diff --git a/notebooks/exploring_onsd.ipynb b/notebooks/exploring_onsd.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..78a1a43d957fc4cceb6b5c119df075a61938d6b0
--- /dev/null
+++ b/notebooks/exploring_onsd.ipynb
@@ -0,0 +1,220 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ea267f3e-e104-4ab5-a3e7-a88245539127",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-07-22 19:18:15.582352: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:15.584225: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:15.585984: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:15.587061: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:15.600518: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:15.602470: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:15.604264: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:15.605003: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:15.606801: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:15.608539: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:15.610332: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:15.611042: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:15.618607: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
+      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+      "2022-07-22 19:18:16.048633: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:16.050425: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:16.052115: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:16.052779: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:16.054531: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:16.056236: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:16.057927: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:16.058562: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:16.060228: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:16.061956: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:16.063637: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:16.064268: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:17.753924: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:17.755787: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:17.757590: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:17.758279: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:17.760024: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:17.761768: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:17.763449: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:17.764100: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:17.765835: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:17.767551: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 43667 MB memory:  -> device: 0, name: NVIDIA A40, pci bus id: 0000:01:00.0, compute capability: 8.6\n",
+      "2022-07-22 19:18:17.768085: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:17.769772: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 43667 MB memory:  -> device: 1, name: NVIDIA A40, pci bus id: 0000:02:00.0, compute capability: 8.6\n",
+      "2022-07-22 19:18:17.770274: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:17.770894: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:2 with 1311 MB memory:  -> device: 2, name: NVIDIA A40, pci bus id: 0000:03:00.0, compute capability: 8.6\n",
+      "2022-07-22 19:18:17.771266: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:17.772925: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:3 with 43667 MB memory:  -> device: 3, name: NVIDIA A40, pci bus id: 0000:04:00.0, compute capability: 8.6\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "os.chdir('..')\n",
+    "\n",
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "from yolov4.get_bounding_boxes import YoloModel\n",
+    "from sparse_coding_torch.utils import VideoGrayScaler, MinMaxScaler\n",
+    "import torchvision\n",
+    "from sparse_coding_torch.utils import plot_video"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "6f1b2c8a-8d98-43a0-a96f-d1ea0a2b4720",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-07-22 19:18:19.462568: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:19.463520: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:19.465541: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:19.466311: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:19.468121: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:19.468851: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:19.470647: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:19.471384: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:19.473172: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:19.473929: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:19.475708: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:19.476443: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:19.485103: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:19.485924: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:19.487761: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:19.488493: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:19.490344: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:19.491064: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 43667 MB memory:  -> device: 0, name: NVIDIA A40, pci bus id: 0000:01:00.0, compute capability: 8.6\n",
+      "2022-07-22 19:18:19.491196: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:19.492918: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 43667 MB memory:  -> device: 1, name: NVIDIA A40, pci bus id: 0000:02:00.0, compute capability: 8.6\n",
+      "2022-07-22 19:18:19.493060: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:19.493691: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:2 with 1311 MB memory:  -> device: 2, name: NVIDIA A40, pci bus id: 0000:03:00.0, compute capability: 8.6\n",
+      "2022-07-22 19:18:19.493823: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-22 19:18:19.495576: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:3 with 43667 MB memory:  -> device: 3, name: NVIDIA A40, pci bus id: 0000:04:00.0, compute capability: 8.6\n"
+     ]
+    }
+   ],
+   "source": [
+    "yolo_model = YoloModel('onsd')\n",
+    "video_path = \"/shared_data/bamc_onsd_data/revised_extended_onsd_data/\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3b05ae07-1df0-4e26-9083-86ab5225fab6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 37/37 [06:48<00:00, 11.04s/it]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib.pyplot import imshow\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib import cm\n",
+    "import math\n",
+    "from tqdm import tqdm\n",
+    "import glob\n",
+    "from os.path import join, abspath\n",
+    "\n",
+    "labels = [name for name in os.listdir(video_path) if os.path.isdir(os.path.join(video_path, name))]\n",
+    "\n",
+    "videos = []\n",
+    "for label in labels:\n",
+    "    videos.extend([(label, abspath(join(video_path, label, f)), f) for f in glob.glob(join(video_path, label, '*', '*.mp4'))])\n",
+    "\n",
+    "all_widths = []\n",
+    "all_colors = []\n",
+    "for label, path, vid_f in tqdm(videos):\n",
+    "    vc = torchvision.io.read_video(path)[0].permute(3, 0, 1, 2)\n",
+    "    \n",
+    "    orig_height = vc.size(2)\n",
+    "    orig_width = vc.size(3)\n",
+    "\n",
+    "    all_nerve_widths = []\n",
+    "    \n",
+    "    for i in range(0, vc.size(1), 10):\n",
+    "        frame = vc[:, i, :, :].swapaxes(0, 2).swapaxes(0, 1).numpy()\n",
+    "\n",
+    "        bounding_boxes, classes, scores = yolo_model.get_bounding_boxes_v5(frame)\n",
+    "\n",
+    "        nerve_bbs = [bb for bb, class_pred, score in zip(bounding_boxes, classes, scores) if class_pred==0]\n",
+    "        \n",
+    "        for nerve in nerve_bbs:\n",
+    "            width = (nerve[2] - nerve[0]) * orig_width\n",
+    "            all_nerve_widths.append(width)\n",
+    "            \n",
+    "    if not all_nerve_widths:\n",
+    "        continue\n",
+    "    smallest_width = np.max(np.array(all_nerve_widths))\n",
+    "    \n",
+    "    all_widths.append(smallest_width)\n",
+    "    if label == 'Positives':\n",
+    "        all_colors.append('green')\n",
+    "    elif label == 'Negatives':\n",
+    "        all_colors.append('red')\n",
+    "    else:\n",
+    "        raise Exception('Bad Label')\n",
+    "\n",
+    "plt.scatter(all_widths, range(len(all_widths)), color=all_colors)\n",
+    "plt.savefig('onsd_nerve_plot.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "51427400-238d-4d5a-b139-5d28b2084f9c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python (pocus_project)",
+   "language": "python",
+   "name": "darryl_pocus"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/exploring_pnb.ipynb b/notebooks/exploring_pnb.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..5baa63839757b86a0df8b72a772559774b66912d
--- /dev/null
+++ b/notebooks/exploring_pnb.ipynb
@@ -0,0 +1,976 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "40fe0f6e-aa6a-4d7a-9175-6b6e6aa02412",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-08-12 01:31:29.832438: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:29.834371: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:29.836208: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:29.838047: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:29.849348: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:29.851260: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:29.853111: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:29.855336: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:29.857171: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:29.858973: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:29.860793: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:29.862614: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:29.866676: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
+      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+      "2022-08-12 01:31:30.290806: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:30.292756: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:30.294584: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:30.296398: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:30.298162: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:30.299945: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:30.301681: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:30.303459: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:30.305247: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:30.307002: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:30.308785: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:30.310617: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:42.727334: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:42.729222: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:42.731200: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:42.732942: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:42.734672: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:42.736372: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:42.738076: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:42.739774: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:42.741476: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:42.743183: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 42277 MB memory:  -> device: 0, name: NVIDIA A40, pci bus id: 0000:01:00.0, compute capability: 8.6\n",
+      "2022-08-12 01:31:42.744358: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:42.746217: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 42277 MB memory:  -> device: 1, name: NVIDIA A40, pci bus id: 0000:02:00.0, compute capability: 8.6\n",
+      "2022-08-12 01:31:42.746913: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:42.748617: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:2 with 42277 MB memory:  -> device: 2, name: NVIDIA A40, pci bus id: 0000:03:00.0, compute capability: 8.6\n",
+      "2022-08-12 01:31:42.749184: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:42.750883: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:3 with 42277 MB memory:  -> device: 3, name: NVIDIA A40, pci bus id: 0000:04:00.0, compute capability: 8.6\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "os.chdir('..')\n",
+    "\n",
+    "from sparse_coding_torch.pnb.video_loader import PNBLoader\n",
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "from yolov4.get_bounding_boxes import YoloModel\n",
+    "from sparse_coding_torch.utils import VideoGrayScaler, MinMaxScaler\n",
+    "import torchvision\n",
+    "from sparse_coding_torch.utils import plot_video"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a9ea96d9-6ef6-4ee6-82ac-c6dc45f7caa5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-08-12 01:31:43.687293: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:43.688181: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:43.689973: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:43.691706: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:43.693507: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:43.694194: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:43.695976: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:43.697709: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:43.699416: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:43.700118: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:43.701987: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:43.703849: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:43.705839: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:43.706615: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:43.708415: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:43.710193: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:43.711997: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:43.712708: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 42277 MB memory:  -> device: 0, name: NVIDIA A40, pci bus id: 0000:01:00.0, compute capability: 8.6\n",
+      "2022-08-12 01:31:43.712835: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:43.714563: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 42277 MB memory:  -> device: 1, name: NVIDIA A40, pci bus id: 0000:02:00.0, compute capability: 8.6\n",
+      "2022-08-12 01:31:43.714702: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:43.716440: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:2 with 42277 MB memory:  -> device: 2, name: NVIDIA A40, pci bus id: 0000:03:00.0, compute capability: 8.6\n",
+      "2022-08-12 01:31:43.716576: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-12 01:31:43.718309: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:3 with 42277 MB memory:  -> device: 3, name: NVIDIA A40, pci bus id: 0000:04:00.0, compute capability: 8.6\n"
+     ]
+    }
+   ],
+   "source": [
+    "yolo_model = YoloModel('pnb')\n",
+    "video_path = \"/shared_data/bamc_pnb_data/revised_training_data/\"\n",
+    "transform = torchvision.transforms.Compose(\n",
+    "    [VideoGrayScaler(),\n",
+    "     torchvision.transforms.Resize((250, 400))\n",
+    "    ])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ad8d82ce-a5f2-48ae-b7e0-29be2522ca9b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "301\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f79141913a0>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib.pyplot import imshow\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib import cm\n",
+    "import matplotlib.patches as patches\n",
+    "\n",
+    "labels = [name for name in os.listdir(video_path) if os.path.isdir(os.path.join(video_path, name))]\n",
+    "\n",
+    "videos = [('Positives', os.path.abspath(os.path.join(video_path, 'Positives', '93', '3. 93 AC_Video 2.mp4')))]\n",
+    "\n",
+    "label, path = videos[0]\n",
+    "vc = torchvision.io.read_video(path)[0].permute(3, 0, 1, 2)\n",
+    "print(vc.size(1))\n",
+    "\n",
+    "frame = vc[:, 300, :, :].swapaxes(0, 2).swapaxes(0, 1).numpy()\n",
+    "\n",
+    "imshow(frame)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dfc484b8-4907-4bb2-8c8b-e13445147fc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1080, 912, 3)\n",
+      "[0.35116956 0.56193376 0.5238077  0.86585724]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f7914112550>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "orig_height = vc.size(2)\n",
+    "orig_width = vc.size(3)\n",
+    "print(frame.shape)\n",
+    "bounding_boxes, classes, scores = yolo_model.get_bounding_boxes(frame)\n",
+    "bounding_boxes = bounding_boxes.squeeze(0)\n",
+    "classes = classes.squeeze(0)\n",
+    "scores = scores.squeeze(0)\n",
+    "\n",
+    "needle_bb = [bb for bb, class_pred, score in zip(bounding_boxes, classes, scores) if class_pred==2][0]\n",
+    "\n",
+    "x_min = int(needle_bb[1] * orig_width)\n",
+    "x_max = int(needle_bb[3] * orig_width)\n",
+    "y_min = int(needle_bb[0] * orig_height)\n",
+    "y_max = int(needle_bb[2] * orig_height)\n",
+    "\n",
+    "print(needle_bb)\n",
+    "imshow(frame[y_min:y_max, x_min:x_max, :])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7af3ea06-6173-4cef-9e40-9750dd8d8d4d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparse_coding_torch.pnb.video_loader import classify_nerve_is_right\n",
+    "from matplotlib.pyplot import imshow\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib import cm\n",
+    "import matplotlib.patches as patches\n",
+    "\n",
+    "labels = [name for name in os.listdir(video_path) if os.path.isdir(os.path.join(video_path, name))]\n",
+    "\n",
+    "videos = [('Positives', os.path.abspath(os.path.join(video_path, 'Positives', '93', '3. 93 AC_Video 2.mp4')))]\n",
+    "\n",
+    "label, path = videos[0]\n",
+    "vc = torchvision.io.read_video(path)[0].permute(3, 0, 1, 2)\n",
+    "is_right = classify_nerve_is_right(yolo_model, vc)\n",
+    "\n",
+    "frame = vc[:, -5, :, :].swapaxes(0, 2).swapaxes(0, 1).numpy()\n",
+    "\n",
+    "imshow(frame)\n",
+    "\n",
+    "orig_height = vc.size(2)\n",
+    "orig_width = vc.size(3)\n",
+    "bounding_boxes, classes, scores = yolo_model.get_bounding_boxes_v5(frame)\n",
+    "\n",
+    "nerve_bb = [bb for bb, class_pred, score in zip(bounding_boxes, classes, scores) if class_pred==1][0]\n",
+    "needle_bb = [bb for bb, class_pred, score in zip(bounding_boxes, classes, scores) if class_pred==2][0]\n",
+    "\n",
+    "nerve_center_x = round((nerve_bb[2] + nerve_bb[0]) / 2 * orig_width)\n",
+    "nerve_center_y = round((nerve_bb[3] + nerve_bb[1]) / 2 * orig_height)\n",
+    "\n",
+    "needle_center_x = round((needle_bb[2] + needle_bb[0]) / 2 * orig_width)\n",
+    "needle_center_y = round((needle_bb[3] + needle_bb[1]) / 2 * orig_height)\n",
+    "\n",
+    "# Create figure and axes\n",
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "# Display the image\n",
+    "ax.imshow(frame, cmap=cm.Greys_r)\n",
+    "\n",
+    "# Create a Rectangle patch\n",
+    "# nerve_rect = patches.Rectangle((nerve_bb[0] * orig_width, nerve_bb[3] * orig_height), (nerve_bb[2] - nerve_bb[0]) * orig_width, (nerve_bb[3] - nerve_bb[1]) * -orig_height, linewidth=1, edgecolor='r', facecolor='none')\n",
+    "# needle_rect = patches.Rectangle((needle_bb[0] * orig_width, needle_bb[3] * orig_height), (needle_bb[2] - needle_bb[0]) * orig_width, (needle_bb[3] - needle_bb[1]) * -orig_height, linewidth=1, edgecolor='b', facecolor='none')\n",
+    "# print(needle_bb)\n",
+    "\n",
+    "# # Add the patch to the Axes\n",
+    "# ax.add_patch(nerve_rect)\n",
+    "# ax.add_patch(needle_rect)\n",
+    "plt.scatter([needle_bb[0]*orig_width], [needle_bb[3]*orig_height], color=[\"red\"])\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c18f383d-86a6-42c6-bbf9-3df2b3a86d8a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparse_coding_torch.pnb.video_loader import classify_nerve_is_right\n",
+    "from matplotlib.pyplot import imshow\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib import cm\n",
+    "import matplotlib.patches as patches\n",
+    "import math\n",
+    "from tqdm import tqdm\n",
+    "import glob\n",
+    "from os.path import join, abspath\n",
+    "\n",
+    "labels = [name for name in os.listdir(video_path) if os.path.isdir(os.path.join(video_path, name))]\n",
+    "\n",
+    "videos = []\n",
+    "for label in labels:\n",
+    "    videos.extend([(label, abspath(join(video_path, label, f)), f) for f in glob.glob(join(video_path, label, '*', '*.mp4'))])\n",
+    "\n",
+    "all_distances = []\n",
+    "all_colors = []\n",
+    "for label, path, vid_f in tqdm(videos):\n",
+    "    vc = torchvision.io.read_video(path)[0].permute(3, 0, 1, 2)\n",
+    "    is_right = classify_nerve_is_right(yolo_model, vc)\n",
+    "    \n",
+    "    orig_height = vc.size(2)\n",
+    "    orig_width = vc.size(3)\n",
+    "    \n",
+    "    nerve_bb = []\n",
+    "    needle_bb = []\n",
+    "    \n",
+    "    for i in range(vc.size(1) - 1, vc.size(1) - 40, -1):\n",
+    "        frame = vc[:, i, :, :].swapaxes(0, 2).swapaxes(0, 1).numpy()\n",
+    "\n",
+    "        bounding_boxes, classes, scores = yolo_model.get_bounding_boxes_v5(frame)\n",
+    "\n",
+    "        nerve_bb = [bb for bb, class_pred, score in zip(bounding_boxes, classes, scores) if class_pred==1]\n",
+    "        needle_bb = [bb for bb, class_pred, score in zip(bounding_boxes, classes, scores) if class_pred==2]\n",
+    "        \n",
+    "        if len(nerve_bb) > 0 and len(needle_bb) > 0:\n",
+    "            nerve_bb = nerve_bb[0]\n",
+    "            needle_bb = needle_bb[0]\n",
+    "            break\n",
+    "\n",
+    "    if len(nerve_bb) == 0 or len(needle_bb) == 0:\n",
+    "        continue\n",
+    "\n",
+    "    nerve_x = round((nerve_bb[2] + nerve_bb[0]) / 2 * orig_width)\n",
+    "    nerve_y = round((nerve_bb[3] + nerve_bb[1]) / 2 * orig_height)\n",
+    "    \n",
+    "    needle_x = needle_bb[2] * orig_width\n",
+    "    needle_y = needle_bb[3] * orig_height\n",
+    "\n",
+    "    if not is_right:\n",
+    "        needle_x = needle_bb[0] * orig_width\n",
+    "        \n",
+    "    distance = math.sqrt((nerve_x - needle_x)**2 + (nerve_y - needle_y)**2)\n",
+    "    \n",
+    "    all_distances.append(distance)\n",
+    "    if label == 'Positives':\n",
+    "        all_colors.append('green')\n",
+    "    elif label == 'Negatives':\n",
+    "        all_colors.append('red')\n",
+    "    else:\n",
+    "        raise Exception('Bad Label')\n",
+    "\n",
+    "plt.scatter(all_distances, [0]*len(all_distances), color=all_colors)\n",
+    "plt.savefig('nerve_plot.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4fe8f149-ec36-4469-bc8a-37297670165d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparse_coding_torch.pnb.video_loader import calculate_angle\n",
+    "import torchvision\n",
+    "import cv2\n",
+    "\n",
+    "def get_yolo_regions_polar(yolo_model, clip, is_right, needle_bb, crop_width, crop_height):\n",
+    "    orig_height = clip.size(2)\n",
+    "    orig_width = clip.size(3)\n",
+    "    bounding_boxes, classes, scores = yolo_model.get_bounding_boxes(clip[:, 2, :, :].swapaxes(0, 2).swapaxes(0, 1).numpy())\n",
+    "    bounding_boxes = bounding_boxes.squeeze(0)\n",
+    "    classes = classes.squeeze(0)\n",
+    "    scores = scores.squeeze(0)\n",
+    "    \n",
+    "    needle_bb = None\n",
+    "    \n",
+    "    for bb, class_pred in zip(bounding_boxes, classes):\n",
+    "        if class_pred == 2:\n",
+    "            needle_bb = bb\n",
+    "            \n",
+    "    if needle_bb is None:\n",
+    "        return None\n",
+    "    \n",
+    "    all_clips = []\n",
+    "    for bb, class_pred, score in zip(bounding_boxes, classes, scores):\n",
+    "        if class_pred != 0:\n",
+    "            continue\n",
+    "        center_x = round((bb[3] + bb[1]) / 2 * orig_width)\n",
+    "        center_y = round((bb[2] + bb[0]) / 2 * orig_height)\n",
+    "        \n",
+    "        if not is_right:\n",
+    "            clip = torchvision.transforms.functional.hflip(clip)\n",
+    "            center_x = orig_width - center_x\n",
+    "            needle_bb[1] = orig_width - needle_bb[1]\n",
+    "            needle_bb[3] = orig_width - needle_bb[3]\n",
+    "        \n",
+    "#         lower_y = round((bb[0] * orig_height))\n",
+    "#         upper_y = round((bb[2] * orig_height))\n",
+    "#         lower_x = round((bb[1] * orig_width))\n",
+    "#         upper_x = round((bb[3] * orig_width))\n",
+    "        \n",
+    "#         if is_right:\n",
+    "        angle = calculate_angle(needle_bb, center_x, center_y, orig_height, orig_width)\n",
+    "#         else:\n",
+    "#             angle = calculate_angle(needle_bb, lower_x, center_y, orig_height, orig_width)\n",
+    "        \n",
+    "#         lower_y = center_y - (crop_height // 2)\n",
+    "#         upper_y = center_y + (crop_height // 2) \n",
+    "        \n",
+    "#         if is_right:\n",
+    "#             lower_x = center_x - crop_width\n",
+    "#             upper_x = center_x\n",
+    "#         else:\n",
+    "#             lower_x = center_x\n",
+    "#             upper_x = center_x + crop_width\n",
+    "            \n",
+    "#         if lower_x < 0:\n",
+    "#             lower_x = 0\n",
+    "#         if upper_x < 0:\n",
+    "#             upper_x = 0\n",
+    "#         if lower_y < 0:\n",
+    "#             lower_y = 0\n",
+    "#         if upper_y < 0:\n",
+    "#             upper_y = 0\n",
+    "        clip = torchvision.transforms.functional.rotate(clip, angle=angle, center=[center_x, center_y])\n",
+    "            \n",
+    "#         plt.clf()\n",
+    "#         plt.imshow(clip.numpy()[:, 0, :, :].swapaxes(0,1).swapaxes(1,2), cmap=cm.Greys_r)\n",
+    "#         # plt.scatter([214], [214], color=\"red\")\n",
+    "#         plt.scatter([center_x, int(needle_bb[1]*orig_width)], [center_y, int(needle_bb[0] * orig_height)], color=[\"red\", 'red'])\n",
+    "# #         cv2.imwrite('test_normal.png', clip.numpy()[:, 0, :, :].swapaxes(0,1).swapaxes(1,2))\n",
+    "#         plt.savefig('test_normal.png')\n",
+    "            \n",
+    "#         if rotate_box:\n",
+    "# #             cv2.imwrite('test_1.png', clip.numpy()[:, 0, :, :].swapaxes(0,1).swapaxes(1,2))\n",
+    "#             if is_right:\n",
+    "#         clip = tv.transforms.functional.rotate(clip, angle=angle, center=[center_x, center_y])\n",
+    "#             else:\n",
+    "# #                 cv2.imwrite('test_1.png', clip.numpy()[:, 0, :, :].swapaxes(0,1).swapaxes(1,2))\n",
+    "#                 clip = tv.transforms.functional.rotate(clip, angle=-angle, center=[center_x, center_y])\n",
+    "#                 cv2.imwrite('test_2.png', clip.numpy()[:, 0, :, :].swapaxes(0,1).swapaxes(1,2))\n",
+    "\n",
+    "#         plt.imshow(clip[0, 0, :, :], cmap=cm.Greys_r)\n",
+    "#         # plt.annotate('25, 50', xy=(25, 50), xycoords='data',\n",
+    "#         #             xytext=(0.5, 0.5), textcoords='figure fraction',\n",
+    "#         #             arrowprops=dict(arrowstyle=\"->\"))\n",
+    "#         plt.scatter([center_x], [center_y], color=\"red\")\n",
+    "#         plt.savefig('red_dot.png')\n",
+    "#         clip = clip[:, :, :upper_y, :]\n",
+    "\n",
+    "        ro,col=clip[0, 0, :, :].shape\n",
+    "        max_radius = int(np.sqrt(ro**2+col**2)/2)\n",
+    "#         print(upper_y)\n",
+    "#         print(bb[0])\n",
+    "#         print(center_x)\n",
+    "#         print(center_y)\n",
+    "        trimmed_clip = []\n",
+    "        for i in range(clip.shape[0]):\n",
+    "            sub_clip = []\n",
+    "            for j in range(clip.shape[1]):\n",
+    "                sub_clip.append(cv2.linearPolar(clip[i, j, :, :].numpy(), (center_x, center_y), max_radius, cv2.WARP_FILL_OUTLIERS))\n",
+    "#                 sub_clip.append(warp_polar(clip[i, j, :, :].numpy(), center=(center_x, center_y), radius=max_radius, preserve_range=True))\n",
+    "            trimmed_clip.append(np.stack(sub_clip))\n",
+    "        trimmed_clip = np.stack(trimmed_clip)\n",
+    "        \n",
+    "        approximate_needle_position = int(((angle+150)/360)*orig_height)\n",
+    "        \n",
+    "        trimmed_clip = trimmed_clip[:, :, approximate_needle_position - (crop_height//2):approximate_needle_position + (crop_height//2), :]\n",
+    "                \n",
+    "#         trimmed_clip=cv2.linearPolar(clip[0, 0, :, :].numpy(), (center_x, center_y), max_radius, cv2.WARP_FILL_OUTLIERS)\n",
+    "#         trimmed_clip = warp_polar(clip[0, 0, :, :].numpy(), center=(center_x, center_y), radius=max_radius)\n",
+    "\n",
+    "#         trimmed_clip = clip[:, :, lower_y:upper_y, lower_x:upper_x]\n",
+    "        \n",
+    "#         if orig_width - center_x >= center_x:\n",
+    "#         if not is_right:\n",
+    "#         print(angle)\n",
+    "#         if not is_right:\n",
+    "#         cv2.imwrite('test_polar.png', trimmed_clip[:, 0, :, :].swapaxes(0,1).swapaxes(1,2))\n",
+    "#         plt.clf()\n",
+    "#         plt.imshow(trimmed_clip[:, 0, :, :].swapaxes(0,1).swapaxes(1,2), cmap=cm.Greys_r)\n",
+    "#         # plt.scatter([214], [214], color=\"red\")\n",
+    "# #         plt.scatter([center_x], [approximate_needle_position], color=[\"red\"])\n",
+    "# #         cv2.imwrite('test_normal.png', clip.numpy()[:, 0, :, :].swapaxes(0,1).swapaxes(1,2))\n",
+    "#         plt.savefig('test_polar_trim.png')\n",
+    "#         raise Exception\n",
+    "\n",
+    "#         if not is_right:\n",
+    "#             trimmed_clip = tv.transforms.functional.hflip(trimmed_clip)\n",
+    "#             cv2.imwrite('test_polar.png', trimmed_clip)\n",
+    "#             cv2.imwrite('test_yolo.png', trimmed_clip.numpy()[:, 0, :, :].swapaxes(0,1).swapaxes(1,2))\n",
+    "#             raise Exception\n",
+    "        \n",
+    "        if trimmed_clip.shape[2] == 0 or trimmed_clip.shape[3] == 0:\n",
+    "            continue\n",
+    "        all_clips.append(torch.tensor(trimmed_clip))\n",
+    "\n",
+    "    return all_clips"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8e1ec8f8-22c0-4138-83fe-dd563438359b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_yolo_regions(yolo_model, clip, is_right, needle_bb, crop_width, crop_height):\n",
+    "    orig_height = clip.size(2)\n",
+    "    orig_width = clip.size(3)\n",
+    "    bounding_boxes, classes, scores = yolo_model.get_bounding_boxes(clip[:, 2, :, :].swapaxes(0, 2).swapaxes(0, 1).numpy())\n",
+    "    bounding_boxes = bounding_boxes.squeeze(0)\n",
+    "    classes = classes.squeeze(0)\n",
+    "    scores = scores.squeeze(0)\n",
+    "    \n",
+    "    for bb, class_pred in zip(bounding_boxes, classes):\n",
+    "        if class_pred == 2:\n",
+    "            needle_bb = bb\n",
+    "    \n",
+    "    rotate_box = False\n",
+    "    \n",
+    "    all_clips = []\n",
+    "    for bb, class_pred, score in zip(bounding_boxes, classes, scores):\n",
+    "        if class_pred != 0:\n",
+    "            continue\n",
+    "        center_x = round((bb[3] + bb[1]) / 2 * orig_width)\n",
+    "        center_y = round((bb[2] + bb[0]) / 2 * orig_height)\n",
+    "        \n",
+    "        if not is_right:\n",
+    "            clip = torchvision.transforms.functional.hflip(clip)\n",
+    "            center_x = orig_width - center_x\n",
+    "#             needle_bb[1] = orig_width - needle_bb[1]\n",
+    "#             needle_bb[3] = orig_width - needle_bb[3]\n",
+    "        \n",
+    "#         lower_y = round((bb[0] * orig_height))\n",
+    "#         upper_y = round((bb[2] * orig_height))\n",
+    "#         lower_x = round((bb[1] * orig_width))\n",
+    "#         upper_x = round((bb[3] * orig_width))\n",
+    "        \n",
+    "#         if is_right:\n",
+    "#         angle = calculate_angle(needle_bb, center_x, center_y, orig_height, orig_width)\n",
+    "#         else:\n",
+    "#             angle = calculate_angle(needle_bb, lower_x, center_y, orig_height, orig_width)\n",
+    "        \n",
+    "#         lower_y = center_y - (crop_height // 2)\n",
+    "#         upper_y = center_y + (crop_height // 2) \n",
+    "        \n",
+    "#         if is_right:\n",
+    "#             lower_x = center_x - crop_width\n",
+    "#             upper_x = center_x\n",
+    "#         else:\n",
+    "#             lower_x = center_x\n",
+    "#             upper_x = center_x + crop_width\n",
+    "            \n",
+    "#         if lower_x < 0:\n",
+    "#             lower_x = 0\n",
+    "#         if upper_x < 0:\n",
+    "#             upper_x = 0\n",
+    "#         if lower_y < 0:\n",
+    "#             lower_y = 0\n",
+    "#         if upper_y < 0:\n",
+    "#             upper_y = 0\n",
+    "#         clip = torchvision.transforms.functional.rotate(clip, angle=angle, center=[center_x, center_y])\n",
+    "            \n",
+    "#         plt.clf()\n",
+    "#         plt.imshow(clip.numpy()[:, 0, :, :].swapaxes(0,1).swapaxes(1,2), cmap=cm.Greys_r)\n",
+    "#         # plt.scatter([214], [214], color=\"red\")\n",
+    "#         plt.scatter([center_x, int(needle_bb[1]*orig_width)], [center_y, int(needle_bb[0] * orig_height)], color=[\"red\", 'red'])\n",
+    "# #         cv2.imwrite('test_normal.png', clip.numpy()[:, 0, :, :].swapaxes(0,1).swapaxes(1,2))\n",
+    "#         plt.savefig('test_normal.png')\n",
+    "            \n",
+    "#         if rotate_box:\n",
+    "# #             cv2.imwrite('test_1.png', clip.numpy()[:, 0, :, :].swapaxes(0,1).swapaxes(1,2))\n",
+    "#             if is_right:\n",
+    "#         clip = tv.transforms.functional.rotate(clip, angle=angle, center=[center_x, center_y])\n",
+    "#             else:\n",
+    "# #                 cv2.imwrite('test_1.png', clip.numpy()[:, 0, :, :].swapaxes(0,1).swapaxes(1,2))\n",
+    "#                 clip = tv.transforms.functional.rotate(clip, angle=-angle, center=[center_x, center_y])\n",
+    "#                 cv2.imwrite('test_2.png', clip.numpy()[:, 0, :, :].swapaxes(0,1).swapaxes(1,2))\n",
+    "\n",
+    "#         plt.imshow(clip[0, 0, :, :], cmap=cm.Greys_r)\n",
+    "#         # plt.annotate('25, 50', xy=(25, 50), xycoords='data',\n",
+    "#         #             xytext=(0.5, 0.5), textcoords='figure fraction',\n",
+    "#         #             arrowprops=dict(arrowstyle=\"->\"))\n",
+    "#         plt.scatter([center_x], [center_y], color=\"red\")\n",
+    "#         plt.savefig('red_dot.png')\n",
+    "#         clip = clip[:, :, :upper_y, :]\n",
+    "        \n",
+    "        trimmed_clip = clip[:, :, center_y - (crop_height//2):center_y + (crop_height//2), center_x - (crop_width//2):center_x + (crop_width//2)]\n",
+    "                \n",
+    "#         trimmed_clip=cv2.linearPolar(clip[0, 0, :, :].numpy(), (center_x, center_y), max_radius, cv2.WARP_FILL_OUTLIERS)\n",
+    "#         trimmed_clip = warp_polar(clip[0, 0, :, :].numpy(), center=(center_x, center_y), radius=max_radius)\n",
+    "\n",
+    "#         trimmed_clip = clip[:, :, lower_y:upper_y, lower_x:upper_x]\n",
+    "        \n",
+    "#         if orig_width - center_x >= center_x:\n",
+    "#         if not is_right:\n",
+    "#         print(angle)\n",
+    "#         if not is_right:\n",
+    "#         cv2.imwrite('test_polar.png', trimmed_clip[:, 0, :, :].swapaxes(0,1).swapaxes(1,2))\n",
+    "#         plt.clf()\n",
+    "#         plt.imshow(trimmed_clip[:, 0, :, :].swapaxes(0,1).swapaxes(1,2), cmap=cm.Greys_r)\n",
+    "#         # plt.scatter([214], [214], color=\"red\")\n",
+    "# #         plt.scatter([center_x], [approximate_needle_position], color=[\"red\"])\n",
+    "# #         cv2.imwrite('test_normal.png', clip.numpy()[:, 0, :, :].swapaxes(0,1).swapaxes(1,2))\n",
+    "#         plt.savefig('test_polar_trim.png')\n",
+    "#         raise Exception\n",
+    "\n",
+    "#         if not is_right:\n",
+    "#             trimmed_clip = tv.transforms.functional.hflip(trimmed_clip)\n",
+    "#             cv2.imwrite('test_polar.png', trimmed_clip)\n",
+    "#             cv2.imwrite('test_yolo.png', trimmed_clip.numpy()[:, 0, :, :].swapaxes(0,1).swapaxes(1,2))\n",
+    "#             raise Exception\n",
+    "        \n",
+    "        if trimmed_clip.shape[2] == 0 or trimmed_clip.shape[3] == 0:\n",
+    "            continue\n",
+    "        all_clips.append(trimmed_clip)\n",
+    "\n",
+    "    return all_clips"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a97a058e-8d5a-491f-b812-37775a37252a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "import glob\n",
+    "from sparse_coding_torch.pnb.video_loader import classify_nerve_is_right, get_needle_bb, load_pnb_region_labels\n",
+    "import torch\n",
+    "\n",
+    "clip_depth = 5\n",
+    "clip_height = 250\n",
+    "clip_width = 400\n",
+    "frames_to_skip = 10\n",
+    "\n",
+    "labels = [name for name in os.listdir(video_path) if os.path.isdir(os.path.join(video_path, name))]\n",
+    "\n",
+    "region_labels = load_pnb_region_labels('sme_region_labels.csv')\n",
+    "\n",
+    "videos = [('Positives', os.path.abspath(os.path.join(video_path, 'Positives', '153', '5. 153 AC_Video 3.mp4')))]\n",
+    "\n",
+    "clips = []\n",
+    "\n",
+    "label, path = videos[0]\n",
+    "vc = torchvision.io.read_video(path)[0].permute(3, 0, 1, 2)\n",
+    "is_right = classify_nerve_is_right(yolo_model, vc)\n",
+    "needle_bb = get_needle_bb(yolo_model, vc)\n",
+    "\n",
+    "if label == 'Positives':\n",
+    "    label = np.array(1.0)\n",
+    "elif label == 'Negatives':\n",
+    "    label = np.array(0.0)\n",
+    "\n",
+    "person_idx = path.split('/')[-1].split(' ')[1]\n",
+    "\n",
+    "if label == 1.0 and person_idx in region_labels:\n",
+    "    negative_regions, positive_regions = region_labels[person_idx]\n",
+    "    for sub_region in negative_regions.split(','):\n",
+    "        sub_region = sub_region.split('-')\n",
+    "        start_loc = int(sub_region[0])\n",
+    "#                             end_loc = int(sub_region[1]) - 50\n",
+    "        end_loc = int(sub_region[1]) + 1\n",
+    "        for j in range(start_loc, end_loc - clip_depth * frames_to_skip, clip_depth):\n",
+    "            frames = []\n",
+    "            for k in range(j, j + clip_depth * frames_to_skip, frames_to_skip):\n",
+    "                frames.append(vc[:, k, :, :])\n",
+    "            vc_sub = torch.stack(frames, dim=1)\n",
+    "\n",
+    "            if vc_sub.size(1) < clip_depth:\n",
+    "                continue\n",
+    "\n",
+    "            for clip, polar_clip in zip(get_yolo_regions(yolo_model, vc_sub, is_right, needle_bb, clip_width, clip_height), get_yolo_regions_polar(yolo_model, vc_sub, is_right, needle_bb, clip_width, clip_height)):\n",
+    "                clip = transform(clip)\n",
+    "                polar_clip = transform(polar_clip)\n",
+    "\n",
+    "                clips.append((np.array(0.0), clip.numpy(), polar_clip.numpy(), vc_sub.numpy()))\n",
+    "\n",
+    "    if positive_regions:\n",
+    "        for sub_region in positive_regions.split(','):\n",
+    "            sub_region = sub_region.split('-')\n",
+    "#                                 start_loc = int(sub_region[0]) + 15\n",
+    "            start_loc = int(sub_region[0])\n",
+    "            if len(sub_region) == 1 and vc.size(1) >= start_loc + clip_depth * frames_to_skip:\n",
+    "                frames = []\n",
+    "                for k in range(start_loc, start_loc + clip_depth * frames_to_skip, frames_to_skip):\n",
+    "                    frames.append(vc[:, k, :, :])\n",
+    "                vc_sub = torch.stack(frames, dim=1)\n",
+    "\n",
+    "                if vc_sub.size(1) < clip_depth:\n",
+    "                    continue\n",
+    "\n",
+    "                for clip, polar_clip in zip(get_yolo_regions(yolo_model, vc_sub, is_right, needle_bb, clip_width, clip_height), get_yolo_regions_polar(yolo_model, vc_sub, is_right, needle_bb, clip_width, clip_height)):\n",
+    "                    clip = transform(clip)\n",
+    "                    polar_clip = transform(polar_clip)\n",
+    "\n",
+    "                    clips.append((np.array(1.0), clip.numpy(), polar_clip.numpy(), vc_sub.numpy()))\n",
+    "            elif vc.size(1) >= start_loc + clip_depth * frames_to_skip:\n",
+    "                end_loc = sub_region[1]\n",
+    "                if end_loc.strip().lower() == 'end':\n",
+    "                    end_loc = vc.size(1)\n",
+    "                else:\n",
+    "                    end_loc = int(end_loc)\n",
+    "                for j in range(start_loc, end_loc - clip_depth * frames_to_skip, clip_depth):\n",
+    "                    frames = []\n",
+    "                    for k in range(j, j + clip_depth * frames_to_skip, frames_to_skip):\n",
+    "                        frames.append(vc[:, k, :, :])\n",
+    "                    vc_sub = torch.stack(frames, dim=1)\n",
+    "\n",
+    "                    if vc_sub.size(1) < clip_depth:\n",
+    "                        continue\n",
+    "                    for clip, polar_clip in zip(get_yolo_regions(yolo_model, vc_sub, is_right, needle_bb, clip_width, clip_height), get_yolo_regions_polar(yolo_model, vc_sub, is_right, needle_bb, clip_width, clip_height)):\n",
+    "                        clip = transform(clip)\n",
+    "                        polar_clip = transform(polar_clip)\n",
+    "\n",
+    "                        clips.append((np.array(1.0), clip.numpy(), polar_clip.numpy(), vc_sub.numpy()))\n",
+    "            else:\n",
+    "                continue\n",
+    "elif label == 1.0:\n",
+    "    frames = []\n",
+    "    for k in range(0, -1 * clip_depth * frames_to_skip, frames_to_skip):\n",
+    "        frames.append(vc[:, k, :, :])\n",
+    "    vc_sub = torch.stack(frames, dim=1)\n",
+    "    for clip, polar_clip in zip(get_yolo_regions(yolo_model, vc_sub, is_right, needle_bb, clip_width, clip_height), get_yolo_regions_polar(yolo_model, vc_sub, is_right, needle_bb, clip_width, clip_height)):\n",
+    "        clip = transform(clip)\n",
+    "        polar_clip = transform(polar_clip)\n",
+    "\n",
+    "        clips.append((label, clip.numpy(), polar_clip.numpy(), vc_sub.numpy()))\n",
+    "elif label == 0.0:\n",
+    "    for j in range(0, vc.size(1) - clip_depth * frames_to_skip, clip_depth):\n",
+    "        frames = []\n",
+    "        for k in range(j, j + clip_depth * frames_to_skip, frames_to_skip):\n",
+    "            frames.append(vc[:, k, :, :])\n",
+    "        vc_sub = torch.stack(frames, dim=1)\n",
+    "        for clip, polar_clip in zip(get_yolo_regions(yolo_model, vc_sub, is_right, needle_bb, clip_width, clip_height), get_yolo_regions_polar(yolo_model, vc_sub, is_right, needle_bb, clip_width, clip_height)):\n",
+    "            clip = transform(clip)\n",
+    "            polar_clip = transform(polar_clip)\n",
+    "\n",
+    "            clips.append((label, clip.numpy(), polar_clip.numpy(), vc_sub.numpy()))\n",
+    "else:\n",
+    "    raise Exception('Invalid label')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2cde7b8e-f331-409a-9833-317ad4a2c2e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from IPython.display import HTML\n",
+    "import random\n",
+    "\n",
+    "random_idx = random.sample(range(len(clips)), 1)[0]\n",
+    "\n",
+    "label, vid, polar_vid, orig_vid = clips[random_idx]\n",
+    "print(label)\n",
+    "ani = plot_video(vid)\n",
+    "os.makedirs(os.path.join('pnb_examples', videos[vid_idx][2].split('/')[-2]))\n",
+    "ani.save(os.path.join('pnb_examples', videos[vid_idx][2].split('/')[-2], 'cropped_region_' + str(label) + '.mp4'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dc3d4b2e-8544-4ff8-83d5-afc098323a6e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ani = plot_video(polar_vid)\n",
+    "HTML(ani.to_html5_video())\n",
+    "# ani.save(os.path.join('pnb_examples', videos[vid_idx][2].split('/')[-2], 'polar_' + str(label) + '.mp4'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cf750844-061f-4b13-b95a-73e4d584a24d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ani = plot_video(orig_vid)\n",
+    "# HTML(ani.to_html5_video())\n",
+    "ani.save(os.path.join('pnb_examples', videos[vid_idx][2].split('/')[-2], 'orig_' + str(label) + '.mp4'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d0dfc03f-7e41-4b28-b15c-79d91a806a29",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib.pyplot import imshow\n",
+    "\n",
+    "kernel = np.array([[-1.0, -1.0], \n",
+    "                   [2.0, 2.0],\n",
+    "                   [-1.0, -1.0]])\n",
+    "\n",
+    "kernel = kernel/(np.sum(kernel) if np.sum(kernel)!=0 else 1)\n",
+    "\n",
+    "#filter the source image\n",
+    "img_rst = cv2.filter2D(polar_vid[0, 0, :, :],-1,kernel)\n",
+    "\n",
+    "imshow(img_rst)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f099f585-e9ca-478b-8b78-8b7d8de8117d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm import tqdm\n",
+    "\n",
+    "labels = [name for name in os.listdir(video_path) if os.path.isdir(os.path.join(video_path, name))]\n",
+    "\n",
+    "videos = []\n",
+    "for label in labels:\n",
+    "    videos.extend([(label, os.path.abspath(os.path.join(video_path, label, f)), f) for f in glob.glob(os.path.join(video_path, label, '*', '*.mp4'))])\n",
+    "    \n",
+    "count = 0\n",
+    "for video in tqdm(videos):\n",
+    "    vc = torchvision.io.read_video(video[1])[0]\n",
+    "    \n",
+    "    found = False\n",
+    "    \n",
+    "    for i in range(vc.shape[0] - 1, vc.shape[0] - 40, -2):\n",
+    "        frame = vc[i].numpy()\n",
+    "        \n",
+    "        orig_height = frame.shape[0]\n",
+    "        orig_width = clip.shape[1]\n",
+    "        bounding_boxes, classes, scores = yolo_model.get_bounding_boxes(frame)\n",
+    "        bounding_boxes = bounding_boxes.squeeze(0)\n",
+    "        classes = classes.squeeze(0)\n",
+    "        scores = scores.squeeze(0)\n",
+    "        \n",
+    "        for c in classes:\n",
+    "            if c == 2:\n",
+    "                found = True\n",
+    "                break\n",
+    "                \n",
+    "        if found:\n",
+    "            break\n",
+    "    \n",
+    "    if found:\n",
+    "        count += 1\n",
+    "        \n",
+    "print(count / len(videos))\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "82a3c9eb-79f7-4a2a-ad8d-fbbbbf336247",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparse_coding_torch.pnb.video_loader import classify_nerve_is_right, get_needle_bb, load_pnb_region_labels\n",
+    "import torch\n",
+    "\n",
+    "clip_depth = 5\n",
+    "clip_height = 250\n",
+    "clip_width = 400\n",
+    "frames_to_skip = 10\n",
+    "\n",
+    "path = os.path.abspath(os.path.join(video_path, 'Positives', '67', '3. 67 AC_Video 2.mp4'))\n",
+    "\n",
+    "vc = torchvision.io.read_video(path)[0].permute(3, 0, 1, 2)\n",
+    "\n",
+    "frames_to_load = vc[:, vc.size(1) - 20:vc.size(1) - 15, :, :]\n",
+    "\n",
+    "is_right = classify_nerve_is_right(yolo_model, vc)\n",
+    "needle_bb = get_needle_bb(yolo_model, vc)\n",
+    "\n",
+    "clips = get_yolo_regions_polar(yolo_model, frames_to_load, is_right, needle_bb, clip_width, clip_height)\n",
+    "print(len(clips))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1ec11de1-200f-4bdf-ae00-b30488780062",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "ani = plot_video(clips[0])\n",
+    "HTML(ani.to_html5_video())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9d5cfd47-fa28-46cb-ac3c-d9b87e604fa3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib.pyplot import imshow\n",
+    "\n",
+    "image = clips[0].numpy()[:, 0, :, :400].swapaxes(0,1).swapaxes(1,2)\n",
+    "\n",
+    "resized_image = cv2.resize(image, (100, 50))\n",
+    "\n",
+    "# kernel = np.array([[-1.0, -1.0], \n",
+    "#                    [2.0, 2.0],\n",
+    "#                    [-1.0, -1.0]])\n",
+    "\n",
+    "# kernel = kernel/(np.sum(kernel) if np.sum(kernel)!=0 else 1)\n",
+    "\n",
+    "# #filter the source image\n",
+    "# img_rst = cv2.filter2D(image,-1,kernel)\n",
+    "\n",
+    "# imshow(img_rst)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "be1d6d70-65e4-4bd2-91e9-117eb2a26df8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kernel = np.array([[-1.0, -1.0], \n",
+    "                   [2.0, 2.0],\n",
+    "                   [-1.0, -1.0]])\n",
+    "\n",
+    "kernel = kernel/(np.sum(kernel) if np.sum(kernel)!=0 else 1)\n",
+    "\n",
+    "#filter the source image\n",
+    "img_rst = cv2.filter2D(resized_image,-1,kernel)\n",
+    "\n",
+    "imshow(img_rst)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "28e0ce00-60d3-4f8c-a3d5-1541849fefd7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "edges = cv2.Canny(img_rst, threshold1=500, threshold2=600)\n",
+    "\n",
+    "imshow(edges)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6f7563de-6e4e-4c2d-aaff-58f3df21fa53",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "",
+   "name": ""
+  },
+  "language_info": {
+   "name": ""
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/visualize_filters_keras.ipynb b/notebooks/visualize_filters_keras.ipynb
index ebc01d7dbd4fccad0e5a8aef10fa4148814dec19..5cda33ca70725d10f2b04f7607d88115e3b08657 100644
--- a/notebooks/visualize_filters_keras.ipynb
+++ b/notebooks/visualize_filters_keras.ipynb
@@ -2,62 +2,114 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "id": "0603e9bd-ac66-4984-b1de-f09367956968",
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.chdir('..')\n",
+    "\n",
+    "from sparse_coding_torch.sparse_model import SparseCode, ReconSparse\n",
+    "import tensorflow.keras as keras\n",
+    "from sparse_coding_torch.utils import plot_video, plot_filters, plot_filters_image\n",
+    "from IPython.display import HTML\n",
+    "import tensorflow as tf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "bb1399a6-43cc-4abf-a4fc-166e35b7f2dc",
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2022-05-11 15:04:36.370067: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
+      "2022-08-11 14:54:36.291285: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-11 14:54:36.293216: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-11 14:54:36.295044: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-11 14:54:36.296878: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-11 14:54:36.306981: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-11 14:54:36.308890: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-11 14:54:36.310725: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-11 14:54:36.312552: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-11 14:54:36.314377: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-11 14:54:36.316161: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-11 14:54:36.317963: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-11 14:54:36.319754: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-11 14:54:36.323172: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
       "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
-      "2022-05-11 15:04:36.854948: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
-      "2022-05-11 15:04:36.856782: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
-      "2022-05-11 15:04:36.858553: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
-      "2022-05-11 15:04:36.859189: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
-      "2022-05-11 15:04:36.860944: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
-      "2022-05-11 15:04:36.862626: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
-      "2022-05-11 15:04:36.864627: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
-      "2022-05-11 15:04:36.865225: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
-      "2022-05-11 15:04:36.866944: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
-      "2022-05-11 15:04:36.868675: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
-      "2022-05-11 15:04:36.870354: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
-      "2022-05-11 15:04:36.870963: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
-      "2022-05-11 15:04:38.397852: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
-      "2022-05-11 15:04:38.399684: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
-      "2022-05-11 15:04:38.401460: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
-      "2022-05-11 15:04:38.402104: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
-      "2022-05-11 15:04:38.403785: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
-      "2022-05-11 15:04:38.405478: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
-      "2022-05-11 15:04:38.407158: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
-      "2022-05-11 15:04:38.407767: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
-      "2022-05-11 15:04:38.409466: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
-      "2022-05-11 15:04:38.411164: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 43667 MB memory:  -> device: 0, name: NVIDIA A40, pci bus id: 0000:01:00.0, compute capability: 8.6\n",
-      "2022-05-11 15:04:38.415567: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
-      "2022-05-11 15:04:38.417449: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 43667 MB memory:  -> device: 1, name: NVIDIA A40, pci bus id: 0000:02:00.0, compute capability: 8.6\n",
-      "2022-05-11 15:04:38.426976: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
-      "2022-05-11 15:04:38.427576: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:2 with 88 MB memory:  -> device: 2, name: NVIDIA A40, pci bus id: 0000:03:00.0, compute capability: 8.6\n",
-      "2022-05-11 15:04:38.436514: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
-      "2022-05-11 15:04:38.438192: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:3 with 43667 MB memory:  -> device: 3, name: NVIDIA A40, pci bus id: 0000:04:00.0, compute capability: 8.6\n"
+      "2022-08-11 14:54:36.766255: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-11 14:54:36.768097: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-11 14:54:36.769850: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-11 14:54:36.771643: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-11 14:54:36.773378: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-11 14:54:36.775076: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-11 14:54:36.776765: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-11 14:54:36.778473: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-11 14:54:36.780176: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-11 14:54:36.781930: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-11 14:54:36.783622: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-11 14:54:36.785321: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-11 14:54:38.485160: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-11 14:54:38.487010: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-11 14:54:38.488760: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-11 14:54:38.490542: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-11 14:54:38.492258: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-11 14:54:38.493975: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-11 14:54:38.495673: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-11 14:54:38.497385: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-11 14:54:38.499084: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-11 14:54:38.500777: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 43665 MB memory:  -> device: 0, name: NVIDIA A40, pci bus id: 0000:01:00.0, compute capability: 8.6\n",
+      "2022-08-11 14:54:38.502004: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-11 14:54:38.503861: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 43665 MB memory:  -> device: 1, name: NVIDIA A40, pci bus id: 0000:02:00.0, compute capability: 8.6\n",
+      "2022-08-11 14:54:38.504394: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-11 14:54:38.506104: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:2 with 43665 MB memory:  -> device: 2, name: NVIDIA A40, pci bus id: 0000:03:00.0, compute capability: 8.6\n",
+      "2022-08-11 14:54:38.506989: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-08-11 14:54:38.508855: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:3 with 43665 MB memory:  -> device: 3, name: NVIDIA A40, pci bus id: 0000:04:00.0, compute capability: 8.6\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
      ]
     }
    ],
    "source": [
-    "import os\n",
-    "os.chdir('..')\n",
-    "\n",
-    "from sparse_coding_torch.keras_model import SparseCode, ReconSparse\n",
-    "from sparse_coding_torch.load_data import load_pnb_videos\n",
-    "import tensorflow.keras as keras\n",
-    "from sparse_coding_torch.train_sparse_model import plot_video, plot_filters\n",
-    "from IPython.display import HTML\n",
-    "import tensorflow as tf"
+    "vae = keras.models.load_model('sparse_coding_torch/ptx/vae_output/best_encoder.pt/')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 4,
+   "id": "be07275a-282d-444c-8f41-cb6e4609821e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1728x288 with 24 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ani = plot_filters_image(vae.get_weights()[0])\n",
+    "ani.savefig(\"/home/dwh48@drexel.edu/sparse_coding_torch/sparse_coding_torch/ptx/vae_output/filters.eps\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
    "id": "a02865e4-6685-4401-98bd-e1a71675d122",
    "metadata": {},
    "outputs": [],
@@ -66,20 +118,20 @@
     "image_width = 400\n",
     "clip_depth = 5\n",
     "batch_size = 1\n",
-    "kernel_size = 5\n",
+    "kernel_size = 15\n",
     "kernel_depth = 5\n",
-    "num_kernels = 20\n",
+    "num_kernels = 48\n",
     "stride=4\n",
     "max_activation_iter = 150\n",
     "activation_lr=1e-2\n",
     "lam=0.05\n",
     "run_2d=False\n",
-    "sparse_checkpoint = 'sparse_coding_torch/output/needle_codes/sparse_conv3d_model-best.pt/'"
+    "sparse_checkpoint = 'sparse_coding_torch/output/48_ptx/best_sparse.pt/'"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 10,
    "id": "72e88838-67a8-4deb-86b0-82f5e4eca9db",
    "metadata": {},
    "outputs": [
@@ -119,7166 +171,9 @@
    "outputs": [
     {
      "data": {
-      "text/html": [
-       "<video width=\"432\" height=\"1584\" controls autoplay loop>\n",
-       "  <source type=\"video/mp4\" src=\"data:video/mp4;base64,AAAAHGZ0eXBNNFYgAAACAGlzb21pc28yYXZjMQAAAAhmcmVlAAYt621kYXQAAAKvBgX//6vcRem9\n",
-       "5tlIt5Ys2CDZI+7veDI2NCAtIGNvcmUgMTU1IHIyOTE3IDBhODRkOTggLSBILjI2NC9NUEVHLTQg\n",
-       "QVZDIGNvZGVjIC0gQ29weWxlZnQgMjAwMy0yMDE4IC0gaHR0cDovL3d3dy52aWRlb2xhbi5vcmcv\n",
-       "eDI2NC5odG1sIC0gb3B0aW9uczogY2FiYWM9MSByZWY9MyBkZWJsb2NrPTE6MDowIGFuYWx5c2U9\n",
-       "MHgzOjB4MTEzIG1lPWhleCBzdWJtZT03IHBzeT0xIHBzeV9yZD0xLjAwOjAuMDAgbWl4ZWRfcmVm\n",
-       "PTEgbWVfcmFuZ2U9MTYgY2hyb21hX21lPTEgdHJlbGxpcz0xIDh4OGRjdD0xIGNxbT0wIGRlYWR6\n",
-       "b25lPTIxLDExIGZhc3RfcHNraXA9MSBjaHJvbWFfcXBfb2Zmc2V0PS0yIHRocmVhZHM9NDkgbG9v\n",
-       "a2FoZWFkX3RocmVhZHM9OCBzbGljZWRfdGhyZWFkcz0wIG5yPTAgZGVjaW1hdGU9MSBpbnRlcmxh\n",
-       "Y2VkPTAgYmx1cmF5X2NvbXBhdD0wIGNvbnN0cmFpbmVkX2ludHJhPTAgYmZyYW1lcz0zIGJfcHly\n",
-       "YW1pZD0yIGJfYWRhcHQ9MSBiX2JpYXM9MCBkaXJlY3Q9MSB3ZWlnaHRiPTEgb3Blbl9nb3A9MCB3\n",
-       "ZWlnaHRwPTIga2V5aW50PTI1MCBrZXlpbnRfbWluPTIwIHNjZW5lY3V0PTQwIGludHJhX3JlZnJl\n",
-       "c2g9MCByY19sb29rYWhlYWQ9NDAgcmM9Y3JmIG1idHJlZT0xIGNyZj0yMy4wIHFjb21wPTAuNjAg\n",
-       "cXBtaW49MCBxcG1heD02OSBxcHN0ZXA9NCBpcF9yYXRpbz0xLjQwIGFxPTE6MS4wMACAAACdw2WI\n",
-       "hAP/hFfaf/CT+QPHuUkWai6v6H51LG5OltYYL/GA0qVZ86xa0DCtW/NlD7zvKNAf8yWq2nMF6Ix5\n",
-       "5QNjmgs0xhFKHzisaxB1IKl9ig/FS1GQQ5Opb2/9/NxVZRHkE16rBsXNDBw0TY2p3gUNfbFMq3S/\n",
-       "dDkM3m4uffu+S1X/QZl7u4QhEjInEhhJpvctqJylIRwLzAbsvPfRHCx6vDAVKsf95waEdPXM4tmR\n",
-       "4kqCpfr7UVAuX+ijIJTpumkGy8fcYCoLd/XUv98oK/f4a9yq6LqHZpBtM0jzMXjRU8kF/EitkC/o\n",
-       "DXIvxUFO5UD63kDcUi9XbrIaKGohzvsnjWUxzzNJjfzYuKGRAeonmR73keO/ZgLIVysdbKAqpAB3\n",
-       "o52e0ieiEg4RjQwo1WLDgifyFWfE9wogVbl5xkh4EVx75cF+Rt00Dyq8AHx0/RfOM/muIa8Yz84O\n",
-       "FQvb9VtBMU0PNefvPAUk/KfFi699x/YnkQdidoKNoA31ukHwNYwaNKCmR+W0h4SVC5fzZ6Qg8WKY\n",
-       "FmB6WLc/5DnUKXuobcVfE+20VjNR2zUM+hPIkhSbc/Gf1hcMWuBG7CxOXK93WsFmIUvzvu97Mqr5\n",
-       "YuFxamMXMcIk2p8SeChqY1BibOBq0KLIdmZ3cru0f/q4n7Ci5TKJw7lGb98IJYb5LYXJWgIjehXG\n",
-       "eYxtvLLVCUBxH+4WWg8G+UnTwjiRIWcit3lITutyOu84S8MMk7T5c9KXgwCrAN+k2gcFlmgvxlZK\n",
-       "fkqFnm3bET0EinlHB6BwNVrtQWf9eyIRfYLR8uFjtn305IhptApM3sAOyGautfYSIwijXkcp47e9\n",
-       "zU6ACEtnSFs6S2mfGsEQ378/qCQXjrMYfFjorx85+NWDAMgRR/cvgJovzxc6ZO+7ea8gccjrTMB/\n",
-       "muloCzK0EHwjkm2JkeUbZ49OnjE4jCaeYGtMkLsrZmnadqaSUGJmNRGMeZXvMl8na0KQ77bbZ+Pb\n",
-       "lOYgu6Hph25n3kWbgeuOJJbqq9N9WsAUvXN2c6drhx7ex7R+R+yHPxiFeQ3CaxqUT7Eexd7qbjUY\n",
-       "tWtBaOr2l8568mZ0XwM3oD3Bz5C60siFx6aF+lyVPUWqjWhDltlbAUvTFEQbl/lUfHE1tRyxhXmN\n",
-       "jM18uCpyFElig7qCfJbj76PVmILvMB1MIom0LZVPf7qPK8tPSrWr/u5Fh++4QLVI/dI/vepCV1sX\n",
-       "+ZU2lfkvsnHOeQn1O3Lr1nqE5Ool1ejbguWEdb76CGYRWzLLLe5C8UugjDyToEiHQkfLksbrYVT6\n",
-       "loDS4ltooE3Zx0kGu+MoEVYHRzaNiIS288OFVroIKnUP3VNR8/u0fItlsXeSWzEyYbWDMR0TtUjC\n",
-       "opX8Zzvmvz6lJw9uFBbX3UMirpqGVpWQKgLGYVB3mI90NT5daaWTKkNdc6y3eCexF31JKZAtrbVu\n",
-       "2Ou3fzyLmA3LLbHFVOLbzY0BuSE95AV/BIc5JLc1c+ySDSYQ72d0Q9axC8Dn0dblm5idKnTU3oM1\n",
-       "OPK9GBUufKSAo4dk1cscFP7u5oEaGtGmHB3+FXfLzjGYVXpySeP5p/7Flbbj5CsogVhsygI2v3Ew\n",
-       "oUhw9JRRzqNJbQon2PfFpnwQGD4asTLW31YL4z5Y8StE1quMxsZhl/iPwYfKR/kZ4aX+C0HWxrdN\n",
-       "Svo+vZtKT4Utq8SXp2Z5xzHcEJxeoWMBljMq42welZYkW9Vm7EMphrzY03vcyS8dSXVHz9SWPhXj\n",
-       "UGnfgBTzKRCIx4Mprl/dJQ4BuqKfc+JuMSxPJuzzHjW72LRmdNlDnr72UBG1+4mFHV2fi+ORtDEA\n",
-       "RNbrMHVO9cp1s5lTTikAOc6V+UGQ9E9iNyduKDogGk0Nj8MiQcuDqm6gcKZ0Yn6MFOBtHVQnUY3c\n",
-       "GHpQdLNtoAQo3GwIDI8qdFVYC29QB13AKydVjRm6IN4Nt0ACdjjmnA8GkafuzmCAq5XPlDg3jqdj\n",
-       "Or29Io07JZEm9LrMm+d0oHakNCg5zlWwSkUy50do5JHFuoXP9GwTSRaUgM7V05J24sjS2KQt1vlo\n",
-       "tG9/pbh45GE7h9J2eCIA2A8s9lgLlWITu3H9INQTYTo3Jsnxr2+TcrHoUwTpieoOAkXxBsHXN2l5\n",
-       "XA8L4rBa1C33gcYxwUPBnCnpxdHPHq95A7Ls3MyaYm5YiI2lhHX/5wPaOBWAZU7Q/Ewtk/kDMktp\n",
-       "qVO8Vcom66zL6AgmwZilCDAPqn4wcJdUmRXXTOJ3RAGJVJbb+e69BK0mDeAH3LnXMe0l6wBWDLaO\n",
-       "AyYbUg1jbrWjxZ8CgIVnEODxEV3l3hcpweyt3b6psEmvKXPTj1v3kBVI+1ZVuXO6okCi1iAdXYTD\n",
-       "7XYRJmic9XOifz5AIeDYtQcZF4LsWsgdxaAg/BRetV5LUvbaWKJOSsazO+txn+M+FMN+SUDd1f0/\n",
-       "6LCwvrEifrDJEbCWZAREysXBaScF/KI2YhwTFxmNE2kuBLF4fgj/GPGNrQeYezfWkZh8YqESIZ1L\n",
-       "5wtv8Kx6McPWdewZYSVpM2Ajz9WmMSMAMpgSAWB9W+i9qS4qnisearoQNI+I6VQkRDoJRc4wcb28\n",
-       "mQLUh3ky84EhDa94S84YQKmOchuLaylNaIDkrdB8bjqBMgHEy+VMuHt85E84Aq5AWbM5xBsE1LpA\n",
-       "WXAj/yt5GD+L5mNfzds3FgI7jpuCbyXkv6y3yNjpbJXrFM0JZbJ87m9sc2k/Zws/7MsiCgB9bA+V\n",
-       "X4pjXOSyVikmbQMIo54z6zi7W7IwL9iepTUOFqIy8sOtAKKbjVxZR5aMo/d+GHmLBqgWc7UR+Xu3\n",
-       "7Z56v3O5yXW6K4aIwAb9b+blzozGQ36QPaD9JD8Tgm9uC/vzbw1XiKowMb7xQKLHWVnUOi13DRdB\n",
-       "y4z5B5+oHpMEjLVRtF10v+XP7Y4btbB1d8KBtCtqc+xmQHrkVPuCWXoFXkMhsDMLrcf4mvZn5FJr\n",
-       "TekN2rBr3SCdznwmXW7G0fVxqkFBmE/xzBjTOmOAZvsOiQP1u5iXSf9BzAoxFR11bRSZO1Uhp8vJ\n",
-       "hMtDehAxS/XavUVyWb+LsYSrL5VFiOYeneDjnf+rBHFM6GbwbnAUB8kl//NLTda8rnkrczom6RVE\n",
-       "BpOFTU3se0K6wfPQDqyV86HzFokMV4m05NukU1FQUQTQv4B0orRbGq9jEWn6wPsxvveb6mhDVpjR\n",
-       "5kkdUKtpvnXKncJRrhVY/3NNGLuNGoRs8vodEmcR83nCLIbwinq83/3/gtQM6bLVoeXTpyLq/7JY\n",
-       "GtVCAjheJjSx+/NteuhiZQvR3NReBIrx9+sVyinNMxUhyg4uQRqc8MYlM9G8Kx9E8qCAGp3YF5Hn\n",
-       "i/ttabl/PuQMEaOJoQx7BH7IE1LVJU+kPB/r4tYCB/TfZKvml1mQ8StfDiB4pPBvyAfAEf7oCfQf\n",
-       "uTDtNqL/TYTok3RM49bTsupVLanNW4nNpjYmAnx30eNz5IeqoAOxHinVdw+fm+jCMW9Ope5r1Xaw\n",
-       "xRlm8pnh9+w2ozqpErinBZ1+lnlkQEddwR7mTmAHlXHJBvsWsT9wkG6N/rICeJc9GpV0A4cz4qgB\n",
-       "S9UNc7v1HUgnhbAHXzBoOdkytya59cRDtMsqRgO885NmJpPEaxdTnSZzbweXZCx6cTBjxcfBHpwG\n",
-       "HR7Re+xZJ7RF8IsGAUyMmnJn4Vf4GdKO3PuOcCPuYkLB6z2qukb4idCzvJfs/wvR2ARHZPsw4hAn\n",
-       "VvrBVGOn+YRnYHIO1a1UuMquLEd2WTQYj45Y8tQTyLbPUI5oxrBKwetAXTNX7wNewtUAo976ED4P\n",
-       "KbuWoxGefmXsVn5F4qCT2czGtC08q6vfBNIAeLaHVUpgRjQFY0pKt6jhq5taoDkKbYNKi/6FPRpw\n",
-       "SSGDhSFIhIDzDaJiZ0NDpC5FrpKPDVUSCAvosa0pFUEtBAHUZvO7FhbsIJxNOdkIQsuRiH51UeFZ\n",
-       "EZVfrkUFHZuJ2D2AoLkZTNJInKr6Ex2WE6BUag2bzdnp/J5kkDCps8JpH2Vku7GqhKQmsprzB4F9\n",
-       "hynHDJt2xrXxBvAaoCZohl4aUllBR42Y+xM1QIeCs//Lk7O+dQPPq+garWO/wBUSGFbFk1i8AzLx\n",
-       "jp0G0zZZIqpFxlfCyxgtr+RdnZtVD5C/qXdYO51mDqG43hO4OZ1dDLNlkxNUpyBnCsAxs6j+0T84\n",
-       "kcRH41oyJ/Dsyya0yTxdY7bJ6k2Wkmi5j93g+K7BqZexG0UNTVmXCj6qan6uFY5nni0/JbSlq+7h\n",
-       "wE8uKeA1+zOCeUTDQR99veqA6DLdR+BTgsAYAXgoi+VapopdD/jAvU/MY6y82ebYtYNHVyvsCjsm\n",
-       "1qA6GlmpVOYiAb/MERnSrbcNIfUng1BaJyz7ht4LFOgq4qe59FfsoGacwmOlSsleX1Rhq6OsqubS\n",
-       "VyDbbNd+V/PT31o2WtS+PNqPumqTX1Saa/hi0ziZu7jwvNeZfaf7S8kTqU4YHrfyHkwCkdv07SeT\n",
-       "fNBpxiHduUSV+WjPCt1NmI2Gvcp1fj5u/wPjF2RclZXRPKHsO9Ms9TvCUGSSAizdyKXCzbuyGuix\n",
-       "8XUe0pXvKJr6HXj6dRERJERkUT22AsaPKMRp+awVcOStQ2Wn8A/49LRuiq26ESPoIJ8YWf9w/eLA\n",
-       "T00Ga+8+csq+OBPdX9PZYeMc3zM+m6+iS5gNExRYPRdlPOzDRhjKnGRnyEg3ihtWx/dtWzKK3V0O\n",
-       "TiMaEM4X2ppdR+d2zcSPywPnjrJPfAw0CDRebt2hp7jOzw69jhiCmKSJ3DOz3rs11XlvxRMvndRQ\n",
-       "73Lr1BULGN8+2AspGdMHr2mC8EuP/XVGmXjEZ4q29eOP0cfbJnjlR0y+EF/tPZl6doDjmdeHzkNv\n",
-       "+It3UfLwfhwM5S1jcqSNv9zK+dGTiKz9N8roK9wdmvP0BLN6zEVH20i297O7kwf9Ddyr63XmlFOX\n",
-       "0+Bk4iX5VrTJLu4UwzjnYgPo5Eg+7QUDpG+QawiYGrKD5gEmCXo/sYfhGKfSSd0Wy2irtDz3Wc4u\n",
-       "iyk80l9XWH2UISWGnxZ717nzd4ZFrjpPr9lnuzZH6wcpxnDnoi9nuHQAhHRNzgFgc+9D+PPodi4r\n",
-       "5GklpEhVCxH/59imQoFzUEJZ8xuCYnkIE72+NYRK/3YeriYEMQuTNi+PC0KiNBRzcsJlUfYpHl90\n",
-       "8Cp+fTsbmBlUd6XftNU5GQ9ut6QGrbdP/BIuzTgLJeIUdaeZfKvbei054YpGRRH9N3Ld7bFmyMFW\n",
-       "SNEtgVOJRfkG7qn59XI9koFFyuUy7nvgQVwU7l4zKNcUo48+rXiTeklaBAAPmcSnyE+CN8LEs00x\n",
-       "fY66cmPjBgOPCaTLuodCogQ29GScUIuATDS3VVWk5E0z3sfDdRaypDBxtucAHa6ziVtt9YEHsnv6\n",
-       "prnplMtelTCrp6cz0RkE/1BvDwZWdXa+SfemIrp1YH2mDuLaYbp7IywjlgmSEaMsqg0QpB61EFTF\n",
-       "ZfDnFL5LTo3b3h2c28DIBI8RI+m8NAKTC4R5ECJAl+tJf1cbfCzPijYn46FbckFg33+Mvbl/XOeT\n",
-       "oec/RZnNq7k1+Js6YhguYbMoYm8bHUSJ7FJUe9XyRijYvnqf02x9ImONX/imEFmVIs+bzXEZs2TM\n",
-       "QuhAd3/G0tLL26YnNzgPgu160yGaffZAm8Kpb4asEyKjPhd4GeDRBxMjTYDwT/a1IxSh4qbP74d7\n",
-       "FUqmAOYAbIXHT6Yv1Qobe5nAmB4VxDG8z7ZWGP77edzWzkBnvdRJtLEigeoYMv8rwIqstyQJu92h\n",
-       "oLaDB2r7n2Kx6KB7nCS510JqZ6qBrwY07Vrc8S1+Ekn3nDmRHewGF202Dk/gRYdYY2AAzEjXSNJo\n",
-       "TLy8aDNAmNhoKixI2WeoYQKq/eimQfBSAveAWWybPcM4SFckBcAHnaghBT9SJn7/Fws1tdbxH+01\n",
-       "27/XCtpoUaN8iUtmPNrrHEnUaZAqrBkAp+qRrILzJD49aXRGOQrQEZj6R9x9jkxecUUn1qfM1OKf\n",
-       "6BtzM+d/B84wfjo6LFp+fvzxsfa+KRLyb44A+rcCMP84TX0mP6iVfJguzOj+n/f91mcZ7eXwIqxV\n",
-       "dAbMn+WmIfQX3ZVIqA1B13nhBCTo900wcVWYThZlf0PiA9FTYBZpAJMGaG1eSf+5TUZsUrSgFZHu\n",
-       "9J6wQiU+f5+emQpEQGlSxegQj1g1g7blZq3fpdo9Ygm/eNQDhbOUEujxGUGbJYi2IQD7k112I5lj\n",
-       "3mUFbAbbcTmCL0YKX7Y4lTCbDybWYrMLyR8WxV9EV9pkeX0X+esc2XHOSs3TP6CCNQ1bE6iSSJEP\n",
-       "tboMjYlRMO5RXaC4ANOEuhgK8lgvAKs2g9i34OOkRuZLqFyUBe2xw5YVh7zK0CFmyd0Dv6Q5do1Z\n",
-       "tCz2rlud8CWvQmkGc8fssJC7st6STXhps3W6MPFlZ5BJMTxX2Li7X10PIDTby8y4a+iVNJPyYVga\n",
-       "LuWEXIxxzXm42U/oIQLIBxF4ZrctnKpwLbD41wh0/5bF2XB8sWSBQRbD7Qhz0UIN4e4rZyee8puc\n",
-       "0j0xPi/+ihqdNiRJcpHzLl+rdiwxDu0X8cVbuskYWz5FHPxhIcM10mUh9h4TR7/E3lzDNULk1B1y\n",
-       "70zU29NI9IHBP3tvoekMOPPLC4inciq/uDg2fSIjydgk8MttXc08cMlgs11r4CC3ij26O3Fo1faC\n",
-       "nSSPzqTHGufCr8eWKWfWe+FblkWTMPmQPa9niRTa/MEPjaX66DRb45fAzR22K3s7LXFNSeM5c/s+\n",
-       "7dwEs7TwjsVHWueSIG5oVIMnBRWgX/+nD0bHw+zvKg6IokIILUiwS83JF2PvnpzUYntLpOEDxo4A\n",
-       "gLaMWQ/9eofpq+KMRU0VEHnL+ysK19YSzRWlqqPbl+mfzKuivJal7bSxRKK1vZ7vrcZ/jPez2lY9\n",
-       "b+1x804wQkFTbDg0849jsOHRptjeiuC1rXXvZHUSgf3KKYZxnb/OrvJ/60zA27KE1mSpU3p7UGtr\n",
-       "MqySNgZVc4D9vJ+eGq0d3VZxovo9DxwHKJAkdHdRNDNcGEApN3Rj1OBjk3Iv1gv1+96cU9T3Rdtt\n",
-       "/u5zUnBns15dFYOS5+AimjHO2Hj4UupZsaoWISZO40QSAqvXY3Y2h9jV6/AFLfxJEJwf0b1JFX2/\n",
-       "fXQaPNQeTlyEf+VOEh+wvmY17Oo+Pz3VmmM4o226xwTKYHB2fw1zxWJHvCLmcdzK0vkk6p+ZmOcj\n",
-       "XQcDKz1Pkvj4JTsm5xGAVxpYaSjv2b1QDIHiOeHOol5HHZqLkq4z4P3l1XaJT6edv7l7WpdJcVky\n",
-       "/h2sHpzliMER9uvrncORLtQ7CiMAlrpWa0ddECAdgMRSEVMnPit4Do5Avu+q0iRgGwkA0KSJ1p4h\n",
-       "sGj8h1cmp/UkGwiADcbL7gKjIgv7gI5y7+o4+a0pyeEJ7lVj+YWKyDH4u51fCLyto66B80TeL5QO\n",
-       "G52QYQsXM5S6APK0ZyGx/c+7QV9idDCkd1omGxn249PA8isZzXfx1W+PCJm5CM4ZpajrPTNcM5fZ\n",
-       "AW14Xbf/+l9XFuKasc91a3f4Tm37dCeN7y2cV2Uur3F6k9bUTGol6+9NAdX19G13E7eMOeEa6RhB\n",
-       "KiEar+sTfNxPL14MKvGfAhVbyQ9OiWXEI40HnI6PIY2RBQi3ABlYjMJK6H5haBwvnuEldCWy/1PW\n",
-       "JPDvFvso6o5Khw13lyUGB/0GhqlvT3UcajH/ZLHMxc+PuAQCgLeEZs/ndZrFfZbtKLQGzUvkX4Mq\n",
-       "50utOTgSOdDpx+dUjuV6LHyU4UvmgCckHS6McXB/kVYeI2UVE9skW9rkG9B/lOP/tewRoOkl1eJC\n",
-       "s9sX9Tfeld3OeU+61sG3Rmw09ONb9FqxwDSHKCKlhyFMRs0yFgQQWPAEV9o3Rm5s+CX93Sg2VioS\n",
-       "PambItQl0RjC0FJMHPaP9xfgRINpsbmX3976eJ/6yK46eHEencryJjEOf5fzVRPPETcZO1ALqTd3\n",
-       "iX9BXZYuKw3tdkaIx2J6chb5IEuJRT4JM3qGAdfsSPXRPmtyOmERsghKqkH8UMODKJHXn5cx0GR8\n",
-       "juX7l6+A7A26Hf46MON5tu8vC+Qs9NJs4v7s5CHytm5EO/tBY3pq6OwobRVs4qIl4TV5dHo1LS0Q\n",
-       "MSJPwHwZRTlEXV5g4A2AWrZ61ZxYNc7YLQV5HU9wtzzTe0CiuIzupGX0ijFCdAjCDjJoyj3Z/cTa\n",
-       "Z9XVG0kuom9xAnanNdb0GKWp07p1X3saXMWGJO+XPn5ogh20Wi23bXQbMoNfg06HR5385Lqs8f1n\n",
-       "jIEBsCZVY3tXczwZvxD0Ma8hErH9eWhymEuxR9tBbmu3s2QV3NBdPe34EkE4HUyz5J6qv5VNzdbS\n",
-       "zWTHJnZJNFqlZo4SWzkAFyDwwEbBBAIXgKSdsx5HQUHegmtCn2FZvR7Jk19QPvhlCouJdC9QoZ1P\n",
-       "jU3FCUqkMxD8xBqxaXRI8Sp9Zc4egJ1onY/DgI1td4NVI1GKsAhbd0rSKbBkkg6iKiS4/goTuHJd\n",
-       "ejOqNJZIEcWSmJnB8bg3p3IFDX99XuzWyUAnuCw/6HISy8IW2P29mQGvmlPT7xTxlBrUKpGuO0jJ\n",
-       "uR2hwF4oi5uj5/yZK/uN7F2rX6PharmW58bB72jGYVRb+675B5bhlVkPeNUY/LGxZgn9LxHYZ7lQ\n",
-       "RVbIUEY/RLA0HnYHHGX8RSaCFixj5+yqFqcVRd1U24YnR7+OgL2k+wtRaYqNSEkxLaLEHZwr2TT8\n",
-       "xku2Mc+lppxfSadgg8SAKXYQc0Osv9cD05ljST5ZaTxw8AQk9KAh1HI9R6ZIKB6b7xS+ykeqCKRd\n",
-       "uFa3rpNuES51cLuSq98K+H7yLslJDAV7A4u3mfkA8N9kKCTOdh+8X+TznlXPUor+HJZBRMpXn5KH\n",
-       "xQ4Ra0gN5s4AL2icsKxxjk1XhckLK4HA79I9XRmGDUYvyB7iUME9A8vpMqNJVBwnSMN3dIKf6qEf\n",
-       "HZhTv8X/cnn82IPD1blYi/xySFHJjbASgVbgqcxG7P0UalgDPi/SRHw0CtXyJklut04NTu/ZoeC8\n",
-       "+ndhVgfcZwxW+slyrKDZmx2jMZiq8jUk60QgRHOYdfuXpE+fs9k4UMquerDTy3ZR1XHEaXyoA7ju\n",
-       "pc+K+xJPO6DDUA3zq3VdZ3P5SJwLO3i1Zs7pjj8fOWWmhwJcE1BbEEa8/VTAIoh7SFBiKbpywmmx\n",
-       "lFurBwGtxOHRmCf5MO1Q2cxtGnnLMQn1bC8od6wwnM4+FLR2TAGVGBaAvK3JBr8WKd9zITWpytTT\n",
-       "Sqma3Q3Mv/q6tu28NPCMZouYZIyOlSmtRBuM6ZxrCQNnb67bjC781uDy0nXpOP4qa9MW1QRbR5Oe\n",
-       "MluF8KOB8LBAZIHspRfsYdKyz+YQVleWYkVaSAYn51NOTBHQfJNzN+eSea3LMx/aSRIuiAL/KXqB\n",
-       "OX96tTNFQt/ivll9fS6AGkExtrc7O8xL+3eQJZP1AFH6KukOGQk4ZJxK4Tf226qyM84KCAO53UoH\n",
-       "dOYy74pLJO9O6UzqAfGr4qqkK4iycmWnKYmv4BviuAdYaPUHc8baeu9tvNErNdwJiLI93CkbvOQW\n",
-       "kghL1kHW8+IwZgMo8lbP4cCdjjGvqIYDNG528IXDyGV+36cC7aUGQyrxlX6S7K9UNYQ5C1FZ+was\n",
-       "V7wF9r0P+JOlKTcszH9KHs10QBf5TDtKostFAKVJkblROyhaOFKCXnMtHeiNb1Of9vF/d262Ek7F\n",
-       "uLFydOcnZDQANWtZGWtRyoMFIlGeX7LbUk2H5fgBWnB6q2MfFjV7FpS69aO2Vr82jzBFdPPIqh/B\n",
-       "OS5EnOgulq816iVIP4wlntbGyTWphBbvMwsrezfLAvciuUO9eFuf6DJO+q1xVKzAKknlwqIX4yXk\n",
-       "flKGPiaU6nJO8hTrBrcxSU472l6ck6bHGDb7Hk8dTMLS5zWiR/zYrvHQNlI3PNSu6C7GCNuJuWV0\n",
-       "kdV70qF/uj9EH5EXoV36CmRAOKxbG2eKQafIYbrdHG6L5Ej/6xWrFh7ieog039+C+T+ob0gl3AcW\n",
-       "LOsXYnJcK7IG8T/zCPaBxmirHk6bI430VdlSTH7R3+Wl3yQrL1yBrdW+1BcVHHYUvqKY16ZPF4GK\n",
-       "xpVOLYEBR6hgfmEpm2dS0CyYwK+bJmFBTW7aTYp49+EPTMR1SVFsrtpva6YXlqwzHNTeCWxqT7Tq\n",
-       "IlIhwPvGdVn0kGwAK8HePCnMuMH1Yl+lk39ki84/OEY3YoC5EYgiB/jd3VIibMPX3SG/QkgTeXml\n",
-       "bIu5aqVTIQ0Me9Xa1Mq8blTcRZipfc+vlM6wwSPoewZ3cKBco/SuuPOc1Xr32YTVi1yFd4i3vHRi\n",
-       "fPTdjD462NS8Z7swz1SekuGaYJaBt5nx1A1/5LtrYGjJr/3rc+7X3lKfiqGy1rTNK0SazuSFz9JD\n",
-       "J8e3dxMYvqGit8WqmhC519e/H52l1KQyCqChnilKNMELqNCCj2oQ2cfA6p49WdwRVd4ORfchfgVt\n",
-       "puCqrqnr5VxSJTtiAFZZZwL0eZq5IQW/Nth/GxU1doo50fpy2poeft8xb8a1p+3vuPXTaiekuJQZ\n",
-       "5Jg5O38Lgwbm56FNuJbHXr/uhjW+T19wtX+oF9SrN/gioucLpbEsP3SvzVbXOCLR7Cr1aD/Dw+/g\n",
-       "wgGtwhwKTysOoDol8NQWbnaTmKpmZzsig3TIlx7BoBvzCtcP5pV3/463IYHvn7udWZVVFo1G4p2k\n",
-       "UwfekbEd9kBdYRHmDjzkGpbeph/n0jh7EBL4Zcec2wT1OovPnPxkkjl6e3IZC1FyGWAwfW+Ur2Xx\n",
-       "60ydd/9CwigaSoY7PzOlBeZ/MCY1huNltXXwt2BuWuo16plT5ZLR8LiiVOIg00niXPc+UeUsEjo5\n",
-       "uJk5eUXJU5FULikvDUYdPhqTVWox0SFW5f+LKhXuy8FjHHIp/XmCBB9C+rYOsmTVzBz5tsd3xAe0\n",
-       "rW68uw0tJg4+EzSG0vmNSmd2d7zK/ppXUT+L/3dk40iZnMLVK+PdEB3ZFn6YtWcCH6WGbclnxhX7\n",
-       "No/0+Z4Xt7Bl8xgAS/uk4Jx9ggRYru/ZZFSwpW/A0IvuTd37+McpnuvCaJwMAaedC8lbXA1R1RUD\n",
-       "sEJqK/at6HM/08q9ZP9GaMbPruV5S/KYy+w2JsrMWp7LyEVVAoSWYnaIPQ/mLGHe7zc9kNtMv5Mt\n",
-       "HciBh1oGUC18cwRrykvzl3OYP7YA0Jxys0Spycyt0GJxmBJQqX3xQPHbsWBnKfY2bjpFUAW7cuNz\n",
-       "w0PNPpRQak6nHCV99kBu/oN5ET66FGkQ3P9+InCBqnm8DyM0BfojXnIKKzkpk34X74yhWHhcHYbl\n",
-       "huho43a1aghJCxe4lZ/pVBlX52fPj0pPvNzU3eLf2D53V5MUW1TMdwMtHSn8ZCiUuZjEolYciIMT\n",
-       "aJHZ4L6LC/SU6J3vlwdzcOG11OsWtkSkbkpkswX5OYCQ+LfbyKBrMXDPnF8XslTrphxa3NNs2N3K\n",
-       "WsFoXfgjsSey+dTLNYDtiEohiEDjMM4ok6vdYJMwJ4aAneVt3f6wF8xuTqUomtzcIbdPX9KIzZLu\n",
-       "cr8yC70X8CqVg2gh/RLYUWJfDZrkc8BydmrIZ4pqunxvos9ySPz4eJYAJxR53/SDeNCpVRsQu7hJ\n",
-       "iXQ58gUtuXTQ692GrvG/mV7K6toRLJqrD7T7pmfnVuRIaGU/XN3WeB0C+iJ0ijiWzdbow8WEoudJ\n",
-       "ZOXivsXF2vroeix4pbg0QtL5RCa6i2fsJgIcTZ92EUSWj8TL9oerK7RGQuQ9UM5Q9a3fxo2eD5aC\n",
-       "H2uVy8oEtiBR9igcVh+1e3fH7QBvwaWUiVRooed2rxCLIxAa/Q2dNm63Rh4rex+7CVY98KUIzfBI\n",
-       "jaMbGPG9d/QcPbLJc0T1k3oBih/Tnh2RLw7s2OiuvqzNqKv3wdGZ0ZMzILUHZfELiUTdv4MJs4pw\n",
-       "2zjVvBMh6lCLA19Qpx0PzPP9qOntMfz8RnFCn35aKBXkucT1PvSkErlYuesfT6nXGRN1r1OTdYGo\n",
-       "Bw20xfJuZ6yAAD/6tn2Rh+BHxZPfv0m20EJrXP2bIvSUkty3fFFrod7CxiwNaGkpKHmj72xFAf0u\n",
-       "w2uOGKFHHgCHkTbwiCBGz1ftzLFA6zEPs7kZ3nM7pp/Ifkgya3s2eae9YdwiO3EqMLef+CvN2taL\n",
-       "w/MGTX89uMA10PBOtMvDjzXAWa2Sc55knFhAGttrXwCbxY95z+DgBzznG9+kVPeoGTJ4BJbId3If\n",
-       "ar06fucFADc3W5UA2loFeIMLJtpuN3pl8mHBJ20j5tuHs/M6F/la+CnPrp3nLZS2FtEpyDXiMh+l\n",
-       "me82bKu8iU/WtSEm8A1Xqk6hA3h0E3bauGWDYVDz/JVoSv1L9qS4OLHQnYnrz7XPf8lDqFmhpYXe\n",
-       "rpC/Css2Cq5Ya9F2p1z0HRdiHrzz0Ogx5FfxnYnrZBeVbBCUc9OClgNDLwPXogpUYAvwzOKGARqU\n",
-       "D7hJL8HtG/w8KU2EZA4stUpm9GycE0fxnn/+J7rIUaIQmWVAQjf8+3njb5NcKylQedHcRCfPaCeY\n",
-       "PWNCw7XJFPUIV8q9gte/oqxzSP+6v7QUTQnBco4TpAslAqFiXzsp0aS3UGAZjzv2ePMVXsQOar1O\n",
-       "IeIWjr3YXp0lpr8jq0zcXD/fuBhL0j0Pu46mLxdhiTnVqT3Nw3nQ+eWGMnuiwsD5dHqV24lkyRcR\n",
-       "FzX4u2XPsnSYRT1+NPjHUgPHhfT6FMrAriO2paNs9WhSGafbxqg2FiWclw86Bz3L6Em6G8ZlGvr+\n",
-       "CgSsed+IoDz7Pm18vMKnCL7ab38hCM6cW4ikRxDUL7EzpNe+F+WPrEOK1Ppzl/vSrRf4TyuvIBN8\n",
-       "5eBZ7DU9zOKkHehMJuLG3/qJsnRNtNjtyn+LKilMSXTsT5n5FSf5XzUrQdUqLhOUGoUr9bXnoEJ1\n",
-       "Q9DCwHV7SNv0HR+1Wst/ET2ACrJfiJn7PzbyHF3jdPoVO9ArNaaEn2erQplBuUehtr+D/pdzCwRy\n",
-       "7pV6nne0lSs52fLS0USLiw8Dv4IFoixPM19SsH2HFsBh6R3Nfbv2qK2SnlZVl+ndPBRF9ieHntIp\n",
-       "Ill92zRa8arULnbYAiCxBOrXj+QRSvVj5Bxn+9fLQDZBNpJuhWLD4eogEpnRbmUTW23klyON+g0w\n",
-       "lQChA98C9kYNljy65F59b18/E7UhrcWJDb7v5gUd/lXZRYdExZMf+JvliPruVn7azY7aDopxGeo3\n",
-       "BoUGWSOlyVdlv6ZwUoZIY6B8J6XWOJHQOpgBI6COZqjxZs856M0oANQM+iC2Orlm3gRP9gGGXbQd\n",
-       "T50M0+Ig7QbcBRyCjBcUoxYvAjEyCjBND6MsQVWts5lbcbylmEZxMRxTCzu6dP9Nm7+UYQpoUs2N\n",
-       "wl3rt75KUVLSYvjz93E94i2mpm7adAoku59nAlyZz83ejOn4luUNJAT2tkVUvzHPLbi5FHoTn/Ys\n",
-       "Eyj8f6S/affDuen2tqL3AB+KCbR7KMR+/oMNPtt71KV5asdDhXCwQyk2uZHe6VEXds/reR72uUMl\n",
-       "QWeKSR6gaadGmYc2mJPrb6Nh+L/vhwyhdAiANm3vB3LWGpVBN6DhmsqPbcDfT/5ksmytJLeByslc\n",
-       "m2nuvff5Bfprmo9J0+zCuH2aBShh5NMn0WN3ZZhcu90CmpI9ePp7ENAGaWans7daNUB/xo7w7vze\n",
-       "qasLIkuFTBwoWFD0On1LZLoF/A26WP1bDkwzenbxa4Vfm0w0CxFFP8QxR8C5AwX1lro1yDK0Qq8k\n",
-       "Vw+n1hYUQ4zDY+9ZP1jLr22HpfaAUYlCcGp7FZ07W70Vybk+Tz4xp18sFjDRvyNcJIz5NJ/fWR+y\n",
-       "7XQwubd/ICvUj/N3FpFPGlhrTrUiS1qTx8ntEwZORhgPrpCQdDLnji5/jca86fzBsSgt7ChnS1JF\n",
-       "1flal8JAkDnM9Or7BUJEmUfJvknS4JoayHLz+503CrpslGAwqIM4US1bW82kC/GFCAFg9j/GX9Jp\n",
-       "QhU7ctBdkW+34K0quc1OmzlatGJ3hE4vZzEQZ+Ieo+odUv+lt0k7BJQoS9hwwuJsFh9rULIvfd3J\n",
-       "V1Rnob6dQceHB414e6icvXUgUf78NJl/92cy6xdjh06yQl37qHRKLwvLvC94po6YgTlLc+iLWFJw\n",
-       "/4X1jd19CPRVLNjMqcQy04OIaBffGP0uIblsd1uvUYKv/1al4F9k1TTOhb1l5hfA0cI8Ch/EyMHP\n",
-       "8yOuFvd9xS4rfyOGz6ey7gtKa/Oz6zYTETL01Hxawk71VdzLKGfTFQuM3fxA6hkRQTJj1bD0EwT9\n",
-       "d3Db0bwnk+Q33P/dgTONRgPTpMse1VlF0CBW93ztYId38lJhTOFNrq5cyna31OBozOcWG8blLpT+\n",
-       "i7nB8QQUfzH8XQ0G9TfmuCYUBZSYqsZGxBVznYipA3baJ6OyYjzjZ/Eg0UjM30VVLEnAFS9Y85WF\n",
-       "rcPiSvGjG1CHaq61FWtIjHPXv2YY/DDQFdLZoEl7ms65NjQ6F7oMJ+uI+rzda7BTwkKKxbB6CMkS\n",
-       "hbHdGsbr4ElY5zeDqgWAY9t1YuCWnHJ0lMDcs177LEdADxDxbjBHtodBoh4dxQBgzlXRcnzpmnTt\n",
-       "PIXrlvPLEOqAKqu5qiNJiMK32HHlezZjfSBcUyZiIb7esSZ/irSV7dbAp+vVFt3zDZKfy5jIahOV\n",
-       "5C6DfmvwfoGOjq0jcxu4PMkT1qFrQTlhLCFqpQCodWyFCu0OUjaQ8M65OYs5Tdmtva/HlXO+8uyV\n",
-       "12kKpubASzCSui7P2vrltIELfO/5YrVJ6Cs8Eh4EyXOMQwd//lppcolzHTMhlnTZKbn3noRTQ4MN\n",
-       "fyQMT/BMu77atRiUDcbKRthgHOYXU1+CSAQMPcejWHZ3vMKZ9gRv9qUTx0QFta/Fc69cGmicf14a\n",
-       "nLfOCcIj01JuDLbS/sJq28ozqoEm8TChp+iGVIvD0j70T94OcsLGcIBtD5jn7qChJgTiZPeCMdD/\n",
-       "EXVKkFByyf4RZKkk4UXdVsIBkJjj8P5FeVF2ffuwbpNiv/qPWWIUtwOyGVPmoxXGIYO//y00qXcN\n",
-       "X3mMs6bJToMzMl3isf/IcIvMtfHvX7dyoCmIx1EuQ+azECPqwXs3QUtfBkC0z0wFN1SvDcXE3/DX\n",
-       "A10g+MNC65sNmXVd3WtLw6oBGb36gfeNxIaswPjpoS4J35KLR/9CCk4O3z2K0koAlwjkxV+NqWJa\n",
-       "VumdcF6oPZjVg7F96XDryxP3v8CjmDXiZsMZCP++qQcf7kfQgEfq1OqZ81Q6/r/kHIavmeDDKbUU\n",
-       "Vdrd+99uUyaUOs0tlqKBf61ekDh4qNpyR1evGOLSf1ONzcRc+Ay/VmoasZoQYZC6aL7O2AOLhpxz\n",
-       "q/RS3mf5k6vQJMHDNVQA51kbFbhT5WXVupMK+Xk7aZWM8l1d31QKaBf1jXHc6NTJzGPwN1h1Gdf9\n",
-       "KpIx+TQ3nltz6iKxeovB1MyPAZwJlGP7n3c50+S9fHqhPphy4q3dCaYrn5xvFBSSpN0/XLCR4dI4\n",
-       "MaF7ehN0tJSynCnI6AB8sKQYUzuEcTCrXyMzoxTN8UvaxrFkW/acLEY7gCukCIuM2lfJj0zimYxW\n",
-       "Syf4u0NssT90xWQk0+gG8JmvwwY8PMQVEPjl2GAC4R7pH+h4u9C09tt218zy8RUrH1s/ORVlKiFS\n",
-       "matRJH+m1f9zldacAQHA7oEJ5KYqlQ7WMya9GYEhl5uzGgmqdJZax5HQwuiPHqnEUBo/wjIjF9b3\n",
-       "2YsjKuCFd4i3vHaR+qO4Om8EtjUo9Muq2sRyWbO8VMEtA28z3tbwv8l21sDNXvTKDfBM6dKecUHK\n",
-       "Zckl4rnYRyLO+/GeHu33/naPbUn/A/rMxOdRDU+nUTJ9S7cXj4G1WFquliol7AWpz2MS0cu7O6oR\n",
-       "Mvd/NcQWwjCBeyHFLoer9c0aDkluyC8AvlBxJ49dlfqHxQbcYY+A9d6qWlB4SVLHAp47rMyhK9tI\n",
-       "u9+LUNtYcMvZOdt1CdNKqvepDLtkKMAG6kvjR5H1xvKNCJDcHO98l6xsNmRUg/qBt3PvA9PTczp2\n",
-       "5t/0UXc6fZyTenFpigDTlASI9pcChHx3YzcHUU1U4rN32M+2+UStNkEiQCvmxrCjOWHTL9H97HJm\n",
-       "WG/UuNDAijTMLx0zLu4dBjHm64Hnye+x8rRqjdnnTzAsnpAN1TRMda7vwGh1WGoLJY/F/ywxzPhb\n",
-       "R/Iik0Xbstg7dDSBBTy7wK/Ow6KqXaybUr7jnXFXsH++jTcuLZJWSENTWc6DGPhIjKytun0VuoS2\n",
-       "/rpZ0ROtJ2YRlx1fUbsHGw6zv9urbdrckfXJdEJd+A9q18ARX/dG+DaPrbSGzEqD8Fy/PVo3+iKJ\n",
-       "/weTT5eEt443W/gvHWRlFuIfKZrcNDjk594imk04C1fT97qIz9rEyuTCaIpZxpuX1xRyG8ENA+2x\n",
-       "i7WC1JB75nyu4Z9KcUMHNXy++AXg+5xJnRbMrvg4Ze33nZkd6CbExMFwOgCuswZqHZcnrJh7J5oo\n",
-       "dOIL25POlBeKyrDbLGC826Xl08zgwnriswDufuV5hDlaO+aRSUzQKK+PSeecLO78yvJCMY+qcf8Z\n",
-       "H3hNeYraJYss+IAZR8f33nOYrbrcpMZbhOGHzUqfoyMdjvrjtJgFCg0tq9AzHNcZTji/kBt6+cbJ\n",
-       "ByP1TwAU0dnmnQ+CpomsefvJ06OwvqE2grQmlBk5/Yhcr+aDlv5ME3dfKjHv9cNEMoNmkyuIIXi4\n",
-       "7XSkhYkQauVToO9Iwus4ahGG/gFNWyMUnvVKeTAVJMeaeGo5hptHkacYkKwB1O5Np7AUq87+koxd\n",
-       "IdNKqF5ziLvwPDzgjr8H/Tw2ve79qnjaLa+oqU4hRZQrv6f7+9jqNutwxkCTIOd0Bu0haZwhRkUb\n",
-       "Et5dmr9AbWPmaHYL2JmLFWoeFuAFT9DH9qcDpU1/WzKZ3xhdRhmLYDR9uX3kwEFSm9HHl3KKoOkj\n",
-       "4v3sgVJl1pGsrL+iO3JWa83hESGCgJePwCJYaDexdvcz7LdIJnwnnQdZ96Ptj56+jmZhW7I+U957\n",
-       "QNxCZa/Z1V0I6HIwkUoHoJKmZl8ttvAD/yWzdtH+V/tFtT7dLdV/SxvPP0ZPv7NnXyNaeQFaFYOa\n",
-       "6lvUNusVD9wnimR9O/laiF0p8gN+Rs0xbcY/7JHRNz6AWvcb7suNymikGh9f9zkqXyBrExXzajUN\n",
-       "kXElv5s844Rygo2MdRUMcfwJzWlGzLdBiEtjhYzXY0Xp3i3NcGzoD2o2SV+XohpQITekZ2xWoRy5\n",
-       "UKJ81vSAH/GkZjutaREbdNSwmBFbeDAQ42kLLOiBsplq30glR2qORIov+xxC14ftC+iCETSSEQNW\n",
-       "+VrVdNrLgqJhhRJTqIWrP/2t+AFVjnVyk0ZloTVypSSRihQR5YXG1VzCFKr5wWXOmhQEZ+6zCkuH\n",
-       "bIXMZogqoC6YVAwg81s8i1uKE6NnZU7OnuFLWXjJvgpD4PuQPc9FniW/wIN5CNVVqEzYuALv+o3D\n",
-       "sqyDLSdwqOJSi6lEiR9VNg4CU6ltfjqIT9soBcyHnpr7/p0a7U0oM1bq7bhN/AEoCYid5rWpYi+J\n",
-       "y+zyv6dyJrVeyDFWyUPQlG871NS3d2DnxgUT3gV8elimZmWEHJ8EpnSyxD57q0uWnNEVy7dn6YCB\n",
-       "QZskPbCyIrJjtah6/R6o7yKppw5FU1zmLqYcNBD8Uw3kHTLzn1HV5P/wjc8/DLlObLlx0BSvcS1l\n",
-       "AxkMs4X8DOd4J0tW5IhaqfsGpRfOlZOOfYzAq0XeXMqLPL+Xth9uTb/qz/aOnvH7rYLsl/Vx+2cA\n",
-       "CclINH6XGisIKAW5WJWn2nOHXgLDPMGWbRNoZOclR+KpdhNnHWZRq6U+sb3eV4h6johBiSGszZWu\n",
-       "qnhdlcZxH08eVm7ypfW6Y6kpGKPILcAhjQ3rSwmV9sNfs2+C6+Aea7yVZUMpemRxQFm/OTwrMwVq\n",
-       "3QxCr846GgaTQkH0zusM9peH+bi1IKvaAJnhO2MRhA9O9DikW+o1oXZLyy19s9NIUY8/vqOjp4ca\n",
-       "0FAg/8Cc5j0XUdhbQMnDknAeFjEdx74GVgrIedyIur22R+ZZtBFnQ7wF8b8q5/0FhUTIeH1/QRlc\n",
-       "ZXZfc3+wUaUXgL+DBqrk+lYWzaIJ1+VlqveQb5AF4+wxaxc1VEsclYcFBvk+3Q3GNanMdjbmKHn0\n",
-       "7Px+TdEY2WuEj0ZtBLaAZCxu5ncKvqch9RTV/rUFZE5T7PoNVUmn39ZCSqeJHLDOgQbuzruAr8r6\n",
-       "FaxM49EP6vdm0deFO/u91yalZySdT/ueYjJqnd3MiZVpwASAIyu1cPRtDsgs8oowBjJaiR3MBuLM\n",
-       "p6D53k/O+kj6q4wkdiVS2N5CI/SxyOQkPotAM5s93VPFxFYTp+GuQIHjaxHaTXZHn+itEmSgOMrd\n",
-       "J3mqEzJy2ZpdlRy2V9boFXvwwULZZBYlRbvJaBmW5kjiXYKnyPtAN6z82F7q5FeXsdZ/ov5dhm0t\n",
-       "56+eIWpY5NTjZUpQcXs6OjXNVWj+1sug6vMkBi9f1vEuhB3NOydar2I/XSRUZE4XJE8usVSTgW8n\n",
-       "YS8uVzFlCMp91qNDuiOdW+L/tcALx77YaWE0W3bYQalDJ7+gi4CdIj1wMjJf2W1R0HuH3iXJLo83\n",
-       "sNKuiWnT6snqt2AIt7FD/xZ+Rr9xA7FSShHUd7XbSRY0U0pOKNA2l/nOtdrN0tFgMLAnFeR8hTV0\n",
-       "FDnRkCveGIDHB43dhrwVwyusGsOOcwOEVCfp7F5by8waIW8VqFEzeUw30tX9oMLatciMl8Uvn3hD\n",
-       "gK3SLOwTHGKKTGARMMHf9q4LfOkAhnIWTt+TYLrb3UmW+hkEk5adstcK1Qjt/2MMNkoXHZuBrt6R\n",
-       "eT2zrSLBjXDyCXZNLDSXOxUdrtCDDUCuGf4XqdUfuE6AMNt2m4vAV9ioDAR8icm1qumFofAaxNSK\n",
-       "/Wlhqe4RyaOKeQTe4ExJVN5dqYSFWLWVJ5CB4gFjK+I/aiZcUfogbzAXoYs9gVDf2Fn4iBugrEXu\n",
-       "D012trEbLVFcskwE9Tb83DwUN+cg5FKojxnqNnqeI2u+3VVwF0Y8pfkdorPEwP03OdBPkelp0ooe\n",
-       "6HEjreCiOFj4Qg8Z4b2Hmn548MibYlnrP6drQHZX8qQKCdyn2EIZSWoT816Tq8wMlEGzVrcOXeCO\n",
-       "ksq0E1H4218pr3AYDP05LZtDDMHpNe0CMXWnQYHv0OaNVpvugSi4lTYfHx/4AwTmyy2RaUwg/RAr\n",
-       "8K3hPS0PqmCFsUeWTefb0Py3/VF6ese+mRX69iGPvmmRKPC9rIkfU52YXKXa2cVetgYH+HtSwnsw\n",
-       "GBqyfh3li0jqktJR3fkN3XNIA5txe28iidjDVe652d3ytKY9yVpWMXp4NDvkiL8eRATPLq4vx7Wn\n",
-       "bBcuVxBF7h1rwxMP5TCWWg7mP1ZAWWNWv8cmFh7h3pL168YT6rgBCOEKfd4GTbzySSP+0CEgft8e\n",
-       "cPa8rn7+pbRWtvr+tmucEv3CTfCS7X5X/koWkXwjc7Gx/bHznUB02VxsZUxTfJcx1EjUfXDr6H0A\n",
-       "9PlD4pbSIzrdwO5YmoAsj5au3bLC5WWvwM4MMS0GuNYe4PtBv09/7ynuTEeXsOpK91PZAYjc/n0d\n",
-       "2pGPhna0voT1YfOKggAihg/BD00C7XrbsZbsk0DkhMNlkzIiZVAgAaR5ltgJGjLVEEx/X6yzBzy9\n",
-       "zmGuUEqTXwIOZwW+hkOCu19FPet4kFIWegT2vGEh2kjND49vmjK9yku0ftxdwN+B4/nk/jTe/Hss\n",
-       "8dHWfxoU6jrhePcXTZ+sCdiS5miVfz7bG/GzRMQfURm0uoI8BRVaDRnkgKvI+RmQrKWxlmHoUBeO\n",
-       "Jt+kkGhWHI8wFw7jnw0TQW8SW1Crmm6o1mVr6GEY0CwA8VnIkQAVKszQylUzemPzX8O78Vzx0qw9\n",
-       "a8bHx03SXJHF827Gp8D+rmKLyfqcoktccFIlx88O5JDUEnJwrB8OxhJuJeIvKzUSo4ReEO5Yfple\n",
-       "+GPPjDbjsMzJv12Fystfe1TYXXxiAO54Z9Rhzma3aCze4nulfbH3YliWvqGnp0et7FYBQMhiRycj\n",
-       "+kTYNZEHkMMh4ZI39XEeqmxREtc9ePbyZ+6cudCKcOQLZkzInamSgFdZ0djgjqyXV10FVMoaj3dk\n",
-       "lWw5IcrOvUCKMrvT7SPpoPqJOf53XjxFyoVcwEjUKuS7uPAjybdWG34iAvw3TwolLvTfz/eVpauI\n",
-       "nH6Yuz101lBsZd5br+zNZTkBdFpOrs3tswrp29LT5Xw4SVEqwrkxWn/wi6zKdobcsuuTxlOfSWr6\n",
-       "Z/u1I1ynS/U9A8F3JH6mzBhZuSZa10eb+f8WIkpeNNnRp5j3zrnqSPYB80bOahxci4/d0VjLgF17\n",
-       "KLWvV5EmHKBAIFFiuDWigkLoX0vwNH6HWU39fK/oJfPQ/2UuWBkTmuI4gvH3ldBA6TSX4N/5jnJJ\n",
-       "YcNrqPzUYYTtWbucPFnwaMDn2ST1zDNlg+aPn58XxhKO+KIV9pkxlHecwiSx9zfBb0vkj9fymwfB\n",
-       "QAO1/fBUo0Xk8y8hEUUrXTR6hp9ZjwsAabLmcqoX4jHTywntlvqAjSmGRIEEmrD8BODSk8cuo8zz\n",
-       "gxXgZk9etQiPrathNNuiiLb/otxDrn5xwdPnVgwM0gC5k378+BT84n88zUAcONvKv/0WjocGcshD\n",
-       "ECZnHctZYcHT2T8WL6jP9zk7bPLgmkosS7OwlY8KJ+Qt7yRE4OoMkgLMwL+xPh1ZWCy0vIlEAHBM\n",
-       "xb+mdDy9UOdLWTv1IfxMRSkGyDbzXM+gVIpN3lZ7pfbdcej8k3O9INvCM2/39xRdBownX2fDSujA\n",
-       "SzvK5aL2nQze0DJX9vrumCcKcU6edtUAMjEkra+J25L7wz/EV3lR4Bi+ovA+I+OlgRedgvtQy2O2\n",
-       "ACsNh7ftGUeJ2tiXMabqjh6x7RbQT8lh6qY2zJ5cuTAbTFPq8+dkUsq1c9OYLFzgA4vGZTckTa62\n",
-       "tMlHXfW0pOHvsB2yL8EIsCqT1zsVYz7u2gGnVIDwxciXvZ+FPHgsRLw4md/qLg9MpWraWL+mOc6n\n",
-       "HuH6GaUmmbCjtg4ArLFzTkW6rdXmvQxucjEfps3AFHsWZwgW51Zwo7TbFsI847NxHSvFKYrDhMwE\n",
-       "WI4n18jov7yNW9CIARl+4B29uYoOikkdUHr1icYeAfBkZk7nQEJgB6L9MPTQmiDqxQsmMYMAOJsC\n",
-       "JNAbiiE5j13szmOhm5IoxihaIy3lI2Ty4G4aqJ6f6n1Iz6y3nBkXPhLhTrmKOj4/qJ+CByakIgHn\n",
-       "YvIX3PclTw46oWsipUjw1be+BecN45RWVtG/lX55mZ4HVEfofm5s28nEDG77z++RLUW04fK/bTsa\n",
-       "eFcj5hZHbhblWdQhSpWW1kGERsriTTR9pkjf17gGqgs5QIAXQDS0H8licS7/hP386OgmHk+b0pev\n",
-       "v97qDsIE7vCJFSCAZxFwevJvOP6SjK6Q7C2CQZa4zBORg2apJGjHn5BLM0kqxi2TtMs0yBH+yFvY\n",
-       "f6VcNAT/jd6d641zdxHw0dEPxiJTJgFxKBdqfOz2/dF86v5LuvBKb8aR1Ymghs2Uv0BtGPT4X3hs\n",
-       "oQztkpIrXheTh75wZhHvkpKkvQp4SkU/wIjmP+XfN/zkoo1McqLLlu9RfsvjKaGhyO7ZHADS/1Fs\n",
-       "8r3faqJsBLXbaGPbZAwUsd+Dq7OCdSc8/iM3MGPdNO4VYaDZKn590lmZ04YW4qbLA704pLceNHRv\n",
-       "TTpG8SICsd0aTxXTOo2ELzcpvxOz+hCD3Al9CFE7rd6v0QlowD90mDqBLJ6uR+S6DQDsgFanjmL8\n",
-       "xC83q59bjh3gRF7Vgyf6iLskkJ8A/LQQXI+iJEa+fd5KRiG3eLHzr4FK4DAnduPY1gmA47iQe+Cu\n",
-       "XtPNfBYkV5CMMuXFb/J+6kCEPWPPAi3X7i1xqiOtjTPGKg2LvC2OAX/6vyS38bxnlu3j+a9bo/++\n",
-       "q0olUMRxYdrdUwOvReUfuYDq9/er2h/xkRuqMrel6J/7IF8dSgip7tv9Y7NQ8xPugJLh4BHSxezB\n",
-       "ClkI97cyDT7LKHGrSRSqrsLVzcNGWV0TlQOz0KUukHjYns2i6wRH89WmghqAGQyBZX8uluh+yfv2\n",
-       "kezsOnIrY3oE3hufCYsJBK9t29XSQqWfR8xKA6Y//hNGQVf5oyQE4vHdy4oI7S9909ZRfpe6wU2g\n",
-       "xjZJKNCi3taBW2ryT2vLn8310gV3pKTjKFJ12ijjICWPeuzB45xkYgr1yvZHpz4GxmmHQU0FHAMc\n",
-       "0y/uPRJDF/aFtZYqaRDEYPYpThT/qMqaHE5E6YymjlckNXxjOuJ4518PR5FQZs8uRw02ld7FKjFC\n",
-       "IKElSDyh9iMWSY+2pHULpkjFIb1nmSrvN+AXxM8sMmHAQjEoT3fwI9Cdn86fyuS5chh9eZgr0SUR\n",
-       "pqVMWV/RV2NxvuZEiNo0EK4anJQLwUzyHgdRqhaHBXtscy3oY+6Oa3tPb388ks9WCCNIugPyxvZg\n",
-       "vviPEQ9npbl9nnbt1ydS+eKsYTvJsm5GXZc4cfPaTxkw0fG6Rr4G8W+k5eHOwvtD/oC9KFr7JicG\n",
-       "1Nn7tD+sLGS5KmXjXvD6AZOFZ1d4ZuKuYgIvJZAsoZq8PLLEOevACl313PjOrfYf3NshcZlT4NAs\n",
-       "M9g0ObG21IiODGZzmdR2Ozt/cUNqzWzV/u1EkBGfuqBjyOFJPTi4jz9KhNKF0nea+pBHEx59/iSo\n",
-       "ddVgYxs5+bkn3ijrkkPOf7iAS5+cCj1xrJg9SXFEL+3cIZnjOYa8CdR6KtN09hAccZoMutZKF6aH\n",
-       "HrOJezzaegdm9XOMlq5k8xJXD42twhuoOE2KQassLjUrx8LF+KJAWV6W7FQtIHUdqLIcD0LBFuY7\n",
-       "UBVuj70n0A7piYM721LibTrrELJlHWtFgWr5wGBLWGeeFAJfxbrK/7OF8wmP2Wbj9QDZbbWiLWw0\n",
-       "Ecdr6ewgCHqXJ9PAOFfEw8CT1V0pzsGBeYti6HmLZuE1BGw38VpXbDC0XKvGJfJUWXi97nlbrMoY\n",
-       "wbAs4UM28OZ0GdwBHr7L934Sf6c7WmqyBLLzWvMgHvoonjIuwalyUYG5rArnAQtvbjsVobHyMZ3F\n",
-       "4pd6+DnU1Nr20oiOVmC1/+ixZFHG4VPb74Xt0DGW/FnP/NEZG4XPVcQLzzudra0wWoqCY6PiddG5\n",
-       "VRjiZ4ySTS972SFx9/gh/yi0fB3oxB5v5ACZU6R6ba0zVTRaOpZ00XBGZRxQpEKfU5Pa2Sx4ZhUS\n",
-       "xjIf0ZlEID0lhn8L5+3LFsqRi29lJ7DHkeCCdUp1It25zjUv7wXsr0K0Sx7LynvcVX6lJvqYfaQR\n",
-       "InRciX8D0u7Hm+Rdfll9/hYmoiDznXq4uxXgZ2+SY6e3PXZLeH/twRhuduv6oXchqNXa0viJVjRM\n",
-       "Q7Umxs5pTcLpKB/LjJgJCjca28/EnVLnLFnEIzybgUnFBsa1QK0oCbfgUzxQ51YSUwzGFeCVjhCk\n",
-       "drkWxfZQr7uCtiCxJDv5UrojfqlJDA6l7tvYpC6Tm4g6I+nnut9N64qn+5uBI6DrxPJwpDJ6Opnd\n",
-       "97+Jmv0exIQayek9bA14LmZY0ncuO2wF6evE9a+puEKnpoEwBcl4YlApQGW3Tr6t7cUJ4xj0xF+B\n",
-       "1MGGTQkMbeqvtbsMsppfuupOD+Kj+9AYiJkhF+OXC6/s5gr1qndrv+6EbA2LgkRy771uLBsgSJL3\n",
-       "Qh52pLqdYglw4NaZUw//JB2HqQ+jmUtCIcc8plIOPziPuJjPWZoPLqUHIb4u/+uY1ypvVsUiFIFe\n",
-       "qdPgdHNGjQOHgtWxzDDRsp65v5o0RkbbS8Z6qFDYEtEq8gea1jaQwIbOnXbhM0fSnBdoVkhAkCXo\n",
-       "D19pREs9++Qow8b/sAk1+Hopbuo5QJFLCIz4qNJF+lzvHV89A2oW2uhkJxKLC5YldTMhAuDa4Fhr\n",
-       "9fVy7M8srdH2+ryUz/187tam/h4by+l5mA9pHxZIHB0m2lNd+5C0v6Iw42uZfOfl8nkAceTMfdjJ\n",
-       "B/5bsmm6O/qJbM/IcPj9tMj4Yn89Are91AMWIPOmmZsvIBM/mK6N3TSvOgKBfZnYi395z2b+sWLE\n",
-       "kgUxCtBgXZDfSEMYuLSB1suTuwbYpjsh1qFoI0PNTI53YRIWa/a3Z0M5RNZcyImt/HI2OKbpMT6f\n",
-       "eJxxo0cMKBWIUdO9ygoMFi1nKAGE910lL2SOnKprToAlk86lPUHj8SDiWt67VBDAXFTu999udRMQ\n",
-       "0DDblZS9MRaxnWolZMZVPZTNA60P5ND3JBEAcLmKs7PefzCz1kE5xEtjqustBPAfxfxCqPM1o7Tx\n",
-       "n7PQYIaLQYHAr8HAce8C/H76pjoHcg+/Bk4Qm9dRjghI31js+KaryeK4JSDbm16Xl8iMipxRzLH0\n",
-       "hHmeLhISRqduxolTzgvaWwlGYeaE4LzqMlRV7XnBlCrZJgZK3NvDv1m+SUlqE/Nek4PfmiBZOJL/\n",
-       "cJwHFHAlpMpLMxMox+RgL2swQOvJumbI6SUCu7jPfVmqU/3jgjAoBy3rXVuEf/Br9wxkzAItEcI+\n",
-       "Lm98mh3/KhGeYE5BuglzaXkP/JPrFZPS69R9637SekDyWj01HowZJUxS2dcWPpk/LG+KnDfc2L8W\n",
-       "kdUlmgwsT4zoZyhhN95T2HBW9BdXseIdeZGcWHtqHopR2kuB/SF4dHRWWXZ/h+zHmGXRv68evtaP\n",
-       "qO5Cn9ql0yjjwfU1qffGqWRWqYidf85xlFGjyGF4i0Dgx4Gs7odsHNtmPllPzv4+t5Q1bijRmujv\n",
-       "E2xyhAsn8riHdgcYgcYKdIjyssXyGJWPs3C3ORaVwuJl/euY6TDrhMT2cB8jh7uxVCB59gsr6l3I\n",
-       "qd9x9O61AF2U8reSRfq49r73Ai5AdLVFZGQzAnI+In/9edtR11WA+pWzi/lmBPR/hV2G7iKxWZ6n\n",
-       "GhNgNUDJJS26DFF78j+jEwuIwBQ3UMTObIcUPirlCf+9j2UYE2S6FCj3zBLi7ij/OuU7aMtGtyoN\n",
-       "qzAqNmO9/0XYBO9LftJyzAzTu/TX6tOsiSCY8YPDjmhvd1tDlVPlwIxhdttZx3epITYFJae2f+R8\n",
-       "7gTqWFNqIlPlvn7APk+VjMu6ejmnFJDy9Nwsx0hEaZ4KR2Hb4i+rKZfvX9LzGi6LSaZteViNTHi7\n",
-       "u1ttNXS6/bPypv13BTCWf8Ldv5w5vA6sYJNWbFDy9H4NKcjx6+x/puRbyokqqVze6NVHctE+HvpS\n",
-       "TqwkSEF1JEY38t97PA9Bt6Si12qeeHQt/mo1XDn4G2FSWZ8kNO2DeB2L50P2djaQFxZwN+B4/nk/\n",
-       "jTe+nn8j3o60WLeqCzsy+7i6bP17cqgqCVaVNqZ+ckgMn/5BFCp2QdLYN+O4sOJMTZYPme4FwOZx\n",
-       "VMSPM39dJpcg6MeoPXmKMxv6uI9VYrVZQfBHyVFsSmyl1bL0+Vkz6V3bPYT/TT1tMIEDNS6uugqp\n",
-       "lDUe7smRBBrXUmaxMpRA3bHXKe8T6iTn+d148ReRwiLpNDFpbvuHT4pGnkwY4dsWIL/xSLVF0ZFR\n",
-       "aQttUtR6mkiGg/rioDwI5G3XoW5su3Z96mQwvXsSf7D/Hd61/35CmPuiLhFiiQbyPWRr2T7OEtzL\n",
-       "+1PDOEaN/QjXnuUKPP8phsZoY5K/axur0p5fwOj5SzFKrC2I1XB81YmLZI0RXpfW+Lt9cTB38S/K\n",
-       "IlesXHrvidRWoZk8fogsk5RiQweNXZptV9ntQbN1zpCtZ+6aR/f+0eMZLGbH6UuOcRN0lFkrTP1X\n",
-       "ono5/ajLD2oWzgFaef/E8kjA57uqzgdUozmeeDlRsnbjSjJAQbTkeuSclB/Dl3TgFMpz2yP03zCV\n",
-       "Jgq+18QBTOmcPv4VdDm6QYz3pDUwwBVrUSXrMr6zKwAAZCrXThMIyhwYBBTul68/azuCUidYnvOi\n",
-       "xcsGPnzNshEkP9PbiLnJi9S1j4kiRS6DsDCWcsiLBEsbEx2zew3IgnBPQX6a+Fu+zIuNQ/GtENwQ\n",
-       "qzNZf7ItkJzRiUUnwOR0FQm7k25v7dnawfWcWCb3EJ+uIdPH3Ec1nCwYGYfGpCdla3uvQxN32T+d\n",
-       "FhhTs/6XM/cK/UPHJ577OkQrQkq5KN4aUD3T0rlz8th1F4TVKGVju7AorUUZyBAch0/d1/ieoQ2o\n",
-       "pQH5sHHhtSSw6JcNerfmkh9P/OPj560/mnu7KLbuayu0Im7IjWRmeI2+6OzLzfNevKA9xKVpURas\n",
-       "X4upfdLowZjpAx81AwNXWr5J8qDebEOUtTtuPnVoMIf/o+MmUM6EhR0ui7By1nNztaJMh9bodfXv\n",
-       "gFm7qXWqMT57PJX7eThNxH2D1kqbuZHBDPLmz2BUCQci+7i6g120kRvBi2GiqFcO98jgX/wRgzu1\n",
-       "hgobTzLqIDEW8xzg0ViRtx+BciKA6WxxvGzmJcMVoBDIUn6sTVHPIO7pN82kCjA0ZBphvJEz00L7\n",
-       "ECLnKrf7MAWYhVG+4y59unoMJG9Cy7A9D/2q9TRc8I54+82En5Q9F6u1BwlpBTP6VvSYQsXtBfFr\n",
-       "O6zmWV2BqtdHuJB9DNXKbhGCkFPWHzc8q3MQwc1epjWH7mfu8ifhoeDyLzynAXb8SmWlT5fNT2Xz\n",
-       "x5bYhomMUVCtxXmMtE6/6+c2dEF7nOIxMImtG1ayNn1Ajw1Ecf9vjEfDfeEndEWxZSPW+rkFKkMl\n",
-       "BvI/pcWC03psFURr1G+Sg3SX5r88gsOXmbOJxJ2l5g5StDwE8V7lZGZdu8PpVN3dNMLhSjaS3XJX\n",
-       "hlhu0lRs8a/F8VgDBMXlHMugcDISjoHFIaiiaftA2N3t1sMlGTTZOK3k69VnwnqDe2LZ+BXfIYu1\n",
-       "xVfviDEqeP1wpKLfyAcchyz9H52W+JGr8NL0pLfHMWZcxg2Q9DRisuCjXwUwZYFz7wDQFwGZrWEq\n",
-       "5RUVmFHiz23QEGFT9nC+eVat6hGwqmW6r9nFb0XI28SJAL3+xUPa6QR38WYYr5sZxWJnjim1fg3P\n",
-       "J3HlzquCmCUFyvlnSPcwQ4hTP/nmavMWjlmMDTCM3HFtgrlcwv41izxfBOH2+YPJNCg9gG9ySNlo\n",
-       "OtnzF1352JYGwwUI8YihrVzyixetj719dC2lL4wcxznR1OkttVldcYlXECXBwya4d2E5/i7fTVBz\n",
-       "GL5k9j6hvxa21e21mlwazU1Zf4Q2eSXLaoEaPAEP0jBeYs9y4yAxhGU7ZCFYWRhOiUNt35DFHPI9\n",
-       "yx/n4XYp8jDgbulsHRNmAdJc9nP23JCzkVUn0mRR5j1hKRN9cbTNdJsPgcH16s/7TAf/9U/t5wFs\n",
-       "0eaLe5HU8pPNT3m8vo1UXouH/xJyD/a/u8ClpczCsxDLlXPcuvkNPMnOor3H0EuptAHSQbghyv6t\n",
-       "3EQHx7xAXS0HXmMdQQ0XXC5wYNR2FyWit0SscewFi3kOz1W2twitriQni0HvGjo6H3L924Ixridj\n",
-       "GrVEBBFAaryFhEJ/+TbuzJqXn/7VLj0YHivRuQd5ngsjsfW5SYq96NkCyQvgF0qZQCwUFi9qPvQM\n",
-       "tnbGiyLGzu0X10T1auD1GSP8oVtVxUOdPI+kQeEhEqGBcv16QyrwQmjZDOOtPbjynmR0OtnAkiny\n",
-       "ftOCw4lOJMc4ALqqMZy7NVdOHPpH75Kfc7YqjUoOVO9aDM/l+7RuJLmVkomGLyIhvAt0zzpWFUE7\n",
-       "CCLX/J3BuZyv+5UsSOdYEcPNscZL3rGkrbgkzvmP2uIRqfr2hrPydaP7N49AYoDv8v8q5rd0L6Xx\n",
-       "OtSj8EMM9IbejXquOHDmrJzFf21CMrfGxeZvsP8FJbCTrGBe8slQlwOeMobH1D4uJRMUC1blm0Y1\n",
-       "xbt8UH5447qovUGHX777ayQBDgT3egsVgRSjPpcCP0WcZ86E89Ael3NUanby0u10j8ognW9SRV+K\n",
-       "KKUJ/SCNBHdtFHSZ314Us6tdImFl1ZyoQOW5Zguer4592HC7qyXuNbrsdldY4tShEnf6zrIVm6VR\n",
-       "/ojAAnN8YQuWHv9A95vcMyO5EO+W38RVGBjdsXwELZypfQmQApNgTosWuxFW2J8h5nDIJ5Um5lgv\n",
-       "8b2qHF0vDqlkyB0aGGPMAhnWh3i7mliI9RTHKmczIgxHMot+kWwKOQbEKZCWgX3/cWpQkVl2Dxmj\n",
-       "N0qj/RGADpfXiSuD9SlPcwXe9fijj5bfxFUYGN3Nx6BuYo9s+63yj0vJe0ZZif5ndMRUHn6gekv5\n",
-       "vJUsHwNeBV5L2fCgbQwNw1MpYiEWabCcHQyTaIpM1NXlpHzRl/uEE2w/bs6ev8fAsZMvZVVRjcu6\n",
-       "dtKc5bce6nmjlB9Z2pDzN2BmiV2Y6DarnC8XIo0YQwMNt+JQTS2DiXWELOH5CGgku6iF4ed4PerW\n",
-       "d/PUB9DNQ1tB0U8r46hS+3bAORV0nh42ct+3UXyac4BJLtzzOypxFkEQdT34p0O5xb5fMuoo9BL1\n",
-       "N+KizcmX33GRndI8DbG4vbnepCQSS590xNSbAAeD5oNB6EgfdnGLAEg75pYlEzQWtn/lJzOcgAPG\n",
-       "gQH8v+LfPHDJJr6IOa10OyzNkkIjo9A9p5IsC1q8kDNnQDTOx8Vno989/Sxf9lI6l8J98vW9zqOY\n",
-       "OwtaKXtaLMoA4FjbK4PlfkOD4XzPG22y32M1Jxt6aV+6xFHissD7YiGB9afHa1nzyV47nAFEQ+27\n",
-       "OmUu0L7aVMfdZFKRsAYHx+YlJErnkGITZ6aM/rsKqJGvL7hIGKzOyjU4Or6fr6aFXHHagsA5qGDr\n",
-       "K8GOklC4Zto1ZiKsGuNXlfDCmIIRi43OiDfs0da4oM5mh5PqcZsb0cBSzFqgyISMz/kGgcHFicB/\n",
-       "wZgwbFk3XGimrosSge9p0aN9u5GQCBIa8tE4OlzZKc/tAiWN13+S/OXaVuhGb/K7WhpXjPR42J5R\n",
-       "GpVxTNHFsYJKw0Ul+BxN4JEFrIZhnkZ6LXfize2WNSvndzXpiwjdsiJlyWC/i/BmNNkpCHu72qch\n",
-       "dPx0IPR4IuPoOL0cekuRANLxMTDd+6eqxXm1YyX3DNCCvAN/l8qLL47TrlSNTF1ynnZLTPzwXIQQ\n",
-       "SPUt7x78sAxevrdDPb0JWnESulD7RMgcoWlZAvIWiQgnYNCRKQYG9jnS3s+ww9YCpTRd31OXIWwV\n",
-       "UvxHIyzAbeN1ObrJlGy3JYRpSAe4tguQMbqJLu5m3GXLQl+gCX11Fe4xgWrEBd1ZSE40jzeVpW+P\n",
-       "tSwjAzPxy+b7E2kkli26NlOPWBXSsatZ82tNoIBQIowGmflE5xe7X49h9yRCYCMfUM4cHASJbZ9l\n",
-       "rPrwoQVTIUUuEPP/ZcloONdJo0SV1tUNc7v25smGLwszIMw4AEEewdUpr0DoQW7JcsXCQtiNT6ET\n",
-       "yo9zVXTidFCQbQcZdF35YtwBUla4/LhSb4orgvqf26ONQjHBx6a/gEuIMGDZMcVFV7Drhw4Pz5zD\n",
-       "p9CjdTH9HRtxdKMI3vjVncDATVE3nSwjXPZnw/V3YP370rHG4obCgYwaUGRWxqJ6b1ATxAEufBld\n",
-       "BxrtuKUhXHMI4pkwxEQ66iOEic+wTqCC1TQf2M2OCeAjkKvMph0TKycTkDBN9QFHeX1k6453+Zk1\n",
-       "UdYhQ5We+XR/Sux0DEATY8B2lTileWv72GC82O1Xec8payEPpGAHcBnh5HAdycMpCZtqnavJlmQe\n",
-       "SXtzQIgmubmRpnl1Ci/QbQ7H5az3eo5m3swIPsZk+krn5Sxq78r+envNhZ2XEjTKydhlxf1lJAnp\n",
-       "SvnsrgPakK/NeZfaf7S8kXBIv06nPyHkwC9j38x/Lm0RrAOEbhyE+BiHOcBq3U2YjYdZ/LaaJ523\n",
-       "e2SF1nkpQsKb1It7zVgrwqjgRLOHcyGN2Rf6icFycRa09bER9SnLKzcmiJmD7k8k0qxa6sTukJ8/\n",
-       "7aWcH4kjndLJbTfiypmvPXF/2Haf5fETb8feSFevxXXAjQe5549zOZR20NFRRVR40fM9+aM7vuhR\n",
-       "oFEszbZce9RnSFaLq+OnM9FuHhIxk9hBhHjEvuL+4nVeNN5ZKZdVomaQ/GguyJq3aUvi6LbTd2QV\n",
-       "2if6YLDTfvvRhnzz8obeqPX+lXPONqPJHvfgiUFUodFlSkP6NA0DJ1iHEmv4d0djxO5tALqeFlf0\n",
-       "fMhkcL2STOujiFPIBMy7Wai8B8NkePpi0z20JLrztl4DTR3tNZPgh4hfQg/zCnC42en2QgBfPnfr\n",
-       "v5z/t3KQBHN/6rah/DbYIeCaKfQaXTU7m6ZmQfvBM3IAswq21D3Wekuo1C/Th+6IngQcPHl73bhV\n",
-       "aFbB4aNaqcDQeRIN9p8BqYteAaWBk1sPZcFjrq+n9JwLfRA3ADVoMXe3cyaJ1f5qXDhVHR968v8z\n",
-       "Cfc7T5rf5juq9zCDnTX1o9rgLlOG7NIYJ7S6xsE9xZlh0oSS4p5sXAgLlijdnBWZrcrvgKSfRbng\n",
-       "bup0yKAuK+lYlI5c+KwXlgFBDND72/rQ4NPoEZju+SQ/ppLxFLCCPRbzbbuRJgQIcYV6zr73kk7c\n",
-       "IvAIHxKAX166+XiMlNzqZ5B8AhIMxxt3kEHquYVY/mpIZMFYwgApp6xkNyi6gmREa1h34hXF9ADg\n",
-       "ly8zAY5KjvXygSBNqAOdFVPcGWbLJ2MEWozaaPu4u5LrYwdtUah74mNcHnPjbphhVmMvI5YRBqwL\n",
-       "3Bg2m6S7R+2finvvch9GGMhiY8hgz7oEsEIcmZBZxnGKp8Hqx67Nxu97ZtGeTorpTYim2HL37wmh\n",
-       "w4PBDhBSsvErkdkP5UrCpiNVTaEkYz373mdhoq/C2mu4aPEilfyfYQLMwz02F0TT3YhalrlhJLi4\n",
-       "JWVfgYR9U3/TNZpu+fPlKALBoRFAEpeeWtHCN5zRQPt2UMZjM1RLVPgEBUlsy669qaPtsf0o18OU\n",
-       "/dIFqpYw5ZbExU2wn6F58gl0Fqfu7KxO9U42WqiKtPRJkagAXO2pGoAZIYLq4mCzGHb/BOqFlbxS\n",
-       "0v4+JaQR9Fg+rYI4tnx2Hsh9zreVhQUkS7k3pepqqxSm7Cy+ToiIQ1QqBWLm10NgUbExXdbHmPWn\n",
-       "mOblz2EDLdy4tW4JcfcFnkBfRtCIPRjzgz7VmVYQL8xe43iShuncb0QtcJwby0c0BqqY2g5/j8R7\n",
-       "VkL4N59m1U/0q7JgNABBZ5Qe6ovUEq01oxuvYHxTfZWPDishOtx1fC7OqxDJii55Rjw67tX3tBz8\n",
-       "sD+QD5/olU2QBYPQZpwTYgAViQQCMASh2X4FO59Yb5agtVWbjJcjhdRiv/Sr9a7s94dm9qOlc6fn\n",
-       "7BgJaeg+MB1FaWZ8l+fU/bzC0hooq6n1UpQiKP7PBW7PorL3r++r4ssSS76vGpujdhMAZnZNhNT5\n",
-       "JX+GHyzDDcMYMbm9hc3gIrBQwVAvo6St0Jz6E+p2pRdPzfF5r2/uSkwYwP4/HItp/EsbC/8PMAAZ\n",
-       "HKuNPBz9rEaCo134RbVeGJWni28uHu82Gp7beqQ4/jCSLGm4R0MkesO7IBg6Rk3Kgox65nAy2zGl\n",
-       "D79hmgTdnOsL6MwF6OSxWKtzuMfzAdcinArOeIXi4M03nAe8Csho85P0ympUoAeEc10VE/bwKAgH\n",
-       "zrOqay/TwpsMlJb1TIezbTUNEfk0czARAA1dMb2HLNkXaMABBp1R4594GBSYwja0Y49aMTEC/QeL\n",
-       "LwzIVjpHy4Mni9EX6isEqs6kQjxi5Y7NnuUjfqUGKQUc6PP/INWxMmexvzqINMDQV3zQirJpzgNu\n",
-       "RIxPdKObhUb8wmGeVeURQot9z3eoAr3XK/UyUl51/rBxx4oKrFx8V9h71LYpTeTNaRnz+AKNgQ2k\n",
-       "b/7nh94diIsJBuWNhhbuAzC8LJg5MpRvRkdZKaivAP1GNPazoD5oOWVxln1Gn9oaRys8YhNAoQt5\n",
-       "r1XLKxZpmHzwnH/gMlblmhCxgrxw7gqUIi+ULoF/vrvb7nu+DgjevER/0EQmHNYVjgYW0xLHNheO\n",
-       "fpzjNAC+BKBk1mvUBfAAd3VMMhTfEDFO4NNEtuRwrhZlEf5m1YZQ/7tjDHgLT82wPbTsBvM0/EK7\n",
-       "LmFe37/YpSFeBBUhQPDPM9Yoa6jICi7ooF+9v7rd5WRhys+3UI0y8ye4YIXCeWypq/Vadxr5jEnH\n",
-       "vx4rbAnM4NLplCVWk+Fo4+iWo7SdMfafzupeEZE/galMlWavXCfSItcM30A1PiVPXR2UFjHBWgOF\n",
-       "hl52ZXNoS3zdu0IybUerO9QfnN964S8OqnQWxgmqIKQiaNDmRD8E9ZccSOjxg0MjsDIidE29lrEe\n",
-       "gPS7pjNTaCC9MhW4TUOi13DNdiBIAW1qLEpX81T6VhzelsOKqwGfp92xaFNQ2n6D6htRHSdhePFS\n",
-       "WU9z8xyQ5L5zKM3SqP9EYARs+PyTQtflRJN8GCeQaDzut7q5c9TtXgqHpTj1b4TobnVgTosWuxGi\n",
-       "93nH75tEOGWD5HTTUWw5gl32ItEcUPV2OnePqTu2Ag7HmsrUsnuR9wnw+aLQL6vPPCaullW2rVoR\n",
-       "3T+OLwgAqcO90SmntX7FCsAqRPo92BfRgdAAN+7op2kGdewZX2yL2494AWM2HSVuAI3f2/+3abKP\n",
-       "8bm/P+lS31gwloF9/3FqUKzG+ex7NGbpVH+iMABxkQyqFoW0PE3x4yoFUs3lt/EVRgY3hqCeULg3\n",
-       "SGJ2NzqwJ0WLXYjHnnVFL/Dz9QPSYAYSllXhNvI5FvsSsvZ55pX1vdWjvFY+axbUH3t1UFU8YEj7\n",
-       "Oif0lG1nnjUu15dps8lBcjucoG2qN8EMXS566KTgFVX047CDOV7pQceeBUnn5UH1CQSoq22ze5Th\n",
-       "93FbXuEhbZhTf2v45+tibOp6wNBlJJa+1k4NUjv9swBHWOBHsxfQS3Fv3aTTCmWX+ioxeSAbY7+i\n",
-       "ZmQ0ww4tPdvH0+dx9awp3dBliET1BMAPNEFAO2EbYLChO2ehgkDwwTw3XapnQ5ZlcvWWUHZT/Xg0\n",
-       "kQ79HhyTt2LBWSaVHw6eCC6XUf9rGhrc8XiYPgLKu9Zcw/564sqnmPtDAWnB06MVx0JGg6x8RA5o\n",
-       "2Hckgar9ZBYx+E02F9awbsS5KM314g1iad6KonO4h0rURMqfQMEpYDIBvnsrb3u5C+Hg6mZX5ua5\n",
-       "cOAWF5kQUZIdoPDEB+YFBxXBX0wxCHI0dMexRywkb2THMBzaRMs7ItDiH/Zty6fs0tMWsr8kc+Hm\n",
-       "WtrtvUiqFLC6Etqo6XodIBYmnXWJx/JpvrayuWhj7JjGkFf9zLJnHHPHB8yQ04DI8M3H38lCrPE0\n",
-       "2GZJNZp3QjFAsCjXk2M4LbsO6DF/rAKrlOkGqNbp70FRsnxyhBTQCoDgOliW1wxEn2MpD7HBsgHb\n",
-       "ZZ9SMTtcMBBn7MDeO31pPF5ohLGPx5ioVCUSfywEM1z89vn5qSbAWWIWZwva1sNTuIdie/K6tOE7\n",
-       "YWNkqNpyRM/ikUS0LFbUWBdxGtlOzWNq0lZswBWGkTHCkCroowx+j13g8QZKB+RVB2rjjArNZD7t\n",
-       "RSc2Zrkq2HRRXfYaeZ1xLI6P5VYl4Nq4EaFbgzDqeqOxbkDo35vtSDcyfOWP/9ucBSKnCcQWV5aU\n",
-       "oIOGcdG2VuPJ5hzQailstNgcl6PkpdlODi6y6Pl8wH3e5q1rmgMYPGBPUmJ6mfRwFQyunC4zoKr1\n",
-       "GklVX4pHSwySBgENxBvin8iiIhfpRm7uj+PKI/87XNA5aimopMrzJK7H2701DHIzD+tDKaReKwG7\n",
-       "Q2jo3jYl2oAfwuHtggMhhRj7gNIvfnYRd3jrewdRHHmdirylMKEP9QoBnLJVHqiWdasyuDf2Zicu\n",
-       "nS1YOxSgrL6543y24EoUEMQ3VFmCp308lJC7PUvlsyiCC6GLPKTGW89saPEYy1m/2km+L2iXZENc\n",
-       "zrwsqdIZiIbvzQegi3FHNT6S8ozwfn7jjpaLrfbUP2+xo3AJ/gGav+OMHJK39Mg+RlOpjoGHPylE\n",
-       "2O4fQVkbZ3aICsvmD2heaHfLidmD/kxP401qCt4TBkbnkymMRPJ6rSczAG3z1qg+Bo+qECkdgYIn\n",
-       "1vM9+7FGt+g1IJePtKcUkBmatbB4QDjxrV+pnY43DAA6YjdUr68OKpc2psqDOZ0wI+0JOcrANTWZ\n",
-       "wIsQjxUjYV8gqBxgAgghzMSLlHR3NFm3RKymKL83275P1D0yRCsG+LUkRRWB3jH73/aZmYsOffSQ\n",
-       "mxTXVFh8i8+or1drpjVZG57koyTTi9onBS+DxvfSjYcxGrXwalSvy5Dafgahs80V1z1onzp7CUtP\n",
-       "kcve2AL+eho5IUXh6JleFPv8j3tMlDRA3lRXgAnLnd7/TeipdM+zMPTqWtx6xgyEhbvgPYXJv35y\n",
-       "4tRsNYmZE7DAN9Ydauqj3yTyzRxvQge+ae3mJZ23zgFR0QS244Mhbjeff3ExrTpUG4VRuNqMPwSa\n",
-       "j5UpOI4MJHX5XS2CoAGD/oXi9vFDVLI4TiOcLeZFOYSFQ6M6J9Q4pRreiB1OTIJH4JSURX5pYsfs\n",
-       "EhBAISRVV7zQr+WB25PbRZP6Z4sU9df/paFNcmziyNrAf92z5WF0fWI4ENUX2GV/JAYGMKNCJTNq\n",
-       "i02Gsff6/JPo5lcILnpfJX4Us5ejGR9N21XTScnF/DL0H5uk8QgIadUz5qh2AlfIRK78SjY2gO4i\n",
-       "SRrBU3YwH6T9diPCaVZBN/qzN7Wvlbd+WRipf3EuWtk5WXfM0L3ZO7v/ZDg0TorrY/y1lb/bX0oK\n",
-       "zgWqm+DKdVeJ1bVqlzspa4PrLE+UkASfRB5VH5yurIsnYGJ2YKfcmR4Jd/6HhqBHjCodgBNaaNb8\n",
-       "zTrtVpyH0KnCfPlwf9iIxEwo47EGowR2ySw9fMJuUsinMJD/ffHDzUTsPFJ4HIqxT7CQZNabh8/6\n",
-       "HQzfxS1fDRtKu4xnsvaK1xbP69d2Wt9OrsvQ+SKuh8zvenPrqL7QhIM99GPgIZ6bipihjJqLQwOy\n",
-       "/SHvJ6WdzBxbbOgHap+02BhO6KBW3fCaxl891M2nKdJXC0iKG7EeE0qbNk5FD4/7NFfazX08Wzgu\n",
-       "tIYlHKFK1vQPHK4qlFSlp+btAADwDScM6S6SGJNJ3Q1gUl0d0kNyEkjAZcBvBoEOpxVduj81ne5D\n",
-       "ofqM4RVQfQT9ZpAgJf348E7URi7SE5+3fetOkbVrP5pp77wcB7gciOe7Dt3OCJpX5zXB2f/ZImQ1\n",
-       "kqFBnA1lr7enLB04eHxAUQ82lpu4B0rqR1R/Oq2l+NINlJK5H07ccKJzrqSvj758Bfc02H3LNpsI\n",
-       "5gaW6Myi5Lu9taCf18XKjelEdGydUIVTl8X7YUUka03I/+NxoCIWp33S/rBD89Ga+yaKrdtrromM\n",
-       "Zh6KOnStKScIT8pQd2iINnVhmJQ5z6sdlT4QAcx+7qAyhcYs45zSAn9GhDJcptY8OW8Nq96BcIcX\n",
-       "w+6Ha41NyLsWt1nzVs/vnEWahE4k3Llf7pxee9DyemLCMTZzVTowEwBX7NHdU1d6MPj1+rtXxP+P\n",
-       "te5+pdnswgkYAOTy4r9IuavGyo71p2rz4gsbxHAzh82KQoj/iosG9K2YQ2ymLcxndIsBPPCJpjP+\n",
-       "2h3GXItbtNjpNCpthnsRhIl+kHrt4t4kn4+NE+L7iMqz6gMRjaq2l9F96MjnxGYtFZ1tQ77Y/855\n",
-       "ooz8Vfal6ZnJJjGQwZTNzWvNzoTpi4I3kGgIvpR/yoRkUkRCU6fmTk0H0axk3w+Pk7s7QDnB2fCZ\n",
-       "bs2FDrXz25X/UDcGtgIosnlc4Bk3ZX7Uuw0yQLXTQURZwCKY1Q2zI1bLScrf0uPNi1fG+stgblv6\n",
-       "L9kPy1S8Hv8uj9upDsqAYgw1EbyGUadOtAYm/hwhiulGiB3Qy97xABYXG9gCKggF75A9WuGa/jeF\n",
-       "pwdGtmHIP9pWqIRkBVqFe6/mqzSOpvnjKgF6u9viJcwTRc1D8UsoZSMDyGaR76qufYstjnFsAaKm\n",
-       "4B0lIcw3Trhx/lZQ73XnZK5qoLqfVhVRAljDWgsLQTZQL6qbcGYSjiNXGYgImkK7MOmQOirT4cSF\n",
-       "dze5ahyJycAGihSm+1kIja/4GX56YZa3gvKTI3csQRkBLAVZk/x3zzECUG6tZyUzgshUNeyuOHzz\n",
-       "/IkHMjwfryhfbc+pd0f5OQxHqxIHwxQk7bKOS4A8StoS8l/fynMUkh2Nu5smfBN0mr52gPsrLY84\n",
-       "2jinsfTFvE3dYbg/sLwVspeTG8iLWCdfL9aRGM2kkKUVoImgEUKhW7x+vQ3YR/krAKwLQEeO49ui\n",
-       "mcktHk+IgHbe5hDTyk07Ezy2rnTqCLMZse1pYczE+kWtTa7gIdhVD0m5ZEzM8uaR3gL9UQY7hnCQ\n",
-       "5GgKtoiXoMtFXuUX++IUIkSRQDs7ovxeDPtgVrAzRCXqs+sNIHiudQdmKi1BFgW0ILgGg87euX0Z\n",
-       "djXBqd7jKvINj1kj1vKtSnoQNmVQBQ9it1Rru7/kZhaoRvOT6U33FRVPdsSxWKuZcydk/BXP1c7Y\n",
-       "aQjh0N/R8mr8YdJtMJl+fvSKzrGhDF/PlcXeoEZixaqaycH+oIrV+BnKqSC+4uC2pZzT8e9Yq3BJ\n",
-       "d1gaTmOezVUWl1/uyf8F8P7ZlMiyvbKdvLUqb/m5oXwRyPcfM/4RZpfPfaMfwKuJdZ8YPbyBZu9c\n",
-       "F8IToFJsZyhG22U7e8CxUyVXhdeFKCzd1FdZZ+aREh/mhmyDHibTVhuEO7Cv1ruz3hoiSDHUne/h\n",
-       "VRaXX+7DxyA9/+jnLntwapOoNn2An9U+19Xu7idm+kj2Vm32NKQvYRaLIpbsLYwb/40GXgJRRrur\n",
-       "H5/fCfI36inii+9Um/fkGDo250XcJFnSTVB17LfEgPII2RP/LjPK3RgP7S0xM0fqcDAFwCp7PXT5\n",
-       "0YjHc7nKyiRHDBFN5XXRReKP24vsGs6IfieblA3sYPhsw1SK5mmz0+FD1qfoNKKdAighDFqjBwLB\n",
-       "XBb+I5XA55Yd9nrVjkFH4ELA5Ny0ISMLBvF9IifFepJVnjQ4TiP2dfdKwSPw3wQtpLlesmpd+Q74\n",
-       "qjzh7LClsJ1j3idf2ih/hHncdfw/CwuThWuZwzA+OktHTcqyqwhvqJTY09onRLto35n7fW2gE378\n",
-       "tBC9qm2gFYNfpsPrdiIsQ+lRhjFTj0UaOZaZ23oNiQ9P80fniS5vMKUStYCp4/7gNWQbBEi0C93L\n",
-       "cGnEypKGx5KzFLYD+HK9USAY50fvgnRIHc7fCDyhvRWSHIAMMoBell1yN4yiHbFmqzL+4ymJ7ywq\n",
-       "rjPWWTTOzJNqv6SJywD8BumI/N6AnueTB58PAO5Sru0Nc2w2or7hTGLm5Bh1WzrcrqXda0W7w6dG\n",
-       "l8F1VD8zhx93AAh5Fq8H/Upk08tvOK6OF6Qf6bAO2apJ5dPFJy14gTbmoqNmtlSnTvo1/zdnoIfP\n",
-       "fsfxn4ETC3Urdrd89FckPA7YGGfvwgvzNv/2dSqr2zWtiS8DiryQySnYvHxtkNiEZmrkprx6+2yT\n",
-       "2xZewhJ6YtVOj0IqpsPKimtdyGmR+7Qd62122P+Y/prIXwlXWf83ZNObzdRJ5Zt7L9c/gp7xZTPk\n",
-       "swmXwb78O221ytHHhmaraWgynpgRNeT0xaqdHoRVTYeVY00yEK1R/R/PW2u2xoVuT+Mv4SrrP+bs\n",
-       "nBamiF9oat2WQkSomVVTfrzx3jemOEXeL2zQ/ztKTenHSNjqp5KhjY54l0BedDufXfYetdCZDE5f\n",
-       "6CJdnnWNAbC0kayCztuAQftp1VvZP54XbCVO7VsaJbf+JZuvO/LdNrqfh4XRg5n4Ku/bvekn/8nz\n",
-       "Py9qMM5wsette9vIHuOePpu6+i9Gglhv+zh8+5nVt/gaTrxQdRAsa3lNdJV5kf0rfYbq/z6MsoRH\n",
-       "EaXax1ytTvTc6g/D/QRCpVtykUgXPyUIE8pR9VkB7IeslsMCs1Q2l1BfI3i9bQx1vKEbyc+dwfvd\n",
-       "4I9YBP7bRL5+VgFnwLXFFl+J8h64qHAm8obK+IVRq32S7p+39oznZtZJFmX+NVJu2s5zM78Vekse\n",
-       "rl3L0c3FPtk/vkKjne/nszcgQg3dya8aoX8XbdTs+bgt9af7JV0yHl10AVqndetU7jUo+zbrYwrw\n",
-       "PyM4JRGQrR5dXz/DV1kPxbdRbPY/rrZ4Omsd+riLVZiCf09KbjTyBn7PVwf5yyBOUOxMM47bHb3S\n",
-       "ZZ7HqlL33tuk/QHLmGB1pFIZivm2aADyc22m/5XTQ6gTnAmihoiHqvwVznQVJM1iJLIIhp7xBdPa\n",
-       "R0k1ufXLk7/rc+DcgUxnnYKsnCAja7A96Lpbq97bAiTUe75PQWpoyc0NU8k5zXrR1con94Jr0ajK\n",
-       "wTJc56XWJmWsd7bxdWTHFZu1BLiKfeFVjBSkMI2vVue01gcOw3gKl4dlZ8naJ86HGSPAB6/4n2lu\n",
-       "U1H+BAWpw1p/Wwn7LqKRUlMg/Ujqqzg1iy95TF0gk1FRbCzh63FJkUjfmHp5Z9wO1wUNwrnKdloG\n",
-       "Go+3V6fpBBBInDgb3EQvRPayOLRLJaYZSOQ6rY8vpcdKtZVZERRJO3MjrD+9ZZR92/X2cjcm0lms\n",
-       "PC9lausy+2VnPI8bbOgX0xEwA6mLcqImiEQ8VfK+qDV48CxUSTDRsaW4tWfCvZHtNUp+Q8Lsseij\n",
-       "yxsz4I3fhvQsMah1zS6ZiWQMzABnEKBSXoxjVFvGwIXXY6RLmErylCO1NZlWjgaR/JbgE/Qoe6ol\n",
-       "hctUFS7UDwNaOZ/uexzrPCKBgIb8dwCs24yEPAwpdumDxV/Odi/5P7qVpb+89wD4/76QhhrmyHZh\n",
-       "xOtgrp+SdLkNr0DrJ2UvoMvywQ5tlDAzgVYtJZqBVcDZjyxSh+MDXp2Q6l2wqgHTg0H7wNymXrE1\n",
-       "63N8/merqrdfCTml0ohRpv3vfcUtIHTMY7flZmUOiS0PLgQuA+SDNfoWNJO1CdQCpO+yvNqnLXut\n",
-       "BcDntlbvkY8lSB4jVWjgDtxa4QP9fJk+bBQpLB5aSVP586Vqoit06z86470tC0vxoTW6Xu9CeKC0\n",
-       "OAkR4ovake1KnUWcIlHvKfg1UPeLFSG//iIClPZUV4ZHaRgJL3KRhXLFL9jnuX/BjxYxoLvWdv8K\n",
-       "QqIVYUnC52Sky/f5WAgBp5RPlVbew9xmsCab7ZH7v2htzTTmHG4vUhUhaoJ1vkSRa1rNf80f0exb\n",
-       "l2bnZ46RWGNqjYr91lgpoc4Ga+ixkrgMT/9bD+HQotqyparZHa4qC8XJm1ntzgWG8SxtQAnQsJP/\n",
-       "84ZfHjmS3mTzDY6rPvRCij6jilfMQZD3F1tJNG5ODCJn3quAxUBNaZ1AK6qWjWP//ni0rmge60Wc\n",
-       "7SmnR/7NUepGYPCOJrq+29noZbstT8USviG39lfO0FP7VpIujuH2CWzUv/UotUtkhFpyBxa4IPF7\n",
-       "1neePicNpI6W9bKfqJZ5d8q4cbEl5BiG5N4WsswIAd+WnRdLKWCbhykngC8YL1HmAQHHpIp9iGAd\n",
-       "Risvo3qr2oJgfMZmH2b1FsWzu9frYh0qysQ2ToPq1pzajBwzZQHMP4mL0UvBt25ZI/Rk2h5/GPJr\n",
-       "zZrhFJHrPUSzy75VwkZJn7ZH2oLnFINHNRW+TUEudLcDn5m73mFM9F99+8hwnCAtrX4rnXrdA/7Q\n",
-       "BJzPkFACdJ01cltgqvMip8w0Goe6uavKGBYXZTG1n3RFYx2Wa4qBELNCZGVTj+MeXv4hbY+XaNeo\n",
-       "lnoJz7Xrnp6Z6xm9jO9pCfrF3AcsQF3huQDBUC47E4skDhDW27FYdbzzQcRq6BOHQYJCeJGRaWPY\n",
-       "r15twj4r1DPrByKkMKH3FoH4lfQxhIjF19l/L3g0wWxbV7rhYrfFbzYJVqHX1f/yrJ1ZrnIQ/Sja\n",
-       "Ku1u/fDXUZ9KHWaWy1D0AwfJboNLi/uAhqMDoOaHLKT7jxVLf+mf/osGd+5U4Y8suKf5R0weOxWy\n",
-       "ZLqJsLEJ7/dyPnLqVU7hWWxBNPxpqpF8EDHJJHr8TIpzSazKFIUmw2f3kiY/2a3vIG+QkKcmQ2DY\n",
-       "LeWqeTphsqqUdgkNmuGCjl1v8Wd9jGh19Zk8SIRE8Xs/6tpjvSs+M6Nv8oCvbnggo1mJ5win/4Ht\n",
-       "Bwf77477xzRl65VQwGnaOtAmN5TihXIheZlorLcgxRvpHCnKqgSawaUcNcU3hU4xf4Nt0NcfleB3\n",
-       "U/0hvED8v6wT7bz7YPWm1gBJejNHLpyQt+aTtdzW5YBsqBrDFODoL2AL01z5F6Im5kSV+SmaM6r4\n",
-       "N653edR5HQMj00ZjCky9GtDAwKkzgtNKLkAzK6xBstclDEAB7L4U7+73X6vp4rdRDZOqEKB0TEFh\n",
-       "RSRrTcceSj7cGIY79wQQlLPPdyzcHkPY/43QrHbUEsNuoHI0lYtcG7TsoUKFzkaPDVsCsPG+Yox6\n",
-       "9uhOz+1XrOaFfVgMkL4etiz2jz5Fgl/MVx2hq92mbth305D//8ddhHIXk8c82b/ZT6gctvUV0GZT\n",
-       "AIHb/xpz/Qff4FvH6iOGRiigRXcLsJvyiqWg+r+tXqt/jhgSfx8dpK1QxKLYhQnJJEJPiXpJ1Nam\n",
-       "npvn/AXDQAmYA8apjuZo9v76HZQSE6lQQRApZ6evfaF3XAiqzVtW9unSX0S/5TYF08IO6tFg9eJl\n",
-       "bLzcXui2NHNykzN45YLkX8+boNaNffh7nbwrgETw/hw93/R4aQJ0dX+KebHWjItv6NI3KvERfsfi\n",
-       "Uq4ODRY7ji8sNdf5EBRPDqCFogOqejwjErw4mim3xr9DAmYVkoe7E4skEHzWwAlEW1wIn7OybaSb\n",
-       "TZCnH/e4jPT85cv6cLp9iL6umReY0/gQhTyzjUlZlGPr7fluT9bGaMXq/eYQXoaY68rbgnXetrTx\n",
-       "yPrfCyYid9yrfeykc4UigfIE4CqeFT2ckiZfcDSHR01FJ/YaLIsDjU9bPSUENDKAaCN+kkbGU3Kz\n",
-       "JaF31ecZzWcEgPqxcUPjc/U0nC6f8wNCqAQjLg2BfzrYUqEerAWhq7p66TixulW+oKh/XR2tzCjK\n",
-       "Bm6vGg0mAU4tiW7+GXCGwfJxaZRJ4b9xfAxYj94anrNyvvTS/VTl+SZeMugD10z7aMeaVzMA386M\n",
-       "3k0jt4UaOqRrZiepxtw35qQskYkRQX60vFlRFtEeCL/E/oOfRMRFUzDomIWCov9+G3fi47Ct1oM4\n",
-       "tEyR4vmun63gesP8RBl9PQwP3m6w59FkaiDBl0j9N/YmcOjhtmGyaStVKMNSv+OOOjcQAAhGZwYa\n",
-       "zSXdnymWQ3boQnnbnw41/bgdvtm+eS4uhD9BHCBEDENs5aYrBVBBuuQ9U865qFSJnsAq3WbKtkcl\n",
-       "Srj3rZjNaxYku88cvVQxkbl1Iqg5HOisRlPpu5zZxnZc6MWGw2bgpf+y5OkhwL9V6knBcwoNPOrH\n",
-       "Zf0GRjclhHrb8RMt35p8yoQeM6Znhm1wCiE64yBnbsXia1MJRZgZouGrKu0DNIJevIcut/kBrF47\n",
-       "ATfRcPcx3gRkpvJyyOx4UeDEpGPe2JVL3aRljBFnv0DnHWzSetrF2rFCUrPK9tddOsk794GeEulg\n",
-       "Zz79xpSwvVR7QJsywE4EcKaDmzwfdwCsUUqrYp1+Lpk+sb8rfSQ+Gu7IHJIpoSlN3F6GSo8YJBws\n",
-       "K+ChtRwHLJJw0y4ZHhW5jkL6r1x3qPDQAHbqsJEuMM2Et1Stf6ujy/hGwAh2v1Qn2NH9+toiVRvc\n",
-       "k2fbi6v8OlMx/3mQ9Vdsx0Rz5+2lZsEzr5KkqopRcjlb1OuKAEh6t3H5+k3jLauv5k8COaaT0xF3\n",
-       "9IEqzxDgL5eaj/9uit8aHS6FQQDc/GKfg0J30xd3F3Lizfli1ENM85KA5/KIGWav3nCTvZMtLtra\n",
-       "A7SaAoZrIK/Y/bjVls1KrFc9cnp9I6v1FMR/ues7EPFdIuWsx1+IOKOUiUUPzMdqZ1tNoQq45g9W\n",
-       "FknneqOdg4Qe1YL/AW7eb2JQ6raqMl4GRj8ik6QPv7+1yX20t9BhOOE7wRtDjFGj8l9N74jJ3622\n",
-       "86tvS+Qufu3SWVwa48q1nx16eMJpbq6nCeSbtNFst0hbzSukxny7WI2a3a17D5xkZxFdL9dTUJnK\n",
-       "cOURicv2wC+gZoW4B+IAoexVhdAWSHNDk+B2JtxHzTxH4qa1m4zZKSaZQhSXGLtZ57A0T7Ra2QtA\n",
-       "K7kaymw3tWm8Aqt2cKSygaFj3Ddx7UGyWy+dSx+/kAZswVcniWlyYgojQ0s70Cm55VfFWXtt/qxS\n",
-       "E2oifFWbhNXrSTMKp6Ec+rcXNKRmYzxymFAkONAbDa23le/hNBgEzHutC/meFAZMSC5Ow0R6ENLS\n",
-       "yr4w8eQ7GP0yveVxhMdLzfpUzG3qoLKngDrJgWNlgH+hvJ3ZYMMs2QHMv/hIZnHkps1TRzAt0863\n",
-       "/QxFjO/DjS01/iEfmDrTrhj8d0uqWjv49Co4L2A4/RNH67tx/R3drqRuG10f/cV5OPy/HvtkVQnL\n",
-       "awS2pXBDYcR28Nk0nGhIvkRTLcwC5Yy8ppdWZLA77dr7qi3IuqtTRvd2MkEwR93t0VKp1V5edcX7\n",
-       "FhSuluWsH/LSOGr22MIbvKtSgBhLeCLOLdqq0UTBgF+CUIqSSdIII0jcQ5UbR4qJWnt2BzQdY8LV\n",
-       "MCDg+QqrI8/xzzD6JnhtZMQnl+BS2BSduPPIt/JvcWU14t3P0730EQs4Ml1h8UOpuHjZaQa0X1+4\n",
-       "UHYXrEVZ30xCpLmhl9UQ+21EGLCcqSXZwc4Hk/8CiiGMNfwg9ufL9I5S2CMvNWv754VFhnHbSzDp\n",
-       "QMp+HbZ6ncnIcWaILAoPtjVXOYXOwsHlFmZYK6YNIB/YXpxSYPmteysoCfz8DSkS7SR/Ubj88Kvv\n",
-       "ZfWu40I6b5N0+btAH8p2DYsvCcFisQFpldhqKhqClC6q2Zq5rM+WocMzwyeUCQNA4hi6YfXF0+ju\n",
-       "5MN647T1iZ05oV0HxWYWTQs2Ip8BAUQfwmFaf65RlxWpJXVm65YIrtGODUi3putFwbnKDLMdw4fG\n",
-       "YG1zfBnrnFcoPtC4VSKzaTahPWMNQQgR+ntZrQ1cGHHw/WHG+kaMFX6+FdEhZ4s2VQFr+jz+L0+p\n",
-       "ILD2WEcHx5yISBNi84TlPTgcPcTERBNsv8neWCGIb6LehznH6Qe6pI1Lq2k5+0Pc3mSiHgm6yfcI\n",
-       "zjxCBMXHflxGkB9GTHpqBsAze9NGJWkcm94swnhtD/0XnEdTMeiZAa6XfhYSGbXIiLd1hR5yQY6n\n",
-       "WEfZzZ+67ZQ0xYcTVEp9VYnLjnOzp6xv/+zoggRUGhGvq7RiNsHF+aNpBuJozVNHxHKyYF2cJAAn\n",
-       "fYZ9bUtUsIoh6UIWf6m496RnPZBN/dZ6NJCs8BQpihaD0XTtiEuBqBbtB6ltEuNXxKy7z0ibVWb5\n",
-       "t+1XcrcLk8rXxyGsWEXKKhQSAmmzO9SZiStSPLtnVCcac1nf7E4lmXVg1pIhgEiInW78CBCNb0o3\n",
-       "ZA/Xu2UWPdQe+GEnFqVoaPLaL7VT9WyLM6D1V0p016ywguSOdbUEChPqxcd1HMwu7Bvf5FYyb60c\n",
-       "pfxFInDtwja8xa9x8eylS9L+vAfbZU9IufIs2q3IjvHceVnwI3T0uSwETjwlDgm7+zKKvMa6emVH\n",
-       "cR6D20q6yNkfN20ZllBBl6m8vKE4IGK5xm0291ApmqLn+vjfI6bOWqq+EFHLYI6U3GGrdsMJZOUz\n",
-       "fIpSnDcKZYDnju7jmNC9XbCClSiuf91ZVPrs0CewgY0s00hushQlRHfLggpRqnF+ASxjFQDAAdvv\n",
-       "SROQrbUxO2jSlFO0c15QlGr24duT9U33Zxawb0O75YLotrRntoudBtGgIViAooWLVMoXEUOxlMvf\n",
-       "IV/bkJpKHx02lz+WfTDIx8z4Efgsnz8EC3IrECwCu/H2EDvBofANYf7TU1FSIsyIldoJYr1p5W2X\n",
-       "9hGOV9XdU/ozdYoXmSo+HtfrCFQ96AkDndzFpKMDWq9fAQJC2saOE4daFRJu/8nM089QU6PEK/tx\n",
-       "DL0fkJywMdLn/co/9U+7MlN7MvBb5K6G/Y6YG0HW2ryF7Lr98KeAU4/5TojehJeSVnrPl6kcmzSv\n",
-       "CXlgPiWEqm+PlRJOlonTTLdiceIpR4K9ymk3F5BTxUBjMtFlU5OTS5if3aR2C1fmQZPP9N2thq3M\n",
-       "WFlawPXW+4DcuUXDgqL1c2VoC8DMYlYnlT62tmnLJ+q32UZ7W5j71oeNKzubwDLIQLoO6KxK1bzW\n",
-       "AbzrfGTOa1mh2D4jiovesaesJ3P5ZYuFLKv5qy+WR5d6CVjLVqlyfOv9WNppfuuI6FyE5k+fDT/u\n",
-       "LPF+9P+d6HFvVZGFLW553P1hpd+3TUOJbPTMHk24Nej0mEuWngemgpYXgK7DcvhUu5+Dnu1RGepP\n",
-       "x1K+QKO4AIvSIl4VQnOL6ut7h1bDfKsKxtq+xetMz4Eqz6u1l4qI8v/2ho4aHmKQos2L0auM8v9T\n",
-       "fminT9ZS1HoYKRcQZ2ydGOrIZ22LwGBMhQEVLLrzuEwAu0s1n4eIBXwhD46T+l1W5dK67T62Au4o\n",
-       "cBkA+IDX4768dWjNEB0POeSRf3ELTABu0H63P9P/8VJSZGCoOAm1H+gedpgffqn831FlYXJvAGzj\n",
-       "A0+1yMMe2nLMBkjVsTPsfZwSi4m3s8rbwbN1/dCyrAoGCXwpY3IS3xbME5E5ILPq5lzgeO7leX+S\n",
-       "EZ3DHbL5ygJpcI3B30sGkFMj5xufiLv5Tq0mcsQhzSeAl31QdWJH+OVJ3aYqmfwSmurxV6xZQPNS\n",
-       "NzQpOQG3TbNv1Em0sA1jiFJyf0LctaHufJ5ihi3eAVDwaRvFPQfKVkQAjlmsbSxGsFcCMkLg6VZ4\n",
-       "J6F7xLAKxydldd5yy5MDmVnmZeQGBbEeQcEIzAVnzfk74mGFqqxjxZqSDzfyOKAowGN9ViAf4aiR\n",
-       "ZkpwoVeYFwnwqnv7N2uC1W+B1SWq94N2hxJahRCiaKeFO56gv4QL785ABO646txzNVVsq+PH6Yuz\n",
-       "102iQ3ZLst1/ZmkKkNXRaTq7N7ZbFEd67VeZSaOdvWH1LJtJXzrhsr/+BQvuRs1/+qO6+H2OrPyH\n",
-       "sNoaZL6MsuB0dwGb4o7/nickrKl0Wpt9IR20dviJZAs/6yokF9hKRPsIlyT4q8dhEff0xdnro3j1\n",
-       "51NGcT+zMq0Z4ui0nV2b2xXIlE0+SjwrqUJZOXmOuw19gVykdchFcMJ2XXdvCP89M+kw9JXplpNt\n",
-       "S57al50ZOE3qWh7gtaWIlAiKrj/MUus/oM0Y29qLsfAWTEjY4Ec3nKYZbJWqoIcUirFmX9ZUoz2i\n",
-       "GUrVzD0iVClUO/2JCMPO/NlX3h+nqTY0L7dFfdatW6haIP/Y9a2GLvv2MxN8F+ImGtLvLMM4uBPg\n",
-       "OWfATj1JCHlVK3XOEkiairHeMm/8JEvXhrIfT1NaiUmIfavzAXluRszi2nPIoqgsl3X/WsGF8mAu\n",
-       "d54G/8cgLrW5pcMva53YjmBMe37qqU7qzk6QSnpxSVieKD0uBaed96kGzcrRw8k6/EYTqxBoEhIZ\n",
-       "zPE2bKadbL4Ast4BXZ/zTN4E+KK5ToE0FHXHcWnckg4qZVtylpHTkaor91siCAifdV6iaSHZNcxX\n",
-       "wxCmiGgkGkqwpDArRDWuunmr2Zd5mzM+5DfH/RxAnIgI+e71N5Cut1wV8d8aaLvn9+JaX0U8f1Bs\n",
-       "2ewVcKvb1DhnwCS55OE8tsQ3zcXagPGYg4U6wzmqVoyUdbaFIBMWcpw9Ct5igFI5OuJ4ByhGYPsg\n",
-       "IMqQtyILqGQDL/0bRajvH9L7YvvjU+74g4Rh2vIb2x/XHT0inpPBktTwwWxf9VPseXTRRBsHv7QT\n",
-       "f+n7N574oaT5XYJNQlVYf7p5gbv04knU+Z6bQ6oqzfroBS4vkBOdW4m/KKnAPl3yMQtgyZE/qfiE\n",
-       "Wa0FAYZTlM7sqGjfpmJBV79sJCagAFy1nQd7T1woqfzeLdV4YsAaVJzwjja/4EBLkfRuBHvXd9Py\n",
-       "qz6MdjFEsSVrvMJi8dbvt0OTLFUSTRLhoW2/a/putjt0ZfwFgBXi0gyWYygSIbGz7WEcnjyS8aEh\n",
-       "7RieH4Xlx56MsfullfCGJh1/f7mQOCEresOFgKVuWPf6yB4N/nH7bv/4fAYFOORf6e5kwKDl9Jwv\n",
-       "khplym4PfdhOr/9WNEYyd+SLDclGlNSnv3NBqSyBBAx3x7qGRzhBFvS/ot6jH3fXn4G5Gt4+bzfV\n",
-       "/+XfqpsRdl2vhLk5dE4NDAPSh+A6n4hgvWESw5tjv4GedDz+V00EPdDt8dcGAWMx3LLX6T1B9jMn\n",
-       "0mNZD0yt3O/K/np7yfWMBl3u15dLKndt7XRuxKiUSxkkPlpN0+BDP55OYTKkcRiIEv0xGkHKbptU\n",
-       "IAKr0/2/FJGpi/ELCSQ+e2oi9vKoIklM8snSVqB4JxGidcgltugvm30vGkm/EEIvvhaEzDFNEyzc\n",
-       "io+4I+vnyVBI73O/K/np7yXcex63t5VvxlraFKOVZILxCNc7Dc7P88dYTMjdGDiq5zH+Z/kWRxzd\n",
-       "L8XrwDQZhVUEm4wMA49Y8UGiprFbnYirnPo/sRM5Rzb3rUs5NSN3o3/cBpi5w2m948xBJx99B7rT\n",
-       "Wqvo9p+zmUWWncZwMKQEJotLYWHQtDHJN9hcz71LFLVskTv3Qv6LdHGN9ZRPTkjGhVN7XQrcpxTx\n",
-       "5+DD3tIm7eizxme7Pqr5xGkyrsvN+dqE89/ViQF1y6Jmn7r3bf3hGpnO+JakSZiG7mP8VpTqKyIx\n",
-       "fjGi8HAbYsnFtilM5djhPkCjerET4ACEkyDuJsutMBBHDd6LqKkSBFbn2rLY8c+iDFT/PQWsn7i8\n",
-       "Wp0KXK96DqQP+K9ByNq20ZMNYwC8Fvf9+yy66g0IDbhhFhdIQox/iwFXJSMGaGMmVfCYq/auQwXc\n",
-       "CdJzza0LSqbCu2H3Lo2+7NPHWAJIoCRw56gOxh6fDhEUKdCv+lYSUM1zRVxiSIFb121bMc20UwmW\n",
-       "YpVEV41sM/9X9um8stXZAhDmIZMvvKi18oYTMmGwCZU9mND9VQAARyvwlZQm9KGpAHZPkHsdkZiB\n",
-       "YtpYrIQhS95tZcpTJiW64jmL/ea67Zx3rT4ZQJ+0oDC8BkPUyvVnELqo+yoPY2ZcE/vixUCWMhzi\n",
-       "W0NKRFaPXXMGdte7NT9DoZ//YIF+AteFNxbR201wTGR/bzwf88nezWLCRYTCgrUf8uHtRThsZe6Y\n",
-       "dHHa8dqvWqo5ElqcEQkCT0g0TCrzwfdMkd/bG5Jo7IwNQX75o1nHImzQ8BOmtHATWD4UuQz00Tz4\n",
-       "aGSpWDxonR5pEsyr2XV6WY8wp5nUAkWXCVQOwSzTEauD/gO9b7fVv7ry/DEZVD8m/ko/6Hzn283n\n",
-       "xQgrS7AgCJu1k/jnNCRjYPEPMHYGI/Hm0JpqhDwkQiQ1kobXmXOoy8c+7ejyfXVO63q7GVOZFqTc\n",
-       "d4kY1iY4zUcdmkpfRyXoEH54yAmdVAmYjvz/DNc+Zc6jLxyrfFuItyX2Fxo7VOu3evtpr5nk+qm+\n",
-       "bFsnLMUEzcln0IE9ssbRzP5SdENAtQztIeAhw53B72/E9p0UmiIS4uqMzeCvu60OkIkwzVR1lx/W\n",
-       "iGD7fzpR3Uxllln9QmDSodLNYmYSRjFuYGx0ZGwBtxNJAVHsTYTecG7wJ53db3HM1LqvhKLPiVJT\n",
-       "5ProAxrkhNaCh9KWnZ4SxMFQ1COEnRa6fvPocW/UK5YlkMpTmMIMYKvQ4wc0xwEcU0AbIqg2wC8r\n",
-       "ZpsB84kA+ecGg/Uh0EA59ApKWL6Jf6bBKPAerXCMfxFp2PO0BG9vsykyN/fW1QPbl5OVchNITIQY\n",
-       "wpkzWecQKgREi4XMWmM/LIFaWSgftjD3wwRrCGZMPwhe/5/G01/fvARoEa7srhzAuYaEHThu/JyO\n",
-       "ENmtQN7QqM0X3ZNIiV7yPxnCyc6GA7r0w7RdV2PHCMDmWlYhux+3NVPQ1DOx1V6bQhnK+Ixu6ad9\n",
-       "E/l1XPRy1P2tqcVPOkhM4zMI/KJ7gUlZ1wZLYE7sBAUR9soPhpz5N0xHlanH5nPnWNJa0WtMm8lS\n",
-       "iP6Djjhu62If7VqpGkuH/tzM5qH+KbVsQrfbUvUfpLVP1SElowSqbF7Wgqqo2zYJaDgjGI+nBW68\n",
-       "9O4eLujGY4uciOk8uEgovR12vb0XfNCbRbNj3N3Jq47Chu3/e2aqmYJB0g0ekyHv5vnUDAUYF1fw\n",
-       "Ue9ArYohpwyIHyDS1U0gAzKHMmS91gh9An+KJGHV7TjAdCSfgmJAMlGRegqVSnI7q6G/4rQFGPNh\n",
-       "qpFDiFIsO2sy7qlWdpf5cQXNNESEXOmMOWDJyg658pwJsP5mQxAwszMho2qmifMrUI2RbgI5cGq3\n",
-       "9AhOe16PvvBeB3CjFsjBSFeWR0E1lAcMRbesbUZ1zx2zPF1thSUcbqja/55RFsQcd3osSEhBT5Yf\n",
-       "3GENkf4SC2aCLQorn/co/8WcLwV/mbRDxUhz8kpz9I8lIcjY1nLUGRmyZFsVxPPdvHQC4c05d6cf\n",
-       "c5EzCCmpqIGOh1Zu157TtSV+sXhhQZWXf36ZyTb2nbjCjidIt/vaDWFQdvGr1TjbZhne9uQBPzQf\n",
-       "5bqubliIIwyMfM97pBt1uKRqhW4uagcT9gWn9uWguJQWNq933r0gZtB0ipa7lSdqguIjBmcFIQcZ\n",
-       "ZtkSDZeTpjGaXTyT5kO57AQPN8oWzQsQ+DoGOKc+PC9Kl58aV6GrGwdZ852fvMjE1FssY7yWBEzF\n",
-       "4LjQlSpl9G8adgn0xvkrob81QNWg621eQvZdfvaQ2XO4uut1Eqffd/+7AHdQ7yyR3hgIuHxLCVTf\n",
-       "HugkXS0TppluxOPEKZ38xImwnbVs2VWJTpsXKubWeyzUPdjZUNRra6FGS/kErZWvLv4Wq84FjGQU\n",
-       "nG8ZYwun0TJ3W21t6obqDwHeet5bgjkyJ6CLorFaZW2ltLcLMG/4+kOO3sCvAOdhw9r6XdzQR+pX\n",
-       "1mNWUeKKmW+6Y38GdIWoKZnTi1jfheKUtmW/wowI8FEGHHHlOb/Gm1Gfh0ZueHhZOw0YTqaASpxC\n",
-       "DyL7ApJrJssFEc1j5iO/SF58sy66Oq84bX7lcSrGsQHmG/fhE/4rsafH26V7398hAYUQxlepCYck\n",
-       "+Rtp0GIRP9iT0G5++HaB/c+6ZRmjsnz5rZQnmwA9sK7Hfm3O48gPrqwfbMy0mNqVQwYFwqntV0U1\n",
-       "y9oCfz1zgQv0cHSYrpAHFZMJEd9GxbYWN/Ttm4wQl2WWYi5XHsQclAW3c4CGsfE5Yxgi67HgMWdb\n",
-       "tFxKI0YeKWIxhk2Zed2cqt2l+q+Ck/9Swd3DCtQFj2X780CIbt3Iuj/6ga2mJ0IKO4Oy1UZ2hb8I\n",
-       "gMcLW9vW0z/vy82B9QRRI2mksJsjCzWwRXxKogrUgdnE6rzdlbRlVgoBoYPJqfr4PofCTAJqS224\n",
-       "n5xP63RYumafyYNM9hU7JZBf3K6FtyvqMRORVngel335kSVh223cgNsjPBa2OQG3Ng4WcQH3bjc5\n",
-       "PwWMVXKLBrEWX4vKgbQjObHHi3BZ2uj+8P35xzyks/oRkHNx8IsVNNijcF8kMBIMpKlQrDeLR0au\n",
-       "1J3i3sdQu80TRtp3K2Bzh1ubhJ8IDERVxlD24q//3UbBfatPlGSfYlG+3mZIanLGN6X8OS4hnk3Y\n",
-       "rJaG0rAb/NCeUpbGu/mEqfGWnbgT1YFqur4qAMAOWPSAPPwNItPNdA4J8i3/ZkWuddHQhZQaOcsE\n",
-       "GN8MVPiPpgoPjoQ6hhmW1Er9DweIFWMXqUCKMrvgu3FlbYstUs15NwXM+pNSSGNGP8dlv2i/pRK5\n",
-       "4Qyg9V4n/Q20GEityXcwhXFKbhCimxTymClPsWyJCddG2MO3wXiDEBg2/F9N8vvBuk5POVqcnQPT\n",
-       "+TtjGVO/+IDkZFVVfTegqQuvPTjP1Xonoj+TGnpGavL+EPsaI/KD97iSmq2Ihh91FGRPWevHLyFr\n",
-       "6yLcffwptnow/qDgeIlx3G/eNHDpSDyxL7yFCqe8bu3PtHis/aadji+rSOBV50HRq4/vr/pGl0O3\n",
-       "c8egsAluIS4YPGrs0oEF7tLf5E7jbVEm2/ni8cRbDOT54Mxt+DMmVx3KI1mxdyoQQTjXlIJL/+Tp\n",
-       "hsZXUPRv9rxTCiyGb7uokD9RMnl8VL28lv+0XOM/b1NdL62uRdRBQE89UKTntvWyn8S0DTKQJzoS\n",
-       "dOMGvuNExvnpb5goQhnKjMwyFwt6SLjyvyNX5wDeRPtx/KVxUsSe5VgOeFX93z7eeINrCcBRp8qp\n",
-       "CefpZ30IazCAsMU6irp9KTXvRJJduCncvNXObPBapGSmTjQ5XHRIyg5pPXCzi9MyYvYN8/cxVNKG\n",
-       "wBuJbJdz3Lie/U9vHYSkci+nEi3Og2ohQ0vSmVJH/8ocgVoQ1nOY9pV4OExMvLcVE6YdXR4UXUqR\n",
-       "t3xkrZM30aCHZMOKZWr0SsD5bUzORkSD1+Tfylgl/yPGZ4QtssatzQ1EhQbgARWzwdOy86FpsALV\n",
-       "C43Xtw5lLi7sHf7iA3hEA/tIPUOoDTj0VypgyAL4fDMjCf5jyWO+q7gcfY0ozqPMOa9QI/CHyhA+\n",
-       "uD64reotc2nKRRjSFsMhpFdHoloFkMV9IpQ6PhgMn9OoXW3EDj8AInfxtDtmSjKVlWaYNOMSla4M\n",
-       "8+7O1SLucBCAVzdw+EfTO/eibzf+mLe+NVVB6V6MuEUaEyDlwMl93J6ptSZb/kdFAEFQaQmD5CvN\n",
-       "AGurtC8lJC+hyNpjx9lMb10P+e4SaD3XsOzRvpv3b60aJLBYxO+g03Fll+ObhdaEqvgdYyD3AAgU\n",
-       "JehowxnFe3sBhAw8Meac3w36o3gqgsDy5rwKwOfZZ0O2ALfo0zCMEFx0vZRsTDUcyaH/jnOk5b3N\n",
-       "WuKVF1FuOrD7B1cYhOPFBmEgNDTsayYkSeUsvcb9c/v30lqDKUXTJo/uP367cWgZ7Ku0v8Wf4Oms\n",
-       "ToRAzWrdtFCw5/3rSvKg908nftmKrw+gqncacWTQj7Kb0FvGwwF5T23cioS2svzsHebYCv9Kln/0\n",
-       "T9x/+1Pi95o+llYlsTaxmmD1QPcQrJT6Uug7S5GIyX67tOoi0EIZLPci7hq6Fa9vqYrXl2IRpaOc\n",
-       "8jRXENZCXCV1df8hF4/C6iw/oroLHEHnM/z30T9x/+1PcyTKjMd3deiYI08qbOYuBKyy1WSCRe3n\n",
-       "iy4WJQbEsSmgQUXAhTvL7kX0mCqcH6reTC7RZZ90dVDcW7CCcljowruJWn94uEQ6BpbPr5rfU9RV\n",
-       "1gb/xysYimjILZHkSXADUNjnap+of/h1TGqNDM7r7vnaKvpJ6VoXn4ZRASEVOsglnFAu7q1MxeuD\n",
-       "wfNJp9H/hoj0Vwec0wYztxiGSlc7z8T88bVpkdBIeTXoSW1vKRJvOyd4SeldYsYE4Cf+Di3Svog7\n",
-       "sucTvx8E1PHyFt18Cihb021TU+4oguN08DqYj9PfH3SBTVFnazy3lh65yx3PH2KLlY/srzwMJJZz\n",
-       "1yyaOEGmn8ATEBS33sDeaWYYCBMGW2oAEpVbxShwGhx8dHQ/G1Cu6zHDAADd9ccom55Q8s/fCl+F\n",
-       "Tb4zM+7fF84UQUGbMpfr8OktcUAb1Ea4cKLuGRepQIPcjYOeY5fzUBC0xUm174vRLTaOqNrPtF0U\n",
-       "3yqXeOdj6nHv/hT6BS1bxf3fTkFbQpmINejKU1PpRqKjnss9eQBtFNR1pyWTJKQl+13pDVsrLV0r\n",
-       "mMRjYX35aPmjc8+CSTQ2J4r6nXm6yj3ExrVsJa5e4dhpsB4lc3lSiFKBO8x/5OjIvzqTe43CLQFG\n",
-       "Rb4jWTgpvP/6Bff9xak5hUz6Jlu4jNiiiByC014teuSsuVEk35YziKOPlt/EVRgY3uyutjTkbnHN\n",
-       "7OrAnRYtdiKYDcfxOsHyNzEVaUFg0D3LIQDMgOaZxwH8ImlvdfGMhSH+L7JPUD1HfRS72Sv64AcN\n",
-       "q7bJYAE5iCjKaOSI7Hpa73yKgdy9wpqJc6Sz/m7OFphrZdVF5ZhqaTap0NkAtCLxVd/y1pZEK2rZ\n",
-       "4VKRbxQLrHOvNf/+0om7zxU56OPq0PQnlrA0Gc/mXiqglMqmn/VS3Dr7Li98GRqQtWStU/o4x8C7\n",
-       "TdcHFk4/cw3L+s6XSrFtrPUVia5B/6VWUTA/VMNM2H/PyL5imslUam4Wcp8KxUZYmsrSfbPXAGwU\n",
-       "XL1p5OqFaoCsh89e4LSnwH6tisuhrgkjNJYKMBJVr5BlBS0PRHyEavljJu6goqqWNCl91KdaeZtk\n",
-       "SqXgS+hX3j2yuiohu5j6s1nze13PfT6fctVD5qYnlEDky2xiEUGHbSzD0biwmeqq9Lfywa5aATWG\n",
-       "ZvR6PzkdNSnc1xSrrELZVcfc3L6gZ6yuxp9njKWm6oKaSze1UWA+6eoyu8ev6N9Ho+8GND2l/QkH\n",
-       "W8ko39rN9SUd0V51d8rDE4OK8QoWrPSDLhjlmXyWgzo57IBp7zZoYMmM68ZG0iSLl8XSpmjuLrdD\n",
-       "+vgYqYiPBGd7depiymFekk9koxqFykfX5fbddpgCS4CUitzdHswA2+Mk71+PisUUmYn30MLNUnuR\n",
-       "zzTOPeFYFiNXSgg13EuOja/LEBggSj0BUujZMhPWt+E+9C3mSPFouDcWve7bdZDrLrmlFJZ+Yz6+\n",
-       "G5s9jEa+YolAW/+YDKHnSudt+Com65rvZ4WrMzLe9u3tJdw65fFht2j8Npg4Slf8Iv1yvPq/AE/Q\n",
-       "empUb4CP2eGZd5q21AmX5cDoDFlDQaL1kp7mtKH/ts8lNqiqERqBnXqSwIhZMORlew+qOZ3MHfGN\n",
-       "1ctzzTLnUb2qWPbfpT8pbDH/bs7NTxsF5KYoTKP7Da0v+f+KqeU1UorKBPtqkGvjNUe53L37ffEO\n",
-       "aHvrAJQG5oBV2fu0L7a+NIMQ5P+B1+dL7Yw9M9GUPC/OUP+MYIyATJncPnr+gX9R4SKGRKrVKLdR\n",
-       "GqQijEUmdt2ggfyjBRt7BBfKfHu+jjpGBuQi67okyjtTqJ84kZuO2chv/3/eegxBNbuckmCGSuHT\n",
-       "tWL9SNyE8Eq9AwT5RAVvx77kdxkyRu5qbZFc8LtNS9O53+AVkJ6ip0JY+orAxXDjfdv/ki1fS8Fl\n",
-       "1cJ2mZs8AiExKyYvV34rZv7rMKz7SyvbOeAmxP2dqMedRgzVqIWrL5W/FUnnL/FD5JHtCuA9/neb\n",
-       "C6/MLYzE+kHo8/mTKs8z2kd41Ye/zn2K01ABowajHBm1kzwZxRyJ9Uk06g9fUVC+kEy3w/v5hgFQ\n",
-       "j3BgrLzN3CS26NlOPWCHSl6ZasJnQd5BAIiA0I5NFE/VSMoPFKFItEP/Lwz3zU1NKUjvvpiaaNiC\n",
-       "5WcYAQrX+FdBxPUbZu69XMI4pkslbRpMSDc5w86bZpMTY3BznB8Q78VTPDq8bXy4CZ+ZGoiT1ULb\n",
-       "9lhzn2sLOdmZGNRulhSDCmR7f/C+p/bo41Q0j5oxoQ3smF1PdfVru4DLQSu/4ChuWT/dRGeAGa9k\n",
-       "/s0pVGidoV5zFxcNUMPwUTaeAyrq7sH79wUfyMb2i+nTaP94jfWC3oh5beGcsJ7g/7hoMiF6XBrr\n",
-       "kcobgy8/Mipe/PU2z8rkc0KzVq2iuQJzPB9M4rLUorFxeocy1l8jf1J37NOa+LCzuJiVknDuakWj\n",
-       "u6cyNjbugHapcWRFW3Cx+md9xaPK2kjx7JSH9WVxP7jrWNA68w0bVvDAVBzsBEiKmuE0XqcPGz3C\n",
-       "VSbVFHEVhJ6V0ttG4XlTFvM6W/0nvECZ1DGh5CpmhdUMxmeBlQ+bZy0qZ2QZ1msp2e6T5b9RX8qm\n",
-       "/jN2o6bkagl1wvBAdESxikvygNJBMEYQEgJAB5RE6kdmwJCbIcH0gVeZYK2P9uLRGk1I1qYb9xit\n",
-       "vfWIxaulDIsKLiqcaexcr7T1w5iRLAVFCICbxwhl7uIXZH8FNJC0nJ0bynZ1yMmWbl1kwruSbFcg\n",
-       "8xCxKTCR87rW9HZ+m9Cqoc/DUqOWZdw5in9WkwzKO5tt2eGM5YfmlzIj7fvJvlHeQPxfVuRNuwwm\n",
-       "l6zVzLuIpoHjBDNCcIP17f44kGa5LUee5yrW2YEAAC5UQZokbH95P3NGdz/c/u13ysGHcvqOLWVT\n",
-       "spBQSLMkTrOZzINNIRPllAL9VRTkDtqwSYjjy5b+zBFywyaCXvCgX6PK7OyM68G2aBuoFw2os+dR\n",
-       "xSbIHDG3bpy/YtR5Loa5G25mLzMx1KO8l1KhO2cIiMsDK5Pvn37ep3wa0kXedcnIUIKxlhhWoZoM\n",
-       "U18mIkCkwi3gRJeJC+Di8AnF2AipQ5+6e1Hi3ZXRhEr4B3bK0tO3S76JsZO/me+F6gmON/IOSDAr\n",
-       "WLAp6O/9Eb8SiiljiK3+THxYWkEWiY6iHDzJq05nLc3PMPdN0epFh5PQFQEO1093Q5Jl109j/Z1y\n",
-       "/NUb13h8RzfzlLD6fDX5IumEI2OYp4blT5nxjesHGQ2wCz26SW9Fr0nHkubXoZGcEK+UtqEQeqdi\n",
-       "ACLwLA0kqkIVFVSgH8iysC7QYgKMzpA0FiVBFiqSMOXPNy/we3f14pHqtwwGVDYmcrjVRZCCijjV\n",
-       "fUaU1j6ujl31nyi4x2/gCyo7sQsH/wMsCmh4g4qhDu7MBJv59CYz32BpaGvLJuAz+oS7d8KOOo6F\n",
-       "Di71Ee7BbNVQdc6w1WczOEflw50vLVDWhfyUYYLDRDTgm94X2exupsqCgDAIKrbvvAnYKJWvTfKy\n",
-       "3fBQQCviKGRUIQusF30QRG/P+Gpa8cL1XsOtS+CWYPXCsYxJjvrRRSsEN6NVu6TjOVrKkacZthl3\n",
-       "N0blE5aBsSjnp3DE0Qs0+IKdLFHpmoyqCKlIS4t1ykMPRv64n7D6SeElUWfvx8oPcqzWQiY5MKVS\n",
-       "ljM3nnfQc01vRIjq7HYLzeG/Nlid8nTZKd8jpXrH25wVgRZZaf2PYZ8kL8fvDkjX379HCK/rBYv6\n",
-       "9ATIkzLuKv+lV4uUEpI+xxKWuOHPyz4aDxPO4Iu+d+VXtCK9DkDpjJ8Izulm3WqlRS3P5wcauOjC\n",
-       "251Ed+bPCRRrhwfKvwPCbkf2KeDaVcxi8N3QX7ma2Ncys2ICtZASeKKzpKxYhEUqaKHgSzVcpcMc\n",
-       "UFPSM/UbQqFXpAFKCC+NupTxyD+a/RayVc0rILyXFlqLkjpIEAI/Xz+P3KqEIvhAtjc4c/AsUknX\n",
-       "Mq8T7kx89jHhcUb5r/XJ58xtHRiTMaIgwVm5+GSK2jK79VnlQRgMeHLaMcy6qtvFfBsGzPvsyJU1\n",
-       "MSGKsaI8PywuqvTq6KgQDsdVzQovXU06qZ9f7bq3ObEpTa0qrosrji18Li1NH7Fsu5YAzK1sAuBH\n",
-       "kxmRIlqcXTUPidLoe5KeWwM1Wda61L9F87xtpyX5FG95T9tccBoyzcMo7FRY8cTCAfggJ1DZ4HRb\n",
-       "EDUR7d5q449jsRxdrY0ndaQvNga5IeWhT6uzg2uuqyeMNuhTxLnFgVhB1TeeDnhiAwBK1+4sC+Xz\n",
-       "F6l7VDajPx0f9T714LGBSmiX/OV36HPaZmYoMu9PalEmce5QChZxMTb485ASOSqXgtMPBLWB9afY\n",
-       "C5Mx02tL7MIT4REmU7fU7mSe3lKIc5W6Xwk/4Ts+rvMl9G2LJYyvvtRcPaEpQu3DiwBTzyBwBN1r\n",
-       "7hjgldMiJ2JhEJ6ubHACGtnmO7Tfy+rDDCWmQExSnecU2yvBTDHK4ngnVyFEbcexC9nSm3t1JwdB\n",
-       "WE3abNEUDuLTGkr7mDSgoX2DjZ0PVw0FlT/R5tUQSuvP52BM6WaVsJyW/+VukySEnx+CfsDtBY5l\n",
-       "7jtgCoIwzAc5EJP9rxODCQ0QmXIE5UXmt9sq8ouZT8S+KNrMfKgDdqRL8JMTG/9+96XlvpzCMZX4\n",
-       "Luj9lyW58mjGy6BKOuL7ciSeBTI7Ss20BYdSDa5PFQJDHVqr1jwyMge11bjxZIZJNybC+TOvP2MU\n",
-       "i50auckm/+OvtdUvehbG7cEQ+Uxb/nEIlN2hrfWCoBJSr/bxuOjBZgjifS2rD/ns0Zt+1iDc8kDq\n",
-       "jPdgCRWcrTh3wBmZ180o1+CznVZHb+dF+sjjrg8fMi80ftMWZR6VHxar+DwtQmZ8D0PjpJF2ebC7\n",
-       "9DN31S/JTbrQNJ/+u/JBvqKvpR+aa7PVb0yLMjJkR0ssizXiF3H1E8zsekMMR3ltl8T5ZOUdx6/Q\n",
-       "VQC09J/HBHdjjkEbcPUG2ePoDgDkOzoHiTzFgs09fa00YQry2e/HuqQR0qlaNzksfDAl2/HngaRD\n",
-       "qBfUuYRPt1dqWP6zLm+ArL9b7fbjafsHT6MJ/AiTDpFZh6LpiVjuuy9+QwKOveHsmGHcCL+wq44Q\n",
-       "pZEsgkU4BbLtERztT3u0R9jgT4UOvwJ4jx1kBMw4ayWaqv08yfI85SsQDYFoUzVZQrWn0p3NXvkd\n",
-       "Se0SpgMNQXoVlRHI/WH8MVOEfqJMbtInzNZYoftxbOdKCQJnam0Tqt3Drajq893b0s57T106MLlH\n",
-       "rLbfRDDKmKxD8Lan/7DK59ZZ5E/SZby/DINfhd5MSDHZsj8s6L9cCl3OCl7LywYlIOP0skX4Qnrm\n",
-       "AK8GElrYrO9mviSFpYiWWFJSK6jnYwq+Bg97TgsTI5uAiztZzHi/pnefPNhoDwDchxLXvGLE3sCF\n",
-       "hSoDj7+fHsnTCk5TgJobkSgn71TdmiLmSb7KPyKRB/+vxQt8V+n9ZVbz9C7KLJBygVWTHjJuPbPE\n",
-       "wHmXsjlaGuqvextPQfNIJ/Tdl1jJjZI0AJ8VJyiOrJYrSWkrIyg6SX2L0xq8AAAU4rkk8SXUtZwv\n",
-       "S318Po0FZmgam6CIhfavZrpLnOZubCHJrASWMSCsVIJZrKV8WWMNn1qzd1qdRS2lrBpatbb1qMB0\n",
-       "mohFu5ctyn8BLGLeXkCQ0ezNuUQitZGAsCx1clSWp6SFg8K2GyxV1VVeY27Rc6nzC9WHRAqwoLs3\n",
-       "fMVpO+uUa5iHnE2h1xIu7C9upfqAdWvEJyvd/TeitBXt+7OfrhlS/K20jR8QNUU9bt3Wn7N/zuYp\n",
-       "/r6GiyKhSCMRr+4FlBTDGDp0R8SsowQhSt3Al6A+GOYaTgaLS6N+FcSgAZx8BvLSa1Fg73ppoRbS\n",
-       "bajavVaTVBYlWX4p7GEWx6LmxXvDPXzohVNHsmH8rgpUSffsNHESiqHCvi2vmF+v2P1j9TsNsKip\n",
-       "3PZF6RkqT5Aa5TG4lHiUd15GxmIeAZgu7eswzRwTgO97caPmt1t3HBGcVBd+OY2YdItNBKZtOOBl\n",
-       "ekFMiaRYilIiM34Bed/DgT0EejDTRgZWEgH5Ny9pFwO0aAAxsBjVrA6Z7AdVzJB929mDN0YnVJw2\n",
-       "R2FB3ZDdElFtm6qMVvzWLVG1uitMkCEUGbzjC/x1ljk99DfyOhA3WshOz4tKSKWf2QR5ha1eBxTG\n",
-       "AUKDnStrPYGKUAjEwrYSAEdSuNJPeMzBeKDzbytqgr2HCdZmx+xG3hD3gtqE3BfdGF+vo3wMdS1y\n",
-       "RDFeKIHQKH2QLM6Q6b4K6KKaPOVnxmryOEjZsXApAKGr3nSsN/4xyyN/dB7a/OBGZO5Kz96cbID/\n",
-       "gAld9eAhrYWFH3YBQRAKwwFrPuTFO0o48c0j85AqLxpIziSOBk2hhp2uWTU9Oewd9lvJANjLW1v+\n",
-       "nyDmZTXX4cGmOZIZ33NvmzLhN/43TcJ5jVwkzOVbNOgXq18ZCulgSGB/Ul+qKGZ9jVmesWJMlgTu\n",
-       "7fLF6O8mhLxfdAksk7oWHuWw3sfyh7ola55yAgiyvftubXJ65SbfVT4NEtYqO7Ts+Ap6lPpuVyLc\n",
-       "rfb59HUj1R66PJHiuZoWuliFTuVD0cyTxMGAtpakhISYaEtOipbq/1AwN8sGlyWeB+FY400dLx5k\n",
-       "NcQeJewWfuNU1sbmrUAlE0m2IoEBY7bKyyuk9A2R/tTOovZmrJtSARBCP2C7auUGBmPavPKbs5RD\n",
-       "GuTgBwf+/XKPhKDQA2m72vmCpUWhMsHH+M8rqYpkbU2tLtP4YZv64GCznDWNVo5CKSUz0kPxuoPD\n",
-       "TR2BYSD9uX3mrWTgCg/M4b3fkEFSR0upTshhoojK9iqKS151iAHYEbSdLU1RqFzIM/LdDIHuJ3wM\n",
-       "MJVf74EhSBDYoTf4lTRh11ZwmeENC5drs4CR/kyj++1lTj5EYvAA1j8EznkRT19L98vkiQr22PzW\n",
-       "DjPH62uM1z6cUR8Lnpu4JRXR5iepRMpEC5HobZ2dBtVwUSZKaGlvqfE2IBodudBDWl+LiBz9WDnQ\n",
-       "beTJBgJczKW7a825M0Bdc2OPIcjls9shYPxlyh75hzKqSvOLR0/MJsIm+C9dXaiWuZ102yvjymPC\n",
-       "y+c2JwFzJ+W78vQR2SZr6WML+gxulBlbrZ6Igjhz6xYDwLYhPxptvG/nGuc1XDHlzIg6FHeDebqq\n",
-       "z4OMZPBalu2nyPsPoORj4KniPKz0Gbu6iEukI2sj1dhvofIOnifwNijZshR1rzc2/TkDhoizRZoz\n",
-       "/vYg76BqlRppeNeYAFScJ13Jnr9Bt3p/gYHNVCYTc8zdvgsuo7nFdsuf/J8NubVimhsGb5YWewIa\n",
-       "VJda3FOU3hU0qdcvwaViIlbQqyxXRgp9tWZloe7VbldLS/JygbVOqfSBj9kqe9H4BQQHVDNuVXXT\n",
-       "JtldieKzTwFd8AQwQbo+gVX+iUARR8BxdAftsX1k8qAm2Kr2pGwxfGpjjPl9pVydWkEl7ScxmHpA\n",
-       "X0Wt1WljWfAOBULP/2eb/CyfHgBgkI16lGJ//MdISSyrcp4UOlk91InB8GA8eUpCC9X0XlVi68LK\n",
-       "YF6UxVP+kBNCO15mPCLFcfIhYGwAP4CUtSGMNvsWlhTb1UOG0NWl9BwZnxOj1d8SW/047o1xeBK9\n",
-       "iTpMm0EtGEOprAdx7TP9YEyG9TGNYJetHYLW0prOlCFU30y2isc+cuS21MSUz8I9t9uB3jcT/rJ1\n",
-       "cHKkS9R34DdoN+3TbSIDwjBdRrKH/NWhnNGjCQ5i3yxp/KC1V3hqhSQ8HL7l6tFScUZklLbtNmKZ\n",
-       "e4I7/Jo/vgdTJFTpWuETcM348r8jHFPLS5znO1llY7RD21Y7FG1ahkr9gL6ortX8G7b+dF8IdCrW\n",
-       "rkZjCkhKCrlPdFkvq/ippUx+zHy3SEV9LVLdO9kde3bflIoglzzm4G9m/HC0YwWTGxsrn2iGvMoD\n",
-       "OGkJVriQoqPM48iNF4uMb7JhQn1/PCx0P34P9oXvpp1pxw08Xt02f/LsdzcTVa0cy+UvWczVT75e\n",
-       "WyX8WC7fGSH6LYqfGOj2O4zDzDiUFLzY09t4ACc807pHxzqFLaLCVog8sLne9TH74ZBrn9d2uC3q\n",
-       "kNpBRRzvg5Cjf9pd4NSoTIU1K6E7A8RPS/7rDGkA3MYjW42i3cErx+djm12sFn+ezJm4s9U6NhI/\n",
-       "NCyhOdCtlTPs/yDc3Je2T7lEEV/WK2uVlhKe3VhbYUPbJ4sfsiuqrv82l2N/cB3wz8i2CNVzZY9h\n",
-       "ngE72OchiN2xTATqRGWgY0fDiTrjHwNP5izTKHq6W4fp4SjV2Mggdt0tD62VKJpL+p/0QJ03AqJ+\n",
-       "LGe2WQt9AOrcEEy68vQJ9GEB/8nUKKMYDHDPJotOc7BuiADpuZQUdkQ7tzg5B9zhQ2GI7jpAVj3g\n",
-       "F+5eUXig96rol5FBcWBaTJk1e5ClR7AOb02mGYutyZBCDMXxl8zoXn/Ia+B8/NVWLq+kwXwO/Utl\n",
-       "6KD0JM243/xZ3ubj3ARMkZ+no6g2p/CZgCrChnHbfXJ6Vds6Mrz/JF4VSIoENo1nOnuydXXTJsaP\n",
-       "9e/eUyqWRAWGucTo9UFbdvKTWQQdbqJCqzPVDikfhPX9x+1VMdyTYXzn9qIGcjtKCqnJuVYfqWaR\n",
-       "acKRlK70CmQcs3rfFaDznM+A3DxjfGEaEYiOEZGpNkooHxHkx0VdQ0mJ80TAxSjyUbvvASdQ2yPa\n",
-       "Y4Z3APQsK3/AsJUAMBA4AgilbCCYLFC//Cok0/M6VhHg5TP3yI2Y0l2lPubmlRbxDuSD/193tzSg\n",
-       "z/1nFSJ4uFO9YeIsndl9x3QVWoSe0P+0kRx8p3aIZXuFaayCdPU08J78fhK87rVGbkA5EjTTUOkw\n",
-       "MHFpydq4aKqnlmQovN0ZMDKRi2EN2Z25htlR17x9XwH4cGqPr66UWlo+pIiL/xdVgPu/E/zoKnZs\n",
-       "9dBI2mONnsZMqxyEGfPNO0gm7pkYJFGgvQY1e9K6i5deY3cg3Of81h6K3/imsDxDwtz+iD1s7aQu\n",
-       "gXGk9Rq6poQFa2VLzBizAqUcFQHmq1JkT34wTaWvqdpUgi9WVagWMzoI+pomGcHB5InZvyy2v3e0\n",
-       "MdjCFj1oSzhF2R2SLQeiaQrXZk05ooTeczP+B+vyBabWXaETzdOTrqdxntwzl0M9uCnUQFYTN+ez\n",
-       "1kkNgP1SjWiVD6yRf8gI4cPnnqPiCIUQRX+I2JdGUzPskdzLU4x8pfwIxtB8r+jYBmiwasr9raDj\n",
-       "+ncaKRR3whe9KJ2Ge3Ybm8jz3LyNn7/UDF+g0TFovoXwe5GjLOmhOctITKSiSOf8DR8TrJ/fudrg\n",
-       "38VvnDGtfA3mgMF3n6OJ2gl8IlpJrkgxYkXxPc4rjo2GoA64jti78f0xnJdkeQVCwHy6sM+EmYsV\n",
-       "A6RopNK/aRK9j9AygmCRnfSDgx8UMjGvkLeJZE0vIMEE569Rn/xULTk632ICHF50GvIir1Y9AZ2u\n",
-       "RLfnetIasqHUMfUOBKY+x8bqaGpFlkkGrzebLnOegSBl96H/+QDTs1LnYcPYkUfqh6Vk4nRX6VVC\n",
-       "ak2l3+2MDSkLs0Nv3Eor9RFcPAmvbFbyCp5G+5hGfPWAZFbCqpMPu6v9E5RF08FlLFuh5BH3YF7X\n",
-       "qYgLW/BZsfLjAk9brKun6y0bXevdl7c55UG9mFC9DQiYrHQgAIgzK9ETR1M8WV8iT5H7byLYB9rs\n",
-       "o9vvlzsS9o7yuB3Tc9Mniw3iNN5d0qFXJ26a/ZxnnmnH/m3MeBEaxoA/0gBiKBKBIOee0wqpfNy2\n",
-       "9grEuxL+PAODJeosHeRFFKRYRq3mP8G8b4CvVtyfzStwjpe8oJ3uNtiFWFOYSR1n+NhIdvsFwMi6\n",
-       "x3NrIEorDC6k5m9sJAMq690XCJoirdVKcc2jUeq92f8a74OfXZtGQDCMlEqUvQVPVWRI63723kvt\n",
-       "KVB6MG8Y+BeuWF7uAxau4VI7BAAOiv6mxhx2MM4QgU3hM1hjcaZH+Kpvn9ExMlgzZrzDNfFQVcPm\n",
-       "VdAGHNCTRdnjFomr2q5i0j60Kz++2RiNWKyv86vwtz6XdbyzC63lgYmDUQ1UK5XEP+11XK2p4bAh\n",
-       "I/44J1zew9CGWkTuiK2/d8agGIEZsI1qUhzEJezq8PWRXY0t0TAG1KRLuNznUM07KIDh960Uo31R\n",
-       "eAAw6F1eXljSEwacPA4bH+PvIcwBM68U1qzXD8xfP7zBxsM82EPGsWDTtkScpa43gtQd8sLbzj93\n",
-       "s/Lz8gdBMSaUYxyiFUTyVNs24BF5lrCgQFWAWG3u4k41XPYPuVEXwsmBNU/eJrSui9j/kEawPcR+\n",
-       "lHQkJ5mvJtew/xqT9ZRHY39xbJEt3x5/4mN7ORMUam5NHd4P2GWV7kFcxDVX/LPmCrrnvjN+xG8q\n",
-       "LQBSjwmu8eRBebgsODa4Yjf1cWMijQMlwvMgu6rdHyCjwDSc4TNNQfr6gNRtHvNUpbwvtl/91kVN\n",
-       "h+qA4Bs0g7tzFbjb4MjKRDkFHfkVDIlHyOjzu5ZrufzEnjgL8CVjvSMftpVaPhMg/cRWFKY6hYV7\n",
-       "u83MZriyeI1x3kRHqnyrhUj8yK7qb7KfAABY5pGAAD4OKP329MVwcEgkC58eAgSQ7MI8/HFwibEa\n",
-       "AOwTFA8aIO3XWVxmHobiiXimOIE1AapsB5klrW0emhnlK3SwKIn2Nvw2ucZX3bx8KoqzdMlpXzfx\n",
-       "Pl1jiccZjJHCGQGnTP9tP91f1zcXFL5sBqXOOuhPBUxPp6k09j2AZwE2aXq0pzXsjxRrdt4M3rMB\n",
-       "c/ZyPIJ07XjOxL2uPpk2gNo1y5NRfWl+YByUhRq9QShlsgmBpRviJTJbnTbmedYW0fBxgghHo0fK\n",
-       "bMGdvhLJ0UvlEv+ayGbTGYGOpSmMNTSNuK57tWLfEqQXalxkDjDLr+8vIYHyv9M7Fp6zDPyjIFlK\n",
-       "49fvD+m3s1F3BuIaynsRzVxFHvUTYI3ghV+S4prC7W75Km5b9wr2e2r6kGXuBohvirKH0T+cMIgf\n",
-       "iRLI9uc+luZ0O5TUdccIu8pCTEbD7FOQNkk5+q/osVz1Ghuc9gYrO0x5bAGsVUjeSZXw+bOC3L0H\n",
-       "Z8uQIIhIdgYdhfUI3UJ9e27U/Oh9xSIobGxI6DbjsVCRSkYwi/Kes3UDmvK7gAiN5CHlqV4eThh2\n",
-       "FD1S6/soIIqfizK72vXEAJgMVXrVSqfZ+/pQwzWLENgCso37VmRzLB2UyBYGGpX044cUWR5cFPH2\n",
-       "RjDZFIot9BTLZYRl88L28qXJU3pl3coMDZu7M/loV+7F/xxhXhCWJNR3jVyFakYp0KpQBGE4g/Qk\n",
-       "uFjDNREPcRHcqog+55YWIMuDIbxO+3Gw2BZDZ2BxdK6UsaJ2qdlvvv/8AyydldPXSa/K1TmyryIP\n",
-       "7UM+7xBufNIbsxEtziSu1kteEidFTMI89XdGtej+FhY+QQyI+6MGAYWBxQGzl27g4f+0iLpW7jGY\n",
-       "TWihprPNZFYxsJysrnt+3UOMocXEZEZwcIuD5jsNKx+W1rXk4lTZQJiY4fVw+5Y9Z8VEShSPUWnv\n",
-       "WwMl9D3uuqjWL3BYdYW2QYbAfzNcgTDwlHfFD+EofayUbyyeg9XoaUWdMyqyWPM1tRA0ib57B/v+\n",
-       "DEoTRFCL+Mc2L1d9mMc7Edj3MS+YUcuLsNn///7CIGhri41vv9Z3BqA3ekKcyBYMaB+hmsf/gAkf\n",
-       "VjcGMDqOZyXBqYztgF6R1Cr84G09b3bacTbr7DbDRaedR4H6JTNeq0utiuWvIbClGtZtF1MCLbkt\n",
-       "i4ZpN5mJmLAgRT1X0n0yycoxjSy2bs0Zmtv1ncnB0FFfZ6u+tu1bnqTPIz331uDUmsDuEjoBVWe/\n",
-       "86IV6/9QYEcjO6dsDTVA3Xo7LyQMo5FHERlNvco8AuDRR4c0U+8OyiyjgrhtmR1DLFaVyCFct+VY\n",
-       "kDvDgVPxPglfvqv8Iq7yTRN+0bwh51P+33VkeRFV7jq9Ii39qWSdQAdh1+MQqySj2KJpxHAVgxST\n",
-       "2jtl9ASh0fAVVIxQK6vYE5PrTw7NsE2G8dHvvNUfswLY7T3FrDlSE/65ijDAm++evtDbmR8DkRKh\n",
-       "RX7UKaZIvw25+b+BA3efo7okuOva+gMCMwL9nXqSEcygWgIZv45xAkn9n56KZ53s9b6QSJNuJODR\n",
-       "QTN51y9ZgeLW6iK+y6mGQb0A2fNL4h5mbympo6P+hpLVbuoEqOfCKJIH57UY91a9PAHCDc1TwLIF\n",
-       "ZehRqRkfyFHUsRKRmfFpdYVcivTehF144xx186DXv7lJ5w3hVhlLNQmhCC1wDjJy83hvBdxsmA8c\n",
-       "Ym1cbtcwDwha3WRerVvGcO6wf5SNoTCKmAVvpj1geg938TUzZteq3uOEaZeGZd28mtzCHJsXseWZ\n",
-       "2L//EyP1rioyWs+HqfduzUj4HsziBAP8uhFp/sURFC2E+K6M+gQroSSkuwc6EkFHesZ5L4fINsCk\n",
-       "tSjy81/2N0z5SluUsQ6N0BhGKOWBK+hM6L3/gDD/yiXpw03spPv+4Eq1YzQ2z3IbgsY8UlTE0qzY\n",
-       "NpwRhQZfWl6LUYR7FeH21YS/M9Lx8sM5qC4Y+kvYCBXVZW+JQNS6nsAGk4Rh+UCpVG5T74PTmvMj\n",
-       "jplXB/yNWkzYuHu0b5MMLvPQOEYVZdNeHeaLlSQEXoqi8XNEmABHn4cvBUirqhKhThI7+AhxdXTx\n",
-       "xqNGrmd5KXU8g15kbDosTuXYO/1YlHZHvYNS5ImqfFw0TN6cJTTNmH4O86fWVZDHEcOJsEUDuA2/\n",
-       "bS1VGzWO36Fdo4gNHwdvEuS2t2EfuHTlsnps//7nv6g6d2DE9kdrfO+n2MOFVciViWCe29sZwVmD\n",
-       "K30V9W2uZauXVsi64sKvruspStGxbFRK/78zBaNwN8JXNKU6vsX9OtgM8g9lK6jROQjOPytKusiO\n",
-       "RBFvqfMGwwmIdFuvy1yd5pLTPWUqVhxkrLQyXt59pBufJ5iF7FBlgwG9Kq2PSm83RGjCoNo9chSJ\n",
-       "9cjn9zMJcxBIfif6fx+0EXd06bRzeRT4N7gqxdHIf1jgGHDr1Y6i5vNAN9WODMIGHbj+VYdY8dCH\n",
-       "vmSHVhIgUgBnoE7dyQbF2+oozJroOsfbnHNfD//A5ykS/gpBHVqV0LSn3lnN5b+QIiixMu070ref\n",
-       "2luUCws2LQ+wE7iI2RB3jmuprEo1/vSRnywpaKTBQyiiUecLu1dqn4Cdle62llLMSw9MbIpKVm92\n",
-       "jksQhrRRLUo3EsdUWh5aK2A2AhF308AV3WME8AFoiA5asAuIi4hqW1k3kFQnidlb5+5ajE+fPPEw\n",
-       "RhQFSGbj5LdrsYW/LYrzbaqX48LXjRirFMbb8kb0SQl3tzHO0Q+2AsQT+EwJz8Du7sTryu1Z0dvU\n",
-       "CcR04CbJaBQWXdsgVWMB0OVpENPT+YQLaC6GfSkFlDIztwAeDEhenSk0wh8i+LYubadZrk2bdCeP\n",
-       "miXRbHsHV+MjWc14lqhrXr0TGl6Ij6cot+R+Tmmi4mHiUHND8bQfCrpfovTeSbDkh9iSRc6jB6O2\n",
-       "JwRPueraIWVfQJfnxoeu5wuJk6hftJWFPIYBFsG/VI65yNdSANJyWXfh0BUjj2RlWKdw/sOrgg23\n",
-       "kHlGjpk1DC0/puFWGuqRB57U0LYsLPb813hBcjzmxE/jCvtYylgjT9Cf/6Gj4SBwRI7J6QbQ4PCw\n",
-       "faPhxOOx6gG/9JAon3/mHPa0zNc/WRwu8aBs6/Upy1LyPXLugbWlYOnqQ40UTgf1iDs1u9KcG8NZ\n",
-       "pLOSztPdPur3fyZgFppZXg7GEcKjKXngEUYH3uhDtuNX/eozjok1127C/3HepdwaI1mmmknBsc5U\n",
-       "E/E1iP6XbDP41MShSncksLfswZOjzfqFWf6mSNwHpvawgeFA39WBRAPshbNF2Hlc/YzF26aM5Xbf\n",
-       "EidGtNzKdP2CocftOMhj11i5XF3CGeW29F93Rc6vWhiOd3IfshFWbsAgJnAxNRydouiOBi1KabgZ\n",
-       "MswlaMylt8E7OuG66VilbZ9VjbiQmzI1dC0AoGhf7KKneJJNGLjlzTaYcdzOKa+4T7RlkX6YcYjP\n",
-       "6HYel1q3NffhniTca005ak7KNCpx68wUdT2LfVITiSCHEglfPB5uzITlI36S1VWbU7kCczupOtYy\n",
-       "MfPhBbjBRLsdJwr5+G+6BAo2naDGqlPXDd6Un+mizLkgHV1KhWAZGdVdWb8BMqB9PKhTjOkhiZZ/\n",
-       "fL+pbwEB3b8OzxZy8maLnfhnbzJj7gFE0FtJjVanIyR/GEiuKg9yOVEBD2KjkwSa81in8hRQAz8c\n",
-       "QP1H3oDN4f3qPE1Jl0dSkmR2vqxN91owPRpFegSB5WtOVv02xCvPVuse67jgkePQXhrGBduLSMqE\n",
-       "njP/g3ZjuPqG7HvLQxp9kjRIwwAF2QQJscSOC4yTn+EgFoBYFNvG8VUgLu6hdfjkHjXKe4shzGJs\n",
-       "SHGoEAdMnhZdxRzDBLzxOOOvVliEZ4lAno51La6ImQTkCG6F56UnA8LphEM5JWoF9kZ4C8je2WBQ\n",
-       "B1I0lgABIgt3jqKwNaWcb5xoQV4OzqIgrjYcc2WZCO2AeaN/YzNNDouDn/JtDHjoIDm/EOxfkP25\n",
-       "u9m3YtHRGaQneHkfHft5j7qFQK0ZfvMTsMLltY3uGLGIrjmPovm7cBhpyAUPFSQKn/LKDkPDanYl\n",
-       "rDyaV+ZxGiguTOGzZf/BxZ8fthlu1UoFCkMYkF0Sa1/bVUafvxGHx7zpCqfZYoMWybIEIhLsQP3P\n",
-       "1P1+AcOqXWTqdUETw+8QWJ9EwE+7+WF5J/KtSunJSqQ77gARFec27GiNBPgqmALSuYTpreX4CeC/\n",
-       "rf5e8LJGqEzYSPU872PD1z7lTkuNYGPnvR+mSNHL5sxzfKzFiCCx3h2Pe1VN1dX9F5cJhaTD4bfa\n",
-       "U0Z5Mc/e07LCtGLwfFJZw8ARQ832rqfkdL8VVrnmirxqFc115lRS2BX9d33nExXUFf+WBphvQity\n",
-       "q61EolAN2P/4c2tkmNIj/RdRSnH8kwuEhXelVkVJOSZmAMXWtxUgbNlElhjqLBxgPfID3qM24O/B\n",
-       "YezoDgrf1BDyZjjLcHuFSelRk2OhgsYCQf8fTrYCD1KubN4S3p38DMMBe0DXF0zwXyiAK9u19vsN\n",
-       "sMI6scfVM4Dbed09pfFoZN8UM23CSR7DfqHm7Dm8xGEFJpwLfdV77y0sfgpuULTr6YmZsQOf8MRR\n",
-       "ISFQzLkuBKeAHSGXR8Vi6plZ5BvvXOF11LTbDKWAPeIUXQ/JP8m8AF86vsyT33e4UzmWg9Xp/IaU\n",
-       "YqBSdo5l4D5/MjhFmwdt9p0TAEsxKNKNwZL8D2NCOJ6fc1QYJ0ATVj1/iEETCcYbi5HLXYNQvxEp\n",
-       "3rPe4olvPcqaL8GK8/WOSVAFrzYzUC4B6GfPP8zVAQ8gc6WIezMnVmFC3iI0dlVZN0aaaMrX91z6\n",
-       "lcX6ULDJ83tMFyPoM0m7nqr1Islo/ooUVum3QwmyyQmARM1BREEGwAjuDAQ7sJPejzNRd9BbUDM2\n",
-       "pQPmxEE6VqATjhjM92n1TrtESKWxX01rN7+uy7cnQQwlmFosEWx4SmI7fdPCy7LRAzyK8+BeOcpP\n",
-       "3c7ephMXwS304v/WinpENYZ68Ywd/DUDgP3qGwpINLwrF9RIAz3kDNDrawVx1ZbzC9iAq+xQoI91\n",
-       "7JzdGG9zEzbNBhi3HEa0om448wjX2XW3RKV7Pwwsb5mETbmZ+YbJbNx1OKjWQjD27us6PKCNvqSQ\n",
-       "UBZw0B1vUPSYIETro7bPsgN+pzKfQsK6FCwESOi14C5mWqvr06zwmJ33ql4WBgH78tyVZ4uVnH1j\n",
-       "tAE7YHvdwtVjeU414dFd05XQdEQVhw3SrCx3LMRyIwlzK9gHxTN31TliBHhxFqgzN5LZtxuijohj\n",
-       "uwDK9GJgoHpCuHQdS+qb/RMRbR+uRSboUyRDpUZh6mLo5nLOWQr2YxzUpqHdPA4UvBcIdiL/f3Vf\n",
-       "en0jzOesn74650E6UgF7fKJrrnTW/zJYNea+8SdrRKZEZqaoFWOzf2fdfMnJ5TvhFIRA6zgLzGPK\n",
-       "CMAuX/zUMhMtqbJTuCgVpd/oipfd9++QWMei1znJqKBcpGqmmEWWSDIcl2JyyBav6LRz+sIfCik3\n",
-       "JVWJ6JmgwxFNModHYI9Kh7DqDO+nU8WHYPDR+492HXY/Vy9E/v6VMXx1Bf16d2CSs92w0UEpLuh7\n",
-       "qDLY5ASqszBwHZPsX5nVtHOX6RBdGTvLxN4l6rJ5K+FugtQdPSWHUDmSFQdAd5hlywM4AONNvg50\n",
-       "PDSbYXa7OSJNvjNPxwxc6kINyeVKWkHEIO53uO9wburxVWQoFyBbT+irk4kK9QWjmpTKmg5v38ZX\n",
-       "QyTRgiY6zgfrVlDqH1083kFIhVXdqTcq123g6H5wMsaaDITTGrrsWjFgeI98d5CD14/5IIMAf7ES\n",
-       "dJyJZpgE+O3bfNfemGbnGkAJdSpwkFjDc6NPmn6nuFuRpd9tSQJkCKMUVaf7Y3081g5MNw6eSvgH\n",
-       "XCF7H43gk2LOrb9/Xra+Uu1cCnHR0Q8K1YKWjCQwf67UP+CJs4F1Mr85bC5G6FWYmSY7lyeeib0X\n",
-       "0lZ2FsHLXiJZvN/lCMUhfLUpGdKqXPrYkfpb9uofo37ZEPMW44rwG/jUvBI4A/8cysxT3ht6CEAK\n",
-       "QvAulFCLDPth6WfQQD7/rm4HtHsvyu4uEoKG3jgnVFu0qA7scDDZd5eyhqxNKB5Y6PDfQZRJYNA6\n",
-       "E3/zFesaKT2I2wvzsRj04n7Vf27nbn9vhuY4/8fZpmz1FT05v14w1YDdvCF7FWh+f7Ci+bvIKP9p\n",
-       "dKmNnAADMfQo3d6VAFPLfkxf4zIZRtLW8s/3Nqz7+Y5pH4UB1lVcbzWGECMEnpD87ho///LavHsg\n",
-       "KllCcCr/apEEjcXJEl9rxt+X9M3/0qOenWpmSO9O28GUoBfHtsNkYqj4w0fNAMb7EY0E/4EhnQKo\n",
-       "aeFTi9uHMCobuIl3hXMB73UOPbKt4P+LQU8wyZIZuNdtsEH8wuCRuBX/3qbDV92z/GgkliSnD8zI\n",
-       "x4mXmraPl+G7113DEgzKQfbTj7kh6Jos2bgOrcClE4JGE16Djf5FJpXmI+kuDvf4GQwkKzjXLx5h\n",
-       "LCLZhaR6mTzelWSvzyFq76f85f0a49YWfcB+giHnbvsM9uuTf1izFxEMHNBhFLKL3bsy7ybSTQvN\n",
-       "hdUsk13bf45fZet8S+N1McjZVFtrcseeS2JpaPR1QgH8wYJ4VwOTcGANAZdj/fQlQ2OoFBr0xJrO\n",
-       "hFj8jc2pVMaNIDO/rb6NF5xiu3caq/nrevsM0OoTvXkhr2Hk5PBJUb2zXIsZNA+auLu7e6ZGShjx\n",
-       "w3jEnPSheEp+3SMjJDPS3Pq9B6frOMCR0REyKVT1QrOLjk5U+82rbpbzApRvab65xsIpyjC2efqI\n",
-       "YeGnZAkG2t0m+0hwa6BcFBl7oxiQNgke3BL7DM/yueL4TqZUgPtqVuM/LieamTAWu/UzRKj8SWhZ\n",
-       "PW0rWT9dAKJmhST0yB6zApz/86Ak6DpxZYyrTQcGpvPKCkGxG+ONCdJmsVW7M7HGp4fSfAVQ3vSO\n",
-       "DcRKKRRi62Zm9V1ZBte6cLw31lHPoswaqnSg7F7uajXt3YswhKCikN9qIvJduTRKbpDBZttjTCKl\n",
-       "UAF8he8hsrXEcawqv5TFlFPYekAtYy17tv/vZj1sdqnZy2wVQbj0Q7ARyX93IcLdsKrRNXQe1gL6\n",
-       "19KaQzGICMJ48a3VxO2511JxCDgN+a7yxBs75eyjvF2xigDPp9t+0tTKwkkqEH9NI0DrbZBo/GIr\n",
-       "tLfj7bz4+w6TkB9P8ctjzVpzI2T+90lKh6DsLgkvNiUuVj88BT6HWkdgvFnjx8Uu3nn+D2nrfdsI\n",
-       "p6zCtsS4KqOaZhDl39wlfA4rEtxlcEMZYqgfvrH3xPTdoA5IQ8Vh7Lw+fj9KDQrLUSY0axNSKW9k\n",
-       "6YSdG7ZrpyeQ1tPwSao4cH+r2Zb9IifIHzvjv8I18GCofX4r3Sik9TFzwZ5yI8nQpqleBQshdWwg\n",
-       "SZoBjaifV2zyHfZHj15kZ962eGwNUinR9Fe/XNSW67Wx5wpKnDbBG8cdGI/zXyLkEF71LMU91l2/\n",
-       "VqTXFSk+r1n4/BfiOEXMryUCJ0XCDuPEGySel5NNvHjLtgUTG9fAzoExmlj0HtR0JwLjj+Wezlwm\n",
-       "Pi0f8slNJsoKCtt3IAM0z+tKVHJNcbysiOTDeMjLgieqVBj5so4guG6VsvRf3XTLFGy+XDSow3i/\n",
-       "7D1KnyIk/pIRLWrsWrSizUuvQJEIiTdxd0mDuJOu+jNz75rkEIc7dQnjm8mC4+Twk7cMgkll8YWe\n",
-       "9d5126w9je9zzniMtzBitRAgcWRI2aPbU8xcR0ThfRnjAe5TWPovvTm306NwMxGWXovXg5mTgcx+\n",
-       "2r1lFJi60nD8zorujvFFWefaA2IEs1Rba7TXl5KcjT18fB5c9ykXq15v7n13MJaIKAEzmrYX9QnN\n",
-       "d0VyE/JsLj1kqhFExZw03WnSDO7ml7vWQymjdth3cTTpA1bPH79Eq782KKVMeOeie4J6rAg466nR\n",
-       "VwCU5VCtBWaV/7uBWe5klMmxaye0OJJMy1hrpfdr40mTOl7j6sdWm7E6GBKYZ3y42zDe2QXueqUM\n",
-       "3bGeTmNHbEpIVqbkc4+qzkQdQgJeSI7MhXAZ/jXyHKewB85q8lKIaL2DVv72+CZ4VsS+fb6EjtV2\n",
-       "DRvptH2sNizzKnimsIsrTph7wiLZoQUGFhymzUyU+iGyuO0mygAAE9FBnkJ4hL/bgURQoxyL0FaH\n",
-       "Sv8Vk7d7P0vyQNlrTPuwYmog2zFEblcv5Y6pyYkGck0w0HxB4fN+hDdTd1CJ5BIYBcd5J/K8rJ2A\n",
-       "xJRwGiIIiEfARuEfQHqtx08gic/Hq9M2Kyqxp6pgl2B1hEKhuMKWEboNmtPDQu3B+oT/sFbSbQ/T\n",
-       "DurAw7BWuZ2q/VrISBFwZZJNjlGm3XCwFvS+lGuOzqAHXg6Jzp6sf4fN3JbNI/oJsApfnctZN/0/\n",
-       "TWh0JuaJ6IDVzzmlKGst4vuQ56CVqDybYlcEbvTTIEvl8w29qdf8l86G0ccuTDY4vL6bsDiXH3i6\n",
-       "2AE8pLRsiIj0gD3iIcrPN0cCaziTv+oOX9kom3jRXSBJHbpJP5Iog0NyrrO808Dnx+djH227UlW5\n",
-       "y/A0IO4h3ZKK+42mWwI/Ptywg9Bim7cQxcSuSQmvYRTglDN3USDRSRjrQifgNFXbUvo66MSc22YI\n",
-       "hDcCiKbeSO/sdWYBCg9OBAJqUEQVY+VScbWaIt5By3Rh54PEf8MpcSso+iG7rMkH9DvvcRQtFnqe\n",
-       "KcSeXsDdEE/BIDWBObSk8qpioqRubn0mODUIpUGJ4dQGQalbUHaY2ob8Tuc9UYIxoiwcULpZC2ie\n",
-       "WoMwgAHHzk/vMqx5KlIFO1laKYcGBinYs4Eu28d7w7TQHTGqF0ULm9Iw6aXNJJVJmAl/1aqKIo8f\n",
-       "9uItXq8pM+6UVO+U0/eY4i8o5+odIjLXffPv9f5j6NkOyvejeT+3JsXiJDatb+F/NuCKRaYj7wfR\n",
-       "JKpLt76RYB036nGrMFqebpLCXdQvthvNd7IfXF1XNCiKYkRUDOT4C5ZuAz6iNZUfYsFbnGf/Hv6C\n",
-       "gazc9N/K9pgs/DvBXKDXfa9jr7Z349Cc3x9WekAf+ZeMdYuvNzEYmhw/Zi77028b6YwK36LYBeA6\n",
-       "AATesDmFVhUKMZW4TmaPcmluj9WpnNotk6sAlZYZdq64amMvoGJx2modGL4GEyOUfiYCHHEHA/ax\n",
-       "YTiJxReKNkbiN9LASm7ktrUy7ZHK7xNP6xcAlQSbJrPdosx7mewcCGSskDDTxpVXStiK6kZP+3wu\n",
-       "gYSsm5cWxQL2Hb972a1xpDuT42Dvr8UuqgtpYcnkToTO67DOai6ZNM/uUUeBhafog1NUqvxYLxPt\n",
-       "btVzgojxZz0R9prTvI86UZ8aapBh9UYPuvIYcPRnthpgwzRCd93/j8D2oIqpb5GZ4tCfBR2+y9xk\n",
-       "ENVR4uslTFPAHaK291wnEcrOFP9BclKjKRpgSN5XwR3xnrC9RpHXiKfDN3Dz3L74by8N9VjkLgft\n",
-       "hszi8tbO4OjNYyVrqgakjGj+UdxbqSkLA7YZx0tq9PhqJ3qbZGNJZlLhcrJSRZp4UR22Pz3PQ1k+\n",
-       "csZpkKRSparWHFvxbcrsOitt9vygb20Dc6pOCXFWVcMStTwGX7yZMv4P5TTN/K7Mbi7B3Dqa58Q4\n",
-       "/ZaBnOyELy/TB16+nDIbGctwRaI2AH9FgAFctRVCc+P84byH//VO45PqpZGEY+BrzwULFg0/lxV0\n",
-       "wDYrOxL3BhtXzzaI/4IVU//5ncazz90yu9kYBjdgZhsg9npWReEZNxvhwOTlirNHXV/flvv50OHc\n",
-       "k4n0oBpSp6OQPX5hRpJ5gBSRK/C+Y7YvFbff/8ymHFXvMqj793GYnn4DzyVXY6Cqx7SPezAD0R8g\n",
-       "ipbCj60DUlgrk2e8et1zk8e3wbHnPQ84lvwCf7DdNmscOkYPsj6Wp9MiSNNkAmHX3gU8/8MXnIDd\n",
-       "ykcpkDvTNiPI5B4AsP52YPiCeAwZrGJ/bBwRvurCJ3NC7qhVbpGXifrwx0ICkThfZx6Awg4L6zFC\n",
-       "0+mZizIcgQomNb4r3bqLI2qUf2BgWoDyh7Q0/DqWnpHckzNq3pOX1h9SUWQEEK6B2Ud0LUGUn7wV\n",
-       "yfAzcgMETfy2cWSocnJql7tlnvz/ek8JGh3jJUl8QY7d/5tpLECDt2PJzshHKsspk6N9AsKf77ho\n",
-       "rrvk5PVHGg4EghyxlTJPzHsirmKmNh2QZ6nvAIFCZa1SQ7anKHb+YL6CEaQOhmvyiWKi927HjXKf\n",
-       "VaQZTiljeWP0PilCAjWUm+8SCqykcgnLC5xHeIBMjJQy3Ijv6rdypFn7fzlL5/NvShgCGgMTCIA8\n",
-       "FibH30mRJO2Yf8bITC5afyd4lfpRVmDzAus/BhMfFoZyZFVt0uFTe5yvU56Ypq3bFVmR60sdPKbt\n",
-       "QHFolD+qrzu5oeHT32wmS5ZDaDcSVC63zQAVjXFvJL8hBZIpHLP2dfq5ZRDXacAfCTAiLZl/Ob6N\n",
-       "QBCpZsUsHd5kjkkYYBNhmFRGEqRIAjpctoKHXrb03vCagcrSw+vfrOKhZx3Csv052VLx+4xgs7Ko\n",
-       "kT0YqpiYrk5BpuXl/SH8wgw3As7fDCR9XlVi9wJF+QgNCps/lb+4XGrF9k0SsLYhV4dnxrfct3Ax\n",
-       "MrZAH/3MgAdcnq/3tALZPSkBAodfbiyUbo5V5UQjzmADgAhVFn9wT7v+xDMZxUYoatarjuxZNJhG\n",
-       "wONX7y1LBsZsd9f9tDc6sWVJeuq7nZQWYyp8OJy4FAyBe+XjyZ7+iNwhQEZpwhho9++qdPdbUB+d\n",
-       "H45yODLDa9MX/5Fj28IrQr6q9l6AXFShhBFfUHxlL8pCoKqRyiFstOq6iE8ZayKPHSWx726UJN7T\n",
-       "EPz9k79eBt7jlBxyS+vxqyZHVVYfxpCBlYzMhj6bNUoO7/FJRJiFUNw64lunS7c6o0ylo5dReSvc\n",
-       "ZENZAryaX0N9/GJyK9ZayEeTwukDsThe782xDflSsd1xmSkogJ/VSnCrcm3YXOnnwpsZPqcNAqnp\n",
-       "QVxzurMEFNeu2jILABdjh+0vq4c2VjO05xpTRFqrLbqQHAgPpHlInk1VHT0yFOj4dQlLd2Xpu8uf\n",
-       "G4Nl5fkoZZw4rVS/34NmQF+Y2P7frmyQXV7d3Ldzg5uKdSfCktQnCIqh2MwHhcNMcHRLRtaSbXhW\n",
-       "tMfEqxbw1AJVLZ1vV3E21GX4vEmjmagjTVw6GbSzWrGrJqMwo6ZT5ppSYrwt6FxHbfZDUqGBa8ji\n",
-       "seVtQdxKYOP8RKa+3H+dxpJGDx0qknxGCchp9SYcxJdrdL2RBLN3mJh0O3xpsxKErcKvZ1tpgMz1\n",
-       "UqA4i15BQYNvgOGMGHGJnnpyt1SvP0RzjiF5z57yF/kvsGNO0mBCra1UjHvDbSgq9EzTmuPvHZO9\n",
-       "Na/4WjtAvgGxERJ30NT6DyOXTtp6H1DAAMox2W/IZ8rW0GNyw+48ZYtEs+RXHmnttmLednNGcuQR\n",
-       "Sy5Jxeb8Joo+dhkR3x/knEQiCZ8luDfFwHafR0pEkvIVjsZpNVCpu0sSPPhjYin9iH+zVTs5qKJE\n",
-       "VMlMr9C0P9KBWi424a/F/cM8Tyg9L5Gz/5gUwyQAEV+0xmzvqlR1rtydMHImpIFnepIbu32vFWUw\n",
-       "BNUoI6T1TuKAVt31Oc/MC9ZSXK7KWrkCQyRNA6HeuA+dlksmWJ/C71YQtfPah1KvboBe3+7sa2PM\n",
-       "okcy8QlYDlz812FqEFSct8OC0i6ImzUjUl6iRHqu8eTsOm3QQJ+S17f71uEaGE1UX1vBPnIpCzDm\n",
-       "IHZIqwMx2UrwT4Y5+6n5m4FT85biKWtHKryCQv9a0Y4XMAOS/A9aiYjCbV6GxegSAfV6CNweBtMn\n",
-       "1ZTIKBvGsBIJGY2Ey4bncAJcRpFNHEt/FOVTuHSiHBryffulZsY6/zAtqVd2OImWDmyGbOQZCVFQ\n",
-       "mGPCwKLeFR9p64kSJRxbR9PvjC8My44zy5VD2lYHJA1nrLDdKr+JLyAJKsaIg0skC8zLiP4ZOlZB\n",
-       "sPNyxRaGC2JoXuWlO4cVEzUZ0EqZdRpIsXcVedv8u3GwR6JhoTjaR0LQwEcQcCpFb0DF3ou2BJ4o\n",
-       "Dnh8KGCL6t2lEPDuNEE5e0vWz2blBccf0wEXCIZNDkctWaXYZukl/tC8bFVrRh4nJzb2A3p9q81T\n",
-       "ngQxoDziUBvoQhG7J/lauJZmL0VQbcwkbAzUytbbaqudukW/120HDUld5VmDoOXTyLVTph/DHl35\n",
-       "6WvhaEKd61aNp0P5r2C9dTdkcwsoLEv6MtpGKx0XLjf3jlioTWmimwtV9vmhh5UPJ+esL/tlcZvb\n",
-       "U/fjyF6u1imS+hL96UQM/ldBN9GYHKZasN/KYkuNKM+bVjHaUKIqdnEfr1zNXmrGsVUo4SrT4y8w\n",
-       "g1ujNEZ7JrQghw1+sfX3DA5bCW7tRkhgvGW0drawNdZV1Oc7ouvRgrqtXKtRuCNTiG0+FpvK3kyQ\n",
-       "oWpfjWQm0BLqutcwydLx+3Fa3VWJvsNhXFScblPCcoOV0d9Q+/j1oZdtc6zOX6QFqfGI949RQO+A\n",
-       "ISIWDh7u+SExGpPeBRmKlbrclld5K/KJp4BlKr1+AMEZdmC/T++3chD+hE9jGkXv//lKTI7/U+F2\n",
-       "Zh3WeuplNDkjhy9Dk6Tf1WWK9DDeP3E8mftG4q4psKlsImRh2V3A8bN/wllo8RCx1lcD0Py/XkoQ\n",
-       "jVFPof3V4XKq8MjVX98eDJfmQdNniPHQZmauD+ng4Gza1z2XJTOeYd2795uCZj6sOuRdZuJiiMIn\n",
-       "qIApYYzyEBSuD0SJRhwfwCrE8z2oi6SaqszJSAJqn6bOuRY51Dajm9fflUNt46IAQvJ6MGW8K5n6\n",
-       "OKheN1ZipvDVAQDlgjJZkt+W/OPQgc3Btm0GzKc7s5IVXqZepKo5n8dOMR3tguWW/TjgQkLIrQfz\n",
-       "x7FCZBrUR+Fgtn6LzYxJJJbc8ZRWXlHMYCA/oGsfIqkmjsiAElVtGmdd8Ly0P0FVqj9Utm4ekfoU\n",
-       "7a/0Z41w5aapLbWai/3NMhu9ZIL41+XLHcVT3pWixHJPczNh3Fpc5nkf9P2D+b8vQ3V9e1Bs0KR0\n",
-       "b5gbP307uV3WvQS0p/lluXfp1GJph54Na2lwfiN7pKlgabq98MLhQbtDndS1fcrmrEMNlM3E9CW6\n",
-       "xmaBgMtE0x4H9DVFncwe+fZyZGxedcCViIELNOplJ8t8YHKUxEvl00BmTiphXusvBHMsOBFYZVeT\n",
-       "p3r+oMt+fmYWDu/gRv9nkiKgZhHRkpfytykBmqUcnGMSbIPR5LqDGpH++e9tQaRUL+OH9yY8U2Pm\n",
-       "X52YKHMFOZpeTcCCkIvLA+65APIzuIFMibT1O6cBQgi+Bz95hqJJ0JvgBNuHHRC80DzI2HadpBBp\n",
-       "dr8NYZvj1jXvO/dDlBigVrSsFPc17ViKP2TrAb1Tfv6YLD2gT56SH14lX4DkEtphF44l2sP/z/dX\n",
-       "BF2CkntGNCeDuDsPrkrT8XGWPSoxo1GEssgzCSsz6VD/6xyx60Y+R1rpGGHjLC9V4pqr5+EOSiQq\n",
-       "Vl+2ytGCQ7Kp6Gy7FzippUB4Ykq9IWRaIvB0j1Ra39TzIk+6qmBwAVTEPi6A14JqijNRNEVbjdkl\n",
-       "f56cCzheCyaISxNbofHQNbH4AuooUshdcG+f0k3lQIfwKZlwZ4exBH1BedH0+opgaPXdRh7eaoxg\n",
-       "Xbj86TdlW9DUQElSsdXEy5uo6z/yccgn6iujnF8glYjBTADq6SD1pq00kkGVn4/D2GjZDqgNAoOI\n",
-       "X2cWYXHeClGadkysoQiZx51jF4NdDrzUketCz+tI/ILDMZnDtQTiUr+h49AGhzYkpn/ubDFV6AvD\n",
-       "t6w0tdVPB7JVRf60C/p+hAd+mg8umfeC17dJ886VBjjDoBazSGWxirh8AVE0h8nJRsE8XCkSGNlz\n",
-       "Oj7i2A8mtVE8H7toN923mKLpAx2wFUykDEY9+9dby77snD8mfWHrD1IP7PZkkgUSJ3qbNsVjqiNs\n",
-       "TY31kChSJxuMealTLxqOS2gRiwTtImnH/vruLuNqOyqIC8na+8VjxA7D6oMqvSmJ6w9LQbI8ECv9\n",
-       "a7URy9Uw89LFdlYmGQ272Rp9u+GS50lEq+KS2mcyDqoSXmA0SK9eni8Bsu/gO8W84jw4mheBAvw3\n",
-       "HZXr1dnsE+Bkp2y+BK1cWr0vWp3Z4v631mnXc8CF8RyGBP5ufm7jEv2nvZek39gBi+bvkXgss8UM\n",
-       "/RFiUtVdGut8TS0KOdpjSjxk99MDIO6VVPu4DkcYH7u5oXiib5T3llZKDNeg97Yfv5UEFHL2XlAz\n",
-       "6I7vTlL1CFz0mOzeGBhev9hzOpkjjSsQQuHPBqDTuXPSy4xXngT90SoDVBSS8PU3Kx9AcdHfPAXT\n",
-       "DEB04/d27f0IcnDw3g3XkwAGUjO8R7XWHmMxmFz3yOcHuBHHVi2VXn9qkF5787ePI66kn4KqTbcc\n",
-       "z1nAe8BYLPewzq+svxjThT6cCJCrLkXwY/rI+DbhV3eHHbjj/NVz7AwNPMNVxClgZxX1aT/Ur0Pp\n",
-       "FLcsjm3mwmUyfvwHiS53X7nOnKjofunGHz0RgRULS7nBeb1+S3cVR+xYOPHBusng4phSgKmVbUp6\n",
-       "ezL9ltxmR1xmtv2rQ0Gy+FYsRJvmBLUBdOvtuWBXMPoj9CM7lkVGA/EKj74KlD3oA1iPh7kBM2ow\n",
-       "oyYslqhzLWRDelTq1W3irB54MmBMolkvYir1uCnV7fH5ayLXdx/v3wS29bii190q/44fDcRPtQKj\n",
-       "i0DGFC3I9ikPW9IIKMpr+QVnGYJB/2EsiLlT/GkF8XiXQbk5ppd1We7bYN7DdYuJXj8pgBNLQvAH\n",
-       "+dgRA6dGJp7M3EajQHob1aipd6n+AjuwCd8OiSJJQ8VFsWeCo5tfraYakEvVpyuzFUH1yEfSMPrp\n",
-       "+NplWYTFAq0OhohVWb0tLNzVITCQkX88u8vDRCTX3Ax4CHoM6TYzJ80AAArJAZ5hdELf5J4H3RF3\n",
-       "h8qTcc3PcMDP/HO3ogXhgx/7KAytILDY+ie1Stbfk2juTRYiHCRSCgWQhKTCTnMSZN/YINsMBVwB\n",
-       "rTFI8b0RNFlAtmALftfqr74atOWPy1NSPRTM/Erh6o+EZY1/kXfMRwyey3HdWedFnHdmjXmtL2bS\n",
-       "2cn/qfN/ThbFDXuh7oM/SJpNem7b8fdaNZFVuX70c6crlz+EvB7X4VOGGs3OpWVK0NTF9qUSOo8h\n",
-       "fftmei2vDQyFhhicMTBZSLbGUWDwbwvsJTHuk8mVC3iW8GH0GBykwAV+xNf5EuVY5wgRFtJSbuGm\n",
-       "heHl0IhW4HsaEIoIUcsVCalQjGYm05fg1Jc75RrFqSJAMxVHHke2HRJB08rUuI3bGK9a/GOtBQsS\n",
-       "k0DqdmSP0LAPNGA1sjnH2bI0kunpsPd5/3KLXZqtbeOjHKfIW/rBfJp7i4k2XgLd/2b/TP6Kq/ZN\n",
-       "PgjOTWaaxVETnT2CjTc/xqtUf7mN92glJnD8X9Qzx5rxXa0uO89SMTgKmuf9AIRJrGjymKitoROL\n",
-       "65UZnriAAPkW0xh90+hx+8EgJPl7sknRH+4oRSc8NDEbTpV7ffvq/nbI2Rkm75vV3VDRdpEKHKbk\n",
-       "kwcGXs3iWr0CmGWsYC/PX4JC8zvenqKHUDa0+rSoeC3amx3y02MhVK6eXnDbO6NEUAnbE6TMXYNT\n",
-       "rqrmzsBJw9u0KgbV8NVqhELW6FKxVhQOoAQngP2Qlcnc9DkbwlU9pw4wJ/c0ojCaR8ua7ruR2M2Z\n",
-       "R9y2u2qwe5+VxJN66GTkNX51o06PiT6JHpcQJU7Ur9gwldsi7xl6wqCteY75iHh526u5lCZNk08U\n",
-       "S0qqWMCcCBBri9WHPrLfSH07Q4424/cfYk1jvKYll43FlkX0rG/RU/jPinplMkThMwj6u0+w5GLW\n",
-       "dy6W8GOLUmSpLA71mJkJSnPJczj50Ymw6J43eDIssi4NFmcznZjm5rGS0Pr35uZ5XFU7pbtFiQMn\n",
-       "mOfvG4Bg+Oaz49jxzLglgABy0+Yf/VJXRAX3JIoMDfq1dJDwhTILsaXlsts3KDYV643QKvbdUN1/\n",
-       "wkc4s4PJ5bDhUkxlOxIQ7TwaUVPFKYKW/sThhbecWOFjgCi8mPlT9tPb7Wwv8VC2lUQFOV0gK+UY\n",
-       "U4cA4HD6mQdV7he+yAl4gw/h2Uu2R2pCbrsjZHskYzwPv6UJjUZ3wa0QiRBzVvcGNWHL0AYtxskp\n",
-       "ffMWycSqfRrZUODf266+k3xsLSh/RXCiu6ZR6d7NRuI2hPPnDiR/6duaNcQqa5eWuDYqbj6HzL4l\n",
-       "YeHrgAKofQO1seKMkFVSrC+7S3w3VNDEA+VdXLS18RZNoM9FN3RRcY1oX7lAJlv14nPFyR2UG1PG\n",
-       "51QcFm5Gg5e+6ISLEZOxwNt0Bnw3+qRtTSrp/1zdhktOJ8Uw9tPWuosZ6JJNA4JXCFb3RqaMj+t7\n",
-       "kbr2xXsNZyuqSPCjgr3j+ISOUj1kPv8Blt89klaT/Iqoi7NsgtMzMyJ+QUNRfl4NywnyLX+gVLxn\n",
-       "UAIsMN7SElX+cysj9mxiKTtHwbow1E/e66xzeT/SG6kWEFDeeeda2BbFhbBt94NC7F0qobx+yEZi\n",
-       "MrWiLNf6cK9Ksrx69qN816FWYg6jflpMrNHJcsr5U11eRRLJSo4W3wUSs7OvWUlBw4RDAHEF4YJc\n",
-       "1EpoNvi95KBR159AZSCg7SNdHvRvrAlvBGmKUrpTHTybZRYX8b8QlskxXoQACK+uOouWSYLdx+Sf\n",
-       "UbeouWyksvIHWHoM0BvVzXE5Qtq03+7b2SvE/t+1ZmQcS1QiAC+9LzoVm+sQ/+2GSEKAoTGPnQAB\n",
-       "jCkFN8mSuaWL3oYbi11ZGsbuXOT1CvtaFZV3OGkzVJZUxmDNsrb8hKy7D4RKeKEucsYOYc7rWPN/\n",
-       "Siol8PYul+8aSbhFByfVbpYggDjaqtYFsw948yJQY7qG93pmYdA4oWB/Gpi03Dx02datFCuB2IAW\n",
-       "SIeVfAKcb2ud1YnbyjPHA26uFg+NXuLMjYw91VyJaFkWIQ31EwrIEMjk1roSVX1SY6/v/486aqS4\n",
-       "8OGKkvOwBKwD9BDsNfnkV2ie3cuXM6BwZm5v7/Df2x26sUsEUKEdkESkxLSnv9bQIXDPBTKC6Z4J\n",
-       "2hNErCHhmM0QxFh4yf8DGK6MhUMB4+Vt61AA1S87baFz/hn1ey2iHuY6iccda87krUSuBO+s4x4v\n",
-       "TQpUDYWCrWwdwHqxeQlI0lPGNIXaAwlyAfzus+RQAqG8hBDUT2LY05vPc7uDI1gP1kgoEFX4Dy4E\n",
-       "KIvUhG6ky9yER4DmBxUWiX0O0b/yE6TQPtwHavJ5vrOms4KSQ2ALyQ0O+zJccZ2C2cHwyMTrAHLR\n",
-       "W8KRoMIx2aTQ0RD5yywQT8kBpZbKK5qmGmcpA2CpZydcnk+iboOSr4lEENl67+TU3gZMvsLL/ZMn\n",
-       "BBW47aaRMOUcqjusvMXORE9s3f6Or2yr+uswQq+i7v4/hNR7AAttfP/JpPd58bRMkz2qVB2sxT0c\n",
-       "A8lvVYXw9TynxYOZBG4943gFizdVuRA3rX4ipP2vP6jaX2Tn+KE7GJ9TOcwqIoFp48ehl2qDR9lE\n",
-       "N0sxIXQN5o5CE6tx9w+E2T8wKWdakEIwV7gugX991ksHJ77Zk7PrH3rgvDWzzEeQDrPIQ/gApUE0\n",
-       "Fq+DTuTS0+3PaSjOvy+KXLJd3ej/2PaYn5NVCGH/XPgHFeXlbUru+48Ug46q6WAAyl8mfc+6h7h4\n",
-       "vf1mmj0kGINZouMKwaTSMQeMfQsHNGi8ilLKjFANEvF2Cum+yZObJo5lW3eeq1mjI7qd+CBLrH8v\n",
-       "pZ5RE7ZLIlCQAIlxYQkcKPKHvdJm1oSxGhUMps17ikcsn8B4op7Ysjnq9xX4peoDjp2rJgACP+Ol\n",
-       "dD2mUI6hJb4bXkzPdt/wzI2uz2m03di+Q8PF4z9HNhzTce4nrhjySh51HPTx1r9Z6ekH5YmB2lx8\n",
-       "8Ack2+74X5/5E6pCEJrJFf+X1lhZEELGFtRYO3+yG9TQX6RoBGbwlNfPrIf6cBBR4D/vWrY4/7KS\n",
-       "ULM8MBIZuyrwqjTos4fOpfCPyOzbpVVWo8y7kYcXIujZqHoowJEkGTVpGC6nfIoHCjCQGBLfACPX\n",
-       "GVEvGB305n7QE80iHi0LzTa9cmRB8TC7Q5lEM4oyn5yc2KygBuOgaGNgF/XIxJZFs4sAxTNIIVHD\n",
-       "AwAAYU6T1zlkQKF/JeA0JIKQGa+hJntmXaPuo4DJkqFUU/i25YEFBWmfcoU+hpeXBtITAVZxLPQo\n",
-       "hJeTFcEhHUc+Z3wnYltffo/4pnUZ/0iSihTuOsDgNzTCCD6UwbXrw5K6r98New+sAD5JHm0P/U2/\n",
-       "hmVvk2eK01Qe/9P+EbMYXQK+BZZHjavVE/jVnPZjLh3pi/lP94Gs752E1Fl/wbQToDVyMjb3DB5A\n",
-       "psfHS68ly8kgRy+3QtFvMV4cBsPVQetNZ8QslwR1d57XtkUaoRyddJo1y5hpziPfoxw3m1HJg5pw\n",
-       "VtW0l730aSjgW2bj3/wYlnHihwi8EWxyW4P9LkYsSBIRRVyJpKiagU+YOjzVIMPlm2pccC1tNK9L\n",
-       "DAl+r+shCPs8QAeBHKUTLxfv07nJZnlp7tjFxak1PKNmNPApMbC0fnOE2BOZjCvIuppmTjs4pve2\n",
-       "aJhKny9JVxqtPLVsgAAACXIBnmNqQt/su4lRcqHzYm0Jnnq25ZYexWn2WSm5s6rHwpi3MQJP/2WA\n",
-       "rz+JLlwo+KqKhgNCE+Ob06VVGRnokHj6byNzChWGfoYcm0ESs7gH48YypfXMuQKLpxqe3Q/Vv98q\n",
-       "xw76JAf+1/PpgVOUYpTzPoDZle0/o7kGMu7QIIdw4c6+057lVRqWPRIpY/uGGM/LOZnWlgSfOzG9\n",
-       "ESme2S5OFC1TpMuvx/nIslyk0mYflsVjhGOGGTHOxIUKPW2/PLZ8nRicdSdO6SpWxzX3r0c7mTPT\n",
-       "ozFPPpL18Gz/ihRyCtVR5MLgUBFm0Cj+amA0koWnt3qpIsR/5as6TRY+Inz7kHXcxRXggUlfQabJ\n",
-       "8ztFWlgxLdEbYde3K3DcYWTGS3ZtcHV1LjgWsWqLqYQUZj1wtfDgbXfm2xSvLshdGJOSaRXDrMKM\n",
-       "fM8lW/nOAHJzCyhkdB8P6q634O7ad2ZBJIZ0uS0WjFunqm+5A8IJjJu+sNnMZ1xJmLgw9Hu+Jji7\n",
-       "mfJF4W1kzbqcJOG8Wv5URSAtdwwTLGQrehfrQsVeTE7qe0d6LRtI7cpZQwRS1MwACAG9Jyy7/32P\n",
-       "SruFPH90rFl/KqBDXS+5Lrb2BZ+Eu+GKzt+ryaIlkfc8yd7r8xLmINLn022ZNl4VBRampiZ93mIR\n",
-       "v4l0rbJTPiUspi3ehGbfXH/yjtbvX/KGkKDpI+jgUlr1acu4k52tcYFrBI7Q2gge5SuCJH+yXQY+\n",
-       "+oLEPmZDgiUyw47nqjSRIyT0Sb8SaKGU3xik7go8xUzD1TvUpKdqFocU4GMfgaiJKNMZlpOa33aH\n",
-       "coZmbreGZWocWmQVblfs7doTz+bDb2guwHIzcD20z8cf2WDsaW9Fig1+Ol4fANanFSlqgUyTqzuW\n",
-       "chevyPnPCntZ5dAUck4KORqJkErzWJ8unCqL6nymjMj4yg41E/0Sk8rIJ690dCHmkbYVZ4l+EtpJ\n",
-       "hAdVrnsZRHSBYvohm4fySo7X04YNBZjY9mzJvLwx1ftluWx465m6ZvQs/aQBL8hTF4jnIgMCdlxp\n",
-       "TNPs4EmIIglf11dMr/UIqy+13GzfaXMi1426ZU/fAuYN3ESbkiwOMCurXjfM+jBk1BESe6dYJrIN\n",
-       "V+kkhV5xu7rfbgchq9P+s6nLFdcoqxe+ZiOx1fyhUr7eR6yNgwa5DyxZAwKPHsna5MM4EhBBB1uE\n",
-       "ksUJ/8k6D3DtFKLb2Wa6s2ktD3UXRQ98yk5EO51i1rRx60WxIW2RFn15bryUMZW2ocUQzRqUQgmB\n",
-       "Ey4MuvjOc1d2+qXwm2gOPQ1ZDcYr3CRRjD9DBsIKUlVWuMmrCid5HGwqM3fYZFOBEkb0qCjFuHdb\n",
-       "t27JFK5xQzEoQ5dChXzSuPj2BMNv/GPuRQaCkY7TJkNDvw8DCKSzZauGlEriGP1Cg340qPiv4Ilf\n",
-       "bkrdZi48Ru7xqHXOA6KIQIjH8lgj3UrTz1PgAEqKOC2WBWvVQOpCh4v86B+2Q7UNnpZHPvdKny4E\n",
-       "XOMDQeaNVS76Jm26uN15Nv72pCh8SJOyuHwLAWTWWo35wZ8kpgHyJpBuPJ9bUbTZPdQEFGTcPPoZ\n",
-       "ZvCN+jy60JZbNbdwCzLPlfZJx33byRT0zSrJhbTP/KCZZ0FEfrosxowgdalKMadFRxCxZdrG/57O\n",
-       "rE/QkHJpUPiD281eBSZrZ4C4A7ZzxAvphYLU9kNA8wTaLAjFacgPNKvJhAsX7XHWrRWPAyVW+ybB\n",
-       "kqCTIibl8w+492t6O9RUYh8uB4d8CH9hKx7rBMLYXDlTkY2LiYHvylugx/z4USUOu6AKfAdaRC+8\n",
-       "t+IbDao+qT4AHEdRI/M2racNJJkb31QtFSIzNHmlcIWf4xgpMXNeJH0kAFv9Zmbp8R6zxuz13xjG\n",
-       "LuasAdKFBa6gaXg9d9yX2819Tbq1IpFrCLeSlM1FA8zy656yM8arSiiOiFput46wFjMaYnF+x+Y2\n",
-       "NlvHH5ejz50s9EKwuB19cutodngJwGngBYiMu0OBWt/wO6Uqwdz90cV6lwCYeJw+3vZyhGX8VPOQ\n",
-       "COnCYdtGwNGqBM50DfQswgdkLSpgPmTLIQQYiRXBvKvurftifLm153agDBKk5o4TdT5560M+iJKD\n",
-       "xDutSeGYyCLsnzM8yt3J2Bf0dhn6cBTsapZ1joncfl/AxhrQRtN4r6GQEoFlymqi9pqGRglTXL06\n",
-       "yzq51LNp5fyC70HBq8fHhHKI/zjP+cqjmBQ2lHJYV4Vatafc5jQ/IcHmjyo7OMNfsG83nuVH82aU\n",
-       "Xxb9QZIQH1isY2DFhZkmU4rPcTlKhnF+JVlFEraFbVH4dnzF12aEswgzYFKb9MxX8jjSiTRGdUwM\n",
-       "dF++Cf4VcnGUBfGOO7YjQs49qV615PunrECS1TyfVYxae4F+RjNYFtxvOMkirHB5S+bf2GQ85O71\n",
-       "mjIb4vzumz9M/jh+jetyxUP5XIEvbUS6d/znAQOGp8ELIu1O5WdUn9ASVmoJBGZ5phCjLdLqIePD\n",
-       "CYOPwJbIMzgWFLT2duBmPTzZ5JYgPCLu+pyw43LkcaQLn4qrdyDlJME0oymnfijzdIuAljbREYat\n",
-       "ClgzIGVgEyzR4/0aVi26HudNmmCPd/CSJSJEIEmKpkKbPzpUqNgYKZ/08yCagJVMom7aveWoXiVx\n",
-       "czZrzGaPZRrf1nuxiajwjo4A1yaqBmlJ/OZxAjSGCE/vQehu+rzK4bPVXe3i4yOdZVGukJNpVp/u\n",
-       "2b1VoY9n8op81uz8j+Me28o5CibuvpAwL270sWPgEmy6k68vhXvwBOVhhWIor4nYCx8sBFIo7rYW\n",
-       "iIrKonKMfQ27hYhgHAdR6A99TUqQSWlypUgO5JYygu64izEiUo3d/NIXFBD7Zx1QTKuj5aeQAdn+\n",
-       "nsrlPbXdwxIsoTl3fGRM1A1MnPM9+hj8uXCsgyTcEDh5JudjQqFsiJuCmMXCp5iAOB0F0864gNt8\n",
-       "w+y+1C+oeL7/6hKYM0ccH1TvyAgr4vJFC2aYJ53sjBkyw5o55Vpj/wTDlxRbZvuQ+uqXQbUHNShA\n",
-       "UaqAw8EiNi7YP77d46k/KDdw97lqmAmGEEbv3zLyw8w/4QRr2tK1aCShyEY0DBXEeQKwkBqrz/c5\n",
-       "k+i1w2ietM1/Uo9tDxx9F7uSct6ZLg9Z3dkXRmFfw1TbQpzQTq0ZN826hDraDVuy40cTiihl3ckY\n",
-       "tHbKDo5oy8fqe44ikyKwUnAVA0tI6/gsIzOq9IUHStxcs6vqnOTWnmcAABohQZpoSahBaJlMD/90\n",
-       "Ng3k+V8MfDnkVXNItsCksGR9KNPiN0IFaBPzKSOCNY2yYDCxgryvsnW7PS+bGhPLY2MmoW427xet\n",
-       "vo5eISmXIhV7tKNG9rXR9GB9tGNuJYeAF60Y0MCL0RY2vIhFF/0rHxCId+0NX3A/2qgjYPMsDWFP\n",
-       "x2NowcbxpKasWi+k5FDQr8WYjIPwk3aEgzm19tbsjR1QCkUyiAOXQFzEallL1YLja44ewFWtL/EA\n",
-       "QsTRv4hoFFVXVqXme4WDBRyIVimbr5fPvoeH5cMKCi+MG0pdvB8oLVleG2EoK6roobKWh5/mf3UE\n",
-       "MiMJLb9pAkr9rkt1k4u+D2yfz24mlOHroL3pMiyndsAxRdr0De/c0d80tHNaxk5Nf+QVjIPR9KMz\n",
-       "vovnmQ09n+l+dd/cGcrVuITKKsZRJnsnAFnF3NEHWF36xztFZpWP3zX+v+15DYo6whGAW3AENekH\n",
-       "MyvPRRZDz7S1zPNUHvHaVUc2l9QAvscrgDn3RJL1TgiSEnLEnpAdPue3ovYB1WELhqCKmUCPjFmH\n",
-       "/y6x/qqFpJXlLfFuJuClkQ7Kadu9l637DY0EKeaa19Tc+dpJiNVZggecOfeINE4svE6QcWoqm1LC\n",
-       "0JD4qWudE3sPId80H7Z5av/jviQYws+C9c/qkyblQ/d2WZoAr0IvtWHA7NnuvIIwC5bdQiDbj9WD\n",
-       "9sMmv+ytQDB6nu0p9rRY+t+s+b+0X9C9RHcVPtrQzcIX5Kwj4cKVGnx/8Fr0+J5DVKwNp0f8DrZT\n",
-       "+B6o2Rr/lUNzMbPpdq75uSmQ6dyzqgamRro7OZOSU+gYrXWak2sQJ6YBNoz2owx9LqRKSup6QheE\n",
-       "7aKu4fkGuy8/Na2xNvGwv9Lzq0E+9+hg2F8ZYXH6YqyM/sdg+dB3g7knXcFMqJLjuMaYgYOdwk91\n",
-       "uVoy26SZ4JoLQg38o4svNT75I11rIRuIiPoa5K0DHihwLY28wMuHu2kFkO/RgYk95RDJhDRrCD9M\n",
-       "LyO1R9+p2i6Ic2137zJMiGId09Yic70PkfM7UfoghHjz2lejDOj5pP2D5P1Fo6XCw4aqyvH3w3/p\n",
-       "HT8pwKiuDl+jv8HBPBDDRnOLkvB5aWVzbWY1PmqA5LjcuhTErc5vXsN6aB1ln3YY8bAzle1TdC63\n",
-       "Yuo6lgVd7Aez4cXZa8IiH7qE3onIfgSlkC+dUgbu4tyQN2KNu3fGBVzbBaggfUI0F0+YM2T+fB82\n",
-       "mr0SCeXE/npmfarJdvTPuLxGARkkoD1Dt+SS9yOAAOQB5qwYckimbSA5vLNg6KlpMRuxmTGxroI7\n",
-       "RcWRvRFWdQxIi0jCJvrR3udNlLAznAFGOsQCr+Z2mPHE+/CbXh9fpzKZG38qPSGu7alErMtffQW9\n",
-       "ePrvTr7xk3hLDFCRjkwpeMUw0GNKdlzaKT9h4vac6v9y9jYLjUZD/4sXItZk/qwQ01Iw7qCW+cwr\n",
-       "03J3xkZlDorMkDo0OLZEy7YahM1ule99DkUqhu5/5VTi8g0zgN5+xBzVjgw0A5lZTI44mNVRr/PT\n",
-       "Bn77whlPbDl0HtaiB5DSUWT97C68nCMktoZVFkO+lxfRl3QqAvAIzpvYryYHypwEwz4x6IiMk2Py\n",
-       "rQkoKblEHWWTUatf6kZza1jxVwDakgkoHCSi7Ea36+TEDUFCuiqNjTtk4M1NJgBHAig9zYWDy3Hz\n",
-       "+KYUwyi5hp0ulUdCEsy1XePeXoXvl2iMkp5a6fEosMeWf0lPrWLTkD1ni+CemgOokmf2cWgJIvHt\n",
-       "b9FNATAcWDz/3qO34hO2HhzqohrNw44WyzBHejveSfnga+ShSJeHdAC4yhDPYe66Ji/ORT++lXZI\n",
-       "jdBjoPxhisqSIsDO9Bo+kCfp9u2/5H4tov7zOohxA8CfnEYWFB1ydcSPMPWTiCcJSoUCylTC+7k3\n",
-       "9L7p/IC2CUBQpNUbSzOHdAABT0MFz9yelWNuQkPjiKNEXCVNaLsfMP6tEGJ5g4crIfzLJPi7kw9/\n",
-       "Bfr2VIpq4UQ45v0EWfwj1BcB4TFMp1aA+GGASEzkTj3KeM8j4cd52NMM9phi4S+swEjiWFl4j2ib\n",
-       "KTU+jomivu2ZWBxC2HAdSgBKBlFAb4hfnIq/3TQxojrxE2KD7uc0n7ZpTc251FRY4HXmSVM2pxKd\n",
-       "by25bSLH9c4UgERQvxbPeJiW6qpCFOEW5kRfZPh38Ojb90ngkVnUOyQSiG3oAn4hExx1rbiNGJjl\n",
-       "oh8yruk+I/MNqurxn2BuGFxejwGmAyVYy94xbQ7zLJnCZPvp9ZzPpeOdaOxzGpTdcJxjYkV+nC7b\n",
-       "6jO1Dq2hV2K7LlXsMnv45nrLVP86F/c2vLKTCpf+mc8aViRx0eZJ9/n1Y3ANTqTfSkLY2iG+QOco\n",
-       "sSXBrCNqQdqd4CJgSmmjQ3Sx0aKhiP2dLxPo1oxmk30v/8vxsj+5kG9jvq9ZNqGhc9mHUaB8MH03\n",
-       "09w6B1dUpPSMKQIOPO07DA/IBaB5V/UJyzQAVGFQhchJiAt2owwTlOsq1+H2U0qfMbbUikGEMqC4\n",
-       "HotvmD2J1Ev0FHTgIaKKNzXV8vNJFQj4z7m0ezBLgDg2XSBd4bO8Mdm3dkVNoRIMAOUSmfcmHwnj\n",
-       "InMuYPqopDBE2NvdVZPCahCxtBp+TEWMKYihJ5aKWxYEX9rMl16WIzWVaiJtC0mvju2rMg069hoX\n",
-       "cgEcDxfk3g8RKtnyhFi0LAeL8wxXVDM6GnMOI7oCc8U/g/A/HlzbqtY/NmWuNnrDdDzRKHj0rLud\n",
-       "nG1icBmgojA+dE6PvuZhiD437K1dJdY7E4sF9Rw8lyuwrQcrP4qotG27cc8tykX5+oUxcdm0S3Ha\n",
-       "TONFv/9eMl04gfo3oB2zlm00VPqbY0N7tA84JyvoJ7hiwuqp0NBYSHgOF8V1xF7fHOk/sYT/pzTq\n",
-       "dYn9LKhWxsjJS+JIfriqlStL/Kl2IbixZENWuMVIYEVCIC56DMAyrXTSMYdKiS5ZeQikHwaXd5aj\n",
-       "f+1sc3F0o4Lh63NJTESsbYO6I+yZZ61OJrvFSMW/vi95NfD9NgVHNkeywcsm+K+gOV6RX1g+bv36\n",
-       "NVg6gCI1PS+D8ef0Ap7ASF/ZBq5X6+vVt/oPcByPle+kM/QFDjPHVhOCiPlrmXRsuj3HtoV3oVUJ\n",
-       "AhAaXV0fJJHYmqRfQxq65/YQjPt5eA+D/p1yiNn48cCUDXzqCn7j9+U/6S/T1y+qfrhbVC6bfBbd\n",
-       "WPw456eab05llt3xPK++w5kEXG8sgmCARndblLDG9/PoYgC0iyy7S0cs/4pYFRub9qCZRXs1gcsp\n",
-       "tvfxOlkPqu62ZfU9NJereAQknv2YW5Ww44emwC1naLKD4uWHtRRso5jqGw/2pRaBdqNTpYr5POUp\n",
-       "WV2tafG2ew/BFpYS0e8y3+upmOzjqPRdz2PIzkeKzlZWxS7UpM4mIW3z67mYYTddbaSRdZK1ihkK\n",
-       "vyzcbzi7dcg+zLkY5pKCgqyVJ17CKikpRUEW6zaC5Rdkp7W6QwjyVFd8mRiZ+WKj/h6TQzw8yAwt\n",
-       "OK4PBlGcbBShe53Uwxy2Xevm/wGfwF3zoTZ129XpXKnPhFWAFcvl0OViLW0PEyB3+cBL4muBx7yc\n",
-       "IWjCqYIGxLePNeZ2IE2UhJM7r4kTWE7MZu0D6l6IWk2fh653I5EdT/N54DDxd22ZPPcQfHimY9Pu\n",
-       "xTBE22HxWbSQRppGbLsWmGS3YfCRyTtsxkyoPivI1WYVQsPdndgBhFhXtOEesesHPXD5GGHMJX7N\n",
-       "a/vLYfGf101LqogN9vzC99YD5S7QhM7FGuaQpJ+b7gx/jlqcqX9SZMFsPUYinRCEnkouru0QAQvm\n",
-       "1KOfCJ/kKoM5Mjccc9O4f4AV0QnqGAD792OJgAuXMRRAkZOMUh3dJ8ePR0y//W/k54SX66m45dFj\n",
-       "vXKMXaNLXvm+nABcebs9Y9HOp1DadUui1/3C4zp8poGTbV8cFi7AOo7zdOxzs91TPWgUuJnagMt6\n",
-       "LSGheg9xgIxjlhqSut0M2yorw4bvXDqhfB0hEJXPCLO1TlLKjeLg3JxngEKpF22Ba1CriRZdyyDt\n",
-       "FV7bQe5zrtMeLVXE5tPiasOy+H9P4raFKfhg8TKaVIuOPW6GYkpSBwdoMs/N1cuUxErQguqjfgsF\n",
-       "ldK/6d5grzL+sgrKe8pM1pwL6FZ99X/KD4ZgOXkkDXZtvhFDuFaBlbL3LNscTI/iqymUP+y3OYms\n",
-       "45VJ6Q5sCrwhuU04MM0hIX3Rs++wYhds2liHxMBteWRrhIiI4r3jTWWJHBhvuMciYAhFrzCXmJ+q\n",
-       "755pj6T8oc7OJmAUObVL0vMXDwUsjxplOUKvdQNbTMzhldtMEX340Z7t1rg9XBfNtZxNYBd0IUDI\n",
-       "z1Sa9Y/V9ZuJDgI9PTM314u6SQiOXdkyD2Km21vw2VigcvdT8bcK7toGKOZP7u+MmN1WTwne7wy6\n",
-       "h586c5kTKIw8S+diKxCwBV3I6DLSq8r/2OzNDdKQZdbhqSYP09/Ve/Sh6wDYPzzpg/22c1cG0pZO\n",
-       "FQfn/EPZ56n+3aFzu6q5u1hedDUxwI23qyuwVcWtJvRxNBQYKCrywkC87laiR2uAkeA83EUtSXFY\n",
-       "PcgpTS/r9F6in+XY8N46m6Q/j7FySArlr/cbIkJmf1M19LDb9FhE61XBl/rXkk/KlfJzF0BZcb6x\n",
-       "7EWvKREL1FrsUCY8qxL08LVLFZXlxUR9yXXnRIY6/phMn9RIr7rC4p+3I2+4kq7Ri4CUU+sNlZxe\n",
-       "MFnKUuD7qcacSpfuRmhxY4NmM0oBAZZ+9maIZdTwkiGcn3Hjl+kX9bOtLobbiT8gEyshrMz9xT8G\n",
-       "+WfaHs0QZE5atzXTwDYIGmOi07JkxvSTjrcl8RB+7e09mNBGhysHHHKOtHNxP9QmDEypbYVkYYNk\n",
-       "N0gzzfSukdlLe8p5QR2AznhzJafqhl9VzGEPWndkO7MSJ1sThdp6GyXjnNo3onN6n88BpVkLqrvz\n",
-       "FbtYxNQ9gyJvGE5c0SqAeC9K6vFuD/IUYPTeSJDUIgf5/uEAIwj6/7lkIcydJA+gi86kz8UMMuIo\n",
-       "bMcjC21+RcftkG4+yDOEbvMHkO9QgNxmta4Td+1z69W6eqzWkm/GNSIvVW0k32zKOl45YAyaJkuT\n",
-       "IwSuk2/kiVMa7PpnoLziYjQBsOiqB5aSED55oB10gUes4Y655W/WHXYwAx3ileE/0AxiozbxoMVU\n",
-       "0C+Zz+1tIsH4pa9sGewM8DYbAK22f9jweekzLN2v1mz6wCu+g9Eeh32g0Diig01oIbj23rSQQv2q\n",
-       "5VvYAhSnl4RfdD4vUUl2j4nNNR+EuPk8j2SKcm30y+NPEsaRWZNRA9G8BGWCHV3gGXXgK19lBXgM\n",
-       "zQYs4zHWTEixeaMpOnqH/ZykeSCvSvFwldvw6jVTt1PLKqyJHB43BCerAkyPHXlWo5hKoh05LXva\n",
-       "dneABBCgyRtsFBTOvSR8w10r7n9ztzIQRFMBkNAA+3RrGDIldoPl1v5HSI6mINHlFKIlBICJOh4Y\n",
-       "LFdBq9uHjbiK69lQib6S3MkSZNk+a7cR/rMNl2VLSYMs8nqUsQrGs4w9pR8jqirUVVVXpoau38rD\n",
-       "kJW1X2XrkeqMXSdl0ZomxSyzXJ1E8YORINWlpkjRqY0z5syvyQBSC/ViPkrukfROw25VP0hzeSFr\n",
-       "ajlbOpFoBre8KEyP8a9iTh3QgU7OOoBTYkgN7nUkp3l2prx73jeXZRYNOmMwej5lhjU1/QBg8RfS\n",
-       "YgCCZAX8DKeaDhtVFKTnGWRG8XzTNxciAopPnbmPBo3wuj2JPAGbhNjAf4uiJgxZj8z4znyFk2Of\n",
-       "7JY355YmY4LdXM3w0eBy5pbMRiW+CqVde6CIqJHgmbhWfiFsaSXHjHl9Bqb8QdZEtGfXE4jryDti\n",
-       "gg79Pd48Tz05zJOeFy1Um8TIexE3Q5otABxzVJh/xLxN68HT+Ny3ya4Y+M5LBB87cODcB99Yd/ZH\n",
-       "/i0+f+5itJ0JH+xFJIWuZkhmiMNN2LWEqxZMjinsFXCSoRsr+wgEyrff3cOE/0xwEycfkcQVROE/\n",
-       "DWhLEqoTci6CG6LPyzZZsruOMP8WQS+NmFBqCxvlGPuiCLrzO5UQYuVcqVAd7DNGK2rcptBzIRIq\n",
-       "UXA96Klf6iH5l3C/qQ6S6gbw1ie8n/Lk9Jhz1eYe0zYRWSZI5OkDBlwTFa4kLd2QrNY9cBkp7fmQ\n",
-       "7NJnal3jDS/AdVzBBuergwbwB5gfwoAq+Frv/DIaxaGyNtxlWZf3tMwwYnkLSp9ob3Eztg1h5QX8\n",
-       "mKZYXaJe4YtqUfM7BL3naJ7msF6dluBkCoCw3WlQJVZZIDQfu1mGpwxIWYZWU8h0vfFEnc337BBG\n",
-       "IK4JbaL6bkQ6ExZK7yJ2wk4us2/j8wsH08Yfo3TvkfJTKkUGM2z0+69Cq85zu4LW9ySvHLID45uI\n",
-       "efgUz0esLnGGwY7VNQ1WnPYPjJEs1UGcWbRqI6xlfZjyrlWKvP6tTjVmXt/SzJqNwvWzDN+PbZyB\n",
-       "MwwlhWjLHy4iqiorhv4tiAv40dTj2+sNix4oxSnrfzk4xgnu1rRU//uEWp9docKxvV5IEP4oDRNi\n",
-       "ImaIcl1NdVH5j3LS6RXFpl4hgjCNckTD+YoytLx2GvMKR5CQWN803uDPO809DVjvU0PY3l5Qe35R\n",
-       "a42he7sHvf2nooE534Wxu5TKaA+MXYhbjKD7DSAZNqXibIcvR6AAFIsh3RgwZbJMuiGwPmF8K4y3\n",
-       "63KEtCaxzfk2mHQVSnAMOu84jF/mZpWeGFb2BAvAb9JaL8Ncy8MFc3qhiCUwmOvUrDsWL+8yYdRB\n",
-       "KKYI5J9RZiEjQAKyns2kDxriL5+z1hWpSHrIrRDqjoaLHPqgt0NiQtlnOrpAv+axUAFHyiIBEgsz\n",
-       "F2vEN4b5we1khbc9YpGIJ+XDgTuT2ey4Te86zNe6ykhq8Qn02Y2wswnClMvX7SDkVbIuLF3wj6jv\n",
-       "TGyms0EofEGRXgmBMwvGTf5uW5LvVIEzRQFtEN444QtQ8QS7vN3LqA0D+EZ9M/k249Mh+P+9u7sE\n",
-       "tsOlJTKQD4H1qw9mCuB++KqVBVzqEmZVYmLAnItRhFuluzF8cj58dFL/453znh5mFinfjW3+wq+8\n",
-       "rUmBiHUSaCgwBqs9ZMcVfBgkwbMQFltDQn5PCA776qH+E004ZNlMV770lE0vkqO3YYgXXILFvfyU\n",
-       "z3plr92ml/evhxRUeNfSooJuSa3oCPSxazKcoXSwTyMNBaX17ncNb8XnWkhyqp7Qy7ZO/oAe9ZN9\n",
-       "kMWaRqY1ZakeB9TKz+HOdpG+7BTmpSYp5jGTcWAHfhKVB851hfoTKHfDt96Yq6Cr3uUMOa90gi3R\n",
-       "0uVnAXz79ioPcItkQaG2nAHb6+ZVnQ2hzlmWs2LUBfd/KloFu3q7kop2+2b3putVc6/ztTe2DSpI\n",
-       "StTWkZymwF/HbKR+IUn6PqswVA8FDO+1iq0TVd4TBBXjWtqwavuDacKEWsRZNW+1NoEu3aHpPPMQ\n",
-       "zPZv3/ECoCSOCGhWtYzceNhFgzI1vOz5h00JIzjZkJKYvY6xy8aqqTxIyrQbr5OEARCJvHOW/r52\n",
-       "qJxqNIlq+sXqlH3XVECzTm5+1j7wRdQhrLtyb1PYpMGdXZ7kuVdv1mlLuVdssEYW0u59YfliMTO9\n",
-       "j/v8+vFPGgEnA3tx/En0hrugyMI8UQgwAgVZYIQ5buUXGMYGiXYm+QApWQffM+xkoBzNZYR9LQcg\n",
-       "cv/8qH0t5h+Jmq+H7pF+645Fn7HHlkxqcYnDWyp0vmeyVbyHloB6Ujxg+aeEO5fj5s8xmODeDmKy\n",
-       "t7ZMxaBPPOsVNGypYjwvh5isoynhEqwTaY6vNCw3NewMTemcWOCibwjFQ1oFRXKkou1Ft0zrt4Ez\n",
-       "F8xFayhAnD4wvJnOZ0MFo6RPXPXCg0ROKacpCCpKeoRtKsWSkS91UubRDyNAsLavhVqMa2NSGKBm\n",
-       "udBvCi+nXS3e0RBy2yiz0F4fdzqWFoanGcWi8hN5FUXVYY7Ixyn3lECU80NGYNXV+WNMkFR1JqfV\n",
-       "BCPKdUPKUqYIj+eqWhrQZaCQuwh4zmJhSR+2ALWdiUW/mmoxUEGNIrll2Cc0v0GfOn+S0A3OGUG7\n",
-       "ET2bw+HYwcbXp6h/fE3eUqWxEj3cpFm7dR7pQ5M0fzXKRyX4Q7LX+KrpF+a6GZpabdaPDxrAtYFs\n",
-       "j536OTQRUCZ/xb2Nrbz7vRAXV7LV4HutREQqP/vfNAXgpU6xh0SL7tpWOCz6CDkvw68K3J6ABkNR\n",
-       "TeJH5oj4QdpIHFH5pyd+XJiqBW3p58vnPb9E5RLTtX58Frq4T5lXs9H5wasqz/wyCzeN8cjXiRy4\n",
-       "RRlnosIgyAYmviATobYuRCcwmy95u30PeqDDsxZxNuJgtGrR3JQca3R8e0iH7B31+A6Hmve4L/y4\n",
-       "haBv4VjyPZAjZFBFu90T9F8PnD0hBHEcce9tRmYTJw5U0sZ+eV4x13119Z4cwvdvXVYMoj0anQ69\n",
-       "MJokfEeZkJGh9GItj9hU3aaTPSSVq5FezXT/t2JS7FNoWxxN94HQadESuDP0NUCxfTMY7xv/gYGK\n",
-       "8Lvws8izEjJNWnlFBg10hmbXehvOTDdvkBJ7hElYLGbXSHqiJwISS7+BT6V8bEdmBClUyHzij2/x\n",
-       "pnd8tdjB7v6stuuiZDh12z8yRjjF1bCQO0OapeL6S7B7BPWQZtJiFS9vJ7hlC6PSe/1fheDeytpo\n",
-       "l+phzVOogkduE8zNeCzSvtVyN/hDzJzVCy2EUKui0k1mbC7gvAtLpeL7x+lpBRShiwGmc2gFM1Ch\n",
-       "Nmw0a1ISxVrw5LpEkuol/T65EVQ4xCp8rVRKyPetIqQdlaV9ErWTl4kNY83RwiHxXwHi9LBwoxH3\n",
-       "S3XzcIR/S3vhfCL442h1NIByRrIF/C2QAHkGt0w5s9wQ34zF2XO4JXGzSZC36pqFq9HkvGNLqob6\n",
-       "L6bencK+OrkAABCeQZ6GRREsJf/SfaAAmtB6bZLE1bHz/Ox8z3AfLUmiNBXuHFNXn3pHbZF1OKVg\n",
-       "Wx3m5RdzLfFwXMvk45ONxpWosc7RRGZWHyKgmlOST9iFbiMXQEQlJPXdLkBsjl/2HF9QAYw9c7KN\n",
-       "+7qxMsRMebOAaZ8S0TxwY8jkhbL0ENUp8/02FtvNXAAgZRusICyt3Eo46W5phJJ7LLgsUIbJw84N\n",
-       "IkbKHdWBfF1tNtXxDLHB9VAGLML6v++MhZ1cSleG9Hd5YbS6fyEr1zdIYXcvLCg9F/mbth/Miq93\n",
-       "AdRzmP69kAYkevGAPHSQigeu7c6jwJlcSXPyqNl8RQVcqwWCFWReoZKIYQdeE+IVbQmWDD5rAISO\n",
-       "7CLiJYsMsGlkP/pl/qMQKZ1nLDysS6fjgfcAz4lzPTB+U1iWJxKU3CrKi63epLt+j6TQ05pg2s8q\n",
-       "KeUSltHQxkq7MOfcgz7iGYlbIHDFQ7oryy93OP/ALUH5v0GZ7JbRC8WkRbArJ3TX0OdQSg2Td7J7\n",
-       "BrTU4YjXNZ5u4jypjv7p//pPpN98pC4Zf/BSOIwjtmzttUkI7kBBT4uXjrBM0IiR+botQ2ci3ejw\n",
-       "lz66rDEYsGNqr568UeFnK6HIAW71+LZvffr03t8RmKLADxaAjnFjGUZ2M/hTNAw94zeTrFxK5Fmu\n",
-       "I+KvTjGgo5pY6CuKrBegdW7dIPIDvXTSMBOJrW3LpOZ8jHoX8MgTNYBvSX7w99gtpTCQDm/mxzjU\n",
-       "0XWoV/tjHdeeYdi9KF0ujfvUmn6Wh8X8Y6+86b5gw7kf++OqY0DfaPzMdPEw2uXK0+r1+IzWm/mW\n",
-       "PJzK7TVb+jucxiwUowZpZulmXLIJkUn2wbT9YrquM9sjbO6HyAP3Fe/3nlbczLNbhGIeA8n+JCfY\n",
-       "UFiLS5825D0NL2yKAy5w5iCfcWOvIKSFNMSOkJUm/CeAJpL39sKpH5xMk153OJb2e6sIUAvAwRQf\n",
-       "INzXJ5pJdrNuzF2sqdqNZdIrGAyGyxSp4Jj0yM4GqoWYE5CCdkiliolKMU/5Pz9iK7w5IAnEw9VH\n",
-       "KkwA9IEinBMP0Py2aGp9Hl6XieTaiWiHu3xRewhRowGwrpTxcuFV/eWyVyQQiTd+JDpm0kevrTpT\n",
-       "ulNu+Pb1076g2eJshq8MFoMKpwELKMChmfYPy4R07alGpNvM6vlj6JuvN/qPhTqiAQmR/yDbpGJ1\n",
-       "ckyTHuvT2x+CrZnEwnrCmdEOc/Llncd9EPun9ubNJ/Ls8tBMihcpjQbLnxT1RZvVktyKgzA5ZRq7\n",
-       "tUkKNcx8qq+ncFtW6IBxAEz8p2dWozrmw6qeCV/mdZgD+CJ40NP+BAjo8pG8V00VANjXElMg+Ooh\n",
-       "GuZKwhhJ11eQWgqPQ3YRk+njZx4xH50TGGm2zrkw3Obp80LH5Phv+SLw7ZCdZCXNaeFhT3Ziu+IM\n",
-       "PpJiozCoWCg2LBJx3Lf7In/p/Eb16m4wOAYhB61UJxgq2EZvmHtxxbjn+zibscnsAgjtKqL1kGek\n",
-       "yiE5zhH9sA6kbnyjGXsAVyk3PIeb7IIXt3v+ubhOTWee9Bsyv8UYq33FgmlPTkrusmaKishDpNhB\n",
-       "flElUOyod38Wq9VdVulUt7Tbp3GqBpd4UVoRcBHuBjtupQdsYuyGa2QKoSRt1IPA7yp5n4MulV7P\n",
-       "A/kH4becuUoarmW+6CLjXR5ge67gE5zMLsbCI58NEv6xiiEDy8ZwX/7d82avMaAUK3V/XLop+pV2\n",
-       "BTIReBrU6ggoDwKKqgFhELEBtbAlSTvFCDpAm5nw/QbvV/TnwvslsDSJk5gjr5fIFtFOk3VM2xgR\n",
-       "FGOCr1yNG5F3m0d0tRph7WvakjTOo2KOZLmhUx44iDyhN71h0D54G5gQnrkzpeulKilqHFuaaNss\n",
-       "h0IrJSaxt0S+8rXVZbf9dfqCt00QgpnV6IzsywxC59pxl2sfI0rl1QD8rELk6MdbwLGjPnuJdnh9\n",
-       "UdDMF50srtWrJa1o3y3xo/6KP98UCRtzHXdlV4lFnZGTgQSjSLSlyO0NsNZJAd9bHPTEvTdkPGn9\n",
-       "9p6YXkFZM04a3R9uDBwtpu96+qRc1N6i3a5eVENJcoWznyKoiGn+6dqjT9Qj2Q0xal66RVIEJ9g6\n",
-       "m5JmlaKBddayX0XnmVpMhZU6FeyRj1U8bpI2PPK5yjmYt3xgFQhuTN0UUBChNMaENGOBkO6SaqLN\n",
-       "aQcaoAHMh+tJQEbrD3uOxuV5mCF2yBS+Jwu5vCD16Rayv4lOKoij3F6W13dTV1hEX0j+rEGdIawh\n",
-       "HjIyfhXrnhQ6Pb0mW61vItzCQjVdOzgcFN1Pxa5l0Y6gSvaDpTgEl/ZfRVeG8zQ8yuf7yI8InDYc\n",
-       "PAwRkU1vbZfkxRx2Omfcd3sp8izsXnxp26cftW9oD0ikfI5373lOySVuwbIiMWet50LaBqJvIizl\n",
-       "4yVLmuQa3SvP88uWLmNMA9k8p7fupAyS+5FeA9O/UxIApJ0E+ADLKLySU3LEc9egbsHFA01fVB8c\n",
-       "Hp0udgGkRw13g0DetJcBozNAEhrBft2p8P8LeZfKC1mw9NkShWLpj5wqq8Vtj9s0WkXfLJ99g7CG\n",
-       "hezP0WjCM3y/lLWpKEZuKIDE+Fo3vPZ7GXE51vH/kHNlABO4a7X25gtFguHJIZl0McHVvdgDcdvA\n",
-       "SEjRqyvGF3ymkvK3dBDJI/xLxwvkunLPQgE49umUvMxrf8fvK+zKrvVjVMy+oFagQYQv1bQBB3b8\n",
-       "oQYTXs/iVeEDmWiv/Us8/qEXr7j38PDZmHJ3aDjL7HuF2/2UTAahYle3Tck1bDpfoqqkblcJh8qy\n",
-       "FBoA0kFwoQ+721LEPmu056GoytLnU+C/McjaJXmXwfC95JEaVuXntLUAZNrdKUdB0At3y1eyJaIX\n",
-       "n7smyXBcUrG2UAfO3Idy771ay+LoODHaGRVh+1/fFKf09oenH2S2kqrfUEM3KI3wDowBn+xrqPPn\n",
-       "uvN4nMON+4ZofxBKHwvFcu2Pxq0QpVsEg78nekkvUm7+A0CSXQxnsJWieuQxeUWr5Axvy00blUUK\n",
-       "cj+kvpNM7lNdtJgEZX9COjuqrYI2khL06mP4ftF66LnJJdRCfb7WqLW1ADuMHYg4cBvnPTnEZF0j\n",
-       "CgxmafPT2yY30aTbaQy4EVXgZbZTDy856r7MxxG6bi7ANQKK4VPhGzDfjSgRgYAfCFFYiqd+Z4pW\n",
-       "Ah958xBK3T456EAC29vWOVff43p1RUwhC9mHii3Uj7U4seRzu5ubpjAC6IRC/HKY5lPszBV/7/aU\n",
-       "D6crpU8Yl4bGPewxVIfjmtmBuiGq4UII9jUr+SADt9NlIW5WRpAi3fuuqMycam1LhN/8lA7/FaFi\n",
-       "QsEPvu3Dt5apakr3GwUO2H9yGXIjIl+pzMk6spnsVVUXe/j//qIL82tBB+nSe1TPvVftyaGuUtHN\n",
-       "rSqk5Hrn58e0tP0+wfHwY3yhBol63UWoSZ6+H5b/rAOH4hE7aHHEk1WeeUlfUFYv2Kx9lfD6HyUP\n",
-       "Gmr/TXO0Y+V9xLW+5iFzLv1PN7oinEl30o3Gw515NOYRT2Ar88DQnawRoUFTtephku+Owpvsqaus\n",
-       "xoVk3fPQnGoii2xViNr2A0lrSj1V+vjeFLsuPYgn7v/KTC/ZbAzcg35iMCek5YQVyMvf7OMeRVgo\n",
-       "8ZvQkzDiY/2l89Uua1cyh1XmmrxvEuVEgEwND3SS7jx+A7qahIbDEU56YlFYTP1utCxrJU84B+9K\n",
-       "aMGu56r+Y2E1WVkNDF20UkOLO3HR6GiFKaJIVBdjG+hZE82CYVzulKiKFJJqsJ7Kxv3cDk6WKK4S\n",
-       "lacQy+XediXCdVAmqjB7IZ6DyW8ZGAyQ/yTlNdeLRL6UKBphB7ysRF64xh2CRXFUPPKkauvu2wCC\n",
-       "A5JfsMjVpnhy1Jt6UsoTGkByruoAJOWX+XsGqoGLh92roDMe/C4H+TxXMqVXOZcYvOaLfMIrDQti\n",
-       "OrRerCmwCccwCDD606ngUvMctC1JnoEclDzgKTOfwflfUv3HVg8rQE1HxtRWdYwKoJmGGiHOapwX\n",
-       "5SR20A364xK9zTSHBRjWdQjq4Zw68gjIO3D22iBpdY0PMV7+FI17LoautkE0b1aahOeLDqU+e6Io\n",
-       "MgSX+53Bm9t1Y7VvL/LAVOKb7snUAKs/yTNfe1pIwIMZf2fT6bZUyUoYwIkqL8T8eki600rvNZcl\n",
-       "Skbcw1EecH0a/9yRJhhPTud9XLdjEBWHw3fBoLJzV4TzFVaNbIbSkcQfQLU9oC8HsNSSoNKjqCcS\n",
-       "56Dun8dIrVaKkI4RokvUcKvmzGi5oebrFnFrEbKSu2yKODQGX3crJzvke2oDuyIFeg1YaTIlUXGO\n",
-       "SlQ45luvjd2BGv3PsJ6zuCvQVh97t+ukTsYd3vfL6AlDJDJfio99RdmPeQiFOVR42u0SEqJed6WA\n",
-       "pGhCnj4Moi2ribIl+TtE10W+dyfWmWzlvlRAhWdwzvb//+YOQsPj92il3BDU1PtkfOB9dE9mJaJg\n",
-       "Fe51l83UBz/h+ASYXNPzzurkMQun/04BEmk5k1Te3QeQx4JfZhXx2KY8v+F7DnVCdjsFmuE2qSB7\n",
-       "NHBa/BRKXCqJ4e7OJfkDhVaN+IfzIS9I1CvYTk/bkXYfXW1L1DeYUk07Hm+s1Ascg5+DynHQVz+5\n",
-       "qPEIU54GegyvQanAFx64x5cL+0SxFv1rjnz8L3ATh/RiUDfpCetAO4msX2wgIdqO7L9rFpqs6SoA\n",
-       "C8kLktE8h/Ge3v04VvhZo3MTNqVsmpuOSlZa/XjvBCZDG44KtzwxzAWfe8IeusDyfq3o7APBSv7p\n",
-       "/2vHyESM6VRFz70KsWyB+6Y29//4ZhaPyCCFvXzmbq0aXJPm743x7FocgxoSYOpSu/b/vYjKehkc\n",
-       "AkaK5tqAvWcFazIZTk9Lkg9S4oEqVP/sGGmOfl4HnABoTpAJojS7K+BZnn6XC+5BlmFH6aeMLbVQ\n",
-       "2WpYuYLc2EdOZ1ow8ffcl8s84gats17+PyYruv94YCeKEE8sSKbHHkWISKZM3P/Y2bSgQexFymwa\n",
-       "KovuL3X/fraBRvrVFykZyTHgl6GgJBHhdLCC54KUncWYc8Sfg7Qig+pP052LXwbCrmakRTWGGrf8\n",
-       "YReJ0vXLs1mTl6JJY/bFBG+V0Wu9BP8ErxT46HoujFIdj9EQpjb+Bsmv7rEcZmjdFTZTsZBczZw7\n",
-       "1k8nBLgv7Gq+kwwpfuRayMG1SLR0t6QncaODubaoPb9ca1BHecpDInp0e++y995Cccw60HlaY1pC\n",
-       "QWEMGvh0lcu9FePoHgqhM0zGeESpP+F5fnHSQyZ/38BzDlfrZYYlUIXOnHC8nDPVQ6rnBUPZLu2p\n",
-       "24BTLOgKkjL3buloNRQNVDaDfkqYJdfrWXsiTGUNw0OaJtiuVrH1fy0XkB1/mPc7+ci4U9rzIL7R\n",
-       "d4Pl1aoYNXFtjwtBxrOMqhpQA7y6I/GAGwpY60kZYbLPcsiiG9D+CtiIRobybaU13xYNaU5GdnFB\n",
-       "y+1Psfu+lQDzpkmKpQwh+ZYgLCPjyD/RTZz4tqiWmmx7fwt5tS6vsBt1G1QvbttKbj/gIH/Z4k04\n",
-       "eTTLsX1avLkQOkyk2UhCnGphUGKuGNaW23sFOW5+1qkRUS2ElwDiYVd7xYXbpr845svfwfODI429\n",
-       "DMW5ZHlSuOaLCVdrLeypp3UnSfZeHc96ixjEEpL9Ui446TJJ7fXll8S1O/lp2tc5AAAQCwGepXRC\n",
-       "395LwgSGfKAjn4WjuN1n8Y2rt+gDlNyz9kd/hKjnPsfuDO7WbU1jEmDLWCnVsX7QypS/lZD0BuB6\n",
-       "8UmzLON8Dw/MdGpSmCVN5ecUWx4ovLAoaZNxJ+ChPjgGkc5imxddjzm40s5UOpp/BwNLWZAsaKqz\n",
-       "MKXex2iSiAuvVgMCICB3BRCe8ywCQu+Yu8j5qJgMg9zf30e9Z4QbkLLMLxhOdNjN8rDtKHzoolS5\n",
-       "NtVhzXEmeS938h+DdGmYnnPRFb1r1e2qM926vcUsZ3VKFDP1qJqnKJfY0lT8zEeV/UoTGvKn6ET/\n",
-       "9JT0b33k3hJSXsNyyAJYEsSdh/ncpMBSIgi7vCdy7OO/eW0i7vpJSkrcJsepmoX3fm9zz1Q08tn9\n",
-       "XmsY/VKpWxr2ESIlmzsW0u/XCb8yMNx2oB433g6OMoWDS2D+O8UDgkex140as/rqDKBZH01ovAxo\n",
-       "V2HwbUTedeYm/cy4Iscos/DKbAEu1v0H6GkyDwa/GQYh9VNO0D4ZOI/HqwruNC8VpXFH0sYgw3u0\n",
-       "kV0lasMX3WW6G8dmkoi1LrWWHBEQMBNCO3YXIp6QjSlYttqPQtEqz4EdVt75SexjYma7aygAOnV9\n",
-       "Vl/7Q75OJ5fqR+g97lqT/tSsc8kdId5VS60wug5F+8A/Wp2VxMBVJ9phVy2lPYt/3uhatH107GYq\n",
-       "/D0zeB9mGx38RGJAIQcW9pU1gYG6AdgA7RCD+aCsIg3XowePRLigRa0+GZtQ2HTUb7Cr2j/1iNs4\n",
-       "sBJz8IBKsCrYk4kSjwZOZlSBHXX0x3oeSjCZpVkoacu+/w4Tp96hr0e3pkiaI8j9tOMLkAnBfNLL\n",
-       "mHNUF2qHvyImSoJ6Sga2MLUJvLW0O68NDgiFAGM+hWMIuFgbyG1Wjjk8WPI2U1ZYdlK0OgiWjzXZ\n",
-       "UZfPa7f3d6ag4hOalOae5dR8Yk5Oe8Fj0g7SLZRHlZVeShJ/iMLwTPH2GLutTfAVVAre3GZbeM0K\n",
-       "WB5K84LMxmxgIhPieSxfYFDF+d1dqPrmSND3DSEjlzDId4/BbSbr9QvYsHjNTh4PBDWxGX7MEdrY\n",
-       "t6ZJMv8W3j0BsWxd3xC6aI/O9xzID0d3bG9LDm4GTbEWcZU6hAN9kG+q6AYhgtRbMjbX0kSkIm3X\n",
-       "B6hj3AYA+Aj9bY+ZG34ac/NdW621CX2n/26oYWZBb/HFyqJlc/iEn5twaTd5yHBxqx2xRd/4fX+/\n",
-       "TUZ63krnZfMvcSH/73Tl1oZ1WTHkfktE5VigdRVIfuaQV11DXE47IjziofupjQ7ILoK1je+cKzig\n",
-       "Q4QtOr72TYKSf0IH1iX3LaGh7u/Jtu6/Xm/220rAcbGQDP/6ejf7Ys/sP9buib1BQ1nw+36QZsDK\n",
-       "YaxI1/PLOB+ir16IRJFvD3EVtYO1fRtegBJn2O2WM1j8ltr4PEdpm4lro/oFBi4VE8jI6bs6eb+6\n",
-       "eA1PHUBnAlFRvUJOI2C8GUAPOCFmqoBpfyLKaDcd2dbr9g/fGARrmT4umioV1d+vmOurJr3iKMPa\n",
-       "myBRJgvGjtTits18W8rfYqCEuXQJrAPq4EAK6xSfu34PyZDDyE6zUfS3xdcCoo4ePazv7qNPBPhg\n",
-       "T5k+aMxx+RrHl8xlZXgcmxGGNK8HCgauyO19wGsIvUN+hrOoogC2jJZIN22z84AVVU+6vmVCxnd5\n",
-       "asJ4C4aRefuR4O9nz0YynhEqhdTuluxUWjaLqmmpKYorl8OU8glDDH6ing1EAOK/Scljzpr9rLFp\n",
-       "taMJg9uwkyiqZWyi54M/iUBIwyXesd42+PuSrxtrfsYTYyfxi9OGkO4lX/ObQbl3y0eU38/luCZl\n",
-       "9yxXl2pn1NoWlmk1X66SNDw1mysFBIZ6KD6qon2wzaMK2MDN1r507HGRT9nDpZBkAz8BVDZzvjPi\n",
-       "vtL/C6O13KvpSYH9tfHWeV4/xyhggsmKAlV9nkY3YyKnwtMgE25skj9Kr2iUTrDzUqgSTlzWf48E\n",
-       "UAcZDzN+0VHP3hoeYddX3JZaL1itmMGWXPiPnBtROY34Pj9tNoMD0hqM8VtpI9D6uZGmieYTxHyW\n",
-       "LG5nVZUHkmWh3+9lG7HNPl0afRS1epSsHRTGkh68bIzkSV6DbV3kcv/+OjJlyCByYC1WUA47e3fo\n",
-       "tfzAQ9OgHBEQ1ZkZMSLkcajA2ZwlXSmbyR0LBaytYhHLZ7n/1K6IJNNOPHBL3DzD26yvAhlJ1bM8\n",
-       "Yiz4Bb/EPmkq7UURA6Uu0uxdm3BxY9v0B4ZvNT72Vv76NstjKw1VLnIH4qJC/8JA5k4D//C6F16Z\n",
-       "2VYC0cD1D7n0PICg2c/V53LGwrdELYUH4Cfk49QEfXF1i1W5YPdV1YayCYlHzAe0AnznCGz3fQdh\n",
-       "illbK3TzOaA4Qn/kkH8tWLN5XY6N37S1QDmme/1ls7iElYGUNimK23GNgOFMOFQff2hSPaUaEts7\n",
-       "Cg0R7varJvPiKqma1ZQCRTXtbmJ/MvzAs/oeCzcI+eYJusosI2IFwlrTTmmR5wqsqP5MCB1o1w1z\n",
-       "NDrZG/lRbOUnd56SZutIS1zD6CLM7+k751NYmXr4HqW9OoIbPVGAnmtiDAwUB6QQrglF2rJtjMgm\n",
-       "Abk1jqfuV/McfwmWqERbFS9C0jHBj/9BLXOHVU15QurrahLIfpzNRjV+W/yU/PP/rqyNoRV6Difm\n",
-       "e9YzewZPHAqQwdIZFiLKaCD+foYjJ91BXkRmPGS5+j/Mi6VzuN4UtNCX5stm7+Wbr2vTLiBpTKuQ\n",
-       "7DOFojpNkRqsKKflKL3r/8pKvGWAgAfWdAPmrc6QbdEga4+UkKyrZ/Ya1LNAijWc5gz+UKeIxyLr\n",
-       "sOKhg+bVcE8sNoEsnuJ05zhmKLYaQ+y8OTPpMuzbR9a5lM45CxjFJJZv6ho1yMw5oUS+tRQ6XeF0\n",
-       "mkwKpjpLqXp7wvGBHWBD4wSnhmCaVOeXrlDNKK+yMi1DP6ctEtkYMAfXg5TH3fTuTp1z0WTbHx0k\n",
-       "DDQBiN5pBVfvH4dWjtK/gNJMolwx2hqysv8UOTOVtyQzWjoVlfIqenShECWacT+WCmEJ8ADGGl2W\n",
-       "lWhfrCjxHT0VPHuTIYT1GKn9ezCBe0ThfWiW9q6La9LSEvNEsbPqffeY4SGCyEGq+2k4LxWAi+Jz\n",
-       "ZY4AxQNldwNWyZaspPFYxQ//TWZKIzQun9o4zGDBYcmrEj9S0eqpL0Z8RjuOnyqcBd16V8MheLBB\n",
-       "Gt8hoIHNIw/BrPHVwl+5jx4asIvsEYg+QKXqOkmlA4vT6+M0VoOCKek+zdHfi77r314TLhpsHwa9\n",
-       "mrdEazxoVWfAWvb3mOjTF59uWKC5iMlH2G98sJr6ZQbvsBm8PPF+shZQxvylKxpQYUYrCPn17ztu\n",
-       "aWULUTRVqYu77/rV7GzbSJXn+kAIQ3Gsrso7sfM4l+/7b67vcYC4KQ7Ar8q926IEvZdXF03Z1G8m\n",
-       "Se/ol6n3drFC2mkKzU1jIhOGqZrSkfunvQskzmaQGfwi9jHFdLzv/5ZCDvzzHQpEDjt3Z1kEweY2\n",
-       "zIwyUsO2whNuOx1V3Ap/+OnmUlIQlcziOVVurjJo/HVPmMbK5mH4VuzBBorw2JFwz2GMTn7grzbT\n",
-       "oxS2NPPxLbjbwRqMgJQSWlqdv4Gxtd0FmuBlLhNguDrrcpglChYBywl0MhibOTispcjLarZl+nns\n",
-       "6A5XeQQONc1OtOmdLHSUyJI7z4/2IugLbWLU/ON7h5b665XR5E0QAeZ1KYBq1c94pN+BnphSxe5P\n",
-       "q8ZtUzqJY1/68fHrTw8dem+chWbRzOe35wqmbpvhFiuNAxKICJTTSy/OnXxChFWG15UX+XhbsSF2\n",
-       "oXZ0US6yfefoFt8d/2u8TJV28RdOGFSfmcWaygXVEqP4TiwEwAUZ7edwZSH5eKYJx/mEaxKlnIxY\n",
-       "BJ5CznXDpVR6npxxI8OBn4fk7JYdK63H0DTJrAQNbA/CC8uNyRKuGrGa/MZgOJKEg5T5jkk80939\n",
-       "0WzH+iUrji856SuI+fBWf2Rf4gGyGq174ryxNgtqznzZ99P0lg7w465ESGiKURgvmjS7LXm9eW+B\n",
-       "qiQWOYm7bC13VkQ07uFGurJEVIJ0uYUddJUdOw3f9h0DF5tF3q0TmbzG4yem56cglBBtVZ5gkhml\n",
-       "tMRafQ7/3h5UT5XHpGWYBg5X+DpXwzjQ9NIzMsxAcnnh8HzILlE94vk1gmX0N40zk4PRzXIZ5Lkw\n",
-       "BYjmWI2A8+Tt1U2FWhsZJfSARhFm+enM7nuYu/elxHmwu9kDvMUwV0OOBOfQQRPFMU6pf2pGsz7h\n",
-       "fZYMGv85hDPvi7/59/iWn90ZNxAs+6dgmJgAS4Qdn484Nt6f10+oA9EoanEHjm6YzG/nJM9wz8iJ\n",
-       "5BwZfZ1RNkat2qqB3bfnVob1UQ58Yewnx21l/7OZrgH+kUxrQQfp+OUwiCRRQZndsC2dyk2WGisP\n",
-       "e+AAdvt32tTP5Zb+/jUltEfWjk8v0D/MgRFPLuT/qlzfOqOD1eoXnNNAK3MFiqKT4LyEI6nvEfs0\n",
-       "h33W7bVdCoYwPKjIZ7NvKJJv0lXgO/XDKB7HYRVvoEvaF3odZjDEFeABT0foZDgUUMY1Nadb33an\n",
-       "K7/xGdVrLe9ljYAANlwiaCKfcESMsgY+fmMH/43wWzrBrd4gFuo6koKOQGvmawlEF6POYSalHDPj\n",
-       "IXMTpasMXKIX8VxPnM7vdinwJjDNgjYnpcMX9kAWfmkpLRu4Mym85gemG01faHZJHNdUQGGTKM0D\n",
-       "bQaHCWItMt70/4QlSJvWRYj1K150idtbz+u+zSB/b/vvhKcgqoVkVm2N8x8TqqMtIhdZw56+qxdH\n",
-       "9kSURVbcLgSQLwhvy21rwtEV1ErlY9TrwjkZda3ZExgcBv7VMVBmLI4AEyx9b9uIRHomThlF+1s3\n",
-       "B1zDX+4TxwicJrpPUJrIKR+5/4fcO9/bzgT/GPMm12J403hoHBHhg4eyR7hjVgQXiAbgdvYAr0/z\n",
-       "2iBGOuhZTZYn4n4CiM8q9HBKmQWvgFeGw3mDOYGRn6bFLtRti7589d+r67zSgrfNeaULEUU1vDZr\n",
-       "wuIYSsLxCmKGcSXCgTOXmmwcSXsBDY3/NxG0qx06cSw8TINJTd7UQYbQTzf24At4Ol30QVBhyGHu\n",
-       "WzASUTvfG/++lPa4+NvwShU1HTaMeyDjIDXuvMH5YFO08sq75701O7OlhU1HIEEpS2HDF9VL1fUg\n",
-       "BknXX48B/8+SB8hbOEXGpUpwZj+NB5eYxn2UIadNnlG4F3JtaeBEX+c5qev4Bg/d14/uH6pmLMuI\n",
-       "k6xitfT5si09yyDMlqv7/6xwVBwchb3Vk5YaMzcavfzc3N/3Zmwe3pLBI9OTOBrM8Hv5vR7VIJqi\n",
-       "nju/jBUW85JbRPxnu0ngp0xglws000Mgzs8qaHV3tk8NaSCTWeBdBW8QrUDb43Is7HU397M5gdR2\n",
-       "1i1GjDiu45ssFEzZgZjPvnDIVQRoKf0ICRgbbIuuSFxkEbWrbVsNnTgruQjMlq1s5xyV1jJBQQAA\n",
-       "CcEBnqdqQt/qdevlCMqvGp4W/OIcRaZ5odrvHecYEMcDRbIT/dHnwaEuEYYUvDfyweHDdFhZ+rhn\n",
-       "UJSD2bnqfNmuQKCoQUozKHQ4biFyGSnzmGCjltRV4guYzyvDTZIQAZ2JzpqJOSu0/1GOtGnwd0Xc\n",
-       "G7o0QHZIHShMcZ4ueecRUA2JC+/hN4Jmio9rewhT7buTdYeM+xi1L200+L9z3/YbAskGLwp3puf2\n",
-       "bCvsdXH8cffaBgYaQXxAQAjhVV8DCUwpJdG+OsDsZxsLzG3VY4uDCtNZIeTCfmdy3vfnMKgsUmXd\n",
-       "UkNlTAKAIh8fW8dc/J8uhmjaw/e6R6KE9XjlVTuquALGEMjw+R4AMWDhoBLeh/kIkMCbP8KS4cea\n",
-       "nOc9gCDwLxfckr0OnLa5uAopPSgrv3V9hujZT18He2mf17pikcSstONJUVFQ4p7b6kCR85oor9SX\n",
-       "1RULp4wR/IN1vklsn66XKmXZ0idJ2OfpXmJl9ZgrAZ2wC7tG5ocrwc7+XyPEFw/+Qe/XStU8Qddo\n",
-       "JH+oZpjbxMbA0poIClMkKC3i3S0imwfy70LlZquUYrLKsluHp5GSCIKvJBabTe8dX0MxOylP1/+c\n",
-       "f9PZ+HtLYDpSmPrlS29pGhiNAVwJrRiOZKspk2sdU4JjvsvMM7mYHHeAbuz0jJLBNBepVyYD64B/\n",
-       "5g+frUUwyfwS7PxVz0bclSWVog+WHbSi7eMkGAQb9ly8OX6iaNOfQ668YIuno2vSc5jQoA/yimcy\n",
-       "VOsBdrrKT9kftdlS/sDWCbKiZHFFwZkavz74x2XELO8WZFd0aK5Zv8LilawfZx9pf2cbhBOCLEzo\n",
-       "jFA3WXu7rtyRuJuCwA3G9BeA2ovnQBTvJr3cJw+eFRq7I1Ow4y2wIDxqj3UPdgoN5Zlvy6Rusvgf\n",
-       "7tMCEREzQB7Uro+E+6ywk+ocqdKGcOkILRP3ZAnFrJLxb9JevR7VrAlD1rEucgBQyfjYf70Em0YY\n",
-       "qD7XFVBoSi0w5m1HVg82DbgUCbloKjnHoYn9xhbqZFJ8UUuXOrunA8M3/GIgd8eMHTaaUgTE4DdG\n",
-       "uqd/j4IxrhfF+sbLd6+9mHVOPg+YIAAHNzp3v/88KLF8bW6Vy+6Ha7V3IFOX7etGRqCNvt0SiB5T\n",
-       "bUoqw3ZkRYfZZmdcj+mwkgnRqmiyfltbe0aKn+XuHMzIDZyWR2ykA6vwDZXxwM+X1jNqIaRRDtQD\n",
-       "An2iQzTn+VqUraHdGp0uuvVp0d/eiHOj5OfGCvU1fWx1OQayVvqTCBPHRefqGTxLHkfOkmx4DoTH\n",
-       "G0FOihPUGGywOXsc42+oNpdOkE3Li+P0mNiIibxqtdD7vIZuBlmYnh4BlU6q5r4o7W3JocnuDIQd\n",
-       "fnuUqezLwy0z1igUV1H44OgKDwaIYPgWnk3MoouqBNqwddHl7LLGeuSrw+HV4ay2OMGoIbH/6eDW\n",
-       "/geLsHuULIQ0BDInOBmpeeJfTmhog1BzpQhVd/AF+sCqXxwgaeOcV7/YESie2akdi9R23hagCyvQ\n",
-       "Do8ydlkjq1cerMshmt0zCP9K0RK06DyqTvOj98CqWZ6WoVr3JiB79Ti5pR3KB6HhnQgu5DI2D0T6\n",
-       "lYKjdNMDMs0cQlg+scUQNTwbOixJokY9+ap4634Oc1xDekCcJjoN9i6KpRrUkPXqoI4yOiEOlBH1\n",
-       "HW9hO85ZAXmzsoOipAoMIXOYl5GpIS14cy/bbGJgVRbkrR2MvkR1vKxPC4Q8pK6i9aPv9nnoITh2\n",
-       "rk0RbSvFPHOD93rGQ6Uuw1rMDq3wB8A6MOxcff7JsVG6THdTAhb74/sdA7eb7WvbhHyBrpDByvCU\n",
-       "lfds8pvwmui9Ot8qzL0uS6MU4fq+5SgevdO0HeDRpeoqib48n2ISo3fshhgofzH/9IoH0tXuAQRq\n",
-       "hqHPqvLZCoOFPqoWspRqQ50c49Wf1rFoB1TqElsQUqhIXASDOX1hh4jHuhSTyxJSf7VHR4fEkn1y\n",
-       "B3oZpD3vcxj7YXKgU5L5hFSGCzDpk5V4NWmDnpJfwOIIfaatWHHIHbWl7DvpXM/fNVtdZHowF6Q7\n",
-       "3QThHZKwLRjHc2wumQUmfHj1DWP/AtUcU1uoaEJiS/45+1QDKH7LOte/UuaRd9bVvy4vxLwB4GjG\n",
-       "J6nbJF/nlVvnufpj796C8I50AhfT73GMf3tIqFAjan/SuMOZCkaiV/lf8sa1oh2U0LvPDY2PVd6K\n",
-       "e0gGEJIyqSO8SSYXBUd5rnxc7bXpa+hxkc8v1halCUk0+OoVy1BL5+w2TvOwmvRrlllNL2nlNhH4\n",
-       "nVFHlgHG+INYVzT0qf3r6ffXvXEbDEqmRPY8xDw+OjYmmtkVz/7hidNDYe3m98PKoE/ZDOJ0KPNM\n",
-       "Nkv4uvAAAMt+yjdzrhlSNwts8wRRQqa3o4DE5HvP+J1gjydN2uE1sYUylIlYq2QUyWH+/bMWmkEc\n",
-       "XueUF8fLAsrmaLgyx7COrZZRwHG3yPHnHLC923gkSuHY0iiwHnJLuz6CX2pW2/6M7r33c5qPSVZF\n",
-       "r7/Wjt5ehpRAAAA0gTm5T9R/GHNZMueUVj5rybNPhglMBr8n5kHuk24ECLkQqq0a8BxiDRLoZ0es\n",
-       "5udn/Q9AcMWUS67yq23Unfs0zxEHa5ZvqzC7j+F9dHA16KZcO1lKywOpstN+T+dXoQ17L1P38cCH\n",
-       "F957zx0BoPyLefy4VWcuk+A8Cr0lZdiQA2wsu1Ni7/yROAPzYKwHaPQCJYAGXccZGYA8CgCVQwSQ\n",
-       "Idia4ggB3ITwIMcBvjbzYA2fE3PBjV3Rl9re3ZeMgPZlkleDJzAa2VQewE+Kmp4rV59QVFmL+HNr\n",
-       "2KfQ4xdx0K1psQJshYwrMlxsBPahd92EuIHj3Do0ftF+ys8sJANiYio4oZXUe9gKA9oExbRhiARu\n",
-       "N+057mo+gaRNSR8k2RMIVeHH+RNHrbpGL5XGhu6RxPrHk2ePxzJ9ElZQzmsrtjkdeBoUBaZbBiJT\n",
-       "n3MDFsLZQXFvZv/7YObtmUzJzKixwrV+BMTkmbiOoWMj9+n24kRmQ9RPIDBryEET1KIRbl2s/apK\n",
-       "rBrMG74kMdKC9bIvg/dBvFsAFlDm3/CwdMFTDQS1xB5viV1G8HZzl6DsAv6KVauuSmxTlo7gMF4L\n",
-       "YapnAh84UG+sIWDPu/9U0eEWDEyhvR3GCC5eyJ05Qz3Cs0LEmb5eS2YjiDrOoG9V5jCJeVg9vrtm\n",
-       "f4vkY7B8dtEJxEFfFigCK2SVyz0cqEJ2Akvgnl9h2abDUnGcgLI2w3Jb/kA+e/ixnBkMGRwCvcSe\n",
-       "HQutnXC8k+3zn6p2/JPTKu3FKDaVOX7OkyyD4lGYTagpNJMI/L8CvmBphAbOmEaAAAAXIUGarEmo\n",
-       "QWyZTA//gLyVhUbXQJNHY45/wLSc5syptYuBfPKYfY+5CNipi1JZYKDzAZ+OsR4OkQFGQc8ObS3U\n",
-       "zKfIIh9Qj8WBXN0iApB8uG3WYpNcb7eZMabpNaF1Uy5XaNMk21MIjC1FgsYX5/96MeWKAsfaKk//\n",
-       "adUXTBGGKqPLcnXyg3vIViAXslgTMJSLhXsfmjPAlHRNVfOu0d7w4R1h50Ot6MZGvaHbgssURdm0\n",
-       "eY8QIt8gRXv3ZkUbmFSKmDjaFwHSD5ESVtRIEZppoMbBe4QCEPXs8tnmDoY2A1yJpdlF6Nzzbq7I\n",
-       "Odk0XF7qkOQMT9kBtGgErJQMfx4AVUjGGO8O/YTeZ/zzZjVZSuj47YNX2tlheVA5Tf2+yJPdtxzJ\n",
-       "VNPhrrGtrTy3HvzfrKbCQui3XAhYrs+e3BbvfUwd1WBjObTBB6KK7axFmp61gr0vqMqMKRK1IksW\n",
-       "7YsCw3nsDu39JtwGZ9AHqTm40akBp2zLA0Cbj1vykbB15uaT6AhghzReT4E9mwa5VdKG/zf5/ora\n",
-       "8BOf0ux9jbSYr8H31+1qBWeO65E6ryxuf0tzLurEaxWJhsQO2svLysNA9pm4XHn6PSCzFDCUrhlM\n",
-       "6kTjuOS31UbRuBIUaWghpyskqtG7WE//xuQlI88aDtq2ASLMv/x9JY8ipeVJGvRbyU7Q5sJH3Gg3\n",
-       "UTBskWkOFobv0+SJR2ZBPMeoeXZRJmmBJP+kKmjsam9Mstzq9ktH/ne/ucj6uP6WpVH4geSNNrkm\n",
-       "qzS32aBlM1M0TG1bQh6lH9bp1zeMe7PG/KF92kGqL0JlgnBRYChK99z0W5qmIHnECN+FgQUTk85I\n",
-       "exzuRUF5a7W7IUerVT8p+ZlVQ7IUpHQn7ZvS1ma77leDwQ+s9d+DtBYnvGSiQlkadBS2e5m9TGYo\n",
-       "jKeBlsp0Ktlc6FxmEG6GZB5tcRcvZFqzqPQUGDdcfOq83Q6of1ETa4OI/fFQNUoZwzRDwGuRsmJx\n",
-       "w1cFsrcp3wrJNEj87BP6z0C1w/dwdrLwObdlYUn0Y2xqKAnX6wbCr/oaWgyy9zjYKkG31V/KbhxE\n",
-       "9KwqD7IUYycexIayunTRjPk0o90WY5CekYPLlwIrMmbNIFfCso1TMJ349oz4D7jpi0MFGMLBJ9WA\n",
-       "GH5ax10nDkQUbb/7oP2L1Wj1jJk4LsmhB6GLdT/saPK61YW3nuZ8tnti0IlgOjHb2thrXjzwjvz+\n",
-       "o3J0fqHtrwm4LIO6OmJq1bIKp1ShXatS0B28gOGD9sgkWOhudFJ05mEzEw+Vum4CU8j95DXn693n\n",
-       "Yjf4SHaqBZnPFzkyB4+0UUxJbqoTw/Bsr/ymlHR4eBw1nj6TASNQ8p6WGXv1AaMKLs/64zROFxAf\n",
-       "6Lo1j+phTgf5BK+9Eq0B77t7n2Y6b0OtFgsnQFlAvhBr1yialJ0j1YM4R7gDKSNGFI969dc3Uo/1\n",
-       "wVEV13hShJp8FdlCo8ROOt0ns8ojX4CE/UmnYAaDyOaSSMz8cT3v8zT2912mbh0ZxzqZKPknxGxI\n",
-       "/8jV8TI3NbEheA3vIeF84Mr57RW+HBYk1+CqzZzMcKUe9icvVi/SW2dFz7dNyvHIncQu/31h0ZE5\n",
-       "plWV4YftWJqgPsz8FdM3TzveMuh9yWeFz5Q7YLcS5emX298on+xD3MRISRZMZ3ZQeCnRfBYlBNTI\n",
-       "h02ZcWWzzb4sEIY8StMCPMcEu6oPLFyRa3CyAQmQ+5oWJI9vxlSvZJpwi4UtEGQ4O8CxEr4fSUY4\n",
-       "Rfga5W2wNn8zgrmuUbZE2G32M3aeJq8gjiw0hbKZvBwwshcxn4Q/mZPMIqsgPWZZ8geQ30ccshuk\n",
-       "REv1G3dSrKVEh4YDSNwKpnnOEw/NQOsmlNL/h/TEn+5x4UcKq+OC4abHAf1ogfXS7X0Bn5Ic4cMu\n",
-       "pfmwSCfkes56B3wTYJbfN2qITELGol4vvlX9scJH+oWS7pnPmUbSF0FwZc93LhKfs0UASl/IQoZV\n",
-       "GsztFGibMCUvCeWeoe4sh+g1aYpShYnuEg6n3Z55Ir5h07vJDlINckmI+LxilsUCvRW9zqNXSWcC\n",
-       "wdm4UBxVR5tn9u5zkAHO09AqSziDi4h6fKaLecn8Hm9KvpEBlpOqmQc21UtiZDprcqqHykADvLMj\n",
-       "1fjNN+11o1sriMujWH/nudHpxdts6yBG9cqtayyxahBICVNQvEGfcYGKcAlbup2S1e7zNXxPU0rm\n",
-       "b9xDBbPItrlSnAavS1lBGFigjYdjYRjN5IGPgwIIEc3TPdv0bpGwzllTaghTRpiq/L81u491PFfJ\n",
-       "BUXxzmHJzsZu6eSUbd0Fim7axoYiWokLD36SkjNtO6V8WCZV7OHRvdqdBaH42viMz8yKJ27eOa++\n",
-       "MJhv1bHcqEf0Ub7G/viBLKBeup31LFLUqE9yUAbVuHZira2i+Bwt0NeK/gzjoWm3zekcFHCgMMNK\n",
-       "WGFeBISFvq3YYrez7ZxBffjMAqtQgYuY7HVr1TsQBmT5wSlZcMBe1/9pz7U3n7TF43tfa1IRWvWB\n",
-       "q6wmMAG25PsiJsV6/D+XulPP5G3rdMgLnj59q8pFhDBsUqXyxfyOHkGZUyp6LrdHUPt/W0dOWqqG\n",
-       "O72/Od3/b2kqyA5wK07Ep/e4vAzbGuxI8oiYJGLoOJyFHoseUuxgAddsD7uiHBOHBSRMhFa44esU\n",
-       "AEl5ARvtLucBjrlmLYi4ol0dO1bbU1S7mfotkQXCt408q4E/3NPSONWMad31t0h/jiVPzjMFfXYl\n",
-       "JbgWyzT+xNgk7OUk/GPGrejZhNOkEo9Cp7CEWABbQ53+XsjKhilYk/xlvqOU/nXidLHZ4ENw+KQ2\n",
-       "iRrDchBMZzWp7p5MICOcFpLWAF2WpJjn5mTzK5geanJSSbLrccvTHLd3FT2mkwc92lKMZ/KFg60u\n",
-       "3gmulKDiUI4vkmOgmaQSpvXT5CnSJCzbd0rK371C5b4BmkX6DM6bLSLziiWDd2fMLv76n8TaizUB\n",
-       "XqTh0RUGO48g16dKBst6I0bcI+/kvuKgT8HvmoefXeEYy8SYzVhlV9mZJmJ+D4mx0KWwKeyeIJi6\n",
-       "X9WLscTd0nTmhjfbIokzJhTzVozFvKoi8bwHpBSsZOntBKkWN716XIACXwxaBf37uUoqrI7qQt37\n",
-       "77ZWIUmC2fobextO/11V0fn8VPLRH2rC5ml5eSfKOa2oRX/ewEcqv7lx6QYEGeG1fypRALghbtA9\n",
-       "a2eZf2t8MiRCMgV8O33ltwrOj1en8M0tcSXcga31t8yJsIleh1m0eSIYjyDHAs591aMLP/BhkZNf\n",
-       "gDvxTkhdCzdNzw6qQgCAjh+MVGFH0OblU2Lf//YsAOJl6Q/MQvw8aukyDQG0VpzXp3TajkdYw3PO\n",
-       "EBh6k5+jj+/05Xef0U1M+yk6foDLkRswrB2l6CUEThu8qAcARkuZpx1Yom4Jzh3dOcATRGKgh5hg\n",
-       "BeGy4L0mtHCRJcR5zIST4oYATZTkJWi6vg8YtnklT81ytqujZFAAlIY+wKlKi1UkXJePfPsBGwDR\n",
-       "g3FRSGGc5mCDFAr/L/UyzriITIhfpgsQhakX3uXxylWQNMn6Q2p4/H9ImQ6juqDGgjvknlyjC9q4\n",
-       "pxLTml3pzdbdW4XYkFR37ZpppZjj4N1/xSB2JZlfx94+byhOfzRA28T4bd+sOo2E+rortHuyyhbO\n",
-       "XQakvl82Zecyd9bC9/UBf6aepSBNntg6tT/KINPmOOFg9zDgYSGCT7sln5FxKFECRGz+9Y/5pSki\n",
-       "P6aO1g6IUGyTZCuNe/66w1BUz535fYBRkGYEUEPBifhY6moIS+QpMeF80HyZteq6/kXVKFhVv3kP\n",
-       "IC/LRIclZcnT5G1mrpZD+exOyQSfDsoUnUY6I1gminD4RmS7NuljWIUmqcEdflpbYc43XwmaEHw3\n",
-       "LqTh7pfvtg0Km/g/jl2NDwPYYO8Z45tyR9w4kFZURqAMA9gzWfrbeDh3xUDNs8PprupoT2Fikbo2\n",
-       "b/USuCamWC2Aqwx4aGdStEJzAxh8smWIcWlVPaKANbCtxoIBW/Caq46Hl8JzunwA4jLc6z7Z65Rf\n",
-       "61K0ttLMUdkNb9jsSDbB6N0nsWvQCvYK1ka3g9/mRO3BnuOXz3lktmBkMqrFDL6CtnrmdgxYtLkV\n",
-       "+eJLA0VDoy83TPwHHwOI7xC3//KS9fE/1dsBpotOxt0M+oP2R5ldsBrOucS0TjmtrFnZJPGxI5Wb\n",
-       "LzKmjAriwjiMbhjzmA+G+cjL+9RNsoxNu6mdEExcHilACrUbcox4S6cXzBnQPEjfnXBspsNTl//M\n",
-       "QGVMxcXqyMPKx76sxLpuwCGsYReTbND0n70H5SA8rsZ3oOZr9abWFojTzLCN5HWUSmeJ15csORcs\n",
-       "rXy3jTEap5XcRcAZJgIoay2kMyC9ZBe9Lg6LOk6+UUtGrrhqhEi8fkRwyMaDaD824WBXAy3tolU3\n",
-       "yax6DnxKJDdw2DmPBJ1cWP+dmNBcpWOzAXMjM8EpW6wK6oFGANQcGIO2Fpl+G/gz1rkLguA62zEs\n",
-       "yUwIycM/2jE/aH8XvtF77rsXfSwBolukxj5LHBbW6pJvdePt3XLPqY14aoPSF37KCyxQiMB+in/b\n",
-       "5qq3GpodQ7Fvpz7D0IaDN1EyfgzEta906oJTIvR3lGPIL/eVS0NAVl0CTELJM4Y2h2h/cqdk68/r\n",
-       "7PyohWcv3lJJUC+Bku3FZyR1lUFtOE1m/I2brNiIxw9a50Cz0l7HMxZKRXpCJR4WidoE5I00z6wU\n",
-       "lmP1kSlyye/8cS2Dj8Nhk3ARUEdl5sY1Wkt3EkzX7rPjIXy2+NLBa6J3THWo2TFO4KhN2mNwXEc2\n",
-       "joEl7e2yVzROcFDLJWLcuqmi+J3iRbQXSGTLzsOZ8nGoHQHgIu/y8QL3PgbZzlw+4Cs4F+vb/wJV\n",
-       "f0Kr7AThwfdclDHjUsRXLpa4Gq/gzZZQqqZPmblXptm4gB4JqjvKbRT76n3s2A70tD+vpMJj6kUw\n",
-       "EuJ25pTNHzoTfQ/rxKf85/oJzU1hloGhWUV61yfwF2R8/yqQMU8lu53ueyFO15w3tsqSwLqLdgeX\n",
-       "S4QoAGPSIXcxBlsdjrpndvgcvbQVmBEWy2cJlu6hMbns+I7RGpro2+Ks/m8SV7w1B8wTpRpyCMNF\n",
-       "7GdOI7ILhCFofgtit376gPBW5agB2kI943sFbgu8UOO9M4cIfjCPwju/5QOPfU92S3HVgc9j2Pzw\n",
-       "kTREy/VvP9UzL1OrQeEFGKsDl+wqjeuoHiEwV44W4qQhrBguIULuMUP/4/rp4mOpI5Ffyhp3dpKK\n",
-       "W4bDm030Rt/A+stIdcgnS12ljCGepylNb2okxZtb9+cSddNfwe5hKozwwAAb7ro1rUgas1WrAuu6\n",
-       "QOGtEy4S1rY9haBobJCoOGb8J9fG4FqQ2d8pt4KjNz5NVS8AI/WZoutq8M4QkHM/EJbxM7d7k/Pc\n",
-       "zEdw70HIIAwuGoHI8wptGtHQRC3faSNDYIwBl9KIPpK/dfqcHJG5M0TLdNy35XrT321gI6qCaxiY\n",
-       "lXK1bTuw4OVUh/tRoQ3gHYptDJPnGvfMUa8zb5Xu9Asvo/cdduR9yfZUTOB9SwMAR9xUBok8cfMh\n",
-       "6qoX9mMRhhSnz8KJ/utCIClAgcvzKpYQtwjuJnOaI9o7yej95vsoYT8tSRcv93zj9ThbF1ddwrv7\n",
-       "sMtxp/UaN0uAHRA+aZRi9TWHy+hEPMBqTGeExsesupJKs3JBPe5z7eRfjwBNZXzIPF1aysFkm4z0\n",
-       "u+MAli+ZXxdN1MI6Qn/oYcqg2qCiiqFh0cu/rSx0mXIqjAe8gGn1f4yqwOfaDLLjOlJwdAAAAwHw\n",
-       "Er7Ccg1LJ4JfgLShXJVIlD9nlkV+CMsmpNkjKvwvv4SV1DVPiekagsYTYMKOhxYgnBhFRzC1nFz0\n",
-       "7NMS0KLloXb0ZV2ow0GA4b3U+d8ipqv0jeazo4ApAbW0Lo7N0SpRd4zzipVN9YDvg8XP9hqz0XgN\n",
-       "gW72fC1ZMEG+PXJ35mjQ/teSBFehJSDMd+l3uE+1UOs2O8HKTGS0AqrDzVV4zn7fMe3dlg5lWJIv\n",
-       "hlc/EfTDfXQriTws/rJuz6lSIvBD8jpJbrb79bTCZSzFMupOKmXocEvHItSp7P87MpRVu1sogEWM\n",
-       "xDMgW70GH/DGSJMCpYq9W7ZUr4U003vniz7oidM0jTjevEGO1XjoSSeoDyM3JBF4APjptyUee+/E\n",
-       "/3dmS5nX9Ibod605Y41Ju3qUXq0YlYogumO3BSUcVnUMoO/7ZKz8Og1UV9jJZRlRez+7C/zzODSp\n",
-       "jZhm5oKU91LSHSvsMKUbManNp1Ly2TBoGyKPznLvv//5KMyRxFIakcM8rIO9n9vkTk+o6USTkYVU\n",
-       "JIuquvEGdsDupG4UUGrpfNqCdP3/XGHa7Qnnb/cUwlxAL3dOlAcODbhsfZOvUuSQNo1JZSRMvC//\n",
-       "9C72YebfFLtqMGoFNSa71WPTeMizNGfcqsTVmcLT1aI3NxUHc56HJb2AIp2OSm5NoxocvPmho26R\n",
-       "PL6p6gGnyZIqLcwMKKZ806F3x9/Ymu9OQSoutGA9bi3XvGu1p/j3jnASM/B4zCtm2GjeD1LQ/It/\n",
-       "GtCnk8nphCLm6pINF27uUR22YNydOqzsu8QzxP6TFMIRvRAB6zEEAAGZFN2VGZHi8avZj2ybEfk+\n",
-       "u+YwR44CFAd5tFddRNSKb5m7y0KwrRmkADFduNV6PEJjLAZ/qCfCj9KXdcMldviBRveAr4nqHN1U\n",
-       "eSR7Uq8tvf17/9h3f6cQBx+yCUFKf/3ONGuUxAU6Bin5zLYGcjI9VlL3x2EXuFVB9snqbULmCxP+\n",
-       "jVvSPg/fhoKS5ZWcKGAmlFQCiXAJ99aondMmMhZyRWw9/XHopS9/CuacPLZZxFtXAJo9BAV1FbvH\n",
-       "AYSp8FyoMj42ejda5og67VGbs/yn12fv7FKRMMgAF127NVIuH/4hP0/kf3mLH7/8gmrZ01K/xf6Y\n",
-       "rpCqZ8jkGKl1iUI87W/bP9FrcEmoa4j0pBScilFDz421RwtJkD/fcWh+CXif4Cqf4t24rOd53uil\n",
-       "f+R5NUlfhBFatbhB/mqShRAsIyrNaYZbw6nUyANdBcaq5795ons2+pmT5WmX+PZzR0ah+pfEWE3S\n",
-       "iS38ONc67S5PySRgR9QQWHnrXZDWN1SQTNJYqVWlaMp6dTZWDEkmDNe5UoaVnGXu0S4pKIFV73bG\n",
-       "9JRyLIZclNNhDXLdtLCWNk5vc2nPypR3Nj+5m5NNc8L3qWs+5pNVIK5ZLg3Y9CMqCLa1/ul9c2IL\n",
-       "OKxzOD21ssw/arIKTz+wGxRVRFAVInMy0yCV/u4ZRq7AXklW23Z9Z6iELlH64ver2nBfpS2mEWec\n",
-       "tZFoEZ3pYuX8EpYtTr0zIOn4uB7dTsr7K15vQVUty/G3uVFGiJv0VLj9VTp20kPbk1uPHKlzD8ug\n",
-       "F+A30glvdT+Kz6pVU3OJfm36RbYmj8gZl0lxgPAHzgO15ts864aDa+RL81p/wrjs6ESQt1oDqFo/\n",
-       "0mM7W3Anl9K6RmbSte8CbsUOuxWp6ZDCca4syUeuGbWZ6zA41Lhv1WqZmkYfrKmTgy9h3szCAPuA\n",
-       "iqNUiAM2RyPpuKJnaT9G7VoRVWigiHoJduYX3NeJiFcbrjN+h9B2aQc6PWjsXhVDm8/hGW7LX8CE\n",
-       "dICWr6PhiUJYa+foRljYiesuBFtsHdeIhx4ygdFWiYF7cMSOhBCGe0wK8wzTMqjBN36KaZzr3LbE\n",
-       "tAwEXFfbsN9joI2vC4lZ2JYxQNFbAFV4qrl4E9xirH5KbSPr658rlZ2uvGkVpDUVFlsFflrDQ7yL\n",
-       "YDFpcuW/4QKG6ZcWmAvVkDj6eHicVcWA4MUETtjDO/gXYTjJWRJNXd3Is/gSbmzYbS6BI9jNT3yj\n",
-       "wQqoaNs9mXMwWiZt72Mc7Mg0wwYJjR8ftGbdOiYuEOHTYRIbekIKyNJiCoCAAAAPhUGeykUVLCX/\n",
-       "pOwUqJIfu08nAdyuyT3V0X9aB5hTYAXHqUZ5kd2LNXJb2adR4LNJ9w3h7qVcjc62tsDFZKL5d/XY\n",
-       "AFibQhxAOqORMA9N/Bj59AAEe7dNYoKAAzu3lQJ/N4hM7VAEdZ86ztWONNUOWfnYIBwGqBYmsxjq\n",
-       "mwOSzVVqhH40ikYGZk6iQQ3U4allY+ie+JzoGckKy5Dkam4yRUSVExBBZTP0KHh0X8KiBxVlTOW0\n",
-       "mo99RWbyBEKj8wI7wJvRPVJxS1awILH/NpZqXfjcGvUhzgqDT2DqFFB8HloCBOWmNo+mmG4OE2Hs\n",
-       "cS7Qi4vwkS2OAdsggUFNBIT0SPAdwG4hGxx3Y+8VlsWG8G9rooWLOAQBlQ28xRx0fnbGycFqLrV/\n",
-       "9o9kFDi2M0A6AJZJMJfUrRlpgGz02nwsOfE644VjCf9wBP7pe4JQemc422rqyfHZvweJvSxkRD//\n",
-       "0VDBvfmRmB4bPplwAcMT+TB6Z1CKRtio+ASSCkh33/homRmsY8ArvdxNiFgiwU7qrFtoYW/E8BGO\n",
-       "BQS9asi0nRO/9/H2HRvcoyq677QOK0/xeq425nj8iVTi4Vv6xKM21V55BhH5UTLYZD7H59JSja6L\n",
-       "3OikP2jgBSmyLBphyXH5Bts1SYXYyjurxdAjMm5ZlLY5z3ShT220mzjQTX9bKjIceP8D7nKn6AZv\n",
-       "d8QfwBIgOp3zdE/m4xICmafnOh8JUd0biQ7yQKzQODql6w7jJ3C48XHMz/UzApm2u3zz0HfWPdG3\n",
-       "3wcaEeqcyZbeBE2edFSoiQDE5TPosFpgehym3ligmu/0Oi0XKdMiYi1iBP8zsbkUh5hMzGs0xyfJ\n",
-       "UnZz+YwJ5A+afe9ezCWrHJBku95VnvAt3YR4KvjYqd8BlTMViJKI4/j0FKwujY66gf41fzsgXfDk\n",
-       "9fjmCR/FHjgccHCX4/VeZmLKz21AdYm+4yMevIvW14FH/zou315OsfRvMDrDkmrlRxtaFikCj75S\n",
-       "Y754Ti2zoUBJpUGCirgxInSYys+yDLvdo+x2dwrLiKppzSixtS9SKNQ6QPJiAS0Dh5PLaLZ1hUmQ\n",
-       "kclnWfGyd6m6EioDQKFDuxJJVgjtuCftVgVN0rWcXqXaOdlPC+U1wkIv1PF+qYp6ycnrVr+Vsla+\n",
-       "JlRjJYupQY8AMwnFc53iBbY8A6mt5WJXfyR8coZGlfC25JZ74ovQ4RMUKhbCIyNeINsPebGtkYLp\n",
-       "gqdoTEmjbRte6YQkCPBkjyvAQg9ase+/YhRSbAC/Qqm1OZ/UYASc0BZoM5Y5SPovzQ55ilYJURmF\n",
-       "jpcZ67aE6DtNde/GPQYujVcEnSDALWg5C9yJ23CLY7yNkn3+KTbSeaKlXL78NdUC5X2UUyCBZl5W\n",
-       "hEZoabMNrko3XUu79Q3wRjM5gbNHzpNHh4KJV/MCkg45lNV3DCMUB8ZwEsR7j1Zm8GOhNF4odFwn\n",
-       "L/zXRUIcb+k3deVdN1jTQTLQ2LpLpSC0HbuqdN0nPwQXpiqxvNElPnkRZOPMCwF0JEdJGDICbT/1\n",
-       "plf04Pwibb0vouCyn/hxFAl2yoD3SmcX1KCEVdjUcP0v1hCVBpCjTUBQujexOFOWd73DiVFARGMh\n",
-       "EfmeXmrcIEoUV6A9LzPc0Qp2+5CXHddG+Mq6jOXNG+AScg5ueAVaJIfmkTGozWjgnzNeXiLp7DHR\n",
-       "D4nfvDJ/YfwbXLB+VGYjXDw4mQahXzxSdILu4olE+RcyY4W20yNOAGZmC4x2urdQsspAV/tyvNC4\n",
-       "rzA4zGqaOlj78ZSwQjDlg7l66/79eO2DR890wFe6QYpZvq40pawk+ZKzjThv2MwNxWYZHO9/rYdC\n",
-       "mJCGeehwteYjuatuT2x4rjzAmswORoTAyHNdQcLpPseb9Cu7Q+dX3W12n5D8oT2dgw5+1jsXGV//\n",
-       "gS1t2v1HdOw+Jg9/1fkVciV4xmy1eWluyenPD3Vd8mAkfqlOuvmzrFDbobF60WOj+B+rmF27lX2z\n",
-       "51MDKzLrqSKrZEDnWkyLkIV/Opgc41Yu+Q3nk3l3VtNYzS2+mf9w2D5NiFgF/WxQxkcDyKzmi1QZ\n",
-       "mq6AcfkRFA5c5Uk94IaF46ni92/OTVCqN/izRA+XawYTNiS9YgJe+81IhBydiQ63XYe+JmtDz5S9\n",
-       "kYdbacApd1q5KETyRPZaNpK3bJGSrhhWWrEVKYO/NM3qlq5XY+iD74hrEnCsVicMXmA4V/GIGn6p\n",
-       "mXme+zj7fKOB/C27HqdZ1wtFVazovaedhb+/PxmjTPoQqwc9I90niXNJvVwJHJxbUL9qRMubsjvR\n",
-       "tWz2q7JilC9UE2J7rYYZrlJZtJJu6DaVawHdZN2A9dbmEF6U1Dp1gMsXgEyqfMtA6KmQkVaK86pb\n",
-       "Eu5OX9b+qiMybZBUHaFSy+4QVAhs7oT19uQR1aQg50jwPFkaK3prpgW4AP7SLmiZxmGvIMB1kScH\n",
-       "YufN2ibPm28Qattf5uahPjv9+nqw2Pd/OspEl/82Z/4qJBORbpJPrkImxplyu62APv1vbfQhzqe+\n",
-       "X7/+gWIrnozv154Jxk4CQC23GXA1BzUYssIcEL4YK61moQNTdJjmIP4XIUITms6k8nlhNA16vHdS\n",
-       "8s97ZWUbyfLcMGsr0GK1t9sSGX1Rtqo+wlSPe5yLqhtGShcTYjJtxr9W3luGZnp6B9CtZbqRyV2H\n",
-       "hRi2lnGupYW+PTrs/g+2DzUjhKdGtSbXL+5lizY0qeDCDE9a7ykAXDhj3iI1Xrm1yQtL45jSvRVi\n",
-       "WeAKKhbvwJrtry7WugEj+lt6BrTW8VL6ASkMSTarqiwz3LnN36JWJ7GSe8I9yJVu7dzPgOtemmYZ\n",
-       "J3iFzVe7vslLBbF/W/SU5mNXBMD55TtMdnhtZMk8g+j23ZQsjjqxm3luxzY44ClMRr4UlyjA/9Dl\n",
-       "CGmfA3tLShVCS9oiXmjKnxW2geNDPgC3gPTckK2FP7NwMA/r1QgForYjToOnuhHfyJsA7m+WouRg\n",
-       "M4fQBuSsWE4K+Yttzckm4N4iXCPSnTDcWeswvF1gL5qizThSXulCNdQZqlpfFAuH7FhmK4dWE25F\n",
-       "RPpi9Q+yp+rbFZ3x9QVs2zdIBtBx4Z9ByP55WeaOykKpNlgpH8scIKx4zkrSDzLOMnDhBFIBBGok\n",
-       "f7LKM8QyJQc57HJ8bGXOw6h9x2Bo4dIhly+M9tmeX0jJr4j2Vmu/TO/3++wkl+iv8J8/M3HrTOdJ\n",
-       "74xidX0IL8rDd2oC7P//MrPQCm31OcY9avBsO4KKfpxiH4ZeT8uSztVW0/GK/sT3cdXaOt8Tzt6O\n",
-       "8Q911CLIz5DA1ZgfjauUPyuGhRWjUWbIFYSInAlcaAiwde0mSIMZhTv2Y7yHmUBeePg6uyT+RtZD\n",
-       "fs6bNIM0RE0BJ0XUOYee3+fkhtu29kQnQmr8jLHx3XM1zeJHDI1WB5wztYtIWrb9su/sMjPOFx/1\n",
-       "gy0vSo94wIZcsoK/UBZKw5SAfQMsZabjpjJfRns/AYHb1vnYvz9zQRe7a9+DLLNNqYNivENVaWDn\n",
-       "dOHfCX/N6yp1eDdr4lEt6iWNSMs9QRoPR9Fhn//ygoM7YqzfpRdwiF+/asqG8DxQ6TW6iw18jdnO\n",
-       "4KOy1bREBVq2q9FR5NLRwC54+da5jv1bqrp5AwNrnhGoqBFf2/q+LxNdIBKJWxGkyYX0TG+IMmd+\n",
-       "5uGrNrEjnaIqyGTrek4s2cb0h7Q+LPyOkAsW7yl/cL2C1tvuF0tv3fjixf+0q9/qUZ7EgSrB1OQN\n",
-       "o0vEImBb2G5hs2snUT7XmKN7uNvQH0A+kCyBUJ4SMpge0ivASA0wvW2yIGvYK0rfLNJK6NMKMWNT\n",
-       "2q3QsS11aAlKwps4M4QmxNGuXE4Z7LoicTXttJ+f3+6RmJtTlCsLjUK8i/UMAMKgjKxcqaHngX/W\n",
-       "VjPqg50bSXpl7G5GXW8whPccMxfmesQXKDKgV1Zw6g8gAIsYk6Exgi8J+hHhxKEvHIyvxzYFz15O\n",
-       "Z0+wLrXiePDdmDygxz8PnnOQilW807slqogNK0z7yIeEnopEPWAyKJBNj0ulycaiyhOvDuLPBtW7\n",
-       "7jy2s3nE1kt67k1oOR5ttX/eyDfAz+dYB9dkMcQFNNa55Yt8yKOcVOW8lvBRYfnWOWMN08BuVGyX\n",
-       "ukZGa++u65mla1G/KzprznBD6Y8UImAfbAbwE4aX3HxIQplmjst2ox/6qCmKpS+t1/+/96vvKIoI\n",
-       "HBOAm/yrnGEnfI1yYqz/MqCXSIQNnMYXo6zsQ6RgOUGI6BCwrVylHUhqLuDqlK8AYnGemZO3/pqT\n",
-       "sEneSD18Uceu4RKUE9mWTXk6iR6MuLTI0oSBqrj7K4EXNQSG8GwxyXu7k82nyOjE5vlVnASXo4fx\n",
-       "+0UanWN2hnIM5lw+LR5Bran9asA2+RvY7pLHRvZetvjKvPgqyD7xBq81JGNtLlkEBFF8kRDz2q8U\n",
-       "xy61EPTjVG2PPtXrbjiWwRtUhAWpsvkD+Hn4Jhqv/1jaM8rShjvgiNxtYhIUVuUlT8QO6XzCHTTr\n",
-       "pHYVBojKTevXRZu2grU8M2SOnfkncSnHLTwlqCBR+9Rrc4gfKbcaZqvVA/+YaVuVZ7PTCv6aPK3s\n",
-       "Yphzr0fZ6u7d/Rx2zyDISeY/ATqNVlE0QqxShWY5FWBFbzhpIFW0IysRgcX6qQjlM4FL1A65McE7\n",
-       "W1oTBcGVEpBquDh3PgDv/u6AQDnVi9UKbjFyzrZacYrcRpdhKiWyFQiqcm17AZuBZOy1BMwKPVOx\n",
-       "eQWjN/payP/Y3pXQtnFOC3rg6Uj2RZhTqxpU5yV0bhulwSjrcH7wKXSA19I5wLmpxAtLKARhN4N3\n",
-       "9zkCmjsp++sdL8CnNJOVxGYza95ehRcrCpTYv4FrCrw5sEFJYJJ/a0dFccJA5QkaOyRo+igYRr8L\n",
-       "PQfoIpizhd6rxN/Lo3Sem11geQ8KJxPcA8t7etlnpcMZV75vA9Kh0vnfoBh0CZ5QOQiaLCJlnMQ0\n",
-       "iH7OcyhsqNCWPJsYJx423KJ747QKrrN+dch66RtezbDhxTUJ09LBfnYe1Ad0Q3Pu0hTNJvUIGSdV\n",
-       "+eEGsPRCsXi2CjlyXxPFKqD65eKCaxptmEKER14aljC8WI3ffMz9dORaoK1F5CIaE5KVE+xcMQU4\n",
-       "kM8qmzlwhDyZu8bzcZCKZNtXekQS8XXX/nnxrQdyKugaYTSaAxKoYft3FX5+uh4FeNgUfoMvJGym\n",
-       "eBqnG8q+HbtP1R13uj1g3JteqJu/zPChL5PTCWuM5FVFSDv0Ry39ol+oWefVBdAfVYc4t4pgqHtW\n",
-       "WvuGvRmtOTjdG9YjzPgSmRj+gwu2ibOpSRa2PzEOpPUAAAuXAZ7pdELf5I/yLbEJ2Q9Q4FbU0q30\n",
-       "wFbH0Ns3fx6TKEeZLZ/cVKOwKk6nXGCRktlQywxCkkWmYo4XWAq1K7T+MF0C+pJY14GpIt3YBt0j\n",
-       "ut0rEvrhtYLNa6yqz2W7AaGbqnLjQJADJR1zu/Za4QgVmLxektsh8vX0XlewthrM3aSlCYyEOXph\n",
-       "sZkjlRcARmCZkqyVEKHCbX5IrhQPqfwgIMXOdzHIfSb8SDx02v2bEtvCmN4ukNhtxTQMAnZ/DjG0\n",
-       "avht1FLxpjzJk1tqM6ACEdpxZshZVUpbwDvpYQ++OdhF2/uuPlycnUPhUeF3Q2qZvPbHc3YGnHYQ\n",
-       "euR7ry+mSJBMCuWhc9281tRYCpD29fpPJqKxMCwblG8q/FA3AYCBiH8LFjw+8RSZ7J3Ds5cbsWLY\n",
-       "VTHF4lrwZoV1aug9vvZ+r/9LEyR1mhVvCBYPKy9tCnGRsZtDrPGckInOOClHI4NH3c7ZfM2rgW1u\n",
-       "D8jHrXI6hOArilZBg5WAPRDxXJL2EMmaovPVdvVth0POGV5R77kYfK+muLdsjVpYOFXobQFcE4C3\n",
-       "E7sj9VaUMb3nNbiLVIb8cgBlmjjov1pxMKvaNhzGkl3PjPfPC/v/J2OFGJ0jBmEq3CmndroHBNYU\n",
-       "tzYYjL67fIe4plWHt6/0HDAGWNKld0FxKiAlXKYRJ1LR2TB7NK3N4E8up8/gYydRzVXaYYL1x4ko\n",
-       "aZ83pqAJVlTcXVLYqJqnHSIPMPGfP+0zS/jDn/hvNfOrEt8Qpn47+DeuZOZewB96E/pKcBYWVJOg\n",
-       "vkJzh+VXs3rOZtd0vMBlhDBnhY9q+4/kHrhwREKN3sMl80MveAzqgSti5XoYlKjFKLt/sjerq0Xm\n",
-       "0kG/g222rM0lWgEmRlgjceYEMStkH0WyOyBrV2GBB3wbqoALPWMwZfulrYXb2vGmmkCkOut28/xD\n",
-       "escgmwUiofBkxMz/yRiNa67LbDgiv9J9Z7aC0fT4yD9z8Ja4/NLJ4zPedIWoi5B+Dq62MGFsPGOT\n",
-       "QldVVs5nbbzHcbV7j+3gR3/7otFmJyJFfeaANXvLPhRm2qdFe9Akbp5XkADkCJpbLOjfHUNuKXsl\n",
-       "aIRP0oquUX+cBjnO5384BsOuOXL/dsMRkowoDeZK/n1KeAz5IqFdVS2n4/5KMDAACUaoUnZvv1ZJ\n",
-       "0RUoDHe9liPZcpVlHRX+b1kivJ7EEWRd/g+ln69At45A9pbj8rwk8sgwlSYD7NyJkCy2DtS96ld3\n",
-       "kxfM2sIj38zYn4t8XQhRoy6VSZUbXx3+G+2cfCIoinbqE4G8jBIEA29/8ArFaXipjJ/CmHuN+4Vp\n",
-       "A6wQA5c36VFe8H2uQKJEgZFpl5/LOVRMjYwf+2FXgKY3RUMLT5PWEleDCRxaimIT8Zjr0K2nJZyI\n",
-       "NN+/MExm8nDhPGReYGH7V789Jaubi/fy8zzQtZi5cVf2XMt5yyMcwkFvBFoDhpYNHKSxxmhdUAYf\n",
-       "+4A44pTVBcbFehvYXrP/FB4WWAse4SBGljETaWRXOFnn2PRyz0UENN00Bn8g10N/BARAAxZSP06k\n",
-       "Xbz1lHAx087qaVhaJkgSgQFYf3TpiKh1C87vydnO84ekHRboDJcvY/ZAR6S6tRGtEWseFAxuQBE4\n",
-       "QoFu+yMlnaI45+/glZXQbz+kOHKWBBmkWhMEiPADU/inoD/mMBEtRWD5z2XffQc9raXvKQW6vpBi\n",
-       "kj0sTS46es5+X0Eu6yvrmvCFZ/A8s/W9mkN0q4eBZNajbE/isDP3GANt3Bhn4B3COdIKji/ssuTK\n",
-       "1/Eonu2yjcmfT+Mt7NUmS0yfbFqqiIr3MJ0q8oMifS8VbnB9ddQ24VcV93tAMmil6GRSzNbjGQ3L\n",
-       "zLqPV3ftMHXLRlNkrDSGeME0/jG7etxc+dDuaPzwiitQJmwtYuy7dJWGLIt70jAoGMnpksfQPCoV\n",
-       "XNMuS0xO6YyAorku6/lJmAOvf3aU+SppuMwMFpmYV3OurWHfNLLEWJbyhnRl4e3rwGF6vOmwAWoZ\n",
-       "ts3wUzrByL7vibL6fmv8AHgMEN/ng5m52vIWh16HcvBguNxs7DCdwlTbDFkkUAXyHW4X3XV4dk/a\n",
-       "qT/IG7xyKVVDznoYX9Ys47z02Oa3a0ZCuaRAAvFQUcFqlR49JszQprvxPSb6y0Onk/ZT+RFpEbQ2\n",
-       "71Wm4BNTZcUFwKqUJ0mNhVdO1xy6c8pXbeTVUBX16CH/uP7TrzlAkYrDZv486rg825Gtx6suGasC\n",
-       "pFaBmBx3JHUXrjomIRBnNLVdYmCJGZ6rrBIzOLPCiH+72vODnJUm/Mo/Ou385+lhSnlNgIv/Iewq\n",
-       "0iuyETc+7OIiH1URp8Imk+uRj+8J0VzWSlQr7I0cJ3G7qL4wPcBqjGjLrj5pAoLKCRusOZKN/r35\n",
-       "aACkDiAOhBGShWOoUTezycMAfKlD3rbCqbyKmGDS8sQXl8ONNFA8d5v0R1e8K6M/ZG6FXEl3qv1p\n",
-       "YJwM4hzqcbgmTkj0igtzyClELM5B24KNgS+tTO4ld4sPBqkijumqpfjqOpv5zfcWgLNZapmhmTpL\n",
-       "wCnTTBbzlpkbIoybBwJDykxQpHbDXA+vgsfvfna813pWLmMeCLHFq/mlFUSr6pTZjOTe4BR5ZbOh\n",
-       "FpJ3QcykwRaMiH/edwpx9ykHOoHpuPVIvkHxY3NxTck657u7t3ydHpDQijFZWrJn4WA2HFE4xBoC\n",
-       "OjDElP6VGTYVs6J1NDNZ2YDysOBqse1+WToM4RXi5pZ8pK/twgijeufAYgXyi6UY7BWNAue1p997\n",
-       "Gttpbvpc4U7LyN2YtYnSCseTuSno0jJa1CBE0gQRwVUT/hESdplKPytqjb2DO88nrkgrVZ8gi8Af\n",
-       "oxBgIHWR4fNZzwBCN9mmPYEML6bt7Lt9eI1jPHi91C+hGfqTa2RYRNJsH67zJQ/yuMePuUMztqth\n",
-       "2WAk7zAH/nH2xma7hXfPZ4e/uYmbJrnz5xtUpAIZk0uZyL8T2HkB+3mIp8H6AcTWwcgi3bTwZnMM\n",
-       "taCWaR93HghJRKqzRB0H8bkDHbJ7SVv5xKziFJQGrJPEOVPvCHvURpReof0ZBu3hmzukhMQID8ai\n",
-       "UKjeQRqa4qjw+RvXaH1vdc42AAnUOS/6s1ULmi9qjanDeivDUIxI15huCFIIJ/mxfOjxj0lGBSSz\n",
-       "qHLduiqlyT9QN+A3M/JlbU95wVnvQdgU0ssWJ6G2O059IqaHRQNtiuFTeZy9KWGg/qDHRiUkpVEP\n",
-       "VoVjbTsolNUKRTDIjGz50rtnAgZyWbtRVEDrUxY/XbshS06NY7JdxyN6Yn5YJuiew9lcwgtHvuL/\n",
-       "+8oVGrRpTx8ge5xFX6znnVrunOprJVQgdBRHEY3Hst0XTckb4/7TPMUoCenLySOA0GAS4Je6XHi+\n",
-       "136yz9ccUmYmTu7Xwo+pNoxAXr15Vki3MxRCaH6J7ckHogDBscNDbpN9TK3wgGB0TBfsFhck1Avi\n",
-       "1GZkk3a13ROZLlKVwLYn9Xh7ZG1a2GxrLS43+jCWs0bKhvsjebpd6zzy9Jxrj34rVVzt0oFuuf/I\n",
-       "BxI+1AGpadwRo2vogVphSrhkyVDRvJLXnMC59DVRHF1GMqkBQN51DfgOvkfVfq9ljfnDT/QzF8NW\n",
-       "7IgIA9HnOCzOnvnxIZL/+72r6ZyYAtRHYrWJH8PBemAw9BZy4jujsbFlwSBipSs4zt3X7dxSCKfV\n",
-       "4ZXm4wkxpsI2IIAtLNiKXsjYQituBT9PGRQpyXj73HCn/sU9XCChTsiX3WGfouB1kxEXEzqE4PPU\n",
-       "wPLTbjJaTtoG8jurEvlyI6rJmna9JzVpIdctsjche618Q05M0Zgnqe2xsEJ1MQIsiTDecH/pYmA/\n",
-       "teSmo2dsdIbI7gAbz3ZwVLRSeJQbQrOevsiartIV39sVtoCYBs7LK6E05KRGF/zPSyfprbaeb8K4\n",
-       "l0S6IdtSIGropPjdkeV+CYwnchi9KBKrnBYBF3XQAGWyCTqopQmnAAAJlgGe62pC36yBJnGGdftg\n",
-       "C/mZBpQdk61GEsU8gO/xtaoGQG90d6dJl3Dbqq/9u08xJ8+n6m5jDFUXuu/voluuGIhUcF//ErMt\n",
-       "GBmK68HQ9snmHbfknvWWCQTI5WkXJE8Mo5f2puPdglXtnGjV0ZvgTgizgEv5ZeA2mMUAxRj42142\n",
-       "lAUkiXRYocLvTlx5Wsf0Zr2hlNWkkjmT14X+iuzuQUlyKkkLnqgrtSlMvujgqtyF0byZ1XHRLWdv\n",
-       "5kn+fbE/pXAgXBsJd/rqKyB/j9C10TE4o+5sintN9+Zb3Fb5h4CP3lg9rJxB3dxfrfL0uS5SrRCH\n",
-       "wA92uKwD3DES/dLvjeB9XHUJ80FwMhE89D7LG5SKLxtYyOjUj970oDpM4lXa7OS5eP9MXOex/xFc\n",
-       "ScetaB5uhm7oXAC/qVMgLeEtsblv5va+RzP3kLzkp2zOlgdtjPuLaUBZXuLsWmFgyGgMcVz3jPAa\n",
-       "RSsVq5dmdIKjnaF9DO+ND0qYzHs2CDC8ySzh/l0+fcKWikWXElYdSaHGurnMckYSDEynDz88YCEB\n",
-       "4s6YgxzebfPLkM1tZJoLDVpFbluZEuYRy+ePa1FbDbHB51BI0saF3jvW9QU3lKoE4PGv+W9qAFq+\n",
-       "E8pQ40s46Ftv7wQ2Yshf/Q6hfmXllxU6korcUg15v6+na9x3Seg5Wz4vebegYrEnHuWx0/2sSQVy\n",
-       "xJtJciDUEi9f4RjVSYjQDfp0c0HlUPERL8ZJyLRgIt8hEk0MeWWs2TF9DcnDG9ZN5TKR9f+jCiKG\n",
-       "ndrE9JJMnUwCvEgXcS5RszYnCUns1QT8gTBdfld+RSiIte9+ASh+qUPunuopCORDF8Ga5JA3Dq9i\n",
-       "KMK9HIXQjEBjOd3dcbcUQA3RjKEgZWzHl/W6soXlSrqdsQzivyEczGXky7e7S17BXYG3LGZAFYNN\n",
-       "gqGg1dYPTQ1pCvwrlIumUBllBcRSbdkRk0sEverWaQccle2ZY1lUV9uzpdVYGAyqITXxvFaBt/fN\n",
-       "maA3tAi6zr4kYa7TJm6COIGB+osusoUVSN5QldZFTcBkQAZkfdoiJKL7Tlat8LsChxd85QlvFNXU\n",
-       "mFBKCM4jvFykaSdpyO+DssYYix2NmqWLSg6zM08OckyW69y+nPUEjWSDhJDGrdAOwZFBZjCXxSle\n",
-       "HdRGzYg6NbTLdNueqt5d6BOoOjlMuN4z0tRCT4jCO7cq4dRv3f6jtoy8f0xKrrzQUXiZlUo82Y9P\n",
-       "pY8H2NKtGK19Po7ZSdlMYft+Lvq9GlJt+whEO+44do8B3xJtchDZTo7S7T1SfHe1yYKlxI3QZECW\n",
-       "Cgu0yvndHGSPbVtmtTE/THUJZI8i/ZR98CNGsH2I6HdpEK9pXMWp5r4n/AkQ9Ir+Lpbloz3mPLnR\n",
-       "wU32VdIC6UJ4K+p7gU00mHfs68FdDh7jazfr64zpTu6CF+ivbcZkmWJDRBZWipt6LV5itHtkvH2R\n",
-       "ex87rI+NrAEX1x8LJPXDms1J4W+zMqbrSzNdGJnWEj4r0spW0TH7XhV/Z2vhVH0BBKBonHdwNnkS\n",
-       "L1l77Ffe6agTl8mBT4JFq2nqAiRjEjmZzGxffKAJxR0x/4Odu8CPm8iI+AZZGZdMKXjSjV3O2ayu\n",
-       "EKKgoLgop5GzWaldiG98yynlVahdtorQxzcn8tOu42blQF1PMNQv2KE+0cJkM7GVEzUdcSECNxFM\n",
-       "YfzRnhjD8oFHT6h7K8BIAN3inBFeHOqe4mPwv2VlBeGxfsAVCTMlpO1cqYEfk/wwXsZZCNS6K+A8\n",
-       "VCvbsc26s+4JzJSDBh/EPx9sbLgq5oYdN22nIfy2QOnSEXvqvg96n27Xs9o+ioMMRP0kogJ1p4/U\n",
-       "ni+0HXdGuhuYCqq3b/B/wLIunz4kHV4jerzdCUutCViPvvdhiN8PtnAJMkaq6ZUs1tLUgGvmh9nc\n",
-       "PHE9OhAzjS6uQ6bjT+3SNYjyIQLXfgbnGTskqMPEb7ECd5RcreYEKbvLhEMsAVVj8l8Wyrl4KE3X\n",
-       "0929g0213sQejmp6YD6uNBMzMnaQ38i//j5lo+s8oQjyU/Mybau2bgroh8sd8vmZk0/zQ1qGR38J\n",
-       "6UUZVjHJgBU0b3stFg2eTTlLF/BoscDCXfKdIT7BRfdrvAFU31ss/5wSD96cExGYD46CJz0UgF+o\n",
-       "2ZeEOHCv3bbKPkrklX/tDvK7poTk36dToo/aLukDn2v+UMMKIwt31Ob8X0hQpX8NyYRdQz34BQbV\n",
-       "VLwgrFMV6L9lCwRB9G1yCvLccms1mc4CW3v+aPTmNrg7Ow5Gv+/bG9At1zjCW1g+i1N2BcAnhSbZ\n",
-       "+w75XEeYFPknCWwCNFR0amFzWDhujH9vm5DH7XBkX2SY2o1bfQuF6RChFuQK9WEmyIzDE+MlAA5G\n",
-       "mQJLz7Jwp9vXqxxH87DQZsPlSYspjy3gS9htH4QHzp0y2o3bmGx2jMUx2mhFClYRv01+iUYYm2ID\n",
-       "cGfR1rZRW8LRQAAEiJkkXVjsOlmSURt9aotK7c4bcyl/hNqJxhapBzaH5knBHzMi11RoM4C3oT9V\n",
-       "YnHgRI3HluSXP1/yFJT06k019OJoCE3dyDJSjeYaIXEj9GQYLjgREmN0gSRhuQoxK/qBvUpVq+gA\n",
-       "AuehCXbbfvBXU7bj/PkVZwrESj7MayeDAXHeCrMAwyMl9alSx/HkIA2tWd6Vj8ACN3V8Qur31WbR\n",
-       "qXd6lY1RDLJRB2W/AjpqR98q9WD/BsDnaWbwWETjzD9R02CV8YrzR0Os4l8WIkZTAoP+7kRcZ1+s\n",
-       "ndJe3mtMCCIX+vRzNxjjV04SSlrLvs0QBw6jHBhPjTk/np0Xz5pPPcGEAOH8XJoNavBWKTwN+E7i\n",
-       "oj3HDMFCJg8sk/FDPB/02YwuG9y4e32qm5VNo8A4nEteQMNfchF4SemGN9n/jM4GMFVCXr7cMrlt\n",
-       "ol0XhqrN+udgAgxxtZB9zIMAUdaZd4T2LRQG5v7uZ73pgC9gHpab1vezafPMFQBDCBBGyAsULxo5\n",
-       "b3OK1OyrHyM7LrUa23Z79FR1IevWXeaDlGxkPK9UiLm7c3pam+Axp7LGCyinOnJxcTdnWZC3EJFQ\n",
-       "NFZgmBvHXZvN4OVu8sKf6Ddvken2jeRfDl3npw7Vcg5q0FYXEb2meSrxpBiI5J6nlYXFXp9cql4b\n",
-       "d19NEtPY/ztU9ROcDXAhKBpM2s4O/F1X3HioMKSMpEWstt7Ih3V3eL10FPOdbSX9OcBgAAADA2ei\n",
-       "QTgiSvjSidtNqDfjJxHxW51Zrk2pR/ZrTsymFizpkUjRnUyZlHIYl+WfqcgEXAAAGL1BmvBJqEFs\n",
-       "mUwP/3Q2zRemlG6jMILc5oGOE4u/iGlISX2/JLrYCKn6MKPFIfzAq8xL73sdegW2/saXuz4rYzn8\n",
-       "jXvyAhtqxx1FOd/Q/9tYjyslg1PRmnKjlSnUmR61H5Tmo10ck44HfHKjuLwrdjakCdSkn4qG0W/f\n",
-       "o6hkNDzaUAAm9pOq98juWQCi7cwBlFUGLymctWEc6Om5NWQbD0+kMIT+B+EP7ZRoaGnEzVzSljwc\n",
-       "ihFxcqDncQ3cCp33V/xJxojxObSaXwh3b+Y7ofEX6vzLGAuF5mySa68icPJs9+PdrgfAhC+yjFbQ\n",
-       "IE0SJiMPBno2tki0JtsiFKVPrLrBgv6DW8nTgsphpi4fjur2AR87FyFM0Y3p4J1DRlriwg+jsEP1\n",
-       "OqH2Mfb6sXwVZxCiWV5L6vrRArFTSjIEaNAwp131A6sSKuQzNBbriHb6iFY1YHDfYM/aToRC3ggj\n",
-       "p8XNmFLlWzcTS37uc5YGjBwxmIAmfFFP0KRZAKrT7J39Gki2oLv9GVHJ6nxYpw1evmJFDK+V4YXK\n",
-       "1VNShchk1y9hHXxI8qph2Lyo+0rojUZuTeG76TyBsufkqsr7sTDdmT6ygoGxRRSmPC9yXeRiHLdt\n",
-       "LxeuMD+J5hCd/gE/3YL8DKORDTIOWIuaiVHcm11X72iw6k0pbOqtaGP0K9N6xLDeM/M1C7zxS0+F\n",
-       "zKLDv7PvvOH/xTEFhY53+288QjhbXaGpDPHxHGOIFBC5563XHM9pkXZZ4GJLFJfbuRfqFbShKjL6\n",
-       "avS44YyZOsUy/KTze+w9DaSlT55PQOFk0R5tdp8jm+Vsqqt4A16GLdUxTchW3ur1qyp/c7HzI7Iq\n",
-       "rW+RfLki7FvRKsJtue2qnrrRYRLEU/gECyK4Rjg6UsaUwQWMzYxwIK14YPia7miVyw5PigscUbry\n",
-       "2+Gpe32jRgFCfjwpruvKSBjjlAPWr0tIm/WbeCVyph1ZNtv67xEqOl5rL/Hnv09J4GYFQH87xx+F\n",
-       "0EGDNzcboFQ94eXbVdhk7LtSy8DxSo3HG1pAITGxehCsSRjF0PnhOa0mS1di6SQEg/CWxn8nrioJ\n",
-       "9R6hMuDR/UxevDI7Wqn7Q2t4W9APGa1IbHAx78H3mM+9CK+M6Huv17dYTZRcKL/WKOI7Vzort3fl\n",
-       "e0kIW4lC2xtY3eIn0r8r7bMRJvUC0gBEqW2U1ZPRoSsXLAe5TEZJPRd90fwl2fbYdCXBIOevipUW\n",
-       "svme37q1ZM5ggKJRxLpaT+DGTM9rVRTIqCMdYd/AzleKCxQyAan5xxbv9J/K/c+HllYvi/d4OCFk\n",
-       "bDVY+9ulWq2s07K78UNIph8EQcaKFA4XRKy9uy5CSrQHH7VfCrSy9yvRUYq85grbOnWwFuREAUzU\n",
-       "FDGBeLQ5zY8pCPlDd71aekmjCwWgLMVLuXeAfAiEVDIrdnNs0v+zwhJbM455K3ife4X20K0wGUkx\n",
-       "OLhRGgTtn+IpcsD2yiw8dma7IDgnVFxBETMjzzEVsd+Ho3T/zBG5JIbadkbS9y2eV2MxamAHG3hd\n",
-       "OV/64HUNq3u0ytgi+LHb3Z7GNFKjMiPxr53LJERAO2OLYuKoBPon6Tkzy1EFUf8lawjx2xfBdND9\n",
-       "tYGPh4A2qIZY8UG7OF/TXp83ytWBSMvy4zCLInB7bndUY5IGA0t7U6Y9irmsExWrq4RI8VMS8B3j\n",
-       "fVicu8fTXB1qWa3CgAIZ1JNB62BF+rHldzzBBrl5piTMrrwxczX6Qss4J9tgtQndMIekQ+jpAVaD\n",
-       "+jNLUc+ovmJFFF5cpTupRk7I2GjcikosqFALigJr/U5IV7+NpWhraT/KC5weRtMUHh9C9KkI2SRz\n",
-       "tWASTZkV4ldAWgyI/EaF/lxJ+rtZRvhb5eaP5JJaVWEz4rM8a/ui8jiRFRmTH9lOFOr6eJDOUr7A\n",
-       "KJPj4FkMSfCuIc1SSsd5VdFgVmgGcE9D6tSNBjr/VPK4UajyRK4QoRw/Bq7b65WljyFY8VkLEUAB\n",
-       "oYKydfMo/ie7pBytZ+TYiWsy0ruDkIzMXbqal3xlZaKb02dtypZQFtlTZI7EYgGAMOyRZA8WUBeL\n",
-       "G/J+4dlIDEmEC0jR4D3lrEHYW9oQub7Kb92XiLJOCRpVzr/kUKtG5Ey19ir1WLspS3YIc2oduxjH\n",
-       "ryDJcEE1JyNFpL7EW7K437bENXIn2Xw+RCd7UdxkWTatlPYxspUrN9u2I5o/h1HFWoPx+6bF0npl\n",
-       "wviIeAGQKpntH94AE6PQM8ZDR5KF99fkTZkeLWICpWisZ2Z85TKSYBFHeQMHSpgfzuF2K77qL/r9\n",
-       "7PSJVjLxp2PuM9VlY51n6luK3hE6GFQwDTq6dER3DHOQQ1WwVypz60yxLpLxwz1jrOZcAGJB3Ino\n",
-       "lcaXk6IzcX5noSRHVUR4LjO2txJPp3+wiM1bcM+yE4VsupzUhx2ZpIofCAqb4I38pZyGHD4snAbn\n",
-       "dQnd70hZZk17UkR++ubVSm7v5Xy7xhLsMpRJY3ynVasxd6w5cTu4rw2u9QOlFMmyA5hBLjQgyMs9\n",
-       "CGfnNVXYkhRmE79eCsodl2gGkq8FG4pnpQ+A7N9PMxMfRn3TqjifTEhhixb+2FgyNiCoKc20QtKN\n",
-       "tWllY8fW/3Hl0AKpAmFtnLEnCcLOP07rjeFouES8t19cSEUxC1zcp0CI079r0EFt3MtbrLXXjbDe\n",
-       "XsZ9PkcMsufN9U0cbdUW9gJErJtawamjjs9UkJs77gEXujJOxqKjz5TaupXKbBjqpH/MPoCtDBfq\n",
-       "q7JpTkmXmXDT56lgDLuGLCnVLkEdPHw5FTnx7yb/f2JkvOkmuC/apxHFnzuKW/gXtITYTHeLYoTd\n",
-       "mLQEQyeLqqO57rMIPHujqTTyWl1oQINgCf8PZ+FnUH0RnuXchoDl3XEnear+ZRfQGkx7n80uS1K2\n",
-       "xDtpJyOPZtmiifzlB5sQNh6+rEwA+ZVm6IcNTzw/6ENpFfULyU9jbGZe4jq6XgAgzPpiIZUCjvc+\n",
-       "AphrRLlYtLOspT3X/Dgr7wRZs0n3nt00rj8wqg3lfuGTvQ4QY14jlJqDKxVQvZMmXC3QD1Lni8/k\n",
-       "xNHh3GGZpRiKKkKyewNQUbulN2JmDflMWMoQn1fgdNzln+r6frYXyWRLwVr0x2s+saIDVnzYnPYO\n",
-       "XVtI94oOnQ/7z55jia9lRqv4bA6xfA+05+6UdIuZOxr74sV5QEzNNglo3XRtR6dW+9jbQhzYFmRQ\n",
-       "uW9Q7ApJX26pSNISbfp2L0TNTJdgTz3zZUJ9mx3D6W9Ya0cAB+bOg6TBVn91pGw/FPZ9XoPZZhNj\n",
-       "+XcI/fpyYVlRKlUE8JSixxKyau0TlA15raN3NurE/PvWazM5ZWxdLNq3B0Fd6TPaTzHPqzvnu0Q8\n",
-       "0XVqhIKuux/Y4TZfiDyoHk9A2yw6vvE4hb89fAX2RcFKfMkg8lLXiq5ltEwGge0qUfsUJE9UyH5k\n",
-       "RPEoQF2efq5JhE2Bd1mPF4oLm0gXJkxnyA5ioPb0q4pMPFVfQW7xEnUNyxVWzkRNDYEbgdYvCItq\n",
-       "uy0ficTxqBwuVNHbphZCqMqzjRo6O90Tuvqx4W1cv97kfq7Ky3nIwgzNNoAy6mW/3ZLSr8rcmN8f\n",
-       "QVKY4zvGgKpm715Fz07/Vnk2BUyY2FEB+NLziWtbIuiEmf1P1yx7jpHbSCSd76STTlANpVbaNFPF\n",
-       "LaIB9x6pVfSguLbRLrhgKJQXC3e3g/bgxsnSBCi2+VjBjlYIzIYzQ14FYKK0ueBprJ+akbABy0k5\n",
-       "FbYq434CqVPLT77P/DFCiyUxMuWkIgPfOFkFlqKqjfHgzdX4Bn96A1nR+SQXDOaV0ypx+ekWZMq6\n",
-       "yKqZ8SZ7/R6TMFOm/lNRIXb8xYL6wK7YGnI2xHb2w03sSU4VqNMXS7xtHcHRbBK68iZ5M+O7qzta\n",
-       "PXVj0Nybotk42xT/c8xWMwScV2AMb3Zz5/KL7AkRgI6i6LImVf25YXkK9Pj23h8Mcow4xODCjJ2k\n",
-       "AOAnIQxWY4olaRqdAsDyovwJtXwleQS3A2NXEikH6e/DnYuG/6BAwGXbQbNOQHNFuxEAbSnT4cjv\n",
-       "MSmJoD2KjECCCc5V3PeOAy+Xf8Ej9a2Jg3e2gnYJFCKcpJOq16TRkaTxq44tuygzW2/h7u4VDDHn\n",
-       "e3dKisOi8WVNzAuTAUH6KT8o2281xLYJ6pcvQ65LCoVe3CwYNuuSPK5OM2/crthBCRp2hX7w8nWs\n",
-       "2FsHmCFqvlw70s6nKS2BwKnnppsd7TjgxsndcTL0UHRtk2iQ0T/lWaE0WfuDUAms+iy/o1V6CaHX\n",
-       "XsNG12MABVU/mgihdzttdV0FL8uKiqm0pejaU71j37Mbf/hrw4dqFnr3G+9sHHZY+4/ZE6ub4Vez\n",
-       "NziMNiHtbFVoySJukv19V3CjhHm4iigaAEjYyOXgKT2QJCGsCbq+2HlKOcFnadnGrkpQ9RiAwOA4\n",
-       "+JAaE9b1GqFj7SNMbrIuTuLuW2wuIHUkzcEGvGfXNu5PXZ2NhvTW1VEH5M8K9Jh56UjYlAULYZsY\n",
-       "JA6rGnxIIR+R2lxXZRxdyjGkOQ31z0C+gdmAEKh9RypFDKaRtZrmobS2QwjfB3wz+gHxmEAnS86j\n",
-       "bXv4Akim0fGeFixc6kG52wTikmoPaI1Bf92NMM9HHLPbpXmZ9ZAd7BKm6eEiuxzPxyO2xUtO0QFS\n",
-       "6FVgJUvql7bHxz7PNtQtSpUBUdk3lliQudgu8JP+naBOucjsaHjZzRFTL2ARl7i/dHYHJKf39/lZ\n",
-       "SXGJ2rWz0henMlGnv2gbngSR28tVSn/mNhoSgwEcUBNtfEfgrhBftvvHyTcRXTqlyLrHa0JL6b0M\n",
-       "m7P67tOiNEQnQAHndsIEUfRrhxPrJXkgLmi2KK6BPO+lLRnKpPBbcxCZeItmlarIUrPT5QE4/NS2\n",
-       "f78Doy9TUv29TYYCkHI008zP8d4gP46w4Rz9Ah8nZoYqXjRbCNcfv+ldcWFjRkIkWJGdJjUCsXBU\n",
-       "p8CA5F3Hh8LKU+rxoOGm1GRcZ9LeNOdNDNLHeGu6mkGZ6eZdVzQaFEBsIr1FCHXcMyqcwQJJr6kW\n",
-       "oIyOK+sZgDLiqxpfHbI6i/3hbWm2fq4GSKuQ+r3CgCbnR4f2ZSwhmJMEshjI/ConJjaiq955oGQV\n",
-       "xeDFlRMoFvXUCvuVJwMZCAG5xphjSLP+sbSPwdWXZdUpdU1HcVLUmyy6yNAzSoOBirMrJYyiOM6j\n",
-       "xlvBqWmiY9YkxvRdpBSnOdGBy7fYnZAheCIebAOm+fME/KlOtb8whDElXArSaSWHW/aH+2mws60Z\n",
-       "WJ2oAh2eIXrQHe8IPATbqhABoH6YsKV/AwNJ1seM1WzZmvkF2aDqrfs/AWTRZ1vzW+V+j+/BfV0A\n",
-       "hFvMKJbQ1+Q5z6imxkRiKv0OFriUjQ+Acuy3GKvVqh9qPFX9bV0Mglj1Czqkw+2Y5t8ksd3KmNMV\n",
-       "hpEsKBwyZkQrpRf/1besB3pWKlbYM/k7oyooG8c4lvx/cTPJCnf5a3Yxhb0LYULI2ooU5UBT2DN9\n",
-       "rAVDs+4XdxcsR99S3lunCgJ0x1iMlO5XngpbqvewxJeGviEjPX3WEJ3Gc3jxa3H9JRV7pRHlh3lB\n",
-       "Uqwqbwdnw0mliHBPYSPccKTGC4D5Vqas2xxihgX4fEyaF9Peax15yXD0uvDMYDROO/CrFh2trr7n\n",
-       "yEecqElbA2bqEBy/uicocoh1eIMuw6EIWUcm/qaEyNLbKpAeBMtVzwvhnHvwh1WjBihiyySk6c20\n",
-       "ilR6m5e7zsSjIHO8e4r/L7icxyEHrA9mVyQdiovIwWsC44lRtKLDdoD6PM694mlX0ooe13IVBXFd\n",
-       "j3mrYtW8v9c5LsciQNdI1IJ9DPpBPuLgeS3zEkI1PVrob1Q3EdUN4Df7u2XYprj5eWZAOcK+e1i+\n",
-       "KhYsddLOyGpD4Y5lL8b+4+2q6ZG8hlNRHG8PZfftLvDf8Od+5znCgr62BL94Igop8jYrLSx5z/HC\n",
-       "yKt0cpMFHYHoPWE/u3tpfNWE4wjknihreGuHufS1VhNESBZu489sE+skU7vd2CejOMCT1KKd4cNr\n",
-       "gf1AmySVtA1aCuVwgB7eGfK4ol0kQCpu4HtwbrqYPpG9w6REALCBvxsEWMXBT+wv4L3w7FrMeBdt\n",
-       "W707ahkQizcgmvYSfnkZt+xPfZVM/1EbGx5C08ti96bpmcsdLFsYnpafwERw0cdqy8Z3JeL9iavv\n",
-       "8BnnMLXKnZea6sawmenqQ4HNX1mtmCpzJf73FtyLAZDPO5qRZEeGND8DgkytlIE+tmsg0KubsPlb\n",
-       "dxFAB0IAb4BfcCtz9xyy4gPUY3RlNyBKcVTQsQW19O0ucB3R2zc1OIe/lkcxvZF9WlrvT4GMtGC8\n",
-       "wFmHe0httWSuvgNGU8ATk3WYkaPwcLVkaDCmL5Zaavroq2y2cg6KcJiVFgR8rqYNMZMloIitBNjQ\n",
-       "tM//2DGTlUV8SifiW5lZh+Uh0LEr1IbbWKwJXTHtUsZf9C9+oBbyNVoW5inMLz38ycfoHd8yfDT5\n",
-       "9egzN1V4KoFLqYA1tJ1BqseoFsB2ABB7mwCILyxXrUz4PoiM+r5SxwUd0lg4T7B9aO3JYRb+MIUw\n",
-       "LkPAW2ueQ/flWtdooysS7zjH9CRYLF9Dp1WcQHEa6NX4J4j8q4MJsJM9Prt/eeLWBlFYEY2J7uq+\n",
-       "4aP38YTVHKtfvJg8l7RoRsIgOuJAUP1RWYTgmgEnTfkxDf21HKCyX5kT6W6Kid8lGojSo3kCP7CW\n",
-       "IcM8Vn+ehnzyeA8gWXr4NO5bhKjKPcD3bmzLrKGjPwRsv9KJIIRmNhIyC+ZaooykSvvNFrZ7JYvZ\n",
-       "ElxFK99pze6VKJT92W5hogRGA7iy9ImAxlyrnXXuQ/YgMrjFWwY6d+VvW11Cx0mOWW2jgb7wFIFG\n",
-       "AYge7hxS7viOThArBnQkCI2fz5Y+RNXMOsPESQcY/c60tWvYkVsoAgy4wq8IYqH5zpYcuCv/0NyS\n",
-       "DuGuA0TfbMmlmvD+gNDSdAArf1Y6EbkuS5vW4o+kUoLVHW+eCUMCxeV2UB9hIH9RK8BXnjJ5njL+\n",
-       "elCuducp69N/O/nTloSkMQncnJSH2Rc810O3wEHkzA0ufD0x0BZB2RH/dw9yjtOnRTvmddy9Lkv9\n",
-       "I6L9+UDabIsVQIiX6CXUv2kIEDCeQIhZwDlikVjD48kFJBSauNjwBzFy9jkbjS9ZLReN6hAT6o6e\n",
-       "0ygH5OjitKWkPERnbgjeSMW4boplCrKFnt7+n2gXdFwlD44z8XVXIiJ/KVMPOadNlU8VKXmUlaEw\n",
-       "ysb6lNwP149oQf8j/O0uRYxu2HlMxvsIdT0z+ywtHLAdCSj6wKBjeQ/rb9viwtVxKIH0RCnU1p1i\n",
-       "ZVV2NJX/fFafv4IucbqD/vaWU4kVifehHL0tRm8YLMUFVvWjc4EuBDT4VG2/nNBtxcvaqLRRKCsH\n",
-       "gtivQK0XuXeJxOgiJ8e0OcbirQQ8pFytvHgALQk9bHYasD+b50eY3IPhjC0bxWWdsO/Sjc/55IWq\n",
-       "gM//eb7f7mmRTrCYk0gW7ImANduzu3wrFWcmN3UM9iOy6k37fzo5+A8ZljC0eA3aQETwGlJRGJMw\n",
-       "Gfr3H+uH6u/sPpyqUHD+c981TCZWur09Rszd9jbbbfuWuQhaLZR/vFBB3kmrMkuFBhtWAMYsehZe\n",
-       "es4rmpT20bpg3RCpPy+n8Nw2vtauas7GbQAQahdbEotH2HQw/7mnF/uHgwkKlpgeSZluThsrowlY\n",
-       "hoatk9H3/pRk3zDU2GmFQp8slMKYIXaDxRARfHKLY6/OJgF/PNMfmCM1hYy8P4F7NUPQlrVVTtSP\n",
-       "Vvk8AB3spdm0IP7XmMZcz0o8stEzhsLxgFUCRpLrDPuXBcdLUSKaTly/FAiTMZCU9t7wNhyd+lZI\n",
-       "80Asj9V2UppOr+Jfjbi4VQBW/L4pvS84jWKUYchi35l0kWnJMtxRHAekjkQTPIjETyLZdDXFeSqL\n",
-       "TpidKbBs2ZnPD7lrxvXaIiaweSRpnR2A0RIm5APBqVyHOzsLJLHVm4m2Ca0hibJpgE45o8ETWVnN\n",
-       "ORDI/Zy8GMQSXhmKGfbDvpow7y8W9l8JGg0a4ZogkLaSYkhbfYxCFYgwX3oiEqVZZ2/CILB/KhPO\n",
-       "Lz27qvJilC1RCNDIJptpnBZYMNVI4SBQx4lvhSTKBCmmquwxRMp3N7yIiaRV9IablXSbwHZCAfB1\n",
-       "XUMTQ9DX2IiDjaAaWgVUKi1kt/w0FEUIj+x/D66rpoITgJX/5I/6izrUk/p2fajcCIucBOPXcmjR\n",
-       "PS5HBGJeg3WXLQMhQNahLrXsrurgC4iChvdbm/GoMAO0ygW6YUWCWYSKtqeIWgVVOMyOVlCGGiVx\n",
-       "2FIrA+2V7SoQOT5aPheaKOfur0UnNQWyWqHK2Qcyh5unku5nZuddDwjCwRtK/bJ63CxWEROv+KTk\n",
-       "Y74gUNLT8nPt5Aa7qcVz7h2OCNqAEaoG6W99n/pQTCXjNRamdr1ZIy3oe+cLiaaFOgVLziTbk3EA\n",
-       "AAwxQZ8ORRUsJf+n1Mm0Si7tFDqAC/DTYIVVs5OTOml9QiwwFg7DMi1MbaaoO4ks7qBv6KFUyClm\n",
-       "jlr2G/DCQfru+Ldb5YJ2qk60ZiZf+8clm7MPaBI92R0K4yGGrTi6hPalMQXVqkxh2t42EFmMOyc8\n",
-       "Yf2tkgvBHmgzB6mHeNuLuf1Np1rfQn1k1WfsdBPGxAnEImbk3BVWytWxyEc100wvUElKkEtYVGzf\n",
-       "32jlrGMNwdfXiRFziyvPMObF4eGmj9Y4nG4iFdV3P3HMOXHBNFHpuejpO47HRFJqpOtUKkT1yZcl\n",
-       "d+7D5BAZlt50en6VBNrur7JLOFcAFpeSF20ccNsGP17FTEcFubF0xNAieHwq9mLnMZ4HY5mM/95H\n",
-       "HpfwvTPa+ckfM6VIg1jiJKOs5VdBdwJVIfuxGf59wHTGWwXSC5wzsJd190ZAUdtTxS3b/EEDzwO7\n",
-       "OB9aTwZcny+T6p6Mrg0yvqVw7x4GRwzwVpALoOidMRohiOGFhlhJU8nYus5J39Aqop9GKI9D+rlJ\n",
-       "2JSlXIBzCi1GxmJrBfQaiLrXDdR8L05Ykbq9dy81Qc55UsH3RURoE9jq+7ngnob3NqgndQ3aM7x2\n",
-       "D4CDqqlaeJC9WnbCHOpAW9zcMNErvDlywabziQkvIT4lXEz5Gnb6xo2/Sz9Tbu23RDaGbNNVUc0F\n",
-       "SGLsnOfwMWkHusRId0ThPZSNXGGktdgB/Rkl24t+32vs230uBCpiR/opcFZ48pax8XP/O6GW1gSO\n",
-       "ffKswiR4uTh7NNbH+xYCsNxBiNj/7J/zhBTKuJHPGz1mJqJuDmyKg8dTTeiUeqiQwnJBRLYLdUr7\n",
-       "wPWjYkD5R/bszU7WxPNsN4f6zNuirVmyZ8ei+WMeBvDI1LNZ/ElkIoL5JYDSW387XbWt1bWR/SOq\n",
-       "uPgynN+h1gG2GqwbllMh2x6yEE/EioYfLjfviKrPhJ2rh8tuag4UhSiYmeu6rQ3HFRoJTBnTqIEd\n",
-       "celT9I8dBQeF7+WGoSrfrFHYiff3AFZ80gWPoA/AeIXmYgccH23H3+M2iUJ6XpZ54Kslv7ULxE1T\n",
-       "ax00puIfTJYVp5GnZ2afn5l/tkmtHRughdm5hsdAWQP6yrwt+z2SIJjC4nghj8ih8+dFRlJpZprG\n",
-       "UZli+O1MZyetcdJtNFKp0Pt1r/yiEw9LtpXd9GjW1O1/YvGCeeWtJHp2o5cVF/mmOVz/FlzRYhqM\n",
-       "zeOAaJaz0RGwoNHSzuNWhnFE+GNuCsCKZzWTTlxvqbe3aFA+Y4PIXLMrtDeTI8Zb38oeGzmpKM4z\n",
-       "v9keQSRaQK1iLdrU8ZFD62d+TySorxbPeIPEtFT5iopZ6hbHAkM3EBG4iOs+IZ8cPoW+cAH++8vR\n",
-       "4k9L0tYq6t0asOu0Twfw07SswpBRcrPL5wN5FMPUXe7yCnZvdRLmMmrF0J7m/WQ4N9JRfatlo21k\n",
-       "S+0hdQrl2j0AQCQRorcCS5XR/Ser0yR12+64SI+aXe2NyjEDDkcW7ZI94LO5dsnxwAHhjatawb4n\n",
-       "7pNJ4Fe8QCH8f6w6YpPxqwJBL0NuJ2flXVI1BmYpvDKard96rWTCXvl/PxDNrXZsD7PG/ZvLy6qR\n",
-       "/PS/6uluKuVndf8lEJlZG/AsYOhFuprvy6xQpxlb8aLUsTY/gwQVshauPAELo0OUOX+EA/0ui3WL\n",
-       "EDGOvePTKVDrzNH5kdA44BkO6qzszMo+7klddcfL7HxpdZ18z6vOLFhEGotd7uEhR5wmtaEeNI3Y\n",
-       "ptAZ6D6i2VvE3d0kUJ5QquqFIhzYNlw5CVD0TcjJi3pq5d1i67GAIPf3J1xtJ4w0Pr8hfNNj4Pps\n",
-       "W8KDyXqc+eT9TZunf9iSwnlND/C3+WbPprEc7H65DOLbNvprHWuu/WZRk1+gpaIs7TM7sEwueLTw\n",
-       "cK77/dRaiKr2enzsM3Z8gfrHWK9mrKBa8SOAbLI9g42lF2fwItuYY85ZkhoUAEam6QF/RFwGZlzz\n",
-       "P/WpGe1WzHQhYa6FQL/CE6WB/Ua0ZVBAUNBIoDzNZNZXnUcyJJSZFyBKtZVrBeu32Vql8HwB9Iz7\n",
-       "5/gCTyrB0mZA5fwfZhRtMY5JpN7Ravetnm8NlPUvMK1DyhxN+gXsafKw3dBFD7Go/pWamfjP2B/8\n",
-       "CcabHR+reaj2uinQW0g5SX1oCL/DzAFjD3oZc9YpGdPSDOzDWWgcb216y+4h8FZdwnAgDrZcpF/2\n",
-       "LjdWlUazOViZ+pPxFljzn9bv0iwyI06DnNuvWv69TGLPXTLPCaDlrv0u0DYL1nnFt4+cB/S+T7E2\n",
-       "JA8YwTBV9UOf4pawxjY3a0OHCnthBgAFV3qI22osgwEB6wIK+DlZYqEEm1LJAi8lEp52M3SNDKG/\n",
-       "qa4OsSerD+WS0QY0ZH3RqKFtxw3lshEbeRUtu3A+1LuQhJh3OjoVvITPm7YLi9csxYANM3hxZ8Au\n",
-       "zEAsYUnX9KzX/nrWb1PGL1s3ERNNI0mgS6cH3ljfFn1LvcqyCjzE4+sjm4ws2Ze5XwEaA4lXiJah\n",
-       "gDpfPM1rwBEthFlWXzt1n3LLqnyPUqS8YtZTvMirVyLXHe0b0An3vyEh+yfUuhCmVlI7k3d+1ox/\n",
-       "q3Yv8Ul3CqWFpX6EnZh0/CPfzWob6Q19hlD1pPMf5kfYbXMCs813zy8f+K2NuxeW83zhHxElBcQP\n",
-       "ZeWoDdvd7EFWsJClsHRnf/N3FUWnqF16PUbWlSBPw5ozQBOjG8Spzl9+zkdmbn7phAlEdWJafKzi\n",
-       "2629I3tFdq/3xVHJJ9eRbsk+/zZsQF2z57qQj8pjatOUipWn/s3MW7yu+5yruHU6vEoBQ8S9IFOd\n",
-       "ej9g0ssn88JbooCGK2Dx/oxXKabbFdRuWk7VudvgFEJdhBoaNW6E1pKSKVQK+rgfbzeBc9h5Vetu\n",
-       "agGaeHLf4/EBaGyTHeaJ7APVkuKNrmaOKn9u+QKhnJn4yyfwv9jf5K5VSZK7fNSB7siOFcRTvBfG\n",
-       "RiYAVrcP7KunV31WQSB6DlNOlOMRgd7cL5w/og7nUmA6YSyz2XTcdgMKkeUEHUCHNxdrEwXxDZhi\n",
-       "gG+45/Jr9NQzPwvHs/BlhFMWX7nN82IUtVI4v5mdvbaWLRWX6X7ntsJ561KpnfyjV1itzYg2pxil\n",
-       "OyqOfwpzjHlS7GIe4iz+8t7JzqeSS3rw2OIp9VTbX9pOrOc2yjoevTgSSEepT317AEhiHRhduZRC\n",
-       "+YOgL9NDs0H10b5OBqb7uOsGjthruu1A8vE50gBkKva2jEboffBqT0mvs90hiyE+XpysfPLRioW0\n",
-       "xRK28DLXEKoixDHN3U2JVMEythIJ3pic0BKMKWwulXLaCxQoPLu1lfCj/W/v1aDMWbXn2RLktN+q\n",
-       "D3GrG5IlTvxMKO1F76pDi+aFCa4Lb0njMh+9yc54rnu7zT8VBGthAJp/j3kyHVKuELM43GKxyJOK\n",
-       "sM3DP9awyjSAiNmzmteA+2pJ0BtA2XJ79m4yUefCtkDDuZwLW3cAw8VwiMxvFBJ3CtRBsTYYqjmg\n",
-       "f8RBF3JlB70CTuQHcR+nFcbOvnHnzvba4NoENj0YD8sdBSUNKbGw6s1qnG500iFUxf8ygKkjsiAu\n",
-       "Ki99fWfuCG1skAHJqWRKId7kpG6yJTPN0Hc68isf1D6wgCHN9SQeqAiZr3OhBg4R/20ZBuv3rgkO\n",
-       "dJlEOrRVH/1R7Cj4q1eKSBlxQtisBZKG7hH33x6o7dfbBi8a8EQ5mu6PfBHqHPYmThvXMj1TwpZM\n",
-       "dQoOWJ+Cprg0f9YClKPoW1hXCMa1D8XTwFLfx4/Lm8pFR3wPaMCNqCcRVX+GHBDy1kRBrB++X1de\n",
-       "GcMpGIvrmg4d58PRz3mB+DCko0jHBKEfD7IH5Uk6rBFT3Gc/jvFIl93OkISgTdWgGtQxqqNYsf95\n",
-       "Nzux6n8Q7bGPQyfAe1tHzGwoTfrtUOTe6eT6vit1yoSOkWNfbbari9aCM4gRMOkTNAR+ssXjh1gL\n",
-       "ahRR0szXD19WxaSN/8EdHw2iBPpjsJju7WwPpp3xrSWqNcYlk6HpbgeUDofsH81a1Lsm8km8FyRi\n",
-       "7GY3t3RPiA9JbYHaDFAc6zlXM6znmmXxtadTCOIMksAvdNt7zbaZaBB+KyOmAMpg7edGsxhW0MGH\n",
-       "DRd1zJdiXtS40uXx5KJy/aICnfZc/47m8a3mN9xLZxww4OVGW/Wlls6ctLsriQAACTgBny10Qt/k\n",
-       "nhHz4YpTT1FDk1FvhMjArZ0PbqZk7D1P3Ab8Eka97DGVamihPfQ32IkS8IEEaUe8IFvb0m75SP0D\n",
-       "Y/aJbYr1Ct9w7ijJLjvU5cavKt6I4GdQxQyPeKRi9hnx8BKK4V0PCGOU13Q31qCD05E6GoK6RkDY\n",
-       "Q/FAaJnCWuriZsv1ANh++4YUg9aRkBxoZQ2py2HQsPtOq/ChKi14YwmHwJ4EVnUKukI+CPjJm+HM\n",
-       "0vBUQxUYY2duXl1gDXpUy7sfg/eVN4M1hWyqggq4amhFH67jqY960212ezSeTj7Y6zMq95RMpStI\n",
-       "XORFyb9GspGLBG5ZaRVhcOek9r9DhsG6rUCl1OsowlbyXuIO8+XeUwGusSMmcZZgZMoM6O1onItI\n",
-       "gOg65vl1CCbwSM/aKQuQdTXbR2rT0hzcCTqIqDm9F/AP3Um8CG7Cx/TqE9hlhDUg+V5hXv+qSfvD\n",
-       "rW/KVYbcQjLvPn6mi/ppfPYWQAk3bOllbdQG74ASnWLXxFKjWl/RyiLNn0YY63sBjZ+sjlqaTr4b\n",
-       "xnRFA/BiST4eiJKyiMrpsnHhrgcnttUOa+tjP2qoKqykQWQK5/FGlS7QqvdGWcShJrfv5TqMHdDF\n",
-       "sHf6yoPrtuzhju7/zwRALH77i4FDNYhrvXzuCia6l9It+Om1/sG9rYFV/TXF+TL93Lj+eVFOPCND\n",
-       "aYIjXU/5HPToMhDVkIhCTVKn39mVVuFQQf6Gym69H4aDHE1lTLbojcG1PscJowfH5wGQxDUQ1KU/\n",
-       "orhChj5GKt5q2m7NdOht8lqgLBqQ+TjwdS7GxDtTPOYAZ5XdINPmNbFCM4/oXnOjNRU7tDNdid/Z\n",
-       "RfTHhoHqjk7wWIbw7BoeXpmQggRQfPsknzDZyy3Ss3/Fw2Hpizjkms0UdDkgFVOwrOOWBiIif+pC\n",
-       "jPa8SXOCIROo3Q5pRzepv2NPmr660mypYvAFzafEuL/Fj59lXzRQildssL/mYrq/LIsF6aOM/wq5\n",
-       "I+D79elteGB8WB4BlGMEJIHtCWHqBFB8B0Ij5bG8+yUjKeR31HUlknBkRg/TjSPyoKkVF4bjC7PD\n",
-       "uLu13xOuyLNti7pT6tr5NZWN/mmG3vKjLmIXGVLmkUJ6qJvhVVo+GGOQn/JyIDkpq86Fuop+7GR/\n",
-       "Bx8TkdipBt8E3FzrZR+qsyKN+XF+pwYU0yY6tcM4MGpqmMqrsfSbQuMae+YVv+BMNC6mJfjgs8GB\n",
-       "3yNLsvDipWY6ee+d1XByTkndXmxMydRp5iQ1JYlBqMN8SiNzWWRuBZKpDh/yoRGi2wReoQ4CGnar\n",
-       "TJb80r9iEdqKU34B8aH9AXmAzktt/7N3cPLnw763/iq+Y3fK9oCnu1mzNAujNZAbl/O9wnVfdctD\n",
-       "hHVMVDzvikgv24fLs0MefZTItxca2f+x/jKZj7i64GPObVNHhmQHcpxVnxA4y6Bzd0iTjEvE+6I/\n",
-       "hfuXAvfmPwXKHmZY7vNWS1c3FvbDgVY29Ejan3uSJztRecszGVjG+p1a6g3UL4ZBDGQVUqbumZIQ\n",
-       "+1KCs37L8eFXbutogpvBZqlqkIlZOhPpY3RUy2lSBOPxFeQvFv2b7RRUeam3YSlGmXFqB9jJwWM6\n",
-       "ipcKpL23lrlAWTl8pSv2ukHn2Edtr9hkQadSRX0mXSzpCR/iS0zBpkN6uIN39j/xekdFAEQvR4Lu\n",
-       "9vOHjpOqb8ZzYzRsPGSd+yBIWIHHWdfDwrA3/vQ44F4601dedUy9udeDsXI1s7HoYamjDonURpdI\n",
-       "IO+d/6YiiuNWNWt9QY73AwbW/rim+4d13IiJfM9Jomp4LKqRN0kGW5a+5+HxQ7SfMrullwNQyRM2\n",
-       "i6p8PdexWAIJg6yaDyBilFIm3VvMBYseSuh2VjojUFAoHLw09a/1t1mK92UZtMkZg/WfBu4s/lTb\n",
-       "9hZfvZVOeMYoaGNygthInoQWNIMeksTRaM3fCZB4TcFIiIQz/kyBn3OKDTscfL7W8lnTBvGx9vla\n",
-       "3eaVBMNpByibThLOkQ307t+zowIwez1y6CJalOVPi0T0hyA3fHX82PzvQ11bwyF5UVXpxR4kl3KS\n",
-       "h14Ah4JiepQEQ2qHJr0SLCkZ8v3KbLCQt2E2ykTFt9UPs9t8++zxrKgBv5dGzdibd/MZ2FZvM+Ls\n",
-       "ym9ejOs//qwc8Y5qj/ruLsWIFSvsqnAaqoL6BO1KzjTMiuFJl2QhfB3HrvKbSi26ml1GiubkZgAI\n",
-       "n5j77g4mZc5xqjU3H+T4l8pDwszXYiZTJneUZ7/jIDd5IfhGYMqQ5prcVmwjPccuk2eNfLsyFivO\n",
-       "PzO/BVX3OXBBnPSz7Rb07kFOBeKirZcVOUBEZDkRrT5f2dxdIOkLemUVI+nlQoDAGCDAOA0Fkx0f\n",
-       "InYf+gNgCnPvsw9NsFEBYFGkb6hsMt5gBqFzGm29ZcWJZFl8Lsw1PiMlpU7JRmUCmkvbBCMG/XZs\n",
-       "Lg1TsT7+Sbk1F5AVUo5Ta0MUkG/VLM9+hf92gVeVzTXHHPaqoHDrO2No/HwPhPn7HwlFBY0u7GlE\n",
-       "Srk1jiMeQylfBD/aCZ86rL3nbsfO8O5drX3VOy0iAqEvPfJyt99pRYenYhypwGoQypWzBxuT6b+Z\n",
-       "OtkNIWxqaambqYiGaGp2Qr4pWlSDHPHN+ODFcTlEsHfpLGJJ//strWV2B4DeclSSw9WJLQSIeKHT\n",
-       "1V4pUuDnQIP4bxP3YpRTeAr7FPmkIIYSAcLV36cBtni0gJIg+vfm9CPuAkFW/gpJ/nV7aiwDkoo4\n",
-       "z0VSfFw7vKtUibkfRCSmdXkVW21eGFhOWYRzlaAKZndbUDzRZZL7oF8Vve3sTRoTuccr+yCzLg4N\n",
-       "uN6O8YdMYaeL0405ht3WchBzMx6VtmLAUoty2ggbuHuNiVm6c/Ww+3cNb9Pdh1vNAvp2E9YTKi/o\n",
-       "yjEE+Gs2mqEPGU/gAcoIi1C3FgSruv3jVYYo4kEcibxlgE9Rcg8oVchsp1HtTFUkYM80g77wWfeG\n",
-       "r5vho5g0h8NMqaBRBXUSh5fbPMr4yYs5P4X6DA0gnH+qczWBoqiZmaV6xrD3KRNoIQFlaOz9eYbg\n",
-       "gvxfTwTDOCm4G5lJy/SMB3aHNc+2DjWDvdq5EwjEG9fOc6o432LS3Fy6R3/P+BmRlwAyPhXX1NdR\n",
-       "m3oyUhQwSRpoXQAlDv6pnwAAD+0Bny9qQt/b7LwWAlvmnprF+F5Q7Rlam1oFrLuOOUZmumGeCuYG\n",
-       "5OrnV5qzO7MllCfPpfhxhN3ZSfHF3XniNxSda6/Is8Kc3BFhHX99BcXG2++E+kOdUodgq/dntP2S\n",
-       "nWbc8o/UPlIxPtQNULYPTNsAlwduAoUtCZtKyPzbjS7S3VAhfL9wWaNe7Ir+GBAsIXqtvE0PTjnz\n",
-       "ITQvLTifw0uZmoakLcebC+hOeUiPVzRzNHQm1klgMoNn2oBBHEXzJNaDU5Wq9CrvYSIEbDqvqGem\n",
-       "4PIM1jVCchMBxzR7Ne5Av8pc+IlICq8fAcUWiz6OdTEKWwwFjh/4KYyo3wLtLLHJg2en7N9/Bw92\n",
-       "ZpVYLg0YC7aUjPwxXyUOc9bSNlW3pk51w0T9zMvuSrQwWHx4vSYbNsA9BKwtNvbJVdOmyMLtfP9Q\n",
-       "eE8erJkOyRFWkMBg4W9qyFCpokc+eLLcm0/T2Pzbk/L0Ty3SdeG1L974SJcITZ8kRvpUTuCQVRIn\n",
-       "H9i0uApMudR4/fpOmkaQJCTMpToMSN2w20RqO0z8lsUUaVmZXVXSJmW6TZidi7qpNVUo2kegTPwo\n",
-       "8YI0kWxPqppsw9LJcgKqksWTTo+JFQQ/iWtF+PLM7cvSluwqRnftAO91Gg/yeM+FuiF8x05fW0/T\n",
-       "W45wmihLJaVpz4tPqAOVHEDQ1pEqHRAoniTupWh4Nu3LPkuTRGaSSHtFiXiKVLnT7qKYe8ssS0Q5\n",
-       "SR5rAJMMLsDotawHpXmXXF9R/3xmKlmd065TizxafjfYUGVAkeu2hJQh7U9Ibtp+3aL6UihlTcMq\n",
-       "aDEr8uC0OrHjBSS/iZW3XhLxc36K9JzLthz1k8CoGWcae/UDfMD/d4CU3CIxM14OmJW0Joi4P28T\n",
-       "qkuC2GUUAtynNUqHcDw0+4xh5tfk3pDfzbBgHiwwyhC5topx2x1nwMqTYi3iI1ar692AukLT47ay\n",
-       "jJryRuj0QJQQjvLnKL03DJe9lkDfNKTz8uBSMDY5+EspbEwyvjzauhqz2n3kxQSiW6PxuePKzge+\n",
-       "r9XOTQmbim+mMftDZdhbvSH7JmBW730FynBQZOkfykHwN0678b/9BjUPEij15GwSQ8+FrDBB7MDn\n",
-       "L9dT26LKxWHs112YsfaGhbWiYmU2BfKBIPo7TzN31ujuAjNaf4X25ZiHNCX0iBsV80oc3pBakhCU\n",
-       "PDfPikb2b/hrkDwVle8sMhl1ILSUojuVP1/Ucwdes5cujL0nMkUQBbjFHeMcFX/BoNCCZ07kqHgQ\n",
-       "s+gi4M6iln/WPr+t2JJpttPaxNR0vT/dzD+WoS2kumVAhSNgPSNgOsMWPxGzq8qOz3T75PQipC4k\n",
-       "ZugKdpTXYQg+Q/v+J1EzrPfS1290bFEWizRxsbuuUX4Wt/Sgkp4BsQHXnaVIsJ1+iYcQDIKfAG6v\n",
-       "h2o0M5yP4WLRA0rO98Ff2kN8M3HEPY2buI19u99nSv3vIbzKvXhqeesF8mvf7B0vpElUXdIT9TCY\n",
-       "NQFuAbE56EGiHNT9M+mz+tJLogX3josDFhO1tznF1cEuKkhHSdwVG8gTx3xGSORkyQH/ey/kmHdO\n",
-       "/H9HZ2u4nysEAArppD/yxHoSTmkvpc/MhWDQv77Hov8ZaypCgqPKY82oGnQKIxChIhvk5nzhUl5z\n",
-       "EIIa2/6DPFecggAAne0n89SPTWxP4GPZ1ts4F0wKJLAYANllVbIVUIJxWneO9vYAE8At6pSuRvAA\n",
-       "byVodrCPE0veKhlx1jbx1Rz1JR3uxcln1ACY+pZ2Yh6FpiwzDZsrqvzK10SeWLLhXNbDuTEAIStC\n",
-       "jzTP2N+coSn2HCFt+kVM8Sme+TCqgrX4sMxAADJ5j4Uix2ev3TzXO2SqZ+6yhArYHSgPJwgPMrbu\n",
-       "mGa/4+mFA67QJdCw0SnHUITseiTiCEJoxOSLm3/8jWIX055ZPvJRN470ZscDVp5ZvE1GQ4vQdoha\n",
-       "R33ed0eaf0i3zA68tAmDT30A+G/HpM1G7es4H9XxIuuycfQW/UL2q70qUsUcHDHpnFFAmWYOsou4\n",
-       "WZ7+zLKI4Hf7ddXkZcwr1p+gL0TDWO/yG4KN+1BMoCX8gTIRZ3Wn1Xx0r8ZRVtcN6P1drB8EOUPR\n",
-       "5c9JFsyI6w/tvaBDh0GtJ6qEyGrgcNGEJK9AFvNd53/9BMrNGz8CavOQ3h7kohtRx9YY9ZV+VgcU\n",
-       "+qCRYuxD1FnWhyF7eiqEVukMosENIVCQMyBCviyDfMOvcfo9ZJLGUnkk6p/ZY2u+i71iYKV8ScPA\n",
-       "rIIc3rKZ/cOfcBbREC1D/ZFXm/lIjnkw0+H7r/K1Qc61hYi4ZLba9spY7v7JRA9gEQ0JYhReVsKc\n",
-       "+nAC13UGDnOP17/NSfv0CJuxzUgyQUiwnalZw9uxJvZLZYEatV5EEe/SKU+z+wi1nNscEUoVrO86\n",
-       "f3B4ZsSGfaOvYOvdNt9uiBTqJmQjG8CJmUE0rjiWNt7S9frvEsJY/1VffGCt7K/v9y45H4uqWtPy\n",
-       "IiLUOmuA6lizuDPbiiZVmNwFs5aW1ZluNLLglSviIddBg0ve3qIQdepdti3TFE7kZ5YIjIt1dhr+\n",
-       "JQjxPb12IgT+89HbgCnGm7afbeEVG+vPJdHK+tLMIZ1/4RUGBHwdAPD8duLV5Ek1mZMzSinv8HJB\n",
-       "Xtq48H+ko3r04pdcZr+mmZjMmElnAyiZmvVb6Fn5a6vF/9dm/zlv/sRPJdulXJE9Yyz40k+9Z+Sp\n",
-       "tUSp6/A/5n+flG5KDERm+FtwYP3NG55qzZChzbG/ESctCdqekobWg0hlMyHObzbqWbd0wQq37qLc\n",
-       "JnshopnjgfrfxEKtkDb5eoaqaC+iLhEKX23OsdbXqs4i7AJHe9nFzhQmKxoArUTld5Hn+qbCh8tU\n",
-       "OD7ZPRq79dtRYNPTkFS2Yxz5Gz7DDerNuGQpv94ceDukT+VAZJUbL2Hq4i4bF2ZLc/n9Mfm+8PIq\n",
-       "R4V3uuVDqU2Va/m15VR3QeL4LWL6QJqZ3ukyNt82nnPsHsD1+eiflsCKj7J6bl3eWAie8OKfxvOs\n",
-       "z+A9uhukrCWE0DenvIDgukAt5nqCYCGa10fiPlmnlPBaSOX6GIe86rPMM4uuR/3gRRsdUI2gTQij\n",
-       "PJ/KHxQlAF0OCDhmjbZz2vQkS7SSoKMd2ofkxA30c+tm8OIE1dxZarMADzKTsj559CJ3qDH2Idbq\n",
-       "zoXGyBIkyiqpIxe9DhOXZLEPbOcA3tznWOvgroXiUrdG7OSCMpJkfs+0oMJMCGgOra6M2MmAiax/\n",
-       "ZPoUDtpkEsZW6j9pxSuANzj1AL9/doiHbQ63coHDqMwSJuaAoWu0JU7g24urs/Lni0SuYbi/fCSy\n",
-       "gzRbUfqLkSoi/b36IaTuLsIdA7/HxS/+c9tzWmHwelyz0/Uv8NiqOhOXu9nUWP6P8/kKbj9mc+08\n",
-       "IWp6HHO5DvKzgcPmRl6xTeGeEukKX1XT2EHy/twVfQDUIUmjuJ0JG0qKg477E13Ay7mxUlS9W2s7\n",
-       "JSBdm+BIJBuIss+9XJrc1y4UNSiJ9p6+LWXmozUKWSAkN7p+3VoLEmX1eZIFnejKXfTIBihVeq9f\n",
-       "b4YVEO2CgQmNOIwfiw7PKv+7M/J8TEDxfWaKRIaBLiyKBc+6iJCs+3/LZyRRtCuA8HawOo3iFU59\n",
-       "BTxoWzlZNpS45DS6+80u+rlXnY8ftwYjRyZyBVRRdIgGl7X10iOkF42MtpGoPhqUUffPlGNEtRYJ\n",
-       "SCWYhvatNThTkRnVXa6RvVhqEGp+H3Nu/k4pD5ebJX14ImwogbzIif16mGT1qjhf0HG7Yf0tqyIk\n",
-       "vRr2ZDVqb7+I0d1SXSU4oaRz9snuZ9BDiic/O51tKxXpT5A7tXy0/10u9n4BOc9jd1BJ8DEbugox\n",
-       "mjJg/KC3El+ViqeJgPn5U21dEJM8q2Q0WYoI3tl7Ofzc4cyPClfbBzX6we7anKzZwvX24yp6K/nL\n",
-       "NULYhzvVuS7OU+rfXDifL1h4FzAnJ928G0QkfktUWyIKHgi7DY2VaLAdpGNZPujeXuUuLbi9G92E\n",
-       "6kYyx9oIAgjmQkZk9l/tYMShvmOdmtVimthzPoqVNQXaPdZohuZdL86/Dy0kPIJ/FvZx2luWW8t6\n",
-       "DGlM0yyt60Y37qWTtC6FuYhmMN1IBmfJH0sEjjXWwTi8Oejj+0Eaz+dcxKEQzjeOAASAyh1LDF9b\n",
-       "NUIgorDkPXKfSsVTcmoiRX266Q03NX6X7AMuNUUo21uc+kLiByd7uQgdYyd/MxmL/jcMKrKmWKpN\n",
-       "u9P2vyTwscSJBT+vCBP38WTlwqntcARsy4Nsrb97gGzlWi6jm6bzI3EJrO1jsd5BQp0MaRHaKoX4\n",
-       "VVhnhjDUiDy22HwJ1hD/ikRu9aF5EsLD7pXAopRh73c/TgN+OcQbeYY4X0PwFU+dpFBu4LjKau//\n",
-       "1BrOqZrv9xzbC06b79uWn/fNRN0jOdgrmlcC1/IhpKYkLGqikQWGAwV2o5bdOIJO3d8KU7lS3mSL\n",
-       "WjkSaU0Kpvro2eHWxXfEBtFleaTygbzWVFbZbS8nWcQA0hdQ7VgzP4IZMYG9PgJv8F04KTm4zqcf\n",
-       "n4popDszQpqr7Ai+60Jp9zjIn9yls0I0uH8T5fmIcmrVad/fOU2CVbDHceKR3p7kBx2CQapW4P+J\n",
-       "tr9kdehAL9t0U9LdCi/XfD2d4r3G3K3qQ0idMxPofwpzUWr6NDqXbyH0/MfWlkFTk1NCwYSEv3Dh\n",
-       "2iDvaMNbSl39WQ16Dyeqc20HalIrxy0XBC6gQjBLZcDw3EVE8GwSiEi0Cj/Yr22xiBEPira0XawK\n",
-       "mWgJomgtVaarh37K9nCIoh95e54/wSWVVAEwHzIDjNj0fgfw4VadQ+x6+AtWhNY/3mjnCf66JJ5r\n",
-       "Q3K8rsisJcQY7zBMg3TTk8h2wsuSfSqBW9EKvpe1yF0Y6NgFn58ckePIcU+8nQit43F9yrU9RlDw\n",
-       "snYY1pQnp+abTm5wmgojkUcmqdyuP2X51R63sZ2rQKs3Cq6bx0KVoaZCkwhnQWSp0l7GHpxpdQ7d\n",
-       "hQwMaO22hrwIULii7iNxv46s1LiF+R+oHg0gVEOUTkJipUR7Ka9JyhAVC2RzkraUGvh7yZnKMqPg\n",
-       "z+jwYXSw0G35LX2ALb36WaNSw2hQvTg9R0o7MPin8w8K+4ogoKFrlzNEhnSXeiSy0vgF/MInw1Sc\n",
-       "A+hm2VrJvNuhE9UbfYRkdFN0xhV1bNR0Jm0v8TY3XxRlvyaA2f0geipKum+hpYQ44cz9Dp0IMWJR\n",
-       "1ofIEcFVi5h5HH7WFJB8ZpOsar8PMiffEo6dvmV7I5/fKbejXPRPXe+n9yoPRNkwvH3j4CfSU8JK\n",
-       "uxTwd6tUlqqSB9fNqahZxrMqMuxWFsyKYFseJajacBfy5UxdbAljRavhUHQyY6OOQOI0pNOyDQMx\n",
-       "dlu98M5jd4u4bdfra9HmXWhJ+yXp/PyFuqZQXxiJhj+/7f9UwBpUltDahC3GA9ou2qYAABnmQZs0\n",
-       "SahBbJlMD/90Ns0dCXHiOWOxnJrgvusHkBJsKl1IY9wOYzHCKDYI8Honb+1fUoLAS0D1efnxTml4\n",
-       "9ZLyACVbi67U0uBW1B5d4+296Q7CvYa8EAaiooVd+EAqQ7VLyk4aILboFJSjxw4yRQwMBfsyc/Fz\n",
-       "5CcpFstZlJtV7lmhmOidhx4YtqugyoOgpjSD57YYmiossmMLaJParvuCQ++eeOE3uwJXT6JEzejS\n",
-       "iIND5cTgXZThGBlsiEDnlRcD7mfoGmNMy30IcO6cstNiPTSJ6ATV9VNeyA1vwOL+dNcmJVc5qdrz\n",
-       "Xx4PYueNUARfbrxrMhxMaCi7AgecvrC1AFGzF0lgTD1gUuUusaq4vViF9HmfIuO4caKx5OLTumVu\n",
-       "hqRDTEMfTA1OCBmmQGNGnlEHahMPCZqbtwbY9qAVxqS+2AUwiyiRT2r3L3LX5rKJFCSaj8k6eLXl\n",
-       "8QD75v2+tTSOyVzHlbSuJm1HivnFJX/hovCkpoCRooQ4PjuoRIRPjNIZ6cS2NQPwKfj+JCLTUJ5P\n",
-       "zUsuqr9zfUAMNC6QCtvuHiWCZYUslky/uSzn6EmoMwu3mfzmL7ZKSy7B6F5ldghd0tN+wpq6snDN\n",
-       "nq00aHCrhoC7VYZLbOneNXdg2a/PODCHLBRdYAX6348R0JUwDZB+GEC5zhm2P+39viFkOre5badA\n",
-       "VCSV/60v4QJvq2fD+LYN04JGAHbVfKiE4olL/Ykhc7zx8ILUwz32qW7yuIPbplQX6tGn9GankTPX\n",
-       "NE6lHbqfjT3gHhcdOdnzHpUk534HQIGs+9Rz+GIfMsWoeGTI4fd1bbq4KZi/giHh+hgP9WVXAcO3\n",
-       "n/bnX9LgUe8CNTz79dHwK+GguqXj9KG4aUWFlIRyeAIDylxHDYCI4/mAsASyEhdJoPGe+iQwat8a\n",
-       "Bfdm0cLR1j9kxCGQJ3m4YQ1BObPpS9g4RSb0WcUBE13wxV/vSrnppLb4F1FKbcZqtRjMHTxDo2ut\n",
-       "CIVH9hK9EPoXpfwsQrSd25qF6BfqHyLD8s70DSRkhwWxionUfHeIOZkyEN6UNke75jCzHf3FQX+E\n",
-       "2tJZ7nFAe9oJv8RA66B1g3goYGL8QOqfYsusfi/6602xv2vY8Yl+TfMQBpV5R1A2EZ1Q3HG6IrBg\n",
-       "UxIzSlPW/XNpPwH57XCy87XhN6OSPUEomAf14KuIF7rR3jxwYyug9gIIo3zZ1b4vYOof30kQmAkf\n",
-       "Xo5RAAzbgIAUt/cKfZo5ndNzpb2857ppGH0oLhyC5k2x8o3M5pc3oq8ffkzAdzQJkvjCXVuglsiG\n",
-       "jDwek1hz4mCDNENL2MHo/YAdE2s/jJOKBqsVLHImBOKkHOOrBzJT9uWhpXPCu56jq2bBHq+2ZawD\n",
-       "bPjXQtJ91mM0GDotiM76BUk7zuIPdDfPIce5RyXDkK/jpL9/gHggJQds79tpOzhsAxIsCrahzqmg\n",
-       "HwC4gfHZFfmBVFnXBkD/Z8dv4H8JfmDvhQlYGx3pF0U3HQ5xOYKMhFsT+DtFiUl9MVq8nJaivPZG\n",
-       "WhSPpvVGpnifD/ivP8iuCTSFUnJ6UXsKJfnCXplH6NsVPCzmmna+1KVWuIpJKyZT2gxpJFBhU6wa\n",
-       "90WdR38NC4bcdOJAlcHGTYetlR7jkHXrIR+tXxlP6xrQvSRGqpBHQ0WimqsodtRXGTX0j2hy/z9J\n",
-       "Ku2eZ0DxZHVaUqhcRwq3Qwg6PpvFI4nbp0r752wXuSTENlntxZ1lJu1YupmvfGhtGIjH5MJ3uCA2\n",
-       "nAF24bhRNv4IKadR9fQXrgPPx7mXk5Vs5Q7D+yHltYJrIT96qlzZet4MI0z/tiur2Zgk6xvPgEoa\n",
-       "3DUv/RZo1W/gDavu3LWAOz/0YCNaUteUEdtEwMe5LMsTe3JoXQFbwQiSieM/z0DyIuJVNQ/nan7R\n",
-       "gROoSeJm3yntSE7LzbaTLSPW8Zqk5WezfMAJRGXtHFMgpKkN2MH63IgAGu4skkrjcpf9ihEU1d5M\n",
-       "ioXm32Ux97cyVq9Xf/ZLohmf1dmTU3b+SI6zmLlS8u7P0GF7SIwYC0w2lG5ykBi/TjhZQKJ9PhKf\n",
-       "Dux2QTbaqduvmvphj0/t0eQrI/mWlp83Zravpg6LqmIB27EdgacqESntqUT+en/w+sCdrTpsaxMK\n",
-       "u6DwZANcXtOKIqmCbuDInKa/wM/edPpD0AlnpIiCoKkCqUFM8Gh/X8R0o3/Uq/tst7OgUU6OQMVN\n",
-       "PSWyaotcd7b7YEBOq+XX9KsjXbXNSS5nngE7tSDln2HCSYTrpHFp1Hr38bil6OiIexN5UZ5Rb0bG\n",
-       "/5e+0/zjS8CebQUGcsaEyranTD3rj5mYJcjF7o6cRjMbUh68F2Zm0cs+/zfpFA1LqciJaewChNNm\n",
-       "DX5s9VEquXPecK4X32REx/noDg8E0wNPhrVHoCmX+CXcIG6CsvXnJdUioaBpz3br4/yG/+KpSx8b\n",
-       "iX9Wp9uUDt5eRfHql3tC3h1W/BVdlIHwn2/9Y5v3uecJM0VI20kGw4p/xE3yTQwg9kMNmcBoPfgK\n",
-       "Hc1mkUl2eYycNWGssHMW/eOAf46Elx1rnYSmjYO/ykbWagUEemZWeDIVZEwlZ6OuP82aUWIh3RGd\n",
-       "fq4NOTHY69dlnnE4n9c+h0PSsrdMdQ1W0vofcwYdyhvP3vzuaui4L2/PvTx3u04lRNXEnrN6sAxu\n",
-       "k6oGQ2ZZ4dBbLuG6mkZ1HM7iN3EJnrKqLDMIm2eGd7keIcwP52ZAYHMMM73/kp3ubL7u9rdh3Rca\n",
-       "az63GpXWzDAslyLtC4fyZTJi3PWaqa5JTuGId1pRRECYaOwU1F8gKAee+zfG9DQzRqW2yfqiEwca\n",
-       "yYrbMn7YoaCAeT6cIU7vX3MKtY7DuUUNQ36GrtzVukKcz09RttA/M+XHiJuTlVyU7w6ubaMsNzVr\n",
-       "15tL6pVhJMA3Jf9hLpCTv1dJSbSUqskqm3KVZfjTP54gFXZEXKYpBkN1gdVTb7zsBne73N15y6Us\n",
-       "F+dmVPfliDr4LS/XCkLWXRh/afK5ZIUApJpeLjvmCd+0Nexa4wprkmnOxSWBJui0xJnV53pfqKve\n",
-       "1ZKzacg0MjNNZtQ1lfXCd5nJmdjHdeNwhtOd/FL8Y3cUmgeoeei9rE+UVDD2cPC7d07UiTI0LAxX\n",
-       "ZH3xC0huCXJwknUFmaLpUHap+Ipu0Mdx4+hjViOn8QaWBj/bc098yypgoxcqLxF5vDcd0jLe+a91\n",
-       "t8kIgyyVTAqsqToyxqtWXfoMZ9MJUvdSb6gFVwNlTSbhz7pbPEyYu419C83EmHEvmVE5ZTnngSXO\n",
-       "hm5AbHvJN8Kv4mMN5hiY+ixAL7WDzDSfx1F3Dh4fDGOLgjwfZ1PVjh9/C4sPlMFRoVzOvtw4y5b8\n",
-       "AUXJLu+ZXRAmF/dyxPRnwL4Zrla6/IV6dM1LGnu5Tf/xNYkfrfceOR4jOflm62YvaK1I2jqQGTB0\n",
-       "+hha8CD7yVkSguJHZTSGpv+bxXlvfYhdaPlobhyQzc6FV0CHXrLOSAmbHkF+nJTvWXPv6vvWQGK3\n",
-       "HRfQY8TvMHXQSRmgcPUCs94jVGvEkWWRXhkhAa///i14M5UgjKCxhbQHFt9Kwwl6z1jJn+7TPm35\n",
-       "xtOA0vErivkRwrefRi/4t828lUr1h/sqQMnAg0AJ2swPS1R3B9v1jUX5UCo6w+/Ch49p6cCWwfLo\n",
-       "wCOdVMS10IzlAsrtpW1uBclqr/vituK2obE1+pGjjION0+5OHzydGJHwj0GoEjSgvcpQQ1NayWXr\n",
-       "z4gDDQu2Qwo5I3f55Ifd63OIiYBU559ZCke/nzik6+H6/SPV2ZN06bXlYMpS/7h63Qyxe2XQtRiG\n",
-       "6XLO1c6MmXJeAlOWN19jahAvaTthTd7bNKlu4+2hH5Y82nmo8Uom7sCczeqsUnr0tREhjHZK0IU1\n",
-       "Ep95rwLy5ZekGcpyCHM/dodwFYuZF5bYWPjRhLgs/Hf10Pwc7xIB67Qp9cdjZuKa04DBiIi9ypwz\n",
-       "gJvmkGdsGbJY7Kxh4J0ouqaSv/BNhDkQx2qS8lYbY1+4z797HbjdlBCfXXFkSAtQBvlZJzgMcray\n",
-       "obABNoMBYvSweha9XyHnKV6aXV+hsqQDuXYnKq0iJpjewFiPATtpSMr2eoADUvzbrxieL0Ug8fS7\n",
-       "RCyXEY+oapQX+9wmQgXKQzZLjtlqw8gvpCdZr89KNPxVkNRb7S4Bxz68ep/bfuNJXszNL1LZMMvb\n",
-       "lCnX6mkE1TuuFw9nb6HysNqyQxuzBOZQDtmEEvEOrl7q37DngyHq8rfOge7Z7HAIMz43Gkupfu9J\n",
-       "sOyq6XqBZ/GvfGJsDWwUA/Aybo0hunVujL9CA39vw4ryJZPyDSuRHVqwBxef2VvmvJAAOiG3NzcY\n",
-       "Nt0bJa3xdddXZtuQ8bhNfyn0AFyVn4f0YSL/5nITVuit7eXqmDbbJ0fn9eEPc3/i3Vog2NEB1D6t\n",
-       "ZsrtDvgP/uQwRE/9jqMD6OChhxBZfIP8YgsV5sBBqnicW+ZUV3e2t0HygcPTIamnbwzb2Xyr6xSh\n",
-       "7ki04QZq/Xd5p0Qr/pPY2GvPaqjKQPeTLtsiTQNFYTDK4n0EIuJ5zw10cvbLQP35b5jUa5cyElCP\n",
-       "MyITyEbNVWvWrVxPO44wO/bDF8EHfoNRuq8MxFZZ/aKgg2uWdS4H6jY2Ka1dbkrSPSvRMGiKD15A\n",
-       "mnTHVoSn4McGharaq/LfmEsh5uS++ypkzMxTQ+VplhjsfPE49xTZMVwAtaDsSKSRIj+cNunmkf7Q\n",
-       "8b4cgjRwMWXGLZfu9BCeiaQA6pxBMbjEQpZ0nuuLBo54lVusDyBFN/ouLGzChMW50sArxzW85wuD\n",
-       "HeaYZas1Jqr+VNeKbFLu/eHYAM/uadHp/Pd+7OH8EfdK+CJlVU8cfT5OrgrIO5c1MX4ZoiX1GCtW\n",
-       "FO0gNZgNg8pnqFMEndBQgGFFjoRVlk631yO9bBSCzlx+3kNrX2yWudj57BRezkSCVWPsjwkbhHPf\n",
-       "CVNyfA5Z6tDiE7+7it51ruSySQBkK11wygT7lDkzpdSE4gF1LRVWZ2FUaMTHmTnrXSIZrrNoRTVp\n",
-       "oJZCBwMGtNLHx4fROvoXm3W2Hr2chjV7GcULti395wvm55kgENqtiHU0B+gooT6uE47jpprTyGhF\n",
-       "hWYAG6C2z9MUzQHUz68T6el5Z610iTVs2yfMuZXzLS0FquFeQ6Z2ktvWBHjkhIlQGcphZuJrL/BO\n",
-       "ZnXNQAKn3oQrFyasvoRZmaN7S/qYQpzjkPfDmT2Vx25vvHhuY0+k2pQZxExQ0zMuNKg98Ww5fyjg\n",
-       "kHhcfz/oqcNNxRaOoHykbCc/0vElzCl6gt0VV0sojqUx3fz26W01hpld2THMK1md4+wKwoNUV5nw\n",
-       "7wVGyhuxNUzmsyPAVGJXhPa0cCpE/3LZHLZbR2KECqpa9+VekRY1zkpqXWxM4V8doZEkKHH87XyI\n",
-       "b9EhWosKVEnNwdK8nhpr6NKp111TyQqiK/QbBYZkVcQT7xGOh9cT4scIrXgSBF0DwCGiwnVt53aU\n",
-       "XF8DOBZ0Mhq8VLhYRgitck/D8ds+p6Q5aVq7StOvzQR/wKJ+FGRW1o+J0MYu31Zu3dCkgmGzSZ/S\n",
-       "krd1YW7YJcBuCeFIer5S/9trJVIi5hdXd5y+wF7SpSvbNWmbq6lH5gDxpe5RqEeOX21nxK70vT1a\n",
-       "3P2DJW8yTsusQ/m0WF/UdqZucJ//6xEMuOR3cBLuwS46smL4PrwxH2jiOTNMJyrVpcUQK1rhfCYK\n",
-       "KMEAbo6unJDnuoP4G06CdkiE7w3bJrzkjJDpuQrBl/vPn4fM2alwShFnSt6wTRJk5Jdy/c7HGUDz\n",
-       "SMFYQpbhL8n2ne9RVPCFseSGCD1eqYR54Eq9fhLaxFqvu9JojdT1Ge3uaNv/L/ZotjvlvkrtSx3H\n",
-       "NUVXC7I25xr0CdRQ4vQwWwXWyJll+THwWiLM/HBXN8tdDXkiJswJqTbnIxWP8dXmcvc1G+vgvBIU\n",
-       "r6y8arTxuuHiA7OFZqkit5uxgs6SRj8WLGNtepYl//8z0AT65cXg+dTlPuPdIMgMpKn9O/UKil0U\n",
-       "El4BvPU4EkkSq7Ed5kNcCNDTayMsW1SDg60aao78IypclU+dB0cltC1i3JxD+Mqnntsf6CBUA1lE\n",
-       "P8ucSHfMzx0w80PCweVBrmngLxOao8nbzFEoaJrHpiyndVjdF6wBQn8mS976iwNws4XcL9Ukl1zl\n",
-       "59wUOrJ/oIqyCvNOBVqcTrUt/RpzpiDAqwPle2geguRE1xDt/zF6cPU3batWRFObKyMwPZUN+OFp\n",
-       "NQnpheZMdMbB3c92y7RDTzDQ/xAr7ekFv1I7/RYQm2rhD9zo84WRGDF7oTsW0RZPYXTnjLkNDvug\n",
-       "Ywb1rC0gwqLXIIFTkxio9Iz0Vgz2SKyCBblNGK3XVuv646z842uP3Tm2/WjLdzSw+j2cAsO891C8\n",
-       "VNTWdIxB/PYaPATpglx1FNeu0OS+T0RqIB/AnGsvwWwIEXP5IGErRv2Ogr+4uch8xgQtJTMcWvm+\n",
-       "Orgwb/ijFTkTBQr9tqOapzOV55zCHQm4czADh85hL89yRzlRE3RWFRyEK7UJUafDUucl+8Ct2CeH\n",
-       "mFRj+51EmZ58ORPQB5Y6P3mZq7uBk76dBsm+495WDlAfa6iks9oY+yOxDXIG4V0Wwg7dZ5s9xgsz\n",
-       "A6WN0Nz+SvassfIa1cHBIGuPyePPiASIWvl2C3okOjX69u4p06B/PluWRjA7p/wtpOfB00Nx02tP\n",
-       "xzZmeKzz3/BGIIVPlSQ7pxfTlQG8ApAhAg9SFQsbmnTJdk3c+DCxWkKjP504ogtZ995BnB2CMCCv\n",
-       "0DWO83YwoHuxWR+W04MyVE2nTzBxn1NLy9rPpbLJqG5+KzfXUPBw9bOatWoH0F5Yhb3P+jXd0uJB\n",
-       "ak0ehkIeBpRdT7UTStYXTZ8DlbP1tL2PCn/Uk5GExH9opMz58B61SKEWiP+/h8BU7XT17RrSvyyX\n",
-       "/YUT/R9R8YGxzfvgSLSjJyDM6qf+v7vVl9yDwEsOE178l0AuQvw9oEo6dPdh5zLjx0SeLsfpEsOF\n",
-       "VT96PogzENB8ClPcunvgkrZ7pmVCjAi9XCdHLWk8RqKKIUoLxlCMTPQzZPVIIK4MDDubGSTMp4ZJ\n",
-       "X8uHjEk3G8GJXigcuMF7BzHI3oelFuh+bE/ID2JmGBraYs5WGala+bDNqsWL0RYsL4GxJkLoZ1Qe\n",
-       "zd/aKLEeuiMspGaFU9GgzA4flRm50SD7NyukllVtEhIY4UXFyhx9fyT5Vc4BOg98hVKPTyFCNef3\n",
-       "8eDFD++kel2M+Yj8rvCtjK446xGIRWTkd7JWzFfzLTKSdiACNTrrkaNwQ9nAyeMMYXWoUIjK1PK/\n",
-       "8rI2+y3wWgSJJ4wqA76QUTQSG0RZi32lxddgmb8mDeyDtFOLoAmzjrWZA65Ojhtq83NO05GzTIcc\n",
-       "1qvJlgdgg5QhfF93h1mMuC6pNPstLlsLTvfWT6zNE33ieo4ggWRwQKA+q9Nbsuqxa/oa1wgrIAYO\n",
-       "GGlw6uuebLVCxCQomn336cXN4K38mZzRi181lj7cwsHenkdhFf7wT51WgKiVGA4cXZcXZCkC+zZf\n",
-       "/vq1ZCyIBkreXYnZ7Z8XKmzxjrKHQCpEhs3oZkM57w2pfiJRE1FK5UwemPiPRiZRG0u1yfHBJS7y\n",
-       "HNuIQeUuKEuaLwMvyhEr5tShIyBxdlfT/DYkN9kJ0VCVc8al8RSwM83lTUeWtYzgtw7fYI70iPFr\n",
-       "SchmL2AHHfhRCOWpUdBYo9UkSpRXBviC07gfzcQjQlpAOlSqNBx/pWg+FxTL0zql6XGsjexyvoL2\n",
-       "BDRy9NKJlcjRBZWuHFnG0updwLRViDSx0TA9vhzP7P66tBbOBtXFygZLm3TKtDrN0X6iwegKCt90\n",
-       "rp4lnAFQq5FdaUkWo/gx6RI1bJDMNCV5BZmn+ULRnCXFghUQE13BVEIG/WD4LZVYAsLLTT44kHY4\n",
-       "hl6sVGthMNR5Vmmh4u+LkEcAhp3aTat7vuphKgAaGef1t9opCT66tBfUkXi5hXz2JpQy6is63So/\n",
-       "mGBSk/TEC+mNYpagkgypqkmv4EELZmwUeOxT/fqzIFJ3dvL+si+lL7+GootvKIVUoqwDzBsI+8Fz\n",
-       "DJBo2YGtDOfWhIChiD5DYDJEKMNXMPXyIscZTQX7q/a/U9C6CDigL/VoKKOHcN1RhKs8PAVXanw+\n",
-       "UnT1fANc5hTnBF2q1S2v60ZcsHiXZI244eGLXuGgxyv7yb+TWhr0hR/+Hg9Xp0XV/PUUQBaaxB4d\n",
-       "y2OL1oV45ihXZAGONX/QUinmUsA3KH4Pf6lFdDcRcIPtJSQiKX2C6Z+uvTY4HRdL4orU6ugFi0mq\n",
-       "7lxN5YR1Ng7S2QVq3Xfjg9C5N9cuiqX/rT5YMPIcIfL05/2OnNjtcqT2qdYBn1AtVzLkSfwaqkZ+\n",
-       "StY0l3qKYy63Zy3wA8xTh7u/QTXThQfQ4j5T3168pD59dM9leTUPysLfFx4zopo/pnvht/xKMPcG\n",
-       "vQL7MMn+GAGy6RHwwvS0LU4QWOKYQTePVYhLbaBfqrByME4F6IiDAu4BL6sLKwyowuW8coIqC7SW\n",
-       "waTSSfSHS8bPBCuFU5y6jv4WMfVrVi59EFjF0MYBg9ULVHPqry1kVds5TlGEaORwT1WxIHVHQfdQ\n",
-       "xn7rV1NOHhdvgvxBe7LbaM1sdQVpyPXHmmrpsGrnmjg5qcDW99QoEsbWSJKbBPZ6kCYX7Ex6vN+3\n",
-       "IUknOYQ6Hf0j0QAJXjiMZVxsqFs8/5xpxMDHj2QyD+JqgWK+mEW0VB/lJ1j0D4eoryeyfi7VahGN\n",
-       "qqOo9qX5cGV4mUD6wIzVtyjOUL1ZL7DyWRho04ir6BNd90e2/97QeZsvUK55Z4N/ioF0TRpci08h\n",
-       "ZXP3klxe7iIKLnvH0HjAAAANJ0GfUkUVLCX/29884Du5WbQA9gFSDgdIlQzlmeY3e7WaWvl22sjy\n",
-       "P4FWyN4Sw2snVhtrN5vfmlTkkv+jlxadIWBV08feVs6t4bwycAHch19DbcDj0D/wL2/wpVD5kDEH\n",
-       "cX78BgiWJikh7Rp2MSmpZoqo305QRnKpphYeqnTkPfjFbZpieErh3b+USNKAu/4IoB1OJOUfobrq\n",
-       "QU03f3l+Vp3wll/+5dDXEPXUXuVvo6Myz8R4/PDoLR9FtKXUWS6hCXz4itmEKdz4VrFHK42sXXEA\n",
-       "rkacnl2zQDFKgTMyPk8VjYqWujBfxT1/hica38VC92k0yzN3hOpiFdwxVSvcfSo1+68sWYmGi+4o\n",
-       "BiFJUx7Ihw2LCrPH/c3me5p+25bqK/Uo5FM+Wq/S7cOBzsQKZ9DoQpyHeSX3vft4+lNU+QDfJqMr\n",
-       "sGDJF/0aSL3rhTsvqn9g1sBWQ0vwneVyvM99buGG9DZshOiVeMqgpgAFI+79D2G8iL1CXXle1L+7\n",
-       "IWjRIfpAsKEEKdGPKOaL3txfkm2nitJepz6JLOVMvxgPS6Ezr3KkRRdU72cycWZaGs8Sc/cJa7J0\n",
-       "+GBsYF9GO317z25bI4mrBAIEI8oI66FUARE2lxA7rFMKZDlasU+dzxPn7sMynFmA2+C2zpb//Ezp\n",
-       "YNRyAHd8SIbvMqg7Hk4pKTovWs4FA9NBU0R72R0Qxi6vrK5Kdlm7FC2mUPCLA4r+kzYUt0jSIc9G\n",
-       "aE1ALL0NWLE40o3SymP7EU03BvXmxZVlTi9QjDWEwnmfbpebeH0GeVtxvOzq936WrexaZ+38sPuZ\n",
-       "9le5iWOvtCnfVz+ePmN3xxWtBwPqIzTH/mhwIBkTicSVp4pWk8eASE87/15P6BiaIo2K7hUS8JKg\n",
-       "p0tLnA2dkbVELmbf3QQk0igAq6wbQugBa7Og5fDTXMNLepYGvFaMf/OgpKTzMOa4N5CNKtsn0EeF\n",
-       "hYaLg60vO1yfVIJ0c4YJRpaJ8VRsS210E27pRAJcdn1yBRvQx9SLZ+vLE5YbhXgAkm9ifv9LW13C\n",
-       "I+Kn5pr6RaM4eCVbhM0OUrpquOZ9OYSB0LYGndEfJlqK/tqNge+z3teDZMXBSoyjyB3bzhwuUBNp\n",
-       "ibhYxhaYt8mBMWjzFGFFZujomfG8Rlh918ZQYsPoj73TPAAhduxiDgOpTTaQeCIb/u7ComUIZmYJ\n",
-       "h/kT8slwehY5m2dwaUZ9XLCC0TMT5wrac9afW5tf8Icyg3OvKN4AGWOBysUWRP5mqV94A6i+T7j1\n",
-       "cATEq8WRSRehLN5KVlDLkh/FzOFoFC1O+68PdvhP46LG0G+i0nmSb+x7N6SK5JDWBx7vHgq9x7NK\n",
-       "Tf17uf8VFwy469gAxeljLgzmGAERoyw7Flt93wU6i12fFGK2vTPy5A1SlqafGMeJUTUqAoulAW4W\n",
-       "uQ9arPExVjnFaz1EuyaGpf+UU0CTDHRwHRmlYzHFw/8Rhn6SNlMrmOT+BhiO0M2eS/81W8dkxvRM\n",
-       "nXTxwsBEh0N77+1NHFz3636cLmNViujyc7RVd3SOYYJUzZkytLRjUzl0SzlNqyAxNaTDq7U+RQO9\n",
-       "6okfjOVMfaKpRl+ETykdPGaIhO1isXFVcFcgkSS7YEyrZDT0cCOfWaqbVNJSIe9ueQfqdDJXcT88\n",
-       "LnJSDvE12O2ocDtxa8DC6Q/iC1vCHyUuu55k4dCRbiyl140CIsplhF7Pv5vaz6jkQowgs1qUBwbq\n",
-       "66Cs3PkjHPVB3I0yaskeJzKnvrNAT6FN5tQ/FRQkJeJOoxdJd3JXSClF2p+gkHXKtYnVwfNvGtVe\n",
-       "DyS0WSUzJ0rU/wT93WbdQwusSZ8TNCkBB82C/FbcMcoJfHXKy23dDMQXzcNdCFA5CpFxwuIx2HT5\n",
-       "Rl9h5fmKpJnE1X9szKzKelH6Yd3/Rcmyt3vaCGoLtFSIHHuCbLOUnh8rwNWTQj/z+rR0U34ePQ6J\n",
-       "hd1/38n3cwwegBF6QzppdOkYJ1t6jKtzS70V9F4N3MrfZMEy7yOuPbmUftN0OcmQQCCEaPYTlgyV\n",
-       "aA34qQHSTq0ltMCGvWs4HJ1gk3TISB4DHL+EwcLcAmchX6EPSsdBE8VM9FNj3LfoRHtlPAZo57pg\n",
-       "lMZuor1H8mG1d68I3XdbcvZ+i+94thMRt0UsZ4k518VJMiSgA8TN9FEwAIO4oBT2L9wM6x5bFRJI\n",
-       "Ym1Pl7zrF5qkg/BkTU+J0DYesqHskdo3BzsGQVPMsOeCJGj8I2zDgk891J2wO82SWYkPjYmaTTd3\n",
-       "vMzqlUxNyeZAq2XumFYYku8PiKHthGM5C+1zsbG6d8VjzfXx7TXuk1C105boO8rNVq8whP0dVBsJ\n",
-       "V/tgTCCHMLx0hWxSFi05NXw1tp8907hBi7ds6bPb99bpSPJhFgpcUgY+Aerb1+tPFxViKVZJpIGN\n",
-       "PZCFnNaGB6D7GiW2V0N+KHnZ1b9XsNZ6OJbBr5MRJkRidbVnq2R4/tIQnw6pX4Fo3YBP+YPvH13V\n",
-       "BjTaIvd1HGq6/J4iml9b1c8RUiJAWiNULcgTHzlE1dzZsgm8nWwS54pW84a55Kq7xB5NWjNJaf/3\n",
-       "rq9N5ZxP2hemc/w8HVpWTb5Cf1JwhZo1g+FbVTNOHci03L+IhRBxj9w4kBUw9OU/apRRk0aMUgwj\n",
-       "w5ZzuHKxAkstyLdvEkS5gVShYxAtX94r6l5zuxwQTmEfgOSrcAwJoKrewNeDALViGmgam77jEssm\n",
-       "zBPIasL1/8nW2TL78JRSTW8n74x2L0qocKin2jZILt8mT+iQMgF/A1mnYzGf0ZwMsT86elieU73I\n",
-       "4D1RRqDNA7IIB48Ql5KZ8BnbfKlSDtptkWzMRgxS2O9DZvz2BqJfxBjxxBBhCQSxXD4t/3OXoWwQ\n",
-       "weEsjcDZ/rkTCkL76LGf3Gl2WpgpoaSWfkgHPqAIK0h9SA1TIrqh6BiCoGNT4GPoCpCjx+apjINt\n",
-       "FE3NXMZ9CHUbfj3BUNW3MR8Euo1Sq82KtCtmMYVBbtKbM9PwPW0B1ymqr8TxVbCD9pdbI7cg5Spm\n",
-       "dVSaZscnUFGKbxhMpxhhPqgvq2FuqMULM1eZs51qhmIlt3PSk66aVMJxaGF3z7Qjs2SbOYHkjuct\n",
-       "vOvfNMYhws74eBcvB3t3hjRsi6xWFGBKyjYJmTiFeNdgV/DjS2w0NN3Bm+GkFTu2y2XVWFVeZOCG\n",
-       "Y+eS0IyYtVO9sf5bSPjG9S/r3zbsTttcSd2481NGskf8AmsOnN/GlEg2/UfSkZw+X/dO5KBeTWnN\n",
-       "xAXJD13Ge7vvmadJ/C2iAgZ2EIHeeYW9Ryr5/ch/u30HyMcDJxPhYFU0XEGj6JBPIVrkT9ObD5kF\n",
-       "PVfFYkU0b5AFP5pSWusj9gl49B+DZ/HtL/kKUfLZ92kp8fNyT7+CZSnI3IBv8cmaFHhGmrwcDRno\n",
-       "PVoWwOvEtre4PrUsHQNkqMp4jlnKne9NXybG/dKdURRk7ff+yGIcY0iX6Ms3wMwy0sTIORxxaGNo\n",
-       "DzE5bewXq2lXjPBJ5mD0CAvRHAN4DFOqbSSGXq8pe6veius/4KjWOsvmFczryOV+6x8AyN4CJZcA\n",
-       "gI1kE3C9BSHROjd8KGGSZwieullRcMVnun6D1DNcTaoNh9IJLw4NlFPZjUDg1CPivnkeIOO9bidM\n",
-       "zHioZzHuH2h8HVAOPBnA8B9M2tgsru8o2+G7GyOjkbRNO7hsoXZmu6wJ//8yUhdMU0xqFlrL3844\n",
-       "zAxufX3hGMySD2BsnhCdQECzEvcgjVKlDF8KKZ/YM0iShP4FcUpMSVE0Ou7MGq1qeqWjayyV5Mw2\n",
-       "RDgQq5sPrE3xvav1Uw5PpnGsNm0kQS40tBUs/Jzk5CXwoX45OTT2Ayk8ZGrb12UF0XnY8GQwkclG\n",
-       "3kf46x9nZEfFWP/wyg3Xaiaz94qIgMXa9WEBhOXHAHw2TDf/6/MOKFf6vqn2gTloguRGCBQ5Td1F\n",
-       "awB/5rYJfQCACcy54XftbTOaVMGGARGJgkktoxCSNYTqkPWoxcEmbbzz+oiEOW/myay03f7L6ffa\n",
-       "bJc1XULAUFkrhpnAGDW0Oywxv+vhr5mcdwcFSpWLLeUEyA2lWBueRNkUSbn0rF/ohG/Eh1kY5PfC\n",
-       "SCX/xYTFScMllHgs3880obwZr+chRQbFkcowwl/DJ5Ie384/qXCt1a4K5nzLDWiE9jIlbAL3U1oA\n",
-       "ak3L+QXFCqJ2uK+2vCIrBQHurvX3K80VKmuy1L1AU8Ebkn1jqpDP8cfcRl44ZhhUR1UD3AfrZfXl\n",
-       "94BEFJxhWyPfXX+TA6K+Abfnl2TC0iWuiPPX9FJuYmD5GybFW1Qh8RoZBfRageW2cocq9ileok5q\n",
-       "bYYBAJwtdsBGoIwYHQxzdKHGpqKbsVSDrN4eWAEqFvBhmTYyuT3gnTqe1lCia6Mt2nxrDAz/RNWg\n",
-       "G0bdH+BGpq8trAl/eR2sIdJoY6u8QmrAMtJe06mh0ZZFwA6Ce6jv4lhYRqc8ciFm54ygp6l25QFJ\n",
-       "aIHOaXwJEgHGLM86Un7jV3qFVCyAroEAAAk6AZ9xdELf5+D+abh+4q6u/Gx5ByXNHna8vFTl2mhH\n",
-       "yyKmpqA7jUZumX+tYtwiRh5rk4v0dgElNff1WNXC8IQdKdSV7aM6cWJzJhX1cagQYArI+XFWkzP3\n",
-       "OVdsCdVvhtDq4NhAucWTh3uUYmIbl/LpImML29TGiswqSnQAmJ7IbKBfxAblmrEb3Fn8rIt+/JgK\n",
-       "964NRm8LyCj+xt/aG+DZrKHdfgeDCdMHBovkbu1LkUS/U4KNjclACtX4egP6sJba2FkP18ljpVWq\n",
-       "uqW9eK1o7vIPwxoa0qYpwfJ3nfB0w2d6QwqcPZhKNY3heIEWEn4kUFBBpoPsacYwK2PtaFVLUl4n\n",
-       "kiXSrLsC1gKVPLqFXNUegXKrRbRv2CyAD/+ut4oY/uj6rdylJy6FAyGPo2Rq5oCL+QV/8k6cJhgy\n",
-       "xuBbhTHdMknomGa3lj7YmOjVY2OWZiz4zdplV9ChcRPYDwHphKqSaNuqZOzshB+lDR3qfyTjrBZ5\n",
-       "j/3a2/1qPeLEOz+ayPmv30nzci3cd9iYRAEe+Pz45cgSfQMtwQ4oEz/O3VZxcmxtyPWWDuUBF65G\n",
-       "/Gc983Xi1wL+tczeNp21X/slgbTnqt37NVUrM5w5UPbx+YSfUlsXYBOqQ3X+OFClzsBNEiv6yy3c\n",
-       "9t2MdP7KVPBoBdTsG8IHfWdpd5yRfGgEO3xUhXyr6Xzq9Sbo7a9/NeQKh+Jq91tkCeniOC3yjzE0\n",
-       "0LiWiw/NOIqe/018FIaoISnI1jC/EteJQCuer12mRPMdxqziNG7rl3bWYkMHCoqieeI+igQnVfak\n",
-       "x15nn9eHOhJZnNlb3DFNA1OhMC7o7K8LOdwzAcWIV9EQuyRzGkpoztwHcF41TeZ1MYEx1qS9HWI+\n",
-       "2gQ0Mnp0RtGnhn4nr9U8tzt94zTOILX+GIM7pQkBiduR5ufLYHK7Li9sRU+doK63nrG/whYgUpHW\n",
-       "41FH89WH1ASJxw6AC+mjOyy+0JiQzwYvRVIyKyPNgTR+ajjIcQ1rGrF88rwMs20uugI59/E3LPNh\n",
-       "zflsVN2VskN7Jq0bP82wvuCiMgSrt3bTDICC0HggKWxF0Rjt8oI1BNI8u88aB9Yn9yqk6stGmHk1\n",
-       "Uhbjb8NKGpSoHHlCQHs5VdBmcavNYpN8u5sIaUBhfse4A9Zn8EiEzlWYP0u8jOr5M8XBXudVY5GA\n",
-       "LS32ksjqC3B6CEcSIYYVWVmBBwxAoBvWL22IMA+iC8uVcsu/OugHlOqtWRWcnJ1gihICGuRahOTM\n",
-       "gRTVp1NmCQI7Ndq97o6Z1xMUQ3U2So1dDV4FzLBiJn3KPv2MrrROqaA8u6pwQyPO2eX/bUwULubS\n",
-       "HgOG4QCLqx6htfbxioZRVtFVevyQfCGRbZw9RnfsFjynhFsJQERZIzG1V/OT50SV7zvYD02VRhYV\n",
-       "uUYiwKQXpBiYJ6zZbypsp4Fgo5WWLX8nj3agXTkrGMJwIAPt0ItqrBacZhGhi89iWyXqccf4Ro4Z\n",
-       "xqbfVpHy3MCQtgJHKe38B8ubN515Telk3/swAAAS5dKY7v9Xm5FsbfutcDZnk6+l+Wne9sqasSJk\n",
-       "V8/fLloqTf7Aw9NgkFJs8CpKzz8MpYP4jcICuFbn56PsllkwFgTpIEUM/2Xr1hE6jUabxtqGV9D7\n",
-       "sNG8y/tMKI+gs5qFcqZWt4gRHZxjLZAI3ps2vrIa6TQmgNEk/eRkrL4MjveQGgGcG0oMji+eAVQU\n",
-       "LxAjOWB7uXwImAYooch0glRvpqz7sCKURb+VdN4h/yds+t6rQSX0/xGxoPdU9aHZN5CswJTL5lzj\n",
-       "Cn6/PFjPXsyXebDgiCBbUSMECyMRibRTtaSmsV2GmYM5K7WySmWPCX/J/lamm8HrCPM7FIeF0VLe\n",
-       "NIww8bXvLokzP0waIGmu0FLuvfB7noyNPKnq6dS8DfSt6MnH2/tFSOoq+zMxCQ+v7lE2g692KC4r\n",
-       "GtR95Xw766K2bMvcYvexlfj50I3td1+LgQXmL1b3GqBWk83y2FV6K5p6D5pjL4F+LOHWwqJRrcND\n",
-       "pM6mQzFhAWOny4tcZ7Yxs/RjFsW/deuOpXnOj5hQ5glze7qQcLFkqRPK2uv94oxvzW50xu05bEKT\n",
-       "srURZYe3BpY6m7ygvwm3jG8aqnShLpBlOIiujsGl6YfxJqpsopjqHW3e8nbtI6mcPV8ss1/+wf4r\n",
-       "R0LL2P2bVmBdMEIAEif5RYEyKkptACwgkN6fbyHn2Gq/8tJtU/SH40buTtu9IBmqAgHZlga7eNCs\n",
-       "er6KR03bIYpv4BdVvzrVBYK/GskOHy+s0o6Gi84IiTzIRRDCB6oaU2YErLbjUCmDZCFtl9ReZOfP\n",
-       "ktjZJJmWU4WGUNdax8a5xQf+SyVqPBs5ZBAvMybNt1YJxGg5YmvJr248uBxo9oKV0XP0Rbe+/p5/\n",
-       "APj7JoStLAwoRqAoBDzkgl2KFh5vTZjody2r+ePUXSjuUN15TKoAVseHFqugfLcC6s0vg7fF1Qyr\n",
-       "hILeQb+Oxh3h3lMkRvXOMOcF7Qx9fZTo85pSRdAkG/8DrWllexoDab/qlfWV811JpaipVHqeW/3c\n",
-       "lLiptL98x89sb+ooP00fM3JMgU+RkJtEHo9vP1NH7q58mjIAmhNLak66prdhA1PkSrl0sh6fEF3L\n",
-       "yAbiAyhKDsV9o4//8yhuOR0b+uoOMbiGQf484zfIqApZEUMVsaVXWbSEqZhIhcYROrD5ZLVQ32ns\n",
-       "HS6iHvdNjdGIdVIcKsEVhNusidekDILSlWBTnFP4wAAAgNFyyXN5nUFVBAMMFhDTU3rS5loXzfZx\n",
-       "NgAWmVoJCK7N87K/jZ8AGhTeUjcnYji27ph6ga5tuuifF+EohlANCoJdvh+580yJXpGVlcWBHdJ0\n",
-       "BFR8BXD+oUmO/g28MV7IM/J5UyWg+m/0mSawE6LTA82bch4Td7EDpYmVny1a93WC1XuE4AIAsqV0\n",
-       "lzf/6hxXzdVezv1Pu2SAa1NdIttMfxPp7DTy3xyWD6a6pOmcUw7WmBoK53gN/nJgAbF2suPABqE8\n",
-       "mBh9sbaLIR4NjtcipUqpCY60G78u8vnJuYF/iGGOBImm/cAaDk1QGonVnCd4EiTqE7H2K/oDf0Oe\n",
-       "MCwnf7BVZOLiyY6cvsq00Unwd4x7nOZ0dBgfVgux7kOb8/f5ValnI6mTHVOKAz7FS4BTQAAAC64B\n",
-       "n3NqQt/qdcmZ8iXmw3y1pPJ0D0XtR1wZaJdyXRHfSq/gjzWXwD6tPRP66FX6EIgZRdn6L9Se1V2h\n",
-       "qFJBKOEQxCnr2kaiNJbgSQWYEWOHBX0F72kklkR9VxIghFrj4IJAu3bqCfKYjCXI/qHTE0oxlYyF\n",
-       "Eli+1T8z+/Okf3dkoqQAXobAtVhStWLHYacQiBDnyKg69GGY2clUtPQSPnTMog32ARixljHiCoRP\n",
-       "lbrMk8bdT58k033KP280YWPbMFkRmYMnt8KYyANhhfeHW22YSMI+uZZ+Gv+xEtFac0BtoTgQHm+W\n",
-       "0a74uYMb4IdYj2sD32AwUU9jMrS8i6FVVSNWTexyqg4dXqi9qg5pnr1oJf57S6K/xl7Fv+LkfJtE\n",
-       "/oL5SMI+NAM7tND2JW6zKRwFRLKy3brxpj4gHWq/0G1qeNlM/CK76n3B4co4PhOp4HHEc/u3V6nq\n",
-       "wIMX+NoRtXd86Wd7q3j2qoDqKCkpSBKwd/Hp+X0KEwv8uMuCHc9hj0w3BPyS0vVPl/VPlg83YN7E\n",
-       "KawXR3EJ3FOv3qTst+0s9n26jIpkZCnNfigGrjkyJbR6IE+qh+/aCflDpLJUGnKA0s3Yp7aoeoaS\n",
-       "AbP0Y4TKrXvCzxBZsElaQaeDUeUptoBI6P/UiZ6ny9HW3WhaKiZc2KKs58llNqflKTz7jKMbG+MI\n",
-       "33huLfK1/rnMWi8y3ld49pWeYgMEURM8n1RzLJKAOznwyifHR34sjcKp/OoWQmmAaf8wUAvsMvuy\n",
-       "NYvHfT6c9tcUnw64Vflz08pDOLIlsnTxYG19x454lJJ/a2fOtvfaLBOWAOr0Jv7LHgNEvuLYlZfQ\n",
-       "9iBFXC7fyP7NK4owMmEwLIIrdHqrtjBE12iYs2vK5WxG+hvftbXpMQkGwPHUABrBl7nII08wE5rb\n",
-       "PZg+p1xyxRqd3cS8T1n1bTgCtx2cf3y4JPzxb36ovjuct3aS0uUOsr2IrjXC/XBnvUPuo0CWYr1/\n",
-       "uevNAh25UCmXZjFUhNFWc15Wk4Nvn/c1mAQAYEY5DsPixcTYc3mGdA/dEVQTyH7wjcDxMKiWBTSi\n",
-       "uoQPKCdN+5LHlQZRQUJbYDhtosIMm6wvfa1BCc5oEuAJBQb+UQa8utFKFQIAwHasX33yB5CF0dPd\n",
-       "VnwA5bGxwFUrsY9ME0TG/iE1RaIo8qmEi+F3CFdnxn8QRPneurQaX6kwMDYdiq7CWZCckEyqK8hL\n",
-       "+2/q7/gbXlLhdWXVasdL58n376Cle/yV/BLq+7vqDOXx2xSRupY2tpvaAcMrx2FHw7THYjkjsEWI\n",
-       "jys37b6lWsa7nU/hPNj+Sq99x7XOIK9JXUn9BY9D6DXT/OSDyiSbj6RUd7almWMwavUGXFbZEIF1\n",
-       "b5X6GdAzV2faspkRc4KB8YcP8q2XI6LA+arR2QwcN8H1Ar01/AoWlt++Xtct8KK6rTul6p5ny2Mr\n",
-       "JaCnBIoNKQmsEcI+Ypp6kdaKx6F1865gqtivayenYhKw53oQ1gNWRsPZ21+r9GNId8Wk51QfTr/c\n",
-       "QLFqIzXZ+uGDkTkdAaLp9OyOI2t5Xc01y4ZP3dsBNR76WcgB1de29qGDxfMIg7oFn4/hfgGGGXMe\n",
-       "rHcOs4VapUbQe3o1kjkwukRVbkjEomRXD4xmS/8c25g5wBprSHj9qcvDcw7Rq4hi4I9XeOopL6bU\n",
-       "4NHNcutnrV1FJ3DXnHIy8y2HCJ8o1hczpWoRMuTHYq3ANa+J9nQZ+i3Oewud7CQ5CTTqG0MLGjIB\n",
-       "zAvKwv2MhoFCzxRIVK28c7a0/NAn85uyLlKhrw1Ic1cXCrJvU3HmGFVnqPxeSSuJkK8VESOnUruH\n",
-       "clok0xPh+gv/acs94B70IT4/vOnVx9Uq9UcWMe5J1yPav71I2vOBxJv2wZ4W+EyCWxLRZsMuzTUt\n",
-       "qDg7EU0XegUAlY9bngwhieZERcWTHXJu3OoKXdJOroFv2oHG0rfC+zW29cRIlN68dfy7p00wyOyl\n",
-       "x1tpwg7vbIiT3FN6QYWZUY/1XrMN+l5o9qgOJHVDvmX6LYk4ZHk4RYP5vl69o3RT65EQCDchEnWX\n",
-       "wQAXsg5PWRwEkABvw9o98pdGQzZNURXJkOb/F4hxyCYVUM0ZvaFsyWWhDeFT8UCcY51FIiS7XVYy\n",
-       "gpyoPYSi4dT0qo+Q5AHPsF9lOtH6REm/ytm2gSAPF3l++3RenovDavxARk4Jy39/zBwRAjH3nA3f\n",
-       "V57SqL5H0A8kXU2IMGQU60a98XEB9z7BsOglqUINk14qAS50s9qExx8Eg3cnagkbqqMDvuQNQZAy\n",
-       "AC1Z4yX9bIIuuQMVQOxDsQKLK6HNCiYRat/FrBa3vtASaGvqGLj7ZdNIq/GrYhTmyzMBMdAOox9Y\n",
-       "tmH0mnynPoQu4qr8MzJ9wrIm4UCd882zPEDMdLLBoBJegCA72ryFddCD64iKcagNitUR5rKK8pEH\n",
-       "9ml8fe5Dq61zxx594r4oovrmfkzAqq9zJd7oqFkkD8zR54+yueZMg+AIFpVdSLefG0A0RYjHiY0U\n",
-       "Q6v2uH+ksmtGA78AubCFwiGZgEuAC/AGJGuadoCzOuOyzME7jrIKU/tovvs0Ecb0qPcohyBOBFJB\n",
-       "n9i4MPiztqDF/1nGl+VdwcvsHpViRx4TaPVdiYW4yF+PKbw3UJhGS4P954257vp3scZW5JSlJrq6\n",
-       "E4glPKUyxKO8l2P9i+YSLYGZ0A9cTMFi9UpyktyAc1gKYfv36ZM5lt7fAM4thMuIc8YIDGpCIZ9U\n",
-       "ujaqCxdscRtnk2OKf1sV9AOIGkRCrj+b8wJsACurqMOd4iyhaM3IXD5rC7gSkdzDCVK3/yeURH8S\n",
-       "DGDHy4a8LgFV5GdDaQWiIazTEePoO2WYaaZxoecOnOsLXfhcjDXrFr8Oq1hcbE8wcUIqA7tNxlf3\n",
-       "0BKQcgbf1Ijgzwkvt2LULm0z/W0vpSa1f3V7VmAzKjENAbL8k3s9NOD3frwieLwF1bKxoJo3v4vT\n",
-       "mjxoBVCqSVvyUwlBnoWK73HpRZFm8zE51jGO61/8lynbSoS1609IVDegLduB6XvjEsXto+oT4MS/\n",
-       "MWNOMQxf64bF7vgAOMqHOuiIMmXmXbelzP0ByyKHOqu/Hey4QVO/1bK4bEYQZYTgqnooQM+6xWlC\n",
-       "6XbRMyyQsN4py1Aj2gAAJ+qdS+Tu8nBIXDHeK30F2mwOk/nIAI8IBGMxkp2F0clJfGSoaQ34AtMs\n",
-       "aBgD9in6pD3R0dnXoYwjg5kcCB4Icb3nH4GUhj7m+kplYcsWiHTYa+HpPr3Ts5sKrJnhBD46l+4l\n",
-       "rOH1Lclw3pqkMH0CHbKR41PjhZV64RJXNx4O7XB4aXisymckKXMvhH0EGYHLWMQNAoyqxP/ETTbX\n",
-       "gA54MK7jpiYZV1dQTmsKA6zkSsGGj3w0W9dxnE1T1C+ilp4mNSgqaHhgbjowuE2zgA5LafJUOCmu\n",
-       "6Waj2q0VwCw1LdMPg6sKqVhhKNA2n48VKDcKC0ItVv9LngBiBTzM5VPSCTzJjvKEW1kWqoxtApWY\n",
-       "8QAXwKhiorlWIXi0Sm+mPhd5yZYYs/iB5UlhbNdqM9grOeKL9xf/fJj/g6H7+2lv86eFu3AOJVkQ\n",
-       "ss7p+PaEm2y8wuqeWCtimSrfgi1i0WpvN7/nRCWp8sIRbRPELhY6xrckLnQoXDhIwKeg74zjq2Rc\n",
-       "0zBt6A/TrCLN86Ji4ogG3cGoQAwcnPb/sh29uF3ajNBtBYqWlg1pFkDhAHFrDvuOYtoUY1BCahoi\n",
-       "dx6MyI7Qf3+LuvU1PJUeDboCf4xbconVqA5g1CynZn+TxwxkNBhGHRYHzSxSPXik1j6g3SCVq+KU\n",
-       "hRxt/wSAoVdI89ksG9xChTy92HPJCIP9tsVfgLgq5S+8xwkk+Dwuw9CKtCLZNzGa0Xk5Pmhfs7pW\n",
-       "8xO6Mjig42mpbqolVz8Kfebqh0ff2Xg/KhpL8ZeZH6J5K+6g64g/lubBhMy9MJK/lAjbxc5r0wCQ\n",
-       "WI8HlkcDnlxcBZvJZY4nisrNJYuoWF9BQQAAFgpBm3hJqEFsmUwP/4C9g2NbgJGf5Grk5N9XKcty\n",
-       "/uRAXu49AUzPL/8vPZgoixpI01lnR8//ELQNIac14Mzm/sGKoGyGmVowpbF3Y4bBkJ7v8X3pAs4y\n",
-       "bvGjI06clJIKIpUV6Ir7dfAwkVc5lmstswl9LeMzH6PqRLuZeNKJIeZZceoYZGrFTFdgWkSR67qa\n",
-       "GkQytaHhtNH4w7Ux9TFN40ChXuQrj2ZNhmUO8gYXsAkhk27751OP7Lz1wvzBRvueAkeD7BnWfn9S\n",
-       "Us4wac51ViAzNt6AtE6BgDUZXDFoTTjU1/7Zbl1ctdy4I1fR0fulGYg0eZMvogMXIJTJXumW/jk6\n",
-       "nrUP7wV6ICgCZSVi5eyS8xzqMDalFkuVXlk94r17e2GJJnx+nVk4bFesCle+Y2/jyrSADO9DFurG\n",
-       "G0q97T5imlo4cWZU3GdGZOVXet+b0JM5da5yOrF0fl0/BZe0i2+/0NhwVcpEQ5KqR5S1hVumqZFq\n",
-       "vERrSsYix7nU+mc/Kqtepb2gYWlf39DuRSdPV4GJ81NbC8rgjNMDWtlRLI/gEI5njZI2vhgXIY44\n",
-       "QdyT2aO9Q3EDd/hhFDiO2ox/1euBBhk2/rcrEYm/16lh3djRl2Bts7B6epOvAuClL/uzL6Tnkw16\n",
-       "j8DuP6NfSMbwZxlMWwPiLEnL7YMwRTpdQI8TvGV9mbifJ4Q+EzX3R6WRqzgl6BtQ9CARFnGy+C+X\n",
-       "C6GtI+d2W6AkE+SgwnfqBjrZHgkEtsR1fXH87fiBJBYKgHvAsiMXRyogWPhLU+oMq2RXWdnf8oUT\n",
-       "t5UAAm4U9HoLc7KTM2bkXgpq9ooCBSykreumKzHxvzs2Hb3Tjb//vRejlcBZwbHDRT1skd2w9/OG\n",
-       "Ks1+ikjtEAWAExJi+tOvwgBMJ3NFa3zieCARHPm1/tAEGpeWs5WUs3mGWZRVbVwonIju7itjLjJf\n",
-       "K1OE/i21mue/HvNv5SivxZt6wZbGhikt22Eci6ZVsBrhQN8e/36k1gdKAa2UKjJk8nqFZfB7sDF4\n",
-       "LNdPlLqrYOUkDm0qOnRHfANOd+yrfGZ+r1wFF4O/MHrkgdY4VeCQSaXN3NAHSUcFIDS1eOYD9/NS\n",
-       "B05UM/3wiGF4XvsCouAfUUk8x0Oqx/MHuCu+Wz799B0UIZSGRa/qR5GLZvNIyV2i7KErxYQ8iQPv\n",
-       "EL+s1dy3N84xYJcfbVV5DosVstx+BT9n93csE9Gctf4fI4cv4ca9ujd0VlwHaFUQNtCGruuJLQz6\n",
-       "FsnE/p5YcPCqPNqeg2cmvuT0ueWSSuxBhX4tFOQ4wqovAQv2Swe8kYgrsGSHzs6MoWRZy9Tm5jzg\n",
-       "SF5b06Z5xCtwvBtYJNUQjBelC1dtJHGnb7AvBp7+mhUJrLcdspK3kL+9ysqtQN2aF1d7pCL+SISs\n",
-       "8IAA2TvqEhYvzl6tyFnjQZbTV5aJEjz7TunADGrwp6m1S46Hvxt4DyEvpls5CYiPqfo58OarirjF\n",
-       "YIAnBvtkjHBkmr1kFpKpgQAkBP2fxbgIxT0oJ5eT4DomoFrOSau+WCcHf6b9sHih9iM2qzJnMCrA\n",
-       "uMdEej/sL9VjoP5PgBlK9N1qCzYAKNcYdKHovSdZOuxvB+kMFc7hy4IvLLqfPzOZ5j65Q/ebWW49\n",
-       "yGtMUHP1k3U5ZuMu8gjI1XPmaDex4H3sO83MsmPyQD4iB+80obzz2ZrQQ5ukrLoWUUzKQh2AoiYh\n",
-       "yCk9c5+xWbQieItg+A/vejyG/oathhhwaDxD0hGnYPlKqkdwP6zSvHQOFemKPfPjPrTY392+diE8\n",
-       "9WOEzs67n8M7vL4/ZvrGpZ98JJCvtIbJHmVlouvha7csfoxyG7XoBqZlL/ViSVq1ylciaUNPDVmh\n",
-       "MXlLlT5Mo3Sjej60LZuhCIdWjDrYCc/7IkGJ28UDm0s7zlTTx0QtHnvY7MvJrOd2D3yAA6bnmDYz\n",
-       "gT8iXWVO7otGwhKWucAeZgSrgqb1BBY5vfvU4sYTBMjIxjKd1IYgTtL8W97VL1596oe5tQGFOFjG\n",
-       "POgdHzKZ53gs26alOVOfRaPKbi8TYRabJ2by0YZcn4tOixN1GGMNG0eqw1+Ywj9KXbf2lYwyi1aL\n",
-       "qCVkNQevcV1zlregT7LSE2Ouq3fDTqYpM1u5LwohsQjGhhcdDZaWyxm9cRD2JNGt8B/QcGCriVgo\n",
-       "kBt0c/oyshFunCq9ozgF+IZHPeYjIPAoHn6/3GNPp4PloLkbb45skW0fRCO+FgnEmK68KwsOJlM+\n",
-       "0htrY/aL6UPmYR1qL/Ij6Y3CaYBjKBgKbxqLKhmDeHr4GteILqdsTAZh/c4qSZ7I2oGVZuTTniOp\n",
-       "Y430UWmUzzIxIv3S4wCWD1/DORPJ6jmm4Mmgn/TC6X+iXp3ctmwx/i4BX23JX8z79+A7+5SJ9Kzk\n",
-       "cK0zo71+N0Uz7ffSj7nUHEfDXHr00qh1Xx/UQcLDdAS+axOWOHxMraaCOhnwbVGBtcj6SfIsmlkV\n",
-       "ona1IHHk2RPAxuV2c2LS9NXZz+KBm5hjBEUMDQnHPo+w8q68mUulHgaqnYU9NON/Bc7ERwXvizDW\n",
-       "IFVNxAXevk/gnJywdmnwsAqaTTHEkl/lLdLNhB9ZcFMxfHsNelBnup7pXdpZVb2pkVNHwB5tN17U\n",
-       "J3oMCpCIsHtgZerhM0AZ6KlcIeAojOGvn4g9E9J4MH3lV5xJKgYdcZolmHQ6WnYpR5OaqU2q/E3+\n",
-       "lqXq7KRN2GzfJMCkGshLXwnZYlWkdX2pIz3Rm57nmPFaElnr9sRiRe9C3/Yof80jZX9xIekhKz5y\n",
-       "neIaI8YkRrDxQN+HubE/n4Oqrv5CNqf3uRANKsuZZ/0lUzlSzMaUqKxbQ+vMuHwisd53O0jEZ5W1\n",
-       "s278GeaEHLT06ZI4akpRSTeZemnHYD0rw9QvlPyzHLm444+TyIXIyhKaCmxKqDfDzdIDEfJ6VGak\n",
-       "mAnSvoSN4yuoJxnNm2qHRPOi/gOUJD60BE2du+rtT/ocozlGAJz6EakcX95MEiKgbZYOfKHEBLjo\n",
-       "OsXjH8oxfy6A/VNG3I1u47B10Owe4vfSc62H/gmI+Q0YCRSYrh7xk9iYdhWLRE4KpZDy3AAcIBNa\n",
-       "osqhqEpsKyqgVQGkk4d1mmFBasV6a4LKMPE2Xv0GRqhvziSG7wrzhoVZFtb9OS8NLlMILS3gy/7h\n",
-       "8UCVW6t31dY+mNcYmN2jnOk8xjIMq4v5expGTOhVeo4t8wRTUB0nruIn2V9rvmlUAAtDwkl4/poV\n",
-       "zT/QZtBHZbP++yTmNCXlGezqa8ywOxfey/ruNMV5YdNgSgGk+cB7315X2K39ffXMuWVFDvcSORJN\n",
-       "/AU2sRLLbPKamshK7vXa+E8KJF9zENRd8Ie0ijOGF8rAK0mkVRXj80vjAw6ed4KSzy3PbS5X77cu\n",
-       "iOCNfleXIQylWH9SMBb/eeN9r/xWm/x7OeSgLjMhMK5jwxiwIMDFtmyys5+fYVSE5U2ZiohqpyZI\n",
-       "e7QS9ZHXiMQ3FcoNgZPJB7p3EhyBLNsm3IVopPT9erl6AwPMm77NSNpeZ2RofyYpIO2ZC3cMRx05\n",
-       "hafwtIqQPxOk2Nb+uUsXnlOb/N8P6QOpXI35Mh5n9n8olnQ9Nm/N7USRVACJfLB7SnsKtlLDFl7n\n",
-       "7wdToVlIa3dZ5af8ZeDLSH6qREIvF2T+3nHFYku1rgyRWH644Asvgi1j9/cLa9aYYhmVpn7KP35q\n",
-       "EZRD5kRzMKrcGrk/ZfD5Y1vUYIxFaeYwTCORDtKqV6U4cJ2fHdClgk/fbrGHPO8WEW2kb2tviU8E\n",
-       "jlqrnJ0CkTYixxA9/mqWO6PnM2xrP8fKjr9g+VBzjSK4BdmkApXmOE7A+bEYxXhuzgAyxRqfh/ET\n",
-       "gPa+ZL2UqoFnscmQdWMrpVOJnMsEyLvqTK9MsMCH328XSCM+B6vBDQJdJG4F6J7n2ymV/2dmk24p\n",
-       "3Cuc8yQ4xc/3wHk6JknxNbZO7Lrnr2Kwsr1DkQztLrwO/pMNN3XZNlyE5ql8OA5QSIIBeWaNWb2Z\n",
-       "KW78faFWdvQhcceaUNM1BuEQ25EijhQYiyLMrgdS5t8SgVIC/7jvwWuVCv2gZvrv2+w/6rQl+O+V\n",
-       "yLoWvYomdHizszTmzdC6YAIevzGepkpKTzjg7ThmceN5VrJdQMYRzxwwlF3MdpFZTn+AL2D0qrDc\n",
-       "L+pY00pV6jlOF77wPjMJOx3hKrN/X/usfswqsfTL7MoAS4lUA5sLWHCkFkZH/EZfNeXDS+D/4QEl\n",
-       "xyHizlbY/tS0l4nAY6FgwQ1w2oHPhP7xFan+KcU8uuuRe1Lb4gWAhhLIIGc7RSDkRJs5YQ81EDtl\n",
-       "yZ/YACeOlfPz0fai2ekvw41eZ+F8+Ajodjqc+zj1SfiA8ptbaPYw1CiKOgG99g/6/0RrQwnVDdmV\n",
-       "T1o31cQ24Mz8CNpfUsduzEVLADdTQgNzF3WRaOUrHFcKdDNBTbmFDtNhFCp2EvSFkP6aJquHD7/6\n",
-       "O0PfzYEisu200wfrS5ahB8IL74LazMf5EYhKn8KG+BDZU0f94j0mCwYLOaLRpKbIx8RUZscUbw9i\n",
-       "ncx51Xe9RTRLyM04eXgVxm0OltYWdvihxa27nIO0l28Scha7Kokm/YohqB24SSqKiXy/2IcYsYCG\n",
-       "2hqA7jqKDTjIkI2U7CJE+pJhiryXarv0b+hM5vw22WdskGuhrZ9rgZLrXQ1nFK5bt61VS3+Hbpko\n",
-       "MUepDWdc0Z56tB7z0PzGM+UVkeKJlPtKJdj+uFMw0stXBqE2QqPNeEnsT7IaLgE+6AI0fTpf3IL+\n",
-       "4Z3OoR6wl6UoKaWOMA7+KSkxWhWyYfMtDHoV9ZTf60wy89Q8yPHfwthygoRKioxUXVScAUha4KmD\n",
-       "sR7QPbrvLXbR7QAUJyBKCtEpYmrsK2LBxefhtORNLERoBZovhSihnNjgnke+TMb5Sk4LigwsJ+cq\n",
-       "OeafG0yYUDnl3OaCjVX/ZnkEgW7Vfm71amURWnwvCbm1EEDlRUKxWDTuko3GpLzN0DwnEsY6i6sm\n",
-       "YDNjmH5npd48qnCCUNA3vaRjcNPzTTWmp6Ws25LvMdsjKXRLbzjhkC7xmUqG/3ITx8vuM6seabBc\n",
-       "DjKKe7EeMPPXoWIMRf6CvfXEiCMgzftHBqk8uJ8bLfIr4tF5TzmcZ0vZeX969fd02Q2e4P+7pNiW\n",
-       "5VgH0LHRCDzwOYRbaJkomEPxMLRHzanjy7k9Ehv+HBxG6yvy/829DbACKyP+vp4fptkmsS8Hc+ga\n",
-       "dUxe54PzDgaPycPu7hmhHRT2o/9cchxZ/RL+NYrYQno407urIY8u+X2leDukigH6DGWxVFFYle2K\n",
-       "k8LBqDGzBlsc7KOfeKKIQxaIjmuY+8s4jTJG0z0HdGI/b/N2nFtFcuv3LHyTYorY05tWop0N7khw\n",
-       "0b6j5M0BG4pRqDTzK6yupMujAY5V2np1o0gD7sjGB0gf93fxNTqW4a5poExNY/oWDv6/mVv9pVFZ\n",
-       "Xxm0whJUuigtFz2qi9MXn0xHGUGxrW7WPnPpmUAZjIERz6/uAKLEe0Z5EWk4fE7uPvN9w/DVMmCX\n",
-       "ABydGmb6QsxEkKr/g6eg9h2nCxWgmjFX+tkT7RA//ujY6Ws8vQCyKps5HKXk6nbdTtHQjVLdJZfB\n",
-       "IDxfZaWWxJcKfuPPtQUWQe5EjetV5FpE9GCzVC9laMtJmRs/p9jaK146Mx/nKWRB//ODsZvE074w\n",
-       "2Q8se/hhnFcUEAV2LNquoBB050GrZH/HIjd4BCfluIVSLyKdZ197zYBnqm7sXzKGfvUAZzNsdoF1\n",
-       "D95WVBU1GJ0tfDC45UnP1EPUw6AazIkSzA/EWZUTROAvA4AnqmuRqOYAPD/GtiuHTUongRkSYvN0\n",
-       "XPL8B9Afr18n5DCRXAcvWlRuCrvPdoZD3G3VK9PVcQgaZV1do96YOyFosXBb6/fnUP/Zsi2Y1Lwn\n",
-       "D8XEOOk04nR933ZKFetJVTGU7FS1z6WI7tBD8j+v893ZIqb27j8jL7/2dSM2MIEobqWTteakCkRo\n",
-       "P+Q55R0ZB5lbd2WpLmNGpGkltTfedV3IzIg3v2/HM8P4tZ3KSQ+JiIpnh76k2FvGudA2yo+iamNI\n",
-       "uB2JQo5pZ/de9aidtH8dh/faiQG/I9qrBwcCtBsLnTlV5buzy08Kqfbtv5nAKxdopZbxJJKcCBdi\n",
-       "g7tdMxPQytrGhy3Px6dfqWC4lBo1Dp0LxopapwSDdxpJkBb1clkplZpads6Bxm+5BZScF/CjYgQp\n",
-       "3a9zg+Toex1EO8vcO0fFEVUlWCJnAv5FC8bxp1AooQEFz3LIGxQmtBa86QrAYEvvKzwKsjBV2g/T\n",
-       "WIBL0YJvcoeLGPeQSjVE5H9J2rFSd8Qgi6VE/08nmUZqhLB6l9W9deRfkEi2gML3izdk1SpjZzya\n",
-       "cDsDtLyLwqzKozrqyih7b2UPxgOpkEHvU0VaPGMiQACqEwLINM/dndRsIAxubzO+oiSm73REusqL\n",
-       "1UDXmS5A4EMDrsxSLtn/kNSeSx8AB4U8RNxIf4geaVHSmNVugIWGAK3rM5jBr7Cwvx0wPD/BnKem\n",
-       "o/rW6g9FC0i+gB4QnNxSK40ToUKuLvdu5Ts529jA09v/eX26exDmn5gtZwYlScc8KtqXRP3DR/xo\n",
-       "h/FT9SbzZnmTOI3Sy1n6m3YWW+0igFud9XBBfaC3okI9w1HID3nsoAKvxfUtuGxCQtR+RCnp+6kd\n",
-       "1pDDUEuZLBvMzuCcqe7ypvUV529Oa+4jBDOaCIzcQ/W7CsNutd+7/3tcghAun+0Vf09OvJWb+vhZ\n",
-       "Fwl/krdNVQFOTpF8roOZJOC4IneJKp+9leYxTzPESmnNv3fG2GSysBaaDFva4aha4/gGrFzGeFBY\n",
-       "RooAi4/l1aWwZX/O1b330b+d2/zei1Kh/i373+Z60ezfLfGkMSwaHRu7su3H7UbbY1VY1VgmNRru\n",
-       "FR7IfmbnXYi3MICHkov3n/iyiGrgmiBt5c0FBy7YITtc1AwY+8J0oIxqh0rix51SH+3g0X5RJsqs\n",
-       "RZDANI2D7uB0hrDBuiZImm3xzrWWQ3KTiYkfUnr/wntor8UdJdM5J84rKPzJIYZMbAU3hRqmxFOh\n",
-       "v9+oWLXv9YrcVypf01jnnwrjKx/0v7Erbcj0WH+wFAQVuWzqNlmlhWH9y/pHlpthK3XTnbuJ43Ko\n",
-       "Kt2Fn6EeZU6LnuItjdC1gHHIAzZR+YP7fHmzlGHfzoYHvoAAOdBLHMbjFSCkDLfmJ2Po+tXt8giJ\n",
-       "0yKFCeznyHS1lY6mNWE92RocrpBiCe5ddMUYbWGYLXFGMDCHPfyJjV2sD6yN/0O3LH8TmVBUq54g\n",
-       "HO1lx7ds5o+K2zLzZfK92P/LZqEIp+fPtENWv47ukd7qjwDrOHtrzDJoaLPA415uVVixBwvTb7v5\n",
-       "7RqnnycNxl+zerw+Zqke4+JGbKIGA3lvpn6zqG8LTVd9yBva27GmMlHHsuPGwAk/ZfgxufAmKYs7\n",
-       "B3j2umeUFKv8SL2mHij90L9bK0fWTWeb6x3p7+BhFmk6Eu3i74L/gfQWqfuB7xttA6nAEzuMZTFr\n",
-       "hCo8gr6lu4SzZ6VbAXZ6W8X4hBmPAPWfdn0dMQAADOlBn5ZFFSwl/9uZSoKNzrdu68paNTG6qqb9\n",
-       "sILdnjppordTiV/AlG2iRe0B8fXABFqfLisDIGQv9h9wCR6ubenrSTtwz+IOSHcBR65ue0wUt9pp\n",
-       "tWWTrEcrz2ZFSOSEh1i1JecBjO8XBstx4UFkeVu3FWvmTLsz0rwmQ7QOK8bgWNiSBDevj2utMSJ8\n",
-       "XXh6toBfkBSwhyKANdEb4JBNvPSvvIBnKhqb0TiG9svJUqPEO+Q3M50n4ZzHt+r/amZi0vkZBVhK\n",
-       "syMPQdoJcz3eH+IdUcvzL6rzYpCPoBxge8jy5+L70b2DID7rKmkcu0VLbXr5OQtDaX8sh9uZMUT7\n",
-       "hLCtbfF/+RGIBiChLoj+RVox8NuSoQqE7CxsKl0PHcsBhn4LVBs8FZ4+T2oMkM9R7yrIqJAFcVs1\n",
-       "NuX8GDBa8bLvgDt9OBQSN5i8A2Dhonj2UtpWFnQOJrGWfuXQ5QmhV/Zw/shCkZSa2Ah6xlaTLsx2\n",
-       "RNQtKsLqQR/v4yUQgI6iJmOLB024KoqMupnO4X2/KJg5rplcoBb5JnV79OxzcCSOaEnX1jLEkfok\n",
-       "KBOK8pn5eZTChHjq3p3ynme5GqrRIPA4T0IpdkY0tfcmDxP4TUls+sKZAsOJxOQq+HBf0vL6i+xq\n",
-       "2RCTxDQqdKU8e3XyUePW4c7zx0gOsc7tbt3gRCPOoMqiuUAZ4zb51JFgIwCS2PJodqqU/zSGMePi\n",
-       "n5Tot94E71L3Nb6sw5sn0MM0ronNVtr3g9/73xgd4nBk6bRLykbHLLF736/Rm7sSNCFZpPm90O/k\n",
-       "vq9nxk8Hvm1K4c4Zey8nDIBTBbXBg1QEmH2Q3+YLyt0q/z9/3oMZ2mHTvcdLvLSQLKP9iYR21Y82\n",
-       "+BMjg+YiamedSxj7I0OqeHLKpN1aMiclpOmI72wPgfoWnOBRn1bRs/TxRjJ31cAPiWrYhSuKkzXK\n",
-       "V/BynoEsiPBH2NOHDHMppCt1dgJ38OQ7fcsUqOUIUVfUfGV//YMKFFloloKmJXzYV6Mb0lepI3hP\n",
-       "AMY7lli9ofHbT87BS5by4gH+cNzN6dTTJoTVkEiApAB759HkJz5h0gSHc50f79ELroYqGcKLV7pK\n",
-       "4Tr61so+gVnVs1UuFa2Hl3Z82Yqg6bg2SPoCP8VsNwOcRwI58gFHkB46hxmT0CCL4WUYxNwOVHeg\n",
-       "LyxSt8Qz5EORW1F8B9btbrRaEpNJ9svgKRA+0Roves3W83wZYbhwLrHtfB5KAwNsFuuX5mIRi5OF\n",
-       "bwgJsE0ZmbpSjpyvf3vUZCcLq1WM0f7uFOFeFLoYrnxxURzl0u3gEMZlDgvrE5RJ/ePhYlblG366\n",
-       "OjutQc9DX5yqFS4TnfAfny+GZh77Y0ZGLqyGZXHrRzdZ5GZwqbZTwGNtg0Wu3VUYLGakCxZJCHU0\n",
-       "5FMLqVRXvbZ407z0X0JAvhTUXDp0u9cKUSNenf+WMVju7NNCyXt9qEVpHgICQTiDRohZmwl/DY70\n",
-       "TCFiUIBwhwBexIjnO9u2Ao4Ts/YTxK8kfRUDAJH1oLQ6fsogLq41xWyOpq3OVhk+GikeL+0PaLoV\n",
-       "enq4P/AS33BlVxCkLtMYjUwPYAeELpfWiOxg96DxCUGOWf9kQH2+6zDPVEc4hUB4vxO1Q/aJz2kM\n",
-       "o3CxADjNoQCpqN9Y7JEZPUvdCpQRV845xOB/Ak3LUdSd09UFkI1ODlOO7R+NFmxCNavdl49N+yy+\n",
-       "hHIKUcHITZ4DhTLPq33wtWsJT2Wuy05RNKrIBuh09HkWxbLKB3v0PcOptB0L+V8mJx2fSwfGn6Du\n",
-       "5O7/8MgIeX5WOuFBhhi7tXU5dbA6jXXHTqSZRxcfchwWBfIz7RNGQzLOTCt7zZnS+/ziZPvgShjJ\n",
-       "D4rYkBYoXT4Rhpub5dEF6A8jE4N6CZs4ifZ2BAIA2d29SD4QLbm3bbuyhENU6uKTkD1j77YNCma1\n",
-       "/eB4eO2vJMsmHJMTTCMOx0Al8HSAvsDL2trmE5/j7+xP/lLIC/NvbbkyFe8SYKyJesmeK2zdrEPP\n",
-       "YebyXrsd0mxDoLlMeBH0zPwVp/knZpDoJjBG29nWCMPq24QAs6vdfIV87Qb6of9wYi37k+yCEKiH\n",
-       "5fHOUyz8vJxOTZ3MTBFwDmow2hEd2gun/vAf1cOL0yCNjVy5jdus/tL3wkaU06vldiQ3jwooBswl\n",
-       "mxzvMxDNxcPMLPFjPQ/zBCb30SiYL0dVxWZNchUhLp0IZHUqoPzIG9W27p+ITI38bQv9U59dc5cp\n",
-       "U/wn4rI521+DoOOTqYMr+gQ0jFuKkNo2zorr99JPgVpSACG+gsgNsNFZ5cxwGZ0OYwaag5agLhUn\n",
-       "tZXcs9pAFSTrGQntdeNAwAs44KHT/ONznoGHat0aJ5oCmJVR2bIX+uyZl1+D9XaXwchQzU+Zlfrx\n",
-       "zMfRrIujM8FFG00x59atYlo+jzY5Nj2jqLYsZ+h9V84WW8b8cO6QGenItPwAcdyQ7uu8vpSOR33a\n",
-       "iM1EPseD5ajur/wve5eWINoe6tyRkSeWZA+4+hSzOyMbsAEludAjuROtb/GxbHSJubhuW2uTTofM\n",
-       "9itABzc50Y8saSR2MjQof7x3xcgSEVJIZ2IhMNjOY3LXd4fcfOOzU+cOn0lwn1n8ywyf/qqFOeBI\n",
-       "OcRFqp85/Z0AFCUFe3CsKJNe/ckIs5MQs0SEqqqJ9L4eOBP/SJM+CUMLaQBlPBWmsazef51K9Foe\n",
-       "1LipnJUcNDdVyA6CF4FzMBcVNYSpgcgDTOfoTwaDnI8GgCujYYBpaBhZYdC5ZKeLkK/UiFIlnr1G\n",
-       "f3j426BZJ/yhybcClATZlZnb0QgNkBSzmHkOwjJh7jgKjL7tZc0x/QRJQ0GNgyaQ4yU18QjNvCQp\n",
-       "baOS2Zu9VkBjOG9LAoPM3im3wD3b9+Qm5gj17fQxX7OCS1VJdKxreMarT4g38F06Z1zCnBeB06OO\n",
-       "xQLS5Q7mmaOsZvf0p9SzaIXhqIKDzW42EzgBUaev3CfdI4HbjrVg2COCND+Qc7Y2TAY/T/fXp8xb\n",
-       "hNPDdL7jB0Hdum73xFGldQEKGnH+bzbY7fyoQT8cstFZc7eZsnS8odwTjhrJFyPf532cUjR2OiaH\n",
-       "oKM7+hEWnCvTJeW/o6aKWT2oFI/RfIZmMUpPWeFt1BVIhpnY0TvJvDjkS6J4nx4aNpt1kcpmt3F/\n",
-       "NS8g3L37+fzQmIQ+wDuzebuXRXcH7KyopcXW5O82QLPelWpxCQ73HW80td+2Hw9xLXy3JH4jqRy7\n",
-       "Ngd2USC9WELAMxvzGjRJnFlnZRQI9Dc3sncA40LiosAhGXYkU9GqTRpS2irLWxL4HoDf6tMfNTuE\n",
-       "aY4tPmub3YXUVfy6Mt0sLMbvcKj7MF/HKGpquFz+W+yDolF+MF45G8zvVBJ9ZBgshHwgrZBT1z4O\n",
-       "9jJI6J9tWvt4pGWu8zKutlAVDGVjot0wHwyUqKDus3ObgImFlJwFZKXJpUi1asBDRPkoKNdr7nix\n",
-       "lgB6qR2NKMd6jsj/AMe17baKv3TwrANvCwZfi4Z+fUSq9nlRy0mF6ljaXzC93DI8i1yW/BZiLcor\n",
-       "S1nnTf0ShOICufHNZKg/aaBow67r1RyFdEa2d1YetEKK7y8EowXNlfmePoPg+UAJYXjuqiNfhvmJ\n",
-       "uONqSlZw552P7KrqkUHhr58uKpLnJ7fmj4uh3lel/llhmCrwyDvu1R9yFTnJ8C8p60O0hLcINhGg\n",
-       "I+0x559sZHgznEn4umcJFRzzq7iLeVp8ZT9YMBqTRDzHzZM0fli6UU8W5xZbRr4lqjEI5w0ayKs2\n",
-       "Hc5SeX7jfbQcrZmdNtAIneN5PEtf+hQ/kylZ3WMLbw50l/21N2KgtPDUrxSTQonHOkF2ctxesEJt\n",
-       "Mgw3zMidFmC60JaJcFYcSge3cBbeCXNgsXCIx36UbHWqffShPti1ukakwGx27MAny0NkrQ84xkOD\n",
-       "eSd8SFFTvGxk1qg7TD1Ix+mbeKcE8ZKchYHKuRSAzUcSxLh7wEcKxlTsG7itjXcShEeCh+BwywN5\n",
-       "h9L7cikxpXZffHrii9mmDevlJl5vQEXOazXgzgsdbAqu9fX96SfZdTpul4hIyuAR14busNT7Opf3\n",
-       "U+2oS8Df6DlUzey2WTgz8d9mXpzNKMKG59JBgoUwsV0/raj8KzAthPgjnvOtC6Q4ZVzJEiGbQ19i\n",
-       "AuuqVh7gRBIMN+6wog3jYMZTupoQe+P+CCliIPfqSROtQcc6QQdH7SisXbEoYdSDpiFRpBkBufr9\n",
-       "ztd9vCyHw3Qjp2TtcRjElcybEdiiOwfIxJvO0h1jXQOmSloFP0kOq9AjI+SCBzQ0OVF7g5G+hjUt\n",
-       "A3u20fH2wup/d2oPjZOEFcowX1Ays7byjjTwhlR8kDODJuELHkx4im2Uc+q3Qr57jHdRHG/7G2rM\n",
-       "WbTPvqtG5OdVHmebNDL59D2Uk2oU+gEl3Z2yFF73QAAACWoBn7V0Qt+2w/13FLpEGaS4xBTKDX0t\n",
-       "wsOoUeJTMNgw1Ntegybt18uggE80SIbvV0kxCHFVqg0haXebHQeZD+4L0DENzgCf59Vv+Hym4qc4\n",
-       "AjESwkXzk926SA7Hk3HM0fHts4COpNmAvIWRZ6p4Icwqc9YMfvlvwp2F0r4ENsv2XibEQz7de/bc\n",
-       "jWmQwYg+Q0O1wCCRA8c05CHvCW9uM+sYA22Lo4p5hmJXkvrXIy+AeSaDy1c0k2qxIyUmK6HKPKYY\n",
-       "zfJ28suTeMsEeM62oLDvo6t7qZtlwfd0E1Y7e5H8OMFxWI8UMkh20q9SIBqr6D8lPUjRWLhdXP1E\n",
-       "NdlsekLQkwSP5QseccYvWTnxKF7KHUPJ+84U0c1x3XsC1V10AaxFChJbrrBOqSWu+Tuc760nVh5K\n",
-       "g+gCGfEy3eaohuRNlHFVeBVEJFuZIbmde17K7BMm9wPLdypCc8QSwm7z1qo7uGcXfOTtx7pwKP54\n",
-       "Bjg3QQ+21o1pQpSotHphGwsS94If96Ue9uzt28UZTShPfcK5S7FBjdZlE6opuQo8n+dIW1BmXMxt\n",
-       "mZBd0ccti3exitJRInYcxU4thn5iGCYESfX5RByu2lhLVc5N2m6IvQSXxlgYxsn4n6ofb8fjS1Oa\n",
-       "13cv1anpFA9EffDzLgXVTt8csvedM1KMO6MPL7lz0OZrrZvt2B9EtP89T6eca3wOWxxCSsapLa85\n",
-       "asr1ZioNJSrCEez1P8EoHBA8XAFVy92g6k3IOxeTbAvwcgCx+Efs8Ihq1q9OFnoAevTZ21iIqlkz\n",
-       "ZDV0I9FuhjtzNddpZ4JuaGQDwBC8udrSJ2MM7+o5JOaYTrWLSir/a/gO7VQ/V+/HN0oNWs7pcOXR\n",
-       "Cn1rfn3N9ve37XyEL4IebufiahAO+2aJkVrntdEn6/XY8rbuuUsm6QfSzs03/Yyd9XtSdg2zpxyZ\n",
-       "QbT593UeQKp/qwXHVXfh94RWusGpf2j8VE1LyQKpZ5s3pTb4JnHjA3vJZ95TmKlSFYoFgrW4Otga\n",
-       "MoU3liL7JVq6RKAdUR3iqYtw8x7d2nbfltbg7Hy50WoMqBuS2BQtauKzvc3gOEcTydIItrWZzzpg\n",
-       "rXAlbpZMPL3L3ow1Xv1Bzk3v99l3NN+UE6DM+5qVJy0LyIzgg4qP3tEomEtH58LXfi0PWSI5jG8v\n",
-       "GGn8W0IUcJLJDnvH3NsuOxWDdmWV21z51QEZGfKr4BUkmqmPdpkM0KrRzam/Ep6GjDg76pfnMhYV\n",
-       "pdOvTlsj5OyJkwWD1FxcXNHbOsX4k08Xa5TUWtAAN+So9sSJkpL6tb5qdTqwyfXlItKbQXTCYBoa\n",
-       "C4lLwtPSz7BlDvJDPNKTeVtwfu6S8vJy9RK1x4d+D/sH/1zAfPgSPauNM1MFzT8KUAO7I643Ciki\n",
-       "AzqB7rg3bBEfROe9n7i4Snjxp4/yOk/GMLBtrYojhIGVCWaX/3kOtc026NzFnuFKLtdiu27lW2ZY\n",
-       "kTrm4wyDI/Qad3QCCaKuiT2UcpNLlodGWAvZh2xUGUl5AoDZegRQcpXVnn8aWyOBN+8J8eyXWpIY\n",
-       "s1udBE3OklAKAppVRtABcXT+yNjJlvoSjaWpHAbdRZYGDBIW5+5pi3KhSl31Col26YbPk5rPBcCN\n",
-       "pUv7yVsSXs3+2Fz1R/qReHz6KOVFRepb6ufzZ/vOkAU4b59sxDLpmNmm3FmwSUk1OcBZPuHihluZ\n",
-       "/WZ5cgSZ7CsUm4pI/1+V/AWKdirKcn0/iWMoWNllahS0ujguiMwG50Oy0Y2/1+poP8bjv7bNZo7H\n",
-       "sa0eQ285MX2/qVfY0VroushbGASknarBNNjENqEtzAHAhdO+iFAPdse2gOqZesGS/98oueQ7wLk0\n",
-       "7itL3IojlZ1OdX3Tsk77DXPsTAbVqWVQdkkhEkViuI93GG0IybshHvX9UPSeyNOJ6Vu/Sevl16Up\n",
-       "5rBWtErls/IKaMxjSo9dZTGC8ub+braT1/5xT+YldIL8zy0maxKjjx4ezQU5uKxgzPoqWvwLLdnG\n",
-       "KqvzVA0IycWBkQjXh9m0UkvTxArH550TQRQ/VKgCZV39IFEsBhfQyqXEKKD2ZVuFWoBpavMMqDjp\n",
-       "8deQGJfvhba496zpTfZy48CEJh6EprkT6uJLgPhAx4gECIOBSp+S4fK9AQf/nTwrCNzL+6KdX7ae\n",
-       "kqNI+ncIJbXkCH2fBfYr6afsq0T4xvwpwZz6VlFnSLq3bpPap2FUyFTz4mDz2nJrlB3+COeeZ74P\n",
-       "9aLqmDxo6uxEbySr3EyhSdbVc41vgVPy5jfP0z+QuNjWv/zUNVDkk0DkofxntaQL8VANVhfUNC4U\n",
-       "1aTd85eOEVl8SIB6VHjM52Og9ttfbamho1wFDIC2pxf+0/jeJfmJ8k7a7jXBdLngv+KmsNQjtXKw\n",
-       "NnnFzlH8esTm2m5uDVN/JxqAmVQd1ky/TgGF1Eg2nbX78HDM2ugAAXenFjJ1GuN1vAFsoHMQZJ2J\n",
-       "68HuX4i3LB7+/EP850dCKeKQgGWCk3ITS2162HMGOp+KN65D3+0YAAKYze20HvQY/KoTuWKb0KZl\n",
-       "yzCQlMFHzpJMwgQFkCstuANt7OaUrkZgRlIJkeQJNauL9wJo2MIcz0WP5ADW6IR5nlUzmw369QJL\n",
-       "wBrNqAJg9pmBbh7MT5kHzsHSrFXhkoW9rE8zL3PwuzY+JW0koRw3o7qkHAeWQbb7JyywjR94fpcE\n",
-       "VHApjf+lAA4wYjXL5AgvY/4eKckPOS4TeM0JG5NVab8AHptbyHYiAYwhhUzH1EEFXQq5KnoHkywS\n",
-       "oJhgh2VCZLekznimiGknB93ESMHzknwByAeCXBslaDpM+kLgYURD/7szfdZ++nw7H5lYlYjOkPnh\n",
-       "CDBfVz5e94dehp0PpVEDceFl10d0ARTw4Wy0AwX3XKnCo7Vc0tY/rQsvqwIZsU6ZLZFRviuru4AN\n",
-       "AkRBqdc6TZ5Ilz+KB7djVbhD/6rr1oN9BQor4raRPbHjSDpjIOa2k0vsgzvNL0zBa0eZFftKD3Ql\n",
-       "u2BF4gZxTdzCzxnU4CmNm+AxcpftWt5p6tTJP8tvi0DXrDYxxG04OKInq6M5fbM8jGRXetPT+T7Z\n",
-       "r6cFAvmCtwxeLIS9H4fVikvVm//ERFx2TXeVvIngLubnhKx5eTpGYb1LGfbFtFTbWyeQtGErGnOJ\n",
-       "jQXOi6noxp1ZHtNwVC0lT9/7FppZtsbLUIaQ2201a+eVNNuQqHmtGbNO1KBXYm25CwRtAAAIqAGf\n",
-       "t2pC3+p/Zp4QPEs+/z2OR+3CuzJsQ+iN1SfK9pEKNScWNM4u5s9JxahQsgjftba2iHe2YhMlzt3T\n",
-       "ENVA9Gnf4fKkY6IK184j45YSJOZ96I+dWMutW6morHHH40C6I8mSl7eP8SnqJogunt9y3Dsm951o\n",
-       "o8RfV0i8nyxS/IP3WnBda9hK+MB46msEE4fy1NQZa7xfcsF6Vwy/Cd4rAUy4aoe81Bw9eD3lzceI\n",
-       "c5aeWk0r6ylr30I58Q/E2koEsAulpoFGes+ZmHf6xN4DJQ6qtVj8My3E1cXdIdZyzi3hWOtsgAQW\n",
-       "KXLrtZ2NhKwTNbhtg2fwXeKusYu7DgZhwAkruPzETYKhkwU3dZteEl6YZboGIvqpmCMyISEOkOez\n",
-       "XG5s32lnj38DFQ0LnXHEYGI+AbOroJuKTIYPCSLIWgGrzTzYFkJ9w1KBYr3tOgqeka3sYR7okTGg\n",
-       "DS4/m130vItiBgC/w1EUH/65OqABtSaYIENA0SrFi52R+FcaXwykA0xDX4bvir1c2UwLBP1xtJj7\n",
-       "OWSubG5N1/GcVoeXADR2Ye4MGY9a6G7LRk6J6NwELKxeqa7tCdcAHpY7Pbfhl8BOcdEKyeE6nDWF\n",
-       "Udj3mwlaf1YKWC+H+iJZcTOeigKpdtEJFYolze089C46Szu9nBJN8lSb0T8ExIivUHq/jGgP7b3k\n",
-       "1g4ZequQ3ynstCZG7h29vfu6nThXoDsL697ra2yTdQPwmBbMSr1HOCUufMWwQOeo54X53T5XKpkD\n",
-       "ggkyd9eLxaz1lLZaEgnil1wy+OVlQZuq7Y41XyRiN7JEeQ2/6Qzfm/+gD8r1vnGKcR95Cy+20XNU\n",
-       "2sG2FHq2qM5w39BNubIUlV1xAb2Bb4Tp5rApKBiLYoys1fIZH/a3Vc8IA0nsyrDUyDo4mvIqk3N1\n",
-       "fI7AV5LBEOQBkw1/W9LbsnVUPhb93uup3vEiqCBaJszdHvqB8s66RoAYfwVVAnHYBz20re1qs4M4\n",
-       "9/eOss8taH2wtD7c6SmYoRJ4RjmXctbsid3A5hC2y3tZF5bW578zjkBK/LOPFqJqw91b6sXKQUdc\n",
-       "8fXLkeYwvgSXo0lMXqBvuDZWZKL7xjvUO0GAdVIOMOsVSPbwll5n6GetDxnLZ1do+LSj8PcOny1H\n",
-       "ADq9e4C50o5+NS3tWdv1TRAaaDc4UGg0sYznbgv9aVZnIbILVD/orgxUUzKcdqaDZnIu+HWyYueX\n",
-       "KCVSqWss4pnvtNR5vXRR/Sga73oYauSleJtOX9ctG0b6v8JHPrgPd4LmB0W6hfijcTguFFVYZvht\n",
-       "KgppOVsX+flglSLx6D9PW3o67wGrZXoy2Zzy13xZcKGOmZms3x3RSBhqN9bCo8O7ALwOfsSNxWpq\n",
-       "No1mrsc/4tx8IGTQdT0emVAtw6U7GwdjKU4ZxfRLKQkPB3SRu+v4hX0QoIaXVgWEBG++7BxhjLHw\n",
-       "Gb/WVbyN0clhFft7Q4VrvIGtbrY3l+91nBudAQf0Pw5nOBYQ6BxJ8TfL8qQMFFt0K9wkR34BW1bp\n",
-       "jQWagkph9iRVj3Uku5aLQ8DBufzXMZB+7qWK0g40Ct9vxjK42Ur7ty127MKwmLhPDBVcS2pqBL/K\n",
-       "kRPocqYwpiRYUTFBsvo8mij7ej9TbMAgYpKBDQfRRdCtmzoeLa4TUb9XRpsIwXuHD6EEgNoJzIxO\n",
-       "wlOl+1/tF1xOo9rGAUkM7euP/PZlXBdg9Szjv0ULvj7VDPbmXauRFYUbEurK72aA77Hio5DQzdfT\n",
-       "yl/N5pu/oo8IBbMtzZLvXGzA+qb7lSNsIbzNXAOE9u1BL7cLEfm855E87RNmHOxi757DvjNqb592\n",
-       "tsyYdu7bdzyeEw8dgSfHr7aSHH7jW1acA/5qDnHGKVPkhjBAIVJvvuUBuBgpsA3XhN+EvvBrexvz\n",
-       "n4fv3op8esJ9SkcFhc6GcKQRkkzH62vMpY9B+67pEna3OEykyRIviI9ly4pWT/rxHIeBhbMnKiDt\n",
-       "5DpOFRgJlp+YvcMV6LpCpP+wYpe1MjXNoNKI4MeHIZz8n1ATdmE01ohgoIS0bbFWqtOM9Cre2LrR\n",
-       "Gqv0QIxZ+HGcXFQYDE18HdRycmGB927/5yFMNL73SZNozYqHS/guF/uVD3YRUeeWWk10QTy2PE/v\n",
-       "4y3Sq1zPG+1C49HJ4kFMi/wtzJagARasbcXaCFJmSG1ELsMTq99ppFPOuU59MSI7mUyQcihFogRX\n",
-       "EXjLvhpB8RlGP/Fx+dWfnBDgzoEk62OLT9VW0S0YZNaB1gsr6QiTUnPyYC/IFfgEQNj9S6VTi2Bt\n",
-       "KQ3ssA9hnaUhnkR1tWOKSigDFBfiFEKslOaupuY4IDST4Sb8TI409Kac0dJND7Abkxd2sQ7PHJjB\n",
-       "j4R7tzBjnZiwW15+78GfSpF2JOHyPPrjRdxhyYKQb3s34/ldvADCw8pi+jqf5QcQs2VDxatSumur\n",
-       "Cfk6Y+5pgf6e4eHx4jM3Vf/8SdW3EWy+NjZN9rIkUhjBSs0mlc9TYgUByGrUE8BeCj5dt/bFb2Pa\n",
-       "GkMbuyWvouVmwpKavoW+Ow+R9WOGTrF91FmHLjv/tW4+vnNZ3BMo/Yhzl6NvHAl3mDP/KKzmMq56\n",
-       "w0TJYAkODWOjzzT3K4TAT6vEVYizcfcuFm6yiL+inXzDMcB+FW3BwO8aQKS4hQDDM61oVjF1vrB9\n",
-       "O9NXDh0jdxzA9AX4g4HrWFNdpeEfgsLPqQI8q9W3IUgAcD5GThThyl/+sTRJ2ggVOB+JkwqJzCnx\n",
-       "zXR3542gNmRYkRnHvtC1EntmhD/RM9Ucpvch8aVe/4EbAkrxeEtpzBL3USjF4eihdhhr4u+oCfkM\n",
-       "1+uzPdq6KZPK/WXlmyNZRJYDcjqpB9zSHoik+EkJp+W0Gy7/Tv5kveXCnU+Ki6+0IuWqZ/+sSGfV\n",
-       "5FrTIEapWczjd17R1Im40j9OW/lqfD2ncmcxq0lwm5b8Vw/g+JltJ2WHtVBVbEHBAAAU70GbvEmo\n",
-       "QWyZTA//gL2GVv0cn9LyyPAJHgyxpZRdyigsFpxjZsLR8LiRsAz5jQ7WoxGYxct91vfdTghhnVCF\n",
-       "jsQAPZVGOS66uCBFU3lX597U1TzqYEch0/NGqNRzCdOMUzZ7xgXp9k+6HqZAJRXsS+as+jNHP3+G\n",
-       "74lO33qBBSqp9oIGXNfe5N2S3zSeIEzMAzxrauzXxFx01Be0ZY173czYN/QPItgG0wbaAqij48vB\n",
-       "VhqZI+8nyUt+SHK1j06C1R4OoEsRMbJg45zwPVrw3va/VT1g8c7rDSI/zTYynBShZyI86nga2er1\n",
-       "JY0I5txd7tZNfgao+JW/Kn31FebY22qst8uc6SID7tuCPken93Cb2Sa5IyW2Swr0auI8eoHJujJy\n",
-       "ND9F+63x8Mo3Z/6FEe1nJ/4uuagH8nXASW1IgXljO7qWges2B8P96xfSZdlDUUyN6Ndq9t6gtU1z\n",
-       "8wb7YhxNcZBIOnGC71clK0pZVhkejPRPg2q3LUo6jY1Cfl7aR8StJr5EBy2in51s7iW073JOW66V\n",
-       "FlVF19Jgjr8dYeYyDhs8Vk292mE+yPurrPPoGsHrW731idHrsR5cQ6bs3Gb4e62XJW4cUufsL3S8\n",
-       "hNIR/26lqGFMwOEar9MbZoUUvNp5Y5X5WUdDTAsMMlikt7W2xx57gjHK8r7xLS6PgEmlySLcyoQ8\n",
-       "KhyJaSaPPY16k8X85wJwFL2PKzfL9G+zPnaX1NKY8rZOCg+CNU8ctgsZWt+Q5WNmpICLHoj7B+qr\n",
-       "7tjamrZYrOnppsh6Fd7gO8LILlAmUo5UuJhnhB+NpuKDhwVK589NByCIEQ3B5WTx61/Lz6zEWNo6\n",
-       "6OGPGQFG8MmWcmwDEUlfQ5cygon9ijVmEeeD1gVCYmT+YL6+dtYYFVn4k8vk0SO9B8K3JTJ1UF4N\n",
-       "Xxoq/7070puBw50yOS+nXp0m2Zz02JDfcg8g/ZN7RCynVTRwmRZlIyMZabFOwUXqs9vNZ0fP1iyD\n",
-       "ya+NWwSG0+JvLnRbG213ee4zbWlHipCDuKgQxe6Tv68zj8MGSbd2iuuQoJMQjodi1ZXVrYlDNyPN\n",
-       "J2K2iIq8gkwWYe58tYwL9kcfFqhfkzfpBnFhB3BcWUYkb8IpbMl2kk++hfonnBKYqyxamz95Vs8R\n",
-       "d97VpLE85AXk6FxYWkXaTYK2FtqQAxHpVvscHBSOHSVo/f6iePiTAyTVBeuDm3qz+YHpGMZpV2EU\n",
-       "62liS/gwZu3Ac3rYCjSltRw9/YP+yfp0zxFaM3Z+VNGqgb3YUz4eiUAZNQ9UFqf5DBjj6BA/etPJ\n",
-       "7PNT8+oGi0kjhor77tzcRoI4tgi5xg6UQRSUbK3eNhLC4gkDyUp0S48fcCHzIsArT6eRTsc+u0N9\n",
-       "OPAGv47TDkYu7HHm8JR0vL9JAf5XOi0kNu77bGQhsE/lMqhuGsBgVEB4U85QpHgqDNZlHfX5UwuD\n",
-       "yyS1AX8mlAipbtDlAUeLSRCx56/wLkZ+A542EIdFBJNAU32RUSgJkFA9IPwpsN+lMvICIEQTy8IS\n",
-       "VrDj6stfVqm98x1tLhJc9RA/dQY4/2ZfFyG+7eOgu3IiNpGhYMZ9B9d/60o1nLTbd0ABWfIhHlfQ\n",
-       "/SuNw/j/1urWFtDAkWtKQ0QQX8zUezSuperYGc7hm/8nk91Mu1xyJ8p9P9lbZ7c5dUU6MebEkqDI\n",
-       "Vt+xx2EKqjk3r16/f29gsYQHD4jfvXZaFJAtB/GcCbbfFpQXCEpmIcu5DCxb76BPwUseXzxb1v8a\n",
-       "OrXf3Nabh4YS+qE4qLqvCC6WGP4bN8xyv9MxTjB2dlOSq+4Zk7la54Q/nCS9EIHOAnn4SOgS6QVn\n",
-       "+gSurRCIKFAuGjTwAgIdVoUVGSXWeix6efB4RpLTbYteP7vSkKjxwfA5sJQv8NRS8WA70BzjxmBd\n",
-       "HyXSZ+7Fl8Bw+Z7FQMkkzYsuR1J+nAbyEgDhPvTITBldhdfsSAjblRXDEJVjYB8QRzL9xALSxDl0\n",
-       "JNqADd0PG6/Rx+LZ/T+Y+6zOXWmYlDIFVhiXbqA7O/S5V3/D0+0JBSbold0NzPP9en2P0DJcz0Gf\n",
-       "z561QON8ejkUAO9iZRM9je39rArSvocUi8iEbpNwVsBVUwrDxjNl10HbOZkaF+HEhfXLn3THmmeH\n",
-       "aad1y3RmggtfTj0qxkXcwRqE+9+7lgHdXsxL2ecjJCN+kVLVyCpDn8T4cPxcdiNw7UXRF9PdfSvi\n",
-       "xnDvYFNSnPhNA4NIzcZ+en+Fwqvksd32z0t+oWRnoojDjqekWgoGtpBYbRKHAMGBCCJi8KHK+GZ5\n",
-       "bwGE92az76DNv+waYTMTXP3isQ1zH2TLDzntIcJelHlbwgdUha7vByMlwJpdrsyNG9nPr4JYFr/6\n",
-       "rscw0UTeOAyFmTNt0WM+XdQUL7cF7vPIw6MnxvjShTF/1YwttgxFOsLajYtlaiSBb+k3+r3lPvKb\n",
-       "M9AlQOK1AH8Izw3RTufotU2iW8Oo8L44H0gFMYxQd/k4xde6vzLXXTo7KrRqeYH9/yd1amu7OEG0\n",
-       "q6sPweQbpZjjbYLdYT7VqX9/wA364/auqzSSsx1NY/iDOxv/BCmXWlRHeMZmpkwu9mw+NJ9/zaj2\n",
-       "qYn82YuqT0czPC2g34nAT7v8bf/2OExne1R/iovosuJZYGkVw1fM4llr36bmOqHkZgmkOYFqdYs7\n",
-       "LYdVeWc1FVBLLURqCNjD+7gmvXzDBIJw/EKx5BbHXnXM010WuK6plKc/n0sSzyAKteILJt20d+Xf\n",
-       "d2f4xnqVjn+kIsTIghXnadDec5414M/0z0dZtZlt9ag/rYO85rxsg/TnnEgV10Viae5pTHaVm7AV\n",
-       "eVvi0TYImi9x9Gu3pZu+xNkHiBUokeBCH/KsQVYpgranbskY28W3kIjLbiec16f7HFCNDzntbDlu\n",
-       "5MwBf/dJpsrJvBUMYpAzut8uM3bZmP6+bRoQFkAzGU3zUu6x3sp1g2iQy6EcLg/KDwcv+Kg596dI\n",
-       "9a9VRHucYrEUbyYPPVjMwIeuA7W02SEhAikpwCgZm4BjI6NTHsBQWg1cGn2llLpXnXVGHgIhpVjO\n",
-       "RnFXtiL1ydera33mRK3U7ArSFEHlqzCWHNQQgrpUdZHwyHIewFEZpD9ptiBBmu0+59VPjKucFg4z\n",
-       "mPMxnb4lOEVOraCbsqa2Lfi4evr8mCfiAjxa0m7geVeujoDOuApZWbjjuOJ06INR+nwkIKySNWjz\n",
-       "vnhemeXArqQyoeFrr3PpqSA+XfaGP2zVRpEzsHXs32O7y9WmK2P59BPnzsryU1WgSpML13+8Rpjo\n",
-       "Q+sqZiDlIumq44Ja2QxZJr264NZdAKqOnUclYL4VBjp1WWxdj9FHndr8CEwhNj6RbsOwLu1fW3Yg\n",
-       "/9bddLVtDsP+FHWMdt+ZvKTJBzz52Xy9qdwMzd2TeAlZ1dfNfAA1EpkEdagOfVoM7JGvoM/NRLgD\n",
-       "VTKsUZtBGyy0HodYpkClABe6jtTA04XXEyUXGo6YA92R5ZbTS45/5GMcC7ieBF2YUOnsvS1T5zNH\n",
-       "+tT3isf6WlVBOuT6jDklN9kBtUhjfoz2Ha5vMmuD0C443z93cbdyCFysYk9wedbO7ETQoKwor7S9\n",
-       "0qB8mYvGqaB35+y/nNjl40vNVNNOPTl6T81GCH7ROGp3nPtknGrzaJim69HIFro/IZypGzhuyS0h\n",
-       "gGRKgqH9gYN0gQH6TvIED2NYoAP5fi4VvRPXWu+wXlRhRT2H+EAhN1b9GdEcj//GaK+/tfvAlqkp\n",
-       "B1Juvtd6HFnm6u6UZms/I2AMDxCEtXLBR5YxqnAycg7QUOevquBM2mHgRQOxN2en4O/UGk3YHgJs\n",
-       "suM2fe/7wBuJKnnrzbJGIe7JRxVnzJeGLxS5OaxoT/M47OQQToQJ4tJufspi46NJ8wDCKonOdf41\n",
-       "4IzfCb3fHEdemwLgffcLCXC0SXktYLUGPAyU8zM9jYh9WNfo4q/mlnpZ8vEDmFgmwrENMr+TGKsZ\n",
-       "0xfdBnYGM76ynysdh75RMzJiSJ337Xo07m7erQO4lmSa+rtP7l1sBJs2KxP4IkrSkXGpytkm3ZT1\n",
-       "VQxecqL6ZGipKxw0zIKnEhM/8OWV0PpMhzvhx/oSaEARfUvnB0Iqt/ZlWPoPwog3oL0LDe153tHA\n",
-       "H7Hepz1DtbdsbjFjBgXTmWXkSVEVt62V+98MtkdZe9XdMXb0GY4hdVJTKrtUYVObOsmTx+LlW4Fu\n",
-       "3ya44TZsrxtHXfgBCX9CahwElJGRLI9ibixAdCub8V5eWGkslnga4eYMAxBxviHc4Us4yAhLqlyo\n",
-       "Ntb+jbuf3c/83R6bxX9BFtfJvCrl8OJmDcU5hSuaNjiD0tsshzJv6JHkuPIFk2wBNssBn9q8drh6\n",
-       "FC//vpTm62komZyPHMnYNK5HiZSVvsrxxDRu+EIPv7RiNmN5tc2OetTFGggCZ1/+f00aPlTbOS30\n",
-       "G3x1R2PakW5XsRKr3CgzbTkHLfrY8VCcp+h5q2AIqhf+CbrJEVOJXS2i2faaOHdaJseLRegyEbc8\n",
-       "8KajWV6gICZSV9955+HIcIO+3dbtuSPBZwvTOhx1THijk5alAGlqYipZFPejEh5E0cq8MrRlgmOu\n",
-       "llp9Ta+p5QAXT7WXpLx1/8Qu7YW+lYAhvzrACRU1XC3SwsI0tPqW245TwZfZNq+MU+cDsrFJSZG8\n",
-       "Y4YP9jE8+At1PvDA8DyQcW7kPVQd/PvE08r4kfbKnLF8lb8NRkMtnpVIdV3ZT5RlV9db46ehBTZ7\n",
-       "NuUDMp+dUSrj7OAXGexKyhdFd68WNyMMdBJocevJacypMR2gkUQetfWeP2jojvWdkxAxjmfHcqtp\n",
-       "Rf3gQw1nJpZoTtvjbwNueR1Sqca4jVJOcEoJc8oBJ5/IBil/W6LpKvfRhzeisHyg4kN/hGhAZ98X\n",
-       "OvFkO9c5BIdFCsCfKHULo7ZfPkzqNnNnrbQX7o10RECpbdwfGpkiBsnRpkhhcYZOAI8U/ie6Nk+z\n",
-       "iNin+EAOTE3fcTd7/n7ovU5c3aaivfQr2vnzys+IoXrsAKcSfzbyZ6w+F0M+li1R1q8fxAnrd38I\n",
-       "8uhWLTY4F5M9sFKYjEIEEPLsvg7/4fKQMGLwj9pv711nUzjnzPjIVPTUwKUJ0Qs3TRmrCQvTAbk3\n",
-       "PG8wANxlbPadq11aw4+OB2agdCiBEsNCBLidChV1Wc/t6T9zB8tjCP9fCFG3xoQGueitgYMFdXnH\n",
-       "bekqk1lQaHUiHqy6QfZFMO/WzhWqz/legtn5RkU69zyTztXYwmD0vk3NPKQ19ajQ32RmYfi8aVmc\n",
-       "WSviN1ocTLy0GeY7MHhlTYAzsW6vo7FB+NTjpgFfHHkudsmFVsdnP4LZTgtER+soBOiiV8tU5hzg\n",
-       "N1ICiKPRkvl6JOATlWKrVhZaned09Nv5/4jzvZGr7LYe2+wHaiA8KSQimyoimRdNYIHXrYjWCieY\n",
-       "Lsm+jcAQQPsNvQ4K02Nqb+ndy8uYxvOo+Fq3g8/rJ1LR+89UW/dGCKhsJ7ofXjLwZRsE6bA55GVD\n",
-       "VG5JVTqK6PQdh+KcOP/Zrv7Hc8RQIhEFnVtnvShqly2dFybkxXQv3tngNqQegsqOiJC+5zvNci1k\n",
-       "6ZoaNbDKMTtX71LqM/tKI/gA6PwPs8JXM/RNp+3o6mzjlp4f9XZVry4DzcHxkiPD9m0Y67VU0BN3\n",
-       "j7MfVfr30flNhQ0vPN1vT7jsTcn1unekwdthR3vdyM0TAXq8nbdyWYbgwqzV5U9GQk+V4n9WognJ\n",
-       "Mq9eeU00aMpBwK/v04yHUV35ldSVMpEPPXumJTwDp4iJvcJ/J6i5S9kK9aSfrcRxXqhAWGZ33lQM\n",
-       "BDgb9Cy5nhM/MFoFUI/pUAsYa11ZnMzHEHVJnQQu09XUmnUt47IVrqLYVgyJxpLl2oGxJgB0vgMj\n",
-       "kD3lv0pzAnW0TfuAmQpge4McfRDSi2VGIiNk8tdRLbMGYZRPK+75RjIsgQDns9fbjxui0MXfzbOD\n",
-       "ztkUxdwWN6Dvb5+30GcG/mzVBKglQ4xGn9ZDCo2WT3ZRjS7suYQw4Qll7zpyI+moYPBa/yFPVPz0\n",
-       "Fy8VAgNCHzIVcME0dd6YTTEiLZz8mkCuidEX5lUcXah6T0e2z+NRO1wJTUv4K7amdIwtFyhg1pe7\n",
-       "GdpXBrDhj3Iwn/eP9VSs9LHzSE44+UHiONajssa5WQRZ/fsZOC7A7i9gw87ivWMfFGOx/MJcpWUq\n",
-       "V7/nzzPSRrdKfar6UOLn2lzXxi2Z1nBX2JftPi6u1XoM/3TKYs7kadjwowSOCeSblzmsWYFi7FN4\n",
-       "FTmF0zrRSCQ3Nf6Cqs4995JuQ0S1gMtjNxISpaWxVmnoqyoLBZpIMZjBYjR1n4+gAL6RUJKarAH0\n",
-       "oMHWNUNPhIFl4Qf8acFgUGoErqATCYGID1cXMFDnI+casgJghmlqJhTUrVBcWYT6Dg4xJJVLzm3R\n",
-       "Wr0zwRDk+HNgZdqdl1IudcQu6xlRgRypKBbIqU35cO1UZ/ytUxP7mqucwirpjdV1d/JwdzCgcVLO\n",
-       "l4IAW1XAgJ3MoftIqLf3t3cmRcL0dBh3yw2MHp3dDgX4qb2qaRUAJ6+t6KGbP7CHukPVukxzDUUS\n",
-       "ayl3qsJqlytmoNLjXzTUmHp7z8nGZxsCzw6iEOC7ErkmnjICMJWpeq71BtKmPpZB/lLP6Z4v2rkz\n",
-       "M7FiPoSqNk5gAu02mpR0EejrzAjYPsyni1tTXOxi+ogjwjTSzQvLTwirAtjNmFME3ie8q+vv224h\n",
-       "1AzeYNKsSgizfoR/xpqYVeljENfHCkzhVtP/aCtWDLQXreLbyIK76eKPCv1O3isNcwpsqM6Jc+uY\n",
-       "mmLFq5scwT46gy+A14HlhHAIpIdmZxiyFjdBYAsNeh5PL/76r1zUJExTPOUKH5mJDup1LNxAJODs\n",
-       "iMCVjxMfwRVfjtW0wu+ibDnet3NVuy8EoU1kCAm5yiJChWB1v8lopx8ilKWgGUpzyoRsk3/x3cMO\n",
-       "N7Xp1tFfS5MiJvGJM0CRpvhiirofGzBe4BeJ7ph1LV5yJnTKEaYsJZP+yPIs3jzJOb72iCHuckth\n",
-       "9t5P6yY96k9xBIjlR/mDyVXqJlR3Ht+SKty5ir+RMWolCi9F2/p6tksCuFXYQXDYFhZAr6cISmJY\n",
-       "3CI1jk46BnBxk3Ia6UoxAKyz0yjl+DTQ/Sy01nxoZ2q7xu5RzpPdTjPpyzfBLYW9Vgrywv4AAA9Q\n",
-       "QZ/aRRUsJf/FS9NUp5GsNDVtWknxbDhKitvOJMJIM3CyX6eFJPWnG1msw+eHwPZ/NukPGm+tjM7+\n",
-       "Q6VzLPCcxmQnDu9YFmABwAAYOofqMNBRPFmlYivGu1bXzTc9IXhxCWl/E7UuHJf5VTuxMWcav14n\n",
-       "J8QEdi4YbZ9igi8ycuRZ/fOBh7GbmDux6vXhWlWkLtMXnQoYplMBB94gHxSlnUUJLzYslAk0bQre\n",
-       "Baj722EWFJ23OQ0yNmrUX1g2KslaQy9DaSlIdxlv8CDKBoM3tikyDfSfaY0G/w8ERuibtBLnWsT8\n",
-       "FWAziINkuD6LljDFl+/FJgLPIjRyCtAbar24hiUHk4HLOQOCzShaLgcf8VYCqvIi3HcxvGoSr13U\n",
-       "zQsM29WZrGUppFxHsnCHMG12nUBQxpjCSoApg0WNEJBPtMnIHUejQkyEJl6a/ji4Pmk4rdy0KCwO\n",
-       "IZgN+oWMYE5WI7cwM1iN04ecdqJPjs24I8HBV2yWICV9SkFijUC1AA0BVe8EBhdq2GQaLbl8kpUf\n",
-       "KnaQmi7C/rfQRyv7ChbBUx8gwZtLgNAwffErcCKG67XmiKBGCXwBlN79v4oKMpE1xs3PywrLgdzw\n",
-       "uNO9OqAd3M9HauYndBBpcW4z9X/82RCMFiPmbWq3+lmr+5J8cm5uUb12+IERA65stwvY+0ikQGjL\n",
-       "FUHaxGzIsCDWiQG7bjyvdsQa8WKpUlxYsI0jQ2FCC6OBJwpGm+EE1NOW8e5ERX59YmencAgo2kUN\n",
-       "VX/UCdAxm5IXQUyN7+bEkrGAcz6oaoSsAV3l7e6mKv9N34vNdjhCAm/XGFqSXMq0zuhDXxCs5iQH\n",
-       "D3fDiwUNozycnfZeJbcaIg+shcnKBKTbT/5iedP0dBKNmmxVZRpk8PFXUJAz23eimISGC0iYv1zH\n",
-       "FEm4GmzhLQVkK/57UxVr63VOyyUPhuwuQ/a3wNc1NAo+TbC6CW/SEKjZi48ZIGzNuLv2EJbaGcoK\n",
-       "ClImBoB8qjlHS7Jw8kdmbYNZtwNrU7tuqoG1bjjWWXqip4sLdgv/A0fVVb++LKePmpnJd4K1NZEU\n",
-       "lLcojjJ12bPruuL0VOO3oiG0+3kH8NGu3i50Nvd4CkNyKPuTI96sSAVxnKUwaVuaeH2Mb7T6Z8y5\n",
-       "Fe8tJKk7mkz8y2f0Rv1uoBl8JRYwtfBEYXJR26a+j1vY4/xu53eIK4S/RWFXv7uXqC4xmK3g5DgE\n",
-       "ydLcCrotKZ5n0ZYBrL0CkeNzTo5sZgeUXR2gRAjY681zfvP6gzmAcoO4O0yK62MW++ll9nT6XBOZ\n",
-       "RNDCbHdef+YgwPP24gUvmaL1pwKHniuPTwHW+NRgyuDTpU7l+8PZFgJrEq7rvW9/Tbe0rZiLXh3x\n",
-       "/IuzNqDXTlHNV0skMiJnYx1tP0PdekpUTz2Pzedy+rE6t4qC+JQ0P1pd58AuL6hKuaNmpAY9yk+l\n",
-       "eKLFZAM3VSRGebfobeBW6XJUYtAkQVfsa4Prkxrv42hVDzRlh7YwU5i3xvzLaE73KAnz/a7k8MSf\n",
-       "zRa5ofiYErSdmoWlKaBm+NoBBfx+UTK9CqZgV0sNzUdscTW8VQOnu8gks57MwGHHD0MkEX5LL2rG\n",
-       "dOw3iX/oc0NhQhSLUnfweKgL8y52WksjyV/eoRGP2N80vNc680JTXBvfLtYZusNO1kzfxtDxKah3\n",
-       "2OGxnlb3IfXH6E9XbLcUQnwkaC8bWeUjqf0JGthsgbAs6bBZh6302XTVX3erZtIungQ54XFA4hyC\n",
-       "EpJkchLQxpJ2TsB4zNeNKpvivN0ep0YV0ERWHFCnHn51Ap76OYVbkZTp5XwOuBjjV8bcxAcAnl/H\n",
-       "kXwZIB78Ut4Vk9e+jpk6ORore8PiWIzi93B+zCtE+ChqhtJXkHMs48r0KHiQtyFZNW00R5GBYdo+\n",
-       "mrLVNarZdEDqmFPS93c9+XoAdSNn4P2hnE7ISC5YVJXa2lTRGF/2tdQKZQb+BJOPOeh49qf5BAR/\n",
-       "IkiaDiuai0evVCgbJaiw0WNDnp8KVrLx11IHjhYDQls8cnl6plFfq9AXqB/zSnG7cztV1+FNWGPl\n",
-       "0jOGFK/pqdp5618Sg6+hgK++SJryTE2RII1kBvv05+/HLl2Z+/2xdEeJVDyZDLvFc2Z57e7kKlz3\n",
-       "vyYEccAIh8Gd5LUz1/OsLj4DP37pbOOop7YQ3LqrxOMEYuv6uiI51KQsGGnPLiCEp7XjIAY5Xv9K\n",
-       "hVMTaT1tPWtiN1d/TE38dVBU0bwU0rfn8/c/iuhSyHPQfkgcubhxf7Y8JVCR+ebQkTNwYZAlXjti\n",
-       "SsZNLo0Y0maCGqhvIh3fDATdzomDes7inroSjRSvdAVLwhIsTNgSkG3Q6md8kjiIKBuCq3YI3GtK\n",
-       "7/0Y46hxHKYjVpjJDYo4ZPBno2zLRDvVo3RDgCrLZUF/boWzkCuPGaXPMsld8nwf+Tz2m2QLzP5E\n",
-       "AYJ18H+b/UQbmB3o6q63XCxkZVxz9Mbmfwi13Kw+ymnLDR2TGZv0VxR8M2yQ5g0uUfEeHSRKv+7I\n",
-       "O3QPz5mW4i3IOHaDESMq7Y4MCwK91SHZAJSKdjigQktpMBO2DMuLB3rFxfgviS2BpnUaLpWT1UCS\n",
-       "XB/xAebNymD5guzde36UgkI15BL3XSzUDAq2MrW0Ny57MVgIhZVXtr56BBz5J+VZacm3aDpl/nq2\n",
-       "xRdoJTY58tHKnBADB9ADCzVxLuEkGWrXdGAkC4K562DwF7keba2qTQLHKRx+uy4Uh/n0qdnu92wz\n",
-       "IL/FeNgo6WEnu0Kz/JYJQM81Ou7oxPTgnOTF4OkTYAyXshHRZ1NDMkrxS3a0SuPu/xDf+dPn60j0\n",
-       "JPXccSc3EGFRYxFM+TtHL09TwGUr+hBEt+zrEMHX+YwCPrcAOr688nQU3p8IxaOQWIw1QPYHJ2yF\n",
-       "+uhozK6tX+wre6u19x9sC4JbX9s3V++Yu9ORCNbyKEJlhJmbckl3A1r9S4AMi/KEd6iLs0f9thTU\n",
-       "xdcCw63gzoSzf9mtMJzgpmXIbVALiOMVs2bBQEaCqFtWYk6vRAMuDZT3xTLpKQj00Qjg0OKxf8l2\n",
-       "7T+iN8jRiPe50W2vpe4dzrjL/K+YKZb9lVXUNvZarmWN00TnH9JrXmDr0W77+1dsK8m7e8c7kBNF\n",
-       "dS6DnOoGgXYEiGlf7HOqFjxroVJ5HyUJwOkbNkykUaOQ6MwH6ZynRUzCsekrD/0WhH4YlLkJ8iS1\n",
-       "XHZ2Z+d0dLB/9Y1H9YPy4a4sElu6f8SzQ4BbnT4npasICFlYLI5rg3dgV2M2rapA9iqwqbAU761F\n",
-       "vKBdzAYrfTOtDhYnm+toFakORdiOs0X04bN1OlI+46i0lOvOPBh5ATfMJzrpve9q4sAXA8+L6O7P\n",
-       "MhJVL3CgUmKmLEm/3df7moq3mlge1MqZ9pEvdQLJrgwKBn92DxXLJIObopU4AHuTAg7FDCvvpv3T\n",
-       "0oKrtloFp5vRWloF6u6ic2dZcLZM7I/UTsCyaMzRNG0tpUOVDi7ui30Eu0qI5DW5RiJliXSgrs+Q\n",
-       "nqezNcuWbxKw6R0sgNQTndTB56GsgK9kjBZ8pP21PeN02ITTGqkulaMzyDu60ddHM/PDfgp5ZyOh\n",
-       "L8KjaGAxRD0e0vDvmReQaJV/5WOfyXNmfZ1TojOXi8dxbNG2BQG3C1zBE107qWBtlRlzeo9zHjQU\n",
-       "wefXa5lDQHaV2BEoLzrciZXN7DeZpgflDFYYTNJh8YpitA3oo6Gg73nWwPCxs+iyWW282MAQmnUn\n",
-       "QOHlI7p8JGkedwtvttHKIlVMDhg+RlRNBLvvkvjpR9OhmUdVod7J4u4hVNoA+BfDZYoz2y6Td9as\n",
-       "LM3XgIpsjUICfy8Fbr6JNGhXKDWdFltLFcOtC9lVY+6OmpeH6THiH8mcMWjh041WmvocuB2qonrT\n",
-       "jgc1ZNRki0/pMeM6IyEANjsxHPHRRmc6YVje3mAuA+LkJfLJ7Cz2vIrF9M/ci4YXMbCYCdJc8X53\n",
-       "JdbGcs2aBhcP1wYEJ4sDEXqBMlDvjUySurATnCETCFcFgVooqDKAxrXsHwD8Swlh0hDDocTC2bGG\n",
-       "HV2r5xzInfHI2f2AtRnQyG8OHHNOUeRh46YHLy9SWMLr1ykuMyiAXcJT7UQ7L+JspN3wANfZNfol\n",
-       "FmvGGP/0/tdrhv/++rd0+vb3Im9Np3Xf4cx4Xy9l2MRstp0Ue/azVAFMrsLuUvupq2XCWFewKZ3d\n",
-       "2nIarYcEo1H0MKsy0R3IPlWlZZhN0GaF/xHqgeauWnvTcZGfulHNlonjW6wd0yQPLCAWWWsS6rVw\n",
-       "7paSxMNI4sMBHw2pFn6UACJglBweRIyTun77QmzwYz7WX0m0gt/lJw5eFpvqwb0PEA+TW914jeOC\n",
-       "xGFaoZzkD5YSb6Ej8+IsEApImE8d+SVJzt0y+eG53uOAhYlzzIkl1Qt7J/P4fQTa16q3cHFKUnZ+\n",
-       "4RjymIeXNjge+LPjh3/bhne7+f1gs+tJIzEdGklHm6uMgFwwJWhHL5UvLku0f8n46MQ7ljI6yYC/\n",
-       "Fan0e0hv+kyWsGR77rZX1FSH/CP9JBbbTQGrFeQDxhypNJ2Y2u4IiWbvD98BO/+g4Gkyibhg3dx3\n",
-       "B7+7bddpFjF0hvti7BDLO7kgYACs9Gm3/XtKVgyXmSUWxstGsNT6y7kEOdudDj/if9kXMjN3qvF/\n",
-       "KCMqnjJmKDq6fqbwyolSszeYUIxTaV2Rz7ItPHb6uAKo2LffdAytzWFk3umZ0AL588bwP6a+Yr8O\n",
-       "URUzXGEPq2O9xznUbJXCj4o1/vg9MxtarXNjjVLd9QkYnV79bOhIQwU+HGWicMqeD6lblV6neoRi\n",
-       "Y0FMzfIfAQa17+P2azEooaX2Wj7YJYNuI2O7x8D4UZnA2/YumhRFOCxZcaheBZq9yPGx0FA9E7vb\n",
-       "YIY6dNefLGNeWhA+o2dsdyVdpAsHBH5aVQproxKdJaC4h7frsUpv7JewbIqhKcmP4NXlAljehQ1e\n",
-       "wG8HiVdSicIn73CjHz6DJcLtSWyho6hkFZHqpJZevKgJqHbBcciTh0dV+KOhfxc9pqLqWzFOibiP\n",
-       "0E3Ytm7MwWSPlvvVvs89+fnFiReiQVRJrCXbSNRCZJeSNXW7VAj9JBw+TzdGHC3SMMJT4T8nAklE\n",
-       "vyLSZDJ85WKG3U4tBcqAWRJ79HAQk1NtxtcssOHuVKng7C9QU+XUiaH1zloJu4+SQGuFWQV6VXkk\n",
-       "y+8gZINs8N5CDuS2UNgNj62v+nzyq+olI0Yiz83pJNZMJLNNDf1w5IqKQl8AAA+oAZ/5dELf3znX\n",
-       "pYtK93mIedwcFTaPGlJbSzGNs7SJBFOA/kaST9z/fTlf3uDRdUUSrDIugfL+y+bl/YGhbZh6hLB2\n",
-       "USAOAxbzf1yGK4jymUiz80ewQOvc3fXXEG5COv/PuCwGeb+9EKxnoMm6Hq2Hz8bpq++i16B7Nisj\n",
-       "V/MQtJaPN+q7O12kODXZjWNFUqJ9E83dM3oSkNjA6Gfh9P0IvC8GXrsfYHxKA3PDYazGGb5vqxIW\n",
-       "jsTWPIZxV+oUOxT/LMg4msnStyLa0chyKKBBommT7NWLXrc72IrCIw9fj3QAGUYitjGxVg+AppLG\n",
-       "mHrNsrOPPfuZBzzyAfBHgs49t0gKuH2Qu8cHgFZEy+2DJa62WQTl6pj2P8KfAUdyVNa4TyyIx6BQ\n",
-       "vpdB8Uf9L5P8AZFzlwdoXah246XVIVIsrBp/FPyhEOKHUwKKKPEYMK2UeMI9XK6HsAEK3gYME+LB\n",
-       "9miv1X+B3n+DG58hM+PNSidcwP+pFc3gXa3whEBusFYJcOzbPVYJHY72XXf+Vhtls4CLa24CbcoF\n",
-       "uDxeOFwxgdlN0YfGBePAVUXvjQl5IGbeWvQqhwApS2uv49CSxuPNe3m3gRPA93Z3R8wAEpvDeHZC\n",
-       "42k2f8rWk25PW3Y3wIr5n8s6uxrxp46A8sQuArWNpqSNTzozJI1Icy6gFMpbnr7BlDhgzgGCouYN\n",
-       "SWWgmYDRUy5hpgMvq2IDfRM2P+dzZVsiy30Ww5wrxUEHiFor5P0KOEiuVorWZ3vIA2ef25xjzJP1\n",
-       "1z26Lao/M6otDnjvIjWAglnXRlr6XeoeOzStN3bndF14oNG1b4uuETJykOI4HHmkALzGRldqHuZ7\n",
-       "UtEOQOIDXMM+HHJ6tljv7KeBmHijU/h+88+LUoiScCxrmPeGFYhzl7pjIVOv1WMyswwpFbsE2mEW\n",
-       "e924qFZxLBnNuxVMBeMnbymikbRlyM03c6yFgIktya6BfXox0Y8wElVgBx7Fbl/IEEJm3FZClg6N\n",
-       "ujLW0PQmq0VS0qfJhTGrwGopbbkkK8Y+I9yI4MFOdfJznbHMGzns2l0OKfNRWFwYTbg++8W8C38R\n",
-       "/nl9TT1C06Xxl65AG94cszsds3xHrqtbkt0mzR7BT8eCJ5UArSCzDsJ3/8hZdt1JDnKbMf5NeOc9\n",
-       "rVNLTBbg7XyDmgSC94wakH13YgDm9NkJMoI6rvlSFsCIiVfVDpulbOunFi2PyUqA8DoECDa/lObT\n",
-       "1I2ngf0+W+wQnK1xepI5t4TfAWIoCBrACfDzkICLFAaMr79zSbZPp6VJcmzSPK7w0FhJ0P9xFECY\n",
-       "RpF5xu/eO/kf6JbJsImj11Q3rxb2wWz4LkLM9GUGsyUrPotlWUSErKKWgKeMdMh7LLAzPIU3Gt6k\n",
-       "nsUjcaMOD4Ryq30dm/FqIlE32s6YgV74yroVMV9IaaoBJHTQbtOFg4mFfbnSdDjyAZusN62hJ9rm\n",
-       "W6F4oEUQl4gqTwuglWizK4O+FJ6BV0cpLqrxj1xvg64Z6bTmA0JsuCtskuVt/SUsh5JAJm3hQcLv\n",
-       "xMrI5/0fE+9K21tZUAM7JJnd6X2e7rdqwwTkASt7gKTeH2dUxKgAAOKe4i9b48Ib/l+tlEJ3Nfk2\n",
-       "2rD2VEY45Tk/mTyyxXpGuun/IHsY2M+sB1ldfSp0Yn4E+8NnGJd5cK1y0Vh/il/3ak4p7gYX+SXe\n",
-       "rBHGPKnG5rGvGjVjPA/S13r1C27CIKNWPVCXm/k9nldFL1jsY1ewjTisC8LQO+SK9Ar11b3z5km6\n",
-       "CNA6CEpLQn6cPo/IFz/5WgEPk9mtJfDdb7SvzUHLeDYtSrkvBO96O7bZZx/gPFDWUL5cNw6HwcCT\n",
-       "ZMYPsvBomsmQZUXMf3k336ZXiMJOzZjDmnlUo5ajgJZ20/lLbKhq7M4XTU9Ai38cp4GCg1lTameN\n",
-       "DwLoWtX6Jva3soODmy6ZNrDy1MgkS7/uAhTEKO2F34RLnTqqNgrqa1GjnL+RooqVLODOZKbAOD3o\n",
-       "vcqMd95JiEIwea4C3hzZHh06KlcRcQgWQQTBFSPZvSaX9tWr5VmAdsf3SF+6+07VvkwOUUNuhbIV\n",
-       "vRMCbtD6oj77+PCh6CxnZR+3gzVEmpfrJw6NE5kaigmQtC4jIk15vzjCnIzqWUjzpiOI1f/Je9m8\n",
-       "ecbct6+fHByIuOJkl8J/TjpRNQOJ86LTKPJio+lsRwzNzdi3G8KqdDFT7zPEzPP7dXdjUQNvWtjx\n",
-       "teqKxHfdcDFXCB23fOur9PUbHQYWUBEmDGpj02mvC8x4ON4Apv5hFV1uSzCuNJh6CeeVMs90RX/g\n",
-       "TDex2tv8b3WjMENn3Z75lX/asulEkgvYJ1wsbcoP8EOUtvNandcJTyt0ij/svSlv/cXm61CiwqmA\n",
-       "ZuHvcJ1rj9BeNmNwL5wRNnoimmhuBASQ0ozhYjURdS0XQpYlWQKrdSVPUEbE7OfituuWCSFLH/1V\n",
-       "uBak3BSug516zQZJl67aGLlCzrYVza+JIy1uQ9r5Bc+0+NT05Gt5H48V7fvwcdby/KgSYn6KgxVU\n",
-       "PbOWvuVKqW8dxI2/DYnGL+sMmr/woFjzsiIzd/ck2WH5I03mZhUxNjEkfetT1QHKm5P5HG2sIPuV\n",
-       "ZgCHU79eABBuqAh0grFTh78aMHnXqTjxboPH/9OV3wPVmz5cVDW/XVq3w98gAj/HFGKXEbMEFNX5\n",
-       "H0LzVmoFjGoIfvP2Qh9lh11wshwVRYjH73Du74+n1cstmt6XibZeKO6PaeMKHcl7FShTHpsDoeih\n",
-       "7ojYf5h9ALR5+JBNTl+TpZ8UdbN4eA/RpKR7X2GPf7/J9VraMx0diyosG81nM0Kv6OBuwEwffLa/\n",
-       "F/ID7AAGfkJ5t9cEgm5CPqvr0wQsrHGoUZDSvyeXvPDxkwEWlwvpUtqzqy0IuxdPAV2Lgl5JNqsw\n",
-       "KAI5B9mDIkWHDUopAuvrEToGj0JLC1EKj0/RJMFQ4W/RBWZorM+bTeM3AbxsiEkXHmI1M8rslxMz\n",
-       "28rsPqRQuBMyAGGVML4OnHvwp6vKk+90Mt6UPpjJ5xKejvPsTCATtrDLIqTBsmoEIKlbSvqzGkIw\n",
-       "TLwssP/96WHpNJ7r61J6/qZtujnBMI6aNSgihGZxkgCu/KK8aXF8zT3kMUxAljra7oa1FvFq5uXy\n",
-       "fXCIEAaaYejxPYpPwuOmBpA5S0KSkNhw4uhF/Xg5zosc8q6yA/T2OEkOTWZ7qHBLfrFpyl0EgpJ7\n",
-       "RmvrjJuuVDAvH6jrJ2Lmz54fHah8rHx5C2je846CB/YXlkvux/JyAdWAxPjuMAZm/y62zVJJq6av\n",
-       "3ZvOQcN/cgqvGLAcfou7FmQF7OIdttyHM4L5uy02B+P2rk9fc1L2GDbHR8kkH+Kk9F0Fhmwv3/OV\n",
-       "um5d0jufbWfGxQfKNJ6ekYG0UwMDT/yS5kPJfDC4FK1TBz4g0eQ5dlXhaqdTXW+wZCmqWVMCtCQg\n",
-       "TKk3Cwe1SlAMcGrYwM9qZk9htynfrJ+yYYAMQuPewJTB8piGxvAzK/x1BRTI4q2e7zRSTFJGmR1/\n",
-       "1w3L3Q3Qh2jofsu38Z0G/sLrTVcCVOM3yK4AH++1dyEI9RT/ty6weCYG4BYQu29idY//U9p1GRoF\n",
-       "ME0/tH+F/ag3uPgu8mWMWfuQyOnA2DPn+OTLsEe6tvD8Li0opDmd+hkbI7weP2G7f+rxnS8mbvE4\n",
-       "U8h9U7LaGv4CFPz+ss/g0Bfj9g4F7gWOVSaF0Uihu5vZ9NO8Ol9hPKixVu1OPDBu41EFs+e8pxqn\n",
-       "SNhK/+Hw8G7B8vpViJVYJFsFcOn9UWP/TucaMBpj44pWH3tqAzvUzBBYcya9daew1jSvuZJ2L6d2\n",
-       "IfBayVQ69XLDLH89OSwJHd+YxChjSxI3L487P6eNAxtrqGOUdf7K1t7TDDm3YBOuuB1eNIm7qgTr\n",
-       "33eLxhIDr5NOlHUJ3x9t0Iv4Sm0syWb9P8nz3OZFcwwMK6rTiUNVs02r/ITY6kVItKgRoPjI2kXL\n",
-       "Iy32F2HQg/lx4nktZxWnTHCdO+heOtGLiX0FsU98/SXuYUKgTIufC7ToGzMoYa543e8LIXgTTQFh\n",
-       "4fCC5A1XYt2Fhxgv4yNFH/jKNUF5JSd9caV56dy1MpOM8Jwxg+ftAn097j+1QUkECK7zhBbFqR/M\n",
-       "+VJDjYWAFpjJGzGuwS2EFddRmCGH3cAKpmShQGzplxGpGGqFAq4xN7vx85FBAob7xZhNnt+7c87h\n",
-       "gA1jk5v0ZRmQlb0D5XLYJ3EQKULo4TWKYObSvEzovFaC5SHkEWtX1OlAb69I3Ow2kgcSTOwCr16S\n",
-       "Y0cg5kowWON4CtrKgJiCIUFv8a2D6m+5B5D9t5WHM38uvcGo9VjGjFoc0cPYOUR6AqU7ofMkMm+Z\n",
-       "fSowHZ6DKMPw3HRMctXuPynwUjQ0T+A63vcuSLMEUECesPKpfEugQMSfjXA/hL1rCTT7CKr5AC1W\n",
-       "4hlbMLlevbsKlrf1aIPvwapjfIoml1S56mrDhkZ1e05b/sA88weXKkZPUjfsxgFmLi6GsrqaO4vv\n",
-       "6KKsoucq7goHjPE78QLvK+22+aSK44JI8KOeo1Hb7PgeDOnCtPmkpiLgJ+U4Az5gqdaDVUFzgJJT\n",
-       "MxLVHnlnPs2FQ+UQEeY/R3i8VX6A42XkNPEMtZbzSMNEgK+Lh/j9Jg+KJwL+BIEgW+eeYda4AdCJ\n",
-       "AuOhwIXX+2iRucuAix9ic4KZ4PyCN+LBjj76Vh6k2y9akpvATYL4pharq2FEVJh/BpSKE966xsDB\n",
-       "CS8EFkXEgmF9thyveV5pQ122i6zOMhoFl7renw06A61j9rNZAbVzT+po+VilcFk7cJKCRBZTA5xf\n",
-       "EZbjNvuNe54iVEqQ9liyur0KHRV1TisNlLbKIZzLfh2/tU6IbfY3MLabNsHAX/1p2vYsUWyW9ALh\n",
-       "dZJ4yCY7OcHyJmEHPiIyhA+VJwqeGbQaOB1MZLUY+Y40MMbyaVA8kt3NjmilsuqLCGEUjHEvubRg\n",
-       "LJSavCSJA0ojye7/lmFCNDZS9g37x7+lFOkvJEL9/SlSc0kYALle0rmsb7xCePk5DF7wSDOs8KZ0\n",
-       "gCQjJqWCISLN3qJrw5SkXIBjiy+vql2CDDADHleVf5DlfTOupiVzg0OmXptfe1c35ZhihvdiLyQ8\n",
-       "TutYuKTPPIlWxnhykTWJ/RXp7owxkXYQ5s93I8YrP4Z78wNsaT//htJyc3Zgv2N0eZE6+MTVvraG\n",
-       "CVlCTIL403pRottF74XFr26s4/4Gh78lz2ZKbbIkwyaM57B/kAQ/rOaChMZQsiqAYZWr5hA2W03K\n",
-       "AbX2p3iroxmNZ8VzFWdFIGEO58a3yNDKQtcZhA1Ni54oafCOonDbuoG65Ku+tTI8VOf+DQU+qiXz\n",
-       "OtDynyAqc8OmAAAJCwGf+2pC3+p34odtk8aFM35BswkTzJojLa0ywK7w0vQLjBBHpMnNAzyTT3u3\n",
-       "j0zdAdXgEUM+qPP9ur1mctb98bd9vrzImstV1k938jTktoBSi0zfqiYXza6BbdsRQA2fh8JxwKWX\n",
-       "AJWbIaBWApdBe0MWQuIJ0a6MyByllle7Dh4scr5Y/0hNE1mZuOzankf1Kc60V6x2N8zsgKgq2rVP\n",
-       "c6UmBLztK/gBx4/b74el89/uwK0FY2B3rzHi+bvUy/603uKVY7vga98/7jnPELka4tZz+Ue09TzM\n",
-       "HgRCaY4mauM2B40shTYZUPHGqvkVoPs80Tu1WBvYhVdd0652/V+5m+VpAZjFXA10Mja/PB3VHZm7\n",
-       "4XTyYltOx9b6bw8w3YHV+HYyJXSM4K/UbvRwm8pwPlAnP+IMl5uGAONEgTjLbOtcQS4yGNkmSXYu\n",
-       "b6E5n7tdcvoQdYx8nILfcQ/p/vs+IYVhf5qt7LMIiHSXERiCmD0XMsPxRCUC6JuzeuYooCYkWg69\n",
-       "tn6rcxPyWfzAVKm3wbj0+gFZkkU3yf5pCHtDYfkaWb95w2UGgShiWF2s4FVobZug0nSydrRx0H4B\n",
-       "AoeYytScI9N0Z20kL7V9Bb51bLckeJ8vjo/WyzZCUEukpEDPML0up8if2NWb1T3OzMGFmoInUt8P\n",
-       "u2dndbyVXOh+q8XgccrGUjxXXcZaHFJns++kWsyKcAjpwE07eN8jtcomxLQccXGb28rNTSRv51ue\n",
-       "Qx6j37uY138RnIEfgEU6baeP+vsiUEKPaG6EikquKZuEvIJtzCr+IvpitqT8A+Qjs9eOohbPgaVT\n",
-       "spZFTnxZ0Xz+uxii8vCK2JXH1KD/fTiV2383fMO70kIYGXCdTNcmd2JK0ERk8q8UaxG9eWmAcDYL\n",
-       "k9uOr3fvU6rn3hhHl410Pvdbq6OhvaW66qDqyuwQ3EXZxnqyx7bJnk4e/LsD9HTp1jieWOytfdTD\n",
-       "PNjfw/zPQhsMCYdaEdjr0YbhUDal2MJ5qVJN+s3d9hV7twZxz0vxY467FP+F6kjzZAAg+hJJTRj5\n",
-       "Iq6ZEWAWO1hNBYw/XndX6LPRaZC6VTMjTPiwC6YOZ5CLC/+WD6XH73SxPpnU3L8qFe2R3FysnnRr\n",
-       "GxYnh6MKAcrt0a26R5WsKda9gxijwXPVjXHMv5ipLgLyVmQ3H6ifK64fFMYBKSBhQtFo8kN8NkJj\n",
-       "CLooSOYFMN4VxL5RsOXDatHjeKcgkRwy0sK1scDJmni5G94HI5HtO+nbCbVV80sGjL1bO5V5RtvU\n",
-       "QkmEZPwX6qlxLmcD8q2hoLNPQkYfLRh8JEosk7zCt/lZ6PSSDnl2+qLTR6ojRaDGRFBg3HuTMNiz\n",
-       "cqyuOHNb1Sry5y2fr93KB0Tg8WZPHhzS1LKdijWOojvv7F/H3ELywbnYtkc3RkcXt50JUr4Mb9HG\n",
-       "hsBz2OYg1kl0MiV99HIIA6LeEV9kvPKEC1OA6mqvtMEQlhLvR0v1xKUAYBASYjTUPdRp/tvLVljB\n",
-       "gUSlkcJITstYtC6GrjAyIFk77lXoXBw6MPOwUQXWDMv+gS/hPVqmEKo5bd+Jh8Qm9oI3PEWc6Bzl\n",
-       "we9kiXSMP0sWkY8zFbUmPo0KOi0Njms1w2oydnr42rCI8y1OOwfQeQ+SSwMguYMCw1Myzmo1G9Gh\n",
-       "IAubxoq2Oek2S8194H0UmtD4z4PPnObvYvfkQCqDB0OzvijijsXtvdl6KwmCRj5PBAJBUG6My7h+\n",
-       "rc49WTUjNJ784/47f8mWaqcaGz+/p2lvMHK5+kTMeHx8M5CcqHLUheJ0D6MtWuR+sF6+WJWXvZS5\n",
-       "7yDoOhsv4sNYynnfXvhZzI8ljVAMavQ7wolPIspuh/NJxFGiDdkwmkoeW0RPzHv7fCY9ECCbB3B0\n",
-       "gHcPZOc71uyfea1yyG+IS0DHWArlVYA0Mor0E9HuwIz8Gnosqp7lDUV7LkumFVT1ECHqCNHjlLNW\n",
-       "mU512wsfQiU3qkWScNO1DSpia20Pv6vKqunajWe1eUXBvtYCk3u4ebtL89kOikbyn63FIPwXipPP\n",
-       "WtKHrJwCkPY8we3rskZuCtYJkM0AAVtr1plQgL8mN1jSpBRuBUcr00ypGwSwseersZYTPItbWokN\n",
-       "IDasc478ybZmk8MzQ/epvbA+Bwq6M80DJAAAPzBIfhQltNc1pHz6kpz177ndpAmW+Jr9IwK3ckDd\n",
-       "1+7ksjS3CmR7p8nVf4cZnXOloUsCThvVJdTYs9+fsN+DNQXU4yomBiPfkER4EekCPeCpiZrOGChd\n",
-       "cj/G9juD8L72IuD7jhd37FFAAdpHavsosH9z5Um7icOLGTLikDWb4nxZ/RqcRYnFJ9iBVBSCKkcZ\n",
-       "dlPvLcr7BWoRhKAyzo2LgzEb7+St7IFxYFnJzb/FwSWlj8DuriK3drl6LMkheEe02bika6Mxkjrp\n",
-       "4yXDObQ/27nXrUhXDTF+e2QQCHovQFApbE0Db7Y0zMx5lUP3AqcuBVax6UIgv1FeRMuzAAsrrq/h\n",
-       "08FDswtWIW2EafJvEbvuceuVDVZ720PRMYqVnL+1EU+J1sV+6eIUwdqTQ946FFe7Eu1qY1Y69xFQ\n",
-       "MLFgwtJvoPUxRpl365eB2aGhgbHqzHUjLSj4D3ig4D7Q1cMtoM2ir+P68NVdzoCq2/6y3mb+njr0\n",
-       "/HeFZajB+yfPjyhT/uVjQfr/UY0eETVvKwtabokwKKa6IcZOdT3h9TUQQWAjeAxsi12a8VpxCdU7\n",
-       "8QBmDI+Fix/maGoWfHd4DKO/WNuvoVw0tWqgM+TdfcTEwI3pqIT4mEvEsZuLCHONF9/keCmBxwrL\n",
-       "KOuqTQekAynkW0s0A/p84f5Rly7/AvJNBMZYjtYfgG6ukAnew06mwU93OgjrdoiuQ8KvvtpxDW3x\n",
-       "FOnjgm0gKOGbbYiuzDUH3D2+/6dIeGAB2CwoUTzhk6ean5Gs6rhpcuaKJ8PoGdt5ueXJ6uzZy+jQ\n",
-       "/ZXdZeIGOOKM3tzi2OXQj6XXR24all0to3Z5NFfNtg+ngTGkobyXIHPND96oy5ltHJXXJiHicX7o\n",
-       "DxOnwljq2eT5CGQkCCvmyieZG37tmCmtSnQEDnyR9h0qXPEhyl+d2AKJK+VjlQwpAAAUmEGb4Emo\n",
-       "QWyZTA//gL15/d1BytJke55AkW2L/iG/zrIf7+RUUy7+wuYQRcpNEnvhR+JVtEP0EA3CHEfpmCeK\n",
-       "iC7/4MIsmmbImcByFMx7SpzHlW9YHRhjsPtyxx4InaxESy4WMwrjMhQGzbNSZwk/OXe6jbcFcdhT\n",
-       "Y7HAvvUdGZO/zdW5hPV5brSPGaJUYvNBIvKBH2lEHhm1/n1lTNYIeacHzzDJU+6f8+tSmz4DKoe6\n",
-       "91ZH776gGknBLAwBLEE7Okk573wK95PPaOwmsw+23iDgcGJZjC5S2zGEIFo1GoM5fc3jn3VUOCQn\n",
-       "jrN0zZY9RtNPPfFACQHuZnJM3EKkGa+Hk+aRRk9JfGJzqu2ndgN9pq5rgMXtairjv00c8yLHn5u5\n",
-       "ncs4WE0GiCWI0GjvqL0Ob6cHZymup4ppSJEc1USZEX2yVTpqGniNU8IQT7lv4DlB/z/Cg0tVYzJH\n",
-       "30/BUC1w5weePJ8xEtCXo2R39X7GAKGrVrcFnQsNctNn8BSJ7S0SMQeQot+F00u+ilrKaByBrfya\n",
-       "YMz00ialmV3Kv9gZfoPMIduQFSqtyN1uGZ3TnAmQyEM8BF9WurWWS9J2LKqZIFs0gRwGBLnCBCAM\n",
-       "JrikhSzjNqR0ElYNDLcbgtgB4xiS5TImyKFayvumXW3rfc90MaRGKVyBCbFbSH23MzbRGRDpYbBE\n",
-       "2mNJQBxnsjhL69epGjdDzi9+matcfL/qEelIWu5+c6ehFUKyPoMWRhXq/TmlnUTeeQK+XEMCCuz5\n",
-       "D0ruKfhCv6q+4Ldu4AHpJN8OCekNoLHgcq1LKcmVOThZIXz5S8dVj+SVnJTN6VKMQOKC0l/7SYW8\n",
-       "3EHcceJuUst8JN/acEQqWNQ0SY8+WAo2rwwKXmY+lD4PVG71iG23gKq9bkhqGZHJ/t99EXSU86xn\n",
-       "aNF7OjJllLbDB7xEl5YqGM2QoAB0B1xDoAMnf9sY4/hQ2wTDoGgutW2qVmo6SwRrST3/zD0LQWN0\n",
-       "3n09oUR2M4Ad31ojCLmDdQ9Dwy1HGHpGHvVI67npuP+/CbxO1WM3d4EPdEKhQW+PmHxKS7j8XoaE\n",
-       "kADMXmedQBVLPA/p/mJ8H4wrRsHfMs8whZHEVNqJQ8ZGOQATrFd8ZJ7w/AqHpe4mkqoRE259gCF6\n",
-       "ibc3ah/Hl/U9Xm19VxaDT8j3Im44/uzxrd962j0aKyRUW4OOzrls9RXPETLjyrIuAxJFs0o6z9Gj\n",
-       "xU7t7LV0W4b9sUDzx65fFyMMZZ2s7xumgWdOgz6KdBN5eZDmXmif4xhKu1wRbWPWxhm1SdaIt1R5\n",
-       "+shpeB0Kz0w/eQBT/PUgD4KpTWTT9k9I2XUQj2KNlUF7Mq7re6/sX9gfjQEtdj6n8tY6XC3uU3dO\n",
-       "21GON1PDMOgtkBDrtal6biiLeA7H+OLANa3aQsd5xFktNsV8sJ5gd9axAcrFUhMuF5+UakzghZsm\n",
-       "uPjmSeuSss+8gAGLNFOsXYqwmtBKnznficMUKrsefXOCbc8b7K9L7fP1Xpy6v8zd74ZeEozHK/Md\n",
-       "nqprob1UWO/f4NXZOchW+8Y9L90aT/tX8NC+cWNh4AX72/guzc4qOhmiQ/R7NxRxLByzZUvxM5io\n",
-       "0MhkwLUxw0hWEBpagys9Nr3or3dfL0F/VQXOtfsBvzef5uevsPh2qS9sRCSX/meRMlZYt30OYw+e\n",
-       "+EL5WGWpZbWh5I7R3uMdCwx39DNKw39lOsx8Gp5iI+m/hUBJ3JwNZ7X5SLH+tNKUuLmmm0+Bgy+8\n",
-       "A7+xB/3WJRQee7HznsyBoRu1916SPAbecQwYHixmnhu3HEfHiERNXWpLNpcbtFzb9x0yDDunuJXY\n",
-       "BJuqXD+lbJ+RxvFKmQnZkVyNKDALg9gRLWGw0MYQvZIG7q6exM1YQqhEQto648lani7yiHlV4IBO\n",
-       "sTXdKh+V+BuNWtpVK4FuX1UycaHGiajcIqiN2yB219HtWzadcB9HTu6dZwUS1HWHzx/VfUvyyn7s\n",
-       "tvgXK9A5FfETGiSO7eYU7NctUkz2Nfdy7F+e7h9aVUQTGZuB84ycNj+H55cSh957gAv3w2GbdeO+\n",
-       "VShmOJ/7Jvz5zZojXq/VUOUSL8zOGG8zAe9IQ2w6gavXh2GeM31DgLbLCjc87lmlm05niYVWnG34\n",
-       "R8xq0pT7e0Z+460jpiTJEYQstsv+G4XNWX21Dj2aqTNn4mzt33ZxdUJEupOksRaou1ePpS26aDV7\n",
-       "Sq0y/2W67nuNmPoGqvlacu13y3LejEol/I92wr0r1mU1JgooqGMQ5q2A318rmBefYlVv8edFd4Z2\n",
-       "lPt6TBhARRJvCIsMefqMP/A8pn6FbRgd/9Ch9FFzomnIf7iWmP8t0ySDVsH1gXBsx/jxNifO5OYR\n",
-       "mKgYeCaKKo0VGTNIqGM8aWLqb4VqAXIPXh1p5ynsP6tRFvh76Qj7VDF/JqWKp3ED9EkX6gH+FLc6\n",
-       "xXczAoH8ikdcraLimzn/nqUMwCs6PuTwCfXndIGLE1adSy5Aq3pjQCqbrOrJIHVZEVHOTlz3pe9d\n",
-       "tUV3NNxknWC9eBJNnzcqF5TKB/j5zfOiZChxZ6eekpUgUw9CzQ+rj2ngTmmvmaEm8VKbKdjtQCCJ\n",
-       "QIzreVty9zGCmjOg2EGCMVReVzjY50d8vrGafix4qNjh5ZzckXPuW6CdvxiyGByaeCqNBmtCwSru\n",
-       "DGJahB1cOC8Z1H/Uwkq1N6NDqp6ZMjL5NHD04gvdFH7iFREMDLE74tzdA9dXV0Hz4aoNb/qnecpK\n",
-       "lujb7AuUYxujbTvTgzyKPvspRHa6LFPI1tEwCr4C2Nw8yVoysNV77/vvm7jaLLT2X7sZaeG8KO+E\n",
-       "z3mZFEr4AVA26YzxkiH+NUh/O9NNZ0Wf924HwynO5uhB0oF2rQcOb61VWuv03eePHUsk7OZUf616\n",
-       "xhXm6SOBqLHxjLDIT0bg8yAxOh11SYCByci3J3/QpGm7dBHbE5xHX1DK30xihVeqhsm+2J/Cw1Ux\n",
-       "+Xbr8/Q8lyDPoxs5jk6YnPFLlH1IBeXi0MwqXJFskJ5C0bn8i9IB46+Y8nrT/p0XF7xT4OOZKNSB\n",
-       "iIN2+3lwuFOjsL7I6lIKCzZgkjQTGnSF9Yw2ubBRv4MdmJthhIv+s3NttyKAAKjTLsVjqFHSTaZ1\n",
-       "tW9ye5giMYM2vGypaWFcW/lTTTw3NSYLuheRZ0UIPuho6da+Pr/RaUh7JFhUvq2t7xSxmG5EID+f\n",
-       "V0/vJaqdhUNPJoAawOPoRwyzLOZYYLBZxmhXmcDggK1XdIh/jJrKirXEaFFrd5oSxTNLKgwtPvmo\n",
-       "KRZn1fUsraR1D2vJI2KR/8Eoj5d/g1LJqIpYJnPniminib03jWDzcHgZgoAC35bAZ8lwfryDOw+M\n",
-       "G93cDVDDCxSC7sMhwFdgXPkJoxCsd7WuqMo0EStyXfVhhTo7Bobej6+uNVSPGZiKK8106yxrI2Ei\n",
-       "iHq95w5HOKsMY3bllJc/KxmCCkzDPkbOJjMUxv4DA4/GUugLoa2II7cnQuAQTVhKgZySV+XNz0ov\n",
-       "lJz7LejLTi26v7+h5KNlfOAcgrKTgAEkO7jN6gHWRWtq2yNxq0lK6/wvaNH7M+srVQFODkfGJAVz\n",
-       "NBBYaFr+4yeU04aSCdkPuaaIqylr4ZohzdlMedTITx8M00quwC7bPRzGkFhastwFurz+VLKnAu40\n",
-       "1nh53shNZb1JPZLiIDZXhhwAQGrlWW15lWL2iIBbcTY8f+UC8HWlo5i5JGlBBeJlVZNk6tQcsePn\n",
-       "kVSd4SmG4JKYcgFkjK0/jRF93md8284N3ComYEyU85zC1SXiw2k5LuH0eAD2QPjntH32JK6ZKOan\n",
-       "Q5/k5+ArZZr6HliqpiNfNvG1R9DXTdeCDd/EtZ5tPj7CQPhCzmLcjprRWIl5fr23xKeXamicG+o/\n",
-       "TzXBXx+yBXx0YMx3e+WrGjuiud+j48AZf7ZSoc/xMhvX/MC9+sXkgMY+glt+09/7vqY7071TVuPs\n",
-       "tX5BKnDz0rCAMswsZLlVlODcG/EF02CmZHdnPH/rcR4iBJline8c6Gw9nctZNq5J4y3OZpob3SYI\n",
-       "QRYbVbHyj+xe20vakwvoxLUQEVZvLECdYnI+1y8KRrNQbJVyn8Znlg2OLmYwcZiWaUxs8KNdVz/i\n",
-       "E2mrovrPgpE0vkjRzbVph6tZ4mgDUcnvduD+sOCHTjZOaltz60jZ4cIrIqxsBguLJJ4WlKkSpF2h\n",
-       "hMdemXRbWkW96kNM3AHQwL/8It3LlU4hctVlnISAKaXX+/K1b/eMVc0sgxVH/P3jVP/RVmc/3+JF\n",
-       "LWVuBsiD4AI/mDQ1GK1AfLConraR0LbeNW4bpqhHYH3Z6WEdPwCgjim6wM+HZ93LxNEG46j3yiLM\n",
-       "EWPOmIwe0brp00huX//Hf+wfSgK3gPW3PRmKm2stQJhlEDArABMoD3HqHeryOP9U8O9PBfJspxuL\n",
-       "L4czD/V9ZHgNt/ure2DERbx/M2IpBPCnx5uFG3sia8e7ki+0qzuoIvwqwl3qabhOzearc43wiGdp\n",
-       "0LKXWEoVu7yWj7kzLIdOShyVmT2LoJ+hmBpIY0nLiG8rhU73WjFDcSizTLFOu8S44ss5/98Dq3Sb\n",
-       "xYcfidS2FzaMlE+Lw7XPUMh/IkfS6OgFd6SJApyP408aX9HMPb4nJOhqzVWn3YZQG1Y1JFW5mr7g\n",
-       "vDuD/dR3IeyOi5AYPy201BWojAs4CH09GKI2U2T7BM2hqrtVivd8qwm/Cy/TSfus50B6zrHRXBUu\n",
-       "SrIzPF4m26Lau9THxTv/GFxU/fkfawghQWJYiCoivaqzHlIgC3tl/mS3RpkpTi9bFljpNChDixXr\n",
-       "MTr5oSpMas23ga/x0J7UnLQvjgwgoUf+v2p7EtX5VJL5b6h14DgW77FUecnWRGoXzqJ7omwbOrh5\n",
-       "/anGBQQ8ocKtEc1pH1CnCrvPKbhY4PUXAiIEHa/TQnXedRKlIsgIW2M1juIql95NeJjSC3Entbpv\n",
-       "9pfifjPnlLf16VpVs8twHr27LP+VigDtypWa4uikbDfMNS7SRrik+jibnxE2LfCJ84p1gw2Dtwsx\n",
-       "5HGwUaPTnxsGRIWMN02PWETL/ygNCv5BCQ3LnyWVVuRBh7+Eri2jEyGdn65HjkfWKQfgoq7dJF67\n",
-       "B1efnXcVqI8cMksTAoVYVMpQDISr0/nT4xp+s7lAIkAAKY41evtQzkyL+7A/lRfti34UDdFY+EDc\n",
-       "g/bBwq/wNgXezdFwkKYf9aUxUjOcEx6o8vx0eKmWJ5EJZY+rmuvnCK3JnV19RTExr5V6k+lgLysG\n",
-       "HgO4kDBABFXBj+mOYCreUCxU7OwmEyQ5TXj5sl1QMenScr6JYg0UA7fkivKt0hZEukSWMgAIBh3G\n",
-       "XC1J1KZIOPmFKYQRtK3MEblwIFZY7EqwTyIrYcU/BDf4Ux0tbmIOK+oEObtcKIxfQb9oqRjbQ+XI\n",
-       "jGJikCvJjVfOxhd/MaxvWR1VrAw9KXa7AV7WAaxm/yrPvCiaHxhfzMxTbUhvdZ6yDHMiqRzLgyFi\n",
-       "cpxil2Ded0StNa4bLAFkT8Vh959azOHpSwvInK/5Qj5jfBOiQtC9U8+kMwSDbkdF7pC7/JJnq61A\n",
-       "Ddn0t2veFdD7NGRo555Ji9QdnNlF2mzGQDXRAAFxEZnVewCYZZfM80/sHP2CUptpYJc/oEw6eyLH\n",
-       "nbNJMPndCRBnJFI+pzJF8QQCnCtce0qVcXZmAPpxZin5KaixLtrMd5/Npy3xjOiDxivGciKya0Op\n",
-       "IvXap/mGDRlk0C6j/x9baGXTLb25d0q5MSgxu9/ND9wr/ovKljaUi+jFzrCvrTluCTkAq5W6F+at\n",
-       "07FngH9VYNr0WT8Lc9rslYqYdndZz/4INxAaYaHNJfrVZb41hwY0ZEGuMHS8WLyOQMhZmU1b0svR\n",
-       "PjTacbNtEmMCVVDfwDrU1MqeChTY0e/QWOmBzS3sPNwxZOHVMwcHPKAS0hACzeeIkDZyYlvYF0qU\n",
-       "feXNmPwikjYG5lu08xCpvBcqnrIZcmpN438bwQ+pvfR57uMiPvuQKx+cCrtek1jcIecdX9hxu9Ao\n",
-       "cb1QobC9GLBD6abCPzyD9UqspkzcaN4YtUrXhnaFaBfM7941a6KBhFu5iW+NApbOGISZMaObK4Xh\n",
-       "EJ2ipQb+6RGi9j53sRkd2e/kdEB6U1oJ1qvSibXcGPj+MRBTK+W2GrydZMyKqqh+9JvMTN89DRQb\n",
-       "rhFvoJWOcNeSf6DHENXqpu44UqPF4moTfnsu//z0nmdJ2W3Dd+Sfknsr62C1/zxyZ380oCnFPJ/7\n",
-       "w+ERLypr1kCoSlHBO11HSbOwRXJQs508BZHnyGrZVz/7pDsSUkY2rW2dw8XtYF0aStQfb0QE8rm8\n",
-       "I7wcBTiuBEilaQ53W2UYv9xLHnSs+l7+sIH0Sh78jtT+4GM3VNSJRutDmSd9aX4Ac75hIGnxUc4v\n",
-       "QRmkwyfce6HWhNMmJP+GwMc1avNIQyaW4dUdIEje6lFwoydwo42wxRSzazytF4BqtcVZfTS3ZT4i\n",
-       "W5L3sW8ROL9MAH9O/2MFkpCDZGz2Y2Q3rEXin+oEt+BWVsYYHXI8FNuLMUB3+soAoqne2SHjIx+I\n",
-       "jhLpy5if0EwT8EO9wbW9/FC/VWLskSc+YcTaq4MpXVFHOcIpTD1bkbgVjU2O/v2f2ZPC2ATpfJ70\n",
-       "ERPVyc+MHd8/6L0BgsjMFdPvW9SSPcgiAJT1E2lT5le1s3a5bxArN87v5KZgMNZ9+8hoG6+S7NQ9\n",
-       "edQWXW6aIkKK335DW5w5rdPyr2WimCAymO8YpDobIHvJuYyv0mITUFaBBd6MUHfsPqSoMeaIMX52\n",
-       "tPH/r/KFRgM0Pf5kMQkERUERY/efzwztsEO0oXVrf/kMX268MHyJwRgXbIxT8in3GREcSmisS2HK\n",
-       "iRheYWQDUe9wJcPi9d7Kbun/riFwdsU3hTFKiQFn6aSA4KKuFIvnyKrVuJoZF7scF5d+XR4jLKVo\n",
-       "ggisHlsLSvJSuHYIvdTqSg0F0OO6qDmESHLWgACjtUJ/crSqBe/wWsb5uLPl6DfbaPxnXLtzHDn4\n",
-       "l/j7dX0HstH8xxPtQOhqxDFgbP8AUMEAAA81QZ4eRRUsJf+lidefdFnf3OGrP/uGP6qRlayeSnDK\n",
-       "utEoh5hjd7FuZoEkx7JiUAbC1hOMteuZlse0w7t6WImiNpuEgett5nAJ02+k3T0W455tUBwRYCkK\n",
-       "QBrJaRtgZQ1GaiUJ6PZkdd/i06qAQYXWtmYGV0Iemw0XzuIF+4ZP3jdCBEtkcFfhGTTKKKOcRYzh\n",
-       "otlN4EF/gNnTtkVjEasLeU28AwOC5/3TTGYTAkomKwfBrEaO/9BKw6PKlqc63kpMWAq3QfWbuvt0\n",
-       "hOgD5xLJx+H7Q9zBVOgvOWLnLQKKU7YsOx+MPau0CIBdYkU/H/OYfkPGGqOa63COUoXsdjHdNueP\n",
-       "Zj/QPc6VciaYozn0ZRHyiQmU0YrWFtNPeGqQUmS7AYrCxIUcYNqQAi3C7HuLQkZtuvxZPHRk6hNA\n",
-       "InaAy60yWMLDKuo/xywY9kQM+tk7qQvNckxJZeOWKTmtveYPXPAHf3buaKzV7kbwCkjJf0E74n2y\n",
-       "dFbK4UQLJdeJnC66nj6RcAJuw4V70XSy0QyvgkXrtA6bwlGiaZwah4J3ate+c0QjBPML7Sc6F9cA\n",
-       "vRqkLm5aN6RWLqf2jUKdkn2ymDuXupowL7FCN+HNNsIDCU7t4E+EkYiNMiSAX3dpyV6K5dmjO030\n",
-       "N2wnCEki7UibibuwSYNc9ql4ojo/TqEad0J0joLOj5ch2OvDNuLR+UcTLBjjsdXMsMtE6JFOV+NM\n",
-       "l4DKkYi7OoExfl/d20tfpxqlQqokc0SdjtcvHCk0KUjK+KzqQaSLQ90IX8drFCM+jxX90MwNMfZP\n",
-       "iRlXGXeBpm9xUeM4vwZHGwVEUEXJfYd1mAucMgKMIP4y1HzExKRpNqqq2e0S3e/wsGtPLDV9+p0r\n",
-       "uqUBlEyKzxC2pj+lWxZxIZP98Ud6Paech2VIRIt/F99nTiFp/8Gs7KI1JJ4kfAclbVnV8+3raG7S\n",
-       "S8LFyQMUZrDqJfQ3rqzvykjstyEwo6GhRXWsnedzbzq9TtxKRRrTv51a/KnwL2G7vkVr+pPqIyF1\n",
-       "u2JlERRrq7BsNW8cI889240M+pQSDyoXdbRR2oRB0MM2CaOPqUkxRApEOIVGoH9CtvHRpDLEpYge\n",
-       "7ip1b1GlR6Jr2FyTGloeby9BOSopNBMVd9plKj2hLEI5izCn+TOAWIZOseyNbl3/Nv0/7nv5F04/\n",
-       "QWKeORbDWz3NTbhyxolEjw3q+WQ+k61wQ7JwCtoWKaY7Op5m5O8y3tHH9R/gMmbDGDgulIj7XXTp\n",
-       "5RzMNjYJe7HYo6d6rVZShW3Jv5SBgBDALJJj7wCE1Ezc9ahhxJnLtydjrLAMLjRjAxo0O2dJkLhv\n",
-       "Q/I1vExHypsjwPLrz4xzbHLSMHk4ud7w1+4LcR2/Lb5+hGUGszp/Y+RlvWFlghlU2VLTSGDFJfh3\n",
-       "QapEw356CXMrmG84HBR7J4TSroIFFhgj7uoV6uZ+E56HpmekFuJk9/QjIvx/+MBLDAwfUb64U5WS\n",
-       "D+oRHqZQyrJ4MlORTcbEbQkWzqqH35SXre7DA5iQQppFgf524Sb/PmApz4pWDo4GXNgCANqZ0AAD\n",
-       "lYd91/WIdolz7+5DGeDX5pR+H63iuLgG5ZXcuTLbtNqLBdgdgoTjhoneqm1TZ2EajUdpS3WWre5k\n",
-       "YBcg8Br249nH1l81SgyUNe70oqmR6XC6pnfb268TCI260IWeCgJJcBsvxTSBDxHE9T7sMiI7RHUH\n",
-       "AlqmKGaq9DQfgh9rThDAUP64J5ADhSgAxx5qMx00gNp2j2OQqcmL/OEq1KISpDLV92heN3s/I3CT\n",
-       "EwhlcawVKFHZxQj+V5Q2fGGXgUjsVHYlNapwJBpWaL+LOAnj9avY92Jv3pxmHYT0wwXbTQv1VC3W\n",
-       "WfWpjmLi9fGObP+a+LDp0PTzDE35IRFZMBIj9nCtOTYHJaAgE0P1eghyHgegV8lQFp4ZAK/7l2x/\n",
-       "zRiGlDUu3ESF+JvnIQ8v5cjJeyGfa2sZRcAUC1czfOVrIChlbLswADdLPMbk3Q7zYx1G6T3+x9vo\n",
-       "pZNMLHIczuqakY8IvgZAs29sfsL+dgW3lRrUU5kZSYIR+jVonQA/ejN4of6XYZy2aGxXdjEn/cSv\n",
-       "KGrmGY/dK/SvULb8q9dCvfzOhOr1Hv9cphwS9gbyC8wihPim6iyBS569gjQMSNcest77Cwn6Rj/i\n",
-       "H+NO3tOkwq6ESzrdVCR8hFh5HUWGIJOe3J+pgseusCx4sBouY0VbWP5e3F3jk/afeX5cTS9ClVB6\n",
-       "37HQ3WcPMB1OqMnbRlDsc/kG1KgUMRtSEMK/o29aK052/RT1nl7Df1MMWWnEYdGUxnQZnnui0OQb\n",
-       "9pm8LzyjvYpk4QFctQleDT9BboklSTibTAjmlQgNJvlrccRMKUk4mbuL3bdn13Eg8rrnVqPXr3sj\n",
-       "VpX/3BWBg61hHTGiD4uKXTRinsbb/uaB5VTljM0RAIK0OZ5ocXVXNb91c9ozixBf2zjCopaVe4RI\n",
-       "fNVrbOGFE6kEjDcevoN5eVLiLVoTkD4qsJkVlmdhnBM/4yfGlusKRxCIidgsXK70jAlIjY1EKPXJ\n",
-       "ABnO1m106IYBSpLn3Uatl7YWQmNla24Erv84cYLVSesnbKldO+11458jACYL3IzQ/rIlo/Gl7Giu\n",
-       "OLrx2TjNvq0Q6khR6QOxj+lr2GP72yDc/FANPCeI08xjF8O/4S0Y1TuKHFkyAxDzyslhGCxBxRAx\n",
-       "XkG3L7JVlV58r06EiB8ctdhOLf/mMVesyKpDdwWPd6MVi+o5eMRIkYJMKOb6Vz28rLSPN6BRDyfk\n",
-       "xKalSraL1D/S39od1Jg927t2DMIoJbQdL1CfFDt7S3q+g8vtNhpIfHiq5BsWAoclr7PmWOdxRW/3\n",
-       "mDKb2454rQ7dM1mtGsHL1uPNval21/Ov4dby3gPZKj8PJ0nEch5/FCZMkAbl9Ru9nfpK8dEesnTe\n",
-       "YhIb9e3uEKaEpEG70Cfrm2iHL9F6joyrZtdj+075sNVYXdzZozguBOSe8MdWyGFqWKsdHe6PVOqB\n",
-       "cMEEiwADsGzhtIXB9nb7oc5QZ4XG3gIiKZJ2HyCNSgm1UWdx9+txlJcrsCHjqebc1075n4icTnTZ\n",
-       "1WKv/MJrC/EMORMcj2+Vrb/dAFLHCiqq860xXqf3p2pkGzwGBL/kms29zlZAmw0mPiqfz048jB9u\n",
-       "o5derAgaay0vOtYCQSKU+uRmH6XlFx3N8y3f0tbfR+ltc53H7J3/x52jSxLqmCq5ZDvcG867VoP9\n",
-       "AycwGVPfndRgH0KHf+WxWefbAnMrTFCoBBCtI7iPGKsRWN90zBi4q9BQ2j3g5CkCQCLQB1pe61Gj\n",
-       "JUGlI3vOf0qNPRH8AIOVkA3k1hzkQ4539uvxf/+YiSGh1mA3rvUdf5aN1DTN6v+AziI/LS7FDp3Y\n",
-       "5WsakSX7Wh6anJFBG2EEGfWKCQajKVp/+1poqKdEniYBoMRl8vS3+zXgbyKyp2mbA1hacq5MA1lZ\n",
-       "06pXy7yNJcl1quPPXViPzo3g29MzJSUAyFseK9qeDDOdBf2LAD9b9Te2UuRBUsnQLxGMTHGoDdyy\n",
-       "tJVGp1P9YY5ea2VRnmhshe8fX+lhBvnGpD0iAs84yKFyU9WEx4WvY4tOjNp9pBoVvnyijHBgs5BL\n",
-       "AH83DB//Je+OGYKukZ/YDqvqOhflSZxOcLBoh4byR0Xy1n+tbxAY1GMQj0dVCnMM2NfEPUQbDxf9\n",
-       "UN7EwJCYiXrSk4pI+fgkbojG3ebEEpb2H3Zbq6nAC0Waw5z1R9WigwkD+SrKQ4uV6LmvF/rKS6k+\n",
-       "MY4XrNh0u5M6sbiQcQPDJ3xO0juw4KS+uVBupqXpCAXjHxFpOyFVdiAVQHBFNHkO8MIj5KWLf+6Q\n",
-       "c+zx6govd3zU+D/0BwqNklCMpzrx6/gqO3nnx6YzkXurA2ni3gqaiDqq1ObhXWeUO7K+xDnUnuPr\n",
-       "Zpfo7dxLkiAkD9xCeOSC1/DOOkHJ2v62sZm1bKD0+YxI5ZGO6tzz9XPuIza0JS/OQXZdKgordgcy\n",
-       "WF1563FFcywuCy/2Uko8XpVSBViLaPJ/3rBvV55e+iEUHiLwG28Jvh6DxjbzH8YvM4PZaFunNZPm\n",
-       "3q9Y1Mh4Ozahkk0/giMPkqu+rGmwA1hFwqj9pMCWLxKvtDsWx6DlnMEF616ZofOHrS3xs53pjHMA\n",
-       "yioJj6N+ulmqaNbF8LDLvlP2V+BN75GIcWm3g2gMZcZgXy01/VHolDcaZDvmlBa+920GEf2HfXZ+\n",
-       "+9jRpeQ45TFaNBycrMmA2qN5MX3WxHWJCNEmRApNgeI4eDsUeD2DeeclvL07lkd8ztBeFEuoRTye\n",
-       "t5u2tDZPchSvNY27fMxruRTMaUWUkdJXURetxdLlqO1BMUNZhU2afBIzrnPnVxxRsA/xByZ/X1Ax\n",
-       "MZrtgOo3ffheGCYPG3sn938lJ8ATCRO1vItUe3DXPhccF/EkPapRzO5li5OZVGn3W6VEfMwaoZKV\n",
-       "73aFSdC8sVmbQD6VN4DvtaPXtpJxVT7Je4qZ1qEMwtBK02s8687jMajR0YdFm/HwPUzAqBdpxNrO\n",
-       "mujbqIj2gWlb0ItaWDAZbyy42TdtvCNUl+XZNVJyAYeLJPUg7PS6GixbPV6NQMJ0K3UReXgMhD+A\n",
-       "+r9WeCxOkrBS3uVivA91Oce6mRAbydDkzdK3EjizF+Jbl3fpii4qvRxN5r2bBl/1dYoONvrSnEWz\n",
-       "tEE9WsTRT+jXb3L4fu2XKvxP5CXTczVtRRqGqBkL4epUlVyx5tgseuBB8uhfh4fbzcN39a+Be6Ej\n",
-       "gIHPxsOTIGyoX+oPq3y3W7hhCPhCjdwyGZKVraX+rvTPD+VQqqp/DoDCaEOyuESnohgA4yeroJn6\n",
-       "5TDuaFoeuDDPNbOJmMiCQCYJEuCPTNhe8NU5bnJoFOhGNB7N6QAXb23aTje9wZBoV0rTpxKBc8Q6\n",
-       "3Sc/8tV7KZsAw6WOP7MyIl4QbXlJcfHG4ZIqKhzpWWpT2pW46ueO0gNVcR8rpU19H2G5Vzksu9vr\n",
-       "JMpAlkMboRSYL+Bm3X7jvwVJciYVwc+5IClXrZyHl2oe80/ftWJpWNxiwqZdQtfFmTgrXV5bAn4t\n",
-       "dhpBw3gCQu7MVS0j+QgnGchWIEDX54sD+f8kD0TUkYYhKDmB8tgAKQi4zlDFvY+OeR3x7gMojVCn\n",
-       "AMCRs255ja6walxaxNilEvqmd0fG9EWS78kHVnDKzQzYRwCe30TuRkBMY7YAAAslAZ49dELf5IzE\n",
-       "WXhV7HluOsqpWMSeW8EZfwUE3i1BgnZ3DoLPPonYOwirg0G3RWY3Kyw+8eD5yUy2UC0g13nYr1JN\n",
-       "qzf/tdBnwFfnXQbxhaXRBdu+9VfBfHoA8BOB6Fi44wrBWkVB2kTdHQvzzTYg5F4qZ/stu9FJvHpy\n",
-       "PHre4AQDjcDjA6LfFd94fs1owrvw61KEbradwPq3cqu4QVNtXEoHH+M8wMpXBzWRBDnq2GKlFGbP\n",
-       "XDUe8EDXCodehEYxDzMtPV/5SLaGmh1HZDqrLr5uNL1C+okVaU5qb1WOmrAla3mXAFQ3VenRzF9b\n",
-       "IWBj69n/5HHiRu1wURCvldj+aekuSLlbUZ94dbHBskU82aPggf02vwXWzBKBC91KxsMTXRyM4Xcv\n",
-       "6emRV3fgvH2iZ6Z4qApPJVETvC8cu5Cxwk5rOJ1Y2HwLaS31wU3AJdsOaq3BDd6rdUNGUdPROV+Q\n",
-       "rVc1AV0nu6EuNYiGRuwIxYKy6pJueJ2yORzXJgto237/YjNpmjY+qxNXxlIuIxSmAA7rmSWARcij\n",
-       "tV9I+cQrGb/ozhGl0DIe2mPtPwVsz7DUZsje1t0QUxnaC1faoppdO/pJUJMBMsGZFRNYjfcHM6PR\n",
-       "BapdlZvB7cXbViNw+X3VliV1yb5Ki9UbZtLL+kP3bNLFWQEQwvuB02ge5Q5BycI2h9H5LjuytRc1\n",
-       "TGBiP05yav32MmV895CG/6PmUpNP/22Jz/kdqTYDhCYtcHsVfxEPDr9UO+4QOMBgLeNB4YPaEt+P\n",
-       "Wg1FGb2W3gIBjSKXU9enfth45DdnAoZfbSmSrYwAGhnToPwx5mHqsCmiqg5OO/ben0ShzKiMa908\n",
-       "uuyt9LWDwik+CvCg+R5aebu13M++289Z12zlaAK5UK1v78I/zbYohj4+sj1PUwGinxbkNYCwGFc1\n",
-       "LLikBSvyVik1Zy7WujoK55P+XQRj/9NNj/008m36JHLXQhzxj4xNKLLT7Lxiq3+4+P2H3tLatUJ9\n",
-       "eOd/zHCkQanrbJrqvT32ddN1MIp99zhHVAB0DDC5aOls0yCVZr+g9gIt2oVbdMm005m0df9pvacc\n",
-       "vf0b6TCbnnEGLY+qGEiF8huGQLRUy6ZjNev+FoZiWp+fR/96ofWndUBraMxjlfEQGRCUgT78r/Wm\n",
-       "hmW6XOuoomKliK/EeNQbwbOUzrf+781Y5KNTlfYFlI+1YpK6li21c7/zylPp4I7aoP7KG1gFCLTy\n",
-       "4J0HP7kFHreGGLuMwPC42U2Mo2k75QxHI2G2FFGnBz1On+gjRK3dhzSGiull4upwULuMh40oFE9I\n",
-       "QCSh7YbOAx2jcafxKzeFJBkcATSekrzUSu/oYAiCBsxz2417cafOdOx+r0iVwZQ3WSLFr2438M/o\n",
-       "ZnuLBK0qFgefVp+ev9/DNtTsKns6BS/4na+/u2OnBlMttOBmrPjSGthhRUD2TwPsk1G0LnYCYL8W\n",
-       "rid4fMDs8sImM7sPPviGFMhCNcDdeOzK1oWObUaXrI4DzQd6ZkBFVtxub5RDvjY/FM6HFgoDyMG5\n",
-       "xHAO+hwmiVU8xtJiuw0nAbGGPNq9qb4g2/swlh6zjq9dZLMofBLZEJeuNfKB9xL+D4pmbPyLkkKn\n",
-       "8cpSNOLJEgZRT4+gOWQZGJeNrcExdyVsydFEMb40f2ZvxH1Rf3/qBseVP8wv2F6ucLDvsf0vJXtk\n",
-       "bsrz1CK9bRmi9K9LCIkxVqsBgXITsW8QIf5T+EhdEUgsUBwqhKVkXMYZYyZwGG84Os9tPu9gjo9w\n",
-       "VB9kkB5Hb+w7GiqiVBvQy1ksi6IAfLYYyK2MIcRQYE6GOLkjIe7Lc+cb6eQ3s5Nzn4tZu4yC14zp\n",
-       "mgUXia0M0pbguFR9pmW1ewmXMQcFW9c/vzUtRShd4bBgAfqSo6xn2ZQkQvHuiZCw2XIB85chpTgV\n",
-       "z+oNyaAK8xsy1v0+3VjZYhZJ9Ndp5wSZYxxrlM98tAvHwOlHZU5NHdRvBrj9a7xSp494cFAh1aop\n",
-       "RnFF0BAGs9v302EKhoiAAE7l/u4SaDNCbQosCeSYj3QL0436WcaJwGPykvxtr+jx6G1/RfV5Ew/n\n",
-       "VjGSTiF8gYQ/03Mw4EkxNbSVyazSaU6SS/LiYtdVeQyWKRipiHJfVyfsPW8OL4mNzNe/BMm0wduP\n",
-       "YWJUhOkGwt9SY7VEUb+ZKKI4zIQHmnmCjd3TDjKqevV1RHVvt+62oTz3qKh2mOVE3j/OBT+KtQj9\n",
-       "2Y8Z1xyLkQ7qmk1jDDTol+UeiUHeA0CxCUOIh8O1JYcrWdpvQoW2zOd9ORN+NDZRvcRTqQQgPZlM\n",
-       "hHNxm1uoOpXO1DdVc/zjLziINvTT6SwHvhj3lq24Vb5dZKtZxBJudqROuPenRgdRv1L1KbHnSTCr\n",
-       "+LYfMw17udQyZ8yFdB7qBgUifwQwNX3IpHWikW00Ih1xnGXjs6WzdLqAIyDYPhphtPQdjXcZwgak\n",
-       "kx6899cINvhd4RjpX3gs4sNSq1+NaimTOcTmLPRuC1p9V9R9TEWKgUtqsok+BiLqFJD5xY+kBgUL\n",
-       "BLUx2YiNolrqdv5NFJoIOGqS0wZOvxDRdWIkn58Mwjh1AplJmmaMiZYkZGT2DpbbbIfHfIe6oVaV\n",
-       "uM2u5r1Yl7Efq7X+CJZLJ5cWAAhy8jwYHumC/Thw9B6luORdNzYyzyaPMuG670F5W3xwEs1/n6cO\n",
-       "uBssr8ABzG7Rw/I6pWv/O0O6GOShZYyhiU3wFMdtIL3aF1lMzGDfduM8QPG2QEWQ/ZlCXWepWi2P\n",
-       "MrRzoQjxHvJjUfyz9VnkusXBt37x2JOCIqB/6irzHghEBDchIQrMVDP6a5p756FxK2V4v/ayqIo3\n",
-       "YhKa7fYaaxNug/3JaCUnTtZwVA9vp7/Y7yVfSSbwfuAJtIUK/fqKL8ufDCXzn6DDmyRa1eefRBZ4\n",
-       "UYbfhA/KwBXTJp7M9U0CDJfnaxSbeKPxn2VkFEW3V9VJtJrmf5ipH/0cGmCa5r/gP1v8eANx7xN1\n",
-       "eEe4KH023jvxwqFA1ECvpsdqSATIU26YLG+jgEUMh2Qs2NC7Fa+vRMS4bgUZnwMeL06wHWrybYmL\n",
-       "F37W0yRqMkUlC28e//YJiKT4prQebTbO3mo+E+U08bDMMW/DeQRm//cX49Gu38vJAnqhDU2qvG0R\n",
-       "1HgmkYzX8ElIbqrEawwz5fHQ4X7MEHWudrNOdveiK3ytnjLpe0/ZRRM/M8Oi5tSm6b/gH1CL30AZ\n",
-       "BPzXluel3KHECyCZDcNoMQEcTbHgy7bFe678SvpWPEpcc8WtPjJunDLhZdtS6o1M2Lah4QZdZtss\n",
-       "SV6bhu4lNM2ht4Td6CEfoEgAeVixIU17qvTY7eG1SqJ6bhHrmVOtd0VC6wdXCqARHbI39Jc3edxw\n",
-       "4s+bnyOTFjHJFOLuhVbko7Ftf/w7MUqoJZdk3s5YyYnCP4Lj+UAlo+9Jn+OoV4E8uztKjqESA6kt\n",
-       "B+s7drE30SVOh/aZ9hC8YZlDnV+FVnUsJalWKn7kulYd4223kxusELpKHnWHRoOq6kc8RuIaw0qU\n",
-       "5vvkEKGTHUdgKszZ4CigSyIQWrwBLsVaMk6gFTnn0NfanE6+PCyv+TVz0NhKZOt5COgYTJqpvFAv\n",
-       "UIB+voSwUAD1VLvh9WtEKGfkhCRXPw2ensGKHcK4UoQBeehxQHRr46F5mIlFBQJeZqlE+Vk9PE1T\n",
-       "puvdacKz2BTGazHdvvGZvVLdQWXVcYir9UR7cbtlomZ68bfLEFAMk7cevCKFcDAHhgDLwjr0uvpy\n",
-       "yXE1a6W0uTVVUuQ+QWenXpTg35QKQQUyb3HtpzIqL9L8xhBOyaguqAUkEOaw8KYvPqMQAAAJZgGe\n",
-       "P2pC3ziNxjnYMAD5AqNpF0s/8AXoRcqocntHnyosjS1YmWyfr/RWSwZ/yM5zEPnz01p/Wu/CJNt+\n",
-       "vSwUDCnWCh3hKWYtOBA/lakIJ4P27SMjfC9VZH+38suILeEm5R3B6DMeBRJgTnk686UDlkq/s9Of\n",
-       "3U/1GFbLtCI1K6WRW7+rF0sfnO50zrXR0EQPr6c0T40vk6UrY6yxJ++do5m96xAlLzAC66vYnHE2\n",
-       "F3dtJ3eVUBmtRHqGrANAYpdhSLgmkAYf/SM6djV5U9nGNno1YFY4O/kGNdppN8ZPHWjTtQcibD61\n",
-       "Q5/0ZSnwxburqUaBbDNJ9bjtA8wv81nN8yHQ23T7+H+JrFosuYlBJWlaD2ySO+Rwtp5i2MRP6gYU\n",
-       "SUUf3XNTLIGvJ87SGHx3ii1ZWQb2WwBmRgxD8CWgiao52lQP3ij6TcxhzBhQ80YDA5GqQexLrart\n",
-       "NuP1nn/dOKz9XpQO0nNNDygDXOw2i+eeWUz+NFL4xJPFn6rXal6xQ+nigpaidG5UORFwDIE0oHSk\n",
-       "N8NNqm/rAnskUcuG1FSV4ZJB/BH1m+O+FOUH5kRGsVHM3tYGvuTVrGeiQXdkdOik18sUwFsx1b1/\n",
-       "tTjQ/1qcMNSEE5HN5fX6Kk3ZveWfKXBtIT7Wfw0dwmKsGJ+Mlxg019Vq1LHA6ta6yHM1o4oB0LpQ\n",
-       "HxETLImX8qpAL8DW6XbLk0r94HXyaAhavA4RI6pX7SO9ltMmbzOqvKClkdMaqj4dhT2rpWlxKG+O\n",
-       "xc4tOl+Mv1QbdHKL6jabPohFQKCnETrrgQPspWHWe4ObZWYhvFs4dvDx38r6G84iEIqmGLpHZqAC\n",
-       "j0HW8ZRQ9A46wAWjJU6Jhen13J4R1bzgS+O3BRsJ/TvqFNTbh/UZ42o4B93iRVlpzUOYz/E50R43\n",
-       "00nfa2c4f7AyojFpoFKqObxmw1N6qIfNI8IcD6d8lCBCbLzL5C9lmnX6WZnADGJjFE5FFY1RrCuS\n",
-       "i8d4o7tBJh/KlUjenaW4dFw2u/5NyP8P5VVSXLhjee2eInI3uPZR7Q30wW+vn1/ZmkcTiI5uTBv3\n",
-       "7PNRJRZw6MIfv+3ZSxUsGil9v0u4Q3tzC+0m74qfiji3g7BHD+oq2icWDrRVRwk4A8Jnm5fm3qu3\n",
-       "sWyvHv8DDH3srpirokTgDsL2mfhsYXjGZlhm9OlEWCXgZfzOjPksBJeON/Qq/5fwYvnFW6BfLLbg\n",
-       "uSFbzhlMQALdlJ6crrk/Ts2c9c7f5FepW2O9dBPZPtQ7P+aRPtni7uaLiySfdz91VcDzN0hfJRkU\n",
-       "JF6HlX5Y5Sg3T8K3Lonydy2RYWl+faJOJtH5cf31pHjxjaxquMSuflQQjKo0OUJ+/7G0GsOqbIYY\n",
-       "wszNfTummQ587RWBXDxC9xkXLuD6rwr6e40jLHXfNbXP3jvzS084eLq8VulrW6r0ZuV6Qx1yQ/4S\n",
-       "MrACrAQRWDNMtRJesQBOSbwYZvxDczDvGMtnN5Wdv9X0getOmdgiq07YuRYAlBsYYAIIAK5Tzjtf\n",
-       "+IWqAAwhAVthco18KLbkB+2wL5bVzm01fRzxOV53NyyKg72Xa4Fo8rclWC5sFZuDwohd8D6xSnp1\n",
-       "lwJX5fcDAv/eIptj2Pbkg8DFfkLUHCorBYf6kFBQLMEncNJBj/wg7ojcz5yJ28hdapXXVEwzAxVR\n",
-       "GyT+mwGVYyKGis5kAm+4anuY2QIDunG2IUYR7U8+36Yvg+8VQCjBPTvf54xcnQHxJHUtWDHGi9tz\n",
-       "CW8dZJj7eznfwNEHQH/lVeqZlU0UQaAbuBNIBmdnT1ZC+kdh4lBZU2AvHnoW7s08qFAzHscI5jlq\n",
-       "e0RfADC3Af2fpMpktLP8W4BAqiULFnyZGU71mIT6vgPZkgFxOHMGvqYoYWDd1Ii0lLmEQgbYxY0P\n",
-       "E21AVeqZJF2AfrjHlOONq1vFcr0qBjBNDhcGHXaKHjc/iEPbgyIrAltjeSeyA2QZY+ppFzKksdTO\n",
-       "85Fs3UFRYwDDjXtH0y8+IvpLJk/PwHb4tfI6kSMa1oAWg0hlj7WRn77zv2R3GBpue8266fM5F56r\n",
-       "fK8VY3CFzKipRoZD6eWmH0H1GrP4/CoOhucU5eM9Fq3uc7i1+kksnDTzDBLy648/LvuCN9WNWj30\n",
-       "T9vqSckEew8qnvNq76EvLefzxcdXNkZF2mNzB1kkf/KaCgYyXz+hacayNV1JE86B1NPXWQzdATu6\n",
-       "G9F8ZugEiTvmqa/PJD6eXbZv8jkgE9n9n3ykf7Ifrm68+1ipCbZn7T8hSpEjA9lTDo04HYcQhuPo\n",
-       "GMl6cAfjWlpQLNo8vAfz9yPh6c7bfbNRxI1iMdt+XkeSokYINFhpipHDDxnjeM7GibjymwpSS48M\n",
-       "9L9asFEUJXfOvGt+RT/uxcdWhJJOOBmU4CAbCj5T3LSZer0Zc5SP9YWRlQVVNB90euc4Yg4N2OIW\n",
-       "c1tLI9j2NcLauU3sWmWqLf9KKXgyw0mit1bYj8svzOhKZHmBXYNVwV8QdoqoYTarvgDmCBqdbTU9\n",
-       "DKsbQARerB6N2VFh3xxFogzBzZ83GTfWxQlAUUEMklW10GsdFhdlRZ9LVfVtFf34QjF5q4sI7V0Y\n",
-       "1AAhPOvkcDmYb6GHSNgR27WnrUUxwoS4fyWu3DaD5h5a9dbNVFBBpvWNjVz/nyJFOY1ij1seNAgD\n",
-       "6SiJIz+oZ3Aita24laWKgAAH1S4AmVvmCtRq93lhZkvQJfHCabwEBBOzA7WUbLAHGNFqdfM7NHy+\n",
-       "H5IUN8zCjxOPhx42fBuFQA2oz1d6jZqHnu9tbqvCuezFqHWqVT9uWunUwRkgHuHpxJKacgDSTuVm\n",
-       "H0sYCEhjoB73B3LHtJx7WZ/dJ/O95LAeQwKtDCLDTOgvgk9iO5pyoc+vEebgQTwg/1oNfa1QrXpI\n",
-       "UgIfYbKq7CVgqappCBc9eDkB2gh4itm6D1biDUKwKUifNIdrkrUnpYlzluMbuIwarYdqcCf63Iid\n",
-       "eKjfVt9r7GskX4lkGjx8PIG/KvZJDeDyXV+nFiOH6+h2x+96sUzNn6NRDKiJyf6etM6r4TO4VpQi\n",
-       "tgnXQt79QWLPPK5rlE9Xf0CJLo2xBMKBwftD71+GK4oIVVGYSqfm8LIZb0XP2dcmOi4tVtRmHVla\n",
-       "J1mtRAv7Bh470C0emh6F9OijvBFxSqdyUaaBL0H6ArdjrVt8PEX+rHcWdR8ygn+B9Yf1zA9jrVXY\n",
-       "U+Ozy1Hv+BgykwAAF7BBmiRJqEFsmUwP/3+EL/eBpBNnyRzQ4WJsyJKTHUYTxeN61jwXLt8/IZz6\n",
-       "5Px/I5elk/Wmwtm9JplsNTfIielIzeCEGucCHnZQRJ4HF86op5OaVCb1yD9DTVzsgCgYBYmO2HjR\n",
-       "6HGNE/gf78t9oahTBUBXdKql+d/gRpn33R/4oujT3nqFx3aWWso/81koaF04GAN9qvd/UcoXIKKo\n",
-       "+Yi73ilBVw/H3nkQKBEDEaoc6Y+V6FiVsZwVA3pcfhestYUzdYTDLQMdL3SRtVYOA9n6T1fuPFO3\n",
-       "/nO56mZxXYUHx08gJesGUsL/LIMlEoi9ZkpkIJuS355ydfqll2IrA9K0BtfAy1DzYPwm3BDNcngj\n",
-       "Zd3PyIucvtVd8n4gCRI20MLOwvn9necqtgO99KHOaBg+Yl3o4C96F+07xxXAvOY2QRddQIORtAjP\n",
-       "4zb/217ooOMQtCq70jj3IxJfHa5uSnc4JBBFlQq4bwtS6tj8xxD8dslp71dB3ju+I4RVfbo0Vzib\n",
-       "oAF0ZxSh62XC9ZylCYEdFXjlnjlaJkCkcIvETii0Y4Dy9k+AvrXn6NZL750xF4zk5AQz8tSgWeLw\n",
-       "b/5dv6Z3ZKNlSKgvnHrc102K24zW5GGgNJM5mjQ34LPVQGUzVodJwbg3RVZ3iPHHOUTk9FCc9w/C\n",
-       "COaklxSqJbuvireSQXhFSYlabISjWXpwTLP8ooZzA9q1nG4bOAsx+ZRqZ0PdsNCJhsJVG5e8sd5h\n",
-       "FUDwI3rL0t+mYDXJCFQNVDxAMBpmcbBAGrnXWEeYS7UztfDuF2RF9Nlygcu3vL51O0eCFGzKNBAT\n",
-       "s/i7IY7aE65JTxSlyXI7u07jMYCoUYXYb8GCc9fHtSgn1XL0wQJdK/ku/5LmXBFxjb12K6ttyrK2\n",
-       "WD424nKMr0k144NTMRX/0sP7exaDca2m8TKZ5ZfJhjNEsu4bAZk9kdtoxeiKYtdYi6dZVAobLRsp\n",
-       "lHTxWcTUdJjt+8c4d2lj2cmfjGicRlbTev7SSu4YVfqLtQygyn99S21mcmgdRdhS+wewxFPOKzKD\n",
-       "UXe5Po28egxg7dUAGkn0pUNkyPKLDPo2mMG7JpRs1XYR2w8T5uRznP31OiuLDPV7bTST+ap4HVNC\n",
-       "IJ5XmZFjyLZARSFO51b6TVxyvmS8IeouQS9g23GDBGmd0K0RbS9w/ycj42DD05pVSpHi6TLOBUFX\n",
-       "m0q0F+KvnJLBJocMpmB3dz0DQUaOzsOyrHqrREsoY1laDNavBozDho0ToC+uie0f/2yfwvVQPgGt\n",
-       "3pvQkG7w5rw/3wBsr0guPgjqCR9PI6dwXSbMEoFZ0IrMlJ54a7rJNcDzsGgXFHY6FJHaYJWO+C5A\n",
-       "II4CeKGS6AET9fklvKB1Idvi4rYJo0BvW5yDTZy9uKr4tvkNf8cKJgMgYUds6MMr8BqA5KSnXoBO\n",
-       "GEDj+7aNzJm7cK+CT1/3X+VslMXWhNWA81iTerFtxLNGohynHAR7CqZpRq+xovG24NNzdtfEOmWq\n",
-       "e1AnpPg2hrDaH8Uzjvj+Mj/m181a+O6UUPjxDhJrooI8dGS5DZvC/Oufgr4uL759J5vm/4DnBAOi\n",
-       "bLxm0eSvgWziPTnzC+mrwwN4CMailfk2KZ0POLABs1AeVSaV585yDrtqtOloItk0A03Mhvqh4gj2\n",
-       "epVwsDUe3Lxb0Gty8dJKohcJ8zn/Oba449C6NJhwYY8fkxIMP3zP43E+W3KCicIW4qOhdqDr2vZO\n",
-       "nn1rMgG1F65W1YmmYpzm/l2lcv78yPQR5VxN3XTDwapc1a7a48v/ZNbWOs4XlRm7Czxa8KR5La9F\n",
-       "10GYz4c8Sr8lmbxMRAKtxG3zVvIBYSsFc1wTk/qO8xOUUiWWX0euh6ZH9vL7TpNTlVOSdIQdNeV3\n",
-       "tsCI+u20Pp7NUYYKA2w6yBHKOkj/xpyjxng8Yfs/smK0viJD+5Nlh9bfPO3N8SbvQpdWV8oI6AEG\n",
-       "aQeaY8i5gniVmrRDKPpiZGLLbDdDG0UbV02T6B7uuVce7OqSAAK7pkcChy9yBkqQipyBbYqM//5q\n",
-       "kSIGNXgfj7OLziL+Z3mxBNahZuBids4seNHTxLWSOBvKSp3jHrfvjVHwK7dOf3RYfR1Dbj5yVDSJ\n",
-       "E4XEATXlwJgnhPrUCVARadQbjp8d8CpYPLOltoOUJypr4rW0pB+MShOehbE6WiZepBhaJ1EP2mwa\n",
-       "odiwluYqYu1U0V7xbIgrmHl6dgcGX/peLTltcsTp217bDycLQG4VZTyznmeQ9eUlINmiRpDwhHn/\n",
-       "lNTFTzkkcUCxUqKRJLckqhrApzW1cGL9WaMp/qOBCDDiA0ZsCB7kGCvFA+mbadBHCWV5pyeLFWpT\n",
-       "fE2Qh8bzT1D82TVmSLRGVSkokqod7mEfKVI1btwo6Be//YneoW1nkp6dBFetm0TcjJwfzhPYDDf1\n",
-       "D/ng5Cgq7fn+OytmDdn5KB+xHgBcH6VpwgOhNf3a3QQoJr/hrr6F9dmQGmgU3s+t/sfXych8r4uG\n",
-       "+9MWNgny/kYbv/CQxkL831fvHEtqw2u/dsRfhLD3k9EPMu1TNpZYrt67WWsLw4Rx+jWA0rRN5pDt\n",
-       "OgYtByjLSKRBsg95YXNmTEU34irVGbfwe09CxSvkn8mE4kTXgWr08mAm8e8NEcqzyxnwtkZW2CcI\n",
-       "WhNjYgZDEaxe49+N2fICvN/4DBqv9kE1pT+1/2LvOllnw+bGzI2KzcF36Z/CrxeCIscH0Nd+APAh\n",
-       "h1mWwhGftodETNDCwz+1IKRmW+W6QM5gYQAN+jt6X2DMZ4L0uCKgetpBxl6TTDzURihvcWsKdIiD\n",
-       "sajJ+Eah1U+hTeWZWbvqrDZRwWrTFPQwlEx0umchjx1kiWHaLufRH3/aa+kjXKglKaak4sM7OLlA\n",
-       "E7cuVjITEHAI+OPW4GDJuvYZlQetyr8n6nUToU+X7N7vDufkilw5imhubH0JPyuRBf+KUfQ9Gub1\n",
-       "L07yUTMWeTNAxnC417oufD26gT/KqyDVh1iTCLwL1IdfwH370O/mwdI2iHFc9ha3T7OLT4GR1i0s\n",
-       "iHCpo0sKB0gBgytyB7XhrWhpJGhlyfcHBjzjOf3nto2QYM/cjYMRfodkYOXpowjoY3plDm9oLevF\n",
-       "YDhWEvX9QjSeTObOCZd8nFM2CML+aRslcbFNdjX88TcrozOmtoxai7rDK9LjUm9BOUxK3TSGjJNe\n",
-       "EjxSCxV3acrz+Z+5JC3jOAR+ERbe4ursNvudY6BSU4xnHEJ6FuV6ygeTac2SpPM0WAA3V7XbsQ59\n",
-       "bklDTQw9kTzQab7n7t48Ii1Ij79bhiAlY3+98z1E9whpXACIoQSj6cQikNiBP/CvyNmla7eVuK72\n",
-       "IScvSwlRHBE/4sdYF98QTDbJ1fW4UmIofJFOTB7UEZsQ/sfdybbfkHhhu4eiak2pJwXY41IpcJlc\n",
-       "g+XVIY698ErBoxS4wOx/3kplMrghV/IEQaiRW8NaqK46zeyWgozDGbWcYeV3wBRl4pzZPMXBVrKv\n",
-       "eMmeG+ChIsViHRXaNjun4GZob5rOe6vrnUlyw+/Nxdi4Ks2oHJXe7W8ebx8g9Aw1BB/iC6x3iyYI\n",
-       "A3BDnDU0gxNOeTrPy99uob9b5I78orW+MDxa4AATq8pH6lj4P2M6uaPq9z3QE9G1Ai0IZM8DzNqa\n",
-       "9X9JitrUw5eMPCq4GCuqPbLRfwiz0MBkjmiB/LSyeDDAypATpPCmX8pho303TbIiFPRJaUAR9Wa5\n",
-       "92gcoFcOXuTpO4iZJmE7jQLaqRZpYt7+g7lxxz3Or8ZduDd/lfHzpGN2vnAdJe72B+ztknCGdTXE\n",
-       "AhO6ffw/0It4tzsfogE5MeB98l0th7V3wkVlnk3riFJ5IBUwdY0EzEzQoM6w4ChW03/taSFCDxLP\n",
-       "/ZgD2eBjw9o4oyZrAiSt43G8CqUtdAUTAHxSrkNfVfqfG3kknO/I1O4Vd6e9VOFaRzwQa0qpCqCq\n",
-       "lEssbf9jB+P3jNhNIjLcCM4NdthqGjezUNhzFn2Wjfs4n4TuTK4uUNg/1mIW4PsQ2VF2wUz0fbS8\n",
-       "QdUTfYbpZpH6LpW0J6Wg3xCxE8Gc4l5ae6DauKdQiTJA/0ASc9KNO0j3AIT7eTBoVhMM7KYU/ynG\n",
-       "5Qz6q329ASg+4AfKc/rqGXrrM2DLBBZbUYy/xUPQNSZAa/Aee+3B8rYje162nFbIOQEQLKLmO/mH\n",
-       "R2PxdgcMYH4dv6bxwjmqb0Lq9haP1HOSy13/T1+bcrnAWANf3NTd9nHLaoTjHYtMmC+RKxW/r7cC\n",
-       "X62Njh4Ej33FBSrSXgv+D7BCtZfMa6QtMfdy/67i/MDDDLwFojYbV7GMx2dOAsGAlLyk+xe+Pshs\n",
-       "t+pvhpGhDBdry0UyxQOFjeO1c/RbIdAV8MMtKo8Wzks7qN+D3RvgalQ7d/OHsUo7w3PhWbK7OgUy\n",
-       "8Own4lTpC2kx+vmEIRjT3odbXQKz7Toncews1ZLNLuBv8bGzVXfzVhNVcm/hh3CQHAcLh2cWaAPd\n",
-       "6eX+HjNs2JgJY9RKHB2UoUv3nNndP/vdfx4EcHe42LzSkZOaGnrSVhb/g8MAoefbLtK+3gbI3IUu\n",
-       "f9Pg/eEIc8/n3mL/stG7NDcr/c3LX8hSAAaRn2oZeZN7J9OiBTRhqx9AB1kAPEcDgGCBwwAA46t+\n",
-       "KGroMI6n74Ns0wF+hYaY5ZrIrUsjUHVv5nfP56KWQm28x2H5EvNQZDOB5PIvTeANUm1csCuStYgg\n",
-       "CRsEsbBS84K8OIB4bygcwX/l9NUH0Zq0duc0fPE72GFGhcsHKtOHeUzQn090xsa67Epkwl6qGVvH\n",
-       "Yza0/69FG/I+GJvUuwbKgDRBfOBqChSsaYsx1NJ/gKl4xJPZ1q8JuzaeATyzCvCiU1wFzV7Bm/i5\n",
-       "rBHhxOpTYqWXTxoLsDq0XK0FcNqmWPFJMbuTXwpwjyQRTDYGeBfIsgQNqNJPj+Oa+p7PYBzihwrP\n",
-       "JIqTmyCwNaTLAOb8LFYQSOoDZxaKXor21oP4TV60QV309m8ia8cCDa0jUcx98EyOAS+CFlRi2tqA\n",
-       "F6bEETXLiAsyI3rzI35NZ4oPIVZRn7DhQLS/0PyLQCluZukAxP90/l3kq4j0YjC/UriZiIV0eY9w\n",
-       "je93JBq4AbzVAgkf2fyM820Z8uQOcriD7+N13UY+e7B1Ru4fMTG4K0tab1mV+In9IwqZm66/lXtF\n",
-       "7039zXu70x7f+TPCHjfF7eAjzIEClPgOLMV15gH5oSm/twsV/EVHYTRw/Xo/kCOJnavEvCurJOxI\n",
-       "eY/LiMFPMsJpMueem8+YN72yUfd+i7XuV+T3Fdw+NZ1KxXXJkeSIB2eATuNN/S4KIVevPZA9dKY6\n",
-       "Zsw+k+auMoOAPW00fMydaHVE+XwXylnJ6lNx742VAxvhAsIu5sfWJprHYeLStq+svOotGy+goFQ+\n",
-       "Ddt9FkjOSLo7ofrit2ku/ufvBQfmcxAqoAZRVBVj0f1SnknGbg1x7K3KtrF73GZ3PtExkklYw12Z\n",
-       "ozv2zfwERuYzAQZk9THJ4z+hkRqQsVGd77cvgiDw/roUUkMgelQ4VJa2BThEY+kykFjmWYdTvadQ\n",
-       "n/A4gVJAuQhg8bqpVqmjOHFFJq9M8JnVx3PDG9nzimj8Hvg+8m1TNrlIv4wffd1Kffhp2LZSUh4g\n",
-       "2P7HxiMbQtHv4WORVsCXE2RvdVSh3/jH5rnE/YYE/R06aMMeOGzP0scC/KPyE89G+YO03HAmI3h9\n",
-       "o+cxY5DvYc1bdj6IFP/dgpiFWFwQX1joei++n4vKYWZKYjcjAYzoIdTJ31o64N4Kzs3mM4cuTOch\n",
-       "nVATrygRQ4kvf+57pDYcDxqOLKTUxVoD45z++JX5E60PE8KRuDhcyzKkPj9H42SXlo7lFivzaJ5C\n",
-       "6IGp/J3OfweR5vjPQJcI7zVM89MvwmEPVvbHgmUOaX8uEUv6DdExQa9D//5QTY+n35kap7t+6Ori\n",
-       "sCWK+Dd/kh5JyhqvelPtWDk5AvBn0WQRAvZT9S2JHteS1wpr14cwFTefrjUiEfTgwrbcMh+aHPCV\n",
-       "KsMLFKAZ1tsKjglBqDa4cjOsqmiRRZmrz9NHvLSdE5xpOaqkueDJRnhjxtUc+T1naJ49PEoCiqJ4\n",
-       "Deqzdo/HjblBF8rWmcGCpHJKzVtUkZjc24bdFwP6LXVXrVSY0r656q13dusvjYchufLt2Bn0R/uy\n",
-       "80OnRw1cgblw2CAZPedEd02j/vPaJElYf7cZNl0SxZDXTA3WDSgwDUAKt/eyiG2vvuHFf3WNkiq1\n",
-       "V+k5zvFPZS0DDwGuBdXCf8oz/7+sk6z4+SipFeKj/oHgTC1XWVnIqShhpDgYSydwm1mPpkFy0SaM\n",
-       "cMWotG1l7mRZ9RuaSU3Ok8Ceq42GPwuXT+VCpBaibC1kkFjXTBKsEFGL9+hCe/2R2C//PSrdTsl3\n",
-       "jMRMzcqHkoDwbLkWF6wD8eA/bXFSzj3WM5i7+QiQRqDCTLsO9izXUyMXMVZ3x7K+1GHyPMHB5cAU\n",
-       "Len0cdj2458ggbSlM2Gr33uS9Wk9zZ2Wo9SlDTGqhoT+VG5Sp6JdppkGP+s2x/YEj+J/rBHf9F97\n",
-       "dUmEzK51I6clxlSSYj0yum8s5Vl3tib7HrJ2+JxdywUTEhljBm8gt/Fqr4EyIjRPyJm6N7+dtVOk\n",
-       "V5Wbxtube2xZJ/w4VFBmBAXGV0H2NVwSNad4zD8qBIXwYfyRtptGRRVvFIiKGdBUvq40MXr9nYEy\n",
-       "tjoVpzuEqxTcbu+UIbp+mSEGkXDYW1PChGGupx3UVJFBWLDu+b1FIPHlBd7vg0y2JuZZjFXeSpHT\n",
-       "PznlN6A9jYSgMv5AG+T0hlqXHsr+kMxEWcaMrj/iGlot2jkJ79sJRLBoA/asUnlaFi0+LCzo2Eku\n",
-       "hZEQUvHvTBj/n6MCuTgeV8+V1mV1TBW6Z9GqjCF/eCcHKGDKJk8HBpElFnLycii2QBSJUPMqUwcE\n",
-       "eUYStiorouZOAJKjnZp7Iv5f/JpD2xwH6OwhDoCHUfLqlCqFbHmP3pGiK4V/u90VxalXGU4Ojfhj\n",
-       "dfY2R5gYo/bhoeURBpOmJw5/9Qw1PbaShUnYutO7ULMQOfJTw92t//CI1HuuczX0F/vMYjWu1RKU\n",
-       "oIoG+JYjIZFmEYlG2TnaDxKYdVNi8FwtD7FKZdin3eIizrzllIci0NLwhVC9J2BSn9iUxnmtcUEF\n",
-       "yC/dxTpXlS4EJ6B8n85aZCO+pGQe/am7A42DhJMnYjOcfIWAmE7qRCi5dfv8OpF730kXoCSGhMlW\n",
-       "+8vInRBHSvd2nwDTFPsuVxLZFErtkFzkGYoITFg/UcqJTbmaBeA+fTgJfG5/UrOAHbKIqJ37AIUa\n",
-       "uF6mE8ZiobJmFwW79kpDmx5kFiL5hHIToNcDYxtV+FX4sO3NWfZeh860VAqV25F6ahBiZkSpZiTO\n",
-       "0b/Ztg04TqDquW1t19I8Rmz/sY2BQnp8Q6F0ljDHHTqrtsh4gb5OHFIMlrkK9UmnEkj860AU5Xuy\n",
-       "XGruVFuCleC7boAb9id77LfaBoHd3ktAjW97/LO61BPHPL7ZLXnhD2dnva/9UkiRUAAZTgMfkVEu\n",
-       "xfMext4Mta6uH9itIyUqeR+n8E4fhW258s6hc0z7rkeUEJE9e/bWMhNIQ6cC3TbP+4TsHiwDXjfX\n",
-       "0nhR1ata/9yn0+PBdwux7w9521UIdO7yj7eneLyy7F239KfTKZHahM4Z3IAl0+qNnwmwmAYzEL6C\n",
-       "/BvclRMYZCixQUSkvYSjCqeZvYWCKur0Lr3BDc3q/YY0NSkuW7LUlKU440FNu/ZOnk+KfiFpWIIY\n",
-       "CoEjdRub5r5OBE229656UApR2wB9MJuBsbOwB3XnhGDnhH7HswWdHlgcR3pRTFw9W86rbd73wPpL\n",
-       "J+pcapGg+LcyXSCxkffeEmFGRs66Fj6P9SwtLLlzxct2qMTDdz0fCjvceHtHH5YAsn7r7SzUVrme\n",
-       "pf9jdHMbtk5xlly8z7GF3ZihtBm1A1e99F1csOaZtyDEE5sGUV+va7NGQZbjTe6UPzZELOF4JZQ3\n",
-       "88tTTf/34NhEOmTEpVItMu0y6LlT9zaoVALFiteGl06Igil2uvuCg+bH6otq1CyrFVS36n1uHNXV\n",
-       "EpP8gV9qAzfBzzIgg3YtIvANkjKNMBoDMNLmw4mocbM/dPiAAAAMFUGeQkUVLCX/w+pPNVTxfYLm\n",
-       "pD6ftmp4Wta7ZRn8owMtRKqye4fj9VeOYu4rCSCYxsNvj8AyjbCLKr7n17Qycry9OKoH5MquKk2Z\n",
-       "dq8JkpUlzE4rsY7T3h5e2XyznxtLX/SL2XhTHYYIoc6X9wIcooUWQHWblUP4smFrManWx98caJ+C\n",
-       "csOZEL6KirwHzfP0V/mT+S5ixB9yclkYJk08O5ToxXXq7twx8MEgR/VUNCVQmhDJbpWiZxqEh63V\n",
-       "ESHX+4uq7+NFBVtUuXkzHAK2SLMsT4ROF9wlvdVLWJOL7qEbbkaviRMW6zQ1S5s4eBqdVaIt2miY\n",
-       "qvAUSF7zjHwr1aysG0joxT5v0QfvChe+a/E+E1nYgUSRVXrvYv5589aKhjYsNU5p9f5c2mrAumIg\n",
-       "TDjeHawrGbfPh2OIUOcc8ysX4m0WKtxMw6wz+nsOGXJvENYC3+9zbFj+V/VlNooZn+8Hy3z6ryt7\n",
-       "pEM4PDjJZsJuok/+nlR0JVBub+9NuPWsOQUZEdFvbufC/Kj4tO/iJkLWdUsJ4XuO2YmzzbYvjraK\n",
-       "utIjugsUaxp4A5HL7LF4dnp84U5FpKB8TddjBsr2afBtnG5mVRmYQwSwcU3lKc3b8nFsvVW2w+pA\n",
-       "tZTXT0k6DSpGd1eKlp4FslP1sAGbcVic0QL34PML4dn0dZwDdUggN5jKbLv+bhnJy7SomFCkVRKB\n",
-       "GFM7NtVqJks4M0xRKw6z3G6CfN0mIC59vVGIrcADYsqbX5giJhh+UDVY2OnV2476uqdK0R0WqjsO\n",
-       "1fvcN0P1RVABdXNtBuNslRB1SCx1OB0EA2XkysRp3KxXQRTSQyJ6ZqGNGl6f43K4xXwbSjuqUGPB\n",
-       "WvdC+eLYTBQYdeeeMZJZGCR8GJXKKvodMD71WAUUx+Dv9Kqu5PT+GiR6D7jcXWdl1xZ/ZsVIgqnu\n",
-       "jFRLpaBQnh5ZrS0rkgznFP81+cLfrzTs5FKSCdxMtmso0ZIRsGvfcseuYTEj+RC3CEa3n86Lt1YJ\n",
-       "itZ/W4tNbhf5/nRvHN/Ls9m1uKtpwmCnCdWcXXMvsA+UDiMxZNkvXrXdw47RWahNW8tt1aD43nZu\n",
-       "bCU7EbmafqrO+8xEWOn2dO8ucuJ+Mjt22FHdq5UjyZLxQDE7gjt40zcaRuHV51yJ5kClY/1SXVoL\n",
-       "o66mb5vQWBfjGw9O8hQ/weuIsp2QOrlrZ9TO5mbkGpIjeVY5JMjqJlN5l39qSh8TuzLuu01lPKEA\n",
-       "fmFklHGE6bS7z11wCBDqzrt85C3TGYECmjlaQYJxyxygShTPQOsh108btH1608/fgAyfxVUPF5v1\n",
-       "5pWSeRrwfTUjCPcWTJZ3RXKCH6+WKXXeOPNltPJEE1mt+xju1Rv23Gtxh3afD8kUi2Kev9PbbTtJ\n",
-       "DuvNbmnNqWnkvxKRehS9mX89Gu04nswtbCRhG1t2YEt/9fp3QZQST5QyCEc6mNMCmhTLBUPv6WFU\n",
-       "D8E4ZAG1qcL2kU2Ki2V4HUJWCzAEvv6kIWCwB846bRYpIb7B/APxcbRbNs4axm/+qLySfvefjz5Q\n",
-       "oDn2rsROGt6P8tnAfuFxhx34ikzswk030QaF7ccEs3D0DI6M44NRfMMMZYlj2RHcOdMfPOOYrQJP\n",
-       "wy4sQVMRyyZ+fpu5akJAAABz7+3c6fs7LdiY3GH5aXbLpfvKPy8WJiEgu2wJ7hyHQLZAN2rEaGO3\n",
-       "6px2MwQOcf1Z14BQB3enOy9ED0keLejF63X1lfZba+G07e/yxpMzgbtiCF5kIKOBJ4QFCnqIVgPV\n",
-       "RMZJ550Mguep41HHc1rpXooVohdoqUWmjn2EhhMcJPqI+7A42UrHizcCX+bE7i89aFYKHpKfeqZc\n",
-       "PGBbGY8lNWQtOwYwss3bPFwIxegGorXxBco7ImpwSLZAz9H9ae4FyfNN4EKc6fGRjWwIh7X9fxrD\n",
-       "GsM8tpGF18eTKr8F508FkelMoUAgRqCNlaqrNdYEMeAaJn/Wibwnht4dObvDePUY/vrfV/W2fHXa\n",
-       "cz2A/SRXfOyIk6o7Ga0d0V/E/Nm8MKB2MkxXfybyixlj1r+mXzHMcPWrYsSuGts2u/zOaocKq7YH\n",
-       "dQYVSzLkcWk9T6Mg+wb44ICrlkf+9hRGQpV0EWLahN8l/IqhB1+FiyYciu7Chv7HuCWWOSoDjBce\n",
-       "+IfntqOxb9DJfy7O66GSpweJK6f/kcwOodNFs59IL0p7hGmESRsORff8NzTtNF8aztHWQSNIihJc\n",
-       "cwEQm2gMlaQElQHJUyVJ0JvzuNAwGCAYd7Siq1rT15p2KUa6e7HiLBlOj/l22XFKohuxxhRUwcpY\n",
-       "9M4+vnE1wBOpGdjRhFDPv+/ALA5zSi/8n4zojQVNUDPIq65jA+U3lazltaJ4Se4eJujrw32DjGs3\n",
-       "EP4SxEDU6kcs9BFMJfyOGIH1/U+kXF1PiXWf6Fca6XVeU7USeuNKwJqxXtWBKYY06QIzBrwtJtrR\n",
-       "EFycfXsvbJIAFPOrGCVYhnUynvrRaEsmg1chv6jCvJTZamVyqvM6f8Id/kIlzX8jDsePQ5ww8jjp\n",
-       "P/qPObv8H/1SGiZkapYQSzh/1DVyiLbtZTZyp8Hj//nvcHFJ6nhC/km9maj2sDzsP1Q/7cOhw2Xy\n",
-       "82mV9s2FEAg8c15wY1zdbwZZfXN9obtxBjfkqG179MMV7wvD4a+he8TIMJ7xlRhl9nVpb0TLpiy7\n",
-       "PzZBE7n8LLh3I1leEZJos+pmrWtkUuZVrB4AtqczNfWxoYB6t/mm1mrqvi3mDScjfOM+t6BrlZ14\n",
-       "ZTvIWAEtVIppW8eMyjySxAieVHTj/+kmfyO5/IXiczsyPqVdkA1v5w3pjr5DcCT7VLC0cPIps42b\n",
-       "lYH38EjVi6sdrahYqP5Med91iY4JeS2FXWdkv/Ck8AG8rb+ZOIo147chTw+mhkds7TrSEnlOFP1X\n",
-       "l0Df0iA9+L2R+h6LRhzwsQ+kbOe5X3m2KyoGDq/b71JA9JRWSB5Rywh6UXs1MAbxkR910vWocjmt\n",
-       "Gi07NQLK7ERrYdmuCuL0Pg2LMtnEPQznLLSCHUJanrfOf1n9Vn6C9iJV/vnCz10cWt1vH338LSbr\n",
-       "5dPyMAosjTFdqfSy9BUgSlnSZlohWtvtq58P8xASxcc/lDySlr1VKI2tWFzwApqYLsEobaFIdIgo\n",
-       "AfRv8DbELUo6A7ycnggwn9rkWf0QwW3YjKtWyZNZrsS2sfhABj29ZPCgcw5BBUtFrd6ZB3HMM5i5\n",
-       "MQy758RmCqAA01vAx2SdzD1v5Omo62DKAn1SCT/LH5sRJwVn+MC7jyjIKtf/qDxoAbAHXEvRxpA/\n",
-       "MOOASj4QichG3RjP+kgarWQuO6XUgAO0BHJTP9vwtz1uEjw6xo1x69Qk3vvoIw49+VeESgPSFuK5\n",
-       "UZNXfCKt1GyajSNuX/RO7D3lnd81nkS5aRXT46f+ySy0j05JWI3XI/6O+KWVrr5WmUQtM6iui6SF\n",
-       "YhPlEaZlZ/fmf93TlZvMh3/VKB7hrkaFwIS9Jrr0ShJiBArd0yz3eRRuuhA4b6HkZ9iocjuZxcDj\n",
-       "PoUERZb1iMO/68d61CW5nuk2ghyCXywmjHH6yQibLac6+oCFtkFBbgw7RzI7yK3vpUcE/tThV+AU\n",
-       "6grBINxb5NqgIjgjspdalmqbon/9561JN7cRPklULoixV3hMOTiJnpyPpHHCajVUsHj0g0htn22u\n",
-       "qt0QugkI+9cMCcrEWteFpi/KGGzPL5g1ZYOB0VEuqvyYMfk1B5t7mEGigYFO6LyOfly9/YC8ZJ8p\n",
-       "3RGk+V9rGu3wN4HVgw1B4WGVXkru75G5SAmpsGuSV51cwhaPc5wFVB16jJNQIuY2FBtQwCgOqEAr\n",
-       "ENpL/xiwOdUllQHbFb5sa9Fg27nk4s5C25qWonE35AYVBLjYl7BdvfuPoU3ENrV2X9hE8Tc8Qu7/\n",
-       "0WBqdaxYPBIlRDBlc6HlF2oawOoy93XpRC1Vsj6C4CbPJjy03cbLwtzKchmb9mJ/9zT1O0eZx7Ql\n",
-       "czeXs5HTPZsghrTnWkGJ7HFz8+nGtjEYbR4yzQgEwJCKRgbAnxh25PxIoK18jagN9PFLiQ7mUZCA\n",
-       "8vYTvDaM/0Scm/SAupgRRxiAthiTLE9z2VhgrXqXwPsBnCyX37DIopXlUT6rtx+uvnyB5wiZNQAA\n",
-       "CU4BnmF0Qt/knhHz4YqtCEdOphMevFkJ3rV3/gSR1wtl9Qq+jngzm+5s0Dt88JQgUFPvOb2RMv/q\n",
-       "ewho14/HYaoWDQSF82PI62Ic1ICGzN97mg0WQdXj0yimRh4h0t1lq0pJkLyWdr6PL/Morgcp3lQf\n",
-       "/WD3kC9ZSGNJGkqj6m1c7pgS03gg2XBEweFY2XvHQzrw9oB4yoq/ZbkWFOUK24UfCbMqJga2RtqZ\n",
-       "hvDd+52vS1X0uLAsL1kXe+g5mxqrO50o9VTPUcbXp2hLrlctMWVIa9rEnARWbTCI/akRxSFS7i+s\n",
-       "PnnGnTzNiYA4ZQH98ADG+jo/BpINwYUKTbzr5wPBYH5N7AFUyXj7wqYK9oht2cM5x7n7qWNNJQ8h\n",
-       "sfMn5NMpDFL1PRzSJxIBnLidvCS5v1z12DHX7/oefE0gp8DuE5spN+fKZJVuL40uoNZf+6q7AeOg\n",
-       "s2wOaIfcUu9k0yH4usCkfleXCGE/Plz+LTomnwVXw1vsuCQgW6X4ZSqWzyFadw0hukNqyIBb40x/\n",
-       "QmvQCeY2URHlys1KRculRjnmDa2FXQnBlipIeSWzJjhAjM7+TKTFwdNLH5mqSHtssJtRGKkHsGWb\n",
-       "6aA3Te7GhOF7JskABHZQI5ClKqJQOWbeXGPyOkbaLkhciW8MAFJrZ6uzgZ1yh6jKlRe6bsgEifhs\n",
-       "QuS+7VqjqD0A4uhZ6SKlNidlF36ThZQaOBtGs7K38O6wYOsYhDySFNtDvMcDVBJMD2Z4PUdqV+oV\n",
-       "JpqprJf838NppPyJ8zbAJXlhzIbApyCeSwvodwgyXJpDavt9xjpbeH1955CZUCPJtZQ3HDyqYy/B\n",
-       "9BT8QLwFy1Ez4066okMQingPMkCstxsKAhgNybF8aNP0iPzw4IX1oazZC81yOti70hhDBMW0Vh8X\n",
-       "EnUqk4Wq5I7mUxRRenvkrgVvWrIEDTVZzY/Z9c2PlJvSKFDXp1HSNIKa5CgaH+mGvd586htHEEcj\n",
-       "F9tW/4JmgVnIRrfzgA3MZm37dipyKm1Bsx8Cg3hBnDEE+grHI92NXfV/95K6tSHCuwhHcYYj0guE\n",
-       "zvXXYBXHPI5TK6Oyd1J5zsACeoDNKrb7yt9oF4dAA7OzySWU4C7M7p3bcjIQMMTr0jJT5a8+V/Ki\n",
-       "5c9OYC4qY6rx5N4kMpDUaXyeBR62kd6qFemQiScCZDzLxK3RV5DOG54WZVwYLsqOFbNvNeUkOr2m\n",
-       "wh7Zhd842Dhcyglo77ojhtfl8cENHw3AZMtxmiDfNDvGBgyU9uiZPgJWKTbfU1G4J5O4RRErJtfT\n",
-       "QEpFOI+2n837AH1Zm+H1E1rE7Fl9syQc70quiRpxjSznN4va8C9R8hFhAYPWzZKXRPTAj0ywgUz1\n",
-       "M0XFcdbrnNrxjMYpoX8wh0ozuE7LBrSDnjRWc62nWd2n2ylaT1tcxmXMIU9S79/2tRMPoa5MhJKS\n",
-       "t4SyUPlMf9+SM72yWEriIkC6iDD+368+arVyeH409rB8+u8+oH04FohTcOqgKSJ5dBKxcv2SXpdD\n",
-       "wOd9wM5YjwBMourGEO46/6t+YNm6c8vrs68oBlayqZdnBNbRWZRRQJVmhqObY1vwt5/07fW6v48I\n",
-       "e6xsT+j2hc5M/YBe1Q2mg7On8tuSctssk63m0sxyC5g+s8sC0WGwzV4QPToy7FidA2F5thjHJtSe\n",
-       "Ll9Urlb4U7tuDZ9RNYhLFsF91dz+HpJemE/hoUo6vI+Yz45lUCPV4CFcxpKvX9BxJLR3Uyko7BtN\n",
-       "CWaMSaPLQ2eGibYhdprEECFtdTx31yEruVFLce4vmEMOdEhpj84okGPjoj/G4mkpAWwjAglmAdWJ\n",
-       "Co9Your4CIUdkNhTblPhEgOrdzTk7wOGQJPXZvzpPkGXhkjScqwJv3hoJlW0QdHRXMjDKU4kLxvh\n",
-       "mxiDJnG6jMXe01VOmpDd5JG/GTLAwmCMrBIEXL2EOZC6zpldCAppfJACohz2Abb4e4b+tY8s1X3U\n",
-       "8mnwm3Qi64Dd30pdiEeKKRQuqKsq+6Ot07bjNIeiPS9eYKpdfuhpGMt6JqLMQLFX6RaPkBfrCEoX\n",
-       "ARLSUrGnARr3jo80fee/zNLqup3XHA+SIwE7fwZiJRz5+GjnPEC9q7wYJdi9Zp5Rm03sH9V+3m1M\n",
-       "yK/XVGKK8oCFcoi1WdW2T/i7y8IK8xHypnFyNzXWrNahrKv2TSkdPPA/QWL2P+Z9Vhcu4gWH5cuR\n",
-       "lQFSDsnJjMO5uXsPrdXQQwSduhP3LmIxyYuvtT4MCssIwFu41Tk8vkvJ5mgDL0ODoZiMah4V9EdH\n",
-       "MHySGstuuq94VTxYMugH74yrWM+BvvpZMJZ6bCLIsZbYH4v/Fwj8xBcNuQoj+LZnSCVy8YW1Ba31\n",
-       "9l7VGQVasjP10UIFmsejdDmtAIkdD8hwOL2rzUTkHMugTT4NYm2B3C4JGSHE7oF2Q/IYW+YRxq4J\n",
-       "NTLInvOg3mGtW6sAkDAewt2dScyqyKNnOItDTCVxLxh5zBmEuzm3VKjCkM5yy2xvHHetjT7OR/58\n",
-       "sknYPweb60oCunGHcm6ToobQEvZvCDJ0/AB1oRVM9yFeOVq+obj7fHdwqLbedzOxjx1+5O33DkYv\n",
-       "Qwt5tnzCmr0E5HiGVulFSrbuE1WwzgYX5/8Sg1wmW2yPFV8VQfnO2Ab3K/92TehV4c9jE/cukOJd\n",
-       "AkX0C3thC6GUZmBbnfe4ItBA1VsI4g2tS8XyRlqoMcjZnAwMMJv9knJiUnl6B/T/aDCBNWV+2+oV\n",
-       "buA81uAu3BWxnVa066jLiydqAw7HMP8kYG3BFrBs412GNPyLciTsefpwqKkYZw6YeUEdrC699LLX\n",
-       "xEcK9Bk7GnYkifTLnyyGkv/1XxY4mHqDdGlN3FeKzmjICrm3I8L0Fw83XZCveb4s3fF+zNevznQN\n",
-       "S3iO7wqf/kM8q4dske8L+9yhbNTKA01mAg9VMQYiBuOfC0AgvCgvD0yeDZKC5E262reHZIg47lnD\n",
-       "d0T5W3wisTG/x3J2X4zZQJ5fEtwiuPzT3cdK/1HkOjzlqGogjHKAinkl4iA9VBI8ssmvscx6Pu5G\n",
-       "aWXjyZOcVtxdr+XFQNj7FvDbee8EhrB0/Z6thA2p4xwMiLlmHN68Mi+gE3anmiIIcoGs1EvZdxQV\n",
-       "/qvdmZQsd/jI6aud9xH9NoKkb+hTEhsc6yCGHmmfvyUwRNf291R+tyjF45ugARkAAA8sAZ5jakLf\n",
-       "4CHHI+bi+KkMGLHmQR79bSW1QnYHZrlA6brSmhOuVhHn2VyEXPOre7OHEiPwIS9W9HFqn2HDEkEe\n",
-       "oySmvyLgDu6QOPWuhcX3tPSwHE6p2tzKM/YS03zAelThggYsBoRB977sBqOL7eLjjiMeR4Skz6tF\n",
-       "TF9KbUmNzF2cXnXS/H48QCyNeYo7K64tnQ0mHz1wJKCqR6V/3GBHFaOiM47ba5PxiT1PreYCNGh5\n",
-       "Ylgj1W2nePR4r/QR2T3+regsYnP2TD4z66j1wuR3fSk4txkTKtbakl/3C14AteeF1lPTwo+SFgKz\n",
-       "DDoYijJoeOT8SqH1oqR7Dzl2hZQPMXcfgGBI21sTb7ry5j3ewmg6oQls8fSZZIl+Int8JJgLTO3t\n",
-       "R88ap3vmzwjIhZoS8j7mxj3ie6szjAMYyDMhERHytZV0iT+fjlG22t+G5m6EMDm+cccQuWY2Xo0j\n",
-       "B3ucbLQZ4xqkTYcUk5AADOdAPrhdkG3LNeZ3m9jNRDefAzaHvXl3J+OpRuo2TMPPG8WnAAp1dEfK\n",
-       "JAXNYnUhwF7Iw4jpz6aEO9WtnbmETkyuO9z8aukU7SWplA92WRK1Al+XCN4HzHci5w6tIz73nzQM\n",
-       "7/pjn3+sB/tUYegKSofMOOZoIeIJYtG914ABi54N0q1NmlclvFzC7ZYHZnkPrmFCha+q3zm96vFu\n",
-       "OuHzePXDy0SI9xCkSLGR657YoqmLTW8bVArToTrf43QfIfcaoaXSebIp1/2XMt2JQaaFNfletH9p\n",
-       "mAkVhB19CWD8t6RWYR9BSRItkSbM5vhNCUrmmh5JKJFDLgIMzwCW5zY/1V3g8ZxCeDloZFsHDHkn\n",
-       "hIL195stG8BgWPfh9DG745N1WBsa+1LS+p6/bwg7fizZlugagEu8zpp0UA8F8NZRZ0ouSMBtUjN3\n",
-       "3+AldGlovH1duAaKfKAS6STlDi5XhJ4xRzyhi+ilus90qmTZH4NH5qbP6n68Y7F7UQeRMYJSO7mN\n",
-       "+pr0n3Mb96Qwue0EDGRbMkycy2ExqIOSVfxnRsbhKL0kqk+hfSyzs/GFkGnmXyWK/gjejbexcsw/\n",
-       "FmF9DBpo45Du5nSSyhjR2oziuH4DAyt4A9J9iBl6/kOHgcOLR0L6ZEFPSyYBoAQXRmRgp8A1o9yh\n",
-       "moTDNXsZWxV0gcsySW//vx5YWEA2poPj/5edQpb2QGemXkS2u1vqdYRACWFnSwmReDF8frd5vTEa\n",
-       "lGEid9ZOWgalaVHz8Q2BLDjdS0Cz3Sg8NVbelkRTZCmkIrv4/Pwqt1eGGd+ulQkJSuZzVvpWO9bk\n",
-       "s104+Q468C5jzmJmeJH7iuo9krHNWaY927Qo+u5XI2pxzr7OSEYK8tt485vvPGr2qUshHkeFY5i/\n",
-       "IctKzXvtstpBoa3I9vGeV7IVevV8/qA8fLhhteUQJZdPTnQlJ2gremGFIceuH5gasS1rxnMtivj5\n",
-       "Uk/s5MTb3284+KpAPHt5t0B12R9PPyhPHXQSZpJtHzvoPR9dDWEh7cGIANGCUa5FjM0Rssp1lkIB\n",
-       "WFNO+DvhHCBiPjYIAAEgMJUv7d9nhrlA7s50HWZRHVeptS731Cag5+XW9CEsPd27/Y5TNbqDOdyV\n",
-       "ZlQJNegfEV03U2GdXO62bkpdx76mNob0hvi2rE1uy4YvL5wM+5khlmidj0veVUxb8NAnj+htP2wa\n",
-       "F567WEpL522KzBeiXDTv6xVKQgwNSXa/GQYF/LQi2iQ1l9Dzc+NORIRzJIcddiE7FPgU7wnVrLhv\n",
-       "JmO8tJ3h2N5WpSrobuyqJB8rImQ4/UBtEZVr5blHi0dy3GGDIippDwq6AvjCGcIwiNYZWsa8p7VY\n",
-       "v3Vh9akcHGVdMirzSC3ggtYpWbgqx5VX/meEZGun/eD186n5W0xWSTgD3Tqd56HGf6FCISh3UHML\n",
-       "ufCTfFaebUb2VsGaBf69vTmAI4z06+eSr2xjW7bfsr92w6sn6b6IEVoemjmJQZvYKNVzWJtbHyCi\n",
-       "Yn+GGKfq2hg91IKVrMmB38qnTNuHki+uvdlEuxraabm6yXjgVI65Y6bAWHlU9a9VBu7UgYgoCZj/\n",
-       "3rGgXDr3EKmVN2X55HxXXbnHl1Y8K9Da/RoDVvCw/O2R5+b7aomYrU3EhbaA5tnYN95qMV9CoWp+\n",
-       "8q+n8NFiSXQzri3qnfEEVs9x/aN2Kob10vv14RtIPiCG2XkgAlSKTgXkY5c+5hKuNwq0CSHWHKk0\n",
-       "Zf/SYFvzhk/wZEsQtJKqKXlKJll4uHRr/9DC1eM0/Rjl8NU8UZUjvzWJw/lQnPQ227uzayLHrJS+\n",
-       "VpEhw2Bk3nlRrhIvDy5LOenUlckB/y3wdvaxHCHsmnuoNKf6FL7992upo9mC+n72AbTusf1x/IjN\n",
-       "aFR9vn1t4BDu6vaLoWrG4+QWRFHkmVeTwQLbeqXao+2N0Ugg4pF1c6awO0xyqeL5+VnupNSoTDHF\n",
-       "Y3uJvpyacpoZ671Go/SKvyn5+PNAVttHPBjU0YmLjUdVa5aYGCAYpnynmz/U99CGdmly3veIxoxJ\n",
-       "K0PG1Cna/XhJtNtb6RLJON5T6WFhK+slEiiD8yxc+3EGz29sJWLrF9JHOxXcE//QWvTryWIQAhOl\n",
-       "8QGVTDeWBmEp7RXSkA0fWLMIpeU91MeJe/RwpDpoN/FNCMwfdIYJgN1IJZkfpmDWsRZtW8iWEUrP\n",
-       "5/B+K/zBTLBC026TJsFwrm3d0/CyAjnKRTeDLj7dFr5x4gQqzE4GTGWCXp0TbD608ld8v25iMm8i\n",
-       "lqXIWIvVCJYpYQFC0ZtDzR9/Wqe4Ahf64mb2/xsFynRVbftdx1czREvNxcmCS9vSo2VRpp/YQESD\n",
-       "DN6jE1sdoh1hVdDghMD3ZtP1JfTzy50ZRqz2Npf2cjEByfUq9Nr8rfvEM/XsYmaocavgVaaAz5l4\n",
-       "+l211Rd4yxmTu/WK/fE8U5hx9k8NtWZ0cp0ifpVSWEN1PMi/Ll/6LFNjhZhlcYOHnxRDMpdwnKBt\n",
-       "lfln6TmshUtc6+xUxNxPGSqesOJjrZnVbdtXXMpjaHQ0uryJ7kLP1WVY0ACOrXZyTwh0pWKHOhG0\n",
-       "Rf+V9DjoKlPQZliyh+YvX6AepY/4Xq34AAHhzXRvS/0c6+7B5M9oXqAAbyt2Mut1jPzA3K1BR0HZ\n",
-       "iQw6FyW7QABVWQH7aPvUSETCzLZWinPbBint+AqJMxGuIk61E3yWUBvtz12YrGuQMQPV+hx4maAA\n",
-       "u8G3kpfnm+xQfCwU+T7hDKwVgYbUjxBrlVCK9+KjY7uhPVTCxCEY2qTmM4v/gx9+/8N1YQp+f3QQ\n",
-       "Ml8yrp1V2pGIMz47+tFJO8oBUVNMyE/JUrXq7XJT0XjGkeJsNTHcDIYGOnmI2uYwo29YtYHpGvZp\n",
-       "1CfzQXz2FQ+LOYWTY9OkKvhm+4Ng2DItJtw4c44yOgjKvFSmdoOU+/SN0BKgh4xsc4mFpe6bLwlw\n",
-       "k2kBn/gm1Qt7SVrCfTFu+PmgLmbm2nAuMu5c8NkUyp1zSLTzyoDbGD5CZQ+IrZ/tHrLP4WNzvhCq\n",
-       "M23cDYZS8Pv3nVwjZUgZqbcmMWZN3iNeIHoiOeD4OfDkOBYetay9gBy+51Alt+NRoNYJHWBm8dpT\n",
-       "/cxHY6ThlYg25ZHQgUfw/j2STgZkS/xeCxChjS8DW0JX481TridBwoKmVhQj/8X2Kaskz1GyHSwj\n",
-       "rThOJaftgFFr7kFrLakNIPuCi3R0eSXEi4a5eOyE3H5hIWs91XY6sB/F23UIHPj56hdc0pbee9K1\n",
-       "WeKNviVRUZ3C0VST1oE0ar+eGBG5knFTw/dnE4FpNDT5Vm/m8DUPHQs+znEUDKD1F/zsR31nSxsc\n",
-       "v3M1bmItzfsdtnizGBcID9sPpZIk9+eF3ivJwgWMardG47ROd5KNFSMGWfOmDsMTMOCTqAyUN1Cx\n",
-       "IwsxCFagFqMd7cMdRuHIk/FwS3/eS4rfAFsVujtLNM/Ue5D3MXep3EYhjUXdZYyd8DdFDLuFvBJ8\n",
-       "8mNnUpTOGt8/j59NmyEkJFxdA/nPFiZDF6j7X6rcritYem2PNeBG+XdKJGct1IZ1k6mCwOMrCypS\n",
-       "f8mECcTj3fHbYosyT40xsKlKMt3z9TnFyOhQlO3X9LInCl9GJFP/SmJYO1HATmXQd9Dml4bvhMhf\n",
-       "nE8xgVk0H7dsesJdXAfYJ9pOGsEp18M+Xy4ujPnFcFC9IuxzJn5kb94SToc+UrEGxG07rBo4SCjj\n",
-       "xJmAyutwmv6CBEZEnZ/A+pVbBe4KvtrTZ1Y5/cRJJX0Aml6ewCr/nZ3iuetWWD65Bp3Q+7SOFjGE\n",
-       "A0XWe1M1Zp8ytLi+VdC3QnXguDsxTvCP6Dwjc8tUcB2OM+mPUXdXkzNO7XKm/PZtSBPenWCjpfb3\n",
-       "6d5e62wVl2HYsRnjHlqFR73Q66uLBSjiUNnOPrO25/7NJ6uN8BIodc9+ki6Xorp59gEt2tH4R9XJ\n",
-       "wa9/xVBBuu2ZzMIejItkCLBbn/uKRKuDhQhvLXOVojp38/E0bvi50mDMp5zCIE9mJMvPGlouSgtS\n",
-       "t1+X+dS8whVsxwjWwLbNSQO6zzEJ8td+k9S7LqN7LudErctuzM5XvsN3TSHB3Z/7vbFglRjXfgJ1\n",
-       "mh13wFIK6li8J6VgwPTaQdhdYiM9FJLjwKwjlb1JMG3G+MvqpBZt92YfcCFZB0rVTWRTSiQwqfrE\n",
-       "oFv1H44p7bvRQh1nIjiOcoT551Vu/1zxaYzulWc8zfm1JSq97vwfcXr1l2zvk/pFDZGOCsYGJ7Vb\n",
-       "IqJZLGCWS/Lxzw2eF/nQQd27WP5ytUlIZHzXOaR9CGdUj8Ev2l9lTqNcDbZiUwnij4BUuOjVbNzL\n",
-       "kCTn6ls02A8+zTjv6e/MSTqIrknod8eqsMr9B2psEllpbnGHdMvRlIoPfpF150Ir+U36FtQfX1Ww\n",
-       "0eIa2dEQLZIaIWNRtdIlSjvureJr9WexH13Rf9cFKqcY3LyyB+BcANs3JxCVDhRgevUfUe2Cec5f\n",
-       "Er8pIgmhpSsYe+KNoI1TdeWof+0vSibkiPk88D8TJmjkvg05eqd8OVZ2dsebOA0TwJtH1Ub7fmOv\n",
-       "BzAW56SD3WHLjSi46P48c+m+GMMqCtaxOwZFtyBP7/2a/SZGQVi1QkNearu/FVw5Nfo7V64HrkJ5\n",
-       "VYkDZ4EEHdgY1j2IAGbq8OyR4FZc6y7/UYYkfdx2zXfB5VctmNSJR/AIkK9Ob57it5N26XOZQbr4\n",
-       "uIEAABnLQZpoSahBbJlMD/90Ns0XI0ryk+hRjs+33U3xuF+LHm6UeJNKgk3U7twn8lgbOW4T2ai+\n",
-       "oT/L5RLQPV5+fFOaXj1kttQj8bpzs6tC8s+8keWxqMXogBITSQesdMdwtQWycCtQzasXLJV5sK/H\n",
-       "iZ8OeRV5CnvcpnRRq/LHxtPAl1VtQ4IQPr2orWsDNVp3RcK4zFweUyKOX21oUpXQti/kIOp9pW63\n",
-       "yFHlLEFF8MKtwrqR3RiedqmXP+PUzPQksb+Y07WmUdYM6x2zb6XZcjVGnXKH8zxCYfp5m6uWxHiE\n",
-       "Wdh7EyEyVhFqFOQUOCyPUz2qhX1NmivTcvZecR7bBGxBFTGcJ5VO4EXTzx5FRWozu16FFMiiH5jt\n",
-       "GzesY1ton8yAnKrfHgLOttf/d6+yoEbwJbVG9zj3d+zNxjEkoONP9adrFL5n8nVEPeHVZHF9NYES\n",
-       "g1O0wLTcpb9kfJPNT9YtqhYw4T9Q0i4ZRGTdoNi5sZtpM/Ul9K/Aogi8eTyC39Vlulb5DPsd4qTk\n",
-       "x2aV0Ta9+RInseXPM/2Cs89fys8G6Tfxx6Ji4Cx7uVl7CXmGVhDAWYYOX+Z7WGdvyhVaqCh3R5fd\n",
-       "sUSph6wlOgk+rjB+sfsW3x7r1s1HOtTrw07bQqyfg2dUR5rRHjSXMd6mmDEifYWcswuYOCrxqesF\n",
-       "lY8MAVPB8EPI/gem6szv3B+z3Rx54JEEW1ZzH2e0+3Mg4baW8iQJ5658QAVzlsDh+d8QJ6Svn9s1\n",
-       "lH8vwnnzWvlOvZXJG6KWBM1g/+LYnGIWnm8XPjU639w5ysulxj2Uvy12OTVk1eAF0wJ+vBfm6Dtq\n",
-       "JXDYKvr/xbLQB5rcaAQ1ZlvPWk3L+he+sc77nPFXlNxl1kzXj+ONDjHCMJIS4Oh8M4bBreAsbOua\n",
-       "cwXnCY8AijQQ2S77pZRmY2T/dTlJ7a5zctUMRUPxQ4xbHit6Y/Q1wXtK0nlVmRJ5eSMsJSJwKUlm\n",
-       "C7BkR90fMcwKMhNiGrIM9ccY2AZ7cB/I869SIH3fS5PFr+Sh8vyLJdC62b8zJcBotg3ym3WpEBS9\n",
-       "BmrJxrApmBbT2OVWCfriEituTDwio479bgC0vYrdyWqOgCKULBTpzTuzm08OPuupNEeZnDlZX1oH\n",
-       "WosPYP6BGtd4CtTVmH3Exx0TRoipIn2e7yTQJvH/ZjlcRtsQSm+TUtXo2ldoAkR8GeU1wEfKbamK\n",
-       "TCeeLLpV8aYYDSOVSOoJ26SlCqNnDZeW1JwO8Ezhybfuxm+pkoa723eOoKxksqvX1OJiocg9jVcp\n",
-       "zMbZh7F9/qYe6ko+5hxIo38vmpTKYZTzuUdvGI/8LSvoI78t8IZboS4sJNu/ffzAXno23s/zytMW\n",
-       "J1UQaPwvUR/UwujKcy4SBvXzVXce5DzGiriAuXEXRW+aOQ+uHk9zVQnVcFnf0ru0IE9lsS9G3WLw\n",
-       "7+zD2NPFSUtIkr/8pmMOA2Sdt4MQsQdXKkX/vEho+mwMCAqJSeuo+SdXh2pNHMULgju8HD63gWde\n",
-       "UBageb5hn6qGTZtrQe1R1PrUXrlRxGvdzL42X28Ycz/xKlnkbRBmbXD2QtkiqXH3iEJ8amFoGzJ6\n",
-       "QwknKiWRmgQmXu7ZuDNsnkzsK5MBu84b6gXpPY0kKIQ0xquU/8X+GdLil1aFYCmgNg55wtmYprqQ\n",
-       "S1yVNEdwJ/Y+X+SzpoUEW6N6MABPVRekgzb4wYSD/XI/PiCyLORi1pwk3hlPw5uitgEy/a1ch57d\n",
-       "qrUpdSdCO1JVz3oJRdkWh+bQwrQJhWk78DePAWPxzaYprAG7BSEm2Um7INKISjS3ucNJ4UF0QlkA\n",
-       "HBFmg043U9bzDPkv92oay/QBMGrM9I3Vfv1Qgnw91KKi4KmjzWyoHM1W5BLMdTeXsey6Sog4gvKT\n",
-       "M6yusZa1ziSbCbEqGbpNHabOXhYkzx5Dy8d/hxIM4VbCYb+z1iVZbAlpQhulSK/+WKYyp6C6kdTK\n",
-       "gikCoWbP71e4OJB+7e9mi5tEMS/E68CaxoXVu+zOWJ6TFFYRlPCWVUSNiyEJmbvGYUEUWLUuaPKH\n",
-       "WuwR1iW5xHnVdZxPYBXnPFP7+I/TiRggd+tv6ksTonGIFc6Uyc4LGRJuBmzXz1iimR6VWpahIUxf\n",
-       "DkSqzjtxnq3sK0qOzxcVzebOtGY1GzE220v44EFlt0RqIMcrzzZhpUaMHRgVsOksXJkcyIVLhKR4\n",
-       "bYVhGMUTfjchcUcbAp9VakjG1/dwLtJEA1+kzxewXbpakDdt9ulNXtkIrGVyf3ocFbehn47pyRd8\n",
-       "dCVlhuodMSQtNhTGNzb8db0L60ONZNijaYsBkYx/Q+415pi7ADhh9kxx/DexmxrBY01QGb/MzREw\n",
-       "6iiSC52aqkWVnkZcPc0fXRnrTekOGiRHfHqzEzjUGjNpl5D4rM1Q9TiRAeM2tDu/pP3ViekXeo1C\n",
-       "2GMtxPi6BIHPEzliYS84OrxMxIXLTJd/ii+Ad39PJXTKqAmsnE+MTHyL2NfcQFya2rUTZCU4UFxf\n",
-       "e8lP44HqoYBoqO6NbiC7CEs5J2Wcjbk/KAvXzRNuTIKZhygKSVpj10+zf/JzeM847syuFt5HOq7N\n",
-       "ykFS95Um85lWfpoMltIOjrffuuboWVY9lsbDEqml1hSX00bcr80vlFVvds/Lkntryeq+F7CMy11W\n",
-       "iRdaaANweyE8GVMNdu28Cvjuz5IWq7ZJLXLC6kLi0peVqQTaJ74ML/lG8V9asIy1ixNY47HjVTBz\n",
-       "lasTs9SYb6zz0oDgn/i1LxzHGVkE6Gy6pC61XfU1ln/M8+fENFTa+Owy83QTRce0CMiTqVuV9t2K\n",
-       "RbgcMBKpmbXIlc4rKTr9rhFuIQO1HNUq5aeqvp5Ah98bPSrl+ULsRIKwudB7DjT/otyqzg+KSJHL\n",
-       "aeZnr+NxAVt3wIE176TdY6iY9kpeJDWXJ1ANSfiOrFvn6YbmHr5zdN/TmoqPHpLYF0uCRei+YVj8\n",
-       "AIpoesGjYOlTABjI53UGwPoRu2g/VBBntqcI1r8DpAdZwOYFBLUBwj8+ziWuHym0dv6dKDFh/A51\n",
-       "YEBSWiwfD7qysqOwNzAFcgcpknnUxedQKbaL7rlnr/JOEMbuPyLofuptoUCiTlEf3uP+bNpFbplR\n",
-       "X40hDLgtN72msyxugTFkG6VYeqdCc9C9cLGtHK7xu3gcVeVRhodhyC25FxWzJ0J6ZPMZIQqPONgz\n",
-       "bZldLgxKOXuxDgWitWBFZ+LmaPv0tloI9g2txSilQFn9xXBV5OAAAW/LMSBM/kJfT246aopKXmJn\n",
-       "Pk3XAK+0fercRowAzHLRqJs8/USWS3b7nhRLWCr1TWfuQpeM2zlQI+uq8ypkbG9iqAvdysUe526c\n",
-       "YNz+vajIYyielEt+S3lOhTS2VqNcXnmS/wvsPhxo8eGUGNd6RQEwxiM6bRiqtm1S5LIIJwsfG+S7\n",
-       "sBNtLW7dexoViCTluFm7661xWZWKX8safAwOflo5IbwSGEhDMllN9JZHHUO/BjiAXUy+iCDPLBE+\n",
-       "VueShFNclvvZuK8NJs061WVyLRQJ879cxc6uO+iNDlgVGxF0LeGu6FF9HxCNi2c4SB/iKSxHvdh7\n",
-       "KRp63YdZsMWNnj71jIqdjXw4NXGA+2HHXmXxsJC57QfH0mn0/Y5bwk0hUhQ91vAv3B5NWkXyhUt0\n",
-       "aY50+9qccc7dlu4fAHn/QRmXq/Xfsr7HyaIeAAuQ78phOIDv6u4PykAhzcLv4PxYM2RydrQLeg8J\n",
-       "R7v0WehwHjrLhKAdaos3mIrHd1i/JJKPeEGMfGWsbpoZw6ijdo608L+31E9aRCMGlmSs+Rrqc2MA\n",
-       "Aa62prJyt0sesQCrNWoHdf5v6WCb1WDexmeco/n5IFiKZkzHgXioLGxwAOY6oEf9r457HhcuQTDC\n",
-       "cWRBjCMMj2G8FvEOq/6HXoyDGM1EDy3TDcri4Ig2+3GgzdJ9jHE3jPuwWQUdtZVWZPnBDpwZ/sRM\n",
-       "/ox1RO1VXmX6oG9vxw36lORvAkLDkZkwLnlNNJGsh4xeDNNPeU1FOI4yloy9W+Dvxhg8ugC77WyK\n",
-       "7KNrFxhUii60Qf1tMyTCvvYjDSgOyGufE5iAEhWApHyeEf79+/CYgSESjAKL2XrEHtAQ1KJzm5jz\n",
-       "jwCVdszPpMHdsooT/CWzucO30LoWlQkZv7ajaR2koJqXRt0NaYppl0ZXrz1Wsi2w3IogDB45Opo6\n",
-       "/yvijnI4QcDQ8hu/kwy/QKiPGf8wOsKWKhYQ15Z9sZ4jsUliFcljivpMBJM6x2yxpiNt3Yf8/W+L\n",
-       "hi4IpWZRTPzUg4ATWKOv9kEAVX3goX6LwfX0x+kWu1gRBaUaTwwq5HQsPt0/S/GaBTNi2R08AkeW\n",
-       "dn7Q0yWJbCRyJVN/YyEiVMMV9uCW31kfJIcKOSUD67Igv3c75m9XRP6rQmb4nhkXqGKb/6H6atEf\n",
-       "+g9aXJo5p0dHfshnNHX2GTJyPZMMplWmYey7zrLchSDmcuvlfZagcxNOGib8M0NIpBw5MjPI9U7N\n",
-       "q4CKzzUAJHpSd6zDah5SjYgEBtG/6j9v4w4jRio2Pc/BasEwEkNBEy95HPiflYg0v01w3cWuRVSt\n",
-       "uvij5MwE/daCZdIuCTxBdFtOTjgNNL2A7gy2qwt5Zl9OgiK+xja1YRm4sIAA+iIBFAq7avsX9LyZ\n",
-       "7/72bgLptykqqZ84LSuHBB5iW2ey+s9Isl2Cv9LW1iQl85zZqRL/xpwTveB1qBnF+Wtcu36UYYdk\n",
-       "E19miPKKekXGwwYHs1K3X9KplRgaAMWE5+o/elWntQxE8BOval9XvQh0DKNkyi/p5lt0JUUCGkau\n",
-       "X/GlhCEUUk46CDPEQ0Y+R/WrE/VjgOhlfvCuxEGuQ7HIgKUf9xzzwCsi/WLvJNq+DO2BOWhmAsl+\n",
-       "LDn3NuVsL23Jb8xjNRNysbF6KPW4VDAvxeB4wgcdT8a8d9eXZRUTU6HOFb8J/BrUMUUVsj0bWzao\n",
-       "8GpFrU2XHllorA0tymeNHvKPfTfiVmnI9atXfKabEJdUcnVPhnvrsge7Vbj/lFh5RijAJEUuXh4e\n",
-       "3P5bvCTjmcnE+DDZMti8hHTe/lZchq/jKWoblnlaKYVDL+sX4KP88NwFtHTFjHfIVVGUDeh+xXgD\n",
-       "fxiFuT2EXyZfNyWStejE/tK8oHw7J4PlH9O2U9UxEwowMR+s7IvhdrDvMryMlayWBOAOmyu3jItE\n",
-       "oUclbHO+qglWEpQAiFQYRgT2X13ZiyUY+H2YgPFKB7YV6gmi7kkHKh8d4go9kzghhCSndeoQzsyc\n",
-       "CCOzjQe3bB3Jnp4OZ4Hu06Q/T+/FNnFkNrTI185UeN66DUQ5tFSWVd6yJpMm69xtxHiGhtxZYbit\n",
-       "XoiR6hnoO8Ul48yBkK6fZAayx7nsji2a3nhTk57D/wj6uYr1vZpiv6Zo7NXqCrAfGcoN+mHGoRXL\n",
-       "9wn4dZINu1XSxs1tO8aEzhVmMcrNLmoN/1qMuSBtxj13hx0YRUSrho9QbVSRHhWDUEpznZV8X+wB\n",
-       "LVbdM7ccT/RDCFlOzeNMH+e8j5Z3LVsegCBh+nVdl6dbcpSdmhs7QtHO1xwT8wINlLtDXUR6fkdj\n",
-       "t8XtwF1AJOb9K/MRqy7lOsGc/79FxC0zC3PuOlVeMLs1aFpmPWAjP/+DOXw+jnxjpPkEVufezA+Z\n",
-       "/IqoRouJ+cbtN9KDtuhsg2fQ0cuErVDJmx8aAKFMw6JN8lqz1Ayav6+3M6JG3RvXwg9KkRZdnuhl\n",
-       "wrzMCTz+ZxMMaIz0fqF4ZK9F/FY9KlbuwxEikAymPObu9iOtOvpG20RJd+hrmwkV/nvK0R0aDIE+\n",
-       "OMge9LEQVAMqx/QoACQrdGe08tRM87Qjl0uxuikqg0V3fvDugjWNnZkPs3aYpnksQotPOw1gwcng\n",
-       "jU7kfOJj6EeFyG9gHzvMLo8vtvqoMkyj5t6GrysAwD8UImMltErn6Co2jbouMeKXJZK0ei78QN2L\n",
-       "yq7tsbqS00kb42uAMthFFDSBEsYidAnQzzjPQlQDM3hnwSmzdwzm21b4el3+9FIL2eQbgIN/vVFh\n",
-       "fclpDT0278cYyaf0yv/g7oWYcs6c24qX3yvRnbOwU8L03dq2UGMYdvcqz/qgZMRUuLK39YJww6Q8\n",
-       "SIEMnK4oi7SlVLO9nl/QUqywBp0W5sXqX/u7dFBFB/bCiLKn0hO2LT8cFLMA0uhR77EQ16eOKfok\n",
-       "3EV9hC//8Cl7yyyQtCcsEW6dtgXt6HEFWKPn8JfJC+Q4WrdsT2fwOKqqQ4DQzU34FL+q/12ZY+J9\n",
-       "HiNPzPFCFj8+MfZwRFyHN8EdjpxZrSb3Csfk0872kyDSsGcNx9L40G1y2YQ3VfWeRms1e9+jyxtU\n",
-       "t2N8hK00Eji6QJVzow74paDe6Knefx179hYsQOc8+oRq+9uE0PwXvDQvsW3yU8lvfN9OuOR6Xx/z\n",
-       "yIZczfPueXdm1ptkTNrmCuxh4j5dCXT5NJifMHbUT7fzYakTMMLZ0oIluxls+YrWKJGh0fMLwAwX\n",
-       "zQtL+QBhI/QB2UmwK/RQMBCV1ergsjuLVkvJzEdDM49E/mRK//8+MO6IOpttHg1THSjeGu7SPMap\n",
-       "ihUfCn+it0B1N0/zPfwBumsZOfIa2a18fINOiYeWTa4CHOQ+XYxbojlsIp2Hjs6YZY8ovlqd/Kxg\n",
-       "Z7V6PfMWO2NHsDhyT5LSxeEyDYhLmD29fwOLJPfbbxe/TKF7gvdS8Q9brvTYv2pJXG2nmgGOWuo9\n",
-       "gJQ0WKXLtRv0Ofer7R32y2sKA1c5lhNxm57SMczvS75Mhmd/o2K6t+EgmI/Ki9SE0Sbs901wSPu8\n",
-       "prHkyqRUvrCUhtVyBipQlDsnY/mwfsBgQg6/Sceun5Isq7sl/K8UF8ZylOy5HX2HTIaW8Mg1hSly\n",
-       "Yl8Sx1JXL1hfBaCK/BOT9JfO+Od+oV2jkf28ps+BL+WIR3mpalDrC/DvT3fv0rMxVDRuz7GzTH7x\n",
-       "3A+aQqaL3VGJvAA8XzL8ipJP5xjVGg9lt2q2r9QORhcgNaqP8b9O/qi9Fao6fiL6zEJ1G5EIbAEi\n",
-       "DdfcCpqMMtXl/0SVwiG3YZY3f5H+oB+I3O+m5d9zzKHT30TtWAbl6wCChuPhcGLhTuYHj2vBMwrp\n",
-       "CYHvlqWB77Otjxsf3pSjl+iNqa0uDPD3mzfY3xzZUg6gXjr08miifR+tCTZB+B8IIm6bLZzSlXRm\n",
-       "iBRwM+KM3Hd7Ln6TLg4nC2ccCY0HgIi/tm0hp8Z9vVarMvo+XW2m/UTTQml9KJV+s/bLbzT8C14x\n",
-       "5rjBejl91ExuBOAZoQtSpV8APlJOsmei+UgVjGHroph8znRynjj9W5XDZsUVCDDvhuyieXl0HPrh\n",
-       "5eNimIuU2RyTgCBn8zPUC8qQXPgkgHkTmz1aCd/eyIRMCNHcAL6guW//8z2bGJa5K+Ps4nIMxU0L\n",
-       "rFL1CgPnBuHdluH2u6I1Nu0hZ/1x9D9QlcUXrzDqnVfrtCWxqGhSOxZ6XLIPLNf3Y+JnZHaI09Ug\n",
-       "FMoC+wNBjwCvprkhWKBjQ8LPss5A9jmNQBcVpHLmL4Z/VnLQ1uahF/UBIM5EgJ9TNqFv/mL/lyqL\n",
-       "MxEnRo4ZnNBvTWFkfVl+pq4agu7/IfQgDSlCPHBqriSYaOV4TrlsLZGHNL6l1BExjgH/eSrbZ0tD\n",
-       "vpXxo2IC/0ePZDeBlRQ+FQjZ99+fpFYx/l07o4Zq4SqczTUiX2q0aaEMNu6PqAa4MC2xzq0S/AqJ\n",
-       "SY06zW7zLYquFCaXBjjZXo5yBo8ydvhkKyYNHAal1NLA77nBTjFRpnNJIUMwy4qWd75e8vcJ22Fn\n",
-       "BMsWrlZG5snIhOFkZpdyFlZEtzOb0K1Ty9em8CcfIi2M7APpprphphVWb5PiW7n2HKE94Afx+B+K\n",
-       "WqCiNsaaf0MJ36Ct1pS+oQ0z51+gG0rHHACmoCy9OgZBYW5TtCa+5DnhtLRg/dIG9YSe6UQsVxWf\n",
-       "NwGc3VAR6QWWy6kYJL0WAZB+8dO0GUfryeEBj71yE2qv0QA7LJZWQ1Xav1DYIgTo+/9yuFGaVHJE\n",
-       "4oFsbRNP+NPrtvEL37ZD4e97Ra6w160pCfi0IXj51nf5LJGDGOFyvoW/a7O8ta1TgsuHaLOoHxpX\n",
-       "1cSHqq/cNlE+GGUBE7uHN/NmXCpZPQquEaI0BfY9T3nEqvHt3UJ1pAoLvWVyLyIj/W/EkPW2qipK\n",
-       "I4FOJVJZpo2XOtct9T5AKzRS4yJd7CWAtZwSpSPhe8pR658zfQPNr933szv7UlrJhHLOnVca0wIF\n",
-       "RZlUaHHL8FfWSnwB0sargDHy1iRAqZ6vtX1i7PGg0zFiEuGAbO+rAK2gzYA5sAnPqd0PkyyaHiTB\n",
-       "qJ6+Lqvi3Ff35XdqnGM789gi1ayJd/hpcdxUbQ5n4WeCt8ar+SLwm5xRTUPTb3fJ72+2iWMkKZh4\n",
-       "yyvJQhju5JILMBAlGgSRLzFOa+iVuwEVjKDcEtDz0j7Bddm6Y+oHat/uKKxWyHp3UmafYX+tkr0D\n",
-       "F3tGHiot/NCUECs1hW594WatCq6fmnuPoD/6VNi+VhJSGt2i/tMCdAz7aqrQ0BMTMKJVXMUcA6hM\n",
-       "X7DqIZ3lCS11+ahJD1br/PF30i1RXuew1FmcrGlAu7ExcC05/BfteHXJn5dDRa+5zU7abYAMnkcA\n",
-       "rWeyTLmcL6LcTiTVO4D3pA8Q7r6/zbZYNXCmXDlvovfuXGlMp3cqprjHfM0/8phtCbZ16LmlSHNC\n",
-       "1HG7HzFvS5f4LlfmR1IiuqtDMrXJhrY16+d2NH+Ax0yzxNKKHx1q7okzaEFokc15T1ClLjfPnC7F\n",
-       "ohax7JL30Xpy2qKphHc4zCloyVNh4gRzUa1KpKgtagE5otpBCHV2pztwPqMaqSp22LIIvUtBAAAM\n",
-       "8kGehkUVLCX/2988Te765aGIfwygxGSgNukSlSTO0ZV2tdpdN5ZuU9z9ogU8dXeMZfbDY0oWHnys\n",
-       "msS8uivzb9xX8XSCn5ZxnV9EL4MHwkm2nshKxN5i4335iure1kfqBoKFH0A8NDLIA4987dCRhIWf\n",
-       "tcwWZZGStZnjeDFKxfId09sNxwNl0FqCpBq1Qu4bCmb8z3+/57E409sRdfsAtE63lp2lHW23RAJU\n",
-       "T1GqW0tYB6wZWehVJ8v3XMSfpUv9F/UfNuN10MiIgy2+X/Smr4Rj1xbbaaupDJMGAjqJYMU2ilHL\n",
-       "Kv0CAVIXj9zqfj0m35/VMBUDfxggpO8oRJxJFpD9zx9Rca9ZchYWlREP7LFkxGqtORN0ZG+1QOiR\n",
-       "ND0t1fQb7tcWyAdO/UXHEMd3BB9HaP0Vvodez92CdWCEwywM5jczkSQFO0X8zI+MfVqgNat27V3I\n",
-       "S0Hxfff59yYPQnk2PFUhmxeeP0a9pRVp5vOem3Hcq3NyidCPbZ3NMYRaRDoLpqbnJNtx+mDhoi4m\n",
-       "Cpvwmfo/EWT2C1fSVnvzrQOdSPLeq9wNUe9RUqcMZSvX6OaM2LirpHBeDX7EcxXCAzjIRWRn+c/A\n",
-       "GiXYAPSYRJaA80VFCGBqZHyXwLT6PNL40nBK9X0N9kmzlCIzyni+vF312vteQBh3m6w/TGZ5dpK/\n",
-       "K0oygjN6SKk0TQGpziQ2R/a+aAMLlbvC8xxHnIcIEkmJBZwOILXZzy7/oroCBaf1VFy9XbNCWXad\n",
-       "8yuSyp4ioP5utnKhB70owWLfOSorjOtCEJQEy1XZsGYuvIiFOT9LNf1Ei/wKNRGLOagG+BaGBwzT\n",
-       "+VD4iTp/WW0m1Ap2YuJEGyl9iAW0PaYSndILqco0DwBWeLNgC1sEz7+0s1HiVBdz0jazD7i//bAX\n",
-       "yoPDyq7jncxA9SvRLwqsDn3fffN+eQv2ASsBusBHUrRe3zWRPGYo07TpBOPUGLfcnxoUekzHhiqI\n",
-       "tdFZ71GQDzEAkWmGck6n3CVi36dmi8rP+1lqbzN9LSteqLM8lYd8omzIGbSI5VHser/Ae3+aRPX2\n",
-       "dOP4bVvV5w1f9wYJrTZRS8dVlImXgdZJKGHpEOXbPqx+fOb6+tuBkk743jPxaJH5+A69Vwnc7jQx\n",
-       "jOkWJhZObar6Zs/CiQ8yaHpa+totH69Qxgkwwja8tgXK5siEQZYHXO9tM8QDzjZBKzByLm3E+DSd\n",
-       "HhZhKpQzSGFNo19uIb3cMpH7t80KfLMijgMNgwSjilZgsvoS4u5hmj3QfVV5YVQI6YwPQb4aNayj\n",
-       "ztjvJdZdIuYBD++MQuwPM+FqrlsdleU595pEyarAKU2H1fY+5jy/QL3DjDcIt0+tIMEcFKb5YqSg\n",
-       "kO4tkGGhRhQ7EhRgag8wHn6LYUgk5GPe7r5k6wsIKdtOtmxrTEjyV+0YOgAMEQ86/hLSeQx9r5yE\n",
-       "EvfSLTm3nU4n+pLa8AzxGzAT7KqF0zDtIorkC5kkHa8dVjPNM5cRW8XXmbC45xRrMd/Wmeu9ZwYr\n",
-       "nzSwVpMgIUnQbVXc5ZV5pF1m2Y0G+Nlco4mbd6rh2RkpQU/I644pBwpgoxQEijYAv1qAxQd7g16Y\n",
-       "jzUB2irzLEPPKowPs+PTcpaQt8m65YBEdqCur54bl6Fyw9KuvkmAKGdDgz/5FGowRD+l1rSwvitk\n",
-       "b4sXV0ArJuHgmXJPs0uyFHcmSQsAOT/gSUdbYuxoXFjst1rq2r4DrMI041eZebnTPRHGe2K9wBYg\n",
-       "ejPBk9hr0nlYc8rUcGpQC9QQulwzUEeDwskzPe1LRYGjJoYeHu+/V2G0PaZ+rGC4dD48AqMhYthA\n",
-       "EmNYsXIvAPqzkYPdxzlSI/co1ONaC99ImBBsAai2FvRNPC1jlzMKQkudaVBNuCw+jWgSWVWvxfT3\n",
-       "wpPHCl3/TkB0yB92gJrn5EuS5AYnddZBwVkkfrfbgGZCXuzbBKNrZ7jYVp8lUB/PSzRq8pK7oTT5\n",
-       "mvDrLwtHiBQ6KKwnqEWdqTgH8R4nSmUW7MozYnMGCi1NcCS3tQ1aRTqrBFyW1qNL4oQX71chivv/\n",
-       "vWxaxdPyOyKNuDFn5xQhAzHQ2QUbospc96PF8A/Yh/A5PAKRv1MFt+v7IafM6vqq/A2r+X6hVQmO\n",
-       "Wnm/iQSGyJ5pZXHSNXPDj8tyK2bZJko8rD4yEYp3+y1J+vihXQy1FoBRHu/KxCnJAZkNyWRWIRuX\n",
-       "fImb6TPsW2cyLB++/IX8ZahRH9+SPGQ4bsRs9mObJWmtcIj4alu3YjoLULjJw5KXOTaMNRh+27tN\n",
-       "0GBShni8TxYfUQOVMB/z1nGfaDERpyaM4N8Vn7qhSwZofz4ZccWwBILzHZSnslG6CdopYiUGCRPI\n",
-       "dC0t+fRVgTRgLH52zaq/JMYvNyeqmRur6/MkzeqAteMZs1ICywlkjrzhwF47gLo2s0i0TuyQvQdB\n",
-       "w+h92uZXFcOSHSO6iY5aUplIm06NYOWQzQEa6kbcgN/NzAjvHLE0+tTLxg4JAwZLSE4qmkCKZ//E\n",
-       "P3Dw4jdpo63B4+lZvTAt7RlB2ypqh8H4piI6FQoeXu64yJ+HlbUhwah0bICLnt9B9AkEmw+1Ka6r\n",
-       "6F4CCM279txJij4/tv0D7aYZmx45qzxDXR1pQ6WkA4ZrVRlit38hf5celXE39VQlbsL2Eqz+y/WM\n",
-       "P+0FCuO8PfFd+9h9rPh80GYHa5PSfBv6bC1Rv+fAR49lY+S3ILx0zcxPb25GJCi03sgoAVhrcfRA\n",
-       "G/rQyA6JSxfWaRfuGJeTXDaV3TfgVq/AalJbwIMk5d/mUR5mTR4hNW67aWArZmZ3DZpCsZF7jAvx\n",
-       "NNo89HIxzzAwrc0zcSWu4Wn5F6OKUoay7uFgVWWvpmK1PO+YsR/Y2gufL2dl/7Xgr1zwAd3Srw8P\n",
-       "TDJBtzVujPEd1W/TyyqFF7Kp2PYlQZ9nII+FySQ0foxg72Hzsh68J8YeuioLeZosOqqdodGA0WRX\n",
-       "5EjeyWjXVcxtWaG+TiUa0uw1EjGTVkd1u/szBmtf1ycMVsDCmYszeqK+Wg8o3Cz6Lfxusc5nLaKw\n",
-       "eerx70Oefdprkfla8Z01E73nUHPB5V0upnsNUWuVhPw8/6s+fl/l7fopwEM0bitLED+cgvS+E4it\n",
-       "rrh+OmX/roOalYQ/zezyt5biLbnerm9tPQ70PVGg2n/mu/S64o/8NUZRwK0FudxyYoAjhoLd3uu3\n",
-       "PY4gtMzudjI430OYPhToWKelbUbPdmIzWcK9S3h+TjvzDTSBeFnK3KUlUdgIR8Yp4dSA5vb08wku\n",
-       "2B5tMlF3KrOShm0yu5leRZWMX6kUgw/Am3aGJtreI3eUTo5PR5EV6xa0gLOfEXLPkX6wnMf8/+SM\n",
-       "VFpGRbcRTPCokeYHdDCZluaM7pwlDnx3EBeIpYyYQ6O6KuTTlGpcY29Hq0gX79VtuxtLqjrZ1+h9\n",
-       "cl5Rl+mQ1GydGnZrNiIZXw0CKgclZnEy6xbmLXSsDUjiFRhJQRpsNywQOdsA9IP7xs1UErEgb+Xn\n",
-       "5HBTBllgTw4SdfXxEoIeFx2yONYd0+/+jzUHoQECD/gDHi+kACiP7gRQEWqn2Kpw7iHu7xmR17N7\n",
-       "NDg8a8xDu3v5hT0AlDPkK+1pJtdldzJWtZyu7VFxcGZOWeSTQAqZ1jkMQf+lqTYFsUEIM7//VJfQ\n",
-       "7MkD0ZmG2SwVRjInlUMBLmjIOnUd5on5xjdfxCOvChlMZ0KKFmz9WwvNpnYNGin90bEJV8ANgw66\n",
-       "vb0X4WIeLi5MXKrPDwaLWCNz7Ndo0jL/wIIQQIIe0LvycIzPCV9xuvK9Y//wTr+/MdRbfc3aYc8s\n",
-       "PCEmaq/W6UIhZdvGKii5XNs4UzQ7Z/zCiQdVxMaC1b8pnm3EBI5nMdTxv1R0IyaV52iwyHM5e3G9\n",
-       "2fqNuHwZOe4c7aKvOPj3jUJi6n0fq4NFNqtY86/UjXaLSzv6Ilq/k6lOt8HoL81pWtbpov++eNt+\n",
-       "cRK0x7qjLJ4FT0Ak+UJ9LJf7QXpRGYGDakImDvPGsJcOGlICZMlYMHLEpiU87H9ZLOAX4Z+T0vTv\n",
-       "8fBcxOt+8p3Uh6VoPqbEvblLrwRmFJ+mWQ31/+COwo28MqTE1e3x3a7yDT/BmVnA8sEVhh/ybdYj\n",
-       "d6jhxSaltFn7tT70nb7sDuZ/ZxMAe7bgTf4UsQkyVxHfEkDfsn9/m1yeSgT5IHIWmpv5lcMkECo2\n",
-       "Lzhxf0ucJOf4wk/qlt1+TWoOsiBpZWTQzlhjouBTz0KiA+cROKtGfEwMmOM6mPSn/Tsv6aVjdKq8\n",
-       "CUH9vjN98ZGrXsTrCb5Yap4VDkSmlB7GpkVj1Gb3DKAKo6KIxB9FiGRsxCaoBLZ4Ro5qVd4zpdbe\n",
-       "UVjUUQPVjKYGIabHKbutG+WZ3KGjpoCuijBXUsK7FCQ5W4QlOL+kS3kY8KGN0UC0RVQP+NRtm197\n",
-       "r0y4UKYfHDChAAAIvQGepXRC3+fg/mm4fuKurvxsd3CgJjRvjj2rhKNe7oIyDKMhy+S57SGOa4Z5\n",
-       "Gkv3Msg7m6W+1jzVcS3oOYF7Qfzff2s5LuBd2sER7rEvfDAngF1qXbhC5hG1tAyZJ88qMFt3bait\n",
-       "R0RGezefOQHjZDu/vPigA+ML25RZ/him0UQInp4InzVbuAFklThowNSpdAMuk/3crNkoeyLq1vvK\n",
-       "DBALIGFAS5m1fH3+JhNhm5w+dRPI2cRFt2bItF6IjIsnXsK6q4TVV8AIEpk4Y1ppr+Ae7+UFfec8\n",
-       "SPZ+AAAHDu+ZSYGA/qVPEyxhnI+TJqGhaQxbMHPbpKtuQ9mVZncDjGHne8ts/UTinBqDP937cgRD\n",
-       "JASVd9ziQKwmy2Nph8Le79hzC8HgYABM6XQTV6rDXZJ7qytIbC42kpT696FyEg+ib8SwOifTOaZR\n",
-       "TN8//smtd0MDtY4qs17uLOcarw0G2Np8gAbRsqN+b4su48nfCMKZzcVZvGa8YGH1ioMXSaB+f897\n",
-       "qTreM3UMDayGMheoN0rMTPz0ATxvlnUMuTsW3gZHGfHa/SHS74kHS0f12+bRf0WEMI2FJSRQW1c4\n",
-       "ZTEThHrBJ6cgCH3DSW+V7rtH4gwShhRnFLVUGK/Hs6BBcK6KLhxOB0kT+mbb0Uy5Y2DAIRSCN7r6\n",
-       "v8PrJN34ylTLyIe5xjdcJuNt+uBTDd6FwIbr8ppR38vBzilxPUDGU3BF0Lpotp/rUQBDeI3A1pyv\n",
-       "NjwxeTVYASS/ED8aVSy/LkvxyCRFuByr3Mu/LJ1G5p/Sme9vr16PhSC7N5MIkmzwWYP1Be/DHFw6\n",
-       "8Meq1JjToChtQ2Lskl9gBrE7L1bqYQM6rbY+aQg9LgXPF1P42hx4duO+Nys1EsRDt6xf5TcdZR6g\n",
-       "kC3lSQe4uYHbLS1BB1bqVSNOcdJfMAF1EYvhwSOPWEj/JjdJG03wS/3TAYOho3tqs7nt2DzuVpFi\n",
-       "NCCdJK2Hz8NIPtfBDYwIRmtgkhJ3EVK+HW1iHFucwH70GdRq7dmxpy8ze0Cy1q/4SHvN/lnRL0L0\n",
-       "ZTdehhOahQom0mU3mIonmzSk1eyFwfoBnu8CHqIKk58MjxKv3a6fX0hQCo30ldXZ77NcZqhqMqie\n",
-       "FWSwbO65yp04MfMnzyU3gCaSHDi97rVpUEm8PrstdjcdrfHqV2M0bJA/DEK99kdJzdggPq52gyw/\n",
-       "t9l5B7r+BQaMO1jMJLRmJ9YqL9wHKJX4ELP9UQpiVRKrdWbFqDDxkdNjQp2+j32fq5ZwmmCY9/xy\n",
-       "/ocjvnElHwvpihJLa4y8P3jo/EtLucI62KUHDle80VZ7Fj5esZxdzAxwkzSQ2gjVLad7cR3LWDJ3\n",
-       "J4NjRk9Xkx08OflPfBtFRXOsSOurZeB8UsMkBulVWEmh5+c0tDYGP1vlW3sGybra558M5Z22vZJn\n",
-       "kgGp09Yee1+ud9ilfM9MbPncYPLOrFruzZQAALTL8q/CcDCiaaMu0CoT7PnJQt9/Y2+IzKbpPewz\n",
-       "q5DuElrORWo5YDLIwa6fs4o3Eue9QjbWao7vSP8qyClfn+mTNEl9AqCTX6wNti86kqocUDfGSWty\n",
-       "caToJNZfjs5AIU7aBb4RgOAAio+Ld6J2VdjZElszSudcfhcg6I6MhJcz59FHKeYdinSX38C3e/3W\n",
-       "veMMyAuAz/jco+yGqw9G+fsVWPP4mYY/jeDl7Yvm1IfdNjmpo4JfvMa3tFOD6g09nij3pMqa8Uie\n",
-       "7y4IAQecjgj0nh8c4PbORciDX9VrXPpf7I4fTQUOkGqYiHcliVC2fc3ZAc2DzqelFkvXeB59xcfg\n",
-       "12O4bQ5xT9JhczaI3ZEsSvDcWiBBXgHGcGiRM44ZUCphJpRT2Ohgbatgah+q/Pzi43KpnhR22dUN\n",
-       "YTKyZ11V9d4fYXqO4RqXgYNwqiF7oJCHw9OMWTFX9SLfeio8aJ/g/ReCwK+v3SG1gKQybH5wjK5o\n",
-       "sRnuQWjP0Ys8eh/+mjs8coze48o0MnS0lImQpuULW/K+8x0HI+hz578+5/+KsjKnFpGdFNkbjAwd\n",
-       "jTo63eEORHTAFwbDYYrQMv4GYWGbxk1cm9NCGVAvVIn/pCx80KMsi+2qKJWfS28OCorljNCeh3lg\n",
-       "r0nhu18tvnfoDUdB6aWTWQtikM4fkySpD5wCTBbs9Ko3fvE23wDAW0TIuVFnElCCsn5nzNBTJJDx\n",
-       "S+ttmuSzjN9dT+mGBXUefs0Bd2ym9Y0uviWq103gwJZxaVyWhOjBnmzAfEVzeMq1Aq6jxNn/IVAL\n",
-       "+EldZcWNsArhmFZB2hFCJqyqbwrsCtcsNtDuYWUYMosohx/cpaPlgF8FBpsGdGZ6ud+a9tybEk3i\n",
-       "fTiNJ1eBNUFmDD0AhWVKz1tlkPs2xv/imfVyltfrq5DyBsZ0f9m81tKbJgvz8637ElFDgFBUTZrC\n",
-       "EPXD+QLm8tJwAcRQuOKMEWHoUZCAGPsBR13b6JMT0m47G9eTZm2tzyPgfShig5CWiynEuy+Wr5FQ\n",
-       "vzOtDYtC3VbH7e41qMwiGKWf/eKnPjc+5576yQG69LHJ9SkxKVfimbehjEhxhUuXjLZQwwDqjzVa\n",
-       "Sr5cbAahdon9xPSFR+J3XKRQnyS4jG3vPs7E+/G5oo0sRXSKZfMdliAZvLAZOekjjRU+iSi+IgyT\n",
-       "rFAkrG7lADs9PrpkwhlgJFUTSAm9YuNjvcqqJsbZcvW7/Sd5HufrQ5+RGpT8CdTwM7kWWmWMPePS\n",
-       "+ZOPGOmrLfgu+GJcVV0nmizLBrWEphD/FBj3WQcgid6rAVugUfvY6lqOZ9pvCLQvHuc/V2O6ErLU\n",
-       "f/AYsUF2vQfRZRvSBxzAA3WSfLFNnww1rhJ42S/ogkWJcQVuY7yR5Kr1rCe0Zht+Xp6ij7GAs4lZ\n",
-       "/ykmZ9uArJ+HXUY3SXpiePoC7qcBa6SDaujf1mHI6WuV6XHywpRZuO0/Nbex851gxy4oDBRxIcvO\n",
-       "lxupHnYbhE5fIGy1iHQO57j9kAzhRVGVIBDxAAAL9QGep2pC3+p/Zgeh3JtRO40C1dWZMnDG6DnB\n",
-       "8wywbKc1r4SQMFbie0og3vFl/llIdz+pMerhNGl1tGjChq0YHjSdWoxew7p8JDE3bMAIcOFK8pl+\n",
-       "VQ4Vo15N2G95KHojY9mCB6H3w5yIF3TgtUacfSSFGRRDeHdishPP+QAxm52Yc9DSoZ9UbKJRLhKZ\n",
-       "H0/rAA7XEY0B/oz/eda1vqPUskO+XX4t4x0ixk8R9nRIoT30ASS5/VEzQzRQaGuE9H38/QuOOjWq\n",
-       "4l5lvs9WInraRTuykwa/0xxieoVtl4xqFFzc+Itr0ZX4K+z8mls4Qk5HpmQDLAQbEPsdX9c1LyR6\n",
-       "VQWEW8AxE2x6nCHBLZJMzwmGcwH3YY+4VvgS07QA9HJO7K7b3bL6cP7dlXCp4nlMBTWUOSSTYp9i\n",
-       "TaZgCdDs2K+RcqN91FCBtJ4NOqYbBYPBzlgiJXs3ZrwbRWXA2Tx7c/hySyfRoUNbZo6jQx8v9f45\n",
-       "grQOqU8qE4y4XsDFfsG6eBOzGMvWILpIUN0qmE15U+MNJc8jIRpmgSn6ytcnjQkfP0NTYYVrSIL0\n",
-       "lI/9JPTYm9EcZiz07/UoAO8WT+v3VNagJLA+XPVYFisUCAQB6cRVMiFdgtN1+dFPsiLkj3LMeStC\n",
-       "pdS8Wi6A95R80g5txjmzMYfbOQru20It467XVqsynLOMsMsTtwCC2yyUv29uq4iIrxvWwlm22rk9\n",
-       "HrvvHdUxaWS90UbpK1erAOFObeeqZIeOGdirIn/d5euf/tI4hTeGoyS5HJ9LjtQtVOjjImg++HMW\n",
-       "NnOFE5kI/ceWh7XzDTY743aJ2dSsVcIy4XJlqtft3Pq5Onj91cgKi77YIOXJFmZkuztiL3bqA877\n",
-       "ov5z9tSAdMmVUzCXgms8hknO+mdX2aeHCtUJf1RuuHNbWVITAwIhla3KdzyKuamseMQ/uxQ/gHSN\n",
-       "ptxOnt03uJK9Jy/XOpeL/zJCCUL+MaNF00iUBYo4Yr1d67UnBd+HK0C0NZ9czh021Ot35p+maSSr\n",
-       "UcKdVGyFTRRi+hutwK+ySYJ350eYIUiKKOgItvfDSYLuUI4gX5SXryp9gZ114U5fBoe4fkRKgLH9\n",
-       "cOlYF7f2EwyzeXMKwfmAehGFOgaoGJlAeqP0x90I2UlXFhTiXxlJJDSVhP6usEwcptehOa6j+OtP\n",
-       "+KKSwjqSoecgpqVLD4XKAvrZDCNWUNarCBgTWS6DFw7UXF/3rlHicJ/oeRkAI6al3536qNITJfqo\n",
-       "DiW0S6uS2OXuJbnyU/PSiPouPet1WHb/nEu4wlN5qrjaG7jPOt//U6UWHzKU50xgGLHZt1o3eCFk\n",
-       "sdp23QezziWpqFBq4nZ/K0bvCDpLvYP6Xkgsy6mWBFmIfPzvZyTMOwtcHUQZ9ajgzTgaMGI8P3eD\n",
-       "Mc81P5fPRX5EJdoADggkkgxaOZNTbwNFYAqcM0YqTK85vC16Lx/jrSiNYL6Ewu7oSg1r4mWICSMl\n",
-       "aGyzhZYFjDnlnYH1uM1Gp34WVrMz9rB7u9qZXB6O8NqLg4bngdPc3V9GSkwzWb7TsCQQ5iweA1nM\n",
-       "I20fCrlepTYjVKKbUadGnqMvplHEsOIedClq0RO12ZHWFjTLLHxdz9NpdJWSXz0+c9HymiwM3FH6\n",
-       "fTIDWL4GepqbFteZgJspmB8XYlKdGSZdWaJ0r4cSHlxJqqWUc9EJKkIoN57wQTylqfgGecgeojtH\n",
-       "UzOxx/afwDoaPbdYD0lAedHwoQF3UrCPJ6uinKCy1T4kW52Ch33T3CAD32hvHonPz4n7Pila7H+R\n",
-       "PdOtOPkebRLnhxr+VkKWBazbNZSC4LTOGm5PJxjwH9xM/B5OQ9q4w94y692mEz69X2cjVEV6UK+G\n",
-       "kliR7Q2CS1ETyoK7HftMhAFaAmiQUUImQNyq3lv9BOqyUn+Thddef+ElpeyO6sUQFU8jzasDKHo6\n",
-       "Dy9PMx3keu8wdmRMOeqoQmNXCJ4hkERuppYCjwWH3QfyGwu7ahn3eD3hnikGZ/nXdkzMAyGNtNHL\n",
-       "lDDakNgNHu7zAYgBp4J8BqDnVo7DK/Hk5sCJi/IRL1Y/De70g6KgwLerQefWZEkbIU1GZXh+l9F1\n",
-       "eH57B6X6qRcEZx4T6XtJvf36j78u+O/o52J0tAYIDtUJKZQSWQ4bNFFsW8RoxB1pR4OH6aS+zGAl\n",
-       "xZZTN6SCUFzrrfXsvvzJoszZl//zn0OB4IAFnqRrYjJlY4xNj2KMiGfDmp73oc1VfLyD3/83khtQ\n",
-       "7hsDzVDMqaI365TUXFr2b/OYovoyCZ7zkATZK/QJJCe3OO2rKIjMSty6u1dyzfhLaPHhxtPeZI2k\n",
-       "mDgxwkTpMglKAEui/KsD+tSMJAidYEQbAIBpOaw4HLZxm6HWaUArcUMXnoHnca5wcasv17ICgach\n",
-       "haXkiPCANtyvrBfdId0BvrzNxzMxxUkdU57e/wMRnQIPgfT/n1Dp7nMP/6PBd7Cf+v6gTX2Fml0F\n",
-       "AHKj3kAjYObvnIKb/v+hC0fttK4hBr4lx1V6vfTxmbScFd8zp4+lufTTQk9ysjhpOnU75cNL2C6C\n",
-       "KU6IQ8wH2pvL6HtErHBevPE0dyCUHTDAh4ZAXTX2eMu4MQtA6lm+WxP5aWO3mL1asHYM6RgVSWDq\n",
-       "4vsOldjEehuJe78JG/0kyuitjv8FlFSmUiH03/hLr+JMktUwxVYfY+GffnLAeMQ8zsZ3kxCYA5TX\n",
-       "6XgmW9PkivBgLUiSoGYB7qc5IljUfXuxvVswrfAeF364R42bRI42J2ENwWilbs/qFzOTIJ3Zn5f9\n",
-       "DE67RIvbRpeII7FNt9+cjoVuJvSO3DyvPyKdZnI4CT5Jxw8kO+CGFSj3P7PXztSp2ZYt3aPWpfdb\n",
-       "YygCPuSB4Xb1S2532DVmndsmARksMXhdmKoKIghkNS1gDH4u6WxD26oaPpAcs9fxhPjIbg+UionW\n",
-       "CizXrQ8EKpU6DFbaF8FngVJv33iMqkuMaekMMW55AtohsrR4zDIdPkAIub3nMm76iRc4lZj4jaHB\n",
-       "LBfM2lKGoruKHsjyLhgYIe0+zFNwMENdiPqk/9CXKfdTmmrsRsVTI8mso+3HBL2Wuf6VI83t3LFG\n",
-       "1AURmqbxVkfoT2Y2I8vaEeEWAB6rUXjPwQM2aKqWHwiJj5pKqWj+tNVzGOLB2N9JwbqYHwrrmCJv\n",
-       "52dwgABV/vnEK0jpkLz7ptWfeGRw9UsN6JBeEOdwmFy1h5q3ywhgOlVYIvFaMw5LhKW0dFvCF2HL\n",
-       "uWADSUbFuyQuGNv8UCUNcSQXGCB10T0sg5+A/715cIIYqR7f/PGyRBYOH2kDd/eMZVHr2UcFp7a4\n",
-       "kcx9+1/IYIzV7uSK7KZDFjBF7l+CWAI6sAJXCTOjGBpj57VTxipNROjMUqCykABGtZQjKV8czna4\n",
-       "tQ+3A8mc1FTcxsLjYS5fbQrnT1Kw0hHGIkVVyT1rF5LILiSZjHk+7aLFZvwclWIUA/0OTowIqBii\n",
-       "r3CkLalSLSMYnq68QrLnCSPLabFs00st78uATpVYAI5tdQ7QkFg9QELJaRQExwr4Wi4eLv1QiDVo\n",
-       "bzC/cPbQmrbOXvZxM+HRRqr48JvVTLVxXoLJNnGXTAV8kARcmH6iOvTJJWkR4NLWwjmmWj7XPKcx\n",
-       "eOrrPmCFltGKD89N4VgyHCYv4JKPResGIYXAuGzBmc5NZA09yk9r6SXDyWkLMg7LktQ8Hc2YPCvV\n",
-       "KUYLyJEdzO/Q36d0wiAfsg2EwhB6yBXdfFbeKbM0uiSEAH0uImTTH4nOahRuHMiUwMUW7itfgQRF\n",
-       "akWVyfshYF2nriuaDtVN4EFlSzpwOeWrvk32/LdC8iLgWwFN8tt5R5kwuCnYU6nVmlGHRwRyrusl\n",
-       "+MJgc22zGSTXLdns2QXY2LCN1Xs5jmWre/ZJ/6nUhCHpzO1SdrNF3Tu0D7n1nFq/R30AwoaRMPH+\n",
-       "3fNSQzaQ8BYv3hssvivpDHmIrieELDPH8v0StLjsb+bkb/rh/4zaxIYoJJKTzBVbkQZGzW5qddUl\n",
-       "gdxdYO32tfy0VPld5GmsCnjkJkFG4fwyQCo+LYybhm84iOgWkYAoXKhevmz+jCIAtaUJV1Rkxywy\n",
-       "5U/H0b4aY7GswKqxsbUAABUmQZqsSahBbJlMD/+AvYNnmiryEp//Vp24/hj7MCFG32udZy3Wkaei\n",
-       "SqvEL/MQEJ9q9pGkc2DLz2wv0RtXIBLkYvr5Jh2oz6dVfI8HP5gNj/e9+4+ypYdRqXub6lhT323B\n",
-       "Ik4FQwktsipVM+ALHWA2NJ9NLqjiFwnWH5wJWGu9K9o6q7tgVcq2FI3ddH4pZ4CaPxx45rhHi7ZO\n",
-       "vkUwX6RWVLFdNbA/4mj0tN5Hkpe7DkYyQqMEMEvxH131L35Tv6YKg1AApo/689jwDbzDxoB102zH\n",
-       "otaQdPYb4KvzSTAwU/eyxpe6Bcte17vlwm8/gvqbPNR2iTPuuzpdY6qoaPqQqo2foeL7Y32u01ub\n",
-       "N3NEX4wHZjJBMVUIzne/J5mn/OVqqXmBSIeGgJrw2/x7DI/OaM0FwHSOsIa30JdTfEwruyEOdZ4t\n",
-       "xtNEOSvM02LlSdr9SS5NZ9BK0hs69pcZw8QfBrZRZCe0Uc/K2IN+jTgsIn4shcU4d6lR1vrotIf/\n",
-       "GxGLvvARFa3VJEMofbCn7OmokBGnXjSfK9rr0GJLccc4d3HfdRjj5SuejacUUYp/sYOLg3AC3q4J\n",
-       "O+nPHXycmUHGJ9QJYNAyTk/u2+F6vnw6bzM80Ui+/G1T3EvV8MDFXy9Tldw+PDkgUpvXIyKOmBOw\n",
-       "VttYC+GaiAFb2yaGMgEtc6zX1+CjPg/aZZY+etj4IQBmpYiObg4Blk6JM6I8AvDWipcdyf3MwXbm\n",
-       "j06S46mJ9HNIL4JiZJtS6vdgzf+mxm6PT4MySJSsG7nvvwBtVhSHtfL96bBYV/2e5Qos7V+gUHxp\n",
-       "MHrDPNnVAZqQeI0d9L33umB1ZP1t4vvQ30vogV522FLa+W4S/z+68qx+OBLe/xFUb9d+Z03SK+ro\n",
-       "xGAtfwLOQ1GQLquNIqfo9t3UfUb8CgVBgRHe1JIPgM3xjNyzjjqec6SxNty7JlYmAgMORjv8RsZk\n",
-       "S2QHni684YXhHZrCjhh9BMe4nMhz0RFnDWhoWk7l80e1wxVNADeZ/aCnGP+IPRJ+t8/rsNVYNte9\n",
-       "4Z05lHekZMiZ9YePfSNEJjfLlIZ7OTGJRvDFn5XwO+phZQvGJJ3PGMrwZr6n0Y4HN/mvO7CiK04d\n",
-       "kr2UfL49hMkoIsjD88Whbf+q+GbntzvPFyvTWMpanQTzPn2FmV3hs2Bs9RcE8wecyWfLKKs+vcJv\n",
-       "zyF+yGEwpZ5kXpGEBIAk/Evpjajm3ZwpozU7KVdxkbXTRVO/PONlItXIcm39a/vB8KZ7N3iLq1qa\n",
-       "MSgoFpMnckDONCJhlS2yQSQzRJwBuaNiQD6SQ+hmwV/Ng8Mvb/u4F+g2jd3meHoyRySS6F8d7a0z\n",
-       "RD96ahiYdohDn2TSEV/JI675kejS9zdDCrrNFBuqkFdtLsTFz5lQi1O8QAQl2ezR5ZL1yaBlHu67\n",
-       "ENELFk1XVDhgqtyRKWCB+AxTLKS7jm6xY8tOCvTXF4ejG5pY5wMxiT2f1ajVRsO+wfa/CjRExcLB\n",
-       "TYSRF35L7W78bV0V2fu7nOFJWf5wnYDY3urqFoDod0UaoMhrpZsB1BhyYfGV0eBN24fb0yN8uNEA\n",
-       "d12rWZCGC/f78dx9SHZOTHeUSFOVX1aETefxs3WKrMkdQLjJSPGHmRWnN9aTyaT2GMc7ALpzJckU\n",
-       "qJv2MbV2hR7G6EIXx5Tq3IdjgqS4nnmcQTDORumyAyYLlhx90tSq9cvSV+RhfDtt5VFXxCSIDPK1\n",
-       "csd205fA5obgXMz9vloIVnOIdMJtArUJzQA5kqmh76+nru0vrZCQkge2bj+bq4VSJ25AhJjuHuvZ\n",
-       "5ptkTS80mVn9IjRsuGUKCq4+w2y74TQs0HwuMDhobjPKGvgWNHmTZEVxjE+ga88ScFy/o02fvNrA\n",
-       "rnx07/Hl8JUVkb5fjoCOet/YO/jX9CroXhlQNdsHStj9MexGiHKgxnGp6ZBQTl+05vEs8iCFT69/\n",
-       "fR5AUBLZNK6b0SAOVJ9l+i8RseIIXTocHbI6B7rhhZsg42kEYcoNX+ZYw+FZWUnTOr5x/s4wsyZ2\n",
-       "J5tuCjucq1OGwpAGNCbrYrQbrWV3thV+HcON/YrWvxIRTwqngCVw60+HjnAo1yHTKqqNRUGifzij\n",
-       "cif7rXYawVaUuYTWCyhOkRFcPveSvLtAIdbAoTw2DFcGVlXIZQEDPEXFcJBcQ+S7wEv2p7x3O+cA\n",
-       "qHCjfySUfEnA0FxICw26zU9si4UQlICSU7MbpNKb2NWaSEP2v1NuIK6QiHXiVR1oYMesK5bd3U0n\n",
-       "ldUIT/AOC802Py5lM/2+lCg6cLMuLWnb7qByDUUjzUKsMkOW7t+6axPbwAZ1V00q8Wkr1vmR9FHd\n",
-       "/5eyepcY9VTGba/hdTamQi/UUwHpLS06yZvLVmQOnTzLOdP/cEy/p6N6RCZqeF4zGg6o14U38YiT\n",
-       "s0sdRRZP4GIcUqYd3RxE6fIAUVERaC/wGRED3lYneIAlCfWlmCjHDH8kEplYBsp2l0VW2hSG6MtT\n",
-       "nw1DEKDqozOQw9h0s0zwXhTdoemGass2Nm6Q8grJJHfKrscBbUSYmKfGs265G+eKFTdvWJbiWvY+\n",
-       "mnyOe+hNjqvZb9+2g7Xb8p5qflEsXhV6WTUWIlpcdm20F8b9l7Sny17xpznkLsC+KtTOxNlaM/YI\n",
-       "NqkwCnWJ0j+5HUfDOGPepEyNuZ8kUBHnbUxLbxs8s4h12yaROj8xlVgrTNyujQgYviCTZdnPgxiu\n",
-       "u3+5+J2nd0bxy2zwGTJUnGtnvLwPdpI4qxDwBCdlQV0fSxMa4wAyqJQTJX8jQBCpfXrbQAdGYEyk\n",
-       "CsSESNIY77G33W0vrLM1jOINT4SEXbN1Dm9JW4b7cNcOIRuaG8fKVelg8BI/DZqZ9UPL0wMnnF3y\n",
-       "nCwA8WjrZzpG5jdHeLhksZnoMJPJi1Ykv8e/JZ5mk1Y6+Sna8T/ShO6k8RvlsoubqqePNMj/b96H\n",
-       "dC5JznT+V9KNw6RnAAANSWmoWqaMRaB7QUQXZxgHiYVz0WVSI37XGPA4ebR9cEXXaJ313BSPlHRJ\n",
-       "F7tcUUcBqsCYaPWvvRYBHhsEjbCYxfDA5MZteuyN2BoIl8uZbiykmUf4vEO3Lws++XepgV1BWImr\n",
-       "Xs1DOMCQ54Aw7Eli4fNrclsy5683ozRgxyMoga8UfGLnoAVGRiXCgXYEuL8FkvbGwqP5DrwoJIvP\n",
-       "TFpHSxin7jYp3G5nSiYFl87iKrc9JWJkejDChtRRLOC34ORynGovpDRyEQWZOiQ+vU+DYoNHxg/W\n",
-       "B8gUNvjeRcozrt9IVpTZan/25xaQXclJBl8zihOvqwiixsUB9xKXtCwHFjnaHWAX4lGsiA6/gADM\n",
-       "xWzGvXXiYyZJzpbzLTFOzWvtL1E7HLAmJMgnjue9746ChF12NQk4UmaVPgL4oWnkLrGcF93rOa1b\n",
-       "Hx80lxGKBg7QLc1W0PO+pYCfYiA7y7XMqlUWa+yzVLPYKuLMyfAJnOaw6fF6iWnqCwxG7Azwf4lP\n",
-       "tfW2gbNjtDB3i7Cwlp3+AgvJH27RBN2DIFU6h9d8GqgJ4ncGo80IRKiVPPW+C1NCpng70gFCAXMt\n",
-       "I4sCtgeSfBdx6Ad1XLumOE+P+lT8k3wLqMHVqWOllNK/TAY2dRxW1eVEPs5CrVlEgkIxIu31IQrA\n",
-       "KacVoF/M8qKfn/2qDuHI69THFUHfscnwbZs3jXIf7/Hja3OmXK5NN//ZOgYy2jx0N6bEwKQnkuC5\n",
-       "vCe/YyZkl/fyRGr7cmHxeVPi7b1QNl+xGYJGp/7m6/lPF/cBziaryqSAmWAkMs/bC52rhFHeb9FP\n",
-       "4+MJEhfd/6s7Vd+2HrtPRvcopztYP/wI2YR63FyBwKmbT78M7X6NKGSrwb6pu1BbfiWDCFjPwwY0\n",
-       "30aEADj6W5329OWfA0dsCEl/Ze6NyeRzxKXMg76ILKi4eJhEl7q4w0Ne+1OCmjR2W7FwSNHTbeHW\n",
-       "9RCWNBwgPyw0z2gTIzH4u3Nt8g15nozRIQSvARYwXhW9eQRvOPN6k/0EiayIuOasOe1LwlnK8Owb\n",
-       "fbUvOv9aPjEpIv5X++CW7vsIFkpL5WhrviiFaM5/pJOv6cxIohy5puHvLMr0dLzwlSEcm2figDiU\n",
-       "nGxbAYKJRE5jBnV1lqSY9dHcwVB1I1mW781+4qaRWSv4XbpJZmJ+AG1trNdCxBFcGuy1pp1IgR+B\n",
-       "JkHC4FXojYDoE+giZh4urPe14GyYUmgyIey3G8k5sMvXhcSFhowVnzqeH6X1fu5HuoCySSEg7KpM\n",
-       "okGuAkEEfC0CLpyboFVCBFGbQ3rEVDlz5D4iFLn3liCKiZZkHk7YUcu982VVJuCImiY4F88gSEML\n",
-       "eoOG0EBlzVeCUtgFcFhfL16nhK3dlbF0GDEf5llXVKBn5WyWdKDuYRrO9M/shMo6NpPsLLKJX/pH\n",
-       "Bl79aF2KXRvHYnddcPBNTj3I0Ae6YREI4/C13dCiJBz0zVD93OPpZ33lsfQoJC9L/Dzy8ZRBvozP\n",
-       "jRcJw6JwxUcKWSlNl/ElenCHushksXrNl0AyJapCBe3tPSKBro74WsTmvmNcAD6iLUJKUmr8OaKQ\n",
-       "s4u3N5kaP21scGPb6MWAUV00oler9XJLJye+XN0PaXfr1VggrfUPM54fxrHvqST40R7XYV4PC5XP\n",
-       "uld+COtNOxEicaUsqabS/F48+81U5QGAiwQf/2EOpjXbVK6vM94a+LyS+DdLOPJkJKd6Btsw1ziJ\n",
-       "5yjbLEBY4U31zuySG+TKkS2To76p2vkoeW/tLDy7EKrdHUdjaw04GoViDezuhnIIurudNe4BT8Vp\n",
-       "eFnDyymCZpGgPWBFKCQcdwSjuJi1WmeZsKt03rQ/4fb/gelx8v3cnXDhkoptTJ4K5u/P5nvrB5Fd\n",
-       "SemhpPBDoAkrjD2eMgErvjLC9sJx6Dva9zkkqc5pRv9raICOX0tV9j2UgE8OnBU0LK8S7Vo4Jier\n",
-       "UYGXceBsffWkOL9DmBiTMMArTKII1NL9KcWCLKcWTE8B1kDuFAWAZmPo+bEC8Ad32NpedSOD7ZUk\n",
-       "Sn3TJgOG3Gs9GHMRzaIXEHvcllxAMSTAVdvcP2MDQXcW9O6dihzsjf0BwWRwKLX6VcuzD4Y6bFtb\n",
-       "o3zLRQBqgDHpn5OHSH67oFoJ01CqXQqCzry0mzTR9ypFMuJJUgop5GmlE1crsdbSvOnVHORcuyYS\n",
-       "h5fjLE4iW8bRXlYbR+Mm4H5+P4lggcJZxY8SuUFxL1kPYNe/AA3F4SflVzPyI5od7QSV4fxjg7rQ\n",
-       "I80JbXHN5X5weX55f1QGrZAmqRrHzYFp5T/jl/SiMbB5uZZws/FJZH3Jxi7zEE8qC82ahvB2qjZU\n",
-       "mv/p4UBAJPuTGgo1fsLnIXIGhYJ7dwW0ySrbmx+BCyWBnrxfR7/F+TROKC80FTTEDfX3wJ2b/Tog\n",
-       "2iCFeAnfv6MCbNvtni5tpGwKJTt4XzLj+wL1xSnbseguKJdv9bwYC+Aw7/U6FgHs4xpjcm6nrllo\n",
-       "rHK5mlNrQW12r03U7xJsfdKh3ZpEsGgRZbAXTfxsmDLFHzxeKlvY/7hHCit8UvcPliS7dSuHPxEa\n",
-       "ah1dnDk4AAs05iABw5a0n+qBXmHBqxsAIOQaPwGuzdE3sBB6W+noU6jCcrEkgIaXWPqqPS96FLpT\n",
-       "bhxX6BaoARwaAeUszg3eWow5mHUdh0tjOlc5jQjjkKrD91zCLLKb3arubKrZl+Xy9g2ZYkN5zfSC\n",
-       "eIkL01WN2kYGVKNe3+EOqnElMcb71v8X99VvYwr8HpUgZoMR+m7mNXrzkenkkPRp+u7pLiWmTnPF\n",
-       "LFTkniKK6mthjqm9cXNpSlenha2+9yR07BhlqjPvt2vKSItNctE64zhRwYQURWaP5BMpiTLtfz3l\n",
-       "MclMHY0L7++D1rmSui0cQpTXPpUBYz2h2XdVTLCPKT8ZmZVaT6KsT1o543Gin5lcG2u1BK/6PxSj\n",
-       "rDzK9p40l8bmVz84GrNGNJPF7Hlh9djTgX9jUooaYNiW3jrIIJE+zR+YdfbHezEOrDO+6LggA807\n",
-       "sIWrKfnnWBiJZ//w70OqhPmVsX5FPyuTKJUcIaSU+doGOJcMvC5zxYgOejlFqb3MH2x6B4bHUXd8\n",
-       "dQZ+rgjsiZy6pyB+woOP19WR4iID1GlD4h3YzHTduu7BznsEZCHQiTj+blMhXqJJ794HPZr70HAT\n",
-       "kCC455TOLdJfTrI8WK5Rgzr2RqX3Q5nJMk/SvLg7XPmVYatXs9c3tgrK0BsNkoi7OXn28hw4vHG/\n",
-       "ykwbfoaEvpS8LCY2S3ZQQqYALnRI8RUkX7Mo7r5cXW02/55UmW1zGAeMsO0gDM4L+BbZ1YUrOHr/\n",
-       "gEcVdY1zrN4ojwwBf+lQxi+hugVWLQ9rn13q1NiFGVO+vA9rMVMwiytyweXbh2v9bE13fQZ+hfKj\n",
-       "1pO1ZNfsm6A7PO//QRtFzI3BEcmaResa68bdONjWrMD0hSCaNWEjJRaypfm2OSLewnnT6FYXlhEm\n",
-       "tUVItULBfNgfCi3qzGPU48I5QYfg/Xy+TwPV/HFUfifAbkmsBSQOxGli5ahAAv0o4ER9cktFPueA\n",
-       "GtswcU1wvX2Q4YwyZldXKBrTs+wYH5P366DXslnITgI/lMWIar59I1KWaEAoSTcyKS8+He467B/y\n",
-       "XtNBd5JFagI2G7qtwDAlrePS/TH0r0OceaohoqiN9mPi5yCB++KczCR5CcomXm0xPhn4o4PPEVkL\n",
-       "YEUYJ612PPKujo+LaQ6ne+PM9x6Ns5aLneOYEmHHfo9iEHXZirLAn0Frbn0ufEsw9EkgYkkia+4C\n",
-       "+vHk1jmaXUry4OzEuy47C3QcvTpq3Tdu0K1JTqw8oHfVZeudPju62ksQSgG2EzqwQEuRm/jobOgL\n",
-       "MeICMcJVU7huWzdkX6ABr+t1T0QhX4iX7G2lNPKWDUrD4lAV0NNyFMAK+wtwyg/A1Glluz3teBZT\n",
-       "PKnIawxs7QNsEedQ7JecWyGxgqNA/2oGZ6ke2jqIGxwWaPVeukL/Z2u75x8bRUI6Vrq1dF99TbGE\n",
-       "IkGUbJWi21qBzrHa7cSI8UqPFbdyJMFEigzAe8BrYWgxmBM+DUYWCf9QNGha61xH02eFsy2ZsUVr\n",
-       "X7ZB7h9MM/I9eAQ1j3XxmaBTyfety33HAJDGa3W030M306s2Qy+01oP2ZD3tRHQglfJBP3BJHued\n",
-       "NPuS4xfQ6kFvFWCDZpWIU9GcvuH7I+W8ZeRfAKwwycThzQ8tGtUb4V0oJnN1tDwwyrduIHtZQjbe\n",
-       "MFYUGCHaawCkETNWzkJvKMAAAAywQZ7KRRUsJf/bmUqCjc6+YSUXYkUF3kVLPuoFM7ZPifqw7net\n",
-       "aDAOu0C7E0x6hlL3DWjCNfAf4YR2g4qKB3cvezxoTw4r4nXvo0YJ0nT6Xtc0eWQHYWXkonvQ0BGr\n",
-       "gCOIDkNG1V3Hk7TBKxi8pY8JszGEY+ZzU45nxhim0XXNBaHbd/dYycvcww7MhJeGy/CDdbdIXwvk\n",
-       "yeVyM387TQdH2W6VSK5C5gUat6E9LsmGiLJ4CzRzioROSpOztgBbODdqNm2vEKP4WQZ+wjuVj2Kx\n",
-       "PRFgwE13IRXl7LgwFzX8Rcc2Vss/y1majDond76PIAehP+XqKEiybd2VIZ1AwWibScrcU57JkRhp\n",
-       "t7JbdqCAaYBax6oEfxMmBWJn+U4LVzt2XaYJAzw61RlNiRAAAC882/aoDjF7RcUMIUSCotWgu4Wy\n",
-       "TjhegK/nTw7VmdLQMFtKiJpazcRMePXiToGrHRVz0WvEbJLxWZAPWBVqZUkqP/DVnnj+/GqPZPLJ\n",
-       "VjONmXqgRO92HhdWILY6S5rAtDLMoCY0rGXr7gSoSBJpRCP7HylOdqjacI2kPOV9rFmLIwQRRElu\n",
-       "uHlxkddH2JnQVAi0CRJsiIq8XtFHZn5sAWHHvGFZ7tkAOWE18GHwVJBLYMa30vpitmYHk4b+mFlb\n",
-       "rpxqaISvd+FyMQd9shTVAKz5AUK/KGgaBvpyPVUBCuNaltjCWMITyHPgqzKQanuJVxxZ4SDlOAXP\n",
-       "L70PYPn5aC+dq5ZpKu5ysXD9fyGGBhacKV5LIg+1+RQCBHDPeYXbaBv3el/seK9MFBO95Jo5ZvgZ\n",
-       "JJS7+4ca9TZN81e/9IkG7vD/zHZnJQud8CRlmRVTCiqeVheisj1q0ZBrj2n/V1MyaqZX32oH7cfN\n",
-       "obLYFYQ5JOTfXHHyHJGmYDRbwG9buGqL2ccv6Mj9aBMrtOYGmmKBr+AVEZvvQWvoop5DkKbKCfsa\n",
-       "Q0D4OxyEqhA+pXJuf/TYxC2ujZAIhgevIotofRbhS+aIbbSrNzdMAzgSTrKCafmgFZeO79CcaxAv\n",
-       "0d6/so6r8UKzo/WScSWCEF13pG6mbhvIIyCH7hUqwMFDSKSHtgkvYvW4VSde3/6FNShn/TOsXETf\n",
-       "PGyu4DdqD7/3F1YR6irkQGoFIxxqlrxQ2FBGWGh2QofzFgn9BuN4+xYmXG+6Nvl0LHt2f28fMkK/\n",
-       "ak2kCYHAwTQiVeU6r+p8is5FlWqz+9ayNvOWORMkXKOvzUI19p60QIkgEEUdNYLDJIKgqF7/zhjK\n",
-       "C1/mbw7fw5tyLaGc/lBBoMB7mS/e0AvwXe4VHjCw4ZQIuGTjxb4/2jOj4xVZnoQhCVV81YVrevMa\n",
-       "I8SEXWfl7BjrbQkt8I9KIOTijOPZAx7J4lqIAsovcriLRMtdOt6HLFNErjXKbgyAkQsJvShNbZXv\n",
-       "fxjY2VY09piaVf+evCl1fW0iJ181Sy97AGWY1N/kqFuGKDJ6wi9GVm4DrN+EMUpVohqRT4+6oYnL\n",
-       "+9oq4o/FgnqRLAnLvycEXt+8cujdFylnv3UQQEbeqTgzLWK/JB4LXeIckNwRKDq0HhIIebWb0GxE\n",
-       "BJOP8T4uugarW3fuZ5+VdoYzohqH8x/1/HM95aw0wwv6MF+SD4XTpGBMCc2vQofP2xVTfTFlF5lm\n",
-       "lvmAxt15SHsiIYx9lh3ffm5FZ10VcmjXIMz7QL+Op+2r798DXQyOtSadDyf1qHGnuJtwR2V5j0Zg\n",
-       "agpPAMs/XYH0NnSMeac8Fuu3oY+TUOeBSQNXE8B4ZXbT7kjT/DtL/EBCsH4sycKPWwH+47GULAnA\n",
-       "FPjqqX3ZGf3hgwA1fGC9zxnSNo/6EIe6SemXNOPzJq2eGB53ZQSijY39AmtbaxtOrGN6DwlO0/M/\n",
-       "6Zq3WNSHxhDV1mxrDtFs0pS+nrRTsvbovfVrOzDv38gE6Eu7hywAWNBj9H/vu+IWoUI0Vc5Uj6Ju\n",
-       "+f8VMp/xVMe6we7naUl0otUx6i1FJua/Q1uD3uhnHzyAAAEcuYiD/8aZBdETQpEZAZ7OCGR4znIW\n",
-       "HTXGYa0UJSer0tfLeaDlhE4eHroesCIPjZ/m/fdMPPTmgV78fLNcqQVWIieFIY8byYRCccEbtbws\n",
-       "J8TPmNoQ/B6OiiICWuca8JCBGpjvDv75NLi1QLcc+/yli8KoQFMRCuS7QZIo2udvs7dd7wVs7Qs3\n",
-       "fnzqma/jK3a2GqS+YUueFYAPz1SfKpnC8gKpDU/QQBZmC/3kKqI8Yan3RXAkoBlF69M6Dz6vqGRm\n",
-       "KEawfsODv3IvqYmDBYQF1iQxdg0xz0Tqz5sdCnY+OOHeEAC8JWg1KmjoOUOi5Vo/DHUXPfOwtov2\n",
-       "oZB1HlxVvBaOoJ3iKLur3WpFm2pLE8jjc9T/RZ35PnAUEPtH5i2GKvFNQhZrtQ0OaJRGg7rnsDGC\n",
-       "OmftkxdM1jn6/s+EGHQBnOmYn9GQhOEy7afmRe+ZO24a9AjoWnt35P1HWpbYkeUjs8y0iIaOUmUE\n",
-       "QD/RDkve3a70IR7o8RdP0Lsn9XSu7egGboqtKTDDcCYfv0f1pCUQoiJtyFF0bowwl+XOirQetkpO\n",
-       "AaUanQm52uhO1cmrYieeTejYpcc/xrA3bEb/ZnhJyDk5FYS2vW4nUv9CDCrZE9CsT9/r9Mc2P9N4\n",
-       "mnI2409Z2OKkP0yvEwlSw81SxmF2jiKZt/IDREtqTJJi1vZwDpA7MP8iVpFFLZWAh0DdaocX+7tk\n",
-       "bDn19mHvIHp4V/OYYGP6KqQF+NO2w/QhyjGuyCBmBfbtWzWkWXRNl3UYgy27q6YGnYWHTXP5As9C\n",
-       "ojriU++RmBZtPwqbFnP0AdLxjqbyrqv7FjUxqDCezN8oz9rBzKc7xp6EDhxqu9cnOMPN++g0ZrRo\n",
-       "rfzLZloPbC3pUJuLGmS6jB/+pHgytDVYkpgsEs6DdMw9PC10D7/JtEgeZSDH5vQr5qOG9DoJYjf+\n",
-       "IoMMeBAtBLm+bbAVKg0W7M5wUSA3VWaBqlGm1ld+V9RszTnXRU4OxrGnZBdV9l5bLooY+gmG0exn\n",
-       "bLZBxGfGWWfVkgrGSVFwSx2Xwv63ItZR7rw4OuHB+Fk5qTQa2BhbAEDnnYiLjD0Xk3VkXQYENUCj\n",
-       "hMEG624/X78CqQ0suKvUhNDSyRZ+NUrQVWnVpda/UlEeMoqLBH2alWgNLK8djLDKTbRzsTPaWpYJ\n",
-       "6ScoEhlzy3zCAHd4942mYlO/R28tIPpbgdyC5Byqo7A6aIAYGcLUeRkMxIWRD6YfLFraSqcG2Fxp\n",
-       "LX3n/LJcB/27hVZpIiCtJRFEc7k9W5IvDCggDoms6/qZhhhZ3XXQ6pf296e/Ol19mqWik2z8t9Sa\n",
-       "dwbI+hC4H8KeWJTpbGnyZX24Ak2H848+2yaXUILgnS18pBjUxSAPcBjOW6xohLoU5oXhA8RSYrhM\n",
-       "kjfRMfPHDbkxISJH3DqTfCFBCeuaGwAEysWeKxATxWs9/nzlxtATkXYJUEb+ss6GDVtyuAaBbTy8\n",
-       "9OFYUNnLqOCPv72HPWSOSuB+gM8PQt0Ij30eQFIaHJqiEVZyN63C51E/vsZFTyMOc95EElqjjpQe\n",
-       "zcOYiK6qUD1shwUVvkSfuD6l5Xb1SMDMwRfg1psfAyU+ti7LEYHTpFyLYnG3LNlUoGVgQHXicxSk\n",
-       "RzAN7eIz8WSKiRenZiM7gSONkEb2Uj5efU8bF8sLffGsyySFiigJ8eBqRSmcgvOR/1G7k3EbpJ1y\n",
-       "gMIun5/jXmHwq6Uuyl8OLMgNzjzl9+Xe+FRTGwspJEqi8NQTjd7C+pKfHBCRb5LJKzh9zh5RzfvW\n",
-       "SZHnVio5JxsCWqi6obH0d2SsGSM1ev/MMq4RGPa+02FFa5z7aqKgSY5NpoJAP+Avd712t/9eF+Jn\n",
-       "4yvcTpKSnYETtT9pWR+En1rofgD3rpuh6ianBy5Lp5cJ/Xd9CtAhWzOpqlppHsw9Ij/TCr8hTLU1\n",
-       "CcK37FnQlj0I84/T0fiZsJSmhQei69sX0foNsUkPUhlIsKfwlVe68FhFKhmRePw3br9gINPvA4tS\n",
-       "hkfS78ZK6STxyNWngEpvZT1/jMj+W90pqg1HzJAz7oXwso4Q2Rkq9rtCa2igfrpwKSdjh+Z9r3YG\n",
-       "nyJ9dVxanaqalXXXPFG3ucLnrtQwOXoyXFwpuPM5zuKKvZLlMuqpkrab/sJ7PcdnH2j6t3qzWt7+\n",
-       "Jsfx5P3SATUxTpIvVwucdupIjUVOa7CyPnyM+cG/hpq1ShJXGzlo3ghJ0li+q0+zLwyCFwNvuSWq\n",
-       "8AJDabIHjMsg5oB8AMEiOK/tvbBmqGuKJXfiuSxqdsY6V8xB74MyUXEfncRLGavMbbtc/qMkelsT\n",
-       "ni8GpvcqgAgiU40NZTDOiPZ+hoEAAAjgAZ7pdELftsP9dzAR6dzJpZnFxrkoNM2Msa/NLrBKm5vq\n",
-       "fdm3nAhzNc13gYdoDZ9rKiuCfDRg836PEFcvbr/yjPLXqpASCPQIYorWbnRDMMyQVJ0fUYIZHP/s\n",
-       "qo05ACqbQ1dO1sNCQLRI9C89IGf95w/6CO2UFLdM0lxydjEKpw1sjGeKi9YNaEaep4mAANVW/wPC\n",
-       "stSRNooUab9d8Q1DG4Kch8gl3GZjXrjEneMLw52s0j7BqeCfh/w+AZuS/WL6b9OpiI8yiFfruBJ0\n",
-       "WCfQa34V+i10slPgHO/p/OSAEuRoTA/jAi5Bfk5clT94JZHgRJdUZP5gagO9GYWRgQynqd7qYzJq\n",
-       "tThLxUvDJuMpwVAtHqSqHvDDqhKCb2fj+595eHAq74TpL7FPZ8cL9V+P+IQ9WeEcF+J4Ox1uuVxp\n",
-       "g6BFMgL1tt/KnFKIRF2c8ryanhkFagIuI6pWOx7E6fAKK/+RWb8kROs9oWORW6rsB4oqhmVD9r6k\n",
-       "6S3qmksIuT5GGt6PkKinBSkrEnZbfZY9Qte2+T/cWElTHYQvnt16sGtiAf1NMsHHB+NaQipvzYNf\n",
-       "0B90UASdc1LW1dWZmopxg49Qcq1LMOnx7v6iT7Le48+zZa9gM8TUj51jKUWkhhGVp86UJWNr3TUN\n",
-       "q2R8bJX71Jhvi/jY3WQsbjtFVwL9L59zDHlBN7thM3XznIqZOOwjc483uNzP1g9ApAuIAjfwIlr/\n",
-       "NIExd8x7NaZ5fUGLgZdqF/LMbx/zsyFQ9QIeNfuwW55STyoqRwEeCajd5BmjjhTDtuii57hLqGgb\n",
-       "XUWlxTUujvZ0vxteEnsZfsA1LVUaV3lKru+no3KH77JNmEwGjWOb3ZUFXdg8ZzwznOCK3aT43GIH\n",
-       "qtoMio7FCdrlB2hJRtTfptrHcdnWCbcrO8v5D3+/8hYC9gcLzax/sE3FrwR6lBxtpsjdmAKLjQTZ\n",
-       "25df0U09G0yWf2UFyWmQ0jeZv3LfXudoBXQO766FFu4Vuzp+24wvTT6ptJsaB8tS6UQO/NansBy1\n",
-       "stoUsy/a8ZfmtwV3boF1zoIaTr0nSDVRPhNte6Pp498PxQhQSn2tpWJtHS73Oy+8XToHry/sTs4y\n",
-       "njTh6LoS3F78DaUdzDC87vdN/FrdtnVpplYXjtnthhcTC5coKGp63h/mp8n2soDcoLh6Jc8c9lqJ\n",
-       "FVMro2LwL4YxTDrQeKr2wsm+YnXIcsFjr6WZLAefaiwZg6P6wHbBgiMyYhX0Lp5+dSTa3Tq7t5gN\n",
-       "6YX6RbpLWc3gXTw1wqM7+pfzTFvUkVDF3sCmENa1bQ1xhc8neQ3oWrWCjf+TArjo9XARYcQs6Rlb\n",
-       "FbTi+xQua6DUpq2Bjbey3G5dMU3rCIW7xOJ6FwvEn0s3O/vuKXJXL/cRmWVPRGpXTgjrZO4tJt1O\n",
-       "qIKakw8XNE8j68ZBUy7zEOMapudqu6Ti0WjMaUn+PSKD9Yi7GFTJP9Cc+Xbw9fcRZNWv28uUc1Ae\n",
-       "mgEPsnBfuOflbSwMrL5nBKM00VEF2KkUtxQUz8NiPettU91cVYeL7STNW65RWin710v7MA1H7w94\n",
-       "TKlWoVPUTKVrSgpEB4EypPX5Q9YUOr8G5mE7gnQmDMOHcajz9XHhGuy/NpnDnQvLCQuEg5CjDjIw\n",
-       "u8SwJn7szvjIqwZcxuiUgUXPXy/WHV4Sw+LyC6c1vscRaUjnhQ4ljz5/FTPRj/J0z+Q8CehPi4R7\n",
-       "toG/qxDOuLezgYOFfo4dmONOVmgor634ge08Qnw1Al2plGI/PVM8dTscnwixCTCmF3B1jMve7N+Y\n",
-       "xbGlKuiNGz2llx13YsGy4wrPokY/DnF7t18TbYWlfUB0OFHvYV+VZ5PvXf8Xh8/DdZEm973PJ0p/\n",
-       "ePGs5sAQZpiFABJstgnED9XYM+XtAucaI8Wz27x/RgwV+xGCLQv7xvDYHn+eDT39t2HODPzeBFMW\n",
-       "RxDPx4SR4MUIAhHuafGwhL9G0Sojk8Heqka5GnEEkr15cSOFv+4tRW0G9apWEXBGcSl8Ervevk56\n",
-       "X6YaoFEn8gf/uGMksHpj7+dDAhz66VTtUhV1clGhJdGrdpYUObVacuN1Lrd7+qDn8tElqCIsYy90\n",
-       "pKzfzdPaZDYBk7l99TPYuD4bXoOv7aWp3ZKcQ8uVOWfLPFqsqGNvC8Jty8ZNtR3RlxDFttH3jwy7\n",
-       "k5rStG27Zzuc3E7L4+ZVk45ZHLle5hwM7h5BNcYCueVuX5qShwgflpH85i/h1OooHnrYwdK5hfxK\n",
-       "XXiyq3NpnfFJ0R7NEXmaKlEee2xNMBwQbK+3zCa1OlWj+z/PwGCihSAAAD1LnBVWP48NWfzwzmAA\n",
-       "y4AbQcYfyXHBQYsi7gu+ArhZmLZWmxfDPpwzc/KM73VnKEvJO9lFSsPR0c+G4AAAZ5/9wrnHK+cI\n",
-       "9g6yQV1du/uldPqHJqdj5SYZPMGy7mL7qkEGOhCjwn2bIy4FlRboQjmC8hxumNx64fgUeT3em3Cp\n",
-       "Ar3bt6S8AdZ1UdOKnehrj3vGwYGxCnkMcojlNKqoJa2SiuJJ/NDkEKCrkwm6g0eNqnpjH9e2QSrW\n",
-       "g4zyEKX3+EixVOdCmEo3+KGLR/58hDjGxs628D1FTnVHbKYReYg0LmYPjBJMaMF+svFM9NG0xMph\n",
-       "MPLdT6jhSQRHsDgEanDKdL6oLCljngAPaTpVSukOCFatR0uktaQQbDttHHcX71NK3sxqGlqbnHkP\n",
-       "jUJlq/IsrQigIpEPFEJhBYIJ9tgsP5nYjcOSFziSMmXm4fUHRsoEp45UjkJhpiSQlHRekHTVqCuq\n",
-       "HBB1enn7S5+/lUxqLuUrsPlwhgitXISM139BQsEYSXfMIqirtGkwUhlDzb+s/WIOKHYMG93AX7Uu\n",
-       "9mV4qLAvGLfpouhNi+v5Inl+X7I4qESWH9y2aOnMNtuvCFUFiIQoDyaUB+55mRPytkBMDNGyJ3+q\n",
-       "mKtjnZfZjXODxL7r468w94Cc5Gow5jKvPXquPOdcwwMRaHGO65+WMv6WPIMlbKfxBJm8GAZ09WLl\n",
-       "llKw++WLkw5E8+80PeluMAAACLoBnutqQt/qf2afQP4pvn+lNmj8BUJsQtwzirKDRyB73a7K4Zo/\n",
-       "dcgJ3Lsiaj6btxTQtyn3L489xjYBrBY5ZKkrG2fH7UiVkcB8147uAGZFiREBjv1sneOVt72nGXNb\n",
-       "xjMJs39OWEGSw5I9HLIAJRI9XDUKd/uEIjGvpYxaE4nRZPDERiXLvhbXNBvEX/UqslABFREhpC2Q\n",
-       "tTbpl1vTgGsSmsMfbFSzWcC2KZHlb7ypfStCwyUyAvp9OyQKlLomZwJCnHgc4XcBgi1eURE7iwZV\n",
-       "O0jDJnp5tVdM7p075dUVnmlc94QyLK0vovwgq5Qnx6FhOKwl3YWgZcRIxfLTjAIYKeywnedNfqfG\n",
-       "aS9gzCNb7wqGlHPtOYxFOCkdO36Ucvcb5Y+qqFiiZpTihu4sl7Fntt11Vs0h3UA0FTVdiYQGCKsg\n",
-       "R8h+D8fNFWLjmkgMQGvQVc+9B+0acPicxaeQMz/o4AHD72nVRF3Zc9TdtNDzqwwYYyXEjGzkPC0w\n",
-       "qcUloTRfwG5Yu4peaznsoX3pDTYJiYklz2kHL9k4LOKLG7Y1EcTuxEWKnuf04oKeuDV/CW8LTkSo\n",
-       "YelIJswhOkGPTQrkFhlYUiDBFg/VSdOosNbk4iIjaxSgWkdZBew9YQTIUtg1G9x7htjDYdeXOhO5\n",
-       "dDmQ4kfCMOInK2gxcG5ucG8o/VJ1NlqQtKzQGaD4nPKK8oUofkyHRkU0D0ps58UZIr7gqE6U++rB\n",
-       "Y06mRSTla+96pioH8W7WWxDJVJaOOXs/hW87niOCG/6bWMVbvBNNqpxfrUquta189hTAGV9mdfIa\n",
-       "8+AZlBxD/K6JpXF6PPTgpoLYaIOzwfShTGnTp2EwWKqbdSnUaaSt4lWodvRnEq5WdATAO8AEmDZs\n",
-       "qjsLwxaGEbMPyjURYhN5LC9M44P1KeA8Vd0/7c4eNrkmsYyt8CXzVJwX1cIoIPdoeCOFXow0eK+l\n",
-       "duyQTWtLl6SSjEz2sxYu9DP7IwxnMr7KXw4aY5l8F/cncW0XXhtkkGeCyGrdJnIB7trdCfX3ych3\n",
-       "sZKU68vKD/yVY1LTtmvIflmMDZ3Ff3cz0/gL19fAEDfjjuSghQyv+iwZADfpPTATzjPYwpuA97zR\n",
-       "jETuGqX59/B9zoU2N39//jPLnGF0BnhOF9ze6BzWfqAkFWAQwBr8aEk4kM0k52T9fig8Z8gN1Of2\n",
-       "Whwd2Pt/Jh1k3G84qdCaNaSXhTgA5iGYCFFM/Y4BaP0hqh4zxE9PkIhaFOAE9DoRqjmL5JW2bNKK\n",
-       "OAVN0isedGiR0GHdaUBJSdOPenB55bDm+p2anUqZoq5oWP1r9gyPdka5SkqRb592Fo+QWCYgGZp0\n",
-       "cZTrjz4CNGkWIwY0Ipd/LdHYpojeeFDjKXSToGRqdLrdyyT+lK5Vii8Yfcklj7nqPWE70Y96lcs+\n",
-       "dmWY8Uu+cPXqxH1YEJWV722M/7HcaPtkIsctuG6GnDDqBmrT+0zpCm0G5lLS5oYY/FsZ3of06Fex\n",
-       "UhepGdbwEpQf6s9I4iXfZDwboKjZPghIwycDVCrI47Ec8ecpAZdfVLKFAc7KBggZb10kTBH9UfRJ\n",
-       "OYVJQ7p77uwdy9nil/aIdHIPcD+2sOBGrzmT3nz2JsmC/vZVGAPXWfThe0XxFSFZKM7uOr9/a01S\n",
-       "L+VVQdFM89AVRrt0ilL5jhT8T7XzamyJSwUbPhQ/wzUvOkUXREgrrSBnDgcxs//sE1UjERiq7jcN\n",
-       "WYMw2Nedzh5wLtnGd4WLYLGoQOBigNNb8xgdOW/QsohfdXAveDeHw2t1uoWVcadNYuWHY+vWcj9n\n",
-       "X/Q+EDFRPNUTYpLGfkV8Ui0LW2+AaeiZMAeKLdIhHwFV4D6v/lCp5S9LmXpvXE2+bAN3Vd0wubXw\n",
-       "8ZAiIOf4ez1bYAmqFERCwOPB7vxhgk+RCs1CbrD8sEX/Y8uB5bqiBeA77Z9gyVcRjXEmhBY3wvwP\n",
-       "mNHGlS6J8wKwX43y6m6OVLbyTAn7aSoOKQx3ORXMH9ZStH3WIGqxl102VT9pfrhIgjGd6jF/Qg2T\n",
-       "a1N3bACDhjDUiZAtiWZ/pw/IdJqK/DP3jG35UzIB7Acx0LdK4HDCWhA88NQN/mb+OL1TWGWtrOSg\n",
-       "7k3/E+8H6K+J+KAG+1psTXBvbxyq5m4efsx44PCypfOukqgLxU5w7Murmar9aQKmoBBpyZCYyQ4T\n",
-       "Uu43d8Qv74bLgiu5kl/n+z1p3hwkQvwO0gS440XizRHPoif24EaCyAEaEC5mW58beExciaXDPLRP\n",
-       "dXNW6CWpV09BkACy+lfYg7RoEb6mOBi8uQf761wOh4k4tDQqUdQs8+/x9kP0OXEU5E0V4Jq5FC36\n",
-       "sBSQCNDU3JsifXXb1Kku8qV0KAyUQcF7wL3b9GsfixmPE+MIeipwJdX3S3gpOHjYgo5TMWF8kVAR\n",
-       "VOOqVwiHAua+fGhmbml4GsoyFjD3HK7Gy+z/Rdx3dumX3GgeQYK7NuoxO8E5deif/9cgJMGbt/x3\n",
-       "0nJ0nSWkySbwa1iuoqGbUQO8v1clHySlrB2NYQpZqSM8cFw/udIy2APBu+NL2jcKdcZTvFfm6xNr\n",
-       "iT+ZLAdyUT+T+kj7IOiVvy0484SwlM+p4bu27+J6SZqQgd9tl2DE5cUAGxVUPwFBVsq3nwzDuORO\n",
-       "JaliGbjoAIDgMQEh8VGVR7VkDJnQsbsWafOinGBhLP43BEYYehAwXHl18HtxcMrQTYrFMySE2vQE\n",
-       "/7ykWKvb9os6VAS/mXkMBSnCJwnxwNauh3I/gvGGN2VAGyNVk2nO0c7VwMzjx5AZr4eGPLiLee9z\n",
-       "0e4HLbfWnYOBWXrXxerfe/8VCN3pPQYbtCZVd6M1s6J0eR4SHgPgPNg4H7UneV4DT/9d5cd6saf/\n",
-       "NpGxIhTg/Q8vN2i2YNa49jvkd9YfeAtTyvh5LQErL9aJpnssD/uRWJOqVehxQ9P8a6fNY6CfMOjn\n",
-       "BudzMqarj/NGmbGnCjK+Fzsm6oyZkUKQII6bJCXMhAAAFA5BmvBJqEFsmUwP/4C9kpVk0f/v6HPL\n",
-       "I8BbdpypB5EOgf8LMjXncMNIhfCr/gKvyKPjGG3YF7VJKhj3poqsmJsAIqQVQZfsfDnqttbdlXrM\n",
-       "5x0rqtWoAwO7PDa/x0+ZZFH888AsRMDLqcCW9t/v3LXON4DBVnOgyNxM8WFqEMp3nRnONYgoKAkG\n",
-       "Pm1bFwTaoMscY1stHAKdKiZcnSQhMXgAEMOiZ6P1IvLcmB7SopdRKp4Z8NxPeTHm+XIh38cZdCzl\n",
-       "a1IB7pDwEqtJVHHfLTpCtRreboImCM9+JNdaY7T526fd3Cx76PYV3LBVV5KMqA5+SUZ43mFqmKfa\n",
-       "U+nt2aXzzo+v1fEuxPNmcySBaARhqfCflvEK7KJjAM5/8W4GCp4bHMvIDQFk3Mz+KdVWHSUMjD0B\n",
-       "4V/4OrBZR+VnxRMZKQ0BrVmq4mbYQGmQmZByFS/JVOYwP8zsJCTFl0/OgCD468826C3RAnYxHEnX\n",
-       "AJV8sYqhxWZ6YW2m/M5XfI7y51DFgNM0Ci6etrkICGb8t4vsXjeR9EJk1pDh0iP1lcHu6e/eV1fN\n",
-       "iHFiemllT25/ePntbmOWGG/+NqAxNDSXVJhlspAZsuKdCptqDJ2OmJekLiAXzfImULIEBHEY7NEZ\n",
-       "R5LMPa6FQt1DQZHeZER4QuZOBj2jd/aEhfyJ5RCPCbsW83E8prkc++9ZNNU/yUbpcDGxXKWcBjsS\n",
-       "qwNSw4Q9CeL55e/yI/pt3vWCIpl3iNF7eytBt1G5V8MMXnUe+Ihe9YAMoFjxERoGQRjN8g5Iqwjw\n",
-       "ALl7q7GZuU8VbdqBl7wLviwjJUrCClwNsAFeLVwvRuxR3NI4n0dw+yvMI97mD5m3U29nndBn5n/4\n",
-       "BrmBaap4XSmyIAT2Dr1bA2xmEseWabNuRoPdTQkswrXPmP8fcNY9LU2+rgCXMPwd+teXl49OQxWY\n",
-       "5Pax/8clGtDeiMYKvJnE+otiEhTc2r0CHY4oT8bprYC7qTBgpWB3ml64fOFKE2EQRt6w2rCtxYmR\n",
-       "9fMg3ZuJkcjjhd7gXjiGBvDJkhSlQzlhZhBgXG4KTRjvroEUz145scUMaQAROxTVEIwi+6TXIohP\n",
-       "WbbzYa90xl5qRwYY/nBBrfluTOEfBM/L0bsSXe5Z333O7J16YaaVwJvEETKXp392sWcH5fF7UR3+\n",
-       "t6yH+JoWUFKLt6xpaYJOVw8ErcPqUZnszh2lR8+EGXiDyspymFGgQl0wnW5sRBQjJCVKwV6SPtNj\n",
-       "auO1uwu1AeS7uUgezJQziXAOa8ms8+DZJigLpMgSboz9UyVpceiR266MHfDSUJWkC4RPMi8X6Bnc\n",
-       "jtUltpEdo+XohYpzKYaCoGer3RnkUvzETgALi0z3AK+8b/1U8/nzDdTLqALWT6U8MPO+MZ48gFlR\n",
-       "VPX71pZByTaf6+CBvjwLYpMdGDFyD5zDl2DrnrkRzrKa4FHWfEf+dr6BjJhPrTO1glDhkGkGwqcO\n",
-       "kTim2y9MTAcsbUjAyFcFFqy18jYAcatPka2Lye1MoQGXimwtq+yN6Kr1lMjiB7FkB2yeWwNhnkpr\n",
-       "2OKzl0Ggbupb7ehAg29L1rjMQmaeX00oyvJvLYxq7/xoJ3v+dYr+oKtiBLkUwkHv3W27lJkORr26\n",
-       "0gVa/LAtuDXYgeK9TBfqb823LYu1AI8gxUvldsu5pRvciQVhK5jDBQRs9Vs/ag0L+V9oiqG6IO02\n",
-       "w9o9z/k/LqWzfdVhxpRAxFiwXrlj8i25jKhJ1uV88mLMkSSURd8LIMnIhgfe0hKDXqMnIX8Gye9o\n",
-       "ep+jxRpUldONuXcXKhdlVee8ulx/tx+K04S196BdwIjpIfuY5+xHDfd7cEuiosLZ4Arqn/nS5cne\n",
-       "Nl84XEeEDYYlHJW5efrhZQkZT/6sfK2GntDNkk09DTeyRekYSuxriiczkhqp/LLtYInnkEJufgcc\n",
-       "O/ez6GRiWZ6LrrJ6kXFm+kbX4romGqF1o05KSdyJEwq+fQYV7eDdyXItf91+QYyZ6KSyMWT3vUqO\n",
-       "vdvReYjliEqVM8S+SCvUQmF+e2dGflOPdkN4KRE28XffJMcXJ0fde3TVMy6fBAhPLPhyJccCWKQC\n",
-       "rKcJSth+ZtXDJ8qj8gEYyp6J1ZJyUkvuiLHnUYHt3Vb0c/xwHuVQgnOtAnve+DieaDgfH1CPQJUN\n",
-       "E8tEgxWwWiz2qFNUs4erVTih+NZ6k38K4KG56p7Lt3Zett3Tc9ZqeedL6khmFrwgQi3MZjI94Kwm\n",
-       "Mrdo1zCjB358xFvmqh9nZn+kYz0zZmjmymdS+V8qZb8GkrrW+u+2z938hn0SBPBZTqm+Uxd5L4Ff\n",
-       "o36wSLvLuFXbcoeWKJegjVW6lwW7U0vHJ+YnBwyTESUOxY7dCSTpjoC+FZTxYdXGzAuwS1WkNKvY\n",
-       "xb2BX7yGKjf155UawIH4FEeWrJJKd/bLP3C1e4Viz/xjJIj8DEJM3kXMRAaIPXFTUHmac281+ZMX\n",
-       "NswYL37CPtS1JIOlYw9eUNKf63vq4aaR0+8mTCJAuupCGSjYrCOIGbiP4lML0eqw9dpJmWuIIVJ2\n",
-       "aC/d/YNZYfASYqv9wswwXbIPmflJ1KGEzLbOSf0TS67FjX3zWxkAsIvz7H4dIgkq9fLBRjcXxnR3\n",
-       "i4bOaEoVond5oT5skQkC4bdj8y4bU366y28QgFprNHgC/dya+FwoN65feap7f4WV4PyKUeDJWh9y\n",
-       "vo0wYdUz9W9JssUt7Uwln6mPd7+2EuKiopwlGpq25xTQcZ4vIbly3YLsxN0XBHsno4+xc95sZywb\n",
-       "AczN1Ct1k3Ns+8ix91aZ3hE//E4bc0dUIbBQge8zPqjiHKn1qRsl3RjmzDvA0685NL/iXjNgCefj\n",
-       "ofEGfauqvgXqzJLO2/3D5f4yO08bTT7EgqKRdpyt5aa1geiuk+pzQgsUfMXIZo+yobavCsel09ap\n",
-       "bvzZEdxs6+RH7e5h/dSkF8wTks6LEEzGgdyIOae7hzyOaSlBJMBhdePcXj+b90KxHp2dCzqjylBM\n",
-       "Li8PVhJLET18m8Wp0fu6gKkloZjVkbHHCJ0X0wEcWzu99HED/9Adj0FP5sPMO5SeZbYHrSZUMVlt\n",
-       "KULABZ1MvBHTLZWiUy7xPhBu/L+5KJvRKgzL46cBiOMjnvnrm0f7WGkYIM7B4x5NWdkQ43mAOHQg\n",
-       "v8zH9XfAP11FFPJKzgwOuh2E2SiWZwmZH+QRgQ3Q9kc6AFHz8zP0Ii6gCiyqa3mUgV1mNHoY6Ut/\n",
-       "W4LkVsKftGbNXTjq5wvYFzcAc64iWcfXXpBZQFN88BdwOcUN/6ZsImSNhcOPLKq+/MFaBRru9uZa\n",
-       "yZFzwuY0h4A4j1rFVJkCHBBmJSbcJduYFKToEBRuH8ZPrKP/ou0QqJvwnMhXQ/+t0kA3AQCvbvFE\n",
-       "kAKC5otb6J07ogpkErzWG0JRVgv7zkLOy5C6t3N/t8Ltzt7lJlsbPGNBpBSGmc/5oSzhPv0pXnKc\n",
-       "GD2vV7Hg41ha2Ik7a6eNb43CDVyewAkwblyxupaOk4lJRBud6ndi3d6Ptzy2fnBAFEH4T8xoFci0\n",
-       "XblqCjlf0IDwNCCaciwIjU7+cEWOSUtXLTLd4QY2VvDumlPDXO2JkHvHeyXNumC4KbeJo8zYj+4e\n",
-       "6mlgvpLj/NYv5/ImZb3TDzI3a1jd2APqyj+9TuYICdSnflI1YoqD+xmUFcC+ykmvdLa1PJdMOsNV\n",
-       "NKLwVsVGRt6xXcpRg1alQ05gx2pkKBlfHKF3Rd97iy5og/BxGghdmbvHD99S1mYl64+thDBDy/zr\n",
-       "a/5LM7unsXj1S2ALk6gQ0yQCoMZvqEoG+THqgcSdV45OHZl3Qoh4olUXEpMbCbBdHkU9NMSxTsY2\n",
-       "ky/Iu+1wl7c0b+lz1KXVGBypp51n2MI52LVmJYW0j7EsH4uGzu2iRqkQEzb7C/PACXELXdVw6mNE\n",
-       "gdJ0izdqWmhFYYr9tTe2Q84owROhOauK1RdGnTu+4XaNZfcTU8QVERaaCzeoxH6VMX8a76oXGYIT\n",
-       "GMltT/bgQwplnWbYv2CHCo3Kffic6mOMszpMjtU8nz6ez2soHNrwA4pFl+YR1U9lghuhujrF+DIf\n",
-       "US1esJ+d8YDJ5a7dLPUdKiNiS/eGfWzsYq7bZPSy3w3xgq9BR3B/8AVMlLC/7YssQo7UImR4pPcX\n",
-       "Kx+iUjsOH17+tpWs8k4D6LzhzcUBn1PhOGNNaI9kvafz+iav6gCQvRrM/dXJyoe9DeOgvQvzdyr0\n",
-       "Dro3TaNwsB+YArQhhiTTP7td5rvbEzp5CpijH4LprvYb+3C2kNeyLwcrgbWZHUxNvZ/WhM9PkBQK\n",
-       "zOXBRvRb00TEEgoIr97MhkrtLvXo18fyzUr75VvQRDPyu8v5TWvSvsrW0gMmYrRQinLYQWFzvdVx\n",
-       "ovD2REmfQwB5foi+7P9GWb5TNT5r+UJSA0yTP9g/IpneXkk3vBam9/LFzZNrbhLRdlikXuv3pD9N\n",
-       "zvg6vm+zmXCVBMhCnmb5UH71z8vDHYxnP4eDdbjVz4/+rjPiCjiRLaO8fb0QDzNgjvGkkCu1T3Xo\n",
-       "6At72HrQB6YBmc2kxGorbhL4cL/BikO93mpTDx2duwz8ZctQ1PGZDfIIJhlk8VWzITMMHc/cJLZJ\n",
-       "05F/HFJzLtfOBztNDzIIDsctjj5zOTLaaaquYVUlvUcUwsIw0rFBD4YR06UVraAhtKaThWi32DFy\n",
-       "Vuv7gT6KLmLz9UqzBLujEoXZUFjjR5JKUbmi9sZaIiaTEWIz5aea8FwACTuUfw2Z1qtLuM8UQDAY\n",
-       "FyidaI5lcwW6SUlP7ZX3ef59GUddWm26LBYRs+x6oQDghKmd7P3lcUkRIR2cnmVMsaCmRePn/qyj\n",
-       "ouCiQJg5sk17r3qU6jU8wYuWTxtWLcMrO6qxebn2A3K7U8bV60eyUI/CPEoD2HPxdMoaxsmgPHaM\n",
-       "cYlXyumttVFW/4MF0dg3wA6hGLJgF1QY2mGPwWffMTb7a2lO/xeC53uGgiM3qNFBLo5bedgRTos4\n",
-       "KK1C7CcYviXQ68+7W246pl0NjQ06KdWji00YUAS11k/CckatTDm6YSfohrU9r0/D6D/fmj1Hp7xm\n",
-       "VPl8GKb3CzRQdu3ktaLkIydvlF5IyfSvhWu1WMoM1hAYIAbsahagS6hQEcdIBxB/lxuERi6coHZq\n",
-       "cicyk+TduyZ9DI78At659yNhNsF67YKWZscwIV8EO8do6bZyun6QRXGOKgS37R2RiZiwszVWkBck\n",
-       "k2hPByaS/+Fr521/8MdDcDeoOt0MymeQQhIPBYW0GcIqXO6+Ot77zvJDiP3v7DBbS7nhFlK9uaw8\n",
-       "pw1oaNNKYHNQrImLms3DYovhRUB2Qqv4QkCO4nPbt41KS65lLxZEyoE7F7/UkgGljGC5+KOJj5BT\n",
-       "LXF9KtkyI0GgNbQ5V+IV6cwI2lNarl23vv2M6VXS/NBrtFBWRlX8lK7vpmtAd4gvIcDubBgPXeVq\n",
-       "U8CgNFf5z5ncJ0HeS820dBjHTJaKt/bTSIbgluVgO5i8Wv/uZbgEkC0Qkna2qhxlzTaE7ScqVRUu\n",
-       "KhH6m2ApJCyLQcvdwi8zGdMFdh/7cw7kfye6FmEh2BeIcpuKOTl8jvCB0iL8UiTDMia+C77m240q\n",
-       "bT1OWaoglgDGHdZbDq7Kov3FghJYQ/vF8Id+b2Q59doWxkZPagc7P9tPxbDsLvaNurwG5C/gvYF4\n",
-       "vQJVZH+MdivNlzJaHpGiyhT/idSPOuItrJApOZV2CP0eTV7jCm2bWD6m/FnlD8Z4RQ6Qc0s9ETDW\n",
-       "G6rEYEp53knK2Az11+BGoDfyvqW2QUx5N+sqb/JqqTRXzFxgbhjkF2Bhkc2D2JTgm9zApp1cpq0t\n",
-       "pgp/wFXmJqIiau6RptJF9ua8U36RSVHiBmv5y5Vgx9UljNFSl0W7Nka2J9xiBwEkP3m4KW6qDQW0\n",
-       "A3n7Kf3XDRb2WrCaOQVEKd+YD7U9Lq1B0/yIjsJqvF9zwHCPGrX+vCebA3EHvE9YJuOtMtnL1eou\n",
-       "iRdSr7NEr7jCaL2P64Oc8r4I6J5CeIWxlK+FEBgiusPfYMlCXgCBgpo/eOaKBwKkrOVG2uuh++YS\n",
-       "8cKsgBSD5fJxgOwKl9QWz/IvEtnYVF3J8z1Z+M83/c1yuCA4s3Zm60PAbIPQFls4mym2ZLAwGQqX\n",
-       "EnFdKt4Rv6esB0KUxgJqwBEX+QEEG78LpzHFrZvAd2Acd2DOagMloS7xO3G72LVetGImoyJQLKMs\n",
-       "h0r2ewgtAY0/ODvxGE8wFftnNRWJHUwGM6CfvueLJbFydwALbhx0tIbHqMRdcdzlKdq9a4DDzqY2\n",
-       "yQ0QlJf1khItJqpneLbIxYS1Zw6IXP88awu+W2NGmDoMNOEeazSJw5G8943QBCRuziIy+MikmWSy\n",
-       "WZ2i0fw6ymwGnwtVe4b0qxctwWfSfxqQjdW5dzJytgz1BLRhL+7SvbB5u/lB7zGYbt3oE8eGUm6E\n",
-       "cyvaFuw9LYimGywBRikgqS1KQ3XpEakqTD9OVG5fwzRm/Jy7rmN5P/jsShO1PXOzl2LS0yI8Eo41\n",
-       "HbQRxUCd5r5cu+EEeKusgBWjnOMqmp/g802WHXV5rhiMrX4V/OwkwaX9louJv9d/DOrzl6sVTP3J\n",
-       "xBWTQ3UpU8aiz6upneKhAsrAG6T/ali5oYXh159WzSJr495xvmR5KdElEfyBwjY6BLKWOazL5vgg\n",
-       "kEyAnC/+30FXfbcqtHKSZ5gIDDuqDT1nsMdamhSfaPae2smkqP0dw34md0KEzuOncIldF27bKbPM\n",
-       "HWjGt7TuxRmRvg8Bo9ioVOA/DwFH6eazv6RBmzvFH8jG/WmptNY5ROUOLWXmZORWZ9yxjAMyri8U\n",
-       "JLrwZy/7fVUS7e1V7QKdB5mBScHWHaflwwrHHuN8gQMRcSUkoFBBAAAO70GfDkUVLCX/4/UWCRiP\n",
-       "iB1RKSHvvR6lgQZ/8I70Rfb49nN+fAjwuVxZ0duUCZ2ftGe+mX1Lequ75+0mz2TIMu1bbWe8kroA\n",
-       "WvPgmNTZYLxWPxrX45fX3L1m4mirbUcFsmvM54UH89H53I0xLcSoSAKj5ALYCAiK6uLUphMVO042\n",
-       "yH3K9iNwquXiaas/t3//QwM4QYopqnq8ShOSBKlexzPLtXgz4+oYZZdf31htvdjSjAhvxpjTReqp\n",
-       "WpWCbLqIF3iKOWqJgRB5DqJmmp5mHE8WOMmEySWF9O1hL8gbrEYFRZg0RT2z7HKk3I/UfcXhr0vq\n",
-       "igMGFVxu9h+1CqBp0E5kGKbn5xJVTjO+MJhKvgxJN76vCZow9npmtgXvz/pPxmTVzcWJsMILzAX7\n",
-       "IB5Q4v7QdldanfgMofFfV+L5TJ/0gpOiqb/k1E6i5Ykv8X3OnwCFX/a4OdC8CojrN2sp+0JOLraJ\n",
-       "sXa9KXQifbFfaHz2w9U3tfIwcZa4tN2+wEmDNb+RUy7Shvv+iyNWEs59iZP1TaFLIPPh7xuIfyoq\n",
-       "wneHHYIvgxjDonhwMe6knpixblaPHOOCpnuc+Cvjyfh72+/9qx/vU3h5v3LRiWSdc5ox1q62u+eA\n",
-       "zkPyMBc1kW402w1PVMqrH6oofaxaphET1JI5nEGgYYLdnm4L5wJWm9G0QyX04rjVDcU60+NeujpA\n",
-       "3uqujKwVvYb2+Nggtjx/7B4UzuITzuXpNtnEiJEpqVcGguCzR2w7NLnep2/HPD1DVdmRgjxIAm/I\n",
-       "vHlXbr92zlpp3nQ77DW3QYQ+ixWecbe3ixEZCbqj+0UIJbK2hefOJpCxkPCHay9e2EFZyDwP+HxK\n",
-       "ufZaWNCDuJ7Y2hLB9j8OZUMsrymUOvOuyXG96rC6o+IzrkHhdiR1PZaY+tZzq3cgTRvspHNW2REV\n",
-       "q24QbY5583ec7R/pd+9HV4WqOYoR5AnGgBOtZcx6JmpILYANWTsVfryqBLqxeKrDEhkTbDu5d8Hr\n",
-       "aag4OlJndSGWTFveCWm/5u0M0XT/wEMQioU0gzOxD+ESalso1chWOhVx3hzcao1lI9ejzuSviz68\n",
-       "s5FG/ZjxrMUnYy702YAXCBgTCMqU/7hOFbEUMAg26URR1wIV0Hwdk2KBP2l7BKprc628ByT67F7s\n",
-       "6r/9yuin/fa+MgP9m9vikBaeOKOXpTe8VNQPQ95i9vIv/vC/OdgvaNGtGBMq/Ph4urD3OjL4A96V\n",
-       "NKdugjrALzi8c07fSwGpVTYEBqyphdmkbKlx/4IRMg0OWC5j5cre7qfh+xIzcjul90ApuvNHWclE\n",
-       "OcvHfbd/koZFcy9bunnAS0sXGG+PbCYQTUz9vRDgFH0+kmBCbYmYrBmNn8tZ+eljqWw3pEXKxROs\n",
-       "87K0mdqkYP71XGjEPDJA5c8VnGw6O4SyZUcyfBEFH/k80lt9GP8Vy+Nx7CWtBtAXR1oFMPRgrbWm\n",
-       "XtZoIju8dxPRvbWyyi2L7UjsiebCMg/lcFeqf/Wq3Nq0xuPTn8Ul+eY4V6pLoQ/cGEireMgDSBGE\n",
-       "tiamWDCbusRbxxZfKNuaqk05bdHHJFe4PaWJfJXA1lqliva6g24MNKWlA9dQpATaeZ5HP2Avnycw\n",
-       "6CuL3BnS84r+l4d0thy3aFYMgpoqbW32wn3SqfyQIESOwuajsq01fmnCafBZkkASyrNNNxaEGIZQ\n",
-       "OFhiTK5MckQW0IKUKyFnwOeWqPcJpmLJFS4wuJUKUNeartSDC8Lo6DW1slbyGGx2u1PaaCpOnaoC\n",
-       "Ar/3hQhNO+TxiXcc6HCLU/bX9jxXWipNHzygFuKK6G70vagiHc4/N6K/yg00aGxhgmM5BXUsiWLG\n",
-       "wzF1BiFpk2RNwXug/1wbzhmjJ8jMmPqCNcb+z5V8oUTU3mExL1baWrjV/UCskkweZkcqs7Y0zokR\n",
-       "ThcYqeAo6VnbGQJKvxTS/y38o5JA/qeEJEFCC2I2h6ksGazmRLXMA36BBIfblQP4JlL4lfral3vu\n",
-       "Gy7IF5BljdQtJTGHIk1faWwTo40fN80BwBkRSwtZL9tkgS6vYo8KpEk3pWS8mJDNi2aI1IWbsPNv\n",
-       "+8ziAZHAtgC0sipLTgUD9d92pyf9/hn+VsltThZIN76WgDaH3l6e3S7WELEdeeNnDn/B51jv5Tc2\n",
-       "iSbA1s4pdHk6VdBB2qLB/DAqJCghZprTDl/goUPBOevvpwh04pwmCZQ3rWyU0zRm85/wvDGLItx6\n",
-       "yyV4IQzig4YzeYyZ504xXNvCaHXfF8WPIqa48p1NViN/gROuYCxWfsdJxXuYIpWhLGG+CIqO1RKH\n",
-       "+JVuSA0MOEANVhWlBu5UPGDs/XBqGjrCl2ebwFlgNxpgeUuNR0WJ/PK1E1W5eDawiTLy/7ZB2UDh\n",
-       "HeKBLivRVBkPTwSVJD3XKsQy6j13F7VDeeNyffjcyJeCs5mchVMmnXnED+lF/g1h/xEJzUJz3qrV\n",
-       "JxYnDsTWam5W8a+1PrPp5lJoy7WoVehg/6Yk9q7wswHO1fr7zKmcNeS2dYlscJjcbZRfOHg6CK9m\n",
-       "XGh1FcNK32pu3J6bI2npfbF649slpvjczNC6uKK4luk03j4s6vSm2QZV0mSiRRoeiM7YROQmSnZ7\n",
-       "wkobO94pZjOYwFJRMgJtXP2BeCkW2GIR6S1VdMGxHpVMs163mPROMuwK5stSrzePJBTINxYBVWxb\n",
-       "pT5egmVUzZkSuGvaAEQx814ziGPQVetwq21mZYPsP6t2zS8Rfgb6x+10Se9NZko+dIwPZmlg+xTp\n",
-       "luUwBBlBEAKfdRbbykHwto+nT1P3wkaXMvPqnANv7Ox8n8V85INYJIP2zFthyIONz4CiNEGDUSKH\n",
-       "azR2UM8uE6Rc2GLbFlSav9Qn7Muj7YU3kxrXA4m9K6DD/Pwye4wyoEdXmunkA7T6Llze/6vefSvk\n",
-       "ltsuDxOPbYX+ro10JdDpP5sNXxBXetyDJF4flZ0D9Ny1wK+NP5rkQKD1tYsjlkQjgHCOVFVGTDE+\n",
-       "zfPOGg2pEgIzr2XWzoEzYFk64zXdSE6IOQ6j5moKSlhoqlxupUm/VhtYqJzCncdOyaxeIkEgTvvS\n",
-       "wGoZhQ1DOCdVwrOiihW/R6/KECPnCcF1WNZsRfhzvfftaG7PDVASw9rmdblXDe0sbAUGNighgZaa\n",
-       "MlPZ3K/PaD498SiKHtxk8k5quHQvYEo+/WvP9dz9EFOrVWsioeMKuTuwen3PrE4gN/qKSTlKCYjK\n",
-       "vpgxiuIhDmaHEs/2V5e8y/jm6sHTQ4JpaISWvb+2j/t+IrNmas0cWf2+1Ri9ky0PLcb5zJ+O+m/R\n",
-       "SUM9ALU0DLCIwvIBepZZ60VaMHR3fik/90elMyGC13xRMsCt8gV4G1klFPZgwmpmQG329tUBYuP4\n",
-       "z3k0bX81vcNEQ+JOS2AqGdN4Tw8AdHSxCLRVxm/ylSOHjW4pwMUf60pkbVVnO+8bE+RIB6uPEDCs\n",
-       "Ke+xmZqz9RvjIkj90Ie+H4VrFWrdMW4aVoYJwcEAMk3uYz9uonox6fSCtWiRpKF+X+IEcWqolzqd\n",
-       "sDYVRHKT9X1VIuMDhd9Wudwx3QB6liMRTKvmXH13igUM33WBIy7mgUTsyf6uMM8MWM7OylbliY3Y\n",
-       "7Rl9wnG5zq9OmOM45jzWt9pfGGqN7NWCNkOZWPQwhiSfkjhD0CrYK+Fx40b/Qwbb60LEWvHALM+o\n",
-       "c4E/uG2HAIn6aDBdJPuD7LsWHre2VgASbFCYYVaxnGgUoTNTz6b+XOTSKLxGgtJkvf+UExLvjz02\n",
-       "8cQcd52Jm/jv3IaEW9OMWmS03DZz/6AIeTTUvsUsNPa0aGdYhYByB+4K2pR7G9OBGmHnTVXscnp3\n",
-       "XomglAWDspGTg6k06nHrJJkDJWGgMVhsAL+6j/QRzoN5jlKBMHkQp56Cvr1eKTv1xZ2ExkoGJF6E\n",
-       "YL/+CJviMoKq0TUUPtqzqfrLK1H428qdZLv/4S4Aoc7syjMUgrpKKP1prZ8Qhk0YaEIRquMDBtTR\n",
-       "UlwL4Cpd946FJELGQ327xYPoLjRPcEL8+3GJSz1p0wFJk9CqO8H+94tWJIVJEP9JkGfQP/1bjUB5\n",
-       "aJ4pTLyseOZBwGlH3rwY+qYyzVKwE0V4Nnj9DhdZTFEOzsvtzawqs0T3wZlMr5BBKoUBVQCcPDop\n",
-       "BpFjuvHE9FkthsxnOeAgVj9vq0a0otwKFp7Mywsk2JN8VvKrgQ2W+0ZG5c9WVH3KfB1JrIsMqnoU\n",
-       "0PfajbY/vUFmCcxADaak7j2efI4ASOwjo9g4HVwTN/I1WoRa0O5LZmbDrM16TzDdJbDNkvIA7hF0\n",
-       "jq/EpKRWkuZKtJ2fjRz1OLuFMJqzYZBYIS7xDMaYzEZWejsGT1rbZ5jkDa182qPWGj+61UuJpIQU\n",
-       "a9V90wdT6HGPb80+HTzGMaRregG2uGZCNCD+eLRUFqcHU24v/reHqkEl9ZhNESZ2Jd7riS7ytrHZ\n",
-       "c4EJ8gg8UOqZbUXs0WZRvis1MnOQp7PrFTKyoEVfP6oDn/hctYZ3/eVK1oe2YAkyJ6dUphQ33PJ4\n",
-       "f5u3ADIPA2LEijGL1IR2py81IFSDBQMkA1lCzrHvOR302oimf4CYGYCVLcZRffBbRlOcKOamztFe\n",
-       "zQhRnYQMnGd4aJAh1hZE/O2c3LNtY+BdehzylLDvppn2TrBqY57rOPXEa7eZ53tYtE/DWZt6p2DY\n",
-       "PwkcmoWk3I8PfSoja3DgEty6mB7+OiVQ80PiLdYD5nLsXfV/vHGJ/nYo8xHp/V2jGPDurVcNSusF\n",
-       "Idki8TjhoaWQVyqfaF7fKPI7SA2tP3kgfRtcmc6sy0vU21BXrUW962M8Y4wbFln0BSB+h1B3CF7e\n",
-       "zt+ovsB0viEVjwvnfceGVMtybrKI9wOygXyEJCo7o9FgtZr1yOapPTrd1xDl73x3Aene9zlvJB8r\n",
-       "4QQ9WQN3TLHfqCDsoEL3q6lIDMyme69tkl9co56TDiKjk74B/5aXkbj/erVevHWQ24SLz/+AfzJp\n",
-       "UUwfMAncM9XiqEtnlQ4xG2+s89AFo/JU7AvfsikzzSG/9HtNMSGRE+uldKWE0hZLciKz2J2LBlET\n",
-       "nfzCuIqNylfC5CDHMMB5n2MEAO7kqKT6v6A7xx76yOMjFYXN1DT4ByiCHvHiDSkAAA+GAZ8tdELf\n",
-       "32Ufeb7QcZzpQ9RUcbZI7WjIoWyGNed511BKjQnxp31SJ6c7a9m3myzjIobHjfjfG+7LrT1523p8\n",
-       "aeGd2X35qjfWwpEOlSqtguwh3dunNLEgQUxGCHQ1Ttx6J7qVjhGY3LQrmlvZ1VcDV5kSMwcl6go8\n",
-       "obS2GBYAjEoWymXJUnYxEWLfz28kB/47tGlId5TyZtY4MM2VK8VaZGL7Lxw5pc5vvgnNac9aS6GT\n",
-       "XhrSbZhhRQEJ0jLnvQ6sfqATa4IIIOEbaNve7EHXrp8yMxoU7reBljNAdDk8l6YORhoWctajlpVv\n",
-       "AC7JCN+woN/B44+Qs8mbJ6pjy5SpVHih8DWYQ6KFmnsl7DVqsMQS/bT4aBin6jSALsX0hWYGGCbt\n",
-       "ZoFKvAL1cPeBVMuLvrTzL7YZBRKnbdLL+5gPEx3Yl2DMnnw7elB/hC+fG6OQx9TxWSICr8+KCcm8\n",
-       "L1fweyD7tLzsM14dwR/hR77b4nEf3Zsy+XubR+AgmgU2cHZkVCSeM6QXRdO6j/pfDXBzeyymsAzK\n",
-       "aYRROeNuMvdHDD3GNtTGG8WmqVvIQh8Nwb5dC4zL+umvgRFIABLIzTDfT8G27nncK/vKZWbBitgi\n",
-       "Bn0q+6NLAgg7UrtOvwVjq4DVqK6U6p2vJ6bAMawtWwKgQCgA3mIOEha73WQkgSg44mwEmQ/E2C+X\n",
-       "XJrRQSjtsN8L9opyQ5gATol/ef0VQlvIyh2Dgs1EeJMImBwAHC/VxmJosuPtJvLcY4IkgxmCwzLi\n",
-       "pChFdlTdgrv55nqHTbEaYyxRZNeM26R5dAxN6CSXcqhv6LRqT8JzPOBVCYtTPIlodjMDBi61DcZt\n",
-       "A9kEczhOqVT8dU4G3T33Q5OEDpMjn9a8+fAuQDkqmaK4R59buKQiLsX4bYoV+022xB3wyiKMW16g\n",
-       "R2arIIkSa237tnAq0n4krz18+EzNswnrhf3SCUhDDRuL/kK/sEyXPw56nJvaxzx6oVsa8/a5F8aM\n",
-       "hmGsdDTILI5bBymq9Bk8lERnMtylijdZSCObuy9Eq8Yo9aAWdcwjWa/C/Mg91toHdqmaeU+C7Vb2\n",
-       "LiGnBKAJVz5T23DWOGh234xjIds/bS0JWOggxO/zFieiBhOICIhdeQzfpF5w8ANjlXvRijFloCg2\n",
-       "Hvv0cPXgxkDlr1yAhHzDiLr7wl4rAMtNdNJ5GU8bk/HZQEkBRdxtUNnx/3p2iAvVeqr36R0bqXtd\n",
-       "L8wrmN4g8oyyetu8VkRJlq+YvNOrGlSYXxfA+OFIJNHG7Xu24EfSYc//x1zhhvhk4ipeh0nMxTGf\n",
-       "zpkRMv3uiuWXwPiimEf+fEs6MARkcX69rnP+2zmD7uZ7GTKfc0rQ92qnv+l9v/aqF0D/iP1mWJ23\n",
-       "fn8LTvHZ1fKEPuRCh+YApVgpQ2R627MJxauQWHrK6Dl3fPr99m68ma1f1xXC5qrtB9OH/vrq/mEr\n",
-       "BMT+q8YB0WRaR3aFtekEKd5z1HLFj4zpZ/6fmVf76BX7gMg2zSYOVLnV9A6ACVqVskSzXIVVUg2o\n",
-       "7KaZcyFktYPGonau1Z9tmShYDcWE/d9U8YwiDx6cKbBPb3CnNbWRwfZip/T7Ue/RUEKlBevcdrsO\n",
-       "InhnKNozEh2YGNuyoCzs6aKBvUBULE1rPYc2DZlsENjyNXLx71O8Tv/UUu+wi+AQy80DoUEgFCOq\n",
-       "QgZlmbTwRWj7kpNMZHe/3+Hn7EHvCRfUML52UJRnjMWw+0epLpCZ/WEl6piWTii4wpp7RPXuxm3q\n",
-       "v7phrAYU16OH79HjtwS95EQIRnY2u7pl+QeJ+OZaH4cF7PFAYhM7Qp7ULggndr+Qahv29pdQ0dTe\n",
-       "n2rabTWwbrfVbKD101W+p9G5+ZPhwf5GuOM60WiysiLYQRPWgp8iQ5MR2wgyfM87rX5p+lmK/Ltl\n",
-       "4bbXo2+bKdYhuzS2PkjuqD7g/bTvWC2phnd0Hb2HSvQB/kQBNr8XgbBMsfOxlRqONQNeAIa2hf89\n",
-       "Jue2MolwAiLLPGVzeO821PRBbuH5m+UogtZsIijFc5MeUSTMI0630nXw/T3XMGYOv//y99XFRQXp\n",
-       "3SM9h3mD6eCchMAoyumScKyI5qFRCzV7SneX2mlBt1ZYAmw/x2DJv2IOrSpr3yEEGvvbwO7YPV9k\n",
-       "JTXwaR1TUZk6KIPhbeksbBS/tkjxWtVG/y6NSr0kIx0LakJ6VbqlAXrjzLNGcxeB0XQmOYMRdNh7\n",
-       "rSTeUYYb/9g5DXu0DzlYu8XHNydDEADaLUNqR6b/91pi5ci0T01zhfZEIagLfgELjn2jKGoQ7PzE\n",
-       "s71crIwmZDJPNIhfgH3/D9tu1NRjNEn6Uu8N7DExcDVTzUpndyx5dhexg9JsQG15N16vlpG07pa5\n",
-       "5xaVtK6A20JEK+wvnziW17xOKIePZDqC7M6+fYbVd/HvnB+qB3bByz33CwsOeso+BxCKSN2n0uZ/\n",
-       "I9rJsPyd+MSPu6pdV/i2xzm4EQp2Tmq0tA3OXf5xPncUiZJrP4DE3mp9I8kyUFLOuknELXKGLlTS\n",
-       "8m2N8lnmb/GCo+u86HOgTiGck52GB3vdBH/xdQvRMHMYk0Lcw294t6aHd6P8Vo2aXKfzF9ykUycF\n",
-       "/C8gOKT359K+JqMMdMIXr/3VAukV9HdFkxz8jsUa8ouXnVUpYFsgRNSAGsI3Z/uN8Nwr2e82/fbP\n",
-       "9Caw52eqndgIr13mogcAVlvQLXLY7pFivtCysom9R+lyGecjG5ASv2GEkWBbJpllDUB0FZjpHHV3\n",
-       "MznfHCDTTH79pXhpXtbKQHZ4zelWPArrweuGIcaXNIDuupdOj0lhrnKZL4Pf6r3qgjfQCfBkkZMu\n",
-       "cSXpERpnv786WQRWmXNmW+vah5K0kwVHenDK+VKDkK4moCcvdYqSTC0PzdaCovaZgmkYdWUtDzy1\n",
-       "XdD8bdGh7RNigF6K9JB+Sag2Ijwt1Kb7Q34bSnbRFvWgnmkOLUq5NcwBSFIWJNaM/TLFYRWyDtRK\n",
-       "/oRj1nFwAUtMvCOgH8VTU+ntkgmYMqTgb0pX4sPAIyaPqv49XaW8rY4otNaNAW3fWUBOSzZvmMAT\n",
-       "J1W63pEHwWjDzrnzt+PBMB3hbJvTLl7Z7u0Srlk4CS6UQXvhOTCKTo/rgzDHhYb5Vb4kWC9Y8BbD\n",
-       "sKgr6Ac1LcSoBBfkyipIGQtTGy52Ubogo3qEk3Yj6Q/+sxsYLDNYGvHCqq3QlS/d5317pA9+5CAH\n",
-       "A5R4vp5lbIGR4tSLwoLcxzdU6mMSVr3DVtG53CqPSvBACCV79EptJleqiURkj1YQ6/0Lhl/nXhiH\n",
-       "5Olh0mriVxaw7qvwBs3oetWxnSCXHLVXWtdz5YK0X8rA7A1VmDffov5R4pMpR2UXlAkYgqiavJdj\n",
-       "+i4iRxIbSGW74YaOPQQH7fki9RgsYhKfX68Cd7hRYZ2bjbnP5euu1w3BQnbMxcVJq1IgrlrMQDnX\n",
-       "+XsJuRd5PcRku///FnorJ415fX5CXJk8TZhUsRVGP8r0OwNHrkauIn0KNSjjqfxKqnuv+760tgHA\n",
-       "5jbdAeb1Y8f6s3/rnfVKjhwmVxveaZGTfQYeJRYCbnvVtm/9AqlpK5Tm5OwOPM9qmJ9qMDzoEhFK\n",
-       "2WH8nyruZB4cNh1yVK5v8znUZ/FxQgNmovGhctaYM7GIW4gqt/E8NKXrgO0HtxS0ZIRz0/6E2+96\n",
-       "Te1uVoig1NOqXAFmzjHlr4oI/K9tYk0n8hrktXNxpO05WqoELdvo5/+rsXjDEUXxJCktzv6+G0hu\n",
-       "rpSAjkQuD7TvTPUoYhKZp9ryuvMbMBefKJ3x5hg5jQhVuacZLq9rpmSlQ+aUse98YapwL1woP2NI\n",
-       "EVP43+o4sbwdjJ9Ilb9bY+O2GZxawr4mtRrMxkjyk0FhVWl9c6KWgWgu2gCJD/WlQCHDNiepwRV8\n",
-       "mSghAQrh374Szbu3Q+KLOsSam9PVe6+PfiZD9OgHqAXjLemtbc5Mfw8IZVRn4FQEtN71CcnTB64R\n",
-       "F+oA+A1PAqcYrhcyAQJQq4D7EPhV4XtofHFVDum4Qt6QpjBRy+ER5jnA0kdr31MJ8mdtam9X3mmY\n",
-       "0k+biZZpxq1aBZ0H9SEToI8Bf2EkqJ8KwXV1cBu/5XfB6Gtvx9EESQ4RHFcgeF0QXU4yCsSeO4qn\n",
-       "iK8eEBLjQsKisXgnARxDTJcgGRNZVcsXClAiUeYUc/ixEOSA/HsNA9XSUo4hzlnlBZRlb4Xph351\n",
-       "7rh/n5ZkWbK0Q75pmU2lOtoswv/lYGza4LUZ/51h6+U+sFK9Cl4RE7/x3iU+cAW+NxiIH06wccwJ\n",
-       "q08fzSaWVypzHqN1aIlTyfbY045+3ak6YvcEEQhUTk7LNoTc6cJ7S84qoTltheEb4Xypnn6oKalB\n",
-       "5WquNfGIku+Msc+nckjKPlxafdNMQuzIgbFWMF2+psIYPYBjiA1NdlMo0EJOSZVBm11d3ZVT0Y+g\n",
-       "FiYA8ZtC44j/FJ4NN7Kd+7jhJNA316MpXGG6IMgSHmUcmUtt/4++Qu6Sf8sbIDylXq2Zp12N2wFo\n",
-       "8IrlI4i4TRY1mJUZSs4fpFzm2BwVfv3ICvJLzFdUkxWVHzidL98VGwHsSebd1dEiBbZPIikDV5YT\n",
-       "PPQnOj5w2JBzfagTmKNt3xtkf1uA6Qrdboo3EzEGt2B6SHAZt1e421OlNZRHnUwvoADTMrhIwFoV\n",
-       "tNxWvTLb5jSNQI0KShtJu3E7qltNb23B4un+VDw3y1Qi9tNY+fWU9W/2Tokunwu3oORKxKC1KOFi\n",
-       "+vBWVtGQ4a5O6gSTjjYQS6uQqtUv/v9j7ZzaoBTP1/5qede7sYpi9opKszg1ZAklpNp9HiJWND3u\n",
-       "e3Q08IIt3vDipIC+qeTtSSR+PqH5tORHabHoWdIgKIDAM3bex7YwnaZseVftTx35CIYeFD6M8qLo\n",
-       "273xrQE7WAnKba1LA1Sd19Mogn1cO/Jh5toFcg38wMLLXJUCSfd5Bv0gPZDNOAB7Nj2beK6Shh61\n",
-       "iVMCnjk/E+OJIfwXtIX23TeGkAJLenIy5QHVjO34Kc7O7kNUnsz/9xkTknRHbNTREI/+3TZJ7W8R\n",
-       "ntCcQ6NlyusnpKCTqmM7n1fny6DDKQFtdO6u9GbNyYMJC3GgXGdq92l8ckNT3Duzf7LLdohVyXHF\n",
-       "vXHd9fNu9MgR35N0lekzjRDDtu5pnQzlhwHkdbeRw/7uxxbhsm6/ldHTedU9y2H+QRBFQw+qHX7E\n",
-       "o/M4B85xFl3Y2DUsu7skxRJf9lzP2uKMplMHVk2p2/yiyrfDrN/Xjgs8VmzhbEmz85i/4M8PcyI0\n",
-       "IQSgn4AUs1cQcIR1b/r93y4+4TPL7YLt738v2PDeTU1j/fMAAAkCAZ8vakLf6nfih22TxqVt7xoW\n",
-       "T6PXpzY4YjAmeVyWvXhWUrKftW7HA3cRqMbPDj9wVeGHnwwLNiU/KzWqSgSeD2RN6tcLTfo/ppvd\n",
-       "GYj2AmW/CgR0V5yh5weFEOcpoGuFD+SITFd6PzPR8PLNDwRvtdAaWeIDjKTrksNycAvl1k6Izp9C\n",
-       "FOn8qTfb7OWQnK0TZFH6JUm0JfXHDFl7rYhBY1sgASXQzVCUdrI9RvGlFnqxJw34nWFK2TnLfdEu\n",
-       "l4/KYFPqBPsYGgYtn1ZPOxVIkvOq82346c8TjaHmK4ngeXVD0CMvb5KeX103bgSAAJZU0a18S3xU\n",
-       "5AfVFWFE9knK10ZjVvyIP6PF0kl116jtxwqEi+ScY0yrVPC/SdW+P/PoTQbVolwWmqsrJ1DhJTUM\n",
-       "avKjzxx82qm+RPrOhMxKRE9ZlkfZ3YrjWygRUH/Arrq8GUsPPWgv/aP+/TKPDrOMmn8bJXnPkMXJ\n",
-       "lcTYN/tVTU7gAqKFzhlLlrBbGfcYMMT5xb739ClQcg1OtZ4zcZmoPzLJ9+L+RUR10CjhiNWzhCaX\n",
-       "JEQYM/s4MB2o2CCE/QrAb3Lp+oi+Zpor7Lmna+v6L+LLyI+9btEXuHziCr4Vn//w7swsL2fDQs8p\n",
-       "C10CzrIUK3L8502IGvAaP7fhunq1xAnDqeBWvSSKhx7gwb9YXE0vODfn+ChWlr73zO5/Zr/nwpYC\n",
-       "8CzHht7hEwhATPA1DqATT242s5Dj2Xehq6euz4P9ed/P9UFjO8+6JXPPv2Pctp8ah3F2iN0kH7fI\n",
-       "1CSHNL9PLpklmjLsTKiEV5vGaCbuHTUvy3LFi4MX/jYDcY7qWZFCTNvyCvPBWFLe29LMEuJuZwCJ\n",
-       "OjmCN23A4PodY3aypymhV6JfGUsruUVuKKzAr+6TlTr/El7EDBAgj7hZwTDBJBiIwvbp6pJwrAR8\n",
-       "WrsQCCec0TNZxjw8kH1e6MCvh+YaOU+9u/roXMtaMqjbHGLWkpKyyRhSaWgsb+SCMHtksqRYbeFO\n",
-       "lo8kypuSNYquMd+ZNEB8RLt7O+aDgBwMP7pVg/Aodrl4oYfD1CEkB6pVDlCCOV2qcbK7CogXoXlO\n",
-       "w7l+icBzpNiv62orVgPe4r/pMtov/vaoJQAlzG89Gtks7HfjuXKwBTbzM6TXLx01fnTgvhn3z5hl\n",
-       "Kk9mhrPD0+XOOj0bbWXAoslsFyHcli7j42m1xtTFGVW/Q094XmM6E6CeiLMBGVxFVdOxpQWeUwvJ\n",
-       "e1mMeeJ7J355rCC8l3598D4IcJmZeLJfqrsF6viGRGbe5bs2luf0suYxDwRauqYAR/Y3dhsSWbY6\n",
-       "w/lVPLUsPuUw5/Rl/x5qv24rFZBfqYz1FC8xiUhtGizfGSeEC/09IYrAD81Sld/1W8fgMa6yn76m\n",
-       "cKh3aiDm2IoGZ3ZKzSeWaQYYmMuW/hiu2YCA5EqfMrQc48prCHxphtd3/wEGJmZTuK7GI2MLLNqI\n",
-       "8B7AOodwzkEJ9Li8AZnZv2cZMZ7D6sBDs+My0Z9mEC85CcuDmVCXqpdc3qG1YnPl0qQLJN5JM24M\n",
-       "LNrjgmoPrrD2MFuDehMLLoERH8+c4TwZOy3rgJJvFHaABnBA1enIVL/s7zpmf3inrhQZjCCf9bZo\n",
-       "weTOIjC62bg/MtM47xwbQEUucbXZMNtSQFuhFZiv4+/yEfIYwHMhDW2TtzXHguaBOEyt8l1im5nT\n",
-       "SebV2BYGQKD2j2NZ2nP6vP1tERqnurODBspOCTuaJKO6Q3cjNTkBJyQrFYeIzEZJvVKORykXIcnJ\n",
-       "eSRn06zdTs59Q5+F/0xAIRGp5UbPXpfZUmOvqWvgYNBNj05+ypKm/t07mLGXbE3vkLc4j066uME5\n",
-       "+mAgEXT4J9owy8qAInbqOiZ0mfXdfS+xSwysAWwRnud+YP2AIVxwOMt0EO8lcwIvER0aOP8ywzr+\n",
-       "UQwfr4kHZFP75HHCSnylk72KcqZbVrcHOHZJW6WwK8iUsDgAqylMQtaH5FGiOWezhNbmHCo/E1dF\n",
-       "ruDLWS8JRNJ3TRpxTGzs12c3tKUsbhTLUiKjD6lg/6CdXBf/mtnZT6R01t5JmyQrcNVw176s2P3c\n",
-       "zs9bEhwNRzVeuYArFCLxxZUzhIAClMaw7rPk1uky8bq0WyqqRQmRwpD0aTweA8Lch2uJHeXrB7vN\n",
-       "PvAf2z7vp9xnbln8Wmr9KwgO8xjriUxrGysodP6NQGQhw9gz/4dEY2rvZ4xD3hq/gGY+UOdF4ZXE\n",
-       "wtdKjSViWJHVOgLWUfRn0lnqfwO6T1xMQG1JvXdBd2Y3DHKpR/d7AfaXhKQPFlQp/cacm6v17UsS\n",
-       "tYR7w0Hunn5+5puapy2Px3AK4BZfPQeojryso+jnLHeLOsSMRZy+lo/HSdQlYbBFbuQpx+aETGXM\n",
-       "2KIbEEaq0oa+feLBcLz8JptaCxUmwBeAXqinrEdnV/l4BF6DWMJRzVJBF6EZTdy38pvTtoOsfmY+\n",
-       "QbEbk/GnfKYlEV9T2xapC9rS+9aj1lBswAB8Xbe/EyDHfWx+lOSXw2K08HCB2KOaU0d1+HVmg2eH\n",
-       "CexRNSW5xrLyYRlXT19vVnpBJmpwrDxh6un5pHf2B97ctJH2KS37d14wMcImxeJgDEAg0C6PbBlp\n",
-       "Jj5r9fPSCPik6nvK9sXRXXAppPnKX+BXdlYcXt0DfknvjJaH+sFs0697KqqSyzEt/DhQN7ARSicL\n",
-       "1u/2l63P3i/gA1oZG9tnvepxwM5RjjG8LMA/KVJ8k7USWTqFB+xla+AYCB+7UyNrsyU+R8BFQzkS\n",
-       "YZSuqbXoVKoLM3cXRSLfcP3StV1JAfZWVLDWJEqdJY4Zrh0Wdi8yg6SfvoI7OrgHMR8IaQO3L2YH\n",
-       "nZ5skoR0ogDdAQxJ88qpvRh8vxbm9l+1qUyR2kZwbRr9Dpf//VOmIZ4PD1t7EXRYE9lKYk6KvBmC\n",
-       "QYdUe9yo90/QQss0D+NrJ34az5atMhi2E524DU/Wtfh4wtP1uCuGRmQh18rUbiq1jy2TAOJGTBCJ\n",
-       "2rqRSQ4AwjeZlKAGLB9nyGxZqyyKIdvCQZwPs9DgDMYRFJXvioPxrCU33qTJ6+o+V3WFHbRESQjn\n",
-       "JRj0I1FhPNoAABSuQZs0SahBbJlMD/+AvXn+llosEp+qYiKv4HTwBeMu+CXnc3TMSSlHMCJhGk3H\n",
-       "j63JLyvOXNMyL8AtuadluJzlSH/zY5D7r6VkzC1usSWwqCOCo6memY6jJX25eakU6y7+dQL/pS8m\n",
-       "1swp3y/2tD2puq0E0+ceQSOIgteP+JcfmfBc9xlOH60B4nSCST5VpC7c0I+03N593Nf2KUCKVTme\n",
-       "iBt+lK6Oe2JYvbIMnBkKWv8MgSD4KvrYyZBmxlLef1PrUBXDRcS91a6MFQ7hicGrtLYXXhUrYl0j\n",
-       "YMDqPnz8nR4cpulhp9OH/3lvnDNQDEO/hySOJbdxVpGOmjoBLvrdB4+dwgALRbtlU4bet8D0ffQq\n",
-       "KmDd8h0d0VoR5sQVvVEL3MYID+8JNWME3N4O8QVLhpoKR3hLJhv/qfR2JXS05hItFUz6y46yUoc2\n",
-       "SWTTvXOOCRFYGAMCPIljB3C2q4GaARfKv2Q/sWHpVTjWAzrmj8SkQDsiFi1s1PWCNkYNmWiSxoir\n",
-       "fbA89Lu9Of6NwpGouJoOl9r3uNeQ1F1dEFhMbBTUXuA0GKKCxAe78IOH+w7Zh4Y/7LUcyMjbzMH0\n",
-       "3wFSMxxybCwklKnJTn3iYQSM+nDKmE/tmYNt5EO2LhrlU0QbetE5qrOKXZ26pnHdUWbTMZynU43B\n",
-       "YZzA8r1i6A0VRqIeY2NQPlA2/YQUHwKH35/revu0oILVZT7ROFtksZXNc+mkBYLhfpSZkPkkv4uB\n",
-       "aGcNOGCzeCCVDMKEa9smmAv/Mx2wkx3pXRF/1dXweylq6A5QGDGmM4hgzZgDUDkdf1WDVM8fsz7U\n",
-       "gG4DW86NBvLtms2BbTG9pm3aFmpGCBMErxfrDWVN0uuN3mpoUpXOd2vrW+EquxnaRi7nhZTVguJH\n",
-       "8rSJaoTU//6hNxdoqNwaU2bCQqtw3h/RpJEQ/hYCmTa5hMYu/ZO0VglPSEZV8T+ipp8WaCt57I/v\n",
-       "naQ7PSDu6ESd7k7xJcyEsUGjWatyVaaDkWSfoEcsnj5bdRWEi6r+LhWDqtc67TPdwmxpITYwtgkC\n",
-       "4/lMRir3hW4yfiZAGjaAHI+1GoiyhviduJ4H6kl/OujBmSUz1yfjIR+Af1D0p4lJYGbjr1+tLkGy\n",
-       "wRoNSgs41syZe8TzJZwgbC9dpQ3oIEULbIyHjGpPu8Jnwx2KggGPVILKaMxmVVWeUoF6K8ne7ljo\n",
-       "8jnHOeiA6+kxb4XF8VSs+hVdji4sGGZHC9Wx3XjNBpVBX2cO4KGZ0253LqjoZkrOpxJAImif0B5w\n",
-       "uR6wb8oQk1gfvGGVvk7ExPnQXie+3Yapnjwv6ict581yNF7JS7RN/HBN7akf34UmftmgN563H2T3\n",
-       "BpV77dxbXDh5kOXfMK/RnbzJx1nXT1MTktACe5KESx3f4zVy6yNfv1dFRbjwYYKXGH92ylwxHb+Q\n",
-       "SA0FS6C5wzDaC8yfhnLQ1oo/bMa8wdPRjPN0J+jN8jDABjQEqW4R9/LhW5m3qY746HVgapE0vzOw\n",
-       "diWV6/put6v627i83ifonqzsZ91HwTHzwfQiEpYMjzH2Vv5O4a47Sr8X88fHsHb/FUb3agYVt7q4\n",
-       "8aRIvRI4XgzcOJiZZv90vhYwujERThVqDTxB1dAHKQwcwUMpfeJHq8QP8ozC7swcuLYIZNV34oXv\n",
-       "lmIPauZh9o8uVLbWwTldfoh9BlFdtEbjgB7iNjSUkdwr9O5a+i7nlD5oAn+1GcjAU9KEDS8fPhLw\n",
-       "d6X2GpHkHniukJw+EgVWb46P/+oDtXApH5PBSE0ylGQAyA+A0ymAiEviLuH9AuPJw0XFT/5d7tlx\n",
-       "cD5H7KaSte8bLtvFcx3CImHjH0+UGekFVoV4YG++RHxsLNq7JbSj8jl5yXF02BfDBgfZfNUESFfz\n",
-       "gQFhF4GbcmcL/xKSVlyjGjxVoHcZY1D7FlYtHp0AdIXhvviVp7F/0LBcpELmF45zqe/lpkOjLWqg\n",
-       "xCNhuVtkVMRkqc4IoWvfSAmlwosImWqUt4AH282KyILvhhiq+FlMP3R99ad3rmRwQ4q4KxfWu8R+\n",
-       "w9aPcZqXgrSLm2rVVwE8B6EIrQDl0Mu4/fe3o8QWVJ28O7d/wT6NIYqqKREXu+Xh7YHr+UgDXZSz\n",
-       "4ECTOmutTaUDIWL8pUyVQxZCESrBk2srcb8UBaOIkUW05R0/VuNgUzR4G42VHrBHb5Hij6LVJPT9\n",
-       "V2etoRV1psdo/IrIZ5t0i/Km79VoonlHOTX4/CRbFi3yL4MeLNc3bsqtDoLRGKBSm3g3C+BTlqFM\n",
-       "OwpSqiccCV05wAISCQDdoHrZKEy1qI0AGF+FmCuUijY6yvpn/nxzFYZQReTru9t/Ng8UHj+2Jk2o\n",
-       "GkjLpAmr6Gt6BPfp/U/zn94KygKLwbA5JIsEgcLFlqRJ2VdXejM9+ROroVk0JULN3EgEh7wjP3wE\n",
-       "kzdvDg2rjh7M/fFqB9h2R6WN4s8+7ozWqLpWvPmEtuWFkXjNwoLioVP3zSj5LvJ/N74U6ZuabQnX\n",
-       "U1W7OwcJkcFf71cpaN0cYW2bcOeijxXiqm2ahnIVBWvaEQC/zpiX7oRiOtWPMqKbw9FwMYUAyZoT\n",
-       "0qVr60pjP6L39gX3pkvY55Hl4oApXQl7EUX9SVm5+x9wu8KBu/xqQ5GjxQEQ7T5goRUufa7apx0k\n",
-       "xZMVhumc98RhTR5sZpykMSsDMIY9esa2DLZTZYVeKbXYeXRYT9MtsPHETewCk0ripcwC/QJpBJcH\n",
-       "se8+hifIE1hcDnhczHxfz/By/kZmK1tvCuGDiisLtrRvEAs28G7nCJ4KngaLiipGmN/50eQUql4f\n",
-       "nd4meIn//Jp5einXIDPQhVUQwoYVJM1qI0f87T8VEFzSdmUHGIMBtwUvcsANMgAmXF6RppOYk5dH\n",
-       "11BTU+9GgBolmnOMDr+So0O4vQiIvs7RjhsvSrmhqKQ/dfxXVs/YLQa+oHMYYjNkI0nr4+sfUTWX\n",
-       "7blcJGl5tjLQX/UBTxIJpmYwaeK0yRdZxa8unZZer2PuCFW9V1lo/EgdMgywQSGISJXSMPI+ZOsD\n",
-       "s8ICq4c0LvLXMmk+x+HBKpMGX1PBCtWjhJWhVQh/4o+KTh9MH1xs/qCG2h1ZIlWI1eLOsYSf4f1G\n",
-       "Bpkt7SVL/In4511WrEHu50x313bCYfBrLHzHZqE3uyA3nk/y4XdvWzWiJxB5dNiQYO67xHK5FIDI\n",
-       "t8WJdpb5ecsucgNVt5xzl7DPAjiqPxdlOXqlVb+zf+yK00jjxv7QzkP6SwblGcN2HgskwLqjwITm\n",
-       "796t9JH4muHPEp2WDMYpLiFWt4TzP59g9jkERW9LR35dlhnYwwA35YUKi24z7hy90vKKllSKwLiR\n",
-       "3pe9r77vYmdmilXGtckBmMgwOpWztuH7VHSlSyX6fp/4bblHIBydftlugoqKpkcsw/HZeIJz98Eq\n",
-       "6mht5QeRor+4MRDtbeub/IEV2Gwornrq3LKXmLeWCW8PDdBCeMAjwlerTgLdBaD8/M0hZu5p44vE\n",
-       "Vb/vh7Nx5AjxBa/HxOAQ3aJrMC0Tfo0TkUn+FihES4VDpsyhybTDdfxvLUCq2CE1Moe/BHhYDzZ7\n",
-       "+OLgKCc1YAR7xWETVFFqxxGc7IGRXpGqtExXgPzcAc/2UItfc+uhVDhMO4wlJsFi/7TLwkGf5NG0\n",
-       "7Ro7FQkTIYwHw0ZVgrS7vt64oVnL9pCefv8jZ5V1gM3yyoBTmkbxyJayDFwhEG7rpX2xnAaV+bXB\n",
-       "LkNKNgAac0dFBq0ZiO5MuombdABRsO01b6WMV2DBs/TDR+QXqsxQ9M3CM5DB8hP3Zc9LzEjepalm\n",
-       "usefloVKas435g7qr2fC9n00x8L3jC5Xd7QiTKU+jDU2Ozc180mYFOaSjBe20Uajo0fTG+l/599x\n",
-       "EBwNFD2wGN5Z+lWNJDRZia6AHDgnrkKjM4dAfs4Ec+NKBiLdl7K2qm3SOlP23zQ+5RE3phOvLtz9\n",
-       "1k8F0Pe8Q4p9KTH0yf72e88PcpfabXGaDGme6tnr0xDvbftrN3Wx48RWsWkf4CJtFMPD9s1Zuwoc\n",
-       "llZmEMXh6YHDmTFp/v5Gp/pnSOt7UjY+/ryG2aTch3Bua8V7D70ibVx/kO4SIbX6AgeFzYnpCjgd\n",
-       "Z6mDwf9aSE1Qd34/qqm+5x7ntAhS5VfEiubH+pGVROWRrOCX9BLUcmTLNRm6WZW2XhflVtE0LXpV\n",
-       "ftSOJXbfGL356BftIj7fpDg9K24XXiR0/H83pkh365vVLUM6USElV1Q4zbjpn0Hn5acqqUEAp4UZ\n",
-       "Y6LrWBQVqfjg2Tj7UqUMuSr/ehNgZP+ql+xjvgSMZRw2Nx0sgZCJZaHR9CgFOA8TbiCj7htpsHv0\n",
-       "Wk0NYl2r29/or0/JwDWqrngSGIVpanS6QhvtXdwcbR2g6SHpKSvOfgxWQDTtbysA/IA6hXWh61eg\n",
-       "QnXVX/oChrqtYrhWU5n5xFzQu1sYMBN2Jzabh7rcB7jUiDGsT21iy2aWhen7e8RsWNY/qP8fL/bb\n",
-       "7ME16HdPM8pUsuTiY+94l2RqQ36IwkZK+AMuLecJFBQpIH7qZjLX375nIyT7bQ4zUKV3EXQtkYLd\n",
-       "y+BDKbeMIvvs1eM+NCrngRyuhvGjARBmI6o6SzdpGlRQVupkOcM/uoYeaInOlXfVLz4thErrwmsc\n",
-       "XEr341i19bX5wRjATX/Rrx/ZnQe11QlfSpHslvOX9NcQjEkTpSaQ27FQLI2wxgktEdaKLOn/koJV\n",
-       "G8rKfy5X5TQxhZ5vePhCOVcT1UrRAAc3TeDUhPA9cqDoA8/zAFxQ2SAB5R2/mIUHmmLPDuvu3Nny\n",
-       "SoZwE6Fq8jGYfLSlpH4ZF/78KJ9fSpwHhzxUkOLpSvU+860G0mTF0vvxBm8MNmmWjHbPwRdrmZ30\n",
-       "RLsHqnrt1b3sbBIpjm6ds4wietpdM9P7ikrDRYpQFCHi2kClWEm2ylaidv8XaSwIQIRT4TdkhJfu\n",
-       "08Gi6kddwOrfSEWnjQ4Qm/7uqqV2Mv9w6KRUQp/KbPgoRiWl//1vqyMb8rbCoL7BuP4SuojNJvRV\n",
-       "9nWrByFpEmfyBnv+Dgp262ew+cPjUmj2trmCtA0DS2uaM+DJrtoh3f1Z0kq9GnEuzFTs5RgQnSsR\n",
-       "T8fMoxVdORL0YhJe5Ez7Hmiyg1pY4h64wYq8ya7Oi64nTDpjPnBCxqFseHjkjLuzVZCZNP5QOREX\n",
-       "bP5r7gNC3SmydRZNY/Vn7dRMWl5CLx/lcmWYrKLrZFjH6a6EcO3bQMKQLXC41RqECgmN8Ox/ag66\n",
-       "mL5FPsHGnryGmRQ5yrJpQkimO8xv51UNsz6OuB0Wz1X/W7GyekSu1GxSH+6KDN5rgxyDLUaJvjSq\n",
-       "VJ6vf50EhYP9VWr/5/d6JMzfqzs75vFtyl+OcL6ui0bmJywgOlwPIGAj8Cl4Zbx2XRx4l82F6XbY\n",
-       "hnXlML3bJxwA1WzWYRULwBiRGRMvxzifzG3UzPLlorRp9s1MIlUuLRRpBRfx/A9nFUk+gZ+mdFDR\n",
-       "SrxOHtjoXVPM7McB9ZQE/8/5aIu/GgfHpGZfS9JSOqvxLDeCojpxTCaHIvEMvoyhkBZ3NQSd0mVV\n",
-       "RbffNuklDPgDaRIPTxgMTXaPkjaClBlHVErkITIBeBFK0mXVxtZ7+GQMXcRaVQYWD1wIWmg+U6ED\n",
-       "mqqeq+LpZR+2ztjuwVvUOcITdSTphMjknQTJJ7nB7UpsdYC6kz903qu7IFIIQMfM/z2Jne22vWTc\n",
-       "Hzl7JiH++BPT5GTOW6LdIAxBwRVZGohGRp88P4gf1euS36ST0yo4GVcVLv2OXdkOHzTj6+Fpojwr\n",
-       "ckEF/DY979plwlcq55RH5/YHudF0vgkXRAZVuEsiZvwkQZ3srXmKfqAhl16TQ/oSHdPIIzEdOnAu\n",
-       "3GOtegPx4njH3f8tG+0KMDxuEuw3EVzErUFl4KdkfsdZk9UbWVBNKbpu26OyPiI8k2XGjY7zjbNm\n",
-       "LdgWjR+9hh8hVaLkiEr8lFcyLaQZuI1cJ+tQr9g+qggleH9rw6Kf0wKUyZ62xZQmL7P3y8lyd++v\n",
-       "gBV3gbUDnnXPBCyJsL0KusSaqYXhht9SP2tdG/+TIB19DcChbtpPpYSqOijPiZkTePfl34WnvtvF\n",
-       "oOymZCPcyt8jeDEnaVy97ltFfHtEdE0KndPX8Ll8rSon493XzFWYNXX/33EqIZih9llWf2VW/9E9\n",
-       "G/ONc+/ZplWItrzlHwjG+OqL8MRJRLusOkpaDleiuZj2gQ+9DrZJebxQWkH5gN4rSOUVLsrL/wdB\n",
-       "aqUitlhTJaNzzQO0Bmf5DcU8vXkixzrD5uG9m8fpHd9N9wa1TFjvdjhFUDXKv1NR7x8uLEwrue45\n",
-       "4LcvuUwCry/AmIyPdoV/Ym/m5c3befp6dNFsOcHmRYdILRwT6Jw1jJJjqF2LdmB5+wlChfy+PaKp\n",
-       "fs6OnFmy+1ngXUsv69knr3Jk3lpUIRM/1p/chTDCSOLFr7rkWSwxI33HXblWJOg2LisY9AwmsQZK\n",
-       "hgdz6wxYvA8eyAR5Qant7F+USFc9u+X9NLbXK+ZyTNcn71XlpTQ5b4ocrpCkjDrJrTyHTp/3YB5E\n",
-       "dj4RUpMpN57vU9iNoMv4MEmU6612T7/i1pzB5P+kzM/72z24kRx9sFhkLGe2Jsaw5odqgMEe9Ock\n",
-       "H33jgCvsV/aX/eUWwNOLeqSOO56y3bXG2LPLktnDtMqXPq8IG0B99GUvpd9yc1w3eLzTrhV0UzD9\n",
-       "Picj85SWw1qDwQ+PeMt7pqjML7/z4CFnqsl0F1ue+vXNxZk/2p0aJ7OgIsSjd3gGS3swq9T7f3Mq\n",
-       "IYFcbQ26HlZOCHO/xJQB/LC05uX06EYZqci1MTC9g1nKSE5evFcoKfHSZG31+1pAwQH/v4yFaCf/\n",
-       "8uMr46rCbSJ56IqGcCfiCB3+L2t14vsE9aGRN50Boy7O5DcDSpFGfwiLa3V32yKtuJ4djLTEm+eg\n",
-       "5dSe3KuuLde8pUFjq8HnlzU4MS3iuvf3KbBros3CIONKw0YrqDCbQ7Vtc3DHGXqBiSxDsw4dUA+W\n",
-       "yxO8tVXSlkpZlZwwWdp4O9bQX0RqIfV68Hm6ehQTOsBLJfl4Q8HqJaWFsQlwHbI4EwK9L3QUVleK\n",
-       "LATgTMAAAA8HQZ9SRRUsJf+k68cYQdYYWHrkWAr/uLCH2su8n0e+kBQq5sKRGdNg0Y9lK0AbaDc4\n",
-       "PJxzTMTpct/VzLf9EDRWcAKifLKA7qAkb0A07PSG5xULpVMTDE0wk7mMQheJdj977NVlkGrVTB89\n",
-       "ulfly5g7toYblL5Fc1WVdWi10k6WTsZhuir55VavIBZQ2IzsfcKnuCSAuQs9RE6ZnBnalOb8rHHp\n",
-       "RjlDHcZOu06cMDg+Zadg3sJbXksqrGA7eMuAZ63HMYUMeWKF47IbD+ilhLai1TIe949OAiVOoJok\n",
-       "Lfl0shwYGACPRQxex4JMtw3c8Nctt0BOfAH6j55HagbsZtdZszh9bbmxI+fP80lzgtTtdfQRejgp\n",
-       "xA8FGEEyxReCw2e7FAiwZ4P/KtF/tX0MsnkuqVK0xMDQQ3TcquvpzqP5vcbPTDwkKx3Z9AaEGQjM\n",
-       "sy1mUwMAwqYR0BlkAYlKYmDjLTwzGmqgEjSREHJXZ5xo3dUYQn+0FG+CzIE24yhdWDolSsXYR4DJ\n",
-       "6HirpgO0PU1c6YyBPRY5qPqQ9uQTSOwFjAi38+dU06Vtc0qa/AyTZwu5kIJXa6zr0uuVY/e6Ve4m\n",
-       "tX80VpJrWsGVeLfOlT3AHNQFvVTFE6/h4lr4q5syx41pmqz7KKIrjCHwzfhXGpjsFIb2I5tugP3r\n",
-       "YDoq0CF8rxaCtSMfrLc9PW4agrAA6yxz79rOMhZsrDtTLzIOz6VSoylxyg6WuKc5Gt//fjAF6Zmj\n",
-       "YNcDqPGLNDusZs0UAQDznM8Jk6Yph1SLAl3vMtiWivOix7jIq/Ws5EoSHNi/k9GP2jQBadQbfeXS\n",
-       "VcEATOgVMhoHqPY5IVM/c+8D2pUlXsBuhB9QknCcYpdjKcNbudQR/SPOg/TkkT85Xe+6O5lGPBp+\n",
-       "fYLQb4Vy91zJGkF020BW8sGOuAfQ6mTsWW1rIJsUGLXuFijtNKEJeEPMl64xDxyhR1B+yCbRWwYz\n",
-       "dZ+a0zMoKPeWsP10L5Oa658o/Nmho3h/WumvV3eIgmnmp9gARSwd4Vzk2eu1Bc0G0Yu4EpjI23Lk\n",
-       "pR66LAwR4Is6O4Qtm1IwXR6lygVmxn0trNzP4AJqDdpj5iZieBeZQH3/e7eIU1pr7tVUuqut3EqY\n",
-       "ugoqz/91SL3uJnaeBZxFrrBeMRjKnEWP9yyGzh/moocOoihzzJt5Q7K+9u51dH5/5cmBzUpzS6w9\n",
-       "HvdNOiIDm5SQ9NQ3h3sedr149kYQnxzZVIaYo4+Quq7VeQ/kO4TofsEeMRj/uUxMOROKUPuwYK20\n",
-       "OvzF+Fi/Q1pz2IvcIlBKwo+Guk2ECb2Y77h41mlc6vUehZRhC6UoCBm9Y3c5Dl/nCqsTageODzTl\n",
-       "fJ00NEhZn8AdL21VbaOSD3mR6ESL5oiiW1oERtQmzl8l0iYxhlbZDPKy7iFbwyBxFr93SfdyFmRB\n",
-       "I0SF+dfZ/mbe+CC8daMSyWwvhI5vk99AURLFRiTgKFgjwe7nDBFdPo+HUhbgAHyGQEXgAEMZkk/a\n",
-       "SpXe/nn6BGOyPQRA+c7Y+lxPv6UVt/wy8+PvhVKMwCg3T/ohYQjIR9AdwMlXIsVXYqwRqvgNS2Vv\n",
-       "ZsscEpcsfI3QdHg4+z1iPIcpzTF0udLAZFitjsWF3XZGc+ubFGfU5RS/Ao+94HgbO0OUHx+S6iql\n",
-       "fr7VmxtqJjNLTxM2MLlWP021zh+DhTW+yE+UMZdXoLJSERkAQTnU6oS46JCe9R8TfwiJex59F4Mq\n",
-       "dkSxJ+Xbub3tFGUQpaam4v8+jqAGXCuGqjZ1fhLclT1imACEzusKJK6E0dH9KuyuuXKfkYTN1T5V\n",
-       "AojoJybhgWbE5Chjn++ELpPe/SR95vbQQK+dmEAfnt6dMpu0+fqy4bG9vUMszLxgBYJ5T133be49\n",
-       "dB1z9f1/kVP+7+JTCJzhkdFsEkOxQfjDlIkXiOVQgqqAVFVae4kKyQIAAAMBZOA1sNmhv69Sui/K\n",
-       "jXTq8EEwGdge/5ZS5q/8VD65Frt5c7OIzf3zdlp9OtHPj38jHrXoO0rRrHgP8FGy5scYNyUKlX9Z\n",
-       "g3nI3dVZ92KJF4m/BDMyRv+O8bvught6/a3HxyEkDqN+BkkRTJ5+vFF3VynwggwjAwoDzQA9V9eo\n",
-       "YLDEv/UsG+MbxWBzhOKRy3P1d/65v0kbCE1F0h5a0IBFxgGM448qwkQ2oc6jfCxIlSISolWkKDYE\n",
-       "0rpQZVwB/AqFA4IcL3D+asbGRLm+ru5KBfs7hgMuUzDyBI1RXXrKbaAhZ1+7yxkUjw8oO2iJXoSC\n",
-       "ARuqA3GcQbrzG/QGGlGUJ0oR+52h9i8O40b/5qqmsIrWQ2coxj+8ePmBuelXJbQN7XuQ0Uz2Egqz\n",
-       "reLFeSY33hfuMMUXMrGB//7NFgWXaYiQaEDlUWDQ0ZxgFmJZmNXiiTAcCq+jyHfom4hfPwrLA9bk\n",
-       "74LevqmRK6kZycYbnSeR2gTNZqQiWHqmMg3i5fl4ynicABGC6roJhy+Eehg15wXxJs+CnS5zccAq\n",
-       "nMmoaR1FYVrtaEoO+VPH/SfYmk/c2Bz+h0273I0nEZyhjO+48MqRmBRi7AJhmDRxqPa6j/dPHtkG\n",
-       "c90711JvkxBSWYVS7dMwuLDbWRLnHZ94npe4I7cpdo/gkQZORcjaiucuHWH44oenqQlrlqYQvQSK\n",
-       "Ge3aC+QWO0inkmj4Yy1MjSjnNQsZ2rvAskMgUA+h8Js9DgrhHinCM8Ik/IEYBIOGknoDmDJUQIpU\n",
-       "jPJvgxU1QXL9U+o2LvYR1gBi5Qq28aWERpEU+T6xZJKBlM0xePQIsPnZdMvtuFPIT/ECVWDrAAxg\n",
-       "4NmRMSeET1q+wYXD6m5jxJ6VPCRdR4lYNuhvyhbkNiPzCQgr/KGUsAOwsaYyVuiW0FKJaC5aJ+Yj\n",
-       "6eJdypAUtZ8u+9hJ1ZApB1SWtmomUzJLgbs5PIMaGu16TP6MMbqrd22ZwHHCPGgqOmmNTdwyB8TV\n",
-       "O8Cwesc5N7MFm4m/sg5ttR/kdxGYlSd9yjlXjDYPowZB7Rloftr49Pcb/m1gy/pXlL6EvLs97Fsq\n",
-       "7+RLHQ1vKfGfbKnl8696kjYEpdcdCZdGHEGns/ESPPXKNj2KlvSTQE4PehbWbxWtXYnSc5jn8SWT\n",
-       "hzr44XyKXwIR/R0R+klugmXkuR7ydDd2aKrOMpaxY2HlU1Wk51AaSO28T2917SPuJFG3+l9JSoSL\n",
-       "jBaCLrJD3aDPRNRtUhfJcHIffyDCeya2dL41S9k+i0NTINrkEH6wravsYC0/6WTTtk5xXDoEeJZP\n",
-       "OeLayO9wXMWl6UAfXDMvRz9eB8NxJCSll182TN0kzlBoflzZcy5Oq0aTw88T7NQdoZ9Kx/L3qMva\n",
-       "QFvOU4sCS8xSdW6enTkYPRdViB10fNVde30PXIKhmN8gw9Hcdfvqm3ghvVPF0b/ZlDqe3vH/YU1M\n",
-       "YfwKDufc36r1AwlouPm05StxMXiWA7euVJG84KPK2qEYIaUp9VdLLQPXGa5de/7dZJzcMq0iy7R3\n",
-       "Lth/AYQfHkyiQgiA2D3gCoQdUG5a9QhyIYkmnMBNWAkOVT0pdSbKfhu52sHbY2JVHeKvCifvmXCI\n",
-       "kYbxOWvqBp3ORcyM1iGLQqWcRp35U3qD0Y+RmLCsHyRHno268uI1x471Ek3ok37K4dhF8Le37erI\n",
-       "b2YwluIScIbeALgcD8Y+VVi79fItNAQElXSB2m+2cbMNRz5AoMyNNKtHYyB2NYZdI7IGVx1QRzyX\n",
-       "S1cLiB1/k27R+PUf75mBVzTTCBb3TrKz+b4E7qxXlTxepJCmr1j/wdUhyOHr3O61n8+LsG4W8BDV\n",
-       "Wh2U4h2pjfMpXQzKFqMN/8Go6Zhmv/S/Od4eLxofdCbJEjPONH/cA5/+vOTCCR2KtTydoFQSgJJv\n",
-       "b3Jn/xju5ORbwNvbr0zhl8qUhO4N6pAb27lYWxqnmd81yev3xu852Ip8cQ3FfSk2tGTqMPmGr+6J\n",
-       "PyNtdjQCyExS5qAkuP//+LQa8djpw8Pu8yRrnLXUdtI37Odb3gwZspJV1eZSi11Ks7BlDiKgV4/W\n",
-       "zFif09INiXEfglDzNAbOpyId8GSDC/xHuFSe1nsoBmzGi0lQAekIbJT7Mhfu7juxci5C5NcnvD8s\n",
-       "T56neItoHsaty2GBMPasylnDyA4t9wRfvJG4KhyuVQL/Eo8QiHu0IvyM61DkT+EpJsdAi91JNw2c\n",
-       "UgHO0AjIRF5XT7bg0ItlbfSDt1eyudh1go31xw2U2I4zjnuSgFuU0OGM34U3AEMSGBNzPVmbBNYl\n",
-       "h7TZLjKLKMJ/rJCHCs7MEoezI0yzcuKeLn8iJVpdu34vZuGKs03d+xHqnI7/Ht2e7f+aKp8ojzqg\n",
-       "68icgZfmqIixJtejN3X1vTuLjn4GvXAx6uCO/5GL4Lw9CMHnDZ0d3ARhwSIJQ07CBRNkjuscKWdt\n",
-       "8D/3+9uj4KHX9gJ11R2yOnqupIZl1aIXBZhJnEX00U0QEnpWhuqz+jTgnDKDYPomk52BFPRhEOMc\n",
-       "StDD1qabkD0Q7SClSTNXRPhPvEBY1PifwqI7mYEsfv98EWGs4ITy+CoE7yUapK/AjwtX9tWAdevX\n",
-       "khFNeFadoAJXm6xBAMtRcDuxI8OkQp6JQmR/DdJnMR29wkOCIqXcVVuHuYHLRGMorP/+H6BluahG\n",
-       "jglFG8y6TMZTlG3ONpVQe/ZzU4oMSk8gMZYOQr9rWAybHgexcUZPNj5WcJfWVJiMZX66cJP+XJ0H\n",
-       "U3LRjOf5sOhdn1Z5+0857qb3zTIyg44/CdcFVjlAweOlo1EGfK665jBNrxMvpmhr+EM32OvchRTm\n",
-       "qJZ26Oskgm1vx4QCL6/Yd89DQf8euK40daGxf8B874irZFT2UbXsBSSOrXf+CGd3M+LxHDXVppfS\n",
-       "EyKr7Zsnj/kzjiTVpCjWcTfq7+DSNxJPsjHXf17FInm3ZBdK94dJhgbnK1Npn3yCZVgKbiUi9KSI\n",
-       "tDHKMLt08hgkMIhfmDChRNLvvDoXe/SsmItzrdSQjysYjcQRQ/FJW0qsrn55wAazCLXBpmU8UfBq\n",
-       "s9uDKGK3xBq7PDb6+RBj3mL/w0DSI1gD0TFeeanyuaLaOyQGufpvaCKeQe9ktBN6jC8fdDlEEd3r\n",
-       "MnfhFnNMyMXCAusSuUX6cPuH5ZgEh7BIPUDAz6NJZXatTgkOiQAACwoBn3F0Qt/kkSMdF4plpZmw\n",
-       "xfY/OoNCBItKPvuMAsYHKcDYbWmntVpC+yV4iEFMPq/6OU9vkCdH6SBFcoC+0dYARmueaIjgYEqe\n",
-       "sD1/3ia6lX1+w0UzENYJCioxw3aTvWFc+4Q2gWeFGtsgS+ISzj+AZzj7Il+ZGq6saisT60FM1Mo2\n",
-       "s06f0EnwKDWpdZt2bgAnxkgOVZ2xz/l/K9aJFfVorWe2isBhLEPGdfBzKVdw9JBNy3blIu2i/skU\n",
-       "55n+yLAaLAeX9v9dhD28b1RX5CZGBKHfN/c+90bTlOzjGsBdpgww9oGPD+gKR1j8o1iqsCD0CoVd\n",
-       "47EP7AVh5b2gdvmnjH5xb98L+Mzfu1S8qv/Hkjkir9vFq7Dw/Xsqi9uvBNHZFjTwBilI8dPvu7FN\n",
-       "hYLdSd6AaBm8XrKs8uPT9o0ZszTCkoCGyHa0aIaBQDnF9Je+dh87yDTYnbXNqce9l9EV3jszuQci\n",
-       "jKBfKcpB2WX6WxMvtBgm1ZZPYdClCspIHpcIzNXPWlpEgkWlerhWbbDUyvYLvFjPfmMrfHQfcZ7I\n",
-       "GggADOBQ4UMjn0YcLuV4uwlgOPYh35svo4uvLiVF3u+L2u5ZMfykW1wQm06zNDq6QCganAxLAKvk\n",
-       "7Ezn3MgpuTRpOMhSN9sT1+Z47wEndX5OeUvD/OEcPyijzkeoVZayIKiQ5YYyktfZJt+hP8HJmtlB\n",
-       "sPdS6za7WL44KCKRo15qE/rhkrM+IYY1JbXm9zDjp3RPtD44bwSlOaFQqs3n8KzYLE7OVuAw0dvq\n",
-       "hZUZmT2F6yEfvvTCA/tW94ZZ3M87nEObn19+Yom6tjf9VYXHrrxmjA56W5N/Q2iZn3HYbrEMtd0L\n",
-       "LcZD5QfAOM8ACCNz3Iw0Un/FBX4dO/SaDP9xSqOyU0DozdvxBVSlOUHhr72M3rE6BC7u3Z0qsfcJ\n",
-       "KWaefMv532M9SHcJ/sgXSnksZCJ6EnvRWk3Isz6i2+gbuXv2kHFUFrC7bNssU/NS69Dex8Ocqk5L\n",
-       "t3n4z6Qs41GPorRNuj3iSN990TieySDuYLdSIhK50Sq46kYM0NM3YDGC0VV2EGTmKJAU+/R8PXLZ\n",
-       "s2tPMsU2K/LBZ6B3EyOmbYdd67m+wnbNv5GFK3LeYrwCCnN9+1DHVM9RdPkUsvs0Kn/n26+MUEfe\n",
-       "q6E3BmcjWS+P04LsD+Uq5ChxbqNHnC5A5mLKrdBWgO9b8+o0oGLOh4myvBTM3MIxm6yAGOPILLmU\n",
-       "sRsMVWVSVUjzlpur8DTc+om7E+gW3o00FQDast5MdE7tFSbp2vs66VTRDAnGsed7YfAnJNqSDad5\n",
-       "avpA0MN+EPGz21FEjrxCyYg8+CRldx1B4XXDAoLo8b4O8zGWUbqBAA2b1+v7B3n35JTxXuxn1HHk\n",
-       "nKEV1iXTN7Yk57ZxlfTADQoJCKr3ndiwl/aOqZmOSUxeMsOE9BrhXe01/L3fN1W0nLRsQF6+nB7j\n",
-       "GgXgfBLUOX6DTfvAOCDE2o2bkVbosBVNCRfnh1iKB4fXz5ZP22+grJeTn4HllOvM4YCQsjsk+yAJ\n",
-       "nvyiYwhDr5PkxGoi8AqmeK5Uh71YXpfGxuADtzaADuraw8GGaox180ZQrn5XdkDiwjrKQ5woW6RQ\n",
-       "GSuM6+FwXGlt6de+1dUP4v9JAmw48UrBluIkGFl0Dv5MWL2ml2IOh7/g6nX99JgR50zTLE2hnP2m\n",
-       "So7VavFX+Jkj0SxmfX/smtYBlGvzukFGr6w0LgxrHEz+xcAWWisKGN86OSZWYC3grNNwSAEZcIT1\n",
-       "MCuSZ60GLbbkC0runDFZoq3LpCerPBfsEM1DuNSg3g9SWVRuL1x06AI14O4xbpErpppiikEC8jx/\n",
-       "BC04krzdVetxI9GArAJdos4kHZX7vRg3TVO5frNfdrQF/NASf99TqRdvWWtqJX7WuBFxT6pQ4KLw\n",
-       "PujNDlhhqqQX0+2Wm4aGKWaBMSL/PGmHWg9EuUCuHDy1wK2ZnIweCP1ZFhiGgbrkmPUHdOw5si4M\n",
-       "tkB3JpT9VPrV42uIir8bL0Bm3mpbqNpu9M8PnFXhfwEbhgwTJJ+FzPT5Ae3utBwyjq5UuVwRajno\n",
-       "dTD2kqfj9fb6hMQS2fNNDfWY3n9342L6Y1FpYZ5UAxE2OYxXVnDVdvVsvNR0TmgI0SOR7XEiQknl\n",
-       "y9PxXPKhoBNBmQnIFuOXkijIAyDLz5P0QqXHV4WREph5W0mbxpHZ3lgt7GX9cuWkLX2B619LoSQe\n",
-       "tDwi7qhPsWu5TEWLSowfq0woTY8fCozfRRYqL4GZYJD8bAbR3Lo4u5vgA7KNNo+ndNc+R8s2ACQv\n",
-       "paueFvXYYv8KgWbyJb1mLsWJStxLyTDxkGe/MKER2BdGyGYO/zIs2Q3Rnhc8/BSnLBJsA2ZZzYtd\n",
-       "F7/hR3w5Q9ynVlaYGsRWb5e0QujD5VVnda3mIy0TQmsYoVWDg2OJxpvPfl+q44Q5xA4GeVdfux/D\n",
-       "tMVkpp4Wj/KRUisvKz6v2iC5HSXztvvs3oRY9pTM19R8M7XHdgVV/Dm1EBoPbg4FF7GCR4SYZoNg\n",
-       "klgSw4xeee82T0ELS6f/uBX09CW3+ABtjz2kaS7MJOn0tNp8pO3eBGWOY+SFAAgE8L6gIg3+v/YK\n",
-       "TXWjCpSOoGXAF2foFMJn4lOSvUGqkWzBvosAAiWU9ctb4DMIhEfarBxYIoDMRAKPN65YAoRxPz6F\n",
-       "KqsZn3lodovqHcgRGVhpqcSgpsz4WUHatp63gGmGvmDCkezl/HQ/sgD3T0h3OVjbLx7eC2oLukym\n",
-       "qtFEN2AAC47hvEiyTIogwp2XPfzYhyHUnxBS081otPw4/SyduEklMT6J7MVzfPh3m03lwFj0d2J1\n",
-       "KvM7LqTnQERNentHYXLh1qM0viTqofG88kVedT/KZ0CYHoIUgaoRS8skfbp9Z4mrEHe8H9RBQxAR\n",
-       "z3Aa3fCqn0ZEd6h3REzFsmQK/ddAL3E85vHyC/oP0X66Ey6ZKeWgEE26ZyCuy0DNVTdDhXZ9SpsY\n",
-       "KjUXVQep/uqnkG89yZGd07mIonle8qJygr8PhwuIut1oFHubB9dmmhCYv1E7AeNit7LMd8tlZheI\n",
-       "rUdk3RAZ/897KIEohpL/KEE4kjIMAxOUhy436gdWJZP8z8wwgcARBcibixxCn/nWDLKhvxO6Qb7L\n",
-       "EgVt8ncSxTJonTqePUKqO9eUQHZyEaTs9xbZj+BjNnsjCyqFEACHstVR6ivzKmg4DSjveluJr3FM\n",
-       "zS7LnIZwrfIOQv1ChlzizDDIfQX2BEh4zMbPjP69DQ7aepiLdFDVVRakMX1FRcyqfZWSmiUiDlEE\n",
-       "0o5nkbYLAH6eel4epzcKkbAhKAfwuwoHXSL9nuYZFFFJQRa7NtHDVzab9xv9gyOa29SnmaR441vX\n",
-       "73undh4MZMHMX7gQASWGKxn1XJUcJYZs1nXQGB9txq/ckf6dTrf6gd1ZRoJ7E1OVbFDCh+gWb/Iq\n",
-       "KTsZArhIJDrRpvyDfqNvbA0BalZQrAclUucA5n2bUwkbj9UjYywZxFzm93s4uCsfqmZrX3AJUVrg\n",
-       "yshoQdMMeNhoiquVDjvAR9D6GeCzPRtoPd53t8SZlAkQaoIFoRQ34eHZIDIMkXWC1oU8qz+f83uR\n",
-       "5aJJXmGDSazcmHJBl2XAAN2YTFW3EU2so94dL/BfqS3USCVEXVwxu1sktbM4kWC1PyEUdBk4hsNt\n",
-       "YoxqLQ3V+DoNN+zPddMxnqT1iv3mfggvmxpS7SblU9RSBXi66DJsvFHA81vN1NyRRcLsy5YiG42j\n",
-       "IQf0DlUPpmASox52BqBsVYAAAAkTAZ9zakLfrIEmfpFa7DUBAoVAI96zV3bTf9vO4SFt34ta+Hew\n",
-       "IHNr0ciMN69CK/9hdLmDX5qV/H/7bQcLyZKDYLdGl4rbnbv/iIrY3sghf2cvA6rGb31m9gEvZEa5\n",
-       "ggu3KEzx4NVuyla/cwqy8OTsGuO+rlSBsLp5+AcF+HDADanygW018W7syVpiXW11PvPvVflGXzG9\n",
-       "01F9JpCYzCrsKBLLCWQm/CQB712vUvS8fcpr3hhjHVVNHhp5VcfdOMs9GKBdCgBUNgw78hujxO94\n",
-       "BD6oNRO+LSxdG0eba89/4oZmqgLuJn2ujup7MZe8v2oZpugvvNwxPBlqBQU8EcS4G1cRUIvO50HC\n",
-       "UlWNg9PuRuaqtTNSsn0DFzhLY3hqizMeGuOCfxJP3BfXnWQjyXr4vbJ/AMyqmYDH4MjZQFUk2WRX\n",
-       "iED8hZwSBCZfe/UrDrOF/BUCmRa8mAFlKuz4JzTAZXJ6sLluV/QlXl+3Vgf+G0vdsGfzMp4xoWmW\n",
-       "Lt7BNyEIVKYQM8ptxGCiVzeC/OYsUzjehISk5W8kiMoxBYI117eM/C4waOU7/++XngBh7oNF4g5O\n",
-       "GteMmogzthcAtJCNxC2otlXm9J0kYR0pcNjat3OONte2M671nngjHqd24Eg4ZlXe9HTlIPHwZVMc\n",
-       "upXtZx48XCOqVAh+y6xZinBltZRgeaDEBBaE9pW3hlQ6guRFEHiQoC/PAYuur77CDwW6zQNSl93i\n",
-       "UhBJTH+Gdw/Ujx/LVgZh9oABk+A5oUIQvupNS08UxVyDboEtBkh4W4J99duOMB63lzOqSWeH9NEf\n",
-       "4ahMob4KlIXOsLepVT111XsmSs3j439agECSE3yyki4/W5bXQe11MCp5aoQ1u7IBtgMMsUPJ6CKM\n",
-       "JiilhHtpcipPGHiA36qsaMNg/gdTBTBtFChnkbUSNnV2hGbADRaMq24AZLUer9yUXr6uUyxLILQJ\n",
-       "JFcDSNRJ2D+6Dits+/ic4gE8VWYbYGQeJeJZ/SEqj2mKvYgnulqJ3Cjhpjzyt+Q/olm7tEnYSuI+\n",
-       "vzO3ZF8EMBJ5c280v4emHKDPKXOLX488Z8NtXCJ1vanhnW3zVUlrg6YFd2Nf/l/ZEWGOULNDXx7p\n",
-       "8FthI20aqMWihM9eVCA2tILYY01mKcJZQ0+KR5K/tD9FR0txv27LDl9nrU334lQWJVdGkjexmn+b\n",
-       "rifJosSjm+nhzquV89pvnMtyC6pK0N9pHXIO6a1ERnKdW9ggUcHvIOPXxCjtlwvH/wBNoSFjzkVF\n",
-       "EW3DUce6gdS5+8QCTkf7nMeZrPmt7/CEbJg2Tu+2seR+oszkPYLia9XbhwBz33tCxIgy/2BIpBVq\n",
-       "9XcBqHjkF09VZANnm+IWV/cpR7RA0T6ax6FUfKgwAz2zbFvZ3bHPKvwSPv9HiPAdy7fnLjxO01wu\n",
-       "AX4+kqwbK78bMLFpa6m7IqkI8zldMiaTbeCrlxTg2TK5mNzOzfDQIaxGh7w9H9ZQWAGj3KuvbAQC\n",
-       "il+BHPBTQFU0OXpBRPY2SveU5MOnmwnDWoAgC9kpzfgyB6p3JusjfraL4WJR9GBC0rATQXwa1tgA\n",
-       "CJbpQgADMpKMC9kBSt+a1aJBLuccYvUyrc/EzOn+6LfG8sqNKkLj8a4apb7IHmK+S/KXw1shw2ip\n",
-       "3vOhK1HO+Rpwzrm00ItCWBSARfxySvDdA9LZa2xgekLugQrZ1+PYDezN3+ZTOO1xF7b8/OkSm18Y\n",
-       "kny7+t82rISM0HpPgYcoTB/Att6XOUDC4l/oX3+fF4n6oRPOJbnDwSMu1D13ExoHNMyab5wHYobQ\n",
-       "atdZq/dCBPaJPiZS1TnGLG1YzR5rR1a3V6OUpaqEblxz9ubZlZNNy73V9XIHClu/YhhpsMOi5mii\n",
-       "HApFQSYJFCzSZGs0x4Ws2KEj+1fg0NxOUH2RrTyhwKLvPzVKcLodaVGTm0iBTG6knD5L8TeBKAmZ\n",
-       "noJiMJbykx1gKosgD5nNxrBQTlMfSo+2d45s1ZmyjyG3P4TeDfkzo5ww08tvTVu358WBQnYOLIHS\n",
-       "EXGOOYsvjiW504XEF+nyfjkNHJmfVbDAUcldxg1l7341T1brEVdPGwENC5QQMP694SWLoNyDd8i1\n",
-       "8JMeZHsYEITtdby1AgdNW0ivdX6bMUnUysC73RwI+hMUQAzhlzWrqoOO0kMX66HXULLCaVSiqhTC\n",
-       "Am3E6LB2fNneXJA9zDvvozNbTn0uK2I5D/agxQHl7WZ4GXYZ15PS8+VCRHK623r+J/3yjD/44j5f\n",
-       "xs0vapqUzRFzudF7m3QjtrYGzOrE97ERo2+rI2vpfkjjvpre4HwVYEJNs1rhHci+5cnuoYPES8ye\n",
-       "GJQcqYABFbKgAZQcBvfEnJZe4fn3I543WRboRcIEwNdPafcbBOBVYGBIshN8f360z7ist8UAAS5z\n",
-       "5aqDjPaI1tD90/flh2gBBL/vtSyqo2X4ueOhmYzf3YFMQMBP2dC72bglAIN5BGiWAow8FUTA1h5e\n",
-       "DwrRXmnlEVxeqOrx+dWWtv8vJ357IkMZzIcMwZ+xb2MbGHFKB58QcNjLCW9rTE6v+BKR+Y3GeWyU\n",
-       "bnXLvO4d8QpHXEdMAAzf4wvYlnuQlVm73G1psbZyYWFtXcEvy4wTbQ1p5S9kkzVPQhtpcneC1QZ8\n",
-       "UsrSCu+S5ABba4+n9AP4LHG9mF57tRY61gYq++Py78Pgb8ZLHxXpHUaOXbixvOUE8gIfn7moC7nh\n",
-       "pFj2eXMPeDVfgu6JwYzOUEEd3C2+P5P6Bcqf6jQEpWGPiU6o68ATCX1kIHxvd+sxhqTNNZ0qnequ\n",
-       "Z/z+uSsPGh/ZJcdJmUsGyewS4Mzi7l7syYuJ8Jn5vwAamREqgq6LXmF+vzCgJTiBkHzErSv8B6KL\n",
-       "aAcSv/CIkrk4AsJQIZN+XPYayW2ak/9rZdJ6IzjXFG15QlQ4+DFex7dAEHKYKaWSrkqRvkMjMf/4\n",
-       "m7fEdlaPNNjQ01QqqlSRc1mOkGqEQOyAVpglljDKYEbWvD4u3/4LCwDnEn8EwIgQewV+FaRKVCdH\n",
-       "OjZfFFUVm0hzPOGM3BDL88X3YPIBxEpIx9zbp6hB/msUbv1u0FeJK/9aynfEo91wOPrIp76Qi0sm\n",
-       "I3arUZm9gAAAF3NBm3hJqEFsmUwP/3+DgONpWQJxmTNb49SktNlPt+SjH84ykBLpYVOFpSD6JJaE\n",
-       "T26pMpcqgIaD1q6SP7Ow5UAXgo/+gaZt6ON4W/ceed+hD6HT1Nv1ymmzcsHiATlVjcC+BC7/lQUJ\n",
-       "YO51iHGHsP5/1wy/GNuBQM5aKnmMVVrSYRUBbj2RybyBa1uaLWngE3R8Kk3yn0pKXVugO5AwZWuN\n",
-       "Fyqro4gswezwM+uYoe/0fWJd8IOocGMbuxhkGc4ESbwoNgfcjo1/XQrBzUnw1it0RAyRbN2FQf3U\n",
-       "lBo96EyssJNu7j0B2ohGdbKnURmHfV3ZCjFmrNT3upPptc/xG6KvL6MgsBTU24LptrWx5geKBRJY\n",
-       "C6s7aone62yR8a9DrlJiAYBTkYJaEvIaXJVfH1lLsp5ns0uwnMKu09Gr17GXWROCLbjxR63ZJc0G\n",
-       "XvWxDYTzt5Pw/JEKJmJwbLsUHLB6QOWYBAeUOjgTdhHkz3dbfY04K9xSyoT2/tHAgLp07imNKosD\n",
-       "w9FyM4VSqz59Wm5bVtkRCEu/kfdSBM5C+Z6GsLSMM++Ps6R7cZ7GuYTe8R+ZEyrqwhkL+FodpMTT\n",
-       "CS5kVXpn3wnz1QX3b4Df/bUJ1F3BI50FYBiSBHFdXq7ydRB1sDqNULgTPabdUXbBPedxhYKH3DrI\n",
-       "QikuNFXrQ4f5OaefQy19gnGY7qyU/LseHhSZVf66C/evUyo+6UmwmumK8Vj9Mfz+HZzY4pKM48HZ\n",
-       "P3Q2xCnbT5aKGeolOBpV2hoVH3fncYDNHktfsOgn9u+lfSH5LvjyBh4etNdzjOlZ7MoEfIm/WCsi\n",
-       "jhtA85scYKzB6sNMAgRzgrjkmtVZWZ3PaogNJUbXQB9dH6lQPJH1aEJPObLrEZtO4rWNB90qE+9w\n",
-       "dbmjnKNFJeDRH8KcXZET0/le2KjehM2mYzD92ePwWu5L3rdL7DAiZamNO1xRuh+PS/MUxowSwgW6\n",
-       "Q1QiDgksbv9Ol7VKVKoQmET8X61gqD44VqnPEqng1rH9W0OM6/gZerw6LdOllaD1qgK5mEtjP+sW\n",
-       "cI7b7UUJlbe8IkWN7br7ATZ6Rdo+ya/gHR5DpkNciDXzKnUu9E87dQG58xO3VsQJxV85V3PzfeL+\n",
-       "HjWpFwjdtmU+OY3iXWYG5HvtHpnLCgoZ1bH5RiDAINXL1frWCFrNlp4TNAixPPSDrj++p/dmnFg0\n",
-       "BmZbAeQwNtXO37mMj/hXCW63UFmuvKsQYj/3zbTljkjp21G8ex9mUvjDpGdUYQCvqovFBeeNf+E2\n",
-       "2A51k+vRfPcc7RlpvzV61O8DGOq5J7xNXrTL34ZZ1YvK82jbfv14HdmYbGvXuc/QPru+ZMd69UhO\n",
-       "jAJ3/IqOBXa67BIqil4mOyJRy8BHGjJDKXO74LhP/6kNbA8ol72yx2Dwvb2rqZXLgy/I/PF0eQpZ\n",
-       "UUr8rkLlF0m7tV2piAurtKIcvjCrAWG22J25JzeWc6701FjZ1rJDUd6qxjLIB7cuYLGh7iSurab2\n",
-       "fTZHhZJ+OqtjDRMAXhZQf0pAqczuJLcdR9zpE9bmsRrbOoGW6iQMDjjxscgO3v+U0t9Y2JZs87bL\n",
-       "Vv920CXpLD3kYYxpW+ncIOQx9Trl/b2jU7pnpaMC9p04HwwUbT2IeVxr/UUQ3AT+tJVPICYfvWfH\n",
-       "nTjvNY8jkpNuOp+idocDOIc+aXBIWOivUk4hgbKQFj4P07g2K794YiYIgn64e4wto2/5NZsCMk6O\n",
-       "uB0d1XWphTpFwRw95CYFFOncTxg+V9sKxmNuRNIeIdTs88OdupAH7iC7E7+4in/TYn2VquGD57zk\n",
-       "5n90Xl41HTWiPuz4jhQiK9xUj9bOTDL0qYdw6qd9nATS6mb2thd9eZZ7hrRX7BUBMvUxo0ZTVF+O\n",
-       "VCzjiXJen+mnE0kDcnrlyyqtDN8CZy3Vq+Hlgek8Z+ncZtu/sILgZkDgjGNlI1C6ICZv8ccFuZgZ\n",
-       "+QuSlhP19ELb8j5APrmaqJzG3FY48WPOBL0ySs3SHE2msYdNAN5jORCCu3Jz4LOavMl290T9Z2+P\n",
-       "SF+kWqIuW9eA351xE2zRcjgr0DYlrpll/f/VxBwogU33VF3iIIgEbEOSu6mZf3gcG2GEcUMthgCw\n",
-       "MCOz3a/YxiV4baQOP7pe/earv+fyL+Dj/D1kkM7K++YOsJXs2femtBV0mnLjOil6L4O57qtiILEK\n",
-       "C96XDpginNB1GSu3CbaJP1OglUNs6+4iQW6GCGx1IBLFhSMS5Bzpv2S1MQJxjIaUwo+K+Ne/4Ppb\n",
-       "GDjzfuQ2l5OacGSeWMoU+n25i2IrUsLC7Os0/Y9Q84Wq/oDCMITmBcmeRDDTpms3X66MosG+nxhW\n",
-       "ZilnFU4EaPNRvaePIvx96Pi0mcku/G3WIs2vAvjXi/gSqJ0a3ENA9he4Pkw6ShOjqAft3VJM2n6R\n",
-       "ZJBel9cNgWY4s/Ch7IDKtvEm9DCjEG3X8USgpwsbzdYZCopf1yKaETDHvWKUFTHGSnDjm2veT/F+\n",
-       "3s6NusS32sjnL3UaZRhsVflzqen5GUEJ6vmyR1yg+3vxUjN0gep55xFHYenzH6YqqAQDrq12Qjbi\n",
-       "a0/QjHBAglmlNndZLT7e52acivktfB0OdsVWZJjC/2wNDVZz4qhfNlnNKAppTGeCRT+jetJnmAYS\n",
-       "9mm5U7WIjUr6KfaUX7HRtmeXl8gjR+xp5grC6FPKzGXrMngz/fRq9roxZMSHnSByJkGk0QHmIqaQ\n",
-       "+x2ryGiSm8znwWd5YyyKMNHZ0aHddp7de84ry38F6rWwAcECg02bNxMsJmioe4UYrGilmZL0DD0/\n",
-       "DxBXK0C7GSJ+p/JM4uY4qcbkEtm49f3eMNWPGV2cabarIV2i7fUQve+zQJXCrLtpsCkpDwQumpsr\n",
-       "m1BzlCK7rkPdLTYhERjBFpqdmP5OLqgLykMHeRLDylBn4dMH3UfagTBFKOWe5qUV3uuMNLRvBlLK\n",
-       "cVjpTklXekMpJihUn4jB2no6L+xO/vOOjCk0uSZIRNR8bb41vQ7GXY56n5lF1V8F9fJl8ExBTxL5\n",
-       "4NAT2fIzroFwnD586To6e46JG8ESmBbdEa9crst7wAjpmuvds03x3BQpmv6FFHDdnffkRKOsmpKL\n",
-       "CE+jE9CoCiOF6XlYWLH3kyKqFQPJbIN9EXPiHnMgWddp6t7BnB69GE+1wBN9/H61H5iep/0LHJk0\n",
-       "nHlTSkDI8xyoXSWPnkpQVe149ziG2IzQJKd2b92+rEob0HFKhvwHSBFXdCjfP7lu1UU03b+D2XkI\n",
-       "lmGPjByp8LAAN0AwxJ6wm6F8TXYvGiihSqo5FX/XkshmffartcEGPIxd8cdydwnPxvDm0HfQ9ONa\n",
-       "QtFGzi7x5h9YhMT3xRdsls6iVnk1JJANoQfCKXssGGYSQ+ZZC8fBBRZlCei4tgEo3G1Tdz2pHK2F\n",
-       "PiX7gcXEeRyit7mDp0jj/RL0bwy7QCP9vs9d3ZlHQN1JpE+YYxxaiJYh8xPVetHB4gqWuJk3pP9w\n",
-       "FQ5oZ6z5WB4xhMmjG/6WDo9ZdBAZDQgZvX5yw8ff6TldQe6A8xRvMWPI8ShmKWqGLJAsHDSZDaK8\n",
-       "UkBjj2QYO7BDjUFFyc3dkc6z1ilzo1faaWyZo7CcYaGRhG1mYmS6/7X/1L095+eQ/88zUuT6XPuO\n",
-       "eTUq1LDrPJKMDuW53pn/pOsixH47fDSQrIs2sIsPMmNEXpzLgBJ0a/gxFNrvAUCDfWsI1KR5iFRi\n",
-       "dU2b19o9vzCheJ28ErKoGa8tuHAFBjjjRWnnc9DUziPp8J26mvN7wXFGmlTFm4c+5CxqLm1a9bj/\n",
-       "fA8c189HqqBIBSV4tM5xoghoxswpTQuz8cljLSUjRE8ZuPciNzFg2GAKfA6GSCkhITdpjzXSGr2R\n",
-       "EycpDoOmyMxHJXH7tfbP3RcB+tSe/MIsxu4/N/PfH9BcMo0xR0FTAUfqDH032pEDVfVrfw0fOYpv\n",
-       "YBKYZ8/u+Z6K/vmhQ1XiiiBDQlcsxAkXsYLCKSXCWQ/ukkajU0xYjCmAwMUGlsaqMIPUVEuJ4oA6\n",
-       "BjX5ruYN6FP9v9GYmB9oJj/iWfhZ96tl/8x6XEdVN1Ifdn658xvn12yXsoPkp03Wjj6bj6oc55Jc\n",
-       "1EW2MHv/mvIVCiF4zONvgmIZaQVvHtvnuhB1MPg8EGgkFd/RvM7b/oVRTF3MoMn523zkErrgG/72\n",
-       "was7o8o8j1yZ4vt4MQcXIXH6WF7XwvjsZ4rz12AFn1MFYZy5e5b7UiuHR10dPD+/Cj39Tt+q1X6J\n",
-       "GP4Md21yksSxxnaOlR6H0Rs4UDiu4yWmiS6YB/7yNy+tjsqhzzNoC3b147Og1lLRsl9xvKXdFD2k\n",
-       "O2d+xGO9as1n84t5J6E88oE/oUp+iLU8g90502ZCj0RocWki3CEWCaDkx+gMfp+/SDSZNxqdMRMN\n",
-       "PgGISEWnFNhY+z6S7HYE+HaQw/DoEEl7GKjGCY8Q4HbcjvXhu8rq4bgoi0cjgGJ99vB+hsR3ACzL\n",
-       "a4hK/KZw4HYNEBQpU7Id+Vv2DII7AAAMCLCrP8EyduH1S4fs1Utx2mxvLFnQn61mjM+2JPYce/dg\n",
-       "mTDnfrkPApkr7+qAPyg01fg6Fn9/bdAxbOHKmLmSq0LL7VQgA/cMU6U2SEUIBU1el44zsSdptTXk\n",
-       "hs/SHtRGDLWKZ/v8bPnAD+TG7Rysj/ggyOQ0WfuFV5s6U7H6IREGktlEKwkT0FFNFnLBf3Sxatsf\n",
-       "Vh0fdXnjkdNbspoDgtUr0lR76T+sT2e0/8fdx0oXWQYVGpZqU4HCPYRqARk4jBenynCB/CMg39pY\n",
-       "baXicD5sl59HWKDdVm+g0FFGjjF19+/I/wcUYabSIEh/gaYDS2DGRFOJgZ0IQwcB3jjJtLlMuT/C\n",
-       "kieyymzcVKat66vAPuSQkZwkHR61jCrCBefOqs2U3GzvEP/SaUSIzwlhKboj+iT0mU/aL3t3+RVd\n",
-       "phmuBQypZyEs9w8AEh+e6HQzLeImbJm6PAWkDw1QduZKJj3demTSTNl8g1y7KLz1cTaAx9qRKH6y\n",
-       "Rm0iKH1gFtvdIAIpEGxEeDQmQvp6KZ7XcZNE2Vcrg06qYCIKZUPtCpCtWrcXAdxQZHYqW7DsIOL3\n",
-       "ZMtsBYSjeeLfnuBp10yTtppjAHi5HD8s13SMBCcWu9/oD+5Ovs2FdHiag9Mw2HPrWWKkb/n/jtI1\n",
-       "p7fSx2mBPAEy5H+SwfxCNJLWtq5A3TmZHYRyNWyXuv54p86iRWmx+MM+xpgPJjit0t0Ot5T00i5u\n",
-       "JnCOe4/ePWkjF9JQdrDnvYLTBxyO5KkbMoUXhx28debFnbe+Y8lqOTzPm+TLFZGtbK5XFBt8ILot\n",
-       "xs5R8VhDmNZKZ5vt1pukQoRr4Mee+2/oGKC16YEstV3/fD0esg/94625vK0s7TvDL2xPx+Ef8uM9\n",
-       "JtO30mHe482PyXIMyIZxzz59EM/blgrxT6Sgs22qOAKxzuznpnEq3PEU1zVHS3OZ6GzKRf+3SNzb\n",
-       "6WBcMM8CGRwwy51S3L8x89lgLc7vMYTLVimJ/ESOalcJ1v6B2ODq8vHgSuKv7FEhL/CkDaIxVzf7\n",
-       "MilyyBr6xyS9cA3QHvFbIVAD2H1uyNZKSreKf2nmspKrgV8Z1AByu41lbcddgk6TJka1jxmEYaFT\n",
-       "HZCAyDPhII/XbCBHWVFd27aGmZYcPF4tWRszMd7ljQ2fh+XDndLs3J6RZOt6YpFsMTPVId/jvGqG\n",
-       "sDN/YRYuO88qqwZWtcPmF0TeWLPbj22RfO1E1lWMO7E4Aai6F7kNsGI8atStydMGjNz09bgGA9js\n",
-       "0ZTIiuMlTYLY1kksUFxjD6ZT5KTJffss4hEx1M7CaDGJHhTBDM2RoOgyNkUaRjMAq7WVeoV7V7HP\n",
-       "1RCfo2n20AFM414Td/E++U6QBbkpM34QLkm93xp/g7lQJuPimpetljo5/pCA77OikE4UBe+XRh0y\n",
-       "0kxvBZTw+AKfsjq+0Z1cPC3cl57UEcRSfrywi60AbQAo4Fma0MoghYbseeHDBEUozHCbUPfdtzgC\n",
-       "lDBch8OnpKJw5nWTWcRHD/g5F6LYhHQhe+WM6p29spD47I4zoO55U75jYVvPQQ41/Y3ah3nT+Fch\n",
-       "jlEYlG5Z3JmXj3TWsPfCBT1/WSM/2ql2ZE4HdKhQZPbtg+hL1PqujhKUJaeEV+RiQFLR0nqCPlXn\n",
-       "cCHSelgg7+TeQcQpEJDhcaQXuHWF7+3tLbctVYkUiNvZunZEZXoJqOtnCMARCLMnNmewmClQDmvn\n",
-       "bsa0YOepV/eulbGPyl0w6n8BT+yqlmLkH8FtKX/MzAKeaIIrZkMrZeDP3xSea9DLTjbcJZ0PUI2a\n",
-       "H0s9xGc500uwkjhY10myGBDaSOWVfRA6XplTJZrG6HmvV/J9Cap3KpmBwWOF1ZCSWJZz7MCBMmNN\n",
-       "PMX/dbmmjhTHQHIZvxwSoRsQjbIASf9YFMDauJ8AgJBh6/jzyCZc1RNkT6pFv1HoSKC/FJoBGOVb\n",
-       "TAABAZHRf0y0+mWq0I6/AS0i0iM+nmsH/7WV38CzfiLKB+L9c+wcDR1nC+kOitc/dAf/7+a8P2w+\n",
-       "DFYC0Qf7f0UBL84hEHqsOGYfG4DfWND9uczuRHe1YfrHb6v2k9e49pQKsEJZo763kNDBwHsvxTMp\n",
-       "tgIY/aaPfT3rju+y2NnjqJmSpuhZp71eo+RPqAxOHJ/4cQUsrmYIOMK38i+X0v7cQwXFc/5/EjLv\n",
-       "6+lsgAcOdDfeG0Ad1x4rOEmXLzEwOAaQH0f3A3TV0LURChiVgq4xJ2do2VVgZr/Z0kp8igtbxZkz\n",
-       "X7YckLgu0nI1kPYc7PiOEOX/mp6MTgObxd4H2sOp6PRA9QF+7TdvV8k8uf072GTdLMI78u394B7l\n",
-       "CJDtQHk455M5AYxibaV/rcW2TrFEujfLgFS1iwQ+Wud5AfEOxnL/PXeIwh1GRuXsNAB0GHlwYrsc\n",
-       "JgrZ9G4lh3fD4RtATjbvWEQotMFbdSfoTdbsRmyKYWZpK+mhEwjsmPvFMXMDnhAluk1Mtk4khsZH\n",
-       "gqPlufBWGPF0OkSnKHDBq61l+Ra9K3ceM59cusXlQrkJB1Fj54Fbu1xugAWF7OSTk68Z2//czdZQ\n",
-       "lMS8b2bz91RRL9Mfv5vkJQ8ffsXR6BDKea5b5eTZjgAAFRe7jPC1Jr7oBiPh8DfbKdryx2McIraw\n",
-       "0t9FRZlq88NgTQ9Ji6yfGmI8zZHlmQwZ+UmWL5fLexhoh2w1F4OZHX0/M2tPWIUXofRvBWvHHjss\n",
-       "pe8erRzLOG6WfZzVnVnklktTQJdmSrtbvDkzJbsLcb9gwei0xaDjbwdIM7tAvKzpmhO7VsB94R8d\n",
-       "qd713e7wyBI1TGWjkMvBmxrk0CHtAjnxGdr+2d7b9AwK6PAiAeQ0naVOFd0JxpOtIhz8BHlnJ0Yw\n",
-       "HyuCyYih4G6Wfxc19GR0XdnhjMr/shegvJHliHCz5nMWgW+HnFbYKOiDm69sMC7mOKr+/PyL0N4U\n",
-       "CdBFmbR8gUgEJPWU0kw7p3wcOoYYuwgsALALZ95+ObIqxT6+2SRhRiVUsZfqYE0DgHKi6WQ00bDU\n",
-       "eLowlrWv4BdMs9t9TP7ZQOqYa80OxRtJU3h27lxTaqUEtN7FGFOWBcqyZDsMyVQVYsAI1RA27Cs7\n",
-       "LOyzGQT7G/YFgJXOcagnB3M7Qyv6wezvHR5HLRb1Sqh02wdi0v77tZeVXWPV0i0xlzDkKaF9u8NI\n",
-       "KsbOgwvtClbwzkvSUc2GuyQcC5JMU36xMVPZdhOZWBBPS2OQBD2woQiGfZbsAIFnFad4cBKnW49O\n",
-       "ygcYeZ/FZdqn7hZToymo0l3D9A3euPsyyW7+vWQaMuGtpUDrB2jJ3yzJ/CFvMm4lFAok8kCVt4pI\n",
-       "ZNQHIX1QSsGr1e3wYjTCTSdLD9GkB/5rMBuWwHoCWPG3V/AybLlyA6mFAaajjProLTBv+8LUQ+b3\n",
-       "wlz8iu0ieQQI4EtsOMejdLhMD2EYFxVMtDs3g+SkGFEeGTnefob6VAIUfARSKJDfSnCUmw2GqT7q\n",
-       "L0+hkJsYYbCSk2aauzTAPeZSZz0xQQOfO/kVUNEAAAvkQZ+WRRUsJf/D4hq66IURjKaPswbI24xw\n",
-       "cPGU3NIEB3j2D+h83zpqsPYUHZg7dgehcXZNqXavCZjkf4FTtm4OTnOgJAE90b9fP8aQqEZIeBiE\n",
-       "554PF2DpXzDg/RLqRnKz8dW1M3gJ9b1tsnivtSoGEsFhhcZF498IuHg3g63wHvkzmoH1pok35DiQ\n",
-       "d6woY2upiP2zKH6R5QFVqslNFtn4vgtJahzehl93Re0uPEAGswUblnazdKD93UrM1wUWbmiRB7Mz\n",
-       "/o5Bn0C6FlqKAc0FEhAtXARNXtAqrremtcJzrSXh9r1nruI21qRc6/OGF2cHoqEuz6P28XbCPCvR\n",
-       "MOr5dhogyV7l0XwwIIz6owXkUbIM2dbOYkNnYUJfB54U+cL5Lb9TQ5qD6KN1axmgsxejzKBZiXT8\n",
-       "GQ8Nvaqn15kY/IfHZRYCw3tAgPnR8Ki96x7Hk7QWy0YUfLUN5m2I0Pjng1apF/VbfHf0zRmyHvRc\n",
-       "XHd5CK57LLrP40I3WGWFHo7w4JqE7IzCqV5nGzDmfVwpzUSd7oMM3MPHzIGrmH2qpnSTJGpEFMHo\n",
-       "CBmlTgqVT8+eDQ6hz7DFWsYSQkuMstBWXzLP5NvYK296lf5R2X2DcKdFCYtdwNGa3BHrfbH7+rD7\n",
-       "d5GOPmKlBaZcfqotwQFV3VRre5mUeHFvysYdgJ02CLkyxU1YMNwPXulqVAjrJ6CPiJ4oXTd2mIC5\n",
-       "+AcUbM6+k/Mz1P9MrhQkhfw3LtWUhSXKYX5J919CnrmrpjD38Mzfd3q/mHbh8cqPHn2QhzVNsA6U\n",
-       "8v5f2pSmT0+L8ZDRcJ1fZ1WFQ8Bw2XdLegVOVxCoXH0FlzYfaLs31IQN9FE4IvG2P+LFU0stLc9I\n",
-       "Vfv0OqxxT1wT7stis2qVhmKrsZjWF2KMLBeWrsiQaYOu6pneuSjtQtf8HI6Ep4/hpyE6BculWkC+\n",
-       "45DN3CdfXEoP4430CKkrI+L/vv6F99OjxJ+zQX18jNl/h2U52B9vyf34PyPjMLWgHyOflx7yeCBE\n",
-       "f4aJoEdS1mCf3jUYdzL3tkNB2U2/S28qyDYHQGVIEC81UVrpL1p1ftQC8N8VmBvYhrpWBtcJ1id2\n",
-       "a9tSy8cGa5dJuKwJdUY4tlBMqzAA5qpDDI9x2OQt7BBiuF8ZOoCew3Iy1DBcShPi9GuC9fxm33cy\n",
-       "Oy+U6ndqrnSbKTeeCTm66YZ+N6psQOYfADle3UC6yRLSWn7yMaQlXzIkp0CC7/DtbJqYRm2WZ543\n",
-       "weyauM3pUWasedTClI7UfD9P0DqPmdQIp0b9eB/ul+rfhjSsf7Y0d+Ag87GgiRmBTwKGS8fjKqKY\n",
-       "mO0QMPgK9kowpkYy51shiEztsHMZd9Xi69n2p7rG7LeIbHfWf917+z91T1PGgEoaSc0uqccSrugA\n",
-       "gdhcGsnIO/r3t+/MWrJsSaatcv9oXZ4gSbM/Uxphq2mXx/tIX2xucsPZdnozK+xAdPKhO2vnflCP\n",
-       "DA2R6Ty8EmrrumkZZGOmb6VzClpYt06pX5pPZOUD4oZn/1+ru4+aCnRUe+LLckLXGeHyMj09F5Uk\n",
-       "OYSg8IOqEuQRi1TKoOyzTlt9X50lG0Nle9lbCU9J5Wk3Dw35xG8dBPMbFd2haJU4wBRBvVWAUtav\n",
-       "8DS5gHo55/eyOAlIvRP5+QtHtmeKvlHQ4N01X9Rb4ArrnB4J/JikJbpSSthn9aA1nHRGw1M3Y6Ph\n",
-       "HHwqRajhiHf2ek+hZPvQTDRsm+hq015hLV4lpquPsfYwVXl3eyhx5bK5QhNyWMrhMPPlNjbUKwUQ\n",
-       "yAv48rlyQMKCeh5naj8xInxsDoZ7Yw9jr8HYY3l83FhVQU0KRxdvU5PNPBBAvSSi9/+yXryRCYWW\n",
-       "ttE9Yw3IITVNa9vIC+ja7SOjQK+tECnwsBuRJ4tZpeBH1ylGpjkdoTB5YEG5VbfBWW6AIeO02EsW\n",
-       "nBCXOgZWVsNIlYxWk2bjmEJD8oHrNSgqk+r0efTih+qv+iF/cykR5lsmvCPiDdq80piaoJou6hLp\n",
-       "GT9/STocPkFyXScmBKwi+sykXHaeBs7eRDZdSf/bZeg8/LF1h+nXDHygolqSaiGLKXYbD4zPFqQ8\n",
-       "VkkYsbKHD8I0/qqdik3wrXx0pMFUbglKVYE6HAePZVCXD36alImyEa1t0ReW1VF0EeGLfAYMPYs9\n",
-       "DOoZ+3eNtGEcy4MnoZ/nLQecek4yeZ4B0+fnkoJgYr+0nJWm/enzjEUeq/XQhaIWw5CC+O17XhU6\n",
-       "SShS9Ypject6B9/uV9zH/7tGTGBQoSDd1EK66Sh1nBvURekH3+deVqTQXc+dyGLlWQATeJo5wMDY\n",
-       "Wke0kv0UkBtI3W9HrJNarlMGcknVgrE7ZIJH3qYoHiIhhFRVjxbWya1bAv7jYfpz4YEVtP1gCPra\n",
-       "rc6AZ+KDvmfm82bw9aZx74dgLZG6hi3mYX7JqeRFoyzpfqQK0AbUe3G+TAi2zqX5B65SCM1l7VVU\n",
-       "Nz33S9fwSwFm+u3NMek8chiI79ucVYGCFDLnaIkPX1nzfoMo1bvXvM2BdpmEWM7nmb3KoySYPlAg\n",
-       "XlAZKiif88VmdwVSlNtahpPM9dORaMR5AojLyRl9UBzvy7T9ek3H+bv7PXcxPO8wcR9sDGAZr23v\n",
-       "J3P5LP9wSrXFrFEO72HZ+HeZ5vCKrHHVuTlwqCCCM3x8a5FLogKshIVw1rpPGXRVC/K9CX3PrSpC\n",
-       "4QVYIjroS+ITzpHKs9Zj9yduZcoX6WjIbKeV5ikeb2X1RNZEXYOMrmuYdovGClq+Un39fmVYKpKz\n",
-       "z9kEwCRs2+aqFmsml0mf1nyjPDzCLvE4R52f97GeK8KHPwG3/X0L1+ngz/nP0SunAA0jM6I+M044\n",
-       "R451Tk1mdz7mxBg/dfw3Z4aVR0ap4i4dqqwLtfXYfUdh/x6QWIyqu3gUmTh+q1EG6LQnzNAKLEjg\n",
-       "47h9mMSZrL9g5P2tO//AkFQZuQHWXOYFc4MSbdIr4Rz6EJGB2FkmGQ8O2RODsxVChQ7Ifql5j/JR\n",
-       "fVV2IatpYRZu7eHEecgpRj5XD+T59ItXOPDyihPpzEKusawLOJsPIy1k+gVIKHHA5eqL13Q+5nzt\n",
-       "R7gfDkkb4GS1j4St8aVreAAGNvmYD6aRO6aHcVqin1lyauKjkK77+WiE7osdr2R9HPKR784tgyss\n",
-       "GOHDyaGiY/pfQBAPRlHVxKU8xzXqUYX4VE8BS9UjB9K4g6yI9MR/fKjPpWqPd5Ny13/+gXF7E/+Q\n",
-       "XhJ8bIrQGQ/fZgsDtvt8+8GpB2C+fnBfk3PXNgH+J8AHi+L/AhywdGXZs809Gy6oOeK2FuNTLQUC\n",
-       "rUyeKkJhJuC2ehrmFo5YU/7j46rFobTcqS3xTWEWsTPaQW12wWb9G4/qP5W+2y0CfUt87zUTpPR0\n",
-       "6NsCz9U15ROLBsvdMLdq5lWcbPTyngMVgCEPZqvGF1pNYEweK1r1Z95xU3x7VuHgcLUvTMb1Zqk/\n",
-       "dOTLe4XCBxUp2D7m0YjECLtcgg6YYb+xDP0ksRkKZzAVn/kotbfPhii7iM5QdlPKKRMoIyZOz3nU\n",
-       "v3FpsR/QY98SEW+wSNHn1PES7CUHcR4UJseJnyk2YottCqdPowHqThJ9SedNcUIHsv9koTgIK5w4\n",
-       "z+JCZKqp+oFU/jUrvNpIhOhGuGkjnfo61uG/YcZc3OlXAuisPk5NZ8B2T6OG+x4GZpC+fjhT6kHW\n",
-       "esvnORd8YujKH/SYiobgjGVR45RmoQ+4z8rqpDE5te0/AxYtcvHaIFhL+FMzRRVuhKDqH7jWH4WI\n",
-       "hq/v/2KIeQWk6ue0EuJVZJ/s7gvzkjKWuN383qy68pK2THvWltYX09i1t5Ev0V/41ao+XEZSIwPW\n",
-       "DAG6mjJ52mKY6xLZDu+HowCeVx/b7BN3gJ4ZCZ/VWDiY78X/xKR3JFcTySMsVEv7pgFZGAMQLKtX\n",
-       "WUOJJbssxa4xASQ9jnLBRhE8J7wO8FCh9VzKqJwUAB0DW0dMLeHQ7hMZyZYAgZeh6h7jXR2tGX0b\n",
-       "Q9XXfqdzOBfa+rjdwMy4TrBU4m4qgDbzvhpWpwFaLXWaguNqppb9BMirhTHffCsPm8hFXkeTlfAA\n",
-       "AAj3AZ+1dELf5J2gLUXIGHe7mq5YUCsxGifB6AGtTPAORqND8ERhHxtfTtU4UoUVnJvW4PX/6mvN\n",
-       "LX8YanZMmJ35B+1bu1gOxUnya1ZtkxIasUYHXf1Gvbnia5qvlRsHCoNxrLynssPt4pM0/6wpwOfe\n",
-       "5aCBbpH6kAeYhtyAoh2tBCED0xWtQUunf+gUIgZvVZUBLd603qilMxgQQJdACdqVF5/WgYaeXUUS\n",
-       "GORylQlAT3WTBIQputz5X4nv9p6cSn8zmXACm2p4kZTWAq7vDA9U0eg4mDNrQWeRS7QyKndVvE12\n",
-       "EyJdGT+jsCK00DnOLj0TohPlrql4jHSN3m/xoKguVYcEsLWccG9iY7FdZjxw3UfrMYDWTWASFksC\n",
-       "mVJePBtrlH8zbqwLDfwECUy5Zp+AnK+86Dwx0y87zczsiGj6rRv0jab75BqRiVwx/ItHAROLZPSa\n",
-       "OgKshJ0iuSENAzrHGhwYvWVtS2Yxl6fE91eakGmBVXofPIOrYLZ6C0FIcXcSjigZA9CYYQLR52aH\n",
-       "HjS0ru9kGY4kmkBl0NFwyR7VWD4EB/p4HkbxJmr//Z/j8WVgK+jp4FxvTgzt7Cf99W3OjjT8NLy3\n",
-       "Gg0Xiat7dzwcrpd4UdrP9ZPxfHPNO2yz2xN6F+VZQeDU7ROaqfcFTdHZbqyVQdzdrQFrApolMIgF\n",
-       "oTIcni48bapPmPVP+GOzV8g7DaBgeTFvZh0YmtDrUTO6hlEYyklPYIX67RukPovmaaXzulpm9Lu6\n",
-       "ecmIcH+YuOjxuL0RJP53xxpTNJ+k3VLUbAOpxlGRCu+qzmXoHNA2yoNJoePXg+kyyeHhwn49BJBC\n",
-       "lX4LLYEpCZdcmmAMWI75nq4jEcTrjb2C88bIL++W3MxrgqvoTCiMNDBkmeStj1pK9Liwu6A2oKKM\n",
-       "bvD/1Tr+0lnm435VnjidNbH6x1eICYpe6me7OaK5wNrKo5HVguFexYAKEPC8W88fzXx834LGAFmM\n",
-       "Ln1tXVrTAM9KfdgRAjyGYibT8DoUKSOHkanXm/CwB5SxH0biv0UUmLFAerABUwCkDE/QIHd0SYeU\n",
-       "xo9FsIDuCS8ixXv7KrdjQtGaAmEocIcJciX8pmnYkaPy1M0y6j7PBNvQZMKVrtLicZODdw9N64+n\n",
-       "tp2aC/GDzN0cOVClc462+RyfLgbKkZs+YSjF430ojAeXcJWG18zlgk4UhHJ5pDPfqd/DpGgQA/pu\n",
-       "HxcVv4bSKgqEiR6BE4boNzEMWuTL8smKFnPzsYsDQ9DN2+UVpPH3AjfLdGQvNEdTTBDyTmvrRu8V\n",
-       "5QXwIe0PM0TG0JzmVtx2akLm4HTqvo3jAho5nyaL2TafBQNp1Xl02FR66HTGkMuUfip7YhSLJZjq\n",
-       "QKxrGRT4mgF7rbcFjDfeINusuxF8qOjxB0h/Q9meEuzxaD3LmYRcWLD/Y1Qo9RzLcQUx4E6allJP\n",
-       "9emnMUwKxlpb/sTE/6+rSaPjKnDtXVgNijmsu2ZesOybhthtTjTdxHKCeLyBNdkNnW5mkowKlZA4\n",
-       "J5iMBvuBqtyieJS86bdpo3eyzzNOvtSUZ9gmHta0uZY0R3OkrogAZyKXZC21MuFt70lovdKGXzXx\n",
-       "hf6gyAbXNF3kQ6jSelPTEl8fKb/wyLGJrR4tQ/gW0vu+nvFtmSAQRh5YHfC2esgF5PEgu19hVihH\n",
-       "0YbB/AytOccQKxmo6oF7eLH0yUSq6gz3y75hIvVpji+UNWGnDDtSq6CGDLDzk1dJX0ktST0MAo9O\n",
-       "jgiqiPJH8wfAbQHRcOYDEPx/z7sGghsk/gI6AF4hZlmJu1ZDbkQC6cMjsaj1sHig20uPvpcXGs0X\n",
-       "j6H7iS5RkysYcJ2FA80CKQ5LbB3YeKHmP/OXgQ0MvR9furJb9VZZisxEWj1RxKHOaqUGLbvcFupS\n",
-       "RAJYrbUbV7G7pPnMCh1b7GmGh1bXM2l094p3UxHyXb64qQ7kTHDU163oXuPDwNPTc88GvNls++mo\n",
-       "OvjE0fPIL62dYh6bTO0FIBBE8hb9vNUsxaudoqmMt/FsSsgTbxROAR6hrbjhkxHCKzBR902hv/pG\n",
-       "lwHAKSxqMV0swyHljciiwzRpC6cJT+ZJhYQroox8+1pSZF287sEEUpM2kAz/dsWLmG1QV8Kt0LHj\n",
-       "2C7cMMGAMoqMb+9GFG5vQPXXcAEY5PJxO5j9K9kXN2uL9p+52+IhXuB/IaFQwL/flBfbEUPxGenh\n",
-       "KPfoo8Wxy/WRdxVQEdSiwY8b/XZvOJpH9xjipadsG2+35JvgRObZYceaxG9GLlwtudAkfXDDsDvJ\n",
-       "rUAMr+PGk+8BHM7t9B8TcfA7yMOUQile5AyUJhDaOnv3C8DnrqxdsPR8o8ycXLLBBSuTW85XO+iE\n",
-       "+QJfEMEg8K/sA6W0gl9idhdxZ6mcWDYte+p1VXaG3EyfQUiUUO3Wv9155W3mXoN4D1LLYTSvZB3z\n",
-       "rSj8O82Q6KGcfm+5BI+94kv4i9h11+QjbXgjNiC34EObfVb7+0hJBfklIp4Dm2bv26JI4TWowRAi\n",
-       "n98Pes/eNFGAOJCqI3DoDOqAgFuO8fUnpGe2qMwRa3MRZtYwR+6FnVT4jaHMMx5Q26DrHfFsPXhM\n",
-       "0J5/bIjjPgHiaFu0hrhz85vs8LIqAWBBVgfV9CwHQbyYuL85LjLRXChs16ewOYhYjoCPCLGjJeZg\n",
-       "j67Bjby90qYlT+lO2Y6IgtoiFvvWAmIQKspKPsWKB42FmnavJ4FMjF2WJKO00N//edBfqhlC0hWP\n",
-       "q9VWKqBruTKzPzBBPXwGNv30QVz134034bNM4gCa7GJGcdcP/t0rJGe083/1aF6+LGOaiKRMNCKB\n",
-       "vFy7H4p0wzGpUSLukwRWYRSVOL2Ci82M4NIH2uHlPfUODWuPTupYGSB4P2BxfCyhL+bSlK2TbP0v\n",
-       "N6oTJS2kB4Hlk5pepDAn3t02dIB1ncLclhFR4Zo8rAEAFHSLO6LdBdhiEhFs/fwDV45mCRn4pRC1\n",
-       "sLFdELDRBM/4t9VYKibQA/IWtdIkC19/YW4ITju3g3qhNP8sEx9ZxKGQ0iYX+C6rN7fF6QATEM68\n",
-       "J8Gbf4lYlP9GmGzZ13e8kAYdAAAP2wGft2pC3+Ah1oWpX9mLPs7TJkx0vUnB+NAtZdxyN7xUuGa7\n",
-       "cIiwnzP/XaWXYOa+HRZEOZIQF6MpxjMYm3JTPjsnGrMTnqAPACeBf+sCOLWxpPzWw5I/VHgvtkak\n",
-       "avF+iAYiFFkuDDKsmqtQqNrse9kpIxEpAzPDaODzxa2sixihFnQHg1A7CfdnBR0gcyLM6J1gvD4r\n",
-       "u4SRUtP+e4bBBSuiKBZFlv3YFEv4aQ4mdF5+3GxSeV9qC0Gb65yUulRc/38jqRlPKDto4043eTjV\n",
-       "qCNlgaTB1gUbadd9JRazKYJCSUTQgC81Fo/uW8vD2XE4SHehSGcPTUpueZByEJ0WQXu1wQWMxlVI\n",
-       "OK/WMKxld38XMYIbEV43wKs8FMMZhsaC4kmVb2XnLHF47ObH9iAXnC5XaHuiq3e5xGajjSQOxwIY\n",
-       "G6/uGmPONyv+a4WzOZGyF6Tsreke4LcbkumhJ1Gv1iVHKnyAJRwmc9Pc6MRJQGeGoXwn3Hl7922i\n",
-       "QgVCp/X27vGBWUyY+ugWh0eRQ1t0gqV0PaqdjN2wiMw1MTWEEEAULX1iEVtM1gK3JgvLFZocsgMV\n",
-       "GsX2w+oNg+QmEthr+JBAvzxde0537PkSW4GM0IQf4Gs6IkTPbeH892wd49sCA53ejmThxN4/ctqM\n",
-       "TT0CIKpVdWm/yFNKIxne1/hbdXSMjaZc7FEBL34qrCOA0uSPgVuqm/4/UO28450QoeSYvZkLIDJy\n",
-       "jRFnuNOprelHnYdtH1euY5edjxffJhs4dMEwAFSg2IlmyZpqphxJNKiOPR7vKyqIJts7jsqEVcnM\n",
-       "5beSLN0+YAZwWSBL+iDoHidcvTZPW6erlzkPC38oZGctPJmobtzN2m+b0VgadAC3sLJ4tfpE0swA\n",
-       "DJLLd5+BQoRfXguAcTdOJ27Yfp1lMrnk3DeYJLXm8EWMKOMLSODc/+WEEZrZYAhiy2Za40ehsqeF\n",
-       "I6XmRTjW/pIbr3qMN4EmIihucHoXHz5bjxKbIycNfbJOzyPBDDe7pY22bXwNd1RG11GH/uhvIM3w\n",
-       "hRAwfLPmEpSbafSUrMX9GDszwgG6CaKKrZDSlwzQzvCnubcPd/D2HblcsB3QMN0ug41NFjuOxfcn\n",
-       "qEg7Yhue2ZzFfqKsHzSo914r/RduYNJR0agiqyOljdJpyrNyKJYLB1yhwM0/W5o+FHCM+eHgSCL/\n",
-       "9CDq27e+vh7aC4vd8ZLTIXLlPdbEFHzZb6S+/mk1IETARno8iiefGSnoreENEQiJIYwO/yYdV/06\n",
-       "hkFHASE8bKDU1PSrWpbZCT/ZQkLy/w9u1jQrUeruMuM4VqvUzsDUgENytRZ+YS7lp+Cq+U4tc9dJ\n",
-       "cqA3LWyRYQ806KalhNY2UGw+QPwzTRwOVvcfGbOE0i3rhfraOfw/Kjl07wwXyjGU2A/mGud69Ci6\n",
-       "pAjbKxDC1utnq4T8kXvhzZxKTPD8OgcL6j4G7SG9lwL7FCBC8LKumQl/PeJXjyM4GjVSi7zPN4xB\n",
-       "US5KZLq3DPRibScWND8utmltka2J28UJq9DM2wQPfxCnkPmj0EwPJJQEFIRLEQe2ZsHfNuc0UqI4\n",
-       "nG88vl2nHjszaIew0YAnZvNt8rYGd0DtpbcH/y1ZULC+yIuwZydUt/HWge3+ybJZ3DBA/DJMAS88\n",
-       "c7dhYJ0/MTlZ7YvhlLSjF+NyS7lGPV9IlSLDL1kjwflTJ3vykWWa11Zfe9IKZdNYit2t5VxVEHMb\n",
-       "E3WFQEq8gVa28MDgFP4p/7hrka8hj8nRIIorI2tJncL247eCmdzrLAnITBEghoqzaBhpGzhoS5ve\n",
-       "J34WWsUyF5MeIRzgjGt5KK9xebm4NtYdF6Pmywep/G363KV3tKHbCj62k7sP48a6i5MVIr05VusX\n",
-       "V+ZxGIVS0DQNV73Rj0ss0sJMfsLwPmQRN9K8alC8mWiKbDXt5wsv4iIgFybCzRYxonPsjHYH4avE\n",
-       "pPkXlWEu7w0z7kQq+6GRqQ8c5bcs7R3V8NP/IVZjiAGxkm1me0VIvXQWrILUpqd+7RkdbXqpqD2G\n",
-       "xrSx9fsyl2U0HPsbdUSOzzF7h4W0Qa9oxNPkgN6JnAIFAo+HvOcVrq/+0pmwA3aEj2laX0Wr90q6\n",
-       "OMID3LZ+ghFKUrV1H6G6y0etAPcYcVgw//xwvlCWclEo7UO+PPYUYWMRjeRQc9/I9HASMtJAd8yY\n",
-       "RnpG8DFHknuXGinidQRU20JfpW+LeCrHWwWLQPn7hVUWXinfzfz12ic+0/KcuS2d2Qy3SYVARn2i\n",
-       "rOZ3anQTb42AsoKZMeqRBxyW4eVfyry6cPdrKFMNf6E0zMrVt85OfBjTqHA8WC6sA/y4bj5FJ5sq\n",
-       "/38Uma+6CLKP3VOmZKdWUXIp+djgC/WAHNSHlGKoyMsuudOqgpVwsOu7MNzn4vvs9HYNuHAsA33W\n",
-       "g8TMV/ESa1Ug/TgU2wiHNLQ+L5KK75iW7testH+QC9ysrBMcKC7Bb+GFi5RAsSMWZUAYqq5LnjJC\n",
-       "u+VWVBmNAGCeUHGlr30ji438C6CZ42WOkQUVSdiXDN3kSNd4ZH3kP/pnJKvI1fBYfnZbA6qAA/9c\n",
-       "yAftCA95wZlaKDYEc1tMN2ZmdiZZYm0jbZlOPcye92Yh+VIs4Mvj20DdsYiHKsZ7IYQiB/0i1zzP\n",
-       "yWdHarDylS3/RqR/7m2832NntSFIqrjlqFUss+QHyAVtzUg3ibzY/duJXihc2Jivk7JwoL4FcP3t\n",
-       "fNwZDahvnmFs8fORvff5vkGeOfQ+XVp88KkuBTaV4WpFcUVADeHZ9+lHENbgn3agYSZdCNbI2ivV\n",
-       "B/uFp6kVx8bp/XbBayXAaQBkvfSrRVThBw6hOMJe/LteTlPchtpZVK04LGoa+1McAGKAN0nkSxod\n",
-       "h+Gp2ZgYEt9mhc8dB/jTJx1phLAqPMFKvLf46m4vB7XTIfnY7Vgt0pTZeCG5UKj1uG5dLk6iSJZW\n",
-       "TZJQwhQK/OBbWDbfbyRjQXS3ibRadTFtRJI3y+SVsA7FGm/NPlVVFXcngD5AI0rnRXouu/Wc9GED\n",
-       "rxJOlgxmzexgWWzJJXdatPOk44GKBM8u/LT7bSPkFDoxJdd2z9j8GwRaTpl/FOI8jpBz1TS4kUrE\n",
-       "Im0hTT4/yjiXzkT1TghT7BmCF9fwwq7mypmRqLf4Hb8znpSH08+myjNy6ihqrWc7G60V397EYUx2\n",
-       "x93neXSusjjcBAXc79k2C627BypUvhs2FJG4lErDXXxjYVE75/EbRGUGCApImvkRLp34ZBJT7KGZ\n",
-       "tsfhgR7Uc2gOJOSwpBWBLIqYGw4ysG1iEJ6+ngqK5nPyJVZHDaYU0w4WRGjFtXQaDx3zcODRe+L5\n",
-       "LMs9HbNp42Nn5Nwz+SCM9KNJ8SDTLm1v915tOVp1Z8XNtdraf4UQ0MnXs5FrBijxee/LN2Vsnap/\n",
-       "/gtuhiOp1mP1DFzFpNG+WSLflV5NiwwYcjbqSmJcfmf/oTOE66x2lNfSA06nJOvqOi1KDm41Ihxs\n",
-       "yoVAuSdQaeZlLrGWt6VGCKgYzb//+UZFaAqL08usfnHKUpVnYVJ2zDy+GWqDngda6hqKk4BYJMuv\n",
-       "qe3j9l/mk333Z3VkzQILWZeDJgSnxBQ3NxRpiXv/ufrNoHr9mTYAavdg95w4/Eusfk06aijEFhIe\n",
-       "PlgpgdjkIQLl/cLE3iWjJEBnKNaLzXclaQ1fdz2WP75Tm4ZNYmkmb+OpBOLGC/vr09nvok3KDbyO\n",
-       "CnpKJb+PjuzfoK7ReaIR3i/rBzSemRyIb34+UKifF3PEowgKlmflGqK9GBqia5JHmNQlIac9W/eX\n",
-       "y5vz9oKKd8Z3hylx2w2r8kYsOTobRdR7FNZPYjJQ3Kpp51x7FUOn7KmWBeburtWbyjPp8fHZJ6CL\n",
-       "Fb+DMe8HufDe7+Hd3rO/dhpGinDYOwE7pBA2SlvLm+Rlep2tP4YEoki8bnGmDkYfKmoJeJin6DFg\n",
-       "wid0fT0SyJb2KIZHayb74UsG/4wi78GftuMqPaixpcS8iMovQWZBdJACxJ73TqFn7xwGNCVSRxHs\n",
-       "xzP7q+6/XRipNL7A+EU0uGProcIOtrGtN1eBVOehwpmBE848hL/1ENeE4puMGhETtziDkVIF2J+P\n",
-       "BDohuagqQJPA+30h3ZiNUlFS+eOUIJ1BqvbNv6BIfZcdS4DGWLP7QQTItb3Q/MGxyUBp3n8yEfNC\n",
-       "F0h7Sb0HvRWO95jBrVimLkPE2VNT+44n7BPbZpLLOhlT4UVO5ed0msP2uN+PpJzMluhNa1TpPo1F\n",
-       "PrfeQekIwtvrkMViZZ9hyomKPtqTysgfkEtxS2ycIFXyIztZacKVX6rp3x1kctaDg6DI9Qx0HEgK\n",
-       "BqNo90Li67k9L2W/nH7fa1KYbTpEsdQB+K5WyIr1auROLNT8sQszfWfR2wgNAuXc6KaeHeR4F/zy\n",
-       "41r3fqMO1o9s2CwZY4GW1Z2mZ1BAOY7Y3qmKhGBwrbhYvtMy9WU1r6lyAeP0TOhcflN7VZs7KgNn\n",
-       "xp7GtYqmleQJlFBP/lemsxBvc2Hqz2JI5Wn8ApZubqZCfqpZMWxbTrpcGUFHGuwUUT4IED/ZZm05\n",
-       "wHGJh+FUkyu0waERzWgSAvXuinTBt7LnL4PGS2zpYqgrHFbG9aac/rnl0+fHin5dOBtP5fUrgJgt\n",
-       "CIfZ5B3WxKWbwm+I6FsjnzbDKD95FA8WLIo65VUQG0rF8MEl9cxAyNzvYlo+wg+uBJIMhFI7XMZL\n",
-       "qJ11y8pc8vDuBxAM9WcLuK+arTDf4v+NS/JJTDGskAL8iuFydk4FZ3zd6lF28+osD8sk/EUD2hgR\n",
-       "sT4fgxlO6cHbuweH4RClThT9APgBt2RkAsppU9IbFB23nyvbV/2irFLA7xrn83Ho1V20v5x4e2g9\n",
-       "tsomim6tQD/gngyoLTAj0PKSv8UKMft8TAwm7Bui349IG2Rx9+Vg2kZm3lksryXgEjb6088v0X5R\n",
-       "n8vbmVnZue3SrS/lTjrcoJ0iJ65NE3S5xeK6GilvXZ/s1vaI/kOmkIs6L8vn9HLN4dGB5pqaxR7A\n",
-       "2zdqJ/2qk3H+kTcVGBltvpDtwYOuaYiMA+rq/2BJpq2ChVstwkhqn1vvCKflFpBdhC90Xsi3e8J7\n",
-       "0xS+MogmaomB9VuJakPTN9QT7X2vUBU1D/yzUH9akQ8ZyVc98xtUoI/aQQZB6fcNDCxedK+vvZqW\n",
-       "WgYBMKQw7y2Wwjs1tpsNouYFSUDi/FdmtpRrNSG9fzsrpBRBOf/WYm/M3nE850IQ7LIV/911Bdr8\n",
-       "Os741RtRZGfwGnFJUTUbC3TusAPSbnOX5NaroI9BmJJugtir7bJpSB/CYhI2JbajyOqllDEuH3PI\n",
-       "AZotEj/uLCZR5QziAxkJAtTsW2qoMXVIYIU+onQ8rI7Szvm+EVYYKwrRZsEYslcdnYmN5qlY2QEW\n",
-       "4yc33KzqlVVdgvxrz9WhJAZQG6V5ePdKO7PgNQV8vgBgwQAAGS1Bm7xJqEFsmUwP/3Q2zR0JceWI\n",
-       "Z4qv4cMMyd1GukdMntCqgkH4Xh5aI88EGVsn4Yuzstn+vCMHgHlkDwhchGZTTwEririKlBn39joS\n",
-       "2Uspab5tCtc6/PcCu+uvRFF2k6zYIHjhsfP6jiUyVvJgSigl2e6qpEY/3Xy39A0qJWlwv1PW0UpU\n",
-       "3INhAF3xwOezO7f2WoKyZ0joGSG4g0rGFQiOw2w0HOycfMdwXg7flxAaUQnSDhRajD/+spy+9TSl\n",
-       "+TbFNOdsKRvpRVMgpIs5m6XGf5/IL+qoDa2DVG/WiurfNnR8EwPQepyhBCWeR8MmMbr1Ij/iguR+\n",
-       "STObmzAObEgwbOmF8goEsxjluXSt3ozDwURjSkv0Q2ODdTC/F7xFdrPk+O2KvSprFpt/t75Qwvmi\n",
-       "jIcFMrzmUzTbf92Umly8vRX9t+oKTCgFZi0I94XXQCvy9b6XL+M/OLX8tIG6LL9LtCZaCOA2XkJi\n",
-       "YD/nabCEwFxVu512lDPu03ZncNN81Squ3kGSjg2CwkzXya4C39OdZfzzq97JbLhGVKxYyvvv0MGr\n",
-       "R0tsacXid9mQzZjzIBPzKbWPGqO9mcz4ZfDfZfbHbl8SqI1+ZkWm3zDtWzjwMckmFWSIIgmqU+tj\n",
-       "nNEgStMrum1WozcVnP+E2sPEVmGLkGslicf4U24o/rlo9l278tKSFawsh2zj9EqiQD7YrqShOSip\n",
-       "k7+aZ0vYKmJynL0o6NGEtZ6IP1z5PLuTiOaZR2Uxy/aewdfqXvryukstr8t4N/LnS/Lsp0kXjx6t\n",
-       "cw84nIR8S7I5W6VLl1OvE0YrKoec7eL8p1QYEDbttLQTmkmiZmtuTAOhjzEGj74nGqVStpBRuvXM\n",
-       "yWDMOSdIqzJHleQG8GELdVj+vQVM/wDb1hjG++Vj1SgboulHDENJ1GmDTWPFFY2IgIllhHgaw6Ca\n",
-       "swRSxyT2Mf3+O5pc0rm+OrwTGSz6N7O6KG5FN7bCELVEOO33GQR16Y4pLA1bPHAGlE+FQYaqnjdB\n",
-       "y2qsmYuiCxKXBtVG1hLoHs6oJFVjHBF/wvTU3mmxL7ZAKScI6Oqr9Hwpkh6jpUnqxNPRlpkFy7fN\n",
-       "/1y8JKMUVo4KKU/0kK+tc04cnKyqD33LO6HAjIAQ0XrIitm9WgNU4symZ3JXRavottjqThZ/xBvO\n",
-       "faa9HsNaBephQ5DPey90e5ZCOhU2j0ClogyYTZTgFWh+x4rAVX3RgQLX3I9azSCU/6Fq7nr0ZJco\n",
-       "eA/Nw0P6um6lc/CJLSLOQJHV5CzKPMunrhc1Qx2yjJzuKgpz1EaoI9MzxwUl8+U9WKocjT0LG3Uh\n",
-       "qXdC/igUl71JH3vRLDvLiOVGt5agzGsNa0mMd/XW/6PCPA1Zrqw4FUmg8b5d5j2eeiALSvZ+5Doh\n",
-       "VvTUTp7he41SNfvLTsFv963zMES3b4gJ3XI2Y3GL8uLALsesvFj87/b3JxPdZ0Z/m6r/HF+HzUKn\n",
-       "awFERMaeE1AXrkrAG8DwEFD3aurX0G/WyHfWop6B0S0PZU3vGsH8dVm0RREpvJ3rcY61zet4sVhq\n",
-       "EQD5EzutTc3x9xNv7K2gs3R4XfM5MXccOJVe1E3oM5s8bFGWUa08HY7uVNCZyn1laEDr/p2Oiz7u\n",
-       "32R/TalI4oIEiQLcZMYcDE2AUasXUgA1ntmhyTXqwxMeKhQbrEdAQRnY2Zaz7OLleCzg7WDztQJk\n",
-       "f9MGYV+XIf9c0ofwo5kJ6tU45gMSSqKriQobV+aIPU/umgsYf3jIHjVVHt9pV9f3oXCFau/wHoRp\n",
-       "AIFjRAlC7YxnZbNmLv+RYrAZaTXv83I+gxdhgx+AAWC0EBexfmjrOTsEKRdd1F+938U80XwNcxL3\n",
-       "Okz52q77RNbRG3WShExI4U7z9xOklvqvBpzPBF5Wp/RuEnh4J2ASOJg0wkPpsy51Tr53RRc9twrR\n",
-       "5950x9BtCnN0K5eljEGEQsqeFJmc7R6Dlb1jRMbXkUU0Qikvdbk0I+NSppUyJ9q/8F3dV92lkDR5\n",
-       "s46djV3ZBGEOu5EUMdgpKKSV2c2lxpHvqH6KUDbpVBzvKLStJ/p5lGTj5YscZNcOsvEJQyyQck36\n",
-       "zsj7lQg/KKhSrqRKZexmSUwpmdW915C/zsHvcAeTD7BBzyCIizboDPet0AJl3Z+lkxcAQ38Eo8gX\n",
-       "xXlaGX/QxKcnPmGWuI4ZH7b1wbQ7fZShS2I7SfyjS41eJLNqmsU7P5nFd366RIlYoBDm8/ePiJMq\n",
-       "jQ66tqsv1NPTs0fqQtG3LdjLbBQluJd0D3xXHd5w2yRM47YriVlZY6eTVPFIcWu0k1vZENfrnEb/\n",
-       "yxp/+4OCnsJHVGaEhtREfHqGIv62V3xDfcEvpkNmb73RYz7LhZsMbP6f2exP4ygts5dVCLfU8eBx\n",
-       "acfNkio5TS+BEDyv3H1bXsDTHqrSaK/CScWQJLKrdkVpQmZCmKPInTEZRrFdCP8bET9nxdgUhZf/\n",
-       "AqP+7VMUlkmf0AlabpIYz2Ozf4PBNCCghx/2e49D/FbEgs511tIHGoL9+MCKA9O7OXEkDH90Uo+r\n",
-       "E/hXAmutNBWusDLTcIosrBk97/lJdKLuS0BoSD65nGWo/vCc3E3NcreX55cjx+UBHvbWxyGT8q+J\n",
-       "0/gxFbi5DzogikTdnonrUdjUxlNr8XPOfRbkNQ2B2hztsS8iZ9C8JfimF/8tfemmYN3MqErQDeAT\n",
-       "d2RwgNyfch3v66r90vqLkfHJLoVelAEEtGPH8aS5fjpUAh5MGryGv0Osdlsx/CrmIzMbNofMP+sv\n",
-       "M4/UXCOxeMMKVeW7txVDASP9Z/rz2U+5K9FT8Rw92K5W+0feElNAGgSFeHrBzJ4yoQYakzRUjjfy\n",
-       "XHXxPyBZ2glmYtN3gkOKh2D0/tFja8y2fxLacPlFzrxyeHcWJBuVea7on1RnH2yd50IfXbRz83w+\n",
-       "BgAJlNeIUYxc57CXZitcmwmosQ3LYN1b0TrZxuFyOJaZOIJ5ANlVyDBF6xuxiP+JC3m+JnDxuXWx\n",
-       "HuMNRdeVjanGfdff2+RQVAwaE+Fpok4g4cNVGDS8j2bt7BuHZUsTDLr59f5LCRPaXp+rbfHYnNfO\n",
-       "+JemMeWo2m7p6mqUldtvmSwTZUQ/fY+PJ5v62Vu4PIiELIdDmRGWNyrTUNLR7eOeHWfRzmQdtPG6\n",
-       "xZijqR0Bf2hVznSppHr19maLcuR4dysT3/ju7dGIVlYfSJ5B/ilpoXkDCTmTSXHemV503hyg2EH0\n",
-       "xNLDVzPYVCRDaMtd4XUn0K3aw9JysY85HdMn5Nm9wwIH4cLr+ERVR6E7e7/PWX4EuDtQ4EGQKZSp\n",
-       "OPFW2lRfbEn7hdJnolEePxw6tHPd5GwUDajNONADWeJCG61WYiEgAKxM7LantQSiTmbOoRRe8Kb3\n",
-       "qFgZCDqA6DhMfcxQbj+T0LDqsZl1OLMUQxMdQ5LmrBJOFISxxXGeYrUAgxPY6xTx24gg+gLCXxzj\n",
-       "XOxlHluW7wZTTXyGuP4yGk1IxRzgGqQ3aouVFiIqDQT8fT0kz+Fcco6FexrFbHuJGL128BoJLZT0\n",
-       "s509B5WfZfMP2BPWN4yi3KEYYKW44Mmas09jQ1DulK8gSh0ikquWWlqj8LhEMVU5B+gl1OLasrVG\n",
-       "ST0viB4EZ+7JxzRWKSdHGr/4lfOvcNkM5sjQpxMGqX0ONlhJHRP8kh4R+1BWowPJjvBZgHODcBHh\n",
-       "sREBtSPT348WKoARXzyxJWs5yFj8ODWkVokoFEcrQjviO3nGqS46zbhuF6W/J7bT2S2GNLb5Ja4U\n",
-       "CDEm1XruJlNFaw918+qafXXhfpmmNxrIlKzTdZXJv3DdmTzOy0Q4tWad3ghouK6b2qSQ0+IbXRlL\n",
-       "85fBSEzfTdEyw0/oIuXnrtMx/wc0cjiPdgLMLIhRi0RvTp4y5/7Qkk28xHcuXH+bkmDnVLcQTDPr\n",
-       "gYiHHItZKK2wDdrgAAHNnUrLdJuv5+x2dxWRYvK+BC+MgrQ6OUicu1dxSmEpuoYUfcv0RGYVu9zo\n",
-       "wzoMfs/VFSTQWl32wW0tZ7J2pPAmGvKHNj+MammKV/nv8nd94oWAgK/VpwAADhBmT0T557+WWpIz\n",
-       "HexBv8GBwfwBZt4w01pupivKZSkDXXg4ewWCUWrDXs8NYgp/5x0A3lRdMq/cO/Si6hhBI1b9v/BR\n",
-       "wBwU84XtFCHvGn8JTTXZDoFYdX3PwHYOlsBlyiepO4oLoTCTl7Vfu+gHFRNwN7Cl/pTDiFCZsjSU\n",
-       "3X+4DvoUWkrLp2Q8NK/L7ydvJ4/w34FUPtObAn7SyaTabFAfr2kqefEfB0Z6i3RiUzVkF2rvzva2\n",
-       "2qHGXVJQlb0bmtEtXyuxWuxfDDBROaBQq4QXyYo9F5/Z0buHK+NgSksdgDgYs3x9/p9JaTbWsEH9\n",
-       "OCRinaqkhbrq3iGEaVB+ndRgNLUA/SyZhv7NYpBA101mKVC/FLiKEAAGgpdbod8xu7Mf4z0qwsq5\n",
-       "Hc9vJCwOT/ZqEXbgcsgwKLp0GUGCKNypfE/l1zpj8pXL7zA7e3+nvXhHqpdORxHI32rYu3k3uVRP\n",
-       "hrDF93WlK0O//QYVPIzWfkLPLSdfOqVcYmTHrnbJmDp9qPmzUeuIp5lNMArfobcUd7B1DOWRE7l1\n",
-       "glpemmXpLNfZEYLSrrK+W15Ic+yBmiOYoGjajnZE+wKs1xX9OuVhxyVhUYDP2+zLNwOQrPKnErYR\n",
-       "ISAQFfXhAxvHSwb5DlSgFV1tv1HVBQWKmc7fmbt8KtfHUIAIPbX4dkpDoTulCUTSV2+oDohUwanL\n",
-       "0apNx3otF67GrP/6SlTLj+mjG7Qq+J9GxQO5BUSH5o/+KpPCL7GaedC6ZdXTq7BOMI9Dw5vk3jkM\n",
-       "Mh29wTJuE0ohdaGf9kPNenYQIZjI7zUrx418FPpLyDM/vKgwhI/cjnG23J0V6aptNTcmyXLz42B1\n",
-       "0LvFJsLxhZP7krLkEbo2xrPsYPbo+upmYsG+cVzoosMwxJYHGNKZwoULOvUmjRQuV4cs16N6iZis\n",
-       "+6fuCHes9pKBtzGsev7PJ0KlCbNqeHojfx5VTtD8czWAhVehFSdXzbd8Q9XOu6hsoRGRABiaTpBH\n",
-       "g5LZrgiItyAZlvBa3T8nCOEgf6Xmpb6crOXV6Uo8YeTqlbwzQ/FzoZ4t+p9Ug3gMEwlZTchQJat+\n",
-       "sh30s8NPLvOx2UPKCvyl84FE71vf2Xfhv+rz2hTVm5Yl2TQ5MkEySNB8bEGrJiGK2VZ1CzS/gDbU\n",
-       "FifFfhD7acivleYFbFTqdnpeS+W7LX1DzqHAaUnQVm3//iqZnUU932YhQD6kmraD8NyvTn2wpIH7\n",
-       "LbURATuzmYBqRgnYa9iwhg/upgwvfxIyJHCwkQ27h5sNFJJAahKUq8KpQBp466eSWAkcXF3V/Ki2\n",
-       "/3v+kILUQjW60ZGBtmCb87s802QvQSAJFDBtsPy9COQptpqEY9dWImLYzveqkg0qv9JU4bZYhELc\n",
-       "wQEx3JzPWPqNX4LeIOIqzcK/R4fE5lr7b1fPTIK5KCPPtsEn/rOkq5o6TCLiLW8qsk3X8P7RruEr\n",
-       "8cAEt5ZBxJw8Ee3/omH2Ww+jGc75Ewpd8uoterpglRbEY5eSJsuCX5GeI5esHbijPkxiJkIwOFyq\n",
-       "GJbZXpPda7AQ35sV9pwmmsZrl/NV3+X2WD0eR5wBXqZxgNivZFsSgcm/YzSuDVqNqAfna4PNAnRk\n",
-       "rKS9+/OnKF2vSKPOgwOFocTD8HJRZWyn7Kks5H/1zkOnZXF8IJ+X6zuBiMqcWvTsfl34zjMYeRdI\n",
-       "DLVb7nZkEwN3eeGLkk4+Er553SORah+R0Bfb44lIvhy1ePvYtaa32oIvhTZvtLS/7YBx1Clk//f5\n",
-       "zG2TBML6pOLpw9I+9Z+/mTHv7ubFc4Odw6VDHWbEuBAoo90RfH505cdTM1IDCwkpBTNGpnGfmQYY\n",
-       "SEq5v93s+UCtylJJbYEq1BMNqIiK8ehYolutqNZm2HIo4n5yJFRA9wsA5tQzHdGMMu9O6/U76wGb\n",
-       "V1rwipdsy5a4jKVHtsJLgIbbi+uNt0kq/jBHsYD479+ScSv1HTjNk1kX+mAwH2JcdNBcfZ6NkAKy\n",
-       "3zFS5egLP7yuaSoDpCxSvq2SyOL1Sekxt6I92sQO0m8gM3+S6hhY1LeSHYNiQY0mavNUneO11aHb\n",
-       "d/s7bIM28rgTE36bA6yi7Cj55ekthbv51rH1/krkEs9/O2pntRp3IGnkW0mOo0hjUHda8M/4ZWKQ\n",
-       "NYj2P6ZUK0vXfUpfxmWxNE1GsMKmbaUOc8HBvGP4jfjK5+tGkhZz5fHZsGzRhCvlp1Sf/PAMDZAL\n",
-       "XJzqs65sa2lXhAFftuQCcxOYHEeFliU1hJfEimo5yCShZZljWrcGJBJ4KoC3mjxurefxHZm6TChy\n",
-       "JxPdl6fL1L4Ya6H+xvq4MYnut2CntBEh8dJBgMpeKirsFCYC7E6zFbXESFIPlv7U871IEqvnYicQ\n",
-       "Byaz39rCOd5ErH7te93gQBJWhC0GUHjwB5ubNQMm+9wC3EqzqYhR9KoVwpRuhHLFAtoLQGdqxTUR\n",
-       "RtobQE7XPYkDQuWT5S8JxWy/O54/sllQd+WwW+lT7hH70ex74l8uPVfGzpo8p4GZuzq9MrpQVV+A\n",
-       "yRhf6getbvPtWITig6mfsy4aBAOjfkNm5RdQ5U+bzU+NO6Qju+94wpcn6dP39QTcZIZz93MNe82G\n",
-       "e22Ha5YUwjOAxGbXo6i156OylP1ZDY5E9NwewcuTa+O7gsSwoZA+qHKZlCHiu7bYIB+6bvhuHhQ7\n",
-       "alKigpzz0GPiqaxbFa0kEK1eJIqCwEnJvJz+7VTrPZWo83LmH8Sp7Sjovow9reWGgOf6PC8tlLW4\n",
-       "4/NnHNwYVEH7kGDzMtkMeF60DYDQ1Lj+2NUiJ4/uHkVVwoZeMmS/KcBDoqGqf7l73nicaEv8y8iI\n",
-       "JMPqoOmVDMsg2MZU2k8hbMOQTDxJ/hNyUkVQDD5soV8NW4DUzAPxoVtNpW3CAWNJLfV26gFeJ3rX\n",
-       "JvneM8X4q0ovofQWv9hfUmWauoA6rj/c/k4+SP2y4fQYjH92jX2wg67rpH313b3JjLCQ6uDKUSuA\n",
-       "JrkaIPQNrvJpE4BiGxCV7wnmhIKm1y1Q5vv/kwXG8D8WlRiE2TJ0hUeUKJ3piqhK1GJJv+fu/D6E\n",
-       "05lOCijOxO6YX29KuyliUcCe+FqNgpyCbK5AAOamsLI3AGC9YzU/9kVXmAgbxmG2vZwMoytZvnlk\n",
-       "iQdeV/1PozTXWYVBTuyBN6Zsqt1CFFhpXJn9jiS7yfqJQAmEHyes0cy03cMsT4cXCrkq/7zDGj1J\n",
-       "3i98v7Uipex9QHw/jOdo5kyPVbNhphFYRgMyaDeyv7N+weAxyubUZITUtTppga2ctYJhceNbOen3\n",
-       "gVQqRYRESk3ibbTC8F3i+usLLPNoy7Es6523qAbVNIaa1oRgugyD7uQnB6ZkkSEJe8jBMM4S0CTj\n",
-       "wUf3HYXea0c77JYQwk/jktxtX/9LEEhH916mj/jZn0llYTkoMaRyh3InMHyM7TAafP0XLN8/yyn3\n",
-       "rLRLbBjksNywQfcBLXYDO89m9HO8Gq1geUlfZwvJSciZHv0ctKKYYzecw6Kfh0CDFP/mOpIHH4EC\n",
-       "Rpd186H/7RG88hgZPiFobYN6S5iHbChfyH7fuHzNdJnPIfxkZtFAqR3z6IW1Lgoo5uaWNyvq449f\n",
-       "c3n9Qhb/n+yIBWXkX6wq5iVrlw2+/nH1gnArEbPRrR0qPylZckUp26GrYPDchukg6pviQPwjzKZi\n",
-       "w+ddSZDI+iNQ9Vfb/b/FI702wXX41OOjnNcGIfJQ0NAVWX91YjspGQPmHhneco4fwwNyu6NGranb\n",
-       "KQJsxc+cVn9PWlVJB9jKhqEvxnDn4V4KaF1BUXyM34ziQV0d4xG/BN21u78fZZgL63hGKFFXeWlA\n",
-       "a/PhkViFSuDw+ZrV18pueu1ynYFHqYFkde6OWIvfdhTaO5JZ0IFP9ndaTF+w+1wlXTmZydTx3WkS\n",
-       "OlxGIrQJR65YWMZw6RmfSt/JergWtnrUOTgAGRFmVd/fGUDw6U5pGBDRQFj0k5AaZj1YVafwaWDj\n",
-       "qVlmwuwSvAQfqwMhNkcDJvESNEhHtaKtFGPapy9xD/TEE1ANc6eIR42FXOa96ODJPjv19b/UXBGi\n",
-       "IpzSL1hh5lBmgja/0GzAFCaFllRYET/hynoauuD6MsmsPEQnybbMXz9Qj9yGaoNV9a4QBJ+X7Qya\n",
-       "5RZYUSi+IqGR7PPFuAveSMrpUS2Sx8KuQ66WAy/E4VcHl4zgIiBh3JzZVCwMCCNyXn27TRhFx7eH\n",
-       "7jhg4pLgpzpIwlxHi9RH/5oklWWn//Jw10HmANTJNhfBwbLSyXGEArkgZ0b+9A7XzqduVVBSYUIl\n",
-       "ed8glovBWnRl2YJf4lIsnPUCYK1QEM1geZNcll2y8Y1p9w9yAVv0Y0Gg02dF9NGOM6IijemiX22E\n",
-       "V/nOUoqTbEDtIWlDnu4PV5ee31PFfkW3EidxRx8QFQLiSrvYeIhisJtrRm/FfI/MgeZRPtR6pwLi\n",
-       "UqvuE2YFgvAoWtPUPFdsS7aPdeQvIjjNpaogzN8kt45Qd4vvIqxCTlb90ln7x721WyAiA/Zx8gBR\n",
-       "sk+j72Tz+cs0WBC20LGXrCYqoy7JsjgPeGhbHHBOaBgbezHtQa6DyCR0AAAMiEGf2kUVLCX/29zx\n",
-       "VV2HBtCQ/ooy2ZzIN1Gs+KeLvloaQBaAikt65iSSWxNnG2tuXWsOV6rQezGGSYCOjx3Gw8lUV+jQ\n",
-       "sjgewcPwgw1tL7N3i8Yvl0V3wUL0QbL8UABSKLAGo74/CeNRZ823FzV4yGM2yKD4wSGMQhBcuFQc\n",
-       "5lbG1MykNjwj6hnLln13j7jifML/vm87FFGu8mJqBOwVU6yAAAN4xkPSpzFqLsw5KnfP61diAa4D\n",
-       "T9qw0oi7qd2yFhAnr0RXg71Bd67mTfywHy1e98VIqEt8vvuVSkmeD+Cnn7X/iSBOPFDYpkMDvsfL\n",
-       "zJ8Khv+kf1N8XAdYhA5p9g721v6oP7mkWkeFLgg1eCNYWX3z2B5OveKfvuSOKDzO0VBhajUreM8R\n",
-       "fYDqmmoFqzqGzluPe/sFajUXVtSGzwqFV+j2oI8iZMbZjC34s9QLgcIQRtVTGoLnM5eSLgNgKflp\n",
-       "ZGStE+4THrix7IcbR1a6C8o0cT6iCpVbsceDrU8WfHFPguFHegEL5lqBYHHMgH+fEzUpPYG+ej9Y\n",
-       "oMUPwhIoCFv8pYLE+eIzx/k53w/SqjukFWSh50VdeB929qg0KhLim8QXMJe1E2ZbXVrdrG6aOFsw\n",
-       "IrHn/uWiLg8nDmKwRzVLs1AMcCYpac1etghQ3eKnFflD3mlPNI3OH93jNsV+G5KG7/GrLpW97q1D\n",
-       "ZXMZVHapEMx/WsxyKghsfw7x0s8wJP6ouE/aHDxxWwhImDP2Y+/AgG02tUbAvTb6lHuF2PH79L4l\n",
-       "vVbJsxto7WgLAnFa0ZaqbSce0hkRfjhhuchTtn/t2sltKnlTeleUnnqGr/DPPQgbpE7nPxdgtmbg\n",
-       "ydMswigBfZ6LHyXDPYtj0SVj7KNzG75oWbehlmucS31Dz/jJNJ/V1w4mqVuKrNT5QqSlvKka2zHj\n",
-       "LTovgWf2BcyZIYmto5uLFDVbdRmPFz4uBU77SR+8G7hV0dB+oAFle3ftSawg/IR2czFlScO3S9co\n",
-       "oTC4AE45H1uk7+Fr/3kkqGTL9AmdBLjK59ZM3J0OxS+ZTgM1/uLI937wHV0gQZ4Y1vgQlW43SpnJ\n",
-       "4z9ZDIN6eNDTuykhfZFtFy+gbOeVSo05ldhuBCtUKq5RcqNt+ou/0Fp6SHU0E6YeT4Iz+OLu6MBf\n",
-       "PVPekNaKZeANSTuSLDn7Yjg/HKMyjaQNw3JShpXAnx66JN+qRPsq5hfe/ExrdyQ3rBNzfUaWMnKq\n",
-       "W4f4GY7nPe+FaMI/A0aUPwUkZk+7HRNKqa5k7cZ8fNGtyYnVLRR3e35r2GQ0+QWfc616Ty0upl9n\n",
-       "oAQOSJQtJWBH3x5qveHypddQf/JtH7F2yg1mCu+b3J3GXdowyQa6JvUfFoMaH5NCi0bfkwfEY5X2\n",
-       "S2wF3GvkubURy5fcHsxqRyjsaMokT6TDapFe4jAcM69AD2tFvzs9zcWpEO8IvkO/bM1COIM8Llro\n",
-       "Jw/OUDHcMa5YOQ+iQjbCbsEPruaW49Oc6ixwcgKnkX2CTkHbcph8EPWkJDwEwHnUjWkH2+wYiF+Q\n",
-       "u5zm9O/cTsER9KT3dJUPzwcUykRkN1et3jbyyaZQsksyDD71RpwltwIq456ITNIUre2EypNLmhOQ\n",
-       "ZTGiz/kvD33gX3y3R35anMm/GgYbeotDa42/bYEJ3EXWxgb8vseQdGK4aQqrVRMMG1ZKw5LBeaE+\n",
-       "3RY8fhBUCnvdO6f8TpYVW0zyC+1O+CTY61sdCwWqfnNtL6XNaA+4Q9wDIEmfoKTa+Gkt2KBPBNB0\n",
-       "G0BblpcwkJzPIctIZ8uRSh8XiIjtByJUFKrWFJqdJ4tWuVuIPijxEcJN7N+4c6+oSTDM/Yn/cqSy\n",
-       "zeprnziyhiqJZl5wQpatn55hcTorFdzCzwpKemv4tdsJDLruHlSrvlHDha/ZEJqQlEYkSEOOko/w\n",
-       "V6POkxVUp2p57L73EB0uRBMu09kGTHWtldIOX5zq0DtNmnaMgPSeYfrG2l62miOZfLhIxP5amrX3\n",
-       "U9H4Qk2pGtl8VVD1vwad+1iWfrHf870DYQiYj9BepMWGPHSe5ftXI8b+RVpTpDYiho5u+ULvP5+N\n",
-       "YqlQQeKzszI8UBmPSIBqLXcCojsYY14clLF8vyCOxQS14IUr0jRfyawaPiVU4k56/PGLhtlaqeFr\n",
-       "bByDQOpwaJoR4l75ksueVe85wznpdnWVmFBVPW/PmgSqg9z2o4lCoqgsNks9rZlGNtrDUHy7WvVs\n",
-       "Up6wYMUzYU028R5lI28M/BOi7nGVaIlkVyla3vVjSHx/Y76vBaf0fPOguTOk9Vlnioi+xN8qcfhW\n",
-       "2TUiDKdUoxwBzfXsbhRK+MQtEy8hQWzx5TnC3ZgLOAhXOqdaV/xbUtfLZK/fDprk964SHnioXde4\n",
-       "VAuxhtdkL+s3JhWKaVc+SpFvMLAlvFiC/e8+6A3P+r+fHbYCxW3u2kgJYFXOvkFmlzjSCt0yojie\n",
-       "xwtx2/or3QGGkfWJ5vu5CuhmkGtOHXd7s4jWYLlG+Yu7Vum8bnuK66z9EG+uQbeGUTHYUg6ajmgD\n",
-       "KiOz6gaEkapKh3kK2yIbXXtN0c5/2wyDreVzjIgGrcQ+vraiEYPISQMcKSkNDlP6lc7quK1eABTY\n",
-       "Hnb8L7iX7PfXv/dkZ6k/zFjH+qz9iey2BaxlAmPGDpdE3RX2t5BAJFIZAI1VU1s1+2aGWouE3CW2\n",
-       "RwnP5N5dWaiuVzrfJEzUaoV0Ze/GQ2mkA71uJYaH/n4Ov2pfV/aZ6/nnsVkEueeRQ6AoXWg+dFjk\n",
-       "Eg1v5gfuEvzd9zWgsDYDhIOn5S1DIhXvCMfex6apHyHdhmfK5o0Z7TuWXX95/LJKvRQxcrPIcNww\n",
-       "d2Pke81t+h6+icRCF5c4HPbWlz4Tw/8jjpmOKHn57Ie2Tgor3UYNa3O37HLMO+relhFgaJTy8FX7\n",
-       "VtDdXp5RozJRFmZ9lg7AbRdVdqGtL34ybDf5PwgY3PecI3WPoxy0adS8HyrYZjIW4dIZc8A+3/8s\n",
-       "vPqVfZ7N72RHRf5dxZArpEWXVMF+UEKC1YZ8ZHf7dv1BwO9YLgA1SBGNGeOJdzqn1iS6cSrkCNHn\n",
-       "g9aO05dyDXQwhZIsPhmtlnsIlzJ/uarhlaUf5nTOAzIkwc9rU4gu9ixeLtxjTEYM2YJdzqpIWqVU\n",
-       "9X5iWLkk1UsscdZVVGajKUSD3NoRmjwq1fPHpVeA4TB1bnAik0UvTEYkxZCRYar+11Id6IHD+Vn+\n",
-       "hFBy9TqJkw9w2FiQtxTRIAQTaMRtBKq55ieVZwD68OwZcFKNIBMmpIdI5EV72HZUVsCDeLWII/wm\n",
-       "T3DcMRfM4jEtmoo74DZ8k21TKMEABEtgc43w9y+GgMAW+vY+qdR58rfrCObv9SSP7cwFu6d5VOWz\n",
-       "ln37SxGMqKAkrg0YAdCRPBSOxoto6U6UHoeL6H/NulCmulz/QVqYMZSc/2hlnyDSPGuSGeWVNieq\n",
-       "XtEqwuFpCqQ6JT6En2i3gjNlWIMNHZ/3DrM76sQXwDmvm03pp1BHzmC6e7Sr9QgAbHiwXJ/mtASf\n",
-       "73kcSbfT8tv2CQdko+eS3071+lxUPmNwon/rzisZUugPnndft1a+uHo82dM0rCtZ1s3mp9n7iCu7\n",
-       "3cpHq7cq3XIrpcCf+x5J3gG9lhtDi22gjDUqgTtmwHkvWhBRUARkqvvHzJnDp6q4lXXdjdFQJWHC\n",
-       "2fTe//3fNUVwpIZWaZmG16byWi8aWOBjxUC0VQ3gL7Wr7nGp0ohdpYE8f5L0Lr+Kx2F89efMv+G6\n",
-       "kfQvYurWhJWVZb1ZsJJ/jzaCgNJKb+c5mEF2VrbAMnTD9NoA2mvylTgKmHzmQ25vDVh2egpOfcVw\n",
-       "YlEJ5dUGfi9xqTgQ4+Qf7XqDmjpo2ZANmmkj6o94yI2knq+5twhQkg7Adh9zDQJGAj+yLEM6sSJD\n",
-       "nYQ08DQd6h+h+30Odfo6OHaiJVRvQlsgdBwqRQGqT4b7bFwPfV6SRtch/P+bwPKNtkmBnLeJeGIn\n",
-       "SzG++fVVpmRuqHgjzHV5B35QTpjZ1lz6pRdZY5RJ6XtuEOV3QmDn6PLk1pECTPDkW5HEQKwjhYSl\n",
-       "tmRhJQlUnK8c975GW7qprzZm4H+C7Nb12i4uMM9XCgPRh/r9c7+LkhGUqAF4BoVjMwqmuui6O5UO\n",
-       "1/v9wxQKC5uFOiIkyxsNmygwdLQ2AJXIBjLPJZIiyiHAqYVFQB7ANiOZk+RDXoCB2MdpcGAg4vHV\n",
-       "wBy4WybuugWTvnYBusH7+1cFqK45BrHfA70JaHmmu8CfQMid635riLr1zBVPCncb0NO49mydgpQ9\n",
-       "owCCAbEAAAjFAZ/5dELf5+ECZKd5bRJ0oPhhvrmtXT5T0skCshT3XzfkVLGewV6ZJ9qYigIurhL1\n",
-       "hFceiGQFtTvr1R/Y+HkFBF40H3N+c4F8auQ7QHtcU/f8qyauTgCYZVVlgJqER/va8aGMVcHB8C6t\n",
-       "XnyJ/lAwoJjoMTfSOfh2gdn0g5S1tKamFa3Vy7jUsswpwECsqhQh1nRKW3U/71twhh4y/sO6A+iS\n",
-       "AgZDHnj2D5Y1HNZzC6XXR5G6LvQKmRMuKb+S3DHJDSwoC+UxWY7xhwSzjh4SdMKzpSl14biNCKgX\n",
-       "JxJ4HXE0xbQlzSGlxwGp8miL7L70G1/YVhiOSBJgTNXTR9UQHw2OGZaUoqoTnEuTg1D5LEsQA3xe\n",
-       "kDwd4IyAdKzB83xVMGXQQ4w9LDIFZ1BjAtefbG22EAAGZt7h9s1AOKTbolnbRTwfy1TI7/EbxAv4\n",
-       "flH7ox68QpEv+g1F3De2a5WrpRDuNvdz6cNugRhj94L8xAUNaZTp8/lEN5dKQBbJDDlGf+WBCLd7\n",
-       "ZsWnilts5l8jLOhEuOq1H3Al5yIR38z9biKRKgTlrRTaJdKS7zhnaYUTQBRuLhYZ5fifm/+a/4kn\n",
-       "KYRmzEM6b1yM6Tn774Wq3nmui0i37fzTNL8MYLI4BSkh2prVa9qhGr1rw1LEP62nisJtCZYL/SPN\n",
-       "mIBvqecBKphqu94ciNALYhSCJKobxBrZaHhPtu79L0arm03MkXMYhLtEr6QnkRYiCsDGlRS4GBYv\n",
-       "NYR/b12/BSS93KUeUfnU1PSIK2CF51zwCmk1eBSBLtX+QLH6+r8IzITnHwolU9meAiH8zq2gv+gI\n",
-       "kpL88IMzCyD8j5O9rw+ikP0bLSU0BJ3czuhB7vX5Y2GGJgSLJiIx50oBGzUjGskUMxsVtLw/uM1V\n",
-       "iEfh/pY+joq5uV9ges4wxO1w0L8YgWnUEDpru6C2LSXM96+xyUi6BgRB+MITc5gYw3XXxUl8lknG\n",
-       "brij5lTdA7ULSWgwEIBNi07Xr24NwXZOChhoKfvaGuGzPM5GJkpLwneVFh/Kr/7JJ0y0RA4TGgHP\n",
-       "pW3R4HPVpIzDYge+Zl1u1NfCqZ/Ji0SAx8qzKz4GcilFd00h4YmlpRoawUlNBAxlKOBN7nU6ortV\n",
-       "LS1eIo4ct6DAZ0lsm+W6vULMlW4IWfZInJiucHKLDg9nm9et7JuRrPx0ZvFU2sy5szqm4H7ZFgsp\n",
-       "JJDowZ/JegkdGrC5BXlwm5uZwyDi3pxOgiLML/gKWEJwVM2poFb4TzuTv2dLD7LQ9K6qQOWFYTix\n",
-       "pvvliVcn7LAO8Oo8FH7KidJQV8sZNwyg51ME7wuc+SOOq7umtDIrtCKwCKMZelmWIp+Pr3EsHDcr\n",
-       "DSKtF85gLslU/nupBiwRtpQeME2a2nbj0uuxN8VxAq25YUb/kp95TzVD/+6MwuBlNSAiezgvpkRv\n",
-       "AdYABAl6qaaYIaXSWfhSFVv7PhBRRgCq3GbmQAAGkdqtz777ATupWkN4sA5CgHCAzxt9/SxT3G4M\n",
-       "BM7r9/RZNM9WbAQOrJtFGwsKbxkOuYb5wvnTFgSZR2HXe93Ol+WucYBwwPpwS+/qLFovnlbMNDlg\n",
-       "p3VMK4kAzffIPv3K3DroyCbIK3PZrzkrkUXB3+Q4Hu7wOgyCy8VM6HuIar4rcZD1q2K+NtV9c7cB\n",
-       "5X0tYJ2QIuHtpbh1znryUXS3hP/C/XANk+LowoTGpZMs99r2A1h+YeAca9FU9Qn1uOZ49w5ztRvW\n",
-       "aRjE9NVAe/GzV9l1u/RAvT4lUJTM5Ld29vR8RAOoHk9yeCOv6F6O7MmYFlHMG36zYb9NmdxqPSxE\n",
-       "Vu7VARdlNng0NzaWWXCtcbu9BSOjhvZWaNDWIPQXym+QawKi3qW+SzIvfdaBngcnZDIvaiwsj44k\n",
-       "Ba2fwf2h4FUDnPrJGEjVoUertyHw8Alce0MqPUuJ9Nh2wy7beeZ5/FgRd70TKPz7TaxZxUOaXepL\n",
-       "L8HzWEI+TQeDArEOTrUh9wfX2BXex8Ku/24R0Q1ZlNsjvBsa8CdaNEMj+I4zC18p+9yUmKdyPq2w\n",
-       "kZbBGAGTczXlsevIdfChCvzdYHpGYuNPFrPGnrq8ehrkiVk+WOLSpsT6AUo8EFglQ/myo4RLnDGD\n",
-       "wCL/YTYc5o1+TVBksojZV57wJt5YLOZ2YdDitgV89s7arOJHHvHtntUr5tKEZ6PJKT5xvTl/r5lU\n",
-       "YmTXsxcAhCUlVew3WVqrzykc5cZ2etTK9TxALi14dcOxNpBXQzcnEH9X2J/8aECurA7x8+WyE5Da\n",
-       "lfii7xjPuWMqZ7PuDOm8d3b3FOPRRyIrP/4V+rf9Ec/PX5zQBNYe3dyvPIW46UX/X8WbfA16KDxw\n",
-       "/ae0fK8EkrAvi5e2dRSjhHL84bO9xdygvNEfDLVM934jf6TnZvIZfII68aIUf2NKa+/hRy0EKc8m\n",
-       "xYCaL83tdhCmn4ItvdrvETkYyAQPa7ULGSvCXaGvLJWDMYGHRjF9hugGTFkDi7PsSrtpmfRJ4QeX\n",
-       "rL8SXrvaoTVvsls7OsdLpdcBUy7m3xTCdQUdZYy26Zaf6a5nUb/C698pBqUtQ61IYRaWBLtFA9pC\n",
-       "/v/tAUmHHAtLnQrAFKUqzEEd1aHxSwak+dgpRiuyX6fjBTpEs8EEO8DP4LmePZzsi3+LAnSGzEna\n",
-       "4JvvVUQO/wYE1L/pJFppyuLCpm7iRMYcQdhPqEVS6grhVaraLW3fDOVOL2k6KUxS9tA4+kQlTCl0\n",
-       "juUKZUDkDD818T7XLp312DOqkuYn4wRPehiKqcMgQNAoJ6BCX8mZkwoGYqy3FVYudQSafZHYQOZb\n",
-       "bOnKBrTLE+7d7/5ARBZg2LySxODqBHP7Z2FmwSf0xvUtCtuvuVf9e+P8rGWDqGiL4NESuXgZaGwS\n",
-       "xQqk0Sl1SsDQAI/FnuzD3Jljoe8YoNgOT7acpbYkQJsHeO2iGq3YcIrHu9UQOS4L19yYUodbuVkJ\n",
-       "8s4jzOTUzr1UQjepv3b/SYRxSJqsZPDCJumdMOB9QAAAC6kBn/tqQt/qc3h7GaafHAQ5YQzPFvPv\n",
-       "GvaOfq/K3CZxSI0PhJ3lH907LukPaThmihNaqvtP9SvDFafOzgWXBu8vp1ImbKixE61CFjrbp0ZF\n",
-       "e6Fzvkz2+fFPBt1tkMAqHrSFYLf4hsFuIFo1NH1fWQUu+k3yvV1HOI/2OfPl+mHoD0EojYb9AqjN\n",
-       "tO91eQ+tmFy02eRmOBJa6TAhQx7muTI2PoiNxSrhfc7+/1jew2IIwsRCMWUMnNtEUgxoC4sgqbGp\n",
-       "kV9bniVIExDrP0z5EqeGkR9uZiXjRHfV8wUb1/CODV9f3kTpfZLOz5+ubQYy4SF4Q5DjB8FIh75s\n",
-       "EAazCgcgNsxgA7t4NsSXardGsb3ahCNrBgU21FxsBwrw54TcB7Bsq473FcQ1/2JxFuwfiVYcuYs6\n",
-       "vLd6YlKIDsR40lKKdyhVZP4v46worMkm6pagwfkZj6L6s/XtpZ2yVKrh8qqqyPIiStF5fqgo63vA\n",
-       "xCWtfRy+RH/kPJf4mhGhXjKJK/K52rPL/ENmTHa5cWIrp5fPBoo1DZiAGLBs9vQQSyI6VuIlVx3v\n",
-       "diGx7GVTj+cGXfnAFnrTVk969SQp+RE7PO7TlHgjZtivcnVAEdmhAc1roMxyrPts+/GfzGN6IhLj\n",
-       "YgMoflQBmkfwS/7yZDBReT1kqQGU3FauUAZ96w59NWsZOj6jhbO9nktlxctYuH6LjZDAP1szVGmo\n",
-       "eT9gr8ExA86qjfIiS8EzFaZsVMfEJX0VhNtEJkfrcGwug+AnJEeWbyVOuLfeeteeVNB2cQZmKe2L\n",
-       "+/hjDAGh2wpBFBcvPoi1rmCXwQqg5zdYDOVJbeDcIKSM0KDNpFC3qtdNaWaQfFIqAJ/u914DH2pE\n",
-       "NkEoJhHh7top0O44K5X8gqYfhAAHoWxuX8cPRKs7On/HTN1zzauA6NuBGiRz+0h2tzHdO3bDPfzx\n",
-       "vxfOfY5wxwni7v3FMGelhOwE2frnCNQmIcEoiCeEdonkveaJFvPbUh4A7+Rz7/KQtiWRQB+oNGUY\n",
-       "eWogQHw+DduqMeuHGRM0tzh6zTESxvren+RenExt9PnugZdJ4aKwz0yIE8EBa/U6UjK5O4n13Cpp\n",
-       "FFMdL3Vw+kL9EyhusTC/dAfMOsEnmPFdmlpdpwvvrNViKCQbAsrjUawtoAEuCBgwVc94IEWahEtQ\n",
-       "Wk7MEfIh0EBMgQEZ1++47AkoxRV1DSZbMxw+J2GrFEP9FH0PQoDvEoG3Xrm+EBERBhO3MTmRMes9\n",
-       "DkN7WhL0qne4aYX4u1v/XBzneYQzl75DQ1xdhQdjqMv25z917Yqozzp0QaOGuDCP+YavHOluw3bX\n",
-       "7x9lY0c0t8cTq9R1mI1HMJhVdcFVIaA0ihzZnwSibFG4fKGFMh/+5eWK3Z3gHx6rBP5fCZjsTJOa\n",
-       "yRWfD2tJ23SrHTWQQ5rgSJW8q/QdVQkHXCOo2CybVHkRrAttdZHIUKc2eV/zRTABHtBZcsGjgjkE\n",
-       "/uuTM0GpQ4uDnJWEQBJwHC9aKuFGgAd/pSp2K16ypblwwxAYmtisKnFdIAz8ntk0wAw+jagGUDiQ\n",
-       "ZoIFcmsm6R7ne6e1nSQv4Pn+P0YlUfXkE6RBMMHIPQwTlOIww/8rf3U0gi8FAXTZ9GfG2h0ii1is\n",
-       "nEMmK6dlC8pSKqO9BtnpkeBrbQSaJTqYh+TP2T6HgM5oMS03u9792RY8hXxf7IUnIKNXxqKVaApK\n",
-       "S3FeFN8jFCKwFZ+rsWDW0gI1MOYFwyIyt5Qk2yNimM2+6wCIdRXWaIok4W/k3TuPpAeNa3w0p9Sq\n",
-       "ZJzHqgvjdPthzwveScQKQk37zQh2f+bRvDgxMwcL+qUb/A7kzeOdhrus2AXbbKdb2sKvYFDUgidZ\n",
-       "yyhjSJ08+glUlhqsGXGGw0FkS67Jy1M/Vg9TrAPyNU0uEp5XoorqofCWOBh8nJxtZv8IrV1+EksG\n",
-       "v5ov5//qmyZyRJzmfFvQZDeDwH3MejsJuOrzR65H7JCyEP36r3W3c1M6go0p4GNQQMP6vhKtHJMt\n",
-       "8mFXSkgxDI9Dy41yLtuXdw5ju9lLH0Eo2E+B0CAvJZ7ePEz1NgMBsENsskTpAAEW83ceCCx8Cnnp\n",
-       "eCocfSid8eu1ytqH7w3QhaSSITnm5kPK/mCOT9FGKk0jXPbO0o2dksI8KR0uJZ66+GHFlb/RwbaN\n",
-       "JxcW4sYF6aVsoMm+bCiYUuEFqmk99Py7tL/uTUUw7F6PrFDRFPMnlLHJyEF/NMMta5eNdG9RMaBJ\n",
-       "kuVJME/4FKuXEym+MtOc0Nld9/Trw5+5eP2rEijIwlaBu7qKo2U5MVmOveWPOKvt82ZWR3bdlgiT\n",
-       "eQXKoDRhIoIL1/xLFYd1n4985xM2+NYmc0t1w2Fd9+T5u6ajrP01LbaHvuDxWUGyh6Y1tYwwqiLa\n",
-       "9f1V/2sCHBtcE/lSoFtYt2bpUqIuPf4jfbdFBtnuj0/eLQFbdUQb3uvmSn67DMH2B7tG8gbYEQGc\n",
-       "erVQVKEPulnPCqNC1k2queKYI/SETp7QSd8OHnLLaatIQfmPgGodJ1Y8bm9KADQqQL+U9RFDKtlF\n",
-       "y6tNwKm+nDe2IG8noh0r6PSwyiqX+TBa0/fOuKo+K0PKINkTlAuwi/7YlhL468h3ieliuVe16Aol\n",
-       "UaM4+/pTxvMw7E0S+7diE4cGZ5altaXVscTnK66o9+et5uLHsAyIQPcGfHKbHnKIWL8GrA9FzGfx\n",
-       "CpZCkkSp9PtXvPunaHU9OchtTJqssZBcBxLImfIKnxetJHwrjdbX4JOhrVTD649fi35+IjjpymQO\n",
-       "JFc4iI0eArMqVAoLPqKPJQgQ20pkn7ydcnfApXJxJNOzgBAAEduwPyepIoSMVGRy4WzcoLRAuZwu\n",
-       "pLiETrUbsEkzsG3RBCkNqn05jHI/iirFgCxhR7eWRAicdD/5Xdt5T5afyG2BHzk7ihBbOQS4ebTw\n",
-       "7FWDj0uQIaYn3ixxm2BPve5D1Z9+CYvEpuN5BniW6hUy9EAaoytt/R4V/HiTfKpBOrMN0kUKfWph\n",
-       "jJqCOULAUa+bVLbnAPh3yoNcgVfiPh/CbITy0CyGuXE4P0WyjCQgdCJnaPQ+em9u5QEsi7IrkO1B\n",
-       "kHGK3eBihx61iNaVwEXGz5TiYOXya+MPmTyXkfI4XFLcrn1DGPJJvADUAiYfLWFt3CjbMlFTrAnt\n",
-       "zhBMBQP/qFMtm6pO87cG7rKrqABNzRYp0dqU7DCx2CwhiPU2+CIiKvFqup5AKg515S7JUTaP9Of/\n",
-       "ggtHi8k8jrzIm1NqaN9jkVE7oMmJUlkIC6ygDQ39LJhz8pXHvwfzuG3+YD9mbSn/o4JoS/ImNsJr\n",
-       "ql2+xHfJAq9cUq+vc9nGx8OjU2jIKEfft+jfu69ci4EHGOT3ttannc1EYBhYVGZZBiepphHnP4x9\n",
-       "6xSQs35jPsrDHdg2u7hHWgPAnxgeOIOoEEEH0LvWFeO98s1FyBMrGsTMcM+uBBCU45XRuhW9h8/h\n",
-       "NLFFM+JQyk7wkZY5bmKoHB/ZlhRD1dTc/O1s5jHIKEtltG5JOz9xsPs/lPTdMj1P2q6gOyhTh7hv\n",
-       "3uwrRAZvZtjRrcZgltozZsFpmRbQ/VQsuc7N4aOgS9fxj+xnjgpcn+qeXAYW6aORbZttW6anMO5Z\n",
-       "KcENWqvwX8E2+IygH/QhL3g1e5RkYu30FIfyJSDrtJJU8ttc1P+NJNKGNdex6aVqE07J8b0OiT/H\n",
-       "7PIWOaMtd9RG9a3pxxSAOjUWO9vJaT3580yW62Pr4f6tpt5ocr4tQv+jqhsE7ytvW1swG1/8KGAN\n",
-       "jyzUDJ0hloMX/czYNDV/7s0mkK2YGfLvZcX6VySxrWuIjQdopRgz+updkk4FIM1deg64p9/lQJ1W\n",
-       "4nXj/dWuvkz8LSVAya8oPz4cq83v0ym8K4zjZBt6Mhl1JKn7mdT7+H4sp9WJXeLZY+1ThRij0oH8\n",
-       "ASxSSkfR+EHmhqeT6Ggy8LK5TdZInTTPJOm3F/gXUGk0sR/jQwjgW6LvoId0eYne1idgBb8NJ+EA\n",
-       "ABR4QZvgSahBbJlMCv9xXzDk1WEIsEQgqeHigyAKh3Pq6kfy7il6FvnzHXkjMwKtJHOIVJErJYqF\n",
-       "3/laE9y2REk1xgnOIUrR8NPR8Y+bnpMCHkWDqaOftTf6J2gGgr7xI6TlBX7s23SMAZdl14Uqph8H\n",
-       "xcmths6LwT075siT7+amRmJOadzlAaIOVeW6S+MlB9L+T9icGjCc3MNZFLit/gaqn+SMl5KHgp+v\n",
-       "z2x7n995Ik4UEGMNREunc1KS+hXT38BWwwiegUYJwFjL8kpagYL/sWbDBY9uw4rwdlMzY1vRyzIx\n",
-       "cjSJvHqUG3OusHJrBRrM7QDVp5F4qoOPVeRZY70RtVtjs6w0lBvZSWTYSfGqidPN8AIZqs8UWy1k\n",
-       "Pu9D0trq0qz5NDEhBZ8TdXw36Ob7RvaPysjqruVeANZ4rkpnW+EH+UvCfzxWYzKYNCjK7gk+Mxee\n",
-       "fFlkXW+dMG9ltr9fu4weJ2vx4KgTWI8RzKZMk/aFFXBe1Ne1B0ZaP9QA5HLeWvPXcekOgqOEwriC\n",
-       "VwFoqLIBtKpvyH4QAsrDJ6HgxJ7sCW8grX/1iZ9NYSetwGhOzRN+DHEnrrZaY0mkwUjiNS+sfUyd\n",
-       "8OyhKPQOE0LjYLaHIJ2rcDehzHjGNbYMY0wfo//pE+//lyPIdXubmawfYhBZwpx/lg2f17guPebW\n",
-       "5H1PYZdujwMTWI7dWBUSyQqZpm9Cg4UG2ceqxdgMUs5MLQkutZFAjkwmJcE3m/X/Y4fjghzCaKNU\n",
-       "PgN5LQJXRG1OFRylE8JMAtWVyO8aK4H4T4gYogsFtAspK1N5K5m2uyrGIQ798Edf6ryo4hGoMngx\n",
-       "zX/wNkmBph+iG69LZTaUcntFa73ly4VCf0sDZ13z/KeCFPkQZglX8/BeFVk3QPsLu9T5ONHHK20/\n",
-       "BSg2y6JZN6jQGkzcrJ2iK6l/srCfINIDGI7X+2+r4rJIL3iwECVIblgO2z+kGWRnhkWyrR0l52Ka\n",
-       "qsOGGwnGyh9e1VesfajED0KtZszfCyLULOJl+ShGMLRMmZkCXZrOD90sZFbSZcwLY0WyUoGUMAt4\n",
-       "tfFtURQA56sgyBETDvWxRELw7iarUwiyMqSv7A+DDw5qQLDKEJOaw4PnConQuZpV8SaWZRJjd1cX\n",
-       "r0DxkGseailEEI0jLHVPn2J1JoSCHi4G3V8Ck37/n3AZreL7OXmoFJRLTkkL8SyvEPbaS5LNhapN\n",
-       "VNshh5iQJCQLfTvHLmZlcLjGKvXAsZu/SY5TqDTpDS6AWQdf3r5qGjY+idIWJHYKRt6NH8KZTgS1\n",
-       "GzMdocdoAJUspOwfhOB97dLN5VCRnISBEAshCyjy4AzK+A27TWQk4r7wzLv1Fs7Rkg8WY2+qSAz2\n",
-       "/g8X2u2iI3GjxqlnrduqsmEL9Qh1p79HWE1SrQdx04t2sWP/j353ioJX4eZr0McSMgJ7DYPaWn4r\n",
-       "jdVyrdGZwjrdAUwOB6Ep1Zop49kJiwTl2P65SY8svoGY8AgwVLBS8O94NtLF3BvWsUZe1r1B1idf\n",
-       "EiyV0NqBZHMXgv3adxoA7eNkKbstANwO/SR9pCToDj71WSDGgWKvwKKmBUk2TEYw2aXSwVPafKRe\n",
-       "l4sEY7wHXdfH3hg+VBhUDDwJTQp0H5osTmU4D7OF5MhOAjq9sKEnvbe+XfqL9EVWKLyuLdBeCKJc\n",
-       "pM5qT4skqqRpKbkPF6+Wvjn/UuPUsv+hR8otG9TkDNr9oiB1b9XrJqwwSuTIcXa0YqQf7DT4vyCz\n",
-       "jFpPQrdVOaLkD5YzdWF5h5uj644dLLD55ldIXsCettU/sKPGJ83lVLjSzp6/8k0+iQcHS8ei+hem\n",
-       "QL7yV2j8+nKrHRSZ4mx9Ea96xjd/e/fO5o1XYl88r4NVWzdGVpgFoF+fDgl1dzKywjhbkvWfRXTM\n",
-       "53b1eQ9WB8Iq5iDeBYPYSZv92i1Br/3Iaawp2dG/CSFkHcvJI2NnWCp6ToRIcmx27g1H3RkAon8R\n",
-       "4kxoHh2UpzJLLi9QkrAJnGRFlV//k4lUw3wXsxvpgk0c5/DzM2OdFumYp7udMSa7B3hd9u5PmHzR\n",
-       "Wr63rn5PTOCgLSu5tAk617s0BfoCK56CdcQSaf59uxQwA6BJe5VIbAmY+jg6baBUEJZWyC31+g2R\n",
-       "JT5Llf/PryvdojSTuEw9LuTwQAWym5sTAvNAG7qW4gVzH3xm+IHMZN0rSvOop/e2dva++JNk+j6M\n",
-       "QFjKTtL2WzgdPaS3fT41CSiSOlosFqGM4aL0Tc8hfArkmZeHyO7dVv/qMGAnf21Bdgqr0ISufS/l\n",
-       "ciXn5sYjtl4XGrYbig4gtqOcnEAr7eZ+4CGCyzeaB0BaX6fS06QMlznsAz18qMPmjBVa4IWzNKhd\n",
-       "t8KcgUf5I8yW2UYY3VCpQ79Es2gOA7wBXOwTXgQ1F25D2LHGmnFddtOhxnhJRT16/6DGJiK01xHH\n",
-       "0vCpouWPbHrH48RPBIyj92djLJEsb2CljTUUJubr+8UnmEW/s1hojwzKXGKNJFOK4v3Wxu0/qAIY\n",
-       "K4WmVRwwDYrzyhNsvnxliYprkkFtiwfVJZQ6MHlK2Aa+1Bv4jyn+HCR/h4ajqEt23k1fnqpy3oXH\n",
-       "PlLlzT05qnmFPMVVKnaFRBbID6t7mHUAoHvRMMjDdlNZCzYCcBRJGb3bXsg1fjx73TPoCjJnswgp\n",
-       "DBJpxFn29h2ykK5ve138Wz656L2Inh09oxjetWSVgcak4et2t0mkHbp3WDNTSjD4xQLxodJEH1Os\n",
-       "KGFkjb9nU7Fb24aUgHuwV2L3f3GegHkw7I7oOZvTrZvCt6djvxQrGkZ7Yx/VavP4Vds2hX76lX6P\n",
-       "pNPFCOd3XInbcvoGVdhNNCcSmvKJAB4MLxz49p5guw4x21L/PqP54UK5tjZR2bVOgvysBRREl/pq\n",
-       "Fp9WRodLHwgyhZXNvVM0RrpajRRM88wfycCnM815suB1OoJuK/NZ8YVRIzPRCF5hD/l31odbAyqJ\n",
-       "PR33TIY7oSfwmqgXNgIcp+KBpfsL+EWzy0c+Al7CoOedUsRrmIsBwJV0EsvGv5NHDyGSXYsJSlhJ\n",
-       "ko2XvX9M+OPQ/vrI8Y4y5EeSBREvfZrOPSw/MS0QjQm7PCLf8JDywvivHO7J78Xm3lXOxV96W0ma\n",
-       "Cl6u0J+bpexZWsYWUwncxj/wTSlcllrYdAxyO+gJcd8R94vZcjxB8ZYCkcD5iu7pdQF7iC7kgoXU\n",
-       "WfKuRe7g6iWBed2sqnPZCoG/coq3EjJ7nciJfOOmZ57y32rQG84Z6F5mEqFKUqOUV8Z+2FEoXL7Q\n",
-       "Aqf6Qfu2UHsKNAYtb9YIOOCEOY0M3MX4Ub10ms/H8X0H8TCGqsyMl+nqQ4IRjF9eGVlK1F2izwOV\n",
-       "nB4hKgwQxwY3TotEh44tlakwzkzFHye5o0bxjHznGC/soSx1tP5b0TInYn4wWLRPM6whuPahr78C\n",
-       "RrvFDxMJtsS2ci2lACLJt2wW8fgfu6roFWmeHW5dkfROSQ1/6RZ+DNYi8wqOZTcIItPbGiJo+Geu\n",
-       "klTFpRASqHZ1XpJgDWDEhoJ19ihj1NOWvf+6FPKrrUlRoe1RH5FUrNIrfoQulOVgQKVGfc5JDnrT\n",
-       "VegEfkC0sza4SmgmirJxKHQgHd87g8r3iDqxzoEDmYPdvB/0cV1yd4vP+ERJEHpktgeDi0RYafEB\n",
-       "o7TsPl6/O7MkYiRlctl1FCor1kic3RqCUk9bZv/FTM8aqBPB8rOccxMG3DnVRWLTjN6SeAG5ynhW\n",
-       "GQVBAZ+0U+PLE+Xe6jX4l5q+xVo8zbCvmq0CgbTzkX07oVjeek+mDGK64vRQSfGAYKPS2FEi/b4O\n",
-       "Cl/lh9oT94FmaIzC6p0gL8CelvTSDaZmfyD6SkpiYH/q6aNT3xLgxcU6150xNc0R8eelacO9OW3z\n",
-       "CkKjMivTe+2Bv/F/bhv+6aKurazWf+yY9f5qKJz5ZG7j/czN5QYgZ3nMioKSiyyN8RZUY+79ACs/\n",
-       "aYTxHzLRP4EonnB53ZfcpybAtz+z2ZRiavhOU3M1kPIMJOiScUsFNyCNhvvEAKbvtqs41sUv7qUh\n",
-       "u5WyAVLS+ZLXoo6JPrx/1W00WFWApRzFAGpjvy9lC+DzpuxdN/ImnuIkd5Zrjh4qT02NOwut9L5Z\n",
-       "y+QXsxsRHAMWEyUcrSFykDDj1PI4vU/Z93W2eK6QiePcMlFVBO8mvVv9rH3TZtKCFLem++hNO1Zv\n",
-       "aqNDfvB5POezQITyT3rlZUTWnpzki0YQL61MpJj/8JfHCKYi9zIn+4uRjj//+qxkFdaPqEyVri3f\n",
-       "GNnzqPX73FdyEu7Dv7JLTcGnJQ3WaMWrRRMyfxQDOU7e0umBpWU1hmZ5bX6lXRMzaF9EhPhP9ZuR\n",
-       "O8QqYLSwdGNZI8InnanNLyzDNCNrgKhW7u1kIatix9lvr7GNo2Nl52VZPCDA4/iubcfnbv0aFvrc\n",
-       "6LRYYBiK0TeIAN/5HN9bqaSVtcr9OQuBfKP4o8AiLP+3J/N3B6zrAj7rTF9XIGL4PQIUSZVN5ajE\n",
-       "cKIi8wRGlbrEMNj0+LYrVKWEliGdgzKBCDxhMgsPyDNWlKdSlvfgsmq/VAcRZbVYUk1MWdyHcNGI\n",
-       "p3KRhVXdx9hx8N1U3aGSN6XaIG1IdLFQNdc84rVRTam9IS6XvtSxK0sovcG0K4q9481TmoYGXHFd\n",
-       "dQNHRrh8+DZWMsLRksImxabMKIcDb0H8s0VGdjhBi1KOIo6WJWp+JfgRyUaAYQROVtJWfMOX2D5p\n",
-       "B7n/797lVbEOWpuyMZY2xYTkod5CwuJKDUJz63eRXzduJsmF2zxLbRLtAEQa+SoY3TlJqsmee3wG\n",
-       "P+iXt4Eykgxn+H/L4w+K22xYQN0gUkptNp4hq66Y+pH/vJYX+QeAJMb5RZO+8S4GRro+UHEBIOZs\n",
-       "3rVsUffIC2uX6PqljbYvWOFtqJPuD7h3TkkpYGboPqcObV/H/7gMhYXRcf4MjovITDEA7k5RhOfl\n",
-       "rahH1xP4u2M9a5jXQ4IciNTWxJhV7u0gxcxaHvSkYg/eb1JvgIWnhnIchv5QSP1D3QBboBU+453d\n",
-       "352qeAbsxD4E5RN3cpkMi75tLHeC2KWixjXx6G0BQDFSMYHg2nlBDsIu0xvw05gQZIB7yLl0j9x7\n",
-       "TvM0/11v9yQpBYocX87hqSz21MJQRn+Bjllz7aKup0uLWAbh1wxzkwz+o2Od86tnn6e+ZTyRwM4c\n",
-       "4Q8dg3Nzo2lk+redJRX4a3EyWPTph7d1VCYiVFKMW6quRb3OMbrZfe+pLtFXcgIoEqdQqylaiNgK\n",
-       "Rb7n71IhWte+NQ9so6uAXVAZZ36GNynXkSRXGvkYcFMx25vO2kD+Pa6Qkc6KyCknLzS6oDR15/8o\n",
-       "ETXMQiyeJYgG0Mz7ceBd5Me2vO3vwv1DlUTCm9/jUfkOavqCnXh6gV4SbmMMgBfnW9g2a2zgyNvW\n",
-       "kAhEB2fcmoWcGtgByhsEI21nUeRQB+wf1TeKZpgOgJWFazyBieRLQK8miUbFWbKLHMqKrefE3j3Y\n",
-       "YEyx5DR2gL73zvDdksoXc+1ruySpGNBAK1S3UcnWt8C0Nr5XnQIGb+1VYPIQFUM9IAPmBVHB05ly\n",
-       "081r9FQtZgS2wySzJ7tuZKkNShS/rbsXZ18bysGHe8Bqkddi/9vIr28+hyHY5vdQZ6AE5GQEbMGI\n",
-       "t/6YjMiGtD2apGC51QRjtzI9Xj2tV4ZQOtXGEc+Ejt/B9DmoFvhoovLf+NR4bEUD7/0lK5BVGAHA\n",
-       "Gfzz3rIg9BcGxmF97UY2SynYqh/chTmJzXa0y9aQ1SLl2pXESBf3J4rh3fWgIg1s3F5GOkEkaBRC\n",
-       "SaYr9sNB1e0SkFCH303nfu8cLSO66RXKcO2g7KbWELj5NH6awzGknKZgdnevIvEd60Edl+2dADHh\n",
-       "EYbYYjkCBaXaANEUNsZrOTW7GaaKBoG47yLR9rZ6TIf21vvgxWaknL2bsXJ2uUO8LQkhoWScdZhN\n",
-       "dzfBh+QrQ8h7MJRRIKVUtgXHmz/5gJQaPumLAI/4eD99q+aq5N7GpUKUFPnch1PB51NSZpLLVRDC\n",
-       "jQ8nXEGaOHqY/M7ByeJuleIMcHc/XN+cmaZszf5UIXetURf1J1FS1WBq2vREV102HOd8CAX1pfX8\n",
-       "0tYKAnzBiPHVhlChveZaYUWV4bQWnKPvP8tI2rqrxDXaXYzr4HriG0fL8AraKVocXm+zU9Qk7xwL\n",
-       "on3fbuLFOg9kpTzc8W0iXmvkAEUWbCOHK9w/cuQjsMGOwDwWdjlFjQukQ+bSoj7IUheb5xNT83rp\n",
-       "/jgwKXQBfyZIY15lNctYsIemCbZKJg2D4qciatm4aK/dHuPeVIJxCTQt2XP57zvFWp2nV3JhIyN3\n",
-       "VOJHjTTok/VVXNR0GT/375T3vvm+wjQRBVxMj6ELQ7+GyOPU+dAcgmR7Q0yrQJ04zVGwCa8K9Tbu\n",
-       "CzMe1p5ROO10jQ9LKrpxiEQv9qiSajajydVip5195eZKBDHruX7q5COLij4Tfmq0k4KYGNwxAljI\n",
-       "CQSyIzNS5b9YQL4B6bJnOvASp+M2GV8KNo9tTRbiYIYl+Zpc91Ir28qjwNEXP+UrqRSFseX7YuPv\n",
-       "dg+6BsARrng4yAo77O7jXLZQ6MYweV1hd/hHtut/wtu+An1qClHhfdo3aL3vcVdfdqQVgMDZD6FU\n",
-       "IUDgHaPxSOLgBa6rzgPz02VI4i1eDvUrbbkM9kcZuis5sUWPp6+ZYRiYs16CJat3klFVXcMPXBxY\n",
-       "+S8sBkQVCjNPnqkixP/8LgqSPpxqPsuoah1fl86CdNAmq/Hh1pR5eqoPQpqtjyLVxFQ/YYpbMwlR\n",
-       "mGK3tKWt6TlvLimmeh2d+NHSbvYLtH26ODUqeZj5j1Vzauo+1i8qqAJtDQozz1aAP0GYyAHD26Yo\n",
-       "oo/MWORcnqbktVThzh+ETAkI3h+CxJYgvttersymddSutBRCRkfyX7ZTAeEdVj/tATbS+umCDe4P\n",
-       "x42d7ugq/MQTrunse2IZUKIH+MkP7vZW7i3CfXxz6Im4CORIAyHkhhwOcJvJoEQ86YR0R7oEB20A\n",
-       "AAyKQZ4eRRUsJf/bmUqCjc63YoA+6bwysKbi2bytDU05GUrZtSXUg2kwDE77TG9hweEd3NAfE09/\n",
-       "rj6p/0rllqyLkNQ71yBAn1P91bmKztxT8T2RagKS2eJ0I83fr/1s5pw5pdLOcKFVH/++45pIya/1\n",
-       "mhdq8Jsq2UfMWz7D+GiS2Cqn0l1KjDhDhYsqck9cgQRpM2DbjNaljmtIurMzfxt8Uw50jzxyLEkh\n",
-       "AQtrmhE3iHqC8/k+6DuiQbRLt3pa6oFDjqGcjzsu/BDlGn7hGrfcupn6v/twDDnizYwB7ktYhvN6\n",
-       "O5tkjy93ViNb4dUBomGE/0YbLvgjozYk4fUaAkW50d/fJiZy5zs8XfGG3/Y9BJEC7xwR2JcJ+Uuj\n",
-       "ZzSDQpKoa+jx9uUTB5+l5JnAXofEOa4OE0X9v4lO3/T3r4kMj7kV+E+AAAD0UzoAGUUbgIcAIJCK\n",
-       "ZfOapuonUkHaSQCO1LEI03E1QkCOBu1RT/Ypz7xmgPbVNNgQUsC0sdOi20kLgFsSywNxTnu+S4WQ\n",
-       "YH4ITCheRF8sQlhxQwHojQ5D+y7i0UJ0aTmYnVwd3kEUdTFr6HXVrSvCnfa/SRd+nUrcF7N/QRAz\n",
-       "O0/25Z/dfTQ0p8RZBoqJbABvo6ZLqqgu8Rpqf/Dqc1HhKUyj5WMk6KW9CoJ5zB9I5oj+LLijZD9i\n",
-       "q6IsQWxFrG0dSZM/pKRHpcB5mgGu4zxGXXCHuXIfXrGxW9/GL7A1xeYNJxGHRCqsfJk4i3cbeVvp\n",
-       "K4FM2MB32WohPB4jNwoFuEzFJe1y09tfGY1//LsJu/EB5RPNzidroagXUfga85ds4EyEuV72ozhp\n",
-       "vAPsMffnre9tz5oT1nkjmlUTH5LeH/MB132w6hwoAQxScnYJMO1ULxJ5a2rync2/aNOVv+/XYp8W\n",
-       "yyvbdOfTaEp9z5qL8ztrZOxZFmUqTX7WlulgaCyLF1m+wW6D1Qvcc8ZEOOtxwkMLq8e2riWQ2sGA\n",
-       "KXgAKHMq1uHsbHAlj4+fLPZTiOynRl30obISCYWpkuzOVMDmcLFsWdlXNkEDUnc7/ZcZLksZ0iI4\n",
-       "T8cqdpj3yJhyd6clvvV3pnbL1TW/7+wguh8kEXWnVXFriHai4JYR5sjrW2Ifk8ciem92JddRDV7F\n",
-       "mOwB8pOJUlGk+6MF79Ita6YtglG5QXta2EBUX4XMWJPowrljJ68/X5iStqBp+FLKOasJrpvipCDY\n",
-       "o/5vxVdjRZYulBqPzPqa6vwWdYmfuUwFpwj58INVlPMonm7Ol/gqZB7Pm8sPF79jWnup50tqjocj\n",
-       "GyhJn+xi0vT5b7PsIkbkpVB4RYcsd0CQJcU2+lXBfKMfYvSuL7Mk5xWaSckbzB6CI7+GX/Oj5MtO\n",
-       "Rk1DWxQXZvt4uAtgSvY6qCSGgc1IwH27cCXspPNxPGlTBMUsr4vGpECeqryiTpeqkjCM2I2flWVP\n",
-       "MCpGn2/mpvwppdRNUSxYPpQMyWvRvjByl2PfUfUdA8Vz71/a+Fcd0jlfoh1/ZpAtKsBo9Z2jZESo\n",
-       "m6rU2HqqnCYvEO0lU6C+cp9vGA9DOFYqhH9Y0SkplPuua8+88egdvajeWgA0GK8y5iwnhWsBpVVj\n",
-       "LGqP5gAZVIMZef7+r0Egsez0cr+iUBGXbspNx/ac0z2rB+TMaBpspFZdYcZmgmhKfEP7dJmmIVrT\n",
-       "KwR/JtzSUG4f5jwvHnymahjEl/UU6csTooQstE6UxluZ9Iplzxt0RGOY8OovUKL9XJOY2FLJc3yj\n",
-       "KhKRfio2j4rWs56uVD4LKYDRAvpV5FHgTQZbSbC/NB2NEp23qBJOJR+k7VYqYXtczP2RjuJoKu5X\n",
-       "5SyelDF64p5UgU6/12F5wCB+yAlkjdGtTqRwEx6aSxwxl/30ukVkHLwsAAAUWFiRl620OCuj8+Bv\n",
-       "biloG6Su/PystCHu84HacxOOe3samzTWEGFC56NGJawZat+nhD0Ygl9mB3wisZJJUE4JJksr8M3s\n",
-       "1OIk53eT958Vk8ro3+Vzv9l5c3VRR5cwBMJtizmgCyi0DSpX85tVY18pQDkwB3jRh8KAySvM4Vqq\n",
-       "lqPpNHG05IO5Cx+ALUQUkAwKXrqqQKNGqIwMJmkrcXYQ1x8MTudGqj71pL1V1Dd0ZgoPo4DxWHd+\n",
-       "dma8rr6j2TKW5nu9t2YeXiSlkO6HQJO6pwdn3gqW1n96qvrY7N7mFDSz96r/NGpSodCosB1z91Ds\n",
-       "577ppwCOsRaa5IR4UsNtV0BL4LVv73+HSB4od19eM+WhNqj2k2H7Xv1hk5C6P/LAoXsaBnAF5OvA\n",
-       "083UucRCLbQViJBIbI9/TeBR16X/lpPNKr5uiTuALellc/jlPwhzCEyBrfWSSTmDRSMQmqCOhoSs\n",
-       "oLl3La05BWyy9Ka4EUrzqAwGx+udx0A8f3cEsRC79dB0zovIIxhyyFjhL+iMoVKTarhLickF/pIW\n",
-       "wrn/8IamSGlXRILP5UluEHunMMkhN82dGzUbN7XsBkV9du+iSQv02/kj/Uihn5wN29U29uKCmk0N\n",
-       "KuoZDUNiKJpBRrNgTFLIxPq82XfJvyxQPBBF3tmSCq9Eo3FAG+vqhyCZ1FH3TtwBSl5S5Wn968B5\n",
-       "i6iO9DMH5+jHefmDv+ySSvcC+yoq4gCz7Lm7AxkPSewx1cD5SB2slVPVfgzJZW1g6pMzpDnkfs3g\n",
-       "3eK5KyGGksV/+f8cA7Ki3PNvOlxnkBFbQXOdK/F7l0bras30h02QzcRJDXORIe/LwEwx8JQ3Kv1a\n",
-       "HWdb30F5qWl0cvu8ROpltXSiQZPbB8O39NKwVEA4Y0Hg7Nhr1tklD0r6FRsgsso6TUdrNtcbY37I\n",
-       "W2XaPSAZHtR7xM6ddY54wEOm5mpGEkCNpIeqJQ8JJ7BR8eoMPoXU/qhk8e3h7p5zjeYe/2zYdvTs\n",
-       "NZcV9zi1kYQFRpEB5RFeBq+fFdHMTy18oe7Sbdn0PN9AKT4EFRzBTAqGO2Sj11I59WudLxgFrlxZ\n",
-       "xpf3gos5RkqcE8mQRrD9+wnRw97L/ICZc2GN9quNqCf60qXoZsRPE1MOkr8OXUlSY3GD2wDkHnq5\n",
-       "Op83XOZbQZMd7SKtTbAobuLY3PHfsz/uMynDMT5iZY1yiiXK4NLIfWzGK7vyEPoUWgBGCNVPa5Ux\n",
-       "AFcQAssivVjl6myU3WHGGOG+N6CUsQrgs3qHYOCzXF1AlhHMMHv+f7YekhMVpUb2PW3vMFM25z3x\n",
-       "Ul5WhKSsDrMwseMN3II85DmWMzE524kIsC1+RWeHc379xCPH+H0hATyQRjw5eSxQvZXrGW2OGvQM\n",
-       "9jyC32mvaNPCJ2gXiloLXjMJpNpP3S78wQPp3dHSdV99rCd/xpj2jOu/rPXg//jAbs5oVERkEigN\n",
-       "HS7yXCaIMQqyhwigcLy0a8lhsN6CkTBZIjcVRfUWIr3vnmPGn0/p0I+WscvFup7C0QUiwi4bJ2Wh\n",
-       "/ut1AK3EXgas6tjlLtYy91FnUsss1osyNf48iCG+1mVFv6qqRaQwIg78H888QJlnx1TBbRj2m5tZ\n",
-       "IJh4khMA2LffhvpJfEJUoUufwzm4f5n8a55ocK76UEMVBKf6mvmXE8Xrhjy/TakJl5K4u/r1uYak\n",
-       "jNkCdarwkHKnimiPBJqtNc44JypGUAIIK/qqwIxbFWw/yIhPr5FDKGZtavGZtkvW0bTEYbmgauxK\n",
-       "CIMHEMXJFnuTGO67vbeVkkXEjqHcLqNWeI9MwKGa+gtW2K/4P4tt4Tp7iQdqTRFiPll10JolosXQ\n",
-       "BivHJrD6al8kR0CS7fu6hzSdGMszZz6qIuqVWtQJOXUybvo/Oi3LYx9nUmfLQkeO1Jz6iqf47cCZ\n",
-       "BhcsdvShhb52xqSzWMz0cvOaGDK3Fb0U+ZkgC6EkJiUQaIIZLHz+kRfMuYBkXTMAC84M8wtpQq7X\n",
-       "3yn9jq4TNDmolxe8NGUq/HYKlkVpGSU4Yba2A+0vkfrF/4uWfE+79sa3UbRwRpHdZiAYxBHelTI1\n",
-       "uClegwHqpb6tJDyoGAFWOaYcq/W15qe30Lzno/a4QcLJrExnkYR3M14uINwl5ncGz5xtvbw3cpqH\n",
-       "A36KmytWU5g9dJfxWNbHnAQOEBRMW5/YAZMK0PZNaEOl8sH8HP26Y6V4DUYYL9O1N+iKhGmz0wx/\n",
-       "Tf/2/FDCR0lGUXEqwbVhsin4a9I/hBW+klv1KBnFNnlXgQoh0H7D5M7q7EXIgLewQYmrtvEPO08e\n",
-       "Ivoq9FVprRgFlYpW7R/ox14XwCAeUDhhvMAYgYl8WlFRr6G8qOJdpenXk+yMux5SdsPfR5AkZ+BE\n",
-       "WVKhL062rzjt8/6MD5s+XbJAfmmVAAAI5QGePXRC36wogXwiMKF3/Gz1jR6x0K+/M7ld2QdwYUOn\n",
-       "U7xdJvjO06xmDLBp4A/oIrXnrWNm6sKesMmnuOiSHYJNVITSJRFM+NKUFvoJ3hArfqjmEIK+j3Ad\n",
-       "HGOC9RFHw37Ynl+P7Hu48ErunOzdp9z6qucAAUQGwiQW8v9xbOfIJiSophXPHqAix2P9LirBPmlW\n",
-       "vpQz7wSwuRRFcR6gud7vl0bIF0dLUOT+LOdxOYMv8Prlqam9DGE/mcNaL6lsPnQjJjb03HSNR7qA\n",
-       "5JSHJ8QecN2zDhGyEYvbGFItFZJSV5122M6pp0x0vS1E5ZA8essPtU5VjD0rFaNTWfsndivocohm\n",
-       "8HVlVyR06cbLmY1eqBKXq5BB2oxU9+bDtkNdHNkGXZE1Du5VCrYDj4KwXStfipHP/io7FlX6vQsH\n",
-       "4EjogwYufnNmI6U/NobaiDRCmA7BnIedybcO1Vx/vNH0vR5CDNsweRWm96Wh5qShTNZnpQGGuTKg\n",
-       "nxEM7g5j4TGrkqncDJf3fT/+TUe6VF2c4meGXeSNxl1n0Lizm5eMg2pTeK0KhVNMRBZ2fhfxDECI\n",
-       "TWDjVvahMfEZH7d6GOgVFgQmL5X7dBb5rvqckvPsHh/5rYEzTpufGs3HSF5THMoPy96mKR5FbB70\n",
-       "pKt3epIyDkxKnrguCBr63ZcuBHULzC2lHowRirYN0JAlDA1VRFSq1IWZUwFs2PP384mmHg3NLoyl\n",
-       "mwGepnbyuLgEvi0ajE3YhKLGYX9b8EsECINSm9Ntbo1OCjSPjOqlLbqA8z1PFOudG3wIh1nmj7wZ\n",
-       "ojlNQYUCdgTDkmd2fiZ7bCyrO+nLvOmXgbmCUmZbs80mN31n6PCa/SfQm0krzPnoyrM4Y8mcPCmv\n",
-       "NZOGunMdgpwTejSSj1CXBPQJEp/FxT2WIGPisqV/bXiKeGZGyba2a9BTus/O1rn3Cpb+sX8N7/dw\n",
-       "+iGlN7/+oO7kGJ99ttrAYxa/nZJZUgAJdwpmZKBEk+5EL7Gpmrk3YVy/KLyt1B9MIIxS/9JszMSi\n",
-       "J+ATY0h32Um6xvpWDwHBrqIp0dUPymEI2/BQEnrd3aotfGmQ0igZtQTg9jlLhD0IWwgBl+c0RxJF\n",
-       "kMpkXkBhnx1wGJ6fHeoSH5Rhpo+hTG/RPkIZeXKQIcg6WwHzMCaJNZ8o0yG78VhQaH/hJIaoWhjr\n",
-       "r2p1UzUiyWZCpkGgnn2hc+aXKrTXMPDQnsDchJrWWdaktgTd4gjq9s8zsiVzlvrlzvXtDaKAtm0D\n",
-       "dDG9ImLjThMP2z3ks5Yr/75yQVp43ppzCbnVwGcqdtLrcteyLvvZWzEzijjG/An0jLOSuGcq+yRn\n",
-       "DmLxtO8o4x3CzHQGN8gJGTZmmJd5o8V/+mMpjiFyaGTqRs5rYjydTwvvzuuUe+KdFUXEE5NIb4CP\n",
-       "cNReI8mAe8qoD8zszOeEUq+Ca32urGM74Vjo29LcGeesfbgzjMw3QSlwbCXBalIkDqGGlkfqGQph\n",
-       "mn6BSeNl8bS6ZLDR282B2O/JQ3TtXZN8dBrv/w6Jry1FwKrEWE52dSBJJ13el9/VnsK7zgop2qsE\n",
-       "xHEu6/WWwDi21DQc8ZBp1u8BMdzJoU2mZC9h4ySkpBImBXHPtlcpyDmBNaYaVHUa8NZrByEp1L+l\n",
-       "C6LjHe9ipErJY7P/ZerfKnfuPvpOrg595qkTbcAe8WSIJfqUsGPohoegAVn0veoU7P+WeT6618eG\n",
-       "POlEn0NRX0zPQOG3Zf09H37zHEuJNyQ8zIbH6s60x0p4irypQjpaLGQD3f0WsQ+b1kQ91gjy8vuC\n",
-       "cWlP7UpKqMQsCMHsKAYcHHLIGYgGt0TBVsKuCuSho3LR9DISA/Z1SoWzWNE1LvphcB5EzF3yddc/\n",
-       "9sM646PVq6eJn73Y5y0xkZEWRUWMcb89YdLmlCORfxZk26oO4pQSrs/wHtBfPvlB/BHlm8it7AlQ\n",
-       "VM18zunlxaks0e90nTsdhKS0HRX4lLSqgTb4sPed/fzbcLB9i/Fg06FegA2245mL032N9ibEnp+j\n",
-       "/OhrOMtan7avD+pNXj80ZHVtcpXwBGsruMpSS2+/7Cry9Fqt4lbgBjI/y5aSqbbgdIaGWg6RZcmA\n",
-       "Puj6PfzPlL7AGfsF9ZZ75vLz0lls3N2q5LS67YdMLObBYNLhBOQrJvbnQN5MtirRNwR+19TBabxy\n",
-       "jWPjaqlvoBBNcVArkAAXr3dOC8lBApCQp4Xod2AL6hbGyeReasmubs1Nw2DOf9VWFo4zhVUCNv0c\n",
-       "Ki7I4q0Idjz0GNp4MP8HIhf5wKTGRTw2GYmmyDqFu2QjfkK4dmXamJ7rKS8HKptKpQi8/M46JTW8\n",
-       "SW4kQuE4U6qV6VPPWpzhpt1pmjKUttfzJRgaoYz0OlWA7woAb7hbCdv7RVdUCkPDa4aGzPxYGuaQ\n",
-       "YjfVn7Vi54pX6DhshmlFzcTZkSmYfLQpeZ6GrFNZH2DeHOIuKAGfSOH4zbP/nIJ1eJNr/VVNAFeL\n",
-       "DSLyns7/UbVE7mhfD0dGIRdPTb75Es80wIdHjHGfjETm1/f9lt5NbMHB6OEskgaP45jAWHUSIUjn\n",
-       "rCjI/d1JjIIABrtW+YO4HM0AA6dOuJsA0hBByvx4C+dVr+YJvKBu96cXs/wmVvul1BLBX176lQjN\n",
-       "n1n8mRrxUW8ZefgiabLaqDypTXBEd1j0nPEXRQTjaRA2bnJP65ZJXG1R2+ASimeGhtUHRuQ5kVf9\n",
-       "Y3TpNR6RJjVl+bxI09gDJsZQKfGk/mlwAfQhE4VHPZYU64Hujnq2HdA4DTn577NY3PPeNXo5BoDL\n",
-       "06A+MNWHZbG7QvtfE0HaYvkU+2a/+Fo8ZkBuLqSjMg44EqloOR3f674E5g8Y9UhfIpcCRhinz6X9\n",
-       "Yg6Peb25jSMjyLyqiDoqLICuNZ1mMhY6FnYAv4J4tn/M3X+kpaIQjN2B4hY9I0iQyYCABeDE7+5N\n",
-       "DAO+YEpgvb7ULoeedr2EvWlRLOzKfLzREAN3m93MPTJdIuo5mEgkSuNn0rzg7uOAimjc/xN/NT+e\n",
-       "T5xXqGmfyCaIrSsfqn85qrGH7hjRgAAACI0Bnj9qQt/qf2ajwcIYcxHvgRWgusdYd/sViLM099Q0\n",
-       "WaawqprFj3c2ek4p1Up4LDi2nr5HazIUG7gBqVJalQTWpuQII4PEE9NmXF5voSlh5VlZPTW7qW6t\n",
-       "zpQkFsbp2k6ifIS47tXcXIjmnJ+OZS4gN59YGXTx/bSzjZTD32UNMka6sejCTbOkNzyT1vKzlPSA\n",
-       "KU5O03MsDLHv/xHEU/wkyHgwvj6bxxE+CFW4Cna2LXNRfpfmKISAg3xU518TEaHIAlD1RNZlz45i\n",
-       "nBvKa8eCpxFrW3XU1okbKXQLpFF3dRSY8U6PMUlXD6rxyIGmVIhAtdizf5Af1lP5IndZ6o5Gae+f\n",
-       "b6J8vnNwuiQTmlNkVlXDDLjqY55YbJkBb88V9DUxpCr74ciImuan/K3HLyYIoUG51hSTHjvdG3me\n",
-       "uj1ED9+4Sg2pdKG1EnkAShGKqoMmmagulRB6oTQQAqSKe5Lr/urD7NtEs3yDCwVKb/yAI2ILN7i2\n",
-       "QePUdWPTGPoGSOOzJHUgA8v9xmjlo0UVodcA1aV6JyBBg4Lf9mPEpPe8Ey5/S/htJ3KGVF4541i5\n",
-       "+phTPEO9fQJZgqzC/hddIWEu/UEkZ3ensCfe63flqEVuW+FcEV39D8OfdMq4WXm6gleS8Q857zEr\n",
-       "uQ8I5lLS9niwDsAYFQAHne4alyx7JqFTukqF/XUniNp6s5in1Ie6l26xDnjqL6/ZmxLEUIwk7U2q\n",
-       "WJnpta9YcpPmNEJPAxubHg1xuVAXEfKDuhHySsPzIxG8T9hhHz6ctW5KwGQBm0+qK1LvTQ/U3kmP\n",
-       "OSPfvdSGi1SzphdC+t8O6sUXT1sh8Plo/QmFD/n95v0cbRQxNi2Vo8h3Ng5WCqpWeTMGl907pXSj\n",
-       "ApV30mxH8GrAZ7AMG2tlyVGSMKAABKL9/+gNkuy84p0rdcIZuVlYtN97oilEdNj6YvgcvpX+q0KX\n",
-       "nDdRC+sOt8GP18MQuSixWWsUdELgNJ65+dWyoo0UpvQ1c9of+HYTrcdC7q4Kf+88pENGfFl0J8kC\n",
-       "gDwFTGzXhLCcQX6nt8ZKq2b30FgLNvbTRB+MlNmWIkmXG5rzRhavs9Hqz9v+mnMT09q9YwmQyjuG\n",
-       "A7WB5yvw4zzwou42H1ee6xdAbUzMM74LOnEhSfR+gejDzPT11RfWHI1zKoLPqtCrjefKqzezdrFh\n",
-       "dsq72BPxc29UtrmG5CwzFCzhb9ZXrkur2Xoq7lGWw/MMUUnskQ7LhgSocHbIn5W570ZWmtTv2HI5\n",
-       "ddfu14m9C/jbzSJ3Ly9bRwhhGXaSM0tgF8iSHWVI34uxeBKcUyUklg7RT6R5USUs63tssSXfj4G2\n",
-       "JGh2+rZpNzYyP/YlKgzMCW0WCE14haanJtVuzj0oF82f8HMh8U+383QJ0RXZeVWxLDPRKyOc2tm3\n",
-       "OlUGqSv2G6M/pyP8Njieb0wbpDStf/yrlNATwhmPN/dZIGcV+AGP6ppCZwsiF0lPqQN8AM6QLnZf\n",
-       "3VVFdikq23H2p+pv6cYrzHDULRANMaqND0FXRK4esZy9Ev4GEy25+QxvZs3RXuGoH1NaR0RaWWOB\n",
-       "v3u3Cm+bu8Khdw9hHQibKtr283hkZmIBDd+ro+XvRCwpsZsMVJ+GJP/cczmw0nsS01I/RxEwg5Nt\n",
-       "6aLWjO72LTuJrY3eNUYgeOLDlwk9LnEyzURyFnVdlYHu9qWhZOgobe43kQzEdSVJrdrLuNSBmSWa\n",
-       "9P6G4UCIBK7Uo0Azdhezwg5cOonD7xW/iMvhm0C/teVTm8vMYhV3Mp00ZgaSY/GXcG7zwH0lIUFy\n",
-       "gWPN3J36VCnalrg8Q29ctbCrG9ugtngTv+fK1paQFyNKMLstHQJZXW2Y9NmLgXTxgC4p56afH58I\n",
-       "DVTB/968mbze6VsEuW0SwrYAZZXhqU9s4QAkqMUIGqxoQeuNjiDJ5ChRFEWVOXytdBdOZ/jwhEjS\n",
-       "1fDV38ER2QsmjkFOB3FBQhNtLhKHtKbqXVnh6J5DTCipfwJQ2/WwO9D8rDHQCtbbueduyRWH2xZ4\n",
-       "JazxlF3XKMayECoYORJtHoyP+Hq9I0kcmRK3k143g6pV1x3zXRWtpjEWbeHKWfs8HUk0lPMUt1qK\n",
-       "J7Nx80hWWgGhrFJsJfzQ/J3myBZLpoGC/A0sGV9hlCUuC6E8QzKU/kcTqBLMli/zRMYXF4urxsX8\n",
-       "qVc3jKR4wN8UQ3t+74TOn0Yga/zgl3J+zbSbb1R3stEhKNSbW5LN/7ScaYg9TdvG4EA7L2lU0C0q\n",
-       "IcPaI+hVrRDBNBIvse0fuJ4XFhFajU5Bx+Qepv1cbNYVq5dMzMtY/JXpYhqVDanH6obTTMqQyMwq\n",
-       "GREf7K6eyUShq1lzBJsh9aqbVh0tC2m7aJvW/KH0PievRmMVHMThq9YCkDx+hCBMADGpCEyTjrAv\n",
-       "KtFOW9UdUeQiI1DzTUzqYlCFCUPsvlRr7SLG/v/0zca5jVbpW0dRao3lNS6JpVvXubD8pExTqUlf\n",
-       "ywQ0sqfaJsn02d+ml+yeQCQ06MphNmhmOLvDmZxu52CRPB45RTlkkucYAsIH4LqfSRN38nSl44/V\n",
-       "x1RT3/HOgTQFKQbSJl1sVQ0NJWWzckQGU4M+0C2W8AfJnQCRiY3NNl5fWngKzhTAUFcaP3aTg/tS\n",
-       "ebjZQWCBLQ4AXOth6TJlaPW4yNGD3zoHhRzB6yiG/Qycw0Tcw8n/9E/rmtvQxh9O7UXXIAvZfDa5\n",
-       "Bxvkhfeu1LKQs7I1l1rPhjCxHe6Bf/sbFaiUn29wcx4gUvGOjA4cN6x2/9Dd9PrGv7WoCoTQmuW3\n",
-       "toxOdRgFC5ovYQ9/JQInDfV4fd9FYc3pwDRLl4ov5TIGHn+72YnbiL5W0K1YY+82+n6oD2VwjrIM\n",
-       "j7F8skKr0duObCInA8KLyDWNxNkdvGD0UT6GzUtM2kMEn5yF0jhnhfKfCsDmMZwDRwAAE7NBmiRJ\n",
-       "qEFsmUwK/3FfAavi98HPltXG4qds43zPb543I+is51vaHWLffW4rRJ4j4BhUaul3wFmNVfgsJyPJ\n",
-       "84zTFQcVrBrFwAGpkm8SJuFM0K6nQ4GHceTz2ITe6WOUrfhQ5TymCfnD5Pv60Vwfhpy040cQaiw+\n",
-       "fnWEkW/cjZq+1MuMm0p5Lf2Lb8IUIEKMbYjoZN/7A//Jo176sgfG0+CB1ayxR64LJXNSyyO37NJg\n",
-       "AeS6FUQeaAHpbGaP/gWLhO7D8up53KB/6okePQg1IWinnhD3eVNy4Kakszjb/AsJLwQ9XI1h1jKx\n",
-       "YW8NT1SPXWRiIUsltHRs/4imIewBZwexzoYLJgwlKzUPJmDskdYJncx2BSuH3GxQF4YWQsfSQRCD\n",
-       "/C2Eq6zCZxjrB9HFN4N7c+kdOwMK3dpAXkOerymX/tj8f+tEbaTlXv7fqdU3nS1BVBaCzaZXlt6Q\n",
-       "IrGsaixV0YIlsgCu3Bbaaj7b9MJQPqORksacAQpahLf7TkmrBZVvYmCjubWZTfYmmJUSaYMqT//8\n",
-       "jbLI9RmWLFqe6W+6tDVo6bqZ6VsWL+xoXvFEIbqZMgxfjzv1mVk63FtTNNAfWBzg5uz3DYA+mdp6\n",
-       "/vTY5a5lFnGJGE2CBeRf0aB7pXLeuVX87AJqzcLM0i2XFR16UhkNu37HFW7VTblNbsPuHHrlOjL9\n",
-       "TPBTBKF+onK/tkqIL6+WGNH5VXw9wwayApgI95nl1BurS7HCPNfB6CgW2rBmX2Ly77L08UlTaQIW\n",
-       "CorLN0w1l4GEsYHcjoW5bRatEwdNsAdIgm0AN8FQz+YLjdKqGgYXJMadnfPjN0BNxO9zT/En3Wz4\n",
-       "4qhkRcgqu4jyFDsW7uFEU/ukzyiVUQRnHnYH9RbEidDNSwhoVypiXJfV3DrSy9hGZlclwfCZZrIC\n",
-       "HKNt+wqT3fgVBsdAgXTKUvzIFg/HPgdY7xjxWnEebL4FiCD4I/3/a+1kl0XrwYuOvQc9ssCGQCuT\n",
-       "ZlD9uOMSkJx9o/i0Xl4PB4iHMpwbnvoAc6YzZdMXW5RvN8cHWtGL4bHQj0pwb1SKlExrno52/6GG\n",
-       "gbWHUqrFmQGqbn7Elyv7IFWqbllQ2k/vykCkhclXsXBghhUILx/5pJepQpGynn89S658xKLV2P2b\n",
-       "JazuAVt9r5R7KyAqYIi2pPnbmFKtCks7I0B/rflZLumPvZuz3QpzkXuz0v2rJshtB5+sUdkIpdBg\n",
-       "M983h02ojSjam4h9Xo34R61+M9Qvzg2kitWiwQ0Lelfvo2mGV7WZHW3EoVlQssHRTOJ52eEHnRdj\n",
-       "NIu8XxxKPN5JYTC4x0OJ+c6joHiB27MjLh55aGKxBZH16lReTiCUcSaAMsEMjcjvEN8H9ylahimS\n",
-       "XfSy1b04h/TXs2YAbrYxX4ap38oAH4AwuPpOTQPwk+U3Y1NUtNd+SHG//8QdDkbXKsC0CqYER97T\n",
-       "EoVLqly2w5e4EzfvcPeZI+qg94cjprJPNRlykpuZPooNmNtbeC9iVm/mOg8QA3GKmBozbSq2A65n\n",
-       "BiyBwd2JGoityUVpUaakxBI7thOjqOjnSIVH1TNjNUWhA1DffXaXkhQAYHdHEcjRx0tbXF+SUkE8\n",
-       "ZUFSpKw45a7dl9OVbein1QBplkYTHsJd+/rCKa50kKntvXQOKC7KHzs+WIm31V3YZ9331Gc7j5RS\n",
-       "xdQBaLfp/rDmbfcXKTaEGK+YcNG8wnY0E+KNWp6gMqCe4z66gJk9IsLqSJK+5E11QIvCWfIKTyfO\n",
-       "3NYzVhZEVJwJoEZPVbcTD684QSflrAsIhSUHQgXxRIEI4pprCQ8bykWDNAP1o4tPw5pD/CX8Bd8D\n",
-       "M8VWuTHe1CL/K5d7FxhBaSzYS3deDjd3epbu3lwd9sIaVgYSsda10yHt0mrnDr/FO9RVK/Pdkvec\n",
-       "Jbm9716NyKhkDSam14NjoaQCPAN+tCQRfLjz7VvqvlehMvyZEhblI9mKBu4MCFjIKhVxE8g8363o\n",
-       "oZ+wDy8So6X3R7sxcvJGokwiyTN8Y1jVfeF6ug4IRFgxOg0oMhN393s9d8uCcF3YL1iH3hNJ5KC5\n",
-       "RgTWU1qiVa1CJ0j+KP40QjRDXnQ6akWKXJXTPfgpXN9Xyjs8spwkIPPV6kW8zL2jaeDxvfp+9O0M\n",
-       "lifAv1ffghGe0Q1lhKMg9WcHpYCgY+S2TksEcHi0oqDYtXOWwDmd59LWtXX/ulGqIctilzn3entE\n",
-       "kq424L0mxSLOdq1pZAdTunu48ne8Ogc6eutGpOLKWaQADJ3V7Au4I/NR9KIs6iZVDbd9qAQLMCww\n",
-       "h/L/TYc5nic7oVQ5atTko0Nf1z/McrwgRPtufqsUGjED6/h0zTIDCM1PHsNilXoMRJ8AXbm01tsz\n",
-       "eimMiKLmOYNZexcoMA8eZVeS8AsQT8ynl7ZT3lUwTYA0m/S0Ulwk9/VGOniggXVV0NhxP9w1a0iQ\n",
-       "93W4pii1SrAXZmCs5kM6S0Eh8r8UK2fkZqoFG8fTLiXTLEBOgVBxmnDkjohZ/bnQFE65UKnLGZYk\n",
-       "XB4+nyYYJau6u6YLdOvLHhyZGiRx8MEezYIqv8XxX9J3Nn0RXvZsm7qKUqe57yHuNsK2MyYpkdUN\n",
-       "uq6PKmO/CQgOMEMbUV4zIpKQ1AGWOPXF8HJ/OuCa4Wu1dbWeobV/L7z3118XRy09qhaRUSbA8cGt\n",
-       "buR3Ze0fhcPgkHvT78G8euAehJxdAxOlWl2NK08wWPy8WlN5CrZv2P/InfNNB91v4m6LtKs2KrJY\n",
-       "8H0AAlAUK75xOWLtlu2iHe+qxL/EqoP0qwWRKbiExAjB6Jbmt6M0RGBtAvRoYC9zUPSpiSmq17Er\n",
-       "EXDmULRbpPR2GywQ0piyIuzFGruiyvmkxLak2hqqaMbJbgbOTSxPyPhTc0pjbcfeHLzxxcw1B73z\n",
-       "ig64H6frZp0vWkzmBmjyBTVu8KQ8DkM1HmDl/5JWuTDr26RxZtJ8o0EeAisK3sAAzi5lxUSKDfAl\n",
-       "LjsTI7wENZuEKUc6Q9pchui+xPmZx5MBIdqUzJc3XU6MLdr5X9BImgTyLEzoPI1yQx7XmUqh/YFA\n",
-       "qdZ5t7NdJTZIBwzk7OOwmBDNEEf/6BYUw3LZI+PfE9IdaZMMs5DaR1q/6gbJphMIYgYZD1c7xecv\n",
-       "GFhmKFMPKfCOPYx/JMWPqFyFVryo0ZS4+1nazurh/TMe7U16c3WiaLte5cKMpCDinMVwc2JFhaK2\n",
-       "uwEOZ/Y8YPrbBZ1cs0hA0medWtHfSvQBU7cvTqqzbbD6TYCCwPaITe+Pb/fQTjUV95EpKL4XT0kS\n",
-       "vUzmV34L5fsc1qJu034+W1HRS5MDfQ/vQYHjO5KrCZVBQh+YJlI1bOXxs8ZIhFk531gM+5XKTZOZ\n",
-       "bAPgszykCHLjyjTkMu9ayw2HO/WioXH35190tk5k93HcOSmOV+Y4q6y1+TehPwMHufAQzUkl+vq/\n",
-       "F8McsHH32MlRS085adKNaPBTC0f/XT+oTjZdQ8ZQQtrfKZFo6XO3aKg9c7TrutInQEpw5S/9yeRk\n",
-       "AdNv4JkR4L4TDFtZjHp2YcF0EIjlYYWs7J8wKjb6U0bKjc9OBsM+E/n7/fAoEB0q9KVys2tdTScH\n",
-       "6oNlpiHJu27+J/Rop3I9ydjT+6YcPdxzS9kt2GXlDwkEUZyxLmib9WtIH5pT4dj+/Q/g9OeVL9R5\n",
-       "Ulo6HI/jJKb4qz/yNKyvK3NEPOPUymVbLEB7VV/wNK/TUA1B2LhpP1wx7GowOv9XAmBDHhp/UosN\n",
-       "YTgjP6xDfcssDrntaCebmWZyyTiONKz6RQCLKBrUh2GwsPe7Mvd6OKCusX+sfZuFCO2yg3dF7lZf\n",
-       "eNrRj/QFH1x/krdDIBd/tUB/XiDFThSiQCm5kz7eDGlpNt+98wBU1E7qKGBt6qJTLSXm2243ndy0\n",
-       "HMF+JJ78uiI7dEKhxwLawuPME8AAoF8Z/MV3XUifk7ANEftPFIb+0Dcd5YnxZjQckJzm+LZYpaIc\n",
-       "9QpyqAhrxKaAUh/g25kDBZWWLbfs59mvAWobSaKVROVgomzjm5FnnwhjSIHgd6cdBVBgn39cm5/1\n",
-       "yuuJMLCKtB6qfexK5S22p9WkmhtjfejPggURC6+FgK1XvT4FxDU55GiTqCMV/ZjFz9iIlkVAZZXU\n",
-       "DbuCKcpkRli/zcnSlSXLp1CsnUNNgmwPckRvk6mRrC3KAWcR2WyRaz/8Qfxmjs/xYeoOYaO86Llx\n",
-       "MOz/B4/4I9cxwv8ZhbRuTUlr2etaGxahejbBl/upt/E98RmHCogcrd87lOQlW4mbxNufV/cVxMw5\n",
-       "vHKWYpLOVI/rxym2BgyVPv+ShG3/HhfUtz3H/WzNMy1yX7Jx2P7Jv763VzyEMs/TmTrxCDz+pDG8\n",
-       "T8GvVCvhMWmA/cyfyXNB9atcqspgADQK4+ONACC+zbT+Bhzj6iWICUNigPadiAm6vYbt4CzeN/0A\n",
-       "0/Q117BunRM35gEgMH4ibt7Y+HvtwRH0ipkN71maoTTubTYcZd6dsVIsWFAgYUPEb8McM7WsY5xq\n",
-       "nY2k1o8/LhScIfbqwWt3lNy4K89q3TzmJ8vMA3e5jZ8w94S8vd/+aEf2PrzmcIjfgSzjv7TR6cvp\n",
-       "b42RYFwqtAqh54TO0iiZiuNA6lDr1Ebd7jz+Hi+/ioo61JgV79Iuiy7AslBRCVcRaeOUCM36SCTI\n",
-       "MDRgszPcAX0HUFscuXaLd5h/vvgfoJv+f8Aa9TbYlfdpvzsqiRacwrwNGeaJu4pa7Y54NszqkDGF\n",
-       "PeO3NWH0mLabz8ywTakgvC7UWTo2OZnlugldd7QiNuS9FB8iOCfbhtjvNGFrDsiIeKvGl9V/+E5d\n",
-       "pSNnR6WoNDH8qSkX9eV7NbC17RVEQhWDgySZej+ZwWRMu2t8CRK8xpwzOzwkaK7/kR3zbTYq5oLr\n",
-       "TrAZZ7JsVjsJN+wQlKw3awa1U2I943NQzctR5DmT+gE9Mo3gYjVymF73Rk7m8oACHd0DGMS1aKr8\n",
-       "qlDBUbxLRjssZCr6XTdb3ATNUHe/erY/9NWCklbrIh/mu+14FglwQSrSWEtJF8ppuNgBLCQfEo2B\n",
-       "IxXT4qzqg5u4zvxqnxpM9AxK6H8zZRZ//BIN6DlXDR353cWn0XEyd2u6/sC7RpuzSy6c80C8I5YD\n",
-       "YKy88ZPVapVdUb+2dFpgNv16/BZvnVMRsGDFDqDdiWPevOmI54iJnim3dniHwPMAZWdl2kE4WO4n\n",
-       "PfJnMm7WtgEO3zYxqOEuIs5TaMaVTNmq2ukXB1uy1GrauoPVNF4wXlcgRFMSfyQDIHGSQXgdSMmG\n",
-       "Sr8ofLKaBVVgNTgFnlqDPuADYAHu24GzBoVX3a5GZYr+B05sUL7XJBd4/t06Yx5Jl5L3lZ6PlA2u\n",
-       "Lgyy7qaOw49KA6DJgbH+A2pqrhph4n2UTVz1GUZiJNhE/rI4Tm1JEU3VXkzoY+pwcEMFfqRLrkZ0\n",
-       "UmAqUk0CXIUFQxzTFUdP806ADwvO4j0QyJHXK2XxHwG8to/42D7J4ToJK7+iG26isKm5dXpuCEQS\n",
-       "hSYqoTp4JKsdaXefH5HQBZa15UUq+n7FgaU4SGuvnKnk33Mz5caPGqfjoxV5+NdMQF0jL+Qe6f2O\n",
-       "JHjVS7f/pyXtHAGNQXTITRNDcrkRukwj5xYfadXUKk6mf7Kluj7nARggK8bRr37V/FgegnsQJFgK\n",
-       "0FHanmnhuZ9+mm6gRWp/yVBNbQDKfp5g2SoqpbAL/bjgaRt6uSB8F8OtEFSlpTz3UgZ8ksvZhhfz\n",
-       "94sTAYZj99WnHb2IlAWcixcpbGp6oxcJq6+cG6kfYXNUiaJjRt8oBfsjIMEjNfhTSk1LNOsrqXuO\n",
-       "l8lq9NvfoIq0rQlCadkidBhdxiaMNcO+ifwPyVvxF0EZe651GEMbXCKKcaC4/8BM4Y4afJ+jdsGy\n",
-       "Ii7iIu8D5iGUpzJO6cin88ZCt53naxdq77CKYYGWXZV0+k1gTQAEqIswexKIBEvu/oNkK81bD4m3\n",
-       "EcvsnEYAzDg7d1MRkkDGvxiu5kalVmw+FahSwHtPH2rA/rkFQyHAxAgOWvq1Xekcv8N9w4W4X0xJ\n",
-       "8C6d7MvfarEVDc0bw+faYzGZk7O38c2XX8j2aEVDxguz4ZWBHzM1XRyioXspxfvZLv6SdzFOoeph\n",
-       "i0fdJ/dlFZ+W3Jg9EoXFLyDx6GfNgDszGikRKcO4S1odLmx1GvRpsCjt9jsK1eKNAGlFtFJn4FFw\n",
-       "3gZnhdHR+UiHuuJ+CfCQHNavC+VGCEQY0NW/bZrA2xiOt2WHPjuZ27ORs0PtUCD9Fnz3ZSAyglrq\n",
-       "nh22QG71o3jVEWtbKFFnKyw6TVigbXrXAuW+Ib7c6V5o9Lgm7Jccr+Qg66VCAVg9qxsA5f2fsHnR\n",
-       "Ov04nSrGW8iZWDXBAye9BOpV2Wpp8U6qCSrRq7+UYUwFHTp80WsOJHQjLJJP+KtuBjb2CwGD6ZH3\n",
-       "ZtSd2H7M4NaBtHonTDof/POQt9r85mVa870/eB7W+RNLVwxkNTYi7rWi6hGBbXpozFuYZQsm2KwZ\n",
-       "MSHVr31B6+7z9vLdYGA5taJ61SbOONVuM9louLOUPD/aT1K6YWUqT5JqcS81j+DWdhlf91wiqMud\n",
-       "e+QJPsbUIjx7Ri507MzYAc0wITksyh7GZ9P/JMctpLJ899Fn2GEpfko+e5YtUyLmng+UAImYdCmb\n",
-       "qlqzvTmEsHdeRK0SwcxuF07u3zissB4ztFfK0JTvn3gdnONgzgLWXaHRJPKi3csQx3ClnAs9VABg\n",
-       "7Q026gaTD1C6qRnuXgSwG0Cs6OfmRNQAAA7xQZ5CRRUsJf/j9RYJTHged7DQLluk8N4S1q/MG/im\n",
-       "FDPu0yrjrEDDSN6qSluX5XSz9kf+zvvvwSki8EmWs/FZi0sc+lILdotMmEP3gxrqm/XC1I89SDYG\n",
-       "Hp5Ho6qcoenOwBfioybIl4pVnhukJUsHhMGtwZHnkYEJPMreUwB+licMKdYOkn4Pg/iFI0hX9TR4\n",
-       "/tNlWl5FVc4TKYHeV0WjdbF61NWSgzSvvLeph8Kb8VEQua1OkuTBmyEN7tGhVLAO2Y8scSbqIvhp\n",
-       "jE+epHHrFgwZy33xoBvZyB6BZmreAaqii/ckjmKxoe8DhG8eZ4AK0JyrYoQ/C6v8yblnAz+FkgIE\n",
-       "wIBcdxDk9w1JHHtyaRqa/ykFhWaC3LaHIprNKcGtzBUTyLoKWOKlpFrcsW6THe788LCxagthsa7C\n",
-       "Ka2gotd8v9kfkv/ltF7hibbG1IFcwwfMi3Q85hh5/43nN20LbqDRencieY5oAm2NP+kH5nULvC8k\n",
-       "EtA86kdTKWOhwYpwzoOk4BoI53BItrjHCLVL0A++VJpuPCMdBvnXVYqZW8rrqmTlsZqmhzE7toeQ\n",
-       "4broYp+7vufDaEBYnNRpTBogcAoj6q7aJuvysaGayJk4L/jDKAdwFq8wSkNYAFHJYgHBO36OkXNF\n",
-       "8VVBXngE37v5ryVG4cG4PqIhuqfyc6NhXw/Lt3fTVwQC/dic6vq+6hGlrMEK2qEn9vXL43968J3v\n",
-       "EYnXcpiunaH+kpJ2Wd/5JIXXV6jNtmr1+E5ju2VAFm08UpIL/tBBJP42WJcOsPvBxvuaoLT/ChSR\n",
-       "XdkfRRC+EhkdIuaFjJBvQU3Uk/SG+hFxsnMt50l9abw+bpnJBdgaWjMZA3rArbwOk8/m3C6ZVqQS\n",
-       "8rh9/7FTULp/QK4oKck6p/nn2O11C8ZQzQ2vnXKgr+XJH+Jg9XJRSkgnj+RNsyDZgSypGX0PVyv7\n",
-       "GemWeDFNPmdP+yqzWbppx3yWfnNPiJwJlGlqbP4pIHZeC83YHGMoMxucG804yZ+PSxHH7aoZgmIS\n",
-       "nLumDcVIcHd3nlmUjmE5pvRXTYW5JmwAtjtoyXHqXFR/C4FFQ40+VomQcv3QK/rJpvuDcWHs2OzJ\n",
-       "MqMYRGBUj7UmtbdvhshkCemmFh901a4cPUpVwMkMN9fVfD1ss6YlL05DvPtWReeCEDxTKMCMQxeu\n",
-       "5SJtVQAV19HyQgeaDfo06X1bVpzvVaVwdKjbfMbiFbC0I9c2RcsV5XRWVMOgEbzzIHxqEDouku/P\n",
-       "hl6q3SCEN65L/Eh9ghZ3n6EZ0GNWpU+xS07ErKsCn2QAEOsndkl2Pckie6S+cxPjSZnbQNIzTC5f\n",
-       "qCtL94r9YuiZrgWn19AiSISoWy1YdA5jEJqDUA4mD8IuGrAkDR9bI53nxV3P+GadTneXRXxgdERQ\n",
-       "zi/3+i4GLZzZ+TTJ1AmDsc5kcWtq32Huyd5cl0PTagCOde4eP7FZQ1CE0vZ85GIiWsfnL062Gc9c\n",
-       "5SlkofFB25PmZLIJJgTacxWxF/v5454IxzR0+/DLXjUL2tb30qG9maz3z7es075c1G3xN5u1LbT1\n",
-       "ipcr5pWNTbM3oGP8+eL3s0ehZi+jGsPNPF2YEX7/l6CVWhEtCvzYxIX1nch/16ojTxMVbTQX9Kjv\n",
-       "TgRICuqPRfhX77zk3S6KzIDcw/5qcjjoOet+SvAdnOKaBC1wHX0TJoB/UqTmrciF90iKzjPABnCT\n",
-       "hq1VLGtCWCjiMg3AbptWLvNqzVqAoiKvv4G85xR1B6IaLZCMqYC4JG0FdqFo5r8RtVq6u3G5+rqg\n",
-       "em2y1ozoj9GygcZF7LsqgUyk77C5r2rwLD7rdUUzmpRuMMNDUQ5GwAreRlGq7vGZGRnzkci5lBaO\n",
-       "r0N355eWblmHh8aVREQnLnnXYd1js34HpxcvruEbPajCq7PAAOp74bILmhVRws2wgpU6pWJsaF8l\n",
-       "xM0C15tn5A/B7szHq2SBNVVydaczcVKG0k91lUjurTIXhmn4WJtouHv48rIppPDeDiIfn1RnbU/G\n",
-       "wgtq0C7qDkKLviMAXDlqcKNRdRidOnSHBHOeTnRi7Id7rLQ6ZT7baBf0bKHJw0zrdgfkb6f1UxMO\n",
-       "FmpYcQP/SQWyqHpx7FITJ7TfIYjgihIg6S6A0ACL0cDfCVrFDy0aqP74+9q8vz54WJR79gq0wfeu\n",
-       "iV4xPtgKNcOGfRduBKMPcwPUHEHy4q5sxGZogqJuDTVWr3ESp+M8WpNys5ZLi0vtdQtUIUk4eNuo\n",
-       "b66VDDEp9qPV1BZ2O6wT9XmJtjWGhJ089GqLVA/tLoodzqBCRJJKT+cJR5NMFNRKXsVxUZD2fzfm\n",
-       "NOEDcvNfE264427PTC064f1IVHiP06tdkYXMk4VEfN6HBLcG4AYe3R0eqW8n8Y5JF9RUc2izlRRu\n",
-       "rfZzfzAzUIveIo1Uc62ErIQEbjaLtpWaxLWCaHF2oTRmsVBCPqsFEcip5p20dxuHtbcyw3ed4zc7\n",
-       "/8U79r9ErE6rNaWWsrWxsFXUpbe32WAbLOylkEOjVorn0tJnpAWfG8eJ8ZoIkWMbia14AAAiDoLw\n",
-       "AIkH6fIMfDUbVelVa16byEeopwyoaoUv1WVX9AQRiOWmTt3arGW4BgQHw1COIyFOaxSoMBdb4b9C\n",
-       "7K9pABONa65f7etjd3AoDT1XOlxrqKRv72ceoJmoShVfQiqPcxb2sBkX+MCulvw5gru0fh+lc0yk\n",
-       "6lTRJQilSTm/UeC2+YVLKFJbsWQjw0Pp4VJhANcA/9zo9shgno05BBKqMwQOcjoDMkaN+kasvPZk\n",
-       "oJf1ObGVEIuUN8T4/Iq71TucCS8s/P/epCm2nILCgv+0bWmjehZI02hHUGOYiAL0I/+rbnRBRNkw\n",
-       "sSZ/F/+zAACVbS7RdksSezpkGj/WmFXjMtg16bV5WOy4ld2VNW77uWyMiWNVvjRGVqCAU69M3DSs\n",
-       "1EWw0YH+etH3YmKzH5koE4TNk0gHeOWngcsmCegWCSBE6JjlQv65q6h//ov9Y/1+p3AsaxjbbdJD\n",
-       "vmMnkD82KSigF3VfpwNb1K0aDXvZ2mifN3i1LCYF21XI97GHw4jSXfccs95S/bbx0/vEZ/40Vv89\n",
-       "gNn8Z5C9wfY401mXEG9ZOgqOpI06YxQ4mZmNHOz29fXTZo7jwmLWl4AMwJmxXiDi4adFXPRaMpNe\n",
-       "BIJNg52cWcTJX/Ud493UxlZTbubZqr99FhBUXJneQoQazjOq58zUQBwsO4RBD1C2GIqfSDPAOts3\n",
-       "Mpd5mqGhglTNjKewuG3Me0LPaekaPA6GaXcGbQx72Parto2+ioEarn652pa8WLxeSJdipBa2Cpl9\n",
-       "DhriPK2gKJzfqszHWd3Oi3b2xr8R5S5/9qbIPe5AW1N3JsRQS50LWSXHQ8+NmHS2KT1dd63+Pngg\n",
-       "PJDp51Z7NLhxXxXuOUykrVFOypAMu8GqkC+SrXFSyrLBKCCU8WwO0Rban39CWf0HD6C8G/qOdOr/\n",
-       "mlGDv7jJJn/lnIutXKPHlOtEeYaNigcQWi4V5u1zKK9kaQWnbjwArXBkJ5QoEytJQ7DXljBUpZ81\n",
-       "sdUNLw2se/VQf3uGrK9TbHYxVaLOBYSYKBUYCsJFuQtSDRP3YF243geyIZ7OTNLpi8cxJQ8jMWOi\n",
-       "xKR6a3idtgNRgh4YLJdJbjPXb1T/9m3bJUUxkJc0zHpLi9J9xSDmC91jHXoC6l1tRb41dkS4XFPC\n",
-       "z03McPyQHv39P6JRLprSv5wAaSI2fh0jnTVGdrSLaTSALDb2u25OdECDIuKfN3IBLxvgXF61/ctp\n",
-       "yjAZ+UHFdskMCdsC0/XW1UjcqBPCvwcbrm1yhjA3O70ZzzWqAT+4hKRHS3Z0MlTInKHZhHB9F1xc\n",
-       "gaOp0q4mPFQ/qtohCwYQPCS9+UbeqRqtOX89sjl3cCrEU/2aAv3j78V4NfXsQMaOP3lJBEUskOl0\n",
-       "1N1s5cbJluhfT+oewhMzrFpkZ/ehG9Y1CNpx0ytJUj+Ol7ThEIdBtMcDFA9utqLAVCoGiX3t0JZR\n",
-       "cUNWG16XFL9m5j2v0P1x8P/8rHCu23WfvJJ22+fOGy7vJSpNFN8XxwChgB+QGKRfw2NvSKx+Erey\n",
-       "BpdEXhy+jxOPOD5FSa4f4lSqc1c77YgJ3T3Jlt9mfH7e1HeaXTGWNuRdNyIQzFcOVKGJKAGBWc8n\n",
-       "LXvU6EyzngXiDCY3JVjmwDu5MfWuGwKM/QyYW10+w+oErpjTBrPiNNRNFet44vfZ+b0wciW9Ngpy\n",
-       "4WKgX7n4xn2EJEAT6BLVff5KwpW5Gp+AmKUWCLAahPrRk7z2h76YJgvKVwWnB7p0T9Vl8Y3D5iPG\n",
-       "THZWG01cl/17el5biyHvcIKtG0WRoTJibDhBnkaVucUTpWTdGadNVvJbppNkSNd5eWJba+LA+lwI\n",
-       "45au1581inXABaMB/5K1VHmKmt/TXTuLhW/anyz6SgQJCQuXZTJ1+k/6E0NVLzMm6rcXdkK60MW4\n",
-       "iAAQtsdYg0Mr4t5SgxREwLEJu3YBtZVUxcL3AAKZUD8OCJW9jMcvUk7D0wqoOb8+rC3WE34peoIT\n",
-       "wh7z7gk5+bIx4HO9OrcRW7YiIyfM0KRG/j5/SfcXMETlH0L/MTBNGrm3vKNpmh38tzKjepr8rwQi\n",
-       "cEBhfGVmfOkfN9c7xSTh08jaN+S+UntDeCfW8e5Rd4B3rCmrjTd7t4IrgQl/ZHKdKP6uRyb/JniE\n",
-       "GbMDfcagEp+aAIbDptfQYzctOXlo9vwirnLQgVUGHisZesi5QIqHtODRJOwGopMpzHQLsaIu4hS4\n",
-       "gnGv6jS2tOYpa5v/e15qVhoWAZtV6dcnhJlWPh2pJMLAX3rxwZFuN2ohYoMReJHE7QdYTB1zREG5\n",
-       "2TNJkv4BruL4p6vrWKTM9DUm4gLpB7s+VjLtKOOAOgZNhzhOd5sPCvdVrRR/X5grNtAPk5Pd1Vkl\n",
-       "vD0jZNapblBgsFYBDrqHdvp9HtbNVTHyR5e2ueAgpx/jEqU4Vqn5RXhYeQGka6+aWyFFK/NQW+fV\n",
-       "NgHaC+PXMzFvIDIcKT6EUiWWInpHX40hc257JkqoLAIK2IER7KKLN1/8Jn5GX8fpJcwG0A2qXoG8\n",
-       "AsDgpn+w+cQYuc1xrx2G+ofAAYce64+vLIzyGpmTRkjBAAAPSgGeYXRC399lGVz3e6AIrklZk+Nq\n",
-       "aKrQHrouJWWPDBZaytuuPErrL9t9L+7k/tAloOconO/kiLmDaVV2VjZjVXIqAIjZTEnCfwRw8ezI\n",
-       "NvgUxkN9H68QgYjEhhVxoVJ6sAMJGIhYNjQ7iSwgN4Xd4Q8XxsQNrDHhQ9gCSIOZZkLQnbCTGiFW\n",
-       "R0w+SZKIxJow6lIxc2R2V5JhVsWNsznzrlBFz9AonB/kkYAPP605K613zM6n1nGrCq7y9D5QJMg1\n",
-       "4FYj3APVbjpKGEdqYrI66t62GEaIM1k+snvSYpkAUFiFGLRLqRw3+0IeB/bs5v5/u4PhS9dBrbqc\n",
-       "WubWcruwioYU0W2n6T4O6Lz1/lsZIui8EmXr/kTWYLbSFtTLhpbDFNPlW40YsoJuX+NtVy9ypm/d\n",
-       "o7uZBaFmJ9ly9/R7OjneUEdvIic0ADxWTZciZERiGVSwf+Oo2NcwSSqzrhsQd76/YHytPkG0pkIo\n",
-       "ogzgZ0Bv+bVMVKIG2hEYJy4cOJnMvDLg6qOyQlz224N+zb4DQo+LkWP7LPdrtsjkEFJ3zePW8g0/\n",
-       "/j6MPRNVZ4e+57Lurdpmy4mCjQAHHrZM2yFFoLGR5N8QvDODCuYbg27cqJN9J6AHJqaRZj52XvsC\n",
-       "cakmCwOPN4tNWwSsrW9b/6bkP0s5mHU+qmSmZQBshpm1mti2WfR25gLi8dg+hXCuaXJLIWBah6/U\n",
-       "xAHQH7osiEPI1oDUofTefPUdidyHdAKuEYP24EpOyK1d6PtPQ7D9r+HNq1yuLv58CL7lF1x/GDvN\n",
-       "67JLTCaoPbaRgVI7JDFqkYwLwMiNbizCCPwwczzPDIc9ItOLg7qtEIWjmYeQxjAg1Yfv5KQu+Xss\n",
-       "kH5+uOvDeKr2Al3JTZr23BRe6iBVdKuuFfv5AGlUvg5GeIkDRNdp4x9zgJpGcTD3xUkXH5nTSSZY\n",
-       "sPOUEIYBEicWCM3bEzD/j7x0lrZK73naIJGsDxF8GeQx3/wRirMdIYgi1wCBLFoXf3TD2jqFxcG4\n",
-       "4fphEaQazM1SQS4L8DiL8BzFiV0tgIHDO2b10OWiv8TDq063BXRZ3GwcuMBf+lgDv+GitqS1cYB6\n",
-       "8JNZPwRiGWKCj7B/9jSe+dQBNWixwej7VGKxcfuEX28iGmh1dW4dFTo9ohmH71HPi2VEs1kp4qcH\n",
-       "7ylgoXx/pX17OUSmci8mU+VcvcmZ7RnqMhpFdmkFkE/+Bh2VXso69CuoPPq2iFSvg55agCFHoa9i\n",
-       "QDAw9X073d1PELtEA7ndR7X/6MEb+Y/fNmG+cEBo7QabIIaV+INWKwcMA904TnMFvbIsh+nDezxX\n",
-       "AF+CR3pB751O0DKOrMpKpMvJBAAHKYrdSt/gMQOV8u8UjmSef248pzGTWYty1CqdOEHASb1GU7Dr\n",
-       "4IDPlAy8m3c4859BSPFke36rqIQ6qA5mFAAeCJPLH22gD9ay/FdC77drNJqo4J95Cz+seH0qTC5N\n",
-       "AZe8Pek4oFtSIzxrc4DmLg2f6ZVZl0RTMO8PAAQSmkC3duw+pHw8wM8k/VqRr0ykbhKD3fuH1i/H\n",
-       "xo25jDzaVER9jmtRXX6PfyH1IABrWNT1PwQZUBdq6R1ZM1vcsruRmjrNsIROex3ppZDh8YxgnPms\n",
-       "Dfqdl1feHokpT4WDRX10L0oFQhGTMeQCllz+HDPAgy0phRItZYekfyAg4SlboCtJtrUqs1pEvIhZ\n",
-       "hiFzV6ANZfr0FYSBbkSt+r3SGJkUMSEecWn3rDavgv4FQKHLwESD9Rws9VXxIcBCPYRYODcP30V+\n",
-       "tm2P1tQpN5vR2q1KBdTL9m1iYk5yNVpS5822GZj5DB1m5ljcDqy6SSRIc4fj3reMu9LNCDTOYnjN\n",
-       "z+yPgqHDkzBcUO/i5vMX4CWIxxGZBraRz6ZAHfz8kxevnmpNSOEjn6bNG+BGmegEwODKjXtCeSZw\n",
-       "i8aeMgygkIta36X6Zg8YpYyIWmOmk8WX4RKzgPEZGb2qNYkrXJb8Wb9dv87Vhpp0bMCFFnOjOHmz\n",
-       "7xHjGGy+Hw0MRWAxDNYtSjRFa2labQkCqWRf/+H2GZTo+XLTPl7k7U+n9gFrCe72gr2UWRAWuMfa\n",
-       "rmJ8cUcCN5m/ulbCO7r6EmOG2cczvam7KN7eF4FnjdyanuaFALiczZya/madF3wV+uRouZydDmZG\n",
-       "81/bdqx7LxbQTTS03n6cn8bnlbSjSeaQ9baWk+4ZaJGMeY1775t/woEuecZCYfJ7M6cclprw9ToQ\n",
-       "AvIcn8essfMrGhq+zRKBkf8pTTHd+KMOZESeoWUlYpATUjUpdfR4dDg/cMi1QsjW96n2fhE11tQ4\n",
-       "Kl+85gweN6d1kTY3VtAvhIqzjsVj/WJlMaf3kegNPh97ar2N3O5LNFH8UIxP+9T6g9MWJvGscBiy\n",
-       "0Kn1WTsY6DWGcuMp3T502uJYE5O6+rrqI3YyyT9LlFyN0ZYS17dfvJkkM9gCOtFXtNvWX/WOZCwo\n",
-       "enLc9PYQM9ZvleVC4u97LcTQmaE2/hDliTmReQIygC5OPesH6gaq4jY7CWD9lqGIzdjKm0wFOVMP\n",
-       "W2VSTWLqtf3Q1eZYHAYVsoD/xaJ92cQOcNRUAoJOzyn3tei6JvPYyOoKgeDFEM3tSpKeZI7IELUe\n",
-       "Mc8ypunFPlXX0PI1zrGeJNhC4l3btIpmXOCOvb8DFxuMjMb1+HD16eLFL7nPuvvOp2gZemkp7HKQ\n",
-       "zOSJq78WQCWh0a/TMbB1xgXQpppriFQurp58dwlkfGTG3/zb2g4kVIDmGQRVm4bm4fq5W40LXvsz\n",
-       "4DODyKnW3vL8lstMKsfT2BN5dqRCkycw2H1ZB7UHAfnJa1VZt7v7t6XiUxqty1EDiUuLzJ48Kvjn\n",
-       "DYLZZy+rrcI2FSqLiGPTRoO9v6Ezkwg1RF85TogTpT0OxevEcK/GRneUnKG3ivm/EbKXuumAHIrj\n",
-       "MEmJW/Bat3JwWvuVzE+BgfCw+yhjgej1893bH6rVSJ/zAFJzfItdQJr2HeXNutnBkR2eU85RUmPS\n",
-       "K0ZtGn2KxpG+k+WInyvnFjeckUxFExWuXCQRofTmNMqqVz4wKO3TeO2YwiGsnxltLs8e2MCY55J1\n",
-       "j2rajPcMgQyAumd695srPYEn7kPovxTZxRnvphOodqN1Qe5liiFz12rYKw1gwKDTQUBqb7fOc8pS\n",
-       "yICkFaZ5K+V5fpCoZBTvcfa5+aTJ/9965cZe5GxbslUDo7tSSFJ3i8RqjgcMvxOURbbAjPA7bJTV\n",
-       "ouRrOm0O6VY7RDJZTMgldHR9NQj9YufpevKAGgsvJPxcH04OMRJai0nEzKEYXmzWjDEX9Dm9h3AY\n",
-       "YiZh9uZN6UOS78xfuDZl3u+wf+eZbEpTXeMSXq4NioNu3C9prmStmJ50tcNRXacz5ztzLTdOdGaF\n",
-       "Mst3qujL/inkFFRu4X3fQTmBl6kkEp0CpZWLMOFAlnmGHgUMAp40XH/QHcuEBVGKc3lE1mNmK3z4\n",
-       "zWqXHaY+aGmB80HZqNUSTMZaP1RaWY1THwZkSrX8nP3KSZZRObU3B5dcn//E7qr8IFCBe0In8LN8\n",
-       "zI68a9OcNo2er2p+gkfEG1Wgc6QJNM/oNXuk2V5OlbJiR2TRkmjjJbGf/5u0HbVhSXBuLEaYf5/8\n",
-       "NeoS5FW+Zbldz9cvDs1JdZfHGtCc+HujDZwSgPVMit5BHyngyHy1vzDdMgp/Hund4xV8eJf6xVs5\n",
-       "DbGw7R/B+g4Jeu70wCoyHaoG4fp+QIhDd4eUy01+GCY0uGUjrEqxgRJhAJKdosVVD8SKe7k3suZc\n",
-       "eS3a+YgIwI38LrYR2EQv6NkipdzUPNoyqs1WfQIo1Eti5wIjEvP++uYxF1n/UDmYCeMkS7nYkLxE\n",
-       "U3ciiCdrtj5gkTPDG0x9jiJbaylN8olSbNv331qby5CHNRVe9ATABZ1k/WT6lB/mPyaKl4p7DoSZ\n",
-       "t95450/GVN3yT5yEGlqYY/MZYB6Ocl3ScA/l+McO9oxjlJFC7P9Cn9iuAtWX19pf0BlBcvp49CD3\n",
-       "bRuy3GeX6FRSU0QjN8X2ModQrTth6C3PrFTEJeB31kd3oVO57AOUvcRDBmJUFZ34RB8KeiFdxG0p\n",
-       "U1DUwkgwE8j5aWvazx6JyJiGUoxR8A6pqQuFXPU0n/gf7WJv1UnueVRnBX/BrBOKrzW5V4FuFSW9\n",
-       "PiVrRyTTw931fC5qVAuu2X75EBh5aILUVrvTjyGki5KrbAYIber2JKR8WTeaz2AeH5lEkrDdhBJl\n",
-       "MaVYgexKu4/6EueRLBc/4pC1cXx2g8P7BjQHNW192eR7Y3jr0lBEZ2HyhKyjefvaNjzIBV9CcZZT\n",
-       "++WVTArpdxTZjGa3BYMYA6zmkZ7FAIKiCL9YoL6s1bR9imrujq4M/SkB5wcXCk8O/zFlcsNBOLnF\n",
-       "vm8XidBefr7Ev2KNkBcRbadq8Sx0IPWyKDT2ZoOOI4tqOf/5fWDsX/6xkZi3/B0ATGxWgiOIgcjJ\n",
-       "y+dFu0D3qwv8bTVUUo91ay+p+V0b9fz4vazZZ0PrZT/Hfj1sqmjpl7dilLG94DNpX6jR319aig+O\n",
-       "onLoDWfyZxt2p4HiQ95Fsf370U6k6pui/+vjMOUWeE1ZI5WugeiJCkLRH6DKbAbVjnHuKBhdf0oo\n",
-       "AfBye7Ay4/bNROg9DhJwYM+qpy/rku+lVwE7mkJM6R2dupuMlNM6lbQfFEVuTeXw8aDh5cVAT1wk\n",
-       "9kMn589dbO/SZDxavKuYVqQ9pMWrDoHY1H+nOBZqtP1fJyevPh2O0abO2PvSPpOu4iWAoDfJDJD2\n",
-       "jxnQleuXDuRVLWxPjIbi55wfewA0KNG7rmM/GcrRySz/smEouzSodb76v8oS9MvQQOEZM4543Ds/\n",
-       "Vjg+ywOyDf6E30kzmIYqms9Ql4e3pq05o9npBmqVOHjxqMyzAmuflUeJ9vmCoRPSoREwGvNjTzYN\n",
-       "r7dkB6PwAhEddi3tKQT7K2kUf2d35K43GhQEYGAtq9qLtG6TUl6+rHoZIB6sxGazE/spCuCvH3hW\n",
-       "cr7Kg91/VZecCVt8ExC96Zf2IoNDJRnpCePX8aYVk7vGxPtr2iyXlSdEpE67nGgg+FpSdABY8zZw\n",
-       "vLPkEV21G5NyoqzBAcqaFfqL7YYEOxwJ0iZwN3TE8OmkM7v/5Ov3UT52+1negL8+KFUvAByGSAn7\n",
-       "gb6DIQOS4hjfyghRbjkNZT1THPvj6rcDD3gNiYZ1YVlD2rjF5hSHNEUrq3usCpxCBFM3zNYg+Upl\n",
-       "gq2AxGSWABZYi+nFzESDWWxAAAAI6gGeY2pC3+p144cbq2kn+lB+xegtx+D2pMw4ZX3KeUcEGhXg\n",
-       "VCnhCYk0GT0VqB6YuoMRNayf2qthMmg6dfQt2d5BcJ6atKuHJLWsvoFT3/99+3L9CsLUd2CcSNgz\n",
-       "4PGzN5zwZydL+02ic6AH8hsCZWkC0O7GiOp74RzSH+R6h2AeHXKaiZCqxBeQRPH0ALCunoTGXmDl\n",
-       "+b4qTWgfMSa/LWgUS4sURS8X0BGq4JyXKa7Ky3TUHpQChfPKzgz7bKAAIgwvFs9/qAF9o4c6XnKe\n",
-       "5o7MTHLaReN7yWDQ9mo+F9GMX3mt1AsccgatvK9M2AVrhQEILtmAODcnV02j4dlxPS3rDdtZkGiB\n",
-       "o/H+WEz05Ow4bLBSfHnLUZ0TG7AJhYGwlLB1srh46pur8cGW75iVnT85kKBq/pwVDj6CRs+3k9aN\n",
-       "n2YRetvmmxbM01QduDGae3tDbQ53bqrijY6WwO1ZHxT3xxEE4FYZ9qar/+oW4OrZDtRtR1y5ELId\n",
-       "JmHdiCYOyF7rL3Xi6XKQFB6xnIr5FYb5Jm7xnTqjYcvveJxprXsZX6PVI0tJ7Z5FlSHThVeuhGF6\n",
-       "ElamNet3fUxHMS8I3+yVaHp9l1OWxlYaFH0s5euNsF+VjbviZaJV/21bhTwi66K7T73lJRBm0xTB\n",
-       "yH5kl//Bbg3UicE6TXmE/yn8Gx2tKRRI+teXga7mq1L+BB613O7GRULxWbXagh/TwdYScqCdSbo2\n",
-       "XGCptJwIs078UC7Vo+r012zMiT3aj0TXr/lVyWSe21OXiuLjYNQTGVYtET8Z8Ggm1KNmPjfE7RKn\n",
-       "cBeM/7YJdNpnW1KhmrPLvTGGpluYCxB0/88FDAZys2jlVKTYFJV5OeHdl60mMHQUBCuhwSk6ZRUT\n",
-       "FZIlv9A8IlS4nU6r43ih0tNvtwbLVIxecGpa/V6p2DWwWOKgVpP+gPyCsNXbXf6k4dRyeemal5lY\n",
-       "xpAv6jOJdvEs2vytayHTa14QGTZm782HrSWiP+6EcnB2n9/fp1h0zAPWZ7iDz3JEkpVrxT+ZLLKR\n",
-       "EaEpSjk3CKBLWJYD/qufohcuT/9KH7vY/ii2yRT6s48ttB6SLkxDjPDjWn5BPxpf91kRS05VLxU1\n",
-       "fnUV2U/vXQtzD/aZMWr3i5HlBmUpXXu0VAEq5+Dapf3pdpjoyqz5Nk1HJi077g3qW1Vf2PTwN4vr\n",
-       "pYv7TJzxyzVC2VaRuaAhZX8FobacVW4XO30NYpLyv1VnHu7VTXf0E1fOMrjYedktLwjV//TUrde0\n",
-       "LWpoid8Aq9mkOW979uRJ42yK++GgKcHjxi5NEBH8ihA/c1Dc2/Q1vq5NvX+YsQBtNpTkXD5C6vRo\n",
-       "FuP/bBYi2vLs2e5XlBzRkbUlV86S5+oWZFNrix7+53sCsZz1exGDkaKMkuhXwaTZM3U4EfzBACmI\n",
-       "8Lat6BFO2PnavEhfL/HlYChOKy21EfBCWoJ+UqPPhINHNobb0wsnIBHrSevKMq/2xcQ2HCHbmfUW\n",
-       "Pv0quzeSjsrf0Y0RLYo9lNM9cfqO545JPdqx27kkh8o/aNnJHLZnXn6UkZ0qBmwcZATdvPYEqWVu\n",
-       "ravOXTmoQGWJa0PbojxyRFW8Vtc3/T/WQaeOms2I2wx7TR5rN9fGVwmoo5QYiCy4MuaQyzRbcR+S\n",
-       "ymeI/d58ZcXu5JjNA8xLYuV/pirkpzHJpc/P2emppNN30nmF5sXvvrlbIqbymPQjrTC1/tz03NaD\n",
-       "ArBFeyupvkjqCPQDvVZCfrlG6f3RcJ5p5THDk7x69mT6AO9nlerz4iPAFBy2nw8x3Km3E0uLKHLn\n",
-       "/kggc4dyuIPfh0hmS+TdRNpF5WPwMQx+eof9lcfpYFN35l0J3o/TAJRLZxmqkLPnog+tyiyPWnfZ\n",
-       "qMerYctsD64RQMOPFoQlKcQwa8uMe6OcgP4EjO8kpictv+wQYfqxo6AEPHhEXE/1YeTOhRVNa6CC\n",
-       "yWbImIq2/tKyvse+i5rgxgPVfh08XT26bb/z60+9mbFq+w0KmIty17vz4iuVYW+FElLfVaYODE8K\n",
-       "3JxX0g1Vy3wqMgvL1qSzSNdX7ROv1xPhzhRfOdCR7BEEB2jmXa96omKV32Gkg1JkA69hAhody5YR\n",
-       "5g20ysPbbKrhHSwjwYETrztwdxwxYNGVlTRno6cN5iO21UOpBqEFUPze9ztOEiXwRAcEMX/AI00D\n",
-       "adCbbzfxwZFo/3SqXAIWtBpU4d/bZorE/qhvNtqmWPi7fXt3QVM1MhIJjFheHzvI1RIQOnnNLYKa\n",
-       "Ff4wq9uO4ZYdyITEzzHqN3IWag4WIPvuMz5rIXxcQlSr2Q5KKbYId0xgzIeXd1CQ/7gkS6vwb6Lk\n",
-       "Doz2NZ06LyYjyXIZR7h6XYGqGxkJaSwwx3gAABlfvDx3/J14zFC4il1i1/GevWJHe6C8GO7OyRfq\n",
-       "HOBbcTHEL5deKf+z1ocS1dVu0iWCgpLUpyo1UFvSZrqj4OZvdiaagVScbQDiOaos4sopzytGSnIK\n",
-       "RITT0I3DdTD1Y22go8j6Ej0ZBnB67e2BUVkMtcyQoCNEjscddi8cka6uXb+4PlM2YqFTRWjK0vvl\n",
-       "yBkvL05XO+DMkCT5bp5YFXkLyXJqa8L3XmJfpEWyMEM+CAcjCdq+JH7u2wrt/53CfoZxYb+xrrg/\n",
-       "ryK36nqCPYJ9l7q9ywiwPE7tLqRZMP9apwWBvS/Vw9JZ+1dECQlyZ5d2A78UEbUQoOsHV5gu6m2J\n",
-       "gjSiEWImKGoU6YtzpfZMMcG+J7UF3P19tmJ3FMLC9E9UBqtfJsjyKyva9avziUTVNXoG9WBTejCS\n",
-       "oUjoZouKPYY0a5R7Wjsjcig6x/xtlrShGkJzOYWtVEi+r0h7sQE0XAMKx0yuyWZ+c761pNQvslzS\n",
-       "bCIgwgI91Bvp1nL2uVkDBQZsESqfyFtH1IWGRCu40jINXmtQzyNbNANeTHUmx8iQZ1P+lLGK9hOn\n",
-       "9fWR3WRUx8tP/HKhgfDEF0v35X7NaQ/mBmJS1j5zRNmDDeUp29E6oOe3GStGnipqeuq+rpgRIsps\n",
-       "+hqalE5Li/OChoVV1+KqGfDr3ni5jFFxAAATd0GaaEmoQWyZTAr/cV3VGBid3SRGZuibbteHYwlV\n",
-       "+Gj5WrgqWLrFgmzAR6SeMPwotcMZ7vZnmMdRx44RQEW4VfKQgCehfOa6IHAoEUMic0lbrRsovI9U\n",
-       "44KQoB1ZtFxxDtazr0GZWSqHz6R3AjkrjZU6lYv15ninoUMIqFhj5VfOX77++H05qc4MUb2bbEG9\n",
-       "0wbLt2E5uvCJvDfjbUfaTXqPDNnSBvpnOUG/PXo3rlGV69zGeo5zn9hDWTKxgCmT0SLtduefigXT\n",
-       "TdV6rPa19qMsS4DdRtH137PB5k1AqtFT3TQWGruX/cFblpl8U4vD6E0+ukhu2XZeW/UKFsyIr7Ka\n",
-       "np34mZCin9Q8Jq3PbZmEkFB9Ml/Kgh79XrOFLnDcTLAQ3fHeoSqql5XKMLxH38mh5cSYaRrUF9nV\n",
-       "6DvNGGLqMH7zKIpW/XH8cfHgThA/lxC/zMco7L8XZbf8KWIV8H58WMUhNLZBYOEHRlVnQ2zmwkhZ\n",
-       "9C9xjdlinuo5Dlhap0W6uqtNfp3ZuOAixsjUyeAUEuIXSnp/7gBCEBalRsYZxsiRCylgXmhLTABj\n",
-       "oPzxHrcv3db03Q9HC4g1yPfmBYdFbbMqY0+L34ZETDX/MCyIC2YfJgKThOyYZz6kV000p/DfhQ8f\n",
-       "nQytJx5Li8htV+nDQv72Y888fB9qBa58ufGekGvoYpeKzQtWjDbP8pDkRKfSEsu94AJT/ORgkwPM\n",
-       "/Ihom4Btiz1G2szJnIgLEJbA4lOIGw4JsbvKpSOziCd6Q4K4GWOWZHLkt8evESGINVnwLhR3o5uy\n",
-       "Kx35ImYVLDbh1vk74EvWcyLfkVXdOPn5fkyNragmYA8boFIfygtHkVQac2onBj778o65kF9L+BNt\n",
-       "9mlO/qcldWtWpbr6F0WXyfL9Hsl1TVeEVM1GEFuJcg+YBvGyOjtzvxpkwMYh7BIdYxP4f5Uy9CyU\n",
-       "4cJMMFFlkCH0BOoUg5FY2KwgrGDKNAZwkD3yHIsqxvWFXKVyMlxML2W7RYUZFkCMF3/ycGQ2GJob\n",
-       "BvpP3OHIUW3CU33sgo11MpXScWuFECYoT+tNwQ+n9h1cYIhOc+nmlur1Yp/l8sZ4HA8Eo0/vHprB\n",
-       "1SOU3lClzpTd+F60AkjzotD68lXk/YM7cdhQxa/M1hGs50YLZDmxMebGnIjLQaHypSstJR5W7Evq\n",
-       "p9dFHxFr8GB8rtxv0GBCpnPbQMHMt+BCsGZxUcq/gQRiJ7zWTrkpGpXlvcEHDcRkMDgNJcpSYev1\n",
-       "IJtBnScHUC5JQ53PXEFoeUuFAlSYAEuL3IDSvZMuPFCkbLQ9qmkot2O1h3boECVPxZzCcMeSVH9N\n",
-       "X9Yvs6Lb9nWVbl9LqOHgPkna3KNxyk3LppyJV4/FQdryHcU4FY1ReF/YrvkRfjFTXNQo3pmn578m\n",
-       "+X+WYWHP0+E4tQy4+hwbmpLyxjZbHyzPHULUdgWW9R1SKfah2gZg7Q1tBKeMLpicVCKmqICme1wO\n",
-       "MQI8iz5RN9bqd6oHKl61rRg1Twq/SCoMQEKDWB8FOLz4P9abGzD542pb8+KBsLYvlqy+Bu/IP/qS\n",
-       "0awCAvmaTEsKrFkIMPudQv2FVNheYN5KFkOoOngegHYm+l2gp0ow1UC2KKyIgFoc70h3SuSCR0Jf\n",
-       "zzT/Kf7k8VfnrXVHUS0uutVK3JPRCRssHV8jfuIc8VhEW9jjCmoGFNOMATSz4Uz5zQAOALx4kFVY\n",
-       "2Y62XKmnbuRJ6G8m3HtkIM7h/lVbmA2+c6Bv25OsYY9fG3hpdCxI+PlxUwncZA5YXVnwdqEhpJ0e\n",
-       "WRLEHwl2d9g/bIf5GdS/VcAeY4Q4HJjuFTl0upQY8HnX+15AWXZEtdvJCwysRlF4qTLvRYBHxOmA\n",
-       "NUNp3mvLCWA7gJ+tVIV6FAUWmOKtL9Un+ukZmHlkb0xxkUbNspzEa1ybQt3xgayKed7M2qwdTwYz\n",
-       "nZcJOwczdJuC+cHhoP8h9TC9c+ZIBGCROsNvUuuI7G30OVkwPCBBiNphsOF4F263ZgL/EPupfXdB\n",
-       "D7MNQsC2LM+w6mhZx+WzhYzhSxKhMDZ0jg1jzZYBq4M8UF4vT1JLiw+Pzks+2vessxn4lcz80W9L\n",
-       "plIUzxns81Kdvj6Zy8Mi6ATY5BTdZcjUIWwxJFO/2bBYEzqogzRymxiXexh3dkw73ix/h+uwtGHi\n",
-       "UMlXC+nSwkIr/FzAC8XiJAxXgwRYVNsB7EfRhbEvej/byeRk6LBncuvNYa7qXHbK3UaVCu2aUvbB\n",
-       "ubh7ekHwtLJBHGc0xPfRZCSNzul6lFnjPFr7G/X1kk8mfSm7WQ8hlTyAU8GK2WEwXXDqmK874evU\n",
-       "y/uKYPjmZb+/tNlO6bym4CLJCPFGhT3s8yp/UjVHAsXn6V+WSVU+da1hxH1sKuau+2naj0///kIB\n",
-       "3h15A/2TqYAspADliTPJn+y9jkQkw3NuZv/iu2qkf/K137wsXxGc+RQuasISfyX7z74LVM98PRK1\n",
-       "K4N+O9HMamAULD983TH6K4kKRghd/MvVgKcDmXA3TSnzB2qOgvvp451OieqEnstBpHFKxZoxEcZh\n",
-       "Rt0Pj0aN8q49aEMG3HlIKNigxEsoUQRKOvB7iTSHoRDh1PClTwAZr9Bem7tbP5oK73jS9R82x8fi\n",
-       "PiyjM8kSFYeZA8wA8t/FJoixty1+gYjNLHsYNGsJcFlhb40Hoo+OV6OTEvDVGt4sao8jkR2KZlh2\n",
-       "Oe41km76xmSDk5xpr5mtppcWH7Zm1Yydm8+NQiLluXwCYeL7oOdnOR6oLRfvZdq2GGFZBJJ17HgJ\n",
-       "OG8iclCyve6L+rsf7IDeXpsrnD/ALJCITqf0fg0vGSzORACJE0Vogtx3gQel7Wk1h3hqPxftgS/W\n",
-       "EghH0by7BflDqiM0DHP05P4gb7ZAFDqMetqFPSR4wWEe29TF15i6wQjW7xKvHZa2k6ICW6lDAJCY\n",
-       "pUAuMv7gOO3qN0PHcVDWba5TEEBm92DnDv+fu/cG1+Zu05pWEFU1fd10hBZH6AC9Bao+scWEZzQt\n",
-       "WkMuUM+VwAbHWhHu3QCG2SQxN99okDLguRYfid+fphsp8ImVeWKG8iz7HzJZppduKb88e4GOKI0y\n",
-       "UcEtlG5LIjqwDP+xuwNnKYFyLLR3HUvMhERt0MyKdLdlgRAXVBgSGb5gVOnQS110w3VTpi3Ib/vw\n",
-       "95fjWvLTzKxp/1hhCKWYzxuapAcKqmvb4isMTmRIHSpo3bAEC2vJYX/e9gchkF0NFvv/1mxnx3Nz\n",
-       "q+cwW2yeV0BctDBSa95bagGlfDHXtWK2KCitzMhJUBcYH7R9fwrCQhaSAsAG57xmQ0/Y9Z8wQUSi\n",
-       "1PdOXb2Xxp7Kh3Nr203U68pr3DAFx9G3DDAZ/Ly90p5eEwkdiJYcmLXIxDFulAyI4p9CsvvtakYe\n",
-       "bi5VNQqI0+ZC5gjEJ75QnrPE+UBPn1FD1EOD4ozuxOP0VV9p7inHt/nKiB1jy+mrbvWrmQNR6rk0\n",
-       "RV26i+qgILJSLgNHuAHZ/l/rvfywwe/dxl7gzzFfHrBdeCVsZnTwHo7fPONMugNllI1ALel0AaP2\n",
-       "0c901FK5gSkdmibJMheponpLa1o01NpDmvB/k1HMnfx8xSddn/GZtP4F0cnuHCakENxoiphSEv+9\n",
-       "/AMftrPCrq4X20SMAPjA6L3ksLoX/TWABYsT7yaHMyMk0T/q4NTyEqZsGC+FZvsfserajJbx8rMZ\n",
-       "LrINNAfWFO7R0UWuz6pwB9+YyBpEJu9XGbGnipv58tAwTpgRK1l5oGlFcMGr3nQ8CJVpCslLitIm\n",
-       "XRbPuiY6TmJq0WvxaFVLtU2FsVNu9Fufh+Ua6Pd6/GktU2qkk8i57wxzTSlPYVxhsDcTtlthsp8U\n",
-       "8uw3gFty+ZrWqAapTOz9t7Pe3uiGX3lczCPEA18Q48vQkzTF6FZZvCaf61UCphoo+5WJM+hkZtKw\n",
-       "RNDs3j2nDPR/bprGdZy3AibyLXakelWONd4OfOKoQCxONdw17Vuv+VJbrL0UajM12B9/Y67SNUSC\n",
-       "pz7hpPtM87cGEUPfyWJMGLt/51bZuR9NabaVH1I40ani/KshK7dlG76hesPIfSOmMIT3CeHrg5/h\n",
-       "F4c2WD9v3GmOrUcr4iJunP4Lt6gUwChm4xwX3En0OfbaML3YOUhSWSyu2SpYkv2Je4nqdVyTORHj\n",
-       "ytsm0QJ1D6vHJyGUP4Cfh80POD8T/0q/4xQSJThbD22S/rw0mgu1MLbvlvNDIInr7v/2GS/wgVIm\n",
-       "NbgpazAQqKVbZ9AoRbnRX17F39hh6O4+KuvOKAPyIsoADPWTmJFzapgwjnQ0mvfYcIP5MeeGKv/E\n",
-       "jzE+ZJzGVVlHtoZ/gWjBf9bZ7KBxOKtC6BpDFW+/297NchHQSVZNOTvlX5J2aHXQsU3wabnnTE/0\n",
-       "j7MDWINcbjx7ZJNHLw959H/8YqbAqZJwWCXFJUdQw1D22r5MTtx/0FIIk29G5T9720b15KbDEf40\n",
-       "BBH9xN5bQf/Sl3/QRLPSgqn+rWFZvkQ6BY6WB+KxciY7mQNOxmjL/z4WSzi/oki+tGl7qcC5RJkE\n",
-       "USpZiAH96DelVBU8v7XKDpqM527rv66/CNPK4H5j7q8rlPZOxUy6NOE5S1LzvrF8obxfhgH8XovQ\n",
-       "Qbpsz0mDeanzk5xhDm8bwcSw442Mc696Ca0HjW4Hm1/nnlPSQHNK083lwoeRNPd9zx/cbfOAzN7J\n",
-       "fZvsPgoHivThILeCiUV5jLFH+ZFrai8u3ZCSnan9/9JpUFDlvl+ohinTD8xFKCL3IV9T7UbUrTxI\n",
-       "BRBo6vf2yMQMTD3IgwT5l2DxgIafVHLyDXtBfegHykNKBoOzQtpHrIL9Gge3P4YHBficn2DBxiiN\n",
-       "NPEDPwnMDL7NthS82bP45iw7qGEp5ttch7x4mdznlI9Fg4FwKDEhYDnuVlzaIKJFYl5SEW02t84I\n",
-       "/nzzFD9nX+WywqXcb6Ntkje8x7oXLa3A0KPm5ARW4iHAr96dxO3LYuvCi4NIuaeWCZs+CB9aMBjl\n",
-       "UdGSxQQLrjT9OGwEKLHp8oCORltuk3uVh4c+KBkHGXp6Tlx+QC3vJD+Yd6hTcgi+5V1LTb/7S7lu\n",
-       "UIt1I6imMsIfAdkP/rI146+1aeHJ5aaXLTMhJ4PnFbq6JTiTnUI3HhmYS9w7Fqj6exQiIaAMf9ls\n",
-       "iWCuJQKnDgcaD2Ijmz8//DKVcnmd1vUoxt9YOpabPpi9OuBgNIW0sA3cK/lX00kd8W1JysWRaUHA\n",
-       "rJycaR0k5IE5bhBauZIHqPHGw05vW977lQqzXJKRZ84VcBNyNOQ5IH6dFn+QVhjCuJFid8xXNzBd\n",
-       "5bin8UWtJbeUd8mCa4Q4s6iK2NW4zC9bVgxU7ZLeJGvoJQHGc55Of3/IixDnjBCwtU7y3wev/0pd\n",
-       "RhRMOS/xxS532sEP35yuVssd3q55ZqtmKGuUua9MeE7+koEGUFXhkWXb2Tifek5K4l2UR8j2Kwo2\n",
-       "Cb2jBYI8+CQn3l4U4cVTg4VJDLVxDMWHHOTVbWWD0JMD7g+Fb2zonUdcymBz26+YwuW70mb3qQ0B\n",
-       "h3YkDkTorOOo7LxY70h4BA1VdVSDTWUD/0g6fmzcI3EZaGGLUu0m5SMK/mkGSGkrpWs32ccDVIYn\n",
-       "slt1GsN2GqlFj0i9ZeZ6OI2/qZYN9qXx2fvgY+xCSo3J9KwQMNOFTrpkwBfwqad7UEsH5ccwwSmn\n",
-       "rc8HK9AHYvCs2ewzfdxcHHT8vS2M9oUtNEulaxfxsIgVdFoS9lG9KOYRQSLtdjGeM1ARBHKpgnMi\n",
-       "IBBfLZLbGvpd09gIz/ToXO/k1Gj4X4CJzZR7Q3LfNrvblTmKgM/U3j6u49gEpcUa3UrJSu9WRjxo\n",
-       "StcKU0VTmlu+Hfd3tY06o3hf80Uo/vxHnB6yAEtaBaZgjCfwUJRvcVFL11MWDzAFxvMnQgM21nDw\n",
-       "NrgvmVu42S4eUaQYX8Oqt93HKB1/5vcmnBw4HgxLfbmT9BivU31XnE4wvIJiMfGhSzzJjNSDaeN8\n",
-       "dQf+wTpvO9tBm/YqwpNCwWFf2wZBb+JzxItGWXNmfrHNsOzvBnsn/bkWYyRV7cP+k2otksCCSfip\n",
-       "eioTebmNd0YhZq1RCgE+Tndk9M07i40ws+L5+Q4f7hUN3ykvNqeufKFVaBIu9PdbcGU1hIAOViDz\n",
-       "hxVLOWryyLiqptdJLQKRuBJiTVrjnCv7HLB46hNMh1LIBpfmCdAV30bHItv0SSxXO95NWIP/BSNq\n",
-       "aSP0mLyx6RAckputO5k9CCjalNm4trTqQTQHFyJ1essSSvAoNrX0dWPt9NlZdxmkvuwZRzN5Ztk/\n",
-       "QQpxCOn3J7U/VHrrBoQnafYdcISKSFosjEo7IdH5En9OPBiQZjbgBzBFj/1/6nScKIzVw8eeHXTr\n",
-       "tLSJ5v8X5pUR70s9amdom3tXzvAfSC4DgTi1ytnycRySTLtCuf/rN6I8V6UH4J8DST553+mZGv91\n",
-       "SjLDnQbzEJZLyOwKlzuAwy4Ffl63qj4xZX+yEKsyrtjEl/rMn8Fud3v3LH0vcWenCpGmh8l/AJ/k\n",
-       "J/fxKuknIFOme+msic1Vovnia1LLCVZwSWtUQf4/2Y6Fiblx+BrgnqgQ7gHFYCJo2443U/bOnhyn\n",
-       "KMUzrihZsCUg++lTg6hTRYY6lVbWjiCLWvq2jPwP7ZJkfmPndtP/bEuaNS7L6QJfECAxIQAAD1NB\n",
-       "noZFFSwl/6TrxxhByNxPtHaHD/seFTjtX+ze4KuE+6p1vgIOWf8D2BEG0zvZGM05dH8o3/99t/vn\n",
-       "LSUEikzC6W2UTcTPiUfwoCkGdySN0pt5e6HP4DVVkaZzbAAm1EngD0y7KnU/QlerXFWCS66uiJ3q\n",
-       "lDh4CZh91FZif2eleDOtCUGmnXQY+RyoP4obalAsiczOhB36sJr8zKABki5uaSHCJH8Lj/yr5pD+\n",
-       "9i4WLeWyDWS0mCOQvJXO5JtzbSj/us8ZVHg5grD+G/VdM8l9mX2HpoBlKIPQd13niTDhgaTWMSgs\n",
-       "qYb7NFJiPaNKA+bKW8Wq4eEWCxg91krwUV5lfnwfUg0/j/yxQYofsgJYASIzF40S8YbIM+0lIik8\n",
-       "6kB5A5v3RrXXfanEp0zCvgtxx20ESsQ99swHdmQ4zqjFwsMTFeHNfKPxOwXHG2axAXoI4yrerGTg\n",
-       "k0ao6Xdu4iFiugI7wSZRndR12pLzqeuFUkAhdhJTMl6ZP6y3VNtTnHEvD47gwotnBN+YFNAiH2C3\n",
-       "O2JnOjXvRl2Jd8VRxK2Ezj7Dynef8wS5TcsYJhGO6la6Gdx5KUCferdg4GCOjcJG5qiO4Fndf1i1\n",
-       "P5iD1wtUD0hY8DnZpqH0KHGKeprhE2s0gsDM+DVCAaCz7sk/HYfo75HeiLct+Fck5p9uSCTLfSNp\n",
-       "pZ4Hr+/CNXPE79HYoy6YBpfSCvyFo94beMtWfzA9f5LYOGtodhHzRQpwIAWr6VG5NNxrvcel4eH9\n",
-       "1TdXzE00e3irA7gm9z6tyqyd6GdHIdEn6q4su0paKet0wgluhCT30WcmKWsV3pnXkBEIybAxmAZm\n",
-       "CWcVcDWddsa/uPBCPGiex61km2oAIWfgFnb6m5WS8Az7QD+0PWZiYvA7AwJE7cQzRmfPJ/tPzeZF\n",
-       "OOCDZUhf2s4dgxy2GIXKJlmcwUaCb1uL7dQd3H8vl2zK0psq3kmuxg5Pt9qDkI2mRvISW22B/QrE\n",
-       "Uxrv6rPSGwtTLURTqyk2do+lw26iLT1zB+XENYQNoEL91UiwUD142PnyJjsuXldrKP+1rB188yfF\n",
-       "cJNf3GFIAQxtIAsJOzsIEbNza6uxZ29WKxHOWT4sUTMaoFawzz0jsJCD1JJyMUEWs93FGb7+IbV2\n",
-       "UeCOt8RJRO+Q8HQ4HES+szfety/5QLWeGibh4ZJyzFLACZ26Hmt6n50njksKkYqPPe2kazoPnH0R\n",
-       "WTHPBXtAy/YolCivbAqUiVoAnIOY93dK3BpRPqxE+El+JPoNcFS+QUuFj/bRd6omHm0o1uFffTG7\n",
-       "H7WXxv7UE+fRdIzN9snflmU/oiAAfXDL0gw4yK+jaBPk8dt731JS15H77X+0wHT0fkO3vKrKBbPe\n",
-       "DAog6tya1Ibdt3V4l4u9xz7eC4UC2r4Tw5gZI1bprGCoGZfILzQL/QfM1a7+d2OQe6RznQA5eQRx\n",
-       "etJ6poFl7Rt9OOAO5FGwfVQre0dXHfZpj79iDE1axQpSP84AX+AeL2t0i68YkvqcSV7MNsf37j0C\n",
-       "jU4qc5t0Utq2U1idszM3GOdXHTcCyrvHlgV6kBFBAHfzawnkoDFlfvtq/Ie613UXDRVCarBTcIld\n",
-       "o1d0KjmiXtrSxp9XHwJaxBksdh8Imk9Mf/YaO9CJh7HUbKRTd/NAvZoXlSBIn1jYQFPaGkpLu8PM\n",
-       "ozokSAClHMjvPJgD/rcLAPlclyte4psGYbQw9WdfuvkFzRfH44S2R0jNYiHDERTfI2gtx1lwIgub\n",
-       "5k4yZ2LwZ4yd/83PeF/wdadsQWJa+HOj+4qCDw6G2D6qpqGxzXudWwHnIruzce+eSqFdd8rsplOd\n",
-       "V4MLXC4x9+WW0X9uNrmAI0ywfP2NrTT4X3Me4EJr9YaPlFZpv2NAMVXA2UscJ/godqGglNMyepCK\n",
-       "6NShgR4pmzusr68VAyz2hu697F7npVAvvKsVveUf6mRSY3lVylNvnp7qP6fjlSiNYlOqwP89y+Fb\n",
-       "/xAX5K7oAG7chHQBnqDmwTXETyqoc818s+IPtUBZ/9drUwyRnsWJr6KBLdBG0YUXLTtTSqwBi5it\n",
-       "Dz8pCo4IAAEGKdegbRpss5aGfxaIT+Qxt0Ivyljokg01+ddnhGZrea8TbFoSQWv6hdnDWQrf8IIv\n",
-       "k16u8xlD9M3lzsXJewtUyBLXYM7/3kuiy+VHn7qznDW5l0kPaNlyyAps3Tfj0uHqYBDVPstqn4Op\n",
-       "b+8S4Ndggm50yGbBNr1G5vTkgVHmHhsnIHB9yVOg19pTAmveqmzUnU9f07ab+D+JVQHxWalPf/+p\n",
-       "L1VS+1JdJvU6MFfizGrMy4sFW5FRHR4QLbsB1ZdKYFsjW+OExNo01p96FUNBuUnUE269vBRcmIZn\n",
-       "xZ13HNu9z7aGQY0JgV2CqBKtJEvYIJPfnJFcfHokmFsvLrnz9X9doO5cZCQUsMV5qIBdzUJImf9D\n",
-       "yYfGKNO/TUCnbIXJIxuFGh9CfucDwn///fX3OzHKH2M0zr/kOryQe96mnUdRmR9Il6qJXGish3m1\n",
-       "MvJIULP5cNEjFcYGJ6kLSjPDCc2IltNjmfklwGv/jusS2JLPPPrx+21RxdmKaZCMRVG58rBqvLnV\n",
-       "udugTOGhoY4iKoSP2kPmWN8tDAvlhLxq/y0NXAZc/eQgIQWzsdhTS2T3OulExKOpq/9zNak9eQRk\n",
-       "4VDwSZfU5SOdqrFg06jPbICNQ2DXUfn7boEcV4Ex37kPDhqKrYFFXEbHwOiUd871wTLs/iAiJFNa\n",
-       "O0lQsriVoh0P+/kaXgsep4h19QPBrRokt+ElZaWPKgLPWm94iYIoFb5z8x5ePwlwBFCi8Wx3df3t\n",
-       "CaDAywUStWvFiW9umyX6hUkLhMzb/OgR5z9yce8hmKB0kpJsqk3jBhdgUHKaPLatYrFMREh0uWzo\n",
-       "BiVerkxnpqnkuMDWX4DeN+2mZSCzk0FTrSE+173S3k9zdx6tzcGyRgiULNU3A48cYFe1Rq3sTD93\n",
-       "tQTOcm6aoQqhkH2qQ3LPSIYx0YMuGfceQmOnIaRieBMqbgwSYgx/SWvmUshMXbk7wn4jJVExwUvR\n",
-       "BFNo1X8c9DroBIelV1svI+N72tefL41bSxkvPY/dGa082/tFjZ+SVjRWspxbkCA4i/9rYK1iaBHO\n",
-       "KHJM2pFxoA8SQaRDpvom1dIaUxJ7ier708qFMvx/FrU5c1Ykhh9sqTHKjhUBhHBtXtE6EuXRVkA8\n",
-       "GgG7/rHIKaw5/DfsqlxS6lDPi/KATGQMvkod4oxUIbn8/g4in/uEFWyjtmt1QHxsnl53df9UCjBS\n",
-       "onVozR3lftp04PsaUweZvP6cfHTGk4r7IkPb/t8ZCe3ePYQFYr9mh1f7n6s3EETvxpqrCGCHZM8m\n",
-       "EKKQPw3qPnXSEDLnHbao2eOlaC0JQGUZ6nnyGWvQkl0uowBLztI2/8w8lrJsAXYrpSmvEuU/LR2q\n",
-       "Ep0zP1Up1Nn2fwd8pjJEnNyPDJAJ8lcank1Cr+xEV0/TwVBnNcMMvrtD3BHeHUAozD3ySGZ7Gx/k\n",
-       "HIRzRljtLlJk8//hCiIQOCWIufUlIS1goAqQfQhQKFUpVcpRCCDB09dx5ErcT0ArSQZ9L1K/4sBW\n",
-       "4XoPFwqAPpS6KjbzEgH56njeOxWPabWK1/RnMAdFJDBPF7NX0rtdg0jGBsHegNS/e++rJHHO49rz\n",
-       "SJY9eDbwG/lMGbPrteQB34W6ozNwqZlFmIN3FKpJLAfjY8pRRhCZ1xGXDpYP9A7u7eBN7cnd1Rqs\n",
-       "js37CuAoiKhdHzNgHo3T6VOcG/DP9UM+O31m8YHxX+3lJrajZrbwOO53bJuPCQxA/VJs+loMkJ4p\n",
-       "WhiqTh1vAWdG7JrJucNX+FON0hkn2w+WWmtZvHpFXyD9xeGDcdKl4cS77zNY74JRgAx4EWRjUGG8\n",
-       "9wCdP9Oh7QavtmWCGv5etQoxMQU4TJ3hRr/cp36vqZvrEWw32GQQfNbIM0CyncaHqqd48yisJeys\n",
-       "q64LN2fdFG9AXmfE0JkJdgI0Q3ioaUiGbUgbRJOPTy7sNcT+wtSOZGIaJr3PT77kG9rSWdpNYrj/\n",
-       "rrsoI7W/WK5LbVJPeQwB/Flpf4Opt7b4otZZworrTv3pG8RX7Jyk77o3GgO08VeMosnKt1UMLZg8\n",
-       "oER5BIFuEEwvrVbycaufTVWV1W6xsoW9Zt4uPq5pdOmpmfFcgS5BZE2VxTQjeHLtPHeDrdWiW3WQ\n",
-       "vQv5SK3hn/Hkn6HQjz3ciKRdqZvdaPA8BGWR0WHO9Wub6Tm1ERgJV2yV9XlMU906nsSdLn+PyzwR\n",
-       "LMXB46kdS3r9UUoQk293lpfTRUUqfHcUJ+/i5c5vAUafxiBn2EBmQ2+bGQXL8/WHhk8NTa4hxdv0\n",
-       "z/hNaf8xnYYwv6DDrJh+hB03sDnTNOdMAEc1lanCWfXGMnixPnIX6uTjwrq3I1PJzspo6jigVaAj\n",
-       "fmCoi70BPjCNUIM12zTWPWVB3btwiV17QZgQ8V9oA8jOzSiGSEIq7mP4FsQyPPm67/+II3txh+su\n",
-       "/ePe//4tgKqMVK5YmU0/WHW3wo8L7sCp9F1s3r69ug6bqJ43l2kdeYl/VkoylqdLAwNv6bxjY985\n",
-       "FZRvjDUZhaqx2SKAdQT2sXLkAwKqWq2sz0XeQPjvwmy+Cv8hCrEsDPMFEZr3KwIVZOpTbf2eXF3x\n",
-       "NxeQNz2HD/QC3w4tUyteFyFhVfagh84eBChk+B8606egaXavD5v3JE8KbO6OQCgNt71tbObyNTIp\n",
-       "ZNafDAEb3QeHk/rHqiaHXrRsgpukDhcbxut9XTA7cqTjrYbRyM8VJfWiqOrQBcma2enGlV+qqisw\n",
-       "bm7+F0Co6PfyAyi0Cx3QYzIzdAnlwtZUsGP7inkh8i0Pdro4Ffd1KD7Co33EZFitE+z7VUlE6x1A\n",
-       "2g9EqQq9MPSPp1eN2Z9SShhVt/k1fNCzjUFnDzYXL3/pETWviy8mw6r7PN+k6IdgYyRES/prwQ8J\n",
-       "vdKofnsyHxQf2nPwzgHsBqa9zt31QR2YAbNe2EPh3GbJBIfJL0Ae7OCFM2hB+f4D7x1Txsp8YaZ8\n",
-       "759jX82egQ+3ayPfvYDhgRK15t7/QxevFoc4wA5MaLibptPEkbe+BoHbl7sAfgR7dlSXgJG7xsxH\n",
-       "hqXSCvXuYNjtZ4RMGbyfieS8q9gvjWr1M3mOHXqmvT0DAfavrlU+0RvVtTHP0+vpEoagfZ4F93vp\n",
-       "aLQOfYzQzeeMnrCMczQ0UwbFTbxWCrt9kmmzZgSV/9FYPcmn6QKVbHsT5aXBEwAACysBnqV0Qt/k\n",
-       "kSMdF4plpZPfAvGOSwS0sDpT4rY085ocxvCqVMbvAXRu6WwGAv7QJfAJIcCQ6Oh/+HdVvr03mBLj\n",
-       "oyIb2/ar5XwkKqDlZJUVtoplg+lC5SOqc0CRYewguQG2qTQbd7nmZxbubGR/2K0c+9uL+9bnTKmS\n",
-       "LcQVmi5Nc/CQoDv9G6u/QgTZIID9df/WM8uj3Q0Ba5UWW99p+ZhP8TNeixCT+gkn/T8SdvP0ZQGy\n",
-       "RYfn5LO1lSkaIM8pR6LWaeitXNTcQksXxxe2TY8Ykb/abuTiGi9O20vLaXA00ZTkQUjgiM3El8tA\n",
-       "loojKYPJd3jKSK2x78F3heC5cjwQPz9p8rlGX3ICwsYvoLJ5RjH/4e6/pxrhwKxqX8zftReFakOY\n",
-       "sGYpn5TuTquxETqDSG0Uc2OnoOvCbAQd4L1ZaZk39jvmpc6rGJV/JSRFAL9HVLCy1fgYg963+AJU\n",
-       "IhwpKwRI0wgVMJs8fdh8hFL+kjP5Gsd1OkAOQDfMiG35vXvm3WWJYqD21hcAUDClgA0gYV6bSbeh\n",
-       "qquTYGY0aVxKDNcCe7U/AAuorFWQiAORAtK6sF9+iINJ9xGQSFmxOIdqQz7Z4DcTha9qXzFMIzfX\n",
-       "XNmTLYzvpWOacTsVlzmNP81qCIAXqbfhTPv9aQBAmQcyCkLuYDct79uOmYxWXUXkZMmPUyCu6ZsQ\n",
-       "DEu92Pan4cuoAGoy2lYMbexe0F0CmTEIHoQBf8pw+CyTFeBdKvRplkSw+8SwChiIfEGMClEsxVip\n",
-       "Niem55j/FsLk2dq1XFSWu1nmUptQvf1A8j7lpSa8M2aMaFFa7+kagYFkO6eKSDOHabgok5BeJLZ3\n",
-       "HgVGRQJCAtC6N4dFsm7QSHNi+7kHMxZ7Qy8qnnTpHf55NYjfGcdsQQhbqKErqwjMopP/XNsCYT61\n",
-       "3BBfkB6Ykavxsuf9A3ws7c7YmlvvIpj5gCiUzJ2pRFY/nCkLL9s4/ToX6T1n2UAPa36GxiukxyjH\n",
-       "gqRqP9npdxOzq/Ki217zr9av8MMuzxqHTpHoU4s9pe5kDJLWG3qhmjhjgHlooxZToXwIBLD35f1x\n",
-       "8rSfvAMEzXlljioZyTcWd6lgToK4TJ2c5EjkF7pHyKgr2mzf9b0JDeviDbYf+yOW6PCmKEcDvQ3Z\n",
-       "Hq/PO2dbsWIbbbCltQ5a+Ju0zSEfPb/W8x1QuqhFhlFgyVbVdCVrPN/d2hqEStWm/25pHxf3hWKk\n",
-       "85CxSq6kezlBCZpR4iym6HKRISbM1jFjOM97dHTvg5C1OGxkknyk+D1sEbu5wKdEB6Q8+Td2/nFg\n",
-       "AMGROZ40uGrSbRLUwhGrBCg4/BQv/gNFjf3YBnW2+0tfPf9EmaL7ZHFwHyaLsB+gYp5xm0aWdVq3\n",
-       "XnQxtYUPHfKLMiZyq3Sid7bzuGiM+SLkuLWqUCGEWengZRyfk7otwqHpcWh9DaEkVYxV2rfC4xN5\n",
-       "r42EwhGxErDUQdzslT7551+UKnpQ53jlL/Bx6yqV6jJ8srFwkUFPWUmZ4lDvpmZJTWkwf5wVm0bl\n",
-       "zIN+lGsAtBU3fXzvB/pRlyeufDRO8arT6gcso0s/DnKHpaE6eNOdyxJ5i7BP+hN5hGMlQ2lds6g2\n",
-       "Cw9pr72Zqw+ji0Fske/yTORjRFSySaqsASI+whVkBKnpVGmOOvhRXphtje77HWGJ9CoA/OUBJeue\n",
-       "hpN92BVQsLtPTHBRExVeR5caHhwEUBGJHXEQZUMAznwLcvnD/Z0K8/+KD+ZI8zdPtjj0lnMrQORS\n",
-       "4psg+I7tmhrpXxPVbUs7fJ8P1vj3aTPQS/kcJeY3vyj7x0YqTdWakJYUv1ngR8jjgVKKjQR96C3V\n",
-       "kVoRTnTr/4p+t7sEDkjY9ryVUC3vFcjO6ZmqOMOWIEzHjyvg7V4opIIX9rWvnqetrLGwJrhvezA5\n",
-       "pdW5fTwH5cEfr0chyWnaD71BcyCYcw6I1dx7huIIBgvtwbjZomr9XHb+pWv7J1GXgwyJKq+oX/PT\n",
-       "aWpblKvaJWXk2rLx60145SPDYlj1qRJh4tzRX5PwMulEDeTfhoPqEkJab7IdpnhAy0SkL9UteUv5\n",
-       "nk3CxO5yzLvYg5UFpdY1m11rwkAix9m8QnFQF7nfguI/09N8ExYi0ybHKUosgd4oMzQm4P15QiQS\n",
-       "r8+zu5O5BLHhIVriJHNKLaKHVb/nrsIX7nPXrwVSNhgEyhEG/ZudHddjg8948nk56h8O8pJaWdu4\n",
-       "67ljpLa/yXHrSOOnn+s6y9j4Evb5TMV3r5JtkTLVdyPHdAjjPZVcEbOE4Cnh+3ZIIOR/TMdRma4K\n",
-       "qqXDoVQw68w4AcJO5F/sO1UwNeXLx5y1thnanvI9FkzIFkYWmpTyAl8Tj4wzT6JlwonLzwgh1aYc\n",
-       "69Utw3+bmy7MCg0/r58a97U1pYVb0OfVzE512wH+sN9r2gI9KUvg8c0kcz/WaEdkc4DMkFbWx+yu\n",
-       "cnG09zMzc6qwKjcAJD+06qRVrReOPfcZVsWZTn166BBKrLAUOKUnDN0Xf+tgAFYokYeu5X/RzCKV\n",
-       "c90sYGw94oAJrZcK50mFRmIrLmczQDmayBWV8VRnaB9Ahz5CQAKOq3mU68ec5bnKqiNJIBKkje5V\n",
-       "uHMrcPBKUz/b10OxCKgrwQk5mJxEMx1exXpa3j4MX1XPsc/y43Urgs6t748ywQ809Ug4aBS9p3vC\n",
-       "cHtBuOTuYVQKnHc48iRq3MxS7xYQvyRM0xOKnFCsTr9NaBJ+dLeUoMngbEDxSxfS/5Sd9mnU727N\n",
-       "IkAB4owAkSxU1iBG49bHy9IeSWXIS87rQv5pxyIqUdR5CqqVTQ8P+ewBg6aR/xuV2K2Wd8M/+N3e\n",
-       "jZmlHhtZmDYX5UTe4v2dDDmnuAqMPJ19+IOo7zHhJzihHk+0D/aiMs6uGibOPzu6wggjOfInGLUE\n",
-       "mTacNfUechjM5AQpKHoe0NNkjrWq8Nr4Xu7x6Anud8F1mmAAof7ho0k48ixti9bGTs508Jubogbu\n",
-       "P5l/hDXSdlOFpA+ACxA55ThJLFEasV1BLg3fR6X4uJ6s6gZiaLRFBu/6Fwmr/4xmgzYdvIiAZSfw\n",
-       "j9ZgBuKiM/Sa/nrC7+UHXBJrj5QM3uZ6isvBNMksha4upzuhN5gKaxqpdqVEIlP6jznEY8K3l1i1\n",
-       "31accL/PGF1S6VpswuGFaRgb1zxYI6eJ0GoXZm9/Qda018U/Hw0P3uUOh9HTn/OKk/SPGipELZ3y\n",
-       "UTYopOcBPn0gPFF+Whmgh4Nrf0qyb2dJ5ND6YteOhvfZ59049eEK7dFRyaBaMcfVZjH9h+ZivLgp\n",
-       "BNve0xS5cgHPPg/Gi1cYlQQgkANQQXtyVCNzFifEUiHJGLHTIvvIaI67J3jIf9jgTq6cgNXXIx3B\n",
-       "3PQDTyjTJ8mj2yBpIDOEtyoR9X+xCj3cA196JC624oYDaCY7ksnGoo1fy19gZ7jA03zDTNAQyc/d\n",
-       "+A9l0oJCQeX7syEp7TWM72iZx7Y/+w3Njqj+QDNqtDiIJhaa0hOtriWjvwMNeNKIED7KKCgOwlVk\n",
-       "NBr4Lev4sy9wwRE5mF+jo4cyx/doH1ETihVlEqBAgwsMuEuqjyO8gwpMnwPzoJV/BfwyI8d0u2iH\n",
-       "wKNDK751BD1DRwGT8deqifSzAKpO0pwJiGuWjL/zIGRJtxc9oMWt8QdnquEBlIjUMXT8hxPvpbwV\n",
-       "1r0OL35qXVmuA3asPS6B/u+JYiFXydhSdr8R2BCbbrVbhseWfrP6AxaKt38cXNmmXKtq7fvlqtDD\n",
-       "UTnz9DtJ9UogZ/QV/cT4Qjt+CkG6QLnXWq23QrOByzYrkl2FhqKGNhFwDQuh7Ds1lMWKTitVYqZx\n",
-       "Z8EAAAlmAZ6nakLfrGrdAUR2AZFTIKVAkBZ0dV4X/3z3Vd50tJh273lsOheB3ZcAv4SElcBMa0Sx\n",
-       "5Drb5166H8Zv9zfOXNlmIU5+/PjXVU2tJ0g8aaVl31x5DKQVVe/spy0Kjf+LoXVqjPZ8goWlP5Zt\n",
-       "5aPjO7o+3H4++OXi1+Sk17a7C6EvFAoRiwp0v+hubMwrs1/YmTXoLAJ39z4ZABfkiYXNT0jH8BeD\n",
-       "NmtxNl3v4OAfulBNF2WuK/henycDgdQiHIEYh20caPZBwhHKvHh5msHDRR+uEQ1XkrxmbWEZiPJK\n",
-       "LDyfhLDxk4l3gA0s4sB7PEjcPrOd0/j4OAEJFToDQJAh7n0Gw2+jZCDya+nvDJcVfhwg4wZ+uQGc\n",
-       "sLPRhMMCIUot+LFOIlrpdrNLYAjaQHbL0twEYV5SIxbEpHiqVBxtvQ96+O9OwcR0mRdgwg7Du2tL\n",
-       "P56/8zdOq8VcmrceshaB9F6mdocBPLb1p8RC4aDbwRAac4poHIle8zUN3CLtTozu9suNTTtDm6Bp\n",
-       "FjBxLMTVO5oKTmUqDyTNxL9R/PcIwlxB9zJLFuVhalXAEwy1eAj4i3MlmHfHMmpV9XrNf6nP2SfU\n",
-       "HZJTRPzLddVqj0dwvWgjWkYB+MPfB5mUZUsIo4ksyoLOIEFpkNYPsLymD7Y44aAp5ImFm0nDw3jg\n",
-       "RS9/EWQ/JqORAjr9VIJMS05IyHDcWoeaDylnRJDZ4BhiyltKRpr7RzuFCNALsL6xJ/5j6G13Bhn7\n",
-       "oLrq5gpaYyNXTHI8IKHQ8J93nFsT9awPweRQY+WyDd5U7B/j2vAX5CGWVVDheaddWjqWgvrq3av5\n",
-       "bKzsSlgcsurX6ELaWNpgfyc/RvoRm5GJ0f4pB6TlBXZ9aoiuh2IDTAumj+BxblHuE2kEJcTZ8CHW\n",
-       "ZympWTcwxzMwgYv1ahpGQ2MvHKLqt4YiZRNXpNYM5GEZqaifJgsbEJuyJIcZ4WT4ZRhdfvkyiykK\n",
-       "I/DbbdORtZ6clh/3WDYdb2BmfPIPwxmX2uhlumaSGQ2ZQe9cAzBF9FgZIKRzZVz6NZHbJM2yh5vM\n",
-       "jlyl8Ek++U4gSStrQAYlP3hMQem2w7nWLXGGJdLY45MqqWSRc3NiaVBguULm85tF+e1Ai4pRrQsw\n",
-       "lTePeTY55TyU180o0GIPR/RGxADI/yTqZFro/oI+rLbFXmxID5xY/G2/WyRUNl+uKdhEqrgc9hms\n",
-       "yMul0dXvu53A91judotfVuUlCllQ9Q2Q8/0Gz0rB+4LsRB4OapN4A3rWdWlZIuAKyRHOPTRo+FfL\n",
-       "3H2kL90gc3dCHBXEue17gjesvVA9MNFvHyABGFYr21VrJrM7WQyEEaaXuHb5EsnMyARk4NYhIBJx\n",
-       "Lrq64nKxCoKdbY3UbqB3vyCqQCDihTcKFLe3EnQkYVExerHvNTO+pu7PIZO08mP4CBe6z4y+iyfG\n",
-       "YRTeQyhceXBCOS+V5o+Lnz5XoxMO81FCQlwnDX2thMFdS7HjinsiQEtAzFUj4UJm1Pe3UElPozk0\n",
-       "LOeaiAXRHgCgLdmt99HdPe2GDyrQH+I0/iI4NyEs0V4ADrbAD7TX15q6oAAAD8JYKJEetM+aKln7\n",
-       "N9OKMr+9/jYFLuyHgX3Ebw7zqZMFB0Mo3qg7hGJn2vxB9scpKLRbciGozYoA9nuMeupIaWEAwGJe\n",
-       "S+uZPwHZRndK0LItFS7ws9wN3kOZnMEyUZO9IuR0MIZqyHuRtjUT69+TWZtbQK37oFvR/D+/5pRH\n",
-       "X6jD9EL/NEmDiMYAPbb8Qs1yjseeCOXicRkM44SIm33FOeAhJQgSK8SEjP2ul1SmKqkn2GhUQXAn\n",
-       "sc4bSy/UzCPiLUWVuPpY3B/zktc80G2sGWRSpccUs0TBiUdmFbiMHbH0VCwLQcYcmgGzLsax+rmn\n",
-       "Z3mj6mXrM26YXQ7hSD70XcaY9B3WtffCeschhLSEdpGhCgoStIJWaP4iqiF2S+RgI5HJIzfY2pgA\n",
-       "RCIUoBBK5T3AkYwPe8eNasUliuU3vxMx8NGiTwQDUFu2/ex68xtswM6eIvoIbcvaMWBC6uPcdfBi\n",
-       "1FAOrzwpf13ROQMbY+ifRcqMxum+R6KaG8qEhiuV1CmdgpWfCwuA3ChJ7Ntzli1l+prsDHW9gLa8\n",
-       "X4taS58TE1IfniTmMOriToY6bJX4GR0WzVoLm5zzfbaRQLQ5v51u9k7UEmrJ3gO7WTLGL0lOy83A\n",
-       "5WZsjRFGYDtTukDq8+bZE+PJC4DPzHtzzT5F5gPrERn+ps5BAp9FDEN3LxnVPdtnyW7gmgCamoSo\n",
-       "5PD5c3FZ0u6DNGkpz+VqUqklnlJVOD45QXaXPC6UAA9dtWJq+TvQjjWnRkeZyc30hKLKUgodZiJt\n",
-       "nSBjBhFzqyzyf7/N1lrB5nBTvDwo8x4eX3j5QzbAS1xAZVH0lZrH84J2YUpZ03+4XU/ZrLRM/K06\n",
-       "LWuGx97F1bMdFwEAgmYXl4y3ihbIsUCLTnGbGnQgS5JW2i4RWHnjr10bXgABzq9lzcUaaNfc8Do6\n",
-       "j+0oJ/ZeWyT48b7S+UPBLNEeWZJo31nxN0TfoIlNyWxAAJlYEryhHG9X1M06HgdFByr5vnGugk5Z\n",
-       "QdoHiPMA0apTtLfM7nKLBTqI+uxr8SP44RjMTQJe3YNNmJM12UmM+jGGd0ji/axeniryOA78j5Jb\n",
-       "1zsYhW58kI75dc5rG4JhgWpV0Y4ILdZ9/IYhA3nTDo8zv6zHaAvxAPlKdIyIfJew9RvpjeyUClAS\n",
-       "JMlOSQVY9D3+oRMUZRaLsowxC39f8FHv2hwx0NexTiF0tVe5M0exSScutYODi/QCUGtYlb8SbVhc\n",
-       "wQzYFxy682kT49WZko4Re5xXcLXaUGM6g+04FmX0C/JMIXpqTAXGT5eCjtn+eo5r5MTrb+EHPXTv\n",
-       "MOoqdbkorHc5dsKszdmO0ryW4GsH+iSpknx7d7eAjPxjMtWwJXZBtvxSU/Ay9TbcqzY4SB0Td/CG\n",
-       "QYuZdsJ/BMVXPsud+/5VMtptklgGmH0Go3RSFUC/99+jnSjmZh9qo8iHkNSYC9aLoPK6ZwfIr46v\n",
-       "497nhKVHOjUtoziD1RENuEqPw5C25YfPx8V3bjv0ChjJZjBp8+7AwPALNdVhVO3jD41dP64gyUnB\n",
-       "GksLAKbDvN1DPhNluVuMRDiBgHIvQyQe28XQxOPkyvyClYFLJpaNK7ykDsnlCexATvfTUIVwqwql\n",
-       "9kLcA6ZkSJGIan+xUalQdZjAAAAWAkGarEmoQWyZTAk/eYpYAC7tmSI+auUZbqxQ3YHmB/i8T1Zj\n",
-       "gO8F+pUpBJDQXU2ulyIMgbSvyoyNnJIgRh/83FL215SihV7jQXtjrE5tsqtO1xFIK1XC630OKqAQ\n",
-       "cOyElniqM2lCxL6iY/EjEJowdMjWoGUdbRUxpFUeSirVfgv+bdJpl0bPFDuy0ToIfmUn4L0GfV//\n",
-       "EOnfyKzQiQ0mDJx48tARDSd0HhJYzA5F1DP6XkVkhVCXqJXwAiIk5W9Chp/k0BsSnYS7heLs4hnL\n",
-       "tv6nNarLe4QMdyt7GecAb3j+40hRq3Y9oKM+QI74nRXHKn4sqheUVIf9J8CQrNfiye3/VpmDKQEa\n",
-       "Z6vuykQmbsAnnvPc/zGZT4QoiH5IGFovsOC9ToX2mePzNNQIyB7QpJLy9QgYocf3i8A0Yh72jq9R\n",
-       "LKbxG6j01X2l/EkYuoAkZAOPQGn4kLOKYm9ln8n18AN1djwac+m4siK27xje/cnxgAq/dCQhHUJe\n",
-       "QSim4uiJb4VWgrsagTyTV3DN30lM9UDcq/V5AvmlMAiGDt91ki9XMiIfGmDBrS/1fVdDZMTpdXAE\n",
-       "CsxP3NcsPc7QWjWUhQUWNlFN/MViyPfeGghCFOEEfPTxA5v/ckGU3Y8yDnKndzwrp7/8VHgRpzhE\n",
-       "ei1IMATmEc/jT3okQ+DpUbfCjjsYMqH0KvGQaxLGZslS5KCEgt7LNKBp9PIRfqxjhphLmWaoehIR\n",
-       "VKzQfslgJyIWOCM9jXSmlInUFGdgeXYsr70lgSvVxK3cVWpfS5mIrIm3WR0ZAy554g/KLLC0tDZw\n",
-       "0hr/HMSyv4PuLUpKAvRMRmkFgA306Taep3SqNC15RHD0GnpnPI8wME9HPfOYXzQkCpWR2Z2QMxjg\n",
-       "lyUYc5Q4XfTDTC7pu1Xr446/4yOW8uY9HwvxImSKatTxz5N6t1GQ8n7rULFd9oXMempnPw30rh0Q\n",
-       "T/Hxv1vJIYsPvOAltXz9Jo3HkbVuz8CZ5OiEE5Tsub/l2nZDEyC7CIyIos41LzXQUHwixt1gbWUd\n",
-       "n2FV8YIpH3GOvyTEsxlPcOcpk9ooiCS1rUyw+Rx1JeCAXDCEnz35zTD5tKvxum+vdID2ds0C9DJV\n",
-       "ibWc/h4Is5PuvUZLo/BBaFpAoimIsWuExc8F2Ejl0aY9dXsAtg34/XuDoX42rG+Lu8kZ6bkmD1i9\n",
-       "z9SZjhA4xu4FAdhapFf9/jSm9eBU3xEk5D/Y94yYL6k08a9nMkMrMqdeqj1zmuGw/3gBD1Ogh2jW\n",
-       "mYFF4xCdopYrb1w7B5WxI45GQGyuwqQipcnodB+Cc9DmYScOaFVnXw3mF3xr4h95ouqbPka53o6J\n",
-       "1sOeIkT8lktRPLAMQZIJKMTY98hqVwHvamthf6ay4z6TgCRvKW4nH5n0Hj/DFdAuw5aHsWf4FEhy\n",
-       "4L3lucq6X/IYHAx2DMWbknQIbHnEOyMrOOObPqAZrOdl8JSx9zDikR262oJN+p4h1hLIIJu3/X8B\n",
-       "L6DmG+VMjsklPIsfiCW7hTzc3z7cSryfmqbbrj41lOYAKnF1Sn98ftrCe/CuAEmYyT7m5qmAuM06\n",
-       "vFRE+ZuFJNeBHo8P4oAFnFQYrSBOymoW/Yo/xq30ORlYFBzeVdzTP+yszd/nY3SgDtqSuw8C/2Vs\n",
-       "LucWv1iwHK0IV+zaUbdGb7LmZJ1wt5KfambnVvPO5IJ1yYdBp/hk8LvWzcQq9lXAILyCDFpFXU+g\n",
-       "K4D7qyfWslsyn63zcVGDj+TQbRNNps1giaW12IlLm4DCnpuRWopK9JK4ukxTY7sil5si4UzbeEu6\n",
-       "JEd6ExuTxvHdFv3oauNOtsIH4mdJ768yALkjnopWcYnja18iAqKzfhay8MXSvyj0O5UovzSJ3CxB\n",
-       "72mK/dkmMeaQnI6lqxO+fbYREToPZlcHxnARoqf9YRJBpE/9lvm01kYpm3p6NfQnIqrcHL8U/NQA\n",
-       "6tJUwSn6V8Udj1cAf9bGExJjFbjIVtrtljlTApryMK0KhyPtZRkD9biXmxrN3zuVu5VwCca4Zebs\n",
-       "VBc/GkurPT3wQesrdhpjbcOSgrwDnJTDkihhyUH8fIXeX6JidBXcp7Vy199uCJ3H8StDGL7zC5jd\n",
-       "ETSaTuBIpGyBRvF7AVg6B9Vy/qqzZvv/g7LGlXRs4M4aNU1PVJ2g5VSU+UTHlWnX4FbY+pY63iIh\n",
-       "sJWnSnzuq4pQDhHTgshP0AtHmfFIH/308MFIQjY1jXS7yHZp9XipaZaETa1g6VkUh0RPt9p9/4HW\n",
-       "SitEnT3cO+chncz0u61kb9C40u4GkYybBF+gTC4vvMd3lmQwMH4xPWdvI1TVW83v31AHmbmeVjvr\n",
-       "4WVDkpe5/APOWWrDiWMSdllRUW3n8RQa6Ji8jiLaSmB6g4K8FS29NRTdev8YyXG3xKuw5g2nevzw\n",
-       "ITBwfZ9l2n7cxBWwL19BXyZncU+3WukWywEQfSii1Mj1x0nrFD7RUva/4Dc7XF2y0uMDpWLrY5R5\n",
-       "U5Tyx140P3jy78BF7zX4ww1GeQl9H6vsnHf4gbZw/IjSDJRi9UO8mJdEJRHm8FxpXRSMYrawzoJ8\n",
-       "5P5qIQjM9giIDvUdkEpbwFygKeU9hf02djn7nuAeMS5IxXP4owH+zA69L4LwC1Or9or5CwhxoV/a\n",
-       "tiTekTWuEdI7CIbrwGJyuTXWeQHbu0SAQp8N4xAllCt0/dPmP9CaRw66hXjic4mZK/7ZgauVcPJq\n",
-       "FBLNDStSD2k26SfrzVKLE2sN6UnU0MZnEvQG0FC9XosKS1Nb5LJeShIovUIn8BtaKkf1D12pFt9w\n",
-       "5zyWsjbBmgZPLti1cgEJC6JbjzSdCiQkUcfhLKU99JUywL/L67DOWHHxc/yw381OUfVoEf+d59DZ\n",
-       "g8riE0DU1U3ZNr+Xh/byB0Tdt/0CnYgwmpshYnn/+URwyejIhi887IN8jTi/1e82s9kHTPS6LteM\n",
-       "VWBtz8TBtiBEISw2+t2fCP/sAeCu9NAFxm6SubYBT9x7E8kXfGg9Mi1snT7CakIuvPempUJAjy8c\n",
-       "OiIFXqj5RaT0k+n6FEEYc/cqJ9oMZskoIkDnaAIidINQ2AMt1rRIVmIxWG0qqx3o7FmChwgCneqa\n",
-       "sStIDWeWVZmyfeJpiMyLR7YNcQZSC1xAcE6Lxvk6qTLX5YgDgDZ2Fxy9zexh8NFPR8RM2YBwa8A6\n",
-       "n3VUsQb19cPjPyk5zGPqXqP0GUHN0VbuH5DCK7WpeHdeOg4aqqhFl/StWxEB5F3murxHz/la5yMk\n",
-       "C5RfSrzjQ/hrR1lSQLaEtrhonHyfQxk8pdPJ76KvCyKI8m9wu87AI+a877n0JU6wXwaTBtDj7P4L\n",
-       "ZPAS9MaVQqTJt0sBG+V7Am4rHUwjfXWl3e7ZC+9B8+EaK4BhxcxwGts+HCDMBDEqnU7SAZqfLdjm\n",
-       "207vg2crxvGZtCtgF6F/IAG30gBkIvMdyxonLgMzra5JoWxbLUh2dJqQYFSZPhChp1c/iKPLF5D/\n",
-       "YnHMdcdAYJ+By1rA5Lw/PYjPk7J9NRmtN/CYeo1McyOZAT5YBd3y+fe7tJz9y0olPQnhxNEaISxE\n",
-       "t+GafxHWiuRQ7tTzXPj/Z6O3QQgzLfOhaM/BawrJPtlsqX8cW+BHIGTwFN0xZD4IbFwaDNBu1kX3\n",
-       "MrNNIbKsOJrpWz4MvxLIkFtGwYvz/CuHn/OLsCCAAVpm5Q3TMiw+KCvadtqX3eyDzo6QdkKJJTgz\n",
-       "1ncwgXF7K9xzjqEwHjISsbIXGzJuVneqTlLs18xMtOD7f9KM6wa8dJBDFojnSuAow2j7EcMCOqud\n",
-       "23BM31xKqOCbNqBROjRGD2NemfQl1+3KS7Az3jBMbOS1p0M2/OQ8N94Fv1DHinF/QLXEj2lCZTWU\n",
-       "7giohp6KtEPTU7YOkI/3GoHVTWborx75LGtkcn6sec5QP8F5svgPzOCsOoqIB+GAf1f890HJiqJ4\n",
-       "g+5zdL6aXcuvbPyCIAUK2CIIBwAGYxpCply9xOjG3A8JypFpDS9nV/cbsYH8MMKiqTkX+6+8Je8n\n",
-       "IfHYc+SusXxWmDrPWiWblfNVD0lKH3xD8ZWd127PPH8ZUrz229jBXUQZTpwI1MSlQARZ8AEEsGFY\n",
-       "DeYJ9irdmLu9Cmt1fCU/ghVIJTFrbqy7dDg4pSOK4HHMJaVMXxd53Fx1a5Lz42MxtniRm3raYtY/\n",
-       "+bdKugMgBilTQVovs84wafrbQmM70FXTyuCHum5TSm6tWWQAbm7nzkJoJv8nqgLejOKwQOrV3htb\n",
-       "RlGxaFuBtIbrb6rx3SrEGukZuoF+EkVEQH5sS97CRfhMF6JpfcXOOrhVL4ESvfl9XwH1ADHtC50t\n",
-       "nkcABNaXjEluSG1K75U272fZ5mxFfrCfaKjEMkfGwK/gIxpFz9cYCMapAXXVB30wUzmwnAKkGM1Q\n",
-       "2Vj5NsZ5serjEVkdd4X/0xwtnLX28RuA+EyV4DQbKKNu06TedqIdY5c/e1WpSNt0jTV+j9X/xdNO\n",
-       "HTX/j12A6nbQAGAbd3gKbisEl2Hgec0rEVJuSjrtmMJN+IExFaiKoqwMsjzWDaXwA+TVxAQpHZFd\n",
-       "lDhlZvJH+ONHyVFNOjHxnTVFAwN82IJ/vvRZ98DrSI632cuxfcabwUac6oeWdOEv0M/3D+sUevh3\n",
-       "G3Eh6UkfsQiY5jFUh2sqo8XVld0OtFw4hkWGcpWTxTmJV2sffMkacveYGd6RDjMhnPZLWGGyySwO\n",
-       "Sp0GeyMElvleaD1pa06h7YrOeSF1xqKt+08nlPdBI597TSgkgBAA6oCh6OXaNPEdtsHTDBxGCjjs\n",
-       "hwkHenSJFaP4EsrzvRKBJoYIfHCYYs/ew2QsnMVW/5fQDAKx8ATVr2o/a9mrFyzH7NUA1zkEYyRi\n",
-       "Ny15A1aGwTXc/zMbMeJlfNNOr1a4UZu4eFKjNFe65jZ8rteWg8/09rydTciOE6sacmRaWPcGak6I\n",
-       "ufTjfkXGAWicwgSydly+WMkP4yrCnhG/cseBec3i0dQ8l9G94mGGJzXmjjXihm1L8ADDvJbckxDr\n",
-       "7vdViXi/b7pbbJ7kFdn+skkKdHxvgF+Cs7XcM+9wECOfbSmAkWAwvHpez6f1X52W365mHA6NyPfU\n",
-       "Q2kFZPggAqAqX9VhhqS+eOtgOLzQNN2JW8yxCXXOOLE92YefIp/ojHyIrR9aYMPRaBA3wNn0plSr\n",
-       "wVD0NNDvXquhKURF7JARx29lkonJiA1rKrlQzyN78BV1Lck6taMXvUPHS1AcmYSBuKzxcPG/Apjp\n",
-       "1DgbO323P19vFNo/SVymkIaUiQcOZIy+t5Np3TyD76sIwsxc86OeOnsaQmJ+erBdPnEMd8VoSBV9\n",
-       "uUN7PFOabBhDy2G3DVuQ+rtSAAUvPO8auU16IX1ZxGQFXcIR7IoD6Fyh73xKEFHGdEWbxV//GzRm\n",
-       "SHCG80SUYwRZP62caxiI3TdGsuNAycNq/CdKzsaym0DFrjd3E/Di0IMeoHEJcbtBXls3+ogKvqgS\n",
-       "Y4J+qp4NceiiYPVgh2oFVhlQd91EvxNvfegxry/KYncPf73eh9blubpSS8bPVN5Jw62npiiBQyuu\n",
-       "wdSVqClx3IAynoJUqkJV1cL7LYA7wqbqhlmZrXAm2kV4S0PIbHQ4FcIMmxsMRBAM2iazk1lBFsKt\n",
-       "XTUNtCBGfnFnsAu3URTy+0mGRz+S1+5leTt/GjfdX17WHE0ZoW+pvQc/iCWYA64MGVBseaHahVUG\n",
-       "vGjuKdf+n29joTgwNmmgU26PODtDgyL3pQGD/0NnYIIiZG0Q1pttnVJlldF3sacrZrpWL2FrmqIU\n",
-       "IxFgE5WiupV4U4u1F5akpiCgh5Hg78j3CIR107RrjCqGtEixCkHG6v7xF8Ch0MdHSw/p7kEXEVm1\n",
-       "2S9I0e2lU0We0mhmtjRCuE9w6X8YDLC8RZKmhghEbw/6H1W9UyN37OFhSQgjFagALZfCdwpQ5e8q\n",
-       "GQgg9E3LJRVYwfRKjxCk6D5R5FctyrKlwFkXtidAM5+XP5YqUVQf0jwcpz8w8aQCoTw9iKtT9weR\n",
-       "Go/dar9Z2vZDrouDZ1GgB1lTJHvsGVwJa7yfbfVQkPfOppUl/0X+hW7/wxx4uPS86Z1vCeSMvOKF\n",
-       "S3+DSB5wb2T5LLftQ3S56qM2zHnHvRDSjogrRts185fvpIeSSQY7iCVjOXL0rTw1HPC57Bh7JI67\n",
-       "lGyEtClrZGQkwIZZeXzftSH/Rq3jAPsTblqsLx8Ogk6sSp741cXdgFWBmgg00JLA61HRWh0VT2FN\n",
-       "eaTYQ39cKXRJtpIIDTs7w0kHaj4GaKnKqnMGmW/8Jnr5MoiSNWVhNi0JNEK2mtjohOqAGvDF9F98\n",
-       "wQ0EZ8E5tAimj146Q+oqwefT/cZ+vWvI2muQAJYFYssELdOrNkFvcaJdMHDlbWPtXqQoATuVYvtd\n",
-       "OtomYag10WRuS5+vBis3IvvnCARaAwyZ7c8G8axKcXmHJZ4l7Rs8FoZllmEA9amh06aopBcFd+9w\n",
-       "Ymtcq0Dz504CFPve909Z5J6wFaG9S8mLFokPNT527iBVoIeVbW6CDfZqtHXp53HLvldDwIU7v+SM\n",
-       "vTgxeEGeqYmEXWnBCMFODBAs1R6fMGC0mPo7TYSorqR1T/4/LfwYY6BSIrqNLc8JW3CtatjC+FwJ\n",
-       "JqoKTVXBif/eCnzqcMD9wS+oHV/+Nfs+5lr/a3xnYRL1NHBzWyGP5lWPaeRB0bqvw0z4J/RmeGNg\n",
-       "S7qGmmJGWJQ3P6bmi6wvRb72Cj35qQRIK9Rz8RYOM1vc7Wkn4dEZvIOU0sdmOS15C3ouDqhkTnlN\n",
-       "Gfv8DGQA4IWXDu9+LpuAvBd5qzXX7wZWTET85Pz0jimahWFuxAtDK1lI2cauluvLt2J85Rjdv/U3\n",
-       "jOFqqQncdGoNtWxY4yqe+nDRl2fV+X1mC1yhVLb0+7vRtA2z6kc8GG1Npz2IdJWmjFBvXh3fi0Qh\n",
-       "rmFZk6NcZQisR7dg+Y3Tu6Duhg1M1kaG3WDLuw9a0gG+M98/eEEKFrCynUHjs9jV6b3tm5B4Q7wm\n",
-       "Do3QpkLkIQrJ+LA9QCk8nHsTzN52cZq26RojRVgZ/f2RYk6+862fHbnOhmsjZcg70/axWP6oxc+E\n",
-       "eR3cZqoFhEaJNeLfZE4d5s4l28vf3pGgOwGCKp+Iy2JEjXj5J4Fz8NAgws5h58JbOzbu8EyzE8Ir\n",
-       "MNDGDTIKfjuJODsfClA9Lnk+5UiPU/iC8SRM9DllK9ZRZP1ZK6W0XXtPBzCm36sx4NsRhP9CQFyA\n",
-       "vBNaNNAxj3ZHIwMXndleOlbQerdunrR2tyZ+ACiulm9VKq1w2OTt2fNwmVLUoSH+ZkluN8gAH9lG\n",
-       "H1CNhW8/muKGT/2+elYYBgmsy/6AvZQ6ynyQATkYA/CpCiBb4UsdIv9hd3JSAflKAmvgNvrhMzLF\n",
-       "UuCBqfi70mQz/vxgnG8DDH+cA2xVmPYOAKSp7Lb2hllcPvUv+KQCeiz1OazlrJesICbophnPKfbU\n",
-       "kZvp4r6ZASIpYePMpzN2mxCsx7VjNWBgWBrR4PKGT9b2nyPOzN2X/zDB7hAbt8TVAsrJEtmgjWgz\n",
-       "4GUXc9AYxGHSsJEJGAAAC9ZBnspFFSwl/6FWPEuX4oqSv1lr4YYwiZTH9F5UCAfhOjzh0ngFnVQT\n",
-       "JrAQo6quwNkaIgSV09eTRtLO7Uu1eEy9kbLp33qKZJZvZn9Y8MOOSpBijnA2XmkyTMDd/FGA9cHD\n",
-       "QRwx/JNp+lqh+utYDAcxzy1nCfAa0WmNdbIn4eFJoXwpjy2hEA6b1SianK+7TYIJ0kD/nABZc8yj\n",
-       "vcAxned3k+ZTTMjGKN4sLZj1HIQOolkbqSx2qCyC+g4ZXZ1fdpgyJadKbSKAz1WjuXacMRsIGW/0\n",
-       "mGBhqF3OF09RTTx/6FQn7HqMQC7wK2A9HPqLxwyE/zCzqWz2SOb/9lAxhzN/ea87mitPKV9l/H8e\n",
-       "QBXiuy6SVsIkcYHF8DsdlUyp7fGCRbijVZeq36G/p0e40LPDRHDgKBMrcxXNKPt232N3SbhmimXo\n",
-       "8aMf4b1vGApMD/Zd8r0h431WJgQjRCRCkUb8Md6inxrQOhei4U1QAMqjvVkhLnvvRIIJzDXgCUgk\n",
-       "S2S6CdanbfPIJpESlLDSHESj9CT+/kLY9xDIH6wb5JaLXFfR/8O9W7iIpWh7Jjvzyu8sdhW8znXD\n",
-       "StxTt8kXXarMdMIzhh/w8e0bN91arZgMz4eariqThdISaMgHagUmQKUXZ8uaTU6SFdz08SKcUisJ\n",
-       "apCJLIElPm4BzYvDrjX3L5L8IL6FQIB1PRsSczW3x6D0VH36phmgeZOlUKDOmefmIaPEnMogxufJ\n",
-       "kHkLU2vnOPhHNSKcmWUF4XZRGIm8yENSgAwutcZ+xUeJjhdkV20i03AM40/cHceOftwKvqL+Mo1x\n",
-       "buUR5aLNl9BTKCDYxJbgit8MwXw9le1nGg0NPvRORtWILrLZIcg/9simkTcz7BlZ7Pq7RtMUhlok\n",
-       "OHEzmQb1QVv8ojlou+ha/XM1T8SU8QzyQc+sp0DAsxB2ZPrpiNLGolaOmhePOGSSsUO6jhsyeG97\n",
-       "2ta050V/8Tyl9Ng+LT4lgFiSKsTCfHIjuTBJ34yb73jIUDJ0yR5lOmG6gN9Xk0jDu77qKWfInf7h\n",
-       "4dPDAA4zSlXCzstNYT2xdEOob2bXiMD0xs2KerxXKZ7jU9iz45IBoDv1Sv+RJPAbq+5p8pr6KV8r\n",
-       "p2b+bRjIeJnyKta/Bk3OfKOxN6vTxtBhbGgcQZC27SHYv+Z7Q0vKFT+vRYlicyncivD1b4f3PLTn\n",
-       "Eh12dRIQlzBbKl08pLwwsPvZZYItl9zA9Kw/NfVT7ZgLQLhquROGEqw36Kx3D7eWUIh7E1r4o074\n",
-       "/Apq95MWCu9FryhA9GXjaadWrIB4VDWAFO0PksD0D0dDMMOOnGUTWz8wiLVZm3HNqRPhdY3oLNx4\n",
-       "9sBeZwjgPAPA8SR7J4WXFL7qnuaySjzIdQm8fFkCEYKRD93qPDfkySBYFfWrb4j/8KYUKbqeLxUF\n",
-       "J3V9SrpQs99e8vBejSirLaUIETFBZCKtYW3W0zLdAWXAvJr3Cnv1nthkd3hynXLU33kpIlVtFm34\n",
-       "MwPfKjDCc1CLM+vb5yqdm5NPk4wMgaF4op8hJ+kJWcBRbybAMcq1wAoYALGBZearXFmOQxZoykAU\n",
-       "77B4SlgZ3xTvZbsxk7esR9ZnFwsTLMvCnACVlbnnDRwe8Rlahheo+e5fdX1M/P876tHFf4uHbLcf\n",
-       "ecfFYKY89/QfW8p/hReQS+ov4DZUf/XHoW4VF7IkMJzjFYAwjK3zrscJt+2wqlUFoGAP9jr6f9/O\n",
-       "1LLmrtrLjw7Go/EgQvl8xrYCN9jjjeVReKSuhHqM40tWai38bVZG9FYV3wcjT0zq0X+hDMK3GIO1\n",
-       "H4hj1hvsBQMabO8StAeCzqPSncSzLaw/zay5y3DuMXQnOW5B3k3GZQwUwJ1mSuJCoTnH8Ycykjs3\n",
-       "8K9a6Se33blymDJ6CwWHVAPB8sBRxqPb7lhxfruMYfvp/VRL241fJTVrxjOoHolJsAGi+hDSa24+\n",
-       "eTkSZANCDV8uH2KZi59ZWjBNVc93U6BpfkRCp6fG/1OrYg803h+gH/YyWOr1Su2SEbiSmuWewV4l\n",
-       "8rhGxYj3MqeYYjXYQygz6qzmvhGcR7j5a45dlUbgVB3j8DiXeFsgI7hxq2+GNFFK0zkgB9TwTySO\n",
-       "m+ovSYZuc9Gp3fxK/b7siRBNML+pwh9laZNPI2mHFLB4pcphdz63fwun8J11WzVB4tTHS7OUM4QH\n",
-       "ru1HOpT7ZVUreacBqZIrovk6kumHiHqM/on+6hliQVKE99+FOr8PLCw05g4WFuOEBrI3zQvZRaLA\n",
-       "BfSQBY4RXxUC0QR05itMveeSQ6KZ4yfHQ6unwPiO6Mcktg/mexVUOMmq3SWyzuwywuqMERWQLgiw\n",
-       "b2o1hbCfPJ3imTYELqMGPjUsJgttStuZ1wfdcrBI1MZ46yLkUYxe0D2h3g/vSeweIvV+stsbSvKI\n",
-       "yopEIgqQMcd8S6sXzMqzUFKpbCBvoi5R30hAWH+ideRdN+hVrnqCd6ncgFiEl6J5sM2R40cFFWX8\n",
-       "qE4TFSEuy5csrwepvzSYmG45cc+tqxRQO9Z5IezFCT+iJzVi9I1t24pgDOHWjftPKLsizNDA9yBC\n",
-       "fY9fw/hhlaDAKc61PWv2jYMfPvoHoGCjlDV2z4Gr8UhXrzWklfCzmOaOd7kHjZAtTY1+DIjwB1aS\n",
-       "dCPOhwHK20aVLdKnavCpN3545UsoDYbBXX8E5ZGJaixTjGkqkdpcfO3mSsyRF9IfAcd5lGTrypAg\n",
-       "eEheBJmhw+79CLpSRoA00ggGwrJt4Z5mO+7hs9gMAKndSZeCfJ+hSwybpcBPwey0srVRvh25jgmM\n",
-       "LhqwsMv6oLbPdAgwwmbmix2w/0GWhrv4oUGbAj0TjyyTMptK8K0A4shaYrWIralj4rRgovXMKOr5\n",
-       "lV6dL+YnB5ZJuzTjRAXIbp2E46IAMuwXL6aXgrIubJetdwG1d70qf0ThmnFAC5ukg/W3KrUqd97w\n",
-       "0226eCabwkaRxQkVcDvzjG7U6rDUhKUFhnzaz6dlrCuqvG6iycw1nqow/DQnCfs+CUq1EdO8Zpt8\n",
-       "6Zy/ollSSKk/i/jqifNOt7nPtKCoCXhB+Zd99Xf3Pg5igziql+AMeDvZWc6k44JrZSj8kAUTgOfJ\n",
-       "Tjb2u+zNEkXWehf8GYLylxeCTwTugoLzYqfXXEmRXDT7aSorncbHORmEAwca6/KC6dE7Y8mur4xa\n",
-       "RwzML43Ppk+3iFqoIRErvKEqChof2PNZ2dlKIkNnOkY3xxPqSMxuoEE2F2ORH5tr96lRAAO/ZIHX\n",
-       "DbMF4vmNHcFqtXeRW0rDBKihzsheoJB6LpMEHwWo6K1lgKgnLpKCNhN7ZdURi5dAa5NxKu1Ssvat\n",
-       "A9FcYYHQQfZinCNgMY2RYgbfByeMyBEO2Fr39t4v/jkCvtHZivCwterBUtxw1IE2GTEgXdr49FQa\n",
-       "b397c5itpOVtX7/0TIGE/HlOFMov692p+W2EkJEwdaTnEuD7jMJu3oeGQD2DWTTs+5Rse4/+qAiH\n",
-       "gbu3B9S/RhbhTHjONaH/nM0pT8Nv70RmLl7/aC9g7zf6yJTvKvL9h6J9WdU8mnTgB2fbOjo9iVfM\n",
-       "G9JzyLN6gBfqhEtJY7aCR3YQnwC73r4RCWL2dJAWySxT44V/B0HS32CWJ0+jaM9hZpQC661p9A83\n",
-       "7StAGyiZ1GlXdOlRXbX5bbPVoAR7HiKAY6rMqYaknMULD+DH1BjMUGxCJUDMojGljP6yrRNBXJ1+\n",
-       "MGHCfKecY9fPmTxZc+YV3TMPTlw2NY/Rp4iV+UAP811nzn4TnoOILPAo3yNWfT8DZZn4MHySRngA\n",
-       "2B4RAr3arLdA5b48VzJLnOuEC1X8tkSmSqzWBOhdCoN1dlHb2IRr3azgagjXDiTSevt1tF7wlwzh\n",
-       "KsN+1bN0S5gM8dOOwyL51cEuiezpmujXxW/VS582/jsSctTi6+ZbGK5AqiAMrakrxr4muxmZlPFZ\n",
-       "YNEHj9KQlnbkBX4ySHUDmCPSR+KzjN1hZpWTAf9qTG6IaHkuwsTEEGTJ7k5pTfKZ9m9FKlvHyLF4\n",
-       "sJJUPqGPd5BA1DkjtI/Fy/NwPlVvtpkrCOkAAAjxAZ7pdELf5J2gLUXIGLNqvcltWBsxCpG3pum3\n",
-       "vxz0OwfVA4YOnhjjLGiFX0DQ9UYUd1EQPLBh48mRx11eoHUEHKJY0iD5o07Tzy53SYI3GZKLhkk8\n",
-       "AbrLKh9nePy1VQcxWKklxS6X5071n7DZ6mxVGwdy9wKODvxDHH+44BumAsPhr3A3Di0sO7ftKZIT\n",
-       "ii4QWZozpciREF6orI+8bL3VKAucc1sGUqhrVAKLCollNuhgARDHiPtmfwqU8xlCJrAGvvq6Abku\n",
-       "9ogFZtOoh//FgAMPsJvSNJh7w1tBZ8ATax50Xn20pjYOc7rXh20wKo8tqRFx9XOhDlE/AikQPFM9\n",
-       "eC6+Ejk5iPXXs+0Er3mpiXZ65bJCVw7Wc7ot0ujZ21fmrbw7CL9Jby2Z7f7V3+f0mdJwR+SFyEpY\n",
-       "2usLXJ3LpeiAc86/1u+DrwFR9035WI4FFjkCJYnPdbPqjJdTJghTI0kINk1TIa5xDKO2q0JPaPEF\n",
-       "VYxeifi6htkvV+B7RxlE4U/ct3IjZCzXoJ9VePbrG1TuofM5PaAZhUzU0HJlmzY0PWk1esOALxIb\n",
-       "QCl9m5PAcig5PCei2D+PAG6Qiip1Sh76xx7/0wMlNq5h+5orHX+FO6suTfuEODT0ESMKkkldchCi\n",
-       "frvt5SlhSZBvg4hd1pt3y5uGTc+6n9M9jM8q6E3TOZtGYek/qKQ4SrEjCmv0yFqEBk0sOWTs58Tx\n",
-       "NpI2fbZ66FSPpHcnFzrPbCXUSsEfsULn7/8tpwEveZNiX/PWjJQWDIkQgKWDcZaabDRSWwRK981+\n",
-       "DHZkupkHRPbVob0BNiaFAhbFnQiRw48Tb590zgCU3cNdckzc65tSk4ZmnqGLb7yTOi6n9X6b4Rle\n",
-       "z80vg3pBQnXZrsOZTPc83KwdTpCR76kmWIA0rpWtjmtnd1OM3SWuVP3d6a0HPyyCmRPME6eBrlkE\n",
-       "flspPxDeorThjXXoKjxlGMWYiAP6roCzCVZ5P2uGsgMUJeccmPItQ/SJajgi7DGJMFkMpg2VPQRi\n",
-       "U0mTlS+2gB4oKgB1fL9ia6eWEGIF9Y23xoQIZkLmmw4MlFH/amZByieo38FXseep/URU1HP/eINa\n",
-       "fut+vLFXBgOp7nY00Y8K69mHJzg2yhB9uKFNQPOvJpjFOUHEEDDz58RfjOKKs5cBdMVDCZWNk69P\n",
-       "od/OqeIeSK9U9AnoW0Ds6IExQQaXW4hwxUbgBZBXKyKlr3xsc3PW3Tw69ILIDkQNc+wflIS8dzKT\n",
-       "9zySTUrfvhHtpFq1tcdvZD4jvELZhEHIJA5u/MXNK/9S9jH9JFQOMmGuJci8Ty4poE1lOLsjBbWR\n",
-       "NjyX+j6sRVV3x9xK7B2YFswfax54azX4tJxohpHOMQneXkI7SpNxbrRnjuxDq01KY75YWRfRCX+m\n",
-       "DwWgSqGcZpRwmgU77eXS38wvQNiTeXCPKysK04fFiR4EQuY6QsdyUAyp1/sqAUM5DtXHzfdaJeoI\n",
-       "xTWwW8cPa9hqGho5AYxUOueu/RkF9iQfuSDOKF3RKurYZCH1DvvJMA1pQanXw8IzhClJWxS5J6m6\n",
-       "aQt7hz5zDEUgOOkF0qrQmhMykwvkgDz/eO/0dpFl7CFVZZUmSaAh8kOwysm8G3SjRA4ty0qUMXkZ\n",
-       "yGd3cUzyfpkSHV+fvI8vcNeZ7Cv04dSeDjbI+1e0Afjg871LLJ5Ohpx+wsKv5WIBQ58xscbzDvir\n",
-       "9FeEP5ok5kBXoVRBCWmBuQ8e/zM/G0F406wzeShzO2C3MPQxsL5+I2IGIMvbUwyZLY5AJNpwP2M5\n",
-       "t1M8RBkFBaJyS1UrP8UMChHoVBrDwrslLW6xAsaQKP2AECVzGKDa/1Ege8+yfQwklS/56IRmkW9m\n",
-       "wkhRzlSOrxkhYlOobSgfrh0nAW7a6SfKaDYsZnLIn6cwi2cADGRTnbg9TJekYUxWj10Fm7iCgufR\n",
-       "MsIaw99blxzRgsnyef2kkxFrSZ7+/qnJop0iIPiTepsvJJp7FeEQsIDvppVnQ5J95TH+BSVke3qY\n",
-       "9K/oNPanvSfJu6Bw72Ny8YnQNnMXEUvCdZNeu65ic0NdpYBHbUR/XgI/qsYLhdwqj5S2jxxdOElk\n",
-       "uFKMz4xgyshhCO9taGBNnwe8fixzd1bNjzZHsvjVvZIBSkGvC4AyQHYODi8kvkTTAd7bvJNIZPmY\n",
-       "zIwPM4nnBf5Hd7J1A5myXel5dqvb3rc2uWmapM0b0yV+54QB0W9bUVhfGekgDYRXdBo/AVcqhkz4\n",
-       "80BnpiHb5iOr9Yyb9g5CXWG6Dw/5gDPHoKdeen8HHyPtfK3dYXUqUW0ZYGIG1WQh4VBcTDZrk15X\n",
-       "dYfdbqnKM7M/yGLoiS9ODPy2G9dDd/1r18M+q20i47Al+2AkE3rAwf+OhDoUQwSxrHwvWM5tT9nq\n",
-       "V9k3Po6FgBE8wZPdi1pXcoeE01bHfJLWCi8a/z6x+bSjZwqIExtFGKoebA87KUxsdpBC+3zsgcVV\n",
-       "/nKIe1/pdvjqH0kCdxQYQADX4t4VPFNtACCJfoO2xntsA4AcmcPGVV6PlKIka4DVHC4IJ+15lJU3\n",
-       "1nFZPD5kHsgIXvMXp/Loe45rC3nn9nraSj+u8HGtnKm//9AzkQBWDwAulLiz+yC9OGz6lLzLaRdG\n",
-       "cTpVh+jSAFwyH1oaX2YK6SDNeIn/Sow6XSW0Vt/QD/YcyyC8IMls45y649H46NF2V3rTgFVSRDVZ\n",
-       "H04lQRa4J2vWHLhuT3hXcLwk9HjtYBILBqgU+EZxU3IXJnaRvq2RXUGJ+UfvQLCiH0SXdSTEPBir\n",
-       "4WAxPOyiAEFjwxrFCMdarljGuxSnDHXEdVI9t6K7YnypqSdRirrQF9LlfS5KbIsIwO/O0NuFuhai\n",
-       "EUO7fSIPKH95SF7yjz0AxIv6FN25vcz2h8hVDW2wMMNmezIa/sX6MrT4wVguZubUbw/0diQ/8M2A\n",
-       "pAP3YSs3UfTS7p2o1iSvRFX2YICAx67XxRS0zXEt6g1X0AZLoH5kZt517rvG0X5Z1oKr3bFAJ32g\n",
-       "h9KAuL8GiPffA+Hc2U6X9xM9LWY6qVlczaEnJ6rOQ+bcVttl0MeAAAAPpAGe62pC3+Ah1oWxd9me\n",
-       "b2Dddw1YsXr2nVh5q6idgdmuaCrJtaNwpIpRAB2uXGfaYFUTEU0s5+cYy02NPRphafTWYqGMGZHX\n",
-       "QZjANryXGWuDXp+jMoA4/cpPxwJamt6Lv84ZHyNjzWtpGcEX50gqTp42gVMAwDEOiJjxRDS7v18Y\n",
-       "jdCESZ7ytpDqHUQqkFMHUmAAw71ZhS95Vf0onnnfApemLsmH5M9ft2AkEYh9ei/007C9aNjfbZ4G\n",
-       "i/Dm4SXLv2jOg2wO1bZSdoZ0SbOjsoMQ1ttcNGKKcqK1mk3tPvmFeQAwti9NL4KS2tzyhMR1g5/v\n",
-       "FmYgja+JQW5vbHgxdZVX8DqU6lLIPD5NtnxUun2lerwU8lnLUyOlZ2VYIelEWr50jhJcbqhLsNwU\n",
-       "myVUn8D2oHL3T4e32PNRYoS+yp5ip4hPsct+icDc5oef60w0L/HfmRvGFVDUPnoD3bErjvgzLgJc\n",
-       "jiVb2YyBbUWarbrCt8xKP8LmkCO1lgcCLXjPS2urR3gPGKR8EM1/H5j4FKCtw6iSXGQuoXWHlMrD\n",
-       "OwnA3z6r6BnzdfwJ44wJh886G+wWKwSjxhEdhdREbZxppUzrjHOJSbg4oBk2CzKmKexTx+PM3Wwi\n",
-       "r+4kBIA3f15/jTcSbFtanluZa751EvYGUkl5EFAOZw7vT8b20QkiuJUNNQGLFPeY1NOsgoHuIqEJ\n",
-       "hk64xjrHVCD9McS/+KPTDlrvrx+AQl4p+VWQp7LD48a9NIxvAk8i7pRCppau5B8bWya1W9BOgGWa\n",
-       "JSbywEwLGRCb9Fd8rS6nJL+5C6i04NJgJxtX/DFL6M9nBSlbtZ/fgAAG4P6Il3yVUdqHTxuuo5yj\n",
-       "2OS04dZ+v0alal++8JL05dKPBY6wG18Of2R7NlXJMNw1yApXwkscFbYz026u9eQaJGr4ODHebAsv\n",
-       "umcoSl8leELQ9yCSKMEKbSw/HxE/1MJ/lkzrxRk/Au31wPDamXcdVfHVeUtZkdcK2q5t6zKTf87E\n",
-       "uh/ESD750eIoyoTEXB9jg9euwD3dNQjU8iTUmuomBRPlg7t1dzdaxlS6aaqkbK5P2CBMOvH3GRY6\n",
-       "hn3bM9ojLFFxebC60IvOj7xbxwZI7c/0ijYSHyDPBet0axK4viY4AxzZbEdC2FKseLOkYps2v9pj\n",
-       "LsGixbScMVsacZTSrkYOQiQ4AN92cbpBa4TpAjRua1XhPLa9AgyDnv1ozEPX11cnl7fYm0yluxpT\n",
-       "OU9XBmbNbhfPoFrKt7hdmdToDrccAQAufS0CEOrK0cRjs9a3Pa2CighfOL4Cw0osraqpgMKn1u28\n",
-       "4Es0/Oi1n14qIZNqw7vdgwUt+FNVObs5Uqb0sqjxhSnVgghpxdEwqGsLnij2LhFPJqGOuZURMdiJ\n",
-       "wLuvvZJVHZ9G5m6cwCFj6xIy6voMTwMIbWjcdKICtbFs+Moty2q1hqysSwczyyJMDPkZYMvJhjBk\n",
-       "XH2wRlgSsJL32PvC3QbJYFRFevRmcHjY3dc+WXW2UdrWMg4TYGxA1tlNg1bpTu2QfuZk+SX8fqs9\n",
-       "85/MQfkCnlDTCRg7srYQZTDylU5d8pv0DRpbqYG5dW5/Sg+Fo9d95Qf2jD2CmxAAx9XuLCYP5eKc\n",
-       "m3hH6rXQRzTgCmsCpEwbYbcnDQ50PTtsC1CeA38qIsZ1Un2/loTcLchuHlOyrGB1DJGO75lnlVZC\n",
-       "jIup4oJT5xNlIuUivpbzcLI41tecyKROBIrUmLArOoisYfteM6JnPN6EkM994S1vmjKLv59f8TsM\n",
-       "H7aW+VQyetjxgJ037SMTTMXrd5rGYvvYzZt80YNrMhjnpJ9DUsYPIroAkETNVBIDhTTFW7+4ADVO\n",
-       "I27oa3sPI/EvPcxc8ylcqxdvn1iyOU3klMp9yelrTwU1VhQzHFujSoffzUuHfR3bNlYSrP05mYtF\n",
-       "669DoDoN9m/8tfHbt6ZBLnbmiF9PDZ+WScCalPO4b9ozJIcUPElOq1kXTn6UYW9yfEjKCNIYCwrD\n",
-       "vDCdXPCLxXb6aTBZYtDT6M1tC9bmcQLSQGyA/JzFuQx1StUKyTi/7G+/OrdNcym4pd9wtQlVNgGF\n",
-       "Bicc7KI34sb0J5GDcHkzRFYwTCGpyUTJYxoFi6/La8NreTtSeSmtD/GsSt9rJBtvHNvmnXP//PLv\n",
-       "R8Qw8SKeSwNobihfM8I+XQoHMMCw+4IW6hWVPQvN5OoZPtM/T2l2ppiDE7o1q4n5QGOyN/VLCXBM\n",
-       "zLXbmqugRvCwQiZnLi3n2otni7eMz0RN2jYrrAElVBYtRQ/kDA1+bZAIIhV585+wErFfuuHgd/cx\n",
-       "+XJDRquseVgfmR7/2WMYcGRaNFpV/lyvYZ2IW8jYzN0NWRmzY/kes3wQ8w36vr1hvsJpmM+euAh5\n",
-       "AJsGqSuHImu4xxcH47HMoIeJMbDzkq0OLzeXZQRLzl3WaUR+/PiCgnluXy/7YNeaouwN0KfNTkdp\n",
-       "vgC4ZnoyEF+xZPFtHC4o4lLhuV9fHmPvax3JkjotMq46ULUywrEM3qeLrPuugz34q75iCCDkhnmq\n",
-       "7PhBvVQkmd5V5lIACW/uYEmyNda4hUusYq6JjwJZqk89z0pP8veYPJxXZz9IfcNeyqOCBOVH2Sw0\n",
-       "KZ6yA0HLUqlHRRra7wKd03OpnA/xrIOs1U1U6lGT843gQLENuPuk122nL6qSrj5dP/8s99nmRfFk\n",
-       "EYIeJjcl7+0mm7p/Lc5al3gkFaFwDBiG6WJCcCk55Cmtykde5NpInzwzXLPmLlOYvOQnm1TfGJmh\n",
-       "skgA5aRDkJrq7tH3JJ2kUNl8dB9/m6U5+IgdhAqrYIFnm9+iaWJX0ee8wLJa/mQDOpYHNti1S1mc\n",
-       "KjFNAcF7j1mPmWZ1vG9gwG1lHSHZ2sYIeL26PIa/Txd11P/Xzu+qDhsQaPIEjvuCk+e3AYAldnpV\n",
-       "IYA+Nzkoch2raF8W0bNPpV/roI4gluFI++GFNTH9By4D9UOeK7IbBHe63zH1Z/mTKKyblz37h8re\n",
-       "6PZDLRaD0SqUv3y9IoWdpTmHeI4iS0an3AaoIYeLm4Pt9IGaWE7IqFrvDhrAm69PfZ75wCXBWQ9L\n",
-       "XFJ62Q+Pje8XAdT35oIPSC5bCBndZJj++9IBxu8LUZY/+V6TRIC0sfh/fQmW9J2SjH5T8oqXEaHs\n",
-       "qTxlqWmtxTD6GsTqYRb/kPcZA5ezbka/i3IGRP34RDu3rCr+vENYbHGOme2+7wporS9n5wgsiC0J\n",
-       "g7D+wVFLayha3RA2f565eJiMwsp023y2xnSAF4Nqov7qbBju8UnPRk4X/oDGtehifZZQAL/SvkO5\n",
-       "m3f4b1VeLmS24A2trrotnRcYWvJhkuzD7TMJT3RB0+MuPRJDSPhzo1VhwT+ft/5Vgu5ZHF2c8gKT\n",
-       "gNRPiLWQ/CcD3SaoGe3Z0yS5oASR6gE+gDUkzrDeMBYuZHwtNP+ArY4dmDOLQjmq+Jyt15fUHktg\n",
-       "yZ44ESyZdz25/5NoPtGs2uZyYgPwTRD6/TaZ8XvS2CkOmiY8C0vk4oXR2m6iHG1Qz1aCoS1/IM9A\n",
-       "AbYkKwA6eoVFTIzXvDVFfA34ZFd4MHgD8PaR6Y8Nnl10T9P3zlDQn2Bc8WF923bEDFm/1rCI6/f8\n",
-       "6lQ2NgE0I0JX5v76u5BSscTsYJIpZxVHxWvBfkVpGA7l3/I6fxT9iimKdBZcxxOVopkwBKkkQJmT\n",
-       "Jrw+xvCqnPam7emChmIjrpnUNSLm/u6GjPWobXd6I7YItS4yhycMyPVbinM5xB4hlMERR0BRLutQ\n",
-       "257bRv9Rj69xC8gI3N+4BlAh+PH6fvR30vI5qBSic/8XMuYURtIVXJK34cC/zBTwCqcJhVLmBE0J\n",
-       "CfoimE7+GQmngZqTaEwRsCZH28EaHiIJDwsuqh205vJNG4m6L4rgpWeHMgBiXxMhJvksNwTUTIbA\n",
-       "Graj1+MenNF9Z7iyO2CfT4Bd5UKZCf6nVjmVbdjv3IhHA39I6vdt+OyPuzwKAAN5Cb1XWgtHZfJY\n",
-       "V6tkL0dy+tlrxk6QP2dpiXNiGGyFNDLKilS5i2cvnK/eMyAdyjiH5n3WYLdx6/nX6nMnPHd5fcRk\n",
-       "ys/nUkt0ItKu9J6d/p4tnLvvvz0pWs6tsTVucLpDnVwg9srEt6esQhaGvuw28s0V1V3l59LbuQzz\n",
-       "flzh0VF3yHKgNXCzgpqYEnvQmkcpgaCaz/Gl9hcGPZWoJi1I7eg2Js3/L6OY8F+fKkqIizun1WMK\n",
-       "45W2tjervcbAHUgDZvEwB1kU4rDKDMMzkY/f9aqUmW0N6fQnZ5hILBrkamQpLqbhet5SOdg/DCRf\n",
-       "LwmDHsixE6eQe26DE3BJxAdzVZiD3RKvd8pIBsOxy+wp/Xm8Re7yKfKLFRiielxWrSMojURPs7nv\n",
-       "bzCOlErBv0zVpvJXl6DbEN7QUBB3AZa46F4KhXovIoJPcCjtrThIHtuNFWFPpVDpN5WgpUjovbkn\n",
-       "4/DW4tcpn1DttXW2sENqw4p02nL6+NhEU+v6nZw9b5QF+G6j3fKn/ex8Mz83KrT3IFxZRrAfU2P+\n",
-       "f8SS7eTUvQG08G4xfoQLVDYVpCP8z1ugip5bNRl3sHdBxJCr9YcTWe3jLjY87NNarnkTB7ZCE4mR\n",
-       "GSYhFtC1/7tDy5nPHlpgY2TibSKLbimF5STf8H+NWbmdfIQ7qRVQ74HmHqor+FY5mX9QHBN0EsUy\n",
-       "Q1A6hPvPdzEtC1irrp0v/OWIu76WHZF5fWmx3eOz7RuSFJCysyYxytiEL8zBO1VsZkR8FNAiQs18\n",
-       "FcdetONg65gffskkh0fJ4O02gX6fcdZbheenCuliJcaOTQ50+XJmlm+hvFD0yAxg18uy7KWwg8C8\n",
-       "XtEjzsyaFrOBSLKINlMwSIl9EvY/RYoUfe4UqsXxhjG67cJg7Qwm/08an5N+2CdOPUAqvCeOVYAR\n",
-       "a3GD1pbT367+vkWNP98NsQvY0ucH3bqbo+0pSTPwFO8p3PNqIMWOEMMy/R9Dlfq8T7Ufjqkg9HiN\n",
-       "822sU7pr1fzAekV9HBZWAEBQWwQrNZlW9bBE0fJzmfDRLrXrmUj/raAQsv1LYxnnH4F/7bEL17sW\n",
-       "Dkeb8spxf9rEzXCgJ5yXk0DGNQDNGhScIBRvCeBaF+JJxKJHmL0LOi/4vfKXcglez+tvlFQR9hPJ\n",
-       "bkqlTlikEmsPn52FD6A+qoN0fEir+4J629MpMDgWKFCSuROnSCu/qtACEKyjfqf8xveRS7qX9GXC\n",
-       "8bzSXjWcJaaCNtymgALz53xg4UJozSm9d973TskqZR6sXc7IalsHlaVc1r2DZlNIUKzemdE/rdkI\n",
-       "lutSBxNL1KxQDWifENekPhc2c8dxLkzV9DeoGINs2UqG3azVr0zp7jhDTWbS5BwXvvtltrUgeFdA\n",
-       "AAAYIUGa8EmoQWyZTAk/bnUEl+oW8x8L25d5iyt1v3WJBAmjDr4ZcN6taWus34RERNTKqKQSgqeJ\n",
-       "nXMsSYAgBfmUV1PmRn2FUcXPmzryqENO/ETJA3YSQy/Ih9cc5foyCy3P80olWydIUehmngnWJOmj\n",
-       "7RDM4VyEPKouU3bIbg2D6tMBdS3POZfZHZ77SWD+fALFt4guHfaOL3BjYLnpEaf4JtUmNCZdH4U4\n",
-       "fFYIPLCNKrv/R75SOJ7EJutZ7rDxFeqO/YPvKgwA5Few8YVVc45t9SJSdkWLjueFaislO57URjVP\n",
-       "/izFomWY/x4zjnRE3ck+06ztwDkx5et1BLd6VgQHdIHzrfEWm0+WJ4FRsXLvuFR8X5vIf/Zq+y/2\n",
-       "hs6pWRMYroBOMfb4nHUzvPIOKWLLaYDLB8a8F/n8//+O5zhHamakA/DshF5RHRaDNutbWG7Siq7L\n",
-       "eu/7r9EEgmHbTDxqrv4+Rmw966fPplOsRItJCugxyoMhVthwoimbKUWj5SW9RduWbDocJvpeqnDM\n",
-       "ynKsxfj5vBjL6dXYWk7ZW9TG+dMr0IjmGOSho6uVJPaHw4RbQo3+YIx1ImQ0w0eNwetmXJF4EPvq\n",
-       "LAH3vf8IwDP7wYzWnUKFJpqwMz02SdsLNGt/lG2Jg9JOQ7i6RXEHl8oUv8JW0fXCzT6DbXP4EGdr\n",
-       "8lF3q7KXiNVYBpa8QKSS6fCOOaVmoFZme22zZMbd+ELnuVbe1aQ//9WOxvwgu5DPtxcFOc0xqlot\n",
-       "dbiWoMeh/gILrJqnT4cWNLCgAH1WvArfXVqJ8azU7d75CeIx8wjuwP8pW1aYv2HUH3Lz/0f0eZLJ\n",
-       "WHkHVdcjjU5/K3ROSWTHI7XWMUCm62zAC1GjoDhKQzfAXRm+8GSEMcTnWYpvXT04qSwFonkZ6sGO\n",
-       "oz8RMPLGek4CWy7pqVY3kHe79JB/lgPjuqv9t0U6uzjxUklRBi/cRMNIjQyVTg6Zju4nJkI7zo+o\n",
-       "JTG2JEkbNAhexafZL/ye2cf6okMjAhyONXnozoLTFk445JSHmoW4Cj0HKgnt9M3PAnQmOO5r7KmQ\n",
-       "Xj6K8xsi4qQWfHLlNZRm/IVkDsKMaC7mGPmcw1kK7gdYjfBkPRn3jV4BxedCGT8XY5GJqEnTU5xd\n",
-       "jKmoczt9/gh0uefxvMG61g8wr3aeqQesgIkirbToYehMALNqBdy8lFsGH8TfSMfiFw/p1u06BZUc\n",
-       "TsWZ4kqnAcG3OWzWT680D08BINq+CnNSPLioMq4H8wY+YBwrNRCRot1MTIzdMIvUb0BrX3+RvrH3\n",
-       "GsZVSovBRUoYKh2yPKLiZ4BXI34+xyG2Ooc6O0+aufYTPGvpQbyCziF5c5P6LyzRklWgSpkewpQ4\n",
-       "F8jiHabgJQYosJFHKX2scQOAYqSthNF/kkDlR0hwR8P31VODWMqlvhntK/Uny6wOtjlSTlMKfwLk\n",
-       "+ArayTaOraWQ1nDZEjxQE+oTT1xVwChVw0wYMw/1EIUDYDRykQyWSGW00c+WMFSshXmTiPhUYTrp\n",
-       "MQ9b+FOmEXRL0LyFgYn1b22AruSK2JB1e8647zqLLgI6IMvAVwizbWNSE2hFdQ34wXb/DGDD6B+R\n",
-       "KyzkaAq5fzOJy47bjINO6cEyPWxtEQvSNiLU+9qDH6CiKTq1xLjmqoEubAPUw+0CDLovW1ASVOzI\n",
-       "clOEi/JesHHs7vxaPrEoci7PqAUGmu4mjVFry6QN+XMRjnyK3RXymphPQXOSlJM/H+hUql2fkpAI\n",
-       "0y8rs5YH4n1JL3syN/QpJg7mTWClFZQAAPrDR8ah2SWjiqxdEcFP50AO57iZGqyn1yRg5hArC2sH\n",
-       "BCRTQ5h7X7e6SR7bXwPg2bC7qZhQ66injzC8bdLEtZX9J/GfF/llJbEA+rCzp8U3Y+MrybP81kbg\n",
-       "NwotR4KxyneHaYbT7FTy23p29t8DFFvCX2HqgWd4D41qCWLCoj5w2uASV4H7MRuHiQzEq9TprSA3\n",
-       "pep3PGqkggFNePo0eAYowvkPy6hQxEJfm/rxFw+t26hCMR4WC60X2/c7vZcwGBTBwxdIbfIMRi9m\n",
-       "5iphXjKz6zfi7MZOo+61yyaRRec6aeZc181kV+wlQcYpQenaX89h0qBVFlCx9877ecAqOlbqh2jj\n",
-       "hKVcqEvf5itFMhaXLGcJM1+zhPiJETuHNWfV5t4wmU4iOGAppOgp2WbPvDyUQiC4YuL+8Gc34Zxz\n",
-       "ARQ9HL8YFiPrf64xyDwLFcH25A5z46gJmfXC3n/gWyV43PhCIq0UPR1phqGVzG3aMBeEbbi3hUu/\n",
-       "H6AI0gm5ixS75Hue4vGKgjVmEkyz0TQ0yFieHmp8nbH0AQAawaXO0JpxIO0aCqlNtVU25bGFYZbx\n",
-       "+NoXWwW0lc0fTRnQvBaG4ErchedrsKOet1xVstRFrAIjvgMjEiYhvqlKmM2EIZeLRKKsL2kOKpPj\n",
-       "zdpz3KACSF8AL9ghyVzgZ/XvBBU+qvWkXmtQv98MFJW6XEQrzJ9YDZX4tzHF6foXdvRT6CHa2Lne\n",
-       "L/4fIfsmTMXEQtKynCZJ+Ltt3Yqgti31y1qT2IxKutCmIibUZQfn+SSdey5JkgRM2xNc3Z1TPHas\n",
-       "JbjDaXNoQ5fCj080TjlX+jzEdNyRd+mYD98DGYHd7V988/2iNummJ54wlFCNAIKJr0CSjHDLH50D\n",
-       "cOr3F7NvY5901oNE4uL+/Xf0umWL8wc3RmbBhmtVTmBQOhTrqb3XKzruwrQ44dbDyF9boyORZC5t\n",
-       "KlutAAJSGerl5m/N7DtC0QwsQ0x7pA45wC7tFDa/2InlcJXA2SG3jt36aU9h+NizqUtGjTphhYs2\n",
-       "YJZ2tYldlLTF3t02mIq0gev7QunfqU3ivqonEetG0E4U5jTuhFxT8y+GU1v7Ocphg9+UTRAb1R5t\n",
-       "mTmt6+Z3zC+NpPjGCX3EP1AAAAMDAy1pFDAb3+XeVSPhjHEzliInoR3OW6m2K1sfEsc86sbFHoJM\n",
-       "gGY6vjr4swj5HBS6kGuSk0SEVTRYZSHyPu8bRSygCqyplgs0RK1HVhIVE5QTQAX3e0Hv6FwAmEGf\n",
-       "JWoF9DLxXUpEIz/2oT8yF6gM7BHy570RAjQGWEHImhpdriTvdkHCyOXSJnrXvkyE3cDueOH5A0Yp\n",
-       "neHNn3ioJwA2pKTRoF6XXmyB+35wd/RiQE2yV3eJzurKGg8peUUCbj+U7H3Iyz4FNX/YL9DwabK9\n",
-       "l0P5NRSQKNPAcLSNSpbTgme2b2UP49NVPo/WA3+gRnIomCOfrAL7WmtIS8okaHbmu4gxGyWM+J3q\n",
-       "GaF+LVMWFHbXrLQOjkHvQSbN+9TF+Sa1UWy1d8q9BrynDyVbSfS/0iN/ppNoEl72vuBxnZSMWN3Q\n",
-       "Xd9y82DXDifUfo7Uj7bbhNl48e0rva4z/oEJT2rcFy55eIYuh7aSA+o8KzO0C1vbjwn8O0bsSIwI\n",
-       "jm9PLEcCYQc62iVDrKdJ0F07PsCSOL4O80Iou4ut6FbVCxqv6RZNUpfkuXnfc/vQDbQ68jbY7DhH\n",
-       "GETjcCspR36Ma/+uhzTprAXgjDyk6weuXePC1GsTNT9jo80/jy8iKMC+CRh3WOk+l+kwzKw4J/JO\n",
-       "rp8QqYwEzjoJELhArWSygbxRjAQRDhCpo5D87BZHYlvzScawvGjJgeBLB8yFykBWXg5Egn+/lD5w\n",
-       "nGxXyagvkElhECxEA6JTfOgv4bqXHBsck5dM1Ct4dPDSRSzjQoX79sas0zc9o4eShOszg00qz2Ty\n",
-       "V4phrNbGN/ghAfpS7Vj/zeaqdB3PVUjx6649oPuR56Vc5dxJxuHqv4LySf1xbLyb3ALRwxe/V8sY\n",
-       "+aSNDhWC8xhnrqZ0xdZb8dZIBjRI7q7y1YfA7N2uXziqug2v1kEZkBxs1iN19wrrfuh5DnDsXuaq\n",
-       "ZGzGBhWJzJs+DuRk24MNqOj26P06ucxaC69OWwgnHzFWOBE61QrXw9XMW65swnRiApMLiK5Luxfm\n",
-       "kZmqHA2Vkke23Zl++hg5uAw8F3VmdcDUqllFHRhTsrpZavrNSCB1z/4g8ZzTXe9pg7eaME0pMRqe\n",
-       "7zMz+EZVizUbwlWWP8YfxYVwCK8RN2tn6AYEcl+veO5E8h2QVa7XrwXocsyKCbisDKJtGyyU6K/B\n",
-       "Sz2IDWw5t6Km3xoBrDYfAaR6ScDoCPnAInx6GJ2XU0FkO8K/1FnpSfPcec+rfEp8PcCY/+08A6zS\n",
-       "DgNXTVMNDbvCv2Rx6Mqi4vc/gbnP1XPAhvk1UzgVMrFU7aoVA9OoSI45qpC4f38yV1ZAiTV+11i0\n",
-       "bbp8y3YNBiOS8XFGH39Zbt0kLhnhxOb7diZEcrYx3AdxlI8D3RNpHodZRiZoyRQmf4vFNjKhSdLP\n",
-       "tQVffNpzfcX0PM1RNE3P53ICZnGIfubOOfenIXpyNjOZCOPx+/mfC9sqLXrwbH/Jh6JGMGnndN43\n",
-       "d2+dje0btV7OQi3knFIUxMgfpzONqV3xYmmyyke4OZ+mHmF6xLsX9EKI8ALi3Ij9D/W3PYmESSOo\n",
-       "oesrxE5mKPJRz+9CrWlpm49JMbEhYidYm3Wb5LFdm0CYcE1uBqdW+oEFBpkUHltKOUtod4G8Dc53\n",
-       "zuLPKOPj7v7EZvgiOdAESeJy0e6sD4URZ8hFUNKs6vFm5lEWeFKHd5qWSEpyF9NVYDMWXQQ/INtd\n",
-       "vCRKvCul8TEaBikVTKTga96zZgNbqezOSmGct1NZAIokOnKui9fl/AqG0fa46by3unccPvuaV13v\n",
-       "WVvmTaBNoSpbBLgPP2o0SGnMEaOY0yBOQwdt/eorFxsixSbcYryjOUR3CMLNXIeui5PDaNvOWBeJ\n",
-       "X1Aw6SGFDVJAhqXCXms5u3c9jXlUE31+RkQ71FBIrsrAvPD2h5z7ttwWU4am1f2QuUuL484rUg70\n",
-       "qUtPM9Ge6vSNo8ia7RrUUCOybYT3PCA3/51+DtlQKI+Jlozr0AeuVgFRVAOskVAaSDpOAwDNR8+a\n",
-       "hVN1JVC2tINIWgMOuLe+1BG4gBuf5PfJg7SPkbtg9Y6wiJJhGVD2g8NCv9fiRi/NIThu1sFncbcU\n",
-       "08wYuZnEE9LXryXQBXfQdiYlxGzeq0Sh6GWJyNfWnl1oJFJ/6rUVUE+7WxbfuRwBosJxiPCBd5f3\n",
-       "802AwVGWb4TPjoz/+wApgPqO62xbHrdi0JKpu1Zhjspd3w3q9npbGxE87c0uloESczJwGaq4+FbM\n",
-       "owzMLciyitf64p44MZo+21tPvH4DglJsFJz5QxFBNM0GeJNZOP/zmxUe/bbFpQQodiVk0q1wIKku\n",
-       "3HhZkYtyOvWeeEUuqKG2axbP7l+3Ror2Epk64E1az5gLd+P78gQql7djBIzdsMVAS+Pf5jkkDf8N\n",
-       "pPYRA3k+pUeVHrsDFxj6XoS4kUx0rlmolcedT1sO36EtDjor3H7JZlpfYNSj0vwXGWERYPrsbGNe\n",
-       "eQpKEQOh6WV0crgBj3FlgxA1Z7KW6mAKD8yV2fJ4ZO6pZoZKdaZViuN3MtPKHcZwwyMcToHF/UcT\n",
-       "I642t8vIjqyzDxw+LTdhAVaazsEpJ7IfBUm6ZiGxXZo0gd97WkeK9Ezylu2mMBKAh7TkQOi1g4ka\n",
-       "pzMx9QG58ChildU5VC1W8lWoquv0wtNSkf2RLKAqcHS40SDSTVqIf64t9aH0WhrOagW3NJ+Xn2BK\n",
-       "pG0ZlM3L97FfpiguCmZLApek0bIifF8dR8BxA3es00rn4ZKcoeMEVSCXf5pFiLF7JoHbVgg6C98q\n",
-       "xZwLVbmyPBuazZ7osZM63Qn2WVvMH6HsVzvo2u41SwCmBvNd20dnJknm0b8u/IDXLrfEA0yk0/+6\n",
-       "RMO07kC766qLEs+3/Q+akUo8376JpvhEXe/xg8AxilLeOvIP6toItQU5W2BZulqWTkxNNQPVv11r\n",
-       "3T1bxqDbiN7PQMXcWM2Qst8Uv4+lDIXxDMkCdEx02f/KLMYOA5/L/IkxIoAAEcUVEGG7chE3L0ti\n",
-       "TJPuVGCbARW/xVV5SX3YvOeIP9eNNxwhSeaLVMqoCXQr0Ts5qZzMBUt6oB6XX6kswELo/5OZMoc+\n",
-       "NXCqCQXDYf//uhnQUMb3O1qRtv7TsVZg6Mw2nk62nn+9cHDe8DjXw/CHlZNdInqSop92T1e3Q+46\n",
-       "I1/3LxeYK2G38Gr4jcX2hBcNVqt8zMwZyoffD1mgY59gnw/0E0ce/myjUpSTq3GMAh1ypMNb3dEP\n",
-       "yxnxY93XXruy9LOtGmOLtl/CqkcENjODJYF7B1KF9ms69X+CfZHgA9NWi/CvKrH63RippV4eJBy1\n",
-       "h0mnKQwm96/3ReU6em8MWzJUZ4u0U5qLRXPxRkLJw/G39rdUNK6bfg8iha7ilpRq0nOLMmMlESw6\n",
-       "Mlhd9nxchrAlEV03JCnedh7WiuYBPIBDNDvWs+dc5Eyg3a//qswFerePyLjx3l3x3/kD438OJgUD\n",
-       "BXGxPIt9sXaKvJ6wdEmC4mcugnAtTWbEzdvUCJr26AEuiT6FGA104Wcjxz+elvTNV0XQ2rfJ5gdh\n",
-       "mIQLzElu7gYM1fkpmxti6G18D2aRgZTFWYFM6TkJavT+xNZIFBkQeRHQOjRFbsery90e0zBH7ZFU\n",
-       "hu0OE2ElQg/Sopkzvj0esnB3KoiWZ9tfyhLrE0E/vRQ8NKeNiXXFgnBoBbD0fhb4gI6EjEaHSRiQ\n",
-       "4v5zPDtzqeJb7zVKx5pMZoR/H+U6WP/JiWzctOVAAImkBx+UTmjmH+GlIiQw5Xq7z/URI9neOS4F\n",
-       "ZZk/v4+PoLSvMq4ylVOFXRXNSX1zkd901tXYpInwZ4hIhzQqqaK2CEicguJRIwHv0YM4mg4vIbfe\n",
-       "aCVQ0E5V0d5QpQMxMkmOzct8FVVxyKIkw0Fm5vyQFpBVrHQ6gK62BjVoM91jjbQG9aKf3waRn8Bl\n",
-       "xejH2fMlqxK8DZAS2u9Oy1IlAcqmRbsX65UxLXFHA5fiu3voPfjY2NYLRgn+2uOrDemh+SjNMI2/\n",
-       "CVSzT57lPhV6V3QHesdA1xjSXrsy2VdoPIo+t+CFS83GT8Jr/Zg4bZQi5q2OE50mDq2M+PtSSfCw\n",
-       "+5uHyH2Yow+j/c280JHjmp2d5MN427zdaSe3khr+lmz8F4mRqxdOh5cj32+yxi81LGwtu5VpnJEc\n",
-       "aSFtr9+Wz8QIDWVHOIWBDkHxece+LoLxNQ/LawrraR9wBAEH6IjCL9DUeXVUOvzMcSX8VMgwNRFu\n",
-       "QHHFmFKhgKnpoUvNVrlohp4xBQ5R1qW/xGHYawtZFt3SL7Z0r5ZVrIP2Gi18dg9be/5xTOimspZ+\n",
-       "TsvHtTAXd6BJAoOdvqy7fYD3BjdivVxRifa1sMCYOFmL5311kIO4CpiPqs+txoocDf9smfwt0HfA\n",
-       "C34+FtYzgJakj/FjDIVk07W3AgXzsvh2GFSRyB247qGuZw1bBDNhtZolfTSHGIA7ZY4U+bOwAHY/\n",
-       "DQU28P9VQk34mDF/O22sY7smTr3VMqBzIBp+vpvPSp2PoPawDMqjr40izfX2D7WLo/34eGdKY4Dz\n",
-       "MLPDmT5CQmpmgQwPuCUyzEj6UiO0LGuEuUMAWQv9Z3MAvSWbR9BoJl/GjbZDtG5YuZFEX6XnTRR2\n",
-       "4F1dVwOs+qHNNHT0mkMzzniG+0b+rPn/pEumiDaFsNXw12mExJy6GiBToURTwJuC76Ufn5XA3EGh\n",
-       "2DiP+7Afmt/hyiWY4hNfJ3d4mlwtdMkeIg7uV/b8QCUhYvUiXOCV3x6pQIUV+Od87eCdV6NUR7tX\n",
-       "gp4ac17ZbithLBnscw2qZVIfAX/ZqsgL0KANB+olKwdzvf2lE9BmUTJSh1+BUAyMSmUrMjB1b2Wd\n",
-       "C1n3NwhvVruTVR8YUqdW3VJkert2XEJZE8R5h62jp7SXednmmfgY/tDsRbFJ4w4hxKcyqRIpJ9rN\n",
-       "569CupUnM/nXIauM/ubiVvLtyapzUcZ63JA/vEFEs2lwd/32o6KXyf00zxf2VYk3alTIZfKYDMGM\n",
-       "SRcEijAJLljeIbqhJ3KRi93JN81Vp4vQA4+ATmU6z206QFvtd76tnh4o2+eaLYaRtlZzN3o5euGy\n",
-       "xad+yA6namz1nbz9EEmRC5dTOCFp4N3ah57/xkhJjlQ7c2/b5P74RAWgq09bgUyimcgTCUiZ0sK8\n",
-       "VXaqmOrDceJ7P3i/dzNg6PgJ1PeH39sfNECcYcoV5yKag/+Q+0LrQL2l8Fl5pTUxwxcd5cLBmke0\n",
-       "iZYlmNoQk9qVh7qPQy3soUtl9LPCI0BXvGhvYemjV1JwNeHQ4mvxgZr0Afrnb3A/cICSnO27R+s6\n",
-       "qA94Bmez3tIvPDyZvRTB93GiJhJCzek4uQAADOtBnw5FFSwl/9vfPE3FWfP2+BBfj+pLA3kRCAvn\n",
-       "5ZWSSwMmvkjXxmmYmD4SPFbZrme2KcN1HgQOWg8S2w420f0OpOzZbk0DoyEUU+4ZLf5k/fvUooM5\n",
-       "da+yaEVaQpDZzxPAE0IuCpOBNkyyhiutgip5Ie6pZI43/E8xYDfcYrAxhcFZ1I36k/O/+tM4Npfx\n",
-       "ttmnO45Q+v6cRsgf9/4ckN1vAxRX3QfDCMHzP4J7JA5ZU4ux7uZTqU73Ws1R0uqFbMkKBorlN/dT\n",
-       "NxR2xPQQQqNYaaglZlCjOQ3LbzeSr2Kogu0lv3RxDjcdUUpQMvel/FBYkvltZ4JvbFSD8Advo6Xn\n",
-       "82rZGjCpBHDZ4UBvFId8UhadQXf8WvOqVtu/II7TE3jH+u96SFxAJZbUf4dC/EoM4+J2v+LdLRq+\n",
-       "EhDNpaYGwSNy9pQGIS32bk31tp58DjBczBSMZaTSwQ1mZ+6qJRAZDlEbh/oeBjMWfyGrPqHWb1S1\n",
-       "rhDyWZ0J7M/EZ6B3NPM/f4Lff5n9GMkXPo1nTyL2KCjW2ReK0CL1JAJBWObv32+f2IEwINPB30p3\n",
-       "b6vsmzjNjp4iWr3JhQ0n++UyPJxXOjdhV1ohOYspuLXqGLYCr0/1jQlzQGq8aWYjV1OtHnxFUaiL\n",
-       "s4rpIEqe+VyixMY+jms/zhYAHhFaLoz10JEso6mwX6XVkgXPNyEWHuxCFWF4rfv+U3mE/p0w3Wyn\n",
-       "3bjeXXw07dUmM+F8wSLXJLYYvbk+KzF7RQW0W3oxoJoSlfWRvshOZWIHGRMFrFJZx87Bw0sxZIk8\n",
-       "05vj7u30ZKywA9krZ+q1NsKOCL7ByPwzIkAbtLyllFAtaWWfK8lst3ZqNMHJ9KeQAaKEW4mH5SxU\n",
-       "H+f454YbUhOsGNSeHkjbm7u550+5kVjIqmYH4iVYgTY7fqdmMLJTyEAY+I8CA9K4NMOSDBgwR5OD\n",
-       "MIVPgpaaf9MRMazP/8S9KS+jmFP+Q62qhDhbjrA3xWthj+oIHH+W5TIRXjVXihAfF0WuUy6/W6ZZ\n",
-       "2+AJvZrWeA/TDiowPtkO0obx+ehDhr7PhftsP6m+OK2M8fHL31ivE9h+O8X1gqYV7CTwNYtt9Qam\n",
-       "ZlaKLaYVtHFP9/d/VogxiAIfZK387DhJzBmH4tPRamr0UOPm4qFUVHCRqseWepNesli0JgZcQztB\n",
-       "Zh0ZF5OLQijuRBGf4vK7fLCAHHCl25o2SM6CjZqBVUu6OxXY5AYoeMyT2WVYrYj+cze5Bu0ugJOj\n",
-       "tEg1FM0fYMkTNR54tLWptjqBZ5jz8KUVRufJFNaLfnpaOMhM3cDSqsNeStHAu5YiSeE3Vu5XQLNk\n",
-       "8HCgjFIetKOrC3GFFPahVl4DbuZqH9Xv9jwzTI18jil8Z6z5jjZV/3g8itKLKssb6PYiaoRYSpnP\n",
-       "Cj5O2fUzdNPWd4+HJ+iT6iy1wt3ySZQ5KfIV1edm+Phx/U8sX/YJQF49/W0S++y+hnnGpYs5JJO+\n",
-       "m3OldarqrDcSRPK01iMZ9g9N9Kbn+zlhkT7d0y9+Bw0W9m7tfI51FA1xyMSN8RRgXl0kokL7KpZh\n",
-       "qOVVOBwHolGv1rLJhT1E9IjpGr8StzXqaT1eku6RrrWZVjzc2V1YaASRiQ6m43HFgF7/Unrm9JA8\n",
-       "9jhiP4A37zi/prYbLGp7UzMYd8nte0gOv5qnf+/0Avyhp6kqNAmCorarRVqnC1Ye8Zzg943BZ0GF\n",
-       "WWSSvzBuHxCyJcMbJSW9TdoU0nmd2zpdQ0LX28bEiBAGl97ozhL/VgVOMeLwU6iPQLhO0QP3L20r\n",
-       "xhZ09xMUVXWYdR+0CUqiJz+O9XHridY4MOW1RuVWrTTGxemLkHuTbhkRbIzCZd3QCIPUT2RsWQlf\n",
-       "ViuZqKdFJe5Ftjz/4LwDtdhMFwSjkO5IQDAv095Y8zYX/OqQ6npT50eXMChxpI29ec2MYNxS6rWE\n",
-       "/mu1EOSdevm3/MJTcCQrNnGy1SoaItw4P3W2FuU0z++xZijrtdHwj5LtV9UKTFNVXv3cp/uYWjbA\n",
-       "mvRaCZWWW/N7zahz3AFvQwCZeMOoIZN/jVO5OymhObZ+2jofZ6Iy8nQ0dZW3Zcxi4XRqJIAppXQg\n",
-       "2bqBdtXiu1Az9dYy34ONkoz0udKZ4hvIQ48QrfqNHf1pw++wlRQH6uEtkqJX5zJ5RTlqOqI3SUkI\n",
-       "4A+mJRhhVBDTPXtDJoNbarz+v9WDF4Mg+6hoOhSygZvONFKotmOfTnX+RtzR9uSWHNfwYOG2tnvn\n",
-       "XxTFCzu4mKIgj4qWZaJEywXtytRvynLYZhEkRx2CsII/D1m6G3uU2TkXLv3VVoOsYIKouqKp/qDK\n",
-       "LlTk1by3H/SmvthUVhToV3m8j2IRl+KKB8aEvUpES60pK//46Fvmri75LMp151nDPiZH2pOT4rF6\n",
-       "pzWedFQRN20Jb0EtfkNVtpbHjO0DWlpuOk0Ytt0YlsfxOEx8I+nW1RKhvAgLvmRv7ta7027kieja\n",
-       "2IszHW//PwBJOOzjjlo81LqYixyvlxgpBo/BYmjTilFgW8ODHVP1z56yjhKyZnoTWkkFNIEYV5Pk\n",
-       "KjdoFj7Vy7Pnpa6DkD0sWFFhpMtwFPxfIn57u6c3KgdZSW/2zCYvbgRZg34I7LbayOB80ivHE8Yk\n",
-       "tkBZ+puKkUUpnY+7MTfQ+Q/D1wxCygLH3762g4LgaJtnh1DCb1m8S+A8M6ywpeTPGiL64gRJ0MUd\n",
-       "GA0ap1Y4jY+6vkgeRc9cJvpv0KdW0glJ+ZFnq/Dy+Xuvzl291kgSkWmOLjIzdnQwb87ncIV+rlzP\n",
-       "p3Z3tHFQyfUPUcGmjdFPsG6Jfxxx6/HnysAQr72Myq9SK2i9bvLbjTtORKZFIZ2NwUSLyEBPTk78\n",
-       "/mTJ0KA9bdQOKYlZrWXL28yUwzzxZ1sankZY/Ug/Q/v7uUU7QvUXjHszFFlFUF6DLEKwJ99hznio\n",
-       "+USGTRXDAp+L/9h+9r2eprpYv13Ec4CFp8RvKKW6vJ9qdBg/oMwjP04DOox2WoJ6RwEXFS6dWlzF\n",
-       "R/mZLV2ccwRGmm4fvOUhVHA+FqnLOmAkPFPt8uyD7nWBa8B8GmzBTf8FSEoS7xo6UIe/RdIedSUB\n",
-       "3anHfxAdT/BytgP6uzPtXLuJDCaQVfIPLa/RIhub1qbiCzDToRCkIimKhYVbJ3K3j1dy9f7zjxqT\n",
-       "G4FhdA+XuLwSlbh/J9+g/n93vktABhsVHoKAM5N81kVi+nbVOGej4g7GjRgg5CRcbL36uDyc1OtX\n",
-       "tl5ceTlU2ow/WU8bPl9UrZMsmY/FsdDZTYTE6j89wQxA5gaVeY7BTYtuIf8Z/gXmuh/VbeiVm995\n",
-       "Kw3m4TdZtPgE3pjVtBVZpyNVUMYkcj6J4xjoDbv8K4BGeJSNo7rQLyt+hCwZDKm7cND3/5aCeppJ\n",
-       "HsknOCAEPdAU5IraG34+Cvk2E2naaRc8tEHtzCQCWlITjuSnxxMQWbfe4kD+AR3ZZLpVtP3+KvCs\n",
-       "dS2gbEUVuBw26YqfnkqSS5GU+9+lnHEJ4iC0Ce08WZkT3UiNpTdoDn6/+8eTBg6OA0I8snqj/YG0\n",
-       "q/eo2oRTolVxeC9WvqC94ShTbV0vt+g0ijn3Q6iNWu6FgsIe5ZodZNb3ogP+q6/6+qzzvdobYcPN\n",
-       "kAMbNeRqIGcBDvP4X729frF0ZKZgwWFz3pgX6is6fpefGdpYCoPQoJOQY6/WIF1l1lyt3RqC3zHC\n",
-       "lelga5M2RD9mLWWGxlJurA5VUiR6vtf/7Mza6UtxKGcojIKkLNLkMP0i6TpMw7dTqQz0R+OscvSl\n",
-       "jjJfo9eKjRrio38O3VTGPkGyT1mukgu5LAhhUoELMg8vkMXEh/ZUXrBd9dwsv3xnMrhTN/09B32v\n",
-       "KJ2k8zOEcBX3/SLKSq1cf7u6r8haISDMMAJxxV1EAgkZzIWitnFWdzUCAQZ54X+Mia8VejE8psad\n",
-       "bSCP7tW27fX476KXGYMn4v3M4o2t148IxDAKOu4+q8SXWfKbf/kxn2ZEn+hbyoWQyMY+OudIQCug\n",
-       "8d+WgGsppfCFYzzp/xC4lePdHXoMsARoQRLIGBahCMR63G3cKb5pSLJuMstPhJeOxhgyYieqrVtJ\n",
-       "bBoy7hU4Za/Zcm++RlSpdFYUDP/R5sgP1yySvqy5MdELnr9paFqrRvVy3o+z8UUIRSge8XY/ldTj\n",
-       "UhnIuTJPH/GnvJ6SdHR6xXL8gG647y7hB1NU/pV/pOGvzf1HSe/fh/nwUavZDeXOqbwhbyamRntZ\n",
-       "qmDedGP/nGDSdstwXTZLm/H+gEhKHJte+0d5IHI/Kp+ZCF6g0nFQrOsoKxa1uV534pSx4F4mhy+S\n",
-       "tVWsr4SindzeWR1JRIyBuNBA3QqQ1fezNeZp7ptIRqXi4JcnagU6A6l+8PefVlZZS3SXw77qQEDY\n",
-       "CfjrOuToTXPJTIkLytStcQVaDJQFwxMqC6NCABwxAAAIpQGfLXRC3+fhAmSneph3giLSsId2Nj+O\n",
-       "VA4nFtMCHZYnHwWG/yYQ94q44nDZ7RLRHm3mQbsraC1uYihs8GYuT5GBLvN7KHtV6dB/e1DLPGVD\n",
-       "Baj9sCZbsdy9VOkus1C4wdUxMG6JUbnQPCl3OwhH/dBEO7XB8jHG0pVi7y0Lj4aBPj68j2b5opqW\n",
-       "WYUp5LH2s/Df15GTbKOM/rI5BazxR+9LOL2Jd+eeeXRHX/ulUXNolYFEL/4w4HS5z9MAuaHObQR0\n",
-       "ackx+btZMtmbqJDY3L1PKTnTWmsB2Pbst+AQ5Hhx8FDTmabjMr3cNxvokJQiOZGC3AKqrqCzUbvc\n",
-       "iYSA31pQnwvj3jqhDoJkqT0px1l2NhhCGMa7O/e0LlrnTbUqmFfMz4yzSBMBl8GIXYNrs3DFtXgm\n",
-       "NA3bvLf8iDELXTqVxf+z9ON/ejfpFMzzInoXSbDrK2snddpvH12cY0TvG+CSyEuyNj3eXOINaWu2\n",
-       "h8v9sA2av9s5rhxvC8RKzio2joPqmil6u96tVhkJZk+opPgIxBL5h6QFs9DSmoJHdxj5JKmBLzRd\n",
-       "zP9P4vj1hMtzlf7ZSmvQ6ioCQAANdo4Au6j3OMHESpPsLTcuP6ClWHO52JVEXHCOiIj976h651Ff\n",
-       "Nx8kytfADpy8ExM/V9YFC9816kR01aPFoE4XsM789BrmV4sc7UgvwiYv4nFTI0qS3yBWs2zGObbm\n",
-       "VkTS+YXanVaOpbap525DrvVcBssNwZ3aZk/zaDDGkAaJIT4xY9TfOCukBgwSwY1u3rYD92nE8rpQ\n",
-       "XSbqg8fng8kcO5LSgv6597hCcN2BijgxfueLzNV45iOeHiiJBzxENhqi9SNFYcSeRzwhDoAjOjKC\n",
-       "j00XLUvuLD7faSVjwmhM0vVcyR751yep0IMKu4payWF6+7ZIqZJXQbF1YcytO8qa3C95h+hnQYjp\n",
-       "RWKOiVIntgwbd+qp6btZMpaPOv4GuftdnYYyHi4KomgxH8cbl3TbP8SeRBS+jSLrkIWHFevZFvxq\n",
-       "6rl51YwFowx8VAfLJGELBCLAAAEgUHPmCfUdGmzNkTAfESLjY6j/XqW/3/pb0YIl8BLV70VBMfLF\n",
-       "wo4YTX2fYdw+pSFQwYm4vX70JECymh5t+A7o0OoIK+lwRloaC8LO0q3UYuvOraoSvdRzAc+bMwrt\n",
-       "6MAR9L9vuNVHiQ9f1b9vGKCkKo5mRcfHmoYMt70XH95r8hhYcMybnzl33oudtH/RmY+MLPAx5ep2\n",
-       "AhUAt11XhK2T73HymlPzTNE4DzJrAjj0j8EvqU3+tgItWm6/jEpfEEe4iZxJnI8FHzGeMvEB+sZp\n",
-       "ySqNVKf/5CuXhmQ01P6uIqS9ixuaiBrLsIRiKf9SbgCmOpkMNftUYWMpX51e01ETWfDR0nJqQMBD\n",
-       "IEbjYyqHQzH+r57xso1S/ThytetW2bMikGCDzk6eP3LZJUT2Q6a7+YH58lycZuRlby1GMxEAhx38\n",
-       "D62rPa76MzQWpffL0z54mWNaVFqbiBiBhtXo426HNSI4pm7fJFm5Aqm3wrT+Bn5FuMDJD4RLktL6\n",
-       "STgXF73nWGQFfD7ZdveHe97P0WJmP25WLk69rETBJ08A1VCw+Y3tJpD2viNgjiMOcWBjGYgG5zPx\n",
-       "eEWwkp1hC6g12ywhpmJ1gPmAPASH2mp2SmMEmqbUPXz8L/UVlMDenqeQDiHJoubyGgPeZA8IUmEV\n",
-       "gVLwvSJAXB/uIGDOR9l2LDf7R+IBMT6bTX8AsK1wx7FvEtR9F/oV9PX+I6o0HzDdCsI7aoNuCjQo\n",
-       "U3VGcZ7sl987zeEN/xA0FqT/CSQtccQVKZi24rN4mM24nN/5SrmyE/c4eSK5tMy4VgP8rsGzpPhT\n",
-       "mjjRlsYXxw6eI5Loco+EVR25f5zrLx74yxTF7ivD7gAV+bGJI/BQDJVV+cLCY3Q82LFGaPOibTJg\n",
-       "GZ67peSv3xWaowNRhX9UzlTM43d0QJyiy3RtE/AOawN7LhY+liTqDwEZL7vZKYipRfiAuM7DCBZ6\n",
-       "X5HArpNhwLleZGoFW0GYITpGHsD357E2hSCbY6G4KYbg/lt3ojJ1Q5AVq5oI5+4AXzfELkd0m7up\n",
-       "zw5++tkMKC1VPRo97I5bg2x6qmY3sKGEMaAht+p7tJLPJtBFk58kXg4JVRTf1mxuNXCWpNrR63fQ\n",
-       "/WbUQyut5uYuMAh59wrn7ZySf0Zwj9dS+bHg3TgnTu2tBoptuJcsLexY9njwwZT0Dp3mWaUoTJI1\n",
-       "Z0YUbQWefS/qhw2jRfEiZfkEM512tLprUaMlVDkkNASHrAcgLvnhHjU4QWN3jzdSQuklzgvX2Vvq\n",
-       "WcKHB8rTUUlAPQkeAzBDarkSZKIazxsLVWY2ZRaN71QXTBsYuQpmkcacTq4dQ2q6DGH34aVQzael\n",
-       "DigZpn2ct/Z1i2zY+ebQKVJWAFqMVZv/qHi3sjFC0/eYyUsw0CrnU5S06er10FRMjc450gO6f+HO\n",
-       "WFV/d/K20rMEg7RoLqJT74CprIbJUjI6xy0I+CGieRpwpQ2+3ACcKeQ0qVOAyYuNzVPC/H9GG7LA\n",
-       "w9pnd3REim8tCSX3p8ALvSJ+oBk0ILqmAAAVnE+33B88yoWUYaTY31lkn7BVVmGL4OEQEqx47rhu\n",
-       "uaIMd/E8Pis7mzVj4NX8WQqgWB5BjzUTt3w0djrEuljuUk3ITaqPxLBEsBFMbtZ7h2Mq8jSWGVPf\n",
-       "QPkfDbxf5rzzBTwQ68NTsTvewigRzSk516+hGVue7lefog6104hSek4RGxXAspQOkb+b/xHXi38Y\n",
-       "bX7/cCVH19NJvTrDboax74Aw4Jpgacz9ajYQ8qdoaq+3OvTLAd8SLmnoGJDOQPnl4lznRNFsV3S9\n",
-       "6vEXNHmM8+t7n9cft9pWITWRLWc7Mxx7HaMKGZMXWY1mMYsS/Nt1/mHPKA1YpofVpwOVwXoV9ch1\n",
-       "TZgTDbRWV+SQXI9SVQWmQEVAITbzuj9hAAALmQGfL2pC3+prWxQSvW7CluZkBkGxJVXydlpCkNQ0\n",
-       "pCBuo0dmMggj3xkAEmZDIenLGaorbF1rJsw6byd21dnlohPAvjZ1N01BbtJ1Y8rzNWff68xUPJHR\n",
-       "MpOA3cL+h8KCB9oRyXPNDSSXkiRUaKsiOP+Z5nDVHzqB+DfYO5GPaE4hAZrVbrg7Te3QoBm0WlMJ\n",
-       "hD96Gu9VkdUYCkYd7ygaEX1RfMMM3I5KVC22jptWp3NjvyU2VVE9V8y0Xb5CWSfb1m/EheLVOWHE\n",
-       "5gjlFvFWLTu4MEn+v2SuKJKUyAZEqUY8NawchFE3JCa7L10CYha5icXwx2AAHSd8iGyAF4rdqrF0\n",
-       "Y1Jjo1t1qS53Mdkf4eQDx+P/bpOh7YXcKDJUeapp+lMZIFnA669OWXsgEiv5b6EuDJxZ0A6L9W5y\n",
-       "cVl9UA3TOqFEG9Ppw28DvpjV4zAg8JwL65FZqmeygx5GtNqMCMK5jwwEoFH5X2jTLZbBAXsedJxu\n",
-       "4UYR37YpHBY5rCyzcKKhH+o14wBfJwQQzJMd/MiJkWMJgaiGZ0FATUh/8b+AJbH9DdjkmyGwR5mh\n",
-       "sZ5oy0f3TLa9Yo9n+bTufEaVS9iNYsrDDTFPSjzJoctC1qV5/8854Go/L66DuUO5DLf49RBAVq7m\n",
-       "kwMUSpA6vYaK2H606agSDqSK81c6iGXZF8bJ7N2QqRLJ4c566UpVqy2jMrl2/AW94mai9FusxcAf\n",
-       "mwfjknkhxiaDTs85wMmszj6JyvnCGj5z6PrQx3fr6Bo/9eJCj9ZbVu1qDl5FQYFFf7mRkkYXRHft\n",
-       "AzXy1uU2w0RseBLegzYnHDhrLj8Y19iWHDDMWMxkQiUrjUU06lCWHQ0KPzd7g3zoGG5tizN2B6YN\n",
-       "fhxj438IhUa/H+O8U2Td2592XYRJ14NQAmR0xiFo/q3DZ+wwmG+F1ADd9Vt+cgYwmNzO3eKhw0IX\n",
-       "rxWG1yf5uzkI9iNNwWpGY13GXiaadkajDE1K5TehgnwOT4+K1WnJE5kDXAsgKUEHAVo+Y90HPs4I\n",
-       "wEMKYwSP6sT5zNlsD75C57sqcmtNy2k7C94B5ZeV9rB3LtO5NQCsXphbPI7IUQROB9OY8SjVIXwk\n",
-       "bgIO8QS9SvdCOnvO6Wa9fIasHC8mdlsAoR2WoUAaYYlWy0KpCHETwbLpzqAfJiMkVh9oO3/kTEHA\n",
-       "p/3DcnkA0JB2UKQGZ+BctGTbmYqcAAB5ZCc5U1O0+UIg7yMoN3ZnUpLVmDQoQY7OW+q0eC1kLLAJ\n",
-       "vEAICW3wI2gs0p7bSMz6swEGkMcAhVMglGatWUjtFb2ccsqjTQJQsVVKFcFHtLrZ1ypW9Q7FVntB\n",
-       "Rvt9rWohwHDCS/81qpic2PyetHeDYa3rsaQoiSM/A+VrZ3fKnGAsT21qOEUrocCTi5AzwXUBx1Ut\n",
-       "hFGc91EdvCh7KMJ05uWI3psR0Dm9uFyVv1J6XRwpcT01NybhIN1bbXWrjIuXM8hcOupbXQzRKeX9\n",
-       "nJCfiseyYNfxlroR30ABTTTmABp0AyWt3x1s+O9XSe6uzt9pMGqauTpegmriHrXhAj3+hgpbQcrI\n",
-       "xIUk+90tfBHTYfATHj/OpizyMi/cp8zlCE2/EfgKDC3AK7piEfiUc5w1Dq1bFC8zUYewI7dncSh7\n",
-       "1XrRvqNUKV+7H7xjf7TmcVrLOZOUP9Pf+/JpOnsNmPiSf1ngQfiP/MLGhpCyf8O4tB0cGCRGESm7\n",
-       "YJ2jmwUMjP8+bR0XvBl/Sxg9UlYG/oBEYqhwNT7Yj1FlzVHCKgoc9UsOngHMPE6S4W7qk2lUKBUe\n",
-       "N9bqC3twvRCD9uOQha0VM2rzu/eCrbYnDG2tIr483UwaEUOz/648niYfuIeGs9Ay3quxo1bPy/zJ\n",
-       "rQ8aYLTYDaoko1MnrU1on6nA5+3fPZQq4tx9mCVdsDwYk86Y5UDome8oqNFf73RZAecSbjwoOh5D\n",
-       "U7bhfADCJAF7WsLXjHarGsI6cHS1GzsiBRn8RRi6POpLhsznS9c5QVP+ys7rj4LeMqCugHA9m1+l\n",
-       "6ml60LDfJ+p7dxUWkE6difvGTsdsws6bmYjqTNFq72w3c+N+M8nO+6OAi8yWgDL5Za0ieMju0cDx\n",
-       "bN6VcdOKS0U1zRdhoU48UvXrucqFyZAQkTwqs4tzsQP0ezEOZumOqW0nQTwAL9kww/9N9l8fR/LW\n",
-       "G3r9LJ4bfkDNAzQJ0S6UDM2TrFqJMzJTA5O/i4H6VTnji2USKdihSBh+vWKIESvDK45DIAdyYvJB\n",
-       "T+deq+LPX33UIdUfMBU/oYCyFoBV5CpoYOtPKH9Z55802FkJHjN94glP6Ftj85eh5xRdnYc2VPTm\n",
-       "zsUjwBQZzjBDmMkD04YRKh3FUO78W6aCsT9c9yugeF7VvTgVc0Z9FNLHYNQG+LbJGz+4kaNXqQDb\n",
-       "FzEARqMzA28BmI7+yMUO6F595N61+b68eBKGbG0Ok4t0vLHVf0E5k6pESlZMq61qvvWiBElBeXLA\n",
-       "IjRd+d040SlcSp3Jwlo6dUTiVKuSzIKWn5G+btuWQzR27WiF49f5Gr/79nbyTYqNhvTH9Cwzyu4D\n",
-       "9StuzQmYw8b6lV6XzI8xFnTfb7saYXztcoVZmnKeCNUYAS5Xwlg4IBVDeDwG9JnvgZN3uBL6typb\n",
-       "hkBb6eFwpfCxeOLNJd+Ar50q3SCg+ilTCrkN0oDDZRYG5Xc+dpQngoLlzV3gnz/I0f3x6OdWzblF\n",
-       "nhaWoBZYj9fBgSyabiFQ4M6HjJ+Wtnf1rLdL+e6OC79lrEU1PpvshnCJF4qYpdCJmEnggU8kYIRB\n",
-       "Higenjfsk9YfR9ml9fxF1bhr/y0EtM29cf5WRtpSzMdeChTRwPHzNmPbUcBQZA+FmwWqX7aRrFZR\n",
-       "/gKs/4v9SKXypeBP1oEkCVL/LbRH2afZ/+skk4NjdpVN3kg3e1Sf5DKIse3AxlPMisiY/+ZMLpKx\n",
-       "FCPNZm17ScJWFxtxklD+1hRzp8VE7Iw9oYLRU7LW3eYFG8RuYhRuQtzBWhEhxWTfuJ8hADES5wX/\n",
-       "BF/GdExfgTTcV2Gz6P0su+LkcZ1MPvRYvK9whSJzu8CxbcdQAO4FRII7b4cv0oiP0tgEYBUOXkZJ\n",
-       "Sss3OL5I/Po3+cbXjYBACPDjV+F4LQEgL6cK24csRlh7ECAA02wVEHagB38zMfEvv6LsWVLIzpK6\n",
-       "KS/dnF8dMoKqI15UyVp77IzZsn+/GueDi0x/anCt5LGqDihoCU1tDb0o2lFFebFTapSO8m6ZRRLZ\n",
-       "cX97Xxy7MFSNG0/KHgqL8SEm+FjsvTdrDX0hMXPfukcavCVssKPEeuZiU7vZc2PekpkFCqWmtPQ0\n",
-       "PABUneO4XRaFpAoe/+IWmC0EI8Oeeg5bfubmVvAKhMipX7fWNAdaxT2KabmjPQCJ/gSFvgrtj3pL\n",
-       "eSF3mhE9qPpPP1DACX+rVwhrmmMu3bl/PLvgtJeJpLf6hcPlQ/4N9KfIfeBxgt+uKZyhvE+anLty\n",
-       "0Rf25u87plAGhOMA97xwGNf/KGovtMXyDO+Jy7qt0pRBVgUE6FJBqWXkZ6TVvc4Av6SCFsvtq3Ja\n",
-       "qcG+3ISfeYIqsCG/VNd4eWOgfYprteNY6VyGqkSU6+uMI8B/VS1tERzORlCWFuapXirDTWWbGYOC\n",
-       "nfcLxMSjo7398pPn6Y88+ObHoisiFuN7/+e8cTe9sl8lax9XqEUs1A69tcluyvUXbXRWwIs9MSlI\n",
-       "GAQ8x48MxXFNqBBvPp96GbzqCoXOg63R2J2mSoOv9Ucdxhl3/fhZB7vuji4j2rA9JMPHqVo+wPN4\n",
-       "uRiiNDFGUwuQAB1GhaTORPdDZk2pxIeO/r09pFxTC9fQEffzY8bMge0slh1sfutgM1X+mgUIxUPb\n",
-       "mqmo3ego8PPA+4+IMdpGqTm0JkraiIiOfGBRAdvn5gua+9NUsxQCoNEHNMQiilT53Vvovezu+rVq\n",
-       "ccdSnIQP6CnuTnDiW92n+VVSL7KHuqYXBNwJneNZc6HRAAAS/EGbNEmoQWyZTAm/hRlPXS8FXy+O\n",
-       "SbOgXuuIahs0wrg4dqgtu/e3Gs1AAMgG97VTkt4Z6yd926TZrWr5BuyOekcO2qH2PUU3AhpD497F\n",
-       "321RrvvlN8EjhIHLCzlZqmWLcvAHbFtQpQMyGFrVEsO3fq0QfigGNXWnlj/TLvlAo4BrlaL59Hih\n",
-       "8by8V8CqzcLr+LkkOz5UjdZT3kTAFDEMknJVOR69agte4yre2xVWhsl0Hh8g//uGSMcoEIWDvWH9\n",
-       "fC/Oyj+3B7ROUWQPmDrDVVela91Es9KMqhQYFrKHU74Lr6N8CJzTk54hTB1tKfRMZ8jKsjcIFaao\n",
-       "XpLtEx8GN+u7ZrvLw0MpQUs66VS0XjABki8FDK9RHfOLuNVmS3SlZ64Efl3qX1NBWH2fvcu9o0XP\n",
-       "XGSe1j/6S3nCupaTloSc7dCGLrDTLNFU9HAwrkhJMscxYe1HmkS2RdU5H5ZMgXp7GGUcfbUPpseu\n",
-       "cQzE+IN1DkUdcxNzH6csxAG6yt2Nu+xx5Kyf2XsO3fGZU9YGm0ArqigBM/pM1bgZ6uZ1plkNZ5h2\n",
-       "W17tIubrP8Ma5DGxBBhMHgBGzoN8CRbA9QGa19urz2LzKajKI+zhavNRtQL9MpsJlzgCXiu43c5e\n",
-       "A2qID0no7u7lkIKiCHgMbmDwsmRNhS/yxbK9bwse5XgTSgFBsXei9oga5uRin56ejHHP3L5YhYiZ\n",
-       "NtHQb17gPPm3UDAWIStzmGreV2gqoOgLx+N8E2cTJgpl1g8EUXJxDsTzr27t47tiVlBYhcje0UPN\n",
-       "/isEDe74Y8xHe1760gHH348NDCSHbwJWkDmWFwWIQZ3R+5h0d+yUBQU+S5s8sO11/PHOwWyHhilu\n",
-       "J58yBcOS8n9Vh2oM+pMJLFPmsBQrZhlJ6sqonS+wieu8vpDjFK3byetEw+nopGuTm/VrzpQ/SkR0\n",
-       "b2OMYegIBbTNAWh/C9JfaIg0oc48KtYyGCMVZNrqlrKp9YiEWEmSc5oXBvjbiMZc6ELqYCMwO//5\n",
-       "ZJRN+z5267tg1I6r051mo3rVZ1EgJTNicgm9uwv1iGMPcWiNd3v5EjoAVcsWAI3ZEhBddxHIZi2C\n",
-       "Bmq0OGdfDIMvkTr5bAnYubCVqRhQOKzfPE5rMMydgtwvJDOzzk4WliIoPrRrwigma/I2gRIOfIBI\n",
-       "Jgg0AL1jARkTiCoMmPnVv9NZbh3zdUJiyvJOYEofrP8vsGmZN4Mmdd9seRQJJamFxDxpIjjL7MGz\n",
-       "ksck2TEVO+aGZ6V5H4SSuq2zspBuuz7rZMcXdCjoIg4Fy1oStRc59fVLdIsFbOpguVGaHPs4q2B1\n",
-       "aKi55vtdaFLA5dnedUTSkK68FC72s0kfbQIFSp2k2YZF4VIWkWvL0AAAKiYiuRZRuldwgPmo53wY\n",
-       "kKl6HLJQJ6goYBlXwA7F6OrbjecDtY6owgfgVos4uG3TTXf6M6o4iXx/hJqZVoghkp7OubFw+M1G\n",
-       "wEPrxQlCbLwyxtJE/d7IC2CQiboosBu0r9YlJPoXgwQ2lH8J16g0288fYpdhfb+EP80qTQC7wjJO\n",
-       "NHQ2rmT/HM8kJlRDL7/8UprVmP0AAGc1rafhsjGBQ4qiO+HvawGcvCumDmD765fyxUzwrbw6jHE6\n",
-       "6rmwZSWZWrWv1c8MgCgbYD/pYc8h2GgKbnPNG1Rng8a2CfccvqBOHHUkyluOb4cnX2CeWUJDsXiS\n",
-       "icox4j5+jOMaDmTjc/3Mi9yhdc2VD42eNpcQvpIHPNbrNdlKnXxci8f+PkV9i+Yzax1f6eoA/qdQ\n",
-       "21qnmp9MmX5iuRdHetSwQ8o5OkHwMGwhIqXn0aWvmnsMQrkocIRKwCImVVGHHBRDrZkfSWRCrs8h\n",
-       "uO7vishGCxxKwE1OSvYKVNbsy1VOC1YuBUSdPuoRhuXrEzoZvo6I3Nfma8gU3O/ZWgl7rfOpRxrT\n",
-       "bghmUcLoE89+k0pHEJQSi/4h9ZsE/eVVOHeeigkFtGCz7NadfyadlhOscKk8S0Lp0aAKPb9acqy8\n",
-       "jiWcLBwjWul1eWaQht+7vrU5+1QlMmc+aRNO/FmFX/pLD53n1BheLKQqc2YS3VcM9DEd6UMh1G3a\n",
-       "dTF/+wVh0ux6WNpJ7OS/k8Qc3BczgRFNUqb/yQ5sKdTDgapYilP/fcF1Ek8PUEaD8F34Ftc1TAy/\n",
-       "g+jtUzOdmtPMYQTf/bSPeYlz2n3CFre+xznmppo7nay+GcpQV3PnK8Uf3U2HjEQjxI55D7rkK0CE\n",
-       "HDTJ0Eo4og6eSVrWfmdxhjt7tuHku3gz4K/eeJPDWOxnxod6lRs2+Ub6evM6TeoAXnIlP8HsCtGg\n",
-       "Bgc/1spT44PithXN4gMUv8uvVW/CCV0hWlKDPpJLabYY60oMn2tVBxYQq7iTB8dx/+bzZqverY9d\n",
-       "Ha11m4oWkcQIT9RdGMjuhMQ8nsP4mMEDt5S7euagUCxLybR1YGSSXGMrdvUnH3hvKMRH5Rfeu9vR\n",
-       "SdG7KG9pmKM+cIf+VBJCsyitmrWgQDjwjW91U5dFiX4CWx4jcz3Rho4IHcEoslCG7qOMyov7PzeD\n",
-       "edsuzjiQfvzVG93x29WeBSHzU7FZaUHLm+ZzsEKDaKLdSMYDTDuclSfjR5b5LlpH1HxNZqMJqRYI\n",
-       "6zRXkAtHAng9w1+5R+cQ69I+iflNYvVdmgbb9SobOXS2A09n9VAKCAtZJ69xPdmgVXcU55FPLVB0\n",
-       "tcAFK9gSe2mF/GoCoLrEigB1WvRGKOfEhd5fENG+Y3u+rYl2at13pvZBLfhJdn28S2Eq9ySZPf/U\n",
-       "J9Mds3PDf6N0DRAIGmVYvLM+CXTpWpJtyok+VBtHbU2lTvyX4m3pZePEYR/VerVPpWDxBJ6j6/DC\n",
-       "hF+luln/CoGPqG7id11UGWGx9a7bfatPnEDd56SP0jUP4lqswG/rGiLjUfUHk9w8K/gFsIvhNdJA\n",
-       "rkX9UyfRe/IbTUcD0U9JR0pHqEf6C9hMdSFeqU1sDpfd4NU6RDScQvlXGKHG4LWZV8Euf6tfYa9b\n",
-       "zHFfbh9Stp/sdqLgaPtvD8SmS5g1GKgsNOHXAQD+0TUtyAuRhRfkP9MwujCkxZfiCm0Ipi/31EiU\n",
-       "JQf7Cfk3be8QpmHKzndsANUL+n0T1i89+pAdCTbO0lf17QGuH1kN28UlptThwlKxX2il4mApAjJD\n",
-       "GUJRhOkXxSSsaEvTTx629HMz7ijG2Z2A4+mUUzuOlJsC1nAj5uqbgrLCN3VyzaANPQ8Fr9eIBr86\n",
-       "ZqFUhe6fP+7DbdcBexGFq/7X0Qp4PXVlgx9NQzHaOW+Mn3JBHxbRsGLDJzqi/pHBo9O5OsUTvOC8\n",
-       "XsEohWBo3LnMU9xtAgVtD8Ne4vxFp2iQl6Mp1srYZlPB+8dRjtcIVucK1iWjx1647SipjuH0Xv7P\n",
-       "o1XPOzT/zByZ4zdxkH0L18syJFu5U4NUL89KDZDteMNeCRl9m4THcQHFac3OCiL70t6/CIjeJ2AG\n",
-       "f1ry301M2fY4tojOcxNmBQBdvBEFXN/NOmg3dklHgpubAAXtXxjQqSQB7VXhYsAtu3SyqUN3WdxT\n",
-       "Dn6WtCIiPxRvo4aAd4rNtMSb3BxfKEWz+/rtF43+TLjSINPxoC1XvGLO77/Rs7WaE685AZt9fs+R\n",
-       "sBxpJi0qKq/mkh4VJTEE76cjZTR8L6mH4DujqcnCTcnwDIpZk2ivwEk9/j8pO5w7DqaQ6GDuzoqQ\n",
-       "r++Xj+gWMVkyuT2P2SFuX7ur+1vcKHeWW0kAwNuF6PnackaqB3yBRBDtg8wSNewwfo/GMV3cK8Sz\n",
-       "fsChHnjrpAFH+010Vbn60NcpBYnPXAXLvUvEUczM690riejphLRMmb8uj4gL/VZ0+Zd54+J7OttR\n",
-       "i45/j0yDXZaz+jS7AMJClnzc5TrAL6FIqkTXS84mJHpBYzN5ZREgvPO9e/V0Vn//5Oih1Gknppha\n",
-       "bUhRfhBsjZklaT4EJj/+tRHlgOt8HxHzYeGQlBxpDDVjydgXb+Cx5G+fW10O+71ioEzc3/F8v4H/\n",
-       "iV/QYOPH3sRhjkyvZP/poTzzPfupQELpDQK+mTUqN6E1ErNUsxgSC4jO0xtpUBz5z+7g1DyEfpBO\n",
-       "IGycCEOT6O5C8MxamtO3JCzprM72mMJKvuizRK0yDcfAyRd/cV3MmnJS6C2Q2U0ai5EKQSk+kPea\n",
-       "QaQmKRCa86fIzlJCz5m90qB+mxZnoQR4uOgy239bg/D7K+oaSJZAR6olCDGjUxqrftA8I1HmZq/2\n",
-       "hRgEKVN2mxaBHaOFrovVS8dLhOoIMzBJr9DewP5hywkUHXeIEVS7uGLvVkGeR8TYOnQUc1En+ptM\n",
-       "amLsx4c64IOds6vMl7geFGz7coqeQh1OkA2lUl//RI1IEYXSqW1LwQQA/jpwukV+oYTrvjam1eVg\n",
-       "JewHmVf4JssN31J1yzzg5L1BuNbAT76HJ/EypSm5tjzsrKj96I52UZ01k5nmF7JuYXKfmPyo0p/z\n",
-       "ozgfYWVSyKezULSECTLLXLrytuASqqXU2toVL0p3yyqhi/0uJtYkGv1J/TyuUlufEJyhIZdb0i4Y\n",
-       "hDoXulTGMoOEehA3/QHU1IFZVn9cu6m3VGkfw4wSLMxhyB2dImsBpikWUqlhs23T8kWX5oDt0cBT\n",
-       "STTrnmPRSaWUY2hb/Xk/KocCqyNbH8+4ne7sEpYkDXhphqm3IvO71vee2mTfUAhHJuWVOp97XH5j\n",
-       "Hov/zmYrSI8RV7PLKFPkpv2acJE5oVLzMtpbVarkPcNoreZTIX3+kDVgwEieA+Nm2qevpKnPKuKh\n",
-       "UcMX2FpkXIV9Px6uvkESj5+venyEm/XeFJnktPU6y+iZ6TQ3v7hIaEX3X2eAekDfd8SgUghFdkAG\n",
-       "5qsRSjD1rFfkflYPuh0/pm/Jrbx4H6vyjjKyMEpocFS57TRxia3WcAC+u95il5Kpuih3LuH0ICMc\n",
-       "/eiYb/MS3qJiAIKVKrBh1BkyDfzL0df2p43u/W2tKJVvvKDJp+AlBBqHgwqgsYUa+U5u2ACmFnkB\n",
-       "vHX7BdR3ErK7IIbUvjsHN/BpqmM8dZIAfdvkEPYp/CJGHwEwh8B1a4zFj27fc1Brw+KwI+ADlcu5\n",
-       "kP/JV3016e65tzv8WpTCXbfiBXaMYhRoMql1nOd9JgByFGxkjCgtreqYM87gE6ZH7QSrUahbQoqp\n",
-       "dJ711hsgvu/neY7oN0U3T9ofrnkBs7oZnwbIa82s4LDumIbSu7dxT7NSZpNlTGHy26Y0ygKNCA1I\n",
-       "eIU+aJYOBwlQ1IkaywAZDPd8vwv+NLE9N3V5246AGFh9VixNIIJzKGWJ38TQ5HjrCuxRNbigyr7P\n",
-       "0Zjtnt360GV6PyolXppzCM2Wmk6LTwNWHipjjiw/XKf0HJZZBmmodYM3/esVKa6LvDPl59g7zd4Q\n",
-       "yape/cpPeApZtGN9eGZpwv776+H5GtrqxJCf9UAiYet1QGR9U4pcbRoz4ckNoLLnkZSHp0Z0jsB8\n",
-       "73tf7uOH45Fy4gUXQ9vHKUp4TwwYvK98nO987apXZbLdvaboY0onzTaazfx5VOGxTZcswYudigBC\n",
-       "mgcpJu6qrxKPULP3UWLJLqNTRTNo2zDa8k45x10kEzbkS+nD4KHOsbogR8JOZ/dP7MHSF2qyEzHj\n",
-       "39t99sDr+H4MUlRObM8rf5edH2O7nELqjmWjhZfNIj414su1X8zPpDJCACk7+WYSqUfHS4av08yT\n",
-       "APuRAJOcbkZzt8cDRjbI3e12SDgddBP9EOUzgODVwv7Aa5B+1AErnTsP1fnU6UIdsppruOKEVWG1\n",
-       "MA7qkMSVBbpYIUQ24JA/O2H3S9pqKL2LVvNLGuqL1rFNvBa/kt/AYp8s9mSHgi6LqnJVJl8bihiK\n",
-       "Z6NXooH6ZPl3i6crfqIuth5bn860VkAi+bSS0NJ5WNwftWOXFCUf9jEx7OMI9n+6zpTNk4RsNgPF\n",
-       "j7gNBQmSvUcRQUcT78vEmmEyEgVyMZi6giR1tze20xYWYgV2BQSNivYfGJER7qX34jC6G/yAKpxl\n",
-       "JzXkKA7DAxFfMDi1mfwHcQjyBNK9mFd0R2zF/lZOeycFPH2SsJEza6weD7WE/PlwFKMcetvXl04r\n",
-       "4iOmGogK+t3tUi7I8ecYtpaWQu2Wu3qpGFCRQLYGuAhLcAZEjHRy0D9W4kPwR7Tv+7SDpU13nSXl\n",
-       "AWFax3tLwDhWAGVw7dYjBv4FRB5WxoAkGqWYQ2ojpe9W2ZqihUexbv7Ayi4ZxVVGKlOrO6KRhVHu\n",
-       "iPkw7HihU/Z30xfCMNwstM9nWAOSRCP3Ugc8xvruut2m3iSLzs5SpvebmB6zUJxeVNodHb7u9QNy\n",
-       "h1LcWv67M/fPRKqtQOLV+40VWmc0LTygU5ali4j9BsYfRjqj90tyPXcU0m3zBW557+v1/2twqDrv\n",
-       "cBJD4y6BEuCun8I42NJhvUK5SlTd9DdgtL2swQtwQTnTbxG2WuneV4Zqw5FYjhmEDYvmhdsOVwsg\n",
-       "61oej/yB4caFh1RQm/UPv03BpyfYESsgHWcCs07dg7lWMNynMv6Raw1uhlcGgKX9hftf7gAADIlB\n",
-       "n1JFFSwl/9uZSoKNzrd8Mw8VHrnYOeYaV7LfPSnRYMAJeB98yU8jF8gbDvBsR8UVdw+JFn8EbdC3\n",
-       "F/67S+vMXfjOX7bVApk6Zl2vlD/ccOpi40Mp7/YitXCpx5ceStSznMXavCaicn0LaB/94DfV99Vz\n",
-       "br1WmP/jMUGJP+dmq9Pj6+t6v/r8bES8F9jyS9hllv7AeHKpUOA3NdjXh71BDDr+Wtzcfo716cP2\n",
-       "qiqE04I34pApjIxCmNgXkJYa3Ig8GWAkMEvgpxQeqYExBLqFjFvBZq3GCoJ+6Z7doWQAAmKCurBI\n",
-       "4XrO7kfTzQOJTQeNVQWnCjhvYZqr8fK5l/MoQ32yoajidD8QiSdrdMAcpYcpHZsZlK+YAINBJ71+\n",
-       "RDMMcNCVlJDKootL+K9ZL+q2AaWn3GOhC6mMy4eB4OwjXRy6BBPSKuEnTfl6UYgUM92l/0aSBDUa\n",
-       "yttTcd3Ip4X4JAPPPwY9JZN7pe6k3OtAQtGyZ9KisY0Hx0ig5lUUU2GskNCvyF9AyLYG/BLpWaz2\n",
-       "MNttSaNxLd4hbtPja4MAFfa7+a5iIyt6siebt/pQ8dDW8EMe9rTgjqHLoPq6qDy0+3D7MLe0wx57\n",
-       "vqqZ5gmQUFmZLICgcRbOG06F9VG6GlS3Sn690LZFP+x5gb9dE9lQrVs0EMZWoBUyUCk6hFh1vzxe\n",
-       "ONu94udTvRThPByRhpmffzqVfGmkLrXui2qTPnRCv24nvVhEtDnQeiXdWvFrWGWc1pwm2HxwnuU3\n",
-       "EIOcUUyzKG9rXzlj3brANLqU6ZFY8OZjiuj9G0/WCaDmyOaNmD/rU9faxORC+wSrSBjXiXwyfzsx\n",
-       "8o2ZVWV4B0sNOHqeX0XyN1o9q6TvE3HxnHIfOBpsr/oAt1T/75T07uZq45LTf6b5PTyXsPXpnfgj\n",
-       "g63W13Daoxabs9NsdWaaHbQkQV2qtQVl4RdvcM/0HXm5+pvvKN44d4j6n6KZRFWgnjeDbeqqWuyg\n",
-       "a9nuWI3JG3h3BcsmnnKcYfg76ElA9s56mEXjdp4YG8JyyS+Qr/b1YoXkSKUEk+T8OqLjEv/n+o9O\n",
-       "MSA/AaoGeZRiHfQ/7/w59WL7hfPtM1UrMra0rCRHdv+pdqYfzP+Tiu+hg2tI3ZwF1dt70IMPAZUQ\n",
-       "JjtfTyXi9a6Fe/ro/dgVB/ZeDmk/A/Rlraq0N+1vEbu9zRk6QgEfaNAzHzZTLYDsrDWFGJTSRFeB\n",
-       "cps0EEHFppag8jClv8j5ujppFa99G248ASaMIXnuRaLQXeg775Xn49t5QzS/79mChShQ1iPFGn24\n",
-       "dgoY1Cuz9GseEgcgb5FeTb6qhQyDPm0aL18NgRuryLu3E2k6HAX3aEj7urApPI/Ii3EECd8vP61h\n",
-       "OZ5E1WroOjWBAxOHDel0AAVjlQn0nNVFXjoUET4HaQgwoCyhfmEfXA8vETxZljc0VVyZwVfQWjB6\n",
-       "yCMVC6/9u4dCYAnaPbosSkgG3shQZsOlWiea/EZ2HkfhZlERQsbnvxRB4EymRj+n8GlrlkMUjSys\n",
-       "1aoGyPOYu+nXUKeQJfUu4TU0C8moca/XnmGtjfT7Wl44t7EFrk0Pv3XKU6bCC4SkTkuaiqy2OhYO\n",
-       "BAG+BtwMgd+spDhBps+SvG9G9wqb1nq+G50v8053K4CTD8qvUj3b8pfsDJezs4OoY4fTF2Q6EbbB\n",
-       "HL4u3/AVABmllFWdjbMNYE0VGfHOCep8IFzzb0nlmb/HJiQvQUW3qGM1IJiN18bhtiPvG66CKhdS\n",
-       "sV9CSb9wdESz96uFwDbU/lb9qXNdPYVwRYPRFU/6rLH35EI42Ky7Ics5VRR7eKZjBQ/BBitVltKk\n",
-       "18Zl7mtfqMABBpk0JPksRuJxejLdE9TQZUp9IHl2814ZAQg6Ldgr8O6JEc+t8D4Sp4DB7NaVxE2s\n",
-       "zF1uwPNtH1Tr9Wlo43KsC8gCg/p6krO+vIDF9B9F8jaPyoHwgI/OoK5dH/EV69R93Cz2fgPmr//K\n",
-       "GnT2Gl7I979IN1ziSIwLqRNprdHhEibgsc3LTWCgaXSeVtK+FMvrt7oj+9iB5lyaR7ytOKlY+I/M\n",
-       "Xvs8Hb5zaS47vDb7KxtKGOSp6Zr7Blanei4oDAL+eUSuu/rO9PtHMqWnwhjnf0F0h2xyjCY9uTe6\n",
-       "Nobky0tQfDTG0fRPscas/qdpohfnMOhsmtX9aItZ7KWebZ45ULzZEkITkXVLS6+6ouIPeg785EXI\n",
-       "ShRoQwwUjyLHQzO1bQnoydbRF8CPJHxbdRh9Hnj7MPi/lYEardzf2I2qa609FoBOJBDHlPgwQWlP\n",
-       "iYhFmxYB/TrE1IqbgeVEwwf/Yt33URfsd1JpuS1gAiONacuih3k3WYRJWITQjFscwi6to/nc0dQB\n",
-       "SGcpPAAHGESaYcLxlluExtu4fJbhLGm2RaGpfLQRFU/jgyTFzY2Cipwjd7YA0wUL/yBESkCegaX3\n",
-       "joibXQAmsDM4TJdiCUupivDA58KplYPz/63FMk4za4b8jLSjKRY/Vc+Jo9D6c8jfxqMXv5d+uUHS\n",
-       "YmpZvbzUm04q4/oUjINb9AAaPE1cXa25xjrBrir35yqH4UTn//8V75fYOe9GeYcYbPsNMiYOz/5N\n",
-       "Gr9PJ/dF9AaEIRNrjoGgKjA1g0/LNwLVbPO1Fn3EQlXtCh2JNWN76LfHPkxFykBTQV9aJuE9/wLF\n",
-       "OaziDvaipLYN0OjqiLZnsgPxm3wuEQsd9wceTsjCg+GM1i5XU9n/3P9TowiTzWs7bXGEfjpe9h9x\n",
-       "9/AHINr1nI18AiIODO0ucJnIAuqddpTguplFlxzPapG+oRBm+xZfAhvwkC5hFuPCfkHuKZn9mbq0\n",
-       "EnZ+3WL3sgc6z/2jGahpCu+mNES1qfX3G7uzl2i1iTNf+0joC/UnSAtE/j9SDENMHpipmHu/6zjE\n",
-       "T7vmI3A2eUO77K9OXFx8lLHOMEMXXLVTc/phzTfCaZZ/KvtxUCw7v3xPcFl9j9mMKoHOb3qPKCsx\n",
-       "E+dEPQ7AamEeUxzMeTCxkB8KsGpB+obf+JpWUitS0t3LVRjr3YCkyMGe/2pUc8Mlvx91Z40lJUoO\n",
-       "9KJui3A5NFAUOSpN1Up4+DMOrxzYw2mAV/7IGFpPjQmAwtHUqeKSvtNQcNcZAAasi4yQMuu4hofY\n",
-       "O2bkfUG62/R94nafrt8fwo4ANQZgXDnYFnFsBVw2IY6xk3I1inR80ipxEZpgX296eNPu3dp1NoNI\n",
-       "uQfKPhvaqa6I55+I1afJ8wMpg6qutvsYUKNvpveJi+BXhyZsRELlJbEbUzJn0WyScypWDVN+3xZV\n",
-       "EknuEBdttdx6cwkJdISLGII726aqdd6lH/Y5SxoQudXROiBw9olh3GrSxo+rXTw8iFQ4r/rPL1mN\n",
-       "tgxOnDK6Zq6rD7zVduOSGALj8tMaRqRg0jLPmDLvtEovcoAWWzDsljUAw4M3MdfM6n+JZyT0smdb\n",
-       "8YBg5DPKc9X6aJAgD+F3Cq4V/Q0BvSlzvgHvsyFrZ4eu/DRJ1iGVDsPvbDgVRxkMpq738cePmua4\n",
-       "MnhUPWvEA4dPrR9pV6VviVcOOeLFnEPiJ0TEClSRFg5KVMuNBq5UE8F0ZbnfLwiqNVgzY3Un4kYB\n",
-       "KJyke10mUuZt+2UfmeXIvnXyYYhvJSoa4UbaTksDZZZqme3P/+PZW+9rqf1VIvALoaCG0u5Qzxtm\n",
-       "raRevmhh3jk1t+WTNDSefNpwaNy4QoYPTVQGssOYItqRdIdExZIBUIOGL4XFVxBedAaPx9wjZs2b\n",
-       "q6R1ddhAm3fwjd6LalPyb5gtjo9ohylAomZ0VQwYVpAQOjReWamuv1hUBLxNy/beAxg3/rnaCsF6\n",
-       "Me1z41S62KOQldlZIT7EqzMAObQocHYQmOAwtlmSjj+2PvrPdIgHJQCP/pBsYtIYYjKlshHsxuik\n",
-       "ob84RaeNgiXYrPL2beIuS/v1CbRXzMippREOaMhWK3U7XGO3kZFS+tcMbwuIw3g5FAAchczKql7t\n",
-       "yroYoxGUxaFF3GmZ1Jf6cDm97yKs/GbuPR9/gi0LrU6zz50NnQ3Uf9i8v7vsoR6MTMOhfFbmtzPt\n",
-       "eqBPtQlTiP5dFZK04wlZ0DGdXqMT7D2b47K3ZEWkGeNAbyNsrHSfh4F9UUAqTXxOQ2t+mS59m5xe\n",
-       "hyoJ+0i7t4yJDfBobYtI/KE0RyfoFPXdVCTZ///4PSM7BWmRjZL19fEoIaSOrYWprrv2UtqRDApz\n",
-       "PB2ovqnBNtcu+hCvHpVlwgV3L18oziHhwaCUquwpSQiEqPHiDI4eVYOt241V+YqivGzi1ArSHIeg\n",
-       "3zzIuF7z4Am7GNHwUGA5YQAACNUBn3F0Qt+YKbp1ZXUhK0bL46Yb1Q3eWI+qyTi+TSTPPJKWmjIn\n",
-       "+BIl8saznT98oett3/uNCd7gvfGfp142B1KqieLRIOiZmBQ+kZawP8DI5j/6wTVxbKwWwubznSA9\n",
-       "AYWwSArQeuqeiTIPeaJY39IiUPx7363gnqpQisNWC3aOODqJCHs2Rz4vNbtuMC5kYi/oH6JeTAa2\n",
-       "6P5ai8gF9mv+yhFps18r3+BPjHNihl6RidxpJHGnokoDprPcJwfg2XB8N8iJFM9Spq8Uuqe0qJCZ\n",
-       "4bsFlZ3Iq48B1f4gneAXID/BdhY6tfc6lNXxQnmHXGGFgL5RQW64d2rBKgNzDaPHe0jIsh47qOH8\n",
-       "CawWXMG2HpDgNgwDjOXslmpPBwoi8XOzWfwN70Q1z4mOGDifi+mPgLtuhLJrvu4J9m5a54RsV1AH\n",
-       "8Rg5h8znL6zbYmlEZDJA6rDRjoyenL7PqeqGykxYwRHTWzfUGGzzPnU+O5+t/VoNC7bfm4bmHL8u\n",
-       "cYVqu4bC1fmTsRggwTr5LC1H+Pqgev3+f6dtbdELnszp868sSoTNwPS2ChRF43vbaul6PrALroSS\n",
-       "VT4X0QSW3tUgugSBz2sKttky3+idg5HrAeaFzYpYA3hdbrsZzO+OYYNCnp8PpTedhfVOees+SjCO\n",
-       "ts1hMVEBe+zUG/DvpdZRXjWLNkcwga6hLRGFCDKvtVW+zxNyCCX/8dq8EYYudKzuEXuqNK29a9ep\n",
-       "CCPkXwzaM32lm4JRb6WDZ40x5VJNvPn0iHlsoUiVUsfM1RP8VnkxRhmb+w/7aQns4ogNDojYq2ll\n",
-       "wFtPKECtrvI8CX+DsgraRRuzX5bHCwsO7zJb+Aa93JDZ1VDZt4mHqQSPYFU+S0f6Nv78N906019F\n",
-       "InatO9ve901CWSXJFj8HfexLc+aIzAUOc8UlUJaM6OaLoRjxqyUGA8naxJf3a0c8835l/FQblPjz\n",
-       "Z/SrNDk1sDylVu85U/Nma5CTBQVEvwtOuAnvI4K3WB7/d5KHNJSGLeMny9h8aKOGDKtJxbbbd60A\n",
-       "BgtT5vHgaHJn+1BJ9rbiAWC5YjFmbpFPoUKuA96+QEfuxlTCi2eZK4WpQ3MD6FPze8WItXyZpmUe\n",
-       "uTafXOnpyc7+jPAK/nZVit65pFn8uuZsdMIdq7VLqgyHyO2XOjEKZPqEudrn35TmOI7+otsXp7KO\n",
-       "mk9pncsEVaigZ2YQc2ZXwvybrAVrwyOO70YbUQ8WuSa6ES6ZP/RqnScj0MewP7iJnRB28dzomWcG\n",
-       "htVTm8VWvukg0Bj1kOPL7gWi26d7rOGQKH5YUTd9ZdqHYwXDLWemQc1WAAiDFGnkUFUDcb+T1sZv\n",
-       "/BGITqxYIxOMLBnbyp4Nu6yUb/8YE+Qr01MXANEAeRvEfXzVO0IcjOrF3B0Xh+NuFKEaSmQ3uMWQ\n",
-       "LElSBGvY0fuZs/BDo7wifWbki6MGrvO9mccUt7SJpw65kX7yDkKg1tUjWJY/xZh1JviweTQgQbrb\n",
-       "vaxe0n+ijh3n51CkL1q2Qcl52D9TUW2oBUNEfFF0t77CtbVoCEAAviYbipDKLdfn30/jyH67LOlW\n",
-       "k2ItHR7mHVxm0S70uAj7LBC6ZYENEnKNXrMF0rpgnKmDfO0/qcwRF/PjN/zNPo9PhPWfJcGAIYy3\n",
-       "aUCvzDGIo/SUVsS+SYBaKOwzNk2ZjBNlBCElRbeSSIQxEJ+1acZ8bCzRDnwoCGezrgspBcRN0QGw\n",
-       "EFtCGqsMLo4Y+B9wXveZ63/uGaHoUqtQc9H8pDDdXos1exghSA4y5+yfEe+/IL50pZAHOpBlxnxM\n",
-       "qB9Itao0hLIvMYOevGwNRJKbyEG6ahnqWJRqMVkO6EkNd7gM8UTq8tjA6L3/idywczWI0QaX+OX/\n",
-       "EORPnV1ByYvE/C6QQYX6p6nihJUgmeTouOwXtZ2ndLPievCRxaaEQUyJkM5P5kaQ9uwJ7JtwRcap\n",
-       "jvFxZlZSo/udBUiDHsEfIRKXIoIEjbqlDpJT8IbjNiaEgag+OhJcjJSzWwKNzsNkIpWYlGny5CT6\n",
-       "18KGX3ZGqb8S+YWRwU9scwePG1kGwY++pW0NztVfR8N+y9l8VUzgRyKUPcziNNhmw1kDuDZNokEY\n",
-       "b5kpRDO2NNYPN541KK4k9G6vcaNaTs6dd3MHrErc1RUMHqRyyYAku/qMWAa8Rl+Hh5sf3rIsjulm\n",
-       "PbMxOocP1iHtq6rZQqRofyzQkzqyZXDuMgGB48XmkzgqMAPrJPvDtG1kvpU2dfKySArb+mTm7rYZ\n",
-       "TGdFeIlu8aFqT5HNLLQdq7eh6JtDhkr36eEfgvmcsPJJO4fewhUP+z69kYopPXDmqF6NJrRu/iSp\n",
-       "ZBXvsfdn2q1xzhOSRKlPRFJU0mwtDXcl2OWaOCs7DnF0OQlbtTeGDCLDZZIjbOCp4m5E8XOGiWTu\n",
-       "9pCYLPJviuMqnN1QZX7tWOAzAMhbh+UsW14ALlUQUft1w/lSYItjcvjv0ny2vDg9jqyb7A1s02hA\n",
-       "gMpTazYcVn72Z66ycD7ob9xTuEIhqcxoCAKETvinnfKc48O06KyfLC+LeHpnPjk0I6CpmJ00/nr5\n",
-       "slNLVla82DjlWkbVtUXcIZTLsgAKf7+dTc4c0nzSUCPKqLXIWZZrPZTEBhkshy2u6sM9cxWSPKny\n",
-       "buiFdQGyFRrUnK/2XJctTjTzS0cs6IWbJfkNl1KCYrKea4KviWUrHZCbpRbJTggGMQrYV7buYt63\n",
-       "AKwpBEmvwZXVS1oSmoIAB4pL/uPdQ+y0Jw16H7UzsyquvSmtN3eQVJhXwBgKBf3+QJ8CUKCZAOlD\n",
-       "q2hmseZUJnCKim2mtpR2Ceb9E5BMzNUcWjalFFNq3siKSZ9g78PA2LwZHUNbDg7UHwkcWObnXh/f\n",
-       "kLauj26e6IN/m7zIKbpFdTM4W0sfxh/ZLZGc9hCnx8/l5uWzgLTwG4WCZq/UCIA6YEqoNHekl6D+\n",
-       "Wm/grHNjstyWVBrmOdsRIz4BHeZ5xvKrjt49sNG52q7x1VqedsBO5fgRLrzWEUjKFQp0zaG9IgyG\n",
-       "DAAACIUBn3NqQt/qf2ak/ZpzaoW2v2+tPDEpmxC2vDwDiWW6EW492+jOZzzW75wvNmioef9orNRV\n",
-       "2321NCMdXSja/HYoGAE7gjIGb2H2wKnChVwE3xQ9Wo+t6pLNOtXxHFyG+PvgEQZZsyNNCnwkl2cy\n",
-       "Us4pU5opTuc2NzhLxXCzkBUTRuLVk9Gofk6AequFD65O9zkjN6BwEW6AZevvJLJZHr2GNxijkgwC\n",
-       "PM6cqiWsWk/sdva/FD4d86IdrqXGNwoDThvxd1T3uMwMzVt8hEs2T1ZeT9PH0zS9AcTkGfagdwfe\n",
-       "0L684g7/Wbg3gM2mDzee9yh6qhuU5kpl7DjcVdELs5Z8LZJC3yF7t+GEbnGSRbJhRDbVh9WnwwyL\n",
-       "U/schXkAHIMXj4RDd2X7Yca11LiGACo2q9rAMEbZ0evcIBoasQPm5Xd8n+9ux3/BqNCkmKbVzhMD\n",
-       "5l9lVklYoPX1oQ9iw+6M3WRGOpso2tgJoA92g7ANnhBWoUkRLyLlZT8NIy2UfQASuY6IIUmupKnq\n",
-       "bBGGEb9nX7IUxeInvWdatn3/u3yoSo8Ouyy916Dq3xmla9PM+LOOG76nckHLeaipusEatRR16PAY\n",
-       "4vwDHH/Z8nPIcgpSrgFnEJYuZ6iXsG7bGFeVETmC8bTHcmimKyA/sQgmVAPt5eDpCHzlbzmiIwkG\n",
-       "z0LlNWvV2Xm7v4GThTtgUv/eveKW7q35QZTO/ye2uCR/95uvaPdwnZNePUgW7EorAvo3bPQoJCPf\n",
-       "g7+9eVUIU/ThOPyMOmDkH+17fetvLUhuKVADyccwsgqQXYfgiPgCZQlRiv/23+oISKUc++I6v316\n",
-       "l2WCwqmLYlNx3p0oVymUKm1zfiTDyzThrLoSHFEEhHMO6+q84b4gwPPIzgNi852EIoDiJ9k+d7rh\n",
-       "EokrgLSHChL66EIb//tXUptZ86cfm8AbEeCCtoqzlnXFuivxRkA4jycNwWjKxrSOkR2Lqba/yaCQ\n",
-       "Qv2vuWWN9uXc+uJBv1RedKTYc28/CJAfIzYJ0XO52/0FNUlhC4Pr5U/pLUewIWAUyFmXIS/UNUhA\n",
-       "hG0eexB/FJSRmoqC6iKhAPsPU0YKSGA1V0v9MsS1Fe0DZPJVMh4ySiQ1BwgZ/+4IgikwtAgu+h9p\n",
-       "qGrN53cIH1jwvicXp99O/jEJi1FiVEwj+WfQgQtMQFwfbYFAq+aFfUtvbY2O4jMqjj/if11DdOD8\n",
-       "FkOt2wPYJtcvARA1hi9Z9cC7MLw1/aEMEnGQt8wd6o6g+LpOIebmlEWdKgdNaJ2ndXmfERRBenGP\n",
-       "t8GVysGup4SWJGMY7jiOtUwcIrWs0etZVgTJ2ZbrnrT0uECFvCKLDDqxTaJzwAFSJI9QCXm4DXoF\n",
-       "RDTIaPjkFDMezeUDGSD47e7tEaoaHYW7W10ndhZv8QCKLYsn/PPPV2uszc4N0XXGzY4rZZB/Bu5q\n",
-       "55MYapnkTOLQpZ+4kHvfL0JjDU1ka6HfgMx5FjzPue1pYqcNlNg+ycseVFep4p5ahWvyKrHujkG8\n",
-       "ay9SA98hy00U/wWTqZpWiHDj2REcgT09a/JJmfqaz3P2VxkAgDqTiJ5+gDgC1i7YEKMKMSmA2icD\n",
-       "5MXDkGLsIcxLjP3/0Mt2oEWBtYd+VMgD0jEVCmlrKF8yindlFDM6yMKmD8XVhNxjT0jolBCuubN1\n",
-       "ZOsEPf/HNaruXn/a5CLA7FgNRLErNG3Zp1F8OEltvBVtF4SbFkUSzkj/c16O+2W3y8156vLEbFRX\n",
-       "1cVDfhYBhkbFzayMsnbHunkhEtLXtglqz2lDJ9HURfKVBSmnOVagERxG8qH85uZtoPHUcxFDkfOq\n",
-       "D3j4PJcHboU72wLympnSz9KYjoIPeeMxPKa4HqkxaKQwuC1hR7447hqKAVG2SyqYNrntG6OCAUCG\n",
-       "jGbsdaBDAXomlSwgfE/BLIdD+Q7Mqt5+VVcYLtbJrfKk8GNux2s2LL38kyXRygVbjSYYHVRYup10\n",
-       "cb6bDndRha3EON+UZ4Q7FRnGOcEtbSe190cPr8GET9bNFBkvWmcRswF91jq3DlkEtzjOup/7i4mt\n",
-       "DzZ6IGNiCP9Cf2K1TYwjfMCA2LqapJfZyV/LI8O09Kq3ye7CgMlxUIHalRP7xvafmzpo/g5vZZcN\n",
-       "ZWmMYpNrNCpvXWC0+YPNOU32yjr++jz1YcxpPk8+hKOfWfWUhsMUWF2tEeyMGlXMAEtvPzJ8zv2a\n",
-       "oTJP5b4VPPNKC35qp2cLa/cXB1cpFhUsA9QP7kizGDVccyrA5he5bN9cA0hee/8C74UVxgmgg4t7\n",
-       "DNsUT0zNwGjyZodc95jzmn8B3Q8sLHQOSxBj4Y2HMid9qmoVGULGDWRWDXPLoCLYTWfkAM5iw10O\n",
-       "+t+L5mT6e9X8eV4GtJ0L8tc7ZTouM6c29N7FKfPB+klE3y8SAhM/kI0eIrS1SdhMUHaRFklX6Bxe\n",
-       "GFHX/+pAO5ncj9uJAFTdfZX99e48Grj36L7sgDK2532umjP7rjL+lC44fZLgFzHfTwe6u1ClAt+z\n",
-       "T0B0fjLzg+2qe8AN9tIetts3Q6lGiqalVMgb189IzGiSW5CJ34Ba7QDhZW7fPq7eD7HCpdNPab+n\n",
-       "WbMpR33hzYUFdXJ49UBSbUBY4q62vAfkPiqmU4XQ8WwiUlkYmi5LWYRzUOm/xG4H/Ivtvl+riGmr\n",
-       "0et3Z2xI0oaiwkCDqDHNidMbaRGZjsAly/UNImDK6BXZXL/kTqh6gUJpuxupUsxTD6kfAzB+D+l3\n",
-       "kSxo4YUpCAUdexAZvbzj8fhVo5Ybx2KR136n08R+DGydCqtFwbRkafL7QfdzoBPS1gMkoyj4bqrJ\n",
-       "lMKi/ifn8eE16pSgZSGTSsLooueDxLUBTF9rxQJwX1ncZnef8zJtbUW5NvZ851ZuQVMmHWEFpaIo\n",
-       "6Rv739r0aYV1lxhNJQ7eTpJqEtAAABHOQZt4SahBbJlMCI+Kt0wAcWl/6CJvLAxd0k0V34dXCvrJ\n",
-       "JlA38+1sFkA7OdbCSRPyc0OzfEEsgDqZK5S7v9S/aMYxxnNUrUoAWkdTIBpfHbhhZMZBCMT3y90y\n",
-       "DDFTKd76kvLCDi2rrI9lPP10qWvycl9UUqPy9CDfuSyeI0igQY5sPnEDs143krJ1WfndVJptHkpF\n",
-       "KtVBjfsLKEUNJayr3Np0kO/7FH17AGqma7Mgwv/fpdHFR3JJdQKXIPbRrWwG874+Wb/vLlJZAgHj\n",
-       "q2rlxZCYYfzw8YhNlT3yXVQHlyAjAlvG+mPhHNDLRXsajrr9YvuaFXq2Wm7tPZgD5OoWYf212Z8r\n",
-       "YGq7GIGt7e0EDxYjRZNUOhQFnojn2AukDKWkx/JzD9c3wPY10Fr4gpDk+PRVhJ5pr8zbp8uQxHRC\n",
-       "ZeuxIxU7GuiqjqjIHiPMdGuwJk/9/g9GDiR2iRf8wI+XwbtVETkx/NNovtXhSRJ8EkZ8ZiOpKDfD\n",
-       "4hq2KlDaEdZ31YqTjOn6PpLpQyoPdNhk/0HNPIYgyh7vzEtf4bUi7BVYd+IJ/jERPxIKlCoRkaij\n",
-       "vue+YYifsdbLcTzPeW83vchOpjQOrZgqoG2hGCMuTqyEyQOupOFvwjFJFfmOajLOPRdX1RiqOx/Z\n",
-       "SYiRDafuIJTbLyFhLMgTtYNxet+rV7PAssPlSSpLrJdCDFpVLulWhVQzqz/ME9l4kwyy7iHc2dEm\n",
-       "GL3ATDHSfBAQS6IRbvLjYjKUbxAYwO5NzYablbuLOHtDYcivGAWrckdxb3Oo3w+wnuTPnfW6ETPI\n",
-       "dYor1MIo6FRWBiKCOWYD/SqiUzayPbOPw10VcYaP/zEJDJLeopyLnoVNTkm2cx404rhnHE1BzgD8\n",
-       "JzflbIgsY87BjVj+XAIzSSuMkYRhQgj0GImgICvQLSzXU5JTqRf67JpXyBd6ne3BldwfLuGrh+GI\n",
-       "MjLwdsQLLMF/tMte8SFaVn7Iw+w0vbdXRLbbf7FX9Z1MqlgZJqU1El7hw3IptAB8rtDAG9J/PQMv\n",
-       "h90ew8up6SQLZTeCdyRD/0Ak9R/q5+S/C9Sss3GFSjCnOkz02+UsFTTOnU9EAW/ZycXWWm4oITBF\n",
-       "4RlA8eKji3I/XLd4fI9AvSE8/PsLdR9boT2wr28BOIGrR1/ZXxYLrISlWirmXJBWE5iPVRGzfivk\n",
-       "4Pbugyi3VOLR9atIChCgUV/TmkEp/+PrWsnewGbIhPt7GGXOJrrOxc+wQNKwNymW7UAHFOZYwp3K\n",
-       "HicWiFZOPsDzYe/JgAeysEoNlTv1gM3mMi/h8W8ntApmWkRO+rC1g+h7O0fLYv8HJyc3Yyj1OdB8\n",
-       "C8B+1qSCQ6VQwt4yI4R5LIByA7+/DW9DNhLAIHBqm7AnLaBx9R1Mi7RA6cH2K7+iwNTdh2RMZRGV\n",
-       "eIJUUksnpSW6Qpmmz0l66vk1lWtSXJYtT3eiI2PSs46jfJP+nlo1ALP9bUSuOjbW58xAIPlPWfk8\n",
-       "al2DN/fQIdetvX6+fki0x+706skQsk58lxczshakvRlbcpB6eJJ8mtuWs1oiTLz/9SG8VMd5cpuQ\n",
-       "/Sumh/ddsewmvNxAaMxswDC614iSnuzMHVmTNQay6Wu3fIlisxTG3D9YbahLdKr/lN4PPzt0bB27\n",
-       "4EPPxLvgKr9HuFZS/Qf6daGvVV0kMacL58LEPxWS+PzzHoLzvGof/0Y9w1lmUwUhOhtqvKvFGbi9\n",
-       "fdbzT/F3118MGLeIf8tx2JB0JlgJdlBdwmUpx0rAaZk0J6mXGOJf8P6nux/w8FtT/2NAXQJVsYET\n",
-       "GlWeF2KcTNrYVlxH4zx6HNFvR7Fvl9eNTiyMig4z7rD6ZUFKytXHEw/GZXhSZUMUkzwdO0bd0OzX\n",
-       "ug8IuPH8I8WiSBCI+lFhkjBbx9LRliYXNxgsGr4BtDwqyBqOYA5cGPLJXd0NRScHQTvfXaoryTWI\n",
-       "u2mPl6OIyTL7o0vIRC3i1vsxWJ3jsMWGXucf4rRL0cl/ISKG7MZ2HjjwuuECCh0UxEAoFgUHBvbI\n",
-       "E4w7QkHxIwSAaEM2mgJYJ5pdTACu5pC3CwV5izxI8kBjkFb/25/GxiLru2WzMmW7nru8AjVP2n9L\n",
-       "c9lTVZfHRXSu7rIFLz/+XfBL2puBSX0Tu+ps57gEt4/oOLgixzm8CBFguAGqf3xDJId+xhy6hH4c\n",
-       "wkwPVj+UPnc7JdymzvYFIOIGun1juGzi24O+Wg7mVTtxVscP5PvDWBPUoQNMXkGMij3VtcymGwiM\n",
-       "LN2Oi7tUj+KbyJLGxMPbzsk6bTFDhORg59lMQgu8Np+ZT1/1JKHO2AR+dan6tqfTXy17x+PZFg8H\n",
-       "wCLvaTjI4wOJTgFg83Wbdoe9xE2nbeIX525V/sI8yqo7ZV9crb02tVZrltIDhyLo/DGufuGzyV7G\n",
-       "RDhonN6Hh+BTJF+b0uaymKy73mvnkyotxbQp/Nr1/CbNuN/BxvvxYg/C8aIaUtFsu/gNxKYlMSr1\n",
-       "Sm7904n1dxoYg4QD5XgiSlejk8hPumZyIKwskOhRjy2tgpKhV/EEkd+488R+w5uuU6lkRmEWRtnw\n",
-       "xU0S1sy258I9PBQVIvqws3QXi8EXU5moljRBowxPkSNzFa5DE13UoIwZZyK4smmpcv8fSFhz5aL4\n",
-       "gsS52j6bxwFReqbuG7H0hnRoRP/cYbXSShbu1xIMO3jJsu9tGT49vP6IzqRZqzUrAU/nHwRTdOAf\n",
-       "fAw7MNtwJo3UNVzpB4k1kVVKb/vK6Nkt3LxU3FPnY1SeiakQn3VsouAJBmQvmAheRuyhvoh5ft5/\n",
-       "Xzkfr1Y3bAeitiRg0wkAPj1j/qiz0L1qcqQdIQ2hgIW7at2NYP5UcsDttpQAyYl8JsZ9QXfezswY\n",
-       "qPf37D26+XkX0wg60UHwDUZ2axh5Lg1jQkC+ulbXANy4mgrBcv36g6Fi1FHCIJI1NSuW7OyCd7s9\n",
-       "M8i4nIazh58qSJrcjKkibaH0+9V22dGaGaNS8UT9ozlqzXo49K0X45v4PiXaxejENdDrqkxPqE5h\n",
-       "ELbRBBU2JFQICTdH03sWnTP/ygtMe5RLlVJLs9Wiqbb9WRN/z/q035ofKIBFAe2u2SqEspiRhaQ1\n",
-       "NDuFjgh6gOhBsaX99iZ9N3z3YRb1htTbn4MuAq37mEN/CXNc7XpJ0EEhAMDD7sDv1Rt+Uq6a5b0z\n",
-       "5SJN8xT5QNTMsU3A7vT1V0YkM8cfMSpP4eXuIsAJpJZmQ5/enH/vMQaF+fJlBh5skpcP41irCbUI\n",
-       "dUT0Csg7o+c99uJVrJF9wfaVyMAGUeT2CDsHhg1M4zZgwIOObs93eMM5p0SzobgOwhQOndiZeL9I\n",
-       "OsQKJ4EGxgtbpIvs9U+td+dAluaSM9Ee+3bGe6fBNiewhbmB7cq1icw2auDQnmAooziK/Z6XFMAJ\n",
-       "nrvDlJ8Dcmx9rVBAhlypYp//z48LxL9Z+6G3r8ql44X+w9OpX2DNKK5GIxYCcnCXqXsg73OO9OSU\n",
-       "EP5ulmWNhjMNKPs0lFkKKOXxUbdMzU1z2N59ng3vPkMVT6RHsBWt2buMDO9sJa3dX/GIXRYlzxfB\n",
-       "RGY+/uTJ8VspjrcUxO39MlWhKGugBbuazaAeJANDr8QcXgbwV2hEMmvWxcgt7vmM0c3jnHdnNvYE\n",
-       "cXeA8IstpKelGZa55VqWocGhCTIjApA7iwbk3f1ZfmpsGRM9wxA3D0PyoZ6XYTGVGS8B2A4kZwhT\n",
-       "5wOTqcBigZTG/UhlcSVAfQJUQNrURS/SvQXO2R1LQUgjkH2etyG0hECXy9VAn8gOOPPu5ALz6yZv\n",
-       "0qBa6zy1fyZSTQW00h8QOFRtbvuIf+JP9QtX8pgKv2+wkpfPJFUsQniKBXR3ZHdFrK+Msj/bvRDc\n",
-       "wQeEdv/LohY68sZ2hmUkO5bG/lj0jYans1rIwVLPUsbZjAocBwaGbvvt41tlEwXTxyaU+RJAG3AD\n",
-       "gZ2UXjra6kEqlHF6hGEJ4X8TYGD4VpRcC/8ZUj+HVTKegdsBI5FqElFdRsVj/7jftcv9OY1tFqsK\n",
-       "pFxZmfljDkeANbaitlxBfEut5GM/0SrFYwFU7RI1CRHH051OodimrvYABj14Ir2eiDy3PzqdAsF1\n",
-       "Z8Uk45VUy4vZcbbskJWt+FtuKj29ZxdcxfbRutbWDBT+st1+JoA1h+KfGHFegpE3kVT+JMwnVKXa\n",
-       "f8Df67j9A/Mi51hDH9+nfzDelWRAeykXVjtzfGA4KPITk3U5LfBG075zcIfJUe6xw4vLg+wvYFP8\n",
-       "NGeq5ruiZWEU6mqcMBXHe0o2AwSAOtc17IrsUEv1Fhx/MvQKnwFAuAz5fMSMxf+V8PWo4D14kdn/\n",
-       "6q9mX00KZ04xIk+Gq/zGm6ynO/K2dPJhYSN/aThXl2jgDevEtdK5wcjmpyXVRzUawxLplZYtcwh2\n",
-       "ODw6L0sXROowqLLy07yxlwCY6pEtpXSf/iqyG54msSkECxBK+zp/rOh49M1ctkRRMNstyLEcaSkJ\n",
-       "PhB6SOwM+GyNMsMOuRk4IeOZlCOt6eBgdpwQK9OPeUkLCbhDpVpU20x8QGL2vtC3xZrc2p3QRxLe\n",
-       "c8UKpqKN955TYvi2Z095PLEbQttHTu9b+UBDOR8kjcO9xj0SZorLY7QAlPDZIaHzptYwPgxU2FH3\n",
-       "QbagxjtIRAl5qccXdjayLYLiInYXMnv1GYSZn+N9pQFetiR9CqpT702QeiwYRWjDQzp8SNFOJ2lI\n",
-       "ZwHPi1mhCgIRA5tZ2h+rhu4Ba8EjOdnrI/NrH8dOXXnOaufbceT+M/5b8AxkOEVJ9muhf+yKGSQ1\n",
-       "2ybEpEyNPMTBQ2p2+SSUEiUOseSk4KBUNW4npa4WWz+QXxYOVq5sGAXlMF22Psab7sRyOwHayNF+\n",
-       "YIsd/PuIC7Dn8lO08/D9bPTn4q34MY8mmuhgTPZQ5Dhc59YYIQ2XPR7kKGi+o8yzTUQ1iiRUqZvj\n",
-       "QkgDgTgME1hePYpWkpppfKa6q40NxW2yllN6sqffYuQjoQqxIyoJrDTzqyCWX8Gp1UbzshaqjRAT\n",
-       "MWUW+7QSGQb1qgc1fhbtTIOCN1FXkTI1NaRjOwQ0scSoEHAs/GbR4MgyyQMeB4u0b8Oe8Ixfkq9J\n",
-       "9cEcRQImxE6XeWB1vFHG6LYCKHjaAowExk4BEWBKHfRYIyaifJI4Zo4mp/o4ENSfbS2yS0SVuNl8\n",
-       "tHshlTD8qQG5sUs5b1bqzoEwS8xlyiMbaTO9CF9YVIH73736Cm2DmBxzW6caRVopHsX5OUikmeiS\n",
-       "e47JLv6t7qHOdpEvKaQSR4G4Ra2eLW8WatX8WjO91rEOqiKtoYLY417ubSlAcgNas6OjkqHiXHjt\n",
-       "tKSMbfGjbl9YgXIN11L18IfArHHh5Tz249iHQQUCQJhH4lBNxh5uwBq1FZIzuEZgol+NSoRZjJwT\n",
-       "GhPWOIB0seU2WBmPHINFmnq2W1Roihek8nFImW2tu+y/7HzrgIxfJLOvyyIg9DZipmGdCU0H33f8\n",
-       "RlAcDhaqzaql6TcOftNLWna0P5W7deogyrurqIajOsRmLs1Msebe3Q0kOapCofuKsomYfqeMT8xd\n",
-       "1SXL61vEB699I0KrsbVEEJn2JqaC9lZEMjx8aI05gYFJNNe5ZISH9uUstaaybgCTnoLz3sD4eIgQ\n",
-       "JaZHKtpAcjVADIiDYCmSbSIDMOlq36sNQMhU3yjnORgHzMNuYHxaj/v3RxXzOMt8u8t2CPLytsxe\n",
-       "TMYlpz8Zt9VTWf6Aj7lAsumICc15o9S5rgDCMj3MERecvlu0QzNy9pLS8xKY/gwUW+VJeWQ2AJPn\n",
-       "D0cn3fPJP/sQuHGSOQnFRK81A6sGxKanaLS+z0qyXEw1tWTBxDao4iX/hMxUJKOcYXWocWcgFL/2\n",
-       "EdSupb1sE5xLd0XesoSGmwgSQePOD7fMgt8XIPQVSwLpzzf8FwcfsjDR+h0ROR2Gikts4v72Rte7\n",
-       "yt60/jyEaG9YBiRB+2nAQWk3QAyjyuyOKuLaB4xrwJOOiR6IOMef0aX/uAiQhoczr9H2dSbgo52K\n",
-       "azCByHs0o2+IR8DMlrsW7Wa2gCTK6mdFyxbRRNigNhobB6tze/cjlyAPUzMCD4mUBACDdBW2sRYv\n",
-       "CKUMIUlMuKS0fAINCG29FN0SOCDYgQAADp5Bn5ZFFSwl/7iO1jji8K7oi07OMCcbnGht1+ZYzkbX\n",
-       "zMCvqv6XlqgxzQzILQFt9+krse+gAARlYSod5+2xoM/xiR7NCLpFEtQ2O+/ymuEQrTgLloBPNfvd\n",
-       "LG/ykHCIp8mTsMaw12Va1B5BGSaK5ImZdVUiiurXhD8GLLhZ6k2DzOID2LhUyBUt0OoY8l7lcJPE\n",
-       "FHEF8YT9mZrQBwfGF4lYYwHi39uB9rlZ9KUccrdon4/hyz8VEW+wd/48lCV2xQLl2zH8PLSCOUmD\n",
-       "VQLIKgxbSr7eL9BRzVeL64g/EUl7btkDmBlFB22DArutHk4Crem+DLxlzryZdn5/iF27EiBSUOPm\n",
-       "VBQGrt+y4tb52VJ75SST5N0etnxr5N7oPDHXZimWFjJ8twa0Y0IKwxilOv5o+bCgA2CcKOTteixj\n",
-       "02+WVa0uEGGYufArvQOcwOZ6ZHb6JHCdrKLRTjaRTbGJftxWNTvIS+WZCEjuViq5XpWVEMiO4ea/\n",
-       "oWfcoglLo55LwLxhxiA5I7vglOBuVoHKp3E6HlKusX4NwELQdHomFAv47hDLDLoFp6LcJ4mA0Jer\n",
-       "PI1G1oocjo3xpidIUMhtMsULH1MkwaSnCEjpE5nFfiUSJnA8eDV3IeESIhka1xKD3RvFDE9RvErC\n",
-       "zyQS+m4YPFKgXmb3asAwZoP5XuhhQYFO/OsQp/Ak0sVOoI64RNUyTGoFP0CU6zbL3AZiDNT/LdTo\n",
-       "EmlXZZBJprb2w2PFhhVR3GHGB0/1xMTc64lN5Ispx5A641Tz57eEt9QF4Kk8dBlvC0PO3BASTZd7\n",
-       "icuqrnr7Vu9g8hZ2AOOaEuKXc7fnaRNasT0rDoPX5rg/OlkmYXIrVULV1Tyzkh4BBkis3j1STUPh\n",
-       "3ZQfn8wyHQvlL/9fkfp78saTtuuLhHn+nno0QEeod/0Svo50IuvPc+Rl7PaF/LrIfImVt9Vpz2g6\n",
-       "O/jYUDMw6iQzqalbd5LvIHslfOqHZT8L2bHGpoFCeItNWV0WJ2MRO2Px9DxRwlK08yB5li8myHFh\n",
-       "jcRwP0XlJWDFuQeH8ZGcTXoGuG9SbvGV3zyDqqMhmm+/lAq8nPfdXLUgrjD+Gk/dThtkzb8ulbW3\n",
-       "s2lCBOocrVkyejqCHsKN5Ssq4LSqAPanmKrR/eNdhc2jzf5z4/3HrIC0Q0BHCAhT/tXB0e5/y3WK\n",
-       "IHPoCwZBlpMvF0qrhfsFB2ZR4zIyQu6wOZhBa4zCp3KwEguD36dCBufgXytDdTOFRwXzm2P3gyva\n",
-       "gS46JU0ym27fY117FuWw0BWKeeBmWImgLsFMVsqkaqCLbERrph9IBhSqY/vWIePN9FJjDdj245BS\n",
-       "59P/W+/zNYGmOAzXLd6/Ras336psEULHif3YO3MLng3MSyIo1oJ8IGhwv/uHEFsSq4TfWamAZOIg\n",
-       "7SsAr6UjaD2ALDfycD+NWXF1hhsYFJuXAnTVceiDRJvttVWhFIIG6Z7yMUtDXlNwiJ0tPoyE8ZGK\n",
-       "SKYaXz+GP629mBCPtZAd7HnIQYlXFvin+BDDf+kg0Avjn8eLaVxY0zwTnZ8jwiS1IlRvV8Ma3J5P\n",
-       "Rac5O2j7g6uS7QgtFZRStfjfBhLV1y4F61DxcbZE7bil4DiWsFx7xje6CqjLtftdumNViCd4AeXe\n",
-       "O2YaVT+ZbLkiip6R9LPfcIjMjQ6InRbGOv6/JzyTRl+lIwD6drcN1DwqUGn8P1GcdlHdUcvub4yB\n",
-       "wRdW0e5hk7RcdbBX5/zVAOyxjZpywuv5BkjS2e0HiOxjHpkaazjU7KpkErtJo455uEf54TUY4TSJ\n",
-       "9levJvcIj+VVPFy8za1v6vZgYJfI5OHLGTy7Gyi/N4MGP0xdkxyEUfVNEKiCeTfmc8nTaa3z3QCb\n",
-       "t8OuyG2p/doDjjO7VOauw+te33EJcMGRNH9tojewgEMUxhflbWpZgGcx/sLUsIQ94C5wKJh+N1zA\n",
-       "6pPDe0fzCJWEG2MzkU0GerdlEhC2HK0aay2d065DV0aRDt58+RZ0dyPSUITBIVYiuElqJegnan33\n",
-       "puE1GyDfeLp02aimTSKRkimGL0xeZVderegeOozWyF27iyZWIpCB1my7PtW7KHdVgqclP27yDuJr\n",
-       "WVN15bA5vmpIeegoBsN/GOoFYN9JJXKbkrtWgiPjs9C0PHoMoi5vCKZ8SpDxHTndxhL5FXcdB/qg\n",
-       "rL/nLbFqH1LIq2HiBiTdXoKqF3Unt41x8l4f6l7xzVNgtHA/VPSCNbkxmSxkL7VVgJexZqHWocyb\n",
-       "qd2v1aq+f5av+4vRG6t29r8TYTPi/PpyBMl6PBwo2a5gxqlEa7o+l9lzC3tNSLwoRjXVSjucNypV\n",
-       "XinIv0XxHc80GMxoy9hlG1QR79plEvN8QNohe7IWctrtcUXVpPogeyKiAvVIIeSxCc+YEfaDDj78\n",
-       "sFGP0kU2fXwfitnPQ6PmgpXY8cw0il6+bfykXOXUsX0A5fcz2t30BlEIzi+oD3Gf6FQ2BxZpdd3N\n",
-       "CtMuFGJVdAENS8oCEm/A9jONAbGKNBMMWllEyyLweCw08GA+idB/biamvHvN3/X6OZdUB/HwH4Y0\n",
-       "nK9tf0rPgXLeQWBvn5ZJ5R/xJFlknu0DveA8n7FJOYdb1K2ckApG6/Gbs60ikx0D/wtQrNv1HcML\n",
-       "GGoIy/wGjJt4OVmJnsETSZNWecQZWMzFnEuep5CJwV2IzfuQ7Rwem3ry7c7U8lryntzq1nvBY0pr\n",
-       "5pQd5up+eZx2e/WoblFaplfClx7T6+HqhqiGUp0Zfx5FWXr2YtRf2YJK+iK6NDMp7vX0ppuQkj9S\n",
-       "WbAxgxM61i8MydZYNl+JAaXskeKjje4HFAVszeu7dSOTwr0vYLO98SsvfIbYk+2p3rasRx1kJOgI\n",
-       "RiHQAcr8tXqZD1HIAqglLuPuO4CD9ET55aVQsTH9HBbYyeoei1E7r+m+ASLw9OehlBTLi0a35Pu8\n",
-       "46e18uxB0it+jTmTiVh84S2KRrtzVbTcMPIJtmN3X9xG4lRTWwPNeykFAXQBufWIZvopskEoPSaN\n",
-       "sZDBkwMrd1m34Ia0IcxpioSI1Oiu8j7xTQ9ifQSsHMbxPXy4rSWVMzasCK1kTToiZr+VgrOrddzU\n",
-       "Uhrx06ckLVd6IqHGaAgg/eeB/FcMuBE/5DcWU0FQF65c8MCZoNU8OBNfOYvHb8a/cJDfZv4feknY\n",
-       "xR6jukmflqkFKqiAVSQRJWqKWHHqfqCe+nekX7+gAtnkmoWY5hiP+dbj8bsDrE9FxOE4J2ABCdxq\n",
-       "brieeGv5N4h38+ET2jjcAhrVbnZMWV93WnCkRz8zbUk7zBR2PZmbncDqskBR3jCV0D0NAabe9cts\n",
-       "rOGrT3hStf/t1T7IYvfSxOgSl9jvrDxJwhRaSmmzFlmaBDPEwRsbZLeLE5d6dWQmDyHMjJuCQD+O\n",
-       "MWaO/LY1hR3ubAJqtIq3MGXg3YX+iUGVbwtUiCeq0+1sugOjZnA46pIK/0SK2Qcue//2bf2GU3Ht\n",
-       "R6zWRMe5fEfe/JbWiwswWFhVDPo7/G4M0j7geVwvFAtEVoMydAQcH/anwp5GNBF8Z4oZYvRf9VFn\n",
-       "rNuw6uH4/+kUaQCRI4MBFMrRsEiK51uxwfI1X6nQI2aBR+aH3rNbLnYUJp+bDkSZ+vWVIYsc7cXA\n",
-       "kgBgIDPUqz+SOGbP6i68V899crrj0f3FpzRO1EaJ2gAxE4wTDCYJxOH8HlrM2hbaeAv7HwmrS/+J\n",
-       "NlaHj7dSdhGfCsJSa7cCm4SCosdqUHPToWZrjc91eg9AuWWgxlyKCzC1JRUvXDE3WBLyHSh1sk3Z\n",
-       "gqbgJeyoUmPcmCc4irhx4ul+YgWwhS+ezkwdmszQjAa+u9mZpQACwOE3CJ5Mc0suLBwptqa/BOre\n",
-       "ojHjUBxf/+U/Uxu70xDYjSQOPeWRHIMPbVUZb2f2FOxV3Z16qssr7KuRLSj65cd2QJnoOawZtecJ\n",
-       "tawzau2sXt9iOtAKRF1NM4r0wE5AaorLxBLxgg/7y2nz4l6oESzXQrRVEQfK/qzEHjSbmh0pR48C\n",
-       "kWoCxgiIEci7vxxFMpsEhXNa5L2PKHZaejoHaXhj4cUDjf0hSqMS0SXmYo7G49Lp8dEQte7GrEXZ\n",
-       "+aXowmxGxRnxJvvVWHUiQpPZPq501qgF2wvWndAH2jS0R0HWtjAOtUDWIAlEY06lb0mLNWl3Y40t\n",
-       "tQYZ8xjLPW7Sz2UYHlOus29PS/S5ptu2gT/E4RMHa+s0z8nSaigTHsV2e0z7QOBgds0quw7LrTA4\n",
-       "lC5EXRR4FTE9AtCQJHDKSalK5K5zFbgUl1PiP4dqFyGGjJxKyJ+ah2rNdYbjOlP448+1FkAep/3Q\n",
-       "05cbqOH44BhkjzMsVeYdTpyyFiiR3fKhVNziZ64SqvLlHHwt2vAUp2hFQiIOzDAczfFLuhf1HNPr\n",
-       "5Jz77JSlDZ/mtqtVFnLq3OX6/fwrJ4CIjg81Fxe56PW0GGHwUcwDI2Hz5jTyNr8eF+f8em6u8/Gc\n",
-       "gShLPLdqHfPDCD756PCSI7OZjwZuRtsVVS5qBpczp+XhLN7tJsErV/0T1W7SPPL0ih2mo1CXl/UK\n",
-       "nl88J/rDVcQ1I0Y1tKOnJaV2IXE3n3MIVu5zNZ9sYjf5961N5tWmLkgQbLZaT904XR4d4aU0YKik\n",
-       "MlTR3L5YxfcUk+QwN1Z6arOlz/v7pCQsF5lonVWfiXsknlekrcXAxuzwAM/BV5GzqpfvMqT6ynvc\n",
-       "nkyIePkJ69LPsiQSR/RnO6iAisJsWysizswfmtJT5iuG2YIC2Q2Gz/JavFZA9nhjoHQabuKvyTPi\n",
-       "LitCMrsWOPKhDKi5EHGtIRaSYWZrXl2p76fnCmrWyX1VJdeWd0oUTXVFGHOHtZKSlTY3xRS3dHH+\n",
-       "9TARrt3KQsvCi0aSHDD7goOe5t6eICQNK0B80IUWIZNeHQie2gN02n4ywBrFkqx72m2BOfuADDPP\n",
-       "YkfyNkGp2Qylx7BRgzND+R0X0lEgSEil1XOpXAdLrMVeIWmDt/n1tlbR2eE3Pw7cR5hFjqMz76b0\n",
-       "y9Qq+Q34AAAPZQGftXRC398QvCZSayZI+y3Vk1jcsxwy02o2548o+bFFPrPNVUrJxyCog4GXT13A\n",
-       "m4bhB3DTODwp7asbcB56JR1ehK04ZR7CFrkXYZ8rJMQlFtEGfkxCGpoWwbTE359/zAFLAabW6Mn1\n",
-       "CPfIlCkUfpq60VUN/uG/uIho+7MEjnHIcdTLi12ooMtYrKdIbsOGslHsv+V5EixwV3KpRFWofNFP\n",
-       "4tS6nW9u4gHYMs1i4ND3KfPyy5VStbw+XXG5DheGAsMN0MMupze8hcWJM77BuLOgbhgA/iUt99Wn\n",
-       "kPN6qCh9Yp/pLEl5oEl3OdBoCApf6VDyyby9Q/D8cf/47tFzlrq3aJHJyoNB4R1QHtYJmpPerbHz\n",
-       "hk4AALErcdQa3HD+BY4lJoRzYffd2H04+8P5YoZ2bwit8+3gKC7sFUACq6HYALbnmRMnIBpqB3xt\n",
-       "yXWNRJc+KClL3F9bag/d0BDSXD8hOQTM4hWVUXbYm/Oe1EVapBapVza+E/vlwG+wv18PX6fRQrMo\n",
-       "CQv43IpFUiATeGHveMXUodL4emp6HsHhR6+jctTDxnrf8msRXHOsjBFP92lhthUPRfg0skJ5vHBO\n",
-       "CXekl/7//bjE81AZsTVqPQ+0HfhHvC+r1M7R3ehupOt2GK12N3vbmCyE7aFyX/jyAuADxNaOdDQz\n",
-       "kBMzeMyOqq4nq8ISNYwlIO27QlH/mBuR8xtiRhIR0wJqDrow6C2NnPY90G6SlAkuJU8jVGycR0AA\n",
-       "kPQg/C7uMhH1fQGmma9+Wj5Xm7CrTqFKeAQlRrHV3v9JysG8lIjV8Fie7mgD348mrhUac6w97LLv\n",
-       "q2iedlBtKrER3jSy8CI4m3fUra1nVo8yq5iCJxePy7M+wLQcpXyNOebKaE7jzrk1IXUkU4IY8/am\n",
-       "2KSGolTV3nB4MipCUhrixNEG1jipk0cjPCBbGQxB4VsEVU++m3kvWNoSeVCbL09dwsKim03jC6fW\n",
-       "wCS6Dghe6ZyEnO8nRLHHQxIvLCmZ6ZN4oEkwbP0V/Lr0YUcOOm7M6hsd1mFQgttDfXGpN7bvKwxW\n",
-       "ZisYcCdVh6F49oXgBLUbA9ZLHM+kfHk/gq6vIdJ43nVVDkquDcdBTUgrK7ABrUkvGcj21l5acgbe\n",
-       "gjVJcBi/uyyB39K/BzA4rvfmMnlFKlBA2V1U8BmZ7W4Gegfu+c80TYEMUEnLSD41cGdbTK9uJiMA\n",
-       "pXljZERDkP7UsB+x/g/K3pl8QZJktqf31OgljH2UVMA8EkDBoOJd5tSRjafNmfQNifk8+Cg2V7TF\n",
-       "CgTMYsvJlNkxzn7iWfzppHLxBuEM40OBk3dm/llB/J0jHY0goCqtMXM9hYFvXFd+ZLbwqCrjbcTe\n",
-       "3rGyjKMaQsoM63eTl2YYU4bIYkN+Z7Ng1PEmoaMFZe22xN0Gi9XT2/iJJyJuMihp0LfjPoceDovi\n",
-       "jn47xGd6fBu2RAHFMReLEVKuGsEdxB7nCMTP9YddupTnl/gXLdBmCBmAkjKvXXeln0cghvzG8PHx\n",
-       "n6TKn3cgWj09AAHCaDZkc9J92zFSEsu7wBFgGw83pJXq0ol4EmAAA56M0GRkuDg8qI4sbEPlHOtC\n",
-       "uLLJqKXH/XTQA9gGzVTBLXL3I3wDygZH4bLYZSzN7TQJwqoE9A5aHzJMqpMkXh3tGrQbLx+bRlgT\n",
-       "e83q7XnPZXQx1xyxWqPQJ8LUrVjcook6TRH4WbAqUvaoLSj/nIf3OcLavkVhjmBnPQZlJ+NUYk+4\n",
-       "MSHa0OStQ5UacDi3TGGOaC9Vi4iUyDSs7G0Ll13QQiHcsyPaIDOPApXLgEHTqZYTgziVoLv/E8Ll\n",
-       "voWOlJQ8aLc863nBumVaBueDbnG2QSWcpuRoCSO3uYkrSRF03Hzu3jkRIdnHdXDyWg95fDWAYdhF\n",
-       "FY33sfqtrym2ds5Sixn5c1TTFgUaiC16aCMszQfMAiRfCvS1UWZsWc8snc6r7TP9CsX8nmcuAFg3\n",
-       "tfJQ4czXeUcxrAePUi22GRcjF4RE134WN4RrbyVfipaMPduBZpBo8TgEIZgx+4hzhORgYMJn0MGU\n",
-       "Bbw1cvBsH21YzMC5O3DIrivsK8V1Ef/WerRR+sEMPejtt3qUBseGwLJFJYzx2eGck3h4kZ1tnXCX\n",
-       "RH9Hpjs5iD7tkrLa3kcSckoQIH90SNUkTj6JGcrSHseuBnqBmaRVIP/jgZ3ddIUE5Gao0TU4agux\n",
-       "rl3qFUgOH1F4Wp1IxHTxpEweh1rQ66iMrAu30xglvMPV/42doxbC5R12ipqSH/IWubTVwRZKJqea\n",
-       "/qtsVgCF2sGh59LI6h2421JQsm8PJUPHJjhN9oAdUR2w5FxA1nC7KjrE8LtW7w+cqCJYV1g50AGm\n",
-       "bajDG+qu3AH6X3TtQbWaXAU5XfJTwP2cJRcoifctGNlyVy1D1tZ9rM7SOdFaJA5/6PQCeg4Tx58C\n",
-       "z2fkh1NWiYGufhuNevnqzyAD0+rYAa+PkFXvGnHCLuXSeqMypyCiFJDrlK/Cpb7ncYbBJCfxv5/b\n",
-       "za9tlWjpHmPUdqjKmcu8ECQqD1vLpomWOCwKWOgpBNYotr2iYxencN7VYdQK+KtgqFt+BbbZTAbO\n",
-       "N1CLJdc3PEyOE2ykGxtNeC8f+rfvqwhpZCsfsG4puSVxDUp7zrBVoS/yu9ibfa9I2prs+SKPQQHg\n",
-       "hdkbvg3sgVsE2PixjXNX5bvqlASd4voKSK8W1AGjvNlaZjLhq3YN0M1ibWewHMYnYiNkhW2icPs/\n",
-       "IaQDMjlSJ4UDnjpRqHBq6lvJYURs2NG5I2rutc2YpxQpx/S23f39jll1G+sbioAFJqF1eXnDfA8t\n",
-       "qnVynmT5mq69W//C/HRhIpdenfTS/DR13N+mM8u9Gf1ybnR4emB5C/Wk9+JK0344A/0lZk/E372G\n",
-       "/KHXxDJNXpmRL0O+FJZjuIKEQzW7RixDSkHfqMyPjmwLaQ/WWpIUJOi1G5+ngJqOYoHzh9WLeOKd\n",
-       "uktkY9/qOEvF14QbD1BPCssfkGOu1bXmS2yFSMo4P8fv68P8+UT4GTHM9rT2DgMXKWi/6hpQdhkl\n",
-       "zeOA8SiXzkW4LKtcQQoOx4sV7U/RkSdmqPhwDQU3L3HdbBo/qyrNDmWvzHGVvmwvSo6fxuQwprTr\n",
-       "W+kjaFWhbWFgp0To6nx/+z2vCwXBZDexZl9x31/purz//+wkA8WiQ6uVJ+ghhePh/EUuif0A99NL\n",
-       "cXIDK1H34sXH7xBertIydtK4DNAsm5J96UY4kmKvvjJ0+hMilSGpdydLikWXcLbl28heVxnrtsWV\n",
-       "cxXfq2NngGcHcqHbez8a8nMHrmyc6qbVx7YCoa0LxGXSn39cNLzu3sHyfd0/1zcVQWrRNQXgnLzl\n",
-       "uK8onG2XJ24PSdUubpd44HtDXRwnTls7pama2pUaZe03+sh7EZMj2CECFbOhehy9JrRtJEzbCTyP\n",
-       "54QXX4NU7B090qpbRp9RrRszgPiDYucx7EwWlFBuhLYV6mEJiC2o0STDIxbxnWTuMK1BmM5HFeT6\n",
-       "+p6hoGHcrihBPJIXce98ieyLCN2d40wzW7vVd+IzksELQbM/Kln8x8T3ElWm8THXFeOPu4xkSAhn\n",
-       "W7f2SKjfIERVTUxqygZO9mRGJiBiv/W1maGq246ZtM2rW1tOu302EKJMuxhYKCs0m70uOKp8uIgW\n",
-       "J59rh3+4D6HK0xzj7j4DauR76WXQMkmtCLYtZfl5L6dZoUDOoU0sqOlAKxzLA+25e3QUssyAg2Ae\n",
-       "jRg8e4cUU9jmDTi57VPhq49JOVTDcDbquHH+aLISdsRxPW2Oh/17NMACYQ55U7k8wjn0h04BDijK\n",
-       "mKO4pDVlIE9Flc4KgIZq2zv6Oi23e0obX9JEXOUYf2KQlHkHQKcYKuSB8rKr/DTPH0OTpYgcvijG\n",
-       "Go/uUMvk+RDjEldkHwCcF2L6ocx/hwymn3NcRpd7GZUN10oeEXRXenQ8zfCreoBFjWwBiZR0HB/T\n",
-       "WY4mrtKVM/QFIfs49V8ia6UP/dxCREuuk4otjeaBndQu8xk8SBQbnJSVgVYul9EllDc1Zub5c/qU\n",
-       "nAifOzugrAQtXBcbAdD6gZwbUhqAoif/eBmoJQ/nSGTegpy7RKHmyNCfD1iXrHBioZP2RP3e/cyT\n",
-       "ktrVVrCCh7LnfjZ07Wdlf64eoIReS5fQft8Y0Z39sTzSW7YJQ83bQGckfj5TK5RWAfwsgSrw0C7/\n",
-       "UkWFHwP9x3KBAxSlxH1p6uWINpCHshYx6D9mp9LJjlzBml29evcTW+UQrx0aIJy0UTPqXFUWwlKh\n",
-       "kckFRvRM6aTAEFyp1v5FM4YD4BkBwlePbzc9Dn38bRpIskV3O2mb8bP+NXGPM1nArnvJTrTVUNsA\n",
-       "RytIOIEjFGKW9sYQ6/Lo0dWjlYQfEm9Xum9CGqf+s/7n/gsmUhWXzGwpUoi1CvCOW8KxhYy6+WoU\n",
-       "4viw3zB4OUszSdCfB1btsSBh/LKZB0sW7baPzyiNM6pdw3pnBJYOpDgrklT8usq1M0RaguCTIbNr\n",
-       "CVFhcxWNE66G6Us3eV3hyLXiUPfKO1M09ex/oLTri9U6vq/ST1RIAhJf5Vy4SHtto07t/pVTylR4\n",
-       "kY3LJ5WK0vE5CT4eAlJKL7uvV85isHV5tG9NMyzPRIIjaXwTddvgJuQ/3a61SgCDMpsEKH5mZpai\n",
-       "vFlABnEYycsdAMXUZQUzasbdwuGb+nQ46yC35J2qFJ8up5Og7+1BGfho7mobWWjVFgF5z0T8jOOI\n",
-       "7zjW6zErXXRiC1BhbWBRbCfOekLHpCM2Jt9pgIw9SG0KHEKaPhzvfRhrGmw6iKAzGUFcvzY4bycO\n",
-       "xFLh9KxCN2Xpv02o5GPxSp421jM01mIUz9wc9opQZOQfG4S3Ek6x/IsL9O7iAA7nv3jTUSXIk4ZV\n",
-       "3l5bumKCHNhHR85oZqetThl6Nw40umEa+tZLKNHpoxoZut0BmvMwrlGOBKv0yupjdRrNqehYSB9g\n",
-       "9bPJZjM3dj7lPyQKo9AzCOpRMoQFlWsOdx2nerXGYGowiifKpDYVh0zOirk4dFfLpHxOOmxYBPNt\n",
-       "yPDIPWj2tsHfcS3wu0y2mFeoR5KuamhyllIhkBnyWpL9sIhrxb3Z05Ulhe+x9KO8KjJSlWrGhs8A\n",
-       "wd8lVJr/EphpC4WEElT6WpZgBhYrcecuxs4EhF+W1ZIaG436eq3uES8FqGm07OIKHmK5o9sBewOi\n",
-       "/A5udgYGh4XVKJ5+wCjKOAEOFJYaeBu/aJmRx2MIcNgt9TsVigp5UFfb4JGBSUJweAH5ljYS7CkN\n",
-       "JOyaf+RfXkKx+NIUWCOvDYKXAAAJDwGft2pC3+p36kOUq9Zlb5QKxmi6npHYGb/TMyssn39hAe9k\n",
-       "0y1i7zw9liYzAcyUYA8WmjTnKf3TQhDeDjmdN1J3raowzPfuPqNFeUukZD8Usmue/amNHtgIzh4b\n",
-       "y0ellMSWbCTl5JxB75LrwiJFCxwWcYtoTS4td0OyxCAEA09WKXZH1OF0rEKmOmr5BBRdwwoLyebY\n",
-       "Da2/MfPapoKU4kASepMYLS+KrW2q8xme+3wnKtcs4tS1Pr4OE1LIHWzJQV1wvmX/9prW7rZRigDS\n",
-       "VcyUMhuoF3wiMCtv6e5LcMX6Q+RtDF9fbtZxDKAD4gC6A/WWLtCUbfVL98iJe8IaGPbBEnc5ODqa\n",
-       "ZP7KzaZbBPbv1cKZ8wblhpB3YFaHG40Irh+naAgNKf4rr8C5WFQHXx6ikuLzGJmxV1tZua80d9+K\n",
-       "ACIOY/uL+4YGX32/946fUug/2v16DBax52FZySctBqnr/K4CJ5w+Fssh8G6361ZfFXDe+6ihlAUl\n",
-       "omdP9ecg8/Qqa2VrIJ5gQYLFf52RU0eGl+11hJw1V+r4/ccUBzi1zkI9e5hgM8SI26SW05PxbcZy\n",
-       "xG8wHoDA+3c4b0A0ipiD+1kn5gk8WO2D6NRxPFwEj+DdDGd4mEw/0l7xTSOZjJnvR1R0dqOikdw5\n",
-       "Vt5xetykLAKnajrc4bvrE34Bpi4d31amTeZClOPKozatMSga9K6k00QbPYri3dCQXAVm/+bn/5+7\n",
-       "tDAC2nW4C+PRTaRmsbVMI09Fy9V/jWw0ZWk4W9JK75GhGTITd7bzDaJTZt0M86J/bn6bK6H1yPNJ\n",
-       "wp+kVj8x+g8RWHhm0EIM2VF94vc78WHQ3wKuAQ5tl9AdIhtvWMVuJsXvZKWfv6GF1BvgmwBQe2sX\n",
-       "4CgN3Kpg8XnzmjrCWsIBC694d22QiW+LAARWXeMu3gKci5PKXlD8MDyz/giVbbZcLWlrplhmJlZy\n",
-       "2BcXboSm2XoXGzmmbNqVoiRKwA13MwvbPL26KQh0ekW42vMmh+DUiTcW9wfre1wQNiUs1Vw3sHCQ\n",
-       "clADYjjY0D2pb+JJhAVF33tvhaaOmuE4ARlU3BHid8aKUsPbgznMyt7G7vtQXPGAuOiJfex+lxuT\n",
-       "TJm+17MUZw2JhQbMCA8yFSKnxERqfhOUBcgz4ozxB6+xjXNrrHZKdk7dbwpPK3u/L/zHekGI2gk4\n",
-       "2Z+w73if0MIXj/x0YF6JKZDT/1d9ZmE8tQ2+h/DSLtNtlzYEaNBAA5+aA0m7onOizwwHRm290oG8\n",
-       "GOZvY0RkyhHSpKuCMUB3JlG+2TB4s5rtwqbXLdz9ysinZnnF+1WhLAZ1SVXOSpcM+PGFWfRsIKcj\n",
-       "iB3Gi9Ia70YNMq8/JRG7f5mQfcGhPx9UmDHz/0Ue0Zk2Gab9qJ8Xq7lhAfuK70ufgrpCTn+mk1hj\n",
-       "KHVKYWud+vgOp3ueVywSQ7Qp+Zi9P9AcJcf8IHT9rBJ8dcJIbtrZxQl7teQ1kAZE8/lFzmoZ6Gva\n",
-       "HjMJlICHNNfW0u6TEQDeg3fiHTL5J+dn+Xy2xq+oS0dZGXQc8UNAWSjsfAW4Fkv7Y6A5PYXGzcaX\n",
-       "w1MB8rexSzEaJwlj/FR2K6BnpyDEEGkNjGEFt2u6v7qoKY0IjJX16H8RmBAsfitDbPwviVS/cK44\n",
-       "mEQ55ZoFVlQymBejg9X6QLgpPS4Ym7SM4+oMPhsHlCe45X3rli7eB3LjT8vMYw7FhYzv4o01fIEW\n",
-       "7K6y/3mdWOYpnai1Oo57Fn7iHRBDfqXl2q/uq1B1sVefxwng692fH9kukD0D/SGKhccj0qEOI2jH\n",
-       "IYENTxXvb6les+58DDF4FKLfp/0e6wdcHOwvgTqePE+KgS73cSV2EZdevOf7JapJANxf/D9bDc2o\n",
-       "QfCL21Gfp8BuitEy9Ac9kkU9X1tJBwwj9h+qz5N8xWR3RF8CTH4CKfzMjdNVt0H3BOb2etfMzLYY\n",
-       "MBBgMgXvK3wfQt4cVH+F9FdC/Jqc2oNdEcPcBNRf+n8GGB8mkERNRH1vtQ2IHTmzzT/k/g++mpH5\n",
-       "9Y2/VABWyAhJBNXFHfjvRMXlgrYlZft7bnqzGko3hJmF9c0E5cAWsrv0GZqRwAxNbVH5gfUbBoTk\n",
-       "zRGNSl+N0oXlrPYVMsqkcBeNxwPrsjGtPSYkXGNN7NBSKXQsKNav0uelrk7FJ83946w3OuRFGJJl\n",
-       "d7MZFRWAfQUqY5ijezI+OSPDCSPMJkrnyRkTl6Vh5fk9bhjLP+w+hEC5sgCMRh2ZSFgmCupjv009\n",
-       "pBAiOPxVTNXH+t55ECeTUyVzJ6nF4384RLmIEG9Knu5dO7Afkze1+m1kUoe+0QTdUrFr1LYAEK9b\n",
-       "c69Zd8DHH3FytMZrTWTZN/ifwKFatiBhhMY2YfVzESB4SRDuRZrtwi6vCiiec6BKzEEkp57HTmeA\n",
-       "XoALRnADpfmM1e6RNbCzdjkKvv8Uch5zjCWyAvipURbudYDdsFw9ZFbZoo5rf+Bkjd+KeeXpXA3h\n",
-       "LLZQWoe2MmFuGWxTwkiVIJeiOzKgEliQJwsn5PzR+v1BBwrk5fxO4cWnZ4VsrL20qqDl9REzxrcd\n",
-       "tAA0wJPjI5N+usxYF944tlLrXHEr2sTOsB7shU8pjjU9+Qp20rN+cU0XZy2TYRREubT2tN2KSLvF\n",
-       "Mt+ydfqfpbUi74X0RdvVwtXYK1o0TaXk36/xSS2eScrJQbxJ2oZ72ekLXvGSqIOAo7r33LspdQzi\n",
-       "ZdayWyk1BjMDGoFRgBt/kheiUgc/YIPEM1WEJEAqqt4p321SYUXsDIg58Ei+BKzAiiaqLEBirpkx\n",
-       "Di7Ogc1VOxIByYXSIWsFe/8fDxJEEJSQ/5wPjxW/BePUD+Q3xu0oRs4rgXZ8etYNEUO/VyEkvGOW\n",
-       "kotx9jCrcYlYDT+Ry74llFjmjHedTifX51xHw1Oy0CjH6EHwVzzTa8POxS3p4+iQWXdgsZ/Rv3Un\n",
-       "ZlQyXkZ+DI0xMNQJBAhBgTTbWwqaI5BFgwPU23OzD5/xNgl2cXmnUDGJTH7AEZhIPiT8gpUYzJNJ\n",
-       "Kj7vGHyZ78lgDlu5j/b63Ht0n2m/i5UYsAVxkTwu3xLEU1DsHUNSzFdvXrUnxuVjLtB8HqTNMq3Z\n",
-       "Yzgs+QAAEM5Bm7xJqEFsmUwIr5i/ITkAATPjK7P9Voh3NZqR3tj7h+OFsh1Dl/58rewa63AjNZEM\n",
-       "ijKbEN+mV1Ji771JigCOA3PWeL3Njitq7HNsX/ZdSSod0DCMQh/uWE9xj0iPaX1DrDFBvz8x7vuq\n",
-       "u299IVfKXyWhB4hbQLTtAfJakZie4YXkzHSHS1rIsA4Mz1comgcqeduUUlAzRJE7dFIDiBBEiAD7\n",
-       "eIwoFgnXUA2AFE0XGjkCvMmsaet58ZK+kOMk4Q0HhfrbJfMk1YROvp0KeGaLzr3TY4U7jcyWH1sP\n",
-       "W+0cuiXSiyRTmcWDLBkgSn5Za6EAfFNnQh85iFJQ2CgNA6tZwtBzsaWhdej2mmsSVttQLPBBdNUH\n",
-       "JRF5Qk91wtH2XOtwLTb6RABlq3m4vtbg/U1EiiSLpLATkao9JguZ8moSH9k6H8tYhlGRXUEB0B05\n",
-       "BpPUMzD6zkFajaGtB1L2LTHN/AuoWMeI98tXdf5tCL3QTCnThc/9RjABfN2/ObURhxJAighRGIHu\n",
-       "3RkjcryypM+oyVN2mIWZ2HwmggiltWQaJmwN5PNiCceNy1FvsRO53xKvCvJRfb6pcMnEgAOhKjrk\n",
-       "hML2A/sOzlzrCB0W0a+WcjcT8D/96PPq4Z+UMMMrTZy8ZXZQ7WiH+SjFgLWNZ90a2iPlbtYWpCM/\n",
-       "hM3NKL8gAILqg8ycFf1s3Nj90PGIOP22Ue2czzGZ0tpTXs+gykT1HYwRIYqtuyXn4IavaVAazFs6\n",
-       "4q8F6+M0FA7ra2oQtEYfutZs09zj1tyGDfBoTZAmM40B3Q0fVLNl7Ui4PDUyDG1LZ1m54y831VNN\n",
-       "sEuYi2Icv54dN84v3xdOiIq/QZoWB9qHosV5a3nCJSTi3iHB2uzt4s4IEezceZAaqJ7rN5VcXW2U\n",
-       "0JLCh1NwEyHxGyiJo4s1Lv9Tm+zWfFVSZyRU6LGqGD2jr+9GtQYwha79C7DwciVcIEHpOxT7i+Nj\n",
-       "P3yFQAkgXUALdGs3Fswl4pFK1qKV660Sit26CgjXqN6l+CYkZRR4eJJnyf3wJscBLve98QlDNPGx\n",
-       "jJCQQMFIVQwz2qIFhKe20ht/S2M/Z7nzTOpEcYvWshuZh8+xQGrwaj0lv/N+D+9KlrqmAwt1EW1L\n",
-       "2pOnaW+VjfjSyQPJRmJkitrZ/Tl0YB6QHO1ypPAeLfN4zV0s4P6IxnZCsO5PDeb0DApOCGKxByZK\n",
-       "rfDqn4gB/sHTMRQdLkFuSuD1r5r8KV6kvyVb6EtByrkiUvmfr2iLnYJTzn27PLaFGL0wIoT/LfpV\n",
-       "duJXmw6nDytxkB25hBfHAWTPALkfSv09JKLTm/vB5YDtTDcwh22/Y/Yl/nuBb0YZqsKvMDTHpZLI\n",
-       "I6F9y2jzEorgiYZWhc168JcyOJHsOrh32AvcRYZ++scA12+yDgx/cfIF0AW1YIBd3ZMrFYE0kHo7\n",
-       "GnitC3e37Gdfv2tDBiS7RGevIplmv8AdOc5Pb96MDWR49VO40sNGX+Z4uT9j28mR3kyuu45s/t6l\n",
-       "Pe3R6j7z+rWq/y6XRZJZ7z+N6AZQNGA1fH1fY0hnX4TeoTjiyvZkdtf+DjocIDHt8x4HplWtFbrX\n",
-       "0FOJfdO6tZDIou6Koqns20c8SoL3f2sKbDZuasF/ismvkM30RIILZ84pZqwIeLdyqUKA9R+7S7Mh\n",
-       "aPPqaIKVYrEftlDF/peaZtYkWBGtR/8CUuEt27LRLzxfJWZskVKwU3Tv2Ff6D4osQBFTno6QGvkf\n",
-       "NqLxGxyHQEnSbm/wPKgyK+iLfuOmdbyZqfmJrXtalREoNFclu1bDKldMPk3M54GK1bLosEChRFqK\n",
-       "o/4oXrsNdkhFbeNDRBa6X3ZzY4Nwah8AYdECYGhE26E3Nqr+De6P6mDmweMX87atrbE3HvCnBj3v\n",
-       "MZYV6Z2Y/CuLcTPPqHYuw7SjPxAGR0vXzFJ7VXJxEzcyw1O0QHxe4SP97iPhEm581jiTPpwLW577\n",
-       "A7IUOJDWKOzybQUuobeGxln9mvniAyMYhKwlv5LauyX+sCKRezj6CAJpj5xtkK4BPtn5Qng+cS4+\n",
-       "H4VqFVc9j3qxzeNwrNag+3fWpwZKb7R8+3Ow0Sg0/WkA7BGF9CUQ4DJcan0OT4FgYvCF5BffQrbv\n",
-       "JUVpqcdQb4iXT7s7VLnzu8ZKWZN1FI9K7HHjoHX6YlNcg7IH1yjFoKv/97gKMIpbbtU/tkaENg8P\n",
-       "Oeu9d2czN+Q9O304sQFEIJ7f9wvspiVkDLVdGOWlFuALnMRL1vC+lDIM4gApdz2ZVfEglWmUIvz5\n",
-       "UjOlnY+IA+lncy4JUyQpxiyG5s3Yrx5lPrGNKHmwHcsS6pc4gtb2qdRX/ukQOMJ8XXEEOY5uVgB9\n",
-       "6A0x1r5HcZ/Mq/jw63uRU8ne6Az4zyPB+Klw1ChW6vqDDLPifVwVgl/WZORvJp7+4n/avyoAzxh8\n",
-       "dSsijDpfWeNWDE8wGJbhRzGX8syUKch0e68f/Og0pnXQ9Ef51Pd6tBZfnlIPjAEv/ztoLIXOUJiV\n",
-       "063eRxu09FSceOYJ3dkTjyv6uQaJaAhQG6EdsaIlvpJLhjIaEUemS5pqH5FsHtXkoKmRzjtfovhp\n",
-       "Y1BO6LyXWmXtqrncIk9xeizS5U9fvZEqvBgJK/YQAFvFC3rmpgXfGtf7lbb/wTu1SQ7ai4O8xGwz\n",
-       "QMttusAxjlB4egVZNYdLBmZCJEL7DKBToBrhFoLVo0laHW8W5siqm6BKURxWE7Ve2VCruZR1seYK\n",
-       "vOAhnB6p9dGr2AFwY4P0teag52cEF1MctQoal2MgthDVvMTpqazY45WnvG5ZHoxq696oo2Qblizz\n",
-       "o/mG6JkEOPAQ7SuvsXrS+qO2JHKoBskSmZ40lNeVTAeKkmJIE/3EjXn81i+zAJ/7LrzeyWIduADg\n",
-       "yXwVDPaj+uYATkhmAya1uOzTC81AJMmUHTyVthPCRNYOfFet6ipTn+xvx+LV9gsK8fiFcAUlLy4R\n",
-       "e5WJAzIRUakx4U0Vt9N/g4clRDCzQa64vRRToZ5jY+EYn6I7GWsouxdhoo6jdzq+/kSh16bhkyaT\n",
-       "nMaYJoo1JU4laR6A5SQCUBVLMCVojITHWlLFxsQtKqn71eAMfUcX9ro/FX00MHuMYTLAFiRSsefo\n",
-       "cXXMolNoDkCvh0A7eGmf21C3uPm5/JNAnFz4j1+ommn/hK1p7tr8bwqFTsYBQuPz80izdgBTAsdu\n",
-       "Eb4OAMTAy7F2Xi/sGyxKcjR/kgL/sNa0Wwc2Dlqg5EOdQUsSBRh28DaEFvoXl2jAH62DFzbkutAq\n",
-       "61sBQE+YEnwpFyYjXRCRS3LML0BubQNDuvbEN8lVRMgOIAowKvsbwJigwxaHAyfkH93H2LF5htlc\n",
-       "s0ZarVxpklNBcgW+fKSHazTcj3xeZrrxXcg+hSgOrGXBzemj+l1ulOwYyqj0hV39xen7JNh1H4AA\n",
-       "T8nw5Lm2d9Lp3YaajJbvADqZKAL6gIo6UsJgov+q7Hnx0ERpU/M8afphVBYf+1f2BvTcuCQAhijI\n",
-       "NGGLFvUqEizcp1t97nGGp8Ryn0Coq3m2yGWAMdvkVN8k1hIDCI36h7D4eRB5ms5GUQfDpAKxwdmt\n",
-       "UWgCDaf2L+9197OUNyjIxOp/NxLEnMSPS2LpT88l2NNIkajOoP+wcdR88Uov4p+AwabjEZ1VPKhc\n",
-       "2fQ4EGoGpKe3gnP8d6LR/wHps2lXE4l3hZa9QsKgGX3tjKIkrWseUX3r7vmgcIEhU8lMf/w/wXH2\n",
-       "UPkFEhPLgdxb0w7DSrreiEOleleK8klgO1Mq4hJIFPv1ay+55TgOX0KmnYHhchEFfTSP9GSL8sTI\n",
-       "cC+iXwE7PpL71rYBRCXSRxNvzl55AIUMrcEHvD1lhO8YNNQlXyx0TLDFrzoRaOM/uNHYXnvMY8H0\n",
-       "MIAIfzkCIt3eiV4EL4npU5iwcYxvxWAByaK1KQiS+4wfTUzF/NKUK1CUryM2Or9HXRSdqBCfZmpB\n",
-       "LtmqKCOl9frev5kF0W8EeoDak09wm2q1QTJb6Xppq2gyqJrOSUf2hd71RSDcFcgWAxKjeJ7M85ys\n",
-       "vknjLG6TIHAOV1U7EPDHdbGlKJrNZPKjqgv98AeJTPsVqcR6eS4YLYYjXWcQdLPUJ5wrzYcykey/\n",
-       "vuCY8OQYBp+l/56Qb/DvZDiTTXa1oE8jZKzDAqGYPhwHCXiLa4ylu5lITqI1y7FXC7w3sf83W9cW\n",
-       "5PNP7GDfLALaUM7XIvKfyC1JekPfVu02110d2h0uQXR8UQADL1tsSAzw0NVX+AJq46RXWyYtzDhd\n",
-       "w9nR7EuObFeqpO10HdGangkdAuJ/V7OCQWx5l6BXDT69HNwTueRxDwOGI5kk3d77VyVMzWJGQX0a\n",
-       "60mTTU1PhcNWINflsADXc3r9oGOf2cbXvoojsGplnpfjZTz8QbBVvBcwXIARv4ZXN2GXmcmiBU9s\n",
-       "BeMNcDFoNk/XdNxlqTteZjTY/Z0UjOqYJzN+eKBENctbb2R6BxhV4BrkVNSa1/fZlxmWgRigI9ZT\n",
-       "RdyQP7RbUHFdQi0acmdgHqhWd3tY4aiex+PlW/Lmb4rNP05JK4SeNNTpqqX5tmUQQX6JIerPz3Gh\n",
-       "w733+8im9+u6xBz7zbgeVClNLg1RgPUGgI1tPbbVaurEVswoSwg9HcIH/0EkCU7+ok6Nb5nsBFlR\n",
-       "1TR/6XfQcUm5+hnmIGzeu+JpMJq1htDNH7ruJTfQBhl/1nRiGNo0Iz1NoYTXKf2VHM0hJ6HMVnp9\n",
-       "RY81mH6Yrn/Akr4j6MsqRMkCDmyBtKcT2KxWzetUbthxnMoKI5Tdkt8hxNMb2YBYF3R1CuM4uw1F\n",
-       "P9+2ODJ72D+qGIxfOwdjCi38q7fDfOyKPLAmToi1iu5YOzZKa4j/j95WCVBB1n/A6KFg6j4S+p+y\n",
-       "t+Fp5Knt7mfaX2pseQ0WNZO6J8lDfq6o7I/tdT8M8LbVwygjEbAGshPoHhgfGAWeu5/tu1AfiMCL\n",
-       "25u0nw+tZXXUgotJqVciDozfyLCvgviZF41we6NKGoc/NfORNFeRLC5d3fNTGTjG+HT/hJjvDr82\n",
-       "87ZCCQbR14RY14rjRn8T+8/rJ6fYF2qZVafgCzY2RR9JRsnTUHJkXL46PsCqu+jmUbBZb3Y7w/Dz\n",
-       "70a0GEpxRQXnUN4l7ovDzYfAXCX8TfjKdRPZ1XvbvCQinYYuB4DNSgRTGC1Ef9P61pJCLAjqAbfQ\n",
-       "BecRFJrNB5EYb6UnR+4RK+f1//6Mdrfybab0yGQpq+7EZQu0B0gvW1NftGYSRERV+2tMBhDW4c0a\n",
-       "bVEnve7DX3JYnkAYg9pV8+JLfnKkiBC4dL4jmDxJ3gvhrKWEN8dmPoGhqZWBMl4vQvMbRoBojRBw\n",
-       "QQqu9jnVSmLZ045k/+Lh3OgOAI3L65kj4ikojLDzixIU30QJcorcS++a/KThx7H/zvbEYCVTxEPC\n",
-       "YO3BjvWrgA+LShzufu6//dLZlgTD+mjpa615IPe2gSL8DCEx5ROTg40j1IKQtckCPOPARX2k4Uzp\n",
-       "jXFgcy5yyqHx67vomdGWv0Uc2Abhl+uEg8+Ht5y0Wo6WPsCb2BuExQBjjYScFgmR1mMCUYOx6BV2\n",
-       "nxQwPzOaxxudHVb/9149SwX8KXuu8V6S046qMo3jL3TB0OGdL2tdwrg4+7ZBezfZeiKHHPSYexef\n",
-       "kdnXQVstrhxO3DRo5tBCu5hHmKT0xVL0FJu3iM2xzkRdEOw9AgJnE83E0CdRkTVLM5VKba+oYAlX\n",
-       "RnHjyUodf9kLAsNW+MTLEAGAeP5OuTLowXmUaPfgVSM6BqQAAA9NQZ/aRRUsJf+k68cYYX3A1dNu\n",
-       "UpGf+MkH/aD13atcpr7FGYHPxXd0y+uSwbQvcb4zBUGO+guKo6Xphtk+rMtEJr7H1W1hKs+qT4SC\n",
-       "osXSKjlQV0QJuRtlxf7ZrIlcowzfJkPKaGbgXncNJ0DZsqv/AIG7jcibx2d0xODLxbtlEVuC0LBG\n",
-       "wNwt5LoPAYudzRRqEUl3OuLBMah5DhC28zBVcuBHqsJYW/oj5eyoSYxD/3CGQBnbA67tpei1mol7\n",
-       "Eyg+8f9OxhmyTSujXcJEfJARTFNAJapyMj7lxYVtwnNuOr/Rtp/2K1K9UIFaxxyDiphiGkJucecC\n",
-       "yk1r06eccUhKeFm+9UtryQyf1iXf7cWm9wUA6RHV7nVnn4OgqnjSyKEsYkPftkGdYuXW1uQyAYnV\n",
-       "pjW2vlydNXBvHQopgJDVkKLB5CdMi8UYZ2wlzeRSOjHkvfmv+M0X70AR9yXYThiGVCSyWbBX0VtE\n",
-       "oAE9HErtND8FIx/BtOmSZ2pueOa5AqPOB0NLHX24hHokrSSRAG0anyomlviV/q1P7PEN8YumebK2\n",
-       "a3jnve4G45NA5fhDv8Kai0BhUdlQmecpuxQh0Rb/ngyEzkQzbeCsXOYvmrAzVErb2suJE29tBuF+\n",
-       "bX8gWwdrkoZ8tT0BPz1fZq65HdOmvVVWG6MXon2jkg7BGUPUrJhL8OSU9L23jc+jvWgIPnvkM/1m\n",
-       "d4j7G8Bcs9oVFA3aLvjKpAG7Xzmt5MhC/I3BqBZJZ6neHUVmjG67RR846aIakr5F1Gh+QC+Mkq9e\n",
-       "IxsnylVTC8+KM3WPIIUVJw2sW/WxMUz99jlHxQV18BTD84rw+ZD7c+NHoZCP3wvigv/XI9jSkyw0\n",
-       "0U3FkqylU2SJIeUG8f6c0i3a4Iczo2VjoO7AerqzYqG4fCe5z7+6jwjGTCeFtPz/V7ipE51n5Aea\n",
-       "HOMT8WhsZNC5940yyetJDlki/XH186myLX+3llFQZe/zqqeuGXobdeXLbaTTbY0URDaaSWVRg1XI\n",
-       "C4jlhhC/9wFJGVCmu81OBtzcCfLkX2pL6EzRshreyjOVXUWyrB5SAxji6Cz8tR0+CV2RtKz2nSHz\n",
-       "uVFegB5dg3QfH0v4OZIgUTYSguu0XRTtegmylOlicqd/60/6zUMlOpiBGv1k5DOubVboD1oZmFMb\n",
-       "cVprHXmDSzMjNVuth/SoAA8gWTsG9Kn9wgm2PdtJd8PcT1YCiyDMXFhYNgqz+wwkz48BT5fTZdTY\n",
-       "ndbnBNdGgyA0qSuJo/uUAykM1qFTju1bJvuicNyVSLvTmCaEecezEjD3HUL1l5UoKptzaDzdxvdM\n",
-       "V64VPGhnG8UqUmOZf4FtVxVYmLMmVCFkTlaxH4b9JUL4QzjTgPKI83EgsLsPIng39RdqOmNOs03i\n",
-       "RBRx38OHZd4gcbJg+2VbU6H9mqiXmN+oSDR0lCjtQ4HEyDMYb9/SM6qWkbKoIUuOu8o/O2CdgTW+\n",
-       "A+bR0s8fBVwDhghIwy9sIHOTBHPLLObHzhKU8Xy3iAN21BhNqxOAmzHLB+PlnMXRTAfV4RhPb5+r\n",
-       "0xXLL48Pgi5rem2UrAK+vuaXm4f9IOu+47ObqsHZQ0vOhBMy4BqFSkg5BIxPAwCkFHZhOEefnZCB\n",
-       "P1njrtMeHl/U3nwFiNIthmnxMpFoJFl/vclPY+dAQWLneGMNWKcUoURhFVYtvhmwlSmE3xLT23YQ\n",
-       "GeuBPfbLRs6xY6Ba9nbsMgIaC0idG0QCp3ZB/drvUBrNcHD2/egEDxVme9cz7lGQ7RZuvNg1Yfna\n",
-       "/FTEEVnj2LI40pG8LN/0DGjMxAJezeXW/0eOfWwA4KpVyOKL0rQ2VAP6Lo84tk50vgsCNEXwBvua\n",
-       "FtnVQHncGf2NWUi4Aq2JZi7KrF3jcWvbNFyLe0wH8iqAquiYqVtSpNxMLCyDv5XCYJ92A96s8qz+\n",
-       "GBn0c3H7xTfGKQ2KKGWmnjGGxmwAlKCVRCNveeYQrtaYmi6Drqz9obUqb/6cwTEX1iFUbedNfRAq\n",
-       "sQdHJBxOh1ipyHEUAAADAMHXA1QACcXQz+pRmZ4cxrsLPkJifPMOpvPI3r+TqtCTodSz9jVnxAWc\n",
-       "WiAp5utUi78sZZJxaZ+MYMznpd20QNiUCqWZ9Zu9Ndj/r8jG0Yzls0u8VwUFdVq5xzSQj6qKlaf/\n",
-       "7blhQVLaZIpzIwuEPo5p7m0ow1pS0QV5aqTFZGL3HxwFfkk1TQUCmmcIv5WYo9/LL7zS1Ky1hfKQ\n",
-       "IGC4UFluPo4nefWbZNmNPp94GCj1p5x1kXx3WMOB/8qBwdVbFuKuPujTnIA/thYNsMAoBHCJAKPl\n",
-       "O546LDZhzaRVzkWgE48pmV6nArHT+qZOp6ZC5MmOyYilR6Ddp9hpB6DFj6mmBNcav7vcL2zWA8Sj\n",
-       "5BHO7lvgyHSeEWff1RkTyYv5+0jr/4/pnfieRPC31R7hvQPraK+gfOsfyLg427U8/o/mz+LqyL6Z\n",
-       "gxyBg1EzpujLnwqPnNjl3Npp+9k3qII+8NaOYhnZCV4ohvAAnOgCHcBTS9UBgXL4lqsHtjmCs18R\n",
-       "yFEsBpR/ep7BuCOrfxA/muWa71MftuNAgiHpVc5nCGxwuqqM53c4eeu7Ovk337Gn0ZWICqolB0qk\n",
-       "2VsqNANwAxfj7duihnXdkHhGVGNTX9xJmes8oeXKDbp8+P38bJLezEtKck+Fvn9xE/rFdGOpPl8I\n",
-       "VqsEL+AUoVcBfZw1MU9aMLHaWVaUcndTSevYF439LFlCQ+ZlWs/rp3jzKtE0Me45wKJIbsc0iaIl\n",
-       "9BYFEqy0KSgsI/6wxZnS9tWP7vWCKGJQZB2BQyDx79cmlgpGz5ldtAew6yYc8njHKnVjriv3lLY1\n",
-       "h3Yuigaaa/WGW+mkW9Fnmt4bpv9HB71/3ZAcmaKm+kR1Vje+DQ1gnivuvwNQnD5hdRNd7NuhtSV/\n",
-       "Vz98RuDSoCazyoxS38wjoL3M4GtpSH0L/a/c+75aHRVhAf9LwdZaRA/P6Fi2oDM/SiFpCSVF2ou2\n",
-       "9ZYGxeXdngy+jqwM27sNCIewWl2TDw4MVvL3hghGJIOU7H9i7XiwCD65NGz+Xkw5cB5zi2Leupy8\n",
-       "M4kT5cMMmJoloUzLAn0qKt1ea1h9gUi7o0vCda+qEwbowtjTCHvX4S7RchPkpEnH54/h2LVs9SqT\n",
-       "jejul17yka7HtEBp26ogq+gZ4AW7Yr3jvvY+r94klNJ48FTNrHIWit0B6/8eMd3uks4IyPL4DBFj\n",
-       "byTFmO62SFKp3l00e+APpRxKUFQKPN55SZWj4Bg09xdL2GpZpz34Khv+g8Z/9vpPtE4i4Zi9oiic\n",
-       "bwOHp2KIzoa3f0vV0pELdKQSvwuipVI73xA720i0BA6SR7FFotImeCpJZZLFBGgPyLQUlXB+7hmi\n",
-       "jECLlS0qWhR73ExWXoZ1GxPbQmWMoOKWjINz4PcxPbhP6VNJonZlOXa8A6jo6A150fSMynRejq+z\n",
-       "qTU3bxGh6TmA451TpTr+Phup34oe0aGe5VVATr0cbdGxVqU7dA8mlvRjtn8fa1pEOO/+8abOpksL\n",
-       "aYIh7QxdrxfvVQtnRfiAmk/r4vfS5B/P2ftGJV8I8L/CTEM8cXB1D+V6CGjhHVIz/SESLl5OB1Ds\n",
-       "36u0UAbp/xw4SPzTdPkiiM271dRbbPfWPspYMKeTLXToqt5oHod3W9tIR5VTYatmb4hP/cgG24L9\n",
-       "ZmBcHlcCN+bAWHKepJzsTK8vfp86Dl8d3rsnd8DL5J/tFvJ9U2i9RM3xkRrJg+CFGYGywXbL593e\n",
-       "9X4hnyI8kwrOyRtL28eqe3HeJ0sVcjG+qJ9LhpZmShPsYFy+yONpeVyr2hytUIpu5L0WMau/o6sG\n",
-       "YvC5rfNC9dAhm4Bwd2YUgrgVyBQwi7+E0XIkzTkcWl0ABH0kksEVtAhZAi2pwTgbnfXsQ/NYcj6G\n",
-       "wZ3yh+/weGHRWF96V8qtZ8Q4sPpgMPriQmCPY4+GaCwc8Yj1vfmGMfQdLjzsaa6NkxhXb2MbXsB4\n",
-       "mxmH+1rAWzehDunujS+x/a6em7soQzmbWyLLL1/IJkeJNwrFZTMsACHwVdlLZOSg88SfJ6tn/xWp\n",
-       "oZzjHugiuPKnaQE4GT0jZ96uNW3P2hcJMZFArZMk6iYBjFHiNCZ9farkZtm4ily4Hc7kqChwepwV\n",
-       "0+BnLDQmrtGNG0fqkTXkMlVZdHIHpa/GY+skMEhl7oujSNcqi8Yq+w1Y/IcbdNeuONw0Z7wwekXb\n",
-       "NHNET2SgmtZy+6u/7SdzX5jadO7+RtP2zMUsEuNrleMMnOHJYfBeqNPlJSulYwQfeDCS9irHFN3k\n",
-       "0mp04mB2sYyoXEG5Z6B5tv4J4XeLwiG9Q9k+XSpwsTO7TMNZx0PPenxfgGhyWm6p7vsqComdx5GF\n",
-       "0EQhRTZj/4LsrjR0mXAAm1d05UtpgG9u5nEBo+M7ERaIFC4fX/C0G+Y0OoebO7BPt9OJYoQ/qPNX\n",
-       "M/mN/buJ1V5kO614xvji3KPqM9ADbBX/+QV3N8YS9xzdL+lVN1yQ92XtNULSN0ylt7o5gvYnkzAg\n",
-       "ViOGrVnu4aUPE1rZEn+G+ba5E//xOHW/pcvwfcxdPwOD5vOVPdW0AH+EQhpj2iH6cmkKjsr+Ff7q\n",
-       "OqSoir97E0gMCxdLNoXd174mnj591rNIGEC52i7zRTirFjYlzKL+rnhB9uA3ymcIJ+Xz7uCNr/km\n",
-       "KE7iKLvUtbRrBdfWYMSfUuxvWRRpxPUpWC/TSnEzAN75QWkdq0RMqMHpHkhX6CjopLEq0zEuE4YM\n",
-       "EhwAsfcvTeQCMk2P6xiWQG8k8CIzqzhsw7RgfaohXePQYzxfmnoVvQvlp+1vmHc3ydUURaz/V/Mg\n",
-       "bOC+/mF8RIJjQKOPHZ0v2dI3l5GGdBnpFZYZjQu+27ismHMT0FZX32s/4mt9TmRetF1v4TkX/y95\n",
-       "+yGjZNUjnpYRImcCfpvvTM7cb9wAVF09Nlyo6vX+OD7VMy+fNgqGO8Be2N86rpDyoIOMS79APRiY\n",
-       "Tw/H4Jqtn3GqAGBqD60hfYDMCTjAFc1Psq/PpB2gtQMtE3EjQZtXoR2FXOu+zkSFsQHXyoUAGLCx\n",
-       "Qe+Qwb6xcdhIwR5jJ8mil32QNrzSHyd0O87vxSJRFC3S3UcG83fSjGE95oInaVrIVW4qPlHEnXlD\n",
-       "6Ikch9a/QclbzyNEzkkkn8Nzzqc9UWCk7K/k4Hv1rpi43zyOEpAO9QeV44mZDCKJVqxa4V4ubydK\n",
-       "ma79WITQUm+0S4gvsQiP6yuBpwPZ1HUAAAslAZ/5dELf5JEjHReMtzrewfnDoCzmbWES3GunVoru\n",
-       "vHEWQpmIslhvO8WIQEhfThW3DBDQBf/kC5k/IHMHbpe2McWSjMPfNX12BJXP7zKMoPd43eqdT059\n",
-       "yorNACxXzvg/MPDZ/xGONQFufO/KPw1oZlYvwWCrqm0WCCJT4VNoIENkTGpLUnbf91MawfwDBiXy\n",
-       "xOquokDn1wtoZT97bvLdmkL8LOq+XRQ0L9TSRt79YZB79sqeTR2/d8Vn96xa0c3Dveo09EnzbfwM\n",
-       "IIOn4BlTT7kawpcZso4UqyYFGsHhFpocbab6lAzy1paWtYJW17dWoKDP7sht25fKn0vGmty32N8o\n",
-       "r5cFjV7MNwARyxBIIFDGNwUiy1WVzJEGzaJuc64xCuvzws7y+SMYGNA9LpuXoKP2J/ScBHwrO5Ke\n",
-       "yIAk5eXA8hWz2tnJaZ4e2akJUQZo6aXO0FpcQMMKl6E4+EDbSF9QWD80ABvtNt44u9pwBDMLgmNR\n",
-       "CrqQcF9NwxS05uAn1amWbZPIdrpvkj3V7AIrUu2XxE8FWp+bDLlTcfbnHfE1KVrexLFgLFxrlXGy\n",
-       "iqZ0EhaErxWBqKjcQWpQgDSNQglhPY1psJZLprcZQn4zaWNPfGDv1GPpCRLSHVogklG5kRK+uDn2\n",
-       "tSGaebFv8CBb4OukTufRIP1vQowMhN7on5dLef+JR/smRHP5gYGvuQbJV0uHAW3XK0s2wTQ0mVZK\n",
-       "QG/Lx38W1h5+AmenW81k9EpoB8Zk95CYLSDg4mbpFVOppIFHAG1kshP++rSh9T+rQiz/xNQQSz+e\n",
-       "PUp0KnIuUCPd7WB8jev+I75v1xD02hp6kXnETxsH4wnWmrWhruEMwU2TyVX9q70jk/4DnY6ip347\n",
-       "yxSS+MhMAnGi8HU9BMcHNQawaDgvuink47SX1JtS3iowg9qVeJ2eXGbZX4pV4qYYqoLCCshu1NhH\n",
-       "EmM8uN36rILvxCYjO5uzFvKeBrXXuv7i+Xe5DkClbpYMcjJHAd27CGjLtThdL5cyuYzgQNjVDipN\n",
-       "I3WHlxPzp3AB0LN6qnsrFY/LmeGeY7WsPhE71F9AX0KJiMtefGLS/iVthYItiFtjvdHYWDSHbtvZ\n",
-       "4kVfsce9MeVaRV/h8R+GOubgZxyZacRh6INdAx/u/T8x7sNCZ5J/DZ7/uX4FTyXEy0XYZGHDnS/e\n",
-       "8hPa+qoEyA3PI8Zem2ovxFaJ454vYJjhxC0iuQZVsKNXXQyNTr5zLuLrx57nBmYwarSffkNTS14M\n",
-       "/qRgTWMWOz+k4MbzJc+LSErZ2UOGh/Y3+kMdtNGaZtQEtgxUYnZ6jPyGUQDwT2J4alo1zksEuNY8\n",
-       "9SzPmxZs0jj0eDCdcYjVozATMZ/COrOCvtu+EJOlcnNWSEMpYfJueV6XqOFhSNedOLiczusJZSeN\n",
-       "QAwA70oJ/VE4B/RADkC83/SwKbvdfmLQbXhLQTTtLtTkjpgHRtYkEAzzzHeheDQ1B1wztPxwW/UF\n",
-       "t4o8qyLNcBVDKFaI3A+k7C9VTsNH8edQGPGVzat21oWyPO6h9T8/Zt/iDSJXYwi+t+TBir0YihoD\n",
-       "22s7v+xPVrz+CVz8u/HmffNaMMerU6vAFqp3Mc6j4A8cOQyzcv4c5W7EpfOS8KlOkekkfIaKY9Qt\n",
-       "egS72M9j233amAz2YDQvJ1SmQOunIWsl11WLd7UoD9D6d3Z8bue8YN8mTC01k9ETJcEenlod8gp8\n",
-       "g2rin7UBY9ESXQZp2LvOG7ue+lJhZfrFBteT+1JwwZGLPKPn4Y91MwC47p6g42x4ZefEXwvL0wxZ\n",
-       "X1H1+oVZN59W21KLGnN3o3xA+qJpWj5/GF9njEujGWDZb/yAlI7yZSmcV6w/4y9ByEjbP52b62AM\n",
-       "pM/BITJ/sswQcLuatKBZfcP6Mon936mQ1YNzPmrxExcUlWMr2ofIfCgtFbzRxmYLBeGKHntE+lSf\n",
-       "cc69PBYE+ObIZLEuS8MzuMYFyPsa2vmIKB1cQG5Q8PTgjwIps4h7b754GsP30fBki/X9+QvqPi/1\n",
-       "CgqLVCG9c1jQuemZurKiChbs+j9xM1IiBPvhNTIeYXUPKyVKbjttCJCLHlv7BsT9lcMnDYuccJcl\n",
-       "SlVH45vxgICoqAMZGePzJ8epschqH0oboTMVFzRIXFv02wXPAiGE0lOc0QACU7vX234QMcTMo98T\n",
-       "lSNzYkmq24YBg5i0PQicM8yVM0mICYWaptL6t7GtFjCqIxMBv4oeSVyT7XK1BeVy6GvkJnLZYKCt\n",
-       "XW8d3qrTiajNs/y4+IMS6BCRwYGKLnzxtLFd9w3a1qIi9kxgYV7JdPScGI+A8qNn9ZLAqPId6zoe\n",
-       "pxabJoWHOONkseISJjaHuv1fTgPwtg1K/EtmGQ1ZGS1Ay81Xlj9df78PGW3SOibolmZFtgocm52b\n",
-       "33oCppwB9shyXOyi1RLdl8xz5N6M1olJTouP5Q6YEHhNsqIA1J+Rg0YQapz1etqtAD7KmyuNpF4l\n",
-       "0YAGkRU0XQH9tpeVX9ztJGOb77Z0vI7iJMnxTu03FnXJsSIFu8Kz5xM7KaK4VgbaiDawP7Rtf0KB\n",
-       "BxMjV0AAEURUc3KY4e3M3HN35BGmgLlAViztEIB0EcdajuFKJmwejL9BVp1lbsoGy9ADmXkiQCZV\n",
-       "kIncMAABJPOLock21YQL/EGs+5UtJtPXUrQPGIvC1GbhEdTMR+mporolqX+BVZ9UNMh4LHOUI1Sj\n",
-       "ibeCu6Nxo3ksFvMpQu3SKvKxz2S9h3geGXvtaNURibzcaaj9u+f0JPVFZZAdMN6AvjLYg+NTZJZm\n",
-       "1T4E7GySUUysgae0SKj/4ACkFTMYxVl9BOVgp/qzjz9iWRZ8fcqhwGGc1UnZVdtrsPUaMEdZrVh2\n",
-       "909GLmMie/2g7AZEtEMnlXKOUk2oZgxQPMsIdVSGexL7ghGgXKOfBRyqSFanSMyhQ8UrYI94ESWo\n",
-       "ux3X7jzYSKiQUhFcbNMZE8JnDFry/7TfQqpifjCTaAAb+ODFtPlXlfP5Xu6UWIKvQMYcvolDgUOV\n",
-       "vkQIJCCerO3LEekgIZYPHGgqESqAsHqOSqH7GXrIUUR2xMvGSy1KSoY7bOyT3cIdNWG+9eVxfNwt\n",
-       "6AhVuZcIM7WGBK6Ss/6ubHU3TPdE3uede7VpwZ5nD/JZPV3XD98d7LmXO9V7+JcH1BcTClPcUd6/\n",
-       "HjPHYb28alF32nTFjXq5ZEojYYbNgkPj67ugq7XNupLilQrPPG60hvvBi7y9ZcGtwAtorv79y2p4\n",
-       "emyEn+lJosqr09KCO5d+mr8CavFGIM3MGblP2LBNUN/FAx8T/ZHri6FZ5ynJlWP32s0NYe1xdpVu\n",
-       "Gz/pIwGibJ9ptUwGi5lFzLxAxdVEbiZlT8lP+eTlPXjUzExT4jdghtLxU9jKyfrHJwUCFY/5mRST\n",
-       "+9NcnO/9miKxbTtU7WF0JnmjlykO7yCEHERNErfedZ7uPMqRD5U9wT+EOOyRCceNrAhKpL33vK8L\n",
-       "exfvqssRosesvoeJmlNUBSR7m4W99eRcFz4Gux/9wveVStVmWrg2LTA8gG3age7m+5NJ8p8dBmTq\n",
-       "YcVvVHFFhqdH7i6alUT2NdF+7+7RhJJ3rOA24t3dhU19FUq36kADe60IMrLTptSjMYyHAfvPujpD\n",
-       "J2pyjeaiJZGqnbKE5C7OR8KQCLa3snUf0aN8Od07wjz7grdLlxqowTOPOFJwhXXiG3GzKNFCID7b\n",
-       "0TkybIBtN7WCMXi9uuxICf/rmElgDimttB2uXWlOZ+E1juegSCO14eybTEK2wo5MzvveSgTk4NeY\n",
-       "NFG6zoe3A31ZWYhUQASx8GTp//ypk4zWlR/cAxKwAAAJHwGf+2pC36wcH/xU23p60w/7KufRD7O3\n",
-       "1Fk1/90VG6TRYl+7qWsi0nu+uM2sCfXznOZPr+L+1uafywKJ2XYlOuH7PR5EKzF0Nl87a+h/tEV8\n",
-       "ZT1FXa5BTcqkmcqEGcaNOG9Ypb4Xf4hnuGRsn4ggi8smWNsbTwPtNNU/8aR8TLVIXZvBM8sgbC3u\n",
-       "zBzWnFNCiH3ffUpqbUz/62qwxSCc1CoqbxAP9ZJH7XlYWFnRPp0MSMmIY/NMrZ9AVBzwhTmqSYg8\n",
-       "cJrDl3TnBwlkBdrahdcaqD/Y7IvTaXFml3v+HPJ/r7jjSoYwzfKjiT9lYmGQoGrAG75QulAd6jX/\n",
-       "bBoyNkeXzrl/A44RSJx3VC796Kv+/2KnTwBePVXJXt7UPGKczIgb+kSIRhFmDk8qTFu3s2Arf0T7\n",
-       "yNnQtP+hkAwCPk8Xey01CB2itDNxxflRcIOKbPHmuV05L3rm7tz+hnoiS0oFoeepCnitj7HeJ2sn\n",
-       "VfzgVZiBpTlZK+d6QQxDWM1DabwwG15LZpBafzqhBV5eQPShVeOriaVJF1jqZHffPWa3XXSjcKdT\n",
-       "xCWbneNy70L4iQ+UbOJzfiiPer+F8jEpqFvgIAAE7XR9pH7xkYqY1C1RIQRu0AwfFuAJ4MQUF856\n",
-       "Duw0AaNeWbHH9eL4AAtl56wuNn9UPC+d5mv9VZYOFalWLpMe/SdsGIdSqZMFEF7ffAMr80SimnM5\n",
-       "M5AFF5lhC2aBILt8X/woRAy6sO9aXrXB1cLczCiiSMx4hUsyAK+0HW0lckUlix7I6AHKPiekwgY9\n",
-       "qKEgnA/f672C7uOipG+YXZRhJoMVIitGw9xttSyNPqYvwLunAwWm2c3/bn4f4H6ZgeEwhh9B8NMO\n",
-       "iU9uBkjnPfFJu1Gj2cfn3qCE6AkfAn+qJSHi/DiJXGdzTBDvdQxR/ee7bqLkvNcvwKkDJbl0Z1gD\n",
-       "z6K4nXhcp9Wr+JHImXxYNldVRSLDYGQzagNWI/ntI58dH3dJdguASn2xUWms+Zm1kSqp9D7nDS0G\n",
-       "T7wQ/vH0TclhEvkWzVUu9I4a9PhIkCocJgHWjvfbDI3513C1kCcMgoV+M9c9S0Xp5OVZu2DsFCZD\n",
-       "gQa1Le+E4vR7Ivc612A0NwJmo+dvboex2CB/ISFQe90kkpwa2c+7KhJDNGBxHR898k+Oera24r9k\n",
-       "6sh/6KP8NJCdB2HDNyMBCpyeCOqBnbgI+Sdb5oTILBeN9Df2q3vLy+bAgdcET+/UGe+i1RKpkk8h\n",
-       "rUergr5ll0QhM+d21YwIJnt+FMqOK3TaRn9F0s6On6gMwRPcLQKswz+u8u5h4ENvGtyb7/oGCB1F\n",
-       "eAngZo/TN81Ing5NerZMWcvvUaGmSDBUGwF4HDVglQ6bMVdfJq0Oj1eYdvF/9TuEJlEULZEKru/V\n",
-       "GrkpVX407dXWna+uSJ+AMJ2i2xKh+OHCt/9bQxBdTzhXEtAhZWchEoCKAJckbSnaMz1PVgAAyZrC\n",
-       "8VBkfed25O/8pA60+FKackwPU8nZyc34G4jDKw6W7oqY5rVghmVQEPSMY/qhl1ZRZLWvKd73Anuh\n",
-       "9g3oEMViPpRe9lJaOPkvCrxcLr/+ajdDvVnID/25oj6vZuFopfaQSl0IBDjkN6IhG7mqQXNBQJ+1\n",
-       "Wg/atfrn1iMpTOH8Bl3eKQxhwVFxyfm2OmPnq7x9A7YuMjwocXcrMU0Ej5dxVH6yjOitZNZjWCO5\n",
-       "jQJPwi7w9NLHCSsQGjM9uX/sLupzBDSz70i1dU7BEvuRF4L8pkm2by+nw5VXAdi9b8G/wPQU13Fi\n",
-       "W1r+f3jxDhtRa4lCcx5L7zDrWKB4qUf4dtY8iuvd0iRSpVmSfrDUbNRFpZM9zZsfhnCnUgWvhXdV\n",
-       "CsZayWxN+fjApB8wBtCNZRbpwmb3PYioI5amCVquiF4LnI5ur1qD/Gu9iYFiudQBnN26FKu5j/0A\n",
-       "OHAmn72xtdWVfswtNEkWGV3VxiZW9M68jmWtzggO/Qpd3iJeB4npud3BJFQw9O39+I3uiqn+YvvP\n",
-       "OOkBYW7HmlVqy0BRyaozuPVTWBU6VcXzvHxTAUD/ihbceog4/3wmepdX+fgivKOsqfVGFtlc4Qdq\n",
-       "pkCs3YY9WX9NNRmTD7ZHD+gnjjwZmLrpbswKamXJ5pvX66AK90c/9j785qKRF3o6yyUv+tW3B6Sh\n",
-       "BPpuhqy/erSYGM/Qi47d4+p6uiqH4FXNpbdezLQ44EbWriBRJ9RtMpqcOt6fv2wj9JDVoyi/3P+T\n",
-       "q64eNKcbRe9zFZGLt1Zpu04YJEi12QHUT6Ji/HL8Cb7KVpqRr20REguweHns0VIFrUXazTX0zHQE\n",
-       "GoOLNpq+6v926Vm4rodvGLXJCAaC8iPLm/lB9/3rNsj9DHuOIZ4IpUGhyPyQXGgIL7q1WTHI4TR0\n",
-       "w2bgtMcBJ+7QLNob6TVOfxQuhSr7xYDMIYPr+K1nM/3GB2HZ73ZiYjmFOD0PaO6fmNl81iH9EN+7\n",
-       "PSQR0SUIVWi0d2hXteNUTbE3BBn2CtqXjiIkoMERqzFicNFwLfnDkb9LrrNiGoAMdmMCm4GB16vg\n",
-       "qsXN4Qphar/IttvzZipj4eoN/w4vSXqy5I08E6jRBXtcm9RmPalL5Eq6JwBY3PaL97jt0CeQdFSO\n",
-       "OtF8/mcLGbflKGT23eZgn7XKjvgBj/jMlA+KkLpNGaksNklUJDmxtq/CrUeZ4quaElDsBZw++7/m\n",
-       "VXphbJCtYYQQH6bjlEKbSex7TPtfvDK/FNbDf1yrKWHsw6kLjBUEr8vYo5//6n3IQtiLt0sOizWH\n",
-       "CAR+3jWSP5pKp6jweiNn8ONazgkFM/cTMxF5Jk26XUHJAoAMo2/AZz/yBnFRMfFsOGFInK7eAJCt\n",
-       "72V76nZPPzaqsyR7xkBvO3LzApwTePzLi9zi1D1OuaMbjEP/Bxet346Pvn9ZJAo2TTndnKaL5dSh\n",
-       "m5qSvvm0z8EwL2bVTzKujVf/vaVlIeIizHwdCdIYSihIqQqNGCd4WQvhe9E4+VlWtPU+SZO/yudj\n",
-       "ftZBTU0om5oKRjbTw5y8ByAXEpPxt/ehP/mjIUYzxzV+Zv9Y/Oqbi04zURVyEOH3N3R0TGILXkTz\n",
-       "wQCHGCmlOIIPXqXco0XVe1VXV/LSKzqfHcygx3ALkrEAABHUQZvgSahBbJlMCO+UcIsTALYlIyOz\n",
-       "LPwRzbTMZqdUhnHb7p8nD3n9UgniTLkti38JoDoKexDO8YQMpthLpciqkdsjfLpKI2LzLRHSdd0f\n",
-       "8NMO6D+gcIHx2BgMO6ElFyw23bfwMhHET2qMdOVn0c9rlYexvPDeJylhPIApakCaSZpfeAZvwyCU\n",
-       "bTWmKOlbiY27c1SP9Lc3Xcl1RKrnglvDac07f3l10zVI/sLnEtr1LqLo9mp/jFeA4GYgZD2xtC6C\n",
-       "Qlp39L+pgqrQiudXj7c3xDVbS3Ed00tzdUYD0MVCo2c4ipI/K6GXLtx2OzIU5vt8IxgwOnAp18xU\n",
-       "5+npw+mZOOCMm2sgTRsYJ2G1R51BfxVMzVpBlJypi5pkdmoGu9eLyAUYDGWyv+hSunGnKp9yVVcS\n",
-       "Tze/lamJqHtOwSodJ7zBkGiDNGVJHczFeZLjs/88EHcVgS29gL19/EX/ZIuSr0t82/iasY7jFT7l\n",
-       "EJEvjhWVsC1yAY+xzycMc5JqPu44G5XzSMWL3EWQfjErO2/mSqNOpjqwMmh4Ld1jyIh0X3Ekmov2\n",
-       "E5kHifoTSGrqJUSFWI3vJdLHF0mINhqR/rlv58Edm6XNEDTheFkru16Vznb36DGTLa16jzZcgCmd\n",
-       "t6BW/GGpwIh9qdgoA5Kxrt0fYi/3J2iSMzRNQdhQTK61CiKWINMmTh5hIzMxF+5HAJZf6t1F/3UX\n",
-       "sFnyr5hucCGGwfsQIAlEDlZ8lyLPbSkvpFPCTIsIf6zi9BzsVVvtOMkfXfeSpC8LMB4M/wV38oY2\n",
-       "AtVCNmSd4JO4SolfCItTtKgIO6Rj81vcDec0uO/hNVcwQobm6iht9IC2eqWnIomuOS8PCtXydluP\n",
-       "flbUlarEv+UY6iyjUdADGvWoxnmHkakDTNc8jhtREUgUwrgj1BSO4h1PXUooBAqoSZe7sLCn3VdB\n",
-       "g2wRj/Ej54oT/rV8FV346/Z7do7t3Hu6o4qPsJo1z3fAr8p8Vtw6/DpsYcowpujYGchtjIyt8j3F\n",
-       "Voz9JyhZuHK8tOfCKZZITFLyf5C49M56+fIb+4f56i7s0B1jT7YXOIiCattWbyUA5PGkJhOcVsDx\n",
-       "iqFi+yNaIRxKJUyxXsx8AFHu9zNHU6Q60SFp1JGrW05bX6LxMTlQOSpqqA5IOwHYHK2XTDQu663p\n",
-       "jhS+iVvmKhp4rXdl1liemu3qHNTvpAaY9S2EnHfFSoBDH+vvyYGPvu8a2l9ZmXbgUN206tMVdaqP\n",
-       "UP5h/+pG0tjGFCgiOt3fbyn9iA13TKza/r5PNgRH7wuHSm6HjUf1OFrXU4BEQowdEWVvnSjp8kxb\n",
-       "o/M+Et9+YOvpC8xvX37921+Wvjwr0LljWdMC0laakfQU9ZZEdNBVfKd/oJD4P6MYaVI1Ne0bjiAN\n",
-       "afAPfcxVHXFGRAEDB2w2e0rw5fNVWYZbMN0r0kajm6Mr0qozoLO+NTo3t+JHNsacBe0l9uAsCAPr\n",
-       "4Ioe8RqBfD7Sh95PiBkyfi4yCJ3D5HO/0WbUZxor4IE7aBqD82YU4nvHQOtilip3AslZzvTcu+6k\n",
-       "oiWQBJpyQ9GMAQKTRYEodZ/tNdet2vGoEkUbVaqPgUuAkuEzjKYFTYuUN49UCuFmJmubpbdhFms8\n",
-       "vj/qvQjkE2G8I98xZa7L334A2Q7BF4vlrpQNzxwUDXM8X4aJxbmt3TIDv0qX18HIcsmGShCUajID\n",
-       "fPl/ZJf5ZXJDk6GDfNOs9EfS6n7V1T75TReUC05XbLw4Yf2mD5pT6MU0baKjQIAnMH0VBvnTjSPq\n",
-       "MITotHk37InBy3Rk4uOHJoBwMBNA5jerh61Tr9VBRhjc3tHX/52KIvRFHNvlbnVXU8qhAWJiEr//\n",
-       "NzwpbZ9bwIpt1gTktn60R5I4E6VgGrzxrEpd9zm4mMRRDgY6DihGK1eHzieiP657BzI6ZSNr7ef4\n",
-       "n5v2Y3umhymYOCYveisvQey8EGmR2Urp+rhuxFoDfB/aARgAHfMpFkLWetnjEt0GRZZ9+zSAqhb+\n",
-       "sgbL/HwSbLW1GNaDCK837Trrjjfs6radQMpwaDUnyMuYglJtcv2mCtQ5WGqqE8IC8yyu6ru1nCDy\n",
-       "AZd/X0ccBlwyCBz29FxW4LInzCrmzQc7+YkOXSRETlzsUKcXSAW3I+5KVZ9X3ZDvPVOpd4QWNHGD\n",
-       "slmZeWL1liDuTtyfKSUMecq73NL37P2CAZG2eY1TMWwdo9/8IMDPxOT61nimvUs9n4H0lcvOchAy\n",
-       "oZUADHTEgV68L4UmyPS5L5vCKL4wKWNWMAYjjcu8G+MrxEkS9U1sxQqwe1DNIeQ1HUpG9hVTSBf3\n",
-       "e1kf1Vo1jzglwky5qKw21jAGzeT9Lby20PaQhBruqnB7M1htvkMm1J7YWKAWtDkvpbF7rZDDJ383\n",
-       "VZuBE0UxR1rksKWZy8Ukw+ROiBrFItI8fQVIh/2wQmDmYGzsfXrNmNoEF6+Jf+WPH5Ssyohan1Fp\n",
-       "thu0KS43mM3q7vPs60cMOq/9e5DbsrkzU/s6Di6rQe51QVVbA2sysy6XoSjCQz5TrjUpWoPwS7K0\n",
-       "OHWgRNdnlEZQ3vF5WGXy4UZwM0sipqUIuEdhyHSJIGxq5nze0qlBraEurz7o4EcV6gRXddK3j+LQ\n",
-       "E1Pr/VXmSaAYTJVTJcHkbZHLDeQNKSBZOb85LTK5vycRrZEJiSVS+MigkEFUOGx6UKTCqKUgGnaJ\n",
-       "ViO5oJyf2aAIG7htIoeOdUEq+MNdFgc+lFgfhPWhzPGBBGMscfsP5/ZoA1sGUCmHgWvSaQFVv67y\n",
-       "ej7jh+LsAi+a/ckH40r51AB7t7W2ZrwepL7GsT0KDGOnqQG7JHI5uMVnzOfojkE+w0cY1jC602d/\n",
-       "0kZEW3bmZZ2pgGKqr6KuUPgJAskxzSZxVdrrKf41bdsdB33YKSCrWzdzV5D9tspNgPL6hwqq/0zP\n",
-       "H4XlsjvHSATARU7IaSvnKKZjZNkWWuqwR/h8iq0ZIP7o45tCVAYNxhT6A1h5XsbJxLLEw4Hum/uY\n",
-       "60NZyrJIdbuzuQDH8ZwZ9RSrau7v2YW58epNGycmBNNFI0WHphJowF31xe1XTK2/t+ifwb03SIvX\n",
-       "9+kdmd2uHOEDnydaww22N6x36UM758wu8qte5gJ42hlQkiOac1bBRcgFmj+j+gy57HHUDV+JO5wH\n",
-       "zmLf4mZg8/zbvWwLyqH6za9ZpARJt6NFrHNPgbrnWMTcc1bUUWx5KRv5QvU8arklx6ja8yVW/auY\n",
-       "2qxYoPLjjCV3mz0MBubQJ3bdvnur58VkSukBEZ8pgmKfnDWZVM0B+eDhWVvAYs/hA5EtcvwRZGW4\n",
-       "GxJ0zjUxRKQ5XDZEemAWDqBenn0mZlrgF/rg3VSkp9LMIgS4ApXO4Megs0NvbRmgZ286+lrzO8Ba\n",
-       "mlcMPGkZGaEInSqf/BzTcCwnOzYFzUNpCkVfzW8LbGrmb2Uv1LUeHUAK/IgPPO9T6EMgU4Kv8gRb\n",
-       "hP0BwJYAGdODA28jMkSt4PtLTt5orcaigxTqH4g8FK3Qqzs/fCQU7TfYPZ0GTXexJRuT6xLLs7Db\n",
-       "Ekr0x7kPbT/xjoc7S80V0Iu7O1bH8t31MvRrEx8Y0wtpBUId2w9vAcxZnNsb06T1y9KRFgoWYD2p\n",
-       "NsAeN5hjzYp7EGTR7GTWMs5BBwul9qZ7nuRtsiLZ2vINrkg+PqDnUERdoSLIRkkspzbbF4Fts1Q2\n",
-       "SxsF3ZsNYO+M8q4G0Mo63m2NRzOFlp5Xu1fj0PoWGQw4DxXKLknI0THgVB/LN74xtdP0tCNnlfRj\n",
-       "5IObnkhYCh/UN8sdYMJQHDPnPB5ELVHW1c9q0mk/DnDicKRMQMhp4wuF9ucNyZs0OYXOB0NQDWL9\n",
-       "hXQRKGf9o6zDuZW/5AQDvNENKfJ2o+ekRu9F7FdoiIjrt1C4HqXMJrhc7CrtqFTbb8/U/xYH7+RS\n",
-       "7RcxKJAseV9Mne+8gKbEkDTkLCgT3xlviAU9iV4ZF/PhsP4D+mkqTBeG52y2U57GTYumCvEH1ob6\n",
-       "rwV+hzSZZkmye9nB/7Lw8Vhpv7vafeyuez7x6olM2bI9X/u9I87Vd/FH0zJu/IQJNfJObOpJLieq\n",
-       "x0i55WrF2KCDXThtfTWPmLVxRLn4I6chJw+9AC0ozaSPxYzrr/0sD0IZtNqgte32QWHkMTS5y6gB\n",
-       "eMgI39/aTGM7UYVnUkGhmMF3tgF7d1zpNMe4JoS+bGAmf7Upw2dVvUYatTVg6/dXlZ58/DOZ/8Ew\n",
-       "M5A4Y/LDIqYmwNAummK5c+rvYS5wEaihMq398aTdmF57k5JsKTPZrWvXrtIO1sg0q4m2fA/JLEU9\n",
-       "qkdkiu+Y9addFqr90bc277KYCVTaAaUn1jmIRSxrZ2Jx6mTWHnHehKU5vPnFjkL2c+Np5umBkiYo\n",
-       "NZphtOBqsRyH3lcFuMt1j9iBAjEhlr8/BKUCDCWas/SNZoJTq8E9c98gN+a7BXJsb9ENknI/mgyK\n",
-       "OJBQrtUI2cY42J+rSDvt48jQVFAdrUBzQ/Q9SplYSaWjUTGGTseuNYhJ7OPj2KvcPFx71uGsVjw0\n",
-       "wOwOPhwespitSr1P1UAlFkSi7A2nxpD+w8gFmvBnOvrof3nFD/mAMMAmrWfWETKAACIMa9Aol1/b\n",
-       "0/jIP9NS7WylB1L7qUtZ8/Qw7qrteX7UKMJpIGiBV9FT1ZL/wYqUgGHvEAww31wfZB50Ec85yzJu\n",
-       "10MX4LkiEFX4FJvtVutsiSyIgeq9YSgZDUAzPQiMy1GY2EzdyJcITRNekzvpraAZXzo9Ub+YwQbY\n",
-       "Llx3utEaDl+6Ds2E5w6m0UfWNy0uFXV2Ewo7HDMjnTjqYDxjZe01FckqFA/7rxInApF0vqJI0Yal\n",
-       "MPnVsdoVzPIrHczhvOe38WP5xQXa5uIuJ3IQ+pAEF4P4S+hK2kr/sdjXJTli7180o6I3CCcuod5Z\n",
-       "4KKT8jrucL40Ja4gzXB3plmTVryRciKIMbEYs74Xjt1sLA77Xvhltxl/njmZcKpv1da0/x/lpK1k\n",
-       "Kq7AR26MnI2rko8fOaqcQ9g/HGnStGFxyn1YbI01P7xSd7xd8UK4j8rGzDgSj//zHI0SLWNLilQJ\n",
-       "ENevhcyYsi3PNyrmlvxJ7aPOEdIs/ZW8NzJBiKb4ROqMbXbhhQ9bifek6OspHZukWLi6hJXsYOxP\n",
-       "kDcCXWFdXTUiOv93R9fRANuepxakYkowbNwVKdUn+ZPguptVnYtiwVis8f6akDduoFNtaAukINaD\n",
-       "mw2OOTGAY3dpXrubVo7BUwyK88FBKEYGDTI4ePo8jgUMjqptYa04L0BYMIHTOS7pnTUjF52dPh9b\n",
-       "gPP5/LTRWr2kgHUI612KxO6bwRaf6LcAcs9AONa6JlsxRtUhYEsXTXt0uDIV76g4S2/DYvBvnf8Q\n",
-       "t+/sgBTS0bo5iKiEX+/uE1ojIv1JuU3s1ideFvz+jjyDdJZoEmSp/AWlG7AT1raWtrrgD3/JOgti\n",
-       "wwx4x8RmtWcAjuzbG4tUtjzUkSZUPoJGjxePEy7uqaduYFfhkGM6d4RH6rDQy74vCahPcd8A2GrA\n",
-       "ZWMkUATxes8G34MJbb1lkPH7lNyMNdraZWXpEbLYrPWKR9LWUaC8huJslF6YaXrtc03Yz2gyXuri\n",
-       "uB1/4/sgz6iI8aZ+cFn6V6GAAjEZKQbRDuV7A4x1zzYLhoHtiXNUPuveYgWZybpSNSP1YjAui2/n\n",
-       "SwqCCzukRJovcwn0jelb9RorbWHpBnzwOFi++b489r5IVSmyviFBjIvyGUJWEncsI3JAdHvv24Rr\n",
-       "Xhp7Cd7HS67afww59287t4X5Uu2Zx3k1su3yXXk3lSmdi6+TlqWrfMT+di/t2zd7kwlh9cGVzYFp\n",
-       "cvSdPUEP/d6ZmM3p5WoUOB5/SP1VUWdFrWAF2rvPVJngny01hXjfoTBA1MhmJBi0yvEnEO/B5Lb4\n",
-       "fuTxB3EX4Oz0PTlIMpUxtgQ3iwvSfmPY4cej+kQtt1X8c+vkbZn5FPkkbZSZRWa81PLJfC5YtE8B\n",
-       "eZruJzmEOBRuwfSVQnKljzCat1N892oNf/vnIkVfj0XzyFPJv/lFsqO4jxqfKx0UEso/HHl/ir7G\n",
-       "aLSQy6utd2dynKBr1TY0kUGQ3kuUURsCPmZhUcwatYE/d+NaBobnewAAC7lBnh5FFSwl/8PjOPVK\n",
-       "rCErWHojhTJTgMKEtA4Ml2lUle3Fp/JmV5CHb54y9JXWCgbRfM4YbLLsrZKHXqFPckJQb+FL393l\n",
-       "sfVKU0Y2bKZTWI9zU+we/pCPdkFq39x63EuhwyKZtLGAqZzJAwwLYGME1kPDQ0CvawUB9czgw4rD\n",
-       "NIhlOW6kbXKIBkhn4jjwGklg/m9JM1u2V0yppvtzE9qyN7bsbETdGrNMDexqWBzgez1rsHUFBUvD\n",
-       "nzWXSlr9i9QxGo99onw0jwx+DK8tHwNWNbZXAMJrhPkrZe1Umdij+GwMawnhfA5Dizl7YZrodos4\n",
-       "ERKa0eeVa3yP6MR1N/Ba3BcP4Y05pTdJhlyBIhTVMgza41Ng2yxjUFfrQGVoAW0JfsIxPJNQ6e4T\n",
-       "FLGbJEiBkck5hWn9qp9TwH94Uy5witNSj0NDt1nvjxxSreuRLeedd7G/I1MXdNnjhjfNfeoxeM80\n",
-       "YRsUWiLwX74fMrFpfROVXVtTrMx1EMikytVKTCMu5b0vEDJ9608/qsSqyGpVk80rle+b91VcgO4K\n",
-       "TgQu2jABEzcrGl/+NofWasuRI7abdHSndeBru2AehM9EwUYNRsAgwzcmwn4QAXiWuUK3qsXoEFxA\n",
-       "ajHsO+Ti56nI3MYqajS1aewfJK9pRfoY7gLNVoTx2tlneYYQMYuuc6rnj1URVKafO+pPxNxJZreZ\n",
-       "+A0pfx3Xnz3LbgT9A1tk7suD/b0bHMZUJU5GN0ep4fgAhq+p7VXdB0t27YWM5ATFbDGHjUTaLlLA\n",
-       "+3dYLYr3D5kOgnUZTPudS/JsIDsrKR3GtGQhi4cKnHWmLLIjxbDEf9H0/fyLPrNGkEjd44KYiQxL\n",
-       "7aXfK+sdP/qkyO4jYPH4HV8ZgXGejq5NkJZyYto3WJjrBH2/rDFERnRvFUeefaIuae+kPuRedGc1\n",
-       "xUzfZ4yRlptlfKNnC/2P7Ygvu6orNYnOOKheHtV8bsaga5XUviSwUzJYwKTEwZYtr3pRUWxfCUKY\n",
-       "cQbdPFZ2f0hCu5OXL8IQ2V2+Z13ibAvP96bfIN6NPr0u4jCaphyWMOOWZ1ZM/6+zFAVo+AtYDkmS\n",
-       "CQylJkPRUVe41umk0305feIPB3entBHZ3QUzIvlG4Jtf/D5Sy+JJJ3FHHHPcpeNNI9P6XhG4iReH\n",
-       "EgJP93QcT0X285w1i/RS4e4tomNCEKC4YSuII8qtkAYH35dC4HxQcM3HKP3XxleHHCn8qzvg2VtY\n",
-       "msoZeAFzWsz06/hQjtlT6uV3DJnE+Ws4PhoDaEdJX6cjvkOpIkW5E8WqFJ35fXOVBNfSgraJ5gAp\n",
-       "JKN889pwYjM6apadgI0czxmZ3F9P8YYVo7UcYMtcE/i8Uy1o5V+LpmNHXInOehylo+SYOMBtXpqL\n",
-       "FuwY5reUXGT4tH/vL/+8Erh2UbOwBx08d+rrFCkj3H9M6hw9m4SJfs+Z+WpBvXmOx8Fr/dJIab04\n",
-       "kUTouccOBs2Ew1NToq5bZ5pXqYMaw1AEUgv0MrvSPMvQB6LLwazdeX04e6fce6o5KzXRy5Q7j7m5\n",
-       "m+tCECDobQENv6sIousZMBAqy+SlhXS6jI4YYG5DmJHCjIM7jYiyhFRwzr9tDfDsrRnwT3ur8mIb\n",
-       "ep8bzI2Jl01LMztu+sUsPyHnYlGwBmSDTLgWQHHMlnh96+1dn91I31yMZR/mi12csV6X4aaKwsNQ\n",
-       "7uXOgbpXj/+AjisIXIfvc0B/xPhvIYogOBF7pJyKEIpYEl1f7CrQMo5DNnBDciT2QDTVY3iU7k52\n",
-       "csbcJmDixZvno7iQJLA5oQI3c4IO6+H8K1H1aIE4kn54FgHjP+uGUE5snT/4j7YupGM9/9IyTUiH\n",
-       "pZXMY/seRDOqBvXYSlomDBLyBFq5aiGngY9blhQSqV0+qeYIv/AXYiHPwPWGYmZn+Kr9VmpjFo+v\n",
-       "Cm99clgfSPYNIv3Fn+yCK6dcGA1sH0LjVLaPlxnC4q7qWw2fZKg+48zsHyyiJiD8udgWGix11bKl\n",
-       "8Xlg8Vnfhx8gxjuHWW1wsvaHi9vVCjZeN6NPCcrrAQ+9c/6qYIpnWaFyMfiVqtfqKv2uomHQSHss\n",
-       "Itbm8osQ2VCqeNjMoNfW1T8FjD3LoLu72UfptmSfDG5YMAYlMqmqunRcF7ypLgB43dvWHqT62HEo\n",
-       "urhsCH+8nHhdlItUtiIlyN8Ky7/OTwYSkbcBKKsiFLST7EE8tgUJlwsIMrr/OpCONG7AGLVD63ca\n",
-       "NJXFiCxfqvOFBR9Y9moEP/WPVJT2tznbGU7jQ+b98xSagTBuGIBiWHRMtd4DB9aO4ckoCQScddwn\n",
-       "UMZ83aIyLzQrASzEXcmxBAwe4+fm81zHi0Uu3DjLdyDQy0KWx7HS35iLguLKcpExgcVA4tPsEEs6\n",
-       "trHvsH4XL6prHdP6+gETLN+1Rm3H3n9/hcQac6C3ncWHgflNixMAp5f0ZJM+IsQLpj9RK+anKxPw\n",
-       "GNqPJ0e0RxXMhcy7pH+k+o3O1m6apGcNqorkNfBX549jw3mXQPWrvBb0zBmA1ZcGz2XkUhK5JIIp\n",
-       "j2GXIofuyoYK7/GWQIxbuZww7XWsELnrY0DdpDQCyCnNmACjpQv9b1oTErCl5LqnLbU1SulXDxiF\n",
-       "UyCozFBq/e7yQa2AUQ3KQDV0RSUY2EvuBI9zzBaX9ti21qNgjCpX2gKaPSRbdiJQwJuIulsHz40v\n",
-       "LJuxVx7XT4UxjX64h2b4maGSH4Tg6uoOT7nSFs+KAgy9vDIEiWzqOzhNcy26+6nU7ZkvTepkrNVN\n",
-       "qqDfhjlRJGVUqK50DOQ6H64AlT/iLdJ+AeakMQvSnyJ5qM6eMB42aoX6G35znt99NCZ1/8/zJeri\n",
-       "2A/TXR1qYbu4W14SultVTDdfAu8GxFU+ZWNgmGiIZqaYPsUSG7Nj2XdtgJuSCj2fT6NIctZnRVM0\n",
-       "9V5AIoV9wKPEtDGpDkAM/aQ12uvJniqEsYtgxCFLwu7iZSJ+qg2eEmdDFvNBFPUTGwe9ZmCy9qHD\n",
-       "qpeQaVqILxWxO0ia/Ue3kDmXe2qSZe50+YzFwY9FAOQnNRC4rGZ//ThNXZbgCbQn3MmetyEJWohK\n",
-       "7Xm4/2uNwibZV4A7OcTpT2DLVDmWQoHYcu4ahq4014iHGr/NiLW2cxpb1W43YpOm/AMqsAbfpobn\n",
-       "C5ylhvfaOZ0tCKJwcn5AbwRo648jrKaWOv+36Xj1RTAGb7YX8laRsncQrpKAF4P6fBBZ/NNuqLRQ\n",
-       "8qAR49jGRXN3ApwvZEvlOCvog/fL7ujIDNpOaMLFOQjRcyVAYUVuZkWY93+FwN4cjGXTqElgpjqh\n",
-       "jqwzUdDDtfiBox2pAV6VGzm1XjxcqBisbz1UJLf5flgWaD5BQJ+p6G//VanDZBZGHxahT5QKbQUQ\n",
-       "cZ5sDvWN5FRshdABLVX7Z9NMk7X2e6k8JfshLMUswW7mfBVxx45MATnLjNMbPalHSovC+7xtGKbP\n",
-       "zvNO1T6fPWDVLhdiRPl3/xeNB0qx0w/yP/ssODYWUL25S/YvV52JNLPTLL4Nvwv1Y4xjiQHJ9KdJ\n",
-       "aRIto59qYLF3e+0AaUe1GgFOCmqGmYAiXfuh8h8CXGvSWidp+IPB3IrHDZ2/NhYB7Gux6abpxWvj\n",
-       "23sWqYk7UQvH9lbY763gIGziAuimzKxbN2Wsk7gSzQpcGZgeXVDOHYOIZMBGk8/DN8cqP9g3au3h\n",
-       "yH+twxlzJPYxAu97aUO++TJeWcTjP93ad+mrCTj/lyFSbkKDPmKzzrvyhOFCn8tv/oS3100uBsn9\n",
-       "Dc0d63OW2bK+sPPP/9PXTSZZusiNiqCNi5X3vNfy9cS3a0CUiCpJZr0aWuoluj7x/I8aLcAcpquB\n",
-       "+ItJAhRg+ji4bVdxCJI3c5BHqWzkgrtqLWx4dWTvHfehH77tBjP9r3NMLyUtul9Nr8wAkhBhWquB\n",
-       "M0pG2KPJf5BAGQ8Uj6/YCmsVOS2mnf+ddGGTmSZNqbpx3U7eNROKt49YDzNzK3IHb+FCUYh/tUZT\n",
-       "L9nWJmgOzPm3njwRP9svqoOLQnzmjoeAAAAI9gGePXRC3+SdoC3divpKrvVi2LdMyoN+RMqerXrz\n",
-       "wne7WOAWosZMsG3A8E2eYU2//4O0hE6yvMQ3Fj3f2YLvoRnUk1uWor/BKgx6/IvxdsGgVLSAUEMM\n",
-       "bOO7sPlzysHPNTpopIw0J1y9NAbTEl3nl87QBmk2J4ievZbstxsqf7/kLuTm7tFZOoKO4oanX9jg\n",
-       "6v9LG+DdO+FD6owd+oRrNyN3kwEXB7XipLSf+aLpWB5mujxEf2/CSFE7s9LfW3sV8ozx3R222Adg\n",
-       "TPK59xcLyxs3h9cNLZILFKRKo1dSXiqmMZlqhj7BZKNKiJvWfc6e9W/wd44eDEJ5cDBNIFPIZ/t9\n",
-       "aFwAFwMyr+Rzrf9Cg1eI+X1UvmNBAqGjgjcBFKgx3kDyu/pg/NNyBNF5H+IImcBMQERxijo9ipAJ\n",
-       "Q+j6KxeLIYnxKRKF/ciYXWuP+M3ky58ykNkUSE474B1/GqLfwqQNMnCmiXkOQ4X6/7fXhldcxio8\n",
-       "EoSwfmRrIGPFaqZsBx8powWZB0TRWaf8ENFPFqUmwIAsCdORSxvJRsLaTvJItzl7AEzer5qda7wr\n",
-       "XYx2QnLaSFOU0QfNPJfgMNMbBXumxbkjWJOpdGomRDsN461YHdJ/tXkwqnzVoGa1Dp3dESf8j2wt\n",
-       "skeHlzLRTjZYIWiRNAKrVQSseOkoEYzgXzBkHjDIJEBAvICgAqdkGoLdbYRTiAo2q5CmEw5zVChh\n",
-       "P//T/Or85KdM2VkEBtckvWwb1UDAQUgB4kGTV0lWoj/IF3bG736dnNWLKCPsRqZ8dq7k1M8NRsuQ\n",
-       "6io4DO85bUR+Ls06ufRmW7e1XbK2ImGHaiE65DCfUKVhK7l2lFiNhjOYdUm5bK4yp9AiCekrINnV\n",
-       "kSQirsKJmS/UpgnkQIdwyrXyliq+ltaRIkWL58WizrXxN9hHrq6xOK6mIPwa+tUm6AHFnq0B8VY0\n",
-       "HV38RaqPCQfDTp9LoQPeA7fgzTOTWlIY6Cu1X1YVsvoCX3vRAcYgeGvpiI8NZiP4O0xcIer6Xhd7\n",
-       "VUcafIi2ZoEq24XLDpXgoo601Cq3gUT+JrZOyX8cNmiPQsr41AMYhA0PKdb3zKXcuYOsAhqdmlem\n",
-       "uUMZ47CZZW+juiUKCz1BI6gxZvZCB4lU9vHt1Tgn2tYlXIYwvRx+BKPWYgdbvMFtCYL2hZd0DDJ3\n",
-       "Zl/6+XlnYsiB1Wf1bBJ+ywB70K87GjQ6mIfnURSo6QIawh1Yrxc096yfw8ZvBDVBewPQ9jb2nV75\n",
-       "tWsVU6PnRAkjsxpAjMuzAbixf+B8QrAV2YGJDBN1ljRfSt2ZgQAVlw5L11ntqWd6NgZ/5ESXEOiK\n",
-       "SBY/hf5sxitVb2BmYfS7EO7eFR2J4BX8DWo7lYMtvBPR2ITykUGssVtCLHh9h5BSYylfECIb5Vrv\n",
-       "AIJjZfue4tnOJbX9BhsjEIcsdYYU3j/FHncOwARPEvFovxnYOmojZudKlvQa7UNMxirFHheiHR7H\n",
-       "vf+j1WmhXgYd4gmwU6QPeJmJbHYEw1Ja03Y6xO3jpc/x7g8QtdsYYwKen21AIkxa3E1Fi6CIK6jW\n",
-       "29lFXnRSpc7qII8m/zEzAF0YXHmrniOr7/jbsKG1OS+qyCH04Y6Nim2KthDo/v2X5iGVx+rViT+s\n",
-       "fpoQCCzi/TtISNGb5Y4uDwGG9UR+AnM9xsvExCeTEEZqtGSchPHre6h/tes6Yk6S2M4c8d3Ts21h\n",
-       "BGr8k2sXmhQXewX1dlfT/859virc3VQKfs/swYW6ygOZf6SZ3NEClwtsHeFbpnfz8WR+v2VwFthY\n",
-       "BJYXTUGF5A7ITruBoElm/qCzWDwYef3BUPGf2orM3yi2qH7V4ijlq0OCBYmSnjpDyEvUt3zfQDtk\n",
-       "M9vuIPO1HlonQ7YrdTRLcMeuEKVoMUFp6vDZALByUAdPX9SQT9Do2SOlSLxiDKeyL7OhJnyeZVkW\n",
-       "rOA9QyCwbKnmzO8YlP2dgvvhljSBqShqH0ck8ToRcVgS12RMCyLIJgVGy1kXFNkwZ6RZ3Bjyo5B3\n",
-       "r8QROsethmwjBDcd5Jn4wSCuBPTQBpJiMzEm8AoqfsOhstAoUH6m7jgL/OCVVeLQ69mO8hVNrb5u\n",
-       "AplEtvPdg0hSngi8+i/eKWvrKEDb3h2O/5xnZo35Qyht25gnkffpDaMH781ioVYAKPVmxZT/HMHi\n",
-       "wcbaq/GlpMnYy9jht93rFOaaJH87Mo4xqO7P6NQCgW3N1oDgR5I+5f8c8DjYCz8KdO5Qs+c25eYK\n",
-       "//KawbLsvZ4sB4bkKLFBJ/A0bHrfPPqOkFDVMUlhEJ3hGaugtXHRZx8s+uzU9KTNhNc79jRx0BxL\n",
-       "qbAsc3F8QIFCRfc0MHr9yTYo1cuD36+/Tg9hmsoNigzuiVOjEZAR5L30uG+eh+RLOZxbJgfvlhT9\n",
-       "LgFxY1iEV88KpmHMdthcXh2KJGZ3LV1pTxCjQ/LiiDV6S3rOwGa3kUIhHFfu6lMQVviQQ3WP5Udr\n",
-       "PW4/nWJdtNTuOrNRJ1g1Q84I7Tno55+FCCJLFF588siOZAOJxZ15o7I5akvMSROC2O8O9nTjGgjb\n",
-       "4ORqBVBn57RcSpRIBRHlpc5zLcyrsmBGKfEgRIpcOdr3IeN2Lv3WHu48mxgFr3OSM8O0G7zGLAfW\n",
-       "5G5dskhrM/btkkwcvtDzjed4Z+VXhrzrvolFgZcK16Tta7xyqJWBcRKXGdYyoBSWW8wdIse41V91\n",
-       "e2SyNIfrefgSr8DZFRO0fWFhqLpQzhp8yoJIyZRhrXvvxfWdgJaB1XN20ZUa2cMWANJPO6gTWDul\n",
-       "Lw32/eWm2uCJPan6vX4+knlHH+95ctJRtI6ES5Vv4Wzvo8yuEUL/gBxY9pfON/PqT7m/XWeW4ea6\n",
-       "RF2EGEskO2F5OQ570SAEcN6s4lR/yuZaCanNbgZvv2FrlhlRa9Pmicv2QVL3znj049yAyvh7emL/\n",
-       "apW6j0o8w6cjXceWYRoa95ItHe79RrsR+r8U2RzK3bRPKE5/mbND5jEDdvwJnBLsdvDVPZS+Cj62\n",
-       "dXJ/WZ1b02THn6O+R8g8T7r7SW1SjDas3XGn/I2M0nV+ase2RjxK9CAgAAAPuAGeP2pC39vsuW4B\n",
-       "cB2aToHirR5NYlHukjKglScwQyHWHCYnL+6H2guGx49syXq9tPpAR6BzY5OyPXrrVOAIYjkmWhQg\n",
-       "Z0K8mLQ2yhLKMjaqSP6Vz7IoZkCMocwocGf78UxWmv78e7Q5lAxw3gNQwiivMA9oh86uvG1B8EHA\n",
-       "L7XGiKILFzN4T8HQGDHoMutxXUtyI5OozHGIeJG5CajFqYvzYdX5BgXMQtUVXI8+rx+lKyqQ/gI4\n",
-       "lXVxnm90ckiNTrGOwEewq3Vp2n3IKFpQ//nG1KI8iRXCTB/swuJy7jNhiKxAg4Hul+IQIp2W0bUe\n",
-       "Kx2V0AbZMmQDOh3CkT2sdL5pH6yUv09PQ9ep7o+GFEBCXCk1kODNx5ibs3t3T4q7dNC7sgu7EBQf\n",
-       "Ph95ZZUCd0JZecEVApMLtQbeb7Hp8BLUo6d1Oa424B1moVcWM3wnatbWrbC4WAU2BgtZwkzdE4J1\n",
-       "BeWe6iGjCJFi1dgh/PDyvSkM6zqUYM/bBb3pyKjwHjTEKFK3ycVwVxlzBXTAhdhOkOTW0lU0PeGA\n",
-       "20Z5pCoXMbt2e4KMsQIh9kxJgQJ7zQWZW4UayT5emPqW19b4a9HjU0IbNA0G0k6r9+yMDjQcA033\n",
-       "e3F955OvDBZgu9IGPF/+sOtXkH7y/khmGwnPI8qbHYytFSm65f45AEwS014vxDdpqRLLS5hpJo+U\n",
-       "SxirldP+qWkDW5n4zxX9jB01a7i4VKnQu8vnREl3C1vwx/fwLomKxC8NbSQMiZGskbVBAYw77lYw\n",
-       "JqnMvdHRkNfv/KjnyZS8xykQuSsR1nhS/9bTMACC+/PC990f9Ep3/ZgV5Fu5QL2DQc4vXkz0+xNv\n",
-       "EEYz0aqgrJTXZizLisdELD7ADnHewULTNhZbZFsAKVu+4ss0l5g6eeYHnbpHnLieAplnUAksVJ2v\n",
-       "a+XrtjbQyw/HpyGZprPcwhXQoBJc9WClCWDlnqhmhFvlR3755UDyMtxkeuGC+Tba7FAxg5dLrMZU\n",
-       "m7sMYYzWmdYaSTe5HiKzjPy1DQVeV27zZW+NsBB0kdRqCzQSL5QLOm24XSDfbJtzfS/BfKme6yke\n",
-       "5wX/kOPgjiXPVj+c9/CdJRI5XHRMi1OQg4BjBD0+8kMBWwvtupxcdMsIAI1EbYKv+0kPguo/SzbM\n",
-       "iuxDvknil7y+E9bNchbwx4si54zzr3tZ0/YoJwkm0Bi62H1MkfZkQ6l+AjPKgrtLXYCkR/WBy1yl\n",
-       "IT/QygprkF60i+YmCQgoCUkoXmsS8v/cttr6ZX/XV8rxiDA/ANQ37iCGZtyBy5I6epu8LKltyZhq\n",
-       "hrih5XNWNw/DAr5fZGvnL4x9CeRDeh9SW01DWBe1uT8l1ctTw0yzPyw54ejlRzyKTJdnfminv2PG\n",
-       "zguknbRjQhNyMC/oGFslCN7/jjdfsWw1pe0GnXPJlNsLabrmeGLo9aDMdZYF+cMj+F1+0l4CUWao\n",
-       "Ai93PxHsBK7/VDXujZnV9c1cPzHDUaS8FsLvqVbFsldgfs/4JVsApqsAL0SpjkpomnQhjQYjaCI8\n",
-       "1Q1/Qd41Id94kZQGaBlBns/2fvpKxjfq/Zh7RR0dT+kWpFMViNBNiN/D/E+ZCLghgT0HsQDO08Jb\n",
-       "YIT0JyPkIfHKSnX/YLjVexncQJdhIq5OpqSRGYJYbd/0UuO0el6Ve9r09GPZZH1Eeo4ZevdmoUcc\n",
-       "b8i/MH7OifCBG/kPYHEi5xNsP2nXdOT3fLr6Sd5X6c9+/RqIv/D2zFqfMWHSwqka+uKsxlR6VzaY\n",
-       "4KP5l2XdgqVw6ZbrbVWrFiUc08x7rIhez5flXTr4Ycp7iJB7HdKMnIzieWn5+n4pYEST6fn7Q8iO\n",
-       "7M+z63rA1icuhMhrBCXb1L2qv/ix7N/+LoJz7SCpZ+PxV34FlbSWZJqihPBkgdP+YA9PJs8r/+fn\n",
-       "Y3QY5MyO/3SEXCnYnS17m4ADhmd+d6yYK7JvaJeCVlUurdfU8s7XbkJiu/boGvGsdwe7G9YlTUIK\n",
-       "bWWZxZSaw1v2WydTFF9RkJI8xU74w7lFHjwwjDG9bwm3uznFkqhYC5cCC3Q5zzzhepO9c8BCO92B\n",
-       "bVcuetNHXgTDe//rSedmO8X+C8bxZIjOcui6oLHkhzg5ey/Puaog65rRQQm3ETWZJd1VtVBGAS86\n",
-       "B+vcrL09t9QTqzq930xMituXPSZ6Db/gY9WLrg8J7TezomV2rITovfP6C/XkZ/wTspPDf98hxG8R\n",
-       "GUB9KuR0ct8hEgXZSidnMvpQhuVvuFNYn/5lxvS0x7SOYwvw65DAGXPbsAk3d1e41ZBwQT7Si79b\n",
-       "/zSfMrYd1Y5Z+oy/PLPlXATa/2LpewtvHEMwYCYMeT6/0uWTNW18lAlifVmCObBQSs5xJ/dw22pz\n",
-       "N32FJ+bbA65DGseDaY6EhrTdcX3y88ImfOUTRtd2BhKuOHBJvbOTFq3Ki/NStAyBsaMwcfpZUAcp\n",
-       "gsAdXcwDHYHzbio9ZU0pmhNF7Q3VMkaeiC1DUaX3+MNBfLrhCzViDV0V6K6vJK/VHNl8uooQ/8eT\n",
-       "fLFlO9jrGp/OVF/ZfBQ+MBJxg2YoqL8CQ+YhQelButdhSXVKRxhl1bBTFO4PF5qnI1SDdLj2+Hqm\n",
-       "PNPejVuvDnVI1h4Bnw14Vb9FzbFnMpzFJaGkb3BQ1NGPNKXIFPK/W4qp6VP9AvIEiNgHe3l6sZix\n",
-       "WFK7c3f1YNLLungvfMVM93ppizYvd8ZJeW7oXsd9u6MNv9EeC1vkSi03Bw23PluvZNr+v+ftIJOK\n",
-       "W9LL/RX0NvgxAbFf2U8URfUT2M7fisvVHUv7SHR8OcVmlT8+cmnKcfGnXzOeSyKIQOmfimfPA70s\n",
-       "qQ4F+bKoEkwHLKIM+M9xroROaPrC+r4BSAiSOZOiG+gJu9qDbK74IMbhv5Hb3BAzLzMr7v4BKyeU\n",
-       "0mDjvI349w2jQ2sbnptRrXCTkZsO5rS55orGp3/sQtBRHwjmEZMlp+9sf7izv4KKOH1jTZs9Hvbz\n",
-       "qf+VvZ6UKMjmNDFRr7mXm5V4TcRnShkEqtu5rknrbp2atEoZtD+3K4dedPiWHY62uD1jIs/rQ6DR\n",
-       "BAXDrXWQwA3402zy8PBVtvT2eC+WUOFeFQg13rAriPubEFwp+GmRNBs7i+2kh0XVYT4qxtxqDsDD\n",
-       "04tL4ARMz1fwS2vVbBYM0pckMc0CZBW4FQetAoXXZ2U6HMd2A5WVfyLEqBoED7WJorozJVRANHyF\n",
-       "t61UjzB6uGT4VYbmitb3dQTlG8GRh3BC5dwBIzTmdC9v3id7uszxArm6UCKL8RoiE5xSVta+rHQi\n",
-       "wluy6/ou5IsHQ3/Kd0+wyLxcunmOGEF2ErjoDAo29ZphEyiafeXRzC2A+PnaSPe0FoDjeYCWOfHO\n",
-       "yGI5jjA2v8V4crTGCPVNxHVX9VP6fZzjnRl1Dtmq+a2iitEHH6bdqfLckqsar6Elm/OYj47X5yt2\n",
-       "YzpMlY1yIQdbfRN6JoZSHg8HOVaM6FlAKsCeS/Z5ZMOHmT3k/NYvZjRL8FhPOmK6L/hr1Hp93bwl\n",
-       "FFr2Sg9AJf/a/jil0IhH4C3TELC51lO2w410eexMilduooXKZCq6f6mUaey4cfDvHmxPiZH3igY5\n",
-       "zbTsxSRsS5zt0zNoc5pnkF+KIgdAHiT11HAKVUBRztPRq/8w95d8A6ltHhEEhO2X20n02cjAq8LD\n",
-       "/cXAmZ12QKTzdJf8s8Na4hYjm0MC9tU9SAFNIcfPX6QXAhghGrGGcElJ+9JiKy7azBE/p5SWIF1h\n",
-       "ylflVCev31DOqj9tKL+7wPBMC7UiFMInKkwphCbshbOP0u371jwCltAI4qZmw/vXN7dEFPSfeDBo\n",
-       "00zTkX/7zG1u+Nr9SrLMSrhHxkJaNimM4ztJLF2oTpVMKh6xpv5dxBaAG6aoLhlyFHaWba9y3wHM\n",
-       "Bimq6PChhjI5rmqRyI3H2K5E3yuVpAj4P+q5vNqv4OGo7J/3/isakDWoaXk9eyy83rneTyqUBDJ7\n",
-       "hBaoOk895T/w8jN312vGPYO+xXWCgaiituE4d8eMxc5TMGWyqbbReAEfXO7rP3GOXPnfV+Q8caRJ\n",
-       "7u+DKMvnxWCaFK0WmkHh9Bs05cfc8g9f6NpEH1DZxZfLn9x46S16HBQWEtS8emAAllcHLHlotGge\n",
-       "kwjqiHlrbHcSBN5zlljIvDHvrK80l0PjA0R/tvVHvQQTMoU8nBSpmgTAukaDO3Gdzz07CWzWz7Hb\n",
-       "chwwOh9iDYKzptqJmD5NglW7dXffCy8sxZNVQviiaaaV/IY9I4nsf3up/NeUDKprduNQB1L6pZ6N\n",
-       "RgfT5Kg0ldZ893PsioV2rmj182+vtQ93VbTkO4LdapVl09HmmWozB26dnLX9S2Mp0iE1V2DksGdn\n",
-       "8xKxsrlea1KwRotJa6TuyJyYRez7WaDcPRbP2b0OU0GGKf+es1Y7IhqpfQMTPYGH1Njy/l3WUnOy\n",
-       "k27P3OkiHrDasWLeCktditBwiLK9KKDFtaJ02jI/UwpbWlN/S82nKddirsr7tvDwO3Z+2xh4vJuZ\n",
-       "bNoGg+FValJXMFuT6t6XBXGFgPt0YFdMFCYxN3SQENjqUFlZ6xnHEnaT1IaB6Qy/f1dfJgq6qFoZ\n",
-       "5TOkQIOxymq8JJVP/QjojsQvK4H7Wf3nB5fvPCFB4I0756AOR3hwF7k5vgZato4abrIhtp8nxfZE\n",
-       "DCiMfXZqRjWeeRQ3pZIaUK2FfiR0U4DG8HiCvrCEdJASNi686DjG0pzxdBq0tZm80zYir86rVyKE\n",
-       "Fri7wCdbd2ajREIeMCrLrbktEknj9MU/8rwICvyyMC1hx2YlQ4TCAexNcdUzfq6ujtsFKU3blY2/\n",
-       "/0n0rmWikDJvU+mIUA85YJVZ/42jGRmSv2VirBhsuWRGTIus8LdHRAXRviCx3m8AX3GXFvEbrmLq\n",
-       "m1Bx/YScefQusaivqYyKDI+VOpopvRMwuWSu1p5+M15om5jXR6O1RZFIr9h+YUSWCchkaVsFebN3\n",
-       "F8bjdyvAJL70zUxCTEJqPY0pUQXcyFMxzll4XpQYlLvUbqoZH+1zjUkTAu2av7w80D7Zs29Nu2Sx\n",
-       "JlxE4vyjROPXw5ceOe7jR5GmE8jKO5XF17opFWgZRdrVzA5DxCTWSzRBMHBXmRQKrCNY49P72PhW\n",
-       "AlvAuR/eDDxXIlM991ylgDfW0eSmzUqCtMScC0aj+k2ky/Fwl0jZjsM9yt7RXUql66meJR4bK2x5\n",
-       "4+lZNlgjniF/F+fkLBWE4Fpu31rCbPBOaHF32uaiTZXYVfjBX6zSywKjJNFYEkwgQokvXKKtSe3y\n",
-       "ntr46+LFwbj/c89r+2gZClLXMovBWE2YVFmCrddpa8mAQu0B9qYGawN4GghOgkaCscITDkG7aTKV\n",
-       "MKetycnDpxXPTtF3Saql8LHULNw6GHEAAANmQZojSahBbJlMCFv/dzDGfzP8OnBkJ9dIZSWZI4BA\n",
-       "yHFXeJEDXh6J5oATCrkoKaL8e22GpKRYjj5lbrZxNm1LKZGY+dDlmsz9dVLI79MrN+CyUQpuHw32\n",
-       "YtryeNplOsFVzV0ap6W+MZoV8ETK7KgKxFWA7xPH4bqKrq6mMuv8nfcm1CTXWRuyNkzgTVzYz7oY\n",
-       "VHBXD+t4r/XvyxRQe4iyW4714V4TCo7gHKhOuyvYixgPbjHomKjHcAvRP9r9ZZx+2OEDeC1wySt3\n",
-       "EAbM3rDxH90CNo/0pb2fiMD7t0Z6uS+8EqCVeI0SuW2XVtb4XDA0a3U8jHO4NwAiJVPtJ4E2YVdc\n",
-       "v+KaFbQXTcVc0JjCv+ZYth6MmmqYKTr5v7jBlPqRHYvf8u2HpvlCd8BTAMzVfJ7nMVBcqIHRzjw5\n",
-       "g+EKn6T4+jp1cQFHmIsOuWtGD636p0SMGzG6AEULBWExhhqGtcgBJj0TRvsGHJ+vxLTnuoSc/W6/\n",
-       "lgQPa3EGz+ZjsEmhlq5GCzbaY9Eac7VaZnSSKKMhpqo+Fg8iaKeJY3Kt9rx6Ygs9OZYZumcDNjIt\n",
-       "uoJacMow5fqyoRb6KIsS5BsAAAwoPP/uWnET9XxHaFb85rw153sPJHdwdCCbQY+megkUWklNNO4K\n",
-       "l2O72UUMKuEC6JMij5fJDMq+2v95/PvkIXtHDmIQ044SqGfNVKayGm+cC/KNhS6KcFEhoDHWeq0h\n",
-       "YbbIR8a1CRWT2vXrJhmBpQ71G16PyZY6lOo8skZyi8j+VDzeJl8c8+2I+9AcATtk/H0klKFJKAKk\n",
-       "crIgphP2i5rH419UcSY2Hje4AmyMPRMxYIcYE0vNM9EJiwJlJ/GE8W2TX1i9UIDSlEmynMAHmwGT\n",
-       "ANmX8GUCV1wWkCIm7+WQ8pRgXYLjXy7uPqdTNsBJLr7h5RKF18cu50ftFKKWraSVgZHs70BWzOLw\n",
-       "cvDep8RPR7YsKKuGBkgEY+e3fFmtWd3cdQXFNdhr86N7iFm3ZB73apzKhBvZgJ9rS2Pv1PbkO8PZ\n",
-       "nfJy96paQNDk8U6tcoZGXuzywNtBuGGsNcrMEdNU0vaiVwmsFVd4Zo1UT/sAScbdcAZrHjsH32/x\n",
-       "4NwTaPdOMr4DA+GjZDZAw4ZVgblHE8S9NQalN1dnA7CsyXHkdES8B4Z8AAAC4UGeQUUVLCn/6aG3\n",
-       "s7l2g1/p70GifOMBuRim0X0umfHHHR4iUJi5dx8EEJRonfeVpJ4wDktdtgeUFrWuYDoQdydHctiW\n",
-       "+AuwqyALHyuKyKlxmjJi4xEiJSfFGqWLnK/7PiqVQ9dZSM3PPJHEJg5oiR4HU0CFIk2Wh8/oTSoG\n",
-       "+0ZO1G1kETjj1evJj1PiESt+TMFTVb9tRgbJIesZFYd6kyas5XHnnCmIpF/PfrwIBCWchSScr5y1\n",
-       "4V7kJnc2RJdMQ1ucxm/Ae/voCPlNpYA4dfI6J+R2IdVovhcq6mOYqzSQXIDOk2jEG1LCOq1h4TaM\n",
-       "kM12LpFV4HWYRWEL6NYyWd1VkQvQp0pgQeZvNGWSSECvOq9Rkac7Wm0j1eXKmjGzhiKT7gLkyTni\n",
-       "/KRQVa4yh7nTJud6eWwHbz1068y9WoQDjkgFq348shmjoA1bYK71jWF8jhwuvj7aKY4CCh7018oL\n",
-       "VbRjoUFTDAv2VG8SL2kv6zXKEI9RPde9Fc++aMM5gqLpf1EnTdiXjTybsSD2xd13q9N1iXVupc5E\n",
-       "lEjxChjpeYV57PGbtZZuLghbUNlDLUghs8nfAUHwAIWiw7QzMA/xc6DjUf8abayKI8XoycmszZK8\n",
-       "L2FReouo7HnjgSFIbFWKfKum3qpTIiaT1sd5pOBn7eqp/cB5SdzOG9+VoXJAN4WcvMzoXRJ0BbzZ\n",
-       "ipiBx/P6dUm7vkX/8Du06j9zOQg1a3MCpGfK6HIW+tDXxbV4tYWZEHZAuoO6E5jm+JEXytuqfj07\n",
-       "nfnpKg6HX4DLiBe5ane2md0R6kYhfL73YJqvbdmxxoxrFoK/txOm9MqaqmDUHuAIm3ZzWeYc9KI1\n",
-       "dkw3TIVCmBDpxLfOw9TyVv84gofdV/dVfWn9ylgduARhEfmEgW+vkhjQ79R0H1Lu2WNPpgnPAyJz\n",
-       "F217jAVjIalRxFCWxaHMxxU5yIlE0sn8WqbBYsdKtZyFnOvMyoRvDO6fAAAJCQGeYmpC3+Af2A/f\n",
-       "q+3nlMJNZWwoac41sudrHlgQj0QWxxgalwa+1U3n+c/0qmEAbKtiFT/jC81KPXJGaNiEGhEBy7iF\n",
-       "hjgPMjwsNbcMP+UIOT0OYTzdnyH4bj+8LIr3PujzdPnRxFG8PsDyHgizQ/tPU72iGZwHU+kIAU8P\n",
-       "oBSCQzRfbzoxvVlvUxr3NoyrNOX+BujSFe2PxRYCv/M82u0yeOhhbDpXVE3N26UK74LtNTKTjD5n\n",
-       "JGsY+btqGRIea5MRI2N1r/KP7YPypr3aNAMYIsTgOOuEix4Ij0V/xU31iQ++sJPbidURV4DSc85C\n",
-       "uwLg3bilts1yBn8QMD/OCOoFAEd4UWk0YvoXnOi4ytLeqpSxAKqkWM+B1imvOQ3ytMIRKaS+zXn4\n",
-       "eo6gC4FvmgFBinIe4+YAaOjRqM0dtYFZNf4Pv+imBbvZlu2H1jR5igvuIyxggFwlTWicI36DFGDL\n",
-       "U4ySTxG+FDJNwkIEZsoyojAJX0OYzk2JdhdVkeYLF7vO1vbjKkWoUUW6rmMofzKVM3ZYQ1lqjal4\n",
-       "KIgLgES9kEx/JUMU/eAyLVjtbevhUmxrBbwVUmtNjfhEyH+witvridcYX1rKTLuvHAXnAUNbPenW\n",
-       "s2oJI3IPFb3UfXzAyMxRuwaJvwmhKzCXUIZ5spBbvibsmxCJbmLmA5Eua6QEMJBCz5/ICUgbzYgQ\n",
-       "Be1b47fx6pxMZQQPGvAnOYGUHzlsY5Gnm8pfHya+dk0QGkCd+hIBQHIVv332JNOoWysM+jrcXbgY\n",
-       "SRQgJPAF1N+my5xuIIHLpHNENqtbpgIZxwpS0PpMTA1ki2DvaFyMF383NQEtJ5+wPdJMTBAgMOFM\n",
-       "mwBHNdra5Obd/kwo+As0vT0725tFRfP6Ar7tpLPjsNxUPQzu7/AyWR7ZCWOFy73t6Lp5QphW2QJJ\n",
-       "A6av976tXbf7DmVjQBaq84wOSFhNNTwkg+q9HiDIvIj4nh2EGfLGTUq7usAv9GXjw9eRa8lQxwj6\n",
-       "ek8gS6r5NSToBsoIhzXbHB1CXrk5KspDZFGUjR1Ib8gbqllYrbSn7iSGuGAUjBTqYfI0kFgfR0Zk\n",
-       "edontM9ZoNIE4X6oXDzyMg+W+zBPrSvB2KHqDzduzVn7ElpOQO+1en5llMir5iy1fmlHsuYJxaOb\n",
-       "zzEWFcwwIkU8H+KaEvyFUyGaVWP3V68bNc1zXmi2+EczkzTGjerMM0IdEpsQ5AKHqP4nhaiaRBEr\n",
-       "74fTgjVtxDrwX3WHc/HyQ6YfBKIn5pn6zwkvYoju6JF9lfoCJVJp5yJsC4TY+zDLjMXFFnOGbX3Q\n",
-       "nvyh2jzUcExvmgaICk0zpBtvTkc6g1PVLk0PQRqDlyhrcoyPtMfLGjsdWyOYFT+E7OBPItVmJEba\n",
-       "J0qmOe8N9GCNpZjyhpxkL2IZV5Ny+HysX+aUZV6GqsOaGFlSN5GOX53I/6yqSRkKON6TR9WF3fL1\n",
-       "Fr35lL75Gjkw1XhatXBV0OV/l//+xFENsBHU2cnGRH+0x+uZu4iglmE+vHS/8yK54Ps/WNH1ksvq\n",
-       "8nPFvxpE9F9+x87IDkPBubyHSr8qpFf7Ztcs+IoWAE4InbITaMY/bD8f4tkwWRw0cBJ8ViSm/WyB\n",
-       "BYzU2aeZ9Pu2GwuBV5tal2of6iALsCcfg8wpT2Lxvx0eMo4SCTRuwKrE7EBs2iYFkTFRZ9e+wQHw\n",
-       "7y5T+bD8Ck6EOf0xUEYEFcv+T8YFlxI2m4P2Ga1VSCADs2wTpxN8YMRSZYuumkyysko3kKXfHaqP\n",
-       "8Si1C4XvfA2tv6qywv8isdMwfPppVyCr3TtjJBEjw5XmmyweIYUY4rt5Qq43KZnVDq3Bfsl/2JIq\n",
-       "ijDAc47yj4vf4UiYX3LhEHJCR/sqUxFXLvX8py/bG2I/nYtF3lfv6AQFzUL+gWaAf3vuf21lXfOo\n",
-       "wX8xdVkRoSGYWssmfnB1yWDoqhh5ZWwYuexq15RgHxlxG0SnPbRbXMvUrqrTGyJ7t/JTV33nwbQf\n",
-       "fw68E4o5QOCXF2dI40cJyAmOsraXbk+S26IQkon930qIKxSBw8TFuEe/WuXY/aijLOz6jmjFIgry\n",
-       "0nXHptAHZfo467qjDkIwdt1gb50etvbE33l3VFrZAzNdLeulluEDOYMqG3/41h1WzIL5mshNBaiV\n",
-       "JkAwY65qVkBHFRu0xrJ0uAlzFc7+1KXHMu+afkLws/asYBGYS5SWHjCukbooPLlaiWuQhrZUiTBH\n",
-       "AVqH3yFVwv1IF5R/pKQi89t+XCoRhxb8AVR1bAFvajupb1ykDbB1nTpRuw8EWhcaqv8nmunIISQT\n",
-       "akTH3i9VmN0V7UjtjIRA22NZitx09rh3KSJvMShzRYIZKzq0odmayr+XlDIeguu1U11Tqhk1Vo7E\n",
-       "h0SBFMV8WqzandBe9/lj8AFy8IjcqXVNm5YlXYgHLl6Q2hIGntV21065kihmGffo/TY8geXi/hNL\n",
-       "ud5drInFQeCaRAfsaQgK5AbzAIrMjnyeSbARjWwdMNCCRZ8PUGlGXG7xyHQe8bRhfZwr0JM955qj\n",
-       "zK5WLMuoFDqiD/hyar9BpbMIE+BqvLg+0rnEOLvn4TygqzhBiID7UrxkNFJvUs0hiXVISIGGSxwz\n",
-       "VnlJU+49C6sn/5dd0dFu7tyXb+3hBOkeGLBc4HgPWCK+/N3YeGuzzwL9bZ5t+Ik7BiC23/nc0lAR\n",
-       "jQjWet7CISVAl93cXA8a6qtKRag8WhRFO0g1nei2ccXz88bpNIcm7QfM5g68oH3ubuI6y/YXhqeL\n",
-       "NLhUuN2xsbgMLuUtAopWEyF7dwvLfXFxW0wFP+Zk+cT1Lm9uwiBfGPkdMS0Ch2Ac00LUlF4JVRkC\n",
-       "/E8Q38oB1Q9gp3eUEAK7YQ74fnfV8pa6186PbCCIZSGhU78NL8FdInpcJM2s9cUXxf0kxb6rVSWM\n",
-       "TS9cmGuyOvbbycX4byFIT4SBWry225YEytp4T1oUtx9oae2+C/PFWR+PRg8RrAzImDz/PaYL0rSE\n",
-       "J+i2i8s0Xl2BEsYjihWpuKy01FfldcB3lXLV6n0IZP0HX+dQAzc+PHWozO53RXLxOUxHD/Nt6IqW\n",
-       "whNHjqIhu0440TfZSFzKcsb0HuMhGQAAB9Ztb292AAAAbG12aGQAAAAAAAAAAAAAAAAAAAPoAAAT\n",
-       "iAABAAABAAAAAAAAAAAAAAAAAQAAAAAAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAAAEAAAAAAAAAA\n",
-       "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAAAHAHRyYWsAAABcdGtoZAAAAAMAAAAAAAAAAAAAAAEA\n",
-       "AAAAAAATiAAAAAAAAAAAAAAAAAAAAAAAAQAAAAAAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAAAEAA\n",
-       "AAABsAAABjAAAAAAACRlZHRzAAAAHGVsc3QAAAAAAAAAAQAAE4gAAAQAAAEAAAAABnhtZGlhAAAA\n",
-       "IG1kaGQAAAAAAAAAAAAAAAAAACgAAADIAFXEAAAAAAAtaGRscgAAAAAAAAAAdmlkZQAAAAAAAAAA\n",
-       "AAAAAFZpZGVvSGFuZGxlcgAAAAYjbWluZgAAABR2bWhkAAAAAQAAAAAAAAAAAAAAJGRpbmYAAAAc\n",
-       "ZHJlZgAAAAAAAAABAAAADHVybCAAAAABAAAF43N0YmwAAACzc3RzZAAAAAAAAAABAAAAo2F2YzEA\n",
-       "AAAAAAAAAQAAAAAAAAAAAAAAAAAAAAABsAYwAEgAAABIAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAAA\n",
-       "AAAAAAAAAAAAAAAAAAAAAAAY//8AAAAxYXZjQwFkAB//4QAYZ2QAH6zZQbAx6EAAAAMAQAAACgPG\n",
-       "DGWAAQAGaOvjyyLAAAAAHHV1aWRraEDyXyRPxbo5pRvPAyPzAAAAAAAAABhzdHRzAAAAAAAAAAEA\n",
-       "AABkAAACAAAAABRzdHNzAAAAAAAAAAEAAAABAAADKGN0dHMAAAAAAAAAYwAAAAEAAAQAAAAAAQAA\n",
-       "CgAAAAABAAAEAAAAAAEAAAAAAAAAAQAAAgAAAAABAAAKAAAAAAEAAAQAAAAAAQAAAAAAAAABAAAC\n",
-       "AAAAAAEAAAoAAAAAAQAABAAAAAABAAAAAAAAAAEAAAIAAAAAAQAACgAAAAABAAAEAAAAAAEAAAAA\n",
-       "AAAAAQAAAgAAAAABAAAKAAAAAAEAAAQAAAAAAQAAAAAAAAABAAACAAAAAAEAAAoAAAAAAQAABAAA\n",
-       "AAABAAAAAAAAAAEAAAIAAAAAAQAACgAAAAABAAAEAAAAAAEAAAAAAAAAAQAAAgAAAAABAAAKAAAA\n",
-       "AAEAAAQAAAAAAQAAAAAAAAABAAACAAAAAAEAAAoAAAAAAQAABAAAAAABAAAAAAAAAAEAAAIAAAAA\n",
-       "AQAACgAAAAABAAAEAAAAAAEAAAAAAAAAAQAAAgAAAAABAAAKAAAAAAEAAAQAAAAAAQAAAAAAAAAB\n",
-       "AAACAAAAAAEAAAoAAAAAAQAABAAAAAABAAAAAAAAAAEAAAIAAAAAAQAACgAAAAABAAAEAAAAAAEA\n",
-       "AAAAAAAAAQAAAgAAAAABAAAKAAAAAAEAAAQAAAAAAQAAAAAAAAABAAACAAAAAAEAAAoAAAAAAQAA\n",
-       "BAAAAAABAAAAAAAAAAEAAAIAAAAAAQAACgAAAAABAAAEAAAAAAEAAAAAAAAAAQAAAgAAAAABAAAK\n",
-       "AAAAAAEAAAQAAAAAAQAAAAAAAAABAAACAAAAAAEAAAoAAAAAAQAABAAAAAABAAAAAAAAAAEAAAIA\n",
-       "AAAAAQAACgAAAAABAAAEAAAAAAEAAAAAAAAAAQAAAgAAAAABAAAKAAAAAAEAAAQAAAAAAQAAAAAA\n",
-       "AAABAAACAAAAAAEAAAoAAAAAAQAABAAAAAABAAAAAAAAAAEAAAIAAAAAAQAACgAAAAABAAAEAAAA\n",
-       "AAEAAAAAAAAAAQAAAgAAAAABAAAKAAAAAAEAAAQAAAAAAQAAAAAAAAABAAACAAAAAAEAAAoAAAAA\n",
-       "AQAABAAAAAABAAAAAAAAAAEAAAIAAAAAAQAACAAAAAACAAACAAAAABxzdHNjAAAAAAAAAAEAAAAB\n",
-       "AAAAZAAAAAEAAAGkc3RzegAAAAAAAAAAAAAAZAAAoHoAAC5YAAAT1QAACs0AAAl2AAAaJQAAEKIA\n",
-       "ABAPAAAJxQAAFyUAAA+JAAALmwAACZoAABjBAAAMNQAACTwAAA/xAAAZ6gAADSsAAAk+AAALsgAA\n",
-       "Fg4AAAztAAAJbgAACKwAABTzAAAPVAAAD6wAAAkPAAAUnAAADzkAAAspAAAJagAAF7QAAAwZAAAJ\n",
-       "UgAADzAAABnPAAAM9gAACMEAAAv5AAAVKgAADLQAAAjkAAAIvgAAFBIAAA7zAAAPigAACQYAABSy\n",
-       "AAAPCwAACw4AAAkXAAAXdwAAC+gAAAj7AAAP3wAAGTEAAAyMAAAIyQAAC60AABR8AAAMjgAACOkA\n",
-       "AAiRAAATtwAADvUAAA9OAAAI7gAAE3sAAA9XAAALLwAACWoAABYGAAAL2gAACPUAAA+oAAAYJQAA\n",
-       "DO8AAAipAAALnQAAEwAAAAyNAAAI2QAACIkAABHSAAAOogAAD2kAAAkTAAAQ0gAAD1EAAAspAAAJ\n",
-       "IwAAEdgAAAu9AAAI+gAAD7wAAANqAAAC5QAACQ0AAAAUc3RjbwAAAAAAAAABAAAALAAAAGJ1ZHRh\n",
-       "AAAAWm1ldGEAAAAAAAAAIWhkbHIAAAAAAAAAAG1kaXJhcHBsAAAAAAAAAAAAAAAALWlsc3QAAAAl\n",
-       "qXRvbwAAAB1kYXRhAAAAAQAAAABMYXZmNTguMjkuMTAw\n",
-       "\">\n",
-       "  Your browser does not support the video tag.\n",
-       "</video>"
-      ],
+      "image/png": "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\n",
       "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x1584 with 33 Axes>"
+       "<Figure size 1728x576 with 48 Axes>"
       ]
      },
      "metadata": {
@@ -7288,25 +183,18 @@
     }
    ],
    "source": [
-    "ani = plot_filters(recon_model.get_weights()[0])\n",
-    "HTML(ani.to_html5_video())"
+    "# ani = plot_filters(recon_model.get_weights()[0])\n",
+    "# HTML(ani.to_html5_video())\n",
+    "ani = plot_filters_image(recon_model.get_weights()[0])\n",
+    "ani.savefig(\"/home/dwh48@drexel.edu/sparse_coding_torch/sparse_coding_torch/output/48_ptx/filters.eps\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "id": "ef944b87-dfb2-4aff-a0b7-497cb86b9742",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loaded 572 positive examples.\n",
-      "Loaded 1752 negative examples.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import torch\n",
     "splits, dataset = load_pnb_videos(yolo_model, 1, input_size=(image_height, image_width, clip_depth), crop_size=(image_height, image_width, clip_depth), classify_mode=True, balance_classes=False, mode='default', device=None, n_splits=1, sparse_model=None, frames_to_skip=1)\n",
@@ -7324,506 +212,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "id": "d69a8ad5-4037-490c-bef5-50d93190ece8",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "('Negatives',)\n",
-      "('Negatives',)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<video width=\"284\" height=\"400\" controls autoplay loop>\n",
-       "  <source type=\"video/mp4\" src=\"data:video/mp4;base64,AAAAHGZ0eXBNNFYgAAACAGlzb21pc28yYXZjMQAAAAhmcmVlAABeAW1kYXQAAAKvBgX//6vcRem9\n",
-       "5tlIt5Ys2CDZI+7veDI2NCAtIGNvcmUgMTU1IHIyOTE3IDBhODRkOTggLSBILjI2NC9NUEVHLTQg\n",
-       "QVZDIGNvZGVjIC0gQ29weWxlZnQgMjAwMy0yMDE4IC0gaHR0cDovL3d3dy52aWRlb2xhbi5vcmcv\n",
-       "eDI2NC5odG1sIC0gb3B0aW9uczogY2FiYWM9MSByZWY9MyBkZWJsb2NrPTE6MDowIGFuYWx5c2U9\n",
-       "MHgzOjB4MTEzIG1lPWhleCBzdWJtZT03IHBzeT0xIHBzeV9yZD0xLjAwOjAuMDAgbWl4ZWRfcmVm\n",
-       "PTEgbWVfcmFuZ2U9MTYgY2hyb21hX21lPTEgdHJlbGxpcz0xIDh4OGRjdD0xIGNxbT0wIGRlYWR6\n",
-       "b25lPTIxLDExIGZhc3RfcHNraXA9MSBjaHJvbWFfcXBfb2Zmc2V0PS0yIHRocmVhZHM9MTIgbG9v\n",
-       "a2FoZWFkX3RocmVhZHM9MiBzbGljZWRfdGhyZWFkcz0wIG5yPTAgZGVjaW1hdGU9MSBpbnRlcmxh\n",
-       "Y2VkPTAgYmx1cmF5X2NvbXBhdD0wIGNvbnN0cmFpbmVkX2ludHJhPTAgYmZyYW1lcz0zIGJfcHly\n",
-       "YW1pZD0yIGJfYWRhcHQ9MSBiX2JpYXM9MCBkaXJlY3Q9MSB3ZWlnaHRiPTEgb3Blbl9nb3A9MCB3\n",
-       "ZWlnaHRwPTIga2V5aW50PTI1MCBrZXlpbnRfbWluPTIwIHNjZW5lY3V0PTQwIGludHJhX3JlZnJl\n",
-       "c2g9MCByY19sb29rYWhlYWQ9NDAgcmM9Y3JmIG1idHJlZT0xIGNyZj0yMy4wIHFjb21wPTAuNjAg\n",
-       "cXBtaW49MCBxcG1heD02OSBxcHN0ZXA9NCBpcF9yYXRpbz0xLjQwIGFxPTE6MS4wMACAAAAWDGWI\n",
-       "hAA///73aJ8Cm1pDeoDklcUl20+B/6tncHyP6QMIpyzqtdwnVCd1ZnhAAAG0BxTVPgn8ksvM/AAP\n",
-       "wts9dHFzfEKBqjuDPp4zqvEbWy5VZFGjFhKt6RO9jkQuMGcti2YUDEtWy94i1Z+w50WcbpL8BkSW\n",
-       "OUuaR3rWhdPFvk7W6c86DmS+0mg+eFOrPjO1NO7v8/3Pa16cAa3tRhJ5Xqsl1UejecI3gvr7V7IG\n",
-       "jrhl8kJA+1acKfJGUnNCaPm0g+Oso8KZYQpwlq2C0qKBe46DgR7e+siOKJeUL4MO9+HucGE4/GFv\n",
-       "Vg+VS490aOc9EAOyJE7XFwQpLlIhMGmNkhj+lSqY2c+IsKLEDwfl2iv7QhUa2az5BXAGob/7WGQQ\n",
-       "IGa5idbkzTn3Nk7ZePY3BY72okMuTya2q4D9GTKzL+dVzZ1zJMcP8+p3NcYnrhhTue+Pf0LtLOYG\n",
-       "hxirN/7H18SQdM+TlnfQONPDY2OMXsak6En61DzIrviFjOnQTlCqkA86dCCh7rWaQU4pKCAPUvBP\n",
-       "/eDtN2IozGjfF9GwjwU84thrAgzAOaL08E9znbjmgaX+yvF1N7DydkA2AxzcyJBFeqzWTJTi7yur\n",
-       "KXFH2Ik7/DHVo9CmN5hFTFpFhg9YCOSYp9docZNcWilX4L5ton/msr2R+ArJsAkf1Cn4f19jQH4T\n",
-       "GpO1R7tP09GxEp47dgPaGZkmdYJimu91A7lr0xbBArThQsLR2tZVxeWTP8LlWHxSFZtlhfm7EHkf\n",
-       "nmGvI631KBYN5cMMeUVbanvhN+IiokLcEM3pqlJG9EsWZEWweZ7ymn7tuO3dVDFahlBljcPxiiCX\n",
-       "sKofHZgguEz0+fS4yzUV6fO9dr+IqTTuyUEmCo6xTd4KmkeiXwD2Px92DYZGX0udR/7WAw5I2dAK\n",
-       "VJ2lOBYiy5xLeFeP08FwuVvUaGyKDVUOg0RUfO+5bUcLR3p/iLOXenB3FQ/TnlMT85zJzR9Uwn8d\n",
-       "EdOAp2X3O13ykc5kiH+ZMOUcWSm0z6OQ9mLz1ueXH/uAJ8U3lVwoWKwoPa4TSooqq9nVL+tJUbfS\n",
-       "dANb8rKgSw0lediFbbV50O6B3Gvm+pHG0s74i7ufhjnFy89fKk/qhEe3mpicL6IjFK2cYdKpW8WV\n",
-       "9ymn57bSmoztBqplYpLZSN15CJX7qM4mAah/oIo8IsEEIH1s0gqsjMy4tadOuHpbYdC6cmMar7Lt\n",
-       "eZmcguJ+5u+/xja6lKS84gD6252nYVIjWo+ZwAVIzfTzN++2fhV43KtppnmJOl4O5JPWWi7MChGl\n",
-       "q7fFO9C5MW5f7v/l1Qz05bE5zUWvO1GcuqRYQ/7Hi9LGko682TeJC9AweVgY96z2Obb3fNvpc5RU\n",
-       "a2DGaYi6oFNC5imKCkm/Scy18RZC9rH6iOsmJjtnh1KId5m133oLVDRQHOfak+DB////SyE1xHVj\n",
-       "4wHms1i8NH8u1gZhqoDiLcxxUQJcSu0y9ANt0n7zciiUEVnerACiU9GtevAfjuaHW2tvrhoWsl0D\n",
-       "c6zfcgw45qlup/7nkKuHSgFiW5oJcbjR4oCSdwvPMN4y47AbuUVbw/iOK52nJiTZbOP7eiOhvCNp\n",
-       "AAeUs4fVjCupw7GUJcKOpfzC+ivGhVKHvJ4Wmr717Tt0yKUF8778lM1Qizc+hY67wJZ2HvK5WamD\n",
-       "Gu5BI1Lqj3p8ufBGvcFkiH8DNyYnbHUOv34GqW5RPQofqI+3XbUw1SjsvTJ9bZu8rVPBEOQjgWKD\n",
-       "ZtfnaAfzviwszz+M2vFoeORysiK1ujtvl/QkyfbBeDlD4LGEmk8BL36rNEiFg/PkWY4zSKy1TO53\n",
-       "DxtkhqB0DfzcLk3qU/HYTN9fgQFxMj1qOs8WxiUWRuTRsugLe88KwHiW1MzgeodBStqB/+GeSoLn\n",
-       "32mQr0EYaHuCBow8m06+hkYJEhSiTHCUyCxuFoVfzCSTPOBMgBvAySxBGOCmO4o27lV3+JjqAcn1\n",
-       "HmDysMtYjzaZ553XSUkF36Jx59nbESm+FlFn4LTGB5nh9yQll5pz+Ci4GAK1UAPYk0Xs1jPxauX9\n",
-       "QnP/47RVsLTNXptBoe9K1K8Z0GA392y3xLJ8uoAuiRRVlxdE90nBvoXZqZfARC6WGMEB5cyzG/Yc\n",
-       "iK1mK6YmajZcXGFf4dwDMtPkA9JH/cqFm3M8n9eA/eC9v1KsLW6iBkhhAuIpPWAeGNa9lsRJYKbt\n",
-       "Kv5zgQ23pC4vjJvfYfdT/lm7zE6tLYWwQDyrHxeJud/+dK1X4f76WJJCSzsN8z+cJ3FPzIJ0NzGQ\n",
-       "gdGuNJ/xgGZv6/6/0mMlHovGjZmReyPM25MsaeuBIYYSG5c8nIjT0ZAOuWXZKhrIcmjQ1WRxsYo9\n",
-       "68H8l12rXGqs3A1VgoL88P1oOdxW8Ovnw+9DWOoSHTMFSrbbTg9MhKExYSwp/RcyeoevWGuEO4Sd\n",
-       "TkxL/yvzmMrjf9zlRv/i+/uYRL4ws36uXPbFCk01EpcyWN6UICaCLY3sDtyInmmZbHx/Eya53UjN\n",
-       "APeY6LGr8e98f9jp3gHwEoOw5HdGOwTqhICmj/73IQxk6zq+BTmcg5fOvKpRNDMSy9r16sv7d7+2\n",
-       "H/fLsPh8nxThfRIWTLdNMBnos+IO9JFNvZOX+cYC5Gg1Ukm9vGPAcHk9EyMhukRiWoSpDmHYehIu\n",
-       "WAwArtHox3ieI/GLHSigwJw9jlWPb1yZfYvtyZM5SWg8EEhTum1fX5skliFIj0rTAGE8IHIFzpkd\n",
-       "Fxc6a5ywGADi1IAJVYRHf0aF39+ylrVfQCMjFPkzuHlkKq+cIPV/rq/LWpEEC5QVv9meLurOK5jJ\n",
-       "VBLzBwVEszcLLNNzlhtCrQr0+5usLfqYv/XrPGMwZqyEGBRyZmUSVrhy6OQJQBIbPch/FiSW76Oq\n",
-       "ZPZ7uTRZsxMapE9dU9MuI405TTZNCZy867E+klMH4v2ORcRq8K+o4S4AOzeFBz/llVmlkHuA2fKP\n",
-       "tegmy6QTR9KbTXdgaZCjyhQg9jtD0x5uOjfvcgDviRJZUDmr6O301AzBK85TXYQACHedWo5Hzp7S\n",
-       "U19/JWCxoaYkiW0RJ7hQ7+OHxIe9U/ZoRWoUlEE9u/4hY8AsXgmRdRYcrNO9TF8FsgeIwAUcfDEs\n",
-       "UdQjAC087IrFIdlNLXFm7OBTpHM8pP/pLB1x2re5bzZL9VxRW1GEzk5xPZzv1OBDaEMwdk32HYvm\n",
-       "BmESUSaeX4EyriyybeC2qh8rpMur0+biqeSmfnJeoWSsXyKQP2oQfy4MuZOh0xgJg9hIT29hjyRA\n",
-       "8CTtwQt5+5XqVJ2JCj4x0YIvJIc9zsGi3YDY0xIZcskesBztlxk1ZCOEulRTrtoOUwIvxmGeuHSq\n",
-       "8+CLd6SC3dVDHWsPTAN7a5+0jjWtyv/gmjRSBC+KQQO83KeVJgKAWSdDlYorOCklhjAjx2bG1Hj5\n",
-       "7KD70eFMiFIb/CaA/LecCZ7JyOj3YZDpi8Rff6dMlogsMfxze1YdwafwInEBzdM69wLvgkU6Hsgk\n",
-       "8+Yh29RJcN2UU+7hl2JUeAvGdsXpzOz/7S90hKMIzR+2NatLb7007h+d/4POci2dNkwhVMQotW+j\n",
-       "uPhNJmVux1FEaVoawAGxt/ZQgLaVXaOrQ8xF+hXjrHJX8fwID7WEcbPaoZ2hPmom4TCee4pLHrfo\n",
-       "mj3sieJwUVyNS+i+u35jPtFneZF6hZyD0qxAg9sXLPu/Bq5Yw6Jx3eRHHBvEm7rGohwzc9IqTJxQ\n",
-       "jXXMZ+0gpcbQfGgzlGC0JJhaQry+67o3P9iHeViuMB7KmFdXhK8WgBY5S6Pvckduv2r4Nym+fxxb\n",
-       "8OLKYewOUw3VErZW9lEB8QEvPzGRGBF2rmPdVNx6BGSyGbvo4pS+oGn0FECZVwl7yCmuRke02+lV\n",
-       "19K3WvHIQqaSpW9R5GXrnxYZSnoNGeHI0lThCgUhHHkDvmvY3dL7s1IuYWBZtkfIEo3Iw7Gb2gJ5\n",
-       "d2WLSV3n7GQkzt7xMAJBxt3mt8p9ITO5pP62o1dvamm84tFWfTdXVlqgM5AwuA5U4GBw6Koiwj2j\n",
-       "9vgocU9fQ/orj0gsiX3GkWFmsqgp5FmptLVjzDoUp1qqUzY3nDu2yWPKC9wfv4HUS7FyHCk15LYU\n",
-       "/2DH1Fekq1/k7dAcg64QPr87Oe4lEXnPvZWd5GdVZ8oPyVOJqSJi/HP7IVRb+tnd0R4qBKfuG0S6\n",
-       "dxqz9CT5RsDmeIZA3nnbnO0xsat3vG6mlA2GZTHEaaFZhWJznFn6DMeOMcf6ubWxvuBxiU5Zboml\n",
-       "9RDbSEfu/yhDMDzxgF/4tj8Wbhc18mm+dAM9trvs8SUABDVF12NBmj8fp0ru7/HHZC+D29oGOqvx\n",
-       "sxKLIFZjM3I5lGr+Zx809KdbIDUaFPaKwVpUJBty/6F7oTrc91ujUczeIl1J3TzCRvc0JpgoOChJ\n",
-       "sgQ87/9IQcx5ZikiBZlVb2LvF35dIsF5RlRx3tiYxb4hiBXjSFGWLpleZBGUwj0KC7iURNsQ45Gx\n",
-       "wuOgM9ll5OPpq0A5dtlzbxne9PxPuFHrX/482j353H+kMV2C93VK/KWtHNSoo256vreHlTwa7oF8\n",
-       "+ETqV/xWcR4+QtJRas+/mTmHwm7wAeSDe4yVFcr2gnS7ofSET2F2Pw/ujspf3FWbCBu4NchZieob\n",
-       "FkTEGoSwioh49dok/3MvFreyEJQGyKYdZz0dbuyMflB4eQkQMoGCkxJm+YeUdmx1BFvj1nOs8ZrO\n",
-       "KUFYgdaq/HIioHgRZ1K2fKOttOP2mx3XVT/tUrkl9JfaP2S9CnGvQ1k1rZaADINFOvsOxvcI5Qch\n",
-       "JDgGe3z3+4Wm1PEAMhMA6gqZFmRamkGTrJM04lLglIKTpCe2R4Y7/CIrK49ujWEFZ2bzFPOgy2/Y\n",
-       "KJTsCnWVY2QQs7RF7ls2brm+12OZv0OAx2A+twVqLbOLuDu5vnJ3REf61ZoP9lr+NZ5G/FlU8wAh\n",
-       "Ey6m+lam250GQq1tL6ktVLdTLV8CKQnSCXlOIM+mg61OChRMEJpevZTy2AFLWO5q64ofATPfuojW\n",
-       "1pI97S3TFDClY9mFtOANhL9lb2f3oNEQcl15ZoVmugduPsJkBtefXrC1KnCcIktEapquJwJaMNcY\n",
-       "TzuazBNPl39Uv5WOxY9i7kjpJ7nCTK+mBkq9RDL+8hivCMXHj92xr2keA7omBlFXFwkJXVVT6a7p\n",
-       "7bSdNfD/8OYSLnUGoiZmGKznArMhjcU6avB5F5cxqBQEjwz4xC7IOdrFxgJnKOOu+XgAGqXp67yr\n",
-       "BCX5B5PF9TlCVgXSRBKriAGTkpX7d4Exk02e7V2XOW4umCdSsj56vnnYhr7Utv5ZhyR8s7m4aJcZ\n",
-       "GHqA16EjAB3LFBHpuc+qlIZrn8hpQKRb+lj++QW26d/J94ZQl7YMdFLEe9ihm4509p2RUUuKDgwv\n",
-       "PVG4MTObJgM3palrWzTQNzAbSmbmNZqPcDeGXqUnu4bv/4XWF+GNr/xBoWHc9wBhrYNbbNieygxI\n",
-       "8AOauICC0sj2QEBIf4M5YhWeKhvbX7OCVpcJS6l0iZXVE6RHxY9an4he4nRuLhqqqmUdj26f6LgP\n",
-       "Dd53yIbKgQ657ZZn4/x0AvA7Uesq8a8IEAqKd982EEtusZ9UOXT9spNZGSkI5WzNBtRnkW8YuuAW\n",
-       "MgZPhgAHYIph+TUhP+LZBMtmX9E+N2UoltCyoY6zscYMmaEaOUjP1yFTjfV/gmMuD4WMwmWwCwts\n",
-       "Oun3GuBS+ZWHQ/Boin8PnH5ubK8BBr0toprWlzcVe2Elc78BgRBB/CmsdONJ211UnF+H8+ugh/fq\n",
-       "GfOljZS17rGmOditPKCOghB4QMR38+46OzqPGgpB/q8bS3ql4RpEgryDmm2D8LlROkMDtzow+dIh\n",
-       "gvtuAolw+HcGqdVNE7lvPEq+r//hrQ64XL+rvONdjNSYgE0h0JEVerjLla9AWM2U2OQ5T8vkp8gk\n",
-       "QD4biNjrJYkDz8gjRtq5vHhLMAFH9Gw0+Np3dwa2nCCn/2fl1VHM//nqRaP90DTbBtHHE8ntLPnl\n",
-       "wA2o1QEE4t2C6ZVbk81VXmg6af7QYTXhtb0xblm+31y+Z5ZOA02u72pmuxXWjQD8Pp3JfkDDyl/R\n",
-       "FO4fepVEJpcgdYwtIDsqq+/JFaHi1qSqO1zqk90C8Zej0wpHYOu4l/c4AqpapcfZaytkAqPaHeik\n",
-       "AeYyWyKvXD2iP4kG6rrz5UHH7gmqv2PmJZDBwXaA6nRvFF2gcT0002D43Bn2IYJCrr6r75sa5Mmt\n",
-       "WRk11uN+atyIMeedWHi1uF9qdOjZxkPAO6CRwVbdjUYR30viBOt3xuguEK7SZqFbagnkarE8SUSQ\n",
-       "/iGwaZIQgrwRuie/1LHWwVp4TwTgSRWl/hAzATHuY9qa+7b/O/5x75fa37ldyAXB5zJJwehN+kqO\n",
-       "19JWjrUkAG6kNqxu0o60oOFQSxhQeUUEcmtY7CYB7iL5cfIN6Th+oncXi9mgOHhynGs1b/4Bsvij\n",
-       "17ck+crzTx1N6mzhCzS4HVVotsnWQRvMn1NTt5AgOte09saMOttuaTeGq/ZhGswf4P/LZLpmYTB8\n",
-       "4ieCSZQpVmuCBoac1sgfSKrzqjwpN1clzx4SWeQCYoujFOi/ec46jS037Hx5f+RSutMwHUJmtnhA\n",
-       "fMisqkFrwkh9C3xrVgGk/CPlKKKF2oBetMB4cIryT6BtFYkKZRLtV9QOOUSV1x4OHfPEbkG6oVYH\n",
-       "/dTnrY+ArIJWCSVXVryCOC1r7n/5Mzio43whCyNDL7kc2SNCXOOGkNecij+QhjUYh82rWGfCGcS7\n",
-       "GYAnwPffhQSaMXXcrLmuBYnKoDMWeTRcBTkGSu8vsa2Zw/UT3n7ttf68rueVLIpPRR5rtkvbiS2Y\n",
-       "HZjdDoqS1nQviJZPudAXZH+1QB3HogfwYHw41GzElFZQpSk//SqSvK29NK8O1tbjs3s6LYdxkNkb\n",
-       "Rv8XKB1HvSFDAMKwMASe6a5WdXTH+i1T93iVyaX25zJuMsOlFcAf/t2RH468vVK9lLeuAUf2OeEC\n",
-       "WU5mWoz6vhpiwOXoGcmOsC/BKuwA2k9EBSdr/DJavt5b+CAF4IkfDJUYAf98/uAjUh67s8Ze2Tpz\n",
-       "ftn9vUVVqIjLY5V5OpVLG47Yq/H1QpU/rUiCkyUp+0LqtI7sDMaZzCmjGt0QzuMqXNrcyCm/livj\n",
-       "6r6ZsG8bMaO8lvXLNwl+LR1JPN3n3JXhPICI7G7SM/mXvzhXLkudKRHjo6GSGTnV/rAHWBllzViL\n",
-       "4u9Luh/IyywdzEzxDAEDef/nHkKLmBGOPjz1DrTbgjAXMM6xhlXIo73vQQvID63QyFJzFQ8R8Op1\n",
-       "pHUYiRFC6lpjBE6m0RGdieLfdDz106wQQElOVW/s0IqWsi58BLPu6ntu1uhiXdPwBl5YipnvhWNz\n",
-       "kFvPbopNIbPi40RPkIlsFseFQrIfUIq9nssuAmHDpPxj8vfYM+HcrJ+5nIF5EYoCSjtvZM8ol9Bi\n",
-       "hnWiJ/+iutZWVpcYIdrVS38j/pXvjAR4JV+v6HfC/Y3ijJB2m1CPTaDECYUYgAAAAwAAAwAAICEA\n",
-       "AAYRQZokbEP//qmWADFXz6AHGIMQPjUlLJA7EAcslt+76E85GhGJj5yrJ2WD4ksDBeQZlw7A4SiE\n",
-       "WNc2ZKRJ6O3y6v15oE1EoEqDE/eNBcGE0K9H7T7c+ucMDX1qpWp6VhHFx6v1zz87TycBDHUw/SDq\n",
-       "8sYirYiVW/MXHEGQ+ofVVzZrZyQVfqYeB6tC53nKnROfJPEj0fKZMXlWydaQxfQK3DJWCD/EbFeA\n",
-       "CIAdHkcCqEpoGDQf2YYIIFs4KkMTXnvZoVEUjh9WPosYskPXcOatsYGjnnBU6qDkqxb9Hb3vVmmL\n",
-       "JapTyUHNn61Lz8PEYH8Bc4ID308hKJF/XPrPcntUKWFkfu29hkaCp3O83tPD2eeUEo5KIZzm5wk9\n",
-       "TktaTInZJRcdTxhqVXc81UpHpqjV4v7/mldocZIkhdGFGKvnHqb2a6tbPTcz/nKvEakpesjHnvtH\n",
-       "yLQYNiXSReki1JfgqYMiJnXl+Dr8/gsxqj0AXJOt+YSLxnbP9kmnIl7509avqkJSbT3XplVrwv62\n",
-       "DyyFVly7U4KrML8+oZce0s/LehG7q5NgDriVyRCkdMdlGgEt1CRIef7yp+EO6sM23h+ytbCdZvzP\n",
-       "AX68nmBM+JpEZFb6MMbp/WdHG/9r2G2hjcJfnfGh91RuTr/DOGns/ucf73cbRw0jyJMJa5Ta9RTY\n",
-       "GFzlWS32E6FEu2cUpT9jlpiuvLNrPiopEfKSXGzGrwR+kt57nfv0bwooMH4HrHBsNQMYPS79btGf\n",
-       "cHTnpJYsjcTLVvVqbuHDvrg7EUOdR/hxbKVrUhZ/w4l83YdOGAiS+5aLm3ciTJxEdReioA9Bkmsw\n",
-       "4TTrxh6odarHXwnwaUvqq+Vuf7sXbamzGJoSdeORTSAJ0oGXUOHP8IaO9iFsvNy3ltMLKByfCH49\n",
-       "NMHdTyjDw2WKOxHMKwxyladFS0TyEtBZT+KMA/yDtyksg6THjTifa4g9In+5lu/Awa0GX/1tFRij\n",
-       "StGHByI3vPIQgpHIzwyZ6JbG96uvJo5c6lM8EPUVbSIrFrD/ofZ/AtW7eTmQsZxocF+qSImwuUAg\n",
-       "5WkqX+Gbkz6YBrDj/3t80DbolScTO0BxEXxE9QHo2yyyhRx2J2oYi+TWmFSCF0jQO0RopCIgAWJC\n",
-       "Lf9M16O74ssqMoPrayW0eFHOQVlw/1NaUOKXkENaebCGmbbwrmYmzJolnVeJfXLJlTh8FszOveTV\n",
-       "HBzXGKtJS/kIi/RO6pXFhX/NQ9y5R/j0R4WOS/RjjHO0dZc2ddhkffcLfm8I/SV7orKmJr2qU7vs\n",
-       "5AdwGZfCHWZpILbEdJZ8SCv9CmtXcRtZ+XMAuozWoxulDKduNZkoVQfCMNWP0yNVT8938qtVHPm4\n",
-       "s2MZawzGnrq0ANxuu/Hu1y06aqWhEPtmcOEXmvpy7yEXNPA9XxlidAiuFlP2PPShnGHGspoPdLvI\n",
-       "eUd2A061kf/YgiaqYiF0KKobLbSNVf3KCoKs/6VMIc0+8L5eUfjNUdv6/SNsVEtOMRlYxlxbNskm\n",
-       "NgD1U9TvYR8JQIYMmjgsYqvoVKwA7NbwIuYrNtlfFKa2UuCFYPKZ28RvoHq5a7G/1KAcynicGKTt\n",
-       "99rMnWdSoquaJ3iD83kgEBHm5VSiV2+k8SXTVKa4W7EB6ZmKLDQpIp7FCLozP3kI0Q6P/CUGJPPX\n",
-       "JEXfGQ8prg211/UcOXMAt2O/dhIG53DUjNjbQNOReDRwua9O+ZG7fN72uCE+46nwS5ByyGtepAg/\n",
-       "iq0deY7t+9PmHXqAtQUsGtpFONI9bwZpQtzJ5HGBjDst8+G7h+kkFFTPAAg5sf+nDBiVSnFmDJVs\n",
-       "1E6OgasGG0IWEr0Vos5Rmben4PqCyf60FXf5EMIJwXonMMYYZ5V5obo/FGnZnOj0vOKWKpxVwilR\n",
-       "VhYyuLcLct43ixiIryNFDl+XozzseGsuvwGkkKXYjqisCJQX8X6RAFkJlcS7QQQjI87rROu70M0m\n",
-       "BqbIK8187q4PsjFIXiUjU4z6/RB9GPZYkJ5BvQcRq+8VQuEg+mFDKNPAhF2yC3Hkg4neCErnvede\n",
-       "O4dQW1DqaV8Dbna5oIRAtoAAAAC1QZ5CeIZ/AB++TJ/lUtwPqPJYYLsAcPegX81BrRReZ+Ynqywt\n",
-       "T9p+q4l+JNvAF74YUGvY+ncjrHK576ZOv1BO3hG8pZkJY4agxe6Waf+cQAA8IuiqdKqKxcX9Nlor\n",
-       "N/xQDuvrjfe28C6tCSUnPxi0t/2sNPyEQz15c7rim/05OHDllhVG48+df08GsV8YnaUP+iU+lGcq\n",
-       "Q1Qvn4S8N0INMy19pSmkQt39Gqih6dsk+rWwFYAIGQAAADMBnmF0Qr8AO1wVtzUqbs/7cV0HowPL\n",
-       "qcoBmMs4BB6MVNB9ZcuVRPhn1K4u5p3BwELjJeQAAAAhAZ5jakK/ACVLJhUYv6L1gT20g/+Fj0xt\n",
-       "OHDkoqUG7DehAAACAEGaaEmoQWiZTAh///6plgAdJdF3WdWTsLazAAkrh8Aw16K3dXnPNjx6K503\n",
-       "+g/PVYO9M4XOe/Sgw8xi7lnTAnhX9Ql3mh60GYaCHK63jdoywquQO6vCwDwAiGPVxr0FYNKpS77u\n",
-       "kTdlx1xuyacWbrZG0mCXK6oGTCw20Ij7YpPO6P4xnZ2grF8+wl1CM1kkVhKWB/7a4I7IFzehfl+N\n",
-       "XS865SSbrFEseLshXOXkft5g7zTxg01hZUAlDB33WsoNgA92pXPKYXjHtU7sd85xeN0Bvvfh9k6U\n",
-       "fimUi57A6oUys+ltndKHLisWmsgKUYnOVT7dF8gXumCbJN4apLPRE4Cp8hF4OkUxvvCSju23r7sp\n",
-       "d0vjqPDJPqanQjctFUS+IRd0lj6dPPO92LKFQzXaPMusUIBUzSYPAq2GZbSgPw+DsBFU/r8MQ9LL\n",
-       "qcx3j/oMNwVC3cuwIBZXCNFsgsPiypiLuDAIsPKDUEgj8iJk3SMFmPxsWs5GwpMhpb9q/jA3ByKi\n",
-       "IFvgNzGd97wkU3sliTrJcFqJHT0Lp3lRMyVMqWEpFSoGz3ejhcww7Dc1JND96IO/dETcOOTyqvP1\n",
-       "R/4DKL51j/j57k7Gk8IZSJPOMc2mURJbE047/NWcOAeXouBo8yC08VUiA3fP9tW0rLNxMsxwet5Z\n",
-       "DRDdssG3hFEbuGDhAAAArkGehkURLDP/ABsGHVlv+1at/tKpoobzf4J1PcbeXTm1DLoXbsBabA3Q\n",
-       "NToB/AW5ESVjFnCrLcyHZKpJTbyAhe2yK/4wMcFjMEA2W/zrjmMbpPI0glCsoWfPaDKjElXJLcFy\n",
-       "cmtD2lrDmzth6LnHx7nhSsTCdTH85S82PXpPbugVCaEM1gZGlBsxW/m0drLLd1+8/1hIQPCRcHzr\n",
-       "Hk0UgBIqzbVVg7EAkDHc4tkIuQAAAKMBnqV0Qr8AMkBnYACFYhIQbqhErBSHMIEinC1ndpWYFYkT\n",
-       "o70iOtPLvkWTVLVx3xmtc4B8vOfPiNTWUepp1sSnGEB5Oc5tMYDRpxKbEWy6J9ERQB44d3qHpWy6\n",
-       "uO9xkhjOiEtKiNC6Sqdhdh4ewWpWDS4hk5ZlZAqYhP9ZfTBMkpPYlZXZKcYQeb+dp+lb8GqQXifQ\n",
-       "G9HgF7GlPwedpPh22EHBAAAAPQGep2pCvwAluwNuinIZrHgAOF1ABDpS+vZE5O8oRf2VeHRL6orO\n",
-       "AIBauT0x3OeidmWXGEnzVsIRqDXAIOAAAAIeQZqsSahBbJlMCH///qmWAB6RgcUJlOqtbM0AXznh\n",
-       "NQD7nWZ/1OUfSQDJZYAi3ZQSFbAt8eG+POXrgDJMl0mOk/vtSdJRU4L8g52X/mP0paaaUKV9gqmU\n",
-       "42zShLOR7hRpQR8yhX7/w+hfbUt3FnK9FAWX33aVuWOlhJw/y1JeTR7wRUrc/F2WD/NswVKK+PnO\n",
-       "uBAnWbLO4y+QGUtFWA6Kg5V34zRtKSom3fE8nDUVF658qZDiNCAxvtD4+E4K+ofoxTFzLxyffMgc\n",
-       "ZsiJOal4Stgjw/SxyneB+HymsNFGoat5XZptbGczXd/H+dimAFSL0pkKrOF0Cd8h2Rcl/OGHCw63\n",
-       "MQu1iI2K8st8/BtT7YEqLaylWq9grx51X5EWQUuHBmUwAE7nnoPPIdiDzNizR+fuTvkxGsoT+jTI\n",
-       "R/EcYJuIf+xYQzIQXAZiwaSoqAEF3NvhlI1erIoryV0N7+rwptFbQKEnotrHp8ID+Qdzx9+QaL5A\n",
-       "6nDOyux8u7YvVfwLe/MCRtNxzFYjv8KFZ/XV3UudYdyVJK5oTKuPtYg2dCTZXUySNxEcQWlNIN5M\n",
-       "oY1CLr1xwI3ipXkxMFVKQhdae5Yxt5+EPu8CmTjaSrnpsM3S1GFuT8oBWiOgb4AQ2bsCIBfGiPkb\n",
-       "3d44EwcFSx8TbNG4Xdk3qovT6AKpG/b5HFTZyzvskTRerJplTUhxJcEY4pP7vaD1IwXYc40nOmAA\n",
-       "AADwQZ7KRRUsM/8AGwYsXIAON2GyYuYfggZFBHzCGljUveTubJgZoeDw/j8nhm6MbkrP+in/kBXo\n",
-       "DMaDPN93c4gjYceVMl6B9Of5KkWniMtTRuj9wYY9IZ8LErnfOyiR/uWhWFo0qnDCm1RvHiRzkiL6\n",
-       "qXwBLYPt+xjgNtqH4Ch/u5wRdHsULKCIpWYk8CUWKWYxV3JjmsD/JhdhkTz7LhLzr9lLKFC+Yoaa\n",
-       "l7etT2YfdAaB291S7QOGnPqJ7M23fefvt0gjwJApQ9ZaqDU9fHMeGf0Qe5XMmhgnKyzsbttav3CC\n",
-       "FpV11QiCBxdnHEMWbj2hAAAAVAGe6XRCvwAfyG5b8UmW3HZefLP69IAakOZpIS2KXKJBMMLrbztx\n",
-       "ceaRrs36lbMoTdtaXFpFecs+IK4k4herWbmVWkVqNNm+zikIZjbJ1P2mUEh/gAAAABEBnutqQr8A\n",
-       "B5gOoXl1E2b/gAAAAY1BmvBJqEFsmUwIf//+qZYAHzN8YgClqIq0tQK5Uag29Z3dqewuabWuzlwB\n",
-       "2AlfDwh8M2Kly87LUkW/z57IGrU4o/lDCqPuHAXoyi9687pSqLT6XA8Gv1UFheYpOVu+ISm2YgTJ\n",
-       "OzeM5giMy2uOP4C76ZyHG6aLcPHVAPn7Ir5KQcS+wyXJlwnzkAOPctbKCz4ZOcH8/SPN2DeMWC+1\n",
-       "Ery4YLLwWSShGIPpxn6CIbQ7J8vkYllsqF3LVVBmice6A8Ya30N4t1D+wMe0Xhs8uNY6HAbI5XbQ\n",
-       "wloeAucllM13tO1bF52/H8yvvTyPXo9hBGDOWw44KCAE0RN0+5WZks4nmTy2dWCG8NAV3uDoOD56\n",
-       "xyvRtT+jHPIR5CNpaj0tKAhYHQ0X8cxdFqLUxSGu0oOqdHhSArxsOc8b9QT5V9DndBujiLqS5vVA\n",
-       "3XpGov+cfTULjt9zMlCy1+TM9MQrnMAFI7QQ13pkCRXT+1LUkfZBH8Evf2tkgPC8RH79bKUub2oX\n",
-       "DfFlFS8ca4tu9kwJAAAAfEGfDkUVLDP/ABQ6o/fl6w/2DuGXv7codpKTS4GgshxRACutUxtiFD6j\n",
-       "hEChAwMV8SfxEB+lUX+mhzin/oQCjcNkYDY1iTsk2qxFdgeS1l7mRpZSbp8HwAmEp1xDWuiMCOkH\n",
-       "kVrI2gg2oIOv+x0CmvJdwmdcF3pLdTbYCkkAAAAkAZ8tdEK/ACWuldcvDHszpppQ6TAJixorWZqC\n",
-       "jWOagJtluARdAAAAdgGfL2pCvwAyREBUeVyBsXZbKDTUc7Ba0ACrLi+TR4PbnqROqm4h5PnJzuBp\n",
-       "tWg5zO1rR+JzHDZJ6747PrO5f1EBGa2tDmUbYvoSya5KHTPAaKwDVlBDcAC4GtbW4Mb2H5UvOjzB\n",
-       "5aipOlOHvOoGp8sZY0pCgm4AAAJwQZs0SahBbJlMCH///qmWAB8zfGIAnBamRdFpDLhuW3qGJKVw\n",
-       "WGgmmq3CV2/Q27wu9bW2CYt45ErCY81UTXiHZgEyw80SROX/B62GCZe8ed11116MS3AeXN7ATYYx\n",
-       "6kylaADJJJevolPr7AvMhs83mktz4+vi9QBJAQN3IRSTmdHg1+PfJ6bPg80VuFuTJBtgtga6+XE5\n",
-       "KbnsY5lmgPdNsvQ59mhflQcOVEDld8TjUJLYr70UmJwsFU+ziECS6XQM+Bvcd8PP0q4O1sJx2048\n",
-       "VFZMJwAu7UXJG+7EYNzWrm6a1sGv/bq3QgDGJLqjKhDrELlaShEafbXFs7t1SWpNgmaOXneli8yI\n",
-       "TEO0OXeGVEE367/eUNzdGWmEO37ohdsjinxFsHABVV0Ce/I1MhQvD1sRch++n5rmKD40yGFah0ls\n",
-       "Qu8hUhqc4/KQNDWKL9DYvj2TvJWNgBQYgUBb4jhD7nn/TGLMP+qRahJJonV5aec9RzdFMyvZOGxH\n",
-       "KX/qygncFIkaqQapXGRPVEX5SSDAEs+0ntFUzbgZjsLvEFny58GmJutMQzIYe4RbofENF6fZgNzP\n",
-       "3wlglcFRUxVjwHwaA3lSkG1lhlLhZoJRzCASUY8peElJqBPl/exZNCWgrKO043P5+lJmHgURzuJX\n",
-       "d+81Q8TiHQnfw0Kx6iT6eHraMDeWWnbMXEg+f/ISsy9IBk7G7QkD0Wq4ZFkrH71h+N+7hRYkuYRG\n",
-       "aPl5oZzAhn4T0vPdwuuaIuo/B07UbjgmrmbCTBu+pHfNHnkNJmRyFBZlpEWfx0SXQZb30ugjw+HT\n",
-       "oMsZMuL8mis/iEQ3QdLvEOK4AAAAbUGfUkUVLDP/ABsE4F6bgnOW0UaP3fAaX3UE0viY1XQ8yAEt\n",
-       "8MJCRYc4PFXI8gV7ObIZ1dQBvZjKuZWpLUjxJoW/GKFxP3FdDvpeFLToismOw9t3mEBRi9RTmp/x\n",
-       "tn6tNy95xbgFQmi9x47vAssAAAA8AZ9xdEK/ACWui2J1H7hWW8aOKPSDagwLuAmxhHDFISMYFq5h\n",
-       "Xe8wADdqryi3rpr9b7A0/w8GdW94AIuAAAAAVQGfc2pCvwAluxARfHmjUZsjjI2UagAIJ8ogBqP/\n",
-       "yrHDNEhyetyj8oY2RDfR6TbISupNQRj5S/ZnFn9rf5VvMf8MhaiCoU+1t+xjbkZhOv1dUkM4EHAA\n",
-       "AAIoQZt4SahBbJlMCH///qmWAB82qwxAFLYgv1+9wd91t+G7oLe0ExttO4XDpil/W0KhRKJM/w6d\n",
-       "x+0NfreFAPI+7CVPHXZAO3jvQTQXQoloqOgTthO0lwL+feE/esxe3TAWCkdkSq3JD5F551XQQiA6\n",
-       "mCha6V9R1gyt6T6dhyWjpGgoj1zCtMpjAEHYforypi9HLA5SBWbTsmh7+Y8ACvZF4OFzRVzUcrvC\n",
-       "L9kNj0FjzVWiNqKfz9npy0PvTwKn6tdGPSQCG6o6Pe4GTQuG9KwsqtNAVdccicrT1Om1qE2Rv+Z8\n",
-       "lV9OGWUUN8wU3p99sBnyZ8Y0UtZf1pftBiRJh5eXVHjJ867kyFWVqhGkozYrysDU1dJArNU5wS5U\n",
-       "mmxlEpC//oSXe1dcwQ4k5hAC2/O6M5dguiQemhu+gTTjaphoqqttRnh2NQL5yOVUSKe0uorB+Sqf\n",
-       "ywgZI0i37rnwGGTR4WlihoV2ZN3G+C/s7m8TJ3Zh7uS9oC//V18XP+Yct/wpevCMRUCKUATMdM//\n",
-       "EJAZKsTzrUsT9h/WcwdYOEke8H8O34+5G+gopLelwtpLxSRXox5BEinkMv1NNoYlckIyjGU6E39n\n",
-       "HTpLTvbtpHq39lUrgXvYTu7Exd+kA/oJhKeImPjfCbO4lZIMIlee1r3xCF9qfO5PAsUqB3RCFKQs\n",
-       "iQcV289qamenpvWqj5daMy3Jw0W0FVwbKkgZhDaR6htX1Ba8oq6zK28XAAAAd0GflkUVLDP/ABR6\n",
-       "a+/EvyW9wbtmMxinU2WwGj9ORcNqT5UDKASrjVTiOV07Rp8pq1YIcMjGgh4JDKR2lqiH8akFfCF7\n",
-       "1VGgamjFjt28Uz8VHO7h3ypJWM4AP/4Cd/2EKYTtIBwcPSQZLgpCB1L2b3ZWmR1TXwMqAAAAGQGf\n",
-       "tXRCvwAlromXzC7675XFc7g7DLNBQssAAAAgAZ+3akK/ACW7AsvVKMfjGBqk3Rsi/gUbLEiX540E\n",
-       "k4EAAAEUQZu8SahBbJlMCH///qmWABQjlLPgFmSEJ3Ecl69KmPVBF/l7cbDdEuiM6UFnBBdjkDv/\n",
-       "j3bO6QLk2wYXAR4vqKLOY2q8gm7ggj+5Gp1EhS0xL3gwslSv6OXZHLA2UQo8hwVEtc9HRPe5w25z\n",
-       "Elh1FNR/urLXjSxSYEVmphpZVb8rq6dc4UG0hHbzggsW22yuGNDet7nwBXzeFV5FLizzXuNyrFw2\n",
-       "eYWq1pF65YOc1xio2SEk9K7Ll3HPTTJBXxDl2UA0l2qi2a/MxkVU04pCeFuqQBeVeEWD1AuVwymN\n",
-       "n5XixNwmboIrolegPrd06wmzOYcNFJOJuNqgo2YBvVzrEO0GgcmiwH8QiK461bvWZ8+Wac94AAAA\n",
-       "l0Gf2kUVLDP/ABsGHWJthi9Rb9yGmuAAW3R/2l7dP3KiM665da4p8aPE3R/n3J5tIo1tO18KL9yH\n",
-       "021KEgihQNvljDUqYn2xXTxzB9ZES43tPBHG8NsSoOR6UTUwEvTPRY4Mp+Unr/ZcolF1N7oL4Gt6\n",
-       "tnYTQiW/+v7O0vjxsj0VHGVMIggwdIYaYTkN/d0OAnLxEy4VYh8AAACCAZ/5dEK/ADJAZ2AAhWIS\n",
-       "EG6oRKwUib7q2YecegMLgwaHzx5LdBFScUuBRRyIg4XobCWqi/tszLi4hN5ZxjWk+so8diSs593n\n",
-       "yU8hsUI/ris64q8eivAcXFazR1jaHW26RSAVjRKxvGls6S6Nb5Mb3TilMhovQL5R4q2k73E0QDeU\n",
-       "kAAAAEABn/tqQr8AJbsETRml6hnjedzGbnsg9aJPaoBUw+HHTHGCa5l86GTeahrIwEFrB0+Dv5Ma\n",
-       "Rq9eUimy0MtvRaH/AAABpkGb4EmoQWyZTAh///6plgAUnzVigFR19DwzOOmA+T33eql5R5RpV396\n",
-       "X5Sh5ARpzsaeYVCtqLiJoKxLxtvgxs5qaYI3HEeNZK3veVMVbcSIPTyK+91GLWtPotI1JIvCgr+j\n",
-       "B2gm35riOq78M8T2Dak08m/gSo4fIkUQ2Z2mZJDlg0DfowMaTimaww0UXPnLxWMgAPTP6OIkS8VG\n",
-       "Mbm3Lu32L6CVvM5EZyZdNGvXlGYyr7p+yq5rUhab1Z6OTK39Z2gcuHojPvS/tuFdeZDlZ9M5VwOR\n",
-       "INoNNlLEHeyVtDHZ3X9QKTrmCq92TqVNetisSlbGznZg9LseyJXxbgojEv96upwBN0HujOvSwoDf\n",
-       "T6e5cSovucjreBR9uVNduVl4g6M1EiyWrRLQn4ZtEoxv7vvrUC+bzP1Ln9dgrn2LaLgCUA2vWDyh\n",
-       "77MR47qgkie4Wrf3RLJX4Ssa8WsoHnLngUZYWd376VuzfsgPN8dCqAedxzyPVutofvcwWPuTLz9k\n",
-       "LyD/rDhMnejlP3hIFSr56Sxp9d0VmjTPBvcrDHMwRM8pDA3DCntZAAAArUGeHkUVLDP/ABsGLFyA\n",
-       "DjOu2mYuYfgg9H6GegU+R0wLyZcHU+6tYpS/F9+JXERmO28OBaG3FIZ13bX3cCdW0SSVwdGyNKOO\n",
-       "pkequeMimgzHECtV4CjZu67WLaIqY+ChL7GrZa7OOOWrZiFQ7k/rcehku9GsveL9hOjvKTtPpxOK\n",
-       "sGoMU/BxGCsTYkgvRW8wZf1fsfuMv7y0r9O1NkmigqOXkOX9WxZMDJJE/2A0AAAARwGePXRCvwAy\n",
-       "Uem9Ljri21sk2mlolskAc/0bGBvXe08JAQvm8Ykx3lxxvLi/8gCoDo2Hc4bu4ECIiHO2Xexy1HVm\n",
-       "YaKXCAf4AAAAFwGeP2pCvwAdBlU4MKZlNO+XEL1GSl3BAAABfkGaJEmoQWyZTAh///6plgAfM3xi\n",
-       "AKWoirPTPbNGBOP+4gesvyrxwCZiORfjesDMoOFc7NG8FsK06gbE1Us3EDUnqvCMQWuzlipoPuiw\n",
-       "5c1UNQQXxiH0OuFvX3moJGbP0IOZAHfKHN8EEPPbA9JC7otcNR5TxnGfn64f6jJ9aa0MLhkgYM9I\n",
-       "ENC8GjvfE0hgIyQ97HpuMl7JYo2YgKaYomC+uDGtxERuRMGnXGnmn2TSmOqjr9hLPTpf8AWoBtyE\n",
-       "iWWosv+IJRZdjVQTnvoHd6oaMOoihdIcgSvgACSB537K/3J8TrQUtmIpnKuXbCwBZusm4HWPeNGG\n",
-       "p2KAfa1In1IAJHWBwJv/1Y6dn11PZkX2HWMHdKwUVWfVl3S9qH33AolezjbyFJfQ1MSrB5joDJPB\n",
-       "vZRY+0dgEAGaxTqhUuxaHSI0+HnWg/E2/rHNv1Yj254czvfpCKsqQBKbkJkwAPsMoqsVkoXLffg2\n",
-       "/tzjA+jixLN0iPu8grRND07VhFwAAAB5QZ5CRRUsM/8AFDqj9+XrEAAn6Fc+HQaLQ5TQ7BTk3xAG\n",
-       "zpJeUQBFiYoMjhik34YqA7LfYXOA0X5a4T5aUFJOcSgCXKUTWwgTp/dUY9l7qyrIKSqkVo88+Wmo\n",
-       "YSh1ze1JdGSLRGJ2NJ9zqQ6L5ksOLxE72xBbySAF5QAAACYBnmF0Qr8AJa6V11KyjQxsXQyDOjoz\n",
-       "LAAGCswoLtibM7KmkP/QQAAAAHcBnmNqQr8AJbsP2JFFFEIvL05U23o+IAqDNgQuPsAfngrzGtas\n",
-       "gscJuJpU3xHxvdbfqOr4wp6QBoE79fvS2Beiux0t/VcZYg+IXmijf/lX7RfxbCVHTOey/nomjVLx\n",
-       "1BqL0+7mm6AKoLYy2+2lnCC0loElZMWTsQAAAhVBmmhJqEFsmUwIf//+qZYAHzN8YgCcFsB1/4Yb\n",
-       "fKWvFZY8sssRge+sEJudMs68oV81sX8pckc6JcVdQIL+Ip3ogj0erbE79IJt2em2tng3wVMD1lHw\n",
-       "xUhj8cXFwEmnkdUhuoUOedNOu2SrXLm90Q7PmrbvJES+2OLWXFdc4WoXl6bUbkjmfneqBD/7Mayd\n",
-       "GZUZG2Oiyi03UuD+v7nuhNsmEXfYz28A7kSYXX8f0eBt5b80QCZ+ZU2ajcBGzZ67AFEV2EjDsp6n\n",
-       "hh0uOcodyC92Uvm30wCZBbCjcOftm6+krbxT9fqBt4eJPGlKbQC1NdkQSlM13sDrUGk6FeY/+gx6\n",
-       "IKZPqARnCUDinrDpTjKIqgyUFRqoUFEWoMUdy4T68SRH8Z+f4KDgzEGLrj/vdN5fT2n4sFk+rufL\n",
-       "EYYzT7zxFSEPDxp3RyzHYufSfFaF5JIb/gPuo15b6SG+MnXwDpg8FfacRo+uoXbXsIeQWCjfGwz7\n",
-       "fFc4Rlox1wndYEWUgNdRTp/Zu7wU+bv8fAPG1fMTq1c72ETGb8JnulsHD00FBx7lbnS7BO8kb9mS\n",
-       "AIzurlVEtKW2Rh3fpIAzp+mkzB/0hyuLM1YLCn59pn/V726a1gk7PFvWJutzot6E/i/0jHijXIEM\n",
-       "SuHlfWM6ytToloeOfzeti8bUJf9c7HIM1lQPbObGRpEJe/kb54I3wGu81W+5UguG7wAAAG1BnoZF\n",
-       "FSwz/wAbBOBem4JBtB7eMZFWvxYwSZJMUiHqf13u7Oyx9XWoBKt5AE1ZVEfppDzAoU0Km1DxyEkq\n",
-       "fxNSL5j0HGs8ExeXfanWSgWOWIMhGnkiCBhCNmHot8LsV8s5GsypoqCKW5bW4AYFAAAANwGepXRC\n",
-       "vwAlrotidR/xbBr4ykSBMajtmqQO7Nvxo5OgDEPvAl4roTeni0+uACIvuWOcn+twAUEAAABOAZ6n\n",
-       "akK/ADJOoKjymtlvhmbONiDTU0AnTk5irt8XpBpQN8AEz1KVoNH2NMFuVB54KgmLlWHWAxrQwxXG\n",
-       "WoUbtW+fYokM2PxWBIQTPhNwAAABv0GarEmoQWyZTAh///6plgAfNqsMQBS2IL6x+rsfDHkjubCx\n",
-       "EPFGsTvzS5MO1TSjpynpo4ddreQaSNzTodUikdmfK0NEyPh4TuT9zeHUbD7eLwY2kVCumtUkHc2Q\n",
-       "SBzUwL69+muH04s/Y2KMIdHWKeq0686Zg03huUiyAcgno/3D2D8Fy01sCnFPwuTjX56ZQh26I9ec\n",
-       "MUWPB8fYJcFRfb1FVRQTwiaOMfu4ixk7izfIZHarPDg7WD5RJ09n35f+yEWTYnBiZZTPpSm/3IcO\n",
-       "z8pbq60VfOIjjXYUTZn2S9QNGX3TfOKV2p0fvNq6oZBgImiNjuCTBtW+3oByO+2dprFRR+E9+baa\n",
-       "Vk4RCG32EfzrCekgrHVOEW9JRoH0QgXccHIshUfcbRLSIlCWwK+LSPBfv9MBlWdBLxAMINGn3WGM\n",
-       "qLTpOWO+KNb0jsCbTJE+YBn9BLmEiBVlfnmgB2BLC050SkyN3k3fJbSo0ftnyrLPceCnYs4DWUJC\n",
-       "P7LF8aBl9n7lVQLV9/37FULArJ+DZTh5xNUkoVN+9qhlWHJZTnbrWTpuP/9F/Au/TC5ArMg/DTgL\n",
-       "UL4THzUwiRfbmbvt4wAAAJRBnspFFSwz/wAUemvvLIRigTn1m57IKrdxgm+LDavW6YjAAbrm/EgP\n",
-       "xMi9L7JivhN531bksQV0B6/NHVi8fJ6t7hT/OC8p/7K+R8C+RSt/s5rGFEYT7nvzBLxGpDDK41Le\n",
-       "NZ7c+u9rXEXITtGzSjoVmtklLebGYANVpcpsoTkEeTaVcJDFPhd5kIJfpIN4Wt9qds+BAAAAEwGe\n",
-       "6XRCvwAlrojdPDC0Q9UHz4AAAAApAZ7rakK/ACW7CKjog19FxIMhSB0KblDhpKSVzuKcN9nO1Xw1\n",
-       "VtiAoIAAAAD4QZrwSahBbJlMCH///qmWABQLQsanAc/72FEXRPoePdiT9qWQDJakbdNX5nWLNu+2\n",
-       "8Em5XOqa7di2OYe0DCUURVN9HfFteDSZQg7gjU46M6nrN9BnslnLhEL0zjJHM4nqFI70YlVXPlPo\n",
-       "54M9V8nimVgdnNlxW14KunmIU6EWYDXhPeaJVj+gOtPmZ0FtcSGtio5BJMoqNY0ic/u52zEA38wo\n",
-       "N9cJ9g2mUe8O8wJs7tlwbTkqDv/ZY8c3UyUMXdxGaru644kTXqX6O7id2Am4exOXjT6pY+3xGjkW\n",
-       "MCERVVz1AXZC8h6+9tmgVqFYuAu/QnWAnDpiR40AAACOQZ8ORRUsM/8AGwYdYm2SRDKC0XmzwGvG\n",
-       "vuF+CQBFkLVVbgcfTypNBLALfO3ruXlz7cAJRwI4ioltjkzjl89DN0I/ueRrUUf3HaLiejrDX2uv\n",
-       "ksIEAj3Htx2AI7xqQlKVj4aYLmvBM/WQbJdm1xk8DbKtJbUD6OGZfhzwMhunTEfBzte3/mTao3rO\n",
-       "SfdZQQAAAJcBny10Qr8AMkBnYACFYhIQ3pyaN96isOVKPNy0bymJU5ulqptnpkn8jSikAF76OWot\n",
-       "fTSU7Xrv2vWvQ4/ptTBaR/EjaHGmU8ONsidupD/CVv957/ucDoEUHn9v/PLO/Xu5oIoICYRpES++\n",
-       "RXs7DG5crCzbRAFLlLvJNCo1ffdb+FjVzsiS7KlcSvZjScZjSOc2mcwcnga1AAAAPQGfL2pCvwAl\n",
-       "uwPiyfMg+B+/0JW43s7etaoBBqi8yzr+b3rt5lHOsPgcmL47vERkmFD2JCa6IBI2Rd1sDjgAAAF5\n",
-       "QZs0SahBbJlMCH///qmWABZOqLAAdEOqrlBFB7v0I0TWCCRygx7Y23iO9eEjt5Ot+EGyqJDNEYo7\n",
-       "mVKnCWez5GvSCsGAgLfyOL+AKwivNr7X2fAeG88C9I4XEDhYDOZYrxfrCgBuMf6H2ue2HpXMSx//\n",
-       "tUHo5wGVPdlpw9wzkHeKmInEJZon3f8EoAEe2fbGdJ/BbED+qqH73rvClVRSlq0y5O4U6b96T/d3\n",
-       "Iw0TG3+x+bXzEhhG3cCR2cmM0Q7ZoFaIczVghjMjsKn4UH3qUEP4UypSdEpynnVtUc+cdlcxkuDI\n",
-       "aWynGm2JJDfpEkdSxW2axI4mSMPsRJrZM/WoZtuyllBZ1eQQUr6ulSRLG9M4DWbEOGWaiXii+2lX\n",
-       "dvCaVDdaeqF8zy+dqOeQ2gn0bj63auf+j7j4V+vu2xbe/aENyg1FWrx70NfQEI8pdMReHI5POVG7\n",
-       "l6T/DWbtfLdVvJ+F077EfMoJiodSGHfwoiD61/wMaLCxyxwAAADEQZ9SRRUsM/8AGwYsXIAON2Gy\n",
-       "Y5e4D4fBQ5mh5XjYtjTmFAden9jSnqlBlkZbGiyV6GSeaKcTh8efQGVeuD4/Rzk8jHFYCF4b5J0n\n",
-       "xwQwRgB1fHec5NMv8mOJkNTRoeLsSgmpWecONn1Au9uqYguqkXizLbnjooyNXcZfadk7znNNM/+j\n",
-       "wjXBETCPiUMKF+42uqwJcZk0uofC+ehkZozPZF+xguhgC9iEl2Hno0dSwrICj4hAHbMyMtTlEKza\n",
-       "YjEGuOIx4QAAAEsBn3F0Qr8AIL4MaC28/bzb09RTyZFczCuE7Kn71NhfoYW7dyeFw/H5CTc8Lbz+\n",
-       "LkrWYUIQ64BNDlhCBHjdJyF9OhyPMJuwJ4MAkYAAAAAUAZ9zakK/AAhuwTcfKyQE0b9EtFYAAAFl\n",
-       "QZt4SahBbJlMCH///qmWAB8zfGIApaiKtLUB94f2HLl/oct3sZxYQlcGs+HI63eTB7MMxrQMhOrj\n",
-       "1IFzgY6aD7Wqb1gdpJqujHvxIsqUPS2LphSqq+lsmRnXcj8z2lMluMm1KZcV2yREfiQrSi6v2MOk\n",
-       "ZUcB1gzWrvLIM8kOoIrNdh+y8Zvr9vIbs29V9I6rj5jDvK6PcnVMFDYgyj2m2PyvVuEyeptEeH6t\n",
-       "ObXEp2R49lskPvB1dDEki+Vm//vudpTIjoM6vWcO0nHINT3oiAviq12SmpIXczqP9Tuec2tmpDDP\n",
-       "qFCFk/kaKQ4mjrVSmLlW/IMpCOnM9TlaR8khN3oghf9NJaYcYziYiyRc5km+SuZWy1eVZWpZ0SvR\n",
-       "tgleRC3FCL02Szx+/cSbwgBVzZIcWV+4o9T0db4eIPIbQhK46Fh9aI+NASfvKA9EFE9A0z9KVGpx\n",
-       "z9bloU1mQ1880GfLO16ZAAAAbEGflkUVLDP/ABQ6o/fl6xAAJ+WA29pvSKwBDWYqOmbhjYDXF13c\n",
-       "qiQMhK+YWL9DhryfJ6ZO7AnBjGSVD6nXaTda11nnQuBeLkFKYD8pWhxg9SKMryX4G0rDtLwL+JQm\n",
-       "IVmOqe50l45iauA9IAAAAC0Bn7V0Qr8AJa6V11KylZex+CAIZHCfzHzPyqFbr3TsxF0i+tnaBiaU\n",
-       "/ahL4EEAAABzAZ+3akK/ADJEQFR4qKcBblZ6uvHQAE1quFry0ZQ8UojFFDX2F58mvxBGxi/7W2FJ\n",
-       "yiyGebpvzcurUYy3o23xjAWw1KVJPeRMdoSkggYANMgwlS+gOOsqDwtCvxpbzh4OIHm5O8Ru07rz\n",
-       "4cs+cf9DRBwSsQAAAkxBm7xJqEFsmUwIf//+qZYAHzN8YgCcFsBXkhFKhTNbQO9lliMD31ghNxdJ\n",
-       "brEASaEIKW3v7qJ6QVfLuQLq/DxgWJCYhxwVPFgbtS2lQCWj+dAyfbFRuaWH8iTYKGVGo9A+SlDJ\n",
-       "CD/2Eks/z8BZ5O/39EalPSyGqgcWGIDlGs1YAl+YuFo28I9EG+Xm6BMx9ta/8BMTG02mfsniZw8T\n",
-       "D5/zJyloaAQU6Zx3KKHUzh0WPEx+MJXAXWv4kJw5tdhSbEbAu2Z/1StR7BO2yh2bzqmL/Hz6pTwT\n",
-       "WDQCOYfkoHdXQXWDM44JmVt20DbqNRYChY0dHk1ykq68SHXE9+RVjz6DUaa+bhvswi7TtFo8wnN/\n",
-       "JRgSIFUEtUK98BUNfEa8I64UjHsiAmaLtiQx0HQKqft3Z4wzC9ExYzTBMYBbhJyJHyVhUu5wfbrx\n",
-       "n/nWhRl2V/QRGbCkYCv/HNJ5dfsz8hfxT+bL5bw2Bkg2MS3aL11d81FhgT58E01u1oYt728gFsbw\n",
-       "RxljfpFhfJp97W5r1bf1IW/CvXyxLSW+c47zH+OBzXcpR81gASj4sap4NoyONf7B/4nSDgMVMAEc\n",
-       "CWjZyp+nCBjKFqHCEIUqQE+cDCQOFHFAV+aC1Hrzobn8NKXdXH+66Zr8G8Kh2aAjpRXPd5FYPCAS\n",
-       "AMHtwssVcaqGy3oV3Nb4tbllisvlaRbgUjBUwWsPHXDw4zI7wggnQAKlmxvxni+FEnBnn/TzkfyW\n",
-       "wsupx/e8ziCGC1E+VQN2IHwWT40ogCuaWGbIU5sy8+AAAABXQZ/aRRUsM/8AGwTgXpt6KoHqZUTm\n",
-       "5QaBWVYNSaYayoFXZbaO/J6XZFoAyKABdg7y1wyp760Ae6rZLIzDJ4uby5XwaSgm5Ko8CR2wa9HQ\n",
-       "QVocrD3OUcPnAAAAOQGf+XRCvwAlroruTmyRgPxFK9dAIOdHMytYtiBm8ZuemFxtiATZG4s+CANN\n",
-       "4AARF0rn0VpUn8Ag4AAAAEkBn/tqQr8AJbsOcEeJaOrOhJI50eUuq4aqQgUlMo6kJRg7J/6I43aN\n",
-       "rnSX7YQY/UCfRAAqw3w8PKGXrTqrDrvT3P86XW0FADphAAABrUGb4EmoQWyZTAh///6plgAfNqsM\n",
-       "QBS2IL6x+rsfC7tlS3sFt+RXpNOXhuEzEFOK40Hyy2fRzi+lPrS9rhhbgIKgtx3KI2OShKWBVxzY\n",
-       "eihf9Qc9QZ1gBpEpuMMeSMcKB2jCSsBTJw3T2kbgOxGbdHfawPkJRYw+i0rAh7Yha2DlEww7PdBV\n",
-       "hYfl13xXeWB75ztYWo+P2Kfja7ioYMrJnV3W0Wc9Bwy54wP3IA24CqmLmzfM/LGAEPn8eMwjQx8D\n",
-       "2b1VnAqkjFu3asEdU4ComSw2D9a4vWRAeqWxeDlSFLS8QsERna8ceDZ4lPEK59oVwd5KmC1xqwLD\n",
-       "3RiYEgbHT5QQ7DKV+GSo+2EdjxEkC99As8PyHT/p8go/cebH+lPgm8CK4ufmV14pJtGZLvxzY+Z/\n",
-       "kJFN6KTQr1JspquhUiZsHtiHudqCWOxcqyqlXjnMZvzMaMOtEkZ8ACW3Mxw8EV6JZMoSJ/XYPITL\n",
-       "cSPvj/RgkxriMbigB0Rk9rvrJUYCs/4KIU03HGRZ6d8DeuxbRhi+aJtgTsEtUgD7V3YF1QmGno0y\n",
-       "A/5Q/Z1a+yKT5QAAAIlBnh5FFSwz/wAUemvvLIRiht2YA9TIKRV5DMG8/oWQqH6AegAljIsHcQCV\n",
-       "fvrWNvZc6rqqXhxfR02H4E87Ja3z8TEq1t8zpKp9mLuxQaJfaXZj0cc7tMqjIykGXgJn+eUJ8VVQ\n",
-       "w8uE5witp2VsYupALo6GnknTE0AG6jwNoOqPhwtreQaWeihUwAAAACUBnj10Qr8AJa6KWpXAjU+x\n",
-       "gq4JxWqKgmB7OABVe1sQ2L/nW4ekAAAAIgGeP2pCvwAluwLL1SjH4xgapHAJ027B0HLBE6D9bJYi\n",
-       "BTUAAADoQZokSahBbJlMCH///qmWABSa7bqWIBh9cLzerbXYD5ZbaCRliM8nsnpekKUUF6Ax9Q06\n",
-       "M/YGllauC0m5yJq99kru/sbr95sTQH2iZOEv410i1uiurPmXhxWbd2p4cIPst7kvvmfVmoCA+ZxW\n",
-       "nnoxmZT7PY/7K2nOk713iUfX/EUOVXueqVz9UTkehBh8uJfwymADD9FPKQpAZpV9JUcrn+6wgzEB\n",
-       "f2eldEt+BXD6sv3ANMZrLfU+eLDhsQDA0JqHOd6lRSSM8r1JApZLA3tDGS1QGML85Yjqc4HqElvC\n",
-       "jwhwWJS7cTAWUAAAAJFBnkJFFSwz/wAbBh1ibZJEDDVQAKtvnWPpoXWCPyDzXXfTJM1h9B75s9MA\n",
-       "O5bJEOcD2C9imIpnauX3aDXjeIiF6FtFb1VaH1AYxPFAcKHIZKlaVi9NKsrEy53AwtG8UI4zC5ni\n",
-       "XwfBE13AtjBS3WmMLOVaVTRxscL+nxTaxuHxQ2Z7DRAapIgXmxA8PPzHIOuBAAAAmQGeYXRCvwAy\n",
-       "QGdgAIViBL/ZqZJsr4DatX2uvmx6e4q0tCtEjtSGwTq2ZxkbeFCXMR6dcpCxzyJ1fUfIcsXBk664\n",
-       "zPrV/P/ZlSGyuHfdoqjqcrmZJ3ley/wyDjMhYy4jvQFkrJrIntgP/43R2pRc2+uJtU6y4Hia5VT8\n",
-       "vm2fZV8vO9gCQPITQoOhZAM6Qz0AS9rLy87cwBcGzAAAAC4BnmNqQr8AJUsmIDGchqwgOH0YxKIj\n",
-       "tGUQ3LfYjcSxUep0ayQaa7aWsg0dStQRAAABm0GaaEmoQWyZTAh///6plgAV1ro6AAr9A3yr8xtW\n",
-       "/I01XW3vVXydYblD8Nt2yTCTJequa0wrxUqA85KfI44ueFAHW1/WgHtJNH0jYkDCISOp3ZXO8yg8\n",
-       "QZ+eDvj8uFKOiaZOlLpOrx8CL9BjEssi3J+qx67bRfWlzZQzPGU61h186d8DQqPWJJCSqFeCQqGQ\n",
-       "phTX4z6Y/P/mA4hosSq3x6qqcwo5DVANsFHZhXY4exnpIlLGfMHNVx8kQKNYlaoJLpPCoA9wTFXn\n",
-       "OC0ynQy9PrjD9H6hYpuBQMrNoMVh590QTtVcLNlPbB4RPsydHjDQ4GOP3Ng/yADjD0kMvgTIf/ob\n",
-       "xSEssJt4nA9ZaKLTbrzE/Oh+u5K1IqxZoPbcFc+aoqw5nLD3vPs2IzNNL0ybbyEjLuyjB1mjECcw\n",
-       "N6gzLyRiLndWKPHLM/z7T+HN5ZejkZ7+mX9eZdI3fAo+EJohbAXTqGZTzdJZIfsHgjWAb6xTeFOG\n",
-       "zjruEYwqZ5yFBNgbEwJLg0KRD6tAsM2nxodILZqT/a48A4zGynHNgQAAAMdBnoZFFSwz/wAbBixc\n",
-       "gA43YAv78PJVs1fYcNsfAqG1GR5qoBw91/WQhzdubE8vPGI8z6mEEX72JAB3znIc7eROz9EFzQC/\n",
-       "CWxCbeikIujIgIB8G87r3Veyu1vb54F5OHDX9zhKFKNVFAoNCPSkEs9FYSEm+I+PJ6vMXR1VxNw/\n",
-       "Z7Js0tX1gOHkDwPcJsIxJz0c4uiKfOj4EZqh6sR9p05c5J6u0eM1LRUfllarVABQ5tQuMRwPln31\n",
-       "bQR+KEW6RzniVcvjUnNnAAAAUQGepXRCvwAlSyaZSIrvqkpGlpZQbm4etOULiX5BcYVhxOdb6RDt\n",
-       "oupmvj0AQtKVPd0kYMTxiYAWOdHDpCgrndMLlmvFyLwvqRu0zdK5n4ZFwQAAABkBnqdqQr8AJUsm\n",
-       "GqLNmBG61vS9RJBfQIeAAAABL0GarEmoQWyZTAh///6plgAfM3xiAIPtNTv4ohx58YUQ448vqmCb\n",
-       "D0m6pb3E6AM5noUsdoSmMCpTBfH5gq7dHNSvzAdEbcbVmM/O8i1LiNYuLTXWmKuOHaEX4eH068Xv\n",
-       "EXZISNjj89OHX+WBvXs77O5GFEUnX7gxe3KzSrtdYjV7msofOh9dtGrLKbw1FWBLMe11oRxwwRd9\n",
-       "ZloV2HSIJXZ/yAh9cKqnoyU29+UM8K2f8Ild4PJiAFfvO8/vGz//p5ZsFiAdO7EfjFg5beOEaQL5\n",
-       "r5Z7ZD66ZgW89798xVsZ34AcBWXVFIUgH4HNRRkIJtLbfiCPzufxu1OrmGOiS/QteNbh+v30+FfU\n",
-       "KfPIwxbMt+xNX4ytkDbtiiROcYz3fTmsjeV53b8sbW0lILA1oAAAAGpBnspFFSwz/wAUOqP35gqK\n",
-       "NEud3lKZ3YAbuGE+fEGyQL9Roz5xlq5xADawG/TTJxAytj+Bj/75wVobNVKkfnMRYCTheeIai+FI\n",
-       "nWSkqcsQH6hnaL3NmDBeutEpPSpGflSwtSoqAzVN7YJfAAAAIgGe6XRCvwAlropqmD/mDhlUiyy0\n",
-       "0gudYShE+IxhiIRgCggAAABvAZ7rakK/ADJEQFRc1TsnkIbaFZ25ltFGFQqw0TQyyg9QgEArxAB5\n",
-       "8ZO+V4KwHsJJGuTm6CjW5pCRjWAkH9DmRp+a9HcksSMNGdmxmkUULIGTuKyF7eeNoOyOrQdz0SGv\n",
-       "WdVEDw/unurWNYHzgPuAAAAB+EGa8EmoQWyZTAh///6plgAfM3xiAGc1rGSbgFhcFV9O18nIJvuX\n",
-       "TbuFQ/vhmS8W96UwrFv6AjMGZKKByvbIZMhP55FrX3RBRVltcKCL9xHIX/bbxs18h+rSp1Go3/Cd\n",
-       "Ok7u/TJRjG8yCJAqEu+v8u0wavH1FcbLw2jtYZbgwvTC2w0PJk1f0OpwgaC4NROgTgeIAaewunoE\n",
-       "BIzFqd0eO/x1ZGneWn9hiAPB9nl6sywt5d+hdbfi/SufrZQB0mpN4qRFCx8Q2osdwZWz5QzJXwjF\n",
-       "JfMp7nZhSwM4gwSf4DLYrxxcXP5Jb8xzeSa8AK0g2gyWXbnR6SAqAd5CVU6zdjhRyusy3jNTDNs0\n",
-       "qPS22P9e/PpNnp1f7GQkaAEjS849O6LR+nTC5PtHXBg3yff5K7T1yE9UAhmCk+sq+/AxjqaHfasM\n",
-       "q2QWF5bjojTXWlRf0mTQvLWcyvsyOmNx1qyQwcOttT1I28RMpEGsHlvP7dvW9h+XgzpBROaI7c71\n",
-       "magghkNfYh+cARGK6Izf1SYSrkHsdwUsrmaOmSqXOAYc/ePFVDoNaSFJt8TwB039/Gyq8VtZ9ixi\n",
-       "AFHSk0fqJ1o/696j7fEcOOaKdcTbt8tbQtHr/9wIpYUiWoAzm8lMj0ZbKjSFiAPoKT7ugnmiPqdT\n",
-       "a1asgFZ8Nx/FUwAAAFtBnw5FFSwz/wAbBOBem5WVTNf3iO2CABc1yiYwh3DcogC5F2K4CSqsSBAO\n",
-       "QmJjm39LXHlxfHi6XsYNl+TUfWPQKKZGi1m+76I7pM/fO1MpbKBxwM7H2eAMv2WVAAAAMgGfLXRC\n",
-       "vwAlroruTe//G8L4sTiLo1sv3bTzTHR822moAROHBloC9Se3XABEVcFyhwHBAAAASAGfL2pCvwAy\n",
-       "TqCo8prZZUm4+2NRBDGfMmUxtNLK6Gi/i1/8sOlvZA7hEm4fnTyvEACrAfxBvD2zo7l8lbFex454\n",
-       "+wbn+zNBZQAAAT5BmzRJqEFsmUwIf//+qZYAHzamHsn0dMpQ/4ACFkqExhBfb517F5v4IGbM3JcA\n",
-       "r/SWw0cK/1XQfO2bVr/h+9rAJBsYHS5D4lhglmUrFkevSzRFvvKYSobLU+AOtyiH6nUo24v82bni\n",
-       "lJwk4f1MzlmHv4iPtP6WVKE8mQ1nyMyFHn1LWDL1CdjAKsZNsMgQvVufBileSyI/HmmZdMZ9amLl\n",
-       "i7QX23ZyQumhEWLX7uq/yqq9SGX/oTTRzdJUS+XtSz1ttyInDuNRSRuNxWMFrf2gTDBNcw7ogd0U\n",
-       "/Q3sJdBu8OFNz9JgVoUQIw9Ld5bGmQw8qIEHkB4Qcnljjh+WN0Hp+WF8RXjPsJq/YffNb151hiC8\n",
-       "MoWuSUQI0b6RxPcl+dyeXlMDPmGozWfP3kECix/LjaN2wPVfu7+G/YbHRgsAAACCQZ9SRRUsM/8A\n",
-       "FHpr7yr3BBtkPPmiaSqhAJADdbe+wMcX3S1HTU9Qutayd9wPmGAbutu/+UIPm5WZo92kc396gBjD\n",
-       "oAKYIcORxEgJkMEQCr19ivufg3lNMck/uIvFuwthZiuAVGVxDIGu/s+OvfzTT7YX+iF14hr1a76y\n",
-       "o2iNMtFB0wAAABwBn3F0Qr8AJa6J/9EAIe9TOfTbkGoDkED0eKQMAAAAKAGfc2pCvwAluwLqrg44\n",
-       "bKDMYaDarzdoAkescO8eDJMtifT1eICVgxYAAACdQZt4SahBbJlMCHf//qmWABQQnZw0TTPV3aiP\n",
-       "xT+xMzwBbxYAEtbEVUp9aLvJUB1UqKfNQt+uHAQf0cAcUdLRhE0Mg/jU1uJk9aaLY6K3qjYYUZ2A\n",
-       "MBBnZ3CoKJzlWywC5doy7y3tWEgy2PFMiZrfHxwSmkSZotTUdY+em+sHuX0e+1aB7kQlndrxqXSQ\n",
-       "wFKU1vZuec8c1dba/YBeQQAAAJpBn5ZFFSwz/wAbBh1ibZJEDDVQAKtvnFoSjbKyl+xjCc3K9xc7\n",
-       "BGY4toaBRHIdIBXMdqiYXEEoIpK/GhfOFVJvIirGX5LbiWCgYaLFnIF1++2iNeiv8v2rSKQ/1OGA\n",
-       "1UgIAa64a6ugZ50P5QfeNQoCHpyNTryiq5rpOBqhM+0ZOUdWYNUJZiE2bkRb04pe1kPVe9jj8PI4\n",
-       "QA1IAAAAkQGftXRCvwAyQGdgAIViBL/ZqZJsr38c8KEg3NKeWh8nN8DgdO21CA0CiwsX5nb+IDHi\n",
-       "ChdXccV3o49Wt7x3yVNi/5h0EPRLbxwG41Ox5VqNZcgBB20xGtV0rBgtRV1avb29IvXwV7LIxU3J\n",
-       "c+9oxIxDmUQqIdswzPe8YK/7awQ1RfJcFwMBO/yF75m+eKqABd0AAAA6AZ+3akK/ACVLJiAxf1zE\n",
-       "ZQE6PpwyqBIA9kIvR/7pzXE15fFKRU9ImLM3C6ExKB75Gq/HRjl4o0C7gQAAANdBm7xJqEFsmUwI\n",
-       "d//+qZYAEpXi//AAmnmtL9WvodCJa9lr80bZ94KO234PQmD3SvWv3bRHxfpisVgsNVThM4eqsr4P\n",
-       "+RvitfnpWmd1S0LzRWa2znJFpfJH0q2npT53dHrZHVq9N6fm5ZFr8ydyC4dAgC4tcGEXY/t3IkW3\n",
-       "riYqDbtzibr9CGinJzZtTgcKh63P9TLGilshL5nAccced0+JXlHxa9HMXk/WLnnbFiHEqkWnxd7z\n",
-       "CmMN5ohZtbAGJ1CBoLd3SvEgPYxuDRi9LnFM0/wISLEwIAAAALtBn9pFFSwz/wAbBixcgA43YAv7\n",
-       "9DL8S6OgQ6bEHzX838UNqCRamRdVvTqL01SlPG+TuhE0f7voifrwrLu2qpWHOwRt5v+3FxKVtnlq\n",
-       "0DXczGMga9jEkRz9Ar828ekazupQAZXZpK7iVC+qeeAODj1QD0RkBuTsQuTazGI/fW5rq8MaYYoq\n",
-       "Qx6X6Ikhvt1XC22sjekweJlFNFk7QwAH3ks8w6WysFbWpVD1WIWxIbrTKl1f1n4GimMnqv2hAAAA\n",
-       "RwGf+XRCvwAlSyasO5ZGLgcOe1sCKY7NUyPCrxy3EznBAUaJLY597Gs1JjRkRMAtuq1zwCVuDGnL\n",
-       "Z6D2klGrZ8BXxKcJjDxgAAAAGgGf+2pCvwAlSyYMnCgGavBCXSdBHSu7UAYFAAAAgkGb4EmoQWyZ\n",
-       "TAhv//6nhAAiqg/D1tLqAGuR9QgiJDJQ41y5Ruf6BMj5CqhPgxhui6tCB1dQgQp5vtgnGLJLyCOe\n",
-       "ouxzN7FvSCEHVnPj5N2+R06LphQi2JC7mLMtRm/89prn9ilgX+bBdI+xu/XiT0Bu/or77mOPjsX1\n",
-       "U0lD2t0gd0EAAABpQZ4eRRUsM/8AFDqjuEBnNb8z6mfzxSQ5XRFznijSxWGUQ/GliDn/1owXCnNO\n",
-       "ytCTt/8ei9JAjgybfpEAQvlYqcKZNHsvfsfpStDf0+L9XtI6oyqQNGYxoK93WJonchMJAyKbf2aO\n",
-       "7C2gAAAAIgGePXRCvwAlropLr8cT01xWiG1dlF5xlySm3im8WcCgA7oAAACiAZ4/akK/ACW7Dli5\n",
-       "iIAirlS8rSwiedhY3M7GS36z2f6WKl3hNqh9z22GIHsvOjMC4mh6y6C6FCxVo79y+G1z0O/PR2uG\n",
-       "wEwkhv/oLPhtT8lYkCtXudWSIx0UQijwM6Z2qM6Rv26SZUrT1nkHaVasap7TU06hdKPDLlVSN958\n",
-       "ubLgpeqSRSRUOZegq1W55Fvtly99eMd92boq3e2LGIimGaKLAAAARUGaI0moQWyZTAhX//44QAGv\n",
-       "nbMXzkiYeCte9XErA8FAFYEzX3W+gznK4AFBd4gLgFtf+WKtVNuUusdOGjKHpH/vpcBNmAAAADtB\n",
-       "nkFFFSwv/wAbqI0dbNUQDmlM70uf0uqZnd6K1Mlvm82vB75SCSYvn6jvUCFWahk3HS+wEFwWdChg\n",
-       "QQAAACoBnmJqQr8AJbsC6q3jvdOf3eLKJBz0xDPEEojZN0HEjzoBmROTz8BABBwAAAfXbW9vdgAA\n",
-       "AGxtdmhkAAAAAAAAAAAAAAAAAAAD6AAAE4gAAQAAAQAAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAA\n",
-       "AAABAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgAABwF0cmFr\n",
-       "AAAAXHRraGQAAAADAAAAAAAAAAAAAAABAAAAAAAAE4gAAAAAAAAAAAAAAAAAAAAAAAEAAAAAAAAA\n",
-       "AAAAAAAAAAABAAAAAAAAAAAAAAAAAABAAAAAARwAAAGQAAAAAAAkZWR0cwAAABxlbHN0AAAAAAAA\n",
-       "AAEAABOIAAAEAAABAAAAAAZ5bWRpYQAAACBtZGhkAAAAAAAAAAAAAAAAAAAoAAAAyABVxAAAAAAA\n",
-       "LWhkbHIAAAAAAAAAAHZpZGUAAAAAAAAAAAAAAABWaWRlb0hhbmRsZXIAAAAGJG1pbmYAAAAUdm1o\n",
-       "ZAAAAAEAAAAAAAAAAAAAACRkaW5mAAAAHGRyZWYAAAAAAAAAAQAAAAx1cmwgAAAAAQAABeRzdGJs\n",
-       "AAAAtHN0c2QAAAAAAAAAAQAAAKRhdmMxAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAAAARwBkABIAAAA\n",
-       "SAAAAAAAAAABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAGP//AAAAMmF2Y0MBZAAV\n",
-       "/+EAGWdkABWs2UEgz74QAAADABAAAAMCgPFi2WABAAZo6+PLIsAAAAAcdXVpZGtoQPJfJE/Fujml\n",
-       "G88DI/MAAAAAAAAAGHN0dHMAAAAAAAAAAQAAAGQAAAIAAAAAFHN0c3MAAAAAAAAAAQAAAAEAAAMo\n",
-       "Y3R0cwAAAAAAAABjAAAAAQAABAAAAAABAAAKAAAAAAEAAAQAAAAAAQAAAAAAAAABAAACAAAAAAEA\n",
-       "AAoAAAAAAQAABAAAAAABAAAAAAAAAAEAAAIAAAAAAQAACgAAAAABAAAEAAAAAAEAAAAAAAAAAQAA\n",
-       "AgAAAAABAAAKAAAAAAEAAAQAAAAAAQAAAAAAAAABAAACAAAAAAEAAAoAAAAAAQAABAAAAAABAAAA\n",
-       "AAAAAAEAAAIAAAAAAQAACgAAAAABAAAEAAAAAAEAAAAAAAAAAQAAAgAAAAABAAAKAAAAAAEAAAQA\n",
-       "AAAAAQAAAAAAAAABAAACAAAAAAEAAAoAAAAAAQAABAAAAAABAAAAAAAAAAEAAAIAAAAAAQAACgAA\n",
-       "AAABAAAEAAAAAAEAAAAAAAAAAQAAAgAAAAABAAAKAAAAAAEAAAQAAAAAAQAAAAAAAAABAAACAAAA\n",
-       "AAEAAAoAAAAAAQAABAAAAAABAAAAAAAAAAEAAAIAAAAAAQAACgAAAAABAAAEAAAAAAEAAAAAAAAA\n",
-       "AQAAAgAAAAABAAAKAAAAAAEAAAQAAAAAAQAAAAAAAAABAAACAAAAAAEAAAoAAAAAAQAABAAAAAAB\n",
-       "AAAAAAAAAAEAAAIAAAAAAQAACgAAAAABAAAEAAAAAAEAAAAAAAAAAQAAAgAAAAABAAAKAAAAAAEA\n",
-       "AAQAAAAAAQAAAAAAAAABAAACAAAAAAEAAAoAAAAAAQAABAAAAAABAAAAAAAAAAEAAAIAAAAAAQAA\n",
-       "CgAAAAABAAAEAAAAAAEAAAAAAAAAAQAAAgAAAAABAAAKAAAAAAEAAAQAAAAAAQAAAAAAAAABAAAC\n",
-       "AAAAAAEAAAoAAAAAAQAABAAAAAABAAAAAAAAAAEAAAIAAAAAAQAACgAAAAABAAAEAAAAAAEAAAAA\n",
-       "AAAAAQAAAgAAAAABAAAKAAAAAAEAAAQAAAAAAQAAAAAAAAABAAACAAAAAAEAAAoAAAAAAQAABAAA\n",
-       "AAABAAAAAAAAAAEAAAIAAAAAAQAACgAAAAABAAAEAAAAAAEAAAAAAAAAAQAAAgAAAAABAAAIAAAA\n",
-       "AAIAAAIAAAAAHHN0c2MAAAAAAAAAAQAAAAEAAABkAAAAAQAAAaRzdHN6AAAAAAAAAAAAAABkAAAY\n",
-       "wwAABhUAAAC5AAAANwAAACUAAAIEAAAAsgAAAKcAAABBAAACIgAAAPQAAABYAAAAFQAAAZEAAACA\n",
-       "AAAAKAAAAHoAAAJ0AAAAcQAAAEAAAABZAAACLAAAAHsAAAAdAAAAJAAAARgAAACbAAAAhgAAAEQA\n",
-       "AAGqAAAAsQAAAEsAAAAbAAABggAAAH0AAAAqAAAAewAAAhkAAABxAAAAOwAAAFIAAAHDAAAAmAAA\n",
-       "ABcAAAAtAAAA/AAAAJIAAACbAAAAQQAAAX0AAADIAAAATwAAABgAAAFpAAAAcAAAADEAAAB3AAAC\n",
-       "UAAAAFsAAAA9AAAATQAAAbEAAACNAAAAKQAAACYAAADsAAAAlQAAAJ0AAAAyAAABnwAAAMsAAABV\n",
-       "AAAAHQAAATMAAABuAAAAJgAAAHMAAAH8AAAAXwAAADYAAABMAAABQgAAAIYAAAAgAAAALAAAAKEA\n",
-       "AACeAAAAlQAAAD4AAADbAAAAvwAAAEsAAAAeAAAAhgAAAG0AAAAmAAAApgAAAEkAAAA/AAAALgAA\n",
-       "ABRzdGNvAAAAAAAAAAEAAAAsAAAAYnVkdGEAAABabWV0YQAAAAAAAAAhaGRscgAAAAAAAAAAbWRp\n",
-       "cmFwcGwAAAAAAAAAAAAAAAAtaWxzdAAAACWpdG9vAAAAHWRhdGEAAAABAAAAAExhdmY1OC4yOS4x\n",
-       "MDA=\n",
-       "\">\n",
-       "  Your browser does not support the video tag.\n",
-       "</video>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 284x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "pos_label, pos_clip, vid = next(iter(train_loader))\n",
     "neg_label, neg_clip, vid = next(iter(train_loader))\n",
@@ -7835,315 +227,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
    "id": "30cdbf7d-9f8a-4c2d-9b72-f504b357ddb6",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<video width=\"284\" height=\"400\" controls autoplay loop>\n",
-       "  <source type=\"video/mp4\" src=\"data:video/mp4;base64,AAAAHGZ0eXBNNFYgAAACAGlzb21pc28yYXZjMQAAAAhmcmVlAAA1Y21kYXQAAAKvBgX//6vcRem9\n",
-       "5tlIt5Ys2CDZI+7veDI2NCAtIGNvcmUgMTU1IHIyOTE3IDBhODRkOTggLSBILjI2NC9NUEVHLTQg\n",
-       "QVZDIGNvZGVjIC0gQ29weWxlZnQgMjAwMy0yMDE4IC0gaHR0cDovL3d3dy52aWRlb2xhbi5vcmcv\n",
-       "eDI2NC5odG1sIC0gb3B0aW9uczogY2FiYWM9MSByZWY9MyBkZWJsb2NrPTE6MDowIGFuYWx5c2U9\n",
-       "MHgzOjB4MTEzIG1lPWhleCBzdWJtZT03IHBzeT0xIHBzeV9yZD0xLjAwOjAuMDAgbWl4ZWRfcmVm\n",
-       "PTEgbWVfcmFuZ2U9MTYgY2hyb21hX21lPTEgdHJlbGxpcz0xIDh4OGRjdD0xIGNxbT0wIGRlYWR6\n",
-       "b25lPTIxLDExIGZhc3RfcHNraXA9MSBjaHJvbWFfcXBfb2Zmc2V0PS0yIHRocmVhZHM9MTIgbG9v\n",
-       "a2FoZWFkX3RocmVhZHM9MiBzbGljZWRfdGhyZWFkcz0wIG5yPTAgZGVjaW1hdGU9MSBpbnRlcmxh\n",
-       "Y2VkPTAgYmx1cmF5X2NvbXBhdD0wIGNvbnN0cmFpbmVkX2ludHJhPTAgYmZyYW1lcz0zIGJfcHly\n",
-       "YW1pZD0yIGJfYWRhcHQ9MSBiX2JpYXM9MCBkaXJlY3Q9MSB3ZWlnaHRiPTEgb3Blbl9nb3A9MCB3\n",
-       "ZWlnaHRwPTIga2V5aW50PTI1MCBrZXlpbnRfbWluPTIwIHNjZW5lY3V0PTQwIGludHJhX3JlZnJl\n",
-       "c2g9MCByY19sb29rYWhlYWQ9NDAgcmM9Y3JmIG1idHJlZT0xIGNyZj0yMy4wIHFjb21wPTAuNjAg\n",
-       "cXBtaW49MCBxcG1heD02OSBxcHN0ZXA9NCBpcF9yYXRpbz0xLjQwIGFxPTE6MS4wMACAAAAc52WI\n",
-       "hAA///73aJ8Cm1pDeoDklcUl20+B/6tncHyP6QMIpyzqtdwnVCd1ZnhAAAG0BxTVPgn8ksvM/AAP\n",
-       "wts9dHFzfEKBqjuDPp4zqvEbWy5VZFGjFhKt6RO9jkQuMGcti2YUDEtWy94i1Z+w50WcbpL8BkSW\n",
-       "OUuaR3rWhdPFvk7W6c86DmS+0mg+eFOrPjO1NO7v8/3Pa16cAa3tRhJ5Xqsl1UejecI3gvr7V7IG\n",
-       "jrhl8kJA+1acKfJGUnNCaPm0g+Oso8KZYQpwlq2C0qKBe46DgR7e+siOKJeUL4MO9+HucGE4/GFv\n",
-       "Vg+VS490aOc9EAOyJE7XFwQpLlIhMGmNkhj+lSqY2c+IsKLEDwfl2iv7QhUa2az5BXAGob/7WGQB\n",
-       "X/G5ikg8Rjmkot8ipChOM+q7pelIRXo9u/4SJ2SlcpIv44ROo3edoO3LDoKOG5tr3oCa76KM8Tat\n",
-       "m/3hHwVD+fxSAKG+mzyhHsDB3RVAcec5kVAwdnzKKoAtO9/mJDm89HgZlEH0/nvWAS/YnB9LZNVv\n",
-       "rTVYexYicbq/j/P8VOUHQUbsSTA32mjcGaOa4RwAEsBXjzxTcekkuVgIvnjzXVlLixOZLhCAzgfV\n",
-       "jZ5pl5BGSceAfykRajtNEaA+mo2f0/4/S0QoyftV4vc6Sq80GowbBpfyt8GTAdSyC8Dxg67DIjOa\n",
-       "PM0iIEO6JulylUvoB71TNYCWoRsc3lOo1DlVTm111Ho8ej8D1I3daJXzQgNekAi6XfAKOcyhKegT\n",
-       "DbEBAEj5g7K+pd8+gWd3gZBx180vECsb1XA69AvKskXDNjNYJCjG0+hd7Fgg93NU5ENU32pteU4R\n",
-       "SZfyB4cu4MYzZwvM0ULfvziYodfTguix8WQ5b80poiPtA1auL69jbfQvvv93Vm9T/l6/tmszrjCW\n",
-       "S4TOAbnoohAWbckUcvvqoxIZ42yVz7CSDnST5uuRJlYdz55BXshH7m87+ljs0jpnLBakHenKALlf\n",
-       "WXVp6PmxbHo/BwnF3+m6x2sW6iEtDpofE9FeoCq1jyyf4rLTQmHE9uRRKO96Lw2F0o2oRFDelUmX\n",
-       "RS5rKJMJFXl/636fjWiQ3Zf6DT1eitSOtKSmpUG+77aQC4eoL+B/iFvuKMCfdZj+nFnMZtnh3LNw\n",
-       "DwbNyhaGwWqLcRwtVH2r0mcn4RMrWNcUywNPwrQpEKlBWu7E0rYDzWKC+N55xgjkqyxdkAg/baDj\n",
-       "cUG/DJkdX+LFTj0ug0U11hY4PTHRTSHFslCIe5orxgDXbM+zryhjSiWNhwWsvtRK6QBKUcJxbk/t\n",
-       "vDeKKcdMjRtp43PKtHkFxasfigGLD5GuXEDoSgK0Pp3iu50VyCIs7sc04PMUK3oHnS5JWmA4zE+o\n",
-       "KbZqAI9VHJCSZszYl3+TRqSjS4P0CA3T3pVE4fyiarvO7XIscxDohyVjyk7BhZ0/VWgItL+MIcA9\n",
-       "wnJAyc01Of0YmJgg+F6V2iF9iBfGX5/lzknSDvBmV8R2XsWnjUwazncwWoU5CyRCIcls//G6JEMw\n",
-       "9cEzRMz6TrsZKRvgqySin6vGc4Tus23AiQKjeU46mAFFoM+fY1suFGuE+MPaIXjf8XgvbLAjEEPB\n",
-       "Bfkxs7Mby9zivErAMuSqTimdzXg3jeJb081wzlKxRFXuX+MwtL7ti9Re4CDyoDAjh7ZKn77imdbf\n",
-       "uXDKbRY02NnReYz7xzkJ8sDmAHDkJXUn9+WguOdqh8tN2VWqz3Q1PIoXuhA9SZNNvuM8DOTShJJr\n",
-       "WC7H9Vi9E+LQAvY7bGOJJtTX06lrBE4xgA+My7DAhPELYAAGWkVdIAUcIWx7VQP1s7Q3o45c7qXX\n",
-       "KzJn6+JHwEah2Ge9Qf+YWi63vVMA261W+Z4LniJPSnkrFD0Wp/DYxRaxyVtZPJmxldSCQXM1tDXw\n",
-       "5wfRKGar6vlq/tsxzFgZU3/14T934O5kB2TNZ7XrfzN/h3fOb1t263WAUV/gXkHidrJ5PEvMyHk5\n",
-       "BnD8zrOrYItTMzMF7W19pQiw6W7ztvViNXA8KUIPm/0xtUlx/PlO7BiSgYrDpxmkmxm/stV13xRD\n",
-       "UQkW3hNJ1LEu1e/LGWdst/X6wT7h6t8WpoPcd9GmvMEv04kJCAkqpJQcEL8IYdESeudFhnnYJjb+\n",
-       "Q5b5uNPxVlXVt6Zm83brn3wheYHYaMRNglkkmuAUAqP5TBszy6tMcLG7kzbAMjzRpeIIIzk7k6BC\n",
-       "R2nq3u9J+MjiKg7haX7+rfvadE+2rXH/0ibaFeS4R7SKCVjDkMpEclpke+/6TPJYUmeUL9cXqm1D\n",
-       "d8AQga6SV+g8TpuJu2JECcG+oLlxxQ9b0dPciv2IzlnsEdPOgD44wKp2CW11Srgy1X4A9K1znkG/\n",
-       "8DhfACL+bdLd8B9XhqbOAUtTHBBT52TytV7S/KGN69lcEJwh0Kpj+4u9HRYeqyg42Pu/0kpa8x/M\n",
-       "3erxA1JeqKis1emNVokte47M4PVhk0tuPNkjF3WZlm71Q0bfK66JoQcTcTgL6ExZT1nFtkSZp3d1\n",
-       "Ptw7XLRXgtwQhHDSo1BCpZpc5p6zU7KB9O0rqmtyM4CsAfoI/9WfrB7nCWPlY+2AkJRZ70dWCWZW\n",
-       "cL6P4uSp/oR3uqOhmUrkTDMs/TMkUhl24CdnY2KfRjfqPyboslFeNBAEsA8xNQDHv2Jxmh0q9JAX\n",
-       "JtAz19qDQWDddvZ9FYzh6I5cAbCO4H2zxyWdtjLKL7q6k6mDBTiBcTru4RfK4ZFmfzlh8YwZ9jxz\n",
-       "0di5u1h5tY/C9+yayzHBGTu/mO/WiIhZtpprphdKfrg0YXjR/FOB2PR+c9Ae29kN8EtKDAGXDlkl\n",
-       "h03OUl8gVbAQrO6+Gza5Eyhv+VQQi0+3Hmztc5REHs1v3kjOFPX6jyyoqOoR86cZBHPWiPpYCbQ9\n",
-       "v/BNmza9wwvxYP3XeiK2MdN6xsqBLI2qkOqPDjXWck80jS5wHqOwkK8PfVyt8peS2IYAfj/Q+xvL\n",
-       "eiSbgUzE2ib4mAg6siVeEYQIIhR1QuZRgr1dxp+LjNntvx6/ICDHhCOzYG0LkuDC67XDoZ7TtVwC\n",
-       "P36TUY723iMENRb2Cx7KWy5TiTVxwp5MhzAogXxjQzJKoTPtbFITYGx6sHtQ6aNjgZWiZZGjGb/X\n",
-       "XNXYYNDWy7LEcpClnyCjGHGxiuTBCwMpa7Sm7rJXq7cNhKYC/DcZQLRyYg6TPf/mykO/MeyOu939\n",
-       "DsqllLjonBoxSMwTTBBhC2VVZ1uFSXHmwbcHz4qbAyPBhg/ryDz5rPNou2qrU2FkjqouZcoQkrR7\n",
-       "OqrlIxZXbL6nKdho4WlofWllushZtWW88lrVUDnY1JaT55b3jCrbYt1SpSHLX057bkue2+CJOMTb\n",
-       "hEFmP0HSnWXH0BDOithHcB4vNPOvmjGQ7n+1540yuWKOOZWDz+IlJO7H49p/+oJDaOrJ6Q3+csTE\n",
-       "R8i2QR+jckMhFmG1/0tPoq28Dr5PHI7JUylQCC+tgmyG1dsmdo3IXvxaAr1Rc038jrACTnwVtsj4\n",
-       "4AKo4CI10QjcuWUnNNtVdVq86bWyXwtoSJItVLbX+6SCCaSyEbif6hQUp5onaQkBAQxAZwdAv4HX\n",
-       "3dm60iZLSgKqCMc78pP9o1R2J1NRWJKDy0u+/lrTFZOg0md3rp5iFN5pfICyHaDBSs4lmo5Ufpi9\n",
-       "FmElxZf7r0a9w7kVj0lunZBLc6dwPyeNDj2c0tKd4D0QdZiBW76xeUrM7JMYgQJCCyWSgMZ9jjLu\n",
-       "RZdajh3CdE72bAWWFbDdiNVJo94W+RXZNNopQEoz3mDLfVqZjNDH2vpyRsDmVXVaBGjUD4d5A29I\n",
-       "JiCQuAxNcLR+lcT5UumdAdBylBKeBJ0xsqnhlsiwNZ4RGM1zrUaIlmlKOd4bwmcGStPwQ2UB0Je0\n",
-       "BMXLUs5kmz6yvnv7QPGH4kcfhdBshWtkuAN7lTAqnLWzStJha/xqv9Ap4dgXqtqKZdkjoo+jYHNa\n",
-       "0lHL/1m2Eg9b/JxY2QhchIpKgNDbsv2Au5/j3Ag09wBtHs/TMcCRMei4y+FS9DpnhVQebKAk3oe2\n",
-       "4zRmhotghWoCZFrmckB0rF9j8n30miti9R4Z983Ylgv2pN/TeIS5UjAEB/wbS4Fz2RK9FLCo5yir\n",
-       "p8src1gq0H8sWIEp5NvV6AXxefHVWmTbSrylPVq41++zyIcpVm7mqA9zl6FHSXNFI2ym3ju1Kbdu\n",
-       "5OA+4LeDAYgbgLT+jMSZQT2MFBBsaXHOBWvgHhifBMX+S3XtxkAKIDlioLzz12BbV4KjZ150M79w\n",
-       "WeZ5SHVyU0vYM7QZ8QydozmD+i3qdXoqvonyR1MZVhWnyCOi7CG8DoeKhrQhn+SdCW14s0FthHNQ\n",
-       "jitttB+Hy3VQ3RkPBKlG0c3SbHvwrYDyV7j8G9pChRHRa9NJZ8HEqsMXo5zB28ewQGTzZxXIYdNP\n",
-       "7N//hN6PY0eYgpxy+IAdHffzyC/4REwHtVwEVbXkkOS1VasHlO5fXoLG3BB8TUL8Bvixdg6yG12D\n",
-       "tp50rE35eGqDTV7gaokw8A3qIvSNulUNGQG4zcV2WtwuCpbnX3R2J7XKxRWcE8VFmBGbQSPrRsRq\n",
-       "oTIKGtESoDmt63rGXgxaUZVzRolFtkYqgzKzk596HlSeIZ6AEO/0vnNM9gmBXTOvcC74JFOh7N5R\n",
-       "+Y8FeBov7xoo7upelavPV189MVJkzn7bSnvtSAeCQmcqwLUcZ8JFPXlvP3UnjwvWjRbYyfTcLCRA\n",
-       "yb7tqblEUEWq4fqNff2dnZPt8ar1Sd43BZh22F8OTu7pZ6r1zlYethtvv2F7LhFIsYs4NL5bnoTZ\n",
-       "C/EaVlI1wDwSxTUQU54bDw6N5Vmf7EomsXBjANNRvShUj7YQRcjRbZlbbgu2uJ9odTCelZCMfw+f\n",
-       "DwDxGC4zvNMgObEky4oWN9AMO+BZHzHlj5gpoGUnfRqPAHZrZJyEaMFSvDL51ZUi0VdzYMrpKHdY\n",
-       "J38PFkxu7sR3rAWUfLt+g/lT6qJ7CP7IfuU+jmzGRtmr8dsAyyjC3hBp3L6c56RqWplosoiHb0Fp\n",
-       "7GF891ow5b3IPeL8CJi1wA+gxNKwRF+JTG2HYkaOKdGldyBP5x1O4BZjL2hMdiuPH95BM3I1e1kH\n",
-       "JYRGaPYLddyHjU035BPGS6BTjUFSp3FCsCK067jMS7TTWJvpp6q9mOnZgatwq5dlGzjAXfolGo+Z\n",
-       "6SB5dv8IhoJ1otoZVt4e1+XIs4urMSKzJx1MCpFv2AoboNnPB2FrVGcjMpGD3qCCPJ8xt2QjpPc9\n",
-       "2tRUq4VuqOA4ByjBFoXaPXgPPFZTjNuv9H1zC6t0C8mO0lsjE2ehHp2Det36SLvYQ6uRvuLWlTVM\n",
-       "yrpDGZLgqkwWFxSm8GJooKM0W4Hhf/8h46a2b+0YnpKWIIeSmQ7yrnLQS8xNfxTHIbeE+qjvvtlD\n",
-       "we0JcpGvCSwMYj9FQKwUE4jUKmIe2xOD3jsHroO1O4cxa5ZLND3lmhD/azOpcWBoZUcwBI9wmRri\n",
-       "V25NH5jjlF0R1sruWeyNKf8v66VkpFaczmbALgGxe/dVK3fM3LqTr/rrNQaIJF23paszn9dV76ae\n",
-       "72vwn4qKtSVpkxt2qRpKoZ8ZybINvXSuhULZkKuuy+zhxJy/zn3g5lzifpDjbxCJv1bSKnkZI1ml\n",
-       "SrCEO7nZTHhy7sd9DMGSM5xXUEN8cO2KBkVjXdpleRXOlr74Bfg73qhkfr5XAS9+jNvlyJ4dM5Bl\n",
-       "MVHUQO2eKOrwwmP+LrVHK6HyZXRKYgFa02CyvxUWXUne2pDWVQpYDvLr6jpGxH7LBjyX6sXA5uz6\n",
-       "WiAxulL9/+t2J1EHREtjZFxt8VqMiVHY0hgF5JwhL857e3h94x9FnJ3pSMsa4qwjPgD1Vly/FfL6\n",
-       "+VW9nLC7P9yNTudnm9oqCez88C3zmb5bkaCTsR0QRQiJKWHXoUFy9r7HSjqbGzWh43bu2Hq2WE3g\n",
-       "4Jnl9lK5nVDCP8ycuRqGi2xv2aEPpaU1+FkFlXi3CS7jC9SAdaw8aA964G8IvB4Qgi0pvFbwU/hT\n",
-       "DDqUEhrDXzwKxEUqulgoRlqUE55JsYr3B7DAdxKBgRwJfiSlHMXqrs3LaZ9wyRkSv/n9fxylL7Yf\n",
-       "T5yV+1Jx8r5TMXxQvSsC+vN3MjaeCVPCO0V6/d9qMaB+gSXVyH3c5uiTfHxMOTraM+Su5xJYS5DO\n",
-       "3ROFWUW0CWk0QL2/CBWXpjX2iyRP5A042RrVPlCUFLK2NyqcZRPRHkJgfM2mubjuzE3gqnzOYSby\n",
-       "cH9jojf8RgS7H3TNQRTgJBN9mmS7Iffs8B624ogE2ik5lPIPuv/NjjqsMrGaDzF2mhcpgiAzMxPp\n",
-       "O1u9c+hTrDos2NklVDxCJfR7xgNJisC8N7oBtz8MUsVQlQlBRpbDSi95/Tu+nPvs4AKLiuJrzvFo\n",
-       "1LmSZCnrmtvSbsB7IVPt9Mx5e8+/Df7fwdImzikuiy0YVGtGlF5EvG0HFvTtLzE/DMhqViUtPPfr\n",
-       "MIQjFsrpypO5l0fqRqiopwDQwVNIghrPQkT2PyAC6EFDE8y5Y0pHv8z2bqd/SGXiEVhl5EKOHyWq\n",
-       "L/dR76mT6EEqQdNLlutmYqdSzH+e7iEZJjY63h1WDs2rqIdnZPUi2EvFiUgSofaG9XXEyvjfdoFR\n",
-       "M3ZlXOm06MqOloaZgUJNPsMCafxGUxbIqKrpPk+iGJNMMR//gpUmGjVHyfMoNsMfBgdpq0R7Br0q\n",
-       "ex8wLFvr553WNfaBrXZj69l4DYqsuwWH5W+51KooKn6tQRtTryNZ4/Ly9dkbvEAf3EDmBCZjoXUQ\n",
-       "ynOfHYN8ArRciuzohpuAucxVDQNCQbyI1pryPYQ4dSuHDLb+qqLHbbuUQiWliW8AoldDafDImFdN\n",
-       "gcDCbiOJmjxihw0J9AFwq+GEwHMmYcfaL6CQK249pFEKaiLlDcu6EmLSuCksxWGc5LeM+N4Aaplv\n",
-       "kgiBH4TbiZD9XiDrvt32iVZBhrXlcLfIgE2DWIQrbWFjI4WYgTFxb5tDeUKnaPjqJQcdKkH3SZdA\n",
-       "ML3pZDSKvlyAieXuM389qsYDvThYCjtWW1W3pU4svJqA2dFwohwRmXiPiZRigBmq5AuarrkGSzwk\n",
-       "iRsqbIIYd7P5ny5di+0LEaPMAVbNAkezHq/9rYT4rrDg+dg8FwVyeuA/PQDo4AaHZXEcnZQK3QgJ\n",
-       "Bu6S29JIGugZVHBRapNdxfw1HOSakDs0nJ68DteT8OxM5V+D1pWXr9rVPCaWjafJruWS9Seu+S55\n",
-       "RuqeMVUnN0kCP8kzx/kMgit1HKsqfgJiZnd3SblXACaD4A/d+cBSM/zothJdXTPt7l78KAXplHvb\n",
-       "uVdBvKMbvn6SqIFV9/PUnCX+fxRLnjRKeHLUx9VKQzUgi3VKtwoRv4mrIUsv3ddne6sTCCahWE0U\n",
-       "C8eTEFUDYbSfgk4yeyoYs6O0aJf/nn5u6vTEb4zyDJonVvnNLvg9lTbADSKlrIz62YLfalOjyaWe\n",
-       "DEt12RdNnSA4FgB7c/OVu8AyZf2nicSWmT4iIxal1qgNH1rssaMjX5MP02EKnUEnj9NlVuaY3LsY\n",
-       "gLiPuH/89H4B6HU2em6+yCqiW/ELv5WhPS1t9RAs4kQzhV5Ja3A9/VoPFNvfrglDjKqDOP9mV59l\n",
-       "VHwvAJWdOAdM6S5QM1f0nPpGDpt4x89ney0vRASLpChMrRYZzkok5fSsoUXjW89fl2ovLrc2Tnyt\n",
-       "W0NX8JyuqnJWvLY461OcEWh2eATpUepycWHWTtHq1JeRaqdewE9AxBcG/bY0VIFiXN1JrPj467iW\n",
-       "UNEioIk1boQQqhSxyosGTRFfZQsraffx9F2mhE7YrfiwBeDknvFubbvSyPcllUR2jkV/9inlxIwU\n",
-       "hPP8wFky6lgSr13GwAhCQJUOuuYnrlzBrWhksDlgvaI/4IQqPdIjp7YJ3ayvXswp4+2nB3xLkOiv\n",
-       "xswRIXkf7TyjdDEf0XaxkVOvmJ/PU8ZQvFzta7Bdqna2Bk0tPSM0PXNf9UKEOi2H+AHZCQt/U8Zw\n",
-       "LuIMvzFQcM1g0R5Dky69mcAvdRc9H88UN5GtTmEXKb+gslpKrg+n5abR8bKEDzbJ6vSQ+D7nAAtY\n",
-       "gaQ0Zl0v6yglAXrOYLzKfsFC0uiCD8yHDVPFuQef26hYSglpnM6nr2ifidJX3N1VrTn+Nlfru5eG\n",
-       "B6eYJ4f1IKBmTAGL3hZZZJ6D551SlB8VS1vQi2ZE3HSXmE3v8fvlhWDM6L69iBOz4eKIThMnHaRb\n",
-       "12nSGMbUtcyWSVhLQwnuoSa06ZiMb+/QUpkq0f+1CfKnu2/jAZejrFB/G1Vma/f19rtXK/blwuQR\n",
-       "IA7tn+iMGAlCsytGU2mRjPqyXx2Go5ai2a/DqqrNRgXh1t0ZBMBp1nUoCtS9injkOU8rG/zoQ77i\n",
-       "2D/C7DRZAVosXtVQwFw4mTCo79DZCfcpatiPWX7Ja5U4F9yPRpE5b1l11epN7gC5A55J485y9VYa\n",
-       "K9F68WGXSRlxqe71xDtCmZQM0rIS8dbrOD+gCawODtKRnTHUuJYesUKB9i0TdWyp3DQGVRzvzsbI\n",
-       "wle+uxBDthejst2ncjYT2ZTptLorn/jtBfJYHvhjvTkMg6lgzhFIRBZs6/RiNIjpWsjMO9C8/Lo+\n",
-       "O6q4t/6+UhcVCoXTpetlgzGa2qxn/hb41ZQzPM353m6NQ/+eNibeMaInaawf9xR09YQ+AerW+Uww\n",
-       "dZxdB4Kzd2RVuxNMNJ8QJVOEi5PaaCmcjadg7XUO5URaWH2/e2stke9rc4YAzEbpOK8JnFhEjuu3\n",
-       "raHqMC3uzrQZleR3+54KuugBAFjWtrhQ80QVhoOqVXKPXV/rhurtyM5G780jAidTtspBWd1mxqMZ\n",
-       "z1nA1u0NgzNdRqd0HIFaMngDRUTyJ/Gu85NSPj5a+Y+EuCfL6zk5AH9LekpuWHt774nkdkmuUkzm\n",
-       "jZHqfcjvTcGux2P0sJAf0mwhxA6wzRh8pe4bFG294esOEZ1v9wmitLC0Rv2XQUcS51jm7nMe+rkH\n",
-       "HUrjvU4p4tnYeYTiQSe2tUhUNpmpdKGb2c3hzigSdwuEbOvBZrgTIh18BubpoEiMCcw+hqywwS0N\n",
-       "1PnBQXWCuWqG/TT/WyzLkLEmZM0MkIIgfRDW8MVkojGs7aW1DOuFbkCt7WgKq9VyjU6psH7Os7KD\n",
-       "nYsjuDlror8hWUS4borTfiFjU03ZwOIYCS1+22EWQZD5MFPjHEUM2YWI0nJ/V5OY5lLkeF63KibS\n",
-       "wtzHCmczDy14uVomiZ8sYYipkuLKWi45wC8ev0SBI3qs16WtQzrwUYgHLQ268fiDJLa7O4xzT5hP\n",
-       "J3jLNIRk4Zedtx/PErh+3fT92AtLYaoUEb1irwqrxnBOezYsGSS0aYHlFXM8cAw4qgXq3ObIb/Uc\n",
-       "rHd+DprxOb1JVLjyzflA+V6WaMHtalObsGP9xXJz90vN6taIC3N8f7ocSd1zuHKwC5ovbkn0fleS\n",
-       "SJcu5A8WKm3NXt0cpgxwqCeDxpaegwG6FTMl+QMgCvxgpRFvppyVWuL20GjJV9Qa13fQg4FTXmaN\n",
-       "wazSfdINW90aL75315zPs8EhevHJliNTB7JqwL+U8o3azITmRmHBKb2BIDw3X9ZYzYBEf371Tlo+\n",
-       "zRC+LUISeg/9MIU4nBXQ0+PPQGsyMedpkbMY+8gBjmqoKZ48gPrdDcYaHa5iPh1OtI6luAu0AThc\n",
-       "YInAUX1ndODyFunM78C4ZYzuVW/s0IqWsi5jrj23e/okK9gzB+Uyyxhw/ELF1Zm5xpfnt0UmRQKm\n",
-       "8LSOQkQMVwiXT9tyEVez8yWnOxlyedFZafrOOZDU9wlDN2doD5MPDEKO29kzyiX0GQIOgIn/6AAc\n",
-       "NRIIzg993JP8XnXZ9WWcBU9bZS28ve+F+xvFHHdujkLSFyCFgAAAAwAAB8UAAAOlQZokbEP//qmW\n",
-       "ADFXz6AHGIMQPjUlLJA7EAcslt+76F3Gq9Y/AdlyZxkii1awmMyZaeFLAb/2Q+vOBZtTpVYri8mS\n",
-       "vTkxYlz6zO9kHQ0Vzkk8Pr/Wj3Qy1tnTjJk3gtqvPDHvSiS3lb4BufHiHFMQTDgdBlh9AzEBRFRQ\n",
-       "eXUPJVhg1x+KZi/feF4WuCY6YYQ4ptkUWJcy1/JXnSNru8/9LcPz2G9KICkx40uN4DOlorZqROaN\n",
-       "ST7OFm+MWLdPkScXn2WoW3rKVCSy9j6bRWr0MjByGjWkVkC1KWzKbffntZ+dRNzNIug3MJPCzgdp\n",
-       "YR2stDFyN4giMMF2dWEjVl4xi4pyewettoj0N7x+JXnqgXqKf9NsZUh96W0N9hiQgoA7Dqrg7aCo\n",
-       "D0xZZswBhhCa2vvLUff/6NLch9Kr/MGEcPe3p0UmaTFsRrS/1WxUi3/9XqXt69ZA0OQBvfU5XdG3\n",
-       "Qde42osEGRpz9LYv29MQNTx4NA+ZZvP+yOb9EjPZ7RZ28oRFpWIalUA3poHRQQ4+ExxH9OGYI9oR\n",
-       "N6kPvKlN3UwHY0k88smOENHh6ALrB+km01uOSRjUbKwB3sUxnAm7H1OykRsNBvtxLBIGiwthoYFt\n",
-       "kxQ6C6WoX9d8HVsQGzUgRPDhyxBqhoniDvlk546eiAjCdlm59VCWEq5t5abqOi1SCuef+D1YC9UX\n",
-       "0CJfsetj5kUmfRrTALKrZUk/eahw27NTrdTKun+vTg1/UysfqZn4GCL7aBdNxjfA0yYCbnD5wZAA\n",
-       "UQkqTBACib1Sf/Y5R43oWKHneECf7OAdwp4b0JDBnycZ+0/ArDvijTTRS1vh6MeQXgXHSc+h5YNR\n",
-       "y54Sr3dFzqYijORRTM6AvTwotGdosByugVKtcMgAVaLcKZ95u8bprTb4V1N6kfdyJP80SPy+WuqO\n",
-       "GLhojLX4Vy2Bdjy27cFGO/8ZXXjDQdyq+LlRLS1BGmD4i3CNmBG5uxvqSZdAgA/JFj+O/zRFXqMJ\n",
-       "5E8C/CZ4IOdVwcrv9XouL6gnKRQPZXs4FW8+0LQLr2xcw4BI/fMZJw2BLIwgvzOFOejXObRktFF3\n",
-       "0U5aYaIXeMQs78eoQ4g4bXm6pKOMxWpPkbg8GI0/ZVL4jf7UbMfpYR5gQLaEvLfZmX8rZ3uAZcZj\n",
-       "c/dhFEQsOa7Vh3VBBbVH53uaJf8M6OeWkQag8fsIU7pDrxA2E8JWHL+GhOAYLdjnk3xAmhJtjPya\n",
-       "/HbWtERUvs8bCiygAAAAIUGeQniGfwAfvkyf70QWJQ6HHHdfYWRGO2wpRP+ZhsiBLwAAAB0BnmF0\n",
-       "Qr8AO1wVtzT86aBRxFkUEMyz60O4C3wakAAAABYBnmNqQr8AJbsByGqRHkA6Fys4mKHHAAAAoUGa\n",
-       "aEmoQWiZTAh///6plgAN5c1Rn//AB9Ayve+QFciAsRcJxqoRV54j+DbAdkCiKZgQxTgd6WgK0r1n\n",
-       "BEUnv2NgP5vcf0cXyWshp2FdfNoc4x0VyZokFnbZ++ZrR7uELbJnxZnijV/mZK3HFf2XCM3WyI6x\n",
-       "3OjXU8m7fF/aKgxGkDTt+biXW0pQIkMSH725l7jlodPnD2nvbzisYfYWADuhAAAAOEGehkURLDP/\n",
-       "ABR6a7AfrUYjm0ET6AYEkJwwAAWUA+99BmaHNJueqxtfYy8rZigRQdKcz9FQ/EF7AAAAKQGepXRC\n",
-       "vwAlrpSANY0bGfjnx6Ep2Ukb+JhDATkAkXzLLbXOMAC5LAwJAAAAFAGep2pCvwAluwFi6g125v6Y\n",
-       "OUe4AAAApUGarEmoQWyZTAh///6plgAUDuU8AHQXnmG8LAXoYGDeB3LBy+8XvIi+UGjAEVCgTYCr\n",
-       "23XnSFL9oePpILu8LnhYUIQoozJ4e/Z6AAADAiko0z2YRvQT4RVKbi2ZzX6I30iMxnn1GoeICftK\n",
-       "AAlGupa7MCXYKxBUCySXplllCnPA0ltBB98AwoRqz3RVe1tTMhqDQlw9WBTqqvimbOcznYJ40wQA\n",
-       "/wAAADRBnspFFSwz/wAUemuujBwjDYNTHmAbxWqoBhnL22TZI6YB6Hn146LK1pb99yykAzhBggE3\n",
-       "AAAAHwGe6XRCvwAlrolV7utmxkMzlSJWLve3KOmIcWlTlfAAAAAXAZ7rakK/ACW7AWLqDX8ge4fi\n",
-       "gcUdA2YAAABFQZrwSahBbJlMCH///qmWABIFt74gPFCDRzebzyBUsWsX8LAbV9Boqb8Y1iv6vMwD\n",
-       "vHKT7aW7+VkE2ofiBT+NIiJCIH+BAAAAMUGfDkUVLDP/ABR6auRnAatV5dUe3HcYr6K7eUb37nbi\n",
-       "QTbZOYZseRLQxFjJxBZ/GfMAAAAWAZ8tdEK/ACWuiOKuU+LHFvKR+6LxgQAAACEBny9qQr8AJbsB\n",
-       "5WhPu7J3irlCE1Egnz5QbW09lme5BNwAAAC9QZs0SahBbJlMCH///qmWABbewJgCPhGPhDnnV97C\n",
-       "vsWrxpaIjcvFApAeKb8n99RZH3SqfYsAzawoklP0MsobQwP8Msk8u4hl+yZVjchaZrAaugjjpqku\n",
-       "6SymDToc8PBvXKC2YJWCTzPoZQMRzhp77r27R+BZAOcgAkyJydkn/TPTzPyD/hvWdX6x2cKTQhSM\n",
-       "6Ls3qKI6PrGOIz3r9ihSeRyDiiviVU7AewZh0nJUel9lIgh4+VwkpBuMwAi4AAAANUGfUkUVLDP/\n",
-       "ABR6auRuPUKj084/qgLSOMCOtQzigwbBst7wJ8vtnjybXkDFjBSbDlPWqD5hAAAAFAGfcXRCvwAl\n",
-       "rojirlPfzd35o7HgAAAAFgGfc2pCvwAluwFi6g14a1f1lZfYvGAAAACvQZt4SahBbJlMCH///qmW\n",
-       "AB6DoKAAq7o98XcXkP3hEhlbPj92Kt886zV7/kcz1x9qbD0iWafIyVu7i55aMD2KEDLmNrtcVaBG\n",
-       "xnnsNOsh5vWYoJeDlFjNk2dlPZ/mG4SeNYYD1OUG2Wg3fnuZjU+3zlX0m1vfjrjAUr3JpdUVhIrA\n",
-       "VSg27MLZ3aIFGD6u/Nbuy1cZqpfWKorP0vpxra6B7uGoS8oPDjwICmbw2/gIeQAAABhBn5ZFFSwz\n",
-       "/wAUemqt49zKGcezUo8W8IAAAAAUAZ+1dEK/ACWuiOKuU9/N3fmjseEAAAAXAZ+3akK/ACW7AXgj\n",
-       "JixxA4uKJcA3iLkAAAAxQZu8SahBbJlMCH///qmWABGHV6OyuaJb/oOSkybZ+93HdJQUAbgCymIC\n",
-       "VFMUODeXTAAAAChBn9pFFSwz/wAUemrcDjhg2bcMcYdeTsn0f89rVJthHOHZWQgYYGVbAAAAHgGf\n",
-       "+XRCvwAlrokBE7qjb9MLUGS4J6cZqCUWOSBSQAAAABQBn/tqQr8AJbsBYuoNdub+mDlHuQAAAIpB\n",
-       "m+BJqEFsmUwIf//+qZYAFA6mqAfC4NiYJTOywJi1tYN/JpgVnIa+xmpciNaAWsAapBGAaBYv4vDo\n",
-       "ePExby5hqx2mkghmGeZVD8Ft/8cqKEP9JUaAFyr/mWGst8IM1oxde1oNG1h5rhEirqai08I67J8E\n",
-       "PyeI5J70U7Tq4IK2IVN3ltxdkAPqBo0AAAApQZ4eRRUsM/8AFHpq3A44WV/KegPHct3ME2xVIgyG\n",
-       "+0ntGmqBmjcAW0AAAAAUAZ49dEK/ACWuiOKuU9/N3fmjseAAAAAUAZ4/akK/ACW7AWLqDXbm/pg5\n",
-       "R7kAAAA7QZokSahBbJlMCH///qmWAAxU4Y0A8yFL6VWjlcVOVxnp4bYXk5lBhhcCQc7b03gACZ9l\n",
-       "bE6cL6c4vfgAAAAdQZ5CRRUsM/8AFHpq0HN8FOBOHjkd3hUD5UbUwb0AAAAUAZ5hdEK/ACWuiOKu\n",
-       "U9/N3fmjseAAAAAWAZ5jakK/ACW7AWLqDXeRQOhKBn7SQQAAAFdBmmhJqEFsmUwIf//+qZYAHohO\n",
-       "nsRZwBW9sPwhzzpBzWbJ585vKcZIrRqF5C+Yp7CHAat23K0ZMcgSXV4MSb0cOUT95eDdweA2+6Qj\n",
-       "q3s4HlULYLYA+YEAAAAjQZ6GRRUsM/8AFHpq0HccLK7wS1lWgVcLWviNXl7uP7wgAi8AAAAUAZ6l\n",
-       "dEK/ACWuiOKuU9/N3fmjseEAAAAUAZ6nakK/ACW7AWLqDXbm/pg5R7gAAADqQZqsSahBbJlMCH//\n",
-       "/qmWAB6F0VWxMCYsb4gPUVQToQDqb2HFAS5qnBlf3yW/vGh4sPTBYlhYBH98qYZdXuqY6jAQOP2Y\n",
-       "XyJ11aUcZ9/SnJeLkuFwSx02QSH40+Go/YrLi2gNwaSs3R/Oap1N4X+DUiJ8B7S/fxuThFuaMApD\n",
-       "T8IfWgEpQgqfbaOgM8n7WdRgoANqFjvOPrRv8xrnFyvkaf7xne7kH8sjip0ww2Au6tM2ewvInMLh\n",
-       "E7YA38bhwFKTKQi215S+x7oYzQprUgClJpCDSkUsr07V/IU3izABLzMGdHHfa8SWpLUgAAAAGEGe\n",
-       "ykUVLDP/ABR6aq3j3MoZx7NSjxbwgQAAABYBnul0Qr8AJa6JAWXfW27v4kflWruAAAAAFgGe62pC\n",
-       "vwAluwFi6g13MQwLX5LcvGAAAABBQZrwSahBbJlMCH///qmWABKHVpEJpFFpllauQ+DFM24q1HuT\n",
-       "l688QJ0UAJ3IOKFGsmtGiETTbEP2KOc7qcMAQ8EAAAAyQZ8ORRUsM/8AFHprZuNk4BxS3lZgfmCW\n",
-       "tcwyj8kjMHQIqJCZHz17gYEMAXCpzGO9J80AAAAlAZ8tdEK/ACWukQljEP8b5v5mQvvNNFWXqjCK\n",
-       "1yUxCMYz7CgPCQAAABYBny9qQr8AJbsBgVG9LLh98SPilFJAAAAASkGbNEmoQWyZTAh///6plgAS\n",
-       "hZk+Zhk9AGagDo0vh15W7Kf0y9jt010dxHTGA+3m5irdpI/ts7sRgkB+nJGnFQZSrWQQqpdrgAkY\n",
-       "AAAALkGfUkUVLDP/ABR6atBnCZlHYjfS6ExZOAJvXkbcku7D3CAisS4YDODqgaUEBcUAAAAYAZ9x\n",
-       "dEK/ACWuiZfMYK3NiFxi/970AOE3AAAAFgGfc2pCvwAluwGoN4pWXFN7HwLmruAAAAAfQZt4SahB\n",
-       "bJlMCH///qmWAAQhZuxLDndn8s8PWO35uQAAACFBn5ZFFSwz/wAUemrQc3yOoNppnUZqRh7vgQIK\n",
-       "dqUMG9AAAAAUAZ+1dEK/ACWuiOKuU9/N3fmjseEAAAAdAZ+3akK/ACW7AurRnaTeE5fzR87noacz\n",
-       "SbAEKCEAAACKQZu8SahBbJlMCH///qmWAB6ITp7EWcAVvbD8Ic86RG0sEmzBmTeUjvxJzYGPtSeU\n",
-       "lgcNvxAAE6DC0iBZqAXTF8owH8ggxtPPZtA0s4ij5xq4+l537EG+otF5FpxwI/HiTMHrH7IgQ72J\n",
-       "OZGgrqBTKSkKqippFA6+p9SW43AThPr5BjKfg6A/IgFTAAAAJEGf2kUVLDP/ABR6atB3GY+631sC\n",
-       "QHDilmJ0d+ovkigPiaUBswAAABQBn/l0Qr8AJa6I4q5T383d+aOx4AAAABQBn/tqQr8AJbsBYuoN\n",
-       "dub+mDlHuQAAAH1Bm+BJqEFsmUwIf//+qZYAHoXRVbEwJeYOWAB9AigAFkLJbCaJJ7DPLRrgHifH\n",
-       "Yk0zJ9g3YOHYTMCjUh4/qbRQaiQzd/TidN41xk6Vmaxce1qNVB+zopBDYrjopW4wnO01GSew5Din\n",
-       "WtatKXAvTLul9Pu1b/deiqpsVgQOOQAAABhBnh5FFSwz/wAUemqt49zKGcezUo8W8IAAAAAUAZ49\n",
-       "dEK/ACWuiOKuU9/N3fmjseAAAAAUAZ4/akK/ACW7AWLqDXbm/pg5R7kAAAA5QZokSahBbJlMCH//\n",
-       "/qmWABKHV8YG3Ent0jccBdIxEPgbhXnMAPJw7YAG4eo3Qda8JOSayoWgANSAAAAALUGeQkUVLDP/\n",
-       "ABR6atBzf3pSEO4AnqW612sp6hL277V9LWXwWl0pLFYBJLACkwAAACEBnmF0Qr8AJa6Kapbp9aTa\n",
-       "y8tnjTBLvtrXtdwTINFTgYsAAAAUAZ5jakK/ACW7AWLqDXbm/pg5R7kAAAA0QZpoSahBbJlMCH//\n",
-       "/qmWABKFm8XjXqnbAz/n6xTGixuuLAu1bSJzL0Co/7gA04J/HkAFJQAAACpBnoZFFSwz/wAUemrQ\n",
-       "ZvPskCp4f+8YVfJDEY9dJHJ4Rb7QxgRJy3cMH3EAAAAUAZ6ldEK/ACWuiOKuU9/N3fmjseEAAAAU\n",
-       "AZ6nakK/ACW7AWLqDXbm/pg5R7gAAAAxQZqsSahBbJlMCH///qmWAAiCEp4AJt4oABZ5yTh9xmkl\n",
-       "solEYqxb6kN48buwNJ4nzAAAAB1BnspFFSwz/wAUemrQc3yOoNppnUZqTBqUhOqjqwAAABQBnul0\n",
-       "Qr8AJa6I4q5T383d+aOx4AAAABYBnutqQr8AJbsC6tFuolBOEUoGftJAAAAAkUGa8EmoQWyZTAh/\n",
-       "//6plgARh46fUAqx8V0HVIgOAG4YRwz7rQXo5ZGgq6M6HtngI/MNAQuUsIsWBaW3VJzSNmqUWe72\n",
-       "zFVlkjs9w+iyFuTuld6Sr1Y49XdjjFJD4lHWYtc+fXJvBbepouvm2oo19fw1M7OM8DpYxHbWnxdC\n",
-       "jPtPK/sgsIz8SxbV/n6ZBPw2Ns0AAAAiQZ8ORRUsM/8AFHpq0HcZmXNDEtt1zB/CiUur+ixClmAj\n",
-       "4QAAABQBny10Qr8AJa6I4q5T383d+aOx4QAAABkBny9qQr8AJbsB7cbyrY3lUaT3hervyhxwAAAA\n",
-       "LEGbNEmoQWyZTAh///6plgAN5jgPZIswHIhSmd1x8gEb/YENAhlAStqB8xTAAAAAGkGfUkUVLDP/\n",
-       "ABR6aroHagqYnDuZEbWWn3jBAAAAFAGfcXRCvwAlrojirlPfzd35o7HgAAAAFgGfc2pCvwAluwHt\n",
-       "xvKtjd6IhILmruAAAAAZQZt4SahBbJlMCHf//qmWAAiDoL2SgK/z1wAAACNBn5ZFFSwz/wAUemqy\n",
-       "XRfMFJK9uvvUbjKRak9DNo1EFkhZQAAAABoBn7V0Qr8AJa6JGOh1hCK2+gT3p8VrCd1GpQAAABQB\n",
-       "n7dqQr8AJbsBYuoNdub+mDlHuQAAABlBm7xJqEFsmUwId//+qZYABIFTnq+FrIOmAAAAIkGf2kUV\n",
-       "LDP/ABR6arJeTZgk5Ro7oCPY/QoqEnYTfY7om4EAAAAUAZ/5dEK/ACWuiOKuU9/N3fmjseAAAAAU\n",
-       "AZ/7akK/ACW7AWLqDXbm/pg5R7kAAAATQZvgSahBbJlMCG///qeEAAAEvQAAABlBnh5FFSwz/wAU\n",
-       "emqt49zKIihLfKxWbC13AAAAFAGePXRCvwAlrojirlPfzd35o7HgAAAAFAGeP2pCvwAluwFi6g12\n",
-       "5v6YOUe5AAAAE0GaI0moQWyZTAhX//44QAAAR8AAAAAXQZ5BRRUsL/8AG6iMSF0Gy8MPcBF0vj0A\n",
-       "AAAUAZ5iakK/ACW7AWLqDXbm/pg5R7gAAAfXbW9vdgAAAGxtdmhkAAAAAAAAAAAAAAAAAAAD6AAA\n",
-       "E4gAAQAAAQAAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAABAAAAAAAAA\n",
-       "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgAABwF0cmFrAAAAXHRraGQAAAADAAAAAAAAAAAAAAAB\n",
-       "AAAAAAAAE4gAAAAAAAAAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAABA\n",
-       "AAAAARwAAAGQAAAAAAAkZWR0cwAAABxlbHN0AAAAAAAAAAEAABOIAAAEAAABAAAAAAZ5bWRpYQAA\n",
-       "ACBtZGhkAAAAAAAAAAAAAAAAAAAoAAAAyABVxAAAAAAALWhkbHIAAAAAAAAAAHZpZGUAAAAAAAAA\n",
-       "AAAAAABWaWRlb0hhbmRsZXIAAAAGJG1pbmYAAAAUdm1oZAAAAAEAAAAAAAAAAAAAACRkaW5mAAAA\n",
-       "HGRyZWYAAAAAAAAAAQAAAAx1cmwgAAAAAQAABeRzdGJsAAAAtHN0c2QAAAAAAAAAAQAAAKRhdmMx\n",
-       "AAAAAAAAAAEAAAAAAAAAAAAAAAAAAAAAARwBkABIAAAASAAAAAAAAAABAAAAAAAAAAAAAAAAAAAA\n",
-       "AAAAAAAAAAAAAAAAAAAAAAAAGP//AAAAMmF2Y0MBZAAV/+EAGWdkABWs2UEgz74QAAADABAAAAMC\n",
-       "gPFi2WABAAZo6+PLIsAAAAAcdXVpZGtoQPJfJE/FujmlG88DI/MAAAAAAAAAGHN0dHMAAAAAAAAA\n",
-       "AQAAAGQAAAIAAAAAFHN0c3MAAAAAAAAAAQAAAAEAAAMoY3R0cwAAAAAAAABjAAAAAQAABAAAAAAB\n",
-       "AAAKAAAAAAEAAAQAAAAAAQAAAAAAAAABAAACAAAAAAEAAAoAAAAAAQAABAAAAAABAAAAAAAAAAEA\n",
-       "AAIAAAAAAQAACgAAAAABAAAEAAAAAAEAAAAAAAAAAQAAAgAAAAABAAAKAAAAAAEAAAQAAAAAAQAA\n",
-       "AAAAAAABAAACAAAAAAEAAAoAAAAAAQAABAAAAAABAAAAAAAAAAEAAAIAAAAAAQAACgAAAAABAAAE\n",
-       "AAAAAAEAAAAAAAAAAQAAAgAAAAABAAAKAAAAAAEAAAQAAAAAAQAAAAAAAAABAAACAAAAAAEAAAoA\n",
-       "AAAAAQAABAAAAAABAAAAAAAAAAEAAAIAAAAAAQAACgAAAAABAAAEAAAAAAEAAAAAAAAAAQAAAgAA\n",
-       "AAABAAAKAAAAAAEAAAQAAAAAAQAAAAAAAAABAAACAAAAAAEAAAoAAAAAAQAABAAAAAABAAAAAAAA\n",
-       "AAEAAAIAAAAAAQAACgAAAAABAAAEAAAAAAEAAAAAAAAAAQAAAgAAAAABAAAKAAAAAAEAAAQAAAAA\n",
-       "AQAAAAAAAAABAAACAAAAAAEAAAoAAAAAAQAABAAAAAABAAAAAAAAAAEAAAIAAAAAAQAACgAAAAAB\n",
-       "AAAEAAAAAAEAAAAAAAAAAQAAAgAAAAABAAAKAAAAAAEAAAQAAAAAAQAAAAAAAAABAAACAAAAAAEA\n",
-       "AAoAAAAAAQAABAAAAAABAAAAAAAAAAEAAAIAAAAAAQAACgAAAAABAAAEAAAAAAEAAAAAAAAAAQAA\n",
-       "AgAAAAABAAAKAAAAAAEAAAQAAAAAAQAAAAAAAAABAAACAAAAAAEAAAoAAAAAAQAABAAAAAABAAAA\n",
-       "AAAAAAEAAAIAAAAAAQAACgAAAAABAAAEAAAAAAEAAAAAAAAAAQAAAgAAAAABAAAKAAAAAAEAAAQA\n",
-       "AAAAAQAAAAAAAAABAAACAAAAAAEAAAoAAAAAAQAABAAAAAABAAAAAAAAAAEAAAIAAAAAAQAACgAA\n",
-       "AAABAAAEAAAAAAEAAAAAAAAAAQAAAgAAAAABAAAIAAAAAAIAAAIAAAAAHHN0c2MAAAAAAAAAAQAA\n",
-       "AAEAAABkAAAAAQAAAaRzdHN6AAAAAAAAAAAAAABkAAAfngAAA6kAAAAlAAAAIQAAABoAAAClAAAA\n",
-       "PAAAAC0AAAAYAAAAqQAAADgAAAAjAAAAGwAAAEkAAAA1AAAAGgAAACUAAADBAAAAOQAAABgAAAAa\n",
-       "AAAAswAAABwAAAAYAAAAGwAAADUAAAAsAAAAIgAAABgAAACOAAAALQAAABgAAAAYAAAAPwAAACEA\n",
-       "AAAYAAAAGgAAAFsAAAAnAAAAGAAAABgAAADuAAAAHAAAABoAAAAaAAAARQAAADYAAAApAAAAGgAA\n",
-       "AE4AAAAyAAAAHAAAABoAAAAjAAAAJQAAABgAAAAhAAAAjgAAACgAAAAYAAAAGAAAAIEAAAAcAAAA\n",
-       "GAAAABgAAAA9AAAAMQAAACUAAAAYAAAAOAAAAC4AAAAYAAAAGAAAADUAAAAhAAAAGAAAABoAAACV\n",
-       "AAAAJgAAABgAAAAdAAAAMAAAAB4AAAAYAAAAGgAAAB0AAAAnAAAAHgAAABgAAAAdAAAAJgAAABgA\n",
-       "AAAYAAAAFwAAAB0AAAAYAAAAGAAAABcAAAAbAAAAGAAAABRzdGNvAAAAAAAAAAEAAAAsAAAAYnVk\n",
-       "dGEAAABabWV0YQAAAAAAAAAhaGRscgAAAAAAAAAAbWRpcmFwcGwAAAAAAAAAAAAAAAAtaWxzdAAA\n",
-       "ACWpdG9vAAAAHWRhdGEAAAABAAAAAExhdmY1OC4yOS4xMDA=\n",
-       "\">\n",
-       "  Your browser does not support the video tag.\n",
-       "</video>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 284x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "clip = neg_clip.permute(0, 2, 3, 4, 1).numpy()\n",
     "activations = sparse_model([clip, tf.expand_dims(recon_model.trainable_weights[0], axis=0)])\n",
diff --git a/run_pnb_only_yolo.py b/run_pnb_only_yolo.py
index b75ce5c6dffa11942a42c1c073169ff33102a7af..8e2bf3da0feed16e8682222ceed7a21474d198f7 100644
--- a/run_pnb_only_yolo.py
+++ b/run_pnb_only_yolo.py
@@ -10,9 +10,7 @@ from tqdm import tqdm
 import random
 
 def calc_yolo_dist(yolo_model, frame):
-    bounding_boxes, classes = yolo_model.get_bounding_boxes(frame.swapaxes(0, 2).swapaxes(0, 1).numpy())
-    bounding_boxes = bounding_boxes.squeeze(0)
-    classes = classes.squeeze(0)
+    bounding_boxes, classes = yolo_model.get_bounding_boxes_v5(frame.swapaxes(0, 2).swapaxes(0, 1).numpy())
     
     needle = None
     nerve = None
@@ -34,7 +32,7 @@ if __name__ == "__main__":
     
     args = parser.parse_args()
         
-    yolo_model = YoloModel()
+    yolo_model = YoloModel('pnb')
 
     all_predictions = []
 
diff --git a/sparse_coding_torch/onsd/classifier_model.py b/sparse_coding_torch/onsd/classifier_model.py
index e11cd4b6eea3b3f197d488ade4cfdd2e6c1e8d39..f97cad64f66d7432d85ad92d0439802a6d2d7788 100644
--- a/sparse_coding_torch/onsd/classifier_model.py
+++ b/sparse_coding_torch/onsd/classifier_model.py
@@ -19,13 +19,14 @@ class ONSDClassifier(keras.layers.Layer):
 
         self.flatten = keras.layers.Flatten()
 
-        self.dropout = keras.layers.Dropout(0.5)
+#         self.dropout = keras.layers.Dropout(0.5)
 
 #         self.ff_1 = keras.layers.Dense(1000, activation='relu', use_bias=True)
 #         self.ff_2 = keras.layers.Dense(500, activation='relu', use_bias=True)
 #         self.ff_2 = keras.layers.Dense(20, activation='relu', use_bias=True)
         self.ff_3 = keras.layers.Dense(20, activation='relu', use_bias=True)
-        self.ff_4 = keras.layers.Dense(1)
+        self.ff_final_1 = keras.layers.Dense(1)
+        self.ff_final_2 = keras.layers.Dense(1)
 
 #     @tf.function
     def call(self, activations):
@@ -39,8 +40,45 @@ class ONSDClassifier(keras.layers.Layer):
 #         x = self.ff_2(x)
 #         x = self.dropout(x)
         x = self.ff_3(x)
-        x = self.dropout(x)
-        x = self.ff_4(x)
+#         x = self.dropout(x)
+        class_pred = self.ff_final_1(x)
+        width_pred = tf.math.tanh(self.ff_final_2(x))
+
+        return class_pred, width_pred
+    
+class ONSDSharpness(keras.Model):
+    def __init__(self):
+        super().__init__()
+        self.encoder = tf.keras.applications.DenseNet121(include_top=False)
+        
+        self.flatten = keras.layers.Flatten()
+        
+        self.ff_1 = keras.layers.Dense(100, activation='relu', use_bias=True)
+        self.ff_2 = keras.layers.Dense(1, activation='sigmoid')
+        
+    @tf.function
+    def call(self, images):
+        x = self.encoder(images)
+        
+        x = self.flatten(x)
+        
+        x = self.ff_1(x)
+        x = self.ff_2(x)
 
         return x
 
+    
+class MobileModelONSD(keras.Model):
+    def __init__(self, classifier_model):
+        super().__init__()
+        self.classifier = classifier_model
+
+    @tf.function
+    def call(self, images):
+#         images = tf.squeeze(tf.image.rgb_to_grayscale(images), axis=-1)
+        images = tf.transpose(images, perm=[0, 2, 3, 1])
+        images = images / 255
+
+        pred = tf.math.sigmoid(self.classifier(images))
+
+        return pred
diff --git a/sparse_coding_torch/onsd/generate_tflite.py b/sparse_coding_torch/onsd/generate_tflite.py
new file mode 100644
index 0000000000000000000000000000000000000000..80903409473f9b2299de26445baaed3ca9f4d41b
--- /dev/null
+++ b/sparse_coding_torch/onsd/generate_tflite.py
@@ -0,0 +1,51 @@
+from tensorflow import keras
+import numpy as np
+import torch
+import tensorflow as tf
+import cv2
+import torchvision as tv
+import torch
+import torch.nn as nn
+from sparse_coding_torch.utils import VideoGrayScaler, MinMaxScaler
+from sparse_coding_torch.onsd.classifier_model import MobileModelONSD
+import argparse
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--checkpoint', default='sparse_coding_torch/classifier_outputs/32_filters_no_aug_3/best_classifier.pt/', type=str)
+    parser.add_argument('--batch_size', default=1, type=int)
+    parser.add_argument('--image_height', type=int, default=200)
+    parser.add_argument('--image_width', type=int, default=200)
+    parser.add_argument('--clip_depth', type=int, default=1)
+    
+    args = parser.parse_args()
+    #print(args.accumulate(args.integers))
+    batch_size = args.batch_size
+
+    image_height = args.image_height
+    image_width = args.image_width
+    clip_depth = args.clip_depth
+        
+    classifier_model = keras.models.load_model(args.checkpoint)
+
+    inputs = keras.Input(shape=(clip_depth, image_height, image_width))
+
+    outputs = MobileModelONSD(classifier_model=classifier_model)(inputs)
+
+    model = keras.Model(inputs=inputs, outputs=outputs)
+
+    input_name = model.input_names[0]
+    index = model.input_names.index(input_name)
+    model.inputs[index].set_shape([batch_size, clip_depth, image_height, image_width])
+
+    converter = tf.lite.TFLiteConverter.from_keras_model(model)
+    converter.optimizations = [tf.lite.Optimize.DEFAULT]
+    converter.target_spec.supported_types = [tf.float16]
+    converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS]
+
+    tflite_model = converter.convert()
+
+    print('Converted')
+
+    with open("./sparse_coding_torch/mobile_output/onsd.tflite", "wb") as f:
+        f.write(tflite_model)
diff --git a/sparse_coding_torch/onsd/load_data.py b/sparse_coding_torch/onsd/load_data.py
index 8ab3b7c84cd2fe25c0482f1f41f0004cf7a33b7a..c4d5dc73f40169aeadb9cdd03b53341c8fb86771 100644
--- a/sparse_coding_torch/onsd/load_data.py
+++ b/sparse_coding_torch/onsd/load_data.py
@@ -13,17 +13,17 @@ def load_onsd_videos(batch_size, input_size, yolo_model=None, mode=None, n_split
     video_path = "/shared_data/bamc_onsd_data/revised_onsd_data"
     
     transforms = torchvision.transforms.Compose(
-    [torchvision.transforms.Grayscale(1),
+    [#torchvision.transforms.Grayscale(1),
      MinMaxScaler(0, 255),
      torchvision.transforms.Resize(input_size[:2])
     ])
-    augment_transforms = torchvision.transforms.Compose(
-    [torchvision.transforms.RandomRotation(45),
-     torchvision.transforms.RandomHorizontalFlip(0.5),
-     torchvision.transforms.RandomAdjustSharpness(0.05)
+#     augment_transforms = torchvision.transforms.Compose(
+#     [torchvision.transforms.RandomRotation(45),
+#      torchvision.transforms.RandomHorizontalFlip(0.5),
+#      torchvision.transforms.RandomAdjustSharpness(0.05)
      
-    ])
-    dataset = ONSDLoader(video_path, input_size[1], input_size[0], transform=transforms, augmentation=augment_transforms, yolo_model=yolo_model)
+#     ])
+    dataset = ONSDLoader(video_path, input_size[1], input_size[0], transform=transforms, yolo_model=yolo_model)
     
     targets = dataset.get_labels()
     
diff --git a/sparse_coding_torch/onsd/onsd_regression.py b/sparse_coding_torch/onsd/onsd_regression.py
new file mode 100644
index 0000000000000000000000000000000000000000..82f1501f65bd06b855d0bdb09b99ad1e1c8c992b
--- /dev/null
+++ b/sparse_coding_torch/onsd/onsd_regression.py
@@ -0,0 +1,161 @@
+import math
+from tqdm import tqdm
+import glob
+from os.path import join, abspath
+import random
+from sklearn.model_selection import GroupKFold
+from sklearn.linear_model import LogisticRegression
+import os
+import numpy as np
+import tensorflow as tf
+from yolov4.get_bounding_boxes import YoloModel
+import torchvision
+from sklearn.metrics import f1_score, accuracy_score, confusion_matrix
+    
+def get_width_data(yolo_model, input_videos):
+    all_data = []
+    for label, path, vid_f in tqdm(input_videos):
+        vc = torchvision.io.read_video(path)[0].permute(3, 0, 1, 2)
+
+        orig_height = vc.size(2)
+        orig_width = vc.size(3)
+
+        obj_bb = []
+
+        for i in range(vc.size(1) - 1, vc.size(1) - 40, -1):
+            frame = vc[:, i, :, :].swapaxes(0, 2).swapaxes(0, 1).numpy()
+
+            bounding_boxes, classes, scores = yolo_model.get_bounding_boxes_v5(frame)
+
+            obj_bb = [bb for bb, class_pred, score in zip(bounding_boxes, classes, scores) if class_pred==0]
+
+            if len(obj_bb) > 0:
+                obj_bb = obj_bb[0]
+                break
+
+        if len(obj_bb) == 0:
+            continue
+
+        obj_width = round((obj_bb[2] + obj_bb[0]) / 2)
+        
+        if label == 'Positives':
+            label = 1.0
+        elif label == 'Negatives':
+            label = 0.0
+        else:
+            raise Exception('Bad label')
+
+        all_data.append((obj_width, label, path))
+        
+    return all_data
+
+def get_splits(videos):
+    random.shuffle(videos)
+    
+    gss = GroupKFold(n_splits=5)
+
+    groups = [vid[2].split('/')[-2] for vid in videos]
+
+    targets = [vid[2].split('/')[-3] for vid in videos]
+
+    return gss.split(np.arange(len(targets)), targets, groups)
+
+yolo_model = YoloModel('onsd')
+video_path = "/shared_data/bamc_onsd_data/revised_extended_onsd_data/"
+
+print('Beginning...')
+
+labels = [name for name in os.listdir(video_path) if os.path.isdir(os.path.join(video_path, name))]
+
+videos = []
+for label in labels:
+    videos.extend([(label, abspath(join(video_path, label, f)), f) for f in glob.glob(join(video_path, label, '*', '*.mp4'))])
+
+print('Making splits...')    
+
+splits = get_splits(videos)
+
+preds = []
+gt = []
+
+split_count = 0
+
+for train_idx, test_idx in splits:
+    print('On split {}...'.format(split_count))
+
+    print('Processing data...')
+    train_videos = [ex for i, ex in enumerate(videos) if i in train_idx]
+    test_videos = [ex for i, ex in enumerate(videos) if i in test_idx]
+    
+    train_data = get_width_data(yolo_model, train_videos)
+    
+    test_data = get_width_data(yolo_model, test_videos)
+
+    train_X = np.array([train_data[i][0] for i in range(len(train_data))]).reshape(-1, 1)
+    test_X = np.array([test_data[i][0] for i in range(len(test_data))]).reshape(-1, 1)
+    
+    train_Y = np.array([train_data[i][1] for i in range(len(train_data))]).reshape(-1, 1)
+    test_Y = np.array([test_data[i][1] for i in range(len(test_data))]).reshape(-1, 1)
+    
+    print('Training models...')
+    
+    clf = LogisticRegression().fit(train_X, train_Y)
+    score = clf.score(test_X, test_Y)
+    
+    print('Accuracy: {:.2f}'.format(score))
+    
+    for label, path, _ in tqdm(test_videos):
+        vc = torchvision.io.read_video(path)[0].permute(3, 0, 1, 2)
+
+        orig_height = vc.size(2)
+        orig_width = vc.size(3)
+        
+        if label == 'Positives':
+            gt.append(1.0)
+        elif label == 'Negatives':
+            gt.append(0.0)
+        else:
+            raise Exception('Bad label')
+
+        obj_bb = []
+
+        for i in range(vc.size(1) - 1, vc.size(1) - 40, -1):
+            frame = vc[:, i, :, :].swapaxes(0, 2).swapaxes(0, 1).numpy()
+
+            bounding_boxes, classes, scores = yolo_model.get_bounding_boxes_v5(frame)
+
+            obj_bb = [bb for bb, class_pred, score in zip(bounding_boxes, classes, scores) if class_pred==0]
+
+            if len(obj_bb) > 0:
+                obj_bb = obj_bb[0]
+                break
+
+        if len(obj_bb) > 0:
+            width = round((obj_bb[2] + obj_bb[0]) / 2)
+            
+            pred = clf.predict(np.array([width]).reshape(1, -1))[0]
+            preds.append(pred)
+        else:
+            preds.append(0.0)
+            continue
+            
+    split_count += 1
+    
+    print('Current results...')
+    overall_true = np.array(gt)
+    overall_pred = np.array(preds)
+
+    f1 = f1_score(overall_true, overall_pred, average='macro')
+    acc = accuracy_score(overall_true, overall_pred)
+
+    print("Current accuracy={:.2f}, f1={:.2f}".format(acc, f1))
+    
+overall_true = np.array(gt)
+overall_pred = np.array(preds)
+
+final_f1 = f1_score(overall_true, overall_pred, average='macro')
+final_acc = accuracy_score(overall_true, overall_pred)
+final_conf = confusion_matrix(overall_true, overall_pred)
+
+print("Final accuracy={:.2f}, f1={:.2f}".format(final_acc, final_f1))
+print(final_conf)
\ No newline at end of file
diff --git a/sparse_coding_torch/onsd/train_classifier.py b/sparse_coding_torch/onsd/train_classifier.py
index 5427e92c9f47d87f6e63aa66d25d86ed2b94eb72..999167536238edb97827caa2a013746260f7eb45 100644
--- a/sparse_coding_torch/onsd/train_classifier.py
+++ b/sparse_coding_torch/onsd/train_classifier.py
@@ -23,6 +23,7 @@ import torchvision
 from sparse_coding_torch.utils import VideoGrayScaler, MinMaxScaler
 import glob
 import cv2
+import copy
 
 # configproto = tf.compat.v1.ConfigProto()
 # configproto.gpu_options.polling_inactive_delay_msecs = 5000
@@ -32,7 +33,7 @@ import cv2
 # tf.compat.v1.keras.backend.set_session(sess)
 # tf.debugging.set_log_device_placement(True)
 
-def calculate_onsd_scores(input_videos, labels, yolo_model, classifier_model, transform):
+def calculate_onsd_scores(input_videos, labels, yolo_model, classifier_model, transform, crop_width, crop_height):
     all_predictions = []
     
     numerical_labels = []
@@ -53,14 +54,16 @@ def calculate_onsd_scores(input_videos, labels, yolo_model, classifier_model, tr
 
             vc_sub = vc[:, j, :, :]
             
-            frame = get_yolo_region_onsd(yolo_model, vc_sub)
+            frame = get_yolo_region_onsd(yolo_model, vc_sub, crop_width, crop_height)
             
             if frame is None:
                 continue
 
             frame = transform(frame).to(torch.float32).unsqueeze(3)
             
-            pred = tf.math.round(tf.math.sigmoid(classifier_model(frame)))
+            pred, _ = classifier_model(frame)
+            
+            pred = tf.math.round(tf.math.sigmoid(pred))
 
             all_preds.append(pred)
                 
@@ -106,8 +109,8 @@ if __name__ == "__main__":
     parser.add_argument('--train_sparse', action='store_true')
     parser.add_argument('--mixing_ratio', type=float, default=1.0)
     parser.add_argument('--sparse_lr', type=float, default=0.003)
-    parser.add_argument('--crop_height', type=int, default=400)
-    parser.add_argument('--crop_width', type=int, default=400)
+    parser.add_argument('--crop_height', type=int, default=200)
+    parser.add_argument('--crop_width', type=int, default=200)
     parser.add_argument('--scale_factor', type=int, default=1)
     parser.add_argument('--clip_depth', type=int, default=5)
     parser.add_argument('--frames_to_skip', type=int, default=1)
@@ -145,6 +148,12 @@ if __name__ == "__main__":
 
     sparse_model = None
     recon_model = None
+    
+    data_augmentation = keras.Sequential([
+        keras.layers.RandomFlip('horizontal'),
+        keras.layers.RandomRotation(45),
+#         keras.layers.RandomBrightness(0.1)
+    ])
         
     
     splits, dataset = load_onsd_videos(args.batch_size, input_size=(image_height, image_width), yolo_model=yolo_model, mode=args.splits, n_splits=args.n_splits)
@@ -155,23 +164,31 @@ if __name__ == "__main__":
     fn_ids = []
     fp_ids = []
     
+    with open(os.path.join(output_dir, 'test_ids.txt'),'w') as f:
+        pass
+
     i_fold = 0
     for train_idx, test_idx in splits:
-        train_sampler = torch.utils.data.SubsetRandomSampler(train_idx)
-#         train_sampler = SubsetWeightedRandomSampler(get_sample_weights(train_idx, dataset), train_idx, replacement=True)
-        train_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
-                                               sampler=train_sampler)
+        with open(os.path.join(output_dir, 'test_ids.txt'), 'a+') as test_id_out:
+            test_id_out.write(str(test_idx) + '\n')
+        train_loader = copy.deepcopy(dataset)
+        train_loader.set_indicies(train_idx)
+        test_loader = copy.deepcopy(dataset)
+        test_loader.set_indicies(test_idx)
+
+        train_tf = tf.data.Dataset.from_tensor_slices((train_loader.get_frames(), train_loader.get_labels(), train_loader.get_widths()))
+        test_tf = tf.data.Dataset.from_tensor_slices((test_loader.get_frames(), test_loader.get_labels(), test_loader.get_widths()))
         
-        if test_idx is not None:
-            test_sampler = torch.utils.data.SubsetRandomSampler(test_idx)
-            test_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
-                                                   sampler=test_sampler)
+#         if test_idx is not None:
+#             test_sampler = torch.utils.data.SubsetRandomSampler(test_idx)
+#             test_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
+#                                                    sampler=test_sampler)
             
-#             with open(os.path.join(args.output_dir, 'test_videos_{}.txt'.format(i_fold)), 'w+') as test_videos_out:
-#                 test_set = set([x for tup in test_loader for x in tup[2]])
-#                 test_videos_out.writelines(test_set)
-        else:
-            test_loader = None
+# #             with open(os.path.join(args.output_dir, 'test_videos_{}.txt'.format(i_fold)), 'w+') as test_videos_out:
+# #                 test_set = set([x for tup in test_loader for x in tup[2]])
+# #                 test_videos_out.writelines(test_set)
+#         else:
+#             test_loader = None
         
         if args.checkpoint:
             classifier_model = keras.models.load_model(args.checkpoint)
@@ -186,7 +203,9 @@ if __name__ == "__main__":
 
         best_so_far = float('-inf')
 
-        criterion = keras.losses.BinaryCrossentropy(from_logits=True, reduction=keras.losses.Reduction.SUM)
+        class_criterion = keras.losses.BinaryCrossentropy(from_logits=True, reduction=keras.losses.Reduction.SUM)
+        width_criterion = keras.losses.MeanSquaredError(reduction=keras.losses.Reduction.SUM)
+
 
         if args.train:
             for epoch in range(args.epochs):
@@ -196,12 +215,13 @@ if __name__ == "__main__":
                 y_true_train = None
                 y_pred_train = None
 
-                for labels, local_batch, vid_f in tqdm(train_loader):
-                    images = local_batch.permute(0, 2, 3, 1).numpy()
+                for images, labels, width in tqdm(train_tf.shuffle(len(train_tf)).batch(args.batch_size)):
+                    images = tf.transpose(images, [0, 2, 3, 1])
+                    width -= 0.5
 
-                    torch_labels = np.zeros(len(labels))
-                    torch_labels[[i for i in range(len(labels)) if labels[i] == positive_class]] = 1
-                    torch_labels = np.expand_dims(torch_labels, axis=1)
+#                     torch_labels = np.zeros(len(labels))
+#                     torch_labels[[i for i in range(len(labels)) if labels[i] == positive_class]] = 1
+#                     torch_labels = np.expand_dims(torch_labels, axis=1)
 
                     if args.train_sparse:
                         with tf.GradientTape() as tape:
@@ -212,10 +232,12 @@ if __name__ == "__main__":
                             print(loss)
                     else:
                         with tf.GradientTape() as tape:
-                            pred = classifier_model(images)
-                            loss = criterion(torch_labels, pred)
+                            class_pred, width_pred = classifier_model(data_augmentation(images))
+                            class_loss = class_criterion(labels, class_pred)
+                            width_loss = width_criterion(width, width_pred)
+                            loss = width_loss
 
-                    epoch_loss += loss * local_batch.size(0)
+                    epoch_loss += loss * images.shape[0]
 
                     if args.train_sparse:
                         sparse_gradients, classifier_gradients = tape.gradient(loss, [recon_model.trainable_weights, classifier_model.trainable_weights])
@@ -236,38 +258,38 @@ if __name__ == "__main__":
 
 
                     if y_true_train is None:
-                        y_true_train = torch_labels
-                        y_pred_train = tf.math.round(tf.math.sigmoid(pred))
+                        y_true_train = labels
+                        y_pred_train = tf.math.round(tf.math.sigmoid(class_pred))
                     else:
-                        y_true_train = tf.concat((y_true_train, torch_labels), axis=0)
-                        y_pred_train = tf.concat((y_pred_train, tf.math.round(tf.math.sigmoid(pred))), axis=0)
+                        y_true_train = tf.concat((y_true_train, labels), axis=0)
+                        y_pred_train = tf.concat((y_pred_train, tf.math.round(tf.math.sigmoid(class_pred))), axis=0)
 
                 t2 = time.perf_counter()
 
                 y_true = None
                 y_pred = None
                 test_loss = 0.0
+                test_width_loss = 0.0
                 
-                eval_loader = test_loader
-                if args.splits == 'all_train':
-                    eval_loader = train_loader
-                for labels, local_batch, vid_f in tqdm(eval_loader):
-                    images = local_batch.permute(0, 2, 3, 1).numpy()
-
-                    torch_labels = np.zeros(len(labels))
-                    torch_labels[[i for i in range(len(labels)) if labels[i] == positive_class]] = 1
-                    torch_labels = np.expand_dims(torch_labels, axis=1)
+#                 eval_loader = test_loader
+#                 if args.splits == 'all_train':
+#                     eval_loader = train_loader
+                for images, labels, width in tqdm(test_tf.batch(args.batch_size)):
+                    images = tf.transpose(images, [0, 2, 3, 1])
+                    width -= 0.5
 
-                    pred = classifier_model(images)
-                    loss = criterion(torch_labels, pred)
+                    pred, width_pred = classifier_model(images)
+                    class_loss = class_criterion(labels, pred)
+                    width_loss = width_criterion(width, width_pred)
 
-                    test_loss += loss
+                    test_loss += (class_loss + width_loss) * images.shape[0]
+                    test_width_loss += width_loss * images.shape[0]
 
                     if y_true is None:
-                        y_true = torch_labels
+                        y_true = labels
                         y_pred = tf.math.round(tf.math.sigmoid(pred))
                     else:
-                        y_true = tf.concat((y_true, torch_labels), axis=0)
+                        y_true = tf.concat((y_true, labels), axis=0)
                         y_pred = tf.concat((y_pred, tf.math.round(tf.math.sigmoid(pred))), axis=0)
 
                 t2 = time.perf_counter()
@@ -283,7 +305,7 @@ if __name__ == "__main__":
 
                 train_accuracy = accuracy_score(y_true_train, y_pred_train)
 
-                print('epoch={}, i_fold={}, time={:.2f}, train_loss={:.2f}, test_loss={:.2f}, train_acc={:.2f}, test_f1={:.2f}, test_acc={:.2f}'.format(epoch, i_fold, t2-t1, epoch_loss, test_loss, train_accuracy, f1, accuracy))
+                print('epoch={}, i_fold={}, time={:.2f}, train_loss={:.2f}, test_loss={:.2f}, test_width_loss={:.2f}, train_acc={:.2f}, test_f1={:.2f}, test_acc={:.2f}'.format(epoch, i_fold, t2-t1, epoch_loss, test_loss, test_width_loss, train_accuracy, f1, accuracy))
     #             print(epoch_loss)
                 if f1 >= best_so_far:
                     print("found better model")
@@ -305,20 +327,17 @@ if __name__ == "__main__":
         gt_dict = {}
 
         t1 = time.perf_counter()
-    #         test_videos = [vid_f for labels, local_batch, vid_f in batch for batch in test_loader]
         transform = torchvision.transforms.Compose(
         [torchvision.transforms.Grayscale(1),
          MinMaxScaler(0, 255),
          torchvision.transforms.Resize((image_height, image_width))
         ])
 
-        test_videos = set()
-        for labels, local_batch, vid_f in test_loader:
-            test_videos.update(vid_f)
+        test_videos = test_loader.get_all_videos()
 
         test_labels = [vid_f.split('/')[-3] for vid_f in test_videos]
 
-        y_pred, y_true, fn, fp = calculate_onsd_scores(test_videos, test_labels, yolo_model, classifier_model, transform)
+        y_pred, y_true, fn, fp = calculate_onsd_scores(test_videos, test_labels, yolo_model, classifier_model, transform, image_width, image_height)
             
         t2 = time.perf_counter()
 
diff --git a/sparse_coding_torch/onsd/train_sharpness_classifier.py b/sparse_coding_torch/onsd/train_sharpness_classifier.py
new file mode 100644
index 0000000000000000000000000000000000000000..e97aa3544a8a230e5bf1c0461aca427f09559238
--- /dev/null
+++ b/sparse_coding_torch/onsd/train_sharpness_classifier.py
@@ -0,0 +1,155 @@
+import tensorflow.keras as keras
+import tensorflow as tf
+# tf.debugging.set_log_device_placement(True)
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from tqdm import tqdm
+import argparse
+import os
+from sparse_coding_torch.onsd.load_data import load_onsd_videos
+from sparse_coding_torch.utils import SubsetWeightedRandomSampler, get_sample_weights
+from sparse_coding_torch.sparse_model import SparseCode, ReconSparse, normalize_weights, normalize_weights_3d
+from sparse_coding_torch.onsd.classifier_model import ONSDSharpness
+from sparse_coding_torch.onsd.video_loader import get_yolo_region_onsd
+import time
+import numpy as np
+from sklearn.metrics import f1_score, accuracy_score, confusion_matrix
+import random
+import pickle
+# from sparse_coding_torch.onsd.train_sparse_model import sparse_loss
+from yolov4.get_bounding_boxes import YoloModel
+import torchvision
+from sparse_coding_torch.utils import VideoGrayScaler, MinMaxScaler
+import glob
+import cv2
+import copy
+from tensorflow_addons.image import gaussian_filter2d
+
+def margin_ranking(x1, x2, y, margin):
+    rank = -y * (x1 - x2) + margin
+    
+    zeros = tf.zeros(rank.shape)
+    
+    maximum = tf.math.maximum(zeros, rank)
+    
+    return tf.math.reduce_sum(maximum)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--batch_size', default=12, type=int)
+    parser.add_argument('--lr', default=5e-4, type=float)
+    parser.add_argument('--epochs', default=40, type=int)
+    parser.add_argument('--output_dir', default='./output', type=str)
+    parser.add_argument('--seed', default=26, type=int)
+    parser.add_argument('--dataset', default='onsd', type=str)
+    
+    args = parser.parse_args()
+    
+    crop_height = 224
+    crop_width = 224
+
+    image_height = 224
+    image_width = 224
+
+    batch_size = args.batch_size
+    
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    
+    output_dir = args.output_dir
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+        
+    with open(os.path.join(output_dir, 'arguments.txt'), 'w+') as out_f:
+        out_f.write(str(args))
+    
+    yolo_model = YoloModel(args.dataset)
+
+    all_errors = []
+    
+    data_augmentation = keras.Sequential([
+        keras.layers.RandomFlip('horizontal'),
+        keras.layers.RandomRotation(45),
+#         keras.layers.RandomBrightness(0.1)
+    ])
+        
+    
+    splits, dataset = load_onsd_videos(args.batch_size, input_size=(image_height, image_width), yolo_model=yolo_model, mode='balanced', n_splits=1)
+    positive_class = 'Positives'
+    
+    train_idx, test_idx = list(splits)[0]
+
+    train_loader = copy.deepcopy(dataset)
+    train_loader.set_indicies(train_idx)
+    test_loader = copy.deepcopy(dataset)
+    test_loader.set_indicies(test_idx)
+
+    train_tf = tf.data.Dataset.from_tensor_slices((train_loader.get_frames(), train_loader.get_labels(), train_loader.get_widths()))
+    test_tf = tf.data.Dataset.from_tensor_slices((test_loader.get_frames(), test_loader.get_labels(), test_loader.get_widths()))
+
+    classifier_inputs = keras.Input(shape=(image_height, image_width, 3))
+    classifier_outputs = ONSDSharpness()(classifier_inputs)
+
+    classifier_model = keras.Model(inputs=classifier_inputs, outputs=classifier_outputs)
+
+    prediction_optimizer = keras.optimizers.Adam(learning_rate=args.lr)
+
+    best_so_far = float('inf')
+
+    for epoch in range(args.epochs):
+        epoch_loss = 0
+        t1 = time.perf_counter()
+
+        for images, _, _ in tqdm(train_tf.shuffle(len(train_tf)).batch(args.batch_size)):
+            images = tf.transpose(images, [0, 2, 3, 1])
+
+            with tf.GradientTape() as tape:
+                blurred_images = gaussian_filter2d(images, filter_shape=[random.choice([3, 5, 7, 9, 11, 13])]*2)
+                blurred_images = tf.random.shuffle(blurred_images)
+                orig_pred = classifier_model(data_augmentation(images))
+                blurred_pred = classifier_model(data_augmentation(blurred_images))
+                label = random.choice([1, -1])
+                if label == 1:
+                    loss = margin_ranking(orig_pred, blurred_pred, label, 0.2)
+                else:
+                    loss = margin_ranking(blurred_pred, orig_pred, label, 0.2)
+
+            epoch_loss += loss * images.shape[0]
+
+            gradients = tape.gradient(loss, classifier_model.trainable_weights)
+
+            prediction_optimizer.apply_gradients(zip(gradients, classifier_model.trainable_weights))
+
+        t2 = time.perf_counter()
+
+        test_loss = 0.0
+
+        for images, _, _ in tqdm(test_tf.batch(args.batch_size)):
+            images = tf.transpose(images, [0, 2, 3, 1])
+
+            blurred_images = gaussian_filter2d(images, filter_shape=[random.choice([3, 5, 7, 9, 11, 13])]*2)
+            blurred_images = tf.random.shuffle(blurred_images)
+            orig_pred = classifier_model(images)
+            blurred_pred = classifier_model(blurred_images)
+            label = random.choice([1, -1])
+            if label == 1:
+                loss = margin_ranking(orig_pred, blurred_pred, label, 0.2)
+            else:
+                loss = margin_ranking(blurred_pred, orig_pred, label, 0.2)
+
+            test_loss += loss
+
+        t2 = time.perf_counter()
+
+
+        print('epoch={}, time={:.2f}, train_loss={:.2f}, test_loss={:.2f}'.format(epoch, t2-t1, epoch_loss, test_loss))
+#             print(epoch_loss)
+        if test_loss <= best_so_far:
+            print("found better model")
+            # Save model parameters
+            classifier_model.save(os.path.join(output_dir, "best_classifier.pt"))
+#                     recon_model.save(os.path.join(output_dir, "best_sparse_model_{}.pt".format(i_fold)))
+            pickle.dump(prediction_optimizer.get_weights(), open(os.path.join(output_dir, 'optimizer.pt'), 'wb+'))
+            best_so_far = test_loss
\ No newline at end of file
diff --git a/sparse_coding_torch/onsd/video_loader.py b/sparse_coding_torch/onsd/video_loader.py
index b644ac915d39f8de86085d250edf97b419afb2da..d6b6b3cf740dcce25d8231276718319f6e938091 100644
--- a/sparse_coding_torch/onsd/video_loader.py
+++ b/sparse_coding_torch/onsd/video_loader.py
@@ -23,46 +23,60 @@ import csv
 import random
 import cv2
 from yolov4.get_bounding_boxes import YoloModel
+import tensorflow as tf
 
 def get_participants(filenames):
     return [f.split('/')[-2] for f in filenames]
     
-def get_yolo_region_onsd(yolo_model, frame):
+def get_yolo_region_onsd(yolo_model, frame, crop_width, crop_height):
     orig_height = frame.size(1)
     orig_width = frame.size(2)
     
-    bounding_boxes, classes, scores = yolo_model.get_bounding_boxes(frame.swapaxes(0, 2).swapaxes(0, 1).numpy())
-    bounding_boxes = bounding_boxes.squeeze(0)
-    classes = classes.squeeze(0)
-    scores = scores.squeeze(0)
+    bounding_boxes, classes, scores = yolo_model.get_bounding_boxes_v5(frame.swapaxes(0, 2).swapaxes(0, 1).numpy())
     
     all_frames = []
     for bb, class_pred, score in zip(bounding_boxes, classes, scores):
         if class_pred != 0:
             continue
         
-        lower_y = round((bb[0] * orig_height))
-        upper_y = round((bb[2] * orig_height))
-        lower_x = round((bb[1] * orig_width))
-        upper_x = round((bb[3] * orig_width))
+        center_x = round((bb[3] + bb[1]) / 2 * orig_width)
+        center_y = round((bb[2] + bb[0]) / 2 * orig_height)
+        
+        lower_y = center_y - (crop_height // 2)
+        upper_y = center_y + (crop_height // 2)
+        lower_x = center_x - (crop_width // 2)
+        upper_x = center_x + (crop_width // 2)
+        
+#         lower_y = center_y
+#         upper_y = center_y + crop_height
+#         lower_x = center_x - (crop_width // 2)
+#         upper_x = center_x + (crop_width // 2)
 
         trimmed_frame = frame[:, lower_y:upper_y, lower_x:upper_x]
         
-#         cv2.imwrite('test_2.png', frame.numpy().swapaxes(0,1).swapaxes(1,2))
-#         cv2.imwrite('test_3.png', trimmed_frame.numpy().swapaxes(0,1).swapaxes(1,2))
+#         cv2.imwrite('test_onsd_orig_4.png', frame.numpy().swapaxes(0,1).swapaxes(1,2))
+#         cv2.imwrite('test_onsd_crop_4.png', trimmed_frame.numpy().swapaxes(0,1).swapaxes(1,2))
+#         raise Exception
         
         return trimmed_frame
 
     return None
-    
-class ONSDLoader(Dataset):
-    
-    def __init__(self, video_path, clip_width, clip_height, transform=None, augmentation=None, yolo_model=None):
+
+class ONSDLoader:
+    def __init__(self, video_path, clip_width, clip_height, transform=None, yolo_model=None):
         self.transform = transform
-        self.augmentation = augmentation
         self.labels = [name for name in listdir(video_path) if isdir(join(video_path, name))]
         
-        clip_cache_file = 'clip_cache_onsd_{}_{}_sparse.pt'.format(clip_width, clip_height)
+        self.count = 0
+        
+        onsd_widths = {}
+        with open(join(video_path, 'onsd_widths.csv'), 'r') as width_in:
+            reader = csv.reader(width_in)
+            for row in reader:
+                width_vals = [float(val) for val in row[3:] if val != '']
+                onsd_widths[row[2]] = round(sum(width_vals) / len(width_vals), 2)
+        
+        clip_cache_file = 'clip_cache_onsd_{}_{}.pt'.format(clip_width, clip_height)
         
         self.videos = []
         for label in self.labels:
@@ -77,26 +91,37 @@ class ONSDLoader(Dataset):
             for label, path, _ in tqdm(self.videos):
                 vc = tv.io.read_video(path)[0].permute(3, 0, 1, 2)
                 
+                width_key = path.split('/')[-1]
+                if width_key not in onsd_widths:
+                    continue
+                width = onsd_widths[width_key]
+                
                 for j in range(vc.size(1)):
                     frame = vc[:, j, :, :]
                     
                     if yolo_model is not None:
-                        frame = get_yolo_region_onsd(yolo_model, frame)
+                        frame = get_yolo_region_onsd(yolo_model, frame, clip_width, clip_height)
                         
                     if frame is None:
                         continue
 
                     if self.transform:
                         frame = self.transform(frame)
+                        
+                    label = self.videos[vid_idx][0]
+                    if label == 'Positives':
+                        label = np.array(1.0)
+                    elif label == 'Negatives':
+                        label = np.array(0.0)
 
-                    self.clips.append((self.videos[vid_idx][0], frame, self.videos[vid_idx][2]))
+                    self.clips.append((label, frame.numpy(), self.videos[vid_idx][2], width))
 
                 vid_idx += 1
                 
             torch.save(self.clips, open(clip_cache_file, 'wb+'))
             
-        num_positive = len([clip[0] for clip in self.clips if clip[0] == 'Positives'])
-        num_negative = len([clip[0] for clip in self.clips if clip[0] == 'Negatives'])
+        num_positive = len([clip[0] for clip in self.clips if clip[0] == 1.0])
+        num_negative = len([clip[0] for clip in self.clips if clip[0] == 0.0])
         
         random.shuffle(self.clips)
         
@@ -105,18 +130,102 @@ class ONSDLoader(Dataset):
         
     def get_filenames(self):
         return [self.clips[i][2] for i in range(len(self.clips))]
-        
-    def get_video_labels(self):
-        return [self.videos[i][0] for i in range(len(self.videos))]
+    
+    def get_all_videos(self):
+        return set([self.clips[i][2] for i in range(len(self.clips))])
         
     def get_labels(self):
         return [self.clips[i][0] for i in range(len(self.clips))]
     
-    def __getitem__(self, index):
-        label, frame, vid_f = self.clips[index]
-        if self.augmentation:
-            frame = self.augmentation(frame)
-        return (label, frame, vid_f)
-        
-    def __len__(self):
-        return len(self.clips)
+    def set_indicies(self, iter_idx):
+        new_clips = []
+        for i, clip in enumerate(self.clips):
+            if i in iter_idx:
+                new_clips.append(clip)
+                
+        self.clips = new_clips
+        
+    def get_frames(self):
+        return [frame for _, frame, _, _ in self.clips]
+    
+    def get_widths(self):
+        return [width for _, _, _, width in self.clips]
+    
+    def __next__(self):
+        if self.count < len(self.clips):
+            label, frame, vid_f, widths = self.clips[self.count]
+            self.count += 1
+            return label, frame, widths
+        else:
+            raise StopIteration
+            
+    def __iter__(self):
+        return self
+    
+# class ONSDLoader(Dataset):
+    
+#     def __init__(self, video_path, clip_width, clip_height, transform=None, augmentation=None, yolo_model=None):
+#         self.transform = transform
+#         self.augmentation = augmentation
+#         self.labels = [name for name in listdir(video_path) if isdir(join(video_path, name))]
+        
+#         clip_cache_file = 'clip_cache_onsd_{}_{}.pt'.format(clip_width, clip_height)
+        
+#         self.videos = []
+#         for label in self.labels:
+#             self.videos.extend([(label, abspath(join(video_path, label, f)), f) for f in glob.glob(join(video_path, label, '*', '*.mp4'))])
+            
+#         self.clips = []
+        
+#         if exists(clip_cache_file):
+#             self.clips = torch.load(open(clip_cache_file, 'rb'))
+#         else:
+#             vid_idx = 0
+#             for label, path, _ in tqdm(self.videos):
+#                 vc = tv.io.read_video(path)[0].permute(3, 0, 1, 2)
+                
+#                 for j in range(vc.size(1)):
+#                     frame = vc[:, j, :, :]
+                    
+#                     if yolo_model is not None:
+#                         frame = get_yolo_region_onsd(yolo_model, frame, clip_width, clip_height)
+                        
+#                     if frame is None:
+#                         continue
+
+#                     if self.transform:
+#                         frame = self.transform(frame)
+
+#                     self.clips.append((self.videos[vid_idx][0], frame, self.videos[vid_idx][2]))
+
+#                 vid_idx += 1
+                
+#             torch.save(self.clips, open(clip_cache_file, 'wb+'))
+            
+#         num_positive = len([clip[0] for clip in self.clips if clip[0] == 'Positives'])
+#         num_negative = len([clip[0] for clip in self.clips if clip[0] == 'Negatives'])
+        
+#         random.shuffle(self.clips)
+        
+#         print('Loaded', num_positive, 'positive examples.')
+#         print('Loaded', num_negative, 'negative examples.')
+        
+#     def get_filenames(self):
+#         return [self.clips[i][2] for i in range(len(self.clips))]
+        
+#     def get_video_labels(self):
+#         return [self.videos[i][0] for i in range(len(self.videos))]
+        
+#     def get_labels(self):
+#         return [self.clips[i][0] for i in range(len(self.clips))]
+    
+#     def __getitem__(self, index):
+#         label, frame, vid_f = self.clips[index]
+#         if self.augmentation:
+#             frame = self.augmentation(frame)
+            
+# #         frame = tf.constant(frame)
+#         return (label, frame, vid_f)
+        
+#     def __len__(self):
+#         return len(self.clips)
diff --git a/sparse_coding_torch/pnb/classifier_model.py b/sparse_coding_torch/pnb/classifier_model.py
index 90cc850c20d7a8a441da9e99f0c6550ad2a6bcd1..112b8813b2ed20edbbe4774c5e05787b3553c7f1 100644
--- a/sparse_coding_torch/pnb/classifier_model.py
+++ b/sparse_coding_torch/pnb/classifier_model.py
@@ -53,44 +53,78 @@ class PNBClassifier(keras.layers.Layer):
         return x
     
 class PNBTemporalClassifier(keras.layers.Layer):
-    def __init__(self):
+    def __init__(self, sparse_checkpoint):
         super(PNBTemporalClassifier, self).__init__()
-        self.conv_1 = keras.layers.Conv3D(24, kernel_size=(1, 250, 50), strides=(1, 1, 10), activation='relu', padding='valid')
-        self.conv_2 = keras.layers.Conv2D(36, kernel_size=(5, 10), strides=(1, 5), activation='relu', padding='valid')
-        self.conv_3 = keras.layers.Conv1D(48, kernel_size=2, strides=2, activation='relu', padding='valid')
+#         self.conv_1 = keras.layers.Conv3D(24, kernel_size=(1, 250, 50), strides=(1, 1, 10), activation='relu', padding='valid')
+#         self.conv_2 = keras.layers.Conv2D(36, kernel_size=(5, 10), strides=(1, 5), activation='relu', padding='valid')
+#         self.conv_3 = keras.layers.Conv1D(48, kernel_size=2, strides=2, activation='relu', padding='valid')
+        self.padding = keras.layers.ZeroPadding3D((0,7,7))
+
+        initializer = tf.keras.initializers.HeNormal()
+    
+#         self.sparse_filters = tf.Variable(keras.models.load_model(sparse_checkpoint).weights[0], trainable=False)
+        self.sparse_filters = tf.Variable(initial_value=initializer(shape=(1, 15, 15, 1, 32)), trainable=True)
+
+        self.conv_1 = keras.layers.Conv2D(32, kernel_size=(8, 8), strides=(1, 1), activation='relu', padding='valid')
+        self.conv_2 = keras.layers.Conv2D(48, kernel_size=(8, 8), strides=(1, 1), activation='relu', padding='valid')
+        self.conv_3 = keras.layers.Conv2D(48, kernel_size=(8, 8), strides=(2, 1), activation='relu', padding='valid')
+        self.conv_4 = keras.layers.Conv2D(48, kernel_size=(8, 8), strides=(2, 2), activation='relu', padding='valid')
+        self.conv_5 = keras.layers.Conv2D(48, kernel_size=(4, 4), strides=(2, 2), activation='relu', padding='valid')
+        self.conv_6 = keras.layers.Conv2D(48, kernel_size=(4, 4), strides=(2, 2), activation='relu', padding='valid')
+#         self.conv_7 = keras.layers.Conv3D(32, kernel_size=(5, 1, 1), strides=(1, 1, 1), activation='relu', padding='valid')
         
-        self.ff_1 = keras.layers.Dense(100, activation='relu', use_bias=True)
+#         self.ff_1 = keras.layers.Dense(250, activation='relu', use_bias=True)
         
-#         self.gru = keras.layers.GRU(25)
+#         self.gru = keras.layers.GRU(250)
 
         self.flatten = keras.layers.Flatten()
 
-        self.ff_2 = keras.layers.Dense(10, activation='relu', use_bias=True)
-        self.ff_3 = keras.layers.Dense(1)
+#         self.ff_2 = keras.layers.Dense(250, activation='relu', use_bias=True)
+        self.ff_3 = keras.layers.Dense(100, activation='relu', use_bias=True)
+        self.ff_4 = keras.layers.Dense(10, activation='relu', use_bias=True)
+        self.ff_5 = keras.layers.Dense(1)
 
 #     @tf.function
     def call(self, clip):
         width = clip.shape[3]
         height = clip.shape[2]
         depth = clip.shape[1]
-
+        
         x = tf.expand_dims(clip, axis=4)
-#         x = tf.reshape(clip, (-1, height, width, 1))
 
+        x = self.padding(x)
+        
+        x = tf.nn.conv3d(x, self.sparse_filters, strides=(1, 1, 1, 1, 1), padding='VALID')
+        x = tf.nn.relu(x)
+        
+        x = tf.squeeze(x, axis=1)
+
+#         x = tf.reshape(x, (-1, width, height, 1))
+        
         x = self.conv_1(x)
-        x = tf.squeeze(x, axis=2)
-        x = tf.reshape(x, (-1, 5, x.shape[2], x.shape[3]))
+
+#         x = tf.squeeze(x, axis=2)
+#         x = tf.reshape(x, (-1, 5, x.shape[2], x.shape[3]))
         x = self.conv_2(x)
+#         print(x.shape)
+#         raise Exception
         x = self.conv_3(x)
+        x = self.conv_4(x)
+        x = self.conv_5(x)
+        x = self.conv_6(x)
+#         print(x.shape)
+#         x = self.conv_7(x)
 
         x = self.flatten(x)
-        x = self.ff_1(x)
+#         x = self.ff_1(x)
 
-#         x = tf.reshape(x, (-1, 5, 100))
+#         x = tf.reshape(x, (-1, 5, 250))
 #         x = self.gru(x)
         
-        x = self.ff_2(x)
+#         x = self.ff_2(x)
         x = self.ff_3(x)
+        x = self.ff_4(x)
+        x = self.ff_5(x)
 
         return x
     
diff --git a/sparse_coding_torch/pnb/load_data.py b/sparse_coding_torch/pnb/load_data.py
index df1f4496a1f07a3000e2ee3d02fece57d7e99a9b..95854057a052aefb847cfb3c8df0dcdbe29082fe 100644
--- a/sparse_coding_torch/pnb/load_data.py
+++ b/sparse_coding_torch/pnb/load_data.py
@@ -64,18 +64,18 @@ def load_pnb_videos(yolo_model, batch_size, input_size, crop_size=None, mode=Non
      MinMaxScaler(0, 255),
      torchvision.transforms.Resize(input_size[:2])
     ])
-    augment_transforms = torchvision.transforms.Compose(
-#     [torchvision.transforms.Resize(input_size[:2]),
-#     [torchvision.transforms.RandomRotation(15),
-#      torchvision.transforms.RandomHorizontalFlip(),
-#      torchvision.transforms.RandomVerticalFlip(),
-     [torchvision.transforms.ColorJitter(brightness=0.02),     
-     torchvision.transforms.RandomAdjustSharpness(0, p=0.15),
-     torchvision.transforms.RandomAffine(degrees=0, translate=(0.01, 0))
-#      torchvision.transforms.CenterCrop((100, 200))
-#      torchvision.transforms.Resize(input_size[:2])
-    ])
-    dataset = PNBLoader(yolo_model, video_path, crop_size[1], crop_size[0], crop_size[2], classify_mode, balance_classes=balance_classes, num_frames=5, transform=transforms, augmentation=augment_transforms, frames_to_skip=frames_to_skip)
+#     augment_transforms = torchvision.transforms.Compose(
+# #     [torchvision.transforms.Resize(input_size[:2]),
+# #     [torchvision.transforms.RandomRotation(15),
+# #      torchvision.transforms.RandomHorizontalFlip(),
+# #      torchvision.transforms.RandomVerticalFlip(),
+#      [torchvision.transforms.ColorJitter(brightness=0.02),     
+#      torchvision.transforms.RandomAdjustSharpness(0, p=0.15),
+#      torchvision.transforms.RandomAffine(degrees=0, translate=(0.01, 0))
+# #      torchvision.transforms.CenterCrop((100, 200))
+# #      torchvision.transforms.Resize(input_size[:2])
+#     ])
+    dataset = PNBLoader(yolo_model, video_path, crop_size[1], crop_size[0], crop_size[2], classify_mode, balance_classes=balance_classes, num_frames=5, transform=transforms, frames_to_skip=frames_to_skip)
     
     targets = dataset.get_labels()
     
diff --git a/sparse_coding_torch/pnb/pnb_regression.py b/sparse_coding_torch/pnb/pnb_regression.py
new file mode 100644
index 0000000000000000000000000000000000000000..1fc624a58584e7e23efdd58691f33f6dcc50fed3
--- /dev/null
+++ b/sparse_coding_torch/pnb/pnb_regression.py
@@ -0,0 +1,258 @@
+from sparse_coding_torch.pnb.video_loader import classify_nerve_is_right
+import math
+from tqdm import tqdm
+import glob
+from os.path import join, abspath
+import random
+from sklearn.model_selection import GroupKFold
+from sklearn.linear_model import LogisticRegression
+import os
+import numpy as np
+import tensorflow as tf
+from yolov4.get_bounding_boxes import YoloModel
+import torchvision
+from sklearn.metrics import f1_score, accuracy_score, confusion_matrix
+    
+def get_distance_data(yolo_model, input_videos, yolo_class):
+    all_data = []
+    for label, path, vid_f in tqdm(input_videos):
+        vc = torchvision.io.read_video(path)[0].permute(3, 0, 1, 2)
+        is_right = classify_nerve_is_right(yolo_model, vc)
+        
+        orig_height = vc.size(2)
+        orig_width = vc.size(3)
+
+        obj_bb = []
+        needle_bb = []
+
+        for i in range(vc.size(1) - 1, vc.size(1) - 40, -1):
+            frame = vc[:, i, :, :].swapaxes(0, 2).swapaxes(0, 1).numpy()
+
+            bounding_boxes, classes, scores = yolo_model.get_bounding_boxes_v5(frame)
+
+            obj_bb = [bb for bb, class_pred, score in zip(bounding_boxes, classes, scores) if class_pred==yolo_class]
+            needle_bb = [bb for bb, class_pred, score in zip(bounding_boxes, classes, scores) if class_pred==2]
+
+            if len(obj_bb) > 0 and len(needle_bb) > 0:
+                obj_bb = obj_bb[0]
+                needle_bb = needle_bb[0]
+                break
+
+        if len(obj_bb) == 0 or len(needle_bb) == 0:
+            continue
+
+        obj_x = round((obj_bb[2] + obj_bb[0]) / 2 * orig_width)
+        obj_y = round((obj_bb[3] + obj_bb[1]) / 2 * orig_height)
+
+        needle_x = needle_bb[2] * orig_width
+        needle_y = needle_bb[3] * orig_height
+
+        if not is_right:
+            needle_x = needle_bb[0] * orig_width
+        
+        if label == 'Positives':
+            label = 1.0
+        elif label == 'Negatives':
+            label = 0.0
+        else:
+            raise Exception('Bad label')
+
+        all_data.append((math.sqrt((obj_x - needle_x)**2 + (obj_y - needle_y)**2), label, path))
+        
+    return all_data
+
+def get_splits(videos):
+    gss = GroupKFold(n_splits=5)
+
+    groups = [vid[2].split('/')[-2] for vid in videos]
+
+    targets = [vid[2].split('/')[-3] for vid in videos]
+
+    return gss.split(np.arange(len(targets)), targets, groups)
+
+def get_splits_all_train(videos):
+    return [(range(len(videos)), [])]
+
+yolo_model = YoloModel('pnb')
+video_path = "/shared_data/bamc_pnb_data/revised_training_data/"
+
+print('Beginning...')
+
+labels = [name for name in os.listdir(video_path) if os.path.isdir(os.path.join(video_path, name))]
+
+videos = []
+for label in labels:
+    videos.extend([(label, abspath(join(video_path, label, f)), f) for f in glob.glob(join(video_path, label, '*', '*.mp4'))])
+
+print('Making splits...')    
+
+splits = get_splits_all_train(videos)
+
+preds = []
+gt = []
+fp_vids = []
+fn_vids = []
+
+split_count = 0
+
+for train_idx, test_idx in splits:
+    print('On split {}...'.format(split_count))
+
+    print('Processing data...')
+    train_videos = [ex for i, ex in enumerate(videos) if i in train_idx]
+    if test_idx:
+        test_videos = [ex for i, ex in enumerate(videos) if i in test_idx]
+        assert not set(train_videos).intersection(set(test_videos))
+    else:
+        test_videos = train_videos
+    
+#     nerve_train_data = get_distance_data(yolo_model, train_videos, 1)
+    vessel_train_data = get_distance_data(yolo_model, train_videos, 0)
+    
+#     nerve_test_data = get_distance_data(yolo_model, test_videos, 1)
+    vessel_test_data = get_distance_data(yolo_model, test_videos, 0)
+
+#     train_nerve_X = np.array([nerve_train_data[i][0] for i in range(len(nerve_train_data))]).reshape(-1, 1)
+#     test_nerve_X = np.array([nerve_test_data[i][0] for i in range(len(nerve_test_data))]).reshape(-1, 1)
+    
+#     train_nerve_Y = np.array([nerve_train_data[i][1] for i in range(len(nerve_train_data))]).reshape(-1, 1)
+#     test_nerve_Y = np.array([nerve_test_data[i][1] for i in range(len(nerve_test_data))]).reshape(-1, 1)
+    
+    train_vessel_X = np.array([vessel_train_data[i][0] for i in range(len(vessel_train_data))]).reshape(-1, 1)
+    test_vessel_X = np.array([vessel_test_data[i][0] for i in range(len(vessel_test_data))]).reshape(-1, 1)
+    
+    train_vessel_Y = np.array([vessel_train_data[i][1] for i in range(len(vessel_train_data))]).reshape(-1, 1)
+    test_vessel_Y = np.array([vessel_test_data[i][1] for i in range(len(vessel_test_data))]).reshape(-1, 1)
+    
+    print('Training models...')
+    
+#     nerve_clf = LogisticRegression().fit(train_nerve_X, train_nerve_Y)
+#     nerve_score = nerve_clf.score(test_nerve_X, test_nerve_Y)
+    
+#     print('Nerve accuracy: {:.2f}'.format(nerve_score))
+    
+    vessel_clf = LogisticRegression().fit(train_vessel_X, train_vessel_Y)
+    vessel_score = vessel_clf.score(test_vessel_X, test_vessel_Y)
+
+    print(vessel_clf.intercept_, vessel_clf.coef_)
+#     for j in range(len(train_vessel_X)):
+#         print(vessel_clf.predict(train_vessel_X[j].reshape(-1, 1)))
+#         print(tf.math.sigmoid(vessel_clf.intercept_ + vessel_clf.coef_[0][0] * train_vessel_X[j]))
+#         print(train_vessel_X[j])
+#         print('---------------------------------------')
+#     raise Exception
+    
+    print('Vessel accuracy: {:.2f}'.format(vessel_score))
+    
+    print('Running predictions on test videos...')
+    
+    for label, path, vid_f in tqdm(test_videos):
+        vc = torchvision.io.read_video(path)[0].permute(3, 0, 1, 2)
+        is_right = classify_nerve_is_right(yolo_model, vc)
+        
+        orig_height = vc.size(2)
+        orig_width = vc.size(3)
+        
+        if label == 'Positives':
+            gt.append(1.0)
+        elif label == 'Negatives':
+            gt.append(0.0)
+        else:
+            raise Exception('Bad label')
+
+#         nerve_bb = []
+#         needle_bb = []
+
+#         for i in range(vc.size(1) - 1, vc.size(1) - 40, -1):
+#             frame = vc[:, i, :, :].swapaxes(0, 2).swapaxes(0, 1).numpy()
+
+#             bounding_boxes, classes, scores = yolo_model.get_bounding_boxes_v5(frame)
+
+#             nerve_bb = [bb for bb, class_pred, score in zip(bounding_boxes, classes, scores) if class_pred==1]
+#             needle_bb = [bb for bb, class_pred, score in zip(bounding_boxes, classes, scores) if class_pred==2]
+
+#             if len(nerve_bb) > 0 and len(needle_bb) > 0:
+#                 nerve_bb = nerve_bb[0]
+#                 needle_bb = needle_bb[0]
+#                 break
+
+#         if len(nerve_bb) > 0 and len(needle_bb) > 0:
+#             nerve_x = round((nerve_bb[2] + nerve_bb[0]) / 2 * orig_width)
+#             nerve_y = round((nerve_bb[3] + nerve_bb[1]) / 2 * orig_height)
+
+#             needle_x = needle_bb[2] * orig_width
+#             needle_y = needle_bb[3] * orig_height
+
+#             if not is_right:
+#                 needle_x = needle_bb[0] * orig_width
+                
+#             distance = math.sqrt((nerve_x - needle_x)**2 + (nerve_y - needle_y)**2)
+            
+#             pred = nerve_clf.predict(np.array([distance]).reshape(1, -1))[0]
+#             preds.append(pred)
+#         else:
+        vessel_bb = []
+
+        for i in range(vc.size(1) - 1, vc.size(1) - 40, -1):
+            frame = vc[:, i, :, :].swapaxes(0, 2).swapaxes(0, 1).numpy()
+
+            bounding_boxes, classes, scores = yolo_model.get_bounding_boxes_v5(frame)
+
+            vessel_bb = [bb for bb, class_pred, score in zip(bounding_boxes, classes, scores) if class_pred==0]
+            needle_bb = [bb for bb, class_pred, score in zip(bounding_boxes, classes, scores) if class_pred==2]
+
+            if len(vessel_bb) > 0 and len(needle_bb) > 0:
+                vessel_bb = vessel_bb[0]
+                needle_bb = needle_bb[0]
+                break
+
+        if len(vessel_bb) == 0 or len(needle_bb) == 0:
+            preds.append(0.0)
+            if label == 'Positives':
+                fn_vids.append(vid_f)
+            continue
+
+        vessel_x = round((vessel_bb[2] + vessel_bb[0]) / 2 * orig_width)
+        vessel_y = round((vessel_bb[3] + vessel_bb[1]) / 2 * orig_height)
+
+        needle_x = needle_bb[2] * orig_width
+        needle_y = needle_bb[3] * orig_height
+
+        if not is_right:
+            needle_x = needle_bb[0] * orig_width
+
+        distance = math.sqrt((vessel_x - needle_x)**2 + (vessel_y - needle_y)**2)
+
+        pred = vessel_clf.predict(np.array([distance]).reshape(1, -1))[0]
+        preds.append(pred)
+        
+        if pred == 0.0 and label == 'Positives':
+            fn_vids.append(vid_f)
+        elif pred == 1.0 and label == 'Negatives':
+            fp_vids.append(vid_f)
+            
+    split_count += 1
+    
+    print('Current results...')
+    overall_true = np.array(gt)
+    overall_pred = np.array(preds)
+
+    f1 = f1_score(overall_true, overall_pred, average='macro')
+    acc = accuracy_score(overall_true, overall_pred)
+
+    print("Current accuracy={:.2f}, f1={:.2f}".format(acc, f1))
+    
+overall_true = np.array(gt)
+overall_pred = np.array(preds)
+
+final_f1 = f1_score(overall_true, overall_pred, average='macro')
+final_acc = accuracy_score(overall_true, overall_pred)
+final_conf = confusion_matrix(overall_true, overall_pred)
+
+print("Final accuracy={:.2f}, f1={:.2f}".format(final_acc, final_f1))
+print(final_conf)
+
+print('False Negative Videos:')
+print(fn_vids)
+print('False Positive Videos:')
+print(fp_vids)
\ No newline at end of file
diff --git a/sparse_coding_torch/pnb/train_classifier.py b/sparse_coding_torch/pnb/train_classifier.py
index 9d631cc626a69c4827a99c8c57c39235aa42b1d3..0cf1f9aa94c2aaa12222501e0c9a9f54d999b1a5 100644
--- a/sparse_coding_torch/pnb/train_classifier.py
+++ b/sparse_coding_torch/pnb/train_classifier.py
@@ -22,6 +22,11 @@ import torchvision
 from sparse_coding_torch.utils import VideoGrayScaler, MinMaxScaler
 import glob
 import cv2
+from matplotlib import pyplot as plt
+import copy
+from matplotlib import cm
+
+tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
 
 # configproto = tf.compat.v1.ConfigProto()
 # configproto.gpu_options.polling_inactive_delay_msecs = 5000
@@ -48,7 +53,7 @@ def calculate_pnb_scores(input_videos, labels, yolo_model, sparse_model, recon_m
         needle_bb = get_needle_bb(yolo_model, vc)
         
         all_preds = []
-        for j in range(vc.size(1) - 5, vc.size(1) - 25, -5):
+        for j in range(vc.size(1) - 5, vc.size(1) - 45, -5):
             if j-5 < 0:
                 break
 
@@ -57,7 +62,7 @@ def calculate_pnb_scores(input_videos, labels, yolo_model, sparse_model, recon_m
             if vc_sub.size(1) < 5:
                 continue
             
-            clip = get_yolo_regions(yolo_model, vc_sub, is_right, needle_bb, image_width, image_height)
+            clip = get_yolo_regions(yolo_model, vc_sub, is_right, image_width, image_height)
             
             if not clip:
                 continue
@@ -90,7 +95,7 @@ def calculate_pnb_scores(input_videos, labels, yolo_model, sparse_model, recon_m
         
     return np.array(final_list), np.array(numerical_labels), fn_ids, fp_ids
 
-def calculate_pnb_scores_skipped_frames(input_videos, labels, yolo_model, sparse_model, recon_model, classifier_model, frames_to_skip, image_width, image_height, transform):
+def calculate_pnb_scores_skipped_frames(input_videos, labels, yolo_model, sparse_model, recon_model, classifier_model, frames_to_skip, image_width, image_height, clip_depth, transform):
     all_predictions = []
     
     numerical_labels = []
@@ -105,22 +110,29 @@ def calculate_pnb_scores_skipped_frames(input_videos, labels, yolo_model, sparse
     fn_ids = []
     for v_idx, f in tqdm(enumerate(input_videos)):
         vc = torchvision.io.read_video(f)[0].permute(3, 0, 1, 2)
+        vc = vc[:,-30:-5, :, :]
         is_right = classify_nerve_is_right(yolo_model, vc)
         needle_bb = get_needle_bb(yolo_model, vc)
         
         all_preds = []
         
-        frames = []
-        for k in range(vc.size(1) - 1, vc.size(1) - 5 * frames_to_skip, -frames_to_skip):
-            frames.append(vc[:, k, :, :])
-        vc_sub = torch.stack(frames, dim=1)
-            
-        if vc_sub.size(1) < 5:
-            continue
+        clips = []
+        
+        for j in range(0, vc.size(1) - 1, 5):
+            frames = []
+            for k in range(j, j + clip_depth * frames_to_skip, frames_to_skip):
+                frames.append(vc[:, k, :, :])
+            vc_sub = torch.stack(frames, dim=1)
+
+            if vc_sub.size(1) < 5:
+                continue
 
-        clip = get_yolo_regions(yolo_model, vc_sub, is_right, needle_bb, image_width, image_height)
+            clip = get_yolo_regions(yolo_model, vc_sub, is_right, image_width, image_height)
+            
+            clips.append(clip)
 
-        if clip:
+        all_preds = []
+        for clip in clips:
             clip = clip[0]
             clip = transform(clip).to(torch.float32)
             clip = tf.expand_dims(clip, axis=4) 
@@ -131,16 +143,21 @@ def calculate_pnb_scores_skipped_frames(input_videos, labels, yolo_model, sparse
                 pred = tf.math.round(tf.math.sigmoid(classifier_model(activations)))
             else:
                 pred = tf.math.round(tf.math.sigmoid(classifier_model(clip)))
+                
+            all_preds.append(pred)
+                
+        if all_preds:
+            final_pred = np.round(np.mean(np.array(all_preds)))
         else:
-            pred = 1.0
+            final_pred = 0
             
-        if pred != numerical_labels[v_idx]:
-            if pred == 0:
+        if final_pred != numerical_labels[v_idx]:
+            if final_pred == 0:
                 fn_ids.append(f)
             else:
                 fp_ids.append(f)
 
-        final_list.append(float(pred))
+        final_list.append(float(final_pred))
         
     return np.array(final_list), np.array(numerical_labels), fn_ids, fp_ids
 
@@ -153,7 +170,7 @@ if __name__ == "__main__":
     parser.add_argument('--stride', default=1, type=int)
     parser.add_argument('--max_activation_iter', default=150, type=int)
     parser.add_argument('--activation_lr', default=1e-2, type=float)
-    parser.add_argument('--lr', default=5e-4, type=float)
+    parser.add_argument('--lr', default=5e-5, type=float)
     parser.add_argument('--epochs', default=40, type=int)
     parser.add_argument('--lam', default=0.05, type=float)
     parser.add_argument('--output_dir', default='./output', type=str)
@@ -202,29 +219,19 @@ if __name__ == "__main__":
     yolo_model = YoloModel(args.dataset)
 
     all_errors = []
-    
-    if args.run_2d:
-        inputs = keras.Input(shape=(image_height, image_width, clip_depth))
-    else:
-        inputs = keras.Input(shape=(clip_depth, image_height, image_width, 1))
-        
-#     filter_inputs = keras.Input(shape=(args.kernel_depth, args.kernel_size, args.kernel_size, 1, args.num_kernels), dtype='float32')
-
-#     output = SparseCode(batch_size=args.batch_size, image_height=image_height, image_width=image_width, clip_depth=clip_depth, in_channels=1, out_channels=args.num_kernels, kernel_size=args.kernel_size, kernel_depth=args.kernel_depth, stride=args.stride, lam=args.lam, activation_lr=args.activation_lr, max_activation_iter=args.max_activation_iter, run_2d=args.run_2d)(inputs, filter_inputs)
-
-#     sparse_model = keras.Model(inputs=(inputs, filter_inputs), outputs=output)
-
-#     recon_inputs = keras.Input(shape=((clip_depth - args.kernel_depth) // 1 + 1, (image_height - args.kernel_size) // args.stride + 1, (image_width - args.kernel_size) // args.stride + 1, args.num_kernels))
-
-#     recon_outputs = ReconSparse(batch_size=args.batch_size, image_height=image_height, image_width=image_width, clip_depth=clip_depth, in_channels=1, out_channels=args.num_kernels, kernel_size=args.kernel_size, kernel_depth=args.kernel_depth, stride=args.stride, lam=args.lam, activation_lr=args.activation_lr, max_activation_iter=args.max_activation_iter, run_2d=args.run_2d)(recon_inputs)
-
-#     recon_model = keras.Model(inputs=recon_inputs, outputs=recon_outputs)
-
-#     if args.sparse_checkpoint:
-#         recon_model.set_weights(keras.models.load_model(args.sparse_checkpoint).get_weights())
 
     sparse_model = None
     recon_model = None
+    
+    data_augmentation = keras.Sequential([
+        keras.layers.Resizing(image_width//args.scale_factor, image_height//args.scale_factor),
+#         keras.layers.RandomFlip('horizontal'),
+        keras.layers.RandomRotation(5),
+        keras.layers.RandomBrightness(0.01),
+        keras.layers.RandomTranslation(height_factor=0.1, width_factor=0),
+        keras.layers.RandomZoom(height_factor=0.1, width_factor=0),
+        keras.layers.RandomContrast(0.05)
+    ])
         
     splits, dataset = load_pnb_videos(yolo_model, args.batch_size, input_size=(image_height, image_width, clip_depth), crop_size=(crop_height, crop_width, clip_depth), classify_mode=True, balance_classes=args.balance_classes, mode=args.splits, device=None, n_splits=args.n_splits, sparse_model=None, frames_to_skip=args.frames_to_skip)
     positive_class = 'Positives'
@@ -236,34 +243,37 @@ if __name__ == "__main__":
     
     i_fold = 0
     for train_idx, test_idx in splits:
-#         train_sampler = torch.utils.data.SubsetRandomSampler(train_idx)
-        train_sampler = SubsetWeightedRandomSampler(get_sample_weights(train_idx, dataset), train_idx, replacement=True)
-        train_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
-                                               sampler=train_sampler)
+        train_loader = copy.deepcopy(dataset)
+        train_loader.set_indicies(train_idx)
+        test_loader = copy.deepcopy(dataset)
+        test_loader.set_indicies(test_idx)
+
+        train_tf = tf.data.Dataset.from_tensor_slices((train_loader.get_frames(), train_loader.get_labels()))
+        test_tf = tf.data.Dataset.from_tensor_slices((test_loader.get_frames(), test_loader.get_labels()))
         
-        if test_idx is not None:
-            test_sampler = torch.utils.data.SubsetRandomSampler(test_idx)
-            test_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
-                                                   sampler=test_sampler)
-            
-#             with open(os.path.join(args.output_dir, 'test_videos_{}.txt'.format(i_fold)), 'w+') as test_videos_out:
-#                 test_set = set([x for tup in test_loader for x in tup[2]])
-#                 test_videos_out.writelines(test_set)
-        else:
-            test_loader = None
-            
-#         test_videos = set()
-#         for labels, local_batch, vid_f in test_loader:
-#             test_videos.update(vid_f)
-#         print(test_videos)
-#         print('-------------------------------------------')
-#         continue
+#         negative_ds = (
+#           train_tf
+#             .filter(lambda features, label: label==0)
+#             .repeat())
+#         positive_ds = (
+#           train_tf
+#             .filter(lambda features, label: label==1)
+#             .repeat())
+        
+#         balanced_ds = tf.data.Dataset.sample_from_datasets(
+#             [negative_ds, positive_ds], [0.5, 0.5])
+
+
+#         train_sampler = torch.utils.data.SubsetRandomSampler(train_idx)
+#         train_sampler = SubsetWeightedRandomSampler(get_sample_weights(train_idx, dataset), train_idx, replacement=True)
+#         train_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
+#                                                sampler=train_sampler)
         
         if args.checkpoint:
             classifier_model = keras.models.load_model(args.checkpoint)
         else:
-            classifier_inputs = keras.Input(shape=(clip_depth, image_height, image_width))
-            classifier_outputs = PNBTemporalClassifier()(classifier_inputs)
+            classifier_inputs = keras.Input(shape=(clip_depth, image_width//args.scale_factor, image_height//args.scale_factor))
+            classifier_outputs = PNBTemporalClassifier(args.sparse_checkpoint)(classifier_inputs)
 
             classifier_model = keras.Model(inputs=classifier_inputs, outputs=classifier_outputs)
 
@@ -282,12 +292,9 @@ if __name__ == "__main__":
                 y_true_train = None
                 y_pred_train = None
 
-                for labels, local_batch, vid_f in tqdm(train_loader):
-                    images = local_batch.permute(0, 2, 3, 4, 1).numpy()
-
-                    torch_labels = np.zeros(len(labels))
-                    torch_labels[[i for i in range(len(labels)) if labels[i] == positive_class]] = 1
-                    torch_labels = np.expand_dims(torch_labels, axis=1)
+#                 for images, labels in tqdm(balanced_ds.batch(args.batch_size).take(len(train_tf) // args.batch_size)):
+                for images, labels in tqdm(train_tf.shuffle(len(train_tf)).batch(args.batch_size)):
+                    images = tf.transpose(images, [0, 2, 3, 4, 1])
 
                     if args.train_sparse:
                         with tf.GradientTape() as tape:
@@ -298,10 +305,13 @@ if __name__ == "__main__":
                             print(loss)
                     else:
                         with tf.GradientTape() as tape:
-                            pred = classifier_model(images)
-                            loss = criterion(torch_labels, pred)
+                            images = tf.reshape(images, (-1, images.shape[2], images.shape[3], images.shape[4]))
+                            alter = data_augmentation(images)
+                            alter = tf.reshape(alter, (-1, clip_depth, alter.shape[1], alter.shape[2], alter.shape[3]))
+                            pred = classifier_model(alter)
+                            loss = criterion(labels, pred)
 
-                    epoch_loss += loss * local_batch.size(0)
+                    epoch_loss += loss * images.shape[0]
 
                     if args.train_sparse:
                         sparse_gradients, classifier_gradients = tape.gradient(loss, [recon_model.trainable_weights, classifier_model.trainable_weights])
@@ -322,10 +332,10 @@ if __name__ == "__main__":
 
 
                     if y_true_train is None:
-                        y_true_train = torch_labels
+                        y_true_train = labels
                         y_pred_train = tf.math.round(tf.math.sigmoid(pred))
                     else:
-                        y_true_train = tf.concat((y_true_train, torch_labels), axis=0)
+                        y_true_train = tf.concat((y_true_train, labels), axis=0)
                         y_pred_train = tf.concat((y_pred_train, tf.math.round(tf.math.sigmoid(pred))), axis=0)
 
                 t2 = time.perf_counter()
@@ -337,23 +347,23 @@ if __name__ == "__main__":
                 eval_loader = test_loader
                 if args.splits == 'all_train':
                     eval_loader = train_loader
-                for labels, local_batch, vid_f in tqdm(eval_loader):
-                    images = local_batch.permute(0, 2, 3, 4, 1).numpy()
-
-                    torch_labels = np.zeros(len(labels))
-                    torch_labels[[i for i in range(len(labels)) if labels[i] == positive_class]] = 1
-                    torch_labels = np.expand_dims(torch_labels, axis=1)
+                for images, labels in tqdm(test_tf.batch(args.batch_size)):
+                    images = tf.transpose(images, [0, 2, 3, 4, 1])
                     
-                    pred = classifier_model(images)
-                    loss = criterion(torch_labels, pred)
+                    images = tf.reshape(images, (-1, images.shape[2], images.shape[3], images.shape[4]))
+                    alter = keras.layers.Resizing(image_width//args.scale_factor, image_height//args.scale_factor)(images)
+                    alter = tf.reshape(alter, (-1, clip_depth, alter.shape[1], alter.shape[2], alter.shape[3]))
+                    pred = classifier_model(alter)
+                    
+                    loss = criterion(labels, pred)
 
                     test_loss += loss
 
                     if y_true is None:
-                        y_true = torch_labels
+                        y_true = labels
                         y_pred = tf.math.round(tf.math.sigmoid(pred))
                     else:
-                        y_true = tf.concat((y_true, torch_labels), axis=0)
+                        y_true = tf.concat((y_true, labels), axis=0)
                         y_pred = tf.concat((y_pred, tf.math.round(tf.math.sigmoid(pred))), axis=0)
 
                 t2 = time.perf_counter()
@@ -395,21 +405,17 @@ if __name__ == "__main__":
         transform = torchvision.transforms.Compose(
         [VideoGrayScaler(),
          MinMaxScaler(0, 255),
-         torchvision.transforms.Resize((image_height, image_width))
+         torchvision.transforms.Resize((image_width//args.scale_factor, image_height//args.scale_factor))
         ])
 
-        test_videos = set()
-        for labels, local_batch, vid_f in test_loader:
-            test_videos.update(vid_f)
-            
-        print(test_videos)
+        test_videos = test_loader.get_all_videos()
 
         test_labels = [vid_f.split('/')[-3] for vid_f in test_videos]
 
         if args.frames_to_skip == 1:
             y_pred, y_true, fn, fp = calculate_pnb_scores(test_videos, test_labels, yolo_model, sparse_model, recon_model, classifier_model, image_width, image_height, transform)
         else:
-            y_pred, y_true, fn, fp = calculate_pnb_scores_skipped_frames(test_videos, test_labels, yolo_model, sparse_model, recon_model, classifier_model, args.frames_to_skip, image_width, image_height, transform)
+            y_pred, y_true, fn, fp = calculate_pnb_scores_skipped_frames(test_videos, test_labels, yolo_model, sparse_model, recon_model, classifier_model, args.frames_to_skip, image_width, image_height, clip_depth, transform)
             
         print(fn)
         print(fp)
diff --git a/sparse_coding_torch/pnb/video_loader.py b/sparse_coding_torch/pnb/video_loader.py
index 10da89dfc49b27ad4871fce24aa56fbe1139d059..a1c6b341cd9b868a2c73fafbf73f7dbd6becb4de 100644
--- a/sparse_coding_torch/pnb/video_loader.py
+++ b/sparse_coding_torch/pnb/video_loader.py
@@ -29,6 +29,8 @@ from skimage.io import imsave
 from matplotlib import pyplot as plt
 from matplotlib import cm
 
+import math
+
 def get_participants(filenames):
     return [f.split('/')[-2] for f in filenames]
     
@@ -45,32 +47,36 @@ def load_pnb_region_labels(file_path):
             
         return all_regions
     
-def get_yolo_regions(yolo_model, clip, is_right, needle_bb, crop_width, crop_height):
+def get_yolo_regions(yolo_model, clip, is_right, crop_width, crop_height):
     orig_height = clip.size(2)
     orig_width = clip.size(3)
-    bounding_boxes, classes, scores = yolo_model.get_bounding_boxes(clip[:, 2, :, :].swapaxes(0, 2).swapaxes(0, 1).numpy())
-    bounding_boxes = bounding_boxes.squeeze(0)
-    classes = classes.squeeze(0)
-    scores = scores.squeeze(0)
+    bounding_boxes, classes, scores = yolo_model.get_bounding_boxes_v5(clip[:, clip.shape[1]//2, :, :].swapaxes(0, 2).swapaxes(0, 1).numpy())
     
+    needle_bb = None
     for bb, class_pred in zip(bounding_boxes, classes):
         if class_pred == 2:
             needle_bb = bb
+            
+    if needle_bb is None:
+        return []
     
     rotate_box = False
     
+    if 1 not in classes:
+        return []
+    
     all_clips = []
     for bb, class_pred, score in zip(bounding_boxes, classes, scores):
-        if class_pred != 0:
+        if class_pred != 1:
             continue
-        center_x = round((bb[3] + bb[1]) / 2 * orig_width)
-        center_y = round((bb[2] + bb[0]) / 2 * orig_height)
+        center_x = round((bb[2] + bb[0]) / 2 * orig_width)
+        center_y = round((bb[3] + bb[1]) / 2 * orig_height)
         
         if not is_right:
             clip = tv.transforms.functional.hflip(clip)
             center_x = orig_width - center_x
-            needle_bb[1] = orig_width - needle_bb[1]
-            needle_bb[3] = orig_width - needle_bb[3]
+            needle_bb[0] = orig_width - needle_bb[0]
+            needle_bb[2] = orig_width - needle_bb[2]
         
 #         lower_y = round((bb[0] * orig_height))
 #         upper_y = round((bb[2] * orig_height))
@@ -101,13 +107,25 @@ def get_yolo_regions(yolo_model, clip, is_right, needle_bb, crop_width, crop_hei
 #         if upper_y < 0:
 #             upper_y = 0
         clip = tv.transforms.functional.rotate(clip, angle=angle, center=[center_x, center_y])
+    
+#         test_img = clip.numpy()[:, 0, :, :].swapaxes(0,1).swapaxes(1,2)
+        
+#         kernel = np.array([[-1.0, -1.0], 
+#                    [2.0, 2.0],
+#                    [-1.0, -1.0]])
+
+#         kernel = kernel/(np.sum(kernel) if np.sum(kernel)!=0 else 1)
+
+# #filter the source image
+#         filtered_img = cv2.filter2D(test_img,-1,kernel)
             
-#         plt.clf()
-#         plt.imshow(clip.numpy()[:, 0, :, :].swapaxes(0,1).swapaxes(1,2), cmap=cm.Greys_r)
-#         # plt.scatter([214], [214], color="red")
-#         plt.scatter([center_x, int(needle_bb[1]*orig_width)], [center_y, int(needle_bb[0] * orig_height)], color=["red", 'red'])
-# #         cv2.imwrite('test_normal.png', clip.numpy()[:, 0, :, :].swapaxes(0,1).swapaxes(1,2))
+# #         plt.clf()
+#         plt.imshow(filtered_img, cmap=cm.Greys_r)
+# #         # plt.scatter([214], [214], color="red")
+# #         plt.scatter([center_x, int(needle_bb[0]*orig_width)], [center_y, int(needle_bb[1] * orig_height)], color=["red", 'red'])
+# # #         cv2.imwrite('test_normal.png', clip.numpy()[:, 0, :, :].swapaxes(0,1).swapaxes(1,2))
 #         plt.savefig('test_normal.png')
+#         raise Exception
             
 #         if rotate_box:
 # #             cv2.imwrite('test_1.png', clip.numpy()[:, 0, :, :].swapaxes(0,1).swapaxes(1,2))
@@ -128,29 +146,30 @@ def get_yolo_regions(yolo_model, clip, is_right, needle_bb, crop_width, crop_hei
 
         ro,col=clip[0, 0, :, :].shape
         max_radius = int(np.sqrt(ro**2+col**2)/2)
-#         print(upper_y)
-#         print(bb[0])
-#         print(center_x)
-#         print(center_y)
+# # #         print(upper_y)
+# # #         print(bb[0])
+# # #         print(center_x)
+# # #         print(center_y)
         trimmed_clip = []
         for i in range(clip.shape[0]):
             sub_clip = []
             for j in range(clip.shape[1]):
                 sub_clip.append(cv2.linearPolar(clip[i, j, :, :].numpy(), (center_x, center_y), max_radius, cv2.WARP_FILL_OUTLIERS))
-#                 sub_clip.append(warp_polar(clip[i, j, :, :].numpy(), center=(center_x, center_y), radius=max_radius, preserve_range=True))
+# #                 sub_clip.append(warp_polar(clip[i, j, :, :].numpy(), center=(center_x, center_y), radius=max_radius, preserve_range=True))
             trimmed_clip.append(np.stack(sub_clip))
         trimmed_clip = np.stack(trimmed_clip)
         
         approximate_needle_position = int(((angle+150)/360)*orig_height)
         
-        plt.clf()
-        plt.imshow(trimmed_clip[:, 0, :, :].swapaxes(0,1).swapaxes(1,2), cmap=cm.Greys_r)
-        # plt.scatter([214], [214], color="red")
+#         plt.clf()
+#         plt.imshow(trimmed_clip[:, 0, :, :].swapaxes(0,1).swapaxes(1,2), cmap=cm.Greys_r)
+# #         # plt.scatter([214], [214], color="red")
 #         plt.scatter([center_x], [approximate_needle_position], color=["red"])
-#         cv2.imwrite('test_normal.png', clip.numpy()[:, 0, :, :].swapaxes(0,1).swapaxes(1,2))
-        plt.savefig('test_polar.png')
+# # #         cv2.imwrite('test_normal.png', clip.numpy()[:, 0, :, :].swapaxes(0,1).swapaxes(1,2))
+#         plt.savefig('test_polar.png')
         
-        trimmed_clip = trimmed_clip[:, :, approximate_needle_position - (crop_height//2):approximate_needle_position + (crop_height//2), :]
+        trimmed_clip = trimmed_clip[:, :, approximate_needle_position - (crop_height//2):approximate_needle_position + (crop_height//2), :crop_width]
+#         trimmed_clip = clip[:, :, center_y - (crop_height//2):center_y + (crop_height//2), center_x - crop_width:center_x]
                 
 #         trimmed_clip=cv2.linearPolar(clip[0, 0, :, :].numpy(), (center_x, center_y), max_radius, cv2.WARP_FILL_OUTLIERS)
 #         trimmed_clip = warp_polar(clip[0, 0, :, :].numpy(), center=(center_x, center_y), radius=max_radius)
@@ -162,19 +181,19 @@ def get_yolo_regions(yolo_model, clip, is_right, needle_bb, crop_width, crop_hei
 #         print(angle)
 #         if not is_right:
 #         cv2.imwrite('test_polar.png', trimmed_clip[:, 0, :, :].swapaxes(0,1).swapaxes(1,2))
-        plt.clf()
-        plt.imshow(trimmed_clip[:, 0, :, :].swapaxes(0,1).swapaxes(1,2), cmap=cm.Greys_r)
-        # plt.scatter([214], [214], color="red")
-#         plt.scatter([center_x], [approximate_needle_position], color=["red"])
-#         cv2.imwrite('test_normal.png', clip.numpy()[:, 0, :, :].swapaxes(0,1).swapaxes(1,2))
-        plt.savefig('test_polar_trim.png')
+#         plt.clf()
+#         plt.imshow(clip[:, 0, :, :].swapaxes(0,1).swapaxes(1,2), cmap=cm.Greys_r)
+#         plt.scatter([center_x], [center_y], color="red")
+# # #         plt.scatter([center_x], [approximate_needle_position], color=["red"])
+# #         cv2.imwrite('test_normal.png', clip.numpy()[:, 0, :, :].swapaxes(0,1).swapaxes(1,2))
+#         plt.savefig('test_normal.png')
 #         raise Exception
 
 #         if not is_right:
 #             trimmed_clip = tv.transforms.functional.hflip(trimmed_clip)
 #             cv2.imwrite('test_polar.png', trimmed_clip)
-#             cv2.imwrite('test_yolo.png', trimmed_clip.numpy()[:, 0, :, :].swapaxes(0,1).swapaxes(1,2))
-#             raise Exception
+#         cv2.imwrite('test_yolo.png', trimmed_clip[:, 0, :, :].swapaxes(0,1).swapaxes(1,2))
+#         raise Exception
         
         if trimmed_clip.shape[2] == 0 or trimmed_clip.shape[3] == 0:
             continue
@@ -192,15 +211,13 @@ def classify_nerve_is_right(yolo_model, video):
 
     for frame in range(0, video.size(1), round(video.size(1) / 10)):
         frame = video[:, frame, :, :]
-        bounding_boxes, classes, scores = yolo_model.get_bounding_boxes(frame.swapaxes(0, 2).swapaxes(0, 1).numpy())
-        bounding_boxes = bounding_boxes.squeeze(0)
-        classes = classes.squeeze(0)
+        bounding_boxes, classes, scores = yolo_model.get_bounding_boxes_v5(frame.swapaxes(0, 2).swapaxes(0, 1).numpy())
     
         for bb, class_pred in zip(bounding_boxes, classes):
             if class_pred != 2:
                 continue
-            center_x = (bb[3] + bb[1]) / 2 * orig_width
-            center_y = (bb[2] + bb[0]) / 2 * orig_height
+            center_x = (bb[2] + bb[0]) / 2 * orig_width
+            center_y = (bb[3] + bb[1]) / 2 * orig_height
 
             if orig_width - center_x < center_x:
                 all_preds.append(0)
@@ -211,8 +228,8 @@ def classify_nerve_is_right(yolo_model, video):
             for bb, class_pred in zip(bounding_boxes, classes):
                 if class_pred != 1:
                     continue
-                center_x = (bb[3] + bb[1]) / 2 * orig_width
-                center_y = (bb[2] + bb[0]) / 2 * orig_height
+                center_x = (bb[2] + bb[0]) / 2 * orig_width
+                center_y = (bb[3] + bb[1]) / 2 * orig_height
 
                 if orig_width - center_x < center_x:
                     all_preds.append(1)
@@ -227,8 +244,8 @@ def classify_nerve_is_right(yolo_model, video):
     return final_pred == 1
 
 def calculate_angle(needle_bb, vessel_x, vessel_y, orig_height, orig_width):
-    needle_x = needle_bb[1] * orig_width
-    needle_y = needle_bb[0] * orig_height
+    needle_x = needle_bb[0] * orig_width
+    needle_y = needle_bb[1] * orig_height
 
     return np.abs(np.degrees(np.arctan((needle_y-vessel_y)/(needle_x-vessel_x))))
 
@@ -239,9 +256,7 @@ def get_needle_bb(yolo_model, video):
     for frame in range(0, video.size(1), 1):
         frame = video[:, frame, :, :]
         
-        bounding_boxes, classes, scores = yolo_model.get_bounding_boxes(frame.swapaxes(0, 2).swapaxes(0, 1).numpy())
-        bounding_boxes = bounding_boxes.squeeze(0)
-        classes = classes.squeeze(0)
+        bounding_boxes, classes, scores = yolo_model.get_bounding_boxes_v5(frame.swapaxes(0, 2).swapaxes(0, 1).numpy())
 
         for bb, class_pred in zip(bounding_boxes, classes):
             if class_pred == 2:
@@ -260,9 +275,7 @@ def calculate_angle_video(yolo_model, video):
     for frame in range(0, video.size(1), video.size(1) // 10):
         frame = video[:, frame, :, :]
         
-        bounding_boxes, classes, scores = yolo_model.get_bounding_boxes(frame.swapaxes(0, 2).swapaxes(0, 1).numpy())
-        bounding_boxes = bounding_boxes.squeeze(0)
-        classes = classes.squeeze(0)
+        bounding_boxes, classes, scores = yolo_model.get_bounding_boxes_v5(frame.swapaxes(0, 2).swapaxes(0, 1).numpy())
 
         vessel_x = 0
         vessel_y = 0
@@ -271,11 +284,11 @@ def calculate_angle_video(yolo_model, video):
 
         for bb, class_pred in zip(bounding_boxes, classes):
             if class_pred == 0 and vessel_x == 0:
-                vessel_x = (bb[3] + bb[1]) / 2 * orig_width
-                vessel_y = (bb[2] + bb[0]) / 2 * orig_height
+                vessel_x = (bb[2] + bb[0]) / 2 * orig_width
+                vessel_y = (bb[3] + bb[1]) / 2 * orig_height
             elif class_pred == 2 and needle_x == 0:
-                needle_x = bb[1] * orig_width
-                needle_y = bb[0] * orig_height
+                needle_x = bb[0] * orig_width
+                needle_y = bb[1] * orig_height
 
             if needle_x != 0 and vessel_x != 0:
                 break
@@ -288,10 +301,10 @@ def calculate_angle_video(yolo_model, video):
     
 class PNBLoader(Dataset):
     
-    def __init__(self, yolo_model, video_path, clip_width, clip_height, clip_depth, classify_mode=False, balance_classes=False, num_frames=5, frames_to_skip=1, transform=None, augmentation=None):
+    def __init__(self, yolo_model, video_path, clip_width, clip_height, clip_depth, classify_mode=False, balance_classes=False, num_frames=5, frames_to_skip=1, transform=None):
         self.transform = transform
-        self.augmentation = augmentation
         self.labels = [name for name in listdir(video_path) if isdir(join(video_path, name))]
+        self.count = 0
         
         if classify_mode:
             clip_cache_file = 'clip_cache_pnb_{}_{}_{}_{}.pt'.format(clip_width, clip_height, clip_depth, frames_to_skip)
@@ -311,17 +324,21 @@ class PNBLoader(Dataset):
 #         self.videos = list(filter(lambda x: x[1].split('/')[-2] in ['67'], self.videos))
             
         self.clips = []
-        self.final_clips = {}
         
         if exists(clip_cache_file):
             self.clips = torch.load(open(clip_cache_file, 'rb'))
-            self.final_clips = torch.load(open(clip_cache_final_file, 'rb'))
         else:
             vid_idx = 0
             for label, path, _ in tqdm(self.videos):
                 vc = tv.io.read_video(path)[0].permute(3, 0, 1, 2)
                 is_right = classify_nerve_is_right(yolo_model, vc)
-                needle_bb = get_needle_bb(yolo_model, vc)
+#                 needle_bb = get_needle_bb(yolo_model, vc)
+                
+                label = self.videos[vid_idx][0]
+                if label == 'Positives':
+                    label = np.array(1.0)
+                elif label == 'Negatives':
+                    label = np.array(0.0)
 
                 if classify_mode:
 #                     person_idx = path.split('/')[-2]
@@ -330,14 +347,14 @@ class PNBLoader(Dataset):
                     if vc.size(1) < clip_depth:
                         continue
 
-                    if label == 'Positives' and person_idx in region_labels:
+                    if label == 1.0 and person_idx in region_labels:
                         negative_regions, positive_regions = region_labels[person_idx]
                         for sub_region in negative_regions.split(','):
                             sub_region = sub_region.split('-')
                             start_loc = int(sub_region[0])
 #                             end_loc = int(sub_region[1]) - 50
                             end_loc = int(sub_region[1]) + 1
-                            for j in range(start_loc, end_loc - clip_depth * frames_to_skip, clip_depth):
+                            for j in range(start_loc, end_loc - clip_depth * frames_to_skip, 1):
                                 frames = []
                                 for k in range(j, j + clip_depth * frames_to_skip, frames_to_skip):
                                     frames.append(vc[:, k, :, :])
@@ -346,11 +363,11 @@ class PNBLoader(Dataset):
                                 if vc_sub.size(1) < clip_depth:
                                     continue
 
-                                for clip in get_yolo_regions(yolo_model, vc_sub, is_right, needle_bb, clip_width, clip_height):
+                                for clip in get_yolo_regions(yolo_model, vc_sub, is_right, clip_width, clip_height):
                                     if self.transform:
                                         clip = self.transform(clip)
 
-                                    self.clips.append(('Negatives', clip, self.videos[vid_idx][2]))
+                                    self.clips.append((np.array(0.0), clip.numpy(), self.videos[vid_idx][2]))
 
                         if positive_regions:
                             for sub_region in positive_regions.split(','):
@@ -366,18 +383,18 @@ class PNBLoader(Dataset):
                                     if vc_sub.size(1) < clip_depth:
                                         continue
                                         
-                                    for clip in get_yolo_regions(yolo_model, vc_sub, is_right, needle_bb, clip_width, clip_height):
+                                    for clip in get_yolo_regions(yolo_model, vc_sub, is_right, clip_width, clip_height):
                                         if self.transform:
                                             clip = self.transform(clip)
 
-                                        self.clips.append(('Positives', clip, self.videos[vid_idx][2]))
+                                        self.clips.append((np.array(1.0), clip.numpy(), self.videos[vid_idx][2]))
                                 elif vc.size(1) >= start_loc + clip_depth * frames_to_skip:
                                     end_loc = sub_region[1]
                                     if end_loc.strip().lower() == 'end':
                                         end_loc = vc.size(1)
                                     else:
                                         end_loc = int(end_loc)
-                                    for j in range(start_loc, end_loc - clip_depth * frames_to_skip, clip_depth):
+                                    for j in range(start_loc, end_loc - clip_depth * frames_to_skip, 1):
                                         frames = []
                                         for k in range(j, j + clip_depth * frames_to_skip, frames_to_skip):
                                             frames.append(vc[:, k, :, :])
@@ -385,40 +402,40 @@ class PNBLoader(Dataset):
 
                                         if vc_sub.size(1) < clip_depth:
                                             continue
-                                        for clip in get_yolo_regions(yolo_model, vc_sub, is_right, needle_bb, clip_width, clip_height):
+                                        for clip in get_yolo_regions(yolo_model, vc_sub, is_right, clip_width, clip_height):
                                             if self.transform:
                                                 clip = self.transform(clip)
 
-                                            self.clips.append(('Positives', clip, self.videos[vid_idx][2]))
+                                            self.clips.append((np.array(1.0), clip.numpy(), self.videos[vid_idx][2]))
                                 else:
                                     continue
-                    elif label == 'Positives':
+                    elif label == 1.0:
                         frames = []
-                        for k in range(j, -1 * clip_depth * frames_to_skip, frames_to_skip):
+                        for k in range(0, -1 * clip_depth * frames_to_skip, frames_to_skip):
                             frames.append(vc[:, k, :, :])
                         if not frames:
                             continue
                         vc_sub = torch.stack(frames, dim=1)
                         if vc_sub.size(1) < clip_depth:
                             continue
-                        for clip in get_yolo_regions(yolo_model, vc_sub, is_right, needle_bb, clip_width, clip_height):
+                        for clip in get_yolo_regions(yolo_model, vc_sub, is_right, clip_width, clip_height):
                             if self.transform:
                                 clip = self.transform(clip)
 
-                            self.clips.append((self.videos[vid_idx][0], clip, self.videos[vid_idx][2]))
-                    elif label == 'Negatives':
-                        for j in range(0, vc.size(1) - clip_depth * frames_to_skip, clip_depth):
+                            self.clips.append((label, clip.numpy(), self.videos[vid_idx][2]))
+                    elif label == 0.0:
+                        for j in range(0, vc.size(1) - clip_depth * frames_to_skip, 1):
                             frames = []
                             for k in range(j, j + clip_depth * frames_to_skip, frames_to_skip):
                                 frames.append(vc[:, k, :, :])
                             vc_sub = torch.stack(frames, dim=1)
                             if vc_sub.size(1) < clip_depth:
                                 continue
-                            for clip in get_yolo_regions(yolo_model, vc_sub, is_right, needle_bb, clip_width, clip_height):
+                            for clip in get_yolo_regions(yolo_model, vc_sub, is_right, clip_width, clip_height):
                                 if self.transform:
                                     clip = self.transform(clip)
 
-                                self.clips.append((self.videos[vid_idx][0], clip, self.videos[vid_idx][2]))
+                                self.clips.append((label, clip.numpy(), self.videos[vid_idx][2]))
                     else:
                         raise Exception('Invalid label')
                 else:
@@ -434,16 +451,14 @@ class PNBLoader(Dataset):
                         if self.transform:
                             vc_sub = self.transform(vc_sub)
 
-                        self.clips.append((self.videos[vid_idx][0], vc_sub, self.videos[vid_idx][2]))
+                        self.clips.append((label, vc_sub.numpy(), self.videos[vid_idx][2]))
 
-                self.final_clips[self.videos[vid_idx][2]] = self.clips[-1]
                 vid_idx += 1
                 
             torch.save(self.clips, open(clip_cache_file, 'wb+'))
-            torch.save(self.final_clips, open(clip_cache_final_file, 'wb+'))
             
-        num_positive = len([clip[0] for clip in self.clips if clip[0] == 'Positives'])
-        num_negative = len([clip[0] for clip in self.clips if clip[0] == 'Negatives'])
+        num_positive = len([clip[0] for clip in self.clips if clip[0] == 1.0])
+        num_negative = len([clip[0] for clip in self.clips if clip[0] == 0.0])
         
         random.shuffle(self.clips)
         
@@ -451,7 +466,7 @@ class PNBLoader(Dataset):
             new_clips = []
             count_negative = 0
             for clip in self.clips:
-                if clip[0] == 'Negatives':
+                if clip[0] == 0.0:
                     if count_negative < num_positive:
                         new_clips.append(clip)
                     count_negative += 1
@@ -459,8 +474,8 @@ class PNBLoader(Dataset):
                     new_clips.append(clip)
                     
             self.clips = new_clips
-            num_positive = len([clip[0] for clip in self.clips if clip[0] == 'Positives'])
-            num_negative = len([clip[0] for clip in self.clips if clip[0] == 'Negatives'])
+            num_positive = len([clip[0] for clip in self.clips if clip[0] == 1.0])
+            num_negative = len([clip[0] for clip in self.clips if clip[0] == 0.0])
         
         print('Loaded', num_positive, 'positive examples.')
         print('Loaded', num_negative, 'negative examples.')
@@ -468,25 +483,36 @@ class PNBLoader(Dataset):
     def get_filenames(self):
         return [self.clips[i][2] for i in range(len(self.clips))]
         
-    def get_video_labels(self):
-        return [self.videos[i][0] for i in range(len(self.videos))]
+    def get_all_videos(self):
+        return set([self.clips[i][2] for i in range(len(self.clips))])
         
     def get_labels(self):
         return [self.clips[i][0] for i in range(len(self.clips))]
     
-    def get_final_clips(self):
-        return self.final_clips
-    
-    def __getitem__(self, index):
-        label, clip, vid_f = self.clips[index]
-        if self.augmentation:
-            clip = clip.swapaxes(0, 1)
-            clip = self.augmentation(clip)
-            clip = clip.swapaxes(0, 1)
-        return (label, clip, vid_f)
+    def set_indicies(self, iter_idx):
+        new_clips = []
+        for i, clip in enumerate(self.clips):
+            if i in iter_idx:
+                new_clips.append(clip)
+                
+        self.clips = new_clips
         
-    def __len__(self):
-        return len(self.clips)
+    def get_frames(self):
+        return [frame for _, frame, _ in self.clips]
+    
+    def get_labels(self):
+        return [label for label, _, _ in self.clips]
+    
+    def __next__(self):
+        if self.count < len(self.clips):
+            label, frame, vid_f = self.clips[self.count]
+            self.count += 1
+            return label, frame
+        else:
+            raise StopIteration
+            
+    def __iter__(self):
+        return self
     
     
 class NeedleLoader(Dataset):
diff --git a/sparse_coding_torch/ptx/classifier_model.py b/sparse_coding_torch/ptx/classifier_model.py
index bd2bde38e343ddeb83943921ec49b2b722c6f0a1..afd58cecdc67c606efcb182fceceb6b687f892c5 100644
--- a/sparse_coding_torch/ptx/classifier_model.py
+++ b/sparse_coding_torch/ptx/classifier_model.py
@@ -10,12 +10,12 @@ from sparse_coding_torch.ptx.video_loader import VideoGrayScaler, MinMaxScaler
 from sparse_coding_torch.sparse_model import SparseCode
 
 class PTXClassifier(keras.layers.Layer):
-    def __init__(self):
+    def __init__(self, num_output):
         super(PTXClassifier, self).__init__()
 
         self.max_pool = keras.layers.MaxPooling2D(pool_size=4, strides=4)
         self.conv_1 = keras.layers.Conv2D(48, kernel_size=8, strides=4, activation='relu', padding='valid')
-        self.conv_2 = keras.layers.Conv2D(24, kernel_size=4, strides=2, activation='relu', padding='valid')
+#         self.conv_2 = keras.layers.Conv2D(24, kernel_size=4, strides=2, activation='relu', padding='valid')
 
         self.flatten = keras.layers.Flatten()
 
@@ -25,7 +25,7 @@ class PTXClassifier(keras.layers.Layer):
 #         self.ff_2 = keras.layers.Dense(500, activation='relu', use_bias=True)
 #         self.ff_2 = keras.layers.Dense(20, activation='relu', use_bias=True)
         self.ff_3 = keras.layers.Dense(20, activation='relu', use_bias=True)
-        self.ff_4 = keras.layers.Dense(1)
+        self.ff_4 = keras.layers.Dense(num_output)
 
 #     @tf.function
     def call(self, activations):
@@ -45,11 +45,11 @@ class PTXClassifier(keras.layers.Layer):
         return x
     
 class PTXVAEClassifier(keras.layers.Layer):
-    def __init__(self):
+    def __init__(self, num_output):
         super(PTXVAEClassifier, self).__init__()
 
         self.ff_3 = keras.layers.Dense(20, activation='relu', use_bias=True)
-        self.ff_4 = keras.layers.Dense(1)
+        self.ff_4 = keras.layers.Dense(num_output)
 
 #     @tf.function
     def call(self, z):
@@ -99,14 +99,14 @@ class VAEDecoderPTX(keras.layers.Layer):
     def __init__(self):
         super(VAEDecoderPTX, self).__init__()
         
-        self.ff = keras.layers.Dense(3 * 9 * 48, activation='relu', use_bias=True)
-        self.reshape = keras.layers.Reshape((3, 9, 48))
+        self.ff = keras.layers.Dense(9 * 9 * 48, activation='relu', use_bias=True)
+        self.reshape = keras.layers.Reshape((9, 9, 48))
         
         self.deconv_1 = keras.layers.Conv2DTranspose(36, kernel_size=4, strides=2, activation='relu', padding='valid')
         self.deconv_2 = keras.layers.Conv2DTranspose(24, kernel_size=8, strides=2, activation='relu', padding='valid')
         self.deconv_3 = keras.layers.Conv3DTranspose(1, kernel_size=(5, 16, 16), strides=(1, 4, 4), activation='relu', padding='valid')
         
-        self.padding = keras.layers.ZeroPadding3D((0, 0, 2))
+        self.padding = keras.layers.ZeroPadding3D((0, 2, 2))
 
 #     @tf.function
     def call(self, images):
diff --git a/sparse_coding_torch/ptx/load_data.py b/sparse_coding_torch/ptx/load_data.py
index 088c18d0ef3bc0927d2ecea3bc834439e4b72863..a6769bd3aa1c25f6f7d0c615e6f0d9220baee7d9 100644
--- a/sparse_coding_torch/ptx/load_data.py
+++ b/sparse_coding_torch/ptx/load_data.py
@@ -17,13 +17,13 @@ def load_yolo_clips(batch_size, mode, num_clips=1, num_positives=100, device=Non
 #      MinMaxScaler(0, 255),
      torchvision.transforms.Normalize((0.2592,), (0.1251,)),
     ])
-    augment_transforms = torchvision.transforms.Compose(
-    [torchvision.transforms.RandomRotation(45),
-     torchvision.transforms.RandomHorizontalFlip(),
-     torchvision.transforms.CenterCrop((100, 200))
-    ])
+#     augment_transforms = torchvision.transforms.Compose(
+#     [torchvision.transforms.RandomRotation(45),
+#      torchvision.transforms.RandomHorizontalFlip(),
+#      torchvision.transforms.CenterCrop((100, 200))
+#     ])
 
-    dataset = YoloClipLoader(video_path, num_clips=num_clips, num_positives=num_positives, positive_videos=positive_videos, transform=transforms, augment_transform=augment_transforms, sparse_model=sparse_model, device=device)
+    dataset = YoloClipLoader(video_path, num_clips=num_clips, num_positives=num_positives, positive_videos=positive_videos, transform=transforms, sparse_model=sparse_model, device=device)
     
     targets = dataset.get_labels()
     
@@ -53,39 +53,30 @@ def load_yolo_clips(batch_size, mode, num_clips=1, num_positives=100, device=Non
 
         groups = [video_to_participant[v.lower().replace('_clean', '')] for v in dataset.get_filenames()]
         
-        train_idx, test_idx = list(gss.split(np.arange(len(targets)), targets, groups))[0]
-        
-        train_sampler = torch.utils.data.SubsetRandomSampler(train_idx)
-        train_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
-                                               sampler=train_sampler)
-        
-        test_sampler = torch.utils.data.SubsetRandomSampler(test_idx)
-        test_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
-                                               sampler=test_sampler)
-        
-        return train_loader, test_loader, dataset
+        return list(gss.split(np.arange(len(targets)), targets, groups)), dataset
     
-def load_covid_clips(batch_size, yolo_model, mode, clip_height, clip_width, clip_depth, device=None, n_splits=None, classify_mode=False):   
+def load_covid_clips(batch_size, mode, clip_width, clip_height, clip_depth, n_splits=None):   
     video_path = "/home/dwh48@drexel.edu/covid19_ultrasound/data/pocus_videos"
     
     transforms = torchvision.transforms.Compose(
     [VideoGrayScaler(),
      MinMaxScaler(0, 255),
-    ])
-    augment_transforms = torchvision.transforms.Compose(
-    [torchvision.transforms.RandomRotation(45),
-     torchvision.transforms.RandomHorizontalFlip(),
      torchvision.transforms.Resize((clip_height, clip_width))
     ])
+#     augment_transforms = torchvision.transforms.Compose(
+#     [torchvision.transforms.RandomRotation(45),
+#      torchvision.transforms.RandomHorizontalFlip(),
+#      torchvision.transforms.Resize((clip_height, clip_width))
+#     ])
 
-    dataset = COVID19Loader(yolo_model, video_path, clip_depth, classify_mode=classify_mode, transform=transforms, augmentation=augment_transforms)
+    dataset = COVID19Loader(video_path, clip_depth, transform=transforms)
     
     targets = dataset.get_labels()
     
     if mode == 'leave_one_out':
         gss = LeaveOneGroupOut()
 
-        groups = [v for v in dataset.get_filenames()]
+        groups = [v for v in dataset.get_groups()]
         
         return gss.split(np.arange(len(targets)), targets, groups), dataset
     elif mode == 'all_train':
@@ -95,13 +86,13 @@ def load_covid_clips(batch_size, yolo_model, mode, clip_height, clip_width, clip
     elif mode == 'k_fold':
         gss = StratifiedGroupKFold(n_splits=n_splits)
 
-        groups = [v for v in dataset.get_filenames()]
+        groups = [v for v in dataset.get_groups()]
         
         return gss.split(np.arange(len(targets)), targets, groups), dataset
     else:
         gss = GroupShuffleSplit(n_splits=n_splits, test_size=0.2)
 
-        groups = [v for v in dataset.get_filenames()]
+        groups = [v for v in dataset.get_groups()]
         
         train_idx, test_idx = list(gss.split(np.arange(len(targets)), targets, groups))[0]
         
diff --git a/sparse_coding_torch/ptx/load_data_pytorch.py b/sparse_coding_torch/ptx/load_data_pytorch.py
new file mode 100644
index 0000000000000000000000000000000000000000..7d57169c69ff69c7b63f68944be3c7674113470b
--- /dev/null
+++ b/sparse_coding_torch/ptx/load_data_pytorch.py
@@ -0,0 +1,116 @@
+import numpy as np
+import torchvision
+import torch
+from sklearn.model_selection import train_test_split
+from sparse_coding_torch.utils import MinMaxScaler, VideoGrayScaler
+from sparse_coding_torch.ptx.video_loader_pytorch import YoloClipLoader, get_ptx_participants, COVID19Loader
+import csv
+from sklearn.model_selection import train_test_split, GroupShuffleSplit, LeaveOneGroupOut, LeaveOneOut, StratifiedGroupKFold, StratifiedKFold, KFold, ShuffleSplit
+
+def load_yolo_clips(batch_size, mode, num_clips=1, num_positives=100, device=None, n_splits=None, sparse_model=None, whole_video=False, positive_videos=None):   
+    video_path = "/shared_data/YOLO_Updated_PL_Model_Results/"
+
+    video_to_participant = get_ptx_participants()
+    
+    transforms = torchvision.transforms.Compose(
+    [VideoGrayScaler(),
+#      MinMaxScaler(0, 255),
+     torchvision.transforms.Normalize((0.2592,), (0.1251,)),
+    ])
+    augment_transforms = torchvision.transforms.Compose(
+    [torchvision.transforms.RandomRotation(45),
+     torchvision.transforms.RandomHorizontalFlip(),
+     torchvision.transforms.CenterCrop((100, 200))
+    ])
+
+    dataset = YoloClipLoader(video_path, num_clips=num_clips, num_positives=num_positives, positive_videos=positive_videos, transform=transforms, augment_transform=augment_transforms, sparse_model=sparse_model, device=device)
+    
+    targets = dataset.get_labels()
+    
+    if mode == 'leave_one_out':
+        gss = LeaveOneGroupOut()
+
+#         groups = [v for v in dataset.get_filenames()]
+        groups = [video_to_participant[v.lower().replace('_clean', '')] for v in dataset.get_filenames()]
+        
+        return gss.split(np.arange(len(targets)), targets, groups), dataset
+    elif mode == 'all_train':
+        train_idx = np.arange(len(targets))
+#         train_sampler = torch.utils.data.SubsetRandomSampler(train_idx)
+#         train_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
+#                                                sampler=train_sampler)
+#         test_loader = None
+        
+        return [(train_idx, None)], dataset
+    elif mode == 'k_fold':
+        gss = StratifiedGroupKFold(n_splits=n_splits)
+
+        groups = [video_to_participant[v.lower().replace('_clean', '')] for v in dataset.get_filenames()]
+        
+        return gss.split(np.arange(len(targets)), targets, groups), dataset
+    else:
+        gss = GroupShuffleSplit(n_splits=n_splits, test_size=0.2)
+
+        groups = [video_to_participant[v.lower().replace('_clean', '')] for v in dataset.get_filenames()]
+        
+        train_idx, test_idx = list(gss.split(np.arange(len(targets)), targets, groups))[0]
+        
+        train_sampler = torch.utils.data.SubsetRandomSampler(train_idx)
+        train_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
+                                               sampler=train_sampler)
+        
+        test_sampler = torch.utils.data.SubsetRandomSampler(test_idx)
+        test_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
+                                               sampler=test_sampler)
+        
+        return train_loader, test_loader, dataset
+    
+def load_covid_clips(batch_size, yolo_model, mode, clip_height, clip_width, clip_depth, device=None, n_splits=None, classify_mode=False):   
+    video_path = "/home/dwh48@drexel.edu/covid19_ultrasound/data/pocus_videos"
+    
+    transforms = torchvision.transforms.Compose(
+    [VideoGrayScaler(),
+     MinMaxScaler(0, 255),
+    ])
+    augment_transforms = torchvision.transforms.Compose(
+    [torchvision.transforms.RandomRotation(45),
+     torchvision.transforms.RandomHorizontalFlip(),
+     torchvision.transforms.Resize((clip_height, clip_width))
+    ])
+
+    dataset = COVID19Loader(yolo_model, video_path, clip_depth, classify_mode=classify_mode, transform=transforms, augmentation=augment_transforms)
+    
+    targets = dataset.get_labels()
+    
+    if mode == 'leave_one_out':
+        gss = LeaveOneGroupOut()
+
+        groups = [v for v in dataset.get_filenames()]
+        
+        return gss.split(np.arange(len(targets)), targets, groups), dataset
+    elif mode == 'all_train':
+        train_idx = np.arange(len(targets))
+        
+        return [(train_idx, None)], dataset
+    elif mode == 'k_fold':
+        gss = StratifiedGroupKFold(n_splits=n_splits)
+
+        groups = [v for v in dataset.get_filenames()]
+        
+        return gss.split(np.arange(len(targets)), targets, groups), dataset
+    else:
+        gss = GroupShuffleSplit(n_splits=n_splits, test_size=0.2)
+
+        groups = [v for v in dataset.get_filenames()]
+        
+        train_idx, test_idx = list(gss.split(np.arange(len(targets)), targets, groups))[0]
+        
+        train_sampler = torch.utils.data.SubsetRandomSampler(train_idx)
+        train_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
+                                               sampler=train_sampler)
+        
+        test_sampler = torch.utils.data.SubsetRandomSampler(test_idx)
+        test_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
+                                               sampler=test_sampler)
+        
+        return train_loader, test_loader, dataset
\ No newline at end of file
diff --git a/sparse_coding_torch/ptx/train_classifier.py b/sparse_coding_torch/ptx/train_classifier.py
index 4152869aed14b87275b95667a876b9251e54e0db..306769b9a16a4decc52e4e0f1dd03bf49c2bd623 100644
--- a/sparse_coding_torch/ptx/train_classifier.py
+++ b/sparse_coding_torch/ptx/train_classifier.py
@@ -4,7 +4,7 @@ import torch.nn.functional as F
 from tqdm import tqdm
 import argparse
 import os
-from sparse_coding_torch.ptx.load_data import load_yolo_clips
+from sparse_coding_torch.ptx.load_data import load_yolo_clips, load_covid_clips
 from sparse_coding_torch.sparse_model import SparseCode, ReconSparse, normalize_weights, normalize_weights_3d
 from sparse_coding_torch.ptx.classifier_model import PTXClassifier, VAEEncoderPTX, PTXVAEClassifier
 import time
@@ -20,6 +20,7 @@ import torchvision
 import glob
 from torchvision.datasets.video_utils import VideoClips
 import cv2
+import copy
 
 configproto = tf.compat.v1.ConfigProto()
 configproto.gpu_options.polling_inactive_delay_msecs = 5000
@@ -117,6 +118,60 @@ def calculate_ptx_scores(input_videos, labels, yolo_model, sparse_model, recon_m
         
     return np.array(final_list), np.array(numerical_labels), fn_ids, fp_ids, sum(clip_correct) / len(clip_correct)
 
+def calculate_covid_scores(input_videos, labels, sparse_model, recon_model, classifier_model, image_width, image_height, transform):
+    all_predictions = []
+
+    final_list = []
+    clip_correct = []
+    fp_ids = []
+    fn_ids = []
+    for v_idx, f in tqdm(enumerate(input_videos)):
+        clipstride = 15
+        
+        vc = VideoClips([f],
+                        clip_length_in_frames=5,
+                        frame_rate=20,
+                       frames_between_clips=clipstride)
+
+        clip_predictions = []
+        cliplist = []
+        countclips = 0
+        for clip_idx in range(vc.num_clips()):
+            try:
+                clip, _, _, _ = vc.get_clip(clip_idx)
+            except Exception:
+                continue
+            clip = clip.swapaxes(1, 3).swapaxes(0, 1).swapaxes(2, 3)
+
+            clip = transform(clip)
+            cliplist.append(clip)
+
+        if len(cliplist) > 0:
+            with torch.no_grad():
+                clip = torch.stack(cliplist)
+                images = clip.permute(0, 2, 3, 4, 1).numpy()
+                activations = tf.stop_gradient(sparse_model([images, tf.stop_gradient(tf.expand_dims(recon_model.weights[0], axis=0))]))
+
+                pred = classifier_model(activations)
+
+                clip_predictions = tf.math.argmax(tf.math.softmax(pred, axis=1), axis=1)
+
+            final_pred = torch.mode(torch.tensor(clip_predictions.numpy()).view(-1))[0].item()
+        else:
+            final_pred = 0.0
+            
+        if final_pred != labels[v_idx]:
+            if final_pred == 0.0:
+                fn_ids.append(f)
+            else:
+                fp_ids.append(f)
+            
+        final_list.append(final_pred)
+        
+        clip_correct.extend([1 if clip_pred == labels[v_idx] else 0 for clip_pred in clip_predictions])
+        
+    return np.array(final_list), labels, fn_ids, fp_ids, sum(clip_correct) / len(clip_correct)
+
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument('--batch_size', default=12, type=int)
@@ -148,11 +203,12 @@ if __name__ == "__main__":
     parser.add_argument('--scale_factor', type=int, default=1)
     parser.add_argument('--clip_depth', type=int, default=5)
     parser.add_argument('--frames_to_skip', type=int, default=1)
+    parser.add_argument('--dataset', type=str, default='bamc')
     
     args = parser.parse_args()
     
-    image_height = 100
-    image_width = 200
+    image_height = args.crop_height
+    image_width = args.crop_width
     clip_depth = args.clip_depth
         
     batch_size = args.batch_size
@@ -192,8 +248,21 @@ if __name__ == "__main__":
     if args.sparse_checkpoint:
         recon_model.set_weights(keras.models.load_model(args.sparse_checkpoint).get_weights())
         
-    splits, dataset = load_yolo_clips(args.batch_size, num_clips=1, num_positives=15, mode=args.splits, device=None, n_splits=args.n_splits, sparse_model=None, whole_video=False, positive_videos='positive_videos.json')
-    positive_class = 'No_Sliding'
+    data_augmentation = keras.Sequential([
+        keras.layers.RandomFlip('horizontal'),
+        keras.layers.RandomRotation(45)
+    ])
+    
+#     augment_transforms = torchvision.transforms.Compose(
+#     [torchvision.transforms.RandomRotation(45),
+#      torchvision.transforms.RandomHorizontalFlip(),
+#      torchvision.transforms.CenterCrop((100, 200))
+#     ])
+    
+    if args.dataset == 'covid':
+        splits, dataset = load_covid_clips(batch_size=args.batch_size, mode=args.splits, clip_width=image_width, clip_height=image_height, clip_depth=clip_depth, n_splits=args.n_splits)
+    else:
+        splits, dataset = load_yolo_clips(args.batch_size, num_clips=1, num_positives=15, mode=args.splits, device=None, n_splits=args.n_splits, sparse_model=None, whole_video=False, positive_videos='positive_videos.json')
 
     overall_true = []
     overall_pred = []
@@ -202,26 +271,24 @@ if __name__ == "__main__":
     
     i_fold = 0
     for train_idx, test_idx in splits:
-        train_sampler = torch.utils.data.SubsetRandomSampler(train_idx)
-        train_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
-                                               sampler=train_sampler)
-        
+        train_loader = copy.deepcopy(dataset)
+        train_loader.set_indicies(train_idx)
         if test_idx is not None:
-            test_sampler = torch.utils.data.SubsetRandomSampler(test_idx)
-            test_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
-                                                   sampler=test_sampler)
-            
-#             with open(os.path.join(args.output_dir, 'test_videos_{}.txt'.format(i_fold)), 'w+') as test_videos_out:
-#                 test_set = set([x for tup in test_loader for x in tup[2]])
-#                 test_videos_out.writelines(test_set)
-        else:
-            test_loader = None
+            test_loader = copy.deepcopy(dataset)
+            test_loader.set_indicies(test_idx)
+            test_tf = tf.data.Dataset.from_tensor_slices((test_loader.get_frames(), test_loader.get_labels()))
+
+        train_tf = tf.data.Dataset.from_tensor_slices((train_loader.get_frames(), train_loader.get_labels()))
+        
+        num_output = 1
+        if args.dataset == 'covid':
+            num_output = len(set(train_loader.get_unique_labels()))
         
         if args.checkpoint:
             classifier_model = keras.models.load_model(args.checkpoint)
         else:
             classifier_inputs = keras.Input(shape=((clip_depth - args.kernel_depth) // 1 + 1, (image_height - args.kernel_size) // args.stride + 1, (image_width - args.kernel_size) // args.stride + 1, args.num_kernels))
-            classifier_outputs = PTXClassifier()(classifier_inputs)
+            classifier_outputs = PTXClassifier(num_output)(classifier_inputs)
 
             classifier_model = keras.Model(inputs=classifier_inputs, outputs=classifier_outputs)
 
@@ -230,7 +297,10 @@ if __name__ == "__main__":
 
         best_so_far = float('-inf')
 
-        criterion = keras.losses.BinaryCrossentropy(from_logits=True, reduction=keras.losses.Reduction.SUM)
+        if args.dataset == 'bamc':
+            criterion = keras.losses.BinaryCrossentropy(from_logits=True, reduction=keras.losses.Reduction.SUM)
+        else:
+            criterion = keras.losses.SparseCategoricalCrossentropy(from_logits=True, reduction=keras.losses.Reduction.SUM)
 
         if args.train:
             for epoch in range(args.epochs):
@@ -240,11 +310,11 @@ if __name__ == "__main__":
                 y_true_train = None
                 y_pred_train = None
 
-                for labels, local_batch, vid_f in tqdm(train_loader):
-                    images = local_batch.permute(0, 2, 3, 4, 1).numpy()
-                    torch_labels = np.zeros(len(labels))
-                    torch_labels[[i for i in range(len(labels)) if labels[i] == positive_class]] = 1
-                    torch_labels = np.expand_dims(torch_labels, axis=1)
+                for images, labels in tqdm(train_tf.shuffle(len(train_tf)).batch(args.batch_size)):
+#                     print(labels)
+#                     alter = torch.stack([augment_transforms(t) for t in torch.tensor(np.array(images))])
+#                     images = alter.swapaxes(1, 2).swapaxes(2, 3).swapaxes(3, 4).numpy()
+                    images = tf.transpose(images, [0, 2, 3, 4, 1])
 
                     if args.train_sparse:
                         with tf.GradientTape() as tape:
@@ -254,13 +324,16 @@ if __name__ == "__main__":
 
                             print(loss)
                     else:
-                        activations = tf.stop_gradient(sparse_model([images, tf.stop_gradient(tf.expand_dims(recon_model.trainable_weights[0], axis=0))]))
+                        images = tf.reshape(images, (images.shape[0], images.shape[2], images.shape[3], -1))
+                        alter = data_augmentation(images)
+                        alter = tf.reshape(alter, (images.shape[0], 5, alter.shape[1], alter.shape[2], 1))
+                        activations = tf.stop_gradient(sparse_model([alter, tf.stop_gradient(tf.expand_dims(recon_model.trainable_weights[0], axis=0))]))
 
                         with tf.GradientTape() as tape:
                             pred = classifier_model(activations)
-                            loss = criterion(torch_labels, pred)
+                            loss = criterion(labels, pred)
 
-                    epoch_loss += loss * local_batch.size(0)
+                    epoch_loss += loss * images.shape[0]
 
                     if args.train_sparse:
                         sparse_gradients, classifier_gradients = tape.gradient(loss, [recon_model.trainable_weights, classifier_model.trainable_weights])
@@ -280,11 +353,17 @@ if __name__ == "__main__":
                         prediction_optimizer.apply_gradients(zip(gradients, classifier_model.trainable_weights))
 
                     if y_true_train is None:
-                        y_true_train = torch_labels
-                        y_pred_train = tf.math.round(tf.math.sigmoid(pred))
+                        y_true_train = labels
+                        if args.dataset == 'bamc':
+                            y_pred_train = tf.math.round(tf.math.sigmoid(pred))
+                        else:
+                            y_pred_train = tf.math.argmax(tf.nn.softmax(pred, axis=1), axis=1)
                     else:
-                        y_true_train = tf.concat((y_true_train, torch_labels), axis=0)
-                        y_pred_train = tf.concat((y_pred_train, tf.math.round(tf.math.sigmoid(pred))), axis=0)
+                        y_true_train = tf.concat((y_true_train, labels), axis=0)
+                        if args.dataset == 'bamc':
+                            y_pred_train = tf.concat((y_pred_train, tf.math.round(tf.math.sigmoid(pred))), axis=0)
+                        else:
+                            y_pred_train = tf.concat((y_pred_train, tf.math.argmax(tf.nn.softmax(pred, axis=1), axis=1)), axis=0)
 
                 t2 = time.perf_counter()
 
@@ -292,29 +371,33 @@ if __name__ == "__main__":
                 y_pred = None
                 test_loss = 0.0
                 
-                eval_loader = test_loader
                 if args.splits == 'all_train':
-                    eval_loader = train_loader
-                for labels, local_batch, vid_f in tqdm(eval_loader):
-                    images = local_batch.permute(0, 2, 3, 4, 1).numpy()
-
-                    torch_labels = np.zeros(len(labels))
-                    torch_labels[[i for i in range(len(labels)) if labels[i] == positive_class]] = 1
-                    torch_labels = np.expand_dims(torch_labels, axis=1)
+                    eval_loader = train_tf
+                else:
+                    eval_loader = test_tf
+                for images, labels in tqdm(eval_loader.batch(args.batch_size)):
+                    images = tf.transpose(images, [0, 2, 3, 4, 1])
+#                     images = images.numpy().swapaxes(1, 2).swapaxes(2, 3).swapaxes(3, 4)
                     
                     activations = tf.stop_gradient(sparse_model([images, tf.stop_gradient(tf.expand_dims(recon_model.trainable_weights[0], axis=0))]))
 
                     pred = classifier_model(activations)
-                    loss = criterion(torch_labels, pred)
+                    loss = criterion(labels, pred)
 
-                    test_loss += loss
+                    test_loss += loss * images.shape[0]
 
                     if y_true is None:
-                        y_true = torch_labels
-                        y_pred = tf.math.round(tf.math.sigmoid(pred))
+                        y_true = labels
+                        if args.dataset == 'bamc':
+                            y_pred = tf.math.round(tf.math.sigmoid(pred))
+                        else:
+                            y_pred = tf.math.argmax(tf.nn.softmax(pred, axis=1), axis=1)
                     else:
-                        y_true = tf.concat((y_true, torch_labels), axis=0)
-                        y_pred = tf.concat((y_pred, tf.math.round(tf.math.sigmoid(pred))), axis=0)
+                        y_true = tf.concat((y_true, labels), axis=0)
+                        if args.dataset == 'bamc':
+                            y_pred = tf.concat((y_pred, tf.math.round(tf.math.sigmoid(pred))), axis=0)
+                        else:
+                            y_pred = tf.concat((y_pred, tf.math.argmax(tf.nn.softmax(pred, axis=1), axis=1)), axis=0)
 
                 t2 = time.perf_counter()
 
@@ -338,6 +421,9 @@ if __name__ == "__main__":
 #                     recon_model.save(os.path.join(output_dir, "best_sparse_model_{}.pt".format(i_fold)))
                     pickle.dump(prediction_optimizer.get_weights(), open(os.path.join(output_dir, 'optimizer_{}.pt'.format(i_fold)), 'wb+'))
                     best_so_far = f1
+        
+#                 if accuracy == 1.0:
+#                     break
 
             classifier_model = keras.models.load_model(os.path.join(output_dir, "best_classifier_{}.pt".format(i_fold)))
 #             recon_model = keras.models.load_model(os.path.join(output_dir, 'best_sparse_model_{}.pt'.format(i_fold)))
@@ -352,18 +438,31 @@ if __name__ == "__main__":
 
         t1 = time.perf_counter()
         
-        transform = torchvision.transforms.Compose(
-        [VideoGrayScaler(),
-         MinMaxScaler(0, 255),
-         torchvision.transforms.Normalize((0.2592,), (0.1251,)),
-         torchvision.transforms.CenterCrop((100, 200))
-        ])
-
-        test_dir = '/shared_data/bamc_ph1_test_data'
-        test_videos = glob.glob(os.path.join(test_dir, '*', '*.*'))
-        test_labels = [vid_f.split('/')[-2] for vid_f in test_videos]
-
-        y_pred, y_true, fn, fp, clip_acc = calculate_ptx_scores(test_videos, test_labels, yolo_model, sparse_model, recon_model, classifier_model, image_width, image_height, transform)
+        if args.dataset == 'bamc':
+            transform = torchvision.transforms.Compose(
+            [VideoGrayScaler(),
+             MinMaxScaler(0, 255),
+             torchvision.transforms.Normalize((0.2592,), (0.1251,)),
+             torchvision.transforms.CenterCrop((100, 200))
+            ])
+
+            test_dir = '/shared_data/bamc_ph1_test_data'
+            test_videos = glob.glob(os.path.join(test_dir, '*', '*.*'))
+            test_labels = [vid_f.split('/')[-2] for vid_f in test_videos]
+
+            y_pred, y_true, fn, fp, clip_acc = calculate_ptx_scores(test_videos, test_labels, yolo_model, sparse_model, recon_model, classifier_model, image_width, image_height, transform)
+        elif args.dataset == 'covid':
+            transform = torchvision.transforms.Compose(
+            [VideoGrayScaler(),
+             MinMaxScaler(0, 255),
+             torchvision.transforms.Resize((image_height, image_width))
+            ])
+
+            test_videos = test_loader.get_all_videos()
+
+            test_labels = test_loader.get_video_labels()
+
+            y_pred, y_true, fn, fp, clip_acc = calculate_covid_scores(test_videos, test_labels, sparse_model, recon_model, classifier_model, image_width, image_height, transform)
             
         t2 = time.perf_counter()
 
diff --git a/sparse_coding_torch/ptx/train_classifier_pytorch.py b/sparse_coding_torch/ptx/train_classifier_pytorch.py
new file mode 100644
index 0000000000000000000000000000000000000000..a72e40ab29ef76d7fd5489954b922ff61c2dcefc
--- /dev/null
+++ b/sparse_coding_torch/ptx/train_classifier_pytorch.py
@@ -0,0 +1,406 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from tqdm import tqdm
+import argparse
+import os
+from sparse_coding_torch.ptx.load_data_pytorch import load_yolo_clips
+from sparse_coding_torch.sparse_model import SparseCode, ReconSparse, normalize_weights, normalize_weights_3d
+from sparse_coding_torch.ptx.classifier_model import PTXClassifier, VAEEncoderPTX, PTXVAEClassifier
+import time
+import numpy as np
+from sklearn.metrics import f1_score, accuracy_score, confusion_matrix
+import random
+import pickle
+import tensorflow.keras as keras
+import tensorflow as tf
+from sparse_coding_torch.utils import VideoGrayScaler, MinMaxScaler
+from yolov4.get_bounding_boxes import YoloModel
+import torchvision
+import glob
+from torchvision.datasets.video_utils import VideoClips
+import cv2
+
+configproto = tf.compat.v1.ConfigProto()
+configproto.gpu_options.polling_inactive_delay_msecs = 5000
+configproto.gpu_options.allow_growth = True
+sess = tf.compat.v1.Session(config=configproto) 
+tf.compat.v1.keras.backend.set_session(sess)
+
+def calculate_ptx_scores(input_videos, labels, yolo_model, sparse_model, recon_model, classifier_model, image_width, image_height, transform):
+    all_predictions = []
+    
+    numerical_labels = []
+    for label in labels:
+        if label == 'No_Sliding':
+            numerical_labels.append(1.0)
+        else:
+            numerical_labels.append(0.0)
+
+    final_list = []
+    clip_correct = []
+    fp_ids = []
+    fn_ids = []
+    for v_idx, f in tqdm(enumerate(input_videos)):
+        clipstride = 15
+        
+        vc = VideoClips([f],
+                        clip_length_in_frames=5,
+                        frame_rate=20,
+                       frames_between_clips=clipstride)
+
+        clip_predictions = []
+        i = 0
+        cliplist = []
+        countclips = 0
+        for i in range(vc.num_clips()):
+            clip, _, _, _ = vc.get_clip(i)
+            clip = clip.swapaxes(1, 3).swapaxes(0, 1).swapaxes(2, 3).numpy()
+            
+            bounding_boxes, classes, scores = yolo_model.get_bounding_boxes(clip[:, 2, :, :].swapaxes(0, 2).swapaxes(0, 1))
+            bounding_boxes = bounding_boxes.squeeze(0)
+            if bounding_boxes.size == 0:
+                continue
+            #widths = []
+            countclips = countclips + len(bounding_boxes)
+            
+            widths = [(bounding_boxes[i][3] - bounding_boxes[i][1]) for i in range(len(bounding_boxes))]
+
+            ind =  np.argmax(np.array(widths))
+
+            bb = bounding_boxes[ind]
+            center_x = (bb[3] + bb[1]) / 2 * 1920
+            center_y = (bb[2] + bb[0]) / 2 * 1080
+
+            width=400
+            height=400
+
+            lower_y = round(center_y - height / 2)
+            upper_y = round(center_y + height / 2)
+            lower_x = round(center_x - width / 2)
+            upper_x = round(center_x + width / 2)
+
+            trimmed_clip = clip[:, :, lower_y:upper_y, lower_x:upper_x]
+
+            trimmed_clip = torch.tensor(trimmed_clip).to(torch.float)
+
+            trimmed_clip = transform(trimmed_clip)
+            trimmed_clip.pin_memory()
+            cliplist.append(trimmed_clip)
+
+        if len(cliplist) > 0:
+            with torch.no_grad():
+                trimmed_clip = torch.stack(cliplist)
+                images = trimmed_clip.permute(0, 2, 3, 4, 1).numpy()
+                activations = tf.stop_gradient(sparse_model([images, tf.stop_gradient(tf.expand_dims(recon_model.weights[0], axis=0))]))
+
+                pred = classifier_model(activations)
+
+                clip_predictions = tf.math.round(tf.math.sigmoid(pred))
+
+            final_pred = torch.mode(torch.tensor(clip_predictions.numpy()).view(-1))[0].item()
+            if len(clip_predictions) % 2 == 0 and tf.math.reduce_sum(clip_predictions) == len(clip_predictions)//2:
+                #print("I'm here")
+                final_pred = torch.mode(torch.tensor(clip_predictions.numpy()).view(-1))[0].item()
+        else:
+            final_pred = 1.0
+            
+        if final_pred != numerical_labels[v_idx]:
+            if final_pred == 0.0:
+                fn_ids.append(f)
+            else:
+                fp_ids.append(f)
+            
+        final_list.append(final_pred)
+        
+        clip_correct.extend([1 if clip_pred == numerical_labels[v_idx] else 0 for clip_pred in clip_predictions])
+        
+    return np.array(final_list), np.array(numerical_labels), fn_ids, fp_ids, sum(clip_correct) / len(clip_correct)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--batch_size', default=12, type=int)
+    parser.add_argument('--kernel_size', default=15, type=int)
+    parser.add_argument('--kernel_depth', default=5, type=int)
+    parser.add_argument('--num_kernels', default=64, type=int)
+    parser.add_argument('--stride', default=1, type=int)
+    parser.add_argument('--max_activation_iter', default=150, type=int)
+    parser.add_argument('--activation_lr', default=1e-2, type=float)
+    parser.add_argument('--lr', default=5e-4, type=float)
+    parser.add_argument('--epochs', default=40, type=int)
+    parser.add_argument('--lam', default=0.05, type=float)
+    parser.add_argument('--output_dir', default='./output', type=str)
+    parser.add_argument('--sparse_checkpoint', default=None, type=str)
+    parser.add_argument('--checkpoint', default=None, type=str)
+    parser.add_argument('--splits', default=None, type=str, help='k_fold or leave_one_out or all_train')
+    parser.add_argument('--seed', default=26, type=int)
+    parser.add_argument('--train', action='store_true')
+    parser.add_argument('--num_positives', default=15, type=int)
+    parser.add_argument('--n_splits', default=5, type=int)
+    parser.add_argument('--save_train_test_splits', action='store_true')
+    parser.add_argument('--run_2d', action='store_true')
+    parser.add_argument('--balance_classes', action='store_true')
+    parser.add_argument('--train_sparse', action='store_true')
+    parser.add_argument('--mixing_ratio', type=float, default=1.0)
+    parser.add_argument('--sparse_lr', type=float, default=0.003)
+    parser.add_argument('--crop_height', type=int, default=285)
+    parser.add_argument('--crop_width', type=int, default=350)
+    parser.add_argument('--scale_factor', type=int, default=1)
+    parser.add_argument('--clip_depth', type=int, default=5)
+    parser.add_argument('--frames_to_skip', type=int, default=1)
+    
+    args = parser.parse_args()
+    
+    image_height = 100
+    image_width = 200
+    clip_depth = args.clip_depth
+        
+    batch_size = args.batch_size
+    
+#     random.seed(args.seed)
+#     np.random.seed(args.seed)
+#     torch.manual_seed(args.seed)
+    
+    output_dir = args.output_dir
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+        
+    with open(os.path.join(output_dir, 'arguments.txt'), 'w+') as out_f:
+        out_f.write(str(args))
+    
+    yolo_model = YoloModel('ptx')
+
+    all_errors = []
+    
+    if args.run_2d:
+        inputs = keras.Input(shape=(image_height, image_width, clip_depth))
+    else:
+        inputs = keras.Input(shape=(clip_depth, image_height, image_width, 1))
+        
+    filter_inputs = keras.Input(shape=(args.kernel_depth, args.kernel_size, args.kernel_size, 1, args.num_kernels), dtype='float32')
+
+    output = SparseCode(batch_size=args.batch_size, image_height=image_height, image_width=image_width, clip_depth=clip_depth, in_channels=1, out_channels=args.num_kernels, kernel_size=args.kernel_size, kernel_depth=args.kernel_depth, stride=args.stride, lam=args.lam, activation_lr=args.activation_lr, max_activation_iter=args.max_activation_iter, run_2d=args.run_2d)(inputs, filter_inputs)
+
+    sparse_model = keras.Model(inputs=(inputs, filter_inputs), outputs=output)
+
+    recon_inputs = keras.Input(shape=((clip_depth - args.kernel_depth) // 1 + 1, (image_height - args.kernel_size) // args.stride + 1, (image_width - args.kernel_size) // args.stride + 1, args.num_kernels))
+
+    recon_outputs = ReconSparse(batch_size=args.batch_size, image_height=image_height, image_width=image_width, clip_depth=clip_depth, in_channels=1, out_channels=args.num_kernels, kernel_size=args.kernel_size, kernel_depth=args.kernel_depth, stride=args.stride, lam=args.lam, activation_lr=args.activation_lr, max_activation_iter=args.max_activation_iter, run_2d=args.run_2d)(recon_inputs)
+
+    recon_model = keras.Model(inputs=recon_inputs, outputs=recon_outputs)
+
+    if args.sparse_checkpoint:
+        recon_model.set_weights(keras.models.load_model(args.sparse_checkpoint).get_weights())
+        
+    splits, dataset = load_yolo_clips(args.batch_size, num_clips=1, num_positives=args.num_positives, mode=args.splits, device=None, n_splits=args.n_splits, sparse_model=None, whole_video=False, positive_videos='positive_videos.json')
+    positive_class = 'No_Sliding'
+
+    overall_true = []
+    overall_pred = []
+    fn_ids = []
+    fp_ids = []
+    
+    i_fold = 0
+    for train_idx, test_idx in splits:
+        train_sampler = torch.utils.data.SubsetRandomSampler(train_idx)
+        train_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
+                                               sampler=train_sampler)
+        
+        if test_idx is not None:
+            test_sampler = torch.utils.data.SubsetRandomSampler(test_idx)
+            test_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
+                                                   sampler=test_sampler)
+            
+#             with open(os.path.join(args.output_dir, 'test_videos_{}.txt'.format(i_fold)), 'w+') as test_videos_out:
+#                 test_set = set([x for tup in test_loader for x in tup[2]])
+#                 test_videos_out.writelines(test_set)
+        else:
+            test_loader = None
+        
+        if args.checkpoint:
+            classifier_model = keras.models.load_model(args.checkpoint)
+        else:
+            classifier_inputs = keras.Input(shape=((clip_depth - args.kernel_depth) // 1 + 1, (image_height - args.kernel_size) // args.stride + 1, (image_width - args.kernel_size) // args.stride + 1, args.num_kernels))
+            classifier_outputs = PTXClassifier(1)(classifier_inputs)
+
+            classifier_model = keras.Model(inputs=classifier_inputs, outputs=classifier_outputs)
+
+        prediction_optimizer = keras.optimizers.Adam(learning_rate=args.lr)
+        filter_optimizer = tf.keras.optimizers.SGD(learning_rate=args.sparse_lr)
+
+        best_so_far = float('-inf')
+
+        criterion = keras.losses.BinaryCrossentropy(from_logits=True, reduction=keras.losses.Reduction.SUM)
+
+        if args.train:
+            for epoch in range(args.epochs):
+                epoch_loss = 0
+                t1 = time.perf_counter()
+
+                y_true_train = None
+                y_pred_train = None
+
+                for labels, local_batch, vid_f in tqdm(train_loader):
+                    images = local_batch.permute(0, 2, 3, 4, 1).numpy()
+                    torch_labels = np.zeros(len(labels))
+                    torch_labels[[i for i in range(len(labels)) if labels[i] == positive_class]] = 1
+                    torch_labels = np.expand_dims(torch_labels, axis=1)
+
+                    if args.train_sparse:
+                        with tf.GradientTape() as tape:
+#                             activations = sparse_model([images, tf.expand_dims(recon_model.trainable_weights[0], axis=0)])
+                            pred = classifier_model(activations)
+                            loss = criterion(torch_labels, pred)
+
+                            print(loss)
+                    else:
+                        activations = tf.stop_gradient(sparse_model([images, tf.stop_gradient(tf.expand_dims(recon_model.trainable_weights[0], axis=0))]))
+
+                        with tf.GradientTape() as tape:
+                            pred = classifier_model(activations)
+                            loss = criterion(torch_labels, pred)
+
+                    epoch_loss += loss * local_batch.size(0)
+
+                    if args.train_sparse:
+                        sparse_gradients, classifier_gradients = tape.gradient(loss, [recon_model.trainable_weights, classifier_model.trainable_weights])
+
+                        prediction_optimizer.apply_gradients(zip(classifier_gradients, classifier_model.trainable_weights))
+
+                        filter_optimizer.apply_gradients(zip(sparse_gradients, recon_model.trainable_weights))
+
+                        if args.run_2d:
+                            weights = normalize_weights(recon_model.get_weights(), args.num_kernels)
+                        else:
+                            weights = normalize_weights_3d(recon_model.get_weights(), args.num_kernels)
+                        recon_model.set_weights(weights)
+                    else:
+                        gradients = tape.gradient(loss, classifier_model.trainable_weights)
+
+                        prediction_optimizer.apply_gradients(zip(gradients, classifier_model.trainable_weights))
+
+                    if y_true_train is None:
+                        y_true_train = torch_labels
+                        y_pred_train = tf.math.round(tf.math.sigmoid(pred))
+                    else:
+                        y_true_train = tf.concat((y_true_train, torch_labels), axis=0)
+                        y_pred_train = tf.concat((y_pred_train, tf.math.round(tf.math.sigmoid(pred))), axis=0)
+
+                t2 = time.perf_counter()
+
+                y_true = None
+                y_pred = None
+                test_loss = 0.0
+                
+                eval_loader = test_loader
+                if args.splits == 'all_train':
+                    eval_loader = train_loader
+                for labels, local_batch, vid_f in tqdm(eval_loader):
+                    images = local_batch.permute(0, 2, 3, 4, 1).numpy()
+
+                    torch_labels = np.zeros(len(labels))
+                    torch_labels[[i for i in range(len(labels)) if labels[i] == positive_class]] = 1
+                    torch_labels = np.expand_dims(torch_labels, axis=1)
+                    
+                    activations = tf.stop_gradient(sparse_model([images, tf.stop_gradient(tf.expand_dims(recon_model.trainable_weights[0], axis=0))]))
+
+                    pred = classifier_model(activations)
+                    loss = criterion(torch_labels, pred)
+
+                    test_loss += loss
+
+                    if y_true is None:
+                        y_true = torch_labels
+                        y_pred = tf.math.round(tf.math.sigmoid(pred))
+                    else:
+                        y_true = tf.concat((y_true, torch_labels), axis=0)
+                        y_pred = tf.concat((y_pred, tf.math.round(tf.math.sigmoid(pred))), axis=0)
+
+                t2 = time.perf_counter()
+
+                y_true = tf.cast(y_true, tf.int32)
+                y_pred = tf.cast(y_pred, tf.int32)
+
+                y_true_train = tf.cast(y_true_train, tf.int32)
+                y_pred_train = tf.cast(y_pred_train, tf.int32)
+
+                f1 = f1_score(y_true, y_pred, average='macro')
+                accuracy = accuracy_score(y_true, y_pred)
+
+                train_accuracy = accuracy_score(y_true_train, y_pred_train)
+
+                print('epoch={}, i_fold={}, time={:.2f}, train_loss={:.2f}, test_loss={:.2f}, train_acc={:.2f}, test_f1={:.2f}, test_acc={:.2f}'.format(epoch, i_fold, t2-t1, epoch_loss, test_loss, train_accuracy, f1, accuracy))
+    #             print(epoch_loss)
+                if f1 >= best_so_far:
+                    print("found better model")
+                    # Save model parameters
+                    classifier_model.save(os.path.join(output_dir, "best_classifier_{}.pt".format(i_fold)))
+#                     recon_model.save(os.path.join(output_dir, "best_sparse_model_{}.pt".format(i_fold)))
+                    pickle.dump(prediction_optimizer.get_weights(), open(os.path.join(output_dir, 'optimizer_{}.pt'.format(i_fold)), 'wb+'))
+                    best_so_far = f1
+
+            classifier_model = keras.models.load_model(os.path.join(output_dir, "best_classifier_{}.pt".format(i_fold)))
+#             recon_model = keras.models.load_model(os.path.join(output_dir, 'best_sparse_model_{}.pt'.format(i_fold)))
+
+        epoch_loss = 0
+
+        y_true = None
+        y_pred = None
+
+        pred_dict = {}
+        gt_dict = {}
+
+        t1 = time.perf_counter()
+        
+        transform = torchvision.transforms.Compose(
+        [VideoGrayScaler(),
+         MinMaxScaler(0, 255),
+         torchvision.transforms.Normalize((0.2592,), (0.1251,)),
+         torchvision.transforms.CenterCrop((100, 200))
+        ])
+
+        test_dir = '/shared_data/bamc_ph1_test_data'
+        test_videos = glob.glob(os.path.join(test_dir, '*', '*.*'))
+        test_labels = [vid_f.split('/')[-2] for vid_f in test_videos]
+
+        y_pred, y_true, fn, fp, clip_acc = calculate_ptx_scores(test_videos, test_labels, yolo_model, sparse_model, recon_model, classifier_model, image_width, image_height, transform)
+            
+        t2 = time.perf_counter()
+
+        print('i_fold={}, time={:.2f}'.format(i_fold, t2-t1))
+
+        y_true = tf.cast(y_true, tf.int32)
+        y_pred = tf.cast(y_pred, tf.int32)
+
+        f1 = f1_score(y_true, y_pred, average='macro')
+        accuracy = accuracy_score(y_true, y_pred)
+
+        fn_ids.extend(fn)
+        fp_ids.extend(fp)
+
+        overall_true.extend(y_true)
+        overall_pred.extend(y_pred)
+
+        print("Test f1={:.2f}, vid_acc={:.2f}, clip_acc={:.2f}".format(f1, accuracy, clip_acc))
+
+        print(confusion_matrix(y_true, y_pred))
+            
+        i_fold += 1
+
+    fp_fn_file = os.path.join(args.output_dir, 'fp_fn.txt')
+    with open(fp_fn_file, 'w+') as in_f:
+        in_f.write('FP:\n')
+        in_f.write(str(fp_ids) + '\n\n')
+        in_f.write('FN:\n')
+        in_f.write(str(fn_ids) + '\n\n')
+        
+    overall_true = np.array(overall_true)
+    overall_pred = np.array(overall_pred)
+            
+    final_f1 = f1_score(overall_true, overall_pred, average='macro')
+    final_acc = accuracy_score(overall_true, overall_pred)
+    final_conf = confusion_matrix(overall_true, overall_pred)
+            
+    print("Final accuracy={:.2f}, f1={:.2f}".format(final_acc, final_f1))
+    print(final_conf)
+
diff --git a/sparse_coding_torch/ptx/train_classifier_vae.py b/sparse_coding_torch/ptx/train_classifier_vae.py
index 6d884b8607bd09d481d0960263f84718fcc915d7..30db2c4cd1619fe29678bf563bc12f78bd37f8bb 100644
--- a/sparse_coding_torch/ptx/train_classifier_vae.py
+++ b/sparse_coding_torch/ptx/train_classifier_vae.py
@@ -4,7 +4,7 @@ import torch.nn.functional as F
 from tqdm import tqdm
 import argparse
 import os
-from sparse_coding_torch.ptx.load_data import load_yolo_clips
+from sparse_coding_torch.ptx.load_data import load_yolo_clips, load_covid_clips
 from sparse_coding_torch.sparse_model import SparseCode, ReconSparse, normalize_weights, normalize_weights_3d
 from sparse_coding_torch.ptx.classifier_model import PTXClassifier, VAEEncoderPTX, PTXVAEClassifier
 import time
@@ -20,6 +20,7 @@ import torchvision
 import glob
 from torchvision.datasets.video_utils import VideoClips
 import cv2
+import copy
 
 configproto = tf.compat.v1.ConfigProto()
 configproto.gpu_options.polling_inactive_delay_msecs = 5000
@@ -116,7 +117,62 @@ def calculate_ptx_scores(input_videos, labels, yolo_model, encoder_model, classi
         
         clip_correct.extend([1 if clip_pred == numerical_labels[v_idx] else 0 for clip_pred in clip_predictions])
         
-    return np.array(final_list), np.array(numerical_labels), fn_ids, fp_ids, sum(clip_correct) / len(clip_correct)
+    return np.array(final_list), np.array(numerical_labels), fn_ids, fp_ids, 0
+
+def calculate_covid_scores(input_videos, labels, classifier_model, image_width, image_height, transform):
+    all_predictions = []
+
+    final_list = []
+    clip_correct = []
+    fp_ids = []
+    fn_ids = []
+    for v_idx, f in tqdm(enumerate(input_videos)):
+        clipstride = 15
+        
+        vc = VideoClips([f],
+                        clip_length_in_frames=5,
+                        frame_rate=20,
+                       frames_between_clips=clipstride)
+
+        clip_predictions = []
+        cliplist = []
+        countclips = 0
+        for clip_idx in range(vc.num_clips()):
+            try:
+                clip, _, _, _ = vc.get_clip(clip_idx)
+            except Exception:
+                continue
+            clip = clip.swapaxes(1, 3).swapaxes(0, 1).swapaxes(2, 3)
+
+            clip = transform(clip)
+            cliplist.append(clip)
+
+        if len(cliplist) > 0:
+            with torch.no_grad():
+                trimmed_clip = torch.stack(cliplist)
+                images = trimmed_clip.permute(0, 2, 3, 4, 1).numpy()
+                z, _, _ = tf.stop_gradient(encoder_model(images))
+
+                pred = classifier_model(z)
+
+                clip_predictions = tf.math.argmax(tf.math.softmax(pred, axis=1), axis=1)
+
+            final_pred = torch.mode(torch.tensor(clip_predictions.numpy()).view(-1))[0].item()
+        else:
+            final_pred = 0.0
+            
+        if final_pred != labels[v_idx]:
+            if final_pred == 0.0:
+                fn_ids.append(f)
+            else:
+                fp_ids.append(f)
+            
+        final_list.append(final_pred)
+        
+#         print(clip_pred)
+#         clip_correct.extend([1 if clip_pred == labels[v_idx] else 0 for clip_pred in clip_predictions])
+        
+    return np.array(final_list), labels, fn_ids, fp_ids, 0
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
@@ -139,11 +195,12 @@ if __name__ == "__main__":
     parser.add_argument('--clip_depth', type=int, default=5)
     parser.add_argument('--frames_to_skip', type=int, default=1)
     parser.add_argument('--latent_dim', type=int, default=1000)
+    parser.add_argument('--dataset', type=str, default='bamc')
     
     args = parser.parse_args()
     
-    image_height = 100
-    image_width = 200
+    image_height = args.crop_height
+    image_width = args.crop_width
     clip_depth = args.clip_depth
         
     batch_size = args.batch_size
@@ -168,7 +225,15 @@ if __name__ == "__main__":
     if args.vae_checkpoint:
         encoder_model.set_weights(keras.models.load_model(args.vae_checkpoint).get_weights())
         
-    splits, dataset = load_yolo_clips(args.batch_size, num_clips=1, num_positives=15, mode=args.splits, device=None, n_splits=args.n_splits, sparse_model=None, whole_video=False, positive_videos='positive_videos.json')
+    data_augmentation = keras.Sequential([
+        keras.layers.RandomFlip('horizontal'),
+        keras.layers.RandomRotation(45)
+    ])
+        
+    if args.dataset == 'covid':
+        splits, dataset = load_covid_clips(batch_size=args.batch_size, mode=args.splits, clip_width=image_width, clip_height=image_height, clip_depth=clip_depth, n_splits=args.n_splits)
+    else:
+        splits, dataset = load_yolo_clips(args.batch_size, num_clips=1, num_positives=15, mode=args.splits, device=None, n_splits=args.n_splits, sparse_model=None, whole_video=False, positive_videos='positive_videos.json')
     positive_class = 'No_Sliding'
 
     overall_true = []
@@ -178,22 +243,24 @@ if __name__ == "__main__":
     
     i_fold = 0
     for train_idx, test_idx in splits:
-        train_sampler = torch.utils.data.SubsetRandomSampler(train_idx)
-        train_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
-                                               sampler=train_sampler)
-        
+        train_loader = copy.deepcopy(dataset)
+        train_loader.set_indicies(train_idx)
         if test_idx is not None:
-            test_sampler = torch.utils.data.SubsetRandomSampler(test_idx)
-            test_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
-                                                   sampler=test_sampler)
-        else:
-            test_loader = None
+            test_loader = copy.deepcopy(dataset)
+            test_loader.set_indicies(test_idx)
+            test_tf = tf.data.Dataset.from_tensor_slices((test_loader.get_frames(), test_loader.get_labels()))
+
+        train_tf = tf.data.Dataset.from_tensor_slices((train_loader.get_frames(), train_loader.get_labels()))
+        
+        num_output = 1
+        if args.dataset == 'covid':
+            num_output = len(set(train_loader.get_unique_labels()))
         
         if args.checkpoint:
             classifier_model = keras.models.load_model(args.checkpoint)
         else:
             classifier_inputs = keras.Input(shape=(args.latent_dim))
-            classifier_outputs = PTXVAEClassifier()(classifier_inputs)
+            classifier_outputs = PTXVAEClassifier(num_output)(classifier_inputs)
 
             classifier_model = keras.Model(inputs=classifier_inputs, outputs=classifier_outputs)
 
@@ -201,7 +268,10 @@ if __name__ == "__main__":
 
         best_so_far = float('-inf')
 
-        criterion = keras.losses.BinaryCrossentropy(from_logits=True, reduction=keras.losses.Reduction.SUM)
+        if args.dataset == 'bamc':
+            criterion = keras.losses.BinaryCrossentropy(from_logits=True, reduction=keras.losses.Reduction.SUM)
+        else:
+            criterion = keras.losses.SparseCategoricalCrossentropy(from_logits=True, reduction=keras.losses.Reduction.SUM)
 
         if args.train:
             for epoch in range(args.epochs):
@@ -211,30 +281,37 @@ if __name__ == "__main__":
                 y_true_train = None
                 y_pred_train = None
 
-                for labels, local_batch, vid_f in tqdm(train_loader):
-                    images = local_batch.permute(0, 2, 3, 4, 1).numpy()
-                    torch_labels = np.zeros(len(labels))
-                    torch_labels[[i for i in range(len(labels)) if labels[i] == positive_class]] = 1
-                    torch_labels = np.expand_dims(torch_labels, axis=1)
+                for images, labels in tqdm(train_tf.batch(args.batch_size)):
+                    images = tf.transpose(images, [0, 2, 3, 4, 1])
+                    
+                    images = tf.reshape(images, (-1, images.shape[2], images.shape[3], images.shape[4]))
+                    images = data_augmentation(images)
+                    images = tf.reshape(images, (-1, 5, images.shape[1], images.shape[2], images.shape[3]))
 
                     z, _, _ = tf.stop_gradient(encoder_model(images))
 
                     with tf.GradientTape() as tape:
                         pred = classifier_model(z)
-                        loss = criterion(torch_labels, pred)
+                        loss = criterion(labels, pred)
 
-                    epoch_loss += loss * local_batch.size(0)
+                    epoch_loss += loss * images.shape[0]
 
                     gradients = tape.gradient(loss, classifier_model.trainable_weights)
 
                     prediction_optimizer.apply_gradients(zip(gradients, classifier_model.trainable_weights))
 
                     if y_true_train is None:
-                        y_true_train = torch_labels
-                        y_pred_train = tf.math.round(tf.math.sigmoid(pred))
+                        y_true_train = labels
+                        if args.dataset == 'bamc':
+                            y_pred_train = tf.math.round(tf.math.sigmoid(pred))
+                        else:
+                            y_pred_train = tf.math.argmax(tf.nn.softmax(pred, axis=1), axis=1)
                     else:
-                        y_true_train = tf.concat((y_true_train, torch_labels), axis=0)
-                        y_pred_train = tf.concat((y_pred_train, tf.math.round(tf.math.sigmoid(pred))), axis=0)
+                        y_true_train = tf.concat((y_true_train, labels), axis=0)
+                        if args.dataset == 'bamc':
+                            y_pred_train = tf.concat((y_pred_train, tf.math.round(tf.math.sigmoid(pred))), axis=0)
+                        else:
+                            y_pred_train = tf.concat((y_pred_train, tf.math.argmax(tf.nn.softmax(pred, axis=1), axis=1)), axis=0)
 
                 t2 = time.perf_counter()
 
@@ -242,29 +319,31 @@ if __name__ == "__main__":
                 y_pred = None
                 test_loss = 0.0
                 
-                eval_loader = test_loader
+                eval_loader = test_tf
                 if args.splits == 'all_train':
-                    eval_loader = train_loader
-                for labels, local_batch, vid_f in tqdm(eval_loader):
-                    images = local_batch.permute(0, 2, 3, 4, 1).numpy()
-
-                    torch_labels = np.zeros(len(labels))
-                    torch_labels[[i for i in range(len(labels)) if labels[i] == positive_class]] = 1
-                    torch_labels = np.expand_dims(torch_labels, axis=1)
+                    eval_loader = train_tf
+                for images, labels in tqdm(eval_loader.batch(args.batch_size)):
+                    images = tf.transpose(images, [0, 2, 3, 4, 1])
                     
                     z, _, _ = tf.stop_gradient(encoder_model(images))
 
                     pred = classifier_model(z)
-                    loss = criterion(torch_labels, pred)
+                    loss = criterion(labels, pred)
 
                     test_loss += loss
 
                     if y_true is None:
-                        y_true = torch_labels
-                        y_pred = tf.math.round(tf.math.sigmoid(pred))
+                        y_true = labels
+                        if args.dataset == 'bamc':
+                            y_pred = tf.math.round(tf.math.sigmoid(pred))
+                        else:
+                            y_pred = tf.math.argmax(tf.nn.softmax(pred, axis=1), axis=1)
                     else:
-                        y_true = tf.concat((y_true, torch_labels), axis=0)
-                        y_pred = tf.concat((y_pred, tf.math.round(tf.math.sigmoid(pred))), axis=0)
+                        y_true = tf.concat((y_true, labels), axis=0)
+                        if args.dataset == 'bamc':
+                            y_pred = tf.concat((y_pred, tf.math.round(tf.math.sigmoid(pred))), axis=0)
+                        else:
+                            y_pred = tf.concat((y_pred, tf.math.argmax(tf.nn.softmax(pred, axis=1), axis=1)), axis=0)
 
                 t2 = time.perf_counter()
 
@@ -302,18 +381,31 @@ if __name__ == "__main__":
 
         t1 = time.perf_counter()
         
-        transform = torchvision.transforms.Compose(
-        [VideoGrayScaler(),
-         MinMaxScaler(0, 255),
-         torchvision.transforms.Normalize((0.2592,), (0.1251,)),
-         torchvision.transforms.CenterCrop((100, 200))
-        ])
-
-        test_dir = '/shared_data/bamc_ph1_test_data'
-        test_videos = glob.glob(os.path.join(test_dir, '*', '*.*'))
-        test_labels = [vid_f.split('/')[-2] for vid_f in test_videos]
-
-        y_pred, y_true, fn, fp, clip_acc = calculate_ptx_scores(test_videos, test_labels, yolo_model, encoder_model, classifier_model, image_width, image_height, transform)
+        if args.dataset == 'bamc':
+            transform = torchvision.transforms.Compose(
+            [VideoGrayScaler(),
+             MinMaxScaler(0, 255),
+             torchvision.transforms.Normalize((0.2592,), (0.1251,)),
+             torchvision.transforms.CenterCrop((100, 200))
+            ])
+
+            test_dir = '/shared_data/bamc_ph1_test_data'
+            test_videos = glob.glob(os.path.join(test_dir, '*', '*.*'))
+            test_labels = [vid_f.split('/')[-2] for vid_f in test_videos]
+
+            y_pred, y_true, fn, fp, clip_acc = calculate_ptx_scores(test_videos, test_labels, yolo_model, sparse_model, recon_model, classifier_model, image_width, image_height, transform)
+        elif args.dataset == 'covid':
+            transform = torchvision.transforms.Compose(
+            [VideoGrayScaler(),
+             MinMaxScaler(0, 255),
+             torchvision.transforms.Resize((image_height, image_width))
+            ])
+
+            test_videos = test_loader.get_all_videos()
+
+            test_labels = test_loader.get_video_labels()
+
+            y_pred, y_true, fn, fp, clip_acc = calculate_covid_scores(test_videos, test_labels, classifier_model, image_width, image_height, transform)
             
         t2 = time.perf_counter()
 
diff --git a/sparse_coding_torch/ptx/train_vae.py b/sparse_coding_torch/ptx/train_vae.py
index 3abe8ade70e5897af9d0a05040784aa2f3b64e46..27b8c288befea1dd3223be265dd6d5bddcfdd7d8 100644
--- a/sparse_coding_torch/ptx/train_vae.py
+++ b/sparse_coding_torch/ptx/train_vae.py
@@ -7,11 +7,12 @@ from matplotlib.animation import FuncAnimation
 from tqdm import tqdm
 import argparse
 import os
-from sparse_coding_torch.ptx.load_data import load_yolo_clips
+from sparse_coding_torch.ptx.load_data import load_yolo_clips, load_covid_clips
 import tensorflow.keras as keras
 import tensorflow as tf
 from sparse_coding_torch.ptx.classifier_model import VAEEncoderPTX, VAEDecoderPTX
 import random
+import copy
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
@@ -21,7 +22,7 @@ if __name__ == "__main__":
     parser.add_argument('--output_dir', default='./output', type=str)
     parser.add_argument('--seed', default=42, type=int)
     parser.add_argument('--optimizer', default='adam', type=str)
-    parser.add_argument('--dataset', default='ptx', type=str)
+    parser.add_argument('--dataset', default='bamc', type=str)
     parser.add_argument('--crop_height', type=int, default=100)
     parser.add_argument('--crop_width', type=int, default=200)
     parser.add_argument('--scale_factor', type=int, default=1)
@@ -48,20 +49,27 @@ if __name__ == "__main__":
         os.makedirs(output_dir)
         
     device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    
+    data_augmentation = keras.Sequential([
+        keras.layers.RandomFlip('horizontal'),
+        keras.layers.RandomRotation(45)
+    ])
 
     with open(os.path.join(output_dir, 'arguments.txt'), 'w+') as out_f:
         out_f.write(str(args))
         
-    splits, dataset = load_yolo_clips(args.batch_size, num_clips=1, num_positives=15, mode='all_train', device=device, n_splits=1, sparse_model=None, whole_video=False, positive_videos='positive_videos.json')
+    if args.dataset == 'bamc':
+        splits, dataset = load_yolo_clips(args.batch_size, num_clips=1, num_positives=15, mode='all_train', device=device, n_splits=1, sparse_model=None, whole_video=False, positive_videos='positive_videos.json')
+    else:
+        splits, dataset = load_covid_clips(batch_size=args.batch_size, mode='all_train', clip_width=image_width, clip_height=image_height, clip_depth=clip_depth, n_splits=1)
     train_idx, test_idx = splits[0]
     
-    train_sampler = torch.utils.data.SubsetRandomSampler(train_idx)
-    train_loader = torch.utils.data.DataLoader(dataset, batch_size=args.batch_size,
-                                           sampler=train_sampler)
-    
-    print('Loaded', len(train_loader), 'train examples')
+    train_loader = copy.deepcopy(dataset)
+    train_loader.set_indicies(train_idx)
 
-    example_data = next(iter(train_loader))
+    train_tf = tf.data.Dataset.from_tensor_slices((train_loader.get_frames(), train_loader.get_labels()))
+    
+#     print('Loaded', len(train_loader), 'train examples')
 
     encoder_inputs = keras.Input(shape=(5, image_height, image_width, 1))
         
@@ -87,10 +95,13 @@ if __name__ == "__main__":
         
         num_iters = 0
 
-        for labels, local_batch, vid_f in tqdm(train_loader):
-            images = local_batch.permute(0, 2, 3, 4, 1).numpy()
+        for images, labels in tqdm(train_tf.batch(args.batch_size)):
+            images = tf.transpose(images, [0, 2, 3, 4, 1])
             
             with tf.GradientTape() as tape:
+                images = tf.reshape(images, (-1, images.shape[2], images.shape[3], images.shape[4]))
+                images = data_augmentation(images)
+                images = tf.reshape(images, (-1, 5, images.shape[1], images.shape[2], images.shape[3]))
                 z, z_mean, z_var = encoder_model(images)
                 recon = decoder_model(z)
                 reconstruction_loss = tf.reduce_mean(
@@ -102,8 +113,8 @@ if __name__ == "__main__":
                 kl_loss = tf.reduce_mean(tf.reduce_sum(kl_loss, axis=1))
                 loss = reconstruction_loss + kl_loss
 
-            epoch_loss += loss * local_batch.size(0)
-            running_loss += loss * local_batch.size(0)
+            epoch_loss += loss * images.shape[0]
+            running_loss += loss * images.shape[0]
 
             gradients = tape.gradient(loss, encoder_model.trainable_weights + decoder_model.trainable_weights)
 
@@ -112,7 +123,7 @@ if __name__ == "__main__":
             num_iters += 1
 
         epoch_end = time.perf_counter()
-        epoch_loss /= len(train_loader.sampler)
+        epoch_loss /= num_iters
 
         if epoch_loss < best_so_far:
             print("found better model")
diff --git a/sparse_coding_torch/ptx/video_loader.py b/sparse_coding_torch/ptx/video_loader.py
index 63fc7b3a204406e3d4311a8863b79825b39fdf37..e490927aef0dd53febfe228154fe3fbae6928b42 100644
--- a/sparse_coding_torch/ptx/video_loader.py
+++ b/sparse_coding_torch/ptx/video_loader.py
@@ -63,7 +63,7 @@ class VideoGrayScaler(nn.Module):
 class YoloClipLoader(Dataset):
     
     def __init__(self, yolo_output_path, num_frames=5, frames_between_clips=None,
-                 transform=None, augment_transform=None, num_clips=1, num_positives=1, positive_videos=None, sparse_model=None, device=None):
+                 transform=None, num_clips=1, num_positives=1, positive_videos=None, sparse_model=None, device=None):
         if (num_frames % 2) == 0:
             raise ValueError("Num Frames must be an odd number, so we can extract a clip centered on each detected region")
         
@@ -78,7 +78,6 @@ class YoloClipLoader(Dataset):
             self.frames_between_clips = frames_between_clips
 
         self.transform = transform
-        self.augment_transform = augment_transform
          
         self.labels = [name for name in listdir(yolo_output_path) if isdir(join(yolo_output_path, name))]
         self.clips = []
@@ -134,8 +133,13 @@ class YoloClipLoader(Dataset):
                                         final_clip = final_clip.unsqueeze(0).to(device)
                                         final_clip = sparse_model(final_clip)
                                         final_clip = final_clip.squeeze(0).detach().cpu()
+                                        
+                                if label == 'No_Sliding':
+                                    y = np.array(1.0)
+                                elif label == 'Sliding':
+                                    y = np.array(0.0)
 
-                                self.clips.append((label, final_clip, video))
+                                self.clips.append((y, final_clip.numpy(), video))
 
             torch.save(self.clips, open(clip_cache_file, 'wb+'))
             
@@ -146,7 +150,7 @@ class YoloClipLoader(Dataset):
         if positive_videos:
             vids_to_keep = json.load(open(positive_videos))
             
-            self.clips = [clip_tup for clip_tup in self.clips if clip_tup[2] in vids_to_keep or clip_tup[0] == 'Sliding']
+            self.clips = [clip_tup for clip_tup in self.clips if clip_tup[2] in vids_to_keep or clip_tup[0] == 0.0]
         else:
             video_to_labels = {}
 
@@ -162,7 +166,7 @@ class YoloClipLoader(Dataset):
             video_to_participants = get_ptx_participants()
             participants_to_video = {}
             for k, v in video_to_participants.items():
-                if video_to_labels[k][0] == 'Sliding':
+                if video_to_labels[k][0] == 0.0:
                     continue
                 if not v in participants_to_video:
                     participants_to_video[v] = []
@@ -171,7 +175,7 @@ class YoloClipLoader(Dataset):
 
             participants_to_video = dict(sorted(participants_to_video.items(), key=lambda x: len(x[1]), reverse=True))
 
-            num_to_remove = len([k for k,v in video_to_labels.items() if v[0] == 'No_Sliding']) - num_positives
+            num_to_remove = len([k for k,v in video_to_labels.items() if v[0] == 1.0]) - num_positives
             vids_to_remove = set()
             while num_to_remove > 0:
                 vids_to_remove.add(participants_to_video[list(participants_to_video.keys())[0]].pop())
@@ -191,76 +195,107 @@ class YoloClipLoader(Dataset):
             video_to_clips[video].append(clip)
             video_to_labels[video].append(lbl)
             
-        print([k for k,v in video_to_labels.items() if v[0] == 'No_Sliding'])
+        print([k for k,v in video_to_labels.items() if v[0] == 1.0])
             
-        print('Num positive:', len([k for k,v in video_to_labels.items() if v[0] == 'No_Sliding']))
-        print('Num negative:', len([k for k,v in video_to_labels.items() if v[0] == 'Sliding']))
+        print('Num positive:', len([k for k,v in video_to_labels.items() if v[0] == 1.0]))
+        print('Num negative:', len([k for k,v in video_to_labels.items() if v[0] == 0.0]))
 
-        self.videos = None
-        self.max_video_clips = 0
-        if num_clips > 1:
-            self.videos = []
+#         self.videos = None
+#         self.max_video_clips = 0
+#         if num_clips > 1:
+#             self.videos = []
 
-            for video in video_to_clips.keys():
-                clip_list = video_to_clips[video]
-                lbl_list = video_to_labels[video]
+#             for video in video_to_clips.keys():
+#                 clip_list = video_to_clips[video]
+#                 lbl_list = video_to_labels[video]
                 
-                for i in range(0, len(clip_list) - num_clips, 1):
-                    video_stack = torch.stack(clip_list[i:i+num_clips])
+#                 for i in range(0, len(clip_list) - num_clips, 1):
+#                     video_stack = torch.stack(clip_list[i:i+num_clips])
                 
-                    self.videos.append((max(set(lbl_list[i:i+num_clips]), key=lbl_list[i:i+num_clips].count), video_stack, video))
+#                     self.videos.append((max(set(lbl_list[i:i+num_clips]), key=lbl_list[i:i+num_clips].count), video_stack, video))
             
-            self.clips = None
-
+#             self.clips = None
             
+    def get_filenames(self):
+        return [self.clips[i][2] for i in range(len(self.clips))]
+    
+    def get_all_videos(self):
+        return set([self.clips[i][2] for i in range(len(self.clips))])
+        
     def get_labels(self):
-        if self.num_clips > 1:
-            return [self.videos[i][0] for i in range(len(self.videos))]
-        else:
-            return [self.clips[i][0] for i in range(len(self.clips))]
+        return [self.clips[i][0] for i in range(len(self.clips))]
     
-    def get_filenames(self):
-        if self.num_clips > 1:
-            return [self.videos[i][2] for i in range(len(self.videos))]
+    def set_indicies(self, iter_idx):
+        new_clips = []
+        for i, clip in enumerate(self.clips):
+            if i in iter_idx:
+                new_clips.append(clip)
+                
+        self.clips = new_clips
+        
+    def get_frames(self):
+        return [frame for _, frame, _ in self.clips]
+    
+    def __next__(self):
+        if self.count < len(self.clips):
+            label, frame, vid_f = self.clips[self.count]
+            self.count += 1
+            return label, frame
         else:
-            return [self.clips[i][2] for i in range(len(self.clips))]
+            raise StopIteration
+            
+    def __iter__(self):
+        return self
+
+            
+#     def get_labels(self):
+#         if self.num_clips > 1:
+#             return [self.videos[i][0] for i in range(len(self.videos))]
+#         else:
+#             return [self.clips[i][0] for i in range(len(self.clips))]
     
-    def __getitem__(self, index): 
-        if self.num_clips > 1:
-            label = self.videos[index][0]
-            video = self.videos[index][1]
-            filename = self.videos[index][2]
+#     def get_filenames(self):
+#         if self.num_clips > 1:
+#             return [self.videos[i][2] for i in range(len(self.videos))]
+#         else:
+#             return [self.clips[i][2] for i in range(len(self.clips))]
+    
+#     def __getitem__(self, index): 
+#         if self.num_clips > 1:
+#             label = self.videos[index][0]
+#             video = self.videos[index][1]
+#             filename = self.videos[index][2]
             
-            video = video.squeeze(2)
-            video = video.permute(1, 0, 2, 3)
+#             video = video.squeeze(2)
+#             video = video.permute(1, 0, 2, 3)
 
-            if self.augment_transform:
-                video = self.augment_transform(video)
+#             if self.augment_transform:
+#                 video = self.augment_transform(video)
                 
-            video = video.unsqueeze(2)
-            video = video.permute(1, 0, 2, 3, 4)
-#             video = video.permute(4, 1, 2, 3, 0)
-#             video = torch.nn.functional.pad(video, (0), 'constant', 0)
-#             video = video.permute(4, 1, 2, 3, 0)
+#             video = video.unsqueeze(2)
+#             video = video.permute(1, 0, 2, 3, 4)
+# #             video = video.permute(4, 1, 2, 3, 0)
+# #             video = torch.nn.functional.pad(video, (0), 'constant', 0)
+# #             video = video.permute(4, 1, 2, 3, 0)
 
-            orig_len = video.size(0)
+#             orig_len = video.size(0)
 
-#             if orig_len < self.max_video_clips:
-#                 video = torch.cat([video, torch.zeros(self.max_video_clips - len(video), video.size(1), video.size(2), video.size(3), video.size(4))])
+# #             if orig_len < self.max_video_clips:
+# #                 video = torch.cat([video, torch.zeros(self.max_video_clips - len(video), video.size(1), video.size(2), video.size(3), video.size(4))])
 
-            return label, video, filename, orig_len
-        else:
-            label = self.clips[index][0]
-            video = self.clips[index][1]
-            filename = self.clips[index][2]
+#             return label, video, filename, orig_len
+#         else:
+#             label = self.clips[index][0]
+#             video = self.clips[index][1]
+#             filename = self.clips[index][2]
 
-            if self.augment_transform:
-                video = self.augment_transform(video)
+#             if self.augment_transform:
+#                 video = self.augment_transform(video)
 
-            return label, video, filename
+#             return label, video, filename
         
-    def __len__(self):
-        return len(self.clips)
+#     def __len__(self):
+#         return len(self.clips)
     
 def get_yolo_regions(yolo_model, clip):
     orig_height = clip.size(2)
@@ -286,62 +321,82 @@ def get_yolo_regions(yolo_model, clip):
     return all_clips
 
 class COVID19Loader(Dataset):
-    def __init__(self, yolo_model, video_path, clip_depth, classify_mode=False, transform=None, augmentation=None):
+    def __init__(self, video_path, clip_depth, transform=None):
         self.transform = transform
-        self.augmentation = augmentation
         
         self.videos = glob.glob(join(video_path, '*', '*.*'))
         
-        vid_to_label = {}
+        self.vid_to_label = {}
+        self.vid_to_patient = {}
         with open('/home/dwh48@drexel.edu/covid19_ultrasound/data/dataset_metadata.csv') as csv_in:
             reader = csv.DictReader(csv_in)
             for row in reader:
-                vid_to_label[row['Filename']] = row['Label']
+                self.vid_to_label[row['Filename']] = row['Label'].lower()
+                self.vid_to_patient[row['Filename']] = row['Patient ID / Name']
+                if self.vid_to_patient[row['Filename']].strip() == '':
+                    self.vid_to_patient[row['Filename']] = row['Filename']
+                
+        all_labels = set(self.vid_to_label.values())
+        self.label_to_id = {v: i for i, v in enumerate(all_labels)}
             
         self.clips = []
         
         vid_idx = 0
         for path in tqdm(self.videos):
             vc = tv.io.read_video(path)[0].permute(3, 0, 1, 2)
-            label = vid_to_label[path.split('/')[-1].split('.')[0]]
+            patient = self.vid_to_patient[path.split('/')[-1].split('.')[0]]
+            label = self.vid_to_label[path.split('/')[-1].split('.')[0]]
+            label = self.label_to_id[label]
             
-            if classify_mode:
-                for j in range(0, vc.size(1) - clip_depth, clip_depth):
-                    vc_sub = vc[:, j:j+clip_depth, :, :]
-                    if vc_sub.size(1) < clip_depth:
-                        continue
-                    for clip in get_yolo_regions(yolo_model, vc_sub):
-                        if self.transform:
-                            clip = self.transform(clip)
-
-                        self.clips.append((label, clip, path))
-            else:
-                for j in range(0, vc.size(1) - clip_depth, clip_depth):
-                    vc_sub = vc[:, j:j+clip_depth, :, :]
-                    if vc_sub.size(1) != clip_depth:
-                        continue
-                    if self.transform:
-                        vc_sub = self.transform(vc_sub)
-
-                    self.clips.append((label, vc_sub, path))
+            for j in range(0, vc.size(1) - clip_depth, clip_depth):
+                vc_sub = vc[:, j:j+clip_depth, :, :]
+                if vc_sub.size(1) != clip_depth:
+                    continue
+                if self.transform:
+                    vc_sub = self.transform(vc_sub)
+
+                self.clips.append((np.array(label), vc_sub.numpy(), path, patient))
 
             vid_idx += 1
         
         random.shuffle(self.clips)
         
+    def get_groups(self):
+        return [self.clips[i][3] for i in range(len(self.clips))]
+        
     def get_filenames(self):
         return [self.clips[i][2] for i in range(len(self.clips))]
+    
+    def get_all_videos(self):
+        return set([self.clips[i][2] for i in range(len(self.clips))])
+    
+    def get_video_labels(self):
+        return [self.label_to_id[self.vid_to_label[vid.split('/')[-1].split('.')[0]]] for vid in self.get_all_videos()]
         
     def get_labels(self):
         return [self.clips[i][0] for i in range(len(self.clips))]
     
-    def __getitem__(self, index):
-        label, clip, vid_f = self.clips[index]
-        if self.augmentation:
-            clip = clip.swapaxes(0, 1)
-            clip = self.augmentation(clip)
-            clip = clip.swapaxes(0, 1)
-        return (label, clip, vid_f)
+    def get_unique_labels(self):
+        return [int(self.clips[i][0]) for i in range(len(self.clips))]
+    
+    def set_indicies(self, iter_idx):
+        new_clips = []
+        for i, clip in enumerate(self.clips):
+            if i in iter_idx:
+                new_clips.append(clip)
+                
+        self.clips = new_clips
         
-    def __len__(self):
-        return len(self.clips)
\ No newline at end of file
+    def get_frames(self):
+        return [frame for _, frame, _, _ in self.clips]
+    
+    def __next__(self):
+        if self.count < len(self.clips):
+            label, frame, vid_f, patient = self.clips[self.count]
+            self.count += 1
+            return label, frame
+        else:
+            raise StopIteration
+            
+    def __iter__(self):
+        return self
\ No newline at end of file
diff --git a/sparse_coding_torch/ptx/video_loader_pytorch.py b/sparse_coding_torch/ptx/video_loader_pytorch.py
new file mode 100644
index 0000000000000000000000000000000000000000..e381c78fd9e8d8e27d6ea45aaa790dd2be6334f2
--- /dev/null
+++ b/sparse_coding_torch/ptx/video_loader_pytorch.py
@@ -0,0 +1,347 @@
+from os import listdir
+from os.path import isfile
+from os.path import join
+from os.path import isdir
+from os.path import abspath
+from os.path import exists
+import json
+import glob
+
+from PIL import Image
+from torchvision.transforms import ToTensor
+from torchvision.datasets.video_utils import VideoClips
+from tqdm import tqdm
+import torch
+import numpy as np
+from torch.utils.data import Dataset
+from torch.utils.data import DataLoader
+from torchvision.io import read_video
+import torchvision as tv
+from torch import nn
+import torchvision.transforms.functional as tv_f
+import csv
+import random
+import cv2
+from yolov4.get_bounding_boxes import YoloModel
+
+def get_ptx_participants():
+    video_to_participant = {}
+    with open('/shared_data/bamc_data/bamc_video_info.csv', 'r') as csv_in:
+        reader = csv.DictReader(csv_in)
+        for row in reader:
+            key = row['Filename'].split('.')[0].lower().replace('_clean', '')
+            if key == '37 (mislabeled as 38)':
+                key = '37'
+            video_to_participant[key] = row['Participant_id']
+            
+    return video_to_participant
+
+class MinMaxScaler(object):
+    """
+    Transforms each channel to the range [0, 1].
+    """
+    def __init__(self, min_val=0, max_val=254):
+        self.min_val = min_val
+        self.max_val = max_val
+    
+    def __call__(self, tensor):
+        return (tensor - self.min_val) / (self.max_val - self.min_val)
+
+class VideoGrayScaler(nn.Module):
+    
+    def __init__(self):
+        super().__init__()
+        self.grayscale = tv.transforms.Grayscale(num_output_channels=1)
+        
+    def forward(self, video):
+        # shape = channels, time, width, height
+        video = self.grayscale(video.swapaxes(-4, -3).swapaxes(-2, -1))
+        video = video.swapaxes(-4, -3).swapaxes(-2, -1)
+        # print(video.shape)
+        return video
+    
+class YoloClipLoader(Dataset):
+    
+    def __init__(self, yolo_output_path, num_frames=5, frames_between_clips=None,
+                 transform=None, augment_transform=None, num_clips=1, num_positives=1, positive_videos=None, sparse_model=None, device=None):
+        if (num_frames % 2) == 0:
+            raise ValueError("Num Frames must be an odd number, so we can extract a clip centered on each detected region")
+        
+        clip_cache_file = 'clip_cache_pytorch.pt'
+        
+        self.num_clips = num_clips
+        
+        self.num_frames = num_frames
+        if frames_between_clips is None:
+            self.frames_between_clips = num_frames
+        else:
+            self.frames_between_clips = frames_between_clips
+
+        self.transform = transform
+        self.augment_transform = augment_transform
+         
+        self.labels = [name for name in listdir(yolo_output_path) if isdir(join(yolo_output_path, name))]
+        self.clips = []
+        if exists(clip_cache_file):
+            self.clips = torch.load(open(clip_cache_file, 'rb'))
+        else:
+            for label in self.labels:
+                print("Processing videos in category: {}".format(label))
+                videos = list(listdir(join(yolo_output_path, label)))
+                for vi in tqdm(range(len(videos))):
+                    video = videos[vi]
+                    counter = 0
+                    all_trimmed = []
+                    with open(abspath(join(yolo_output_path, label, video, 'result.json'))) as fin:
+                        results = json.load(fin)
+                        max_frame = len(results)
+
+                        for i in range((num_frames-1)//2, max_frame - (num_frames-1)//2 - 1, self.frames_between_clips):
+                        # for frame in results:
+                            frame = results[i]
+                            # print('loading frame:', i, frame['frame_id'])
+                            frame_start = int(frame['frame_id']) - self.num_frames//2
+                            frames = [abspath(join(yolo_output_path, label, video, 'frame{}.png'.format(frame_start+fid)))
+                                      for fid in range(num_frames)]
+                            # print(frames)
+                            frames = torch.stack([ToTensor()(Image.open(f).convert("RGB")) for f in frames]).swapaxes(0, 1)
+
+                            for region in frame['objects']:
+                                # print(region)
+                                if region['name'] != "Pleural_Line":
+                                    continue
+
+                                center_x = region['relative_coordinates']["center_x"] * 1920
+                                center_y = region['relative_coordinates']['center_y'] * 1080
+
+                                # width = region['relative_coordinates']['width'] * 1920
+                                # height = region['relative_coordinates']['height'] * 1080
+                                width=200
+                                height=100
+
+                                lower_y = round(center_y - height / 2)
+                                upper_y = round(center_y + height / 2)
+                                lower_x = round(center_x - width / 2)
+                                upper_x = round(center_x + width / 2)
+
+                                final_clip = frames[:, :, lower_y:upper_y, lower_x:upper_x]
+
+                                if self.transform:
+                                    final_clip = self.transform(final_clip)
+
+                                if sparse_model:
+                                    with torch.no_grad():
+                                        final_clip = final_clip.unsqueeze(0).to(device)
+                                        final_clip = sparse_model(final_clip)
+                                        final_clip = final_clip.squeeze(0).detach().cpu()
+
+                                self.clips.append((label, final_clip, video))
+
+            torch.save(self.clips, open(clip_cache_file, 'wb+'))
+            
+            
+#         random.shuffle(self.clips)
+            
+#         video_to_clips = {}
+        if positive_videos:
+            vids_to_keep = json.load(open(positive_videos))[:num_positives]
+            
+            self.clips = [clip_tup for clip_tup in self.clips if clip_tup[2] in vids_to_keep or clip_tup[0] == 'Sliding']
+        else:
+            video_to_labels = {}
+
+            for lbl, clip, video in self.clips:
+                video = video.lower().replace('_clean', '')
+                if video not in video_to_labels:
+    #                 video_to_clips[video] = []
+                    video_to_labels[video] = []
+
+    #             video_to_clips[video].append(clip)
+                video_to_labels[video].append(lbl)
+
+            video_to_participants = get_ptx_participants()
+            participants_to_video = {}
+            for k, v in video_to_participants.items():
+                if video_to_labels[k][0] == 'Sliding':
+                    continue
+                if not v in participants_to_video:
+                    participants_to_video[v] = []
+
+                participants_to_video[v].append(k)
+
+            participants_to_video = dict(sorted(participants_to_video.items(), key=lambda x: len(x[1]), reverse=True))
+
+            num_to_remove = len([k for k,v in video_to_labels.items() if v[0] == 'No_Sliding']) - num_positives
+            vids_to_remove = set()
+            while num_to_remove > 0:
+                vids_to_remove.add(participants_to_video[list(participants_to_video.keys())[0]].pop())
+                participants_to_video = dict(sorted(participants_to_video.items(), key=lambda x: len(x[1]), reverse=True))
+                num_to_remove -= 1
+                    
+            self.clips = [clip_tup for clip_tup in self.clips if clip_tup[2].lower().replace('_clean', '') not in vids_to_remove]
+        
+        video_to_clips = {}
+        video_to_labels = {}
+
+        for lbl, clip, video in self.clips:
+            if video not in video_to_clips:
+                video_to_clips[video] = []
+                video_to_labels[video] = []
+
+            video_to_clips[video].append(clip)
+            video_to_labels[video].append(lbl)
+            
+        print([k for k,v in video_to_labels.items() if v[0] == 'No_Sliding'])
+            
+        print('Num positive:', len([k for k,v in video_to_labels.items() if v[0] == 'No_Sliding']))
+        print('Num negative:', len([k for k,v in video_to_labels.items() if v[0] == 'Sliding']))
+
+        self.videos = None
+        self.max_video_clips = 0
+        if num_clips > 1:
+            self.videos = []
+
+            for video in video_to_clips.keys():
+                clip_list = video_to_clips[video]
+                lbl_list = video_to_labels[video]
+                
+                for i in range(0, len(clip_list) - num_clips, 1):
+                    video_stack = torch.stack(clip_list[i:i+num_clips])
+                
+                    self.videos.append((max(set(lbl_list[i:i+num_clips]), key=lbl_list[i:i+num_clips].count), video_stack, video))
+            
+            self.clips = None
+
+            
+    def get_labels(self):
+        if self.num_clips > 1:
+            return [self.videos[i][0] for i in range(len(self.videos))]
+        else:
+            return [self.clips[i][0] for i in range(len(self.clips))]
+    
+    def get_filenames(self):
+        if self.num_clips > 1:
+            return [self.videos[i][2] for i in range(len(self.videos))]
+        else:
+            return [self.clips[i][2] for i in range(len(self.clips))]
+    
+    def __getitem__(self, index): 
+        if self.num_clips > 1:
+            label = self.videos[index][0]
+            video = self.videos[index][1]
+            filename = self.videos[index][2]
+            
+            video = video.squeeze(2)
+            video = video.permute(1, 0, 2, 3)
+
+            if self.augment_transform:
+                video = self.augment_transform(video)
+                
+            video = video.unsqueeze(2)
+            video = video.permute(1, 0, 2, 3, 4)
+#             video = video.permute(4, 1, 2, 3, 0)
+#             video = torch.nn.functional.pad(video, (0), 'constant', 0)
+#             video = video.permute(4, 1, 2, 3, 0)
+
+            orig_len = video.size(0)
+
+#             if orig_len < self.max_video_clips:
+#                 video = torch.cat([video, torch.zeros(self.max_video_clips - len(video), video.size(1), video.size(2), video.size(3), video.size(4))])
+
+            return label, video, filename, orig_len
+        else:
+            label = self.clips[index][0]
+            video = self.clips[index][1]
+            filename = self.clips[index][2]
+
+            if self.augment_transform:
+                video = self.augment_transform(video)
+
+            return label, video, filename
+        
+    def __len__(self):
+        return len(self.clips)
+    
+def get_yolo_regions(yolo_model, clip):
+    orig_height = clip.size(2)
+    orig_width = clip.size(3)
+    bounding_boxes, classes, scores = yolo_model.get_bounding_boxes(clip[:, 2, :, :].swapaxes(0, 2).swapaxes(0, 1).numpy())
+    bounding_boxes = bounding_boxes.squeeze(0)
+    classes = classes.squeeze(0)
+    scores = scores.squeeze(0)
+    
+    all_clips = []
+    for bb, class_pred, score in zip(bounding_boxes, classes, scores):
+        lower_y = round((bb[0] * orig_height))
+        upper_y = round((bb[2] * orig_height))
+        lower_x = round((bb[1] * orig_width))
+        upper_x = round((bb[3] * orig_width))
+
+        trimmed_clip = clip[:, :, lower_y:upper_y, lower_x:upper_x]
+        
+        if trimmed_clip.shape[2] == 0 or trimmed_clip.shape[3] == 0:
+            continue
+        all_clips.append(torch.tensor(trimmed_clip))
+
+    return all_clips
+
+class COVID19Loader(Dataset):
+    def __init__(self, yolo_model, video_path, clip_depth, classify_mode=False, transform=None, augmentation=None):
+        self.transform = transform
+        self.augmentation = augmentation
+        
+        self.videos = glob.glob(join(video_path, '*', '*.*'))
+        
+        vid_to_label = {}
+        with open('/home/dwh48@drexel.edu/covid19_ultrasound/data/dataset_metadata.csv') as csv_in:
+            reader = csv.DictReader(csv_in)
+            for row in reader:
+                vid_to_label[row['Filename']] = row['Label']
+            
+        self.clips = []
+        
+        vid_idx = 0
+        for path in tqdm(self.videos):
+            vc = tv.io.read_video(path)[0].permute(3, 0, 1, 2)
+            label = vid_to_label[path.split('/')[-1].split('.')[0]]
+            
+            if classify_mode:
+                for j in range(0, vc.size(1) - clip_depth, clip_depth):
+                    vc_sub = vc[:, j:j+clip_depth, :, :]
+                    if vc_sub.size(1) < clip_depth:
+                        continue
+                    for clip in get_yolo_regions(yolo_model, vc_sub):
+                        if self.transform:
+                            clip = self.transform(clip)
+
+                        self.clips.append((label, clip, path))
+            else:
+                for j in range(0, vc.size(1) - clip_depth, clip_depth):
+                    vc_sub = vc[:, j:j+clip_depth, :, :]
+                    if vc_sub.size(1) != clip_depth:
+                        continue
+                    if self.transform:
+                        vc_sub = self.transform(vc_sub)
+
+                    self.clips.append((label, vc_sub, path))
+
+            vid_idx += 1
+        
+        random.shuffle(self.clips)
+        
+    def get_filenames(self):
+        return [self.clips[i][2] for i in range(len(self.clips))]
+        
+    def get_labels(self):
+        return [self.clips[i][0] for i in range(len(self.clips))]
+    
+    def __getitem__(self, index):
+        label, clip, vid_f = self.clips[index]
+        if self.augmentation:
+            clip = clip.swapaxes(0, 1)
+            clip = self.augmentation(clip)
+            clip = clip.swapaxes(0, 1)
+        return (label, clip, vid_f)
+        
+    def __len__(self):
+        return len(self.clips)
\ No newline at end of file
diff --git a/sparse_coding_torch/utils.py b/sparse_coding_torch/utils.py
index e2398c3646eaa6afabdfc9ae1cd76761fb0432f6..8baf7254a64e23d740622f3bde85b102b0f64895 100644
--- a/sparse_coding_torch/utils.py
+++ b/sparse_coding_torch/utils.py
@@ -154,7 +154,7 @@ def plot_filters(filters):
 def plot_filters_image(filters):
     filters = filters.astype('float32')
     num_filters = filters.shape[4]
-    ncol = 12
+    ncol = 3
     T = filters.shape[0]
 
     if num_filters // ncol == num_filters / ncol:
diff --git a/yolov4/Pleural_Line_TensorFlow/onsd_prelim_yolo/yolov5-416.tflite b/yolov4/Pleural_Line_TensorFlow/onsd_prelim_yolo/yolov5-416.tflite
new file mode 100644
index 0000000000000000000000000000000000000000..85f3b83a1987012197635b4c71e379e18b28315e
Binary files /dev/null and b/yolov4/Pleural_Line_TensorFlow/onsd_prelim_yolo/yolov5-416.tflite differ
diff --git a/yolov4/Pleural_Line_TensorFlow/pnb_prelim_yolo/yolov5-416.tflite b/yolov4/Pleural_Line_TensorFlow/pnb_prelim_yolo/yolov5-416.tflite
new file mode 100644
index 0000000000000000000000000000000000000000..6281408c62d41916ea5944f6c13da0b9ac0e1978
Binary files /dev/null and b/yolov4/Pleural_Line_TensorFlow/pnb_prelim_yolo/yolov5-416.tflite differ
diff --git a/yolov4/get_bounding_boxes.py b/yolov4/get_bounding_boxes.py
index 54ec72c24777a8cc63f1dbf1ad6b00d0548f953f..0db254ae40ea3828e3779f8f1a20a7efa0a83434 100644
--- a/yolov4/get_bounding_boxes.py
+++ b/yolov4/get_bounding_boxes.py
@@ -11,15 +11,126 @@ import numpy as np
 from tensorflow.compat.v1 import ConfigProto
 from tensorflow.compat.v1 import InteractiveSession
 import time
+import torch
+import torchvision
+
+def xywh2xyxy(x):
+    # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
+    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
+    y[:, 0] = x[:, 0] - x[:, 2] / 2  # top left x
+    y[:, 1] = x[:, 1] - x[:, 3] / 2  # top left y
+    y[:, 2] = x[:, 0] + x[:, 2] / 2  # bottom right x
+    y[:, 3] = x[:, 1] + x[:, 3] / 2  # bottom right y
+    return y
+
+def non_max_suppression(prediction,
+                        conf_thres=0.25,
+                        iou_thres=0.45,
+                        classes=None,
+                        agnostic=False,
+                        multi_label=False,
+                        labels=(),
+                        max_det=300):
+    """Non-Maximum Suppression (NMS) on inference results to reject overlapping bounding boxes
+    Returns:
+         list of detections, on (n,6) tensor per image [xyxy, conf, cls]
+    """
+
+    bs = prediction.shape[0]  # batch size
+    nc = prediction.shape[2] - 5  # number of classes
+    xc = prediction[..., 4] > conf_thres  # candidates
+
+    # Checks
+    assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0'
+    assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0'
+
+    # Settings
+    # min_wh = 2  # (pixels) minimum box width and height
+    max_wh = 7680  # (pixels) maximum box width and height
+    max_nms = 30000  # maximum number of boxes into torchvision.ops.nms()
+    time_limit = 0.3 + 0.03 * bs  # seconds to quit after
+    redundant = True  # require redundant detections
+    multi_label &= nc > 1  # multiple labels per box (adds 0.5ms/img)
+    merge = False  # use merge-NMS
+
+    t = time.time()
+    output = [torch.zeros((0, 6))] * bs
+    for xi, x in enumerate(prediction):  # image index, image inference
+        # Apply constraints
+        # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0  # width-height
+        x = x[xc[xi]]  # confidence
+
+        # Cat apriori labels if autolabelling
+        if labels and len(labels[xi]):
+            lb = labels[xi]
+            v = torch.zeros((len(lb), nc + 5))
+            v[:, :4] = lb[:, 1:5]  # box
+            v[:, 4] = 1.0  # conf
+            v[range(len(lb)), lb[:, 0].long() + 5] = 1.0  # cls
+            x = torch.cat((x, v), 0)
+
+        # If none remain process next image
+        if not x.shape[0]:
+            continue
+
+        # Compute conf
+        x[:, 5:] *= x[:, 4:5]  # conf = obj_conf * cls_conf
+
+        # Box (center x, center y, width, height) to (x1, y1, x2, y2)
+        box = xywh2xyxy(x[:, :4])
+
+        # Detections matrix nx6 (xyxy, conf, cls)
+        if multi_label:
+            i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T
+            x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1)
+        else:  # best class only
+            conf, j = x[:, 5:].max(1, keepdim=True)
+            x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres]
+
+        # Filter by class
+        if classes is not None:
+            x = x[(x[:, 5:6] == torch.tensor(classes)).any(1)]
+
+        # Apply finite constraint
+        # if not torch.isfinite(x).all():
+        #     x = x[torch.isfinite(x).all(1)]
+
+        # Check shape
+        n = x.shape[0]  # number of boxes
+        if not n:  # no boxes
+            continue
+        elif n > max_nms:  # excess boxes
+            x = x[x[:, 4].argsort(descending=True)[:max_nms]]  # sort by confidence
+
+        # Batched NMS
+        c = x[:, 5:6] * (0 if agnostic else max_wh)  # classes
+        boxes, scores = x[:, :4] + c, x[:, 4]  # boxes (offset by class), scores
+        i = torchvision.ops.nms(boxes, scores, iou_thres)  # NMS
+        if i.shape[0] > max_det:  # limit detections
+            i = i[:max_det]
+        if merge and (1 < n < 3E3):  # Merge NMS (boxes merged using weighted mean)
+            # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
+            iou = box_iou(boxes[i], boxes) > iou_thres  # iou matrix
+            weights = iou * scores[None]  # box weights
+            x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True)  # merged boxes
+            if redundant:
+                i = i[iou.sum(1) > 1]  # require redundancy
+
+        output[xi] = x[i]
+        if (time.time() - t) > time_limit:
+            LOGGER.warning(f'WARNING: NMS time limit {time_limit:.3f}s exceeded')
+            break  # time limit exceeded
+
+    return output
 
 class YoloModel():
     def __init__(self, dataset):
         flags.DEFINE_string('framework', 'tflite', '(tf, tflite, trt')
         if dataset == 'pnb':
-            flags.DEFINE_string('weights', 'yolov4/Pleural_Line_TensorFlow/pnb_prelim_yolo/yolov4-416.tflite',
+            flags.DEFINE_string('weights', 'yolov4/Pleural_Line_TensorFlow/pnb_prelim_yolo/yolov5-416.tflite',
                                 'path to weights file')
         elif dataset == 'onsd':
-            flags.DEFINE_string('weights', 'yolov4/Pleural_Line_TensorFlow/onsd_prelim_yolo/yolov4-416.tflite',
+            flags.DEFINE_string('weights', 'yolov4/Pleural_Line_TensorFlow/onsd_prelim_yolo/yolov5-416.tflite',
                                 'path to weights file')
         else:
             flags.DEFINE_string('weights', 'yolov4/yolov4-416.tflite',
@@ -51,6 +162,103 @@ class YoloModel():
 #         print('model loaded\n')
 #         print('Took %.2f seconds to load model\n' % (elapsed_time))
 
+    def get_bounding_boxes_v5(self, original_image):
+
+        start = time.time()
+#         print('image resizing\n')
+        image_data = cv2.resize(original_image, (self.input_size, self.input_size))
+        image_data = image_data / 255.
+        images_data = []
+        for i in range(1):
+            images_data.append(image_data)
+        images_data = np.asarray(images_data).astype(np.float32)
+        
+#         print('running as tensorflow\n')
+
+        #print('loading model\n')
+#         print(FLAGS.weights)
+#         saved_model_loaded = tf.saved_model.load(FLAGS.weights, tags=[tag_constants.SERVING])
+        #print('model loaded\n')
+
+        if FLAGS.framework == 'tflite':
+#         print('running as tflite\n')
+            interpreter = tf.lite.Interpreter(model_path=FLAGS.weights)
+            interpreter.allocate_tensors()
+            input_details = interpreter.get_input_details()
+            output_details = interpreter.get_output_details()
+            interpreter.set_tensor(input_details[0]['index'], images_data)
+            interpreter.invoke()
+            pred = [interpreter.get_tensor(output_details[i]['index']) for i in range(len(output_details))][0]
+            
+            output = non_max_suppression(torch.tensor(pred))[0]
+            boxes = output[:, :4].numpy()
+            classes = output[:, 5].numpy()
+            scores = output[:, 4].numpy()
+            
+            return boxes, classes, scores
+            
+#             if FLAGS.model == 'yolov3' and FLAGS.tiny == True:
+#                 boxes, pred_conf = filter_boxes(pred[1], pred[0], score_threshold=0.25, input_shape=tf.constant([self.input_size, self.input_size]))
+#             else:
+                #boxes, pred_conf = filter_boxes(boxes, scores, score_threshold=0.25, input_shape=tf.constant([self.input_size, self.input_size]))
+#         else:
+#             infer = self.saved_model_loaded.signatures['serving_default']
+#     #         print('batch data\n')
+#             batch_data = tf.constant(images_data)
+#     #         print('computing bounding box data\n')
+#             yolo_start_time = time.time()
+#             pred_bbox = infer(batch_data)
+#             for key, value in pred_bbox.items():
+#                 boxes = value[:, :, 0:4]
+#                 pred_conf = value[:, :, 4:]
+# #                 print("VALUE", value)
+#             yolo_end_time = time.time()
+#             yolo_elapsed_time = yolo_end_time - yolo_start_time
+# #         print('Took %.2f seconds to run yolo\n' % (yolo_elapsed_time))
+# #         print(boxes)
+
+# #         print('non max suppression\n')
+#         boxes, scores, classes, valid_detections = tf.image.combined_non_max_suppression(
+#             boxes=tf.reshape(boxes, (tf.shape(boxes)[0], -1, 1, 4)),
+#             scores=tf.reshape(
+#                 pred_conf, (tf.shape(pred_conf)[0], -1, tf.shape(pred_conf)[-1])),
+#             max_output_size_per_class=50,
+#             max_total_size=50,
+#             iou_threshold=0.5,
+#             score_threshold=0.25
+#         )
+
+# #         print('formatting bounding box data\n')
+#         boxes = boxes.numpy()
+
+#         # remove bounding boxes with zero area
+#         boxes = boxes.tolist()
+#         boxes = boxes[0]
+#         classes = classes[0]
+#         scores = scores[0]
+#         boxes_list = []
+#         class_list = []
+#         score_list = []
+#         for box, class_idx, score in zip(boxes, classes, scores):
+#             sum = 0
+#             for value in box:
+#                 sum += value
+#             if sum > 0:
+#                 boxes_list.append(box)
+#                 class_list.append(class_idx)
+#                 score_list.append(score)
+#         boxes_list = [boxes_list]
+#         class_list = [class_list]
+#         score_list = [score_list]
+#         boxes = np.array(boxes_list)
+#         classes = np.array(class_list)
+#         scores = np.array(score_list)
+
+        end = time.time()
+        elapsed_time = end - start
+#         print('Took %.2f seconds to run whole bounding box function\n' % (elapsed_time))
+        return None
+
     def get_bounding_boxes(self, original_image):
 
         start = time.time()
@@ -78,6 +286,7 @@ class YoloModel():
             interpreter.set_tensor(input_details[0]['index'], images_data)
             interpreter.invoke()
             pred = [interpreter.get_tensor(output_details[i]['index']) for i in range(len(output_details))]
+            
             if FLAGS.model == 'yolov3' and FLAGS.tiny == True:
                 boxes, pred_conf = filter_boxes(pred[1], pred[0], score_threshold=0.25, input_shape=tf.constant([self.input_size, self.input_size]))
             else: