWtFGtEcRxQbs/061Q6YvsnD+PDMEYBLOO7biYyqevDyJlSKXArkAqcHYTCBwhgZDFRxBFt/4IgTQXnVE9LpLxJOUDMQ/8GkRN4X+H8C+MoKxne1Aqibd1bSgWIdKUzMr3pQzYVoLD8NpQSEGtAJkcdC1DqltmMKiRYGdqEoQ6cbATxDDZutliJi2zA63qemx5xcxGLolCstdVq0I6gzIVfmOW6mKZ6qJD99btpIdzRLu3sHjyBG6jgJ7rJ2rmMF2T6koZq97EdzRUchOx3iLNmclOFeWyXJOC7jw0m6JjvW4XtBbyvEQMKXBoZqBGrEnQH4mCoQV8shzUDJk5ZDYF8ZjLy9BR5UI6IetcIil8jqgp8V4yDlv3waF/g9pFeFWhbH3BlnWoDHQHiUbITDv/8l/odjAVvGHfdjYN9aMpyHXliGYiKKUwurootU/yJ//wKzzz5cVOpdmTPLpWhVqxQqW8QmNwEV/Zz8PbEKFYz2/gu22clvDGanXpFHlNZJ00QIvCxkEoSLOPrVdHefgLNvXas6NRD6i2FG0UtqZRsz2uuuYWyktTtFzQ9B6irolbOoHSo6jo0JqzcfmV4qTZw7W793G3+ZgoOF/GJl520L1CilcGncA9vCFqPDeb/UoVzUNkW3juLqTdsm7nm4/vr5+l15pdDqFr3c61fA7KlReAzogBvaxW+VWAffZ8SQxqAc/VAlY8UZ9aKUPTg1IDtBqM5mG5KQ42FYFYQxITuwLJoP1uuRCxgqFQTUhnoCdHMJTjQlfeB/+oeDojje/EaReqxAzY0C8BglWFxWWoeIGDR/azG0kytgJb4pAalvY/dWAccZwlRCAoKfKXRhbSJpAQNBRVUFWg5AoUphokFL2iNOTXAlF7rYrCl3JftSltm5UC5DPSxklloNgF2a5gumJf8KVtUEkyPTvY1r9EYXOFXHyZM0s2M4tw/DScPB7Ica7by29rb0EFmKAlLZJdDQw9h+/EKS0vgnIoVo5Rt+NkcjmSm01alTbJuIupqjTri1RLddxcikRWo1mfpmU6nZEbl/t8Rk0olzswsHW7oDXsQF0qmBEUNYJhdAi8MxaV9zlKOrKuJ3CnZFQSEVuTokg8KlBKkA5HKg3JmKA5R3t1zGieZzaVOXzwubWOPCSGbCLD8WrB357v8itEY2LnaDdbR7uw23XKpRKGaWBGoyQyGVQ0wtmJx/nwB47QWotA9eVLmwVYWa6zsjBFacM0I8kVNDXMc0WtPou0WMGyW7htgcaaDviOFIY8DRpuoK1Rg81bYLjP4Ortt/CVv3+S07Vnjx72gLmqzy4jTm5wiO179pPt2kyzukSua4hYqhtFDbTcqlrcizGFwXve+2388V/8I5Vjrcsa6nxZyYZSsHOrQa3kMLdyac90qMUbzjQMhPKeF5bk0+lo6Gv+9nKEt2u3uR6MXZp5vsL11jFU6/bNY7s2wDMnpINwyaaBmRSn6mkQT0hFLZ8RDkbVgEID5hRYMVjWwIxBvQnzDVipi359w4SuOEyvSFWwPxnQIWyIVKHUDrTfAyhDvC3c6dQgwiZn6gI7V4PFr0E6C4aJN3+WmfvGaNchmYSpJZiZkyZDLgkjTVj0JLjYCGxX0GNAbjMYG4JNLiOlxpCvYSEtkGjA0UBIpb4Hqo4kJCVkAlcDwAOnAF0K7EBSJgY4NRGfd4FaSbQynbpEOYU41PthuSDC9YPDkBuFaAr0HlS6zuCm/dxlu2zNnWHb8AIThSqZpMtKBZZmWMfIXmmTQi9+A5qFFmiLwvXWHYgovLkSLa+EM7hAti9Gw2vTapWpFHy8loff9rHLGnpUh7qNP81lVwnNpJCarfJ6r/dSrIUUZp2Ag+Z6Au/0kaRC0wXuY+hgaYAuiYWuSzLQ9ETEwjURzoYBiZSsjck49OZhz6YErcRWBodPcvRQ8YLQofMtVMgLvjJkop1jEQUxXf4/nDG4/dbt9OUGiJpp4qletu7cgx7PoWtZogObOTP/CH/wvv9B8ewF7o0mNJZgbt5iYX6cuaUJNsQn6dZ7kb7z+ebjU8bmDBV/hmqlTKsRnMcA+2z4oAUqV1YLonGYWwBcxSYvRrt18VjL82F2eoFnnnyIa265gZ5bN2HqHlOnnmLLvtvwvTJmLIdS4VycFxe39Sf2kOw1qJzgstbNy+tsKLj2eo3NBvzNZ8QRXkq3MkRQhtwLBYF07qXZuh949ZnngeNwWZntuq3ba9liqUCq9XKSjUBhKqNBIg/ZrMywyCahKy+whMlFqFTB1iGaE+dRtwQ2VazLvIzlKjQb4pS6EHIlChpliMUFgeQ6ELOhPyUKVnZE9O193wY1foGdq0N9AeJ58OrU772H8eMOfcNgWlBYEAiXMuHGUYjNCHygjdCxB2OwJQfNCsQmZfigcqWLsSoTWQS/AtpwwE+MAg1QcUk6aIIqE2hqKkj50g3piaKaLSj7EkQ2kBJgJg6lMiRdSESgEEiwNmyIV6QltFyAfAGGh6A7DmYWbWgbXbpHbriP0dYyfTMncbQJFutt7m1Dc5FvSKnBC0FKXjXmS7LhNNbUow3A9FdB9u6CS9Or4xUlWF07VNvHw2kHn3t20fc5zYjD/tdHWBizmC6/+EP5ZjAn4N37ARTEciRA1g2pLVgIHUf3pfARiSDDSQ3puvqedEWISqKSSMhPPCGJh6ZBq9WA2AR7bvF5/AEoXaIiT3gHxZAGshbwOIo+JHTYNxxh/65emeedzXL1LXdRb+v09YywZdf1jOzaT3lpEt9rQVxxz1c/yhc/MyVUoPOtCe4CLMz6nJ2aYWzgOKO9W8imNqEziDovmPdx8DhDgSMstscolso4VtDNCUj36ZQkXLoBRgraOsQj0NOlMTdVp1y5eELs+dD0fJ558iH+/i8shvNDNOoLHD96mFjCZGHyFDv23Ui2uw+iG1BahheTcERUD5G0fvEZhhexy5sgvinG3V+wuG4ENFc65RarRYqL2hq422p34vwEYq1I16t2cVy3VfNcH9f+JrlSr2qPvW5XylpxKbZflrnSGUBHsg5NnHbLEblcpctk3mZLUECeK2tpvQHlApSrMiCroIlGfT4liX7TkZhspSwQBTMiKJCUBxlDIFatZZipwMYdcVLJfeftWCBcWChBdAlv6kMc+/CjeD6MDkgMX6jLLJCqB60yaC3YpcnjkFMwPACpqHC6Z09B1oNcF6ACRdIcqB5JQFZb1T6Qh8UTUHdg0ybZrtqmYCglRHDNBc/DtwO2ZzTYhqHwHQ9KbfylFspuQiSBiqfAUmBUpHQ6twTpBZibheFu6E5Afww1MIzemyfXWGBjq8ZUfpE9m9ssN+H4CahN8Q1X2YppQZD+WrFQlUXJLAenLUMsB/JyHMXnSipiQTAcckPC4zbolL110RPAgFrFYmH65TyYbyyrtaTI4Ruy3oSzHD2k8FgPkglNSYJhmEEX0xBYleHLlHDflWubSoksbiIRjArSoOq4JDLzbLghzlV7NR5++PJu3iYC8RxMwsAgRGNpuru72H71EFdffT1dXb2YWpbujdvIDYwSMWJoegKn3aS6Mk9+sIvF0kH+5RNfp3r6Il/iA0VYmYaT+SpD3U8yumEDfYkt9KkUvkqvhvI+Nh6zFPwjTHqHmFk+xtKCQ70hxSHbDjjnDUkaYglIeoIM7e6C/myShWc82taFkw2lIGpo9I4Mk0+ZHD82wZf+9Z/I9uRI5/P0jVxNYXGZxbOnSCaiGJEqq+SQF2i+79GyfPFnl9EUvKxv3H/HKE8+Bvcf7cRf4TOtgo3ZnNuoCd/jn/f72pZXoE2xSgq3gp9gRui6vQrNd31c6zVwdV6KRGE90Vg34PEHJPi5XGvXYXYBIm1IpsGqg7YCDQO8JFRrCDHQENlbx5VBf4VFQQo1TSh6YOYg2iXykWYUsKFtix57PAnRqOjJV1vg1kB1icMyoz3AnRfYsxIUCngT8zzx9wXueaLBO26G3g0mru0yEPMYyQUNhSiQhX5duNmmDX1dAtmKboa2E3DN21L59OugLFADSKciLH22AFcCkiUXkjPQn0BwFo4l5c+mK+OFl5GOxoh81q/5+NU2KgrK9vHnathanUhXG2V7oMfwPQOMEooCnJ2GXBwGs7BzAHrikIrgLTWpz1ZwSw4pF27cJcHPE1WwLqQQ/Bq2tYnG5lFR0VkqXvrn43EY3CT3b6twgTeEcIWX0PoHhZZTKcv8BqsJflI6gmSR8nUbCTY0wADdFrqPMkHrFXhUPCuvm6ZUkRtNkSyNx4Q3VSjK82TXWQ80LsEaAa/AD2RktUgw0NkPxHx8+T2yJvgzNAmiDb0jmWshMrcRM1izosL/MKPCWYjEoGdbgm1Xx3jqiSLtyxhT7QEoWSMHRhNcf+vtbNt1HcnuDKOj2+kf3ooZ70czIlh2g6nxszSKdahWKS3OEDN04gmCLOk5vqgOzgJMZuFE7wwjIwfpym7E7DbJsg2NFNDEYYkSJ5nwDzFZOMzMzByFggh3+IHquGFIVyOdkcRLxeRctlowkt3Lk6faWO0L74yhwQ037edN3/I2tu3cwsLEYR5/4hFuv+v1DGZ6cL0IV117C81GESO/BaWyvNiARtNd+jbBwsNc1nNzWcnG8YPzlIudxEED9mekQjXXkHWnRoebEYqNBOsBGsEgEjqa7dCRtPWRpCNKJ+mosS7T+Wo011NYzqufs7HelFi3l8peSKIBSHdjWeBGzS5kUbMk8FMZmRgbGQI8aNZFFKewBM15oAVuUmLuhiFBvRt0R7RgWne1BG0PBpIyHKvpSJXQNKCvHwxjNzDEua1zH5zT+LNVlr6wyL9+Fa6+2mDPt6Qxdo0wMLDId3rLWDV31UkUSnB6HCarMlCwawxG0zCYh1h/oD4aqFD5ulSYtUUkMIxKgIchmvg9g1CahUoLeiyBPOC1gwNFHEFMdlM15TxQQgZFtwEtIFRWfbAq6NUGvhHB8yLokRR6TUnAkI5AbxJV2gDbNkBPFs316DV62NVl4dlzpNpVJjwL0/jGUmq7ZmuSvBnnXT/8bj53z8d56x1phrd/H5+9+9N86pOnnxMPrzT48Z8d4ea9IxxbeYR/+gzMPSyv9Y5o7L8dvvpxT6Bwz2dhRfFC4wjCzwdDgrJpKC7DcJ/idTdrjJ1xOXYa5iaD9/UG24izCq1QSALxljdqVGsJjp6oEU1LYq9Mgf55Dgz0SFCHkmd5ZhKaKxfYp3W7oMUS4EVFylYnENXRhGegI1w014W2BihZg1xPLr0KIC1xXYLrkN9hKkiYkmj4uigxEQM9bbNlXzfpbJH28qXvYyj0owGJeARci4ihyGf7yGR70KIxlOlTqZQ5fuwoRw8cg6ZFZXEOzbEplmpstbfy+pv28cRXpqktXCSadoFlqCThdK/NwNljxHPDOBGDLek6eZXGo0HJX2LeP8VE8SBnpk8xPWezUhayvKcC3oYvPBbbgJotSoVeE4YGY+zuexvvm/7gRW9Rz4fl5QVSiSg9PTnymWtxfY+267Hxqh2YiQyaipLUPXyvDVosGLcRlv4v36reOLbldE74JdolJxs+MHWwRrsqo99tT/6WGVS86Sr44N0+TaeTOARJLDE6pHCFrBFZZJ2IITdG6F+adJKN8DS8UIGJdXt5zdcMfP1CZKhXl62r867bq8LChVEhgbMNzgwyYdEAfQCaFrgr0q2ol5BZFcHi1/KhakrAnywBqaCorGQoluPLhF9fyUDwaBJIQ8zUUFyNjHNcay0onMI+0+SzD0Jqb553/da1xLYNQzaK3nOGAe0QZ7+8QjYPtaKPpsORKpx1YZ8Gk2VRGuyriYxjPMdqJUpDjsutgVOEaDeyuHtAJMhJfDkVzQKkg2MiSKDQQOVZRXsJZ0OO1SpJVdAEvDo02j6uaUPEJhJvoYwmkWUXXelo7QTKreMbBiqRAy2O6s2R3rqXPQPD5LvPcv+Rg/RqK2zpgzE3OPevMYWqTZtgdlpw9KEN5mPYxToHHvoiu4eH+B9/epy282e4ro0Xwg8u5Fg1IA61Zptf+7XHsTyB9IF8ZvcdMbrzGiItTAciGFYFA7ggIOcxjTh1Vz6Cg0Qe0TXvi8i+mEgn8Oysz9S8i+dJUKYC+A1tIAZGDzhLMs06k4B+C77zqh5e/223854f/zRtH5yWfMXwgAhOLTagaAt0ZW4SnAq4TZ4dMIWwi8uoqH8zmG9ANA140rX0fEkuXF8gU5FIp6tht4OEQheIlRvcZ2HioQXE3bAYHf7SdEGzIOpY6AkTpV9gR57DokBfEoZHTfqHN5DvG8HFwHZ8as0mmt0iacaolpc5dvgEn/nYp6FSo1kpYderjI6OcFfhbdxw8x182w/O8PG/O4BTukgQUQdvBeYW4dTMAvHMgzhmjca2BfrTXXi0KVjLLJVPMzl/mOnFOkvFYCZRMDW91RZyvaOksx2NBgR3A1KxDGdnZxk7M3nBr9eDhM3XFKVyk2Q6Q2GxyvZd23nki1/k4a+mecu395HNDNEot/HsEtHcKJd9Us+zYnsF3/DEpVzGOnnJyYYDuEEpxA5uHB948JTP4XmRXlzbTQ0JOwFPkCBhXX2uneD18GZTwfvCn2bwExLLQz+1Dq16dZjvrQ9oXbd1uyxLI1WWueDfHhI8WWCXoVITx+26YC2veZ8FblugVCoiKjoggZbjgRkXJ11qiQpVNh9AWdMEnryBRE7hoA8f/GWoTeG2PTZvyfLuP/7PJPYnwXBh8ST+0dPMPLjCAw/67N0Lh44L/+NkG67RYG9CqBXNJixOgrMCeQ1SMWhbkvTE+0EfFIEhDDqLt4E4ajvII+qQWgKVRCBTaTrkvpC4rUmV2veFTOq0oDIPjao4anSIZCGSd1G6ixGLoikd33Vol2voZgE9NoOqWqhKDNUXx4jFGeoa4uYdDr46SjZVIJ7xOXAK2osvzy3wcplXhZs2JohaDdp1OFaAmTMrXNNjsnBgnsdLbfIKZm1LqqmhPGRYFQwdc/C734aP/d1Sx6mv6U488OmGaOyHHYYQ/xw679SabQf8i1WHHwYBQUKz+r1BYF8sCWlWM0REwKvKvvrhdmz5Dqcu//Zb0rV43VaN9pEiP/C3dzO3BF2bIdYlqj44UK/DcgPmHQn23DBxCQUfYnTkloIui0zPDE/wi7xA3wDmRkCPSrLQcoMhf64Qwj09uPxmAIlvSVIXMeWzCiGAu0HBxXME9ua7sob5bgeC72uA7qF047KKhSbQrUMuDbm+bnpGtrJx59Ukkz34po6KJ4lGM7Qch3K1RjqapDvbzZm5ArRcersG2LR1Jzu2XgV6lO96y/ezUlrmyx+bxq1f4AuDALfVgMWiy/jcWbx4k3Z0iaW+PpTSqNaXKJQnmCsssFTzqXrgxYRIb7mBAhXgGGDpYESC4X0axHSfI88cYnHx3H5rJKrT15+jqyuD7Vjku3IYMY1MJk5pWRHRkwxs3srK/AKFmWNoXpVUrgtNz6FUGG2/cGs1StRXfCkcXAY26pLfeqEOKEjGWngONYcQLhWuZ+GQvrVwqvD3ULKsTWcdC7sa4ZyOEPr7Dcbje82ZpvkYLy5BfnVbWHFbdzLrdr7F6YykvRxLIezF84mHPjjzUAzgtH4DGc/dokOsRkiWpSgs9YhTd+NgO0JAVyY4GpTbMpvDBewiHFQeW4cfImGsIFE8yE29BK0qsVv28cbvewfa/u+E+qcg5eJNzVM7M8dSwyc/KrCpTy9JDJZE1vN6C4Zz0B8VuEoiB+6MBA21liRGXVOQ6wctKd0N34JIRjjcRgxG6mBWZFtWAaIbkMXdQBxDWDUPCfaOnHerLspctZJMWbcQnHcsDn5TEjLXsbBbbcoOVHVItD1UpY5+JkVuUw/m5n7USA9GIsXG/m2Ua21qZRvHreC5cMKXeYH+a8HRGHIufvu3b2ND8VGOHa7ysa/Aogt3DCo+cDrOgt1maxyqESi0CKY+0qnyKeR8R5BZKWHyAKvTosO10PWQZ6CBXJe1DjuEJYQJCHSCh3BaLnSCgLAqGXSuXFceLaXJZOoQcri6TQu5HyoBGTwCQ/2Q3ejx/37Oo16Dri5Y0QWiY1WhviTXsZmAlkWH7xEGFOHxBZhbPSUVersminGLi3RwQ9/E/iDdBbE04EontdaAdgCbtBHYphmoSnm6XB/Xkx8VcHtcT7qxvhU0QYPOiA9YHqTi0qnK6MO049vR9dM8H7gxRqfA3p3X2XXVKJu3X8XA0Db6R7YxvHE7hWqBRtNidm6KesOitFyhUqlQqdWZnJ8npWsMDw+jxeK0XJeclmJL7zY2DQwRic7SrF/gwq8RHbA9qDVclkrzGEs2TW8ZA526VaFYXWKl4lJuiTBINCLdDCMKkXSg4q3AM+V+dhwppsQSCWolG9c9N/JOZ9NcfeNt3Hj9LUxPnWHy7HEatQJTp05w/NAzDA6McsMtt/HwPV/g4EP3M7J1M6Obd9K/AXEUBGSmF2hd2SwbRxTz/RC5jO7fJScba0nf51s6J7jhC1mcDhQqlEyPrPlbEblR7OBv4ZoWIA1WpbPXY79Xl8UiJplk7Pnf+Bo1MwN26ZXei5fGLqQ7vm4vwvJIwHKBkxpLy5w590KLsIkschciEi0HFT0HqHAuhCMEIjfATsDSihBgqxGp9Pua4KltQ5R74lFRkKo5MDML+9RZ9m9YQaZjBCu5L9In6s63o3q/B4qPCZA9nqRxZp5Sw8FJQHIQnjoGM8A2BDJf8SDfDYN9MJCQwFClwBwFpiAxLapBUzYUZyCty75EdVGJMQyZuZEZAX8MFlrQrELEEXgGRnB+K3QcQRiYBkegB9ODHQWaJ9s2PAkQfRNqDR/PEQWvpg7lxRrNQp1IvMIm36LHdqQ825NGiyl6tRw7BzaQ7Z4lnSzQk4CjZ2E2IOm/anH9CkjAVYNJbupaZP54G8PK8d9/77s489AEJw7eS1dXF6X5Mo+3/HMPIyRfhlwkjU4SHXYfFJ0BjWEA4NJJRMLq5hoVqXOSwzAKXFtQDbsiFzMt2K16sO2wY2LTSUYdmUeTz4pQwdFlmA2UqpID0EgJV8MJ+T5hQlFAkqkw6VBCyFUO+J4il4NrbvI5ehCWS1BZgd39MFYSeOOr9Ta4EqZSwqlQHpieKMS1K0J2NkyoOKC35fnzkUq97kqXUzcECaEQVIRti4re6u2hyURyIwZJQyfv7GOykZfY+GL7QwDlR5aGVBQ2bUgytH0jW/dew7U33cHglr3EYv1Ek4OMjx9i7NQYjZZLeXmFM5NnKJaK3HLjzXzL69+A5rSYnBhjdnqOVLqXHXv38xu/+AdEUn/Ah9/3VcqTrML9cFkNZvU4RDOSPLgomo7DSr0MbYeGbVH3mpRdKDly7MqT7oVuQiwr656HJBu2CmLhOMyVahTHp59102lGhO7uIW674w3s2PHjLM2PUy3NsTx3hnpxnq89c4B3pr+ToY3DtFstlhcqbNjahePkMSPZYHt+oEV++WbVbPLE6Y+36E9d+ucuOdkIYLQXtFTu4slGiQ4cSkNOZFggcOl0KcLzGRZYQh8T+vP1YOnVZTo+EfXqXnqjGVkML8fiCZO3vPMa7v3qIcrfINIEYaHgQldLN2TNcdbxyZduizKMym6BvaboZsQk2WhfSJ5TIV7xYoRUnw5k6HwLg+82UILaklQDk0HdJPkAAJ0cSURBVFFRC0okgyC9IRX+ZBx0C1aaMhCwtMFCIqz/j73/jpMsPcu78e9JdSqnzmF60k7a2aRdaZOkVZYWCYkcBDJgbIKxMX7BBmxsY5vf6xdHwNjYGGO/2AZeLECAQEhCcXel1eYwu5OnZ7qnc3d15XDi8/vjPqerpndmd2Z3ZnYWdM+nPz1dderUCc95njtc13UPfJkGTO4B6z75v60gvROM3diFL5IZGyXlb1J3Q45Vwq1E+E5DnIa1JvTaUE3DdAEyDbAOAJNC+Ny1ASurojjV8+UUTCVwGOVEAUoOzJxUNtbWITcLRmbgfNdBxdmoHpAVQn3CFl6KqkG6KdUdIyuE8VpTrk29IU5PsiyE+koDNnsKK9nF6K3irDex1zYxJ0poI3nsfJKZ0gTjdoJMokMh2cMIpGF5tXOJ+3IjmAKaUHd7fPzhHu+c3MUXfm+WYP5h3vez38fhD7ydh3/9DwlCRT4Lw6Mwu0SEi0YcphhuEHsDOfoyxS7SpHFwgY75F4MykzEB3EICjQ4XLtqXo9QR83Xi84J+5rEr8B1bh6GkOGoP3GehTI9iOctK3SGR9HB7UF8D14vItxkkKm1GPw361cKY49qCiT0pfv/jP01lscfXHvpDHvrMKX7wO+/he947xCc+8TC/8dmmqCn9FTY9HV3TAFwVEbotgVOig6Og40vWXovgUbobKVJFjrYbiMx3aEEyCZonzrcRQtqU3hI5fQTbO8zi3JlXVLzMIeISaVvmvh379/CWt72TTGEUZacxLIlWDcNi1647KZRmeOLxx1ldOUmv12N6ZiepRJJGt8qhm3aSySY5v7rOwvkF9ju3YSdm+MBbPoAdKB76ynF6WpOzyy06QcT30cBKQz4LpXKK8lCZXDaNnUzRVT0srU1Sc7H9DrohwYRvitpaoAlvQ1dyrVwgmYCRYbB9UJpH27mw0Ui2kONNd7+du+66l52T48yMjjEzOoLrtVlb28P+m/bz/BNP0FjfwE6YDE/tImnbbMw/j+6ukJ/aR0LXQR9FS2Re1TjYWb6Z9x2eIXmiin0t1KherrKxfO7SnwuIGsNGdjHoW2z+wLavbm7/eg73eplhaCSsV1+Kux52pYHG2MgQP/WT38bEVIUvfvrJa3NQr4O9HBJE41UnOP7qmg/3vk1n9qTi3On+TKVCqK1w8ckrAYlhyGZg89gltrnUpBfDPUIke7MA3Rp0NTBHIFmC/JD4VKoLeh02V6SrbtaAxT1dwvA4uh7L33oCqk7uRUgSeUjvB7WKWp3F9MAxM6x3qnztJFR60i18XcEXA9iLQKUywLkO3NGB26eQybsA2obo8A+lwI04xDqiw7+2BmOeZEC1UcmUJhHogFuH1DKwKdWacAOBy4QQ9KSCYUTwHC0hwUpSl4WZtHBFHEckTj0ke6g70hul1YTFliTiK+suI0mXcqVJsVXHaBbRhvJ4KZPiuM6+oRGSWp16rcGxs9IY7IY2BS/0An7ht07yO2Mmy3Uf//mT/PJ3/GOyFozY8oCHmty3LfxyzElII56Ojzj8cAGsaItcGV+HLhfyPOIJZnAfV7oM64jnONgC2ukHGKYOu0s6N5fhfe/bg2f6/MHDSzx3AjbqLRJJKA9ZPPjAbXz14SP0XJeuCdWVKJES+1VDyODo0M9yJqCd8PkX//h3OXrkNCtLISqEd91q8yu/fY4//mLzQu7AX1GJQzsQhSkQGJWvomx89IArU1SWHOSZdX0plhq6wKWcrnBnOl1JGCQNaJlCdE5FCYmWCRW/TeP8WdYWlvCcS69esQ5BNgVTUxYrtYD55TodzWD3zAyeUtSaDbqdKplMlmJ2jGJhgh1T+zl19BRhsMy73/cexsfHOX/yCEdPHiGtZQk8j+pmldpahZt23MHhPe+nsaSzO3MHG/4Sz55+iIXGBlXl0UmAn4J0USdTLlAqj5HLZdBtE8N08Hwb0/dxwi71tiKIoLEdR/gaYeSBG7YghMbHog7sDbAzXZpuf6ilUhZvesv9fNf3/U0euPfN7BotR2u3jp3IsWP6dkbKk4Suw5FnnqRRd1BhjampYRbOvMjZZ5/knR/9MRL5IYmQXq2pLhkXDgyZWFfwoL/6zh7XyF79M5wA7gTOIMvs1+2amlJvEEDzSy2RSqAbOr3WhZWLqfFhvuPBu3j0sT8mcP9qrCb+G0x150YxvxIKvGbAgkFoceSQaAlQHuCBVRf4UZWLzHOx83axYTe4Xw/ZwTrgg9+CVkM6iZt5SDpwdhkaZyL9+3X4ZCbgPXctMJon+gILCRlMUA2gBloK5WYJn3qIxguLrG72+MITHn9+HEoKZgwhoX8ZgVTlgH3ABEKQnKjB9EZ0XG0Ie6KutYL8xLHIigfJOpQSAl/xlSxCRV2UWHDZ6hiuKhLA+dH5JzVQaXFAVZ0teI3jQbcuXdcrbcGRZxKQS8o0pSvpC2IpcYTOeHIcU2cDSr1NMpsu6WGHZLZALjHE8NA4SmkcGmqzIx1wnhtcmChyzs924OzZCx/opgdNT6Gb0O5CKx6zg2NKIQFHHNSaCL8okhjeqnxAPxgJubjTHXMwuMT71sA22rZtu8CIrORZHYpZODAKb9mjceftI9x95wQvfGqJ27/1Y7z7O/8NL672T8Lpwf23TmGpEpsLLrYO40nIOrAEOIlo/zEhPA5uRoAe1AOPT//Zya3j1YDf+vhX+MwzQf+FGNKTQAZzDMP4K2K9dUhkhPSdMgTKaSSln6ZhSMM+wxTejWZI8OGb0i/CdaSfULsj/08HkGhKAOz4kOxKFcRyIFewyNZ01lZW6HUvvQ7ndCilIZPTyA+PUxg1GZ3ZRRBopNJZ9uy5HRVq+G6FJ594CtcNedMdbyZfKNDpeLRqHUK3Sbtp4nRD1laqtDbOMLdU4fCtd5O2hwGDmR23YtyqOHPkBU6tl1HTJtPei5yrnqabd1ClkB07iuQLQ6QLedKZDKGugWZiBQrlt0grg2TORynQOgL7VAoSgcyD6Rzk85DLiQBG24LVisdCRc61XCqx//Ct/OCP/G0++J4HGMldPFiwU8Pcfs+HmJ65hcraEmuLc6TtFLmpg8zsmiGZsMEsvKYEY8I6wDd+8KM81fhVurWLlfEvbpcdbITc4MG8dgfwMUg00a2Po3pV8jsP0Jz7EqH/lwMOcyNZqCl8Ldyqer+RzLB9tlUnARjNZZgcLdGpeQR/iZxwQ5Ms7+Dze3DXXk7OzRJ+XRv4is1KSQBRzptkMwbHzzgvmRyHJwRamh2WTrOE0HkRzi0Lhnl4JyQzsHA0+oAd/TR45axwDAUBWACqonDijshCTguYRRzKApRmNAJzAaWOoWkHEI9xHEiA+zB0ZqH07ThP/QmP/8bn+ORXNjjvh5xtCVxC0+BIIF/bBo4gflqcHG4Dj27AAz0YG4LqOmx2oRLCU8ALwG5gP+Kf1QMo2UAZjCU5DUMT4vhW5tmVo+x1RR4yGytURQ5q6IETOXyOkt4i1aYERB6CCgs94YIoGwomTEQk1cXomJtd0Fah2mqTWvXI5V2yWsDo7gS2kaCgWRQJXgOV8jrZZSQXd90sEI7zz9FvfBX76h4CMYoDgDp9mFRcpYj/HgQPxGMwfi1ONceRWVwRIfpts8W90DxR3hnbC0Ojsu9GExZOwv5dsGsIjBV4zwEYTyjGu13+8DfO88t/XqPw8V/i+NogXgIyaYON5Qqf/sKccNWj4Gsa2AMs1USFylACqwuMKKsc93Do0W8WqMQRfPj5KJKwo+M3o3OLG4jF5/lXpNKxdB7yJSgMQc4WXoEeCple04R3gSbXVleiJqbpIlzRCaFnQFeXAETpYHchZUVk854EGuOawXDpLZw/n2FzvXXJddhA5tFUDkamMjR7HZJ2lqmde0nnini+jqln0Q2TidEC+u0ZfuWXfon/+mu/yTvf/W4O33YrzeoGjz/5BKVMCT3Q2VivsHB6lqW1BiNjN+F7cv+DVo+0ncXxE+TMMW6fnmDDPcDkxGk6yUUayVXyhSyjOydRKUWoC1nAMEx8dHTfxtWhqKTKVq9CuwlOFVImmEWBwmZSkLRE/ENpkPThzW9LEswnOXToPXz0B3+Ub3nvA9gJ65IoI03T0Iw0I9MHGZ7ay9jMTtx2nazpYwUtNk99haFD78VIj736gRDWMVcXGVEp3Fhu7DLssoON1y6YdS1NB3UQuJ3M3jvZdf/H8Ds1MMdpLd4qUi9ft6tqIQpfU2+oYEPTYHLMpuW5dIOXrg63Tw+zcPwI62u1l214dcNZ/GBe4pgv9tweP3fmWh3NX3qzxuGxWUhbGral9THrEcG7HDUP21gGZ5Gt+6I6EHSguBtu/QAsnQBiSJWHZJNfDglajLaLibJE/68N/D/mfkTSraENn/wTxXD9j/h73zlGaejn6csCBbDxPIRFyDuc+NSn+NSXVvl8Q4oncaJ7U8GqbM1OpKKxC6lWTGjQ02AxhBMtKKWksrAa9sW0NKS6kQdmkCawTh1SVQkIqshnChUwdwJp4WbgCuHb9SWDGrjCRVGeYMBdVzL1QYT1931RIIKouZjTJ/UmC5LppioBiYtUTdodqLYU4bqDnVin1q6y30uTnx7CDi2mR3pMLMFc4w3oT8ZOsA5ry/QngpCXlmoGA4jtubm4I29cebPpj9kk/UqFTZ9v06MvdxxBsQwNCmPS86I8JhnvXTthfAIefxyqc+BtwnwPCtNweFyqUSN7R/ipf7fO85sSE9G5UP7SMOBHf/RNnD1r0Hnusa2q2zoybrtAzoG3pSDw4agH65Y0wgxriDhPXCmLA6oggnQPKnUpRPHGiR7T+Fl9ww2MV2drC4An3IqgIOpvdkIqHUEgQIfAlwAjkQAjIX1xPHOgiGWA7kVD0xKOVlwdsW2YzOVItW5j+dQilWp7qz/HdguAZgBoOslMjkJhhE7PZaO+SacbcPL0HKZdYnxkmkK2iJnMcujwXfz6f/5NHnn4K7zn7Q8wOlLkuedeRHchmzJZODfHZschMCxmz82ytrLMjnA3vu9y5vhRkimLjJulVakyNXETh6Zvoa1XWOzN0tE3KFk2WrZNYK8Thk06XQdf62C4PZp+KNK/SirBGxuikpYMwdwUdT2/C+GUKKClUmBnDW56+x1MmAd4653fx9vuvhvbMi/TD9fQtASl4ZtgWKG8JrTOUlAe2mvEM6neMfzKInktiasuP5F/RU39btxnSgFzaFaXxHCK5Mw01dY0s4+64O9Glrqv29W0UIUEgdfPXL0B7O7bZ/jJv/s2/sHPf4p6pXbBe0ld48EP3MOxE0/y4uk5IcJdS4szhlfjoXqFfcTSgm94u0EyiJ1FhCQZehFGii2Hq1CCN98FX/oCbGl9b7PUCJytwPkn6J+PTz+jHJke9SQI4z4Ak/Tx9nO8VH63haTtByvbq3D+IXgm36HxIZdSmYHoMxBvrvgWvNOf4snPv8gTDSku5BH4iY8kf+PD6tL3PUNgpwU3D4v/126L8z86CeloAdXdvmJpF4mBpojOaRn8ppx2RcF0BYoLSDM/Qw5CM6Nr4EtW0OhKgOL74uSEAbQj8rwVZUxDX/ghoddP1juOqMZkTChF8qoB0A7kcjUD2ffmGZ/QbLI3YaLnNPbtgnea8BdPCOfjDWHx3JJBTjQNrS4SzMY69N1ouxx9lZaYNK7R75URyw/HErE6UI72X4lej3tnNPufNYOoX4AmkJHJHbBjJ4xNiFT+uVlprDf7WCRhi8DId+yBD3wAdozBkYfhyUV4+Mw6X9289KOfy0G13eGpZ2cBCRJySDXt40gVqwt0evBtaRj24JgPzwb0ZazjZEGKC6s5OUTBKuasNAXmsutWOHmUPiHJ4S+9tdYhl4COLZXDpAmlnJCb/VCSAW5PpqeMEZG/fegEgAmeLZc1oQQSaaRAL4GdlDGgJaDR0llpNpg9eYL19fbL9vISjr+i03Ox7C6ZQoF6o8UXP/8IupHkxWPn2LV7D7ccfhPVapuTR09DoNNoNXnqq4+RT1kMl4psbK5xcrNCT0UxuB8wNTVFq9agu97ESiRoVzbZqG0SYHN+YY6e51DbyDBzcC+HcjvxcpusbDxHwvYIEy6huY6W3KDjdakHDp1eSLMFtTqsrUBtQWCing5mEwhkbirYwlGyQihk0pSH9nLPN/0Ad+y+j7T9apsoa6LEUdyPnR2RUsprMC25F2tkiF7QwWlvXvbnrqip341LvVbAIxj6v+KmycOMzkxiAcst6D5hveG6wb6c6YjyQtfjIlG/SV8A/dqCSTVCdO2NBVg19YCeW8FxXxqN333LLkolj8WTCzR69Wvv017tjJjNxRc8TWRG1Y2sqHO5FsMX4uY7r5fFuv/brqeZgB0H4NFHRQ3nUra2AuFpUcF5yX4HzN4lXZJDB5HbVYhnPsHFg/yoQeAFx+XIZ1bWXDbWX2RmRxdtq7lfCYbvQfkV5v73r3P0xQZ54KYCVHXo1cRhjNE0IH7Xi0gxJQdMefAmGyZvgc5ZCJqgF0SdJbkIuzYFkhKvH7FSaiKCpiQtcTQqCjYdyG0IQZIIkpxMgd8BLw6sfPl/GEgQbSVE+cYLJNDwA5H9bQKp6DronlRHtK6oYWWi82nSl12P1VBDD5JzisxYi/zuLGXb5PBOn/OL0Dwtsr83vMWZwVgRKn5mOkhgEEOaAvpSnnFGP4YJpekTx0N5LZGJYHoN5EZ6yHLj0ZfItYCeXMdcUgivt90Mhw5AowHHTsBTzwtJ2LRgz24o5OHw7bD7IHhJOHYcZp+ClQ3wGlDbhP0z8t7iqqi96cgYMGxotOF//MbRLbWoAPhMdNguMk5vBfYoeKELfwE0Ati1WxpV9ixkAMQqXJ3oXFJInjKSqy5OQG0W9u0w+L5v2M2/3jxNZSXaNn7e48DsjT7XXsTqbUhWhZth6fLsmhGUyVcC09N8MD3hJTjdaElSfXlqPSn+S9qW/ai0/JCQxMKYNcNznznJCy8cw7tYb4sBMzQwTEW71cW3Wmj5IYZzJZ782lGOHZslYdscPHSIgwduZ+HsAppS9JwWeStByjJI6Qk21zdpdDq0VL+gp6F4+JmvcujAzWSyCYZyWXzfoNbo4QdtglDR7rbRDZvVs+uMTA2T0scZSXSYP/UoyZtMwnyAH9ZoOR6bDRnL587DyhzU5kCdAaqgenLtqqPQrkCvIfCp0ek0o0O3sm/oPdw79na019j1W9M0ICUY4Ndovl9hrXmGhcYym3M97r7Mz10RjOrGxq4G+M6XOfmFf8HQHf+S7HQZ3XdBnX69D+yqmoGovHRs2IxkHsUs4DuEu6L+X+DoJfZwdUzXdUzjhtMXeFl77vgyX/tKk07nwmCjlLf55g/dg67VCbQW3d51SFPF9y2WeXutTsylAmoNcSx6vPHJjDGE6Aa1zBAsnIZmA9DEob5IXEswv+2FuOnQNnMdgV0BsmovIcFG3IDoYnYxJ0cDlVD49iqKBTRK0RtJ0G9CLf0W8187Q9CD23KQGYVORzLAbks4tJHgE7noMJ5D0Cc7FRyqwJ2jkNsJzkkIV6Ome03IaLBTg1Oh7GM0PsQI+mRkobMOywrmAlGqysQOXyAOqWVJp3IrIZ3UHUcCmFYPLBsyGXGE2q58bJM+kifmAodKIB4ZBGOeUP3HLr6ccUuGjQ5sLvukywrLTBB2Q6xmSAYJUG54PzLc9ruO3LgU4lTHlbIYBhX3ySjQr3oFCL6tFv2tS9O1Cywmj/cEo18ehomdOpNDIUNZyCbg9tsEUnP2NBx9RqBTd+2DO++y2b3LYmhEUVlrc+IkzD0JS4YEi5sVWO9K74LsTiG617rgl8EsSZ+VMIThafld64DZhvZ5QF2IBGshAfJzCHdAIducmY3OYU90HQr01bTK0XVzQGuC5YJ/Vs53lw1/+7vvQ5u6mX/y9/8Ep04/IrcGrotF1KGQCwfNYFX7Faq1OvBd311idi7ksUfrl97wOpgRQm1deC8pU/rauG0JNhJRvwWvBcoRVbxeR5r7oSDsgnJB1yQx4Foim+uGAof0NUhrELaGmD0xz9pyl84rLMMJDXRNZ3W1R3vJ5507DlIolEhnSrTbHdrtNo9+5Sscf/4EWStNvVulkM7S7dXpuV1MrUet1mEzCC8YLwpY3Vzn13771/naE1/m/jvuZGZ6klalhuO2GZ8exw1h6fw5wuA8YXgrqXaO1OgE49kH6FafoeN2yI00mXOXWVqBM2fkGeieRAbjPDJZ6aBKyFxnQHMVNlIad07t574dP86h8jehod9QFAaFwUavw1rosti+/Nnwsr3FGOV7Y5tPfe2/8dC/8yg/8DN0nn5EWDhvUIshrzFcNuanzdVECjCtQVuXyRZ1M/DPoDwDrSfBubbBhlKKMAxv9AgU6I/boZTi1CmH1gA5vFw0+A///m/yXR/7SU5/4ZfZvXs3dmqRV+paetXsauETLxVIhBBefqXz63a5tv2eGZKhakfqIXYa7vl2ePTj4HVe8ukL7RJDLTg38D2D9/AKJZ0xZJ44WTnBHf7D2NYt9J+KMSiNUB7ROTwJZ9ahviHZxmwREl3IB+JzriNrZMwtdoAngXQD6k/C+/cJKRRLgoFOIMPyrLzEGjJdJBDSqOlJMJHWBT9/HjiwGUEsDCAj0IxOR+QzKzXZb0h0rYkCkwgLXY8uVxb53tjf05Btvei7m5EzGqOHtGi7BoIMKgVw7GTIRr2KWYKaBrtT0Ip2XOtA740ATYwb4nnICccwqZhjEb8ew6ta0TZFLiSQI9trUYZf+eI0miHsHhYY0/gIfMs3wpvenOaJZ9o88Zzi5Ck4tyCBYioJpQl4820ab75thFsO7WFpcY0/+P1znDwmMJxGF4405TC6bREMKIxAz4Sugk5UUTSGpCnc0JiQvRtrokblbAq8x+QlXWW4pJseIoP7QPShBqCDkYZgUa6H5sDt0xozmuLPz0DpbMDmH5zknvu+gbT1SZxQ9ZM5CSSijZtQbifjI+dAmn7HYi4dc2Q1+J73fgiVv4nveuYX6PZev4xRogeNjii/9SowMgS5PBTKkCkIX6rbBasHRkd6ENkpuX7dmvCnXAXVAFQWMkXpyRNa4JiQHIel9TVOPLtCbf2Vny7NgMDUafZC1lsuX3noGRw3QaPWRNM0VCR+Um1uoCWSHJjaSSFt8vRsk9V6gyX6tyXSjtgyhWKpXmH1ia+wvniOD7z7fVhaAi9sU1v3WdhosL5eJ58vkcln6J4LSWVzHLzjZsrqneSCIVaDGu21FRbOKk4dge6LCEdvhX5yMIYjatB+TJLI+8bL3Lbz+zlY+CYsXnsl4mqbYY7STqQ41Q45fwXaS1c9Nb39oUlyvVEPAZ3138L78ilUdZZLpwBvfIvHY/wQxPxPEHJja+BvtJvRh3cTDmnoqb2Ei1ydjPklLAgCHM8ldQMWNzQNxkc0bMtgvDDGm2ZGqa6eYnxa47EzrQvG555dZd7/4PsxjE2G8mXs7E3cdss8n/3C6YtDYaLIz8iIQ+TGKi6v+mCj35eaW6+Ap1AqJjCVyXr9lbzbr9urNdOE+96l8+LzIZur/dcz46LSQg/aNcjfBE/9KbwmIbyr5c024OwT8Pv7uty5/7McnvheJI0LkEXLHObAAyOszJ3nS3MCeRgZl2AgkRH4S52+A5dFksE300fanApg56xULtI2EMp8tRz9lJCh3EAS7G5dYBhB5PgOIfOcp6T3BjqQFPif5wmJ1NRhze/7xCDEcj+Qv9tI5cGij3Sz6QcTCaCky4pwLpTgSR/YNiVfKeR4H2pLkF4TbPnIBNx3CHbbMLsCL85B7UbF6Q/OGSn63XMHHd4Y6tOin333EK+rRh9ONWAKSNqw4xa4ea/GLdMGB/f7PPIwLC3DJ/4M/t2vtqg0wLUhW4DJcdh9K9z/VkinwQp0zq42+MyXnmF93WV9XdHoQacJ59ahakJgQ3JEjkUlJLhzOqDnJADJlCPOuQ2tTXAq0bllBZo4noEzL9fQa5ulAO9U1LAuGgyqCvkk3P++aX7gW9/M3Ycy/K+f/yx/dGadP2zBs//pOR6cXSCdVVTbyIMQk+XjwC6+nq6M9VxSqgO9XvRs2WD6cHC3xl33JPn8Z7rMbULd71dmmgp+8p98grt2DTGWDjj3Ogpr1tai+cyExbqozpXz0sE9m5HqVhDIGNGaUm0czkv1MdGGnivn67WhvgIVPVr+krKeHgxN1s7B5trlJfpME0ItxDRksC8s1qj82ZexDBO1rQOjE7gE3XWObrSp9ZytNjMu/Qb1gWbgRHL+8aPtA+uNOovzc0xOTaLpLrW6x9rKMrlCiUIxy/zcCXquj2FmaFRqfPCb38vePW+lVX2OxReOcvaYS/cEUtGIlTa22wYUDiT49h++l295zw/z3sPfSsK48QINAE3PMbVrN/boIwQblw81uCquYpzcTiNJkbh8HRMDFXIzbfrP4LWNzwO8jYev6TdcD4sTU3EGLq5uZLV+Zm/LkhrWpImTDgmbuWuedjMti1TyNTSGebV2Gc63UvD2+2/iH/30N5F3crzw+LM8//g5mmGH47MXfljggUnQpiiN7Cbh2AyVR9H1S8DvIgy0PQqZLKwf5+KZ6atFZr7MfSRtnR/5roMU/T08c+Q4zy+c5fiSc+NnX99AlkzCyCj8039k8RM/5lwQbChbNONjebZOA9pVrl+WZXtqbpv1qvDQV+CRe5/j4AeXMLQ42NDQtAKpPaPM7Fjk/gPgZhOo0QSLpxu4mszZNuJLjSBhyi3ADuBWTdSozofwsCvok5wr7+XZEu/hPBKkxLxlMyInm0kYWoMNV77HUdKcTxnSrE+3Rd3GcSUwaSEwLiUf34JLxcirmGOfok87iGH0XaRr74gN9U5f9bSFFI4qSNIxjM5zhxJSejKAsR6UMiLb2c7Aekr6A3g3CocjLn/HcJ5BQnc0Low02IWIixMFGJoVaRzEE0XAVvCRzElm2s6KetSdd8Hd9ycplAyWTnY4ewT+9D/CmdOACeOj8Ja74fnjUsXKjkPFhe4SmKdErWhxPmDlWECtIiR/z5XrnctCcQZIQMOFdFb6xzQqctyjM8JxrbclmeS6wvsIuvQhUGvgZmDeFdKxF0aPX8BFn41symZodJz7RgP2HzBZq4S03Q5mos17v3WG6YPfwuGDD7J69jw//Q9+jj///PpWk+IX1nu88D8XpdKSlwoLZSSadujzYBBC+Y9/T55vezDL6ueXUV2DdspGq3XYOKvYkYWVdZivweo2OKwCTi23Ob3cJv86+56pDEL+z8s5+Y7wZuobUD8PzU3hUeTzkIiqlUkfClmwHSj4ApvyfFG0Cg3pxeP0IJ+GtKOzvrBJq3lhFH+ppdRzBZqVtvtbdHs9LNvCNnScAVJrWgs5tb5JK7hQcG1QA8FGx7nIQNnsdllYXcRxqxSKeQrFMQq5HJ1Om1VnnnanQ74wxurqMmsbDQIN3uXfxeTuD7EncYrj4bMsLSrUOhcdh1oCxg8nePO7D/CzP/hL3FS880pvzXU1XTOYHL2Fm++YxDW2Y4Ivba862Ihvr4Y8Y4P9fyI47pZKXh5ZYPLR9pvIAxsHHfH8GNBXdRxYt7ee3VcyPdpPDPm63M9tPy+4/iXyOGAbPF6fl0LxDWTBfInCkGHg6KANa1ibk+L/XsOT0NEwXw8M1fZzusRMNLnrFm6794N4557m6FfPo6s6rqtouGxlTfEhnU6Ry4yjMYwxcwf+mS75VEJgHNu/J64UBdKa4GXrBzEu95UG4FW6R/t3m3z0A/dgVsd5+6FRHnoxxz/7rSdwbhRn6C+Bfc/3Jzl1usfyuiN47wHbPh7a57h+k4iJePfz9J+H7YuaAbkSZPMmShkDmFgleITpMfbeV8YaSXHeSTPb7tHsNlhpyabT0VdMIYnvvUjwUEzAbbtgdQOeqsAziMOeBe6PPldE2oGEyHy2CwkcNEuCCgxx/JNAzQO9CkqDISWZ01QKNhviyERIni0Y1KAKcJycWUICkZiDESLOr4NklqcTsKMn+P0Wct9qSFVkdWC/Y9HngxBWV2AoCeEQ5FNwz22wswKrrzdKNyZ7D+KcY0c3Sf+CAVkbZkbgVCNytpQ4a6W8KDVZUWA5PSXE7sI46FnYfwB27TE4fRR+77d6PPuUwNpuOewzNg3JLBQKcMutGrWaIjcGswvQWhXHNOXDxnPC3VAebHoSSNhlyI2JCEC+ANW6wOtyeWkOl7XBDSQz7hMpnm30A6UtnYMYClYEUpAPpXnmhgMNH8IUcoNdMB1I9XT+xg9/jHfe8w6+4ds+iqE/jLbyDL1jG3zlyeO4vWO85cC9pMpFPv/xX+VjP/FHtL1LRPJKBArw6Ee6WWSNqcPNu+BHPzzC9/3kD5EfOkjzmb9N63STz37Jp+1ApgZf1RVfcLsvm4BVQP11BmmM7RLFsMwQ5DLgdqHbkgpgdRnqJyGswloKcqMwOi3wqJ4n6mSGBpqCIQ12ZCCVBmzohpDIww4rz7NrHp4rVQs/EH6IoQn8art1AqjVFe62ZF/b8RhNWlQHuBgb/sVdhRjF3AN66uJZ+noQ8NkTJ5mxTe7aP00+mwO3SXuzTmDabDaazC9VqNZa6HoSK5vi8MId5BI3cVf+Yyym21RnTjK/AuHawI4jcYzd707zIz/8vXzbu3+cnflDV35jrruZlEof4M13PMb65uVPgK862DDok8ZjGCj0s0pW9LtIX2+9SH8ejJX1tmBB9BcNhcyRBpK9rwM1TBS7kGlnEG3XL39b0QmVos/GGN54QRlUtIv9zVjG0WCLr4NCFqY4gGLguOM5fbvoy2uxiRQUMxJAOK40N6rzUj81XjeaF3UgO2BBYgiUmb1KR3Zpu2H4Oxe5CZYBN41l0DQLQ88zqlLYKY3nT4R9Kb1oUFq2gZU0QNPRyKE1e+wslsmkkrRjInkSjLJkcdQ6lw4gBnskXGcFtHffM4PfaVMuumiWR3rZ/HpV4yrbl77scvP98L8/IWpTyRHpqntRu54XP6Q/YVziwTTzcMtbNG679RZMvTDwjhKZpr03Yc6tkllp4XdcTpxYYm4dFgNJGlvAqC4V1aNK5qIHdai5wl84dACGjsNwFZ5UcAb4EySwuAmZZzcRh76KZK/zLXFY1h15HEeRObDRlkfJ1iCrxBlNRryN4Qj3XUGSyHECX6Nf3Yj6sm0pXwXRd68D6wrsunRFL0Xb55FKRjfaphId45novQSw6kDprHQ6n9qTwM5b5LNdDP11jubjjN/g4hXPPdF8ZKgIJdWAuaMCkxtPC0E3nYGJESgPa9x3j8bQSEhtE554Ap58GlZ68OQTkM0FaMBQGUZ2CCk/W5YKQm0NXngSvviQwkhAbgZ8A7w1yE+CloONUzIG88OgT8D0jASbIcLn6PoRHE5Js73NuigbKV94QL6D3MhadH6xfG+W/oKtAUMiHNBDnFEVl9PKYNXhnv1Zfuxv/gjf8dF/hmmZ4DwPf/b3afyPo6ytBfzxM9KD4+f/4ywrpkWnG1460IjMb9F/7uIM4Yio033fgzp/6x+9HbPxImo1R/1zisQO4R8960DCh9/TtqEUXsGSeei9HkoFJuhpga8FOchPQMkRmOP5oxBuAB3wmlBty+G1qxEaIwVDBSGC60DJgEJ0zQIdDB/shkG76jI2kSCXNzk/1yF0LyJMMGCrdfWSyxAAK72XBg4ZLoSeG8ConSVhJVnt1OiFF1+046/vOD7NWpPnn3qO8ytraAmT3Tftxw98mm0HHIVpBlTOzXHsmSOUjDTT03dxeOS9nBg6z8r+riiftUArQHGfwXd/x728847v5N23fSdDybFINepGNw2NYcZHbmXf9JHL/tSrDjZizFtcQUgDw4jjH+Nfc0jSJYYBpaPXJ5BFJ04EgNz4OFER0hfIqCCDo6sfQkv+R7qdNvA/sPkDLEJSSGUF+kHEZPRaDyl715Qsmu1o3/lo3250HrGiZhzgxCV3Ozpe6MskxudscvUEfjZ78sVKE0nbLhefR14WyagFMhlkr4+fq6J/18uMPARt5MLoyElqkpRVMcYsskwmwfve91ZgJ/qwwdTOg6SWn2DDD/qbxTOIaaFZAdCGoI3p+GS0BKY+UNroQbD0Gg5eF9iVYUJnkBwWWyyfHUe3CV7aWOtlLJuEPRMjBD2Dntmip9VpBZ2X3J2f/snvYiSZ5hN/9AU22x1OnV+/ZNOkr9tLbfZ4SH43HH9EOFO92uV/dvxNUDsncKarbiF9AvklHkllQcdX1LsbqKIzEJN4oPUgMQGaR8rq4NWbnDntserIUEwg8/BzEQ/DR5zyc6FUOp4+I8HA2E3wrjXYswwnHHiaftJmg35bhhbSB8N1hXOxqfqwqLwhc6tpRBCsunA1UoZsk9FgXMl7C4ivGfNvV5AKRot+crlFn7O7imj0GQpup4/VjlE4ZWTtqMtV4RzyPYloXykXpufBMjVyZR1LNxk1r3NWYbuF9D3VeDGKb27ElNajl3KaNNQrZKXb88wu4VLs3CWd2s+cUTzxMMyelcTK0JhUNfJF4aetrkKtCo0anD8Fx0LQE5Aahvy4fE+mCL4NvazAZRpV6BjgFyAxCmEajADqEWkYpD+K40XKa4koCRkT2GMiDshNtKPzSiIDJibkxHK1NcmmO3ABtMzqwPvvg31Jg2/6wBCdY79E1jmHVpuFT71I/YWQFyvwuAtPOxBckP58ZUuOgtsQyV8UUIV0Cm6b0TA2KvAnx/AWHuW5F1rUF+BLbWmJEwJXIOgDwMxhkQb2r5OGSWxBAJ2WKIOtN6E0LX03PEsUqAYvl2rLfNcsRGOvBL0A8pnoM82oP44FegZKOR23lcUwuiQTXdpNjzB4ZZjipS5d/Hqc/1PRb4s+b39XschwuUw+mye3oHFicwPvZXwaD3B8n4X1FRZ7PkHPwzt+CqUbYKTougEjo2MUCwXmZ0+RTSW4LfUm3vm27+Bzxz9PKnuC4HYJoN///hG+9Z0/yHsOfQ8z6VvQ3whKO1smtSAdA6OXfMWtY7viYCN2ysfoBxEFJJCIfcB4TohLVEPR52KlvRgDnKbfEyieO7bz2AJkYZueeDd73vtWnjim0zydZXrz0+RoUkQWlgb9PkVxAHFnAYbGoe7B4qZkpzqeELCWo23y0W+TgcoBMsfFi0xcmfG5sJlqjEeOr8OrXXYcJT1BXpPpgA2+pWHZBt7LtiJ+7RaEAV7gYF4ngngwqMATn5YBVk4mLjVw8ZN2hulDDwIltGSGoFgkm0mSy8QMwEFLRXXwDpgp7GyWcqHEcFFj6VJZ6+0WR9SXutwhOCsv8/lBiGpMMLwCKxZMykWddtdlOG/TbPZYWKldMFGn0/CO93yYb3jwW/h7//AITz33MH/jR/85L77wRulUdgOYBWdORVlFuHwZXh2aPfBySMr8dbDkQbCH4djCOe4s10nbAyB9ZUOnil5bIWH22JhrovVgXBNSa0PB+Z447jbwdmSefYFoLgzgmVW4rwiZGdiTA+84rHnC1aggsdBJZN4dBSYCGHXAHoLahhC/PYRIm1ICo0ibQFcgPtBfG0ykElElasxHX7q2Qp/fnInei2G8MaT+eeSR24esObF/aEXHtk5fMGZAuA6A1TZUXnQ4UPSYnEiyd2roNd6ZV2kmF4dp6mAlpQ+ClQYzDQVDZEL3TEplYucuOH9eoGFPPw3PHYG5s4rRUXjXezJ8x/dorK62ePaM3Lf5Oel4XFmXe6UpyE7IuqUZkB+VfeZy0HVgvQ69DbBnJCtt2BDqcj1rm6B6Ao3zQ4FVeV3QkmAOCQFZxUGGizgGLeTGdRBnI5buDemXsXLIjY59tjjwijDbBw7Cyil4/GSdB82fozNr8A13g50I8JVJIwj5w7oEyK8mgWgCroE4NVGVKezIddDWfbw/XOOzz8OvufDUWh+qZ72K7zr56Kv40FWwpdPS5TqwgBKs1SV47R0F/wwvcYBUK6r6mLDZlgpYqghDJcjo4l8VC3Lr0o6i0arSbnXZWO/R6YG6yu5LHJOngbKuc8vePXghLKyu0vM9dE0TwuclLGnBer3NfM+nBRhK4ZsmUyMjbNTbLLprNBd67JjZQSqZZHlxCU1ZTLb28q7bPoJu/x7zxjyuBffdO8Z33vm3yGozaDcOTuQyzUExT3tjjdXK9hny0nZFrqKFVCUy9KsQBuLs5+h3m43748QVhxh+FJMMq9Hv4eh1M1Il6ITyHTkNyiqqSNrQs4bZ9Y5v4lv/vs6uJY2v/NooE5+0OaCaaEoqAhVNouAYvlWyJIpuVgSatKsAO7KwtgmLvhxbin5D1fhcBjXXY/6Jg8x3Lfp8khg6lonej5PTm/QbYMVE+StJXMSfvdIJT8+USB6Cbgkso4zMytfOswkJ8dW1pfkPmhYpjVxgPrgXOcXJIR1dywNJCAN69Tq5oTJGIpZlGTSFrGR50LP4YYidsDhw0OD5U9sPgn5WrUt/cr1cTN120Oh2nHVctYm/xxt4/WUsl0/S6/osbS5TTOZpt1zB5Q/Yrbfs5h0P3IGm25jZPIVynVL58pUkvm6AD82Iq5HISGYxvJxLqCL9/0vNtjmufR/OJJyagz/dXOdDuxqkR2ALYBQuoR7+Io2vbRAkdHotxV27YIcPJ9akP0WRvrpTmr6zv4xkaJ/34MQx+MYJmNkLY0Oi2vQkwtsYRpz4U0jFoInMy7ojuOUV5GeyJ4+T7UkmFU/m0BiGtYw8erG66Br96vegMFzMGy4h61OcEIovdewblZF5/Xi0/7g6fqlUjYP0DNncDLnZcUhor9MzFM89UUYs7UjFxlSQU8LDmJiCfftg7z5Ri+p0wHNgZVNkZlNFGJkW6No7D8Gdd9qEyuKZp5t89atwdhk6FmimKJIpBbmiVDrsknRdz+Qg8GC9GgVnLYFBpXIR5l4HtwJY0rCMNmRKwhVRCswU+A3QPGn4NnhOWxhqGxlAXvRToo+PqyEDoYDMpzFJJ4YetGWbk08KoXhnzsDrFsjt9Pna2QYPfNso3uQoZ556gcfnB/tWXYbF87kBrRp9SIcrx6xa0H0hoLH8GI+fgX/YlMraG7WY7DxPH3c4JM0VHR+Rc914mQ9GUXuYgLYmxH7NBa0u9394Bibriqy2Sa0ihPGrFWjEyNLB29pD+oStrq6w2WxzvtWhHbxcTUNsw4Oq52/5gFlNYyiZoFQqEKIz0unQc3yWlpekdKhpnJ+fZ1d9g1vvv40PvKnLMefjFG/yuPfQm0mSeQMGGqDUWSorn+a5F57gyaMLfPQyP3dFwYaOPNMjyMSeQUiDOjKhx1WLUJOJL6MNyBkCOVMwnqWelE5zGZmQlALbhm4PVhtyUBOadJV1QsjMvJedb38TnZTG9CQc3p/Bs3NM9DZIabBpC6M/4UHWkcnRMqUhlaZJFO1uilRh24cZQ8hwgX/hIIwrt9urFHHGLE0/CLHoy5Z3kPlwFFFhmCjCQhOWAzijRL7uUmbQrwBNG3DHDlHpOLcOS44c06Bfe6m9DE3fzgMfgUfPwEYnZq+IDXKbL3UMl+HTXmCmbmCbryYv8+rsAu7WKxRt3vPeHZiWKGWpYAO/20RL2tRbF6N0a6Dq4CfAH6FTqaBpipFCkouqswdcCPy82FwRl/bjBTDO0kV/G5pkbi1bxqDjQBhj7uOH6ArKZHtvUiyd32AiNYHTDVk4v0q1cuGxZ3NDZLJTcuBKI5NyMbTXadmLCVMxDvuNYvGDCgzthsY6tFdf9hP9zzlcWlGgzTUnQbUfgbNPQ+6eMCJbxkuwB91V/BdXOPKlkOH9IUEPimnJgi+vytxcCGHRkUrFQ8gacDg6reOyFx4LoLcI390TOFTsH04g3I1HkNsdJ6QqTcCVbs5nkQr4rmifhVAy4LECaywg0kGOwSPiUiDzYyy8lKHP/1NIkJRHnmQ92i5SVUVDoFgbwGkk2IjnwZebC0MkcHqiHdA7XrmyG/FazBQZXpAEnZ0Gvw3DJdibhNGE9C1wHGjUpf+JWpbgbqMHwyMwNCVqQbUqFIowtQOOHIPF8/Dof3M4dlz6EHVd4VSYabDz0lfDsmHHLqlKVB1psmhnYGNdukPXO1BvgtIleOicRRbNFSSqGwKSovDlrQFtCEuyRodtWa+3eBjx89JBIsQYExdCca80k+ucid5Xcm22Frm44tEGAoEWu1Fl/O69KebNGf71bz+LocH/06vypaNNPnEaVi53AYySQXoasrtFljedgsYcBE225n8vAZ/7Asw6Pv+fL8qnr8V0XZpTXktJ+5e1mAQbP8Q+knE4z4XzeEzoHSTz2sA0pMYlOdw7AzgQ9GCxKUGpmYP19UiC+Cra9tuqAyjFZr3BSrtNK7y8G+/RR7UooKUUpzc2UJbBWqND2/foBi4vnjtGJp1heGSUudUVzj3yZXLlUW6/54Pcf98DTEwkKCdvwtLKL/+FN6ApQmZXnuMzf/6nPPvESR57/PJVC64o2Cgb/XJ0Brn4RWQuACjYUnZ3ItJQObqWnY48KHaU/le2TDC6Llrcmi6RbC2EVU2c8xjjO+eb3HrXt1MsFzh1Gu66BSbel+HpT+5n4/hZJoG0AYkklJJQtMFLQbsnONNkSr6r15WBMpISlYsgEL3rC/g4QURSi8JhO8JVdVzI+v1ApBNduC59xav4uiQT0AoERz8SwIoPzW3JdBOZT2PeSy/aV86Ae2+C0TFYW4Evfg3m2vJ9CU2e4aaKMM+DO9T2kt31zbxtD3RnFX+xdprYUbbpJ4LqwA4dLBV1wlX9QKSGvKbTT6rHC3Z8iS5MyptYW/Wc62DxZBZ7DM6lN739LQ9gGCbgENRmWV6cY1l12di8mGcbg/8UeHXcdhvX8AkvNgENeiFWdExxhi2eIOOqRAq5cfHCF7LlbAZeFGRc7HBeheJIKqloNDrkvA7nZheorNfBCy+IyXZMxwGoD6FOwg/Yv2uCL395/sq/8HIsJkDFKeVBG7gWF7Xtqagb0NbnICxcwQdeLgEe36Qs4GiSnbnatii/KhmPdidmhZlIimSE0LU5W4OyD7kUNNvScO/gMLQ9gUQ5DswSOa9IvDiBEMCXkDnpEQXJDfiWLLwvBae78igs0X8MFhFn3TVk3p8NxV/ZST8QCZA5NUDWGKLvG0KCggVkvornLA2Z56J6JlESfasKMhjHx6jHWBTkKa6MoBtbBzjiX7+BWijJWpnJSC+T6Z1w970wlAP/OSjr0GrByiqMZKDagNkzwnVLT8FwSmN+TbH4FJyfg8qGBBSNljRji4XJsiXIhFAaATMj+5zZKWtoOi3reC4pmer1NnQNqWi4y6DZUeKkgswBcaIlzuQh/RgyWWg5Eak4QZ8o2aRP4IyfrxKyuEakyaAimXGSyOCKVQdq9EmW8ffaYJXA6cr3H13XePwTR5hzZMz89T/y6Fw2HlLMSoufkNgrUtjtFlRXIxneCLunJSWh+cfr8j1XIyS1ElElIcErSCFeI4txiQnQShF0OUoYbFkBCgfl/OuLoM6yRdC3d0BxBJQJa10I15FJwYfNTWnm6V+H5JMC1oKQoNm64phtu9thhopOxwE/oO10hc/mNDm7eI5yeYid09PMrS5T2Vzn8KGPsXvPMBD7ndszTCpCcUlEp2mD7vmNUQHxVZuHn/sMv/Lfj7BxLqS29sqfie2Kgo3JdB9alDchDGE6BWVBq5DNRVmPigyaZEJec3Pyfsy5TSQke69pUCzCmWU4swIJBWNJUS6gK9kVdJ/P/eEvYDxeJ3tzmcfGYPGLz9E+8xDvBfIaJFzJrKR8sF0hH6KBYQm+NGFBaVwWxnpdfopFGBuViRQQX7MXZV08mdSzWfmd6gg5UFkRNcaNfpTMj3FZrQcs9wSfXUEWvMF7kdBgXxb2pWBnGs7OQUsNLIo+1JahlIaRnKhUddpQtGDvTtH5X6/A8gbMeZINdKxhjF3/kIVzk/zuL0Lv/DpB7fcAFw2Zr/PIxPe995l8+PYEiQ1FvalwfQO36bCw4jPXhGUn4igrqLqi+R27qC797KBgTWM80XWyONMfO++XsJtuynD3ve+OHuYWYWWJduBCOnHRJHoy6YNqgiaTQBCE9DyH3XuLlEbWqF6KtxHFJxdFZW0nHsV2pfSIuALwMguLZkK5ZJJ0M5imzWalgeMoxofzWGZVFidgcz2ukemgD9FrFMnYI4he6kXM5qXXOU4vX3AAXDwwiCEPV+KLxQHcGwBn4MfErqtpXRi9b4qNZ1YI29eGeOw54IdxRKsBabCmqC1pzDlwjwG1GlRasFKBN81INjXjQKsj6lA1xMeIKwzTwH5knjgCfByBQ33jDHx4Ds4E8CgDYh/IkHaR6rbniPzlGlIlKSJzTYl+rwwrSkJ59EVI2vSRhlVk/k3Q11mYp9/pPA5e4iTZk/QTKa8lXLiePt833AyVTZiZgZ3TkLIh3BQM/+IJGC7A0ARMHBYJ2WIImy/CkTqsPQXe55Rkxlv0cc0+W+UcIwPlCekc7/Wg0QOtI2v1ZgumJqXvhtOTiketCrUFQI+g7oGskVst3F1RoiIPqgNaCLovSm56GlJ5WW/dODABtjo/9hC2/jj9eWgcaEtgEzuwFzSg6iEDQdEn6JjSWTy+yS8s9DHmild3/7wOkBSBiDPn6ZfUCsjg2inn2zWu7hThxFy+10mTIDENpAV+NzwOlRUhgW9ZEibu17njcAKv53CyqDjvgloBewomdkAiJVyf5BB0YsiZBYUZGBs2OX/CxzYlGde7yudpArahoylAKcYyWYrpHKc3KrTDK2fbK8A0dXKFJHbSZt11aPg+OhpO12Hh/DxaIsHkzAzJYimSur1U0KAQcLpD19sgbY2iQodQOajQQsPAtOKZ7/ULPNYqa/yX//pl5s6HOOtcPm+RKww2SpY4vAULrGEpHw4FUMpJ4GCakg3LT0Sl0VAUeAJDlEd0XX5SaXAdqFZhpQ7LNSim4OZRgZi0WlDTpEGPoUF3/Tk48TdondARnq1kbXcm4HAWElnwDMh3INWSchypCPOLSOvpSr4TB1RXEogZQxx7VASXz0q5OAxkQgzC6JiDqFt0NB4ztkj7KSVzXtOHiifzXh7Bnp5Nanyloxgcw/vy8MG9cOsYdBciRoUGKU16Z6QUrM0KXnlkRLgrM6bIAi4vgm3KxJ8LYQaY03QKb/5+3vTD382zn7B4/Hfn0Z1/Thh+fgtbnYm++55p+LGf2MdorgDnOxgEGLaNpkL8zTb1k0s0PR+zCJpm4vZ06tWQbhfa9YBqR7HpKNY3A+bbilYmQT+NdOPY1PRB9u1/O+ISdehUFmi229QDh85FqgY7dwGhD0EVjGFyxRKWrnHvPQfZ96UFHl+/xHJ0KQ9Foy9pFv+9/TOX6+HE8cHLWCoL6bRB2NHQ7SSdThNNmQwNm9g2W8FGKhl7/xpoOeqbebxOiGH0n5MLbPtEHy+o8UK//Zy228ud38sFKG8ku9pBUQDrTy6/prUkOQa9Vfr3axt/L502SVxQkPRQjQrnlnsc8XTuWzN4btVjM4TzGtwVijznSLLPwY1hnT0EITMHHADeg8BqP4lAqt68BjPRHHkU+bzUGyNxDUcSNgnVr0YcRZJCITLURonERqJq9yJ9KP4K/Q7gK0j1JBX9jES/15B5No+ImiQRvzaNVEdMBO61yI2P6Lt1F2ykhZex8KysZyEwPArGblhcEanRo+vw9Enpup4pwYYShSZGZF1WGpAAvSRwY9WQ3hXlaYFWbaxDuy6f37NLvlspQQv4SBa6swItHVRMnhlA5ZkZ8JPyt+qAVRZotQol4PCb0F5HnK88fQhUhv7NIdpvTK706Oe2WkiU0I7eH44+F29jglaEXAFaL8p6rvlynlfFFHIhlqO/Y7J+HNH6cmxhDKd9o81rl7DdNwvkMZsF0xa1scGkmrkDbr9jhD1TZSq187RosTwnksuZNExOAxlYaYLZQ0qiTRGJmLrV4EBpmpVjS5w95V71CocBHBwtsu+mnZhaEs/xKZaK9HoebuhzfGP9FZcseCnsfiNUqGoDK1R0I6JJgMLXQ8pDQ1i5PMlCmUM338LwcG77blFK4fkt/KCDpoc06pu0202GyjqaUjTqc3TqdbKZLEOlMRLFA7ye6riucujqPZwlrjjovaJgIwWUbdhZhl5GVAkKLXGWzUSEQ09ImdU0I5gSMvkohKDluaLzbRvSzVSFMFGQwahrsgCZCXATMkG4F/h6/dVdA3aUDSayAS1XJuB0IPwMlQKSUkkxTamkAPRMgSqNZiRwsEIJZoJQvteJHhzThFRCAqcwFBnG+JtVKJk+y5TjDUJRuLKReaYEtE1YNnRc1WdCJIARTd5POhJQ7UzDsCVNjFIpqFeFt6K3IEjIAm/YUOlIYOQHcu0KGRgzNebsW7j3h/8W93/UJrUR8tk/+xOy4f/Gw2UcSQxZ0bViE/7k/z4GnkgPDuehUEyQGs6TGUqi/BR79pjkrQACDW10HC1ZQqGj1XuQNCChUKpNR7NYM2+D6wmjukx74K1jyNVeg7BKrdGm2XZouz16F4kbhoc00E2Brhgp8mOT5O0h0nqWn/7hNj9x4ossrr2MVxkvsoPEFz96vUgfIpAQyILaRLyteAFK08d1xPjj2OLs4yC3YRtfJZ+DYiZNopMmX86g42EGJeyCRiph0ogy5Lt2Ru6b8oAGtmWya8Ykk5a+Li+xi0GfLrVYx+cYH1eE1nqJaQPvv5EX4MsQensFYZNLmuq9+lXWHIa3/UiWz/1CS7zt+BrHUD8FnYZPpzGLmoxK+UGV8MjXeOiFVZSr+MOzIY+FUVNWBafOwkxWsqoZ1Ycx5REfr4HwJiLRGd6PVDdOKVhoiS+o6KsNpgc+00Xm60z02Rr9wtYQfahTCal0bCDwq3q0jzryyGj0u4eDPHYusl6NR8e2HO0/ER1HFrlEtWj/ccByI1urI+Rapw3VddgxBcoQLkW7BWdXoDov8KR2CEOZCMUY4+t9QIfsDnGEA1/gSOUJSaq5nnAuum3B0I+Owfg4oAnk6uQRQIHTFKz9VoDgITcsBBJgFSGwo/EfStO/wIfOkig0benlJ6Ppbo1+ZaBAH34ZH3eWfl5rkMEfK7w4yI005PvJixTt+z8Inw2hcT6CRGcikvqrqbput3gfscVz6Cb9JmJ+dF160XH3eEPPe++4D06vCO91cS2qSA2UbhI5jdJQGr1g0nYCGkCqDBNDMDQi0sYrLix1JTnMQaAHI5Ow+2aDMYrY1tI1gVJpQEIL2DE+jusoAhfQfM4un6PWe2XIQTzktt++TqDo1F5av1qur3H63Ane/Pb38K73Pcjb33Uf6dR2jquUA2u1RVZqKyRMnc2VNdbPL1DZWCOZSHPo0H7Kk8O0Wk3SqTwJPFFX0OI08vWNPDa7DVYWamhlmXuuRDnzioKN8bxFOgFGR5zjTB6mhyTb3nYl0xJokIwqHZ4mDrmhCW+jVu1DmEplKOah3Y44HUqcfGUJHrXsQVklWKpfvLy134JRMyCfACMBri6kxnIARlpwgYYpMCjLEH8yZYERBTheVLnwXMnsBhFOOcavtptQcUS6L6+BmZTgw7KlymElpATc7kBag4kcjJsSHM37cMYJaA6MTNOAqSFIepIpzNowNiz8lWxWAqyECx1dAhnbhz1jsG9Srk+xDNVNudajYyaLxgE6wz/AHW/ZTbetce6FDXbkHmWn49LtShavZMoiYiI640efl4e8p4nmtae5JLUNkrqMmf0JySTmbRgeqWBndIJAI2XD5HSe9FgWfSRJOGJBOXHR+/J627d88/uRlaoBXpON1SqrG0262e5LlIM0IJ8NAT0auHmMHbsgnSTv1Pjw+28i+S9H+dlf/AQvnL7EUxUvWIMTpEZfz7mNLMIl0O2oV8jg3BZV27bwIoPzlk4f6K4jC+kofcWCUGAovfUWXs2galo02w1yuRyqY/K2vSX+6Ml1AsDxFf1cNIxM+Lzlvr0U/udxGs2LRRtXYNsf0UtlPOJr9QZecIFXrmhosO+eMqefqBJejrxNTICtv9KGL2/jd1k0ei5bAmshJHdpPPDPRnn4/16lewrwQHXiG+bD+vM0nz7GXCUgEyj+eDFgGfHjDgDHHHjYgfcm4c17dJorioWOIovApwL66oI1xG98c/TanyPwqhri7BeQ4GMN4X3sBCY96KgYRND3ydaQREkF8SFT9PsSxIGFQx8RFPPeamw9GlvVDIN+Xycz+v9Q9L4bfWaA0XTD2ucelrUglZQ5uhXKevTkC3BsKVqvkHUODapNCTTCKPlhZAVa7G9COg+dBmBJE0WnCVZeOAi7DkDChm4TThyX4KNdiRpYxsPZQJxqH8hJlSTcBNbkc1hIIGCBU434DLEQQszhiNVRLDBLoJeFb6libHKBfkv4ZPR93Wi/WfplMgcZcDHe1xHUxWd+D9rLoAURBCkEsygIgVBBb01ECC5qGv3O7HEGf3siKFYZiLgkW5i+bvR6ItpHzO3LR+dSirZfpy+B+Qaw23Ymsa0eL85DZwO8c1zQQNIJFEfOzjHc0Gg0A6rzMG3B4aL4hGd7sOmIX0gGKIrfU9wBwbCHZqxQGtclW3GVzQdeXGuy8akvYCdsml2HTMIi1GC11X3FuDPgyiqfLiEb7Srzc3MsLa2wsLrCyGga0zS2tlGAF7Yx0xaJToLZUyc4dfQYy+dnMZTOnt0HcHwNx4d8aZRUNo/yltFUANaoNCjZOrqYuXZtbefwbj7yA2/lyWcfIjA9FpcvP2K/omBjaqjA0IQ4xaoImSQMGQLVSPkQN9q0tOhZU4IxDUNx6N1uRIhuijpGoSglTqcDJCBRkM/qNiRN2JsYobW8+JIDPmRbfGAUxpOeKE0BQUxcQzS94wxZ4EfOtgFJXYIMFUqwYBgCnSKU0rLri0a558DZc9BYhV0jMJSXrrW9CG8aBJLoNxJQyoA7DEUTRpVkmJYacHLbJNYJhBA8mo1gAxlphpRJQTIizKf0fiXFTsDkDnm/WmOrJB0qSKUMHjnb5FNf+2W+ulLkLX/jg7Tn6jidF/E9uA3J7ilNGu5YJhSzMne3dFlse1GVqdOT0nbBlDL5OrDagec2A3oEW7LAQ09skDE30Gyo2eDeDv/qo1cyel6jDXoCMWxxm9N38CaDkdFDwsHw16HbZHNliWq1TivwX6pyocHoeIjSOmjpA+C2UM0m5CbQkjmsVpsPft/PMH7oTfxfP/Pv+erXVggulbGPFyWBwIMpYy1e5AM/6hXSlgqHlo7GuwXNZcRr2j5XDC5CcaywjWLh9qQhltZuc7p6mtHRERzXxTBNvum9d/H02c9ztuLhNOKySwLCJtl8gt2TO/iOD+/jV3796a8397vKtniycXGRge2mIZ705mv8Qh38sYAX/zj6zh6QhkPfrTOmJ/Ei1aykAWktgUwoddyjz/DYF55iqRHQ02ExGgdxce6rwNsnbT76tjSpfJHO42sce75NS76Su5BAIgDuBsoaTEWQp1i18AmEhL0TmYPmEejVLFALJLCIdQQyiO8Ywe23ZMgn6Vch8sg85dNPUNfpFxZj/zPmhyQReNcKfT8wFsBII0HMKjd2oAFwZCUqorZgJAtHNiV46HREhGQLpWlC6ESJDQS2HKqIa2AJCdzzIJ+WddO3wCoIr1EhybbVJdhcEXlTd50+NyK2ONqsAxqEcSRYRi5qfEPaUQbcQy5wHrRx4VKGIWBCekKavnVa4NXBGIGgRV8hIOZxGAPf79InTMbolLhqAoQ9aB4BUpDZJ0iK7oY0EvTr9LuQZ+gTLuOEzzoSJOTpa+FfrBo7CJeFvkSlSX+9igM0Gwk+JqP9tdiCnV1UEj0OpKBf/Y5JSXVenYTka7RSKsdwwmVXOmTVgpp/4RIcrMGJZ0M2JkQWvDknQbFpQ2kCznnSpHPrIRdEL3Uf6pbCLayTKITXTB/EUXC+56Hi7uLelUV5MVrucpbKhK4zOjHDzL793PKm27nz9v3YlnhTSik6TpXKxiqbtWVa7Qara0ucP3eOaqVCcWiUoeEhhkbGIWnQabbIGTbL82fJF4oUSnk0VUG3FWjZKzqH12or7jzlcp577r4ZT1+nNL/4yh+K7IqCjWI+y8yEjumEdG2pRliOQH0MQyoFKhTHWdMgMEVvPZmSsmy3KxOjhlQcOm1AiSOsQpG+i50eLYSgtbL1TGnAqA7vH9Z413AaO99gtANeCzLD4Nny4wRCbvN8qQKEoSho6KYcUyIhihlmhBPVdfm/pkMrwsf3WqIm9e7bYWJSIFebLSGsN+oCD0tYst9kCkIDjDbYLYngx1ORjN+2J+bEApxtQ9uWICBbBsuHZCiLRtboV058X4htjiYwF8OQyk23A9WGy1fmV+h0XE58+W9w4qE3gZoizyyTGtySAjuAqhnppCeF2JkPIakkMOx2xUkdiio/oyOiuqU0uX5GT/aRt6RBVNGUqkwjgIYjsLXraoOTeqxWEo2fGNby9gd2MzY+IVFZexPV6xE6XXKWTt3RXjqBabBRX6bTPk86tQMtyLB65iiV40+SKxRJtuuUD2ncee+H+N3f6/Az//i/8nv/ewl/+xwVQ6cMMMpy71QsP2JBsEqfOO6AXpAqWW9RKkwX7OcKbWkBRkcOk067zJ+b59Dhm6lsVEjYCcbHhvnAAzfxG398DM0jwnABvR5WcZiJRIof/U6bL/zFLM+erl35l18Pu1Yrz7U0Be3Ny3RdE4hnHs97OU0It/UrPOkQVv7nwDI4Atod8NwnA575j3Nb1bQwhOaZs/CmGnjLBB7MLvU46b6UilMH3rkjza/92g8wujsPp+rsX/wT3nqszdNeXzEwRgWmgG8rSGO3p7vi3J9Agorz0T4nov2fiP4fC0/EpPEqEqCkkSJeXMlQ9EnhKcQPTCOBTozkGaQ3bSIJlwYS4CQRf20t+r8RHVOMYHwjJJddIo5KCJuNCxW4B60dJbrMnKg9ljPSyduyJCGXTMHCeUiYYOYhOS49OaqbsLEEp45Gne4dLi4KkYl8HAUqh1zkuAobV3JzkjQMq/R5Fx3ZTgXRZzzAFH5HfV2qK6oHYcx76AIF0KdlXVIGcmNjzkZ0DFt6pNBH9mrIAGqDMyfIC3ygKPDqrU7fdnR8XaTq4NJvDthCBlk2+juGdWn0JSWhnygYph9IxFWOTLS/mGQUX8+4smPSl0WLbXsavTPw/uAgv87zYrXmY6KT00N2D8PKDqg06Ksurolfs9Jh68FaDeB0BfyyNFeOA+BY0tNrQK0L1RzMqwCzLO0RelcAz7kSu16XzA0VjUaLVLaAYSW2gmClQpqdFY4df46TR4/Ra3dQmsfyyiLzc/PM7NxN2s6xcG6BidEJ9CBguDzMytISCd3Czo6AkUApB7wqykpAbxmSO9A0n2sNbZ/fOINyfXZMTNPwfJZXly77s1fG2TAMEp7CdMDRIZHUsAxFx4NmXQKFhCWydmHEhygXox4avihkDGelmhHzNwwdhoYFXhUHKZompeBsN+D/F8KzSHfSQ6NwQFeoTl2ezxpMTkI7CVpKvtsIJZr2PAkSYoRIvFhpukTapiUBjmFKJaHbE8J6ryuT8uQk5AsSUGQyko1u9wTi5bhyDokYl+qLbFvCkOrHSivSw95mGwF0W/ITJCT70mlDIynXSNflQVOBHE/LkklWNyOyX1m4LI2eYnULCqFAPQ08ja7B3QW4pQS9NmR16CbA1yCnS1YwdKLKjynnEIZyDkM21KxIxhCpLJVLEhwGnjSJSoeSREoloDt0JSPnKtggTj4uv8c3VQms78FvOoSVSEDoRtwEj3whScrSSTkBaRM6Az6gUvCv/k2NvaOP8KF356E3wtzCGR5/8jEmJyegpzhYr7D/znOMFSz+yd9/J8ee/X2efuYi0D5TOjQnixEHooFcrBL9qsWSHGtQu3pkVM+Hhx8+xTfevRcrYdJptzEMA6fnEKqQ7/+2t3Nqfp1WBbkmWhm8VTQzh5XLs+tgif/1H/45P/Rzv8HXnnnhKh3VazSNvk77GzHYuFxLIKSCuf5LelZD08G/0mBj0HTI3wGt5yHcRkRoVWB9uQdY4Gso30LPmCzr0ByINkzgnskEv/hrf4fRb/gGtLCFWniEcsrknp0G3mzAbChVizbi0HeAuitBxBDSwO8z9Ltx1xFHXyF+2zPRZ87Sl7v1kAAih/h9Ed1pq+dhPfquGOJfjV5PcCEXvoVULOLvno5eryMVkiRCEL/cTOWNYIPHeqlAA01k4I2McCKzJUmcbW5AzxHuBdFne47IxlebsPo1aC5A2ORCuGmM0Igz+5YEGpoNYYu+/nucpR9mqw9DmCNi94OZFaK405EqiEIqDq4LbpM+yVwHtQ7GkCTJ/E4UsMRVCEU/2QRbAntbsqxRtWSLF+IJbCw+By0T8UaS0TYWEgzY0UWNA42YJ2cMfKeFDM4EfQ5bgn5wsM6FjWEcJBqPgxGTfoUixgy+2vntdVAz+OyXq9y0xySfS3D7zQor9HhcwdoceDW2lCL1AOysJH/rXXi+J+gXPQv5AKp1+hG+EVXmx6IANiW+1pVwAa6XxbHh5RyaQtHzfXw/xOm2aTd72KU0XafNkSNPsTB3jkAL8HE4fvQo1eoGnc01NgydlrmKZdlsrK5TyhRoul1cz8NO2WQyJdB1wt46uu4Tdrt0Vk6T3T1OX9Lt2lmhNI7jdlieX2SpMseZc9cIRmV5LmZHkXCkTX2AznIjoFcXB3xkJMr0R7OiZYAVZcxCG7AlwLBCUVwazkOjA72OyOR6PuSiQMVzYWQYvqsMH+xGUJRA/KVAB9+RDMixJTjdkgDjwA6RZQscCTY8Txz44bEEOiGaFqBZiiAQuJPrCf7VDyTY6XZgbFx4Jioq87quVBUSuvBAdEMCFs8HzZOAKq2EwO0moJ0webSj420DspvAO9PwtnEo6uCZ0A0gNOX7NCTo0TxotYGanK+vgRcFDeE6eGn4/c2LL5CTJtybkWaKVVMqFEUd7JyUzhOA0oVrEiN2NC2Ck+kwmZDz7qakDGq4kp1SplwHD9ku44NyLnIA19IGx7RLnycQLSK2rXP7rXmgCRooUydodiiUbQ7fPE1wdpbxos/sxoX7XD6nOHLsFB/85nFoVRgfzzE8NUy316O22iB7Pk26aJAuJpga3sPf+b47+enZx9moD9yByDF2K4JPpkyfdbqILDoXpx5dFXNcl2K5xPLSMiOjo3Q6Hc7OnmVlaZndM7fxE998Ow8/eo7OwiypHbvQTANUAvQc1vAkt3zg/fyiKvLdH/tRVqrdV/7Ca2VxkKHoL6ZvFE/w1ZhPJEnXt6AZsoVdfJWmaWAvRkHvtsx0owkL6yrKvKZw2y5LHZNWeGF2f7Kc4J/+229k6t0zaMyCb4JjMJYexZzu0dpYpVOTYCOPDPdNYLEjh5+O/q7SL0o26KvjhUjFY4ktqf3+8SPH0og+HyNy8tG2m2xB/SkhgUucfI6HS+z7xejDGhKMrNBHn7yRh9YFS3w0mRs6pAowuUsq4l1P8PHnl6HlweikrGn5vFQxjAhuvL4M4TwXSl1HvDPNQvopxLBVUyBQeiCJq7iXBTnE2beAtChEujFJJpD1OuiCarPVBbd7TtYjcmAMs5VAClpAT9CwWzymPP1qAPTVA5pynAzT525AP/DII/NxBOlSdaAowZJhCeTMNxGlKj3aV0z+jonq7ej8UlzYzTcYuGZx0BDfmItVIGKS/uDNiwOaeO67XGjU6wCj+sqXwDBC9u/JsHemQM5cppTz+PKjMPs8qBrQlSSmSkFxVODanTbM92AyL+qfnVYkxhMRpnwduhXwcwJjT2Qi3s81slcb37lcvsNs6AY37TvETfv2c/DgPkrFFKEKOHv+JKdPnuTs7AmOnjhObWONbqdJ0tLYPL9Kc22VQmGYBx78INlimT37DuOEbVJjFs1KlUwujW5YtNpnyCfzaF6V7MReNGyuB2fjQO4gtxy4lT/70yc4cbbD2hXAf68o2FAqxDRlnNc2oNsLMEMZHOWIyOy5ESnciNScfOkmbibk764SgrSpi3xsaMPCBuzZKw59uwPZDKRz0ojHNiFd0gn9EF9FKlVJkUxcr8PiBtQUpE1o1QWXmjDFUfc8Gfib6+5WxUS3Ip3vaEGfnxcHe2xMmhDqOjQakXqF6vNNwjDqcmlIBSVh9SsDIFCqQMkcc7Gk5A4T3jEqDbOKCdm+40iDoGxWKjeuKxURpaBbleZFHtAxoGtLR/V1XzpuXszusIR4nspCPRRsqtuDbFoqJroh5x+v7Lou1Zl4iFqGVHu8VP+cY5k1X0eaMaqoj8m1rda91OJMN7x0sg6EA6H8HlstZzUDI6Gz6+AehssZfMvnrsXznKt4L6k6Lax28IMsobVOtpQCU+PM3DwFLcfcuSUcp8tb7n4T5xdX+PA738bjD87zX35voHwYjQEVZf50JQu0qnNdrKuSzOzcSbWyyU379pPOZui028zPzbFwPMMd4yMU3w29Wp3U+DoqmUIzdgp4mhqwzgMPvoOf+r/+Dj/zz/7t5XENrpbFfJcAWcgTyKCPF/FBD/IGsv333cfIuz7I1/7XH6FWXiT04vTuFVicHR00l9e+ZqRh/TgU74faw9u+UsEfff4rfPgDn2SkNMTp00t8dbH+Er6CUoqFF46xa3yVxME7IH8TOA3cVpORcp6xUoVszcdF/Lu99LuJryNBxTEuzAKGyGiLr1LAxfsQdJGgwEW4FCGSIN6FVEzix6pOH+lT4aVXXw1sd4o3vBjQxc2UnhW5iIjcdSKeZEJETZIJKO+B0lREfViH9ZoEH14NydTHBJZBy0J2lySc6rOyb7MIfhfCtYFHctD5jrB0xpAgWdGieTnCx6n4WY7I0mEEKyVAAhtdAoDAg2CZfkBhIINiXbbb4oV0EQWAONBoRMcy6ABFywHQl9b1xBkOuxDG5KB1+mWvdHRezejvkH7v13T0Pduf0SioeknvpUH+xcWmB0VfqaCFYP+a9CPuS9nrUO2tLMALJ0NSBYede00yJZOpaY/du2CjBfVjyLWZAzcPdhHyY9B0pIrmpcDwBcXhpsXXwJJKWn0TNjNQsmFmv/CTrpWleKmY2OVYXLy7HAvCgBdOvMA7Gt+Aacpgqbc2OfL8Uxw58ixnTh2TnhuhR2ezRscPSWTyJNNp1iubeKHG8MgYtWYDOwUq8MnlcuiJBGCRG9oNhkXl/IsUd5Wwrn2cAUClcZ5nHvsqi/NNmg1E1esy7YqCjY1KC05LdNo0BbqiBZBMQ7MLyysil2eYYGri3CZssDKgpTVCQxFq0iDP8EUyNjQhl4aOCXbSBNOno4kzbmKimzoqDPF1yBV1Wg2f1RWoNwS2tWsH3FlGyscJkQIl4mt4vmTxY4dai3Cfq2tSTvZ8CR5yeWkU1GrL52MSeSYbcU+CKHgy5Xcqgl/F1ZPQj/hvGtQ6vsjrbbMp2+DADpudZodCAlxTgg1dRdfIkmDMMOT/fga0vECeuro0/yubkNyE+1fgtH/hfJNCYypv4Bs+hg3DwwL/arXA60aqgAmpcGhRoBXLc9q29E9JJuVcW03JRrQ6koXQDOlem4gqO4lA7v11tdgZjbPesVMarXq3vEm6eUIPtFG0RA6KIZn9KdIjRQ42NnjQhEeOzrJcu3DXv/f/1Uk4P87P/eMfJjc1xa5bbkLpCcaMEbrdDq7b4YVnT3Hb4UOUhvcznP+TS2dHvJdCV661bTYDisUik5NTrK2tsjuzhze/5W5SqRSVuQXuGr2FWw/dQnb3XXjdUxipCQxzBJl2u8AZNH2Sv/UTf5fPfvGL/MUXn7y2B2wi969LPxsYL9LuwDY+N2SgAeDmduFNfZTb/9634Pkhp//736Z74uFX/uAr7vi178IugTkCI/teGmwAPH70DP/rj36b3Ykh/tNv/ilfXW++5DLPVj0++u+O8ZtLWb7xn98OrSrtsy/w3Kk5ymmTRMbCwieqJXKeftuBfUgAEgcKscVQqsuxKv2mfQohlceP+7novTZbPNOXcHUHLYy+N8EbHJWXBD0la5OKHfFIVtWyZe3qOBFMOC9ztqegVhE53NaaONf6ZASBCukz82OoVJwgHZH1LFZaskfAafBSR9oa2M9OOcagIus6PVDnom0AhiFdFj6ImQa/xVYfqqCJQJ5iIrgN+gRYWfDaENZBKwihmLQcV7gR7buBBBgu/T4XcRUkrkzosj9tXJAPdCBc4kJlAYUM5lhVIPYs48pDTFCKB7VGH7kSB1zbLb62L2cx5yEY2M8rfeb1mBeTAoev9nrMbp4nG3r4JkztgTOrUD8RbdcGbRkyO6Gch4QjymmhIfBxI0m/q2cIdGHxJHhNuGMCSsPiA10hf/uyLW3p5BM2Z9uvrEL1as3UDQ7uv4PpnTvJZFMEoc8LLz7J6VPHcX2PiakdrK2u0Khs0Kp2yI9kuOWeO1g8fYpDB2/l5sNvwsdjvbpCpm3gdnpMjExEGeAumjkEmkN6dBQzOUQ/IoZrWeF48eyXWVk5Sb0VkrBgcubyP3tFwYZumyhTeATDE9AwdEZvPUxnZYFqs8PtP3A/tce+guu4uKFkKAoZSfU23YB2xG9wAzA98BM6G82QHbceotXqUtlYY62RZWZvll6zSsIF0jsoJDvYrTqtdpt2Sy7p2BjkixJU6LpwLXRdggC40JHeOn5DMid79sLUtEjxNhqybXVTHOhEQpSDrAg6pGmy77hSEzcm9P1orrHANfqJnZ4jsCxZ1OTmW4RM6IqsHpA05TOBIRCsBPIb1eer+L44+JkyYEBK16AFlmnQtn3GjUFxDKm/dgk4UVW0RnSK2ZBySs6/0xayvRVVpHpeXzXM92BpSc5XIUGPbcvfvi+/swUJQGoNMH2RWlU22xqDXQcbpZ9d6iF+cgNwQCvB6dmQRx+tsmu/D1RAd2UwpFOwqVEulrnl4B5+8Pt7/PJvLNPu9KeZSkXxK7+5xPs+cIwPfvt3cYAyqv15eudbjORy+EGSwOmweX6RcjrDvXfvRfvNk6gbxBFutzr4pRHu/vDdHHn4DyjunGRoZhQnbDC7PkttdY7nZxdZfHqWH/rpn0I3TSm7aElktR4FfDL5Hdx/32187ktPvqoeEZdtMZwgtsFF2rrI+zegnXvosxy+90d59MttWstr7L33o5w6/zR+52K5+lew2Jm5GudsSFPVXW+G+Scuvkmt0eOX/+dn2FXO8MRa46J+i0KkVJ91JvjGsQdQp/6Q5TOn+MyZLr0Abs5oLCM+WRXx0VKIauUicIYLqxivxgb92pjnkUSCmO3vvdKli1BBb+zKRpp+1U9J5SI/Ca2qJM7sjKwr7R64a1DdEJGRXqxiFHEYwkX6z1icxe9E/0/Ib3sUnNUowAijqoSFPKsxRCh20Acy87omlZagyZacmFaI4FMVWR+VJapTW0321MCx5GVtcVeEu+i1IEzKe3pJtg08+o0PYpxePNDiIGwICTyW2UpqqKr4LthIcBJE28UVkXhwBHIuW708QAa4H71mDGwbR7txxBvSh5zFke0rDbrBKSNGLcR8jpeLoq+zpaegPCzy/mdnPQ5Mwtgo2AmNs3OKxRFwG4CCRAdyTeHfdJExEBhSUfLbEQogvqZp4dF0XEkgT4xoDA8plq9B0k4Hal5Iw792gQaAnczwtne8i1vuvJlUzmZjc4mvPfJFzp05geOHWJZJo9XCC0BpJul8mfX1Zdxul9379lGprqM3oTxSorFcIZUrURgp43cq+GFIKr8T1a1i6hk000IGfhz5Gi9zZK/NqpUVKutNMkkJ/MsTr/yZ2K4o2ChPFdl9K1CHDQMWNnRufvBjzH36t2muL5D9az/JqadepDa/Rior1Q2aCsdROEnFRkvkXXekIO3A7GLIaiLJm/7aj7H3pl08+gv/kvf/4PdgT97M4q/8C1ZePMNdv/hrLP3qz7Nx9GGSFgyXTMbHFZV6QK8rE5PnCUwoYYnKUjIiiBmGVC6C6MHVoqy4nZQsfSYJE2NS0eh0JKjotITHMVQWZ13X5Tx6PeFAaLq8rumyneOAq4mEbCuA5U1pWiMDWVhou9Ip7tqdwbQ20EIH14kC+6gao+sSCEEUbHgyoZpKXu8GitAHp+VTNODtezQeOanxtW5IEDHcNBrU9AzL6QyFxAp66GNoInuIK763rkE6JsYhQZHvynm329JnpNOVXiPrClo+pFeluWA2I/txesKfca83gStWn+rQXygjj0NVoe5oZEcMoBGBjFOSwksWYDRPyQvJuTu4+d1v5fiL/5FPfL5yAZxKKfgff/gI7/pIm1L5ENnOJ8npPXZMD+F2G3TaLl6wRHu9RSF5ifTzIO4WrlsKdWmjwy/88v/h7/34u9FG2iw6Jwn8OrONx/ni7Dk++/R5Pvdii2/71t2gH0bTHOTRT8qPinR9NI0f+/Gf4H/9zv9h9lzrqhybHs1/W+ov8PINDQYXZmvb3/H1fR3IkS+xXoWf+0iO2nfez9/9B3/A6pGnGL/zXSw88qeX/syl0up6nIl67QMmPQqH7oehnXD60xffxsrAWz6yA2MzT3ji+Uvuazql883f/I2QmITREgurG3ymJ4pOe2uKDP3b4dD3zdaRba62uQip+0oChvhx1AZ+3rCVjZirlwAjJZA4XYehSWhswsKaVD0cF/xq5NCB5BLKCEEmhiWNIJn6GOKjy34TEyJR71VARdwJ9EhdsU7/4sUOdUyiKUAiJxK6lg7NTlSM3iFQC9VCqhEeffnXOGEVB9uRIoBbkfeUI3M7KWBKzlUFSDdwos+PyTFpCUQBMMbV1ZGAIYKX4SNifG1EBxlkLWny0mQH9CVpu/Tn85jTkYzOOZ6P4usRZwDj/SW5kLtxpRYHYfGgTfDSyudr5HddiVkuOBuwUZcgcHdeIzdapFy2qfbWWO+FHF8Tro3bhMa88Edzo1LV6IWw3BJVT6DfLwXxzVJJGJ2y2Zcc48wzKyyvXH2io0lUiLvGk0A6lcNM23ihR6PTZXlpgbOzZ9lYWaHarNNs1AhcB83TOLDvAEM7yizOn2Fqcie1dpdw/hxDw2WU7xG26qTTeQLH5fzKCcojQ9jpJL3qCol0FtxNIVdZY1zrATEzMcWOUZONNR8jHcHyL9OuKNjoOC5NF1IGKBMmbx1H3zNDe+f9HJieR3dMKsmddG+/ner6Cre8eSdnH3mCm991G83mEuaxBQ59430s/M4jBKpLpqxz8P43U3zLN4Py2Xv3vVSfPErDXGXsPQ+y6z0ltEyZzPgU/rhNCg+351OvCbwoVDKx6roEFmhSJVBRlcC2QdM1FIrABy+UE9bdqIGgEgiUbSJqVoZ8zjSl10YyKX+nMsJ7GOxFYJoaSoGlKzaasDAvvTtCFzJbzpRIZTgqT1MvcK6yCgEMpaTHgp2OvjPaZ0xK9wM5N0+TQCcIhFxnhbLfMUPjmyYy1OZ7nPRDPGoo4IluHfukz48d1rm5qIMKsULZlwUkNCHXe9EXakA+Kz9qRK4pSMUnWITZFnzrbgk0PB/aUXASBkKmv56WL4qMr2/S7xZbYGsBTBUsbrvtMPWNOQr5CUjcAdpuII2WH8FO2dhhC2UP8au/Pob9I/+U3/3cxgWOx8NfWeLIsU/yltt+gKHpDD4OXlCl4zc5Of8CxaEUYW2I//0Hz168qhFDvK6z+Qr+4JOP8ZkvPCW4Z+NTKKXwfR+nJ4IIIRBoIDCzyYGDNWUF785BKkW5PMS3fujN/Nv/9KWX/9J4AYw9zDh42CYcoFuSgdWUaNxjQHJKJA+DqvytJwQ/DfSdmYtl9HRgTCp1ap3XPeh45qjiBz9m8nP/6lv42R9+km5tiJdzZzNFaaSmth+3pq6agECQEJW+dhOqF1MlTIJ9q04nbHPsieWLbNC373+wwKEPfQC0TagdwxlJUNMcukrI3bsQP1bjwiFQuTqnclG70ssUz60xlOqNbNld0FmTeTpwJOHV60J9WYQ8vDaEcYUsTm56XIhJKyGBh448q12E0xA55O4ifYc6LgfFzznRa+lo/5GEmJaD1E5J7LXOQrsebefIurHVNEUhc3ceWZBWB/YXOy02wltIRseUQoIGF7xZ+Y0Vve9CYo98h6rS53wp+hK0Fv2KTKwStUY/17Kd/6iQwZJGlOI8ZEDHVQ2PfrMWk748UZo+KT22VyuiEl/r7Umri60517G6Xn8amjmZ0wvTkDVhcjTH3ulpZnZ32N9osL4GlRch2ICzy5BPQTEH4aZUNvxYxzqqYsVBXXoISiXIFy0mRkcYn1zHNAVhcTXNQB6BKtdOs8UwTA7cfCt79sxQyGXR9YB6Yx07aYmwhebjNbt06l103aSRqlJ5fomx0Ry1zU2Gh9vs2LWTcjJLs1nDcB3CXpfN9VVStomdSuD3amAqtJTOVoMZNRaRbBXXCkqVS9tYgUV7rUewJsHn5doVBRtOENDsSqfwpTYkRyzCoQl237qD+pElGJ/hnT/7k/jdVbIHPkyoukzd83/Iv+2b6T30B+z81t2kbt7LxpdO81sfP83B6YAHRqbQzDyqtcjQ5DTrR0/ScmocuH8P9bkNnvwPP8+tH/xWal/7A3rdkIQhyJjQFDK66wlxu9USyJBts9WNPB0FEHF1I9z2YOoRHEnT5R4lIpiUFVcb9JiroaNCFZVGIAw1HBcMXeH7YOuwfxfkMhqTZoaFU3DyaIuuSgFFFrvw2GqX6RGLvOOAozBaYGb6vBAQnCJEKloAdlSVCSVbZPqC1bX9kJszHb5vf5lPN0s8vLiKH3p06fGlapPpF6B4QGM0I5mklBJpXgMJPOJxqGuQzvSvh+PD+oZI/mbW4NY8DJciSeO0lD8JpdzpXmc1KnsE2gMcjUE4AQY02i4f+ch/5Zf+xYO8+0N/C1mx6G9gFEA30biVid238HM/s8hDT/0/LFT7g2J1XvHP//V/5q9/7+Oc+OpxOnMtykNZQs3i05+Zo+LaOO5Zzixe2xLsqzEFNNsvPzN//qk1/GAew5xCVtAkIprvgj8P3fOYqQSjY9bFXeY4TTz4W0cW26FomzVkoEULsx/JV24p5pQl09UIRQKYpEA2uufpp6x12V9yCJzFAbiFBjQFxrcFWXidbGbXPh566LN84o/+lJ/51Z+kPPKNHHvxs/TTtPGDmxMwMtCubt9L5LG4MS7ktZ+QMwfPfhKyE0gn5gHTdyLNzl4IeXJ+Hct/+el/4q17MFIaqA3CjSVy5YBkCuodueVngXcgPpmN8Da+xg2D+gBe93j0qloiBZ2ErBdWRpy+9lp0n+Mse5Z+Nn6wkgh90soKxK3azfGo8tDjAjnSmHPRly2M9pekLw+bADZlTXLaQkoPPS5UBahGx2LRnxfaoO+WyouKIUtIwk8l6Xdl9GVbEvSDo3hfyHveUlTIrkTv2VxIeI/XjHiuigOe+DHdPsnFJO84SMtH3x9XGCIpX+xofxPR9YyWmQse4Vf7OF/qc6/3g+VDWI0EGzbhoU1FqK/ygGVgZTRmdkDnbnixCCsvgL8SSS4PCZIlAPwesjRnkftYF7Tz+CTsnoJ8NkSZGsPjNqbZverBRkRxuqbzglKK2TPH+cqXv0TKNjDMQ1SrVXzPo92oceyFBRK2IFhqDY9Ga4lMEnrNFknDpFxcoFOdoqlpWLbF8vwZEpbF5MQEyfIkZiKNZtgk05NodKTU59Wis4sjbrgWAUe10aTr9lhfhbVVaF0rgrjSxWPVokzBs4/OceihT/Cl3/40QWudiR/N4BT30dvskbITrP7PXySZywMO9vv/HppqwIn/Qs92wJAKgOpsoDrPwjNPEM6fJn/vgyTCGiuPfQ3j0NvI3DWJN/tprKyHFgq2T5ek/VYVwoiyOArhJlgGJG2NZMokDBWO46Nr4sz3L79UJpRS4hoEff5HGAUrsn8NpTRQGioCshu2jWlorC93KRQ0SjmF7ilQik4YcHhyiqkzFU73isAuAlyeWT/HPXmbt+xwGFOuVCyihGacJW+3hSfheRH2NSXH5HuiYpVJQsITgrZtBViZFr2RaU518ixUNoAsHgGz7Tb1rkYSRcqJ1LOyYKlt5UPtwqJbOg1jk7BRgcUG3D4D9XYE7eoAlsDWeg4EfsiFWoTX1tafpJ+ajLNSMQ65LZny55/tUmu2QR+L3ozYjpoH+g4p12CBlmH3Pd/O++7/7/zWny1LRSfCH3/qd9b41O/8+eA3D/z/NdTEXwm/ETsLcWZt0BLIBB1LTbqX2G67v5pCqj81oAcLlR7PHP1D7rplDNNMosI1NH0U1BdRzNJ47LM46YBTT50WeEwBmbs26Tsv6Wi/vegnRx9e0IrOI8NWA68tKcoIwxI0RClpixzeg+7stvPIAr5gxvV8JIUZRtchg4yDGNP8OpiZSPBDP/WT/N5/++9004f4qZ/9LerFbwE+i0RdHuIlZUnd9tfpPvWb9JmoIBdwL1uDIlkDfR90viSZD7TIg3p15m0IXv8C0wSW6VaiuSWv8+bbpvjMX8xd/ByBtN9BcRSa88w/tMiXHu9uQSBihMkx4FakwrHA6xr//aW32qasCegiytJaEaiRkYuekdhxj3BjWjriKMROsAl0pAoS0y1SRWivRtvFjsMgZ0pt+x1zqlz6z3qsCKiBZoJKIOnjCDqp2WCWZFxSlNdNBUGkREQIegcMD9wq/X4Xyei4Y0Jxm361JcLrqbmBYzPoBwpxNSYWo0gMXIdW9H5c8RgctPG+PKQiE2ME4wBGox/sxIGLit5LcWEjhriaE/Dyc/92i6aAGzpS9qG1AI8+5mAOnWV6hyBE9k6J+qilw0I6muK7wvMI6jJe2YmMj4hEpSfBTkEpB4HuUHfXCQ1vKwl7NS2+VdfSwjCg3eqSzecxMel2upw9c5YzJ06wen6BrK1IlGyqQU+aWzuCpNmoeEwN+awsnuPwLbdx7myFqelxlmZnSWfTtDoeeQ/QkmhYaHoOug2UFUBooFEBZSEdXa8A33QFtm/HPdS7FnOrHr0lrmjCv6JgI2Fa5DKgO5IMLZcV3bknyVo1epbG5v/7j7HSaQrlAvP/9veZO/IcpdIErmly6nOfJswOU958gcRyjb/9Vguz5KMWj9D7lz9CbWmDjZUeE+4q+uIZFk6fpVifJThzik89ssCwBUYNLC+StkV6eiQsqVhYVtQQT0nAoRT0eiGNekCnIxUPKwHJQWKzUoRBH7aExlbfiZgkrekKOxGQTJtYpkSMPSfg+DEXXdMoj9qYnoNpKkIXshrsHR3m7p1TzJ90cZUJFKj4p/n0bIWdgSTZd5al2VJL9Yte5bQEOY4rx68cKUlbCXnddyQLFXakuWBWdcmpeawtz6sEaCTsLoWhJFbYIaHADwXTqrlSnFGa/A6Rcyc6Bjshi9iJp2X/aKAMqLekuqEnIGOLSonjw/UMNrawGi5Yw4IFDtvg1iRbG0/MxaEASXNN0a+p50ALwOjXuZPZm/gH/+SbePjZ/8bpZV+c5iZXtijEl1xnoL0vF3aEldsvNgg9GwS629E2O5AFtRJtGwdXcckeZNGLIQ5t+lm2QU5EbHG2MnI+1pZdPvDeX+Fj3/W73H/XARZn59j9prtJdBY5+9wpPvPpVdY6iiOrAsfZyuYV6evpxwHeELJox8cS48FzCB58jb7KCojzoNEnY8aZwPTAdYhXgigzqzSBD26dV4d+vLedz3EdbWzvzZw+3+Kuu9/KD/3Y3+EjH/khdn7Lu1ktnCRobiAnfQjQ6T3/28jNGwU2QRuB5F+H7seBA8BnwdsF4RNAGVSF5MxBes2A/NQEjbVz0Fh7bS11I6y3Oy9/6haEnZATL55/6bYGEETIlsfOoDafYO5zR/jkn3f5/WPQ8Pu79JBqRhd5dGI0ydVh+3zdtpvmC1fD2YDGMqBLNcBKI52ZBxzsRB7Gd8v6UTkdJeXzwjtMIPBhx4XmC/Qjj+1m0FeNi4OWgH61IsryaylZN912BGeKBTwizqqqQ5COji0jn3eP0M90RcRg0wZtFKGQxYFGnn6gEfMvNqNjir1GE5kPxvrcDS0llZOtuSaeLwdJ4DG5e+sCDxyTRT+5E5Pj46qGMbBtvJ8M/YZ98Vwdw7RiYRNFn682+P/tNsgRgX4Pk23bTh2y2HfLtXEqL8uU8E6bHsydk6GyYwTG02DshEwKSMLyKlTnkcnCQ65TCJTAHoOhjPSHqfdArQWcqcyztqQIbjT4wBWY67i0Gk2Ghopsbsxx8vgzLJ0/R08pPA2aK12aXWkCnbUgnxQ/qzBkkCuX8d02G8tz1DbO4fYcxmZuIl0q4bY7+G4PwzLQwyYqcNCUAj2JcptoWgKscWRQJq/6eY3kZhiamsCtnOmT4S7TrkyNiiwBOo4f4mowVNDJNevsny5wfmkT7/TjbDo9bnrLW5go+mRmMlTmj3Lk3z9Dox1Q3pWmMJ1g6J4R0gZ4uQlMEigUqbbPzokUc5/6NKmEIh04bH7qL1Ae7BuCtAWBLc1efEfKc0vzsB7C/qQQrZNJ4Wzk8mCZikAFuBEx2nCkEWF5RFSnQqWkO20KjCDKqngRH8GL+BkaJC0wMzrK1PF0Rcc1ee55B3S4+YDCtUKUYdBqhuhtUFaPpDrLPTN7+erCGufaDjAD3Mvz3hH+88oGvQK4rSZTPYWRiKRuHVGKwpZmf8qAkQSYBcGkhqbwLQILQkew31kXivUqZoPorgsIdoWQmu6xIymViFML4GZhzzhoTpR9CmSx6bpSObGTwofYbMKqB6VROBfINemEkDREfrAwBJsB9BKDHvB1sAFogLcRVQ0HyXiRPfzQEe65989J5w+jabGGYKTW4K+IpBgeWthlODVE1rJl5W1w+RYtvMYOKL9FAsOUBaunRAUGFwqHpALk9GRbVuUw8pOS9anORwFupP5CB9H0nEKCjib94MemT86MHf5RpHlADDe4mMMQZ9XiikcAjU2f//yfl/h1bYkAsJOzglDoRcdjIpjuALkmbbYygpoOyVGwIniiPglrL7IF39AKCBE0xk9HcR4+/RbQAf3MoI8EWe3oJ0J1bWUdY8WYArKIx85BnKWNq8XX0TQjQS9/K42Rw/ydb/swE8N5brrjDkz3WYLVzyPR5wzofw3CNZR3NDrgFPAF0KbAf4w+gH4PhIXo3t1FdkcPu9iit/EMblWDXivKCFyhJdi6Z1PvSrDwmX4+r5CKlPgGIIRbFo2hHvD8rMf7/uLT/Kt/WuNPzsBy9J4G7LTgvCe3bQ25bTlet2LTXwkLItz7oDSq6khT3Fgu1kjL3J7KQ7UiRN3ckChWFTLgRDh4L4JI6TkII4dYSwgkigB5nvNIYz3ow4bi4AO2eAyqDkGGvnMeE5lzyHPfgnAN6IFugsrL+kYg61G6AOPDcP4UqBXk2d6gr76VQHppDIqDDHIt4iL7XFSh0UDlomtSpc9h6dGHYcWBQhxUiM5Kv4obJ28GY/yYNJ6hX9obos99qXFhwNKjH9yk6FeHo2O8INi4GIoyDh5jeFy8BkTWqoQsHXsdsVXDkJqQ9c93oZAQXl5PQSYNB8YlcVt04JwHG7WIE7TAVuU7MwXDI5LEbLqwfh6qc4rOSYHJvxEtlynx4W/6Ht7x7neRSVl89YmnOHvmBMoLCQPIF5LUnC7triJpwMSEjYFP4AVk0ikMQ/HiC89iJ3zarR733vcNTOzaQ211AWUXYPdeetVV9MIIpmWiej3IZuitLpBI2OjDY2gYXItgww82cYOePJs2V+QCXpG3qGHhKQ3SAukxdY1EMcPUeIap+w/SO3ES90wVo3oav9umlPbJ78uzY6+GmbSwd02LZ9vtoaXTJDLDYA2hNJ1CMo3K5UiePkej1kF3FCOGwJq0UPpyBFbUDTsLQyVILUF2A/bdJMdnRVWOel0yLZkMFAuiNFWvSnOZyibs2gPj42CnNJSmobohKiJS+2EEoVbSEMkPodsN0SyXnqdz5PkeqRSMT4jTvrHmYOZ0NDRyhiJjKcraBgdzOd6x12b1xZBuEAIjeOzkmVYX/0QNc5eFVQ5J9HwygZCttEAqKqHWlzA3lBDbvQA8Q2BQfloaEC4cg735kOzWJCVOyXoLnj/qiWy6BvMurHVBb4OZkgVGhaKkFcQOpgHrDfjyUxBa0DVkcuh4EJiQjfgtgQ8NE9T16iITmWZHC18MHwq4aFHll391jZHsb/MjP/0jGIZNPx3mgFYDZx20DCpwcXrrVNxtM3zs+L+cRaRDvQT5EWhUYPl58M+AOYYsRpYsrLqPcLCLEvilknDgMLQPwqkXobFIH5vsIQtenKXM0idJxotmvGCdRRb0Mv1S/mC2bdC2YYkV0X3XpXHmBWbSR47F8M8ou6fbEKxLYFIajdbJOIiwEOy4T7/bblzRibHg8THGDskoMAJGSZIEbg1Z7DugjUX47UK0fbzoxwovDv1mXdfREsVpDDcLzZN877f9Gz7ywQd59737+dNP/zZ4VeRi50FPgzYH4RiorwIvoO/4COFaHpzPQvmDsPk8cCqCTH0voNFuTtPe+B3ortPrrL36A50QdSD3FCx85cJV+66dcPQ0LFwMFTgwTvbdnSExMQbhJuuqX3zamYSfvsfgH3w52FLtXEb8w+1826/bVTRF3zmN5wUgOwlo0GsLDHbxKLR0EStRrT5PeXVh2/7ibH8IegEK41A7I0iMVFme87g539bzNzg3xo60CfkStM7IWoGHoFUteY7DJltV2rDLBVUJ5YqYwVxTXIOtc1TRd8XOtU8fehlbPDcNBgdxgiN+dOIAA2Rud5EAoYzMNXEiyKTv8Mf7GYRBRee5Be/qRscXk8fjBbsYbRfP5zEkqoCU/AbLfoMQ+zT95Eps8XdXuajV1wLqa68j1kpBcwNOnIFRS4QfHVeCSc+BUh6KSZiYgX15WMjByVlYXECaKW7ImLVGBaFSqcPCOXBnRa/kRpGWv1JLmCnsVAIDRbWxytEXn+Hk8XM0Kg6FIYtWp0ulpdA02DFsUMjqbK5IJcdWCWafP00qm6BYtggdg/LoMPW1NWaf+wp33P1OnF6D+tJ5rFQaQ9Nwuz2MwEX1XPRiCU3VgJQ4ylc5/RNiY+hJjHLEgdZf8SNbdkXBhus1cfwA0xScmW2EBJ113E0Pb26ehK2RCDzIatiWCU0HkyRmvozpR5qq2YxorGZysLwMQyFaqAQv0fJIBD66G5ACkoHI2JqWRM+eAk+XzIzrwWgWSgkoZ2WiMk2BSe2akJKUgXwukwM7gLOzUCiAakN7HZIZRdJWqLaUoQ1NAhvNk/8nNckaNTuw1oK1zfD/396Z/Vh2Xef9t890x7p1q6pr6KG6ObVINjVQZCJbUiJHhuE4gG0gCBIHUAIYSYAgBoIYeQjyZiBA/oggTgwkUB6ixLYSRA9WFA2AycgSLUvdIqkmu7uqu8Zb053vmXce1t733K5uSiyK3SSd8wGNqr51zrn7THuvtb5vrcUnPy6VkSZjGbJKYHCck6bgK+mOnkcZC/U9PrW4xGtLFW50dijEnsu8OZzw5bspaXWep5pHnHNCHA+qiUy+1Yo4Ca4nrEs1MonrFYlapTFsTyB24NUNeCPDHL8FDJmrZbzwhOIamloFllHEoSbIRXIUppIf0u2KnjKMIalA14E7A2g7cqtqtUI6PkklzzV3JZqxeOksT87PDz270Fja3JTNm9L8QHcAX/1an9/+Z69Rb38cWREWQd8Ed4vx7g7737lB++Iqr1x/lcyfMcRs7sFPczZcSar0zkGUwOaPxQDXI+CClCxevCClKJMBsAtuG1ZfhuNNkTXceA3OPwFXPwNvvArjY0R61EMWlmXgPKhL5rz7yGLVoXAsKkwJG1bMvhUK438WCdM8iGnTKOu0nYadm2wOuTVIc6kwYqnto+Gp7WclTT3wM9ANSI8ptM5rTBPJnRZU21KkIVBw/G0z9gCpxmvlGCYPxFsT4yk/oQjY9HjsiKOMcOzw2d/4h+zfPuYrr77Or81d4/VX/8RskUnSWPafqb30JaKbXyaf7IA3x8pza+x1vw5EkH4f6EDt70C+ZyYwB937v/LSOS3RwsRHUNVSW/vdQoG7ItHt+AYPVMUZ7LxzYl9rUebNJ9vw+b/7WdLhOkfxFikDfGAugIt12PGyKRm1gqjoHnc17P+f4TSgugjjHQkgpZkwjoMjIDOV3uoUMkWrtLV5CmYOyI0SNp/AcMdIvc26Y999tSLb6lDW4nRU7A/AgsxheYQY8WNgHvQO6NqpgVsJlo3Ym/yvaREJI72hS+FYvZtUOZvTZQM3CnE6tDn/gELnNzB/sxWvXIoAhpVZW2mnndes5HNMEb+CYh61c6llmaKZY1QpZFBW/mnzSSy7PSs5tVCcTdb7uHEg8ZX9NnQrEC5DuwZ+G467EtAd1uBCHS4quNyQlgLf60uBofkqnPdgLpPqarvbcPjnoO/x4c5X+RnIVcziYpt6PWCns8HNn7zJqBehXagEmiR0ieKUORficcbEmXA8hGoDkizlrb2ca+shd++GrK+vs3/QYTI+5ni/g+cHxNGIfveYercDtRqjXg/P1zTnlyB30cpB5QPR67/P7Eazvs6nXvgF/si7A72zeYNncjb6oy5uD+bmIFLQHeRc/8YGizXF8pMN9m5MGPZS6vpNmi0Naca4p0ncQ5wso9pQRKFCpQn+2/voNCXnTaKhptGCIHBo5iH1eVlf/chUjDITk/YkXyD3JXAYVuVdrDXFqXB9YTSSUGwru+9BB8JDeP6yVK4KcjHsg1DYhIoGx1WEI02WSF5EnEBvW7pp90dwkMLTHxOHIuwXjf5qvlRJihwIFTihyLguN0e85MPghRX2ByccTDKkKPg1Ei7y3f7r9N/y+eWLdX5xxafOCDfPqSGJ3J5NEHNEHlapQGqqaiWpSL26CWyNYFHBjq4iVmpK4Iw5V69wsR4SzDks1ueZ9EMaowlRInr84IJDphXhKOPwxOHODnS6OUtVoS8niRjTQy1MS9VMfMrcC7/y8GfkkWGZIvpko1k20jRColMZUIc/vTnkS7/9T/jn/+LXuHPjTa59Yh3f6bN5a5PvfXPE17/R4/JShe9sjDkJkYclRG6PlerYSb5CYaAbw1r50q131IPJDugu09KHemBKf6ZIuHdFkjcPfwBJANEJsAjDbagNpekWHgRXTUMkT1gc1wN3GeI7oLfMeY8ROYFlOALz+SVzTWbDyooi8jcPwWWINygqeMHDqXs7ye9RSA7sgvuwhc9+poprFazAwhVplEluxh0ihsaSMBnZAMYH8h4nbXDXILtJYWDMshqZKN30mjle35ybdTYfI6r1Gn6tyr1bMb/3734P540Ov/Wbf508nfG2klug9gnf3ESPdsHJIXXZ+9+/b7qO5tC/AVQh/Y4xWG5AnBodCxLWy4bieLwHpUQeQf97D//b7aMH/TQXyB3wAvh7fx+WXTjpN+jeXOZ4VAEGZMBf+TR868+g+4rc0gYPEGclHhVsFFGJkzA5gMoiZErycLxIksinUsNUqr/lXRO0cM2710eMa8O+5plsm8RQXxUpzOjASJIyCaQ4Ddn23GXovCHv47RghCdVoXDMca10VCNzkk+R8GyfZav3tiyC3aaJvNunJEMPNbyrFHPhPPIwDiikVzY3wEiqFj8Dx3aOccw+dykqVtk8FLu21ZC1xRbkqFLknBkZ0LREt83LGFI4EZaZsPSfPQ9m9p1NRFcUDtAsE/xhdTg0Ms4DWVZu70rlT1wJSu3MSd7GvAtPLsLqosixL1dlnayfwOquJJanNWgcgTqQgKotbfxRnFief+5TrKyt4LgpB/v3yOKQOBJ52XE3o9vX1JS0kIhC6ERwEsOFBrx1p0ekIRmD8hTt1SVOjveJB5rWuVVqtTkc7ZBkGYPBgFajRpqlJElCa7VpdIoNcM/xKGTuFTXHi9d+lXNr/43tjfhMeZNnGk232yM/EWJi14G6B4ednEkEh7f7oKVW8mQnpBJK9L93BK25AZMIEsNSzC97TI5iqlVNHIEXizxnkEo/i9wVh8ABMTQmYuRqQ5e6LnhzximIhGFwjQ5cA24ujsZgAEe74nCsX4BaYCTQoZmLU9PLIlDUfEW9odGpdA7PFaQOJBOIhnBJiRGYjQ3LkEswMk1B1aHVgKpJwM5z0CeaZ+bHeBcG3Ht6gS+/vs8kV8jsvEzKx7ne+zGH4z5BsMSlCxmtdIzSoKrCsGAkOMok1yUTuaaDCUwUbA0l5yQfYTLwVoEaLXeThh/gpiGMczKVoTONa6qYZC4kYc4kFPbi6lWXZ59XfLafcDDR3OvArQ68tSvOWJ6bNULByQnsHMJiK+NL01DNY4CNxplI3H3J3CPuC6uOTuCPvzrgj//nVyCHRus6biByJzuB//neWBabefNZDVRjRqoFhdTH0uwKSCDZhM4ds52LSO+tVMgDtSD7aJjmLySHwCrMPS+OrNeS42eLcpz4FtNFUf/QkBAL5rNrZjxvUUTAEooI2fe5v0qUXTDtYtCFeIkiX97CRhgrFNHDeOZvRl431SrbRdheM0v7W6lAS36P52D/BOiBcqH9i5LUOr4B7gCu/C2496rpC9CTffwaeJch6pp7myMLt0lSzzsUEgTr/K3w2KHzDJ0u8F//zR/wV1/4XX7lSkA8PJ3wk4Puo+3nGUyb9KSzlEJorLSHfRFGWM/Zy6do0Nd5RyPF3vbZ4GGGFKI43oPvfhtefhYOt7f5H//lu+z0jgFJYvyLG7CgZA4CefXunHF4Jd4jmlK5R+dAFdwxtC6JFDMaiKx3GmBQQCSs69SwTyXvw8qcmK4dxVckkTH2bHEIkJyLkazBR3cMgwFFAnlEIR86McebDXzMHmsWdn7CjMdKiayUaTYY8rBn2Z5HTGGYNiiS03tMgxU0YWlNnOTcysdmf1pYx6CBiVhSOBwnZhxDConoojnX8cy+5vuIebDXhnUgLMusZj7TPPium9yrD6oYxlmQZfLPoh+KH7Wn4N6+2Ha+K3L1SSy21E4ItS1J6K8MpPhPCMV6/rCA2Iccnb0jbt/aYG21yubGW/RPjsWhz+H4RB7kdkuK7AxjkcZnwGgoChJXQ+cErlyt0KhX8B3FeDhgdf0JaUgYJziOz2QyIkkzDvc71Op1dJKh3FQ6LlcXzWhmo4E/PzKdsrl9i95GLvfoDI7wmZyNrd0EtoV16PuScL0zhvkcXvoYPP8p0d5pJQnMdzehcwif+rTkT9SaDp6rydKUTMFwLCXwfE8YCc+RC+4ocAOJnsSh/PMr4mRoD7QjTsNoItumiciZskzyM7QLuwPRJb94AZ55RvpZ9DtiyHuOUfHHYry7vqa9oHHM54mJDHm+NN7za3KukStOTJZKp3G0jHEYStfVNIe2L4HMJARX5SxV7vHF8w6vvHXA65HlVdtYDnkv8XjtMOeTATgDGKVQj4VSW2qbBoOAU3UYjnI6E9g9gptHcDiEoyaIbPMCkojep+YMCOOQxAeVKKLEQXkVyCJU4KKqAX7dwdOQTGLizMVViuqcw/lmzrkVzTXgsJ9x462c69vQHYtj14nE0RmHdgZ8TM5GjyLyZKM+loq2dLmNalmYyNfI2oIVpiUKVQDBFYi2mDoJug/OMuTjmePWkQl/VmtrKHWnBSxAviPfY6Np+k2K6qbbyCUKgR1I6jLU0W1zbIXMyCEFO+OZsR5RROnWgatIRY8BhXTMLrCu+U6bxFlHFkc7WW9SRM9mF3IbnbSLqoHzrHEEhhQyA0AtynXRJg3GuSDvm74l12IqV8iYNnPvvYo88nOQdWB4XYbcPZaE87lAGtDpWQenAmpONOdTw8VEZ6f9Aj4AZiM82uTZX1nlx1/f5V/+1h/yT//1p2m//BsMv/kfHv9gfhp+yiJw+A5/tr7N9R/CwQY8t7DDD7Y6/CTPpaVAA+714bwrhTlKPGaMkVKxCbhVYTQO3hYZopPB3DL09yiaRprouVeH1M6BCdLbZllknl5bflpCLZllFGbnWG2kzH2KfIaqOZ7NPZiNRNtjuBS5Z9bAnpUd2W3CU597CIvbpQhmWHbEBkYCxOg/RuYpu7QemfHYY+VABG/9Rx402o1sc8pU5BSBrZBClmpZ9RpFVTxlfibiBHqXTaf1PtM57AFnw7Itp9ni2Rdy9vfT+38EobXYaqOY+1jaLrB9DNyRXMaFFjRNoNhtCGs32BE77aMDn2vXXuTpy+ucnOxy585Ndnf6TPKC7Ko6YrtF+f23epAUqr6jBF5aW2E07lFtPsHYzVhYWiLPUsJJjFetMOkcM2q3iMd92osrqKAOynrYVt9nX9b3B54KON+6TJo5Z2bczjSKL3zuCc7V/oIkyen4IieKDySnYBjDT94Woz6Kod+DzS25qPN7sLoGeT3HVZLwnDpFEz2FPJA6F89WpyJdSiOJqueuRPUdV6omBcbg0JikbsQQrlQkv2J7F97eg08+Cc9+QrzpSSwVM/oDQxsj3+cFUJ+DgaU888KWDRBDSmmZX6LUjDM2c40W6UFkjNVuDvc2YXQkigi/Cm5txEn1Jl5mudEJIohfABw0isOJx95Q4XdMhb8c6rl07J5rQ9CAfj9nqw+HIWwMYaRg9Qpsj11cKuSsYS3MVDlMfIfIFRZj6Ij2qeqA6zmkfh3fC3A8F7cak2lNmqd4KqVOTu6IbCpop7ReTHn66ZS9fdi8J07ObgLKcu+PC08i0p66uXQ+YoQeUJQnXBdGSB+ZfSzbYGEXgAnoSBrGTatQLci2ji8yPVqyHYfmb575vhqwKFFEvU9h0Futcdt8VqNwBvpMF75wg6lxr1yonIfQQxZMG5W8wv3UfojMzDYh8Zz5p834UjPGOiKePzHb50zr2rtzkO1SOFEVJAckQRy5GoUh3zLnZlmeC+aYc0aDnZt9Q3OtjTKIXSQno2++d1nOKx+be+fI8Tr/R/ZRGtIjyWXJQgonwsgU9IRCOtIWSQguZHvm3uzy+KFjbn/793nq1/+AebfP1/77t/jktWfY+uYHMJafAa8O1XMwvHv/5z9rjUhz2DqBH76xy61JLilCjpTq1sfwySfgG3f5SOuqP5KwzGJDZIiAsKIBBMswHEkw7j4jO5F19D5EJsiCyFeayxB2ZY194OGwxre1W6zUJ6KQgxtj/qERaJu/kCFzjM0rs6ywTa5WyBzWoTD2LcvRoqgaZRdmyzRbVgGKRPaMojdRl2nNBlwK1mdYXJ+pXbYEzmXD3Oyb4yYU+SlWmgoSLRkyDdBohTQptXN9yMODydZwfljEXj3ks78sOE2lYoxvY4GPxjCZQEtBy4EXrjVYf3GZr35lg+joIcf7kMJzPSaTHv3+EYwO6Gzt0x3k993W006GhUvx6C/PwYVLqwy7A5ZXzrO/q1g5/yRZnhFHY1wPOp1tqktV4miCzmJ6e1s0V+fxq205YHYiVWwUFJo++7K9NyiluLx2lVrgEZ6Rcj+TszFXW2Btxcd1I6oKorai2obWvMNglHFyJMbwJBQvNvQhq8GrP4G5bXjuBXEwKnWoB9CoOKACXNdDOQrX9fBrAckkQnkTVJoRjlJGI6mK5AFzDjSqitZ8BTfTJElGmmbs39EMO7BxAJt9+MwVuPgxiKs+k9ShH0Y4gQ/tzDzhZu6o+gSNgDiKmYQJaapJzb2oVtQ0EJz4itxx0S7kUU44yqZJbs0KxBkcDmDowqQh8qpaAPNzcK6WcvUcvHWQMMl63N9AQXOvl6KfqHPuokMjzRk4MAlEJ5tUZf7ZG8BhCj0NXSUMTerCd/czEiqIZSeca4+EfZ1woMVIGDsaFDQcyNKUSRLScBWB65JXXeI4Js1TmpHGyR1y14wsd2k3m9TqGSvtjKev5Fw9HPJ8H/ZWKoh1+5iwRVHMP0UWkkVQYxPJq0jxBcc1ZRi7FFWP7EIYipPgrZsoro3ObSOLi2MSFS2rYOUHLuIfjpBTPgA9BueK+e4J0AT3sjjCYQ/0tuyvnjBStAMz7hpFpPBQFvmpkW1ZibcparcvIXKhHCmNG5nxrlMkHLrmM7twL1L4gStyjv6SSRq9BMELEO9iPGhkka/NXLPUTIbWUdsx37Nrrrsv2m/HN5HQfYqFO6Vwjlyz35I5F+uQpBDMg04k4qptICaauSceRalL5HcnEOd+OldaI+MxY7D7fRa/97tc/fw1nvrbX6Ky/22+9sEM5afib/4DGHXhW3d/5qYPxY0bOUPzDOgc3tiVXLJ5/wOpOlwCxF6wBrOZ9/BNyW2P+6UNjryjjmvmNYsZZUU+MVXFjWTXyq8e+E57w619obk/ifs0ZmVBiqIpqY25zY4DiiRpD2FCrIzSBh8i83cT7KFK0dfCMWOZo2B6m4izYvedmM89iiIbcN98zATy69LD6Yv/SnINjrfge1+H/o/M9g0KpsMWKokkeJU9TDI1ez0sKwIPT3Q6Y6T4QwMHuR+zlb9O410EJnINXS2XuFVRrM8HrC8rTo4+QhfG0TgNSHXI7Teuc3djn/TUNXmns7HEoAss1mA87tOsVKlUoX+0x1yjQRKPqNY8hlHMQWeX5StrjCYD+oNjBuM+Tyz9Ar4TiB487EPdagFnNeg/JzKp1HpWnMnZODyc0NyLqdZg6Dk4ukq9qTgcjXEDWFiXfAe3D9RFA590pemQ68EPbov8SDkiD1qq55xrhLRaita8T7OpyY4TojBhOI4ZhJo4lhKtOpNeGF4Kfk/j7oa4nsighmORWN06gI0uPLckEePj3GUceYyHEaMxVPwc5VsySxgOPY5RUYJONePQ5IBkEnWt1sVIVxq0r3GcnNyFPISTvki3cgd0CP0TODblWYIGol13TQQ9hgttaI9hcp+8W8Zxkib82x/0eNmBX23DyiIEqkKvG5EdwCCEcQpjBTfvQWcMV9ry/YepufvTMMwmY9Vn4Pj0XXBSzc5hD7Smm2qCmgZCQt+jkjqEplmY1uBlDpVIzilTimqjRp4qHMeBJCCexCw0qzy34rN6yYSZHydaTKlp14PgHFTWYLwN8R7oXakqZpO1p5HALtNa5Y2rxi6+TSEDALwLpvTiCUWvBy37UJEKXJO3kcjVUD7LrfTpCtCRak1hBemgOy/762OgDd4VSLtmLF3ur+ZiHZoVZFG0LEdkxmOlC5k5bg1hdFzEkLe6GCufGs0c+03ZLsR8tgPOEji7oNtMAx26S1EXft4c08rOnjE/N814PMhX5b2wpTOnC/nEjEEjj+QlIBUWR9uIZi5Flu4rLwlTyYbbkjniPgMplcR8z4PFNTjZhPSEDwgZm6//CZuvfx3/P/17XPXhDEe+9Dn41h+dfT8HaLjwTAteP4FLLdjqS0TuORfGh3BeF9VFSzwmWHbBVjeCQp40a8TPbu9AZeHUu2Rh3rvp395JH2+DISBzj3U+3slqqiKGvw3YzJv/20Roi2zmp5VjWbZDURSIGFAUAzlvtj0C2lBbgnTesDKWpW1hLFYkBhcizKpnxt9A5k8rUR0iAZc+kufShY3vwst/Q7H6PDj/SxfnOjHn1KVoaPpOLMVpadTs/XmMooBHjgpyX/a4Pxn+PWKUw2s3hpw/f5eVxxjPfD/geQ7HnQ4333Q56uwSTrJ37UPaRyhwpF/OnTc2+MIXvsjtt9+m4rkM0zFRf0BrYYGDTocoCUGnjMcDJuEIVynyPJPIUHogyctTR8Pox98HNJtz1KpnZ0fO5Gy0Xvgc6XyfUIVk1EgSj5GvaM43GCcxwzgRhiLO8MOYeQ01NSbKJriuyyoOg25Md5jT78K9LuymcK4Ni4su7YUmo+GEpJ6SLmgSrWk0fJq1Cr7voZQi17lYw6qK63m4E/me4+MR4cKQLzzdZvlcBddXDLKckyzHd2sorchcRaXu4jhy+7M0YzwJSdIU3/PwlcJJc9I0xVEOlaopb6UVOZ5EsR1wEs1ckqAyhXYyxsfHzMcpC3VZkFXg4qDwPYeFWkqc5rjVeQ7u5ry9MTh1VSsooum8lTQgbDuM6gHNaohjkpLjoek2uQLtCjgJuG7Ai4OcfraOdCwW6+3p9YTll+YJ3BMgo57WpIdQOiBTGqfeJK83SCoB0XiCpxx83yMJIYyE8kscyMgJsxinEuD5c9SjHJIhqdYsrD535oft50HwLKxdlqT/MJTqYvF1aVqVKYqwQCrSvGm1FEuTZ0APhj+goPaNoa0WofEUDLaRmvBWwqSRBWgslc+mToJVw1l98x6wKEnfWV8eGWdZHOD4FuCB00QWOCsFsKVrI2GpdAVhT7pyDlxEFsktCn1wQlEta5VCNmCN/bHZbweZ9E0EzlkG7RjJ0xKEN8wx18yYtDleYM7lkEKCdQzcNjfB8rwmQT/LZv5vnbvZ3I8lZME/MEyPZVGGFNHTTK6DPydGj04kr2MK64z4Uvra0fIetJ6E4w88vK5JotGHNnfzT78Br71ytn08YNmBpUAYjJYW5tbiaiBs6fAjFGz8SwObqzArt5k1Wk8buBjm4vhdHt8e0xrP9n2fTfCep+gQ3jt9AAObTH2fdmTm99kkdjs3Z8gcNqGYP+YocjXWEOmoQuYkgHtSFRJf5nAdmn1tDpz9Hk3Ru6NCUemoR8HGzkh18gO4+Ydw86ta5ujZfL3JzO+W+a7xYIOZh70f72WieMg9/dDBXtP3EVtH8LVvhdKNnI+OYjOcjNnf2WV1eZFBLyR8D07lnCNSbh0qFpbXuPGj1/hrn/9lukcD/CxjOIqYjEIWl1YYjyPSXJHlOVmaEvcP0a0Gyd4W/srT0lEcKLr/BkyTVN+jnOpgr8fRztkDbO/a2VBK8fI/+h3gdwCR0Jc4G/7xBz2A9xlKvT8VDt4NXAANT30M9vZgd89o9z0eqOwRLEKYQfWSJO7ntymaJ3mIETxiGr3XgUliNqejPgb6LoX+VkmyWvMTpoLE60g52gRoQPWiNFy8/CQcHUkxgSCQogjJZel2X61B7zxMzH7OU6AqUuVl4Rk42Zdz0BmyyNp8k2fMyXcRoz+Tsa/9khRX2H4F6pdh6Qrc+45hUobmO87D4qfh6BWp3la/CLEnMr8pa1JBFt0RNK/BxIFsG7ggzJm2C6SLOCSH4KzA+YtSAjvR0O/D0S3IbWKmlU0sIY7PnPlnE7xtNLAm/6rn4KlPSKXX3g5MBtDbAOalVLhyRJaoXch8GIXgRKaiV4l3xI/+TMoLvxOadWGFZ9E21WJGEbx9By7m8JOZYwQ5ZEnRTbzEY4QnHZdHD8tVsrlNNrdg5v7kMwayqgnbTg5OBVrnobfFtJ+S7GB+riCyx9mCEwOKHI1ZC3DWIsxnjmELRthCE3bJ8E7trxAWdEhRQQoK2VEs8nO9Z+a4VTlnPYTGokhje3sUeR4L4K5D9iMKx+kEmesWofqkCbpYx8Yy2XVk3rUMyyJFgOi0Y6coimucdjagcNpswOQsFrPdt0Kxvj0Mj28JfmeYHLv30yPIgY096KiPYBqLgl5vl/39vWkRwjPsSt2RPCu3HrC7t8O4P+DchXVG/QFN36Wzs0kWpcwvLjMYjfErVZTjE5NyvL9Lc86hs32XtYUn8CohBQVnH8Sf76Hpd0fEvbMvAEprXS4bJUqUKFGiRIkSJUqUeN/h/OxNSpQoUaJEiRIlSpQoUeLsKJ2NEiVKlChRokSJEiVKPBKUzkaJEiVKlChRokSJEiUeCUpno0SJEiVKlChRokSJEo8EpbNRokSJEiVKlChRokSJR4LS2ShRokSJEiVKlChRosQjQelslChRokSJEiVKlChR4pGgdDZKlChRokSJEiVKlCjxSFA6GyVKlChRokSJEiVKlHgk+H/ly8q2YlO5TQAAAABJRU5ErkJggg==\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "transfer_model = resnet18(weights=ResNet18_Weights.DEFAULT)\n",
+        "\n",
+        "transfer_model.fc = nn.Linear(transfer_model.fc.in_features, len(class_names))\n",
+        "\n",
+        "transfer_model = transfer_model.to(device)"
+      ],
+      "metadata": {
+        "id": "HT0h4yF4VDzo"
+      },
+      "execution_count": 21,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "criterion = nn.CrossEntropyLoss()\n",
+        "optimizer = optim.Adam(transfer_model.parameters(), lr=0.0001, weight_decay=1e-5)\n",
+        "scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.1, patience=5, verbose=True)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "flp0KTUOVI_y",
+        "outputId": "3901ebd0-54b9-4905-98c9-4f664907236d"
+      },
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.11/dist-packages/torch/optim/lr_scheduler.py:62: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.\n",
+            "  warnings.warn(\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "best_val_loss = float('inf')\n",
+        "patience = 5\n",
+        "no_improvement = 0\n",
+        "\n",
+        "def train(model, train_loader, val_loader, criterion, optimizer, epochs=15):\n",
+        "    global best_val_loss, no_improvement\n",
+        "    model.train()\n",
+        "    for epoch in range(epochs):\n",
+        "        running_loss, correct, total = 0.0, 0, 0\n",
+        "        with tqdm(train_loader, total=len(train_loader), desc=f'Epoch {epoch+1}/{epochs}', ncols=100) as pbar:\n",
+        "            for images, labels in pbar:\n",
+        "                images, labels = images.to(device), labels.to(device)\n",
+        "                optimizer.zero_grad()\n",
+        "\n",
+        "                outputs = model(images)\n",
+        "                loss = criterion(outputs, labels)\n",
+        "                loss.backward()\n",
+        "                torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n",
+        "                optimizer.step()\n",
+        "\n",
+        "                running_loss += loss.item()\n",
+        "                _, predicted = torch.max(outputs, 1)\n",
+        "                total += labels.size(0)\n",
+        "                correct += (predicted == labels).sum().item()\n",
+        "\n",
+        "                pbar.set_postfix(loss=running_loss/len(train_loader), accuracy=100 * correct/total)\n",
+        "\n",
+        "\n",
+        "        val_loss, val_accuracy = validate(model, val_loader, criterion)\n",
+        "        print(f\"Epoch {epoch+1}/{epochs} | Train Loss: {running_loss/len(train_loader):.4f} | Train Accuracy: {100 * correct/total:.2f}% | Val Loss: {val_loss:.4f} | Val Accuracy: {val_accuracy:.2f}%\")\n",
+        "\n",
+        "\n",
+        "        if val_loss < best_val_loss:\n",
+        "            best_val_loss = val_loss\n",
+        "            no_improvement = 0\n",
+        "            torch.save(model.state_dict(), 'best_model.pth')\n",
+        "        else:\n",
+        "            no_improvement += 1\n",
+        "            if no_improvement >= patience:\n",
+        "                print(f\"Early stopping at epoch {epoch+1}\")\n",
+        "                break\n",
+        "\n",
+        "\n",
+        "        scheduler.step(val_loss)"
+      ],
+      "metadata": {
+        "id": "DuzgRiZTVL-f"
+      },
+      "execution_count": 23,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def validate(model, val_loader, criterion):\n",
+        "    model.eval()\n",
+        "    val_loss, correct, total = 0.0, 0, 0\n",
+        "    with torch.no_grad():\n",
+        "        for images, labels in val_loader:\n",
+        "            images, labels = images.to(device), labels.to(device)\n",
+        "            outputs = model(images)\n",
+        "            loss = criterion(outputs, labels)\n",
+        "            val_loss += loss.item()\n",
+        "            _, predicted = torch.max(outputs, 1)\n",
+        "            total += labels.size(0)\n",
+        "            correct += (predicted == labels).sum().item()\n",
+        "    model.train()\n",
+        "    return val_loss / len(val_loader), 100 * correct / total"
+      ],
+      "metadata": {
+        "id": "QVQaGQJTVPQU"
+      },
+      "execution_count": 24,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train(transfer_model, train_loader, val_loader, criterion, optimizer, epochs=15)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8ZHO6U7HVQwG",
+        "outputId": "a8e19532-6efe-413f-94fb-57ee05b34448"
+      },
+      "execution_count": 25,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Epoch 1/15: 100%|████████████████████████| 328/328 [00:44<00:00,  7.36it/s, accuracy=93, loss=0.242]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/15 | Train Loss: 0.2420 | Train Accuracy: 92.96% | Val Loss: 0.1360 | Val Accuracy: 95.86%\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Epoch 2/15: 100%|█████████████████████| 328/328 [00:44<00:00,  7.44it/s, accuracy=98.8, loss=0.0444]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 2/15 | Train Loss: 0.0444 | Train Accuracy: 98.78% | Val Loss: 0.1505 | Val Accuracy: 95.84%\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Epoch 3/15: 100%|█████████████████████| 328/328 [00:44<00:00,  7.39it/s, accuracy=99.6, loss=0.0148]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 3/15 | Train Loss: 0.0148 | Train Accuracy: 99.65% | Val Loss: 0.1350 | Val Accuracy: 96.10%\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Epoch 4/15: 100%|████████████████████| 328/328 [00:44<00:00,  7.35it/s, accuracy=99.8, loss=0.00791]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 4/15 | Train Loss: 0.0079 | Train Accuracy: 99.79% | Val Loss: 0.1526 | Val Accuracy: 95.99%\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Epoch 5/15: 100%|████████████████████| 328/328 [00:44<00:00,  7.36it/s, accuracy=99.9, loss=0.00561]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 5/15 | Train Loss: 0.0056 | Train Accuracy: 99.89% | Val Loss: 0.1516 | Val Accuracy: 96.01%\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Epoch 6/15: 100%|█████████████████████| 328/328 [00:44<00:00,  7.45it/s, accuracy=99.7, loss=0.0109]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 6/15 | Train Loss: 0.0109 | Train Accuracy: 99.68% | Val Loss: 0.1876 | Val Accuracy: 95.30%\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Epoch 7/15: 100%|████████████████████| 328/328 [00:44<00:00,  7.29it/s, accuracy=99.7, loss=0.00963]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 7/15 | Train Loss: 0.0096 | Train Accuracy: 99.67% | Val Loss: 0.1677 | Val Accuracy: 95.93%\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Epoch 8/15: 100%|████████████████████| 328/328 [00:44<00:00,  7.41it/s, accuracy=99.8, loss=0.00685]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 8/15 | Train Loss: 0.0068 | Train Accuracy: 99.78% | Val Loss: 0.2224 | Val Accuracy: 95.07%\n",
+            "Early stopping at epoch 8\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def evaluate(model, val_loader):\n",
+        "    model.eval()\n",
+        "    all_preds = []\n",
+        "    all_labels = []\n",
+        "    with torch.no_grad():\n",
+        "        for images, labels in val_loader:\n",
+        "            images, labels = images.to(device), labels.to(device)\n",
+        "            outputs = model(images)\n",
+        "            _, preds = torch.max(outputs, 1)\n",
+        "            all_preds.extend(preds.cpu().numpy())\n",
+        "            all_labels.extend(labels.cpu().numpy())\n",
+        "\n",
+        "    cm = confusion_matrix(all_labels, all_preds)\n",
+        "    plt.figure(figsize=(10, 10))\n",
+        "    sns.heatmap(cm, annot=True, fmt=\"d\", xticklabels=class_names, yticklabels=class_names)\n",
+        "    plt.xlabel(\"Predicted\")\n",
+        "    plt.ylabel(\"True\")\n",
+        "    plt.show()\n",
+        "\n",
+        "\n",
+        "    print(classification_report(all_labels, all_preds, target_names=class_names))\n",
+        "\n",
+        "\n",
+        "evaluate(transfer_model, val_loader)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "F9tkTOH0XsB2",
+        "outputId": "6ba65df8-f031-4ed9-a4d3-7b6cc7f6fa30"
+      },
+      "execution_count": 27,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x1000 with 2 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "   butterfly       0.98      0.96      0.97       435\n",
+            "         cat       0.93      0.95      0.94       333\n",
+            "     chicken       0.96      0.98      0.97       653\n",
+            "         cow       0.94      0.93      0.93       374\n",
+            "         dog       0.98      0.94      0.96       969\n",
+            "    elephant       0.98      0.95      0.97       281\n",
+            "       horse       0.95      0.97      0.96       501\n",
+            "       sheep       0.93      0.94      0.94       350\n",
+            "      spider       0.97      0.99      0.98       980\n",
+            "    squirrel       0.95      0.97      0.96       360\n",
+            "\n",
+            "    accuracy                           0.96      5236\n",
+            "   macro avg       0.96      0.96      0.96      5236\n",
+            "weighted avg       0.96      0.96      0.96      5236\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "from torchvision import transforms\n",
+        "from PIL import Image\n",
+        "from google.colab import files\n",
+        "\n",
+        "\n",
+        "model = resnet18(weights=None)\n",
+        "model.fc = nn.Linear(model.fc.in_features, 10)\n",
+        "model.load_state_dict(torch.load('best_model.pth'))\n",
+        "model.eval()\n",
+        "\n",
+        "\n",
+        "class_names = [\n",
+        "    \"butterfly\", \"cat\", \"chicken\", \"cow\", \"dog\",\n",
+        "    \"elephant\", \"horse\", \"sheep\", \"spider\", \"squirrel\"\n",
+        "]\n",
+        "\n",
+        "\n",
+        "def preprocess_image(image):\n",
+        "    transform = transforms.Compose([\n",
+        "        transforms.Resize((224, 224)),\n",
+        "        transforms.ToTensor(),\n",
+        "        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
+        "    ])\n",
+        "    return transform(image).unsqueeze(0)\n",
+        "\n",
+        "\n",
+        "def predict_image(image_path):\n",
+        "    try:\n",
+        "\n",
+        "        image = Image.open(image_path).convert(\"RGB\")\n",
+        "        input_tensor = preprocess_image(image)\n",
+        "\n",
+        "        with torch.no_grad():\n",
+        "            output = model(input_tensor)\n",
+        "            probabilities = torch.softmax(output, dim=1).squeeze().cpu().numpy()\n",
+        "\n",
+        "        predictions = {class_names[i]: float(probabilities[i]) for i in range(len(class_names))}\n",
+        "        return predictions\n",
+        "    except Exception as e:\n",
+        "        return {\"error\": str(e)}\n",
+        "\n",
+        "def main():\n",
+        "\n",
+        "    uploaded_files = files.upload()\n",
+        "\n",
+        "\n",
+        "    for image_name, image_content in uploaded_files.items():\n",
+        "        print(f\"\\nProcessing image: {image_name}\")\n",
+        "\n",
+        "\n",
+        "        with open(image_name, \"wb\") as f:\n",
+        "            f.write(image_content)\n",
+        "\n",
+        "\n",
+        "        predictions = predict_image(image_name)\n",
+        "\n",
+        "        if \"error\" in predictions:\n",
+        "            print(f\"Error processing {image_name}: {predictions['error']}\")\n",
+        "        else:\n",
+        "            print(\"Predictions with probabilities:\")\n",
+        "            for class_name, prob in predictions.items():\n",
+        "                print(f\"{class_name}: {prob:.4f}\")\n",
+        "\n",
+        "\n",
+        "if __name__ == \"__main__\":\n",
+        "    main()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 353
+        },
+        "id": "jLjP3L9AX79y",
+        "outputId": "b7bbbc97-b7f5-4204-ebd9-9ab9cb154c6f"
+      },
+      "execution_count": 28,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "<ipython-input-28-ab0c475d5f09>:10: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+            "  model.load_state_dict(torch.load('best_model.pth'))\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-de97a8eb-6f40-4179-b0f3-4f2b04741ab7\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-de97a8eb-6f40-4179-b0f3-4f2b04741ab7\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving horse png.png to horse png (1).png\n",
+            "\n",
+            "Processing image: horse png (1).png\n",
+            "Predictions with probabilities:\n",
+            "butterfly: 0.0002\n",
+            "cat: 0.0003\n",
+            "chicken: 0.0003\n",
+            "cow: 0.0000\n",
+            "dog: 0.0020\n",
+            "elephant: 0.0000\n",
+            "horse: 0.9962\n",
+            "sheep: 0.0004\n",
+            "spider: 0.0006\n",
+            "squirrel: 0.0000\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Iron hack Triple project.pdf b/Iron hack Triple project.pdf
new file mode 100644
index 00000000..10017761
Binary files /dev/null and b/Iron hack Triple project.pdf differ
diff --git a/Triple pixels.pptx b/Triple pixels.pptx
new file mode 100644
index 00000000..9280dac7
Binary files /dev/null and b/Triple pixels.pptx differ