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Created October 2, 2019 06:09
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processing_nii_files.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Goal: Reading *.nii.gz files and prepare them for fastai/PyTorch/TF modeling. \n",
"# What will you learn?: you learn about how to preprocess, prepare and load the data (as training and validationn sets) using medicaltorch and PyTorch's build-in DataLoader module.\n",
"# in otherwords input would be a 3D volume data, pass it through a neural network, where output should be another 3D volume of the same dimension/shape.\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# 1. First understand what is .nii format?\n",
"\n",
"# NIfTI stands for Neuroimaging Informatics Technology Initiative (1)\n",
"# Why Kaggle competition use .nii format instead of DICOM (2)\n",
"# Because this format preserves 3D or 4D information (height, width, depth, sequences)\n",
"# It is important to appreciate two main representations for storing and accessing more than one Nifti images, that is sets of MRI scans:\n",
"# a big 4D matrix representing (3D MRI + 1D for time), stored in a single Nifti file (3)\n",
"\n",
"\n",
"# sources:\n",
"# (1) https://nifti.nimh.nih.gov/nifti-1/\n",
"# (2) https://nifti.nimh.nih.gov/nifti-1/documentation/faq#Q2\n",
"# (3) https://nilearn.github.io/manipulating_images/input_output.html"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# 2. Are there any python packages to read this .nii format?\n",
"# Yes, but none of these are helpful to prepaare the data for fastai/PyTorch\n",
"\n",
"# 2.1. Nilearn (4) - interesting functions to tranform the data.\n",
"# 2.2. nipy-nibabel (5) - nipy also has other interesting libraries to do statistics.\n",
"\n",
"# sources:\n",
"# (4) https://nilearn.github.io/manipulating_images/manipulating_images.html\n",
"# (5) https://nipy.org/nibabel/coordinate_systems.html"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# 3. How to prepare .nii files for fastai/PyTorch deep learning moodels?\n",
"\n",
"# I think we have to write a custom dataloader since nifti is not supported as an extension yet (6,7).\n",
"\n",
"# sources:\n",
"# (6) https://discuss.pytorch.org/t/how-to-add-new-format-of-image-nii-to-datasets-imagefolder/46457/5\n",
"# (7) https://forums.fast.ai/t/custom-data-loader-for-3d-data/27290\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/10 [00:00<?, ?it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"/labs/mpsnyder/gbogu17/kits_2019/kits19/data/batch_download/case_00000\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
},
{
"ename": "FileExistsError",
"evalue": "[Errno 17] File exists: '/labs/mpsnyder/gbogu17/kits_2019/kits19/data/batch_download/case_00000'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mFileExistsError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-5-2085d33ef271>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0mcaseDir\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataDir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcaseNamePattern\u001b[0m\u001b[0;34m%\u001b[0m\u001b[0mcase\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcaseDir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmkdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcaseDir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0mimageUrl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimageUrlPattern\u001b[0m\u001b[0;34m%\u001b[0m\u001b[0mcase\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mFileExistsError\u001b[0m: [Errno 17] File exists: '/labs/mpsnyder/gbogu17/kits_2019/kits19/data/batch_download/case_00000'"
]
}
],
"source": [
"# 4. Download KITS_2019 data and move the image files into images folder and segs onto labels folder.\n",
"# git clone https://github.com/neheller/kits19 [WORKING!]\n",
"# source: https://github.com/sharmarochan [If you just need few files use the following script]\n",
"\n",
"# From 1 to 10 files [Just for Testing]\n",
"import os\n",
"import requests\n",
"from tqdm import tqdm\n",
"\n",
"dataDir = r\"/labs/mpsnyder/gbogu17/kits_2019/kits19/data/batch_download/\" # The target dir you want to store the downloaded files\n",
"caseNamePattern = \"case_%05d\"\n",
"dataUrl = \"https://github.com/neheller/kits19/tree/master/data/\"\n",
"imageUrlPattern = dataUrl + caseNamePattern + \"/imaging.nii.gz\"\n",
"\n",
"segmentationUrlPattern = dataUrl + caseNamePattern + \"/segmentation.nii.gz\"\n",
"\n",
"for case in tqdm(range(0, 10)):\n",
"# Use tqdm_notebook in jupyter notebook to show the download progress\n",
" caseDir = os.path.join(dataDir, caseNamePattern%case)\n",
" print(caseDir)\n",
" os.mkdir(caseDir)\n",
" \n",
" imageUrl = imageUrlPattern%case\n",
" imageR = requests.get(imageUrl)\n",
" with open(os.path.join(caseDir, \"imaging.nii.gz\"), 'wb') as f:\n",
" f.write(imageR.content)\n",
"\n",
" segmentationUrl = segmentationUrlPattern%case\n",
" segmentationR = requests.get(segmentationUrl)\n",
" with open(os.path.join(caseDir, \"segmentation.nii.gz\"), 'wb') as f:\n",
" f.write(segmentationR.content)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 5. Copy multiple files in multiple folders into images and labels\n",
"# https://unix.stackexchange.com/questions/544538/copy-multiple-files-in-multiple-folders-into-one\n",
"\n",
"#for pathname in */*.gz; do\n",
"# cp -i -- \"$pathname\" \"$destdir/$( dirname -- \"$pathname\" )_$( basename -- \"$pathname\" )\"\n",
"#done"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "Number of source and target images must be equal and non-zero",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-9-7b4203bf2933>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0mtfms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch_tfms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCompose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mRandomCrop2D\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mToTensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNiftiDataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtfms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtgt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/gbogu17/.local/lib/python3.7/site-packages/niftidataset/dataset.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, source_dir, target_dir, transform, preload)\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpreload\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpreload\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msource_fns\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtarget_fns\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msource_fns\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'Number of source and target images must be equal and non-zero'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 45\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mpreload\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 46\u001b[0m self.imgs = [(nib.load(s).get_data(), nib.load(t).get_data())\n",
"\u001b[0;31mValueError\u001b[0m: Number of source and target images must be equal and non-zero"
]
}
],
"source": [
"# NOT WORKING\n",
"\n",
"# 6. How to write a custom dataloader / in fastai?\n",
"# Use niftidataset library https://github.com/jcreinhold/niftidataset\n",
"# https://github.com/jcreinhold/niftidataset/blob/master/tutorials/5min-tutorial.ipynb\n",
"\n",
"from niftidataset import *\n",
"import torch\n",
"from torch import nn\n",
"import torchvision.transforms as torch_tfms\n",
"\n",
"#data_dir = '/labs/mpsnyder/gbogu17/kits_2019/kits19/data/batch_download/test/crossval_niftis/case_00209.nii.gz'\n",
"data_dir = '/labs/mpsnyder/gbogu17/kits_2019/kits19/data/batch_download/test/someones_epi.nii.gz'\n",
"\n",
"tfms = torch_tfms.Compose([RandomCrop2D(32, None), ToTensor()])\n",
"ds = NiftiDataset(data_dir, data_dir, tfms)\n",
"\n",
"src,tgt = ds[0]\n",
"plt.imshow(np.rot90(src[0]), cmap='gist_gray')\n",
"plt.axis('off');\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 6. Try this if 6 and 7 fails\n",
"# https://github.com/wolny/pytorch-3dunet/issues/33"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 7. Using MedicalTorch to process *.nii.gz files\n",
"# ----------------------------------------------------\n",
"# create a DataLoader using medicaltorch for medical dataset containing data in NIFTI format. \n",
"# https://medicaltorch.readthedocs.io/en/stable/index.html#\n",
"# https://github.com/perone/medicaltorch/blob/master/examples/Dataloaders_NIFTI.ipynb\n",
"# https://github.com/perone/medicaltorch"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import os\n",
"\n",
"from medicaltorch import datasets as mt_datasets\n",
"from medicaltorch import transforms as mt_transforms\n",
"\n",
"import torch\n",
"from torchvision import transforms\n",
"from torch.utils.data import DataLoader\n",
"from torch import autograd, optim\n",
"import torch.backends.cudnn as cudnn\n",
"import torch.nn as nn\n",
"\n",
"import torchvision.utils as vutils\n",
"\n",
"cudnn.benchmark = True\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [],
"source": [
"# Data Exploration\n",
"# Before we do any modeling stuff, let's investigate our data first. \n",
"# Let's look at one sample (MRI image) from the dataset.\n",
"# We will see the preprocessing module mt_datasets.\n",
"# SegmentationPair2D which is used to read and format the data in a way that we can better explore it in our environment. See the example below.\n",
"\n",
"ROOT_DIR = \"/labs/mpsnyder/gbogu17/kits_2019/test/\"\n",
"mri_input_filename = os.path.join(ROOT_DIR,'images',\n",
" 'case_00003_imaging.nii.gz')\n",
"mri_gt_filename = os.path.join(ROOT_DIR,'labels','case_00003_segmentation.nii.gz')\n",
"\n",
"pair = mt_datasets.SegmentationPair2D(mri_input_filename, mri_gt_filename)\n",
"slice_pair = pair.get_pair_slice(55)\n",
"input_slice = slice_pair[\"input\"]\n",
"gt_slice = slice_pair[\"gt\"]"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(90, 560)\n"
]
}
],
"source": [
"print(input_slice.shape)"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# As you can see above, images are 90 X 560 dimensions. We can also view the image in our environment using matplotlib.\n",
"\n",
"img = input_slice\n",
"plt.imshow(img,cmap = 'gray')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"img = gt_slice\n",
"plt.imshow(img,cmap = 'gray')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [],
"source": [
"# The SegmentationPair2D class gives us a easy-to use wrapper for Analyzing the NIFTI images and thier corresponding segmentation mask.\n",
"# The 2 most frequently used methods are\n",
" # get_pair_slice - This method is used to fetch a particular slice from the NIFT image. We can also pass the desired axis (Axial,coronal,Sagittal) to view the image\n",
" # get_pair_data- This method is used to fetch the entire image/pixel data and gives us a tuple containing image and mask data\n",
" \n",
"img_data,seg_data = pair.get_pair_data()\n"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(90, 560, 560)"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"img_data.shape"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(90, 560, 560)"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"seg_data.shape # As you can see we got the entire image and label tensor by using the get_pair_data method"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[('/labs/mpsnyder/gbogu17/kits_2019/test/images/case_00002_imaging.nii.gz',\n",
" '/labs/mpsnyder/gbogu17/kits_2019/test/labels/case_00002_segmentation.nii.gz'),\n",
" ('/labs/mpsnyder/gbogu17/kits_2019/test/images/case_00003_imaging.nii.gz',\n",
" '/labs/mpsnyder/gbogu17/kits_2019/test/labels/case_00003_segmentation.nii.gz'),\n",
" ('/labs/mpsnyder/gbogu17/kits_2019/test/images/case_00001_imaging.nii.gz',\n",
" '/labs/mpsnyder/gbogu17/kits_2019/test/labels/case_00001_segmentation.nii.gz')]"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Creating a Dataset for 2D segmentation (slice-wise)\n",
"# Next we will see how we can create a Dataset using the NIFTI images.\n",
"# medicaltorch library has a built in generic Dataset called MRI2DSegmentationDataset for this purpose. \n",
"# This dataset takes a tuple of input images and thier labels.\n",
"\n",
"ROOT_DIR = \"/labs/mpsnyder/gbogu17/kits_2019/test/\"\n",
"\n",
"img_list = os.listdir(os.path.join(ROOT_DIR,'images'))\n",
"label_list = os.listdir(os.path.join(ROOT_DIR,'labels'))\n",
"\n",
"filename_pairs = [(os.path.join(ROOT_DIR,'images',x),os.path.join(ROOT_DIR,'labels',y)) for x,y in zip(img_list,label_list)]\n",
"\n",
"filename_pairs"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"# load data\n",
"train_dataset = mt_datasets.MRI2DSegmentationDataset(filename_pairs, transform=mt_transforms.ToTensor())"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
"# PyTorch data loader\n",
"dataloader = DataLoader(train_dataset, batch_size=2,collate_fn=mt_datasets.mt_collate)\n",
"batch = next(iter(dataloader))"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Let's visualise the batches\n",
"for item in batch['input']:\n",
" plt.imshow(item.squeeze(0),cmap = 'gray')\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
"# Using built in Transformations\n",
"train_transform = transforms.Compose([\n",
" mt_transforms.Resample(0.25, 0.25),\n",
" mt_transforms.ElasticTransform(alpha_range=(40.0, 60.0),\n",
" sigma_range=(2.5, 4.0),\n",
" p=0.3),\n",
" mt_transforms.ToTensor()]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
"train_dataset = mt_datasets.MRI2DSegmentationDataset(filename_pairs,transform=train_transform)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Now lets analyze whether we are able to see the transformation getting applied or not ?\n",
"dataloader = DataLoader(train_dataset, batch_size=2,collate_fn=mt_datasets.mt_collate)\n",
"batch = next(iter(dataloader))\n",
"for item in batch['input']:\n",
" plt.imshow(item.squeeze(0),cmap = 'gray')\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1698\n"
]
}
],
"source": [
"print(len(train_dataset))"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'gt': tensor([[[0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" ...,\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.]]]),\n",
" 'gt_metadata': <medicaltorch.datasets.SampleMetadata at 0x2b90c6117ef0>,\n",
" 'input': tensor([[[-1024., -1024., -1024., ..., -1024., -1024., -1024.],\n",
" [-1024., -1024., -1024., ..., -1024., -1024., -1024.],\n",
" [-1024., -1024., -1024., ..., -1024., -1024., -1024.],\n",
" ...,\n",
" [-1024., -1024., -1024., ..., -1024., -1024., -1024.],\n",
" [-1024., -1024., -1024., ..., -1024., -1024., -1024.],\n",
" [-1024., -1024., -1024., ..., -1024., -1024., -1024.]]]),\n",
" 'input_metadata': <medicaltorch.datasets.SampleMetadata at 0x2b90c6117978>}"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# You can see an example below of how a raw image was converted to a tensor format.\n",
"# sample of the training dataset\n",
"train_dataset[0]"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [],
"source": [
"# Now that we have the image loaded and transformed into tensors, \n",
"# we provided it to the PyTorch native Dataloader function to do its magic. \n",
"# The DataLoader below basically creates mini-batches and shuffles them.\n",
"\n",
"# PyTorch data loader\n",
"dataloader = DataLoader(train_dataset, batch_size=1,\n",
" shuffle=True, num_workers=4,\n",
" collate_fn=mt_datasets.mt_collate)\n",
"minibatch = next(iter(dataloader))\n"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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WGJ4KsfaVjhp+S18uuycB0FnIZnk3+kqKLsHdIJfBsgBt14CCWHp3H9PNrC0AjqVrsVFv+9qrnVhjnDqZREASHhhRmaRTa921LrRZs2WmDnRgdpbrioZ6UqRGI22PkAukfx7GH6tjG861dhTp53r32M2C5E364JQ0X9W/yfRmp/v9O8AYLAAyaz5Ima80QjMWxqblMuv1w6bgHg1eiuD6LcHH37zEvR8tmJ8s4KUBq4IJrQ3CDDIvBgDgY9WJetWlNhlr5MOK/ccNz98m1D3UhXDfIM8L0IA//OQruFknHcdnyF2MM+ntl9tRXG8nQd0DdaceEwGMgAKj6bZUGNJGN1Esa/cOAkbPpAb18gBUQsneC9oB3f0LTc+dkovn1rNJmko3jgFuo3C/58/PA8+WVxt0CR3kkmfBiccCkzYSrJPyzJX/dvBF6cC5uWaAvDSorc//lJNziCbImR6g3Q7YzfjkT1yivrYAB1/zO0tDDMJYevsjeac6472sqrWm5SfMiOMghDA2wXTZdI9VL0oCtIsGuai9Ls5MHcwF4SkDAfiGg62E8cj8cqWMempmQG12JmuXN8NhM8DK7l9UaWTI3gZb74gG8MGe02xCYl0e7q/sIIwA96G9M5jOOnGVa5VPGgPshMiWHs6ydQKwWYdEDZoAeG4mFZL56Y7ajpgHAGfQFaDsTE8lPUeEwkVMWFDmCuaGUpo+w3Y+rDobHN9Z8NF+Bq8T9h/cgK9Nz2UCHVfIboJMxvxEQDcrqBBoqQrI5l5FdcXVjxc8/foey+sNdLlivlixPJ0gQvjej1/TCWvrthfNItHWqNr/AHSMLBTeLwSg7YC6B2iV7pvb3FXMJDrTdmVZ429ZV31lJndZO4LmSc+p0kHXgTs3WtHJR1yaAMJQrkCfpENilTSK/z2Z50d/h31yftnyaoNuLhtN9aQwgYTMEHaq3QIwYL7t8nQK5udY7GfoODRNoN2M+uZDPH/HBsKagh6A7oIFH680LtsM3MJFzGmdn0sCuZTOVt0o45KAPwbbTE2CZkCNzJCDAfaPRKCMppHqV4mBK4Bt6urPIAn0pD+j2LkDKzajXtQzswe/j09K/Ue/Ria2ku4B6HU5TYUhMvfrA+i+x5IOySuDdKwTtTapH7NquOMx2m8yni8YBLywCZVmwQ/Wf9EQ/biTlYdVJAx0LOBdxfqY8OydGRcfXOLijwX8tHaUlm0/iRqSiUAWIOGAMz1bMT3bazs0wnos0YftWE7ab1uvPGluNXKStMIgHUO00XKHEu+h9FXrGM7XXc1agxD1NqxiQGr+velawVy5mFeIGYzP3mNTpw2W0Dlt+HOWVx90zzzY5/GfHUA0Sj0vV4Q2U0fGm+QGPS8FPxB3C2l2SdvtQPOM51+7h2fvNeDIXa9sFIajbIjIhBdQhiC7Fk7lDozEAjlolMPF4xs8vv8cTIIPn9zD4Xruhi8HFF8KF0D2VZlebroTlNE60pHBRxqCM8Ss/yRAnRCRbe5N4NeiRjbwjamW/qghq01QN6UGBSVK762BLtyVztstlYgS80khvBowuMlFExu79baFAHSk3g9wcmvAkQDP6yQMtH0DXa16vrFtB3hpBJ6aSgwR+WYAatdiFogFUCixc53WZygK/9h1TTqvmKEutQXZ5MlXK57+EmF6PqMcL7C/XvxmKiOYbCET6/KYgVYm9XJIwFueHLD/5EqjEg8F8nzSVQi8jcU8TLZg5KuH1GZuFK3J3a31dqYtoLE2cESerXYBjzybpgiWkHW1AAnWCcP8B0tM0A6GI0kLI/o0Gbv2VXIBXDN2X0S/bhjGfYIYn/tly8+vIe2zROzt95vokpPrbj0Xtufedj/Xdu0aYAIuL/Ds7aJGKrPkZj9ZX+6f+Dg6gJi3gi7/t8fod7t5xdW84P581Ognt+BHdJmB9ySQqamRrbSfviqqBtoLdQnAwCh0W9n+TKDlf27qGyQoMR5gPJ4SMA4X9F+p32s8GbdGxg3H+XP4ccHcZLhX6O3JEyP3iRsc3UWJ8vVDu0RnsIbaAZqbSY649Sg1r5qo+6ID8IlHE0lM2KU0lMdHXH9V8OydGetrV5CLWQG2NmCt+jPhEAAF4at9gA0tK3ZPG/hgz5ICJtxQG5q5j1vX6G3S7EEuaezkn95UHikHnLBcZacOiFuWAGW2OX+E99mmDHad2wb9ICWi67n++xCt8g+uvPJM1xnrSXBC/j15FFA5PXc41mOwXf/NgFqKvUyjdjv8NM8HYhPbzbNB2hr3aI/u4ckvQqPFFgq2BGDwKugBEj6IoIazXQNN7SxIqaeBoDbG+0/voZCEjydWBh9ZgbaISRPokoLXgSzooaQ2baTBGwa4nquAKjr6uKwVnxnrdH1ZAM+/0Cmif27v7GrhntyvB/98TvWk3mZR7WZ64QkIYXRLS8U16JwPwo9rVu8IkBCkgBSrX9M+kbmp94kx2XATMwYvQpAVmkvBQSrXoxGazVyNGpgE1SY2EfVYcE09gi0qgYooztl3kkDYh+g0VRzeqviICoALPPgOWR6GNjI2QHMvtIp2f4fl4Q77Hz4FHY4AEfYfLpifXWB9DMhOog2IlFnrKsEnGGP5k4Tvewfe7qVDxnV40ogxmQQRyQAFx/BuCUaprFfMqKZt0YBljDDFskKmCRA1pMU7trXltMR4VpOetriQDefVCVpeBdvElYjSi3hW5fKKM93+dn3mA26jyfJVsuvX1rLp52YXkxdwBaEU0UJEwG7G8fVLLK/Xkd3a4wRrc8aQWQIZgE3J9YvG83lukVTl5mbG9WHu+p9AQcLi3T1/ApUE4OZ2BgMPWVkZbiX1r1wxaqr+gqeJI2us2Hw+uJNlFzV/zoZg0W7g2pLNbZuctEMqA/klGVhvGNHOtH+sAkpmvzQc67KCWPCIGvAomCsMcOMl9CCVNFnEXCsU3gu+1M6uYl0jHk2zTIlwWH9EeDYltvvwiOPbK569wzi8MUP26rVAWyZpeq4UxnrFaiSym09PjyiW+MtDwYfO2b4W3o6c2srHsU9eeQUAdPfCaJjbdVHK/rI+gXhpMrDZF4K/phPPdvWrF5LTzxw/RIYbvayf7ivPdOHRXnnwpBDc0Gmz21hzd67OfDUOnkdx/bZbFu4akJdgyP1aUdy1ZZ6BRw/w8Td3QFlB1wz16bPzHMycUZUOECB357LlqL9n/qIV2ej8/vKP46RdNXiiGrBoUIZHO1XW2XotykzdB9cj1QDzm02sz7WB7PcL+9wEywA3Z4CUPhsQRMITAo3Cz9P9dX25HiHB8bAIA5xHqkWiG4KlyuyeBlIQ0XPuGSF5AieMAEKInBjZPY4aUC9b5KGQSGXYJ8TORrW9ox9WBk/KaCOHgsDGsnostFWDBJgFZaqDl4M0Blvdc+bDMiWJDArm66rCOV+ueP5uAR8nlJsrXKwNuD6GsUxKMYauEWnTc9N8dzM8kGJ6bm1xkZ8XEF/JpDbjIqhFBmLhk+swkXqIdZ6FgJGBJ0Y7eB74u8X6fRAjI0mURHOd9AnkLl+Avaet3y9cwmwl4+46TYAyj8dlt1CLaPPAHXlJwAVeeaZ7vkjOOvZZFkTXWbdZwnLJeR0AeA7c4ZBt5iL7PUfU0DShPbrCs/egL4sZEjy/wsjK+qwf3yXvAg8RdTsfG7sJUib9ZyQgIYAuagcjoLuUCRScxCSPhcCWEKdcE9jc2gbtNtUvM3TZvjznDHKJ3cRH7rrVMBqxnJ27RmsT09gB3e8zmDKlunmbEnqEWu7CrXdIAk4HWG0vu0gzXdv9pJ2pJRc/og2YGMAO18F4nmOENJ0EPVprmhrmuWKyrGWAejnE4zurnSq4NHBpoSf7tae5Am8ecP224Oa1gnY5A1PRKDVALfoTq7tYbSjXVfVVT5LTGsrRJJfSkO0JEsbZ3o7Mauw90W5j7FGMA/dhTt2pQUxZK9lkDpPkvRAAm/MzWHayfgLMVXDDVnMwSHYPgx27lRo8LWTWfO1FC9vDy2Puq850rfGyVssEMj02ghW8nJMFslaznZ22SW5S7l3XbTu455c2dUYFhFh9BncznvzSPRwfCejQ3Ww8Bj6W3xlVHMwmW8KmpWbWBSknTfEIIA8t9eCIqXUPhkaq/zWCVLuOpY3s5nRL226GvfBI8DpKz3cricGG1pnnMn+kAGcJV7nMmIWgblfJIyEizozhOsB332AEuGZw9twJfv9g0KlK2VUpyw0kBFSAcr7dOEnPa/Z2dFxQ6UeBtq9AAkBFtUv1NtHVSsugQIhgCL+eJhSqOB4nlNIBF7AhZxMas0BK6z7BlbpdgQRcKjwX1PK44tm7E3ZPL3D19AiqFWJApZFpBAEjUoVWAWqD7GcsV6T69XbC8D99/JCy7DCych/n3n5hLHbGnL6DALLogKN5A0NeV0vRmkObSW+sYFwlPCFEyKQgE6HP5c9FIkkRdVq6zaPagMp5XvxY0oGQPWNetvzcMF0qZrRKOs9JrgTPSAQk/7x2aiG9JamNf+dJcAZJ4xZfwsjdSwSaZzx7myEPVtCBYpm+TaSSmVnct5hlfLOE6+Q75Vq1NZy6HHVrOk2iLHIh8A2p4/9C4CMrkw3tl06YNoAI280lwNFnd2ew5o8lmUFuvADYk54kbRgkukuBAWxmTzlEOhsaPSlQ1MOZrBnIul7sk0cC3tzWsmHyFRGuOuRy8Gaaxj6nogELXGwFQrrEVm28Awv5vZyFujtTytHgExOaHteOBbVyj1pLXg9Ady/L3+mroDsyTJNNyiTgBwtu3hTcPC4Gqq0bQdc2gFUPhW1AIdQLn9hS47XUh7m/rQ7hV75lgD7eTbIZ5h+RnmPh3OYDOezX9dzWkjyg/1wGFJ8ALDyXIh9uunEK6R2ImGGLkqiUh7t2HZkK9xXDFyyvONPtACtbppln1c9r+DLWS3l54vqvff+ZwQ+lJ+nITJvmCfUrj3H9FVGvhQS0AE5dovLYnUSNFkD4Xzob8hdNhDrzbfoC10qotaA1jeXHTenAmQCrM25lX0NYr8/ag88XBmY6MPNBbIUBXz9On1tf0AGyBJqpTIwRxX3HNsp6YDDr3Ga0qeIQNWagaW3YSp8jvFD1a+unzXVlYGDXMRHNbWPYTJWDASCRTnjeT6bvopEmOXcDn5/DmuS8QrPecdHvnemqny55ldAq42iTjEe5NdF+dgliXTWRDpGAi2B5VPH0axMuP3iAy+8+Ua+FZP0XIvAh5SRgxuHNS9RLINKFCoWNQWURGTqouWRAGJLzD9KUjzNb7bSiHiihiaYkUYBFkjnpcbtKTuID6Hua3mFqEkmDyFersQzb2F+25URaCGNK13y3RSy44yVzL/zcMF2fpU6WB7eVE6tkslRmvWbDlCMz0ZCfN93rttmuFBzfuMDyuIV2Chhb2/abzf4BFJOyVDH2OAClJbpWzx9bfjrjbYxWiwLuyuAbBl/zuMxLgIKVNLm5Ay/G41R77oOQEtsZGK8fkp7DUzb678O9gcSMjVFHHgn7zsGY+rVP7+t9ltrOl8jS6ysF9k+wzYEau204+BrohuywCT/m2XeDsLDczWNF3tupbfrN9F77nS2KzYkXm2812Q4Tpaimy9xCKw6NWwh11clVzAtCNVKylY+5nIXRVED3Vtx8peHZVyf1ZDBDWYAJADqkgTIVHB4V1AuTfpIEovquJ2Pf9JGP8xzanSbykKVY80rIpCudNhEi7NdLynUQ2cb8mtvQXiZgnlTrzWM5u4n5+3yOSHl6yK32OyS/OoMRufzDmmVsmyV++Jz4vARwziXMSgjyufiyIy8/TnJxulk6XcvkDb7Ygx7cx7N3d5B9GzJm6bU21XGgASKhTXY90ot3tuFeabr3lqcLnNCqWcCvi+5KUTHe11ipa58Btk1BLxulAGfAHRxjiU8ysNLBWs3Du6f2K0t4kxmjth1FtXL9/HnDre224vXYHnPGljooHbnOQ338IU6JPgAFd1LPAgdXJKDzi+v8rAwTQLC/YQLb/C5CYNI90nyHidYI+3kNcHf3sZYT+nglfTW8ltD6vYgQeG5oDyqefp3w6TcfoO1niBnSwAyUFIpLhPXRBZ6/xcr8fSUk6G5w/lyATS5JOtmEP8fWUCFfaQO77vpZRqgh9PcbTZUAACAASURBVDeB7bDaDUKkbUG5HjlgKb/TAEJyAEbDeHL9PLfVz3BvWPv8w+mniw6QW9/b7E2QtuiJ76zhB08H4mGJEj893eO6hEY7bOXj1/AZ2OvjARmXF2j3r/D8LQWsCBSQ7LCdK+8vTwfdbIXOTDJres28EKQR6qGY9Zttk0oV++N+HtcfzK4DWoBw7bqzu4UFS0meBl7nAGHxiDrqnhL+TGyMmdG1WH+GtT/bVv8LphmRcDQAZYB4nJCa0ycLfz5B8pAAPC1jsPnUDT759DpJ1Mv16q6lYpR50oUICH3dAwdcKorDclKgyuqFYJosRHPx7ucVxVglu65rWzL59T0lpF6HQs8ktrwQfr99xc07Kz75E4x2NUPmov8KYawYYXm4w+F1oG+1ZMcMO0lTb5+0Cg8U8XbLG6qmfo6n9188uuyMb7zvpO22nKE4EWsCsOapZteU7Xvy9zxjgwOup3fUTskd1B9K2u3A/QXLK67povvV5gY4p8EmT4ToyG0OBtnsm5a8FXIU2knyG+lvpTRRy7fdj/bKco9vXuHwhgwv+ODPuQEZZwJdTkgjkzBGiwGa+Npyq/q26m5QC2Yt6Z7xjCOTzUzD3yH/I7wDbpmKA8S8vg7EBsZ9gknPQzJs8NjbwoDMEvRIZpt+DaJ8mfF7oW7Ey/RUCBDpk8Lmwp72MtrLL+igPOiQDrYyrI7IAI4NHAkjk/XCW9CFXpO4Rc5c97ut7tYHhMTgMlKAt/TELsrsCK2qhswkaPZsgAI2TQ3y6IjrSnjyCxe490eMcm0eA6smigEzhIGb1wvWS+lyjE9kmQiIDXuCgjFDdd7cMT6+qJ/THz71o4PqVjKo/Xe1g/jA2USjJYOaFF01DOUkVDiPEdWPe7pHPy69c9uI1MHlFON3L1hecaYrloA4eSDclrDG9JgA6cRuKTPeJl0ryuG+KVn5iV7sSwz3WEhgTRd7tPtXOLw+Y73fIv2dR+p0rdSvlRg0n/ncXiye+ssmAqxLQTsUzfgEPY88T4IP7gF8ENpl7OqbmKwD/rAnGamblPu5xn5i/sKYMfBk1wVgYMSxKaX7DttkENFdzo4FYVQ7g1mIDSD9fTCtcQje8MonhhrWcknHJZaeJYphMrS+2koR24xfuuptmOYkO5hxK2y/gviuv/e+S7DNBeTGM98EFDiuBYV1Cx/XfgOYbVv2+GdGs36+dFBmz8vbgPsrPv0G4+b1GcKkCbjFALYQ2sw4PKI+Ifp29WnV0R9+M9GFNi99rm19leBjM3zMfWwAY35cpM+2xVOyEqW8Cx0ThG2S8X7bRKtZgwzvOrYrZcByVKTzMiMGcJKo5yXLqw26+RlzIgpb5kukakuDIovj21nKWOsAnvBDKBIon3RGrkOOhpMGurxEu7/Ds7cYctEUBFt/mYeQxwQMACwOvS8LAYQemF8k8W1bLFRXVtbl81GDHIZltS8Dfclp7ShkAOc6HEkHXgchkm6tzsdG21rlt8tJY3vZxSiW7GdGWA8RTdp3epd9UghN0KWG9GK5BKLsPLVzntRiuYmxjfzaZXz+OD+BjKCDrgNlJBk3HTbfcPCtDlaKkAD8PNh1Y3dg+3ypBYUbpqnn3I0gCemELbKTZUbMACUXQjd+TRcLrt+tuH6TIXPSL1kzj/n+ajGJEoCLClxU0OWKq4c3mHZ1fB+9Mf34NJGdrCCg329TWOqD0PjOnrO55GO9+LsvajhlbkO/9e29KDWaP7fOjINbWS6bcGM/N5KY44sB8KsNusDoTQB0eWADwuFfF7tKpOOlDQyWNrMpJU1JpLuj9CCJkQFnSUPuXaJNjJs3DeTWzXIfnXkBfVwIIfIs8NRBF0LhocDcwlo9aGcHBq3cDURuWDGw6tvj2DvhngW1fxdaphj79BenIdI6+rW30kgs0+M+ox8tBcvV6/rOxpFxzSeGJL/ktrlNe42VgzPmqItdw+6nIcXU8ypIfw5arZ12ovvDWdL3LLP4dXPSbk9q4wfWquG3rRZNtuI+sual0INGlM0K+rufQdw3sYyENva3G9OaBV0QklwhKa9uKlnicN03tnC/rLh5g1Bn1v3SWBkuCKh71h0j0k4Yr73xFG+//TEePX6Of+rd7+Lxw+fDZO5kYQi2yV2ad+RIxfstckL4O7usnVnOU1+1eklsN/8NomFiz7puaMU5Qo0YmHQftdEwRyOBc4I1eDd1EkHZVvSC5dXWdMmWFIwTC+Q2wMG37+k7A2dNRt1GQouNiBP7fmvt3K5wsq4UE0AFzRPkcgeZGIc3m9bXNcpGEelCm0k46OdkMgKJzqKNQvOLFxQYHOzReshuZouDZ5q/m8XZAOIfOXg0DIw1tNNGEdTg79fZoZWZDSOWk2psQ7deE4Z2QFMJg/wa1M8dWKYtcSVf2zbnHDaFJChbBUWdYvsfRrfE2/WpEcBNo66KxIahLoF4/6n0QWgro6YMdz2WwCUn6LiDGb5IdJYhiegxTpq9G0IzwPqkGi5pJCgmS9TKKM56zXWwCYEbeqBFrJpbAGPOhEYAymXF4Y2G5eGE6fkKFIaYj2u9ZNQLievIZcWvvvE+drziw8M9/JnX/g88WfZ4/0ePev8XA12RwOLBZpFXJBjHik7uop5AQaIMGJuBogdAAJ1YZVCM1WsGzrzKICNQDEkGdmGokU3OSRDKnIe8DPleiemq+y+dSM2fp7ziTJdOGiaz0rNnBHgqmAZDzZqwdSJtlyvJkyGXYMs5sEIa+P49/ZVJgSDnBACS1poYQHxGfpnYxiWs3v4IacdYDe8llRQqTtykOsNIpC8lkaZGkBndSJV02Xw+pDPTE1Cm7jo2WLkl/e7XNQCMnXgrhVwQGi9llooY0IOmyoJ2Kaj3GlCAthNNO2hSSNvbrFAEbddGndfvn9i8yxLqt8zdqZ/0GQYf5wbIswnrYYI0DgCtldXn1JLduA9vsagwfzeHkUtdjuh5cm0yIk3PeLlbcG9/RCHBaqCuKoBNOo2VUTs7BizVJAfzVdatwNwqD4a3diF4+k7BzVt7yNQzkC2XTmyAtm/4R77xI7y1f4Kf3NzHZJ3x7uUn5kuONMDRvWQkjRNgBGKbYFpTWUx3Uzbgs3fNt+HRFWv1ZUA0n4h5DkUa1r4yLUdgqaWP41ZP3ctSsitZK4bEV30JYj+bASynWVb9nD1xvkw/HYd+WnnFQRenvrPm7HwS/puTWjB11y5nsXm2q7dPT2JSBW2WGb4LcIQZA6AH9wERtNni1d2nNS19t5Fpoy+uhZDav5GuwrJN2TlVw3nLDYdr11hxu2S4q6Eb0QjGMCUAJ1IgOjpIv4ZMMjDMvGwcwDKBmu+HNujXQEgUXhd/uTPLjdSAxa+VVjBFIFcr6Gq1HTUE2OvMIkUge2V3UkT3LgvPCq+fhQnPHjQhAcC02iTm9Rb9rLMzwvRpgdyUITzXt9qRRh1wi+qwPqlln143Iuly3wIYeDSoTdxwb3fEvd3Rcu2mcRY8grrboFfRQodDqqh9PPlW7l5v2Vc8+5rg2VsFdbZdgwlYr8gysxHoasVffu9v4+3dp3j/2X0waeTcNy4+AM9dW/Z6EUtP0uQgbGMmflrbNl8Fup4OKMhaoIM+Tx2CIqI4K3bSlDaoLAfRTGuANqhLFV62ssG6jnkVvLhE6ZGmTvAMP2ip6vbIglbSNV+wvNryAoCth0GUbBzbOoeb1BAl+9zKeTczv1b+zpPMDDKGLYP48hKym4EqOLw2aSpHz2+QIpP0pIxaSEDnAwHBGvqW3VBPhZU0rLhStwonnZUXjOwONDLISI6DDnIZ250Rh4UfA+hE/QbXMwm2G9oG0HVev6/LCslYlQM4eqYx6ZFhZG3lMoI/m5CGSlu0n8wSbDZSRBbR7eoXO97CgNuDCtpr9jVZWSPzDiU05lZEsUIAD1f19pQJoJXRnk1YpLt4eaJxZ5PZw0Fz4Gp+g9YoJALmBkEZgl10cwQ1oD057E4kB2fHgg6+eUx5Ah5prMeYhOmTQqtpB+wiWO83HF4rqJcF07MVMvOw2nnt9ad4wNf4O0/ew7ObHZ5f7bBIwYfrvWFMUNEw4PBy2I53H1eQGGw8NQ1ddn9tT0yTCiUw1Wttl3R2seMygGmzgB/JANssAtKlBvZgquYdle6THmAbxWYgLEnzk0IjsL9AefVBF+gNnzK8ew4EWQRAHYxfwzl23qABb3VdO142Md2xw7BFxci6xvF0dQm53IGOK67fYNW4ltRRifYpNhgYNzLNEScSgWuMbdXr4WihvccOarqsHxnouIx1JqGfsnSgzbqbzwNuYGs7MRC3eppR0PPcsu2uoGG86Jb/AvDBrml5bIXtUT0yySYVPnrWMWOa9rIKEKAbHh5CkKK6KxF0KT83iNiGie56tlLs6kBF2RzqFO1FBOwf3eDe5QEPLw54dtzhJz95AP5kiudqQKwetrkc2k4sDWbRrr9aLbNYetmTDETW4aV0MFxXYLerKNxw8AEBhLYLAMdjwbqygrmNHQ0Vbj1oxhizeAcKgYugLdyNreaxwNw0L4cZLokV9PD4iMP1BZYrxh5Am1lZm3XEr73xx3hcnuNb738Vh+sZz5cdbtqM94/3rZNt3E0NbbFV1+qDS2Li9nEFEBppGkptExsvAkTww/AO2B6EBTYzWUcw2+cFwAI5HkFXV2FYa2YYlon1mPBKKMDE6g42l64Ti/peizsf1zXVgUf3tPg8rTDd8+Elys8H6OayjUzbuHedJKwxgCVnIuFUnU2eLZhtMOuNv174phow04P7wNrQ7u1xfIjzsz0QL7JehzqobJfBZMag1s/NRqAMmL7FdWzCmL0A+vuZqxHhvPqhsdQI40RozQE6/rIaMcj31/wEyWMBGIQqB9Yhl0JLdQ3kyCxd26TtrF1SDlsNFLFVRIMxJAPdya7lG06yygf1Sp+rAViXCTelobDg2c1ON/e0e7rhz9tK/HrepLlfq2YDI0vfCGOsLie4VAAgItekdlBorcSNpHVG63ui1YYAyZzisYm6ljmbdSMZzOvBPQkiXwMBERpsbFmaAjSRoF40HB8w1gczjg8LnMSCgHvTAX//+CZuDjrIqhAWmfDB4V6XuqhPGBG+ThaQ4jtf+5ggDyYyBr7qREcCDLl0AUtgrjovaSMq8NW0w7FLDOKrVDOEWjfxkvRYtBHUHWjNdWzYWNaNYtIASrC4yVQW78HPwmWMiL5ORP8DEX2LiH6fiP4t+/x1IvrviOgP7edr9jkR0X9ERN8mor9DRP/kZ99FMITx6nX6LLQJ1dUDuHeis+LoBASjzbpu+PsaI3bD2eDv6/WQphnFHt8HlhXrgx2WR8pQc06CWEYbwMWuBrHM9+fp4AKhLiX4TwfC4iwXqmNO0pN/p3lmSLXoS3wHK/vMXdrUyNUju0JmSEzP65W3tYmcDQ6endTb93odXijkBvezjbZw42HWllmfTeY2bBdPjL6ppgdIFGW2mmgGEUhCtrUO3VtBFxWyb6jXBTfPd3jy/ALPP70APy+hIUvxyUFBXKaxPWNnW4KGWh8Y4gyP1K2vFEExRu5gCMACIfTvdSk4Hqc0nFQyqJWxLgWa1EZXOe1YQmaA6bSxeaV7JqADHpsxD4LOwA3syaPUmgI3lwa5bDg+IhweFdy8xjg+lCAHl2XB/3X9LtalAATUxriRCR/c3EvbEUmfsJ042Fjgo/aPuyLGXmnO3o8EWkdCE2VZdDUpAllXfQ9bgxyPpreaHjtNCqae7Y/7JKX5gg1ES1FPiJAsDJB5Y1zPxbBg2PwyBUn0cHXpmPKC5YsY0lYA/46I/BqAXwfwV4noHwXwWwD+loh8E8Dfsr8B4M8B+Kb9+00A//Hnuck2Z+453z0/7tbPznxOyY9vu3fa1igXv4fPMIGPK+hwxPHRhPWqbVju5hkS6wzvBTuuZ4pyZtyZVYTkehUSWA+a8Oa+bozKxq/BX5JH5np67ukzbBk0gDFFpP+9eeZupMtIhm7Iazoh+EQz6Mh2IXez8uTsIX9Q8m/221UamZ89Z9ZcA1hT3XsgBmKCioxjO0F9vEIeLqD7K+Z7C6g0tJWxHgvqylHHzGDjfgagm97SKphee5LwCD4hJ8Zvf+dnDj/cTbhu6KisCc69zQCotnsBA1zSlZN9dWwTqsb3Rjm0GU8Pe2PVnXR6/8Qq7wyGbYNj2ixq+Ud6l1MehSGhVFBp+MPocR45VruXguveuXLbIAvJ+6zlXAy5ZJ33tvLyJBfAF5AXRORHAH5kvz8hom8BeA/AnwfwL9hhfx3A/wjg37PP/zPRVvrbRPSYiN6x69xSRoYKYFjq98Oypdd0ms3nw7HZ0boYxTLNyCPTTqybDswWaEEffgJZK65fK5CrBTimgWquU2Gw8vcmh9cS0g66QCSKdkNUETUEVQu4aAQhYw4NwVZdSgNwkl5vcOWJJCSiOyJQnwSc7YppzpxfILKLSQdVgTHElYAphRLX8TquzQLOyu18FshM4CNC98MC0ELdAk6dxbeFdSVx1BvRrsXQiJB5f5cWBu2asrKjUnaaW7DBsmuoVXVfXtFXA95fvsIgCe1aLiveffdDJeMkKNzw/fdfQ3uukYIr6YvUjJW65tuy3siWQzd7JVgOhmhUGFO2P9xdrBECxNld5XxyFg1xb+A+gVTWLGONUCbLWiYJnElQLwTXbxHqhYQWDwAfHy/x7v7j3rYAPlkv8fR6Dwj1nCDJbYyTHBr9lyMfk7G1XgrWSzu3Vt2fTSsGX/JTf3iACLTboTeI2VZaA1YBTROodcOmzAWxR9qUHoIJWNcOzMFgWwfqtA+idpzYPfs53bvm5ZH3H4imS0TfAPBPAPhfAHzVgVREfkREb9lh7wH4Xjrt+/bZTwHdfBMefGwHJ+k0O53oNNvi4PkiJVg0qz9hKZBnz4HdjOWBGnjgOuFQZ9NpbyuuwW3W50N+gtvWIuMpm/uijwlnhg6+JQGw/QjjiINXYkr9folJOUuOY60yfg/qL3EwZ7sGiZwOV3NDc2InQN/iJSL17DqM+KynV0Q3SjUFPqyal0L8nRZdyrsVP65pdWxsk5xX1aOzCsBzxdW8AACKzTwiBDJPCKmk7C33obFdB8f4ZtNpYpPskN9BFHxyFJwz6PC7zV0iBghRBWe11K9rfdTs77bXcaATcO/w7z55DW/un2J/ccThMINIcGiTJVs6R2WRPFsk+tLiJqzP/ZnIculaPX0niNiKKzS3znr95xCaa39vPz9t3k6esuvpNsuaf7/xfOgG/NPj9Zl+RoY0IroP4L8C8G+LyKc/BdDOfXH6/hH9JlR+wAWuxkbI5Zyfne9Zll3GmkAkHdsEw2vvrNcHQL7XhilLreD9HpgmtOfPUR7cx3IP6Aae/kgZbEPPdbAywCIxFgT4m6/fTd0VKFrOlplDpjD3hvCtb/wldJLJMuwi0XaCdtHi5ZGZFICMIfO1bjOtSV8orjU8h+nIg9sZcl066JKrLh4J1LRdcn5fnQSgWq6xbnU3ggJaUXYlLSGXY6brwgSNLHMwvS6qv3r/H/W7o0wKQhZC7c2OlYC9QHYNtK/hbeFsjYvgj5/chwCYS8VaC+qzST090OcekKBMBjwWyCBuGCvN9kfrXdqqare+m3E8kwFcNd9TJrEE5opBImRRaow65HKA/p6GbbU92ZprwaY518sGPihKU1qV/OAHrwMA3n38KX7w0SOwgW6TNLii4bTdYwUnOJG/eBU0RvcbtlOlUIT6kvvAk2x2CFbXLUmSg9Q2ArIPPw+1zkFQHlDR7P0vmfkWAFXfuW048Habn1xk8/MlyhfRdEFEMxRw/3MR+a/t4x8T0Tv2/TsA/tg+/z6Ar6fTvwbgh9trishvi8ifEpE/NdPF+RunpDUAhlnptnjok1SN/s8T6Nxmjcw5HsJlBRorvptRL6UbeIDzU0sutDkm3lj9KSyx5TcA+K61PUmMHeqBBgbGkv4N98q38gCCYmxvVpDBvgJzC2OdWKYx3768X8/u5aAfILi5EfXzfL7IL2b+Cb/PrMZBmVpkrHLZIIyNhPBT1onOabV0iYFg/rdeDQMx8zyQhZMG2dm7FJUh5v2K3eWC3cWCab9ivljBpeFwnHBzvcP1YYfrm1kzvNXTR/fsYNG1yd+2s04EUAKdEeZJxZlteEHY8wk6uIamnScjQkTEqSeARYK5x4QbuSY52SkEJOBPJ7z/yX08mG+wn1cQgOs6D+5tw/hKZOLETgCE4XNrJ5BioJrJDhBAFzaWMxIhJenBHza8Pew9DvKX9z0rKZ1jTn4zXLxP1p9VXjb3whfxXiAA/wmAb4nIf5i++psA/or9/lcA/Dfp83/dvBh+HcAnP13PBQBRcHNvgsRuh6Q1yY+XSlFdNm/Nsw2qyGneMvhufHsD3GPzSZUs5OYGkIZ27xLrpR1+bgaUMUl4Xr67tRiA+uUCtpz2yTYhqKR/AOioA02KOu8DCL/ZyJ7v2FTUDateNvV6sHpp20i/pmvIs7LhttewUXH91wG5CGTffw8d2Rn3qqDn4BzRZoxgwfGyehcSFAiLaKAD6xIUllZSKiOCJWZfdsNYZDIseRj2ZUO7ahZZJ71tzfvA/am9z9pOYnXB5tvr/rI9H4KeoJtHMoREjUKzZYOzdvXkNU6WAdhKmIwJt8ilwMVzKviAsN8n6e5YgGUUSxcEggWTt2U23DWo+2M6PtJMOttsdNI/bdb+X24mfOeT13CxW8Ak+PBwL7TgyP/sE0cmwP6asfUVSZfKvJ+RxgAreIYkeKbIuvoDIDPSAN6imlBrKZKw9g0rt+kjs9fDkFOBCeDSXczOSZO+mvXJ7SUj0r4I0/3nAPxlAP8iEf2e/fuXAfwHAH6DiP4QwG/Y3wDwOwD+HoBvA/hrAP6Nz7yDUoXYyUG2wAic/m0N4bPQyQ4T21tsr0ubRs9Jzv3aNwdABO1q1uV61PW2Z0AAXTA8QW/9bHmOFzB1qOli8c9ALSea8RSF2bAGIDRc2RnLpTxw08vqbmm2SaYYoERIsO0G0fcekx4KHJMIwhDlgC/+PIT+8kVfddCHLX0jYffUzx02SZzMKORMF+jgYsDg7H1w/zLmrAw1tTcQ7mm+O4Nv/Ahg2IXZ28uTzMR5xdIsIuFC0l/jb2hE22RRbbwBI4i3QQsQ1n6SmFzI+22jH29/+uRxUlyS8bHmGjmJrTQEciz49MkV9jYpPD3uo61zzggvYayN1Rl6HubEfnN9ZFu5IWw3sc1a9e8zexPmrbe6p0oiXGQ7SGQMONcoxB1ws3tpVHYzbsUjQl8OPr+I98L/hNPFhJd/6czxAuCvvvCNzKUrQnY3/rbDbONWS2LVdDeeDnp8AtdSQBi35oHnd0j3j1+nKRgxTRMOr+01HDUtG+PYZoMw188HI9AH/sIavurLMAcVG8l5jzK3qoe3AxChrOERUVP4rb23A9sIK4cN1Aa44SaszqtuASQXddiGXMyv19NX5meOJabnNfDTvE4wrdg1b9N4AWXucR8zdEn6PVh4NVoWk450ndKlkAzkRTo78d16p6oMWQCZSHVN648sBURqRbe6V0atHHlu2161BbJk5qU0LJbsZpoqFmuUYl4EIIsmE4RxbF1LuLZRUfqedXypDDLg850Rmui+eFQsP4M9q4cnCxIok0A9AmyysDSRPlm7DDPIAnPTEN9G+ODZFeZScVwn88pAuLZFdrPMY0wiGZLdO+X364eYjmCjVNi016TdWiADErBimszA2AlV/O4SjB2nebE3RrgmEK/wSbKbBlmqyg9sk/o2TUBJCaTOEazPWb6Qpvv/eXEK77HQGXC3mcC2jDUb4DZBFBHkcIvRbxsp47/TPPXrl4LlflH26APL75F/zz6VxgYyI/WcCpm1Sb6eg4YNZjd6kPm0KrNE7CARbk7ZmSKzTNn83kiX5b68ZwUq9vwFFjCQgzBo2YBuMHREQvGhHfxFzUw3sTtOidgBu9bUgn0Sy5hz2OtM6LquBUqEfy5gz6PX47nqMReqX2MWNd4Nk3IOeDDGG8EGahRjNolg11AuVpRZd/H144kEu6kGi51mTf7N7jss3aOgLWyGLUQQwygTJHaZ/GxDl13ZJiJoboei271zDh229m+rGfWMTQ+rLEL87VvOy8q4vt7hsEy4fr5T+SgDriXWid2ac//lCVegdgkaPwOgLpqefyGleSRNTGFEgLtXwXZHYLuGSx2kJ4PmGaH3um+vNtz4e8YKT5SzrmMwlRdiCHMf74IRkF+gvOJhwPbwrqc603Umu/Xh9XJGa/Ete7YJy7ef+efeOTRPo4uafc77PY73GZCqTvueg5YxLvujAjbQhXoy8UphKMug6y8i6jioe4KZfk0PsRxcvQjBYGQnPbKLUr28iTySx7TOtm8gGR3m3cNBZjvWc84am48Ukr4qzs3v9QbgbmWenyG0XWeh1V4UY2NeTw8gAKBhpquxeVd2/JFYImqKCBAH84KezwJ2bVI3P5k0x64sauV/zntlufYg4XVgD7UuJcCfTaNVdzSd5VojLLb/mWrAZD6vyd2LgOU4hVzAU9JaEyv070NKEN0LTCYHCwPsopnPmox7hTmzBvSYYfJlgeyAJupDTav2iawcLnuNBUeoES1yYPiE5x47fv3EbsnGghvXSMzH3Cb5GB+RPk1G+YAJZDmwt3m049mSoU2oXwoAZK2gSX3tpTX1WnD8cOCdino1EHVbkXtL1IazgMp9VXBuR5TPW15pphtkaKvleiTZuZ1C/dytZTLJFDRr5vjB7Sz76LnhDlBJwfPoek5OANjNakRrpMwvAaRa/oHsUuCpEmMbdAPfrmluHtwHtVvwBQD1UOBBLjQAC+OIM2GGJoNJS/ecr1dFRgVlPhCmp6SuVs3Oy6Br/3I4sD9XGBZaAuRcHNjFn3vsszYbIKSotLyFEQSRw2CwhMeERJvlhdGp8G6AToxJeuCpdcBvsDSPmk1s4yQRJgAAIABJREFUPUxojUPXrcnVK6LPrD+mqaI12zQUgDSVIboG7FnIMvlqaCaR6HY8tU+ayQWMNmw7dv31ZEYpi5vKDpk42DkO5mYAY/eMMX1cbKXmATioBCzGZCuhLhxMPKLPcl8E6KacC9bX1ADfWcINfvmYcaOB9Dexpnrcss1zdhru7diPS4nR3eMobDTSz+uNpT+LarvhMuZ18TpmZH9JIxrwijPdYcXijNSDG4ozA0t7l1My5lDAwgNzBTC4qZBlLPMOF9Tu68u65FHAJZslTUve77Hcp0C/rPWIM7p0S1pp1ON5BK9+YAdID+WNnLDOVlybGM7r5/slxI+l1nUqjMwQUEATRgQS9Dqm3z1YIPyaMXofENTTocrAuKP4szsVth9eBzUOeq4IQNLNaVbBWtC9JXSCaSnQYRMIkMtmg8UsQThY+NKZVmW11UCx0tTBhqwuQBiqqm+nw8Y2G2E5TpjmNbl9AQQGc7W/dZlKbAnR0+rDOb27mDUhyFrQ9z5Thl3Jl/vjsznb9V0kyA2EJKjgWBQN7ZGbK+wGNjbcVSxpsWF8c6LhXRormnQTMbJhEkO5JkzPAV76TUPPzSBcceK2FTrv5l2XcwCayzZ9Y+Hx2ls5MfvoOjP2gAq/1Uu6iwGvONMdYRe2cWQXKyXPRuYakgEUAIZtnt0NrG404ezzFxf3xDjN6YnfVDtiv0O9gA1A9EEpaZbPLmIrghGHO5VbeNMgdUbQdVvEsn0EsW48sz87Bvt9w3/VfD0zYwR6SGdT6WC9aj1bV3YRah0cQcbWU/O54U5mi3DKZYODQ3J0+zuMcyYH0KJsiw4MusmiKw1uOzQl5u5Wwzzj+ERhuqUzVLK0i+7TO7JkaGLzm6L/nkxq7Dyy/nSiZG23rgq0bniTSqhHjuW+CEx7tYisyqhmQGOytIyZ+VP6af3VNiuDPNa6j6+Mk4m1CZtHhqaJNE259XF16klj946E5RuyQEC4jXkgi7kLepCOr2h8jIZeTMD0jLB7Ipowxg1nETXWgdffURkiyroH0RiVOjbPsLU6YMDpq1rqqR8BbUB3K7stECsb8L0ZXk7OBfCKM93PFKvzDJRLsmoCCZwjR25ixrH9jsQ1x5y8RfdCqyNYt/sXqJd98JujJkYqgfElwoYR0Ob4rQ5sgBlaUiLB8T1JDHrXTx2gnRVKA86FI4f7E2Bygr1ESrl6esWFIdSC5URyGGfVhBHo4S41QPZS2LZF6L/2ooc80UjTKZkE0w55EoVuVdcMjLJeHJSLIJ7XwgGZRIMkinQvCJcbsmHJrkWL1bEBtZnsklg1zGjWagkADoNXZt1ibLE46HeDVBPSCc/d5fopvSrWJznAIZfYMTpOsHOa/oydhHPJumxM/tK9GNIKxI1vUSvp34u1jzj73RCIuKYbOAWYnwPTjU3c/u5lsuOZwIb6yvh7KRBZe/QZdOUh2mCje1jYgmh4x+Gr33MJbrIGrI3snXHali9RXm3QBbq/rckKw8wnultu6LMZQD0XQ1v9QiCaEMPTkluEn9/SjxuKe08sqyUx1/vX+3tldaYHaiU3IIPO6oSoSwoEMybZyyvJoJYYi7NNTwYe101amQOezOhLftsax5PJiBuRwqnd3g13OUpeC7CkNcHgjfkJWCPXGOrPWai/ZATwQpGiOIxk/rt0gM2uZMML6uWMLkgHjpdaioAX1pSLC3fW5fdz9nXg2OIermEeWHVMB1CbVML3OFUNlnC9Tan+QY4IBGONQOyb5sEPKZWtPp7rsIk1qsGv9WW8XRNI+JAmqdBrifr8bhMfl6bBEg7qkEgAwwb2rZbe5A66jNhtAwv1SdYmjh7UkCabSn2nDve64ZTkaENApMC8UIBWCeVa+irJ3ld/zMidy7aK89Wlg+gqmmt3nswVrAWhYg8GaeiBE4AmHxIHVtORPC1AuJgm1sspmCLqmWbkMxzvRcsrD7q5RKfY8iDnWBh+t2XJ2YiRs5EmLY4f8vICXc8FlAWbJ8V6NWkkk7t8BatCYq4IINYOTd/Zi9wMdAN4fUlaOQxubqnn5bTubifq3gtORaCacJOBMcMNYfkFyeG++TlCfyWboBDacrN8vmpMk+6ulsdnhW530zDqfeknWb19h4pInp7qx0vP6RC+t2JyBFEH16lfm9Y0eQgpO14JsqPuHkcIj4xgsc0+Ew4girqI+ro2AxF4M5n+6kv5WrO/LYWUkNs3ksH4JOy2AcHAeuP4ZDSMCRoYZB9Jvnqe8yH+Fk8hitBixeQjNyZm6UAJY88QJj7YmlXeSIHbK6ON0pgU135TkULdGBvvoi33a2KinAxzke+6g6l/7qRpeDM2TLnvKG4T3ZAuksa/nZxVO+5sMMWIES9aXnnQpay/JJa7jXt2gA1g1g8DnPV8WwaaM7Z4Fno/34Il4lwm0G6O8zJgt9kSrGyWe92dCz1TVX6BDJwUrHR2DgMR95cClgB82J3WCzv4Ia7vXgPhHWH3d0OcHmeTikjPP7DNr5BdytzwtmsnLw9gIOasxycUP91Ajkh6UEdLjNeAuO8UrF9QlWH3hsyaqUGB0CeE1j0lNGeEaStukAPgSdvFmXwwOpvgLMghGPGRNQ8FSzAtT8FJAsiBbRshHzfNAE0ZbykaDOEBBuGFYffLq91YohsTznJPKS0SxTgIu8cEk2AVhqwAT2rMC1LswLrFitzHOf+tT9TcQZNYdHW0PT5dRxYe+tL7yjcXjdeCtQ9a1bFweB0oR8LuU+pgGrJf60lwQKPGam5lZEYwCt/9VDcBSATSmhER0dWpp2xkSoBqOm1oOgawtamUFpjjRmjd16nbak/fh89bXm1DGtGw3YaHA/d1nhuCNrPpbbv9bjahDMuk6USDUzSTuotN0+i0baXtCCiIvLahh+ZJ80zrxq4LQNfBslTgSzg3iABdWkhMwt3H/KBwP3NWC6QdV82lbVG3KPd1pdWZi//UutCUwnEBXU7miDGrO0UF0XefsDbwKLns3pa1XUrMLc7JE4TjlBne/OV2KacDNvS5PJENEM8RBjcP5nCGn1zReF81ECO5qPHUwBfVdhfudQQAtp0jmrFHZncHs3PNVcw9EpjEAie0ccK1yYDUgyL8vAx8nK7J3JMgeQk9/owh7iRc13Vm65uhDxg9ZNvuHdow0DXgzJ597PhkGbmh7Z4xsUElq5VBDBxea1juUR+bkbPWxrsFRlhF4n0MzwXPde2+9aWEtquTAHVp0S2n2dXMje235dp2o1tNWGEYQSIx3k9ksRcorzbowhpum6QG6Ls4ACOYNhmOy4xYcmfkpDnncuwmmSE6z+/NhDbZ8sxf7IFR+g3t+LX/nn3Let5cGXsiRX/5RpCD/6tRDF4TkDHU0b+Tqp7T1o+RDlIO7LSQap2WKPzE3SpYGtKFFQSbL/n9HWnpJSVogAX5M4wTQWQSc2OLNU3YbJzJm6FwcFmy543red0qaV7jI5vure3bLhvkwnxS3TsjuUTFDsNe78zyiuagiDpXAEcej7FSqwYW1Ermq4uod2zJzi0mtBi2Jk+4IS5LBm5wayGY6++ep0LPwbBTRXZB6ztL2D/X8fNw93FsMk1syxMXTONT0mfOem1CDkKRxgOl7Xu8v9oEtWcUVgC1nSBONqk0siOWOzdrvjlBlRo7nSzZfRy4kyeD1KbbAK2rAqu0EVyBMQUAF62jkS8h98Ywm8ZLllcedGOWKhuA9S13stRwztUDwGl+BuvgLF1wCnzw49wS2jYdQ4y6y9ekrutKDxjoQRD92MGQ5KBr7jaZ5bjG6BLGVmrobJK67DoBo8O616+fw6uG3Tpg00K64/AND/Xc+vIGMgbIS0gL+T4Z7NuuZyPz9jmJiDNQlgxQJj1I0lm9CmGY9J+uLcPa46DP4hnbMGnoL1+toH1TVyhzoQtQcqDw518SMHk6TEBXNlXbSxZnYD0FY7MMZK2W2CHCmbAX3U+txbnudysOyMZ6ez9o5zafGAS2VFdG7mHLHsKsfWaSB/fEPZF7wRippECR2EByryG1oQcbCY1x6gPNAdr+7NsfkbpGJoNvOSYWDD2vzQq+NE3jfoQuGYTbWA9S8j3S3OAtnq7RIupiRw7XfH11mgOoWtU91ny7nyaxJ1tgx2SKq/vvlwKa9F+MRYKC7ksGSLzymq5rL1KbgadbFmsEMAwJcYDRIHbbZa2hpQkIVcdENqJZfgdaV9Bup5vj+XdMCromQ0XJDNeXsiTdeTsGO/XjE7MbXKok/9SBqgYBPV8NEUkvlR5e7Gw32GJWVaxeUiQlybFjiYCbom3hkwE4QEjqlEB3fO5Y/rtBxr+yXBBhMFp7W0QJIAUoJSki12+Bvj0RoBnQvB0dIAgqm/hkx9Aw4KmzalkTqxWybF7oMo0bfkjQlqKBEM13PNCJoweoKPgJuG+3DvXbjS5slvDGE81QT0sZ0XHObqWvIpon1jb2y6WCPMk3oN4jEemmYD3Nte8K0VgBGcqAG0M145V0NSN5fBGo1G6E88nPwdUFWoHKMNC6++ac7uXSph64Iozol5Y0f0n3bRMlYxV39goFY40KlTCWSdqCXRbbvLIqiEoab1g3q97MSF0Yj/SOKZVjIbiL6OjCJpBWQUUT/JPA5LoNEXuB8oozXWdZo8aTd/PdgutWTsg5FwbGu5Ud8qyaj1nWmP1EJFzXdOmc2AISlvb3R4u7agl6/gT/fFtci3NstsHf5p50JpblOT+tGaxI0FmlM0M6uYU9H8WLh6ag7aw3NDzTUWk1Rnzkbvl2/LQ65PrGvSw0OYDZtN6sKcT3sW1LZ+XOmjixJbGcuygyJBLv+Yq13nzk0Kxjh4ZkRHIf12DYrGkVyXJN8GQzWFj4e10BKHM0oAnAM9cxTw/pS31prv9S3At2KU2G0+I7aSpxeKgws2CaK4jVJW23q90n2M6fd6sGQRjr9QQ8nhaTbTIZQrC93Thp+K4tk/R7+IBxN7vEkpX8yphgifvkG7sB+zk2LtUVLxmoPeKTGCh91RnGbqK+a4TvDGygO463ZATbvs++A/DWbuPSZd3gjAdCtdqj5gQIr6CXLK8+0/XiHZITlm9lAwDndvLNIcLbjghWnL9jUh89UQ2ImNQ3EEBbVvBuh3rhgAWE9RcIID5ZeBDC+KMht2axdpBwYOb+opAgBnhsqYIEbs0YXGbFQM/74F4ReWKwpX4Ar7OtZMACEEY2MeOBzKLh0a4/+3ENo3HK6x/+yRgi53p7SEgj4ckQK4R0nGCgBhE44W26k1jy5WvHuQLd9mf1fYTsq5UtPFUrSbMa08R9f0mjxdytyyfRSLazasAIz4g+DGOXJ1cXwmLbqxOrMa1VcyHjhialywOiaRyJBMtxisBHAMq0Wydq4etrkkJz/TgBequsWcds26dWGXIsOl52Eqw7T/yh/4rfyybd8LKwY0TCUOa7OCvIplVKyEnan54ask+o3lfUE1pFRYzdbn3oc74UZl2J1qo2EzjJYFBizrJlpJNlCnQ/f/egyEbzaGAyXKghezhpOskH/ALlFWe6n1HcspllgTMp2YbP8u8ZlLeJzp1RLyvkuIwhyLsZdY/OEvuP4bMtC9YBSLrUmo1VhSHNfvr5qd8BdCBykBQkLZXGFIXZ0T+8K3r9JINmnrCz54B7O7jVf2qa3cqSnAezbQQ2Qx+AntYx8jJIvJDdFcPbyQ6p4/M6Y5fcllFHmxD8fpZ4O9o+//Nnq+a14aBun2HtTNWTy2QrvVhym7i2oGeIM1Bh7jOK7/hLgEWVy7DDQ96ypyRXPfXvFczziv28WprGmuzEFHICgDCqtaYI1pqGIru2rHuqdb9hiD4L3M/b0ma610aPkrNOdcOlKJsHoTNhMbBPMktotoTB4NZXXqm/jERE7oXYVj0ZLMKO06VEbcMUHOWAXBvYT2XoDsBMIE8JmVmv67RVWbIsK+Rw6NefPJJEOvgay45JIL3nL7zBrZVXnOn6THPLcgEwxnv6+VbnjfMz6G4Z7rl7ubZrehKVApom1B1iqSVMZ/32FNyMcTrwsTuG28DKGi3sxcTITpX1SidqGzDP3gUS6/gOzAJKBjF0FzcM5C8u6DKGhLHM2C2kv1QxUaDP+l6P5D7WOwRJVjhtK+2PXA8EQA9x7n4rX0USQDZJDaw6QJI6MFRSDwa/fqNwsWs3kwVeWNubnKAh0KITUEOsItT3V7OLqbunMXtvV/G+7c/q3g3ZvSw+d2McAk8AEiRzADxvAnvqySQFSO9euJzQDKzbsWiWtcVoppACruvbluEwojWD0aaRkXyIAUTEJDX0LaOo90EmHjGB+r+hz1v3hwZ1FhoZ/wDfOYLcXQyIv2mahvzNUsje60TEEgbklACE0r2jcoLzlC4gyxHxjtt9bt1X8TPKzy/TPQfA2z3OOA+S04CKsJp6zl4H6u2xaZlCs27B3mZ7qcyQ4Ew2j9MhOMGMTNm3Nn5mhusfS196B2BvB+zm70jxWNN3Pv7c79ddxTLzzddxrdjDQ6fWt/lxPTp+t/PTXmQk6Ma77eTg9xUK97Jcj/67Iyj66mD7zH69hS0jVUKnXCrFDgl83HiSeFtUAj03V7M0IUGgSXcObDvnIiYhsuu2QzHJQPPqNvciMCNYdIPAdgOm5NKluu+6FDV4NcLqRjergyeqISjQqfuYSh5k1yVrYAdc3y24VkumszDo6B4rFJJBrKgsMIcsZy5Z2w67FOeUjkCMZ+9bT3gT7bYdr8kVLmwb0TB2IXvPxKNOS4Eno6LCwDyBdruQ+tyPPidZEibVYHNGspxzITPqedLgJ2fEeT+22zwT7LlO5LIXKK8400XMOicBD2f02c9Vttqwf5bv58uN3PC1quZTCjCVk213Nja18ZY+dn3ZZv8isXWw1ZQK8ITNYjQURX174MAJoMcFznzmxRhhzvwVOrF9n5ODb0eb+xHHtRqlxDCbW8lPGaxbNuQvpuNfcqmLNvdz2VYKxZg+OiD4rsDRfrVrtGjOhAm8AC1Zuj21JK/K5GMbJO+LCn05DwVSCasoKPJ0+5iM6htwOjVsAFgk2G60lx9vnhG6rbvd1wxdYozMXcu8tMqoh6JbLx1td46WIghjzNm9kvSR23j427VzlyD63KHtngZtjCfpkZe5EcKjpzVgnns4L1G/bWQWcwOYmG8vtDIR6JDGq/h5gv+Xu/d7tW3bzoS+1vuYc+19ftx77k1SISbBWBi0HkQtgiiCiHmyFFMPFghCBQnkRUSoBy3/BJ9KfSkIFSSCD0peKogIUmU9WhAp9cEoFYNWrqmqJN7k3nvO2XvNMXpvPrT2tdb6mHOfH/vEZN87YLHWmnP87KP31r/e2te+hi7r+apObu82ns9a3WeXwWl1XJOU3lbe8R03up/9UFUMJ/R2o3baDMGKymJY6qSV7wAsjbgE3wBzMQApplGCNUzrrdVR4YiHy38u4x8aHkUGScbaoe0eNM95GghVwJyIOTiYjwwckUtYsxMq8cElKobgnd9p10fOIM5s0AbTRnC00w5YwK1GsMt4tH1kZXdw0orECUCgEVwzFEUDiKSmqRsRRTIMmgcF6atlenUHyOnUQcQnJaDj93Gsjcb7jAmNQb9DLJB5E8itW8aVApMVl91X2jqLXXoTiyVPQCWkH8UbKSoJ00fsboNJYwukYWbgaqZBCHdLUxx7B557ME8i1bprZhfSt9+yf6Xxkvy8fjaQxlrKpCvIhAEp/fJA8fNK/LrL1lyC4rbyDEoZ4JxdhaCUzAr1sNJmpyW/Vv2EGhSjn7YhhW2YSFFVydQGRJ4fvqp6O4MLvPNG1zeyCXwLnYWYpfrDw86FK6XREd8/cz/fef2f12oC7UQXcL8WDRjREJduNmKXDlYQ5Pp/hW5Y36mUMbAYSEJCxE+gxelCNB49jnPGNXiPeaoFEcLRCBMHGpJu5ciP7AOcX0FBSdW/yr/DD+03UwN7dwHEB20l5ffS9XlKyVdQUftZVCc2si2mFCSTwUkVN4DFJRr3Mcx1oYddbDQx15CLuqgMQ8pICcaaXVb9t4v0YzygOEAr74rHAsvCjX5YFYW+7uYWORCuF1aGrhxhaPr7Q/eDF4nqJ7WNJSdfvhvXVajlbJZAJg8t76dSzJb0e53Jna1JSSF2dRqXzYShknBwGsd3cRs3pvTTViQMmK05dN33vPkYeNvtHTe6KwJ9mPTAxmv54oyaIhCsLgktTnH6fOs+yioRwELWzu+tCnBKSBZmAHflkpidgJeriEJzn0CcjlAADjBZkSCQflMaO1J25PQ57b4fEyiYVqj6UyMYBUTSBJBcRCp5Nc1ADA25IhIRItrdsFDjHhtGjbaxQIyGsat+QvD+j0SkVb8hEjyaI9e9fOeGnDXcICawTrQcRWGvCtkRXp+7wCLPQ/nHioTjWdKoUZAdCujFjSicRidqrjJFsFYEAEv7MPCF8tmYEgDtzodfl0/wQJcnLdCHy9XR3NxHfz01sCADZHQb+CS0FPkUJD/XtTwW9wL7HdulvivvZ+LnsOKlkn5bAJGRNhD6CkvBAtjnOqyg7FIdpkwC2iUMcwS6ODMxucp9t/k9e+jMAFqcfLVBC3D4gc1I8y3qm7XqYymKYEBSwNwFcFdbrZynfIDg69XPzpvO9OtuPZFYMbi0tbm0Ll+4T3AJFvH4QDuOqO4CftmjIzocBhirpVigmN8Lzz0R+RD13FG7rQweWksut0UsxRNckrmRimUrT0q+8WT23GkgLs+rbuixTF7ivsc6T7D4JUnpMZkVY9Rufm/VF9xgqalDvBrCityU9dlu/nkFRWcjXOhQsQWy8/NMeMKGGS89mtn3Th8sECVwAOfm8r230FDooc9Q9o0+4z7e+hyjuf+2ZRUOsB/6pOGVfrOyb5mMFVZRY3SfkGR9WQQMNLYM8NZ2AHJVJcgYRg2EhtEFpFmFXXW3T6yWiHwjbmIWW3ozzm7VR2DCArd+Gt9x3TTUynPOtoKrVh5gqhWvfNMmeGvK2PcHe2Hx4SQrQXpPjm01uNyKgV2q/lYfrp+zzqqPb0E9/XAi+HrFpnIs1+BP9FsaKD7GlqR8u+dTxwSyk7uxrNH+Jd5RQEou7YoqF5Eox5YvMesanWMrvBXN9lt6Bwev+/AiQ6we2/Ic0TAc0KcBmMadD8W2idsKTm4+W05UVRYScNRLkj43ts3mvnV1w+0C3rqpyTi+GFk6iZcRPrNPHC5VGeLuJZ3bStT7sXUVcQhwa9DXHePTDcenm5VC9xI99tONleDMBLYp/b11syQLfx17y59bh96aIdC9PIS7REJcaHHj0Hj6/W4TLz94xuWDW7abZ9ct4MHdZ6ZKVk7Fvhc3i2AzSH3PjyYuYPW91oSGSvcaE9K7qQ3SRaAmBSoEDsfpxGWsq2peJ3RVNO1HdSdU3j7dil/BpbDc0h/JWf4YtkB/FdIT/Z6WG+dtKVrZW2r01oyU83kf0MuUzv3acaqBAYpfy7+oxpQdnfze6IwAKx5EBLkMEKnXAZLrq+Vv37EaXgpN0+DEsYxgA6s/tRrgrstAMkRr/xtdKp+nugJQBuISOSzPQgO97FcNGK9Jo1rbs7TBElikES7UsbivLc/HScyCSmZs+nsHWP1gcWuEgYJda8l8ymeJFG1gFSW6NbRXVudNPu2QT7ZQc9O9GQo+TJFszqyjxvRi02AododzzgT0uRnF7dagN6O1BUuB+7nbJ95xP83YZet94kc+/ARf//BVAgpnetD1xYrC1n9RNCuyP63vCHmuQpNbtuq35YxSRXBi3DdEjbRCM1O1yiq9O+I9rW7DLsyxXucR8+DhCrfs185fPTjHF9jefffCWaxcCim5SDXG0iEySs4vs2a8+AvjC6ppiL7sWPy756QKvkjvVLZcQRooLvm9s6WhcUQXCBcIVf6ylqauaiiIAYky6/KN+ylCAIbRWyLSEIYpgy/0EE76oGGg6QfluUNiMY1kaEhwheF0LKu6UCaKgp6X4AuNe0v0KwpXuNL1+9K20QbVsCiCMRFi5YyqxzsrKNmrQSgU+mrDuGm4MLgspq+YzIzFfYC8N+FE6Ua73ZqJ4/AQUrwmPJXaX+hz84CbJV/ABXVyJdAgl5mKXxPA0TA2ewmyt/V5Rk6AMbl4uzGpg3XK6GOSrmjXgctloPeJj55eQUTx+zzwQQwi6HZn41nBhz93HReBiajnMb3/eX8Vai8A60q0rGAVyEKxAKDu3/VAowApROMVIOzgGk1GXicSLU6r3DcF0Ph8X3F7943uXcJDMxGayK0ekOaP4S8pJOGoz0AHPQDmbC/COGMspX5sJKWTqbIblmgnByLfaUHAXB7PzQd/8GkLQV+M/yoonRJIfy39lW5MYnCdX3xFkfzIKVaz0/jCA1YSyl816AXNpTlL7MRA9qWkjEKDo7QfEZ7TaKYX61SKjfvYnZtRxeo9092xoEM+M7/ftBg+pMH3QCODaYAsuq6zA82NjX1YLs13MmXxA4cimoMs3dREy6/+rIKgu8VqYWa2YKLoMuloXkuf1NKRu6I9F+6vAuO9CSEzgM9fmCMmKSmYF3v/7TknCN3U07U1VgAMTsY9s6QRkxz8//fee8Y333uFT25X/OjL76LJxP9Fw1nTyWtQje0ppkGQkwtODBGNPhB/uAtIVFNEhgE1Si5ybKtCxFelzV/8vmfSBIsPkL2guJ8LHhlPVo4g+NlagjX6k0fRqOR37E930eEvt33fuBfCd1tVhJoEGtXio6n0L6Xqez2eqmPUU1jqoRV2wwOpyFA3a2XCfbC8WgJtQEaw3B8WFRu4W32Jvu9KNavUNP+pkeWKCok0AvXoYkSpKAYU4wAzjGhY5ReJ7pTnkcVI8hraTX6SQRbeVyDmmfvzuqI2Mdy1XUXF9AnHMyNQXNWEDRSu6/nOOsTLd7p+loyH+/0D4VNlKz7TYHKEr7mi8Lof75UTUi8T8SGZWefsgODYHo6UpbRHZRk2FdyMAAAgAElEQVTE+ynPxbaATRCzshZ8v+0y8GMffg+9TXzw9IwfuX6Mb79+f808K0GERcuf9wA+u8Tqiv3h7l2cXE1VO3dx7xU6111ZdE8Jjm3O+6SgkgCxuB2ZNFH/B5aVsn1QAwNYEZE/61fZvrLRFZEuIn9HRP4b//8fE5G/LSJ/V0T+KxG5+udP/v9v+vc/9UXOH4jUG7C6FoQZKbUcx9kV4L5Y+n6jPpIfK9u2VpBwf28Y7qLhkBKQK/c2gmfVcHrqZl3ixb7NtE0X5OCBExE1356svldRFD+qLJUkghNKNDrdtyekiyE+kwkTqDmkdFLbd16t4GQrcolhuGmM/Pi6yTS9Wb1OF8pp0RbNq2aQfVBlAYH1PtKfWs/tX5Q05rv9OAH5c0ZBRDf6ubI4GcLT5BYotyC8+Nv3mxuiQjDvgenEcERfhd7jPHSDiBq6FvqCEQFDahkYMoYJzO+GcO9oJ5xYXVi9tp9dL43lvCrwglH5fAfXpwN/5qN/gE0mvvH0KX7s+of4/Y/fNzTO9qdLgsZm1nfBi/mqo+VzVD2GiMOdYKhEzjHPM+/jK/wqdA8KC0EVOqet6sIVwv3a6dyKqBZRdVlEikGX3PfBJADkWDgj6i+z/VEg3X8fwG+U//9jAH9FVX8awB8A+AX//BcA/IGq/uMA/orv9znb/ay0zlTzzr/LLQxpkYDU+kJ4hQfLjwXxnrm69EHxHVdAFj7TsI35CI8mxyUyTPEShPFgE3zWxBryiOU2K+Kr6b3BTqANmlgpPkRzfL7Yzw39zM/OBiC2yiDgOQKJlOCclu+JogsiXiLmVUCn+GYz6AcTNmedr5hMTu3B+yxGMdruhBaXNuG/E/V1JeOBASYyG3hMNZScAJi04kZMFKmSVlcIinBLQNbrivOpw5CXd6r1/fCghhQ8Kt//yIcf4+vbK1z6wDeurwAAz68v2c6cuGoQzNtqXWWx3csK4LQtCR+A8XTdd1qN750UIytEzJlU0DJmHwbQOWbJVqiJETTGJ1nYcC8U434XWCuI+quA3a9kdEXkJwD8awD+mv8vAP4VAL/qu/wKgD/vf/+c/w///mfl84hu8aJaNkxBsnocQeNaj3MkS+FyshVqCQ/3GcHLtS8RzVrGp4ricJuAHFiRqrMCIoIuiZKUf/N5qkFzJCECq2QAWMdkGqsmkr1bm/u91IAa74XXnVfN63uiRJTZqTqoxVBV9BTXAAJFhrBPvaZKJjG48TQlLoSBOHNoARSfLI/LIBYA92XzPpLTW9G3bor5Yi7UrTpxqUfta1kfACmUMnm/WNwDZxdPIFB+v9lzzYtGNQv7gftSsQa2DjvHvNiztN3/f1Ghf3mfTGy4arqDvMadbjbRBNqkYeS74eStsFUTbUkRK/+nvvE7uMjAtR344aeP8fF4geO5W3tdNBgLpvsseeyUeLcx6RHZMiOzdOVlSc5jGiK+svBuaWS9KvBihMm1nZa1FsZaEdl+dV+97TmmRbIc0LkKMGAJE3OU/b3ycA3wSQmqfgXL+VWR7n8C4D9AztE/BOAPVZUZ7d8C8OP+948D+G0A8O+/4/svm4j8ooj8uoj8+o7n+FzP/pjzpvMetbqW7tm2pyJ8SUQ46+ny+PjTv58KUV2oOctSo+omVONS0aj71QLZ1pMollXRGW1VBBjuuXOz1Ouf7iECVIH+qnEtRv18z9xIqzq5V8JnPMtSU5E0LA86xrX9Am/0wwYKlrju8oh8XW50iTSTk7si/mViqc3IicNvLCaUOjI4Yfjnull2m24T+jQt0HadwEXtf/+ZTwp9Uswn86nqxnMXOtoJyS6IvL6e0q+Km9U/qA3D/cvzVzeMt2N/GvjTL38Pn44rtjbxze0TfG+88OKka0MHm8bfoRQXQ/i6F4T9oN9Ul8sjpPim8jcBlMrqc8kK0WAvmEFfEWwazEItPSdIxT0S1D3Agk5Vkzfc5pfZ3pq9ICL/OoDfVdX/SUT+ZX78YNcHJujuu/xA9ZcA/BIAfK19UwFk8Io0LvJopRnr4EwB4Xb2y/AlhC/KA2XFp6u7IlKDqasZCRR+zDHQdmRKqBP/FzoX1r8544v/Iz2jp1DxS1BpyhCJjmqUstJCGFwxVa1zRwiGg9q9VX5qLsvTupN5EHGO5q+mohNH5PRB0jBFGq+zDmQACjHDUyL51aeJZm23UL/IsuCKwGkPRHLaAYH9rxvM0O2SJWRoWDeP7B+S0Xq/7/DPFjdQZtT5Oeqk4u9Mr+a6mN3aVS/TnvU6vAaaYLuOVBCcxkgh53oOFxa/dZOQLO3MfiSHQK+aK4QilUgtXyuVDkA10qLzXeX7QHPGR4cndOQw48rqG1/7FP/I5Q/wv3zvJ9FE8cPb9/Dbr7+Zk9Qhphnr/Sw6olPyIp26K3DzpA0tri5/h+Hu4Ripqx4R08M9Dkevpo9rD2adWseAPD3ZWNyPRMaxUp0GrD2uMC8N7ZE/9uxirBt1etV4+HJpMHlIp0VsG6Q1yDHRb943pz52bXyB7atQxv5FAP+GiPw5AC8AfA2GfD8Skc3R7E8A+B3f/1sAfhLAt8QIdF8H8O2vcP00qidbu4hZ0K/Dz0OF7M0NtnxPsZ16rTkzwFO3JTCG6MALwqXlpVGs162R3YIc7nyNWP+PlFt+XoxdipNofP2Qgshrl8ARrUOgU3cVZDDGDRoZD5qHLb7f6gs+3Xv4ntkmbrjJ8wwvSgjS2LPEfcjpfIVbqh0Q12MI18ZIw1ClMtUJ/7Y8d0Q3zHCxdDtVtORq9cpaV7x4ecO+d1yvRzzaKFUeAHjyg+DoirG3pHdxQh5rm4laX6DBqtV0zQgi+4kbsCxQijC2gfr7/Qv/0Q++h6+11/iD5/fw3nbDh/0Vvn17z9tKC7vGJnamDYfW7jShHM3L5rsEwrWzvGfAUo25ahALXEdp9d6By7YExiN5iQh14MRIGIAbXXNbPDa4EYSvyVC9vxnVHqclR2vAKG6eR2PoC25v7V5Q1f9IVX9CVX8KwL8F4G+q6r8N4H8A8G/6bj8P4K/737/m/8O//5v6BVI66JeVS2EZcBnA2YY8vyqgwZmQbom6zKg0MQoYF2rZGxXH3NcU+gO1MzvSssZBBp2quyH2w2JRomxPNVaKhddKwyf1fCq5AhcsxjGCRFXrloe6X4LLfq33Xq4XgTi6Brr5rfWqyzKbPORAtEDm/lO7oRpe2uta8oVo3JEs9w2/qz/XfJpBFTOj4tzXgOmOaCis7l+JwtkAbP+UoyRany+m3dNmP/piAB/d8OKj13j64BnXrz1je3/H9nRguw5caGj9XmvVCP69tVnK8Qy093foZaaMJcV+/NbnBuNWa062LElUE0MiiOffE10uBSKBqMUnzEbz1/zPfPQt/O7xIT45rnh/u6FD8TuffN3OSXlKR6bhShjGuOGEpWWCzxWLppZGBM0U6Io5ejzzAhqerpAXT8D1ko1JQapLqVt2SQHzoHl5Sr4IImPQOo569mmL/aOQpX9v9dJoH9Rsw/WSBQ22zQ2wlewSLfddwdyX3P4o2Avn7T8E8JdE5DdhPttf9s9/GcAP+ed/CcBf/kJncyMqLnixbHQPuK9GJHm7ABZju/huT0Y1MtPOAbkHqcdSZ8fzdFc+XmZ6dvayvzKyrYiAhZ6R8tl/REhxRtiVFSAlnZVGWpJStiBvoq1yX0sQqRwDgeWKNA2Dpr60jzI2QCBu02aoTIbTsyENNFCMMlEn96v0LUEI1FDvF4C5Enj68B0CTCrhuYQ6upL7MACEDvfL+s820V4MvHj/ho8++BQfvveMr73/Gi/fe8blMnC9HrhsAyFEXx6tioGzQm/UQHu5m+83AoXlnkQdUWu2kb8DJrPUtov2K4L6SXEzqx0awwyueh/7My9/B98eH+D52PDUDnSZ+INPX9o9Xwjn4BWM2baCUJcjQKi/o90fGKOWwyv85r6xAkRU5gXS7+quBEy1GmZ0L5Z9Ir58RteVksYsVQbqdELI960lglpHRLZ5nf1IZTJ/T18F6f6RZKSp6t8C8Lf8798C8M892Oc1gL/wpU9eCMw1WyXOO+a6fKh+3Ue11e6U4k/KZY/qqAFprMPgPr5dLou1dkTuTyTYWBrFlm5hrMNwCCrqleGIlv6xEgWaRTi6Lun1oneC3LH8qwjdCfaxwxTP0c9zMjtLL9NQYAhZ2xJ4ilfWPVienfehGdmelGGUQLYx+IjO/RhqAdtaXRxNe5vsnsl18ey8Sk8D8voKLCXTxVgD7dBkgzjSHsz22yYuTza45mjom/WlT5+v6M0KVybQEQw13YRadeF229wvb+Llr56vsc+2mRGLCbiuODz1OmLwbmzDP8rmObmLODmH7GfhJEMFupnPszW1Atcu/fhhe4X/e/ywlYmXiZt2PN82O5dKTHaTLBqCAufwpsrbaTy4vnC41qatMMYQ4OKfDaSvnhkXqtDnm31I4zePDHzphO7uK+odUsalqL2v8Cc3Yx+ZQluxF72lIY0kqDJGpj6GoZcNaB26ZZBxdlns0JfZ/v9Aun+EW2mgBzKNAHKWAtKVwM9jl/JZCNecBDCAFQHT7fBIeAP3YNM+LAggzv0ADTdd0FEVsl4UxzggJ8K4ZKALGbmvBpefV7QoAXzso0qr8rEfaCGMV16vKnKJV5KVq6FBeFIE6VOhzBUuCeSSc+TSNJI7aNhJr+MSOiYceMFIRJCJGVqRwRUPwefVYFHQ56nNJo2gsF2mu0uMaYBtonXFB++9xvsvn3G5Hrg44tuPjlppN8DTZL0yje+Pw1TDWPFhv20YRzejoPCg1KlrSLoOSBMj9ezMZoAWkZ/z5FLeW5yfWg086GiAAu+3Z3w6rxZ/E8WuGw6WqecKQxMZC/3LO42uhgGNe/H3taZ7yzo5wt59JNjMHFt6HK6DchqH3I7DEWiiXWn2PBrOZc1j5skF0DtCZ4HCOUD+BrCwF2hPKCvZElV/Fcv5jmsvPDC052SFqZCOtXFPLoWl9E5Fsly6BPvBz9cm7sSMK8KmzuYDyxuiKwXZytRAlJVaw/LfoTYn2cn5+AJkvvepaYLPqgh9Bfr4Yrfik1vUyfwc5veVDIzxUoKI2uuEDV6UYwGLRB8SA9sMLJBBKNJ5SrS73jddH9dcUge3tkwCocb2yPhMQK/l440sj5kouhxvxTbVtXWBfp0x5rbLYeXMi5EKBHv0qPgwjuwbqoL9dF8KRA204UauPaVQOZE9dQrCdXBu3zIBzot/0Ngezlw5e9wcXcZk7ToLVvJHQi3uHxwf4dW4YHc/67du37SaauptG24ZMaM+GnBrwcNmBH+JUfAQ70ucSGqfh08oM1wiasa0jvPjSDfCbvoqaE79VH/pwUbyMS5+zoJspTfXdxiOnCvQqYbdUHXcTyBjLKwHmXMdhj+QIuZKIyiPGQV1ewT1q4F1srMOgDXSTLzGB9fi/82y7st1+T2XGURVBWZWnVELMAEk1J9dTlCnh3lGk/GwyyhiJw5NhPXwjFh7QoEjpnPqavNVWyiDhdE1Yyt7RvQ1Boaf5zptyXiZNvDimX3Jyd8T7o/0S09Yp3VXCalZlgXn1z0k9X27evVZvzei5kCv9cEzcLQMaqBQvjRrgXE8782utSm2J+tA1ydbso7RcLkMHDPLoVcLeBwd47AI0XRDar54YB490Fbr3ibuelA30M1XN2P0RPd0KRDlk3XAZXIYXa7TQY9EtIOGkz5XRyGSw7YWxeTqYAICwe8dH+LVvGI/OqYK/v7t6yY76eiWYufsh0o9iCjZlPdHQx/XFyR6j7hBfUf+Uys7xHcC3Q8DKteLfX0cFkthIUk4mi1jheWU7LX5uK+INSa1uSZj+DUDVJ1A2KL9MDQnnAUBfLnt3Ta6KCi1bnXGohsgPwAA3FVfAFJ3oez3yICvRSnHes05F+pInvz+/5APJBqlvStRbrtXG0lcltJI2T34Po6OZKbBSR+uox6iy1Lnikvs8CfzMxQUSYRcBgtLu9CwngNu8cw0+s3dDGK6rmk83aC0nIxQKmgstLZ5Orc/ePgI/T7udC94DrIhSjUJaeYKEYEL+ij6NnG5Hh4E8wHrL+N2syHRnDPNIpKq4sDKtG6hDgRo4P19hKsBbqic/WEgrUyypwn1rmR9z2dgRh186WN/3gcpF/8vd9/F/LgN5lpwyt1vvfoRvBoXPF0OfG17jb/36ht5cTfWy6qLhUir0Wf7u9E3FgjSVVINlAABVOMeZXEvRK20CHDZexFnJakqhON311LqB/fbI7tBZDw1fefnOBC3zypqIAotYltfdnvnje6S1FCrH9bAF9LISiuld+7WXvkdXRIiAvVgmjIRglsTi5JLloAXVcgYCwE8ISLsQyXK8H0afAb1Xef9sTqRSv2aKDkCJDSysUSXO4RnNC1Z9mPdM/r9RGFuFO7GPH43GHYuM7qyUc81kW1wNYGkSE1Bu07gOs327H1Bb/G7TCBS1MUik43tcgISZ9obalP30pbXYYaFRs0R33YZ2LaBcWmBOLc+Mbw8Tn0eolnAcv25LI/nLdlY1EKWS7aPtGnIl9bR+bys7EvtXb7D+WSRVwv4SDx7GlqE1CapiuEyH8h+R3dAfZ9w5Hw0m+g87Vsh+NanH2FrE+9fb/iwv8bH+1PpN2I12VDeQ02pLiniC7tFTagpvnU/vOksFKDEsVFWrqx9BsBYDEHj9LqEvk9N31/UBN3dFJM5LTwFbmynNLBTLbBWkbPfi5Xx6Yi0YD4jN7qG3jI54h0PpJXtpBx2h1g/b6u0r7NcXD3PI+bCcsy0LJja4U++3YUyVmf2CPZIOv7jWrJ2cM3sM6LBu6U0zx8XdoQxsSDxudlgiYmCwRop5xMb3PNqfFVcZmQiEQ0StcXEwM94D87HXIJ4KBNGIBxS2NJQ3JVC58QV7SYLwo3z8hj4GNgm5GnYz3VC+kT3n23Lv5soepuOZlsExTIJwBHrdKOlCMO5GH+izK523qbo27Cy632ibUYZi3vmcifeqU/s5R3eLcd5jL/bpU894i6VriUTtvLwumlEoN/63kc4ZsP7lxt+9/Yhfvu738CSxFJBhUomaJz63+oGWe/HAoJAiAGVd2V+bUe1jwLZbwqcA46OC0Key63G9pBHu9iR0/eVnVQTMuL7fDB18Z232d59pAsgaF3AgmxZ9RfFB5uHpAZuLBV6R6T4Rpohlv8R/+p6TqYBN4tk1MyUALr0Y7EjSjEcceJ6Sg20G7xduMGuxmxK9nVfQqffT5ZrN+eRg7Qf9xcrVjEaHlMDTUEL8+V4ZMhxv7hx2NK5a7of3JhD7Tw4iXSzrapW7tmI8v+awLCsIqpB4K1MRKUGHWKVEJ4yaWEcSekio6A1Re8D3d/vHO6SUhd4gVOsRou4TDAFo938Pezm3+99oruB3TZEgUkGpEQsQWL6ikYYCAwRdY3JiAkwKQdqfSMmerpqhG1WU9A5keW7aruYPT0Q2Yvf/eQFjg863ttu+D++96P49h+8DzkadJt5DqLyvZmbgq4cv8zkBEnAwH5X3ykz/YoxVr9v3XoYtghsnSmb/l4wSiKUB9ZqHwBgE/6W7zLKr7MxGrU7HeVS7+Hsiliy2uQe2PHr+YZJ4XO2d9vovgG+V3+tMDvMXQP6CJ3WfU9uibOxzWrBY3FFpKN9QsZ8KHyxRKDLrLuk6S4Xw50RZnD2DIRjHADpCgij6J81tUQB+kHbaQxUlIZynJ9TN4U8GS1MC/KWljSpM0IXDv743E54VxEiUCsCbSV1zZED1mfmPVapzHO7a4OVunHuc/fEBTbvDVv4ZlXFjeHEaIJNjEs7Qp8VhlDdiE7qBXjbCozTy/ZofWBINzekI2ieQ7WhuWocA3PCmy92LfiqhKcxsUg8d7SJf8bIfyzt/e9FY6M2odc6C2bIpri9vuDbr97D7drx/3zn69DvXhGusPOmyIoep8n3zE4IHz2BQ3GR1eOovXAWMbfik77f1DejSTfWUUlYC7e7GslzzIZG+3yuM32suihPuQHZLg8++wLbu210ub2JnxtR5kdfazGg/tnnLTcw1v8LGhYtyLmUfT4vaRb0ySASrRqQhjb8hPZdBGRqpQXvnMGx4DUrQwB+HMEGgxiAGU7357YdESCTvQyWQkGKgpkeAFIQzZ4b1ycIZzXQxdA2xWCW5dOEzJYDlb7hhtSJ7WoBu2os6isqS3ECTPpxY855mmgvD1va90TozBRrbWLOhuGGh37V4+iQDdjcJZAG2Sa0rU3IVTGODtnMHQGxPtU9bftyGRhd3dDajbPIZFyPtDgtRp+G0tsf4pS67oZjwpgXKvdGUOwdtptkaZyJSJdmMsK8um+1+P5plPTJqE//8Ntfw++2ieN5yxVKXVm0bP+6IkKd8P2awXZRN/LDJwdHwlr8wBHMPbkJnApyB7ZqoQLz0a6uQFF7B4m4Pf5TfbkB/Xv5DG6UZyLsYD4UUMZS7eyDfs233d59o+vKX8AJuT7wvQbt6zzTPThn0E0e+Xc9gBaBNS1BNhVTGaMPkiRxD6qc/ZZAQaUnGBf83CHAxcj549Q5gRVB2xJdw7Cna8O+p4vBb8HGyQTaIRhPnhY6M+gwBRn1Z8Bn2joyKGFCo5/WQvp06pTE4DTU6BbxqtBby0Cg3yNpUtbm/r/D7aiMQWRUVgIRBFcYNQ5iCPdp4OnFjss2jOQ/uvtmBaotEOvYewS0rOKTuRmu24HNjSLpYiK236VNvG7TEpn8nmZTNDfUlz5w6Ukzm9MCcXRl9G14xle+97qCWAKle4tqvSFViHx2YZ+ChPuGyF8O6z80VplSXfoL+1GDlZ0fDccn2xp7YD+YCLcUdi+gSVRbYoQVgQO6TAL5nf+mvxzFcDEoVkEVGQueYRouBXglbroZThsnVBrDpcqMn/c++0w4AP3/9T7y5H6+slr7Ktu7b3Rryu9UE76R8qIeGdrz8U7vWLLQzsa4uhsGgN7RNouY6lnTcwz0m3fkfnqZALhMXAI95xfVSv52MmTif3I2me64iGtXw74hGAk8L8h/rcaYA3SXO8pZiJp3g8jtMjNOKZniGog4kCQwRwad5mxOHasTmL+GqyUtULnLWCAW3KlLav6q81MI43g7hF/zvYHLy93KbwM4XN1rjG7uBGcnzGEMhAlz62EaIj2OhtYaumSGII3uPjq2NvH0dESwja4D7rePbmhaFMe0fQZpWc0FXtyXy+PMBdRyJTTFsvnIOPCJc1nObwodgF4EMvw+OXIJ+grHmWWHUPuf7zO3GXzcSlWL4CcnRGcpyN5S26ICxvqeWoodpR/ar3lGynzNCmgNXNHolbEUxvXkDsjqLdPdiyXjbqwDzb5/QO3qLZGtG/U7INd9GTiHhzZKchOR+lts3xfsheDieiNLb672VShelc1QaWR+zJl3J+eidUAcY0G6DrmaqlEoj7l/R+dEf42gJC0eDncJaO1kD1q5eRCKyvw1PTQq/yoesBqQFxQsVRUiHZSDiMZ/ciB77bRZz6GGkpyTCxhCCw5rQ+Tf85p2k3ZslAh3A4ym6NcZVQaimoJnnVEzl4i83WR5xoXpoPmMNfUZsIni+sENH7z3jOZI/DisMczQdoxb9xRcy6ianqgwpyUtjKNj3zccp+xDgbsJVPDicuDpssfE0nvyd/e9xzXDMO/+M8QmJCBUxuB/y80njaDVKUIoxp+5alPwe71adQwozE1UjOVSLHQjPazIcsINYjNULXudxZHBLnd/SBjdwizRx8Y8+lzt81Xfg5/RHcg+9Aixnsdl9afSyE26/TwQ2hH+c6FiWN2Y/FCB1rmKxCMGBTdWFy/PbM/zdpD33Ue6ZCeEK0ZzaUBt3DCoXFe7r6c651thLQzvBycVsXBfsMLwGPmiq0N9DLTD0037jGViBDJoLLGitOrst1VVpltWtSrxkRKlz72jk3wO0agYq4x4iy/bD0n0MyVnfvcXq5irwQpJqkerAdlmGNhZMs/UjfkEPIBUNAH4TIy2d2bVIYyx0PfnkXOgtA2QKGxiGfTw7yKVlNcU9wO/GNFu/G0ZpW5oXbN2smhkQbLkyI89RW320Zd3sPnnx2jYR8exW5/YncernjzQPT16HGbUJUTV/V22GUh8DPP1ogOUpxSFibF7u4Sx5fPWQpdA+IADdfr/IcYOJIp2KEoUyr6oogvlPUvspEXRW4uEiHg/XGGI+6BpaIFlJbaADPLEy//MpJQx13E4mQxRfLhMUOI4d9ADwDJMXW4xQMsJKYdhZEmuePkPCh7QR1EN/QPAFvGHH0TBG0EuMWTbIvKoLs/2SK7xYQabTqulNi2PW6dCqUJPEraX9oG0VK8fA1ZxVIPWomMAtx19z8EQgi4laFFLUXMZRlX/MABTloGuhCUTERwJHVU/jC6D5OMm0hT1wcD24/coiFjgmrUKfTnQ3jvALC2K7UzPuGrlPhc0Wn2uAJi9xGW0jpbL1Gk//XULn22gWXejwCeMFTH5fTeEMTCNCABPE0/v32zsaGodWHJDw7hl+q2ONYijCk9nFuhzCtnse8cYLZ75abMl5T46brdu5x0N43XHvNmPvjI0fXvecNw69NaMm+vBRRFF7xaUo893jh6SmDSe7cbnQ6TZRrCQGYb+jisHOt6tL++rOysyxfwaKQ/pn11mnAdE2lxdAMDN0HCyLHJCWNxlPK64RAIsECj0PLfpP/jrnWYAtfxgTkv35eZjOypGFKnXqCDh9tJAjf2jqqfaaxMmeO6gbUwPoBVkTC0WXpfSki73qJLc+a+yvdNGV5FLCGaj5JcTVXBcZBUfPzMXQjWsfl+3qq/gCDd8R0X0Al6LbakcQYQSiJYXQRoZ/5/ZV8pMqJkMjIU4XweRrqetaDCMOZeSHFwUjBJHmB60gt+2bkx2QPhmiRbVfZIrT9f3ZZMKCkq361uCQYt71AuXfJKGVDQmlZiQCqUuyqBfNNqVDTFfJCrftv4r75AAACAASURBVPStztlwuJLXYHAPZbJgW9EAhcHQ4Oiy/ZsopoqzA83QqrMwdLgmQXUFsb24bBannTnrI1YvfAZPUqiTWKqzrQY0Pmd/cIW1CEQ2RRRBRdk4qdE4ajGYNLqcOPn4Q1L7gui2ZKWFsSHKq8bHH7/6cUNs3bUusLkb7dOO63cNHJCFELXL4J/VZbsjVKnFYttpXN+xa4ic3WY8QqQPdLXj8/P+VB7rEpOMvCXKBd5xowvVpT5aVO2tW/2//k0XQXBte6JZ7lv8wbl0UasyfBz5wngMlyRjREAjlxoSHVlmVnSo6cDKVN2SShpL85bULMr7hWA0DXpFD8VvHCikq7kNPCssBtksAwWOPChuLQBEMZ97IFy6PbT8TW5qGJFWMtTcOOsQW9aXABGAjIbzNXGSCD8iAhnpxSa0eZ2RWRcR8/cG1FH5pdu7mFNwuH92DsG89TQ6HtSKKstAribE0CZpYvS7tjYL7asFv1cE0L1h81I9C9rxSZQTWPfMN4qXA3BGhJ2jv2r2Tjg59HRN1coPSv8r+8guaM9utGkI2Rd66Q805ueJ2TMGyWmmkYd6ttre4kBRcSF6RHtRHD/rylVjXNxO7kKQ6W6sbaJdBlpTbN9tePn70yiMTI7wsRfGtwbP9wNRWsfHYxQzmBo8feVzV/bCzPF9Tu83cWM/Z/Uhi+Q45yrYBDOgrRkNzkHM227vttEF1sb6rN0+z6k95/25lmjlfDwjPrr+nNheTfTX7NV84aXDx/GIffJ4BH/zzHkEToNlSH7AUxH9EMn4+c++tFwbYjmHVV/gd5rf0fifzrs0B3KiABCJA3BWgR7NhbLzHmu8Zjmf5AQjp6YnjSwCZ0U5jUGT1lIu0Vx1OZlRuS2er/grzW9gLhCKDJEmdoyO1jSCa3P0XIEKMhBE5C/8zq5lK2RztdSHEsD8ue724OqDRi2CZjUIKxqTL7mvLFFfNZf5Hu/YLecXRwPJybDGG4AEDXRxVDSbl6nMwfy/fBjuI8B8x476j+eOl78rePn7zv7ZOs7JD9RVCHfDAzEaHc44YOWYAWOotMxIy2cShJtgSSvEOrYfafiebIpwhQtYvby3lHZ8943ueXtoBDUijJnAYCi5BtPO56kqQfV7MhgeXkvMeb99b8flYz93QZxEtLU2V0UUrBAAT0Bo2wzC/uTyjqhwyl0Rw1hmurtA/HoLEh5M+fX74SDjab0GFpfTACBOG2sX0wzgpBCpykAspfVoEUgC/Bguu/cGPDe0W4PUct6KCMIEAle4nzfbLlDvLuErB9zoesS9NXMB9D6DVxy8Vi6Nu92TbM6wGMkThQLy3ICbsQxU3R8timM09DZxuxk1cVLu0NOel2QcGnagBBEFx94XlDvcPz727um0moZUYajvUtDdtrqvwuAW3zR9+tVnm4U7fZ9qhMM4w8rxTEtsoUA8BMGMwGHtFTKZtKxlMkiLa//TGCVlTJN54eeQ72346P888OJb38P2WqGXbqiVK9gm5tajmLkb30CfdAmMYbKPHnjrzxbItJp9XG70xW0hl8sJZCk79H359+rbBRDR4enVgJtiXopL5Etu777RPTER4u/qKniU/vvAYFZR8zvpxwf+3kfykPyuv9pDpzYi1VyKafooz6gjUMyb0Ei9lq7fLT7jgMPIAeDfVRC7IFxGxU+FAdUNYv7vCJCTiE8KDIQEq0HL8QD0aGtlB/72Byc5P8ZsMSwV3YfxjQGP5JTGylKCFaDAGtyjr7pMXoEoI8AnMflFyR1/dhPCwRrgrONLfUWwabgNHm3182M2c30ErW99L4s+AY1tsW1Bn2M7se3KxL5stY/5eZbaa2R1DE8EKoG9CALXc9bOSBxQ6Yya38Xv4vuOQ6er9E2FPkjxld5t1Wm+LDNsYz5cyfKzNnwybEj0SVpZDZKRoVBdi190U11Wb/bO3s58vttGN4yMI7NqbB+V4+E+/LtU/V2+7/3eMJ98w6um7mkbA7KPe51XILNxOEAEKVRSB1VkTaWKVzAXGGwj+qCB4vG8JRVMR0XnAVDLtxAt61WjNA3chzz9+o01sSgFCGDuPSL9AKDDkGG7rCNy3roN4mcTRpHDXQYDiyh3VNWgfYzoO1GWhM+Qfkiey55FAM/aO46OJ6d1NTGFL6YWt0tJBybyo7/x2UTE9erv/tawP1OfoYUhp+uiXYetCDzdV3zCkabYXFhnQXxNsV2yYCX5vc/Pm9UXa/k+yUzRJzUaV4NnDyITJMoWvvroW5op1bwPb88KAMjPZQmeuXcTdH/dITdxCpnGO6kouiY7hKvHjXOjbjK7QlPMTQvKBdpzrhT0wwPf+akLjm++b4i0IZOPmOB0vdj4vGyQ69V0dI8DSyKCCPD87H9KTIJ2/2rVe1lPrXBsgwHF1F+RZEqwaCV5u72tLIqgkKGM+R9UpAs3qm/KOPssP+1Z8MaNdogln7eqqXn+/JQZh2Mk2igIxHY43dKUBX0GcilohMyBMLw+iLUY2CSjJ7JeObPlflr9DIjl40Y6ExKxSn22gk5notjcQWLZbPcN45l69lIMWBrNEsALPygZBbxOHbgV2WEFWFBEUMyEojgZa5LjN2opwIwR0bafM5bojuzkEEtmcBYE3S2ZzGD85dYHqJcLAaTnNSvKtHHsGYzCtGPBcdsy+84HLoXdK4918dee2v6hj7X6quX0ux5LmppKFPeUG91XWFxaNcYgddKqBkfzPS9MGwIN73MMHOsUbE8Dr/4U8PzNqxndCdxldVHEnCXYiXyBQudqi/4ufet2v1qCX5IdmNzeKPvzAOkuqb++35tsgt/H22zveHLEG9TZC+tgKadD3t6SMIE0sI6QdeSxucuESUTKYyX5u1ptMzJ1MvtL7nUSeIkJQBx1QG2wu2i5CAxdHmVQAgkp4pq5DJSpyYVtCaTr0j0ynbi0v5RMMR9c4mNs7g0s1R1+y+4+51FYAOLonIN0iPluD4Sojvi9AojsOmXpIvpugRicWpe0mpNKNIEA8tyg14nLdeDq6PPm2WBXrwJxXIfZjz4xbhfjze5tPR+X2D4JiSMxFpjc+ohgGl0Hl+vhOrxmzK8vdszRcOxb0v3YbsLMtRkUuv22QV91a5c6rt3HD1+qm1vGPhNngURtuXiA8mezD2r6LfcxtOurlycEBS/e2bNPzq7QZu+R6nTlnETJ/ls4mZExMP2dTjEhJe+TZoSd/nZrxqzpivFDA9/9RzdcPlbIbrzZKL3eT8t+8uPBrD8T8Qlj6PGVcfFrLpmTYkaRRSxVoc/P5tu1Fwbc9mRG1FThu2QIawChiyH6/gM78QW27wuku8wybiwXfyuTGj4P7kdQ7b5Mu56RbPm+njcrC2sEgQIFEDEw+FRvEdmJo9ZUlIKBIzIJgwAUVIz8nyeLJSZyqVl3rJlNQVbfZtKn6oQAWIFJIGhgIWdI5TFlM6s1mRsX8wvCBbIl76feEtuHbcBnrM95rO1VG06bGXR0xeV6mEhNm8EwuLg27rYNFxDP4JkQ1dGH6aprpIzxhukO6D6gouS6KK6XA5c+8PLpZiI51wPSvO4YsLwzbhu1eqclbMitrX2CSD9oVlh8qZlAwJmpGrNTu3oQrbZ7lX2cV7VadwW9kg4W1aR9hSOjJAD4V8vqwydIDcMs4VJrR3FNELGro11niuDDHa9+BDheAEKVMQbIpK2dkuOULoF4bkkjDZRUeL/Hmt7bBOLIGAfdQfa50VANSAjLs1Nq4CyY44h7mdx+IPV0uTmToKLa5bf7g+4UiyoDwcu4r6V8RiJi6irsPKYHIVtpcTwhQ6elMG6vEzU9XBJ656XfVZt1TOWAgxuxQzJ91ztq8CeLmllUc3UEYeR4nEYiwijXYIg+zRhAPB/gfVxhQaHg4iKX8dOLLUpJqfVNOet3QKEli4pt7ywLIpCSbRVuFU4IshqKJWro0WJKOO6jWyl02LNT5cvQpQdet2luA6bRRqZ4JhdoNx93f3+PZzpcc4FGs3tZH7hEpGnmCi6XYWj3uftEZOifCJcoed89PRiING69qOsee9ura2JwSV7Ea9a2yPebqwJ/104ZCyZNsAkkjREAJsmM92ZOfM56CJTqfWyh7Pk1z1ziefExUGvzAREYjDJNfl8QOHdXkt7FlegcwLZBdq8OTLcehyg5vFMBjGAy9Gd3JzWY22LbkssLpCHvHXqMOK9c0vxF/UTVZDJVucdp9sMXtD7Z/SAG0mJkpitg0ckl/47bGQF/zsag2nKOM4m6CmFUJsWcoTQGrAbOPsiAR2SIbalSFMZSNKlhBU1EIMzRFBMJ2sgBRaSxtFhBwAsCu5bnchQb+6hlWtHPyyAbxI2uZ1jBGQ1RQJPZUCTKuzENWhiXzZTE4H3XKHoZlEuOft0agE1xfWEId05ZAaOridEloIp0o3TXyODENGG+zGc3RteJFy/2YCochX7Gag8UvzmOHsb40g1Vh4tHsRhdHj9nu5uQlQkKM9+5uPg8UW7d2E0Wv2n9+7wcqoh6ZrsH06XZJKwMqLrGQqV9VepXbJr9kNcKPYeTJdF4p+s5xA3x3LAaLSY+POLtcitol1IAGBN9t35Knq65KwQoEpF2vNc9Y3Ctjv2aIjyH7zfz83BpIsb2227fH0i3bHfpu1Xc+E37cHOd3GX/evxsaWS5Uerx/Pkwzl6I3sTFgVqDTHxStn4q6wA5mnVWdzMwd72sJgtTAQgSfUSnE0XHPoW+xvRafcqU36B68e8SSGMgT4cL3EQgqMX3UtTLiL5xmVC0yEKSmci+EjbS6GIN1vnADCToegx18tCrZXnRpcCsMantCQ3Obd8G8H5hVhAB1r/VBYMUxWAbSp0q6OX/4+iBYAHXfFDjuopPIs1dElQie37eMD7dnF9s7zK0FIhAN3v/81KI9xcEWo1CkNGQD4xyTH45Sccc86TRJ+EMCQiMwbK3xa2QB0r4dNmHxF9ipL8Goi6GsLpuYpyh/C2ZzaeAii39A1EWMRu7hr9fZoY5sl2X/S37iQDU6BUAkcr/wEYs4/ls2HmtKoJDXYgYh/7dW2zvONKVRLm+tLePP+e2TzJtjxIdtL4ILbQVIHm8Z3H0euwYaLsmmjy3fzGEFJ6JSLE/Gn1dpEiFqlMFF6dzR3AMCNpR3XkRJHH7aKXROQoLjWpmmm8gbDfEerigt4+JqlfAFOC2uTvCSfZ0m9xR5ID0Xer6FRWnrLxPovqo18Z726yQpBlaQ7cmrcgJIRupNcW2TVxfHGjXEW0kfDa2KbuAGjc3EkViEsqEinG0JXONVYIb3TIN2Hy10NrEVBNOl+du/lzJdxcFIguytCxBe/YZZcxlWabHI55WN+ZjLZNcZaNcNZIxgjbmDJbwK7vhjXaJ9mF/rB0wulpcOz5r1j/0HIfS3MdU6nTpGslK6JkEES/T+4Rq/p70rRharShbhhahqrm6GMlooD+4JFL5zfl+ftyZ5RT3gPvx/iW2r2R0ReQjEflVEfnfReQ3RORfEJFvish/LyJ/139/w/cVEfnPROQ3ReR/FZE/+8Uu0nAnXlO3N/B1P+t89qvM0NW41ms+OI6/dd8tFfgVnfm6Ghku+zSXjWf0FsE0FCR4fqHlmDg9O7FKpgnXQAuRbNVfYNBLUr/XXNUa14vAmCAM68Kioe4vbTYDSQJPuLDvFuPJZX4ZzCGGwsNrAJHNOAuKElgWFQzh2r3aSedsOHZDofvOqhF2mJXwUcjLA/LeAX0apt/wcmC+nNAXA724XZojW1XBfnS8vl0wZ8PttgXan7O5S8N9uF75lxKRgBn959uG+dxz8vU+IufgW0He0c4z2+Iux9/70VIMkp+XdtaG0OFIQ6qg3gYn3kwvXi9TpSDzfstLi4udjpG6LxbDzhTpAAf0F9OAckkfJ5xFc8EMYdLEJF0S0yfK5hlpb9rOiRG9paEloiU91RkPeZz9iMcqVPAn5tP9TwH8d6r6TwL4pwH8BoC/DOBvqOpPA/gb/j8A/KsAftp/fhHAX/1idyiLETwzC+6YDDyGWzWWXDYUGcd1JnM0HTQS75itRDe53XZsrw70V9b5wzdZB4GUzlwRADvrRKLfik54S8XALZumQYpBzP2La8N8yY6giHAFlgjhSEa8rhiAIP6bYTY0xCBYaCzUMbdn24nr8vI2uJSdJwdW+CxJlxJDYUsQiBoD5dlat4yxWVAe73ccLSlcUxbDLG3i+nLH03s7+suBywc3tPcOtPd3bO8duD7tkY3WCuNgv224vd6MffDcY/ztowc/uPeJiwu5XC7JzQWAnckQgqQY+fKfSL+yFGpyS7gPygQWE6kb1FrcknMuRW24j1419RWKbbGHlPi9KOaVFY3WzEX1yZj3eEK8975lhNsj3V+2ghIa46ga4cjSU3/t+BZoU315X+UapfdAvzI9BrFZgO4zV8JnIfPKjOA90BgTJYdYlqD6x//Y04BF5GsA/iUAvwwAqnpT1T8E8HMAfsV3+xUAf97//jkA/4Xa9j8C+EhEfuxLX/cBWbkyGaQmMpS039geuBre6LZYmA7r+XQMtOcRqcA1SAacOmN8yM90QXznbY3i5yEhfF1dUxx8NNDs+BWpREdxhOvXvVPL8utFdJYflWX2w2eJm/HBzgY4HZPLehu9fM5E53VCRT7H04S4Ji0AHEeLYFpknqkpnA03vvttc+TbQj1suxzYLgPXpwPXFwcu1wPbNhbXnCmLtag2MY4eBkqnlfgZpZYaV6yVYjanYO7d4gfV307kytULJ+b4vDSWsL1WdHkmqnBlEG2o2Wbtgx1yHRG4W8jcgLm2qsGl3S19N4Jg5wuXiTXug0CgglW+79IH82EEtfH1nO5bZFsjJRhYEabOYHJYuxYDSpAV13uDuTsL4JyrSFT3gwODrxJI+ypI908D+D0A/7mI/B0R+Wsi8j6AH1XVvw8A/vtP+f4/DuC3y/Hf8s+WTUR+UUR+XUR+fdfX94bvUbruo+BXlXNjhPIs8UZais9kkTYcWS3FdTHOxxq5W9jJa4SWf/LlEIHSj/fA0BFJAEhkpAxMIUW+vRMHMAlUnChIOVgEYH49ubcAMN1fG0vNOp5kvb49uxlQ/o5x0rCwGmj0SR2jNOPdxOMTBN0f6XKR+isSK/p7B6gmBsBlHLv5o6W4QTwIOG7NSvXsHXM3Q7nvPc7Ru3F6W9OojyYw4z1cJpLL73lIitk4g+EYGcBj7TTAg3HwskEuf7gwFMKVkO+6otbF2Pl7qMFIZqvd9SGef2bfkBcDX/vwFfplJtKNoeST/l6RW+lz6tepFLDyioRZazEx8PnKTg4SCAiEKyiK6EzYGOo9UKtRwvyGOK5dUYzSq5U3T1qYxdrE2+YeVNk9zUJB83E+RnJ3rWHiGhZddSR8HHFOPq8oHupBfJHtqxjdDcCfBfBXVfWfBfAJ0pXwaHs0N9zdtar+kqr+jKr+zEVe+IcPGpGz2KOMNSDdDp8hfLPcXBU1Ph9zNtbSDOm+PgzpMkgBxMgRNwLLcqz4Xu+2KtjCTkwEVJEIP3cfVhWqgfvJqtHPAJ3kF+ygbW2HRUwdqzE+l8WxZihGW10VbMN9dYNAxbjvBSeDy+cIn9820wVAZsGQVP/y+4j7nGKskL2ZdsTerF7absUqj31LgZsAMLr4c6sLg1mD1SgtlXpVVvcMYKV9TlzbCKTSOAbSVTzyp1Y0HIFHzX60pH/ToLK/iUK2ics23Aef7yPaeaQub6ySalc6yoS5oFON/eorC0NdXAnrQ2GJSy3A+U2MpKqPUlHnI3ZSGF3cg7BT0kVktFE6MrQ77/e1e8970LAR97fwRbevYnS/BeBbqvq3/f9fhRnhf0i3gf/+3bL/T5bjfwLA73zuVQp7If6vZXbesD2kjZ11GTjb0Xn+SLvzkc/Yry2vnnH9nqb4M079cyJQql3zdJ5yi48K+S2uBXJbuVQjqiWypT0PQydh8MSTL+jXlZ7L4uVe9P7apPkQUS7sB/GsLFqF4gecxR9YS71U1wkrStyNaUFmi3GlF1oIjpLdqM197cLxTKIx2aia8TycwnV73rC78QWALqwWIZBGvjLCv00D0npmvPGHqBkwA3y7bZive3BfQ7DIo/wygebyjoGEQQSJaCe+k6CNLZzoYkDU/OY0thEu2Bu+8/FL7K83OzmDm+wDr1sg70iwoAHmOwsaGQJBx/9O8WsHEbYdKy4JaSud7KuxCtKlSZNvy621RJAcl2d652k8LjojQNK9zq5IJllpCcjx8/MYn6d7iNnCn/NPwuiq6j8A8Nsi8k/4Rz8L4H8D8GsAft4/+3kAf93//jUAf9FZDP88gO/QDfH5F8uAGQ1ulEV/lKJbGAqV+vVG0eGzy+H82aN9dQKvXpvRvVXrifXvU8Dj0X7U343PH7zQapziNEJEWK0zAhFRd1eoj0Cmw8n407Wm8759KsMhQEC9d6Z6CjyQkcZyuTfeXzEMi7+zojG2iSIMPnm0sdFYHWdUw4fPtqRMpd46sAvmc49KvlOTe6tA+H+pupZJJFYJeHO2Av23vU+rgYYMwMFF3EUl3+2UCBAuQj8nSpbRxE7PWd794u/nVtKZ4/et4fbJ1VTVgHz309qsPRdUGm6rPPxN8YZwY7khjpXMKdAWVEkaWMHyTiKQ9qYlegVaPob1zNHlpdgefq1wP5x+h9vgEUXsjKjr59WAC8rK5g325HO2r5oc8e8B+C9F5ArgtwD8O7BX+F+LyC8A+HsA/oLv+98C+HMAfhPAp77vF9oWsXEXpYnyPUwP7j0qBttB7p+Zx+lkDcCwTJV0cuX3pKiU8wZ5224gjtE50XZFOxomgwql89YpLTqG+7QM4biRrG7qIWa4aLSA8I8SYdSkBgrWmI9JEhkREB1iHNABq1pwycqV0c+cGQC1zLSFuwsYMnYR8FZYCtYMKfQiXaGz5cBqwHyhQJTwlvSBl8nBToRoOFEYtxVlvnSO7mAOfzFM82jh1xVIcFvhp9S60rjY73nrkG582jl6aDmIWLkdJgGoAnqIr0QFqm2ZAFQFh9doG8PLBRUD0/YWojIyJLm6IxMQ+MwUsInVCd//LIbO2y4DYOtECwBtB2Zv0DYtCWKaAlwEVrk6ENh30BXFKqwsjU/iteCqCoCLehklRYjOs72q2wN2flwM1dOFw2eeW8O2bQ+Q50y+Ln2pjxIY/AUFQucKhbSzXjQU6NvgczC1v4z3vL7mSngOMIinFeA1PJwAvsj2lYyuqv7PAH7mwVc/+2BfBfDvfskr2K9iFKXNqAoMKoYBALLOkh5HON+VedzeuFKVjM7G1EVztME0GACf4WqqccvZbyr6TcMgmo6oFFTiU68ijMQqxejf9QmS09sB6MWP9YAUPNOLIiNRedWhhW7I6vNDLAtJESibxhcC4CXRaVE5uwyICgaAtimGU51oWJooDqcl9AsHgYQ/1aQ3HRXSLzwEczMuLG4tfZpccnPwOUqKiWL6ffsylYi2i+s/OGplcAtiyE0uXOIjJiMiQC3yhcKCls8dekEogW3X6dlsMzQcmImm3hXnNIs3+8TmE5X65/veMY9WNHPt3babYFw0RHvm1d8z5R7qEqYwP9ohmFfNIBGDj8UlESiSSJOT6BToDdBNIC98jLwSm3AA8+Xy0k7fW5JsgHB3hWuormA2c6npNQ0dwpBqui38fc5W9lNkwK0JcNlM7cs3A0Pwqi/bg2X/9PpmJ5dDRdNuKC25p/hoz9Qwy2hxqlp9+AnaE9BHLNlmgI/jtxS8+So+3T+Grcx+VYkoxIbLTyuzGoAgVHMr/hwAC+evGt9al0nOL6kG7zxHu5VS7Hc+gFyR+DPABiJFxbuudKGmmJc8trolYjnHWR1AVHZla9HA0n+6rdlBMgT6bALWUBooIMrd1KCLD+y2pQGCIEqds5lVxbVlHQ2XwJAMAXY3uPRjdgQjY1kV6NpmummIqYgYP5Ypts01A+wdabgA6t/V77wE8MrgJEoXUezDGmrrRNTG/SXCr4LonHCYJszr6bMXxSxaC9oUcFQ4mRmneS/VY1LdT4zwq6DIeSLZEO4TNn9x6Wil/ehDjSKhU4qaHfsSlgwxcwWU4B4neuS56R9mcO8u2MvNV36hH8L2I6fdx9XiIrScbPvxlN7g43avqXbSR9Em1ifg97Jw+TX9smca2dmFeKaOMiuN51kiy3jr7R03ujAUVfl6/Az2EoRCx0C+KPp/RqYO6xhr5llxHSwpwXRbAPmiaipyT/Ux7AfabUbnrVHpRyQF9QHCqHBoEERnVOh1QotPkoMAKJ38TddwFCEKS8vtsKwk9qUBC6DsLTpNLVOzsAB8YLVuwbKQdTyaVw3Oa/ZtovVh/tXmRpX3cjO00HaJIoVMFa52PgyAf6YXtWW5UvvAMsFMvtF9rqJRC40ldYRC3W4sFy7ywpAwA009BaOUKZ4uh3N9jeMblTM024ToeEkXHhmcqowNpjfbJJLGNG6H75MvuPydamj+Gf3BCter8F29ukYEKhXhogo/Lmuh1coQvI9qBcT+Z2o6+1DQ3BShI10DvfE83l/Vjbk2tXc/rf0oeGNjYWa138q75zgPADWDcSA1fdfU5TF7Cse3Ub6r5wgqmJ/rbESrlkJVKKONqJlyf1KBtD+ubUnX5YzHJAj7J3f2ir9n5kL4fCnpdvp+Kd1TI5Xna0iza0xDxjoGLh/vuH5HAl3aBXk/shjHVBbDuswqyMcQL1LyUJGo5IQowgiznw33xQHLgJIysLMAZKI2WrpIDSVaHC2MGQ2YsBhkuQ8F0pfrCD6qW0wzOONJ875qe8AHZ/ksDJPCdALoLvCtdUu7DQPcrEROSCxuM3RzddB9hNQSjlRnG2fBRvCRRPnGSX5zM391u5i/pjXTgdhcUWzfu004bmhAZFfrjqncDdT6XmIlQ4N45mMVl0Jdzt8FK3lo9WvXmIFn+qkHPZcSPGFU5b4vi66BT2DtHng79wAAIABJREFUg0AwNuI+/XrcN+h2PslqT0R73rIcexaQ1TED/doHM641XNDent2vWdOGiXZ7j/Ms1z2j43pcWT2HO+YBqPqi2ztvdJdlAR3e5/RdwBFqMZzAsnyQXo4JObeZL6LkXC9G2Gdh8ngja6YJ9Laj/78f48XvOxLwaqqB3jgwQnDF/wdAvx1gA5KGAMweOguHVP+sIn1uS/qo+QGTNoaoi8ULhx+1ppfGNcwAC1OHhxkeBtUEhiTFo/Hh91Xntvr9VxRmSloKfW/k/Z4MbHByed9EWkPQrxNdnFHgjIHLZeB6PXC9elbZZeDpxW50sikWCNumBRG9gjCcKkc2g5Cl4OfdtuEFKQWXPgwUuSGVPlO83I301fcfR8d4bYwFMhVaEbePpBkiy+ru4AQD+z/KHIUqHSLwuGR5NUTfqRWTw6i3/C1Nk90g9t7bAcyru7JKijJXGW3P/hV9RLBWI1YEw4QHGrc332/1M4crqfRX3driElwCauECUOB6sX323VahJ5qZTGDc3Og2QVaLmGk8PQ4j22Y+XzITKi0t1I2G7QfYvmXrHuvRqs3wJbd33+h+VtICtwc0r6q5K+2kAu/70kiLpFH1g9NlcZ4RKz9YJ+TVMy6feGS6Ls/PM2FBBA9bXXxw1Fz5ajALEomZ9kHa5Wcue4i6S8eH+2d11sHiqLD8H7/4YEsEH8t9JsLL7CYUFLRMAkRu9XjJ89Awbn1iqC3rmcQA2Jjq3Q3nZUA2Q8HBsiBKrn7fbQZqrQkhqoIxyde1Y1vnzywlfKxU++vbBeNoViMO6wRYnzPaQE6T5ESwWc4+woUtwDbiM4x1/9inDo96PrqnptyvruL+su9U6dC7vkUjq36+mhBR+nwNb+QJUC4CGwdeKh0AlioS3GaO0TxPQaEAoL5K6wrtdb8y0Ojbrbz8UxA9uWDt3l7wHA585Y7o/sW3d97oxjLjzJ/j7zepjJ3Fx8c6u6Xfd4CF8EKZvh7P6xBxc+M5Xr3C9eOx1DeLAcOI/EkJ7C5lODqgurFzY8YEA0+HnRXRSCLFqupVRWLCJQG7hilHwfmTUnaCMwKmT/guWUhXwnSJR0e+1VdaS7HH85fADwDILuiftCwFUww+ToaK5H8VANdproRmvlYGuI6juf6CpeBeulmSF9cd16cDl8uwqr6bQjygJNsE1HzUTy93vHh5gwhCoNxeqWBM01ZobaK5r3q7DNdoUC96KbgdHd/75AX0dWozsPw8A0WW8WXPGEkPxbhFewCL4VpWOC0ndIrEE+UuExsNJA3sWN+vPSCSqnYUYzm9coVTw2K1FNRExHsi91YUq0SlyurqqME43t+JxWDPJ0kBq1lhdWPQ+yR2Q3WyWCH0iXkRLKtUX5UyOK5M6WW1XyJcroQZQzqO1c9bkTDb4g3ZsJ+3vfNGt844j9J33yjn+EiTsyLUB7PpUu7nTYkUfnycYwz0V16kkpFeHlqR44I8/OMF4enaKc+oAFhP8uizR+c/7xbsBIQ7geglAmnVD7fMdQWl1c+JFjlhbEVS0M/3yN9X/76rhuHLYho5AJGea3XHTEs3FMVgaLUax3YZaJeJtrn2b1eI11K79BFVg6OApCdLULaxdWNLdFdi43mPo+H5+YLxastn402UBJPQSZayYolGW9/VYoiA1U87kXq6fF++/xJc9cBZ6G7Etcr9AV5NA2kMCyJn8KzS0tYVyRvGhej6VX0uKedzAAEAGIkWtSLHN6YFt8ff1356+up+hVsMO43ykhFX7QIRseREUK/zWTbiM7Z3vHKENQiTGKI+WcyODcAogbI0mJH8UP3A3NdpX1SlV4pezOaG2TmClZpWfcknIvX26Y62XzE8ODG1oNAwPH6oRx/cFWpPSX5rmz6DegcerSRSwDo2l35AGtfKsSxIO5BnDYoQkShClIWpsjFHTFPsCkMg2VeDVaGIIBv3aZfptDE4hUp520ZLGgIpg4BtkMtvBTnIutlnrPo73aVgyLtj7FlW5+ZL/9th3Zn7bVdDqKqmPna5DOx7opPL5cCcF+PiThO7eXq5Z2Xgy/B9TNXs0hUTJts4P7k4UlVzndBvX5fvbCtSpzyIqAAaEyWA4OfKbpMNA11sK1ELkEaGl7s+aMuZqKCwvjIvMAF4oBhpviTNFVHphMa9lfCt875FEzXreohRHCX7VNuxBOQWVoSzF9rVeeUCFxxPd12tWcbxitZWYfMaeCNYanZjVkNQk9LJU/UGJaqNcyM4wfa5G2GUZCgmRlwuQGvQy5ZC7W9pcIHvA6QbLoE3+XPvdj/PbKufN1KD6y6kkxG9vknXgcE06jHw5T0PyA4sQZKKBAviXbaAGP6rQA5W5WWEeAG5tfMvRlaXTv/wmvUkFMI5oxcFakrwWhLHd6Ffr3zXmvlRyR7QrU4sCJWsR20QoP7BvStSvJz/6zRXB+Uc58zfRMSXy8DVWQ7bNnDdjvDL0qWgCkzKOd76cnxrM2qiqZqvl/u1181qrVVmiZT3xFUBjS/K39Uo+XuMyaeiyrpF0Mr/Lu1Uy/XAJ7hIDKmvzo3jMnHzq5b7nPvMnVSj98sMgp6RddmXG10VQLqfKmcewOJWCF7+2hAReDslJogbfxM0l9VenCtAfIbBjIKWxSURvGE5Zea95faOI13b7qoAF3+tnJ6eS34llzaOn3GctLmI2yy+GWmWTtzEslQ8m03EUVTh64X+w5xe0lqCB6mydtZzp4wOWz+uo89Rn3bTvw0RG4Vl/QR6KYjRzw1JJFMjzroh5BZ1A7ALFA14OVaRl5YledaAjKEVuZw+59f+PYOA8+WAPFtYXIrbheh9RUUaEIC+TwyTUuyOZAErEikA2pb6utMTGSCKsW/ol4HNyfJDBQJLehizBZ/zGJ5FNkhHsvseh6+AQIMgUY3i1aurCcjsLd8v3zWz7YhqvXsFs4AolSuHEqyin53ZXGt6LdujiAZ13AfEaPfqpD8B4cSnxa8c78A6jyL9u+XRo2PVYpTBDWafPNHVaqC1Gl8J7CTpO94aus40oNRWGM6l9WQIq3kmKeU4vZv7uO23Ge8v/OE1EE63Ya0Eodl/alDtTmzH7wtzAsdAG9mWb7u9+0gXAJMTHvpvH9DDWMPsYUUJN9oMolXEGvvwBdTja/ZavS9VYKiVSldkZNkNy53vjn8THfDSlQlQ9+n3SCj8exUVAssszOwlFousfj0aiLZ7plgx4DXgkck4KV+I+veDLXRNm0JejMyMqs9wdxCRmibK8wDUHFwKalR2EMmSQxAEAtVJtEqGg917E8XWB2pp9eEC5XNIGgO4fzdQsIRbAwCOW4d8umWp8mKgmKBABLgoy9EPwPdHtKiIiVR8hUv3Af/nJr5vsB3YR3g+3vP53TyIA8RH1bgyYHZG5/59AIRWnnGWc5RjpD5jvba6whn37evNRjZoBLjT0AJIHdz6P5C1CoFY9tcA/GJIgfUcb+rMTPWHAS2lHgSDdmdf8JfYvi+QrpBHyy2inRXJprGkbzc/c19uROrNV7ug5pL6p2NA0NOHLLoi4+Ki0NsN/ZNXuHwCvKJISe2MOCFRN0DLgB1iVJd5CmaJId3GJSzgvj6EHzT8pRzw/n2kAE+LdstAcDHJ+9QO03eoiHNKGjRuRGhukC3m4Qip5WemU1tenIqVftdu7hcgJR8ZlVcUtoO4f9LaSQ6yJfKkl8sBYDN/NBqaMxeG9nALnNXShrobQBSH6yTsNyvtU5+T+sACZHVg2LOOo0NHy3LyJ2OX78x4zFW7wP4oqLeO1abJ1+VvR8I0VKbZoLGCaoddKyoLA8l57p7JB4SBjBVRpXax/etEqMhjFbEaYYaaPaOWc3Iy9z7HWATRe2Fn6KaR6JLtBVtFlrGtRoTOa1EvpaT/L1lm0oyx4JNYXE8VobsQHHwNlLt8juncXbnX51bNyUHyXc4ubx1Ie8eRboH/wF0m2Rvro1VD+kiqrTrUzw33yH/snN5QJqpf7Qfw6jW2T9UMQZntbQf/IdqryzsiHOq+0uhqQZ9EIRwLPc8bvkSVQNWkqc3NDe+lGHkaJQbuHF3etTp9dhV1RvuqG1/upy51CF/iaf4AkOtMLWAgsqDSzy3hRpFRBqqjRS2+XAHw5P7Z1ow7+/S0m8tAMqNOZ2pDAAjkK4BTzrqpgfH5AjkWAyxZLYLniMrNJc23+tvjfSonDpiRZgCNj1LpYTRoRI5BuSrn5OqpTNJxTdLEiCSZag1OInXyTJEhGsfKYgCAc/CrBjfzeqf9fBnDJJeYNNmH4WCAE27tciImehMauC3RLSVcq6uAm/tY0cQM4Pm8p01qEcrzufg/g3qVw8sg38IT5sTzdubzHTe6b2jFE+qMzVkL/oX9foNLQh4ZY37Nl1zdDKfKERUl63Hg8omivW4WQCJKAXJQ++BdbFIZrGtHdMQlsPOxqiuwoOhH1KCKfOSQ8LtpRwzoRzq3qZnr+5DiU5eJjhaZIktB7FmMcojP1IymyzRFKo/M323uCqGvL8oTCaB7iwCZgi46O4fRt3oE2aQp5JJauK0841Dj4M5p7gYW0tSRkx2z1KTN1NX1Z59DohoEK+WGmycMkU8oFw3a3DJJVqQJrIEovhdOpP4O4xxEyiXQdRczaBosCojmxHP2OfiExmDerP3L7zXOyesFSPBzFTS/3IvyWavouSmjVcqhofKCnrmro9rICOP3HK+10IC7I9qhS6WOcFOUdOElW/WsucCisxFEH8FcCBeDazV0rtgY93mL7R03usBC0ToZQRq+1fCekChfUjnHUtzS3Qnnffj9wyrEdR8AGBOXV4p+WzmSta+fDS2RSXxftRD4sUeh9aJLAMP+wMoJpjEvARbxkisWtCgc4jNSEaKiYixj4J3QCf+eaXRT0St/Gn2ZAOQyodtMX6eslLmQc3RkFbXVAGC4v7b4Vql0RiGcORisMwUy+n5bQXmV2WDaDe6aGi3dOoIl86yLRuVfpTQl26TBswczCSCQ3sXRfanGXNHkshV/KY3qstxv5Rx0I/FQ+mHLOw23Rl2hLJN7omqBnbP2r3PgNJDx6XvlJMATc9XA5yyovVIU12dHGM5VxEZW0Zkar6mqZB4ka/tqdAEAXJlWMStuZxGt6lLgMZWjC1hq8Jhm4EE0/4NqdOt2Qpv5MVGgZimf8/Yg4BZLh+qgr/vx+yqwc6ag9W5I97sH+mtJxoFHfO/K6QDZQfn3aUCKay8A9l4pfJ0PjEBOUb5FHiBf7hsnPl3b0XAtwRP+XTjaDcuINKJ+zTO7oRpgrdcDgItivpihpUv/40IZq20CRBsE0lVLYujdNG8BhJsBYoI1vVsWm4GUTNu1ShEt0C8/52woDVF+pyLk27Hh9nwxnVy2t0q6CMJNg/SZn1Kel0KStAFkIZRMr2iHMGCl7R8wYWqfqq4reDmmsNDVx13cF/GUZdIMo14n85hUckJe/NI1SFt+AIQeL+r9+PdaM0DjXHNBqRIyqjz2DH4kjTz7kpjRfljZoQTI7HQ0sJJGtIK4s13gx48m0C+4vftG9026CvbH/b6nOmc0mHfBOO5PgfNH/uJ6Liod1ZmzdyNe327YPvUilY5wAoHQgMq5p9aLlfsN10L5kIjBB1YwE2amnAJYWQ1nNOvH12Vg6NwOCd3Y833VABqNKOlfdyXc6/LRZfzIQpBtAk8TeikMhYbgjYYBYRTejRjPRZQ7nIHApAcaYIHdD7PH6tY8EDbnGujLZCM3zi5gToMOAPveMV73rIPnhqkWmUzCvBvwvZXvPTX4tCoJd4+/h8X9AGRwVMt7Y2ArJs+TEQzXDlkesHYjNYx9QpEUPUUI7ISvnRM4X68HBbIaNXJlAiTzoroY2MbKicUnJyJ57kMj6MY1FMCIfgtraNHC5sbU4LqsZMpwdTW+ibHwSESryg5UlyV8/AlyJfYW2ztudB/MOOe/T1SxJbh2cg2QdbDQxKTMiI8Ca+frcXOhDvqP+sfP2D7x+zjzW4EwJuFGcCQcG5dxha4UCIQDmoecESFysOQHRE2Pl0CBSCZMjetoGYDS9Twc6VXghsG2RXZxWfuu90Oal15mDmy5368aEt7H9LLoh5eOF1FsXqeMyLQazO6aEaR7BQVsZmWI8N9u5mqgH1f8XsdoxnR4Nl6uaRUURFcZKvUZPFhV057X5+OPrufi4YEmZX3nNUJLl0855/L+OXnJuYNwwi7HYH2WBWnX07ixjACwwvpny9XcfZ8vt8jzyzlduCWKZSap6qpfy12JRktihDjdRGs//KIVHSKwfgZjD8CRJ2qoFHDyA6syVrfzjPUFs9TeuJ0Qbs1GCbHyU9pvJGlQxZ4G/dUN1+9qZiYx2wsIatdD3m4ZrDQ0NHyLr7T41JRIRHLALHXRyqC4U4CS9TwQWNFKZgzxugWZWNaW5EB05EkhHAZtpGv4hFtj9N+/57NsmohM1/si2gKwlCzHEIxnKySpHhDrxe9qZXY01Mbo02XgbE5ZqglfXZ+hb2aAt8tYVpeA83hHgz73FJ6PwQYEY4TGhisHvv8SDIxkFiSyv4u2L24cb4v+YL+CRGsl4SUwWvuYApTKJA0vJrRqvMlsqOeURL9hZE+ThXZY4PA84ZbHWtxqyPPaC2Gn8o5K1TEazhpn4Ziszcb89cUs+DGtBMh4DjvoHu1SwpEou37H8za+b6r8/YAb3buS6qXiw8K3fdP2oDTHQ5/PWcrx/B03Ll8opPzqGS++M82AxbR+GggFvZ2RSXRqlTR6PLxp0eQ93XMJpiz0n4rGCkiKaLt/oALLuR9iBRhZKaHct9SBTxSFPEf8eaafEX3UY3xZG/dxRtb18fjdEGA3w7vvLkqjKzuBwa8aPKt+YGkTtTyPIWRDuZcoq25Za/u+Yb+ZL1duzTMTyz1Jtl28x5NvFiCi1NVwLki+BM18Eg3EWLtrgZda+kzdMkiqy/uJya7wixcD/2jjysDPd+7O4QaKiUTzHZ9B49nFxUCxYOHnxtbfsNoE0g1xd7tiY4SnC/3t+zG/IOu7EznyfZMNYBrw29navI2vdvgf00aES0rH2c9bxcdRDPTJrbBsjxTLIl1w5rUeGfSzwZYGfb5h+2Sg/X/cvUusbUuWHTRmrL3POffel+/lPysr07jKhZHNpwMlY6qFbGQwCNlIWIIOBbLkDhIS7uBeSdABCckSHSRLtjAdC4SQ7AbIsgwyAmRDYYRdYNlVdrmy8lPvZb7P/Z5z9t4rJo2YY8aIWOu8d99LO/M6l3TuPWfv9YkVa8WMEWOOOeetAaWJ2Z2qF0UUkwEcjO/cpAxEQOeGK3rIsU/oMFIJatmgrCocA9lZ7FK+txUNAQXFQA6aciqGBacBpoOq+DBBuOhX6yrSrKGTI8w1OMtMMegY8xFrljJSLeeC8+0RtRouUTONtdPMvKV+FJUDK0IwO9jxuOJyKThdDpPfpGuNT6cF9y+vUG8PWF8cWvpC5WS95y+ARNvt0T40ThsevQoqjnuth/5eAETK2CDSYR4nh+wIaVkYbh7DCcuRvKoawcGAc3+uQkR9kLXaNhMusqw8eedhNecmCgZPDjklcZcKsFyPOsaDWtiU2hkQaJykTu+J7p/7xj2po4zpIvl3Lsswom8fVRDk73+Cw4AfnlLSiCoPq57GOQgC/NW2qBnxQJQP3kv35nX7HWVsd3c4vrhguY1ZN2RDdF5sEIqjO4rkpclCghDkaJ76XwNGrtD7/5aDrSOVIXcvB5IYBWYZs0tzquFiydeScmgaVkisevvpmt44nxg8BjWUuN5Af1kfgFqaRvXN6RQE97NWgfZVoxnOQTVcLr1WGZPeUGKW1YyD6mgZw9o+KSuThDbrWnC5PwAvD7D7gvJqaUY3qlnklqjUu8HC9JzlVnmfyaHzb32OogxIDl2CIHKbVwVElyyZLqqXnKxmQ01kOr9/JsiUH3oLKkmVx3AudO7a+r0qIMhtQQb+9AoZntGh/aQTQtXxuIc+c4z3e9jUQHuAhtxd6QLb9mQubu+OtB8C7b7hRrfNgCkDk2iRgW8VHkbTw6lTbVA8xHF9Jt3pBk1cLgaeGuGWDNn7rHo+o9ydsdxzNt2/nT20AKBHbd0v6dBSjhRAR1gx29YpwscXDF7xlNCkl7kNwiEJC3k6b0jZ7tVhKe1Lw4oxdFZvRZfO1jN5tc/inli/jKeg0aIqY7W+QgBg5E4rmsf83Jxb59MB9/eHpBBa2ywNMcOS1zT+UYrnuCYqBlrVidUNp9MBd0+vW0XfQJiZA4H9uKKlTIw+L/clExG5IfjqfmxPkyj8NJ8T9+NrqcU+gRHdJr9t/R2KfiZd4AmRx2cCRjjyOZKjlcT3+Yj4DGRyGKoBh3Ov0yYd5TMJjWqFnRN3cbjy+uxSQzNkTCwOiFNt38Htl0v3t0i7czUGbGupAeN4nzcmxQG2mt1scJF2/xAWF2+80Y2N4YCaYHzSzrJq8IPZ3KekNr43c87BEMV2db++ri38l/k8q6OeziivTlju5+uio4DhJIow2gCz1Vr1XGYUC7F/b18ztBkNdfQcBObo3xUgl5KGcXlb0UJxDWnMaADKxVDuiiDaDldywLici78KAjYZ/Kyeq8cVliSiARI5XM+9MHWW959yBvxuweV+weX+0Mul157escnDLJKcx6ohmnIV/C0zhy0WUW23BywfHWCn0p2gXHLzNqoFpdC+Wu6sS4eMfe+j/Cq6UJfhm7Lm8f2ISHvfDMEiQ7/YViEhyHv8nA2JzziJCm2CWPX0/u6OQJ5CKZbhWvQtFB+CJmyVaww8dTwUJpIBRvA0c7ua8YuSMEG5xtqCwOAUM6Z11EAJUgVSE21Xq6va4LQ1GP//DNsbnvAmHrYWjuS2yRjEju60QnK5M83ABDmMSMM6LDWUYjBVOEwc8ixrsbsTji88Svc4EBVy1bdB/o0ZpRpqmDJ9lc6XcknGyKE0zLQGRBNAowHONkQxzcvdNO6D0QfIqfoC+KWMWs80XJ7vL7wt5/XlozFTTS9VD3asYLJ08oxD6kJEezRRSiAk441wQK8G1KVRlueK21eHMOTdYPix8dHlWHFCy60LGC5R4qdFsi24q0C9XxqfLYaQjk1OUED06SrXyP29Le/jkST6I2I2AEyxyAnujO0mxo+GmyqGXaNrjtnpNFBSAHAh5Tah2kiGNFQAyUnTMogFQK6INmWmdtrPe2dpITNkOLpfoVdkBvujdDohx1kfp+4OY2IrOjg0qTla32QodwVQFrQE6JJhjNrbPWXCkBJADLNXtNyYnPy89/E8rj7F9mYjXQd6EIIYPzWkE+p1F152TtGojjjV5zKPpxyjDrWZ7+0lodcxgOL2DlfPPYX0OVBS+tWQU1teOrJkTm2DsMe1t1nbL50ktOLA9dpLrRClGlFU6y+LIolUO+hyjwYg2zWpDTJvAKsU0/BlbgMM1IJPCWmWw5r8Lwd/PS8pz0rpWKY9RBgZoqim0rBL9FUgJF06s8KB3RcsLwrs5QGHD46wZ0eUZweUZwfY3QJ/dYC9OqCeFqznJaVvKTu7LFhfHuAfXOPw/rHRKjoaAl3Wo/TPgqZmCB46+zXCfZP7JV996aHD9arNvqygUVg/jY8mEGgarLhfIFYwRJWJNqM/plWBGjUYel9H3+WEcfCRV562geIJ3pYrouRuc2f2T59s/eiZ3Kis0Y4y0mUbnR6pQ4431jU7X3oR2WXpY47jcfVUwtjqsMOcKax2KdqUUxdAl47uhQ1rkIXLe2h4WGXxCdubjXQN++gW8fkcFjzzt/J7pmgMjtgyEUnt38Xf80aDnNK0QMh6HV9X+N0drj+qWF4suHyxcX+bl5pGbwNBATqMXJJDDwgY6PQBBEFPNGzyiLn0tDT2uoxNNCL0Q6La4mNXxDvJCK5MVh7KgZYEpzfMaxjk+M7jon4p++XfaUzYLW5wVqLItIKyf2lQcJAd0YiYpWHEqaCuhnsag7V0tC05cLsTSNpFQxfnG+RvxQfkalHYkUtqoCP5bLvcX5WRx+eSlAL5VDGu7JOMYqs26H8tVhFdYx3PRVYAzK/cqBBpX56D7fGuMHBpQ0FO9HxmRMAZqs7jTraLBr3a8DGLTLboM2QuXTsegBWZGyGrRTCx+VobBbFEtrhLaUmGIlNZQ7vYUoiD/nFno5JBVxa0LR7pNUN3/2PR6ZrZf2hm/6+Z/YqZ/TkzuzGznzWzv2Zmv2pm/42ZXcW+1/H3r8X3P/MaV+B12p+UcOkMQ25WKYA5l8JeqY6JWNek5jSuiZDJ69qYdm6WktXbO9z84A7H52HohbtMh0fxWG5Bvohfw1kEQbiDMXJrFRmY8zQePrnERMVhSLs8KQz0wVtu3Srti+UfkVm5tGslWhXqwiPTlwFdliXSrsz2BaLgnvDcg6fGuakQdEJy/Z1BH3nfnkY6E7IT8cZ9qgQN3lQY5dTy0Ja7ArsrqC+OqM+PwNMjykdH2O2CEk7DNDKz8WHb08kHkH8lT8lnXE7R18zsFgaYFEGh7IzvyjU9VkgVgMvvuZyXyTkrkkwTZUsPiqbu4KTC465qv7faUGdmGJPaehYGFGgoVyuOJEfNlVGqEPrkxjSi3Jb7eMcHuYP19wAAS7DDOg3gkcS8gZswqnSOe9ANS0kDbIWrSstnhcMyJj+H2BCCtXU7fhsFsXTkq6V7Qr1gHsDBsQvQXmf7zEbXzL4B4D8A8PPu/k+jzVP/FoD/DMCfdPffCeBDAH80DvmjAD50938cwJ+M/V6jhTvT5SznmvfZy7HA/5V62JmpHtL6Dvs8IDXx86U708RQ5nunjow82c6JKnpuWkCQl/fUkXnRETRz4G5Qm36vyEm/4HkruuPWuld4r1Za+7y3Bd4Mc6KyMNyaiFzvO08ZbdWUgDRew2XlnioTtQOb+8wb5XkvhnJfUCLzmjqMfHomNGyb56dtMPmRaL5hX+vnywACRgnuvAd0iCoNMZ0qkadr35nEFnuvAAAgAElEQVQYyQDmXGEAHeEO/jWuBOJeh8fj03PR55EnoAGWTqt6D8iTJjWiN8KNOXTTgVVSTdQN5ceM7z1FwmwfSDOudfgbD43xdKyVDuq4zGP/F2yN9mtuPyynewDwyFoBsccAvgfg9wH47+L7PwvgD8fvfyj+Rnz/++1BoVzfTGZB/g0gDenmFLNGdych+fD73HFUPyytckQi59hfud/kloQHtvsLDnfoekmiV2AYhAN/F3+nsTkXINBgO61lMALTCe55qIlmffHNi99OhB6yaR25pcQnqxC3XAzt1lwkaDL4FaHbmCybyXPKoXYqglvIe/JauvlY4cJOlmhU5W80In7tqFfxHhBJsr08JRUc58ajMrtX9kGma9T2od8vk5DLufLcgZC1mgTY5UXuz7xV6I3zcTJJeicUG4WaYGtccjnJxMs2cmJbkKuQZqDR75vyKfLnElKd716sgqiwUP32kMIylAqWaN6yb5JWANpkdib67cdY7cnN4W1llCuU4G5tKQ3Neu3JxidnGYCeYGpdu18lKnm7RqQFIm4UYBjcFssOZiBLXw0rUpQ+xjFI2NauhjBLOqsli/8RG113/w6A/xzAt9CM7VMA/xeAj9w9aprj2wC+Eb9/A8BvxrGX2P9L83nN7I+Z2S+b2S+f/a59qPHPjBrbbVTd/S5L60wz2vz3XtDERlo26/94XZ7jfMFyHy+wDNIBYeZx0/8xxkyXgEQLRC2GqDu2aWpeR39XQ6DICMAGwSnCHDSVpaMmZ1s4WHWzCXgYRhQMAIdI7yjhwKQFAHSnI9BpmBi8OTHFREb0qKqDUYaniAgjV/rQtoN8be8YIm7lzocD4phc3fQySz71SX7GZzXOM/2SDzy3YcKQz8jvpsPMbeijnnu5V+tIZK9dJ4Ebbn3/sQIIrzG2lRPQ0B/ZTTGWVcoVtEJWaZGENPupGncQ9Ceh30/aHtpXyvX8WNQLZvYFNPT6swB+GsATAH9wZ9dpqO9+1z9w/1Pu/vPu/vNHu2lLgp2QPtk/lwCbDGIfx7m4lFsHupNMeVzZd4OIeTzEeFsB7k+9dM8imtqs/DC91HV8WZWjI3c05DQoDlzVRGuNA5SBOifbptKBXOHZ8jqdH+wDMzNkeRu0zPZPtMrS54x42tWbGzoH7GjZxYLjXK5q4xlDY5xosUJmiAk1OmAXZCiplsxp6S2FZpA2sMou0WOXgqEjtqFv23cpTRMjmPKtMDa2oiFTHs7IO50EHN1hJUiTK5Fy7kaQvCm11vlMXRCpSJWSL+VcdUSXCYqsLX/UCOaqwYYabu0z6cNsmOW9MER8yBEsfb6lD2agE463Atg5ymCxGCWjvxAG1mtTLtzdg0UmI+lGH/dlfPDmGJKiu0hIlTsewNOyNDSrUWztYOBwaNc4HOBLQT1YH48/hixj/xKAX3f377v7GcB/D+AXAHzeWK8c+CaA78bv3wbw2wAgvn8HwAefeJVZoSCJbmYU+lo11GK/h9Avr6efD/vMvLAcY8VaQvNbT4mMzvQjf2njZ3l+ohMDE+BosnCzVpImjXgiO0EwD801Lt8RrWZ7ept6IpotPNeSK2z8cAs6xapBis9KqbBjbdKmBV0uJoZhk/Sa7WWznfdi/ThzQWv9sKR35DulfDYrAyJqNRZ7qNe74U+kuLN/Osr0HtnG+Lt/5zki09BO957PWC7hhk476U3J5J7GVv0K3j8a7k83k8/MB4plTmajJ9GmDH3MIAqg5V6ogWTngAUaWBrCjd9mAmNKbw0F8qYb0ugzfrdM/h3hfWnsM62kIvcfg3rhWwB+r5k9Dm729wP4/wD8zwD+zdjnFwH8+fj9L8TfiO//J38wLk+2WX0Q2245dgkP3P1+L0WjanypC5wMcgZSSBgw91eUy9Lvx2cXLLccPejOE0dKvlLNIBP1oGhwDBrLdkHLpX1KgQypqaQXP89Xbajuyn02HnBeQ3hT0JEX58vIOEd3rEVOg8GAA112RoQbv8O7woH3kP4uhsayrd7QWxoFa6kMzbsDLUNrq+S65blNvp/R13DPYvTIjerkCGTZIwCbyDH0but9xy4RvlcVGlQ51KuYKBRxBgpPfnpWt0AMGR2F2WeWz805ac8lcvR4Qb+b+9Z+mfqw1VVDnyRsyqmr56VjTaSIyJUVjR7lXc0Ik0Zwd9jh0OqlqfOr1nS2tVUo8tytP7072OciBarvzxvSB2i9PdYRta8Vdr4McsAfuXrB3f8amkPsrwP4m3GuPwXgPwLwx83s19A42z8dh/xpAF+Kz/84gD/xWa+9x732HLg7tzR3zpAlvoz7PeCRnCPWNsUqebw7Di8vzQnCQauoUWdKoAvK03j5iITQjVzXdXtWfAUElfHFl+PTaRSDYVMLazZKRHvJIdPAolNapbVlyKWrCNPF8efov0MMsjlwqIPzJ7lPGtCD3qP0CQ1STGSkDFKCRWO6p4cmSnVsHGMAuhKBp3Mgq3SEgcwvd+b1bDufp04GwlszHDu5XjHI+azkUonss2FyPb2H9B+396/TB4rk+t+ufS7vRBpQNezah6HL7Rz6ti9G/lb7iPfpyOq/aRQ7iEldrlYDngtLuvTFA5FiZpIVUIMjgH7dIeRYfuc495gQqvTbZ0S6P1RwhLv/EoBfmj7+ewB+z86+dwD+yKe+iMo6igGVVmMqJlkLXBTNpBBsaemq8nceM1EDyQXvzV5aqJLnKgZgyfMmOj6dsbw64fj8Cc5fjoF1sm5Tc+keNmYvUAAxrlaDnws8VADcz2GNEz02w2M05rGMdgRaOgRyC1veEE3nmtuF4rSSpIThxlwKWniGfWWIb4TmAqGsiG4zaTw48K11tybvoaFfGIEW3u5wvyaVeLHRgBIRW2sv0y5m2W/tR044DA2FGAhOBh7XDANjF0v9b5eUdb1sR9zoE6WhO/QOAFbv1AAVErm/CxdrgXCRmd5yEyPMFYHBMq8Dn5etPTQ3680ZGqpcHDg47HYyyuyjmTYqcQ15ji360UZZGOKdC79BPp/an8tAaxTpo4ds1F7eFI7DdW3KBokUy0rBDKiAPAsaYKJl6oArxzxnt9LPwUg1NbaSJqBNDu08gz/mM0ak/bCSsR/9JrrcwZGlXGtsSQtQ/qUIV/V7D6LWnaWIleFcNiVW9tMJdnvC4RUaCpTQyz6Y+/kerLXkyKipkQuMew8VgHkfALM6QUX37T56chxNnZjOn6QriCIDLXE/GmE2kS9gLGd72Z74nnXX2ObSES8j2zJNYvERybFv9L71e0rByKsykCD6yuKznku493neK5AIj/2Yoa6BkpPDVV699lplma+ioDv0BO32TGX92KQ/JiffwB0LZTLQAWl0kffanq20k8+CdAmfiZwjBRZyPp/aMeTEUKQa/cOgG6Df82byY7umc3QfxWTojoch+98uAxljLp1pfOY7NpCSsQRteyvZ3c8EHSeF2AfMhvv+FNubb3QnuRYYsTJvOnPp3/M5xBE3Hz9UoFADTU/nnvMteGQ7HnKmtvsTrp56JhvR4pH9eugDyLafM+OWMb+BGLg8jsEB01I3l6qqMeX1OOAZpUYE5ujluxlVdbFUICg6cqLXYS7ymKeacRyCIRTluJwTiGAP74gyDGwm5UnNbL/BLKgpfOKIKDHSETRG5p1fp1ZYjOYmpy2dc/yMv0b+i0SCgUxbKHC0UQx945fleCJCw8gRYnxXku/l/VcZ9JrXQybVzFehE82A3JFt9zLdXyDleuiVm5koPXN+yNbah+1maA7S2tvpR2/RlLcHXD21JqvkpqBmXZPDTVpgtgGTk6vdH69tw/m8VrB8z2Dg97T8ql6w0pVTA/8r9/lQRsNP2N5wozsh2DCYr6OvjS86JxPH7+7Dc9BozufTrPXTuejVzPy+1YH7E65e1DYIOeDRDVwO5gmZ9uu1wWshbO/SLRv3PdZBQzn8b5GRbL9nhqAAL0CK9Q1dHyuRVoNO2JEBG2yvFeTkkAoHt45oYz/X74GOWAV9J0erk4wa+BUo95ZGgmh54xgURDIYZhrzoghVHIzxMxQPZd8bUaalxIycbGEVjP4Isi0ZpspzHHygC5KSWXyYaJRmGBxxzN423bMtXMrEu0NEnSsXhhh3Qz+0FciQXiasyf4Loz2oKOYhpd97v28ca6OY7gqOz7FNgUolUvUx/FaTVAHN2a08LAMWROEBYJu2cc9xRp3wThHM9n0oK8SQG+2yYx9av8b2hhvd7XIgS6rPuTb36IApWGJQIyhBzm2Whk3H7crXODvWPiP65YKr5xXLy0C6BaMEac9hRoOsiDUGM84SrECjRYQTBqAwfwLPF92niakHvVGgKF1qDiia6oBAu8bJQs/NbggDkcY4Ml1psh5m+QLQuN1oQxrkQF/kMLMPMuqKfYduMBa0PBTVeg0wGutwrA3H8b4x9zufCY/Vd4v77aDeh/4XBNlzSvRTDs5K6/eUX/t4v8qRDsfQeNPhaOiTcyhQhkkH7JPe/7np3ML34tIneV1FdEWJGONJj6vOPo/sYn4qePztBZ/7zooDke7H8aKhGsgUqkxmMye+4i1wAhT1w/D9Q4FVjILrO/b9xiz9GCpH/Bh0uj+abZZ6qDcTaJ06c7uyDfIvUgsPxUxPx9p87b1tXYE6ZaVfKw4vLzi+nIwcgIEP4tc7A6PTDGjFLhMCoqFSIqdlJ0VfGBFyjoNnWvbJlJCDxx1gIUy7WCZUBxAOM/7eTlhKBZ2C5F3LQUfwtsvKoQr/67KEnvoB6A40NUIFiRQzRSGRuwR6eOynE0su62mceZ06/q4GdlAIaD9OCHxAwuxSeU55mAGQNg4GP3hplQRqXyYlFBNBPYznBsvnuCTeyfv23j62d2gU4nkAdiqRfjLOFUEoG2422r+n2VVVDYrD7hd87lsVT37zVaMXKsDVoTN0F0hjlsEStY99Wx5wdgOx0vJuKPc0uroxDHjPHtBgC5XhxVAXGYd7NOdrbG++0VUtn3bOTmcNUWU7zq8s8zFwPp777BltKwY7HHPfrlzof7M8CNGzXy5YXpxwfIFw5kg+VCKmMIgDf6fGWX9nPlJFqtxCAbBBYebpSNJsVRywbtg+fZ8GtaNlrjpFNQlxguXJjKkbeX/dIHdLh8jPMLWPyHhCnbtSLjl/vzayCsUYSSWGUhEZehcC6NIwFomU/fs1p2PUOIH9ZL1PZ9Q7/w5pq8u54/9ymdoQvw+Sq8HoRZ8cau+fgsy5MCgo5m0PqAUoyFB0XnDul7x39OtO7YzTgVGRXhyXR4Z6tewaac1vTYnWx24cb6VfeOPg+jgk/XEpBWgXqM+fw40/G8htp/zsh/6INnKnh0OXcGiHAD0VXCYj7rIQDWyA6nhnDnemLIBExVl3bU6Woz/xmVeHn04or+5bFQnV4fL0EqGVS2U1OED3jBMJXVhdAsHbIQ1rvfaej8F4bDfkQ4gsJVTmnT+MTRExB1y5oCX4rtaSldCxkvxsUCgRGpxJtA0RPBHKBjq9qqFeSrNvId5vxsqGF3k2klxy93BndEMgaoR5mUtFQiZgcUATsuS5gMFBlte08XklIqeRmcYsE35vgiR4iopM6pMXUSehVEceNnWIptEmEgXs4F0NEhnKOJE0R178wmdcpV1Cd83UxrAKc7k2m17lO924OvJ4BqvBHq149rPAi2/eYL3uY9fdM29uu1fvlIJZD4TQsa/Jp3L8jEg5t5mKzD710fDu0YuSFiCDOaZ39dNub7zR7VUfohjlXlIb6UjOlpvkGAzlK+O+Mze8UTYEWb+hJQRpD9QF0B7QXRhdR38p5nubENXuF/Fi28VGGoDIsTj8qrZlJlHZBMbU2Ht4lQE19rEjnS38Hc2IlFPTC7M97fvG9VYtqEiHljFhfJx/uJ9AhfyMOX+JmjjQZ45wQsjpTNo5/dx383mUythwuMCgCEnDG/2Yjpq9e4t90pARBbpMLADm0jd6vsFZJmPc2NYZyrEd2ffx95xVzLuRzFNwIuBqYA8U5iTzAJKkjvchJI54TucCO1ScvnbBq68WXK4NNtNyvKR+VnolCTWEWirLGZ4b/TeEEmc7FblOn89t2PPh5LXkHn8MYcA/ks0UzQbFMOhyi3WeZw4rpCGMn0YD7C8n0lhLXgYA7bhIH2eh902H3LGHJ5J2SBXDZcXhzoFz6fQCB2M4wjLBOD3q8/jVfbhc5LEx0IZ0j8CgX020Q2RrGPk/QNBiRzrt2ugDyQG7L6jnEnIxGuaGWhv69sEwuks7AVSqMByw4qiinuC1eL8sDdMrHeRpu9EuGKo1pFEgTRD7+wHZh2r4gA7w0vFVhZvkbmxT9EMm6NFJIPves4hjnkP7VpBn40l5P9Y1xTxu4/SU77gfEwYJ1cGqGKqhzcAFnWj0HuVybhj10dplOZH043Ryyq3adsVxNtRzAQ6Oy5N4LmdJblMs0zkOEWTAaDxLT1rT8+32+7LJEG4KIOjGROXkdmdEPCXbajRGv+fXyWKwt73xRjc3LV4nqDLLechMOOTgdakkzCUJjbBWjpiTakjEmp9bfs2W6/PjkTJlLn464/hyRbkrw0uafBmdHaqflP/bTEpDZOmNVo0rw2uNpVJkYKWx9TDYhEv6xHW8yiAzGkC1HyeD3xf4OYwvnXmkIQb+trVNE6E7I9yiHc3pYdI+DMgrJVxi8Ia+Keh6Yl1JTKsERkip8R4274i/6XcxTIBpGIHsv6FN/JyOywxa8GyO9m1y+71rxhUJu4j9oROO3qIhZW/IABzr+XMVW1AOqNtsUPP60+z/gDN0UIPMyHYGENaiB3EpsOK4PEIrV3RZR9VPVoyY0YfQC3lPO0YU6DkyZsOdx/E93TGYs4FWeSjBFd8Vxydzzg9sb77R1SX9nDR8+tzpBU0+qGxLsrOY5OxM408cs5e2cU4zqbXTMk0kz3O5YHl1aXrS0qu7bjbf+d0BpjEEZMzTMw107a619yfL3ERARMtZi3xBEk1exAvNc1cb0bZGQmlNtlMB7gtwtwBEmaUZ2Err4r1dAHo1YOuTRBql1UKZEW0pQL1qHnnE72k0YyJo0jJ0g4p2j5sIOy2SqFFSomsuDFgQo8iN9AUlWf0Z7D+3cYXQ+y83cp/xRaJJvqqXuDcxvGy/8Z75bNShCLToRK4+LqVN9EllxE7CS49OOrXm/D6eUZXfdeLhs9jbBgChCFX2ScecjcaP1J8CG8ml4qG5taX0FSUguubYUQrNarrIfiKf+NsO2AAMCW/yf+95F36cqR3/4W9En7Gk8NMpO4tGjlITpSFcBdGy/NdENanlo6SMD0Ycb3MmMr9I3ewImPCZ0ojv/HLB4cUJh1vLF35AG8OL339Xx8VQkwoYJVwODKG3VJUFrVAPGCKOks+7jAYKiM+JuG2MPMpKCwDKvWF5VbC8LK26BQelYUDg7kxHyTppapW8VxBmfl8Z7IxcggHOvMHefzTfQJ+UvBdGjL7tWa965Fo642i3WDnZ+nHZJ6yswFzIOvHt7J8ondSD9WvyOObRVQeU8blfQmtt/djcqqEycESylnG/cnCwvL2dDeXOsk29Ht4OAn0IxZrcx7SlY1cde/x/MsZ6vRnhA+jAR1etDILQxDdEnjSmQ9aw8ZytfQRQFhGsO4h0DqnkPjxO2zddp2cz+0lEuoaOHNVgznkU9jbheDdpHEUCshtsQapCDC+AnF3zs/h9MLz8u1bYyzvcfB+N1z06Cm22oUcjxSw95ESgZ52IJ6VNgTYj0i25UVqRqMiQSVu8UwRVygKR3lAHlktimBoJxgcFg/e2J0JbDbhfUO+WTh2gGV0m0jYDfC3R1tIR+qnATpHwhSXMq7XPzvF/GKqkTgpSv5rqhbVPNDqwVbucRlA0q02VgOzXRHY08IGe1ZDYBQO3qtxnlmBXJxk5UBp9Fp/k6zMl9El9sCoCOF8xjJlGjxMUYjVRvIWdB5XgR+/JgbQApVIjFsaakzWvKyudPsH4cBzPmZSMnt/7s9JyPtzaO24N2TKTGNDVSSoX03Lrx+OYRTDAVbl476uKSDweKFoNdAIr0fqfzsA5GscaakB7cYvIVCNZermgcdMA7OoKn2V7s40ugFmaNSBSYONB1GCITQ2z+D0j0vjdjvphqAYhxw7/T5ySLcuQQs5e3eHR+zWX9CngB5BL30Bj3UHUl9Q0ImnwalMS2IX6J2QtM/AlNwxL8kEcj36e0bOCQDfNmLc6ajJARUrm1tGOXZpxxLl0Y4V4VFM59jR6IROzS4jvw+nSEsUAJWqZlZNFtixpalAuXPqngoGc97xUTpjV5yWgG6GRt+0TEL+rDKxgyaBLzxanyYRSksaJs986UrnA6yTURQ/gILLUWmsTAu3huHwvMVSKsOIZgebUbvMYze4miDy5SfMw+tafu1uunng9fRc7ZTMpMWo0PSfJOJxZydg/BU11AIihKyM9COSqNasAD7IyGmQ+FIKrXugyM5JJXgWTCDRXunG2F1z10lkPwFZvY84AXB3xWbYfKrXjP/ytPwwAsIyk6U6uwWDuCaHFwLY0jHL2COsbarAFb5sc7YyoJRWkLQgjHqha1+W1wk9nXD1bYesCv65dWjO9yJ2r68aE67XMeqX82qUhRT/2dIkZoRZhsRpVNZdaz5wCBMk6AQDjkh9tX0YkccmYBt0AnAyOQ0c46GtZD6NYAeBUQIogK/0KqtMlN42YcxUQFEhOSDoXhh3znLy89y3EuHHiE+PbJ8LGJecqgAYq+qEpIbwn45b+TWeSzmMp7+vpNTPjHHq/p5EsgtTDwA8KAjrwwkjWA1pARFzLzyVSXYrRz/von2dODcS5SC0BwxyM4iNtrdeftlwJiEHN6+Wzir4rve+sVlT3xpVSM08kS4O8LD0qzQyol0SvVg553XRcLoCfY0nJGPWytGKTpCSTl490r4q0AWQhSnHO+7L0eZDP+aGcDZ+wvflIlxuDGfY8mXvbjGA/LjJFt71QYU2wAfQZVq8l/DIAsPro8cUFmee2CIJFRy2ZLUtQxrCc1/08EN65NOPLZSdfOi77pGJAohKiKRrcGCAJ3+IcLfyTRoPfj4YO6IOwnIFyW1qJ87uCcts5XzuXZkBOpYWW3hUst6VLmhR5KcdIbrmiozAd9LOOV6Rdyt3yO/aDrWjcKTCif6AnTZ+89+Qrh2xxtffXwFuSf8+VSp+L6wHQA1z6NXMoxLMolwG8dVkbOWpKBZeYZE8l1RypghEEb6QgiKo5mQEoWrEESKNCHhjabFk95MQmK6JGHbTJzMKoZ75j0i08lpGcrMbLZT8pgeOho8kYW346RRtLoxHiO7bfizXKgBV9iWKHHCtrP2ZZ+nmAMOzeDa77vnE1jOf8FNsbjnSBQULyMaLlB/nd2Tir/MN9mM2GAAc9Jz8TymGgKIq1UN1J3wsAy4sTDq9ucHrHh2QZTi1jtXwBelIRh0NebqIV2S/lWpFgxoHOgZp3B1Te9ziI1Du+qxAyl8kB6fAZvPjVYOi5HVhQsR0jBiCO7eXHgZY8xDuiEaOf7aGxQAzkaNycGyEnJp1X1ZhzhQAaTiLhOC6Rchwr/T9wljb2lTofuwGy7CsaysEIsv2I/nIJQd7ZMil45qfwZsSuZAyEg1Wj4JQS2pswmZPDvd9f+7sb3rafHMR7NHnXIIZZjPawqtP3UDPuFRujPWcqLwwynWet/HoRQ8p3g7OEoOPZ0aYlfwh1zaKddT+PggZZMIjCgKxQ8XE+pY/Z3nykqwaXvC6dagA0SGJzDLfZKTad88F6arkc6TpfAF2uEp8NAuwpYq68usfhlfUX0YBBXA95VzXirEwv9NC2QCkXkWfFeRNVMz6ES/cwihtPfRqNbojmAIG9fLXDEpVA22iImoEoF2R9MYa3DsiJjjH042fPfV+aT22e+mNIFgR0SiKq6loY/Z6DeDqOqCzvdzSYe1sm8p6ycQ2J6XNysx6gEfslKtRE8zPYJl8s5/TFW0Vl9vVZQoeJrlmqKNo/oProTzoTdSU0tBvYGOukpHKlYuM7lYZQH3Sf/IaSOqX08H7Ktoh8OY4yMKnxuXaQJOcc9/GO9xzNZXSCAbDjEXYIY8wikwAoW8v8KfMWFSPGz8QYf4btzTe6Ox2xm+92NsL63UPblL5x1/jK93HxzWdDZIpqg81g9yccnyNf/HlwAeiOLYwTdkeJ8/0ycssatysGUJGhGsthgMlgHIzd7o0jDdCmHXnP01dccnq/1iB10rcuaIAcpHwkw5odeU9DZNZkFEdvu5yHaCt+V72sPochm5hcfq4QnIiTu+vyQQE3DbvJ+eWWNhOqTFh0lCUfT+NJbpr3dLGer4FNKNtJPfuJz4rPlUnkSTVpP3OStD6J5fnGRzNspCaGa2vf6r0PAEiMqUaR6jaDMOlr473TkFO1xPD/h3S1of3drZcGjGPerEvGPuP2hhtdmf0+iT956PtpCcCKvZ90vM9Uws65cqPBZnIcJtax0hKaP/cWTaZ1rPjiCyIYHuRDCEukO4l2qR6wbnTHKK3xeuT4BnmPIj42hm1wGzlUtpt0QPzeHFaeg3RGrYOMSBN4y/UHTlv7g0621XIyUZnQsLTWviQ3zLbMYzgld8hVwsANq5KEfRQGhNpZXU0kk6HPjtfmZ+k8bQ1NY64OPEMmEu8daGkcPVFu6RF9usnE4IYBjbOMj/Le9RAhzDRcFcN50/lJ4y/9u7vpO57vVL+N9rkY2WnF2tFs7bkXQtq1G2lmDltqpF60briJVGvk5d3zxxRr9AO5XYYl6wqWn8vHP5mpHR1b9Km1yfZQ556BfshY6rnawfHxA7Npv9jwedIP8aA0sY6fTrh+VptjicZinki7/RrfYV26RX+M7UBTGpxLH5CyzBwQnaCu7rCT887oi8v0MGobWkJ+TyOoSE4dcbq/ybnIBevnPvLAeZ80iHtjfEKsej/DfZd+P+po4j1szu1yb/LcumzKuwFx3eQAACAASURBVDNSN5kUUtCSaHf8rnv3MdyDl9DhKqoyOtu8p26cqlVsbkKvO71bm2cz8LDW+wrSXgnl3rwz05bvAXmaMu2/l5gGYnCp5Z0/H+6vv2emQINbRpbW7kDTwAtuqmKwsjW4m+vuf/w625ttdHWbpVtKlk/RY8Om8dPzpl7NvWALkYfl3/Nn8XlSCiT+a/DPpzOOzy4or0rLfctZPo+VWxRvc34WiIX82YweAQyJspM+qBjKtGf5HwPmkFBbR76xl0OP9lz66JoVAeSPuyGXwZwGvP+/yVvA+52MfftuUnE4elAJd5GJRXNa9B2mNvGaXCXo6sORiBpA0gHU62rKxnQSCuUxTB4m51CjX8eMYzMlwv0ahx03mvpr9PDuiVbIPtI+trEr8trsZ/a9YQxvnhFpReeEk/f24Tnv0mJ8V42Tk/SzAYlIVRer0WnAGAwBjPpcjlkFDTUMbJzPGGq84+RWCuK1N7HZn3X7R8PoikiZ22aJsZv9fesk2zjT4thNVFqewofflfvdJLthEh3+H3HfVx/d4/jcmhEkb6aDDegv+0MIERhkSPwbQHqvYT4Y2iF8VVDLRvqlRm8aQMAWrQ3HOE+O0YBmY+VYR/fCx3eDZ1+usefESsphMNLISWkwuNqX05aT2ypIVr9LGiFugwBIZHjDMjt/erJ6rV3XeXNPA9Hvt0+A6cBbiHLbPTAqzw/etLmOjNrrz0BApRpiTlI2Xi+RvfOY/jyGfA0x8flo28b3Uz8T+ksde1k9YqBZbJRflmUER8yRDaQBdUWq8bm+D0ZaQbbmJBPFwt42H5dKh8l+VO8+lJ9I9YJNhm0OYpgRqMpO1OAKOt2lK/YuPSFnDRceDLSVrCyxR8Tb4YDy7BbXHwFcHmp45IBGxGs+GLDMf7szxQpqS0fRsabm0yM0OPtzWmY2BOUjYhyMJwbPd6oJBMl1yVh8loNKnpcBRb3sQFdoBAqnnSbnm/1Dztf1ujKJLL2NKcTwfnlK2calvAFlmhtoIMSgD3Y7EF+i0aU9s3pdG/9Kw2j9OkMyFj6P/Fz6rmA4R96P5Az2xZs07FTyOWW7tU90ApV+00mY37PPVO43TpZqiPrE0KMAMWxzAvTBcbxGlegyTQxTWtZBDaQ5VGZQpFFktVWodo0im+qbtXPK+QCRg1IxsTPGZKNTsPXzJ/iZHtjebKMbT3Q3TZsu9SOUb0Cle4R5HjrNlLLP4DSb+eO5ZBAPkyWQsTIw0B7o8QB79gI377e6YClZotde0MFQMDAdWePgml/kRIpR/tvNm8g/uD8NB21tmpxURqMVf1IVEddWT3RDhxPMFi93qjMk6Un3lKPlLhAEmvef6FfaoyuCNLq8trSFBit55d7WNHbhgFMPejrJ+BkN02TcuS+dj7nsj3BtNwBHhx9acp4MgGB7qIeNe+3fe392QQXV6wq/cviVw27WvmqJ8yCMbrnboRWgE6DlO6ShwNmvaZRpiOJzot2JLlHeOyeDMPDcR9/RwdDKhIBU1vD5NNTqq1aBmGogzukeNVcD0I2ko+X8KOhhwBpmPAQ7jcmpkuZQILdHUzLwiePjkwK0Htje/OAIoMtGdhQGs0Nt4GinbUDNfJjjCeW8dXveWnZnt6wyuqKFC9b28Px0gj26Acxw/bTCb5de02sVYwZ0QwvPQeawwchyyd1j42VQog26jGpiXTQal4lr9c2xI6jJZSUHFeew4mE82Zi5k/u1st0F3Vt+8BEZx6RTVYcrg96ia4tjg7pmJ5EXgBHI+v24Lka2LUNbMRqQ2RGp5XxScbA049gE8+3imQCH15S+Lud2v0NbEaj50Kp/2PWKz3/hJd6+ucdxWXFeF3zw6hFuX13j8uyqPRPy8mrkaPzl/agLUGrv9wEBGzbPus/g6JOF97bSt5B9w8l8mqCGtklfD88j9+PEHQ+BFIAGG4lT29c6ONaG7zl5r2HI3cdXMwModoyqfjag4/BM0+hbJAdigMdnJHf/0TC6l/OAQrU4JIDhc1djOke5qLNrbetNzVjm6xootaFmO9qu5ne4fhjj5HkXRNnoFTihGd1lwfX791hePsL6+QusNqnSqnltlykLVSzj7DwZV0d38BjGaKaKRGG2RBJxR8vSH5wvkVA9jCkch9+JEuOazfi1470gcgSL4Xrg3WPYLQpgZ4MfHfXoWF5Z0n+VuXGP3pLc0MBzB++JbeaKurztDLNdkIlfnBMP0CeZQO4WN+kHRzlHUc1q6bAqNIqkP87MqwuUsxjoqwqGuWY7dGKjcbNGrVyOPhhwLwYElWA3K24en/Cv//Zfwc9dv4s7v8J9PeLv3n0Fv/z9fwzfuftiC6WOe8t8Eorw2V+OpE7K2bDe+PBMeXzSLfN3fHZnmVg5cXFlwexhZxvRM6QPeD4xuizj5IZAlmFItSRPSLd8XVt+hWJNLrausKvjJhiKE4JXQ1n3jaGZwa0AHjQCRKmwRpYxRcDcso5iwVA5YgYbn2L7RHrBzP6Mmb1nZr8in33RzP6Smf1q/P+F+NzM7L8ws18zs79hZv+sHPOLsf+vmtkvvnYLJdhgdmBpWsUHHWa6aYrI3Uv18w5hvtKW3XPPTrhivZ5baAMPz+5w/UGzVH6IdziW5uo8ArrRG5xLfPEjdeCQPhBAVplgUnBKdOInM1IJjzp40Oe0hztIL9sggy6PJ1CicRbUNURouRhsNbCuP7aRn6VxKYKAF1EysDuyzWKV2XYNQY17Tvoj9mFGsWyfoGamJ0wjc7a2ZD6VfI5DXxgNVG9X0ieBbv16RXnrjH/im+/id331XfzW/dv46y9/Bt+6/xKerze4XY/4/M0tvvZTHwHXqyzdtxF+rZ8kfJuGbu1tMnlO2laddPsqSAyubjHxD4nMCQD4PvMHMkmpkw2IyLEwuEAHSWo0OWapCFKDG9RAPvMMsPGtMy25XNvfZ/bHZEVi2peWxDzrsX02kNsu8Rr7/FcA/pXpsz8B4C+7++8E8JfjbwD4gwB+Z/z8MQD/JdCMNIBfAvDPA/g9AH6Jhvrjt8gSfzhslQIi8drNl6BnmdHqrMcl8Z7iaEHKuu0FSVgZK5m2+02e10+n9sCevsCjdx1a1WHIAwv0l7JyIHTDk+ik9kGsDq9MDnOy5Cmzflrx0YEDDMER7W90/jaMOg1wJojh+0peWjhWUgZEe0zV2CaC/uzKBXlu8NyGXlJoNgLWK/S6TBpZgiaj8azfn4uhl7lpTuCSeRniOl7C2SfJYXhON/TKwqSI7ltiH7svjdoJrj6vSwO79NppzWFWgaMDVxXL4wveefsV/t1v/O/4177yN/H3n38J/9v3fgd+5elP47v3n8f790/wlZsX+H1f/zs4PjpDlRpzmSOgXw+OlheZfeyyr+Zx4PumUZEaQg65Dp+1xzHMQ8zP6Qzl+8NVEPfZ9E0ELihlsJSeWwHo1B0PFY2tr81IJwLVOnOaDF0DotKBtnbnmdiBTKS+ilNtOF7u4R9WEnN3/18AfDB9/IcA/Nn4/c8C+MPy+X/tbfurAD5vZl8H8C8D+Evu/oG7fwjgL2FryD+uDcPfWkadxSBZYmc2zg9JwQZdrs6me98/oHDIKsCbks+lv0jr2uiM+xMefVBhpzKoFzZLc3UyMbrLx+8AoHOD3g1nGt8YJLTKYXwTISqSJUIBtsjQMBjm/HxeTsrAGiLL0K8185D8fyjaaT60T42eHpfLSaEP0hjJNTb0B42F/L2hRzwmxdpLtw+IWtvBemRqWGSy7I3tkxkWBw4Ou16xPLrga198hm++8xTfv7yNX7//Cn7w6jGevrjBu6/ewgenx/jC1S2eLCe8f36CWkvX2gqKHDbH9Bz3IdmGh9XdDd0hubPPcC0ac/5q48m1rfsN8W689nJX6++SJ3cbABX/VTlG+VnV6s66XSD3HW2AjdfR6hGOcbL4FNtnOwr4mrt/r7XDvwfgq/H5NwD8puz37fjsoc8/fvPWCX6+NB6VUSOqDlgW2PEwVObllolpmH9TVQZDJqLgYDXRhsZuL2KcgUEpkWHFKuqWyBZfa6aae/TuHQ4vC1A8qzO0/Xi/1pfbBahHdIMEDJKnnp5QPlfEs8q9Hrwhq0BrWUWWWbA0d6t3YwX072jQsmCkpANUh0tOJmxfhJdqJQoXZOyHPE2/B/YPJ4djD+XN1QFvj+oAVnm4tP1q6Fw12xqznHUFBpKuUK6uXnmjEkJKt6nJFsc2bp4d3q+xSRzjaNxt/NhVxeHqgrffusUvfPXX8c99/lv4H9/7p/AXv/278fzFI1zuD/joxWM8O9/gdz/5Ht4+3OL/+N5vx3p76DRGBitwgh3fC+1ThmnTGA4o1+SZyuQxOOxiFTAoSsS4JmsQemKm4vxYXTgAc+/jrpRAueK4opHVHLvFNnJRS2cmwpHWH0BWGjbryLZ/2f+XrGYAokpwB3cD7RH9tBuO/BrbZzW6D20PzYcPfb49gdkfM7NfNrNfPuN+X5bBJcfe8n9Gper9zMzxI2G+oSfmfA1SWXhv2w2W4Owtxxw+usXxabzMXMIpAswTbj9LwEJkOyNN3Zchom7hxGgv5aYg5XDQdJ0474gS45zCByfSEfQ6HKdIRyYP/p2Dfd7knGmkoX/LfUzHa1FP7kS6RieIYTKjEWF7qUMddkDnZ3ldomeWJ0pNdRgz6naZ+3ZxlGPFl955id/xhffxjesPcfYF33n6Dj589hjrpc0C62pZYfnd+7fx/OVNR9UmbdD+fMijuTf69JnEO6JRZmmAp1WHXmZzuSKfxwpp2NfRZF2G7fvHbc9g6rjVtKzz/fDdfCCwYUiqk9eTsf2QTje1wuMq40edZezdoA0Q/78Xn38bwG+T/b4J4Lsf8/lmc/c/5e4/7+4/f7RraIgtZ8VZkwugk+wakiup4nJGBTp3KyGIM7/bkytbFqDcM66kNuKLdtnTCc4kygBwfQ0sBfbhMzz+LUfSAoZAanG4t0FdIryzXDA6vIZosziG38sgsih144Ea03hdNQTZpDXWB4XG99MwxbnTqMW+LV1jt5RMgJ1LcAe0eGSWoFEtLo1tRadJpve3nEbElgbO0FQHFxsRrnDJRM9ge6NN/Ezbmzl+S9xvqB+Axu+2Z2CJ3vxY249oqsvFOr8Zz9CP3qqFHFvgBI4VdlVhx4rlsOIXvvrr+De++n/jvh7xv773c7i9O8KrNZB3rDgeV3z90VP8rZdfx19/95stf7KsQABOLi5O0ODbBZH2FYRtJ7uJx9UglLwGr1n7+2AX24aG7038QEvtyf65GFhBeijXczyOqJErR1EbtQci7wT9JuR0lWoC0FavQv1xZbuxG2GI+cN2rGKEw474YWkry4cmt9fcPqvR/QsAfjF+/0UAf14+/3dCxfB7ATwN+uEvAvgDZvaFcKD9gfjsNVvJAVP7ch6BMKsDdYdXnbehvtoDjrJJl7tRRkwaYJtjtwdNYZ8Y+HLUFy/x5L1LS34js/0QE08ky3pi00DpAym+UP4QQFIUwe0aQy/j3AOX7A3NdY+4dAWdK/qCRbsGaRQwGIOkIdiVO+3jTXAJusdLzoh7+JqJvZ0XlO9K75uB356VIHrPiXTb51QFZEAFL+HoihDrt8KJRpfZHgEqdqzAocKWlgWrHCuePLrHP/Pk2/i543t47/Q5/NZHn8PldGiie3NcXZ9xc3XG0Sq+9eILePbsUX43Gt3+PuS9PTCiN3ZC3qUB0W4ORH8ePv09PJT96w0OWxp4/lowOJ3zVLOWVtOqOmVmht0oNcNgLz7VpnkgFPWm84463Q56Psv2iTpdM/tzAP5FAF82s2+jqRD+UwD/rZn9UQDfAvBHYvf/AcC/CuDXALwC8O+1NvsHZvafAPg/Y7//2N1n59x244PcoRhs4GOHL9K4dt52AURb6+dAofHQDIKM1+018rgV3eDqLCwOuabvtP6ZlS5XWVc8+s3nuHn3i7j9xiW0kLHbNPDzgz0+LA1xN4Aef+d+5xLJrpEl0fOwgzfUgY4QYazlFQYryq3Uo6DIQOaOmCQWb/EinDxWytmkrYhjBZGx7WnEV7SorlgiwlqwRJsgRoTKJXw90FhbQ+8WqHqZULMgVw+UDG9IWg1WcumhXOhyMao4or/uSy+R40A9ANVCY+yND/ajA9e1TXjFe3SgGw6HC37h638fP3V4ir/y8nfhr37/Z3A+HVqxQwfK1QVfe+c5DqXibz39Gt57/lY3htHnOMsqBdJ+7qPzZEGi0horhAHtX3r/5jO/kmCLCkkM1M5Xr7wrPDjJLg3V1mM0oXjqtDPM3NrEkw5aKheIarMMeu1jcVng5/suwcywXQeuSos+W+MZHyuqaus5BjlGzYDD0rldN+GLC4BY2dbmi7HDIcOSfa2w86Wt7Myx3thY5udTbJ94lLv/2w989ft39nUA//4D5/kzAP7Mp2pdHvwx3s2Z2+VsCADl0JctxXqRu/xevptQrU2Gs/0vb3Ma8ChQyTjwWlpZEd1vXdvLs64o3/0+rt//Im5/GsDibRAPqE55o2bgRosst6roY9rKBVgZ645xn3GwEboArMzbkBtykKmkrc30RCJADQdZ12j2ApbaZ4wW07an9KhugFLK6rIwpX4ZsrtmJBw1Ul/QSdj55jA6clEqE8qdtYiyXG2gBZG4ABw+h1h2W23Gul5J33OSCTqEKLccV7Cqhy01Q0evri74F97+NXyu3OL/efZNvPvB262kd0wMy+L42uPnOK0L/vb3v4q726v+/Cye3Wm72tCSSergypwKROjqjF8AMLrQIz+FPiD+uiJkWTF5RLANDS1XCmW1FjACy3cn+fP4MSZlp9HV8Xm+jMi1entnRLrFhDfuDrPSnG2r56quHmyLcNfaYyGWAq8DskI6ziukWG2Nr8MR72fgsubkVo8Yw5E/xfZmR6QZ2o3VMGxzmC7QO23PIK9r0wJy29Pzas5cK+N5Jw6IFYK9ekPHKkfj0qRYlIvuCgu/P8Gur9oDvT/hybsrnv9cwfq4dq/+LDOa0AowRhUleixIzpHGOaVgF4PfLyiPLu3lB8KAOPzSjSX5vLICPYMSOmKt2KDXzNRF5QO5OhNEyyiwUpGVfHk+axcZuORQQmRIcEUaDIYSE3GXcz9fuaCnPbRQFHhvM7Wrg1PH+vl6P7cvLIx6+xBZf4wGTVEluUuqJawaHA3VwpBcbGu04+e++D4+V27xV178bvzG8yZVt8XhqFiuV3zl7Rf47ot38OL+qiHgYXWATmMYZInruQJon4W2mfsdW+SdXeQYovgo2kfVQw19tT4n7UeurszRwt6lgjK8PRfdPFUkMfmuLRqxPa/Su5/+k2VpxjcoOuNngXQNYaiLNcSKY3K6bWKL57YsLYqs1gGRZhbA8wVWHbgKcEXZ6PkcjrM247e/A2EflvZuuqHcI0sJfdrtH7R64R/wZunBzJr3EhCxcaZNocHJ+Q6n7OoEi9RyQ3IcXuMBbW8mSZ9LBOn14wWyJSaE87nNtqXATyc8eu/UQmkFDWxS/aUn3RNtJRlGIFc6Et2UmqFBONsoVywcBPxAjgGGKKdt4UEkqrHgnEs4R7ohHc8xUMISPJDysuCZqS5QY2l0+LHd3icnu8j1WFpeKiHTOZaSKJGgpWph5putf57VeWnsvffZUHAzJobMDJafR7ujn5qPxvFPvv09PCn3+BvPv4H3nz1pRizCtm9uzvjpt57ig5eP8fTZY1zOS+eUORF7fwYZhBA5LJz3SqmbGEs6DOcMblohOJE030WuovI83flIaigjGHlKSX9J2kHVJL6W9hgXhLGM8UxqoSxtnNCxVaWUDuWf/KkOVnNI9YIDcB8KCZAztqV0kEW5GoDMqx35r9Nxt67NsIZT3A9L61cHlrNnJYtPu73hRrc/jLlybwY9aApH+f7BYpPcilY+lW5Q5Ku/M2pNQ5Ln9JGikMiNWt2IGzczHH/wAo++z+Uo0jgANDzx8qgh1eUkV5BynIeRnaVUdjGs9wvqiRYDbTQwoQnPQaM5GfS43KC5HHjBQLcZIVd7LL5dLNs1b6paIBdcWZ7GIuMW1Rp7j1IdSowQC64zpXFi+Ls3PRDdoWdE6/kLwoiIpjgTl/N8XGLHxJWTTQnDe3TgeoVF3lu4JVp99OSEr189xXfOX8BvPPsCamipl8OKd955haVU/MazL+B0WhpCBjoHWgA7dIPab54PDOkcTbWFGmlNrMVJTaIhh8lf5IBJGyknb32C4wrHYtKjEzMVDysnaD54We0sPdiB/CkAUSbIGNWxCIimXiajnOnjuIwisw2IssOhjWVqft3B6FKn0SZAK20/crqu9/MZtjfc6G4N4FAgcs4CNnE5NMq5v37HBBt9534eud7mGOF6Z8P7cejb3dsyZ1mAD57i5gexXzht1JOsKDGNp+s+cU4xkk0NIAPNei00nEor6aNImLer16b8B+iGKPZXtKvhnylRYgUFefkbNdCTvCgHrSkIExEdvKc+vAqjO3iLBHmX/hUNPieDVDAo6qQigdTD0k+piD5DrFPq1ieSVGUQUUZgRqd62qSxXK9YFq7NEVwh8M7jW3zt+BG+d/4CPnrxOG/rcKj4+tvPUMzx/kdvYb10b2B7zR2l1GaA9bmgT168VoaDS4hw0kPD+4JhIu+Kj3gg/E/oAKW3aqS41BWOpgjNSL9Uj3SDne0OINNKrIejy+sYAMWNMjKOywRa6s/pbcmsZHvgi447Qbi5zTkYqFDyCpwvQ4TmZ80y9oYbXXSJGLlUQbgAsKEbptDfXYph/0LjNXeMdaLth9op2zBjAkkx2LIA9/d49IML7L50eZEkKFfxeGaDEkQ7GDUxhqPxlmUenWYcZGHI9VybTQ0cl4N6nHC8eYrJIKQTjpSIGMkeJdXPixjwVg04t7wGeW9E3lM/qWHdSKBK/36+TU4Isz44ka1QG3kCtl8mjdTrHr2pFIL3rXriAthS8bXHz3FXj/hrH/4MTvcHuBuurs94cnPCs/sb3N4fW3AE0DPEVcBXw3qJqs8l0DTYBmzDpFOT3NvAcOtBohfvQk7e+t6tlqsflRTOKSI3qxATg+vTcRWRZSz6jPTBHK4LJK9KSsCdTrRx5euXtSP8EpnbxJneHGdifMkRl51rxrGbSLP015R2L1IB5rNsb7bRdYD62gcR7qzJk858sEqEUAsbiiLO5fqQNFhipjf02jzf3q1cLqJiqLj66ITlztLJ4MPL3y2ZTQhBk3okmsnlbucVh4GVHJ01o2Ce5X0Swc3NloEGIJOv08mUtcqIZLwP7ETii9yPnqsgkoF3pNm5wrY0LrclAySSE7SOpBSlGe+39Hsnv8mkNl2B0VFxypji/j0CVcjZMqS4P0SiH3TVRXCWdr3GYHTU1ZIeaNRA42x/6tFzPF2f4O/84KvwS1O4PLo+4e2bO3zw4jFOt02GYdGHXq1xoGtBPS8ttDsmafaXObokTZQnsyHuNFXPyZuT4s6klqum0t8pvnMMdNlzrma/q0AgjTWfp0zgpACEwwXQ6LgIqe8ZxmpHr9Tm05nlQJFAoPacDEPKSGAMKwa6TaCSgkZZN6/tmEP7fFlqX5F9hu3NNrp720OCZyJccjGvfTrf7j9zvLLvJ4YMk2rQWmk8Ngh5WwoOH73C1dN4Q3Spu4M6krOCIBGXlznbg9FYxk9yaty3oOUCKH1fIlpeI2kNNd7xpfK9iapFpkRjPhS4zH37fWYU1KytBXoU0zwZOI12GAJRa2y2RFozcrFhAsi2of+uK4shPJaqDR5/bMizLF2DCkeoF2LH4nj8+B5fuXqOv3v3Fdy+ugKK49GjE26OFzy/v8b97bEZ2HC65S3IpJbP4VCDM52+0/uwKUmPIv7pmO3B8+fT3zlB9r/1vdTrDasZsF8Qk59cjytSlXTm5+OY1/Lsue/cdJuS47hv6YA9O0GjTDTMcywLnJkD+a6/IbkX/sFvatiIQFUiNulq1cjt6nHlmA1CzSXJ0nW6yh/pvnr8TiRbBmAwbhsIpUQFDgfY7T2uP/REiMrFzrpYABLEMI0NvgAPEfs0dEIxWPGWy3Wh4ZYUk+LZ1iUiZUFqnDeD3WNJWtAbyQEng94uHeF24TxScqbtYAhrxvKHUqBeRyiwY3DgZB8FYs2lroyP5Di9XZMOtTTgifCQbeAkYmKw06F0rCnPyqUz+zDa9cXHt3hnucXfe/FlAMDhuOLLb71EdcMHH73VHJ3WZE+DTIxb0DFWvCUw0mtk6kaufJDPeph4wUmkP8vhXZLnpBNvbmI8MzIR4zF6rf4hupyQ7eN552x8YhgHZxibKM43hIbWCHSAptnVJlexFRx/3A6H0bCqY70s/XMx4FZDDujYVzi9xvbmG92Htj3DNxvXvW0HKW9y9e44y4Z9VXa2dy3KT6g1tKAiqG5YFuD+hCfvViwvFqR0TFBuOznEeNnDAyD+Hv7nKWisLgaLkeSOnmdXz8GBJgazlwzfnhs7YwvAyO2GYUsukMmvxThsBn62Y/w+Q5Pl2kobsE86L7ntn/m8OsEpsnZ5JoMki9+Tlz7UZgR1AjIJvQZQFseXH73AXT3i20/fAQA8eXyPz13d49X9FdYXB1DcDyC0vf0Z9PPG/0vtkwywUQZskP/8iobxG9DqtLGmmrYh6YZcIVn/nP26h7zJfcsxn7gJUGm/OIZUivw96YN4OHt8bKJh+X1WNGjeBVKKmmVM7ufTRhdvbu2HO/xHsKl30aRI5BS0MMyA/YsRlZJKmBHq3jKDuRqE393UY9Prk+MNgzzn9+1Jdlo7vFY8+t5dl44dOycHYGMMdbmovCmX8InQBquD5N9S+lMBXEoLA+bSvkSSbe+DZBiMkteXvOe2jd7RYRjXQb/rXco0e9mbhhQbA9DvKS7BUjUXg51aySEUT6mYTlqDpIw2LCYAlipK7tYD/a3SXvTP9RYzUo7ysARJIwLuizNDWVZ87niPV0MWzwAAIABJREFU37j7Il68vAEc+PJbL/H8dI0XL27aOdjHUXxyQK7Rn1l93NBrwcU9wbxnQJtShpJnzXdI3pdeGmns/57Mnh+0n04bxZiQ/uoHY7u5dQWDtC2pg+BS5zSq/aYxKI5yrCtyNW/BHZoThf/v2IbNpqvnNZKcy/mtdqTuy74teJ3tzTe6H7c9IAdrHz2AQIGBl90rvZP75AxZt/vkw9yS8rvGmSJw93iYK47f/QBPvlObMZmr9u5taoC8/aPOkBnRKEK1NILWBjUjxMJBNlRV4OCk48PkXPF/op40xugBDz43IrYqTQxKwy7SLr1PYMhcpp/biohI68YyLyU/mdmMy1qPtILJB0NQoqBxNTSzE1Dy4kJLmjuCfw3EKkj3+vqCq3LB33/+JZzvDiiL4wvXr/DRq0eoL45gTTsL+qBXw5D+G/oUY8Y5Qd/qC9DV0967pVnGxmtZP2Z+p4qe16fjsaUs5H1VNUj3V3hHlZrDFjSyobdjknGJXGOVB72eF3wy1/pQ8JN+lvrdOk4Acp3Pur3xRldzaAJiMKdlvkcpZ00onvsT4RLlMpBhztmwufiWq93jeDNSbW7v0DYGbXvnqarj8fcvODwvA4fYr4k2iFcZHOjoIvcPQ5hcqMi9yIkmbzy0u3OqXe+JPnC5DJ0HEmQACfIlgk1DFweVC//GwCmWKHw4cLIR5daDEyyvl9K64F5ZJFLTV2o79H7gNtwHI8jI72ZEFh95GoW+Ukj7QoM7GVeTJT952XKo+NKTV3h+vsH3Xz5puXQ//wLfv30Lz188yhy7SQ0w/wXPS3lSoGArjnopTcUgYdAZEEFFA88D6Qsg+eu0l6GvTs5+DgqJviBC1n7VMPF8J5KOshEIyIQ+rpDYqXU0wDSAS4tQyyg13YoBh4MY8ChMGQZzCH7aW9UOiqiOojHnZgAyQi6pLscWjL3m9mYbXQN0qQ4AA90A9IezrvDLOSmBzanoedwLZFDE/IDMLAMh1umBKOrV9uhGLpfn4pLlcsHNt5/h6qMYIIKciDQHhYJbfp4Kgam/NkaDPKHE1OfnBTnoky+d1QAcwDynDj4QaY5tTsMdy17mZ9DQX0qeWtu8o9O1/wBiDCpaQU8agOJTMIS0T1DXMMKJbN06upcKCc0IccKRc8wI+FhTk2u6lOf1gZSMLYeKn3ryDB/cP8azF49wvLrgZ975AO+/fIz12bGfw9AoimrtM/ZHCVVEZCyz4o0eOtTMUWGZf7nz5YOzsI7vjaLOjCRcx3dN01qmoxHyXDLPA4b3dD+fRe+XpFIAZD5dDWSYZVuh1bWsnTYZ0AxyaDZ7qByRQGjy91AC6hXqLJuPG8Z29cixK+/Y6+j/d7Y3O+ENN0WZ3DI+em93RR+jUbRi8BXdiMusarmEGR1nXvtDnqtODNVJudFpFm3ZqB3OZyBKQtvdCU++47j96gH1psJNEmYXZhlDR4oFKbUZkoBzgLHdgm4GrzFT8iU315BbXS2dP7MMTVbnQQOMXN+sWWRym8HoUdjPJbAkjZmTpNB5lQ6aQE1abDIpDz7GFZ1P5m5sO40Qke3B01AwXDWTCmk4vSH1wF6QvLsxeUvcH6vF0FBSgWDFcfPohALH+6+eoJjj82/d4sX5GrevrvNe8n8f5WL57AwRrCUzMoMoAqS5o0vb2G/cNDrPx9zNifRNuk5oA6Wc8nrOdyHOzcCUYdKX9JjSl34pGzXJUJnFxSACjV5gzgSGB+/ZAZ5vRzI6KBYGPT4f2s42qBpqtxM6uf5kqheawZwNLlHq/DN3NrlVRoft0gmRT8HMgOOxzaR74cRAVImI7ydjm5KziLChcmGvvfV0Rr2/B84n4PYO7/y9W1x/WPoy8mJ98IuxnUM555pfQ7SRDDLIy28rUj5mi7ccAVe1SchY5bdOlAbPWTp6ZHVbq0CVFIn8rB3XEbKFcc4gjnDeuRhdpRSTGw5jSwfaQIGwT4Ce7QwQVCcP0WmUidgseW2mKWRFCs4yKjdr1SBqqwCxxI91wwu3jLZikhu44fOP7lDM8fTFDWCOn37rKV6crnF5dQhDZHm8C8c61kf0NG7D5Al0bjf6oh5pHNs9WNx38ttATLqSqMba/qxxpttQCTieJbnuIQgDcg6+o2kIvUv/tD4dtznwQaVd1LsT5R4OrSIE95VMX2k/Zycaq7hoock9amBHrdAQMAePDff7k+9I2wlE2N1NVQPCxwxBE5MsLKNb1rX9PvOzgnB9nbjdOQJOMidpW/aqEvvdPfx0wvLyhKuP4rOj9yQ44qTIAVMxeIGJ9gZUKQqGRCVSnicNDg0YEPRGHGDew3R1ZieaQj8HqQYtVDmANXFEDahHJgIOfpUepbHj9dWTHhwsnWxqLJLPNNduEE8/jVTPbgbr9EpKxYCB/vDroBSi/U6Ea3HPDlQawlhNLFftBN952WRin3tyh5fna7z/8jH66sXz+IH+ycCHlhN5MOh8/JI7t0f1dSOeVMEFPRvY5BjchBLzM5PKzPoezrZKgWMZnyfm5wn2tfV71VI5wKjLVbTJIpU6G1GbX6z3ZdZTUz0vXwI2MJBr0htMnsMOm/YXY81Vge3kZnnd7R8Zo2tzx8VnyfcqLzunZ1SORo8jzxshulrbLGkJHnNsa+iUkcX/s6c0RdTkpcRwU8LGumr1/h7+8hXs5R0e/aBZEY+aWl3mFIOPXJ2mFaQxmhQDIw8cn0nOUy3nk1WDjx3pMEw3tahADmKPRCclzkf1gPKryYvSUHIAVjZOJgLEOcIRlNesfRmcUWtc1sugtopWGISKBIr2E/V0RJ50As/JYwJNZ5UD0onMWnZw4HpFOa7xjJGSuPzbDfW8tN9Xgy2OR4/vAQDf+/Dtlpz8rRf48O4RXj67aSuMCHJIxQKTzRAR0oaI8Uo9L8OB6ZNlVWWpBsx3p1ysU0Dsu9wHGc6cK4Uq+5nU8KvWAzHiXAyz7ogaeQ9zxjtYTI66IpMtU60SJMU4GRxosZJMELWU1vbFWxdV5LHdeNr2YsBYVVyNf/LJW5vjtAcVI23xKbY33ui2NG9LGiwAGyT7OtvGk0nK4WPCfDfSk0kzPJx3bHR+P5R6f6Bddjrj5sMV5eWSXmlKwZrTKE6784w3Gkl+Jj9DAhSnXrYbjUQJCxEuOvL0nixcJWvtOAzoN/nf0mVoAJIWaZOBQfMmAMilLg11vfIse1OvPOmIDLoQfnF23Cl6TsUB0WsaahvvY9Nv/fz1xrtjK+4xH3nZOUmc6HB1wds397g9H1HXgnee3KK64cXtdSvNw/7b+5+8Ox/bnr6VzywmqcGrPvwhNnJq7sytJlcev2/apbuqw3H+3i1VJpvr0m/ggA0a245o541Gdi9Cre8kl2euBi1KOduKPRUSlU6pYpg7zJoO2Jh35CfU6KKUhjLJ7QLdcKqmNpUBa89CD4xi61j+52dTAnPlbvPasbTw8yVoBe8TANUOw/XreF3OyorGAaDWziXfn3D97itcv18SwWSy6doRnsmyebOFwaLRSFRYvFVWkBUYPBARuWCiwWONJWsYeEqLNHl3JKUhCLfVmmEsfR8vaPrfAxGQd1RZ0dUMNIx06pHvva7wR2v//yDfKYcYiV6SJ4y2z1KmViW3NbgHY7QGDBxdOgejfQcH3rrArtcIZmrGhEUiy6EmOKLKgOP8yaMTvvzoJV7cXmM5VPyOd97HR3ePcPv8GukczPcuDuL/q40aYE3TeKgYqnsEHaXRd0Ngg7cacmOuhHZ/PX8w8rnSkPdIxM7fDxNV6dcaec7+TJPSmeemRNSefG6ThcnYBJBKhlQcVPjlssk4Bmlec4y24KN2Chl/de2GOPXydVBAZVAE0GkH2pmoBlw4i+xMEK+zvflGV7aBE52cWJuk5pp4BhgM3oN5MGctn85kM9+jn0vmsQc31QqT96WhPp9Rnr/C4/ccuF3CgPZxOUQFAQN0MHmpcxwLsO3LbDkHl4X84W0R7UoCnjzfACGJOjvC7VpJJAIe2izt7Ym/PY1bjcQxTMaDCNyARn7JeVUaN3PF2XY9JpxHirpGBIahf/3g8KtmSG1GtDnjTH3Hv69XPLk+4eIF62q4Ol5QYXh+e93yGiPGPo9RTSsnBpWIudS60z42mYh20XDvh42ahaey/v3QH9k+H/pVvxeRQ9I6qWrI98GG1Qjb1VY/MQ6KjDsrfdzNqVQfMnLe79+XDm4eXIXOW/Utqs1zy3eRMN1VAfIZtjfe6Pq6tuQxVRAmMPK0U0CCKgnG2Ov4X3SzaihtKcndIpLnuHI83G+OC9ffiwEM1MjvhJbIk7RZ3d3hpzPs9h6f/9V7PPpucMc0SJRBaQCAeohcUIk6MlKbicziledkJNlqzQjocrCgIcxYjrcqCoJowhOd5XOCj0ybHGn97GStJBG5PUGgkNuox0C2j1f4o7UjvOQ30YodxgSh6HWgWxytZlfpDqWaCL0fk0ZKHYJxK4kWF8BvVvjjC+r9AlZTzkYbxiW/BHagGt56+xZvX9/hO0/fwTES2/ztH3wVr57d9H3dUA41gh10poAY1egLKhEWT+clDMChth8eKjy48uimwSbaXzKZMCJQHWvK8/Yd4784Z+trNI5fV/6192n6GAQM1COwXi+9YgT19YcDMiPY+dKQbSgV7HBo+xMdh3bW4j4bKpemBiWRKSA1sY0ZUFrdNacCglQg9cJTnl87X7CcgFoN9TCh8k+xvfFGd6AAXkMXt+F6Z2OnCJjezzTgk8dTZ8CPy9MQ1+llg8TI6jJoo+eNc53P8Ls7XH3nIzx6L0rF0M4Xyfc6j81EGXMnTE2bkRrRTYTh0oBwmYyDlMoZjqER6AZUudPR829Z0pvH+2HkbrOY5MFhV7U5qiQ6KwMEpPTN0AnSL8kXE7E6RloGfbJJZK5ok30VhtmuKpar2pf2e6+eb4GTLY63b+5xKBW390dcHVYcy4rnL2+A+6X3o/Qp6Yq0CaWHBJsi6MnYmwa4yGk5EedliHIFabbrCFqVY/sFdj7jOWe6Yr5m9G22VlYGecghKAWG+wKjGkjz6LK6xI4NaDQFn/H0/czT5kFlVDDx2vq5KhzMgLWiXPzh9+E1tzff6BKN8uGovlYiWjLr+0H434fCfPeQayxrnBzP/JBC0aAqiqE6BTdJT5ftW5ae8GZodyyD1tpKgby6w+f/7gnL89L1jsDo4EnEKgO39IHFAZQSIW5DVipLmVfLfYDRgLh1XhkYIo8SFXJQBfrMnARHb8oHMdr1ylvl45vaAkCOkVryunPIfm7JuhNREsGxfDmB7+LpRfeCVkZdcynwxzBOTmG0yemWc/u/3rQ0kVbj95vaIs74+hwi3eJqvX2hIMicuWzfUnH95IRDqXjv5Vs4HhvN8O7zz7VG0DhyIsk+DSRNOuG6ttfVkNe04q09FhFqvMfS+rJmNQl06sjRc3qY/A5MjkU+KCJcF/TYJ9B2YJ+nyNNT8ZGrJ0HcWXGkeK6OiHxt9eYrcW8a+WUBLpeoXxYAqJTu+6C/5HiAHQ8Jxsq55S/J4p2qza8r7NAQLdb4Ub3u1RF2fdUrBlOmRn6ZDvHDATgsre2Lh6GfbMRrbm++0QW26FINF4CUbgVXmpFlAx8kCEHjrOX8Izk/0wGl//B8qvvLpkwPIxxmg6GP8s55zLq2ZdTdHa6/9SGOL2306ANbA4z+96ClpLHZe7L0JhP5MOsXRflK6hHNCn+VEjZgnO2rdWdXOgGlfUcHrirsem3StEP7G4dudLHagPgY9TU4nXhfYQi6ISHkbfsaMIafioSJy/DU5R5rFlnM4Idjg+5eLVC2gXXWULzzsdEHRJ+2ON561GRiL+6ucVxWPDqe8ZLRZ+Lo3PDEhtTilqs10kMi+6SpJiyEMTTebX+LwJZUnKiOO7ztKbsTLrzvJB9Zl/nxeasm2mT/Tn0hs5ixFl0+fA3EkGu2/ve2tK81QnWXsRT7kH8hDCV9I0FDoFgYQMDKjhqieh97Go7PUkGlwI7HwfGd45rXzfaU7CereNg39Anbmx8GnDws4KKrmyT4yZs60GVmezkVinW+dSNwXsZ9NVm6hAr7pctNfKYf5Dhfa+OIZ2nJ9LeVvryyF6/w5NuOu68U+LHCasmIrHR45KDygWcF0GPfhf8cJGDaZ8y5cG5lse1Y+yA/OPzQrlPOOljHSKQ0cHGNcl+S7shCk4HarTj8UAGUtmynl54cqweyW2peC8yK1m4Xg1PH0dM6arKZMDCZ/IVNDyUIbSiAVqfu2JB4JrHhtd0Go0eD58qP8vzVcHV1wZOrE57e3uDqcMGXnrzCe8/fwuU0rqzgDEUPoxkZxlwmTgdasIU6TYu2r//4asB1Rb3ECuDo8CP6akn4gx7Ewsm3fdcUDjacl/3qhijvFP0mipHGifcJiAaYPrjKdzYmSqeyhrcaWtzWh5KsivfNcTytJuG1oU8z1KV0Oo5dfIkbZc6GZWn7VO/qBK/wc4zxDHYC4GtD/eo3isApI9P1Q8DVNxzpeobTzgjSyfXSU8l9KK5myY08YCqXPifOATpaBsZ9IS8HedjXiUZRBEyyflZVAKDTDgD85Ss8+a0LlruQj4V8J9EIfx+ML7riIB0i4/dtp+nPcJTYWQwbHR5pDBGJVKS5HJxUWMTylUaQDpZ6CPTIZXUJBHuogq6bMUnelucGOoqMz/aUB3axTPPYucpOjQwwvfZEN6zRVk7Rn0F1GBEnkFFiRN52qN2/MixDWjuvjxfcHM7J5X7p5iVevrqGn5YehGINKftaetmX4m3O5j3xvhkIwb5lX9HwAbmktus15GOhb74OGicCMHL5X/t1dCLmCoW8vCpn+o93KiH5cnQ+1bhPfDZMeAascW9KfWVZnFhtrt0gtvuzzZjJjIIZkdbb095L64aVqLYsUY27dK0vHWyXS1APYfTXFThfeogxM5atFVyhuvLQn3J7w40uxlSJaiyDY9UE46ZodJabpAxsQshqyMUQDvKvGtxTzHZycLbxwYKUGke+8/IkH82HXQyPv/UMb32rDWo/1lAQCDwDMvn44LiQwaS0ADAOnFyGE5HG+ZsOFT2hS6CcysoJqpPltYiIIkmPOQcwwMxpdqjBzaIZEkWUDEeW8FY/l/b53QKcCubKtBz8g/wM6EtcIAsoDmV/aIwPjnpVsT6qqI88J40e0iuGP/XS0/MtvSaaFcejt+9wc3XGR3ePcH11wc3hgm8//3xDofxxpARMCeeWIAc5CQ3zufdERHXtTk9bGuJ2vcdjhUcwSTokSxjgyK+xPmk/SakQyKcSYZrd2KXU/XbQjCHIRbvH+/uYt8kwc9nqsWUP05SsJgoCX+s4JkPtk+qFQKhlbe/0srSVlcXMaIcFdnXEZtPwXrUtEoG6qUwzjf32fv+kqhemvAWb4pAQw6mca1AHm4xjH5foQtG0oORE1VONpcwgRrJ9zq1AQ/pxSTaAIQADywJ89z187jcv7eVmqKhyZYbOD4YBxTRQtxdBHzj8iMcUtGxtFdslLDAgIOpzZ02nJkZxCSnGEsvnpTuk0uuO3v4hp0AoKuxcmgOHoac0isk3y73xPghsNSw59skQ5cVbyfSbCr8WDkK4VnKqfemv1+uqAgvH1hfeeoXrZcXz22s8ujrjuKx4//mTQKzI52Oc9OLeGXTBgpRJM7Av83868zo69kuDpGnImVzdPCkdlLjXSEdpj1YYA06ImB1DKs29TdN96kQ/OHXZPROa/v/bO9dYO6oqjv/+597bB6Xa1lKtlNjWgEm/KA0mra8oYnmE1JjwoYTE+vqiifERH22amPgRNIaYGIvxEaMVwVqRNCGNIvFjEdBCEUqvUsutYGkMtalyufee5Ye99sye6emhF86ZOWn2P5mcmb3nzP7Pmtlr1l5777UxVaxRWZx043UnbbVG6zVVrlBaw+lMMxeQfETB+Pict3icqLdMz/G9xqhl8bo9Ryv4i1MJvONGFlzM7gWqLoVelmuKdFB0P+VaSeptoRbnx3gJ6RfwQqYf18qvfCzSX0u+5v61t+lplhw7w+KpsHZWnPFVWHmd8jjtHKsvNRMKjgnpfVF2uBjEMY7MdoqKr1hh60PHKsrdrxuLiOW729YmukQXgSVWWqGI0nGpEXNCsx14pVN+COq+2cJ6ijKgcp1i5p4r5nokrOCz7gaXxlj1Hq32wSqa9/H2a1GyNGYsWfoyYzLOTC9gZmaMxRMzTM+OMzMzVg7xckdyMRKjUO4U7opYVjFMzF0JqWFWsYTTloxbo5X7qT3zohMQYCz4cbsLfZugjDFcm2hh9evE3/pHj1LWxQcaKCdYBNl3XlZVucc+mIhiiGitnpnP4hwfp4gGGP2wKYd0McpuYvD0iERYGY8bRyuk/TjFaCWVHWmdrrcIL1ZL163VunWbuh2im6EYJub+3cp/0k61+lYrL14DKHsuY/yHxO9buDfm6d9NOwbSlS1i4B0kdOIkS4+HAOBh5V4rFYnKrRKMJK0sdQMkbe6pmq8YJMab/4XiiUFVKufWrknCxwhW4GzoMGEiulUUrLLIL/pz473EziqChaoZoekOMah6Os60XHbI/9tJZJB0uEV3QjGSo9JKoHChyI/TD4ASU640fPx5edyE0pjqctmlZ5kzcebsIrrdDgvHZvnfzEShaNOOP5vrJEqoLNcMt6oplHIR0jHy6VjFEi6WBSr8LqKIlezPgsRqBspRIp0w484WdsstrqYxR42jyyXqr9hqqI29TUfNxJWeK9fwrTMdp5MDc3PV0T3eh6MYOzeBzXVhYrxcqicaLUp4dKn4bO2VGcqANokOKEi7DkiNqbTczlhyXiik0/G12OqxtS8QI690z1G2PUIkvtp/+qJHJKFe6DUkrXD+10cyXChqbgeLDvtOh+7pMyx6KfSikr68lPWr4OYK9YKLTZRA6pqoLi9upTJKr58UHOt0BfFDoFKxFZ1h9Y9AL9QWqqwMU0vKLYZrpVmpxRvzrP4fV7bpkK2KD7N2Q/UhH4XvNcil0zGWTkzTNTE3GyzbjozZuU41Qli8drdU2OH6CafEqBLl/wrjMirCSKkyS/Hc61XkEpPiM3bLN061LoIdpR+ohGPigu6LIj+6gKi+r5L58kIQg/Gfs/pKGpyqDqlQikWfSKwXhXPf/9/tlh1qUfHWUS87LTcxsCphJsHl/CrCOA/0WseaNQFJZ4AjbfOoYSVwqm0SCTKf/hg1PjB6nDKf/ujH521mdtl8Ljbq43SPmNk1bZNIIemRUeKU+fTHqPGB0eOU+fTHoPmMvHshIyMj42JCVroZGRkZDWLUle4P2ibQA6PGKfPpj1HjA6PHKfPpj4HyGemOtIyMjIyLDaNu6WZkZGRcVMhKNyMjI6NBjKzSlXSDpCOSJiXtaKjMKyQ9JOkpSU9K+oKnr5D0O0lH/Xe5p0vSd53j45I2DonXmKQ/S9rvx+skHXQ+90ha4OkL/XjS89cOgcsySXslPe1y2jwC8vmSP6/Dku6WtKhJGUn6saSTkg4nafOWiaTtfv5RSdsHzOdb/swel/QbScuSvJ3O54ik65P0gdXBXpySvK9IMkkr/bgVGXn65/2en5R0R5I+OBnFNYRGaSPEI/obsB5YABwCNjRQ7mpgo+8vBZ4BNgB3ADs8fQdwu+/fBDxAmJ+yCTg4JF5fBn4B7Pfje4Ftvr8b+Kzvfw7Y7fvbgHuGwOWnwGd8fwGwrE35AJcDzwKLE9l8okkZAR8ANgKHk7R5yQRYAfzdf5f7/vIB8tkCjPv+7QmfDV6/FgLrvN6NDboO9uLk6VcAB4B/ACtbltGHgN8DC/141TBkNHAFMaCKtBk4kBzvBHa2wOO3wEcIs+JWe9pqwqQNgLuAW5Pzi/MGyGEN8CBwLbDfX8RTSQUqZOUv72bfH/fzNEAubyAoONXS25TP5cBzXhHHXUbXNy0jYG2tAs9LJsCtwF1JeuW818unlvcxYI/vV+pWlM8w6mAvTsBe4J3AMUql24qMCB/q63qcN1AZjap7IVakiClPawze7LwaOAi82cyeB/DfVX5aEzzvBL5GGd/rTcBLZjbbo8yCj+ef9vMHhfXAi8BP3N3xQ0lLaFE+ZnYC+DZwHHiecM+P0p6MIuYrkybf+U8RLMlW+UjaCpwws0O1rLY4XQW8391Of5T07mHwGVWl2yuSRGNj2yRdCvwa+KKZ/affqT3SBsZT0s3ASTN79ALLHLbcxglNsu+b2dXAWULT+XwY+nN0X+lHCc2+twJLgBv7lNvqu9Wn/EZ4SdoFzAJ72uQj6RJgF/CNXtltcCK838sJLo2vAvdKMe7b4PiMqtKdIvh6ItYA/2yiYEkTBIW7x8z2efK/JK32/NXAyYZ4vhfYKukY8EuCi+FOYJmkGDcjLbPg4/lvBP49QD5TwJSZHfTjvQQl3JZ8AK4DnjWzF81sBtgHvIf2ZBQxX5kMXVbe8XQzcJt5e7hFPm8nfCgP+fu9BnhM0lta5DQF7LOAhwmty5WD5jOqSvdPwJXeA72A0OFx/7AL9a/aj4CnzOw7Sdb9QOwp3U7w9cb0j3tv6ybgdGxSDgJmttPM1pjZWoIM/mBmtwEPAbech0/keYufPzBLwMxeAJ6T9A5P+jDwV1qSj+M4sEnSJf78IqdWZJRgvjI5AGyRtNyt9y2eNhBIugH4OrDVzP5b47lNYVTHOuBK4GGGXAfN7AkzW2Vma/39niJ0Yr9ASzIC7iMYNki6itA5dopBy+j1OMaHuRF6MJ8h9A7uaqjM9xGaB48Df/HtJoLP70HgqP+u8PMFfM85PgFcM0RuH6QcvbDeH/ok8CvK3tZFfjzp+euHwONdwCMuo/sIzbFW5QN8E3gaOAz8jNDL3JiMgLsJ/uQZgvL49GuRCcHXOunbJwfMZ5Lgf4zv9e7k/F3O5whwY5I+sDrYi1Mt/xhlR1pbMloA/Nzfo8eAa4chozwNOCMjI6NBjKp7ISOJKwy1AAAAOklEQVQjI+OiRFa6GRkZGQ0iK92MjIyMBpGVbkZGRkaDyEo3IyMjo0FkpZuRkZHRILLSzcjIyGgQ/wdgJVhJf1W9kwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x2b90c1ec79d8>\n",
"Traceback (most recent call last):\n",
" File \"/home/gbogu17/.local/lib/python3.7/site-packages/torch/utils/data/dataloader.py\", line 926, in __del__\n",
" self._shutdown_workers()\n",
" File \"/home/gbogu17/.local/lib/python3.7/site-packages/torch/utils/data/dataloader.py\", line 906, in _shutdown_workers\n",
" w.join()\n",
" File \"/scg/apps/software/jupyter/python_3.7/lib/python3.7/multiprocessing/process.py\", line 140, in join\n",
" res = self._popen.wait(timeout)\n",
" File \"/scg/apps/software/jupyter/python_3.7/lib/python3.7/multiprocessing/popen_fork.py\", line 48, in wait\n",
" return self.poll(os.WNOHANG if timeout == 0.0 else 0)\n",
" File \"/scg/apps/software/jupyter/python_3.7/lib/python3.7/multiprocessing/popen_fork.py\", line 28, in poll\n",
" pid, sts = os.waitpid(self.pid, flag)\n",
"KeyboardInterrupt: \n"
]
}
],
"source": [
"# Visualizing Batches\n",
"# Below we show you how to visualize batches.\n",
"for item in minibatch['input']:\n",
" plt.imshow(item.squeeze(0))\n",
" plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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