train_pneumonia_solution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hackathon Day 2\n",
    "Today we want to train a Convolutional Neural Network for classifying pneumonia on the [PneumoniaMNIST](https://medmnist.com/) dataset\n",
    "\n",
    "\n",
    "**Learning Outcome:** Learn how to\n",
    "- define training and model parameters and analyse their influence on model performance\n",
    "- write a training loop\n",
    "- evaluate classification performance using different performance metrics "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 1: Load dataset \n",
    "Use the code from day 1 to load your dataset.\\\n",
    "If you worked on this yesterday, you can also use data augmentation methods. Data augmentation is a technique used to increase the diversity of training data by applying random transformations, like rotating, flipping, or cropping images, to create new variations of existing data. This helps improve a model’s ability to generalize and perform better on unseen data.\\\n",
    "For data augmentation you can use the [MONAI library](https://docs.monai.io/en/stable/transforms.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/admin_ml/anaconda3/envs/namt/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "# import some libraries we will need \n",
    "\n",
    "from medmnist import PneumoniaMNIST \n",
    "import numpy as np\n",
    "import torch\n",
    "import random\n",
    "from matplotlib import pyplot as plt\n",
    "import torch.nn as nn\n",
    "from monai.data import DataLoader, CacheDataset\n",
    "from monai.utils import  GridSamplePadMode\n",
    "from monai.transforms import (\n",
    "    Compose,\n",
    "    RandAffined,\n",
    "    RandFlipd,\n",
    "    RandGaussianNoised,\n",
    "    ScaleIntensityd,\n",
    "    EnsureTyped,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using downloaded and verified file: /home/admin_ml/.medmnist/pneumoniamnist_224.npz\n",
      "Using downloaded and verified file: /home/admin_ml/.medmnist/pneumoniamnist_224.npz\n",
      "Using downloaded and verified file: /home/admin_ml/.medmnist/pneumoniamnist_224.npz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading dataset: 100%|██████████| 4708/4708 [00:01<00:00, 3753.66it/s]\n",
      "Loading dataset: 100%|██████████| 524/524 [00:00<00:00, 2048.65it/s]\n",
      "Loading dataset: 100%|██████████| 624/624 [00:00<00:00, 2011.49it/s]\n"
     ]
    }
   ],
   "source": [
    "data_transforms = Compose(\n",
    "    [\n",
    "        EnsureTyped(keys=['image'], data_type='tensor'),\n",
    "        ScaleIntensityd(keys=['image'], minv=0, maxv=1 )\n",
    "    ] )\n",
    "\n",
    "# Optional: Apply Data Augmentation:\n",
    "# You can also randomly rotate or flip your dataset, apply gaussian noise, etc. to create more variability in the dataset \n",
    "# see https://docs.monai.io/en/stable/transforms.html\n",
    "train_transforms = Compose(\n",
    "    [\n",
    "        EnsureTyped(keys=['image'], data_type='tensor'),\n",
    "        RandFlipd(\n",
    "            keys = ['image'],\n",
    "            prob = 0.25,\n",
    "            spatial_axis=1),\n",
    "        RandAffined(\n",
    "            keys = ['image'],\n",
    "            mode = ('bilinear'),\n",
    "            prob = 0.5, \n",
    "            shear_range = 0.1,\n",
    "            spatial_size = [224,224],\n",
    "            rotate_range = np.pi/180*20,\n",
    "            scale_range = 0.1,\n",
    "            padding_mode = GridSamplePadMode.BORDER), \n",
    "        RandGaussianNoised(\n",
    "            keys = ['image'],\n",
    "            prob = 0.25, \n",
    "            std = 0.1,\n",
    "            ),\n",
    "        ScaleIntensityd(keys=['image'], minv=0, maxv=1 )\n",
    "    ])\n",
    "    \n",
    "\n",
    "# If you want to use Monai data transformations you have to use this class\n",
    "# to convert medmnist dataset to a format compatible with MONAI\n",
    "class WrappedPneumoniaMNIST(PneumoniaMNIST):\n",
    "    def __getitem__(self, index):\n",
    "        #data = super().__getitem__(index)\n",
    "        image, label = super().__getitem__(index)\n",
    "        image = torch.tensor(np.array(image)).unsqueeze(0)\n",
    "        return {\"image\": image, \"label\": label}\n",
    "\n",
    "# Load Pneumonia datase for training, validation and test\n",
    "size=224\n",
    "train_dataset = WrappedPneumoniaMNIST(split=\"train\",download=True, size=size)\n",
    "val_dataset = WrappedPneumoniaMNIST(split=\"val\",download=True, size=size)\n",
    "test_dataset = WrappedPneumoniaMNIST(split=\"test\",download=True, size=size)\n",
    "\n",
    "# Wrap with MONAI CacheDataset\n",
    "# Attention: If you use data augmentation, only apply it to training data and not to the validation and test set as we want to keep them unchanged.\n",
    "train_monai_dataset = CacheDataset(data=train_dataset, transform=train_transforms, cache_rate=1.0, num_workers=4)\n",
    "val_monai_dataset = CacheDataset(data=val_dataset, transform=data_transforms, cache_rate=1.0, num_workers=4)\n",
    "test_monai_dataset = CacheDataset(data=test_dataset, transform=data_transforms, cache_rate=1.0, num_workers=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Next: initiliaze train_loader, val_loader and test_loader using DataLoader from MONAI\n",
    "BATCH_SIZE = 64\n",
    "train_loader = DataLoader(dataset=train_monai_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
    "val_loader = DataLoader(dataset=val_monai_dataset, batch_size=BATCH_SIZE, shuffle=False)\n",
    "test_loader = DataLoader(dataset=test_monai_dataset, batch_size=BATCH_SIZE, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 2: Define training and model parameters\n",
    "- We want to train a classification model. You can for example use the **ResNet18** model. To initialize the model you can use [Monai](https://docs.monai.io/en/stable/networks.html#nets). \n",
    "- Moreover, we have to define our training parameters, as optimizer, loss function and learning rate. \n",
    "- For a binary classification problem you can for example use binary cross entropy loss (see [here](https://pytorch.org/docs/stable/generated/torch.nn.BCELoss.html)). \n",
    "- As optimizer you can use stochastic gradient descent (see [here](https://pytorch.org/docs/stable/generated/torch.optim.SGD.html)) \n",
    "\n",
    "Carry out several experiments and vary the model parameters. What effect does this have on the model performance?\\\n",
    "You can use, for example, a larger model (e.g. ResNet 50), and vary the learning rate and the batch size. Write down your observations.  \n",
    "For quick experiments, you can work with the small data set (28x28). Ultimately, however, you should train a model on one of the higher-resolution data sets (64x64 or 224x224). \n",
    "\n",
    "**Solution:**\n",
    "When training a Convolutional Neural Network (CNN), several parameters significantly influence the performance, efficiency, and final outcome of the model. Understanding how variations in these parameters affect training can help you fine-tune your model for better accuracy, faster convergence, and more effective generalization.\n",
    "\n",
    "1. **Learning Rate**:\\\n",
    "    The learning rate is a critical hyperparameter that controls the size of the steps the model takes when updating its weights based on the gradient of the loss function.\n",
    "\n",
    "- Too high learning rate:\n",
    "    - The model takes large steps towards the minimum of the loss function.\n",
    "    - This can lead to overshooting the optimal solution, causing the loss to oscillate or even diverge.    \n",
    "    - Training might become unstable, and the model might fail to converge.\n",
    "- Too low learning rate:\n",
    "    - The model takes very small steps, leading to slow convergence.\n",
    "    - Training can take much longer, and the model might get stuck in a local minimum, potentially not reaching the best possible solution.\n",
    "    - The risk of overfitting increases since the model might focus too much on the training data details.\n",
    "- Optimal learning rate:\n",
    "    - Achieves a good balance, allowing the model to converge efficiently.  \n",
    "    - Helps the model reach a good minimum in the loss landscape, resulting in better performance.\n",
    "\n",
    "2. **Model Size**:\\\n",
    "    The size of the model is determined by the number of layers and the number of neurons / filters in each layer.\\\n",
    "    Advantages of using a larger model:\n",
    "    - Can capture more complex patterns in the data due to a higher capacity (more parameters).\n",
    "    - Often performs better on large, complex datasets.\n",
    "    Disadvantages of using a larger model:\n",
    "    - Requires more computational resources (memory and processing power).\n",
    "    - Takes longer to train.\n",
    "    - More prone to overfitting, especially if the dataset is small or not sufficiently diverse.\n",
    "\n",
    "3. **Batch Size**:\\\n",
    "    The batch size is the number of training examples used to calculate the gradient during one iteration of the training process.\n",
    "\n",
    "    Advantages of using a larger batch size:\n",
    "    - Produces smoother and more stable gradient estimates, leading to faster convergence.\n",
    "    \n",
    "    Disadvantages of using a larger batch size:\n",
    "    - Requires more memory, which can be a limitation on certain hardware.\n",
    "    - More prone to getting stuck in local minima, leading to potential overfitting and reduced generalization ability.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from monai.networks.nets import resnet18, resnet50\n",
    "\n",
    "device = torch.device(f'cuda' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "model = resnet50(spatial_dims=2, num_classes=1, n_input_channels=1).to(device) #resnet18(spatial_dims=2,num_classes=1,n_input_channels=1).to(device)\n",
    "# You can also use num_classes = 2 and change the activation function to softmax instead of sigmoid.\n", 
    "# With one class and sigmoid activation function the output of the network is transformed to a probability x\n", 
    "# with x < 0.5 = no pneumonia and x >= 0.5 = pneumonia.\n",
    "criterion = nn.BCEWithLogitsLoss()\n",
    "\n",
    "NUM_EPOCHS = 500\n",
    "\n",
    "lr=0.001 \n",
    "wd=0.001 \n",
    "# You can use weight decay as a penalty term for the model weights. \n",
    "# This penalty term prevents the weights from becoming to large and thus helps the model not only to memorize the training data, \n",
    "# but also to learn general patterns that can be applied to new data (prevents overfitting)\n",
    "\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=lr, momentum=0.9, nesterov=True, weight_decay=wd) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 3: Evaluation function \n",
    "- Use [scikit-learn](https://scikit-learn.org/stable/modules/model_evaluation.html) to evaluate your model\n",
    "- For a classification task you can for example use metrics like AUC, Accuracy, F1-Score, Sensitivity and Specificity\n",
    "\n",
    "What exactly do the individual metrics mean? How can you interpret the results?\n",
    "\n",
    "**Solution**:\n",
    "1. Accuracy\n",
    "    - Accuracy is the proportion of correctly predicted instances (both positives and negatives) out of the total number of instances.\n",
    "    - A higher accuracy means that more predictions are correct.\n",
    "    - However, accuracy can be misleading if the data is imbalanced (e.g., if there are many more negatives than positives).\n",
    "2. AUC (Area Under the Curve)\n",
    "    - Specifically, it refers to the Area Under the ROC (Receiver Operating Characteristic) Curve. The ROC curve plots the true positive rate (Sensitivity) against the false positive rate (1 - Specificity).\n",
    "    - The AUC ranges from 0 to 1. A score closer to 1 indicates a better model that distinguishes between positive and negative classes effectively.\n",
    "    - An AUC of 0.5 suggests the model is no better than random guessing.\n",
    "3. Sensitivity (Recall)\n",
    "    - The proportion of actual positives that the model correctly identifies (true positives out of all positives).\n",
    "    - High Sensitivity means the model correctly identifies most of the positive cases.\n",
    "    - It’s important in situations where missing a positive case is costly (e.g., diagnosing a disease).\n",
    "4. Specificity\n",
    "    - The proportion of actual negatives that the model correctly identifies (true negatives out of all negatives).\n",
    "    - High Specificity means the model correctly identifies most of the negative cases.\n",
    "    - It’s crucial in situations where false positives are problematic (e.g., false alarms in a security system).\n",
    "5. F1-Score\n",
    "    - The harmonic mean of Precision and Recall (Sensitivity).\n",
    "    - Precision is the proportion of true positives out of all predicted positives.\n",
    "    - The F1-Score balances Precision and Recall, making it useful when you want to consider both false positives and false negatives.\n",
    "    - It's especially helpful when the classes are imbalanced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import (\n",
    "    accuracy_score,\n",
    "    f1_score,\n",
    "    recall_score,\n",
    "    roc_auc_score\n",
    "    )\n",
    "\n",
    "def evalModel(y_true, y_score, thresh=0.5):\n",
    "    y_true = y_true.squeeze() # ground truth class / label\n",
    "    y_score = y_score.squeeze() # probabilities \n",
    "\n",
    "    y_pred = (y_score > thresh).astype(int) # predicted class\n",
    "\n",
    "    metric_dict = {}\n",
    "\n",
    "    metric_dict['Acc'] = accuracy_score(y_true, y_pred)\n",
    "    metric_dict['F1'] =  f1_score(y_true=y_true, y_pred=y_pred, zero_division = 0)\n",
    "    metric_dict['Sensitivity'] = recall_score(y_true=y_true, y_pred=y_pred, zero_division = 0)\n",
    "    metric_dict['Specificity'] = recall_score(y_true=y_true, y_pred=y_pred, zero_division = 0, pos_label=0)    \n",
    "    metric_dict['AUC'] = roc_auc_score(y_true, y_score)\n",
    "\n",
    "    return metric_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 4: Training loop\n",
    "A training loop is the repetitive process in which a model learns from data by iteratively passing batches through the network, calculating loss, updating weights using an optimizer, and repeating until the model converges or a set number of iterations is reached."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 74/74 [01:12<00:00,  1.01it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 000, time for epoch: 75.08\n",
      "loss 0.5091, validation loss 36.0087, Val AUC: 0.500000, Val F1: 0.000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 74/74 [01:12<00:00,  1.02it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 001, time for epoch: 152.14\n",
      "loss 0.4191, validation loss 18.3662, Val AUC: 0.910378, Val F1: 0.708197\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 74/74 [01:12<00:00,  1.02it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 002, time for epoch: 154.32\n",
      "loss 0.3610, validation loss 13.3787, Val AUC: 0.954508, Val F1: 0.156398\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 74/74 [01:12<00:00,  1.03it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 003, time for epoch: 151.89\n",
      "loss 0.3291, validation loss 10.1149, Val AUC: 0.917195, Val F1: 0.902564\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 74/74 [01:12<00:00,  1.02it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 004, time for epoch: 152.38\n",
      "loss 0.3073, validation loss 8.1406, Val AUC: 0.953213, Val F1: 0.936118\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 74/74 [01:12<00:00,  1.03it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 005, time for epoch: 157.76\n",
      "loss 0.2912, validation loss 7.0666, Val AUC: 0.647425, Val F1: 0.414487\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 74/74 [01:12<00:00,  1.02it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 006, time for epoch: 162.67\n",
      "loss 0.2792, validation loss 6.1014, Val AUC: 0.972522, Val F1: 0.912863\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 007, time for epoch: 166.42\n",
      "loss 0.2691, validation loss 5.3719, Val AUC: 0.946330, Val F1: 0.932806\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 008, time for epoch: 74.38\n",
      "loss 0.2593, validation loss 5.9985, Val AUC: 0.710159, Val F1: 0.015306\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 009, time for epoch: 74.75\n",
      "loss 0.2505, validation loss 5.4302, Val AUC: 0.969199, Val F1: 0.906077\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 010, time for epoch: 74.29\n",
      "loss 0.2428, validation loss 5.7616, Val AUC: 0.566714, Val F1: 0.163934\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 011, time for epoch: 168.81\n",
      "loss 0.2367, validation loss 5.3009, Val AUC: 0.957726, Val F1: 0.926893\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 012, time for epoch: 163.66\n",
      "loss 0.2301, validation loss 5.0177, Val AUC: 0.747263, Val F1: 0.547135\n"
     ]
    },
    {
     "name": "stderr",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 013, time for epoch: 163.89\n",
      "loss 0.2260, validation loss 4.6905, Val AUC: 0.968266, Val F1: 0.883191\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 014, time for epoch: 166.25\n",
      "loss 0.2203, validation loss 4.3941, Val AUC: 0.979996, Val F1: 0.941606\n"
     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 015, time for epoch: 169.23\n",
      "loss 0.2159, validation loss 4.1501, Val AUC: 0.967828, Val F1: 0.878745\n"
     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 016, time for epoch: 173.89\n",
      "loss 0.2120, validation loss 3.9147, Val AUC: 0.980358, Val F1: 0.959079\n"
     ]
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "Epoch 017, time for epoch: 175.32\n",
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    {
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     "text": [
      "Epoch 453, time for epoch: 174.16\n",
      "loss 0.0338, validation loss 0.2570, Val AUC: 0.998572, Val F1: 0.985770\n"
     ]
    },
    {
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     "text": [
      "Epoch 454, time for epoch: 176.83\n",
      "loss 0.0337, validation loss 0.2566, Val AUC: 0.998153, Val F1: 0.987080\n"
     ]
    },
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     "text": [
      "Epoch 455, time for epoch: 174.57\n",
      "loss 0.0337, validation loss 0.2562, Val AUC: 0.997686, Val F1: 0.988417\n"
     ]
    },
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     "text": [
      "Epoch 456, time for epoch: 175.05\n",
      "loss 0.0336, validation loss 0.2558, Val AUC: 0.997163, Val F1: 0.981818\n"
     ]
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     "text": [
      "Epoch 457, time for epoch: 174.48\n",
      "loss 0.0335, validation loss 0.2554, Val AUC: 0.998153, Val F1: 0.989691\n"
     ]
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     "text": [
      "Epoch 458, time for epoch: 173.64\n",
      "loss 0.0335, validation loss 0.2550, Val AUC: 0.997867, Val F1: 0.987013\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "Epoch 459, time for epoch: 172.45\n",
      "loss 0.0334, validation loss 0.2546, Val AUC: 0.997239, Val F1: 0.988357\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "Epoch 460, time for epoch: 174.83\n",
      "loss 0.0334, validation loss 0.2542, Val AUC: 0.994487, Val F1: 0.987245\n"
     ]
    },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 461, time for epoch: 176.43\n",
      "loss 0.0333, validation loss 0.2538, Val AUC: 0.997705, Val F1: 0.987179\n"
     ]
    },
    {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 462, time for epoch: 175.99\n",
      "loss 0.0333, validation loss 0.2535, Val AUC: 0.997372, Val F1: 0.991014\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "Epoch 463, time for epoch: 175.59\n",
      "loss 0.0332, validation loss 0.2531, Val AUC: 0.997544, Val F1: 0.985770\n"
     ]
    },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 464, time for epoch: 176.15\n",
      "loss 0.0331, validation loss 0.2527, Val AUC: 0.993878, Val F1: 0.984655\n"
     ]
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     "output_type": "stream",
     "text": [
      "Epoch 465, time for epoch: 175.97\n",
      "loss 0.0331, validation loss 0.2524, Val AUC: 0.994040, Val F1: 0.979798\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "Epoch 466, time for epoch: 177.47\n",
      "loss 0.0330, validation loss 0.2521, Val AUC: 0.993373, Val F1: 0.982188\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "Epoch 467, time for epoch: 174.46\n",
      "loss 0.0330, validation loss 0.2517, Val AUC: 0.997982, Val F1: 0.983226\n"
     ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 468, time for epoch: 176.23\n",
      "loss 0.0329, validation loss 0.2513, Val AUC: 0.997420, Val F1: 0.988357\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "Epoch 469, time for epoch: 176.22\n",
      "loss 0.0328, validation loss 0.2510, Val AUC: 0.997525, Val F1: 0.985843\n"
     ]
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     "output_type": "stream",
     "text": [
      "Epoch 470, time for epoch: 175.33\n",
      "loss 0.0328, validation loss 0.2506, Val AUC: 0.997610, Val F1: 0.987179\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "Epoch 471, time for epoch: 174.89\n",
      "loss 0.0327, validation loss 0.2503, Val AUC: 0.996315, Val F1: 0.984416\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "Epoch 472, time for epoch: 174.13\n",
      "loss 0.0327, validation loss 0.2501, Val AUC: 0.989955, Val F1: 0.976040\n"
     ]
    },
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 473, time for epoch: 177.62\n",
      "loss 0.0326, validation loss 0.2497, Val AUC: 0.996839, Val F1: 0.984456\n"
     ]
    },
    {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 474, time for epoch: 178.23\n",
      "loss 0.0326, validation loss 0.2493, Val AUC: 0.997801, Val F1: 0.988447\n"
     ]
    },
    {
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     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 475, time for epoch: 172.75\n",
      "loss 0.0325, validation loss 0.2490, Val AUC: 0.996382, Val F1: 0.979487\n"
     ]
    },
    {
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 476, time for epoch: 174.22\n",
      "loss 0.0325, validation loss 0.2486, Val AUC: 0.993945, Val F1: 0.987179\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 477, time for epoch: 174.30\n",
      "loss 0.0324, validation loss 0.2482, Val AUC: 0.997125, Val F1: 0.988417\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "Epoch 478, time for epoch: 174.37\n",
      "loss 0.0323, validation loss 0.2481, Val AUC: 0.992355, Val F1: 0.977836\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "Epoch 479, time for epoch: 176.45\n",
      "loss 0.0323, validation loss 0.2477, Val AUC: 0.993564, Val F1: 0.985879\n"
     ]
    },
    {
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 480, time for epoch: 173.25\n",
      "loss 0.0322, validation loss 0.2473, Val AUC: 0.997658, Val F1: 0.985806\n"
     ]
    },
    {
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 481, time for epoch: 175.94\n",
      "loss 0.0322, validation loss 0.2470, Val AUC: 0.997125, Val F1: 0.984576\n"
     ]
    },
    {
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 482, time for epoch: 172.11\n",
      "loss 0.0321, validation loss 0.2467, Val AUC: 0.997486, Val F1: 0.984456\n"
     ]
    },
    {
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 483, time for epoch: 173.99\n",
      "loss 0.0321, validation loss 0.2464, Val AUC: 0.997029, Val F1: 0.987113\n"
     ]
    },
    {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 484, time for epoch: 173.48\n",
      "loss 0.0320, validation loss 0.2460, Val AUC: 0.997829, Val F1: 0.987147\n"
     ]
    },
    {
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 485, time for epoch: 175.33\n",
      "loss 0.0320, validation loss 0.2456, Val AUC: 0.997486, Val F1: 0.988447\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 486, time for epoch: 174.21\n",
      "loss 0.0319, validation loss 0.2453, Val AUC: 0.997334, Val F1: 0.984615\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 487, time for epoch: 175.62\n",
      "loss 0.0319, validation loss 0.2450, Val AUC: 0.997286, Val F1: 0.981865\n"
     ]
    },
    {
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     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 488, time for epoch: 172.99\n",
      "loss 0.0318, validation loss 0.2446, Val AUC: 0.996858, Val F1: 0.985915\n"
     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 489, time for epoch: 174.96\n",
      "loss 0.0318, validation loss 0.2443, Val AUC: 0.997220, Val F1: 0.989770\n"
     ]
    },
    {
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     ]
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 490, time for epoch: 176.22\n",
      "loss 0.0317, validation loss 0.2439, Val AUC: 0.997020, Val F1: 0.988447\n"
     ]
    },
    {
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     ]
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 491, time for epoch: 174.77\n",
      "loss 0.0317, validation loss 0.2436, Val AUC: 0.997010, Val F1: 0.988417\n"
     ]
    },
    {
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     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 492, time for epoch: 175.93\n",
      "loss 0.0316, validation loss 0.2432, Val AUC: 0.990612, Val F1: 0.987212\n"
     ]
    },
    {
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 493, time for epoch: 174.51\n",
      "loss 0.0316, validation loss 0.2429, Val AUC: 0.993440, Val F1: 0.985879\n"
     ]
    },
    {
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     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 494, time for epoch: 176.56\n",
      "loss 0.0315, validation loss 0.2427, Val AUC: 0.997420, Val F1: 0.973753\n"
     ]
    },
    {
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     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 495, time for epoch: 175.42\n",
      "loss 0.0314, validation loss 0.2424, Val AUC: 0.994230, Val F1: 0.989770\n"
     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 496, time for epoch: 172.92\n",
      "loss 0.0314, validation loss 0.2421, Val AUC: 0.996963, Val F1: 0.987179\n"
     ]
    },
    {
     "name": "stderr",
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     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 497, time for epoch: 172.36\n",
      "loss 0.0313, validation loss 0.2417, Val AUC: 0.998458, Val F1: 0.989691\n"
     ]
    },
    {
     "name": "stderr",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 498, time for epoch: 172.00\n",
      "loss 0.0313, validation loss 0.2413, Val AUC: 0.998020, Val F1: 0.984496\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 499, time for epoch: 174.09\n",
      "loss 0.0313, validation loss 0.2410, Val AUC: 0.994306, Val F1: 0.982188\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import time\n",
    "from tqdm import tqdm \n",
    "\n",
    "final_model_path = os.path.join(os.path.dirname(os.path.realpath('__file__')),f'finalModel_{NUM_EPOCHS}_Epochs_size_{size}.model')\n",
    "best_model_path = os.path.join(os.path.dirname(os.path.realpath('__file__')),f'bestModel_{NUM_EPOCHS}_Epochs_size_{size}.model')\n",
    "\n",
    "# to save performances \n",
    "writer_dict = {'epoch': [],\n",
    "               'loss_train': [],\n",
    "               'loss_valid': [],\n",
    "               'lr': [],\n",
    "               'Acc': [],\n",
    "               'F1': [],\n",
    "               'Sensitivity': [],\n",
    "               'Specificity': [],\n",
    "               'AUC': []}\n",
    "\n",
    "train_loss = []\n",
    "val_loss = []\n",
    "\n",
    "min_valid_loss = None\n",
    "scaler = torch.amp.GradScaler() \n",
    "# https://pytorch.org/tutorials/recipes/recipes/amp_recipe.html\n",
    "# Gradient scaling helps prevent gradients with small magnitudes from flushing to zero (“underflowing”) when training with mixed precision.\n",
    "\n",
    "for epoch in range(NUM_EPOCHS):\n",
    "\n",
    "    start_time = time.time()\n",
    "  \n",
    "    model.train()\n",
    "    for batch in tqdm(train_loader):\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        with torch.autocast(device_type='cuda'):\n",
    "        \n",
    "            inputs = batch['image'].to(device) # move to gpu\n",
    "            targets = batch['label'].to(device,torch.float16)\n",
    "        \n",
    "            outputs = model(inputs)\n",
    "                    \n",
    "            loss = criterion(outputs, targets)\n",
    "\n",
    "        scaler.scale(loss).backward()\n",
    "        scaler.step(optimizer)\n",
    "        scaler.update()\n",
    "\n",
    "        train_loss.append(loss.item())\n",
    "    \n",
    "    lr = optimizer.param_groups[0]['lr']\n",
    "    writer_dict['lr'].append(lr)\n",
    "    \n",
    "    if epoch == NUM_EPOCHS-1:\n",
    "        # You can use this code to save your model\n",
    "        state_dict = model.state_dict()\n",
    "        for key in state_dict.keys():\n",
    "            state_dict[key] = state_dict[key].cpu()\n",
    "\n",
    "        torch.save({\n",
    "            'epoch': epoch,\n",
    "            'save_dir': final_model_path,\n",
    "            'state_dict': state_dict,\n",
    "            'optimizer_state_dict': optimizer.state_dict()},\n",
    "            final_model_path)\n",
    "\n",
    "    # Now we want to evaluate our model on the validation data\n",
    "    model.eval()\n",
    "    y_true = torch.tensor([]).to(device)\n",
    "    y_score = torch.tensor([]).to(device)\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        for batch in val_loader:\n",
    "            with torch.autocast(device_type='cuda'):\n",
    "\n",
    "                inputs = batch['image'].to(device)\n",
    "                targets = batch['label'].to(device, torch.float16)\n",
    "                outputs = model(inputs)\n",
    "            \n",
    "            val_loss.append(criterion(outputs, targets).item())\n",
    "\n",
    "            y_true = torch.cat((y_true, targets), 0)\n",
    "            y_score = torch.cat((y_score, outputs.sigmoid()), 0) # sigmoid to transform outputs from logits to probabilites \n",
    "    \n",
    "    y_true = y_true.cpu().numpy()\n",
    "    y_score = y_score.detach().cpu().numpy()\n",
    "\n",
    "    \n",
    "    # Save best model on validation set \n",
    "    if epoch == 0:\n",
    "        min_valid_loss = np.mean(val_loss)\n",
    "        state_dict = model.state_dict()\n",
    "        for key in state_dict.keys():\n",
    "            state_dict[key] = state_dict[key].cpu()\n",
    "\n",
    "        torch.save({\n",
    "            'epoch': epoch,\n",
    "            'save_dir': best_model_path,\n",
    "            'state_dict': state_dict,\n",
    "            'optimizer_state_dict': optimizer.state_dict()},\n",
    "            best_model_path)\n",
    "            \n",
    "    elif np.mean(val_loss) < min_valid_loss:\n",
    "        min_valid_loss = np.mean(val_loss)\n",
    "        state_dict = model.state_dict()\n",
    "        for key in state_dict.keys():\n",
    "            state_dict[key] = state_dict[key].cpu()\n",
    "    \n",
    "        torch.save({\n",
    "            'epoch': epoch,\n",
    "            'save_dir': best_model_path,\n",
    "            'state_dict': state_dict,\n",
    "            'optimizer_state_dict': optimizer.state_dict()},\n",
    "            best_model_path)\n",
    "    \n",
    "    # to monitor model performance  \n",
    "    writer_dict['epoch'].append(epoch)\n",
    "    writer_dict['loss_train'].append(np.mean(train_loss))\n",
    "    writer_dict['loss_valid'].append(np.mean(val_loss))\n",
    "\n",
    "    metric_dict_val = evalModel(y_true,y_score)\n",
    "\n",
    "    for cur_key in metric_dict_val.keys():\n",
    "        writer_dict[cur_key].append(metric_dict_val[cur_key])\n",
    "    \n",
    "    end_time = time.time()\n",
    " \n",
    "    print('Epoch %03d, time for epoch: %3.2f' %(epoch, end_time-start_time))\n",
    "    print('loss %2.4f, validation loss %2.4f, Val AUC: %3f, Val F1: %3f' %(np.mean(train_loss), np.mean(val_loss), metric_dict_val['AUC'], metric_dict_val['F1']))   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 5: Final model evaluation\n",
    "- Plot the loss function and the determined evaluation metrics\n",
    "- Write an evaluation / inference function to evaluate the trained model on the test set\n",
    "- Check the false-positive and false negative cases\n",
    "\n",
    "Take a closer look at the results of the model. Are there any reasons why the model was wrong here?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot training and validation loss saved in writer_dict\n",
    "from matplotlib import pyplot as plt\n",
    "plt.figure()\n",
    "plt.plot(writer_dict['loss_train'],color='blue',label='Train loss')\n",
    "plt.plot(writer_dict['loss_valid'],color='orange',label='Validation loss')\n",
    "plt.legend()\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot evaluation metrics saved in writer_dict\n",
    "plt.figure()\n",
    "plt.plot(writer_dict['Acc'],label='Acc')\n",
    "plt.plot(writer_dict['F1'],label='F1')\n",
    "plt.plot(writer_dict['Sensitivity'],label='Sensitivity')\n",
    "plt.plot(writer_dict['Specificity'],label='Specificity')\n",
    "plt.plot(writer_dict['AUC'],label='AUC')\n",
    "plt.legend()\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Performance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save writer_dict \n",
    "# see: https://stackoverflow.com/questions/11218477/how-can-i-use-pickle-to-save-a-dict-or-any-other-python-object\n",
    "import pickle\n",
    "\n",
    "savePath = os.path.join(os.path.dirname(os.path.realpath('__file__')),f'writer_dict_{NUM_EPOCHS}_Epochs_size_{size}.pickle')\n",
    "\n",
    "# write dictionary\n",
    "with open(savePath, 'wb') as f:\n",
    "    pickle.dump(writer_dict, f, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "\n",
    "# load dictionary\n",
    "with open(savePath, 'rb') as f:\n",
    "    loadedDict = pickle.load(f)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model inference\n",
    "Now we want to use our trained model to predict new data.\\\n",
    "Therefore, we define a function *infer*, which gets a data loader and the trained model as input. \\\n",
    "You can use the code from the training loop for help."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def infer(data_loader, model):\n",
    "    model.eval()\n",
    "    y_true = torch.tensor([]).to(device)\n",
    "    y_score = torch.tensor([]).to(device)\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        for batch in data_loader:\n",
    "            with torch.autocast(device_type='cuda'):\n",
    "\n",
    "                inputs = batch['image'].to(device)\n",
    "                targets = batch['label'].to(device, torch.float16)\n",
    "                \n",
    "                outputs = model(inputs)\n",
    "                \n",
    "                y_true = torch.cat((y_true, targets), 0)\n",
    "                y_score = torch.cat((y_score, outputs.sigmoid()), 0)\n",
    "\n",
    "        y_true = y_true.cpu().numpy()\n",
    "        y_score = y_score.detach().cpu().numpy()\n",
    "\n",
    "        metric_dict = evalModel(y_true, y_score)\n",
    "\n",
    "        y_pred = (y_score >= 0.5).astype(int)\n",
    "\n",
    "        print('auc: %.3f  acc:%.3f, f1: %3f, sens.: %3f, spec.: %3f' %(metric_dict['AUC'],metric_dict['Acc'], metric_dict['F1'],metric_dict['Sensitivity'], metric_dict['Specificity']))\n",
    "        \n",
    "\n",
    "    return metric_dict, y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1699051/3997562727.py:3: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  checkpoint = torch.load(best_model_path)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==> Evaluating ...\n",
      "Validation performance:\n",
      "auc: 0.994  acc:0.973, f1: 0.982188, sens.: 0.992288, spec.: 0.918519\n",
      "Test performance\n",
      "auc: 0.958  acc:0.877, f1: 0.909942, sens.: 0.997436, spec.: 0.675214\n"
     ]
    }
   ],
   "source": [
    "# You can use the code belwo to load the best model\n",
    "model = resnet50(spatial_dims=2,num_classes=1,n_input_channels=1).to(device)\n",
    "checkpoint = torch.load(best_model_path)\n",
    "model.load_state_dict(checkpoint['state_dict'])\n",
    "model.eval()\n",
    "\n",
    "print('==> Evaluating ...')\n",
    "print('Validation performance:')\n",
    "metrics_val, val_pred = infer(val_loader, model)\n",
    "print('Test performance')\n",
    "metrics_test, test_pred = infer(test_loader, model)\n",
    "\n",
    "# Now we want to convert our dictionaries to a pandas dataframe and save the results as csv file\n",
    "# see https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.from_dict.html\n",
    "import pandas as pd\n",
    "df_test = pd.DataFrame(metrics_test,index=[0])\n",
    "df_test.to_csv(os.path.join(os.path.dirname(os.path.realpath('__file__')),f'testPerf_{NUM_EPOCHS}_Epochs_size_{size}.csv'))\n",
    "\n",
    "df_val = pd.DataFrame(metrics_val,index=[0])\n",
    "df_val.to_csv(os.path.join(os.path.dirname(os.path.realpath('__file__')),f'valPerf_{NUM_EPOCHS}_Epochs_size_{size}.csv'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we want to have a closer look on our model performance and the predicted results from our test set. \\\n",
    "Write code, to visualize false positive (prediction = Pneumonia, label = Healthy) and false negative cases (prediction = Healthy, label = Pneumonia)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 80 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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DPdPpdPaH/tAfGu77Nb/m18z+4l/8i7Pf8Tt+x1z6aDabzf7aX/trs3e9612ztbW1uVTSWPpoNpvNft2v+3Wzqpp93dd93Wj//9gf+2Ozd73rXbPNzc3ZvXv3Zl/0RV80+7f/7X979vM///ML+TabzWb/1//1f83e//73zx4+fDjb2NiYfcEXfMHs277t24bfbzruf/AP/sHZu9/97tnDhw9nm5ubs1/1q37V7Du+4ztmp6enc+399E//9Oy3//bfPnv7298+W11dnb3jHe+YffmXf/nsh37oh25d1115Y5TJbBax5l25K2+SMp1O6+WXX66v/MqvrD/+x//4LzU5d+WuvCHK3ZzCXXlTlOPj4yu59D/1p/5UPXr06MqBdnflrtyV8XIXKdyVN0X5kR/5kfo9v+f31Fd91VfVSy+9VD/xEz9Rf+JP/In6h//hf7j+t//tfxs2rN2Vu3JXFpe7iea78qYon/mZn1n/0D/0D9V3fud31qNHj+rFF1+s3/7bf3v9B//Bf3DnEO7KXblFuYsU7spduSt35a4M5W5O4a7clbtyV+7KUO6cwl25K3flrtyVodx4TuGd73xnXVxcDCs8ZrNZTafTms1mw3eKM1L5G9dyhyX1+FP17ATI6XQ6fPwbv7vO6XRa5+fnw72/HMtkMqnl5eV6+9vfXm9/+9vrC77gC+rTPu3T6t69e7W9vV2TyaRms1mtra3V8vJyrays1NLSUi0vL9fy8nItLS3V0tIlXuD/1dXV4bO+vj48u7y8PLS5srJS29vbtb29XRsbG3MnkjI+VZdy4fFbXl6u1dXVWltbG9qfTCbDd8b44uKizs/P6/T0tM7Pz+vo6KjOz8/r/Py8zs7OBhmgbnb5np+fD7TS/vn5+VAnMmMal5aW5ja/TSaTWl9fH96+tr29XWtra7W1tTXci6yen5/X4eFhHR0d1fr6elU9Oz0166x6drgf/IcOyzE0QsPq6upQz+npaZ2dnQ198DPHx8dzfKdftM9v6Db95xrFNHEfzzAe/nt2dlZHR0d1dnZWp6enw28eH9o5OTmpi4uLoR8867FhfPlwfW9vr87Ozurk5OSXrb7fptyERzd2Cgitja2FcGlpac5hVNWc0vu6DYF/G3MK/LUyU7jGfWlAsr5fDoX+npyc1P7+fj169Ki2t7draWlpMOYc93Cd80zjBf9toDDcyMLZ2dmwRBQZ8H0Uy4xlxWPd9Y1n7byoe2Vl5YoBtJGzQTY9S0tLgzybLv8OXWtra7WysjLoxGQyGZzQZDIZDFYaS8tvOjuMJEZ3zIkm7+C7x9397YCU//dY+nnf0wHA/NgW2DFR0vFkva6nczIdDenM78p8SX26abmxU9jY2Kiqmht0BNnXqy4VF2HzbxSfeulBTYFE6FOAUtCtYKurq3OK2Qnkm71cXFzMHduwsrJSFxcXtbW1VRsbG3PHW6fjhMc2uDbU5+fnc4az6hKNw2vQ3ubm5mBAMabpkJCNFNzrhJk27XCgCwBzcXExh0SJhNwnitEwxc6HZx0lwSOQqg0c0czp6enAT9rmr6Nm88OG2rTyOTs7G2hKlG9jal3JT/62SM8cFaSRTkTfRV4Z6fseOwQABVGgfz89PR2eJaIgQrorz0pGtZZbA7JF5cZOAUW28mN8U7AZfAjEiIwVlJBn0nj7EDPasiNJw4HCWnARHofkb3YHcXZ2VoeHh7W0tFR/7+/9vdrb26uVlZW6f/9+vfDCC4PBdul4aj7ym1OJ3JMRw9LS0oCQ19bWBqewubl5xUDyndSREbCL7yfiYWxtsHgeeSVKwCnZMWQ6J+UinWaXSjFtafj40D48t/NIPbIzyAiBv0Q3adTT0SVy7xD9mLPIVJZ1Lx2F0zxnZ2dzDqKLJBLt82zy2u3acThl9su1ICNO4aZcW79uUm7sFDoPZETG7+kUrjO+1OGwOlHrWApqEcL0/XnfL5c5B1I5R0dH9eTJk7q4uBhe8LKxsTGHdKuu8pHSjWVGiHYIXOcZp2YwiBjHlZWVQZgt0GP05O+ZXze9fGiDdnAoRlKu1zKXBiyNpdNo1NPx7+LiYmg7I7KxNEiXZoWvXSqmk+cu8rCO2rmnM8j7xz5jqD+dSDoh/2/+JvK3TKXj+eXoEDL96Wigi4Q/ZU6hSzfw3cruVJGRUA62BcOTXv49r1FwRIl+qvo8WqYTQBiE9W9WBwHvj46OqurZW7mWlpbq5ZdfrpOTk3rrW99aOzs79dJLL80ZaEqiaMbK+fCLi4srxtRjZmdBxEj0sry8XOvr67W6ulrb29uDcbdQu5gezyVtb28P6QZPanqeAfqRu0xhdSg6EXKiMtO0uro6pMnMl5Rj89OG1A4rU1s2nJ63yDQPY0CxsXU04nudCuqe5zMWKXQOwHrftZ+/eRKaCKOq5hY+kIY7PT2tw8PDudT1L4di2YcnRL2OPi1z/P8pm1NwCscoMCfMuokvrpHXtoPw/XnWPc8kCrJC8luH8syINDS0leHtm60wXqenp1X17H0JKysrw8qg2Ww2vDvAwjWW7ksDirOt6pE9jsD5fo8b15ggttFO40vJa6xa8uSr56KgFeMGMHAKyH1zH42wc5La6R0bQu7nPiIlj4f74/k1y6jndDJi5pqNcfIdQ5upNetSTjJ3TiX50kUPrru7r3vOYNEOpRtvA4tMXb5ZS+cIHNl69V/3nhCK5egm5VZOgQZpxII4FupW1dzcgyeGjDL9TCpFhrSepEuUk87DKAxj49QXisMLQd5sjsFo8OLionZ3dwdkxrjgFMaQehpyp4tsOPysEWYi9hw38u0YMsDAxsZGuyTUxQrj1IpX8WBMQKEgTpyDDVVGRw6/uQ69mb/NaHg6nQ797pZy8rz5ksXO2QYWR5PzZBRHTpmmsYybprzmsbWz6xyCowdHBGPpJt/vSfGu3077cu+buWTq09GAZc4pI0rK0G2jhKpbnn2Uxtr5UYf0id75nXvpYIaSKFHXQQtoGqxMV7ksQqcYQZibRuLNhEboz9HR0dCvj3/843V8fFyrq6vDxN3Ozs6w7h6+UHIMKE4lGVEbPNgI8AwrZzDGXk6KE0dWMnJx+zY2Tg1SN87g5OSkTk5OhnmWbpUMhVSaU2pd5AldpME8cW9+Gd3yjBEf99OOUydG2gZVmfKxjKex9YqqBGMduh+LCDJNlCjfjiqdlnW9W6VkA+c+HB4e1vHx8SCjb8bitCRjn3uI7CyQPcuI66q6mh25abn1gXjZCIKYoW56fSu+DTMCjdB2nfP3DC8dWtJW1aVQLUo/JM38Dy1vNkQC4mP1xv7+fi0tLdXu7m5NJpPBqM1ms2HTGQj5pvUzzk6teJztGDw28NxpJlI8jvgSFFTVnOHBaDptkssXu1UxVfOy57TXWLFjSKWlvuy7AYrroRgF4pByRU4W88UppYwM0vhn9Nd9t8FPJ5ET75meyvSd6+jq7sDe6enp4MzfjPMInYF3SqiLBvwM/9vGVs3rrZ+9Sbn16qNFobyvg/TsAKquhqbcv7KyMhiBROlcW6Skqdgp9HmfUZsN2Orq6mBATk5O3nQRg5f07e3t1fn5ea2srNTh4eGQVtna2qqLi4thw1uH1D0WqczUyf9cg9fT6XROAezIqZt6nf9nTHPMSJXYyOREpq8tLT3ba0BEkTJig9XJLbQ5sr24uLjSZxtJR9LmYSLCLjIherUjc5omoznn6p1G6xxU1eWChNS9jBC6yMEOgWfGInzz9TqHcnx8XCcnJ/X06dM6OjoaxvjNUpwe8sdAwymitLupB05hpmO4Kaij3MopdHk+G3cTb8FysXI5jeMUTjfplArlNq2czu2SjrAjyHkKe1gfgUAbVqw3Q8EAERVghEDZpFpItziNkkKbQtmlJDIK4zeMiI+eqJqPGvjOfEM6cozw2O5XG/Y0zow3k+2muWpevjLyTUTN/chu9pH/rcjZXueY8q/rwLHaOXVyik6lU0tw1k04dw6AvmZ9PGsDn/xKg5VRPwCAFUanp6dz+xHeDGUsKsj5AstolwJKg2+ZyizKp8wpeEmqS0YCmZLxBHXV1Z3PNhzkmC8unu1CzRSTBTNDJ5fp9HLVEvfSrlMRifa4117X+dA3umNYWloadhZvbGzU+vr6cI4PqBzjSprp6Oho+M35zRTGLBiITqjhoyMK/57Hqcxms2GOgN+XlpZqbW1tzqDkhKXHzQjf4+05ABtGSjf2lmF+YzLZdZO+NNBwP50S8+SzDWYaXqcEDGpw6i6ZynK7NtRjn4wGTH86Sq4lDzsHaoPmvqH3x8fHdXBwMDiFN8vKQGSP88McKWZEkCmiLir3SsEE476P9NGYrma5sVPIEKbq6galsYYzHZDpJNfHh+WricCskJQ0OnYmGH7uQ4HddqIeI2DQl3dbvtHKZPJsp/Dq6mo9fPiw1tfXa2NjYzjuwjlMUgmsTjKKZFKa3ck5MUixYejCXv+OcfCSO+QqJ0cxfLQFDaD9Ls3h745AEnl2ES9I7uLiYkgrWv4uLi7mNl7m8lbT34X8lkWn1qr6CJt6PPlMuzlHkgaeOnnGziIdQecUzJ+87jYAdhSnCDNNRRRzfn5ex8fHQ6ro+Pi4Dg8P55xBZgreSMWpIpZPw5P8VM3bn9wQ6ftSdjoZdmR203LrOYWsvPNMY8/yfKIf6iEEnkwud6mm8nbCkbRZ6PN6/p8TNF0bpKHeiIKJYHGyKfsTVldXr6QxQHqZ93WqhzExzzz26QjMry691MkCv2fKJtMZOBMvUsBYZc6a/jgKdJSaxtJKlUemdIg60XCmr6z4VnjacvTK8+ab+UH/aCOX1XZpn6TXjimjkrE+5j2UMRTvqMbXDPAcmXJKqp2p5yYzLfVGKU4ZGfx0MtGljhJwG6Cn8e+igk/ZRHPOJ3RCldGEw32ew+iPFaeSLPSeLMxQiWKkj+B7zsGhdiKvrh6U2e0wMflGKDjX9fX1un//fm1sbNT29vZc+mc6ndbx8fGcAax6tvvZvMJJ80zVJVIBAZn/VVeXFEOT5QZDbaPTpQO7lSdGyL43DYgNKM7QhsaGs0tLoqBEuYm4fS3bQ3at4P7rlVgJOjy35mgnI1z2Itip5z6FdI6Mk6+5HfPY7aX+dFE2JQ+m9Jgzb8ARLKwyInpyWo82fUT6G6U4QnB07eWndgxVl9FAnktmB9DNFRho8H/V1ejtuvLcZx8t8kTptfyM0w1p3KfTywPM3HGUB6U0M1IZ3Z4VfVGUAK2JFrnfHv6NVOA3cwfr6+tXED0Kd3p6WisrK3V8fFxVz9bdp5O1g7Ri2ulaNmzocqxtGBPB5gRtl8pwBGMjZgNIsWHL3y2TqXD+biVl/iA3DZk3s9nl8t+zs7O5SXraWllZmTsAMCMuz/HYMWY0ZodgI+9FEvlh7Md+y7G3nnkcuygBvuUqKOYLWP1Gqoh2kDnzMh3JWFTyD2JBthZFCLkBMlNNWV/VJRhzask2k5LpqJuWW0UKifZMKKW7B4JNtO9Lg+16bdRBhu4kk2tdKiDp8LV0LL43Q2yvFX4jpZDsFJgH6EJJlO74+HjYn9A5BRvPTAFgNL2+3jKSPMsxSCPg63YKvocx75yI60NGOsSUO0RTrh3602a3AzlpQ15ZTZOGvOoyLdlNMkI3xjBXwaVTddvdstXkqenuUkKdM8/7rDfck32AbiaNDw4O5o7HZmzSCHqczs7O5qK1N0rJubKxD/d6rsBzfPyeoMXXsFd+xnMO6dQXlefa0ZzC3UURvmZiOrTdGQ2nI/gd9OFow2g/UwKE7Sioc8mOCNx+5lt97Tq+/IPmLKzAuU2eYmG8uLiog4ODOj4+rvPz89ra2hqOwIB/GDwEnhTBZDIZlNfLV3P1URpsfnOKBUNmWfJ9mTKyQR1DrmnQ8p0IriOdjuVkeXm5NjY2hpVT3g/Bs0b2fNIIcC8rbqifHeeW027CGvrsrFwPqwXhWepINx7Jw86QpC4nv91vDP/u7u4QGbCTHN3d2NiY47fH3v8bFL4Rig24wVJGCFWXwMSR6HWOJB1A1dVd9shAOvzryq13NLvTndI4jEkkYeIpiwwu9TmfiyJ29WU+1PVnBOGUiBXGbbtf6fiSTtObdf2DVKA1UQrXUTqWBKO4k8mz1z5y3+rq6qDYdqI2Nl422UUFfDfPuvmIdAxc63hNfQYAnRNKRfWYXpeisGLjEHnOkUKXp+8iZs8rpJxm1Js8zDYzwsn7Oyfg0gGisZLRhx0CEZJ3JPOyI881mh8AvrGVYUn3P6g6RumQvfXF0WmXUrLe2HFkajMjNM9XVH0KnUJnFLNjGabSIQuZGeLwf8wxWJGNuNK4V109MKzqcrIOdIXgjgmWaaY4BMx702FQp8Pyv1+lc2Qel4wabDg644LBYmkq6Q7SGLwwh1CXSVwMogXXgm6UZESeE9ZOraytrc1FfJkasSGuunq8utv2ssC1tbW5lUpjJVFzF/14JY0jGke33UuNeItatsfv/E0Dje6YBn/PeZgs6SC66MG0dM/z1xPbpIqOjo7q5ORk2HPAcuJ0wjZ8njf0MmSn4Lwi6e+3jt2mQKt3rVsn+N1ymd8pfjmUnWnOMbn+PIfrpuV1O4XMhdqo5jOZEqBknjs7kGg/6/N8B9+7SCDDfK51YXAiant9p5gSkVF3GuNPteCmsPkawoNx6lIjnuC3MyGnvbS0NDfPwDJNC71p8AqXHBNo86dD0+m4l5aWrpzkamPs8c318FZMoy0blrH2XSx/1FtVc+3wO33O498NMrjfy1Nt0N2uHZO/pzN3ms+rq8ZKOhM7Od+T1813nAIpIyIE5kW66CT1Ih2Rl9tWXc7BJID5B9k5GDgb4Wd61Smj3H+QziOPQkm9dTtjYHtReS6nMDzceKIxpJp1ZKdcT9cWStdFFwiI0aeVw1FDelgrddduGlszOz8uCKnbui4t8bwlBcLCYKPsdIYRW44Z3+HZyclJTSaT4Z0MCDF12NhS6Gsi/2zTkR8869JNHb/hLfJgQ2H0nwpnpUojYz52xpL+G91CcxqldML0t1uR0jlnf08Db4eWc2hcy/FOg2zdG0Pe2ZbH0qkiDqw7OjqaSx15HiDbt5ykLnsOwfyww8tn/n4AL5ebtNdFBnYM3fyB9YjnDGRyXtD3GWAjo7d1DrdafZSpoS70XGRgKN11C+eikhEBApLLA53CcERh5Nx5UxsGoyIjQ4wA1xN1ux7oNK3dHMbzlkQSOXHKRrXOaBvNJ+J3nVXPnMPh4eGcsee75xcyT58TwnaWXgbsDWKdLDlPyj3r6+sDHw8PD+ccWUZpLImkLSO1jDLySJccJ8aVM7uISugvzi+P0ECHSFvRjmWCVFKOpfsBQjeStkzOZrO5lIsdQzcOY87BBhj+eA8ER1DYKbBSypPb3REg9DtBgVcr0a6BBPc4Yk9A+MkqY/bL5SY6jE74/SCdvlrGU/YNZHKS2vSa1owYbuoYnmui2YOb6L+LBm5SbFwpFloLATQgvEajVfMGmf/tRHzdTE2n53tToToeLOr7GMp4vY7Bzs4hZgpaGnn32fSn0OIoqi7fUnd2djYYNIfFOWE65hicsuscpx3uWB/tzIkGvAghUT/tZVppaWlpDmknf1xMV451RmKZNuyUMtM//p5O2vyxnoyBr0yrmJYuGuj+T4cwnV6+mwKnk0eVJ2I3Ks4FIh6/sWJn4E/KiZ3DJ0OnxvjaAYROl/3depQAp/vutq87hDKL9cLP8NtNyo2dAkLbeZ1FUUESY0EYy+Pn/w6jLayuK+tzm0472cHwbNZDf5M+O5F0FtCbjsp0Z/ibQn6bYucIkmAC1QbUp5wuLy/PvSchnaeNbu45oP8+wphlnaYFw5DG2zxhQjEnFSnmMbS5fxQv/QSJOUL0+MBjjJl5aP7byeXHDtPRqDepOYduJ5FODNQPXy1j9JUX97g+I3DT1Bn8ztjThvuVEVouA6V/jgrOzs6GdyXnUlF4ZBrscK175oMLToQoFD3KdGFOOr/eZatj4Mml42ny3X/Rp/X19SuG3XMKBgCMbZdmTHuadjPr/5RFCh1KssHrHMMixqZxznY6h5EeMFFuhxISZeT1DD3t/Koud+4a8XV8cL1c88e05WotK2vHp/wfQ200nqsTEk27rgw5odcGzH10GoT7fbic+2WFcKTiPqa8UB/3eJWKNzWlXGGkbJycSrExx2A4/dKhbxtaK5XTVjaqGE5Qs495dr8ZDw4hTGea/LbDTR6bv0695BgahBj8ULpIxZGBX1REegin6ny/+0AdHa3IDOPh9CUyAN3Um/rnSDNB1U2N3lixkeZ//+1KOoWUf2i2TnYfyx3PTCaTYUVfOpHU/7TJGXmYz9eVW72juWNIClt2yowbu4+SCKhrL5/N5axWINAGypseFvSSzqRDkC6mr3MM5k0KS94Pal7kydMReemnDUknfItQRBrnRDhGK/k7RtVzCHkMdhpH8w36jCSt+NRt1JkFY8xchx2saWfVStX8ew+cHsl0k53JWJrCvDCaxpDl8e2cMAu/ujHJiM98Nx12Cj5OIvVvTK4MVDJy9VJajL8Pq7ND6JxNyks6UesVOpj6Z+Ns/cmUo4Hc63EKHnv/73rH7EwXndlgpwPwZHNGxaYjddi6TVt8N88dfWT/blJu7BR+5a/8lXPnl6AE6ak9+MngZGQyukOw3JupoRSUfGbooBQzGcNvFxcXgzK6nUQzGdkkEh2j0cqW/UuE6rZTsDBwm5ubc+g3c9DQ6jNSbDQScfg5aEulTCfO3+Pj44GOqhoMH7Sz4c1Im/Z8OJ2NDKtoSKWAUv3MZDKpk5OTuSMgHL24P2lUbZgcGdpxZJqQ9tPQUhd7OTJ6JaojFbSxsTH8Bl+925zfPZns+yjs28gluJ58hg9cd8Qw5gjsELyCKA/Ws/HpSkYhgDPLOjbCR124T+kUHGGkXjkl2OmRf0vQwjgn6k7H2hlW88N6YoNNIUJ0OtwylVGDQZ7vsQPo6BpLGX3SncL29nadn58PqyYIIxN5fSpLZ6RSCCgWBj+fg24DkKiHAciJ26xrzAujfK6vixjsWHwtB93G1AbcuXnTaUSS/LLRHJuYNo0uidydQrEBc9/skAwcTFeOmXPNp6enc7y3Eb8uLHZOPh1tN2ZWyuvqT4ft41Soy2fZdP00sLIMOLc/mUzm0mPWu9wsZyOVkXjOX/BxHV7Z1M0XWFbdV+r0Xz8zxr9MN6GT1Oto3/ICL9MBZjqN+x3VVl2+5tRzPpnC4e+YTlhPO6eQRtly5X50ANr0dHV1NjABsO+5abmxU3jw4MHwnTCZjSqOHjj+9iYOwoKVRCf6zzRLF7Za0fy8r2eoynNdXZ40XVm5fIVlPm/hSRo8WDYIRhZd+Jn9Qah5a1MuqaR9L3HzpHPy269AhU47i4wUeC6Fuupy1zigYTqdDmmSPJSrE9REWUyG2yjmkk5HdSsrK4NBo14bKS85TaeVRtL9GwMeqbR2AFWX0RPtIDs2WpYNaLezpa828vkdw20DiFFPVO/nuwjBqaFcSURUkJPHKetG8+irDXZGY5b91NM06vArx4hnLi4uhgMfkxfQtb6+XltbW8M9vPuZM7s6UGSD3IGk1NG87ufgYZe9gEd2HEnLokjGNKQjuY1DqLqFU2ADkz07xmlzc3NuJYhXZHAtBdPMSyNL51L4HP7ze2fks55M76SSdwOLYMNYBtP3d6jfv+XKCN+TH9PWpcNMr9MNVfOnf+IYjEwzN0//vbIo+5/CtSiKcP8ABqTkeN0n6RXzBNmws5lMLpewsqrINDpv7jQG9xltI3cYzuvkLiOIsXRahxb9rHfeGtGbZqP4BA60l8bbE9l2mHYwSV+eQ+TUy3Q6HXTUUYZlKR02fUgjbhTvvlhnoS1RdtKOk59Op8Nu+gR2gB/zlb+ko5wFWFtbG46P78YVejOdw31peMfkgO8dKO4iBffJ7XYpo3RWpoH70Ye0cdxzk3Jjp2BvOjz8/yuiBdgemujBaSaU3gK9iMkZhtK5LvXgZ12saN19VgLq6kLVTJukYelQ/k2cQkYUXZopBRPFMN18T4eQ8wUpfKafOsy7LkLwWNmBo5TZr0yd2EinwjMeGFfzwIjf9HqS2+DDaNp9XyQnKQvZ507e0kF0SNfPWsb43k0k8qzz/HYy9AvdSlnLCWPzZTabzUX2naPskGYHxFIn05Gkjvt6pmd41nNEHhcDH67DDztMyysRNhGb+2F+pjHu+rCIN9cB1KzXi13Mj0T5qbdZrN9J902dAeXWTsEfBsW5Sq6vrq7W5ubm0HGEzqsZHK6mYPK9Y7qRojvs8JiSwmZFc6okjXvV5U5EdqCmUTRKMUJ1HTaCKWAIZIe6snR98KSriyMJaM5+WNhy5UK3fM7KasF0SsDjQN+qLt+QRmTpl5bTf6fpjH5Jq0CnFwV4PKgP/jkScdrIvDdvq2puUtxGvkNo3TjTlk8DTWXu2oZ2eJB0eh9Ezt/BOztCEHYeOwFdfgdJF5U6ouyMXKJh8xb5dAqrW2CRqTI/Z6fveRo+Tk3y1sCqGo4O97gRUWxsbAyRAntdvAQW59jR2DmDRO7miYFdN9/q5xMMdp8xQ5/2JPVyTGavK8+1ec0dMVJ05+ypUxlsqFDyqvkcMh+Ei7rdLnSlkXPprsO8zHFTd2eUEym7zczTZiidDoPnoT3RXefZUwBNo/PrY2Fo7mNwX8fQSCdYHXIyvZmiMVr3792EPbzA4UJbpp1sZNxWOk3aQa6SnzznevN6Fx1148IzY9ftmDx+mRrISCrz/Zni8f3+fdEcgZ9LWsf65v6l0+rSSXbQCRwSYBg1pwO2vMIngx7kAgOf8szz3XEStOlVTxnx5bgbEDN+2XfbJtuITj6uywoscghjTqGr5zblVk4BBXVH3Rmu5WRQEp0DY2THxzsoiSSMQC1sRlid0cqSxswKacNASZrTMSZdTovBs3QAaQjMB9NpI+r64JedS9VlRODzfXwdlI7hRSncr+xrJ1we43SWNlRGS1ZcJts8bwPvrWg2npljN4/SaRmN+XjsjECTvqqrSxVtpLOQo+6M2dLS0lzaxg7PaQPLivvVnTFEPXle02w2m4tQaJf3Ghu4mJdG7OncbLgp6XRdn/mTxg4ZyXtyrDHydiq0453eyCy2wcdoeByQeyIEH4iYMg2PU8fNnw7Fm2ceQ/PLum30PxZBdMCsoyPHrnMilveblBs7BSssHbMwXyc8SXx33Z3M0wNRABvAZD50YXDc/pjAZnGUk8bRzsdOzaF/F97zLEvrOsUyKvUgmt/uC/nlpD2dArRmSsnC6351DqGLXJLvlgEbE0cxzmPDJ5wUE9EoOAbVEUWX1uK3dKyJnCwTia4zEnX0majNcpZRF+1yNAPXuzRL54CrakD43iPg3cQYQe9LgGaDJzsiaM8D+jon16Fcl+uMS+cIvCLIe2e43zn9fNbzZt4rYmObWQWPuxc6ZMrRfFlbWxscaRpk2rfe5/xiGnd0nf/TqC8CQ5aLsaxBfvezSdtty42dQoadncfv7rUn7TrjkDoNUKI5riWSSAOfHrhLHSxyYOnobPxz8DqECvqzY4Buh8h2AlbCjldJbyp1GhkLcTdPYHSGU+jaSoO4yCj4Nxtsj4fRLcWK7noSiIyNi/nSGeB0sNDhMbLM8nuX33ddGe2675bnpNOAiv/57mMdcAp56ByRAP31JCt9snxkNJmy5JL6lb93qaJFBR504KGjIVFwRgxdxJHjbp7iEDwW5oX1JZF5R0uXkTDf/J1oMMGe64XXOeGcTsl9pw2XjCi8IvG25dbpo1RcD1KXUjCC8v0WFAtFLjGzd+Y5M9apFEoaPUo3kPw1esx7HH1YuHgeNOMziTItAR3eRe2ookOk5nWG+SkgfucwQu5oq+u7xymRN387I7xIOE2r68kjHxhjop2Tk5OB9s3NzbmlqEQbHJCWxp5iefIOXBR/Y2Nj4I35nxvvMoTv+m8ZS51I5Xf+2e8kzrH0JHW+nKYzwshRgjBQddLhOjrjmnzEgOZzaYAT4FjnzbcO3Tut5boMvPjd8ynolB0nbfrYlY2Njbn9Mjm/BH3ojzdMIo+WRUdb5peBnftiJ28bYODmzETKX6J+83RsXi4jtNuWWx2dPRYBLBKsRYWOZCqlaj687Dwzzxt1mQldBEF9NmpWyE5YfF+ixzSOHlA7Bof/VopuYxd1mRfpGN2HDkWP/d+hoKzD7VxXxpyGee3+XFcAFRg0lDHThtBsZfWKFQMF8yqNUqZYOlozpKc+y24Cn45HaQDtTDzf4TmIpCcNu+eyHHF3fYGG7FvndPI5yiI+jcmA25nNZlfmBzvbkXqd/eoio3w2U6YutOsJbFa52ejjXKw7Bli2NfQTml0/v6Us2TEn6MsxGMtkdGOR993WMTzX6iMrQSp+IvZFRNtTjnXCggHCoxgBmUFZH2110QT3eYNd93vVfH4zB21paWk4AoQlb9R3cHBQJycnw8Ftnhz0fUkzv4F+aDvznp1z8vcMi/M+6PW96RwSPWZ6KPnbyUFHYxZQvp0rvLXcLS0926FKXX4pCzxOnjoq8yKGnIfwM16xtSg15e/IpMcJ+fI8kB2UQQTf0wBZPxLVkn5wX67jeecociz8LPR2UYZlzVFQ1aXR9diY9qSJZzJFbDq6FVUGOt44ST2ZhWCs/JxfOZv6lUDETi0dWO6xSfTvyW6DlhyrHH9fW6RHvi/177ryXC/ZoXSCW3WZSnI6xQ4gDb8HPzvSrfGFsYtyZom+zZhE3emFM/rI3LLr5zv7MkAnKM/y8nJtbW3VxcVFHRwc1PHxcR0eHg4Oohv0riRqod3umUQ2PhIjFSedSiqCxyrbtVAmkndJJJSTmP6+vLw8TAqyWgQabGgnk8nwljKMm9fys4qE+tNY+qA3HyoIffTTfc+UppXZhh35905i1tDn+vaquhIVYNBIfWBE85QA2uZZT8Tm3E0HnNDLzujmuHRGKpc2u/hZOzrTsygyd8kIiZ3us9lsbnIZepAbQIP5BH9dt+U6+WFHYHDmPtgx2fH44wlqA698PmmFjkwVWQetd+xDMt23nXi+tVO4zjt1ApUC4Pts2DoB7bwm36+LRPK3NKpdtJN0JtNNL4PqcNWH1WHYHM3Y8PhkTyutIwg7waSl40n+TeSf/HAUwTPXjbHv62i5LlzNlEsHFlz/ovSPgYiXYtqA5yF1VTXk7nEKbh85pE4bUd+XwCIjFb94J4+3zvQBBUe9trY2vHvh7Ozsytr65GV+p79pcNLwu54xgNE5lOuMuPtk3vlatjtWn/mfgAcddP1sbnPE7kiqi5qts05XGlTxu+np5hEsQ8he9ieBVWZAOh6krqccmV4vMBlbgjtWbvU+hfQ6nRK7YSsmDPSaeP/1wFqQcwAt7HjuDCNTKMccS1XNpRS6Z933NKieOEKJuW7EhvC+8MIL9fDhw7q4uKinT5/W8fFx7e7uDobDfLOQG6V0xsS/GVHAu0UrJrJ/6Xzcz7HcrMfQ/HEbHmPyyh0dRlqmyUtWUXJ2szLh6JN7c4IvDTdr/zHW0IwSwWdQ6ebm5txchZWN9p0iMrq3k8KwYaTMA/raRafLy8tzB75R/8rKytzpAJ48N889Jua5/7fhhdaMRm0DurrtNMxbp8kY6wSQXerJ7TqCoziq5DecAvJkR26HNpbWJArxvgg75IyMPLeTAGZpaelKJGie2VF1TtN21XyCTqfZcISeILeTuGka6VZvXruJBxtDHIm0svP5N78ziBgLG61EnElfIgxKGiC+d8gm+ZAOwog7l5hRl/OoXhWRSp35cdqwwU7aMg1kA8P9FjTfm8qZ4zfmVPLeTmm7kgjbSotBh376Ra7X0YGdo1d4Zb/sSHDAflvbmFMgb+88M/VZ2atqbrmoj2ROfps3XpUEj4ks2Z+QixOydLn31LHUt07XLFOdHnXjx302eglo/NeO3vV02YJOhhJM+sPvk8lk7igXA5cEf5kBcMrFEYIdNA7fJftFfy8uLk9rWDR3YxvVRWG+bj5Yv73iMFNUvu8m5XW9ea0raWBcEr1X1Zx37ULOqnnld6Tg8HFReJQGxsaom5fIfGDnpU2XHUJ69ayTASWqeOGFF4Zc8eHhYZ2entbBwcHcC9LdRserpMHOCSTlFAk8WSSI5p3r7sbG4+Gx9ffumW7C3fl41721tTWHkNOR83yiMWig3pOTkzo5OamnT58OTgFDZgXDOWxtbQ1jkWNsnuCYfJS1ZS/BDO15lQxtAwwYe57nGRttR4cZxZsnRtqd7OT1lIUObGFozf8u2u8ASUagGZ2kHeG6+ZifzKGnU0hn5PsYC1J2XjCSfHMUNsYfA4cxJwfvnYLqIhjzMG2ZgYz/cp/7NmYfs9xq89oYGs9OWjCsBFXzDKVziyapjD4J68xsh2kdwk9kkXUb2VvZOwdkQ+AIgIHJIyRMQ1XNoT7nGnl2c3NzzvCdn5/XwcHB3HHUTmFYedJZJTLIiIr+Jo15bez/LgcKLd6006Ee+Du2pLBrj/5vbGxckUMbEBsol278O8NnZ1NVw96IDv2m4cu205EnimeMEmHyLPezh8J8MmjxkRf+20UN9Nuy7rYzKun4aBrS2WeknXTTrvetmA+elE59Ni2OshI1e3yIvjyu3kVPymVra2tIAxswdqCRo0fgkyefO767r/DAdHfG2raley6PpgH8eUFKgpCblte1+misLHIcHrAkeBHSr5oPLc00Gxobhg7Bj9Xv5zvEf5Nr3SD7/i4fmd6fenAKq6urw3uIM0ftM206+sh9m4dGJOZ9x+/sg+t2TrgzIF2qIA0Czt08GTNWpN5YYQOvxgxf1zfT4EUCHgc/2xmjrv6unaqracWun0k38o0MIM9G3xisdArO23fppgQG0HGdwchn8rd83s5yrA079kxP5kT6GI+dTvVksOn0/AD3MjfjJeS8fIeoLOci7FCq5uU/kb3tkKOiMV2zvC9KM7lvi2zQIuBzk3KrOYVsqOtkhnip4H42UVmHDkh9mMFZUBIjVXtRG323nQOVoT305jLCDH+5Pp1evnHMjg76QCFJO2kHvP3m5uawc/fhw4c1m82G/DIO4vT0tI6Ojubyzl3JiCINd4aW6WzhSzpXRxoZ7TkqNO8Ibb0rmXFjyaYdJDTOZrM6Pj4eFNGTaclLio1wfgdR+Tnzx0YAOWFzU6Iwy68NMjSYnqpLY5IRRir5+vr6IC9sfpzNZnOnn0KXJzqdfuM7RhO59KFv0NxtljM9Hg878i7l63stV56g7g6vs8POVV/ca0fr64xnOjvGCH6ura0NS8fX1taGNuGp5/M6x81bAb2DOoFF8ioBUtXVOUI7Ed9vQOH+I0vmO+OY8wz53HXlVnMKHcLtkA6dtmIlOsl6uN551ExD8IyPnvBKg5zAS1SJQGakYiGDkR6MpDv7TR7cCpbGluvddxSNftgYg25ANuyUPj4+rrOzszo6OhoMRNJpntoBdOkd9yf7nGOSY5gOJ3kFXzwmHisbsbH6fNCYeZzGy86oi0ISneYYgDydYuh2uCavSZ0luKF9t5XfkzfuOxFBd3wF99g5wFPLDPtnPDY2QqbHjj4ddFdslFx39nMMXWdd/mv7kEDAE6yZWnHu3XM2dgb0yRF4Orc0/K63s0vp9O3AKJ3epdPIaynrpm3MmacjvUm5lVMgpE1iq/oB5HuedskHBcyBTqRlj8hfP89fp5dSGGx0qCdPzszQLZlqWrIwOeh7Om+/yNDaaeGEM0SeTCa1s7MzKODx8XEdHx/X48eP6+TkpI6Ojq4YFPeHkuciLaKpu5YClk4v881Gl/DdOVCcqZfmun2eN8p1Ksn8NrIybRiK6XQ6ONbOMZg/q6urc2kG2srUUNX8YXaMY9Ji+jreeTWRf4c37ktnOHAOXAMdAyoADtYDAxnXZwCRm+VoyzS6zpR76/SYs0wHm/fkhHoHljD6REZeo5+RYy4d7rIHgE/LCXqZ9aVzRQ/SDnSG3bz1fQlU+b2rq+Nj3n+TcmOnkILc/W4ifN2hnZGWjXH+lgLWtWVkBgqounRgCIfDM4Qf9Ef7PmCLiT2UYgy9Jm9wDIlYzQ+vCDLN7pfv7yZt+X9ra6vu3btXFxcX9fDhwzo+Pq4nT54Mb9tCEfnr1QgZySWfbRjy+Q6xuSRi6qJB/hJdoXhra2tz6M19xylQfDxH9sE8TPlKnuRY+l6v6PB4paFjrCzT7r8BTSp6Lhl0VMCkJkteLfs2YpPJZG6ykXq9wzXH1I4gjVy2kykdeJzO3/LpOoy0xxxi8sX0ehz8oX/03anBjAD88R4Vo/+MtCwPVZeviPXKJOt/2jN4neDA/czrtjfpDFJ/c8VRghXr4SfdKXjAF02GWBgSHbmernRecdEzyQSHws47e1Bns6vnkvDdwmM0ll54zDlaoWwY3T48gpbO43eOwv9b4Oz8yHeurKwMEUSXLkhhS6HpFDXbz74kkjJ6SsNISQOa49TRSv1pjDpjk/zq+uR6u+ez7pwEdYTqFE5ntKE96fJ3O0wMmpfNLqLZoAq5x3hlfw2ofN36bUfAb91qHBcDB48lpQMg3XjYsYH4cQB2DukoPNa5kom2HS0lrYynxyx51AE6fs++pOyN2bZuHN2mbUPavM55dc7nk+4UzKgUUCO17GxHnOvje9XVM1LSWFZdNVh44kwVuV7udV8mk8ngHOwojApRyLHSOYdOAC08OfnjUJdQGIHLFQ4ZZZ2eng70bm5u1ubmZt2/f78ODw/r5OSkXn311To8PKy9vb2BtjEjm0a8+z0VvjNq5kGiJ0dpaRScYszIL4tlB/oSrGQKhTa6MJ/nOgeYUZvl36lP60QeVJcyZN4ZTZoeGy6nNmjPCytcryfzq+Y31VlPu0jB9WS93a5c87sbq4wW3LYdoNEszzAxvL29XRsbG4OeeJwSrJnn3usxBmJtB5wqo40u8rP+2G5ZP9LBJNBJXc7obAwAOyWW0VjKU/f3puW5lqSOEd1d86Bf93uHJn1fotmq+dUcGIsuZK+6fIF8osv07lauqqvLxMYUw4Pkdr1JK/PYp6enc05geXl5eMH4xsZGbW5uXkn3dKGihWxra2swhOzGZVXFWDEvJpP510J2DiFRfPbdfKJ++JnKzb1WPhQqeev+esMOdfB8TiQb/fKxIc9cco5XKnHKTreM0gprfiKreUxB5xDc1hgSTV5DD+lQOwDLrOvrJoihE77ZqCMTHqsO8Zof6bhTpjypz45/5nO6tF2OlX+HdqJn82dswp3i9E/Hry4KHXvGJQ24/y6KwBbZ29RBOwbbTQOh68qt37xmQrqwb8yo85ufpQNdWJ/Pj4WuKFdVDecgZQ6x6hIZkcOm3s6we5I36fKzyegxptvQZUkDglNgMhmn4DSAjWIabfgwm82GybfT09Pa398f5ko6HpqWzkkivImGMlrg+QxjLS8YkzRE6TStqHbW6RS8uSzD/hwnnmNuwmPifjsqBu3TfxvYdDpdMUI3v4z6KJmGuonMpHOYzWYDQvYSy+55j7nrMe3p9DtjnzYhAVynG5n+BMysr6/X5ubmHMo2b3w0CfXbWNvBs9LIkYzPS/O9BpdOI/laRljJkzEASbFe0H/a6pYFj40x/MvfHRmY/2O2tSs3dgqPHz+utbW1YZNHejeHv2koUoj9vRsYd9ID6nRPGoE01lXz6SiHg/xu1GcHwe/eoey2EgF3JfOxXR+h1yHq2dnZ8O6Fx48f1/7+fm1tbdVb3vKW2tzcHLbhr6yszL1PgPq9Oswo6+Mf/3jt7++3Cjo25wAK95JGj0vyII25ozfGz4pgRe2iwETXKfj5Vq2qmlvPn8YwnSsAghSLka/Hp4vIFiFNF647muHwxO3t7bmoxWkVVkdNp8+O5nA6zbLMd4ylC/IwBtRwjpb/Tqccea2trQ1ndY2h29TPDnitr6/X9vZ2bW9v19bW1hAdWD7cz47+qqtzNFWX6UJ+n81mc87R6Tjfk87B9fvZNN7puMZo7YrpRlc6x+w2EqR6oULnCBK0XVdu7BSOjo6GnHseOAXhi9BG932RkUwD4xDN5br2LZQ5YIvQHXViCCx8XV/GnjWNmbriPvOx6tKwXVxc1P7+fk2n09rY2Bj44G36KKtTYzbCRB2gsVznv4h+r5SyM8iowH95tvs/Det1pVPSDvU4ejEKrLo6X2Il6lC+74ffiZY72rtrtElbyKfnkTIVZ1m1caQNR50p2938V6Y+7PABdotQZCJRg7QOYLnv6RDcPnNgW1tbg1PI00gTAJqWlIN0anz8/oku0jAvx+q24+3SjB572ynT20XOY/y2bmUdmbbt5DH5cttyY6ewu7tbq6urdXR0VA8ePJg7KnoREWkc0xN6ANJrpjKO1c/9FnArvE/QXITyjUJBR9AOqsz7b4MCoDGV14MLf4gadnd3a39/v/b39+vhw4d1//79+rRP+7RaWloaoobl5eVh1ytnBJn+lZWV2tnZGXZDQ086RQsu6/NBb0Yi7lfm5Tsn1xlykCol5wB4zukblNNLKuEra/GXlpYGQ+AVJokKOQ/H6UK3Bw9tuJ3OcGphTB5SZqGfiVMDK0dlaVCRX3bcIodsYLTR4rl0vNaLrg3z2/R3CJRlw+lwocl1JQrH+L/00kuDY1hfXx/kNBGu+WxA0x3w6FOGibg5It0b+1y/5dPOkjrdD+QtoxPzL21Vyojz/Z2B5x5467kHy0imiQx6oAfnPeY8xsqtJpoRwqdPnw6CkRtEssDsNNaJ/NKzJjrK8DzRYxq4DsFmGaOX30hX5e9GR1mP2/PAOkfZGRTXk2iB3PDe3t6wdv/k5KS2t7frhRdeGOYdjo+P6+TkZBBib+zBaOIUOodgFM2rDNOw0Z9ULH5LI8NfUFtOtPvZBAQ2wKDIVIxEiGmsvOrGa/9trBLJed7A+X+36f66jzZUKdemL49JsJJ3QMbOiqMu7IQxiowt/clVRZbH7DtRjfvs56CfZ9bW1uZAjsc9ZZv+3bt3rzY3NwdgSWoywaONt1NJdvb+fnZ2NjgAgJH5ZP5bzkzvZDIZQEf2GXnoUoYJbP2bHZvlPMGD5cr1+lpGl9elhWjTS49vUm7lFDBOMB5l7QjsGJXokdKhq67trmS4mNdcrvOU+UymGDpvjBHpnucavGFwbHS64giJOjgxlc1x0+m03vKWt9QLL7wwjIEF1yiYNBJGDYWz4NImgsccAr9D16J0GqVDnkZ6i8Y70bmjHa8cytUVY3JgBOsDBfNeozGQOzzi47XzY8bezp/xNqLkr2XJfCb9NbaDOBGveZAgxuOWyNTO0/fTd9KTXSRkJ+K6uoKsIFc7Ozu1vb1dOzs7cxGo6aVfrtfG2c6B88COjo6GTZsGRl16K8GE2+xATrew4CbZC/cJ3ib47canK5b/sTRSVzqwcl259fsUHMrt7u7W+vp6nZ6eDkfPZpjdMd+M4PeugzyXE9vpbHBW/GaB6BBtF1obAVHSUCSyTCVxfWnULeiJJI2IfL9p5ZmLi4th38HP//zPD+E3k3YscWXdP8q4ublZ9+7dq6dPnw5tuV3SUDgPoxKciT82FkmjV4GlEUzBTN5ZEf1ydhzVgwcPhuecRweoWN4YA6cTQI+58snyVXW5MsYRiMcr5Tfz6dSb8miHjKKzQsYRglMIjpAc/ZhP6AB02rnBVy+0MM3uh/tg1Oyokb4YEILSkxeTybP5g52dnbp3794QIZBpcIrP+gRw4qVIGH5SpIwp17L9jOYZ0zGDzJg66zGWhvEY83v227JFqtILT6yb5lVn4KGrcwYZNfiZHIdPulNwJx2WYIRy+/dY5zoiO0RjA5LXKRYAG23nV7vS0ZVKmExeXl6+cpZN1wfqIE/tNdGdw0ujkvWOCQohM+cdMRG9trY2LLf0Myz1A/3hRKEd4U1E4iM50hGmg3e/EgSM8b+TCTsrG8rZ7PLsI0dbdg428GnQ/UkFTmNx0yhzTMb4zQa4Q9129k57JbDpkJ6Rc6bEzENk26CKT+reonHp9BFHhXEjOqLu5eXl2tnZGT6ZljQd/tCv09PTOj4+nnMKXOP3PMxuLGrJfo05+e6Zsd8S2OUz2IE04I54x+irmk9fpvG3TOT4+O8iGe3KrY+5yE0/FxcXdXh4OKCKe/fuzeUIYRjfjbjSoFbNrySxMqQxQsjzGGG/rcw0Zz/4m+kDaHVe1nQfHx8P1zzwSXPV/MonK7oVBx46nwxPPOhGK0bxBwcH9eqrrw6rwnhDGWk+aNja2qqqZ0uLeTcDbflZo1fmIuCDo0U7QUrmfNMQwycvz+wEOVdp2fjt7+/XxsbGnNFL1GZ6nG7oxiYjmEyP5b4MStblZYo27AlQkBenwJDZ5FmmHhwZOPpxdGG55v4uMrbM0wYlAUr2l/vTWDKu9GNra6s2Njbq5Zdfro2NjWHcOmc5mVwublhZWalHjx7V0dFRPX78uI6Ojur4+Hh44ZQ35Jmuzp5AX6Jmj7ttlXnXpVfNP+tnOle/RItrRLvs1naK1jJkHvPxijXoSyBkHqStHHNqXXmudzRn5SC46XRah4eHQ7iUeWAz2akVC2EKcYeOEiHlpJKXnIFKUqnH+uFBttBjhHMVEvc5rZJRgNGg+06bOFrQbxdd+ONVG1XPjjJ48uTJsPwUBaN96F1ZWakHDx4MKRWUinDeJ036qGg7pFRoL/WzU0gjYqcAbR0CslJ2wnx2dnYlPTWG9PhOVOXjxRet3zc9RFbprCwv6fz4zQeupZw5uuGa+21kz+9OqZFW6SLM6/iYvMp0b05Op7z6Xq7lm7+Wl5dre3t7bm8TfbDDQt5XVlYGp8Gqu729vdrd3R0iBeYKDPrS2XscO1nwfekQMjXaORk/N6bnVZf7lOAFfMW4dw7GcoAjS9pMY4Ja/nYO+zbl1i/ZceP2vkbSELq+vn4lj+xOOlw2msRA0k6ilczR4hj8sbe3Q0qh8LVkoJnLSgsGdCzVlMgkEZr75r4w0AiTVwxQB/3gGAy/8GN/f7/Oz8/nzk/CiRn1bm9vD9EdUQ8IxnlO77R1n+CVnXEa2jSe1ME8B+OSvDPfbehdPMaWiTR6LsgJjjAdeyI/Pp5Q9sZJ2rAhSOSbyK+TA08mp3x2KVFHPbk5Lw2gZbszWgZKnUMxXZ1h6dqoqrlzvNh/4LkQp8T4Hx1l9z0r7fb29oad+J5fcntdRNQZxA4AuB8JTG5SUuczarFjAGDkHpmudHMsVVfPBAOopI3N8ckMxnXldb1kx16VksrHHEPmHtNDO22SyMSGiFCbyIBNdWkkMkfr1+hlKJltdc4COsnZe+ewB21MSc0D53ndrqMDT165fQz75ubmMEF5cXExd1w2m9VQJOeb2XewsbFR+/v7A+rykRE2htA5nU6HiVqj1JSJLIlgkBF4cHx8PDhdO6NUeuqmP6enp7W5uXkFVKRRzqjSb9hy8Xjwv533mBHlntxHQR0GAnwnwrHzdqrVfZ1Op3V0dHQlGsn5BuiCh7RnZ54G3kjTY5QGqXOyPAftzFd5Ipl8/8HBwdwzRAM4D1LOJycn9dprr9Vrr71Wn/jEJ+ZO+k2nRrE+p0HvwKjvMVjs9sn42a5++sJf2xunoJEHp4gXOan8P3UBmpjnzOjLNFDHonmWLK/7Hc0dEyEAg4NB7pwIzOR3K0fe44gAxaaNTmiox2jc9SejOiWhWHjshOyN+dt57uy/narrygjK9WcoSj3mOWE2v6URqXrmALa3t2symQz5aa6bR9QLz8lf45jTad4EiZpmf895FitxKlJnELO+/JsoP4FIJzvme4IVt+f6cv6qoyflLNuk3Vwt1JWO58gJxjdlzPWbTj6dAXXfrQtLS0tzeXKf1eXcv3WQZ4lQqy4B3/7+fh0cHAyABpptyB1FO8rOyNp876IBR4YGqNxv/cuooBvbHJOxv8lz+pF15Rh0emag5TZuGhV05cZOoTOg/m4iMr3DUsfsOP934ZK9n3cq8p0849irNa300+mz5XqclWP6utIho6pLQYYmC6qLUVqGbum8GEwbRS9h80SnVyukQXSaqOqZQ97c3BwMuvPPPnvn4uKiHj16NPDDG3dwxLn+Ox25IyGPYRoSngHNJB+cRrBhs3Ez2jZPHYFRElml0lteuMdjiMHD6FFHzinR96S1Kx1YSMX33wQgYwYCoMBkLeCFMWTVFh+iiYyaHcFMJpdvNuuMJyf5Erk6xYbOsWLI8rW8vDxEuqurqwPAe/LkST1+/Lh2d3cHvaZOo12DhzTejF2CAfrlvvi6+cizTtN0pQNFbtf8Mo2ZFaDYLlnvl5aW5iLo1DHbMuuM672Nk/ikHJ2dobeJmUwmdXx8fMWo2TB0SNJonsgApAq6TcZDm1F71m3Dx/9jXtq08J30l9e6G3HwbKYcKB3y8+A6BHTfLMx2kPyeEVOHWt0WBgSE5ZQQjoSogx2iNsQ2Dl5QkCXz8vDcEQL1UezgSNnBF0cINpi0ZTCRBs/GwryycvOXfuXLmjpH77rSKaWz7lDlmIzYcHTAIuU80xMGRe5jLjHO/vtZHyfBoXUcr4IjAAw4goTu1dXVuSXS3g+DLO/t7dXR0VE9evSoDg4OhqM7zJ8xlH0dsHMqr+MZfBgDba4rx7C7z6m67vkEQ/m8x7Jzxp2dGaNxzAZdV153+sidqboaUTBwhIe5HHFswBMpIXA+/2U2u1zR0q2YSLoYfLfdedEcRHt/DItzxPlsfjwoRj04GUdKiSBsxClnZ2e1vLw85GUt1D4aPF/FmCgQZSbUxwFUXaahjo+P21U01NG9WnQsNTKG0j0m8MD9r7pUbv9mh8442VF3NGTbY7+bb1ZOI0LT7zE0rU7LZITsdpMfHXjwb+ZpLvW1UzEPE2G6Lx09yW8c9NbW1rBhkva8Ksr02aGiN8gMen1wcDC8DMob8JKWXKDh0gHTHM80rmlobUiTF7YjybPufp6xYV6UVqZuX0v6su7sK6Xrw5gj6spzbV6zEN+kQUJJnveks4XUxs3LS4kMMFCkbgjvmbDqmDeGKKCDkojVQmbBsIATubgt7qf+VAqnc9y2vT+G0YIBH+kPk49sCGIlEr+zyzPRrnfPclQJy1sfPnxYjx8/roODg3rttdfm5mwwdjgheLeysjI4DcY4jZzHJZct2mBCq40CvxEteAWHnQBywTg5rM6ozvUmfaYTAwa/LDuZHnOUR5020rkPwTR4hYojIC+7zfGn5JEgPIsMbm9v19nZ2bB8OY2kJ707/UGG2XNw7969YVURPDZg89h5kQl0ofvHx8d1eHhYBwcH9corrwz/W38onSP2WKSOJ/DzWCTPkrcJVDvwYD6mTCX/nJoisjevE8z6ezpufzJVSvtcB7At6sNYudVLdm7ibWxMk7kWcu7LcJu/Vow8xIy6M8RfRLfpMPNsQFz8vwfOzih3kfK7EbSXdnpwjOY6r26U0fVpOp0OztLGy07WbRjRG9nyO9fhKTukTZfXV1OcL6Z/TtOYN3b6uSzPKSijMTtLnoPGDqCM8coKn+Mw9tzY/5Yn+uqVZdxjJXbqC1647i5KSL52xiZz5Nxn/cAQOf2XRimf4TeDLu9atuFGzlPHPFYsR8aBMKHM0mgiDevKdfYmf7fc2cFlFDuWiskPNHTOqGs/f+sikJTn60reZx6PZTluGxlkudVEc6KMzpsagVBQChBTEm50bqTrKMGTnBZYNsawoSnzzBQzKZF5Ts50k1P+nfa8k5rBRwDX19evPOuJNugwsrTzsSBlpMM1Jn+ralBaOwc7Vyu7ry0tLV3ZEcvSwo2NjbkJak9ecsyAf3eInkaOMaXfOFYMhzfPca/HDFp9LlMnmymznmNII2jnSP+Q1THHZmOYablsz0Y9J1sTpSZNBkM2KCm3TmM62nXunro4eaCrj/4zCcyENcdSQCPjDR+ZhMcpHh4ezkUF1hWcweHh4fD+8KOjo0G3kxeWd8bB8uCUXRpwjws0ZH3+m+khy3E3b9G1n7/Ttt94mGNom0f7Y2lYnk1eZb8cdVJuEy3c+nWcGUYnwdnBRHMZ5rlOT1IxkcrhVygHxmFjY2NYG23E2+X8oI/CfeTmvaoojXQqjg0I4Zk37FGXJ7PNQz9fdXl4nPnT5R/HEBATwnt7e8OqIhwE/PERFrTlsbEx4/f79+8P7ZIaYBIQh+AoAyWwQOZY892GCUQJTU5n2MDayMIHp8LSGOR4Jx8Z207hPc6mgfGExi7SRa4sj/mxITCiBkylHFZdpjszwrJcJQJ2tGk0j2zZiSPP+Qa0LncNfaQLeRbnnsCM87mOjo7qyZMndXR0VIeHh8PcoOlI/tM/fxjTLpL02GcE00UFWTp75XacljR9phvZdCRl+enat83qoiTrqGnLtLuBTn5uWm41p+BByZRLt/KEztjzOexJhed+8pSgC6/gmEwmQzgLGkqDwX05WMk48uW5GslGuUsvcR30AVqHL5mvTB4axZBnTFq7lJZ/s+CyhwADwwcaWf6XOfD830iTFyhNJpNhlykO2mPp/nqlRxfamgcpvFzLlVdjztFOyIi8Q38dL3NViut2HTnu8Kq7L6OCsYi1Q4tdhJ19sPFNR2mjNZZ6zPw1MkhkQRTmaDP7bj5ZL50qNejyh/d9sAnS8mbbYtrzOvxPY20a7WRSX8aMZPf/GNiwPHXg01F5Vc0dGW/H4Gc6Xenso/nhehZFBbdxCFXPsU/BzOoUKkuiDZTFSmPjwKSgl59yH4LHfgOjyTED2gkP97Nbcjqd1r1794b76Z+PBibV47wrKQPvlE10krzoBnURaulSM1Zo1wWaPz8/n1Nw/93c3Bz6zDiwYsmGlra5Z2lpqXZ2dobIg/XnTvNAC1FdOgajXDtGp20cwbifPFNVQ9rKUR7G0uPLuJMOog5HLE6f0XYe85ERSi6nhnYjfJfZbDYYWkeSnrRP3lxcXFw5WLIzVH7ndPLIDgr+ozNOb9hYXVw82x1vPnQ0VF1OQpOmInIDPHBEBVHBdDqdczpegJE6nJESOsBf5M6Rhses40k6ii51030fc44eXwrjysIP8zr/eo4v9cJ8sHzBd4+ZeWOaukzHTcqtnELXMB3pSqLlRITUkUwgB5oRAgxHEHl+zCmM0UYbGHQrIatcZrPLPQnUkykS6kYxE9UmDZ1zch8yRM46uvAz0RGGAGEjzEdArOj8joHvxsxjnDvTmR/yGT6J6m4ikBgaG9lEeNl32sdgp6HxShecd8dHO0Bk3HLmTWluuwMb1/GP9EoaB7+bGLCVxoq/dqbUmzx322P32gmNRW30h2jfCHgyudzYNptdnso7mVzukveqL6IcFjCsr69feTNaV8b61kWR1xXoHjtKBWfjMesQuAFZF4Ha2HP/ov65nU6f0w7yO389XotA5k3Lrd+85mV2Yw1bOfmfvwgJApGhvdNHrq+qhhDXyy+N4pKxqci+7kleK8Xm5uaco8HIul9prLxVn+JBtcJRj+c+aMd5Z+pwf6rmN4N1KMr9Y0/H4eHh8DvLT+knCMRpoU6o7FA2NjYGRAiv8lgC/qbhSSVLxcznMhVnviJD5ifRnVMloGPX7Y8BAm04RclvyeN04EahtGWAYd7xO4slqi6NPpENkRrne02nl69Y9UZAz3uYPtNkumxMjUSNnvmLU0TOOdfIkfLZ2VkdHBzMOTH0C6cMT6ueIV3mGezwujRfOqjkdfY5o3CuO8I3gEjZHDPC/Gb9g2brvaNW89n6NKYHdiTIsE9zcCTcLWLxOL8ex3Bjp4DQZBopFZjfbIi7aKBDw0ZsXTGK6+7rDJmRBs+wQ9e0IsSsfkkDjaCY8Qwu+XdPvtqwd8bESlN1VVAcEvNcN9ium+teyeN5F889WMBypYoFzevlnR8mLwzvfMicacy9KOkI/Ca0Lgfs5Y+ewM8VYRkVwRtHf+abx9EGFmPB6jHq7lYj5Zgk7ZYRKz2yh3HyAgDX78POUv6THs9p0D87x9RJ5/1T9iy30DuZTIZ0od9wx9hz/9bW1tAn+Obzl0gx3bt3bzCw9NPZgY4mj51lKeXAugBd5l2OE3JKfT4dYMzZ+nfrKrrntlM+LK9eZJFjBhixDrrvBg6OfjzGz+MYbrX6yOFSl7vrUkxjJQcd4q1EtJvIMxH3bWjP/LG9vpnOUjI7gDQqFKcbvHehQwnQg/CnQLs/7q+Fj5IOAVoJ01kq6OdQYI4eofioAvhEm1wjV8zHjs28hbYMcTvewVcbFjstSm5qS96aT3YwpC1wYPDZz5lWnnFqBBo9Np1ToK9pBCw7CWa4J+ciUs75ZKSa9MCfzCcnz52mtRwaVWPQTScOwY4FMOX9DJ6vwMHZmQNALi4u5mQRkDEGDs0Lj6ezBamnBhtj9sl1dvNCyevkP+DCMmww0EVA2T78wtinztj5Q6fH3KcbjEVNNym3fp+CCUqGd4wyWu+YYYI7BvKblaETlq7eZA4rIHzYliesQcMIdrd806Fmevatra25A8A6noDOuk1fmUYzD2nDaRErgyfhHz58WFtbW7WzszPQRz20fXh4OPRhe3t7ONwMJX/69Omwy5QJQ06vJFJIhwZvvVQRZ5OTkqmgXo/vyU8bIJSFaM47tf2dsSYCAr1Cf8qXUwMYNq9I4zfLYxaPY8q9jZcNDkpsNNgZBx/z7ajGCzFsGLzQwYbbiJzveXjfZHK51HcymQyrzl555ZUhSqAPnH3EfgaiK1Jk0IqDoK3l5Wd7IdyendTZ2dmwp8KpmSweHxvJzpl6jiijed8LrQCJjLy6iITn/KpR142sW0b8PHQxt+SFK8zB2D5VXZ69Znrpk50ybYxFXV259TEXNgR0NJEgJQdn7HfXnQZ/LD/WecKOhlQKG+tE0SiaT+nM3KIRqdt1esPOKNMCdgSuOx2ikZN5kfyjD96P4L0Ki5w2CndwcDAcIwJ9BwcHg1Ng9YgPxuvGwDtevVoLQafPDqtNY46zgYcNGrRsbW0NfeY+6Ep+Q1+XljDK98fG3Pd1/49dG1NGp2ZYmZORkOWSZ7wBMFdr5QorR29G34nEU8agw46EV7jaUGKUvEGOuSojZ+r1O014nrbMc2iANwZi3Zh1kdyYzeG3Dr2bB47KrgOhGGWMejpkA0ecFjQAZnCkKUtpd6DHqSVozmc6nb9Jea59CqkAixQtjagdCsVRCPd0zEgaujbzGsxjg9lsNpszpHnv6enpkH7ByDn1QO6xSyd5EjxfleiUURb3sUOYYw4VwQWtbWxs1M7OznBgGTlreOyxsOH25jvGkmjg5OSk9vf351CKaYAPk8nlJC999DyMnaRph6defsnz5jHC77mgi4uL2tzcHN4N4WKnC4Ik+st0A/WhvBklpDNwHyzXacQzQoa3NjbwZGzsqy6dt+/xUeIg9HyxEg4UA5vO0nV6jsIT70dHR8NiBerhmdPT01pZWRkiRJalwh82wWH4aMegAn45feiU4mQymZu7St54HPxsAiGnc25SMjVLyZQvkQ6LN9IWOhUEX6gfx8nrdT2/5rFxXYwPDhr56GyfF/vcptzKKSwyUnhEwvwcgFQsSpfjo4PZnlF3V5+NCQLs10UyCEQCpo9BOz4+Ht4a5dQEgjCWk4QOT+ym8Dii6pBo5ygT/VLP0tLSkOYAra2vrw/o2SkIp3GYGzg5OaknT57U4eFhPX36dEgLEWE4vDc6gZbMr3MvvLVRSsPpFUOMi1eWeaI4kaONK2+OI1rY2toaxpEVV8hSImT3BUPtMUtnlvKZY2+jY9RmQ8s9GHkbMH9on706NsQU56+dMsOZOz1pQ5bgwzLI8+x18WnEXqqZThG9JN3BeDq9gS75HcWOxjFyzj4wV4Gx5DgMt29+wdO0PXa87G+xnqEnjjaM6F2cs+dZ0miut2reIVhPGD8f0wNwzYn/zuZC69bW1lyqyRkRT/R3mxEXlVunjzqPRIdT+TvU8zyei5J5OtrK32GOU0a03YWpLg6ZHVFQp9etJx8sKF4ZkFGPC/9boDojYUH35ieckJcLGu3mnAL9Ozo6qv39/drf369Hjx7NzadYoel7lhR0O0wbdRtD9yUNMP023WNoj2dAkMfHx8MBhVXzJ6YmOu7GzoAmkWCn0B0fnEZN+e/kxMjRfbQDdD86uXC0heG2geiMS343LT4u3W/kyz54HB2NuX0vN+djucwolzoW9dPAIPsD7x1RdGPVpWdTxlLek18eKzs82xTTA+0J+uCF5TknjzsaaMdgNVdtZeR0m6jhxk6hM/JGs+mxszNjXnfMydiAe9BADF2h/S7E80STDWTSaYXEi/tEVO4BVaRBoZ6tra1hYjt56Lx8Z/zMJ19HqbxWncHmvcykjayA5NLPz89rd3e3njx5Ur/wC79Qjx49quPj47l3AB8eHtZkMqn9/f0h+tjc3KylpWdHX9jZWoBBJdAJGsTB+LiSLsrw2FXV3EYnC70LfTo8PJwbW9rr2oIO5NHgIo1J50D8TCq5Zajq0shllJfI3akb59Lhrx0lfXRkQ2TgSeCk2Xrm9kkP8T5knIHps/Oh7rxm/vJBBvzWPpYMA2iQKcbNtMGL9fX1ms1mQ9Tgdz4ngp5Op3MpXvcDHYaf3khox5h123Y5wgJAMZfQGXKMt2VlOp0OERMy7PF2BGne2lZUzWclDg8P57Ii0EMbyM9NynO9jvM6j0PHOjQ/htCuq8/fc/J2ET0wMgdmDPkl/UYUCIudAvWQMuF5hM2rAhI1m76xPiAEXr3j1QUOz+0MUFjSECjq7u5u7e7u1t7e3mAInG+msIoKA0H9aRAyV20U2UUpRo422omYqmrOgXeou0sPZeTheu3wed75a/eB72NOgfEfm3vIZ7J0URzjBe9SXi175mXuZ3CbTllSv1EpxsSpqgRUNoSuO42l++CIxTruVU+kcqgbAJEAi/57U1iuuuIvcxbpCF3gMekqj3N3fxf9OTq3rnUAGVo8h4n8sAowHdgim2C5o79elUY9nnfyPNl15bnOPvLf655Jx2Bh6yILE55Cwd9uEJORVopUvkUOoarmvK1TIamY/p86c1esIwNPIhmppgGwsYQGUIlXdNg5MMnn46iNQjii+NGjR7W7uzssOTVN5jWpBPrPS1qMWj0e5oHrQRHoh41y5luhF4VB8Rwm2wnzsVOgXsuSJ6nTWDnM71IPndxazsjX2hClU0qn5sgP2XT7dqiuz3VYpryyi2K+0B51Y5QXLSKA/q4YyGQk7/oBMualJ0c9z2DgZhnIdokqiHAyJQiASbm0XbCcJH2dY+3o8IojO17LhvtilO/IhLkvp3cyKrmuAAoTlHnBgO3GdeXGToFt1ih3GjGjpy40998xJbEgZKhUVVdQRHpTDwbMQtC6qMVhuyMCBgh0nGubMXZMgGXO0Gmlzc3NId1lBNgplWlhEFkF5cljeMZ9Xi21tLQ0hKJ+fSkvM+H46xR0o2mP68XFsz0LGGacj51sN1bUR146nRz3d1EK4+idsE7fub4snhClLvPZy2Yp/JbvdEh5yTrTqTsicrTk9CKya3lDFu2gEgnmGOWcWeoN90MPqRFPaHrSNfuZcuqSjtx8cfRChDq2K9+npyIXrNenHqey7ESXl5/teyCiTbQ9VjzuHhPoskNJfebaysrKsLcH28CzyYdciorss/fn5ORkLso3EHU/EgDnNUfuuRdnNrtc3HCTcutIwYjZ5SaRw1ixY1hUXxdZdN7U0UE3wbIo/LWiI6QsZUwFMdJNlGgkitNwG4siLgTPqzW8vLRzCjakpAL4kCfOY8jN11R056wRMl7fmZOFKGyOEf3EsBsRJ6jg/ryeK458fxfyd/+b5wYCnbwYyS0yLq7PffIYGSiM1eF+p6Owk8gotUvB2dnmWDjNlE7EPKUe13ddceTM/7SXQGEM7NkYmhdOPVGQexwLf/PeNKJjxePslGKXGnSqNn+3vNNupyPwJs8LS764pKwk3f7NfBiLdhaVW5191BnAFOZFAzCGOJKR2YYNS2fQsg2/nYv7zfxEQIm+yRVubGwMCByj6snbyWQydwYQ1zzR4+sdSk1hArEyaWxU7lCY+3hXwvLy8vCyHdJC1IkQdoJhPnTID3qn02nt7u4O6G5zc3MuvUOEYlQFnbRvflDMs0TdOSEPLaaTqI02MyVlOoxAQU5WJNBa1aVh7ABERpvQZUddNX9MfCd7/J+pTeTAqZ9MkdioO7K1UTBqhJfw0xPWTq9lNAZdTMo6as4oxc/x22Rydb7GUcHS0tJgHE9PTwedu3//fi0vP9sd3e2+9iILdkB7jsCGnX4kmOiyAx7zTAtV1dyGSfOTezxG8M11nJycDNG6QRryb7uR9tTyMwaQx6L260COy62XpLpYQNNrmukQnM929eW9yeju+USCPgPENC36DAzRtv00lh4EjADhZtV82srP8Ze8vA2XC3Tn3EA6LTsZQkOEzO+79Rh5KV+iDk/OeeIveed0kJ2dozIroZWLkpFmly7MKMS8XxSlolyWiURenRP0vTayXRv5vx16B4jMhw6YjKVpumiIcTQNjoYd6Sxalum/XUlHZB30nBlLSSeTyZBK2dzcnONJ6mknW5lywkA6LWqUbf3BAbNSrTvGpEtr5ng42k4+eIw2Njbm1v07mkuZSCPudG4+M51eHk6YzukmttPj1V37lDgFmNktQ6uaX0lkRajqJ05uEtrZGF3XIYwUTsFtuK5u8ByqEhr6TWUoI+FwhpsOe6G36nJik9CRpXVd3ruqhgjH73d2eJ8Tcxa2o6OjuaV/iSq7VFfmsHHkNgRpRD2PMpvNBkeHUeI+o7Sxsc/xs3HMENlGMkuXLnEqJSNSK2LKhuu7rqTym76ursnkMs1hQ+P7uz52im2Hks7ZtPh7x/8xINa1a/SNIaX9ra2tAcg4UkhU7vE03xzN2AAbIFXVEFFQnL7zUSjppLPPaSR9pDn32P7AV5xUJ9/deDtyyyW/jn5xCrPZbKjf/EmZdVvWjUyffUqdAjlhBq0T3Jw0zA6l4HVIIr2rEVLnQPyMz/txCsMDljTk+UBexYPnJpVBQYAt+I5QJpPJkG5CCNIAcKAcfEWxSMnY6EMXKTE7AviXy/OMml2owygv+WnhstGwwXXozH4G5KJbDWP+e/WHV88wHpkGcfopJ+GcvkzZswzZecMHz1nwfCdrODwbS9qwUeoMYDo1HwaYTr8DP/AEWmnLK886vcF4ssDA7znPFJH7QnFfukjm4uLyZNTNzc0BQWd6KaObTPOZP54vAeRcXFzUzs7O8B4Q7sn9JoCU1dXVevr06ZXl3x0Ic5rXkX8aXgNFRyzd/Xz3qbHYiv39/WFi3FkA+gzPjo6O5vQ0QUQHWuwQLUs3dQQutz46m+8ZNaQntnGxsFb1k8MpdJTrogR7Y5Qk6+uihKp5NIlgOfVE2ziFTB9lTtFpNEcrzotbaSyIfvdBpl54DnpxOKzaSP53PDLv6aeNsR14GkkLow1k5mSdGrKxofC8U27moxWc8bThTgXn3jz8L8d60ffrUJQRZzqKMbmi3s4YeUmvxznnHeyEkBf3z6vqLCvcCyDJieUsnnhP9GvZyHboS8qrx9wLNFyfAaSdhOeVSFOdnJwMffXLcRy1U6ePdkhnlONjOqGt+512/Z4Eg4Ksx3zl4/kdxsNp2gQG3fik/Jun9JW6xyLCT3qkwMDZoxuJpwExIWkgMrSzYlnw8/mu2Gh6pRC/0U43cWjl9CCCdlNpPZgOkdmleXFxMYSXeTy0J8mMmFFwDg+jLXjg44iJOp48eTI4BU8YeqySty4YUiN2jIENC/WYNxSfYYNQ2iC5LfPB17zvonvZjuWOE1ytSCjr/fv358bJjsv997NcQ14yD20Ea1m0nLpfdohjvJ9MLo+ltvE1bxIYZB2J2q1LyCJHmHiXLHxPw9BFB2mErOueiGexwWRyuVuYfnJPgizG0JEL39Ejon6jXpZusgCk6vKsJ0frS0vPloGTqkkAkUuRE3nn95WVleEIevoJsHGkB5/gtzeS+kh5R2uJ/tO45zygbZVlYjabzfGhcwqLgEGW55poTqNvwrMsQmBZlztvBOFn3OnJ5DLfh4FKGjuaxtBd0kPbyUwLGDRUPUO9GGqvRmEQ6SMHh1XNv9PBg8lzoCNSAUdHR0MI6mWeXUkExzUMqnOlFOqzgHU8gS9+TSCT8zY06eR5zk6R/joaShRv5Ge0zHsgEv0nCID+VDAbjjTqRsy0b7nJPiW/eNZ8txHyBLH530XhBl3U57w8zpjXXGZ6BVpzXiAXJFRdXYVkB+j0HqkN84T6csEC/PImK4OwqvlJYsAB4Kuqhtd3mhbLEobVc3fd3IZ5kIbW8mEHbbnN39xHp7EsY/lWQo8FHztY2uMexmjMsHfXE1h/0iOFrnQdy99tDF2canGhnjGjlPXzjAcoB36sDb53/fIAgA5swGjT96JMrEF2HpIUFPWjEJ40djoHR4eDwRns7+8PqNkopSvueyfwNp7cjxNKHmB0jIa4Rv+IjpKGlA0bIPKrXmFi1EVxlMY9zP/AQ9875uStgAkyxsJu1+c++fnr5NXRqMcE42a0z9iaR64v5dzRqN97kfqS0RAymcYGObQTgj5HeD62wY7TUVPSkKknwBWymOmu5eXlwVkcHR1V1eWkcGewJ5PLl/asra214DKdsB22nYU3lDmVbADTzTVV1VwUDXiyHuQYZ9vWu5S9biPadQZ/kUPJ8twv2clG8myhqvlJy845mDGpvN0kqZHZGArOVSdGclYmD4DrQUARTtM1mUwGI+gX8WCUEGpCRTZ7eVcy6NYrjHAiCJKP9j48PKyTk5Pa3d0dziqyk0ohgk/89X02XtPptLa3t4fQ03l7X6uaN0qgPRsJfsOJLQIIaYjS+FddzsdYnkCFnrxksn5/f38uykg6aMurvqAnDyZjHFMZzVfTDj+d6jCad1oz0w2MPy+pgdfIf2ewO+UmTXR8fDy8MtWpB+TUk5EU8xNjyXlY9IF+ACSm0+kAVmzcLRvWK4OhBCqOiEH4tge+n5MBuJfnnPZEDjnCezabzR3f7rYzSqZQv48w4RmOyTbgQ8YcmZH+8tlinpNIu5TRbZfS5Fn/798tG9Z1p6NuUm61o3lRJJDCamZ3kcJYSZSWTOgQvgXJRirRQTqCLj/rPK/Rh9u6uLiYmzC28TViYsBtbBJV06Ydmuc1ePcBOfexMNAOk/8zorEAZqSGsTbCTFTtTWc8YwTscJziPLNDaqNBI6ZFpYvwLOwoFvV5fDHgmVt1H32vx5K6zWcbgc5QW9bMkzR4pjcVOnPEHRCCB96t7GJHj0OwcyR9Bh8BPu43ssKKpkTOOamaE9D+LBrXlFPrs/lsAOV74AV1YbzpW9qhzvGkTCdKZxVgvv7V8uFI3IV6u3HK+9I5ZhmLSPnfY255ukl5rtVHed0C5cnSDLM7r+xixNuFn2kQENREeZ7xpw4rfZfSoKQ39f1WThtpio2OkRY7fZmAvn///sAL0G7VZcg5nT47PfHg4KCePn165bwa87JznEZtdjZVl0jU592jjN1qFSs3TsHjMOYYjJq5D4WCzz7XhvX7rtsrV9w3yxdIMMN6lAbjQJ9zbNwPp7/MJ0cGlhF4x5g5DQYt9Mly7d8zsrIyI7/msdMl9CGPTMhojN8BF8gj/bR+Iq+OChlPxiwPeUN2l5Yud+NvbGwM4+2NoNBv3cTBOHLo0jrI1MHBwVBPtyHUPEbOO6cAPXZKXgVoAMf/LBv37+4D15kz9MuemE8hykrDnpG4bc4iB9BdTzm9Ceii3Hr1UWeEaNiIj0G04CSaz+L6x5wQhfotMHYMtGVhTAOTUQm0LWrbht/CbAHPfqTAUBA4wma/+crvRgbdeQlsok1HQjkBZuHjHht48sA5CZhjgWHwyaoOeY+OjobQ24Johev2kbikA86/tJfKnGjUy1w9p+Nle/Spy/NDg+UhIwc7XKf8OjnrZHqs3pTdlD/4YOeRqU7LDR/SShnR2yl47LtiMOK/rDjzuGOQj46O5ubQMPbWDfiVCNnynJPvLNiwTQF8YQ9Y1Wew4/b4eF+BHVzSjGzRviNWH51iec0+wP8uPZ267Mi9s7v+nvJkPbtNeV1zCi5GLibYSNSI3fXl3+s6kiEdJVdbjN2X/enaT8XO/o+FYhjOjHi6iGMymcy97Pvg4GDYlEZ+ONebm1YjSM9xOM+J4eiiLNeVERTPeyzI4xMlIXjUc3JyUmtra7W9vT0XbRix89dRmxXuulRSpmFsEFKuoAFj4mXGaYQpiYCTD/npculdtJuGwMbEspkGy7S5bqLVBCi05Y2TOA6ceQIg9z2BUeqR5crP5p4AUDrPe04CQ9ulyMwHX0PG4Vnyyk4hdZjJavYKdM96r5Cjzox08kRWimXNtBu4WNazv77HICf7muOwqHSg4ibl1nMKMKQzsjAmDZC9Ic/a61Mc8mb9Rg/pYd12pn8S9ZnR0JaGwANjBE07GFobHvfPRntl5dkxu+zIrHo2kUeqgpd9g+TYfcqEIStJaAOEz2Ta1tbWMKlmXoMy+G5+2MjbcbDs1WfQjCFNp4X4Tl/glx2Sx9zOxwgNFNWlDHK8/H9GSR43p2joc0YLiUCt1N1vHHJG/zo5HKvPdFpObOxxnG7D91bV3Dk6yMre3l4dHR0NUSZpI/QiJ3zHIhLLiIFHB5Asc+mUMOJ+U2KmwuxQO5uQu7AZV9sCgyKnsvlcXFzU9vb2kKpNXi8vL8+9Z9knB+BI4B8bRvOgO/7iCDmpuKqGhQQGZx5Lisci0+DpHCxTKWMpmwmerivPPadgb9whfwxxojIGbswbjnm/TiDtpBLRJhI2ak0vngyzYe0E3t/TEaVD8EmnrNawIUW5QeA2KBnq2lh6viBzzdn3HENoJ7eMI8IpIfyJMHm1qBFyGlbnwq2A7kOmL/zdaD5RF47OKRQbGK7Z8NqYdQAgxy1Lgo+UhbHf/Ky/W1+QG6c2LFOePLYcozNegsr4eRWU67IOmq5OL+ws7BA8qdoZIb57JZP5kgabdhm/zOXn8+mATTPPOC2Z4AO6Uke9Ogy9YlxMN04WWs27BL52JNCHDXAaybLTAZ5Ms42h/zEZ7GTxuvJJcwqpPPmXkuGRw+Q0IPndCMb0gLTNuIw2PDnGPd1uYBsM6Bxb2WGjCZ022kwqk5NEoHymCUddszEHo0gfrTTQZ0NI2Eu/vPGlGz/TzNur9vb2BuPiY4ypw0vaFhl2+JY7ScdCXejFQXHN4Xs6UU8We/XHWBqty487+uvSQu4TNNlAjDm8lPkEGo6sZrPZ8GY7Oxd471elOrKw0cl3K/uE3M7g8Nfj5XRSgjevxeesrK7vKQvIsXdvex6K8SPFRf84NptPRiiMfUZ+s9lsThbcd88v5jiih55IxtgTHXMd8Aa9Xj4LL43soZV0leWT5+C/5cPf7YgzPWc56wx+pm9vU26VPkrBXBSOpAO5rnRK1oU8ji66ep1OMJJ1btn3dv3M73nIFM86FUHBKG1ubl55vaDf00B4iUF2amY6nQ6C6pxwRg1ONaQTgH4L+MXFs01wTguhnJ7ItlFLo5kO3KgSBeBvCrUjAJ43X1OQnef1Sh47uNlsVsfHx3N1W/G6VSdWQDucLuJN8NGls7J0Sms+Gi179dxsdrnYwGlDxsE89zuVO8eWAAVD7/rov50dvxv0WQ68x8HtJjBExoyITT/99HHROLm1tbW6d+9era6u1tbW1lwufox2v/5zc3NzkGvkxUtEqYNJZTsUp0sx8G7HEY3HB34wtpY9t5upQ8tJlox4LINZuuzAbW0w5VYTzZ1xSGXK+zvibkPgbWijGAmNtTdmBFBg/laNz1eko+B5b25xPTZuIPP9/f0rSwozlHVu2IrqVEmmGKyYoMrz8/M6PDyci27SoIzx0vfxP/caLNj48/xY9Oa2HI1kGwYHCUwsg0Rj3Ef9piUVqpMB/598sbEck5+uPvfJ9BD9eN6GdGLnmKijSy11/fJfHIPrMb1ddOlnkZsuO5A2AFntgCM8zJVsicY5OpuxzPSV23KaZn19/cqGzAQLRAqe28Dp2SlYthy1dzaPur3ZM22DPzbkYzbR2ZFuXFx3V8fz2NrnPhDPJUNSSoeobDycl67qw21/NzNSMLuBsiHLJWGJaty2lzAa4ZheC5wHeWtraziPB0TE7sitra06OzurR48e1aNHjwY06NwjNBG28uzKyuWppig3kYiVixVCpIcwMI4aUCKHrqQI4DET2jZmPgI8HYf56t8cPtug5vg4SqLkGTCeNPYKJiu00aGjOEeYDutTjgwEquZXtGVuPuUolXXMAdnIwDvGhTHzvg3LhhFt9i0dVRpk6M9jM/K76eI35AW6sn7aJv3p8U4aLKPmodH3xcWzo7mPj4+H9yHv7OzMRaoXFxcD8PKbEX2MN84BfVlfX6+tra1BBqiHo2PoCykj9NupNEctCYpyVZ6zCQYxCejMJ0eudvq2Nx63RU7lJuA9y+vap5ClcwxdPWMfd2SsrkRAtOuo4KbeMRncORXfy18bV9NgA059k8lkSIGgDAcHB1dOsHRx7pW6ECILVtWl4UT4vdGNVJFPUHTUs4hPY0go0aWv5zXzuDOgRniZEjANvr8Lne2IEjhkash1+T4U1XLu+q7jUXd/5yicruJ78i1pdB0YONMIzzLNYr66ztQRxsg6Z34ZDI7RZYPlFJRlFQBj3nM9eYrRPj4+HsbF5y3BQ0cjOC/6wr1E3dTP3JlX2HVjnrwzvzpj7evJk+SrHX4X1Y05+utKR2v3fVG5lVOouppG6Aw6fzuDnoNogbMCd4qc9Tk6MIK4ST862vO6fzNyTNQMkmDpKageweSI4YODg9rb26snT57MGUEjBtoAcZC/ZI4hFZtjMHgml7VieFPA3b8O1XXhaOd4oTsjHY8Z/fNqG6NP15Vpn5QBbxhKeUk5dHToOuxcbBC9DLGTl5uWzlikI6C9qrpiJFP2HNV0dNFXy4fTMwYy9M88p47OSDu6sOylvqZjMoDIdK7by/m/HPPj4+NBzi8uLuZOxJ3NZnNH1FvW4W9uyFxaWqqnT58OZ4rBozGblFGko0anafk9waPTWgnoPJ62i2NOcszBWIY6p53fryu3mmgey8mOofmxdFCnmG7juk6kgxjzqum8uHcM4Rh9QE/V/MvZE1VMJpNhBQMrNOgHDgHhYO24FbjLpbtP3EMoi4L6GfMPp+DX/nlMbJApTkF5JVXyMZ8ZQzGd0EOHDVWuBPH9zt060iA9ZAXqEJiRcqYozFsbbE9mu58pNx1AsV4gH7ns0LSlI0tnn4Y4+WkeY4TZB2MZcd3c7yOvUz9cd6Lf/Ov0o6MTH2Vi3mUaxbLRARCDpbOzs0F/Li4uhjQtcw/MGaBX6J9fzGNH5bEyn6yP3MsqsS4F7fFkwYfrIaXlfhmk8NerrFysky4dKE9bkPy9DjBTbj3RTIO5kmesdIjTdVnhOyNyXeh0m9Bq7JlO2W1MEn1lf7yCgfu9ZNQTvnZ8RiSJ0qGHtrwTk9RTLsPFGHhlB/XZCOR8ArTkCifzx0KVCN10d+kafkvUk8ZqLAJwMTjJFGOiqE7ukhdGp+lQaA+abpL26Nrr+ph9Sp4k0jSSzjZdr08NhUY7vqr5vRxdKoj/E7xlP21gsx7qNp/d1+uK+wYdzvHTL+bVNjY25nQB48v3PG8o5ZtnfUBggkfsnsfTk+CdXqNvXVrUfR2Tn1zdOFZSdrrrNy03dgp4Kxs6M73q6sRyCnIqZKe4CLSFK4sHnGKE5fv4zUqRhst0JS389dEO7h9pI0/MTiaX8whVdQW1r6ysDBNt3mXpNfYO24kUfM1GPFFBd8ZNrurx792S1g69JdoyuoavTOa5Hj9vxUlnbESXfPZRA5YBO2Iroyc8GV9P6Fo+bExSDlB6y1zHG+9Adh+9dj75wTh0k+z0wXpnGjId5T5ikA1UrI8ef7/8pds0Bi+8IAJww7PQCp0ZgfmIiTyyxY7JTr7rJ+0wGY9MwGcO42Pl3/r6+qBnufGSPUS8oc7voEjjiq1zNOSD81hUYsduXsHDboLfJR3RZDKZ43eCHds824Gx6zctr+sdzXmdAbJRdwerru4SzjZch0uH3sZo7BjoZzqE2SGlNKoorhWUSSxvosI4ItgZknrZG23ayaUCdu/bTSTpvqFE/i1TE+lIkodjfKBAM0rIK0V9wB99ga48avgmNOB4crKwM9JJt2kdu2YH0KXE/IxTm4nSM/LgfstMFsvmoqiGOuyg7ARsQCmkKa2LNnqOIOyQXE9GDfSJc4uow5OlGRHgnL0vJvuaYAzZcERo24ORxtgDwkgnnZ+fz4E0omoKqSb2C+Uy56Qpr9u5ety7aK/jYY5/93/KRqcfY3V0APk25cZOgYFPgjp0PpZmMKrJDsHA7m8K91j0YKSY92BQbbx5rotYukF25IBR4nhg6sD4+aVDCOZsNpvbM2DDZ3qJSogkEH4cRRpCCm2b13YgmaZIAadviwyqx5aNQsynbG9vD0tvaYsJeRSRHC910u+cFIcmUnO8qCjz8Y6YOnqt3HmfP3mNOjoH4LRJzlFY9twfp1s9HmOOOp1N0uD0ZI55NweGLBkouJ+np6dDZOu3ASZtua6fup0qod9ecur/kZ/U55Tn5Id/Qw84CQDaZrPZsGLJPOY9EPBsaenZ8vGTk5M6PDy8Mn6mz/Jo+0XfuiibZwBnKY92GmNGHn52S6Hz3i5KSNB1Uydx60ghjYYN5HUlFapTyqqru0zTcKfimkYbtzSKDHRGDVxHaDoGIrjkBznGwkf3GrGQ48S4Hx8fz20cS8NdVYOzIDwGWXlTE8graU1DDq8RLPiTk9OMHdeM7D2BZh5MJpNhLfj29vZchANScxuJKjPXu7R0eWyAjUymBRLJp2F136ElxzP55D7ZMWeKo3PEnfzaaHR7KSyLrt/LLi0b8Iv6WfKMk2Rs6Jv77IjUSzU7nQOwzGazwSk4rWjaDWT43zLCvQZL1ksbV/N9DKXz123nXAuO6ODgoNbW1uqFF14Y+MpxFtSFfJL6nU4vXzVLOhf5SR5BI3qIjGY628/YrpmXHRC1zqALOReSAC3LGMj9pDsFhKoLZ64rnQB2DKxaPMnYlTGn0N2XziE/eW/Xj8zfYtT4n63zYxt/En1SMPysze6WE9opVM3nsbsIwDxJYUvlzj6nUzA6nUwmw54MXjto9JtIJvP05mfylPuphzZsYKnb90F/Z8A7fueY8t08ojhNkOCkq6+r22PhOlyXUb7HwfoDLyxnaSTSOXDNzsb3dSArwVLyIg13Ar2qyyjKDjoj+KxrbJw8ll65RxqJZ6fTae3u7tbKykrdu3dvWCKOg0uQSLTLe529oS35MiZHgLzkQ8ebtDMet66vabc8Dq4j6cr/F9nGLLfevOZc+MXFxdxE46KS6QwItxe1M6DO3PXMczdpr2NUt/TLA9c5iXSES0uXy92IDIgcvHkN1H90dDQcHwzao14ONOMdCp54TISf4SqH1oEKLcw2jjm3wF87suS3XxoDyrKx5D5vwmNJLv20Y/Hci3nsyMXLCnO+okvTZJ/GZMRy5bCeDzJsRGaHMxY92shnFJrAoeryfRRZLFfmNccymH544Zx5piK7TXhcpx1HYumIOrDA4gfadP2WL48jfPW7nQFG3XJqR0Spv8nblAXk//T0tH7xF39x0I1P+7RPG/YPZVqLsd3a2qoHDx4MG0y9ygnack7RByoaKNEf5l28QCR5DH8dcaTeUzJt6nHwvZ1Dsf24SXmuSCFTSB4oOpCDl0jkJqVDFS7pJc0YC1nSl04gvft1CLBb6QAacZoJ9E8bRvZOFZ2cnAwbziywRqZeaeTB9b4D/4bD7hACxir/WuAyvLfw2gBZFjBWOSFcNX/uU5fXtgJirDn3xhGK0Sv0joGS3OtAv/ibH9ofM/5G3pR0SNfphRGl9cRRlh2vkXl+OkeV/DUYS36l0elSRXYeXYSQ/LJBs3HnujeTZfGEtekwjYvsCX28uHh20qxfZ+vVQ9CQk8+sWKIutz/W98lkMpdKcn35TFc6wDtmK00L45xOIvnWych15Vab15jV97rbTijdsTF0ngyzER8biE4RuoGiLnvKDONdOseQtNoQERUwwcpuYys9DoFle/yOwPJmtb29vbk5A6Mp8wBh75yC0YV/89lBFNC6jzUGfabRz7Eywkx+Y8T5wHccgZdHWnbSWIM04bHbdkojQ+mUu+4a7dhYZUQDTztZ8XyO27aB4Bk7r1xm6/7z3Tpl5JuprQ4lW0ZTht0/P98500TunuQ0eOCeXHVkXo05BWTAwIHi+gyOPHbul8cnAdTp6WkdHR3V48eP6+TkZG5lHG0zfzCbXZ6iurW1NfTf7xC37GR/cQw+LNBjnysEs6TtcdTq8aBk1Jx8sF52QPK6cmOncHp6OhzfABpEkNOQWMnMxAxnKYkAUqgpHRobE2wbcT/jibsuJOtSEVbi9fX12tzcrPv379fW1tbcKY5O3eSATKfTISo4Ojqq/f39udcl+mTGbpLTaDIdhg2qx4GVPnxslPOEyCxGqJ43cXqr6vLlIcvLy7WzszMoH0Lt+YgOsXi84KHRpPuYYMNLfLnmPmPEcsWTDRafsXXxlgfay8MCkz7z2/xMQ2zECm875e1AkmlL+U2UazmGx/QH2SN1iTGEbzznqBMachc6dY6NL5spkybSjl71RCrH6RzzhjH0hLD/zmbPjsZ4+vTpsHDj/v379eKLLw7R/ebm5qCD1M3CkZWVlSHKSOfc2alM3cKHBBjduKRB7+7LSK6L/vOZLuq9SXmu1UddB8YQdjLOYXh6yLE6koakywpvBXEd3bVEXdcxEOUw4silhqbFxp4JZM8f5KsSM30Az6ouUwkeaAsqv3Oda/ndSDyR36J+03cXp3r4vTPe8KOLQlxMRycTvpbCT+nW7NvxJQ3peEzHmIEdk1O3M+Zs3WaX3uuONOgc45g8Z3rNPHD/6VuubuNZb1C1QbLh80op86CLGFzs5KAnU4RJv09n7Ywfztr3TKfTYRkuss45ZFWXmzZTVnCGRA8+byv7kg455T3vu84xeCzHSgdc084+jzOg3HpHM7P9zmnlyhATygDbk6ZjSMFZJFSdgCWqpDgcT3oSOWZ7vgf0urKyUg8ePKjt7e26f//+8LuXmMIL0kDHx8f1+PHjuejAxwZbSbv+Gd1xf+ZCl5aW5g4Gy2eTt570zfGx8aA+0mNEivQh6To8PGzPXEqDbOeS0QrKa+XucuBG+6yAIm1AHbnZy8UymIifZ+kXz+dZRNCf9WdEm4beBsrGFlScaQrT5DRk8sRGFcOGbDoSZZUb5wkdHR3VkydPht9B7GmovZx6MplcWRaL8/Ax09Qzm80G9J/8g6eWPepj8nZjY2MAUdad5C2yjKNbWloa5JW07Vvf+tba2toa3m++uro6nKsEnevr63N7OzJCz/Qliwjou9O5dgQGxpaNBDwdoLZ8mWeLZC+fvUm5VaTgcB3nAAOMEBLZGCWaAZQOWbrzY44hw/GunhzAznlkvd295Mp58UeiNSO/8/NnL7PZ39+vJ0+e1P7+/hAlYFSdM8yw3MjMfONeK5udkcP8HDvzy4ru+R6nFHxuEuPMd5SM+32Sq18ulDRAh0GEnVNODHeG2/WZbor75muWxe7ZRHCdInl8kp5M5XRo3vxPB57OKSM/94H7HfWkTDuiQGedLvJcVGdouwiB3xwRdJEubR8dHQ17KkyXI4z8P4v5ylwVUUzm3eGR6YKepaWlYVObJ5/pJ3WzQ7qqanNzs5aWlub2DeU4efzSAXSy435XXV1WnvJiW9r9jo5k212kcNPo4bmOzjbDc6mWmWDB7DzbIueQ7aYDMFLM5zP/R/EKhBzQFMhUNjak4RS4x3QsLy8PYfj+/n7t7u7WkydPBgTtvO3YAFoAUlAs/J4sxohwLc9YScVxv2ycPLZ+6YhTZEQC5GFBpT4pFpDgsfe6b5Cl0ydOPTl6cR3d+Lif8C83E/lapufskOlnF55Tj7+bv+5f5xCSPvrrOjKizT50cjudXl1l53FOw++UpuewbGBzpVDVvFNIlExUTKRmuVlZWan79+/PLeEGvDiayyjBfKUvnhti179TrglCnXIyL9HT1OnZ7Nk8BLwm+t7c3Bx0GLmz87N968a9s3d5zfY1Hc8YwEqbnM+bljFH0ZVbn5LqENIvovfuR3fEQpbXqxZPMmMseNZ0jD3TFaNqK1c3WFZUjqvwm5y4l3wrwjqdPttzwFntr7322vCdCMHph0QHadg6I+FowHT4WXiWDtOoiWhmNpvNvZyHZXxnZ2e1v78/53ystJ78g56O1zg3K5AjABsqKzmpCviaO6o7Y23nneNLP9bX1wfed2vHiY4ctbk9K9aYQzfC74qjIWjA2HnJruXQaDejFTtHxpEXLLECx0YfGkyPndiifkOjd1UznklL1eXOYVbaQTfInAgT+eKvoxSDo5zEZnED97How4Y600uz2ayePn06LJzZ3t6unZ2dAdxsbGwMoAe79vDhw0H2SI0auHW8MkjI9sfSZ/4YrHjMM3pOe5nOYqzdReXWE81dhNB5yERBVf0EnUv+3uXE+X+RQ0hFHat/rG2UD8HNfL3745VHHGVxcHAw5DAdstshdDnhNJ4ZZjqvnAgl9xukA7ZQMH5eskpKCLo5KMyrh+ys0pklzdzfGbQOJaWjsAA7LZAy5f5xr+cSMvXC947/WVcayO7eLN198I367Pj4HZoXTfx39FiWMgpwWid1wmMyFll3zgL+5iKLlFvLWicryCDpZ+piLoTSpQ891v7rlYB5L3JoAHR4eDgHfGjf+pp2IGWzAwljMjWG4LPcFNVfZ08z5XTd/ZTnep8CxsRzCh789F5dBGEFcRg2ZkDcsS7V4+gh7zUynM1mc/shLMgUwtx79+4NaMGHflVdRhGTyaT29vbq8PCwHj16NLzRiclkKyY02JEmr4g8rHidY4MOaOBFI97OT9sYec9nnJ2dDaH80tLlZBxRDW0YYSJkjLnHC8Wx88p0n+m3IXQ70+l0COF5v3XVpbGkT17Tbtmi3twNXHW5PHB5eXlueWQqrNFYOt8xx2B05/uSD1WXL06Bdzkhy5h5UtZR1nQ6nfvd93XPUqeNf7cElzZ8hHuiS/TBL7Thni4CTyMN3wFR6OPy8vLw4hzSkAYlthGJ/qHHbz1ERjy+/M+E9ZMnTwbduHfvXq2trQ37FGazy0P0NjY26t69e7W6ujpM0ndy4FQX0a2jc8uq+WY7ZTm1fCGL6N6Ygc92AB9jALort3IKNGrhz9CUe9KbmjHO/Vnp0kj62ecpnRMau4+/pBlIGYEu6A9KgWE5Pz8fVhfx7mWELnlkIU0k7DyzN8OZD13fcqWOT2H1aaudkbGhm836d06YP2NohzkFH0dhhL7IYfv/bokvDozfzFcDC+rwZkHahQ8oSCqkZS+RZZfqSyXPMTJ/bJit/G7PfLH8uA2iOehwPt36Z+drR5PzAClTndO2niITjlQt211JGTI9phe5BJCcnJzMpZYAGz6oz3RnCtqbJz1vktdSfphbWFp69tIey4rtAuPUjX8C0JSLBHWmO3nF95QRRwApZ0lPpkJvUm7sFNwZPI9DU5ZtmiALVHqwTqGvC5nGnIbpoyRqua5ffCdXurW1NSiwzy2yU2DPAbsmPZGc+cZcipdC4fmCfO9sh47ooyfrMBZHR0fDxLZTCVZub8CCXtrE+I7xLwUSZO6jLNIpuK9uy04IB5dG1cc6m+ZEjpPJZBgr3+/zhix36exSYdNp2nn6Ppd0+Jlv7wyFja/fnWE+dMg/ZY06LPvmcyLndAppgNKAe17L+p8r6cxr18nzyKnbRs9OTk7m2mEimJVtKVPQ5T6bRk+AO2UKzV5pd3FxUTs7O0Pk4bPIiIZZIptnWBk4oLedQe+KnUKOXUYaXV2djUX2zKNPWaTgBnn/aK7f7jxmIkOKryfiYPBz0PP5fMZC0THTbVInaJeVEisrK3MrKXAG5B5ZWXRwcDAsWXMoz/+JfN0extRrveFDIlOiB8LHi4uLuRRVruH2XIZRjSfwCM3hMc4OQ2vBTMOI8HtViZeXms8prF4FBup06N0Jr3O5acRxNDhArxO3MXS9+Tw8QG65x2lDO7BO9tIhcK/5YGfURWDo0tLS0pwz79oCebttp2sxZFVXj+NwXzpEn7rn9BQ0Zp/TOEG7x9t8SH7Bd/fh9PS09vf3h0nljY2N2tnZGTaPguzpA3KQcx/YKZyu5QU9Pzk5qbW1tdrZ2Rn2MACyqp7ZgJ2dnVpeXp7T+TFwYNnKcbYccR//j2VgkjdZ5xiozrmF68rrTh+lweiI7YzjIg869tsi5J+KmgagK/bQXlaZSBCj7fXROIOMDpIvSRMCnGcCdeF90mikjeCAhHhJjZ1xGgnXZ/qcKsn7zYtMpThS8QR38j0RuGnpxiQjizGeuB0jP4xp3pfPGB13ypV9pg76nv27ieIZGGR/03gkrelgO1m3I2fsDCby/o4+y47lgtKteEuaLFtZFul91sk4ois4GepgLq1LS2U04TkQwMJsNhvA7dLSUh0eHg6Tyl6wAE2rq6t1fn4+d/Jr57AXIfuxPufznTzepJ7s+20cQtVzRgpWJqNTRwhV/bkfENmh9UQwvoZS8Ft21AjHa8ATIeUzGOnt7e3a2Niozc3NuR3Hy8vLtb29PbS9u7tbR0dHw7I2jJANtRGeJ55ms9kcujE9XX+gjULdLDfM82o6haBeKzkKQoF+zz1UXa4zNw/TAPhc/0S+BgOTyeWOYI+HUWZnxG0MMBCZZjLv2VSHUsP7RGKms3NWRnOO2LiWcmdn2+0boHRG3b95/imNgXXMztq8yN/tSBjXRdEfMgo/M+I2Ham/yMxsNp/uTDSdcto5N6+MI53Jqr6Dg4O6d+/esCvZ7/VwMQ8dkeMgOZnYu/Pp08rKs/cxoAPoHhtYt7e358BIFgOHlCV4bTkwOOnAtu1XOjffx/+my/fcpNz66GwLAIR5bsGEZTFz8nqiJ9/va1VXhQyvn549kR6GwQ7Eb0oDBTAozu2z1hpncHR0dGUQPZgM+vLy8hCGGl3nYJsm88Ibjqif6IDVGzbWnYJR0sDlLmjzy04/kfBsNruSMkpj3v2fzjzThWNGYgyJ2hnY8HUpGv/NyMzpDe6xjI3NIXTOpAM6/J+K2YEU86FLTXT96fjWjWkaKUe41NPpYEd3B+IonjugnbQbHQ86HuUROvwGgHny5MkwB+jjWKiHyXmfX4SOgfY9N8IqvIODg2FH89ra2qBzyM/29vbcQo50mjknROmcY/bd9pTf7NwT7F4XCYyBn7FyK6eQyDWNoQmounpcwyIExTUbgm4OIttIYe7uHUvHMOA4BC+vnc1mc4ieY67ZIm9UkUJgw8uqBZbKmS9GY+msKAje4eHhHAJIR7EoH28k4uVsRh7Q4kLduayQZ4h60inkuKUByfHx9wQIOX45rh3vzf+UKW/8M70p29zva4msu5IGM2nwfIXrTZoybZVtuK3OCdoBeAVS8jOBiet1n01z8jDlA6dgWR0DfJ1T8P+ZFuZ3wNHBwcFglJFJ75TnN6ehkHcf+ucIDdB3dnY2rH4yH5aXl4ddzsw3mJ8ewxy3Dqi6GFjmsx6vTm9yDMZWhV1Xbv3mNRPIhE5O4HB/ooecgEyhTsNhY9fRQ71ZOsHrDO/m5mbt7OwM7xlGKFiBtL6+Xqenp/Xxj398OK6C9EQqugeG1Qu0B5o4ODi4MlGXE2Wz2WzuwDIO5PJZRCghz2TkYZ5Z4PmfsWNFx/b29tDGZDIZ2mcMqMuT4Zubm8Ok39bW1vA/fbdBgm5PfneCjCykjGS6BiWHvkSBmXuneGWUDVA6VE+ypvFahOCT/2OI2kDJDstyhV6lAc+oJfnTAabcGGZkjPzYMRpxQ5tl3CDA8lh1aRNy0pi+eBFIp9fwp9sYh8Fn6ShGnROHkemHDx8OkSzHrgDk2Htg/rMpjWs4BYAYKxHpF1HG5uZmXVxc1NOnT6+k++wIXRylWdYWAQ3X2313JJFRhssYcM7y3HMK9thGbNlZE5KIY6zODPNu8jzPdfencoJk/PpMr2/nGss6OaqCJZ42WEm7DTX/U7x2HrpcD6EuQm5Ha6RjIXDfU2hS+Tr+TSaTQXGqalA0jjCxEXV9PjMGPnrTHbTk84nkLEfZXqLLjEbSYI5FJEm7r3dys6iM3Qv/F9GQYCLHZCwN0KFq/+++GRykTJrvFL9SlsIY+qwh6zn1ecw6JM//vmaA1kWGOJXUXSaTkR/qcBoFUANwyzFzpJRpl9RXHJlXg3XpV0Bdh97dZ1/LkvMHXT3duLr499dbnntJahogNp34zBAG3OvevUzNBjKNmAee0uWWXXx/MrdzCCw/ha6qqu3t7cG4/cIv/MJw0ikvyEERHELTNxxKtxqi6hJpW+Cdujk8PBzOH+rSOZmvzhw/45BOirkS0FLW5fXfnnSDXyBHTyozMf/gwYMrTiFTF+ZXGkQjOApI1PKR6B4FMFLunKTlwkYz0a7H0fRR/JuNS0bDjnr9fD5jA2AjNabwnbNBftwP6jSYgEbSmHb8oHIAHf8naAKF5m530jG5NNb3YKwBEzgcbEEujXY7yKfTsI6uLcde8OLD7myPlpaWhgUa0OWNo+7L0dFRTSaT4Wwkg6fp9HLOcW9v74quZwrTpbNdjt46WTBf7UQ8b5Mpx8yijDmlLK9r9ZE7R0ec5rByWGAtUFlHly4aixA6ZqfhSMW0QUMwOXed0JSD7TjQjk01PgTOH++0tIBDA4aZySkfg0H0wcqZPBqDgvOoutyMlek8+sB1L0N07p/7UXju5b7kHYpC/X4/dcqBV854nDE2jIH5hxJ3KMlGn/qNWDHCKEQX2fieVJQuCvFvY6gtf896Ukf8fcz4M4bwqMtJu18ZWaV++Z6qyyiQRRNpxIyenQIiOnaqLp0Ucs9LbEi9dKAuAY/rcoqGer05MtNvthfWGXQLOYUuAzZvZIN/lnPG0SfDJhihfqd4LS8GNmPRqCOA/N5FXn7OY+f73RY8/ZQ6hSTOHWPipiMAZnYoi9/z2lh7ty0OQ0EQS0tLc7lIjPrp6elwnpHzlbnMzgLkv1XzE4pco03ylrPZbG7XMQ4hFbRzvh78RKuJrm0wXZ/3F/C/88iZLsBYdcdaeJwWhcNVNeeIOpSbxtAo1MVOwdeuAxaOSDskt0jWOqWjpEPI6/m9AznIKE4howQ7Vl9Lp2A54RnPDTg64/90tjh4p04wkNBg2gEMs9ls+GuD7SjjJvpMO96AVnV1/oUx5Rpp16oajsyYTCZzR81DP/ywbUpnaidgh+TTbbvMh51CBxqypM6MgeEOPPm69X9M1haVW79PYUwpPIufgtwRSelCHAt1GjzazijEBsuhOveTBtnZ2Rm2siM4KyvPznyverYP4dGjR/X48ePa29sb9gPgGGiXY3b9wh0jNCOd5CGpouPj4yFV5NA3FTR5nU5hNpsNxwPYKKJEOMLp9Nn5MhTqtXHwZrruHB6jtkwZoRg4xESAdjpVlyurjGo9Se9JavjrVEeXXvQZSRTXk4YbepwXdgooU3imhefgdZfys+Gumn9LnyM6Rx02Ql61ZgO0vr7eOjTLJ7IGvezcdSRKm7RjcOPVeJQuMkF+LSPeBOo2DQR8Phe0Uw/P0087yfPz8zlDjCy5fcsYx9Ew/0XUsLa2NpfhQN+YjN7e3p7TB+gw7yaTyaBbR0dHV0Ab9xgIZ/rQkXimnhfNEYwBxC568nzvdeW5I4WqapUyUW6WDpV118bQ1lgIlsWekcH0jmUQtNMZFxcXwwYZGyyH1EYu7ruVmv+55qMnbGStUGPrmjt+XIcy6K/nZRzJ+fkun09xGoN+Y7SdQoKf3URmx7NEy3zS6Xf3+nfqt7KankRoYxGCI6GMPFDQRfJsA+n2sp0c36Stk3sjZDuFPGI6ecO7KIyE+eQponZaNlzJE9+XbduR2Slnbt1RX9oP89vRQcc/f7dxRK+zEKkwL8iJqHY41OXVSqbT42QdznetmC9dtODx8n2dvaMYRHTP84xlvOPVTcpzOYVOYRE2vL+PLh7L43bMSA/bhV58H2Ni5ivJ/W1ubta9e/cGBk+n0wE5XFxc1NHRUb322mvDW5a8+gfhIc2EcDhdhgDZQDJXwM7JRAyZMsriaCmjNQuAC0aDpXTwJFEfjs+HkBESG0mDwNnxzVLejBR8DAF0otRWcBTXCNF94pqjCvcNpXWqi3EiRWcD5U8a4i7dlPNdOHajr5RNy1znfAwAOqfnv8iRVwRRPGGfTj51hvH2GT9+cx60IC+07TbMy6Wly0ULlk/LtMcYxM3vHk/a9dh7DPJQSOfU3V+3bx74O1ESoOzk5KQ2NzevvA3OvCL69hllk8lkbj6PNtfX14fVenbCSe+YHqfj8MIc8yvr64rHjBT1omWqXbmVU0hFcLGnzcEyIsuSDOt+77xsIhCYlcgexMxhWoTJeHgQ7+PHj2t/f38w4D7uwbt2+YtidAan6jI0ZklrnnyJEWV7PqmsDmkn+jFPfKwwv2V4b8UzwlzUDxs2lGJnZ2dwCltbW3PIPxXCRoUoLY1kl56w8HfFRsfGhFSADRq0Ox3SARG3nbnyHIfkfxchmCeUbn4gUR2GNceMejv98/Njv6WxsbPE0AOMLi4uTzu2HuOkvTvfH4Mkj5WLDSX0IRuOaKsu5dQO3/2iHs9TcD11JdMsvGp2aWmptra2BoCTKSmAHCDXL9wyMPDxGQl00m5ZZlzMa8taOpIsed22Ih3Cp8QpuOGOsGT+dNrvhr1pvfmbw7FkuJ0QRslpDx+9y31+b8HJycmwi9FLNxn0zPXl5hQrHcLKfASoLCMYG3MPpJ1aZyTT4JEWq6o5umyg+W6jar4aMdtoEWWhELwAxZPqVkbQayp51m3DcNMwN9MNaVwsE44CGGOH4OaB63FdvndsLDwmrsNOISPA5O9kcnlKLDxIpNmBsQ5JmqfpeDIy4lrKhVNJ/M18t0snu6bXRil5jUNnjPidCNXzR2ORdPY5f89IHFDYGXX3I4+xABx1hrtLjaZjp+5FZQywjN3r+qwbGR3c1CFUPceOZoytjb1THCbEXs4MtMJYCdKBpHKkQTCSR7FcF87AyBZEvra2Vvfu3avpdFqHh4e1v78/7BPwZJdpHFs/DV3eq8HRukQBFk4EbDKZ1NbW1nC2it96Rru0Be+YNCaV5b0DpKqg1wfddegU9ONoDv55nmFzc3OInE5OToad3kaboCfu4TcQGOmezpmkg3K9aQwNPCzwRrmZxsg9EvlcGkwbPvjhccn8v52VjVciNo8n6SGPbc4B2JDaubsP8N1gyP0EFKUe8r/TpESzmfdH55F/7ktak49V8ye0Wv68n4fx8XibZlJWnPXl/mfaCLlhzAzITN/FxUXt7+8PR3O/5S1vqa2trSG9DK9pl8lkgCS8Q8dwbtY/0+livjq96yixi3os++nsbDsTZDmiu0l5rtVHSbTRhg151fjSPX+uKw5PE0mZJooFkxSRV394ApYco1dBdAy24PseG2KUxhNa3RJT88F5VuetO4SNwbIz4ncUyxGJBSLTUDZUTmvRT+YZjOYw7svLy8MmRX7vQnb4lQ7d3xNVWy6o0443EWNn6KgneW1jN6asY892UUYXsXZ1dijZE8g3WUOeRtP0Zv3ZB+SQtGi+j5v/+e5UqNOJnqPgL/LDfblyzFFvVc39nsjavPX/1MVeFgCgQRoF2ed3HFgCVwogkSNoSCezpJZxT4dOhE996KDl+CZRQYKPHLvuftMDb61r+Ts0fsqcAkSn0C3KfeUgZyph0f1uw38XMRz0QQ4UZI1wMKFa9cwQs1uZJXkMMGjZ3hlDiBJMp9Pa398fUAmF5X/Xhc5etUC+loLwGe2ZP0aZCKSVwstcKaCfqhrOVeKvd0Oz25PJY78S02gr+YHCY4hyzDwxDz89kWeZ8KYhT0qSu12kRAYtXeTayZHBRxajxzEDk8VO2eACfpEiYUJwTKbNV9ed9Ps3gyhAChsx9/f36+joaHiF7NnZ2bBEmslh65Db9hJZG95E/p6Hgx7n5NFHj5FlyIV6McSke3NJM8UROhEuumpwAa8uLi7q0aNHw5wju/S9E5qxhBaivKWlZzukcYipmx4T/9bJTTqejN4dSSYgGQPLtl3XOSnKJ22iOYnI74ue8fcO9dF2MtJRi9E/QufdtzYMGDaQkdeOI/QOjxHYXHUEuvLafyOkdARcN+0YVytOF1pXzb9CkLYcASTvjei88ilTWT78zkjHqAklyQiFe+0MJpPL1yCar2koMm/rehMF54qgLA7HMxKhsKM300HwFvrz2S4aSpBiJ2FFzLosn3ZAnm9K5R3TuQ5oULyyjMPd9vb2hmOhSauYXoBBOkZo7aKGlCe+AyrgCbJNpOn3G3gRQte/DkAB6tbX1+dkfTqdzh0imdFd1uvxPzk5qUePHg0O5YUXXrgyf+bxhy/wzYs1nJJ1f8acBvKVmYn8mxGr67ZtSbCT8rio3OodzWPCaWOSRHXeyYhsUejN8xiJRfeavnQMVvY0PAiSEUoiPNfL4DEHQBheVXPLcK8rnYGhftNtmmyYEk1YMb381GG2o4c0XoxTOleiqqSH9jOM7UJY+pQG231HJogCcK7uf/LWziPHvpMJLxTowEjK5XVjmYjN/2NkU29y3Exf3p/3mB8Z6aTOGRCAmJ0yyj04jFk6p865jRnwyWQyB6S4ntGh9RGEnWnBHF/67ms4F2jODaA5j5P0+jp1HB4eDrK+s7Mz0Ot+07/U1wRLCWpvApBzPG+L8rv6OplfVG7lFBKpmbHZYCJlrnVlUY7ZwpKK7IHhXpSfzSnMJyCYPhvI0YPRtZeIZuqGzW17e3sDGsaIzWazuaMfqDONUyoZYSd/jTIQIqe8KN4Ux65rL39NNGxnR1+NDqtqSAltb28PE2+5NyMRIgjOk4OesGQ8nKvmPkcU8JtnQWkYG9JN8MRG3ggyHRdjaKTLnA/3ufj/Lv2Wht/X4Qv9TsPuSJL7Njc3Bz7w22QymTPcPNtFH0bnBgQ4ByaRiYwNEijIV+fIzEvL4cHBQbsogOdY+mnd8ngRFT948ODKm9G8xNqTx/Qzl5VPJpPhjYSHh4dz/M3iSJu/diTT6bPFJ1XPIq6XXnppkE10wGPLfqDj4+NB/rzD23I05lQti37OO7Xz3rFrCXj9uUm5VfoolWOMsM4hVF2dEBlLB9hA2JB2YTV/MywHRZge6rQAWxishIlqcRasUCJXabSF0qS37/poOt1P+pMIOFNFoCAvfT07O5vbam+aMifsiXWPnccmVxeZHgsd6TeUM/uT0YF5k2NqZOqVXl0kNwZQ7MiNRK9rd1G9iagzmrTD9Jg7CqEfNm4pjx4P5CtlgnG33FqOM5VjOjtDZd3AQFq3KMghssQem855pNN2m/yl78znASxI8zEnCNBizs6pqYykLYce1xzPqvmD8CiMI3ONBwcHw6rAjBr8DLQaEI7pfsrHGILvIo5F/clrGXl8SpxCNsrAdkrUEYtCVM2fR1N19VyjNNxZJ8/ZaDmn51ylB4j7TC9ICsNP/Z5kAoHs7e0N92Wo7JVNvp6pAKeHNjY2Bloc+sIn6iOKQDkwBqSwWF5HKgv6l5aWhnkBUjMnJyc1mVyeO4/TtUHzuxGg1blVo1n46Jeoe0LSq374a8fm8bRh9f/IGktoU0EtG8vLz96MxbXcn2J077aZt3AxHd1yURu57hk7UP/mPgBenI6zjNFHt08KyCkT88qgwb/7vk5GV1dXr5x0bDDnSVYiTEc5XpHmlFFXoJO3lxFxr6w8ez8yJxAQqSKzpgnZhWbGHrlFVyxTfCfyymwDfDs4OKiVlZXa29ur7e3t4Vj9HGdoxoF1ef+0kQYLXLODy2fGUkhj/F2ka9eVGzsFjIA7ZGSVXrFDJjaOmTK4iWPpUAHhbM78O1UEw7nOwIJQMhw1Hefn57W/vz+HGPyhsOSP1IfP4OGvt+53cxugW7+5zPfgBDAI5IiZTKQvRqTQBv+8Wc4rsOAdDoQ3z2X+G6dqIw4fTk9Ph+fzqASMqsPnLsefY4585aoL000/ZrNZHR0dDSgz5WkyuZwA99HR9AUD4f51EUKH1GzQvQLM45fo0gCA35BFr14jHWFavPclDX46styM6dVeNiiMhwGaQQHXDQSqLg99zJRlLgWl3/z1ZDu0okfwYmnp2RsSvcGNXD+y5yMs6Cdppc3NzcGJsvTW7VGQ14yYz8/P6+nTp4MjfNvb3jbwhPEhxUN7W1tbQ8SeUVTKlGUjo8ucB8ti+5lyanDgaPEm5dbpo5t6m7Hnu3CK33wPwmSl67xl5jSr5ifD/HwqvMPtdHYIBBPKXqmUBsKGnU1dVTUIj42g6a2an2TONEIiZgQ/P04luD4rmmm1wOHEMKR5LLYNBMW8Be1aEKnf99IHEFrW47pTLjqkzfg5z46xBMVSv5GvU4QpSx1ydlSRimVj1+mFx6LrC22AkGkvjWsXZaQMpkMwzV2/0mGP/ZZpRPhuenx/Z3jS2KELdpiWcxt7VvZdXFzMLc/OiLWTP+41TV6A4fEYs23T6XRYzru2tlYvvPDCAPByPIhkfUJs8sH2wrxJXuV4XFdsHztwMBZRdOW5nMIi57DoNyNF7k1vynUGGk9JyWjAiJznMGi+j0ln3p4EWsxla1WX5xbt7u4OaSNQiCd+0thhlKzQHK9N5AIf3CcE2Bt7oIFJM9CiJ5X9ilDqw8DbgB0fHw/XKY6Y2IMwmTw7LjidQvaTNBm88vhCi50hzyTf0ljameDQMo3YyYrTKii8lzkiY+aNeZEK3EUaHq9uNUrSSj/NO//NPpi35il1ux+UbKtT/tQd+OC0iaMt8wID5zfzVc0vcLABhnepo9Zzr9KhEBX5XmgjjcNLe6CVVKX3Lnic7PRT/33gZdLitol4j4+P6/Hjx8PO5gcPHgzHb1fVsNN6Op0OacunT58OUbxBXsrvmKGGJvOkA6EZIeQZahk53KTcavOaBS+F+ybezHWN/W/hcJ2J7JyaSVRqJIExdl65Q9UoCuiEj0+U9MRfFhs0DHyuuMlBgVYbDujyFnqjoYwQLChG/umoWG2zKEz1OyI8j+BJcY9ZFw7775hMjKHJzpheh5SoK/Pq3vfg9roIwSmO7IONDWjQtPHX8uj+Z2rU181P87BDedCSfEmnQ+mMQtJshE9Jvci+puNBZrkHZ2ok77maDmT4/owiMlqeTqeDbnpObmlpaTiKInXUkQ1zIk4BuU/oK9GRQQ0v31peXq6HDx/OzbnZNuCEcGpdtJZj1Y3hWBlz/smnrPtTFimYgDTcXcjU1THGlLHrFu5UQAs1g4khm81mc+kQ6gTRJnqpqis7PhEGD24qUiIjBNSvu0zlwWB5u7zzxN06cudcQZTQ448RM44OAXVKwEiKo7Z9pDDOgbCdulPpunRGN+78vQ5QZH+uK3aeS0tLV5y3HUfSQ3upMF3kYvrTGBt1p8E1cPGzLGQwrWkg3N5tgZfHpCvIq+n0ktm8FwPZteEFH+ZbLsawc8jdyJ2jylQPET738Wrdra2tIe/vI2bQAWR/ZWWlNjc3q6oGwEekYsePDQEsko7c3d2tyWRSL7300tAmc4BeuHHv3r2aTCbD3MJ1emFgknw3r/N7jgV1pRNYJAdZbr366LqKUyggKBFTp0RV82u7qy7X6aYwGm1lPh5Dtrm5OWxddx3Os1NYfvbo0aMrwpJGL9Me0GFBQrk88Zu7guENBp4XiptHCFwnWOYxiDjTCyhSt7vTaSvelWAUxcd0w5fOWEGnERN8MGLvULGNZTr6MYWyfDGmyI93qedKt0RaRuxZOkRuBTMvE9xkJOvfMt1B3Ti1ro3kUV6HNq9col7Pn3nS2gsr/AxgwuOVdBuYWJbMW3jgeRLTSqrOztPp4O64GDszjufY39+v7e3tWl9fr42NjYE36BT0OPVJCooUEXWjD04hEqXv7e3VbDarj3/84/X2t7997sQEFjmcnz97ve/FxcXggMyrsdKll8yvRP2O/A0CLd++9imJFCgpzLdBMB3isaKksI9FJY4U8jpCkxvJqq6uioJZLOf0CqMxtJXKSmrKG6SST44iPPlFGsgv9IEX/pvOycWOuEN40OJVSP6dyCDPunEKDuXB8KLgOWbud/KpE26PC22NyVcHDKwg3Vh1csJ9GLh0QEmzn0207HtN1xjt/m0Rkk/++XpXzHPnkMccajqXrDtXRWVbOJ/sAzJoOXQb5nvnWPl0fUlQgxxiiJ0dQGZxel4tlKCSZ5w5YHUWtFhfySaQXs5FGdTHnIzPX0IPEzT776Lx7e5LnfIYdav2rivPdczFmDcbcw6Z/++EPFHCdXRY0WEABo0XwXituouFjAF++vTpkDZyOiLbRviqakgRefclQufTJj2vgbAeHh4Oy0ot/Fk8GZx7I3w/CM33Wuk64w3fOTQw5xLsmNIxoEBeZgvy8hh67HEknbHif9Cs28zcexp1p918UBlt58mdVVdX0MAn6racueBEEqiMKVwaR55Z5LxzbA0MPKYJWoiq09E4kq2quRVbRuX8n6DA9BF1EtnyXO6293PwK7MA/q1ztPTFO7WRM+QUh3B+fl67u7u1tLRUh4eHw2589HN9fX0Afa53MrmcUKbvvDeEQuoJmeOMpI2NjZrNZvUrfsWvqPX19dra2rrCkwcPHgzpXo+hU8nwHd1OHfX9/N/JgH+3jqEfn/RIwchsjHAUZmxuwV7aijdWFqGsTmFZjcAEr5FOpi/Ozs6GVQ3esegwu+MBwsi7DDyBnVEByNtb3xkgjgDojtNIp2WvTz+TdxY2v0LQBhQUZCdBjtUv0IGPRluOckyDX1RkB9GhP8YE4XfO14Ld1ZFok/GATzlfk213xs+yZQPvOnL8+ds5k5RR19vpi5/rUPNYxOTvyHU6gS7KwzBbF3IhhD+5kMGGFFlIA8XYGmEb2Rvte1K647nHI/lvGfBf5hygHwcCcCMicPoYQMnENU7Ckft0Or86itMN1tbWhklvr4TECTDnQH0Y/zG7l9fHQDTfuyiBkrr1SY8UukaTuERFVYtDokV1Zj18tzI47K+qOVTgZXe+B8ElP4hTsDPoQmf6iKH3Ji33PSfu/KpBnIJf5IMC8SzF+V/fh3KhdKnQmQ6wotqI4xyWl5evOAVPLpMnhkbvdKZ/fKdOG0KcAcoIPSim89v0oYssDQD4bkfu3/xsOlDT5/9T5hyFWk7HUHzKKvdSR67cSuXu2u5KGkrfj7E3v+wU+N20mD9pQDw+s9nl7mhknlegup+56stRgvuKU3DevuNHLqXm98yfm6fMex0cHAzzig8fPqyqGs53WlpamjvqHvnd3t4eHESXNjZfDg8Ph8iEzZ7wkzQrTsHOd9HYdiDBAMay62uLQMmnzCk4D5YRA387ZGalsWB48q9zKGOoiE8ulVxdXa3t7e1hYseGlPvIBz59+nQ4siLPkPcAGOEYWTtXaQdiRG1UAXLx8RTJE/MLXniCzLSQIvHOZ0Ls09PTK3VZgYgCWDnB9nycQjfph4NIBYGGRLaeUIRnyU9HUtPpdO74CqN5o1LkIOd8zEuQXipiRrqmrzMs+X/KpY1xItm8H5my0bZcJ0/hTTrJrIv2Ml+eq3X8XgFkpDOo1GnAkXl4jOry8nJtb28PdBlVI2O0gzPhCA3k0HrEfcgq/QRV20iDuOGh93Fkn9jnwB4D9iuk/qKz1nWMPEfHwM+qGpauLy0t1SuvvFL37t2rl19+edDPp0+fDuO1tbVVs9lsWInk4qihc0BdVJb20kY/x9+fseX0WW6VPkoFWVQ6o77onuuKFSnzniBbwjTXa9SCQngPgg2v6/fGHLdjhwdfaMdCxW8+EoNUkI0mz+YgJ2JIXmSunZAV551O2YqOMoJizDvzz/SlgzevXDoU7JVAiXz8fzpFG8M0yNlWOppMOXU8zP/TIZjG7tmMKDLqSP4vaj/HGVCRdYzx0MYgU0oYcddtg5/tL0KbllMvSqi63MjoyDGdoMeX+8xjRwFV8yvLfM1Ocsypmwfuv3UaujyHxrP0gbYdSQHuOISSc5bQLYMN73Lu5DF1xiVtxCK0PwZqFtXflU9K+iiVwamdTsEt3HnNdWbbRhc2wuvr6/XgwYO5M3f4nf+Pj49rb2+vHj9+POxO9FwCNBKukjYBfTraqLpUNLezsbExCClHa3tJnSMFG0mKc7nd8jLnQnNzGbQ4t873lZWVufONHF05/TMWvjK57vOlrKBWog6FV12+9jCLFZO/OW9hBTPytMJ7tYd3kJumzvm75NwG9405ZtNvQ2VjTh86xTRdHciw8zdi5JrRsJ0CuX9vuMz5G7fNMwZEaVD80h74Yz2E7+YzY+DjKby6Dz562bnn3eiT+5e8Mz/8W/YTfV9aWhqihocPHw66vbm5OegM8oUh397enjt+HP4cHh7OrcZbW1urnZ2dun//fu3v7w98ox7Sbc4UWJ8sS2nMkWM75g70ODNgR3Ybx3Drieax0v1mT77ovq6eVEyMnp0BBttrimFIhuC7u7u1t7dX+/v7V17lZwPD8QiEwI4QMFjc69VE0Iby+dRVD2YOUK4McB12EKxmwmnlewMcBaRCUC/3eiWUDaOFzcZ9UeRiBSS6SgTNfa6Tv16ql5GJ2/PEoPvEfdknf6gnI0hocFon2/X/dpypkFm3nZvvSSdSNZ9WzcgJMJRynzxJI24QMUZfOnKKnUuOF/1C1rjfiyDsWGjP6WI7SxdQ+Ww2G/L99Cf7SrEzy2iym9AlnWQjzv4G6Pb5WZkloE0+HDeztLRUjx8/HiLu9fX1QXe5xhE7pKPgzSLdymtjGYVFz90mSqh6zn0KY2XMMVTNb2BLhOaCYlggU4GtIF4tYAOP0DLIBwcHw2mnftmIw9J0BM6Xd7TYKdBPdkB734GdTiJRG9SqS2Pl8BRB8soRJvzSsLte9yujBDs6G85EkZ3RGBOyRLlpYBfNG40Zbfcl23WKhbSRc8Jp8F1vGvSM2pIeO4Kuby6W0zHF7dpLR2FDmlEy8tKhehutjjbzzfqSzj91EPocpabBzkjDziOjyMwOXFxczNVpQMUENvLg/iSYSABCP9H1o6OjOjs7GzZsEmHiCHBKtEPkSR/QcehkGerS0lI9fPhwONV1Nnu2y5nn19fXr0TMTqc5lZeOwmNumUl7Qslr3T1j5blPSU1hN4qwYnKvBRAk73syhO4EyCE0Oxe3t7cH5UPIfP46K4yePn06TDhhaK08KN3W1tYgQKxK8jI8FJNU0WQyGZaX+kgMI/10VDa0dnIINAgkJ4iNWhJpEZYbhcA7lI0QljkEzydkntU8YcLNa9HddufYoM9hvXnOc66Dsac+8ykVwobfq8FyE14nhwlecmwcbdFXL+9Nx5ByasfuNrp7LevmlaO9TIV2xfSQLnRf7Azs8Hz8C2ODoSLNMZvNhv078Jq60I8uNWcdT8cOYEPXvELOq9im0+ncGWTwI8fP48Lv6Xgp5uXJyUk9efKkZrPZcGQ8su4FIl6K7iWmFxcXA9Csqtrb26vV1dVhwcvBwcHA052dnbq4uKj9/f0rTnssokaPSD11q678XMqty6dkojm9sDtDWRQFuKSiZnFkwf1WujxwzobTE1/Hx8d1dHQ0pIwyOoBmBJjVFUZHtM/vidhJExEdjK0P7lI1ngOBHx5UC7gn9jKH2glE51idZsn0l3mZY5RC7N8QtkQjiQSz3kylLAqVs1guuuvud9envA9jgzNMei0LY/R0Toi/Tg9mJOF6SXnaeCfNiSKTjoxqTFc6hQ5ZE3Xn+GMYiaaR/a5/BnKLZDMNHcVy77mKrIv6HNWOjbF1AL0jCmd+wPNZCdTcJ5wtPJhMJnPvwd7e3p4DgNPpdG5V3Jhzuy7y7GxlRgzcP6aX15VbOQUY4zwwxYbVnbVh6xCShT4ZYsThSVU2XBElUMx01hE/efKkDg4O6uDgYDDYFiwjaGj3tnSukaIizJxOp0Pk4clkL1G1oaE//DUCoO/25PSZOQ7us9KnUYVX3JfpCHKoRFJ5RHaG/iBFlrl64tc0p2GjLpxoykPm2mnXAt/Vadkx8mRJMbKZCgRP7NgS7dNn7kVxMTbJez9jdJvj3Cm5HbH5bH1g7NIA2pi6rUSN/OZJakdjlDwG2/S5r7w/AOeAEczJZzuZ3EeS45f3ZVTNuLNk2sfe58S5U0xO++Z4k0lALrEJR0dHAyDc2tqqra2tqprfDGc98wkCZAomk0k9ffq0VldX66WXXhrSUvCAZd8867Sb+WY5MKDsdNR2Z1Hp5HCsPPecQhcOWxnsHFKRHAXAnPSAVmqnWEjdEMJheB3WstKAHcu80DtXGnWpLht1trHbMORbzzxv0B0tQVtcc7GS+DeEGiPnZ7N+p3xwmN0YeSWOVyI5FeV0jWm04bPjYew6FAji7oyW+7ooGvQ9Lhkyd5FM8ngyuVxj7/FMuhJ1Vc2vDBkDMLn3JOuCh8iwNz12fDf9XVSThjV5QQrT6UwbYd/ndF7+nsW8cISc0bdL7n0xwISX3fh1zjdlHIPoFUuuO1OapE0zVcfn8ePHQ6oKtL+xsTE4oW5ug/mD6fTyhTykk9jxDE0Ay5xb6PSE//k9bWquwksZ6mTjJuV1rz5KR8D37t6sZxGRdjRmiDdbOcxF0ZaWnu0wZHMaDsHGmzqde7ZgVF2eUGrhIvpgtyTXqi4330A7aYDOIWSIi6IjqFYi8qiZhsp0En0wYrXRMDK1gfAZTaYxDTo86DbRJF+h0RFlTohSMNg5B5DylvRlPjYNpIFH8su8zGjEqCtpYCz9O39TMceQuQGMNxp2dbnfnT6530k3sulFD/xmXUC+7MxtYE2H+8532ukMFDLneSv3MeXIBnDM1iArdrwYai8XTeeOPnsuMI3vdDqtvb29gRafbeYNf4nK2XgJv5m3JDr3CaxOv1X1qR3bDSJf+u4I9nkM/k3KrTev+ZNI3ornjqQyp5Ikgk1USr0cOvXgwYPBOM1mz1JAW1tbwxzCL/7iL9bR0dGVc42q5ieZEoHSTiJalrExYe3VB9BN3yj0xUY6kTu/eVu9ldspEvrpuQA7TK/aSEVFyLyUELSVSJ2SabbJZD6FlbxLB+6UlI1jfpyiSiTt1xp2NLo971HJAo83NzevLEXO+yipZBk5pIz4mdyHwj3mSZcKuImSp/NIGbGx5819OAU7j64dR7xOx9APHIbfiYycMRFtZE7k7iWZHT+RR2SStjwBzX3IiCN8duhjjD2RS9337t0bZMqLQLJMp9Nh4ng6ffYmtQcPHgxpLGSW14ROJpO5dOxkMhl4xHUm85HD7e3t2t/fH3Zmj0VYWZApeNOlFi0HjEd3HMyi8tw7mrlWNb9yyEpgFNx55kURg42MHQPhHx03amB3Ibl+hMRoIfPNlEz10K+OuQ7HHV0kWs6+oWRp4MynRDjmg9NdRiyZivPeBjtwBMNo2Sh+DLW4fRsK6KM+9z2RfPa3Qzs8T4RhIV4UfWZEkBGrncei8TGN+bvl34jc8nKdHKfhtnyb152ejSFrG3qDABuO/HT1dePk9hPYsT4fOTs5ORkcRKY0TW8upZ1M5o+ed6o4QYXHuxtL2nJqDqe0CDR0ffZSU7ITCd7MDwNZdI0+Zz98cN6Y/oyVRVEUY/V6o4Zbzyl0Qu7SGVyKwyI7FAtOLrW0cPilOXjynZ2dYcD39/fryZMnw5lGR0dHc0g6V+24OFVEv0BYpKEcVmeelr7T/y5n6Z3BfoeBc7TcB93eVQ19Jycnc0aAUBfayYmyqSb7bN7bkViQbcBzVddsNr9ENgXahopn0tlyrVumyvPw22k19wGFy+W6Ni4ZcdqB2jibL3aaXjaZ8g4fkib4Zr64bXhhmXMK0yDKY+WIy0bfx6gk2h9zVGMOwWOez5j22Ww2ROFeLkqb6LHHlUlbkLZlFxnnwyISAFBVtX3KcSEq4Bp6zxLRqprT4w40UJfTz/fv36+dnZ25Zdm0zTsVfFik5x7gJ5HB5ubm3Bwf45/6k9E4dSHPHYjrgHYHeBeVWzuFDmmYWEoiQN/XeWUXmOg0z/Ly5cu3zUivB2Zz2snJyTDBhnAajeRqDwQHWpg4I/RGkF2sTIlI3ScjH9PAPax6GNt4heHwZJ5PdbUDs3Nx2G3DaWW10XK4zji7PxkZ+LqNgceYcRxDMf7NOW/TTmRjmuADYMJ1w7PNzc0rDsOpNaePjLgTjSf6ThCDXHSI1sgxkbZl0yvGMm1nnlkGbfwzVWg6Mv1GyWjJxtCO3oWx8cdHVmRJ/tqJM+dm5+vIYzabXdELHwCZUZcXeTjFalQ+nV6+mtaGMnmRUcDJyclwbA3OisliH8GBczBosi5g6L2S0pP6qVt2qFk6YJ5RZsr1TdNUzz2n0JVOiJ4nlDGy4v/l5eW55VxVl8vViAqOj48HA47QevCNII1WE+WCaOxcOie4qP8oGkbExsmo0KsQcge16yRqMcrKicWqy01BKSyZNktFdX18514UmJLpGYoNSecosxgJ5jK7dDCWBytRx3PkIh1aGiHzyAbG9WU6pqrm+OS+Z9SYqRT3D/l0JJWOYJHOuQ4vz0w64GFXMCL+m79Dt/nRpaW6MR8DhanbOe4XFxdzR61jZFMGkF3n1wGAHKEPsLSuOWLr+ph8xkkxeewUNnXPZvOrgTLF5fYMVpLfi7Is7vPYfVmex/6+rgPxMqQcMxJc61a5uGSIDYpn9zKh2/Hx8bDmFxRN2ojVQSANUkvQj7fuaGZCGQeTu5NZfcMz/G6ngTGlD94sZmOfDs8rI4hOTk9Ph4O1SBmBSBAO6jg+Pp6LsByh+H/ut2PKSVeHtSnETs9YJrox7Qyav2M0uWbDy+9GTRn+TqfTK30DCRq9YngyGuI3OyHkDoPN2nhHKZTcb5HRl+XOkRx0O8qEBo5XNsrmGYwaiJfloKRSHWFZbl0Hf7sI0GNGesKGzuMzmUyG93BkxMLvGfl6jBP8WC7o6/n5+fBO5Koadq07jQwNfjcCeoRsOYJgeWnKZe7KTjlF53h3wssvvzw4B7ftFYmAE8bUE+1ECh2CN0DIYn28rhgk3KY895LURCK53r9q8fyCSxqrDLs8s099GHwUwxGCo4PMbWZ6hr4418nKDc/ae+Cc9uiQS6LbDjHb+TmXSfiMsvvsIyufnULVpdHEcXp1U0ZC7kv2w3XlmCRacx1uYyxCcNvJlw5huqDInRHL+h05ca/RaNJNn02/+25nQCTWRSkuXVrCoCSNow1vprESSS+KIAxOsq6xtBD6mxFYOpOqmtsPhF7ZAdtpmPdd/8YiINB/1fy7M7oIFrsAQKyafzVopk7SESUtnWOgjwYUBwcHQzrbc21ul/bgrcfEaeQsiwBW14eOh88TIVCea/NaNpgGozO8PNd1OJG16yJttLW1NWfAQAvepIa3xziurq4OAuVduaRpUPCLi2fnkeT+g7HdnnZURBcMeCLXjmfQ53kTIh7qs2MAGdpRJbKHRvZwVFV7hrsnsT2XQiHd5RC4O1Mow2+PtX93/6E5eURbHWoyXUbWlhvzt8tzjyGurMv3OoLx/xiIlGPz1fLfGUTPG9igjDkE7u/m3jrgkzl/GxNHiPCTOmzIDIaI6Piwu5nNm7wnJFMbSRe0daknF/QSvvrIeeYEj4+Pa2Vlpe7fvz/38hye9R4N2oHXft/BIvBHn6iLMQaovfjii3Xv3r1h97NXF3p5LnSZT4BaCulGvptXVfOG3mPOb6Y5r922PPeBeFXz+cE0hCnYN4kY8l4MF6kiG3zaIt1DeG5mW4ltUKDVcweknRxG2nBY2avmjy3mzUpj4adXxnji20iLiIfQmQ+Tb92Ep+lEIZ1+2N7eHn4fGxOQYRoG6CaF4tUSRrsec7eTxoD6nFapupqL7wy+/09D5ojIaK9bztohrJRn0w9wSFozqrJTWxTyp6MgAhlzhoyjF0Zkn5LuRdED31NOMT4dkLGzNTI3j3AaGZ26frdtx9hFWIC4qvk0GMCH36bT6QDmqBsakFuMejpmfrfzx8HZaRK528jboR4fH9fb3va2K2lV9BlHYaCLvKAP6FI3hpbrHH87T49Z8rOL2heVT9qO5tvUk8/lNf9vYTOKg1k25BhelC1XmFCnDbuX1DHwiX6NgHKQEvEa1dkYeTUCTg2BRRgRSBCM02Lmv41FGhoj1o7XLuTNk06K5xHsEK0UN0UkmS5LBD3mGPx8jmOXmrNjGKuLkvR3TsHXzFPu93zDTfrvksqe/y9C013pjEfnpMaetWPoUhA4dN+DDKGfuWw4/3Y0OjVHsVGGNs8R8AwOguXn7BaG1lwckPJDm+hNzg/aabqO2ezyJTsPHjwYjvA34PRiAuhxNOa0cY6Fee7x8XXrbUaYvodVTjctr+t9Ch2a4npGDxa6MQXtHIU9P0dYb2xsDMbU73ytupyEM7quqrnlbTgR0jS+z1EKQugQH0Gh32ksjb6rrqbWqi5XEjFpTJrIb6VKlJgbY6DRdPGd9FE3Jj5XqTPCdgReIZFjk4bKSDyjh5QH6PUE3dgKr4zSGI8uOvUzXSov+9ApH/RbDvJ+10db6eCyTtedxcDF6ZVcVZMGItHlotSMgYIjFdNEm7m8k4JcWIZ4jvQHBiiXRS9Cq/w2lnoFbXOEu+8DQO3v79fx8XEtLy/XvXv3hr0ORPE+v8igEPvCi3aWl5eH1YzcDyDwKQrYmel0Wp/4xCeGNlkwwj22F2QwcBTIced805GmvBiQplwkwLItu0l5XTuas3QDnwKdCIxOds9V1Vw4akMAw/kfL5yK6RCtan5pZ76j2ejTfer66DrN8ES0fLeyEeE4xMzPmDD473Vj0N3X9dF0I6gW2qyXuhOFj4WqnTEYQ8ddX1J+Mqfa1dEZ50Rl3fMduu3ozYJMjvU/r4+1bYfgFEuOWzoijzVI1W27TUfaCTTS2VAfRtvXDYxIPaKHTomk80lk67pcZzomRwHuAzxAb9iJXFVzjgQa/XFKCMNt58j1lAM/z/zG4eFh7ezstIbacutNq3bUtpWLIs+0QR7vRWVRnS6flDeveZCSGfb8uUTNjHCYxfMWkHweY0pucGNj44qRz5QF6Zj9/f05xGCGdSEs9OTvdjgomENF0BJzHkw0TaeXa5+dM81lffQBp5c7OrucopU0HST9nEwmc/s9fN2Ca8fnkkjEdaRj6EJjo5kO2Yw5GfPCRxY4ZYZceXLU8ua/7oujLRtJ0225yNLxyc+OOYQxnviIEmSrqj+Blc+Y80md8sYxb6TMTX3ul+dtbCipjzk9olHGxY4HfYaHrguZTKfPpHbSkGNhHhI1HBwcDBPR9+/fH+gDkB0eHl6JxImysTEAOPPb4zqdPnuTW1XVo0eP6sUXX5zTGybHsREsi3XmIqO3HMMEmNCH/neR+espr9spjCHJNAZd/rf7nbr4iwBfXFwME0MYAYxXGkYGwEJ4fHxch4eHw45np7I8iJ1C5H1GJWlM01j51EQPPhNYCBSGzHzrUhfmr/Ou9Aee5AmvVn7+4kC6eRf/dXF/E736Hq573XoawzQy2abrGdsdnHSNydgYDxdFKqax+31RdJP9vK5YLrqIN52no4gx545sIKcZESMTjjo6B2Y9Mi3ep+PTWO1YxqLNzhh6P4/BXGdboCFXmeGEHHFj/FmlRF84HM97C+DTZDIZjPdkMhmWqufcBN9JYT19+rTW19dre3t7bl4BPUNH2XPhOqoubRfj470p2X/oxO54Mtw8S9m5rnxS5hTGUNJNiXBdnYGpurp7tGr+nQhuL+uZTp9tVc9D8kx/rprgOaesxlCsB4Z6HAl4AjvDTzuCLgSEhkTWacgpmfpJFAod0+nlEr1EX88zXunU02HkvNNYnfkX3joqy9/z+e63qvHFEt3z+Vw+60iY/8fadd2LHMyY0+qezfu7aMtovnMeTtF09CfKdztOsQBwxpZMj4FAp1WqLtE6zip1z7ozmVw9MDGd/GQyGRZqUKdXCeVcYdLNghCWto8tXuF5AKBfXdqNJSDHANJlTL5dV95vPo7Z0puWWzkFE+vB8uB1Hciwq2o+wvA5Je6g851MNlU9mxdgDb5RDsUpg/39/To5OanXXnttbkMYtHU00K6/J+15Lgvt4gRol0gAIfAE3Nj680SDzn0iWNl3lIlVEBmxgIScM818pnPBCK6Rm4UavuWcD0aca4yTJ+lsQDqET/20nwjSfGA8nHLoZDGNRTogy2TKbWeAKUbf5hHFNPp+j2/WnfLsutyGJzJd6L8jsGzDskV9XllEPam3y8vLV+bDvHw6l3RCt+cLDBTs9EHOySuPLbSzVLubt+hAHCcn7+3t1f3792t1dbW2traGJe+cIHB4eDjQjf6TUlpdXR1Sz0bwTpvxtsf79+/XbDar7e3twX5B52w2G97Kxgopj0+OHf3vlvaax07dYRPGoq1F5XXtU4Dg6+7Jkp4wFTMNsQ2jjc9kcrmEzNfs+f1OBedM01HNZpcnKnaGKJGhjSb1eAkpx1JkP9xHr+6xY/D3VMwOHaLUuTIjEYR56XA2lTTHylGHBTVXouTyVoMGzrJxhIayjOXoXUdOaHr80nB3iDuRcfaxQ9L5l++MT2c00yGkXHbj6Po7I97Vnc/AozQSHlPLLsUGKZ0R/Uu6GRdPPJPGtbFEti0HmQb02FIcPXtsGHvL9FhqKqMLynR6eaAkQC1XPWXEgG2oqmHlYycv8J/jaQCBfi+zeQiIy5V47k8nY5Z9+jgm289Tnjt9lF6fskiYOwTV/Z6KxCCdnZ1dQTPkMG1EQDBMKoNgHO7mmmPCPZaUcZxv1VUhRdn8nld2WfpNb1XzaRPn+7roKlEihrPqat6X36nTO6RxNrmao3NQXYogx86b7nimi6wcLtOmQ3UcuyfYbOSp2/KQq6E8x5STjGMIO+WsAy7mdd5vI9TV7/Fw8Rh3jirvzd9zrLOepM8GlPq8ez9f82o+pgOwQcr0o8EO44JRc949HdJkMj9vkLytqivp1u7dAzaq3kVMsdw7EoKvzA9UPdt4mhkB7IEXMDBBvLy8PHdAngECm+VOT0/r8ePHtbz8bBfz5ubmnEOD39ga5ha7MehsZ5alpaU5G0gfrkvhjZXnWpLaIRQ3aiS3yKOnx+8QQKY2+IAwfbwF9TKh7PcgsKbYQu5nMFSdAaSPNq6kYXi7lTfB5QqO5AsFYeN7KqDRluuArsypuj7q8lyHkaMdBykeFC0jJZ91k8jdKMvKbWSYiMcox3LhSCodJ4rbOVHPz7CXpUv7dMgxy02jXI+h27pO+Rgf0gFd1GparV+WCxtl88L3eJI5nY71N8eNkvJqo47xcyry+Pi4VldXB5CUbTny6yZsDdqgeyxi87wDPOpKNy44tidPntTh4WHt7u4OaRefhuBVR8gx+rS9vT0c84GOeOKeI3Nee+212t7eHo5yN12TyaR2dnbq/Px8OIQTvc5+d2DA4+Q6OwcKr25Sbj2n4M8YkZSbELGozhRWG2UcQW6SAQmw2sgb2TJMthHqUkVJTzpA2vZSUhv2LorKwaVOXwdVuP9GiR2/7bS66+kM7AAcXfhaZ5yzPiu66cprRr4Z/nYoF8VPg2KwkG2mk+jQdo5HVzoFpD0rmenvFgkkbfCFgvLzSadox5D/m8+dvGbfqy4naDvHuogH2Ve3SbTnhRWdDOZYuf+ZIkkHkJGAAQwgIOUC45q6mJsDOQ6bF984MjVQSyTu3dKOwsxTvw4Vx5LO2ymsHL/rooNFkXFnS68DPJTX9ea1RG4m2EbER1RY8WF8l9pwdMDZ6LxQxw6BEI5nzs/P68mTJ8M7mkEsmaIYGPD/p4y2trYG5T46OqqTk5NhIiojCO4hDLWAIQAIvENYp4/cvnPtyVPvTTBCMq+4zof3TjDpvL6+PiAZjhw2wjeaZ4OQBZiSb4yrmj8+ICf9GCt+py+ejEyHgJFJ+fAa9m5iznXgoH3dzjeVI1GZeWvD3aHONHbuE+M4Fsaj2Ea5LJOEd/AjUb3RITLkY1JszGzUQPs5Jjm/5L7ZoORf6nb/AUpG8R0YMPpGlpibyHk1O6NuYUbu7XHK2emxLnU8nU6HQx/v3bs3HMDJ/IEzAfCRCBtnQn2WXWzRkydPamVlpX7Fr/gVdXFx+fY55hu2traG+chu34H/9/im48/IwPxJ2VtUbuwUMpTr0P1YGUMdRgFjIREGygdxZb0ICukbo0V+TyROmwjDIudk+hyd2Njn5BTPdGkMD9xkMhlyl0ZOViiUzIYJJ8L9vHOCDwLLAYG5wiujh0Tj9DeNs509fe/GNyODDsV3BsjGootOTJvrsvPIiMn3W2a6SKND1x6rlFPLJLxMOco+UgwAqMerTFDyXEmWznoMUadBSUTtqMzPZ4SKHPOcZQ+6PbYGOf7dxtxjnDzKdLRl1c7IKbJ83n20M/AksWmjj+gjekQ66fj4eI7/1iFsk1PctH9x8ewNjqxaoj922EtLSwNYGzvNwFEI42Jnl/dCh8fhpuV1RQodSrpNHZmeoCQKBO36tzQ2nug1w5PBtAeiBjUnbfTHSK3qcmKbj4XOxgGFdvsWQhtWUAXPJDKczS5f0oJDog/wCKfAeS9ra2vDqwNzTsHG1uOQKTR+y8leo7Ycs+RdIhrfl0AjowL6limuzhjRP+Y/OieSy3pNr0s6rETRnWNwn5xb7uSq++sJdG92Qh4o5LETIDF+3aII+tRF9kSELACA39Dv6DidO8Yt5zIceVg/cuc5bRlQwSdo6BytHYIjHQM9G1Fy/HkigMfRJxNzH04Bx2BASDsA1qrLl0alUzg4OBgiIOSS6IaxBsB5qXYHtlzsGHLT2hjIu0l5rtVHFvAMJ3MwfC2jAguIBZjfQbZcd4hIG6xZ3tvbG3YrY0Sq5ucPuO78YSq0BRBm7+3tDUqRW+35m7n2yeTyKA7fm2jVhsNpNvMkc6N2BqS/3vKWtwxOgfSRJ5AdFSwaV/56TsGTz8k3lppmyWgAJIXydtFTGgzuof8WdKNnGxjOurHzsix4EtN0mSbzyWNih5nFRszX0jGY/0aOFFbFWEbSIVlPzCe/XIb+Jo1pHDzOjrIwWqSw/JwRLnLhN5Cdnp4OK3ESCKDPqWd2gh2Iswx3yNfPeMIWWnNS3PxJnZ3NLt+/srGxMRx2B6+6NPDy8rPVSdBiwHhyclIHBwf15MmT2t7eru3t7eEe5jR4o5yBQTpKvqcTRUY8v+nsheX3JuVW6aOxQuc7xeD3vNaF93zvBIF6XKcF16/PdDRg45So0x7dToHvMNUrinjDUyIc6srURSoGCgfayDSGr/G/+eCoBlTNXAHOwP2z4tmgdPx2W6m88NN1jtHcRRDJ5zSuGObkm431WJqA7zm2dmpVl6AijSVjYgNrGrtr2Yek3XRm5Os+ewz8Pa/5eTsD5NTOYyyq8fgYqME3OwU7Sk8eWzd4FgNIJEsd6IzfQ567ltM2XIeQ3e+MWB2VZLagW+lllO2UE/ceHR3VbDYbdJ6oIJ1LAoks2Kijo6NaXV0d3nNSVXMT2KR6Dw8Pr9RBfzsAZhnI1PnzlOdafUSxV0uU0jkGK0EX1ljwCNuY+My6CeX29/eHM0eYqKmqYQIHoXad0J79cug8m81qb29v2LoO0zPlhDI4heHdu55oNrqFVtNh4c7lhN6pTP2klJaXl4f11rmaAWG1QU8j59QM8w/b29tXDITzwDYwThdwjb7Qx25lVucEUn48Jh63dCi0bTCRkYKRYDreqstJ/8w7u50OsZv+Dij42Q7NY5TMk3SSlDReXn3nT6JNnjUgMY3IqfnoVIfp7uQUY7m9vT0sAWdCdX9//0qKhz7a8U8mk7nNjHZAaXSRZ/RgOp0OR9j4lFTGqZv3ow/8nmO6u7tb+/v7dXR0VPfu3audnZ168ODBEEk4jWUbYLrtEF599dWqqtre3p6ji36R7uW91JbBrNffGQPastMzH29aXvecQjK4Q3X+vUNBiRSWlpaGt63xnPO6MIAX0zhf6EE3kqZu6qu6fIG6c28Owfw2NtNnNGQHR7GzzByv8/PJA+fHrbQZhXA/m2mcNsFxuB33z4rl6MNGwKH8ZHKZT14UCaJcec3vjbBiwosEBYvSWx67LmocizwsY349YxZkK/PkBjQ2mulUuK8L181/3+f+dHrTIefUIRuGfDeHnSh1eUUVeuUXxDga9cRq8t31JUI1wPM426lV1RXHZVDlku0a0CCbnqS3zjn1CL/8FzqgL42662TOamtra7A7rtdADt2hHpbJEwk4NT6dTgfHioMei/Y6Oe1kNnl3U8fwuvYpJIryx4Y8CesihQxPPfNPXZl7ZT8CS/Fyspe0CoI71icbI5wNA5jIpUPYGWGASI3gLJQ21u4zApZLRjthxnFyL86AFEBGB+Z3plfScaAQ8JJNPPQ/0yGOihIFYpxwCowFDhu+uM9ZMj3Q/d4Z2DTg0OZFAmPFKNA0e7zSoSW6y+iFcU/lHQNR5uXYb/xOfpoVeBzKlssvAVTw5PT0dEgXQoPlIftnY5UOjmumz+NgtO95Iq+2ymj2ujQINKytrQ2RM21ldOL2O2CQ48nYn5ycDM+88MILw8IXG2j6gUNlYhmABchcX1+v/f39uXlN+MEYeA9EgoLrbK5LXvv74hTGvHfV+MagRZFGVQ0pHs/G+wgHdv+x/tcb1CgIIiuMGFgEsZvsIiI4PDy8guIY3ERM5KgxzlWXCkyaourSKOEoWD2C4WCizoYjDYKROnV6TwIfIoZ0uo5aOqdMlIHBcFt+zkYPxwg9RmTcm6s6HC77QDzLDAAg92RQd9LAb9RhgzKWsvRkaaak3E/Lgg81S9lNI5hyjgFIJNlFAp2DSyTolTR87BgYE8Y+jUdOqHaHUTpyTEAAiHGkUHVpDEnjeMe/nZEBC207Vez5CANHG3boR6/ICmxubg7X8uA8ZB05shzkuDpqwKi/+uqrtb29XS+//PKwnwHbYh2jf+vr68NYHR8f1+7ubk0mk3rb29422DqeA9jxjOXCQLubUO7SY0RON3UGlFvvU+gUYhHiShRkBNUVp0KqLg/sckhHdOC0kYsF2qGWc3gIJM8iXKCCVAJHPdkve+Q0MNTFNecu0zHR50yhGG25jziG7sRW05ghfqfwGEqUxcX9s6Pj/zTqNmQ5wZdhbRpzj5Od1k1QdcpnOr4cD49XjllnmBfJeVe3++d6PP6mfayfadAzclrEDztrl3R6if6znbG+plPwAoyk2cttATmOtC1bfGeeJx0thfqNrKnT56Ll/o7kj/mS45i2Z2VlZW5zqxeNjI0zNHIS6/Hx8VzkaCeQdssf63HO0+XYdN9vUm7sFBLFpiDDGFCeBcYDcF0bLKuEkSAa/j85OanHjx/X06dPa39//8oxFyBeaHCO0yErdJMu2tvbG7yv76dOG6yqmqvbg5eoJnnEs9TrHcZON2VIbmQFwsaB8jKQqquvU6SPnjzFkWxsbMwpkUsaBPpLnzI3bMVaWVmZe81o5lptqLlmB5XoBsX1WJoGOygc5WQymdt9jSwtSkdkms703cQZpQPqosqzs7M5RJjG1+8GzkjA6cNuIhkEntdNC/zFGGV6zAbOSJ2xd/Rmely3VyOR2mR8iepPTk6uACPSWF795qWtGclUzacCE5yAxDHmjioMtFxfLnufTCZzy5xPTk4G2UIvNzc3r8xlwE/6j/zhENgtff/+/cEGsb8CXSbCxgF4foKUbNLq1WDXOfWxciunMBYppGB2hCSSS0QL8z3BXHV5UN1kMhneT7C3tzesbDAKzc03FhR+c/6VOQm/QapTKJfcTk87DFa3ioSCYPnT3Wfj5xCbMTA6Yp0zfe3Quv/awMILT8R3yLYTrkSfiWz9Fi4rdMpEV8wTpwUzz829jn7oJykJo7DcjZopQfjHGJuniSjdH/+1bBtAmC9Vl8aMguwh192KIqPCdAp2SgZvdibmY0a+aXD5PwFPJyPUZx5nnZZp6nI/DJTSOVhXMk1pMNSlUOiLj2/xicljssezXpnn7MLR0VHt7+9XVdWDBw+GyWcf4WL+WAaIFlZWVuqFF1640i8frme9Sn3JifUuyuQ3/72uPFf66PWULspA+Zhc9is2jSBPTk6GE1Ax5GaAjWgy08vrmDfgjCO2sBuBpUOhWAmJirhuBO++cT8RQXesRg6i+2L0Sz+hE8fQIfY06KnMGfZ6jDohs2HgPof6VgR23noScZET6GQLGp2aAPWn8hqhOX/vMNsT5v7d7adSeXwcvY31xQbZoIGPI1/33cs4Mx0AD+3gMq3g8XDbOWa+z/KbMmdeGPC4vs6hOhq20+1o4363YxBEJE1EmycP2Ck4KrWh9KIL/iZASln0WFr33G/eAV1VtbOzM0Qls9msfW0n9ILwj46O5hyVU6ZEVpYd05e8dESZ9um2UULV69jRbJRhYTKaYZBttCk2FgwWh1FVXS4X5f/T09N65ZVXand3d5hc9uB6staMcKqB+1lZtLu7Oxemd+E4tHQGC4dlxEefjb5JYXgSOI9lYBIdOpzSwcgi8N6dyaFam5ubc4qa9HoPRSJuGxcvv01BcxlLIzq8TecKP4xI80hso8iMTKyg7ouNpE+lpK2MHO0QPM4GAYl2zRNPOCev+O6/6fjzGIPJZDKsFvKz0OfJxMwj21kZ0BhsmV+mx/UwpuhjjjvPWta90ih13gbX4+MoyukctwMAxFkeHh4OuuJ0FB8/67X6tOs2ptPp4HCoP9OKdgb0I+fsLi4uhgn0lZVnR2m/8MILwzMGmiljS0tLA8BF35hcvri4qM3NzeEQSwM+yzNjm7JgvsOPDnguKs91IF5XErW4XEcMKMCH3jkMzgmaLmx2RDFGNwptVGGFsiKk0XRI3RU/a+XqJnRNl++j70ZrNgjk6UG8Rno+76Vrp+MHdFtpc0w9fn7OE8EZ8Ti1kegQ3iYCWxTudga3UwbT5ZUuY+PW/ZYOIv8CBsZkOo16Rq3uT9aRz2aU1q0iyvo6niX/kasEUBQbUPfdNHb8HAMApsFOI9Gu6zEN1MFKRIy09cNOj+cAKPyfPPc4Wq4sN6YxIz/qPjg4qMlkMmz4dFbC/aIvgA5HfsvLy4ODoC2nl7MsssXmfYLWm5RbRwqdcUyGG6UkoV1deEXvNgY9r6ys1N7eXu3v79err746IGbqy23zmbNEeJhD4M1o0+mzzSKgFRvFRakOHzs8m13uKjYymc1mwzpkG2s7jVRYr9F3ioCXBB0cHMwt7TQKnM0u34SWym9k4WugDOgy4qZ045jOyg4QJfHENuNIaG0UY+VIpAZfHGV6XJwmgKdMstopjKVE3JajFAOSLHYM6YzSiHf883h1RorfuonmTBctWmqYTsLo3wY32++AXWcMO0fPcwY3vscAyO0uLS3N6V86QHSM6HllZWU45t6pIZ+AAOJmSep0Oh3marwc3DaHZxwp5/wGhtsRH+ASO/DCCy/U1tbWgPyxL47GiOqhj8UxHObJfbYd8DBXaTl9zb0Zdef168qtIoXr0PJNShpcnMLGxsbg1QjxMPQHBwf1+PHjOjg4mFvrbqa4w91kL84Ah4AhtrFMj9ox14bBTsSCQ90Or7kvIwfXxcfpIeZOuC+NKOFrKprpzbHrENBYlAU/XdKQ+dpkMplL43XzJ4liMm2V/MGBZZ8o/ObUiY1mpsuSbjtH8znvy776e4f8u2d9ryc6bQShJY17V0e243vps9OcY/3lPjsu922RA8pJb8sWRtApRcbGab2sI+/PCMkTvxhQz9lhOzDkBopOy1HHxsbGYHe6PQIGfvTRPDg5Oand3d1BF7e2tga59apG+AuNPh3BkcHS0tKQbs5DCReNe4K6HKublE/K6iMTliUNaYaUeGxWi9Ax8oaz2WxYcXR4eDgnEAiDkRf1MtgIFUdiYHw6BO3lXF3fM1dKAU0wIeYBTsNEOzmgThOBbjIfaoeQjiT70bXbOYpULrdjXiLgHW/cD9rHKeSuahsrC3Py3akxy0UHTPjNxqxzXB67TqE6A3xTp9ClTX1P1143ph39Hd/dtsGPox1HitxjZ9kZfiPPbMfFdXguw78z5pzWm0bZKb80/DgEvxLTbTkqXl5+9o5oMg5eQko7Pn3Wcw7QSsTgNG3yGtoSZKCnBwcHw9EzTiV5DBkLR7ZEXzk/4g2FNynp5FOWP+lOwWXMAXQEUmxcfA0k4V18S0vPzhefzZ4dSre7u1tPnjwZNpYxsZqH5Tn0op29vb1hpUCmXdgNjKc3MrcAYNjS0JIi4v2rGEIPcp7jkvlCJg9xXF5NlIqZG/UydUHfPXmdRsHFyIpIh37bKNJ3T1R6NVQuocwQFxrtgJwG6RwC99iBJP8s9DZ6gAGMEKG9V5506R7zkXrTcNL/MdSehevQQ7/G0kOL6rBDdXRIPQkAkv+JdEnduGQUl+NoWuiHVwFab5AFp3hpz+NPioXVgJYJAwGKTzlAh6iDNvySKQw1q7vYCwJoyd3VvOGRiD1PTHD6zrw+Pz+v3d3dYYMbGRDArIEeNHmlETxjcjgXpnhlV9X8juV0BNBDfTd1CFXP6RS6cp1H637DuHiW39cRFC8/7VIBPOP8JWije6eB6aVdBsaGsap3bhhab8oxTUZJGSlBl706QnZycjKHZGwUoWVscPM3o3OjkG7SCeVCKPnrvti5EP5Cf5ejTqPUGVEbxYweEzx00U6OSYb0yatFaDt/y/q7urrri0oXBVxH3xgvFtWXutZFQtY18yARe0fPdX1D/9wnOwHG3dGxwVFuyqL4uXSOTglVXQIVRw1E9Nxn/XIU62gXo2u+uM/mIbpbVcPJsKRPvbrQY9YtkAGUjS2eyfHo7GE3LouiWZdb72jOsNrEZZiS92Z4D4rA67PGnGtnZ2e1u7s7LNfDuNnAJaLFyJ+eng4v3oHJNnZZjKYRVAu0nQHRgVdAOAxECGwYjWR9gmXmkY1MLi4uhrDWqS2jY9fr3x3m+jRGt+WxSwfmUNaKgqBCFygcPqS8eIURxQgz89BJi8c86aC9XOXlMXNdHkdkoUPQKdfwLQ3fbRxKF8J3yp5pDfrrCLSrr3su+U1/fXy46WAsOHqa9jyertfIlTZ8L/W5PW8U9YdFFCB+xgg5q5qf2K2q4VA6+kGq6fj4eHievU8PHz6cexe76/OcA4BneXl5OEyTRSrdhtmqmtN16P/EJz5R9+7dG+zF8vJyHR4eztkLnIafN8jK65Qc5872jsnUTcrrihQ6xGJhcxhJ8T2Zb0zj7cO0JpPJXM4eIbcz8BwEu5XHmFRVc0aUAppw3cvLy3MnkjLQ9Mf84H7zBgFAYHwWu40QimSH4XSIhdF8MIJPRE5/+c3L9hBI75bukH1GK6bHx4tneJsRk3nt8coI0SFz1pE8zwjD0RH3ds7ZqM88tcO1rKTiZXSTRsKlq8/1uA1Ha9m3qnln7T5cXFzM5aCzXqNd05VynAYP4MWE6NjksEFJRm7UR2o0D0nszi9jvOzUAYbk+92eI3x0xvI5nU5rc/P/a+9dl9tIlnRLByiJV6mqq2u3tfX7v12fXSWRFG8igfmhWYmVHz0SIMU6ZjPGMKMBTGRGeHj45XOPS57W6enpzFjjsAwwUvZ95IaPIUmQ674SqaxWq/rjjz+mo2hMo4/U8erDDlQvRQuWHcu0ZenQKKHqBU5hKez1750DcLHypQHlN0JCp1RsJIzwchA3m800qZzhmhnma+kAMh/vU0hzhUYqnvtDWwhRroCqmhsQ+t2tNsJppgBnSGu6OoNjo+/UXTqVRLs5hlW7Y8ZRcIqdgHlj2tLYEyUaWS1FpyNZ7JxjPpvKk6h5Cdl39XahedaTMjpqu+uDn81JYwwl9VIXYCEdWPItDXdGTv7NUYujDz/raMEOJPcHdfuFzLtu/A0wM2obySiOiD4xr+QzsYzIu7qcevIcSqbIaHez2UyLRdbr9fQ6T/gBgErdpO19BjzHcAQGR/3ZV95kTsFC4gmwdAruPOibpVsIDmEbxvPh4aFWq/mxukbfGKOjo6NpLf/Xr19nCCaVNpWN3yz4TIAbHXEP7Wc6y/3DETw8PNT19fXsSGOXnIB0MRIBKaVDQ3G4ZofCrlAmqTzph3Ib9XRGiLqg1XTj6BIl2nFSt8+zot0uKusm9TsHZcfnevPNZrS13e5WfvBcB2Ky/5Y5vnsOJYvrZHwcAdio0RY0pUH2b5atTPVlNMQudwqyZwNqMIScAaSyH/7fhozD5nBMBiyOGjGkmS7qon2Whtox0l/as1O8vr6ewE1nTKGbRSrI6snJSf3+++/TSiGfh+T9NgaGHz58qC9fvkwpYCbGzUsvpcUxXFxc1Gazqd9//31Kn3ke5fT0tM7OzqZd0Hw6JZpyY/ngemc/XlNefczFob+lp3LKxMbJiuDJIwalahyNgAY43M4OIVMupskCaQPLQLB3wm3RhzSi/t+pIo74JlQeIZFR4ZlMh3TIxkrpSIq+wVdPqttwux33GV55nsWpgzTOrsuO03z3HAn05rwAMpIpyJHzgt5EcJ0x5nnn6LPezuiOIhRkKJU0jXDyqKOR37tIy/3k99E8CgVkjGODVtNb9Tydmg6Ka+mU3H/Tb7RuR5w8gH6Mfhr+HK9ceYVjcn+Tb14aCjhjlZAjZu5x39N+kFJK+wId7sPj4883rrF03sCMuqEVOjoQdGjpQM5IZkflxfsUXFKoRsQlAyzMGC2ukaZx+OVdyR4A6mKZ2fX19SxfD3K2I+ocgweGSIX1zlXzydTc+WhjR6796empvn//Pp1v4qWwPEN7if6skAgdAkNxm6RaHK05hPc9rJgit5mOkXttLK3Myf90FO4LY9ul3jK1VbU7fTblzMCB8bbhTYX1sQYGE5ZLj7cdTuewLTMGJZ2TwDDakGU//d3GOeXaz1q+fL9pyTkhGzi/4c5pnkT+yD96k8aEZ72ihuyA9cG0E80nHxJseSw8rvncarWaHFzqOcW72ckweB/Uzc3NhPDPzs7q/Px8moAmislNo+hVAsYPHz7U5eXlLAIw3U9PT3V1dVWbzab+9a9/TRkI+oJ9IhKCPvf3JY7BspxA5NDy4kghvb3Lvg6kAiUaYOCqdg7n+Ph4thoi28ZAf//+fUplpAOgPrdteler1fReAy9hoz0Mk42oDQPKxitCWfnkUNQ5UitEOoROaa3opsWOjWeSr0QCHQJ36qpqd2ie+ZPOIaMdRzk2LJ6bseNyNGO52GeM0wj7/oxCHMEcktLMaKtD5lXVGkrzEyTocevAh/s9ilxMR2foR6UDb9lGThhTJ0aUt4EZYFnO/C4BaEIunG837U4z2RHbKVnWmAdwDt/3GRTZCDMG9DPnpHiGtrAtACYmhLE7mXFAx7BV2AzqMp/pH2ncr1+/1vn5eX358uXZHAVyCphxStb9zoyH++UyAhqHlFfPKbzGA41CR+fnjZaraoYuR3V648tms3lm3LJ96rKRBkH73avdagqnXrJOL12zI3Mev3MKafBoN40nNHeGDGHp5igyDE6k6bqttElXN7Gdjsa84i/pHAk293RGbQQ2UP5DSvZ5qZ2OLreZNPCZUdMhKC/Hs+NFOmobjI6OqvFSRBtW2sEpAIIwlDZOGMhcjFE1j/RydY6jHacqk14vM4Z+kLTnKEaLI7r+dd8t4xhtXkvKRlRk3Sk1f3ddBkF2cvCWM5Bubm6myMURO39EHvAhI6QuwoXfLh0Aeom9ftU7mhkwM+BQIjqloR48pfPoKDxGzgaLiabv379P71jwhKojj64wOMfHx3VxcTFLc+XAcx2aOHjr8fFxCkevr6+nPQgZIZBCMT3mJxGPl6rxvAViu93Otv4zAUYbXINXppfUGDstWWbrl4h7iaInVI3ucjmdjX9GEz7i3EKdymrlSLno5CkNpo1cKmr+7jH2+NqJdo7IeWnf52uOENL5Qxv9TGPipZRGp8kDO3bX36XZrG+ebzKfeJ7+sDeAc4AYb0depEGQE04kYLLac3uOnhwBJA9J3zBJ6+XbyHWOlemDrnzfNJ9ErRhmG26O0bm7u6uzs7PpDZDb7XYCeavVaooe8hiMjx8/1ufPn+v6+noCpwZnLID58OHni3VYkQTfn56epvObmGz3knr4x1jZ4XZAx8/5vkPKiw7Eo4zQXHc9jWCWfQjMTsjtgEbyNXvuPKgiEQnK4NNZzUwj40TdmQtFQLzmOo/T8DuUu36jJBkyutjoWKHSSXMfysGnBQ5HmPsUMrLKcHVpnKDB4MC0uu/Zjuvxn5dfdqmTRPYoPp8drVzbF324pMJlZDByUHaknQGnT14MUFXP0HLS7dRfXrfjSBTt+TsDBhucpNFAII2RDR8yZDk1P6yXT09Ps3mOnN+wHGV6yTzPSNi8Hzllg007aHju9yDwWmBy/avVbnVR2hn4i0MFuEHfdrudLTwxvQApZNZpuZSdUeRnXo7KmzuFLlfZEdf9n6gQArtURCpsGj0cAob4+/fvk2OwkHjpWebTMYQXFxcTerARg04vg7Uy2TFxngpOIfvjvHp3Xg0C0b3sg3ZRzlzS6rwhmwBRNk+Yd1FBnvcC3yhWOit7GhqPk/PJ/BE12IDYuFjJaatT7AQlaThA3MwJpfx0xjtlNeUVHns8u2czwrG8ddEz93lS3/Kd6Y2uZP/clvua/XKqwmkS89XOiue8DwXd8gIGIuHksccqgRUG1ueNLRkuP9fZjgReI55lBGl7gdH2+UVnZ2fTM/DB5yd5zHOTmvtOxMFZSAYvDw8Pk076PCSPrb/nfEPqpfu8z25nOdgpOEeYJYXeaR7+T89tpmU+ze3YeFnB2LFMmNkJFIYUYwEyPj8/n+YPMMoZdmfukU+MPxvR/GIM0BIChnFzyOr2CCvTodgoIjRewcE1OzGnifhDUYkSch6ENjDcLk6DpAB6ohJkZNAA3ThfaOjSKjxrw0QbptG8ZKx94m2myYyKrWCjcDvBSq6Y6SILgxjzC6VNOcd4pjwjK8gJbTNuyJ/ndJLelH2nptxX2kt0SR02MH7OxhO6ycOnAfNKOeQ9QYXTwtBrp8u4mY48GsZ6mujadPuTMUlb5GfQx7///nt698FqtYv4HaVVzc9RYowvLi4m5+JCWuj+/n7aYW3bYh3uVlnZGfpajk2WJYCR5UVzCqMG855EL933RA9Vzw9m45pDPeohFOscAu3n0kC8sJdIOhXDs134aWPBO1ad7rEBtwA6VOVQrC5ayhUWRh8dWu14nqt9Mp8+iuTcrlGkx8JCv91uZ6FuGtau7fyODNgQYOwS6VupM1pLpOY/88Zyl6F/ymvuwejqSUPj/ni8uuXI0GpwwLVM55gPXmWVSNny1KF12k7Dn3KaxQ43eeBNa9YBaGV1jZ9zqsirhOAfTjBXIqIjBpHQDfjoaO/k3u072vQ4kprebrcToDT/06HSBrrns8EyHYjTGa0WdBS3ZEtNe/7m/uyz2VlePadAB42UYKaN0Ejp8LgOQ/H2zvERwrH2/+npadqTwGF33Of6oYlcHcfonp2dzdJCRr30KRUUJ3B3d1fX19ezZXJOG3jppRE1QmUj7DNX7OmNJhO1QZfRZf75We4BUWcdjo54xn1n3O0IGBuvBsni+u3sUCzatIHy+BkFci3TTx5zGy47ajtjG5hUpEReXo5peU99SCRqGswb+of8WU49vo4IuvRTly+28+LgSIy1+XJ0dDTbEW+DDApmnLux5fmjo6NpMvn+/r51Jm4TGfDSS/M8nZ3Lp0+f6vz8fDZPRz2eD8N24DSgK0Es3z3/aHlG7pAPbMzl5WWdnp7WycnJlB46OzubUk22BbThY68BkPCD/UvHx8f1559/TjrvBSpEjZ1Ds/xlSdBj53Goc3jR2UcvKR0R6RwYEB9Bnc8y4D7ylldUetmWEZrRD8+yD8H1J/LjEyFFWG5ubqaDtSzY3NsNQmekUZYuXYPhsFFw+Oz/c+VD1mWlTNTuPxskaLewpSHNOYMlgTNPnBrxGnG3Z+ea7XfF0UDVDqGZZvpknjgNmijR/bAsmje+ZsfgSCbHJccjUykZcYwQoa85mvEnRtPja5BF8Z4B88uoNyNextGGHbnNqCURNTJvRM8zBgopm44+zs7OZilB0zYaD48DbTLm2IwELx3SBhh++fJltpiBZ7k/l9wSLeEUsCsc4YODc0RuQJyZgS4ySL6n3OTy1n3lxemjjogU6M4ZWGn49HrmkVPAsMKoHz9+1NXV1bTkzWgvlXW1+rn5jcmifL+yhSa/M6nERjTPXfget52GEARgI7/ZbKYd2xQLsSMFnrOxSSTQjQc881xC9+eIIo1ZKhKT4Slo3sUJb51HN4IGZeUxJihnomTT0Y2XowlP5HNPRh3k63Onaiez1JX5dviRIb5TEbTvsTXPqcP0p9z7f9OYupPOwZECdIxy6Dau+Qft6dhI8Vh2jdI7Y+66nGK1g3bk7py9dYf6LBNe+mp6RnJs/cyMQs6doe/39/dT+9vtz/ev//bbb1M9Pu+IugwKq3YRHeNxe3tbJycns6jWtGy3uxcAGcyOZGUELl7iDCgv3qfgT5f0zk4lZYrFBD8+Pk4TK9xrtGKlZvb+7u6uDXEtSBjXL1++PKs7lRovbSF6fHysy8vLaWWTBZeS0caSobZygBpwENvtdmbEvSoKZcs3wlXVM+GnTyw5ZeWVN/sY2diQUA8RlQ2315bbODw97U6gzJxsonB4akNoWeEoERwP4+d+ZX1pUBl782S1Ws0mn1kZ4rqcOkhD0uWdjWg7RXRkyDWjwA4kWe5SjjKq8WdGbuaXx6OLhvg/QZENLNfROadaeD4jdI8HDpM2SEExqYqT2W53h9bZMHosWddPPY+Pj9NiEXibepp8zUgQWm3Quc8rolgQ8u9//3s6xBOQc3FxMdujQXs4KI7EoH/UjT1jySt998ZX9Nd98ZibP+Z7V5Z+c3n16zjz+xJhI2IcVqF83TM2HqnQaai5n8kev3LT6JT2O1SKcPhlONzjNJXpTJSYCIV7MqKxwrBvgtNZP3z4MNv/YMfidhxpZaoox8TOFHTDS03cPwuenQhoret397+vuf+053owmCimJ9qNkt1GGruMJpIXmQaBJvOqm3B1+1l3ylNnmLOedBojcOH+pgPIP9c7QsgdPdk/gwRoNBLO+jJSNE83m82Mn914EB0ga6R8ky8+v8i6y5iRxko5Sd5BZ9LsucKMILmX+Yrb29vJbqET6JZTzNCWNsigyvyjHjuFdHLdWKbN6e45NGp41eqjRCqpqKPnUmgRQBByrsyp2s0V+LWaoL1uToGQ7ezsrD5//jwdW4GXhjnb7XY6lturBdiOzjHcCJ/7Z6SMADHAThdV7RBWOpyqOTJniSyT4k5L8H4IrjEBZQdAZEB0QNhLsfOB9hQ+L7fjPvfTQkf/OgNkNGyZcLsoVI63Q/mq3QGJ8Jl7EyU58qvqHT7y4aWejna6Yh7xl4bNfUjkTbHjgF4bCurinBx4Yf77WhrmBBgUg4GM0JLGBAPIjfvmdJrp4x6DKPh8fn4+/Z5RmdMm3LNer2dLQa3nT0+7d6xsNptJv52q2m7nb0vLCMdjUTXfT+HNaylfABYWuPz48aPOz8/r999/n/p5e3tbVfNNcMj658+fpwiB54kucBw4JvSAd007m5F6lePIWDnqeUka6VUH4u0rS4h2VI89YN7HbD0owGkV7sHQrNe7V/AZMRjhe5CNhMj1WZBMR4eAM2xNFORw3AqFEXZUYGfpialEIx3SxLiR6vIElg141fwEVvOftjuD06HQNE4jFEOhvdzxbXnIsXe0Yn6ioE5nGfFbgTx+NqpdmiRRehrFlJ8snUOomq/Dt9y6z52zTqeaUZPHLcehG0P66z66+P9EqJbtbvwYF3SNfnBERFVNq7o8djZ4TpW4D+hXVU15+tyEaVlHX4i2SU86UrbDNAhFN7pIjwJ4rKppRSNH31TVLPqmbi8kQY7Z72Tw6bkhp1HhQ8roCJS7v91Yj8ovLUldusdoJAfMIZ7vtWFFcfDeHEFdVdOEYeazQdzn5+fTBKsNX4b2CNdqtduwcn19PVsa5j6b8R5AC5rTEwgFDseTywgRuyXX691rBhFI6vXks40Rz1jJjDyenp4mntg5pTFHSI2YOjTu73Z0FBvQTjYSmdvZ0OcuF24kmoY+0wU4UdeXfcr+dxGN5dJ9yO9ZT/7msUqZMbK3MTB4MO9SBtMJpxG14U7H3hmSJb3mOaeC/C7lRN1V9Uw3iIQ2m820g9fyid6YV/AjHTERhmXXKdSnp59HabBslI2mjgrd/9GYWyZszDebn6/6fHp6qs+fP9d6va6zs7MpciEL4fpsE+ADS92dZTDAM388RpbpfdHAqE+j8qIlqaOw0/ckIYnQPBHIMkWjvDQMt7e304F3npUn9PZM//n5+TT5UzUP/TEWfPKdE1ZJFXHglZEJdbg+56OdW/TyWvfZHp++Y7wQaiuHV6twD+vQzYtpID98mKIEhGuz2cxSJVVzI2YlSidtA2CDmoYkoyMvLsApwUNHCuv1era1nz4wdpYD+J0OKZ2kx8X0cc3hdAdI6KvTfV3KpVNIt2nDmVGiDbLTlxm9uO2si9+6SMO0dRFD9iGjEmj0PJ/b8Y5yywrfbRscQfMb+u4+sY8GcOPVU+4PbXhpZxe1octEB8fHx/XHH39MhpZNrwAoO2HXT9+Z53BfHh93x3P8n//zf6YUGfOYCUDgBQtKoP/29nb6Q/9JFz88PExpYbIjOddhvUo+2Pll9L9UXpw+WvKkh9xv9OKoYLOZH3kNIz1wqXhersW8QG7CsTC5zapdftI7o31YFc+lE+D/7KeXe1KHnYtz6Bla8nsXCpt3VhwiCxsIHAMGNpd/ppJbqbr/qd8Gx2NqhObxNc3+bmXhf6eAfM8odZPGx0bNUYUNcDdJuoSg7CTgW5f+6ZC2DbLHzU7Njq5zLJRRP7v7l+rp7uv64HZNa/5msMW1jBhcUgdGsp2y4eh2VKeLjbB5brSNLntuwrLo/nZAKNtiHoEX9Rjs+gVdzmh4o26+49z67RWE5tWodBHFS8uL0kedgU+vv8RECxIdPT4+nhCT1+fj0b9+/TqhdyMiHxnNM0zUprEF4TgUe3x8rG/fvk0H6plGjJwHwnMYIwfo9ACCwIoiGxTQRz7LLkgbcqIY9mZ4aSr0socAh8bRuxxn7K32DmEzIrGjNdo10uZ6HqdxdHQ09clpO3jhNA4LCpxCMXqyAcgUkp2Cd8LzWzpvj1m3kixRro1hOkDu8ydyllFlyr/rSsPusUwkDt9zspTUjWWqo9N8p75R6iyNIjxLvfVSzQQBLj6ckTptrO2wiSqpCzk3SOsWMHjFkSNRIgFsBLaFydzVajWtLhwBJdo2n114jnewQyeTztvt9hnI5PvZ2Vltt9tp0poswHq9np2HBi0AXu+ghjY74w44vNQxvCh9tA9dpCHO634Oo75araaDtWCYV9wsHTfAjH4iDx/rgOLbO/P+AxvaDLsQMoRkaY7BQmojmekPKyc0Exr6ZEQmnp6e5q/19JuwbPSo06m5nNg0ek2HkJN1fibbyBSQ0z8jQc3icJdPp506Yc7UicfHffLzXm2yhOizdOAnf+/qXapndJ/rMb9tLG2EecYGOY1+1s996dDgZVfviF7Ln2XZhfExYDNPMs2VdGATTIdXpJm2BCamwelPcvi5n4C0bqdHdmYZlZD7T97c399Pm2XR7xw7QK0jGJyAdcnGvzs5FV4tReYJ2g8pr96nQINpEJLQLozBYzuv70m39Xq3UY3BdNtVO+N7cXExKQZe1B62y+ez5JRzjIgOzDgbKiNfGwKjVwptscTVCNV15Oojz5GAYH78+DG9d5pJrW4MPA5WRqNe+mTFM2/43ePmiUIrqIV8SVCtOJ2B9zWihpxk7WTI/aFP2Z7RXZeqMK2+Bu0dCPI4jxzCPlQ2eiYNMfzP94QkL+z8XgLUKKPcfUer+e/orUvjpFOwQ0pAk+koz+mYLr5bDwCXJycnEy3IhFOS2IV8/zLgiTQP/CQiSgdKH7z0GXBrp8P14+PjZ7voSY0SAeAEf/z48SxNhKz7BUPmseU2y0sjBMqrzj7KfPcorEIhjdwwROTKGLCcxL26upqtBDLq8zk6hGEokIUL54MD4iC9f//731MU4hUF1JN9tFDbCHKdJbDn5+dTCqvq+QFmRuPch+BylEauw+7eQJUONHlvZ4Cwwa/NZrcBDVpySV6mOToZwFAxhplqyHkJxtkOwYY2FZD/3Uc7OL4/PT3NxjHHJ1egWTaSdx2S6pBiB4AS8TqayOdGiNxOG3nOKMHGdckRuC8ZJXRjbLmxwc42MOIGMnZMjuDcNpE69WJ0s39En6ziQa68MstR++3t7UzPWdGHo/C7TqAb2qzr+dIfvwXR/bIcuF9EzOxGPj8/r9VqNTuyH7qZQCaNVFUTGGS/EtkOaCS1nGlRj0/nGHy9c95d+aUD8Q5tJJ91eiOVg05jzDqhraqZsbchQticHyf9c39/P6Vj/MIa0g4uNj6eI7DBs7f3OUNpQLhmhUQ4OwQDbUbrncGycYMuI6tcsZMI/Fyg/QAAdf9JREFU3cpvWtPQ5RhaKK3U3pSXzxkoZF2JelEEz2FkXaaD59z+SIldsm2nC82DLkQfGfwcn6Xn3XZHW9LY9SMd1GjMMsoZtWdeJI1ZV85XjPpiMISujepn3EjpOMJ3eoVnvIwbWgwaq3aLUgzscDY5Rl4tx3fsEO2kvbCj2WyevyPFk+u0B0DmiAueS6dtJzwy+gkA/Gne/iPpo5GQ8VsnTPksSMCv78v5AJZd+gwQ2sQIs2GElAEnoTofiIfebDb19evXury8rK9fv047fQndOtRlYacNp1w4itsvkHHahnZzUsyK8fj480UeXhmDcFC8RDbzpjbg/IFybBQ9x+EVS4TMDqU7Y9eF/jyTYTHjawSZ6YIOlXrXsleC2bGbHhtxeLbZbKaxcASY6Qsj2w4FexyNeimOAD0HA305Lr7PNHQ64iWSPDe6N/Ut24RWG29+N1AxCvdkttvtUqXpPHk25xAYD+TfAI4J4XS0RN1MDG82mymV6qgU3rPpdL3+OUF8cnJSX758maKAdGBOVWYKFr1erVYz456OwKcXWKc3m019//59ihR4qRdtelHI6enpVDepcuyYAalPGLYMpEMwLXYsHWgZlTfd0TxCsy4Ig4/QtQfNl+fYoKRDwJNj2Far1SwMJJx7eHiob9++TRvgjNqtwO6j++m0F2kfn5dOPxF87kX4jWYJS40OrOBWSpcUiHRC8N8re7bb7Uxws59ppLvxHaF712Hh9Cf0dDIB//1/Ogo74qSJT/jlVFjVeK6DvmZueoScR+m5jlduY1+9+Zv51Y1/yqcBVTfXNHpuVCf8yBVa/JbgINtjPE27x4KxzQlWO1Uv0QVY2Dg6/Um7jLNBCHpuUIQeHB0d1dnZ2SyV4xd1Ga1T98nJyYT+HX13vOBZ7Njd3d3sKBsDK/ht3qW8OFLodHhpfDMSO9QxvOnqoyzp1RBmGG4PzQBwZglCYoOE0YXJqRAYYjucq6ur6UUZKBzPelLHIaGdEP+DVv26SxdHEQghxsooDGFxO/4cITU7ABvLDMkdTmcqK40xAtcZOqPZLn1lZUynYWSVRtrI2U6hKwkIshgpmn9Vu6Ov3W/3q0P7qXBJm8eiM74jpRwBjrzerTTqisfQkZP1s+tD97sBySiS870jAJVRioGCowz0yCkV+u5nbcx9cq5XC+USVeTUO5jtCAB0LO+0/MP3dEzr9c9zmLjHhj8jdxf0nEnnqpoBRQMfp4A7Gaqag0CPK7Skfua4HZo6qnqFU0hFsFFKxfU1OuDUEUpwcXExDdLV1VV9/fp1WpbpcJeQ0su8HNZBy+np6bRi5/Lysq6vr6d7QPIWQE8opbEgOmG/AUb57u5u+t1r/7OQCuN9rVZ42kvnYCPcKRnPmd+ekIf3pNcsEE51mXdGXkbrdjQ5N0I7pi/lxKgxo5tEPPyWZzY57WGeJO2bzWaawHN9aayQA8ulDRV1p7yb38h2jk+HqPnN192O0zdL0YfbxCD5HDDvR4GWjocdHaTvUpfsKLjuMU7+0QcvWbbBBen73d7eW2QZTj7RxvHx8fSsV+rl6kPvM7i8vJxsxx9//DGdRoxxpx5SWWkHyE44tesMx2azm/+gbLc/z31iMpx+Hh8fT3y+ubmZ7IuNvLMpdpJpu9yWdTb1Me9fKi+eaO6cQ5b0SnYO3hjl6AFjizF3zpDf8fA8mykAe24QAwJpQ2ZBM+O8GgEH5rkJe3hHD11B0XEGIJfkods3WqNAc5fqMNLplnJ67GxIjAITybpt51yhpUO+/u4xsdE07TYiXPNhYXnOU9aRQu42AReHFCthou+lZ5IH/q37PqoP/nf7EA6h2femLLv+RPg2Ko7cUkc6x+3owbzgPjsG2jBdPvcqQUbKi/UiHWb3jHUYvnZjADjz5jpAQm7Wo0CrJ6LN+24iGLpxWKSh7VjZo4XMG2Da4TBGS6nUfXJ/qF788pvXKCmkXRgEEvWOXdI5R0c/N6xdX19PyzMZGBAvG0KMrpyTY4KGV+d9+/ZtijY8kWlDi+EzOiE6OD8/nwlMhpBpUGysCR19DHcizXSwFkivnqqqKTKhXeefq+Yb9qrmqB7B9/yCUXYKIXwyf1IgeT5TN9BlEJCG1KkDpwT49MRgRkgpT/4fRTfCHSF6io2T60rH6zY72R4p3Aj5M978Zd0GRI4aHU1xXy4p9jzV0jJh64LlPPPmpgca3GfLD/z38Q6WKZ9W6mNpHCnAd0eqjoS9yswLOszf1GnrzV9//TU7OBMZzcUH0IRuYYPgC0Cvm4SHPz9+/JhOeD45OZneBAl9ZDHIoBAxwIPke7dZdCR7qedvHim4o/uQTEcog+IzPyAcQeIYWSZouJ/wiufcQQQKx7Hdbqd5BM6mB+kjlNBnhwDTGRgvMXXfzYvOACMo7EDmL8P3DEOz2GDiJPJ+5+5tnBEeeOf3U9NnI0QbTerMHCt9znH22Fq5GWeHxSnYXinG/17Wa+PVhcWd4TLYcAqkM7aJhu288r50Gp1RzGKwYNmxQ3W7aZCSv5l+M4JOx220C++yvynT6fi7yCf1r0OvWTqnngsEquYvysmIJekYgRfaS0dqmbPe45R4O5r3PaED2KcEghy+eXt7O4vkoY/IgGjAUUHSzvJrr3yyY7PjsYxYNtPpd077kPLqfQqdYnTGLQmxoaMjeGpm+Am3jo52h9yRz09acBww8unp59EQnCViY5WMtoFxqggnlArmNlN5jR7u7+/r8vJyckJW0FQucv6Z2rIxwtBDS6LoLrymzjxCww7Q/bEBzhx3ClSG+RkBeJy71U/dMeCME39pLMgPewmsn+2MhRcz5Fh1qa0s6ThduonRNOpux0bfY8ezHYjK8cGwJd2Z86dwzbQu9bnT36Snc16j5zIaTsfIGDGWGG36mCmZzrClQ8h2/Qzj6OiaI6uRSeYrvQqran5YJWPBuCKbdgw4YaeRaSsdWDoF+m3dhvfdmPj7CJy8pLz6zWtGJ/yWua80PEY6KINXE7AUFWOKYGD07TUTEX/8+LG+fv06HbVtJD51VmkqfgdB814DJrG6FTem33MNvO/BKa18py2FATc6ToUhSnF+8dOnTxNNPvogBaeqZiG4Bdi/0Z80WJ6DIc2Hg2TdOILrsUha6Bvv1bWCG7k4ynC0kMYvkRgyl7LlM7UyleV+I7uWX0cTBhJccz0ZteKIHMU4FQCfSed0aS3qTRBiQwQNjvbM94x8kCP3yem1zllRp+lyO9xn+RnxEb64L64LPTd4QU8Ba0S63F9VM3TOOBtQ0hb6DQ8c2RgAPT7+PB778+fPs3fGuy/uP0dSHB0d1e+//z4tPvESV+tSVU3ZD6IVUufb7Xa2SsmZCsYh52n47KK/tCfwYsnpu7zq7KNfKSOEmt7ZyDIHhGddH4aYCCFDc7dpo15Vzzx0emTa4TkGlnZzQw3Ci0KYZqcnurYSYXYIYDSp7DoxEpkn7YoNgg0s/cYpYHAzIvAyvoweN5vnk3Adyk/hzpLX8/6MNFK2/FxGD11O1gY2AVAa8ozafE/mdG38zWMb446ejKqy/dQN6wf8zondlCHLgGnhmp2P01HdWCWN7nP326gPqWujVKvv7yIknM/j4+Ozl9ZYb0k5m1YQfzeOtF9VE3DFaZhnm81myn74sD/odcTj6Nr8ccloc59+w9NDyi8diNcZWxcrEvdwn4WPOr1hzcKLwXfaibpYx3xzczPNJfgIC+6lJKK0oYcO30s/MDYcvFX10/Pf3d3V1dXVM8bnLsiMntJQmYdGdDmQdjhLY7Ja/XxZx6dPn6YjhLvozUoEP/mkz6TuHNYnokTocZJ2sj6OxGNreegMhvtu+UpAAE2k/tIpODoaGczkZUZfRD95r/PpGASvgEswYxrok3e6Zw7ZSNN9p2QbneOw0aJ0UTxj6ZdU8Zvz8JbtzkBCczpBO2yedR2ZsjTSh57kp6PZblk59bL6h2fSYBNpIMdnZ2f15cuXaWw4Sblz2PSPeYk8ooc2bm9vZ3TDD8YR/WEymqOyLVuOJA0OE5DYznSRw1J51Y7mDBEPbXiE7qyECJQPliNdkkam6iezHh4epmOwMTpd+ETbfvH9Uh9srLyqhVCRw+oQAKNiG6I0UqMCjfTBSMJ/IyPAd4ff3OswvOr50dgZKbhkqqNbcohiWXFtMG0cEgxwr40JfUo6MhXB2Jyentbp6em0OsSGqkPrIMKc1E5e+5qjB/PC/cs2+Y37cw/GajV/L4QPiTMPcozyr4ssk89Gzq5/xHM7W8tTymLm0pGFqt2R1464ckzsSLzR03WxYCPnzsxDJnAdBRmUOVWUdsdRCc/f3NzUer2eUkk4S+iyblr36IcdFLQzthcXFzO74PHzvObt7W2bFcjijMC+e/eVV0cK+0oqdCqTr6Xns6JUzVcKwXQUixwiL6OxEPCs20pDb6GwQDk68JZ8DtNj3sJLCm0IHF4jbJ2RSyW20qZjS6dgJ2ShMgLhGe/kpI5ufXyHmv2XRsW0wssRKs6IqHOS2U73e44nK8ZOT08nh59GoTPwVbsjTJwTT4OepXPwNtaMq5dl5tjY6SOPpA5Atzm/kHz195wfsdOhnYw6uM/XnMqqqhm6TxosRwkSPIHs+/M+j5WdguXJUUqmY+k7TpWIzs4n5aEDCR4ffvN7XljuTv2dEbZcn5yczBZFbLfb2TEZrHLy2MEr+uKVmkvGvrtuvU4d2Fde5RQ6geRz1HA6AAuuPS/MIBSr2oWNGDGvNvrrr7/q9vZ2mugdMcIT1lxngOyATI9RHZHBt2/fZpviLNAOdSmZnnIBsVqhvaohJ8sTgdt4ff78+dlqn6Ojo8lQEpKirFYwH0hmA7Xd/jwypDtN0uNoZ07bOCCf+5KritIYdQ4EPjntQGEy7uLios7PzydFY7z9/Gazm6DPlUPpBNLwQWs3hnZ0ozww6I8zs2zgqnZn8huVUoxsDWgcvTGmoygidTLHDn6Noh/AF9+TT14QYUdGSodJVOuLwVo6PcYZwOfJW8uZx4N6MdqknWkHh/H09PNoeqN0ZNVLT3kOdA/N2CUb+U5ezs/Pp/aurq4mMEldnOIAzwx2cSYsw4eOBIQdaDSP7bTtuPeVX55opuEMDUeC2BXut+H2hhKKhYBUEXsbXGCuBYhcM2mKqv7Mm8x9bzY/J59ub2+nVUYOjzPsdJ+WELHD625ntAedeqDP+X7PdTiqsbECeeYRwM5Jenwy9WMaOqfAvd5N7NVTyWejI/iQK7QSvfO88+/ICtFBGngbzVSURNVpQDsUaV50PLHc0Q8bchx16oGjL8tTnhPUpSHtwOlTylwX1Rg0dbLHZ+py8sh9zT4wRkbvnitxH/jEYXcHKSYdIyA6cuDUgfNwlO35xS6dCDDy0nV4nnNWfCZt7ptBAe1CD/fhpLL/nazleIz6fkh58TuaR+GgFc0EpJJ1BS8IkmLAHGJaUaqqLi8vpx3QFuwMc/0yHl93iEuxx67a7Vj897//PdtY58Fcr3cTTCwhGwm8r+O9KRbGRJIII4Lg+Q2ElKWfmR4C0dzc3EzIn3YSNcOfNHaecKa4L14+aEfRpbG4n/6iID6pkjGjL0asbBjyRB0O3zy1ooHODWAsJ4503b9MPZqW5I0R8lJKDzpdt3nka+hClg50ub1cCJBOgjHPehy5OwLMCMR1d5FVgiSPY/fnPiOTpG4MILLPjLHTqPlHHU7FmKdeTZeO17aLEwqqajpDyTQlgLD8YLfQSUcNdkrQSOqQSCHTiAYXnSzAT5d9DsPll1Yf+f8sozwYxKWxWK1W05G22+1uQ4gZwR6CHz9+1Ldv36ZdyzbMNjyr1WpKXzAoqWRelePJvqen3SY4VjR5S7vRW1dwZlZAC4qjlVyF4HwlvzPxnikwijf3Ga174tvvu7bQ5ITXCDE5neUdz8yt5IQ7dGdUw56Qjx8/zg40s9OzDNkwOf9qkAIa7YojxDSarsfHmTinnWG5x8yRS4equ+iI57y7vlPiNMqWb/PeO+Yz/WF+ZUrNNBotpwFJgJepVqciU+676I26HLl5DHD09MMRsNMgjDlgxGlK15lj6px98sTpJ/hj3fX+CFYIHR8fT9GEdcWv4eTIf/jFqQ2ACK+aYzxJa3nxQabhRjb20KigK68+JdUDuvSMFQWmZ50MIHlvBN0OAaEm14ixfnr6eYaKhcBCDupMpTOCSOVmEBg4vHqmshjENBod36p6BGYDx/1JJ0qBkHjewYJOG36W+hFaDAxKlAYro73OKFbtVpV4ZYjnWeAnUZ+dL4Yf508dRlSWHUdMieAMKjpZdASA8TCfE01SaNt9YtxwCLmePKPBDrHyHVTcIf7sm2U7HRbo004Ro0yfLQPJw+SBZTF1BrrSyWS6ifsMUtJBjuyGEft2u4tsnaZxOpDx2Gw2s/kv2u6AQkYFyW/LVMqDF7NQF07BhtoRD304Pj6eViHZxq1W81QRkYLtUoLKblyyHBoZZHmxUxiFK/uehUmgQgrenwOq8LAYfCbhYNDNzU399ddfdX19PXla6gRNc8yt9xQYYWAMQRY+yvv29nba70CEgPBnOsBKSb2eKLeAoAgYwKrnqwysbAgkkY7nRKzE3cqnzrB7DqQzNhmSesVHV0glEUHlwWA2xlXzs24Y/9Vqd7QJB/55EQGo1eiN39wHz8u4TSsV/9vRmAc27K4Dhe947LC+Q9aOtFJ2quqZHqD8IwNqx2x9MnqkfcabdwpgVOFdruJJwOExc9+cjvKkOcbO7wpJfqSh5Vo6EhvJBD5ElH6HMnLHrn/G2U6e8bK+2XlkqtC8txOmf1dXV1Ok8Oeff9bJyUmdnJxMy9Q50QBeEyEfHx9Pp7Te399PsntxcTHxnt3SOBL2aSHrHR+T5x6vl5Y3OyX1kPsQ1qenp1nu28Yao2AjyXdy+jc3NzMjVLUT/hxU0+PrRnoMBMxHwFGAjDSck0dgEn0larShyFVKRtJGdG6r420aGa7ZGVXtkIcV3N9tjGnDRtPXMDBdyiNRsZ9z2sAOKtNZdkp2eI62GBc+zXPTYifaIcHuL/s1Qp02rtDV8cLj5vodFdnQdkaTYgNlVGpeppOyXFpfXE/ynn7iFDy/ZV47Zdah64zqRn3L+QI7hwQtGPusD/kFYI6ceOcQEkSlPKRT5D5en8nKR3jWjSX9Y/4v5SZPVEC2sIncmyvDDikvBfEvcgoZWiYyc0e4h98d7qXBoaOkSPwiGE8ocqw2S7xYqmfEnuFihsLO6xrNc6rp9fX1lDIwevRuUIpzwgi20zAO9xHi9Xo9pbucu8dIoNwjA2FDYCFyaE2bdm7U6XOTEGpymn5Bjdt0G9DtF53YERCBJcqHT/Atlc0OHYeQoX1VzSbnjNQZY0cVLFzwOHRGNR2MDaD5YQRNsVx3z5iPyL5XsORkf0YqtMuzlnXG1PNlachcl51rRoI+/weQ4fZsjJAZO0bu6+TWPOi+ZxrKumLZ8bgmcKr6GSlst9tJNs0DA6gOzJhH/p7ON8d6s/n5/neiJox7bhLlGbIIt7e3U6RPZJTp6KqdTaQuL1J4SXmJY/ilOQWExcYtn3F4bgGiIGAecDOCgdxud0di55ppb8w6OjqaQrfHx8fZuu+cHPS8wffv32epnc6hZb+6JWMMXA5uVc0mXZ3HNu8Qdq75RfT8bkeJIbQjgF7nP60gjgRWq1Wdnp5ODtW7vbnHB3zBa5/tkgLXIWQrcaaZ4IOV1k6+izJcH2NqulilhOI5+rAMWkY71L2kgEZ1Lq7PaQjGys95rKt2O18TrWYbOeGY381fRzTIgA2w+ZC6ibPwPBgRo1NFvo+2DY7ML+uWx9e5c/5nBRayvd1uJ+MPjdwPqPmP//iPaRk5/EyEzZigDwngzPOkF/4ji+x7uLq6qt9++61OT0+naMAROjSs1+tZWtiHTJIRscNnWa/3G4yMfNrp15RXHZ09chD7PJhTLnk9w0T+PPh3d3fTklArXE76ZagL8x0lVO2OuUZ4coOHkar7buSfk5PJByuBJ4xzbqCqnilUrp22IUGoocUTv3a+PINhJAR3KigjuBx35695HqHtlMfj2cmAn2UsEhnCD9djIwlNnu9x1DJaIdNFqu5rN8Yex5SNUTFPfM3t+h7uy3RM/iV9+/QwI6mM9JM+xgZa+LNTqKpJFngO+TKo8lyZeWEdTxryf+9X8uQzYwgAsxyxEpG68gVU/qTPjuw91siTo6CMPOkzZ4xBAxGNN7nxhyNwhAANHj/Pdx5iY9+ivHhOIQVrqdjDUnL5oVNEDBYIYb1eT2vsb29vp8llt31ycjK9b9XpDVbEwHgcw6dPnyYa/v7772kuwc6H71YEpzbMC4ej6RR4lvbPz8/r5ORkWpJZVW2Kx07OO7ttCHACTGxxRkoadqPERJ+8eYr6/YJ0r6hIR75a7d5/ayNix8s4Jlq0shqx2nDw3Zv0bJxNz3a7O9rbcz/wB9RFP3IXaqag7ARNVzrkURSRzsOy6vCfSDWLj05xPp/27dwSEDgtB1gxgPJqM0eb1Mun+2j66QPXPAbww+kNrwSybK5W813EjoadeqFYjhyJO+3H/09PT3V6ejo7D4tjcDgy3xGPgRL9MrBwPw02AKm532iz2UzvXT47O6vff/99WrACoL2/v6+zs7M6Ozur33777ZkeGrSQ/sxorXP23VyG6To0enj1gXj77uHTxBhRVD3vjK970vPx8XF6bzMMc3olPW2iLhvpp6enaaKanHpGFrSbYaMNUxf2phPE4OC4zs/Pp7PSSc9keqGqZhECxWjXE7AWTBsQnEuOVxcNeMxydYOft4G0QTCCynFwqmxURoJs5GqnkfIFT3BqCVxstEaG3EY/kWTHJ3/vIsqMcChuP3nY1ZeOPGkyEOloy+9Vc3m1g8txzr4kXR2dXbGO2WF4/BwR2yA7zWoQ4b0sNsrW281mflQNJwb7qHs/kzTzm6MQro0QO6epXl5eToCQtrsMR648TJ6NZLHTl5GOvXRy+lVOwd8PbQiPnp21MNgpePOKD6Cz1+ZsHZhhI0qbRqibzWbaBY3npj2Uw8rpuYiu2KCnY/Az5Bg/f/48oSMQSodcvVSVfoAEbfwcmvLn5Z4eJ/qSCtSNY4eU4YP54zrSKSDkeXa9n7MyegxsBN2XNPTU4/dfOw2Q9HU0p0HreJEGMb/7c2REbAzsnAA9mWJJWgxwOj6CfF38rOnLKM0O0zJsfrtPrjf54MLzXaSKLLPk29EAOfdMCzOhTl2ALutDVc3shJeIEzmi/12k5v44Ik0HmjrrqG273da3b99qs9lMWQw2K/K85xL9PQFMAqAOeDhKMiBbcl5L5cUTzXmtK9khBhrBBdV6V6EZu1rt3lrFElEv+zo6OpqdiJlIEyON02BSjIPzvJzVtDGolJxcom0rSVVNB8rxO8LMs+ybwMh71RL3OLRHIKEHVOOdjRYSK0rVc2U0wvfYeb8E9XgHpuc1qNNvrON3847+Q4edRBp7aLMRymgER2fnbjTpkBsDk6jbz3ItFS7lu7vm8UqDi/x2EWOWTtEzPWeH4olyz4vYkCdPsx9V1fLHOpr6PIoMO94Y/SK/PmurAw9c99ll/MbkrY818XuUvSchT6Nl/5IdukHK2dnZbOez/3KiPfnidJGdJkaZPrHParPZ1J9//jkd2gjf/E75qt3pAx7Xbh6hiyih1XbXutbp3VL55fcpdL/7s/udDmd6wU7BjiI9Nki6WyZKsTGqqukwO78Qx0tnGdBcpVH1fGlbRgIgGISOJZ4MmjfMpFKlMpnHzoH7TW5egTAKGekL9CbqdG7WuWj3H/46V2/hytyvxziLkanHqPt0O6bb6IcVGchNt7qG+zM9mTQStSUNed9ItvfphOvd53iy3s5opcHw8+mMOqeWEYUNtSd/M6Lo6M6oKR1m3mNeUEYLHHI+CSewWu3SSexJ4GVbRsvdeBnQmZfIUs5X5XzaSIZc0K37+/v6/v17ffnypR4fH+v8/Hzqi+crknepq/49+d8VP/ua8stvXjNxMNEe2EIK0wkbP3/+PKV/7Nkx5j6V1BNR5+fns0PhquZot6qmNeoMzv/+7/9Ox19T/8XFxVSvFdDhV9Vu8xc5yqr5HAPGkQlaTxptt7vjJBh0I/71ej2bTGL+JJfSVe2MOZGJlaNbyZFr/T3xyu9VNR2W57XvtM1Kj8x5wjM7aadHUiHTqRu9dU4SHncyt17vdkXbCTtyhLddFGn0lY4/+9BFHDzn6DbRXNV8IxNjb7q64nbQFUcK+f5fy4eddEYAjC//Z8RrAJVGaL1ezybsXbfbQi54lonVHGP301Gxf7cjNwgapZS8J4Ul6QZ7OX+A3pk3uTvckZlTydBogIYu0oYj/dPT09psNvWf//mfs/Qx0QX3eiUSf97smyk8TzxnSUe4L3p1efXR2SO01F3rrjsNYUFBOBF63pDF5pBE6S42UCgsjsBnGNFG7nfI7+6nldk5cytWnsnOs1ZAhDXb2Gw204op6PSqKBvqzIUayacTpv4OjVnhXK+NVn53eszpv0wLJLLJcUoDlPfBv3TQHd/MF7efhry73t0/MpB2JOlwuvSL+5SG1PT49zRGXVqgixDMs2y/a9t8T4NhI539MM0jB2GZdbqqq2N0zWPbRXrokfdGYJRPTk4m3U+H7bFNvrNCsapfFehIteNzN3dSVdNeqB8/fkwH6BmE0L5To1znGJmkO9teKubnIeVV+xSysX0FpnpdswfSnhgDx2DjFJyOcb0UGxE2vDw97U46ZZWOB9Qv6bZhH/WLfuTgeYeqc6oUG5AcXEdPOC6/us8Oxsixah4dGbV342ThxsBhRI1G05Da4fAsgu0jSdLIjnjHPb5vSYbMNxvYNCBOR3ab3txeOgaPjx19Fx3k/a4zeZ7fO2eYsmankEsQR0sSE313bS9FKEmXl0R2DiCNo6+7TkeTGHc71pGjqJob/aWozHQBBplU5tgL1225cD/8LpDNZjNbqm66fA/1OQrpDO/Dw8Ok28yHJM9IYVqGeZazkKwHo3Hk8yWRQZYXOYVEKId4oG7gbTQzfAd9Xl5eToiZiWWO1mavguur2iHyx8fHur29rcvLy7q6upodYWtG2RCz2cQ5zDQa6/VuCSwRCc6A31PRQTiE4IS8dgSsKCLczjXJ8DE32NkYwjs7OKfqfL/HE95zb4blPlDt7OysTk5OptNsO8SMUKdRtiJCm+XCv6eRzlxr9tsHy5l2y1hnQD0+IyVLRGtj2Tkaf8+UVIeO+b0z/F5S3KH37ln3zb8zzh6TLvLB6afRd9qJ500vdedeiBw3ns3x73QOmpH57tDDjj+khi4uLqqqJqDlPifQ8b4fbIV3U8PbTIPa0VHMV4/5169fq+rnakTvTUC3cQykvb2pFr12FmVUDCjMo0PLm7xPgWuUDK9G93YOw+kjhMErWqp20QXPpEARrvHe5ky3pGOwZ07j5UjCS0UzL54orPPmFkoGzpuVnMt03tu86lCZUaf7yF/Hf5CbaUl0SKHv5O292ahzCsmLURSRgp2/p+HlWgdKkm9pjJI/WZK/o3JIhDOKlpZ0pgNd6RQta12x0bcxSmTtvHiO+cgR8pvl6SXjmfV2vMPAphPlmmU2+YhM2zkgt11kQHuuAxmCNoNM6LDjZTxyPtN9xpFtt9sJgN7f39fp6elsLJFdz895Dil5mH33OHW/vaT80us4k8hEtnTS/8MgPrtBYAkpG0EwwixN5PcUEgaN9zazLCyLlafq54Dc3NzMJq18Fo8NIisIQIJWVveT70bJVbudtqyV9g5tjIL3LsAnRwg2+NmPRM0Yb4femX+38CCUHJB3cnJSFxcXs30WeeChFYPr1NulWjKNaMG18+d75q4zhKZP5iHXqaczANkHO5ksaah9jbZGkQBj4/bcL8bXq8y41ykYR1lezNH1xwjUumjD63F/fNwdhucJzUw7Vu32AThVik6Z9oxg+D0Bj8fV84FpNNF3p/ac0sNm+J0FOAcOocwUssfHwALZZSmsT0+2rnCPZZm+cHw/mYSbm5tpmTyZCRbaOFI/PT2dDv1kUYkjsJRJvnf//193CoeULnxE2KtqNgCZP4cZFlLnF3OdMVvZ+UyH0Bkt/0aqheukSBhIzk+vmr/pK1cvVT1fromxJzIgivFZMYlWOqRGe663qp4prFMuNi4IvZGzEQ3Cen5+PjlCdmTbQaViJrIzvcl/ip+xglkWujKKwBwpYOT8e0YZycMOMRul2zjvcyDpPMwT2vY4Z7jP52iFidNAlG7OLnXPepP1uU/+numTNDY57l16h/us9y4ZIXT9w0n5/9QF+s7RFqxEQp5yE5l5t93OX+pDvzDwtOHI3v01/d0qxM1mM22eu7u7q0+fPtXnz58n2becdvwzr3NcOztrvnQyNCovnmjuQhjKKLQboZmq3YRS1S5S4HrV7jjczOVV1cRkfr+5uZn+ukki6EG4MuyDTvroXZU4hS5U9IBOjBWaRgj9Ag6jGYqRWadgNlg5Jok6O2ONUtBXT/bzx0QYy35Z6perSDJ9Zj4kr02r6ff/Ts05AurSWVmHDRgGIM/HGcltGsGs3znjdM4d4s2+p7J2Sp31+/ekkWIdyHFIg96hyM4RJq1dHaO5gKyX37iWqa90dq5nH5gw3zsZwy6wyOTp6ed5SLl/KO2RF050oMQAgZRSZivQL6/OgzaWx2OjWEJL9DOSQf/fRQo5tqPy5k6BSjsBS5RcNZ5XYBAJ525vb2eH1VXNd+J++vRphqadzqn6ucb++vq6vn79Wn/99ddkcF069GfByDkHVi7gCHhjEmv2HVKvVru3n2WKAEHgXdK8PYlVVXmYFgJkg5iRh9HEyDkTcoJAnFtlV6X77XkSIgVHSQ7ZHeYnoku+u12jKSsmcgENqbTwy1Ffh/y5blm0k6APlM7Qd6mu3EHu8fG9mcbjd9Obht7ovEPpnWG27Hl58HY7P/KENjo56YBHGtq8j2tOTeWihzTy3gMBHeg98ucICdpzsyT12YF58hl5pF6nCzkE7+7ubraXgT47/QkvzTu+I/+kgz5+/DjpMH3MPUPJc5ak393d1dXVVR0dHdV//dd/Tf2gj7e3t1PUksDCxfLvDINl7zXlxZFC522WvFsWG+ZEqwyQB8vFBiZ3I9vIWtj3ocyuMLBpUOmrc6juf6JL0lHkOu08EiUiNB0SSBSVxqXrUxe52MjjzDabzQydJ4+t0FaezKe6P66n21zX7UQ3OltC9fAqDW6nPAlWMoWTf7lTfDRWHY3+bqfQRUq+v0N/7mfnMPI509rxJPnnutKwuD6uJcDLOZERejVoMM/S+XZRjH9z/fzmcU1HYQfGeKCPlneDMPfZdOeY4RCJbE1jviinK44YyBwkyHMKuFtinvVZz5fs9KHll17HacSSpcsJUmwYmEzGYDGAIEcrhgcTY8RSTs40AoGZhq4fnREFyZBH7/qAMPo9qp2BvLm5qbu7u2lCOSMpr/bJwmooTwbTn3RONmKr1W5SnnvsYDnOl+OKHalQjx2hjT91s0ch+1y1WxrKWHq5rp2R51u86oo6eMa5VtPYpQtoFz7YSJqnyB/3OFJLRUzk3iHmdAw5XtYPG8Y0QpbDlDcbNcsbz8JDNkWRN0+H1qU53W6mhni2W3HjOZYuInE76RhyPPnOb0lfgiWPKzLnCXpHwPAK9H11dTXZjz/++GOKHvJtgJm+tBzQf59n5mN3DEbSOfLbzc1NrVarur6+rouLi9k9pLy8GdaO0jymeEwsM91Y7yu/tHltqXTpow5tMKeAQfJzKVhWCPLxHAmBIaraCUpn/LM/6/V6ZuTIqTtvCH0fP36cED9hrkPOzWYzRQVMdjtlZAXN0BteOIfdKVkaKReEIldPkZZxvpRQNh26jT3O4+zsbJbi80oIn4JKGsMG3cKeSJtrqXCdsXSKI3+3M/QKGhvtLheeaRT6QGqyQ+nQ6DSO++T/O1CSEZGv20GOlNmGeUSb17/bOPBnupYQbeqi/3L5r+nLOnyfkXY3nhkpuF74bnpy1RCFRRw5gQ+gI00Dv5BZNrGZXs8f2I4ZpDIuXjW4Ws1fD+sogr1J7EUwH8wz6nZqdgQmMiKmzZc6hldFCmngfa2LJrqShsIIP5WR+61MMJUIAUMAwztUaUPkaIKlYc6lIww2Jg5BTReb0XAK3es9U0HSGFK//0/ltfNIRcRJVdXMWRm120g7N2xDw31+XzZ5VFZUEME5ArBRg/YOFVphbNj2gY5EmSlL9NdOIVMUTq/4WqZB7CjTGdOeaTb/PG4dKDHvu0gj8+zZ33wmHYNTfxgVjGbOh1XNj1KHBkoaKn5PI2s+OHLgNwyjDSZjluCG/rif6Wx9L0s5faYXy9dzE5fHkZQzYAfj67kJ62PKDu2gFx7vnP/q0mXQzRyo+WiZtFNIuzgCvB5L6/uhwP5FTmEfEVXj1Rz8ZoHgvqU8nA2j7+N9zSztXK12y8Acbnft0xecAIaP+hEInmfgPEnlqIWzlb5+/frsbUwWpowW8juD6HylV1FBV04oHR3tDgnk7W4WVvcFBSD8ha/5ekAiC78UyIYWmn1ssaM4L+3NFIrRE/3xGI/Gf7XanehqWTo5Oanz8/PpbVbwzYVnzPc02h1K55kEBwYVI8NvBIm8+O10phHeb7fbKdpMlI88eQxykyf98vHs8J818b4voxODqowCqC8NLXR5LG0cSXtVzdE1Mug9GhSj9xwLzx3yLJ/IFsCM9C3PQ9t6vTvk7+7ubrIdRMU+ddWAlfFmV78nxZEL+J9L4j3eOK7v379PdF5cXDyrj13VRDUJtLriNPNrypsciGcil0Jfyig0NHJ0sadFEDhBFeYj/Bh7C2K26UnX3KHsPrkf9AtnhkFjTgMhzBd++FnXm5FQomjT0Q2+J8I/fPhQp6endXx8PHMKGBmiiO12d467J3v92ktSab6WK4gyouNaKoHRTDpkPp1GdDTjvw4YcD+0stkul2rSjg0ev1u+kvcd3b7f9PvTztG/QasnJxN1VtUku6PxR/aWAFqi3ETdufzRYMMOz6g567XD8P+p+ynn9LUzWqbJe2EYSwMr7vdz/Na9e33EI8+pYRcAiF49hZzhdPzGR57PaMDtdH2lfdLNHSAxqBjxqisJfjoHPyq/NNGcv2WHutIZPaPuzWYzeXDSE77/x48f9e3bt/r27dvsbWw2KjYORlG0jYdnqzlK4n54aZ/nDGwAmC+4vLycJrspRjgOm0foK9NRGDA/SwHts6ns06dP9eXLlylS8DG7mTJA4D0vYETLszZe7pPHz46yC5O7cR6lTxw9oABpXM0/xhR0x9vt/Judpo1F97Y7Pm0M6EMquGXAS3a5ZrRnvhKZdikD+svLYxxFGWxxnbHIvHEabY8PbXgi3U4MHjg9RrtsBnW06yjWepMAz3N83jntNnOOYrVaTY7Buj0ysKYJ9H15eTnbqOaIzWkwp3lvb2/r4uJieilOOgb4yUmsGHYbcGQuJ69tyPnOZra//vqr/vu//3uSIaIfjwnXs47khcc+l3MfUt70Hc3pzYzyOsTDPVZ+0Hei/6qaUPn19fW0BLVDKQyQV7mABAgNc0KSeyycTotYkLfb7QyFMH+QRxTYQFgIk9aMSKrmr8SkHv5wajgFnIFf/eccomkC4XRRFO2YHx4jI8yRQFoWbOAdjdlgd3KRiBsD409HMflyc2iBB96DgdPt5p0Y51wym2k7RzD0yfJko2tHkRFp6gtjj3Mg/ccueAMHGzhW8Dk6ou9dPpt0qw238++5pNYgAfkwere+eEwzmrCh8so6xg2e+lnTZzlL28IyT1IyLEAxDRkdwgc7G/cr01xETqYhJ3IdHfo+99G/8zZIbIjrcCSTmQzLTNrAzj53Udyo/PLR2VXjpXhdVMBnKhd/5Badt7Tw+KA7C3BHowV4s9nMVtRkaiRDZG+iSecBciJ9dX9//wzVpUHkuRR4D5SVgoIwkMPkdYJ2Cv7OhLkRrsci3/fQ8SwdmA1NCt0ojZERQaaGuFY1X3VlmoyQjHptYEHhoCgjWc9j2GCZz5ZD6ssNdLTtFWOWW7571Vfywv0fpX86tIk8ppxkv6A1f7cc0IaXtiaitMGmT52ejoy19S0BhOXdKZ1cOWT543ePZdXcSEK/U7mdI+XerN+8pw9VNc2nAbS4Dz7RPpGor3mMmXfz77QFvURi1jf4lNFyJzdd6um15dXpoxzoRHtLRGXnfPIm6SO/XII6v337VtfX17PcvVELdBnpOIcOYoO2dAqm28aHCbr1el1XV1ezJadORWSYhiA7XLX3p18oqA06qJbJJ/7HaH38+HGaR3D04yMpHDrb8I0cAoY0jWammzDEiaidhlqSD6OsTp4Yc6cOPSFKhIZDtHGzfBn5M9bdWvyMlGzAfR/9MMp1RJjIvzP+yOR6vW5fAlO1SzcSMfCcHbUnZbu3onV0+NPGHXno5BiH7Ync7LMNpt8M14E7+us3INqR26i6PffNOs95YhyA6XcX5Gon96lqN2eSxhhD/Pfff097PzgU0vsiunE2H+ENR/6TXiZKIZ15fn4+Oy3ZAMMpWuumbZ4L/UmZGoG3rvzygXhLxr8zPFXPCcw8btV8TTCMZlWGlcN1GsVRL0bBCDk9apd7s+NySsLH3/qVmZ1ToJ6MWFL4MfJsLOOZDx8+1MXFxWTwoQkEg8NwOszGib52Yeto7FLAibA8n+CIrxtnjO8+cNAZTRsr151RoRG020/k77SOx5V+LCF28+GQ6GjUt4yiU9ZpI/ubfaTfSY9RfsqgHTAG3gYm24det9cVG2j3kXYs807VdZEx9Zl2G74Ekb7XaS+uAyC4x87PPDE/oatLHeLgDHpIG3WRdKZfkWmMPMACY8+4ZMqY31w6fRnpWI7RoeXNTkl1+JIhP8VoxQYcA9gJOugjD7hKoXUKyINwdnY2Q1zUbUOz3W6fLaG0QwDxPD4+1vX19ZTGGoXJ1EEhd+ljLlBMDp87Pz+v3377bbbHgCN23beqqvPz8zo+Pq7ff/99cghp7EGaVo7OCOa1NP7b7fzdsWmkR+mNPGbYPM18q1NMRp92tihRGhFHJdBEJGOHyTNLxVGAjZon/ZCJTGOYhi7/awPouZXcxEi/MEZEzFzrJi/hDSeCdkjRKSyvuqOfHhsjVGjOqMJjan5xXzoqp2Wrnu+BsDzZmJnvjmgYEx8VAdL2BlPrsbMOaS+g6/HxcYq+cd53d3cTwGLy+dOnT7Nlp2nbPD5OPbN8HTvGEmpkgQlvxouIpAMSaRf2lUOdw5tNNCMQVc/TRxBuZSG8wgDaKVjxf/z4UZeXl1O6JncXug3+JzIgB2/mOW2BYHUIBQW0kj49Pc32IViJKYnIeQ5Bgk/MARCWnp6ezn53H5zmOjo6miKKs7OzFm3TV3jKLk2H6COUSEn0RX/gWa6jpz3Xm0i5M5TmO7SjCLe3tzN6XHKOwg7GxsrKhPHsnLjHDqVOum1sPSeV7cJTjE9Gozzn9JiNC89zP8bHPLcjMg/TYcAXP+M+dss1zUuDFDugdAz035PsTpcakafR5NN7Duizl3h3fKQcHx/X7e3tzA7Z6eQegQQyTts6MqcuT0o7tZzjnWOIDMAbR6wpQ6TNDWDhV/L3kJLR5aEO5FVO4dACavLAZz1GMFVzIUNQbm9vZ2cIdbRYgT2hDDNTKChp0KnXDsPCzZ/RtxFH9sF0YeiJDlhKymmslA7pkj9nKW23SsaKakFiLBzu2iBbybv0hYsNlSMt978bE39mMT1Od+UqmUPqtkNIA5FAwjwD0ZkHjqIwrOZfOoKu2Pi5b54LITpx3R4Dy6Lz1SOA5n6NaKIdO8mMGOwYMFQsme10L1NjXURp+eIeRwD0E/DVpVzMdwO4bjLfckXpIlWuO1Xq+2j//v5+toLPtgUewpOl8XGqiWdwhnYKdqRV83dmJP+7cbYMHWq7f+l1nFao0aC7pKJiIC0ULDH8+PHjdMTsX3/9VV+/fp2lhVIpNpvNlObwPoVMnVj5aJswDWHkz0taEVK/v8HtO4yGBmjF6BMVsPs2HZd5hDPgPr9D2hPYniRLBcg6MzowGsv8qJG2lYJ7iD5SIG0YeIbv0Gw6bcTdbtXPCdSc9PWmxPxEQeF7buCz8+rARUYQHt9Mq/g5F8u/+ck1T/6bFx4D+gsQ8SQkxUbHbSE7vo/6iEw6wOK/TAObLi86oL4uLWF74P8zujQdtiOkwgw+iJQcqdI2CPvx8bEuLi4mA47Mcpy8++G5D79i9unpaRZ1kMrkt7u7u0l3neXwAgRHiKSEcHjr9XoCgTgYaETX0RX4Qt28X6aLEi3jCe5G8tqVFy1JHSnBvue6YhTCfRgJrwjilXQceuc0Q6LEXKpoYUvB5zkGBCEjHwkzM9TO1JgdnY+JwIiT5iE6ODk5qbOzs+l3O56qmtUDQnO9tGXh22w2s2WvCCQlHVhnoDoHl4jMjtiGNfmRPE6jlfdSB32zke8+LVuu17zxjtNctOB+dvUmTd09dirJq9QVRzodakMOodV9G43PPr0yzeaNdS6jE+fEKZmOM++RibyP37uFAZniyrrdVwyxn8VRVNXMJnhRycnJybTxzXML8D4BUsqd2+MaBppniWKfnp4mI57y7bkPo3bLK5tJDQAcTcIvrjmrshSJ/Er5pWMulgTT91HSoLKEEgFwxxlEb1SzYcL4g7i8HNP5wAwbbTg8ODgEv7XNvzFgHcLBcDsfeXp6Ol0jVYRzYPWQldAIhmdxLuR0U5jdB9CKjXB+GjnQpzSsjhDov52JeWnn0Al8otmUiS616PmTkTHKvqBAoCgjv6r5XEAaK/qS/esch2nBOHlHc0drx0N/d8rCaK9zZBl95xgbVFlGbDiZ27IR5r5c8pgO18/wZ2OZfHAUm8DKzq9zqI6UKETr9/f30/1Ek/Rru91OiN5G9vT0dLanyHpsuqHNMuLzwswLJqC32+20RwgeeX4EunP+6cOHD9N7z+kX3x2JEDlh9xzBOfoalS6SWypvuqPZ9+RnfvdgWOhJt2CkvScBAWGNOmv1E0nbwGa0YE8Ls+/v76cdhQ4hLcQwHQH0iaE+YdUCenx8XKenpzNn4HkCGxxQA/UY1fHZIdrumkNXoy6jVfqSz/LpVIMjpmwbAcWgedxpA95bPjpkmNFconF/+jrj5vA9DRIFenM3fPbFzsOG0UYH42ke5R/FPOd7GiBSDV5hZCBl5baTgD6njexkHckCmswPjzn3Gp3a0WTER8RpZI3BMw+4Rh1pqPy/I4pckeQVZ6vVagKM6C19RKaclmPM0WlvbktH7L5yn523HTh0ejGJU15efgovPT/o9z+TKvQENDTAp0zBJjCA352uHFLe5EC8UWTQPZNOweigatdpkBhpnaqaDS7tGgF1HhEBQdCs9Gx8YVlbrgCx8kJbRgL+7tVDbMgjVeSUkJd4Uq+dgjcsedVRphC6gR/xO51596wNRKYDnK7jXhvCVCa348+u7VEqIY12OjzzBAPm6CDbzf87nhgRp9wk7TZQS8VjZmNuZeY+G8HR2HWocCQLNmIADK+A8TEK/Oa0yZKcdXqSKRfXU/V8z4B/gweOVoyIvZLQef4EmC7MA1E39gP7kjufbYds9Ktqmvi2vHs1mF+8BT9yPG2zcv+U6csIlDq4NwHdCKzDr8yaLJU326cAYV2IW1Wzga7aTbBU7ZY5Vu2M+PX1dV1dXU1IIFf7JJIhzKItmMsApbI/PT3V1dXVNKns1FDV/ITI9Xo9Lf/88uXLLFS0U/JkcEYPOJTuRE+e8fEb0ImQcUJjGsnOgCRyyJVXFjSuUWx8s8Bjh6u+z0Yv0X6iXO53f22w7Pxdn1ODlqncDGS05fSQUxfQkAW6zN8uauiWUY+Kx8Y88HWQInLN9YxWTbdRt9MTNh5c63YgZx0eG/fPDtjALqM8G0oit9FcmP9o2xPRnSOxU8BIczgd11y6pb5eYJHAkDRNyob7VLU7zZboAwDrbABOyzpBvX5PiVOdRIsGhqvVfN+P91Ck03aEaVlxtLWvvHpJqj2bDVOWURiTHrVqJ3gode4hyDSA0zx2EN39RnyEdDmpTLEimd4PHz7MNq/YUCGcfh9BojNvGPKmMyOdNIxL+fVuXEZowd+zrhFqc31+xs49o4dD5KBTOhsz15cRCsbBsgey6gxth+Rdr9Gl+9hFLaPrHd98baSMvp66VDU/3jn5mpFcGlmnKvg9F2Akb1K+Rn3s+GCn0PUTPe1AWspQ8qfTbf9OZO6owo7WDoz2GH9H7kxg+7iJTMF6n5TnfzyHcXR0NL1i03N9/suVh5b/NOwGBq5rJEu2ZQZB/4hTyEo7hOoyMmJ2Cok8yKn6RffpdGxgYRiGoWsvQydQgXeLUlw3aJ75C0I+I9WqfqIZRGtk699cZ4bqFhTnFTM1kuOSRiXro39cG6HXrHMJDSciSVSUCpFj0aFct71arZ711zRagTJVkfT5N997iGH0904hobWrI9F090zKuR0exippsYzYGXSTopZFdA3jlNHdCFhRjNxTvrpIqJtUHqUqc1zsbLy8OQ0+B0ZW7TYpjpZak3pyNoAVSx8+fKibm5vZkTqea2BJup/F/vgd5avVappTdPqGPubEtHW9c2zOEmQ2IeWXcehAxyHlzdJHnUHJYkF1CGvkfH19XZeXl61HTMQNA73rEcPtAoMY6Jubm1mIyjNEAj5t1PRmCgr6HAZCF4rqZbIcWeE5hUTu5k1Vj1479NsNOnVb4R2qp4G00rq+kTHK9jypnIJq59YZTyMlP5cor5MtkKT74pRYZ7DTeVbNX/Sehn9fTnaJLyMnkdFWJ19Gj44cHak6KnA609e69pO+jD6gKcd0NIYJJFKW0vnY4C7JVFVN6aEEHOiKsw5ey5/AEpqpJx0w+skCFL8HPtPcRAisLrKj//r165Ry5mh3VkoBCp1aIiuSQJE2TD+/Wx6zjzxrB/OPOYV9FXcRg0saBgYbAWIHs9Ep9yLsTr3QltcjJwKz88jlrXYKvJeA00cRSCOxFG4G3WGo0ZCd2NLyxc74c73j3yFj0RmbfDaV0Uq4bwyTPvgzmtxPw+06DnFAaUjdFxuY5E3Wmbwx/SPHmXQulZS97jeXzon4e9KTMtQ5ADuUztEnfZ3sJTBL2jv+dP1InaTYMI/kPq9nhOFI3PVCH6u9Rsib+7xoIAGTI4404qww8u+r1Wo6BZbVRdiABJi2I6OIspsjSPC0VP5xpwChI0FOIfa9GAkjHzx71U/D/v3797q6upqY8+HDh2lHoJfUGRlsNj8nGDmTvGq3A5XB590HbDrxcjCfme4UkAcovTO/QU/OM1TtkKedhhXV/HLON1GujaEFNgUxx4M+pJP0PTlu/A+fXNwe9LsNO9nOiHdIlO+Mm1G/eWPkl7+5UE+mVNIwpXOBX+lAnddN/no8OoWzIbbDSeQ8SgdyP8gXOQfNuv/Iezr75Ll573FMlJ+GJ0saMOuj+cL3nLinPe9KNn0JVlarXaqL/j89PU22w7ppxOzUD5PLq9Vqtnx5tVo92zTGXqKHh4c6OTmZogWWyJPN8NxC2kUmrv/rv/5repsbGQXPZZk+xpg/g53UpS5jkMXj8Y86hSyJPjKM8T101vehjBwIhXFHIZzHGwmqBfDh4WG2w9eeltDtw4cP0/4B/pwCsmFOQ54K4VUuTgvhMHy0dSKyRHUdGktDnoqfRqlD2x2ffD0NbBoLXxvxvzM8ndPpaBg9M6IhESSlM1YjBJz1dGNgxGheJ21us4uy0gCkETEiTOSMcYAOO5iR0V9Cj0v3jBwcBeN7SDvZXne/jV220/HYYAxnCS/gXbc6juvoqSeSq57PhfDdYM3zjz6RNidyV6v5PNjNzU2tVqs6Pz+fwGiXTjWPaNfpI/PmUN6/pvxypDD6zPumBrUTmd/tFFgiiuBxUijPJiKpmhsr8n54V++gxHhfXFxMx054mShoyyizapcTdzvuGwPsZ+0QvBa5Q8FpuDoeplGj3x3fk0edgvn3bvLZjpvSCXGmAHxvOqo0rFz3p+sZOSb/no7K49I53/zN9XSRjOvP1FoXobkvSZfpy0llr6JLB9BFlY4QOhDQydISfb6ez2A8MxLsFgDY0HbPgsa7ZztaUwaRt9z8iTNG59P2OCrD2EKb9xrY6WB/fvz4UcfHx9Nk8na7ra9fv7YGG5qcCbm8vKzNZlP/+te/6ujo5ynHXo21BFAcSVp2nSocAcIshzqRN5lT6IjiGugHg0NuDU9NNLBarer6+rpub29nb046Pz//Sej/mwqipDFw7nCz2UzvcuZAOSaQvbOYg/c8R2EDbiPBILhfaTxwCsxR0L8uwnHdiVAzfZChow2XJ+7gg51GGliPWaZVbEjs8ChGsdTh71YoGzz64b5badIR2AjmBKEViL4nD1arXXog+dyh9DSkNjjeOMm9OWdi/pjX7lei2M5J5MSgaco9CPxPMY/dX69nz/FJ3kJPyg3P8Lt5kX03PVw3Kl7im+uDLr/33PwzT1KmrDOmg3axFUdHR7Pja8wD/rdBxkmcnp7Wn3/+Odmp79+/Tw7CwIH+ctTF7e1tHR8fT2+UTGdPMa/ZXJd8sezk9e7/EUDoyptNNKdwVdWzwa/anVsOYrCxZYmo9yeA4q08IySc64YREA6lc1RA+JkTxN6eb/poo5vohAbPd3STfi5psHLQulTSqPj5NBSjsRmhZz47Y+ExTUNsRcoUQ9Vc0BM1dsY5+8Cz3T1p5DtlSV6OHFs6xUT02d8sNgad00xauz50cpDgoUujefyWeGlD6s1dpvdQ2XtpgfbRMmPKKKJyKnikQ50uJA2pb369pkFs1fPFLj7JlAwHzy2BZhwQZ2YZwHWlS2kt8aqT/7znkPLifQqpNFW9gUxDyP9ECmzy4PqPHz/q+vp6Wt7FwLC8lOgiZ+Kr5i/jfnr6+dpOcnccU/3ly5cJvWO4OWzO+X6H5XYUyQcrqJcJZnhv/tioZcqCflTN0zk2vvylokAvtLldG6eRU4DXVrDOGELTSPjsFOB1h3ISsXdOyPSnApsWO+oco5xPGjkG7jcq5blcaph1eOVP59zSudD/lId0vq4n05MjR5DR2IhWt5XINotl0jzL8UrQ4zHxZHK3us0y5WfsFBgPVvt42W1na3jG45cOBN4aqLI6kUhztVpNRn+73c5WEHJG28ePH+v29rYuLy+fRYLm0d3d3UTzn3/++Ww/iXkH7TghSmfszUs7z7x2aHmzieY0Dkb1HgA6n8dabDab2Q5jCyhGmU8Gumq+eoV2SQuxusgC7VRRLh81GsjfKV1+12exdwrbCWOmi9JA5aDmd9fdhZA2Spm79O/wNWnyuCbqZYxz7LOfSV8WnF4qdH6mU8h7TZMVzfz1qqbsmzdF2YhXzVevdE6B63Y+yJv7lQ4gx8L32xjyezrtjl+j+n1vJ8vJwy4S7+pyPdbDrnRZg67OUYTgsfQ9I55YbnKMcBB8Vj23Me6/+e+IF5t2eno61c/GWL+pDWfOkvgPHz7U7e1trVar2XE5/sMp8DxzG11U4M8cm/ztkPJLTiGdwahxEIlz6z6eGgZ4D4GLEbuFparPQ/JuYw6qQwBAW7k01GjM7XUrBBKZMTfRGfl0Cr7HaNG0I+xGksnvTuGhyQ7ViHxkjOyssy0rVaJgG0uPccrByPGZ1yk3o/65/azb9SevkacEGziLLvp0vaM+5XWKozuDiKrnjhMd8NxNtp2pouRFBzRGKVbqNq+6aC3bXjIsIyfU8cW6kBO0ljfvOcn6DZq6CCfvz3sycjT/7dSc+8d2pH5tNps6Ozt7lk3wuU/8OUV1enpa6/V6+kw9dKSA7RxNzicf/dtrypu8ec0EjQiBqQ4FjcYI2bjGABB6rlar6d0Ld3d3U70WdhC79xo4zIR2mJyo0splpwD9bjPnIdLIZ0QxQnLJRxtyfs/6lpRl5JxshD0mOa5dmE97uUzXz5nezqC5rs7J+J5RH7Je/2YHzb4T9zMjgBH9pgnnl9c7uc/6aCMdA3KVk6Q+sROj4F3/6QxyT0vnODxuls+R8TeaN1DqDKrpWooEOxmj/uR9ApiMFEZAMGUpdZW+rVa7BQjpGL2Pgn6yAIZIkpf6sN8BG+bn1+vdZtaq3bsYqnZ7cO7v7+vq6qqenn6+5+H09HRKZVO/+cA8qGVjNH9hHmfE1QGfrrx6ovmQCMGEwbiq56mHLvebeeeq+TEEMM1G3a/jxHB3CNhtdA6hQ2QUe24LQgpkFxV0DsbCn6hyVFLpuZb1JyLqFCbr7fruDYH5rMdwH5rk/kzlVPUpF9PUGWKPGePiVySaR0t0pgPK3zLy829L8nKIU8jfvb+GaNoHwiVw4S/54r53tFPyniVQt/QbZZQiGoGgpetuM4HTPh3J+p0usv2gbuuIQeF2u51OB7Y+eUycemIzHWA2+cF4kkrinc+m1U6B60tL4qv271p+SdTw6kihI6xqbNBgspG/Txf0MbQjo8ngcFTuZrOZveyGw+o4a8SGm998fgkTOIk4R0YeFODJKffZ93cOo3OkiYpsJHxfNy/Q8b1zSjkOviefz0iByMr3pZOFNu8S5beUj0SGDs255vNqaJNlhNDoCG29Xk9LjVlQQKRpGkFZ/p8FDCirI9XOSZoHmRZMuXUdvgZ4Md+RxdXq58QmOef7+/vJiLlto/9uLguZ6ib6O1DRRQ++17wcpbOybsCEdcXt5V6YjAw6g5pLcSm2Ky4Z+fA/8mpQ6j4YpPiPt63RJrJPX7A/X758mer+/v37dBQG9TN5/ffff9fR0VF9+fJl4ieLcJD57fbnm904OsMgOsdwlAo91IlWvdIpdMqy5KV4pjNCICJPErqNTKd41n+73Z0tnqmiVBoLQ6YSugnnNPApHJ0j6AzFUgg9+j+dRPI8lZtr2Y4NTzoNeDEyeKarcwqJ4Gws9q32yTX3XEvjnUKfiJsxZ4MjAAHFoXA8AkqWqIr78yx+15P8XXIIdlp53dFrGuzVajWlLKDv6Oho2szpYoczAjHmOUY6o6XOQeR47dNtP59OxfV07XV65Ospe8hH5tfTXtCG5am715tcM4tg+Sf7sN3OV6lV7QCbgQ0A1pPO9/f3E03ML/gd9B3a98S45SmBeRcFj+Ya9pU3f8nOkvDYwCM83io+8vReS81SU69SslNI4+18v1GG84HUPUolgQS6UN3f/VymDsyj7vuInyOlSRr2GawurO9Sa04LLfUzIxie5bNzDDZYaSBp02cXeUVZl1rwmvHj4+MpL2veIRtVu2XNNiimtxsrGwe3bZ7m2PjgszTUOCgrsRGwF0ZQlw36iI6MLpNGOzf+3wfifP8h6YeU7U7mOsCTTtz9667lmHU6kkazm/z3GIzS1/CKVYzekU371OfjcdhbxeKZqt1GPK8swiGwWTfndaDPMmUej9JGnawcWn7pdZydt2RX3yh36+dgzu3tbT09Pc3yqQy2kd1m8/OVfBcXF5NyE4bZcI/y/ony81Cs/O6J5ERiiSo6I8zn0gSrhTV52xkg+EOfqcu0UEYo1mPBtUyrJOIyPVkSnXbpjpSd5CfXPe6uy4CCcWR1Gas/nBKo2hlUo28bFvqL3GW0kuOcfe7uAbjk/cik317nMcBRIZPUwREvTnt6PJD3rnisqQ+dy/X0IxldKn6+eyb5Q0lZ6laAJYCEdve30xk7DMaRMeUZG2bvkHY7jFnqDsBjtVpNR2vbwTmlaTnyoZ88g6O/v7+vy8vLOjs7q81mM6WSRgtkuOYxy2NSPK5cP8SxV73xgXj7ooS8H+J9qBTFSuw6VqvV5LW7/FnnANIZULpcJnXkcyPkn6Uzdm7jkDK6L41v58hSKTslzc/uXv+e49DdU7UL9ZdWOFWNj+XO/iXCs9KjIMwfWFGyjyNEn+NpOdjHKz651+0ujUM3Xt1SQ+bJMAZJUxrIRNvZZwMdR+ppVJIH+6KJJVntnEJ3/z69SJ7iMDrgOdKzlDmDTeu7nYodr+Vuu92lh+Cd05kZ6TFf5JcAud9MPDNn8Ntvvw2jP+vGofbkpeWXJprzO8VerjOODru22907U713Ib32drudlhuSQ3bYt9lsZsiePytDVb+r06jCBicjBA8Ka4jpb/5RMtJZMg5dyft8raqPBrpx6erJe1PJUwm70s035L4HGx3GCfTiflBHpgQcRToSJEJgDbdRm6NF6hs5t46vXaTjNKTHM1GynVi251ToaMyravYmQUfe5pNTY1X9+UN2ltCPcaKf6CDjkxP97kc6jTT8nZxm6orfOgecAM11ZkoH+5B0pFNLe0Xq2edjsTDBUYKzENSRe5zsZC1rLF81/zKiNSj4/v37dBz4//zP/8wcvlcjEXEsOb5OtpZkLcubRQpLQp6pDxsFp5uYUDs6OppNwjEAvA2tamc8eI2eD9jL+QXutVJXPTdoHux9EUEavq7vXfTh9vL7iIc2/Gm4lpyKf0vFWLr/kPptfNMQ+G8pKuh4kG16ziLHqKvDY9ytVLFxsRFIY+tztDpaoHUJsaWhonTyZ/RoGhPJUqcBSSLcJVqq5pvruO6NoU6/OO3QGfVuLLsoIx2n6+miuw7U2MgzXp48d32dw7LNybHjM9Ov6fxt6P1qXu438OE+RxJc89EZzCcAcDLVBe1J32g+wSWj7kPKL80pZDnEGyW6t5Ngdh5G0wm8ax5ZsV6vp1UBVTvnseQQfG9nzNLgLDm6kaF2yYkj7hkZ5VEbpjGfGSE232Ph5//OELsf6fA8JkZYieL8TKfg3T0dLUlj19/8zcAgxwgD737QRqZe7BS4ZkPtdvk9+zvqg+W0annH9VK/PV/m5+3IzGfXmekldAZ59byHI91ObtzXrr/7xr/j1dJ97rPbdbQPn0cOoUs98ww053ib58xFrVarZ8uJvQnXab7c9ZyOl9cAO5qElkx18fwhxv4Qu+zyaqfw0oaM3qyU9vi8hnO73U4M3263szeXZeGcI3b8eRByxVAafKcYcC65PDVLh9xyQsjGhL6PjLb52E1QdsZypJT5OTLs/r1qN8Ga9/PXTbQnDzBGI0Ro/lfNI0bzJTfp2EhRr8fRfPJ11+e8ux1O0mnkzhjaOWT6z22abvMyZchy79TDyKm6LfMr+02fnCbq5CWjj64up3ehcTSRmTSavqzf8pVO2Ly108p6kkd5SoL75zHkL+XIDqUDhJa/jGjgDSkdRyjov1Of2DWuPT091bdv36bUOSvobm5unh2d00UFXZQwih72RRQuv5w+6hBMEnNICsEDWLXL3fm4Cp7xZxr+VIrOeHDdcwcdusx+8NnV193b/W6DtMQP93FfGTmSUV1LaMyfNgydkenGvuNNGr3ucx9Pss38bRS5Lo3NUknZSVoNBtL42/DbQOVfR+vIMZgm65Pry7mrpfHqHFEHCtx3G7zRJO9Itka/uV+JkEf3Zp0ZmXmsMlXUAaUlHthhmSe2EywzxtgTRTil7XkoniVt5CN+WFFJlIaMdfLSRa3OwLy2/JJT6AwJZaTovo4hRsBYrmXHcH5+/mzwEtF7DiHf4dsNJOiR5WUsFXTez+0lclnqm7/bWVU9fyGOaRrxcZ9R6wyf+TUam+5588lL67rd25kmyHy7nTZtpLB2k/AjI2tEbToT3XdOzdHbyChXzXfF0j6Tf6Zju93Ooo/OIfh7Kq7nyzoDBC25Z8H8MU1MsOemKj/jMbXu2JhAixGxc+OMk/cIpXPoZDcN2FKxTPHXATt4P4p66IcjnJRN6He/01ZUPX8XCHTZZvCsl56alykn9/f3Mzrh6Xa7rdvb29nS5I8fP87Oe/Mm3wQFXDs0tdSVN9mnkCHLPmLSEPGcjxzgmpd+Ve0MAYbKKJ9Ukl8677yrlaPbyzASrs7o5kY435d1pTB2vEwBHSHHEerLMYFX2YdOiRyF0S87gi6N5n4ljR5/VrXkksfMp7oOUkugq64wjjjxbqKW8XV7iSqzpBIjH149BX2OLJdKh6g3m83snRNGvQY6+ZzryqWs/N8ZaeTLkV+XLqkan1xrp2sDZCM0kokRL5LGvG+kj8mLDjx1/OjqsvOjbp/PNQJNaQe22+1srxWy3+kc86e5b4W5VeYmDFiXAGFGj6N+HlJ++c1rGZpBYIcMchATJSeC4VqXy/PqEp7Ll1E4T2jhzyMxMqIYIW++jwy+B79D1KatuzZqM9tOOtx2pyidY0nEbUNqBJ5OpktdZD88/t6ok3nfDHW5ngYo+2J6+Z7AwX3Jtkc87sbAeX87sm7uqQNCyGDHI+TVS05zHGxkO7A1QopZzFOnMBw9cJ/RsOvrdMSRjcd+RIfpPsSZdjKc97iPo3v3RSnpkJCzvMc2xLqHbsBHUkq5mqhqbh+ZW3Bkwaa4x8fHWRRinnQ2KOU7x+0fcwpmGsRaiCAk847pPKzQeF6OOz46OpoOnuNwqLzXwkyH8y1tmUryRqdRvtzMRCg6x2KB7YxLGlM/Z+XvDN9oEF1Htpdl5IgwdI62LPwItY9XoFiYc5wzBLagmqYuR4v8OCWSfEieJGpDHumXj7xg52o6o86pWXHhS4ckzVP4QH3dxLbHjv7Bh7Ozs+k4BEfAXXST421HYNo8/pZd7vVxDU6ruq+WGffV8kNdfPceh1wuallwFOTobDSh2n1PHemAUfIi70duu7lEjzOygC369OnTDCRkn0D4FO+N8DHpGXE8PT3Vzc1N3dzc1N3dXZ2cnDxL9bmkjibYSn09pLzp2Uf7igWWNb5VO8X2e0+dN3MqIL97IBNBVu0UzznyRMud0YFOiieLRvd0pTPso3vSMY28+6hOo+iRAc0/O4V0hnxfShXkMr8OzSbNI+NFmuYQRGPhh05OH/WKo33IdURLR7ev+Xo6PugZyVUHqjAMlq9Res31dP3qxt//02467s6xLAEUywZ8JupxW46COvqXeD26/1Ajl8CDawnauue6epxlMC0eI9qDF9g6gKrnN9KmVf10IA8PD3V/fz87VsNtjehOfr2GZ/+4U7ChsEf1i6uPjn6eg//bb7/V8fHxdGIgnpVJYV5dZ8/qYiQDAiJ94Z2IiVK7SCCNFX8jFEtfO3qqxpO+fh5B87k42cbIWXROoGonOJ6M7wwXz3WoBOXPsSRvaoew5ASTV939jty6381PIkHn/620dlg8435mWO9ih+m2fH8qmUFMyocNLn/eUetxssN1XrpbSdMZ1hE4GPExEaVTiF7azb12YqTn+J32cQzoMG25byMe2ml1DnfJQbodP5fAp2q+uzx5WDXfP4JckcEY0cRnrkx7fHys6+vrSWe4hz7bmXLkxc3NzXTkhee17BSW9O2lEQLlzU9J9Wf+hsB5gtCTX35Pwt3d3RRqffjwoX78+FGnp6ezeYCqHmXDbNrKuYWcaHMqBcHOHKsVegk9ZRkpZQppOoGuvqXffE8XCaQTHfWhmzNIR9CF+DnfMOo3ffRcgPuTjiDRq5f5YVT9Iveqmhkp+jRyDHmto8H9s2yk4UkeYuA7frpNI3WDoYeHhynN4Kgh28syQpDds52M5bwPOtgh/RxnAw6K1+l38jOK4JJvNrLU4XExTaZh5EBz/Lt7HIl2oMr9Moiw7qXjIKpN+2OHst3udj1btnEoI/1N+kd9Xypv5hSWHIH/MmVhphvNOl/nc4a4N42jB2C12s0vuC6npSxgpom8tJ3OPsZ3v+0ziPvqWjL6o2JhtRAbiZq+pboyMgDZGuW5T9Q5MpQuRp1G0vxGvVZWI8d0VCMD7r6YznQMNjrJz3R8yaOl0rVpBJ1j4D4578y17GtH99KYjkpGMqYl5dPgwjrUtW8HmiV516F7/5b0Jojy/x0dHdBYAh9JYwessv60KdSb6Tr4Ql0GyfxfVbNVaTiKbo7prcsvTTSPriO8GHkMCp4vl/JlyoB8Gorw8PBQ2+3P9bvsbu5QO4PIe1HJ5VFntmcnxb1dfV2fPRG5ZNhT+BCYNIJuJ51W1ud7uealpEYWScOSIBnJ2SlnBOAli6k4psvKwb0p1M79J30dyjYqZJOPETTpDD/v3xKlpyHkE36mc3E/s28GOiNQYSeBPrj4cMjklWlPXo+Q46h0jjSNsvnlHbrIp+fpsi5K51S22/lSzFy6af22E01dSIDp/RPZnuvr6EzZdHbh48eP01/yvNOzPB7k9vZ2Somv17vz2khLffr0aXq/PHWytB5ADNDhfLgcH9Od4OklUULVGx1zsa9RM9wonOJUgtGSc3qEsev1ejofvysZttnhOOTsUGEa7K5vVupDUJrR+757ujJCsHya7gxxDxWGRN7+34o0Qn0v6U9Vv7Q1ER/Xk0Yjui5iyEm5rp/Zbkd/x7scwxF/u7FeQrKmLY9jGNE1ahv57vq5NC4dfSOHye9O5e2T833I3XOOeZ8d0SE0+3qiao9djrOftS6R6hnxzBmGbI85N2yfecQYc0xP6jN88XLVTi5GY5SO4VB78OrXcY4Iq3q+e4/fWYKVB0PBfGbm/b5mM5BdgEQCKYC0mzP6RALMUZhORw883+XOE4mZyd3vSVfy4hBFNV9TmBGe3KuRhpu2OoPG9zwbZskpZulSB93zWY8RfSLdzngvFRSHfuau9C53TLvpbPxcXusQ8Mh4Z10dms95j5y4z/ucSx85vE7W0nha7vOMrgRNHtMu0jNiznz6El+gJyOBRLmWiY7Wjhb/bkA4Gpfu/u12Oy1h55j+5Lv7bllgH0tVTVmOqpqiDWjB2XEQXkbLm81mdpqqHUSWlIcuYji0/NL7FDrlTW9mtO9TKjuh9pZ0igcRZHJzc1NnZ2ezkKvLodt4Vs1X90BnnrffvZVqhCgpqQQdGhwhMF/r/s80hAXRUYH77jHK0kUBeX5+R7dL9mEJFWbJSdpDHGSHBJPnyA+piJQDt+VnLB9L7Xqi1Tw0OnS/kg95TzcGjtL8rMelc7xdW50MLpVuHsZtGsHym/mJoTNISf3raKU+Zws6WpyXt9Psxg77Qn1Vu6PBk+409uab0zbm9yiN6L6T2mTnMiss3R7yA1BOg09/ncq1jHTOFzoTwPzjTsGN5/el+zuj499trCmd4j88PNTx8fHEVO5Lx+DnLaApeKmAXZi21Md9DmHpvqV7OvptEG30RugsCwJux+d0hR12OqaO1kTdo9LRRrTQOcUlo+e+pmFGafzejTSk+4xm0tI520x/+bMrnVPw/o6cQ+ieM22JkPOZJVmwEV4qGeGYhq4/KS/dhr9ubNHFDjB1/XFEZNBp+vJ752S7Yln3mHfP2MmkbtqI077rI2LId784QrRTyGNiklbqHYESfj+kvNlEcypGeuXufqc7CJUyr1j1PNzdbrdTKqk7xyjRPwUvTvu5g7Zqt/s12+0Uo/vuezuHtPR897sjgw4l5/8dn/1nQzTKUXb0pXHd1/+l+nJMRqFvV2wMOOPKiN/LUXEMaUA8Fkv9rHq+CII/71sxcnX/RrLvNKnncZIeT5gnms0IMsuhjsH/JwLdJxs2hnbKTgWZVreZoMara9LYcr+jqywJjpyZ4HnLfEYfbs9pWehO3ua1dIB+f3NGFv4OuPXpC9ZzZISFN6YXGbQMjED1S6KFf2TzWjqIkQOh444AuoOoUoCraqZUVfUsnTIyZLSZdSbTGEwLcSKiEYryp+npysiApiOxwHf9y5KGxCFrKh905vNLNHbXO1TWGbtOOLtrnaPraEnltoGueu5scxyXAE6ClE5O0mBzfTQp6PmbTJcuob9E3Es8cUmn2LXT0env+xx9R1sXVVmn9hmpBD4py/uezdRPpzv5W6Yc9xWDLerrNpmZB3YgnH2UDo/22YvTRZL7aDy0D1ne3Cl0CmaEZlTlkOnx8bHu7u4mpOIBxcubmc4fr1ar2blGabSr5kvpElEsIdP0uiMBc7FD8L1piEbGM51ClyKyw+jGwHy3wKVDGOUlOyF23Ym0fS3rOqR0BtT85v/kWaJ089rGwzw0XYkSTc92u52h+RGCzjYscx1vbTRG+Wr3b4knyRs7vJSZzjlkHe6PafAS3c75+94OnXa6kvdbLju52pd9yL7gSDoQOuJXtzqyi1xcPM5O9bjfTpV6vouXgyEX2S8mmr1nJctLooBDyptuXlvK1cFwVhZ5cxkbdWAAS8AszN2EoZVqhN4TLTt36bpzQJLJ6RxygpHrWYxwqDcjgPy00OQO7q69LiIwzekUTFtX58ix+tP9GPFh5AQ7dJ1Rm8sI1ee1kZHq0KKf6+gxAux45PmY5A3FsrY0L9P1zeiyM4LZXv7eofScGB2laZKWfeO9D8W7fhvlLKP0qYGIjS/OKoEBbSYY7frl+Q+ns/N+y1Cnz9vtdtqBji1ZrVbTBDLzB7TpPR787zkI6uY9CgZz9DNTXAkwOjt5SPm/kj6q2u0I7CZffFCUjeJSR6xMS/eNhCGRe/7f1TFC+6PnRnV0ffD/FpbOyY0QV/fd1zqHkHSP/h+hwyzd86YjjWY+Z56+VemMVP5V9ci+m4Dv6hkhyMz58pz/9wTjkizl2O+T1UNLd+/S84mwfd2/mabRuPraEqg0TXZqIx6MnGRXZ5ebt23JersCQHD2AlrT2aHjBnzZdhp5+JQZE2hc6nNnA5fKL795rRP4joD1ej29xYoJQnbuXV1dTRMp3uCRk3hGK6z5pW02tnX5Y+jp0E9GJHnqJ3S6H+53IkyXnGDq0LC9vSfdkqdepWMaEsl0hjeX+I6Us+tDOs7u96yjM14dYunSGX7WUeCSQFs5ugl5PjtD1SlU14+OB4yVx6fjS/Ixx4/0qSeWO5Q7oiN5kTo4kkuPgX/rjk33/cwLdOg8ZW/JMGfqcuSIPB6mOY1i0mObgTxZj7zqx3V1BnqfQfVkMPxZrXYnJXDdbZMtITVJJIFjIXPCb1W7pbWdA8h5qg4gHlJ+ySl03gkGuRj55o5NGJaGi/DKr62jLq82smIlLUvIIxXeQpATQ267c4RZOuXP30dt52d+H7VrI5dGdWQclv5PY+R2umf9W3etcx5cN787mkaCvWRE7BwOAS1dHTYqSW/VPMWTCxKq5nMxS+ONTKMflC6SGhlI09cZWt/X8bszuKPjTLJu/58gInliOnMhx1IZOUenf7AlWUZOP1OzCWYo8IvVZtybfck2XU/OZXizH3z2SkzmEDrgNEplmdZfKb/8Os5usF2M2kZOIV+Is1qt6vj4eDovyaE7DPVrNPGSHRIc0Z5KmoqSCpr9GZWRA8jfOyPRORHTl3V3/c3IKCeVl2hN3vj/bG9EQ967zyl09e4zJkslxzSjs45HnazYsXb9GzlwaO7QuulJBAs6N8hJfrj9UepkSTYtB4n0O776t6VJ6tGznSNLvjjFNqoz66EPnWzk/CB/qcvdPOWo2EEabHksMrqjzm4RRNpDrgGE0dmHh4fZ3MOSvtOnfc71kPLL6aOqfkLP/xvZEyqz/NRe0cXeMxXLh9dRqHO73U4v6xnR6//tAPy/+2Vh6N7S1AlUKlEnEJ1TyLpHk8EjdEKb5lkeK+J7k4+JpkYTpKm0IyQ3SgHl810EgiKkcnW0wLtPnz5Nu0e9I9V1m55cPtg5686R5H1e+ZapH+TYskX/si1Cf79ThP53xzwYbbvuUYrIbVrOXXIe5bXI0+OXumIjlykX35cGNX/LVFw6EPjniKQDWk5R00bHF9pwepu0UeekXA/OhXfJmFbbOXjjN7PtS2EZ/HU6vQSSs7x681qn5KOGc+CYjbfAZ1iWBtt1eTDdeefSDvGYybzRbx1Cs2FYai9zlK47jX/ntFyS71273TiMEOVS/7vncszzviVedhFD/paOqDOuo/qRC5Ym+wgUO5ZRGqRzCtm2lTb73BlbKzOpAaeU7ChsoH0iKTQjR7kChTJy3t092bcRuHmJQ1iSjSWZqXq+1HSpjdG15KH1zg6ocw6p50t9Tp7Z5lAHJR0CtPjIFNNv25JGfokHb11+KX3kidhDwi8MPIibA+os/M7fue7tdlvHx8dTlNChddb7ctysnzUt/OWJmjm3YTRASQ+fueRUOLdph8anX0CSNHK9M0weh6Ux6uhK47WEwkfOcWTc9zk2Fyuu37PcpU5GdTKOnB/z+fPn+vjx47RTtGp+6Ft3rlWnvIeiKkqmhehfRgr+Q5bzt81mU+fn59N7m9momZ/JD7ftM/nhg4/+4P6MFiwzfmNaGq/RWPD/aNOfkXYXRYwMOnyinRwz+uFjThJ4Wu/oC9+9tNUpnCWQhR3r7uv6goz6DCTbD2jPjWrWC9MxmnBOvr/UkbzJ0dlL13MgPNgOiTtDXzU/QM2vW8z7UKbVajWdhJpzEVnM0Jzr6AS/c0SdoewEz06hSyUkakk6O2fj37M/6ZTcRpZRm6P2Rm3z2aXffE/e3znZbkLPfPQnToEowajcwCLnB0bRS/Js6XPEh7ynQ6gAF5Zqp8yxX4eVdexuNdhJ/vGZyDhTCzb+fiZp3gcYlkrSk3MYI3kYXU/6l+6xjCyN7xLNlM5wG0R6LLp+2DFAU7cikD9+646B7+zZSBZH/NlX3uzo7O6eNKLZeaMRIwI64FALp4BhHU28gqLwzEt0m1l2CunFXbqJyy43m0KFM7Ah45nOIeTgjwa0E8R9hn/f+C3Vk/SlcbLCYghGfUnD5HtywtDXbUztFBxJbjabWe4WVJcKPHI+/m5lzmsGFSPj42fNp5xrs6EAADmvjxwRZfNMR+++HDS0p7FNXqRT8RzGS1JLydfRhPUhTiH1rZOhvDaaHzkk6vHzRvM+2bZD7dmvtInJe9qzUzCdnh/ZlyZPHh7qEKp+cU4hUUd2zBMznUH26qKOqTgF6rAxyO3oHlzW/f748WO29KtqbpgcgndIze3mpGAaCOhNHtkZsC65Q2hpVE1np6wjpNs953s6nmX9qcRLAjXK1+bz3UquqmpRkZ91WpH7iAiICk5OTqbxMSrjOYAFYKEzEDakXS6X/z1GS2PDZ46p18Zbho0qkU3q9pvI+MORJNAy76nHTsU0dg4hx7b7nuPdOYqcNKbvI6OfNPmax4w2kt6OvhEQyhSin/PYj9JIAIzRXoCuT934pA3xO7lXq1V9+vSpPn78ONMfyx7tez4F/rzUEbj88tHZKVz+bamAcrwU1QVGGe2nYU56YBgD6RVFnYEc/Z9l1GYOcvd7pjpeUzphG9GdTmVUEkl1vx9Suj7zmc5vifbROCHs5iOGnkggX6qTdaez6vpgg+PnbAxy/mgfn7vfDSpyItz0p3Hqnku5yu+uq9PNQ2Qkn3H/98nQUl2dnGZ6ryv76E8HwjOWrVxptNTeiEdJx5JDoHgJPc9l/w2kvXqui6w6xzZaSPGS8ssv2cnroIZUJv78LJPMDoVsVI6Ojur4+Hj6nwjA9bttI0QQUrclnP+tkDkhlbR0xYaq+y0RYSKGEZq34BiB7TNESa8NXc5fdPfZ8HX15n3mUY5F5nQtrP7u82DSUDKWRkKr1WqaSOYVhh6/pMm0sPnIypT9Rc68kYj6R9EOZcS7THdCI21kWiuNAHKcE9IJktLIAKqS/pShkXF1yegp5XeUDlqqNw0z4519t2wk/daP1K2kyXwfGfKMOLp5KEcvpsf2pLvXG+w6gPHw8DAtRa6anwHnbAp9z9Vp+3h/aPnls4+SAVlGSJLwxx3qFHTfmSSdQKeC5SFTrjvbS+VaUrzOgSSiHUUIFsiswwPf8XQJRaVhyPqXUEOnpPn76JlEQIl8u7at8E7R+Td/T6PaKWBGFiP6E611eWr/nyUBT45dykPSYL6lE1tqr9O1TgatU+bfdjtPm3btuN40eC5cOyQlaZnuHI7b6ZB+lyrr6DVdS/0yuFyaG+FegGummbPuDmSyPNr8qdqNE38+6qRLkWebpsVtjvp+qKP45WMulkoqsa9VPT+zpDPS9rDuMN/zGdcN8vDg23MvKVjXlyWBzPvysCtoGuU+O4PlkoLn9ri2hPhGDm2pHT+/dG2khFa4NFrQi+B77TbPj5yCr3tcs519/cjf0hhVPX9pSbZr0JA05PiPSjqkjjZHEDk5nrzlk/scLXdI3H1KR2XHwH3p5PJaGsB0CCNZdX9H+u76acP6PHI8I+dhPiXfOloz0nT9pjWjMi/b97sXDFy9JD6zKx0IthNIZ7XkFPeVf+R9CqmsHkwUybP27CsgVVRVs+WFXoWRy8BySWuW7ozyTtG6JWL5Xt59aNsrRvK+Lq/t+jr6aDevdZN5nQN220uo2X3MkDhTfkacpi/rzGs2kIkKl/jJGHfzSUaPpsU0ZW7dPOqQNzQZtbmuqp1S+/9uU9qSo0fmHCV1CD9LOgLf4wlp05y64XXy+ybXu7bSUI+eSQPr31xHLrF13f5ugNfRlk4hzyqyfJs3q9XqWdaiK4nKc97HOpNO2TveUx55bQBpROwe6aPkq52IbWvem7JwqGN4U6eQRFQdtmytm2y2ICdKHyF8BsWfnqHvDErn9Zf6l8UIzQLRIZZ910aOq/u9K4nwltrt+tM5GLf9EhSWzszjl2ORaG2pbcpmMz9COJFuIt40gh3v/L1DkaO5mS5FNAIPbtvR2hLY6PqRdXUlgYid8lK0MKK5G/d0fKN0R1dG/VjqV+fcO5o6mvN68t7jPYoEEjzkpHWCoJwTSyNtYJyriTzRbKcDjUuyvARI9pU3PxDPpQtzu8K7TO1pP3369Az5eBOS709H4HvyTCMrSneWzCH95v40dN5Yl/21k+vqcNn3P8XpCxenEGgvabIwp3LvozUFboQUMyKA1kTSaaQojD/38J0x9QFiWU86HCtQl27qnMhmszu+GF5n/d3O+9GYZt0eA+THuuAJRwoGpOOXjQV99xhm6pJ2QZ6Jfl2Xx7lzjKnrSw7Y93ffRwAh+bJarabFBku0VM1tR/bHBtx2LR24kbnBzSjKWq/Xs+XTtkUGpY+Pj3V/fz/1HVlg3BMAEVXkcdzIYe5dSV7sK292dLaVIQmC+Fw95N+rfjoHH2SWf9zbOaOOCVaGkRNLlJsItkMUHgAbJzsWOx8bwXRe+5xRKsTICGef0siP+py/H1L3UtSQ10Z8SAV+CcKs+hldfvr0qapqShOkEfBclA2N+Z9ywu+MJ/sBlgpKXDXnfbaZIMJGjjr8Bi7L12azmR0Jk/qTcjSS2Y52yztAKvvisgTyLK95zyg9lk7ZfXE/3G8DhEzXdMVOfKRvyKhBRILMES/cD/riPTWpY/zvTXB5xA602Gn5mZF8OYJxOSRrU/VGTsGlMxbp/Tsj9fT08+U75NHsCDpHwnMjYed3PjPM6+4zU0fK1DmG7ujf5IW/ZwjJZxdidk4hedgp7+i+Q8vo3n1RX2f8/N19TAVPg5Lt5v30u0vf2AHl3Jbry6gh++Gx5d7RfVl3yhXfTW/nFGjLkZWRateX5CPPmK6RjGbEcMjYHAJOKJnHt8Ea8bPTga6/I90eOcCq5Zf/jOxXgqHO9nV1dfOSvtcTzNjI7rhsy1C3AqrrdzrdQ/X/Td68lkT4Ow4Bz4bRd64Nou/u7urs7GzalET4lWiD957aKVjwzDDqd+jm+2BWpjYy1eT6uJe+dAjYaCGjiORTZ9hGkU0alBTikTMbGdRUHitOV3eH4LINO0tvLEv+ZNrHn50CpRHnjKvT09MJAWJYc5dzghLnaLsjKuADO0qTRopBzAgkjcAIx7BD99HRUZ2enk76Ag28w/z29nY6prkrliPGz5P0Rp3ZV6476vJpxl1KZtRXp0HouyOE9Xp39o/lqJsrSJnJP/Ta8tpNzuaxMl0frDe+jq3p+O1P0w8vvVgiDflm8/NtbbxpzbT6+BPsIGPCfoYuuu6impc4hKp/8B3N3UTWUo6R36vmy7FSKKrm6YJDEHK23RnnUT86FJNItDPEndFcanPk1TukkPR07YwK9Xf15X3d/0vtmQc5fiNkiazsO0Omc5KsWgMRMy6ZzhsZr3199b0jhNz9bqc2aseGBMeQ0QHXOPpg3woZg61MO2CIDXRG48/1LvrJe0b8NXhJIMa1Jf0byRdt5j0dkHA5BNV3DgKal5xhV7dtWM5nUbr5IetORtnQsyQHLzH+o/Im+xRy0NODofTeuUnxQMMgFDtTMww6eVcrEb8b+dswGMGMUkl49C7vaDQFCsi9CKn03cSelT9/M9pcMlq+NopCXEc3ceffR+PRleyn77Mwe17IJflA6MwLkjJ6yLFFPjByjkBzoj8dDMbWE3HmewdA3GcraN7TObN9Y+hn7RRpg8jg7u7uWe7Z45x89uQjxctoU3Y7w279AdVbd2g7Ha95ZFnucv6dDnZgrisjGd5un7+YK2WJ6xlxdXV2f/mb/6ceH8OS98JH5D4jJEcKDw8P0zWe9Zsq/4nyJm9e666n0aMzZqIVPA2ClTDDdpjmweY+twktHXK14LpY4Y100th1k8dpqEeOIfvYGfTkcRomXzPdo3pcOhry/i4Ed5sdf43W01BZMXDoRr8jBYRGG0JPxGIEnp6e6uPHj9NqFI9t1m8DNQIHS0jYfOsi1o7vHVrOOrmOwfD7FEYoeARK3JadZKZZPEbd2HIfn3YCIzDTOdSq55O8naHmM2lw3anrS/zNPmW/urHyWJjubNPXkv+jo20oecqq7+vGhbq88mhf6RzXIeUfSR9VzR0DhsDebWSYqvrziax8ZpznFvxc0pHhrMPojoZOsEdpLRc8eqZOsk73Pb/n/+kQfN2/Z92HCoH7eeh1X+uig1H6xgCBXOrIGTid2PUHxWDZqMcojc8IkY4M+tK10f+j0hm7zsDYafrEzK6uru6uJO+r5iv4uonoBBvb7Xa2CqubW0ma0kF09DnNNXJ6Xd0ZrWTpnILv71JbySfTvaTvIxrziArqgTbsYS6Lr3oOKg1A+BvJ3MgOvMQevOmS1KWGnT9LwjPkH4VGjhQwQqQdqp4rX2cI0kGkwc6BcLidueoOAXRGpkstpDB0qZbsk2nyb1X9kdijenxtyZl0RjB5aF51+w/8x/izFr9DcUlX9r9TXqcmiT67Cf6unyNQ0iHLjnfwPQ1Rfu/SaJ0RBgmybt200h4LLajXS289Lh67bNt5adebTiIdhNMhfqdD6j/pvREN5h9HRafejuyJ+5dHSjMW+VyuEEyeWkfT8MIbnnfkhtO0Tq7X84nhrg8PDw/TJHPqlVOhXGPCOt/d7f504/zSKKHqjSKFbHhkWLoVDFXPd15mzrR7hudSkDoj181zdHV2KAxjd8gJlVl3oqVRO1lGdHTGJvuydE8a1hHdozoyGkgH5T+PG4bDKcTOqR7CpxHvMG5dtNgZ7qV6O9DgPvo7femMcAcWsl4/h7EenWPj+jrj3f0/inxGBtigrauzmxSt6qOHbqxeYpxGdXT94N78vnRPBzCWnBDjbD51zqdL5SbdHufUo4xkLGtLUcI+J3Ao799sn8K+BhMxu9NOueBdvXmn84oOPavmE9we4M4h5HsWRoKHEWRZbDqFERozX0Cu9HWEHHluSSH9fxYLaRqqLKnc+VsaMD6NXrqDCJNOT6jl/AEFBRgJezeGSZf7YgRp5Jx1dnR3PE70a5pNX37P+jq5T5q6lELS0NG6L3rdZ1jhVxoif3d+HHosA15F498PMVQ5Cd0ZcPqZfKR40Uk+ZyNuA2xabS+cqs50MfV0S9yxZY7cRo7R+xNMD1FB5yS6iensQzrnpbEflTeLFEZelpITzR4c56E7Lwvzu63qCIpXV6Tx7nLd+5iVgzPqt+mjn919IzTs0jmCDnnasdj5dMaja2MkrEvPOX2W0QH9ctt2BnzPfiWKHSlsGlf6zv+OEnJSdsT7kXFxMfDooiPkLu9LWlM+Onlh4pF0qA1pOr00Sp2s+H5oSOOcxp/+uB3uzRU9CYhysyA0UleH2Jf6NYo40s50jszOo9PHru6uXsuax9d2J+vJFFsCmu12O82ljSbLHX114CJt5UjP3a999tnlH5torurX9y4Zglx+ekj0YZSxhHTyb6nYoRziFJbyxXnvS8pI0fnN99jQHtK/Q+kB8aVTHdVlw4ORGQl/N7/U3ee6HXn5ehoBK+S+lIbpzvvc33QKHf0pv+mEzR/X43RCyv7IiB8izzlW1De63rWbdPNM9i+NX4KYzjF0crhvjDr6RzrXyWfaINe9NB+X19Ihdw7aEYxR/Cgy7mjqeGAbNaLxNTan6gVO4bUNvJf38l7ey3v5/0552Slk7+W9vJf38l7+f13encJ7eS/v5b28l6m8O4X38l7ey3t5L1N5dwrv5b28l/fyXqby7hTey3t5L+/lvUzl3Sm8l/fyXt7Le5nKu1N4L+/lvbyX9zKVd6fwXt7Le3kv72Uq707hvbyX9/Je3stU/h+wt4EGIJ7uwAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check false positive and false negative test cases\n",
    "# save indices for fp and fn in a separate list\n",
    "fp_cases = []\n",
    "fn_cases = []\n",
    "for cur_ind in range(len(test_dataset)):\n",
    "    if test_pred[cur_ind] == 1 and test_dataset.labels[cur_ind]==0:\n",
    "        fp_cases.append(cur_ind)\n",
    "    elif test_pred[cur_ind] == 0 and test_dataset.labels[cur_ind]==1:\n",
    "        fn_cases.append(cur_ind)\n",
    "\n",
    "# plot fp cases:\n",
    "imagesInARow=min(10,len(fp_cases))\n",
    "rows=int(np.ceil(len(fp_cases)/imagesInARow))\n",
    "\n",
    "\n",
    "if imagesInARow==1:\n",
    "    fig = plt.figure()\n",
    "    plt.title('False positive cases')\n",
    "    plt.imshow(orig_img, cmap='gray')\n",
    "    plt.axis('off')\n",
    "else:\n",
    "    fig, axs = plt.subplots(rows, imagesInARow)\n",
    "    fig.suptitle('False positive cases')\n",
    "    row=0\n",
    "    col=0\n",
    "\n",
    "    for i, idx in enumerate(fp_cases):\n",
    "        orig_img = train_dataset.imgs[idx]\n",
    "\n",
    "        if col>=imagesInARow:\n",
    "            row+=1\n",
    "            col=0\n",
    "\n",
    "        if rows > 1:\n",
    "            axs[row,col].imshow(orig_img, cmap='gray')\n",
    "            axs[row,col].axis('off')\n",
    "        else:\n",
    "            axs[col].imshow(orig_img, cmap='gray')\n",
    "            axs[col].axis('off')\n",
    "        col+=1\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "# plot fn cases:\n",
    "imagesInARow=min(10,len(fn_cases))\n",
    "rows=int(np.ceil(len(fn_cases)/imagesInARow))\n",
    "\n",
    "if imagesInARow==1:\n",
    "    fig = plt.figure()\n",
    "    plt.title('False negative cases')\n",
    "    plt.imshow(orig_img, cmap='gray')\n",
    "    plt.axis('off')\n",
    "else:\n",
    "    fig, axs = plt.subplots(rows, imagesInARow)\n",
    "    fig.suptitle('False negative cases',y=0.8)\n",
    "    row=0\n",
    "    col=0\n",
    "\n",
    "    for i, idx in enumerate(fn_cases):\n",
    "        orig_img = train_dataset.imgs[idx]\n",
    "\n",
    "        if col>=imagesInARow:\n",
    "            row+=1\n",
    "            col=0\n",
    "\n",
    "        if rows > 1:\n",
    "            axs[row,col].imshow(orig_img, cmap='gray')\n",
    "            axs[row,col].axis('off')\n",
    "        else:\n",
    "            axs[col].imshow(orig_img, cmap='gray')\n",
    "            axs[col].axis('off')\n",
    "            \n",
    "        col+=1\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Optional Tasks\n",
    "When you have finished everything else, you can continue working on one of the following tasks, for example, or come up with something of your own\n",
    "## Task 1: Multi-label classification\n",
    "So far we have only dealt with a binary classification problem. However, we often also have to deal with multi-label problems. Multi-label classification is a type of classification where each data point can belong to multiple classes simultaneously, meaning the model predicts a set of labels rather than just one. For example, a photo might be labeled as both \"cat\" and \"dog\" if it contains both animals.\n",
    "You can use and adapt the code you have developed here to solve a multi-label classification problem using the ChestMNIST from the [MedMnist](https://medmnist.com/) data set.\n",
    "\n",
    "## Task 2: Large Language Models\n",
    "Use Large Language Models (LLMs) to generate radiological reports for the PneumoniaMNIST dataset. See for example  https://huggingface.co/microsoft/Phi-3-vision-128k-instruct\n",
    "\n",
    "## Task 3: Generative data augmentation\n",
    "For generative data augmentation you can for example train a conditional Generative Adversarial Network (GAN) to generate new healty and new pneumonia images, see for example: https://github.com/qbxlvnf11/conditional-GAN/blob/main/conditional-GAN-generating-fashion-mnist.ipynb \n",
    "GANs are relatively difficult to train, which is why you can also consider using a pre-trained GAN to achieve better results. You can take a look at this: GANs are relatively difficult to train, which is why you can also consider using a pre-trained GAN to achieve better results. You can take a look at this: https://github.com/Project-MONAI/tutorials/blob/main/modules/mednist_GAN_tutorial.ipynb.\n",
    "\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "namt",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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