{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "provenance": [], "gpuType": "T4" }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" }, "accelerator": "GPU" }, "cells": [ { "cell_type": "markdown", "source": [ "**Install All the Required Packages**" ], "metadata": { "id": "wmefx8HFfmDi" } }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "collapsed": true, "id": "BcnNMXkefhux", "outputId": "290b3bdb-0db4-494f-c8f7-d4fc2af61c2c" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Collecting ultralytics\n", " Downloading ultralytics-8.3.12-py3-none-any.whl.metadata (34 kB)\n", "Requirement already satisfied: numpy>=1.23.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (1.26.4)\n", "Requirement already satisfied: matplotlib>=3.3.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (3.7.1)\n", "Requirement already 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packages: ultralytics-thop, ultralytics\n", "Successfully installed ultralytics-8.3.12 ultralytics-thop-2.0.9\n" ] } ], "source": [ "!pip install ultralytics" ] }, { "cell_type": "markdown", "source": [ "**Import All the Required Libraries**" ], "metadata": { "id": "bLDyuxsHf4qU" } }, { "cell_type": "code", "source": [ "import ultralytics\n", "from ultralytics import YOLO\n", "from IPython.display import Image" ], "metadata": { "id": "biuqf9lyfvNr", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "57e2f189-abc3-4693-90a2-60184994c249" }, "execution_count": 3, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Creating new Ultralytics Settings v0.0.6 file ✅ \n", "View Ultralytics Settings with 'yolo settings' or at '/root/.config/Ultralytics/settings.json'\n", "Update Settings with 'yolo settings key=value', i.e. 'yolo settings runs_dir=path/to/dir'. For help see https://docs.ultralytics.com/quickstart/#ultralytics-settings.\n" ] } ] }, { "cell_type": "markdown", "source": [ "**Check Ultralytics Version**" ], "metadata": { "id": "lJx0BkZegEze" } }, { "cell_type": "code", "source": [ "ultralytics.checks()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "4E-fU5_af8sv", "outputId": "52828dd0-9e7b-43ae-d3f1-192901d6ca6c" }, "execution_count": 4, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Ultralytics 8.3.12 🚀 Python-3.10.12 torch-2.4.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n", "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 36.6/112.6 GB disk)\n" ] } ] }, { "cell_type": "markdown", "source": [ "**Download the Dataset from Roboflow**" ], "metadata": { "id": "ST42rfqmgKtT" } }, { "cell_type": "code", "source": [ "!pip install roboflow\n", "\n", "from roboflow import Roboflow\n", "rf = Roboflow(api_key=\"8joVH7TL2k5V6uScFBEy\")\n", "project = rf.workspace(\"rockpaperscissors-adtav\").project(\"plants-classification-zcckg\")\n", "version = project.version(3)\n", "dataset = version.download(\"folder\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "collapsed": true, "id": "lgAJkcakgIN-", "outputId": "9ed9e668-9e37-47ed-83c3-fcec4e591b6d" }, "execution_count": 5, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Collecting roboflow\n", " Downloading roboflow-1.1.47-py3-none-any.whl.metadata (9.7 kB)\n", "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from roboflow) (2024.8.30)\n", "Collecting idna==3.7 (from roboflow)\n", " Downloading idna-3.7-py3-none-any.whl.metadata (9.9 kB)\n", "Requirement already satisfied: cycler in /usr/local/lib/python3.10/dist-packages (from roboflow) (0.12.1)\n", "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.4.7)\n", "Requirement already satisfied: 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Model**" ], "metadata": { "id": "Mq0hNIF2giGM" } }, { "cell_type": "code", "source": [ "model = YOLO(\"yolo11s-cls.pt\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "wgPqQU_wgapF", "outputId": "9e49d364-1b30-4c80-de35-1624b66a0535" }, "execution_count": 6, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-cls.pt to 'yolo11s-cls.pt'...\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "100%|██████████| 13.0M/13.0M [00:00<00:00, 196MB/s]\n" ] } ] }, { "cell_type": "markdown", "source": [ "**Train the YOLO11 Classification Model**" ], "metadata": { "id": "E3S3tZZWgo0T" } }, { "cell_type": "code", "source": [ "data_path = \"/content/plants-classification-3\"\n", "results = model.train(task = \"classify\", mode = \"train\", data = data_path, epochs = 60)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "OYATBP_qgnLy", "outputId": "70dd8bfe-db7f-4518-ed12-ec45a1c0764f" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Ultralytics 8.3.6 🚀 Python-3.10.12 torch-2.4.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n", "\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=classify, mode=train, model=yolo11s-cls.pt, data=/content/plants-classification-3, epochs=60, time=None, patience=100, batch=16, imgsz=224, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/classify/train\n", "\u001b[34m\u001b[1mtrain:\u001b[0m /content/plants-classification-3/train... found 7905 images in 27 classes ✅ \n", "\u001b[34m\u001b[1mval:\u001b[0m None...\n", "\u001b[34m\u001b[1mtest:\u001b[0m /content/plants-classification-3/test... found 376 images in 27 classes ✅ \n", "Overriding model.yaml nc=80 with nc=27\n", "\n", " from n params module arguments \n", " 0 -1 1 928 ultralytics.nn.modules.conv.Conv [3, 32, 3, 2] \n", " 1 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2] \n", " 2 -1 1 26080 ultralytics.nn.modules.block.C3k2 [64, 128, 1, False, 0.25] \n", " 3 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] \n", " 4 -1 1 103360 ultralytics.nn.modules.block.C3k2 [128, 256, 1, False, 0.25] \n", " 5 -1 1 590336 ultralytics.nn.modules.conv.Conv [256, 256, 3, 2] \n", " 6 -1 1 346112 ultralytics.nn.modules.block.C3k2 [256, 256, 1, True] \n", " 7 -1 1 1180672 ultralytics.nn.modules.conv.Conv [256, 512, 3, 2] \n", " 8 -1 1 1380352 ultralytics.nn.modules.block.C3k2 [512, 512, 1, True] \n", " 9 -1 1 990976 ultralytics.nn.modules.block.C2PSA [512, 512, 1] \n", " 10 -1 1 692507 ultralytics.nn.modules.head.Classify [512, 27] \n", "YOLO11s-cls summary: 151 layers, 5,477,595 parameters, 5,477,595 gradients, 12.2 GFLOPs\n", "Transferred 234/236 items from pretrained weights\n", "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/classify/train', view at http://localhost:6006/\n", "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks with YOLO11n...\n", "Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt to 'yolo11n.pt'...\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "100%|██████████| 5.35M/5.35M [00:00<00:00, 107MB/s]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/plants-classification-3/train... 7905 images, 0 corrupt: 100%|██████████| 7905/7905 [00:01<00:00, 4159.00it/s]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/plants-classification-3/train.cache\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/plants-classification-3/test... 376 images, 0 corrupt: 100%|██████████| 376/376 [00:00<00:00, 2933.72it/s]" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/plants-classification-3/test.cache\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\u001b[34m\u001b[1moptimizer:\u001b[0m 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... \n", "\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.000714, momentum=0.9) with parameter groups 39 weight(decay=0.0), 40 weight(decay=0.0005), 40 bias(decay=0.0)\n", "\u001b[34m\u001b[1mTensorBoard: \u001b[0mmodel graph visualization added ✅\n", "Image sizes 224 train, 224 val\n", "Using 2 dataloader workers\n", "Logging results to \u001b[1mruns/classify/train\u001b[0m\n", "Starting training for 60 epochs...\n", "\n", " Epoch GPU_mem loss Instances Size\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ " 1/60 0.508G 3.45 16 224: 1%| | 4/495 [00:01<02:36, 3.13it/s]" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Downloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf'...\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ " 1/60 0.531G 3.4 16 224: 1%|▏ | 7/495 [00:02<01:51, 4.39it/s]\n", "100%|██████████| 755k/755k [00:00<00:00, 22.5MB/s]\n", " 1/60 0.541G 1.565 1 224: 100%|██████████| 495/495 [01:28<00:00, 5.57it/s]\n", " classes top1_acc top5_acc: 100%|██████████| 12/12 [00:01<00:00, 7.17it/s]" ] }, { "output_type": "stream", "name": "stdout", "text": [ 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1.416 hours.\n", "Optimizer stripped from runs/classify/train/weights/last.pt, 11.1MB\n", "Optimizer stripped from runs/classify/train/weights/best.pt, 11.1MB\n", "\n", "Validating runs/classify/train/weights/best.pt...\n", "Ultralytics 8.3.6 🚀 Python-3.10.12 torch-2.4.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n", "YOLO11s-cls summary (fused): 112 layers, 5,468,715 parameters, 0 gradients, 12.0 GFLOPs\n", "WARNING ⚠️ Dataset 'split=val' not found, using 'split=test' instead.\n", "\u001b[34m\u001b[1mtrain:\u001b[0m /content/plants-classification-3/train... found 7905 images in 27 classes ✅ \n", "\u001b[34m\u001b[1mval:\u001b[0m None...\n", "\u001b[34m\u001b[1mtest:\u001b[0m /content/plants-classification-3/test... found 376 images in 27 classes ✅ \n" ] }, { "output_type": "stream", "name": "stderr", "text": [ " classes top1_acc top5_acc: 100%|██████████| 12/12 [00:02<00:00, 4.29it/s]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " all 0.971 0.997\n", "Speed: 0.1ms preprocess, 0.8ms inference, 0.0ms loss, 0.0ms postprocess per image\n", "Results saved to \u001b[1mruns/classify/train\u001b[0m\n", "Results saved to \u001b[1mruns/classify/train\u001b[0m\n" ] } ] }, { "cell_type": "markdown", "source": [ "**Download the Model Weights from Drive**" ], "metadata": { "id": "_EdVUROF2dNw" } }, { "cell_type": "code", "source": [ "!gdown \"https://drive.google.com/uc?id=1vQ4W4nksiq6yKyNiUkVUGQ7Bb-SxYJSB&confirm=t\"" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Zd2ap2EK2deu", "outputId": "dc8c8624-612d-41db-8df3-48cea666191b" }, "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Downloading...\n", "From: https://drive.google.com/uc?id=1vQ4W4nksiq6yKyNiUkVUGQ7Bb-SxYJSB&confirm=t\n", "To: /content/best.pt\n", "100% 11.1M/11.1M [00:00<00:00, 12.1MB/s]\n" ] } ] }, { "cell_type": "markdown", "source": [ "**Results Analysis**" ], "metadata": { "id": "QExnK8IL1gTG" } }, { "cell_type": "markdown", "source": [ "**Confusion Matrix**" ], "metadata": { "id": "XaUdhO5420Yj" } }, { "cell_type": "code", "source": [ "Image(\"/content/runs/classify/train/confusion_matrix.png\", width = 800)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 617 }, "id": "UoyzmdTvg67h", "outputId": "2730372e-a840-4f33-c39c-607797ae9288" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": { "image/png": { "width": 800 } }, "execution_count": 11 } ] }, { "cell_type": "markdown", "source": [ "**Normalized Confusion Matrix**" ], "metadata": { "id": "5dEPlRPG4EH2" } }, { "cell_type": "code", "source": [ "Image(\"/content/runs/classify/train/confusion_matrix_normalized.png\", width= 800)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 617 }, "id": "J25YNgAY3oQr", "outputId": "10ea027a-0d25-4c03-d622-364d17d18a87" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "image/png": 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VqFQ5w7hatesax/v37cnWGudDz+nqldQNlWrWrpNpbK3/v37l8iVdOB9qdm1/8F5duXxJktSoaTPjfFxcnM6dDdGVy5eUlJSUrdoAPFxMJr4exBcAAFmhwR0A/t+PP/6oDz74wBjny5dPS5cuVc+ePe8pn7Ozc9ZB/8/X11evv/66MV6xYkWGsVu3blVQUJAxfu655/Tiiy/eU413yk69JpNJ48ePN8YXLlzQnj2Wv6lUtmxZjR49OtMYLy8vdep0e8eGbdu2ZRg7depU49jZ2VkTJ07MNLe9vb2+++47C6u9N1FRUerVq5diY2ONc7Vq1VLZsmWNsYODQ7Z2+R40aJD8/G4/8jCz75O72djYaMqUKZmuZzKZNHDgQGN89uxZnT9/3uI1MpOd768nnnhC3bt3N8bZeZ2S9Pnnn6fZKf9ur7zyitl4+/bt6catX7/erPl98ODBqlatWqa5W7dubfa9CwAAACDnVfAtJEk6cf56ps3gR89eM44r/v8cS92MiTeOC+RL+wHqO7m5pD6C3tHBTmW8PbK1jiTZ2dqofiVvSdLFsCijYR4AAAAAHkVnTqU+JcvbxzfTjbd8S95+4vWtOZY6ffL233f87siT1Tqn71rn4P7bm2yVKVteZ0PO6N0RbyigST11e7atnm3bXO2aP6WPxozSubMh2aoRAAAAQMZocAeA/3fp0iWz8axZs9S6detcWz8gIMA4PnLkiCIjI9ONmzdvntn4zt2lc1OZMmVUunRpY7xjxw6L5/bt29eixu6GDRsax//++68SEhLSxERERGjLli3GuEOHDipSpEiWuatXr57hLtz3KjY2VsePH9cPP/ygGjVqKDAw0LhmMpn0xRdf3Fd+W1tbtWjRwhhn5543b95cpUpl/uadZH7PJenw4cOWF5iD7vzvITuvM1++fGbN8RkpVaqUihYtaowzep13N9db+mGSl156yaI4AAAAANnnaG8rT/fUJ5iFXkn/d+dbwqPiFPX/jeolPN0yjb3bkZDbzfGNq/tmGFezbBG5udxugPfxyvgJXRkZ8HQN4zX9HnQ02/MBAAAA4GERFxen8PDrkiQvr6KZxubPX8DYROnSpYyf3pWey5dv/+3Xyyvzvx0WKXK7jst3rXNno/yZ0yfVr2cXBa5fa/Z066ioSK38a4n69XpOO7dvzVadAAAAANJHgzsAZODtt9/WuXPncm29YsWKGcfJyckZ7px9Z9N0qVKl9MQTTzzw2jJyZ83ZuVd3N1FnxNvb2zhOSUlRREREmphdu3YpOTnZGDdv3tziOrITe7dmzZrJZDKZfTk7O6t8+fJ67bXXdPLk7d0dTCaTvv76a7Pm9HuVm/dcksLDwy1eIyfd+TqvXr2quLg4i+bVrl1bDg4OFsXe+Vozep13NtcXK1ZMFStWtCh306ZNs7U7PwAAAADL3dotXZJuxqT9IPTdbsamxuRztux3hVtW7zyphMTUx8wP6VJXhfKnfTKVySSN7dfEvL5srlOyaAEjR2R0nL6Ym/ETzAAAAADgYRcdfdM4dnZxyTLeyTk1JiY6+oGtc2uN9NaJiLhhHE/68jPFxMSoW68XNO+P5dq4bY/m/7lSPV/oJ5PJpOibN/Xfd4br4oWceUIyAAAA8DjL+FlPAPCY6du3r4KCgozG5GPHjqlp06Zav369/Pz87jnv1atXtXDhQgUGBurAgQO6ePGiIiMjs2zYzajh9uDBg8ZxTu9ALkmJiYlat26dli5dqn379unkyZOKiIjQzZs3lZKS8WPfs9MIfWfzcmZcXV3NxlFRUSpUyPyR8nc2kktSlSpVLK4jO7H3qlq1avrmm2/k7++fadyePXu0cOFC7d69W0ePHtX169cVFRWlpKSkDOfk1j3PSWfPntWCBQu0efNmHTp0SFeuXFFERES6u/PfKTw83KKd+S19nZL5a83odd75/ZWd7xcXFxeVKlVKJ06cyDoYAAAAQLY4Odx+SzM+MePfmYyYhNQYZ4fsvRV67kqk/rdsr159tra8Pd20flIvjZ62UYH7QhSfkKQaZYto9PNPKaBuacXFJ8rx//M7OVq+jrOjneaO7SR3VydJ0vDJa3XhWs7+HgYAAAAAeUn8HX8jtbe3zzLe4f9j4uJi730du8zXcbijjrvXiY2JMcv54iuvq99LrxrnvEv4aNAbb8nNrYCmTJ6kyMgI/fbL/zTi3THZqhcAAACAORrcAeD/+fn5KSgoSM2bN9exY8ckpTa3NmnSROvXr1eZMmWylS82NlZjx47VpEmTLN59+u75d7u7ETg7zbyW+OuvvzRkyJA0TeOWSK/ejDg5OWU7v6R0G+zvbvL28PCwOF92Yu9WvHhx45GItzg6Oip//vzy9vZWzZo1FRAQkOWHEA4fPqzXXntNGzduzHYN1rrn9+L69et655139NNPP5ntuG8pS19rTr/OO7+/svv9cj/fXwAAAAAyFht/+zHwDna2WcY72KfGxNwxz1Ijp25QyWLualu/jMr7FNKCD7ukidl99IJ2Hb2glzukPmEtKjreoty2NibN+u+zqlEm9cO8U5b8o5mrD2S7RgAAAAB4mDg4OhrHWW2AJEnx/x/j6Ji9vwGZrZOY+Trxd9Rx9zoODrfzuLt7qHffAenm6PlCPy2cO1PXrl3V+rUr9dao/8pkMmWrZgAAAAC30eAOAHfw9vZWYGCgWrZsaeyUHhISYjS5V6hQwaI8sbGxevrpp7V+/fp0r3t4eMjFxcWsGTcxMVFnzpwxxuk13EZGRpqN795t+35MmTJFr776arrrOjs7q0CBAnJ2dpaNjY1xPjQ01Gg8zqlG6Oy6+8MDjne8WZWV7MTebdasWVnuyp6VnTt3KiAgIN2d2O3s7FSoUCE5Ojqa7V4RFham69evS7LePc+ua9euqXnz5goODk5zzWQyqWDBgnJ2djb794iJidH587cf35gXvr+y+/1yP99f6ZkwYYImTJhgcfzw4cM1fPjwHK0BAAAAyAsi72ggz+ec9W5/+ZxSY27GWNZ4fqf4hCR1+e9C9WlTXS93eELVS3vJxia1QeHS9Zv6Zfk+fTZri8a/2sKYcz3Ksg/oThvxtNrWT/0w/cKNhzXsuzXZrg8AAAAAHjYuLvmM45jo6CzjY2NSY5xdXB7YOrfWSG8dl3y3x7Vq15W9vUO6Oezs7FS73pNavWKZIm7cUOi5syrh45utmgEAAADcRoM7ANylaNGi2rBhg1q1aqV9+/ZJks6fP6+mTZtq3bp1qlKlSpY5xo0bZ9bc7uvrq8GDB6tly5aqWLFiurtMnzp1SqVLl840r5ubm9k4KipnHlt+5MgRDRkyxGgitrOzU//+/dWlSxfVrl1bhQoVSnde06ZNFRQUlCM13Kv8+fObje/+EEBmIiIicroci8XFxalXr15mze0BAQEaMGCAGjRoIG9vb7MPE9zy/vvv68MPP8zFSu/fsGHDzJrbK1eurNdff13+/v4qW7Zsuo+f3LBhg5o3b56bZaYrf/78CgsLk5S97y0p57+/IiIiFBoaarX1AQAAgLwiLiFJV29Eq3ABF3l7umUa6+7qKFfn1OaDc1ey9zP9LSkp0vQVwZq+Iliuzg7y8nBRTFyiLoZF6dZncct6336C05Ez17LMOWlwK/Vomfr+wsodJ9Tvs2V6SD7DDAAAAAD3xdHRUQUKuOvGjXBdvnwx09iIiBuKiYmRJBUpUjRb63h5FTGOL1++lGnspUu36/C6a507x15FM6/hztjw8Os0uAOPCBuexgAAgFXQ4A4A6fD09NT69esVEBCg3bt3S5IuXbokf39/rVmzRjVr1sxwbkJCgr799ltjXKlSJW3ZskXu7u6ZrpneLt53y58/v+zt7Y3H9V24cCHLOZb49ttvFR+fupOdjY2NlixZorZt22Y5z5KaHzRPT0+zcUhISKb/Pnc6e/bsA6jIMkuWLNHx48eN8ciRI/Xpp59mOS8v3PPsuHTpkmbNmmWM/f39tWLFinQ/5HGnvPI6PT09jQb3kJCQbM09d+5cjtaSP39+eXt7ZyseAAAAeFQdOXNNjaq7qExxD9namJSUnH53eAWf2x/YPhKSdeN5VqJi4hV1107wNjYmVS/jJUk6ef66rkXEZJrj4xeb6uUOT0iSNgWHqMcHfygxKfm+awMAAACAh0XJ0mW0b89uhZ4NUWJiouzs0m9dCTl9yjj2K5X5RmHprXHLmTvyZLVOybvWKVW6rHGcnMXvbsnJScaxra2tRXUCAAAASF/arWEBAJKkggULat26dWrQoIFx7urVq2revLl27dqV4bydO3eaNeeOGTMmy+Z2STp58qRFdVWtWtU43rFjh0VzsrJmze3HoLdq1cqi5vbk5GSdPn06R9a/H3c3s9/6QIIl/vnnnxyuxnJ33vP8+fNr7NixFs2z9Pskr1i3bp2Sk2+/2ffpp59m2dwu5Z3Xeef316FDhxQbG2vRvFOnTun69es5Wsvw4cN17tw5i7+GDx+eo+sDAAAAecmWg6kfKHV1dtAT5TPeQa9xdR/jeOvBnP0Q6i1Na/iqcIHUR9YvDDySaew7PRvozW5PSpJ2HTmvzu8tUmx84gOpCwAAAADyquo1Uz/0GxMTo6OHD2UYt2f3TuO4Wo1a2VqjuHcJFfZM/TDy3t0Z/21Xkvb+k/r3RU+vIipW3HyzoZpP1DGOz4dmvnlW6Lnb1z09i2QSCQAAACArNLgDQCYKFCig1atXq0mTJsa569evq2XLltq6dWu6c0JDQ83GNWrUsGittWvXWhTXtGlT4/jUqVPas2ePRfMyc2fNlta7c+dORURE3Pfa96tChQoqVOj2jnyLFi2yaF58fLyWLl36oMrK0p33vGLFinJ0dMxyTlxcnP7+++8HWVaOe9D/PTxoDRs2NI7j4uK0bNkyi+YtXLjwQZUEAAAAQNLSzbefiPV862rpxphMUs9WqR8Svx4Zq8C92Xsqk6Xee6GRJCk+IUk/L9+XYdygTrU1tl/q+wv7T15Wh3cXpNk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ew9X+3d9Uv3597d27VxUrVtSRI0dydMLC9OnT9eKLL8owDM2fP19dunSx2T969Gi9/fbbMgxD9913n3755RdHpZ8r77//voYOHSrDMPT555/r6aefTjfO3gL3MWPG6PXXX8/L1F3K448/rvnz58vHx0fnzp1T0aJFnZ2S3U6ePKmmTZvq/PnzslqtatCggTZs2KBjx46pdevWunz5siTJ19dXP//8c7ZWhCtoHPk8f5OPj4/q16+vjh076plnnlHp0qUdOr4j5cX8b/L391ePHj301ltvqWTJknlyjNxw1Ml8ktLchxaLRR06dNAHH3yQ5soIruTo0aN64YUXtG7dOpvb7Slsd9Xn+YwU9sd8SidPntTUqVO1fPlyHTp0KN0Vq7NSUIpd9+7dq/r168swDA0ZMkRjx451dkpOU9g+z4iLi5O/v78SEhLk4+OjS5cupbsCe3x8vKpVq6awsDDzb1tgYKCqVaumI0eOmK+JDMPQ3Llz1bVr13yeScby48T1mwrK7z21TZs2acqUKVq1apV58mZOFJS5F+bnusuXL+vxxx/X6tWrJWX+miblfZReXEH4937mzBnVr19fly5d0q233qpVq1apbNmy2e7/8ccf6+WXX87w+fGbb75Rz549Jd24P7755hs9+eSTDp1DTn399dd6+umnZRiG6tevn+uTGQqKZcuW6cEHH5R047PJI0eOqESJEnaPc+nSJVWvXt1clGHRokV64IEHHJ2uw6T+u5ZXn92nN74z5eX79dQKyut6AACAwsI1Ti0GAABAgfPOO++Yq4KkXMmrMHj11VdtVu6R/v0yyJ4imILqv/h7HjFiRI72FRbHjx/Xr7/+qhkzZmjJkiWKj4+XdOPfQmRkpCZPnqzJkyf/Z1d1f+aZZ/T111/bPN7/K4+DvXv3au/evTIMQ48++miOf29PPvmk+vfvL6vVqjlz5qQpcO/atau56tPBgwdznbejpFzZ6/r167kaKzIy0tzOyZeqBdmdd96p+fPn6/r161q/fr1LfxmckUqVKpmF69euXdPu3bvVoUMHHT582Czk8vb21k8//fSfLG5PyZF/32JiYrR161Zt3bpV77zzjoYPH64333zTpVe9TDn/1K/XMtqX0X128/nin3/+0SeffKL58+fr+++/d8l/Q6lf06a3L6WM7pvU4yQlJWnp0qX69ddf9cUXX6hbt24OzdsR1q5dq86dO+vatWsZzjX17Zn92yhoCvtjfsyYMRo5cqRZvFOQf5fZUbduXdWoUUNHjhzRkiVLCm2Be2H8PGPv3r1KSEiQYRhq0aJFusXt0o2rO90sbpekAQMGaMKECbJYLIqNjVWPHj30448/SpImTJjgUgXuUuaP4f/67zgz8fHx6t+/v3m1gYze22b2+ib1ye4FSWF7rktKSlKXLl3SnLjn5eWlkiVLKjw8XIZhyN3dXWXLltXly5d15coVM84wDFkslgJ1FZ6BAwfq4sWL5ns2e4rbJemll17S7t279dVXX+nDDz/UE088obp165r7//e//2np0qVasGCBJGn16tUuU+B+4cIFc/uRRx5xYib56+TJk5Ju/Hvt2rVrjj+HKVGihLp27aoZM2bYjOvqMvt77MiTuVxNYX4uBwAAKKwocAcAAIDdvv32W73zzjuSbAu7y5Qpo+DgYPn5+TkzvTw1efJkTZw4UZLth7wWi0WBgYEqUqSIs1JziBYtWmT44XVm+wo6Ctyz1qZNG7Vp00YXL17UV199pS+++MJmJWur1arNmzdr8+bNevnll/8zq7pPmjRJs2bNkvTvF/re3t6qV6/ef+Lv3V9//WVu52ZlXX9/f5UuXVpnz57V/v370+yvXr26ihQpomvXrtkUgjtbqVKlzO2MVmbPrl27dpnb9n6ZXtAFBASY26dOnXJeIrnUoEEDLViwQJ06dVJSUpI2b95sfnnq6empefPmqV27dk7OMm+lV+Sc1RfIWcXe/OI9Pj5eI0aM0M6dO/XTTz85KGPHufk6Jzk52fzd35yPxWLRLbfcosDAQHl5eenKlSs6ffq0oqKiJP17H4SEhKhixYqKi4tTZGSkjh8/brPi3fnz5/Xggw9qy5Ytql27dv5PMgM9evSQYRiKjY3VggULbOZepkwZ1a5d22buYWFhOnDggHnCm2EYqlWrlho3bmzOfe/evTp37py5PyYmRj179lTZsmVd6nLvp0+fVteuXRUdHW3+HitVqqQ2bdqobNmyeu+99yRJxYsXV//+/XX58mX9/fff2rRpky5fvmwWgD3//PMuuTp/VgrzY16SBg0apMmTJ6db4JzZCcz23F+uaMiQIerdu7cOHTqkadOm6YUXXnB2SvmqsH6ecfOKXJJUr169DON++OEHSTfukxo1amjixInm/eTj46NZs2Zp/fr1unTpknbs2KGzZ8+6zGvfr776Kkf7CoOePXtq/vz5af5mpSyQTPn8f1NB/3snFc7nus8++0zr1q0zn9vuvPNOjR49Wi1btpSbm5tZjF+pUiXzffClS5e0Zs0aTZkyRVu3blVycrI6dOigKVOmyM3NzZnTydKlS5e0aNEiGYahBx54QNWqVcvROIMHD9ZXX30lq9Wqr776yvz896Z+/fqZBe47duzIdd6OkvKEpeDgYCdmkr9iYmLM7ZQnI+REyv65Xfwgr1WsWDHDz+cz2/dfkJ3nIXtORP4vnbQMAADwX0aBOwAAAOxitVr1xhtvSJK52s/AgQP1/PPPKyQkxLnJ5bHr16/r7bffNj/8dHNz03PPPacePXqoQYMG8vDwcHKGubd+/foc7UPhUbJkSQ0ePFiDBw/Whg0bNHPmTP3000+Ki4szvwj4r6zqnpCQoFGjRpmPeX9/f40dO1Y9evSQr6+vk7NzjPDwcHM7t5ffvVnEeebMmXT3lyhRQteuXVNsbGyujuNI9evXN7eXLl2qCRMm5OjLwLi4OK1atUrSjefGZs2aOSrFAiEiIsLcjo6OdmImudeuXTvNnDlTzzzzjM3z/Zw5cwrkyvT2uLnC4/r16zVq1CglJydLkho3bqzWrVunW+S8c+dOLV261Lyc+2233aYPPvhAFovFLHL+5ZdfzCtFWK1WLV68WO+8847LnUC2fv16RUREqHv37ubcH374YfXp00ctW7a0ueLDTaGhofrhhx80efJknTlzRhEREXr33Xf11FNPSbpRdLFy5Uq9//772r59uwzD0NWrV/Xss89q69at+Tq/zMyaNUtHjx5Vly5dlJycLE9PT/Xr1099+vRRzZo10+0TFxdnrv68e/duHTlyRE8++aSGDRtmxuzatUujR4/WwoULzSKwF154Id0ToZxlzJgxioyMlGEYcnNz00cffaR+/fqZhV8pC9xHjhxp9ouPj9eXX36poUOH6sqVK1q2bJmWLFmiOnXqOGMaOVLYH/MrV67UpEmTzL/1xYoVU//+/dWuXTuVKVNG1atXl2EYCgkJ0dq1a82TG9asWaM5c+YoOjpabm5ueu+991zyygSZefrpp/XLL7/oxx9/1MsvvyxJhabIvTB/nnH27Flzu0KFChnG3fzbYBiGevXqlea1cZEiRfTggw+aBeM7d+5Ux44d8yBj+/Xs2TNH+/7rvv/+e82bN8/8XVauXFlvvvmm+ffO09NThmGoatWqOnLkiPn3bvXq1Zo+fbpOnjwpNzc3jRkzRoMHD3bybOxTWJ/rxo8fb263b99eixcvzvIzyxIlSqhbt27q1q2bxo8fr9dff13Tp0/XxYsXNW/evLxOOVc2bdpkXqEiN+/Fa9asqWLFiunKlStpVr+XpKZNm8rd3V2JiYk2q6Y7W8q/6VevXnViJvkrZTF/bk/CSNm/XLlyuRorr4WGhuZoX0GX3mMytZt/62+esFWtWjXdf//9qlu3roKCgsyFNyIiIrR3716tWLHCPMnHzc1Nb731lktebQ0AAKDQswIAAAB22Lp1q9UwDKvFYrFaLBbr0qVLnZ1Svlm2bJnVMAyrYRhWd3d36/Lly52dEuASIiMjrRMnTrTWrl3bfIxYLBabvxUlSpSwDhw40HrgwAFnp5tta9euNefj4eFh3b59u7NTcrgpU6aYv6d+/frleJwLFy6Yv/PixYunG1OuXDmrYRjWokWL5vg4eaFSpUrmffDpp5+mG3Nzf7Vq1dLdP2nSJDOmSZMmeZmuS2rRooU5/y+++MLZ6TjEqFGjzOf7OXPmODudfDNt2jSrh4eH1WKxWFu0aGHdu3dvln3i4+OtH330kdXPz89qsVis9erVs166dMkmZsmSJdZSpUqZfyf8/PysFy9ezKtp5EhcXJy1UaNGVovFYi1WrJh15cqV2e4bHR1tfeyxx8zHwbx582z2JyUlWfv27WvzHPnrr786ego59s8//1irVKlitVgs1vLly1v37duX7b7JycnWQYMGmfOaPHlympjRo0fbzH3RokWOTD/H4uPjrb6+vuZrlUmTJqWJyerv/7Fjx6zlypWzWiwWa0hIiPWff/7J67QdqjA/5ps3b27z+z116pTN/sx+9xcvXrQ++OCDZsz48ePzK22HSUxMtHns1q9f3zpx4kTrjh07rFFRUdakpCRnp5gnCvPnGWPGjDHnPnv27HRjDh48aPP3+q+//ko3LuVr308++SQv04YD1KtXz/x9NW7c2Hr58mWb/Zn9vYuLi7P269fPjHnllVfyK22HKWzPdceOHTMfxz4+Ptbz58+nicnq9Y3VarWOHDnSjJs1a1Zeppxrn3zyiZnrl19+mauxKlasaDUMw1qyZMl095cpU8ZqGIbV09MzV8dxpIsXL1o9PT2tFovF2rNnT2enk2927drlsL9NAwcONMfavXu3gzJEfvr000/Nv/W33XZbtt9vr1271lq3bl3zs9/p06fncaYAAACwFwXuAAAAsMuMGTPMD3w7derk7HTy1eTJk825d+3a1dnpAC5p8+bN1p49e1p9fX3NL1VTFklYLBZr8+bNrXPmzLFev37d2elmatq0aWbejz/+uLPTyROLFi0yfz+lS5e2XrlyJUfjjBs3zqZAKrXExESrl5eX1WKxWG+55Zbcpu1Q77//vnkfeHt7W5csWZImJrMCgE2bNll9fHzMmO+//z4/0nYZ06dPt3mM79mzx9kpmW655ZZc/RiGYfXy8spWbOXKlZ093VzbvHmz+YXwo48+ak1ISLCr/9atW81i4Y4dO6bZv2vXLpti4vQKoZ1pyJAh5r/jnJzEmJycbL3rrrushmFY/f39rWfPnrXZn5SUZL3jjjtcskCsT58+5hf6O3bsyNEYN4t9vby8rMePH0+zv23btubcn3/++dym7BCbN282cwoJCbEmJyeniclOAdj69evNuBdffDEvU3aowvyYj4iIsHnu+uOPP9LEZPW7T05Otnbu3Nl87Pz+++95nbbD3Pyd3PxJ/Vrd3h83NzdnTynbCvPnGWPHjjXn/vnnn6cb89lnn5n/HjIq7rRardavv/7aHGvs2LF5lTIc4PTp0zYnbR87dixNTHae655//nkz7ueff87LlB2qMD7XzZ071/xdPfbYY+nG3NxfpUqVDMeJj483i71r1aqVV+k6xM339BaLxfruu+/meJyEhASrl5eX1TAMq6+vb7oxN0+Q9/HxyfFx8sKjjz5qNQzDGhAQkOZkjP+ymwttlCtXznrt2rUcjXHt2jXzhNXbbrvNwRkiP2zcuNH8W9+2bVu7/y3ExMSY71c9PDysmzdvzqNMAQAAkBMWZ68gDwAAgIIlMjLS3G7Tpo0TM8l/165dM7cL2+Uq9+/fr3fffVfvvvuuPvroI2enAxfWrFkzzZo1S2fPntXHH3+sevXqSZJ56W6r1arNmzerR48eCg4O1uDBg3XixAknZ52+y5cvm9t333238xLJQ61bt5aXl5cMw1BERISeffZZ87Lt2bV9+3aNGjXKvOR9+/bt08Ts379f8fHxkqSQkJBc5+1IL7/8sipWrCjDMBQXF6eHH35Yzz77rA4dOpRpv6tXr2rMmDFq166drl+/LsMw1LBhQ3Xr1i2fMneeuLg4bdy4UU899ZRefPFFGYYhwzBUq1Yt1a1b19npmUJDQ3Xy5EmFhoba/XPy5EkZhqGEhIRMx0i5r6AbMmSIEhMTFRQUpC+//FLu7u529b/zzjs1fPhwWa1WLV++XCtWrLDZX79+fb344ouyWq2SsneJ8fwSHx+vzz//XIZhqGXLlun+HcuKYRh67733JEnR0dGaPXu2zX6LxaLBgweb7U2bNuUuaQeJjo7Wd999J8Mw1K5dO91+++05Guett96SJCUkJOirr75Ks3/gwIHm9tatW3OWrIOl/DvfqVMn83ksPQkJCRnua9mypZo3by6r1arvvvvOfL5zdYX5Mb9lyxZJMh/zjRo1snsMwzA0c+ZMFS1aVElJSXrjjTccnWaeufma/Obv5ubzeMrb7f0pKArz5xnFihUzt8PCwtKNufk4NQxDTZs2zXCs69evm9uZ/e2E8/3++++SZD7PV6lSJcPYzB7LH374oUqWLCmr1aoRI0Y4PM+8Uhif6yIiIsztO+64I9PYlI/l1Dw8PNS5c2dJ0uHDh/XXX385JL+8UK5cOXN76dKlOR5n1apVio+Pl2EYKlOmTLoxUVFRkiR/f/8cHycvTJ48WcWLF9eVK1f03HPPOTudfDNq1ChJ0rlz5/T888/n6DXJiy++qLNnz9qMh4Jl8ODBSkxMVEBAgObOnStfX1+7+vv4+Gju3LkKCAhQYmKiXnvttTzKFAAAADlBgTsAAADsUrx4cXM7ICDAeYk4QalSpcxtb29vJ2aS/1atWqWRI0fqnXfecekvteA6/P391atXL73wwgsqXbq0pH+LZ6QbX55HRkZqwoQJql69up555hmdOXPGmSmnkfIxb++XIwVF0aJF9cQTT5hfAi5YsEBt2rTRgQMHsuybmJioKVOmqG3btoqNjZXVapWnp6d69+6dJvaXX34xtxs2bOi4CTiAl5eXlixZoqJFi8owDCUnJ+urr75S7dq1VbVqVT300ENm7IULF9S1a1c1a9ZMJUuW1FtvvaXY2FhJUsmSJfXTTz85axo5UrlyZbt/ypQpo6JFi6pVq1b6/vvvzcI2i8WiCRMmOHtKaeS06C67hXsFqagvM0ePHtWWLVtkGIYeffRRFS1aNEfj9O7d2/w7n7rAW5J69Ohhbu/bty9nyeaB9evXm8Uq7dq1y/E4LVq0kI+PjyRp0aJFafbfe++95v0THh6e4+M40oYNGxQTEyNJuueee3I8zu233y4/Pz9JSlP8Jd0oAndzc5N0owDFFaQsdK1Vq1a6MTdzjouLy3Sse++9V5J05coVbdy40UEZ5p3C/phP+ZozqxOXMyv+CwoK0oMPPiir1aqNGzeaxVEFSUEsUs+Nwvx5RuXKlc3t9E6yio+P1/Lly812Zif4Xrx40dzO6d8P5I+Uz7nNmjXLNDazv3d+fn56+OGHJUk7d+7U33//7ZgE81Bhfa67cuWKuR0UFJRujK+vr6xWq65evZrpWDVr1jS39+/f75gE80DKQv4///xT33zzjd1jXL9+Xa+//rrZbty4cZqY8PBwXb16VYZhqEKFCjlLNo+UK1dOP/30k4oVK6aFCxfq/vvv16lTp5ydVp57+OGH9frrr8tqterbb79Vx44ds72Ixv+xd99hUVxfH8C/lwVpiqCiIARRrKjYG0ZRUIJYYg1qrMSCscSuqLFGsWvsYkWMJUYsaAQEsSNWYsGugGJBkN5h5/2Dd+e3Kx22sufzPHkyuHcuZ9idsjPnnhsREYE+ffrw+/W8efP4QR1Edbx48QKhoaFgjGHIkCGoUaNGmfqpUaMGfvrpJwBAaGgoXr58Kc0wCSGEEEJIOZRuqDohhBBCCFF74pV3xasCqQPxyn4VoVJraYhXrWzVqpUCI1EeT58+xfv375GQkICMjIxSJYWIPwCtiO7evQtPT08cO3ZMYuYDIC+JRiAQIDc3l/+33NxceHl54fTp0/jrr7/Qq1cveYdcIPHqdqqYsFRS69atw/nz5/lElStXrqB58+bo0KED7O3t0bRpUxgZGUFbWxvJycmIiorCvXv34Ovri/j4eHAcxw9ecHd3R/369fP9DvFEgPIkUMqKjY0NLly4gJ9++olPOuU4Dm/evMHbt2/5hIbk5GScPHmSf13075aWljhz5ozSPeQuTkREBF+xtSxE21+pUiXs2LGjXInBsjB69GhFh6AyHjx4wC83bdq0zP3UrFkTNWrUQGxsrESfIs2bN4euri7S09MlkosVTTz5o7BqjSXBGEONGjXw7t27AhNKDA0NUa1aNcTFxfEJ9Yr2/v17frl69erl6svIyAgpKSkFVgbW19dHtWrV8OXLF4kZUhRJ/PpWlJz/rcqVKyMxMRFxcXFF9mVmZsYvF1YZWZmo+z4vvv+Jv3fitLW1kZmZyQ8AKUzbtm3x119/geM4hIaGqkRilCpVX5Y2db+foaGhAaFQiMuXL+POnTsSSaF79+6VOD6LBu4URDyJV9Wuf9VNYmIiv1zYNY7oOJ2SklJkX82bN+eX79+/LzFoQhmp67lOfHB+YbPKGBgYIC0tDSkpKUhOTi40+V80cBNQ7nsijRo1QsuWLfHff/+B4ziMGzcOWVlZBQ6+L8jHjx8xbNgwiST+4cOH52t39epVftna2rr8gUuR6J6Lm5sb1q5di4CAAFhZWcHe3h62trYwNTUtdbEWVbh3GRUVhUmTJkFPTw/Lli2Dv78/GjduDAcHBzg5OcHGxgY1a9bk99GYmBg8evQIfn5+CAwMRE5ODgQCAZYsWYJRo0aVaFCAhYWFHLZM+uLi4vD06VPEx8cjJSUFlStXhpGREZo0aVLu74GKJH5cLu+MgqJZSIG881yDBg3K1R8hhBBCCJEOSnAnhBBCCCGlYmdnB0NDQyQmJuLq1auYM2eOokOSm5YtW6Jhw4Z4+fIlzpw5g+XLlys6JLkRfxCqzhXarl+/ji1btiAgIKDYSldFUYWHRKWVnJyMw4cPY8+ePfjvv/8A5K9sbG1tDTc3N4waNQpPnjzB7t27ceLECb4CdkJCAgYOHIh79+4pxcPCLl26wMTEBJ8/f0ZQUBAWLFig6JBkonr16ggKCoKDgwO+fPnCJzyHhoYiNDS00PVE76+ovZubGxYvXpyvXWhoKPT19dGmTRtoaWkVmSyjSJ06dcLDhw/h7u6OQ4cO8RUMxRPZRT+LaGpqYsyYMVi1apXKPhAsT8VWbW1t9O/fH7///nuh1Y8V6cCBA4oOQWWIJzlrapbvdqGmpiY4jiu0Qnn16tXx/v37cp1HpU08ebm4RObiiBJnC+uncuXKiIuLU5pqyeLJjJ8/fy5zPxzH8QOlxCuHihMlXIkfUxXJwMCAXy4sZiMjIyQmJiInJwfR0dGFJkOLJ5CV5+8oL+q+z2tpafHLoir936pSpQoyMzMRHx+PrKwsVKpUqcB24lXAlTn5T5w6J7ir8/2MGjVqwMHBARcvXoRQKISzszMWL16Mhg0b4ubNm1i9ejV/fG7cuHGRA9tv3brFLzds2FDmsZeUvO/RFPTdR9mIJ7QKhcIC21SpUgXp6elISkpCenq6RFLzt+1EVOF4p67nOtEMeoDkbAvi6tSpw1f3DwsLQ5cuXQpsFxkZyS8XliyvLDZv3ozu3buDMYbs7GxMmDABnp6e+OWXX2Bvb59vIH5ycjLu37+PkydP4uDBg0hNTeWPgd26dUPfvn3z/Y5jx47xy507d5btBpXSmDFj8t23yM3NRWBgIAIDA8vUpyrcu7S0tMy33dnZ2fD394e/v3+R64q+jwmFQixZsqRE10eMMeTk5JQvaDl6/vw5PD09cfr06SKL9VhaWmLAgAEYP348GjVqJL8ApUD8uFzeY7349wJlmXGNEEIIIYRQgjshhBBCCCklbW1tjBs3DuvXr4e/vz+ePXsmMWVtRbdy5UoMGTIEjx8/hqenJyZMmKDokORCvNKdKjzIlLasrCxMmTIF+/btA5A/sVeksCRY8bbKktQlLbdu3YKnpydOnDjBV7gU3/ZKlSph4MCBmDRpksRD006dOqFTp07YsGEDPDw8sGXLFuTm5iIrKwvr1q1TisRUDQ0N/Pbbb3B3d8fly5cREhKCTp06KTosmWjWrBnu3buHiRMn4sKFCxKvFfRZFv2f4zgYGBhg3bp1GDduXIF9d+jQAXfu3JFN4FJmZGSEXbt2YcWKFTh58iSuXbuGBw8eIDY2FomJidDT00P16tVhbW0NBwcHDBo0CObm5ooOu8xKm+DGGIOenh4MDQ3RpEkTtGrVSqJCIFFdGhoa/PLz58/L3E9SUhKf3CvepzjRMUVbW7vMv0faxKcxv3z5MmbNmlWmfu7fv4+UlBS+kntBRIlP+vr6Zfod0iaeBOXv74958+aVqZ9r164hPT0djDHUrFmzwDaiKrKFVUuXtzp16vDL4klw4ho1asQng4SEhGDw4MEFtgsLC+OXlemzXRh13+erVavGLxdWbdfExIRPDHz27FmhFSHFq4Ary+wEpHDqfj9j+fLlfKJjXFwcpk+fzr8m/h23qIG9YWFhfNJr1apVlSoRbunSpXL9vq0KCe7i1yOFHe/MzMwQExMDAHj8+LFEZX9xHz584Je/nalNGanruU58nxQVH/hW8+bN+cHs586dKzTB/dSpU/yysbGxFKOUvq5du8LDwwPz58/n71XcvXsXd+/eBZB3b6pq1ar8zHTisxuI7tVxHAcrKyuJGehEXrx4gfPnzwPIO0726dNHPhtWRuU5FqrivUvxmQXF/60wpWmripKTkzF37lzs2bMHHMcVu31v377Fpk2bsHnzZowfPx5r1qyRGAyszMST0p89e1auvsTXL+x4TwghhBBCFIAjhBBCCCGklDIyMjgbGxuOMcY1bdqUi42NVXRIcjVjxgyOMcZpaWlxO3bsUHQ4cpGTk8MZGxtzGhoaXPfu3RUdjtwNHTqU09DQ4Bhj+f7T0NAo9jXx1zU0NBS9OeWWkJDAbdmyhWvevLnE9olvZ7169bjVq1dzMTExJepz7969fB8WFhYy3oKSEwqFXPfu3TnGGGdmZsa9ePFC0SHJ3LVr17iRI0dyxsbGBX6uGWOcpqYm17p1a27jxo3c169fFR0yIaScTpw4wR+D69Spw2VlZZWpn127dvH9WFtb53tdKBRyurq6nIaGBvfdd9+VN2ypuXnzJn9809LS4u7evVumfn788Ud++3v27Jnv9YSEBP71Zs2alTdsqbh//77ENYq/v3+p+xAKhVyXLl34Pvr165evTUxMDP96y5YtpRF6ub1+/ZqPycHBocA2Cxcu5Nv06tWrwDZfvnzhjIyM+HY+Pj6yDFsq1H2fv3r1Kh/3L7/8UmCbYcOG8W02btxYaF9OTk58O3X5bqjq1P1+xubNm/N9jxVfHj58eJHrz5o1i1+nd+/ecoq6ZL79Dl7Qf8V9by+uvfjfTBWEhITw8Y4cObLANqNGjeLbLF++vNC+vv/+e76dp6enrEKWGnU912VmZvLxmJubF9jGx8eH/0xXqVKFe/jwYb42K1askPjcP3r0SNahS8WuXbu4ypUr57tHVdh/4m26dOnCvX//vsB+v379yj1+/Jh7/Pgx9+zZMzlvVfGK287S/qcqxzhpb3dF+Lu8e/eOa9SoUYHn+KK2S7x9o0aNuOjoaEVvSomcPHmS347atWtzaWlpZeonNTWVq127Nv83+Oeff6QcKSGEEEIIKSvGcRVsSCohhBBCCJGLjx8/ol+/frh37x5MTU2xdu1auLi4lHsqSFWxcuVKLF26FEKhEE2bNsXIkSPRoUMHmJiYSEwBXRIWFhYyilK63N3dsWbNGjDGcOXKFXz//feKDkkujh07huHDh/PVnOrVq4cFCxbA0dERJiYmqFSpEhhjsLKywosXL5CQkIA3b97g4sWL2LVrFyIjIyEQCLBq1SrMmTNH0ZtTLtevX8eePXvwzz//ICMjA4BkpSuBQABnZ2dMmjQJP/zwQ6kqPnEcByMjIyQlJUFLSwuZmZmy2oxSS0lJwbBhw3D+/Hno6+tj/vz5cHV1hampqaJDk7m3b9/izZs3SEhIQGZmJgwMDGBsbIzmzZtT5W5CKpDY2FiYmppCKBQCAKZMmYI///yzVH28fv0aHTp0QHx8PABg8uTJ2LJli0Sb58+fo0mTJmCMwdbWFteuXZPOBkiBubk5Pn78CI7jYGZmBn9/f1hbW5d4/blz52L9+vUA8ioC7ty5M99MPwEBAXBycgJjDD/++CN8fHykug1l1ahRI7x69YqfleOff/5Bjx49SrRueno6xo4di7///htA3rZ7e3tj+PDhEu3OnDmDAQMGgDGGn376CUePHpX6dpSFmZkZPn78CB0dHcTExOSrLh8WFobWrVvz1zTu7u5YsmQJtLS0AACRkZEYOnQoXwVVU1MTHz58KLSCv7JQ930+MTER1atXB8dxsLa2xqNHj/K12b17NyZNmgTGGCwsLPDw4UNUqVJFoo2/vz+cnZ3562F1+o6k6tT9fsalS5fwxx9/4Nq1a8jNzQUAWFlZYfLkyfjtt98K/R6XnJyM7777DklJSQCAbdu24ddff5Vb3MUpabVVVsqqvQW1Z4zxfztllpaWBkNDQ+Tm5qJevXp4+fJlvjaHDx/GqFGjAORVfA8LC0Pt2rUl2hw8eBCurq4A8rY9NDQUbdu2lf0GlIM6n+scHBwQHBwMxhiuXbsGW1tbidczMzNhaWmJmJgYcBwHPT09jBo1Ci1atEBGRgZ8fX0RHBzMt2/WrFmh1eCVUWRkJJYvX47jx4/zMw0WxcbGBtOmTeM/46pINLOGNInPdqSsvLy85P47R48eLfffWVKpqalo2bIlXr9+LTHraMOGDeHk5AQbGxsYGxtDX18fqamp+PLlCx49egQ/Pz88f/5cYp369evjwYMHSjPzWGHi4+NhYmKCnJwcAMDIkSNx8ODBUvczZswYfvaGSpUq4ePHjzAyMpJmqIQQQgghpIwowZ0QQgghhBSoJDf1U1NTcfLkSQiFQjDGoK+vjzZt2sDU1LRUSd6MMezbt6884cpdTk4O3N3dsWHDhnJN28oY42/AKrvMzEy0adMG4eHhqFmzJgICAmBjY6PosGSuZcuWePjwIRhjaNu2LQICAlC1alX+dQ0NDYkEd3FZWVmYOXMmduzYAcYYpk2bhk2bNsl7E8pt8+bN2LNnDz9Vq3hSOwCYmpril19+wYQJE2Bubl7m32NjY4PHjx8rVbKAvb09AEAoFOL69ev88Q7IG5xSluNdUFCQTGIlhJDyGDBgAM6cOQMg71g1fPhwbNy4EcbGxsWue/bsWbi5ueHz58/gOA4aGhq4c+cOWrVqJdFuy5YtmD59Ohhj+PXXX7F161aZbEtZ7NmzBxMnTuTPb9ra2pg2bRp++eUXNGzYsMB1srKycO7cOXh4eOD+/fv8+bFu3bp4/PhxvvPDxIkTsWfPHjDG4OHhgblz58pj04p16tQpDBo0iN92xhgGDx6MX375BXZ2dtDW1s63TmRkJE6ePInNmzcjOjqaX69Fixa4e/duviTDn3/+GUePHgVjDJs2bcK0adPktXlFGj16NLy9vcEYw8GDBzFy5Mh8bbp3744rV67w538DAwM0btwYGRkZePz4MYRCIb/9Q4cOxV9//SXvzSgTdd/nW7dujbCwMDDG8OzZMzRo0EDi9bi4OJibmyMrKwsA0KRJEyxZsgQ2NjZ88t/q1auRnp4OjuNQq1YtvHv3Tm0SpFWZ6F6HOt/PEMnJyUFsbCx0dXUlvuMWJj4+Hg8fPuR/btWqFQwMDGQZolRxHCdRrKBq1aoYMmQInJ2dC0z6e/jwIS5cuIATJ04gISEBAoEAS5cuxYIFC8p1H0jebG1tcevWLTDGcO/ePbRs2VLi9ZSUFJibmyM5OZk/ns2YMUMi2dnLy4s/39WtWxevXr1Sib+Bup7rtm7dit9++w0A4OLiUuDAQtGgBfHrP3Gi+z0aGhr4999/4ejoKPvApSw5ORlXr17F7du3Cxy437JlS9ja2qJ58+aKDpUQqZg+fTq2bNnC79c2Njb4888/YWdnV+y6V65cwfTp0/nBLKp0H3vEiBE4cuQIfxxzcnLCtm3bULdu3WLXjYiIwJQpU3DhwgX+WDh8+HB4e3vLOmxCCCGEEFJClOBOCCGEEEIKJEraLSnxKlalIbpxqCzJrCXx5MkTuLi44OnTp/y/lfWyWtW2/ePHjxg0aBBu3bqFSpUqwc3NDWPGjMn3gLSiiI6OxnfffQcgryLn06dPYWVlJdGmqAR3kUmTJmH37t1gjMHX1xfOzs4yj12aRNsontjOGEP37t0xadIk9O/fHwKBoNy/p1WrVvjvv/+Uar8o6Fgovr+X9jipTNtGCCHioqKi0LRpU6SlpfHHK11dXfTr1w/29vZo2rQpjIyMoK2tjeTkZERFReHevXs4efIknjx5IpEY4+bmhu3bt+f7He3bt8fdu3fBGMOxY8cwZMgQeW9mkURJUN8m+piamha4/eHh4cjMzJQ4P+rp6cHf3x+dO3eW6Ds+Ph516tRBSkoKGGN48OCBUg0UnDJlCj8gT3zbBQIB6tatK7Ht7969w9evXwFIDnozNjZGcHBwvsr30dHRsLKyQlZWFhhjePHiRb7rKUXx9fXFjz/+CACwtrbG48eP87V5+vQpOnfujMTERACQ+PuIb7+pqSkePHiAmjVrym8DykHd9/nFixfjjz/+AGMMc+fOhYeHR7428+fPx9q1a4tN/lO2gRukaEVd36vD/Qx1NmrUKPz111/gOA6jR4/Ghg0bUK1atWLXS0hIwKxZs3DgwAEwxjBixAiFVA0uq9WrV/NJ+RMmTMDOnTvztVm/fj3mzp1bouNdQTO1KCt1Pdd9/vwZq1evBpB3Lbdy5coCBywuXLiQP/8V9J5raGjgzz//xOTJk2UfNCGkXNLS0lCzZk2kp6cDAHr06IHTp09DV1e3xH1kZGSgf//+CAgIAADo6+vj8+fPSj+D45cvX9CkSRN+tg0g7z6+g4MDX7m+Zs2a0NXVRXp6OmJiYvjK9YGBgXzxIY7jUKNGDTx58qREA6EIIYQQQoh8UII7IYQQQggpkHhCa1EKegBc0ktM8QdnqvJA+MWLF+jYsSMSExPzVbEuC1XadlEl66ysLNy8eRPA/95/fX191KtXDwYGBiWuWqgKlaxPnjyJIUOGgDGGXr164dy5c/naiPaVwqb7BvIqotWrVw+xsbFo06YN7ty5I+vQpUr8eFCtWjWMHj0abm5u+Spdltfu3bvx6dMnAMCSJUuk2ndZlfRYWFKqtM8TAuRVvUtJSUHlypVRpUoVRYcjNaKpp6Vp1KhRUu9T3oKDg9G3b1/+oXhBCU7fEk944jgOffv2xcmTJ/NdD4SEhKBfv34AAC0tLbx8+VLppjvPycnBqFGjcOzYMYkEZqDoa17RthsZGeGff/5B9+7d87V98eIFLl68CCBv+ydMmCCrzSgzd3d3rFu3jt+uwgZ0ffs34TgOVlZWOHXqFJo1a5av33fv3vEVf7W0tJSqAmhOTg6ePHnC/9y0adMCr2Xv3LmDn3/+Ga9evSqwnxYtWuCff/5RmsT9klLnff7JkycYMWIEgLyq/AEBAfmS/zIzM+Hs7Izg4OB814Pin//CquOqkri4ONy+fRvv379HQkICMjIySnX9u3jxYhlGJ13fDt79VkW+n6HOxGdqmTFjBtavX1/qPubMmcPP4rd7926MGzdOBpFKX0REBGbMmAEA0NbWhpeXV77jHcdxGDlypEQF3G+P90De32DNmjVyjL781PlcVxKXLl3Chg0bcOnSJWRmZgLIu8fn6OgId3d3tG3bVsEREkJK4ty5c/zxyMjICC9evED16tVL3U9cXBwaNmyI+Ph4MMZw+vRp9O3bV9rhSt2DBw/g6OiIuLi4Iq/zviX+fKd69eq4ePFihS3kQwghhBCiqijBnRBCCCGEFMjS0lKu0w2/fftWbr+rPLp3744rV67wNz61tbVhb2+PNm3awMTEpEwPskaPHi2DSKVPHStZb9++HVOnTgVjDCtWrMCCBQvytRH9XWrXro13794V2tfEiROxZ88eMMbw8uVL1KtXT5ahS5WGhgY6duwINzc3uLi4FFj5q6Lq1q2b1I+FwcHBUu2PSNfbt28RGhqKp0+fIiEhASkpKaVK8GKMYd++fTKMUHY4jsO5c+dw+vRp3L59G8+fP5c4TgsEAjRq1Ajt27dH//790adPH7leK0hTaWeqKQllP6eV1IMHDzB27Fg+IfnbJCdx4q9pampi7ty5WLJkCbS0tOQXsAycOHECCxYswOvXr/l/KyoJUktLCy4uLli/fr3KVO8uzK1bt7Bw4UJcvnw533te0ICvGjVqwM3NDQsWLICOjo48Q5W7rKwsnD17FhcvXkR0dDQAoE6dOnByclLp4yHt80XLzMzEypUrsW3bNiQkJEi8VqNGDbi7u2P69Okq+/4fPnwYW7Zswb1798rVjyqdA2Vxr0NV7meos3r16iEiIgL16tXD8+fPyzQDWU5ODho3bow3b96gbt26EtcJFcX+/fuxceNGhIeHS/x7q1atsGjRIgwYMEBBkZUPneuKx3Ec4uLiAADVq1dX2fMaIepK/B52YbN1lJT4TKR//vknpkyZIsVIZefNmzcYP348f9+1pMd6IK+wj6enp0rdryeEEEIIUReU4E4IIYQQQkgJhYeHo1mzZvwN0J49e2L//v2oXbu2giOTD3WsZL1q1SosWrQIjDHs2bMHrq6u+dro6+sjPT0dVatWlZgK9Vvbtm3DtGnTwBjD8ePHMXjwYFmGLlX//fcfWrRooegwiBxxHIeIiIgyJXgDQNeuXWUUmewEBwfj999/R0hISJn7UJXBOwU5fvw45syZwydtFvWei86DZmZmWLduHVxcXOQSozSVJcG9sIeiqvy+FyY3NxdHjhzBnj17cPPmTQiFwkLbVq9eHcOHD8evv/6KRo0ayTFK2bt06RIuXLiA27dv482bN0hISEBmZiYMDAxgbGyMli1bwtbWFi4uLiqf2P6t169fw8/Pr9htd3JyqvAJX+qA9vni5eTk4M6dOxKDG9q0aQMNDQ0FR1Y2CQkJGDp0KD+zREnO+4W1q2jnQFLx3Lp1C7a2tmCMYe7cufDw8ChzXwsWLMDq1avBGMONGzfQsWNHKUaqPD5+/Mgf7ywsLCrEdQ6d6wghFdm6deswb948MMawbds2TJo0qcx97dy5E5MnTwZjDGvWrMHs2bOlGKnsnT9/Hp6envD390dWVlah7SpVqoQffvgBEydOhLOzsxwjJIQQQgghpZF/vlVCCCGEEEJIgW7evMkv161bF2fOnFGrStZdu3ZVuwpO4pVIC3v4WaVKFaSnpyMpKQnp6enQ1dUttJ3Ix48fpRuojFFyu3rIysrCoUOHcPjwYdy9e5efwr20GGPIycmRcnSytWzZMixfvhyA5PTE6iAnJwcjRozAiRMn+G0u6TTO79+/x/Dhw3H69Gl4e3tDU1O1bjOV9T0uLtGvIhAIBBg5ciRGjhyJ5ORk3L17t9Ak54qc9GNvbw97e3tFh6EQVlZWmDx5MiZPnqzoUIgc0D5fPE1NTXTq1EnRYUhFbm4uBg0alG9WIW1tbdSoUQPR0dFgjEFTUxOmpqZISEhAUlIS344xBg0NDZibm8s7dCJD2dnZSEhIQEZGRqmubywsLGQYlXS8ePGCX65fv365+rKyspLot6ImuJuamsLU1FTRYUiVOp3rHj9+jGbNmkmtv6lTp2Lr1q1S648QIn1mZmb8cllmKREnvr4qFvbp3bs3evfujaysLISFheHZs2eIj49HSkoKKleuDCMjIzRu3BgtW7ZEpUqVFB0uIYQQQggphmo9eSSEEEIIIUSBvnz5wi8PHz5crZLbAeDy5cuKDkHuatSowS9//fq1wDZmZmaIiYkBkPcQsV27dgW2+/DhA7+cmpoqxSgJKb8HDx5g2LBhePnyJYCyJe2qalL4X3/9hWXLlgGQnKLYxMQEZmZmqFy5siLDk7mBAwfi/PnzEon9jDE0b94cNjY2MDY2hr6+PlJTU/Hlyxc8evQIjx49kmj/999/Iy0tDWfOnFH05pTY27dvS9U+LS0Nnz59QkhICA4ePIhXr15BQ0MDK1euxLBhw2QUpXKoUqUKunfvju7duys6FEKIHNA+X/Ht2bMHwcHB/Hm8Y8eO+OOPP2BnZweBQMBXpa9Tpw6fGBwXF4fAwEBs2bIFISEhEAqFcHZ2xpYtW8qdREUUIzU1FV5eXvxMJbGxsaXuQ1UGtn769IlfTktLK1df4oOAxfslqqWin+t69eqF0NBQqSSmzp49Gzt27FCpBPfk5GScPHkSt27dwtOnT8s0Mx1jDK9fv5ZhlNJVr149qfanTNsvKkYgsnjx4kJfkwbx/lWJtbU1v/z06dNy9fXs2TN+uWnTpuXqS5EqVaqE9u3bo3379ooOhRBCCCGElAMluBNCCCGEEFJCRkZG/HLdunUVGAmRl4YNG/LLjx8/LrBNs2bN8ODBAwCAn59foQnu//77L79cvXp1KUapfOLi4uDj44Pw8HBkZ2fD0tISAwYMkKh2R5THixcv0KNHDyQkJPBJy2WhisntHMdh/vz5AMBXKp0xYwbc3NxgaWmp2ODkYOfOnTh37hz/nuvq6mLOnDkYP358kckQHz9+hKenJ9avX4+0tDRwHIdz585h165dcHNzk1f45VKnTp1Sr9OkSRN0794d7u7u8PDwwO+//46FCxdCR0cH06dPl36QhBBCiAysX7+eX3ZycsKZM2egpaVV5DrVq1eHi4sLXFxcsH79esybNw+7du1CbGwsjh8/LuuQiZQdPXoUkydPRmJiIgDVvI4vDQMDA375zp075epLfH3xWdqU2evXr+m7uJqJjo6Gk5MTrl+/LvH5L63Fixdj48aNKjObY3Z2Nv744w9s2LAh34x0JT3OiQ/6ViURERFSLTqgTNu/dOlSiXjEE9C/fU0aVDXBvWXLlrC2tkZ4eDj+/vtvrFy5Enp6eqXuJy0tDcePHwdjDNbW1jSrJyGEEEIIUTjGVfQ7V4QQQgghhEjJ+fPn0bdvXzDGsHnzZkydOlXRIREZS0tLg6GhIXJzc1GvXj2+urW4w4cPY9SoUQDyKr6HhYXlSww9ePAgXF1dAeQ9JAoNDUXbtm1lvwFScv36dQQEBADIqwo1ZsyYQtseOXIEkyZNQkpKisS/M8Ywbdo0bNiwQakelJG8xKaAgAD+YWidOnUwevRo2NralrmCeVmShxXh1q1bsLW15T+TZ86cQZ8+fRQclXxwHAczMzN8/vwZAGBpaYkLFy5IDOwpzsuXL+Hk5ISIiAi+6n10dLTa7ON//PEHFi9eDE1NTQQHB6Nz586KDokQIgVxcXH4999/cfv2bTx9+jTfdPZNmjRB+/bt4ezsXOEHLZKK5/Xr12jQoAEAQEdHBxEREahZs6ZEGw0NDTDGYGVlxVdw/9ayZcuwbNkyMMawf/9+jB49WuaxE+nYsGED5s6dKzEbj4j4bEbfEr++E73OGENubq6MIy6/gIAAODk5AQD09fXx5MkTWFhYlLqfqKgoWFtbIy0tDYwxXLhwAY6OjtIOV+o0NTXRvXt3TJw4Ef3794emJtU+q+hEx/Fu3brBz8+v2EFMBVm5ciV+//13AKqxr2dlZeHHH39EQECAVJK8VWGbxYlmXymtb7+7K+PxXfR5Fp23xOMSf00alGm7y+LUqVMYNGgQGGP4+eef4eXlVer7M2PGjMGhQ4fAGMPJkyfRv39/2QRLCCGEEEJICVGCOyGEEEIIISWUmpoKExMTpKWlYdiwYTh8+LCiQyJyYGtri1u3boExhnv37qFly5YSr6ekpMDc3BzJycngOA61atXCjBkz0KJFC2RkZMDX1xdeXl4QCoXgOA5169bFq1evVCoB1NnZGf7+/gCAHTt2YOLEiQW2CwoKwg8//AChUAggfxIEYwzjxo3D7t27ZR+0jEVHRyMkJATPnz9HfHw8kpOTUaVKFRgZGaFRo0bo1KkTzMzMFB1msd69e4c6derw79WIESPg6ekJbW1tBUcmH56ennBzcwNjDM7OzvD19VV0SHJz7do12NnZAcibtvn27duwsbEpdT8PHz5Eu3btkJ2dDcYYgoOD0bVrV2mHq5RycnLQsGFDREREoEOHDggJCVF0SISQcnj79i0WLlyIU6dOISsrS+K1gip5VqpUCQMHDsQff/xBszsRlXHs2DEMHz4cjDEMGjQIf//9d742omQx0feWgmRnZ6N+/fp49+4dGjdujPDwcFmHLnO5ubl48OBBodf3rVq1gkAgUHSY5RIaGorOnTvziYACgQBDhw6Fo6MjTExM4OjoCMYYateuDW9vbyQkJODNmzcIDAyEv78/OI6DQCDA0qVL8f333wMAfz2pzLKzs1GrVi0kJiaC4zi0adMGfn5+pRqk9PXrV/zwww+4d+8eAMDQ0BCfP38uU+KwvIn2aQAwNjbGmDFjMH78eKrqXoHp6ury1zIuLi44cuRIqdbfuHEjZs+ezScOt2zZEvfv35dFqFLz+++/Y+XKlRIDderUqcPfmynLwP0lS5ZIO0yZGTt2bKnap6Wl4dOnT7h//z5SU1MB5B0rBg0axFf9PnDggNTjLAtLS0uJ6/C3b98W+po0iPevitzd3bFmzRowxvDDDz9g+/btJfquEhERgSlTpvAzkM6fPx+rVq2SdbiEEEIIIYQUixLcCSGEEEKIVGVnZyMhIQEZGRmlqp5SlupZiuDm5gZPT0/o6enh2bNnMDc3V3RIRMZWr16NBQsWgDGGCRMmYOfOnfnarF+/HnPnzi10KmPxCkje3t4YPny4XGKXhtzcXBgYGCA9PR1aWlr48uVLgVNccxyHZs2a4enTpxJ/B2NjY8TGxkokvfv7+6NHjx7y3pRyEwqFOHToELZu3YqwsLBi27dq1QrTpk3Dzz//rLTJMH/99RdGjhwJxhjq16+P8PBwpY1VFsT37w0bNmD69OmKDklu9uzZg4kTJ4Ixhv79++PkyZNl7mvw4MHw8fEBYwy7du3C+PHjpRipcps5cyY2b94MxhjCw8PRqFEjRYckdbm5uUhKSkJKSkqpK+OpyvVdQd6+fYvQ0FA8ffoUCQkJpd5+xhj27dsnwwhl5/Lly7h161a5tj0oKEiGEUrfrl27MHv2bKSnp0tctwEosroxYwy6urpYv3493Nzc5By1bKjLPl+vXj2p9CMQCGBgYABDQ0PUr18f7dq1g5OTk9J+T9y6dSt+++03MMawevVqzJkzJ18bUTKsqakp3r9/X2hfv/32G7Zu3QrGGP777z80a9ZMlqHLzOXLl7F161b4+fkhIyOj0HY6OjpwdnbG1KlTVXYwX+/evXHhwgUwxlCzZk38+++/aNWqFf96UdX7w8PDMXr0aNy7dw+VKlXCwYMHMXToUHlvQpktXboUy5cv57+rmpmZwcPDAy4uLkUmqefk5ODYsWNYsGABoqOj+e+5ixcvVpnk128rHDPG+Orebm5ualnVvaKf644fPy5x32nmzJlYt25didbdsWMHpkyZwn9mmjZtisuXLyv1rDXp6ekwNjZGeno6AMDMzAyenp78zA2kcNnZ2Th27Bjc3d3x4cMHtG7dGmfPns03MyVRHlFRUcW28fLywrJly8BxHDQ1NeHg4AAnJyfY2NigZs2a0NXVRXp6OmJiYvDo0SP4+fkhMDAQOTk50NDQwJIlS/jZSlXhmFeYin6sJ4QQQghRF+p1x4IQQgghhEhdamoqvLy8cOHCBdy+fRuxsbGl7oMxhpycHBlEJ33r169HUFAQXr9+jSFDhiAgIABVqlRRdFhEhoYOHYrQ0FAAQHx8PDIzM/NVt541axbCwsJw5MiRAhOfRGbPnq1Sye0A8PjxY6Snp4MxBltb2wKT2wHA39+fT24HgIEDB2LPnj0wMjLC+/fvMXz4cFy/fh0AsGbNGpVLcH/58iVGjBiBu3fvAij4/RURvXb//n2MHTsWO3bswOHDh1G/fn35BVxCnz594pddXFzUKrkdAKpVq8YvGxoaKi4QBfj69Su/3KVLl3L19f3338PHxydfv+pAPKH9/v37FSbBPTAwEIcPH8atW7fw6tWrMk35rkrXd+KCg4Px+++/l6sivyj5TdUS3A8cOIClS5cWmdBanIIG+ik7Dw8PLFq0KF/sHMfB3NwcxsbG0NfXR2pqKr58+ZLv75OWlobJkycjISEB8+fPl3f4UqGO+3xERIREwmd5iD43ly9fxt69e6GhoQFnZ2esXbtW6c4LSUlJ/LKxsXGBbfT09JCWlobk5OQi+2rcuDG//OTJE5VLcI+Li8P48eNx5swZAAUPZhH/9/T0dPj4+MDHxwf9+/eHp6enUid8fispKQn+/v78th06dEgiub041tbWuHbtGnr06IGbN29i3LhxaN26NRo2bCirkKVq4cKFOHfuHO7fvw/GGKKjozF69GhMmzYN3bp1KzTp7/Lly0hISJA4R7Ru3RoLFy5U8BaV3JIlS3DgwAGJpEihUIjg4GAEBwerTVV3dTrXubi44N27d5g7dy6AvIrsFhYWmDp1apHr7d+/H1OnTuXPjw0bNkRgYKDSH+suX76MtLQ0AHnnsODg4Ar9WZYmLS0tjBw5Ek5OTujWrRsePHiAXr164fbt22ozs5+qKU3Veo7jkJ2dDX9/f35mzqLaAnnnhyVLlmDJkiUqc8wTp07HekIIIYQQdUEJ7oQQQgghpMyOHj2KyZMnIzExEQCkkhyg7CpXroyAgAD0798foaGhaNGiBdauXYsBAwaoXWKoSGpqKj58+FCmyv2qUPnO0tISp06dKrINYwyHDx+Gvb09Nm7ciPDwcP41juPQqlUrLFq0CAMGDJB1uFL3+vVrfrmoBIjjx48D+F8y2JEjR1CpUiUAgLm5OU6ePAkrKyukpKTg8uXLiIuLU/qHpCLPnj1Dly5d8PXr1wKT3wojeih8+/Zt2Nra4tq1a0qX5KShocEvl2TK4orG0tKSX/7y5YviAlGAmjVr8stVq1YtV1/iA1/E+1UH4tUuo6OjFRiJdERFRWH06NG4evUqAPW4thO3bNkyLF++HMD/ErXV5W/g6uoKLy8viQFc6rDtly9fxqJFiwD8b5vt7OwwceJEODk5FTj4KSEhAX5+fvD09MTly5f59RYuXIiOHTuiW7du8t2IclD3fb4kAxbFFTZL07f95ObmwtfXF0FBQdi3bx9cXFykGnd56Onp8ctZWVkFtjEwMEBaWhpSUlKQnJxc6IBuXV1dfvnjx4/SDVTGPn36hM6dOyMiIqLIGbgKwnEcTp8+jf/++w/Xr1+HiYmJrMOVipCQEAiFQjDG0LZtW/Ts2bPUfejo6MDb2xvW1tZIT0/HrFmz4OvrK4NopU9LSwuBgYH44YcfcOfOHf7YnZCQgDNnzvADHb717Xmxffv28PPzU6mK50uWLMHixYtx4cIF7NmzB+fPn+cT9ziOQ0xMDNatW4f169dXyKru6nqumz17Nt69e4etW7eC4zjMmDEDZmZmGDhwYIHtjxw5ggkTJgDI+xvVrVsXQUFBqFWrljzDLpM3b94AyNtPhw4dSsntZWBsbIwTJ06gRYsWePz4MRYtWlTiqv9EMYo7lolm6yhJ+2/blqR/ZaOux3pCCCGEEHVQMe5OEEIIIYQQuduwYQPmzp1bYPLPtxWsxZX0xqqycnV1BZBXsfXx48eIiIiAi4sLqlSpgtatW8PExAQ6Ojol7k8Vq3sCQGRkJLZv344LFy7g2bNnEAqFpe6jIlZDcXV1haurKz5+/MgnOlpYWKh0wqd4wma9evUKbRcUFAQg730dM2YMn9wuYmxsjD59+uDYsWMQCoW4f/9+mZIq5C0tLQ29evVCXFwcf/zS0dHBwIED0a9fP7Rs2RK1atXiK7t+/vwZYWFh8PX1hY+PD9LS0sAYQ2xsLHr16oUnT55IJAMp2nfffccviyqeqRM7OzsYGhoiMTERV69exZw5cxQdktyID2goyRTXRXn37l2B/aqD58+f88uqngT08eNH2NnZISoqSiWrcJfXX3/9hWXLlgGQvJY1MTGBmZkZKleurMjwZGrz5s04ePAggP8l8Ono6KBFixYVftunT5/OfyfR1tbG3r178fPPPxe5jqGhIYYOHYqhQ4fi6NGj+OWXX5CZmcknjz148EAeoZebuu/zo0aNAmMM6enp+Oeff8BxHP9ZMDExQdOmTWFkZARtbW0kJSXh3bt3CA8P55PCGWNo0qQJ2rdvj8zMTHz9+hUPHz7kZ8dhjCEtLQ2jR4+Gqamp0gzsFU9ULGz2tTp16vDbERYWVuhML5GRkfxyYcnyyignJwe9evXC27dvJY73nTt3Lvb6/saNG/xx8s2bN3B2dsadO3dUYrC7+PtV3GxamZmZhb5Wt25d9O7dGz4+PggICEB8fDyMjIykFqcsGRoa4vr16/Dw8MDq1auRkZFR4nW1tbXh7u6O+fPnQ0tLS4ZRygZjDM7OznB2dsanT5+wf/9+7Nu3D2/fvuXbVMSq7up+rtu8eTPev3+PU6dOQSgUYsSIEbh48SI6d+4s0e7kyZMYPXo0fy40NzdHUFAQzMzMFBR56aSkpPDLbdu2VWAkqs3a2hq9evXCuXPnsHfvXvzxxx9UxV1JleSZSmmeu6jiMxpx6n6sJ4QQQgip6FT7yRshhBBCCFGI0NBQzJs3D0DeQzKBQIChQ4fC0dERJiYmcHR0BGMMZmZm8Pb2RkJCAt68eYPAwED4+/uD4zgIBAIsXboU33//vYK3pnQOHjwocZNU9HA7KSkJV65cKVVfohuuqpbgvmrVKixduhS5ubkAVP8muCyYmprC1NRU0WFIRWpqKr8sXqVZ3Nu3b/H+/Xv+5379+hXYrk2bNjh27BiAvMrwqpDgvn79ekRGRvL7eu/evbFr164CH/QaGBjAwMAADRo0wJAhQ7B69WpMmjSJr2oYGRmJ9evX4/fff5f3ZhSqY8eO0NDQAMdxePz4saLDkTttbW2MGzcO69evh7+/P549e4bGjRsrOiy56NKlC2rVqoWYmBj4+PhgyZIlZe7Lx8cHQF71dmVJ4JOH1NRUHDlyhP9ZVRJACjNz5kz+eAfkzVozYMAA2NraVvgkZ47jMH/+fAB513aampqYMWMG3NzcJGZ6qIiys7OxYsUK/n03MDCAh4cHRo0aJVHpuSJ68uQJHj58yG+7l5cXfvrpp1L1MWzYMGhqavIVuh8+fIgnT56gadOmUo9X2tR5nwfyvte9fPkSgwYNglAoRKVKlTB58mSMHz++0GuBzMxMnD17Fh4eHggLC8OLFy8wfPhwLFy4kG/z4MED/PHHHzh16hQYY8jKysKkSZPw5MkTeW1akcRnE/rvv/8KbNO8eXOEhoYCAM6dO1dogrv4LFfGxsZSjFK2du3ahf/++4+/vm/Tpg12796N1q1bF7pOz549MWfOHDx48AATJ07E3bt3AeT9DXfu3IkpU6bIK/wyi4+P55fr1KlTYBstLS1kZ2cjPT29yL5sbW3h4+ODnJwcXL9+HX379pVqrLKkpaWFxYsXY8qUKTh48CB8fX1x7949iQRZEX19fbRt2xb9+vXD6NGjUa1aNQVELH0mJiZYsGABFixYgMDAQHh6euLs2bP8QJWKVNVd3c91jDEcOXIEDg4OuHnzJjIyMvDjjz/ixo0b/Png3LlzGD58OH+Pz8TEBEFBQSp1DSw+k8a3xRZI6XTp0gXnzp1DUlISgoOD4eTkpOiQyDcOHDig6BCUjrof6wkhhBBCKjyOEEIIIYSQUnJ2duYYY5yGhgZnYmLC3b9/X+J10WsNGjTIt+6TJ0+4tm3bcowxTltbmzt69Ki8wpYK0baJ/v/tf4yxEv0n3ocqmTFjhkTs325TYX+Dgv5Gqrbt6mrVqlX8+7Vv374C23h7e/Pva9WqVTmhUFhgu4MHD/J9rV69WpZhS02tWrX4z+6IESMK3bbCCIVCbuTIkfzfx8TEREaRlp2joyMfW1pamqLDkbuMjAzOxsaGY4xxTZs25WJjYxUdktz88ccf/D75559/lqmPrVu38n2sWrVKyhEqr9jYWH7fYYxxAoGA+/Lli6LDKrOYmBiJc3WPHj24mJgYRYclNyEhIRLXK76+vooOSW4uXbrEf461tLS427dvKzokufHy8uLfd1tb23L11blzZ76vgwcPSilC2VH3fZ7jOC4xMZGzsrLiNDQ0OHNzc+7Ro0clXlcoFHIzZ84s8hwqOseK2pw+fVqa4ZdZZmYmp6ury293QXx8fPjYq1Spwj18+DBfmxUrVkhsX2n+fopWp04d/rPv6OjIpaenl2r9jIwMiWsAS0tLGUUqXWvWrOHfrwMHDhTYpkaNGvx1TU5OTqF9iX+v2717t4wilh+hUMhFRkZyYWFh3PXr17mwsDAuMjKSy83NVXRocvPlyxdu7dq1XMOGDQu8b6WhocHVqlWLmzdvHvfq1StFh1sidK77n69fv3KNGzfm39O6detyHz9+5AICAjgdHR3+PTc2NuYeP36s6HBLTfxafsmSJYoOR6UdOHCA/1vu2rVL0eEQUiw61hNCCCGEVHyqNdSeEEIIIYQoXFJSEvz9/fmKGIcOHUKrVq1KvL61tTWuXbuGHj164ObNmxg3bhxat26Nhg0byipkqbKwsFDbaS79/f2xefNmfvurVq2KKVOm8JX7GzZsCMYYLC0tcenSJYnK/YcPH0ZKSgoEAgFWrlzJV7kkyq9KlSr88sePHwtsc/nyZQB51cE6dOhQ6D6SnZ3NL3MqUPn/3r17iImJAWMMNWvWxK5du0q9/zPGsHPnTly8eBGfP39GTEwM7t27hzZt2sgo6tLbvHkz2rVrh5iYGMyZMwfbtm1TdEhypa2tDT8/P/Tr1w/37t2DjY0N1q5dCxcXF5WrUFhaCxYswKVLlxAcHIyZM2ciNTUVc+fOhUAgKHZdoVCIdevW8VVru3fvzlfAVgVXr14t9TppaWl4//49QkJC4OPjg6SkJAB5+7mLiwtq1Kgh7TDl5vLly/zMMrVr18aZM2cqfPVucQ8fPuSXnZ2d0adPHwVGI1/Pnz8HkPc5HjRoENq1a6fgiORH/LqmvO957969cfPmTQDA58+fy9WXPKj7Pg8As2fPxps3b6CpqYlTp06hWbNmJV6XMYYNGzbg1atX8PX1xdy5c9GnTx/Uq1ePb7Nw4UJcvnwZQUFBAAA/Pz/8+OOPUt+O0qpUqRI6deqE4OBgfPjwATdv3oStra1EG2dnZ36Wl5SUFHTq1AmjRo1CixYtkJGRAV9fXwQHB/PXxc2aNSvV30+RwsPDERUVBcYYqlatisOHD0NHR6dUfWhra+Pw4cNo1KgREhISEBUVpRIzNxgZGfHLCQkJBbYxNjZGXFwcOI7Dq1evJCr+ixNfPy4uTpphKgRjDBYWFrCwsFB0KApTo0YNzJkzB3PmzMGVK1ewe/dunDp1CpmZmfx3d1Wr6k7nuv8xMjLChQsXYGtri8+fPyMyMhL29vaIiopCZmYm3yYgIEDpj2UF6dixI+rVq4e3b9/iwoULWLp0qaJDUlnix3fxmT8IUVZ0rCeEEEIIqfiU864DIYQQQghRWiEhIRAKhWCMoW3btujZs2ep+9DR0YG3tzesra2Rnp6OWbNmwdfXVwbRSl9ERISiQ1CYlStX8sv169dHUFAQvvvuu3ztNDU1+amMW7ZsiYEDB2LFihVwdXWFr68v3N3dIRAIMGvWLHmFLjNxcXF4+vQp4uPjkZKSgsqVK8PIyAhNmjRB9erVFR2eVIhPXx8aGprvdaFQiPPnz/M/f//994X2JZ78oArTw4aHh/PLP/30E/T19cvUj76+Pn766Sds3boVAPDkyROlSnBv0qQJ9u/fj1GjRmHnzp3IysrCpk2byry9ysjV1bXYNvXq1cODBw/w8eNHjBo1CpMmTUKbNm1gampaqsQnxhj27dtXnnDlhjGG8+fPY/LkyThw4AAWLVoELy8vuLq6wsnJCdbW1hIJKzk5OXj69Cn8/Pywf/9+vHjxAkDe33f79u0qNQCsW7du5YpX9AAVyDtObtiwQVqhKUR0dDS/PHz4cLV7IPz161d+2cHBQYGRyJ94EktR5/CKqGrVqvyyiYlJufoSX9/AwKBcfcmDuu/zKSkpOHLkCBhjcHR0RNu2bcvUz++//w5fX19kZ2fjwIEDWLFihcTrM2bM4BPcQ0JCyh23tPTv3x/BwcHgOA5bt27Nl+Cura0NDw8PuLq6gjGGtLQ07N69W6KNKOFVQ0MD69atk1vs5fXgwQN+edCgQTA2Ni5TP8bGxhg8eDD27t0LAAgLC1P6pFArKyt++eXLlwW2sba2xrNnzwDkJYwVluB+7do1fll8QDSpGOzs7GBnZ4evX7/C29sbq1evRkxMDIC8fZ/jOAQHByM4OBi1atXCpEmTMG3aNInzqjJQ93PdtywtLXHu3Dl069YNqamp/L4O5F27XLhwAS1btlRcgOXk7u6O8ePH4+7duzh79iz69eun6JBUkui6BQCqVaumwEj+R3wAoawxxvD69Wu5/T5SfnSsJ4QQQgip+CjBnRBCCCGElEpkZCS/3KNHjyLbiqoAFaRu3bro3bs3fHx8EBAQgPj4eImKYkS5xMbG4vr16/zPR48eLTC5vTDVq1fH6dOnMXDgQJw5cwbu7u74/vvv0aFDB1mEK1PPnz+Hp6cnTp8+XeSAB0tLSwwYMADjx48vNDFAFYgSsTmOg7+/P169eoX69evzr584cUKiUqm9vX2hfYknjJuZmckgWukSPcQH8pI9ykN8/S9fvpSrL2m7evUqTExMsGjRIixduhT79u3DiRMn8NNPP8HW1rbUCd4A0LVrVxlFWzYHDx4scTIzYwwcxyElJaXUVb5FSc/KlOBe0ofBGhoaEAqFePHiBdzd3fnBSIaGhtDV1UV6ejoSEhKQm5sL4H+JbQKBAJcuXUKTJk1U8mFwaWaTEH2GROtwHIcuXbrg0KFD5U6OVTTR+wpApc9ZZSWevGFoaKi4QBSgZs2a/LK6JQOYm5vzy+WtQCy+vni/ykrd9/krV64gLS0NjDF07969zP20bdsWlStXRmpqKvz8/PIluNvZ2UEgEEAoFOLTp0/lDVtqfvrpJ7x58wZA3nk8MzMT2traEm3GjBmDly9fwsPDo9B+NDQ08Oeff8LR0VGm8UqT+PV9eRM5xdcX71dZNW/enF++fft2gW06d+4MHx8fAMC2bdswduxYVKpUSaLN/fv3cfbsWf7nJk2ayCBaomgZGRk4e/Ys/v77b3z+/Jm/DhZ9VxJdD3/69AlLly7Fpk2bsHLlSkyaNEmRYUtQ93NdQVq3bo2///4b/fr1Q25uLjiOg56eHs6dO4f27dsrOrxy+eWXXxAYGIjjx49j1KhR8PX1RZcuXRQdlko5d+6cRAGLFi1aKDCa/4mIiOCPPbIi6l+VBu2TPHSsJ4QQQgip+CjBnRBCCCGElIr49KTilZ3FaWlpITs7G+np6UX2ZWtrCx8fH+Tk5OD69evo27evVGMl0nPz5k0AeTf87ezsylR9mjEGT09PXLp0CSkpKZg/fz6Cg4OlHarMJCcnY+7cudizZ4/EA93CvH37Fps2bcLmzZsxfvx4rFmzRiUqen6rdu3a6NSpE27duoXs7Gz06tULGzduRMOGDXHz5k3MmDGDfxD03XffoXPnzoX2JZ5I0aBBA3mEXy7i73F5H6TJ8kFceX1byZrjOCQmJmLv3r18VcrSYIwhJydHmiFKTXHvg3jiRknX+XZdZVOSh8HfbreobU5ODmJjY4tcRygUIjIyUiUfBpd2vxRto5WVFTp16oQRI0aoVFJfUcQHHWVlZSkwEsUQzTwDKN8gJFkTr+j78eNHBUYif3Z2dtDV1UVGRgYuXryI2bNnl7mvwMBAAICuri7s7OykFaLMqPs+//79e365vLMuGRkZISUlBe/evcv3mr6+PqpVq4YvX75IzJagaLVq1cKmTZuKbbdy5Uo4ODhgw4YNuHTpEj+AXV9fH46OjnB3dy9z9XtFyc7O5pe/TeovLfHEb/F+lZWxsTEaN26MZ8+e4cGDB4iOjs436HjIkCGYM2cOOI5DeHg4nJ2d4eHhARsbG2RkZMDX1xezZs3ik8kMDAzyzQBAVNvDhw/h6emJv/76C0lJSQAkvyNoa2ujb9++ePjwIT+bE5A3I8yUKVMQGhqKgwcPKiL0fNT9XFcYJycneHp6wtXVFbq6ujh79myFmcXn8OHDqFatGnbu3Al7e3uMGTMGrq6uaNeuncTMZERSVFQUdu/ejY0bN/L3DszNzZWqKIms76kp8z07UjQ61hNCCCGEVHz0bY4QQgghhJSKePLat5W8RAwMDBAXF4evX78iNzcXAoGgwHbiyQTqllCjaj58+MAvF5e0k5GRUehrxsbG6NevH/766y9cu3YNHz9+hKmpqdTilJX379+jR48eePnyJZ/gWNKEUaFQCE9PTwQHB+PSpUuoXbu2vMKWmkWLFqF37958deb+/fvzr4n+BowxzJo1q9A+Xrx4gefPnwPIqxBb3oro8iBe1fbp06fl6kt8+m9jY+Ny9SUr4p9t8X8rKVlX1CoPCwsLlUu+lqbi3peyvG/K+l6X1Nu3b0vVnjEGPT09VK1aFVpaWjKKSnFat27NL4snK6kLOzs7GBoaIjExEVevXsWcOXMUHZLcdOnSBSYmJvj8+TOCgoKwYMECRYckN1WqVIGLiwsOHjyIoKAgXL58Gd26dSt1P5cvX8bFixfBGIOLiwuqVKki/WClTN33efFkc/GZiEqL4zh+IJgoEfRbopkRVPU6xN7eHvb29uA4jp+poHr16iq7PeLX9y9fvixXX69eveKXlfX6/lu9evXCs2fPwHEcvL29MX/+fInXzc3N4erqir1794IxhuDgYHTs2FGijfj3v6lTp0JXV1du8ctKdnY2EhISkJGRUaprXAsLCxlGJT9paWk4evQoPD09cffuXQD/+24o+ntYWVlhwoQJcHV15e/lXblyBdu2bcOpU6f4dby9vfHDDz9g2LBhitkYMRX9XFfYvdbSyMjIQM+ePYttp8yD2EXEZy7T0NBAbm4u9u/fj/3790NLSwvVqlUr1cx0qjY7WVGzKRYmLS0N79+/5+/Ji98TKmoGF3lbsmSJokNQOmV5v8UxxviBmObm5ujUqRO+//57VK1aVUoRyk9FP9YTQgghhBCAcar+RJIQQgghhMjVnj17MHHiRDDGsGHDBkyfPj1fG2trazx79gyMMYSHhxc6PeSff/7JV3/+448/4O7uLuPoSVl5eHhg4cKFYIxh9+7dGDduXL42urq6yMzMRLVq1Qqt+AtIvu8nT56USJZWRqmpqWjZsiVev34t8YC3YcOGcHJygo2NDYyNjaGvr4/U1FR8+fIFjx49gp+fH54/fy6xTv369fHgwQPo6+srcpPKZNasWdi0aZNE9Tbx5W7duiEwMBAaGhoFrr9s2TIsW7YMjDF0796dr3SqzO7evYv27duDMYZatWrh1atXfJJSaaSlpaF+/fr49OkTGGMIDQ1VqmqXhb1nZcUYk5gimCjW2LFj5fr7Dhw4INffR6TH1tYWt27dgqWlJV6+fCmVpBlVMnfuXKxfvx6ampp4+PAhGjdurOiQ5GbNmjVwd3cHYwzXr19Hp06dFB2S3CQkJKBVq1aIjIxEtWrVcObMmSJno/lWSEgI+vXrh7i4OFhaWuLBgwcqkxiizvv8wYMH4erqys9OdenSpTL1c/XqVX4mnO+++w4RERH52lSrVg0JCQmoXr262s0QoYyuX7+Orl27gjEGS0tLvHjxokyf/dzcXDRs2BBv374FYwxXrlxRiQrIoaGh+OGHH8AYg7GxMR49epSvkn1CQgLs7Ozw6NGjAgewiv6ta9euCAwMVMmqyKmpqfDy8sKFCxdw+/btIu9fFEYVEn6Lc//+fXh6euLo0aNISUkBIJnYLhAI0KdPH0yaNKnIWYsePnyI4cOHIzw8vNzHVWmryOc6DQ2Ncg0y/3b2ruLaKvt3fNHf41ul/fuI/qaqsM3iCtv+4ogPWhItL1myhJLKlVxZ3++i6OjoYMyYMZg/fz6+++47qfYtaxX5WE8IIYQQQqiCOyGEEEIIKSUrKyt+ubCKZ6IEdyCvomFhCe7Xrl3jl1Wh0qE6E69WW9hN4ipVqiAzMxPx8fHIysoqtMK/oaEhv6wKlfsXLlwokdxuY2ODP//8s9hK9ps2bcKVK1cwffp0/PfffwCA169fY9GiRdi0aZM8QpeqDRs2oGnTpli1ahXevHkDIO9BmJ6eHsaNG4fVq1cXmiidmZmJnTt38uv06tVLbnGXR9u2bWFsbIzY2Fh8/vwZ06ZNw969e0vdz7Rp0/Dp0ycAedUdlSm5HQCCg4MVHQKRIUo4JyW1bt06dOvWDZGRkfDw8MCiRYsUHZJcrVixAv7+/nj06BEGDx6MK1euSMw2VJHNnTsX/v7+uHz5MoYMGYLg4GA0aNBA0WHJhaGhIfz9/TFo0CA8efIE3bp1w+jRozFhwgS0b9++0PXu3LmD3bt3w8vLC7m5uWjWrBlOnjypMsntgHrv8y1atACQd1165coVBAQEFJm8WRCO4yT+ZqI+xX358gUJCQlgjMHc3Lx8QROp6NSpE6pWrYqkpCRERERg6dKlWLFiRan7WbZsGT8bjIGBgcoMDOrQoYPEDAYFMTQ0RHBwMKZPn44jR47kSw7V1NSEm5sb1q5dq5LJ7UePHsXkyZORmJgIQPVnJSqtlJQU/PXXX9izZw8ePHgAIH+1dlNTU4wbNw7jx4+HmZlZsX3a2Njg6NGjaNmyJTiOQ1hYmCw3oVQq+rmuPJ/fivjZl8Y2qfLfpbyfh44dO2LFihVwcHCQYlREFXAch/T0dOzatQvHjh3DX3/9BScnJ0WHVWIV/VhPCCGEEKLuVO/uEyGEEEIIUajmzZvzy7dv3y6wTefOneHj4wMA2LZtG8aOHZsv2fn+/fs4e/Ys/3OTJk1kEK18VeRpratVq8Yvf/36tcA2JiYmfOWzZ8+ewcbGpsB24pULi3u4rmhpaWn89OwA0LNnT5w+fbrE07Db2dkhJCQE/fv3R0BAADiOw969e7Fy5coyVQJXNFdXV7i6uiIiIgKfPn2Crq4umjRpUuhgBpGEhASsWbOG/1mVHpK4ublhxYoVYIzhwIEDSElJwdatW2FsbFzsul++fMHUqVNx4sQJAHkVsdzc3GQdcqkVN1iDkIrmyZMnOHnyJABAX18fs2bNUnBEyqFz585Yv349ZsyYgSVLlkBDQwMLFixQdFhyo62tDT8/P/Tr1w/37t2DjY0N1q5dCxcXF5VM4CsNxhjOnj2LYcOG4fz582jdujXmz58PV1dXmJqaKjo8mXJ1dQWQN0D36dOnyM3NxYEDB3DgwAEYGhqiefPmqFmzJnR1dZGeno6YmBg8evSIv4YVVbi1traGh4dHsb+PMYZ9+/bJcpNKTJ33+VatWqFBgwZ49eoVOI7DTz/9hH/++Qc9evQo0frp6ekYO3Ysrl+/zv+bi4tLvnY3b97kl9VpVghlJhAIMHLkSGzbtg0AsGrVKmRlZWHFihXFfqcBgKysLCxatAgbNmwAkLdPjxo1qsJVCq1WrRoOHTqE9evXIygoCNHR0QCAOnXqwN7eXmUHgG3YsAFz587Nl9ANFF3NWrxKrqomv96+fRuenp74+++/kZqaCkByVjYg736Hm5sb+vXrV+rPdPPmzVG7dm1ER0cjKSlJqrGXR0U+11GFbUmjR49WdAgKVdrtZ4xBT08PhoaGaNKkCTp16oR69erJKDoiC9I+H4nOB/Hx8ejXrx+CgoLQpUsXqf4OWanIx3pCCCGEEAIwTlXvxhBCCCGEEIURVWjX0NBAZGRkvopO79+/h6WlJX+jtXv37vDw8ICNjQ0yMjLg6+uLWbNmITY2FhzHoWrVqvjw4UOJk4aVhTpNa33t2jXY2dmBMYaxY8cWWMV6+PDhOHbsGBhj/E3lgvTq1Qv+/v5gjGHbtm2YNGmSrMMvs3PnzqFfv34AACMjI7x48aJMD/Pj4uLQsGFDxMfHgzGG06dPo2/fvtIOl8hAamoqmjRpgujoaD4BoHLlyhg8eDD69OmDFi1aoFatWtDT00NaWho+f/6MsLAwnD9/Hv/88w9SUlL4Y+F3332Hp0+fquTgBkIqkk2bNmHWrFl8UhpVuc8TFRUFADh27BgWLFgAjuNQv359jBkzBra2tjA1NYWOjk6p+lSFQXwiokTn1NRUnDx5EkKhEIwx6Ovro02bNqXefmVKZC6Ovb09AEAoFOL69ev8tgN572FZtj0oKEgmsUqbhoaGRHIfgAITHkvzemFE1xG5ublliFT61H2fP3XqFAYNGsQnuTLGMHjwYPzyyy+ws7ODtrZ2vnUiIyNx8uRJbN68WeLasEWLFrh7926+2Yx+/vlnHD16FIwxbNq0CdOmTZPX5hXJ1NQUDg4O6N69OxwcHGBpaanokOQqLi4ODRo0QGJiIv8eWlhY4JdffkHv3r3RrFkziRnMsrOz8ejRI5w/fx779+9HVFQUfxwoz3dEIl+hoaHo3Lkz/94JBAIMHToUjo6OMDExgaOjIxhjqF27Nry9vZGQkIA3b94gMDAQ/v7+/ICmpUuX4vvvvwegOgOFW7ZsiUePHgHIX629evXqGDt2LCZOnCgxY2NZtGrVCv/99x+d6wghRMlxHIeUlBTEx8cjPDwc169fx+HDhxEVFcWfI0xMTPD8+XOVmHWXjvWEEEIIIRUbJbgTQgghhJBSmzVrFjZt2gTGGFauXIn58+fnazNhwgSJytffEl2GMsawcOFCLF++XKYxS5u0prVWpgd/RUlMTET16tXBcRysra35h6Pidu/ejUmTJvEJAg8fPsx3E9zf3x/Ozs78Q9UrV67wD4eV0fbt2zF16lQwxjBhwgTs3LmzzH1NmjQJu3fvBmMMf/75J6ZMmSLFSIksPX78GHZ2dhLVWkuS0CaePFCtWjVcvXoV1tbWMo6WEFKctWvXYv78+UqXcKho3yb6il+rlYWqDOITKSrRubR/A2VLZC5OWZK8C6Oq217cub0krxdF/Hcoy99G3fd5AJgyZQp27NiR7zMgEAhQt25dGBkZQVtbG8nJyXj37h0/k5X4NZ6xsTGCg4PzXeNFR0fDysoKWVlZYIzhxYsX5U4elZZv33tLS0vY29vDwcEB9vb2qFmzpgKjk48rV67AyckJWVlZAJDv/a9atSo/gDUxMZHfb8Xfex0dHfj5+aFr164K2w5Scr1798aFCxfAGEPNmjXx77//olWrVvzrov3CysoKL168kFg3PDwco0ePxr1791CpUiUcPHgQQ4cOlfcmlJn4Pi861tva2sLNzQ1DhgwpcEBPWShjgjud6wghpGRycnKwfPly/PHHH/wxcs2aNZg9e7aCIyseHesJIYQQQio2SnAnhBBCCCGlFhoaih9++AGMMRgbG+PRo0f5HoglJCTAzs4Ojx49yjf1M/C/JI+uXbsiMDAQmpqa8tyEcpHmtNbK9OCvOK1bt0ZYWBgYY3j27BkaNGgg8XpcXBzMzc35JIEmTZpgyZIlEpX7V69ejfT0dHAch1q1auHdu3dK/d6vW7cO8+bNk0q1+Z07d2Ly5MlgjKnMAwLyP+Hh4fj555/5B/ZA0cls4seGVq1a4fDhw2jSpIlcYiWEFO3QoUMYM2YMGGPYu3cvxo4dq+iQlEJRib5luX2oStc4QPGJziX9GyhjInNxxLddGlRp2y0tLcuc+FBWb9++levvK4y67/Mi7u7uWLduHb/NhQ3u+DZRhuM4WFlZ4dSpU2jWrFm+ft+9e4eHDx8CALS0tODo6CizbSitgpJdxbfV2toaDg4OcHBwgJ2dHQwMDBQSp6xdvnwZo0aNwvv370t8fS9qY25uDm9vb5Wp4K3ukpKSUK1aNf799fPzQ8+ePSXaFJXgDgAZGRno0aMHbt68CT09Pdy/fx8NGzaUS/zlJZpdokqVKhgxYgTc3NzQvHlzqf+ejx8/8veD6tSpI/X+y4LOdYQQUjrz5s3DunXrAAD16tXDq1evFBxR8ehYTwghhBBSsSlvJgkhhBBCCFFaHTp04CsZF8bQ0BDBwcGYPn06jhw5ku9moqamJtzc3LB27VqlTnD+VmhoKObNmwcg72ZnYdNam5mZlXhaa1XRp08fhIWFAQD2798PDw8PiderV6+O3377DWvXrgVjDOHh4fmqmoknULi7uyv9e29mZsYvCwSCcvUlvn7t2rXL1ReRP2tra9y5cwcHDhzA9u3b+YSlwnAchxYtWmDy5MkYM2aM0n/WScGys7ORkJCAjIyMUj0Uo6mMlZulpSW//PHjR8UFooQKSvBUFxYWFnJPdFYWXbt2Vdttj4iIUHQICqXO+7yIh4cHfvzxRyxcuBCXL1+WeK2gAc0cx6FGjRpwc3PDggULoKOjU2C/3333Hb777jtZhl5m7u7uuHTpEu7evVtgZfLw8HCEh4dj69atEAgEaN26NV/d/fvvv5datWdF69atGx4+fIh169Zh7969+PLlS5HtOY5DzZo1MW7cOMyePRuGhobyCZSUW0hICIRCIRhjaNu2bb7k9pLQ0dGBt7c3rK2tkZ6ejlmzZsHX11cG0Upfy5Yt4ebmhp9//hn6+voy+z2mpqYy67s86FxHSMV16NAhqfc5atQoqfepShYvXoy9e/ciPj4eb9++xYcPH1TiPjYd6wkhhBBCKi6q4E4IIYQQQmQuJiYGQUFBiI6OBpBXycne3h7Vq1dXcGSlp87TWj958gQjRowAABgYGCAgICBfckNmZiacnZ0RHBxcZHV7FxcXHD16VH7Bl1FYWBhat24NxhimTZuGTZs2lbmvmTNnYvPmzWCM4f79+2jRooUUIyXyFhUVhZCQEDx//hzx8fFISUlB5cqVYWRkhEaNGqFTp06U5KyCUlNT4eXlhQsXLuD27duIjY0tdR+qPJXx27dvcevWLTx79gwJCQlISUkp1cNBxhj27dsnwwilIzc3F6ampoiLi4OdnR0uXbqk6JCUgpeXl9T7HD16tNT7JIRIB+3z+b1+/Rp+fn64ffs23rx5g4SEBGRmZsLAwADGxsZo2bIlbG1t4eTkBC0tLUWHW27Jycm4cuUKgoKCEBQUhCdPnuT7/vZtJUxtbW106tQJ9vb2cHBwQPv27fnq0KosKysLt27dKvb6vlOnThXivVc3np6ecHNzA2MM8+fPx8qVK/O1Ed3LMTc3R2RkZKF9DR48GD4+PtDS0sKnT59gZGQky9BJOdG5jpCKTXxWGmmhyt3AsGHDcPz4cTDG4OPjgx9//FHRIRWJjvWEEEIIIRUbJbgTQgghhBBSQuo+rXVJZWZmYuXKldi2bVu+Sv81atSAu7s7pk+frjKVQps1a4bw8HCYmpri5cuX0NPTK3UfaWlpaNCgAT59+gRra2s8evRIBpGWT7169fhlxhhev35d4GvS8G3/RLHs7e2l2h9jDEFBQVLtUx6OHj2KyZMnIzExEUDZqz6p4lTGAQEBWLJkCW7fvl3mPkQJcKqy7e7u7lizZg0YY7hy5YrKzapCCCGESNuXL19w6dIlXLp0CUFBQXjz5k2B7cS/x1WpUgVdu3bF2bNn5RUmkZJXr17h8OHDuHXrFp4+fcoPbiwNVRnYuWbNGri7u4Mxhp07d2LChAn52mhrayM7Oxs1atRATExMoX1t3LgRs2fPBmMMp0+fRt++fWUZOiGkHHJychAaGioxeCk5ORlVqlSRGLzUoUMHmnVPRZUlwb2gez3ig/pU5Z6GLK1btw7z5s0DYwy7du3C+PHjFR0SIYQQQghRY/RtjRBCCCGElEpGRgb/sI8xprTTrsuCuk9rXVLa2tpYvnw5Fi9ejDt37khU7m/Tpo3KVfhbsWIFBg0ahE+fPsHNzQ1eXl6lfnjy66+/4uPHj2CMYcWKFTKKtHwiIiIKrNL47WvlUVj/RLEuX74stfdEVd/fDRs2YO7cuXz8hc0+8S3xbVXV+gGzZs3C5s2bAfxvG4ra5oKo4nu+dOlS+Pr6Ijw8HIMHD0ZAQABsbGwUHRYhhBCiMMbGxnBxcYGLiwuAvBmLRNXdL126hE+fPgGQvD5ISkrC+fPnFRIvKZuEhAT89ttvOHz4MP9vqnodW1Li16qVKlUqsI2BgQHi4uLw9etX5ObmQiAQFNhOfCbCjx8/SjdQQqRk+fLlUulHIBDAwMAAhoaGqF+/Plq1agUdHR2p9C1LycnJ2LBhAzw9PfH58+di29eqVQtubm6YMWMGqlSpIocIZSspKQknT54s9+xsqlKYojzFCcrbR0Ulfq6Lj49XYCSEEEIIIYRQgjshhBBCCCklb29vuLm5AQBsbW1x7do1BUckP+LTVPfo0aPItpmZmYW+VrduXfTu3Rs+Pj4ICAhAfHx8hZzWWlNTE506dVJ0GOU2YMAAzJs3D2vWrMFff/2F2NhYbN++HXXr1i123YiICEyZMgX//vsvAGDevHno37+/jCMuu6Ie6EjjYQ89MFJupX3YWZb1lFFoaCjmzZsHIG+7BAIBhg4dCkdHR5iYmMDR0RGMMZiZmcHb2xsJCQl48+YNAgMD4e/vD47jIBAIsHTpUpWrAv7nn39i06ZNACTfUw0NDRgZGUFfX19RocmctrY2Ll68iEGDBuHWrVto37493NzcMGbMGLRs2VLR4RFCCCEKZ2FhgbFjx2Ls2LEAgKdPn+LQoUPYunUr0tPTAaj+daC6SUpKgoODA8LCwgoc2FlRid9v+XaWORFjY2PExcWB4zi8evUKjRo1KrCd+PpxcXHSDJMQqVm6dKlMBiELBAJ069YNkyZNwoABA6TevzTcuHEDI0aMQFRUVL4B3AXhOA6fPn3CsmXLcPDgQRw+fBi2trbyCleqMjIysHz5cmzevDnffenSDF5XpcIFb9++LVX7tLQ0fPr0CSEhITh48CBevXoFDQ0NrFy5EsOGDZNRlKpHKBTyy6pWqIYQQgghhFQ8lOBOCCGEEEJK5cuXL/yNbmdnZ0WHI1fiFUvq1KlTYBstLS1kZ2fzD/wLY2trCx8fH+Tk5OD69es0rbWCRUVFFfn6pEmToKenh2XLlsHf3x+NGzeGg4MDnJycYGNjg5o1a0JXVxfp6emIiYnBo0eP4Ofnh8DAQOTk5EAgEGDJkiUYNWoUoqKiYGFhIactK7klS5aU6TWi+rp27Vqqh5eiB4Lv3r0DkPcAlDEGW1vbQisdKrPly5fzs3PUrFkT//77L1q1apWvna6uLrp168b/PHPmTISHh2P06NG4d+8eVqxYgYMHD2Lo0KFyjL7sMjIysHjxYv69FwgEmDBhAkaNGoVWrVpBS0tLwRHKlr29PYD/PazNysrC1q1bsXXrVujr66NevXowMDAo8VT1jDEEBQXJLF5CCCFEERITExEcHMxXcn/+/LmiQyLl4O7ujgcPHvDXf4wxdOnSBba2tjAzM0PlypUVHKFsWFlZ8csvX74ssI21tTWePXsGIG+Gq8IS3MWLPKh6peenT5/i/fv3SEhIQEZGRqkGO4waNUqGkRFpKW4WsuLafzuwPScnhz8fODg4wMvLC6amptILuJxu3LiBH374Aenp6RKDeDiOg4aGBgwMDKCvr4/U1FQkJSXxSbyi7YyIiEDPjIqargABAABJREFUnj1x8eJFlUtyT0lJQa9evXDz5k2JxP7SDmJStUFPhd2fL0qTJk3QvXt3uLu7w8PDA7///jsWLlwIHR0dTJ8+XfpBqqAvX77wyxWxKA8hhBBCCFEtlOBOCCGEEEJKpVq1avyyiYmJAiORP5rWuuKytLQscYIvx3HIzs6Gv78//P39i20L5FW+WbJkCZYsWQLGGHJycsods7RRgrv6unz5cpnW+/DhA/bt24e1a9ciLS0NBgYGOHbsmEolxiQlJcHf35/f/w8dOlRgcnthrK2tce3aNfTo0QM3b97EuHHj0Lp1azRs2FBWIUtNUFAQkpOTAeQlt589exZOTk4Kjkp+Ll++LJHcBfzvmJ2SkoJHjx6VuC9VqnBHCCm57OzsMiX9KeNARkJKKjMzE9euXeMTGB88eCBRxfPbfcHKygoODg7yDpOUQVJSEvbs2cNfs9jY2ODIkSOwtrZWcGSy17x5c3759u3bBbbp3LkzfHx8AADbtm3D2LFj8933uX//Ps6ePcv/3KRJExlEK1vXr1/Hli1bEBAQwH8XKAtKcFd+BVUvFyV7F0X8u1FhbTmOQ2BgIOzt7RESEgJDQ0PpBF0OX79+xY8//oi0tDR+G0xMTODq6op+/fqhefPm0NHR4dtnZmbi4cOHOHv2LA4cOIAPHz6AMYb09HT8+OOPePHihUol9s6ZMwc3btwA8L/E9ipVqsDGxgYmJiYVena2smKMYcGCBRAKhVi8eDHmzp2Ldu3aoXPnzooOTeHEz5XKNIiFEEIIIYSoJ0pwJ4QQQgghpSJ+U7M8D8NUEU1rXfGV5EHftw8HS9q2JP0TxXB1deWXGWPYt29fga9Jw7f9q7LatWvj999/h4uLC+zt7XHhwgX07dsXly5dUplk35CQEL56e9u2bdGzZ89S96GjowNvb29YW1sjPT0ds2bNgq+vrwyila7Xr18DyPtMDhw4UK2S28UVlbRBiEhFT3S+evWq1Pvs2rWr1PuUtdTUVHh5eeHChQu4ffs2YmNjS92Hsg5kJPktX75cKv0IBAIYGBjA0NAQ9evXR6tWrSQS6JSdUCjEnTt3+IT2kJAQZGZm8q9/e8wzMTGBvb09HBwc4ODgoLTHOWm9vyW1ePFiuf6+sggODuaPT0ZGRggMDESNGjUUHJV8GBsbo3Hjxnj27BkePHiA6OhomJmZSbQZMmQI5syZA47jEB4eDmdnZ3h4eMDGxgYZGRnw9fXFrFmzkJubCyCvuIEqVXjOysrClClT+O+ihVV4Lup+h6itqnzXU2dCoRAcx2HlypVYunQphEIhateujREjRsDe3h5NmzaFkZERtLW1kZSUhHfv3uH+/fv4559/4OfnB47j0KxZM5w4cQJ169bF169f8fDhQ5w/fx5eXl5ITk4Gx3F48eIFfv31Vxw5ckTRm4wVK1bg69ev/Od00qRJWLt2baGJ3dra2mjXrh3atWsHd3d3zJs3D9u3bwdjDF+/fsWKFSuwceNGOW9F2cTGxkoMYKpduzY2bdqE/v37l3hGMnU2f/587N+/HxEREZg9ezZCQkIUHZJCxcbG4uLFi/zPHTt2VGA0hBBCCCGEUII7IYQQQggppc6dO0NTUxO5ubm4f/++osORK5rWumC5ublISkpCSkpKqRMClSkhoiSxl2b7KDlSdRw8eFDiYb14ArroNWkoqP+KoGHDhvj777/x/fff4+rVq1izZg3mz5+v6LBKJDIykl/u0aNHkW3Fk72+VbduXfTu3Rs+Pj4ICAhAfHy80ld7S01N5Zft7OwUGIlidO3alZJziiEUCvHgwQPcvn0b79+/L3WCtyof79Qt0blbt25S3R9UadtFjh49ismTJyMxMRGAel7Hqds+v3TpUpmcBwQCAbp164ZJkyZhwIABUu9fGp48ecIntF+5ckVi4Pq373fVqlVhZ2fHJ7SrSrVvWb2/hVGFBHfRdS9jDD///LPaJLeL9OrVC8+ePQPHcfD29s73fcXc3Byurq7Yu3cvGGMIDg7Ol9QnnhQ+depU6Orqyi3+8ho9ejT+/vvvfPu4eMJ6QRW7SzrAX1Wo07luzJgxOHz4MIC8BN7FixcXOADLyMgIRkZGsLGxwZgxY3D37l2MGjUKT548Qfv27XH9+nU0b94cJiYmcHR0xNy5czFo0CDcvn0bHMfh77//xuLFi9G4cWN5byIvNzcXXl5e/Od19uzZWLt2bYnX19PTw9atW6Gnp4d169YByLsXtH79emhoaMgkZmkKDAzkZ1sxMDDAjRs3lOp+q7LT1NRE//79sXnzZty+fRvPnz8v9H6+Opg9ezY/E0KzZs0kZvNVVlSchBBCCCGkYqMEd0IIIYQQUio1atRAjx494OfnhzNnziAuLg7Vq1dXdFhyQdNa/09gYCAOHz6MW7du4dWrV2V60KlMyU8HDhxQdAhEyVWEh/myZGtri+7duyM4OBh//vkn5s6dqxIPguPj4/nlOnXqFNhGS0sL2dnZSE9PL7IvW1tb+Pj4ICcnB9evX0ffvn2lGqu01axZk19WpUqz0nL58mVFh6C0srKysHbtWuzatQsfP34sUx+qPKBHnROd1WlbxW3YsAFz587lP7cFVbIt6G9TUZL+1H2fL+69La79t5+DnJwcPnncwcEBXl5eErOgKYPmzZsX+tnW0dFB586d4eDgAHt7e7Rt21YlrukKI499U1UGzGVkZPDL4vc21MVPP/2Effv2gTGG/fv3Y8aMGdDW1pZos3btWoSGhuLRo0f5zgfA/yqYd+nSRSUGNYgcO3YMx48f5z+r9erVw4IFC+Do6AgTExNUqlQJjDHUr18fL168QEJCAt68eYOLFy9i165diIyMhEAgwKpVqzBnzhwFb03ZqNu5bv/+/fD29gZjDCtXrizVAPS2bdviypUraNu2Ld69e4chQ4bgwYMH/IAOMzMznD9/Hi1btkR0dDQ4jsPx48exZMkSWW1OsW7duoWEhAQwxlCnTh2sWrWqTP2sXLkSJ06cQEREBBITE3Hz5k18//33Uo5W+t6/fw8g7xg1atQoSm4vA/GE9vv376tlgntiYiJmzJgBb29v/t8mT56swIhKjoqTEEIIIYRUbJTgTgghhBBCSm3Dhg24cuUKkpKS4OrqCh8fHwgEAkWHJXM0rTUQFRWF0aNH4+rVqwBUO5lH3OjRoxUdAlGgoio5U5XnknNwcEBwcDBiYmJw5coVdO/eXdEhFUv8vf12MJKIgYEB4uLi8PXrV+Tm5hZ6vhMf7FXWpAl5atOmDb8cERGhuECIUomKikK/fv3w6NGjEp3ji0r+VUXqnOhc1rhVfdtDQ0Mxb948AHnbIhAIMHToUD7pz9HREYwxmJmZwdvbm0/6CwwMhL+/PziOg0AgwNKlS1UiAepb6r7Pi1djFv+34ravqGrH4v0EBgbC3t4eISEhMDQ0lE7QUiT6/LZr1w729vZwcHCAra1tvqRfVWRhYUHX8N/49t6FuunQoQMSEhKKbGNoaIjg4GBMnz4dR44cybd/a2pqws3NDWvXroWmpuo8Xl29ejW/3K5dOwQEBKBq1aqFtjc0NETr1q3RunVrzJgxAzNnzsSOHTswf/58fPjwAZs2bZJH2FKjjuc6Dw8PAHlJu2WZXc3Y2BirV6/Gzz//jJcvX+LEiRMYNWoU/3r16tUxZ84cTJ8+HQBw5coVqcRdVuKzbA4ZMqTM+6empiaGDBnCV3F/+fKlSlzfiX9WbWxsFBiJ6hL/zERHRyswkpKJiooq1/ocxyElJQXx8fEIDw/HtWvXcObMGaSmpvLfhevXr48xY8ZIJ2A5Kc1sHGVZjxBCCCGEKIbq3IEhhBBCCCFKo0mTJti3bx/Gjh2Lc+fOoVu3bti6dStatmyp6NBkTp2ntf748SPs7OwQFRUlMY01IaquqErOVOW55MSrk75+/VolEtyNjIz45cISXoyNjREXFweO4/Dq1atCK3mJrx8XFyfNMGWiZcuWaNiwIV6+fIkzZ85g+fLlig6JKFhaWhr69euHhw8fSpzj69WrB1NTU9y4cQOMMejo6KBdu3ZISEhAREQEkpKSAORd12hoaMDW1lYlq/2qc6JzcHBwqdqnpaXh06dPCAkJwYkTJ5CYmAgNDQ0sXboUXbt2lVGUsrF8+XIIhUIwxlCzZk38+++/aNWqVb52urq66NatG//zzJkzER4ejtGjR+PevXtYsWIFDh48iKFDh8ox+vJR931eKBSC4zisXLkSS5cuhVAoRO3atTFixAjY29ujadOmMDIygra2NpKSkvDu3Tvcv38f//zzD/z8/MBxHJo1a4YTJ06gbt26+Pr1Kx4+fIjz58/Dy8sLycnJ4DgOL168wK+//oojR44oepPzYYxBKBQiMTERycnJSE5ORmZmZoVIcKfBe/k1a9aMX37z5o0CI1Fu1apVw6FDh7B+/XoEBQXxiY516tSBvb29ys1gGB0djYcPHwIABAIBjhw5UmRy+7cqVaqEbdu2ITc3F7t378aWLVvQs2dPODs7yypkqVLHc11oaChev34NxhgGDhxY5n4GDBgATU1N5Obm4siRIxIJ7gDQv39/PsH99evX5Qm53GJiYvjlBg0alKuv+vXr88tfvnwpV1/yYm5uzi8ry0yZqub58+f8sioMYLK0tJT6fXnxe/16eno4efJkoYUglE1pBzampaUhPj6e318YY2CM4bvvvpNViIQQQgghpByU/wqdEEIIIYQonUOHDgEAfvnlF+zYsQM3b95EmzZt0Lx5c3z//feoX78+DAwMSnVD+NsHJcpKnae1njlzJiIjI/kbxpUrV8aAAQNga2sLMzMzVK5cWcEREnl7+vQp3r9/j4SEBGRkZJSq4o2q7POk5FJTU/nl2NhYBUZSclZWVvyyeNU3cdbW1nj27BmAvAEPhSW4X7t2jV+uUqWKFKOUnZUrV2LIkCF4/PgxPD09MWHCBEWHRBRo69atfPIPx3EYOHAg1q5di3r16gEAn9RjZmbGD/7hOA537tzBxo0b8ffff0MoFMLAwABHjhxRmf1ARJ0Tne3s7Mq03tixY7Fx40bMnDkTe/fuxcqVK9GoUSP89NNPUo5QNpKSkuDv789f2x46dKjA97ww1tbWuHbtGnr06IGbN29i3LhxaN26NRo2bCirkKVK3fd5ABgzZgwOHz4MAJg/fz4WL14MHR2dfO2MjIxgZGQEGxsbjBkzBnfv3sWoUaPw5MkTtG/fHtevX0fz5s35wTBz587FoEGDcPv2bXAch7///huLFy9G48aN5b2JhRIfdP3s2TM8e/YM27Ztg0AgQJs2beDg4AAHBwd07txZZZKbSNFatGiBZs2a4cmTJ/D19eUrPFd0kZGR8PX1xb179/DlyxcIBALUrFkTnTp1Qt++fWFsbFzgejVr1sSwYcPkHK303bp1C0Devu7o6Cjx/edbRX2fX7duHU6ePInY2FgsWbJEZRLc1fFc9+LFC365bt26Ze5HR0cHtWrVQnR0tESfIhYWFjAwMEBSUhK+fv1a5t8jDVpaWvxyZmZmufrKysoqsF9lJj4zaHh4uAIjUU2pqakSAxFVacYTaVUeFyV4cxyHOnXq4OTJkxID45RdWQY2Zmdn486dO9i3bx8OHToEoVAIJycnbN++XWUGNBFCCCGEqA2OEEIIIYSQUmKMcRoaGvx/jDH+P/F/L81/FU1cXBw3cuRITiAQSPx9GGNcpUqVuGnTpnEZGRmKDrPEYmJiJN6vHj16cDExMYoOiyjAtWvXuCFDhnBVq1Yt8/5eEfd5wnFDhw7lzwM7d+5UdDglEhMTw8fctm3bAtts3LiRb9OsWTMuMzMzX5t79+5xWlpafLvAwEBZhy41M2bM4BhjnJaWFrdjxw5Fh0MUyNTUlD9Gjx49Ot/ros93gwYNClz/xIkTXKVKlTgNDQ2uW7duMo5WuhITEzmBQMBvf0BAQL42xW1/eno617lzZ44xxunr63PPnz+XddhK5ddff+UYY5yenh4XHh6u6HBKxM/Pj39f27dvX2Cb4t53juO4N2/ecDo6OpyGhgbXp08fWYUrdeq8z3Mcx+3bt4/fRg8Pj1KvHxMTw1lYWHCMMa5Ro0ZcWlqaxOuxsbGcubk5/zuWLl0qrdDL7eXLl9yuXbu4IUOGcMbGxvm+r4pfs+vp6XE9e/bk1qxZw929e1fRoZNyOnPmDP8e79mzR9HhyFRGRgb366+/clpaWoV+J9XX11eqfVMWtm3bxr/nK1euLLCN6HVzc/Mi+5owYQLf9vXr17IIV+rU8Vy3Zs0afru2bt1arr6qV6/OMcY4XV3dAl+3tLTkv0sq0uHDh/ltHj9+fLn6Ev+cHz58WEoRyp69vT3HGONq166d75qEFC42NpZzdHTkr4EEAgH35csXRYdVLNFn9NtruPL8Z2pqyq1atYpLTExU9ObJ3dWrV/n73C4uLooOhxBCCCGEfINxnJSGdhJCCCGEELWhoaEhtWkwuf+f/jI3N1cq/SmbmJiYCjGt9YkTJ+Di4gLGGGrXro3nz59DT09P0WEROcrKysKUKVOwb98+AJJVH8W/VoofG779uilqW5H3eXV1+/ZtdO7cGbm5uWCMISgoSKLCsTITVWjX0NBAZGRkvmpd79+/h6WlJf957t69Ozw8PGBjY4OMjAz4+vpi1qxZiI2NBcdxqFq1Kj58+ABdXV1FbE6ZrFy5EkuXLoVQKETTpk0xcuRIdOjQASYmJgVWsi2KhYWFjKKUvcTERFy7dg23bt3Cu3fv8PXrV6SlpcHMzIyfvaaiCg8P5yu0GRgY4N27d/kqVIqu/6ysrAqs4ggAu3btwq+//grGGNatW4eZM2fKPHZp8Pf3R69evcAYQ9u2bREaGpqvTUm2/+3bt7C2tkZWVhacnZ3h6+sr69CVRlpaGqysrBATEwMHBwcEBAQoOqRieXp6ws3NDYwxzJ8/HytXrszXRvS+m5ubIzIystC+Bg8eDB8fH2hpaeHTp08wMjKSZejlpu77PAA0aNAAr1+/RuPGjctc7fTo0aP4+eefwRjDgQMH8s1QtGXLFkyfPh2MMdjZ2eHSpUvSCF3q/vvvPwQFBeHSpUu4evUqUlJSJF4Xv743NDRE9+7d+QrvqjJjAfmf2bNnY+PGjahUqRL279+P4cOHKzokqcvMzISTkxOuXr0q8b1VnPi/u7i4SFTvrUhWrVqFRYsWgTGGPXv2wNXVNV8bfX19pKeno2rVqoiPjy+0r23btmHatGlgjOH48eMYPHiwLEMvN3U91+3YsQNTpkwBYwyjR4/G/v37y9RPVFQULC0tAQA1atRATExMvjampqb4/PkzDAwMkJCQUI6oy+fJkydo3rw5gLxZV168eFGm+65xcXFo0KABEhISwBjDw4cP0bRpU2mHKxNhYWHo2LEjsrOzMW7cOOzevVvRIUnV8uXLAQDVqlXDlClT+H+/evVqqftKS0vD+/fvERISAh8fHyQlJfH3KocOHYq//vpLanHLytixY8u1PmMMenp6qFatGszNzdGpUyc0a9ZMas97VNHZs2fRv39/MMawe/dujBs3TtEhEUIIIYQQEcXk1RNCCCGEEFUmzeogooojRLlt2rSJf6/mzp2r6HCIAgwdOrTQ6kAFzeZQUAVI2ucrnvT0dM7T05MzMjLi3+MaNWpwOTk5ig6txGbOnFlsBdfx48cXOUuJ+Gf7999/l/MWlF92djY3e/bscs3EoqGhwQkEAkVvSplERERw48aN4/T09Ap8bwuq6JiRkcFZWlpyRkZGnLGxMff582cFRC494lUPR40aVWAb0etWVlaF9iMUCrlGjRpxjDGuTp06MopW+nbv3s1v34IFCwpsI3rdwsKiyL4GDRrEz9jz9etXWYSrtCZNmsRXPoyMjFR0OMVavXo1/77u3r27wDaVKlXiGGOcsbFxkX1t2LCB7+vs2bOyCFeq1H2fv3XrFr99CxcuLHM/6enpfHXoH374Id/rkZGRJT52KIucnBzu5s2b3IoVK7hu3bpxOjo6RV7ff/fdd9zYsWMVHTYppTVr1vAzlzg4OHBHjhzhIiIiCpypSBXNmzevxN9TRcuenp6KDlsmxM9PhVXtr1WrFt+mqMrPBw8e5Ntt2bJFViFLjbqe6/7991/+c161alXu06dPZepn7ty5Rc50k5mZyZ8D69evX96wy83CwoLfr/v27ctlZWWVav3s7GyuT58+/N9OFd7rbx0/fpx/T1xcXLiPHz8qOiSpEX0Wv/1uXt57GKL1GWNc3bp1K9TfjJRe+/btOcYYZ2lpqehQCCGEEEKIGE1FJ9gTQgghhBDV8/btW0WHQORMvNp2o0aNFBiJ4gmFQjx48AC3b9/G+/fvkZCQgIyMjHzVygvDGOOroKuKY8eO4fjx43wln3r16mHBggVwdHSEiYkJKlWqBMYY6tevjxcvXiAhIQFv3rzBxYsXsWvXLkRGRkIgEGDVqlWYM2eOgreGfKugKn7FEVW8+u+//5CWlsZXu2KM4ffff4dAIJBBpLLx008/Yd++fWCMYf/+/ZgxYwa0tbUl2qxduxahoaF49OhRvlkLgP/NTtClSxcsXrxYnuGX25MnT+Di4oKnT5/y+3hJj2cVgbe3N6ZMmYKUlBT+c1yS7dfW1saAAQOwefNmMMZw9OhR/Pbbb3KIWDZiY2P55ZYtWxbYRvT5SE9PL7Qfxhh+/PFHrFu3Du/evcOdO3fQrl07qcYqC+LVSuvUqVNgGy0tLWRnZxe5/QBga2sLHx8f5OTk4Pr16+jbt69UY1VmoiqpHMfh1q1bSj+jg3iFwkqVKhXYxsDAAHFxcfj69Styc3MLPb+JVwn9+PGjdAOVAXXf58Wr9NatW7fM/ejo6KBWrVqIjo4usPKvhYUFDAwMkJSUhK9fv5b598iTQCBAp06d0KlTJyxatAgZGRm4fv06X+H9/v37Et8N379/Dy8vrzJXCFYmSUlJuH37tsQsLkZGRhKVYlXdt8cwjuMQHByM4ODgMvXHGENOTo40QpOa2NhY/voMAH+N7uLiAisrK+Tk5ODZs2fw9vbGw4cP+Wu/JUuW4JdffoGGhoaCt0C6atSowS8XdhwyMzPjq3M/fvy40OP4hw8f+OXU1FQpRikb6nqu6969OypXrozU1FQkJydj2LBh8PX1hb6+fon7OHPmDDZt2sT/ffr06ZOvTVhYGHJycsAYQ7169aQWf1nNmDEDM2fOBGMM58+fh4ODA3bv3o0mTZoUu254eDgmTpyImzdvAsh7z2fMmCHrkKXup59+Qq1atTB69GicOHECp0+fhoODQ5lnZ/t2ZhpFK+p7emnuYXx730N0njh06BBMTEzKFyRRac7Ozrhz5w6ioqIQEhKCTp06KTokQgghhBACgBLcCSGEEEJIqRWW+EMqLjMzM345KytLgZEoTlZWFtauXYtdu3aVOXFJlDypagnuq1ev5pfbtWuHgIAAVK1atdD2hoaGaN26NVq3bs0/ZNyxYwfmz5+PDx8+YNOmTfIIm5TQwYMHyzwNsXhiO8dxGDNmDKZNmyblCGWrQ4cOxU6nbmhoiODgYEyfPh1HjhzJ9/BUU1MTbm5uWLt2LTQ1VedWy4sXL9ClSxckJiaWKrm7ojhw4ADGjRvHbzuQ95muU6cOatWqhdu3bxe5/ogRI7B582YAedNZq3KCu3iSUrVq1Qpso6+vj5SUFCQmJhbZl5WVFb/88uVLpU4AElHnRGdp0tXV5Zejo6MVGEnJGBkZ8cuFnQeMjY0RFxcHjuPw6tWrQgd6iq8fFxcnzTBlQt33efF9s7hBK8URrf/p06cCXzcyMkJSUhIyMzPL9XsURUdHBz169ICDgwNu376NkydPYseOHfx2q/p1Q25uLo4dO4bt27fj9u3b+bbHysoqX4J7ZmYmRo0ahezsbGhqauLQoUOlThpUFPHtE13Df/vvqu7IkSP8PQvGGLZs2ZLvPezduzdmzZqF2bNnY+PGjQCAz58/w8/PD87OznKPWZYaNmzILz9+/LjANs2aNcODBw8AAH5+foUex//9919+Wfx6R1mp67lOR0cH48eP5xPUr1y5grZt22Ljxo3o1atXket++fIFHh4e2LZtG3Jzc8FxHKpUqYJx48bla+vr68svK8PfY/Lkydi3bx/Cw8MBANevX0fz5s1hZ2eHPn36oEWLFqhVqxb09PSQlpaGz58/IywsDOfPn8eVK1f44yBjDM2aNcPkyZMVuTllZmFhAVtbWxw7dgxZWVnw8/ODn59fmfpStgT3wu5blfYcJvr+b2VlhU6dOmHEiBFwdHSURohExZmbm/PLz58/pwR3QgghhBAloTpPXQkhhBBCCFECdevWxbhx4+Dq6gpTU1NFhyM3rVu35pcLqk5Y0UVFRaFfv3549OhRiR6cVKREgejoaDx8+BBAXsW/I0eOFJnc/q1KlSrxD0d3796NLVu2oGfPnkqVOCDPauPKWOVQpKyfV47jYGZmhmXLlpWpGryqqFatGg4dOoT169cjKCiIT96sU6cO7O3tVSLJ41sTJ05EQkICf8yqVKkS7O3t0aZNG5iYmJSqyp+qefr0KSZOnMj/XLlyZbi7u2Ps2LGoVasWAEBDQ6PIwR+tW7eGqakpPn78iJCQEGRlZRWaHK3sxN/rwpI9DQwMkJKSgvT0dMTHx0skB4sTH+RRWMKnslHnRGdpEp/lSSgUKjCSkvk2Wa0g1tbWePbsGQDg8uXLhb7v165d45erVKkixShlQ933+cqVK/PL9+/fL3M/UVFRfEVk8T7FZWRkAAD09PTK/HsUJTw8HEFBQQgKCsKVK1eQlJSk6JCkKjw8HCNHjkRYWBgASCQ3FnVdrK2tDcYYTp8+DcYYBg4ciKFDh8ojZKmqCN9VC3L9+nUAee/jyJEji6zAv379ety5c4c/hl+/fl2pvqdKg42NDTQ1NZGbm4uQkJAC2/Ts2RPe3t7gOA5bt27FL7/8gtq1a0u0OXjwIG7cuMH/3KpVK5nGLQ3qfK5bsWIFfH198fr1awB5iZp9+vRB7dq1YWdnh6ZNm8LIyAja2tpITk5GVFQU7t27hxs3bvCJ7aJBMOvWrct3/zMnJweHDh3if7a3t5fr9hVES0sLFy5cQOfOnfHu3TswxiAUCnH58mVcvny5yHXFB3vXqVMHFy5cUKmB6yKHDh2Cm5sbMjMzy1zEAIDEAHBlV9pZZhlj0NPTQ9WqVaGlpSWjqIiqys7O5pc/f/6swEgIIYQQQog41ft2RgghhBBCiAJFRkZi8eLFWLZsGfr06YMJEybAyclJ0WHJXMOGDdGxY0fcunULp0+fxrp16+SaFKxIaWlp6NevHz99uUi9evVgamqKGzdugDEGHR0dtGvXDgkJCYiIiOATQBhj0NDQgK2trUpOd37r1i0Aedvh6OgokQz2raKSJNatW4eTJ08iNjYWS5YsUarEAXWsXC2ua9eupXp4KXogaGhoiCZNmqBTp07o1q2bSn6+y6JmzZoYNmyYosMot/DwcFy5coV/7x0dHbF///58ySwV1dy5c5GTkwPGGOrWrYugoCBYWlqWup8OHTrg9OnTyMzMxJMnT1Qi2acg4kkrMTExBbapV68ePnz4AAC4d+8eevToUWC7V69e8cu5ublSjFJ21DnRWVpycnJw7Ngx/mdVGAjavHlzfrmwGRs6d+4MHx8fAMC2bdswduzYfANZ7t+/j7Nnz/I/N2nSRAbRSpe67/N169YFkHcN6OPjAw8PD35wU2ls374dAPhzybeysrIQFxcHxhiMjY3LF7QcvHv3DkFBQQgMDMSlS5ckEnsKS/42MjJC9+7d5R5red2/fx/29vZITk7O9x2gJN8NXF1d8ffffwMA/vnnH5VJcF+yZImiQ5A50eBsAJgxY0ax7adNm8afu8XXrSj09PTQtm1b3Lp1C2/evEFYWBhatmwp0aZ///4wMDBAcnIyYmNj0aZNG8yYMQMtWrRARkYGfH194eXlxe8XlpaWaNOmjWI2qBTU+Vynp6eH4OBg2Nvb4+XLl/x7Fx0djaNHjxa63rczW61atQoTJkzI1+6///5D+/bt0b59e2hpaaFbt26y2pRSMTc3x507d+Dq6iox4wBQ8P0q8fsgHMehd+/e2LdvH2rWrCnzWKXtwoULcHV1VYlBptJEs8wSabp79y6/XNjgVUIIIYQQIn+U4E4IIYQQQkgZ5OTk4MyZMzhz5gwsLCzUoqr7unXr0K1bN0RGRsLDwwOLFi1SdEhysXXrVj65neM4DBw4EGvXrkW9evUAgE/qNTMz46tCcRyHO3fuYOPGjfj7778hFAphYGCAI0eOqFyym3h1Mltb2yLbiipUFqRy5coYMGAA9uzZg/v37+PNmzf831AZqGtyO4Biq5mRiunmzZv8ct26dXHmzBloa2srMCL5SUhI4KdpFwgEOHPmTJmS2wGgWbNmOH36NIC8GU5UNcG9cePG/PKDBw8KbNOiRQu+KqqPj0+BCUA5OTl8wh8AmJiYSDlS2VDnRGdpyMrKwsSJEyWSv7p27arAiErG2NgYjRs3xrNnz/DgwQNER0fDzMxMos2QIUMwZ84ccByH8PBwODs7w8PDAzY2NnzS36xZs/hkNwMDg2Kvl5SBuu/z3bt3R+XKlZGamork5GQMGzYMvr6+pZq55MyZM9i0aROfHNenT598bcLCwvjBVMp03Svy9etXXLp0ia/SLqr0CxSe0K6rq4suXbrAwcEB9vb2aNWqlcpUeRX5+vUrevXqhaSkJImBfmPHjkXHjh1Rs2ZN6OvrF7ldos9QSkqKSl1Lq0OCu2j2FD09PbRo0aLY9uLH7NjYWJnFpUj9+vXjB67v3r0bO3fulHi9cuXKWLRoEebOnQvGGD5//gx3d3eJNuLHhBUrVqjEfq/u5zozMzPcu3cP8+bNw+7du0t8z4PjONStWxe7du1Cz549C2zTpk0bnDhxQprhSk3NmjVx7tw5XLx4Edu2bUNAQAAyMzMLbMtxHLS1teHk5ITJkycXOsBB2XEch6lTp0IoFPLn7ebNm2PEiBFqMTsbIdLw+vVriQFA9evXV2A0hBBCCCFEHCW4E0IIIYQQqXr16hXevXuHr1+/Ii0tDVWqVEH//v0VHZbU2NnZ4cqVK/zPHMepTVX3zp07Y/369ZgxYwaWLFkCDQ0NLFiwQNFhydyff/7JP7wdNWoUDh48WOw6jDG0b98ex44dw+DBg/Hzzz/j33//Rb9+/RAcHCzjiKUrMTGRXy7sQa6uri7S09ORkpJSZF/iCYT3799XmkSfAwcOKDoEQuTuy5cv/PLw4cPVJrkdAK5cuYLc3FwwxtCnTx80a9aszH2JV/wtrDKkKmjatCmqVKmC5ORk3LhxA0KhMN+sDH379sX27dvBcRz27duH/v37w9HRkX9dKBRi6tSpePPmDf9vnTp1kts2lIc6JzoDQFRUVKnXSUtLw/v37xESEoKDBw8iIiICQN410A8//ABzc3MpRykbvXr1wrNnz8BxHLy9vTF//nyJ183NzeHq6oq9e/eCMYbg4GB07NhRoo140t/UqVOhq6srt/jLSt33eR0dHYwfP55PUL9y5Qratm2LjRs3olevXkWu++XLF3h4eGDbtm3Izc0Fx3GoUqUKxo0bl6+tr68vv9yuXTupb0dZzZkzB0FBQXj48CH/+S0soV1TUxMdOnSAg4MDHBwc0LFjR2hqqvZjpSVLluDLly/8LFze3t4YOHBgqfrQ0tJCx44dERgYiPj4eLx48QINGzaUUcSkNBITE8EYQ40aNUrUXrxSs2gWtopm6NChCA0NBQDEx8cjMzMz37X/rFmzEBYWhiNHjkhU8AYkq1zPnj0bw4cPl1Pk5aPu5zogb/DC9u3bMW/ePOzduxcXLlzAw4cPkZ2dna9trVq1YGtrixEjRqBfv34qP2tjz5490bNnT2RnZ+P+/ft4/vw54uPjkZKSgsqVK8PIyAiNGjVC69atoaWlpehwy+XGjRt48+YNv6/+/vvvWLp0qUoMRCFEGVy8eBHjx49Heno6gLxBcnZ2dgqOihBCCCGEiKj2nUhCCCGEEKIUbty4ge3bt+PixYv4+vWrxGtWVlb5EtwzMzOxcOFCCIVCCAQCrF69WmUenAQHB+PVq1fw9PTEoUOHJJLZKkJV9+ISnAYMGIDMzEwsWLAAv//+O7y8vDBmzBjY2trC1NQUOjo6pfp9FhYW5QlX5sLDw/kK5gYGBti6dWup+xg8eDBiY2Px66+/4urVq9i4cSNmzpwp7VBlRvw9LWyq4ypVqiA9PR1JSUlIT08vNLFLvHr9x48fpRtoOYwePVrRIRAid0ZGRvxy3bp1FRiJ/EVHR/PL5a0yLX5cK26QjzITCATo0qUL/v33X8TFxSEoKChfxUYHBwfUq1cPb9++RXZ2Nnr37o2ePXuiRYsWyMjIwIULF/Dy5UsAeYlQtra2KlX1TF0TnQHA0tKyXAkwHMfx6xsZGeHPP/+UVmgy99NPP2Hfvn1gjGH//v2YMWNGvqS/tWvXIjQ0FI8ePcqX/Av8LyG4S5cuWLx4sTzDLzPa54EVK1bA19eXr1r+/Plz9OnTB7Vr14adnR2aNm0KIyMjaGtrIzk5GVFRUbh37x5u3LjBJ7YzxsAYw7p16/J958vJycGhQ4f4n+3t7eW6fUXZsGED/7n99jPNGEOrVq1gb28PBwcHdOnSBXp6egqMVrqysrLg5eXF/+zl5VXq5HYRGxsbBAYGAgCePn1KCe5KQjRrQkkTVsXvReXk5MgqLIWytLTEqVOnimzDGMPhw4dhb2+PjRs3Ijw8nH+N4zi0atUKixYtwoABA2QdrtTQue5/LCwssHz5cixfvhyZmZl4//49EhISkJmZCQMDAxgbG0sM3FU1GRkZ/P1Zxhi+++47/jUtLS106NABHTp0UFR4MhcWFsYvd+rUCcuWLVNcMIQowPLly0u9jviA7YiICInv8jNnziz1Mw5CCCGEECI7lOBOCCGEEELKLCYmBuPGjcP58+cBoMTT3WprayM8PBx+fn5gjMHOzq7AKd2VVf369bF27VqsWrUKp06dwt69e/kH24BqV3UvTYITx3F4+fIlFi1aVKbfxRhT+gfIommsGWP48ccfJRIZv1VY8jcATJw4EZs3b8aLFy+wZcsWlUpwF6989+0AFhEzMzP+YeLjx48LrVD54cMHfjk1NVWKURJCSkv8ob8qJ2aXhXh1TvFE/7JIS0vjl1UlmbkwAwcOxL///guO47Bx48Z8CUACgQB//vkn+vXrB8YYcnNz4e/vD39/fwCSSc5aWlrYtGmT3LehPNQ10VlcSa/lxYlXeW3QoAGOHj2KBg0aSDs0menQoQMSEhKKbGNoaIjg4GBMnz4dR44cyfd30tTUhJubG9auXatSla3VfZ/X09NDcHAw7O3t8fLlS37/jY6OxtGjRwtdT3y7OY7DqlWrMGHChHzt/vvvP7Rv3x7t27eHlpYWunXrJqtNKRPxyu2NGjWCg4MD7O3t0b1793KfG5XZtWvXkJKSAsYYOnbsiMGDB5e5L/FrKfHBc4SoMldXV7i6uuLjx4/859rCwkKi0r0qUfdzXUG0tbVhZWWl6DCkytvbG25ubgAAW1tbXLt2TcERyZf49/mKNIsqISVVnhkLvp3FqGfPnmV+1kEIIYQQQmRDde64E0IIIYQQpfL27Vt07doVHz58KDAZpqCkH3Hjxo2Dn58fAOD48eMqleAuoqmpiSFDhmDIkCGIiIjAnj17cPDgQYnK1Kpa1b24BCdRtcKStldVsbGx/HLLli0LbCP6O4imMS2szY8//oh169bh3bt3uHPnTqFJ4MpGvBLh48ePC2zTrFkzfjCAn59fodv277//8svVq1eXYpSEFK8sFZ1KS0NDA5UrV0aVKlVQr149tGjRAtWqVZP57y2Lbt26QV9fH2lpaQgNDcXUqVMVHZLciL8ncXFx5epLfOCOqh/XBg0ahOTkZAB5563MzMx8Cd69e/fGjh078NtvvyErK0si6Ud07aenp4fDhw+jbdu2ct+G8lDnRGeg7Ndy2traaNu2LUaMGIFRo0ZV2Ep31apVw6FDh7B+/XoEBQXxSX916tSBvb29Su7/6r7PA3mDNO/du4d58+Zh9+7dJd4POI5D3bp1sWvXrnzJkiJt2rTBiRMnpBmu1Jibm8PBwYFPaq9du7aiQ5Kbt2/f8su9e/cuV18GBgb8smhfIqSiMDU1Vfr7ViVB5zr18OXLF/59c3Z2VnQ4cidefV+8SEVF9PXrV5nf21HFgcokT3meT2hra2P27NlYtGhRiWeBIYQQQggh8qFaT1oIIYQQQohSSEtLg6OjI6Kjo/mHPo0bN8bo0aPRsWNH1KxZE02bNi2ycoaTkxO0tbWRlZWFS5cuySt0mbG0tMTKlSuxfPly+Pr6Yu/evfDz85OojKcqVd1LcjO4oia0f0u8ynhhSar6+vpISUlBYmJikX2JV8h6+fKlyiS429jYQFNTE7m5uQgJCSmwTc+ePeHt7Q2O47B161b88ssv+RJlDh48iBs3bvA/t2rVSqZxE/Kt8lR0Ko9OnTph0qRJ+Pnnn+X+u4uir6+Pn3/+GZ6enjh9+jTev38Pc3NzRYclF+LHp9DQ0HL1JX5cU6Wq1QWpWrUqfvvtt2LbTZw4Ed26dcOWLVsQGBgokejr5OSEGTNmVOjPUkVMdA4ODi5Ve8YY9PT0YGhoiLp160IgEMgoMuVTs2ZNDBs2TNFhSAXt83kqV66M7du3Y968edi7dy8uXLiAhw8fIjs7O1/bWrVqwdbWFiNGjEC/fv1U9rMfFRWl6BAURnwAc3kT+3Nzc/llDQ2NcvVFCJENOtepB/H7dSYmJgqMRDHE7zeKZlesqOLj47Fs2TKZ/g5KcFc9FhYWpbrfJ/59tkmTJujUqRMGDRqEqlWryjBKQgghhBBSVpTgTgghhBBCSm3NmjV4/fo1X8V73bp1mDlzZqn60NPTQ4cOHXD16lV8+vQJERERsLS0lE3AciQQCNC/f3/0798f79+/x969e3HgwAG8e/cOQF5iuDJXdT9w4ICiQ1Aq+vr6/HJhFdoNDAyQkpKC9PR0xMfHw8jIqMB24pVcP336JN1AZUhPTw9t27bFrVu38ObNG4SFheWrZt+/f38YGBggOTkZsbGxaNOmDWbMmIEWLVogIyMDvr6+8PLy4qufWVpaok2bNorZIJKPtKpfCQQCGBgYwNDQEPXr10erVq0qbCXf4ogPArp58yZCQkLg7e2NU6dOQVdXV4GRSRIl6L5+/RpDhgxBQEAAqlSpouiwZO7777+HQCBAbm4uzp8/j5iYGNSsWbPU/URGRuLKlSsA8hIk27dvL+1QlVajRo2wfft2RYehUBUp0dnOzk7RIRAlpw77vIWFBZYvX47ly5cjMzMT79+/R0JCAjIzM2FgYABjY2OJCqlENYlf56SkpJSrL/FkeVUc3CQSFxeH27dv85/5jIyMUg1oV9ZEwLJU+S3tOsq67aRs1OFcV1GJ309Vxxk1vv/+e9SuXRsfP35EUFAQ5s6dq+iQVJL47A1EtURERCg6BEIIIYQQIkOU4E4IIYQQQkpFKBRKPPDZvHkzpkyZUqa+bGxscPXqVQBAeHh4hUhwF2dubo6lS5di8eLFuHDhAvbu3Yvz588jNzeXf2AsXtX9xx9/xNSpU9G1a1eFxTx69GiF/W5lJP6QrLAqSPXq1cOHDx8AAPfu3UOPHj0KbPfq1St+Wbzanyro168fbt26hf9j777Dori+PoB/L1URUFQQ1GDBRMWCUWwYwQax964RCxpsiSUWLNFYY+/GAlhQNAYhikFAARFF0diQIDYsiKIUaQJL2Xn/4N357cqiLGzF83keH4fdu3fPbJmdcu65ALB//3788ccfEvcbGhpi2bJlWLhwIRhjePv2Ldzc3CTaiD7zjDGsXr1aoy8aBQcHIzg4GNevX0dCQgLS0tKQk5ODJk2a4OHDhxJtCwoK8ODBAwDFCeAtWrRQRcifpKjK5tra2ujWrRumT5+OIUOGyL3/8lDF7BOigR0XLlzAhAkT8Ndffyk9htIYGhoiODgYgwcPRlRUFGxsbLBx40YMGTJEY6vSlkWNGjXQpUsXXL58Gbm5uXB1dYWvr6/M/cyZMwdCoRCMMQwcOJCqtxLy/16/fl3hysiEqJK+vr5ENVRSeYgPUoiJialQX//++y+//NVXX1WoL1U4duwYdu7ciVu3blWoH3VN8pa1yi/HcTI/Rl3XXRapqal48OAB3r9/j+zsbBgaGsLExATNmzfX6IEbpKSkpCQ8ePAA6enpyM7OlvnYeMKECQqKrOK6dOnCzzx4+/ZtVYejdFpaWpg7dy4WLFiAixcv4urVq+jSpYuqw1KIL2VGUUIIIYQQQsj/UII7IYQQQgiRybVr15CWlgbGGFq2bFnu5HYAaNSoEb9cmadJ19LSgrW1NZo3b44rV67wr5+4wsJC+Pr6wtfXFw4ODti+fTtat26tooiJSLNmzfjlO3fuSG1jY2ODK1euAAB8fX2lJrgXFhbi1KlT/N+aNmXy6NGjERUVBaA4UUAgEEBfX1+izfz583H37l14e3vzn2/xpHaRX375BWPHjlVS5PLl6+sLNzc3icEK4hfXpF1o09HRwZAhQ/D8+XMwxnD//n00b95cKfHKSlr8n0p8/7i9eFvRbBUhISEICQlBz549ceTIEZXOVBEWFqaU58nLy0NmZibi4+Nx+fJlBAcHQygUguM4+Pr6IjIyEnZ2dkqJ5XMmT54MoLhaYUxMDJ4/f45Ro0bByMgIbdu2hbm5uUxV+Blj8PDwUFS4cvXrr7/y2+szZ85g5MiRcHd3h7Gx8Wcfy3Ecfv75Z5w5cwZA8XovWbJEofESokhbtmzB/Pnz5dLX27dv0aNHD8TFxcmlP0IIkaf27dvzy/7+/ti5cyf09PRk7icrKwvBwcEAivf31WXfrizS09MxevRoXLhwAcCnkwU/3r//1P2aTNb10PQqvw8fPsSBAwfw999/f7LqbcOGDTFkyBBMnToVTZs2VV6ARG5SUlKwfft2HDt2jJ9ZsrzUOcG9du3a6NWrFwIDA3HmzBmkpqZ+cQM05s+fj5CQEAQGBmLo0KEIDAzEt99+q+qw5K5evXo4duyYqsMghBBCCCGEKBHjaKgrIYQQQgiRwaFDhzBlyhQwxrBs2bJSq1tpaWmBMQYrKys8evRIahtPT0+4uLiAMYbff/8dCxYsUGToSldYWIi///4bBw4cQGhoqNRE2Dp16iAlJQVFRUV8lV8A0NXVxZ49e+Di4qKS2EmxoqIi1KxZE1lZWahduzaSkpJKVOgNDg5G7969ARS/b/7+/nBycuLvFwqFmDlzJvbv3w+g+OL5w4cP0aRJE+WtiBJ5enpi69atiI2Nlbj922+/xbJly9SmkrcshEIhpk2bhkOHDgGQntjNcRyaNGkidXu3adMmLFq0CIwxLF68GGvXrlVK3GUl/pn+XBLLxz4ezCDtftF9TZs2xbVr11CjRo0KRKt5YmJi0KdPHyQmJoIxBldXV4mZUFRJ9FstTtrAlLIQJfpo0gwVY8eOxcmTJ/l1NTExgYuLCwYMGABra2vUqlWL35e5d+8eEhISEBoaip07d+Lhw4f8Os+fPx8bN25U8doQUn46Ojrw9vbGyJEjK9RPamoqHBwc8ODBA43aFnypGjduDKB40HFISEi5++nfvz9iY2PBGMPTp0/lFR4hCmNtbY24uDgwxrB8+XKsXLmyRJvPnc9Yvnw51q5dC8YYunXrVqHvkDIVFRXBycmpxMBPfX191K5dm99f1dHRgYWFBdLT05GZmcm3Y4xBS0sL9evX52979uyZ0uIvC2XNqKNp+71A8cCMhQsX4uDBg+A4rszHe4wxTJ06FRs2bCjTYFCiHoKCgjB+/HikpaVJvNeV9TjvwYMHsLW1RV5eHvr37w9fX99KPSuZNPn5+ZgxYwY8PT2hq6uLqVOnwtnZGW3bttX416Is1xkIIYQQQgghlRMluBNCCCGEEJls2LABbm5uYIzB3d0dkyZNktpO1gT3devWYdGiRYoMXWmePHmCgwcP4siRI0hOTgYgmfyppaWF77//HtOnT0ffvn3x+vVruLu7w93dHa9fv+YTQrW0tHDlyhV06tRJVatCUJy0ExAQAMYYAgMD4ejoKHF/UVERmjZtimfPnoHjOGhra8PR0RE2NjbIy8vD+fPn8fjxY/6ioJ2dHSIiIlS0Nsrz5s0bJCYmAgAsLS1hZmam4ojKb9q0aXB3d5e4rWnTpujUqRPq1KnDJ7aWluD+4sULNGrUCIwx2NjYqOWU2RzHYe3atVi5ciWEQiHq1q2L8ePHo0ePHmjRogVMTEygr6+PzMxMJCQk4Pbt2/Dx8UFgYCA4jkOLFi3w119/oVGjRkhLS0N0dDT++ecfHDlyBFlZWfznf9SoUfD29lb16iqdn58fhg0bxs9+cu/ePVWHBOB/v9WlVaEs6ykj8T7UPfFBnEAgQK9evXD16lWpr0Npyf6idhzHoV+/fjh79qxGV/EUefHiBV+V3tjYGBMnTpS5j8OHD/OJcEOHDpVIgCPqS0tLC/r6+ggKCoK9vX25+khPT0ePHj1w9+5dtdsWiGarUAZNmslClARa2v5LWX377be4d++e2r3vpOxiY2Nx/vx53LhxA69evUJ6ejry8vJk2g/QpMENBw8exI8//sgn7m7fvr3EzHSfOp/x119/YezYsfwg9YCAAHz//ffKXIVy27dvH2bMmMHvx3Tq1Alr1qyBg4MDtLW1pa53amoqLl68iJ07d+LatWv8gM2dO3eqZcJkeHi40p7LwcFBac9VUa9evUKvXr0kzk0AZavgL2r/9ddfIzQ0FHXr1lVKzKoQHByMI0eOIDY2FgUFBWjYsCGGDRsGZ2dnpQ2ekIcbN26gW7duyMvLk0t/mvIbf/LkSUyaNAn5+fmws7PDrl270KZNG1WHpRSigYtA8XGd+PdcV1cXNWvWlHl2NnX6bacEd0IIIYQQQr5clOBOCCGEEEJksnPnTsyZMweMMezevRvTp0+X2q4sJ543b96MhQsXgjGG/fv3a3S18oKCApw+fRoHDhzgL6iKJ8ABgJmZGSZPnowff/wRDRo0kNrH/v37sWTJEnz48AEAMGzYMJw6dUp5K0JKEA3EAIDvv/8e58+fL9Hmn3/+wcCBAwGUnK5c/HOgp6eHK1euwNbWVjnBkwoLCAhA//79+fewbdu22L17t8TAk7Js71q0aIEHDx5AS0sLqampqF69urJWoUycnZ35aZ4XLVqEX3/9tUwXP//9919MmDABDx8+hKGhIa5cuYJWrVrx9ycmJmLYsGG4ceMGgOLXKiYmBs2aNVPMiqip/Px8VK9eHQKBALVq1eIHP6law4YN5Z6YrW5VPD9HIBBg7ty52LdvHwDJWQdKS3YXLc+aNQvbtm1Ty+Su8pg9ezb27t0LAHBzc8OaNWtk7mP58uVYt24dAGDevHnYtGmTXGOsKPFE548TkRWRBK2trQ0jIyOYmZmhVatW6N69OwwMDOT+PBUlStiqUaMGrl69iubNm8v0+KysLDg6OvLbenVLgpI2W4UiaNpAH3klClWmBPekpCQ8ePAA6enpyM7OLnOCt8iECRMUFJliPH78GNOnTy9RzbuyD3ATCoVo27Yt7t+/z8ffo0cPzJ8/Hz179oSurq7U70dcXBy2bt0KT09PCIVCMMbg6OiIwMBAFa9R2TVp0gTx8fFgjOH777/HmTNnoKury9//ue3C5s2b+cIEw4cPx59//qm02En5ffjwAW3atMHTp08l9mW/+eYb9O7dG61bt4apqSmqVauGDx8+IDk5Gffv30dgYCAePnwo8ZgmTZrgzp07qFatmipXqcw8PT1x+PBhAEDXrl0/OZuaq6srDh48CKDkeR1bW1sEBASgVq1aCo1XXjp27IibN29KDGaZNm0a7OzsUK9ePY15/2Rx9OhRAMXJ/Xv37uXfv1atWuG7775DkyZNYGxsDB0dnTL3qUm/65+anU0W6vrbTgnuRFaFhYWIiorCtWvX8PDhQ7x//x5ZWVkwMjKCiYkJmjZtis6dO6Njx44ybRcIIYQQQogKcIQQQgghhMjgxIkTHGOM09LS4ubMmVNqO1Gbr7/+utQ2Y8eO5dudO3dOEeEqXFxcHDdv3jzO1NSU09LS4rS0tPh1YoxxjDHOwcGBO3nyJJefn1+mPs+cOcP3YWFhoeA1IJ+Tnp7Obd++ndu+fTu3Y8cOLi8vT2q7ffv2cfr6+vz7Lvo8iD4L1apV4/z8/JQbPKmwb7/9ln8/+/TpI/V7XJbt3fjx4/l2V65cUWTIMvPw8OBjW79+vcyPf/fuHWdpackxxrimTZtyOTk5EvenpKRw9evX559j5cqV8gpdo1haWnJaWlqcjo6OqkMhUkRGRnJDhgzhdHR0+O24tH/a2tpcnz59uMjISFWHLHcWFhb8Or5+/bpcfbx+/ZrT1tbmGGOcpaWlnCOsOPF9NC0tLan3KfKfoaEht3DhQi43N1dFr4B0LVu25F+XBg0ayPT+f/jwgevatavE67ds2TIFRiu7j/fNFfnv48+VOivL/ktZtGnTRuPWXVxycjK3dOlSrkGDBhX+jmuSkJAQzsjIqNTvhrTbP15fTfzci7x48YKzsLCQ+E3Q0tLiDAwMuNatW/O3GRgYcO3bt+fbiq+3lZUVl5aWpupVKbMnT57wsVetWpV7+/ZtiTZl2S6sXLmSb3f48GFFhkzk5Oeff5b4XtvY2HCXLl0q02MvXbrEb+fLci5Q3bRv356P29/fv9R2u3bt+uT2jzHGderUiRMKhUqMvnwePnwosV1bunSpqkNSCmnn4Ur7/aqMv+uVfZ9WXvutpPLLzMzkVqxYIbHv9ql/FhYW3G+//cZlZmaqOnRCCCGEEFIKquBOCCGEEEJkEhcXB2trazDG0KhRIzx+/FhqRcTPVVYRCASoX78+UlNToaWlheTkZJiYmChjFSosPz8fp06dwsGDB3HlyhUAJau1V69eHRMmTICrq6vMVTABoHbt2khLS4OOjg7y8/PlGr+8CIVC3Llzp0JT2YtXTq0MHj58iJ07d+LixYtITEwEADRo0AC9e/fG3LlzUb9+fRVHKB8FBQVITU1FamoqsrKyUL16ddSqVQs1a9asVFVvnj9/jsaNG4Mxhho1auDRo0dSK7aVpZLUunXrsGzZMjDG4OnpCWdnZ0WHX2Zff/01nj59imbNmiE2NrZcfZw4cQLjxo0DYwyHDh0qUelMfPYPBwcHhIaGyiN0jSJeMVOdKqERSdnZ2bh27Rru3LmDlJQUZGRkwMDAALVq1YK1tTW6d++uMfsrsnj48CGaN28Oxhg6deqEq1evlrsve3t7XLlyBYwxPHr0CFZWVnKMtGJE22tOSlVC8fsUQfx5HR0dcf78eaVUFS+LV69eoVOnTnjz5g04jkPr1q0REREBIyOjTz4uLy8P/fv3R2hoKL8uc+fOxebNm5URdpl169ZNqa/1x9Ww1ZW8KmGK9iOqVauGrKwsOUaoeEFBQRg/fjzS0tIkvvuyfl6kbVPU2atXr9CmTRukpaXx26YGDRqgZ8+esLCwwNq1a8EYg4mJCWbNmoX09HTEx8fjypUrSE9PB2MMWlpacHV1Re3atQEAK1asUPFaye7x48cYNWoU7t69W+oMLiLcRzO52Nraws/PD/Xq1VNqzBVx8uRJjB07FoyxUmeKE20XGjVqhCdPnkjtp6CgAE2aNEFCQkKFjh+IcuTk5MDMzAy5ubkAgF69euHvv/9G1apVy9xHXl4eBg8ejODgYABAtWrV8PbtW7WclUbchw8fUL16dQiFQlSvXh3JyclSz1d8+PABDRs2RFpaGgDw+0LNmjVDdHQ04uLiABR///fu3Ysff/xRqeshqyNHjmDSpElgjKFNmza4deuWqkNSCnnO2KNpv+sAMGnSJLn3eejQIbn3WV5UwZ2UxdWrVzF+/Hi8fPnyk/t1IuJtGjRogGPHjsHOzk4psRJCCCGEkLKrPJkHhBBCCCFEKZo1awZLS0u8fPkSz549w759+zB9+nSZ+9m3bx9SU1PBGIOtra3GJIvNmTMHx44dw/v37wFITlssutDt6uqKMWPGyHTB8GP16tVDWlqaWl5Myc/Px8aNG7Fv3z68efOmXH2IXrfKluDetGlT7NmzR9VhKERQUBACAwMRFhaGmJgYqQmAjDHY2NigR48e6N27N3r27KmCSOXn2rVr/PLw4cMrNB15zZo1+WXRhXN1EBUVxU9VP3To0HL3M2TIEOjo6KCoqAje3t4lEtwHDx6MOXPmAACePn1akZA1VmlJQkS9GBoawtHREY6OjqoORaliYmL45c6dO1eor44dO/IDAO/fv69WCe729valXuD+1H3lxXEccnNz8fr1a37gG8dxuHDhAk6cOIGxY8fK9fnKq379+ggICIC9vT2ysrJw//59DBkyBIGBgaUOXCsoKMDQoUMlktunT5+udsntAHDp0iVVh1Bppaen4/nz53wytCa5ceMGhgwZgry8vBL3VfaaQOvWreOT27W1tbFlyxbMnDkTWlpaAIC1a9cCKN5/XblyJf+4/Px8eHp6ws3NDZmZmTh37hzOnj2LVq1aqWI1Kuzrr7/G9evXsXXrVuzcuRNJSUkASiazi6tZsybmzZuHefPmQV9fX+kxV0RycjK/3L59+0+2lfa9ENHV1cXgwYOxa9cuPHz4EDExMWjZsqXc4iTyFRoaipycHACAiYkJvL29ZT5XVaVKFRw/fhzffPMN3r9/j5ycHISEhGDAgAGKCFlu7t27B6FQyA+yLm2f5vTp0/z5SQDYtGkT5s+fz9//008/Yffu3QCAPXv2qH2C+7t37/jlihzja6LK/vv9KeqUjE6IKly9ehXff/89cnNzJQoRcRwHLS0tGBsbo1q1avjw4QMyMzMhFAoB/C8B/vnz53B0dMSFCxcoyZ0QQgghRM1QgjshhBBCCJHZjBkzsHjxYnAch3nz5qF+/foyXdi6evUq3Nzc+L/nzp2riDAVYufOnSUudFetWhWjR4/G9OnT0a5dO7k8jyi5QN28fPkSAwcOxP3798t04Ug8+Z9opoCAACxfvhx3794F8On3kuM43LlzB3fv3sXWrVvRoUMHrFmzRmMT3d++fcsv29jYVKgv8ep2oup56kC88lWjRo3K3U+VKlVQp04dJCYmSq2mZWlpCWNjY2RmZqpVgj8hpFhCQgK/XJFtwcePF+9XHXwq0VnRSdDPnj3DtGnTEBISAgDw9vZWmwR3AGjdujV8fX3Rt29fFBQUICwsDJMmTYKXl1eJtoWFhRg5ciQCAwP5fb1JkybxyV/ky5CQkIAFCxagqKgIjDG0aNFC1SHJZPbs2cjLy+OP7Tp16oRp06bBzs4O9erVQ7Vq1VQdokIUFBTgyJEjEomcs2fPLtNj9fT04OrqCkdHR9jb2/PHhvfu3YOxsbEiw1YYPT09LF68GPPnz0dISAgiIiJKncWlZ8+ecHR0VPuq1aXJzMzkl01NTaW2MTAwQE5OzmdnY2jWrBm//N9//1GCuxp78eIFgOJzMyNHjiz3oO1atWph5MiR2L9/v0S/6uz58+f8cuvWrUttd/r0aX65bdu2EsntALBlyxb4+fkhMTER//33H54/f46GDRvKO1y5qVKlCr+sSbNMVNSzZ89UHQIhREXS0tIwaNAg5OTk8Pu45ubmmDx5MgYOHIhWrVpJbBsFAgGio6Nx9uxZHDp0CK9fvwZjDLm5uRg0aBAePXqkcYN3CSGEEEIqM0pwJ4QQQgghMvv555+xd+9eJCQkQCAQYMiQIZg4cSJ++eUXiQudH8vKysKuXbuwdu1aPpmgbdu2GDVqlBKjrzhRgq+1tTV+/PFHTJgwAdWrV5frc6xatUrtkkBzcnIwcOBAREdHS1Q4bdy4MSwsLHD16lUwxlClShW0b9+er+goupAumsrezs5ObRP4iaQFCxZg69atAP5Xdf/j6rbisxiI3wYUVwd3cnLC0qVLsWrVKuUELUf5+fn8sviFkPIQTyiR9/aiIsRnYaho4r3o8aLqlx8zMTFBZmYmBAJBhZ6HECJ/osqeQHEV+4oQTwrNzs6uUF+VSaNGjeDp6YkGDRoAAG7fvq3iiErq0aMHPDw8+Fk4vL29Ua9ePfz+++98G6FQiPHjx+PMmTP87/+YMWPg7u6ukpjJ5+3YsQM7duwo9f4XL16gcePGZe5PKBQiIyNDYt8GAL7//vtyx6hsjx49ws2bN/nP8JIlS7BmzRoVR6UcN2/eRG5uLhhjsLS0xE8//SRzH1ZWVvD29kb37t3x8uVLuLm5afwsVrq6uujduzd69+6t6lAURjwxX/w4R5yxsTFycnKQnZ2NrKwsGBkZSW0nXgG8vLO6EeUQ38f7VJJ3WYg//lNV/tWFeCVzCwsLqW2EQiEuX77M/+3s7Fyija6uLgYMGIB9+/YBAO7cuaPWCe5fffUVv/y5wSqViWgfmxDy5Vm9ejU/OxHHcZg+fTo2btxY6oBVfX19tG/fHu3bt4ebmxsWLVqEPXv2gDGGtLQ0rF69mj8fTgghhBBCVI8S3AkhhBBCiMz09fVx9uxZ2NvbIysrC0KhEIcOHcKhQ4fQqFEjiep97969w8iRI/Hq1SvcunULhYWFfPJr7dq14evrq6rVKBc9PT0MHToUrq6usLe3V9jzqONUz7t27eKT2zmOw9ChQ7Fx40Y+IUaUtF6vXj2+CirHcbh58ya2bt2KU6dOQSgUwtjYGN7e3qVeLFelZ8+eYfXq1fzfjo6OGDNmTLn78/b2xsWLF/m/V69erTEVtJydnXHs2DGJxHbRd1dXVxcWFhYwMTGBoaEhsrKy8P79eyQlJaGgoACAZPX+tWvX4u3bt3y1N01hZmbGL1c0cePp06f8cu3atSvUlzyJJ7JWJNny5cuX/KCc0pJjRUkQmlr1kpDKTLzybmpqaoX6Eh+gJ54AR4oTjiwsLJCUlFTh11lRxo0bh4SEBCxZsgRAcXXnr776CjNnzgTHcZg8eTJOnTrF/84PHToUR48eVWXI5DNEg04/noUKKN5PKygokKhyKwvR58DCwgKTJk2qaKhKc+3aNX65TZs2X0xyOwDExcXxy/379y8xUFWcaL9eGgcHB3Tt2hURERHw9vbGtm3boKenJ9dYiXzVqVOHX05JSZHapkGDBvxg1bt376Jr165S24lX7y4tWZ6oB/HzD9ra2hXqS/zxdevWrVBfylCWAZzR0dHIyMjg/+7Tp4/UdtbW1vxyYmKinCJUjK5du0JXVxeFhYW4deuWqsMhRG5odlAiTVFRkcTsRL/88gs2btxY5scbGBhg165dMDAwwKZNmwAAhw8fxubNm6lADyGEEEKImqAEd0IIIYQQUi6tW7fG+fPnMXLkSP7iDsdxiI+Px7Nnz/iTillZWfx0v+KVnhs2bIgzZ85IVBbSBAkJCaVO513Z7dixg3//JkyYgMOHD3/2MYwxdOjQASdPnsTw4cMxbtw4BAQEYODAgQgLC1NwxLJbtmwZTp48CaD44n5Fq7X06dMHy5Yt4xMAtLW1cfDgwQrHqWi7d++Gl5eXRGK7iYkJfvzxR/Tp0wft27eXWtE8Ly8PN27cwD///IODBw8iPT2df7y7uzvatWuHadOmqWCNyke8KltYWBiWLl1a7r7EP+82NjYVCUuuGjVqBKB4++zr64v169dLJL+UlahqJ2OM71Ncfn4+UlNTwRj7YrehmqagoADp6enIy8uT6UKypaWlAqMiiiI+8Ob+/fsV6is6Oppfpu97SVWrVgXHcSgsLFR1KKVavHgxEhIS8Mcff4DjOMyZMwcWFhYIDg7G0aNH+f3Bvn374sSJE3ThX0MoIimI4zjY2NjAy8tLrWao+Rzxqr5Dhw5VYSTKJz4IqXnz5lLbaGtrQygUfnbWHUdHR0RERCAzMxMRERHo2bOnXGMl8tW0aVN++d69e1LbtGrVClFRUQCAc+fOlZrg7ufnxy/Tb716E0/MfvDgQYX6Eh8gI17YQl3p6PzvEnhps5VduXKFX7awsICVlZXUduKDQdW9KnqtWrUwcOBAnD59GmfOnEFaWhpq1qyp6rAIqZBnz54BKC64QYi469ev8+efGzRogHXr1pWrn7Vr1+Kvv/7C8+fPkZGRgcjISHz33XdyjpYQQgghhJQHJbgTQgghhJBy69y5M6Kjo+Hm5oajR4/y1XnFE9lFf4vo6Ohg4sSJWLduHWrVqqX0mCvqS714Gxsby1dyMzY2xq5du2TuY/jw4UhJScGMGTNw+fJlbN26FfPmzZN3qOX28OFDnDx5kv/8Hj9+HDVq1KhQnyYmJvDy8uITA44cOYKlS5eq9XTWKSkpWLZsmUQF9tmzZ2PdunWlTu0qUqVKFdjb28Pe3h6//vorFi9ezE/xynEcFi9ejBEjRsDExEQZq1JhdnZ2MDAwQE5ODi5duoR79+6VKzk9MjKSn/3AwsICzZo1U0C05dO9e3cYGhriw4cPyMrKwpgxY+Dv7//Z91rcmTNnsG3bNv4z079//xJt7t69i8LCQjDG+FkfiHr58OEDjhw5gvPnz+PGjRulVvb8FMaY2iXtrlq1SuHPoaWlBUNDQxgZGaFx48awsbHRuCSSb7/9ll8OCAhAXl6e1IFMn5Obm4t//vmH/1sTkp+U7e7duygqKlJ1GJ+1a9cuJCYm4uzZsygqKsLIkSMl9ul79uyJ06dPSySPVSYxMTG4fv06EhISkJaWhpycHJiZmWH9+vWqDk1mbdq0gbOzc4nbRZUOjYyMMGTIkDL3p6urCyMjIzRs2BCdO3eGra2tPMNVCvHtm6bMriQv4lXZS6tobGhoiIyMjM/ONCH+2iUkJMgnQKIwrVq1QpUqVSAQCBAZGSm1TZ8+feDu7g6O4/DHH39g/PjxaNWqlUSbNWvWSAxma9++vULjJhXTpk0bWFtbIzY2FqdOncLatWvLNaNWTk4O/vzzTzDGYG1trVaDtksjPvBKfNYBcaGhoQCKj2Ps7OxK7evDhw/8ckUr4SvDjh07EBYWhvfv32PatGnw8fFRdUiEVEiDBg1UHQJRU48fP+aXR4wYUe7jUx0dHYwYMYKv4v748WNKcCeEEEIIUROV8woEIYQQQghRGhMTE+zbtw+rV6/G6dOnERERgTt37iAlJQUZGRkwMDBArVq1YG1tjZ49e2LYsGGoX7++qsMmMrpz5w6A4ot+gwYNgpGRUalthUJhqff9+OOP2L59Ox49eoSdO3eqVYK7l5cXn9zer18/dO7cWS79dunSBf369cM///yDoqIieHl5Yfny5XLpWxH27t2LzMxMAMXv986dOzFr1iyZ+6lWrRp27dqFb775Bj///DMYY8jIyMDevXsrVAldmfT09DBo0CCcOHECQqEQY8eORWRkpEwVSnNycvDjjz/yf0tLMFOlKlWqYOrUqXyCenh4OGxtbbF169ZSp2cXSU5Oxvr167F7924UFRWB4zgYGRnBxcWlRFt/f39+mZJg1M+JEycwc+ZMZGRkAKhcU3+vXLlSYtCdsnTu3BnTp0/HuHHjlP7c5dG0aVPUq1cPiYmJSElJwe+//46VK1fK3M/vv//OD44wMzNDmzZt5BtoJVBaQqm60dLSwsmTJ9G9e3dERUVJ7N999913OHPmDPT09FQYofxlZmZi165d2LdvH16/fl3ifisrqxIJ7gKBAD169EBubi50dHRw8eJFiSqv6mDQoEEYNGhQiduPHDkCoPi7eujQIWWHpVLis4ipeyVeeRP/fIr2+T9mYmKCjIwMFBYWIjExsdRBAPn5+fzy27dv5RuoHFy+fFlpz2Vvb6+05yovPT09dO7cGWFhYXj9+jUiIyNLJPT27dsXderUwbt375CdnY3OnTtjwoQJsLGxQV5eHvz9/REWFsbvW7Vs2RItW7ZUxeoQGaxevRrDhg1DUlISXF1d+QFOspgxYwbevHkDxhhWr16toEjl65tvvuGXRYns4rKzsxEcHMy/FqXNWAAUH/uKaMKMJXXr1oWvry8GDx4MPz8/9OnTB/v37/9iZ9t68uSJxKBFIyMjDB48WNVhyezj3zXx3x5F/OZpwm8bIeIzM3399dcV6qtJkyb8svh2nxBCCCGEqBYluBNCCCGEELkwNTWFq6srXF1dVR0KUQDxSr6lJauJLgqWNvWzqM2gQYOwadMmJCQk4ObNm2qT7Prnn3/yy/JOwF66dClf0dbb21utE9wPHTrEv5djxowpV3K7uNmzZ+PatWs4efIkAMDT01NjEtwB4LfffsNff/2FoqIiPHjwAB06dICHh0eZqvi8ePECo0ePxn///QegOOlfnQZ1iKxevRr+/v54+vQpgOLZDPr374+6devCwcEBLVq0gImJCfT19ZGVlYWXL1/i1q1buHr1Kp/YzhgDYwybNm2ChYWFRP+FhYU4evQo/3ePHj2Uun7k07Zs2YKFCxfy76N4crv4TA4fK22mli+Z+OsQGRmJa9euwcvLC35+fqhataoKIysbFxcX/Pbbb+A4DmvWrEGDBg0wadKkMj/ew8MDa9as4T8bkydPVlSoSpOUlIQHDx4gPT0d2dnZMn/WJ0yYoKDIlKNKlSo4d+4c7Ozs8OTJEwBAx44dERAQoBGfaVmEhYXB2dkZiYmJ/Pv88TZRGn19fTRv3hyenp5gjOHUqVNSB3qpqy91+921a1fo6uqisLAQt27dUnU4SiVeAfXVq1dS2zRt2hTPnz8HAFy7dg3Dhw+X2u7u3bv8sr6+vtxilJdu3bopZZCbOs5gU5rBgwcjLCwMHMdh165dJRLc9fX1sX79ekyePBmMMeTk5GD//v0SbUTbDS0tLb7KKVFvQ4YMwaJFi7BhwwYcP34cKSkp2LNnDxo1avTZxz5//hyzZs1CQEAAAGDRokUakxjcrl076OnpIT8/Hzdu3MD58+clBnFv3rwZOTk5AIq/x05OTqX2Jb6904RK0qLjb1dXV2zcuBHBwcGwsrJCjx49YGdnBwsLC5lna9K0/dqrV69iz549uHDhAtLS0iTus7KyKvE5FggEWLp0KYRCIbS1tfH777+rXbV+8d+1j3975P2bp0m/beTLpquryy8LBIIK9SU+eFO8X0IIIYQQolqU4E4IIYQQQoicFBQUID09HXl5eTIli2hCBSXx6Zhr1qwptU21atWQnZ3NVwAujZWVFb/8+PFjtUhwT0lJwdOnT8EYQ506ddCxY0e59t+pUyeYm5sjKSkJjx49QlpaWqmvoyrdu3ePn7pbV1cXmzdvlku/W7ZsgY+PDwoLC/H8+XPExMRoTKW/Jk2aYP369ViwYAEYY3j8+DEcHBzQvn17DBgwANbW1nzb3NxcBAQE4NWrVwgNDcXff/+NgoICAMUXBw8ePIhatWqpalVKZWBggLCwMPTo0QOPHz/mE/oSExNx4sSJUh8nSogWLa9btw7Tpk0r0e7evXvo0KEDOnToAF1dXXTr1k1Rq0JkFBUVhUWLFgEo/oxqa2tj9OjRcHJygrm5OZycnMAYQ7169eDl5YX09HTEx8fj4sWLCAoKAsdx0NbWxsqVK9V66mZVJHCKvkcXLlzAhAkT8Ndffyk9BlnNmzcPu3fvRlpaGoRCIVxcXBAUFITFixd/shL7vXv3sG7dOvj4+PCvdc2aNbFw4UIlRS5fKSkp2L59O44dO4aEhIQK9aVOiUDiA41kNXr0aKxevRpVq1bF6NGjcfr06c8+Rp3W/XMCAwMxePBg/jdbREdHByYmJhJVAaWZOHEiPD09AQB///23xiS4P3v2DMCXmbxRq1YtDBw4EKdPn8aZM2fUdt9cEcT3Xf/991+pbdq1a4egoCAAxYNTpSW4p6SkSAwQVueEzy91IIc0I0eORHx8PABAW1sbAoGgxOCEiRMn4vHjxyVmrBCnpaWFHTt2fDIhmKiPly9fYvr06TAwMMBvv/2GoKAgNGvWDD179kTv3r3RunVrmJmZoWrVqsjNzcW7d+9w//59BAYG4uLFiygsLIS2tjZWrFiBCRMm4OXLl599TnU4z2VsbIz+/fvD19cXHMdh2LBhcHV1RdOmTREZGYljx47xx7O2trZo2rRpqX1dv36dX/5UO3UxceLEEgOSi4qKcPHiRVy8eLFcfWrKvt27d+/g4uLCF5ko62+Avr4+YmNjERgYCMYYHBwc0L9/f0WGWm6fWqeK/uaVZYAnIerEzMyMX753716F+oqOjpbaLyGEEEIIUS3G0VEKIYQQQggh5fLhwwccOXIE58+fx40bNySqnJeVplTE2bFjB+bOnQvGGPbs2SO1Un/9+vXx+vVrMMaQkpICExMTqX15enrCxcWFr/asDhWtg4OD0bt3bzDG8MMPP+Dw4cNyf46JEyfi6NGjYIwhMDAQjo6Ocn+Oitq9ezd++uknMMYwfPhwiaSViho5ciR8fHzAGMOuXbswY8YMufWtDAsWLMCWLVv4i33Sqld/XC1L/HB7zZo1WLJkiXKCLafs7GwsWrQI+/fvh1AoBFByncSJ1q9Ro0bYt2+fWn6myaf169cP58+fB2MMZmZmCAgIwLfffsvfr6WlBcYYrKys8OjRI4nHxsbGwtnZGbdu3YKenh4OHz6M0aNHK3sVPis8PFwpz5OXl4fMzEzEx8fj8uXLCA4OhlAo5LcXERERJaqkqqOQkBD06dNHYnYGoPh73qFDB1hYWMDQ0BDZ2dl48+YNbty4wSfJirYJurq6+Oeff9CrVy+VrUd5BQUFYfz48UhLS5M6m0FZiV67oqIieYdYbqLvc3l9/Nv3Oeq07p+SkJCA5s2bIycnhx/oM3nyZEyaNAnt2rWDjo7OJ7eFACAUCmFmZoa0tDQYGRkhLS1N7Sp+kpJev36N1q1b4/379xgyZAh8fHxUHZLS1KtXD2/evEGVKlXw7t07GBoaStx/9+5dtG3blv/Ou7m5YcWKFfxgCNEsRVFRUQCKB4O8fv0atWvXVu6KfIb4dk+Rl8HUbXsvL6GhodiyZQtCQ0P5iqjVqlWDk5MT3NzcYGtrq+IISVl9vA9Q2vGrNLK0FVGn81wPHz6EjY0NCgoKPnkcf+7cOYnq7uKuXr2Krl27AiievfLt27eKD7yCKrrfJ04d92tL8+zZM9jb2+P169elzkLGcRyaNGkidZ/O19cXw4cPB2MMY8eOhZeXlzLCLjMtLS1++eP3RPw+edCU95yQ//77D61atQIAmJiY4NGjR+UqLpKamoqvv/4a6enpYIwhOjoaLVq0kHe4hBBCCCGkHKiCOyGEEEIIIeVw4sQJzJw5k69WXtnHjVpYWPDLpVWxbNy4MV6/fg0AuHXrVqmJbU+ePOGX1eViyatXr/jlxo0bK+Q5xPutaEVYRRGfdrtfv35y7bt///584tCdO3fk2rcybNq0Ce3bt8eMGTP4xMfSkt3FK17VrFkT+/fvx7Bhw1QVepkZGhpiz549WLRoEdzd3XH+/HlER0eXqGgLAHXq1IGdnR3Gjx+PgQMHUiKfBsrMzERQUBD/+T169KhEcvvnWFtbIyIiAr169UJkZCRcXFzQtm1bfPPNN4oKuVwcHByU/pyLFi1CTEwM+vTpg8TERADA8ePHNSLBvWfPnjh27BgmT56M3NxcAMXbtfj4eD6RXdzHSU8GBgbw9PTUyOT2GzduYMiQIcjLyytxX2XfzyuLsiZJyZoIr2pubm58cnvNmjXxzz//oEOHDjL1oaWlhU6dOiEgIADZ2dl48OCBxsxU8yWrW7cufH19MXjwYPj5+aFPnz7Yv3+/WlQdVrRevXrBy8sLAoEAfn5++OGHHyTub9OmDRwcHBAeHg7GGNavX489e/agWbNmyMvLQ0xMjMRgyBEjRqhdcvvHLCws4OzsrBGVl9VFjx490KNHD3Ach9TUVADFsx9o0jaeSBL9RktL8pZGlrbqqmnTpjh27BjGjx+P/Px8qfspCxcuLDW5HfjfLDiMMdjb2ys0XnnSxPerInJycuDk5ITExET+PW7WrBmcnZ3RqVMnmJmZoUWLFp/chvXu3Rv6+vrIz89HaGioskIvM2nHY2W5j5DKrEWLFvjqq6/w6tUrpKenY9KkSTh9+rRMs1QVFhZi4sSJSE9PBwB89dVXlNxOCCGEEKJGKMGdEEIIIYQQGW3ZsgULFy4skcgK/C/5p7RKQSKadqGpWbNm/HJpyck2Nja4cuUKgOKqR9KS2woLC3Hq1Cn+b3NzczlHWj6iE9iA4mIS71f8+dTJ48eP+eX27dvLtW/x/qRVytIEI0eORP/+/eHu7g5vb2/cvn0bhYWFUr/PNjY2GDNmDGbMmFGiKqa6s7S0xKpVq7Bq1SoIBAL+IpFAIICxsTFMTU1Rp04dVYdJKujatWsQCoVgjMHW1rZcFfirVKkCLy8vWFtbIzc3F/Pnz4e/v78CotU8LVu2xM6dO/nBLaLfR00wcuRItGjRAjNmzEBERESZHsNxHBwcHLB37140b95cwREqxuzZs5GXl8fv23Xq1AnTpk2DnZ0d6tWrh2rVqqk6xArTtP1PRfvw4QNOnz7N/3369GmZk9tFWrVqhYCAAADF1WIpwV39iRIWXV1dsXHjRgQHB8PKygo9evSAnZ0dLCwsUKVKFZn6nDBhgiJClbvhw4fDy8sLHMdhw4YNJRLcAWDv3r3o0qULP6A7IyMDN27cACA5kMXc3Bzbtm1TXvDllJSUhA0bNqBLly5wcXHByJEjZX5/v1SMMbUfwEA+T7QPIMu+QGXZbxg+fDhatWqFDRs2IDQ0FElJSahatSpsbW0xc+ZMDB48uNTHJicn81W8OY5D3759lRR1xXyJyc4bNmzA06dP+YEZ5Zkx0sDAAB07dsTly5eRlJSE58+fo2HDhooJuBwaNGiAVatWASgupjBr1iyJ+wj5Us2dOxfz5s0DYwz//PMPevbsif3795fp3ERsbCx+/PFHREZGAije75k7d66iQyaEEEIIITKgBHdCCCGEEFKqly9fKu25NKVSXlRUFBYtWgSg+ISntrY2Ro8eDScnJ5ibm8PJyQmMMdSrVw9eXl5IT09HfHw8Ll68iKCgIHAcB21tbaxcuRLfffeditem7Fq0aAEjIyNkZWXh6tWrEAqFJaa/HTBgAPbs2QOO4+Dh4YHBgwfDycmJv18oFGL27NmIj4/nb+vcubPS1uFT8vPz+WV5T+srrV/x51Mnb968AVD82RYf1CAPTZs25RNhRJX+NZGBgQF++ukn/PTTT8jJyUFMTAxSUlKQkZEBAwMD1KpVC82aNas0SSD6+vqwsrJSdRhEAV68eMEvf67atkAgKPW+Ro0aoV+/fvD19UVwcDDev38PExMTucWpyfr16wd9fX0IBAKN2+61aNEC4eHhuH79Ov7880+Eh4fjv//+k5jRQVdXFy1btoSDgwNGjx5d7sRgdfDo0SPcvHmT/51asmQJ1qxZo+Ko5OtLTHT6nMuXL0MgEIAxhl69elWoKmvdunX5ZdH+lKZ59uwZoqKi8ODBA6SnpyM7O1um5EbGGDw8PBQYoXxNnDixxADkoqIiXLx4ERcvXixXn5qS4N6nTx+JQcuFhYXQ0ZG8VNS8eXMEBQVh3Lhx/Axc4p8HjuNgY2MDHx8fmJmZKSdwGS1evBhHjhyROMa5evUqrl69ip9//hljx47FlClT0LZtWxVHSohiHTp0SNUhqFzTpk3h6ekp8+MMDQ3x4MED/m/x2Q3V2ZeW7CwUCrFnzx7+7+3bt0skf8uidevWuHz5MoDixFd1SnAHgJUrV4IxBisrq3KvIyGVzcyZM+Hh4YHY2FgAxQUGWrVqBQcHB/Tv3x82NjaoU6cODAwMkJOTg7dv3+Lu3bv4559/EB4eLjEzXcuWLTFz5kxVrg4hhBBCCPkIJbgTQgghhJBSNWzYUCnTTzPGUFhYqPDnkYdVq1bxFW/NzMwQEBCAb7/9tkS7qlWrolu3bvzf8+bNQ2xsLJydnXHr1i2sXr0ahw8fxujRo5UYfflpa2uja9euCAgIQGpqKkJCQkpU++3ZsycaN26MZ8+eoaCgAP369YOjoyNsbGyQl5eH8+fP8xXCGWOws7NDkyZNVLE6JdSsWZNfTk5OVshzpKSkSH0+dSKK0dDQUO7ffS0tLRgZGSEzM1PitdBkBgYGGp3QSb5s79+/55dLS4DQ1dVFQUEBcnNzP9mXnZ0dfH19UVhYiCtXrmDAgAFyjVVT6enpwczMjJ8FQRN16tQJnTp14v/OyspCVlYWjIyMYGRkpMLI5OvatWv8cps2bSpdcjvw5SU6lYX4YN7PDfT5HGNjY345Ozu7Qn0pW1hYGJYvXy7xPZCVqKK3JiW4f6wi+77iFc01gY6ODmxsbD7brn379oiJicHZs2dx4cIFJCYmAijenvTu3Rv9+/dX6/Vet24dVq9ejXPnzsHDwwPnz59HUVERgOKK9Pv27cO+fftgY2MDFxcXjBs3DtWrV1dx1ITIn7Ozs6pD0FhVq1alfSgNcO3aNaSlpfHJqRVJ/G7UqBG/rMzCL7KoLLMrECIvurq6OH/+PLp06YKEhAQwxiAUCnHp0iVcunTpk48Vn6G3QYMGOH/+fImBn4QQQgghRLUUU5qQEEIIIYRUGhzHKeWfJsjMzERQUBB/Ef/o0aNSk9tLY21tjYiICNjZ2SE/Px8uLi549OiRosKVu6FDhwIo/kxs3bq1xP3a2trYsWMHgOIEkaKiIgQFBWHjxo3YuXOnxLrq6uqq1VT24tW2FVXhVLxfda3unZubC8YYatSooZD+Rf1+LlmWEKJ44glpenp6UtuIEjbT0tL4hDBpatWqxS9rauViRdHV1QXHcRAKhaoORS6MjIxQt27dSpXcDgDv3r3jl0X7O6TyS0tL45crWoFafKYLXV3dCvWlTL/99ht69eqFa9eu8cdklfVYTpov6Vi2vPT09DB8+HDs378f586dw7lz57Bnzx4MGDBArZPbRbS1tTFo0CCcPXsWL1++xOrVq9G4cWMA/3v/7927h9mzZ6Nu3bqYMGECwsPDVRw1IYQQWYifbxw8eHCF+hIf6JSVlVWhvhRFE35/CVG2+vXr4+bNm+jbt2+J/fPP7b9zHId+/fohKipKYmYuQgghhBCiHmj4ISGEEEII+STRSfPKfuG+LK5du8ZXb7e1tS1RwbwsqlSpAi8vL1hbWyM3Nxfz58+Hv7+/AqKVv2HDhvEXdxhjEAgE0NfXl2jTr18/7N27Fz///DPy8/MlqhmKqqEYGBjg2LFjsLW1Vfo6lEaU5AAAFy9eVMhzXLhwQerzqROBQADGWKnJrhUlSvgqKChQSP+EkLIzMTHhl0urLm5qaorU1FRwHIcnT56gadOmUtuJPz41NVWeYWq8J0+eqDoEUgZVqlThl+vVq6fCSIgyiQ/oy8jIqFBfb9++5ZfVdSDjx44fP47ffvsNgOQxn7m5OerVqwdDQ0NVhqdwihrUStSXhYUFli5diqVLlyI0NBQHDhzAmTNn+AEqubm5OH78OI4fPw4rKytMmTIFzs7OMDc3V3HkslPW8SZjDE+fPlXKcxFCSGnEB6s2bNhQbv1WlkHKhHwpzMzMcO7cOVy4cAG7d+9GcHCwxEBkcRzHQV9fH71798bMmTMrPKMXIYQQQghRHEpwJ4QQQgghZWJhYYFRo0Z90VN2v3jxgl/+3EnP0k6eAsXT3fbr1w++vr4IDg7G+/fvJRIN1VX16tXx888/f7bdjz/+iG7dumHnzp24ePFiians586di/r16ys6XJnY2NigZs2aSEtLw4sXL3Djxg106NBBbv3fuHEDz58/BwDUrFkTbdq0kVvfhChSQUEB0tPTkZeXJ9NAJ0tLSwVGReTBysqKX378+LHUNtbW1oiLiwMAXLp0qdQE94iICH65slX2Jl+Gr776il9W10qNRP7Ek1Zv3bpVob6ioqL4ZXkmVikKx3FYvHgxgOIEVR0dHcydOxeurq4aEb88NGjQQNUhEBXq0aMHevTogbS0NBw9ehSenp6IiYnh93efPn2KJUuWYPny5ejXrx9cXFzQt29fjama+/z5c36AuSJpyutBCKncqlatyi/n5eVVqC/xGX7EZyojhGgOR0dHODo6oqCgALdv38bDhw/x/v17ZGdnw9DQECYmJmjatCnatm2rUbNvEUIIIYR8qSjBnRBCCCGElElSUhL279+PYcOGwcXFBfb29qoOSenev3/PL5eWEKGrq4uCggLk5uZ+si87Ozv4+vqisLAQV65cwYABA+Qaq6o1bdoUe/bsUXUYZcYYQ/fu3XH69GkAwJIlS+RayX3p0qUSz0OIuvrw4QOOHDmC8+fP48aNG0hJSZG5D8YYCgsLFRAdkadWrVrxyzdu3JDapkuXLvD19QUA7N69G5MmTSoxw8Pt27dx9uxZ/u/mzZsrIFpCFKtr167Q1dVFYWFhhROdieaws7PjkzPPnTuHrKyscg3SSU5O5vcbq1Spgs6dO8s1TkWIiopCYmIiv/6nT59G//79VRwVIcpXs2ZNzJkzB3PmzEFUVBQOHjyIU6dOITs7GwBQWFiIs2fP4uzZs7CwsMC8efMwb948FUetWjTLX+UjFArx/v175OTkQEdHBxYWFqoOSSYvX76U+Ft8sPXH98kDDeZWP2ZmZvzyo0ePKtTXnTt3+GVN+y4Q8iXKy8vjZ3FgjEkMXtfV1UXHjh3RsWNHVYVHCCGEEELkgBLcCSGEEEJIqUaPHg0/Pz++GnleXh4/ZXeTJk3g4uICZ2dniQsJlZl4dbKPE/xEjI2NkZqairS0NBQVFUFbW1tqO/EqQG/evJFvoKRcpk2bxie4h4WF4ffff+crW1bEpk2bEBISIvE8RL2U9j1VBHVO/j5x4gRmzpyJjIwMAJS0UtmZmpqiWbNmiIuLw507d5CYmIh69epJtBkxYgQWLFgAjuMQGxuLvn37Yv369WjdujXy8vLg7++P+fPno6ioCEDxb6CdnZ0qVoco0IcPH/D69etyzeagKQMia9WqhYEDB+L06dM4c+YM0tLSULNmTVWHRRSsTp06aNu2LW7duoX09HQsXLgQf/zxh8z9LFu2DPn5+WCMwdHRsdTjBHUSHR3NL/ft25eS2yupy5cvK+25NGV7/ymiBKgdO3bA29sb7u7uuHnzJv+79/r1axw7dkwjEtzt7e0VUl09Li4Ob9++pcrtGi4nJwfHjh1DcHAwrl+/LnFOqkmTJnj48KFEe4FAgKCgIADFx879+vVTaryf07BhQ/4z+fHxtvh98qDOx/OfEhsbyw9if/Xqlcz79YwxPH36VMFRlp/4LIn+/v7YunVrud53gUCA4OBgAMXrTMe2hKg/Ly8vuLq6AigewCw+wyAhhBBCCKkcKMGdEEIIIYSUytvbG+/fv+en7L5//z5/35MnT7B48WIsW7YM/fr1w5QpUzRqyu7yMDEx4ZfT09OltjE1NUVqaio4jsOTJ0/QtGlTqe3EH5+amirPMEk5OTo6okuXLoiMjATHcXzV9YokuW/cuBFLlizhvxddunRBr1695BKvIqWlpWHVqlUK6VcdcRwHxtgXndC9ZcsWLFy4UOpr8akqjeLb/C/59dNUffr0QVxcHDiOg5eXV4ntXf369TF58mS4u7uDMYawsDB06tRJoo3ofWeMYfbs2RLTwxPN9eLFC+zZswfnz59HXFwchEKhzH1oWgLQjh07EBYWhvfv32PatGnw8fFRdUhK07hxY7n0o62tDWNjY9SoUQNNmjRB+/bt0bt3b9SvX18u/SvC4sWLMWLECADAgQMHUK1aNWzcuBFaWlplevzmzZtx8OBB/m83NzeFxClv4vtkPXv2VGEkRJG6deumlONzTdvef061atUwdepUTJ06FX5+fpg0aRKysrJUHZZMLl26JNf+Xr58iSVLluDy5cuV+pzPl2DLli1Ys2YNMjMzAZQ8hpN2TKevr4/ly5cjJiYGAHD9+nW0b99e8cHK6FPHo1/qserjx48xffp0hIWFSdwuS2K76ByBOmvWrBksLS3x8uVLPHv2DPv27cP06dNl7mffvn1ITU0FYwy2trYS54EJIeopOTmZ30717dtX1eEQQgghhBAFoAR3QgghhBDySSYmJvj555/x888/48aNG/yU3aILvAUFBThz5gzOnDmDunXrYtKkSZg8eTIaNmyo2sAVwMrKil9+/Pix1DbW1taIi4sDUHxRubQEd/FqIkZGRnKMUnFEyU+NGjWSqEguq/79+yM2NlYtK0Bt27YN9vb2EAgEfJL7+fPnsXLlSnTv3r3M/Vy6dAm//fYbLl++zF84rFq1KrZv366gyOXr/fv3+O2331QdhlKV5QKvtIu6n0v6lqV/VYmKisKiRYsAFMeura2N0aNHw8nJCebm5nBycgJjDPXq1YOXlxfS09MRHx+PixcvIigoCBzHQVtbGytXrsR3332n4rUhshg5ciQ8PDzAGIOnpyfmzp0LfX19iTYbN25EVFQU7t+/L3UgiOi2rl274tdff1Vm+ERB1q1bh5UrV/KV+dV5+yVPdevWha+vLwYPHgw/Pz/06dMH+/fvh6WlpapDU7jnz5/LbaCX6Dfw0qVLcHd3h5aWFvr27YuNGzeWul+sSsOGDUP37t0RFhYGxhi2bduGc+fOYe7cuejXr5/U5Py8vDxcunQJW7du5feJGWMYP348OnbsqOxVKBfxGQpq1KihukCIUnwp23F5EQgE8PHxgbu7u1Kr4KujzMxMrF27Frt27eJn9gOKP1MmJib8oHCi/vLy8jBw4ECEhISUOqj5U9uK6dOnY8aMGWCMwdvbW+0S3Cm5vaTQ0FAMHjwYHz58KPW8hbRjO3Hq+NqJijHUrFkTs2bN4m+fMWMGFi9eDI7jMG/ePNSvXx8DBgwoc79Xr16VGKg4d+5c+QVNCFEY8eMac3NzFUZCCCGEEEIUhXHqeHRKCCGEEELUWk5ODk6cOAF3d3dERUXxt4tPCdy9e3e4uLhgyJAh0NPTU1WocpWcnIw6deqAMYa2bdvi5s2bJdps27YN8+fPB2MM1tbWuHXrVon1v337Njp16oTCwkIwxhAcHKwRlRNFlSybNGmCR48elbufb7/9Fvfu3QNjjE+eUyfHjx/HDz/8UKJSVZMmTdCrVy906NABjRs3Ro0aNWBoaIjs7Gykp6fj+fPniIqKQkhICP/6iFc29vLywtixY1W2XmWhpaWl8MpcotdUnd77siTzv3v3Dvv37wfHcfw6tGrVCq1bt4apqSmqVauGDx8+IDk5GdHR0YiJiYFQKARjDFpaWvjxxx9hamoKAFixYoWiV0km/fr1w/nz58EYg5mZGQICAvDtt9/y94s+F1ZWViW++7GxsXB2dua3dYcPH8bo0aOVvQpEwdLS0jBnzhx4e3uXqOStq6sLV1dXbNy4sURyPNE88+bNw44dO+Qym4O6bes/5+jRowCAuLg4bNy4ERzHQUtLCz169ICdnR0sLCxQpUoVmfqcMGGCIkKVO/Fq5eUdzFVagpTodgMDA3h4eGDUqFEVjlfe0tPTYWdnh7i4uBL7f0ZGRsjMzARjDDo6OqhevTrev3/PbwtF62dra4uIiAiN2Q4GBwejd+/eYIzh999/x4IFC1QdElEA8X17RV4G0rTtfWnu3LkDDw8PeHt7IyMjA4Dk62Zubo758+dj/vz5qgpRaQoLC7F3716sXr0aaWlpEvsFenp6mDVrFpYtW0YDZDRI//79ERAQwL+POjo66NmzJzp16oQ6derwyevSjvkA4O3bt6hbty4AoGnTpoiNjVX2KpQqPDxc4m8HB4dS75MH8f7V1atXr9CmTRukpaXx73mDBg3Qs2dPWFhYYO3atWCMwcTEBLNmzeIHsV+5cgXp6en8eQxXV1fUrl0bgPqcxyjt/IRAIEDTpk2RkJDA78dPnDgRv/zyC5o1a1bqY7OysrBr1y6sXbsWubm5nzznqw5E62BiYoKffvpJoc9FA9iJJjhz5gyGDBnCD1ZW9PeCEEIIIYQoHyW4E0IIIYSQCvnvv/9w8OBBHD9+HKmpqfztogvpJiYm+OGHHzBlyhS0bNlSVWHKjahCu5aWFl68eIF69epJ3P/q1Ss0bNiQvxDevXt3rF+/Hq1bt0ZeXh78/f0xf/58pKSkgOM4VK9eHa9fv0bVqlVVsToy+VSSqyzUPcEdAPbu3Yu5c+eisLAQgGSy3ueIt+U4Drq6uti2bRtmzJihuIDlRDzBTZHU+b2X5tq1axg+fDiSkpJQpUoVzJ8/Hz/++GOJ77+4169fY//+/diyZQtyc3NhYWEBHx8fdOrUSYmRf15mZiZq1qzJf24DAwPh6Ogo0eZz3/28vDz06tULkZGRMDAwwO3bt/HNN98oJX6iXO/evUNISAgSExMBAA0aNECPHj1Qq1YtFUdG5CEoKAh9+vThf79q1KiBWbNm8bM5fPPNN2CMoUGDBggNDZWYzeHYsWPIzs6GtrY21q5dyycxN2jQQMVrVXYfD/KS5be/NJryWzdx4kQwxpCbmwsfHx9+MBdQnNDZokULmJiYQF9fH5mZmUhISEBsbCzy8/MBFL9GzZo1Q4cOHSAQCJCWlobo6GgkJSXx94uSIoODg2Fvb6+ydS1NamoqnJ2d+eQ/QPpnQNqp9IEDB+LYsWMwNDRUTrByIBAIYGFhgYyMDPTt2xf+/v6qDknlYmNjcf78edy4cQOvXr1Ceno68vLyypwYro6zU4lv18zNzeHs7KywmRScnZ0V0q+iZWZm4tixY/Dw8MDdu3cBSH7PtbW10adPH7i4uKBfv37Q1tZWUaTK4+PjAzc3N8THx0sktjPGMGrUKKxbt65SzthXmXl5ecHZ2Zl/LwcMGIA9e/ZIzFJSlvM97dq1w507d8AYw9u3b/nEZ6J+ZsyYgX379vEztG3ZsgUzZ87kz/mU9n7n5+fD09MTbm5uyMzMhKWlJc6ePYtWrVqpalVK+FTxjejoaNjb2yMrK0tiwGKjRo3QokUL+Pv7gzEGIyMjODk54dWrV7h16xYKCwv5bb+pqSn+/fdffPXVV8pdsTJSRmEKEU05liFftpSUFNStWxdFRUX44YcfcPjwYVWHRAghhBBC5IwS3AkhhBBCiFzk5+fD19cXHh4eCA0NlVrxs3379vj5558xZswYVYVZYfPnz8e2bdvAGMPatWuxePHiEm2mTZsGd3f3Ui84iCfLLF26lJ9eV919SQnuABAZGYnRo0fj1atXn01sEvm43VdffYWTJ0+ic+fOCo1VXhRR3aw0mlD1DAASExP5ymeWlpY4f/48X/2rLB4+fIjevXvjxYsXMDU1xZ07d/jKd+pAPKHV1tZWYlYOkbJ89589ewZra2vk5+dTohwhGsre3h5Xrlzhv+8hISESiR2f2hakpqZi8uTJfNLIxo0bNa7CrTyTRdRxtpLPefz4MYYNG4aYmBjo6elh5syZmDp1aqm/eQKBAGfPnsX69etx9+5daGtrY+XKlVi6dCnf5s6dO1izZg38/Pz4pLrmzZvjv//+U9ZqyezkyZPYvHkzbt++/dm2zZs3x7JlyySObU6cOKExxzoLFy7E5s2boaOjg+joaJn2byqTx48fY/r06QgLC5O4XZbEdnX9zkvbrnXp0gUuLi4YOXKkzLNSVCbh4eFwd3eHr68v8vLyAEi+540bN8bkyZMxadIkWFhYqCpMpYqMjMQvv/yCqKioEp9/e3t7bN68Gba2tiqKjlTE119/jadPn4IxhgkTJuDQoUMl2pTlmG/KlCk4dOgQGGMICQlBt27dFBw5KY+CggLUqFGD37Zt3boVP//8s0Sbz73fT58+hb29PZKSkmBpaYl79+7B2NhYKfF/zudiv3btGkaOHInExESJGak+dY5W1K5hw4Y4c+aMWiX0f+xLnXmRkE/p27cvAgMDUb16dTx58oSKMBBCCCGEVDKU4E4IIYQQQuTu2bNncHd3x5EjR/D69WuJ+9q0aVOmhBF1FRUVhe+//x6MMZiamuL+/fvQ19eXaJOeng4HBwfcv39f4mKKiOg2e3t7XLx4ETo6OspchXKTV4K76OJqtWrVkJWVJccI5S8/Px+HDh3C5s2bS1Rk/FTSu5WVFRYsWICJEydCT09PKbESxRg2bBj8/Pygq6uLqKgotGnTRuY+7t69i44dO6KwsBBDhgyBj4+P/AMtpwMHDsDV1RWMMSxevBhr164t0Ub03a9fvz5evHhRal/Dhw+Hr68vdHV1kZSUBBMTE0WGTgiRo5SUFJiZmQEo/n27ceMG2rVrJ9Hmc/sBHMdh6NChOHPmDHR0dBAREYGOHTsqJX55kPcsJpqUFJKZmYm2bdvi2bNnqFu3Ls6fP1/mmZc4jsMvv/zCDwCVNi382rVrsXz5cgDFr4uvry8GDRok9/X4lD///JOfWaAs4uLiEBERgTt37iAlJQUZGRkwMDBArVq1YG1tjZ49e8LGxkbiMT4+Phg7dixf2V7dCQQCdOjQAffv34e1tTXCw8O/uGSQ0NBQDB48GB8+fJCa0F7asZw48cQ5dfvOL1myBEeOHMGbN28ASMZubGyMsWPHYsqUKWjbtq2qQlSqpKQkHD58GJ6envyxnfj7q6+vj6FDh8LFxQXdu3dXVZhK9+TJEyxatAh///03AMlkz2bNmmHDhg0YMGCAaoMk5fbgwQO0aNECjDGYm5vj8ePHMDAwKNGuLOd7Nm7ciMWLF4MxhoMHD2Ly5MmKDr/cXrx4AX9/f9y6dQvJycnQ1taGmZkZOnfujAEDBsDU1FTVISpMZGQkvvvuOzDGYGlpifj4+BK/XWV5v8PDw9G9e3cwxuDq6oo9e/YoI/zPKkvs79+/h5ubG44ePcon+gOln8fT1dXFxIkTsW7dOrXfFxKtv6LTO9Rxv4aQ0jx48AC2trbIy8tD//794evr+0XMukMIIYQQ8qWgBHdCCCGEEKIwQqEQJ0+exIwZM/jpYTU9wb2s0tLSMGfOHHh7e0MoFErcp6urC1dXV2zcuLFEcrw6k0eCe3p6OkxNTSEUClGvXj28fPlSzlEqTmxsLC5duoTr16/j7du3SE1NRWZmJoyNjVGrVi3UqVMHnTp1Qrdu3WBtba3qcIkcpKSkoF69eigsLMSgQYPg6+tb7r7EE+Vfv36tNhdNN2zYADc3NzDG8Mcff2DatGkl2ujr66OgoAC1a9fGu3fvSu1r69at+OWXX8AYw99//02JMIRokLNnz2Lw4MFgjMHBwQGhoaEl2pRlPyA5ORlNmjRBdnY27O3tS1REVmefGsBTXg0aNJB7n4ogmn1IR0cHkZGR5arQO2jQIPj7+0NPTw+xsbFo3LixxP2Ojo4ICQkBYwzTpk3DH3/8Ia/wy6RKlSoICAhAjx49FNK/v78/hg0bhqKiIo1KBnrz5g0GDhyIW7duwcLCAhs3bsSoUaM0ZgBuRbx69YqfpUeUKNagQQP07NkTFhYWWLt2LRhjMDExwaxZs5Ceno74+HhcuXIF6enpYIxBS0sLrq6uqF27NgBgxYoVKl6rkoqKinDu3Dl4eHjg/PnzEp9PUaKfjY0NXFxcMG7cOFSvXl1VoSqEUCiUuv7il8Vat26NKVOmYPz48V/UAM3U1FSsXLkSBw4cQGFhoURiu5mZGVauXImpU6dSgpiGO378OH744QcwxjB79mxs375daruy7OeJD47etGkT5s2bp8DIy0cgEGDevHk4ePBgqb/HVatWxYIFC9Rymy0Pnp6ecHFxAWMMM2bMwK5du0q0Eb3flpaWePbsWal9OTg4ICIiAtWrV8fbt2/VooCDLOcmk5OTcfr06c8OWhw2bBjq16+vpDWoGNH6161bF8eOHVPoc2nKzIuEAMUzcU2aNAn5+fmws7PDrl27ylWkhBBCCCGEqJ/Kf6aaEEIIIYSoxNWrV+Hu7g4fHx/k5OQAKH062MqoZs2aOHr0KDZv3oyQkBAkJiYCKE506tGjh9oktypTQkICFixYgKKiIjDG0KJFC1WHJBNra2tYW1tjxowZqg6FKMmVK1dQUFAAxhjs7e0r1FfXrl3h5+eHwsJCREREYPDgwfIJsoLEt8ulXaw2NjZGamoq0tLSUFRUVGqSi/h2TVQplBCiGcRn3PlcIoN4FcSPmZqaYuDAgTh+/DgiIiLw5s0bWFhYyC1ORdKUZHR5y87Ohre3NxhjcHJyKldyOwAsX74c/v7+KCgowKFDh7B69WqJ++fOnYuQkBAAwLVr1yoct6zy8/MxdOhQhIaGyr1adWBgIEaOHInCwkKNOt4RVd5t3Lgx7ty5gzdv3mDChAmYPn062rVrBwsLC1SpUqXM/THG4OHhoahw5W7dunV8cru2tja2bNmCmTNn8rM5iGa1qVmzJlauXMk/Lj8/H56ennBzc0NmZibOnTuHs2fPolWrVqpYjc/S1tbGoEGDMGjQILx58waenp44dOgQ4uPj+STve/fuYfbs2ViwYAGGDRuGKVOmVIqkNlH13qSkJACSSe1GRkYYM2YMXFxcyr3d01QCgQDbtm3Dhg0bkJmZySe2A8WJv/PmzcPChQthaGio4kiJPIg+/wDQvHnzCvUlXqBBIBBUqC9FEAgE6N27Ny5fviwxu4Y4juOQk5ODVatW4eHDh/D29lZFqAqVlpbGL5f2nmtra0MoFH72fXR0dERERAQyMzMRERGBnj17yjVWRTM1NYWrqytcXV1VHYrcVa1atVL8VhMiD0ePHgUATJkyBXv37kVkZCTatWuHVq1a4bvvvkOTJk1gbGws0yDeCRMmKCpcQgghhBAiI0pwJ4QQQgghcpOcnIwjR47Aw8NDooqO6MKSvr7+FzXVNwCYmZlhzJgxqg5DJjt27MCOHTtKvf/FixclqnJ+ilAoREZGBjIzMyVu//7778sdIyHKIBqYAqDC1SyNjY2l9qtq4lUq09PTpbYxNTVFamoqOI7DkydP0LRpU6ntxB+fmpoqzzAJIQr2/v17frlevXpS2+jr60MgEPADF0tja2uL48ePg+M4REVFqc2AHiJdeHg4cnJywBir0H66ra0tDA0N8eHDBwQGBpZIcHdwcOCTqcST7ZQpMzMTffr0QWRkJKysrOTSZ0hICIYOHYr8/Hy59KdMhw8flkj8E1Vuzs7OxuXLl2XqS5QgqykJ7gUFBThy5Ai//ps2bcLs2bPL9Fg9PT24urrC0dER9vb2ePnyJQYOHIh79+5J7O+pIwsLCyxduhRLly5FaGgoDhw4gDNnzvAJjrm5uTh+/DiOHz8OKysrTJkyBc7OzjA3N1dx5OWzYcMG/nMtYmdnBxcXF4wcORIGBgYqjE41jh49iuXLl+PVq1cSie2MMUyaNAmrV6/WmIFppGzEP/8VrcafkZHBL9eoUaNCfSnCihUrEB4eDsYY/93/eBJz0Wee4zj8+eef6N69O6ZOnaqKcBWmoKCAXy5toIqhoSEyMjI+e9wuflyQkJAgnwAJIUTOJk6cKLFPI9r2R0dH4/79++XqkxLcCSGEEELUByW4E0IIIYSQCuE4DufPn4eHhwfOnTuHwsJC/naRVq1aYcqUKfjhhx++qCm/NVV6ejqeP39eIhkAKH5fCwoK8Pz583L1LTrZbGFhgUmTJlU0VEIUSjyJ8+XLlxXqS/xi8OeSQ5VJPMHv8ePHUttYW1sjLi4OAHDp0qVSE9wjIiL4ZSMjIzlGSSpq1apVCn8OLS0tGBoawsjICI0bN4aNjQ1q1qyp8Ocl8qGrq8svl5b8ZGRkBIFAgPfv3yM/P7/UWR/EE55oNgf19+rVK365ojMMmZiYIDs7W2oCVLVq1VCzZk0kJyeXOqBK0RhjSE5OhpOTEyIjI1GnTp0K9RcREYFBgwYhLy+PT6YTVUXXJB8nuYrfXhaaVLVe5ObNm8jNzQVjDJaWlvjpp59k7sPKygre3t7o3r07Xr58CTc3N+zZs0cB0SpGjx490KNHD6SlpeHo0aPw9PRETEwM/74/ffoUS5YswfLly9GvXz+4uLigb9++Gvl+M8Zgbm4OZ2dnNGvWDADg4+Mj1+dQ9ySoixcvYsGCBYiOjpb4znMch969e2Pjxo1o2bKliqMkimBmZsYvVzRB+cGDB/yyqalphfqSt5SUFGzfvl3is921a1eMGjUKVlZWKCwsRFxcHLy8vBAdHc2f71qxYgWmTJnCz95RGYgPtvq40ISIiYkJMjIyUFhYiMTExFIHuIoP4Hv79q18AyWEEAWpyP6q+H4SIYQQQghRD5TgTgghhBBCyuX58+fw8PDA4cOH8fr1awAlp/weNWoUXFxc0KFDB1WFSSqgrEktsvZpY2MDLy+vClfEJkTR6tatyy/7+vpixYoV5e7Lz8+PX1anqoitWrXil2/cuCG1TZcuXeDr6wsA2L17NyZNmlQisfX27ds4e/Ys/3dpU6ET1Vi5cqVKLtB17twZ06dPx7hx45T+3EQ24oMR0tLSpLYxNzdHSkoKACAuLg6tW7eW2i45OZlfVlUiMyk78feoIolLHMfxn4/SkqlE1ZJVsT1atGgRX8352bNn6N27N8LDw8tdcfv69evo168fX/0eAMaNG4eDBw/KM2yFsrS0/GKTN0QD9wCgf//+n3wdxCvhfszBwQFdu3ZFREQEvL29sW3btlIH/6irmjVrYs6cOZgzZw6ioqJw8OBBnDp1CtnZ2QCAwsJCnD17FmfPnoWFhQXmzZuHefPmqThq2XAch6SkJGzYsEFhz6GuCe4xMTFYsGABgoODAfwvaYvjOLRp0wabNm1Cz549VRwlUaSvv/6aXw4ODsZvv/1W7r5CQkL4ZVtb2wrFJW/e3t58MjZjDDt37sSsWbMk2vTr1w/z58/HL7/8gq1btwIo3vcJDAxE3759lR6zojRo0IBfFh/IKK5p06Z84Ypr165h+PDhUtvdvXuXX9bX15dbjIQQIm+KuI5BCCGEEELUAyW4E0IIIYSQMsvPz4evry/c3d1x6dIlqdP9du7cGS4uLhg1atQXOeW3SE5ODiIiIvDw4UPk5ubC1NQUbdq0Qdu2bVUd2me1adMGzs7OJW4/cuQIGGMwMjLCkCFDytyfrq4ujIyM0LBhQ3Tu3FntLoQSUprOnTvzyzExMdi1axdmz54tcz979uxBdHQ0/7ednZ1c4pMHU1NTNGvWDHFxcbhz547U6m0jRozAggULwHEcYmNj0bdvX6xfvx6tW7dGXl4e/P39MX/+fBQVFQEorhinTutIlEt8vyAyMhLXrl2Dl5cX/Pz8ULVqVRVGRj5FPPlJvDqnuJYtWyImJgZAcYJTaQnu4slP4tXciXoSr2IeFBSERYsWlaufiIgIviK2eLVYcRkZGQAAQ0PDcj1HRaxfvx7v3r3DoUOHwBhDdHQ0Bg4ciKCgIJkTtm7duoU+ffogOzubTxQdMWIEjhw5oqDoFaO8MzJVBuIDeUoblKetrQ2hUAiBQPDJvhwdHREREYHMzExERERodLJwx44d0bFjR+zYsQPe3t5wd3fHzZs3+d/2169f49ixYxqX4C5e0Vne/aprlc83b95g2bJlOHr0KIRCoUSc9evXx5o1azB+/Hi1jJ3IV8eOHVGjRg2kp6fjxo0buHTpErp16yZzPwEBAXjy5AkYY2jUqBEaNmwo91gr4sqVKwCKv5c//PBDieR2cZs3b8bNmzf5GciuXLlSqRLcra2t+eV///1Xapt27dohKCgIAODp6Sk1wT0lJQV//vkn/7d44rw6SEtLU/hMZb/++qtC+yeEyMezZ89UHQIhhBBCCFEgSnAnhBBCCCGfdf/+fbi7u+P48eN4//49AMmLw7Vr18YPP/wAFxeXSlu1NysrCzdv3sS7d+9gaGiI1q1bw9LSskS7wsJCrF27Ftu3b5davdLKygpbtmzBgAEDlBF2uQwaNAiDBg0qcbsoacfMzAyHDh1SdliEKF3jxo1hZ2eHa9eugeM4zJ07Fx8+fMCCBQugra392ccLhUJs3rwZS5cu5ZNH7Ozs0LhxY0WHLpM+ffogLi4OHMfBy8sLixcvlri/fv36mDx5Mtzd3cEYQ1hYGDp16iTRRvSbwBjD7NmzKZFZDamimpUo8evChQuYMGEC/vrrL6XHQMqmdevW0NLSAsdxiIqKktrGwcEBJ0+eBADs3LkTLi4uMDIykmgTFBTEV4kFJGeJIOrJxsYGQPE2Ijw8HMHBwXBycpKpD47jsGzZshJ9iktOTkZ6ejoYY6hfv37Fgi6ngwcPIiUlBf7+/mCMISIiAmPGjMHp06fLnOR57949ODk5ISMjg9/GDRo0CN7e3pQoqkHEq7KXNuDC0NAQGRkZSE1N/WRf4gMDExIS5BOgilWrVg1Tp07F1KlT4efnh0mTJiErK0vVYZWLIvd/1LlSaJMmTZCXl1dilj03Nzf8/PPPqFKligqjI8qkpaWF0aNHY9++feA4DuPHj8fVq1dlSlZ+9+4dXF1d+b+nTZumiFArRHxA+dy5cz/b/qeffuIT3MUfWxk0btwYFhYWePPmDSIjI5GdnV3it2748OFYt24dgOL992XLlmHFihXQ1dUFALx48QKjR4/mZ/rR1tZG165dlboen/P+/fsKzUhQFpTgTohmULcBOIQQQgghRL4Yp85n4QghhBBCiErt378fHh4euHXrFgDJC7haWlro1asXXFxcMGjQIP4iSGWTlZWFBQsW4OjRoyWq99nb22Pv3r18Un9ubi4GDBiAsLCwT17sZoxh/fr1WLhwoUJjlzctLS0AxRfLHz16pOJoCFGO27dvo1OnTigqKuIrH3799deYPHkyevfuDWtra+jo/G/seGFhIR48eIDAwEB4enri0aNH/PZAR0cHN27cQJs2bVS0NtJFRUXh+++/B2MMpqamuH//folqtunp6XBwcMD9+/f5hD5xotvs7e1x8eJFideEqF54eLhSnicvLw+ZmZmIj4/H5cuXERwcLFE1NCIigqr7q7G2bdvi7t27YIwhLi5Ooqo7AKSmpqJ+/frIz88HUFz1eMWKFRKzOfz+++/Izc0Fx3GoU6cOEhISNHZ7EBsbi/Pnz+PGjRt49eoV0tPTSyQLfgpjDE+fPlVwlPLRtGlTPHnyBBzHwdjYGD4+PujVq1eZHpubm4tJkybh1KlTAIrX28vLC2PHjpVod+bMGQwZMgSMMYwcORInTpyQ+3qUhUAggKOjo0Sl1ylTpuDAgQOffWxsbCy6deuGlJQU/nevb9+++PvvvzX2c/6l2rNnD2bPng3GGHbs2CG10m/jxo3x/PlzMMbw8uXLEjPciOzfvx/Tp08HYwzr1q0r9ywI6kQgEMDHxwfu7u64fPmyxH02Nja4ffu2iiKTjbL2f4DiQWDqREtLS6JyvYWFBebNmwdTU1O5P9eECRPk3ieRr6SkJDRp0oTfR6tduza2bt2KcePG8Z8T0WfGyspK4nxPZGQknJ2d+X0aMzMzxMfHq92MjaampkhNTUW1atXKNCDnzZs3qFevHhhjaN++Pa5fv66EKJXH2dkZXl5eYIzh8OHD+OGHH0q06d69O8LDw/nPgLGxMZo1a4a8vDzExMRIHMeNHj0ax48fV/ZqSCW+fVMU0XqLZqlTJ6V9VwkhhBBCCCGksqIEd0IIIYQQUirRSXPxXcavvvoKkyZNwuTJk6VWMK9MsrKy0L17d9y5c6fUZKaaNWsiKioKVlZWcHV1xYEDB0qdAl38dsYYzp07hz59+ih2JeToxYsXAABdXV3UrVtXxdEQojyHDh3C1KlTwXGcRKVyoLiSWY0aNVC1alXk5uYiPT2dvwj68aCggwcPYtKkScpfATlJS0vDnDlz4O3tDaFQKHGfrq4uXF1dsXHjxhLJ8eTLFRMTgz59+iAxMRGMMbi6umLPnj2qDouU4tdff8WaNWvAGMPChQuxfv36Em0WL16MjRs38vuHHyeXiG8jt23bhp9++kkpscvT48ePMX36dISFhUncLktiuzonxUjj5+eHYcOGScQ+fPhwTJkyBQ4ODlK36y9evMDp06exfft2JCYm8o+zsbHBv//+yw+MFBk3bhxOnDihFp+NjIwMdO3aFf/99x8ft5ubG9asWVPqYx49eoRu3bohKSmJf5169eqFc+fOQU9PT4nRE3k4d+4cBg4cCMYYFixYgN9//71Emz59+iAoKAiMMfz5558YPny41L6mT5+O/fv3gzGGLVu2YM6cOQqOXnHu3LkDDw8PeHt7IyMjA4Dkts/c3Bzz58/H/PnzVRUiKaOPE9wVmQyqKb91X7oTJ05g/PjxAP73mTA3N0efPn3QokULzJ8/H4wxmJmZYfny5Xj16hVCQ0Nx8+ZN/jE6OjoICgpC9+7dVbkqUunp6aGoqAiWlpZ49uzZZ9sXFRVBV1cXjDE0bdoUsbGxSohSefz9/fmZGa2trRETE1OizYMHD9ClSxeJ7f3H2w3RAJk7d+7AzMxMeSvwCdLOVSuCuu7LU4I7IYQQQggh5EtDCe6EEEIIIaRU4hdFzc3N4ezsDCcnpxIJK/Jgb28v9z4rqiwJ6xzHYcCAAdi8eTOaNWvGtzMxMUGvXr3QqFEjcByH+Ph4XLx4ERkZGfzjrKys8PjxY6WvFyFEdn5+fnB1dUVycnKp2wRx4m1MTU2xf/9+DB48WBmhKty7d+8QEhKCxMREAMVTAffo0QO1atVScWREHYknzrZs2RL37t1TdUikFP/99x+f+GRsbIzg4OASic0CgQB9+/ZFWFhYicQS8e3eqFGjVFahuyJCQ0MxePBgfPjwQeo2vrQZLMSJJ/mrY1JMaWbNmoW9e/eWGLygra2NRo0awcTEBPr6+sjKykJCQgLS0tIASCZAmZqaIiwsDNbW1hJ9JyYmwsrKCvn5+WCM4dGjR7CyslL6Oop78+YN7Ozs8PLlS34dSqvkHR8fDwcHB36wDsdxcHBwwPnz51GlShUVRE8qKj4+Hk2aNAFjDN27d8fFixdLtFm2bBnWrVsHxhi+//57BAQElGiTkpKCb775Bunp6WCMwcfHB0OGDFHGKshNZmYmjh07Bg8PD9y9exeA5D6utrY2+vTpAxcXF/Tr1w/a2toqipTIQlqFY3leCtTEwVwE2L17N+bNm8fPTgagxLFtaZ8bPT09HDx4UGolcHVQnqTfypwoXFhYiP/++4//u0WLFlJnm7l58ybGjRuHJ0+eSO3HxsYGPj4+Kt9vEyd63+rWrYtjx44p9LnUbXYOgGbXJIQQQgghhHx5KMGdEEIIIYSUSrwqjiIrfjHGUFhYqLD+y+PFixewsrLiKzbr6+tj/PjxaNu2LYqKinD16lX89ddf4DgOWlpamDp1Kvbt2wfGGCZOnIgdO3bA0NBQos+srCzMnDmTvwDDGENgYCAcHR1VsYqEEBmlpaVh/fr1OHLkCFJSUj7bvnbt2pg4cSIWL16MmjVrKiFCQtRPfn4+qlevDoFAgFq1aiE5OVnVIZEKEggEWLt2LXbv3o309HSJ+2rXrg03NzfMmTNHofuOivDq1Su0adMGaWlp/P5vgwYN0LNnT1hYWGDt2rVgjMHExASzZs1Ceno64uPjceXKFT65VUtLC66urqhduzYAYMWKFSpeK9m4ublh06ZNfDKbtAEM4reLJ8VZWVnBz88PLVu2LNFvQkICoqOjARTP+OHk5KSwdZDF48eP8d133yElJYXfpz9+/DhGjRrFt3n58iUcHBzw4sUL/nNhZ2eH4OBgGBgYqDB6xYmJicH169f5gQw5OTkwMzOTOquDJqtXrx7evHmDKlWq4N27dyWO3e7evYu2bdvyn3M3NzesWLECurq6AIqPF0ePHo2oqCgAgI6ODl6/fs1//9VdeHg43N3d4evri7y8PACS3/nGjRtj8uTJmDRpEiwsLFQVJiknRRQlkIYS3DVPZGQkXFxcEBcXB6BkQrs40TahWbNm8PDwQOfOnZUSY3lQgnv55efn4+zZs7hw4YLEIPbevXujf//+ardP/6W/bzS7JiGEEEIIIeRLQwnuhBBCCCGkVB9X/VLUrqM6XhTduHEjFi9eDACoUaMGLl26hNatW0u0uXjxIvr164fCwkLo6uqioKAAXbt2xaVLl0rtl+M42Nvb4+rVq2CMYdasWdixY4ciV4UQImcFBQWIjIxEVFQU4uLi8P79e2RnZ8PQ0BAmJiZo1qwZOnbsCDs7Oz4JipAvWYMGDfDq1StoaWmhoKBA1eEQOSksLMTNmzclEmHatWvHJ9WdOHECY8aMUWWIMpkxYwY/WFFbWxtbtmzBzJkz+fUpLZkmPz8fnp6ecHNzQ2ZmJiwtLXH27Fm0atVKVatSIdevX8fSpUtx6dKlUmcvEle7dm24urpiyZIlGlnN/NatW+jevTtftV9PTw/nzp1Dr169kJiYiG7duuHp06f8utva2iIkJARGRkaqDl2uMjMzsWvXLuzbtw+vX78ucb+0JDKBQIAePXogNzcXOjo6uHjxIoyNjZUVcoU5OzvDy8sLjDEcPnxYalXi7t27Izw8nD8mNjY2RrNmzZCXl4eYmBgIhUJ+MPjo0aNx/PhxZa+GTJKSknD48GF4enri6dOnACSP8fX19TF06FC4uLige/fuqgqTyEF4eLjSnksdKxyTzzt79iy8vb0RERGBN2/elLi/evXq6NatG8aMGYMRI0aoXZLzxyjB/ctB7xshhBBCCCGEfFkowZ0QQgghhJTqS676NWjQIPj7+4Mxhg0bNuCXX36R2m769OnYv38/gOL18Pf3R9++fT/Zt7+/PwYNGgTGGDp37owrV67IPX5levDgAV69eoX09HTk5eXJNBBiwoQJCoyMEEKIOmjSpAni4+PV8vf+S/Tnn39KVKhWBB8fH4wdOxb5+fkKfR55KSgoQI0aNfgqxlu3bsXPP/8s0eZzyTRPnz6Fvb09kpKSYGlpiXv37mlUsu/Hnj59isDAQNy4cQPx8fFIT0+HQCCAsbExTE1N0aZNG9jZ2aF3794aP5jr4sWL6N+/PwoKCsBxHAwNDXH8+HEsXLgQDx8+5JPb27Rpg9DQUNSoUUPVIctVWFgYnJ2dkZiYKFGdX3yfvkmTJlI/9y4uLvD09ARjDPv374eLi4vS4q4o0TEZAFhbWyMmJqZEmwcPHqBLly7IyMgAAImZzUTLHMfBwsICd+7cgZmZmfJWoIyEQiHOnTsHDw8PnD9/nv8dFn9/W7dujSlTpmD8+PEwMTFRVaiEEBV59+4dUlJSkJGRAQMDA9SqVQt169ZV2jlBeaAE9y8HvW+EkI81btxYLv1oa2vD2NgYNWrUQJMmTdC+fXv07t0b9evXl0v/hBBCCCGkfCjBnRBCCCGElOrIkSNKey5nZ2elPVdZiCfjPX/+HF999ZXUdpcuXUKPHj0AFF9kycjIQLVq1T7Zd3Z2NqpXrw4AMDc35yufapIrV65g586dCA4ORlZWVrn7oURHQgghRLmqVKmCgIAAfv9F3vz9/TFs2DAUFRVpzO98ZGQkvvvuOzDGYGlpye8DiitLMk14eDi6d+8OxhhcXV2xZ88eZYRP5ODUqVMYO3YsOI6TSFwW/d+yZUuEhoaidu3aqg5VrgIDAzF48GB+dg3RpQJdXV2YmJjg3bt3AEpPcL9y5Qrs7e3BGEOfPn1w7tw55QVfQYWFhfjvv//4v1u0aAEdHZ0S7W7evIlx48bhyZMnUvuxsbGBj48PrKysFBZrebm5ueHo0aNISkoCIJnUbmRkhDFjxsDFxQW2traqCpEQokAvXryAv78/bt26heTkZGhra8PMzAydO3fGgAEDYGpqquoQ5Ua0n2ZiYoKffvqpTI9ZuXKlzI8BgF9//bW8YRI5oAR3QsjHRNsFeaQ9STsP0LdvX2zcuBFNmzatcP+EEEIIIUR2lOBOCCGEEEKIFCYmJsjIyICxsTHS09NLbZeSkgIzMzMwxmBmZiZ1amdpzM3N8e7dOxgYGCA7O1tOUStefn4+Zs2aBQ8PDwAotcqj+Mngjw85xBOGNCXxjRBCCKkstLS0YGxsjNDQULRt21aufQcGBmLIkCEQCAQa9Tvv6ekJFxcXMMYwY8YM7Nq1q0Qb0UVzS0tLPHv2rNS+HBwcEBERgerVq+Pt27fQ09NTZOhEjvbs2YPZs2eX2K/95ptvEB4ejjp16qgwOvlLSEhA8+bNkZOTA8YYtLW1MXnyZEyaNAnt2rWDjo7OZ5PIhEIhzMzMkJaWBiMjI6SlpUFbW1sFa6NY+fn5OHv2LC5cuMAPTm7QoAF69+6N/v37l0iEURfSkn3s7Ozg4uKCkSNHwsDAQIXREUIURSAQYN68eTh48GCp+2JVq1bFggULsGLFCiVHpxii7Z0sxM9nyUJT9m8rK0pwJ4R8THzGEWnbdGnpUB+3k3b9Qvx2AwMDeHh4KHw2PEIIIYQQUlLJkiSEEEIIIYQQZGVl8ZWcPkX8fkNDwzL3b2hoiHfv3iE3N7fcMaqCs7MzTp06VeKkryhhXbRc2klh0f2EEOVbtWqVwp9DS0sLhoaGMDIyQuPGjWFjY4OaNWsq/HkJIbLJzMxEnz59EBkZKbeqwyEhIRg6dCjy8/Pl0p8ypaWl8cvNmzeX2kZbWxtCoRACgeCTfTk6OiIiIgKZmZmIiIhAz5495RorUZyZM2ciOTkZq1at4pOCraysEBYWVumS24Hi6t6i5PaaNWvin3/+QYcOHWTqQ0tLC506dUJAQACys7Px4MEDtGzZUkERq46enh6GDx+O4cOHqzqUcmGMwdzcHM7OzmjWrBkAwMfHR67PMWHCBLn2RwgpH4FAgN69e+Py5culJnBzHIecnBysWrUKDx8+hLe3typCVbnyJMSr64AmQgj5kk2YMAGMMeTm5sLHx0fi2oS5uTlatGgBExMT6OvrIzMzEwkJCYiNjeXPXTDG0Lx5c3To0AECgQBpaWmIjo7mZ0FijCEnJwfOzs6wsLCAvb29ytaVEEIIIeRLRAnuhBBCCCGESCEUCsEYg66u7ifbiVcolOVClyZeFDt58iT+/PNPPvbGjRtjyZIlcHJygrm5OfT09MAYQ5MmTfDo0SOkp6cjPj4eFy5cwL59+/DixQtoa2tj3bp1WLBggYrXhpAvj2gKdmXr3Lkzpk+fjnHjxin9uQkh0jHGkJycDCcnJ0RGRlY4eTciIgKDBg1CXl4eGGNgjGHy5MlyilbxCgoK+OXSBiwaGhoiIyMDqampn+yrXr16/HJCQoJ8AiQVVtZBXlpaWqhTpw7evn0Lxhjs7e1x8OBBmZ7r119/LU+ISvXhwwecPn2a//v06dMyJ7eLtGrVCgEBAQCAhw8fVsoEd03HcRySkpKwYcMGhT0HJbgToh5WrFiB8PBwfn/sUwUIOI7Dn3/+ie7du2Pq1KmqCFeuqJiCdBzH4dKlS7h+/Tri4uKQnp6O7OxsmV4vxhhCQkIUGCUhhJTf4cOH8fjxYwwbNgxCoRB6enqYOXMmpk6dyg/u/JhAIMDZs2exfv163L17F48ePcLYsWOxdOlSvs2dO3ewZs0a+Pn5gTGG/Px8TJ8+Hf/995+yVo0QQgghhIAS3AkhhBBCCCFl9Pvvv/PL7du3R3BwMKpXr15q+xo1aqBt27Zo27Yt5s6di3nz5mHv3r1YvHgxXr9+jW3btikjbELkJisrC6dPn8b169fx4MGDcl8Yfvr0qQKjVA/ir0lkZCSuXbsGLy8v+Pn5oWrVqiqMjBCyaNEibNiwAYwxPHv2DL1790Z4eDiMjY3L1d/169fRr18/vhI0AIwbN07mpGBVEl/3zMxMqW1MTEyQkZGBwsJCJCYmSiSyixOvYP/27Vv5BkrKrTyDvDiOw+HDh2V+Lk1IcL98+TIEAgEYY+jVq1eFqhDWrVuXX37z5o08wiNyJp7MKu9+qaIxIeojJSUF27dvl/jOd+3aFaNGjYKVlRUKCwsRFxcHLy8vREdH89/hFStWYMqUKdDS0lLxGpRfWFiYqkNQSwcOHMCqVasq9PtM23lCiLoTzVD37Nkz1KtXD+fPn//soFt9fX2MGDECw4cPxy+//IJt27bh119/hZGREX766ScAwLfffovTp09j7dq1WL58OQAgLi4OZ86cwaBBgxS+XoQQQgghpBgluBNCCCGEEEI+KzExEdHR0QCKq9Z7e3t/Mrn9Y3p6eti9ezeKioqwf/9+7Ny5E46Ojujbt6+iQiZEbgoKCrBmzRps2bIFubm5EveVNVFIXRKAVFHVTrTuFy5cwIQJE/DXX38pPQZCyP+sX78e7969w6FDh8AYQ3R0NAYOHIigoCDo6+vL1NetW7fQp08fZGdn89/1ESNG4MiRIwqKXjEaNGjAL7969Upqm6ZNm+L58+cAgGvXrmH48OFS2929e5dflvX1VBeiKp8VGcyl6VU+y/N7rQ6/82X18uVLfrlXr14V6kt8gEh2dnaF+iLyp8h9P6qWTIh68fb25gfaMcawc+dOzJo1S6JNv379MH/+fPzyyy/YunUrgOIBeYGBgRp9fsbBwUHVIaiVwsJCjBkzBr6+vvy2WlGDnVShMqwDIUR+fvnlF8THx0NHRwd+fn4yzSjFGMOWLVvw5MkT+Pv7Y+HChejfvz8aN27Mt1m6dCkuXbrEH+MGBgZSgjshhBBCiBJRgjshhBBCCCHks65fvw6g+KSvk5MTrKysSm37qQtNmzZtwunTp5GSkoIVK1Zo9AVU8mXIz8/HoEGDEBwcXKGLqOpwAVZZVe3y8vKQmZmJ+Ph4XL58GcHBwRAKheA4Dr6+voiMjISdnZ1SYiGESHfw4EGkpKTA398fjDFERERgzJgxOH36dJkTdO/duwcnJydkZGTwye2DBg2Ct7e3xiT5ilhbW/PL//77r9Q27dq1Q1BQEADA09NTaoJ7SkoK/vzzT/5v8cR5TXDo0CGsXLmy1CT/slDnJG91+C1WF2lpafyymZlZhfoSCAT8sq6uboX6UoTLly8r7bkqUglfEaiiMSFflitXrgAoPm/zww8/lEhuF7d582bcvHkTERER/GPp/EzlsXLlSpw+fRrA/waccxyH+vXrw9zcHNWqVVNxhOX37NkzAOq5z0EIUb7s7Gz+HISTkxNsbW3L1c/y5cvh7++PgoICHDp0CKtXr5a4f+7cuXyC+7Vr1yocNyGEEEIIKTtKcCeEEEIIITI7evSoXPrR1taGsbExatSogSZNmsDCwkIu/RL5S0pK4pc/l5ial5dX6n2GhoYYMmQIDh48iNu3byM+Pl6iIgoh6mb16tUICgoCY4y/MNygQQN07twZ9erVg6GhoapDLDNVVLVbtGgRYmJi0KdPHyQmJgIAjh8/TgnuhKiYlpYWTp06BUdHRz4Z6syZM/jxxx9x4MCBzz4+NjYWjo6OeP/+Pb9t7Nu3L/766y9oa2srOny5a9y4MSwsLPDmzRtERkYiOzu7xPZ9+PDhWLduHQAgKCgIy5Ytw4oVK/jkmhcvXmD06NFIT08HULyf27VrV6WuR0VMnjwZR44ckajyWZkSwinRV1KNGjX45YyMjAr19fbtW365du3aFepLEbp166aUQReMMRQWFir8eWRBFY0J+bKIZt0DihPxPuenn37iE9zFH0s0W1ZWFrZs2cL/9hkZGWHZsmWYMGFChQe1qQNNG0BKCFGs8PBw5OTkgDGG7t27l7sfW1tbGBoa4sOHDwgMDCyR4O7g4ABtbW0IhUKJ6ySEEEIIIUTxKMGdEEIIIYTIbOLEiQpJErCwsEC/fv3w448/om3btnLvvzzS0tKwatUqhbTVJOKJL+bm5lLbVK1aFbm5ucjOzv5kX61ateKXb9++TQnuRG3l5uZi27Zt/PauXr16OHDgAHr37q3iyDRLy5YtsXPnTgwbNgzA/yoLEkJUS19fH/7+/ujatSv+++8/cBwHDw8PmJmZYc2aNaU+7tGjR+jVqxdSUlL4JOhevXrB19cXOjqae6qxV69e8PLygkAggJ+fH3744QeJ+9u0aQMHBweEh4eDMYb169djz549aNasGfLy8hATEwOhUAigONF1xIgRapnsK8327dtx+PBhAP9LbK9SpQpsbGw0bjBXaSjRV5L4/vytW7cq1FdUVBS/3LBhwwr1pUiVacAGIYRIk5qaCgAwMDCAjY3NZ9uLDzpOSUlRWFxEuS5cuMDPrqKvr4+wsDB8++23Ko6KEEIUQ3z2sVq1alWoLxMTE2RnZyMhIaHEfdWqVUPNmjWRnJzMD2onhBBCCCHKoblXnQghhBBCiMqVliTwcfL7p9qJ3/f69Wu4u7vD3d0dkyZNwo4dO1Q+be779+/x22+/fbYdx3FlbquJqlSpwi+Lkrc+ZmRkhNzcXGRmZiI3NxdVq1YttZ3Imzdv5BsoIXJ06dIl5OTkAChOEggLC4OVlZWKo9JM/fr1g76+PgQCAV6/fq3qcAgh/6969eoICgqCnZ0dXr58CY7jsH79epibm2PWrFkl2sfHx6Nnz55ISkri9+McHBxw9uxZ6OnpqWAN5Gf48OHw8vICx3HYsGFDiQR3ANi7dy+6dOnCD/zLyMjAjRs3ABTvC4r2gc3NzbFt2zblBV8BBQUFWL16NR+7sbEx1q9fjwkTJsDAwEDF0RFFsbOz49/zc+fOISsrS2IfvaySk5Nx8eJFAMXHC507d5ZrnPJU2WYlIISQj2VkZIAxVuYBduLVvDMzMxUVFlGyFy9eACj+3Rs3bhwltxNCKjXxZHPxmaVkxXEcP9irtN9E0fGxMmaHIoQQQggh/0MJ7oQQQgghpFxEyQHiJ/REt5Ul8Z3juFLbcRyHQ4cOIS4uDqGhoWqfMPUlnNQUv0BaWvX5evXq4d27dwCAmJgYtG/fXmo78eTWDx8+yDFKQuQrPj4eQPF3fPTo0ZTcXgF6enowMzPDq1evqNIRIWrGwsICwcHB+O6775CSkgKO4zBnzhyYmppi1KhRfLuXL1+iZ8+eSExM5BNF7ezs8M8//0gMhNNUffr0wZ07d/i/CwsLS1Skb968OYKCgjBu3Dg8efIEgOR+L8dxsLGxgY+Pj0TSmDq7cuUK3r9/DwDQ0dFBcHBwqftwpPKoU6cO2rZti1u3biE9PR0LFy7EH3/8IXM/y5YtQ35+PhhjcHR0VPvjNgsLCzg7O6Np06aqDoUQQuSusLAQjDHo6uqWqb22trbEY0nlkJ+fzy936NBBhZEQQoji1alTh18OCgrCokWLytVPREQEcnNzwRgr9VheNNC9MsxwRgghhBCiSSjBnRBCCCGEyOzZs2cAAC8vL6xcuRJCoRBVq1bFgAED0KNHD7Ro0QImJibQ19dHZmYmEhIScPv2bfj4+CA2NhaMMbRs2RIeHh6oXr060tLSEB0djYCAAJw7dw5AcYLQtWvXMH/+fOzatUsl60kV/v7nm2++4ZdjYmKktmnZsiWfGBYYGFhqclRAQAC/XNGpQwlRpOzsbH7Z1tZWhZFUDrq6uuA4rtRZIAghqvP1118jICAA3bt3x4cPHyAUCuHs7IxatWqhV69eSExMRM+ePfHixQs+ud3W1hbnz5+vNFW+dXR0YGNj89l27du3R0xMDM6ePYsLFy4gMTERANCgQQP07t0b/fv316jBjw8fPgRQPJhr2LBhlNz+BVm8eDFGjBgBADhw4ACqVauGjRs3QktLq0yP37x5Mw4ePMj/7ebmppA45SkpKQkbNmxAly5d4OLigpEjR1aKATqEEEKIiIWFBb+sSfukhBBSHqJjeI7jEB4ejuDgYDg5OcnUB8dxWLZsWYk+xSUnJyM9PR2MMdSvX79iQRNCCCGEEJkwjrJ2CCGEEEJIOSxfvhzr1q0DAIwePRrbtm0rU6VKX19fzJw5E+/evUPdunURGRmJr776ir//7t27GDlyJJ4+fQqO46Cnp4fHjx9LtFGG8PBwpT2Xg4OD0p6rvHJyclCjRg0UFRWhcePGePz4cYk2x44dw4QJEwAUV3y/e/cu6tatK9Hm8OHDmDx5MoDiC21RUVGUOEzU1pEjRzBp0iQwxnDw4EH+s0sIIZXVxYsX0b9/fxQUFIDjOBgaGuL48eNYuHAhHj58yCe3t2nTBqGhoahRo4aqQyYV9Pvvv2PJkiVgjGHnzp2YOXOmqkMiStSzZ0+EhYXxCXBff/015s6di379+qF+/frQ0tICYwxWVlZ49OgR8vLycOnSJWzduhUhISHgOA6MMYwfPx5HjhxR8dpIt2TJEhw5cgRv3rwBIJnsZ2xsjLFjx2LKlClo27atqkIkhBC5+HibrajHEPX28OFDNG/eHIwxzJ8/Hxs3blR1SIQQolBNmzbFkydPwHEcjI2N4ePjg169epXpsbm5uZg0aRJOnToFoPhYwcvLC2PHjpVod+bMGQwZMgSMMYwcORInTpyQ+3oQQgghhBDpKMGdEEIIIYTIzN/fH4MGDQJjDDNnzsTOnTtlevyTJ0/QqVMnpKWloUOHDrh69arE1MjPnz9Hu3bt8P79ezDGsH79eixcuFDeq0FkZGdnh+vXr4Mxhlu3bqFNmzYS92dnZ6N+/frIysoCx3GoU6cO5s6dCxsbG+Tl5cHf3x9HjhyBUCgEx3Fo1KgRnjx5QhWliNq6fv067OzswBjD8uXLsXLlSlWHRAghCnfq1CmMHTsWHMfxyavi/7ds2RKhoaGoXbu2qkOVm5iYGNy6dQvJycnQ1taGmZkZOnfujMaNG6s6NIXz9PSEi4sLGGNwd3fHpEmTVB0SUaL09HTY2dkhLi5O4rsOAEZGRsjMzARjDDo6OqhevTrev3/Pz8Qiuqxga2uLiIgI6Ovrq2w9PqeoqAjnzp2Dh4cHzp8/j6KiIv4+0fra2NjAxcUF48aNQ/Xq1VUVKiGElBsluBOR9u3b49atW2jcuDEePnwocc6VEEIqGz8/PwwbNkzieGb48OGYMmUKHBwcpB6nvHjxAqdPn8b27duRmJjIP87Gxgb//vtviVmtxo0bhxMnToAxhm3btuGnn35S1uoRQgghhHzxKMGdEEIIIYTIrE2bNoiOjoalpSUePXoEPT09mfvYt28fZsyYAcYYTp06hWHDhkncv2bNGvz6669gjOH7779HQECAvMIn5SRe4XPatGn4448/SrTZvHkzFi5cWCJBRkR0+FFaNRRC1E2TJk3w7Nkz2NraIioqStXhEEKIUuzZswezZ8/mf89FvvnmG4SHh6NOnToqjE5+/vrrLyxduhRPnz6Ven/btm2xadMmdOvWTbmBKVF4eDi6d+8OxhhWr16NJUuWqDokomSpqalwdnZGQEAAv+8uvs8uIu0ywsCBA3Hs2DEYGhoqJ1g5ePPmDTw9PXHo0CHEx8fzt4vWtUqVKhg2bBifEEMIIZpClKxuYmJS5sS7lStXyvwYAPj111/LGyZRgrCwML568a+//ooVK1aoOCJCCFGsWbNmYe/evSWuSWhra6NRo0YwMTGBvr4+srKykJCQgLS0NACQGMxvamqKsLAwWFtbS/SdmJgIKysr5OfngzGGR48ewcrKSunrSAghhBDypaIEd0IIIYQQIpPo6Gi0adMGjDHMnTsXmzdvLlc/mZmZqFmzJjiOw8CBA+Hn5ydx/6NHj9CsWTMwxmBpaYlnz57JI3xSAc+fP8fcuXMBAPr6+jhy5EiJCigcx+GHH36At7e31AQZ0fKCBQuwYcMGJUZPSPl4eHhg6tSpYIzBz88PAwcOVHVIhBCiFCtXrsSqVav4328rKytcvnwZFhYWqg5NLhYtWoTNmzdLTdoVp6WlhZ07d2LGjBlKiky5hEIh6tevj7dv36Jbt24ICQlRdUhERU6ePInNmzfj9u3bn23bvHlzLFu2DGPGjOFvO3HihMTfmiA0NBQHDhzAmTNnIBAI+NtFxzFWVlaYMmUKnJ2dYW5urqowCSGkTEQJ7rKQNqCpLMRnwiDqadu2bZg/fz4YY/jll1+watUqtZ5thRBCKsrNzQ2bNm3if9vEj/WlDdwVv3ZhZWUFPz8/tGzZskS/CQkJiI6OBgDo6urCyclJYetACCGEEEJKogR3QgghhBAiE29vb4wfPx6MMezbtw9Tp04td1/16tXDmzdv0KRJE6lTIRsZGeHDhw8wMjJCRkZGRcImSubp6YmtW7ciNjZW4vZvv/0Wy5Ytw5AhQ1QUGSGyGzNmDP78808YGxvD398fXbt2VXVIhBBSbqtWrSpz2z/++ANv374FYwwTJ05EgwYNZHouda3uefToUUycOBEASgzIExG/XUtLC2FhYZV2+79hwwa4ubmBMYYrV66gc+fOqg6JyNGff/6JUaNGlbl9XFwcIiIicOfOHaSkpCAjIwMGBgaoVasWrK2t0bNnT9jY2Eg8xsfHB2PHjkV+fr68w1eKtLQ0HD16FJ6enoiJieFvF6/82K9fP7i4uKBv374yJ4ISQogylCfBXVaiSreU4K4Zjh8/jpkzZyIrKwu1atXCiBEj0LFjR5ibm6NKlSoy9WVvb6+gKAkhRH6uX7+OpUuX4tKlS1KP8T++rXbt2nB1dcWSJUtk3i4SQgghhBDloAR3QgghhBAik02bNmHRokVgjGH37t2YPn16ufsyNzfHu3fvYGBggOzs7BL3N2zYEC9fvoSOjo7GJkt86d68eYPExEQAgKWlJczMzFQcESGyKyoqwk8//YQ//vgD2tramDhxIiZPnoz27dtDR0dH1eERQohMZE1+Km9lT0A9q3sKBALUr19fYkpyY2NjODk5wcrKCoWFhYiLi0NISAjy8vL4i+A2Nja4c+eOiqNXDI7j0LNnT1y6dAl169ZFWFgYvv76a1WHReSkSpUqCAgIQI8ePRTSv7+/P4YNG4aioiK1/M7LKioqCgcPHsSpU6ckjlFF20ALCwvMmzcP8+bNU1WIhBAilZaWllKehxLcNUdmZibc3Nzwxx9/ACjf/rzocYWFhfIMjRBCFOrp06cIDAzEjRs3EB8fj/T0dAgEAhgbG8PU1BRt2rSBnZ0devfuDV1dXVWHSwghhBBCPoES3AkhhBBCiEx27dqFn3/+GYwxTJ8+Hbt37y5XP8nJyTA3NwfHcTAxMUFqamqJNqIK74aGhsjMzKxo6IQQIrPGjRvzyy9fvoRQKOQvCuvq6qJmzZoyVfhhjOHp06dyj5MQQsrqS6/uefLkSYwdO5Z/DUaMGIF9+/ahRo0aEu0SExMxbtw4XL58GUDx9jsqKgq2trbKDlkpsrOzMWbMGPzzzz+oVq0aFi9ejMmTJ8PCwkLVoZEK0tLSgrGxMUJDQ9G2bVu59h0YGIghQ4ZAIBCo7Xe+vD58+ABvb2+4u7vj5s2bEve1adMGt2/fVlFkhBAiXXh4uNKey8HBQWnPRconNDQUY8eORXJyssTtsqQFiAZ6VrbfeEIIIYQQQgghmoMS3AkhhBBCiEzOnDmDIUOGAADMzMzw+PFjGBkZydzPhg0b4ObmBsYYWrduXaIiZlFREapVq4aCggI0aNAA8fHxcomfEEJkUVoiqKyH0nRhmBCiLr706p5TpkzBoUOHwBhD586dERERUWrC/4cPH2BjY4P4+HgwxrB69WosWbJEyRErnqiyt1AoxJUrVyQGc1laWsLCwkLmwVwhISEKiZXITvSdNzU1RWRkJKysrOTSb0hICAYMGACBQFDp93H8/PwwadIkZGVlAQBsbGwowZ0QQojaunHjBhwcHCAQCAD873xEeVXm33hCCCGEEEIIIeqN5lInhBBCCCEy6dGjB/T19ZGfn4/k5GS4uLjgxIkTMiVL3bhxA6tXr+YTZ3r37l2izX///Yf8/HwwxtCwYUN5hU8IITKTx7hwGltOCFEXYWFhqg5BpcQHVa5YseKT1eyrVauG+fPnY+bMmQCAu3fvKjo8lbh06ZLE6yCeBPXixQu8fPmyzH2JEp2JemGMITk5GU5OToiMjESdOnUq1F9ERAQGDRqEvLw8MMbAGMPkyZPlFK16EAgE8PHxgbu7Oz+TAyGEEKIJfvzxR352FY7jYGpqimHDhqFdu3YwNzdHtWrVVB0iIYTITUZGBqpXry63/k6cOIExY8bIrT9CCCGEEFIxlOBOCCGEEEJkYmRkhDFjxuDw4cNgjMHHxwfv3r3Dnj17YG1t/cnHFhYWYu/evVi2bBlyc3PBcRz09fUxZcqUEm0DAgL45bZt28p9PQghpCycnZ1VHQIhhMiVg4ODqkNQqdevXwMAdHV1y/RaODo68suJiYkKi0sdlDYYiwZpabZFixZhw4YNYIzh2bNn6N27N8LDw2FsbFyu/q5fv45+/fohJyeHH8wwbtw4HDx4UJ5hq8ydO3fg4eEBb29vZGRkAJD8Dpibm2PcuHGqCo8QQgj5pP9r777Do6rW9o/fK4VQEloIECkBQpEqUkQjECABBQUEjyCoh3qODSx4RLCAoliwooIVVKQoIKIISAktVDF0pZeAFCGkkJBGkv37g1/2m5gAmWSSCfD9XFcu12Q/e82z3zlv2DNz77UjIiK0fft2+9/ogQMH6uOPP1bp0qVd3BkAFI6ePXtq2bJl8vT0LPBc8+bN04ABAwi4AwAAFCPG4hsKAAAAOOjs2bNq1KiRoqKiJP3fSo1t2rRRp06d1LhxY1WoUEFeXl6Kj4/X0aNHFRERoQULFigmJibbyo5jx47VmDFjcjxHo0aNtGfPHhlj9PPPP+uuu+4q0mMEAADAtadUqVJKTU3VDTfcoGPHjl2xPiUlRaVKlZIxRo0aNdLOnTuLoMui1aFDB6evun693ymguBkyZIi++uor+3Vu166dlixZIi8vL4fmiYiIUGhoqOLi4uxVYe+77z599913V/XK/efOndP06dM1ZcoU+04NWb82cXd3V9euXTV06FDdddddcnd3d1GnAABc3qeffqrHHntMxhg1adJE27Ztu6r/jQaAK3Fzc1OfPn303XffFWieRYsWqVevXkpLS1N6erqTugMAAEBBsYI7AAAAHObr66uwsDCFhITozJkzdrhh06ZN2rRp0yX3ywwJZNY/8sgjuYbbN23apDJlyqhly5by9PTMtnImCl+nTp2K5HmMMQoLCyuS5wIAAJAuBtaNMSpVqlSe6rMGgFNSUgqrLZdatWqVq1tAIfviiy8UFRWlBQsWyBij8PBw9evXTz/88EOeQ2/bt29Xly5dsoXbe/bsqZkzZ161wbnVq1fryy+/1Lx585ScnCwpe7C9Tp06Gjx4sAYNGiR/f39XtQkAQJ7FxMTY4z59+ly1/0YDgCPmzJmj6tWr65133snX/suWLdO//vUvXbhwgb+bAAAAxQwBdwAAAORLkyZNFBERoYcffliLFy/Otu2fNwnK/FAwMwhRtmxZvf322xo6dGiuc7dp00abN28unMZxRatWrSr0D3KzruIPAAAAoPC4ublp9uzZ6ty5s9auXStJ+umnn/Twww/r888/v+L+f/75pzp37qyYmBj7PV23bt00Z86cq24181OnTunrr7/W1KlTdfDgQUnZ3796eXmpd+/eGjp0qDp27OiqNgEAyBc/Pz97XK1aNRd2AgBF6/3331eNGjX05JNPOrTfmjVrdM8999gXtFeoUKEw2gMAAEA+ubm6AQAAAFy9qlevroULF2rNmjV68MEH5evrmyPcLl0MDLi5ual58+Z69913dejQoUuG21F8WJaV7QcAAADA1cnLy0sLFixQkyZN7JD6lClT9OKLL152v3379ik0NFRRUVH2fqGhoZo3b548PK6O9XMyMjL0888/q2fPnqpZs6ZeeOEFHTx4MNv7nGbNmmnixIk6ceKEZsyYQbgdAHBVCggIsMdZV3MHgGvVk08+aZ/XP/PMM5o7d26e9924caPuvvtuJSUl2Qsz/XMxJwAAALiWsUiqAAAAwIkOHz6sQ4cOKTY2VikpKSpbtqz8/PzUtGlTlS5d2tXtIQ/c3Nzs1dUrVKggHx+fQnuuw4cPF9rcAAAA/5R5nhMYGKh9+/YV2j5AcXXy5EkFBQXp6NGj9l2VJk6cqGHDhuWoPXTokIKDg3X8+HE73B4cHKzFixerZMmSLujecaNHj9a0adN06tQpSdlXa/fx8VG/fv00dOhQtWrVylUtAgDgNKmpqfL391dsbKx69uypefPmubolACh09913n3744QdJUsmSJbV06VK1bdv2svts2bJFISEhiouLkyR5e3tr8eLFuv322wu9XwAAAOQdAXcAAAAA2WSGuCzLUsmSJdWrVy8NGTJEnTp1cnVrAAAABZJ5nnPDDTdoxowZedqnQ4cODu8jSe3bt89vm0Ch2r9/v9q2bauoqCj7blszZsxQ37597ZqjR48qODhYkZGR9nuDoKAgLV269Kq6cDnre5tMQUFBGjp0qPr06XNVHQsAAHnx/PPP680335SHh4e2bdumRo0aubolAChUqampCg0N1dq1ayVdXLRn3bp1uvHGG3Ot37lzpzp27Kjo6GhJUqlSpbRgwQK+/wAAACiGCLgDAAAAyKZ06dJKTk6WJHsld0mqU6eOBg8erIEDB8rf399V7QHFwu7du/XXX38pNjZWycnJcuSt9b///e9C7AwAcDlZ71STV5l/4x3ZzxijtLQ0h54HKEoRERHq2LGjzp8/L8uyVKJECf3yyy8KDQ3V8ePH1aFDBx08eNAOh7dq1UphYWGFenenwpD1/+erVq2qAQMGXDLoUlCc4wEAioMLFy4oKChIERERqlu3rlasWKHq1au7ui0AKFSxsbEKCgrS3r17ZVmWAgICtGHDBlWtWjVb3Z49e9ShQwedPn1akuTl5aV58+apa9eurmgbAAAAV0DAHQAAAEA2cXFxmj59uqZOnaqtW7dm22aMkbu7u+68804NHTpUd999t9zc3FzUKVC01q5dqw8//FBLly5VfHx8vudJT093YlcAAEdkDbvm9WPRrMF2R/a52v/enz9/XidOnMjXxVysXn91WL58ue6++25duHBBlmXJ29tbM2bM0MiRI7V371473N68eXOtWLFC5cuXd3XLDsu6grujF7c46mr//3kAwLUjJiZG999/v5YtW6by5cvr+eef17///W9VrlzZ1a0BQKGJjIxUUFCQTp06Zb+PWbNmjby9vSVJBw8eVHBwsE6cOCFjjDw8PDR79mz17NnTxZ0DAADgUgi4AwAAALikLVu26IsvvtB3332nuLg4+/eZ4ZAqVapo4MCBGjJkiAIDA13VJlCoUlNTNWzYME2ZMkVS9pV8s76lvlwAMmuwivATALhOUV2Yd7X+vY+MjNSkSZO0ePFi7dmzRxkZGQ7Pwer1V5fZs2erf//+sizLPlfJ+t8mTZpoxYoVqlSpkqtbzZd/3rXB2V+HcI4HAChuOnXqJOnihVdr167NdpFXQECAqlatqpIlS+Z5PmOMwsLCCqVXAHC2bdu2KTg4WAkJCZKkkJAQLVq0SCdOnFD79u119OhRSZK7u7umT5+uvn37urJdAAAAXAEBdwAAADiFZVk6cuSIYmNjlZCQ4HBwgFUei7ekpCTNnj1bU6ZM0dq1a7Nty/ySrH379vrPf/6je++9V15eXq5oEygU/fr10+zZs3P9u3a5VYBzC1MRfgIA13rllVeK7LnGjh1bZM/lDK+//rpefvll+9+p/H5szL91V59JkyZp+PDhOS7eq1+/vlavXq0qVaq4sLuC4aIWAMD15p8Xd0m65MX5V8JFXACuRkuXLlX37t3tC6979+6tbdu26eDBgzLGyBijKVOmaMCAAS7uFAAAAFdCwB0AAAD5lpqaqmnTpmn69On6/ffflZSUlK95WOXx6rJ371598cUX+vbbb3XmzBn795lfkJUrV04PPPCAhgwZoubNm7uoS8A5vvvuO/Xv398OfNWpU0fPP/+8unTpoqpVq6pEiRIyxigwMFD79u1TbGysDh06pGXLlunTTz9VZGSk3N3d9frrr+vZZ5919eEAAJCrESNGaOLEidlW7s7ExVzXh5dfflnjxo2zX//AwECtWbNG/v7+rm6tQFavXl1kzxUcHFxkzwUAwKVkBtydFQHg3A7A1eibb77RoEGDcryfNcZo8uTJevjhh13ZHgAAAPKIgDsAAADyZevWrerXr5/2798vKX8rPHI796tbWlqa5s+frylTpmjZsmXKyMiwt2V+cHzzzTfrP//5j/r166eyZcu6qlUg35o3b64dO3bIGKNWrVpp6dKlKleunL0984vjzIB7VqmpqRoxYoQmT54sY4yeeOIJvf/++0V9CAAAXNaSJUvUtWtX+9y8fPnyGjZsmH0xV/369WWMUUBAgFasWGFfzLV8+XJNnz5dCQkJcnd31/jx4+3buwcEBLj4qJBp3Lhxea795JNP9Pfff8sYo4EDBzr8Oo4ZM8bR9gAAgJN16NDBoVXa82LlypVOnQ8AisJrr72mMWPGZLvo5/3339eTTz7p4s4AAACQVwTcAQAA4LB9+/bptttuU2xsbK6rPDqKgPvV79ixY5oyZYq++uorHTt2LNs2Y4xatmyp3377zUXdAflz/Phx1ahRQ5Lk4eGh3bt3KzAwMFvN5QLumR599FF99tlnMsZowYIF6tatW6H3DgBAXrVv315r1661/z0LCwuz//2TLv9v3dmzZzV48GAtWLBAxhhNmDBBzzzzTFEfAi4j8/XLq6wrGzqK93QAAAAAipNHHnlEn3/+uYwxGj9+vEaNGuXqlgAAAOAAAu4AAABw2J133qmlS5fawfaAgAANGDBAQUFBqlatmry9vR2ek1Uerw2WZWnJkiX68ssvtWDBAqWlpcmyLDVv3lxbtmxxdXuAQ3744Qfdd999Msaoa9eu+uWXX3LUZIbG6tSpY9/R4p8SEhJUp04dRUVFqWXLltq8eXNhtw4AQJ5ERUWpcuXKki4Gmn/77Te1bNkyW82VLuayLEu9e/fWTz/9JA8PD4WHh6tNmzZF0j+uzNGAe35wVy4AAAAAhaVTp0753jc9PV3h4eEqVapUnt6nGmMUFhaW7+cDAACAc3m4ugEAAABcXY4dO2aH2yXpoYce0ueffy4vLy8Xd4biwBijUqVKqVSpUnJ3d1daWlqhB2qAwnLq1Cl7HBQUdNna5OTkS27z9vZWr1699MUXX2jLli06dOiQ6tSp47Q+AQDIr/Xr10u6eA4XHBycI9yeF8YYff7551qxYoUSEhI0atQorVy50tmtogBY4wYAAADA1WrVqlUF+o7BGKPk5GStXr36snWZF+4CAACg+CDgDgAAAIesWbPGHtetW1dTp06Vu7u7CztCcXDq1Cl9/fXXmjp1qg4ePGj/PjNMw/9GcDWKi4uzx1WrVs21plSpUkpKSlJCQsJl52ratKk93rJlCwF3AECxcOLECXscHBx82drLXczl5+enHj16aMaMGQoPD9fJkyfl7+/vtD6Rf1xsAAAAAOBawIW7AAAA1x8C7gAAAHBI1hWN+/btS3D5OpaRkaGFCxfqyy+/1OLFi5Weni4p+wfNzZs319ChQ/XAAw+4qk0g30qWLGmPMzIycq3x8fFRUlKSzp07p6SkJJUqVeqSdZlOnjzp3EYBAAWW9SLOgnB3d1fZsmVVvnx5VatWTW5ubk6Zt7DExMTY42rVquVa4+XlpZSUFCUmJl52rlatWmnGjBmyLEubNm3SPffc48xWkU9XunABAAAAAIqz9u3bs7I6AADAdYqAOwAAABySNaRTu3ZtF3YCVzl48KCmTJmib775xr7gIWuovVy5curXr5+GDh2qFi1auKpNoMAqVapkj6Ojo3OtqVatmk6fPi1J2rVrl1q3bp1rXdYVcs+fP+/ELgEAztChQwenf2FeqlQpNW/eXHfddZcGDx6sKlWqOHV+Z/D09LTHl7pw1cfHRykpKYqJiVFqaqpKlCiRa1358uXtMRdzAQAAFB9xcXEKDw/Xxo0bdezYMUVHRysxMVHVqlXTtGnTXN0eAFzWqlWrXN0CAAAAXISAOwAAABxSo0YNe3ylVRxx7UhJSdGcOXM0ZcoUe4XTf94StF27dho6dKj+9a9/XXIVa+BqUr9+fXu8a9euXGuaNGmirVu3SpJ+/fXXSwbcFy1aZI99fX2d2CUAwJmcecvzxMREbdiwQRs2bNArr7yiF198Uc8//3yxWtW9YsWK9vhSF3NVrVpVUVFRkqQ9e/aoWbNmudadOXPGHsfGxjqvSQAAAORLZGSkXnvtNc2cOVPJycnZtlmWpbp16+bYJyUlRTfeeKPi4uLk4eGhXbt2qXLlykXVMgAAAAAAtuLzbQoAAACuCrfeeqsdyrlU4BPXjq1bt+rxxx+Xv7+/BgwYoDVr1siyLDv8VaVKFT377LPau3evVq9erYceeohwO64ZzZo1k4fHxevCN2zYkGtN586dJV38Yvijjz7KtlJ7pq+//lrr1q2zH998882F0C0AoKAyz2+MMfbPlVypNnPO1NRUjR07Vv/617+c17AT1KtXzx7v3r0715omTZrY47CwsEvOlXVb1tXcAQAAUPS+/fZbNWvWTFOnTlVSUpIk2Z/pXe6iTi8vL/Xq1UuxsbE6e/asZs2aVVQtAwAAAACQjbGcuSwRAAAArgt33HGHli1bpipVqujQoUMEmq8xcXFxmjFjhqZMmaJt27ZJyr6aqbu7u+644w4NHTpUd999tx0ABq5FQUFB2rhxo4wxioiIUPPmzbNtT0hIUPXq1RUfHy/LslSlShU9/fTTuummm5ScnKwFCxbom2++UUZGhizLUu3atXXgwIE8hSYBAEVn9erVki7e+vzVV19VRkaGJOmWW25Rp06d1LhxY1WoUEFeXl46d+6cjh07pi1btmjBggWKiYmRMUaNGzfWhAkT5ObmpujoaO3YsUOLFi3Sjh07ZIyRZVkyxmjMmDEaO3asKw/XFhcXJ19fX1mWpUaNGmnnzp05aj777DM9+uijMsaoZs2a2rFjh3x8fLLVLFmyRN26dbOPcfXq1Wrbtm1RHQYAAACy+OqrrzR06FD73Czzc72AgABVqVJFv/32mySpbt262rdvX479t2zZolatWskYow4dOlz2IkcAAAAAAAoLAXcAAAA4bPfu3WrdurWSkpL06KOP6uOPP3Z1S3Ci0qVLKyUlJcdqTrVq1dLgwYM1aNAgVatWzUXdAUXrzTff1PPPPy9jjP773//qk08+yVHzzjvvaOTIkdnCi1llXRH422+/Vf/+/YukdwCAYz799FM98cQTSk9PV9u2bfXxxx+radOml93nwoUL+uijjzR27FglJiaqadOmWrFihSpWrGjXLFiwQEOHDlVUVJQsy1KZMmV05MgR+fr6FvYh5UmLFi20bds2GWO0Z8+ebKu6S9LZs2dVvXp1paamSpIaNmyosWPHqlmzZvbFXG+++aaSkpLsi72OHTvGRZAAAAAusHv3bt10001KT0+XZVny8fHR6NGjNWjQIFWpUkWS5ObmJmOMAgMDcw24S1K1atV08uRJlSxZUrGxsSpRokRRHgYAAAAAAATcAQAAkD+zZ8/Wv//9b124cEFDhgzR+++/rzJlyri6LThB5pdclmWpZMmSuueeezRkyBCFhIS4ujWgyB05ckRPP/20pIu36f7mm2/k5eWVrcayLD300EOaOXOmHW7PGmrPHD/77LN66623irB7AEBerV+/Xh06dFB6erp69+6tWbNmORTQ3rhxo0JCQpScnKyuXbvql19+ybZ927Ztuv3225WcnCxJev/99/XEE0849Rjya8yYMXrttddkjNHIkSP1xhtv5KgZNWqUJkyYkKeLuYrTsQEAAFxvunfvroULF8oYo1q1aiksLEy1atXKVpOXgHvv3r01f/58GWP0+++/6+abby6C7gHAMZGRkfrpp58kSWXLltXAgQMdnuPrr7/WuXPnJF3821e9enVntggAAIACIOAOAAAAh61Zs8b+78svvyzLslS2bFn16dNHQUFB8vf3V8mSJR2as3379oXRKvIh80suSapQoYLKli1bKM9jjNHBgwcLZW7AFaZOnar33ntPf/75Z7bf33zzzXrxxRfVq1cvF3UGALiStm3bav369apcubL2798vHx8fh+d444039MILL8gYo4ULF+rOO+/Mtv3ZZ5/Vu+++K2OMevTooR9//NFZ7RfIH3/8oQcffFDSxUDA0qVLc1zMlZKSom7dumnlypXZLt6SlO3irr59+2rWrFlF1zwAAABssbGx8vPzU3p6ujw8PLRlyxY1adIkR11eAu5ZL4KcOXOm+vbtW9jtA4DDhg8frsmTJ0uSRo8erddee83hOV566SW9/vrrkqQRI0bo7bffdmqPAAAAyD8C7gAAAHBY1gC0lH3FxvwwxigtLc0pvaHg/vn6SlJhvG0wxig9Pd3p8wKudvLkSR0/flySVLNmTVWuXNnFHQEALmf//v1q0KCBjDF69NFH9fHHH+drntOnT8vf31+S1LdvX82cOTPb9p07d+qmm26SMUa1a9fWgQMHCtx7UUpJSdH48eP18ccfKzY2Ntu2SpUqafTo0Xrqqafy/Z4AAAAABfPTTz+pV69eMsaoZ8+emjdvXq51eQm4T5o0ScOHD5cxRh988IGGDx9emK0DQL7ccMMNOnXqlNzc3HTs2DH7PbkjTp48qRo1aigjI0M1atRQZGRkIXQKAACA/Mj7fXYBAACAf7AsS8aYXMPuefHP1R9RvPDaAPnj7++fry9TAACusXXrVnvcuHHjfM9TuXJlVapUSVFRUdnmzNS0aVOVKlVKSUlJio6OzvfzuIqXl5fGjRunMWPGaPPmzfbFXAEBAWrZsqXc3NwkSbNmzVK/fv1c2SoAAMB1KfP8TCr43TKz3tEoISGhQHMBQGHYu3evTp06JWOM2rRpk+/PY/39/RUUFKS1a9fqr7/+0sGDBxUYGOjkbgEAAJAfBNwBAACQL5nh54KEoAlQF0/t27dn5U0AAHDd+Ouvv+yxh0fBPi718PCQZVnZwkVZ+fr66q+//lJ8fHyBnic/vv/+e/Xt27fA83h4eOi2227LddvcuXM1YMAAAu4AAAAucO7cOXtcoUKFAs2VmJhoj0uVKlWguQCgMOzatcseX+o9al61adNGa9eulXTx7msE3AEAAIoHAu4AAABw2MqVK13dAgrRqlWrXN0CUGxduHBBZ8+e1dmzZxUfH69y5crJ19dXFStWLHAoEgDgGpkrj0sXV4DLr3Pnzunvv//OMWdWmRd4enl55ft58mvAgAHy8/NTp06dCmX+BQsWqH///kpPTy+U+QEAAHB5FStWtMdnz54t0FwnTpywx76+vgWaCwAKw7Fjx+xx7dq1CzRX1v2zzgsAAADX4tt3AAAAOCw4ONjVLQBAkVmyZIl+/fVXrVy5Urt27cr17hPGGN10003q1KmT7rzzToWEhLigUwBAflSvXt0ez507V2+88YY8PT0dnmfWrFnKyMiQMUbVqlXLsd2yLJ09e1bGmGzho6KSmpqq3r17a8WKFWrRooVT5/7111/Vp08fpaWlcScgAAAAF7nhhhvs8aZNmwo017p16+xxvXr1CjQXABSGrHea8Pb2LtBcZcqUsccJCQkFmgsAAADOk/tSQgAAAAAAXOcWLVqkli1bqlu3bvrwww+1Y8cOZWRkyLKsHD8ZGRnaunWr3nvvPXXp0kW33XabwsLCXH0IAIA86NChg9zd3SVdXKntf//7n8NzHDx4UC+88IId7s7tQqd9+/YpOTlZkhQQEFCAjvPv3Llz6tq1qw4ePOi0OcPCwtS7d2+lpqY6bU4AAAA4rm3btnJ3d5dlWVq4cKFOnz6dr3kiIyO1evVqSRdDo7fccosz2wQApyhbtqw9LuhdK6Kjo+1xqVKlCjQXAAAAnIeAOwAAAAAA//Dss8+qe/fu2rZtm71iuzEm209uv8sMvG/atEldunTRmDFjXHkYAIA8qFSpku6++277b/jHH3+shx56SGfOnMnT/j///LPatWunmJgYWZYlY4wGDRqUo27JkiX2uHnz5s5q3yHGGJ05c0ZdunTR33//XeD5wsPD1bNnTzu4b4zR4MGDCzwvAAAAHFe+fHndfvvtkqSkpCQ98sgj+Zrnqaeesu9M1KNHD7m5ESkAUPxUqlTJHu/cubNAc+3YscMe+/n5FWguAAAAOI+xcru3OgAAAAAA16kBAwZo+vTpdkhRkh1y9/T0lL+/vypUqCBvb2/Fx8crJiZGp06d0oULFyQp2z7GGA0dOlSfffaZaw4GAJAnR48eVePGjZWYmGj//S5VqpR69OihTp06qXHjxqpQoYK8vLwUHx+vo0ePKiIiQj/88IP++OOPbP9mPPLII5o0aVKO57jlllv0+++/yxij7777Tvfdd1+RHuPo0aP11ltv2Rdk3XTTTVq9enW2Ve8csXHjRnXp0kUJCQn2sT/wwAOaNm2aM9sGAACAA1asWKHQ0FD7/Ozee+/Vl19+me2cz83NTcYYBQYGat++ffbvLcvSk08+qY8//tiu27lzpxo2bFi0BwEAebB37141bNhQxhhVqlRJkZGRKlmypMPzJCUlKSAgQFFRUTLGKCIiwmUXpQMAACA7Au4AAAAAAPx/H3/8sZ544olsIfUKFSro4YcfVteuXdW6detcvyhJTk7Wb7/9poULF+qLL75QbGysHSA0xuiTTz7Rf//736I+HACAA1auXKnu3bsrKSlJkrKF1i8l610+LMtS9+7d9cMPP8jDwyNb3YYNG9SjRw9JFy+W2r9/v8qUKVMIR3F5Q4YM0VdffWUfV7t27bRkyRJ5eXk5NE9ERIRCQ0MVFxdnH/t9992n77777or/NwMAAEDh6t+/f7bzsgoVKmjo0KHq3r27GjVqJF9fXzvgvn37dh07dkwrVqzQhx9+qL1799rnwc8884wmTJjg4qMBgEurUaOGjh8/LmOMXnrpJb388ssOzzF27Fi9+uqrkqQqVaro5MmTTu4SAAAA+UXAHQAAAAAASVFRUapbt67i4+MlXQwtDh8+XK+//rpDIcTz589r1KhRmjRpkh36K1++vA4ePKgKFSoUVvsAACfYunWrBg0aZN+e/J938sgq6zYPDw+NHDlSY8eOlaenZ9E17KCMjAz16tVLCxYssPvv2bOnfvjhhzwH07dv365OnTopJibG/neuZ8+emjt3rtzd3QuzfQAAAORBSkqKQkNDtW7dumwX32fKepFmVpl1lmXprrvu0s8//8zFiwCKtVdeeUWvvPKKpIt3nfjiiy80aNCgPO8/ZcqUbIuSjBo1SuPHj3d6nwAAAMgfAu4AAAAosOjoaK1cuVK//fab/vrrL8XGxio5OTnXIFBujDEKCwsr5C7hbIsWLdLixYtzvO55ZYxRWlpaIXYIOGbcuHH2Kj/GGE2cOFHDhg3L93wfffSRnnzySfvL4HHjxumFF15wRqsAgEKUnp6umTNn6osvvtD69euVkZFxyVpfX1/1799fjz32mBo0aFCEXeZfSkqKOnfurLVr10q6+G/ekCFD9Pnnn19x3z///FMdOnSwb91uWZa6deum+fPn51i1HgAAAK6TkpKip59+Wp9++qmk/7vjUOY40z/vSCRJw4YN0/vvv8/FiwCKvfj4eNWpU0fR0dH2RTr33XefRo0apebNm19yv+3bt+v111/X3Llz7b99FStW1MGDB1WuXLki6h4AAABXQsAdAAAA+Xb27Fk999xzmjVrlkPB5qwyP3RMT093cncoLGvXrtWQIUN04MAB+3f5eVvB647ipnbt2jp69KgkqV+/fpo+fXqB58y8LXjm/AcPHizwnACAohMfH6/ff/9dhw4dUmxsrFJSUlS2bFn5+fmpefPmV02o/Z/i4uLUrl07/fHHH/b5+OjRo/Xaa69dcp99+/apQ4cOOnXqlB2ACg0N1S+//KISJUoUYfcAAADIqw0bNujtt9/WggULLvs5nJubm7p06aKXXnpJt912WxF2CAAFExYWpq5duyo9PT3bHStq166tW265Rf7+/vL29lZCQoJOnjyp3377TYcPH5b0f99reHp6auHChQoNDXXZcQAAACAnAu4AAADIlx07dqhbt246efKkQyu1SznD0ASdrx6zZs3SgAED7A+Ls7rc65tV1pWheN1RXGzfvl0333yzpItfaERGRqpq1aoFnvfkyZMKCAhQWlqajDHavn27mjRpUuB5AQAoqJMnTyooKEhHjx61QwCXunvJoUOHFBwcrOPHj9vh9uDgYC1evFglS5Z0QfcAAABwREJCgjZs2KCtW7cqKipKcXFxKl26tHx9fdWoUSN17NhRFSpUcHWbAJAvs2fP1uDBg5WUlCRJud6xItM/71xRunRpTZ06VX369Cm6hgEAAJAn3DcWAAAADouOjlaPHj104sQJ+wPC0qVL69Zbb5W/v79mzJghY4x8fHx0zz33KDY2VocOHcq2QqSbm5v+9a9/EYi5iuzevVtDhgyxg7qS1LZtW3Xp0kX+/v76z3/+I2OMqlSpojfeeMN+3ZcvX649e/ZIktzd3TVy5EjVr1/flYcC5BAeHi7p4hcb99xzj1PC7ZLk7++ve+65R3PnzpUkrVmzhoA7AKBY8Pf319KlS9W2bVtFRUXJsiw99dRT8vPzU9++fe26o0ePKiQkJFu4PSgoSAsXLuRcHgAA4Crh7e2tzp07q3Pnzq5uBQCcrk+fPmrcuLEee+wx+3PeK8m8cHvy5Mlq2LBhIXcIAACA/CDgDgAAAIe9/fbbOnr0qB1wefrpp/XKK6/I29tbkjRjxgxJUuXKlfX111/b+506dUoffPCB3n//faWlpenAgQP68ccfVb16dVccBhz02muvKTk5WcYYlSlTRjNmzFD37t3t7f/5z38kST4+Pho4cGC2fX/99Vc9+uijioyM1CeffKIffvhBHTt2LMr2gcvatm2bPb7rrrucOvfdd99tB9y3bt3q1LkBACiIevXqadGiRerYsaPOnz+vjIwMDRgwQL6+vgoNDdXx48cVEhKiyMhI+9y/VatWWrx4sUqXLu3q9gEAAAAAkCQ1btxYq1ev1saNG/X9999r9erV+uOPP3ThwgW7xtPTU02aNFFwcLDuv/9+3XLLLS7sGAAAAFdirMz77wAAAAB5kJGRoUqVKikuLk6SNHLkSL3xxhvZatzc3GSMUWBgoPbt25djjg0bNujOO+9UQkKCmjRpos2bN6tEiRJF0j/yJykpSeXLl1daWpokafr06erXr1+2miu97mfPnlW7du20Z88e+fr6aseOHfL39y+S/oErCQ4OVnh4uIwx2rVrl1NX7dm9e7caN24sY4zatm2r1atXO21uAACcYfny5br77rt14cIFWZYlb29vzZgxQyNHjtTevXvtcHvz5s21YsUKlS9f3tUtAwAAwAmSk5MVFxenihUrytPT09XtAIDTxcfHKz4+Xj4+PvLx8XF1OwAAAHAAAXcAAAA4ZMuWLWrVqpWMMfLz89OxY8dyfPlxpaCzJM2bN0//+te/ZIzRqFGjNH78+KJoH/m0cuVKhYSEyBijG2+8UX/88UeOmry87jt27FCLFi1kWZbuv/9+e7V/wNXq16+vAwcOyM3NTRcuXJAxxmlzZ2Rk2H8n69Spo/379zttbgBA4UpPT9e5c+eUkJAgRz9GrVmzZiF1VThmz56t/v37y7IsWZZlh9oz/9ukSROtWLFClSpVcnWrAAAAKIC//vpLEyZM0IIFC3T06FH799WrV1fPnj319NNPq3bt2i7sEAAAAAAAycPVDQAAAODqsnPnTnvco0ePy67sk56efsltvXv31s0336ytW7dqypQpevXVV+Xm5ubUXuE8Bw8etMddu3a9bG1qauoltzVr1kxdunTRr7/+qvnz5+v8+fMqU6aM0/oE8isqKkqS5O3t7dRwu3Tx4g8fHx+dO3fOfh4AQPG1fPlyTZ8+XRs3btSBAwccDrZLkjHGvvPN1aJPnz46c+aMhg8fbofaJcmyLNWvX1/Lli0j3A4AAFBM/P333/rhhx/sx7169crTnRLnzJmjQYMGKSkpKcd57rFjxzRp0iRNmTJFH330kQYPHuz0vgEAAAAAyCsSRAAAAHBIdHS0PW7atGmuNZlB9eTk5MvOddddd0mSzpw5o/Xr1zupQxSGrK973bp1c63x8Lh4/eyVXvcOHTrYdeHh4c5pECigpKQkGWNUvnz5Qpk/c96kpKRCmR8AUHBHjx5Vx44ddccdd+jbb7/Vvn37lJGRYa9o7ujP1ejxxx/XmDFj7JXbJSkwMFArV65UlSpVXNwdAAAAMs2ZM0fDhw/X8OHDNXbsWPn6+l5xn2XLlqlfv35KTEy0z/f++SNd/Oziv//9r77//vvCPgwAAAAAAC6JFdwBAADgkKzhzHLlyuVa4+3trXPnzmULReemZs2a9vjw4cNq27atc5qE02UNaZUqVSrXGh8fH8XExOjs2bPZQlH/VLlyZXt8/Phx5zYK5FNKSoqMMSpRokShzJ95t4sLFy4UyvwAgII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"text/plain": [ "" ] }, "metadata": { "image/png": { "width": 800 } }, "execution_count": 12 } ] }, { "cell_type": "markdown", "source": [ "**Model Prediction on Validation Batch**" ], "metadata": { "id": "6NNwzcoE4peG" } }, { "cell_type": "code", "source": [ "Image(\"/content/runs/classify/train/val_batch0_pred.jpg\", width=800)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 817 }, "id": "nfXZe4ju3x3R", "outputId": "b129a3f0-1e7a-429e-eb7e-21e1a39763d4" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "image/jpeg": 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"text/plain": [ "" ] }, "metadata": { "image/jpeg": { "width": 800 } }, "execution_count": 14 } ] }, { "cell_type": "code", "source": [ "Image(\"/content/runs/classify/train/val_batch1_pred.jpg\", width=800)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 817 }, "id": "bwtDOd613-Ph", "outputId": "3fbd3d01-63e2-4b9e-b95f-682b24f60881" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "image/jpeg": 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bGiLyV6s9VSlLqXXSVnE0bv4havZ2d7cSamgjifashiQLn+6Pl5PFNh8ca5qXhaHVLXWXjkBIkC20TAnOP7tcxr+i/wBreH3sLNSiJOrQn1AB5/U1l+Hp303wjqtrbb/Ot2LKr8nPfj6itOfsyOV8qZtzeMviBdMP7N8UQPs4dTZxZ/8ARdaieJPiBaOjXWtxSxHax228QJU/8AHeuFsZ9TuJCUieB5NqK5XaASMsT9B/Ou1hRYrSOF5HdlXGTySM1m6rW5uqEp7FKx+KPjB7hYhK10W54hjG3nocLXSr8RNWiVftU6h+6oE4/Sucm2GY2+lRI1xIPmULyce4rOm8NyyypDcEQXDqXJEn3RntWftG9ma+yjFe+dlL8Qr2e4SKLWZLdmGAv2eNgT9dtXrrxXq8liY7bU7mK6Xgym3hYE/TbXDWmgWlkyiSV5JMjDshH5c1uyRoXLeYykY6n2pqq1uS6cZK8TUi8SeKGhMb6wPMZTskW3jGD9Ctcjc+MfiVa3Mkcms5jVseYLOEjH4JWrLezNCs5AGxtobGARUV3fQGFHc3DKAN2xPen7ZkSpuKuya18beLxZiSfVnZskfNaxKTjHONnTmuP1r4seOrC/2Ra7iMdV+yQH/2SumleW7lSeF90QjPyMmOo7+9cdrnha71H9/5QSYKSVBwG/GrhU7kMsQ/F/xvJEM64Nx4JFrDx7/cra8M/FTxZeS3Frf6pvmT5kP2aIcfgtclo/gS+RkkvJYoos4f5s8e3vXSNFZWOpNJYabEOwkYtk++M1UpaWTJT1OjPj7xUzlU1D8oI/8A4mp9I8ceKbrxBptpPqIMct3EkqCGLlC4BHC5GRXPhri4iJliWKU/NGduARzU/h+2ul8T6N9oCFheQksn++OtZKo77mzg7XSM+X4IeJYnKNfaSSPSWT/43UR+C/iMf8vulf8Af2T/AOIr36+JF2/px/IVVYhhXrvG1Tk9nE8IPwb8Qj/l90v/AL+yf/EVE/wj19DzeaZ+Esn/AMRXt05ZOR0qlJKGFc1XM6sFe6NIUIs8y0/4d+JbDTLu0tZdESS7QxSXRkmMvlnGUHy7QDjnjPJGccVseBPAWqeFtbnvr64spIpLZoQIHYtksp7qOPlNdkk+3vU0kxaHK15dbPJTpT1T7nRHCpSRFOAGJqHTbT+0L5oGl8vAz05P0qi+on7SYWFWrecQajaSg4KyDP0NfK1cRTniYOWsHujvjTk4O2jR08fh+3Tq7v8AWj/hH7VJPMSLDetb4AIpCor7FYLD2+BHme1n3MBtHiyTtqL+x7ZH3+WA3riuhKVG8QPal9Rw/wDIg9tPe5hrZRRknbk1KAEGFGBWg8I9KrvEK1jQpxd0hOcnuysxJqM5qd1AqButXoSNBIPBNWbebDAScj19KrinrihNBYvu6g8Gk2mQYAOKgQtgYANXoHwvzL+IqriGCA7TUHmx2AkMjBQW6evFX2mU/LGN7HsP61F/Z8clx9omUPIPu56L9Ke4FJCbqUPIpES8gHuasvK7cIvFW2iTqajdkTpVJCKTQySH5jTlt1TrUjSk9BTeT1NWkFw4HQURA+fH/vD+dGQKWNx50Y/2h/Oq6COKuPD13bTtE8kBZcZIY46Z9Kj/ALFuf78X5n/Cup1X/kJTfh/IVTq3mFY5YcJ5c4ptP7zC/sW5/vxfmf8ACj+xbn+/F+Z/wrdoo/tCsV/qllvZ/eYX9i3P9+L8z/hU9vptxAHVorSZXxkSbuMehGCPzrWopPH1noVHhTLou6T+8oJa3TX0M8phEcS7FSPOFGDwM+5q/RRXPVqyqO8j2MDgKWCg6dK9m76u+tkv0CiiiszsCiiigAooopAZOuWL3lwr5BTy+RjrXjWuWIsr+QINq7vu+lfQM0YMHI5214t44hC3+/HfFeLltZ0q1lsckoJxZB4M8QjQ9W/fMfssw2yY7ejV7PFKk0ayRsGRhlWB4Ir5yU84rr/C3jK50Vkt7jM1kT909U9x/hX01Wlz+9EzoVuX3ZHsNFQ2t1De20dxbyCSJxlWHepq5LHencKKKKACiiigAooooAKmtP8Aj9g/66L/ADqGprT/AI/YP+ui/wA6BS+Fnl/iTxRZeF9fudGvYriS4t9m9oVUodyhhgkg9GHasv8A4WPo/wDz7X3/AHwn/wAVWP8AF/8A5KlrP/bD/wBER1w9eh9erHw/+ruB7P7z1KL4l6NFKHayu5QP4HRcH8nBouvilpVzcPPLb3xdzk4jTH/oVeWGom6UvrtW9yv9X8Hy8tnb1Pcn1y3S2gnaKYLPGsiDAzgjI71G3iK1QAmG4wf9kf41k3luYvDPhy9JPlz2ir/wJeP5VSbUIfK2thxR9erHRDhfL5RvZ/ebeoeIbQ2MsaxzF5Y2VRtHBIxzzXGXEjvyygbBk49KvSzAAGQHLKSM+1Zs0hMcpPQxk/4VjUrzqtOR6uX5ZQwKcKF7N9XcxJSZrgmQ8noPalcyRycnIHY1nTTP528dByPatqdVuLaO4i/iXP8AjWTPXoOPO4tDYp8NuXArUtbkkBhxzzmsOMPI3GAR1rWtLd/Jbc2WI4qUJpKViTX0RtPj5OMg1h7/ALRciCP7uRmul1ZEbw2Tj5gcVzmmxiIu78P0/CtZrYeIWqa7Es/LrgcA8D6VoRuI7QyDAZe1Zskpa5wO3SrM8xNmQqZd/lwPWsmZX0KlgXubuSYLlV/maY+jSQX+XBKMcgVsWtstpaCNQc4DNjuavvG7RB8/vByM9qtaEppSTZTdZrG0xCge5ft/dFVI02lIZiN+fMk9hVuOQgli+WJ5JqO/tmdA8X35SE9yDTUrs39q5yt0NHS2e306W6Qf6RfS+XGMdEFd18PSn9qzuT8kAKg+rdz+ZriJJo7Rnl/5ZadCI0HYyGpfD+s3Fkj+Svyud2fXvVydlYvETtTcUfQ9vcKx4YGrqHNeLab4yuoLlGfJXIz9K9h0q7j1Cwiu4vuSqGFZHiTg47l4dKWm84pee4oIFNIv31+tFCfeH1oA8m8YavqGl+Kr2ysZhFbRbNqFVOMopPJBPUms+PxZem2VTJmQ8ligB/AYxTfiLP5XjbUhjORH/wCi1rClYzact1CmZIRyo/uivEngsNzNezj9yPUhh6Ps03BbdkbtzrWstCZrbUSMLkoYUOP/AB2p9Hvddn0SXVrrVvMhJIiVYEycdT0rK0I/bYBOGKw9M+pParGtSyWOn28MWVjedQFzhV9v0/Wh4PCq37uP3Ix+qUeZ8qRJDqba23mvM8hhyFDRhSucZzgewqxHEZ5thxheCexOKqabDFA0ijaHZsuVzU0dw9tCkTD5izMe/fC11U6dONowSS7IwqUlCldG/AWZvKwFEYB68Gq17q8MZKrIMqRhemf/AK1Zs2oG0jIj+a6dgFA9PesC/mi1R7pTKLeQQlk5wc9hWzm46BRpKrqamoLHq0w866fYfu+WO/1qBb3bqMccCMViKxkN/Cuen1PWudiuNWsbSNp4leGP7zKeR71tW2o+fD9oRVedFyF6Bj2NZqrJbnRWwkWvcOlnuQkjqvOcs7exqpGftF3JFaBftEMDNExPB7YP51yGo6pdJZR6hKGDsxRo14C+3vWp4GvJZ7i/u5UIEaCMZ7nOT/Smm2/IiVJKlaW5c8PxXNlp87XwffnAXOce4qxa28LXsjxnZMwAYY4bnvV2XYEw7cnkYPSqY27/ADH+U5zWbepsqN4JRHalvELAqSykHaBWbO16IysMRy3AJPH1NXp7t3kEY+ck/e9BUN3ci2jJLAY6VDZ001ZCaZbxaGktw05e6kHzSA9PYU7ToHu799RkuHISNhHFnBx681zV1Nd39wkEausZPztjtV671aOIRxxttEQwD0IGMYpXu7DcU1qb0+oCVgUZlx2Zic1RutZhSHZuXdnr3/OuOuNbkkbCsSTxir1l4W1vU086RVtYW53S9SPUCtOXuQrR0SJbzX3lUIjs5H3fc+wrorTxVo8sEVt+9kvAoVowMEGsfRtJttP1WbJ88xnajP0z3Ira/syzglknjtoo3kbc8h6574o0RFSDnZFqHXpo7xbVUiVSCwL8Zx2Bp0mrlrCR5oYuOXJQHiq1rbWt3d7nUOkWQCT1J7U8QBp5FhX5Mcf3cCp5ugLDxW6M+61O8Fo0qyQSoo3KsUY6emKhtJdR1IRA3EsDyHaqBQDiteKztxFLII1whAXnHNZ9lqVtaaxHdyOfLt2CsD2znmmpEyw8N7HdLodnp+iJbXMiyuy8zSZYhj/KuU0N2j8U6dGrhkF7EpP/AAMVb1DXLJDcu9yXaRCYkB4JIOK53wzqyz+LNJVgN730Iz/20FUmm0VFWi0e637EXcg7cfyFUHbHIq5qJIvJPw/kKznau6bPMSGuwcEGsm7DRNkdKuSvg5FQybZ4ivevPxEuZWN4KxnyOyqCehpbW6ZpPL65qtLI28wt+FSWaeXKS1fFTrTo1JPq+h6XKppW2H3lou4SKPnzTTbPJKhJxt5NPM7m6wQdqmpHmJuF4xu4rg5pG0ny7HYaPqq3NuIpGHnxjDDPUetanmg9xXmRv2sLrcXwSOD6Vr23isxwB7yIhSMh05yPpX2eXZ1GUFCvo+/c8qrh9bxO0Mq+opjTL61hW2uafdoHiukI9zg/rVwTxuMq24exzXtxxEJfCzndNrctPOO1V3mHPFRNKvofyqtLcKM8gfWlKul1BQZI8hJqrNcJEDkj8azrnWIVcpHIrN32nOK5DXfEEsV6kAP3+FHqa8vEZg9VS1aN40rfEdnJqAHzK3FPtdT81wp5964qO7uY5ZLafhlAP5iul0CAzvvPQVjhMXKpJK+rLnTUVqdQr7YTIewq7Yu08Y29PWqk0IkhW3BxvIz9K1YESCJUQYAGK9+CZxsmVFQYAx6015MdKY8oA61WklJrZIkdLKT3x9Krsw+tIxNRkmqQDzJTDIfWo2JphNO7CxKZOOtLA5NzEP8AbH86rkmn2x/0uHn+Nf50rjG6r/yEpvw/kKp1c1X/AJCU34fyFU6we57NL4I+iCiiigsKKKKACiiigAooooAKKKKACjGeKKdGMyKPcVM3aLYPYs3A+Vh6CvHfHi4ucbT159q9hnPyyE+9eT+OkyzN718vhJWqowS9088xhqmWopOHHvUiHNfc4d3gmcMlqztPAviJ9O1FbG4kP2Sc4GTwjdj+NesV88qSCCDgjkGvbPCurf2voEE7NmVB5cn+8P8AOazxNOz5kdWFqXXKzaooorkOsKKKKACiiigAqa0/4/YP+ui/zqGprT/j9g/66L/OgUvhZ4H8X/8Akqes/wDbD/0RHXD13Hxf/wCSpaz/ANsP/REdcRWh4g01G4qU1E3SgD3/AEzSjrnwPsDDFvureNnix14Y5H5V55a2RZ3kYFXHVCMYr2L4Qyb/AIbWGeQrOv8A49WN4r0PTdO/tG8mvVjkbMsSjgr7YpX1sdFGbXunl18zNIIgcFVwT6GoZkZ5HiTqyhPanXkg+SVckOcn3qxaFJLqOTPy/eJ+lNI7Ix6k1l4dsbO0JuIvNc/eLdqLzRoIrMmx+XPRC2QT/St0OJkY4BOOR6isy4glhzsO63b7y9wakE7O5wksphnYSK0UinkGtC11AqAzMpU9quanbpOPKu1Akx+6lA+8PQ1hm3aNyscZKr2p+Zs23HmZ0lwzXOnRxLjYTvJz6Vhl98ZK8Spzg/xL/wDWrTtJhLaGGbCP0T8qyZ3jtZ3IUlyuAD2PStByTcbjrKMzXBbnqOa6eHS47a2lnOS5HGegNYuhyJDiSRSfmxn0rptTmCRRxKceZzUpGXLaJmM4iG8nGeOKmjmMkZK49yarS3EUgEfylV4BqO6vRbxERjGVoMZMpW7Br2QbsLndj0zWtDse9jYsCkKmQ/XtXIG7aGUt/EwINX9Pmnd1GSS3L/SiO5eH96VzY17ULe408RRxeW00vmMB09BU9kiW1oqA5IUDisfUZBLqsMScopAH0FbMaL5OAeSetEndk1JXkXYnGRyDX0F4dgFv4fsI1PAhU5+ozXzbCAjkK+T3r6M8LXQufDenyjp5Cj8hikctfVI3OgoNN3ZFG7FBzC54pV/1i/UUgORTlHzL9aQHhnxDZj8Rr4fwqYieM/8ALJKzIZGt7xtkTiAJumJGAD6Y96t+Opg/xc1OPzNoiWItnoB5SGlkkj1WOIRyqkZwSmOXHqa4pqzbPVhK8YpdidLbTSm+6v2sraEb447dD+8Oc1b13W9Mm0CFpVdnUho1ZepxwSapW2nzojpdxFrQHCSHsD0qDXtMN7apAjBdvIAFYuz3KUVTu4i6LKt1GZACGHX65rSuYdsysScN0I9aztBtmtIJEdQuffNXNXupLbTZJ4lDMgBwe3vQmwSU42kij9mk/tH7OELSshII/nn8ahj8IWIuZLq/uWZQAAmcfrW7a3Pm28cyEGTYMkDt6VBcWcF/G207yvJj3cE96HNsKNCML2Fsm0mORoF2NGowSzbh+dJLY6S6b4AsTLwGj4H5VlHUobaPBiEcYOMEdDVJDmG8uFf9yf8AVZ5571Ovc1aVzXto45nmhnhjkZGwpbpnru/I1Lpa/ZbOX5Cplncn3A6Yqp4akmvLd5bmIqIzjI5DD1zVvzLq7vj5CgW8ZI5HU+lXdpGc4qUk+wXMjFsRjOOKS6lSOAEJsG3Byale2Qz7twJQ/Mq9Kz9Y+e3ePDYIOQvX8Ki1zZaIrz6tbw2xMbAlepzya5LUddupr6JY1zhgSD3rrJdMtF8NNDbQEypEWEp5bd1OfWuP/sy7nZGtbd5HVgXIrSKRE5N7HpPh/Umu0Xz4VuXUbWQjO3PpVbxD4S0251QXvmMlu6HdDGcYf3rBt7e/0y5SZ/MhDfe5612dv5V1YMzSZkK/K2eKh6O6Baqxw2lBdIvhJ/Zp2byqSSJkjPTk13dhfecoGcueozmqFre2t3PHa3BSEDIYuc5I71RupDpUss9s5a3jbBb+6fb1FNttAoo6E6HbPKbiBDFIxyVPQ/SsXxPY3htYUg3xAuN7gFto+gqFPFQ2K7y5I9+lX4vF9tHAQ7ZJ54Gee1Smx2E0ey+yFFllEwRdwHqQM1na5fSvE/2WGZ2UneFjIC+wx1p82oSaozfZWCuq7s9vpxS2mpzPIFnVUZONmeD70upa2JEZotNQOzbdoLg9elcJcTvqDXCxykRlzvC9QB7V6E6Leafcw+XhnU7JM9GrhZPD11pEKCWQCWaT53A5A9Pxq6bT1ZlNPY6vS7WxuNBtZWmiDldpTvx3qjo1vBH450XyznF/BllPBPmCsifQb+OxMlldF+4hxjNN8D2OpyeMtFknZUSO/gZkY/Nw4rWMU3cyk+W9z6Q1MgXknPp/IVlTMVrV1NVe8kB68fyFZM8ZA9RXRVbVzz42KE0nFV/OMbAnoafcZUVnXE2UxXzOOxM6LVtTvpwUlqWJgrXccmOO9NkkUTgDjJwKitn3ryelTC0+1TcHhe9fPYqrGpVc3sddJcsLMfK0aSplxz1psl/Ct0pyDjis27cvcG0QZcc7varcFskdqhYbnByxrLkUUmwnZvQY3lXV2GkHCpkZFAiLolqyny3bGfarhMUjONo3YwvamYd4Mn5WiPX6UKbEojILGG2t5LaOIE7iQO/1rAWSe21We3kSRZmAZAhJ3D2rqV8qOfftBby/v55/Ksu2jt21+e6mC/uFAR93Qnr+ldFCq1zN9hPyMmW71CXVYbYvOkTx7sFyCR2puprPD5WJ5dxG0AuSGq3q6Sahq9tDaf66PdJuJ4GB0/Gqer3LWFrFLewAzhwygNkA9RXZCrOfIr/ISfK7lq2sJNPs5LiaQYBXOeuTUc0UFzfWty6grC28n6VPM39s+HlFy6q0yCQsn8BGTmq0llLaabEk4IaSPBz3OKiFSSUrvVuw5wu0/mMjvF1DVZZY+UJ2pjuBXoukxx2dkpYgYGSa8x8PyW+mqZLg8qeB6V6FoCT69IJnQx2UfQH+M162AUYVfc1eyOOrJyjqdHYB7hjORhTwufStAjApwCxoFUYAqNjx1r6uC5UcDIn61EcU9j71Gx4qxDSajY0rGmE0DEY1GaUmmk0AIelOtj/pcP8A10X+dNNLbf8AH5B/10X+dMBdV/5CU34fyFU6uar/AMhKb8P5CqdYPc9ml8EfRBRRRQWFFFFABRRRQAUUUUAFFFFABUkAzOn1qOpbcfvQfQZrDFPloyfkTPYfcnEZ9zXlvjk/J+NeoXf+qz6V5T44f5se9fNYf+KjP7JwEv3h+NPSmSn51p619xhP4aOGpuTDpXcfDbUfJ1K4sGPyzJvUf7Q/+t/KuHXpWp4eujZeILGfOAJlB+h4P866aseaDRNKXLNM9yoooryT1gooopgFFFFABU1p/wAfsH/XRf51DU1p/wAfsH/XRf50Cl8LPBfi8M/FHWf+2H/omOuHrtvi/wD8lT1n/th/6Ijrh+a0PEBqiapTUbDFAH0l8F5N3w5hX+7cSj9a4Xx1jU/E98XkYmNtiqemBxgfrXX/AARkz8PrjB/1d1J/IGuWvrYTyzzM4Du7Mx78mqgrs7MHBOTbOKvJ0t4YoZFIaMbfw7U7TbqJpVOAVzhvpTr3TraSaR5pyB7nk1mrCtopETEKTnnrTlC2p11KUlqdjC/ls0RJH93HcVLLKY4fMI3oTgkVnaXfCRFD8yAbVNaMCB4mRhhF6e5rNkWILlLK/tCCwAxnB4INc5c2ElspuImDmMZ6/eHpW3q8aTlYo0247jjmucurdoHUBpGU8detUhwly6dytK8chVg+xTySW6U24ubaaU+UTKEGWdh1qRbK1jVzcbiQeF9az5ImR3iihYF8fKOwpp2L9tLZFi5uzbmKFWB4DSKPU1ej1d5QZCQ+3hR1I/CsC5tpoW8ySGRQf4mFWbe2ae2LJ/COcdfrRzamaqybdy39vUbmWMbyc8DiqdzfvIdpbPOTUDpPbnMmSnrVMlpJNqck0mYVJdEiYZuJRxkk4Ars7DT/ACLcOwwxGT7VR0PSI4ds0wy/8Oe1bc8wC+X37kVrGPKrs6KcHShzPc5u2Tfq65PClhzW9C3KjH8VYkETQ3Mk56NJ8v071rqeCynoc1kzBq2pdSINxjkd8V7X8OZGfwpErfwSMo+leIwSHcrgng4Ne2/DqTf4ZUY6StSMa3wnX8UL1NJSgfNQcw9cE8VKo5H1pqpindGApAfO/wAUdF1C7+I+pyWbxrHJ5LSHzBniJAARnOOKq6VC+kwQfbZQ0hzn068AV0HjtkPxI1JR94JHuP8A2yTFc3erJfTx28GDJkH6VxVHq0erRilFM6dNTM85LN+7lQRtGOmPb371grr+RJBKwWWElME88etLGp06dba4wJsjcPTNM8V+GWeI3FsoF0BuDIfvrWCs3Zmz0Ro6RcpLaPMoLfNhivNSavtl0W6GSMxt1+lc54Wv5bbSkjcFZQ7Bgeo5rdlZ9Qha3VlUuCpY9s8VVrMV+ZGf4C1lbuM2srhWhXJ3DOR/nNaYeS11a4e32ywE5Az6/wA6x7Tw8PCd3FqC3aTucoU24GCKsRW15LO9yZI1V/uoAeB2okrvQIXS1NXUHW902ZmtjLg/NEOpFQ21ktxaRKB5UKjkEfpVq0077PJ58l4zSMPmVRgUt/K8KfKhx0XHQ1NrDumOjMakWdsgjU/eC8ADvVySVIbc+W2yNOMetU7YLbxBG5mf77dT9BWT4i1E21jLHG/QZI70730FZI07eTNs0553tkfSo5ZoJ98iDlV257CuZi1yRNOR1BdBGCNgzx/jVzQJZptDEz4VpXZsHtzxQlbVlNrY0tAuDLbFH4PKtnnHaqCax9l1iSyMSI0fG5QBuHY1T0S6Ca5JZTOUUyb8+meDW3dafY63cFshJ4yQs6dSPQ020tyFd7GwtxHtKPsc4y2D7VXDw208aRsVVvnUf3eaZd+RY2ytdjEMeGzjlgKwR4w0e1ymyVpWyEbAIUH0qEr7DcrbjvFWnXvnpqOm27zRtjzY41JKn1wPWs+11G5m32d5bzRLIpUl0Ixx711Gga0Gh3LIGJ4z71rNNFcIfPjWQk8DFWnYSTPHm0HVo7zyJmIh7SZ4YV2ml+GLL7J5YZ5JmHLM5roHsFYvG0W5PRuwqwlv50JACKwXEbf3acpcyCKsQaRbppGkSoIVDsM5B3FhXnROqtczXMVvOx80hUVSSF7V6S1uyRYeXLn+6OtMkaS4YPBauCNoBIx9alPlHZtlDQn1GG133EQjBwSH6n/Crk80GoTeReRK6qcozdQfarDWuIHLuS+Oma5rUoNQZ4pbeMEbsEE4wKlau5TaSNmfTXtQHtZdy5ztbqal0CGFvFFhKI8SC6iDN/wMdaZZS3FvA8t4ygBBwvzGptKlFz4l0oR+bCovIWC7fvDeOtXC9zKpPdI9R1NSb2Qg+n8hWdJuAORV/U7qFNQlRpFDDGQfoKotcQEf6xfzrunY8tGdcRBs8VjXEBSQEglc810E1zaKMmRfzrKuryF428ohvpXzOaeySXM9zuoKT2IlRXYGNNqY5o06TF46B/lbjFOjnU26ADGeM1FFYARtOCVZSeR3r5XSzTO56xItU2w3sTx4DE7SauoFRWiwTv6GsURtqVxKXJAj/U9q24S5gQhSChC/N34q6i5YpdSFuRmXaS6rzEMEetNG6RmjblZuevIGKSRG+0sg+63L+oxVNDNJe7IjkRDOfXPb/PpTjC6uaokMUM800Vw7Hy0CqCcHnv8ApU1zpFotmQF2l1wZM5PHI5qtbxxSW1xLO4aQMwc55QDsKgn/ALSXSQsjiRScg/xbM8fU4rZKTdk7bGUtyHwxewxPeQyyFpfMA3P6AUmt2sWqTeXIWWE4xIOwB5A9+1auoaPZ/wBneXCqxsqHY4+8D16+tcqbW9v5bb7MJZbaFgJJEYZznJyK6KbjUqOrF2sTsrGjeGPw9pKW82ZBK+1CepQdM/Sruo3iSWiXOCQFGwt/P9azvFwGofZRHG8kwkCgICTs7nH1xT9Quo/s6WkKKGRkjKv1IGGIHv0pKHPGEnu73L2un0Cz086rrdtCkYIkIY+mB1Jr2K0to7K1SGMBUUfnXKeH47a2ut8USIzZyR2B7V0v2hpn2R/nX0OS8jp+0trt8jz8U3zcq2J3mA6HNRGQtTlgx9480rKFr6JXZxkTUxqexqJiKsCNqjJp7GomoAQmmk0jNUZegdh5NOtj/pkH/XRf51AXp9o2b23/AOui/wA6AsT6r/yEpvw/kKp1c1X/AJCU34fyFU6xe57FL4I+iCiiigsKKKKACiiigAooooAKKKKACpoOjH8KhqVMhAB3Oa4Myny0Gu5M9rDbyTELD2ryHxpLunA9zXqeqShITz2rx3xTN5l6RnpXh4RXqIiWkTmpP9av0qRe1RSf67HoAKlWvt8Kv3aOGe5MtOjbZKjD+FgaavSiu17GR9BQtvhjcfxKD+lPqCx/48Lf/rkv8hU9eN1PXQUUUUDCiiigAqa0/wCP2D/rov8AOoamtP8Aj9g/66L/ADoFL4WeCfF7/kqWs/8AbD/0RHXEV2/xe/5KlrP/AGw/9ER1xFaHiCNUbU9uaY1AHvHwRlP/AAgurqOqTs3/AI4P8K46aWbzH3SkBiSfauk+Cc2zwl4iXP3W3f8Ajhrjp5ctJk55PFXDc78CtWQX32dACiln/vN3rGeO4kvI1jG85y4z2rXEYdlLDJ6gUmj2F2L6W5kUANwqnritJtWO6vK0bFX7BNBcq9vN7qCa6q2uojaq8owy/eA9aztXtlm0+3EagOW+Zh1A9qxrO6m0q+O6MyRv97J6+9YHGnY6RrV7qJ7hzszyBjtUUVmsoXzV+cjcPYCrn2kXUSyQEFCMEegpyxq0obd0GABQkU0mcbqQaLUpUJBbI61q6QES1dmVROwyHYZ4rntX+03Pia5kt1MmGwFHsK6q1tZINLtXVR5gUEj3z0qrlJu2qInjjniaOdxMrdVPFZOoaeulMl5ZljAeHXrtrrmWzuIfOULlv4cdPaqcyxOhh2gxnqKkhxuc4J4NoGEK989KZaSWLXTC3tI92efapbjTYoLtkz+6blfamQ2sdpqh2ZIkjzz6imOEmpaoutK7Ntzgc1Wlkd4Sqj5jUy/MT65/SkeLZNx060NinNtla7GLYD/nmAR71X0y4eS6EJbKt2qxfj/Rx1HFVNFhLXjSDonegwbadzoIz5UpTHB717v4BtGtfC8BYfNKTJ+BrxDR7OTVNYt7WMFvMYA8dB3NfR1hbrbWUMCDCxoFH0FIxqSvoWduaeq4NCpUg60GIuPQ0i/e5paOhFAzxbx1YNceOdTMSZd/K3MeAB5SVgiwa2uEMGWlX78hHBPp9K7nxp/o2v3srlPLmaNdvc4Ra5qGRr6ZlhjLFRkjPSvMqP32exSXuL0GS2q6ncxSahInmxjlkGC3oCe9SX4uI5I3UtLAoChcfdFcxqGoz6dqgW4UrycfWtC18V25tir7llOQWP3Sp/rUWe5TdhbmySeRJok2OcbuMbiaksbSWOe8iIRjtXyyG/i9KJdVRHXy2WXDZLDoeOlWtNmVhJIVbzJDz9KrpqD8iu+lzKVkvnByd3lg5arMts/loyzrGvQ57Ul5exwL5kuCwyAe9YFzeXE8RumkEaofkj7tRca7nSXbwWNnukkGMZG48n3qSJpLmxVpZN0asDEM8YI5IrzLXNVu9UuhaEOuPvDOSfaug0q8uLO1jt5JJGlVRwx4UelOUfdIUryOskuEit5YViBkYfI2elc5/YN7q85E6vHbAZd88v7ClWy1DU7gMkght1+ZmH3j7Cq8niDUNMc2txHIUU8Mvce/pSin0HJo6aztRYQ+RDZ7UXgBVGNv86q3Yght2aMhSCMx46Z5rCj8Zg6ukjAJERsABz+ddfFPDf27XO1JGXBII60S5uoJLocNdaVNBdPqNxOkUSjmPPLZq/Yz+RDHeRNuhP3gOdtXdbtGvUke0Yq7DBDcnHtXP6dL/ZMospslGBPPr6VVuZCT5Wd1HqNtqVvHbSqjRMMMT6Vgat4MsRf77aDFu6H5gTwc1Gs32OZXQBY2GSPQ1v2OomfYuQRnkHv7VmnKDLaUtTl7Dw3qdtfJHbz/AOjE/M7dV9q6w3Vvplt5bOMt1duWrQltlETfZtpYchScA153r2uXGm6wI7mIhSCCoHI+lUnzkt2O+hkWVfN8wbCM+uaYJF3EKTs9DXAReM4o08uDzBH0+epj4qt/Lz5nJ9TR7OV9gUo23O7e7ijjxtwexBqp/bLKxEkrlQv8Ncg3iJZY8hgfoaoX2sSpEDEwAcZVs+lCg2PmSO3GuIzMJWC/Lk54rIfVbGyjae5uPMfPEYOQ3PH5VR8LwC5WSa6PmPJ94N/SmN4Tupb2XLRpbB/llfsD2HvVxjZmNSVlc2oNQN3iUHcz8qqjHH0rodBguG17TZ5HKoLuL5SRn7wrNtrez0i3AQhmRB856n60ujao134q0mOKJ9n22LJYY/jFVGyZlObkrRPTNetVl1Wdu52/+giueurCVUOwmur1bH9pzfh/6CKz2ANaVoJ3OSDaPPL8TxE7tw/GneHBLdXsis/ygc5rrr6wiuEIK8msfTtOXT7tm5AavlMxpexhK3U7qMryTLU+23idlOcGpbK7M1usJG1mOee9R3luJWCg7QakubXyIYmi/wBagGMV4N4tJPdnbUtfQrX7RWVwkygKoYBwO4q6HmZAY8FZfmX2HrWfADe37C5jGUTdg+tXIrh1jDyx5RMohFXJaJPcziR3DyxKLlhwcjbnqtUXZrGI30rscjMiqvb/ADirTvc31sqqnlcfNuHTPpUdzYvcaeEnm2yMuCwHBwfT3rWFlZS+foU5WIcrqOpCMeasIiJK4wG5HFPLS3Mn9nIwRI0y8gHQdAB+tU21L7UoFpGTNBj5wPlT+8DV2eD7BLFLBIdzsBMzDlsnj8q15eVpNeiIbuQBNQvtUbTmk22oXesv8WKlvrEaJbM9jkKThlJ4PHJq3JbHTyt8XLyxphz0BGf/AK1ZGo69bSJEZnVlA3SRq2CeOBSjzTaUFp1BWS1MnRrnUtZ1GSaC5jiSAqSr/wB32rYvNEWC8/tCa4DoxJRQuME9Oc81n6ba2WiTXJac+VeMBFt7IeQCfWta5hl1VrVIt3Tyo1J6k9zXRVbdZKnpF6bf11CFuW8tzc8OOdQuDbWeSqf62TsvtXew26W0QRfxPrVHQNGt9C0qOzgAz9537ux6k1osSa+vwGFjh6SV7s8qrU55XGM1QuTUjVG1egjIhao2NStULVYEbVG1PbrUbUARsahY4qRqhekykRs9SWT/APEwtv8Arqv8xVVyQadYv/xMrUf9Nk/mKBmtqv8AyEpvw/kKp1c1X/kJy/8AAf5CqdZPc9al8EfRBRRRQWFFFFABRRRQAUUUUAFFFFIA68VOMAfSok659Ke7AR14WbVbzVNdCGYWuT7Yn5rx/WZfOv3+uK9J8SXYSJ+a8tmfzLkn3JrnwMbybIqdig/M7keuKmUcVAhyxPqc1ZUcV9tQVopHDLckFKoy6j1NIOlT2cZlv7eMclpFH610PYz6nvNuuy2iX0QD9KkpAMAD0pa8c9hbBRRRQAUUUUAFTWn/AB+wf9dF/nUNTWn/AB+wf9dF/nQKXws8D+L/APyVPWf+2H/oiOuHxXc/F/8A5KlrP/bD/wBER1w9aHiCHpTGp56UxulAHr/wWSW40LxLbQ48x0Xbn3BFY97pb287mXhhxtra+AEmL7Wo/WOM/qa9V1bwhpOrzCaeIrJ3ZDjNOMrM6KFb2b1PBxbXMTR3Bt28o8BiMCr9uSqF2+836Cup8X+K9Is430S3tGkS3GxXHYiuNgv4LmPKuAxGNh60SvI7E5T95kk0bNZo45wCCv41lXmHg6YYECtCd5FtTtPXIrMvCZBHgbSByPWkS1rcgjuZre3/AHbFWzxj1z0ol1e883YMI3dgKpXt4lqMKdz9h6VQe9V4wSf3mOTTvYIVIwfmdBamCEcH96/JbuTXR2xH9nBychc8/hXnkd6xcYIOMCu/t7qGbRhEjAMFww9DQiudTRUiuI5eVYKSe1P2ts3bgR1571kN/oUe5z8oGTjtzVlBcXNt/o5CgHIJPUUx8xFqT71VY1JfrxzxVdllFzbuycGNsfXIqWa/j063xI6NKASQOp9qbaTSXUJmncIpz5aHrzTSuOnHnkRyziCQxquX37D7UsUm8KXK7ieuazYnX7cVZ/kR+vqaqMm5pUWQkgtkZ6HNHLoZuOhvW/2G+1Bba8mKQKfmda6LVtNtYtdsdI0oJvnQcr05HBz9Oa88iby1KZ7YrX0HXLzRdXjv1RZ5Y02R+byFHQflUtGcqbex7f4P8Jw+HVkvryVTJt+aRuAoq43xNsftckNpbPNChwJd2A30rz7VPG2qeKbGKwjiW3hKj7QV/iP+FTeH9Nin1a0tCHaKSQBtv60WIdFQjeZ7tZXK3lnDcoMJIgYZ9DVjvUcESQQpDGu1EAUD2qTGeaRyMO9OHWkxSjrQB4X8RV1CfxxqMUYCwL5RVmPX92ucCubT+2dJuneONntygZ3HUfSum8e+IYbP4ganaXAJiRYiRjjJjT/GuQ1nxdLJEkEMLKnUL6jtXnTi+dnr02lBa9C9cXUXiC1UllLpk5IGSa5m18OyXc5RJmBU85ORjPWjTNM128u2ureH7PbscF5DgE10lsn9hKUuGZ2mcEvjAB9qTi4dR35h1r4Nv0lijN+iW4yzDHzHIFXtVhvfDtq0iJ9ot+vmjqD6Grsd2DceaZCwI4GMVqXF5b3mmzWzgZljKjPuKlPuOzRzGgwrfWU0t8PNmkcFFHRKb/YUVw929zOQtuNpQHAcnpitPQtNOmaZFC7B5Vy0mO5NVbxby41VbQx7bR/3hkHtxzQmr6DZQn8LQCAzacPLnI6schj6ZpND0aQF7jUUbeDkruz0rVkuo4rxIEVhEibjjqO3NQ6iZ7jTpVtpSsjDK5/lScmx8q3Qt9rlvafJAuMjAQc1zc0Gp62ZdsfkR9zJxuqDTcWzl7h905JBLdvatiPU085UODvcLn8apKxLs9zGHgaFmUy3rqzY4Qd/Sux0fTmsLPNpAZ4YR++lkkIKj2rSuraxnaJ96/aGXcVDcqRWdNa3ptpbexunt0kOWDciTjpmiUnISSWwJG1/rkdvbMY5JELMQPlGK53xRo07TlISrTR7QGX+/wChq1oU19YeJ4G1BxBEwbkngDBrptL0+31zXknednSNGfYx4Y54J9OaE2mhPVM5ZtMnRIrK7k/eFAxZamttP1C1IkjzNbqSpfowI7GvQtX02Oa0t7kQQr5XDSJ/P6VgLDN9rcySAofmJHTPtUvVjhLQy7bWFl/dvJyDjg/oakutHtvEDeZNGrbQY1kI6VRv9AkvtUD28nk7zlyO+K6eztYrCyWBOgHLHqfc0k+V6F3XU8d1Xw/PpepS2L/Nty6t2ZfXNLpfg7Urq7ikeFUt85LM3BHtXpeqXsNsRJKoZk4UnsDVOzkvNVtnuIiscKnaNx6/St3VdtDFUFe5J/wjlh9jSFYY0dOskY5qh/whmlu5YvMzvzt3/wAquQw6yjOglRYH4YZ6+/1qlea1NaZggt5hjKmYrkms4uXcuVn7qRqraWGkxeVB/rQB8u7JHvToLksu1nUKGJQdTn1Nc/8A2jHBAnmI/mSYJJBJAP8AjUY1q2jmbKSgD0U/4Um30JjC+stTpUa3nDcRMQfnWTgt7j1p+h6gq+KNKgc/N9shXGMY+cVhQ6zBJ5P2WLcWUiRip+XNRQOR8SfDmDgPf236SrTgm2ObUYvQ911lgNTm/wCA/wDoIrMZgK0tYH/E0m/4D/6CKzHWtqzep5kSF5PWs+6YOyqOuauyrhaotA0koIOMV8tmtpP3pWsd1BtaJDJi2xST83SpYZ2MyGTGFBBNOkhVoG3HlRwKgtEW5uBAw+7z9a8FWcfQ7ZxdrkbwvPeSSRMVAUqzdjTILtxBHanczQMC7AfdGavK6WZmj7Nzz04rMs71Iku0lXy8vuTI5IIraGsdPIxW5da7kKyMkZKSEBOOc+v0qrNJfxKtuFSaU7jx0A9DVn7UWFutojFkGcMOg7g+9RJdyyXUlx5TLFGTGw7t3/SnFW1sVe5k6ZKdOtZrOe1Zp5JCX8vnOecn6f0rXlsV+zx7p2Z1IYMT0xyBUVvcwmW9cuCwYL07Y4qKS/upo4Iordg8zFFY9CuODW03OcrrT/hiWRahrP2m1dEKq4cQNvzyzdAKxdR0SGDUrLzFlaKSULcgHhMkcZ7CujtdKTT7u3+0sHwWKtt6ufX+lZmr6zb205ufs6ySghXBOQfX9K1oTtPlorQUorluzb1U2ohNkCnkyKqheox6r74qXwjEba78qX5o4SVic85J/wDrYrmrnUH1vS0uLZFhSKfgOcbUAwOfx/Sr0Pi3S7HRIY47xHuQwBQdevX8qKMKlHlsru+q7eYTlGUX0PW45Qy9aeTkVzXhrVf7ViV8kLjpXS8Yr7TC1faQTR5U42Yw80x8VIxxULGu1EEbGoW61IxqFjVgMaomNPY1CxoAYx5qGQ9akJzULnigaK8hpLFv+JraD/pun/oQpJDxSWP/ACFrP/run/oQqC0buq/8hKX/AID/ACFU6uar/wAhKb8P5CqdQ9z1aXwR9EFFFFBYUUUUAFFFFABRRRQAUUUE4rKtVVKDmwYqnnFRXsmyI1J91cmsnUbrCNzxivkqtR1JOT6knEeKrsbCAeTXDE4SWT2wK3fEdwZLojPSsC4+S1Re7nNevl9P4V3MZvX0IIhxVhRxUSCp1r66COGQ7tWt4Xt/tPifT48ZAlDH6Dn+lZJrsPhzZ+drstyR8sERwfc8fyzVVXaDY6avNI9Toooryj1QooooAKKKKACprT/j9g/66L/OoamtP+P2D/rov86BS+FngfxfP/F0tZ/7Yf8AoiOuIzXb/F//AJKlrP8A2w/9ER1w9aHiAelMan9qY1AHrHwDfHiHVU9bZT/49Xvh5FfPPwJfZ4zu0/v2h/RhX0ScEUCPnDxlo93p+uXK3SkGWRmQ+oJ4NcsY2ifKs24f3BX0h428Lp4k0gqigXcPzRMe/t+NeJDRZIpGjusxsjYKY6Yq49j1cLVUo2ZjQ6pdLmOSLfGf73FTJML3zWKcxDCgHNa0ul2pi2hST2JqG70iTT9OilETIs+cP/exVSikbVVFK5yU+kXs1wXMD7T0qpLpcsLHzFZPqK2lvpYY48uzYPStNLtLxRwp+orMwg4KXM0cb/Z7Fx5TEtnpWrYw6j54ZgwUDJPQcV0yWtuHXy41BA5OKg1CVY7cxoQDIdmR700+xbdNu8EZC3aufMkYrDyOecH0qLTLyOeaSAM0YIO35v8APNTWmmyxTvaXKnDA9ejVFf8AhuWNoprQkIRkgt938aqUbClCUbPuQJp0gvCZjv5+U/1qO+vP32ImOBhQc1KJ3it3RXLE/LuzVRbfLAt1p9DoVOTiuUS2hdnUe+TnvU1/A9vqkjqMRy4cH61cgUKC3Gegq9JF9ohRcA7RgZq1G6NJYZNK3Qw3jxhvUVq6Lpr6pdw2sIJllcIAO2au6doJ1LUba0eQRpI4QsBnGTXuvhP4YaV4auFvNzXF0v3Hfov0FZPRnLWqRovzM3TfhWLa1VGvArdWwtdboPhOy0NjKh82Y8b2HQe1b4FPAGKg8uVSUtWMwM07FIaM8UECnrSdxQelIPvCgR4d8RfCNvrPjjU51nmWd1jJCngYjUfyFcppnhd7K687ULpbgIMRDHp616L4xulHizUYosrIPL3sPeNa5yZ1UEuAVxXnym+Zo9anD3YvyLUVxBBsdzmEcYPSsjWtQtwJDMolt/4QOintz+NULzUAWdOAg5rn9Wu/7QtGSDcRGwwucA/41CTbNG0kW7m+u9NlU/NJasAUk64z2NXY/EETgZlwT2NY+naib+B7AjomcEcZHatqDw4dQsYfK8tiq+Y4PBI7AU2l1JUtdC/ba9ukBjkGRyM8itNNYl3F2OcnOAvFYUOoaRYv9nuLGJHX5TuXmrbHRbiTdb3TWxPQg5H5VDXY0uasU0E6kFPnfgnGMiq15EbaMSRl2TzNpU/wk9Kh0vTZ5pHnupglshIWXP3/AHFF9qyTMlnYnncN7N0B7GkVsc3rei6lPeST2cOQQCylsEH2rkIL2+u7pLZOH38exFepzaW9zaFlYsoGTMWAJ7cVa0vwvYSWF3CbWKGeOMPDLkZGep963jUSWpz1INu6OetdQYyyvN/r24JHfFa9tqJC8kkemarabY2WozSQIuJT/rHzjp3FW28OywZ8m6D47MMGs2aJC6jaJrlmI2BDj5kZeoxVHwpr66Fq7f2llFOYmYD7p7E1otaz22H8wZXj5TWB4p0mZLBL2Es4z+8T0PrRHUJKyOl8U+PbSLSpbPTVVopQd7B85J4GPTtVjTXkfRY2uGUuEG4nr0ry3R9FvL2SK9eMC2VxtBPLe9ekTObXS2Y9ApzTqJRViaSuizpcwmvoI15MnAq/ekw3MsTEZQ4OKo6PGRDBO7xx4IGTTtWdYb6WMMWO44YnrWRrYx9cu7e2tHuJ7R7lVG0AA7VJ6E4ql4dub6/iSUQFIx0hUY/HHYVs202+KeI/MsigheuSDxV/w7GixPKsYV5W3SfXpj6VpdKJlJSbsjNtdWhZ5rW6Qx3AAUjn5W6kflVl3W2iO8bhnIP+Ncl41klsvEqTwEAzJ+99MDHNTaV4jWVVjmBIboX6kGiSaipDg03yGzLcQSTq2wMCcHauSKur9l8kh1Q56jaKx7vTC8huLCXbJtyYx0Y+1YEOtT290tvdh45TlSrDoe1TGJp7qO7gtNOnkaAHytw+UoBya5u+sprH4n+FI2zgajbAMOjDzVp+hazFcakEGd6Ecepr0CK1h1jUdKchTJbX0EwOORtkUkVVOXJNXM60bwdjtNVhZ9SmIHHy/wAhVP7GT1IrS1J9t7IM+n8hVBpfTNdVRK+p5UWyB7IEYzVOS2KDjtV95Diqc8pINeFmVOjyOUlc6aLleyZltE7Ocn5c4qrPP9iuVcY5+U496tT3QB8vOAT19Ka0ED3UZmwcLkbuhNfIrR6rQ9JXkmjMkuJLnV4rfJZN3zA8ZFX72CJNQtwYgUAIyT044qlIPL1IyLgKufmNSTwtqCxTCVgHJ8sDoMDk5ro6x6KxEo2diVbr7NNKpUkS4AIHBPTrUUfmWF6sEzNILhhkY+63pWnciKCKB9qeXGV3A9BUF3FFe3lvGrKwDFztOeg9qmM09OhJW1iwtw6SbSd7qrhOOM1Nqfl2ywzZwlu4O0HBwKpazfzRoYZYHSJMM86noo7/AFzVS4+2S21nJKWmidTKYymGK43Acd8VpCE5Ri5PQNjT1e/tpIInU7wcMCvp61yUEMer6PdZiRrmOQ8q2DyOGx39K3bv7PqGmxyWtvIi3EZBODlcf0p+i6ZHaaC0vlhbpnYs7DOecD8MVvSlGhT03uDi5tdhbOxtZPBRW7t8KkREqDgnGef615zJoz2utrHKjSQyfNG49K7OCz1G9uLy1lv96zRLtSMBVx1AA9OK3tJ8PNdhLqeLbcIQSp7Y7V2YSU4TlGLvza+lzCvFOKbRv+DLVbe1jChsFOpHSuw7VQsYBDCu0YOOlXN3HNfS4Cg6NPlbucNSV2DnmoWqUmoj3r0kZkLVExqV6hbrVARtULmpmqB6YETGoXNStVdzSAhc8Ulj/wAhaz/67p/6EKSSiwP/ABNrP/run/oQqWaI39V/5CU34fyFU6uar/yEpf8AgP8AIVTrN7nq0vgj6IKKKKCwooooAKKKKACiimu4QZNTKSirsBWYKM0xTk5NRFi5yaezhVr5zHYz20uVbIQXEu1MVyur3YWN8Gtm8uMI3NcNr95tRlB5NefTjzysJuxyt5Ibi8b3aqF6wa52DogxV+EcvM3QCsvO+RmPc5r6rLqd5X7HPU0j6kiDpUwpqDAp9fQRRxsQ16n8OrHyNCkumGGuJDj/AHRx/PNeXxxvNMkSDLuwVR6k17rpVkunaVbWa/8ALKMKfc9/1rDFStHlN8NG8rlyiiiuE7wooooAKKKKACprT/j9g/66L/OoamtP+P2D/rov86BS+Fngfxf/AOSpaz/2w/8AREdcPXcfF/8A5KlrP/bD/wBER1xFaHiCY4prDin0xhkUAeh/BKQJ49K5+/auP5V9IgivmP4PSeX8Q7XP8UUi/pX0yrAgYoEyYdc1xXi/wSdZc3mnyrDd/wAQb7r12g6UHFJMqEnB3R5RpPwz1J7pJNUukWFTkpGck+1Q/FpYoLCy062xG6EMo9F6V67ivPPiX4bjvbCbV/OKSQRgbSMg8/8A16HJvc0dWUnqeIMVMDqFzxycVXsZgT8pxzgD1q9PG32JiRz7VBp+nLt+0yAjAwg9vWrtpc6r2gi59paMY/OmJm4nzsJjVTknsanaBTHuBx7mn3btBZRpDE37w8MR96nCydzWg1cnmnjtbZFncNJH09q56TULq/uioyYQcBRwK2oNN3I01wWZjy1P+wpHOCqYX+EiqlK5tVqp6Iz30Zpf3jMEVRwoFKuiZlx5hPqMVstxGgPODzirECgDeRknms7sx9vNLQyb3S1gto/KXBXljVeHIkVenvXSzqrwgMOo5rAKhLgj0OK0g2dmDqOSaZ13gWyjvvFtpG4GFzIcd9vNe+gdq8J+GmP+E5iB/wCeL4+te8Cpm9TzcwVqthMUZpRTe9QcIh70o6UjUDpQSLSD7340UD7w+tAHkfjSL/ir9RdQcsY88df3a1x2ozyBGyrBF6nFdz4z1WOz8V3qHkoEJH/bNa5K58RR3MAhCg725X1FeZOXvv1PapJ+zj6HL2hutSLraoHjkO1srngda0RZ6Vo7qLxU3t0XPArSU2+n232bT7YeYx+6nc/X0qLWNAmn0Ri0q/a0HmYYAg89PehK7HtuZ9z9gubyG3sQFuGkXiNR931J+lSX2oz2Ov3KWzqR8sZjUcqAo7VY8L6LBpd9GyqGfyyWdxySR2qjFNb2PijUZ5ZBl8fLjJbrTavoiUraiaisWvQbfsn79P8AlqB396TTPC6K63V6W8tOAo43VJBrC6hq8dlbx7IyS0h244FXL+9N1eQadaHGD87A/wAI5NJ3Ssi1a92R3+rSXapY2abYlO07OigVRhsfskkrl2Cqd27PJrp5tN/s+0Vmj2xuNwJ54rnLqPUr23aO0tyylsEnjI/GpQ3qN0vVD5otJZGDMxZEb0/ya3Zr6K0hWRwXBZQQGPPPt2rnZdFvYpYrmeBopFxiRCD+GBWk58yBInTL9QMck02kTG7Rr2WsWq391MLeG3yVXbGOvvz9aNav1sbm3uAyokw+6x5Y4rnNa+y6LPb3Gx3gdsyKT/F/hT9Xsk8R2dtNaNJCsC7gXJ+bPXHpxxV2vuQ207I6C5uDcWAmH8WMHGKt+Wl/YzWrr8kiFfccdajeESaTLAoAXysJ7HHFU9GvzIwH8YJVh7is0WznNHNwdSTTXJ8uAuCMY+7gA/rXU6tGv9mICeHcKQPwpGskj16S5C/66PbkD35/pVLX7j7Lbxs7hUaZec9KctXoEdFqXldFgChxntVG7aYxsAN0hHy+9U59Yjn2RI/AwNwPX3pYNShm1KGPzcFR+p6VNmW2XNN823jLOpJA9elbXhZ/MsAWPLAk85xWbOwt7Wdyc4BGau+GSkWhrKwwWTdn1okLbY5rxBbvrGtTQRYKQp8zY6iucv8AQLvTVN7CzShRkr7Cu7l8PSJepq1usgW5IWRnfjJ6Ee3appIFeGSJgCFyrEflWinayM+VNNo4jRPE7xIFLK8bEbgeo+ldJJ9g1ZBJKiM38LfxLXmlxbtaXs0MRGVkK7fbtV2z1OW3l8s5RlPKk1cqet4kxqaWkdR9ggsLh7qCJhcZ+Yq2AR64ru/B9402rWD7sbp4wfxYV5ydYLxrIyEuFKgAdRXRfDq/Ems6dHuO77XGCD/vis2rtGja5Wj3HUx/pspwO38hWY7YrT1PAvJPw/kKypc11VkeTFjC2arSjOan9qQRZNeZXjzKxvHQ52+iZuFUk1YMDSaWXkP7xRXQCzQIXZRkCsaZXSXzARsY4218tjsOsPyq+r1Oug3OTa2MS5SRtJVYhufqx71d05c6LBFcAKIwenfmrcipAXwBhhWNqd5cQvFawqvluwBYjJFc0W6q5F3udLioe8iWGUX11Hpu/cANx4/h9P1qe4gGhzxG1iLFztKA/eB/z+lVbbSLyDVGvVkVEQ4AP8S960LW4i1DWQWbHkg4QkEgn2q5OKfu6xtqZ2fUpXRk1O4NrNH5ERjzIXbhsMDgVLqN55FvFLbOgSMgFcjCrjHHrxUXiiMyW5gj4eR1AP49KqXPhtxpC2wkZ5g+/LfTmqh7Nxi5Oyvt+o7SV7Fq61e2a3htrKdPMlgzGp64xXO2ia5qt5eQifyzGgUheB36fWtuLQIdOXTXcR+asqmWTdjHB4H41LeapBpviMwwwMwlXLv0H4VrTnGN40Vd2bu/JiabaczDtL+XTNWt43C7jbgAt0YZwR9RXovgvUk1WK9fjzILgxMo7DAIrze8t7fXb+JC/lxlmQOvXk8mt74bCPRvFWqaTGsy28qJJEZmyXIHJz+J/KvYy+NJ11KWkrfqc1fmUfI9VK4PtSE08jIqM8GvqYqx5whNMY0pPNRsa0QhjVC1SMaiY0xkTGonNSNULGgCNqruealdqru1A0RSHim2Df8AE3s/+u6f+hCo5XpunPnWbIf9PEf/AKEKllrY6jVf+QlL+H8hVOrmq/8AISm/D+QqnWb3PVpfBH0QUUUUFhRRRQAUUUfSonNQi5S2QEcsnlrxyewqrks25jk1LKvzHNR18xisfLESsthO4/OBzUU8mAaVmG2s67n2qea4mxGfqF1gNzXCanM082PU10Gp3JLEA1z6IHlaVvurXZhYa3Fa7sZ+oMILZYR1brWci1Ldzfabpm/hHApEGBX1+BpclPU5a8k3ZDwMUUtAySABkngCvSOZnVeAtJ+364Ll1zDajf8AVu3+Nes1heEtH/sfQoo3GJ5f3kv1Pb8BW7XnVp887noUIcsQooorI2CiiigAooooAKmtP+P2D/rov86hqa0/4/YP+ui/zoFL4WeB/F//AJKlrP8A2w/9ER1xFdv8X/8AkqWs/wDbD/0RHXEVoeIFNp1NoA634Xy+T8Q9NJ6NvX81NfTsTgnjtXyx8PX8vx9pB9ZsfmK+pIPvGgT3Lw5FGOaRelOqRh2rkPiSzL4Qm29C6hvpmuvrlfiGm/wddezKf1oGjwS7H+itj+6amjwYEHbFR3QzbN/un+VPgObaP/dFadjsl8KHD7i5/Gug1lVPgvRJABlbl1J/CsAfcX610Gp4f4fae39y9I/Q0x0f4hRPNq/0qOb/AFcZ/wBo/wBKkXm2f/dqOT/UJ/v/ANKS2CW7ImH7sfjVmD/VD6VWf7g+pqxbcxL9KliexYYfulrBlGLt/wDereP+pFYVxxeN9auB3YHdo634dPs8d2n+1G4/SvfRXz14EfZ460w5xncP/Ha+hBSkcmZfxhR3pvenDrTT1qTzwPShaD0oWgkSgfe/Gigfe/GgDwT4gaPfXvxH1aT7SY4D5IjUf9cUB/XNcvcWUWkoS8wkmBwOec/SvQfHV1CnjW9SS4SHaYgSx65jWuWafw+kpfC3E/J3uc8/SvNqX52e1St7Nehl+G794/EsX2vcYpgVLMMBeBj9SK9E124sbbTZNkZkuZF8uFV7+prmWsrbWYWxhfJcBQBht3Ufh0qzZ2WUaSeUtIvRi2SDnoPaiT00IjHUW1uooruNZlCuYunoa4LV7LUk1ybVYLZ3tFkOW9u9dFrFs19rFqIbgxrhvMZT1WtmSSC1sxESMNhefSlF8urNJK+hjwXFsukSap5Kq8qeXEw4JrE0SRR4rt0dyVP3j6DvVnxS81zpcUmnHyrS3JWMDq/qad4P0WGGT7fqlwd8o2BQfuj1JqkrrmJk3ex1Wu3wu5LawWfmRioVR6c4/KooZJDB5ECbinA5rlLOC+l8W3eWYW9tuSOTPr0I/CupgKwDaAQoXg+9RKNio6kS3729wy3eBsIIB5+lRal4hsxbGFFQzTEKmMZBz2qTXL14INLntYY5GkZ0uFIGWCkEU1/DWmahLaa3aQmNovmkRejfh7UJJPUL6aFXUraO/it7WUjmVT+Vat5EsGlTLGACqcYqpbkXWru3GyIEfiam1gLaadNPuIATkZ4Yf4073dh2VrljS7yK70kOD1T9QKySn2C6FypxHOAfo1YmhXcqwpDauzwyAkb+Nh9K3havqPhqSLcRcIhKt6MKfLZi5k1c0471JZo9pyen4VheNrc3dlDbB1Ql85ftTvDMb3dnFdSEqwj2sP8AaHek8Q6bPqlxGkE214RuVW53Z/8A1UlpIN0cfdJNoumq7gyvkqrL90VX0ET3GoI0zukDsA8qjJFbd9eW0MP9m6rE2/oydPoRW74dtoVt43ii2R/w5rZytHVGXJeWjL+rK39jyqrgqFPzHjjpmrejsI/DKc5ARRkfQCsDxY0ksEdpBIVLtyvsK6HRIVfwgWd/3o+UKe+K5nHQ2TL8+ozXGkW2lJHtQMNrDrx3qCOMWs5RvnC/eU9feuf1e4khe2EUxRy4+6cfWta3uC8nmHIyuM9zTs0NpbI5G80ST/hKmnWJjAzB84yAfSlsfC9lrviW8S6nkt1hjGPL6k811WpyLbtb3LB0TOGI6GsfzI11Rr+3JVzHubn72D3rRTlYzcI3NNPD1tpaiNlLbB8pI+9VDwlb7PiLYpD/AKj7TE5wOmHFbljqEWpwIJmC54UnrUnh21js/FNoocMWvIfm9twqIS97UqUbRPVdTyb6QD2/kKz5EHc1pamf9Nk/D+QqiI2kPHT1ruqRueRF2KwXJwBmrCxrEMv17CnnZCuF+961HCjTyjPTvXM6Zd7ks8q2+nzXMuMBTgGvL9Q1XUHkVEkwocsAPf1rufE915iLZRH5V5bHrXLRaeHlUsO9eDmKTqq60idFK6jp1J7KSQwxyzku/G7PNXZJYPKkcKHYDIB9axry7az1FYP4A3zfSt3bBAYUbIMwLY/DPNfO1YtNS7nfCdtugbpLm0wAE+UHj19K5fTbWc6y1xlo1DsCw+hreQ3E92Xgl2pjgetWblRCkQ2hSWwcd806dR07xXUrk5leRiw6pbeasNx5kt2sxKqBwRng5o8YvcxG2ghkkC3JIcg9QMcZq7b6XbNqMk55lCdFHHNJcPFd3tssrhvI+XZ6N71rGcFVU4rZakuPu2bKN3qEOoaXY2FxIkF1LIIwWOQSO/8AL8aPFdpNpVist0wLx7V3AY3Z9PxqnqGjNe+KLFdmG3AkDt3re+K1m8ulWLKxwjjePXivVwmEhUpe0j0d/v3OWpWcZNHAafY3N/Ol3DIymJwdqnjFeiy2lxBfWOqowKwjDBR2bvWT4JtUiYIyjDetd99lWNDBtzCw4z29q7qFH2jVXtt5GEpW0Na2uBPAsgPUVIxzWLpsptZDbOeB90+orX3ZFfRU3dXOR6Ma1RMeae5qFjWoDWNRsaVnAqBpaYCO1QO9K8lVnegBHeqkslOlk4qlNLSbKSGSy80ulvnW7D/r5j/9CFU5Zc0/SHzrunD/AKeY/wD0IVBZ3Wq/8hKb8P5CqdXNV/5CU34fyFU6l7nqUvgj6IKKKKCwooopAFTImFz61Se4RbhIs/M1ayJlBXz+bYtSfsIP1MpSsZk0OHLVXf5B71oXRCg1i3U2M149ki4u6Ipp9ueaxb65+U81Nczknr3rJvJPlyTVRV2My7otI20dTWRqUi2doIIz8zE//XrYkIhRpHODjPPauTuZjd3TSHp0Ue1e/l+HcpK+wTahG/UijSpQMUKuBS19RTVjzpu4V1XgbQTqeqC8mX/RrYg8jhm7D+tYOl6ZcatqEVpbrlnPJ7KO5Ne1aVpkGkadFZ24+VBye7HuTU16nKrLcujT5pXexdooorhO8KKKKACiiigAooooAKmtP+P2D/rov86hqa0/4/YP+ui/zoFL4WeB/F//AJKlrP8A2w/9ER1xFdv8X/8AkqWs/wDbD/0RHXEVoeIFIetLSGgDZ8GSeV410h+mLla+rIfvmvkrw2/l+KdLb0uk/wDQhX1pCfnNAmX0+7Tu4pqfdp1T1GFc547Tf4OvsdlB/WujrC8Ypv8ACWoj/pkTQB89TcwH6UWhzaRn/ZFOkH7o0yz/AOPOP6Vd9DsfwomH3P8AgVdBdfN8OFP9y9X+tc8PuH/eroT8/wAOb0f3LtG/WqHS/iIow827f7tRtzaj/eH8jT7b/Uf8Bph5tfxX+RpLYqfxMjP3P+BVNa/6sVCfufjUtr9wfWpZL2LX/LEVhXX/AB+H8K3QP3B+tYd5xdn6Crpnbgn7zN3wg+zxppB9ZcfpX0WO1fNvht9ninSG/wCnha+kh0FEjnzNfvEKOtIetKOtIetQeaIelItKelIKCQo/iH1ooH3vxoA8j1HRLHUvi/rLarH5kZji+zqfuhvKTJ/nXm/idLCfWZbK1RRLFKcMnHANdj8QNSEHxA1KFi8ZURbXRipAMS9xXF+bY285KhEmWQGN2PJB65NcM377PUpr3V6E8czaRrryDzWEjguhOVUAYzTbvX2knlt7SZ5IGDFTt2nggn19av701CKU5Uzo2GQjJasS9HmaTNPbKEuYJCC2MHtx+lSmpPUtx5VdEunXc6XFxdFWlhjhIBA6HPT61b06W81C4W+lQfZwCFjbvkYzWh4ESJtHXygoMhO846nNM1uaSx1L7GJFiDkFUZev41M1rYqDurkV7FNqMD2dpH8sQIXsMmsX+y9WaQRufKVMAkNkGrEfiT7BB5FwRvZicRjj8TWReeL5DHt8sjsKcIyehUpxSuzq7G8trS5ht7iQkMwViOuelQ+LtWGg6o1rG37ooGQnkmsaw0i5utM/tS5Zw2Q6RE9VHOTV3VbK31qwtJpWZpISQx9Qeg+nSmlFS1IlJ8t0Hh29m12/LuuYLeIkn/aOP8K6Dwjqp8+5sJQyhWZkX1U96bbW8Gg+HHMSBGKEnjqaj0gWom03VLltg8oxuM4Den9amVpXsON1ZsdpcL22r6pCclUmyrY/hxx+lYfjnWhHp/2RCAZeCB1A71t3mrWUWqX9wS6RuqeVJztJA5XPrXHeMdOlu4otSRSFJCY9AehqqaXMriqN8jsYNhqF3aovktwORmvRPAmpNeWkiTMGlDHdnv3rkINNKaaZNvCjBPpU/hS4ew1ESFtscp2/0FaTaknYxp3ja56DYwpbyXUa/KNxOB/n3rI1DUvsXiFAT8rRDOPqa3YMTSzuvJOOn0rz3xmbi31yKWIkZhBI9OTWMI8zOmUuXU3fFFhaeINONwqlLq3XKv6j0NbGjRiLTISoG0LwTXAR+JZ5bF4MYL4Rj3Fem6asJ0YbY8/LgFqJqSiosmDi5cyOYu0MviC4luvltvJMcTA8BgcnNbOgXaXdncRL91CVH+NZ6xx3L3FtOgZGYgir/hjTJLWS7VQPJT7p9RilKzQ0rO5zPiTUfst5YxhQVZiN3cYrodHuROAFXrjA96y9X0q11RbxJMrcQN+7b+7zTvBzSanDJHAQZ4/lY46f5xVS1hdC5mps6TVFjvbKaCTDHlQQfutXOeGtGlt1nFy29nIGCegHauiTTL6zgZCrFGG5i3Rj7VBpz/6QREMESchuf51KdlYE1J3OZuppdA1TCH/R5TjHXaa7nw8mda0p2+8buE/+PiuM8UITqNmuBl51GPxzXZ6Lka1pWCN32yAEf8DXNNR2Y297nqt9Huv5SenH8hVWWUIMLirGqTbLuRQeeP5Cs1Q0r45xXos8cAGlbAqxLItlalhgueB9aUBIFzWZcymeTJPyjpWE/dWm5a1M+ZC5LNyTzS2lr5jO54VRmppBnAA5NXTGLXTH/vOMV5NXCqpfm2N1O2xxr2Rubh5W+Z2bArpo9NaOxkuLk7pFiPUdBirOkaWo/fyLx1ANX9ZYQ6Q4wMynbiuHD5dKEJTmtehc6ickkcYjnT4rbzGHnSHCgdAKn88XjsfLbg4AFSvZG6liLIMIuAK1LaySJDxSwuTRnDnqbl1MU07LYzone3tpIyhLZ+XtUVvo7QtFPIMyM4duK2/s6M6lhwDmroVX612wyakovTcyliJNmbp+lK2tC9fnuM+wwKq+OMXVokXX5hXSKQi8cYFc1rINw57gGtlhY4bDunDqRzc8rsydIiMDoVGMV3MEgmgVjya5eCHaowK6DTDldhp4Gl7NcoVXfUj1OMxFJ06A1ZtLwSoOeanuYQ9u8bdxxXNRzta3OxjgZxXqR91mG50zNUDPTIJxKuCeaZOCp9q1ERTTFc1Re6x3ou3IUmudur4RuQWpgb32kNxmmPKMVzi6oufvVbjv1cY3UrjRdlkzVGWTrSvMD3qtI+eaTLSI5HqfRif7f07/AK+ov/QhVRzmrWi/8h/Tv+vqL/0IUiuh6Dqv/ISm/D+QqnVzVf8AkJTfh/IVTqXuenS+CPogooooLCiiigDASTfrK5yCJGGD9a7EELCDXJyIkeqxDo25m/M5rovN/c9e1fFY12xEm+5zcrbKV9L15rnryYk4rT1CYDPNc1e3iqxAOW9qygnLY6UiK6lC9TzVCRtxLN2p7FnO5qwNX1bOba3bnozjt7CvVwuFcmu5qkoK7KusX/nSG3ib5R94jv7VmIuKcqYHNOxX1WGoKnGyOCtU5mJVixsLjUrtLa1iMkjngDt7mr+i+Hr7XLgJAhWEH55WHyr/AI16vomgWWh2wjt0zIR88p+8xrpnWUFZbmUKTm/Ir+G/DkGg2e0Ye5cfvZMfoPatyiiuJycndndGKirIKKKKQwooooAKKKKACiiigAqa0/4/YP8Arov86hqa0/4/YP8Arov86BS+Fngfxf8A+Spaz/2w/wDREdcRXb/F/wD5KlrP/bD/ANER1xFaHiBSHrS0h60AWdIby9bsX/u3CH/x4V9dQHJU+wr4+tG239u3pKp/Wvr60bdHE3qoNApGlH0p56imR/dFPqXuMKyvEyb/AAxqK/8ATBv5Vq1Q1td+hXy+sL/yoA+b3+4frUVif9EX2yP1qaTow96hsf8Aj3I9GP8AOrex2fYRN/A/1rorcb/h9rIH8MkbfqK54fdf610WmfN4J19PRFb8iKpbhD40ZtmcwD/dpo/49W+q/wAzRYHMCf7tKP8AUSfh/wChUkaVPiZF/AfqKltPu/8AAqhH3G/CprXow/2qlkPYtD/VN9axNQGLnPtW2P8AVt9axdR/14PtVU9zrwXxsu6O+zXdLf0uU/8AQq+mEOVFfMFi2zUbFvSdD/49X09EcxIfYU5meZr3oj+9IetL3pD1qDyxD0pBSk8Ui9aBMKP4hRQPvCgR86/FZHufHmr7SA1v5J4/iUxJn8utefT/AOqkhJ3E4Kt6nrivQPiaB/ws7VCSdu6HPP8A0xTr+Fcdb2MDztFJhtrZBIrilpJs9SOsEi6+q+RqkeBuVlQjbwRlRVrU41ksbm4gGXlAYehIqay0Sz1C7jgf5S7gbwcbfSr11oz2LzWZcHA6HmsnJX0NoxdrMzfA9zIsU8UilHWTIH1rpvEelRakkEu5knQ/e65HFc1osL6fqheRSA+OT/F1rr5ZCbcyY3DqRSk9bhFaWOWu9It3CxmPIZsYo1HwbaXjWTRfuUj4lH97v/jW/NAGngkTmN1z9DU2pI4tQqcNKyqv4nFPnaV0NwT3M27nS0ubbzIvLtJcwoezDFULqL+zVfauYCQ0Z7H2q78QHlt9JsYAq5hO/G3kgVDpd5BrmlpaSnczr8uOoNC2uLrYsahaPqdjavDL5cbSfNk5ycHiotPs4LrT5tKfLbMqN3UHtUen3cmkXX2G7UmMf6tz0P0qxIY7e+ivIiyFztkwePY1N7FOJDYSWQtG02827lJVlbsR3rS1S0gm8OTQgA4UMv1HIrlPGen4uYNbgDBCQJwPbofxqwnipNRUwQEgGPaC3X6VUovRohSV7FGclNHdF438n+lRw6c3lRiM/cwMenvVq6spEls7aTcEldQXHYZ5Nat1afY7y3Rz8hcZb1FNvQVrs19JgktrVWkP3m6/hXM+NrbfqlrKAN/l4ZfUZruZZI9qQRD7oHJrB8RWpkvrKTAOQw+nT/69TB2Kmr6HlyAR6kRjarHgmvY9Jm83TUQKAir0H06159rmmeXK0iRhRjePY12fhS5NxpMagL/tHPJ9quo7pMimuVtGUlykepTRsRneTXWaDMphnGeCemOteY61fiy8X3cB4QlSO3YV2Xhe5MzLtydp/OpnDlVy4SUtCjeRMni++tMlY3RXz65FcvpWoS+GfFE8LO4iLZOBwfr7V2fi4eRrOm6gBtWXMT+/cf1pv9jwrfS6k6gmRAigjoO9VGSS1JnFy0Ru3Hi6yvtJdTKodVz14H09q5rw5ercpcT+ezfvT9a5/wAQ2Fu5LxExED7qng1N4EISWeEHIyDzTcfduRHSVjS1vbPrembctmfOfoK7HRQv/CQaYBkf6bF/6GK4/UQV8R2Pou5vxrtNBQPr+nk/8/cR/wDHxSv8Jo/tHpOpxmTVJR2+X+QpAFhT3q7fALdyOe+P5CsW7ucnANegzyCO5n8xto6VVLCms3NN3c4rJq5SZZto/MlBNXpIxcTpEPuJyfrVKCQINxNW7eXapY/ebk0vZq1guaQ2gLGoxWZrUnnTRwjonJFWIpxlnz0rOd/NuGkPc0ShdWBMWCEAc1OxAGKYHwKQtk1cY8qsJjxyasRiq6daspwKYDpXCqQPSsaaPcWq+XLmQn+9gVXZck1y1VzFxK6JwK0bA7XFU1XirVr/AKyppxsxvY17g5jzXJ6rHhywrqJGzHWFeoHDV1tGaKVhfEHDH5hwa3gVngyOTXHS7oJtw7cGtzSr7cNhNEH0G0F/Edhrz3xHM1vlvSvUbqLehI9K878WaezwyYHaraJOEbX9j/eq/Z+IlOMvXnmqtNa3bIeBniqkGoywtnccVF2Uj2qDWFdR81XUvA4615TYa8cAFq6G11sNj5qCzuPNVh1q7ohB1/Tv+vqL/wBCFcjBqYYD5q3vDt4H8RaWuet3EP8Ax8Ux9D1PVf8AkJTfh/IVTq5qv/ISm/D+QqnUvc9Ol8EfRBRRRQWFFFFAEE1rHMwcqN69GqGWV4oyucYq7UcsKTKVYV5uNwEcR7y0YJK9zktRuZH3KWIFYjskYMsrgKO5rtn0G3lfLu5Hp0rntZ8DTXc5ktLwbO0cuePyrnoZdKG5qpJHGanrD3AMNtlI+hbu1ZCp3rvLf4cXDHNxexqPRFJ/nW5Y+A9JtSGmElw3+2cD8hXsUYQpLQwm5SPNLHS73UpRHaW7yt6gcD6mu50b4exxFZtUk8xhz5KdPxNdtBbQWsQjgiSNB0VBgVLWzqt7ERpLdkcMEVtCsUMaxxrwFUYAqSiiszRKwUUUUhhRRRTAKKKKACiiigAooooAKmtP+P2D/rov86hqa0/4/YP+ui/zoFL4WeB/F/8A5KlrP/bD/wBER1xFdv8AF/8A5KlrP/bD/wBER1xFaHiBSHrS0hoARDtmQ+jA/rX15pjb7G1b1hQ/oK+QCcHPvX1voL+ZomnP/eto/wD0EUCkb8f3RT/So4zxUhqWMKq6iu7S7pfWJh+lWqiuV3Wky+qH+VAHzRKMNIPQmq9l/q5B6O386tXQ2zzr6Oaq2X/LYekhq+h2L+GTjo9dHoQ3+GvEEf8A065rnB1ce1dJ4YG/Tdbj/vWTH9DVLcI/EjI005t4/pTl/wBVMPY/zqLSzm2j+lTD7sw/2WpI0qfGyH+FvpUlt1b61H/C30p9t99vqKTI6FwfcesfUv8AWp9K2U5D1kan99KcNzpwf8QZE22S3b0kU/qK+oLU7raJvVQa+XM4iRvRga+n9Obfp1s3rGp/SnMM03iy13pG60vcUjdRUHkiHpSDrS9qQdaBMKB94UUD7woEfP8A8TNKkn+IGqTRSAs/k/IeOkSCuUbSbz7UjABVkABY9FwDzXo3jWKM+O9SlkByDEBnpjykrDZ4xC24gnoP/wBVcE5e8z2KcbwXocRbX8plMDA7s/QmtZNWis1Mdyc7uBtOdvuaklitJZJVSPdKTtJQdDWRc+FdVntZHhbGCWAfALe1QlFvUqTcVoa2nOLxVnWTcGlIX3ArfvpNmnsoOMKa5/w5A1tp9skgCyIMlT61c129Menz7OGKEYqna9kJN2uzZ06T7RpEJLDzUG7Hr2pwlN5q9rEHzHGokJHrjp+dYfg6+N9YyB/lZGKsfYjP9a3fD9obe1uruYkSMxVeOw4FZSVtC07o5vxxcy3uoNbr8zRx4AHpXHaNf3WlzDCyqA3ysOxrrHhl1XUpmiUGN3IZh3A9/St+20G3aELLHuiQgEgEKD9a1jJKNmZuLlK5Shlg1qz8qZsSBflPfPrWJNcz2rNaXZ46pJ/ex/WuqfwvDbDfZGQOPm2iQNjv35qjeaQL22VL1DFznIHzA+1Qty9bEEF2t9ostsyh2ZCCtYdv4TuEmgniARcguHOMe4rUbS/7PmiltpWUAfMHOd1SXuuQM0UPmlXJwQKq9thcqe5sWpgvBHDcxKGXhXH8PvTdUhtLyCFHuAjRENuIyahtsqgfYTxyR2rMjCNrFyL2Bym7MbZOGFQ1dlWsdPazJKgkjbdtUc+uB/8AWrO8RXISG1nP3TJj8xV60SCNS1qxwV4Q88e1ZF8sepRyWLtsDHAY9j2+nNNKwi3/AGUmq28chYLHjBbGaNK0xNLgndZ2dUbKBRyOelXtLnbS9CWO4lRZkO3Y2Off9KisovOtZDuCvJyST3zQBy/j3QXvXt9TtYdxI/eBRkmjwhPJZ3hhlRwCQybhgkV2tgBcrJbtIUlibjHYVFqMVpblJim9wcCXoQKbqNx5SVTSlcXxHp0V5o4DHcYmEkeP4T6VU1S/s7fw5I4nDNsDp3LZHQfjmtSyaHVLZk3cn5f/AK9Ya6HBZa0HWSKUA/LCx4z9KiHZjkux5jf63PduYVjIJPAxya2/Ccc2n3/mXBw0oxs9Oa72XTbFbvzXtbYSN22A4/GpHtdKVvntkMiHhlOK6HUVrIzjTcXzSOW8Q3Q/t2xZOq/Jgdea7Dw3Mza9pQw277VEGz/vCqA/s8SmV7ZXkU43bQSPoTVjQblD4q06NcBTexYHf/WCsl0NHsz1fWrnZeSIOvH8hWC755zWlrp/4m8//Af/AEEVkOa9FnkIRm96j34PWkY1GcmkMsCfoM1YW44xms4DmpkzmmI0fPxHtBpiHBqFKmUc0WESA5qRRTVWpkWgY+Nakc7VpCQopkjbtv0rOb0BCYwp9zmoSOTU7fdqI9TUNFJkSDrVu2T5qrJ941ft1xTjEGyaT7lZNwPmNasp+U1lXH3jWpBi30PUiqlpOYJgM9DWtcJvQ1iTqY5M+lS1Z3LR2VtKJ7cH2rE1uyEkTZFT6Nc7kCk1fvYg8Z4rZaojY+d/GmjmOVpFWvP24JB61794s0oSxP8ALXh+r2ZtbthjAJqOoyirspyCauW+pSREZJxVGiiwXZ1llrWQPmrsfB+p+Z4t0VN33r+Af+RFryRHKnIOK6vwHev/AMJ74cQnrqdsP/Iq1NrMq+h9W6r/AMhKb8P5CqdXNV/5CU34fyFU6T3PWpfBH0QUUUUFhRRRQAUUUUgCiiigAooopgFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABU1p/x+wf9dF/nUNTWn/H7B/10X+dApfCzwP4v/8AJUtZ/wC2H/oiOuIrt/i//wAlS1n/ALYf+iI64itDxApDS0hoAjfivq7wnJ5nhbSH9bWP/wBBFfKL19TeBn8zwVozf9OyigTOvj6VIaii6VKaljCmyDMTj2NOpD900AfNeort1G7X0lYfqaz7PiSf/f8A6Vra0mzW75fSZv51kWn/AB8Tj3B/SrWx1x/hllfvN9K6Xwf8z6lH/espB+hrml/1hHtXSeCfm1SdP71s4/Sq6gviRh6T/wAe6fXFWAPnm+jfyqtpJ/dAejf1q2v+ukH1/wDQaSNqvxldfusPY062/wBY34U1Pu/8BNLb/wCsb6ChmfQvx9W+lZWpj7h9zWpGfmb6VmaoPlQ+9EDowb/eIqn/AI9q+mdDbfodi/rCh/SvmX/l0b6V9J+Fn8zwvpretun8hVTLzP4Ymx3FI3UUvcUjdqzPHE7Ug60vakHWgTA9aB1FKetJ3FAjxPx1qVqnj7U7eSRVkh8pwD/FmNOPrWQLV7xzczZhtmOQpHJqt48095fjZqEt0D9jbydpB/i8hAP1rP1TX47RnszMHZDjGcmuCrH3tD16E/cXMb66xo8V2YIbJbYjkt2c+tW/ttqxWItu3/dB5FeZz6jdzlIxbsQ3Cbhz17GtS1urnTb63hugXyCdxGMVk6b3NFUV7HZppcDzbwBHGvXHcZ9KwPiDdLNq8EdvAVg2As2MD6Vsx6sssJYMuG46dKsfY4tWiMRUMmMc9RUxk09SpRT2OR8HxmKSR0PEp3Y7eldhqeoGx0qWMLiRhtH1NYGl2L6XftbOvCtwc9s1H4t1M/2hb2ZYAAtIcjGfSrfvMVuVFqzZII0DfdyCwAxWhca1EpuEgXbDI+4qOw9q4JtbCyAmT2wDVabX8Mctn0qvZ3I9pFdT2nTvEel2mhXU0wHmI2dh5O3tzWZd6pa+JIoXtyY4+WDEdT/hXi15r0k0DQxs2H4bmuv8G31w628ahTHAuX3HChO+TVyp2iZxqx5ro07jR76TW5LW63JAqh969GB6VT1TwslpdpdQl3hcgHnOytnUfEen3erRW1jcMWAIlLHjjpj9a2AsMlvNCzeYBwGHG4Vk3Y3SUldmXCGS1+XgYqKWGHU9DmwAbiJXMf8AvZP+FQtdhbNmXI64B9q53S725sdLvMlzO4zEOcEMf6ZNCjcU5JHXaBYxwaYhcu8rpuZs9D/Sq13Zq9wZI8iST5SO31p3hi7+0aOjsTkjHB71Ztw76jJv5hHOPU0PRgtVdGJ4pW5MMcdoqu6gBvn5NXNEkuEs4I5wQ6gb/rjP9ar6jFGb+B43VWEjCQHstXLh1hWOeGYfKR5gJHfpxRfTQFozTnGy7t7jorHY4HH0qbVol8iSMn5HXg1Vt54ryJ4pD1GVNTTXga38qVAwU7RmpWg9x3hwi3s25A3Hr6CodRsJZb15rcQZOCGfI/KlsNZtluBaPGg3AgDpUGqzvbTRzFwBkoRngHtTu07i5U9zNuFu7E7ZgcDo+OCPTNQPqiDOM5z0rfjmivbcRzjcrfeB/wA8Vzmt6Bcw77ixDSQgZKDlh9PWmtWGsStNqJ8w5c+uBVzwvcyzeMdDCHg30BbHp5i9a5CCSeecxNG6AHncMGuu8JlYPFeiIuF/0+BcDv8AvFrRKzRDejPbNd/5DE//AAH/ANBFZMnFb+sWdxLqczxwSup24KoSD8orMfTbw9LSf/v2a7zyTOPIpNtXf7Nvf+fO4/79N/hSjTL3/nzuP+/R/wAKVh3KQWpVWrY0y8/59J/+/Z/wqRdOux/y6T/9+z/hQBXVamRanXT7vvazf9+zUy2NyOtvL/3waYiFVq1DCWOe1Sw2ExPMMg+qmtKG0ZY+VIP0oA5+6fbOyjtThyF+lPu7O5a8kK20xXPBEZxUv2K4Cj/R5en9w1i02xkLdKhPU1aa0uf+feb/AL4NR/Y7rP8Ax7Tf9+zTsBDGMua0IRgVDFZXO7Jt5R9UNW1t5wP9TJ/3yaqKBshlPFZlyec1qyW9wc4glP8AwA1QuLK7bOLWc/SM1YjOJyCKy7yPk1s/YL7P/Hncf9+m/wAKhuNLvnXiyuT9Im/wqWh3MrS5zFPtz3rqd3mQ59q5kaTqcdwGXTrvHtA3+FdHZ2175YD2lwv1jYf0qodgkc9rdmJI24rxTxjpBVndVr6IvdMupUIFrMf+2Zrz7xL4R1W7jfytKvZD/s27H+lNiPnogg4PWkrrNR8AeK47pvK8May6k9UsJT/7LVP/AIQTxh/0Kmuf+C6b/wCJpAc/XQ+BD/xcPw1/2FbX/wBGrSf8IJ4w/wChU1z/AMF03/xNbngvwZ4qtfHXh64uPDWswwRanbPJJJYSqqKJVJJJXAAHOaAPp7Vf+QlL+H8hVOtHUraeTUJWSGRlOMEKSOgqp9kuf+feX/vg1m9z16Uo8i16ENFTfZLn/n3l/wC+DR9kuf8An3l/74NBpzR7kNFTfZLn/n3l/wC+DR9kuf8An3l/74NAc0e5DRU32S5/595f++DR9kuf+feX/vg0BzR7kNFTfZLn/n3l/wC+DR9kuf8An3l/74NAc0e5DRU32S5/595f++DR9kuf+feX/vg0BzR7kNFTfZLn/n3l/wC+DR9kuf8An3l/74NAc0e5DRU32S5/595f++DR9kuf+feX/vg0BzR7kNFTfZLn/n3l/wC+DR9kuf8An3l/74NAc0e5DRU32S5/595f++DR9kuf+feX/vg0BzR7kNFTfZLn/n3l/wC+DR9kuf8An3l/74NAc0e5DRU32S5/595f++DR9kuf+feX/vg0BzR7kNFTfZLn/n3l/wC+DR9kuf8An3l/74NAc0e5DU1p/wAfsH/XRf50fZLn/n3l/wC+DUtra3C3cLNBKAJFJJQ8c0EylHlep8/fF/8A5KlrP/bD/wBER1xFemfFPwxr+ofEjVrqy0PU7m3fydksNpI6NiFAcEDB5BH4Vx//AAhnin/oWtZ/8AJf/ia0PGMOkNbv/CGeKf8AoWtZ/wDACX/4mkPgzxVj/kWtZ/8AACX/AOJoAwHr6b+HEnmeAtHPpFj9TXgDeC/FR/5lnWf/AAAl/wDia+gfhxp9/Z+B9Pt7yxubeePcGjmiZGHzHqCM0CZ2sR4qao4o3A5Rh+FS7Wx90/lUsYlB6Uu1v7p/Kja390/lQB86+JF2eJdRX0mb+dYVt/x9TD2Wu18U+HNZm8T38tvpF/LE8hKvHbOyn6ECsCDwt4hW8kJ0LUwpUc/Y5P8ACr6HVBr2ZSAxL+BrovA5/wCKgQf3o3H6VTHhnX/NB/sPUsc/8ukn+FbXhDQdatfEVtJPpF/FGMhne2dQPxIqluF1dHLaeNpkX0kYfrVtf+Pph6n+lW7fw1rqXVznRdR2+axU/ZXwRnr0qz/wjut/a8/2NqGMjn7K/wDhSW7NqrTluYafdH0/pRBxL+FaieG9dBA/sTUv/AV/8KIvDeuiXJ0XUQMf8+r/AOFIhNWIo/vn6Vnan/qx/vV0aeHtbDj/AIk+odP+fZ/8Ko6h4a114sJouosc9rVz/SiG5rhZJVFdnOr/AMez/SvovwS/meDtLb/pgteFR+GPEAhYHQtTzj/n0k/wr3LwNb3Fr4P0+C6glgmSPa0cqFWHPcGrmbZlKLgrM6OkbtS0NWZ443tSDrTsU0A56GgTFak7ilIJowcjigR4h4+dB471MSJ8oMRyR1/dJXN3Og6fcBLyONFnUknA6/Wux8daTq9z4xvrm10u+uIgI9rR27sj/u1zggYPOa5R9O8SJIII/DureXIM5+xScfU44rgnGXM7HrU5R5Fcp3SrBHG6RhmiYOAfUVnTWV3qUbXUm55JO2eAPaulvvDmvy26Rx6LqO5jgsLV+P0r0TUPCcdp4ZWK1sZXuIYl27IyWY8Z4FCUrBJw5rHiEek6raIzpIjx7iRHnkD61reH9fkimCyNjHyuO4+tbN3o/iCG3Ih0LUnZj0FpJ/hXKXXhDxbBdtex6DqbyueUSzkIx+XWp9nzIrnUXodRcSq+rB1O4mMN+tc344sXutSimDFXMWFH41v6D4e8QP58tzouqRyDCgSWki5Htke9WPEHhrXZ54PL0e/lwvLLauf6URi4taDcoyW55TFBJvZpcAocHFVXh3MxYck5FehXfgnXRbkpoOpeYTk7bSQ5+vFZK+CfEpkVv+Ec1bYMkg2UnP8A47W6b7HNKEV1Ob0/Q3v7iKNBsDvjcRx71100DWMH2a0k32yLjco+8emffHpViLw/4kSGSZ/DWrxyOTDEsdjLiND95sbeuOAfetmTwdr0Fmgg0zUCseHVfsj7ify9O3vSm2xRcIM5iXRobHT0vInJm5UsR1OAaWw8R3LW02U2mNQC3uSBXSXPh/xHLaJZroOpCKVt/wDx6yfu2xgjOOlZ1r4N123Mif8ACPak4Yg/NayYPX2qLX3RtzJbMibzjpyxkZLj72emaXVLiGDRJXhRAzw7So65Axmra+G/FCnjw/qJLH+K2kwB+VaX/CD6xNbutxpd6A/YQMSP0qUmipNS6mH4AS5uNIKRo7MJCo9K6PUEm06JlniMZK5Ac9fei603xHBbRWunaFqFukA5aKzcbz+A+lQXFj4rvo1S/wBA1OVWAG4W0hYfpSmnJ3sOMkla5z+ok21l9pdCcpuIB685zWQk91cx+XGcSvJmTPb0rtNQ8L63e2Ahi0fUV4Aw9nJ/hVw+GL2BD/xT+oPI45ZbWTOcewp2stg0b3Oc069MN2LeRv3inrW7qLx2ypeP/qgMSf0rm9W8O+K5tShmtvDeqqicHFnJyPyrrbbQtZvNMaC60XUQJlKlXtXG39KUqbHGotjiL+VNU1e3htXx1ZtvpXZSaZDqFrDZvO8YG0lsZwRXMeFPBniSz8Q3BudC1RIo1ZVkazkCtzgYOOeK6C+svE1vdgwaBqsiDpss5D/SnKm1ohQmmrtllNCuLVP3d2synkbuDSFb6JWAhJHGNrdafbQeJZEBfw/qoIHRrVx/SrJh8QyK8i6HqasvCj7JICf0rNKXYvnjbczPMhuZ/IFoA5G5y4+7VnQLa0/4SjS/3KCRLyEjjvvGKi0/R9fhE876HqRkkPIa1kzj8q1tL0TVW8Q6VPNpmoRL9tikdzavhVVx9444HXk1UVLmFKUeU4e80DR9MttNa/1a9Wa9tEuwsFgrqgYkYLGVST8p7VU1Pw1fWWsrp1qj37SxJPA1tGzGWN1DK23qODyK6DxLqVtZ2nhtJdGsLx/7GgYS3DTbh8z8YSRVx9R3qzomqt4g0jxALiCe51K4NuFtrGVYJGt0yNkeUbKj5cqBk8HtX6/HEYiNP2r1W2tv5kk1bole93/mfHOEW+X+tjhjp18L02RsrgXYzmAxN5gwMn5cZ6c/SlvNMv8AThGb2xubYSDKefEybh6jI5r0rTZHXxn4VtRB9kvLa0uFxcXYmmjBSQoJSEQKRzgc4BGcVQ0SzvLLTpLTxBcNYyXeq2r2j3WGKuGYySgNkFcEAsflOR1qv7Rkt0tk7X1d21dWvorX66B7JHD3Oj6pZWyXN3pt5BA/3ZZYGVW+hIwajOnXwvUsjZ3Au327YPKbzG3DIwuMnIII+tek3FpMuieLzLpFzaF4A/mXlwXluCJlO8LwpA67guBkc1Ts7uF/C0Hi1pFF9ptm+mBc/MZjhYX/AAjdufWMURzGbjfl62+bSa2b0voDpK5w8OjapcWj3cOm3ktsmd8yQMyLjrlgMCvT/gfI8f8Ab204z9n/APalLodm8fiDwxItpNewpBABqck3l28YI5jRUAGQSVwxJY9Qc0/4PR+VfeJY9u3ZLCu3GMYMvFebm2MdfA1otbcr/wDJv+B1s/I2oU+WrF+v5HrP2iX+9+go+0S/3v0FRUV8EemSfaZf7/6Cj7TL/f8A0FRUUAS/aZf7/wCgo+0y/wB/9BUVFAEn2mb+/wDoKPtM39/9BUVFAEv2mb+/+go+0zf3/wBBUNFAEv2qb+/+go+1Tf3/ANBURpKAJTdTf3/0FJ9rn/v/AKCouopp60wJvtc/9/8AQUfbJ/8Anp+gqE0lAE32yf8A56foKT7Zcf8APT9BUPakpgT/AG24/wCen/jop0V5O0yKXyCwB4HrVWnwf8fEf++P50Ac2dNkM1vFEd7TRCUkjAQHPU+gx1qKS2BuTDas9xjuqHn6D0rXlv44IrO2mjXyJrVRKw+9jnGPp1qKKxe2ju4iHnzsKxxHHmKc4bOCcfSvbVWS1l/Wp+VVMBRk+Wlr330926VvPvr5LTXJMEwl8oxSCT+4VOfyokt5oQplikQN0LKRmt8Bk1LTW2BGEDjAbOMBuM+1VNMlQWxe4OY1u4mJbnH3smn7Z2vb+rtGbyymp+zcrNt69FaMZa/f5Ga1rcIu57eVVxuyUIGPWnzWTxvEke6VpIllwq8jIzWikN3Fbam1xuCumcsfvncOR6/X3qeVlntI7OIbLl7WNgwP+sAH3P60nWdy45bTcXe6bV0nve7Vl626/j1wfJl3onlvvcAqu05bPTHrTo7aeX/VwSPzj5UJ5rZggkm1LTJ0UmJY4wz9gRwRn19qpyOy6IyhiAbts47/ACiqVVt2X9bmEsvjCLnJu2vzSUX+NyisEzymJInaQdUCkn8qjunutOhe5SJ1niUyRgrjLL06+4rfug80M8cOWuHjgZgPvOuzn684rN8QebHplv5z4kW1bLE528n+VZ1KzcH6P8rm8cvjSqcybdmtenx8tvXr/WvlWu/FLx5Zs7rqItVyAqfZ4T/NDmus0bxt4nn8N299c6v5kryYL/Z4gMfQLXnnjbTme3hadCZc/JOOQw9DXYW8EcHgJRIhxDGH46EjmvjKmIlKiuV2bP1Fyvoaeu+MvFcV/Glrq62lqq7pZZIIiAPxU81Uf4r6hDZrKNTkkGCoZoIwZCO+AvArzO91O6vUijkYsozheSCfrVO4kFvppilw0kh+QD+Gt4Qm42lN/JnQo2jqeg+Hvin4x1jUXtzqIMSHc0n2eLgen3a6mTxr4mtb8Svf+bZOoG3yY8q3fkL/ADrh/CmlDSNFeZ1PmbS7n1PYVRtPEbW6ImGKlysgPO0k9cVzzqTquXs5Oy03Oa7d7HqF34p8SSRrJZ6rhf4x5EZI/NaqweMvFNywigvmc5wXaCMf+y1zmJ1tkuLa42PncjA5RgexB6Vqadqss8htLiFYJwNxK/dYeoPevPnVxCTtN/eyHJ9zpoPEmuQR5udTMr+ghjAH5LTz4p1knJvNo9BEmT+lYU1xFbjLMd3bP3j9KqFri7JA/dRnr6n8aiFWut5t/NhzPub8/jXVYztS73v/AHVjQ/mcVHH4q8RcyTakI09PJjwP/Ha5q51Gx0tMBg8vYe9YV5fXd+DJPJ9nt+wPBNbxqV39p/exOT7nYX/xL1aJvJtLsyy+ohT/AArMf4jeJ7UGS81kJ6RJbxZ/9BrkBeNITBpcJOesp71PFpsFp/pOoTB5OvzGtlVqR0cn97Fzy6M6KP4ieNtQkzb6h5EI/ia2iJP5rVyT4jeILJQkurtPKeii3iyfwC1yct7NPHiAC2tv+ejDBP0FZr3kcBK2ylpG6ytyxq/aVW7uT9LjTl1Z2b/EjxYreZPqwgT+GNbeIsfrlam0P4leKNS8UaXaNqGbaa8hilXyI+VLgEZ255B7VxNppd1qEm9s7T1Zq6fwyljZeKNJggQ3Nx9shDso4Qbxz+FdNNVNHKTS9RqTT3O0g0+xntLi4F5chbdVZx9mXPJxx8/qfaqn2OSd3NlFcXEKdX8nBH1AJx+dXNO/5Amsf9c4v/Rgq/Eq3GlaaLa3uZjFu3/Z5wnlvu6sNpxxjmvoLtH55DD06sY2Vvdvpdt+8159NduhgxWdzOpaG2mkUEglEJHHXpQlpcyQNOlvK0S9ZFQlR+NbmrXG/S7hoiiK+osSIn3KSEHQ4Gec9qlCXMuq6dd2rEWMccX7wHCRqAN4Y9B3yD1o5mDwNPn5E29tvPr6L9Vsc/FY3c6horWeRSMgpGSCOmaZFbzTy+XDFJJJ/dRST+Qra1KfFppZt2ZIfNmeMA4x+8OP0qxeI0sut21opNy10GKJ950BbIA78kE0czE8FC9k27W+d482n3W/qxxviHVtR8J6PPq1sJLe6hA8pnjHdgh4YYPUivPE+NPj8yKDr3BOP+POD/4ivRviwkw8BxJcMTKLeMNk5I/fjA/AcV88Mdso9jXNW1dz7Dh+kqWHnBfzdfOMWfRvh34heIr2wSe81DLEcjyYxz+C11ln4n1KVMvdZP8A1zX/AArxvQbjFjbpnk8mu4sbzYgDHFZHvndSa/epDv8APHT+6v8AhWFdeKfEEwnSzvVjfYfLJiQ4bsTkVmSah522FTkDqfes+PUFt9Q2O7NlskjoAB/KvOxWJa92DHawn/CX+OoL+2in1eN4pGIYpaxjHGR/D61o+EPiJq+oXzW2o3gkO4qD5SLg/gBWXc3Kqk0gI5jVxjrXPS2zaXrkV7Fnybk7/o3f8+tLDYiU3aTA+hYbuSQAl859hVgSse9c3oV99psYnzklRW9G2RXpJ3RBY3t60u8+tRg0tMB+9vWk3t602igB29vWjzG9abRQAvmN60eY3rTaKAHeY3rR5jetNpDQA7zH9f0pRI24AnqaZQv31+tAHAy2Nrbx25nupg80QlwkAIAOe+4enpUNxYywXCRL+98xQ8ZQE71PQgVf1CyurmLT3gtpZE+yICyoSAcnvVqPaZFtkcvJb2RRo4iN0jFslAeeme3PWvV52le9z6pV5JJ3vvfyMGS1uIpVikglSRvuoyEE/QUSWtxFGJJIJUQ8BmQgH8a3mjKxaRmDySt3jYWLFeV4Oeh9qrxyGSbWfOJdSNzAnriQU1Ub/rzKjiZNXt/V7FG102e4+Zkkji8tnEhjO04UnGfwqvFbTzqzQwSSBfvFEJx9cV0Kx3R1a+uVJNpJDJscH5WXadoH0447YqCJRPptgIILiUx7t3kTBNj7urfKe2OaXtGSsTLf0+W5lx2W+zjuNzHfP5OxUyegORzyeelRJazSyvHDDLIVJyAhyPqB0rfeRZZYXQIAdUB/dtuXOFzg4Gefaq1xHLc2UsVoGeRbuRpUT7x6bTj060Ko+o44iT3/AOGMhLaeRmVIJGZTghUJIPp+lIftFlOG2tFPEQyhlwQRyODW/eSkWF8FkzKIrdJmU5y3Oee/YVm6llrPTWPLGAjJ9nbFTUqNwfo/yuVGs5rVaPT8LkDeK9dHS9/8hJ/8TTB4t13PN9/5BT/4mqMsfNQGOviPrFX+Z/eef7Gn/KvuNk+LNb7X3/kJP8KafFuuf8/3/kJP8KygOMVHJxR9Yq/zP72Hsaf8q+41D4v17P8Ax/8A/kGP/wCJoPi/Xv8An/8A/IKf/E1iMcU3dkVXt6v8z+8n2VP+Vfcby+LteP8Ay/8A/kFP/iakHizXe99/5CT/AOJrBQ1MhBNJ16v8z+8PZU/5V9xrnxZruf8Aj+4/64p/8TTT4t14f8v3/kFP/iazWHFRMvNHt6v8z+8PZU/5V9xtL4s10j/j+/8AISf/ABNL/wAJVr3/AD/f+QU/+JrHjGOKmA60vb1f5n94/ZU/5V9xfbxZrw/5fv8AyCn/AMTTP+Ev1/8A5/v/ACCn/wATWc4qIrzR7er/ADP7xeyp/wAq+41/+Eu17/n+/wDIKf8AxNSf8JZrv/P9/wCQk/8AiaxcU4EEUe3q/wAz+8PZU/5V9xq/8Jdr2f8Aj+/8gp/8TUumeK9cuNZs7ea+3RSXCI6+UgyCwBH3aw2wOal0U51/Tv8Ar6j/APQhV069VyXvP7yZUocr91GC/wAF/EaOVN7pWR6Syf8AxFN/4Uz4i/5/dL/7+yf/ABFe7XH+vb8P5VHmv0H/AFlx/dfcfOfU6R43o/wu8U6LqcV/bXWjvLGrqBJJKV+ZSp6IOzGqX/CmfEX/AD+6X/39k/8AiK9xoJqf9Y8apOV1f0/ruP6pTtY8N/4Uz4i/5/dL/wC/sn/xFaN18MvE9xpVvpiS6Jb2sTeYywyTZlkwBvcspycemAMnAGa9gopS4ixsmm2tPIFhKaPDv+FNeIv+f3S/+/sn/wARXcfDvwZqPhH+0vt81rJ9q8rZ5DMcbd+c5Uf3hXc0Vlis8xeKpOjUas/Ls7jhhqcJcyCiiivHOgKShiAMk4rLmu5JpGVDsQHGfWsateFLST1Glc09yhtuRk9qXNYE0jxzCTcSccGlS9lEitvJA6ivInntKnV9nKL3NPZO1zdopFIZQR0IzS17id1dGQlFBopgJRRRQAU09adTW60wENJS0lACUlLSU0AhqSD/AI+Iv98fzphp8H/HxF/vj+dMDIPha+H/AC1t/wDvpv8ACoLnQbu1j8x2iZe+0n/Cu1J+cisrU9Us7SNo5ZU3EYCk96qrmVeCvdHzf+quXdn95w4mBuZLfawlQbtp/iHqKS1voZbpUiZSy/MSwyox61HcJc3MMlztAlViF2A52n0qu2kLO0M9szwkITICcEmuGeeV/s2+4X+q+Xp3Sf3ly58RRRwOq2nlpIfnMSdQD7twM+lV5tTihhSUxysr9NoB/rWCtrqk8l0hiQs/zEyH5QPb/CrbvLHom+FA8y8Bc4rned4yLtG33Cq8O4GbvJP7zQOs2ohEnzknomPmNEmrRJaG5SCeVQMlUA3fqa5y60o6hHbRfbEtXEm4yMcc+3rWku+wuvKWZJ5AoDheBJj+RqZZ9jklJNeliVwtgGr2f3kdt8RtJQlDY3Eu84KyRrlSPcPkfnVp/EcHivRrkWqyQloHgSNlA8vIOOMn1zWVNoFq9y17YoDHK2ZVxnaf6Vk67YXXhu/Ot25jWz2qJIwfvH1xWqzyrWfLG135G8OH8GklBPTz+Y/U42h0W1t7uaOS6kPMTYBx6iukt4FufDTWjHarpsb2rAu7XTvFWmJcZxOF3RyKcFTWzpd2tr4eeWRS/lHLAck4rx6r5UnHe+x7F/vPO1jTTNZNvdRKyK2FVx29aZqOiW8/ie2S1kaSAYLheQh64ruNa0W18R2UN9ZS4Ych8cgd1NcbDqMekzOkdsxnLfvCx7A+nrXZSqynHmi9dmjZzlONkdfqNsJtNbT4LoQ3DLlSTwSO1cNo1hPBrk6Xh4MZGwc7vetXUYxqUCajpUjAxHc0QPKnuatWN0ms2yzm326lbHy2ZQR5i/41NHmpwaT337kaxjYvaHaXFmZYnvoXs+ojJJdR7ipLnUP7Kk3RsZLcnKq3DAeqmufu5brSLt9aibEYPlNE4/1nqMdq6dJ9J8TabGzpsYDJHQoazqU2rT3T3JdrXLiXVlFF9pllDHGSCeay7vWrq/zHZr5cI6ueKpahcWEMvllvPKYA2HJPHeqYjv8AVDtx9mth0A4rJU0tenmZ3GyXcFtLtiBuro9T1Ap8emXF4wn1GXanXYO1TqbLS8Rwp51wegUZOf6VVupnk+a+l+U9IIz/ADNWk3tp5i1Zb+2xxr5GmwhiODJ0UfU96z5p4o5N8sn2icdCfur9BUD3U1wRFAm1OgVelaFroixx/aL6QRRjnk8mt6VFvZfMpIoKl3qUu1QW9AOlakWn2emKrXjeZMekSck1OLlmi2WMYtrfoZ5By30HeoXe102IzzSGInrI/wA0r/QdhWjnTp6R1kS5diaR7i6xHKTBDj5YIfvsPc9qtaBe2dl4r0W03AO9/Aqww9iZF5Y1yt1rFzeAw2aNbQt1brI/+FavgvSyvi7RpNpJW+gZv++xyTTjTlKSlVfyBK71PX28FakpwZ7T/vtv/iaT/hC9R/57Wv8A323/AMTXeyf6w0yvb9tI87/VzA9n95wZ8HaiP+W1r/303/xNcFqPiqw03UZ7KWOd5IZDGzRqpUkemSK9yuH8uB3P8Kk/pXybqM5n1q8mPO64Yj86aqyD/VzA9n956rPdpb2H2tlZk8sybVHzYxmoNN8VWbxORZedG4ziZB29CrZH51lPcD/hEzL/AHIiCM+v/wCuue8PqW81FPyspxn0pyqSvYcOHsFF8yT+8teMvHdnrulXOmpb3EcgCRxjy1EaBXDY+8T2P515PKf3x+tdTf2+24mbvkmuUk/1p+tZSbb1PTwuEp4WLjTvq76/d+h6B4fuDuQE8KAK7pJ1+z5bpivL9GuPJfJJxgGt671O9eIR+V5Vu4K5J5b/AArKbsmdiNePX5JLuVYMeWh2+uW7V0EkSmEzKPndc5rlPDmmZgBIwx+fkZzXdz2gghSJWzhcAn24rwcS+WdkbTWiMK4ElvCrqm/AAwTjHFWby1aWyeBVOxV82HPJQgZx+WaTUULxXMUTYlRARj6f/WqcGf8AsiKSVdsyKN2Dnp/9anRly2ZFjqPBN95mnxjOccV3sD8CvKfBUvkvNBnISQgH1FemW0m5Qc170djNmmDTwahRuKduqhEuaSoxIA2DT91AC0UZooAKKKKACkpaSgAoX/WL9aKE++v1oA8/162js9ZuLe3XZEm3auScZUHv9az/AJsda2vEq58QXP8AwD/0AVmBOK8GvjMQqskqj3fVn0FKrN04tyeyIAG9aXBqXbzil24rL67if+fkvvZp7SfdkWDRg1IwxTKaxuJ/5+S+9i9pPuwAqURjHSmoKsKOKf1zEf8APyX3sPaT7sh8tc9Kd5aYzjn605xg1HI2OKmWLxDVvaS+9h7SfcikAwaqvViR+DVRjzXOiGKKhmPNOZtoqGVsiqSFcjc5qM5B60uckUMOM1aExVerMLZqiWxU0LEUNCRfYfL1qP8AipQxIzUe/mpKJlHNSkcGq6vzUm/AoADzUbYpjSYaozJk0hEhcCq7SlW4NEr4FVTJk1SQmyw0pYcmrWiv/wAVDpo/6eov/QxWWZKuaFID4j0wf9PcX/oYrSmveRnN+6z2S4/17fh/KoqluP8AXt+H8qir6M8YKKKKACiiigAooooAKRmCqWPQUtUdRm2oIx1PJ+lc+KrxoUpVJdBpXdijdXTzSHJ+UdBUCvhcd6ryy4ahWJVmHQCvz2WJq1KrqN7ncqdojpZ9wwfwpIW3VTZyWqxbnisZuT96RpKCUTprVt1rEf8AZFTVXsf+PKL/AHasV+lYZ3owfkvyPNe4hooorYQGkoNFABTWp1I1MBtJS0lACUlLSU0AGnwf8fEX++P51GafB/x8Rf74/nTAp6/ql0t3LY2Iwyx5llY4WPI4Oa89v7tbm4EFmsl4xIO/cTk9Cc1ueNLiRdVvoA58t2TevQY8sd/6Vx0mqSWE6wWwaOJkA4HIPrnsOa8XEVOaTjITZ09teQadcxPcXpXqJUY9D2FXrm9js1kcbGDkGNuCeecZ9K8w0qC8v/EqRahJi3iy6ruADtnj613sNiLozWmoGOOJsGBkPKEdK8+ulDSPUlnE6t4g1BteWCdmgQODGUGBn+oNdFp2o/b4pILlPKnXIkAPHtV+98KXFzZ4V7ZdRjBWOVuePWuO0+wvPDmtsmo3lvOLgYzFJk5HUHvmqjy1qWm6IqLS6NvUNO+0W4gIzEOo/iHoQa5ya+udGkFrqrPPacGKdM70HY5749K2LnWbu3mjmht/OsJTtLg/MhzjkVLdzaffNJazbXdfvBh2qYOVOykrpkQqOGvQfo+omYmSFi7MPmGMeYnZsdjVTxNoranCZ4nMqleYXY4H4VWES2GoI0Uzo80RWB1GQCK6C2v1lbyJNqTgA46CT1IrKrzUp+1poFPld+h5fYvqXhtZLloW+whsSA8YJ9K7vQb2HUfDkkkbfIx4/wDr1U8c6PNrGj4gdgbclhGOh/xrN8GEW3hq4hmymzlsda7J1IV6KqL4rlSaeqLNgl14c1F7mOXzbByTNETnA9qm17QIdfh/tnQZFlVl+eIEZrDm1vz3mgeVYrUggF+pFV/BF1qVprsktirmwfIcNwp96uNKaj7W9pJfJlKLguZmXp1zqGnauiWaSGY8SxMCFI967a1imgs5p4dq3JPmuq9Vb0rej086jeSXHlxJMV6gYzWDc6eumXzXcWoKk/RoG/iH+1UPExrvl2dhOXMyDxDKNd8OxXdrDl0k2zR4wyt9PSudfQ722K3V/fLbRSYDqp24X0rtrOSyW5W+tomaGVPLuYMHJb1FYuveHL7Wr7yw7JZJzGXHSroVlH929F+RN0RWmp6Na2u2DaecA4yWqWS7ub6FnUNb2q9WK4Zvp6U6z0nSNGfyCnmXQHLMP5elO1nV47G1LwyIZFGGhboR7VFoyn7qb9TO2pkteLEDHaJtz1c9WqWy0e4vW8yX5U7s1P0a4057JLpo2kncnbAozWjILi7YC5YhO1tEe3+0e1dLUKavN3fYG0gie3tCYdPiE86/elb7q/U00xhybi6lWZl6u/ESfQfxVTvdWtbICBAs8g6QRcIv1PesaVr3VXDXLkoOka8Iv+NL95VWvuxJ1ZoXniAFyunjzpOhuJB8q/7oqhFZT3c/nTs8sjfxNyfwFadrpSRRCWZljjUfffj8hTbjV0iHl6em3PBmcZY/QVUeWCtSXzZSiTC2tdNiD3T7c9I15dqueHNUnuvGWiQwIYYRfwExpySPMXk+1Ztpot1fN51wzRo3JZuXb/Cuz8L2NvY67pgiRUzdwjcerHeKz9vGM0lqylZPQ9zk/wBYaYafJ/rDTTXtHUY/iS5+x+Hr+fOCkLY/Kvlq4+W5LD+Jt3519F/Eq5ktvCcyxjiZhGx9Bgn+lfOVwT5qe1NCOquroL4Ln4++yL/Wq2hOyWbN5ZYAYyvbPeqGrXSL4ejh8z78m5R6gCtjw7g6TIe5Wr6gjGv7dVglcrglSa88m/1x+teo64oi0wkjBKHAryyU/OaUgRv6XJ+8U+wrqGie+gVFB3J82fQY5/pXH6c+3Y3bAHFen6RClpp4Zk3zSDOMZOD2rmrSsrI1prXU09Bs5bKzt9RdkVYyN6AcMh4yfzrq9Ult/s1q0EWUdsA+xFcRHeb5Ggt5CyufLkhY5wx6AfjV+21CRtMht5ZUjlgZgFPPToK8ivTbdzacdCgGu7jxC5gOI2l2uSP4c4xW7fyPEfLEY8kR8n1NYujah5In81lDCUh2Pr/jTNR1u4vJHtrSIOwyFbPTHXNZ8krryISNnweT50hYjKsUOPY4/livTrJ/3YrzLwmym5njP3gVY/iP/rV6PZH5QDXu09Yoye5sxtkU8tiq0TYFNlm9+KskZcXQibJ6VYtbxJlG1wRXI69r8FpdLabJHkKh2K9FBJA/lXO6f4ifTtcdGkPlSN0PSo51ew7M9cU06qFldrPCrg5BFXQcitBD6KQUtAAaSlpKAENKn31+tIaVPvr9aAOP8QIDrtyf93/0EVm4ArS8QNjXbkf7v/oIrKd6+ZxH8WXq/wAz3KP8OPogVQ7ge9SzIqx7gMYqk0/lOpz3q3PdLJEVXpislsaFV2qIPUDzc9ahE2DQkK5oo9WBJhRWWk2GqwJwaYXLLy55qB5M1G0gPGaiMlA7iyPzULHmkZ8tSVVhDXJqu59aneoJKtIkFHenuPlpsf3ae33aQFRqlhPAqOb2pYjxTewupoIQF5qE4yaTfhRUJk5NSh3LKHilMnFVll5pGk5oaC4ksh3UgkqpNLzSebkUcpHMWJXytVQxBp4bcMVHjmqjoF7iM1W/DzZ8S6X/ANfkX/oYqjN8ozVrw2c+JdK/6/If/QxWtP4kZzejPcrj/Xt+H8qiqW4/17fh/Koq+gPJCiiigAooozQAUUUUAFYl/Lunc54HFa8r+XEznsK5u5k+UknmvnOIq9qUaK3ZtQjeRRmfL1dgAFo+epUms4Zd6s2sksguVddqopCn1FfPYWCu35M9CpskUw3Jq1bt71RJ5NSwSYOM1hKOhpON0dbpb77MDP3SRV2sjRZOZUJ64YVr193ldX2mEg/K33HkTVpNCUUUV6BAlHaig9KAEHSkal6CkJzQAlJS0lMBKQ0tIaAEp8P/AB8xf74/nTKfB/x8Rf74/nQB5x8Qb2f/AIS2WzSMLAxUSSHgFjGNoJ9K5HWbe4tNPeYRmSXChzjoB6V23jdEuPFF9DMreUVQZU9G2Lg/hXP2d68AFvqaKUztRiQN1fN15t1m+zd/S5g5NSMe60+DWY7d4pPLniZd5B5K4BGKpzSzQ+LVgluZPIibzGYuSAvXmuvi0+1jmNzahfmAUr2wOg/WsG+8N3F1d3c8ZKtcqEPHRaVPE03Jxei6Ap2Z3el30V7JHbTuPtMPz28mf9YncGuZ8V+Fo/tL6tp5jheTLOT1Q/T3rHknutJ0m2umZRcaW4UKr8ke/wCHauivdXQxWWtNl7C7RVl28iNvXHpUOM6UuenrcfN2OYS9MVvdQploz9/jo47j2Nad3Zm9gd4ceZJFtyT7U3xZdy2FgJra3intJR98DsfcUunXu/ToLhOV29O/0pVKk3BVIowknF+RDYQGyS3tbm4Wdwdy4HKE8flVW4t7q7tJrJ5fK1G2JktpAeWUH1p2rahbwwyXcThLiDBdSM4B9a1US312xtr+GQJdKMxyDpnuppqUox55q3+ZUXbczfDvig6oDZXgEWpRHaVPHmY9Perv9mR291I7bks7rhjjAB9K5rxTorSXUes2zi2liP70Dghx3rodH8T20sR0zUZd7hVJDDOc9xVVKSS9rRV0912Ka6oW7+HulahcpcJu8tOcK/X8KuHSJrZfslosNvbFNofncD9Kw1vNbtNXkVLiNLDO5dy87a6X7RqhjR0tmlRgDkEYx64p1JzSir3QtZbk1nbxWFtbWVxfGWZT8kvQn2qcG0ubua3uok+2qvysQPnHqKiYzgEyfZyyYzuOMVy+peIdupwWl3sWXcPKuInyF+tcvsZVJNxevkF7FbWPEOo6Pq6Ws0CLAxwHHAxVqHUru2vGCAzw3IyI252/Q/0rW1jQk8QQwfapFR48MsiD71ZkFjp9tIYJ9QlaWVhtUNjGK6oSpyha2q3E2uhk3mjahqV2Zrm+EJX7oQcgelT23hawXD3LvcP6ua2dRnt9LaGSSCWW0kO1rhWzt+tZPiUXUgigtHaOKT5klj6SL6Z9aFOrKyvZdxal4w2dvDsgjWMHoUwM/jXI3t/fXUjWsafZoQcFEPzN7k1q3t+NJjht5E3xRoAXY8ljSXP2ayVLidd4k6JGec+9a0IuEryXNfYSRnWGjFz9zjqT2/E1clvLPT/khUXE47/wLVSW9vNTk+zwIVj7RRf+zGtOw8OpHh7w+Y45Ea/dH19a2qVYx1qP5FaIzI4b/Wpg5yyA/eYYRfoO9dBYaLb2eJCPNl7yP2+lWLm8tNOixKyrgfLGtV4LPV/EJyoayse7sMMw9q5nKdXf3Yg22Ou9Xgt5PJgVri5P3UTnFavhfQdRu/EWmX2qTeSsV3FIkCnuHBGavabo9ho8eLaIPL/HK3J/OrWnavD/AMJJpsCHzpGu4lOOi5cVdPljJKP3jW567J/rDTadJ/rDTa986jg/irLs8NpH/fk/kK+eLk/vOnINe7fFyf8A0O2g56Fq8Hu/llIFNAN1GTfBAp7dK6/Q28rQmPQ9K4i5O5kHpXVWUxXSSnGc+tUBB4juy8Eif3UNebyHLV3WukSWzybjwvY1wr/epS3Eja01xBbidlDKgJIPetKHWNQ1CYTSXDIq/dVPlC9u1Ze3Z4dD93cKK0/DsYZ1DdzzUcqbuyk2i3FDc217DcGVw6OrbvTBFe2/ZbC9sWLwxeY/7wyIOd3rXAjSkuIsYBJHWuh8M3Mq6fJa3Q8t7fMYU8hlxkVx42leKkuhpGTaOdg0ZljnmnfMU15IxA4AQHjHpnFcRb+IZbfXGlU4t2mOV9FJ6V2vibUEsNDiEbksVwo6Hnv+oryknaQQec0YenzJyl1ByaPbPCdykurmWNspNAGHvg//AF69Ss2+UV4t8OVaUpOZPliQoFx/eOa9itHwoz6V0wjyqxDNUy7cDPWq9xNhTzVZpwzZNU7+7WCFpGPyqM5NWxHmvjS4vLLxOdQt3LJIFhZCTjgcdPrXP3mqzXDm4kiaLkKVJztP1rpr3UEnWSeRB5m8sAR0yeP0rE1Ei4mzswkwxyOMj/GvNjVvMpO6selfD3xEb2zFtM+ZE4B9RXo0b5HWvnzwfcSaZrSwsSAeRXuljP5sCse4r0IO6JZqinVGjZFPqxC0lFFADTTk++v1pDSr99frQBw/iWTb4huh/uf+gCsZ5xzzVjxfdeX4pvY89Nn/AKAtYPnk96+ZxC/ey9We1Sf7uPoiS9uCDxSpeP5IUmoCN/JGaZINuCtSrWsWycSbj1o3Yqqr5YVN2p2C9xzPg5zTlnPrULY70xuGosK5cSbLdac0lUEfDVI0nFFguTl+aXd71TMvNSCQbetOwXJJHqJmyajkl4qATc1aRLkXFfAqTOUqqr8CrKMNtS0UmQyg0R/eA9acxFRIwElJg9y22CuKz5H2k1bZ+OtZc7kuaUBSZKJwi7mYBfU1KZOtUmUTW7xt0dSp/GoNMumlgaKV1a4t2Mcm3vjofxrXlum0RzFqVjzUSS57024kxnmq6SDdRYlvU0Ubk0EnIqOM4GTUn3iMVL0KTEn5Aq14bGPE2l/9fkX/AKGKrODV3w+MeJ9L/wCvuL/0MVVKWqJnsz2u4/4+G/D+VR1Jc/69vw/lUVfRHki0ZpKKADNFFFABRRTXcRozt0ApSairvYCjqc+1BEDyeTXOXMu44Bq1fXRd2Y9TWauZJOlfn+NxLxWIdTpsj0qFLljdkkPyAuf4RmrNjPb3UE8cMyO44YKehqpf7k09/LGWrK8Jzypc3MU9t5DORt+XG7Gc114SmlF83VfmObb1RfmjMTFTUMT/ALytXVIcL5gxWKH2t71xTpuEnE6ab543Ok0eUreR+jArXSGuO0+bEsTejj+ddjX0nD870ZQ7M8rEK0woNFIa98wCiijvQAjU2lNJQAHpSUppppgHam04000AJUkH/HxF/vj+dR0+D/j5i/3x/OgDx74t3MsXiK5Ec7xnz1XCdT+5jP8AU1kWZt/GWipBM5TULJgTjjevqK6P4p2f9qeI7iCEeWYGVpJGHDExpgD8MVxGhW0+j3Euo9Zoo+EH15BryayjLm5PiTZMuWS0LkWtLFc2+npNIjMxUseCCDjFdRePIdtiLpo7yeEmBs4BcdvxriNdt7fWdesL/Tj8kxUygcbW7/Su21zRf7W0mFoGaO8tx5kLqecjt+NefUjSjODel9/JnPZHn32u4j1BLO+VmmnDxusnrnAJrtdD1C0tYR4Vu4wIGUiOV8YZj1/+tVP934lt45J7SFNVt3CTbgVbII5yPpVTWrIXd3FBBIyXqsQFYZDehBHSumry1bQWn6PuU/IuWrzaFqj+HdTObC5ybSVgCFPpzUVqh0y4l0u5c79x2NjhgeQa2pdJl1/Ro9O1MCLUYQrLKOfxBrnvFMUtq1sJpAby1IzJ/wA9F7GudONRcmz6/wCZEtSK00mybUr2zvvNhe4CkK7ZEieorRt7ZfDMhWBy+nSnpn/Vt2re1OyTUPDdvqUEQa8it8Iyjnkc1w2l+HZUjMup30kgJyYQxI/GrnKM4vnlptbzFLVG3rNvNqcPl2j7jJjc5OAB7+tX9D8OWcBLBRLcYAeY8ms6DUGlsJ5bS3cW1uDgKOXx6VOl2194We+0i6eOdSC6k5Oe4NYqNSK5do9/8wV+pqalYw6ksulNNsuFGY5AR8w9KzZtVj06I6cDcvLEAm1UJ3/j2rL1S8mstN/tLYGvhtOVONvvVyTUk8V+HY7u0vfslzFgXCqPmNEKLVm9Y3+4paamB4g1O5lbyoy9uZj8sCNudvc4qpP4beHTFuJN/wBtPzLGpyfx9K6PRPD0WnO15O7S3DdGfqBV3Ub2C3RwxUSSjao711OuotQpoTkReEfECahpotZ3/fQ/KwzzVrWdJimkSUMUkhO6MjuPSvM4pJdF8ReZbZdd2GUdwetetWN7b6lYxsXHI4J/kawxVN0ZqrDZkyVtTlotbYTzW4g32o/4+InHGO5FWrOyRGURXEsmm798QYZMWf6Vc1LTkd13NtVTzt/iHvWC/ie3tdRbTmgYQN8jvuPC+uKqD9qmqa9RqXNoXNStk+y3STxqLiMbkkI3JKP6VzawzG+tZZD9oWVRiIHaFNdcLeBrmPRp2P2aaMtbTs2Q+e2ayWWx0vVE06/i+zPtIDDn5u2Ce1dNKTS5SldGpYy2sFozBUgVT8/1qJb2+1WY2+jwEr0adhhRVXw/pn2uS4juZTJumCtG2flAOf1rumlttPttiCOCFeMDgmuZxhGfdisZWmeFrSwcXF632u765bkKfYVo32pwWi4kbn+GJOtZVxq1xdEpZIUj7yN1NZss1tZHdKxlnPYck0Nt7ibLk91d6hwxMFv2UcZqTw/dwReKtKt7ZDK5vIQzKPujeMk1Qisb7UsPcsba2/uD7zCun8NWkFprOnR20QUfaY8tjk/MO9SqsYySZKlqewyf6w0lLJ/rDSCvpDuPIvitMZb9YwPuR4/OvF7z/XH1zXrXxDmM2uXY67CAB+FeT3w/fk4poCi/3l+taguP9FC5PHasw/eqVZPlwaYBqU5NowJwMVyz/eNbuoOojxkkD171gnnmkwNm8fbomnwbcbi0hPr2FaugkDGDznFc/czGZoE/hjjCj+f9a6HSUCKD3NCA76zuD5QyeD1rV0mP/SJomZ3CWwO5u5BP681yEd2FtshsHsK1tGuwty9zvYvJBhlJ4Cr3rOsrwZcNzl/GtxI2rTWikeRCFAH+1jn+dcTKMNmum1nzbu6uJy2Gdyx4rnrhFVTk/N1FOEeWKQm9T1H4ZP8A6C49Gr1ZZwkIGeWryL4Yt/oEgP8Afr0dbnzJMg8LwKbA1XuAq1yfirWY0uILZixjA3OF7nsK07y7EULOxwAM1weszLcTPcHJ6E8fUfpkVz4ido8vca3H6hcwou4kN5pHHpxTTseFQ7bcpjr3Hese+J8hJE/1ecdOQPrV4IsiRqjbiFGR7EVxuHJqU1Ym0+UNdpMjZMEnlk+or23Q7kyWkeeuK8Ttogs6KvyBgc8derD+VeveGX3adF64rvoO8SJanXRNVgGqcLcCranitiR1FJS0AFKv31+tJSr98fWgDyLxxIV8a6gM9PL/APRa1ixzZrW8dg/8JvqJ/wCuf/otaxLWMyXEaHgMwFfO4j+JL1Z61L4EaUMuEA9qjmYFj9a37u3iGnuFRVMa5U46YrmHfuaxjqbS0FDYap1lBWs9noSY461bWhClYvs2TTGOBUaSg4pJGzmhIdxQ/GadvytV1OeKSW9i0+2854hKzNtRWPHHUn8xVRg5S5UTKXKrsdI+CKkEmVxVSO/tL/hY2hmPQBsqf6j9afvxV1Kbg7MmE1LYdM+FNVopCSc1JKflqsGwc047A9zRR+lWFk461nRyfLUiS1LRSZceQY61WaT5sg012I+lUpJSH61KQ2aazMyYzVR1+fNSQHemafImEqNmO10QIfmrH1GQ6brUF4EPlzjy5T2Hp/T8q2EYck1BqVuL2wlg4y64BPY9jWtOXLLXYiUbor3jlSQeCKrW0m6QDNRxTG509WkbM6ZSbn+If/WxUduxEw9K15bXRm9zdJ6CnxPmQgVCh34+lTKmxt3rXJKXQ0sWsZFXNBH/ABUmln/p7i/9DFUN+Fq5oDbvEmlnP/L5F/6GKmlL3l6ik9Ge1XP+vb8P5VFUtx/r2/D+VRV9SeSFFFFABRRRQAVl6rchV8oH3NaUjiONnPQDNcpqFyXZiTya8HPcW6dJUY7y/I2oQ5pFGeXfIRmpY2WBC7MBhSfpxVVBl8npXK+JfFJs7jybdAxjOHDdCp6185hKEqtVRij0qrUY2NHSPE8eri4gkRYzG5UENndzXXWaIdrsnzoNoNeLfa7Wz1qP7EpSNznBPTjvXd6F4mumgvrm5VTBCh6dmHQfjXvPDuM9Njmb90iXUb6XxrqNvJM7W+MrHjhAOP8AP1q8zAP71R8N6k+urc6jc2ypcD5PMQYBHoferrD97mvLxn8ZpnbQ0jY1bQ7Y8/jXcocoD6iuFt/9Q2fSu3tzm2iP+yP5V6XD71qfI87F/GSUlFFfSnIFIelLSGgBtFFFACUGikpgIaSlpKAEp9v/AMfEX++P50w0+D/j5i/3x/OgDzzxzFu8V3m/IRtmPr5a15z4rl/s3Sl8hnim3nLKcEjB/rXqPjuCC71fULZ5ShZV+ZeqEIpBrze9hi1e2l0ieZWuo1ykgH3+OD9a+ei/38n0Unf7zBNKTOX8CXcsmqiKTLK2XyfWvV9TuLiz0yKaFSzJtwvqCeleUeD4jaeJTC4OUV1Ix3r1DWpJU0IvF8zKinB+tTj1F14+ZlP4jCid/wC37i72+WJ1BKdwa6/SJdMizE8CQXpAYuf4s981xNm8gv7kTcFcBSOOoFaVvfm11P8AsjVT+9PNvN03Ke2fas6sJtO3T7wTaZ0WtO1ssOoQvvMLDfz1QnB/KuI+IEkkU9rcIQ1tMOuP61o6nobIlzNDeyhTER5LMdtY80n9ufDsWsp3Xds+IsdwP/rUYelCLjOL0vZ/MpanoPg6UT+GrQHkNHjB74rF8RafI2peQp2W7LuIX5Tn0FXvh8zf8IjYswBKsQR7Zrd8QaSNQtuSOOe/P40VbQqtk6Hmr6uNNvYJrGN5VjPky24PAHuP61at760069uLspthuTg2sa5GD1/Gsi4sv7Ov5IxZIrsfvNKfmH9a19Fv7G6gNk/+i6gQQqgcH0IronFOOnzGtdCHWVur+Qw6XGI7Nx80kw5H0qjb2+n+ErX7RK7NI/BHPzfSt/w9bXthPPaarPDdW8jZVz95D6VQ13yte1ddEdW8q3BO5F79jn0qY3b9m/h6sfqamqRrqOjQalp8rS20i5Kr1B9DXLQzyapqKLDbtvAId2GCtbHh/UJdC8QnR1gzp0ifOD/Cf71amv6ZPaBYYblIIrg5WZU5YHtms42pz5G99iXZnD64sFnck2gBuEU7pP4V/H1qPwhqc0LyQzbjCzcMema0Nd8O3bS2trHhIzySMkn3Jqa/totJ0ExxR/OuPmx3rrlOEqapvW4Nq1jp0cXEe0n5x0PrWDqujQXR80xgTDoasW915ccWJOSobJ9cUahqdvDAJpDgA4PvXmU41Kc7R3M1dPQyNPufOtbjR7zKy27b7Vu6fQ+lVvESjWZraGWUfaUXaC5xniuouPDH2iKO8FxGjyx/uJf4W9ia5PUNMY6tb6fdytFd4+Sd+hbsK9KjNTlzbM2i29zrvCUM6IJLoASE4OPYVBdaxp9zdlJXO9WbbnpxWzpqXa2ZMpX7UIjnb03DgV5Vr9pc6Rqa7iRO3zEN6mssPD21SV2r9BLV2O5SS81I+XZR+TAODK3H5VpWWk29kdwUzT95H559qq+FdQOraLCScSJ8jjpyK6SOAKBxXDiKs4zdNLUxd76kCQM/LflWrosQGs2GBnFxH0/3hUIjVFBkO0dlHU1Z0y4H9tWCDCqbmMY9fmFZUqfvqUn1CL1R6bL/AKw02ll/1pptfZHonhfjGbd4o1BCDkPj9K831NMTscV6d48tvL8R3R6EurE+vFed6pGGdqpAYJ+9S7hikfKsRmmk8CgCpftmE4rJrW1DIt/rWTUsCxDl3+prqbFAYtjcKwxmuesosuhXk4ya6CL5Ez6DFNDLsjBEJAwvc/StrSnZtOiYAZcMM+1c153mMyDjA6V1+kxqmkwttwdpA/Ona4I5zU4hG8meOa467brzn5jjFd3rka7ju54z+NcJekFiB2PNEhHf/D65EGkzkfeL4Feg2ku2LrzXmPgRS9o47CSvQ1k8uKpGUPFeqrZ6NNIcEjG0H1zXmVhr13cXmy4m/ctkcjpmtPx/qjSzxWSNwvzMK5iGBhbZA9zUOCluNSsdfJcl0UBcHIVuOK3LiZNNtzMkZXZEHDkZ7c4/WoH0+KTw7DfoFxIiMOcAHoc/jUXiJpI/Ds7PgfJgH6kVwySm0vMqbLxnMmo2jp8yPn5j3yOteseFm/4l8R7EV4JoOsLJYpbyNmeAh0J7qP8ACvcvBd9b3+lI9uwYIdjAdjXTRXI3BktaXO3hPAq0nSqkPSrcddBJIKKSloAWlX74+tNpy/fH1oA8m8cR58Y359fL/wDRa1gohVgQcEcg10/jJd3i29/7Z/8AoC1hGIEivm68v3svVnsUl7kfRFuTUZrm38nYFyMMwPWqws/MHSnRALitC3AOKyjoatXOdv7VrdSw6VlrNg12OoRrJGVIHSuOlh8udl7ZraLujGasW4ZTkVsW2k3N9ama2UyMM7owOceo9awYztYeld14I1WK2ufIfyYywJ3lfmb2Bq6cU5WZMpNRujBtdNZpykwKsBkxng49/SuQ8RFJ9Wka2kcwogRVJ4z3x7V7RqES3Wr3k1uBIwiTeTwAD+HNeP6+UW/dokDF5iAijA2g4/DvWsOWE9TOUnJFG1R4BCd480/Ng9K6aW3licLIuDisDT4JrzUrq6eFkS3QBEPTrwPyBrsXZJIlWXJjx+7cDkDsD6jFGIqQk0o9ApRa3MqRPlAqm4w2K6q/8OzWemrdTSxAMoZVB5Irkro7c4rNJrc131Q+N+DT42O8VTickkVbQHg0SdhxLTNuFUHUu/41cI+WmRQ5BNZRZb1LVov7sVLOMLTIMKMU+dsrUfaKWxmO2AfrUiuTFuAyeuPWqk77WaljnIgO0bmAOB61q0RfUy7R4bpZrhRGk7ZEsaHoR0JFOt1JkBpkVkJ9WhuLUMn2jcHQ8HeATgj3xVu3jIbBGCD0NdEtDLdmlarnI9KtSjbFUdonz/UU6ZsBgTXBNXka20IGm2rVvw7N/wAVTpA7G8h/9DFY8j5BFX/DSkeKdGJ/5/oP/QxVRjaa9TGd7Hv9x/x8P+H8qiqS5/4+G/D+VRV9OeYFFFFABRRRQBQ1Wby4AmeW/lXJzvukNbOrz7pnHZeBWCTk5r4PMq/1jFSl0Wi+R6eFhaNzF8WXz6foEskUhSRztXHWvLm866gM91K5lzld/XbnrXf+N5Wt5tOMyg27OBjru55yKxvFUSXy213YQnyjG0TEDA47Y/KvZyyDp0k2t9Sa0kzlrFzdXc2Ds2oTnH3uf/r1tQX39n2E2mSScTPuYleDxwM/hVG2a0sZLH7TMrxyMjygD35H5Cq+pyxXF3Kyqwt45QfkOdqk8V6bjzMwTPRPA00S6Rd2sYAdJcvjuSP/AK1bJA3nPWsTwho1xpdlcXEswZZ/ubT1HrWyn3+TnJr5fGtOvJo9Ch8LNGPi2P5V29r/AMekP+4P5Vw8h2WefcV29pzZw/7g/lXp8P8A/Lx+h52Jd5E1FFFfSnMFNNKTxTaACkoNFABSUUGmAhpDQaSgBDT4P+PmL/fH86YafB/x8xf74/nSA4bxfJGniq/8yMEfuxn6otc/PFp3mqdyJKh5zjIP1qX4lDHjhmcuIC0ayYP+wuKzbnSrZc3Lw+Zjqc/lXy9aKjVk27Xb/M4pXuzNurWxttZguoGj812bzCp5Oa6WW6txp6oWUjA4zWHFJYCYx+UocYzx0/GtEW8LukYiUs/3RjrWdfVxTeqI9Tk/EAX7NKEaYTJ86lDncSeKuLcL4t8JE3AEWrWY+Vm4JHrXSSwW0eElt9rHgjOD+FR/ZbLDHlOcHIHNa/WUko21WqZftLKxzFidR8SabHb3IeLyvkaYHBkWpL+/sdDCaVBF8siEO59x6+tatqYI/EdvbSSn7PcRlVVTgBqn8UeGZDpEstrtM0GX+Zc+YvXH1Faqa9olLSL/ADEndmh8OJxL4bkhP/LKUgH6813Ejb7dcHtj6V5V8Mb0qLqN8gEK230Neli5U25KnjPWubGvlqWY27M4zX9Ngvpj/qxIp3Ruw5UjqDXn+owSxapBNHd/6TC2VKLgKK9D1S2866mZdzI53MN2MEelUfsVuib9iDjghcn860oYhUopbhz2OZ1yG81WytLmykcXZbbKQSC/vW3o9kNNgCvIZbnH72TOSTWpFpwaxe7D5cvsAz0rJ1C9XR7V7m5XBBAK+vtTqVpVF7KCJcpPQbqEkVhC7xwnfMfmdskY789q07PW7PUfDKW98cW4l8lZM5MbdVNZes6rb6xaxpprL9oKhg442+//ANaoDAtjoktm6iW4uskhMKNw74rT2cVBRl8VyrqO51cLC4iQSNlojsdsdR2NLq/hcanZtDFJtJweRkVzPhZ7m+8uBbpC0hwSe23sfetLU9XuLKSWKAFr+IYktS2A3uvrUuD5uWO6LfLua1n4eFvEsE4ikCgDOKh1Hw7atbsI4lI64YZwaxL8z6pZwyQ6jPZyD5mjzkBvSr2maldWiCC8k89SPvL3rOcZR99PXsS7NaDfC32pZLnw3qfME2XtZuyH0rndXt7j+110jVEZ7iBg1tNj7y57n0rtbhrdbc36yBRCN5/z9K5fTrqTV9UNwd0iqSkLv3UniqpzbvUStf8AMaemptSmIWsls8joPJxvT8Oc1jahaLqumNp+sQb7yIZtb1eki88E+tbUV3JA1zY3Fsd6j5o3GAw9QadthvdIlRLowI6ACNuTGw7irw0vZ7hF6nIfDucw3l3YueRhhnqDXo7TLEo2gZI5Y15tottHpPjWOFLwXPmIQz7ce9d+5zCRwdrflUY/SSnHqZzWojzF2OPzNWdHYDXtO3HJN1F/6EKx7m/jtx8x5PQdzVzw9aX134g024k/cQJdRMA33nwwNc1GlOU1KW1xxjroeyy/6w02nS/6w0wV9gdx5T8R7XZrSyY4lj6+4ry6+jyCce1e4/EbTGutNiukGTCfm+hrx68hzG645FMDjZox5h4pnl57VoTwfvDx3pUtskHnPpQBh6qmLZe1ZKQM6O4Bwgya7DUNKea0O0cjmtiHwsLL4Yatqk0WJHeNEJH+0M0mBx2lRbo92PQVqHIJHHvUWlw4tFOOtSS8E0AKD8w4rsrSdY9OgiGB8nrXCiTbKF5wTwa6GC4JijBABC4BpoZX1uYSO8W1iAAd/Y+1cVefe+vWut1aTK4zniuQun4K4HJ60MR3nw8j3WE7Y6SD+VdjeSiKBieABk1zfw1h3aJcP/02/pV7xpefYdDmIOHk+RfxqRnl2qXbajq00xOQzcfStr+zJo7GO42MYmHXtXOWql5h7mvS9Ek+16eli5BRfWmkIn8NF7zwlc2TjcI3G32DMDWF46uwkFtYoxxuLsPYcCunRJdNa8ht4NoCoGfPBDd/wrjPGMkT3yxopYgDErdW9a5FTftm+hb2uZ2hDF2G744r6N8BW0MOgx+SoUs5Z8ev/wCrFfPGix7bmMetfR/gUL/wj8RX++2fwOK6rdSDsIqtIeaqxjirCcUATUUmaWgBacn31+tMpyffX60AeYeMWx4uvR/1z/8ARa1hu3IrX8atjxhfD/rn/wCi1rCZx1z2r5it/Gn6v8z2KT9xeiJC+1Sc1b0+8Q5Unms15AYzVJHeOTKmhK5pfU6G8lG01zdzhp+Kvy3JaIk5zWar7pMmtIImWorqFA4qWCcxOrqcGoZGycVEpI496aM2dIdcmj0ySBWl3zglmV+p7frWHZ2SzuSxVniTPXuTjj8qYX+TA61qaHD5vnSHACgDgdetYV58tNsSSvoW7O0VbaR+7yIpIHopAFb1h4fhktpFup2VokAG0D8D+HIrGtriSEEKvG7eGx3HH9amk1WbezL8nB249PT9BRS934jRxfQj1q6aOJLLzVkCKFLKeGA6fjXK3aZq/cvl/mqhIdz9a6U+oNWVipbA7iO9aMa/L0qtFERMTitCLAPNZ1XpcUUKI/lA71pW8axR4x25qipG4VfLgrkd6xi7o2SKdyFjkyvAbtUDtuUCi7lDTKo7VFvFWtyG9Shdjk1UhcqxFXbus/kEmuhaoyloxb+yjkjN3FJJHcJgqQ/y5yOcVctbgXflO4xMyB39z3/WokYSJsYZVhg1Hpc4lklD8TxOUcYxn3/HmrbfI12Glqb0XysKguTwaerfMMGo7jJBNcfUsz+rVo+G8nxZow7fbYf/AEMVSijDOc1qeG4gvivST/0+w/8AoYrRfGjCWzPd7n/j4b8P5VFUtz/x8P8Ah/Koa+kPMFopKKAFprsEQsegGadmqmoSeXaMO7cVjiavsqUp9kNK7scxqE2WY+prMd9kTSdlGas3cm5j9aqTCNrSRZjiJhhj6A1+fU4tyV+rPZguWBw954ls72xvBqEBNxDLthOfuVzEOs3MllcWhl/cxHzlJOOvBA9eop2p6aza09pbTM8csuQD/dPSn32mWY0aVUlVLqGQrIPUV9nTjCCUUcMrvVnN2EH9oXLo8pCxKWHPPWuq13RDpoa2sbvzY51RpMr29c1zukWrpBPcrG0hJ8tQO/rXRLetJqCOYmhhCCNgeccYwM1vUlZ6GUdtT0jTJlfRoRGwKKiqAO3AqaFfnFZuhWZsNGjiaQO7EuxXpzWtajdJ9K+PxTj7STjsenS0gS6g/l2qD1Nb9z4q0zSNKt3nuE3lVG0HnpXM6y4CRp+NebXlhfa1Hfxx2UzmFyFkAPXPFenkk3BSt1PNq+9I6vUfjWRcOljaAqsm35j1HrXeaL42tdYgaSNGTaQPm4zXjK/Du6i09Gls5WuiwJMbdPw/rXcabod1b6w58spaeWuPn74x0r35V2tEZxhfc799egWRFOMscDJ61cj1C3f+MA+9cMmkWMXltJLLNJExZWkfOCauvfxRDLtgCsp4v2fxMt00dorqwypBp1cRpmu+fq9vbwZ2s+CSa7aujD4iNeN49DBhSUtJXSAhptONNNACU+3/AOPmL/fH86ZT7f8A4+Yv98fzpAcV42t7e81nUgXBdNqMAORmNTj9a4vTb+Yaa9pMD5uCkZ7kA4BrW1+4uIvi34gtpMi1uVgCMeiuIY8H9MVbvbeNrI3EUCi4i5YqOTjkivl8XHkrSjvd3OSS1Zx15N/Zi29vFEJGkkG+Trj61f1C/ksbH+0IRnycMCpyMio/E0aC1try2jUwSsCzD1qCzvYLSwezu4iYLku+Sv8AqzjiqUVUUZW16kKKZ3lu9p4i0e3u1VS0kYcMOq1zninQbiS0WOzkb5hhsnvWT4H1htHmFlO3+h3Mh8gn+A56fQ16Je2kd3aODwrdcdq5K37ivdbdCrR6HgAmvtB1iNLvzR5T5Qt6V7xp80WpadDIrBklQMM98jpXjPi3QNZt7+Gy2yTwu37uQnOcnp7V2nw4vpUjuNIuWzNakEZ9O/616uJpqrSU477jcVui0fDcmharcXtk2bef7yf3TVwaq/2ZkYd+MV1zIk6HAGM4ZTXG+IrX+x43u40ZoX42gZw3+FeXVi6zTe5m0ULyTULlV+wTCNwcvu6bartqNrpAFvqcu9Z8yJIRgEnsKuaQIrtUWVwguVMZU8EEjGK53UHtZdDtLC8X/SNPuHjLnjCg8fXNduGprl5ZIpJHUWb/APEidLWCYWhl83zSc4NZ/iCK117SzAE2Oxysh6Zro/D7wT+CyA6oJVcZY4HfGK4+zkltdNhtrl0zExC++TUyhyy5ovVDkuqI9K0H+zbXyo2idv4nY1bjs545YpnZGaE5BB4Psai1jWNOgig1Kyn8xuUe1Ucg96rRavrGq2wiSFLS1ZQHkZfm98VThXfv3WpDi73JtL0dtM199RiZVVsuIVbjJre1eL+2jAzwxwXDHbFPnnPpmqMNotpYGQSAuCFYt94g9xV+LZeWl1p7uWeMrKuRg4/+tXPUqSc1O+27HzXdmYlrZXE96LPUZJIpouRcbcZx/e9avReFS1+18muQsDnKDgdPSpdXSfUIopf9XcQAhJk5yO4Yd6dpdpY3SgSIlveEAExnCufXFdPtko86sVeKMfUbKLTrZYZtRaeIPypHyn2Nb2lxaammOylY2DcEDHOMjFVNW8NXdwTE0qi2IwwA+Y/Squm2sUU32OzjkUIuXMhz0qYtTgrvXeyFvqa7RS61exM0vknyjEGYcsw5/Wua1vRNcW/ijsrZljUktKz4Diu48HyQ3rajNcLkBgkaoucYHX61U1n7PPC9veyzQjcfLIO0j2qaTdOrdq6LS6nnekaPcWXitTczozohc7G3Ae2a7wTqLd2z6DmuSt7Oz0q9mktJJJA/R5DmrGs3U0VpEFLbWbkgcCtMSnWmktibczOjtXtIw969uXl6BtuQKtaTfXF14j0su3lxfbIsLnr84rn9L8QtdWp027mSKeHgBhgMOxqPw5cz3Xi/RzIWkIv4eBwoHmDmiOHnzrm2LVon0TL/AKw0zFPk/wBYaZX0Z0lbULRb2xmt3GQ6kV4Lr0I0m/ktLsFCGwrY4xX0HXF+O/CcWtWLXMceZ416AckUAeIS2iyMZIyGU9xTodPcsCv5VeTQ7nTZ3iZXUH+FhWvYabPPKqrGWYnoBTAZo+gm4uEjYbyxA212nxF0hbH4WSWcS4/exkgf71dF4X8NLp6C5uADMRwPSp/HcC3Hha4jYcZB/WkwPnBbUW9qqAfd61mzDkiuk1Bdq8Dr1rnbgYf2pgVAoaRSexzWvHIFRcemKyo+ZKl2vHcb1JKMOeeBSRXQbfsX3M2R2xmuZnPzc10F8+Yya52U5Y0Mk9l+F9mR4TaUj/WTMRXG/EfUxc6qLONsxwn9a9J8LbdC+E8N9IACIWlGe5JOK8Ivrp729kuHOWdiaQEtiuJBnpXZaHdNbI8oQsQOgFcnYJ8pYity1uDCvBIqkB1uma00suWU5yQyMMZFcz4xi36orBABjoO1aOkZk1HJGdowSvJb8Kg8VJm/3cghehoAy9KX/TYx2r6N8FRbPDlscY3bm/WvnfSRuvolA719L+HYPI0SzjxjEYpAbkfSp17VCnSpRSAlBzS5qMHFPBzQA6lT76/Wm5pyffX60AeUeO+PF16f+uf/AKAtc3vJBGa6TxyN3i++z/0z/wDRa1jrZbbH7X6vsAr5iu/3svV/metS+BehUBOMVHwGFSNneR2qFzg8c1MJXZZYkQFMisp32SEVpg5Tms26iIk3Ct4BMejBuSaYTh6Ix0pzjAzTMx0abzgV0ukRCLSp5OpJP6D/APXXP2oJGQO1dNCBBoAJ/iBJ/E1w4p+6o92vzNKcepFBIDGRng9qqyYyeabAx2nFNbqSa1luaGfeZ5NUox5kg/Wrly2QajslUynPeuhP3SHqyyIVC9McUg4NWzGFHPTFUCf3px0rKo/dKaLNupd6kuBJBESjGlthhc+9WbhPMgrKk9C0vdOaEjGQljznvUoY9ajlj2zmrNjZveOdpwF6k112VrmGt7Fac5FRfZswH1NWL+1ktJgj8jsaaj5XFTzW2C2upTt1IYKfWiWNodWEqjKSx4fH94Hg/kTVtUG7IHQ1VnvUS/S3mGxHXCP6k9QapNzb5ew2rI0Y5AzCppuUNZ8L7JCG7HFaDsrxH6Vi0CehTj++RWroL7PFmkL63kP/AKGKwkmBkPOCDyK0dCuBJ4w0XB/5f4M/9/Fq1H30Yyfus9/uf+Phvw/lUNS3X/Hy/wCH8qhr6Q8wWikooAWsbW5WClQcADNbGa5nXbjczoOa8jOZqOGt3NqEbzMFm3HNUNXuBHp0kZON6kCrJk9K5Hx3qj2cFuirjIOT3Oa+bwlH2tVRPWk1FXZyMxEM73cFwfOB+RT1XFZt6Zd3mTSj94oLAnv2qVIJjcLHOxQSNuLHjHGetOitbaW0uJ7h8iFwiDd1+tfWxXKeZKVzttLgtoPhvY3Cou9ZWkYkck7iCPyrl4Y9Q1LXRDBbyeTKTkgdB6/hWnZ6mx0EfZyptlXJhc5+bof8a0vB85uNUFwqkIIGBx0znArmqydNTmxxV7I62G3Szt47ZCSI1C5Per9knOapAb396048RQlj2FfJVXf5noVHyxsYurTb77APC8VsxX1rbxcOqt3xXK3chbzpM9c1iy3LgDJJr1sHKVJe4eapbs7q48QwL92QGs6TXvMfK7ifc1yKSOzEHtVqJjvIHYVvUqVZfFIXM+htTarKSADjPpUEs7OuCSSeaqLudxmpZFZRk1ytakyuzb8GxNPrqNjiPJP5V6fXG+BLPZbz3RHLHaK7GvpMvp8lFeZigpKWkruGIaaacaaaAEqS3/4+Yv8AfH86iqS3/wCPmL/fH86AOP8AFGnW3/CRave3LEKfLwx4C4jX/CsHTL+3v4UuoZcC5JiYE9HA/r/Wo/iRrPm+LdQ0MyAI3lsRnkkxrxXI+ArXUIr28sLmCRbXd5scrDhXXkYP0r52th+dzm3Zpv8AM5ZLVnVxR213FNpbjaCSyqO+DzVPUtGMzQ7J1yibDu7r0x+VXLswefDetkzwyYPkjjcfX8KZcXv9oRQ3lkoa2eQrMCMmM9M1yR5oWkmR5GRqXhe5/s5BbFOPnUKfuEentXTeEdf/ALTsmt7ri7hAV1PG73FZl/eXmkjEUkTRscAMOA3oaoxa3p8c0FyyPDqI+VoohuA+vsa2qQdeldr0HbQ7i+hSSL94AQOhxyPevKr64m8J+M49RU7rWY7ZAvdT1r0GS/uJbVpEXEbr/F1z9KxfFOg2+v6Ir2ABu0iEhizyR3I/GubASnTqWns9BLc9AspYrm2juIXDRyIGVvUEU6/skngKMA0b9Qa4L4W68brTZdJuSfOtThd3XbXoyNuBXORiuivT9nNopo85s9CNr4onuruMsA2+Js8Z+lcv44kNkkD3Sr9onuHcqBgbQeK9Zu4FbIJAIOQa8z1XwrPr3iJ57uZ/sisFjXufatMPWjz3qOyJTS3Or8LrFF4Zt7q5t1CMpcKegFcLq9rPrN/JJauIrUyEoCMceteoraxx6fHYqB5EYWPHtiuP8X+FJp7y1SyujbWewhwOpNZYevFzlJvS+g1qYmm6TZ2q7mk+0OOTjkA1YcyXF5aGIbY1BLgNw3YcVs22lWul6Z5UZz8vU9SfU1kzR4NuFARDKMeuPrVU8Qpzf5kc12P8WSvp/htZ8EPJMoRwfTqDWqHEVtaamY/lYKkpH91hWdeXMV7oWpaXqUJKxjzbdx1B7GtTTY/P8P8A2d2+9DtFKpyqnFPe/wCBUuVIz7JL+fWL/ThsV4gJIyf41NSSWt4LmVRBvaJgrbTypqXR43k8WWUzNgRWpScjuR0rOs7i5XV9S1G4nEdvMzBYhyTzj8K0lRpuKlsPkTV0bFhrTXCyQzg/u22iQ9GPpSXpUxk54yCccHg1j6tEU00SY8qKAhhGOM/j61zK+IZ9V1OC0tNwikdQST83vmojhpT9+OiQuRnsvg4Jp2mS3HG15NzZ9+a5bUPEum6jcXcOogRxvKwikHPfFbVzeJpfh5UzgBew7155ql3DPgQqpyxPSow1SU/davqPmurE1zaOun3M8LF4bZeexI9a6O3todT0KJvvB4wwGK5XTLn7Tpd1cyvNHawjMq4+WQDov54re8D6zHqOmvEQFaNiNn90Zzj+VddSlKNK63KiupyOqWHl3wEysBH91lPJrf8ACl7nxRo0KLtzfQZOO3mLxWvqtlbpM8jx7lwTgDJx7VleG7aE+M9Ilt5zsW+gzDKu1x+8Wqo1PbJLqgtdn0ZJ/rDTKfL980yvdOoKCARg9KKKAM+50ayu5PMlgRmxjkU+20mztTmKFVPsKu0tAC/SsXxbGZPDV3jqFzWzmqupwfatMuYMffjI/SkB823+HPSufu0ADV1OswGG8dCMYODXN3QwxHrTGZKkCQHjNTb+CMkU0JhiSpNSRwM65bI4J6UFW0KV0CYyc1hshaQL3Y4FdHPCTCwAyQKqeGNLfWfFunaegJ824UH2GeaCWep/E66GhfDfRNFjO2SaJN4HoBz+teHqORXpfxwvPN8c/YkcmO0gRAOwOK85to/MnUe9IRr2sWyIA9+anZsDFKBgDFNbkj3qgO38GQIkcly4yyjI7E1T8WwbBHKTky81FZ3TW+mrHG6qzA8kZxR4pnMj26NkFUHB9afQTKXhyDzdZgXHcV9NabtNjAU+6UGPyr5z8Gw+ZrMZ/wBoV9K20YjiRVGAoAAqWMsKKk6UxRUlIApQcGkooAkBBp6ffX61CDzUsZy6/WgDyvxspbxff4/6Z/8Aotao+Zu0oW+OUfcPpWj4wYDxpqAP/TP/ANFrWTG4JK57V8riP4s15v8AM9ij8C9DOcdT3qlMWU5HpWw9uAckcGsu7jKPt7dc1NLRlND4HLquaS7TMfA5zUVq2DnsKtMGlbCqWPYAVtf3hbobYadNeyxxRIXkbsBXXweClTUre2uZd6upLbeORzj8iKdYaRJoq2ut2kpltwgaRSPmAPBFdJJdxz3ME6MM5O0+xXj+Y/KtoxX2jNt/ZOd0Lw3H/payqVIbyirdhw2f0pl7bC5t5ILWPOceWi/UVum8H9rXVsnHnsCG/wC2f/1qzvDrk6zIACyxq3TrgVz4impTpxXf8ilJq7Zzf2Ga1iWV4yqMSMH2qhdPtfjvXpUgg1l5otgEcalVXHIYj+n8682v4XjumjKncjEEY5reVNIanzGRcSHJBospBuDetNvhtz9Kq2rlSPSkl7oN6m/LMxQCqvQk0nnZUA04DOaxlqrF3uWoJVWPmpnuB5O0duazskAVZQho/fNTGNkUmZs/MpPqa2dCwIHXjO6su4QecR6U+N5LYbomIOK2eqsRFal7xCB+6A+9jmuWFwyTlM8VszzSXEfmy53H1rCZCbs4pRh0ZM9zZiUGENWNqKebJtJAIIKsexHetyNMWoPtWFfA+bVUFZ3RVT4R8dz5hGTllcxlvXB4P5YroVSIwbQOcda5VvliV1GGDAn3rfiuo1tw4YdOB3qqqu7ozic/csRMxrQ8KTZ8X6IP+ohbj/yItZd1JiRqt+EWz4z0L/sI2/8A6MWumENjnk9z6cuv+Pl/w/lUNTXX/Hy/4fyqGvYOEKKKKBDZG2xsfQVxmsPycdc9a6+6/wCPaQjstcZqx49STmvnM9m7wgdmE+Ixo3zgeneuX+IOnG5tbSbdkJkkeldLny7iNF53HnPpXMfEbUJIba3slhJ835g4PpXn4G/1iPKejXdo3OGurl5I4Ip+eQAxPIXjA/KquoFFvPstswKs+0kdCfWrtnYvrcst5P8AuoYQM7RgFvSorexW2a6mlTcUIVA3b3r6dNJnlNNmsGXT7eS2UBYymUbGTuAP866/wLbSweHmllGDPIWT121i6fpjXukRusOb4TrH83O1MZzXdxr5UUcSgAKMYFeNmeI5YezW7OrDwu7liCPMnSn6rN5NpsHVuKnt02ruNYeqXXnXO0HgV4FOPPP0DEz6Gbeki1cdzWWV6Z7Vf1GXZCo9TVJcMPavXpaRuYU1dEDq6suCMd6txKQm7vUMrBDkiriY8tT2xVSeg+XUtWqqBuPU0XPzuAtMjPAGeKu6Tam91aCAdGcA/TvWMYOU0kRUdo2PSPD9qbPRLaMjDFdzfU81p8U1QFUKOgGBS5r6+EVGKiuhzoDQaTNJnNWAGm0ppDSASpLf/j6i/wB8fzqI1Jb/APH1D/vj+dMDyX4mWkk/jW+aCK2SRWibziPnP7tOpqjZXzzQCIyFJEXlVPU+tbPxP02wuvFczsz/AGhvLDhGPGEX+lcwbR5dVgjt/wB3IRhcdRGB/EPevBxFqkpJuzTf5nFUfvM3WkVbAOWUGSTc5HTiqHguX7Pb3kTusiCdsgHggjtTgSGltZ0KNghlPQg9xUWlacmk+ckTMUdtwVux5rib/dSi3r0M+Yk8S6RNJfRXpmH2FhuXAzz7+9UVsk0+AzRRA5Ikdm5LjPPPtXTaLez3NteaY+3z1/eQo4yrj0/pWRb6hZ6vbz2cETRSRgq8Z6o3Q/hV051FGz2W5pvqdKUa8s0+zSbUZQ6kDtXL6m93p9oJYXZJrOTG4HnDcjP61veEpmk0hIZuJbdjE2evB4NZXid7eS6vNL3o8lzCFVO4bPH8zU0IpVWuhSsc/Ya3Zx63Dr8AWNz+7u4k4wT/ABY7ivT01RFVJY2DKwzkd65Lwz4HsPDwbUdUVC6rmOGRuP8AeNVpr+ZJMwxOsczHYgIKxjPrW+Kaqr930Cd1sdbfaooQOpz3Jz0rP0y4a7eS7Zv3QOI89z61z93bC+mjhWeQQR/NKc48w+g9q37dooYlVMLEgwory6kVTjvdmO5oz3KoF5yF5Ncx4i8QTzTKsEbqE/jc4Wr8t4DOQ5wp4Oa5/VYBf3CwwgMUPVj8o+ta4NdJrQpDrCS6vMPNJ8ucdMZp95IDqltbr/yzRpG9vSqVpJ5AaEOfNQ/Ng9KIlLPcysC7OPLBbuK6owip81tAsQ63O15eQqi7UjTMuDjp2NdBYT+XZ4JAxHisOy06KaQWKThXcZdmOTWhYWcj6+2mzxl4Vj3h1bH4VdaHtEkug7X0NTTIjGt9eAfNNEEX2PPIrlrz7QbyOJl/cOpE/wDtHsa7eG2ZVKxSbwvylDzx7+lVbnTo51PMeQORu+ZazVZwtpcuzSOZtfN1yyudMBbCkICzc9Kq6DoI8PaoV1JEQIdyTfxEntV2600wPI1tOqu2CMPggjoaZcz3M8Xn3YSe8wMF5SR9fauuM+eLUXo9yr3VjoBcQ69I5S6VbaPCkAc+lc1rOhWpvVtUlaNIH3mcN8rDrgjsazrGW7t7qVYTHGjKNyxk8kd66aNLc2DpdASwuo3noUJ6EVMYKi0oMSstjL1Wcq0Gk2aKlsynzMDquODWfollN4e1IXSy74ZDtkHpnoa0JQumvEWP2iJgNsnfHofpUGo6tYpbvCwVGI5+bJFWpy+BK99xxlfQ7HVpQLeO5XaQB83PauS0uGebx9oEsUyLbnUrd8FvvHzFyKNA1tNZ0S50i7y80cTbCeNyjkVz/hDUZz430C1IAiOrWxAH8P71a3wuFcJ+Zooa3PrmX/WGmU+X/WGmV6xqFFFFABRQaTNADqCMgg0lLmgDwnx3Z/Y9cuFxhS2R+NcLdxq45r2P4paSZFivFHDDYx9+1eQTr1B4IpgUUhx1G4UBSpxjircQ5C569qla3IXIQk44FKxXNoY96rJatsB3EYOPeu5+BPhoz+IrnWZ0/d2sZWMkfxH/AOtWbp2jSagwjCHDcN7V7x4Z0GHwz4WMEagPsLufU4oYrnyt8QLxtQ8d6vcE5zOwH0HArK0yL5i5HtV3WIGuPEN65/ilY/rU8FuILfBHvQhDenJpo5kApzdKIh+8BIpgbELfuljONnUg1X1W4NxdAnPAxSGXPTtVVyZJSadxHYfD+Aza5bjH/LQV9FoMCvD/AIWWJl1iOQjhTmvclFSxj1FOpBS0gCiiigAp8Z/er9RTKdH/AK1P94UAeT+OpvL8a3/t5f8A6LWqFsy7Ax5JGaf8QZCPHepD/rl/6KSpNCtHu4wxwFTjJ718ridK0vV/merQd4r0JY0MkfzL0NZepwjII6gVtqTDI8TjBU4NZV78zOxHtUQ3Oh7GZbLkc11vgqyWfXt5KHy0JCseSfauVhGGx2zXf+CdHjnifUWmeN0YoMdMYFdNKN6isY1GlG5s3MkCPLA2BEw2TxegPRgKhi0lba1jzJuWFQSx7gHP8sitO5t7S4mC3CHePuyr3+hFcr4o8VReG9kBkMfnArG4Tdt+ueMV1yjrqYxba0L9tbRYuL/KumFeIqeOhB/nWd4adorPUbiM4mOEDn+HqSa4LUtZ1H+17S0gm3WsgDkxcAgHJ4HQV2Pg2aG+tL3TppFjwVkZmOODkY/SsZ07VI28zRvTU2NJe6TVhAzsqzorDPYDPT1JzWVqWm/ZtVm/erKZPnDKOOSeKTX9VtIrxYbC4YmIrG84JJYnqAR7Y/Wnz31vd3UCwDY8ceHO3APsBUV5JRt1KpJuSa6nGa5ARdsoGPWslF2MBXU65GPPDZyfWsN4c49jWMamlipxsxwQNHnvU1ucrT7eDcvtTol2llx3py2KsQT5RQakgDOpIpbsgDFWLELscdwKlP3QS1Mq4cicg9a0rKPcQWHQcZqldDN4G9K0rR9qjdzTY4ble7UNZtJjGGxWNawhp2JrabBgkhcHlqo2kRMjADkGqvZMUldokkbZCFrn78s0gZJGUjqB0b2NbWqboYc1zk8h8rcTXRQWlzOrLoXY40mtw8Z3IeQcVK6bIxjrUeg753eIgeVICUPow6j8a0bmAxMVYdOhqKjtUt0IjrG5zt4jgkkdelWfBuR400LPX+0bf/0YtWL6EGIkelN8LLt8baCP+ohb/wDoxa66c72MZxsfTV0f9Jf8P5VDkVLd/wDH0/4fyqCvXOAdkUmaSigBso3QuvqDXF6gNx9hXamuQ1BMSugHRiK+a4gi17OS80dWEdpGCQPPyMHA61X8QaJaa3pqfaJPJeE7hIBzjuKulNuTjnNRXtv9tsZrUkgSIQa8ihV9lVjI9OquaJ5cVbTYjbQSrMpnyWU5Xr/hV+00S41q/wDslqGKF/MeUjhVPrVO108vp8lukEj3UVyVO3oB0FetadBFp+mwwxxhX2Dee5OK+jxGJjRjd7s89U23YYllBZRJHGq7kXaXAxupYo979Kc2XYip12QRF24xXyteq5ybe7Ou6pxIb+4FtbEDG4jArmC+6Qk9Sasahem4uD6dqqqO5rpo0uSOu7POnLmZR1KRTIsZ7DNRW/EYB7etV71hLdk574p0MnIBr0FG0Ea02kTTDdIi+p5q+ihl2nGKz9+6TeozirCyEnk49amadrA5pMnY7ZNqmuz8D6eTNLfOPlUbEPv3rkLCzkvb2OKJSXkOB7D1r1rTrOPT7KO2jHCDr6mu3LqHPU53sjmlK7LeaQmjdSZr3yAJopMijNAXAmkoJpM0CFJp9sf9Kh/31/nUJqS2P+lw/wC+v86dgOI8XWlje+NNTs3mxdywqyqOCAI1A/lXn3gG0lt7jUbi7dmaNzHuY54BruPGWlzR/ESTVoZgu1oy6t12iNRx7cVg3s0aXk9ta7VjlkMkpHGB6fjXzlaVp1ILrf5anLUlZsuWIjv9TKTg7bg98ZCjoBU+q6LcaYskqAy2w53Y5Ue9Y+hTtNqBuDnYshKZPQDivSLO8t9TtGHymTad6etedUTU+Uzirnn9lciKVLm3dS+eCRVZLRLfxJLq8R8sTj97EPuknuKw/E8F1oOo3kWnlv3T+YFAyCh611nhrTm1zw5DqHnETEZZAvbNdVWnKlDnT0ehSjK1iwNStIVlNuWSWUE5A4zj0rF0qWW2uXu3s4Wu8kRsw3f8CPqa6+Dw7YPZJcmeRieqggY9aq3GixJp0z2z7pVPyM3c56GsaVTkXItw1sZ99a3mqaNdLeSySPPwxVTnHtipdGijk0qG1ntZR5GI8tERkDvUmm6wIDm4VkIX5hjkGi78cW1tFuuIpYiThAR96t6c5Si4MuLvuSvo9uZMxRzoSeBjrWZdadqgkKwRh1zwXOMVr23iH7XOoWM7SAQw5q5cMTGxViPl6ehpcii/eQ+Vbo4+TR7mDfJc3McIfBK5yQfWoYnsrZTDHcMS53F36t9BTdS005dpLuUqMkbu/wDjVZP3djBHKVZo8iF2X5hntWloy0vYiyJZJ7Ml2DtJMBnGBk1qaRprXEMb70wHG+Mnk561i2trJbQ+bfQBJdxO7GS2fWqU/nLIl1ayyM0Tq5UdGA6imoq/LcSetjr/ABJo1tHqEd0khh2R7QAvBA7Gq2gXX2m5a5kQbt5Xfnrgd6t61N/bXhs3FowMoTcoPVT3BrD0ORdO0qJHOZ8Ev6kmprP9038i5aD9UuLw6q8lmsi4TIaPHJ/2vaox4phx5Oq2wMw4LwHB/EU93uNkksaCRcYxj5hWFNBFJL54jCSKedykHPvWtLlcFGSEnpqbxt7adw1jexs7fMIpWwRTJbe7SIrcWxw3TaMjH1Fc3LcSzvswFXHzGNcZ/GpLbV9TtrjKF1hXgIW6/WqdCN/dY7J7F2Wz8jzHhJUEcgryKuWp83TLksR86BVLd/Sql74xnjQtJDEEXhiy5Le1Y9p4li1W5ETaYqxr0KSEVaw8muZdB8jOu0q9sk05LO5VVcn+M8Mc9qzvEXg6OeN7uxUpKecdQ1c3r2q2LRi2it33JzlX6GtnwH40UzjS9Xk/dniKVv4fY1UaFSEfaxNYRaVzkLG4k0u+W6XPmISCvr2NXvCCsfiD4dkPG7VLY4H/AF1Wtr4haONL1UXMUeYJ13ZXoTWZ4H2S+NfD5H3hqdsf/Iq16dOalFSNbdT66l/1hplPl/1hplbgFFFFACGiiigApaSigDP1zTI9X0qa0ccsPlPoa+ftb0aewvZIJoyrq3p1r6SrA8R+GLXXIMlQs4+6+KAPAYLUjkjkdOK0bSwNw4DHArvm+HNysmElTb9K3dI8B21pIsty3mMOcdqYFPwV4cMMpuHj/cqBsz1Y9zXbahxptxj/AJ5t/Kp441iQIgAUdAKivF3WUy+qEfpUgfK+tWCQalK4H32JNZlyoVenauh15i9/MhXBjbH1rBuDuTNUBmN1qSMYBOegpkg5pAcUAShy1TQx7nFV0QsQAcetaFso86NcZ3ECgR7P8KdP228lwVxgYB969NArnPA1h9j8OxfLgvzXS4NSMKKKKACikpaACnR/61P94U2nR/61P94UAeN+PlDeP9Sz/wBMv/RSVoaBPHFZCNmCspJOTjIqh48IHxC1HP8A0y/9FJS+WrIoH3ccV8tio/v5erPWw+kUy1d3CPdyTIQUJwPes24fzRk96tbF8sJVKc+WGAGcUoo2kUw+2RlxXoXgZbqTT53+0ItojnfGydePWvOVkDy7jXa+FdZj0/TLuCRwvmfMNwyM4xXTTcYzTkY1E3HQ2brxBZWh2oHlgDdNg+U+mc1xXi28tdU8i5igjuJPL2AkDEfXpnqRVe+vWklltvLdolkOWAwCR9aWaNr1I0SNERcIrD+LOMCupyjF3ZEYvoYK6VbW80c8Ez/JnO7nPtU9sJftBdJSqkDIBxnHr+NMlSSGURvgcHp35xTrWaKK7BaPLKuMr3zzWM5xk7m3K0asMyxanKrqphniDIVXBDD5j/6C1W495u9yNjHJNYN9NJFZ+ZC5jdZFZcYyQcqR+TGtuynTq/y71HXsa4KknO0n6FR0ZBqjss+12znBB9RVA8nA9ava2hLRuOQFxWbuwBUTT0aCW+pqWK7xt70+W1ZJG2nI7mmafKquM9KvzMOxyD1NaRlrYpao5+9yM5HSuj8I6bFfLLPOCyr8oXPeud1XKSBO+K2vCWrppwkjmBMb88dQa0mlGxC+IbrejC11VljOI2GRmqG8xkIvLitvUL9NSvWl4UAYAJ7VjqAbssejHArOMrysUlYjndWQPwGPBFV7dwruR+NF1AYY2IYkbs1ViuPKbn7rcGteTsS5a6kuqr51oSe1cpdAomyurXxP4fto57K+tZppCuBKDjYa527WK4uFEDbo2Pyt7V1UoygveWhjValsavhu3E+nNGhC3CMWjfup/wA8VqahHvK88AYP1FM0m1WAKEGMDtU9xb/areeHJVmOBz/nvXLKV6l+hpFWjYxeJZNmM4GafokWPGuhMRgjUrf/ANGLT7eMNc71HDKDj0z2q5p8JXxloJC4H9o2+f8Av4tdMHaaRlNe62e9Xn/H0/4fyqCprz/j6f8AD+VQ17x5QUUlFIYtYeqQrHcNLj7wyPrW3mqWpwGe1JHLJyK4Mxoe1oOy1WqLpy5ZHJSwFjmmrAQRWgUzzik8vHOK+MfwXe56Kq6GfYaZb6etx5cY8yeUu7YqZ4+athaYwABJ6U5V5zinInms7kCoEUselYOr6mH/AHUZ4FS6vqqqDDE31IrnGbexNa4ag378zmrVXLRD1yzZNF3N5VsxX72OKI+MVQ1C4HmBB0HWu+MeaSMHKyuymfl5Y/NTvNAC+uOtQSPzmmY3kANj2rs5biVQtpK2TtrSs4JJCq7GZmPyqByasaH4bvtRZTFbsEzzI4wtel6L4ctdKAkP72f++e30q6eEnWe1kNybIvDOgDS4PtFwAbqQc/7A9K6DNJnmjIr2qdONOKjEQGijNJmtRCmkpCaTNAgNJSZozTQC0+1/4+4f99f51FmpbU/6XD/10X+dMDj/ABxKjavqpacwiGMLlRySY1I/HJriNNs3t9FluJW3SsPvN3J6V0HjsCXxvf2ytlnkjeTB6L5SDH44zVO5Hk2KRKpKFs5r5rFXhUkl1ZyVVqxmi2629o7AA7V2/nVC71WPSdUS9TU1gaJdptyOJAa2ov8AR9PXPRjk/SvP9cs4dS1uFgrAq2XXPUVnhEpzbkKmjtdav7DVoINUtcIskRSTcOhHrUnw8vHOjTW9hKsskMpGG4Bzzj6VwniC/wDsGnm1hYjdxgetdV8LJHt9OlbyyFkyQ3rjrWk6VqD7X0Limzq7m6+xSmBFIRm34HOCeoqOzaW6trowuGw2Nn8QPv8AhVXwr4gj8Q6lrekTMDPDIXtmPXA7fpUhlNheLfKoGT5d0o9v4se1cv1Tlnee4+S2pZNr5i75oOSP4h1qleaLa3ke2ZcgdBngV1sE0U0IHDBxkEdxVW5s08pzEMPg7Qe9TaUXdC5bHEQ6VdaVOsllcEqpJKP0NbUHiIoFS+sSqnq0YyKxLvxHFaTeTewmCUEgZI6+1MTUmuYVktVDQklWfONuOpq5Kq9WTqjc1HUtH1OAQB180DciMuCSK5G9ljlnEIby44zk88sfan7zqGq2dxbKZIoi29/QU6/ubG2uHe4wGABJPpVqDhbS9xSRWh1H7XdrayXCxI/GXPLe2e1X7WCzEUmnNIVuIRuR+5HofU1kyajo5y+9PlAIyOufSrckUQUSeVh2XPXkitG3CPw6D2WpREl6t5OJbny7ZflCqQN/uau/21p9m0amPP8A00UZA+tZ93c29vHuZEJ6BDzk1eSzhudIYhWdlUu3ygDGPu4rRJTs5bCjd7m3BdWtyMxxmLd92RQQGp82lbVe4mcyQ44JGV/Suesb0R6NYyM4jYqQF/GppPGK2EX2fzx+8+Xy25BrN0p8/LE2iruxm6rqNvY/u5bm2MQ5WOJNrH61i3OuWLx5izx0GOTWFrd2bvUZJCAADgAVQXIyB+devTwqaTe5r7NJ3OnsvEsUEEkF1YQ3CHkE9azbvV980htbdLVXGMJ2FZhIH8WaaTXSqSWxXKOLEsSSST3NRyBkYSKcEelLTgcjBqou+lgZ6l4Z1K38Z+GX0e+IN9An7tm6sP8AEVzPhuxn0j4naHaTKVI1S3H4eatc1pupXOi6pDe2zEOjbvY17Rp9paeKtV8N+ILLAlh1C3aZR/DiRSc1zW9lPyYJ2PdZf9YaZT5f9YaZXYMKKKDQAlFFFABRRRQAUtJRQAop1NpaAHY4prAMpB6HilpKQHzt40svseu3sWMASEj6GuLkQgMD0zXtnxS0IsV1KJMhhtkx6jpXjk0ZyR6VQGRJHzmovLJbpWi9uR24piwnd0oAWBAFJbAA6mtjSLEXGp2ygZy4wPWq0EK7cMAQe1egfDPQjqGvi7dP9HtRu9i3YUAezadbC0063gAxsQA1YIp9JUgREYpKkIphGKAEpKWigAp0f+tT/eFMp8f+tT/eFAHjfj1d3xB1ED1i/wDRSU5I5IrcMcHA59qXxxIsfxGvy3TMX/otKfOwTTHOeWGBXzeMX71+rPWofAjPW/XzihPPSpbqBxBvK8N3rm95XU0Vm/5aV2Esoawb/dNZyXLY0Wu5y67ll9q2rUyoh2OVJGDtOKo2qCWQrgZHrW1b2UyxF9hK+tOroEFcytrh5FzhJPlbIByM1sWgjjRXcDanIJ7VQl+acqO3NSuSYNg6EVk90jRKzMjWVEMofIy7FgB1AJPWq9nGS5ePaWx0YZBo11/kGDkqAKp6bfBQAT7V6EYXhoZyfvG5dQRXNvuUIFUq45+9z2B54IpnmNFMij14FI8+1fJG3EvIPf6fjx+NDgP5LdyBXPOFg6mpqJD24IwSQKxZV2YrfuIc2KsBziufuuW21gkrFTLFtLtkAzwa0I5wFkTr3FYsW7j1AqcOSQM85q1FN3JTILyV7jEh7DFOsZMcZ6VZubfyYiWAway7ZyHyOlEoubHszbhw77jTru3ubGSFrmB4hIu+PcPvD1p9nHm1z3PApmsapdapcQJO4ZbVfLjwMcVFGUeeUXuJ36FbUkA0tZw3LNyKzbGP7U65B8tTl2A+6PetF1+0+VZM21GJ59DWZdmTRNEv1abm5f7OgA4cDkn8OK6qSu+VbkT097ocPqUqzahPIgwjOSPpmtrTM7Ldm7c1gHDPXRWGNkQHpXbX0gckNzt9LQNOpP3cZqOV1NwxH8QJ/I//AF6p2UzQYfkgdR7VJ80hVwPlJI/OvDUrydztUihDLsuXBdSRIy4AxxnI/nW3p6q3inQ2H/P9bn/yItYNwqrqGSyqSnA7sQR/Q/pXRaMoOv6Me/22D/0Ytdyl70X3sRL4Wj2a8/4+3/D+VV8mp73/AI+3/D+VV819GeOOyaSkozSGL+NIfTtSZooA5+9VbS6ZGO1G+ZTVU3EbNtDA1c8RIt1B5S/fXkEdq851F7+KFxazYlB718rjcOoVuVbM0VWx3bzRom4sAK57VNZGDHCfxrJF3dSWcZmfDlfmwazHdi3JzXGqSlK76ClVb2JJXLNk9SahBbzAB070okDfhSqjyNtUck11JW0JWpbt4jK4A6d6gu9MididrAmtu1tha2+3+I8saR1U5rrpUdLvcib5mcyukwb8uGI9M11fh5dIs3G6zjL/AN5xk1TaAGmiHB4NdNOLg7kpWPTbW6hmjHkkBfQVZDGvOrC+ntHGGyPSu007UEvYs9HHUV6VOopGiZpbqTIpmaTNbDJM0ZpmaM0CY7JpM0maQmmIWkzRSUwDNS2p/wBMg/66L/Ooe1S2h/0yD/rov86YHEeLI/sXjfU71oS0cjRb2HUfukH9KpzSQSwHy9rxHPKnoak8e31y/jDULGEYjHliSQ/w5jU8e9ZmmWyxgKo+RAeCe9fNYipapK/dnHKXvNMvI8VyI4lYAAhDmrGreFba5f7VbBVuduN3ZvTNZNxcC2ZZF2MC4EkeMEKR97PtUlpqmpJNgyxi2cHYvXIz1zWMOaN2uo1ZHnXiuyk0u6tZb0E/Od1v7fX3r0bwpbG08JLcMpR7hy8cZPQdqNXOgeIB9ivlZJYV8056gD0arVrceaqTBGitUUR20ft6muupP2lKMbamqemhwHhmZ7Hxfd61DuUwzANH03bj839a9M8Vw71S8so98NyAWHZSR1rPGn2puXaOJFllOX44P1FaOgySy2lzos6iZbc4bb/CjcjH0NKtPns+wr30Zl6Rd3Wnwbbhg8an5RnGz6VvJrFtKRiQBsZwTXHvpN9a64kF7KTb+YFV89VP3aqvJ9l1F2DHCOY3GffjiueUSea2jF8YWMOtXkCm6VYIzl8jv7Gn27aTpdiLOZ/KgxtO7Iz+NP1O3huod7gmNvvEdjXO+J44brRDEZwrw4KOeDIPT6itIJ1eWDdlcSbbH6KIIfFdxDp8pksxGSMOWC5rbvNFk1CQ3EMsakAKUdchhXO+C7dbPSprmTeHnPGR/CP/AK9Xpb6XcWMpVT02saqt/vHu9EN/Fcl/4RaJFj86OBjG27AcgZqK8M0TGGMx+YeCd2QoqK41EwbDIkrI5+Zu4qld61amZktIZmQcguOpqrVJq8jTllJXHXmlmwtftUkqyyP3znFaGkaubbT2a8cKm0jJPWn2mm3moWkcrIscbtzu7D1rL1thc36WNnFssYOXc/8ALRv8K2hDmXvdDXltEr6pqkdvYRB4BIrDaBkj8q5yS50eZlMqXKP7PuAqXUNZ2RT2DJHLGzfK3dD7YrnWzvzXoUaVoijHqbEp05gds87Z5GVFV1FtnG+Qk+oFV0GVyKRge34GtnBx3K1L1zaqkYKcjHWqQPODWhZziZPLf6c1VuoTE5IFOLsUmR0Z5poOadUzVnzITQrAOvSu0+FPiKbSPGenWDEtbXl1FEV/2i4AP5muKBwa3fBaZ8f+G2H/AEFLX/0atNpTWpJ9kS/6w0yny/6w0yrKCkpaSgAooooAKKKKACiiigApaSigBQaWkooAqanYRanp81rKAVkUj6V89eIdBn0jVJIJEIweD6ivpCsHxJ4YttftsOAs6/dcDmgD52Nv8uOh96Z9iY8jknvXcap4G1SzchYDKoP3k5qtYeFNXuJBGto6g92GAKYHO6fpM93eRQRIWdiFAFfQXhXQY/D+jRWygeaRukb1NZ/hXwdb6GgnlxJdH+Ij7v0rqs0mAtJRRSAKQilooAjIxTalIphGKAG06L/Wp9RTadH/AK1P94UAeNfEOEt49vmXg/uv/Ra0AA2G0jtVjx64Hji/HceV/wCi1ptzE0dpCMjdMgZAQRke2a+exl3NvzPXo2UF6HGXA3aizD14ragvnlgMTNkYrMubK4jui0kLoM5yRU9njOD1rOVpRVha3NC1xCRKw69fpXao6CFSuNuOPpXHMV8gH0Na1lqBS08okEkfIxPSplqrG0PdMu7mWK7l28guen1otbsPcdPlUdD3qG5iaGTceRnNZ0kvlXvHAccYpqF0HM0LrEP76cKcpuBUdwCDxXMM5imwua7efy7uFpEXnADH1IFcncWjrcFgpIFdGHrJScGZ1I9TVVmlsImz+8QgjnpW0RC4iaDJiYZXPUe34HNYOmlkKs2Tj1roGijt7a2EUyOpDOUXrHyMA0q2oR7msxJ07b3rmLpTFMQepro4G8yz61narbAQBv4l5ri20NJaor6bCtxvcn5RwKguV+z3YUnoQc07QJwZnhLY+bPNaF/aJNM8hGVUDHvVxdpWEtY3I9RdZNOK98cVz9kCXVe5NbFwmUKr0K1hxyeTcqR2NdVKGjREpapnbRQrDbwAHJILH8KyIgpu/n6FuatfagXB3fdiAx6ZNUFYyCQr1BrzqKtKU31LujQutLSW5tfO3QwSHlh1x6iuZ8bWha2tJYB5NmqsqhmyS3qfwrrPEGvfbdItRbx7JIlCk56Hvj8q4LxDcvc6YskivtX5EI+6Wx/PFejhrqrfoZVWnHzORWQhq6LTph5cZ9Kw7eJT5b/5zW+0SwwxIp5wCa7a8k48pywXU6OCUGLHqK07dR9lc+nzD8KxLMhkAHYVqWzlXSMn5WO0j6187Vi4yOmD0uZl48RuoDITtE2MDrkqwH4ZxXXeHIIm1PSpHGX+1wkc9PnFca6fvGkcBmicSBT3KnOP0rqvDl5F/b+mw7wNt7GqjOcjeMYPevQSu42By3uewXv/AB9v+H8qgzVm8ika6crG5HHIU+lQeRN/zyk/75NfSnkjKMin+RN/zyk/75NJ5E3/ADxk/wC+TSAZnimu+1Sx6Cpfs83/ADxk/wC+TVe8guTAQlvKSfRDSk7K4GHO/mSMx7muX1jR3ldp7Y4JHzLXWvp96T/x6XH/AH6P+FV5NPvwOLG5P0hb/CvLq0nVVmhNHm0hkRTG+Qw4warfWu9vNDvrgktpdyx9fIb/AArNbwzqCNldIu//AAHb/CuX6jOOxBzVtazTnbGhx3J6Ctm1tI7UZPzSdzWjHo+sxjaNLvdvp9nb/Cl/sXVj/wAwy9/8B2/wpLDzi72LTKTyZFRFq0Toerf9Ay9/78N/hTP7C1c/8wu9/wDAd/8ACt1Gb6CsZxJzSbiDWn/YOrf9Ay9/78N/hS/2Bqv/AEDLz/vw3+FaqEgM9eRV2yvJLaZWVjx1qYaFqg/5hl5/34b/AAqQaFqf/QNvP+/Df4VooyTuB1lndLc26yL3HSrOawtFtNTtZfLksLsRt3MLcfpXQ/Zbj/n3l/74Nd0HdFojoqUWtxj/AFEv/fBpPstx/wA8Jf8Avg1YmR0Gpfstx/zwl/74NIba5/595f8Avg0CIqTPtUv2a5/595f++DSfZbn/AJ95f++DVARZzUtof9Mg/wCui/zpPslz/wA+8v8A3waltba4W7hLQSgCRSSUPHNIDzPx5qcK/ES7sY/nuGMXyjsPKXk013SztAhIEj/3utXPE/h6/m+JmrapHpN7LDtiCSx27sHPlIPlIHOMYrkbjT9evPOln8N675iSERKLGbp65xXgVcNz1m1F76nLKHvNmpoyK/iqW0kbMV7bFCSejDnisJ76e0lvtLyvnWbEwyOpAA7rVi28O+K2vI7mLw9ewojYHm27hsHv0rRi8C61c6vHd3dpfuSvzK1u4TI/n+NaxpcvxIvl0sZ2j2ZuWM8xZ4id7nJBmb/4kV0jzySl47fa8iru8sN0FaaeFbsoB9ivNw7tC2MfTFMn8IajhZYLO5S4TlWEbc/pWc1J9A5WlocSusy2mv28knmKW3RSq6nCDs30rt/CchjttS1J2/10mAezIowKqX2i6rqGnyLLo98swG0/6M5z9DjmtO507VbXwwlsmm3kk8kYjZYrduPc4HFZVLyXw2YoprVlLTlTWre6+1SztbNJuhbP3WH9046VnS+DonvZp21SaRpDkhvl/lTo7LxKlstvHpWpRqgwoFrIB/Kq72HiwNn+zdUPPe1f/Cs37VbIbs9bF0eHbWCBo5LyZou6mXiuf1nQNIQb2aWaQfcVpchfyrXuNK19/Lc6TqZI5YC1k5/Sn3Hh/WLuAO+kX4cDG37M4/pRB1U7qJN+yMC2he5jMcQUBRwM4FZeqWhnuba3clHjbcqLzurr7XQdahKgaPqIx1P2V+f0rV07w/fm6Ms+kXe9PuO9uwI+hxW9GE1O7WpcIPmOHPnnUd85dIdu3ywnBq6bKyZd8c5DehiNegto94c7tOuSBz/qGP8ASuG8V3PimRHsdJ8Ma0UPytMNOl5HoPlrs+rTludTpvuYV3qkFtIYnu92OMAmub1jxHGsTw2mPMPUgVE/g/xi+7/imNbBJ5P2CXJ/8dqIeBfFe3/kVtbz6mwl/wDia3p4SMNXqZ8i7mAJDJcB5ccnnHFS3MIwGXpWu3gbxb/0K2t/+C+X/wCJqzb+DPFhTZJ4W1v2J0+X/wCJrouXY5iJyhwanIBHsa2ZvAni1XyvhfWiPbT5f/iadH4K8XYw3hbW/wAdPl/+JraL5lqIwlYxPuFagK3dv23AVabwP4s/6FfWv/BfL/8AE1Ja+DvF0EmP+EX1vB/6h8v/AMTWTVnYWxz0iGJyDRmuru/A/ieVdy+GtYz6fYZc/wDoNUB4I8Wg4PhbW/8AwXy//E1Sd9CjErf8Dn/ivfDoP/QUtv8A0atN/wCEI8W/9CvrX/gvl/8Aia2vB/g/xPbeNtAuLjw5q8UMWo27ySSWMiqiiRSSSVwAB3qPhduhNj6ul/1hplSSKxkJAP5UzY390/lWgxDSU4o390/lRsb+6fyoAbRTtjf3T+VGxv7p/KgBtFO2N/dP5UbG/un8qAG0U7Y390/lRsb+6fyoAbRTtjf3T+VGxv7p/KgBtFO2N/dP5UbG/un8qAG5op2xv7p/KjY390/lQAwqD1ANARV6ACnbH/un8qNj/wB0/lQAlGaXY390/lSbH/ut+VABRS7H/un8qNj/AN0/lSASinbG/un8qNjf3T+VADaQin7G/un8qTY390/lQBERg0sYxKn1FPMbf3T+VIkbiVflOMjtQB4p8QTIPH+pkA7R5XP/AGySudlu5cKruzDGBk5xXe+LtG1O68bX88Om3ksDmPbIkDMpxGoOCBjrXM3nhfWTMNmjagVDZ4tn/wAK8Won7SWnVnpwa5FqZcU9wvzGRmUdEY5GKlivLSZtkmIJs5DgZB9jWyPDOsFDt0i+HHQ27j+lcxd+F/EqXZKaDqrAHqtpIR+grGFHme1mOUlE0rkTQoA2CM5BByD9DTrOdnI55U9BT7XQtf8AIVZNE1QLj5l+ySf4Utr4f1+G5Vv7F1LYecm0f8jxRKjJbIcaivua2pRhbPLdT92uWvCrJH2dTiu0v9H1e4hiI0u+JHb7O/H6VgT+F9cmk+XR9QHPU2z/AOFKnCXVGk5R6Mfo8INu653fN/Ssm9gYzq8fr0rrtD8PatAhEmm3iZb+K3YentWZc+HNb+0nbpGoMocjItn/AD6VyxjU9u3Z/wBIJSjypXMckIVDKFUgcjsfepQQGBOcgEVpzeHdbKg/2Nft7fZXOf0qvLoPiJDCTomosecsts5yPcY4I6V3KnKXQnnj3LuhXCXEk9ux5HIqPWJSImQjkcE1Do2ia7aaqryaJqYQggn7LJj+VbF3oerTMxOk3zf9u7/4VhWoyUrpD9pFrc5LRyFv2Y12cnliy6Ag96wP+Ec1uKQSR6JqIPQgWr/4VpjT9daDy/7G1EH3tX/wqfZzbvYITilZsypnDO20fKowKx7aEXGqYI+RTuNdRJ4d1oQMy6TfEkdPs75/lVfRvDOrpJM1xpGoIGB62zj+laVJTjSk0ncmTV1qUZd6TzB0C5YbTnqMU+wILMB/FxUj+H/ETwpJLpGos7DoLV+PbpVm18P61EVb+x9QHIPNq/8AhUzoNU+VLoEZJPcoTxYt5U7iseJBPGIZcuisXCHoDjrXaXnhvVWWQx6ZfHI6fZ35/SsjTvDGt/aHMmjaggCNgtauM/pVQclTbswbj3OS0q0iNu6FRhp93TpjtVjUYwN0hUK/nYwOgAArak8Ma9BJIsOh6ltJ6i0k/wAKqXHhPxLsDHRdTcMc4FrISD+VXyTlJSM3KKjYr6dMmwhTz0rR81YxuJ6ciqlp4a8RQsc+H9V5/wCnOT/Crb6D4iBBGg6ocdvscn+FY18PKUrpCjUSRWvdq3co/hY5/A8/1q14aYt4l8PPgDN1ApAGMFZAp/8AQc/jUl74c1+RLeVND1MlowGX7JJkEcc8e1anhvwnrtrrWlXVzpF3HGt9DIWMbfKu8ZyP4QMZ59a1w8Jq112JlJM7NtLlM1tFCd7TQiUkjAQHOcn0GOtQyWwN0YLRnucd0jPJ74HPFbcuoR28VlazRL9nmtEEzgfNg5xg+3Wo4LP7JBfWhR5pXKMnkuFMsfPKnByOmR/hX0dz8gngKMny0/nvp7t0kvPvrrstNcQwTCYwmKTzR1Tac+vSpUsLppoomglQynC7kIz71tQuV1TTk8sxyRRScNJvcDDEBuBgj09Kp2EhOnO0jEhbuJssen3smncyWApKSTber8tlGTWut9bfIpTWFxFeyWqxSPIjEABDlh649KiNrcCRozBL5ijJXYcgfSts28gn1XMUsjNLn7OnBdSxIbOM4+lWJN8UYcKI3GnOPlYnb8/TPqKVzX+y4O8tUlf7rtf1rfy6nNywTW7BZonjYjIDqQf1pzWdyib3tplXG7JQgY9f1rUtJI1tNMkuDmNLtsluw+U/lT0hvYrXVmudwV485Y8Odw5HqPf3p3MI4CEldN2tf093m19dumz9DesLmZdNtQH4EKAcD0FWftU39/8AQVQsf+Qfbf8AXJf5CrFZH6hhv4MPRfkT/apv7/6CsPUPEk0EjCKYADj7o5P5U3xDqYsLIgHDv/KvP7nUHlODyTXJXrOOiPZwWD9oueS0Nm78dawJSIbsKM4/1SEfyqqfHPiAHH2/6Yhj/wDia50ttjYtyB0pA2MHANcvtZvqz1VhqK+yvuNubx14lXBXUxgjp5Ef/wATUbeP/EoGf7TBHtBH/wDE1gyxkO5GMMMgelVmdAmeSemKuNSb6spUaHLdwX3I6yLx54jd8f2lkf8AXCP/AOJq4vjXX+p1AH/tjH/8TXExEeYCOmKuJJlT7UOpPuUsPRf2F9yOxXxjrboWGpAFeSPKj5H/AHzU0HjLWHG83YK+nlp/hXGedtQncOQQafaXQGEPp60vaTXUh4Slr7q+472DxZqLOFa5BGMk7FH9K1bfX7tiCZ1cH1Uf0FecNdCFA3J+lWrLVZBwWBAzVwry6nLVwcOiPTrbWhcEpv2OOCCBVv7TL/f/AEFcLDrCDqBnHPFbFlrIyEb5l/UV1QqprU82rhJR1R0X2mb+/wDoKPtM39/9BUCOsiBlOQadWtzi1JftM39/9BTo7iVpFBbgkA8Cq9Pi/wBcn+8KYz5h0/w7oGoaNqWpLrepJHp0cbzodMjJO9wgC/v+eT3xxWRPpcMkSy6X/aV2klyLeFpLEIJGKg7QVdvnycbRnjBzzgbHhv8A5EXxn/172n/pQtavhX/kW/Dv/Y2R/wDoEde65ON9ev6HNa5yDeHtbRYWbR9QCztsiJtnxI3ovHJ4PAqGTStRhuJ7eXT7pJrdPMmjaFg0ScfMwxkDkcn1FdwdRvW034jSG6l3yTxBjvPObgr/ACOPpxWhBuureztlO+8vfB7wwqT80ridiFHqdqHA9sUvatb/ANdQ5Ueaw6bf3CRPBZXMqSlljZImYOVG5gMDnA5PoKde6TqWmpE99p93apKMxtPCyBx7ZHNep+FbG90a18Jx3UbW90t5qUnltw8Z+zDAYdVPGcHnBFcZazS3Pw01ozyPKV1S2cF2zhmSXJ57nHNNVW35f8Gwcp9K+DP+RF8P/wDYNtv/AEUtbdYngz/kRfD/AP2Dbb/0UtbdeLP4mdC2CiiipGFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAVdoAGTjPtSbTnA5+lPG4AYG4fTpS4HzAD04BoAjwc4wc0bW9D+VSD768YIB703J8r/AIFQA3BJxg5owc4wakIyW6npwKU8D0+WgCIgjqMUbT6GnqRhM9N1ADAPn0oAaykMR1xSYJGcHFSSfNuA6jk+9Ko+ZeM+9AGFquo3dncrHBLsUqpI2g9SfUe1WF1C6MUR83llyflH+FZuv8Xqf7q/zarKf6iD/dr57G16scQ1GTS9QjuOtdUvJLiVHmyFBwNo/wAKrX+sX8NvbvHPtLNhjsXn9KZZf8fk3+6f51U1P/jztv8Af/rXj/W8RzNe0f3su3um5Y6jdTGbzJc7cY+Uf4Vm6zrWo2sE7QXG1kjJU7FOD+Iqxpn3p/oP5VleIR/otz/1yNdCxVey99/exdC/4b1rUNQ0gT3Nx5kn97Yo/kK5vxP4u13TtZjgtb7y4iDlfKQ+nqtafg050EfWuS8aj/ioYfof6VvRxFZy1m/vYuhvXPizW47GzlS9w0iZc+UnJ/KtXR/EGqXUBaa63Ef9M1H8hXIXfOl6d/1zrc8Pn/Rmr0qFWbhdyZFTQ37jWdQjA23GP+AL/hWc/iPVgxxd/wDkNP8ACnXfRayJPvGuunOT6kJs0P8AhJdX/wCfv/yGn+FSJ4j1YsM3f/kNP8KxT1qWP7wp80uV6lSZ10Oq3rwhmmycf3V/wpYNVvXkIabIz/dH+FUrf/j3H0pbf/XV8vmGKrwfuza+bOimk0S6trF/awFoZ9p/3FP8xVDT/EOqT6jaxSXW5HlRWHlqMgkA9qdro/0VjWTpX/IXsv8ArvH/AOhCuTC43EylG9SW66s83GzlHEJRdkeVXfh/R9Nh003+sXkc17aR3QEVgrogfPBYygnGD0WqGoeHNQs/EFzo0ML3tzC2MWqM+4YBBAAz0I+ldtqY8Rra+G5dI0YXkK6TbnzjpUdwA2W48xkJHbjIxnPep7y1tbi+8SvYRS30kn2b7TpthKP3khBMh3YZiivnIXueeAK/oiGOqx1lK979tPeSV9Fbfq9e6OV009v60PN5NL1CKeaCSwukmgQySxtCwaNB1ZhjIHI5NRmyuxDBMbWYRXDFYX8s7ZSDghT3IJA4r0q/mWw17wiLqBLe3utPaxuEWQyBEd3QqWJJJXcMjPBGOOlXoIraEf2VctER4REN43PEjCNnlUeuZSgq3mklFPlv1/Fx/O3yYvYrueYQaFq9086W+lX0rQMUmEdu7GNh1DYHBHvUNtpt9esy2llczsjBWEUTMQxzgHA6nB/I139jDPc6PoN39mn1Vnlkmefz/Kt7JzKS28oAd38RZmHGAOBSa28toPiIsJaLffQA7eMq0jn8jVrMJuThZXvb/wAmUfXrfZfPcXsla/8AW1zgX0zUI78WD2Nyt4TgW5iYSZ/3cZrV0jwnf3+vWmm30N1py3EjR+dNbN8rKm4jBxk4xxnuK6+2d5/D9lFA/wDxNrnw+8NqxbDvi5bcin1KBgB+FXfDltqNnbeEbfUxIkqX14EikPzxJ5IwpHVecnB7EVlWzGooStZNXXnom7ryuhxpK6PPJvD8sXiv+w1mViZxGs+3ClDgiTHYbSG+lLrugSaT4lm0a1kkvnTZ5bJCQ0m5AwwoJ9fWuhLxt4Si8Tb1+0R2baRjPPnfdDf9+CR9Vrbklt18f+JIWV2u59MjjtlimEUjsY4tyo5BAYrnHHPI71TxtWMu9k01/eTim/TUPZxa/rbU83k0jU4b0WUunXaXbKWEDQMHIAJJ24zjAJ/Cnf2Jq321bL+y737Wy7xB9nfeV9duM4969K0N20+80CzawuLGeKa7mhjvbsS3Ma/Z2z8oiTYhYAjPJIPHeuY8Kwy3mha0+y71GQtCrafbuBJKuWO4tguVB6hepIJ6VosfUak2lpb53k13t07+V1uL2S/r0MjTvDV5e3Oo2s8c9rdWdo1z5DwHe5BUBdpwRnd15+lZt7p97p0qxX1ncWsjDcEniZCR64Ir1S6M9nbLPHELSZfC06YilLmMicDbvJJyAcHng8cVxF+zS/DnR2kYsY9QuUQk52rsiOB7Z5pYbG1Ks7vZu34P/L8dwnTUUbMXxM8ZSTqv9sHax7WsP/xFW7jx/wCNYkyNWIx62sXP/jlc/YaFcyWd3qLRstraH95KRwOe3rT9U1mK40cwQWkweT5I5nXCt64r8iqOspK2x6kozuki6vxT8Zyz+TFq2W7/AOjQ/wDxFXofHfjtlLHWE4GdrW0Qz+S1x9kqWKLk/MP4j3rYtEn1KNnt+cDnjnOK7Iq3U9ilhqSgnLUvzfE/xtGWVr9FYdhbxn/2WoZPib44TAbVmVvQWkP/AMRWTuNtdQT3EKyxDBZD3Bz/AIGpPFN5ayXomjlneN0VozJ1C9MH6EEVFRNLRnnY2hKm+aGxt2fxB8dXkoH9tLFH/E7WsOB/45Wj/wAJv40hBK6wl2AOqW8QP5bc15zYa1HaymIqJI3POetbiz28/wA1vMY27c8D61hKU72OS8u50C/EzxRvKvrARgcFTbxD/wBlrUtfG3iy9VvI1X5wpOPIi5/8dri7hndQt9biVO0qDkfjV3Q9tvdho5soezcGpcpLZgpSR3umeMNdW33X+oF2Ay2YYxj24Wt7Tta1PWNKuZrfUjGxceVI0SfL6g5GDXDapDG1nJcpOEZsBl/vU631jy9O/s+I7ERAEZe568mumhJyV2ephaPto8yPU9O1Nigt7i8Sa4A+8oABp73Gox3kXziS3eRVOFGQCa8z0242PDcSyHcgznPJFeg+HdZh1JWi3AOpGBW5Vaj7J3SuY0nhm9jcoZbfI9GP+FN/4R27/wCekH/fR/wrrbr/AI+X/D+VQVPMz5D/AFYy/s/vOZ/4Ry8/56Qf99H/AAo/4R27/wCekH/fR/wrp6SjmYf6sZf2f3nM/wDCPXf/AD0g/wC+j/hR/wAI9d/89IP++j/hXS0UczD/AFYy/s/vObTQLxJFcPbkqc4bJB+oxVmTS71oZI40soVkxv8ALL/NznHOcDPpitulo5maQ4dwUE1G+vmRW0bQ2sUTEFkRVOOnAp7uscbOxwqjJNOrD8U34sdMwD8znoO4rOcuWLZ7+Ho3caUfQ4bxBqsmp6iwDEIDwPasqTIA5p8rEyvIwxu5qDzCWyVO3tmvMbvqfX04KEVFEjOPKO7HIwQe9V2fC8fLjoKV5ATtPXNVZmIY7RkCkkSPmm3R47qc8+lVUkyjMDg5FJNISrZ+U44qlG6njdgA/nWsInLXm4rQ1EnMwB245xirKsVHH05rNhdVOTyOxHY+lXBLx1wTRJG1CpzRuLMxHTgZqJJWDAjvzVh2VosE9eRVH3x0oOi5ca7byyrHpiiK/CMcg7j0NViQqFSOexquCo6nPp7UlFGU1c6qDUo5YBGTtmTof71Sw61snRCw56muRNwVZHBxjg1p2gS4gcs+JVPP07UNWV0clSdtGel6PrRV/LbJXvXVJIkqBkYMD3FeQ2F+yNsd8svAI710+m649qwOcqfvD1ranWa0ZxYjCc654bncU+L/AFyf7wqta3UV5As0TZB6+1WYv9cn+8K7E76nl25XZnN/8Kh8C/8AQD/8m5//AIuj/hUPgX/oB/8Ak3P/APF129Fbe2qfzP7yeVdjiP8AhUPgX/oB/wDk3P8A/F0f8Kh8C/8AQD/8m5//AIuu3oo9tU/mf3hyrscR/wAKh8C/9AP/AMm5/wD4uj/hUPgX/oB/+Tc//wAXXb0Ue2qfzP7w5V2ILKzg0+xt7K1j8u3t4liiTJO1FGAMnk8AdanoorMYUUVHM/lwu+M7VJoegPQfmjNcp/wlEnkq/kp8zYxv6VIviORpbeNYFBlXcdze9cf12j3OP69R7nTZo71zX/CSMEncwr+6bGC3PWiTxOFVykSsqyBAS3XNV9co9x/XaNr3Ol70tc+2vYuLeFolzIAWIbpmopNfniW4ZrVAImC539SaHi6SvdjeMpLqdLRXMp4kllRQlupkJYYL8cUo8SMWhAt+Jh8vzd84Io+u0e4vrtHudNRUUZJUE8HHSpa6kdaCiiigAooooAKKKKACiiigDNmuUgkZGDEj0qH+04cgbZOfYf40zUf+PyT8P5Vmn7y18JjeIMbRxFSnBq0W0tOzOlUouKZqHU4R/DJ+Q/xpp1aBRykv5D/Gsx6hk6VlHiPHNbr7jKcUloah121H/LOb8h/jQdetQjP5c2FGTwP8a556Q/8AHvN9K6KefYyUkm19xyKpK5snxZYrafavJuNmcY2rn+dX7DWYNStTPDHIiE4IYAH+Zrz9/wDkA/8AAq6TwpzpH/AjX0jxdRQ5jZMtnxfp4meLybncvX5Vx/6FQfF1gCR5NzwM/dX/AOKri3/5Cdx9TSv/AKxv90V1UqspQTZHMzpr3UYdTkE8KuqgKuHABzk+h961E/494P8AdrmNP/49G/3x/Wumj/49YP8AdrwsY7122aQdyvZf8fs30P8AOqep/wDHlb/7/wDWrln/AMf0v0NUtT/48rf/AH/614v22afZNLS/vTf7o/lWX4g/49rn/rka1NK+/N/urWZr/wDx73H/AFxaumOyJ6DPBn/ICH1rlPG3/Ifh+h/kK6rwZ/yAh9a5Xxx/yHofp/Suih8Yug66/wCQVp/+4f51t+Hv+PdqxLnnSdP/ANw/zra8O/6hvwr1MP8AwyKhp3f3VrIl+8a1rv7q1ky9TXbTM47kFSJ94VHUqfeFV0ZUjobbm3H0pbf/AF1Ja/6gfSnW/wDrq+RzLc6qWxBrn/Ho1ZOlf8hey/67x/8AoQrW1v8A49WrJ0r/AJC1j/13j/8AQhXFhPij6o8rH/7wjy/VPh7rek3slrKbaVo8ZeJyV5APcA9/SsF9LuI3KMY8/wC9XufjGJ59XvYY32OwUZ9PkFeZXOnrazSB3LTLjYFG7JzzX7DV4ozGEkk19xM8PCLMO38O3lwVVXhBYZAYnP8AKop9Du7ZiJNn4E/4Vox6hdW0s8j/ALpz8oHoKzm1m4inyXLo3UMc5+lRLijM7XTX3B9XgQDT5Scb4x9Sf8KvWvhy4ugNl3aKT2dmB/8AQauS2jzWaXcK5Q88HO364qoLh42AbPXOax/1tzNPVr7iXQidNpngHV7ItcBtDvY5IyjRXHmOCDg8YUFTx1BBqS68F+INaW3so/7KtbW2DPHBA0gRC33mJYMxJwOST0FVtF8U3WnSYD74j95GPBr0fw/rVne28j2jDzSPnRj8yjjp696ceKcbKfO7XXkaU6EJOz2PMD8M9aDujXFiGQgEGR+4/wB2pbn4Wa7bRPI9zp7bFDFVkfOD/wAA9q9Z0q3Nw0k8oPAAIPdlPX9KqRalHd6tqFs7EOFAUeuM5/U1o+K8xtuvuOtYCizxq98G6jYwtK8ts4UAkRsxOD9VrCSEyPsDLn3r16dVH2i1cD92SuT/AHD0P+fSvK9TtX0zVpYypVQ2V9xTp8V5jJ2bX3E1sBSiuZFqLw7czKSlxa8dcueP0p9v4Wv7mXZG8H+9uOP5VcS/gbRXnkZPkQqu7gknsK3vCystqTK6bWbKhfTrWi4nzHq19xpTwGHlHm1+89M8P6Wlx4dvLSSPEEyMuPqK8w1nRZNf1KGw0ZGSDT0ELSNny9/cA98frXqej61DJamwhhO/GGOeAO5rlPEPiKw0pvsmkoFMY+aQfxGvFvdHarym2jHh+FDTRE3Gtqpx8yxoGC1q2vgqfSLZEt5VvVXOZY+31HrXMw+KruQ7ZWDlmHOdprsvCfiBrudbaZzEoOPl6ZPAP49KaipKw5zqRWpy2q2UX2HMsaxNDy7EfwjP+NclpGkR+IpJY55SphUsGB6jPpXsHxE8NyajoMt9p4PnIMyxgffA6/jXjWhXk+n3MkkLIrMMMrg9KymnGIptTossX3hi3EqiwlVZUHRuj1jXcF5p8h3QuuOS4BI/OuiubiMAspjVid2RJjBrO/txoTiaWKQAfUmuaM5Pc81R01I9I8QskgSRiAeu/wC6a2rmfTNvneVPA7c+Yh3DPuKx11HSrtj9pgQN6hcVp2aWDRsLefdGeqOc05I0VO+qKc+ryMohacMmOCO9Wra6Mdssbk+cn3RnqKytVs1h1KOSIMYdq4PUD1Bpl7dFJhsPA79xXRSSSPcwiUKdjuLK4F3EJQ3zD7wrb8J3M1v4ks4+QDMFY9mBOBXGaFPgB88H7wrt9DmjGt6avGTcRgf99Ct76F1dmj1C6/4+X/D+VRVLdf8AHy/4fyqKsjwhKWkNKKAEpKU0UAJRRRQAorgPFV59q1Rowf3cPyY9T3rvWbapY9AM15Jf3BkmuJSeWckE/WuXFS91I9LLafNNy7FS6KhsjJGKoPMx+UYAFFzckDPtiqbz5AOOa5Ej3ldKzHySfOAT3odsNn1qBn3P9KWQ/u87qpESkRTuACCRn0qojAsRzg8/jQ7ZbBJIqZYkCjoTwAa0WhzNc97jY5cTALwByfc1IZiCvze9V5CRMMH8aUq3DfhVDoRcUaUdxwN3Iz0p0LK0pXIIPXJqnFkjAGW7UsfEm2psdieti7IuX/dnK+9VmjKtz+VaUMe07TnB79sUstueCR1xjFRcm92ZEvU5GQaWOd12nqMYYfjVqa3zAzckhhg9u9VlXkDHXg1aZlOKlqX4LtJpQpOGznPfrW/bzkyYyQuM5rlrVhHLnG7II5rTa6aB15wNtTJa6CSUL3O80HWEtDh5AYz1rubd1kaJ0bcrEEEV4ZaXUisi7sKeprv/AAjrRS5gtJJC6SSBVB/hOa2o1bPlZ52MwvOnUjuek0UUV2nkBRRRQAUUUUAFFFFABTJFDxsrdCMGn0UAZJ0SyaGKPYdkZJXmo/7BtAyMPMBQbRhscVs0h6GsfYU+xi6FL+UxxoNl8+VZi5BJJyeKr3lrpIdkmkCOzBzhu4qzcX87vJDZxhivDO7YANNtII418qQqxPMjH+I1wYith6TUbLUyVGM9KcUV4rLR7iUBHWSUYIJbJ4q9JpNrMkqsD+9IZsHuKhuoIpfkQorDmJh/CadbX0qTRw3UaqX4V1OVJp4evQqNxsh+yjDScVqJ/wAI/ZEABWBBzuDcn8aVtCs2CjYflAAwfu1sUV3KhS/lNPq9L+UjjTYoHJwMc1JRS9K1NkFFFFMAooooAKKKKACiiigDE1H/AI+3/D+VZrfeFaWo/wDH3J+H8qzW+9X5Vmf+91v8UvzZ3R+BCNUUnSpX6VFJ0rmhsc9XYpSUh/495v8Adoej/lhN/u12Uvjj6o85bmO3/IAP+8a6Pwn/AMgj/gVc43/IBb/fP866Lwl/yCj/AL1faS/hI6Y7HLyj/iaXH1NK/wDrT/uiiX/kLTj3NK3+sP8AuivQofw0QaOn/wDHs3++v9a6aL/j1g+hrmNP/wCPZ/8AeX+tdPF/x6wfQ14uL/jmlMgtP+QhJ9DVPU/+PCH/AK6f1q5af8hF/oap6l/x4Rf9dP6143/Lxmq+E0dK/wBbN/urWdr3/Hvcf9cm/lWhpX+uk/3FqhrgzFOP+mTfyrpjsiehX8F/8gQ/71cv45GNdgPt/Sun8Ff8gVv96uZ8df8AIat/p/St6C98T2C550fT/wDdP862vDv+oasW4/5A1h9D/Otrw7/qTXq4f4CKhpXf3VrJl++a17v7i1kS/fNdlMziQd6lT7wqI1InUGrezLkdFa/6gfSlh/19Ja/8e4+lLD/r6+SzJanRT2Itb/49XrI0n/kLWX/XeP8A9CFbGtf8ejVj6T/yFrL/AK7x/wDoQrhwnxR9UeZj/wDeESeMZHi1i7dHjT7gLMeR8grz2e5+ZorCF55CRvYAk816F4ssILrxJcPKm7bsxz/sjrVZNlhps80McSvGyMDt+tfocqWvMzacNbnkt/Zyz3DA72wMuJBtyfesj+yr4MZIyjN/dDf413usWz6hY3V6243BZUiCDG92PStHQPCkEUavqCyzMBkqvQH8BzRCDkXQoyq7HPeGdWe1sp4tQikXPRgmVP1Iq8PD6atbPPalEkHO3OQT6V6NFBpz23lRQJgDBUqP61nxeGoYZzc2qtFhw7xDgEAED9TTnhtLnW8JaOp5PeWFxYzskqFCrbckcE+1bemPeWOm/wBpbDGrxybHB6kMo/xrqNO0ebVbPULHUYwZI2aRVJ5Tk9T9P51R8QWh0/w+NLgfcIUVmDjkFiW6/SuT2VkcfsrO5s6J42+12LWspRbgL8knQMfes/RmuovFkwuB/rUZ1Y9MHHevO43lgYEknHXFdZ4e8TukkcUgVxnaAeoH1rHmaepdOs4uzNzWCEeO7HKhvLmH+weh/A1xHjW0/dxXKrl1O1j6jHB/KvXzpml39gow7STZjZA3rWNr3gGSfTpbe3u45WCgDzODn8K6IQd0zq9rGcXE8e0kxXSGzuM+VJxn+6exrrNPtL3TY1hkIe3df3cqnkHOMVnr8PNftpxza8HgLLzXUWukalGtsmoJtiXPzKwIJHpXVysmhGUdGjWuZJ9E8NmeC4jFzcnYMrk7e9ecXs0jMWbO7qT6mt/xLfyyXKx+cxjjGFUjG2uXmu2APRqZ6dGGmpSN9sc5PI71v6RrapIv7whj8p9Sp6/41yt26y9V2/Sm2U7R3KsvYjinF2ZNWmmnZH0lofiOG608Qyyq8mQrYP3u2fx615v4k06S31PUE3gWAYKi7cEh+c59QQag8MX5XUYoVUb5lxyeAw6foBXbeINKhvtJmgnuPIaW4AVwu4kAZP8AWqqNPQ41TSjaR5RcfD67jgSZL23cvkhN+Tj1rT0j4c2JgEmpX8g3DOYl4Wr9rZi31J7ZnkEMQDL5gIY5Gc1vzXNu1sIxMFJ9j/SnGKR1wwUFG5RtvgzCLiK9hvvtthncY9u1m9q6kXtnpgW0i0yG2hiGCDGDzV3wzri21stvIwKgcMDkVg+I/EEN1a3U/kIGikCMyk8qcjP5im7dTmjBwlaS0OY8YDT9SVZtPg8mRiwkT+Fs9/Y5rhUhKQb5wPvbTzyT0r1TSPAsuqbdQvZmghYbo4epb3PoKv614f0a1tvJTRleYcFxzzkYzz39qxWrNPaqErRPL7OVbRcxsCAehNb3hzUbi68ZaQhIVftcJJPTG8cVSn8I389yzrAsAZiQmT+VaHhS2k0rxHpVle27xTvfQkeYv/TRcYNW07G1abUT3i6/4+X/AA/lUNTXX/Hy/wCH8qhqTyBaSgUtABSUUpoAbTc8089KibigCvqUnlaXdPnpEx/SvJJVBQANyOW+teo65vfQ71YwSxiIGK8oZtqthtxYmuLFPVHtZW7RZkXsmGGDgk9aryNsQ02/DtJweh/Kom+bAPI9azS0PSlK8rEolDDipuGhIzzVZECdOhqbJCGhoz3KEhIfHenHIlChgdyhvpUkqhjk8VBt2yKQeccmr3MknzkjZ87H0qQhgoyaiLAufTPWpiwKYPNFzsjTsh8cuyVTnFOjJMhNVicnipIifMUDk0MrqdHa5EBB+8QMGp45XkOwqGVe471RSUwoY+p25570oElsFZWGH5AB6VlY56krSLc8JbgJ8jEE81UlslH3fxBq1vD4aOQ7znIJ/wA9qpy3DRDHXnr+NC7CdVLcb5KwKpOBzQ0CLukBDKqk7TTpSJYfNBJHfFMhkSUFFcYIwR3FUrk1KitoTxNvRTjkAVt6C+PEmmYGM3UYOP8AfFYUIKFU4z04Oc1u6EP+Kl0zHT7XFx/wIUL4kavWk/Q9xooor0z5UKKKKACiiigAooooAKKKKACkYZU0tB6GgDnY18p7q3YYZX80Edwad2qW+Ah1KGU8JIhjY9ge1MMMgGFQkD2r4LiLB1JVozppvpoVhJJJwfRjBSODJNaWy8sW80/7KipBDJ3QgHvin6f++1OaZeY41Ean19aOHcFUjVlOomug8XJNKC6s2x0FFFFfekBRRRQAUUUUAFFFFABRRRQAUUUUAYmo/wDH3J+H8qzTy9bF9CXuHb6fyrOa2IOa/Ls1oyjiqsu8n+Z2wfuogcVE/SrJjz1qOSIbeK4YbGNSN0Zj96b/AMsJv92rPkhiQaqSq0sy2sBwzn5z6CvQw0HUqJRPPUHcyuuhvj+8f510PhIj+ym/3qx3sW1K++yWpK2yfIWHc96076RdCsFtLY5kx19TX2D1gom6Whzs3/IXn+ppW/1hz/dFMjjd7gFVLSMcmtaDQbiWMu4IJG78K74TVOKiyEriaf8A8ez/AO8tdNEf9FhHfBrLsLWOGEtIwIXnH0q/BcQSlA7AynOFrxsZNe050awQy0/5CL/Q1U1P5dOjJ4/ed/rWpBERLtCAKSeadfWVtc25ikILdQDXlcl5ORsl7pBpLAyvgjlFqlrnEcwPeJse/FYLeIIdCupYWDMucKKutrthf29u5f8AekkBT2yO9dEYSsrGPNpYn8FgjRmz/ernPHKM2rWzqCV6Z/CtfRfEEK3XkMEhUcZX7tJqtzBNrcStkx7chcdT2q4XjIXNoZU1vK2iWWI2O3OfbmtLw/PGoMZYBj0BrRW7jlsZ0aPyrhMkK2KyrCFLmwebyiHJOGTt7120MS4xswnqbd1yg9qyJfvmpobmYDybgc4yjn+IVWmb5mr1KM1JaGaViM1JHziqxarNoN8yjtW0laLKZ0Vrn7OPpREcT81YijCxD6VXZDkkV8jmPc6aa0I9aI+xsaxtJdTq1iP+m8f/AKEKv6lJvtWRvSsPRyw1ywGf+XmMf+PCuHBq84+qPJzCf+0RRt+JWx4guh/uf+gCsssWsrhChcHblfX5gP61qeJgRr90+OPlGf8AgAqvpyFUluGIaMDr645/pX6U3pY7Z7DbLSYbSzSScKfLBYJ/tnqfw6fnVSLUwuVV/KI7k4q34h1EWMHlW+NwiBLHoM815m+tPJNky7znggZq47Hq4OklTPQ7TUmN4Ip8Op5V+AR+NdLc3ZsdMlvUi83ygDKg+9t7n8OteY2mqMyBZdxTuDESPzGK09R8X2en6YyvduzhQo2YOM/wn1H4cU5NcprXp3tY7+zt43nNzEoMVwquzd2IHf8ADFY2raSlxNLK0HmGfCMrdFwOKb4a1cXOiQSRsNgVQMt0PfP0rfW9EiFPMRdwIDf1rgctbHmyjaTPG7/QV01Z1kurZZJD91iWKj2wKWz0WxEsMkF3KXT5mJQBW9qteMLa7stYHmqrxSD5XjTH5/pWNBdyWW5JMqM8ZHWosrWZi49jttK1NrZ5JjgY+SPcenqas6trkkWksI5G+0P8zPnHHpXn0uuHKqowB0qW71vzLXLBiAO1EG0VTVpq5Ztteui482aJQWySwJroNT1aOaG3ijuE+RMkqcAk9xXlN5qxMu9Dgf3WHH41Na6xG0DLOxKtgBvTmurmdj0ZK9rG3rV6xIbIYnqx71gNKsn8ODnqKbdXUjTgwSb044HIpUcTXG1oytNbnTB2RHKinABFRxxqkoq3LEiSYxj61BIFX5t2MU5IvRo9Q8LRJewLeww2q3QkUOjE8gEZK/hXobzWcNr51ygYRZYAjPJGK8Z8GQ30bpeDzTDI21ABwBnr+Jr1qNYjbxvdspX76humR/e/wrC/vHnVLORHFpuh3M5eS1kSRgHaQk+lQXvhi1m+bS7lZJMcRyfxY9DWdrWro026C4ZIlOApAwx+oNZK+IZLZ4JIpNpWT8q64s7KdOcoqUX8hiXkum3TqR5MyHDKBwD75qwHe7tpGmsYWfIcjJVZeQQPr046e9T+JntdYsE1uzYBlcR3A/ka5aTVLj7b9pYny4gscQU9WZhkkfSsqtyanM4+Z6lb3F21sJJ4MsFHyIwAHHSo78vNan7QDFIo3xk9eOx9TXP6f4p+02cM0QVwVwy/xce1WpfE8M9rMkrI5Xrngg9scVz07JnEk072MObXl84ILhix5xxXU6TrNnqc9hb38Mc0yXERhlAG5WDAj9a8n1BfK1afYrGNjvUA9A3I571seF4Z5PE+ky4ZE+2wkgn/AGxXW5s9SpCM6V5Hu91/x8v+H8qhqa6/4+X/AA/lUNZngBS0lFAAaWikoADUUvQ1LUco+U0AZ058yOSI/wASkV43eFrXULi3YEYc17HMMMTXlXjbSp7LUzdxyForg5C46HuK568L6no4GooyaObuG3uxHHNV164/KovOcqQRk01GIYcAVzpWPai9C8GGPwpGbioVfHHWkkcBcg8mixCY4ye4IqA/e61EZvUU3zABVJGkY63LCn5sGn7sDPUVWWRfXJpfN6460M3exODnnOKkh4lQ9eeaqrKCehFTGTEf+0T1pCNmWZIrgFhklMYpobzMjHB6YNZPnO7FixJAwKm+0MFOGOD60rHNVXc10LRShd2OKsC2hltHbdtYLlR6nPNZEV22wEnJwOfpVmG8cEjuT3pNM5402y9YRFXCFsq55zU9zZW9uFSMYbzDtz3WqEFy0NyjY3KpLda0JHSedJlJ2ld20+5qJJ8yH7OzSI/KUMCvBB7dK6jw3bf8TexcgbvtEZ/8eFc/CqtKvHGea7bw7ZsupWbd9ynH0NaQV5I1ry5abPSqKKK9I+ZCiiigAooooAKKKKACiiigAoPSiigCtPbpcRlJVBU9Qapf2NCowksyj0DmtX6ikxUSpxlq0Q4Rbu0Zf9jQvw0szD3kNXoYY7eMJGu0DsKmxS4ojTjHVIFCMdUhaKKKssKKKKACiiigAooooAKKKKACiiigCtMoLGqksfy5q9IPmNQyjK4r5zMcLCpzP1NoS6GO4+Yim+XwasOn7w0m3Ar4T2U+ZpdzpdmZlxiFGkbgAfnVV5BDEiQjddTnAI7CpNQX7Zfi0Q/JGA7/AF7Cn6JB5t/Pduf3UXyoT+tfT5bhY04Xe5xTS5tDRggt9G0/n74GSfUmuTmd7u7JJ3MxrW1TU/tMzQxjcTwB6Vc03RxBHG8vzO/J46V9BQikuZkS1JdI0OCFBK4yxraMSpCQR8uO1NVgiqq8DvVS7vBGdm7ANXyc12NGZND9mviIxujccgngVBcXUMKrJHEjDOG29abqN1H9mJX7x71yst/JBI7q2cjiuWrg1U94lysdCviOytV5dlHfd60q+LdJupFQSgSc8mvO79Wu7h5GYgNzjtWS7LbspAztOKxWWxs9SfaNGp4m1Rb7UCUGMHtWZDcTRJmMnirsWnz3I8zy9gYDazA8irf9g3sn+pQ7IwC5A5rppyo04KJGrMB768jkbGfmOSfep21/UJJ4ZWLbolwCKu6jod7ajfndnBxis1IbmUDbGdx4IxxVJUXqGqHz67qBmMxlcK3J561Y07xVfWN4XWTNvIfnXsPU1JJoN/Nal449wTqKxJ7Vo3McilGBwRVwhQqO0S0z0y+1aLbDdriWCRR0PK0+OdLlQ6HhlDKfY15glzcQKsXmN5eemeK6HQNbFswt5uY3I2n0NZQoToq6GdRIcHArW02MZQ96zWVZcSIRg+launAhwK66lVShoF9ToS2IgBVcNk4qTfmICowMEmvlMe7s7qa0MjWlZF3DpWRpHOu6f73Mf/oQra1N96EVkaUuNdsP+vmP/wBCFcuD0mvVHh45XrpnXeIbaK6uLqFuN4AJ/wCAiuQ1SZ/DXgxLduZnkC5A5OTXbaqjNqkjIpJG0H8hXCeLJDfaiFKvIlghlCKM75f4F/En9K/Q3vc6pN8xjeOb9H1F43Z9mxThfpXHwzqjny4lHoxrY8fTui2DMvl3CwgT7+u7p+fFcfHNvkBLl+Olaxkme/Rf7tHQpqDE/wDHxIzdNqHAqhrKedYvIIAGUdTgnmo4p1QDKqoz0HU1LcXiSW0i5C/LwFpS1Nb6Gp4B1m4hjktUZGgZPmjc4+YcYr0X+0re5VI5EAki4dFPC57E+teJ+FNRj0vxFi5XdbygrIuemRx/SvRIobWezuIIblZGcs8bD2x156jpXJOnrdHnVPiOo1y1GoRQWohO2IrIrsxJcKD8mfxrhNZ1XTzkTIAcsNnQg56fyrptK16S1LWt6zPbsB+8kHzoexH61514stlTX3hklU724kHQ56GhxvqcjlZmdc6jAzkxR4xweegqba0+ESULkZXPQ1mwQJuZG57EnvWnbReapQDJjUlSPT0o5TekuaRnPbSF2UQmYZOT0APpUAR4oikkQTDda7DSbWM237znJzyhI/nWfrtkVV8BdmMjaSf510cuh3+xSOZhLwS70bbnuK0I5pjKZPMzj/ZrJyVfAPSrccjBeTxQmWi5NcO+S5yTUMBjNzEJY2eMt8wzjNQswzmo2l2sGBOfQU27g9j6D8JzW97ZwW6xRRJFGDtiOQuDt/mKwLzxP9i1/U9HuEVi0/7gyfdIz904Ixnsa5/wNrM9tau0MaovnKEGcfKqsxH1/wAay/E8n2rxrqkhBYedgZqYx7nLTgnUaZ0Wr65dW5RHs7JoCdyAw7R9Dg5BH1rJi1DT71is0Mtk+c74/wB4mf8AdPI/OpLRbnWIY7S6uGKbvlwg3YHU5/KvQdG8CaEbRPt0Zu5iobzFYgMD0Iwabmkzp9tCkrPc4m2tZ47G7SxuobuGTqqPgj6g45/Gsi4F5YiOO7hdQr9xxn1yK7vxH4V8O6fazG3DxzAERxSMWDNj07fnXk15q91MsdiHmYx/Js8wkEgnnFOWpPt/abEkOsPavFD5pjLOw3/3T2/Cr66vJJseaPEgyroScdcHGP65rb0D4XnXGbUdUuzb2pYlQmPu5z1NegQ+AfCtwNqSuZQoBkD8nHQ0lTV7mF7O7R57E8dyIXMezaMZJySP8mui8PXUY8S6VB1JuY8Y7c1Z174eX2kIbnTna7tx/AR8yj+tL4F8LXi67BqV/mPy5BsiI5zkYNOVo7s3lWg6Tsz1a6/4+X/D+VQ1Ndf8fL/h/Koak8cKKKKAFFBpKKACkYZGKWigDOmXDEEVzHieBLjTZIiuSPmU/wB09jXXXMeVJFc7ex+YkikZqZbFwdnc8LuhsncO/wAwPIqNHHXOa6DxLpQ892UYOa44yyW8hSQcdjWLp3Wh6VPFNaM1/MHeoZ2JAINU/tHHXilacY+XgVnynZTndjjJikQtNIVXoASarPJxXQeFrBblJp5FDKGCgEd8U5NRVzZyctImKkpDYOQR1qXd83BrX8U6OLSSO8gX92/EmOin1rn1kwetKLUldB7Rx0ZbViG60GUlhznFRMRuBHTFIjDk0WKVTYuRSnbkHFTSSqxx2HFUi4IUAdPSpo/mk56UjV67l6Fw3CjCjtU6SYmBqvFgZNLHkt+NSJJI2IYzKcDspNLDMZNu05UDAP0zTtPJMhGR9006zg/0gKBgAdP1pdQtdm7p1vkgnr6V6N4ct/3iTsOmFH9a47SrbeqHHU4H1r0iwgFskMS9FI/E1vSjeVzy8wq2XKjYooorrPHCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigCJ/vVBKRU0h+Y1TuJlU4714WY1oU4SbNYK7IduWJqvfTraWjzN/CCasr0zXIeNNRO1LKNiGchTj3rwcHho1J87NKkrIdpkry6bPescT3cm1M+nbFX764XT9OSwhI85vvfT1qC0gT7VBAmDHYw7iD/eIrP/ealrTgH5EIyR3HpXr0FerZbHK2X9HsQ863DLknnmurLBOWxjGBVa0gjjhXaOg6UlxJuOzoor1PZqbshElxMrJhTisLUGc4VAWatB08z5cnHtShI1+8cDHPFbNKnEZzl3bSG3d2BCpziufKQ3V4kJyqMeSOwrqdaZ5bbyYGZVY4LEVmW0NvYWLTyoXZWODXLUr/ALt8u7IkRTaDazgqnzkLwOlZEHhWK5uQn3Chwdx4NaF/4gt5BGF+STGOO1YEep3Au1IZ3y3GDXBGFflbZJ6La6fbxWcNrNiR4lwjY4q7NBb21uxdVBfAYrXOWusXUF4klzA6QnAGRSat4gX7tv8AMrKQw9D2rjhQqT0NrpI02l0u6ZVlVQ6kDDVgalDZfaHe32KsfUL39K52a4nJL+YSx4yKxp7iV5s+a4OQp966I4GSWrM5O52r+IYtJCI0I3thiB0NYWradBrOn3OowyhJlbe0dVL/AEnUWsVu2xJCMYfOSB71z/224tkdQ7AMOx61vh6FtYvYgbDaS3pKofnXnpTIlkjYpICrA8HHSr3hnV4rHUC9wpAcEEgZ5rUnvrC/uHW4KxtyQyjqPeuqVeUZuLQ7mh4c1UBvskzcE5Umu3svlck15KXEd4vlSdOhr0XRdS+12Mbk/Ovyv9azkrLTqM6cPhRTZZwkZyap/aBgc1Tv7nCnBr5/Gxd7G8ayjEivZ9+QDUWkjOtWJ/6eIz/48Kp7y7HJrQ0hQNXsf+viP/0IVy0Fyzj6o8mpPnqXLNz4uWL4jat4elJ3gxvAfrChK/rmunttMsdMtpLworysfMZm5wcV4J8UNQk0z4x315ExWSF4GGDj/ljHXp+o+JlvPDUV9C4Ec8as656HHNfoE5WPYqUtpHBfFie1lkW4j3u8r7gAPlB6E/59a81hvMEAgkkdq6zXL+HUo3hlkGCcg56GuEIktZ2Q9QcfWnB6HbSk1GxtxEyDcSVXPPPWpyQoG4fIByKy7WUmQGc/L6VZuLhXB2/cB5rRs3UtCkwX7cHPRmycVuaCbifTb22gypBVkfJG35sHHvVXRdFl1y+IRSEHJI/QV2miaKulh9/JfqvoRS5bhGkpM6qzSW60mBZfs6zxqAXlG52IHU/Wp30uFt01zp9jdEcg7OfwzVWC5HlnC87cfWoRcPt2mRsgZGT2puCCWFg9LEU02hbnWfSIBIAOAhBGenQ1y+pyW9teSJAsUZIwFUdKt6lck3TFCVYRdj1xXLyzymUedIpLHJLLyc+9Ry2ZEcK4VFJbHZaddwx26xOVLAf3RVPWoIrmLcBg7cZHeorGdmt1wm5VHBXBqveXjxkhwVB7MK0Nqm+hxN5E0U7qeOeKiSfacE5q9qBMkpbHB71Q+RGyy/jWZzqor2ZMGaRsKM0sa75xGepOM1A12qD93kA9T3qbSpQdXtgQGXflsjsOTTG6sUjqbO+W1kigiVQ8cE+Is/edk4J98fyrJvdXN1qBvPMw0yqzhezbRn9azLSe4uNZW8hGXWTeFPcZ6H8OKXWbb7BqkkUYPktiSPI6KwBH86ZgptPmO98BStfa6Bj5Y0Ixnscn+YFenaF5tvqM0MErMsefKVuQF64+nNeO/Du5+z3dxcAjcQEH1z/9evStF1qK3upLW4dRcLLlGPGEboPzP61i1dkVHfU29feDUdNkjkeOCUuVAErLlz2BUjP0NeXWdjb2urxLCsTSYPmkchPUA1L8StXC3ttDar5b3DCSWQNjeUOMOvqD371g2Nz5CFd5DN1PrWjdtDqy+PM22dvrOuSmFLeGTy7eMYVE4H41nWXiCa0nQrcHI96y5plktjGTl8ZBrC8qX7RnJIxmqV9zulCKZ7VY+MJ1CuZQ24YZG6Z9qpReIJV8YaZBGT5d1dxk46DLrkfzrgbS4laJcZJA4rpPDsUv9qaZNKMSJqVuoHsXGaVTlkkceJpU4020e13X/Hy/4fyqGprr/j5f8P5VDSPICiiigAo6UUpoASiiigBkgyKx722xlgK2j0qvNHuUihjR5l4k0/crOBXm2p2QYnjmvc9WsRJGwxXluuaeYZm44rPZm0Xc4F0KHae1J5pJCt2rTubbcx4qhLZSH7oyadkzop1XHRkbNnpivRPCNns8PxueDKxb8Olea7JFba6EH3r1zSwtvoVtGOoRRiubE6RSPQws3KTZbW0guYHt2+ZOjBuc+tcTq3g67guz/Z0ZlhY5wSAV/wDrV2tn8srkDluavgAsBgVyQnKD0OqpTT3PLpvDOtRRGT7E7KOTsIas/wAh4BiVCpIyM17GwxLtUkYHUVjaroFnqjEy7kkXjenH51tGv3MOWz0PNFI61bgxgk1Pq2i3GkSjf88R+7IBwfrVRHO3itd1dGim9maCsCwA6GrKxFiOeB1qhHOqBdx7Zq9b3UbSY3DGM1LTNlNM07PKRgmr1v8ALKzjq3NZUtxGkO4yqiAZY56VSHiRJ7iOCyUkcbnI/lS5WyJVYxdj1TQizqHYYVBwP6131k/mLC3uK8y0XUHmttyowkx8wFd74dufOjVc8qw4rShL3rHmZhTduY6iiiiu08oKKKKACiiigAqCe4jgTc5/AVKxwK5HXtUMUhCOAcHuK5sTX9lG4m7GtL4ht06I5/CqknilEP8Aqq4yTVrg/wDLdKYNQD5LGM7RknvXlPH1HsyOZnZL4wtz/DVhPFNmw5OPxrzmW4RDuGAR0wetSGXeVClwSOTjil9fqp7i5melxeIdPlHEwH1q2mpWbjIuEP0NeW5jCf6wn8KcLpAg8uTGfXIrWOYz6ormZ6ot7bt0mT86lEqHo6n8a8rE0jpkSA/8Cpy3d0n/AC3I/wCBVosy7oXtD1TcPalryltavEGRPJ/wE09PFGpxqMXDfjW0cwg+ge0R6nRXmieONTg4k8p/qMVch+IbDAntF+qtzW0cVTfUftInf0VyEPxA0tyFlSSM+4zWxZ+JNKviFhu48+jHFbKrB7MakmbFFNV1ZQVIIPcGnVoUFFFFABRRRQBm310sEj5YDH+FYa6gLq7Kg5Aqj4ovGj1W4iDYA2/+gis7RrgG6xnNfnmaVKlSrUUtlJ/mZfWl7VU0dtEQQM15nqtytz4tIfLJFNk/QV6G04iEGTjzHCivKdYk+yeKL5c/ekI/OvUwMWqCb6m+IlZnTG/e30e6u8EPcyEDHXHaui8OaWsGlJNLxJKNxJ6jNcmgju77TNNjY8KJJF9AK6681KO3hVScZOFVfQV04aVmYpmi8iW8QCHJNVCxds1FGZJl8x+h5xUqEKOeB617tJLluUTqyIue4qJ9kxzJJsX0FULi6HITJPTFU9QWdbPeA+48D1FZ13Hl1APEN/bW0TLHMTIo4XtXDXet3D2bwE4iLZ9+a0GsJpr5ftLMVl4AJqzrOjaf/ZrxqzLLEmFYjgtXFGtSi+Vshs4dpwzABsnHetnQZManGzoCqnOCOlZUWmNBcAkmRV+9xXd6FpTPbGRIkLuMLkdKvE4qMYWXUSN3Vke70/Ea5LEciuKuNMkgcrISMjIzXZrqI0y223DIWGVx9K5q5uWvJGmPrxWWA5ndls0vDujwTxv5gDgrk5rL1zw5p8dvLLA7CZTwvrW54dvUgheNlO7pml1N47izeWNBjoQevFZ801WaWwmtDk9Iv3S0m02ZQyS5G1qxvEOhGKzSaBArBTuUdRU5A/tPJYqucg1t3tza3KQRxr5knl5bBqaylQq3jsQeZWRCXDBv165roLvRJrjSk1G0jJjAw4PUGsbVJIU1RbmBAiZGVr0C01qLUNId4iIEiGSqgfN9RWmKqTjaaW4XPPYopwvmnOVONoGTXYeF9QEdz5RPySj9adby2l/cXZgjCDY3Ud8dqw9K3rLuHAR9pGKFV5vi3RS1R6R5mehqvdOelNtXyik+lEw3GvMxcbu5z1HoVlOGxWnpDf8AE4sAf+fiP/0IVS2KvOKs6U3/ABPLD/r5j/8AQhXBBXqx9Uc0V7yPLvjIoPxQ1c9/3H/omOuq8AaZbar4VS51O4Z7SN2jW3VsE49TXK/GEqvxQ1skZP7j/wBER1pfDm4UeG5038ic5GfYV9/ZdT6ZpOnFGl4oh0qGCdYNPgiQjCHHzDHvXlF026Z1CHKnOfavVtctxdRbSNzZ7VyK6Ji9nRWV8Q4fPRckUm0aXXIcezMvDZH1pDMduwHOePrU2pW86zTXBibynlbZu9AaZpcRm1KJWXK9aaMlPWx6D4Im/sxQZBlGIyMdPeuv1ZE3C6gP7uT7w9DXDadKjEiKTce6scVtJq32e3a3lOUYd+xq7noKDvdF6K8EWO59Kptfs4djxtUj9ayo77zGOT8w4pPM+RwTgknik2URXlwdsrDrjaDWBcT5w52nBz9BWjqRaPEZyC+W59M1W0q0ivrthKxCphyP72OgqbinI29D0q41K3eZoRawL/y2kwBn29fpUeoQwpMY4pZZVAILZ4J9cVfvNXmnGxyBGo2qg4VR7Cqgh8qMTD5gxxScjNQ1uzlLu2liY889h61EW+zlPNwXKBgCOBmu0gsY7/UbWALljIp49jmp/EWg6ethKI2VczbPNcZPygA4/LH50m9TDE003aJ5ldTpKzeVEq8849as6ZC6W15c4b5Itit23McY/Lcfwq/ruhiwtre+td8lpPlQx6hh1FEpMOmR6cw2yswkkA/h4+UfXB5/CtLWODlalZkGnYtZfMweBitrXvKu7xEZssIo0Ht8grKNsSgWNgN3vS3N39pv5LhFC54VR2AGB/Koe52RitDa8PwPpNzEkqny7sM6SDowC9PzFZXiXWTd6sJYJTvCKkhHZlPB/QVZt9TdptOhnYm2gkJRR1G7g1y8ylZpEPJVyv15q0le5yVlyysTveXN7ffaLqZ5ZT1Zzmt2Js7ZRyo61zKffBzjFbVhd7Bsk5jbvSerOvBSsjfgnDNkjnt9KnljklZTAn3sjPbPWssSoozFyvXFWtP1S1tt/wBpnkCFh8iA/N/h9apySR1zmOt9SnEm1JTtAxjt+Irv9Dl/4mfh9Q5QteQvIBxuJcAD9awE0ixubYXMcbiQAFpTgEjOf4eCfetnRJ0PinSkVY+byD70gBHzjt1Jrnc+ZnBXquUT2q6/4+X/AA/lUNTXX/Hy/wCH8qhrQ4AooooAUUhpaSgAooooAD0qMipDTSKAKF1AHB4rhvEulB0ZgteiOuRzWRqVmJomBFS1cuDszwe9tjDMcjvUUSKx5Fdd4h0opIx21yhjMT81KfQ6HqrnTaDb2czG3uoIpY2HG9QeatPPGkLsPlROBWRpk+yVDnGK0zF5hyQGQHcR2PpXJidWkelgYtqT9C5asdw46irEjZOM9KqRXIWQnjPf2NNnlKuVz2rmsehe7JvtDhvXI9amW5HlhNuGHDH1qiMrGABknkn+Qp8JO/71FhNXVzRmtYL20MFxGJI26givPdf0WfRmEax5t5GykuMn6GvRoZA/GMHvRdQRXlu0Eqjawxk9verhPlZzyjzI8bZtq/MwA9c1A2pxwD92C7nv2qz4g0ybTL2S2m5YHIYdGHYisYQM2K9CMYvU4ZVJXsh013Pdn52IX+6OlbWgRFJkkxgfzrNitQpwTk102jRRrsRu9TVkkrI1oQbneR2/h6bZK6lyOODXeeG5/K1ONW/jYD615vp25LjjO0HGccV6BoYxf2ZJyfNX+Yrmg1zKx04tKUGejUUUV6R84FFFMZwoySAKTdgHcUVk3WuW9udq/O/oK5vUPE96xKxFbdOmT1rkqYylT0bJc0dtLtMZ3MB9TXlvia2gN+/73a3qp3D9Koajf3M5+a9nlJ/2sCufmhuBL5iIwb+8GrzsRjKdbSxnKVxZlMch8qcbfr0pN0wyy/NxjKmopIZpj8yA+44/Sqc1he27F4y2PTkVyqKk9CNTRF3Moy4Zcc5xUy6pMExuz9RzXMS6rcWr+VNJz6ZFIutn7xIY03RfYNTqv7XfowB9sVJFqoY7WjArlhq0Q54596cNVTsD+BpeyfYL2OofU1MmM7UHoetTpfRyY2nBxXJLfxOOpBpyatGvyK2cfpR7N9CUzrGvo9o4J/Gj7XF18yuZXUkUbnPWlXUoQx2n86FCQ7nQtdQuqrkcHuKqymN1OXGT0Oay11NGB4zx2qF72N2GOOODWihNAWXkaLP71TtNMS/aJ8+ZjHINZjybj9/bnJ6ZqFVkZhvKsPSt1GW5J6p4P8TXf2pLfzi6Nj5WNeqRtvVW9RXz54c8walA0O4HcK98sFdbKESH5sDNelhnLl1Oik21qW6KKK6jQKKKKAPKfGk5XxNeIDyNn/oC1n6FOy3IJPtTvHMhTxjf+n7v/wBFrWRp07C8RV/iYAV8RjKPPVqJdZP8zz1TtX5j0TWJXSx06cZAWZWb88V554phd/Gc6KD80qkfjXa67eLLo0sS5D27qMe4rIvYVn1y3vSBiaKN/p0zXa7wpqHZHZUfORaVK39rapcFQGRFgQ+nqav6Z5moTPezN+6VtsWfbvWTC5kM1tEP3l3O3zDsueTWkFkub1dNscrFCPmI9ajDu8iFodQLkyRqkQztHJFNkeWRlXHXioLOQQwLFwHHDE1bjnQLgkdete7TqdDRSG/Y47WXzC27HO33qBriS6DvlQq9c1V1C8cKSh49+9Ycl88Vs/mhgCRhR3rPEUZSiJs1LsRKslwELeWCOO5x2qBpNK1TT0bzD56tl1LEY9fauYvPE8sUTRq2A4wy1zM+rzFysJ2K3FefHByTbaIudZrBgtdShjSTKOVAPHI/Cu4t7+2jtY/s7gDb6V4xaXDNdxvMzSMOmeQDXTJqF1JcHCkBoyAB0U1NbDXSjfYUXYt6rqcl3qbI4GzcSBirFsFaEEdBWFbmad3nkTJU7c/Wt6zgkEY+vSvWwzjCCijRO5IsrwhwhwCKQ3TvaNGxwMYJq+umTyRI+35WqhdWRj3ox5J7VbVJu6GzmtRXYARwR3FYsepS2N2JBknJHXtWzqsLQjaeR2qkNKaaDzSAOOams6aV5Eto5S7LSTu5zjdkfjUsGpSW4eJXIRxhh61Pd2xjuGiI6c1k3AxPQ4xmkhWuX7PVJ7SRpFc7c9DXdJNZX+lwyQBIpG447mvMlYtCQfWrlpqMsCxruO2Nsiuavh1JpodrHrWnOTboG6jirb4FYOh363IBAwCua15ZQFNeXioyjoznraDXl461PpD513Tx/wBPMf8A6EKypJx61Z0ScN4g00et1F/6GK4KNP8AeR9Ucq+JHnnxiy3xW1seggx7/uI6g8JXosrBwTgM+TXb/Em6t08dalE0EZb90WYryf3Sd656Dw3c6raSX2lkeWgw8VfdLV6H00F7qbNaO6W6x5ZyxHy1QYNplrcMV3TytkD0x0rO0+/XSLmaG6hnS4BIT+6apXGvpNdSRPE0ro20uWIA+nqKwqOV7WHU93QxdbUs3zOXY/M2TwT6CqWj3AinYkjcq4Wti5lQQzXLxgSRgiMA8ZPfmubtX2tLn+LFaxvYyjbmubsN7Jbz7xtf1xzWgt0l6wJJBrlDJvO5TgD+dWYb3ynG4bhjk1R6Eauh0DyPv8tCAf4Se9a2kabPqjFmnRApxIh6jGP0rmbfz7i8hFsA6Mww/QL9c9K76LUUhMUSoVi5Vgy7d5HUj1/+vWVWTS0Mp1uiK1x4anmE99Ohu2ZgEgt1LMq+nHH49uao3ME9gW+2wxQSuAEiiIIjX04rd+1RR75Jkm3H7rAHaQfSuRubSW8vpTOI4SRuOGGAv0HephJvcKM25aj7cCcMok/eg/Kp/iHt711mg7Luz+zS2zIT8sucgn0OP61S0HTL63hWTTrUztIRsuZWVdo6HAzkiuiWwmjcs7s8w582Ndpz/wCzUVJWNJ1UtDN8OWjxXeoyBFVoQY43xnBPBP4CoPEkaFooJJvKtoV2RqersenHUkcn3rore6hcMjeVA7PkNn5mfoN31wKxtY0e7NuWtgvndJLrBkbn72xVyR2H4UQk2zB1ffuzAtruys7K50/CyXci70jck7GUEg8cBv8AJrg5Y5lhabaxLEkvyRzXbXmlWXhy18xkjvr+U4RZCPkz3MfU/iKoWyXNw0huYis8Y+62UCjsuB0HtW7lYzqxv7yOWiuSDncflUn9K0NF0i51SQeUQif3j2/DrT7+1jhkJeyWCaTgojZUr61veC2uF1QPCqfKON69falF3dzfDQvrI7bw38MtJYpNdztcydcE4we3FX9f+CumX1vPJpRktr8kuqu5KP7e1bOkaibxjDu8m4AyFY/y9DXRx6v5ds8c7nzFPynHIPvXS0raGOKpuUrI+TNRsbjTb6azu42jnicq6sOmP6UsAB2gmvUvihpkWq2x1iFMXUPEgHdK830fTbrUry0hgTJuZCiP2OMbj+GRWMtDOCdF8sjRhtx5KMwGGJUHP61t2CaTGw8u3e4nUcTuOMj0H5Vja4wstVksWkPlW2Is56+p/GtXTjajTsQOxdm2GYjCj1x68H9azm7o2qVE0dHNdlBFBEPkmYlhjpwMfzqbRdPtrnxVo04+/FfQMpB54kHWsy8uoIRbKhXMD7XJPqBj+dO8KyPb+MdLhuJgd1/D5bAcnMg4PasIxadzlm0z6Euv+Pl/w/lUNTXX/Hy/4fyqGug5goopaAA0goo7UAFFFFAAelJS0lADSKrzR7gRVrHFRuKBnGa/p4kRjtrzrULHax46V7HqNvvjPFcFqtjiRuKzlodFN30OMhDIxWunso82CjnOOfeseW28ucEjjNdFp7xoyxNyAcYrixL2PUwkuWEjLeNI5GYE7mGSPSlyHG5zwepJq1e7VkkIAz0qnGgZgCfuncf6VjF3OqinYmB2oq5yRQn3hnr7U4JucsetOjTo+RznAqmb20LMJbd9KsvIScYOcVDDGSCwPOKlIG8Mmc45zWRk9znvF+jnU9NM8af6VbruHqydx/WvNokZn4r3g7ZoQ0iBtvXb1Neea/4dGn6t5kcTJBcfOoK42nuK6qNb7LOSdLmloc5a2hdskfnXQWlsVKso+tNt7PCgBfpW9Y2hkIXGQOuKc53O+jR5Vdm3ptiklgs0f314auq0df8ATbTI581M/mKxrPy4LYR8gknHuK1tLJGq2mOhlT+YqKSvucldtpnotFFFeofPDJH2DNc1q+oFZlhMmGbnAPQe9beoyGO3LDtXn+pXgmu3+dd7DB+leTmGIlBckSZBe6hEj7YW3NnBY1kXDOwaSZsKfxJpJo2i5jTdKemeRW9p2grZWn9o6r88pGUjY8L6V5FOMp3fYycWZNnZSSqJVhAiP8cwwM+wq1PZwR/L80p6/wB1K0bu8iWISSFSx5VewrEnuJbk8EBf0xQ2l8JOpXn2xAiN0QdxGvNYl3HNIhAV3H+03UVqNJBGzLhncH7oGamMd3cA+TbCEY6t6U7zYHD3Ph9bkbvKAP8As1lz+EioLJMFPua9E/sO6c/vJgqnsooPhqN/+WkvHtW0KtSOzC55RJpV1bnhy3+7zRFHIv3y4+teqp4YtGDcSt9Kjfwrp/3ZIGx1zurdYn+YOY87igVj/rmH0qyNNkOGS5U59VxXeDw3p6HKQJ1HBPT3qyNFs40C7YxjkAdqHiIdEK9zzxdJv5EZkETgf7dVZLa9h5a3Ix6MDXozWdqu4KeQcYA6VHJpdu4PyDp3pKuuwrnmj3zRn95lSOxFSQ6krY+eu5fw/ayfeiUj1A5qIeEbPzOIAO+Sa1jVi+gWuczH5s44jZvwrRttKupXU+Uyj3rrLPw9HEQecEVsJYJHHlUJZf1roix2J/A3h2zjuRcXM4Mw5Ef/ANevT1xtGOBXnNo5hZSI8H613WlXRu9Pjlb72MH6120mrWN4PoX6KKK2LCiiigDyPxzAH8UXp9dn/oC1h6BD5viGzhxx5g/IV0vjiMrr91L2Oz/0Baw/DRCeIoJSMhQzfpXycmniZ/4n+Zk0uYk1O8kXXr63ZvkckqK0rcmaO3B+9CGjI9uo/lWO7Jc30tz94sxA9ua6DSJ7e10q4kvWx5TDDY554H86qfv6LcIJuRiQN9njZ1cLMVIDHog6k1Jpmo3ZUW+mQNIxJLy4zXQ23h61djNeOqwEfKpbG4e9bNnLo+mRtHbiONf9nn9aiC5WPl11Ofh0W6JDX11szyQrfMPwq7O0NrGiQzb89c0/Wtbso4gkMkZkbn3Fc5bC4v5flbLg5HvXfCrKKuS/I0JJWlIUcktVLV7LKH95+8Cnao9cVuWdvLbvIDbeYxGFJPQ1jzpd280t9LCRGAQd/apqV6jad9Aex5pqZkjuRGDubGTUNvbTTE5HTnpXcWehi91COUwkqw3MGB5X2rTXQdOt2MkEqgO21VZgCD+NOeNstCDP8KaJDDbx/bVQtOc7XOCoFbOo2UWnmWSxiWWYjG0nIHuKowWN2s7rMA6H5FcsOMfSti4VI7uKOIowVAAd2c15U6s3LmTKVjI0+zeSIQ3CLGclix4ro49O8uJGjG7kVSkltwoW4lDSFsZHarTasIJfJgKumwHPoa7aLqPYa0OgiCxwBFUAE965+809xvZ1P3+vqKng1QNbtvYb1Ip91qRmtx8yrgda61GcYtlvU4zxBYHbLtUlVJINYqPJFbRF3HlF/u9yK7adIdRsZFWXLDLYHpXGzWpa5WIZCdFDDHNY+39pHll0M5GVeWLXt8zBhkLwP6Vzd9AwlztOc4PFd3FYTLIA65BfkjqKr6p4c3MZIuVKhiSejelOOLUJpEptHncoMR2kEZoBIzgda6a+0GVpIV2EgqMnHXNQXugTWloryRkEsQD7Cuv63TZdzX8LzMqxgnrXSzz5U81h6XZG1it3LDDDt9KfdXZUHmuWvH2uqMqsbq5JLcj1q14fnz4m0oZ63kP/AKGK5aa+O4/NV3wzeb/F2iqG638A/wDIi1zwwzU0zmVN8yLnxagkTxrqV1HuOZIYzkYA/cpjHrnmuShvLu2uTaW13LHHvCuFbhjgZzXpvxBXb40vorstc2N3sUIekLiJdoB7EkZ59TXlut2sumamLyM5trlxPC+O3cfX1r6W9mfUYeaskzTvNfhmsJ4LuESuqgJJ3XjHFc5bbWt7guy+fIBgk9eah1Bis80YOAW/MdRULEpHgDjgiiT5jWtTU1oOaRJi6TBomC4Xbzu9sdulVGs/KUTAttbgKeorrtH8F3+v3AmjHlRABnd+gHWust/ANnCqma9Q85ZdpNVGJy0aDk7yPIQhAIIOBzinqwDCQfLj0617TJ4C0y42gOgPc7fyrnNS+Gs3lyG2lj3oCSpbBNNxsdkqKS0ZwkeobQeAS3UnrXR6Lc/b7RI5jkrKTGc/dI//AF1yVzaS2szRyIQwODkdK1NDu/KTy+MZww9iP/1VnNXRwTeupvCe50+5WAS7kK5KhunJOR61T0+O51bVG+zogLvnErBc/n/IVBeXIfZbhS7MRtVRlueQR/I1Z02CHRil1O4uL+Vv3MEZ4X39/T096iMbFU6lnY9LWARW6wz7EKDCRKu1V98H+dZt7qwWFordzJJ0/dHv9ay5b+OBUkku5CWwhSBt7SOf4dx9OlS6rqywRHywwfAGB2PpWcotlN32Rn6QjWWosJZBvb95KAclSOVHua73TNSleCM4EaM3ylMN78jtXmK/bMyXqI52qSW64btn8v1qzp2uywXkzoisXGDl8K2fX396pRZHLfdHdXuqWA1CKO+so7S+ZyvneXvEq9sseOfbkZrzfWb+S11udI4rSNRk5t0x+eec11Lan9stZUmicMo+SZGwyjsD9K8rupCuoToC/wB4r8/3se9bdAtY3rYSapdmaST5VHAPpXR6WsVqm1sk5zkVg+H2CQyfKCx7mtA3gVsjg0RPTpQsjrBqscW2WUthOcjr+db7+I7O/slEE5eaMAbum4f4ivMXu9yH/aFVbS7ls5twbKZwBWynymsqSk02dte33m+YpAZWB3A9Md81R8LafHpGnS6iQrPBJMIh6KQpPP4AVT026E17bo+xke5RCH5BBBOP0rs7qyjjeGCNMWsbeYfTA5A9+f5VhOpqefjEuh5zqmgC10lb3Vpdt3fu0roq7pEA5wo9TnknFYxu00+MCKGeOPaRGXOcHtn3PpXp1/p9vOJ7y4UPO6iNQ3BjXqMflzWR/ZVu6kMowO5H86lzPNd72R5/NNdSRpAzuxlxLJnu1dX4PVW8U+HllYlk1C3KDPT94vH8jW5aeGrW+uAIbR7gkdY14P49K6Lw94VsYfEOmSPptzFLHcxyLIrArlWB559qFzSG6NS12j1u6/4+X/D+VQ1Ndf8AHy/4fyqGtDIKWkFFABRQKKACiiigApKU0lABTSKdSGgCpcpuQiuS1W3BY8V2coyprnNUh6moka03qcTd2vOcdKhWRorlZQASDwPWtmeHcDWJKfLl9dpPFcdfoerhle6GXLmSbeeCWJxSQDEr+54+lQyyFrgN2qaA5OScnFYRVkepCFkSzy7EyB06ipUXAG48nk05Ig67jg5OakZcDpjFKTuJkiPtXgg+oNWIlDDG7NUImZmP90dBXZeGtI3EXcyDyhzGD3PrUxi5OyMa1RUoczNDQNH+yQCe4X964yEI+6P8az/iBaJNoKy7AXjmXB7gHIrretYHjMA+Gpv99P8A0Ku504xhZHjUa0p4iMn3PKfJ2LwOtX7Jtg+Unn0qJsZAH4U+PCYwevU1gtUfUS1Rs2rl3RWJLYzzXQ6WQb+y9RcIP/HhXJ20xEmSMgcZrp9Ck36nah+vnJg/iK0p7o8/ExtFnpdFFFegfNEM8ImhZD/FXBax4V8q4+0RbyM5IHevQzUE8PmJiuavho1lqJnlsBih1mz84YjEgyDxit/X73OI3IwCCBWnqfhuS8GRKAfULWRqOgS3MKid2EyjHmDo31rzKuCnCm1HUnVHPTxrKfMbc3fA6VWtLW61XU47RPkVjyR2WrVxbfZIGRmJcd271B4f1CG31gmRmUyoVDMcc150aVpWZLSudUdPt9Jt/Ks4E3jrIw3E1jzaldqzKVOfUR1Vvbu5N08ZdwenFUXnvUHMkmM/Wt29dES7Ft9ZutgyWA9SKqnUbslv3rnPtVGa5uwHIkOD/nNRx3V2sY3zOQfUcVLFoaHnXKHIdwDzjNMaWdzhi+enrVV7qUoAJGz65pouXPHmNuHfuafKySwHn64J7cjrS4kc/wAXuPSq/nOwJMj8VEXf1b8+aFHUC7twwVcZ9z/OpkMW/DtwOBjkGqMZBwFRvr61K28HBUgCqSA0t0CJjOfpxT45Y96/IfrWSbmOPhmz7Ux9RCEhPmzjBraN7hdI6RZQfm+7jtSvexRkE9K5v7dOwwuQD6UkUF5dzBEVnrohGT2BXZ0yagsjhYxlicAV6FpERt9Ngif7+Mt9a5Lw14djtGW4uSJZh90dl/8Ar120K47V6NKDirs3gixRRRWxYUUUUAeceNEL6nd8dAv/AKCK5fQMDWIs90Yfoa6vxg+3VLsey/8AoIrkNJfbq1uenzYr4vmaxdT/ABP82clSVqhoaNoj3Vz8zFY2PUdjW94h8NrP4dks7OQi4MiMxHXg81Z8PxfZ7Wa5k65wo9alubpreSRujlMn6mt6dTlqOTOxK2pz+r+G73Z5tvdyzRAYAz+lcpJe3tjE8UhZSD0YV6XoN+iXlzYTOCQ5ePPoam13w9Z6tEVeICXPDDjitI2vzMzlDm1R45LeGZftRkJlGd6npx0rc0LxItv8su1WJ6mpL/wDdC6kFsCyMezAYFczqGi3umTI88MhRuAT+VdiqU5LlRDi0em2niOC2LLJdKz5yMEHrVPVfE2mXVpJFdS/MDlNpxk+9eVzAxFf3rbj1ANRsfNEgV8kEYyetKOF1u2LmZ6b4e1qPUpmQzKjopVdxAGKqahc2UU03DOqA4O7o3tXl7XFxBc4iZlYdwa0Bq9xLZC3fk53Fu5NKWCi5XuTzXOkGu3aRSRGThCTj1rL/tm4SXzFmYMpPGazbG523JaY5BBxntUYxLLLITySeBTjh4xdrCO0sdTF6jTE4x15qzYX268+ZiARxXKWEhiZ4gcDr+laNnMWnWTsB0967qdOMbGkTtVm8xWYHIFSXFwJbIqBhscVlWlyZlYZwfag3GzIY84NdDSehZBpuppDeTMz/NGpJXsc0pE81xbo0m4MVZSB0z2NYFu6x6hO7fxcc960rLVEiuTC5+Rl+U+hrx8XScbuBDNltNa1uvszSEZ2uDn8xVTxBqyafd4jjBgmjGeehFc7ruu3Ml+ztJjaNgK+lYuras09jFbMQzqd27PrXNTws5NNk7noem6lp1zokkkuF2AKGPbHSud0/WIG1PZcL9oRiyqre461zlrqyx6QbMnlpN35CqNvfNHqKzDoDkCtoYRq47HVtNLFqAjfCgE4Udqrai5GQKzrO5e51EzOxJJ71c1B85rphDlSRUttTn7iVt5rV8HvnxnoWf8AoIW//oxax52+Y1peED/xW2g/9hG3/wDRi10KKuiLHo/i/WI4/iNqlrPApRDDEGI4O6JGGffOea5nxJZxX/hmWFHzJA3nKCMeWT1H0OTXReN5re/8e6vpLyxpNiJ1VwFB/dIRz356YrBmkmtHZpogsnl7XBHDD39a6pysz047Jo831fMeoqCMLsXB9QBiizQ3dyihchDk+9Q67Dcf2rNI6uQ7/Ix/QVoaEjW8xeQ7SvGPetU9DuoTu7HpFpqssWmw2qnYiLzt4JzzzVkX3Rs5B4IrlLa9LPsJ/OtmN1aM8444raLOxJdDd/tAsAQSazNX1KSObKuw3DkjvVGC9K/IBzUOoXLS2ys6jLdKJPQFFcxx2tPJLIS2JOT82PmIJzWHua2svPXlhNjIPoMj+ZrR1OZ45FdW6d6p6ascrm3l5SXp7MOhrHoeZiYPndiWHV2tbtp1t1cyYILHov8AdHpRAtzqV9577vNkHyqvGB7entVt9OUhtwxg46d69B8N+H7TSIHvdTj3SMP3cJ+nBprUww1F1JXbH6V4RmKw6lcFbe0UeVCspwQnc49T61qHTNPErpERIhPyOapav4glu4fLLBVX7qjtWbaawjFIhMGZhkhf4SO1Wkk9T2qdJR+I09S0wJbMts2N/DoBwa5bU/BOq6JbfaynmWUiBwynJ2nsfpxXYX19HbxhC2WKAnHaqEvi2aC2ezYrPbSJsYMM7QeDj8/0q3GLM69KU43gjzy31qeC58kSfunG0SDhh6Z+lZFzulvRNL8sjZ8wY/iBp2uw/ZdQlSEkxB22NjqM8GqqTvPOZH6nqff1rG1jypNqrZnSWTJFbYDHc3p2rTstPmvptm9Y1AyS+efYYqLwhos+s3gGCIU6t6+1en2Xh2CGZCzrtUYC4wDVRjc9bmsjI0v4bnUo1kN8UbqFKfeH51ieKvB934fiMzxsYTkHbyF9MHuK9W88WK7Y02t254HuK3baWw8R6U9vcpHIQPmUr0NaTgrHLUrzi+boeJeFNORtWlllVfLTb8rdRgDaw9P4q7Bpc7mKF1TiNF7/AF7Vla5b3Og+IJLVYQYH+YOGAAQe3XIyc9elYN5r7Rg3ED/ukBO5W7Dtj1J4rinFuVjjnNy1Y/XNaWPUDA7hJF5dQc4Y0mhwzeI9XjsPMEdsMvM2eoHJxXH3Granrd9AL04BQSP8oAZcnFdXouqNpcMxiRVkZdoc9getVy6l4ejzJzO2l8RjT2jtrVVjjiG2Jk7L6Ed66PwtrNtPcIJGHnNMvsvzHAxXjkmpbrgvMxDdA3atvwzqGfEmjIh3q99ADg9P3i1tCfQ6p04yptHvd1/x8v8Ah/Koamuv+Pl/w/lUNI8UWkpTSUAHaiiigAooo7UAJRRRQAUGkpaAI3HFYupJlDW4wyKy9QQ7DUy2Lg9TlHTkiub1UGK6degIBrqJfllNc34oQqkFwvY7TXJUV0ethJWmvMyVO4nNWITx+lUUfcAw71ctcswx061zvQ9tmpDxgCpGUMBu6AZx602DoBU7EKcEck8VmzGWpZ0bTWv7+OEAhern0FekRRrFGscY2oowAO1Y/hnTfsWnCZh+9n+Y57DtW4K6qFOyPBx1f2k7LZC9K5fx3OsOgKGOA86j+ZrqK8z+K+pCEaZZhvmLNKw/Qf1rer8NjHCK9aJg/KTvH3ccUZwRsNUrK4Dp+HerAYecqgHJ744Fcex9XdWNG0GQGGc55FdJoLH+1bNf+m6H/wAeFc9aBwAAuWzjFdBofOr2Z6fv0/8AQhWlN6o5MR8LPUqKKK9I+WCiiigAqGWLf2FTUUAY91pzSpjyo3+ori9c8J3dwT5UUK46FUAIr0ukKBuorOVOMt0JxTPGmtr+yi8rU1E2PuuoO4fX1qjvkj3PHc4B42tXsV5o9vd/fQVz174Ft7g/IcfWuWpg4y1RDh2PO1uXV8s3PvgiiS/wu1VV19lrq7j4bSP/AKuXFZ03ww1D/lldEVzPASuTyM5+bUHZdiRxA+u2okvZAMsEZ/YVvH4Xar/z+GlX4V6meGvSPpR9RmL2bMNr5j8uxGCnIyOTUZ1CZx9yFQP9kA11EXwnnH+svZPzrUtfhXZx/wCtmd/xNUsDLqx+zZ541+zfK0gX0xQHlcgJ5kmfTJr1y1+H+kQDmHd9RmtWDwzp1v8AcgH5VrHBJbjVPueNwWF/cDEdq+fUitiz8I6ncDLKI/w5r1qPT7WP7kKj8KsLEi/dUD8K6I4aCK9mjgbDwCAyvcMXb0Y101l4ctrQABBW3ilrZRS2KSSIIrWOIfKoqYCloqhhRRRQAUUUUAeWeObjGu3UQP8Ac/8AQBXK28u27icHG1wf1rb8dP8A8VdfL6eX/wCi1rmVbDZz05r4ytG2IqP+8/zPJqzftfmeqK0XmxwIVEcA82TnqcZArEuL3z4ZpW/ibj8f/wBVJLfC20C5uif31wqqp/SsW8ne302JyOBIvP4U7XZ6bldIL65khltNRgbBxsf8OK6rTPE8N9bAyP8AvACM5rhXuPNSazbGD8yn0NYyS3djI4V8Ct4QclYiNRo9cWcLbNKw3f38Gqd8lrrFu0Vu6OVHINcTpPiWaEmOd8xsK6V9Tt7Sw+3WsauzYDhT29aU4Sgy+ZM5TVfAkkDCbzWKn+Edj6Up8KxvBEIsGTb849K6Y37zSw3GR5DkbgTmrd8kdjELlEG1j69c1M68+gmkzyi88MTpPN9nYu8fPNc+Yp42xsbeOtes6rexRkiGEoWJySOa5W5tY52bywfOLcAdxXZh8VO2qMnucsHCxhiCGz0NIj/vMg4zXQXGkZtWlki2jJAz7VjJavK4Arsp1FNNgXLNgjgnnNaWnytCSQN2W6VkPazwOOuPpVy1uHjIJFbxmpLQtG3BcyQyFsEDHNMnvt7ZwaqS3DSkNGeDwRTXDR7iSMY6jtVOaTsWRSyE84IJzVC4nYMhXIK1M9wf4iDVCafe+3rRZSVmJq5DcOZX+ckk8mqkqHeQxyR0NWnBL5x3qGQbnUdyKlS0FoiMIAcmgjBVVHJ4qeK3knkUbTzzVqOyUzqWOQDk4rJ1UrhdFrRbVwxduwzV3UoiSSB2qxZw+XMIlwSwFX9RsW2sAtc/tNbsU2cHcqQxq/4QP/Fb6AP+ojb/APoxaZf2skZJK0/weP8Ait9B9tSt/wD0YtdNOSk0Stz0b4iSw2vi/UWLDz5Gi8vcgcr+7UcDqPxriLvX3OnyNcGWaJpQisy/MuQePpxXXfEseV4x1OSGAfaHMYEsjDaAIUrh47bZo3mSv5jJOJMnoMK39a6ZK8nc9GLskR6vHPZact0hWZsrw68KD7eoyPzrEtpNjEYJx1zXRxgS2klvISRPHvOTkZ//AF1zJBR2XuDg1pB6WOrDbsuLeBZFwcYPFbthePdfu0Vnbr8gJP6VxjyEOR2NdloOnq1sh+0S2V82PJkZsRTA9tw6GtU7Hf7RRRaUObgRgEO56EYwamuYJb5nFsjNHbxnkD05/wA/WtV/P024todakR/ObanlQ79ueOXBGD9QagvoZbCDV0gZ1hgtysWTycryc+tRKojGWI7Hml6ryyFCjKR/CRgio7MxWjtJKCWCkpj+92q5FdPPb+ZO+9sYBPU/U96xrybDEjp2p2IrNcrl1Ou0CeK/1G2Wb/VqfMYH+IgdK3tR1p5pDuYlQ3GfSuN8PuiXVs0zlIm4Z/7vvVq9uh9olRJA0YYhXHcUXsa4eChFGkL9bqdIpCVj3DJ7gZq9Y6X5GohVkjLiX/Vg/Nj8eK5OKQEu4YgpznHWuv0tRqmnmRQWu4hsBHU45X8cBh+App33Oh1EtiLX7s2zlEJMjqOvIXNUIrS4nthOGGcbhH3YDv8AStnUdIl1PWYYo7eY+YvQDBHOP/r10Gh6RZB73Undk0yABPNfuVAPA9zgY+tJz1sY1MSoR0OBe1MniNbMwecwykiqMgH39MV0i+DdPsI5p7Vo3vWXALDdFF68dz+n1qzfaPfGW5awiGn2ABIaVgJ7xyMnLdue3FU9IvHnthaAlWc7XVuq46ik27nnTftqyZ1/g/TINL0UyGPJPRgf4u5q3LcnzCwIHOSKhkb7PYxqmFCqBxWbPdufnZQR/eHWui9tDvNo3p+yHaQ8OcfMfmRqr6XqMttqC7HIWYYIz0YVgNdEMcfKG7A9ferNtMfMRu+4c+mKOYaSs0b/AIjtU1SW0MnyyK4AbHOG4x9K871vwkVtooLV0E0hdpXz8oRQAT+e6vUIQbx0uZ+VjVkYe68j9KxdU0qAWQs5JXhWSPyQwOCqE75CT2AAxn3rGbTdzzMQtbI8g1BtmrSFQyxiNFhB6mNRgZ9z1/Grks+23Thvu1ma7cRy63cyW5c227bAzLjcg4B/SnRX7SQ7Qc4Hepe500LKFhDcOzH5wfZq6XwTqIk8X6JBOgJN/AFdTg58xcD3FcqxXAYjk1s+DCreOdA2/wDQSt//AEYtCHUl7rPqq6/4+X/D+VQiprr/AI+X/D+VRVR5AlFFBoAKKKKACg0UlABRRQaAEpaSg0AIelUrxd0Zq9VeZcqRSexUdzi735JjWPrMX2rS5Yx97G4fUVv6vEUcmsV23IQa52uh305OyaOFgl7E/hWvanbHuPWsi9ja21GSM9myPcGtS2bNsp7ZrlqKx7dCqqkbm5ENpQd+ufatDSbX+0dXiiPKg5b6Cs6OTeCw/hUCuu8LWixwyXYHLfIv9awbUdWZV6nJBvqdYpAAA4A4Apw9arg8YqSNsg12Uat2kfOyj1HSOEQsTivn34has2peM7nDZjtwIV/Dr+pNe46vdi1sp53OEiRnP0AzXzVJK95fTXEnLSOXP1JzQ58032R3YKn9o3rCcKg3N2rdtts2wFd64wWHbFclE4ViGGRtx1rpdKmHlJt7jgVlJHuxldWOitlMYDDqDkGtnRGA1mzzwxmTI/4EKyLJi5YdVGNpHcEVs6Mn/E3sv+uyf+hCrprVHNWfus9Qooor0j5kKKKKACiiigAooooAKKKKACiiigBMCjApaKAExS0UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAeMeOzjxnf/APbP/wBFrXM78E10nj3/AJHS/wD+2f8A6LWuYbjmvkMR/Hn6v8zy6i99+p0WsXOfDWmqnVjg/hVW/uWk0aRSOFmx+gpR/pHh2EryYZiCPrVK7P8AxKps5GLjd+lapLQ7I6xRRdm+0xOWIUjLH6VYkjW8t1lU5ByP8Km8lHhLt0xniorC+htLrynUG3fHX+E+tWttOg1Exp4pUVVb5WqzZ6jJFtR2I29Aw4NX/EQtfOEtuAEYDkHvXPPK7SBSd3oa6or2sdRWN0akUV2VyNpzgdK2YtcXUoo7Z3C7Oc571xvmsocEfeGKg3y2x8xTye1Zyw8WO9jt7y+iubYs7APGMZ9aoW8Jh1GCTIKk8Nn2rlP7Qmddh6k8/Suk06VYLiHzP3oAygHPJrCpSlTiB0F/aG7t2RxjALcDiuf0XTIY9UCXX+rz19a6+2QT5kJUQxgbs989qx0jtZr5rZnKqj5JI6VhQqSgmmVYfruiw2cSPAS4J3A+1cxNBlzt6jkgV3Wy1MN1bySLIAoEbHsM1Rmh0+KB5kUDy1KN71tDFOOgrHK6ZYGa/WOThOpPStDWNLaC2blRubKgHsK1dEhgeKWWMhnHzbWqzqLK1q0U2wTFSsZNRLFSlVTC2h5q1tO0uzBz0rQsNJNxcsBgbRgZ7mui03T47vU4S5OwsAdo43dKuakbDSr6RA+JTnaV5CmuieKk7RiF9Dj5tNdb14sfc4/Gp4NISeF5hGWK4AwO9WLK6VtQl+0SgKx+Yr1NayX0VioKQgQBNysx5POKmdWpsIWw02zS3d58AWwO4njcTzisSeO2tZROzBzIGIQH7vpTr+/aQSyF8K3G329a56S5aR8AZUcZqqVOTd2WkdN4Zie/1dB2X+ldzPp25TuWsX4caZu8+6YdPlB+tehTWO6MYHairFvY0cLnmWs6JvhZlUZxXH+G4TB4+0JDx/xM7f8A9GLXsV/Y4jYFe1efQaaYPH+hSBMf8TG3P/kRaeEqtVFFmLjys6zxxo1tqni3VW37pVRAYt2Mny1P8sV581uEtLvTpkZLleVyMbsOGGPzrf8AiJftbfEnVwCV5h5B/wCmKVk6tqC6pp0XmAGaGNmZhwxHTrX03JGSPcjRTpp+RmyyRvczFBgKVQgHge/+fWubuiWuf3WWeQ8ADqen86v297HHA8YXErbR8xyGIxt/TNZ+qubaZZ4CUUsHiI6qe4/l+Vc6jZnLCXs5WZu6NoM09wI7+2htoWGRJcENlj0GAwNdxoPh2C0jZYrxJYHGWtmXIVvVd3Qfr715ZB4jkkmjmurS3upVORIy7WH/AHyQD+NdGnxEuYoVC2i7xwpY7v8A6/605LQ2k20ej3zWcNo6TwJGQR0kJzjof5ViWOpS6hqlxbmNFifiOQlWVh6EZ/SsW5nvdR0gzXgRJ87woHUdcVlWdosSDULQhF4faR91s4I/WuXqZIy/FNj9iuJ2itltwkhWRFJIAPKtz2NcfIGYY7V7TqJstf07F2qLJs8t5RwTnoP6g14/qunz6RqUttIfmQ4DDo47EV1QldGdWpK1jTs8GEYOMDFJGxW5U/e56djVW1dvJwKuwyybY1EK5Rs7gOWpvc9SErwVi8sM8MqXEMZKSZJAXcvXoRXZ+EYYbuXzIoiCf9ZFHJs2upDKRnPXBH41ytrPLAGkiMmDy0PmEZ/LrWnpes6fHcfaEWWKcuM4PBPoR0/EYqZaIiq/dsey2dxp9w9w6O0E0ycvtG5BwCBx6j9au21lpVtpy2aQrLAr+YwkOcnOckfWuMa/hgjEqxyRy3shIVmBZQMfdPTHPTFXVk3IJDcSb8bgkZGenvXMm2zglC+poeJLSy1pFS5tiILc71kVvvHHHHr6Z4ryvSZFk1O7m+0SzKJdqvKgVjj1xXY6v4n0qa0lCXeXxtJwFdT+II/SvPLG9mN1PLIvLykgbs8fXvXXGzNcGv32p3lzfbo1UtkAcg1WZw6nHTHNYi3cjnIXb65qzBeq0TZDBh1rax6E1ZkzqQeMkfyrQW0ZbHdG+6Rhu8vuB61QtUnuptkKNIx/hA4/Oul02zaSW2iFpvMClpbwtjZ6Ko6Hn1FZtmM6nLZnT6PAl1pqTwkFXXLK3BzjB4/OsjWYTqq/ZDE6CU4lJX5vKB6Z7A4H4Uajfx2Vw4tVaEw7HeTcXVx3wO2KlPiG1jtluGRlLjI3HA/AVg5e8edN3fMebar8Nry5DX2oaxbW5AISJUO1FHRQc1wBQ2d5JDvSQqdpZDkV33ivxB4d1E3VtfX139oBKZUFhGR1GAQPzzXnEvki4K2srvAD8jsu0ke47VokVSmr2NJipQAVpeCw3/Cd6B12/wBpW/T/AK6LWKkgRQo+Yiu18AaPG/irRr29uoY0+2QvConAYuJBgY2nP04+tUazldH0pdf8fL/h/KojU11/x8v+H8qgoPOAUUUUAFFFFAAaSiigApKWkoAWkpaSgAqNxxUlNagaOd1mDchOK5NlwxBrvNQi3xmuLvY/LnPpWM9zsoS6HJ+IrQlFuUHzJ19xUOnOJLBSOeTXQ3UImhZSMgiuZ06Nra8ntjnbu3LXPWV43PQwk3Gpy9zes2yxTHoPrXp2m2otLGCDH3EGfrXnnh2D7TrcURGVVtx+gr0tWyCa4JvWwY+W0R5apo+E+tU3kAcKOpqw8youxfmccbRXTh2k3OR5c07WRxPxK1T7F4ee3RsS3bCIf7vU/wCH4145BCS3AJ+gr37UfDNhrV5Fcamhm8ofu4s4UZ6k+tXYNI0+0QLb2FvGB6Rip9qlsdtGvGlG2588y28gYblZQe5Xit7wjE092UJLADg9hXtr2NtICsltEynsUFUG8MaZlmtoRaSMQS0Ixkj2p8zasafXY817WOZsbUIrADGOQPxzV/RWzq9oPS4QH/voU/U7eTSIriaXBh2/Kw9ff86zPCkxn1W1cn/l5X/0IVrF+8kjocvaU5SR6/RRRXpHzwUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAeJ+PTjxrqH/bP/wBFrXOPyK6Px/8A8jrf/wDbP/0Wtc+ihjzXyWJ0qzfm/wAzzKj99mroJLw3NsejAOufUVFeRnypxn+MMR+lLYyC1uY3xxnmtK8t1kdtvPmITVU5KUTto+9AzLR0ntFLLhlJVh7Vm6naCFCVHbNSQLNbzlOzmrUqG5tmyDjPWnzcsrmljk5ZJCux2OO3tUtqnmMRtyUGc06eL7NdESKWTBANNgn+zMsir1HOe9dvN7uhDQ24hdv9WdzHqKi8uXafMB3A4CV1EMFrrC7rVVguAAdmeTSXOnyQOFuI9sgXAOODXP7drRk2OQnyp27SKns9Ra2lQjPy9CavahFvO04Vx1rMuLfy4wCOvQ1upKpGzA7E60JtPCxuVbIYjPWsefWZHv8APC5HzEd6wIXdH2hiPUUrK5bIyfeohhYodzoF1AvIpDHoFPNdNfrbHQ4yhJYHeU75rziB380DseK2ptZkETRA7m2bcVlWoPmVhHSaBfpAjM0QG5yS+f0rF1vUmuNT+clRngdqz9Mvxbs63cbFCDgFsYPrWZqV79rucruCrwMnPFFPD++2Gp22m6hLDp0ptHysZG04zg45rBu7x5r1vOYNK/f0p+hXttbWU32jdllOAKwJ73fMXAwc1VOi+dj3LMc3k3EsjthgcCnzavNKiRu+Y0XBrIMpf5mbnNSLGWh9MnvXXKlHdjSLAknu8Ih4NS29i5u1i65OTipNFtz5+c88muq0DR/N1mBWwWY/MPQdzWVSryNpFxR6T4P0sWGjRKVw7/Ma6Z4d0YwO1Q2qKiqq9BwK0guY6VBc6NrHO30AKHNce+n48UaRKOdt7Cf/AB8V6BdRAg8Vi/ZQdXsjj7txGf8Ax4VhOm41E/Micbo8n+KZ2/EfVvcw/wDolK49rjDMC3ysNp+ldl8VFLfEXVgqkkiEAAZ58pK499N8lzBc+Y04HzRwnkZ9TX06lY9yk/3UfRGXrKrLiZCFYLztGOazZb03dlHbTPiRZeGbgAH1NdNcaTFHsSVJpXEbE+SfvcnHXtxXJyQ+a7lB+HpU31PPxUdbourpMnBjvrGX/ZWb+mK29C0XzLn7RdFHETDbGhzk+pPYCuSWzmedYYlJZjhQO5ruIQuhaOlpJIEfBM7D+96fhwPrUyZhGq7GneayI5nukU/ZLdjGdp+8Mcmo9P1zSJ7Se2Bw83zKh/HH5cVxtzrs0e+OIJ5coBIPO3GRj+dUImWG6EsZIjIyuex9Kj2fVgqp1EGsm2+2WkqhpPKMYZTkFex+ornrme5ayWK4fzI1I8tm5ZR6A+ntW07RXcMcxQBv7yj5lOOh9RTJdIE0JQFSpHBHahaMmbbMGFwg44rQtm4DFuOwzUEunvZDDHI9adbHDdh6Zq9zuw9X3bM0xIwQMmVI6EVd02yuNTmK2tuFZuHdc5P4dqq2VvNe3KQxAvuPNel2dlDoWnxeWAs+PnxwadrnVGKluWLfTCtvHFdSKzRKAqocFWxyaswwSom7rFkfOx5/GsNNXWRJHLH5Dk5PI/xFV7/XWWMCGctbyrtZCeVb/P8AOtoxgtbHRaKXKkX/ABBpVlHEu4wl2OSFHQ/UV5gZJNL1GSCRm2ltyljkkfWuke/e5UlySQeuay/EFmZ7ZZ1UbkPWolboZVKCh+8juadjqOVG7BB7GtQTK0RVVAPrivN7fUZLd8FifStqHXZBHtLUKRHtVPU61dUurS3aJZ2RT/dOO+a1vDl2jvFbLeM8t0xeYbuI1BwF57k15u+pzXU6Qx/OzkKoHfNb1jrcGhsbS1to7m5icNPM3O98YCoPY96l7nPXkmrI9S1GIDVYt6brd7WdSQOCQDjisPS3SG2WMtvCjBUrW0+oI+lx2/l+bfRxn/RxJgkEZ6/TH51XOmiS5j8iGdpCm7G3n3U+je9Yypt6o5LNLUydY0TRLyH7QlnBFctlhI0YKZPdgK8Zv7a4tNQlS4j2PvPRcKfce1e3/uxcGAo6Zz8kgKN/9emXWg217HteEMuflDjOKXM46Mxc+WWx4hE4zwfmPc1v+DG/4rzw6ATn+1Lb/wBGrXY6n4D064gJjxA6/wAScY/CsfwNoLWfxD0rzZUlWK8hKMp6neK0i7nVTbqK1j6euv8Aj4f8P5VBVm5jdp2IRiOOQPaofJk/55v/AN8mrOEYaKf5Mn/PN/8Avk0eTJ/zzf8A75NIBlFP8mX/AJ5v/wB8mgwyf883/wC+TQBHRT/Jl/55v/3yaPJl/wCeb/8AfJoAYaQU/wAmX/nm/wD3yaXyZf8Anm//AHyaAI6Kf5Mv/PN/++TR5Mv/ADzf/vk0AMppqUwy/wDPN/8Avk03yJc/6p/++TQNFS4UMhrkNXt8MTXbPbzEH91J/wB8msTVNLupVOy1mY+0ZNZzRtSlZnGEZGKxru28u+WUDrwTXUNouphuNOuyP+uDf4VWutB1R0yNMvCfaBv8K55JtM9GEkpKVyx4KtT9su7hhwqhQfc12MsyQxZJxWVoGm3lho7GSyuFldyzKYmz6DjFTW1hf3l4bi4trhIl4jjaMj8SK8qop82iCu1Oo5N6IdEk9zN5zlok/hA6n/CtSGNY1wox/WnJaT94JP8Avg1OttMB/qn/AO+TV0qc5fEjknNdBgGKd3qQQy/88n/75NBgl/55P/3ya7VTstjBu5HjNIV7VKIJcf6p/wDvk0vky/8APJ/++TV8l90FytLBFcQtBOivG4wysODXP6boUWk6zbRwhjEZw6k9hkcV05gm/wCeT/8AfJpY7aRpoi8Tja6tkqfWpinzJWNadZwTV9GbNFFFekcoUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAeK+Phnxpff9s/8A0WtYKcGuy8baLqN34mu7i20+7mRtmHjhZgcIo6gVzq6BrQ/5hGof+Az/AOFfJ4iE5Vp6Pd/meXNS53p1K3VcVr2kjmyjYjLJwfpVePQdZPXSb8fW2f8AwrotI0W+WMrLYXKg8ENCw4/KsqcKkZWszsw11ozAvrLzoWmg6oc/hVTTvmnEErbVY4OfSu3sdDuo7iWJ7SYIF2hjGcEGue1LwxqUUh8jT7pyG6pCx/pWslN6NM7HAxrnTEa6eAngZ2tjr6Vzl/YTWj5YZXoPSu/fRdVljjxpt4rFcN+4b/Cqy6Jqjxtb3ej3zgfdYWzn+lFOdWL2f3GbgcBE7wTeYHIKH0rqbbxA09qkMyhgBgueopNR8G6qQJYNMviwH3RbOf6VlQ+HPEaMQND1QD3s5Of0rd0nUV7EWsTXMEE8255ApJxmqeoWflgAMrx9cjtVp9A8QkZ/sDVNxOD/AKHJ/hUD6H4mCNGvh/VcN1/0OT/CqhRqJ6ImxiSwHafUHj3qOBJZX2jrWmfDvil5CD4e1YA8Z+xSf/E1taT4X1q1IefQ9Rdjng2kn+FdEueK2FY5OS3kjyem3kVY06xe+Z8/wjk12Wo+HtRmEajQNRzgElbRz/SmaL4d1q3lmB0XUEXHG+1cZ/SsZVKnJsO2pxmrWz28nBLR4wGrOiZuWwMCvStQ8J6nf26hdJvkJbcV+ztx+lcteeCvEUV0Ui0PU2jzjK2jkfyrSjOTWqCxzrTHyzgnrz9Kj2SOpZRx1H0rr7jwRrKwZ/sXU2YYAVbRz/SpV8Ea8tqzto9/uUgIgtn/AMK15uVXsGpxcUDu6hkyPat660+K3srfacuwB57V0I8IarBZLING1LzVX7q2jk5P4VjT+HvFNy4DaBq5xxk2UnT8qi1Sp0C1yrb3DWYkCYKqMZ969B+HttJO0uoy9W+VM/rXG2ngzxFdXMUMui6lGjN8zPauAB9SK9n0TQ5dPtYoFtZFWMYGUIrkxcJK3KtWaR3Nq15GK0lHyCq0Fu6AZRh+FXVVgv3T+Vd+CpyjHVGrZUnjBHSs0Qf6fAcdJVP6itp42P8ACfyqsbZ/tET7G4YE8e9XVoNtMVzyPxtp80nxE1K4SHzJCYhCmOCfKTLMfQdh6isRdFa1tnMjb7qVh5k5HzEbui+59a77xVpWojxDqN5ZafcTedsIZI2bJEargEDgcfzrGfRdWazKz6beSOeZXW2fcTj+Hj8OK7W3c7Y1fdSON1maGxhe4IKRAbI1DH5uOQB39M9fyriBey3Lyx/Z4IEK5wkQ3f8AfR5P51v63ovizUbt2/4RbWDGnyxKLCXCr2x8tWfDHw78R3tw0l5pV/ag957d1OPxFWotjqwU1ZM5/TtPnN3GbeBpJgcrxkA+tdMnww1zV4Faa4WAMckOCx/z1r1fw94Vi0q0hL2Fw8yrkgxMOfyrq4Q7uENjKgPdozxWsaa6mXs4QXc+c734K6xCMxXUchPRfLIJrnLnwL4hsra4E1g7qnzApzyP/rV9XgXADF7eVmU4GIz09qa2mblJ8txu+YDbyDVuKIcabPkbTLgKpSUMMcEdDWzasEJXIwOPwr1Dx98MJrmV7/RrOT7UOdiRkiQehx3rza18JeLFcSSeGtXUZwR9ik7f8B6Vzzh2MpRsQXsK3UZwMY71zskTwyBXByTjNekWvhbXmt2P9gaojns1nIM/pVTUfCmvGBETw1qrOW5IspDj/wAdqY3Kov3kit4alh0qMXcqO3HBAz+NXtQ1QX7GRJCfRgf509/DviNbREi0HVxgY2mxk/8AiayLnwt4p3fuvDutKTz8tjLj/wBBrQ9f2kV1I/t8jXiPHII7hT6cP9RVO5kkFzISoG4/dHRfpViXwj4tIVn8NauW6grZSEj/AMdpyeFfFnVvDmsknrmxl/8Aiad3YmNVN7kdvlwR3NXJo/NsiG4zxVmz8L+JUf5vDurgH1spP/ia0z4Z15rdQdB1TjnH2OT/AAoR0SqRcbXPKLqMxXEi/wB08U1ZHYccDpmunufAviy6vmCeGtYAcn5jYyAfntrSt/hrrMUQhudF1mR2O4+TZOAhA9SuDSPGlJxqNJnN6Bb3X2+K8ibYIjncVyP1r0PTfDumXsgnkQ2rghmEYzGfp3BNdd4f+Hc2n2EayWMiNjdtMbE/8C96vT+Gr2KMJBZ3CsX3bUtm2/icVcFc76fs+XV6kdl4eJu/7QhaOWQ8gg4OOw9enFdNY3m87JtyOn8QHAz6is2y0rVEG82lxEx+8vksfy4pb3TtWi/eWsOoMpxlQr8fQCuhcqRM0paXRu3ml6d4ktAt1EBNGcCRfleNuxFefXt02j6hPptzKpkj5EhXAdfWukaz1aOJbmG31AyuuChRyVOPfsa8w1/QPFF1fPJDoursxPLC0kIP6VhVpq10YLCp6toi1rxDHdq0SDa6/wASkjNX/AEAbxTpzs33Z4+Md9wrm7Pwf4pm3tN4e1cOjLjdZyDd69RXofgvw7q9pqdnPc6TewgXMRJeBlwNwyTkdKyirHVScIwkrrY6mGxs5rWecXc4ECqXH2cdzjj56pyxBcvF5jQZ2iR025OOnUj9au2P/II1T/cj/wDQxToYZLrQvKgQySJclmVRkgFQM/Tiva5mm7vqeqpyjJ8z0vbp2RVWxf7HcTybozDs+Rl+8Gzg/pTWs5jO8cMM77SAQYiGGemQM4ra1FdlrfqSDiO1GQcj7tS3KTySa4lvkuWiG1erDnIFQqr3/rp/mYrEy3/r7P8AmcnqN7eaHbyXcMos7mHGHlUDZkgHIYY6H071yOo/EXx292E03XbeRAmTsjt25z7rXQ/E/K+AjDMzC4jSMS45ZVMy4B98dvevB5Uh2DY0gGe6c1xYuV5p+X6nkZnLnqxb7fqz1fSvF/xO1e5aEeIUs/LwGaSzgIJJ9k9Oa1tQ1n4l2Ojtqi+JRcRI0iusdjBhQjFSSdnfHFeKIXQL5dy64HUEircep6hCDEl9MI24ZRIcHPXiuVyPLt5np978TPGMGlmZdVKybwu42sX/AMRVub4j+KlWIrq4G5lB/wBHi/8Aia8nk13Ur1Ftbi7kliDbtjdARWg3i7U5tqzm3lERDDfAp6evHNPm0LS1Z6UPiN4qF0VOqnaEBx9ni65P+zUE/wASvFiWMsqasdylsH7PF2Jxxtrz5fGM63Kytp1g7ONvCFQAMnPB96e/iKCW2aB9PQNJk70lYY3Enpz60cyHys6fVvix40tnTyNa2gxhiPssJ5z/ALlWbX4oeMrm2kmXWuApK/6LD1/74riLu/0W9dS1texIq7W2TKxPpjK/WktJtFjjkH2i/jYgiIbVYH03YI70cyIcZHTr8XvG4uZY31gYH3f9Fh/+IrTHxN8aG1M/9sjB4C/ZoeDnGfuVwcsWiy3AdNUlQY+ZntjwfThue9aENzprWHknUVV94wrRtyN3X8uadxJPqbafGDxiXVW1hgdpB/0WHr6/c96lb4teMBAD/buHwBj7JD94n/c9K4OWylguPm2lSMhgcgg4INMnOAB/tr/Oq6ifqeiD4qeMmYAa4AcEkG2h69h9yrEfxN8YnaTrRIJB/wCPWHkHt9zsa81ed/PBWMsFOTx096dBrEsUSObYbfmcE5xnpimhX8z0NfiZ4480Z1o7eD/x6w9Bwf4PWmf8LO8dh9n9sAkAZ/0WHrjn+D1rzz+17hHbGByV9egzU0er3HmYYBiOeQPUf407IV2dtJ8VPHSMpOtEDft5tICPX+5WvYfF3X5tIuY7u+KXpXNvMkMZUnPII28HGeteere+bsd7YBHfAYHg8ZPH41KsUP2ZpIXBUkAr0IPbinZBdtnUW3xW8bSXMqtrWUXHH2WHj/xytRPiX4ufpq/X/p2i/wDiK83tVAuZsj0FbcQCqADWbNo6nodl8QvEr2Ikl1NTKbpEG6GMAr1K8L3/ADqHwn8Q/FepeLLSyu9Sjlt5bzy3j8mMfJuAIBC54rmrIA2trk5J1GLjHWtHwTbQW/jK1k83962oEImOxYZ/mKpEy3PQJbG1t47cz3UweaIS4SAEAHPfcPT0qG4sZYLhIl/e+YoeMoCd6noQKv6hZXVzFp7wW0sifZEBZUJAOT3q1HtMi2yOXkt7Io0cRG6Ri2SgPPTPbnrXp87Sve59Wq8kk733v5GDJa3EUqxSQSpI33UZCCfoKJLW4ijEkkEqIeAzIQD+NbzRlYtIzB5JW7xsLFivK8HPQ+1V45DJNrPnEupG5gT1xIKaqN/15lRxMmr2/q9ija6bPcfMySRxeWziQxnacKTjP4VXitp51ZoYJJAv3iiE4+uK6FY7o6tfXKkm0khk2OD8rLtO0D6ccdsVBEon02wEEFxKY927yJgmx93VvlPbHNL2jJWJlv6fLcy47LfZx3G5jvn8nYqZPQHI55PPSoktZpZXjhhlkKk5AQ5H1A6VvvIsssLoEAOqA/u23LnC5wcDPPtVa4jlubKWK0DPIt3I0qJ949Npx6daFUfUccRJ7/8ADGQltPIzKkEjMpwQqEkH0/Squpy3elWV1cRboLu3iaWMsnKsoJBwR6gda6i8lIsL4LJmURW6TMpzluc89+wrA8U/N4YRm5J0+UZPoC+KUqjcX/XS4e3lOLuvL8Lnnh+Knjcf8xr/AMlYf/iKb/wtXxx/0G//ACUh/wDiK5oximNGAK+bVSXc+XidI/xY8cjprn/kpB/8RUa/Frx0T/yHP/JSD/4iuUlWmRpk1XPLuXyo7dPir43I51r/AMlYf/iKG+KnjcDI1v8A8lYf/iK5RI+Kl+zMydKOeXcvlVtjoG+LXjgf8xz/AMlIP/iKB8WvHH/Qb/8AJSH/AOIrlJLQg80gg46U3N9zOUTsY/it43brrX/krD/8RVlfih41I/5DP/krD/8AEVxkSfMBV9YsjisnVl3I5WdFJ8UPGy9Na/8AJWH/AOIqv/wtbxwD/wAhv/yUh/8AiKxHi+XFUZY8U1Vl3GonUj4r+N8861/5Kw//ABFDfFjxsOmt/wDkrD/8RXIlCKaICzdarnl3Ksjsh8VvG2P+Q1z/ANesP/xFL/wtXxtj/kNf+SsP/wARXLR2wx1qVrYBaTqS7hym+/xY8br01v8A8lYf/iK0vDPxO8Zah4o0izutY8y3uL2GKVPs0I3IzgEZCZHBPSuGktumBWt4Rh2+M9CPpqFv/wCjFqozlfcnlPqPaABk4z7Um05wOfpTxuAGBuH06UuB8wA9OAa7iCPBzjBzRtb0P5VIPvrxggHvTcnyv+BUANwScYOaMHOMGpCMlup6cClPA9PloAiII6jFG0+hp6kYTPTdQAwD59KAGspDEdcUmCRnBxUknzbgOo5PvSqPmXjPvQBjajf3NrcKkMu1SgJG0HnJ9RVP+2L/AP57/wDji/4U/Wv+P1f9wfzNZxqWefVnJTaTLp1nUAf+Pj/xxf8ACm/21qGT/pH/AI4v+FZ7Piq8k4HSgz9pPuzVOu6gP+Xn/wAcX/Co/wC3tSz/AMfP/ji/4VhvcZ71F9p96sn2s+7Ok/t7UMf8fP8A44v+FL/buocYuf8Axxf8K5sXGe9SCfHepD2s+7OhGuaj/wA/P/ji/wCFL/beof8APx/44v8AhWGk+Rmp1fNAe1n3Zrf21qGP+Pj/AMcX/ClGtagf+Xj/AMcX/Cs0HK09aRXtJ92aP9s3/wDz3/8AHF/wo/tm/wD+e/8A44v+FUKTNAe0n3ZeOs6gP+Xj/wAcX/Cm/wBt6h/z8f8Aji/4VTPNMxQHtJ92aH9tah/z8f8Aji/4VNaavfS3kEbz5VpFBGxeQT9KyasWH/IQtv8Arqv8xTHGpO61PONV8PnTdI0/UFufOF0gZ02bfKLDKjOTnOG9Pu0250I23hq21drjLTSiPyNn3QQxBznvt6Y6EV0bQHUxZaKGAa70iB4S3QSIWI/8d3j8aWG9spLaK4mx/Z8WuQou7oIkj2qT+ABNUfrCxtZRSerTu/NdF+a+Rxk+n3trCk1xZ3EMT/ceSJlVvoSOatXei3ENxaQWyyXUtxax3ASOIkjcM4wM5x610t8HtbfXJbixukS5Qgy3d8HjlYsCrRgRjcR1GDgDPNS3ySXOgfY7HLX8ljZMY0+/LEEbIUdTglSQKC/r9R8rsrXt0t0167X/AOG6cSbK7F0bU2swuBnMRjO8YGTx16c0tzYXlmqNdWk8CyDKGWMqG+mRzXbae8lvrHhyC4b/AImVvbXHnZOWRdrlFb3A7dsiufgkebwXqRldnIvYWBY5wSr5P1NM2hjJyeytp87ycbry0v6M95oooqT8xCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDyLVJ10nVJtPnBaaLbkx8qcgEYzj1rOTxDaSWhuRHPsClsFRnj8a3fHWkumsXOpLKrCRkDJjmPCAZP5VwNvCz6FtVHZvIYjaM9jXV9aqHe81xSfT7jfufEVra2Ju2guGjADYULuwfYms6L4n6WlrNbpaXpSYqWOxMjbnGPn96iXRrrW7SPTbdljeZUQlv4Rxn8eK6jRPhzpFprcaXljCzW2NvBKvx3Hf61LxU9i45hiqi1tbfbsed+IPHula3o93plva3gmYpgyooXhweoYnt6Vw0jfL/qwPYGvQvif4ekPi+51HS9OdLGSFMyRphWYcMR+lXfAfw807WLNLnWRJmRyAhcqAMe3euarVdSV2Y1/rGIlz1FsvTQ8ryu0ZQnjrmpFRCAcNniuk17wddaL4ml0sb54UPySov3lPT6Gi+8OwQaYJ7adzcxkLJG/U5rBzinYdLLcRVpOrFaL7zm1jgyro7FtxBBTHb1r1b4XeE9F1KzmutUtlvDM7RxhiVCgDkgeuT+lOs/DemaP4ctFn0UXVxqCNG13Mxwh6/IOx/wqn4POtafcwaWsflP5jBd3HXufw71XtIrRmtDLql7zWjT/Dv2MDWfh9fnV9Ym0e1eTS7CZx5hPQZHA9SK5YW3zdULADq3NfUMl1HpdubXEYWWKSVzFnBbkk/XNeJ6N4dsp9QtLmeYmXzvMdGA2bRyP1FE5JGcMuq1bumtEcbJpVzGdjRbTIodQSBketRNYzKE+Q/Lw3IOOa+gNb8MWHj7RCbdJLa9soyLaQR7UlHYcjkEg8ivI38H3iaBPqazRu1nKI7q3XO6L5sZ9xmnc5p4epFyTW25zYs2Abcjc81DOPLVQF5Knn6Cuns/Dks8wWSVkSVN0UnY/Udhniuo0r4UHU9Ai1G71BreYHHkhAQFJxnP0zShNSehrPA16aTkt/8AhiHw/wDCO/1LwzDew3sfnyQ+akDEjJPIGfp/OuL1TR5LMvIzRKEkjzEJdzAn/DBzXvNrfP4V1Wy0ct58aR7lk+7uU9P5Yrzj4heGbXTfE0cVjK5jvytw6ud3lncS2D6cVqpruFfBThyuOqkcbPaSDUEt7VCZrtk2Rw5OQw5H1zWj4m8G6x4a06ya+tjHBNGxQqclSeSGx3r1jTfB9k1lp/ia0hSKe1t2zCuTjqQ2T1bGfzrB8Z+Ln1cpbBQYYY9gOerkYJ/pSnVUY3NsLlU68+S+2/keSXckBeF4SdyA7wwx8wHb14xTzLFJeK4UBCm3H+1gDP58111/8K9Xis57y2lgn2fvPIjJMgVlHt1Gf0p8vgGDSraSa9vjLMX2RwRKQecENnPbA4x3FU6iSuzkhgq1Sfs4rUwZLOWLS9PuWlBV5CojA6HaMn+VQWuWYjkkivU9C8E2135CaoLq1hD4hVkHz8feGRxXPz6FHoXiK7s7X9/tm8tXcZG1ug9M/wCFQ66Ubs7IZPUnUUIyTdrvsvn1+RytjGz3zqFOAMnitqK0keZY1jZmboAOtdzafDySz1MXz6nCLe5UhjsOUIxgYzR4m0lNG0aG+tp3neU+UzkAbNwPIA+hqpSSVznoYac6qp93a5n6X4Z1W4uYLOONIpobiOdjI4xt9eOtVfCthdWXxNtNPu/klivWdgfXIP64FbHg27WN8NNkJyzD73HQc1f16aNvitoEsY3SCSJHZCASpfgn/vr9KUJ3imdGNwDoz5Y6nN/EHXNQ0PxxqOnadceTaQ+Vsj2K2Mxox5YE9Sa5k+MNe7X/AP5BT/4mtT4r5/4WZq3p+5/9EpXH44rGVerzP3n95isTW/nf3s2T4y1//n//APIMf/xNM/4TXX/+f/8A8gx//E1iuPaqxU5NL29X+Z/ey1ia387+9nTJ4y189b//AMgx/wDxNWF8Xa4V5vv/ACEn/wATXMQ9s1aU8UnXq/zP72J4mt/O/vZ0C+LNbJ/4/f8AyEn+FSjxTrPe8/8AISf4VgoentUm4Cs3iK387+9k/Wa/87+9m0fFWsgf8fn/AJCT/CoJfFGrTwyQy3e6ORSrDy0GQeD2rIeTjFRA4NHt6zVnJ/eyfrFZ6c7+9k5YU0kHg0wsMUzdmsrGSVhsig9qbGoLCpDzTV+V807jUtS9BFuPI4rRSMBcYqpangVoL0pcxsncz54QWqAwYFaUiA81EY88UNiZTihJfpV+NAFxTo4gooAHNRuKxFIBVOSMNV2QYFQYzQFikY+elJsI6VdEYzTjDgE1cWTykMPvVjAPFRhNop4NDZQ0xgmtbwrDjxdopx0v4D/5EWs/Zmt3wum3xRo3/X7D/wChikt0DWh7tdanDaXLwSLIWXGSoGORn1qH+3Lb+5N+Q/xqhrf/ACF5/wDgP/oIrPr07nlSrTUmjf8A7ctv+ec35D/Gl/tu2/uS/kP8a5+ndhRcXt5m8NatifuS/kP8aX+2bf8AuS/kP8awl60tK4e3mbf9t2wP3JfyH+NO/ty3PAjkGfQD/GsAigdaLideZvf2zbf3JfyH+NH9tW39yX8h/jWEetFFyfrMy3qF0l1OsiBgAuPm+pqiXwOtK5wKpXMoQUzKUru7Ce4Azk1mT3WO9QXl2BkA81kT3Zz1pmMpl+S9wetQG+B71kvcjvURuKZlzM3Fu+etWEusnrXOCc9jU0dyc9aYcx1Ed32zVyK4z3rl4rvGMmtCC6zjmk0Wp6nSRS5qyGHGKxrecH+KtCKTpUtGqdy5TaAeKKQwooooATHNWLH/AJCFt/11X+YqDNWLH/kIW3/XVf5igcd0Xv8AhX3hf/oGf+TEv/xVH/CvvC//AEDP/JiX/wCKrpqKo+i/tDGf8/Zf+BP/ADOZ/wCFfeF/+gZ/5MS//FUf8K+8L/8AQM/8mJf/AIqumooD+0MZ/wA/Zf8AgT/zOZ/4V94X/wCgZ/5MS/8AxVH/AAr7wv8A9Az/AMmJf/iq6aigP7Qxn/P2X/gT/wAwooooOMKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPNPF16YtV1aMYZcRRuDzwUDGuc0HxdbeH4/sMVmhCR7XnY85Hf6Vo63pCaz8SdajubiSO0i8kuI3wSxiX9MVh614HvbvVLwaQm21MIkjMrfeJHKg+tc83Nu8T6ahHCOlGFfeyf3pfiWvBV7cavrLSxJHDCqksxHIBPUeh/pXQt4pP/CT31kYpFaKJURiOWweSPY8U3wfpUOiaCFmQGd/vn046CmXbWX9vQfdN1HAWdh1YK2FB9skn8K1p8yjqcuIcKlZqmvdWn9fMo3N39t1mKPxQJY7RpGW1tx0CjGGbHUmuiuNNS3iE9k8UdqjFkTPUYzwfXrWRqka6rJpryLukjmJ/DHP8quXV0yaZK0qkR53DjgDB6fhQk02x1Z+7Hldrbrp8jnrrUdGN3qOuanE88bssNtEp6tjkmrDx+DNQ09YoVIvHIkAbOd24Zyfw6elc9/wiV03hT7fPOQ8A85LYL2HJLehxziubsrq4nuBJEm3ykycZIye5rncuSdmj06VOniaHNCb06X8trHrc8n9uWctjuRRKo8kuOEIHB/SqGn+EbnSFuNTmvUlulBymOAp4yp78Vg+EtQu52aW6baVQ7QR1BwMj/PeuvcW+p3Ngkzyb7cGT5GIBUjGD7e3tW8oxl7557qVKcXTg7Rfl/W5F4jggbwpPcRM/2i2XbuJ6qW+b+deYypNc3h+yxFLeNSu5T1JHQetdh4v0ufTbWbULa8eWGfMc0TOcbWORgdumK4XT7mdgWMhKRMwjXPAz1rmrO8z1suj+5dne7/pf13Pd7bVba00i3llDCTAhRAfT+lZOl+H9MD3k7bmW+cNKvRQAwIXH4c+vNcJb+JLu+iXzGBaGQBOO+3v+PNdY2pLb6OwEuHMbFTn0HWuiDUo3PGrYSVFuN9W9TV8R+HdPvZBNLKlt9niMQKgKC2flyf8APWuBtfFN5BcG0u2xGICMKflyOn16VS0zVL/VbuzsJLuaWSWZnyTkZJzk+tdd4n8GwHw41zZH/SIB5kgH/LRR1IHbA7VipOXvQR6MacMKo0MRO99vIqaZp194n1SXU7yRYLVVWOPfyxXj7o/EfrXC69qdwNZu7e4ZWeNniY55IDEf410WmeLorfTba2hjdisn73J7A8YritR0XUroXWtyMvly3pXaHG5QxzyPpRo1v6hXqVcPPmcbx2SXTt956v4d8SRTWTWtvHJMFtizBB0HPWs/xjpmnW/h6G6trZLa43oriPo46nP5VoeAsaXoUUjCPzbhicgfwkkgH8D+tcf488SC4EljAuFiYyMcfLgc4H54/CtKi5onDQxCp1Paz0XXzOq0bxNbxz24adBuwjlvTGOa5izvptT8T+YjLnefI3nABxwx/ACua8LRRTPLc6hceXtfavmNhQSQCfwGa0obmy/t5ri0cJatMWh91HAqaibsjpwOJhWnOUVbR/8AAPWPEGtQWvhuC8mfc8EhIY9ScdPpmuB8GtJc6mbu6lYZfzS3Ulslh/I1oaxrVoulxxO6SxgFWAOcEqR/Wuc0C4vNFfzLqFvs9zGTbyY4cDjI/OnUV5JvZBhZQhCVKL96X/BPRfE2q7rW1nji2wyM5kOfuvgY/MZrO1SCa/0OS04WTasyq/GcdPpmsm0119ZihtmjURxSb3X+9XUwX6S20i3ahVljLOOu3A4/LFaXU4uxlKDw7jG2q/pHPfD/AOxWNrfapqCb1CsQrdAE68fXj8Kr6f4hTXvGulXc9lHGGurdI0Q8IfMXkViahq11A1xBDtRHYhlx15yf1zWx4MsbWbxDptwrIFSaMmNjkrJvH+RXPe9oo9CdNQjPE1N2vu0Mj4qpn4k6sf8Arj/6JSuOCV2vxU4+IuqH/rj/AOikrjxUz+JnySWhXdDUBQg1bk61CxyaSCwyMGrABqNQQamBwKGS0AOKcWNRbs55p4IxipAYzHOKGJxQ685FNwStArDDIQcdaPMwaVk71HtNBVidWyKd1NQrkGpl6Ck0Q0aFqcAVoowIrLg6VbXd26VBrEnmcDgdaapzSBO55pwqWUxwJoVcmnKu5qk27AaExIrzDA96iWLJq15ZY5pfL21aRoQJFzSvwpqfAFNYAg1N7CKLtgcCmBm3DirDoKPLGKVyGgjYgg1veGTu8UaP/wBf0P8A6GKw1Xitnwuf+Kp0cf8AT9D/AOhirjuiraHsWtf8hef/AID/AOgiqFX9a/5C8/8AwH/0EVQr0jw5/EwpaSlzQSOHSjFItOyKQDaUUtHrTE9hD1oziimsRSIIpnAUk1h392ACM1dvbgKpFctfXRJPpVETkRXVyMnms2SfJqOefJ5qo8mTQQo3J2kyabvHrVYuSetBcjvQhqBbWT3qRZKorJgVIsmetO4pUy+k2KtwXWDWUGz3qaN8VRlytHTW13Wvb3OcVyEFwRitm1uSMDNDRcZHUxTZFSqayrafIHNaMb5qLG6dyagdaaCTTx1pDExU9j/yEbb/AK6r/MVFU1iP+JhbH/pqv8xQOO6O0oooqj1gooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA8L8T6jNbfF3VreEEmXyV2jufJSuzjjlXTGDOwmtwGdR/n0rkdWiEnxu1eQjiJIsH3MMY/ka7SSZRGVXaGk4Y+vHeop3V/U9urUU404rol+SMi71FQpYuM85FcVc60lhr+oySnLSbVjPtnJH5mquq6hcTQTy2xLGKQB1A5IyRx+OK5yWWe8lhuJl3MxKKMd8+n1rOVZRasdFanKgoyeibS+89G0zVpLoXcpAVYINsfrufir3iLxCLWzi0e3OZVVZZJBzjuAPrVvwv4Nhaz26jLPHcKySskbDHA+UHj3zXEa6ktn4k1JblWVzKdue69j+VEm7X7m1GFCriHFbLX12Ov0zU/t+iyl2H7z5GJ/iyMH+dWdKtbfStJi0+1RIww/esQN0gXqT9TXm9jrj2qQwryjys2T0yOn613MeoRzRBw4LMqLn8Mn9aqn+8szLE0PZ1Wls2dDqm27023gRViDK65VR8uF3f+y1yfh3U5bq1kuXG0FFjHvjJ/rWlqepm0tliI2skTu+7gqCpA/nXn8Gtf2bZRW5OEOGz9QKrmSnys5acoQlySdr/wCZ13i+8A8LOp4LSIM/ma81s79IYnQ/3jWlr/iB9VEcEQ/0ZGGW9T0qxpfhE+I5X+zWZmZcmWRX2Bf6Vy1NZ6Hq4epUo01aOju227JbW+8zNJa/nulFnC0nmk4A7sOmPeu1svCfinU9Pa4ZIkRUZFgd8SPzyMdB+dQeGtCubC9ltpVaFrZlU4/hDE5OfoK9hsopoIFjVQbYrgbeqn1rSim426HDicVKyqWtJu55N4Z0HUPDl4upapZSRQ8Rw5xnLHHPpgV6FBcIrmMncmxgR6jFct4n8Q30Ooy6XNAJYogkySBDnI4JJ/A1m6H4ibUb2eEZBZWTI6qf8a0pcqTiuhpXjUqr2tW2q6djg7XZGsrqCMuxUHqBnius8IaXba0stpe72VpAYyGxtIByffGRXKXWj3+lX01td7AE+YFWzuHb+VdT4Pv4dL8xXdi3JQY7nHH51yxSU7M9SverhW4a3R3OneHIXgisbG+lVY/vOwBOM4AH4CvNvF/h8aP9r0/ezpOvmRSSDlhnrn9K9L0afyZIjgg9SPc/5FYnxNia6fSbeQKjvM0YIP8ACSP/AK9b1YtRT6o8rCyareynrCS1Oe8NeAI/E1jIjzmKN1flV53HoT+vFZHi/wAKv4a1h7TaXgeENAw42joT9c16loLHTLW3hBHBLnaMDnp+lYXxRlW50/TpicyBZB+BxSnFwp36nC4RoV70tIt2+R5zp2ntPcWcBJbzJQSTzxnmvb5rDTrzS2srqFHQRqqKBgxkDjB7dq8o0hvsd3ZSXeYyihQjDBHfJ/Su3t9cikKSvKAZMseegGcU6C5o6io0m6k5Re0mk/TY8+jW50rUbm3hdGJkMfmBugB6n8K2I/E0Tzzrw5WNlCjucYrntQui087s+GkJY/iazLRxCWkUfOxzWMKzS5UfSzw0arUmyxNdG4lLNINwYk5PfNdz8PLG7v8AWrW9gh3W0NyjOxOM89R645NN8NfDmx1VXe/ld5GYMfLyPLyclR6nHHtXoFpp0fhr+yrLTnb7N9pjhDNyQC4JyfU9M1pShy+8zz8bjuZSoQR5X8VX/wCLj6qPTyf/AESlccrGuo+K74+J2rjP/PH/ANEpXJqRUTXvM+djsic4Ipnl04DNPAqSrEWwUjRkVZVM0jpgUCaKRyDSZJqSVdpzTAuec0GbJUIIpwWkQECphwKljREUyaa0ftVlVBFBTmhMsq7Ao5pY+TUjpwaZEh3UBYuwKTWhGowBVSL5QKtpytZsYrjjimRj5sVIeRikjwDUsCZVPWpghK8ikTBxVgEAVCCxAEwKjcYqw2DVaQ4FbLYshd8VE0tRyuc4qIkk0miG7ExfNSpytVsgCnJLgEZqbAiyTgGtDww3/FW6MP8Ap+g/9GCsgyZ71p+GGz4u0X/r/g/9GLWkN0X0Pa9a/wCQvP8A8B/9BFUK0NZH/E2n/wCA/wDoIqjivRPBn8TG0UtHaggVaWkFLQMM0vajFHakS2JVaZ8A81MxwKzNQn2Jx6UyXsY+pXON2TXLXVxuJINaOo3O52BNc/LJgnFG5mo80hryE96gaSrlnpV5qLhYYmP+1XQ2fgeR8GdyD3FctXF0aPW52U6Mjkt3vTS1d+PAsLDOTxVG78C4k/dSkVmsyot2vY19kzjw5xxTlkq3qGh39gCzRFlB++BxWarkE5HNddOrGesWZSp9y6knvU6PWeretTxuK1uc06ZoxSEVft7khhk1kxvViN6o52rM6m0uenNbNtNlRzXH2s/Ymt2zuQMZNJo0hI6RDlRUgqlbygrVxag13HVPZf8AH/b/APXVf5ioKnsv+Qhb/wDXVf5igcd0dnRRRVHrBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB4f4rvk0/4pazMe0UXy/wB4+Un/ANasnU/EN1MsECy4CpvkZTy5PSmfE3afiJqu5mQBYzuXr/qUrlLMyKXSZtzqxTPrg1yyqWi0u7PcyOdKpU9m1qrv8Tp/D1rc6xrK28A3OBlwOMrnk/hxXaar4MbT9Z07UQ6yRNJ++ATAD4yCPY0z4aWqWtlPelf3k5wD7DgCu6u5RdafcQygEohkXjuORTpUla7OjMsXOWIUUvdiZ1heR2/2uYMd5k7+gGK434t2/m6Daa5bxbpkfyJdvdSDt/X+dWpL7y9NhfcA0g3Nj1Jyay/H98W8CRDf8puV4z1wrVtJe5qc6puE1Ug7O9v0PMLBxL5aTIQBu475NeueC9JTR4A90vnSgg735w3oPoK8Z0278m9SXAIRw2098HNe02N0ZDGu75XcuB6A/wD6qwor3mdWMcZzSv01+8g+LVu/2S1vrZ9huAIJc9+cj+v5V5n9na4CFQV+ZAWPTA6fyrvviFrSyR2+jgKzkrISeqk9P0z+dcwWlS2aRWUxoVUL6+lZ1pe9ocdDJKmK5ZzlaPTuaXhHw3a3V0dSvE8yKMq0cP8ACzkZ/IZr13RprczOkdtHGMYYouMtXGaA+dNglKbGI3YAwAx/wGK6XT8wW7MvBPqa6qMLQ5iatNwp+zm7taBc6a04iMW2ISTP9qz1bado/QfrW7a3qSlrc/LkYBHase4vyyQHBXbuRwe5qvZXQ+3A56mrila5m4OpH3ul7HD+NNQktvEDBW/erH5be+Hb+lc5oco/4SgSxlhCUaQxqM9RyB+Zq748lD+Nr9cgKNpGPQqD/Wux8F+HlsZLO4IJuWh82XpiND90D3JrjjzOroeti9MDStvp91nc871+dpdanc9CR1+mf6021mWHUbaRvuqwY16F8TtBjutO/wCEgs1UfZcJcKBjK54b8M15N9vDszHgZwPpU1IOM7s78HiqdXDxheztZo9QsdagBnmd/lTJHv6CsPxzq73d5okny/uvncg/xcYH6VxSX8zSEIxWEvkntkdqr6pqU8wjjf7ygndnk5//AFVvOspo8yKUcQoNa6/geswa7by3ZBcBeuc9BWB4j1p7p7O5iYFYd7RgjOOcj+VcFp16GlK3VzNHCV+YoMk+3Nbml2t94gzBZxtK8KHy0LAfnU1qnPGyOTMKL+rSlHp/nYz9Q1WbVNV8y4cKdxJ2+pOT/PH4Usc9zbqr+YxiwdpJ478VFdaTPYGS21C3lhuP7rjBB/rUTSNc3McCZCBsAf1rFycVpud2T4aSoQ9ottfvZcuNM1DUoN9qok+QfMzBfrxmq2jLI2qwpPGQqOWcH0UE/wAxit6CX7PMUX7vlsuPoM/0qDRrmFNcg3Iv7xgH44wSP6D9aULN2Nq8Hh6kKdNvllf5bv8AzPYPBF49ppSQTJtbqwPXJp13q8KeKYrZJA8BvYBgjgOzrgVjQajGl24DD5mArHu7yW48XaSI4yY/7btfMZRwvzLjNdtVcsUkcahGVWdVrdHMfFhc/E7Vz3/c/wDolK5WJCcZrs/ilFu+JerHHXyf/RKVysceDXNN+8zw07Ilihz2qcW+e1SQLgdKtogI6VjKdjP2hSWDHakeLjpV8pyOKbJHxzUKZancxbiP5TxVJSQ2K2JYwc1QMYDnitU7oGhUGRUpTgUR4x0qQLUSYr2GoMVLjJphHpUiA4oRqtSFlycU5I8HOKk2jNX9O0+XULgQQAGQjKrnlvpROaguaWw7dirEnrVkcCupt/CkIvZ7aZpQ8e0pwMOCOv51Si8OkXMsNxMVKRiRGUcMCcH9a4FmOHlfXb9TT2M+xhk1GGweK3NS8N3NhDbyLIsxmIUKvYnoKzJ9MvoJHSS2kUoCx44wOpraniKVRXjJEyhKLs0NSbAqcTZFZfmfNxU0cvGK3S1EjQDDFV5TmnIxI4FSeVxk1YzMdDkk1Gauzp6VTkGKlsxmxhbioy3PFDniqrydhQkOLLIl5xmtfwtJ/wAVjoY/6iEH/oxa5zeTjFbfhLnxnoRP/QQt/wD0YtaRWpbeh77rQ/4m0/8AwH/0EVQxWjrI/wCJrN/wH/0EVRxXeeHP4mNxSEU+ii5A0cUUppKAHUdqKQ9KRBFIcKTXNavcgA81vXkm2Jq4jWbvcxAPWi9iXroY15MzyEg1s6D4Ze9KzXK4Q9Fpmg6K+oTLNICIweAe9ej28MdnbqwwBivJxmMv+7pnoYTCuUrsgstNislVFQKoq+pgXqRmuf1TX0UOI+o4zWCdblZMbjwK81UZSPqKGWNxuzvvtMIAJYcmkEkUrcMprzx9VlaFTvPrT7TVpYZkO44q/qz6m7yvQ72WwhuUaMoCMdK4/wAQ+BYp182yURzDqPWtGz8SjzfmPHSuhhu4rkKQ3J7UU5VKLujysTl8lqjwq7s57CZorhCjA9x1pinaM17Lrfhy11mEiRQGAOGHWvJtY0a50S8aCbLJn5Wr2sJjIVlaW541XDuO5HHJxVmN84xWcj8CrMMld+zOCpA0on2mtiznzisBHq7bzbSBVXObZnaWc2QtbEbAqOa5KyuDxzXQ2s24AGoaNos0BU9l/wAf9t/11X+YqupqxZf8f9t/11X+YqTSO6Ozoooqj1gooooAKKKKACiikJ4oApW+p2tzey2sUoaaL769xV2vLPDl75XxU1RP4JiwznpivUs1z0KvOnfoxtDqKKK6BBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfOvxLKr8SdTMgLKXg49hEhNcdPdKtw0o+4zsR+Jrr/ig2fiFrAxkxrG3HXmFB/OuJbyPs0McnMrYO3HauOaV3cMDi3hMR7VLyPZNHvjo+h2iogdm2qQfTHJrpbO9We7CE/K67SfY1wen3sE13ZRM+VWPJyehI/8ArVo6bqqf2z5atxvAHPvXXBan0cqSm23u7syPGUF14ZWBJZVmt2ZlSRRjBB6H3xXIazqza5o7KzMLaBdsfP3mxyf5V0vxIuX1WG3tWZxAXln4P3jkAfkM/nXCsB9gjgAC5CnA/wA+lYVJ3djysVjnTg6L+LuT6TpdsLdJEaRmYHcc8jA6V0+n+IEspoJLhiEWPk+4rlrGTyYI3BxlzG4z1B6flXpPhP4cpq2jG61eWVFlPywRgbtpOeT2qKTldtHBhMRVVRy37nEXF3LrWszXsp5P6DsKuSMFtfL6D7xrQ8S+Gj4R1drZWZ7eYeZC7dceh9xXPy3OVlbsPlFYSTje5+i0JwnSjKn8NtD0u0uEEMEYPOwZHua1dQ1dNLssqFadlIjU9B7mvONL16Pz03vlUUZP0H/1qsLrX9p3ckkjAE8KP7o7CvS5k4Kx8/OgnOz2W52F5rCRaXbSzyZmePJz3YAA1kWPiFPtLyFuEXJrkfE+qfapEFvJ+7t8qceprM0uabyZQz8HDH39qxdZRVgwkY15uEen/A/zOl1mFNZlbV2nWORWAeIj70YPY+uO1dBY+NTa3Muoi2DW7MqbRJ80UY46Y561wZ1BlyFwVxgZHTPWpop1MMi9A6suO3Irj9o46o92WDp1FyyV1se42sUes+G7+yyGW6gcAnpyDivC4beCclprVFEA8tVB4JHGTXdaF4vW10G8XIVoIfLiJP3j0FcTE22PHuc/WuitUUrWPNwWFjDEVFNa6WHaaIxdNC8YMHChMcY/ya53VdPkh1SaJFJVTjOPStlJdkzEHBqC48+eZXRiZ5JFWM9ec1zJu9juxdCml7d6cqex1Xww8ILqVw2r3SqLaE/uVZc73Hf6CvabSxsLSN3trOBJnG99kYBJ9eO9cJYS3Gm6bBaWS7UjCjCr1B6k11NnetJfJJnAAUEfWvQppbI+axNOpUbk3pqcZ8V9PkuNItr1WVhauTk/eKNj+R/nXlOjwEzGdh06V7P8RpAnhfUYcgnAKj0GQTXiz6zHZWzmNAzfdQe9ctWN56Hr5XVjHDtyexfu7k2ZEgB6EDjqSKoQiRbi3MLF225GOxq/pl611pVzZzMrRudxDDoxGMj6EU3RoxG6jJ255NYJ2ehtisNVxNSnyu0Vd3X3WLJ1q7t71leGQSqeUI5ye1dP4b1SNb/TbKVfMml1O3eYg/dcyKV/lWTr0SiOK/tY1UlisgPZyB81UPBz7PFumrK4z/aVtyTncd4GPrzXV7RydmeHj8RPDz9hu3+RtfE1M/ETVT/1x/8ARSVyoXB5rsPiSufiDqh/65f+ikrkZSFrmqS99o8dyLUABqyoANUIJuKtpICa55amVncsKo606TDLUYfjFDNwRSVzaCKMy7WNZ0ow2a1JufpVGZRjNdEdjexDGaeWPQVCW29KduoaMpLUmBAFSIcColORVm0tZbu5jghQu7kBVHc0aRV2bQWh33g3w7o1/ZRXjy+ddRvveNuNhHbHcd63p/DMEF82oWDCJzgmIL8rEMDn26frXL6N4O1mC7huHPkKrjf5cmHK98V6BFBPGMPN5ijoXHzfjjrXxeZYhwruVKtzJ9P07HXTV9GgCxSqjlBlsDJHODVc2NvvBeJWK5XJHbOf5gVdIyOevH6U0L859DXjKbWzOpFKaxjnuFlclVhPmLt7nkY/Ws690iW/i2rHF8jvu3nqGBAx+YrcKHdjsQBUoA+bI6kCtYYicLNPYJJNHh9xpM0WqPY28ck0qnGFXqe+MUiRmNijLhgcEHsa9Z1DTL/yni0eGCCSYfvLqRvmAPZRXC3/AIfGkEpc3sctyRkRxDOP949q+wwGaRr6Sevbd+bdtjglGzM6GMBckU5jTugxUDvjNesQRS85rMun21cllHIzk1Qm+bOaFuc8ldlVnL9sCo2WpcHOKcUrQ1jEgZcVs+El/wCKx0I/9RC3/wDRi1nMny1o+EyR4z0If9RG3/8ARi047lPY9/1n/kKzf8B/9BFUc1d1n/kLT/8AAf8A0EVRruZ4NT42GaWkpewoJENJSmkpgOprHinUx+hpEGVqsuyA4rjI7Z9T1RYAPlzkmun1p8DFS+HNL8mD7TJw8nP4Vx4qtyQaW5vh6fPKxtWNnFZ2oRQAFFc/rOtOZmhjJAHFX9c1P7MBHGe3NcZeSmWYkHk8mvKoU7vmkfcZXgVBc0kMuJS8pbPU8ioTny8984prnDH6YqQEYAruske6klohr8cDpio3kKtgZ4Gad7GkYAEZHbmixXQI7nDnPTGRWxY6vIjIdxwDXP43MOwB4p4k2sQDis5UlIwnSjJWZ6jpOsJdjhhS6vo1vrFu0cqDJ6N6V59pmpSQTKEOAK77StWW4YITk4rgnCVOV0fP5hgLXaPJda0efRr1oXyUz8jetVI24HrXsniHRINX090ZfnIyrehrx+8sp9NvHtrhSGU8H1Fe3g8Qqis3qfK16XLoTxPVqN/Ss2NxxVuNxXfc82cTesp8EZNdLYz521xNvIQ/Wuj06bkc02iYy1OtjbcuRVux/wCP+2/66r/MVm2rgrWlY/8AH9bH/pqv8xWZ0R3R2dFFFUesFFFNZgq5J4oAXNNLqvU1TlvQmWJCx4++apNcvcOGyUjPT/arlqYiMdtRXNRrlc4X5jVd7ppSY0GD0J9KpTXSQRlARuxgc80+02xrlifMIBNc8sQ5aXsK+tjLt/DemWOpPqSwN9pJyZC3U10cdxuzxkVWc7o2XGSelQ2zknawA9eazptw+Ed7aGqsqk4zzTwwPSqAfkh+nr7VGZZbcFgfMjB5HcV1QxH8wXNQGlqCG4WVQVPBHWps10xmpK6GLRRRVgFFFGRQAUUUUAFFFFABRRRQAUUUUAFFFFAHzr8TEWP4k6s0m7MywquP7ojTJ/SqGheBdQ8STsbPEcHlbWuZBxkdh759K6H4oxef44dbgFIgVCSAdjGmf1zXo2i3Ftpul2tvAq+SsahMD2zn9a5IrnqNPoexXy6jTwkJKN5T1vf5ngNyNT8O3aW17G8c0HDhvQcZHqK29Atr+fGqN8sBcquDk59x2FbvxhtUeSO53HfG4j+qOCQPwIP51wdldTeVcwh22lARhiMVUp8pzLMKmGdqmq6fcbPibUo7q4t4YW3iBHUkdCTyf6Vzro72sLKhdtwGF9NtW1t2CxzkDyzlfqcV6R8JfD+y3uNTu4FkEh2Qq/Ixnk4/D9Kxj78jz7PFVpVZbbv/ACRw3h/QpXvLeOby3heQOwH8OBmvedKnWG3Cg9AOK5/V/DKabqBvrOMC3d8MoGNh/wAKspcCNcDsOa7aUOWNmeu6GHUUqGz7mT8UUW98LvejG+wmVs/7LfKf1IrxJZ2uFjhRhmQ5yTgD617N4hl+2eDteRuf3BOPcEH+leRWsIKQxnahLAE9ADiufEpKSNamOrYGlKFON137X/pmpa+GZptAu9VabyooYyFU9XOcce1YtoGiaQCRgVQNn1yea0JFvLCS6giuG+ztmOWLdkHDcHH4VnyXBM1yMDiPOfy/xqE7aHzqr1Ix5lJ3bLt7ZCAMisCzAvj0UZx+eKp20rKCqg8qBwPatKyi3wvPM+TJ8oB9DxWjDCYLdIogowvPH3vrWcnZH0vD1GupPnVk1f7/APhjI8mSJAXQruGRkdaie4xlQec1sa9DcwWEc0ihRtBwOwP/ANf+dcvHJ+8DE9eTWfK2fVOaiac08a28VsSwdyWUjp1A5/Wp4Zvl2k9BzWDNdCTUowOke0Z9+prSW2um1KOwVGElwR5fHUE9fpWnK1oj53CZhCWOrcz06fLQt2kM95OUt4nlkckKqDJrtdH8EarNcWl5JZskMJ3fOwVicHsea6nwroMPh+1KK4eQnElxtwWPovsK7pXjKIzHHQZzyDXRTofaZOPzOcounBaM5KOYQpHbqOByR7e/+e1Lby7bgBf7wxWVJcPba5qS3LDzN3A/2ecY/A1DpmprJqCAsPvV1xWhhGD3K/xEuQmlSWjNmSWQ7vpkV4rqmnta6tBCGA3YeQdRGT/9bFeg/EC+P/CRDzSzRhYyoH0ya5ia2WS5Mr/Mx6E9645ycZsnB4ZYqtJXa5bL53bIFsZbTTGugd0Zk8tmzzuxmtPSQFgjJ6nrWZcoQsieYyow37exI/8ArVdt5ZPLRIhtJHGfr/8AWrnsr3PVyqdVRnQqu8oyf3G5HfEWU53gBXU89s5FZ2h3n2z4g6BGoG2LUYCTjG5vMWptRsZoLWGeZU2Ku1tnc9s1l+E3A+IGgIOv26DJ9SZVNVFe+ZZhjIuusMl0bb+Wh2PxMkC+PtU/7Zf+ikrh5p811PxVlI+I+rL/ANcf/RKVxbZxWdRe+/U+WUS/bNkZq9G1ZVs5UAVoRvWbQnGxa8zHWkaUetV2cEZqNcluaOUuKLH3gearyoSDUoB5pG+7Vo1M9k9aTOKtbATUbRfNxVoEriRjLVo20jwSJJE7I6HKspwQfaqaoRwtWoVIoautTRHo2hwaxqlhDe3WvSxwZJCx7d2Bxya14Nc0ltXW0XUZZLjG0BmzGTjpnpmvLDNIsJQSOEPVQxAqNQcgjjHTFeBVyaNWUnKVlrZJJW/zLVS2x7jNJFAyiV1Rn4UZ6mohIM5wcZ61wng24tZL2WfUbwmeJMQiZ+AvcjNV9S8Y3R1CX7O2ISSEXtgdDXgzyqcarpRd7bvZfI6I1YpXkejb1HLYGPWlaaGNNxdVB7scVwUXxAZDF9ss1dc7XdOCR64ov9b0e6VbZ7lbjTrs8AgiW1f1/wB2ojlVe6507eWoOrFrQ3LnxD9g1bZdm+gjPIDwqUYexHOKzr7VZ9QEoiv7OKBhygBUkfiM1hQ3eoaaTY3MouYFPCSfOjL2Knt9RSzvBI4MERhQj7pYt/OvoMLllODUrdN9Gvuet/vOaU2ylIOSBWfPuBPrWnIQvfk1XEW9s17aZzznYyHhcfN61AyHBzXQNbArjFZ81ttJ4o5iIzMfbzzUoAK1JNHtPSolbAxVp3N07h2xitHwtGB4z0M/9RCD/wBGLWcPmbC1teF4SPGGiMe1/B/6MWri9UOWx7hrP/IWn/4D/wCgiqFX9Z/5C0//AAH/ANBFUa7z5+p8b9RKM8ClxRQQApaQUtABTHp9Mkxgn0pPYDCuoPtF0qdt3NaV5fxafBHFxnoKqCRYZJp36KMD61zNzdvc3DszZ54rxKr9rV9D6jJMv9pHnkSavcNK59qzH5Ab2q1OrOm89warOR5A9RxWsVY+ypLliokBINAI2H17VBIwDqM9TTpiVX8RitOxXVosKvy+/Wq8k6NctB/y025FTQkh+TweKyESQeKmbqoh59hmqikznr1HBxt3NFQpIGOnFMlHze1WRsCsR1zmo3iJZc9xUo6HZlZSyNuBxW9o+pLBN5jPjFYUikE81GjFZAc8Up01NamNWmprU9Y07VUuUG44B7VQ8XeG49W09poVH2mMbgR3rlNLvmV1IPTjFenWUqz2a8DoK4FejO6PnMywcVqjwNg0UhRlIdThlqzC9dZ4+0BraUajbR/IT+8A7e9cZE/T3r36NZVY36nylanY1omwc1s6fPhhXPwvxWnZyYbrXStUebLSR3FhLvArc0//AI/rb/rqv8xXK6XLwOa6fTW3Xtt/11X+YrNnTTd2jt6KKKZ7A1mCgk9Kyri8EpOW2xqePejVLpgRAnf7x9q5fXLxz5NnD/rpjgY9PWuDE17e7EmTsi/Fcpql5Jhv9Hj/AISPvEVceYriNex42/w1Us4orG1WEA8D5j6moby6EMLOxAjB49TXBKWgorqMWcz37kyZCcbfWr0V6GIUq744JUZFc9YMTA1zvAZ3JIY9q0bWdUDLgoDySGwD7is7spJct+puLMA/7uN2z68Co/tMcdwiEYDZzn1qk8qKxEcoLkc5JY1TvtQNtNAw2DJ2ndz+IralU0syG7K5u+eAdoG1TyWJzTkkxkBWIPTJzms7zS6qQI29s8j6etOSXYcSykjoVU9ah1PesXcsQTGC4Kg/Ix79jWmdQt4EzPKqf7xxXIXeqs0jW9nDtfOCzc4qGKz81vMumLv/ALRzTji5Qdoav8C6dKpNvlWh1cnibTI22+fub0UZqaLXbCUgCcKT2YYrnorW1Ug+WuR3Iq0scJH3Bn3rWOMxD7HR9Tl1ZuvqVqqn9+hOOmag06/e+MjmJo41bahP8Y9azPsdtIMlFz64pWtREmA7Y7YNCxlfmu0reRDoSWh0oPFLXJsZ0O6O5kXHbOaeuuXts372NZo/9nhq6Y4+G000J0JrodTRWZp2tWeogiKUCQdUbgitPNdkJxmuaLujJqwtFGaK0EFFFFABRRRQB4D8T7hx4x1SEnKBomUeh8pOldDo14W0fTSTk+UpNcv8U8f8JxquT/BHj6+UlWNOvfs+mWMRbBWFfwrmoL97Jn2c1fCUV5L8jofiLbw3Phm4uSoaQRoV9cBwM/hn9a8jW5gt2ZHwjMgUyduTXqXi+cP4SgnVwfMBiIz1B5/mK8fuYw32gvyrSAD2wP8AGpr2UrHyOaR5ZK7/AKudTplsL+CK0JAR3BVj6nivY/D4Szs4oI+EjUIPoK8Q0658vWNFtVOB8rtj8cCvZ9KkzHV0I+7zBgoXpSl3Z0f2hZJngkGQ64IPevO7idtO+220km5op35z1BOR/Ouwlm2Xyt9K8Z8Sapc2ms6rBPkP9qf8s8fpiujazZ6lGMYNXdro6h73zfDOqKCC0se0AnuxAH868w89RauxH3XbIP5Cr8mszSaa9rGTn/WNj26VTvtrSJdqB5c8e9h23f8A665K0lOV10MM8rNRjRXXX9F+oy0MhBV2yuOe+KYIx9pulBzvjz9OBxVuzjhNlLIjFmbkE/Tn9aisImn1C4xyFQk/QYrN6SseDyt8se5c3GGFU7IqjHvW7az20tkyYIuYjuz/AHhgcVz9y3OO+RVm3YpMrg/e61imz9WVKMIqK6W/A6VmTUbJIZjnzFeH8xx+RxXm4sbqOWSOb5GiByDXdW822GPH8L5Fc7Kr3N3dscvI8oTjvzwP0rVS6Hzmf1p0IQlTdr3RkrZJCkQB3Tyvx9P/ANdeyeFbGN1ubpgoKosCy4yQFAyB9Sf0rymW3kju5bh9uyDgYPp0/WvS/CFy/wDwjVujn5nkZj7jP+NdFKKlM+WwEHOudrC7TzAnovAHoK0bq48i2QE9W9fSs6xUquRyTUF+4kuxGD/qx831rpqaaHsWU6qXRHF+OruaLxHBJAjsby2CqFGdzg4wPfG2ptB8J64im+vYvsx2/uopPvN9fSo/EFzBD4/0vzZMpawKxGejFif8K7PU/FEdjApmk8xJATHt5JFZKduprSVefu0+/wCR5R40kdfEMAlBSWGNQw9D/nNY7k+WjH05rb8RTSeI9VW6Cxo6YUL/ALPb6nmsu+haCQQFWJQAk44x9a4qkrycjry6jVwdebxGim1btfXT1K07QJGu9Qzjnk9BU2nxhgss5ZQ5GNp+6KyXzNebCeM810qwbHCkDaANv0qFdn0EKUIzc0tXv8jQt5XaUWcoEgyYnGeGHr/I1heHdPeHx9oQXl4tVh389vNWuguYoUSO8hyr8eameAexH5VX0WaP/hY+mSJghr+IdO5dR/jVptSR89xB7kadaO6dhvxVOPiXq/8A2x/9EpXGlix9q7b4px7viRq3/bH/ANEpXILDzwKKjtJnz6loOhB645q0rYWmrEQPlFAikJ6Vje5LbJlOQB3NShePpSQRMTzVpocLxTuZ8zTK/vS7gRjvTgp6VGy7Wqo6m8HcYUIapFi3DFOABbNWIttNuxskRxQ89Ks+VgdKkjUbs1MyluBU3YXKBXdnjig/LVp02iqz4BJJq1G4BEjSzoijLMwAqSeIifB/umoI7ryLmOUc7GB+tXXdZZOvH3s+orgxqlFp9CJsoXibIIx/d4qpAu589qs6ndblWMEbs5xjoKggbYBnrXThYv2SuUmatu20AVaE+KyUuCDTvP3cA81pJClJFyWfe+RVi3kBHNZ6HjnrUnm+WwGazaOaWprAA1Vu4sgmpYZAyZzSTHchzUNmd7GJNHkGqfkkt0rSmGCQKbFEM81EajuaKpYhgtivatrw2uPFejf9f0H/AKGKrqoq9oAH/CWaN/1/Q/8AoYroi7tFxk5I9d1n/kLT/wDAf/QRVGr2s/8AIWn/AOA/+giqFeoePU+N+otFJRSIFpaQ9KB0plJC96iuHEcEjHsKkqGZPOeOH+82T9BWOInyU2y6ceaSRz+sMYtMXnlsua52Mhl3Dqea3fEE6ySPEOikj8Kw4AIgRjIxXkYde7dn6Xl9GNHDpLsWpGwjqR0FUWjJgY471M8pdju6EVGJyY2U+uRW53RjYwdVlNtd2YHRpOavPvIKnB7028tPt9xDuGBFIGqdBgSuR1b8q1esUctNT9rO+w6AAOD7ZqOZfLeR0A3OAC3tUseQwPtTJgWUp+NTextKKluRR5P5053Z2wMgimAMlCTB5Mg98E0La4OVnYHQgdKrMMEE1cdiSewHT3pJIt6Bjx6CqT0G3citJjHMGzxmu38Pa8fMETHjPeuAcPG4/Kr1jcmOQOONprGrTUonPXoqpFpnrt7bRanYyQvysikGvEtV02TSNTmtZMgK3yn1Fen+H9aa6m8pjkAYrO8f6MLm0+2xrmSLk+60sJVcJ8rPjcwwrpyPP4X6CtC2fDVjxSDOQK0rdule5E+arQszrtMk4HNdfo7ZvLX/AK6r/MVwmmSfdrtdEbN7a/8AXVf5ilJDpPVHoXeo3YIrMTgAVJVHVJfLsXx1bgVnOXLFs9owJpvPmeZmwG5/CsfTI2u9Snv5OVQ+XDnt61Y1GQw6fK6kBlXgkcUaWnlabEp6kZI9z3rwZVHdyZk9WXmlLcEisrXONMZE++xAUntzVudt2VOdvc+2KxdYuN4t1BwGcYXHOKxjK7sxv4WWrW3e3tViwM9QW6irSGXYqtscgfdAGT6U1gSqFz8wxhhSPHvIYoS3UkGlOVnYpu0bEiSMv3S0YB+YEcfnWX4htJJ44ZxlxuXO3nirUrbEDyMwRMYQHnNU9U1iS0tcwQt/ewecY61cJcqJaTRpxXHnW8XlorqF+8pw6/hUrXTtGqqhNx03EY4rK0y5S/t45kj2lsYO7kHrWpC+CzbBycA+gqHJOWptSh7SSigSIRr0+Y8596XccgUMxJ9qY2QQc8UpNdD36cFCKiibzMDmnrJu78VAJATgZqTcFGSPxqU33HJIuw5DcEmrG/cpU9az453UDaMipnuD5ZfGcDkCtoySOacHcczc4qKUFRnrWO2r3EMwb7NK0JzlwOB9a1YriG5jV0YBu4zUKammkzV05Rs2tClcWiT4kUGOReVZeD+daul69Ja7LfUGLA8LLj+dVJQVBJ61XfDqysoNTGrUpPmpvXt0MquGjUV1ud2jrIgZTkHkGng1xek6y2nzi3uXzbscIf7tdijq6hlIIPINe/hMVHERutGt0eROEoStIkooorrJCiiigD5x+K0u74iahCD/AM8s/wDfpDXNza5lo9oYblG0VrfFjzB8TdYfBWNRCNx7/uUrlRCWvbeFjhvJBU+hKgj+dcibhKTPYxWbezhSp09eWKv9yOgvdamvYIbBnJEAzjtkkZ/pWVJGrzANwmdxPt3qx9oju5YpXRRPgKSoxkjAOf51VvZI5IGVJNrSARqWGMcc1i5c87s+exGIniJc0jT0xYpvEVpcn5VSKMovuScfpmvX9JmRLcu33Rya8UikNteWByCVjQsR7Z/+vXbv4g8nTYURv9Y43f7o5P8ASuqhL3GeplsfaUnFd/0O+km3OrkAbucCvHviu6r4wcxD5pYI2P1xj+ldnL4iTcilhlffrXBeMrn+1vETXSY2xosQPrgH+ua0rOPId+KlKhThOK12XzMWK2uBpvmxHMkz7MKMnHPA/Kte20yR4ba1ucoCpBYc454/nTtO2i337RhBtX+pq9FKGdTuIdeVrz3PSx10shdf97jJPmfRfkVTpz6c32O4jKhE2gnowPcVQ0hltrq6LdGR48+/+RW5rFwZLGJixYocZJ7ZrFZAWDxqAHyGPvRzdT5zHYeOCxip30TT/UZJ+8u8dsirUb7IznseKzknA3E/eBokufkK56mhI/Rfaxkrpm7DcDavbmqBhuEguhEhafzuAOvHXFUkumdmCHhRmtWaSQWkdxtDNuVic8g45OO/ehu0rHyWeYilWxFKhJ6Lf5mcsUzWHlsp3NJ0PX8a9A8LMTBbwEj92uDg8dSa5y7kgn0mGVXUThjvHd19fwqtZ61PYW9xLF9A392uihVSnqGGyueElVk9lZJ90+p6df8AiBbdvsNmcz4G+T+4PT61Xmvk0yx81zlzyATyTXA6Pq8W8yyPk53MSeTSavrMmosxQt5QITj+EE4zXRKWl2XNQowcui19TJ1C9kuNRluH/eyzyck+/pWoHKW4QsTtXuay7GEmUTODwCACO9XnYeWxPeuCTOnh/D+7KvLrovluLBuLtIOgIFW9SjiubEzDImiXrnqO4rPtSU8yTseAKn3sbOVByWV+PwpLax9DUhGfxK9tTF06I3N2z44zmumjTBUH05rJ0eMQk5xnFaksgSFnzyeBVxjZXFzdBPOM7XEYIG+I4yfSovDmB4x8Pkgbn1GDOPaQVWdxEks7dETIH1qfwzuk8ZeH2c/P9ugYADgDzFBH6fzqY7o+T4lq/vKdP5mt8Tv+Skatn/pj/wCiUrmI1BPSui+KT4+JOrDv+5/9EpXNwvyBRVXvM8mKuXRGAuQKQkAUokG3FRk5OaySN1FWLcOAKkbmqsclTK/qaTMJx7EZXBpGG4c1KQDz2qKZgEojIUXYrtLt79Kat0A1Ubq4IJAqh9ofd1rZK5tFnXW9wGwM1eVwVrlLS5bjmtqC6+TrStqTfUszyBRWdNPjOaLm5znmsm4uwDjNbRLbLLz5roNGEd1pRlfjySwPvjBFcQ95k8Gun0yd7bwbdTdGlkZB+OB/jXJjo81NJdWjNmYkjXU7zv3OQKnJwMiqsJOBipXY106JWRoloTLIApNOtm3SZNUyxLBB+NXoIyuDWM5WZhN6l1X/ACpkzj73pUTPgYqN5QVxWbZHU0rS4+XFWd+QaybfO7ANXxnbxUsmpZEUpG6lRx6VDMxzToUJFTGF3ciEeZlncChK9aseG3ZvF2jg9r6D/wBGCqhyDxxV3w4P+Kt0c/8AT9B/6MFdEdGjsUOWJ7FrP/IWn/4D/wCgiqFaGs/8haf/AID/AOgiqNemePNe8xtFOooJsIaKXFFAAaIEH2hpD/CuBR3J9Keu3bjoTXLiabqR5TXDzUaibPP9XkLX86/3jiqseSRGnJ710+q6KWlMgHUiudnt2tZMEEdq8+MZR0P0zC4iFSmrMgmUI5yOKjdQioe54pZWLYBOVz1pnmCQ429uKs7EtCTGyUcc1FcxshK9FJ3Gp4hmJiTznqafIA6B2OcfLQm7kNa3I0RUjAPXFV3+aRcfjVooBkE9OBUO3MuR0qrkkV0oW1kdeqqTWVoAaXT97n5iWJ/Ott41kiaJujDBptvaxQRCKEYVB1q1L3bHLOEvbKa2SZRLM65xgjjFTEmRVFSPGACfWmxrtXn0prY3K0yHB55J71ACBnJx2xV1hubcRnA4FU5o9rc8g9/emgltc19F1JrO5V89eK9MUJqelMrDO9MV43FIyuuBwDzXp3hbUVmt1jJ5ArjxEOV86PJzKh7SHMeXapZNpuqzWrAja3y/Sn2rnoTXW/EfTQrwagg77HP8q4u2OCOa9nDVOeCZ8Hi6bi2dNprciu40Fs31p/11T+YrgNObBFd14ebN9Z/9dk/mK3kcdF+8j0yszWji0A/2v6Vp1m60CbQH0YVz4j+HI9w5DVnKWSgqG3sFwe9XRkBc8HFVtTjD28Qxn94ufarn90Hrn+lfPz+Eze7ICvy8dumayNQiLalbhsFhzwO1bLghRjAwevtVWaLdepMeig1NPdi1ashZSBtPJIHPoOKgkYf6zGCeAVPORUzMSpkU5IyGX0qtMQYVIXknOB0rCc/f0ZTZE6jHOVy2SW5yccVS1MqllLlS7bOhbA+lW5QQIlHuc56Cs7UpljsJXKnCg8t3q4SvK/kUu5P4eilh0p5HKLhcBR/Ca34zi3Gea5/QXaXQHlkXBYglfYd66CL5oFK8qRmplpVS8jqwK/e28hEIPNIMkndjFKRyBzQVB6/pVO57gB1jBlCkhRziuNu/E12NZcxWxez6B15B9z6Vu67cSW2kyT286xSRkE5HX2Ncvaa0j2vlyyQyCc7twIQj2HFO2h04eCs5tX6Hb6bfJe20cyMNrDpnmtE7RGwJzkVwtjfzQXkUbhVSU/Lt5wB9B1rs4MlVIO7cO9JO2jOfEUuR36HNX3nS2txHvcRoxPzZC/8A16bpZe2EM8REqM3zBTjH+NXLm7aDUJLaS9liaRf3aBQV/lVKCYiQwSusrBukJG857+1ZNWOhOTg42Or82OeFSpJYjvVOViHwQeag0xWtYxEFZRjOXOWq9I6yKcdRWz96N+pyL3JWWxSmQSKVbGK3PCupPhtPuGy0fMTE/wAPp+FYcmBuyaiW5e2mS4j+/Gc/41NGtKhVVRfPzMMZhvaQ51uj0vINLVWyuku7WKdDlXXNWhX1UZKSTWzPDFoooqwPn74yReZr12+OQ6L/AOQkNcJqCgXlrKuNslpGwx24A/pXpnxdtgPEkECAl73bKSeihVCk/kBXmt/bhNQaONyVWIFQfTPSuOo7NpnG9JSuPCg3aTDG2TDH2bPP+NYl3KzSoqjO3JA+prUtXLHn7qsp+h6UlzppTUVkd/nYrIuANuATn+lYUrL4jKFupZkj2yw99sKj+dV5J55XuYlZdsMmVJbnnPb8KuMC8qAdSigVlyYR5pP4pGIH0q4NrVGuHr1KUm4OxYF5cXNykaNyRnJOBUt2AlqhJ+Zm/wA/zo09I44TKVzIwCqf7o70zUPmnghH90fmaU5tux72WSnicbFVHdRd/u/4JoW6FbSFP7w3H8amIMZ3DtSuRG4XsqgVHITgZzhqye59wmWUCXENzCwBbyGdc9iCD/LNZFkxkRkzglSRn1FMurl0eQoSOAnHp3qKy+8B68VSXuHwfEySxl11SKl+gkhEsLmMsQrk84NUpPOiRSzAkttyO59f1rQuyPLdAMYPJ+lRXkYFtZswPMjf0rWEtDzaOPxEIe7LbQmsYWEEjkHZggH1PGa1JJov7PiLzKihGD5P5Vm2V20kDWwP7qMMw9yep/QUxgbiCKAMVBLF/cDH+fxrK37y7OOUnOpzSZHbXMrxQGQ5GGANaNuZjbOqxuUbdyFOOnNWdL0Zta1Sw0u0CpuYkn+6gGWP5V9A6fYWVjYpa2kSeXAm1VKj/PNdEKfM+Y+po5q54NQteWq/yPmiIoiqjA4wchepPar9gfLkYIQwYDcOor0j4h+FbN9EGr6ZZxQNA/78IMHDHGfzrgp9Pk0tLe7gkYx3EQVgOQGCnP6UppxuePmNbnmoxVkktPMYkiiMYbLEnI9Of/1VDeXTJHsj6nvVd5RFIVDZyBx6Ub0kVnbOe3tWSjdI+vyerD6lG3n+ZeSQGJccD+tO+2rZHzm5CDgepPaqMMwCKM9KcYhfL5A+aR8nZ6imkdWOxkcLQlU3sQWl7wSD96rsl/5jKpPyrzWRd6RcafdJb2z+duiEgycbR6fWnW9rP8wlIV9pYjr0Ga0kklZHnUM5oSjzTdmX1me7Z4lAJfG3c2BxWp4bcL4v8PoBlzqNsevQeYBXOCbYqy8AKcEHkVs+CLjd460qTG5Wv4EQeg81axgm5HyOMxksZiHVfy9DZ+KQP/Cz9YP/AFxx/wB+UrmEfH1rrvieufiVq5/64/8AolK5VYuaqo7zZvGVg+0FetI94RjimyJ+lQEbiKmyKc29i0l2SeBVuORj1qiiCPnvVuMEioaKimy2WO3iqkxYjipScDGacIvlyetCgVyGJPGxJ4qsbZs10DWm4ZIqM2wx0rRaFqJl24ZDyKui42qeaVoNhJxVUp83I4700rg4lm2tb7VroW1hbyTzN0VBWfruk6hpF6ba9hZJBjr3r1/Q9Yg0vRrSDT7eOFnj3M5HzMT6mqHi2xTXtLivWTM8I+YquWI7fhXDHH/vlC2gtNjyOO3PVua6q6RofCGn2+MNK5kP0yf/AK1VotPLMqIuWY4A9TXQ+KbUWlza2A/5doVVvrjn+VdFZ81SEfO/3IfKcxBGQvNPdeOan27enWnrDu+Y1s3Yb0RBbW+fmPWtBUITpToYxjirG3AxisJO5yTZQkTHIqDy8nNaDp14qEpiskRCQQDFWgxC1WQ4bmrH8PFaWKlByIJeWq1B8q81CYsjnrUsf3cGiKsKC5WSSBcVZ8OHPizR/wDr+h/9GCqLkgHHNWfDEhPi7Rwf+f6D/wBGCrjujp500e06z/yFZ/8AgP8A6CKoVe1n/kLT/wDAf/QRVHNeoePN+8wo70ZozzSJuKOtBFA60p6UCG0Ac5pc0UPUkfuVsBxmsbW7FZYTIiDcT+lawGTmlZQ4Ib0rGpT5kengswnQmm3oebXVv9nYpIMoe/pVVHWMjccL0BrstV0nfl1zycbcVy81qEkKSLkDpXBKLifoODxkK8E0yKMbotm7jd+lWEKrGFPJNVQqhcE4JP6VOnL7geMbvwpI6mh7YO1cfWoz8gAC+tSli5XK4wKimY9h1pkIaFO7rmppFCEKO45quj5cgdRSSz4Rpj25b6Ua3Jm+W7ew+5KiNinpVKObDgHpiq+jXj38UkrD5S5x7irskIEbEVo9NDCMueKkiMtubjg54olEaAK/C4OfalUKNp/I1m64JvsYjjOXlkCZ9sc1UdWKrV9nTc+w1sKQynK9jW/4c1H7Jepz8pPNY5tQlokQ6ooqOFzG4ZelTKPNFoJR54cr6nq+uQx634buFTBJQsv1FeOwEqcHqOK9U8IXQuLJ4GOcDoa861u0/s/X7y3AwqvlfoearAStJwZ8JmlH2dRov6c3IrvPDhzqFl/12T+Yrz/TDnbXe+GjnUbP/rsn/oQr03seFT0mj1SqOpReZZvgZxzV2myKHQqehGKyqR5otHuHG3MYeFl/iBzn8acuSRwM9as3EflzOhHQmqzoCqliwxwSK+fqRtF+Rm9yJpMRtnnY2CKjcEuM/dPb1qq0jRztGOecls9fSrK7mcMSCfasKTTbK5bajGTY28j5fb+vrVSfIhC44DZwOw6VfJO8gE7sZHpVSQYJycMh9OoPasJK8mupK3KkwzcHALbUHArD1pidMlXblmz16ZrcuAVRpmbAXJZRzWDqbFrScM21QC4H9a0glzaGsY6OxseGEYaDsY5JHetmykBtVUdFytYnhOczacVcd8/Qdq1EdYbp0AIB5C1pPS0uxvh3y1U+5bON1BJHbOajB3Hk4qUkbcCnbqe2znvEjGKOEtBcTEnhIujexrkNRgtbjTmtZI5NIl80bFbPH4dDXot2zeSyqXDEEZU9DXExanJc29zbz21xq0YbbJHNFlkx3Bqo76HTTbdPTp5mx4R0aCzDma6NzJ1Uv8p/CuuERChsYHQVwcDQWPlTWlpJbbmB2h/NwP8A2Wu4jmEsAfdkHke1ZylG/mY4lSlad9Gc34z0+SUx3SyyxQxA+YYjhiPQVydhc6erO1oJoQxUPJJJl257DtXoWsos2muEP7wA+9eexalo9rAttFFN5it86KhyTnnJoUnJNJHZhJL2fveh3UVwkaRFUUAjjLc/SryylFwwxu9O1YVmZLq3LMIWgwNqr1UfWtUlFjUDnArOLa0OaUEpWGSEFjzUDeg/KpWIYVHvUMPWsmbJXVjpfB16zQz2T9YSCp9VNdWDXAaBc/ZvEkMfRZ42X8Rz/jXe9TX0eWVfaUFfpofKV48tRpdx+aM03b70BeeteiZHlXxIs/M137QzBv3QjUf3R1P58V45f3AbWmdSAsfyj3xXpHxE12WLxTrttLjZaeSYT/vRKSPzNeUwHfGXJyxJx6k81xVd2clR3kzSESxQu6Z2vMmPphjVa785pYJVcbFkKNuP14/lV6wIudGugAGaPDAntjv+pqWfRz9mnd3xhVmiPY56j+f5Vkk1qZJ21KyuFjaYvjYgxjuaoDM9o5779w/DH9CaZdShYY4weT8zf0qW1YC2jz3LURVlcEWrUgW67ZVL5wY+4HPNRs6yasT2VwM/T/8AVT7JMqCwwTgA4+8KqeYFuXOerUvtH0eRVYQxcubqn+hZv9TWO8ghJ/1jjd9M9K0tQkVZBjoBXC38xk1MFm5WTGPoa6q183VL63tI/vyEL9PeqnTskfT4bGKpOpd6LY3PDnhd/EFpeTyzrbwxsAsjrnc2Ogr0bT/hvoX9jpbtv+1HBNxnn346Vl6FA00trptoCYo+3+yOpPuTz+NekwJDCoD/ADMe5rejBW2PAzWEKlZzl8T6dl0Pnjxj4bm8NavPZM/mRP8AvIpMfeU+vvWRdKG05Fx8ytkfjxXuvjnQE1jS775QZY7dZ7dwOQVPzL+Irxi1t4ZHcTNlWTbtBxt5znP4VEo8stD52vS9le2zsZljFtd8DACnJPAqa1TeywF1Dc/d5P8Anip7+7sYEMMC5UenOT7msSW+cA+WojU++SfxqXBSdzl5bvU9W8A6O+nPc61MyujxmOD16/Mf5CvRNNmJi3E8k5rjtIuS3hPSEwFzAmR+FdHp8wWHLNgV3Rgow0PoqVFU8OkupoalGk+g6nAy/LMrLg+6/wCNeJpcZ0FrSRXMiHrjgc9Ouc8mvVX1dZpLqFQQmzjJ6kd68cuIsXwcMWUsfbjPANcldrQ4MdDlUZPf/hiCSxKtN5o7MevTv/WsRnuIZWG9WhdTgHgiuiuZ0uNPe4TgSrlh6HIB/lWZtNzdB2IKog7ccdKzhJxvcww+Kq0buD0WpQFw4ITac9631ngk0qOOFNs8L72nxhm9h9OKpzWqWt6HiOHgQPI7/wAbnsPpVsXgNhEjQptKugZeCSSME/lTersjTF5hWxS5Z7Ef2wzXE81zhpyqAEDHA6/0pu5hc+YP4iMZ7g1o3GmRReDLfUCuLlrltreqnjB/75rNt0LrHsYAlvlyfr/hUS3uccvifkZiqz3CQEZDsGP0712fgLTkbxVos4fasN1G5TGMkvgf0rmS32eaznODKuS2V4yD6d67PwfeoNa0hcjedQgjOABxvXH16mqi9USviJPib/yUnVv+2P8A6JSucCgiul+Jif8AFx9Vbt+5/wDRKVzcZzgVFT42d7GNBu4AqIwbO3NaaxjHFI8QPFSmaU43M4RDr3qUfKKmMe2oXHOKqx0pWFXlx3q6qDAJqnENp5q0kmTimMsbQBUDLg89Kc8oCgA0hb5eaLFFOZC9VihBHFXjzTSnfFUgNSzummtUMhVShCBVXFbE14W0ySI5CuMAqetcoisDwSK24ZPO075sbo2HT8q86thl7aNTpcycLTUhNLtBLqdsmOsgyfYcmq2vXBudZuJOuWxWzooH2p5v+eUTMPqeP61zd1JuumbqWOa3XvYh+S/P/hi3uRJHk5NWliOOlS28O4Amrnl4HStZMTRSVMdKRnxwasSjiqTnnms2mYzhoLu5NRyEYphfFRs+TUPQ5EmpDsZORU8fA5NVfMAHBp4lAHJrRbHdD4Sy8gAOaiSbFUp7g44NV1uDu60HPVkjXMuaveGcHxdox/6foP8A0YtYqPlcg1reGNx8XaKR0+3wf+jFpx3RnB3PbdZ/5C0//Af/AEEVRq/rP/IVm/4D/wCgiqOK9U8+o/ffqJijvS4opEXAUtJ3p2KClsNNFKaSgbQopaQCl6GgNNhCAc5FYeraUJcvGOcVudTSEA9utZVKXMd+Bx08NO/Q8+vbRo0IK4IHFVJCsYUIeQMEV2mpacWYuq5XFcre2bwzghMgda8+cXF6n3+CxcMRC6Y1csNx68ZqOcAz/L0pY2KjcwI55qDzMu5po7ktQCbXLA80wp5sFxBj53Uj8xS5+UHnOakiGJAeenNCdncVSKlFplXTrEWNisI4I4P1q3P86gdBwKsBP3mB90881G2HdvQU27u7MIU1CPKjOckSeWBwKeke8qzDIzkUuzzJw2TyOBU6hwpGPcGnzWIce5TlwMswzuOBVGVnQsCAMHAx6VdbJURt1BqK52NGMgA9iO9VFlyvub/g26ZNQVC2A3Wq3xCthDr8cw6TRA/iKo6ROLfUI2zgZ61v/ECMTafp9315Iz9azp+7XR8xntLRTOc0s5213/hn/kIWf/XZP/QhXn+lfw16D4a/5CFn/wBdk/8AQhXsPY+Liv3h6pSHpS0Uj2jG1e24E6jp96sRiST6HBxXYyIJEKsMg9a5LVrOWzkJTiNuhrysZQs3NbPcTRTnUeYGKBvXNNgcND8vGG4pqzJMmcYYZyKZKfJtyydRkgGvGh7tTUV9CWTkjBOOcfWq0/IycEOAMk08TLIiSZ255Y9qhldTlVXgnke3qKiWkmxLuVpxuhki24Y+/WsLU1jkI89isJITg1tSsImUKWYDkHuPasTU1NxJDFsyocsSK0p6msdEaHh9kivpIQSVICx574rfvoGZBJHw6849cda4kXTWkq3CDlSCSP1rv7eZbqFJU+ZWXcM963spKz6lVFazKSyq8YYHip0mUDABJqtNELa5xtPlschv7poJK5PasFePuy3R7WFqqrTV9+pYnYBPc/pXPz38Gkb7m7mikjckDK7T9MitgyFhgY/GqF7GjKY/IiYn7rOmQD9Krmtr0OuMNLGM2rTX9q17Y25mtZG2LDK4Rl9/cV0Hh13OnLG8PlFScruyB+NY7SzxzqkcQvJU+V/I2xso9Ap6j3q94bIh8+EwyJvO4CTGR7HmnNPexUrezcVubN9GzWr+T97GcDqa5y1sZ5LGSYwTRiRjtREG9j3z2rqGYqCR09BXOah4gljtxHBbTTmMkSLs4H15FZQtzaEU3U5bRIbUQJD5VwY43VdnlbgSo98dDWqroYY/LOVHHXNcvY3SatEVgs009zLz8vJx35rfeZ1jizl+OWI25/Cia5WzplB3V92Wi+TwBVWRxuxnBzTTIcA9BSHLEVjdmkYWLdmSuvaXICf9dgn6g16divNtMgabW7FVOQr7z+Ar0odK9zJ/4UvU+TxtvrErCbfegDkU7AoxXr2OY+bfi9KyeM9YjUcNNBvPt5KVw8Tm08guzIwfkAckYrtPis//ABcjWYTjEoh6+vkpiuAuZmS0QdZCK5Z6yscT+Jmzp06RrfWyAqQmf94ZP+Ire1S9WDSreJiMuu0+4z/+quHinaCaOcN/rECkeoxg1r6pcNPp1kxOSFP86SjZkSWpmTvvuWYdzjFXlI+z2+33H61Rgh8+ZwX2EKTk+oFX4YHjsbbeOS7ZHp6fyNTJWVgS2LFnM0l1HGTxGowP1rKij+0TMxJAY4BHb3rRs42GpOSCEMYwccdP/rUlhCN2QBhRxSja9zSCk6i5N29Dnp41/tcoQ/2gydX+6T68V6F4H0m8tbjUtTuY1JtrZwmCMbjx/LNcm9iZbtZWXlW4NemaVIlh4Ev7hyN8/wC6X37f41t7TmtofXLAVMOrqV7yS9bnY+FLCK1tIb6ORna5iDEnt7CtW5uyZsA8Csfw7IYPC9mjH5lSpJpuSSe9dEY2gZSi5VpSlrqbaSNLuycr5RQ/jXzrqsnk3E0C/KqsVOO+K+hLSQNp7MGz82CPTivnfWkMmu3i9EErZ/OsKi1VzxswWnzMuKWOa4ZDzhSR6E4NUmPmPs98U9U8qQkdQa1fK09IYZlTDycBd2SO2al6O55mzPWIZVjgs4l+6kKgD8BWxBKXhVc8GuIXUNpi3ZVggGD1HFb8WqR2unm4kbhFLfU9hXa2nG59U1+6jYu6ji3keReAE5x9K82u0EccTiRWzEzFQeR16/lXVaprRn8OiZiPNkTBx69K4Uswgd2PBjAH8q4K7V0jx8ylblg91cbaTI2iPAwy6uWA9RxVjT/Ka22gfP5w3Z9MDH9axF3bFION0mP61q6SN8czEgAyogLdMn/9dOK1uedFe42W5QbpUiZxvLsNrHB5J4/OpNRsGs7G2DJs2hjj8akhMVpqsLXADLC4diPQd8/gK6Xxx5D6TaSwFWS4cFCPQ81EHuTHWWplXbLbeFLSyDJLJId6q3YHrj3z/OsFoXtERGQGVX6A9D/k1S1W6lVIXDMfLJGAfXFTyXTrpcUknLyAncTyDxRONndifchuGN5dXcxx+6TdgHIyTz/Wuk8NwLB400NIpd8TX9sR7fOM1zFnG7WE2B80p/EgccH8a3vCx8vxpoS84GoWwBIx1kX+lDXvJFJapnRfE1sfEXVR/wBcf/RSVzUXJGOtb/xRk2fEXVfX9z/6JSubt5Aeama99npcpoo2OKefu5qqsmT1qQy5XA6VNjeCIpGy1QNgMMU+QE/SoS2Dx2qjUnXmpSmEyOtQI/IFW1wyUCZXJyakVh0qJjhjQkgJyKVxXLAUY6VIsee1LGu4VOgAouUVniIOauaWrSxXkQ5KxF/yqGUjtUmkTNFqXlg4EyNG3uMVFX4GyHLoXtPuDDpd7cA4PyqD+f8A9asFvmcHHetd1MXhwKOss5/Icf0rLiBDc1lQ1lOXn+Q1q2alsMqKusg259BVO2wuD2qxNMApANaPct6IpTuADWe7bjxTp3O481UVmLkU2jnlJN2FkPNQM/NWGU4JqlI2CahohwW4GTnrUbTYJ5qEtkmmjlqcULm6BJIW61CCc96c5waYrc07Gbjc0rXccc11HhZR/wAJRo/qL6H/ANDFctZuAea6rwswPinR8f8AP7D/AOhikviQcvKey6x/yFZv+A/+giqNXtY/5Cs3/Af/AEEVRr1Ty6nxv1CiiikQHcU6m9xTqC1sIaSnGmmgYoNKetIKU9aAEooooAaRuXaelYeoWDNIccq3P0rcprKG4NZVKakdmDxtTDSvF6HB3kXktIvOCOtVSo8qTgZI4rsptIWWRwwypFchfwtBM0S8bDiuCVNxZ99gMxp4lLuVM9ARz3qdCWUEDnv+FUiJHfJPQ81aWQug2nkA5xSPVkidhudSeuMU4YBdSeQcYpkGXJlY4C1JkGUgLkk5zTMWhv2Qeaz7hwPypFiLcFuAKmCxg5TPPLZpDkocdKEZtFDyh85xgE4Oap+V85U9Kmm1CCK6+zO3zselSTKi4KHI6GrasY06nO2iiE8qQHnAPNdXr4F74HilzkxkE1zUrhiePrXQWzi48FX8H9wbhUS0nFnm5zS5qDZzuldVxXoPhr/kI2Y/6bJ/6EK8/wBJHK16F4cGNSs/+uyfzFex0PzyP8Q9SooopHsgaguLaO5iaOQZU1PSYqZRUlZgcDquiXemSGe3UyxZ5Pf8axzqMbhhn0BHoa9UZcjBGawNU8IadqZL7WglznfGcV5lfL760xqxwkV+kLNbsQWX5k560sl8gcktyB+npV7UvhxqMnzW19FIynKmQFSPxGayJfBHi9E2mK0k2945fvfoK4Z4Cq90OMV3IZL1d2SOM4HPasY6hFPqE2CSIwACp/OrVz4O8ZiN1h0ss5H3vNTAP51Q034feMbcCNtOAZyS7mUYyaulgaii9CtFohbiXMLDO7ccYPpXWeEdSBthZSMNyD5D049KpWvws8Q3Lj7Xe2tsmefLBcge3Su60PwDpejASEyXFxtx5kpzj6DtW8cFUYVJJqxXmRZVK4zkVmykRPscZA6H29K1LtG0668qXoT8h9RUUkcVwhWRRx39DXDVTT5WtUGHrSozujK355wMdqhdA5Kh8HrVuSPYdhBYD7tQmFNu7jOOcGuVt7M+joYmnVV0UJLR2d3ZBJnGWJOfwqxbwQpIJEB8wcHrmpVn2EgEbcdKkiuY2wVXDelStdLnRLm7F5N7x8qRmue1KxJvWnm4UjaoBPJ98VfuLuZWHPHpThPwPMUAdsmoc0na+pNOMoPmOesI5IZPK+xKpDEh2Ynr3HerrsY2EZbLAAA4zVi6WJX3qwyODg1VSJUlbaXYnnLHOKcndHWved2TFCRknmnxRO7g54XqBTEQhwCcgnpV9I2fbBbrumk4UDt7mpV5e6tznxWIjRpts2/CsBlvpJtp2RjGT/e//VXY8iqelacunafFAOWAy7ep7mr+DX1eCw/sKKg/mfISk5Scn1GZNKG5FO20Bea6ybHzJ8YG2/EzUWHbyf8A0Ulcbc2qXcG7cY3Odp7deldh8X+fiVq3sID/AOQkrlIp8RRxeWZO+2uOekrnFN2k2T65BZQ2VmqN9zAjZOVxgZz+NSrGs+l2/ILRylDj86S4t4xYedeCNGY/uIQOp7k+1R2U3kmUZUqB5j4HAI4H481pOrztOwpS5iRoILS4nhYrKS5O4HjHp+tWfOSXcMAAKMY7Y4rANyzzHHLMeK0LMSKknmfex+VZ1V7ole6uWrGdzIQcFFUkAjvg0W53SDACgckDpmpEt/JjjcOpWSN2AB5A96fZ2rNYvdAkruAIx0PP+FYt8qPZyDDqvjE3tHX+vmyUBSenWteSZrv+ydIQ4QAMw9Wbkn8qxccitjw6hufFMcjH5YIt5/BQv9a1pvmdj7TFJQjzdrv5noqkQWscI/hAqrcTFnRR3PSq8l8GlbkcnioYJvMut/UKOK9OSsrHiJWV2bEErW7g7jtZMMK8Yv8A95qszD+Js16euoq+kSzZGYTIrfzH6GvL5p43t5GWIm7ZAVYNgLwM5FcM2tGeHmeijF7mRfQ+VO4/HioomC7XP8C5/GnxEyRtGwLMDnf7+9aUMMEVosiQs8xOGLDI9sVMpJLU8puyC0luJY2kjYkDBw3r3A/KrF1rUs1tHGARCh+du2fSpI3YRHcMbV3EYxjv/QVhXm4aVCgzh5WJ+vFKnUb06HdQx1WEElsjcXUPtNj5QztQAKfXk5qCYFdN3Y65X9c1WsTm3ODxsGfrmrcpeSzggTrI7Bj6AAGsJtuojirVZVqjlIpWkIm08SlgCkhIB/i4xWn5ccSRoQVBYMcDjJ7/AMqqQQYkREHyjCqPxq/qrLAptzzIRk47GteYTfuoztf1TzHKRII4gBhe7H3q3purPfaZplpdAutoZF5bGQfu8+2ax9RaMwO7hjI23bjoOTnNW9GjzbohGXdCw/OhK0RrRXGzqzj3FPu0BEUZPCryKmnQQy/P0HNUmkZ2LMcknNTVeqsezkmWrFVHUqr3I/iy9aSOIxDGQkagjOMnnrXT+FLYR+JtFZnLN9vtypbnI8xa5uxgdlwB711PhxDH4o0VTg4v7fkf9dFpRve7PsXhqFKjKNOKS1K/xYdv+FnasB0Hk/8AolK5uByEya6v4qQ7viTqzn/pj/6JSuSjUsdvatJ/Ez4xK6RchkLn2q2ozxVSGMqMCrkYIIrJjtYVoyV4FVniIzV/dxio3XK5pXKTM9Ww1XYXzVORcOangxwM03sMW4U59qrKyq2K0ZIwyYrNkiKMaIq5Fnc0YbgKOama4ULWZExAGadIzAZFNostPNnoasaYf+JpbNn/AJaCsYSOTWv4eJbXLNWxhpQvPvxUVI+6zLl1NrV4jbwwW3dMkj6nP9ayVTmtnW1aTU2JPQkH88f0qnhVFY4Zfuk+93+JcBqMFQCop5wo4qORyucGqM89aBNkM85LGmwZJJqKRqfCSO1NmHK7lh3whrNlYEnNWrhyEOKymkJbGaSiW9hGfa/tQH4JoMe4VCQyHaaZlbUkzuJpQvFJEpxk9anSM/hVWNYxEjYowNdZ4Rfd4o0X/r+g/wDRi1yewg810vg1j/wluign/l+g/wDQ1qbaozqHuusn/iaTf8B/9BFUM1e1n/kLT/8AAf8A0EVQr0zyai99i5pc802ignlHE04VHTx0oKQp600049aaaQC0E0dqQ9aACjNApKAFPQUw8089BTKaMxcE1l6jpEV1E7quJK1BRyaznBSR14XF1MNLniedXFlLDI0bLgioV2xKWxjIwR713uoadHdKxxhsHBrk7ixMbmN0+YVwzpOGx99luaxxUNXqVbYmSPHGTT2ba/yjkd6iUGBijfKB3qQbfJdRyx5z6Vmtj1ZO+xJnChSeSKbLgR7SPu4walTFxGmBzjOajmZQxU9e9NGfLfQ5tdGH9syX0rbnY5A9BWomx3KkUkoP3hnd0NNRn8zdtGRxVyd0Z0qKp3aGugDOSOMYFamhqX0fVIyeDCcflWfKS8QyMZ64rV8PqTZ6gpH/ACxYfpWdT4UcmZr/AGaRiaQp3KPavQvDoxqFn/12T+YrhtKiO8cdq7zw+uL6z/67J/MV7C2PzGH8Q9MooooPaCiiigAooooAKKKKACiiigBPwopaKAKOo6dDqFuY5V57N3FcRdQXWk3DRTfNH1Vsdq9Eqre2EF9EYp0DA9+4rixWFVZXWjA8/wDOjlw6OCeowaN8cmcr83cipda8I3dpvmsS0sfXC8MP8a5htTaBzHI7RyJ1D8GvErYecXaSN6crO8XqbkltE5yJl+h4qI2xUZVlJP8AdNc/c618vzMKzV1NFmMqSbfVcnmuf6vJ7Hp0sbUirPU67Y5bkEgdaS5kUAMwIQetc9a65CjmU3rIwBwpfINW21zEf7yTeHGQSo4rJ4etzX0LeOqKSfQtNPE74jyT7VIod2B4wO3rXNf8JBY+a9vAYpbgnoRnJ/Cuh0fRdc1tl2GS1gBwzLHsBH4jJrWGCqzlZDq5vJRtCKuadraTXFwsUYCl+x/wrudF0GHTELnMk7dXb+QpdH8P22kRfLulmP3pX5Y1sAcV7uDy6FD3nrI8WrWqVnebHUUUV6ZmFFFFAHy38W2Z/ijrkajJxbgD/tjHXPwSRWcJmlOcLxn/AD0rvfiV4Z1q8+ImrXlnomo3MEvk4lhtXdXxCg4IGOCDXG3vgXxXcGN/7C1MxjDOgspM9en3ea5nHmkzjlHmkzHknmvZDcNknHyDsq1TkvCkTQxnIY5c+uO1blz4T8WsuxPDOtBfQWEv/wATVNPBPizIJ8L61j/rwl/+JpRi92iVF7mVDuMqMDzn8q6K03f2e0jrhvm4B9KqL4N8WxTBl8Ma0cH/AJ8Jef8Ax2ukj8K+Iv7LIXw9q4JyQrWUm4Z/4DU1E2gknbYxY2KRSOScCBsfy/rW3p1ysnh8hUx5kv8AIf8A16l/4RLxD9jDroOqI/llCv2KTIbcOcYq1p/hXxPZwTRXOlX80bkNGEs5Mo3f+GsHB8vmevkOJhhsTzVdE1b8jEPBPtVvR7wW9+ZQcZjaNhnt1H6irF34Y8RIT5egaq/+7ZSf4VTtvD/iiATg+GNWZpV2qxsJfkwQc/d+oq6KkpJ2PpM4zKlSpqnB8zfbsbi6hwTmrcGoJbWUs7sBxhfc1zh0PxUq/wDIuawenAsZf/iaSfw74xm2GXw/qoiHRRaOT+gr0HU5loeY8ZRlFa7k0+sfZNDvSeTPMAB+HP6VhTMIDE6BlWQE5Xtj6/StK+8K+JpoIYx4f1faCXIFlJ1x9Pes+bwz4wa4Zf8AhHNbaJeFH2GXGB/wGuVw5tzxcfWWIquUdlsY13dmDckPyeYpWTHcZzz+lWtOup7lDbDIOMq49B1H5ZrpNG8D69eW96174f1NJJI/kE1nIvzDnuO+MUlv4Q8RqXc+H9UXahCj7FIOTx6VEo6WaOBro0ZTSYtLh8/eyAf0qleoBpCnoUYH9DXQy+EvEn2NYx4e1YkkZxZSev0qHUPCHid7Axx+HtWJZxwLKT/4mojGWlu5VnZIxLXbbWSqgZ2cBsvx17Y/Gtq1Uyab5jY3x5XgY6kf0zUh8H+JA6qPD2q7UUKP9Ck7fhWnF4X1+PTmRtB1Xczg4WzkJ6fSnKLvexm+a5i2KgXqN12ndz6gZrKupbibUZ53Rgm8xcjGD6V0c/hfxLDY5i8P6sZZFIO2zkJGT9PSs1fCXiyeXM3h/WiOX+ayl6/l1qorfQvlfKY9zEZrYkEDYc49a1dOdocIDwkZOAOrbTjn8DVlPBniSRnV/D2sAEZ/485B0/4DWppHhrxLHdYk0DUhDJ8jh7GTj36U7JxS6oF8JzV/O7+XG4XKDqBgn61UiG+dV7Z5roNQ8JeJWvJCnh3VmXJwRZSEf+g0y08IeJhKWbw7qw472Un/AMTWUovmPtckqwWDim0nd/mPsG/ej5QcjAz2rd0Cb/isNFVR8v2+AH/v4tV4/DXiJdz/ANg6nuxgYs5B/StDw74d16HxTo802ialHFHfQs7vayBVAcEkkjgCrSeh6tWpT9nL3lt3E+J6F/iJqoxx+5/9FJXMQWxJyRivRfHnh7VL7xtqNzb6Xezwv5e2SO3dlOI1HBAx1Fc8PDGuqP8AkCal/wCAj/4VU0+Znx0WrIx1g2jgUhG3mtseHdex/wAgTUv/AAEf/Cq8/hrxCw+XQtT/APAST/Cs+VjbRjtN6UhmyOatyeGPEv8ADoGqn/tzk/wpq+FfEhHOgar/AOAcn+FLkbIujOlYMKbbyYbBNaj+FPEe3/kAaqf+3OT/AApkfhXxGGz/AMI/q3/gFJ/hT5X2KjIfH8y5qOSDdk4rTg8O+IAAG0HVB/25yf4VdTw1rbddF1EfW1f/AApWa6Fto5k2zL2pmwjINdXJ4b1rbgaLqJ/7dX/wqlJ4Y14njQtS/wDAST/Cl7z6E3RzZjbdgDitLS43S8gdchg4IPpzWivhbXcf8gPUs/8AXpJ/hWlZeG9aS4h3aNqCgMMk2rjHP0pO/LsVdEF7u88hid2OaoykqCK6C+0HWXvXK6RfsoA5Fs+Dx9KzZ9B108DQ9SP/AG6Sf4VnQTVKN10ITSRhysQD61WMe7k1uP4a18/8wPU//AST/Cm/8Iz4gzj+w9Sx/wBekn+FW4vsZykYi2+5unFWkthjOK2V8Ma5tH/El1HP/Xq/+FTJ4b1xV50bUP8AwFf/AApcr7Ac3c2wKHisk26+Zhq7SXw3rr9NE1L/AMBH/wAKzLvwl4gxlNB1Mn2tJP8ACrSfYhyuYqWxyBjNNuLHdh1HTrXRWXhnxAVHmaDqan3s5B/Sr3/CLa2ykHRdR/8AAV/8KjllfYSepxQhxgYp5QhK6G48JeIA2U0PUz9LST/Cq7eFfEZX/kAaqf8Atzk/wrTlZ0aGCT+lbXhA/wDFX6IfXUIP/Ri1C/hPxKenh7Vv/AKT/wCJrV8KeGfENt4r0aWfQtUjhjvoGeR7SRVVRICSSRwAKFF3WhhM9s1n/kKz/wDAf/QRVCtTVrW4k1OZ44JWU7cFUJB+UVS+xXf/AD6zf9+zXeeXNPmZBRU/2K7/AOfWb/v2aPsV3/z6zf8Afs0E2fYgAzTx0qUWV3/z6zf9+zTvsV1/z7Tf9+zQHK+xAaSrH2K6/wCfab/v2aT7Fdf8+03/AH7NIfK+xBSGrH2K6/59pv8Av2aT7Fd/8+s3/fs0C5X2K9FT/Yrv/n1m/wC/Zo+xXf8Az6zf9+zTCz7EJpuKs/Yrv/n1m/79mkNld/8APrN/37NBHI+xAKXp0qf7Fd4/49Zv+/Zo+xXf/PrN/wB+zSGoy7EHbnmqt1YRXEf+32NaP2K7/wCfWb/v2aT7DdgjFrN/37NKUVJam1KrUpS5onnWo2kkFy0b5APG71qsbWTZud8rwa9FvNElvExJaTHHT92awbzwzqKbljs7l0zxiJj/AErhqUnHY+5wGcQrQUJ6M5lJCE8tG+UnJb0prEMzZbJ6GtCbw/rbfINLvQvtbv8A4U6PQ9Zxk6RejHY278/pWfK+x63tKW6kvvM6II0hjY1XIZJX/ugGtV9A1kOJV0m+zjBAtn/wpsmgayUBGk35J6/6M/8AhTUZdh+1pfzL7zHySCgOM1v6FhrK8YdfJIP5VmSeH9e3HGjaiSDx/or/AOFdJoeianHZ3Xm6beRlkwFaBgSfyqasXy7HBmdSDw0kpLYx9MtiH6dK7LRE239r/wBdU/mKoWGh6hHvLWNyMnvC3+Fb+mafdRXluzWsygSKSTGRjmvVi3Y/NowfMnY7SiiiqPWCiiigAooooAKKKKACiiigAooooAKKKKAEPpWNrHhnStcjKX1kkh7OOCPxraoqHFSVmgPKtT+Esi7m0q/PtFPkgD61zE3wn8VBmKvaMpPAEx/live6MD0rL6tTvdItVJI+eIfhT4vEvFvZjJ5aSbj9BW7b/CPXbxlXUtUt7eEdVhUsT/KvasUhA9Kf1envYHUkzkvDvw80Pw6fOt7fz7nHM82C3/1q6tU2jgY9hT6WtVFJWRAUUUVQBRRRQAUUUUAf/9k=\n", "text/plain": [ "" ] }, "metadata": { "image/jpeg": { "width": 800 } }, "execution_count": 15 } ] }, { "cell_type": "markdown", "source": [ "**Training and Validation Loss**" ], "metadata": { "id": "j9pbdNav5WYN" } }, { "cell_type": "code", "source": [ "Image(\"/content/runs/classify/train/results.png\", width = 800)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 817 }, "id": "AsPEPmsc5AdK", "outputId": "690a9dff-ada7-4700-9e2d-e3d5dd273ea7" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": { "image/png": { "width": 800 } }, "execution_count": 18 } ] }, { "cell_type": "markdown", "source": [ "**Predict on Test Data using Trained Model**" ], "metadata": { "id": "Y9qHuR1h6lfR" } }, { "cell_type": "code", "source": [ "#Load the Trained Classification Model\n", "model = YOLO(\"/content/best.pt\")" ], "metadata": { "id": "ThC8d9256yUv" }, "execution_count": 7, "outputs": [] }, { "cell_type": "code", "source": [ "#Specify the test folder path\n", "test_folder = \"/content/plants-classification-3/test\"" ], "metadata": { "id": "gBGnYlYa68H4" }, "execution_count": 8, "outputs": [] }, { "cell_type": "code", "source": [ "#Loop through each class folder in the test directory\n", "import os\n", "for class_folder in os.listdir(test_folder):\n", " class_folder_path = os.path.join(test_folder, class_folder)\n", " #print(class_folder_path)\n", " #Make sure its a directory\n", " if os.path.isdir(class_folder_path):\n", " ground_truth_class = class_folder #This is the ground truth class based on the folder name\n", " print(f\"Processing Class Folder (Ground Truth): {ground_truth_class}\")\n", " #List All Images in the Current Class Folder\n", " image_files = [os.path.join(class_folder_path, img) for img in os.listdir(class_folder_path) if img.endswith(('.jpg', '.jpeg', '.png'))]\n", " #Predict on each image in the class folder\n", " for img_path in image_files:\n", " #Perform Classification on the Image\n", " results = model.predict(img_path, conf = 0.25)\n", " #Loop through the results and extract predictions\n", " for result in results:\n", " #Get the predicted class index (top-1) and confidence\n", " predicted_class_index = result.probs.top1 #Get the predicted class index (top-1)\n", " predicted_class = result.names[predicted_class_index] #Map index to class name\n", " confidence = result.probs.top1conf #Get the confidence score for the top prediction\n", "\n", " #Display the prediction results for the image along with the ground truths\n", " print(f\"Image: {img_path}\")\n", " print(f\"Ground Truth: {ground_truth_class}, Predicted Class: {predicted_class}, Confidence: {confidence:.2f}\")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "srOVdTUs5hha", "outputId": "42b1b90b-9606-4802-bd7b-5583e13782d4" }, "execution_count": 9, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Processing Class Folder (Ground Truth): Bayam\n", "\n", "image 1/1 /content/plants-classification-3/test/Bayam/aug_0_7916_jpg.rf.d4a95b39484e5384c13456192f68fefb.jpg: 224x224 Bayam 1.00, Kangkung 0.00, Semangka 0.00, Mawar 0.00, Padi 0.00, 6.1ms\n", "Speed: 62.3ms preprocess, 6.1ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Bayam/aug_0_7916_jpg.rf.d4a95b39484e5384c13456192f68fefb.jpg\n", "Ground Truth: Bayam, Predicted Class: Bayam, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Bayam/aug_0_5411_jpg.rf.721073c2a7c97cf170fac6b725c2a0aa.jpg: 224x224 Bayam 1.00, Mawar 0.00, Kangkung 0.00, Semangka 0.00, BungaMatahari 0.00, 6.4ms\n", "Speed: 7.8ms preprocess, 6.4ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Bayam/aug_0_5411_jpg.rf.721073c2a7c97cf170fac6b725c2a0aa.jpg\n", "Ground Truth: Bayam, Predicted Class: Bayam, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Bayam/aug_0_3781_jpg.rf.f7af6e35cb00468d75e24fbb0fc73ba4.jpg: 224x224 Bayam 1.00, Daisy 0.00, Semangka 0.00, Kangkung 0.00, Mawar 0.00, 4.4ms\n", "Speed: 4.6ms preprocess, 4.4ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Bayam/aug_0_3781_jpg.rf.f7af6e35cb00468d75e24fbb0fc73ba4.jpg\n", "Ground Truth: Bayam, Predicted Class: Bayam, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Bayam/aug_0_8513_jpg.rf.524aa0cfa25fc717e5a792a83349eeb3.jpg: 224x224 Bayam 1.00, Semangka 0.00, Kangkung 0.00, Mawar 0.00, KacangPanjang 0.00, 4.2ms\n", "Speed: 5.2ms preprocess, 4.2ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Bayam/aug_0_8513_jpg.rf.524aa0cfa25fc717e5a792a83349eeb3.jpg\n", "Ground Truth: Bayam, Predicted Class: Bayam, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Bayam/spinach704_jpg.rf.8e25f504895f01a6e37f0b4082ffb5f8.jpg: 224x224 Bayam 1.00, Kangkung 0.00, Jeruk 0.00, Timun 0.00, Kelapa 0.00, 4.1ms\n", "Speed: 4.6ms preprocess, 4.1ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Bayam/spinach704_jpg.rf.8e25f504895f01a6e37f0b4082ffb5f8.jpg\n", "Ground Truth: Bayam, Predicted Class: Bayam, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Bayam/aug_0_3905_jpg.rf.928ac14d2bee3f82b34c7dbc8bd46b8a.jpg: 224x224 Bayam 1.00, Semangka 0.00, Mawar 0.00, Daisy 0.00, Jambu 0.00, 4.4ms\n", "Speed: 4.7ms preprocess, 4.4ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Bayam/aug_0_3905_jpg.rf.928ac14d2bee3f82b34c7dbc8bd46b8a.jpg\n", "Ground Truth: Bayam, Predicted Class: Bayam, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Bayam/aug_0_1216_jpg.rf.0f0023e9fb39f3a67e852ec33ff7de5d.jpg: 224x224 Bayam 1.00, Kangkung 0.00, Mawar 0.00, Jeruk 0.00, Dandelion 0.00, 5.4ms\n", "Speed: 4.8ms preprocess, 5.4ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Bayam/aug_0_1216_jpg.rf.0f0023e9fb39f3a67e852ec33ff7de5d.jpg\n", "Ground Truth: Bayam, Predicted Class: Bayam, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Bayam/aug_0_3573_jpg.rf.216d0d0970246cff7832d3665871fada.jpg: 224x224 Bayam 1.00, Jeruk 0.00, Timun 0.00, Kangkung 0.00, Jambu 0.00, 4.5ms\n", "Speed: 4.7ms preprocess, 4.5ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Bayam/aug_0_3573_jpg.rf.216d0d0970246cff7832d3665871fada.jpg\n", "Ground Truth: Bayam, Predicted Class: Bayam, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Bayam/aug_0_2197_jpg.rf.03269410a7d2748e10d053e55551d88f.jpg: 224x224 Bayam 1.00, Kangkung 0.00, Pepaya 0.00, Jeruk 0.00, Padi 0.00, 4.4ms\n", "Speed: 6.0ms preprocess, 4.4ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Bayam/aug_0_2197_jpg.rf.03269410a7d2748e10d053e55551d88f.jpg\n", "Ground Truth: Bayam, Predicted Class: Bayam, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Bayam/aug_0_1366_jpg.rf.ba7b7c850ac8a63fe401c60eb444aaee.jpg: 224x224 Bayam 1.00, Kangkung 0.00, Jeruk 0.00, Terong 0.00, Kelapa 0.00, 4.1ms\n", "Speed: 4.7ms preprocess, 4.1ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Bayam/aug_0_1366_jpg.rf.ba7b7c850ac8a63fe401c60eb444aaee.jpg\n", "Ground Truth: Bayam, Predicted Class: Bayam, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Bayam/aug_0_527_jpg.rf.8749c7e5d6c89b8d68d3cdbd3df64d57.jpg: 224x224 Cabai 0.50, BawangMerah 0.49, Bayam 0.01, Kangkung 0.00, Jahe 0.00, 4.1ms\n", "Speed: 4.6ms preprocess, 4.1ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Bayam/aug_0_527_jpg.rf.8749c7e5d6c89b8d68d3cdbd3df64d57.jpg\n", "Ground Truth: Bayam, Predicted Class: Cabai, Confidence: 0.50\n", "\n", "image 1/1 /content/plants-classification-3/test/Bayam/aug_0_3347_jpg.rf.6c7e7e4ce612086edd12107a7d8f15c7.jpg: 224x224 Bayam 1.00, Kangkung 0.00, Padi 0.00, Kelapa 0.00, Nanas 0.00, 4.3ms\n", "Speed: 4.6ms preprocess, 4.3ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Bayam/aug_0_3347_jpg.rf.6c7e7e4ce612086edd12107a7d8f15c7.jpg\n", "Ground Truth: Bayam, Predicted Class: Bayam, Confidence: 1.00\n", "Processing Class Folder (Ground Truth): Jambu\n", "\n", "image 1/1 /content/plants-classification-3/test/Jambu/aug_0_2157_jpg.rf.c3ec94510cbcf074f7fd5be58a092081.jpg: 224x224 Jambu 1.00, Melon 0.00, Mawar 0.00, Semangka 0.00, Bayam 0.00, 4.9ms\n", "Speed: 6.6ms preprocess, 4.9ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jambu/aug_0_2157_jpg.rf.c3ec94510cbcf074f7fd5be58a092081.jpg\n", "Ground Truth: Jambu, Predicted Class: Jambu, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jambu/aug_0_7662_jpg.rf.e0afaee9f58cf2c88d38ce69c9c15e60.jpg: 224x224 Jambu 1.00, Jahe 0.00, Kelapa 0.00, Mawar 0.00, Bayam 0.00, 4.0ms\n", "Speed: 4.6ms preprocess, 4.0ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jambu/aug_0_7662_jpg.rf.e0afaee9f58cf2c88d38ce69c9c15e60.jpg\n", "Ground Truth: Jambu, Predicted Class: Jambu, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jambu/aug_0_905_jpg.rf.e2a416c7ba42218ac7b12d63d0995604.jpg: 224x224 Jambu 1.00, Semangka 0.00, Kelapa 0.00, Kentang 0.00, BawangMerah 0.00, 4.0ms\n", "Speed: 4.5ms preprocess, 4.0ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jambu/aug_0_905_jpg.rf.e2a416c7ba42218ac7b12d63d0995604.jpg\n", "Ground Truth: Jambu, Predicted Class: Jambu, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jambu/aug_0_1555_jpg.rf.4aaef86d8594f5980db70043c576db18.jpg: 224x224 Jambu 1.00, Semangka 0.00, Mawar 0.00, BawangMerah 0.00, Pepaya 0.00, 4.3ms\n", "Speed: 8.4ms preprocess, 4.3ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jambu/aug_0_1555_jpg.rf.4aaef86d8594f5980db70043c576db18.jpg\n", "Ground Truth: Jambu, Predicted Class: Jambu, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jambu/guava737_jpg.rf.3edd065f59ec9806f3f587c690a34417.jpg: 224x224 Jambu 1.00, Mangga 0.00, Kentang 0.00, Semangka 0.00, Melon 0.00, 4.2ms\n", "Speed: 4.5ms preprocess, 4.2ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jambu/guava737_jpg.rf.3edd065f59ec9806f3f587c690a34417.jpg\n", "Ground Truth: Jambu, Predicted Class: Jambu, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jambu/aug_0_4754_jpg.rf.87e6bd8cfb27d59b2f122be6beba59b5.jpg: 224x224 Jambu 1.00, Semangka 0.00, Pepaya 0.00, BawangMerah 0.00, Melon 0.00, 4.2ms\n", "Speed: 4.4ms preprocess, 4.2ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jambu/aug_0_4754_jpg.rf.87e6bd8cfb27d59b2f122be6beba59b5.jpg\n", "Ground Truth: Jambu, Predicted Class: Jambu, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jambu/aug_0_3467_jpg.rf.ab55140da65074933f92946d83dcd2a6.jpg: 224x224 Jambu 1.00, Semangka 0.00, Melon 0.00, Kentang 0.00, Pepaya 0.00, 4.2ms\n", "Speed: 4.6ms preprocess, 4.2ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jambu/aug_0_3467_jpg.rf.ab55140da65074933f92946d83dcd2a6.jpg\n", "Ground Truth: Jambu, Predicted Class: Jambu, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jambu/aug_0_6873_jpg.rf.20d5628d350a644b2cf6f5c4e9f685b3.jpg: 224x224 Jambu 1.00, Semangka 0.00, Kelapa 0.00, Terong 0.00, KacangPanjang 0.00, 4.3ms\n", "Speed: 4.8ms preprocess, 4.3ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jambu/aug_0_6873_jpg.rf.20d5628d350a644b2cf6f5c4e9f685b3.jpg\n", "Ground Truth: Jambu, Predicted Class: Jambu, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jambu/aug_0_5402_jpg.rf.9a43a2f43549b5822e3ed67681552f6b.jpg: 224x224 Jambu 1.00, Semangka 0.00, Kentang 0.00, Kelapa 0.00, Melon 0.00, 4.2ms\n", "Speed: 4.8ms preprocess, 4.2ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jambu/aug_0_5402_jpg.rf.9a43a2f43549b5822e3ed67681552f6b.jpg\n", "Ground Truth: Jambu, Predicted Class: Jambu, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jambu/aug_0_6472_jpg.rf.4ee05db90aa2b54782135510c9bc166e.jpg: 224x224 Jambu 1.00, Semangka 0.00, Kentang 0.00, Terong 0.00, Mawar 0.00, 4.2ms\n", "Speed: 5.8ms preprocess, 4.2ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jambu/aug_0_6472_jpg.rf.4ee05db90aa2b54782135510c9bc166e.jpg\n", "Ground Truth: Jambu, Predicted Class: Jambu, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jambu/aug_0_9984_jpg.rf.a6a8a4621f2de89b8ecf77e12173eb98.jpg: 224x224 Jambu 1.00, Semangka 0.00, Kentang 0.00, BawangMerah 0.00, Mawar 0.00, 4.4ms\n", "Speed: 4.8ms preprocess, 4.4ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jambu/aug_0_9984_jpg.rf.a6a8a4621f2de89b8ecf77e12173eb98.jpg\n", "Ground Truth: Jambu, Predicted Class: Jambu, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jambu/aug_0_7937_jpg.rf.300da8ab3c045f467500a47e40581a50.jpg: 224x224 Jambu 1.00, Mawar 0.00, Jeruk 0.00, BungaMatahari 0.00, Semangka 0.00, 4.5ms\n", "Speed: 4.6ms preprocess, 4.5ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jambu/aug_0_7937_jpg.rf.300da8ab3c045f467500a47e40581a50.jpg\n", "Ground Truth: Jambu, Predicted Class: Jambu, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jambu/aug_0_491_jpg.rf.5fec999aef660517e8d816565bccc583.jpg: 224x224 Jambu 1.00, Mawar 0.00, Jahe 0.00, Semangka 0.00, Bayam 0.00, 4.3ms\n", "Speed: 4.7ms preprocess, 4.3ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jambu/aug_0_491_jpg.rf.5fec999aef660517e8d816565bccc583.jpg\n", "Ground Truth: Jambu, Predicted Class: Jambu, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jambu/aug_0_2401_jpg.rf.9a7965c5f1147e212b21aded4e1109fa.jpg: 224x224 Jambu 1.00, Melon 0.00, Semangka 0.00, Mangga 0.00, Pepaya 0.00, 4.9ms\n", "Speed: 4.8ms preprocess, 4.9ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jambu/aug_0_2401_jpg.rf.9a7965c5f1147e212b21aded4e1109fa.jpg\n", "Ground Truth: Jambu, Predicted Class: Jambu, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jambu/aug_0_9166_jpg.rf.e558ff20de0016d98c9cb356b8b44c32.jpg: 224x224 Jambu 1.00, Mawar 0.00, Semangka 0.00, Pepaya 0.00, Kentang 0.00, 4.2ms\n", "Speed: 4.7ms preprocess, 4.2ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jambu/aug_0_9166_jpg.rf.e558ff20de0016d98c9cb356b8b44c32.jpg\n", "Ground Truth: Jambu, Predicted Class: Jambu, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jambu/aug_0_2541_jpg.rf.615f8a3781cccea97e91406a707cb5fe.jpg: 224x224 Jambu 1.00, Semangka 0.00, Pepaya 0.00, Melon 0.00, Jeruk 0.00, 4.5ms\n", "Speed: 5.0ms preprocess, 4.5ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jambu/aug_0_2541_jpg.rf.615f8a3781cccea97e91406a707cb5fe.jpg\n", "Ground Truth: Jambu, Predicted Class: Jambu, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jambu/aug_0_8540_jpg.rf.05275bc90c61d7e4e5f31d26726d829a.jpg: 224x224 Jambu 1.00, Semangka 0.00, BawangMerah 0.00, Melon 0.00, Pepaya 0.00, 4.5ms\n", "Speed: 4.5ms preprocess, 4.5ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jambu/aug_0_8540_jpg.rf.05275bc90c61d7e4e5f31d26726d829a.jpg\n", "Ground Truth: Jambu, Predicted Class: Jambu, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jambu/aug_0_7143_jpg.rf.f4f6b73cc135ac8dd471ea3946e51926.jpg: 224x224 Jambu 1.00, Semangka 0.00, BawangMerah 0.00, Kentang 0.00, Melon 0.00, 4.7ms\n", "Speed: 4.6ms preprocess, 4.7ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jambu/aug_0_7143_jpg.rf.f4f6b73cc135ac8dd471ea3946e51926.jpg\n", "Ground Truth: Jambu, Predicted Class: Jambu, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jambu/aug_0_8042_jpg.rf.fce74806b78617365dc8f180a43025fd.jpg: 224x224 Jambu 1.00, Kelapa 0.00, Melon 0.00, Pepaya 0.00, Mawar 0.00, 4.5ms\n", "Speed: 4.8ms preprocess, 4.5ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jambu/aug_0_8042_jpg.rf.fce74806b78617365dc8f180a43025fd.jpg\n", "Ground Truth: Jambu, Predicted Class: Jambu, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jambu/aug_0_3883_jpg.rf.cbcc35b23259182a1a80e1338ba1de53.jpg: 224x224 Jambu 1.00, Melon 0.00, Pepaya 0.00, Semangka 0.00, Kelapa 0.00, 5.1ms\n", "Speed: 4.7ms preprocess, 5.1ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jambu/aug_0_3883_jpg.rf.cbcc35b23259182a1a80e1338ba1de53.jpg\n", "Ground Truth: Jambu, Predicted Class: Jambu, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jambu/aug_0_5927_jpg.rf.b4a7e30de2da61f602c5070d70e69f0a.jpg: 224x224 Jambu 1.00, Semangka 0.00, Pepaya 0.00, Terong 0.00, Jahe 0.00, 4.3ms\n", "Speed: 4.9ms preprocess, 4.3ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jambu/aug_0_5927_jpg.rf.b4a7e30de2da61f602c5070d70e69f0a.jpg\n", "Ground Truth: Jambu, Predicted Class: Jambu, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jambu/aug_0_5651_jpg.rf.f55792c2e3da81f2331c22c88521fc03.jpg: 224x224 Jambu 1.00, Kelapa 0.00, Jahe 0.00, Mangga 0.00, Semangka 0.00, 4.5ms\n", "Speed: 4.8ms preprocess, 4.5ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jambu/aug_0_5651_jpg.rf.f55792c2e3da81f2331c22c88521fc03.jpg\n", "Ground Truth: Jambu, Predicted Class: Jambu, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jambu/aug_0_7345_jpg.rf.8652b9d660c904334bcee5d711aaeb5f.jpg: 224x224 Jambu 1.00, Semangka 0.00, Terong 0.00, Timun 0.00, Kentang 0.00, 5.0ms\n", "Speed: 4.7ms preprocess, 5.0ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jambu/aug_0_7345_jpg.rf.8652b9d660c904334bcee5d711aaeb5f.jpg\n", "Ground Truth: Jambu, Predicted Class: Jambu, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jambu/aug_0_2677_jpg.rf.1fbad18158d05a28a89a40b566f2001f.jpg: 224x224 Jambu 1.00, Jeruk 0.00, Mangga 0.00, Jahe 0.00, Mawar 0.00, 4.5ms\n", "Speed: 4.8ms preprocess, 4.5ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jambu/aug_0_2677_jpg.rf.1fbad18158d05a28a89a40b566f2001f.jpg\n", "Ground Truth: Jambu, Predicted Class: Jambu, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jambu/aug_0_5871_jpg.rf.12d742d6e4fbf2679bc724ce2ae97405.jpg: 224x224 Jambu 1.00, Semangka 0.00, Melon 0.00, Kentang 0.00, Pepaya 0.00, 4.4ms\n", "Speed: 4.7ms preprocess, 4.4ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jambu/aug_0_5871_jpg.rf.12d742d6e4fbf2679bc724ce2ae97405.jpg\n", "Ground Truth: Jambu, Predicted Class: Jambu, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jambu/aug_0_8147_jpg.rf.92ec20626fcc4bb262c73b90df65b1a2.jpg: 224x224 Jambu 1.00, Melon 0.00, Semangka 0.00, Mawar 0.00, BungaMatahari 0.00, 4.6ms\n", "Speed: 7.2ms preprocess, 4.6ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jambu/aug_0_8147_jpg.rf.92ec20626fcc4bb262c73b90df65b1a2.jpg\n", "Ground Truth: Jambu, Predicted Class: Jambu, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jambu/aug_0_9959_jpg.rf.bb847450ccc55e29e497c2a09ff8836d.jpg: 224x224 Jambu 1.00, Semangka 0.00, Melon 0.00, Kentang 0.00, Mawar 0.00, 4.6ms\n", "Speed: 4.9ms preprocess, 4.6ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jambu/aug_0_9959_jpg.rf.bb847450ccc55e29e497c2a09ff8836d.jpg\n", "Ground Truth: Jambu, Predicted Class: Jambu, Confidence: 1.00\n", "Processing Class Folder (Ground Truth): Terong\n", "\n", "image 1/1 /content/plants-classification-3/test/Terong/eggplant982_jpg.rf.daeb4d75687ad6bd93a9567691205d68.jpg: 224x224 Terong 1.00, Daisy 0.00, Bayam 0.00, Tulip 0.00, Mawar 0.00, 4.7ms\n", "Speed: 5.0ms preprocess, 4.7ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Terong/eggplant982_jpg.rf.daeb4d75687ad6bd93a9567691205d68.jpg\n", "Ground Truth: Terong, Predicted Class: Terong, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Terong/aug_0_9603_jpg.rf.34f82c77625aece0861ee3b9ceb6d81e.jpg: 224x224 Terong 1.00, Bayam 0.00, Cabai 0.00, KacangPanjang 0.00, Timun 0.00, 4.6ms\n", "Speed: 5.1ms preprocess, 4.6ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Terong/aug_0_9603_jpg.rf.34f82c77625aece0861ee3b9ceb6d81e.jpg\n", "Ground Truth: Terong, Predicted Class: Terong, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Terong/aug_0_6894_jpg.rf.081b8fbe4a0c8658bf7566d12a37bef9.jpg: 224x224 Terong 0.99, KacangPanjang 0.01, Cabai 0.00, Nanas 0.00, Bayam 0.00, 4.4ms\n", "Speed: 4.9ms preprocess, 4.4ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Terong/aug_0_6894_jpg.rf.081b8fbe4a0c8658bf7566d12a37bef9.jpg\n", "Ground Truth: Terong, Predicted Class: Terong, Confidence: 0.99\n", "\n", "image 1/1 /content/plants-classification-3/test/Terong/aug_0_868_jpg.rf.451e5de8c9eb75646c5b839ccb46ef72.jpg: 224x224 Terong 1.00, Bayam 0.00, Kangkung 0.00, Jambu 0.00, Semangka 0.00, 4.3ms\n", "Speed: 4.9ms preprocess, 4.3ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Terong/aug_0_868_jpg.rf.451e5de8c9eb75646c5b839ccb46ef72.jpg\n", "Ground Truth: Terong, Predicted Class: Terong, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Terong/aug_0_9333_jpg.rf.a424e4992ed5912fa579db0c41da1898.jpg: 224x224 Terong 1.00, Bayam 0.00, Semangka 0.00, BawangMerah 0.00, Tulip 0.00, 4.2ms\n", "Speed: 4.8ms preprocess, 4.2ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Terong/aug_0_9333_jpg.rf.a424e4992ed5912fa579db0c41da1898.jpg\n", "Ground Truth: Terong, Predicted Class: Terong, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Terong/aug_0_7314_jpg.rf.1994144f5aece95585934356063e1341.jpg: 224x224 Terong 1.00, Bayam 0.00, Jambu 0.00, Kangkung 0.00, Cabai 0.00, 4.1ms\n", "Speed: 4.7ms preprocess, 4.1ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Terong/aug_0_7314_jpg.rf.1994144f5aece95585934356063e1341.jpg\n", "Ground Truth: Terong, Predicted Class: Terong, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Terong/aug_0_5444_jpg.rf.d675a5e97faa5a02aaf1c454a8993c64.jpg: 224x224 Terong 1.00, Bayam 0.00, Cabai 0.00, BawangMerah 0.00, Mawar 0.00, 6.1ms\n", "Speed: 8.8ms preprocess, 6.1ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Terong/aug_0_5444_jpg.rf.d675a5e97faa5a02aaf1c454a8993c64.jpg\n", "Ground Truth: Terong, Predicted Class: Terong, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Terong/aug_0_7615_jpg.rf.d894ce279e82f4c8d4f90e64d84cc7de.jpg: 224x224 Terong 1.00, Cabai 0.00, Jagung 0.00, Tulip 0.00, Semangka 0.00, 9.7ms\n", "Speed: 11.6ms preprocess, 9.7ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Terong/aug_0_7615_jpg.rf.d894ce279e82f4c8d4f90e64d84cc7de.jpg\n", "Ground Truth: Terong, Predicted Class: Terong, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Terong/aug_0_7959_jpg.rf.9b9554ab985d03de7f6aa3615711e3b0.jpg: 224x224 Terong 1.00, Kelapa 0.00, Kentang 0.00, Jambu 0.00, Melon 0.00, 9.6ms\n", "Speed: 7.8ms preprocess, 9.6ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Terong/aug_0_7959_jpg.rf.9b9554ab985d03de7f6aa3615711e3b0.jpg\n", "Ground Truth: Terong, Predicted Class: Terong, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Terong/eggplant902_jpg.rf.b728c7e621aedca30753bf87b9a42001.jpg: 224x224 Terong 1.00, Daisy 0.00, Semangka 0.00, Jambu 0.00, Mawar 0.00, 6.1ms\n", "Speed: 7.2ms preprocess, 6.1ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Terong/eggplant902_jpg.rf.b728c7e621aedca30753bf87b9a42001.jpg\n", "Ground Truth: Terong, Predicted Class: Terong, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Terong/eggplant915_jpg.rf.c33144e2f8d5411a266da27f3bf56946.jpg: 224x224 Terong 1.00, Kentang 0.00, Tulip 0.00, Mawar 0.00, Daisy 0.00, 6.4ms\n", "Speed: 5.7ms preprocess, 6.4ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Terong/eggplant915_jpg.rf.c33144e2f8d5411a266da27f3bf56946.jpg\n", "Ground Truth: Terong, Predicted Class: Terong, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Terong/eggplant904_jpg.rf.6d55b1bbb4f912e142e62f2f39840994.jpg: 224x224 Terong 1.00, BawangMerah 0.00, Jeruk 0.00, Daisy 0.00, Tulip 0.00, 6.4ms\n", "Speed: 6.0ms preprocess, 6.4ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Terong/eggplant904_jpg.rf.6d55b1bbb4f912e142e62f2f39840994.jpg\n", "Ground Truth: Terong, Predicted Class: Terong, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Terong/aug_0_6897_jpg.rf.79cdf73a4f43be442c12db63e4f10a1f.jpg: 224x224 Terong 1.00, Bayam 0.00, BawangMerah 0.00, Semangka 0.00, Jambu 0.00, 6.7ms\n", "Speed: 5.7ms preprocess, 6.7ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Terong/aug_0_6897_jpg.rf.79cdf73a4f43be442c12db63e4f10a1f.jpg\n", "Ground Truth: Terong, Predicted Class: Terong, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Terong/aug_0_4204_jpg.rf.3f074af5f2a44a3042eacdf2f7aa9df6.jpg: 224x224 Terong 1.00, Cabai 0.00, BawangMerah 0.00, Semangka 0.00, Jagung 0.00, 6.3ms\n", "Speed: 7.3ms preprocess, 6.3ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Terong/aug_0_4204_jpg.rf.3f074af5f2a44a3042eacdf2f7aa9df6.jpg\n", "Ground Truth: Terong, Predicted Class: Terong, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Terong/aug_0_5295_jpg.rf.4f2cf567eae1ed907e51cdc94e1ad8eb.jpg: 224x224 Terong 1.00, Bayam 0.00, Semangka 0.00, Cabai 0.00, KacangPanjang 0.00, 9.0ms\n", "Speed: 6.4ms preprocess, 9.0ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Terong/aug_0_5295_jpg.rf.4f2cf567eae1ed907e51cdc94e1ad8eb.jpg\n", "Ground Truth: Terong, Predicted Class: Terong, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Terong/aug_0_3278_jpg.rf.47008f6344e63c3f1f4487f408a32991.jpg: 224x224 Terong 1.00, BawangMerah 0.00, Semangka 0.00, Tulip 0.00, Timun 0.00, 5.6ms\n", "Speed: 6.4ms preprocess, 5.6ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Terong/aug_0_3278_jpg.rf.47008f6344e63c3f1f4487f408a32991.jpg\n", "Ground Truth: Terong, Predicted Class: Terong, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Terong/aug_0_295_jpg.rf.d7e8c39e0c88a1e0aa9845736262a102.jpg: 224x224 Terong 1.00, Semangka 0.00, Kentang 0.00, BawangMerah 0.00, KacangPanjang 0.00, 7.8ms\n", "Speed: 7.2ms preprocess, 7.8ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Terong/aug_0_295_jpg.rf.d7e8c39e0c88a1e0aa9845736262a102.jpg\n", "Ground Truth: Terong, Predicted Class: Terong, Confidence: 1.00\n", "Processing Class Folder (Ground Truth): Nanas\n", "\n", "image 1/1 /content/plants-classification-3/test/Nanas/aug_0_5551_jpg.rf.59374a242d480b2579cb88358f948c41.jpg: 224x224 Nanas 1.00, Jagung 0.00, Jahe 0.00, Kelapa 0.00, Padi 0.00, 6.1ms\n", "Speed: 5.9ms preprocess, 6.1ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Nanas/aug_0_5551_jpg.rf.59374a242d480b2579cb88358f948c41.jpg\n", "Ground Truth: Nanas, Predicted Class: Nanas, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Nanas/aug_0_9670_jpg.rf.b2f92d40e5077b8096a5192f6ab1df11.jpg: 224x224 Nanas 1.00, Kelapa 0.00, Jagung 0.00, Padi 0.00, Pisang 0.00, 6.0ms\n", "Speed: 7.6ms preprocess, 6.0ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Nanas/aug_0_9670_jpg.rf.b2f92d40e5077b8096a5192f6ab1df11.jpg\n", "Ground Truth: Nanas, Predicted Class: Nanas, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Nanas/pineapple472_jpg.rf.139f186380eddbc55da49f1abb6f95aa.jpg: 224x224 Nanas 1.00, Tulip 0.00, Dandelion 0.00, Jagung 0.00, Jahe 0.00, 5.7ms\n", "Speed: 5.6ms preprocess, 5.7ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Nanas/pineapple472_jpg.rf.139f186380eddbc55da49f1abb6f95aa.jpg\n", "Ground Truth: Nanas, Predicted Class: Nanas, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Nanas/aug_0_9394_jpg.rf.0ba5647c39f9f8846fd14f86dd3419ec.jpg: 224x224 Nanas 1.00, Pisang 0.00, Kacang 0.00, Jagung 0.00, Padi 0.00, 6.0ms\n", "Speed: 5.6ms preprocess, 6.0ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Nanas/aug_0_9394_jpg.rf.0ba5647c39f9f8846fd14f86dd3419ec.jpg\n", "Ground Truth: Nanas, Predicted Class: Nanas, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Nanas/aug_0_3695_jpg.rf.97080201efca8cb7a33ff9e7ce15c25d.jpg: 224x224 Nanas 1.00, Jagung 0.00, Pisang 0.00, KacangPanjang 0.00, Dandelion 0.00, 5.8ms\n", "Speed: 6.6ms preprocess, 5.8ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Nanas/aug_0_3695_jpg.rf.97080201efca8cb7a33ff9e7ce15c25d.jpg\n", "Ground Truth: Nanas, Predicted Class: Nanas, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Nanas/aug_0_7284_jpg.rf.a904234908df0007dfd86294a35fe2bc.jpg: 224x224 Nanas 1.00, Semangka 0.00, Dandelion 0.00, Daisy 0.00, Tulip 0.00, 5.6ms\n", "Speed: 5.5ms preprocess, 5.6ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Nanas/aug_0_7284_jpg.rf.a904234908df0007dfd86294a35fe2bc.jpg\n", "Ground Truth: Nanas, Predicted Class: Nanas, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Nanas/aug_0_5711_jpg.rf.6528d8ca7fa2ca9d42a489fd1d4a8773.jpg: 224x224 Nanas 1.00, Jambu 0.00, Kelapa 0.00, Daisy 0.00, Pisang 0.00, 6.2ms\n", "Speed: 5.6ms preprocess, 6.2ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Nanas/aug_0_5711_jpg.rf.6528d8ca7fa2ca9d42a489fd1d4a8773.jpg\n", "Ground Truth: Nanas, Predicted Class: Nanas, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Nanas/aug_0_2384_jpg.rf.4606411faac68a79758c92f45fd7a87e.jpg: 224x224 Nanas 1.00, Daisy 0.00, Tulip 0.00, Dandelion 0.00, Mawar 0.00, 6.0ms\n", "Speed: 5.8ms preprocess, 6.0ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Nanas/aug_0_2384_jpg.rf.4606411faac68a79758c92f45fd7a87e.jpg\n", "Ground Truth: Nanas, Predicted Class: Nanas, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Nanas/aug_0_504_jpg.rf.ad817aa68765b123df88ee21f9b35cb7.jpg: 224x224 Nanas 1.00, Jagung 0.00, Semangka 0.00, Kelapa 0.00, Daisy 0.00, 6.2ms\n", "Speed: 6.7ms preprocess, 6.2ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Nanas/aug_0_504_jpg.rf.ad817aa68765b123df88ee21f9b35cb7.jpg\n", "Ground Truth: Nanas, Predicted Class: Nanas, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Nanas/aug_0_7990_jpg.rf.40b17db419a09e19a9f4fa56a5a2bfd1.jpg: 224x224 Nanas 1.00, Semangka 0.00, Jagung 0.00, Jeruk 0.00, Pisang 0.00, 5.9ms\n", "Speed: 6.9ms preprocess, 5.9ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Nanas/aug_0_7990_jpg.rf.40b17db419a09e19a9f4fa56a5a2bfd1.jpg\n", "Ground Truth: Nanas, Predicted Class: Nanas, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Nanas/aug_0_1489_jpg.rf.aef442ce2e88d4b9eef5a73846f150c8.jpg: 224x224 Nanas 1.00, Semangka 0.00, Jeruk 0.00, Jagung 0.00, BawangMerah 0.00, 6.0ms\n", "Speed: 7.6ms preprocess, 6.0ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Nanas/aug_0_1489_jpg.rf.aef442ce2e88d4b9eef5a73846f150c8.jpg\n", "Ground Truth: Nanas, Predicted Class: Nanas, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Nanas/aug_0_3297_jpg.rf.35fbfa18a9c98e7e2e232b1fdeaacb5b.jpg: 224x224 Nanas 1.00, Jagung 0.00, Tulip 0.00, Kangkung 0.00, Semangka 0.00, 5.9ms\n", "Speed: 6.4ms preprocess, 5.9ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Nanas/aug_0_3297_jpg.rf.35fbfa18a9c98e7e2e232b1fdeaacb5b.jpg\n", "Ground Truth: Nanas, Predicted Class: Nanas, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Nanas/aug_0_6515_jpg.rf.d77f7ace6b6c2c9d4051c5fde06be88e.jpg: 224x224 Nanas 1.00, Daisy 0.00, Dandelion 0.00, Semangka 0.00, BungaMatahari 0.00, 5.8ms\n", "Speed: 7.2ms preprocess, 5.8ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Nanas/aug_0_6515_jpg.rf.d77f7ace6b6c2c9d4051c5fde06be88e.jpg\n", "Ground Truth: Nanas, Predicted Class: Nanas, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Nanas/aug_0_6083_jpg.rf.ead321130bd697a45c56ba2e3e61437b.jpg: 224x224 Nanas 1.00, Semangka 0.00, Jagung 0.00, Terong 0.00, Mawar 0.00, 5.6ms\n", "Speed: 6.9ms preprocess, 5.6ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Nanas/aug_0_6083_jpg.rf.ead321130bd697a45c56ba2e3e61437b.jpg\n", "Ground Truth: Nanas, Predicted Class: Nanas, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Nanas/aug_0_255_jpg.rf.a1ea6a8ddf5f4ebe4788c79a31b12d33.jpg: 224x224 Nanas 1.00, Semangka 0.00, Jagung 0.00, Jahe 0.00, Kangkung 0.00, 5.8ms\n", "Speed: 6.3ms preprocess, 5.8ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Nanas/aug_0_255_jpg.rf.a1ea6a8ddf5f4ebe4788c79a31b12d33.jpg\n", "Ground Truth: Nanas, Predicted Class: Nanas, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Nanas/aug_0_1260_jpg.rf.8d2597cfd744b5893b62386e1c34df16.jpg: 224x224 Nanas 1.00, Padi 0.00, Pisang 0.00, Jagung 0.00, Kacang 0.00, 5.7ms\n", "Speed: 5.8ms preprocess, 5.7ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Nanas/aug_0_1260_jpg.rf.8d2597cfd744b5893b62386e1c34df16.jpg\n", "Ground Truth: Nanas, Predicted Class: Nanas, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Nanas/aug_0_651_jpg.rf.42042f475dbb46b9e299992d73bcd510.jpg: 224x224 Nanas 1.00, Tulip 0.00, Jagung 0.00, Mawar 0.00, Dandelion 0.00, 5.6ms\n", "Speed: 5.6ms preprocess, 5.6ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Nanas/aug_0_651_jpg.rf.42042f475dbb46b9e299992d73bcd510.jpg\n", "Ground Truth: Nanas, Predicted Class: Nanas, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Nanas/aug_0_3646_jpg.rf.161f22bb6a8bf4057f80c3c77ba280b3.jpg: 224x224 Nanas 1.00, Dandelion 0.00, Tulip 0.00, Jagung 0.00, Jeruk 0.00, 5.6ms\n", "Speed: 5.6ms preprocess, 5.6ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Nanas/aug_0_3646_jpg.rf.161f22bb6a8bf4057f80c3c77ba280b3.jpg\n", "Ground Truth: Nanas, Predicted Class: Nanas, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Nanas/aug_0_4115_jpg.rf.da0b6b73d4bfd971f3e778605b721f62.jpg: 224x224 Nanas 1.00, Kelapa 0.00, Padi 0.00, Jagung 0.00, Jeruk 0.00, 9.4ms\n", "Speed: 7.4ms preprocess, 9.4ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Nanas/aug_0_4115_jpg.rf.da0b6b73d4bfd971f3e778605b721f62.jpg\n", "Ground Truth: Nanas, Predicted Class: Nanas, Confidence: 1.00\n", "Processing Class Folder (Ground Truth): Mangga\n", "\n", "image 1/1 /content/plants-classification-3/test/Mangga/aug_0_9168_jpg.rf.a1485d8d889241dba153aaf2f546c2ae.jpg: 224x224 Mangga 1.00, Kentang 0.00, Pepaya 0.00, Timun 0.00, Jeruk 0.00, 6.5ms\n", "Speed: 8.1ms preprocess, 6.5ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Mangga/aug_0_9168_jpg.rf.a1485d8d889241dba153aaf2f546c2ae.jpg\n", "Ground Truth: Mangga, Predicted Class: Mangga, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Mangga/mango248_jpg.rf.a814ee1937e787cae883a18e2e364805.jpg: 224x224 Mangga 0.99, Pepaya 0.00, Pisang 0.00, Melon 0.00, Jambu 0.00, 8.5ms\n", "Speed: 8.5ms preprocess, 8.5ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Mangga/mango248_jpg.rf.a814ee1937e787cae883a18e2e364805.jpg\n", "Ground Truth: Mangga, Predicted Class: Mangga, Confidence: 0.99\n", "\n", "image 1/1 /content/plants-classification-3/test/Mangga/aug_0_5384_jpg.rf.818111011f8f1b79b15c2ba092f9d882.jpg: 224x224 Mangga 0.97, Jeruk 0.02, Nanas 0.00, Pepaya 0.00, Melon 0.00, 6.4ms\n", "Speed: 7.8ms preprocess, 6.4ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Mangga/aug_0_5384_jpg.rf.818111011f8f1b79b15c2ba092f9d882.jpg\n", "Ground Truth: Mangga, Predicted Class: Mangga, Confidence: 0.97\n", "\n", "image 1/1 /content/plants-classification-3/test/Mangga/aug_0_1580_jpg.rf.15c9b7df40091b85e9076fab7a291ceb.jpg: 224x224 Pepaya 0.50, Melon 0.25, Jeruk 0.13, Mangga 0.07, Kentang 0.02, 6.4ms\n", "Speed: 5.5ms preprocess, 6.4ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Mangga/aug_0_1580_jpg.rf.15c9b7df40091b85e9076fab7a291ceb.jpg\n", "Ground Truth: Mangga, Predicted Class: Pepaya, Confidence: 0.50\n", "\n", "image 1/1 /content/plants-classification-3/test/Mangga/aug_0_4058_jpg.rf.c7098279faf5a3ca453cb6c92a4cc629.jpg: 224x224 Mangga 1.00, Kelapa 0.00, Jambu 0.00, Jeruk 0.00, Pepaya 0.00, 7.6ms\n", "Speed: 7.3ms preprocess, 7.6ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Mangga/aug_0_4058_jpg.rf.c7098279faf5a3ca453cb6c92a4cc629.jpg\n", "Ground Truth: Mangga, Predicted Class: Mangga, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Mangga/aug_0_3478_jpg.rf.235e1c0337c467be4e94e03c609943db.jpg: 224x224 Mangga 1.00, Kelapa 0.00, Jeruk 0.00, Pepaya 0.00, Ubi 0.00, 6.0ms\n", "Speed: 6.3ms preprocess, 6.0ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Mangga/aug_0_3478_jpg.rf.235e1c0337c467be4e94e03c609943db.jpg\n", "Ground Truth: Mangga, Predicted Class: Mangga, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Mangga/aug_0_4602_jpg.rf.c38605c2406ce45cbda31149d6af9d8f.jpg: 224x224 Mangga 1.00, Jeruk 0.00, Kelapa 0.00, Jambu 0.00, Melon 0.00, 6.5ms\n", "Speed: 8.5ms preprocess, 6.5ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Mangga/aug_0_4602_jpg.rf.c38605c2406ce45cbda31149d6af9d8f.jpg\n", "Ground Truth: Mangga, Predicted Class: Mangga, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Mangga/aug_0_7550_jpg.rf.e09fc125c9a4bd54fb89b2ade04b40d9.jpg: 224x224 Mangga 1.00, Pepaya 0.00, Kentang 0.00, Jeruk 0.00, Melon 0.00, 6.4ms\n", "Speed: 5.9ms preprocess, 6.4ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Mangga/aug_0_7550_jpg.rf.e09fc125c9a4bd54fb89b2ade04b40d9.jpg\n", "Ground Truth: Mangga, Predicted Class: Mangga, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Mangga/aug_0_1882_jpg.rf.09d0995f614b344dbb368a7fb0fb353f.jpg: 224x224 Mangga 1.00, Kelapa 0.00, Pepaya 0.00, Semangka 0.00, Jambu 0.00, 6.2ms\n", "Speed: 6.4ms preprocess, 6.2ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Mangga/aug_0_1882_jpg.rf.09d0995f614b344dbb368a7fb0fb353f.jpg\n", "Ground Truth: Mangga, Predicted Class: Mangga, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Mangga/aug_0_5074_jpg.rf.357d3eaa69df37c3e9130781207c2ecc.jpg: 224x224 Pepaya 0.51, Mangga 0.28, Kelapa 0.18, Kentang 0.02, Timun 0.00, 6.4ms\n", "Speed: 9.6ms preprocess, 6.4ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Mangga/aug_0_5074_jpg.rf.357d3eaa69df37c3e9130781207c2ecc.jpg\n", "Ground Truth: Mangga, Predicted Class: Pepaya, Confidence: 0.51\n", "\n", "image 1/1 /content/plants-classification-3/test/Mangga/aug_0_6734_jpg.rf.7086ea753784107623cff3444c1e8aae.jpg: 224x224 Jeruk 0.39, Mangga 0.19, Kentang 0.18, Melon 0.13, Pepaya 0.10, 5.8ms\n", "Speed: 7.2ms preprocess, 5.8ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Mangga/aug_0_6734_jpg.rf.7086ea753784107623cff3444c1e8aae.jpg\n", "Ground Truth: Mangga, Predicted Class: Jeruk, Confidence: 0.39\n", "\n", "image 1/1 /content/plants-classification-3/test/Mangga/aug_0_6425_jpg.rf.b265925fb66137e39296d555dab27f93.jpg: 224x224 Jambu 0.85, Jeruk 0.11, Terong 0.01, Mangga 0.01, Semangka 0.01, 6.0ms\n", "Speed: 6.7ms preprocess, 6.0ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Mangga/aug_0_6425_jpg.rf.b265925fb66137e39296d555dab27f93.jpg\n", "Ground Truth: Mangga, Predicted Class: Jambu, Confidence: 0.85\n", "\n", "image 1/1 /content/plants-classification-3/test/Mangga/aug_0_1556_jpg.rf.4bec96c50835b4b26a0d0b216df39f80.jpg: 224x224 Mangga 1.00, Ubi 0.00, Pepaya 0.00, Jeruk 0.00, Kentang 0.00, 5.8ms\n", "Speed: 6.4ms preprocess, 5.8ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Mangga/aug_0_1556_jpg.rf.4bec96c50835b4b26a0d0b216df39f80.jpg\n", "Ground Truth: Mangga, Predicted Class: Mangga, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Mangga/aug_0_7750_jpg.rf.e63ed7ba06e37ad86bc45f8519dd374b.jpg: 224x224 Mangga 1.00, Pisang 0.00, Melon 0.00, Jeruk 0.00, Pepaya 0.00, 7.9ms\n", "Speed: 7.7ms preprocess, 7.9ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Mangga/aug_0_7750_jpg.rf.e63ed7ba06e37ad86bc45f8519dd374b.jpg\n", "Ground Truth: Mangga, Predicted Class: Mangga, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Mangga/aug_0_3446_jpg.rf.dd4e3f1fd053c6ead9873d0388990190.jpg: 224x224 Mangga 0.47, Timun 0.36, Kacang 0.07, Kelapa 0.04, Semangka 0.02, 10.8ms\n", "Speed: 8.8ms preprocess, 10.8ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Mangga/aug_0_3446_jpg.rf.dd4e3f1fd053c6ead9873d0388990190.jpg\n", "Ground Truth: Mangga, Predicted Class: Mangga, Confidence: 0.47\n", "Processing Class Folder (Ground Truth): Pisang\n", "\n", "image 1/1 /content/plants-classification-3/test/Pisang/aug_0_825_jpg.rf.d417f81b91491f3cc2a8de9ede16274d.jpg: 224x224 Pisang 1.00, Cabai 0.00, Pepaya 0.00, Melon 0.00, Jeruk 0.00, 5.4ms\n", "Speed: 5.7ms preprocess, 5.4ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Pisang/aug_0_825_jpg.rf.d417f81b91491f3cc2a8de9ede16274d.jpg\n", "Ground Truth: Pisang, Predicted Class: Pisang, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Pisang/aug_0_8561_jpg.rf.3ecc6fe1d0b4a8f2efed4e8cd17059fd.jpg: 224x224 Pisang 1.00, Mangga 0.00, Cabai 0.00, Jambu 0.00, Melon 0.00, 5.8ms\n", "Speed: 7.4ms preprocess, 5.8ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Pisang/aug_0_8561_jpg.rf.3ecc6fe1d0b4a8f2efed4e8cd17059fd.jpg\n", "Ground Truth: Pisang, Predicted Class: Pisang, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Pisang/aug_0_9391_jpg.rf.fb8e8c17e2b06d1975a54e341eba37b0.jpg: 224x224 Pisang 1.00, Melon 0.00, Mangga 0.00, Pepaya 0.00, Cabai 0.00, 6.0ms\n", "Speed: 7.0ms preprocess, 6.0ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Pisang/aug_0_9391_jpg.rf.fb8e8c17e2b06d1975a54e341eba37b0.jpg\n", "Ground Truth: Pisang, Predicted Class: Pisang, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Pisang/aug_0_5012_jpg.rf.d9d484aa5c58c90144d4bcf498f32a4a.jpg: 224x224 Pisang 1.00, Cabai 0.00, Jambu 0.00, Kacang 0.00, Melon 0.00, 6.4ms\n", "Speed: 5.6ms preprocess, 6.4ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Pisang/aug_0_5012_jpg.rf.d9d484aa5c58c90144d4bcf498f32a4a.jpg\n", "Ground Truth: Pisang, Predicted Class: Pisang, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Pisang/aug_0_349_jpg.rf.d93a981ac2d6663fadbf74c2670975b6.jpg: 224x224 Pisang 1.00, Nanas 0.00, KacangPanjang 0.00, BungaMatahari 0.00, Mawar 0.00, 6.6ms\n", "Speed: 7.8ms preprocess, 6.6ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Pisang/aug_0_349_jpg.rf.d93a981ac2d6663fadbf74c2670975b6.jpg\n", "Ground Truth: Pisang, Predicted Class: Pisang, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Pisang/aug_0_193_jpg.rf.135134a96085f4584366ebb74e5429d4.jpg: 224x224 Pisang 1.00, Padi 0.00, Melon 0.00, KacangPanjang 0.00, Cabai 0.00, 7.0ms\n", "Speed: 7.2ms preprocess, 7.0ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Pisang/aug_0_193_jpg.rf.135134a96085f4584366ebb74e5429d4.jpg\n", "Ground Truth: Pisang, Predicted Class: Pisang, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Pisang/aug_0_7523_jpg.rf.2daf351b029512235e25a8a5392b1087.jpg: 224x224 Pisang 1.00, Melon 0.00, Mawar 0.00, Jahe 0.00, Timun 0.00, 5.8ms\n", "Speed: 9.4ms preprocess, 5.8ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Pisang/aug_0_7523_jpg.rf.2daf351b029512235e25a8a5392b1087.jpg\n", "Ground Truth: Pisang, Predicted Class: Pisang, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Pisang/aug_0_8490_jpg.rf.01d86a07dacabec314625fdf18075d6b.jpg: 224x224 Pisang 0.99, Pepaya 0.01, Mangga 0.00, Kentang 0.00, Nanas 0.00, 6.1ms\n", "Speed: 6.2ms preprocess, 6.1ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Pisang/aug_0_8490_jpg.rf.01d86a07dacabec314625fdf18075d6b.jpg\n", "Ground Truth: Pisang, Predicted Class: Pisang, Confidence: 0.99\n", "\n", "image 1/1 /content/plants-classification-3/test/Pisang/aug_0_9931_jpg.rf.f216134e27ec7c11f2fe8ded04271f33.jpg: 224x224 Pisang 0.53, Dandelion 0.47, Mangga 0.00, KacangPanjang 0.00, Nanas 0.00, 6.2ms\n", "Speed: 6.1ms preprocess, 6.2ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Pisang/aug_0_9931_jpg.rf.f216134e27ec7c11f2fe8ded04271f33.jpg\n", "Ground Truth: Pisang, Predicted Class: Pisang, Confidence: 0.53\n", "Processing Class Folder (Ground Truth): Padi\n", "\n", "image 1/1 /content/plants-classification-3/test/Padi/aug_0_7233_jpg.rf.3b40ec61f7f5b60367e108c131001a44.jpg: 224x224 Padi 1.00, Ubi 0.00, Kangkung 0.00, Kacang 0.00, Melon 0.00, 6.0ms\n", "Speed: 6.1ms preprocess, 6.0ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Padi/aug_0_7233_jpg.rf.3b40ec61f7f5b60367e108c131001a44.jpg\n", "Ground Truth: Padi, Predicted Class: Padi, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Padi/aug_0_9774_jpg.rf.44650d2b41fa0035f51d09bf2042a36a.jpg: 224x224 Padi 1.00, Kangkung 0.00, Nanas 0.00, KacangPanjang 0.00, Bayam 0.00, 6.0ms\n", "Speed: 5.8ms preprocess, 6.0ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Padi/aug_0_9774_jpg.rf.44650d2b41fa0035f51d09bf2042a36a.jpg\n", "Ground Truth: Padi, Predicted Class: Padi, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Padi/aug_0_713_jpg.rf.c6b1c6d3f73150bb52df90f710839439.jpg: 224x224 Padi 0.99, Kelapa 0.00, Ubi 0.00, Jagung 0.00, Mangga 0.00, 6.5ms\n", "Speed: 7.3ms preprocess, 6.5ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Padi/aug_0_713_jpg.rf.c6b1c6d3f73150bb52df90f710839439.jpg\n", "Ground Truth: Padi, Predicted Class: Padi, Confidence: 0.99\n", "\n", "image 1/1 /content/plants-classification-3/test/Padi/aug_0_2975_jpg.rf.2b97231a48cd597f9ef934fdbe956cc5.jpg: 224x224 Padi 1.00, Kacang 0.00, Nanas 0.00, Pepaya 0.00, Mangga 0.00, 5.8ms\n", "Speed: 7.9ms preprocess, 5.8ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Padi/aug_0_2975_jpg.rf.2b97231a48cd597f9ef934fdbe956cc5.jpg\n", "Ground Truth: Padi, Predicted Class: Padi, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Padi/aug_0_2438_jpg.rf.72e502d4c8cd91edc1731036d1eb1f5f.jpg: 224x224 Padi 1.00, Kacang 0.00, Kangkung 0.00, Ubi 0.00, Melon 0.00, 5.7ms\n", "Speed: 7.3ms preprocess, 5.7ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Padi/aug_0_2438_jpg.rf.72e502d4c8cd91edc1731036d1eb1f5f.jpg\n", "Ground Truth: Padi, Predicted Class: Padi, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Padi/aug_0_8063_jpg.rf.ee85648c9390f9ed12267ff34ca95f6e.jpg: 224x224 Padi 1.00, KacangPanjang 0.00, Kangkung 0.00, Kacang 0.00, Nanas 0.00, 6.0ms\n", "Speed: 5.6ms preprocess, 6.0ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Padi/aug_0_8063_jpg.rf.ee85648c9390f9ed12267ff34ca95f6e.jpg\n", "Ground Truth: Padi, Predicted Class: Padi, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Padi/aug_0_2377_jpg.rf.6dbdcc303748cc443e645f5c2947abbd.jpg: 224x224 Kelapa 0.40, Bayam 0.21, Padi 0.16, KacangPanjang 0.09, Terong 0.05, 9.6ms\n", "Speed: 7.5ms preprocess, 9.6ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Padi/aug_0_2377_jpg.rf.6dbdcc303748cc443e645f5c2947abbd.jpg\n", "Ground Truth: Padi, Predicted Class: Kelapa, Confidence: 0.40\n", "\n", "image 1/1 /content/plants-classification-3/test/Padi/paddy995_jpg.rf.609b85f63ba505eaa929ad91a333288b.jpg: 224x224 Padi 1.00, Dandelion 0.00, Ubi 0.00, Kangkung 0.00, Jahe 0.00, 8.8ms\n", "Speed: 9.8ms preprocess, 8.8ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Padi/paddy995_jpg.rf.609b85f63ba505eaa929ad91a333288b.jpg\n", "Ground Truth: Padi, Predicted Class: Padi, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Padi/aug_0_1571_jpg.rf.ebbb845d87e81ff53b9c85bc4bdcbb27.jpg: 224x224 Padi 1.00, Kelapa 0.00, Pepaya 0.00, Semangka 0.00, Kangkung 0.00, 8.8ms\n", "Speed: 7.5ms preprocess, 8.8ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Padi/aug_0_1571_jpg.rf.ebbb845d87e81ff53b9c85bc4bdcbb27.jpg\n", "Ground Truth: Padi, Predicted Class: Padi, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Padi/aug_0_791_jpg.rf.60adbc3878debf28ec516299be2fa531.jpg: 224x224 Padi 1.00, Kacang 0.00, Nanas 0.00, Kangkung 0.00, Jagung 0.00, 6.4ms\n", "Speed: 6.1ms preprocess, 6.4ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Padi/aug_0_791_jpg.rf.60adbc3878debf28ec516299be2fa531.jpg\n", "Ground Truth: Padi, Predicted Class: Padi, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Padi/aug_0_4373_jpg.rf.2686d002264d5bee119eeada8ed38823.jpg: 224x224 Padi 1.00, Kacang 0.00, Nanas 0.00, Mangga 0.00, Melon 0.00, 6.4ms\n", "Speed: 5.9ms preprocess, 6.4ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Padi/aug_0_4373_jpg.rf.2686d002264d5bee119eeada8ed38823.jpg\n", "Ground Truth: Padi, Predicted Class: Padi, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Padi/aug_0_1671_jpg.rf.93a475cbff06ade2cf881cac3a5fdd44.jpg: 224x224 Padi 1.00, Kacang 0.00, Nanas 0.00, Mangga 0.00, Jagung 0.00, 4.0ms\n", "Speed: 4.6ms preprocess, 4.0ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Padi/aug_0_1671_jpg.rf.93a475cbff06ade2cf881cac3a5fdd44.jpg\n", "Ground Truth: Padi, Predicted Class: Padi, Confidence: 1.00\n", "Processing Class Folder (Ground Truth): Kentang\n", "\n", "image 1/1 /content/plants-classification-3/test/Kentang/aug_0_8045_jpg.rf.3f385e4293de1da5c5db7575d65d2fe6.jpg: 224x224 Kentang 1.00, Timun 0.00, Ubi 0.00, Jambu 0.00, Jahe 0.00, 4.0ms\n", "Speed: 4.4ms preprocess, 4.0ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kentang/aug_0_8045_jpg.rf.3f385e4293de1da5c5db7575d65d2fe6.jpg\n", "Ground Truth: Kentang, Predicted Class: Kentang, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kentang/sweetpotatoes726_jpg.rf.c57354297bdccfdd5b258bdfd7230991.jpg: 224x224 Kentang 1.00, Timun 0.00, Terong 0.00, Ubi 0.00, Pisang 0.00, 4.1ms\n", "Speed: 7.7ms preprocess, 4.1ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kentang/sweetpotatoes726_jpg.rf.c57354297bdccfdd5b258bdfd7230991.jpg\n", "Ground Truth: Kentang, Predicted Class: Kentang, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kentang/aug_0_2617_jpg.rf.40ee2a56eea78a3a0594ab4484daa7c5.jpg: 224x224 Kentang 1.00, Ubi 0.00, Padi 0.00, KacangPanjang 0.00, Semangka 0.00, 3.9ms\n", "Speed: 4.6ms preprocess, 3.9ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kentang/aug_0_2617_jpg.rf.40ee2a56eea78a3a0594ab4484daa7c5.jpg\n", "Ground Truth: Kentang, Predicted Class: Kentang, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kentang/aug_0_6446_jpg.rf.4ec75253306ef3a0feeeed1e097f09e1.jpg: 224x224 Kentang 1.00, Ubi 0.00, Jambu 0.00, BawangMerah 0.00, Timun 0.00, 4.0ms\n", "Speed: 4.8ms preprocess, 4.0ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kentang/aug_0_6446_jpg.rf.4ec75253306ef3a0feeeed1e097f09e1.jpg\n", "Ground Truth: Kentang, Predicted Class: Kentang, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kentang/aug_0_1964_jpg.rf.d89384debceac14e6d67be52f35f8cde.jpg: 224x224 Kentang 1.00, Ubi 0.00, Mangga 0.00, Semangka 0.00, Pepaya 0.00, 5.4ms\n", "Speed: 6.3ms preprocess, 5.4ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kentang/aug_0_1964_jpg.rf.d89384debceac14e6d67be52f35f8cde.jpg\n", "Ground Truth: Kentang, Predicted Class: Kentang, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kentang/aug_0_813_jpg.rf.f17198fca7de4c7ea77b7b7f4b2ea10b.jpg: 224x224 Kentang 1.00, Terong 0.00, BawangMerah 0.00, Jahe 0.00, Ubi 0.00, 4.0ms\n", "Speed: 5.7ms preprocess, 4.0ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kentang/aug_0_813_jpg.rf.f17198fca7de4c7ea77b7b7f4b2ea10b.jpg\n", "Ground Truth: Kentang, Predicted Class: Kentang, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kentang/aug_0_1068_jpg.rf.6c5390a68a838cf8ac2c909557f15a8c.jpg: 224x224 Kentang 1.00, Ubi 0.00, Jambu 0.00, Daisy 0.00, Semangka 0.00, 4.5ms\n", "Speed: 5.4ms preprocess, 4.5ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kentang/aug_0_1068_jpg.rf.6c5390a68a838cf8ac2c909557f15a8c.jpg\n", "Ground Truth: Kentang, Predicted Class: Kentang, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kentang/aug_0_5541_jpg.rf.b8cfe956ff302c355b4d2017243b7618.jpg: 224x224 Kentang 1.00, Mangga 0.00, Jeruk 0.00, Ubi 0.00, BawangMerah 0.00, 6.4ms\n", "Speed: 6.5ms preprocess, 6.4ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kentang/aug_0_5541_jpg.rf.b8cfe956ff302c355b4d2017243b7618.jpg\n", "Ground Truth: Kentang, Predicted Class: Kentang, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kentang/aug_0_9283_jpg.rf.12e8bf624533a461dca53c54224cd418.jpg: 224x224 Kentang 1.00, Jahe 0.00, Kacang 0.00, Pepaya 0.00, Ubi 0.00, 4.0ms\n", "Speed: 4.7ms preprocess, 4.0ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kentang/aug_0_9283_jpg.rf.12e8bf624533a461dca53c54224cd418.jpg\n", "Ground Truth: Kentang, Predicted Class: Kentang, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kentang/aug_0_1519_jpg.rf.7c5996c4d7e67bdde312f867c47a62b4.jpg: 224x224 Kentang 1.00, Ubi 0.00, Mangga 0.00, BawangMerah 0.00, Jambu 0.00, 4.2ms\n", "Speed: 4.4ms preprocess, 4.2ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kentang/aug_0_1519_jpg.rf.7c5996c4d7e67bdde312f867c47a62b4.jpg\n", "Ground Truth: Kentang, Predicted Class: Kentang, Confidence: 1.00\n", "Processing Class Folder (Ground Truth): Kacang\n", "\n", "image 1/1 /content/plants-classification-3/test/Kacang/aug_0_5591_jpg.rf.df0fe95756429c62b356ccc72b8d6ebc.jpg: 224x224 Kacang 1.00, Jagung 0.00, Padi 0.00, Jeruk 0.00, Nanas 0.00, 4.0ms\n", "Speed: 5.6ms preprocess, 4.0ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kacang/aug_0_5591_jpg.rf.df0fe95756429c62b356ccc72b8d6ebc.jpg\n", "Ground Truth: Kacang, Predicted Class: Kacang, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kacang/aug_0_6709_jpg.rf.4fd2281f3b1ce8d14528ed37709392ff.jpg: 224x224 Kacang 1.00, Jagung 0.00, Melon 0.00, Jahe 0.00, Daisy 0.00, 4.1ms\n", "Speed: 4.4ms preprocess, 4.1ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kacang/aug_0_6709_jpg.rf.4fd2281f3b1ce8d14528ed37709392ff.jpg\n", "Ground Truth: Kacang, Predicted Class: Kacang, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kacang/aug_0_9953_jpg.rf.0f39e2b696b4817ac01ed100c7fae5d4.jpg: 224x224 Kacang 1.00, Jagung 0.00, Padi 0.00, KacangPanjang 0.00, Melon 0.00, 4.2ms\n", "Speed: 5.7ms preprocess, 4.2ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kacang/aug_0_9953_jpg.rf.0f39e2b696b4817ac01ed100c7fae5d4.jpg\n", "Ground Truth: Kacang, Predicted Class: Kacang, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kacang/aug_0_5996_jpg.rf.6514b2f20f4e212db39156bdbb6ca045.jpg: 224x224 Kacang 1.00, Melon 0.00, Jagung 0.00, Mawar 0.00, Semangka 0.00, 4.2ms\n", "Speed: 6.9ms preprocess, 4.2ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kacang/aug_0_5996_jpg.rf.6514b2f20f4e212db39156bdbb6ca045.jpg\n", "Ground Truth: Kacang, Predicted Class: Kacang, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kacang/soybeans917_jpg.rf.af15201c6b8754451cab189c8221f0e3.jpg: 224x224 Kacang 1.00, Jagung 0.00, Daisy 0.00, Padi 0.00, Jahe 0.00, 4.1ms\n", "Speed: 4.5ms preprocess, 4.1ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kacang/soybeans917_jpg.rf.af15201c6b8754451cab189c8221f0e3.jpg\n", "Ground Truth: Kacang, Predicted Class: Kacang, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kacang/soybeans791_jpg.rf.b70c176ab00346474d978270d4ca5e41.jpg: 224x224 Kacang 1.00, Bayam 0.00, Kangkung 0.00, Timun 0.00, Pisang 0.00, 4.3ms\n", "Speed: 7.8ms preprocess, 4.3ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kacang/soybeans791_jpg.rf.b70c176ab00346474d978270d4ca5e41.jpg\n", "Ground Truth: Kacang, Predicted Class: Kacang, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kacang/aug_0_5204_jpg.rf.45e14d96f6ad81a0f85866dd9d720994.jpg: 224x224 Kacang 1.00, Bayam 0.00, Jambu 0.00, Mawar 0.00, Pepaya 0.00, 4.6ms\n", "Speed: 5.1ms preprocess, 4.6ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kacang/aug_0_5204_jpg.rf.45e14d96f6ad81a0f85866dd9d720994.jpg\n", "Ground Truth: Kacang, Predicted Class: Kacang, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kacang/soybeans290_jpg.rf.d952dfd7b34700b6b6103cda8bc9dd46.jpg: 224x224 Kacang 1.00, Padi 0.00, Semangka 0.00, Jagung 0.00, BawangMerah 0.00, 4.3ms\n", "Speed: 7.2ms preprocess, 4.3ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kacang/soybeans290_jpg.rf.d952dfd7b34700b6b6103cda8bc9dd46.jpg\n", "Ground Truth: Kacang, Predicted Class: Kacang, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kacang/soybeans296_jpg.rf.721700943c5fbd58a5e3a94e2adaf34c.jpg: 224x224 Kacang 1.00, BungaMatahari 0.00, Mawar 0.00, Daisy 0.00, Padi 0.00, 4.5ms\n", "Speed: 6.4ms preprocess, 4.5ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kacang/soybeans296_jpg.rf.721700943c5fbd58a5e3a94e2adaf34c.jpg\n", "Ground Truth: Kacang, Predicted Class: Kacang, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kacang/aug_0_1448_jpg.rf.5629f5d49280439d03e5d384e02891d7.jpg: 224x224 Kacang 1.00, Semangka 0.00, Jagung 0.00, Padi 0.00, Melon 0.00, 7.6ms\n", "Speed: 7.1ms preprocess, 7.6ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kacang/aug_0_1448_jpg.rf.5629f5d49280439d03e5d384e02891d7.jpg\n", "Ground Truth: Kacang, Predicted Class: Kacang, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kacang/aug_0_9345_jpg.rf.4bf23280056189c23a15bb031188ec34.jpg: 224x224 Kacang 1.00, Padi 0.00, Jeruk 0.00, Jagung 0.00, Semangka 0.00, 5.7ms\n", "Speed: 7.3ms preprocess, 5.7ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kacang/aug_0_9345_jpg.rf.4bf23280056189c23a15bb031188ec34.jpg\n", "Ground Truth: Kacang, Predicted Class: Kacang, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kacang/aug_0_9987_jpg.rf.b6af6880d8173940a41e9a7520bfe106.jpg: 224x224 Kacang 1.00, Padi 0.00, Jahe 0.00, Dandelion 0.00, Jagung 0.00, 6.1ms\n", "Speed: 7.0ms preprocess, 6.1ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kacang/aug_0_9987_jpg.rf.b6af6880d8173940a41e9a7520bfe106.jpg\n", "Ground Truth: Kacang, Predicted Class: Kacang, Confidence: 1.00\n", "Processing Class Folder (Ground Truth): Jeruk\n", "\n", "image 1/1 /content/plants-classification-3/test/Jeruk/aug_0_1985_jpg.rf.a34069bff3783a63b4de24eedd88d7c3.jpg: 224x224 Jeruk 0.96, Melon 0.02, Pisang 0.01, Pepaya 0.01, Mangga 0.00, 5.6ms\n", "Speed: 6.0ms preprocess, 5.6ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jeruk/aug_0_1985_jpg.rf.a34069bff3783a63b4de24eedd88d7c3.jpg\n", "Ground Truth: Jeruk, Predicted Class: Jeruk, Confidence: 0.96\n", "\n", "image 1/1 /content/plants-classification-3/test/Jeruk/aug_0_288_jpg.rf.7740a68c020ada3e562c717a89b2a187.jpg: 224x224 Jeruk 1.00, Semangka 0.00, Jahe 0.00, Melon 0.00, Jambu 0.00, 5.7ms\n", "Speed: 5.7ms preprocess, 5.7ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jeruk/aug_0_288_jpg.rf.7740a68c020ada3e562c717a89b2a187.jpg\n", "Ground Truth: Jeruk, Predicted Class: Jeruk, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jeruk/aug_0_1626_jpg.rf.45674a9daee42c016c712eb13057603e.jpg: 224x224 Jeruk 1.00, Kentang 0.00, Jahe 0.00, Bayam 0.00, Melon 0.00, 9.3ms\n", "Speed: 9.0ms preprocess, 9.3ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jeruk/aug_0_1626_jpg.rf.45674a9daee42c016c712eb13057603e.jpg\n", "Ground Truth: Jeruk, Predicted Class: Jeruk, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jeruk/aug_0_9212_jpg.rf.0ccb5dd688315b1e35115018483a200f.jpg: 224x224 Jeruk 1.00, Nanas 0.00, Melon 0.00, Mawar 0.00, Jahe 0.00, 13.0ms\n", "Speed: 7.9ms preprocess, 13.0ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jeruk/aug_0_9212_jpg.rf.0ccb5dd688315b1e35115018483a200f.jpg\n", "Ground Truth: Jeruk, Predicted Class: Jeruk, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jeruk/aug_0_7728_jpg.rf.b750e580d8c1c3e396808a495f746dfc.jpg: 224x224 Jeruk 0.91, Pisang 0.04, Mangga 0.01, Pepaya 0.01, Kacang 0.01, 8.0ms\n", "Speed: 7.0ms preprocess, 8.0ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jeruk/aug_0_7728_jpg.rf.b750e580d8c1c3e396808a495f746dfc.jpg\n", "Ground Truth: Jeruk, Predicted Class: Jeruk, Confidence: 0.91\n", "\n", "image 1/1 /content/plants-classification-3/test/Jeruk/aug_0_4428_jpg.rf.80c32cd78e8fce8d63d6859db5bbad9e.jpg: 224x224 Jeruk 1.00, Dandelion 0.00, Melon 0.00, Daisy 0.00, Kentang 0.00, 6.4ms\n", "Speed: 7.1ms preprocess, 6.4ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jeruk/aug_0_4428_jpg.rf.80c32cd78e8fce8d63d6859db5bbad9e.jpg\n", "Ground Truth: Jeruk, Predicted Class: Jeruk, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jeruk/aug_0_115_jpg.rf.9292ea9ce019b6f73c9e6db186d27616.jpg: 224x224 Jeruk 1.00, Mangga 0.00, Pepaya 0.00, Semangka 0.00, Pisang 0.00, 6.4ms\n", "Speed: 7.3ms preprocess, 6.4ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jeruk/aug_0_115_jpg.rf.9292ea9ce019b6f73c9e6db186d27616.jpg\n", "Ground Truth: Jeruk, Predicted Class: Jeruk, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jeruk/aug_0_2709_jpg.rf.03237c5d573cf4f22c46aedf3ed324e8.jpg: 224x224 Jeruk 1.00, BawangMerah 0.00, Mangga 0.00, Pepaya 0.00, Melon 0.00, 6.0ms\n", "Speed: 6.5ms preprocess, 6.0ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jeruk/aug_0_2709_jpg.rf.03237c5d573cf4f22c46aedf3ed324e8.jpg\n", "Ground Truth: Jeruk, Predicted Class: Jeruk, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jeruk/aug_0_557_jpg.rf.a7f25648a3ee5eb438d4dc623098f393.jpg: 224x224 Jeruk 1.00, Nanas 0.00, Melon 0.00, Ubi 0.00, Dandelion 0.00, 6.2ms\n", "Speed: 6.3ms preprocess, 6.2ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jeruk/aug_0_557_jpg.rf.a7f25648a3ee5eb438d4dc623098f393.jpg\n", "Ground Truth: Jeruk, Predicted Class: Jeruk, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jeruk/orange537_jpg.rf.e33fbdbb64b75c75f6d4164ddc821aa0.jpg: 224x224 Jeruk 1.00, Mawar 0.00, Melon 0.00, Tulip 0.00, Dandelion 0.00, 5.7ms\n", "Speed: 5.9ms preprocess, 5.7ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jeruk/orange537_jpg.rf.e33fbdbb64b75c75f6d4164ddc821aa0.jpg\n", "Ground Truth: Jeruk, Predicted Class: Jeruk, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jeruk/aug_0_2479_jpg.rf.8af37cbaf488210b192e92553d0bd500.jpg: 224x224 Jeruk 1.00, Melon 0.00, Mangga 0.00, Pepaya 0.00, BawangMerah 0.00, 6.2ms\n", "Speed: 5.7ms preprocess, 6.2ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jeruk/aug_0_2479_jpg.rf.8af37cbaf488210b192e92553d0bd500.jpg\n", "Ground Truth: Jeruk, Predicted Class: Jeruk, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jeruk/aug_0_9744_jpg.rf.dff3b6630a9a62cee5eb18be4531fe03.jpg: 224x224 Jeruk 0.85, BawangMerah 0.14, Kentang 0.01, Kacang 0.00, Jahe 0.00, 5.5ms\n", "Speed: 6.8ms preprocess, 5.5ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jeruk/aug_0_9744_jpg.rf.dff3b6630a9a62cee5eb18be4531fe03.jpg\n", "Ground Truth: Jeruk, Predicted Class: Jeruk, Confidence: 0.85\n", "\n", "image 1/1 /content/plants-classification-3/test/Jeruk/aug_0_6060_jpg.rf.1cd432eb4dba8e78d515150e3d94d719.jpg: 224x224 Jeruk 1.00, Pepaya 0.00, Terong 0.00, Cabai 0.00, Timun 0.00, 6.1ms\n", "Speed: 11.3ms preprocess, 6.1ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jeruk/aug_0_6060_jpg.rf.1cd432eb4dba8e78d515150e3d94d719.jpg\n", "Ground Truth: Jeruk, Predicted Class: Jeruk, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jeruk/aug_0_9081_jpg.rf.76c6ef261cf93c0ee8fcf6206cfcf04f.jpg: 224x224 Jeruk 1.00, Timun 0.00, Melon 0.00, Daisy 0.00, Ubi 0.00, 6.0ms\n", "Speed: 7.4ms preprocess, 6.0ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jeruk/aug_0_9081_jpg.rf.76c6ef261cf93c0ee8fcf6206cfcf04f.jpg\n", "Ground Truth: Jeruk, Predicted Class: Jeruk, Confidence: 1.00\n", "Processing Class Folder (Ground Truth): Daisy\n", "\n", "image 1/1 /content/plants-classification-3/test/Daisy/16291797949_a1b1b7c2bd_n_jpg.rf.4cf59438455feba213e5cc2d88955b29.jpg: 224x224 Daisy 0.98, Jagung 0.02, Kacang 0.00, BungaMatahari 0.00, Dandelion 0.00, 6.5ms\n", "Speed: 7.7ms preprocess, 6.5ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Daisy/16291797949_a1b1b7c2bd_n_jpg.rf.4cf59438455feba213e5cc2d88955b29.jpg\n", "Ground Truth: Daisy, Predicted Class: Daisy, Confidence: 0.98\n", "\n", "image 1/1 /content/plants-classification-3/test/Daisy/5765646947_82e95a9cc9_n_jpg.rf.f53dd297e1747b3d05afd750f15e181d.jpg: 224x224 Daisy 1.00, Jagung 0.00, Jeruk 0.00, Mawar 0.00, Dandelion 0.00, 6.0ms\n", "Speed: 6.0ms preprocess, 6.0ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Daisy/5765646947_82e95a9cc9_n_jpg.rf.f53dd297e1747b3d05afd750f15e181d.jpg\n", "Ground Truth: Daisy, Predicted Class: Daisy, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Daisy/14307766919_fac3c37a6b_m_jpg.rf.ba72f9f0b72b17fb46d01bbafe41caeb.jpg: 224x224 Daisy 0.99, Mawar 0.00, Tulip 0.00, BawangMerah 0.00, Jeruk 0.00, 5.7ms\n", "Speed: 5.7ms preprocess, 5.7ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Daisy/14307766919_fac3c37a6b_m_jpg.rf.ba72f9f0b72b17fb46d01bbafe41caeb.jpg\n", "Ground Truth: Daisy, Predicted Class: Daisy, Confidence: 0.99\n", "\n", "image 1/1 /content/plants-classification-3/test/Daisy/301964511_fab84ea1c1_jpg.rf.123aa9249e434e7d4cd62a9eeed1f522.jpg: 224x224 Daisy 1.00, Jeruk 0.00, Pepaya 0.00, Pisang 0.00, Dandelion 0.00, 7.1ms\n", "Speed: 7.6ms preprocess, 7.1ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Daisy/301964511_fab84ea1c1_jpg.rf.123aa9249e434e7d4cd62a9eeed1f522.jpg\n", "Ground Truth: Daisy, Predicted Class: Daisy, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Daisy/3456403987_5bd5fa6ece_n_jpg.rf.a3ee01c23bbd5f890de16622702fd37a.jpg: 224x224 Daisy 0.99, Dandelion 0.00, Tulip 0.00, Mawar 0.00, BungaMatahari 0.00, 5.8ms\n", "Speed: 6.3ms preprocess, 5.8ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Daisy/3456403987_5bd5fa6ece_n_jpg.rf.a3ee01c23bbd5f890de16622702fd37a.jpg\n", "Ground Truth: Daisy, Predicted Class: Daisy, Confidence: 0.99\n", "\n", "image 1/1 /content/plants-classification-3/test/Daisy/5944315415_2be8abeb2f_m_jpg.rf.1980a0b4448f2d04f722c9f59a8f8491.jpg: 224x224 Daisy 0.87, Dandelion 0.07, Mawar 0.05, BungaMatahari 0.00, Ubi 0.00, 6.3ms\n", "Speed: 7.7ms preprocess, 6.3ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Daisy/5944315415_2be8abeb2f_m_jpg.rf.1980a0b4448f2d04f722c9f59a8f8491.jpg\n", "Ground Truth: Daisy, Predicted Class: Daisy, Confidence: 0.87\n", "Processing Class Folder (Ground Truth): Pepaya\n", "\n", "image 1/1 /content/plants-classification-3/test/Pepaya/aug_0_3805_jpg.rf.1b796e0031f8725756ab56907e2b8882.jpg: 224x224 Pepaya 1.00, Timun 0.00, Pisang 0.00, Mangga 0.00, Semangka 0.00, 5.6ms\n", "Speed: 6.5ms preprocess, 5.6ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Pepaya/aug_0_3805_jpg.rf.1b796e0031f8725756ab56907e2b8882.jpg\n", "Ground Truth: Pepaya, Predicted Class: Pepaya, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Pepaya/papaya796_jpg.rf.b35cf15e1a3fe804f9ad7b124f3dbe95.jpg: 224x224 Pepaya 1.00, Jagung 0.00, Tulip 0.00, Kacang 0.00, BungaMatahari 0.00, 7.6ms\n", "Speed: 7.2ms preprocess, 7.6ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Pepaya/papaya796_jpg.rf.b35cf15e1a3fe804f9ad7b124f3dbe95.jpg\n", "Ground Truth: Pepaya, Predicted Class: Pepaya, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Pepaya/aug_0_2580_jpg.rf.846d01f136ff0b83219c1e3e4339ace9.jpg: 224x224 Pepaya 1.00, Melon 0.00, Jeruk 0.00, Mangga 0.00, Jambu 0.00, 5.5ms\n", "Speed: 6.8ms preprocess, 5.5ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Pepaya/aug_0_2580_jpg.rf.846d01f136ff0b83219c1e3e4339ace9.jpg\n", "Ground Truth: Pepaya, Predicted Class: Pepaya, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Pepaya/aug_0_8713_jpg.rf.1995c9efd16a652f66974b1652dffd17.jpg: 224x224 Pepaya 1.00, Melon 0.00, Nanas 0.00, Semangka 0.00, Mangga 0.00, 5.8ms\n", "Speed: 8.7ms preprocess, 5.8ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Pepaya/aug_0_8713_jpg.rf.1995c9efd16a652f66974b1652dffd17.jpg\n", "Ground Truth: Pepaya, Predicted Class: Pepaya, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Pepaya/aug_0_3374_jpg.rf.9bbdf40c2fe91ceb0b3829a1eecb08a0.jpg: 224x224 Pepaya 1.00, Kelapa 0.00, Melon 0.00, Timun 0.00, Semangka 0.00, 8.0ms\n", "Speed: 8.2ms preprocess, 8.0ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Pepaya/aug_0_3374_jpg.rf.9bbdf40c2fe91ceb0b3829a1eecb08a0.jpg\n", "Ground Truth: Pepaya, Predicted Class: Pepaya, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Pepaya/aug_0_1582_jpg.rf.06fc36627b952d796285ca848ae80747.jpg: 224x224 Pepaya 1.00, Jambu 0.00, Jeruk 0.00, Melon 0.00, Mangga 0.00, 6.2ms\n", "Speed: 7.1ms preprocess, 6.2ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Pepaya/aug_0_1582_jpg.rf.06fc36627b952d796285ca848ae80747.jpg\n", "Ground Truth: Pepaya, Predicted Class: Pepaya, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Pepaya/aug_0_8443_jpg.rf.0ca656e4eaecd7beebd609f955769434.jpg: 224x224 Pepaya 1.00, Pisang 0.00, Timun 0.00, Mangga 0.00, Semangka 0.00, 6.1ms\n", "Speed: 6.0ms preprocess, 6.1ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Pepaya/aug_0_8443_jpg.rf.0ca656e4eaecd7beebd609f955769434.jpg\n", "Ground Truth: Pepaya, Predicted Class: Pepaya, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Pepaya/aug_0_3125_jpg.rf.4781135f7b0176f98bca5c280fecced3.jpg: 224x224 Pepaya 1.00, Mangga 0.00, Kentang 0.00, Jambu 0.00, Melon 0.00, 6.1ms\n", "Speed: 6.2ms preprocess, 6.1ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Pepaya/aug_0_3125_jpg.rf.4781135f7b0176f98bca5c280fecced3.jpg\n", "Ground Truth: Pepaya, Predicted Class: Pepaya, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Pepaya/aug_0_8356_jpg.rf.bbaec8df64d08b5124f1f591d2adc996.jpg: 224x224 Pepaya 1.00, Melon 0.00, Jagung 0.00, BungaMatahari 0.00, Nanas 0.00, 8.1ms\n", "Speed: 7.5ms preprocess, 8.1ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Pepaya/aug_0_8356_jpg.rf.bbaec8df64d08b5124f1f591d2adc996.jpg\n", "Ground Truth: Pepaya, Predicted Class: Pepaya, Confidence: 1.00\n", "Processing Class Folder (Ground Truth): Kelapa\n", "\n", "image 1/1 /content/plants-classification-3/test/Kelapa/aug_0_4249_jpg.rf.5bc40639327d96913ddc7d6e8be7c2da.jpg: 224x224 Kelapa 1.00, Jambu 0.00, BawangMerah 0.00, Terong 0.00, Semangka 0.00, 6.0ms\n", "Speed: 8.9ms preprocess, 6.0ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kelapa/aug_0_4249_jpg.rf.5bc40639327d96913ddc7d6e8be7c2da.jpg\n", "Ground Truth: Kelapa, Predicted Class: Kelapa, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kelapa/aug_0_71_jpg.rf.39f82ac5bb7b831665c3f27769b92996.jpg: 224x224 Kelapa 1.00, Jambu 0.00, Jeruk 0.00, Ubi 0.00, Mangga 0.00, 6.5ms\n", "Speed: 7.4ms preprocess, 6.5ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kelapa/aug_0_71_jpg.rf.39f82ac5bb7b831665c3f27769b92996.jpg\n", "Ground Truth: Kelapa, Predicted Class: Kelapa, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kelapa/aug_0_9391_jpg.rf.6e197b82535d43c2ffabe9df9e4f7ac4.jpg: 224x224 Kelapa 1.00, Ubi 0.00, Jambu 0.00, Jahe 0.00, Jagung 0.00, 5.9ms\n", "Speed: 10.3ms preprocess, 5.9ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kelapa/aug_0_9391_jpg.rf.6e197b82535d43c2ffabe9df9e4f7ac4.jpg\n", "Ground Truth: Kelapa, Predicted Class: Kelapa, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kelapa/aug_0_9471_jpg.rf.55df2368153a8ded31cea455918223d5.jpg: 224x224 Kelapa 1.00, BawangMerah 0.00, Pepaya 0.00, Semangka 0.00, Jambu 0.00, 4.5ms\n", "Speed: 8.0ms preprocess, 4.5ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kelapa/aug_0_9471_jpg.rf.55df2368153a8ded31cea455918223d5.jpg\n", "Ground Truth: Kelapa, Predicted Class: Kelapa, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kelapa/aug_0_5303_jpg.rf.d97c86c64322a6f5402c643b636865af.jpg: 224x224 Kelapa 1.00, Dandelion 0.00, Jambu 0.00, Padi 0.00, Melon 0.00, 5.5ms\n", "Speed: 7.2ms preprocess, 5.5ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kelapa/aug_0_5303_jpg.rf.d97c86c64322a6f5402c643b636865af.jpg\n", "Ground Truth: Kelapa, Predicted Class: Kelapa, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kelapa/aug_0_310_jpg.rf.b3e8af47256efc26120c531db9492942.jpg: 224x224 Kelapa 1.00, Jambu 0.00, Timun 0.00, Pepaya 0.00, Jagung 0.00, 4.7ms\n", "Speed: 5.5ms preprocess, 4.7ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kelapa/aug_0_310_jpg.rf.b3e8af47256efc26120c531db9492942.jpg\n", "Ground Truth: Kelapa, Predicted Class: Kelapa, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kelapa/aug_0_4062_jpg.rf.b0f75cdf03ba48edf4182cb18bbc9458.jpg: 224x224 Kelapa 0.97, Jeruk 0.02, Mangga 0.01, Kacang 0.00, Pepaya 0.00, 4.3ms\n", "Speed: 7.2ms preprocess, 4.3ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kelapa/aug_0_4062_jpg.rf.b0f75cdf03ba48edf4182cb18bbc9458.jpg\n", "Ground Truth: Kelapa, Predicted Class: Kelapa, Confidence: 0.97\n", "\n", "image 1/1 /content/plants-classification-3/test/Kelapa/aug_0_2295_jpg.rf.1bdaca0ebe3bbc4ce2f437d847a8b321.jpg: 224x224 Kelapa 1.00, Mangga 0.00, Dandelion 0.00, Daisy 0.00, Jambu 0.00, 4.2ms\n", "Speed: 6.5ms preprocess, 4.2ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kelapa/aug_0_2295_jpg.rf.1bdaca0ebe3bbc4ce2f437d847a8b321.jpg\n", "Ground Truth: Kelapa, Predicted Class: Kelapa, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kelapa/aug_0_1071_jpg.rf.44d1eed3276c8002264b763349314bc0.jpg: 224x224 Kelapa 1.00, Jambu 0.00, Mangga 0.00, Pepaya 0.00, Semangka 0.00, 6.5ms\n", "Speed: 8.2ms preprocess, 6.5ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kelapa/aug_0_1071_jpg.rf.44d1eed3276c8002264b763349314bc0.jpg\n", "Ground Truth: Kelapa, Predicted Class: Kelapa, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kelapa/coconut631_jpg.rf.98fe5af41b8719287cbb2b152b5cfe3e.jpg: 224x224 Kelapa 1.00, Mangga 0.00, Pepaya 0.00, Kacang 0.00, Dandelion 0.00, 6.1ms\n", "Speed: 5.5ms preprocess, 6.1ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kelapa/coconut631_jpg.rf.98fe5af41b8719287cbb2b152b5cfe3e.jpg\n", "Ground Truth: Kelapa, Predicted Class: Kelapa, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kelapa/aug_0_2740_jpg.rf.f1ec55ef64be5da3eaee2e20b9e3e202.jpg: 224x224 Kelapa 0.87, Kentang 0.10, Jambu 0.01, Semangka 0.01, Pepaya 0.01, 7.3ms\n", "Speed: 7.4ms preprocess, 7.3ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kelapa/aug_0_2740_jpg.rf.f1ec55ef64be5da3eaee2e20b9e3e202.jpg\n", "Ground Truth: Kelapa, Predicted Class: Kelapa, Confidence: 0.87\n", "\n", "image 1/1 /content/plants-classification-3/test/Kelapa/aug_0_2909_jpg.rf.8f79d321f82b73cead6f1b3e10107bee.jpg: 224x224 Kelapa 1.00, Jeruk 0.00, Kacang 0.00, Mangga 0.00, Jambu 0.00, 5.7ms\n", "Speed: 5.7ms preprocess, 5.7ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kelapa/aug_0_2909_jpg.rf.8f79d321f82b73cead6f1b3e10107bee.jpg\n", "Ground Truth: Kelapa, Predicted Class: Kelapa, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kelapa/aug_0_5743_jpg.rf.4ed2b4977c03e91076e4fc9977f852cd.jpg: 224x224 Kelapa 1.00, Jeruk 0.00, Jagung 0.00, Jambu 0.00, Mangga 0.00, 5.6ms\n", "Speed: 7.6ms preprocess, 5.6ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kelapa/aug_0_5743_jpg.rf.4ed2b4977c03e91076e4fc9977f852cd.jpg\n", "Ground Truth: Kelapa, Predicted Class: Kelapa, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kelapa/aug_0_5510_jpg.rf.c66ca67cf2c04318e6ba6c291fb09cd2.jpg: 224x224 Kelapa 1.00, Mangga 0.00, Pepaya 0.00, Kentang 0.00, Pisang 0.00, 10.2ms\n", "Speed: 7.8ms preprocess, 10.2ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kelapa/aug_0_5510_jpg.rf.c66ca67cf2c04318e6ba6c291fb09cd2.jpg\n", "Ground Truth: Kelapa, Predicted Class: Kelapa, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kelapa/aug_0_2900_jpg.rf.ea16478c9c71536cd921d2f5b6867668.jpg: 224x224 Kelapa 1.00, Jambu 0.00, Pepaya 0.00, Kentang 0.00, Terong 0.00, 8.1ms\n", "Speed: 8.4ms preprocess, 8.1ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kelapa/aug_0_2900_jpg.rf.ea16478c9c71536cd921d2f5b6867668.jpg\n", "Ground Truth: Kelapa, Predicted Class: Kelapa, Confidence: 1.00\n", "Processing Class Folder (Ground Truth): Mawar\n", "\n", "image 1/1 /content/plants-classification-3/test/Mawar/5335945781_e45852fa49_n_jpg.rf.4e8427874bb9ce80ab19f40b69d08b17.jpg: 224x224 Mawar 1.00, Tulip 0.00, Daisy 0.00, Jeruk 0.00, BungaMatahari 0.00, 7.8ms\n", "Speed: 6.9ms preprocess, 7.8ms inference, 0.2ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Mawar/5335945781_e45852fa49_n_jpg.rf.4e8427874bb9ce80ab19f40b69d08b17.jpg\n", "Ground Truth: Mawar, Predicted Class: Mawar, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Mawar/16001846141_393fdb887e_n_jpg.rf.4338c1eb301b2788c3eb08d6686f6833.jpg: 224x224 Mawar 1.00, BungaMatahari 0.00, Dandelion 0.00, Tulip 0.00, Jeruk 0.00, 7.8ms\n", "Speed: 8.1ms preprocess, 7.8ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Mawar/16001846141_393fdb887e_n_jpg.rf.4338c1eb301b2788c3eb08d6686f6833.jpg\n", "Ground Truth: Mawar, Predicted Class: Mawar, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Mawar/7420699022_60fa574524_m_jpg.rf.593d24601b9c8c24cc475d18deed90bd.jpg: 224x224 Mawar 0.50, Tulip 0.45, Dandelion 0.03, Jeruk 0.01, Daisy 0.00, 8.1ms\n", "Speed: 8.5ms preprocess, 8.1ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Mawar/7420699022_60fa574524_m_jpg.rf.593d24601b9c8c24cc475d18deed90bd.jpg\n", "Ground Truth: Mawar, Predicted Class: Mawar, Confidence: 0.50\n", "\n", "image 1/1 /content/plants-classification-3/test/Mawar/5212877807_a3ddf06a7c_n_jpg.rf.8a40b5b72eb42ca7303c3fa37679c4e8.jpg: 224x224 Daisy 0.98, BungaMatahari 0.01, Dandelion 0.01, BawangMerah 0.00, Bayam 0.00, 6.1ms\n", "Speed: 6.8ms preprocess, 6.1ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Mawar/5212877807_a3ddf06a7c_n_jpg.rf.8a40b5b72eb42ca7303c3fa37679c4e8.jpg\n", "Ground Truth: Mawar, Predicted Class: Daisy, Confidence: 0.98\n", "\n", "image 1/1 /content/plants-classification-3/test/Mawar/1402130395_0b89d76029_jpg.rf.be311a6e950c842b3219f2f050deb594.jpg: 224x224 Mawar 1.00, Tulip 0.00, Kacang 0.00, Jeruk 0.00, BungaMatahari 0.00, 8.8ms\n", "Speed: 6.9ms preprocess, 8.8ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Mawar/1402130395_0b89d76029_jpg.rf.be311a6e950c842b3219f2f050deb594.jpg\n", "Ground Truth: Mawar, Predicted Class: Mawar, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Mawar/9309388105_12c0b8dd54_m_jpg.rf.a52f8e6afa94d83ea2f666714669e4fe.jpg: 224x224 Mawar 0.70, Tulip 0.19, Daisy 0.09, Mangga 0.01, Jambu 0.00, 4.7ms\n", "Speed: 5.2ms preprocess, 4.7ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Mawar/9309388105_12c0b8dd54_m_jpg.rf.a52f8e6afa94d83ea2f666714669e4fe.jpg\n", "Ground Truth: Mawar, Predicted Class: Mawar, Confidence: 0.70\n", "\n", "image 1/1 /content/plants-classification-3/test/Mawar/2888138918_402096c7fb_jpg.rf.83e24a23015758fd72e5f9106d4a7bec.jpg: 224x224 Mawar 1.00, Tulip 0.00, Jambu 0.00, Semangka 0.00, Kacang 0.00, 4.1ms\n", "Speed: 5.4ms preprocess, 4.1ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Mawar/2888138918_402096c7fb_jpg.rf.83e24a23015758fd72e5f9106d4a7bec.jpg\n", "Ground Truth: Mawar, Predicted Class: Mawar, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Mawar/14176042519_5792b37555_jpg.rf.35a8dbc99940fda0d332c3e83945060c.jpg: 224x224 Mawar 1.00, Daisy 0.00, Jeruk 0.00, Dandelion 0.00, Tulip 0.00, 4.2ms\n", "Speed: 7.4ms preprocess, 4.2ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Mawar/14176042519_5792b37555_jpg.rf.35a8dbc99940fda0d332c3e83945060c.jpg\n", "Ground Truth: Mawar, Predicted Class: Mawar, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Mawar/5159317458_bbb22e2f65_n_jpg.rf.b0b0b7cd55f7e88ccadf3c0d94dc8c1d.jpg: 224x224 Mawar 1.00, Kentang 0.00, Tulip 0.00, Daisy 0.00, Dandelion 0.00, 4.5ms\n", "Speed: 5.6ms preprocess, 4.5ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Mawar/5159317458_bbb22e2f65_n_jpg.rf.b0b0b7cd55f7e88ccadf3c0d94dc8c1d.jpg\n", "Ground Truth: Mawar, Predicted Class: Mawar, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Mawar/5002446614_bb2a5b4bc1_n_jpg.rf.bb34a66368f21868e85bcd977d011d62.jpg: 224x224 Mawar 1.00, Dandelion 0.00, Jahe 0.00, Daisy 0.00, BungaMatahari 0.00, 5.7ms\n", "Speed: 5.5ms preprocess, 5.7ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Mawar/5002446614_bb2a5b4bc1_n_jpg.rf.bb34a66368f21868e85bcd977d011d62.jpg\n", "Ground Truth: Mawar, Predicted Class: Mawar, Confidence: 1.00\n", "Processing Class Folder (Ground Truth): Timun\n", "\n", "image 1/1 /content/plants-classification-3/test/Timun/aug_0_8824_jpg.rf.a07cdda81823020db654132345d23e7a.jpg: 224x224 Timun 1.00, Semangka 0.00, Mangga 0.00, Kacang 0.00, KacangPanjang 0.00, 4.3ms\n", "Speed: 6.1ms preprocess, 4.3ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Timun/aug_0_8824_jpg.rf.a07cdda81823020db654132345d23e7a.jpg\n", "Ground Truth: Timun, Predicted Class: Timun, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Timun/aug_0_9751_jpg.rf.f071ebec9165fb39fca485df7fc1a3ff.jpg: 224x224 Timun 1.00, Jambu 0.00, Ubi 0.00, Jahe 0.00, Terong 0.00, 4.3ms\n", "Speed: 5.4ms preprocess, 4.3ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Timun/aug_0_9751_jpg.rf.f071ebec9165fb39fca485df7fc1a3ff.jpg\n", "Ground Truth: Timun, Predicted Class: Timun, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Timun/cucumber931_jpg.rf.55b3eae8ef44db737495d874d57a93f7.jpg: 224x224 Timun 1.00, Mangga 0.00, KacangPanjang 0.00, Jambu 0.00, Pisang 0.00, 4.3ms\n", "Speed: 6.3ms preprocess, 4.3ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Timun/cucumber931_jpg.rf.55b3eae8ef44db737495d874d57a93f7.jpg\n", "Ground Truth: Timun, Predicted Class: Timun, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Timun/aug_0_9863_jpg.rf.3853c329ccc08e2ebb7df30da4cece3d.jpg: 224x224 Timun 1.00, Pepaya 0.00, Mangga 0.00, Jambu 0.00, Ubi 0.00, 4.3ms\n", "Speed: 5.4ms preprocess, 4.3ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Timun/aug_0_9863_jpg.rf.3853c329ccc08e2ebb7df30da4cece3d.jpg\n", "Ground Truth: Timun, Predicted Class: Timun, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Timun/aug_0_4886_jpg.rf.bfedf94d011d1b413e5c9c49df244c7e.jpg: 224x224 Timun 1.00, Terong 0.00, Jambu 0.00, Pepaya 0.00, Semangka 0.00, 4.5ms\n", "Speed: 7.6ms preprocess, 4.5ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Timun/aug_0_4886_jpg.rf.bfedf94d011d1b413e5c9c49df244c7e.jpg\n", "Ground Truth: Timun, Predicted Class: Timun, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Timun/cucumber901_jpg.rf.2a54452b57cb66b34b7abc8ab3b505d5.jpg: 224x224 Terong 0.59, Timun 0.38, Bayam 0.02, Dandelion 0.00, Cabai 0.00, 4.4ms\n", "Speed: 5.0ms preprocess, 4.4ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Timun/cucumber901_jpg.rf.2a54452b57cb66b34b7abc8ab3b505d5.jpg\n", "Ground Truth: Timun, Predicted Class: Terong, Confidence: 0.59\n", "\n", "image 1/1 /content/plants-classification-3/test/Timun/aug_0_7065_jpg.rf.9c62da13e3f6b38466f509e9b2e893c3.jpg: 224x224 Timun 1.00, Terong 0.00, KacangPanjang 0.00, Jambu 0.00, Ubi 0.00, 4.7ms\n", "Speed: 6.0ms preprocess, 4.7ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Timun/aug_0_7065_jpg.rf.9c62da13e3f6b38466f509e9b2e893c3.jpg\n", "Ground Truth: Timun, Predicted Class: Timun, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Timun/aug_0_553_jpg.rf.64be98a671939613652e7816cf41d5a6.jpg: 224x224 Timun 1.00, Mangga 0.00, Pepaya 0.00, BungaMatahari 0.00, Terong 0.00, 4.1ms\n", "Speed: 5.0ms preprocess, 4.1ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Timun/aug_0_553_jpg.rf.64be98a671939613652e7816cf41d5a6.jpg\n", "Ground Truth: Timun, Predicted Class: Timun, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Timun/aug_0_8725_jpg.rf.40d829a401c78e3370b8dc02498b79f3.jpg: 224x224 Timun 1.00, Pisang 0.00, Terong 0.00, Jahe 0.00, Jambu 0.00, 4.2ms\n", "Speed: 6.5ms preprocess, 4.2ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Timun/aug_0_8725_jpg.rf.40d829a401c78e3370b8dc02498b79f3.jpg\n", "Ground Truth: Timun, Predicted Class: Timun, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Timun/aug_0_8167_jpg.rf.b169fc4c3bff11951bd0efaa206ddd20.jpg: 224x224 Timun 1.00, Jambu 0.00, Terong 0.00, Jahe 0.00, Mangga 0.00, 4.3ms\n", "Speed: 7.0ms preprocess, 4.3ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Timun/aug_0_8167_jpg.rf.b169fc4c3bff11951bd0efaa206ddd20.jpg\n", "Ground Truth: Timun, Predicted Class: Timun, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Timun/aug_0_9438_jpg.rf.9e4ac824160324ccc242737528e3e5b7.jpg: 224x224 Timun 1.00, Pepaya 0.00, Jagung 0.00, Terong 0.00, Padi 0.00, 4.1ms\n", "Speed: 5.1ms preprocess, 4.1ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Timun/aug_0_9438_jpg.rf.9e4ac824160324ccc242737528e3e5b7.jpg\n", "Ground Truth: Timun, Predicted Class: Timun, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Timun/aug_0_8608_jpg.rf.8fd532dff45e4c86a726f5fc4a767ba2.jpg: 224x224 Timun 1.00, Pisang 0.00, Kacang 0.00, KacangPanjang 0.00, BawangMerah 0.00, 9.1ms\n", "Speed: 7.0ms preprocess, 9.1ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Timun/aug_0_8608_jpg.rf.8fd532dff45e4c86a726f5fc4a767ba2.jpg\n", "Ground Truth: Timun, Predicted Class: Timun, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Timun/aug_0_9233_jpg.rf.1dbba77b3c6b89614bebba6379f6a745.jpg: 224x224 Timun 1.00, Terong 0.00, KacangPanjang 0.00, Daisy 0.00, Pepaya 0.00, 4.5ms\n", "Speed: 5.3ms preprocess, 4.5ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Timun/aug_0_9233_jpg.rf.1dbba77b3c6b89614bebba6379f6a745.jpg\n", "Ground Truth: Timun, Predicted Class: Timun, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Timun/aug_0_3898_jpg.rf.fdc92c22fbdfb97c984e0fdd95dd5de4.jpg: 224x224 Timun 1.00, Terong 0.00, KacangPanjang 0.00, Ubi 0.00, Kelapa 0.00, 4.7ms\n", "Speed: 5.4ms preprocess, 4.7ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Timun/aug_0_3898_jpg.rf.fdc92c22fbdfb97c984e0fdd95dd5de4.jpg\n", "Ground Truth: Timun, Predicted Class: Timun, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Timun/cucumber924_jpg.rf.5d556ee47345e5e9853fd53c9ee4cf0c.jpg: 224x224 Timun 1.00, Terong 0.00, Jambu 0.00, Jahe 0.00, Mangga 0.00, 4.8ms\n", "Speed: 5.9ms preprocess, 4.8ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Timun/cucumber924_jpg.rf.5d556ee47345e5e9853fd53c9ee4cf0c.jpg\n", "Ground Truth: Timun, Predicted Class: Timun, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Timun/aug_0_702_jpg.rf.bd26b2898b674189ec670c85842fd023.jpg: 224x224 Timun 1.00, Semangka 0.00, Cabai 0.00, Jambu 0.00, Pepaya 0.00, 4.5ms\n", "Speed: 7.7ms preprocess, 4.5ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Timun/aug_0_702_jpg.rf.bd26b2898b674189ec670c85842fd023.jpg\n", "Ground Truth: Timun, Predicted Class: Timun, Confidence: 1.00\n", "Processing Class Folder (Ground Truth): Dandelion\n", "\n", "image 1/1 /content/plants-classification-3/test/Dandelion/2596413098_7ef69b7e1d_m_jpg.rf.c844b28784dc256f67daf9506473c332.jpg: 224x224 Dandelion 1.00, Daisy 0.00, BawangMerah 0.00, BungaMatahari 0.00, Mawar 0.00, 4.7ms\n", "Speed: 5.5ms preprocess, 4.7ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Dandelion/2596413098_7ef69b7e1d_m_jpg.rf.c844b28784dc256f67daf9506473c332.jpg\n", "Ground Truth: Dandelion, Predicted Class: Dandelion, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Dandelion/34732908685_a219574ffe_n_jpg.rf.613c94fc500242d62c49cc474a688def.jpg: 224x224 Dandelion 1.00, Daisy 0.00, Pisang 0.00, Jeruk 0.00, BungaMatahari 0.00, 4.8ms\n", "Speed: 8.1ms preprocess, 4.8ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Dandelion/34732908685_a219574ffe_n_jpg.rf.613c94fc500242d62c49cc474a688def.jpg\n", "Ground Truth: Dandelion, Predicted Class: Dandelion, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Dandelion/7998106328_c3953f70e9_n_jpg.rf.624cf331dc0315f6aa99303beaab0888.jpg: 224x224 Dandelion 1.00, Jagung 0.00, Kentang 0.00, BungaMatahari 0.00, Melon 0.00, 5.3ms\n", "Speed: 6.4ms preprocess, 5.3ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Dandelion/7998106328_c3953f70e9_n_jpg.rf.624cf331dc0315f6aa99303beaab0888.jpg\n", "Ground Truth: Dandelion, Predicted Class: Dandelion, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Dandelion/4496277750_8c34256e28_jpg.rf.07793e5032c70410b0689739f7716077.jpg: 224x224 Dandelion 1.00, Daisy 0.00, BungaMatahari 0.00, Jagung 0.00, Jambu 0.00, 4.5ms\n", "Speed: 6.0ms preprocess, 4.5ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Dandelion/4496277750_8c34256e28_jpg.rf.07793e5032c70410b0689739f7716077.jpg\n", "Ground Truth: Dandelion, Predicted Class: Dandelion, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Dandelion/34695057206_98de825612_n_jpg.rf.f67100ecbe90a66f15797280443677eb.jpg: 224x224 Dandelion 1.00, Daisy 0.00, Nanas 0.00, Padi 0.00, Pisang 0.00, 4.5ms\n", "Speed: 7.1ms preprocess, 4.5ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Dandelion/34695057206_98de825612_n_jpg.rf.f67100ecbe90a66f15797280443677eb.jpg\n", "Ground Truth: Dandelion, Predicted Class: Dandelion, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Dandelion/1193386857_3ae53574f2_m_jpg.rf.fcd60ecf50774af812fd3f006d5a5c64.jpg: 224x224 Dandelion 1.00, Jeruk 0.00, Jahe 0.00, BungaMatahari 0.00, Padi 0.00, 4.2ms\n", "Speed: 6.3ms preprocess, 4.2ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Dandelion/1193386857_3ae53574f2_m_jpg.rf.fcd60ecf50774af812fd3f006d5a5c64.jpg\n", "Ground Truth: Dandelion, Predicted Class: Dandelion, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Dandelion/8701999625_8d83138124_jpg.rf.c70ad97a79637ccd2171ce570aa2d125.jpg: 224x224 Dandelion 1.00, Bayam 0.00, BungaMatahari 0.00, Daisy 0.00, Nanas 0.00, 4.4ms\n", "Speed: 5.1ms preprocess, 4.4ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Dandelion/8701999625_8d83138124_jpg.rf.c70ad97a79637ccd2171ce570aa2d125.jpg\n", "Ground Truth: Dandelion, Predicted Class: Dandelion, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Dandelion/19064700925_b93d474e37_jpg.rf.5b47f1af4e3f2e1e19767988b826870e.jpg: 224x224 Dandelion 1.00, Jeruk 0.00, Melon 0.00, Mangga 0.00, Daisy 0.00, 4.4ms\n", "Speed: 6.2ms preprocess, 4.4ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Dandelion/19064700925_b93d474e37_jpg.rf.5b47f1af4e3f2e1e19767988b826870e.jpg\n", "Ground Truth: Dandelion, Predicted Class: Dandelion, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Dandelion/3475811950_0fb89845f5_n_jpg.rf.33ee75bfdaf42c5bb2a3b9cf1fc2025e.jpg: 224x224 Dandelion 1.00, BungaMatahari 0.00, Padi 0.00, Daisy 0.00, Tulip 0.00, 4.8ms\n", "Speed: 6.6ms preprocess, 4.8ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Dandelion/3475811950_0fb89845f5_n_jpg.rf.33ee75bfdaf42c5bb2a3b9cf1fc2025e.jpg\n", "Ground Truth: Dandelion, Predicted Class: Dandelion, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Dandelion/19397467530_1e8131a7cf_jpg.rf.ab06e4e7eecf0c56d95bad027ff42687.jpg: 224x224 Dandelion 0.83, Daisy 0.17, Mawar 0.00, Kacang 0.00, Tulip 0.00, 4.2ms\n", "Speed: 6.5ms preprocess, 4.2ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Dandelion/19397467530_1e8131a7cf_jpg.rf.ab06e4e7eecf0c56d95bad027ff42687.jpg\n", "Ground Truth: Dandelion, Predicted Class: Dandelion, Confidence: 0.83\n", "\n", "image 1/1 /content/plants-classification-3/test/Dandelion/8717787983_c83bdf39fe_n_jpg.rf.d865ac3c1fa3ffdbf1beb437b589500d.jpg: 224x224 Dandelion 1.00, Melon 0.00, Jeruk 0.00, Nanas 0.00, Jagung 0.00, 6.6ms\n", "Speed: 7.2ms preprocess, 6.6ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Dandelion/8717787983_c83bdf39fe_n_jpg.rf.d865ac3c1fa3ffdbf1beb437b589500d.jpg\n", "Ground Truth: Dandelion, Predicted Class: Dandelion, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Dandelion/2780702427_312333ef33_jpg.rf.ee7b59bf69913c7d2955a52fbc21648c.jpg: 224x224 Dandelion 1.00, Melon 0.00, Daisy 0.00, Ubi 0.00, Jeruk 0.00, 4.9ms\n", "Speed: 7.1ms preprocess, 4.9ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Dandelion/2780702427_312333ef33_jpg.rf.ee7b59bf69913c7d2955a52fbc21648c.jpg\n", "Ground Truth: Dandelion, Predicted Class: Dandelion, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Dandelion/5598591979_ed9af1b3e9_n_jpg.rf.578f16384bb719ae335b5e218ab4f418.jpg: 224x224 Dandelion 1.00, Padi 0.00, Jagung 0.00, Kangkung 0.00, Melon 0.00, 4.5ms\n", "Speed: 7.8ms preprocess, 4.5ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Dandelion/5598591979_ed9af1b3e9_n_jpg.rf.578f16384bb719ae335b5e218ab4f418.jpg\n", "Ground Truth: Dandelion, Predicted Class: Dandelion, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Dandelion/8842482175_92a14b4934_m_jpg.rf.fccb458098ef84d88c22125c3172de25.jpg: 224x224 Dandelion 1.00, Daisy 0.00, Padi 0.00, BungaMatahari 0.00, Kentang 0.00, 7.2ms\n", "Speed: 7.1ms preprocess, 7.2ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Dandelion/8842482175_92a14b4934_m_jpg.rf.fccb458098ef84d88c22125c3172de25.jpg\n", "Ground Truth: Dandelion, Predicted Class: Dandelion, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Dandelion/22190242684_8c3300d4e6_jpg.rf.701b449b2ead7bb2ca7fabb610be1562.jpg: 224x224 Dandelion 1.00, Jeruk 0.00, Daisy 0.00, BawangMerah 0.00, Kentang 0.00, 7.3ms\n", "Speed: 7.4ms preprocess, 7.3ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Dandelion/22190242684_8c3300d4e6_jpg.rf.701b449b2ead7bb2ca7fabb610be1562.jpg\n", "Ground Truth: Dandelion, Predicted Class: Dandelion, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Dandelion/7188112181_571434b058_n_jpg.rf.5edeb69a8e6bea272c33562a7fc6c7cc.jpg: 224x224 Dandelion 1.00, Jeruk 0.00, Daisy 0.00, Padi 0.00, Melon 0.00, 6.2ms\n", "Speed: 7.5ms preprocess, 6.2ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Dandelion/7188112181_571434b058_n_jpg.rf.5edeb69a8e6bea272c33562a7fc6c7cc.jpg\n", "Ground Truth: Dandelion, Predicted Class: Dandelion, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Dandelion/34540904752_ae86e5f6ce_n_jpg.rf.bb6e5ab3daa196e08a079d08cddc5a2d.jpg: 224x224 Dandelion 0.95, Daisy 0.04, Jeruk 0.00, Mawar 0.00, Tulip 0.00, 7.8ms\n", "Speed: 6.8ms preprocess, 7.8ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Dandelion/34540904752_ae86e5f6ce_n_jpg.rf.bb6e5ab3daa196e08a079d08cddc5a2d.jpg\n", "Ground Truth: Dandelion, Predicted Class: Dandelion, Confidence: 0.95\n", "Processing Class Folder (Ground Truth): Cabai\n", "\n", "image 1/1 /content/plants-classification-3/test/Cabai/peperchili820_jpg.rf.29dec5d311bccc23be455769025b7ecb.jpg: 224x224 Cabai 1.00, Padi 0.00, Tulip 0.00, BawangMerah 0.00, Jeruk 0.00, 7.9ms\n", "Speed: 7.7ms preprocess, 7.9ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Cabai/peperchili820_jpg.rf.29dec5d311bccc23be455769025b7ecb.jpg\n", "Ground Truth: Cabai, Predicted Class: Cabai, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Cabai/aug_0_8206_jpg.rf.7b62a63b1d88f1af42399f22943dc878.jpg: 224x224 Cabai 1.00, KacangPanjang 0.00, Padi 0.00, Jeruk 0.00, Nanas 0.00, 7.9ms\n", "Speed: 7.5ms preprocess, 7.9ms inference, 0.2ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Cabai/aug_0_8206_jpg.rf.7b62a63b1d88f1af42399f22943dc878.jpg\n", "Ground Truth: Cabai, Predicted Class: Cabai, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Cabai/aug_0_4091_jpg.rf.a46ca71d10b9ffdff2f5ccc999d8b319.jpg: 224x224 Cabai 1.00, KacangPanjang 0.00, Nanas 0.00, Jagung 0.00, Padi 0.00, 6.1ms\n", "Speed: 5.6ms preprocess, 6.1ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Cabai/aug_0_4091_jpg.rf.a46ca71d10b9ffdff2f5ccc999d8b319.jpg\n", "Ground Truth: Cabai, Predicted Class: Cabai, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Cabai/aug_0_2512_jpg.rf.4086f34b94b8e469270ed0401cd476d9.jpg: 224x224 Cabai 1.00, Padi 0.00, Jeruk 0.00, BawangMerah 0.00, Mangga 0.00, 8.2ms\n", "Speed: 7.9ms preprocess, 8.2ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Cabai/aug_0_2512_jpg.rf.4086f34b94b8e469270ed0401cd476d9.jpg\n", "Ground Truth: Cabai, Predicted Class: Cabai, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Cabai/aug_0_7301_jpg.rf.4b5d27a85cc48e8702d742600aafceff.jpg: 224x224 Cabai 1.00, Bayam 0.00, Semangka 0.00, Ubi 0.00, Tulip 0.00, 8.4ms\n", "Speed: 7.1ms preprocess, 8.4ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Cabai/aug_0_7301_jpg.rf.4b5d27a85cc48e8702d742600aafceff.jpg\n", "Ground Truth: Cabai, Predicted Class: Cabai, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Cabai/aug_0_2300_jpg.rf.2deb39a6a9b96cac05b2eb271582a9cf.jpg: 224x224 Cabai 1.00, Melon 0.00, Pisang 0.00, Jambu 0.00, Semangka 0.00, 11.0ms\n", "Speed: 8.3ms preprocess, 11.0ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Cabai/aug_0_2300_jpg.rf.2deb39a6a9b96cac05b2eb271582a9cf.jpg\n", "Ground Truth: Cabai, Predicted Class: Cabai, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Cabai/aug_0_553_jpg.rf.bc87adb017e47ae34ad0dc6255f5bb0c.jpg: 224x224 Cabai 1.00, Semangka 0.00, Jagung 0.00, Kelapa 0.00, Padi 0.00, 12.7ms\n", "Speed: 7.7ms preprocess, 12.7ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Cabai/aug_0_553_jpg.rf.bc87adb017e47ae34ad0dc6255f5bb0c.jpg\n", "Ground Truth: Cabai, Predicted Class: Cabai, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Cabai/aug_0_8358_jpg.rf.f2a503c3cd543adc12b434840f2739ec.jpg: 224x224 Cabai 1.00, KacangPanjang 0.00, Kacang 0.00, Pisang 0.00, Bayam 0.00, 7.8ms\n", "Speed: 7.6ms preprocess, 7.8ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Cabai/aug_0_8358_jpg.rf.f2a503c3cd543adc12b434840f2739ec.jpg\n", "Ground Truth: Cabai, Predicted Class: Cabai, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Cabai/aug_0_6693_jpg.rf.ff57469331f67d6ab06f99823b45ccf5.jpg: 224x224 Cabai 1.00, Terong 0.00, Mangga 0.00, Tulip 0.00, Melon 0.00, 8.2ms\n", "Speed: 7.6ms preprocess, 8.2ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Cabai/aug_0_6693_jpg.rf.ff57469331f67d6ab06f99823b45ccf5.jpg\n", "Ground Truth: Cabai, Predicted Class: Cabai, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Cabai/aug_0_3517_jpg.rf.0977b8e6b16c42734a12c310c8f8e808.jpg: 224x224 Cabai 1.00, Tulip 0.00, Bayam 0.00, KacangPanjang 0.00, Mawar 0.00, 6.6ms\n", "Speed: 8.1ms preprocess, 6.6ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Cabai/aug_0_3517_jpg.rf.0977b8e6b16c42734a12c310c8f8e808.jpg\n", "Ground Truth: Cabai, Predicted Class: Cabai, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Cabai/aug_0_3067_jpg.rf.1256f9c0160a8f1a3252cc205d613095.jpg: 224x224 Cabai 0.99, KacangPanjang 0.00, Padi 0.00, Jeruk 0.00, Nanas 0.00, 6.4ms\n", "Speed: 13.2ms preprocess, 6.4ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Cabai/aug_0_3067_jpg.rf.1256f9c0160a8f1a3252cc205d613095.jpg\n", "Ground Truth: Cabai, Predicted Class: Cabai, Confidence: 0.99\n", "\n", "image 1/1 /content/plants-classification-3/test/Cabai/aug_0_4042_jpg.rf.5b2637f8788d989459a2d477f8ab1316.jpg: 224x224 Cabai 1.00, Nanas 0.00, Padi 0.00, KacangPanjang 0.00, BawangMerah 0.00, 7.4ms\n", "Speed: 7.7ms preprocess, 7.4ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Cabai/aug_0_4042_jpg.rf.5b2637f8788d989459a2d477f8ab1316.jpg\n", "Ground Truth: Cabai, Predicted Class: Cabai, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Cabai/aug_0_6680_jpg.rf.e20e65a06b37d6d77e3fc6dac004e225.jpg: 224x224 Cabai 1.00, BawangMerah 0.00, Melon 0.00, Bayam 0.00, Pisang 0.00, 8.1ms\n", "Speed: 7.6ms preprocess, 8.1ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Cabai/aug_0_6680_jpg.rf.e20e65a06b37d6d77e3fc6dac004e225.jpg\n", "Ground Truth: Cabai, Predicted Class: Cabai, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Cabai/aug_0_9997_jpg.rf.0fa3f46ff18eb83919049e12c8a3b162.jpg: 224x224 Cabai 1.00, KacangPanjang 0.00, Kentang 0.00, Bayam 0.00, Semangka 0.00, 5.9ms\n", "Speed: 8.0ms preprocess, 5.9ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Cabai/aug_0_9997_jpg.rf.0fa3f46ff18eb83919049e12c8a3b162.jpg\n", "Ground Truth: Cabai, Predicted Class: Cabai, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Cabai/aug_0_4805_jpg.rf.573d1c78f0ec20b652e234e7cfd9c4c5.jpg: 224x224 Cabai 1.00, BawangMerah 0.00, Terong 0.00, Padi 0.00, Jahe 0.00, 6.5ms\n", "Speed: 6.7ms preprocess, 6.5ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Cabai/aug_0_4805_jpg.rf.573d1c78f0ec20b652e234e7cfd9c4c5.jpg\n", "Ground Truth: Cabai, Predicted Class: Cabai, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Cabai/aug_0_8065_jpg.rf.01f9c896f65dfff741da8b63a184ec9a.jpg: 224x224 Cabai 1.00, Semangka 0.00, Bayam 0.00, Pepaya 0.00, Jambu 0.00, 5.6ms\n", "Speed: 7.3ms preprocess, 5.6ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Cabai/aug_0_8065_jpg.rf.01f9c896f65dfff741da8b63a184ec9a.jpg\n", "Ground Truth: Cabai, Predicted Class: Cabai, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Cabai/aug_0_9011_jpg.rf.c7ac8d5427a2e99e1646721364aba585.jpg: 224x224 Cabai 1.00, Pepaya 0.00, Kentang 0.00, Kelapa 0.00, Kacang 0.00, 7.1ms\n", "Speed: 7.7ms preprocess, 7.1ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Cabai/aug_0_9011_jpg.rf.c7ac8d5427a2e99e1646721364aba585.jpg\n", "Ground Truth: Cabai, Predicted Class: Cabai, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Cabai/aug_0_9175_jpg.rf.846de08ca42dedfca4aa90ebda35357a.jpg: 224x224 Cabai 1.00, Padi 0.00, Kangkung 0.00, BawangMerah 0.00, Jeruk 0.00, 6.8ms\n", "Speed: 7.1ms preprocess, 6.8ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Cabai/aug_0_9175_jpg.rf.846de08ca42dedfca4aa90ebda35357a.jpg\n", "Ground Truth: Cabai, Predicted Class: Cabai, Confidence: 1.00\n", "Processing Class Folder (Ground Truth): KacangPanjang\n", "\n", "image 1/1 /content/plants-classification-3/test/KacangPanjang/aug_0_7323_jpg.rf.2660a11953cf2ec104aae16e263cecb0.jpg: 224x224 Cabai 0.96, KacangPanjang 0.02, Nanas 0.02, Semangka 0.00, Padi 0.00, 7.1ms\n", "Speed: 6.8ms preprocess, 7.1ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/KacangPanjang/aug_0_7323_jpg.rf.2660a11953cf2ec104aae16e263cecb0.jpg\n", "Ground Truth: KacangPanjang, Predicted Class: Cabai, Confidence: 0.96\n", "\n", "image 1/1 /content/plants-classification-3/test/KacangPanjang/aug_0_4008_jpg.rf.328fe36ccca1e1c3b2875d8fb401bcec.jpg: 224x224 KacangPanjang 1.00, Semangka 0.00, Jambu 0.00, Kangkung 0.00, Padi 0.00, 6.9ms\n", "Speed: 7.2ms preprocess, 6.9ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/KacangPanjang/aug_0_4008_jpg.rf.328fe36ccca1e1c3b2875d8fb401bcec.jpg\n", "Ground Truth: KacangPanjang, Predicted Class: KacangPanjang, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/KacangPanjang/aug_0_7133_jpg.rf.f5771e2e922b5b46333dcccd8a9279bc.jpg: 224x224 KacangPanjang 1.00, Kangkung 0.00, Bayam 0.00, Padi 0.00, Semangka 0.00, 7.5ms\n", "Speed: 8.6ms preprocess, 7.5ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/KacangPanjang/aug_0_7133_jpg.rf.f5771e2e922b5b46333dcccd8a9279bc.jpg\n", "Ground Truth: KacangPanjang, Predicted Class: KacangPanjang, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/KacangPanjang/longbeans620_jpg.rf.bb296df40a75ee8d0a16fad28f14924a.jpg: 224x224 KacangPanjang 1.00, Timun 0.00, Pisang 0.00, Melon 0.00, Semangka 0.00, 6.7ms\n", "Speed: 6.9ms preprocess, 6.7ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/KacangPanjang/longbeans620_jpg.rf.bb296df40a75ee8d0a16fad28f14924a.jpg\n", "Ground Truth: KacangPanjang, Predicted Class: KacangPanjang, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/KacangPanjang/aug_0_5729_jpg.rf.18f86ba0133b570ea4bb0820aec07faa.jpg: 224x224 KacangPanjang 1.00, Jambu 0.00, Pisang 0.00, Padi 0.00, Kangkung 0.00, 6.0ms\n", "Speed: 8.1ms preprocess, 6.0ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/KacangPanjang/aug_0_5729_jpg.rf.18f86ba0133b570ea4bb0820aec07faa.jpg\n", "Ground Truth: KacangPanjang, Predicted Class: KacangPanjang, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/KacangPanjang/aug_0_644_jpg.rf.5a7b2cfba17163dd9b5c617e71e61af3.jpg: 224x224 KacangPanjang 1.00, Cabai 0.00, Pisang 0.00, Semangka 0.00, Kangkung 0.00, 7.0ms\n", "Speed: 7.0ms preprocess, 7.0ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/KacangPanjang/aug_0_644_jpg.rf.5a7b2cfba17163dd9b5c617e71e61af3.jpg\n", "Ground Truth: KacangPanjang, Predicted Class: KacangPanjang, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/KacangPanjang/aug_0_4986_jpg.rf.2bd631cdc6b357eecdfaf780e9ce0356.jpg: 224x224 KacangPanjang 1.00, Kangkung 0.00, BawangMerah 0.00, Pisang 0.00, Jambu 0.00, 6.5ms\n", "Speed: 6.8ms preprocess, 6.5ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/KacangPanjang/aug_0_4986_jpg.rf.2bd631cdc6b357eecdfaf780e9ce0356.jpg\n", "Ground Truth: KacangPanjang, Predicted Class: KacangPanjang, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/KacangPanjang/aug_0_6021_jpg.rf.9331fd9a43a4119d783d2a8d8762def8.jpg: 224x224 KacangPanjang 1.00, Kangkung 0.00, Bayam 0.00, Pisang 0.00, Jambu 0.00, 7.8ms\n", "Speed: 8.6ms preprocess, 7.8ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/KacangPanjang/aug_0_6021_jpg.rf.9331fd9a43a4119d783d2a8d8762def8.jpg\n", "Ground Truth: KacangPanjang, Predicted Class: KacangPanjang, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/KacangPanjang/aug_0_3386_jpg.rf.7769524ab1a12601495e583ca1cd78d9.jpg: 224x224 KacangPanjang 1.00, Kangkung 0.00, Bayam 0.00, Jambu 0.00, Pisang 0.00, 10.1ms\n", "Speed: 9.5ms preprocess, 10.1ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/KacangPanjang/aug_0_3386_jpg.rf.7769524ab1a12601495e583ca1cd78d9.jpg\n", "Ground Truth: KacangPanjang, Predicted Class: KacangPanjang, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/KacangPanjang/aug_0_8732_jpg.rf.b23036efbe78f8939e3630aef79c0a23.jpg: 224x224 KacangPanjang 1.00, Kangkung 0.00, Semangka 0.00, Bayam 0.00, Nanas 0.00, 7.9ms\n", "Speed: 9.4ms preprocess, 7.9ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/KacangPanjang/aug_0_8732_jpg.rf.b23036efbe78f8939e3630aef79c0a23.jpg\n", "Ground Truth: KacangPanjang, Predicted Class: KacangPanjang, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/KacangPanjang/longbeans700_jpg.rf.f8da7d9fdbf5727e58ae5b1526d853fc.jpg: 224x224 KacangPanjang 1.00, Cabai 0.00, Dandelion 0.00, Padi 0.00, Semangka 0.00, 8.5ms\n", "Speed: 7.5ms preprocess, 8.5ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/KacangPanjang/longbeans700_jpg.rf.f8da7d9fdbf5727e58ae5b1526d853fc.jpg\n", "Ground Truth: KacangPanjang, Predicted Class: KacangPanjang, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/KacangPanjang/aug_0_4304_jpg.rf.f9b14c018fe5eab60c223203a2e50503.jpg: 224x224 KacangPanjang 1.00, Padi 0.00, Cabai 0.00, Kangkung 0.00, Bayam 0.00, 6.1ms\n", "Speed: 7.8ms preprocess, 6.1ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/KacangPanjang/aug_0_4304_jpg.rf.f9b14c018fe5eab60c223203a2e50503.jpg\n", "Ground Truth: KacangPanjang, Predicted Class: KacangPanjang, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/KacangPanjang/aug_0_9942_jpg.rf.f81c9c80e4cd6c768340bb58fd0d4c51.jpg: 224x224 KacangPanjang 0.58, Kangkung 0.42, Padi 0.00, Mangga 0.00, Bayam 0.00, 6.3ms\n", "Speed: 6.9ms preprocess, 6.3ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/KacangPanjang/aug_0_9942_jpg.rf.f81c9c80e4cd6c768340bb58fd0d4c51.jpg\n", "Ground Truth: KacangPanjang, Predicted Class: KacangPanjang, Confidence: 0.58\n", "Processing Class Folder (Ground Truth): Semangka\n", "\n", "image 1/1 /content/plants-classification-3/test/Semangka/aug_0_7754_jpg.rf.70fb6dfe34772721909b5be1889d1fa0.jpg: 224x224 Semangka 1.00, Padi 0.00, Cabai 0.00, Jagung 0.00, Jambu 0.00, 6.1ms\n", "Speed: 7.4ms preprocess, 6.1ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Semangka/aug_0_7754_jpg.rf.70fb6dfe34772721909b5be1889d1fa0.jpg\n", "Ground Truth: Semangka, Predicted Class: Semangka, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Semangka/aug_0_7053_jpg.rf.b1073ebedba8f3b32c9d9161a8f0d657.jpg: 224x224 Semangka 1.00, Jambu 0.00, Jeruk 0.00, Jahe 0.00, Pepaya 0.00, 5.9ms\n", "Speed: 6.0ms preprocess, 5.9ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Semangka/aug_0_7053_jpg.rf.b1073ebedba8f3b32c9d9161a8f0d657.jpg\n", "Ground Truth: Semangka, Predicted Class: Semangka, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Semangka/aug_0_8242_jpg.rf.c21b32075a6ad7cb391cedaf7a6fc7dc.jpg: 224x224 Semangka 1.00, Jambu 0.00, Daisy 0.00, Jagung 0.00, Jeruk 0.00, 6.1ms\n", "Speed: 7.9ms preprocess, 6.1ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Semangka/aug_0_8242_jpg.rf.c21b32075a6ad7cb391cedaf7a6fc7dc.jpg\n", "Ground Truth: Semangka, Predicted Class: Semangka, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Semangka/aug_0_1683_jpg.rf.9c0940384430c628cb067caf0330b8ea.jpg: 224x224 Semangka 1.00, Nanas 0.00, Timun 0.00, Dandelion 0.00, Mangga 0.00, 6.0ms\n", "Speed: 6.7ms preprocess, 6.0ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Semangka/aug_0_1683_jpg.rf.9c0940384430c628cb067caf0330b8ea.jpg\n", "Ground Truth: Semangka, Predicted Class: Semangka, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Semangka/aug_0_6609_jpg.rf.7273def89c13c1db628418667190d2e5.jpg: 224x224 Semangka 1.00, Jambu 0.00, Pepaya 0.00, Jeruk 0.00, Nanas 0.00, 8.1ms\n", "Speed: 6.7ms preprocess, 8.1ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Semangka/aug_0_6609_jpg.rf.7273def89c13c1db628418667190d2e5.jpg\n", "Ground Truth: Semangka, Predicted Class: Semangka, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Semangka/aug_0_5104_jpg.rf.6a9b103d1de1bb5482bace02abf685b8.jpg: 224x224 Semangka 1.00, Dandelion 0.00, Pepaya 0.00, Jambu 0.00, Jeruk 0.00, 5.9ms\n", "Speed: 7.3ms preprocess, 5.9ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Semangka/aug_0_5104_jpg.rf.6a9b103d1de1bb5482bace02abf685b8.jpg\n", "Ground Truth: Semangka, Predicted Class: Semangka, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Semangka/aug_0_2390_jpg.rf.65b2e72449b1a2ac942ed876e65f81cc.jpg: 224x224 Semangka 1.00, Melon 0.00, Timun 0.00, Mangga 0.00, KacangPanjang 0.00, 6.6ms\n", "Speed: 8.4ms preprocess, 6.6ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Semangka/aug_0_2390_jpg.rf.65b2e72449b1a2ac942ed876e65f81cc.jpg\n", "Ground Truth: Semangka, Predicted Class: Semangka, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Semangka/aug_0_9745_jpg.rf.ec32af863dc03fae0035715ef6275d9a.jpg: 224x224 Semangka 1.00, Jambu 0.00, Kelapa 0.00, Pisang 0.00, Timun 0.00, 6.2ms\n", "Speed: 6.9ms preprocess, 6.2ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Semangka/aug_0_9745_jpg.rf.ec32af863dc03fae0035715ef6275d9a.jpg\n", "Ground Truth: Semangka, Predicted Class: Semangka, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Semangka/watermelon889_jpg.rf.22b174495d856d0e9758971ae1a5f8ad.jpg: 224x224 Semangka 1.00, KacangPanjang 0.00, Timun 0.00, Dandelion 0.00, Kangkung 0.00, 7.3ms\n", "Speed: 6.5ms preprocess, 7.3ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Semangka/watermelon889_jpg.rf.22b174495d856d0e9758971ae1a5f8ad.jpg\n", "Ground Truth: Semangka, Predicted Class: Semangka, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Semangka/aug_0_5858_jpg.rf.3ea9e9be87dc960c2315c6951ec43b23.jpg: 224x224 Semangka 1.00, Jambu 0.00, Cabai 0.00, Pepaya 0.00, Jeruk 0.00, 7.0ms\n", "Speed: 7.0ms preprocess, 7.0ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Semangka/aug_0_5858_jpg.rf.3ea9e9be87dc960c2315c6951ec43b23.jpg\n", "Ground Truth: Semangka, Predicted Class: Semangka, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Semangka/aug_0_4409_jpg.rf.ca0276c4173ad97e884230c57940c964.jpg: 224x224 Semangka 1.00, Jambu 0.00, Jeruk 0.00, Terong 0.00, BawangMerah 0.00, 6.6ms\n", "Speed: 7.4ms preprocess, 6.6ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Semangka/aug_0_4409_jpg.rf.ca0276c4173ad97e884230c57940c964.jpg\n", "Ground Truth: Semangka, Predicted Class: Semangka, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Semangka/watermelon99_jpg.rf.ca932e316ff6788f7d2062e4e5b7288a.jpg: 224x224 Semangka 1.00, Jambu 0.00, Terong 0.00, Dandelion 0.00, Jeruk 0.00, 6.2ms\n", "Speed: 6.9ms preprocess, 6.2ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Semangka/watermelon99_jpg.rf.ca932e316ff6788f7d2062e4e5b7288a.jpg\n", "Ground Truth: Semangka, Predicted Class: Semangka, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Semangka/aug_0_4762_jpg.rf.6436085f8c67b6361e84f0625f22c9ad.jpg: 224x224 Semangka 1.00, Jambu 0.00, Timun 0.00, Terong 0.00, Jeruk 0.00, 10.4ms\n", "Speed: 6.9ms preprocess, 10.4ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Semangka/aug_0_4762_jpg.rf.6436085f8c67b6361e84f0625f22c9ad.jpg\n", "Ground Truth: Semangka, Predicted Class: Semangka, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Semangka/aug_0_116_jpg.rf.2dcf31ba22b32a638992b05086179a55.jpg: 224x224 Semangka 1.00, Dandelion 0.00, Pisang 0.00, KacangPanjang 0.00, Mangga 0.00, 6.4ms\n", "Speed: 8.5ms preprocess, 6.4ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Semangka/aug_0_116_jpg.rf.2dcf31ba22b32a638992b05086179a55.jpg\n", "Ground Truth: Semangka, Predicted Class: Semangka, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Semangka/aug_0_6100_jpg.rf.9cc3a7fdace5db79b4214d29056caee8.jpg: 224x224 Semangka 1.00, Jambu 0.00, Nanas 0.00, Pepaya 0.00, Dandelion 0.00, 6.7ms\n", "Speed: 7.4ms preprocess, 6.7ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Semangka/aug_0_6100_jpg.rf.9cc3a7fdace5db79b4214d29056caee8.jpg\n", "Ground Truth: Semangka, Predicted Class: Semangka, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Semangka/aug_0_5977_jpg.rf.85b3b7f806dece12bf78f6c137e11ce0.jpg: 224x224 Semangka 0.97, Jambu 0.03, Melon 0.00, Pepaya 0.00, Padi 0.00, 8.0ms\n", "Speed: 9.1ms preprocess, 8.0ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Semangka/aug_0_5977_jpg.rf.85b3b7f806dece12bf78f6c137e11ce0.jpg\n", "Ground Truth: Semangka, Predicted Class: Semangka, Confidence: 0.97\n", "\n", "image 1/1 /content/plants-classification-3/test/Semangka/watermelon408_jpg.rf.3b36ce2c63af26c9dcc3a1941eca0363.jpg: 224x224 Semangka 1.00, Jambu 0.00, Pepaya 0.00, Timun 0.00, Nanas 0.00, 7.4ms\n", "Speed: 12.2ms preprocess, 7.4ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Semangka/watermelon408_jpg.rf.3b36ce2c63af26c9dcc3a1941eca0363.jpg\n", "Ground Truth: Semangka, Predicted Class: Semangka, Confidence: 1.00\n", "Processing Class Folder (Ground Truth): BawangMerah\n", "\n", "image 1/1 /content/plants-classification-3/test/BawangMerah/aug_0_9926_jpg.rf.3badadac5d98116d37d6f09f9ef5c771.jpg: 224x224 BawangMerah 1.00, Terong 0.00, Kentang 0.00, Kelapa 0.00, Jambu 0.00, 6.1ms\n", "Speed: 7.1ms preprocess, 6.1ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/BawangMerah/aug_0_9926_jpg.rf.3badadac5d98116d37d6f09f9ef5c771.jpg\n", "Ground Truth: BawangMerah, Predicted Class: BawangMerah, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/BawangMerah/aug_0_3182_jpg.rf.46e3e4d5647a47fb76c2c1783358f4a2.jpg: 224x224 BawangMerah 1.00, Jeruk 0.00, Terong 0.00, Kelapa 0.00, Semangka 0.00, 5.9ms\n", "Speed: 7.4ms preprocess, 5.9ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/BawangMerah/aug_0_3182_jpg.rf.46e3e4d5647a47fb76c2c1783358f4a2.jpg\n", "Ground Truth: BawangMerah, Predicted Class: BawangMerah, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/BawangMerah/aug_0_5963_jpg.rf.49183b751d4568153f7d4e6e48f5b915.jpg: 224x224 BawangMerah 1.00, Jeruk 0.00, Terong 0.00, Kacang 0.00, Kentang 0.00, 6.1ms\n", "Speed: 10.2ms preprocess, 6.1ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/BawangMerah/aug_0_5963_jpg.rf.49183b751d4568153f7d4e6e48f5b915.jpg\n", "Ground Truth: BawangMerah, Predicted Class: BawangMerah, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/BawangMerah/aug_0_4081_jpg.rf.2ccbbb9b34db1b2ef9c17a28129ddf70.jpg: 224x224 BawangMerah 1.00, Terong 0.00, Jahe 0.00, Jeruk 0.00, Semangka 0.00, 6.5ms\n", "Speed: 8.2ms preprocess, 6.5ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/BawangMerah/aug_0_4081_jpg.rf.2ccbbb9b34db1b2ef9c17a28129ddf70.jpg\n", "Ground Truth: BawangMerah, Predicted Class: BawangMerah, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/BawangMerah/aug_0_2886_jpg.rf.65d1cc2ae77fe4061c6a032a793e6192.jpg: 224x224 BawangMerah 0.99, Terong 0.01, Jeruk 0.00, Semangka 0.00, Jambu 0.00, 6.3ms\n", "Speed: 8.7ms preprocess, 6.3ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/BawangMerah/aug_0_2886_jpg.rf.65d1cc2ae77fe4061c6a032a793e6192.jpg\n", "Ground Truth: BawangMerah, Predicted Class: BawangMerah, Confidence: 0.99\n", "\n", "image 1/1 /content/plants-classification-3/test/BawangMerah/aug_0_731_jpg.rf.67b13c562f9fdf0c6fda35b11386b684.jpg: 224x224 BawangMerah 1.00, Jahe 0.00, Jeruk 0.00, Semangka 0.00, Terong 0.00, 5.7ms\n", "Speed: 7.8ms preprocess, 5.7ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/BawangMerah/aug_0_731_jpg.rf.67b13c562f9fdf0c6fda35b11386b684.jpg\n", "Ground Truth: BawangMerah, Predicted Class: BawangMerah, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/BawangMerah/aug_0_6511_jpg.rf.e798c2c2588281706f8727577aa51277.jpg: 224x224 BawangMerah 1.00, Bayam 0.00, Semangka 0.00, Jahe 0.00, Ubi 0.00, 4.3ms\n", "Speed: 5.6ms preprocess, 4.3ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/BawangMerah/aug_0_6511_jpg.rf.e798c2c2588281706f8727577aa51277.jpg\n", "Ground Truth: BawangMerah, Predicted Class: BawangMerah, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/BawangMerah/shallot925_jpg.rf.7446cf9bfe59dee01696314e550a5fdb.jpg: 224x224 BawangMerah 1.00, Semangka 0.00, Terong 0.00, Kelapa 0.00, Dandelion 0.00, 4.7ms\n", "Speed: 7.6ms preprocess, 4.7ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/BawangMerah/shallot925_jpg.rf.7446cf9bfe59dee01696314e550a5fdb.jpg\n", "Ground Truth: BawangMerah, Predicted Class: BawangMerah, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/BawangMerah/aug_0_1531_jpg.rf.71bc88e13f280ab7b53ce2603a44cd27.jpg: 224x224 BawangMerah 0.82, Kentang 0.18, Terong 0.00, Kacang 0.00, Mangga 0.00, 4.6ms\n", "Speed: 7.6ms preprocess, 4.6ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/BawangMerah/aug_0_1531_jpg.rf.71bc88e13f280ab7b53ce2603a44cd27.jpg\n", "Ground Truth: BawangMerah, Predicted Class: BawangMerah, Confidence: 0.82\n", "\n", "image 1/1 /content/plants-classification-3/test/BawangMerah/aug_0_1774_jpg.rf.da6d70280aca3473a153a848c48112c4.jpg: 224x224 BawangMerah 1.00, Bayam 0.00, Jahe 0.00, Terong 0.00, Kentang 0.00, 4.1ms\n", "Speed: 7.0ms preprocess, 4.1ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/BawangMerah/aug_0_1774_jpg.rf.da6d70280aca3473a153a848c48112c4.jpg\n", "Ground Truth: BawangMerah, Predicted Class: BawangMerah, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/BawangMerah/aug_0_8866_jpg.rf.cbd9cde54ccc54c43fa25ba22bfa7400.jpg: 224x224 BawangMerah 1.00, Jahe 0.00, Semangka 0.00, Nanas 0.00, Jeruk 0.00, 4.2ms\n", "Speed: 6.5ms preprocess, 4.2ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/BawangMerah/aug_0_8866_jpg.rf.cbd9cde54ccc54c43fa25ba22bfa7400.jpg\n", "Ground Truth: BawangMerah, Predicted Class: BawangMerah, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/BawangMerah/shallot938_jpg.rf.c1e7eb7fc0949e876ac62e3fe6c2f9e3.jpg: 224x224 BawangMerah 1.00, Jeruk 0.00, Semangka 0.00, Kelapa 0.00, Nanas 0.00, 4.3ms\n", "Speed: 6.3ms preprocess, 4.3ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/BawangMerah/shallot938_jpg.rf.c1e7eb7fc0949e876ac62e3fe6c2f9e3.jpg\n", "Ground Truth: BawangMerah, Predicted Class: BawangMerah, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/BawangMerah/shallot974_jpg.rf.454c96adea3f207b78d5ba968a7b792b.jpg: 224x224 BawangMerah 0.63, Tulip 0.22, Terong 0.06, Daisy 0.04, Bayam 0.02, 5.4ms\n", "Speed: 6.9ms preprocess, 5.4ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/BawangMerah/shallot974_jpg.rf.454c96adea3f207b78d5ba968a7b792b.jpg\n", "Ground Truth: BawangMerah, Predicted Class: BawangMerah, Confidence: 0.63\n", "Processing Class Folder (Ground Truth): Tulip\n", "\n", "image 1/1 /content/plants-classification-3/test/Tulip/7166626128_8e0983ac8e_n_jpg.rf.870de0d765a9d6542fa08b15d02be6ab.jpg: 224x224 Tulip 1.00, Jeruk 0.00, Mawar 0.00, BungaMatahari 0.00, Cabai 0.00, 4.7ms\n", "Speed: 6.7ms preprocess, 4.7ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Tulip/7166626128_8e0983ac8e_n_jpg.rf.870de0d765a9d6542fa08b15d02be6ab.jpg\n", "Ground Truth: Tulip, Predicted Class: Tulip, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Tulip/14270573963_f122c40438_jpg.rf.07b06031dce3f48054b7dbd243cb2393.jpg: 224x224 Tulip 1.00, Jeruk 0.00, BungaMatahari 0.00, Pisang 0.00, Mawar 0.00, 5.2ms\n", "Speed: 6.9ms preprocess, 5.2ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Tulip/14270573963_f122c40438_jpg.rf.07b06031dce3f48054b7dbd243cb2393.jpg\n", "Ground Truth: Tulip, Predicted Class: Tulip, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Tulip/16568530400_f7e4ffc633_n_jpg.rf.cce090e0b6d8200ac0a56e684c80876a.jpg: 224x224 Tulip 1.00, Jeruk 0.00, BungaMatahari 0.00, Mawar 0.00, Mangga 0.00, 4.3ms\n", "Speed: 7.0ms preprocess, 4.3ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Tulip/16568530400_f7e4ffc633_n_jpg.rf.cce090e0b6d8200ac0a56e684c80876a.jpg\n", "Ground Truth: Tulip, Predicted Class: Tulip, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Tulip/164578909_51f245d3fa_n_jpg.rf.42b1c7224bd6c916a4ec3beef076bfd0.jpg: 224x224 Tulip 0.64, Mawar 0.36, BawangMerah 0.00, BungaMatahari 0.00, Bayam 0.00, 4.3ms\n", "Speed: 7.1ms preprocess, 4.3ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Tulip/164578909_51f245d3fa_n_jpg.rf.42b1c7224bd6c916a4ec3beef076bfd0.jpg\n", "Ground Truth: Tulip, Predicted Class: Tulip, Confidence: 0.64\n", "\n", "image 1/1 /content/plants-classification-3/test/Tulip/112651128_7b5d39a346_m_jpg.rf.9e80418dcfe6e46c527dc044eb62e2d7.jpg: 224x224 Tulip 1.00, Mawar 0.00, Daisy 0.00, Jagung 0.00, Mangga 0.00, 6.0ms\n", "Speed: 6.9ms preprocess, 6.0ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Tulip/112651128_7b5d39a346_m_jpg.rf.9e80418dcfe6e46c527dc044eb62e2d7.jpg\n", "Ground Truth: Tulip, Predicted Class: Tulip, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Tulip/16938892686_3613ea68e8_n_jpg.rf.e94e68db62b61b8a891bf87f34140c31.jpg: 224x224 Tulip 0.99, BungaMatahari 0.01, Mawar 0.00, Daisy 0.00, Jeruk 0.00, 5.2ms\n", "Speed: 7.3ms preprocess, 5.2ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Tulip/16938892686_3613ea68e8_n_jpg.rf.e94e68db62b61b8a891bf87f34140c31.jpg\n", "Ground Truth: Tulip, Predicted Class: Tulip, Confidence: 0.99\n", "\n", "image 1/1 /content/plants-classification-3/test/Tulip/20265958876_edb70c4553_n_jpg.rf.8833a546a34d758daaea134c65014d5b.jpg: 224x224 Tulip 0.99, Dandelion 0.01, Jambu 0.00, Pisang 0.00, Jahe 0.00, 5.1ms\n", "Speed: 7.1ms preprocess, 5.1ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Tulip/20265958876_edb70c4553_n_jpg.rf.8833a546a34d758daaea134c65014d5b.jpg\n", "Ground Truth: Tulip, Predicted Class: Tulip, Confidence: 0.99\n", "\n", "image 1/1 /content/plants-classification-3/test/Tulip/19413898445_69344f9956_n_jpg.rf.31170c3cf51aae5e2c875cfacf31724c.jpg: 224x224 Tulip 0.99, Mawar 0.01, Jagung 0.00, Cabai 0.00, Daisy 0.00, 6.0ms\n", "Speed: 7.1ms preprocess, 6.0ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Tulip/19413898445_69344f9956_n_jpg.rf.31170c3cf51aae5e2c875cfacf31724c.jpg\n", "Ground Truth: Tulip, Predicted Class: Tulip, Confidence: 0.99\n", "\n", "image 1/1 /content/plants-classification-3/test/Tulip/5811004432_266f0f0c6f_jpg.rf.cf23ffb3bee5b577f6d7f54e28121906.jpg: 224x224 Tulip 0.95, BungaMatahari 0.04, Dandelion 0.00, Kelapa 0.00, BawangMerah 0.00, 5.4ms\n", "Speed: 7.6ms preprocess, 5.4ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Tulip/5811004432_266f0f0c6f_jpg.rf.cf23ffb3bee5b577f6d7f54e28121906.jpg\n", "Ground Truth: Tulip, Predicted Class: Tulip, Confidence: 0.95\n", "\n", "image 1/1 /content/plants-classification-3/test/Tulip/15052586652_56a82de133_m_jpg.rf.ebab304e2bea20c546e3daa8a4b8fdb2.jpg: 224x224 Tulip 1.00, BungaMatahari 0.00, Mangga 0.00, Daisy 0.00, Mawar 0.00, 4.5ms\n", "Speed: 7.1ms preprocess, 4.5ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Tulip/15052586652_56a82de133_m_jpg.rf.ebab304e2bea20c546e3daa8a4b8fdb2.jpg\n", "Ground Truth: Tulip, Predicted Class: Tulip, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Tulip/15090146325_b7e1249e60_jpg.rf.eed3810fbcc3a57ff111569da9faf151.jpg: 224x224 Tulip 1.00, Mawar 0.00, Jeruk 0.00, BungaMatahari 0.00, Pisang 0.00, 4.0ms\n", "Speed: 6.5ms preprocess, 4.0ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Tulip/15090146325_b7e1249e60_jpg.rf.eed3810fbcc3a57ff111569da9faf151.jpg\n", "Ground Truth: Tulip, Predicted Class: Tulip, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Tulip/14651385476_7ccb20e594_m_jpg.rf.25e48b6a27c6674a917766bd58882fc9.jpg: 224x224 Tulip 1.00, Dandelion 0.00, KacangPanjang 0.00, Daisy 0.00, BungaMatahari 0.00, 4.0ms\n", "Speed: 6.5ms preprocess, 4.0ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Tulip/14651385476_7ccb20e594_m_jpg.rf.25e48b6a27c6674a917766bd58882fc9.jpg\n", "Ground Truth: Tulip, Predicted Class: Tulip, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Tulip/15516715153_08abc9bb20_n_jpg.rf.386d59b85dc376a1b9dbaea237ba342b.jpg: 224x224 Tulip 1.00, Jeruk 0.00, Jagung 0.00, BungaMatahari 0.00, Semangka 0.00, 5.0ms\n", "Speed: 7.6ms preprocess, 5.0ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Tulip/15516715153_08abc9bb20_n_jpg.rf.386d59b85dc376a1b9dbaea237ba342b.jpg\n", "Ground Truth: Tulip, Predicted Class: Tulip, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Tulip/19915714271_3d8d6a23a5_n_jpg.rf.e06a27a1ee62e0cf639eb8fe79d9b994.jpg: 224x224 KacangPanjang 0.99, Tulip 0.01, Kangkung 0.00, Kelapa 0.00, Jagung 0.00, 5.3ms\n", "Speed: 7.2ms preprocess, 5.3ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Tulip/19915714271_3d8d6a23a5_n_jpg.rf.e06a27a1ee62e0cf639eb8fe79d9b994.jpg\n", "Ground Truth: Tulip, Predicted Class: KacangPanjang, Confidence: 0.99\n", "\n", "image 1/1 /content/plants-classification-3/test/Tulip/14278331403_4c475f9a9b_jpg.rf.552a2aa3c8149debd7913fd0939d7723.jpg: 224x224 Tulip 1.00, BungaMatahari 0.00, Dandelion 0.00, Mawar 0.00, Padi 0.00, 7.0ms\n", "Speed: 6.6ms preprocess, 7.0ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Tulip/14278331403_4c475f9a9b_jpg.rf.552a2aa3c8149debd7913fd0939d7723.jpg\n", "Ground Truth: Tulip, Predicted Class: Tulip, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Tulip/13910471347_30c8bf4de1_n_jpg.rf.683634c7ee398308e9359218407685db.jpg: 224x224 Tulip 0.98, Mawar 0.02, BungaMatahari 0.00, BawangMerah 0.00, Daisy 0.00, 6.0ms\n", "Speed: 8.0ms preprocess, 6.0ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Tulip/13910471347_30c8bf4de1_n_jpg.rf.683634c7ee398308e9359218407685db.jpg\n", "Ground Truth: Tulip, Predicted Class: Tulip, Confidence: 0.98\n", "Processing Class Folder (Ground Truth): Melon\n", "\n", "image 1/1 /content/plants-classification-3/test/Melon/aug_0_5392_jpg.rf.b8b6970c65f9521888baeddf6580613d.jpg: 224x224 Melon 1.00, Jambu 0.00, Jeruk 0.00, Timun 0.00, Terong 0.00, 7.3ms\n", "Speed: 6.8ms preprocess, 7.3ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Melon/aug_0_5392_jpg.rf.b8b6970c65f9521888baeddf6580613d.jpg\n", "Ground Truth: Melon, Predicted Class: Melon, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Melon/aug_0_8103_jpg.rf.7baa0c4b6eb1714f688892954c4809d0.jpg: 224x224 Melon 1.00, Jambu 0.00, Timun 0.00, Kelapa 0.00, Tulip 0.00, 6.8ms\n", "Speed: 6.8ms preprocess, 6.8ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Melon/aug_0_8103_jpg.rf.7baa0c4b6eb1714f688892954c4809d0.jpg\n", "Ground Truth: Melon, Predicted Class: Melon, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Melon/aug_0_6778_jpg.rf.34ebc6abf9ce741bd0ddac2807c130be.jpg: 224x224 Melon 1.00, Jahe 0.00, Jagung 0.00, BawangMerah 0.00, Jeruk 0.00, 7.3ms\n", "Speed: 7.5ms preprocess, 7.3ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Melon/aug_0_6778_jpg.rf.34ebc6abf9ce741bd0ddac2807c130be.jpg\n", "Ground Truth: Melon, Predicted Class: Melon, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Melon/aug_0_6483_jpg.rf.14dbd7372601a14c31badcc10bf614d3.jpg: 224x224 Melon 1.00, Jambu 0.00, Timun 0.00, Jahe 0.00, Jeruk 0.00, 6.4ms\n", "Speed: 7.6ms preprocess, 6.4ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Melon/aug_0_6483_jpg.rf.14dbd7372601a14c31badcc10bf614d3.jpg\n", "Ground Truth: Melon, Predicted Class: Melon, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Melon/melon607_jpg.rf.6ed8d5c985bfb26f965e26657a531d81.jpg: 224x224 Melon 1.00, Pisang 0.00, Kelapa 0.00, Tulip 0.00, Jambu 0.00, 6.8ms\n", "Speed: 7.3ms preprocess, 6.8ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Melon/melon607_jpg.rf.6ed8d5c985bfb26f965e26657a531d81.jpg\n", "Ground Truth: Melon, Predicted Class: Melon, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Melon/aug_0_4963_jpg.rf.5c512f0d08ece1d6da48108c5272fe68.jpg: 224x224 Melon 1.00, Jeruk 0.00, Pepaya 0.00, BawangMerah 0.00, Nanas 0.00, 6.9ms\n", "Speed: 7.5ms preprocess, 6.9ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Melon/aug_0_4963_jpg.rf.5c512f0d08ece1d6da48108c5272fe68.jpg\n", "Ground Truth: Melon, Predicted Class: Melon, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Melon/aug_0_6057_jpg.rf.c9268b526aae3c2c49f9839e36e7f608.jpg: 224x224 Melon 1.00, Pisang 0.00, Terong 0.00, Jeruk 0.00, Timun 0.00, 7.7ms\n", "Speed: 8.8ms preprocess, 7.7ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Melon/aug_0_6057_jpg.rf.c9268b526aae3c2c49f9839e36e7f608.jpg\n", "Ground Truth: Melon, Predicted Class: Melon, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Melon/aug_0_3329_jpg.rf.255705379f41965d8d8466963e73376f.jpg: 224x224 Melon 1.00, Jambu 0.00, Kelapa 0.00, Jeruk 0.00, Timun 0.00, 7.6ms\n", "Speed: 7.7ms preprocess, 7.6ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Melon/aug_0_3329_jpg.rf.255705379f41965d8d8466963e73376f.jpg\n", "Ground Truth: Melon, Predicted Class: Melon, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Melon/aug_0_5183_jpg.rf.d50b5df681aee444a1c3c78176f2df35.jpg: 224x224 Melon 1.00, Pepaya 0.00, Jambu 0.00, Semangka 0.00, Timun 0.00, 6.9ms\n", "Speed: 7.4ms preprocess, 6.9ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Melon/aug_0_5183_jpg.rf.d50b5df681aee444a1c3c78176f2df35.jpg\n", "Ground Truth: Melon, Predicted Class: Melon, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Melon/aug_0_2007_jpg.rf.5519ff8de67ba23bcf89e1f36f86277c.jpg: 224x224 Melon 1.00, Kacang 0.00, Jagung 0.00, Kelapa 0.00, Jeruk 0.00, 6.1ms\n", "Speed: 8.0ms preprocess, 6.1ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Melon/aug_0_2007_jpg.rf.5519ff8de67ba23bcf89e1f36f86277c.jpg\n", "Ground Truth: Melon, Predicted Class: Melon, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Melon/aug_0_1334_jpg.rf.4a7dd9d7f29a9f92bfda1cbd87ccbd5b.jpg: 224x224 Melon 1.00, Jambu 0.00, Pepaya 0.00, Kelapa 0.00, Semangka 0.00, 8.7ms\n", "Speed: 7.5ms preprocess, 8.7ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Melon/aug_0_1334_jpg.rf.4a7dd9d7f29a9f92bfda1cbd87ccbd5b.jpg\n", "Ground Truth: Melon, Predicted Class: Melon, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Melon/aug_0_757_jpg.rf.2416f79fbaf590ec9b8982a70c8bf89c.jpg: 224x224 Melon 1.00, Ubi 0.00, KacangPanjang 0.00, Jahe 0.00, Timun 0.00, 7.4ms\n", "Speed: 7.6ms preprocess, 7.4ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Melon/aug_0_757_jpg.rf.2416f79fbaf590ec9b8982a70c8bf89c.jpg\n", "Ground Truth: Melon, Predicted Class: Melon, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Melon/aug_0_8696_jpg.rf.9c780fd2acba7417e0322d76657bde41.jpg: 224x224 Melon 1.00, Pepaya 0.00, Jeruk 0.00, Tulip 0.00, Pisang 0.00, 10.5ms\n", "Speed: 7.8ms preprocess, 10.5ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Melon/aug_0_8696_jpg.rf.9c780fd2acba7417e0322d76657bde41.jpg\n", "Ground Truth: Melon, Predicted Class: Melon, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Melon/aug_0_2943_jpg.rf.880339a0b297355fd5f03d146239e35c.jpg: 224x224 Melon 1.00, Jambu 0.00, Tulip 0.00, Kelapa 0.00, Jagung 0.00, 10.5ms\n", "Speed: 7.9ms preprocess, 10.5ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Melon/aug_0_2943_jpg.rf.880339a0b297355fd5f03d146239e35c.jpg\n", "Ground Truth: Melon, Predicted Class: Melon, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Melon/aug_0_2691_jpg.rf.834702f5fb7ad75475b527169d5dc7f6.jpg: 224x224 Melon 1.00, Pisang 0.00, Pepaya 0.00, Mawar 0.00, Tulip 0.00, 7.8ms\n", "Speed: 9.7ms preprocess, 7.8ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Melon/aug_0_2691_jpg.rf.834702f5fb7ad75475b527169d5dc7f6.jpg\n", "Ground Truth: Melon, Predicted Class: Melon, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Melon/melon596_jpg.rf.b1c1ad9a73fcfbddd49e93350d016c15.jpg: 224x224 Melon 1.00, Jeruk 0.00, Mangga 0.00, Jahe 0.00, Nanas 0.00, 8.4ms\n", "Speed: 7.4ms preprocess, 8.4ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Melon/melon596_jpg.rf.b1c1ad9a73fcfbddd49e93350d016c15.jpg\n", "Ground Truth: Melon, Predicted Class: Melon, Confidence: 1.00\n", "Processing Class Folder (Ground Truth): Kangkung\n", "\n", "image 1/1 /content/plants-classification-3/test/Kangkung/aug_0_4144_jpg.rf.46adb6694377fb3fa104c95abe6485cb.jpg: 224x224 Kangkung 1.00, Bayam 0.00, Padi 0.00, KacangPanjang 0.00, Nanas 0.00, 7.3ms\n", "Speed: 7.0ms preprocess, 7.3ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kangkung/aug_0_4144_jpg.rf.46adb6694377fb3fa104c95abe6485cb.jpg\n", "Ground Truth: Kangkung, Predicted Class: Kangkung, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kangkung/aug_0_6955_jpg.rf.34cb489dde8f65de08ef26f388f6f379.jpg: 224x224 Kangkung 0.99, Bayam 0.01, Nanas 0.00, Jahe 0.00, Jagung 0.00, 9.0ms\n", "Speed: 8.6ms preprocess, 9.0ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kangkung/aug_0_6955_jpg.rf.34cb489dde8f65de08ef26f388f6f379.jpg\n", "Ground Truth: Kangkung, Predicted Class: Kangkung, Confidence: 0.99\n", "\n", "image 1/1 /content/plants-classification-3/test/Kangkung/aug_0_4590_jpg.rf.e6c784883ee186965e8123ef13f03fe5.jpg: 224x224 Kangkung 1.00, Bayam 0.00, Nanas 0.00, Padi 0.00, Jagung 0.00, 10.0ms\n", "Speed: 7.8ms preprocess, 10.0ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kangkung/aug_0_4590_jpg.rf.e6c784883ee186965e8123ef13f03fe5.jpg\n", "Ground Truth: Kangkung, Predicted Class: Kangkung, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kangkung/kale726_jpg.rf.07bba88a1d2de4521677f7336f70d915.jpg: 224x224 Kangkung 1.00, Bayam 0.00, Dandelion 0.00, Padi 0.00, Jagung 0.00, 8.4ms\n", "Speed: 7.7ms preprocess, 8.4ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kangkung/kale726_jpg.rf.07bba88a1d2de4521677f7336f70d915.jpg\n", "Ground Truth: Kangkung, Predicted Class: Kangkung, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kangkung/aug_0_7073_jpg.rf.4070ad52830da184d2973756c4da6bde.jpg: 224x224 Kangkung 1.00, Bayam 0.00, Terong 0.00, Jagung 0.00, Nanas 0.00, 13.8ms\n", "Speed: 7.9ms preprocess, 13.8ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kangkung/aug_0_7073_jpg.rf.4070ad52830da184d2973756c4da6bde.jpg\n", "Ground Truth: Kangkung, Predicted Class: Kangkung, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kangkung/aug_0_5627_jpg.rf.6d87436d4fd52ecedfa71af1719a332a.jpg: 224x224 Kangkung 1.00, Bayam 0.00, Terong 0.00, Padi 0.00, Dandelion 0.00, 6.1ms\n", "Speed: 7.7ms preprocess, 6.1ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kangkung/aug_0_5627_jpg.rf.6d87436d4fd52ecedfa71af1719a332a.jpg\n", "Ground Truth: Kangkung, Predicted Class: Kangkung, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kangkung/aug_0_6732_jpg.rf.7f4fd517921d4603cf03121e0796cb91.jpg: 224x224 Kangkung 1.00, Bayam 0.00, Nanas 0.00, Jagung 0.00, KacangPanjang 0.00, 7.6ms\n", "Speed: 7.3ms preprocess, 7.6ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kangkung/aug_0_6732_jpg.rf.7f4fd517921d4603cf03121e0796cb91.jpg\n", "Ground Truth: Kangkung, Predicted Class: Kangkung, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kangkung/aug_0_5185_jpg.rf.faf015181b418b3b718efe654238b037.jpg: 224x224 Kangkung 1.00, Bayam 0.00, Nanas 0.00, Jagung 0.00, KacangPanjang 0.00, 7.7ms\n", "Speed: 7.5ms preprocess, 7.7ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kangkung/aug_0_5185_jpg.rf.faf015181b418b3b718efe654238b037.jpg\n", "Ground Truth: Kangkung, Predicted Class: Kangkung, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kangkung/aug_0_538_jpg.rf.d10761744194cb52f527048c8c45f86b.jpg: 224x224 Kangkung 1.00, Bayam 0.00, KacangPanjang 0.00, Mangga 0.00, Nanas 0.00, 7.1ms\n", "Speed: 9.5ms preprocess, 7.1ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kangkung/aug_0_538_jpg.rf.d10761744194cb52f527048c8c45f86b.jpg\n", "Ground Truth: Kangkung, Predicted Class: Kangkung, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kangkung/aug_0_1073_jpg.rf.2900458bcd71c54ef1b29726cbf08f59.jpg: 224x224 Kangkung 1.00, Bayam 0.00, Pisang 0.00, KacangPanjang 0.00, Terong 0.00, 10.3ms\n", "Speed: 8.2ms preprocess, 10.3ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kangkung/aug_0_1073_jpg.rf.2900458bcd71c54ef1b29726cbf08f59.jpg\n", "Ground Truth: Kangkung, Predicted Class: Kangkung, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kangkung/kale572_jpg.rf.6eb6f95384b1c0283bc35c6fe73610a4.jpg: 224x224 Kangkung 1.00, Bayam 0.00, Jagung 0.00, Tulip 0.00, Timun 0.00, 5.5ms\n", "Speed: 7.3ms preprocess, 5.5ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kangkung/kale572_jpg.rf.6eb6f95384b1c0283bc35c6fe73610a4.jpg\n", "Ground Truth: Kangkung, Predicted Class: Kangkung, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kangkung/aug_0_2728_jpg.rf.400ca3345a1e98ae6d7fe4cb59232ad2.jpg: 224x224 Kangkung 1.00, Jagung 0.00, Bayam 0.00, Nanas 0.00, Mangga 0.00, 6.6ms\n", "Speed: 6.8ms preprocess, 6.6ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kangkung/aug_0_2728_jpg.rf.400ca3345a1e98ae6d7fe4cb59232ad2.jpg\n", "Ground Truth: Kangkung, Predicted Class: Kangkung, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kangkung/aug_0_3334_jpg.rf.3d3b1f7bca3b0cee9dc39285451f6a4d.jpg: 224x224 Kangkung 1.00, KacangPanjang 0.00, Bayam 0.00, Padi 0.00, Mangga 0.00, 7.2ms\n", "Speed: 7.2ms preprocess, 7.2ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kangkung/aug_0_3334_jpg.rf.3d3b1f7bca3b0cee9dc39285451f6a4d.jpg\n", "Ground Truth: Kangkung, Predicted Class: Kangkung, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kangkung/aug_0_6925_jpg.rf.5693d5c97f2215f82a3b10d9a93b5476.jpg: 224x224 Kangkung 1.00, Nanas 0.00, Jagung 0.00, Bayam 0.00, Mangga 0.00, 8.3ms\n", "Speed: 7.8ms preprocess, 8.3ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kangkung/aug_0_6925_jpg.rf.5693d5c97f2215f82a3b10d9a93b5476.jpg\n", "Ground Truth: Kangkung, Predicted Class: Kangkung, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kangkung/aug_0_9247_jpg.rf.23c1f829cce4c9ba805695786f9c197d.jpg: 224x224 Kangkung 1.00, Nanas 0.00, Bayam 0.00, Jagung 0.00, Ubi 0.00, 8.1ms\n", "Speed: 9.0ms preprocess, 8.1ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kangkung/aug_0_9247_jpg.rf.23c1f829cce4c9ba805695786f9c197d.jpg\n", "Ground Truth: Kangkung, Predicted Class: Kangkung, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kangkung/aug_0_3716_jpg.rf.c997e5c42ffdbd86fe8c67e62397af0d.jpg: 224x224 Kangkung 1.00, Bayam 0.00, Nanas 0.00, Timun 0.00, Jagung 0.00, 6.4ms\n", "Speed: 7.3ms preprocess, 6.4ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kangkung/aug_0_3716_jpg.rf.c997e5c42ffdbd86fe8c67e62397af0d.jpg\n", "Ground Truth: Kangkung, Predicted Class: Kangkung, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Kangkung/aug_0_8670_jpg.rf.d5253de32f582944b4fc36e7e2e86b44.jpg: 224x224 Kangkung 1.00, Padi 0.00, Jahe 0.00, Nanas 0.00, Melon 0.00, 5.8ms\n", "Speed: 6.4ms preprocess, 5.8ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Kangkung/aug_0_8670_jpg.rf.d5253de32f582944b4fc36e7e2e86b44.jpg\n", "Ground Truth: Kangkung, Predicted Class: Kangkung, Confidence: 1.00\n", "Processing Class Folder (Ground Truth): Jahe\n", "\n", "image 1/1 /content/plants-classification-3/test/Jahe/aug_0_2923_jpg.rf.e24e8b1562925349f41f7fa4e57d7bf7.jpg: 224x224 Jahe 1.00, Jeruk 0.00, Kentang 0.00, Melon 0.00, Terong 0.00, 5.9ms\n", "Speed: 7.0ms preprocess, 5.9ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jahe/aug_0_2923_jpg.rf.e24e8b1562925349f41f7fa4e57d7bf7.jpg\n", "Ground Truth: Jahe, Predicted Class: Jahe, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jahe/aug_0_2865_jpg.rf.0987254acfb85bf3015d060255e96d4a.jpg: 224x224 Jahe 1.00, Kentang 0.00, BawangMerah 0.00, Nanas 0.00, Terong 0.00, 8.4ms\n", "Speed: 9.5ms preprocess, 8.4ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jahe/aug_0_2865_jpg.rf.0987254acfb85bf3015d060255e96d4a.jpg\n", "Ground Truth: Jahe, Predicted Class: Jahe, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jahe/aug_0_3954_jpg.rf.3c7d9f8e93b18d9e81e0fe3a04202ddb.jpg: 224x224 Jahe 1.00, Ubi 0.00, Kentang 0.00, Jagung 0.00, Semangka 0.00, 8.7ms\n", "Speed: 6.9ms preprocess, 8.7ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jahe/aug_0_3954_jpg.rf.3c7d9f8e93b18d9e81e0fe3a04202ddb.jpg\n", "Ground Truth: Jahe, Predicted Class: Jahe, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jahe/aug_0_281_jpg.rf.adad4207f6f03143971da26ea953e135.jpg: 224x224 Jahe 1.00, Bayam 0.00, Nanas 0.00, Jambu 0.00, Jeruk 0.00, 8.1ms\n", "Speed: 10.0ms preprocess, 8.1ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jahe/aug_0_281_jpg.rf.adad4207f6f03143971da26ea953e135.jpg\n", "Ground Truth: Jahe, Predicted Class: Jahe, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jahe/aug_0_4020_jpg.rf.70289d7295c2cbb4d70dfb01f5f5fa80.jpg: 224x224 Jahe 1.00, Kentang 0.00, Ubi 0.00, Timun 0.00, Pisang 0.00, 11.7ms\n", "Speed: 6.9ms preprocess, 11.7ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jahe/aug_0_4020_jpg.rf.70289d7295c2cbb4d70dfb01f5f5fa80.jpg\n", "Ground Truth: Jahe, Predicted Class: Jahe, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jahe/aug_0_1684_jpg.rf.638b5bcf61b881f75061e4228e0b4c28.jpg: 224x224 Jahe 1.00, BawangMerah 0.00, Nanas 0.00, Ubi 0.00, Kelapa 0.00, 8.0ms\n", "Speed: 8.0ms preprocess, 8.0ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jahe/aug_0_1684_jpg.rf.638b5bcf61b881f75061e4228e0b4c28.jpg\n", "Ground Truth: Jahe, Predicted Class: Jahe, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jahe/aug_0_252_jpg.rf.38917ebb9995ba4b19175a405124079f.jpg: 224x224 Jahe 1.00, Ubi 0.00, Kentang 0.00, Bayam 0.00, Jagung 0.00, 6.8ms\n", "Speed: 9.0ms preprocess, 6.8ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jahe/aug_0_252_jpg.rf.38917ebb9995ba4b19175a405124079f.jpg\n", "Ground Truth: Jahe, Predicted Class: Jahe, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jahe/aug_0_6398_jpg.rf.1d21c8ee724cb77a03e14d67253728d9.jpg: 224x224 Jahe 1.00, Ubi 0.00, BawangMerah 0.00, Nanas 0.00, Semangka 0.00, 6.8ms\n", "Speed: 7.7ms preprocess, 6.8ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jahe/aug_0_6398_jpg.rf.1d21c8ee724cb77a03e14d67253728d9.jpg\n", "Ground Truth: Jahe, Predicted Class: Jahe, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jahe/aug_0_8644_jpg.rf.5d9a0c68d8a303a73c303c1174a50f07.jpg: 224x224 Jahe 1.00, Jeruk 0.00, Bayam 0.00, Terong 0.00, BawangMerah 0.00, 7.4ms\n", "Speed: 7.3ms preprocess, 7.4ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jahe/aug_0_8644_jpg.rf.5d9a0c68d8a303a73c303c1174a50f07.jpg\n", "Ground Truth: Jahe, Predicted Class: Jahe, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jahe/aug_0_1252_jpg.rf.dc341968e8e24c5157606ddcdc8881f3.jpg: 224x224 Jahe 1.00, Kacang 0.00, Pisang 0.00, Ubi 0.00, Kentang 0.00, 7.3ms\n", "Speed: 7.7ms preprocess, 7.3ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jahe/aug_0_1252_jpg.rf.dc341968e8e24c5157606ddcdc8881f3.jpg\n", "Ground Truth: Jahe, Predicted Class: Jahe, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jahe/aug_0_986_jpg.rf.80abb1fc245da7700cc468617d57d0e0.jpg: 224x224 Jahe 1.00, Ubi 0.00, BawangMerah 0.00, Kelapa 0.00, Terong 0.00, 8.8ms\n", "Speed: 10.7ms preprocess, 8.8ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jahe/aug_0_986_jpg.rf.80abb1fc245da7700cc468617d57d0e0.jpg\n", "Ground Truth: Jahe, Predicted Class: Jahe, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jahe/aug_0_38_jpg.rf.f049d446e145ccf0ac13c32f3c533bca.jpg: 224x224 Jahe 1.00, Kentang 0.00, Ubi 0.00, BawangMerah 0.00, Terong 0.00, 10.8ms\n", "Speed: 9.9ms preprocess, 10.8ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jahe/aug_0_38_jpg.rf.f049d446e145ccf0ac13c32f3c533bca.jpg\n", "Ground Truth: Jahe, Predicted Class: Jahe, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jahe/aug_0_907_jpg.rf.6d116a4b841b6cc8ea1bac004eba448d.jpg: 224x224 Jahe 1.00, Mawar 0.00, Tulip 0.00, Bayam 0.00, Ubi 0.00, 8.0ms\n", "Speed: 8.1ms preprocess, 8.0ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jahe/aug_0_907_jpg.rf.6d116a4b841b6cc8ea1bac004eba448d.jpg\n", "Ground Truth: Jahe, Predicted Class: Jahe, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jahe/aug_0_8391_jpg.rf.14888be909615bd5f99f53aaf17f56bc.jpg: 224x224 Jahe 1.00, Ubi 0.00, Kelapa 0.00, BawangMerah 0.00, Jambu 0.00, 8.5ms\n", "Speed: 7.9ms preprocess, 8.5ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jahe/aug_0_8391_jpg.rf.14888be909615bd5f99f53aaf17f56bc.jpg\n", "Ground Truth: Jahe, Predicted Class: Jahe, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jahe/aug_0_6267_jpg.rf.01b02226e236beb6bff766dae4e6e2d2.jpg: 224x224 Jahe 1.00, Mawar 0.00, Ubi 0.00, Nanas 0.00, Bayam 0.00, 7.6ms\n", "Speed: 7.8ms preprocess, 7.6ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jahe/aug_0_6267_jpg.rf.01b02226e236beb6bff766dae4e6e2d2.jpg\n", "Ground Truth: Jahe, Predicted Class: Jahe, Confidence: 1.00\n", "Processing Class Folder (Ground Truth): BungaMatahari\n", "\n", "image 1/1 /content/plants-classification-3/test/BungaMatahari/1240625276_fb3bd0c7b1_jpg.rf.bf58fa471c29f97c19ce41cf203e1204.jpg: 224x224 BungaMatahari 1.00, Daisy 0.00, Dandelion 0.00, Mawar 0.00, Terong 0.00, 6.6ms\n", "Speed: 7.3ms preprocess, 6.6ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/BungaMatahari/1240625276_fb3bd0c7b1_jpg.rf.bf58fa471c29f97c19ce41cf203e1204.jpg\n", "Ground Truth: BungaMatahari, Predicted Class: BungaMatahari, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/BungaMatahari/6140892289_92805cc590_jpg.rf.9d265ea4feee3d5a8c2dc2553806f3fd.jpg: 224x224 BungaMatahari 1.00, Dandelion 0.00, Daisy 0.00, Jagung 0.00, Tulip 0.00, 7.2ms\n", "Speed: 7.9ms preprocess, 7.2ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/BungaMatahari/6140892289_92805cc590_jpg.rf.9d265ea4feee3d5a8c2dc2553806f3fd.jpg\n", "Ground Truth: BungaMatahari, Predicted Class: BungaMatahari, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/BungaMatahari/20812318934_82f10c45a1_n_jpg.rf.cacfd0accbe5a52a413a567b70c2a055.jpg: 224x224 BungaMatahari 1.00, Daisy 0.00, Pisang 0.00, Dandelion 0.00, Semangka 0.00, 8.1ms\n", "Speed: 7.9ms preprocess, 8.1ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/BungaMatahari/20812318934_82f10c45a1_n_jpg.rf.cacfd0accbe5a52a413a567b70c2a055.jpg\n", "Ground Truth: BungaMatahari, Predicted Class: BungaMatahari, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/BungaMatahari/20938724084_7fe6bf87ae_n_jpg.rf.bb748a15a7759ef904fb27ce9a1f377a.jpg: 224x224 BungaMatahari 1.00, Dandelion 0.00, Daisy 0.00, Nanas 0.00, Mawar 0.00, 7.4ms\n", "Speed: 7.8ms preprocess, 7.4ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/BungaMatahari/20938724084_7fe6bf87ae_n_jpg.rf.bb748a15a7759ef904fb27ce9a1f377a.jpg\n", "Ground Truth: BungaMatahari, Predicted Class: BungaMatahari, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/BungaMatahari/200557977_bf24d9550b_jpg.rf.941500b73d67af82da4e3d32a42c42f8.jpg: 224x224 BungaMatahari 1.00, Dandelion 0.00, Melon 0.00, Pepaya 0.00, Nanas 0.00, 10.2ms\n", "Speed: 10.9ms preprocess, 10.2ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/BungaMatahari/200557977_bf24d9550b_jpg.rf.941500b73d67af82da4e3d32a42c42f8.jpg\n", "Ground Truth: BungaMatahari, Predicted Class: BungaMatahari, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/BungaMatahari/20753711039_0b11d24b50_n_jpg.rf.77c8496018ad2f7942721d3aea9867df.jpg: 224x224 BungaMatahari 1.00, Dandelion 0.00, Tulip 0.00, Jagung 0.00, Nanas 0.00, 13.1ms\n", "Speed: 9.8ms preprocess, 13.1ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/BungaMatahari/20753711039_0b11d24b50_n_jpg.rf.77c8496018ad2f7942721d3aea9867df.jpg\n", "Ground Truth: BungaMatahari, Predicted Class: BungaMatahari, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/BungaMatahari/15054866658_c1a6223403_m_jpg.rf.283a3a4a0dd471258708363f9ed03220.jpg: 224x224 BungaMatahari 1.00, Daisy 0.00, Tulip 0.00, Dandelion 0.00, Mawar 0.00, 12.1ms\n", "Speed: 7.5ms preprocess, 12.1ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/BungaMatahari/15054866658_c1a6223403_m_jpg.rf.283a3a4a0dd471258708363f9ed03220.jpg\n", "Ground Truth: BungaMatahari, Predicted Class: BungaMatahari, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/BungaMatahari/4664737020_b4c61aacd3_n_jpg.rf.881925654d9f1c10d91910b642a71f75.jpg: 224x224 BungaMatahari 1.00, Daisy 0.00, Dandelion 0.00, Semangka 0.00, Pisang 0.00, 10.0ms\n", "Speed: 10.5ms preprocess, 10.0ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/BungaMatahari/4664737020_b4c61aacd3_n_jpg.rf.881925654d9f1c10d91910b642a71f75.jpg\n", "Ground Truth: BungaMatahari, Predicted Class: BungaMatahari, Confidence: 1.00\n", "Processing Class Folder (Ground Truth): Ubi\n", "\n", "image 1/1 /content/plants-classification-3/test/Ubi/aug_0_4136_jpg.rf.360d7f3e47f89567d03dd75455a243dd.jpg: 224x224 Ubi 1.00, Dandelion 0.00, Timun 0.00, Jagung 0.00, Kelapa 0.00, 6.9ms\n", "Speed: 9.9ms preprocess, 6.9ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Ubi/aug_0_4136_jpg.rf.360d7f3e47f89567d03dd75455a243dd.jpg\n", "Ground Truth: Ubi, Predicted Class: Ubi, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Ubi/aug_0_9224_jpg.rf.a71ea3c5d462ea7c015e9463c8f6fdd5.jpg: 224x224 Ubi 1.00, Pepaya 0.00, Kentang 0.00, Cabai 0.00, Pisang 0.00, 5.5ms\n", "Speed: 11.3ms preprocess, 5.5ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Ubi/aug_0_9224_jpg.rf.a71ea3c5d462ea7c015e9463c8f6fdd5.jpg\n", "Ground Truth: Ubi, Predicted Class: Ubi, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Ubi/aug_0_5990_jpg.rf.ab768a26a33c3f88189a3a202e22c9a7.jpg: 224x224 Ubi 1.00, Jeruk 0.00, Pepaya 0.00, Kentang 0.00, Mangga 0.00, 5.6ms\n", "Speed: 9.9ms preprocess, 5.6ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Ubi/aug_0_5990_jpg.rf.ab768a26a33c3f88189a3a202e22c9a7.jpg\n", "Ground Truth: Ubi, Predicted Class: Ubi, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Ubi/aug_0_7317_jpg.rf.11049f9244454106e978d36609cd823b.jpg: 224x224 Ubi 1.00, Kelapa 0.00, Jahe 0.00, Jagung 0.00, Semangka 0.00, 10.9ms\n", "Speed: 6.9ms preprocess, 10.9ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Ubi/aug_0_7317_jpg.rf.11049f9244454106e978d36609cd823b.jpg\n", "Ground Truth: Ubi, Predicted Class: Ubi, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Ubi/aug_0_8118_jpg.rf.ac68cf1baa6985ce5262c2fbb0457478.jpg: 224x224 Ubi 1.00, Kentang 0.00, Pepaya 0.00, Jagung 0.00, Timun 0.00, 10.6ms\n", "Speed: 7.4ms preprocess, 10.6ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Ubi/aug_0_8118_jpg.rf.ac68cf1baa6985ce5262c2fbb0457478.jpg\n", "Ground Truth: Ubi, Predicted Class: Ubi, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Ubi/cassava746_jpg.rf.4636a5adfc5e9598a9c57dca5beaaae8.jpg: 224x224 Padi 0.56, Ubi 0.44, Mawar 0.00, Dandelion 0.00, Kacang 0.00, 5.7ms\n", "Speed: 7.4ms preprocess, 5.7ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Ubi/cassava746_jpg.rf.4636a5adfc5e9598a9c57dca5beaaae8.jpg\n", "Ground Truth: Ubi, Predicted Class: Padi, Confidence: 0.56\n", "\n", "image 1/1 /content/plants-classification-3/test/Ubi/aug_0_5816_jpg.rf.b32825fcd57931cba1555b9ddc9e04ef.jpg: 224x224 Ubi 1.00, Jahe 0.00, Pepaya 0.00, Semangka 0.00, Padi 0.00, 6.2ms\n", "Speed: 7.1ms preprocess, 6.2ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Ubi/aug_0_5816_jpg.rf.b32825fcd57931cba1555b9ddc9e04ef.jpg\n", "Ground Truth: Ubi, Predicted Class: Ubi, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Ubi/aug_0_7666_jpg.rf.25b53e085e16723c74fc269e903044ee.jpg: 224x224 Ubi 1.00, Kacang 0.00, Mangga 0.00, Kelapa 0.00, BawangMerah 0.00, 5.5ms\n", "Speed: 6.9ms preprocess, 5.5ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Ubi/aug_0_7666_jpg.rf.25b53e085e16723c74fc269e903044ee.jpg\n", "Ground Truth: Ubi, Predicted Class: Ubi, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Ubi/cassava59_jpg.rf.d1aa722784ead5ae35d23172d0d239fc.jpg: 224x224 Ubi 1.00, Dandelion 0.00, Timun 0.00, Jagung 0.00, Jahe 0.00, 6.4ms\n", "Speed: 7.0ms preprocess, 6.4ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Ubi/cassava59_jpg.rf.d1aa722784ead5ae35d23172d0d239fc.jpg\n", "Ground Truth: Ubi, Predicted Class: Ubi, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Ubi/aug_0_9582_jpg.rf.c42885bca4b815697009bf636ca09202.jpg: 224x224 Ubi 1.00, Kentang 0.00, Jahe 0.00, Pisang 0.00, Pepaya 0.00, 6.0ms\n", "Speed: 8.3ms preprocess, 6.0ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Ubi/aug_0_9582_jpg.rf.c42885bca4b815697009bf636ca09202.jpg\n", "Ground Truth: Ubi, Predicted Class: Ubi, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Ubi/aug_0_8665_jpg.rf.7d74948f1fcffc8278975078badb3e2d.jpg: 224x224 Ubi 1.00, Kentang 0.00, Jahe 0.00, Daisy 0.00, Timun 0.00, 5.8ms\n", "Speed: 7.5ms preprocess, 5.8ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Ubi/aug_0_8665_jpg.rf.7d74948f1fcffc8278975078badb3e2d.jpg\n", "Ground Truth: Ubi, Predicted Class: Ubi, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Ubi/aug_0_2874_jpg.rf.ea6580d7aaa0968585b2472469611536.jpg: 224x224 Ubi 1.00, Jahe 0.00, Padi 0.00, Melon 0.00, Jagung 0.00, 7.4ms\n", "Speed: 8.4ms preprocess, 7.4ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Ubi/aug_0_2874_jpg.rf.ea6580d7aaa0968585b2472469611536.jpg\n", "Ground Truth: Ubi, Predicted Class: Ubi, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Ubi/aug_0_4554_jpg.rf.62b085ea005618cb3f6edf10e63e5ece.jpg: 224x224 Ubi 1.00, Kelapa 0.00, Mawar 0.00, Kentang 0.00, Pepaya 0.00, 5.7ms\n", "Speed: 7.2ms preprocess, 5.7ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Ubi/aug_0_4554_jpg.rf.62b085ea005618cb3f6edf10e63e5ece.jpg\n", "Ground Truth: Ubi, Predicted Class: Ubi, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Ubi/aug_0_572_jpg.rf.b31cf7d0493b36f493b8b1051255aee2.jpg: 224x224 Ubi 1.00, Semangka 0.00, Jahe 0.00, Jagung 0.00, Mawar 0.00, 7.7ms\n", "Speed: 7.9ms preprocess, 7.7ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Ubi/aug_0_572_jpg.rf.b31cf7d0493b36f493b8b1051255aee2.jpg\n", "Ground Truth: Ubi, Predicted Class: Ubi, Confidence: 1.00\n", "Processing Class Folder (Ground Truth): Jagung\n", "\n", "image 1/1 /content/plants-classification-3/test/Jagung/aug_0_2986_jpg.rf.4c1a22dc03ce4c092d3e0106f922fe90.jpg: 224x224 Jagung 1.00, Semangka 0.00, Pisang 0.00, KacangPanjang 0.00, Pepaya 0.00, 5.3ms\n", "Speed: 8.9ms preprocess, 5.3ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jagung/aug_0_2986_jpg.rf.4c1a22dc03ce4c092d3e0106f922fe90.jpg\n", "Ground Truth: Jagung, Predicted Class: Jagung, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jagung/aug_0_5116_jpg.rf.ad2822f5ee18003205a3580881a52e77.jpg: 224x224 Jagung 1.00, KacangPanjang 0.00, Kangkung 0.00, Timun 0.00, Kelapa 0.00, 6.0ms\n", "Speed: 7.6ms preprocess, 6.0ms inference, 0.0ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jagung/aug_0_5116_jpg.rf.ad2822f5ee18003205a3580881a52e77.jpg\n", "Ground Truth: Jagung, Predicted Class: Jagung, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jagung/aug_0_345_jpg.rf.70a5c85d8171403e6b43ccbdcbeed9a9.jpg: 224x224 Jagung 1.00, Kacang 0.00, Dandelion 0.00, Semangka 0.00, Jahe 0.00, 6.1ms\n", "Speed: 7.4ms preprocess, 6.1ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jagung/aug_0_345_jpg.rf.70a5c85d8171403e6b43ccbdcbeed9a9.jpg\n", "Ground Truth: Jagung, Predicted Class: Jagung, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jagung/aug_0_2164_jpg.rf.153ec98db597eec06006a6fc4a3ad03a.jpg: 224x224 Jagung 1.00, Pisang 0.00, Daisy 0.00, Semangka 0.00, Jahe 0.00, 6.7ms\n", "Speed: 7.9ms preprocess, 6.7ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jagung/aug_0_2164_jpg.rf.153ec98db597eec06006a6fc4a3ad03a.jpg\n", "Ground Truth: Jagung, Predicted Class: Jagung, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jagung/corn938_jpg.rf.e61d2e38022198280cac4528708481f9.jpg: 224x224 Jagung 1.00, Dandelion 0.00, Timun 0.00, Tulip 0.00, Daisy 0.00, 7.6ms\n", "Speed: 7.5ms preprocess, 7.6ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jagung/corn938_jpg.rf.e61d2e38022198280cac4528708481f9.jpg\n", "Ground Truth: Jagung, Predicted Class: Jagung, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jagung/aug_0_3874_jpg.rf.93e37d21f03ee2ed51747a8c2ef64472.jpg: 224x224 Jagung 1.00, Kangkung 0.00, Daisy 0.00, Jeruk 0.00, Pisang 0.00, 7.1ms\n", "Speed: 8.9ms preprocess, 7.1ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jagung/aug_0_3874_jpg.rf.93e37d21f03ee2ed51747a8c2ef64472.jpg\n", "Ground Truth: Jagung, Predicted Class: Jagung, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jagung/aug_0_8422_jpg.rf.704a570bbea8e55741de166da818ddde.jpg: 224x224 Jagung 1.00, Timun 0.00, Padi 0.00, Pepaya 0.00, Kelapa 0.00, 7.0ms\n", "Speed: 7.1ms preprocess, 7.0ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jagung/aug_0_8422_jpg.rf.704a570bbea8e55741de166da818ddde.jpg\n", "Ground Truth: Jagung, Predicted Class: Jagung, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jagung/aug_0_4925_jpg.rf.0fe0e8d0aad62c9fe8ce010037e8206c.jpg: 224x224 Jagung 1.00, Padi 0.00, Pisang 0.00, Kangkung 0.00, Kacang 0.00, 11.4ms\n", "Speed: 10.2ms preprocess, 11.4ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jagung/aug_0_4925_jpg.rf.0fe0e8d0aad62c9fe8ce010037e8206c.jpg\n", "Ground Truth: Jagung, Predicted Class: Jagung, Confidence: 1.00\n", "\n", "image 1/1 /content/plants-classification-3/test/Jagung/aug_0_9453_jpg.rf.c1b06ab1fc323ca8c7926d553cc0b753.jpg: 224x224 Jagung 1.00, Nanas 0.00, Kangkung 0.00, Jeruk 0.00, Cabai 0.00, 7.0ms\n", "Speed: 11.0ms preprocess, 7.0ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n", "Image: /content/plants-classification-3/test/Jagung/aug_0_9453_jpg.rf.c1b06ab1fc323ca8c7926d553cc0b753.jpg\n", "Ground Truth: Jagung, Predicted Class: Jagung, Confidence: 1.00\n" ] } ] }, { "cell_type": "code", "source": [], "metadata": { "id": "34fQ6Nd98A02" }, "execution_count": null, "outputs": [] } ] }