From a0a2d0d3eda572e9e0ab788f3375d4bebc784a5b Mon Sep 17 00:00:00 2001
From: gagewrye <95107220+gagewrye@users.noreply.github.com>
Date: Fri, 22 Nov 2024 18:46:02 -0800
Subject: [PATCH] Formatting

---
 .../Schrodinger Diffusion/i2sb/losses.py      |    2 -
 .../Schrodinger Diffusion/models/__init__.py  |    4 +-
 .../models/how_to_build_model.ipynb           |  128 -
 .../models/model_testing.ipynb                | 2419 -----------------
 .../Schrodinger Diffusion/models/models.py    |  226 +-
 5 files changed, 9 insertions(+), 2770 deletions(-)
 delete mode 100755 Super Resolution/Schrodinger Diffusion/models/how_to_build_model.ipynb
 delete mode 100755 Super Resolution/Schrodinger Diffusion/models/model_testing.ipynb

diff --git a/Super Resolution/Schrodinger Diffusion/i2sb/losses.py b/Super Resolution/Schrodinger Diffusion/i2sb/losses.py
index 2ce20be..308c26b 100644
--- a/Super Resolution/Schrodinger Diffusion/i2sb/losses.py	
+++ b/Super Resolution/Schrodinger Diffusion/i2sb/losses.py	
@@ -2,8 +2,6 @@
 Helpers for various likelihood-based losses. These are ported from the original
 Ho et al. diffusion models codebase:
 https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/utils.py
-
-
 """
 
 import numpy as np
diff --git a/Super Resolution/Schrodinger Diffusion/models/__init__.py b/Super Resolution/Schrodinger Diffusion/models/__init__.py
index 327dd39..1358891 100755
--- a/Super Resolution/Schrodinger Diffusion/models/__init__.py	
+++ b/Super Resolution/Schrodinger Diffusion/models/__init__.py	
@@ -1,2 +1,2 @@
-from .models import ResNet_UNet, ResNet_FC, SegmentModelWrapper, ResNet_UNet_Diffusion
-from .loss import JaccardLoss, FocalLoss
\ No newline at end of file
+from .models import ResNet_UNet, ResNet_UNet_Diffusion
+from .loss import JaccardLoss, LandmassLoss
\ No newline at end of file
diff --git a/Super Resolution/Schrodinger Diffusion/models/how_to_build_model.ipynb b/Super Resolution/Schrodinger Diffusion/models/how_to_build_model.ipynb
deleted file mode 100755
index 1afd545..0000000
--- a/Super Resolution/Schrodinger Diffusion/models/how_to_build_model.ipynb	
+++ /dev/null
@@ -1,128 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Example of building a model with a wrapper for evaluation.\n",
-    "\n",
-    "The wrapper takes a raw image in the form of a numpy array and outputs 1/0 array for mangrove/nonmangrove classification."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import torch\n",
-    "from models import *"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = ResNet_UNet()\n",
-    "model.load_state_dict(torch.load('ResNet18_UNet.pth', map_location='cpu', weights_only=True))\n",
-    "wrapper = SegmentModelWrapper(model)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([1, 1, 256, 256])\n",
-      "tensor([[[[1, 1, 1,  ..., 1, 0, 0],\n",
-      "          [1, 1, 1,  ..., 1, 0, 0],\n",
-      "          [1, 1, 1,  ..., 1, 1, 1],\n",
-      "          ...,\n",
-      "          [1, 1, 1,  ..., 0, 0, 0],\n",
-      "          [1, 1, 1,  ..., 0, 0, 0],\n",
-      "          [1, 1, 1,  ..., 0, 0, 0]]]], dtype=torch.uint8)\n",
-      "\n",
-      "\n",
-      "\n",
-      "torch.Size([4, 1, 256, 256])\n",
-      "tensor([[[[1, 1, 1,  ..., 1, 0, 0],\n",
-      "          [1, 1, 1,  ..., 1, 0, 0],\n",
-      "          [1, 1, 1,  ..., 1, 1, 1],\n",
-      "          ...,\n",
-      "          [1, 1, 1,  ..., 0, 0, 0],\n",
-      "          [1, 1, 1,  ..., 0, 0, 0],\n",
-      "          [1, 1, 1,  ..., 0, 0, 0]]],\n",
-      "\n",
-      "\n",
-      "        [[[1, 1, 1,  ..., 1, 0, 0],\n",
-      "          [1, 1, 1,  ..., 1, 0, 0],\n",
-      "          [1, 1, 1,  ..., 1, 1, 1],\n",
-      "          ...,\n",
-      "          [1, 1, 1,  ..., 0, 0, 0],\n",
-      "          [1, 1, 1,  ..., 0, 0, 0],\n",
-      "          [1, 1, 1,  ..., 0, 0, 0]]],\n",
-      "\n",
-      "\n",
-      "        [[[1, 1, 1,  ..., 1, 0, 0],\n",
-      "          [1, 1, 1,  ..., 1, 0, 0],\n",
-      "          [1, 1, 1,  ..., 1, 1, 1],\n",
-      "          ...,\n",
-      "          [1, 1, 1,  ..., 0, 0, 0],\n",
-      "          [1, 1, 1,  ..., 0, 0, 0],\n",
-      "          [1, 1, 1,  ..., 0, 0, 0]]],\n",
-      "\n",
-      "\n",
-      "        [[[1, 1, 1,  ..., 1, 0, 0],\n",
-      "          [1, 1, 1,  ..., 1, 0, 0],\n",
-      "          [1, 1, 1,  ..., 1, 1, 1],\n",
-      "          ...,\n",
-      "          [1, 1, 1,  ..., 0, 0, 0],\n",
-      "          [1, 1, 1,  ..., 0, 0, 0],\n",
-      "          [1, 1, 1,  ..., 0, 0, 0]]]], dtype=torch.uint8)\n"
-     ]
-    }
-   ],
-   "source": [
-    "test_input = np.random.rand(3, 256, 256) # C, H, W\n",
-    "batch_test = np.random.rand(4, 3, 256, 256) # B, C, H, W\n",
-    "\n",
-    "wrapper.eval()\n",
-    "output = wrapper(test_input)\n",
-    "print(output.shape)\n",
-    "print(output)\n",
-    "output = wrapper(batch_test)\n",
-    "print(\"\\n\\n\")\n",
-    "print(output.shape)\n",
-    "print(output)\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "mangrove",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.14"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Super Resolution/Schrodinger Diffusion/models/model_testing.ipynb b/Super Resolution/Schrodinger Diffusion/models/model_testing.ipynb
deleted file mode 100755
index 123a74f..0000000
--- a/Super Resolution/Schrodinger Diffusion/models/model_testing.ipynb	
+++ /dev/null
@@ -1,2419 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import pickle\n",
-    "import torch\n",
-    "import os\n",
-    "import gc\n",
-    "from torch.utils.data import DataLoader, Dataset\n",
-    "import torch.nn as nn\n",
-    "from torch.nn import BCEWithLogitsLoss\n",
-    "from torch.optim import Adam\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from tqdm import tqdm\n",
-    "from .models import *\n",
-    "from .loss import *\n",
-    "from torchvision.models import resnet18, resnet50, ResNet18_Weights, ResNet50_Weights\n",
-    "from torchgeo.models import get_weight"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Load and prep data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "TEST_SPLIT = 0.15\n",
-    "NUM_CHANNELS = 9\n",
-    "NUM_CLASSES = 1\n",
-    "INIT_LR = 0.001\n",
-    "NUM_EPOCHS = 30\n",
-    "BATCH_SIZE = 64\n",
-    "INPUT_IMAGE_SIZE = 128\n",
-    "THRESHOLD = 0.5\n",
-    "# define the path to the base output directory\n",
-    "BASE_OUTPUT = \"output\"\n",
-    "\n",
-    "# Pull data from data folder\n",
-    "cwd = os.getcwd()\n",
-    "with open(cwd + '/data/128px_images.pkl', 'rb') as handle:\n",
-    "    images = pickle.load(handle)\n",
-    "with open(cwd + '/data/128px_labels.pkl', 'rb') as handle:\n",
-    "    labels = pickle.load(handle)\n",
-    "with open(cwd + '/data/128_mlabels.pkl', 'rb') as handle:\n",
-    "    m_tiles = pickle.load(handle)\n",
-    "\n",
-    "# Setup the device to be used for training and evaluation\n",
-    "if torch.cuda.is_available():\n",
-    "    DEVICE = torch.device(\"cuda\")\n",
-    "    x = torch.ones(1, device=DEVICE)\n",
-    "    print(\"Using CUDA device.\")\n",
-    "elif torch.backends.mps.is_available():\n",
-    "    DEVICE = torch.device(\"mps\")\n",
-    "    x = torch.ones(1, device=DEVICE)\n",
-    "    print(\"Using Apple Metal Performance Shaders (MPS) device.\")\n",
-    "else:\n",
-    "    DEVICE = torch.device(\"cpu\")\n",
-    "    print(\"No GPU found. Defaulting to CPU.\")\n",
-    "\n",
-    "# partition the data into training and testing splits \n",
-    "# uses 85% of the data for training and the remaining 15% for testing\n",
-    "trainImages, testImages, trainMasks, testMasks = train_test_split(images, labels,\n",
-    "                         test_size=TEST_SPLIT, random_state=42)\n",
-    "image = trainImages[3000]\n",
-    "\n",
-    "rgb = image[:, :, :3]\n",
-    "print(rgb[:,:,:1])\n",
-    "\n",
-    "\n",
-    "print(f\"Image shape: {image.shape}\")\n",
-    "for channel in range(9):\n",
-    "    plt.imshow(image[:, :, channel])\n",
-    "    plt.axis('off')  # To hide axis\n",
-    "    plt.show()\n",
-    "\n",
-    "# NOTE: we are not using transforms, because there are too many channels for standard PIL transforms\n",
-    "trainDS = Dataset(images=trainImages, labels=trainMasks)                            \n",
-    "testDS = Dataset(images=testImages, labels=testMasks)\n",
-    "print(f\"[INFO] found {len(trainDS)} examples in the training set...\")\n",
-    "print(f\"[INFO] found {len(testDS)} examples in the test set...\")\n",
-    "\n",
-    "# calculate steps per epoch for training and test set #config\n",
-    "TRAIN_STEPS = len(trainDS) // BATCH_SIZE\n",
-    "TEST_STEPS = len(testDS) // BATCH_SIZE\n",
-    "\n",
-    "# create the training and test data loaders #config\n",
-    "trainLoader = DataLoader(trainDS, shuffle=True,\n",
-    "                         batch_size=BATCH_SIZE,\n",
-    "                         num_workers=0)\n",
-    "testLoader = DataLoader(testDS, shuffle=False,\n",
-    "                        batch_size=BATCH_SIZE,\n",
-    "                        num_workers=0)\n",
-    "\n",
-    "\n",
-    "del trainImages, testImages, trainMasks, testMasks, trainDS, testDS\n",
-    "gc.collect()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Training Pipeline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def evaluate(model: nn.Module, dataloader: DataLoader, loss_func):\n",
-    "    model.eval()\n",
-    "    total_loss = 0\n",
-    "    total_TP = 0\n",
-    "    total_FP = 0\n",
-    "    total_FN = 0\n",
-    "    total_TN = 0\n",
-    "\n",
-    "    with torch.no_grad():\n",
-    "        for (x, y) in dataloader:\n",
-    "            x = x.permute(0, 3, 1, 2).to(DEVICE)\n",
-    "            y = y.permute(0, 3, 1, 2).to(DEVICE)\n",
-    "            y = y.float()\n",
-    "            \n",
-    "            pred = model(x)\n",
-    "            if isinstance(pred, tuple):\n",
-    "                pred = pred[0]\n",
-    "            loss = loss_func(pred, y)\n",
-    "            total_loss += loss.item()\n",
-    "\n",
-    "            pred = torch.sigmoid(pred).view(-1)\n",
-    "            y = y.view(-1)\n",
-    "            \n",
-    "            TP = (pred * y).sum().item()\n",
-    "            FP = ((1 - y) * pred).sum().item()\n",
-    "            FN = (y * (1 - pred)).sum().item()\n",
-    "            TN = ((1 - y) * (1 - pred)).sum().item()\n",
-    "\n",
-    "            total_TP += TP\n",
-    "            total_FP += FP\n",
-    "            total_FN += FN\n",
-    "            total_TN += TN\n",
-    "\n",
-    "    avg_loss = total_loss / len(dataloader)\n",
-    "    precision = total_TP / (total_TP + total_FP) if (total_TP + total_FP) > 0 else 0\n",
-    "    recall = total_TP / (total_TP + total_FN) if (total_TP + total_FN) > 0 else 0\n",
-    "    f1_score = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0\n",
-    "    iou = total_TP / (total_TP + total_FP + total_FN) if (total_TP + total_FP + total_FN) > 0 else 0\n",
-    "    accuracy = (total_TP + total_TN) / (total_TP + total_FP + total_FN + total_TN) if (total_TP + total_FP + total_FN + total_TN) > 0 else 0\n",
-    "    specificity = total_TN / (total_TN + total_FP) if (total_TN + total_FP) > 0 else 0\n",
-    "\n",
-    "    metrics = {\n",
-    "        'Loss': avg_loss,\n",
-    "        'Precision': precision,\n",
-    "        'Recall': recall,\n",
-    "        'f1_score': f1_score,\n",
-    "        'IOU': iou,\n",
-    "        'Accuracy': accuracy,\n",
-    "        'Specificity': specificity\n",
-    "    }\n",
-    "\n",
-    "    return metrics\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def train(model, trainLoader : DataLoader, testLoader : DataLoader, lossFunc, NUM_EPOCHS=NUM_EPOCHS, print_all_epochs = False):\n",
-    "  opt = Adam(model.parameters(), lr=INIT_LR)\n",
-    "  # loop over epochs #config\n",
-    "  print(\"[INFO] training the network...\")\n",
-    "  training_loss = []\n",
-    "  all_metrics = []\n",
-    "\n",
-    "  for e in tqdm(range(NUM_EPOCHS)):\n",
-    "    # set the model in training mode\n",
-    "    model.train()\n",
-    "    totalTrainLoss = 0\n",
-    "\n",
-    "    # loop over the training set\n",
-    "    for (i, (x, y)) in enumerate(trainLoader):\n",
-    "      # send the input to the device\n",
-    "      x = x.permute(0, 3, 1, 2).to(DEVICE)\n",
-    "      y = y.permute(0, 3, 1, 2).to(DEVICE)\n",
-    "      y = y.float()\n",
-    "\n",
-    "      # perform a forward pass and calculate the training loss\n",
-    "      pred = model(x)\n",
-    "      if isinstance(pred, tuple):\n",
-    "        pred = pred[0]\n",
-    "      loss = lossFunc(pred, y)\n",
-    "\n",
-    "      # first, zero out any previously accumulated gradients, then\n",
-    "      # perform backpropagation, and then update model parameters\n",
-    "      opt.zero_grad()\n",
-    "      loss.backward()\n",
-    "      opt.step()\n",
-    "\n",
-    "      # add the loss to the total training loss so far\n",
-    "      totalTrainLoss += loss.item()\n",
-    "    # calculate the average training\n",
-    "    avgTrainLoss = totalTrainLoss / TRAIN_STEPS\n",
-    "    training_loss.append(avgTrainLoss)\n",
-    "\n",
-    "    # Evaluate on test dataset\n",
-    "    metrics = evaluate(model, testLoader, lossFunc)\n",
-    "    all_metrics.append(metrics)\n",
-    "    avgTestLoss = metrics['Loss']\n",
-    "\n",
-    "    if (e + 1) % 5 == 0 or e == 0 or print_all_epochs:\n",
-    "      # print the model training and validation information\n",
-    "      print(\"EPOCH: {}/{}\".format(e + 1, NUM_EPOCHS)) #config\n",
-    "      print(\"Train loss: {:.6f}, Test loss: {:.4f}\".format(\n",
-    "          avgTrainLoss, avgTestLoss))\n",
-    "      print(\"\\nValidation Metrics:\")\n",
-    "      for k, v in metrics.items():\n",
-    "          if k != 'Loss':\n",
-    "            print(f\"{k}: {v}\")\n",
-    "      print(\"\\n\")\n",
-    "  return training_loss, all_metrics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_losses(title, training_loss, validation_loss, training_time=None, y_max=0.3):\n",
-    "  # scale losses to fit graph\n",
-    "  valid_loss = [min(x,y_max) for x in validation_loss]\n",
-    "  train_loss = [min(x, y_max) for x in training_loss]\n",
-    "\n",
-    "\n",
-    "  plt.figure()\n",
-    "  plt.plot(np.arange(1, NUM_EPOCHS+1), train_loss, label=\"train_loss\")\n",
-    "  plt.plot(np.arange(1, NUM_EPOCHS+1), valid_loss, label=\"valid_loss\")\n",
-    "  plt.title(title)\n",
-    "  plt.xlabel(\"Epoch\")\n",
-    "  plt.ylabel(\"Loss\")\n",
-    "  plt.legend(loc=\"upper right\")\n",
-    "  if training_time is not None:\n",
-    "    plt.text(0, 0.3, f\"Training Time: {training_time}\")\n",
-    "\n",
-    "  step = y_max / 10\n",
-    "  yticks = np.arange(0, y_max+step, step)  # Generate ticks from 0.025 to 0.3 with step 0.025\n",
-    "  plt.yticks(yticks)\n",
-    "\n",
-    "  xticks = np.arange(2, NUM_EPOCHS+2, 2)  # Generate ticks from 0 to num_epochs with step 2\n",
-    "  plt.xticks(xticks)\n",
-    "  \n",
-    "  plt.show()\n",
-    "\n",
-    "def plot_metrics(title: str, metric_dict: dict, metrics: list = ['Precision', 'Recall', 'IOU']):\n",
-    "    plt.figure()\n",
-    "    for metric in metrics:\n",
-    "        plt.plot(np.arange(0, NUM_EPOCHS), [x[metric] for x in metric_dict], label=metric)\n",
-    "    plt.title(title)\n",
-    "    plt.xlabel(\"Epoch\")\n",
-    "    plt.ylabel(\"Value\")\n",
-    "    plt.legend(loc=\"lower right\")\n",
-    "\n",
-    "    yticks = np.arange(0.0, 1.1, 0.1)\n",
-    "    plt.yticks(yticks)\n",
-    "\n",
-    "    xticks = np.arange(2, NUM_EPOCHS+2, 2)\n",
-    "    plt.xticks(xticks)\n",
-    "    \n",
-    "    plt.show()\n",
-    "\n",
-    "def plot_loss_comparison(title, training_losses1, training_losses2, validation_losses1, validation_losses2, compare1 = \"Satellite\", compare2 = \"ImageNet\", y_max=0.3):\n",
-    "    # scale losses to fit graph\n",
-    "    valid_loss_sat = [min(x, y_max) for x in validation_losses1]\n",
-    "    train_loss_sat = [min(x, y_max) for x in training_losses1]\n",
-    "    valid_loss_img = [min(x, y_max) for x in validation_losses2]\n",
-    "    train_loss_img = [min(x, y_max) for x in training_losses2]\n",
-    "    \n",
-    "    plt.figure()\n",
-    "    plt.plot(np.arange(0, NUM_EPOCHS), train_loss_sat, label=f\"Training loss {compare1}\", color='orange')\n",
-    "    plt.plot(np.arange(0, NUM_EPOCHS), valid_loss_sat, label=f\"Validation loss {compare1}\", color='orange', linestyle='dashed')\n",
-    "    plt.plot(np.arange(0, NUM_EPOCHS), train_loss_img, label=f\"Training loss {compare2}\", color='teal')\n",
-    "    plt.plot(np.arange(0, NUM_EPOCHS), valid_loss_img, label=f\"Validation loss {compare2}\", color='teal', linestyle='dashed')\n",
-    "    plt.title(title)\n",
-    "    plt.xlabel(\"Epoch\")\n",
-    "    plt.ylabel(\"Loss\")\n",
-    "    plt.legend(loc=\"upper right\")\n",
-    "    \n",
-    "    yticks = np.arange(0.025, 0.325, 0.025)  # Generate ticks from 0.025 to 0.3 with step 0.025\n",
-    "    plt.yticks(yticks)\n",
-    "    \n",
-    "    xticks = np.arange(2, NUM_EPOCHS+2, 2)  # Generate ticks from 0 to num_epochs with step 2\n",
-    "    plt.xticks(xticks)\n",
-    "    \n",
-    "    plt.show()\n",
-    "\n",
-    "def plot_comparison_metrics(title, metrics: list[list[dict]], titles: list[str]):\n",
-    "    metrics_wanted = ['Precision', 'Recall', 'IOU']\n",
-    "    extracted_metrics = []\n",
-    "    \n",
-    "    for i in range(len(titles)):\n",
-    "        metrics_add = []\n",
-    "        for k in metrics[i][-1]:\n",
-    "            if k in metrics_wanted:\n",
-    "                metrics_add.append(metrics[i][-1][k])\n",
-    "        extracted_metrics.append(metrics_add)\n",
-    "\n",
-    "    print(extracted_metrics)\n",
-    "\n",
-    "    # Create bar positions\n",
-    "    bar_width = 0.8 / len(titles)  # Adjust bar width based on number of titles\n",
-    "    r = np.arange(len(metrics_wanted))\n",
-    "    \n",
-    "    # Plotting the bar chart\n",
-    "    plt.figure(figsize=(10, 6))\n",
-    "    \n",
-    "    for i in range(len(titles)):\n",
-    "        plt.bar([x + i * bar_width for x in r], extracted_metrics[i], width=bar_width, edgecolor='grey', label=titles[i])\n",
-    "    \n",
-    "    # Adding labels\n",
-    "    plt.xlabel('Metrics', fontweight='bold')\n",
-    "    plt.ylabel('Values')\n",
-    "    plt.title(title)\n",
-    "    plt.xticks([r + bar_width * (len(titles) / 2) for r in range(len(metrics_wanted))], metrics_wanted)\n",
-    "    plt.ylim(0, 1.1)\n",
-    "    \n",
-    "    plt.legend()\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Model Comparisons"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Loss Comparisons"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We compare BCE, Jaccard, and Focal loss. Jaccard was found to perform the best overall."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[INFO] training the network...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  3%|▎         | 1/30 [00:03<01:48,  3.75s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 1/30\n",
-      "Train loss: 0.286346, Test loss: 0.3842\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.17238836271200705\n",
-      "Recall: 0.06542655167871816\n",
-      "f1_score: 0.0948533959754596\n",
-      "IOU: 0.04978797735307396\n",
-      "Accuracy: 0.8660676090265909\n",
-      "Specificity: 0.9622616450568134\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 17%|█▋        | 5/30 [00:14<01:09,  2.78s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 5/30\n",
-      "Train loss: 0.079786, Test loss: 0.0645\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.8080546379886181\n",
-      "Recall: 0.8433665151101094\n",
-      "f1_score: 0.8253330445480618\n",
-      "IOU: 0.7026102511162626\n",
-      "Accuracy: 0.9617119354593539\n",
-      "Specificity: 0.9759306959839719\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 33%|███▎      | 10/30 [00:30<01:04,  3.21s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 10/30\n",
-      "Train loss: 0.064878, Test loss: 0.0591\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.8211500293860302\n",
-      "Recall: 0.8301689234450625\n",
-      "f1_score: 0.8256348474818359\n",
-      "IOU: 0.7030478090323493\n",
-      "Accuracy: 0.9623899678573371\n",
-      "Specificity: 0.9782758332585296\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 50%|█████     | 15/30 [00:47<00:50,  3.37s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 15/30\n",
-      "Train loss: 0.059055, Test loss: 0.0551\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.840960261563625\n",
-      "Recall: 0.8611069315554296\n",
-      "f1_score: 0.8509143626322772\n",
-      "IOU: 0.740514314130247\n",
-      "Accuracy: 0.9676352258082227\n",
-      "Specificity: 0.9804342027791539\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 67%|██████▋   | 20/30 [01:05<00:34,  3.47s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 20/30\n",
-      "Train loss: 0.057861, Test loss: 0.0536\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.8523134601173005\n",
-      "Recall: 0.8297773853223175\n",
-      "f1_score: 0.8408944574290393\n",
-      "IOU: 0.7254684121032575\n",
-      "Accuracy: 0.9663200864605096\n",
-      "Specificity: 0.9827251828441308\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 83%|████████▎ | 25/30 [01:20<00:15,  3.13s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 25/30\n",
-      "Train loss: 0.054345, Test loss: 0.0514\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.8516368923132257\n",
-      "Recall: 0.8760006718712615\n",
-      "f1_score: 0.863646988607702\n",
-      "IOU: 0.760016456109473\n",
-      "Accuracy: 0.9703313042689506\n",
-      "Specificity: 0.9816647777078205\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 30/30 [01:36<00:00,  3.23s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 30/30\n",
-      "Train loss: 0.051109, Test loss: 0.0499\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.8566534713662922\n",
-      "Recall: 0.8550574675948277\n",
-      "f1_score: 0.8558547254215074\n",
-      "IOU: 0.7480297689791253\n",
-      "Accuracy: 0.9691069196023848\n",
-      "Specificity: 0.9828095359434713\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "loss = BCEWithLogitsLoss()\n",
-    "\n",
-    "sat_resnet18_UNet_BCE = ResNet_UNet(input_image_size=INPUT_IMAGE_SIZE).to(DEVICE)\n",
-    "sat_resnet18_train_BCE, sat_resnet18_metrics_BCE = train(sat_resnet18_UNet_BCE, trainLoader, testLoader, loss)\n",
-    "sat_resnet18_valid_BCE = [x['Loss'] for x in sat_resnet18_metrics_BCE]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[INFO] training the network...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  3%|▎         | 1/30 [00:03<01:37,  3.35s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 1/30\n",
-      "Train loss: 0.642387, Test loss: 0.9610\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.12349163868835103\n",
-      "Recall: 0.054197376628036885\n",
-      "f1_score: 0.07533299501366235\n",
-      "IOU: 0.039140794131396824\n",
-      "Accuracy: 0.857293438689954\n",
-      "Specificity: 0.9537824344985317\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 17%|█▋        | 5/30 [00:16<01:22,  3.30s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 5/30\n",
-      "Train loss: 0.224126, Test loss: 0.2108\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.85703547646791\n",
-      "Recall: 0.9113955877954422\n",
-      "f1_score: 0.883380039653836\n",
-      "IOU: 0.7911196924869395\n",
-      "Accuracy: 0.9741894194345934\n",
-      "Specificity: 0.9817338639504584\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 33%|███▎      | 10/30 [00:33<01:06,  3.32s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 10/30\n",
-      "Train loss: 0.209889, Test loss: 0.1975\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.8612532730677893\n",
-      "Recall: 0.9238227197119055\n",
-      "f1_score: 0.8914414224430838\n",
-      "IOU: 0.8041446257244038\n",
-      "Accuracy: 0.9758662109322406\n",
-      "Specificity: 0.9821190421061318\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 50%|█████     | 15/30 [00:49<00:50,  3.36s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 15/30\n",
-      "Train loss: 0.216988, Test loss: 0.2019\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.8706533169371493\n",
-      "Recall: 0.9082630788086669\n",
-      "f1_score: 0.8890606260164073\n",
-      "IOU: 0.800278257154956\n",
-      "Accuracy: 0.9756873926455739\n",
-      "Specificity: 0.983788172273194\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 67%|██████▋   | 20/30 [01:07<00:34,  3.45s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 20/30\n",
-      "Train loss: 0.191392, Test loss: 0.2230\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.8075334448489832\n",
-      "Recall: 0.9571924676028928\n",
-      "f1_score: 0.8760169783792886\n",
-      "IOU: 0.7793863088039609\n",
-      "Accuracy: 0.970938735274208\n",
-      "Specificity: 0.9725902980363036\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 83%|████████▎ | 25/30 [01:24<00:17,  3.46s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 25/30\n",
-      "Train loss: 0.183049, Test loss: 0.1845\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.8573944802488759\n",
-      "Recall: 0.945616237305446\n",
-      "f1_score: 0.8993470026613676\n",
-      "IOU: 0.8171031240872276\n",
-      "Accuracy: 0.9772971757451708\n",
-      "Specificity: 0.9811035222754798\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 30/30 [01:42<00:00,  3.40s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 30/30\n",
-      "Train loss: 0.181101, Test loss: 0.1780\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.8892722772221734\n",
-      "Recall: 0.9179440480421696\n",
-      "f1_score: 0.90338072155872\n",
-      "IOU: 0.8237870146171179\n",
-      "Accuracy: 0.9789392068402695\n",
-      "Specificity: 0.9862675474712086\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "loss = JaccardLoss()\n",
-    "\n",
-    "sat_resnet18_UNet_jaccard = ResNet_UNet(input_image_size=INPUT_IMAGE_SIZE).to(DEVICE)\n",
-    "sat_resnet18_train_jaccard, sat_resnet18_metrics_jaccard = train(sat_resnet18_UNet_jaccard, trainLoader, testLoader, loss)\n",
-    "sat_resnet18_valid_jaccard = [x['Loss'] for x in sat_resnet18_metrics_jaccard]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[INFO] training the network...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  3%|▎         | 1/30 [00:03<01:50,  3.82s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 1/30\n",
-      "Train loss: 0.158965, Test loss: 0.1231\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.1332952115372917\n",
-      "Recall: 0.01765086707802495\n",
-      "f1_score: 0.031173728825085425\n",
-      "IOU: 0.01583366154824937\n",
-      "Accuracy: 0.8823237718722378\n",
-      "Specificity: 0.9862109935757145\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 17%|█▋        | 5/30 [00:18<01:35,  3.82s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 5/30\n",
-      "Train loss: 0.120989, Test loss: 0.1194\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.9550611176931455\n",
-      "Recall: 0.20452678137783992\n",
-      "f1_score: 0.33690516532191533\n",
-      "IOU: 0.20257724231771068\n",
-      "Accuracy: 0.913645753860586\n",
-      "Specificity: 0.9988437504843879\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 33%|███▎      | 10/30 [00:37<01:14,  3.72s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 10/30\n",
-      "Train loss: 0.120640, Test loss: 0.1190\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.9395940072156462\n",
-      "Recall: 0.337384952414945\n",
-      "f1_score: 0.4964919382939912\n",
-      "IOU: 0.3302223319844584\n",
-      "Accuracy: 0.9266017879211927\n",
-      "Specificity: 0.9973939917723808\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 50%|█████     | 15/30 [00:56<00:57,  3.86s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 15/30\n",
-      "Train loss: 0.120334, Test loss: 0.1187\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.9652026850162048\n",
-      "Recall: 0.3785119483840619\n",
-      "f1_score: 0.543777286947479\n",
-      "IOU: 0.3734162927644617\n",
-      "Accuracy: 0.9318758701388412\n",
-      "Specificity: 0.9983604786312831\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 67%|██████▋   | 20/30 [01:16<00:40,  4.03s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 20/30\n",
-      "Train loss: 0.120696, Test loss: 0.1187\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.9636043656883803\n",
-      "Recall: 0.41786075940224865\n",
-      "f1_score: 0.5829353846098044\n",
-      "IOU: 0.4113682455117196\n",
-      "Accuracy: 0.9358672192979026\n",
-      "Specificity: 0.9981037638599449\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 83%|████████▎ | 25/30 [01:38<00:21,  4.30s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 25/30\n",
-      "Train loss: 0.119842, Test loss: 0.1184\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.9784183384965172\n",
-      "Recall: 0.4878849539206963\n",
-      "f1_score: 0.6511007490211834\n",
-      "IOU: 0.48269042224518843\n",
-      "Accuracy: 0.9439165393157957\n",
-      "Specificity: 0.9987070313513309\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 30/30 [02:01<00:00,  4.06s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 30/30\n",
-      "Train loss: 0.119848, Test loss: 0.1182\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.9764450329444468\n",
-      "Recall: 0.5688281580644384\n",
-      "f1_score: 0.7188753843303632\n",
-      "IOU: 0.561128383248347\n",
-      "Accuracy: 0.9522809392167774\n",
-      "Specificity: 0.9983513591150851\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "from models import FocalLoss\n",
-    "loss = FocalLoss(alpha=0.25,reduction='mean')\n",
-    "\n",
-    "resnet18_UNet_focal = ResNet_UNet(input_image_size=INPUT_IMAGE_SIZE).to(DEVICE)\n",
-    "resnet18_train_focal, resnet18_metrics_focal = train(resnet18_UNet_focal, trainLoader, testLoader, loss)\n",
-    "resnet18_valid_focal = [x['Loss'] for x in resnet18_metrics_focal]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_metrics(\"ResNet18 UNet BCE\", sat_resnet18_metrics_BCE)\n",
-    "plot_metrics(\"ResNet18 UNet Jaccard\", sat_resnet18_metrics_jaccard)\n",
-    "plot_loss_comparison(\"BCE vs Jaccard Loss\", sat_resnet18_train_BCE, sat_resnet18_train_jaccard, sat_resnet18_valid_BCE, sat_resnet18_valid_jaccard, compare1='BCE', compare2='Jaccard')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Jaccard outperforms Binary Cross Entropy by all metrics even though BCE loss is much lower. This indicates that BCE loss is not as relevant to our desired model performance. \n",
-    "\n",
-    "We have a class imbalance, so we need to weight the classes differently or else the model will always predict majority class. Jaccard does that by emphasizing the IOU."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[0.8566534713662922, 0.8550574675948277, 0.7480297689791253], [0.8892722772221734, 0.9179440480421696, 0.8237870146171179], [0.9764450329444468, 0.5688281580644384, 0.561128383248347]]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Loss Comparison\n",
-    "metrics = []\n",
-    "metrics.append(sat_resnet18_metrics_BCE)\n",
-    "metrics.append(sat_resnet18_metrics_jaccard)\n",
-    "metrics.append(resnet18_metrics_focal)\n",
-    "\n",
-    "titles = ['BCE', 'Jaccard', 'Focal']\n",
-    "\n",
-    "plot_comparison_metrics(\"Loss Comparison using ResNet18 UNet\", metrics, titles)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[INFO] training the network...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  3%|▎         | 1/30 [00:05<02:49,  5.84s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 1/30\n",
-      "Train loss: 0.673210, Test loss: 0.8859\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.11151507922786547\n",
-      "Recall: 0.9927951848270795\n",
-      "f1_score: 0.2005082037117182\n",
-      "IOU: 0.11142490570131859\n",
-      "Accuracy: 0.15080570660499856\n",
-      "Specificity: 0.049643814443344925\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 17%|█▋        | 5/30 [00:28<02:25,  5.81s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 5/30\n",
-      "Train loss: 0.438492, Test loss: 0.3929\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.6418421691438556\n",
-      "Recall: 0.9199972349497549\n",
-      "f1_score: 0.756150753193711\n",
-      "IOU: 0.6079118953805742\n",
-      "Accuracy: 0.9363548810434464\n",
-      "Specificity: 0.938320191207522\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 33%|███▎      | 10/30 [01:00<02:05,  6.27s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 10/30\n",
-      "Train loss: 0.386395, Test loss: 0.3571\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.6971925467083907\n",
-      "Recall: 0.8932412227586152\n",
-      "f1_score: 0.7831336769574487\n",
-      "IOU: 0.643565905414628\n",
-      "Accuracy: 0.946937155563493\n",
-      "Specificity: 0.953388521643196\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 50%|█████     | 15/30 [01:32<01:35,  6.37s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 15/30\n",
-      "Train loss: 0.368363, Test loss: 0.3488\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.6986218117072867\n",
-      "Recall: 0.9065739866773208\n",
-      "f1_score: 0.7891278579928792\n",
-      "IOU: 0.6517020506267774\n",
-      "Accuracy: 0.9480314456105474\n",
-      "Specificity: 0.9530124046341174\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 67%|██████▋   | 20/30 [02:02<00:59,  5.97s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 20/30\n",
-      "Train loss: 0.357496, Test loss: 0.3515\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.7489733023458656\n",
-      "Recall: 0.8334628883945034\n",
-      "f1_score: 0.7889625572977993\n",
-      "IOU: 0.6514766013653375\n",
-      "Accuracy: 0.9521750041039303\n",
-      "Specificity: 0.9664378220438694\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 83%|████████▎ | 25/30 [02:29<00:27,  5.55s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 25/30\n",
-      "Train loss: 0.334581, Test loss: 0.3281\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.7449156478889473\n",
-      "Recall: 0.8757586064288511\n",
-      "f1_score: 0.8050554119240627\n",
-      "IOU: 0.6737177773409042\n",
-      "Accuracy: 0.9545079647023879\n",
-      "Specificity: 0.9639694061965899\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 30/30 [02:56<00:00,  5.89s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 30/30\n",
-      "Train loss: 0.320339, Test loss: 0.3215\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.7445986637279962\n",
-      "Recall: 0.8871447336722723\n",
-      "f1_score: 0.8096454188546232\n",
-      "IOU: 0.6801716326202317\n",
-      "Accuracy: 0.9552566490626591\n",
-      "Specificity: 0.9634400417697692\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "from models import ResNet_FC\n",
-    "loss = JaccardLoss()\n",
-    "\n",
-    "sat_resnet18_FC_jaccard = ResNet_FC(input_image_size=INPUT_IMAGE_SIZE).to(DEVICE)\n",
-    "sat_resnet18_FC_train_jaccard, sat_resnet18_FC_metrics_jaccard = train(sat_resnet18_FC_jaccard, trainLoader, testLoader, loss)\n",
-    "sat_resnet18_FC_valid_jaccard = [x['Loss'] for x in sat_resnet18_FC_metrics_jaccard]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_losses(\"ResNet18 FC Jaccard Loss\", sat_resnet18_FC_train_jaccard, sat_resnet18_FC_valid_jaccard, y_max=0.5)\n",
-    "plot_metrics(\"ResNet18 FC Jaccard Loss\", sat_resnet18_FC_metrics_jaccard)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This is without a UNet and just putting the ResNet output through a fully connected layer, similar to how the 2020 paper setup their architecture. It does not perform as well. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Imagenet vs Satellite Pretrained ResNet18 "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[INFO] training the network...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  3%|▎         | 1/30 [00:04<02:08,  4.43s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 1/30\n",
-      "Train loss: 0.692403, Test loss: 0.9502\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.11232754731440303\n",
-      "Recall: 0.0829327439737497\n",
-      "f1_score: 0.09541757477845021\n",
-      "IOU: 0.05009894742011536\n",
-      "Accuracy: 0.8313402876093876\n",
-      "Specificity: 0.9212586618370882\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 17%|█▋        | 5/30 [00:22<01:54,  4.58s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 5/30\n",
-      "Train loss: 0.248509, Test loss: 0.2240\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.853375897052128\n",
-      "Recall: 0.8977708246149801\n",
-      "f1_score: 0.8750106125586881\n",
-      "IOU: 0.7777945483990936\n",
-      "Accuracy: 0.9724899833618297\n",
-      "Specificity: 0.981467211466041\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 33%|███▎      | 10/30 [00:46<01:31,  4.58s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 10/30\n",
-      "Train loss: 0.234818, Test loss: 0.2285\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.8890937170785222\n",
-      "Recall: 0.8570593562755615\n",
-      "f1_score: 0.8727826906541147\n",
-      "IOU: 0.7742807739180151\n",
-      "Accuracy: 0.9732011278191248\n",
-      "Specificity: 0.9871551279029329\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 50%|█████     | 15/30 [01:08<01:06,  4.41s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 15/30\n",
-      "Train loss: 0.207884, Test loss: 0.1982\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.8545808576615159\n",
-      "Recall: 0.9304993275006432\n",
-      "f1_score: 0.8909257073812935\n",
-      "IOU: 0.8033057057680704\n",
-      "Accuracy: 0.9755621762584138\n",
-      "Specificity: 0.9809763094364661\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 67%|██████▋   | 20/30 [01:29<00:43,  4.32s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 20/30\n",
-      "Train loss: 0.200235, Test loss: 0.1949\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.8861640949514953\n",
-      "Recall: 0.8996613122715684\n",
-      "f1_score: 0.8928616978204146\n",
-      "IOU: 0.8064590449654468\n",
-      "Accuracy: 0.9768417807195955\n",
-      "Specificity: 0.9861147260904838\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 83%|████████▎ | 25/30 [01:50<00:21,  4.25s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 25/30\n",
-      "Train loss: 0.182018, Test loss: 0.1904\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.8487433992036875\n",
-      "Recall: 0.9478395579038618\n",
-      "f1_score: 0.8955584990856459\n",
-      "IOU: 0.8108700174198663\n",
-      "Accuracy: 0.9762873820185808\n",
-      "Specificity: 0.9797052817916413\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 30/30 [02:11<00:00,  4.39s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 30/30\n",
-      "Train loss: 0.183155, Test loss: 0.1803\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.8760664550198718\n",
-      "Recall: 0.9294181308165127\n",
-      "f1_score: 0.9019540277253904\n",
-      "IOU: 0.8214173636619119\n",
-      "Accuracy: 0.9783268498607187\n",
-      "Specificity: 0.9842030499161358\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# This ResNet is pretrained on ImageNet 3-channel RGB images\n",
-    "imagenet_resnet18 = resnet18(weights=ResNet18_Weights.IMAGENET1K_V1).to(DEVICE)\n",
-    "loss = JaccardLoss()\n",
-    "\n",
-    "imagenet_resnet18_unet = ResNet_UNet(ResNet=imagenet_resnet18, input_image_size=INPUT_IMAGE_SIZE).to(DEVICE)\n",
-    "imagenet_resnet18_train, imagenet_resnet18_metrics = train(imagenet_resnet18_unet, trainLoader, testLoader, loss)\n",
-    "imagenet_resnet18_valid = [x['Loss'] for x in imagenet_resnet18_metrics]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#1:49\n",
-    "plot_metrics(\"Imagenet Pretrained ResNet18\", imagenet_resnet18_metrics)\n",
-    "plot_loss_comparison(\"Satellite vs ImageNet ResNet18\", sat_resnet18_train_jaccard, imagenet_resnet18_train, sat_resnet18_valid_jaccard, imagenet_resnet18_valid, compare1='Satellite', compare2='Imagenet')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Imagenet performs slightly worse than the satellite foundational model with the rest of the network the same. The difference is negligible, though. I don't think there is much benefit to using a satellite foundational model based on this, but it would not hurt to use it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "torch.save(sat_resnet18_UNet_jaccard.state_dict(), 'models/sat_resnet18_UNet_jaccard.pth') "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### ResNet50 Backbone UNet"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[INFO] training the network...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  3%|▎         | 1/30 [00:30<14:49, 30.68s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 1/30\n",
-      "Train loss: 0.593423, Test loss: 0.9963\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.08969134562010761\n",
-      "Recall: 0.003831558368772413\n",
-      "f1_score: 0.007349164990814303\n",
-      "IOU: 0.0036881348511719693\n",
-      "Accuracy: 0.8889804404698736\n",
-      "Specificity: 0.995327773835648\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 17%|█▋        | 5/30 [02:25<12:00, 28.82s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 5/30\n",
-      "Train loss: 0.225915, Test loss: 0.2117\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.8500418140506137\n",
-      "Recall: 0.9182545649124266\n",
-      "f1_score: 0.8828325221998667\n",
-      "IOU: 0.7902418748693737\n",
-      "Accuracy: 0.973856881306235\n",
-      "Specificity: 0.9805372920501354\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 33%|███▎      | 10/30 [04:56<10:08, 30.42s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 10/30\n",
-      "Train loss: 0.198286, Test loss: 0.1960\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.8471780581228489\n",
-      "Recall: 0.9423135776182779\n",
-      "f1_score: 0.8922169524406011\n",
-      "IOU: 0.8054076602871652\n",
-      "Accuracy: 0.9755802625425886\n",
-      "Specificity: 0.9795771306738752\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 50%|█████     | 15/30 [07:36<07:55, 31.69s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 15/30\n",
-      "Train loss: 0.185020, Test loss: 0.1789\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.876001572012641\n",
-      "Recall: 0.9309969471523106\n",
-      "f1_score: 0.9026623769689387\n",
-      "IOU: 0.8225931181285925\n",
-      "Accuracy: 0.9784637933726418\n",
-      "Specificity: 0.9841667579856473\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 67%|██████▋   | 20/30 [10:11<05:10, 31.07s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 20/30\n",
-      "Train loss: 0.173546, Test loss: 0.1701\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.8986116030176723\n",
-      "Recall: 0.9175626749141564\n",
-      "f1_score: 0.9079882654353314\n",
-      "IOU: 0.8314821505075692\n",
-      "Accuracy: 0.9800536115186796\n",
-      "Specificity: 0.9875616644858737\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 83%|████████▎ | 25/30 [12:50<02:38, 31.78s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 25/30\n",
-      "Train loss: 0.179605, Test loss: 0.1822\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.9073232514744808\n",
-      "Recall: 0.8945017442236648\n",
-      "f1_score: 0.900866879920424\n",
-      "IOU: 0.8196158076423011\n",
-      "Accuracy: 0.9788843288143245\n",
-      "Specificity: 0.9890225814226375\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 30/30 [15:27<00:00, 30.93s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 30/30\n",
-      "Train loss: 0.159527, Test loss: 0.1522\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.896740833411397\n",
-      "Recall: 0.9411674149021525\n",
-      "f1_score: 0.9184171764759627\n",
-      "IOU: 0.8491417915491254\n",
-      "Accuracy: 0.9820654215325207\n",
-      "Specificity: 0.9869791645476664\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# U-Net model that uses a ResNet50 from SSL4EO-12. https://github.com/zhu-xlab/SSL4EO-S12\n",
-    "# The ResNet is pretrained on Sentinel-2 3-channel RGB satellite imagery\n",
-    "resnet50 = resnet50(weights=get_weight(\"ResNet50_Weights.SENTINEL2_RGB_SECO\"))\n",
-    "loss = JaccardLoss()\n",
-    "\n",
-    "sat_resnet50 = ResNet_UNet(ResNet=resnet50, input_image_size=INPUT_IMAGE_SIZE).to(DEVICE)\n",
-    "sat_resnet50_train, sat_resnet50_metrics = train(sat_resnet50, trainLoader, testLoader,loss)\n",
-    "sat_resnet50_valid = [x['Loss'] for x in sat_resnet50_metrics]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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OllJ/f3+++eYbjh07pm+RBXVmY+77QjyIjAES4i65f1Xebc2aNQDUq1cPuN1CcGfa5ORkvv/++3uutbKyIikp6Z7z/fv3JyIignXr1t3zXlJSEtnZ2UW6hzvLzc3rTs2aNaNWrVrMmTOHGzdu3HNdQkJCscp9mODgYGxtbZk+fXq+Y1DuLP9+9/Ag+f1sdu3aVaJTo/v06YNWq2XKlCn3/L4oisKVK1cANXB2cnIiLCyMzMxMfZqFCxcW6p7y895773HlyhW+/vprQP19ysnJYdq0afekzc7O1pd37dq1e+qcGzDk1610p9yxQF999VWe887OzvrWodw/Eu5UkJ9peno6169fv+faadOmoSgK3bp105976qmnMDEx4YsvvtCfUxSFsLAw3N3d9bMdhbgfaQES4i6jRo0iLS2N3r174+PjQ2ZmJjt27GDp0qV4eXkxbNgwALp27YqpqSk9evTglVde4caNG3z99dc4Ozvf8wXQrFkzFixYwP/+9z9q166Ns7Mzjz32GO+88w6rVq3iySefZOjQoTRr1ozU1FQOHTrEb7/9xrlz53B0dCzyvdSqVQt7e3vCwsKwsbHBysqKwMBAvL29+eabb3j88cfx9fVl2LBhuLu7c+nSJTZt2oStrS1//vlnsT7HB7G1tWXBggU8//zzNG3alIEDB+Lk5MSFCxf466+/aNOmjX5xu2bNmgHwxhtvEBwcjFarfegqv08++STLly+nd+/edO/enbNnzxIWFkaDBg3yDfiKolatWvzvf/9j/PjxnDt3jl69emFjY8PZs2dZsWIFL7/8MmPHjsXExIT//e9/vPLKKzz22GMMGDCAs2fP8v333xdqDFB+Hn/8cRo2bMjHH3/MiBEj6NChA6+88gohISFERUXRtWtXTExMOHXqFMuWLePTTz+lb9++/PDDD3zxxRf07t2bWrVqcf36db7++mtsbW154oknHlhmhw4d6NChA1u2bLnnvfnz59O2bVv8/PwYPnw4NWvWJC4ujoiICC5evMiBAwcANdjSarXMnDmT5ORkzMzMeOyxx0hLS6NJkyYMGjRIv/XFunXrWLNmDd26deOpp57Sl1W9enVGjx7N7NmzycrKIiAggJUrV7Jt2zYWLVokiyCKhzPI3DMhyrG///5beeGFFxQfHx/F2tpaMTU1VWrXrq2MGjVKiYuLy5N21apVSqNGjRRzc3PFy8tLmTlzpvLdd9/dMz09NjZW6d69u2JjY3PP1Ofr168r48ePV2rXrq2Ympoqjo6OSuvWrZU5c+bkmXpPEabBK4qi/PHHH0qDBg0UY2Pje6bER0ZGKn369FEcHBwUMzMzxdPTU+nfv78SHh7+wM8odwr67NmzH5guv6nSd9q0aZMSHBys2NnZKebm5kqtWrWUoUOHKnv37tWnyc7OVkaNGqU4OTkpGo1GPyX+QXXQ6XTK9OnTFU9PT8XMzExp0qSJsnr1amXIkCGKp6dnnrR3f6650+ATEhLyvZc7P29FUZTff/9dadu2rWJlZaVYWVkpPj4+yogRI5QTJ07kSffFF18o3t7eipmZmdK8eXNl69at+f688uPp6al079493/cWLlx4z8/1q6++Upo1a6ZYWFgoNjY2ip+fn/Luu+8qMTExiqIoyv79+5VBgwYpNWrUUMzMzBRnZ2flySefzPO55342d06zz7Vp0yYFyPdne/r0aWXw4MGKq6urYmJiori7uytPPvmk8ttvv+VJ9/XXXys1a9ZUtFqtfkr8tWvXlOeee06pXbu2YmlpqZiZmSm+vr7K9OnT812GIicnR/9zNjU1VXx9fZWff/75oZ+nEIqiKBpFyaetXwghhBDiESZjgIQQQghR6UgAJIQQQohKRwIgIYQQQlQ6EgAJIYQQotKRAEgIIYQQlY4EQEIIIYSodGQhxHzodDpiYmKwsbEp0jL8QgghhCh7iqJw/fp13NzcMDJ6cBuPBED5iImJwcPDw9DVEEIIIUQRREdHU7169QemkQAoHzY2NoD6Adra2hq4NkIIIYQoiJSUFDw8PPTf4w8iAVA+cru9bG1tJQASQgghKpiCDF+RQdBCCCGEqHQkABJCCCFEpVMuAqD58+fj5eWFubk5gYGB7N69+75ply9fTvPmzbG3t8fKygp/f39++umnPGkURWHixIlUq1YNCwsLgoKCOHXqVGnfhhBCCCEqCIOPAVq6dCljxowhLCyMwMBAQkNDCQ4O5sSJEzg7O9+TvmrVqnzwwQf4+PhgamrK6tWrGTZsGM7OzgQHBwMwa9YsPvvsM3744Qe8vb2ZMGECwcHBHD16FHNz87K+RSGEEOVATk4OWVlZhq6GKAYTExO0Wm2J5KVRFEUpkZyKKDAwkICAAObNmweoa/B4eHgwatQoxo0bV6A8mjZtSvfu3Zk2bRqKouDm5sbbb7/N2LFjAUhOTsbFxYWFCxcycODAh+aXkpKCnZ0dycnJJT4I+r+L8bhZZGHp4F6i+QohhMifoijExsaSlJRk6KqIEmBvb4+rq2u+A50L8/1t0BagzMxM9u3bx/jx4/XnjIyMCAoKIiIi4qHXK4rCP//8w4kTJ5g5cyYAZ8+eJTY2lqCgIH06Ozs7AgMDiYiIyDcAysjIICMjQ/86JSWlOLd1X/tXhNIoagqH7DrRZMzyUilDCCFEXrnBj7OzM5aWlrLAbQWlKAppaWnEx8cDUK1atWLlZ9AAKDExkZycHFxcXPKcd3Fx4fjx4/e9Ljk5GXd3dzIyMtBqtXzxxRd06dIFUH/Rc/O4O8/c9+4WEhLClClTinMrBWLhXBNjjY6qSYf480AMPRq7lXqZQghRmeXk5OiDHwcHB0NXRxSThYUFAPHx8Tg7OxerO6xcDIIuLBsbG6KiotizZw8fffQRY8aMYfPmzUXOb/z48SQnJ+uP6OjokqvsHeo36wCAp1E8s1ds5+K1tFIpRwghhCp3zI+lpaWBayJKSu7PsrjjuQwaADk6OqLVaomLi8tzPi4uDldX1/teZ2RkRO3atfH39+ftt9+mb9++hISEAOivK0yeZmZm+kUPS3XxQ3M7FIe6ANTMPMHoJVFk5+hKpywhhBB60u316Cipn6VBAyBTU1OaNWtGeHi4/pxOpyM8PJxWrVoVOB+dTqcfw+Pt7Y2rq2uePFNSUti1a1eh8iwtmurNAWhhcpa9568xf9NpA9dICCGEqHwM3gU2ZswYvv76a3744QeOHTvGa6+9RmpqKsOGDQNg8ODBeQZJh4SEsGHDBs6cOcOxY8eYO3cuP/30E8899xygRoajR4/mf//7H6tWreLQoUMMHjwYNzc3evXqZYhbzMu9KQB9XNQWqk/DT7L33FVD1kgIIcQjzsvLi9DQ0BLJa/PmzWg0mgo/q87g6wANGDCAhIQEJk6cSGxsLP7+/qxdu1Y/iPnChQt5trRPTU3l9ddf5+LFi1hYWODj48PPP//MgAED9GneffddUlNTefnll0lKSqJt27asXbu2fKwB5N4MANfrR+jt78aKqBjeXBLF36PbYWtuYuDKCSGEKC86duyIv79/iQQue/bswcrKqviVeoQYfB2g8qg01wEiOxNCqkNOBjde2cPjP10k+upNejR247OB/tJPLYQQJSg9PZ2zZ8/i7e1dPv4ILoSHBUCKopCTk4Oxcdm2ZWzevJlOnTpx7do17O3ty7RsePDPtDDf3wbvAqt0jE2hWiMArBMP8unAJmiNNPx5IIbl+y8ZuHJCCCHKg6FDh7JlyxY+/fRTNBoNGo2GhQsXotFo+Pvvv2nWrBlmZmb8+++/nD59mqeeegoXFxesra0JCAhg48aNefK7uwtMo9HwzTff0Lt3bywtLalTpw6rVq0qcn1///13fH19MTMzw8vLi7lz5+Z5/4svvqBOnTqYm5vj4uJC37599e/99ttv+Pn5YWFhgYODA0FBQaSmpha5LgUlAZAh3OoG4+JemtaowltBdQCY+MdhziWW/g9dCCEqM0VRSMvMLvOjMB0un376Ka1atWL48OFcvnyZy5cv4+HhAcC4ceOYMWMGx44do1GjRty4cYMnnniC8PBwIiMj6datGz169ODChQsPLGPKlCn079+fgwcP8sQTT/Dss89y9Wrhx6Tu27eP/v37M3DgQA4dOsTkyZOZMGECCxcuBGDv3r288cYbTJ06lRMnTrB27Vrat28PwOXLlxk0aBAvvPACx44dY/PmzfTp06dQn1VRGXwMUKWUGwBd2gfAax1rs/VUIrvPXuXNJZH89lprTLQSmwohRGm4mZVDg4nryrzco1ODsTQt2NeunZ0dpqamWFpa6pdwyV0geOrUqfrFf0HdI7Nx48b619OmTWPFihWsWrWKkSNH3reMoUOHMmjQIACmT5/OZ599xu7du+nWrVuh7uvjjz+mc+fOTJgwAYC6dety9OhRZs+ezdChQ7lw4QJWVlY8+eST2NjY4OnpSZMmTQA1AMrOzqZPnz54enoC4OfnV6jyi0q+ZQ0hNwC6fABystAaaQgd4I+tuTEHLibzyYaThq2fEEKIcqt58+Z5Xt+4cYOxY8dSv3597O3tsba25tixYw9tAWrUqJH+uZWVFba2tvptJgrj2LFjtGnTJs+5Nm3acOrUKXJycujSpQuenp7UrFmT559/nkWLFpGWpi4E3LhxYzp37oyfnx/9+vXj66+/5tq1a4WuQ1FIC5AhVK0J5vaQngRxR8DNHzd7C2Y83YjXF+1nwZbTtK3jSOtajoauqRBCPHIsTLQcnRpskHJLwt2zucaOHcuGDRuYM2cOtWvXxsLCgr59+5KZmfnAfExM8s481mg06HQlvzivjY0N+/fvZ/Pmzaxfv56JEycyefJk9uzZg729PRs2bGDHjh2sX7+ezz//nA8++IBdu3bh7e1d4nW5k7QAGYJGc0c32F796Sf8qjEwwANFgTFLD3At9cG/vEIIIQpPo9FgaWpc5kdhZ/mampqSk5Pz0HTbt29n6NCh9O7dGz8/P1xdXTl37lwRP53Cq1+/Ptu3b7+nTnXr1tXv1WVsbExQUBCzZs3i4MGDnDt3jn/++QdQfx5t2rRhypQpREZGYmpqyooVK0q93tICZCjuzeB0OFzaDwG3T0/s0YDdZ69yJjGVccsPEvZcM5kaL4QQlZCXlxe7du3i3LlzWFtb37d1pk6dOixfvpwePXqg0WiYMGFCqbTk3M/bb79NQEAA06ZNY8CAAURERDBv3jy++OILAFavXs2ZM2do3749VapUYc2aNeh0OurVq8euXbsIDw+na9euODs7s2vXLhISEqhfv36p11tagAzlroHQuSxNjflsUBNMtBrWHYnjl92lszGrEEKI8m3s2LFotVoaNGiAk5PTfcf0fPzxx1SpUoXWrVvTo0cPgoODadq0aZnVs2nTpvz6668sWbKEhg0bMnHiRKZOncrQoUMBsLe3Z/ny5Tz22GPUr1+fsLAwfvnlF3x9fbG1tWXr1q088cQT1K1blw8//JC5c+fy+OOPl3q9ZSHEfJTqQoi5biTAnNqABsZdAPO85Xy99QwfrTmGuYkRq0e1pbazTenUQwghHmEVeSFEkT9ZCLGis3YC+xqAAjGR97z9Yltv2tVxJD1Lx6hfosjIfng/sBBCCCEKRgIgQ7pPNxiAkZGGuf0aU9XKlGOXU5i19kQZV04IIURl9Oqrr2JtbZ3v8eqrrxq6eiVGBkEbkntzOLIi3wAIwNnWnFlPN+KlH/fy7b9naVfHkY71nMu4kkIIISqTqVOnMnbs2HzfK7VhIQYgAZAhPaAFKFdQAxcGt/Lkx4jzjF12gL/fbI+TjVkZVVAIIURl4+zsjLPzo//HtnSBGVK1RqDRwvXLkBJz32TvP1Gfui7WJN7I5J3fDpTJHilCCCHEo0wCIEMytQLnBurzB7QCmZto+WxQE0yNjdh8IoHNJxPKqIJCCCHEo0kCIEOrfntn+AfxcbVlQHN1J+B1h2NLu1ZCCCHEI00CIEMrwDigXF19XQDYeCyOHJ10gwkhhBBFJQGQoeUGQDFRoHvwWj+B3g7YmBuTeCOTqOiy2S1XCCGEeBRJAGRoTj5gYgWZ1yHx1AOTmhob0enWNPj1R+LKonZCCCEqKC8vL0JDQ/WvNRoNK1euvG/6c+fOodFoiIqKemjemzdvRqPRkJSUVOx6GooEQIZmpAW3JurzSw8eBwS3u8HWH42T2WBCCCEK7PLly2Wyx1ZFIQFQeeB+a9O6AowD6lDXCVOtEWcTUzmdcKOUKyaEEOJR4erqipmZrCOXSwKg8qAQA6FtzE1oVcsBgHXSDSaEEI+kr776Cjc3N3Q6XZ7zTz31FC+88AKnT5/mqaeewsXFBWtrawICAti4ceMD87y7C2z37t00adIEc3NzmjdvTmTkvftSFsbvv/+Or68vZmZmeHl5MXfu3Dzvf/HFF9SpUwdzc3NcXFzo27ev/r3ffvsNPz8/LCwscHBwICgoiNTU1GLV52EkACoPcgOguCOQdfOhye/sBhNCCFFIigKZqWV/FGLYQr9+/bhy5QqbNm3Sn7t69Spr167l2Wef5caNGzzxxBOEh4cTGRlJt27d6NGjBxcuXChQ/jdu3ODJJ5+kQYMG7Nu3j8mTJ993+4uC2LdvH/3792fgwIEcOnSIyZMnM2HCBBYuXAjA3r17eeONN5g6dSonTpxg7dq1tG/fHlC75gYNGsQLL7zAsWPH2Lx5M3369Cn1YR6yFUZ5YFcdrF3gRhxcPgg1Ah+YvEt9Fz5YcZgD0UnEpaTjYmteRhUVQohHQFYaTHcr+3Lfj1EXwC2AKlWq8Pjjj7N48WI6d+4MqK0kjo6OdOrUCSMjIxo3bqxPP23aNFasWMGqVasYOXLkQ/NfvHgxOp2Ob7/9FnNzc3x9fbl48SKvvfZakW7t448/pnPnzkyYMAGAunXrcvToUWbPns3QoUO5cOECVlZWPPnkk9jY2ODp6UmTJur418uXL5OdnU2fPn3w9PQEwM/Pr0j1KAxpASoPNJpCdYM525rj72EPwAZpBRJCiEfSs88+y++//05GRgYAixYtYuDAgRgZGXHjxg3Gjh1L/fr1sbe3x9rammPHjhW4BejYsWM0atQIc/Pbf0C3atWqyHU9duwYbdq0yXOuTZs2nDp1ipycHLp06YKnpyc1a9bk+eefZ9GiRaSlpQHQuHFjOnfujJ+fH/369ePrr7/m2rXSX+qlXLQAzZ8/n9mzZxMbG0vjxo35/PPPadGiRb5pv/76a3788UcOHz4MQLNmzZg+fXqe9BqNJt9rZ82axTvvvFPyN1AS3JvCiTUFCoBA7QaLik5iw9E4nmvpWcqVE0KIR4iJpdoaY4hyC6FHjx4oisJff/1FQEAA27Zt45NPPgFg7NixbNiwgTlz5lC7dm0sLCzo27cvmZmZpVHzYrOxsWH//v1s3ryZ9evXM3HiRCZPnsyePXuwt7dnw4YN7Nixg/Xr1/P555/zwQcfsGvXLry9vUutTgZvAVq6dCljxoxh0qRJ7N+/n8aNGxMcHEx8fHy+6Tdv3sygQYPYtGkTEREReHh40LVrVy5duqRPc/ny5TzHd999h0aj4emnny6r2yo89+bqYwGmwgN0beAKwI7TiVxPzyqtWgkhxKNHo1G7osr6uM8f5/djbm5Onz59WLRoEb/88gv16tWjaVN11vD27dsZOnQovXv3xs/PD1dXV86dO1fgvOvXr8/BgwdJT0/Xn9u5c2eh6nd3ftu3b89zbvv27dStWxetVguAsbExQUFBzJo1i4MHD3Lu3Dn++ecfQG24aNOmDVOmTCEyMhJTU1NWrFhR5PoUhMEDoI8//pjhw4czbNgwGjRoQFhYGJaWlnz33Xf5pl+0aBGvv/46/v7++Pj48M0336DT6QgPD9encXV1zXP88ccfdOrUiZo1a5bVbRVe7lpA185B6pWHJq/tbE1NRyuychQ2n5DNUYUQ4lH07LPP8tdff/Hdd9/x7LPP6s/XqVOH5cuXExUVxYEDB3jmmWfumTH2IM888wwajYbhw4dz9OhR1qxZw5w5c4pcz7fffpvw8HCmTZvGyZMn+eGHH5g3b55+YPXq1av57LPPiIqK4vz58/z444/odDrq1avHrl27mD59Onv37uXChQssX76chIQE6tevX+T6FIRBA6DMzEz27dtHUFCQ/pyRkRFBQUFEREQUKI+0tDSysrKoWrVqvu/HxcXx119/8eKLL943j4yMDFJSUvIcZc7CHhzqqM9j9hfoki63ZoPJOCAhhHg0PfbYY1StWpUTJ07wzDPP6M9//PHHVKlShdatW9OjRw+Cg4P1rUMFYW1tzZ9//smhQ4do0qQJH3zwATNnzixyPZs2bcqvv/7KkiVLaNiwIRMnTmTq1KkMHToUAHt7e5YvX85jjz1G/fr1CQsL45dffsHX1xdbW1u2bt3KE088Qd26dfnwww+ZO3duqS/aqFEMuJxwTEwM7u7u7NixI8/gq3fffZctW7awa9euh+bx+uuvs27dOo4cOZJnMFeuWbNmMWPGDGJiYvJ9H2Dy5MlMmTLlnvPJycnY2toW4o6KafkrcHAJdBwPHcc9NPm+89d4esEObMyM2TehC6bGBm/QE0KIciU9PZ2zZ8/i7e193+8AUbE86GeakpKCnZ1dgb6/K/Q35owZM1iyZAkrVqy47y92brPhg37xx48fT3Jysv6Ijo4urSo/WPVb44AuFmwcUBMPexytzbiekc3OMw/vNhNCCCGEyqABkKOjI1qtlri4vF04cXFxuLq6PvDaOXPmMGPGDNavX0+jRo3yTbNt2zZOnDjBSy+99MC8zMzMsLW1zXMYxJ1bYhSgYc7ISEOXBurmqNINJoQQoqS8+uqrWFtb53u8+uqrhq5eiTBoAGRqakqzZs3yDGDOHdD8oPUIZs2axbRp01i7di3Nmze/b7pvv/2WZs2a5VksqlxzaQhaU7h5VR0MXQC5s8E2HI1Dp5PNUYUQQhTf1KlTiYqKyveYOnWqoatXIgy+DtCYMWMYMmQIzZs3p0WLFoSGhpKamsqwYcMAGDx4MO7u7oSEhAAwc+ZMJk6cyOLFi/Hy8iI2NhZAH5nmSklJYdmyZffsRVKuGZuBq5/aAnRpH1R9+PoHrWo5YGmqJTYlnUOXkml8a4FEIYQQoqicnZ1xdnY2dDVKlcHHAA0YMIA5c+YwceJE/P39iYqKYu3atbi4qDOcLly4wOXLl/XpFyxYQGZmJn379qVatWr64+7pe0uWLEFRFAYNGlSm91Ns+vWACrYgormJlo71nADpBhNCCCEKyqCzwMqrwowiL3EHlsKKl8EjEF5cX6BLVkZeYvTSKOq6WLP+rQ6lXEEhhKg4cmcMeXp6YmlZuJWYRfmUlpbG+fPniz0LzOBdYOIuuXuCXT4AOVmgNXnoJZ3qOaM10nAy7gbnElPxcizYZntCCPGoMzU1xcjIiJiYGJycnDA1Nb3vdkmifFMUhczMTBISEjAyMsLU1LRY+UkAVN5UrQnmdpCeDHFHwM3/oZfYWZrQsmZVtv93hQ1H4xjevhyveC2EEGXIyMgIb29vLl++TEyMAfb/EiXO0tKSGjVqYGRUvFE8EgCVN0ZGaivQ6X/UcUAFCIBAnQ22/b8rrD8aKwGQEELcwdTUlBo1apCdnU1OTo6hqyOKQavVYmxsXCKteBIAlUf6AGg/BNx/C487BTVwYdKqI+w9f43EGxk4WpuVciWFEKLi0Gg0mJiYYGLy8GEFonIw+CwwkY/ccUAF3BkewN3egobutigK/HMsvpQqJoQQQjwaJAAqj3IDoIQTkF7wjVlzF0VcfzS2NGolhBBCPDIkACqPrJ3BrgagwOWoAl/WpYG6dtK2U4mkZWaXTt2EEEKIR4AEQOXVnfuCFZCPqw0eVS3IyNax9WRiKVVMCCGEqPgkACqvcrvBCrgzPKiD/KQbTAghhHg4CYDKq+q5W2LsL9Rlud1g/xyPJztHV9K1EkIIIR4JEgCVV9Uag8YIrsdASsEX72ruWYUqliYkpWWx59y1UqygEEIIUXFJAFRemVqBcwP1eSFagYy1RnSur7YCSTeYEEIIkT8JgMoz/UDogo8DgtvdYBuOxiF73QohhBD3kgCoPHPPHQdU8JlgAO3rOGFuYsTFazc5dvl6KVRMCCGEqNgkACrP9CtCR4Ku4AOaLUy1tKvjBEg3mBBCCJEfCYDKMycfMLGEzOuQeLJQl97ZDSaEEEKIvCQAKs+0xlDNX31eyG6wzj7OGGngSEwKF6+llXzdhBBCiApMAqDyrnpuN1jhAiAHazOae1UFpBVICCGEuJsEQOWde9ECIICu0g0mhBBC5EsCoPIuNwCKOwxZNwt1ae44oF1nr5KUllnSNRNCCCEqLAmAyjs7D7ByBl02xB4q1KWeDlb4uNqQo1P453h8KVVQCCGEqHgkACrvNJpidYPJbDAhhBDiXhIAVQRF2Bk+V+7u8FtOJpCelVOStRJCCCEqLAmAKgL9lhiFbwFq6G5LNTtz0jJz2P5fYglXTAghhKiYJACqCHIDoGtnIe1qoS7VaDTSDSaEEELcpVwEQPPnz8fLywtzc3MCAwPZvXv3fdN+/fXXtGvXjipVqlClShWCgoIemP7VV19Fo9EQGhpaCjUvIxZVwKG2+rwQO8Pnyu0G23gsjhydbI4qhBBCGDwAWrp0KWPGjGHSpEns37+fxo0bExwcTHx8/rOWNm/ezKBBg9i0aRMRERF4eHjQtWtXLl26dE/aFStWsHPnTtzc3Er7NkqffiB04ccBBdasio25MYk3MomKvlbCFRNCCCEqHoMHQB9//DHDhw9n2LBhNGjQgLCwMCwtLfnuu+/yTb9o0SJef/11/P398fHx4ZtvvkGn0xEeHp4n3aVLlxg1ahSLFi3CxMSkLG6ldBVjJpiJ1ojHfJwBWLb3ogyGFkIIUekZNADKzMxk3759BAUF6c8ZGRkRFBREREREgfJIS0sjKyuLqlWr6s/pdDqef/553nnnHXx9fR+aR0ZGBikpKXmOcse9ufp4aR8ohe/Gyu0GW7InmkZT1vP8t7v4eusZTsReRylCfkIIIURFZtAAKDExkZycHFxcXPKcd3FxITY2tkB5vPfee7i5ueUJombOnImxsTFvvPFGgfIICQnBzs5Of3h4eBT8JsqKa0MwMoG0K3DtXKEv7+rrwtDWXlSzMyczW8e2U4l8tOYYwaFbaRXyD+8sO8CfB2K4liorRgshhHj0GRu6AsUxY8YMlixZwubNmzE3Nwdg3759fPrpp+zfvx+NRlOgfMaPH8+YMWP0r1NSUspfEGRspnaDRe+ErXOg1/xCXW6iNWJyT18m9WjAf/E32HIyga2nEtl15gqxKeks23eRZfsuotFAI3c72td1on1dJ5p42GOsNXhPqRBCCFGiDBoAOTo6otVqiYvLOz07Li4OV1fXB147Z84cZsyYwcaNG2nUqJH+/LZt24iPj6dGjRr6czk5Obz99tuEhoZy7ty5e/IyMzPDzMyseDdTFoImwfdPQNTP4PME+HQvdBYajYY6LjbUcbHhpXY1Sc/KYffZq2w7lcDWk4mciLvOgYvJHLiYzOf//IeNmTGtazvQvq4THes5425vUQo3JoQQQpQtjWLgASCBgYG0aNGCzz//HFDH79SoUYORI0cybty4fK+ZNWsWH330EevWraNly5Z53rty5QqXL1/Ocy44OJjnn3+eYcOGUa9evYfWKSUlBTs7O5KTk7G1tS3inZWS9RNgx2dg6Qiv7wRrpxLNPjY5na2nEth6MoF//0skKS1L/55GA2O71uP1jrUK3LomhBBClJXCfH8bvAtszJgxDBkyhObNm9OiRQtCQ0NJTU1l2LBhAAwePBh3d3dCQkIAdXzPxIkTWbx4MV5eXvqxQtbW1lhbW+Pg4ICDg0OeMkxMTHB1dS1Q8FPudfoA/tsI8Udh9WgY8LMamZQQVztz+jf3oH9zD3J0CocuJbP1ZAKbT8Sz/0ISs9ed4GxiKtN7+2FqLF1jQgghKiaDf4MNGDCAOXPmMHHiRPz9/YmKimLt2rX6gdEXLlzI06KzYMECMjMz6du3L9WqVdMfc+bMMdQtlC0Tc+j9pTog+vhqOLCk1IrSGmnw97Dnjc51WP56G6Y+5YuRBn7bd5Hnvt0lA6aFEEJUWAbvAiuPynUXWK6tc+CfaWBmC6/tAPuyGbS9+UQ8IxdHciMjGy8HS74dGkAtJ+syKVsIIYR4kMJ8fxu8BUgUUZvRUD0AMlLgj9dBpyuTYjvWc+b311pTvYoF566k0Xv+dnaclk1WhRBCVCwSAFVUWmO1K8zEEs5uhd1flVnR9VxtWDmiDU1r2JOSns3gb3ezdM+FMitfCCGEKC4JgCoyh1rQdZr6fOMkSDhZZkU7WpuxeHhLejZ2I1un8N7vhwhZcwydbLYqhBCiApAAqKJr/iLU6gzZ6bDiZcjJevg1JcTcRMunA/15s3MdAL7ceoZXf95HWmZ2mdVBCCGEKAoJgCo6jQaemgfmdhATCds+LuPiNbzVpS6fDvTH1NiI9Ufj6BcWQWxyepnWQwghhCgMCYAeBbZu0P1W4LN1FlzaX+ZVeMrfnV+GB+JgZcqRmBSemv8vhy8ll3k9hBBCiIKQAOhR0fBp8O0NumxY8Spk3SzzKjTzrMrKEW2o42xNXEoG/cIiWHekYJvaCiGEEGVJAqBHhUajtgJZu0DiCQifZpBqeFS15PfXW9OujiM3s3J49ed9fLnlNLLclBBCiPJEAqBHiWVV6DlPfb5zvjo93gBszU34fmgAz7f0RFEg5O/jvPf7QTKzy2atIiGEEOJhJAB61NTtCs2Gqs9Xvg7pKQaphrHWiKlP+TK5RwOMNPDr3ov0/zKCQxdlXJAQQgjDkwDoUdT1I6jiBcnRsHa8waqh0WgY2sabb4cEYG1mTFR0Ej3n/8s7yw4Qf11miQkhhDAcCYAeRWbW0CsM0EDUz3D8L4NWp5OPMxvHdKB3E3cUBZbtu0in2Zv5YvN/pGflGLRuQgghKicJgB5Vnq2gzRvq81VvwI0Eg1bH1c6cTwb4s/z11vh72JOamcOstSfo8skW1h6OlUHSQgghypQEQI+yTh+AcwNIS4TVo6EcBBlNa1Rh+Wut+WRAY1xszYi+epNXf97HM1/v4thlw4xXEkIIUflIAPQoMzZTN0w1MoHjq+HAEkPXCAAjIw29m1Tnn7c7Muqx2pgZGxFx5grdP9vG+ysOceVGhqGrKIQQ4hEnAdCjrloj6HRrIPTf70JStGHrcwcrM2Pe7lqPjWM60N2vGjoFFu+6QMc5m/lm2xmZNi+EEKLUaBQZfHGPlJQU7OzsSE5OxtbW1tDVKb6cbPj+cbi4G7SmULUWONVTD8e66qNDHTAxN2g1d525wtTVRzkSo3aF1XS04oPu9XnMxxmNRmPQugkhhCj/CvP9LQFQPh65AAjgymn4qTcknc//fY0R2HveERT53H5uXnafQY5O4bd90cxed4LEG5kAtK/rxITu9anjYlNm9RBCCFHxSABUTI9kAASg06lrAyWehITjkHDi9vP0ByxQaFNNDYRc/aD5C+BQq9Srej09i3mb/uO7f8+SlaOgNdIQ6F2VYF9XujRwwc3eotTrIIQQomKRAKiYHtkA6H4UBW7Eq3uIJdw6Ek9Awkm4cddmphoj8OsP7d8Bx9qlXrVzial8tOYYG47G5TnfqLodXRu4EOzrSm1na+kiE0IIIQFQcVW6AOhBbibdaiU6AcdWwan16nmNEfj1uxUI1Sn1apy/ksr6I3GsPxrL3vPX8szo93a0omsDF7r6utLEwx4jIwmGhBCiMpIAqJgkAHqAS/tgyyw4uVZ9rTGChk+rgZBTvTKpQsL1DMKPxbHuSCzb/7tCZs7t2WJONmZ0aeBC1wYutKrlgJmxtkzqJIQQwvAkAComCYAKICZSDYROrLl1QgMN+0D7d8HZp8yqcSMjmy0nElh3JJZNx+O5npGtf8/GzJiOPs50beBC+zpO2FmalFm9hBBClD0JgIpJAqBCuHxADYSOr751QgO+vdRAyKVBmVYlM1tHxJkrrD8Sy4ajccRfz7ugYjU7c+q52lDP1QYfVxvqudhSy9lKWomEEOIRIQFQMUkAVASXD8KWmXcEQkCDp6DDe+DiW+bV0ekUoi4m6ccNnUlIzTed1khDTUcrNTByyQ2ObKlexULGEgkhRAVT4QKg+fPnM3v2bGJjY2ncuDGff/45LVq0yDft119/zY8//sjhw4cBaNasGdOnT8+Tfvny5YSFhbFv3z6uXr1KZGQk/v7+Ba6PBEDFEHtYDYSOrbp9rn4PNRBy9TNYtVLSszgZe53jsdc5ces4HptCSnp2vumtTLXUcVFbihq42dKzsRv2lqZlXGshhBCFUaECoKVLlzJ48GDCwsIIDAwkNDSUZcuWceLECZydne9J/+yzz9KmTRtat26Nubk5M2fOZMWKFRw5cgR3d3cAfvrpJ86ePYubmxvDhw+XAMgQ4o6oXWNH/wBu/YrV7wmdJ5bJrLGCUBSF2JT0u4Ki65yOv5FnYDWAo7UpU3o25Ak/V5lyL4QQ5VSFCoACAwMJCAhg3rx5AOh0Ojw8PBg1ahTjxo176PU5OTlUqVKFefPmMXjw4DzvnTt3Dm9vbwmADCn+mBoIHVkBKKDRQtPB0HEc2Lgaunb5ysrRcS4xVR8Y/X34MqdvdaF1aeDC/3o1xMXWsNuGCCGEuFdhvr8NuhlqZmYm+/btIygoSH/OyMiIoKAgIiIiCpRHWloaWVlZVK1atcj1yMjIICUlJc8hSohzfej3Pby2A+o+DkoO7PsePmsC4dMevAK1gZhojajjYkOPxm6MDa7Hmjfb8UbnOhgbadhwNI6gj7fwy+4LlIPeYyGEEEVk0AAoMTGRnJwcXFxc8px3cXEhNjb2Plfl9d577+Hm5pYniCqskJAQ7Ozs9IeHh0eR8xL34dIAnlkCw/6G6i0gKw22zYFP/SHiC8jOeGgWhabTwYVdsP5D+DdUfV0EZsZaxnSpy+o32tK4uh3X07MZv/wQz3y9i3OJ+Q+uFkIIUb4ZNAAqrhkzZrBkyRJWrFiBuXnRuyTGjx9PcnKy/oiOji7BWoo8PFvDi+thwCJ1f7GbV2HdeJjXHA4sLXKQoqfTQfRuWDseQhvCd11hx+ewcRIsfwmyM4uctY+rLctfb8OH3etjbmJExJkrdPt0K19tPU12TjHrLYQQokwZNABydHREq9USF5d3n6e4uDhcXR88PmTOnDnMmDGD9evX06hRo2LVw8zMDFtb2zyHKEUaDdR/El6LgB6fqZutJl2AFS/Dl+3hv41QmO4lfdDzvhr0fNsFdn4BKZfA1AZ8ngQjYzj8O/wyADJuFLnqWiMNL7WrybrR7Wldy4H0LB3T1xynz4IdHLssXadCCFFRGDQAMjU1pVmzZoSHh+vP6XQ6wsPDadWq1X2vmzVrFtOmTWPt2rU0b968LKoqSoPWGJoNgVH71dlhZrYQdwh+fhp+7Kluu3E/igLRe24FPX63gp75t4OeRgNg4C/wzn8wcBEMWgomlnD6H/ihB6QmFqvqng5WLHopkJlP+2FjbszBi8n0+Pxf5q4/QUZ2TrHyFkIIUfoMPgts6dKlDBkyhC+//JIWLVoQGhrKr7/+yvHjx3FxcWHw4MG4u7sTEhICwMyZM5k4cSKLFy+mTZs2+nysra2xtrYG4OrVq1y4cIGYmBi6d+/OkiVLqFevHq6urg9tWQKZBWYwaVdh21zY/RXk3Oqq8u0Nj00Ah1pq0HNxLxxdCUdWQsrF29ea2kC9x9VVqGt1BpN8ukQv7oVF/dRuN4c68PxysK9R7GrHpaQzYeVh1t/asb6WkxUzn25Ec6+iD8wXQghReBVqGjzAvHnz9Ash+vv789lnnxEYGAhAx44d8fLyYuHChQB4eXlx/vz5e/KYNGkSkydPBmDhwoUMGzbsgWkeRAIgA0u6AJumw4ElgKJ2X9V7HGKiIPmO8Vmm1lDviQcHPXdLOAk/9VaDJxs3NQhyrl/sKiuKwt+HY5n4xxESb2Sg0cCQVl68E1wPKzPjYucvhBD3OLoKTCygThdD16TcqHABUHkjAVA5EXsYwqfAqfW3z5laq8FQg15Qu7P6j7+wki/Bz30g4TiY28Mzv0KNwBKpclJaJv/76xi/7VNbp9ztLZjS05fO9Z1lAUUhRMm5ehY+8wcjExh7EiylxRkkACo2CYDKmbPb4HQ4uDcvetBzt7SrsHgAXNwNxhbQ/weoG1z8fG/ZejKB91cc4uK1mwDUdrZmSCtP+jStLi1CQoji2xkGa99Tn/f9Dho+bdj6lBMSABWTBECVRGYaLBuitjBptPDUPPB/psSyT83I5tPwU/y88zxpmerAaBszY/o2r87gVl54O1qVWFlCiErmp97qpA6AxoOgd5hh61NOSABUTBIAVSI5WfDHSDi4RH3dZRq0eaNEi0hJz+L3fRf5MeI8Z+9YOLFjPSeGtPaiQx0n2XleCFFwGTdglvftySKWjjD2FBhV6KX9SoQEQMUkAVAlo9PBhgkQoe5HR+s3oMtUdb2iEi1GYeupBH7YcY7NJxP0Sx15OVjyfCsv+jWvjq25SYmWKYR4BB1bDUufVWex3kyCjBR46R+o3szQNTO4CrMXmBDlgpERBH+kBj0AOz6Dla+rrUMlWoyGjvWc+X5YCza93ZEX23pjY27MuStpTFt9lJbTw/lw5SFOxV0v0XKFEI+YU+vUx7qPQ82Ot86tv29ykT9pAcqHtABVYpGLYNUoddPWOsHQbyGYWpZacakZ2ayIvMQPO85xKv72CtVtajswuJUXQfVd0Er3mBAil6LAx/Xh+mV47ndIuQyrRoJbU3h5k6FrZ3DSBVZMEgBVcif+hmVDITsdPAJh0JJSn2KqKAoRp6+wcMc5Nh6LQ3frX6W7vQX+NexxtjHDycYMZxvzO56bUcXStMTGD+XoFFJuZpF860hJzyJHp6A10mCk0aDRgFajwchIg5EGjDTqea2R+l7ucyMNaDQabM1NcLIxK5G6CSFuuXxA3TLIxBLePQvpSTC3nvre2FNg7WzQ6hlaYb6/ZT6uEHer9zgM/gMW94foXfD9ExA0GTxalFogpNFoaF3bkda1Hbl4LY2fd15gyZ4LXEq6yaWkm/e9zthIg6O1Gc62ZjjlPtqY6wMkS1MtKTezSbqZeTuwufWYlHY72Em+mcX19OwSv6+WNavSv7kHjzeshoWptsTzF6LSOXmrq6tmJ3XxVxNXqNZYDYz+Cwf/QYatXwUiLUD5kBYgAUDcUXXBxOuXb59zbgA1WkKNVuqjnUeJD5bOlZ6Vw+bjsVxKziThegbx19NJuJ5x63kGV1OLvrP9g1iZarGzMMHWwgRjrYYcndpClaNT0CkKOoVbjwo6nfpcfe9WOkVBp1O4npGtH+htY2ZMT383+jf3oFF1O1kUUoii+iYILu6BHp9Cs6HquX/+B1tng28f6Pe9QatnaNIFVkwSAAm9pGj1P5bz2+HKf/e+b+t+R0DUSt1Ww6iQLR0Z19VVXa+egaunbz2ehSun4UYsuPqBXz91oTO76vrLsnJ0JN7IID7ldlCUGyTF3wqUbmbm6IMZOwsT7C3VR/1xx2v7W+lMtCUzNyIm6Sa/7bvIr3uj9QtCAvi42tC/uQe9m7hTxcq0RMoSolJITYTZtQEFxhwDWzf1fPRudUNoczt454y60XQlJQFQMUkAJPJ1IwGid8KFnXAhQm1y1t3VbWRmp3aV5QZF7k3VlavTU24FNvkcN+IKXocarcHvaWjQG6wcSvb+7nQzCeKPqQGdhX2xstLpFCLOXOHXvdH8fTiWzGwdAKZaI7r4utC/uQdtazvKYG8hHibqF1j5qvpH0av/3j6vy4HZteDmNRj2N3i2NlwdDUwCoGKSAEgUSGYqXNp3OyCK3g2ZN/Km0ZqCmS2kJT44L0sHqFoLqtZUD4daUNUbrJzUfv1Dv6mtUNz652pkDLUeg4Z9wecJMLMp3r1cj4XzO9T7OL8D4o6oZVk6wlPzoV634uV/S3JaFn8cuMTSPdEciUnRn3ezM6dvcw/6NauOR9XSm3UnRIW2bCgcWQHtxkLnCXnf+/0lOLQM2r6ljlmspCQAKiYJgESR5GRD3OHbAdGFnWoXVi4rp9sBTtVbAY5DLajiXbBWluRLcGS5+p/c5QO3zxtbqAGKXz+oHQTGD5l5pShqy9OdAc+1s/emM7NVF1gDCHhJXSW7BJcEOHwpmWV7o1kZFUPyzdtrLrWp7UD/5h4E+7pibiIDp4UA1HXJZtWCjGR4cYPa0nyng7/C8uHg0hBe226YOpYDEgAVkwRAokQoClw7pwYRVbzU/vmSknhKbRU6tEwdN5TL3A7q91SDIa+26ngkXY4amJ2PgAs71MfU+Lsy1IBrQ7WLzfPWeCaLKrBxCuycryZxrAdPfwPVGpXcfaAO9l53JJZf90az/b8r+vPu9hZM7NGArg1cZNC0EOf+hYXd1dbisafuHWuYekXtBkOBt46CnbtBqmloEgAVkwRAosJQFLgcpQZDh3/PO2PN2hWcfeDS/tstObm0puDeTA10PFurf03eL0D7LxxWvqaOVdKaQudJ0PL1Utl3KPpqGsv2XWTpngvEpWQA0KmeE5N6+OIlm8eKymz9BHWV+kYDoc+X+af5pgtc3J13hlglIwFQMUkAJCokXY7anXX4NziyUl0gLZepDdQIvB3wuDVV1xApqNQr6grZJ/5SX9fsBL0WgG21krwDvbTMbOZv+o+vtp4hK0fBVGvEqx1q8nqn2tItJiqneS0g8QT0/U6dEZqfLbNh0//A50kYuKhs61dOSABUTBIAiQovOxNO/wMpl6B6c3VcQGGn599NUWDfQlg7HrJvgkVV6Pk51H+yRKqcnzMJN5i06gjbTqmDyKtXsWByD1+CGriUWplClDvXzsGnjUGjhXfP3H/MYEwUfNUBTK3VdA8bD/gIks1QhajsjE3VgdEBL6qrxBY3+AF1wcfmw+CVreDaCG5eVXek/vNNdUZcKajpZM2PL7RgwbNNqWZnzsVrN3npx728uHAPF66klUqZQpQ7uas/12j54AkTro3A2kWdjXp+R5lUrSKTAEgIUThOdeGlcGjzJqBRW4W+7KD+9VkKNBoNj/tVY+OYDrzWsRYmWg3hx+MJ+mQLoRtPkp6VUyrlClFu5O7+Xqfrg9MZGUHtLreu2VC6dXoESAAkhCg8Y1PoMlXdM83GDa6cUpfo/zcUdLpSKdLKzJj3uvnw95vtaVPbgcxsHaEbT9H1k638c7wQi0kKUZFkpsLZberzusEPT18nNwBaX3p1ekRIACSEKLqaHdQ1R+r3BF0WbJwEP/ZU1ywqJbWdrfn5xUDmPdMEF1szLlxN44WFexn+416ir0q3mHjEnN0KORlgXwOcfB6evlYndaHUK6fU9b7EfUkAJIQoHsuq0P9H6DkPTKzg3DZY0BoOLIWUmFJpEdJoNDzZyI3wtzvySvuaGBtp2HA0jqCPt/B5+CnpFhOPjpO53V/BBdt42dxOne0JcGpj6dXrESCzwPIhs8CEKKIrp9Ul+WP23z5nZKJu2mhfA+w8wN5DfbSrfutc9WLPVjkVd50Jfxxm55mrANSoakmvJu50qudEo+r2ss+YqJgUBT7xVWdzPvvb7e6th9n+KWyYqI4Heu630q1jOSPT4ItJAiAhiiEnC7bOhgO/qF1hSgFaY6xd7giOqoNdDXXGSyFWnVYUhVUHYvjor2PEX8/Qn69qZUqHuk50rOdE+zpOsgO9qDhiD0FYW3W7m/fOqhsrF0T8MfiiJRibw7tnS3QLm/JOAqBikgBIiBKiy1FXp06KhuRoSLpw6zH69mP2zftf79oImjwPfn3VrrYCuJGRzZpDl9lyIoGtpxK4np6tf89IA01qVKFTPSc61nOmQTVbjKR1SJRXW+fAP9Ogbjd4ZmnBr1MUCPVT/40982vBBk8/IipUADR//nxmz55NbGwsjRs35vPPP6dFixb5pj1y5AgTJ05k3759nD9/nk8++YTRo0fnSZOTk8PkyZP5+eefiY2Nxc3NjaFDh/Lhhx8WeD8hCYCEKCOKAmlXIflC3qDo6mk4sxlyMtV0WjN1wcUmz4F3xwJvw5GVo2P/+WtsOpHA5hPxHI+9nud9JxszOtZ1opOPM23rOGJrblKitycqgQu7wMpR3di4pH3bFaJ3QfeP1TW9CmP1W7D3O3Uj4+5zS75u5VRhvr+Ny6hO+Vq6dCljxowhLCyMwMBAQkNDCQ4O5sSJEzg7O9+TPi0tjZo1a9KvXz/eeuutfPOcOXMmCxYs4IcffsDX15e9e/cybNgw7OzseOONN0r7loQQhaHRgJWDerg1yfte2lV1h+vIn9TNXA//rh52HuD/jHpU8Xpg9iZaIwJrOhBY04Fxj/sQk3STzScS2HQinu3/JZJwPYNl+y7y577T+GvP0b3qJVpaXsLOqzFOXcei0UpAJB7g+BpYMkjdoHTk3gK3UhZI2lW4uEd9XpQWnDpd1QDo1Hr1D427GgBOxF7ns39OMaJjbRq4Vc4/9A3aAhQYGEhAQADz5s0DQKfT4eHhwahRoxg3btwDr/Xy8mL06NH3tAA9+eSTuLi48O233+rPPf3001hYWPDzzz8XqF7SAiREOaIocPkARP4Mh36F9OTb73l3ULvI6j9Z8PERuhxIPEnWhd1cOb4DLu3D6eZptOSdrbZb04g1PjMIbFCTNtI6JO52PQ4WtIK0K+rr5i/Ak5+UXP4Hf4Xlw9VtbF7bXvjrM1Nhprc6hX7EbnCqp3/rZmYO3T/bxpnEVLwcLPn7zfZYmD4ae+xViBagzMxM9u3bx/jx4/XnjIyMCAoKIiIiosj5tm7dmq+++oqTJ09St25dDhw4wL///svHH39832syMjLIyLg9aDIlJeW+aYUQZUyjATd/9ej6Pzi+Wm0VOrMZzm5RDzM7dZxQ0+ehmn/ev3avx8LFvXBpr/oYEwWZ1zEBXO8oJtvKlUtWDTic7kTH5JW04CBOR4bxUtRYRmqq06xGFTrUc6JDXSd83WwL3KVeUIqicC0tC3MTIyxNDdo4Lx5GUVD+eB1N2hUyrNwxS70Ee79Xu2jdm5VMGSfXqo8PW/35fkytwKstnA5XW4HuCIBmrzvBmUR1+5pzV9KYve4EE3s0KG6NKxyD/StLTEwkJycHF5e8mxq6uLhw/PjxIuc7btw4UlJS8PHxQavVkpOTw0cffcSzzz5732tCQkKYMmVKkcsUQpQRE3M10PHrC9fOqzPNIhepY4j2fqseLg3VLoPEU3BpnzqF+J58rNQut+rNwL05uDfD2M4dT8ATyLg4kvRfBuKdeok/zCYxMnMkm8/5s/vcVWavO4GTjRnt66gzy9rVccTesuAzy5LTsjh7JZVziamcvXWcu6I+Xk/PxtzEiF7+7gxp7UX9atICbUhZOTpikm5y/koa56+mcT4xlfNX0/CPWcqI9I2kKyY8efVN3jL7k+5sg7/eVreJKe7eeznZ8N+tNXyKM4C5TtfbAVDrUQBEnL7Cd9vPAvBy+5p8tfUM3+84S7eGrrTwLsEuvArgkfsz49dff2XRokUsXrwYX19foqKiGD16NG5ubgwZMiTfa8aPH8+YMWP0r1NSUvDw8CirKgshiqKKJ3QcB+3fVVuBIn+GY3+q44XiDt9OpzECp/q3g53qzdUVdR/wJWVWvRG8tgV+HYz1hR18bzaHfXXfIiyjGzvOXCXhega/77/I7/svYqQBfw97OtZzpkNdJ/zc7UjLytEHOOcSU/MEPNfSsh54W+lZOpbsiWbJnmgCvasytLUXXRq4YKyVdWtLQ45O4VT8dTXIuZLK+StpXLiaxvkraVxKukmOLu8okbqaaOaZfg8aCMl+ljgzLyanD6S92V5sYiJh/w9qd1hxXNytdvVaVIHqAUXPp04XWPsenI+A9BRuaCx557cDAAxq4cH7T9QnKS2TX/de5J3fDvD3m+0qVeujwe7U0dERrVZLXFzePXzi4uJwdXW9z1UP98477zBu3DgGDhwIgJ+fH+fPnyckJOS+AZCZmRlmZsVbiE0IYSBGRury/7U6wc1rcOg3uLRfbfJ3b6a29JhZFz5fayd1r7M1b6PZ/yPNT8zlm8aXyXh/Dnsv3mTziXi2nEzgZNwN9l9IYv+FJD7ecBILEy03H7IStbONGV6OVng7WKmPtw5PB0sOXUpm4fZzrD0Sy66zV9l19ipuduY829KTQS1qUFXWMSo2RVE4EpPCishLrDoQQ8Id60bdzdzEiBpVLalR1YqaVYx59eRkzG5kkebZmQ+e+5jxwNhlB/j4SF8mmfzEzb8nYurTE621Y9ErmLv6c+2g4rUmOdSCqrX0syqnn/Dm4rWbVK9iwQfd1S6vD59swLZTiZy/ksastSeY3NO36OVVMAYLgExNTWnWrBnh4eH06tULUAdBh4eHM3LkyCLnm5aWhtFdU2S1Wi26UtqgUQhRjlhUgRbDSy4/Y1Po8ZnarbZ2PBxYjNmVU7QZsIg2tRvwQXe4lHSTLScS2HIynu3/XeFGhrrukIOVKV6OVng5WOHtaKkPdLwcrLAyu/9/vQFeVQnwqsrl5Jss2nmBX3ZfICY5ndnrTvBp+CmeauzGkNZeNHS3K7n7rCSir6ax6kAMKyIv8V/8Df15azNjajlZUcPBCs+qltRwsMSzqiWeDlY425jdXitq3Qdw4yRYOmLZLwxM1ODk80FN+HzjCI5t20J9LrA1bARNRv6MTVEHzuduZFq3W3FuV1WnK+xawOV9q1h8pDcAs/o2wvrW76CtuQkznm7EkO92s3DHOR5v6EpgTYfil1sBGHQW2NKlSxkyZAhffvklLVq0IDQ0lF9//ZXjx4/j4uLC4MGDcXd3JyQkBFAHTh89ehSAJ554gmeffZZnn30Wa2trateuDcDQoUPZuHEjX375Jb6+vkRGRvLyyy/zwgsvMHPmzALVS2aBCSHucXoTLBuidk3YusPAxerA7DtkZus4fyUVZ1tz7CxKZtZYelYOqw9e5ocd5zh06fYMuOaeVRjS2otuDV0xke6x+0pOy+KvQ5dZGXmJ3eeu6s+bGRsR1MCFPk3caVfHCVPjh3yGpzfBT73U54OWQr17g5Pt//xJm63PAfCG1SzeefF5PKoWchXmpAvqIoYaI3jndPGn1v8XDj/3IYEqBKTPY2hr73xbecb9fpAle6KpUdWStaMrbldYhVoIcd68efqFEP39/fnss88IDAwEoGPHjnh5ebFw4UIAzp07h7e39z15dOjQgc2bNwNw/fp1JkyYwIoVK4iPj8fNzY1BgwYxceJETE0L1nQsAZAQIl9XTsMvAyHxpLo9Qa/50PDpksv/Rry6k3c+X3qKorD/QhI/7DjHmkOXyb41NsXF1oznAj0ZFFgDR2vpygfIyM5h0/F4VkReYtPxBDJz1B4AjQZa1XSgVxN3ujV0LfjSBmlX1Q1+r1+G5i/Ck/efVXx10YtUPfUbh3VeDDWeyYLBLQjwKkQQs/trWDNW3dD0hbUFv+5+stLJmO6JmZLOSxaf8PlbQ/Kd8n49PYvgT7YSk5zO0NZeFbYrrEIFQOWRBEBCiPtKT4bfXoT/Nqiv278DHd8v8OrUeWTdhPM74PQ/6hF/VN081n8QtH0LqtbM97K4lHQW7brA4l0XSLyhjl8x1RrxhJ8rtZ2tMTfRYmZshNmtR/N8HvM7V5E3jdXpFPacu8rKqEv8dfAyKXdsgeLjakPvJu709Hejml0B14vKpSjw6/PqAHvHuvDylgfvrXUjHt3nzTDKSGFC1lCW0o3pffzo26x6wcpb1E/tAus8CdqNeXj6h1h3JBajJc/QRbuPS03fwb3nh/dNu/VkAoO/2w3Akpdb0rICdoVJAFRMEgAJIR5IlwMbJ8GOz9XXPk9C7y8fPthaUdQg5/Q/atfE+R3qQnX50RiBXz9oOwacffJNkpGdw9+HYlm44xxR0UlFv59bnG3MeMKvGj393WjiYV/iax2VpIzsHM4kpHIy7jqHLyWz5lAsl5Ju7yvnamvOU03c6OXvXrzlBPb/BKtGqoHpSxvv6fbM161WnDQjK9qlzeEKdrzaoRbvBtd78N5zmWkwyxuy0+G1HeBSvFaYKzcyCA7dStebfzPd5FvwaAkvrnvgNeOXH+SX3RW3K0wCoGKSAEgIUSBRi+HPN9U9y5x9YdAv6vT8O91IUBdtzG3luRGb931b91uz2DpDzY5q99rWObdbmNBA/R7QfixUa3z/qkQn8dfBGFJuZpORnUN6lk7/mJ6dQ8Ydjxl3vM7Kyf8rwKOqBU81VltN6rrYFPkjKq4cncL5K2qgcyL2hvoYd52zian3TFG3MTPmcT9XejVxJ9DbofgtWldOQ1g7yEqFoCnQdnTBrtPlwFcdIfYgR5y60z1aXYeuSwMXQgf4338Q/Ml1sLg/2FaHtw7fs31FYSiKwojF+1lzKJY2TjdZdP3FAo0rup6eRbfQbVxKusmQVp5MeaphketgCBIAFZMEQEKIAoveDUuehdR4dU+ovt+pXzS5Ac/lA3nTG1uoK/TWegxqd1a7VfL7oouJVAOh46tvn6vTVe1y88h/w+iiyNEp+kDpQHQSf0RdYv3RONIyb0/l93G14Sl/d3o0rkb1KoUc1FtAiqJwKelm3kAn9jr/JdwgMzv/Wbw25sbUc7GhjosNbWo7EFTfBXOTEtrSIScLvgtWF9P0aqcuiVCYKenRe+DbIAC2tfuJFzeZkJmtw8fVhm+HBuBun09X3Oox6mKeJbCtxqoDMbzxSyTGRhpWjmhDwz+6qa2PT3+rLiT6ANtOJfD8t2pX2OLhgbSuVYwp/WVMAqBikgBICFEoyRdhyTP3Bju5XPyg9mNq0OPRUl3RuqDijsK/H6sbwSq3AgGvdmog5N2+WK0E95OWmU34sXj+iIphy8n4PK1EzT2r8JS/G0/4VcOhCIOu0zKzOZuYypmE1FuPNzibmMrphFT9EgJ3Mzcxoq6LDXVdbKjnYkNdVxvquljjamteet10/3wEW2eBuZ3aHWVXwDE8d/pjpLpti0tDIh9fyfCfD5B4IwNHa1O+fL45zTyr3E6rKOrsr+RoeObXYq0AHZ+STpdPtpJ8M4vRQXUYHVQXNkyE7Z9CowHQ56uH5jF++SF+2X2B6lUsWDe6/QOXbihPJAAqJgmAhBCFlpkGf4yAI8vBykkNdnK7tWxcHnr5Q105Df9+om7/obsVKFRvoXaN1elauEDoZhJcOwtXz97xeA7MbKH923n2s0pKy+Tvw7Gsioph59kr5H5jaI00tK3tyFP+bnT1ddWvKwNqq9Klazc5k3iDMwmpnEm8oQ96Lien37daJloNtZysqeNiQz0XazXgcbXBo4rlg8fOlLQLO+H7x9WAs+93RZ/pl3oFPm8K6UnQbQaXfIby0g97OXY5BVNjI2Y93YheTdzVtHFH1c1Vjc3h3bMPHmj9AIqi8OIPe/nneDwN3W1Z8XobdZmEc//Cwu5qK+XYUw9tzbqzK+z5lp5M61UxusIkAComCYCEEEWiKOrmq9YuRZsVVhBJ0bDjM9j3w+0B1K6N1EDIp4dark6njjXKE+Dc8Xjz2oPL8OsPnSeCfd4tgWKT01l9MIZVB2I4ePH2mkRmxkZ0queMgsKZBHU7idyp5/mpYmlCTSdrvB2taGt8lHYx32GbfhmjRv3QtnwFbIq+G0CxpadAWBt1PZ7Gg6B3WPHy2/sdrH4LTG1g1F5STR15a2kU64+quyCM7FSbt7rURbv9Ewifogazzy4rcnG/7onm3d8PYqo1YvUbbW+P38rJglm1ICNZ3a+sevOH5vXvqUSe+3YXAItfCqR17fLfFSYBUDFJACSEKPeux0LEPNjznTpIF9Rp81pTtTUn+/4tLQBYOUNVb6jifevRC85sgQOL1feNzaHl6+p0fPN7/x88k3CDPw9c5o8DlziTkHrP+6bGRng7qKtf13TKfbSmpqMVVaxM4dx22BwC57blvVBrqgZgrUaAiwF2KF/+ChxcAvae8Oq/+d57oehy4JsgiNmv3tfTX6PTKcxZf4IvNp8GwMvBkmWmU3G6th+emFPk1cwvXkujW+g2bmRkM+5xH17tUCtvgl+HwNGV0OE96PR+gfJ8f8UhFu+qOF1hEgAVkwRAQogKI/UK7FoAu75S/7rPpTECO4+8QU7VmurzKl73n7IfEwXrP7wdmFg5qV+WTQaD9t4vv9x9tTYdj8fa3Fgf5LjZW+Q/C+vCLtj0kbqBLagBT7Oh4BGoTh+P3nk7be0gaDVS7UYsiyn5h3+H315QP7thf0ONliWT76X98PVjgAJDVoN3OwBWRF5k6p9H0aVdY7/ZK2g1Cr+2XUOP9i3zXazwQXQ6hee+3cWO01doWsOeZa+2vvfzj1wEf7yu7o/38uYC5XsjI5vgT7ZyKekmz7Wswf96+RWqXmVNAqBikgBICFHhpCfDfxvVQbtVvMG+BmiLuB2HosCJNbB+grqRJoBTfej6P6gTVLQ8L+6FTdPhdLj62sgEmj4P7d7OO8A4eg9EfK4uPJg76NvFD1qPBN8+6v5spSH5orrac3oytH8XHvugZPNf/ZbaHebko7Ys3frZpGZks2vVlzx25H1O6KoTnDkLBytTXmznzfMtPQu8n9iPEeeY+McRzE2MWPNGO2o65RPgXo+DuXXV52NPgbVzgfLe/l8iz35TMbrCCvP9LRvICCHEo8DcTh2sWztI3QW8qMEPqK0tPt3h9Z3Qbaa6yWzCMVj0NPzUG+KOFDyvmEhY1B++6awGP0bG0HQIvLFfnep99+wqjwDo/yOM2g8tXgETK4g7BCtegU8bw7+h6iDukqTLUbu+0pPVAeAd3i3Z/AEem6AOQE44DrtujyuyMjPmMaMoALJrd8WjqgVXUjOZtfYEbWb8w8frT3A1NfOBWZ9LTCVkzXEAxnXzyT/4AXUwfjV/9fmpDfmnyUeb2o4817IGAO/8dvC+s/UqGmkByoe0AAkhxB1uXlPXJNr1Jeiy1C6iJs9Dpw/uP8Pt8kF1jM+JNeprjVYdVNx+rNodV1BpV2Hf92oXX+4ikqbW0HQwBL5678KTRfHvJ7BxshpsvbpNDSBLQ+6q0qbWMHIP2LqpwdfsWupnPOxvsqu3ZNWBGL7YfFq/Y72lqZZnWtRgePuauNjmXUIhR6cw4MsI9p6/RquaDix6KfDBM+Zyp/c36AX9fyhw1VMzsgkO3crFazd5JrAG03uXz64w6QIrJgmAhBAiH1fPqIHC0T/U16bW6urILUfcnrYdd0QNfI79qb7WGKmDfzu8W7zAIjtDHaOz43N1QT9Qg6oGT6ndY9WaqLPisjPUlbmz0yE78/a57Ixbz+86l54EGyapgV3PeWq3XGnR6dTFFS/uVrvz+n2vTrn/LhjM7dVVmm+Ns9LpFNYdiWXepv84EpMCqPu99WtenVc71NLvMv/V1tNMX3McK1Mta0e3f/ju87kLNJrZwbunC9VSuON0Is98rXaF/fxiIG3rlL+uMAmAikkCICGEeIDzEbD+A3WVZFC382g3Rl1r5siKW4k06orDHd4DxzolV7aiqF1pO+bBmU0ll2/9HtD/p9IfbH35gLpNhqJTV5c+s0Vd6LLh0+qaQ3dRFIUtJxOYv+k/9pxTly/QGml4qrEb3Rq6MvKXSDKzdczo48fAFjUeXr4uB+bUgbQrMHQNeLUpVPUnrDzMTzvP425vwdrR7Qo8RqmslHoAFB0djUajoXp1te929+7dLF68mAYNGvDyyy8XrdbliARAQgjxEDqduujjxsnq6sV38u0NHcbddxPXEhN7CCLmw6Hf1BacO2nNwNhMnWVmbK4Ontaa3fGY+76Z2iXX/h2wsC/d+uZa8w7s/goc6qhjohKOQe+voPGAB16268wV5m8+zdaTCXnOd6znxPdDAwq+Kvbyl+HgUmgzGrpMKVTVUzOy6fbpVqKv3uTJRtV4rWMt6rnYYKwtH0OKSz0AateuHS+//DLPP/88sbGx1KtXD19fX06dOsWoUaOYOHFikStfHkgAJIQQBZR1E3YuUMcHeQRAx/HF3sW80DJuqF1eucGO1qRsps0X1c0kmNccUnMDGY3a/WXlUKDLD15MYv6m/1h3JI4qlib8/WZ7XO0Ksb3Kod/g9xfVDXxf31Ho6kecvsKgr28vV2BhosXP3Y7GHnb4e1TBv4Y9bnaluE3JA5R6AFSlShV27txJvXr1+Oyzz1i6dCnbt29n/fr1vPrqq5w5c6bIlS8PJAASQghRqqJ+gZWvqs+rt4CXCj4rK9fFa2mYGWtxsinknmxpV9WB14oO3jpSpH3O/oi6xLK9FzkQncT1fGaFOdmY4e9hj7+HPU087GnkYZ9nu5TSUpjv7yLVJisrCzMz9QPfuHEjPXv2BMDHx4fLly8XJUshhBCi8mg8EPb/CBd2qEsOFEH1KkXbLwzLqlA9AKJ3wboP1O4/18Lt9fWUvztP+buj0ymcSbxB5IUkoqLV43jsdRKuZ7DhaBwbbm35odFAHWfrW0FRFfw97KnrYm3QrrMitQAFBgbSqVMnunfvTteuXdm5cyeNGzdm586d9O3bl4sXL5ZGXcuMtAAJIYQodWlX4cTf4Nev9BZ4vJ/9P8KqUbdfuzVVlxZo+HSxt/+4mZnD4Zhkou4Iii4l3bwnXZvaDix6qYRW276l1LvANm/eTO/evUlJSWHIkCF89506cv3999/n+PHjLF++vGg1LyckABJCCPHIO/2Puqnu8b9uDyI3sVQHsTcdrG5PUkLjeOJT0vXBUFR0EgcvJjOohQcfdC/Z/d7KZBp8Tk4OKSkpVKlSRX/u3LlzWFpa4uxcsOW1yysJgIQQQlQaqYlwYInaKpR44vZ5x7pqINR4EFiV7Jo/OTqF9KycEt9ctdQDoJs3b6IoCpaWav/j+fPnWbFiBfXr1yc4OLhotS5HJAASQghR6SgKRO9WA6EjyyErTT1vZAI+T6jBUM1OYFS4jVrLUqkHQF27dqVPnz68+uqrJCUl4ePjg4mJCYmJiXz88ce89tprRa58eSABkBBCiEotPUVdeXv/jxCz//Z5Ow9o8hz4Pwv2Hoar332U+mao+/fvp127dgD89ttvuLi4cP78eX788Uc+++yzomQphBBCiPLC3BaaD4OXN6m717d4Rd1wNzla3eok1A++fwLCp8LJdeqA7gqmSJ1vaWlp2NjYALB+/Xr69OmDkZERLVu25Pz58yVaQSGEEEIYkKsfPDFLXTX62GrY/wOc2wbnt6tHLofa6sDp6gHg0QKcfMp1d1mRAqDatWuzcuVKevfuzbp163jrrbcAiI+Ply4jIYQQ4lFkYgGN+qnH1bNwdos6Zih6N1w5BVf+U4+oRWp6M1twb6YGQx4twL152W03UgBF6gKbOHEiY8eOxcvLixYtWtCqVStAbQ1q0qRJofObP38+Xl5emJubExgYyO7du++b9siRIzz99NN4eXmh0WgIDQ29J83kyZPRaDR5Dh+fUt6TRgghhKgsqnpDs6HQ6wsYtRfePQvP/ArtxoJ3ezCxgowUdcPaLTPh56dhphfMD4Q/RsL+nyDxP4PeQpFagPr27Uvbtm25fPkyjRs31p/v3LkzvXv3LlReS5cuZcyYMYSFhREYGEhoaCjBwcGcOHEi3+n0aWlp1KxZk379+ulbnvLj6+vLxo0b9a+NjUt/CW4hhBCiUrKsCnWD1QMgJxvij6qrTV/co7YSXTsLCcfVI/InqPUYPL/CYFUuclTg6uqKq6urftXn6tWr06JFi0Ln8/HHHzN8+HCGDRsGQFhYGH/99Rffffcd48aNuyd9QEAAAQEBAPm+n8vY2BhXV9dC10cIIYQQxaQ1hmqN1KPFcPXcjQS4uPt2t5l3e4NWsUhdYDqdjqlTp2JnZ4enpyeenp7Y29szbdo0dDpdgfPJzMxk3759BAUF3a6QkRFBQUFEREQUpWp6p06dws3NjZo1a/Lss89y4cKF+6bNyMggJSUlzyGEEEKIEmTtpO571mUKvPA3tL1/L05ZKFIA9MEHHzBv3jxmzJhBZGQkkZGRTJ8+nc8//5wJEyYUOJ/ExERycnJwcXHJc97FxYXY2NiiVA1Q9ypbuHAha9euZcGCBZw9e5Z27dpx/fr1fNOHhIRgZ2enPzw8yt/aBkIIIYQoOUXqAvvhhx/45ptv9LvAAzRq1Ah3d3def/11PvrooxKrYFE8/vjj+ueNGjUiMDAQT09Pfv31V1588cV70o8fP54xY8boX6ekpEgQJIQQQjzCihQAXb16Nd9ZVT4+Ply9WvDFkBwdHdFqtcTFxeU5HxcXV6Ljd+zt7albty7//Zf/iHMzMzPMzMxKrDwhhBBClG9F6gJr3Lgx8+bNu+f8vHnzaNSoUYHzMTU1pVmzZoSHh+vP6XQ6wsPD9VPrS8KNGzc4ffo01apVK7E8hRBCCFFxFakFaNasWXTv3p2NGzfqA5WIiAiio6NZs2ZNofIaM2YMQ4YMoXnz5rRo0YLQ0FBSU1P1s8IGDx6Mu7s7ISEhgDpw+ujRo/rnly5dIioqCmtra2rXrg3A2LFj6dGjB56ensTExDBp0iS0Wi2DBg0qyu0KIYQQ4hFTpBagDh06cPLkSXr37k1SUhJJSUn06dOHI0eO8NNPPxUqrwEDBjBnzhwmTpyIv78/UVFRrF27Vj8w+sKFC1y+fFmfPiYmhiZNmtCkSRMuX77MnDlzaNKkCS+99JI+zcWLFxk0aBD16tWjf//+ODg4sHPnTpycnIpyu0IIIYR4xBRpN/j7OXDgAE2bNiUnJ6eksjQI2Q1eCCGEqHhKfTd4IYQQQoiKTAIgIYQQQlQ6EgAJIYQQotIp1CywPn36PPD9pKSk4tRFCCGEEKJMFCoAsrOze+j7gwcPLlaFhBBCCCFKW6ECoO+//7606iGEEEIIUWZkDJAQQgghKh0JgIQQQghR6UgAJIQQQohKRwIgIYQQQlQ6EgAJIYQQotKRAEgIIYQQlY4EQEIIIYSodCQAEkIIIUSlIwGQEEIIISodCYCEEEIIUelIACSEEEKISkcCICGEEEJUOhIACSGEEKLSkQBICCGEEJWOBEBCCCGEqHQkABJCCCFEpSMBkBBCCCEqHQmAhBBCCFHpSAAkhBBCiEqnXARA8+fPx8vLC3NzcwIDA9m9e/d90x45coSnn34aLy8vNBoNoaGh96QJCQkhICAAGxsbnJ2d6dWrFydOnCjFOxBCCCFERWLwAGjp0qWMGTOGSZMmsX//fho3bkxwcDDx8fH5pk9LS6NmzZrMmDEDV1fXfNNs2bKFESNGsHPnTjZs2EBWVhZdu3YlNTW1NG9FCCGEEBWERlEUxZAVCAwMJCAggHnz5gGg0+nw8PBg1KhRjBs37oHXenl5MXr0aEaPHv3AdAkJCTg7O7Nlyxbat2//0DqlpKRgZ2dHcnIytra2Bb4XIYQQQhhOYb6/DdoClJmZyb59+wgKCtKfMzIyIigoiIiIiBIrJzk5GYCqVavm+35GRgYpKSl5DiGEEEI8ugwaACUmJpKTk4OLi0ue8y4uLsTGxpZIGTqdjtGjR9OmTRsaNmyYb5qQkBDs7Oz0h4eHR4mULYQQQojyyeBjgErbiBEjOHz4MEuWLLlvmvHjx5OcnKw/oqOjy7CGQgghhChrxoYs3NHREa1WS1xcXJ7zcXFx9x3gXBgjR45k9erVbN26lerVq983nZmZGWZmZsUuTwghhBAVg0FbgExNTWnWrBnh4eH6czqdjvDwcFq1alXkfBVFYeTIkaxYsYJ//vkHb2/vkqiuEEIIIR4RBm0BAhgzZgxDhgyhefPmtGjRgtDQUFJTUxk2bBgAgwcPxt3dnZCQEEAdOH306FH980uXLhEVFYW1tTW1a9cG1G6vxYsX88cff2BjY6MfT2RnZ4eFhYUB7lIIIYQQ5YnBp8EDzJs3j9mzZxMbG4u/vz+fffYZgYGBAHTs2BEvLy8WLlwIwLlz5/Jt0enQoQObN28GQKPR5FvO999/z9ChQx9aH5kGL4QQQlQ8hfn+LhcBUHkjAZAQQghR8VSYdYCEEEIIIQxBAiAhhBBCVDoSAAkhhBCi0pEASAghhBCVjgRAQgghhKh0JAASQgghRKUjAZAQQgghKh0JgIQQQghR6UgAJIQQQohKRwIgIYQQQlQ6EgAJIYQQotKRAEgIIYQQlY4EQEIIIYSodCQAEkIIIUSlIwGQEEIIISodCYCEEEIIUelIACSEEEKISkcCICGEEEJUOhIACSGEEKLSkQBICCGEEJWOBEBCCCGEqHQkABJCCCFEpSMBkBBCCCEqHQmAhBBCCFHpSAAkhBBCiEqnXARA8+fPx8vLC3NzcwIDA9m9e/cD0y9btgwfHx/Mzc3x8/NjzZo1ed6Pi4tj6NChuLm5YWlpSbdu3Th16lRp3oIQQgghKhCDB0BLly5lzJgxTJo0if3799O4cWOCg4OJj4/PN/2OHTsYNGgQL774IpGRkfTq1YtevXpx+PBhABRFoVevXpw5c4Y//viDyMhIPD09CQoKIjU1tSxvTQghhBDllEZRFMWQFQgMDCQgIIB58+YBoNPp8PDwYNSoUYwbN+6e9AMGDCA1NZXVq1frz7Vs2RJ/f3/CwsI4efIk9erV4/Dhw/j6+urzdHV1Zfr06bz00ksPrVNKSgp2dnYkJydja2tbQncqhBBCiNJUmO9vg7YAZWZmsm/fPoKCgvTnjIyMCAoKIiIiIt9rIiIi8qQHCA4O1qfPyMgAwNzcPE+eZmZm/PvvvyV9C0IIIYSogAwaACUmJpKTk4OLi0ue8y4uLsTGxuZ7TWxs7APT+/j4UKNGDcaPH8+1a9fIzMxk5syZXLx4kcuXL+ebZ0ZGBikpKXkOIYQQQjy6DD4GqKSZmJiwfPlyTp48SdWqVbG0tGTTpk08/vjjGBnlf7shISHY2dnpDw8PjzKutRBCCCHKkkEDIEdHR7RaLXFxcXnOx8XF4erqmu81rq6uD03frFkzoqKiSEpK4vLly6xdu5YrV65Qs2bNfPMcP348ycnJ+iM6OrqYdyaEEEKI8sygAZCpqSnNmjUjPDxcf06n0xEeHk6rVq3yvaZVq1Z50gNs2LAh3/R2dnY4OTlx6tQp9u7dy1NPPZVvnmZmZtja2uY5hBBCCPHoMjZ0BcaMGcOQIUNo3rw5LVq0IDQ0lNTUVIYNGwbA4MGDcXd3JyQkBIA333yTDh06MHfuXLp3786SJUvYu3cvX331lT7PZcuW4eTkRI0aNTh06BBvvvkmvXr1omvXrga5RyGEEEKULwYPgAYMGEBCQgITJ04kNjYWf39/1q5dqx/ofOHChTxjd1q3bs3ixYv58MMPef/996lTpw4rV66kYcOG+jSXL19mzJgxxMXFUa1aNQYPHsyECRPK/N6EEEIIUT4ZfB2g8kjWARJCCCEqngqzDpAQQgghhCFIACSEEEKISkcCICGEEEJUOhIACSGEEKLSkQBICCGEEJWOBEBCCCGEqHQkABJCCCFEpSMBkBBCCCEqHQmAhBBCCFHpSAAkhBBCiEpHAiAhhBBCVDoSAAkhhBCi0pEASAghhBCVjgRAQgghhKh0JAASQgghRKUjAZAQQgghKh0JgIQQQghR6UgAJIQQQohKRwIgIYQQQlQ6EgAJIYQQotKRAEgIIYQQlY4EQEIIIYSodCQAEkIIIUSlIwGQEEIIISodCYCEEEIIUemUiwBo/vz5eHl5YW5uTmBgILt3735g+mXLluHj44O5uTl+fn6sWbPmnjTHjh2jZ8+e2NnZYWVlRUBAABcuXCitWxBCCCFEBWLwAGjp0qWMGTOGSZMmsX//fho3bkxwcDDx8fH5pt+xYweDBg3ixRdfJDIykl69etGrVy8OHz6sT3P69Gnatm2Lj48Pmzdv5uDBg0yYMAFzc/Oyui0hhBBClGMaRVEUQ1YgMDCQgIAA5s2bB4BOp8PDw4NRo0Yxbty4e9IPGDCA1NRUVq9erT/XsmVL/P39CQsLA2DgwIGYmJjw008/FalOKSkp2NnZkZycjK2tbZHyEEIIIUTZKsz3t0FbgDIzM9m3bx9BQUH6c0ZGRgQFBREREZHvNREREXnSAwQHB+vT63Q6/vrrL+rWrUtwcDDOzs4EBgaycuXKUrsPIYQQQlQsBg2AEhMTycnJwcXFJc95FxcXYmNj870mNjb2genj4+O5ceMGM2bMoFu3bqxfv57evXvTp08ftmzZkm+eGRkZpKSk5DmEEEII8egyNnQFSppOpwPgqaee4q233gLA39+fHTt2EBYWRocOHe65JiQkhClTppRpPYUQQghhOAZtAXJ0dESr1RIXF5fnfFxcHK6urvle4+rq+sD0jo6OGBsb06BBgzxp6tevf99ZYOPHjyc5OVl/REdHF/WWhBBCCFEBGDQAMjU1pVmzZoSHh+vP6XQ6wsPDadWqVb7XtGrVKk96gA0bNujTm5qaEhAQwIkTJ/KkOXnyJJ6envnmaWZmhq2tbZ5DCCGEEI8ug3eBjRkzhiFDhtC8eXNatGhBaGgoqampDBs2DIDBgwfj7u5OSEgIAG+++SYdOnRg7ty5dO/enSVLlrB3716++uorfZ7vvPMOAwYMoH379nTq1Im1a9fy559/snnzZkPcohBCCCHKGYMHQAMGDCAhIYGJEycSGxuLv78/a9eu1Q90vnDhAkZGtxuqWrduzeLFi/nwww95//33qVOnDitXrqRhw4b6NL179yYsLIyQkBDeeOMN6tWrx++//07btm3L/P6EEEIIUf4YfB2g8kjWARJCCCEqngqzDpAQQgghhCFIACSEEEKISkcCICGEEEJUOhIACSGEEKLSkQBICCGEEJWOBEBCCCGEqHQkABJCCCFEpSMBkBBCCCEqHQmAhBBCCFHpSAAkhBBCiEpHAiAhhBBCVDoSAAkhhBCi0pEASAghhBCVjgRAQgghhKh0JAASQgghRKUjAZAQQgghKh0JgIQQQghR6UgAJIQQQohKRwIgIYQQQlQ6EgAJIYQQotKRAEgIIYQQlY4EQEIIIYSodCQAEkIIIUSlIwGQEEIIISodCYCEEEIIUemUiwBo/vz5eHl5YW5uTmBgILt3735g+mXLluHj44O5uTl+fn6sWbMmz/uTJ0/Gx8cHKysrqlSpQlBQELt27SrNWxBCCCFEBWLwAGjp0qWMGTOGSZMmsX//fho3bkxwcDDx8fH5pt+xYweDBg3ixRdfJDIykl69etGrVy8OHz6sT1O3bl3mzZvHoUOH+Pfff/Hy8qJr164kJCSU1W0JIYQQohzTKIqiGLICgYGBBAQEMG/ePAB0Oh0eHh6MGjWKcePG3ZN+wIABpKamsnr1av25li1b4u/vT1hYWL5lpKSkYGdnx8aNG+ncufND65SbPjk5GVtb2yLemRBCCCHKUmG+vw3aApSZmcm+ffsICgrSnzMyMiIoKIiIiIh8r4mIiMiTHiA4OPi+6TMzM/nqq6+ws7OjcePGJVd5IYQQQlRYxoYsPDExkZycHFxcXPKcd3Fx4fjx4/leExsbm2/62NjYPOdWr17NwIEDSUtLo1q1amzYsAFHR8d888zIyCAjI0P/OiUlpSi3I4QQQogKwuBjgEpLp06diIqKYseOHXTr1o3+/fvfd1xRSEgIdnZ2+sPDw6OMayuEEEKIsmTQAMjR0RGtVktcXFye83Fxcbi6uuZ7jaura4HSW1lZUbt2bVq2bMm3336LsbEx3377bb55jh8/nuTkZP0RHR1djLsSQgghRHln0ADI1NSUZs2aER4erj+n0+kIDw+nVatW+V7TqlWrPOkBNmzYcN/0d+Z7ZzfXnczMzLC1tc1zCCGEEOLRZdAxQABjxoxhyJAhNG/enBYtWhAaGkpqairDhg0DYPDgwbi7uxMSEgLAm2++SYcOHZg7dy7du3dnyZIl7N27l6+++gqA1NRUPvroI3r27Em1atVITExk/vz5XLp0iX79+hnsPoUQQghRfhg8ABowYAAJCQlMnDiR2NhY/P39Wbt2rX6g84ULFzAyut1Q1bp1axYvXsyHH37I+++/T506dVi5ciUNGzYEQKvVcvz4cX744QcSExNxcHAgICCAbdu24evra5B7FEIIIUT5YvB1gMojWQdICCGEqHgqzDpAQgghhBCGIAGQEEIIISodCYCEEEIIUelIACSEEEKISkcCICGEEEJUOhIACSGEEKLSkQBICCGEEJWOBEBCCCGEqHQkABJCCCFEpSMBkBBCCCEqHQmAhBBCCFHpSAAkhBBCiEpHAiAhhBBCVDoSAAkhhBCi0pEASAghhBCVjgRAQgghhKh0JAASQgghRKUjAZAQQgghKh0JgIQQQghR6UgAJIQQQohKRwIgIYQQQlQ6EgAJIYQQotKRAEgIIYQQlY4EQEIIIYSodCQAEkIIIUSlUy4CoPnz5+Pl5YW5uTmBgYHs3r37gemXLVuGj48P5ubm+Pn5sWbNmjzvK4rCxIkTqVatGhYWFgQFBXHq1KnSvAUhhBBCVCAGD4CWLl3KmDFjmDRpEvv376dx48YEBwcTHx+fb/odO3YwaNAgXnzxRSIjI+nVqxe9evXi8OHD+jSzZs3is88+IywsjF27dmFlZUVwcDDp6elldVtCCCGEKMc0iqIohqxAYGAgAQEBzJs3DwCdToeHhwejRo1i3Lhx96QfMGAAqamprF69Wn+uZcuW+Pv7ExYWhqIouLm58fbbbzN27FgAkpOTcXFxYeHChQwcOPChdUpJScHOzo7k5GRsbW1L6E6FEEIIUZoK8/1t0BagzMxM9u3bR1BQkP6ckZERQUFBRERE5HtNREREnvQAwcHB+vRnz54lNjY2Txo7OzsCAwPvm6cQQgghKhdjQxaemJhITk4OLi4uec67uLhw/PjxfK+JjY3NN31sbKz+/dxz90tzt4yMDDIyMvSvk5OTATWSFEIIIUTFkPu9XZDOLYMGQOVFSEgIU6ZMuee8h4eHAWojhBBCiOK4fv06dnZ2D0xj0ADI0dERrVZLXFxcnvNxcXG4urrme42rq+sD0+c+xsXFUa1atTxp/P39881z/PjxjBkzRv9ap9Nx9epVHBwc0Gg0hb6v0pKSkoKHhwfR0dFlPjapspZt6PKlbClbypayH8XyS6tsRVG4fv06bm5uD01r0ADI1NSUZs2aER4eTq9evQA1+AgPD2fkyJH5XtOqVSvCw8MZPXq0/tyGDRto1aoVAN7e3ri6uhIeHq4PeFJSUti1axevvfZavnmamZlhZmaW55y9vX2x7q002draGmxwdmUt29DlS9lStpQtZT+K5ZdG2Q9r+cll8C6wMWPGMGTIEJo3b06LFi0IDQ0lNTWVYcOGATB48GDc3d0JCQkB4M0336RDhw7MnTuX7t27s2TJEvbu3ctXX30FgEajYfTo0fzvf/+jTp06eHt7M2HCBNzc3PRBlhBCCCEqN4MHQAMGDCAhIYGJEycSGxuLv78/a9eu1Q9ivnDhAkZGtyertW7dmsWLF/Phhx/y/vvvU6dOHVauXEnDhg31ad59911SU1N5+eWXSUpKom3btqxduxZzc/Myvz8hhBBClD8GD4AARo4ced8ur82bN99zrl+/fvTr1++++Wk0GqZOncrUqVNLqorlgpmZGZMmTbqnu07KfnTLl7KlbClbyn4Uyzf0vUM5WAhRCCGEEKKsGXwrDCGEEEKIsiYBkBBCCCEqHQmAhBBCCFHpSAAkhBBCiEpHAqAKICQkhICAAGxsbHB2dqZXr16cOHHCIHWZMWOGfq2lsnDp0iWee+45HBwcsLCwwM/Pj71795Z6uTk5OUyYMAFvb28sLCyoVasW06ZNK9D+MoW1detWevTogZubGxqNhpUrV+Z5X1EUJk6cSLVq1bCwsCAoKIhTp06VetlZWVm89957+Pn5YWVlhZubG4MHDyYmJqZEyn5Y+Xd79dVX0Wg0hIaGllnZx44do2fPntjZ2WFlZUVAQAAXLlwo9bJv3LjByJEjqV69OhYWFjRo0ICwsLBil1uQ/0vS09MZMWIEDg4OWFtb8/TTT9+z+n5plH316lVGjRpFvXr1sLCwoEaNGrzxxhv6vRlLu/w7KYrC448//tDfyZIuOyIigsceewwrKytsbW1p3749N2/eLPWyY2Njef7553F1dcXKyoqmTZvy+++/F6tcgAULFtCoUSP9YoetWrXi77//1r9fWr9rBSUBUAWwZcsWRowYwc6dO9mwYQNZWVl07dqV1NTUMq3Hnj17+PLLL2nUqFGZlHft2jXatGmDiYkJf//9N0ePHmXu3LlUqVKl1MueOXMmCxYsYN68eRw7doyZM2cya9YsPv/88xIvKzU1lcaNGzN//vx83581axafffYZYWFh7Nq1CysrK4KDg0lPTy/VstPS0ti/fz8TJkxg//79LF++nBMnTtCzZ89il1uQ8u+0YsUKdu7cWaDl7Uuq7NOnT9O2bVt8fHzYvHkzBw8eZMKECSWyntjDyh4zZgxr167l559/5tixY4wePZqRI0eyatWqYpVbkP9L3nrrLf7880+WLVvGli1biImJoU+fPsUqtyBlx8TEEBMTw5w5czh8+DALFy5k7dq1vPjii8UuuyDl3yk0NLREt0EqSNkRERF069aNrl27snv3bvbs2cPIkSPzrINXWmUPHjyYEydOsGrVKg4dOkSfPn3o378/kZGRxSq7evXqzJgxg3379rF3714ee+wxnnrqKY4cOQKU3u9agSmiwomPj1cAZcuWLWVW5vXr15U6deooGzZsUDp06KC8+eabpV7me++9p7Rt27bUy8lP9+7dlRdeeCHPuT59+ijPPvtsqZYLKCtWrNC/1ul0iqurqzJ79mz9uaSkJMXMzEz55ZdfSrXs/OzevVsBlPPnz5do2Q8q/+LFi4q7u7ty+PBhxdPTU/nkk0/KpOwBAwYozz33XImXVZCyfX19lalTp+Y517RpU+WDDz4o0bLv/r8kKSlJMTExUZYtW6ZPc+zYMQVQIiIiSrXs/Pz666+KqampkpWVVaJlP6j8yMhIxd3dXbl8+XKB/k2UVNmBgYHKhx9+WOJlFaRsKysr5ccff8yTrmrVqsrXX39d4uVXqVJF+eabb8r0d+1+pAWoAsptEq5atWqZlTlixAi6d+9OUFBQmZW5atUqmjdvTr9+/XB2dqZJkyZ8/fXXZVJ269atCQ8P5+TJkwAcOHCAf//9l8cff7xMys919uxZYmNj83zudnZ2BAYGEhERUaZ1AfV3T6PRlNleeTqdjueff5533nkHX1/fMikzt9y//vqLunXrEhwcjLOzM4GBgSXSHVIQrVu3ZtWqVVy6dAlFUdi0aRMnT56ka9euJVrO3f+X7Nu3j6ysrDy/bz4+PtSoUaPEf98K8v9YcnIytra2GBuX/Jq9+ZWflpbGM888w/z58++7IXdplB0fH8+uXbtwdnamdevWuLi40KFDB/79999SLxvU37elS5dy9epVdDodS5YsIT09nY4dO5ZYuTk5OSxZsoTU1FRatWpVpr9r91UmYZYoMTk5OUr37t2VNm3alFmZv/zyi9KwYUPl5s2biqIoZdYCZGZmppiZmSnjx49X9u/fr3z55ZeKubm5snDhwlIvOycnR3nvvfcUjUajGBsbKxqNRpk+fXqpl8tdf3Fu375dAZSYmJg86fr166f079+/VMu+282bN5WmTZsqzzzzTImW+6Dyp0+frnTp0kXR6XSKoihl1gKU+9e/paWl8vHHHyuRkZFKSEiIotFolM2bN5dq2YqiKOnp6crgwYMVQDE2NlZMTU2VH374oUTLze//kkWLFimmpqb3pA0ICFDefffdUi37bgkJCUqNGjWU999/v8TKfVj5L7/8svLiiy/qXz/s30RJlR0REaEAStWqVZXvvvtO2b9/vzJ69GjF1NRUOXnyZKmWrSiKcu3aNaVr16763zdbW1tl3bp1JVLmwYMHFSsrK0Wr1Sp2dnbKX3/9pShK2f2uPUi52ApDFNyIESM4fPhwqfxlkJ/o6GjefPNNNmzYUOZ7qel0Opo3b8706dMBaNKkCYcPHyYsLIwhQ4aUatm//vorixYtYvHixfj6+hIVFcXo0aNxc3Mr9bLLo6ysLPr374+iKCxYsKBMyty3bx+ffvop+/fvL9HxGAWh0+kAeOqpp3jrrbcA8Pf3Z8eOHYSFhdGhQ4dSLf/zzz9n586drFq1Ck9PT7Zu3cqIESNwc3MrsVbYsv6/pDBlp6Sk0L17dxo0aMDkyZPLpPxVq1bxzz//FHvcS1HKzv19e+WVV/QbgTdp0oTw8HC+++47/WbgpVE2wIQJE0hKSmLjxo04OjqycuVK+vfvz7Zt2/Dz8ytWmfXq1SMqKork5GR+++03hgwZwpYtW4qVZ4kpkzBLlIgRI0Yo1atXV86cOVNmZa5YsUIBFK1Wqz8ARaPRKFqtVsnOzi61smvUqJHnrzFFUZQvvvhCcXNzK7Uyc1WvXl2ZN29ennPTpk1T6tWrV6rlctdfnKdPn1YAJfL/7d1fSFP9Hwfw95y6OcmnpZma+A/DzNKLlFj/pAalXVRmSCAyKTA1zZsKQaUioi4qL4IEobwoKTDSzCgjtQghLVBnYDpR6CKlpGj+KRX8PBfhePYzaz/dmY/P3i84sJ1ztvf36JfjZ9/zPa6jw26/nTt3ysmTJxXNnjU1NSUHDx6U+Ph4GRkZcWrm7/LLy8tt/eyffc/Dw0PCw8MVzZ6cnBRPT0+5cOGC3X5nzpyRrVu3Kpo9MTEhXl5e0tDQYLffsWPHZO/evU7JnO9c0tTUJADk69evduvDwsLk2rVrimbPslqtYjAYxGg02kadnWm+/KKionn7W3JysqLZAwMDAkBu375ttz4jI8NpI67zZff39wsAeffund16o9Eox48fd0r2/75vTk6OS/ran3AO0DIgIigoKEBtbS2am5sRGRnpsmyj0Yju7m50dnbalsTERGRmZqKzsxNqtVqx7G3bts25XbOvrw/h4eGKZc6amJiYc/eFWq22fVJzlcjISAQFBaGpqcm2zmq1oq2tDQaDQfH82ZEfi8WC58+fw9/fX/HMWVlZWTCbzXZ9LyQkBKdPn0ZjY6Oi2d7e3khKSlqS/jc9PY3p6WlF+t+fziWbN2+Gl5eXXX/r7e3Fhw8fFt3fHDmPWa1W7NmzB97e3qivr3fqqPOf8ouLi+f0NwAoLy9HVVWVotkREREICQlRpL/9KXtiYgIAXHa+m5mZweTkpKJ9zWEuKbNoUfLy8uSvv/6SFy9eyNDQkG2ZmJhYkva4ag5Qe3u7eHp6ysWLF8VisUh1dbXodDq5c+eO4tkmk0nWrl0rDQ0NMjg4KA8ePJCAgABFrk2Pjo5KR0eHdHR0CADbnJPZO60uX74sK1eulIcPH4rZbJYDBw5IZGSkUz4d/y57ampK9u/fL6GhodLZ2WnX9yYnJxed/af8X3HmHKA/ZT948EC8vLyksrJSLBaLXL9+XdRqtbx69Urx7OTkZImLi5OWlhYZGBiQqqoq0Wq1cuPGjUXlOnIuyc3NlbCwMGlubpa3b9+KwWAQg8GwqFxHsr99+yZbtmyRTZs2SX9/v90+zhhpXsh5FE6aA+RIdnl5ufj5+UlNTY1YLBYpLS0VrVYr/f39imZPTU1JdHS07NixQ9ra2qS/v1+uXLkiKpXKNl9noYqLi+Xly5cyODgoZrNZiouLRaVSybNnz0REub7mKBZAywCAXy5VVVVL0h5XFUAiIo8ePZKNGzeKRqOR9evXS2VlpUtyrVarFBUVSVhYmGi1WomKipKSkhKn/eH/p5aWll/+fk0mk4j8vBW+rKxM1qxZIxqNRoxGo/T29iqePTg4OG/fa2lpUTz/V5xZADmSffPmTYmOjhatVisJCQlSV1fnkuyhoSHJzs6WkJAQ0Wq1EhMTI1evXrVNBl8oR84l379/l/z8fNHr9aLT6SQtLU2GhoYWletI9nw/EwAyODioeP58r3FGAeRo9qVLlyQ0NFR0Op0YDAanFNuOZPf19cmhQ4ckMDBQdDqdxMfHz7ktfiGOHj0q4eHh4u3tLatXrxaj0WgrfkSU62uOUoko8K9tiYiIiP7FOAeIiIiI3A4LICIiInI7LICIiIjI7bAAIiIiIrfDAoiIiIjcDgsgIiIicjssgIiIiMjtsAAiInKASqVCXV3dUjeDiJyEBRAR/etlZ2dDpVLNWVJSUpa6aUS0THkudQOIiByRkpIy50spNRrNErWGiJY7jgAR0bKg0WgQFBRkt+j1egA/L09VVFQgNTUVPj4+iIqKwv379+1e393djd27d8PHxwf+/v7IycnB2NiY3T63bt1CXFwcNBoNgoODUVBQYLd9ZGQEaWlp0Ol0WLduHerr65U9aCJSDAsgIvpPKCsrQ3p6Orq6upCZmYkjR46gp6cHADA+Po69e/dCr9fjzZs3qKmpwfPnz+0KnIqKCpw4cQI5OTno7u5GfX09oqOj7TLOnz+PjIwMmM1m7Nu3D5mZmfjy5YtLj5OInMRlX7tKRLRAJpNJ1Gq1+Pr62i0XL14UkZ/feJ2bm2v3mi1btkheXp6IiFRWVoper5exsTHb9sePH4uHh4cMDw+LiEhISIiUlJTM2wYAUlpaans+NjYmAOTJkydOO04ich3OASKiZWHXrl2oqKiwW7dq1SrbY4PBYLfNYDCgs7MTANDT04OEhAT4+vratm/btg0zMzPo7e2FSqXCx48fYTQaf9uG+Ph422NfX1/4+fnh06dPCz0kIlpCLICIaFnw9fWdc0nKWXx8fBzaz8vLy+65SqXCzMyMEk0iIoVxDhAR/Se8fv16zvPY2FgAQGxsLLq6ujA+Pm7b3traCg8PD8TExGDFihWIiIhAU1OTS9tMREuHI0BEtCxMTk5ieHjYbp2npycCAgIAADU1NUhMTMT27dtRXV2N9vZ23Lx5EwCQmZmJs2fPwmQy4dy5c/j8+TMKCwuRlZWFNWvWAADOnTuH3NxcBAYGIjU1FaOjo2htbUVhYaFrD5SIXIIFEBEtC0+fPkVwcLDdupiYGLx//x7Azzu07t27h/z8fAQHB+Pu3bvYsGEDAECn06GxsRFFRUVISkqCTqdDeno6rl27Znsvk8mEHz9+oLy8HKdOnUJAQAAOHz7sugMkIpdSiYgsdSOIiBZDpVKhtrYWBw8eXOqmENEywTlARERE5HZYABEREZHb4RwgIlr2eCWfiP5fHAEiIiIit8MCiIiIiNwOCyAiIiJyOyyAiIiIyO2wACIiIiK3wwKIiIiI3A4LICIiInI7LICIiIjI7bAAIiIiIrfzN+uqNFNyIjJvAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_losses(\"Satellite Pretrained ResNet50\", sat_resnet50_train, sat_resnet50_valid)\n",
-    "plot_metrics(\"Satellite Pretrained ResNet50\", sat_resnet50_metrics)\n",
-    "plot_loss_comparison(\"ResNet18 vs ResNet50\", sat_resnet18_train_jaccard, sat_resnet50_train, sat_resnet18_valid_jaccard, sat_resnet50_valid, compare1='ResNet18', compare2='ResNet50')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Better than smaller ResNet, but it takes about 10x longer to train. It does not seem to have fully converged either. From the way I built the decoder, I have a feeling it would perform better on larger sized images."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[INFO] training the network...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  3%|▎         | 1/30 [00:31<15:24, 31.89s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 1/30\n",
-      "Train loss: 0.630888, Test loss: 0.9639\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.11431885646817753\n",
-      "Recall: 0.050741229890809786\n",
-      "f1_score: 0.0702856699625184\n",
-      "IOU: 0.03642283672171995\n",
-      "Accuracy: 0.8560176451819385\n",
-      "Specificity: 0.9527686016314711\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 17%|█▋        | 5/30 [02:39<13:15, 31.83s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 5/30\n",
-      "Train loss: 0.223765, Test loss: 0.2160\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.8807862994952873\n",
-      "Recall: 0.8795340863008362\n",
-      "f1_score: 0.8801597475138303\n",
-      "IOU: 0.785969021527649\n",
-      "Accuracy: 0.9743102766997637\n",
-      "Specificity: 0.9856972821082527\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 33%|███▎      | 10/30 [05:19<10:41, 32.09s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 10/30\n",
-      "Train loss: 0.205977, Test loss: 0.2096\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.8942369612470468\n",
-      "Recall: 0.8743337795788829\n",
-      "f1_score: 0.8841733769734683\n",
-      "IOU: 0.7923931538532979\n",
-      "Accuracy: 0.9754295133402618\n",
-      "Specificity: 0.9875757883288412\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 50%|█████     | 15/30 [08:00<08:03, 32.26s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 15/30\n",
-      "Train loss: 0.192620, Test loss: 0.1906\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.900946754611249\n",
-      "Recall: 0.890358643187981\n",
-      "f1_score: 0.8956214066075108\n",
-      "IOU: 0.8109731680476466\n",
-      "Accuracy: 0.9777403927607772\n",
-      "Specificity: 0.9882389838308969\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 67%|██████▋   | 20/30 [10:41<05:21, 32.16s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 20/30\n",
-      "Train loss: 0.178805, Test loss: 0.1725\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.8949789352270718\n",
-      "Recall: 0.9181256122547224\n",
-      "f1_score: 0.9064045247712731\n",
-      "IOU: 0.8288298052638681\n",
-      "Accuracy: 0.9796623651922102\n",
-      "Specificity: 0.9870557765628517\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 83%|████████▎ | 25/30 [13:23<02:41, 32.38s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 25/30\n",
-      "Train loss: 0.195213, Test loss: 0.1977\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.9016901590005655\n",
-      "Recall: 0.881517785141734\n",
-      "f1_score: 0.891489873020493\n",
-      "IOU: 0.8042234809795056\n",
-      "Accuracy: 0.9769829069040349\n",
-      "Specificity: 0.9884526848072648\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 30/30 [16:07<00:00, 32.24s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 30/30\n",
-      "Train loss: 0.167344, Test loss: 0.1623\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.9007822221742975\n",
-      "Recall: 0.9245049037675342\n",
-      "f1_score: 0.9124894048623159\n",
-      "IOU: 0.8390625424176124\n",
-      "Accuracy: 0.9809801292488866\n",
-      "Specificity: 0.9877654166629358\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# ImageNet ResNet50\n",
-    "loss = JaccardLoss()\n",
-    "imagenet_resnet50 = resnet50(weights=ResNet50_Weights.IMAGENET1K_V1)\n",
-    "imagenet_resnet50_unet = ResNet_UNet(ResNet=imagenet_resnet50, input_image_size=128).to(DEVICE)\n",
-    "imagenet_resnet50_train, imagenet_resnet50_metrics = train(imagenet_resnet50_unet, trainLoader, testLoader, loss)\n",
-    "imagenet_resnet50_valid = [x['Loss'] for x in imagenet_resnet50_metrics]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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kk0/O+r0UoqHRKMo/+l2FEOI08+bNY8aMGRw/fpzIyEhPN0cIIWpNQo4QwqG0tLRK0W1ZWRm9evXCarWyf/9+D7ZMCCHqTmpyhBAO1113HdHR0cTFxVFQUMAnn3zCvn37zjitWggh6jMJOUIIh/j4eN577z0+/fRTrFYrXbp04YsvvmDixImebpoQQtSZDFcJIYQQolGSdXKEEEII0ShJyBFCCCFEo3TR1eTYbDZOnDhBYGCgU5aHF0IIIYTrKYpCYWEhLVu2RKutXR/NRRdyTpw4QVRUlKebIYQQQojzkJaWRqtWrWp17kUXcgIDAwH1mxQUFOTh1gghhBCiNoxGI1FRUY7P8dq46EJOxRBVUFCQhBwhhBCigalLqYkUHgshhBCiUZKQI4QQQohGSUKOEEIIIRolCTlCCCGEaJQk5AghhBCiUfJoyPn999+56qqraNmyJRqNhqVLl57zOWvWrKF3794YDAbatWvH4sWLXd5OIYQQQjQ8Hg05xcXF9OzZkwULFtTq/CNHjjB27FhGjBjBtm3bePjhh7n77rtZsWKFi1sqhBBCiIbGo+vkXHHFFVxxxRW1Pn/hwoW0bt2aV155BYDOnTvz559/8uqrrxIfH++qZgohhBCiAWpQNTnJycmMGjWqyrH4+HiSk5PP+ByTyYTRaKxyE0IIIUTj16BCTkZGBuHh4VWOhYeHYzQaKS0trfE5iYmJBAcHO26yb5UQQghxcWhQIed8zJo1i4KCAsctLS3N000SQgghhBs0qL2rIiIiyMzMrHIsMzOToKAgfH19a3yOwWDAYDC4o3lCCCGEqEcaVE/OoEGDSEpKqnJs1apVDBo0yEMtEkIIIVzAZoNyk6db0eB5NOQUFRWxbds2tm3bBqhTxLdt20ZqaiqgDjVNmjTJcf59993H4cOH+e9//8u+fft48803+fLLL5kxY4Ynmi+EEEI4X9EpWDgEXusJOYc83ZoGzaMhZ/PmzfTq1YtevXoBkJCQQK9evZg9ezYAJ0+edAQegNatW7Ns2TJWrVpFz549eeWVV3jvvfdk+rgQQojGoTQfPr4OTu2BwpOw5HYwl3i6VQ2WRlEUxdONcCej0UhwcDAFBQUEBQV5ujlCCCGEylwCn1wHqcngH6YeKz4FPSbCtW+DRuPZ9nnY+Xx+N6iaHCGEEKJRslrgqzvUgGMIhtu/hRsXg0YHO5bAX+95uoUNkoQcIYQQwpNsNlj6LziwArx84ZYlENEdYofA6GfUc5bPhNSNnm1nAyQhRwghhPAURVEDzM4vQesFEz6CmNNmDA/8F3S9Fmzl8NVktShZ1JqEHCGEEMJT1r4Am95Wvx6/EDqMrvq4RgNXz4fQjmoh8ldTwFru/nY2UBJyhBBCCE/Y+A6sSVS/vuIl6HFjzecZAmDiJ6APgGN/QtKTbmtiQychRwghhHC3HV/BL/9Rvx4+Cwbcc/bzm3eA8W+qX69/A3YvdWnzGgsJOUIIIYQ77V8JS+9Tv+5/Lwx7pHbP63INDH5Q/fr7aZCV4pr2NSIScoQQQgh3OZYMX96uFhJ3nwBjnq/b+jeXzYHYoWAugiW3ganQdW1tBCTkCCGEEO6QsRM+mwjlZdA+Xh1+0tbxY1jnBTcsgsCWkL1f7dG5uNb0rRMJOUIIIVzDVAi/vwSf3QSZuz3dGs/KOaRu12AqgOhB6kJ/Ou/ze62AMJjwIWi9Yc/3kDzfqU1tTCTkCCGEcC5zMfz5KszrAb/9D/b/Au/Hw4FfPd0yzzCehI/Hq1s0hHeHm78Avd+FvWZUfxhjn5m1ag4c+eOCm9kYScgRQgh3UBSwWT3dCteylML6+Wq4+fVJKM2FZu0gagCYC+GzCRff9gQluep+VPmp0KQ13PYN+IY457X73Q09bgLFCl9PAeMJ57xuIyIhRwghXC19C7x9KbzYBta9DpYyT7fIuSxlsPFteK0nrHwMSrKhSay6uN2/NsLknyDuVvXDeNn/wYrHGn/gA7VH67OJ6o7igS1g0lIIDHfe62s0MO5VCO8GxVnw5WQoNzvv9RsB2YVciIZo+xLY8YU606JlnKdbI87EXAyrn4MNb4JiqzweHA2j5kDX6+peeFqflJvh74/hj1fAmK4eC46GYf+FnjdVrTlRFPW83+x7MXUcC9e/C3p/97fbHcrN8PlEOPQb+ITAlF8gvItrrpV7GN4ertb79L8HrnzJNdfxsPP5/JaQI0RDk7EL3hkONgt4+6sFjP9cCl543sEk+OlhdZgC1OnCMYPVZfwLT6rHWvaG0f9TN2JsSKwW2P45rH0JCuzvLygSLv03xN0GXvozP3fXN/Dd/WA1QYs4tT4lqIVbmu1WPzwIWz8Ebz+Y9ANE9XPt9VJ+gc9vUr++9h3oOdG11/MACTm1ICFHNGhWC7w7Qp2Kqg9Q18rQ6GDsy9D3Tk+3ToBag7HiUTUEAARHqUMK7S9X75tLIHkBrJun/v2B2qtx+VMQ2t4jTa41azns/EoNanlH1GMBETD0/6D3JPD2qd3rpG6EL26Gkhw1HN3yJUR0c1273e3vT9Sp3Wjg1q8q/+5d7bf/qbPZvHzh7l8b1/cUCTm1IiFHNGirE2Ht8+DbBO77U72/7RP1sSEPq8NXDXn4oyFTFLWX4pdH1JoUNDDgXhj5hLr30D8VnVL3LdryoVqrotGpQXX4TPAPdXvzz8pmhV3fqv/2cg6qx/ybwyUz1DZ7+9b9NXMPw6cTIOeAGthvXOy+MOBKJ7fD+6PVtXBGPA7D/uO+a9us8OkN6hBZk9ZwdxL4N3Pf9V1MQk4tNNqQU24Cnb5uK2eKhuXENnjvMnWl1BsWQbfr1Q/W31+C1c+q53S7Hq55s/a/UZ+Po3/CvmXg1wyatlZ/mDZtrQavi1V+mlpQe2CFer95Z7j6jdoNUWSlqFOA9/+i3tcHwtAZMPBf5xcenEFR1GG21A2QtgEOra7sufFtCkMegv5TL7yepjQPltwOR/8AjRaueFF93YaqNA/eHgb5x6DDGLjpc/f/0lGSq7ahIFUNzi16QMwQ9RY9EPyaurc9TiQhpxYaTMhRFCjLh6IstWq++BQUZ6u//RVnVd6K7MfNhWrB35AHodftrv2Qc6aj69RCxI5XwsD7z39xrMau3ATvjIBTu9X9a278sGqg3f4FfD9drdOJHgQ3feb8H2bGk7Dycdj1dc2P+4SoM2pODz5NWqvHgiIbZw+TzaZOiU56Sh160unh0v+ovWpnq0upyZE/1O/vyW3q/aBWMPJx6DHR9d87azlk7lSHkVKTIW1jZd1QBZ9gGPwADLgPDIHOu3a5GX6aUdkjOfBfap2SVue8a7iDzaYWGh9YCSExcO9azwX/kzvgm7vUFZGr0EB4V7U2LGawGnwCwi78eoqifh7lHoG8o+otoLnTh9Al5NRCvQ05x7dUFiQWZ6nBxWY5v9cKbKFu4tbnjgtfcMqV0jbBR+PBUqzeD+8G4+a5vkCvIUp6Wp2Z4hcK0zbWPJxxeK36W7GpAJq1V2sBmra+8GtbLbBxIax53l5DooHuN6ofQrlH1N/wizLP/ho6vfqDv6k99LS9DDqOufC2edKpffDDA3B8k3o/agBc9TqEdTr/17TZ1BCZ9DQUpKnHInqoH/pthl14myuYCuH4X2pPTeoGOL658v9hBa2XWhgcPVC9tR4GPi76mako8Odc9X2D+kvPde/WPMxXX619Ue1R9fKBu1ZCi56ebpHaw5iaDMfWqb9Q5hyofk6z9pWBJ3YIBLeq+bXKTWrv3ulBJu+o+v8/7yhYSqqe36qfWhfkRBJyaqHehpwvJ8OepdWPG4LVD7SAMPVP/zB1LDygufqnf3P1mE8w7P5OLWasmMrpFwqDp6sLRjnzNy9nOLENPrxa/UBu2Uv9T1KaB2jU9H/ZbOctmNXQpW+B90apU5AnfKT25JzJqb3w6Y3qB6RfqFrQ2arP+V/7yB/w878ha596P7KvWuTcslfV88zFkHdM/YFXEXzyjqpf56fWHNh7TIQrX3bdB6erlJvU1Xx/f1l9X/oAGPUk9L3LeT0ullI1WP4xF0xG9Vj70dBmhBouNVr7nzo1jDi+1tr/tB8//VhJjtpDk7oBMndVndIO6s+a6AFqWIseqM78cvcvSafPvIroof77bQgzrw7+Cp/cAChwzQLodZunW1SzolNwbL39ts6+1cY/IkBItBp4msSqIakiyBhPVD/3dBqt2vvYJEZ9bkQPGHCPU5svIacW6m3I+fBqOLJWLeTrck1lgPEy1O11yk3qrI4/5qrjwqB2mQ78l7p+Qn0IDqf2wgdXqquhRg9SVwC1lMLKJ2D7Z+o5AeHqkuVdr7u464wsZeoictkp0O0GuOH9cz+nMEMNOhk71FkW178HncfV7br/HJrya6Z+kMfdVvcPcpsVCo5X/rA8uR22LFY/ZENi1N/YowfU7TU9JW2T2ntTEfo6jIGxr5z5t98LVZyj9vBufl+txXKmkJjKXpqogdC8U/0YUkzbBJ/frBZvB0XCLUsgorunW3Vm+anq/9HSPOg9Ga5+3dMtqr2SXDX4VvT0nNyuFsGfiT7APgRtDzKnD08HR9V9iLaOJOTUQr0NOe9eBumb1UK1Tlde+OtZLepUzz9eqZwNYQhSg87Af3mu4j7nEHxwhTq80bKXun7E6b/JH/ldHZ+vaHO7Uepv+84YdmmIVj4B619XQ9+/NtS+zsZUpC7zfmAloFED48D7z/28moam+t6p1oY4s8YndSN8e7f6AaHRwqX/VWtZdF7Ou8b5stnAeByy9qvhMitFrW3ISlGDOai9ZFe+6L4QnnNIXVG4NFcNOzarGhJtVvVDyWZVj//zmOMxq1rE3KqfvbdmYP3uIck9om4Bkb1f/WAdNF3tVdJo7Td7b5ZGc9oxbWUv1+k3QxC0Ge6aD+ByEyyKhxN/q0N7d65oOPWQNTEVqiHz2Dr1Z3RI7GlBJlb9ZceDv3RKyKmFehtyFgxQfzuc9INzx95tVnUY6/eXIWuveszbH/rdCYMecO4S4+eSnwqLrlA/QMK6wh0/1fzBWTEc8McrYDWrY9zDHlGLHi+mwuTUjeoPUJTzC7/WcnWoacsH6v1zFXQe+R1+/s+5h6acpcyoXm/HF+r9Vv3hunfcF2jLzeo05uwUe6Cxh5rsA9XrCypotNDzZvX72IBnqTQIpfnw5e3qv8sL1aInXP++89ch+vFh9f+XbxO493d1qEe4TIMMOQsWLOCll14iIyODnj178sYbb9C/f/8az7VYLCQmJvLhhx+Snp5Ox44deeGFFxgzpvYFjPU25Mztqn74T/0NIi+ghuJMbDZIWaYWx2XsUI95+ajdq0MeguBI51/zdMaTag9O3hG10G3Kz+eu6s8+oPbqHLXvrtu8M1w1T+1eb+zMJbDwEsg9pH6oXrvw/F5HUWDda/DrHPV+p3Hq8NDptRY1Dk09pe415I7hi51fq3/PJqM6fXrsy2q9jrN/YyzKUgNV6ga1VybvyJmHgLTe0KwthHaA5h0htKP6ARnavvFuQ1AflZvVLTGy96u9VDXdbFb13/mZHj+5XZ2p6uWr9mj2ucM5/7a2fQZL7wc0cNvXaq+zcKkGF3KWLFnCpEmTWLhwIQMGDGDevHl89dVXpKSkEBZW/QPwkUce4ZNPPuHdd9+lU6dOrFixgoSEBNavX0+vXrX7bbPehpzno6GsAKb9Bc07uO46iqIOYax9UR0eA/UHeq9b1d6SoJbOv2ZxNiweq/YQhMSoe7jUNlQpijo9euVjauEkqMFs1JPu/U1aUdTrG9PVUGBMV2fC6f1dU9i9fJb6wz2whTpMdaG1VDu/Vn8gW81qD83NX6iv+c+hqX53wYjH3N9LkXcMvrtXnQkCav3R2Fcu/H3bbGqt25bF6to+/yyA1gecFmTaq2GmeUe1a/5i6jVszIwnYel9cHiNer/jWLVu5kIWXDy5A96/XF3wb/ijMPwRpzRVnF2DCzkDBgygX79+zJ8/HwCbzUZUVBQPPPAAM2fOrHZ+y5Yteeyxx5g2bZrj2PXXX4+vry+ffPJJra5ZL0OOosDTzdTx84S9rgkaNV3z8Bp1Iblj69Rj3n5q4XPF+LczlObDh1epvUeBLeHOX9QPkLoqyYVVT6jLpYNaEzEmUZ3KfKG/lVktarFu4cnqIcZ4Qr0VnlQDQk2axMK1bzuvh+noOjUUosCtXztvFdhj69WCzrJ8tc1ePpVDU636qbVPntzs02ZVC+bXJKr/F4Kj4bq31emtdoqicKrQRDN/PV66s/QyFWaq665s/UgteK4Q2UetownvogaaoJYXd2F7A2Ass5CeV4rVpmBTFGwKlV/bFKyKgs0GNkX9WlEUrDb1HKXimM1G7MGP6LJnLjqbhVJDKH/FPUtG6BBsioICjtdWFAVFqbzfOSKQwe1OC0SleerecXlH1RlvNy+pHwXbTvbP74Otyv2av1fKaed66TSEBTq3PqlBhRyz2Yyfnx9ff/0148ePdxyfPHky+fn5fP/999We06xZM1588UXuuusux7HbbruNP//8k6NHj9Z4HZPJhMlkctw3Go1ERUXVr5BjKYVnI9SvZ6a5f0rt0XXqYmZpG9X7Qa3UfXS6XX9hHwCmQvj4WnU9Dv/mag/OhY6JH12nDm1kp6j32wyHYTPVdpqK1B4Jc7H9z4qvi8/8mKlQ7Wk629TI0/mHqQWbQZEQGAEHVqnTtTVaNSAOm3lhBY6mIlg4RP0B2ut2uGb++b9WTbIPwCfXV86882sGlz8NPW+pPz+oj2+Gb+5Wh5M0WnVfpGGPsPGYkReW72Nraj7+eh19YpsyoLV6694qGIMWdWXeLR/A/uWVQ1GGYOgxAfpMrt+zdEQ16w5mc89Hmyk2n2XGTx101hzjNe/5dNCqy2y8X34FL5ZPxMTZ/8+O6hzOk1d3oVWwj7rn1v7lav3NPWsbRW2WudzGnpNGth7L4++0fLYeyyM9v/SCXrNPTBO+uX/wuU+sgwYVck6cOEFkZCTr169n0KBBjuP//e9/Wbt2LRs3bqz2nFtuuYXt27ezdOlS2rZtS1JSEtdccw1Wq7VKkDndk08+yVNPPVXteL0KOUVZ8HI79evZeZ75sKnYd2fVHLU2CNT1MsYknl+NkKVUncZ89A91Jdw7ljlvs7hyM6x/Td0B2Vrz33udab3VoaGglqeFmIr79ltARPUAU1ag7lVUsRljRA+15uV8F4Rb9m/46101aP4r2TWBtygLlj+ivp9h/6mf2zGYCtXv67ZPATik78SUwntJVaoXyoeTyy3ea7lFv5bm1lOVD0QNUOsvuoyv34tiuoHVplBus2HwajirCK/dn8U9H23GVG4j2NcbH28tOo0GjUaDTqveNBrQaTRoNRq0Wg1aDei09vv2rzX2r7X28/SKiZsL3uPyIvUX6ePerXkn7DEyDK3trwMa1Ne2WG0k7T1FuU3Bx1vLh+1+Z8CRN0FnUBf882TP5wXIKCjj79Q8tqbmsTU1n53pBZjLbed+4lloHN9j0Gg09IoKYcm9g879xDpo9CEnKyuLqVOn8uOPP6LRaGjbti2jRo1i0aJFlJbWnDobRE9O7mF4vZc66+mxE55ti6UU1r+hzm6qmGHS82Z148faTjktN8EXt6gLZOkDYfL3rimmzjkEKx6DE1vV2hi9v1pjoQ+o/NoQcNpjgZVfG047LyBcHf66kHC5eyn89LDale3loxbu9r+nxtcss1hZtuMkP+44wWWdwrh9UKz6wOG18NHV6te3L4W2I86/PY3A8bwSkr5eyPjjLxGsKaFI8WF5dAKXXP8guUWlnNj8A2H7v6BryUZ0qD+g8xV/vrUO5StlJP6tutO/dVMGtGlGn5gmBBjqwfR0J7FYbeQVm8kpNpNb8WeRidxiM9nFZnKLKo6rx/JLLWiAts0D6B4ZTLfIYLq3CqZLiyD86+H35bd9mdz38VbMVhuXdwln/i29nB/Q9q+E7/+lrjCvM6g9mgPurdZ7fSCzkMeX7sL72Fo+8n4erUbh0KBE2sb/y7ntcRFTuZXdJyp7af4+lseJgrJq5zXx86ZXdBN6RYXQO6YJ7cMD8NZq0Wo06uz808IiVL2vsQcbV2tQIed8hqsqlJWVkZOTQ8uWLZk5cyY//fQTu3fvrtV162VNzskd8PZQ9cP23//ca8RDjCfg16cqp/d6+8ElCeoKymfbNNBaDl9Nhn0/qc+57VuIcW6ar7cKM+D7aWq4A3Uo7Zo3HUXWR7OL+XTjMb7acpz8ksoC2Fcn9uTazkHw1hB1U72+d8G4uR54A/VDbrGZBasP8nHyMcxWGy3I4cMm79GhdLt6Quth6rBbYeUvBKUtB7AldDzflvZm3bEiMo1Ve/h0Wg3dWgbRv3VTOrcIIsDgRYCPFwEGL/wNXgTa//TT69zyw7qu9pww8uKKfRzLKSGnyISxzDkLA2o0/wg+kcF0benZ4LNydwbTPtuKxaowpmsEr9/cC72Xi3q3i06p/2cPrFTvtxul/p/9x9IaSn4q5gVDMVjy+aJ8ODPL7+G63pE8emVnQgPquGCrGxSbypm/+iAbDuewO92I2Vq1l0argU4RQfSKDqF3dBN6xzQhtplfvfy3f7oGFXJALTzu378/b7zxBqAWHkdHRzN9+vQaC4//yWKx0LlzZyZMmMBzzz1Xq2vWy5BzbL06vbppW3hwq6dbU9XxLbB8ZuX+PMFRar1OTYug2azqDJmdX6m/Gd2y5OLrjVAUdcPGlU9AeSmKTzDbez7JKye68seBbMdpkSG+dIoIJGnfKbx1Gv7s8gPhBz5Xx/nvT3b5nj1lFiteWs3Zi3fdrMRczqI/j/D22sMUmtQP8cFtmzHzik70aBmoToVf/WxlrY1fM7WXsffkKjMSFUUhNbeEjUdy2Xg4l01Hc0jLrV19gUYDAXo18PgbdAT4eBNg0OGvV0NRiK+e63pH0i0y2OnvvyaKovDpxlSe/mlPteEErQaa+OlpFqCnqb+eZv4Gmvrbv7YfqzjeLECPzaaw60QBO48b2ZlewK70AjKM1X+j12igTah/1eATGeyWnrCfd57kwc//ptymMLZHC+ZNjMPb1f9GHf9nH1dnS/k1U7dm6HiF+ni5CRaNgRNbKQ/vwdNhr/Lx5kwUBYJ9vfnvmI7c3C8arbb+BIRFfx7h6Z/2OO439dfTOzpE7amJDqFnq5B62YN3Lg0u5CxZsoTJkyfz9ttv079/f+bNm8eXX37Jvn37CA8PZ9KkSURGRpKYmAjAxo0bSU9PJy4ujvT0dJ588kmOHDnC1q1bCQkJqdU162XI2b8SPrtRXbDqXicsfOVsjnqd2ZX7YkUPUut1KhaKUxT48SHY+qG6Z87ETxv+BowXIPvoLsq/nkpEkfqDZql1MHPK76B3h9bcNjCG4R3D0AAPfP43xt0r+Fj/vPrEyT9B66EubdumI7nc/eFfeOu0XNsrkgn9ougQ7rm9zSxWG0v+SuO1pANkFao9MF1aBDHzik4MbR9a9bfL9K3qv7HWl6pr/tRy25MT+aVsOpLLxiNq4CkylVNsKqfIfis2lWOr5U9CnVbDfcPa8OBl7V1a41JQamHWtzv4eWcGACM7hXHPpW0IDdDT1N9AiK/3BX+wZhWa2JVewE77bVd6ASdrGMrQaNTi26eu7krLkLP05F6AH7ef4OEl27DaFK6Ja8krN/Z0bwg/tU8teM/cqd7veyeMflZdvmLzIrW28N610CSWral5PP7dLvacVPcVi4sK4dlru9G1pXvC77nc9/EWlu/O4KZ+Udw/vC3RTet/L01tNLiQAzB//nzHYoBxcXG8/vrrDBig7mMzfPhwYmNjWbx4MQBr167l/vvv5/DhwwQEBHDllVfy/PPP07Jl7adc18uQs+tbdQn+mCHqInn1lbmksl6nvBTQQNwtKCMfh3Wvodm4UJ0Nc/370O06T7fW7RRFYf2hHD7ZcIyVezLR2Cw84LWUaV5L8cJGeUBLvK5bWGVF67LCXIpe7UeoLZuvvMYy4uEPXNr9nXwohzsX/0WppepslZ5RIUzo24qrerYkyMc968MoisKynSd5ZeV+jmSrO2BHN/Xj/0Z34KoeLd36m7GiKJRarPbAY6WorGoAqvj679Q8VuxWd1zvEB7ASzf0pGdUiNPbsz0tn+mfbyUttxRvnYZHxnTirktau+WDKrvIpAae45XBp6KGI8DgxSNjOnLrgBin/v189/dx/u/L7dgUuL53K168oQc6T/SMlJvUndCT7bMaA1vah0U1cOtXVZZzKLfa+Cj5GHNX7afIVI5WA5MHx5JweQcC3fR/qCaKotDv2V/JLjLz9X2D6Bvb8Gd/VWiQIcfd6mXI2fqRuulf+3i49UtPt+bcCtLh1ydhp9pWC154Yx9CGP8WxN3iubbVUbGpHAXw9dad9w/VghILX21J47ONqRy2f1gD9Ittwm0DY7giJA39D/erBeYAA6epu6x7+8DSabDtE45rWnB56bN0io7g86kD8fF2fg/BuoPZ3PXhX5RZbAxtH8ptA2P4Zstxftunzh4BMHhpuaJbBBP6RjGwTTOXBY11B7N5Yfk+dhwvACA0QM8DI9tzc/9o19VfOMkvO0/yxPe7yC4yo9XAvcPa8tBl7Z3yd6YoCu//eYQXlu/DYlWIaurLGzf3Js4FQaouDmQWMvPbnWw5lgeo/7YTr+tBu7ALH1b9anMa//1mB4oCE/tGkXhdd88P/RxaDd/dB0VqLxrDZsKIWTWemlFQxjPL9rBsx0kAwoMMPDGuC2O7t/BI78mR7GJGvLwGvZeWnU+OblAz6s5FQk4t1MuQk/wmrJilrktzwyJPt6bWtqxbgdfKx+ipOQDAy15Tuf6+p2gdWr+XvT+SXczK3Rms3JPJ1tQ8Kv4HGLy0+Ol1+Om98NXr8NPr8PXWVT+m1+HnrRappmQW8uP2E5js9RL+eh3X9W7FrQOj6RRx2r8vc7E6E6xiH6nmndVVplc+DmhIv+47rvzOQkGphSu6RbDglt5O/UH/+/4sptqn4w7v2JyFt/VxfChnFZpY+nc6X25O48CpIsdzWjXx5cY+UVzfJ5JWTc5/CnZusZmUjEL2ZxaSklnIrvQCR7jx1+u459K23DW0dYOa/ZRbbOapH3fz/Ta1+LldWAAv3dCDXtHnPx0/r9jMv7/aTtI+dRr8ld0jSLyuB8G+9WPlZZtN4eMNx3hx+T6KzVb0Oi0PXtaOe4e1Pe+6mc83pfLodztRFLh1QDTPXNPN8wGnQkku/PaMOoHi8mfOOfty7f4sZn+/i2M56qzUoe1DeeaabsS6+efhl5vT+O/XO+gX24Sv7nPuOjWeJiGnFuplyFn7olpQ2Xuyutx4PacoCu/9cYTEX/aiKDbui9hPvknh87zOhAUa+GzqANqFea7G459sNoWd6QWs3JPByt2ZVT7InaVTRCC3DYxhfK/Is39Y718B30+H4tPWcxk0HeKfZePhHG5/fxNmq417L23DrCs7O6Vta1JOcc/HWzCX27isUxhv3ta7xt/uFEVh+/ECvtycxo/bTjiKfzUauKRdKDf2jWJ0l/Az9lgUllk4cKqI/RlqmNmfWUhKRhHZRdXXMvLWabh1QAzTR7arl7NTamvF7gwe+24X2UUmtBqYOrQNMy7vUOdenb+O5vLg539zsqAMvZeW2eO6cOuA6HpZR5GeX8pj3+1kTUoWoP7bf/GGHvRoFVKn1/k4+ShPfK/Oir1jcCxzrupSL99vXZRZrLy15hBvrTmE2WpD76Xl/cl9Gdq+udva8N+vt/Pl5uPcP7wtj4w5z/W66ikJObVQL0POyidg/euOD7v6rMxi5bHvdvHNVnXBwJv6RfH0Nd0oKLVw+/sb2ZdRSDN/PZ/cPYDOLTz3/TWX29h4JIeVuzNZtSezyiwSL62GQW2bMbpLOKO6hBPiq6fEXE6J2UqpxUqJ2UqJuZxSs/p1qf1+icXqOKYeL8fP4MX1vSPpHd2k9j+gi7PVIu19P6lbC9y71jEtf+nf6Ty8ZBsA/xvfjdsGxlzQ9+Gf640suKV3rYaDSs1Wlu8+yVebj7P+UI7jeJCPF9fERRLfNYJThWVqmMkoZH9m0VlXSI1u6keH8EA6RgTQITyQ/q2b0iLYNQWs7pZfYuapH/fw3d9qUX6b5v68dENP+sScu1fHZlN4a+0h5q7aj9Wm0CbUn/m39KZLy3rys+kMFEXh+20neOrH3eSVWNBq4O6hbZgxqgO++nMHvA/WHeGpH9Wi/Lsvac1jYzs3+IBzuiPZxTzyzQ42HcllZKcwFt3Rz23XHvnyGg5nF7Pojr6M7FR98cyGTEJOLdTLkPPTDLV6/yzjvvXBqcIy7v14C3+n5qPTanhibGcmD451/HDKKzZz+6KN7Eo3EuLnzcd3DqB7K/fNNigylbM2JYuVezL4bd8pCk9bS8Rfr2N4xzBGdw1neMcwzw8BKIo6U6hZ22qbUL6edIC5q/aj1cD7d/RjRMdz7NZ+BqevN3JFN3W9kfMZVkjLLeGrLcf5enNajYuInS48yKCGmfBAOkSof7YLC2iQ01Xr6tc9mTz63U5OFZrQaOCuIa35v9Edz/ihf6qwjIQl2/nzoLq0wHW9InlmfLcG9b3KKTLx1I97+GG7OmwX08yPxGu7V93r6R/e/f0wz/68F4D7hrXlkTEdG1XAqbArvYBxb/yJv17HtjmjXT8VHnXoud+zv6LRwLYnRhPsVz+GOp1FQk4t1MuQ881UtYh39P9g8AOebk2NdqUXMPWjzZwsKCPIx4s3b+3DJe2r/yArKLUwedEmtqXlE+jjxYd39qf3BdQpnEtesZnluzNYuTuDdQdzqix6FRqg5/Iu4YzuEsGgts1cUszrCoqi8J+vd/D1luP463V8dd/gOv9mv3zXSaZ/5tz1Rqw2hfWHsvlq83G2HMujVRNfOkYE2ntoAukQFtjofqjWVUGJhad/2uPo6Wwd6s+LN/Sg3z9muPx5IJuHl2wju8iEr7eOZ8Z344Y+rTzRZKf4bV8mj323yzH9fGLfKB4d27naLxNvrjnIi8vVfeceGNmOhMs7NMqAA2ovXa9nVlFQauHbfw126c/BCst3neS+T7bSKSKQ5Q9f6vLruZuEnFqolyHn85sh5WcYNw/6TvF0a6r5cfsJ/vP1dsosNto29+e9yf3OWlxcWGbhzsV/8dfRPPz1Oj6Y0p/+rZ07jdFin745b9V+R+0IQGwzP+K7RjC6azhxUU08Mw3VCczlNu74YBPrD+UQEeTDd9MG13p4Z9mOkzz4xd+eW29E8Nu+TGZ9u5NMo9qrM2Vwa/4T3xFvnYbXkg4wf/VBFAU6hgey4NZe9aqG7XwVlll4cXkKH29QN39tHmjgmWu6Mqabuh1MRQ8lwIxRHXho1AVu1tsA3PvxZlbszuQ/8R2ZNqKdy6/3zE97eP/PI9w2MJr/jW98m9FKyKmFehlyFo9TN7K8/n3ofoOnW+Ngsym8+ut+3vjtIADDOzbn9Zt71WodlRJzOXd/uJn1h3Lw9dbx3uS+DDlLF3ZdJB/K4ckfdpOSWQioHxRXx7VkdJdw2oUFNJrfDAtKLdzw1noOnCqic4sgvrpv0DlnIH2/LZ2EL7djtSlc1yuSl27s2WCDXkNXUGrh2WV7+HKz2qsT28yP0AADm+3TsG8ZEM3scV0aTA9jbW06ksvMb3Y4llMY0zWCmGZ+vP27uoSCuz7w64MP1x9lzg+7uaRdKJ/cPcDl17tm/p9sP17AazfFcU1cpMuv527n8/ktv97VB2b7bB+9a5fyr4tiUzn3f7rFEXDuubQN70/uV+uF4vz0Xiy6ox/DOjSn1GJlyuK/WL3v1LmfeBYnC0p54PO/ufndDaRkFtLEz5vE67rz80NDmTaiHe3DAxtNwAF1yfhFd/QjNMDA3pNGpn26lfJ/7EFzuu/+Ps4M+4qxN/ZpJQHHw4J9vXnxhp4sntKPFsE+HM0pYfOxPAIMXrxxcy+eu7Z7ows4AP1bN7X/n2yLl1bD8t0ZjoAz64pOF03AAXVbElBnz5nKrec4+8KUmMvZdUJdgbkxLQB4oSTk1Adm+wJy+vqxvkxabgnXv7WeFbsz0eu0vHxjTx69snOdPzB9vHW8M6kPozqHYy63cc/Hm1m5O6PO7TGX23hrzSEue2UtP24/gUYDtw2MZvW/h3Nz/+hG/UEe1dSP9yf3xcdby9r9Wcz5YTc1db5+veU4CfYVY2/qF8UL13toxVhRzfCOYayYcSl3DI5lVOcwlj14CVf1rP0q7Q2Rj7eO/8R34ofpl9A9MhitBp4Y14V7h7X1dNPcql1YAKEBBkzlNv5OzXfptbal5mO1KbQM9iHSRVtvNEQNp4y/MTPZe3JcvCljbWw8nMP9n24lt9hMaICBt2/vU6upsGdi8NLx1m29efiLbSzbeZJ/fbqVeTfFMa5H7X7I/74/iyd/2O3o+u4dHcLT13Rz2waJ9UHPqBBeu6kX932yhU83phLTzI97Lq38sFjyVyozv62nC6oJAIJ8vHny6q6ebobbdWkZxA/Th2AsLb8oi9I1GnW5ih+3n2D9oRwGtmnmsmv9dVQdBpVenKqkJ6c+cAxXebb48ItNqdz2/kZyi810i1R/OF1IwKngrdPy2k1xXNsrknKbwoOf/8239tknZ3I8r4T7Pt7CpEWbOJxdTGiAnpdv7MnX9w2+qAJOhfiuETxmXxzwuZ/38fNOdQn5Tzce45Fv1IAzeVAM/xsvAUfULxqN5qIMOBUqhqw2nLbelCtsPpYLqFtuiErSk+NpilIZcjzUk1NutfG/ZXtZvP4oAGN7tODlG3rWalGv2vKyD3vpdVqWbE7j/77ajrncxk39o6ucV2ax8s7vh1mw+iCmchs6rYbJg2J5+PL2bts4sr6665LWpOaW8FHyMWYs2cbmo3ksWncEgClDYpk9ruGvGCtEY1MRcv5Oy6PEXI6f3vkfu+VWG1sr9hVz8kzWhk5CjqdZSkCxF5N6oPD4zwPZPPPTHsdMpf+7vAPTR7ZzyYelTqsh8bru6L20fLzhGDO/3YnZamPSoFgAkvZm8tSPe0jNVfd+GdC6KU9f042OEQ1/eq0zaDQaZo/rQnpeKUn7TjkCztShrXn0ysa1YqwQjUV0Uz9aBvtwoqCMzUfzuLSD87d42HuykGKzlUAfLzo0guUInElCjqdV1OOgcWvh8ZHsYp5dtpdf92YCEOLnzfPX9WBMtwiXXler1fD0NV0xeGl5788jzP5+N1mFJnafMPKbffZVeJCBx8Z24aoentnFtz7z0ml5/eZeTHwnmV3pxka9YqwQjYFalxPKN1vVLVJcEXL+OqoOVfWNaeLR4WqL1UJmSSYni0/ipfWiV1gvj7WlgoQcTzt9+rgbPqiMZRbm/3aQD9YdwWJV8NJquH1QDA9d1p4QP73Lrw/qf/rHxnbG4K1lwepDjmnq3joNd17SmgdHtm9QS9u7m7/Bi2/uH0xqTgntw+W3NiHqu8Ftm/HN1uMkH3ZNXU5FPY4ri44VRSHflM/J4pOcLD5JRnEGJ4tO+7r4JNml2Siosz/7RfRjUfwil7WntuSTxNNM6jCRq+txrDaFJX+l8crKFHKKzYC6uN/jY7vQLsz9w2QajYb/xHfC11vH3FX7GdIulDlXdfVIWxoig5dOAo4QDcQge13OzuP5GMssTq0vVBTFMbOqYvuQ0vJSPt7zMYXmQjRo0Gg0aDVax9ca7PdP//of55WVl1UJNBnFGZRZz753HYBeq6dFQAsi/Fw7KlBbEnI8zQ0LASYfyuHpn/aw96S6UFTb5v48Pq7LeW/86EzTR7bnrkvaOLXIWQhxcfvnWlKeHs5tGeJLbDM/juaUsOlwLqO6OG938NTcErIKTeh1WnrYN0Resm8Jb/z9htOucbpQ31Ba+Lcgwj+CFv4tqnwd4R9BU5+mHv9+n05Cjqe5cI2c1JwSnvt5L8vtC/AF+Xjx8KgO3D4oxi074taWBBwhzo9NsVFiKaHIUoTRbKTQXEiRWf26yFJU5X5peSlWxYrVZqVcKcdqs2JTbI6vrYrV8Xi1r21Wwv3D6dikIx2bqrd2Ie0w6AxOfT9Wm5VjxmPszd3Lvtx97M3dS0puCoVmtce7YigEKoPM6cdq4qPz4caON3J/z/sJ9OAyHYPahnI0J5XkwzlODTkVvTjdWwU7VtBedWwVAMOjhhMTGIMNG4qioKBgUyq/VhTljI95a72rBJkW/i0I9w9Hr3NPWYOzSMjxNBf05BSZylmw+iDv/3EEs1Wdhn3rgGgeHtWBpv4N6x+oEBezsvIydufsZnvWdnZn7ya3LFcNLvZQU2wpxqaceasPZzpedJwtmVsc93UaHbFBsXRo2oFOTTs5AlCob+32qDNbzRzIP8C+nH2OULM/bz+l5aVObXeZtYyP93zMssPLeKj3Q4xvNx6txrW/5J0qOcU7O95hddpqHh/wOCOiRzC4bTM+35TKeievl7O5oujYvj5OZnEmO7J3oEHD7IGzae7n/ELnhkRCjqc5MeTYbApfbz3OSytSyCo0ATC0fShPjOtCB6nfEKJeUxSF9KJ0tmdtZ0fWDrZnbSclN4Vypfycz/XSehGkDyJQH0iAdwCB+sDKm3cgAfoA/Lz88NJ64aX1QqvRotPoKr/W6vDSqF97ab3QaXRVvtZoNBwvPM6+3H2k5KWQkptCvimfQwWHOFRwiF+O/OJoS1Ofpo7Q06FpBzo26UiYXxgH8w+qvTM5e9mbu5fD+YdrfG++Xr60b9Kezk0707lpZzo160Rz38oPag3qUMg/h0T+ebzi/q7sXbz414scNR5lzvo5LElZwqz+s4gLi6vbX1At5Jbl8v7O91mSsgSTVf0Z/O7OdxkRPcKx2vHek0Zyi81O+4WzYmZVvxi1Hue3tN8A6Nm850UfcEBCjuc5YbjKXG4j+XAOL63Yx650te6mdag/j13Zmcs6h9Wr8VEh6jtFUSgpLyG3LFe9leaSZ8ojtyyXAlMB/t7+NPVp6rg18WlCU5+mBOmD6vR/7fRemu2ntrM9azs5ZdV/y2/u25yezXvSo3kPWvi3cISXAH0AQfogArwDMOgMLv9/3iusF1e1vQpQv0enSk6RkpfC/rz9avjJTeGY8Ri5ZbmsP7Ge9SfWn/M1g/RBdG5mDzNNO9G5aWdigmLQaZ03hD201VAGthjIZ/s+Y+H2hezJ2cPtv9zOuDbjmNFnBmF+F16baDQb+XD3h3yy5xNKytV1vno278nO7J3szN5JelE6kYGRdAgPYH9mERsO53Bl9xYXfN2cIhOHstQtbyp6cpKOJQEwKmbUBb9+YyAhx9POoyfHalPYe9LIuoPZrDuUw19Hcim1qDvcBhq8ePCy9kweHIveq/7U3YiLj6IomG1mSi2llJaXUlJeQmm5+rWvly8tA1rSxNDEbSHcaDZyougEp0pOkVeWVxliTrtVHK/4LbwuvLReNDE0qRZ+mvk2cxwvKS9RQ03Wdvbn7q/Wk+Gl9aJz086OUNOzeU9a+Ne/9aI0Gg3h/uGE+4dzaatLHcdLLCUczD/o6O1JyVVDUEl5CeF+4Y6emYpA46735q3zZnLXyYxtM5bXt77O0oNL+enwTySlJnFPj3uY1GXSedWalFhK+HTvp3yw+wNH3VCXZl14oNcDDGk5hLtX3s2mjE2sPLqSKd2mMLhtKPszi0g+5JyQs9m+ynGH8ABC/PTkl+WzOXMzACOjR17w6zcGEnI8rRZTyBVF4XB2MesPZrPuYA7Jh3MoKLVUOaeZv54ru7fgoVHtCQ1wbjGgEGXlZaQWppJqTOWo8SgZxRkUW4odoaW0vJQSS0nV++Ul56wX8fXypaV/S1oGqLfIgMgqf9Y2BCmKQoGpgBPFJzhRdIL0onROFp8kvSidE0UnOFl0kkJLYZ3es4/Op7LHxrcpTQxNCDYEU2wprhKIcstyKbIUUW4rJ6s0i6zSrFpfo6KXpmfznvQM60nnpp3x8fKpUzvrEz9vP3o070GP5j0cxyqKowM8sKL7P4X6hvL0kKeZ2HEiiZsS2Z61nde2vsa3B77lv/3+y7BWw2r1781kNbFk3xLe3/U+uWXqcFG7kHZMj5vOyOiRjteIj41nU8YmVhxdwZRuUxjUthmL1x9l/aFsp7yfynocdahqzfE1WBUrHZt0JCowyinXaOgk5HjaGTbnPFlQyrqDOaw/mM36QzlkGKuuTxBg8GJA66YMbhfKkHbN6BAWKBszigtisVpIK0oj1ZjKMeMxjhmPqV8XHiOjOOOCXluv1ePr7Yuvl3orthSTVZJFaXmpo66jJjWFoFDfUHJKc9QAYw81J4pOOIYJzqaJoQnh/uFVhpv+eavogfHz9qv1+zNbzdWCzz97h3LLctFqtHQP7e4INhH+EfWul8bZtBptvQg4p+sa2pWPrviIZYeX8eqWV0krTOOB39Tel//2/y9tgtvU+DyL1cJ3B7/j7R1vc6pEXaE9KjCKf8X9iytir6g2zDYqZhTPbnyW3Tm7SStMY2DrCDQaOJRVTKaxjPCgCwu0levjVB2quizmsgt63cbE4yFnwYIFvPTSS2RkZNCzZ0/eeOMN+vfvf8bz582bx1tvvUVqaiqhoaHccMMNJCYm4uPTQH/7sdfkWL39WbnzJH/aQ82R7OIqp+l1WvrENGFIu2YMahtKz1bBeNWjaeCi4cguzWZ/3n6OFBypEmhOFJ84a89LoD6QmMAYYoJjiAyIJNA7ED9vP0dwqbjVdMxLW/1HjdlqJqM4w9HbcnpoSS9Kr1UI+qdQ31BHKGoR0IJI/0hHQGrh36JOwaUu9Do9Ef4RRPjXjwXQxLlpNVquansVI6NH8s6Od/h4z8esO7GO67+/nls638J9Pe9zTDm32qwsO7KMN7e9SXpROgAR/hHc1+M+rm53Nd7amhf3a+rTlP4R/dlwcgMrjq7g7u5307VlELvSjSQfymF8r8jzbn+p2cqu9AIA+sY0pcRS4qiDuixaQk4Fj4acJUuWkJCQwMKFCxkwYADz5s0jPj6elJQUwsKqF4N99tlnzJw5k0WLFjF48GD279/PHXfcgUajYe7cuR54B05g78lZtr+QB1O2Og5rNdC9VQiD2zZjSNtQ+sY2cayBIERtVKw5kpKXUmVWTHbpmbvKfb18iQ2KJToomujAaGKCYhy3EEOIU3sd9Dq9ep2g6BofN1vNVYacKsJPTmkOzXybVfbu+EfSIkBdx6MhD/UIz/D39mdGnxlc1/46XvrrJdYeX8tHez7ip8M/8XDvh/Hz9mPBtgUcKVA3xG3m04ypPaZyY4cba1XHEx8bz4aTG1h5dCV3d7+bwW1DnRJytqXlU25TiAjyoVUTX1YeW4nZZiY6MJr2Ie3P+3UbG4+GnLlz5zJ16lSmTJkCwMKFC1m2bBmLFi1i5syZ1c5fv349Q4YM4ZZbbgEgNjaWm2++mY0bN7q13U5l78lJK1IDzKjO4Uzo24oBbZoR7Ou8pb/FhVEUhTJrGSWWEvVWXkKxpRhvrTcdmnZw+qJodVVkLmJ/3v4qBZ8H8g/UWECrQUNMUAxtgtsQExyj9s7Yg0yob2i9GT7R6/SOdgnhajFBMcy/bD5/pv/JC5te4KjxKLPXz3Y8HqQP4s5ud3Jzp5vr1CN4WfRl/G/D/9ibu5djxmMMatuMd34/zPrDF1aXc/r6OBqNpspQVX35P1wfeCzkmM1mtmzZwqxZsxzHtFoto0aNIjk5ucbnDB48mE8++YRNmzbRv39/Dh8+zM8//8ztt9/urmY7n70iP9ei/kYwsV8UlztxNUxRM5tiY0/OHjac3EBeWZ4jtJRaSikuL6bEot4vKa8MNWcayvHWetOlWRfimscRFxbn0vUpbIqN44XHOZB/gP15+9mfq07fPV50vMbzfb186dCkQ5WVatuHtHfZsI0QDd0lkZcw4OoBfLbvM97a/hYAk7pM4vYut5/XislNfJowoMUA1p9Yz8qjK7m54xS8tBrScktJyy0hqun5/V/861jlflVmq5nf038HZKjqnzwWcrKzs7FarYSHV/1ADw8PZ9++fTU+55ZbbiE7O5tLLrkERVEoLy/nvvvu49FHHz3jdUwmEyZT5W+zRqPROW/AWcxq7U2ORe21CfLxeJlUo1VsKWbDiQ2sPb6W34//XuOaJLXh5+WHn7cffl5+FFmKyC3LdUwL/nDPhwBEBkQSFxbnCD7tQtrVWJdyJoqikFWaxcG8gxzIP8CBvAMczD/I4YLDZ1wRNtwvXA0y9kDTqWknogKjXL66qxCNTcWU8xs73Ahwwb8UxMfGs/7EelYcXcHUHlPp0SqYran5JB/KOa+QY7UpbLWHnL6xTdhwcgPFlmLCfMPoHtr9gtra2DSoT9Q1a9bw3HPP8eabbzJgwAAOHjzIQw89xDPPPMMTTzxR43MSExN56qmn3NzSOrAPV2WZ1b+KIBmicqrjhccdoeavjL+w2Cqn3vt7+zOoxSCigqLw8/LD39u/8k97iPHzrnrcx8unSmhQFIXjhcfZlrWNbae2sS1rGwfyDpBelE56UTrLDi8D1GDUvXl3R+jp0bwHQfogQF2/5WDeQQ7mH2R/3n4O5qtfF5gKanxPeq2etiFtad+kPR2aVC6pH+IT4rpvpBAXIWf1eF4WfRnPJD9DSl4KRwqOMLhtqBpyDucwoV/dp3rvyzBSZCon0OBFp4ggnt6gDlWNjB4pv9T8g8dCTmhoKDqdjszMzCrHMzMziYioeYbCE088we23387dd98NQPfu3SkuLuaee+7hscceQ6ut/pc7a9YsEhISHPeNRiNRUfVo/QB74XFmmTpcJXU4F6bcVs72rO1qsEn7vdqsnKjAKIa1GsawqGH0CeuDt+7Cvt8ajYaooCiigqIcq8EWmYvYkb2D7ae2sy1rGzuydlBkKWLjyY1sPFlZP9Y6uDUllhIySzJrfG2tRktMUAztQtrRPqQ97Zu0p11IO6ICo5y6IqwQwrWCDcEMbDmQP9P/ZMXRFQxuewPzVx9k/aFsFEWpcw3NX0fUepzeMU0AG6tTVwMydbwmHgs5er2ePn36kJSUxPjx4wGw2WwkJSUxffr0Gp9TUlJSLcjodOoP+4odaf/JYDBgMNTTxfFsNkfIybeqbZSenLorMBWwLn0da4+v5c/0PzGaK4ckdRodvcN7M6zVMC5tdSmxQbEuL8oL0AcwuOVgBrccDKiznA4VHGLbqW1sz9rOtlPbSC1MdczWAGjh30INM/Yg075Je1oHt/Z4QbMQwjniY+MdIeeOK6ei99KSaTRxOLuYts3rto5QZT1OE7ae2kqeKY9gQzB9wvu4oukNmkeHqxISEpg8eTJ9+/alf//+zJs3j+LiYsdsq0mTJhEZGUliYiIAV111FXPnzqVXr16O4aonnniCq666yhF2GhRL5Vo4Rfig1YC/vuG8D0VRSC1M5e9Tf5NTmuPY/K9i47+KP//5tZfGC51WV2WDQJPVVLn0v6XqFgCnr6Rb0/GM4gysitXRrmBDMEMjhzKs1TAGRw52DAt5ik6ro0OTDnRo0oEJHScAkFOaw+6c3QTpg2gb0va8ChqFEA3HiKgReGm9OJh/kPTio/SODmHD4VzWH8qpU8hRFKXKSsdJqd8AMLzV8DOu13Mx82jImThxIllZWcyePZuMjAzi4uJYvny5oxg5NTW1Ss/N448/jkaj4fHHHyc9PZ3mzZtz1VVX8eyzz3rqLVwYez2OotFShp4QX+96PfWv3FZOSl4Kf2f+zdZTW9maufW8i3edrV1IO8cwVI/QHvV+OKeZb7Mqe/4IIRq3YEMwg1sO5vfjv7Py6EoGtx3FhsO5bDiUw+0Da79MwvG8UjKNJrx1GnpEBvPEVvvUcZlVVSOPFx5Pnz79jMNTa9asqXLfy8uLOXPmMGfOHDe0zA3MlasdU6ohyKd+pfDS8lJ2Zu1ky6kt/J35N9uztldbOt9b60230G5EBUZhVaxYbVbKbeWUK+WOr63KWY7Zv/bx8qlcMdfLr9rquY5j3tUfD/cLl5VmhRD13pjYMfx+/HdWHF3B43ETmbsKkg/nYLMptd6W5y97L063yGCOFKaQUZyBr5cvg1oOcmXTGyyPh5yLmn1zznKdP+D5ouO8sjz+PvU3WzO38vepv9mTs6faLskB3gHEhcXRJ7wPvcJ60S20m9SNCCFELQyPUoeUDhUcwi8gCz+9jtxiMymZhXRuUbth9cr9qprya+pKAIZGDpXVvs9AQo4n2XtyLF7qNMUgX9f/dSiKQr4pn2PGYxw1HnXsKn0w/2CVQtgKYX5h9AnrQ6/wXvQO6027kHb1fihICCHqo0B9IEMih7AmbQ2/pa2ib2x/ft+fRfKhnFqHHEc9TkwT5h+QoapzkZDjSfaaHJPWHnKcOFxVYilxbLxYEWYqvj599tE/tQluQ+/w3vQO603v8N609G9Zr+uEhBCiIYmPjWdN2hpWHF3BlW3G8Pv+LNYfyuHOS1qf87l5xWYOnFI/N0KbFnCk4AjeWm+p7zsLCTmeZO/JKdVcWMhJK0xjXfo6UvJS1GBTcIxTpafO+pwI/whigmLUzRgDo4kNjqV7aHea+DQ5rzYIIYQ4t+GthqPX6jlqPEpUp3wANh7Oodxqw0t39oX8ttinjrcLC2Bz1loABrYYSIC+blPQLyYScjzJHnJKUMdSaztcZbaa2ZK5hT/S/+CP439w1Hi0xvOaGJo4NjiMDY4lJiiG6EB112dfL1+nvAUhhBC1F6AP4JLIS/gt7TcOlawn0KcjhWXl7D5hpGdUyFmfW1F03C+2CUmpMlRVGxJyPMk+XFVsDzlnKzzOKM5whJoNJzdU2b/IS+NFr/Be9ArrRWxQrNo7ExRNsCHYte0XQghRZ2Naj+G3tN9YeWwF/VsPImnvKZIP59Q65LRrYeGn/XvQarQMjxru+gY3YBJyPMnek1OkVPTkVIYci83C9lPb1WCT/gcH8g5UeWqobyhDI4cytNVQBrYYKIvJCSFEAzGs1TAMOgOphakMbVVA0l5YfyiH+4a1PeNzyixWdqar+9kVe20DoHdYb5r5NnNHkxssCTmeZO/JKbBv6aDRFbL04FL+OP4HySeSKbQUOk7VarT0CO3B0FZDGRo5lI5NO8pGbEII0QD5eftxaatLWXVsFcXeW4Bu/HUkF3O5Db1XzT/Xt6flY7EqhAUa2JL9OyBDVbUhIceTzGqIybf64B2ygRf3zqzycIghhEsiL2Fo5FAGtxwsu0wLIUQjMTp2NKuOrWJz1hqa+Pcir9jC9uP59IttWuP5m+1Fxz1jdGw89TcgIac2JOR4kr0nJ8+qxytoH6DuTD06ZjSXtrqUrs26ypo0QgjRCF0aeSk+Oh+OFx2nV+tCft/lQ/KhnDOGnIp6nICmKShZCl2bdaVFQAt3NrlBkvEOT7LX5ORa9KBTC4kf6PUA03tNp0fz+r//khBCiPNTMWQF4B20A4D1h7JrPNdqUxzTx7NsmwHpxaktCTmeZO/JyTbr0WjLAHXbBCGEEI3fmNZjADhath5Q2HosnzKLtdp5+zMLKSwrJ8DHwu68LQBcFiMhpzYk5HiSvSanUPFBo1NDTpC+dkt7CyGEaNguibwEXy9fTpVmENosE7PV5uixOV3FVg6x0amU28ppE9yGNsFt3N3cBklCjieZKqeQV/TkyFRwIYS4OPh6+TK81XAAwlukAJB8KKfaeZvsm3JqA3YCMlRVFxJyPMlcDEAxejQ6E4Aszy2EEBeR+Nh4APK1mwGlWl2Ooij8dSQXNBbSTdsAGaqqCwk5nmQvPC7UVv41BHpLT44QQlwshkQOwc/LD6MlC61PGtuPF1BkKnc8np5fSoaxDH3gAcy2Mlr4t6BL0y4ebHHDIiHHU2xWsJQAUKJVd/n20fngrXPeTuRCCCHqNx8vH0ZEjwCgadhurDZ7z43dZvtQVbOw/YA6VKXRaNzf0AZKQo6n2HtxAEq1CiD1OEIIcTGKj1GHrDQBOwAbyYcr63LU9XGsmPVSj3M+JOR4ir3o2KbxwqJTuyYl5AghxMVncORgArwDKFPy0PmmVqnL2Xw0D53fYcxKMU19mtIrrJcHW9rwSMjxFHtPjlnnV7lGjhQdCyHERcegMzAiSh2y8grawe4TRvJLzBSUWEjJLMQraDcAI6JGyCKxdSQhx1PsPTllWj/HasfSkyOEEBenillWPsG7UBQbG4/ksiU1F7DhE7QHkKGq8yF7V3mKfSHAMo1v5Ro5MrNKCCEuSoNaDiLQO5BCjOh8j5J8qA2+eh1a3zRsOiMB3gEMaDHA081scKQnx1PsPTnFVK52LD05QghxcdLr9IyMHgmAV9BO1h/K5q8juXgHqkNVQ1sNRa/Te7KJDZKEHE8xV4QcXzRaGa4SQoiLXcWQlVfQTvZnGtmWloeXPeSMih7lyaY1WDJc5SkVCwHaDNKTI4QQgoEtBhKkD8KIEZ3fEWxWP7T6HAw6A5dEXuLp5jVI9aInZ8GCBcTGxuLj48OAAQPYtGnTGc8dPnw4Go2m2m3s2LFubLET2IerjDYDSE2OEEJc9Lx13o7iYq+gnXgF7gLUeh0/bz9PNq3B8njIWbJkCQkJCcyZM4etW7fSs2dP4uPjOXXqVI3nf/vtt5w8edJx27VrFzqdjhtvvNHNLb9A9p6cfKv05AghhFA5hqwCK0OODFWdP4+HnLlz5zJ16lSmTJlCly5dWLhwIX5+fixatKjG85s2bUpERITjtmrVKvz8/BpeyLH35OSVG2SdHCGEEAD0b9GfYH0IWq9idD6ZaDU6hkcN93SzGiyPhhyz2cyWLVsYNaoypWq1WkaNGkVycnKtXuP999/npptuwt/fv8bHTSYTRqOxyq1esE8hL1J80NjXyQnSB3myRUIIITzMW+vNqNN2Ge8X3pdgQ7AHW9SweTTkZGdnY7VaCQ8Pr3I8PDycjIyMcz5/06ZN7Nq1i7vvvvuM5yQmJhIcHOy4RUVFXXC7ncJ02uwq+3BVgLf05AghxMWuYsgKYFSMDFVdCI8PV12I999/n+7du9O/f/8znjNr1iwKCgoct7S0NDe28CzsNTlFVA5XSU2OEEKIfhH9aBXQCn9vf1nl+AJ5dAp5aGgoOp2OzMzMKsczMzOJiIg463OLi4v54osvePrpp896nsFgwGAwXHBbna5idhV60NgACTlCCCHAS+vFp2M/xWw109yvuaeb06B5tCdHr9fTp08fkpKSHMdsNhtJSUkMGjTorM/96quvMJlM3Hbbba5upmvYe3KMOvWvQKfR4evl68kWCSGEqCea+jQlwv/sv+yLc/P4YoAJCQlMnjyZvn370r9/f+bNm0dxcTFTpkwBYNKkSURGRpKYmFjlee+//z7jx4+nWbNmnmj2hTPZC481GkDtxdHYvxZCCCHEhfN4yJk4cSJZWVnMnj2bjIwM4uLiWL58uaMYOTU1Fa22aodTSkoKf/75JytXrvREk53DXAxAiU69K0XHQgghhHN5POQATJ8+nenTp9f42Jo1a6od69ixI4qiuLhVLmYfriqx5zepxxFCCCGcq0HPrmqwrOVQrs6oKtWqRceyRo4QQgjhXBJyPMG+ECCA2R5yZLVjIYQQwrkk5HiCffq4BW9sOjMgw1VCCCGEs0nI8QR7PU6pxrdyB3IJOUIIIYRTScjxBHtPTonGp3IHcm8JOUIIIYQzScjxBMfmnL5otOrmnNKTI4QQQjiXhBxPsPfkFNoMlZtzSuGxEEII4VQScjyhYksHm49szimEEEK4iIQcT7D35BTjAzp1uErWyRFCCCGcS0KOJ9h7cooVH7Q6EyDbOgghhBDOJiHHE+whpwgpPBZCCCFcRUKOJ9iHq4zoQWsBJOQIIYQQziYhxxPsPTn5Wr3jkL+3v6daI4QQQjRKEnI8waSuk5Ov1QFqwPHS1osN4YUQQohGQ0KOJ1RMIdeo334pOhZCCCGcT0KOJ9hrcop06rdf6nGEEEII55OQ4wkVU8i1GkDWyBFCCCFcQUKOJ1Rs0Gn/7suWDkIIIYTzScjxBPsGnWU6GyDDVUIIIYQrSMjxBHMxACaNFZDCYyGEEMIVJOS4W7kZrGYAzDo15EhNjhBCCOF8EnLczV50DFAuqx0LIYQQLiMhx93sCwGWKd4oFZtzSuGxEEII4XQSctytyuacZYD05AghhBCu4PGQs2DBAmJjY/Hx8WHAgAFs2rTprOfn5+czbdo0WrRogcFgoEOHDvz8889uaq0T2KePFys+aHT2kOMtIUcIIYRwNo9umLRkyRISEhJYuHAhAwYMYN68ecTHx5OSkkJYWFi1881mM5dffjlhYWF8/fXXREZGcuzYMUJCQtzf+PNlnz5ejC86nfTkCCGEEK7i0ZAzd+5cpk6dypQpUwBYuHAhy5YtY9GiRcycObPa+YsWLSI3N5f169fj7e0NQGxsrDubfOEqtnTAByTkCCGEEC7jseEqs9nMli1bGDVqVGVjtFpGjRpFcnJyjc/54YcfGDRoENOmTSM8PJxu3brx3HPPYbVaz3gdk8mE0WiscvMoe01OoWIAjYQcIYQQwlU8FnKys7OxWq2Eh4dXOR4eHk5GRkaNzzl8+DBff/01VquVn3/+mSeeeIJXXnmF//3vf2e8TmJiIsHBwY5bVFSUU99Hndl7cvK0BtAogIQcIYQQwhU8XnhcFzabjbCwMN555x369OnDxIkTeeyxx1i4cOEZnzNr1iwKCgoct7S0NDe2uAb2npwcjR4AvVaPQWfwZIuEEEKIRsljNTmhoaHodDoyMzOrHM/MzCQiIqLG57Ro0QJvb290Op3jWOfOncnIyMBsNqPX66s9x2AwYDDUoxBhDzn5WrWmSNbIEUIIIVzDYz05er2ePn36kJSU5Dhms9lISkpi0KBBNT5nyJAhHDx4EJvN5ji2f/9+WrRoUWPAqZdMFSFHDWqypYMQQgjhGh4drkpISODdd9/lww8/ZO/evdx///0UFxc7ZltNmjSJWbNmOc6///77yc3N5aGHHmL//v0sW7aM5557jmnTpnnqLdSdvSfHqFU70WRzTiGEEMI1PDqFfOLEiWRlZTF79mwyMjKIi4tj+fLljmLk1NRUtNrKHBYVFcWKFSuYMWMGPXr0IDIykoceeohHHnnEU2+h7uzbOhRpNYAUHQshhBCu4tGQAzB9+nSmT59e42Nr1qypdmzQoEFs2LDBxa1yoYptHSTkCCGEEC7VoGZXNQr2mpwS+3deQo4QQgjhGhJy3M3ek1OqkzVyhBBCCFeSkONu9p4ck1ZdpVkKj4UQQgjXkJDjZop9g06LPeRIT44QQgjhGhJy3ElRwFwMgEVrASTkCCGEEK4iIcedyk1obOUA2HQScoQQQghXkpDjTvaiYwC8zICEHCGEEMJVJOS4k30hwBLFgEZXBkjIEUIIIVzlvEJOeXk5v/76K2+//TaFheoH94kTJygqKjrHMy9yFQsB4oOisYccbwk5QgghhCvUecXjY8eOMWbMGFJTUzGZTFx++eUEBgbywgsvYDKZWLhwoSva2TjYp4/n4gMatTZHenKEEEII16hzT85DDz1E3759ycvLw9fX13H82muvrbKjuKiBvScnW+sDgAYNft5+nmyREEII0WjVuSfnjz/+YP369ej1+irHY2NjSU9Pd1rDGiV7TU621gCUEaAPQKuRsighhBDCFer8CWuz2bBardWOHz9+nMBAGXo5K3tPTo5GDYhB+iBPtkYIIYRo1OocckaPHs28efMc9zUaDUVFRcyZM4crr7zSmW1rfOw1Ofk6b0C2dBBCCCFcqc7DVa+88grx8fF06dKFsrIybrnlFg4cOEBoaCiff/65K9rYeNh7cvI16rddio6FEEII16lzyGnVqhXbt2/niy++YMeOHRQVFXHXXXdx6623VilEFjWwhxyjVu1AC9BLT44QQgjhKnUOOQBeXl7cdtttzm5L42cfrirUqSFHanKEEEII16lzyPnoo4/O+vikSZPOuzGNXsVigBoNIMNVQgghhCvVOeQ89NBDVe5bLBZKSkrQ6/X4+flJyDkbe09OqU69K4XHQgghhOvUeXZVXl5elVtRUREpKSlccsklUnh8DopZXSenVKsA0pMjhBBCuJJTVqJr3749zz//fLVeHlGVrUwNOWVadZ0hqckRQgghXMdpy+16eXlx4sQJZ71co2QrU4erLDobILOrhBBCCFeqc03ODz/8UOW+oiicPHmS+fPnM2TIEKc1rFGyD1fZdLI5pxBCCOFqdQ4548ePr3Jfo9HQvHlzRo4cySuvvOKsdjVKGvvsKqvOAkCgt4QcIYQQwlXqHHJsNpsr2tH4KQq68hIArFoTID05QgghhCvViy2wFyxYQGxsLD4+PgwYMIBNmzad8dzFixej0Wiq3Hx8fNzY2vNkKUWj2CgHbBozICFHCCGEcKVa9eQkJCTU+gXnzp1bpwYsWbKEhIQEFi5cyIABA5g3bx7x8fGkpKQQFhZW43OCgoJISUlx3NfYF9er1+xDVcXaylwphcdCCCGE69Qq5Pz999+1erHzCRtz585l6tSpTJkyBYCFCxeybNkyFi1axMyZM894nYiIiDpfy6NMatFxhkbtdfL18sVb6+3JFgkhhBCNWq1CzurVq11ycbPZzJYtW5g1a5bjmFarZdSoUSQnJ5/xeUVFRcTExGCz2ejduzfPPfccXbt2rfFck8mEyWRy3Dcajc57A3Vh78nJ0qohR1Y7FkIIIVzLozU52dnZWK1WwsPDqxwPDw8nIyOjxud07NiRRYsW8f333/PJJ59gs9kYPHgwx48fr/H8xMREgoODHbeoqCinv49asW/pkG3vyZF6HCGEEMK1zmsX8s2bN/Pll1+SmpqK2Wyu8ti3337rlIadyaBBgxg0aJDj/uDBg+ncuTNvv/02zzzzTLXzZ82aVaWmyGg0eibo2HtycnR6wCYhRwghhHCxOvfkfPHFFwwePJi9e/fy3XffYbFY2L17N7/99hvBwcF1eq3Q0FB0Oh2ZmZlVjmdmZta65sbb25tevXpx8ODBGh83GAwEBQVVuXmEvSYnX6PW4UjRsRBCCOFadQ45zz33HK+++io//vgjer2e1157jX379jFhwgSio6Pr9Fp6vZ4+ffqQlJTkOGaz2UhKSqrSW3M2VquVnTt30qJFizpd2+3sPTl5WrXzLMhb9q0SQgghXKnOIefQoUOMHTsWUENKcXExGo2GGTNm8M4779S5AQkJCbz77rt8+OGH7N27l/vvv5/i4mLHbKtJkyZVKUx++umnWblyJYcPH2br1q3cdtttHDt2jLvvvrvO13Yre02OUad+y2W4SgghhHCtOtfkNGnShMJCdeglMjKSXbt20b17d/Lz8ykpKalzAyZOnEhWVhazZ88mIyODuLg4li9f7ihGTk1NRXva2jJ5eXlMnTqVjIwMmjRpQp8+fVi/fj1dunSp87Xdyt6TU6RR34sMVwkhhBCuVeuQs2vXLrp168all17KqlWr6N69OzfeeCMPPfQQv/32G6tWreKyyy47r0ZMnz6d6dOn1/jYmjVrqtx/9dVXefXVV8/rOp6kmIrQAEXSkyOEEEK4Ra1DTo8ePejXrx/jx4/nxhtvBOCxxx7D29ub9evXc/311/P444+7rKENnbnUiAEo0SiAbM4phBBCuFqtQ87atWv54IMPSExM5Nlnn+X666/n7rvvPuOqxKKqcnvIKbN/x6UnRwghhHCtWhceDx06lEWLFnHy5EneeOMNjh49yrBhw+jQoQMvvPDCGRfvEyprqVrHZNapu7hLyBFCCCFcq86zq/z9/ZkyZQpr165l//793HjjjSxYsIDo6GiuvvpqV7SxUVDs6+SYtVZAQo4QQgjhahe0rUO7du149NFHefzxxwkMDGTZsmXOalfjY59dZdGWAxJyhBBCCFc7r20dAH7//XcWLVrEN998g1arZcKECdx1113ObFujojEXowAWjboNhoQcIYQQwrXqFHJOnDjB4sWLWbx4MQcPHmTw4MG8/vrrTJgwAX9/f1e1sVHQWYoo1WhQ7LOrZBdyIYQQwrVqHXKuuOIKfv31V0JDQ5k0aRJ33nknHTt2dGXbGhXv8hJy7Ysaemm88PXy9XCLhBBCiMat1iHH29ubr7/+mnHjxqHT6VzZpsbHZkNvK6HIu3JzTo1G4+FGCSGEEI1brUPODz/84Mp2NG4WdbuLQq0abKQeRwghhHC9C5pdJWrJPrMqX6P2gEnIEUIIIVxPQo472Hcgz9EaANnSQQghhHAHCTnuYFYXAszR6QHpyRFCCCHcQUKOO9h7cvI0lYXHQgghhHAtCTnuUFGTo1XrvKUnRwghhHA9CTluUF5qBMAoIUcIIYRwGwk5bmAqVkNOoX0xQCk8FkIIIVxPQo4bmIoLACjWyTo5QgghhLtIyHEDi324qtS+ULSEHCGEEML1JOS4QXmpOoW8zP7dlpAjhBBCuJ6EHDewllWEHBsgIUcIIYRwBwk5bqDY18kp05YDEOAt6+QIIYQQriYhxx3MhZiBco305AghhBDuIiHHDbTmYsf0cZCeHCGEEMId6kXIWbBgAbGxsfj4+DBgwAA2bdpUq+d98cUXaDQaxo8f79oGXiBdeTFF9pDj7+2PTqvzcIuEEEKIxs/jIWfJkiUkJCQwZ84ctm7dSs+ePYmPj+fUqVNnfd7Ro0f597//zdChQ93U0vPnVV7ZkyNDVUIIIYR7eDzkzJ07l6lTpzJlyhS6dOnCwoUL8fPzY9GiRWd8jtVq5dZbb+Wpp56iTZs2bmzt+TFYSzDaQ44MVQkhhBDu4dGQYzab2bJlC6NGjXIc02q1jBo1iuTk5DM+7+mnnyYsLIy77rrrnNcwmUwYjcYqN3cz2Eoo0qqrHQfpg9x+fSGEEOJi5NGQk52djdVqJTw8vMrx8PBwMjIyanzOn3/+yfvvv8+7775bq2skJiYSHBzsuEVFRV1wu+vEZsUHkwxXCSGEEG7m8eGquigsLOT222/n3XffJTQ0tFbPmTVrFgUFBY5bWlqai1v5D2Z1jZyKwuMAvQxXCSGEEO7g5cmLh4aGotPpyMzMrHI8MzOTiIiIaucfOnSIo0ePctVVVzmO2Wzq2jNeXl6kpKTQtm3bKs8xGAwYDAYXtL6WzMUA5NtnVMkO5EIIIYR7eLQnR6/X06dPH5KSkhzHbDYbSUlJDBo0qNr5nTp1YufOnWzbts1xu/rqqxkxYgTbtm1z/1BULVTsQJ6n9QZkuEoIIYRwF4/25AAkJCQwefJk+vbtS//+/Zk3bx7FxcVMmTIFgEmTJhEZGUliYiI+Pj5069atyvNDQkIAqh2vL0qKCjAA+Vr1Wy0hRwghhHAPj4eciRMnkpWVxezZs8nIyCAuLo7ly5c7ipFTU1PRahtU6VAVpUX5NAGMEnKEEEIIt/J4yAGYPn0606dPr/GxNWvWnPW5ixcvdn6DnKisSB2uKtJpAUUKj4UQQgg3abhdJA2EqVhdl6didlWQt6yTI4QQQriDhBwXs5SqIafY/p2W4SohhBDCPSTkuFi5PeSUahVA1skRQggh3EVCjovZTIXYgDKNup6P9OQIIYQQ7iEhx8UUUxFFWg2KunWVhBwhhBDCTSTkuJqp2FF0rNfqMeg8uPqyEEIIcRGRkONiWkuRbM4phBBCeICEHBfzKi+WkCOEEEJ4gIQcF/MqL5GQI4QQQniAhBwXM9gk5AghhBCeICHHxXxsJRRq1alVAd6yRo4QQgjhLhJyXEhRFHyVUunJEUIIITxAQo4LlVls+CMhRwghhPAECTkuZCwpxUdjcayTIyFHCCGEcB8JOS5UaMxT/5SQI4QQQridhBwXKjHmA2DU6gApPBZCCCHcSUKOC5UUqzuQF9hDTpA+yJPNEUIIIS4qEnJcyFRUAFQOVwXopSdHCCGEcBcJOS5kLlV7cqTwWAghhHA/CTkuZC42ogDF9u+yDFcJIYQQ7iMhx4WsZUZMGg3l6oLHUngshBBCuJGEHBeylRU5tnTQarT4eft5uEVCCCHExUNCjgsp5sLKomPvALQa+XYLIYQQ7iKfui6kMRfJQoBCCCGEh9SLkLNgwQJiY2Px8fFhwIABbNq06Yznfvvtt/Tt25eQkBD8/f2Ji4vj448/dmNra09rKZaQI4QQQniIx0POkiVLSEhIYM6cOWzdupWePXsSHx/PqVOnajy/adOmPPbYYyQnJ7Njxw6mTJnClClTWLFihZtbfm5ep4UcKToWQggh3MvjIWfu3LlMnTqVKVOm0KVLFxYuXIifnx+LFi2q8fzhw4dz7bXX0rlzZ9q2bctDDz1Ejx49+PPPP93c8nPztpZIT44QQgjhIR4NOWazmS1btjBq1CjHMa1Wy6hRo0hOTj7n8xVFISkpiZSUFC699FJXNrXOFEVBbytxzK6SkCOEEEK4l5cnL56dnY3VaiU8PLzK8fDwcPbt23fG5xUUFBAZGYnJZEKn0/Hmm29y+eWX13iuyWTCZDI57huNRuc0/hyKTOX4UyarHQshhBAe4tGQc74CAwPZtm0bRUVFJCUlkZCQQJs2bRg+fHi1cxMTE3nqqafc3kZjWTn+lMpwlRBCCOEhHg05oaGh6HQ6MjMzqxzPzMwkIiLijM/TarW0a9cOgLi4OPbu3UtiYmKNIWfWrFkkJCQ47huNRqKiopzzBs7CWGohgDKMUngshBBCeIRHa3L0ej19+vQhKSnJccxms5GUlMSgQYNq/To2m63KkNTpDAYDQUFBVW7uYCy14K8pdQxXyb5VQgghhHt5fLgqISGByZMn07dvX/r378+8efMoLi5mypQpAEyaNInIyEgSExMBdfipb9++tG3bFpPJxM8//8zHH3/MW2+95cm3UY06XFVGodYfkOEqIYQQwt08HnImTpxIVlYWs2fPJiMjg7i4OJYvX+4oRk5NTUWrrexwKi4u5l//+hfHjx/H19eXTp068cknnzBx4kRPvYUaGYuLMWjKKbLPrgrQy3CVEEII4U4aRVEUTzfCnYxGI8HBwRQUFLh06OrT3/7m1t+Hc1lUS055efHFuC/o2qyry64nhBBCNGbn8/nt8cUAGytTSQGAY3ZVkLfU5AghhBDuJCHHRUwlhZQDpRWzq2S4SgghhHArCTkuUl5qdMysAgk5QgghhLtJyHGR8tJCx1CVr5cv3lpvD7dICCGEuLhIyHERW1lh5b5V3jJ9XAghhHA3CTmuYi6SLR2EEEIID5KQ4yJac5GjJkfqcYQQQgj3k5DjItryIse+VdKTI4QQQrifhBwXsNoUvMtLKoerpCZHCCGEcDsJOS5QVFaOP5Wbc0pPjhBCCOF+EnJcwFhmIUBTVjm7SkKOEEII4XYSclygoNSCH2WOmhwpPBZCCCHcT0KOCxhLLfhT5hiuCtLLvlVCCCGEu0nIcQF1uKpU1skRQgghPEhCjgsYS8ur9OQEeMtwlRBCCOFuEnJcwFhmwV9TJuvkCCGEEB4kIccFjKUWAiiV2VVCCCGEB0nIcYGCEjN+pw1XScgRQggh3E9CjguUlJZg0dqwaaQnRwghhPAUCTkuYC4xOmZWeWm88NH5eLhFQgghxMVHQo4LlJcaq0wf19h7dIQQQgjhPhJyXMBWVuQIObLasRBCCOEZEnJcwFpWSJHMrBJCCCE8SkKOK5iLZI0cIYQQwsMk5DiZxWrDq7y4sibHW0KOEEII4Qn1IuQsWLCA2NhYfHx8GDBgAJs2bTrjue+++y5Dhw6lSZMmNGnShFGjRp31fHcrLCvHXyNr5AghhBCe5vGQs2TJEhISEpgzZw5bt26lZ8+exMfHc+rUqRrPX7NmDTfffDOrV68mOTmZqKgoRo8eTXp6uptbXrPK1Y4l5AghhBCe5PGQM3fuXKZOncqUKVPo0qULCxcuxM/Pj0WLFtV4/qeffsq//vUv4uLi6NSpE++99x42m42kpCQ3t7xmBaUW/ClzbOkgs6uEEEIIz/BoyDGbzWzZsoVRo0Y5jmm1WkaNGkVycnKtXqOkpASLxULTpk1rfNxkMmE0GqvcXMlYZiFAU+boyQnSB7n0ekIIIYSomZcnL56dnY3VaiU8PLzK8fDwcPbt21er13jkkUdo2bJllaB0usTERJ566qkLbmttGUvL8T9tuCrAW3pyhBDCE6xWKxaLxdPNEHWg1+vRap3X/+LRkHOhnn/+eb744gvWrFmDj0/NWyfMmjWLhIQEx32j0UhUVJTL2mQss0jhsRBCeJCiKGRkZJCfn+/ppog60mq1tG7dGr1e75TX82jICQ0NRafTkZmZWeV4ZmYmERERZ33uyy+/zPPPP8+vv/5Kjx49zniewWDAYDA4pb21YSy10JpSWSdHCCE8pCLghIWF4efnJ1vrNBA2m40TJ05w8uRJoqOjnfL35tGQo9fr6dOnD0lJSYwfPx7AUUQ8ffr0Mz7vxRdf5Nlnn2XFihX07dvXTa2tnYJSC35IT44QQniC1Wp1BJxmzZp5ujmijpo3b86JEycoLy/H29v7gl/P48NVCQkJTJ48mb59+9K/f3/mzZtHcXExU6ZMAWDSpElERkaSmJgIwAsvvMDs2bP57LPPiI2NJSMjA4CAgAACAjxf/1JZeCzbOgghhLtV1OD4+fl5uCXifFQMU1mt1sYRciZOnEhWVhazZ88mIyODuLg4li9f7ihGTk1NrVKE9NZbb2E2m7nhhhuqvM6cOXN48skn3dn0GhlLy9FThkmrA6TwWAghPEGGqBomZ/+9eTzkAEyfPv2Mw1Nr1qypcv/o0aOub9AFMJZZQFcG+AMScoQQQghP8fhigI1NQakFm84EQICXHzp7j44QQghR32g0GpYuXer0c+sLCTlOZiwxU65Vx4QDvP093BohhBANxR133IFGo0Gj0aDX62nXrh1PP/005eXlLrvmyZMnueKKK5x+bn1RL4arGhNTWQklXhVFx7LasRBCiNobM2YMH3zwASaTiZ9//plp06bh7e3NrFmzqpxnNpudspbMuZZrOd9z6wvpyXEyW5mxcmaVIdjDrRFCCKEoCiXmco/cFEWpU1sNBgMRERHExMRw//33M2rUKH744QfuuOMOxo8fz7PPPkvLli3p2LEjAGlpaUyYMIGQkBCaNm3KNddcU612ddGiRXTt2hWDwUCLFi2q1MCePgRlNpuZPn06LVq0wMfHh5iYGMfM5n+eC7Bz505GjhyJr68vzZo145577qGoqMjxeEWbX375ZVq0aEGzZs2YNm2aW1ehlp4cJyqzWPG2lsgaOUIIUY+UWqx0mb3CI9fe83Q8fvrz/6j19fUlJycHgKSkJIKCgli1ahWgTpePj49n0KBB/PHHH3h5efG///2PMWPGsGPHDvR6PW+99RYJCQk8//zzXHHFFRQUFLBu3boar/X666/zww8/8OWXXxIdHU1aWhppaWk1nltcXOy49l9//cWpU6e4++67mT59OosXL3act3r1alq0aMHq1as5ePAgEydOJC4ujqlTp57396QuJOQ4kbHMQgCVm3NKyBFCCHE+FEUhKSmJFStW8MADD5CVlYW/vz/vvfeeY5jqk08+wWaz8d577zmmXn/wwQeEhISwZs0aRo8ezf/+9z/+7//+j4ceesjx2v369avxmqmpqbRv355LLrkEjUZDTEzMGdv32WefUVZWxkcffYS/v1p/On/+fK666ipeeOEFxzIwTZo0Yf78+eh0Ojp16sTYsWNJSkqSkNMQGUvL8aMMo0425xRCiPrC11vHnqfjPXbtuvjpp58ICAjAYrFgs9m45ZZbePLJJ5k2bRrdu3evUoezfft2Dh48SGBg1V+oy8rKOHToEKdOneLEiRNcdtlltbr2HXfcweWXX07Hjh0ZM2YM48aNY/To0TWeu3fvXnr27OkIOABDhgzBZrORkpLiCDldu3ZFp6v8HrRo0YKdO3fW+vtxoSTkOJG6OWcpRRrpyRFCiPpCo9Fc0JCRO40YMYK33noLvV5Py5Yt8fKqbPfpgQKgqKiIPn368Omnn1Z7nebNm9d5N+/evXtz5MgRfvnlF3799VcmTJjAqFGj+Prrr8/vzUC1VYs1Gg02m+28X6+uGsbfegNhLLUPV9l7coJkdpUQQog68Pf3p127drU6t3fv3ixZsoSwsDCCgmr+vImNjSUpKYkRI0bU6jWDgoKYOHEiEydO5IYbbmDMmDHk5ubStGnTKud17tyZxYsXU1xc7Ahf69atQ6vVOoqi6wOZXeVExrJy/DWVNTkBehmuEkII4Rq33noroaGhXHPNNfzxxx8cOXKENWvW8OCDD3L8+HEAnnzySV555RVef/11Dhw4wNatW3njjTdqfL25c+fy+eefs2/fPvbv389XX31FREQEISEhNV7bx8eHyZMns2vXLlavXs0DDzzA7bff7hiqqg8k5DhRQamFAEplc04hhBAu5+fnx++//050dDTXXXcdnTt35q677qKsrMzRszN58mTmzZvHm2++SdeuXRk3bhwHDhyo8fUCAwN58cUX6du3L/369ePo0aP8/PPPNQ57+fn5sWLFCnJzc+nXrx833HADl112GfPnz3fpe64rjVLXSfwNnNFoJDg4mIKCgjN2752vBasPYvo1kT9j/+SAXs/bo95mcORgp15DCCHEmZWVlXHkyBFat26Nj4+Pp5sj6uhsf3/n8/ktPTlO5Cg8linkQgghhMdJyHEiY2m5rJMjhBBC1BMScpzIWGbBV1O54rEUHgshhBCeIyHHiYylFry0pY770pMjhBBCeI6EHCcyllpAWwaAQeOFQWfwcIuEEEKIi5eEHCcylpWDzgRAgJdU9QshhBCeJCHHiYylFhStGnICvfzPcbYQQgghXElCjpMoioKxzIJVZwEg0FtCjhBCCOFJEnKcpNRixWJVKNfaQ45B9q0SQgjRsGg0GpYuXQrA0aNH0Wg0bNu2zaNtuhAScpzEWFoOKJi0VgAC9cGebZAQQogG5Y477kCj0aDRaPD29qZ169b897//payszNNNa7BkF3InMZZZ8MNEkU7dtyrAJ8SzDRJCCNHgjBkzhg8++ACLxcKWLVuYPHkyGo2GF154wdNNa5CkJ8dJjKUW/Knc0iHIp4mHWySEEKKhMRgMREREEBUVxfjx4xk1ahSrVq0CwGazkZiYSOvWrfH19aVnz558/fXXVZ6/e/duxo0bR1BQEIGBgQwdOpRDhw4B8Ndff3H55ZcTGhpKcHAww4YNY+vWrW5/j+7k8ZCzYMECYmNj8fHxYcCAAWzatOmM5+7evZvrr7+e2NhYNBoN8+bNc19DzyGmmT//uyL2tC0dpCZHCCHqBUUBc7FnbhewB/auXbtYv349er0egMTERD766CMWLlzI7t27mTFjBrfddhtr164FID09nUsvvRSDwcBvv/3Gli1buPPOOykvLwegsLCQyZMn8+eff7Jhwwbat2/PlVdeSWFh4YV/j+spjw5XLVmyhISEBBYuXMiAAQOYN28e8fHxpKSkEBYWVu38kpIS2rRpw4033siMGTM80OIzax5oIL69Pyt2y5YOQghRr1hK4LmWnrn2oydAX/vZtj/99BMBAQGUl5djMpnQarXMnz8fk8nEc889x6+//sqgQYMAaNOmDX/++Sdvv/02w4YNY8GCBQQHB/PFF1/g7e0NQIcOHRyvPXLkyCrXeueddwgJCWHt2rWMGzfOCW+2/vFoyJk7dy5Tp05lypQpACxcuJBly5axaNEiZs6cWe38fv360a9fP4AaH/c4UxFG2ZxTCCHEeRoxYgRvvfUWxcXFvPrqq3h5eXH99deze/duSkpKuPzyy6ucbzab6dWrFwDbtm1j6NChjoDzT5mZmTz++OOsWbOGU6dOYbVaKSkpITU11eXvy1M8FnLMZjNbtmxh1qxZjmNarZZRo0aRnJzstOuYTCZMJpPjvtFodNprV2MuplCrFh4HekvIEUKIesHbT+1R8dS168Df35927doBsGjRInr27Mn7779Pt27dAFi2bBmRkZFVnmMwqFsI+fr6nvW1J0+eTE5ODq+99hoxMTEYDAYGDRqE2WyuUxsbEo+FnOzsbKxWK+Hh4VWOh4eHs2/fPqddJzExkaeeesppr3dW5iJH4bH05AghRD2h0dRpyKi+0Gq1PProoyQkJLB//34MBgOpqakMGzasxvN79OjBhx9+iMViqbE3Z926dbz55ptceeWVAKSlpZGdne3S9+BpHi88drVZs2ZRUFDguKWlpbnuYqbC0wqPJeQIIYS4MDfeeCM6nY63336bf//738yYMYMPP/yQQ4cOsXXrVt544w0+/PBDAKZPn47RaOSmm25i8+bNHDhwgI8//piUlBQA2rdvz8cff8zevXvZuHEjt9566zl7fxo6j/XkhIaGotPpyMzMrHI8MzOTiIgIp13HYDA4uvJcTZGQI4QQwom8vLyYPn06L774IkeOHKF58+YkJiZy+PBhQkJC6N27N48++igAzZo147fffuM///kPw4YNQ6fTERcXx5AhQwB4//33ueeee+jduzdRUVE899xz/Pvf//bk23M5jaJcwPy2CzRgwAD69+/PG2+8AahrAERHRzN9+vRzFhbHxsby8MMP8/DDD9fpmkajkeDgYAoKCggKcu4079KkZ+h//EsANtyyAX/Zv0oIIdyqrKyMI0eO0Lp1a3x8fDzdHFFHZ/v7O5/Pb4/OrkpISGDy5Mn07duX/v37M2/ePIqLix2zrSZNmkRkZCSJiYmAWqy8Z88ex9fp6els27aNgIAAR6GWJxWa8gF1DNDPq27FZkIIIYRwLo+GnIkTJ5KVlcXs2bPJyMggLi6O5cuXO4qRU1NT0Wory4ZOnDjhmCoH8PLLL/Pyyy8zbNgw1qxZ4+7mV1NkDzkBWj0ajcazjRFCCCEuch7fu2r69OlMnz69xsf+GVxiY2Px4OjaORnN6qqRgTr31AAJIYQQ4swa/ewqdyo0FwEQqGvc1epCCCFEQyAhx4mKyksACJR6HCGEEMLjJOQ4UaG1FIBAb9m3SgghhPA0CTlOVFheBkCArJEjhBBCeJyEHCcqVNT9P4IMwR5uiRBCCCEk5DhRoa0cgAAfCTlCCCGEp0nIcRabjUKsAAT6NPVwY4QQQgghIcdZzEWV+1b5hnq4MUIIIRqaO+64g/Hjxzvup6Wlceedd9KyZUv0ej0xMTE89NBD5OTkVHlebGws8+bNq/Z6Tz75JHFxca5tdD0nIcdZTg85Pk083BghhBAN2eHDh+nbty8HDhzg888/5+DBgyxcuJCkpCQGDRpEbm6up5vYIHh8xeNGw1REkVbdyiHQ4NyNP4UQQlxcpk2bhl6vZ+XKlfj6qgvMRkdH06tXL9q2bctjjz3GW2+95eFW1n8ScpzFXOjoyQnQyzo5QghRXyiKQml5qUeu7evlW+e9DHNzc1mxYgXPPvusI+BUiIiI4NZbb2XJkiW8+eabsk/iOUjIcRZFoVCnAyDIW3pyhBCivigtL2XAZwM8cu2Nt2zEz7tuq+AfOHAARVHo3LlzjY937tyZvLw8srKyCAsLc0YzGy2pyXESS8uelNoTdaAsBiiEEOIC1ecNqRsK6clxkiL75pwA/np/D7ZECCHE6Xy9fNl4y0aPXbuu2rVrh0ajYe/evVx77bXVHt+7dy9NmjShefPmAAQFBVFQUFDtvPz8fIKDL+512yTkOElFyPH18sVb6+3h1gghhKig0WjqPGTkSc2aNePyyy/nzTffZMaMGVXqcjIyMvj000+ZNGmSox6nY8eObNmypdrrbN26lY4dO7qt3fWRDFc5idFiBGSoSgghxIWbP38+JpOJ+Ph4fv/9d9LS0li+fDmXX345kZGRPPvss45zZ8yYwbJly3j22WfZu3cvu3bt4rHHHiM5OZmHHnrIg+/C8yTkOInZasbf258gvRQdCyGEuDDt27dn8+bNtGnThgkTJtC2bVvuueceRowYQXJyMk2bVq6sP3jwYH755Rd++eUXhgwZwvDhw1m/fj1JSUl069bNg+/C8zTKRVbZZDQaCQ4OpqCggKAg5wcSm2JDq5HsKIQQnlBWVsaRI0do3bo1Pj4+nm6OqKOz/f2dz+e3fBo7mQQcIYQQon6QT2QhhBBCNEoScoQQQgjRKEnIEUIIIUSjJCFHCCGEEI2ShBwhhBCNzkU2cbjRcPbfm4QcIYQQjYa3t7rifElJiYdbIs6H2WwGQGff8PpC1YttHRYsWMBLL71ERkYGPXv25I033qB///5nPP+rr77iiSee4OjRo7Rv354XXniBK6+80o0tFkIIUR/pdDpCQkI4deoUAH5+fo7tD0T9ZrPZyMrKws/PDy8v58QTj4ecJUuWkJCQwMKFCxkwYADz5s0jPj6elJSUGreQX79+PTfffDOJiYmMGzeOzz77jPHjx7N169aLfmVHIYQQEBERAeAIOqLh0Gq1REdHOy2YenzF4wEDBtCvXz/mz58PqEkuKiqKBx54gJkzZ1Y7f+LEiRQXF/PTTz85jg0cOJC4uDgWLlx4zuu5esVjIYQQ9YPVasVisXi6GaIO9Ho9Wm3NlTTn8/nt0Z4cs9nMli1bmDVrluOYVqtl1KhRJCcn1/ic5ORkEhISqhyLj49n6dKlNZ5vMpkwmUyO+0aj8cIbLoQQot7T6XROq+0QDZNHC4+zs7OxWq2Eh4dXOR4eHk5GRkaNz8nIyKjT+YmJiQQHBztuUVFRzmm8EEIIIeq1Rj+7atasWRQUFDhuaWlpnm6SEEIIIdzAo8NVoaGh6HQ6MjMzqxzPzMx0FI79U0RERJ3ONxgMGAwG5zRYCCGEEA2GR0OOXq+nT58+JCUlMX78eEAtPE5KSmL69Ok1PmfQoEEkJSXx8MMPO46tWrWKQYMG1eqaFXXWUpsjhBBCNBwVn9t1mi+leNgXX3yhGAwGZfHixcqePXuUe+65RwkJCVEyMjIURVGU22+/XZk5c6bj/HXr1ileXl7Kyy+/rOzdu1eZM2eO4u3trezcubNW10tLS1MAuclNbnKTm9zk1gBvaWlptc4YHl8nZ+LEiWRlZTF79mwyMjKIi4tj+fLljuLi1NTUKtPJBg8ezGeffcbjjz/Oo48+Svv27Vm6dGmt18hp2bIlaWlpBAYGNqoFooxGI1FRUaSlpbl9arxcW659sVxfri3Xlmt77tqKolBYWEjLli1r/XoeXydHOIcn1/+Ra8u1L5bry7Xl2nLthnXtRj+7SgghhBAXJwk5QgghhGiUJOQ0EgaDgTlz5nhkurxcW659sVxfri3Xlms3rGtLTY4QQgghGiXpyRFCCCFEoyQhRwghhBCNkoQcIYQQQjRKEnKEEEII0ShJyGngEhMT6devH4GBgYSFhTF+/HhSUlLc3o7nn38ejUZTZU8xV0tPT+e2226jWbNm+Pr60r17dzZv3uzy61qtVp544glat26Nr68vbdu25Zlnnqnbfiq19Pvvv3PVVVfRsmVLNBoNS5curfK4oijMnj2bFi1a4Ovry6hRozhw4IDLr22xWHjkkUfo3r07/v7+tGzZkkmTJnHixAmXX/uf7rvvPjQaDfPmzXPbtffu3cvVV19NcHAw/v7+9OvXj9TUVJdfu6ioiOnTp9OqVSt8fX3p0qULCxcuvODrQu1+lpSVlTFt2jSaNWtGQEAA119/fbUNk11x7dzcXB544AE6duyIr68v0dHRPPjggxQUFLj82qdTFIUrrrjinP8mnX3t5ORkRo4cib+/P0FBQVx66aWUlpa6/NoZGRncfvvtRERE4O/vT+/evfnmm28u6LoV3nrrLXr06EFQUBBBQUEMGjSIX375xfG4s/6tSchp4NauXcu0adPYsGEDq1atwmKxMHr0aIqLi93Whr/++ou3336bHj16uO2aeXl5DBkyBG9vb3755Rf27NnDK6+8QpMmTVx+7RdeeIG33nqL+fPns3fvXl544QVefPFF3njjDadfq7i4mJ49e7JgwYIaH3/xxRd5/fXXWbhwIRs3bsTf35/4+HjKyspceu2SkhK2bt3KE088wdatW/n2229JSUnh6quvvuDrnuvap/vuu+/YsGFDnZZ5v9BrHzp0iEsuuYROnTqxZs0aduzYwRNPPIGPj4/Lr52QkMDy5cv55JNP2Lt3Lw8//DDTp0/nhx9+uOBr1+ZnyYwZM/jxxx/56quvWLt2LSdOnOC6665z+bVPnDjBiRMnePnll9m1axeLFy9m+fLl3HXXXS6/9unmzZvn1O2AanPt5ORkxowZw+jRo9m0aRN//fUX06dPr7LdkauuPWnSJFJSUvjhhx/YuXMn1113HRMmTODvv/++oGsDtGrViueff54tW7awefNmRo4cyTXXXMPu3bsBJ/5bq/UuV6JBOHXqlAIoa9eudcv1CgsLlfbt2yurVq1Shg0bpjz00ENuue4jjzyiXHLJJW651j+NHTtWufPOO6scu+6665Rbb73VpdcFlO+++85x32azKREREcpLL73kOJafn68YDAbl888/d+m1a7Jp0yYFUI4dO+aWax8/flyJjIxUdu3apcTExCiv/n979x5Tdf3/Afz54RzOgXMWCBKXIzuIo1BRTIOxAxXVKZDYUjOpSXQsN0TRsC2NAhblhP4oWbPEMKEbSeKETrKiuGhKiCUcLhtxCbZawtR5Qe7Eef3+cJx9D0qSfj7n2Pm9Httn83zOB57vgy/evs77vI8nL0/U3Jmyn3vuOXrhhRdEz5pNdkhICL3zzjtW51asWEEZGRmi50+fS65cuULOzs5UWlpquaa9vZ0AUH19vaTZN3P48GFSKBQ0MTFhk+ympiaaN28e9fX1zer3QazsiIgIyszMFD1rNtlqtZo+//xzq+s8PT3pwIEDkozBw8ODPvnkE1FrjVdyHMzU8q2np6dN8lJTUxEfH48nnnjCJnlTjEYjwsLCsG7dOnh7e2P58uU4cOCATbIjIyNRXV2Nzs5OAEBzczNOnTqFuLg4m+RP6e3tRX9/v9XP3t3dHREREaivr7fpWIDrtScIAubMmSN5ltlsRlJSEnbs2IGQkBDJ8/43t6KiAvfffz9iY2Ph7e2NiIgIUV66mI3IyEgYjUb89ddfICLU1tais7MTMTExomdNn0vOnj2LiYkJq3pbuHAhtFqt6PU2m3ls6rON5HJxP2f6ZtnDw8NYv349PvroI/j6+oqa90/Z58+fR0NDA7y9vREZGQkfHx9ER0fj1KlTkmcD1+vt66+/xqVLl2A2m1FSUoLR0VE8+uijomZPTk6ipKQEQ0ND0Ol04taayI0Ys6PJyUmKj4+nqKgom+QdOnSIlixZQiMjI0RENl3JUSqVpFQq6Y033qDGxkb6+OOPycXFhT799FPJsycnJ+n1118nQRBILpeTIAiUk5MjeS6mPXusq6sjAHTu3Dmr69atW0cJCQmSZk83MjJCK1asoPXr14uaO1N2Tk4OPfnkk2Q2m4mIbLaSM/UsXqVS0Z49e6ipqYlyc3NJEAQ6fvy4pNlERKOjo/Tiiy8SAJLL5aRQKOizzz4TNZfo5nNJcXExKRSKG64NDw+nnTt3Spo93YULF0ir1dKbb74pWu4/ZScnJ9PGjRstt2/1+yBWdn19PQEgT09PKiwspMbGRtq+fTspFArq7OyUNJuI6PLlyxQTE2OpNzc3N6qsrBQtt6WlhdRqNclkMnJ3d6eKigoiErfWxG2BmV2lpqaira1Nki5/uj///BNpaWn48ccfRdmL8G+ZzWaEhYUhJycHALB8+XK0tbVh//79MBgMkmYfPnwYxcXF+OqrrxASEgKTyYTt27dDo9FInn03mpiYQEJCAogI+fn5kuedPXsWH3zwARobG0XdHzEbZrMZALBq1Sq8+uqrAIAHHngAP//8M/bv34/o6GhJ8/fu3YvTp0/DaDQiICAAP/30E1JTU6HRaERdTbXlXPJvswcGBhAfH4/FixcjOztb8myj0YiamhpR9qH82+ypetu0aRNeeuklANfnuurqahQWFiI3N1eybADIysrClStXUFVVBS8vL5SXlyMhIQEnT57E0qVL7zg3ODgYJpMJV69exZEjR2AwGHDixIk7/r5WxOjGmP2lpqaSv78/9fT02CSvrKyMAJBMJrMcAEgQBJLJZPT3339Lmq/Vaq2eWRER7du3jzQajaS5RET+/v704YcfWp3btWsXBQcHS5qLac8ef//9dwJATU1NVtc98sgj9Morr0iaPWV8fJxWr15NoaGhdPHiRVEzZ8rOy8uz1Nn/1p6TkxMFBARImj02NkZyuZx27dpldd3OnTspMjJS0uzh4WFydnamY8eOWV23ceNGio2NFS13prmkurqaANDly5etzmu1WtqzZ4+k2VMGBgZIp9ORXq+3rCCLZabstLS0GestOjpa0uyenh4CQF988YXV+YSEBNFWTWfK7u7uJgDU1tZmdV6v19OmTZtEyZ5Or9dTcnKyqLXGe3L+44gIW7duRVlZGWpqahAYGGiTXL1ej9bWVphMJssRFhaGxMREmEwmyGQySfOjoqJueLtjZ2cnAgICJM0Frr8+P/2dDTKZzPKsy1YCAwPh6+uL6upqy7mBgQE0NDRAp9NJnj+1gtPV1YWqqirMnTtX8kwASEpKQktLi1XtaTQa7NixA5WVlZJmKxQKhIeH26X2JiYmMDExIVnt3WouefDBB+Hs7GxVbx0dHfjjjz/uuN5mM48NDAwgJiYGCoUCRqNRtBXkW2Wnp6ffUG8AkJeXh6KiIkmz58+fD41GI0m93Sp7eHgYAGw615nNZoyNjYlba6K0X8xuNm/eTO7u7nT8+HHq6+uzHMPDwzYfiy335Jw5c4bkcjnt3r2burq6qLi4mFQqFX355ZeSZxsMBpo3bx4dO3aMent76ejRo+Tl5SXqvoQp165do6amJmpqaiIAln0gU+9gevfdd2nOnDn0zTffUEtLC61atYoCAwNFeZb7T9nj4+P09NNPk7+/P5lMJqvaGxsbkzT7ZsTck3Or7KNHj5KzszMVFBRQV1cX7d27l2QyGZ08eVLy7OjoaAoJCaHa2lrq6emhoqIicnFxoX379t1x9mzmkpSUFNJqtVRTU0O//vor6XQ60ul0kmdfvXqVIiIiaOnSpdTd3W11zZ2uGt/OHAqR9uTMJjsvL4/c3NyotLSUurq6KDMzk1xcXKi7u1vS7PHxcQoKCqKHH36YGhoaqLu7m9577z0SBMGyd+ZOpKen04kTJ6i3t5daWlooPT2dBEGgH374gYjEqzVucv7jANz0KCoqsvlYbNnkEBF9++23tGTJElIqlbRw4UIqKCiwSe7AwAClpaWRVqslFxcXWrBgAWVkZIjyj/t0tbW1N/37NRgMRHT9beRZWVnk4+NDSqWS9Ho9dXR0SJ7d29s7Y+3V1tZKmn0zYjY5s8k+ePAgBQUFkYuLCy1btozKy8ttkt3X10cbNmwgjUZDLi4uFBwcTO+//75lA/admM1cMjIyQlu2bCEPDw9SqVS0Zs0a6uvrkzx7pp8LAOrt7ZU0e6avEaPJmW12bm4u+fv7k0qlIp1OJ0pDPZvszs5OeuaZZ8jb25tUKhWFhobe8Jby2/Xyyy9TQEAAKRQKuvfee0mv11saHCLxak0gkuC/aWWMMcYYszPek8MYY4wxh8RNDmOMMcYcEjc5jDHGGHNI3OQwxhhjzCFxk8MYY4wxh8RNDmOMMcYcEjc5jDHGGHNI3OQwxv7fEwQB5eXl9h4GY0xk3OQwxuxqw4YNEAThhmPlypX2Hhpj7D9Obu8BMMbYypUrb/iwQ6VSaafRMMYcBa/kMMbsTqlUwtfX1+rw8PAAcP2lpPz8fMTFxcHV1RULFizAkSNHrL6+tbUVjz/+OFxdXTF37lwkJydjcHDQ6prCwkKEhIRAqVTCz88PW7dutbr/4sWLWLNmDVQqFe677z4YjUZpHzRjTHLc5DDG7npZWVlYu3YtmpubkZiYiOeffx7t7e0AgKGhIcTGxsLDwwO//PILSktLUVVVZdXE5OfnIzU1FcnJyWhtbYXRaERQUJBVxttvv42EhAS0tLTgqaeeQmJiIi5dumTTx8kYE5koHyfKGGO3yWAwkEwmI7VabXXs3r2biK5/WnJKSorV10RERNDmzZuJiKigoIA8PDxocHDQcn9FRQU5OTlRf38/ERFpNBrKyMiYcQwAKDMz03J7cHCQANB3330n2uNkjNke78lhjNndY489hvz8fKtznp6elj/rdDqr+3Q6HUwmEwCgvb0dy5Ytg1qtttwfFRUFs9mMjo4OCIKAc+fOQa/X/+MYQkNDLX9Wq9Vwc3PD+fPnb/chMcbuAtzkMMbsTq1W3/DykVhcXV1ndZ2zs7PVbUEQYDabpRgSY8xGeE8OY+yud/r06RtuL1q0CACwaNEiNDc3Y2hoyHJ/XV0dnJycEBwcjHvuuQfz589HdXW1TcfMGLM/XslhjNnd2NgY+vv7rc7J5XJ4eXkBAEpLSxEWFoaHHnoIxcXFOHPmDA4ePAgASExMxFtvvQWDwYDs7GxcuHAB27ZtQ1JSEnx8fAAA2dnZSElJgbe3N+Li4nDt2jXU1dVh27Zttn2gjDGb4iaHMWZ333//Pfz8/KzOBQcH47fffgNw/Z1PJSUl2LJlC/z8/HDo0CEsXrwYAKBSqVBZWYm0tDSEh4dDpVJh7dq12LNnj+V7GQwGjI6OIi8vD6+99hq8vLzw7LPP2u4BMsbsQiAisvcgGGNsJoIgoKysDKtXr7b3UBhj/zG8J4cxxhhjDombHMYYY4w5JN6Twxi7q/Er6oyx28UrOYwxxhhzSNzkMMYYY8whcZPDGGOMMYfETQ5jjDHGHBI3OYwxxhhzSNzkMMYYY8whcZPDGGOMMYfETQ5jjDHGHBI3OYwxxhhzSP8Hdo2kgJZVMjoAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_losses(\"Imagenet Pretrained ResNet50\", imagenet_resnet50_train, imagenet_resnet50_valid)\n",
-    "plot_metrics(\"Imagenet ResNet50\", imagenet_resnet50_metrics)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[0.7445986637279962, 0.8871447336722723, 0.6801716326202317], [0.8892722772221734, 0.9179440480421696, 0.8237870146171179], [0.896740833411397, 0.9411674149021525, 0.8491417915491254], [0.9007822221742975, 0.9245049037675342, 0.8390625424176124]]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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yhIoFCxbo2rVrevLJJ2+6jxtHnVauXGmzrm7duipXrpzefvvtbAPOjVNGFypUSJJy/YVx4sSJOn/+vF544QVdu3Yty/qffvpJq1atkiQ99dRTOnXqlM0sa9euXdPs2bPl5uZmvRQrv5w4ccJmdrrExER99NFHqlWrlnx9fSVdP2//DlfLli3Lcr9UXiQmJmY5F9WrV5fZbLZONf7UU09Juj5b3Y1mzJghSWrVqtUt9/9vmZfgTZs2TTVq1LBe2ti4cWNFR0drx44dubpMr1ChQvkSKI1kBrgbXxeLxaJ33nnHpp23t7eaNGmihQsXKi4uzmZd5rZms1nt2rXTt99+qx07dmTpK7Pd7b4PSpQooVq1amnx4sVZpvz+/fffc7UPIyEhIXJyctKsWbNsav3www+VkJBgfc/k5v337+n7peu/Cycpy3T4AB5MTEcO4L5UvHhxjRs3TmPGjFGTJk309NNPy9XVVVu2bNHnn3+uJ554Qm3atDHcT/fu3TV58mTt3r3bZrnZbNYHH3ygJ598UtWqVVOvXr1UqlQpHT9+XOvXr5eHh4e+/fZbSddDliSNHj1azz77rBwdHdWmTRtroPq3Ll26aN++fZo6dap+/fVXde3aVWXLltX58+f1ww8/KDo6Wp999pmk65MHzJ8/Xz179tTOnTvl7++vL7/8Ups3b1ZkZGSuJ5nIrYoVK6pPnz765Zdf5OPjo4ULF+r06dNatGiRtU3r1q01adIk9erVS8HBwdq3b58+/fRT62jGrVi3bp0GDRqkTp06qWLFirp27Zo+/vhjOTg4qGPHjpKkmjVrKiwsTAsWLFB8fLyaNm2q7du3a/HixWrXrp2aN29+28efqXz58vL19VVsbKwGDx5sXd6kSRMNHz5cknIVnOrWrav33ntPU6ZMUfny5VW8eHE99thj+VZnpsqVK6tcuXIaNmyYjh8/Lg8PDy1fvjzbe6VmzZqlRo0aqU6dOurXr58CAgJ05MgRrV692vo5eP311/XTTz+padOm6tevn6pUqaKTJ09q2bJl+vnnn+Xl5ZUv74OIiAi1atVKjRo1Uu/evXXhwgXNnj1b1apVy9WIrHT9jxhTpkzJsjwgIEDdu3fXyJEjNXHiRLVs2VJPP/20YmNj9e677+rRRx/Vc889Jyl3779JkyZp48aNatWqlcqWLaszZ87o3XffVenSpdWoUaNcHzOA+5g9pvIDgPzyySefWP7zn/9YChUqZHF2drZUrlzZMnHiRJtpti0W2+nI/y1zamhlM9Xwr7/+aunQoYOlaNGiFmdnZ0vZsmUtnTt3tkRHR9u0mzx5sqVUqVIWs9mc66nJo6OjLW3btrUUL17cUqBAAYu3t7elTZs2lm+++cam3enTpy29evWyFCtWzOLk5GSpXr26ZdGiRbk6vsypoP89zXLmMd843XTZsmUtrVq1svz444+WGjVqWM/nv7dNSUmxvPLKK5YSJUpYChYsaGnYsKFl69atlqZNm9pMvZ1T3zeuy5yO/O+//7b07t3bUq5cOYuLi4ulSJEilubNm1vWrl1rs11aWppl4sSJloCAAIujo6PFz8/PMnLkyCyvd+ax/Nu/a7yZTp06WSRZli5dal2WmppqcXV1tTg5OVmuXLli0z676chPnTpladWqlcXd3d0iydp3duc/u/OSk3HjxlkkWS5cuGBd9vvvv1tCQkIsbm5ulmLFiln69u1r2bNnj0VSlvfL/v37Le3bt7d4eXlZXFxcLJUqVbKMHTvWps3Ro0ctPXr0sHh7e1ucnZ0tgYGBloEDB1quXr1qsVjy531gsVgsy5cvt1SpUsXi7OxsqVq1quWrr76yhIWF5Xo68szP7r8fjz/+uLXdnDlzLJUrV7Y4OjpafHx8LP3797dcvHjRuj4377/Mz2vJkiUtTk5OlpIlS1q6du1q+euvvwzrBPBgMFkst3BnMADggePv769HHnnEepkg7l3h4eF65513lJKSYr3XDgBwZ3GPEwAA95lffvlF5cuXJzQBwF1EcAIA4D6xaNEiPf/88/r5558VFhZm73IA4KHC5BAAANwn+vTpI19fX7322mvWSSoAAHcH9zgBAAAAgAEu1QMAAAAAAwQnAAAAADDw0N3jlJGRoRMnTsjd3V0mk8ne5QAAAACwE4vFokuXLqlkyZIym28+pvTQBacTJ07Iz8/P3mUAAAAAuEccO3ZMpUuXvmmbhy44ubu7S7p+cjw8POxcDQAAAAB7SUxMlJ+fnzUj3MxDF5wyL8/z8PAgOAEAAADI1S08TA4BAAAAAAYITgAAAABggOAEAAAAAAYeunucAAAA7qb09HSlpaXZuwzgoeXo6CgHB4fb3g/BCQAA4A5JSkrSP//8I4vFYu9SgIeWyWRS6dKl5ebmdlv7ITgBAADcAenp6frnn3/k6uoqb2/vXM3aBSB/WSwWnT17Vv/8848qVKhwWyNPBCcAAIA7IC0tTRaLRd7e3ipYsKC9ywEeWt7e3jpy5IjS0tJuKzgxOQQAAMAdxEgTYF/59RkkOAEAAACAAS7VAwAAuIsSEhKUnJx81/pzdXWVp6fnXesPeFARnAAAAO6ShIQEzZo9Rxnp1+5an2aHAnpp8CDCEwz17NlT8fHxWrFihSSpWbNmqlWrliIjI+1a172CS/UAAADukuTkZGWkX9OG1ACtTKlyxx8bUgOUkX4tTyNcPXv2lMlkkslkkqOjowICAvTaa68pJSUl386DyWSSi4uLjh49arO8Xbt26tmzZ673ExMTI5PJpPj4eJvlGzduVJs2bVSyZEmZTCZrELhRUlKSBg0apNKlS6tgwYKqWrWq5s2bd9P+JkyYYD03Nz7Wrl1rbZOYmKjRo0ercuXKcnFxka+vr0JCQvTVV1/lOC19VFSUzf7c3NxUt25dffXVV7k+F3fCV199pcmTJ9u1hnsJI04AAAB3WUKGi85bCt35jjJubbOWLVtq0aJFSktL086dOxUWFiaTyaRp06blW2kmk0njxo3T4sWL822fmS5fvqyaNWuqd+/e6tChQ7ZtwsPDtW7dOn3yySfy9/fXTz/9pAEDBqhkyZJ6+umnc9x3tWrVbIKSJBUpUkSSFB8fr0aNGikhIUFTpkzRo48+qgIFCmjDhg167bXX9Nhjj8nLyyvb/Xp4eCg2NlaSdOnSJS1atEidO3fWb7/9pkqVKt3CWbh9mceF6xhxAgAAgA1nZ2f5+vrKz89P7dq1U0hIiNasWWNdn5GRoYiICAUEBKhgwYKqWbOmvvzyS+v6ixcvqnv37tap2CtUqKBFixbZ9DFo0CB98skn2r9/f4513KyfI0eOqHnz5pKkwoULy2QyWUernnzySU2ZMkXt27fPcd9btmxRWFiYmjVrJn9/f/Xr1081a9bU9u3bb3puChQoIF9fX5uHk5OTJGnUqFE6cuSItm3bprCwMFWtWlUVK1ZU3759tXv37pv+AKvJZLLur0KFCpoyZYrMZrP27t1rbfPxxx+rXr16cnd3l6+vr7p166YzZ85Y1xud92PHjqlz587y8vJSkSJF1LZtWx05ciTHmpo1a6aXX37Z+tzf31+vv/66evfuLXd3d5UpU0YLFiyw2SavfdxPCE4AAADI0f79+7VlyxZrOJCkiIgIffTRR5o3b55+++03DR06VM8995w2bNggSRo7dqx+//13ff/99/rjjz/03nvvqVixYjb7bdiwoVq3bq0RI0bk2PfN+vHz89Py5cslSbGxsTp58qTeeeedXB9XcHCwVq5cqePHj8tisWj9+vX666+/9MQTT+Tl9FhlZGRoyZIl6t69u0qWLJllvZubmwoUyN3FXunp6daRuDp16liXp6WlafLkydqzZ49WrFihI0eO2FzaeLPznpaWptDQULm7u2vTpk3avHmz3Nzc1LJlS6Wmpub6OKdPn6569erp119/1YABA9S/f3/rSFl+9XGv4lI9AAAA2Fi1apXc3Nx07do1Xb16VWazWXPmzJEkXb16Va+//rrWrl2rBg0aSJICAwP1888/a/78+WratKni4uJUu3Zt1atXT9L1kYrsREREqEaNGtq0aZMaN25ssy43/WReSla8ePEcL4HLyezZs9WvXz+VLl1aBQoUkNls1vvvv68mTZrcdLt9+/bZjBxVrVpV27dv17lz53Tx4kVVrlw5T3VkSkhIsO73ypUrcnR01IIFC1SuXDlrm969e1v/OzAwULNmzdKjjz6qpKQkubm53fS8L126VBkZGfrggw+sv2u0aNEieXl5KSYmJteB8amnntKAAQMkScOHD9fMmTO1fv16VapUKd/6uFcRnAAAAGCjefPmeu+993T58mXNnDlTBQoUUMeOHSVJBw8eVHJyslq0aGGzTWpqqmrXri1J6t+/vzp27Khdu3bpiSeeULt27RQcHJyln6pVq6pHjx4aMWKENm/ebLMuN/3cjtmzZ+u///2vVq5cqbJly2rjxo0aOHCgSpYsqZCQkBy3q1SpklauXGl97uzsLEk5TvyQW+7u7tq1a5ek65OIrF27Vi+++KKKFi2qNm3aSJJ27typCRMmaM+ePbp48aIyMq7fxBYXF6eqVave9Lzv2bNHBw8elLu7u02/KSkpOnToUK7rrFGjhvW/My8vzLxcML/6uFcRnAAAAGCjUKFCKl++vCRp4cKFqlmzpj788EP16dNHSUlJkqTVq1erVKlSNttlhognn3xSR48e1Xfffac1a9bo8ccf18CBA/X2229n6WvixImqWLFilpnvctPPrbpy5YpGjRqlr7/+Wq1atZJ0PRDs3r1bb7/99k2Dk5OTk/Xc3Mjb21teXl76888/b6kms9lss98aNWrop59+0rRp09SmTRtdvnxZoaGhCg0N1aeffipvb2/FxcUpNDTUehnczc57UlKS6tatq08//TTb2nPL0dHR5rnJZLIGuPzq415FcAIAAECOzGazRo0apfDwcHXr1k1Vq1aVs7Oz4uLi1LRp0xy38/b2VlhYmMLCwtS4cWO9+uqr2QYnPz8/DRo0SKNGjbK5LC03/WTed5Wenp6nY0pLS1NaWprMZtvb/R0cHKwhIK/MZrOeffZZffzxxxo/fnyW+5ySkpLk4uKS6/ucMuu5cuWKJOnPP//U+fPn9cYbb8jPz0+StGPHjizb5HTe69Spo6VLl6p48eLy8PC4pWM0cjf6sCcmhwAAALjLPM0pKmq6fMcfnub8+e2lTp06ycHBQXPnzpW7u7uGDRumoUOHavHixTp06JB27dql2bNnWyc0GDdunL755hsdPHhQv/32m1atWqUqVarkuP+RI0fqxIkTNtN856afsmXLymQyadWqVTp79qx1lCopKUm7d+/W7t27JUmHDx/W7t27FRcXJ+n61N9NmzbVq6++qpiYGB0+fFhRUVH66KOPbjoTn5GpU6fKz89PQUFB+uijj/T777/rwIEDWrhwoWrXrm2tLzsWi0WnTp3SqVOndPjwYS1YsEA//vij2rZtK0kqU6aMnJycNHv2bP39999auXJllt9Yutl57969u4oVK6a2bdtq06ZNOnz4sGJiYvTSSy/pn3/+ueVjvtHd6MOeGHECAAC4S1xdXWV2KKCmTofvWp9mhwJydXW9rX0UKFBAgwYN0ptvvqn+/ftr8uTJ8vb2VkREhP7++295eXmpTp06GjVqlKTrI0EjR47UkSNHVLBgQTVu3FhLlizJcf9FihTR8OHDrdtnMuqnVKlSmjhxokaMGKFevXqpR48eioqK0o4dO6xTlUvXf7NJksLCwhQVFSVJWrJkiUaOHKnu3bvrwoULKlu2rKZOnaoXX3zxls9TkSJF9N///ldvvPGGpkyZoqNHj6pw4cKqXr263nrrLXl6eua4bWJiokqUKCHp+qWIZcuW1aRJkzR8+HBJ10eSoqKiNGrUKM2aNUt16tTR22+/bfObUzc7766urtq4caOGDx+uDh066NKlSypVqpQef/zxfBsduht92JPJcrt3st1nEhMT5enpqYSEhAfiBQQAAPemlJQUHT58WAEBAXJxcbEuT0hIUHJy8l2rw9XV9aZf2IEHXU6fRSlv2YARJwAAgLvI09OTIAPch7jHCQAAAAAMEJwAAAAAwADBCQAAAAAMEJwAAAAAwADBCQAAAAAMEJwAAAAAwADBCQAAAAAM8DtOAAAAdxE/gAvcnwhOAAAAd0lCQoLmzpmltGsZd61PxwJmDRz0EuEJuE1cqgcAAHCXJCcnK+1ahtpbvlM/yyd3/NHe8p3SrmXkaYTr7Nmz6t+/v8qUKSNnZ2f5+voqNDRUmzdvzvU+oqKi5OXllat2JpNJJpNJZrNZJUqUUJcuXRQXF5frvow0a9ZMJpNJS5YssVkeGRkpf3//PO3LZDJpxYoVNstOnjypbt26qWLFijKbzXr55Zez3TYyMlKVKlVSwYIF5efnp6FDhyolJSXHvm52Dm+s48iRIzKZTCpevLguXbpk065WrVqaMGFCLo8u96/bw4oRJwAAgLvMWxdUQmfsXUa2OnbsqNTUVC1evFiBgYE6ffq0oqOjdf78+TvSn4eHh2JjY2WxWHT48GENGDBAnTp10rZt2/KtDxcXF40ZM0YdO3aUo6Njvu1Xkq5evSpvb2+NGTNGM2fOzLbNZ599phEjRmjhwoUKDg7WX3/9pZ49e8pkMmnGjBn5UselS5f09ttva+LEifmyP2TFiBMAAAAkSfHx8dq0aZOmTZum5s2bq2zZsqpfv75Gjhypp59+2tpuxowZql69ugoVKiQ/Pz8NGDBASUlJkqSYmBj16tVLCQkJ1tGkm416mEwm+fr6qkSJEgoODlafPn20fft2JSYmWtt88803qlOnjlxcXBQYGKiJEyfq2rVrkiSLxaIJEyZYR8hKliypl156yaaPrl27Kj4+Xu+///5Nj/9m/WSOTrVv314mk8n63N/fX++884569OiR4+WQW7ZsUcOGDdWtWzf5+/vriSeeUNeuXbV9+/ab1pMXgwcP1owZM3TmTM6B/OrVqxo2bJhKlSqlQoUKKSgoSDExMZLy/ro9jOwanDZu3Kg2bdqoZMmS2Q59ZicmJkZ16tSRs7Ozypcvr6ioqDteJwAAwMPAzc1Nbm5uWrFiha5evZpjO7PZrFmzZum3337T4sWLtW7dOr322muSpODgYEVGRsrDw0MnT57UyZMnNWzYsFz1f+bMGX399ddycHCQg4ODJGnTpk3q0aOHhgwZot9//13z589XVFSUpk6dKklavny5Zs6cqfnz5+vAgQNasWKFqlevbrNfDw8PjR49WpMmTdLly5ez7duon19++UWStGjRIp08edL6PDeCg4O1c+dOa1D6+++/9d133+mpp57K9T6MdO3aVeXLl9ekSZNybDNo0CBt3bpVS5Ys0d69e9WpUye1bNlSBw4cuK3X7WFh1+B0+fJl1axZU3Pnzs1V+8OHD6tVq1Zq3ry5du/erZdfflkvvPCCfvzxxztcKQAAwIOvQIECioqK0uLFi+Xl5aWGDRtq1KhR2rt3r027l19+Wc2bN5e/v78ee+wxTZkyRV988YUkycnJSZ6entaRJF9fX7m5ueXYZ0JCgtzc3FSoUCH5+Pho/fr1GjhwoAoVKiRJmjhxokaMGKGwsDAFBgaqRYsWmjx5subPny9JiouLk6+vr0JCQlSmTBnVr19fffv2zdLPgAED5OLikuOlcUb9eHt7S5K8vLzk6+trfZ4b3bp106RJk9SoUSM5OjqqXLlyatasmUaNGpXrfRgxmUx64403tGDBAh06dCjL+ri4OC1atEjLli1T48aNVa5cOQ0bNkyNGjXSokWL8vy6PYzsGpyefPJJTZkyRe3bt89V+3nz5ikgIEDTp09XlSpVNGjQID3zzDM5Xk8KAACAvOnYsaNOnDihlStXqmXLltarfW68ymft2rV6/PHHVapUKbm7u+v555/X+fPnb2madXd3d+3evVs7duzQ9OnTVadOHesojyTt2bNHkyZNso6Gubm5qW/fvjp58qSSk5PVqVMnXblyRYGBgerbt6++/vpr6+V1N3J2dtakSZP09ttv69y5c1nWG/VzO2JiYvT666/r3Xff1a5du/TVV19p9erVmjx58m3t999CQ0PVqFEjjR07Nsu6ffv2KT09XRUrVrQ5xg0bNmQbtJDVfTU5xNatWxUSEmKzLDQ0NMfZS6Tr13LeONR84/WyAAAAyMrFxUUtWrRQixYtNHbsWL3wwgsaP368evbsqSNHjqh169bq37+/pk6dqiJFiujnn39Wnz59lJqaKldX1zz1ZTabVb58eUlSlSpVdOjQIfXv318ff/yxJCkpKUkTJ05Uhw4dsq3Tz89PsbGxWrt2rdasWaMBAwborbfe0oYNG7JMBPHcc8/p7bff1pQpU7LMqGfUz+0YO3asnn/+eb3wwguSpOrVq+vy5cvq16+fRo8eLbM561iGh4eHLl++rIyMDJv18fHxkpTj/VRvvPGGGjRooFdffdVmeVJSkhwcHLRz507rZZCZGFnKnfsqOJ06dUo+Pj42y3x8fJSYmKgrV66oYMGCWbaJiIhgdhEAAIDbULVqVeu96Dt37lRGRoamT59u/UKfeZleJicnJ6Wnp99SXyNGjFC5cuU0dOhQ1alTR3Xq1FFsbKw1XGWnYMGCatOmjdq0aaOBAweqcuXK2rdvn+rUqWPTzmw2KyIiQh06dFD//v1t1uWmH0dHx1s6ruTk5CzhKDO8WCyWbLepVKmSrl27pt27d9scx65duyRJFStWzHa7+vXrq0OHDhoxYoTN8tq1ays9PV1nzpxR48aNs932dl63h8F9FZxuxciRIxUeHm59npiYKD8/PztWBAAAHnZnVeSe7Of8+fPq1KmTevfurRo1asjd3V07duzQm2++qbZt20qSypcvr7S0NM2ePVtt2rTR5s2bNW/ePJv9+Pv7KykpSdHR0apZs6ZcXV1zPRLl5+en9u3ba9y4cVq1apXGjRun1q1bq0yZMnrmmWdkNpu1Z88e7d+/X1OmTFFUVJTS09MVFBQkV1dXffLJJypYsKDKli2b7f5btWqloKAgzZ8/3+YP8kb9ZB5XdHS0GjZsKGdnZxUuXFiStHv3bknXR3XOnj2r3bt3y8nJSVWrVpUktWnTRjNmzFDt2rUVFBSkgwcPauzYsWrTpk2W0Z9M1apV0xNPPKHevXtr+vTpCgwMVGxsrF5++WV16dJFpUqVyvEcTp06VdWqVVOBAv/7ql+xYkV1795dPXr00PTp01W7dm2dPXtW0dHRqlGjhlq1anVbr9vD4L4KTr6+vjp9+rTNstOnT8vDwyPb0Sbp+vWszs7Od6M8AACAm3J1dZVjAbO+vpZ/s6kZcSxgzvWXXzc3NwUFBWnmzJk6dOiQ0tLS5Ofnp759+1onMqhZs6ZmzJihadOmaeTIkWrSpIkiIiLUo0cP636Cg4P14osvqkuXLjp//rzGjx+fp6mthw4dqgYNGmj79u0KDQ3VqlWrNGnSJE2bNk2Ojo6qXLmy9bI3Ly8vvfHGGwoPD1d6erqqV6+ub7/9VkWLFs1x/9OmTVNwcLDNMqN+JGn69OkKDw/X+++/r1KlSunIkSOSro/mZNq5c6c+++wzlS1b1rp+zJgxMplMGjNmjI4fPy5vb2+1adPG5l6u7CxdulTjx4/X//3f/+nEiRMqXbq02rdvn+09TDeqWLGievfurQULFtgsX7RokaZMmaJXXnlFx48fV7FixfSf//xHrVu3lnT7r9uDzmTJaXzwLjOZTPr666/Vrl27HNsMHz5c3333nfbt22dd1q1bN124cEE//PBDrvpJTEyUp6enEhIS5OHhcbtlAwAAZCslJUWHDx9WQECAzT0yCQkJtz3ZQF64urrmeD8M8DDI6bMo5S0b2HXEKSkpSQcPHrQ+P3z4sHbv3q0iRYqoTJkyGjlypI4fP66PPvpIkvTiiy9qzpw5eu2119S7d2+tW7dOX3zxhVavXm2vQwAAAMgTT09PggxwH7LrdOQ7duxQ7dq1rcOb4eHhql27tsaNGydJOnnypOLi4qztAwICtHr1aq1Zs0Y1a9bU9OnT9cEHHyg0NNQu9QMAAAB4ONh1xKlZs2Y5ziQiyeb3Am7c5tdff72DVQEAAACALbuOOAEAAADA/YDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYMCus+oBAAA8bPgBXOD+RHACAAC4SxISEjR7zmylX0u/a306FHDQ4EGDCU/AbeJSPQAAgLskOTlZ6dfSta3YNq0tsfaOP7YV26b0a+l5GuE6e/as+vfvrzJlysjZ2Vm+vr4KDQ3V5s2bc72PqKgoeXl55aqdyWSSyWSS2WxWiRIl1KVLF8XFxeW6LyPNmjWTyWTSkiVLbJZHRkbK398/T/symUxasWKFzbKYmBjrMdz4OHXqlE27uXPnyt/fXy4uLgoKCtL27dtv2lfPnj3Vrl27LMsz+4uPj5f0v3PYsmVLm3bx8fEymUyKiYnJ9fHl1CeuY8QJAADgLrvkeEnxzvH2LiNbHTt2VGpqqhYvXqzAwECdPn1a0dHROn/+/B3pz8PDQ7GxsbJYLDp8+LAGDBigTp06adu2bfnWh4uLi8aMGaOOHTvK0dEx3/Z7o9jYWHl4eFifFy9e3PrfS5cuVXh4uObNm6egoCBFRkYqNDRUsbGxNu1uVYECBbR27VqtX79ezZs3v+39IXuMOAEAAEDS9VGKTZs2adq0aWrevLnKli2r+vXra+TIkXr66aet7WbMmKHq1aurUKFC8vPz04ABA5SUlCTp+ohIr169lJCQYB19mTBhQo59mkwm+fr6qkSJEgoODlafPn20fft2JSYmWtt88803qlOnjlxcXBQYGKiJEyfq2rVrkiSLxaIJEyZYR8hKliypl156yaaPrl27Kj4+Xu+///5Nj/9m/WSOTrVv314mkynLaFXx4sXl6+trfZjN//uaPWPGDPXt21e9evVS1apVNW/ePLm6umrhwoU3rSe3ChUqpN69e2vEiBE3bXfs2DF17txZXl5eKlKkiNq2basjR45IkiZMmKDFixfrm2++sb5ueRmtehgQnAAAACBJcnNzk5ubm1asWKGrV6/m2M5sNmvWrFn67bfftHjxYq1bt06vvfaaJCk4OFiRkZHy8PDQyZMndfLkSQ0bNixX/Z85c0Zff/21HBwc5ODgIEnatGmTevTooSFDhuj333/X/PnzFRUVpalTp0qSli9frpkzZ2r+/Pk6cOCAVqxYoerVq9vs18PDQ6NHj9akSZN0+fLlbPs26ueXX36RJC1atEgnT560Ps9Uq1YtlShRQi1atLC5rDE1NVU7d+5USEiIzfkLCQnR1q1bc3VecmPChAnat2+fvvzyy2zXp6WlKTQ0VO7u7tq0aZM2b94sNzc3tWzZUqmpqRo2bJg6d+6sli1bWl+34ODgfKvvQUBwAgAAgKTrl3xFRUVp8eLF8vLyUsOGDTVq1Cjt3bvXpt3LL7+s5s2by9/fX4899pimTJmiL774QpLk5OQkT09P60iSr6+v3NzccuwzISFBbm5uKlSokHx8fLR+/XoNHDhQhQoVkiRNnDhRI0aMUFhYmAIDA9WiRQtNnjxZ8+fPlyTFxcXJ19dXISEhKlOmjOrXr6++fftm6WfAgAFycXHRjBkzsq3DqB9vb29JkpeXl3x9fa3PS5QooXnz5mn58uVavny5/Pz81KxZM+3atUuSdO7cOaWnp8vHx8emPx8fnyz3Qd2OkiVLasiQIRo9erR1lOxGS5cuVUZGhj744ANVr15dVapU0aJFixQXF6eYmBi5ubmpYMGC1vvafH195eTklG/1PQgITgAAALDq2LGjTpw4oZUrV6ply5aKiYlRnTp1FBUVZW2zdu1aPf744ypVqpTc3d31/PPP6/z587c0zbq7u7t2796tHTt2aPr06apTp451lEeS9uzZo0mTJllHw9zc3NS3b1+dPHlSycnJ6tSpk65cuaLAwED17dtXX3/9dbbBwdnZWZMmTdLbb7+tc+fOZVlv1E9OKlWqpP/7v/9T3bp1FRwcrIULFyo4OFgzZ87M87m4XcOHD9fZs2ezvQRwz549OnjwoNzd3a3HV6RIEaWkpOjQoUN3vdb7EcEJAAAANlxcXNSiRQuNHTtWW7ZsUc+ePTV+/HhJ0pEjR9S6dWvVqFFDy5cv186dOzV37lxJ1y9Lyyuz2azy5curSpUqCg8P13/+8x/179/fuj4pKUkTJ07U7t27rY99+/bpwIEDcnFxkZ+fn2JjY/Xuu++qYMGCGjBggJo0aaK0tLQsfT333HMqW7aspkyZkmWdUT95Ub9+fR08eFCSVKxYMTk4OOj06dM2bU6fPi1fX98c9+Hh4aGEhIQsy+Pj4+Xg4GAdkbuRl5eXRo4cqYkTJ2YJe0lJSapbt67N8e3evVt//fWXunXrlqfje1gRnAAAAHBTVatWtd4btHPnTmVkZGj69On6z3/+o4oVK+rEiRM27Z2cnJSefmu/VTVixAgtXbrUeqlbnTp1FBsbq/Lly2d5ZE7AULBgQbVp00azZs1STEyMtm7dqn379mXZt9lsVkREhN577z3rpAiZctOPo6Njro5r9+7dKlGihPVc1K1bV9HR0db1GRkZio6OVoMGDXLcR6VKlfTbb79ludds165dCggIyHF2wMGDB8tsNuudd97JcnwHDhxQ8eLFsxxf5m983c7r9jBgOnIAAIC7zD3N/Z7s5/z58+rUqZN69+6tGjVqyN3dXTt27NCbb76ptm3bSpLKly+vtLQ0zZ49W23atNHmzZs1b948m/34+/srKSlJ0dHRqlmzplxdXeXq6pqrGvz8/NS+fXuNGzdOq1at0rhx49S6dWuVKVNGzzzzjMxms/bs2aP9+/drypQpioqKUnp6uoKCguTq6qpPPvlEBQsWVNmyZbPdf6tWrRQUFKT58+fb3Hdk1E/mcUVHR6thw4ZydnZW4cKFFRkZqYCAAFWrVk0pKSn64IMPtG7dOv3000/WfYeHhyssLEz16tVT/fr1FRkZqcuXL6tXr145nofu3btr0qRJ6tGjh1577TV5enpq48aNioyM1Jtvvpnjdi4uLpo4caIGDhyYZX9vvfWW2rZtq0mTJql06dI6evSovvrqK7322msqXbq0/P399eOPPyo2NlZFixaVp6fnHZu+/X5EcAIAPLQSEhJu6Z6Me42rq6v1L8a4t7m6usqhgIOCzgXdtT4dCjjkOrS4ubkpKChIM2fO1KFDh5SWliY/Pz/17dtXo0aNkiTVrFlTM2bM0LRp0zRy5Eg1adJEERER6tGjh3U/wcHBevHFF9WlSxedP39e48ePv+mU5P82dOhQNWjQQNu3b1doaKhWrVqlSZMmadq0aXJ0dFTlypX1wgsvSLp+edobb7yh8PBwpaenq3r16vr2229VtGjRHPc/bdq0LDPGGfUjSdOnT1d4eLjef/99lSpVSkeOHFFqaqpeeeUVHT9+XK6urqpRo4bWrl1r83tKXbp00dmzZzVu3DidOnVKtWrV0g8//JBlwogbeXl5adOmTRoxYoSefvppJSQkqHz58poxY4b69Olz0/MXFham6dOn6/fff7cuc3V11caNGzV8+HB16NBBly5dUqlSpfT4449bf3+qb9++iomJUb169ZSUlKT169erWbNmN+3rYWKyWCwWexdxNyUmJsrT01MJCQk2P1IGAHi4JCQkaPac2Uq/dv9fluJQwEGDBw0mPN1jUlJSdPjwYQUEBNjcI3O3AzvBGg+7nD6LUt6yASNOAICHUnJystKvpWtbsW265HjJ3uXcMvc0dwWdC1JycjJfju8Tnp6evFbAfYjgBAB4qF1yvKR453h7lwEAuMcxqx4AAAAAGCA4AQAAAIABghMAAAAAGOAeJwAAHgDnzp2zdwm3jdnfANzLCE4AANzHnNOdJYtFX331lb1LuW0FHBw0aDDTqgO4NxGcAAC4jzllOEkmk4K2bJVHYqK9y7lliR4e2hbcgGnVAdyzCE4AgDy52z/eeac8CJe23cgjMVGFL160dxnIBX4AF7g/EZwAALmWkJCguXNmKe1ahr1LAe5LCQkJmjN7tq6lp9+1PrkEEsgfBCcAQK4lJycr7VqG2lu+k7cu2Luc23JA/lpvamTvMvCQSU5O1rX09Lt2aeWtXALZs2dPxcfHa8WKFXe2uHxw5MgRBQQEyNvbW4cOHZK7u7t1Xa1atdSuXTtNmDAhV/uKiorSyy+/rPj4eMN2vXr1kiSZTCb5+PioSZMmeuutt1SmTJlbPRQbzZo104YNG/T555/r2WeftS6PjIxUZGSkjhw5kut9mUwmff3112rXrp11WUxMjJo3b56l7cmTJ+Xr62t9PnfuXL311ls6deqUatasqdmzZ6t+/fo59pXTeyezv4sXL8rLy8t6DkNDQ/XDDz9Y28XHx6tw4cJav369mjVrlqvju5vvV4ITACDPvHVBJXTG3mXclnMqYu8S8BDj0sr8denSJb399tuaOHHiXenPw8NDsbGxslgsOnz4sAYMGKBOnTpp27Zt+daHi4uLxowZo44dO8rR0THf9nuj2NhYeXh4WJ8XL17c+t9Lly5VeHi45s2bp6CgIEVGRio0NFSxsbE27W5VgQIFtHbtWq1fvz7bEHcv4necAAAAkKNmzZpp8ODBevnll1W4cGH5+Pjo/fff1+XLl9WrVy+5u7urfPny+v77763bpKenq0+fPgoICFDBggVVqVIlvfPOOzb7vXbtml566SV5eXmpaNGiGj58uMLCwmxGRjIyMhQREWHdT82aNfXll19mqXHw4MGaMWOGzpzJ+Q86V69e1bBhw1SqVCkVKlRIQUFBiomJkXR9RKRXr15KSEiQyWSSyWS66UiVyWSSr6+vSpQooeDgYPXp00fbt29X4g2jiN98843q1KkjFxcXBQYGauLEibp27ZokyWKxaMKECSpTpoycnZ1VsmRJvfTSSzZ9dO3aVfHx8Xr//fdzrMOoH39/f0lS+/btZTKZrM8zFS9eXL6+vtaH2fy/aDBjxgz17dtXvXr1UtWqVTVv3jy5urpq4cKFN60ntwoVKqTevXtrxIgRN2137Ngxde7cWV5eXipSpIjatm1rHXGbMGGCFi9erG+++cb6umW+pncCwQkAAAA3tXjxYhUrVkzbt2/X4MGD1b9/f3Xq1EnBwcHatWuXnnjiCT3//PPWSS8yMjJUunRpLVu2TL///rvGjRunUaNG6YsvvrDuc9q0afr000+1aNEibd68WYmJiVkut4qIiNBHH32kefPm6bffftPQoUP13HPPacOGDTbtunbtqvLly2vSpEk5HsOgQYO0detWLVmyRHv37lWnTp3UsmVLHThwQMHBwYqMjJSHh4dOnjypkydPatiwYbk6N2fOnNHXX38tBwcHOTg4SJI2bdqkHj16aMiQIfr99981f/58RUVFaerUqZKk5cuXa+bMmZo/f74OHDigFStWqHr16jb79fDw0OjRozVp0iRdvnw5276N+vnll18kSYsWLdLJkyetzzPVqlVLJUqUUIsWLbR582br8tTUVO3cuVMhISHWZWazWSEhIdq6dWuuzktuTJgwQfv27cs2DEtSWlqaQkND5e7urk2bNmnz5s1yc3NTy5YtlZqaqmHDhqlz585q2bKl9XULDg7Ot/r+jeAEAACAm6pZs6bGjBmjChUqaOTIkXJxcVGxYsXUt29fVahQQePGjdP58+e1d+9eSZKjo6MmTpyoevXqKSAgQN27d1evXr1sgtPs2bM1cuRItW/fXpUrV9acOXPk5eVlXX/16lW9/vrrWrhwoUJDQxUYGKiePXvqueee0/z5823qM5lMeuONN7RgwQIdOnQoS/1xcXFatGiRli1bpsaNG6tcuXIaNmyYGjVqpEWLFsnJyUmenp7WkSRfX1+5ubnleD4SEhLk5uamQoUKycfHR+vXr9fAgQNVqFAhSdLEiRM1YsQIhYWFKTAwUC1atNDkyZOtdcfFxcnX11chISEqU6aM6tevr759+2bpZ8CAAXJxcdGMGTOyrcOoH29vb0mSl5eXfH19rc9LlCihefPmafny5Vq+fLn8/PzUrFkz7dq1S9L1WUfT09Pl4+Nj05+Pj49OnTqV43nJq5IlS2rIkCEaPXq0dZTsRkuXLlVGRoY++OADVa9eXVWqVNGiRYsUFxenmJgYubm5qWDBgnJ2dra+bk5OTvlW379xjxPwgHpQpoyWmEoXAOytRo0a1v92cHBQ0aJFbUZIMr9g33ip3Ny5c7Vw4ULFxcXpypUrSk1NVa1atSRd/3/U6dOnbSYacHBwUN26dZWRcX3WzoMHDyo5OVktWrSwqSU1NVW1a9fOUmNoaKgaNWqksWPH6rPPPrNZt2/fPqWnp6tixYo2y69evaqiRYvm5VRIktzd3bVr1y6lpaXp+++/16effmod5ZGkPXv2aPPmzTbL0tPTlZKSouTkZHXq1EmRkZEKDAxUy5Yt9dRTT6lNmzYqUMD2q7mzs7MmTZpkHeX7N6N+XF1ds62/UqVKqlSpkvV5cHCwDh06pJkzZ+rjjz/O8/m4HcOHD9f8+fO1cOFCde7c2Wbdnj17dPDgQZtJPyQpJSUl24B8pxGcgAeQPaa7vZOYShcA7OvfkxOYTCabZSaTSZKsoWfJkiUaNmyYpk+frgYNGsjd3V1vvfVWniZPSEpKkiStXr1apUqVslnn7Oyc7TZvvPGGGjRooFdffTXLvhwcHLRz507r5XSZbjaylBOz2azy5ctLkqpUqaJDhw6pf//+1tCRlJSkiRMnqkOHDlm2dXFxkZ+fn2JjY7V27VqtWbNGAwYM0FtvvaUNGzZkOdfPPfec3n77bU2ZMiXLPUpG/eRF/fr19fPPP0uSihUrJgcHB50+fdqmzenTp21m3fs3Dw8PHT16NMvy+Ph4OTg4WEfkbuTl5aWRI0dq4sSJat26tc26pKQk1a1bV59++mmW7TJHz+4mghPwLw/CSM25c+fu6nS3d9KtTKULALCvzZs3Kzg4WAMGDLAuu3GEwNPTUz4+Pvrll1/UpEkTSddHSnbt2mUdlapataqcnZ0VFxenpk2b5qrf+vXrq0OHDlkmHKhdu7bS09N15swZNW7cONttnZyclH6Lf3AcMWKEypUrp6FDh6pOnTqqU6eOYmNjreEqOwULFlSbNm3Upk0bDRw4UJUrV9a+fftUp04dm3Zms1kRERHq0KFDllGn3PTj6OiYq+PavXu3SpQoIen6uahbt66io6Otk3VkZGQoOjpagwYNynEflSpV0pIlS3T16lWbcLtr1y4FBATkODvg4MGDNWvWrCwTiNSpU0dLly5V8eLFbWb/u9HtvG55RXACbpCQkKDZc2Yr/dqDMVLDdLcAcG9KzOFL4P3az79VqFBBH330kX788UcFBATo448/1i+//KKAgABrm8GDBysiIkLly5dX5cqVNXv2bF28eNE6euXu7q5hw4Zp6NChysjIUKNGjZSQkKDNmzfLw8NDYWFh2fY9depUVatWzeayt4oVK6p79+7q0aOHpk+frtq1a+vs2bOKjo5WjRo11KpVK/n7+yspKUnR0dGqWbOmXF1dc7zU7d/8/PzUvn17jRs3TqtWrdK4cePUunVrlSlTRs8884zMZrP27Nmj/fv3a8qUKYqKilJ6erqCgoLk6uqqTz75RAULFlTZsmWz3X+rVq0UFBSk+fPn29x3ZNSPdH1mvejoaDVs2FDOzs4qXLiwIiMjFRAQoGrVqiklJUUffPCB1q1bp59++sm67/DwcIWFhalevXqqX7++IiMjrTMp5qR79+6aNGmSevTooddee02enp7auHGjIiMj9eabb+a4nYuLiyZOnKiBAwdm2d9bb72ltm3batKkSSpdurSOHj2qr776Sq+99ppKly4tf39//fjjj4qNjVXRokXl6el5x6ZvJzgBN0hOTlb6tXRtK7ZNlxwv2bucW+ZzxUfV46sbNwQA3FWurq4q4OCgbcEN7lqfBRwcch0A8sv//d//6ddff1WXLl1kMpnUtWtXDRgwwGbK8uHDh+vUqVPq0aOHHBwc1K9fP4WGhtpcSjd58mR5e3srIiJCf//9t7y8vFSnTh2NGjUqx74rVqyo3r17a8GCBTbLFy1apClTpuiVV17R8ePHVaxYMf3nP/+xXh4WHBysF198UV26dNH58+c1fvz4XP94riQNHTpUDRo00Pbt2xUaGqpVq1Zp0qRJmjZtmhwdHVW5cmW98MILkq5fnvbGG28oPDxc6enpql69ur799tub3m81bdq0LDPGGfUjSdOnT1d4eLjef/99lSpVSkeOHFFqaqr1PLi6uqpGjRpau3atze8pdenSRWfPntW4ceN06tQp1apVSz/88EOWCSNu5OXlpU2bNmnEiBF6+umnlZCQoPLly2vGjBnq06fPTc9fWFiYpk+frt9//926zNXVVRs3btTw4cPVoUMHXbp0SaVKldLjjz9uHYHq27evYmJiVK9ePSUlJeXpx3PzymSxWCx3ZM/3qMTERHl6eiohISHHIT/cmgflErevvvpKa0usVbxzvL3LuWV+SX4KOhekFj/8eN+POF0sXFhrWoaqX79+1ksIYD8nT57UggUL1M/yyX3/A7j7VFlfmZ7i836PeBA/6ykpKTp8+LACAgJs7je52/+/vF8m2MnIyFCVKlXUuXNnTZ482d7l4AGS02dRyls2YMQJ+SIhIUFz58xS2rUMe5cC3LMelD8uALg9np6e90WQudOOHj2qn376SU2bNtXVq1c1Z84cHT58WN26dbN3aUC2CE7IF8nJyUq7lqH2lu/krQv2LueWHZC/1psa2bsMPIASEhI0a/YcZaRn/Z0KAHgYmc1mRUVFadiwYbJYLHrkkUe0du1aValSxd6lAdkiOCFfeevCfX35zjkVsXcJeEAlJycrI/2aNqQGKCEjb1PE3ktKOSSoruMJe5cB4AHg5+enzZs327sMINcITgBwFyVkuOi8JevvWNwvPDOu2LsEAADswmzvAgAAAB5kD9k8XMA9J78+gwQnAACAOyBzWu3U1FQ7VwI83DI/gzdOdX8ruFQPAADgDihQoIBcXV119uxZOTo6ymzm79XA3ZaRkaGzZ89e/w21ArcXfQhOAAAAd4DJZFKJEiV0+PBhHT161N7lAA8ts9msMmXKyGQy3dZ+CE4AAAB3iJOTkypUqMDleoAdOTk55cuIL8EJAADgDjKbzXJxuX9/hgDAdVxsCwAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDpyAEAAPBAS0hIUHJysr3LyBeurq7y9PS0dxkPJYITAAAAHlgJCQmaPWe20q+l27uUfOFQwEGDBw0mPNkBwQkAAAAPrOTkZKVfS9e2Ytt0yfGSvcu5Le5p7go6F6Tk5GSCkx0QnAAAAPDAu+R4SfHO8fYuA/cxghMAAABwHzl37py9S7ht9+O9WgQnAAAA4D7gnO4sWSz66quv7F3KbSvg4KBBg++ve7UITgAAAMB9wCnDSTKZFLRlqzwSE+1dzi1L9PDQtuAG9929WgQnAAAA4D7ikZiowhcv2ruMhw4/gAsAAAAABghOAAAAAGCA4AQAAAAABrjHCQAAANlKSEhQcnKyvcu4LQ/C1N24NxCcAAAAkEVCQoLmzpmltGsZ9i4FuCcQnAAAAJBFcnKy0q5lqL3lO3nrgr3LuWUH5K/1pkb2LgMPAIITAAAAcuStCyqhM/Yu45adUxF7l4AHBJNDAAAAAIABghMAAAAAGCA4AQAAAIABuwenuXPnyt/fXy4uLgoKCtL27dtv2j4yMlKVKlVSwYIF5efnp6FDhyolJeUuVQsAAADgYWTX4LR06VKFh4dr/Pjx2rVrl2rWrKnQ0FCdOZP9DYifffaZRowYofHjx+uPP/7Qhx9+qKVLl2rUqFF3uXIAAAAADxO7BqcZM2aob9++6tWrl6pWrap58+bJ1dVVCxcuzLb9li1b1LBhQ3Xr1k3+/v564okn1LVrV8NRKgAAAAC4HXYLTqmpqdq5c6dCQkL+V4zZrJCQEG3dujXbbYKDg7Vz505rUPr777/13Xff6amnnsqxn6tXryoxMdHmAQAAAAB5YbffcTp37pzS09Pl4+Njs9zHx0d//vlnttt069ZN586dU6NGjWSxWHTt2jW9+OKLN71ULyIiQhMnTszX2gEAAAA8XOw+OURexMTE6PXXX9e7776rXbt26auvvtLq1as1efLkHLcZOXKkEhISrI9jx47dxYoBAAAAPAjsNuJUrFgxOTg46PTp0zbLT58+LV9f32y3GTt2rJ5//nm98MILkqTq1avr8uXL6tevn0aPHi2zOWsOdHZ2lrOzc/4fAAAAAICHht1GnJycnFS3bl1FR0dbl2VkZCg6OloNGjTIdpvk5OQs4cjBwUGSZLFY7lyxAAAAAB5qdhtxkqTw8HCFhYWpXr16ql+/viIjI3X58mX16tVLktSjRw+VKlVKERERkqQ2bdpoxowZql27toKCgnTw4EGNHTtWbdq0sQYoAAAAAMhvdg1OXbp00dmzZzVu3DidOnVKtWrV0g8//GCdMCIuLs5mhGnMmDEymUwaM2aMjh8/Lm9vb7Vp00ZTp0611yEAAAAAeAjYNThJ0qBBgzRo0KBs18XExNg8L1CggMaPH6/x48ffhcoAAAAA4Lr7alY9AAAAALAHghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAICBAvYuAFJCQoKSk5PtXcZtOXfunL1LAAAAAO4YgpOdJSQkaNbsOcpIv2bvUgAAAADkgOBkZ8nJycpIv6YNqQFKyHCxdzm3rJRDguo6nrB3GQAAAMAdQXC6RyRkuOi8pZC9y7hlnhlX7F0CAAAAcMcwOQQAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGChg7wIAAAAeNAkJCUpOTrZ3Gbfl3Llz9i4BuKcQnAAAAPJRQkKCZs2eo4z0a/YuBUA+IjgBAADko+TkZGWkX9OG1AAlZLjYu5xbVsohQXUdT9i7DOCeQXACAAC4AxIyXHTeUsjeZdwyz4wr9i4BuKcwOQQAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGLB7cJo7d678/f3l4uKioKAgbd++/abt4+PjNXDgQJUoUULOzs6qWLGivvvuu7tULQAAAICHUQF7dr506VKFh4dr3rx5CgoKUmRkpEJDQxUbG6vixYtnaZ+amqoWLVqoePHi+vLLL1WqVCkdPXpUXl5ed794AAAAAA8NuwanGTNmqG/fvurVq5ckad68eVq9erUWLlyoESNGZGm/cOFCXbhwQVu2bJGjo6Mkyd/f/26WDAAAAOAhZLdL9VJTU7Vz506FhIT8rxizWSEhIdq6dWu226xcuVINGjTQwIED5ePjo0ceeUSvv/660tPTc+zn6tWrSkxMtHkAAAAAQF7YLTidO3dO6enp8vHxsVnu4+OjU6dOZbvN33//rS+//FLp6en67rvvNHbsWE2fPl1TpkzJsZ+IiAh5enpaH35+fvl6HAAAAAAefHkOTseOHdM///xjfb59+3a9/PLLWrBgQb4Wlp2MjAwVL15cCxYsUN26ddWlSxeNHj1a8+bNy3GbkSNHKiEhwfo4duzYHa8TAAAAwIMlz8GpW7duWr9+vSTp1KlTatGihbZv367Ro0dr0qRJud5PsWLF5ODgoNOnT9ssP336tHx9fbPdpkSJEqpYsaIcHBysy6pUqaJTp04pNTU1222cnZ3l4eFh8wAAAACAvMhzcNq/f7/q168vSfriiy/0yCOPaMuWLfr0008VFRWV6/04OTmpbt26io6Oti7LyMhQdHS0GjRokO02DRs21MGDB5WRkWFd9tdff6lEiRJycnLK66EAAAAAQK7kOTilpaXJ2dlZkrR27Vo9/fTTkqTKlSvr5MmTedpXeHi43n//fS1evFh//PGH+vfvr8uXL1tn2evRo4dGjhxpbd+/f39duHBBQ4YM0V9//aXVq1fr9ddf18CBA/N6GAAAAACQa3mejrxatWqaN2+eWrVqpTVr1mjy5MmSpBMnTqho0aJ52leXLl109uxZjRs3TqdOnVKtWrX0ww8/WCeMiIuLk9n8v2zn5+enH3/8UUOHDlWNGjVUqlQpDRkyRMOHD8/rYQAAAABAruU5OE2bNk3t27fXW2+9pbCwMNWsWVPS9anCMy/hy4tBgwZp0KBB2a6LiYnJsqxBgwb673//m+d+AAAAAOBW5Tk4NWvWTOfOnVNiYqIKFy5sXd6vXz+5urrma3EAAAAAcC+4pd9xslgs2rlzp+bPn69Lly5Juj7ZA8EJAAAAwIMozyNOR48eVcuWLRUXF6erV6+qRYsWcnd317Rp03T16tWb/qYSAAAAANyP8jziNGTIENWrV08XL15UwYIFrcvbt29vM7U4AAAAADwo8jzitGnTJm3ZsiXL7yb5+/vr+PHj+VYYAAAAANwr8jzilJGRofT09CzL//nnH7m7u+dLUQAAAABwL8lzcHriiScUGRlpfW4ymZSUlKTx48frqaeeys/aAAAAAOCekOdL9aZPn67Q0FBVrVpVKSkp6tatmw4cOKBixYrp888/vxM1AgAAAIBd5Tk4lS5dWnv27NGSJUu0d+9eJSUlqU+fPurevbvNZBEAAAAA8KDIc3CSpAIFCui5557L71oAAAAA4J6U5+D00Ucf3XR9jx49brkYAAAAALgX5Tk4DRkyxOZ5WlqakpOT5eTkJFdXV4ITAAAAgAdOnmfVu3jxos0jKSlJsbGxatSoEZNDAAAAAHgg5Tk4ZadChQp64403soxGAQAAAMCDIF+Ck3R9wogTJ07k1+4AAAAA4J6R53ucVq5cafPcYrHo5MmTmjNnjho2bJhvhQEAAADAvSLPwaldu3Y2z00mk7y9vfXYY49p+vTp+VUXAAAAANwz8hycMjIy7kQdAAAAAHDPyrd7nAAAAADgQZWrEafw8PBc73DGjBm3XAwAAAAA3ItyFZx+/fXXXO3MZDLdVjEAAAAAcC/KVXBav379na4DAAAAAO5Z3OMEAAAAAAbyPKueJO3YsUNffPGF4uLilJqaarPuq6++ypfCAAAAAOBekecRpyVLlig4OFh//PGHvv76a6Wlpem3337TunXr5OnpeSdqBAAAAAC7ynNwev311zVz5kx9++23cnJy0jvvvKM///xTnTt3VpkyZe5EjQAAAABgV3kOTocOHVKrVq0kSU5OTrp8+bJMJpOGDh2qBQsW5HuBAAAAAGBveQ5OhQsX1qVLlyRJpUqV0v79+yVJ8fHxSk5Ozt/qAAAAAOAekOvglBmQmjRpojVr1kiSOnXqpCFDhqhv377q2rWrHn/88TtTJQAAAADYUa5n1atRo4YeffRRtWvXTp06dZIkjR49Wo6OjtqyZYs6duyoMWPG3LFCAQAAAMBech2cNmzYoEWLFikiIkJTp05Vx44d9cILL2jEiBF3sj4AAAAAsLtcX6rXuHFjLVy4UCdPntTs2bN15MgRNW3aVBUrVtS0adN06tSpO1knAAAAANhNnieHKFSokHr16qUNGzbor7/+UqdOnTR37lyVKVNGTz/99J2oEQAAAADsKs/B6Ubly5fXqFGjNGbMGLm7u2v16tX5VRcAAAAA3DNyfY/Tv23cuFELFy7U8uXLZTab1blzZ/Xp0yc/awMAAACAe0KegtOJEycUFRWlqKgoHTx4UMHBwZo1a5Y6d+6sQoUK3akaAQAAAMCuch2cnnzySa1du1bFihVTjx491Lt3b1WqVOlO1gYAAAAA94RcBydHR0d9+eWXat26tRwcHO5kTQAAAABwT8l1cFq5cuWdrAMAAAAA7lm3NaseAAAAADwMCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYOCeCE5z586Vv7+/XFxcFBQUpO3bt+dquyVLlshkMqldu3Z3tkAAAAAADzW7B6elS5cqPDxc48eP165du1SzZk2FhobqzJkzN93uyJEjGjZsmBo3bnyXKgUAAADwsLJ7cJoxY4b69u2rXr16qWrVqpo3b55cXV21cOHCHLdJT09X9+7dNXHiRAUGBt7FagEAAAA8jOwanFJTU7Vz506FhIRYl5nNZoWEhGjr1q05bjdp0iQVL15cffr0Mezj6tWrSkxMtHkAAAAAQF7YNTidO3dO6enp8vHxsVnu4+OjU6dOZbvNzz//rA8//FDvv/9+rvqIiIiQp6en9eHn53fbdQMAAAB4uNj9Ur28uHTpkp5//nm9//77KlasWK62GTlypBISEqyPY8eO3eEqAQAAADxoCtiz82LFisnBwUGnT5+2WX769Gn5+vpmaX/o0CEdOXJEbdq0sS7LyMiQJBUoUECxsbEqV66czTbOzs5ydna+A9UDAAAAeFjYdcTJyclJdevWVXR0tHVZRkaGoqOj1aBBgyztK1eurH379mn37t3Wx9NPP63mzZtr9+7dXIYHAAAA4I6w64iTJIWHhyssLEz16tVT/fr1FRkZqcuXL6tXr16SpB49eqhUqVKKiIiQi4uLHnnkEZvtvby8JCnLcgAAAADIL3YPTl26dNHZs2c1btw4nTp1SrVq1dIPP/xgnTAiLi5OZvN9dSsWAAAAgAeM3YOTJA0aNEiDBg3Kdl1MTMxNt42Kisr/ggAAAADgBgzlAAAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAIABghMAAAAAGCA4AQAAAICBeyI4zZ07V/7+/nJxcVFQUJC2b9+eY9v3339fjRs3VuHChVW4cGGFhITctD0AAAAA3C67B6elS5cqPDxc48eP165du1SzZk2FhobqzJkz2baPiYlR165dtX79em3dulV+fn564okndPz48btcOQAAAICHhd2D04wZM9S3b1/16tVLVatW1bx58+Tq6qqFCxdm2/7TTz/VgAEDVKtWLVWuXFkffPCBMjIyFB0dfZcrBwAAAPCwsGtwSk1N1c6dOxUSEmJdZjabFRISoq1bt+ZqH8nJyUpLS1ORIkWyXX/16lUlJibaPAAAAAAgL+wanM6dO6f09HT5+PjYLPfx8dGpU6dytY/hw4erZMmSNuHrRhEREfL09LQ+/Pz8brtuAAAAAA8Xu1+qdzveeOMNLVmyRF9//bVcXFyybTNy5EglJCRYH8eOHbvLVQIAAAC43xWwZ+fFihWTg4ODTp8+bbP89OnT8vX1vem2b7/9tt544w2tXbtWNWrUyLGds7OznJ2d86VeAAAAAA8nu444OTk5qW7dujYTO2RO9NCgQYMct3vzzTc1efJk/fDDD6pXr97dKBUAAADAQ8yuI06SFB4errCwMNWrV0/169dXZGSkLl++rF69ekmSevTooVKlSikiIkKSNG3aNI0bN06fffaZ/P39rfdCubm5yc3NzW7HAQAAAODBZffg1KVLF509e1bjxo3TqVOnVKtWLf3www/WCSPi4uJkNv9vYOy9995TamqqnnnmGZv9jB8/XhMmTLibpQMAAAB4SNg9OEnSoEGDNGjQoGzXxcTE2Dw/cuTInS8IAAAAAG5wX8+qBwAAAAB3A8EJAAAAAAwQnAAAAADAAMEJAAAAAAwQnAAAAADAAMEJAAAAAAwQnAAAAADAAMEJAAAAAAwQnAAAAADAAMEJAAAAAAwQnAAAAADAAMEJAAAAAAwQnAAAAADAAMEJAAAAAAwQnAAAAADAAMEJAAAAAAwQnAAAAADAAMEJAAAAAAwQnAAAAADAAMEJAAAAAAwQnAAAAADAAMEJAAAAAAwQnAAAAADAAMEJAAAAAAwQnAAAAADAAMEJAAAAAAwQnAAAAADAAMEJAAAAAAwQnAAAAADAAMEJAAAAAAwQnAAAAADAAMEJAAAAAAwQnAAAAADAAMEJAAAAAAwQnAAAAADAAMEJAAAAAAwQnAAAAADAAMEJAAAAAAwQnAAAAADAAMEJAAAAAAwQnAAAAADAAMEJAAAAAAwQnAAAAADAAMEJAAAAAAwQnAAAAADAAMEJAAAAAAwQnAAAAADAAMEJAAAAAAwQnAAAAADAAMEJAAAAAAwQnAAAAADAAMEJAAAAAAwQnAAAAADAAMEJAAAAAAwQnAAAAADAAMEJAAAAAAwQnAAAAADAAMEJAAAAAAwQnAAAAADAAMEJAAAAAAwQnAAAAADAAMEJAAAAAAwQnAAAAADAAMEJAAAAAAwQnAAAAADAAMEJAAAAAAwQnAAAAADAAMEJAAAAAAwQnAAAAADAAMEJAAAAAAwQnAAAAADAAMEJAAAAAAwQnAAAAADAwD0RnObOnSt/f3+5uLgoKChI27dvv2n7ZcuWqXLlynJxcVH16tX13Xff3aVKAQAAADyM7B6cli5dqvDwcI0fP167du1SzZo1FRoaqjNnzmTbfsuWLeratav69OmjX3/9Ve3atVO7du20f//+u1w5AAAAgIeF3YPTjBkz1LdvX/Xq1UtVq1bVvHnz5OrqqoULF2bb/p133lHLli316quvqkqVKpo8ebLq1KmjOXPm3OXKAQAAADwsCtiz89TUVO3cuVMjR460LjObzQoJCdHWrVuz3Wbr1q0KDw+3WRYaGqoVK1Zk2/7q1au6evWq9XlCQoIkKTEx8Tarzx+XLl1SSkqKCqVdVHpGsr3LuWXODolKsaTosDx1SRn2LueW/SNXpShFLokucnN0s3c5t8wxxVEpKSk66eqqy9eu2buc25Lo6qqUlBRdunRJhQoVsnc5t4zP+r2Hz/u95UH5rEt83u81D8pnXeLzfkdq+f+ZwGKxGDe22NHx48ctkixbtmyxWf7qq69a6tevn+02jo6Ols8++8xm2dy5cy3FixfPtv348eMtknjw4MGDBw8ePHjw4MEj28exY8cMs4tdR5zuhpEjR9qMUGVkZOjChQsqWrSoTCaTHSvDwyoxMVF+fn46duyYPDw87F0OgDuIzzvw8ODzfn+yWCy6dOmSSpYsadjWrsGpWLFicnBw0OnTp22Wnz59Wr6+vtlu4+vrm6f2zs7OcnZ2tlnm5eV160UD+cTDw4N/WIGHBJ934OHB5/3+4+npmat2dp0cwsnJSXXr1lV0dLR1WUZGhqKjo9WgQYNst2nQoIFNe0las2ZNju0BAAAA4HbZ/VK98PBwhYWFqV69eqpfv74iIyN1+fJl9erVS5LUo0cPlSpVShEREZKkIUOGqGnTppo+fbpatWqlJUuWaMeOHVqwYIE9DwMAAADAA8zuwalLly46e/asxo0bp1OnTqlWrVr64Ycf5OPjI0mKi4uT2fy/gbHg4GB99tlnGjNmjEaNGqUKFSpoxYoVeuSRR+x1CECeODs7a/z48VkuIQXw4OHzDjw8+Lw/+EwWS27m3gMAAACAh5fdfwAXAAAAAO51BCcAAAAAMEBwAgAAAAADBCfADkwmk1asWJHvbQE8OG787B85ckQmk0m7d++2a00A8DAjOOGh17NnT5lMJplMJjk5Oal8+fKaNGmSrl27dsf6PHnypJ588sl8bwsgf9z474Kjo6MCAgL02muvKSUlxd6lAbiLevbsqXbt2lmfHzt2TL1791bJkiXl5OSksmXLasiQITp//rzNdv7+/oqMjMyyvwkTJqhWrVp3tmjcMQQnQFLLli118uRJHThwQK+88oomTJigt956K0u71NTUfOnP19c319OV5qUtgPyT+e/C33//rZkzZ2r+/PkaP368vcsCYCd///236tWrpwMHDujzzz/XwYMHNW/ePEVHR6tBgwa6cOGCvUvEHUZwAnT9txd8fX1VtmxZ9e/fXyEhIVq5cqX1L01Tp05VyZIlValSJUnX/+LUuXNneXl5qUiRImrbtq2OHDlis8+FCxeqWrVqcnZ2VokSJTRo0CDruhsvwUlNTdWgQYNUokQJubi4qGzZstYffP53W0nat2+fHnvsMRUsWFBFixZVv379lJSUZF2fWfPbb7+tEiVKqGjRoho4cKDS0tLy/8QBD7DMfxf8/PzUrl07hYSEaM2aNZKkjIwMRUREKCAgQAULFlTNmjX15Zdf2mz/22+/qXXr1vLw8JC7u7saN26sQ4cOSZJ++eUXtWjRQsWKFZOnp6eaNm2qXbt23fVjBJB7AwcOlJOTk3766Sc1bdpUZcqU0ZNPPqm1a9fq+PHjGj16tL1LxB1GcAKyUbBgQevoUnR0tGJjY7VmzRqtWrVKaWlpCg0Nlbu7uzZt2qTNmzfLzc1NLVu2tG7z3nvvaeDAgerXr5/27dunlStXqnz58tn2NWvWLK1cuVJffPGFYmNj9emnn8rf3z/btpcvX1ZoaKgKFy6sX375RcuWLdPatWttQpkkrV+/XocOHdL69eu1ePFiRUVFKSoqKt/OD/Cw2b9/v7Zs2SInJydJUkREhD766CPNmzdPv/32m4YOHarnnntOGzZskCQdP35cTZo0kbOzs9atW6edO3eqd+/e1kuAL126pLCwMP3888/673//qwoVKuipp57SpUuX7HaMAHJ24cIF/fjjjxowYIAKFixos87X11fdu3fX0qVLxc+jPtgK2LsA4F5isVgUHR2tH3/8UYMHD9bZs2dVqFAhffDBB9YvTJ988okyMjL0wQcfyGQySZIWLVokLy8vxcTE6IknntCUKVP0yiuvaMiQIdZ9P/roo9n2GRcXpwoVKqhRo0YymUwqW7ZsjvV99tlnSklJ0UcffaRChQpJkubMmaM2bdpo2rRp8vHxkSQVLlxYc+bMkYODgypXrqxWrVopOjpaffv2zZfzBDwMVq1aJTc3N127dk1Xr16V2WzWnDlzdPXqVb3++utau3atGjRoIEkKDAzUzz//rPnz56tp06aaO3euPD09tWTJEjk6OkqSKlasaN33Y489ZtPXggUL5OXlpQ0bNqh169Z37yAB5MqBAwdksVhUpUqVbNdXqVJFFy9e1NmzZ1W8ePG7XB3uFoIToP99QUpLS1NGRoa6deumCRMmaODAgapevbo1NEnSnj17dPDgQbm7u9vsIyUlRYcOHdKZM2d04sQJPf7447nqu2fPnmrRooUqVaqkli1bqnXr1nriiSeybfvHH3+oZs2a1tAkSQ0bNlRGRoZiY2OtwalatWpycHCwtilRooT27duX6/MBQGrevLnee+89Xb58WTNnzlSBAgXUsWNH/fbbb0pOTlaLFi1s2qempqp27dqSpN27d6tx48bW0PRvp0+f1pgxYxQTE6MzZ84oPT1dycnJiouLu+PHBeDWMaL0cCM4AfrfFyQnJyeVLFlSBQr876NxY0iRpKSkJNWtW1effvpplv14e3vLbM7bFbB16tTR4cOH9f3332vt2rXq3LmzQkJCstwvkRf//rJmMpmUkZFxy/sDHkaFChWyXmK7cOFC1axZUx9++KEeeeQRSdLq1atVqlQpm20yJ3L596U8/xYWFqbz58/rnXfeUdmyZeXs7KwGDRrk2wQ0APJX+fLlZTKZ9Mcff6h9+/ZZ1v/xxx8qXLiwvL29JUkeHh5KSEjI0i4+Pl6enp53vF7cGQQnQLZfkIzUqVNHS5cuVfHixeXh4ZFtG39/f0VHR6t58+a52qeHh4e6dOmiLl266JlnnlHLli114cIFFSlSxKZdlSpVFBUVpcuXL1sD3ebNm2U2m60TVwDIf2azWaNGjVJ4eLj++usvOTs7Ky4uTk2bNs22fY0aNbR48WKlpaVlO+q0efNmvfvuu3rqqackXZ9w5ty5c3f0GADcuqJFi6pFixZ69913NXToUJs/jpw6dUqffvqpevToYb2Ev1KlStq5c2eW/ezatYv/X9/HmBwCyKPu3burWLFiatu2rTZt2qTDhw8rJiZGL730kv755x9J13+nYfr06Zo1a5YOHDigXbt2afbs2dnub8aMGfr888/1559/6q+//tKyZcvk6+srLy+vbPt2cXFRWFiY9u/fr/Xr12vw4MF6/vnnrZfpAbgzOnXqJAcHB82fP1/Dhg3T0KFDtXjxYh06dMj6GV+8eLEkadCgQUpMTNSzzz6rHTt26MCBA/r4448VGxsrSapQoYI+/vhj/fHHH9q2bZu6d+9uOEoFwL4y73EMDQ3Vxo0bdezYMf3www9q0aKFSpUqpalTp1rbDh06VKtXr9bUqVP1xx9/aP/+/Ro9erS2bt1qc/8z7i8EJyCPXF1dtXHjRpUpU0YdOnRQlSpV1KdPH6WkpFhHoMLCwhQZGal3331X1apVU+vWrXXgwIFs9+fu7q4333xT9erV06OPPqojR47ou+++y/aSP1dXV/3444+6cOGCHn30UT3zzDN6/PHHNWfOnDt6zACkAgUKaNCgQXrzzTc1cuRIjR07VhEREapSpYpatmyp1atXKyAgQNL1v06vW7dOSUlJatq0qerWrav333/fOvr04Ycf6uLFi6pTp46ef/55vfTSS9xQDtzjKlSooB07digwMFCdO3dWuXLl1K9fPzVv3lxbt261uUokODhY33//vb7//ns1bNhQzZo105YtWxQdHW293Bf3H5OFu9wAAAAA4KYYcQIAAAAAAwQnAAAAADBAcAIAAAAAAwQnAAAAADBAcAIAAAAAAwQnAAAAADBAcAIAAAAAAwQnAAAAADBAcAIAIBtHjhyRyWSSyWRSTEyMvcsBANgZwQkAcM9o1qyZNazUrFnTZt358+dVsGBB6/oRI0bker9RUVHW7XLL2dlZQUFBCgoKkoeHR663AwA8mArYuwAAALKzd+9ebdy4UU2aNJEkffDBB0pJSbkrfaempqpEiRL673//e1f6AwDc+xhxAgDccxwdHSVJs2fPliSlp6fr3XfftS6/UUJCgoYMGaKyZcvKyclJpUuXVnh4uJKTkyVJPXv2VK9evaztM0eeJkyYYPP8zTffVIcOHeTm5qZ+/frleKnejh071LZtWxUtWlTOzs4KDAzU9OnTrXWOHDlSgYGBcnFxUZEiRVSvXj299dZbd+I0AQDuIoITAOCeU6tWLQUGBmrFihX6559/tHLlSsXFxemZZ56xaZeamqpmzZpp1qxZOnPmjKpUqaLz589r5syZatOmjSwWi8qVK6fAwEDrNpmX35UuXdpmX2PHjlV0dLQCAgLk5OSUbV1btmxRw4YNtXLlSiUlJalChQpKTEzUpk2bJElz587VG2+8obi4OFWqVElFixbVvn37tHr16nw+QwCAu41L9QAA9xyz2ayBAwfqlVde0XvvvaetW7dKkgYPHqzPP//c2u7zzz/X7t275eTkpL1796pChQras2ePatWqpXXr1mndunUaO3as/Pz8rKNOOV1+FxgYqC1btqhw4cJKT0/XsWPHsrQZM2aMUlNT5eXlpW3btqlixYrKyMjQvn37JEkHDhyQJPXq1Uvvv/++JCkpKUl//PFH/p0cAIBdMOIEALgn9e7dW4UKFdLs2bO1fv161a1bVw0aNLBps337dknXR54qVqwok8mkWrVqWdfn5R6lsLAwFS5cWJLk4OCQbZtt27ZJkp555hlVrFhR0vWQlzmRRevWrWUymfTBBx+oVKlSat68uaZMmaIiRYrkug4AwL2JEScAwD3Jy8tLzz33nObPny/p+mhTTpycnFS7du0syzODUG74+Pjkvch/CQ0N1a5du7Rs2TLt2bNHv/76q2JiYhQVFaWDBw/Kzc3ttvsAANgHI04AgHvWoEGDJEne3t569tlns6x/9NFHJf1v8oj//ve/+u9//6uYmBi9+uqr6tatmyTJ1dXVus3ly5ez7Ss3U5UHBQVJkpYvX66DBw9KkiwWi/bu3Svp+kyA3t7emjp1qlatWqWdO3dKkk6fPq3Y2NhcHTMA4N5EcAIA3LMeeeQRnT9/XgcPHpSzs3OW9V27dlWNGjWUnp6uRx99VI888ogqVaokLy8vPfPMM4qPj5ckVa5c2bpN1apV9Z///EebN2/Ocz1TpkyRk5OTLl68qGrVqql69eoqXry4xo0bJ0n64osv5OfnpzJlyqhu3bqqXr26pOvBrVy5crdwBgAA9wqCEwDgnlakSJEcf4DW2dlZGzZs0EsvvSQ/Pz/99ddfunjxourVq6epU6daL7+rUaOGxo4dKx8fH8XFxWnbtm26ePFinmsJDg7W5s2b1aZNG7m5uSk2NlZubm5q1KiRJKlJkyZq2bKlMjIytH//flksFj322GP6/vvv5eXldcvnAABgfyaLxWKxdxEAAAAAcC9jxAkAAAAADBCcAAAAAMAAwQkAAAAADBCcAAAAAMAAwQkAAAAADBCcAAAAAMAAwQkAAAAADBCcAAAAAMAAwQkAAAAADBCcAAAAAMAAwQkAAAAADPw/TZGgjyS+kUgAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# All ResNet Comparison\n",
-    "metrics = []\n",
-    "metrics.append(sat_resnet18_FC_metrics_jaccard)\n",
-    "metrics.append(sat_resnet18_metrics_jaccard)\n",
-    "metrics.append(sat_resnet50_metrics)\n",
-    "metrics.append(imagenet_resnet50_metrics)\n",
-    "\n",
-    "# The FC model is the architecture from the 2020 mangrove monitoring paper, \n",
-    "# so it is used as a baseline. It uses a resnet18 backbone and a fully connected layer for segmentation.\n",
-    "titles = ['ResNet18 FC Baseline', 'Sat ResNet18 UNet', 'Sat ResNet50 UNet', 'ImageNet ResNet50 UNet']\n",
-    "\n",
-    "plot_comparison_metrics(\"UNet Comparison with Jaccard Loss\", metrics, titles)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can see that there is a negligible difference between the ResNet18 and ResNet50 backbones, despite the large difference in training time and size. The ResNet50 is over 2x larger and takes ~10x longer to train."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Shifted Window Transformer"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Model that uses a Swin Transformer backbone from Satlas.\n",
-    "# The Swin Transformer is pretrained on high-resolution drone imagery.\n",
-    "# This takes too long to train and is low accuracy, avoid using.\n",
-    "\n",
-    "# loss = JaccardLoss()\n",
-    "#swin_satlas = SwinB_UNet().to(DEVICE)\n",
-    "#train_swin, swin_metrics = train(swin_satlas, trainLoader, testLoader, loss, print_all_epochs=True)\n",
-    "#valid_swin = [x['Loss'] for x in swin_metrics]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Foundational ResNet Comparison"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[INFO] training the network...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  3%|▎         | 1/30 [00:03<01:30,  3.12s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 1/30\n",
-      "Train loss: 0.588781, Test loss: 0.5754\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.45127320499444495\n",
-      "Recall: 0.8675423752793748\n",
-      "f1_score: 0.593712470517723\n",
-      "IOU: 0.42218426749208077\n",
-      "Accuracy: 0.8726457141444366\n",
-      "Specificity: 0.8732588612662006\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 17%|█▋        | 5/30 [00:15<01:20,  3.23s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 5/30\n",
-      "Train loss: 0.422721, Test loss: 0.3991\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.6939310493560565\n",
-      "Recall: 0.8238188918573639\n",
-      "f1_score: 0.7533171210652514\n",
-      "IOU: 0.6042572123144397\n",
-      "Accuracy: 0.9421293355076429\n",
-      "Specificity: 0.9563438940433023\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 33%|███▎      | 10/30 [00:34<01:11,  3.58s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 10/30\n",
-      "Train loss: 0.400882, Test loss: 0.3648\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.6919937964187086\n",
-      "Recall: 0.8870386214592374\n",
-      "f1_score: 0.7774700712712563\n",
-      "IOU: 0.6359517693605375\n",
-      "Accuracy: 0.9455355869805474\n",
-      "Specificity: 0.9525637791592476\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 50%|█████     | 15/30 [00:52<00:54,  3.63s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 15/30\n",
-      "Train loss: 0.398418, Test loss: 0.4670\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.5751453959710945\n",
-      "Recall: 0.8805867012950847\n",
-      "f1_score: 0.6958222436042567\n",
-      "IOU: 0.5335332857748223\n",
-      "Accuracy: 0.9174215344246657\n",
-      "Specificity: 0.9218471021281839\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 67%|██████▋   | 20/30 [01:10<00:36,  3.67s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 20/30\n",
-      "Train loss: 0.356665, Test loss: 0.3485\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.7493234053786169\n",
-      "Recall: 0.8361426157538008\n",
-      "f1_score: 0.7903559254727084\n",
-      "IOU: 0.6533789088179233\n",
-      "Accuracy: 0.952422044231405\n",
-      "Specificity: 0.9663925834895616\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 83%|████████▎ | 25/30 [01:28<00:17,  3.56s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 25/30\n",
-      "Train loss: 0.342626, Test loss: 0.3303\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.7296990151214192\n",
-      "Recall: 0.8931978427722467\n",
-      "f1_score: 0.8032125800346939\n",
-      "IOU: 0.6711405606669715\n",
-      "Accuracy: 0.9530560054947917\n",
-      "Specificity: 0.960247740377747\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 30/30 [01:47<00:00,  3.57s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPOCH: 30/30\n",
-      "Train loss: 0.323515, Test loss: 0.3224\n",
-      "\n",
-      "Validation Metrics:\n",
-      "Precision: 0.7415952260351034\n",
-      "Recall: 0.889851269934126\n",
-      "f1_score: 0.8089869392527949\n",
-      "IOU: 0.6792427101893083\n",
-      "Accuracy: 0.9549282393004608\n",
-      "Specificity: 0.962746994325669\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# ImageNet vs Satellite ResNet18 FC\n",
-    "# This might be a more fair comparison than the UNet models,\n",
-    "# as the FC model is just a linear layer on top of the ResNet backbone.\n",
-    "imagenet18 = resnet18(weights=ResNet18_Weights.IMAGENET1K_V1)\n",
-    "\n",
-    "ImageNet_resnet18_FC = ResNet_FC(ResNet=imagenet18, input_image_size=INPUT_IMAGE_SIZE).to(DEVICE)\n",
-    "ImageNet_resnet18_FC_train, ImageNet_resnet18_FC_metrics = train(ImageNet_resnet18_FC, trainLoader, testLoader, loss)\n",
-    "ImageNet_resnet18_FC_valid = [x['Loss'] for x in ImageNet_resnet18_FC_metrics]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[0.7415952260351034, 0.889851269934126, 0.6792427101893083], [0.7445986637279962, 0.8871447336722723, 0.6801716326202317]]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_loss_comparison(\"ImageNet vs Satellite ResNet18 FC\", ImageNet_resnet18_FC_train, sat_resnet18_FC_train_jaccard, ImageNet_resnet18_FC_valid, sat_resnet18_FC_valid_jaccard, compare1='ImageNet', compare2='Satellite', y_max=0.5)\n",
-    "\n",
-    "# ImageNet vs Satellite ResNet18 FC Metrics\n",
-    "metrics = []\n",
-    "metrics.append(ImageNet_resnet18_FC_metrics)\n",
-    "metrics.append(sat_resnet18_FC_metrics_jaccard)\n",
-    "\n",
-    "titles = ['ImageNet ResNet18 FC', 'Satellite ResNet18 FC']\n",
-    "\n",
-    "plot_comparison_metrics(\"ImageNet vs Satellite ResNet18 FC\", metrics, titles)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "They're the same."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "mangrove",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.14"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Super Resolution/Schrodinger Diffusion/models/models.py b/Super Resolution/Schrodinger Diffusion/models/models.py
index 30ba012..e2eae9a 100755
--- a/Super Resolution/Schrodinger Diffusion/models/models.py	
+++ b/Super Resolution/Schrodinger Diffusion/models/models.py	
@@ -1,26 +1,13 @@
-import math
+import os
 import torch
 import torch.nn as nn
 import numpy as np
-from torch.nn import Conv2d, Module
-from torchgeo.models import resnet18, get_weight
-from torchvision.models.resnet import ResNet
-from typing import Optional
+from torch.nn import Module
 from i2sb.diffusion import Diffusion
 
-"""
-Pretrained model Weights from SSL4EO-12 dataset
-@ https://github.com/zhu-xlab/SSL4EO-S12
-
-Imported using torchgeo
-@ https://torchgeo.readthedocs.io/en/stable/api/models.html
-
-SWIN high resolution aerial imagery model from Satlas
-@ https://github.com/allenai/satlas
+cwd = os.path.dirname(os.path.realpath(__file__))
 
-New weights can be imported from torchgeo using:
-torchgeo.models.get_weight("ResNet50_Weights.SENTINEL2_ALL_MOCO")
-"""
+from ....DroneClassification.models import ResNet_UNet
 
 """
    _____   _                       _    __   _                     
@@ -30,132 +17,9 @@
  | |____  | | | (_| | \__ \ \__ \ | | | |   | | |  __/ | |    \__ \
   \_____| |_|  \__,_| |___/ |___/ |_| |_|   |_|  \___| |_|    |___/
                                                                    
-"""                                                             
-class ResNet_UNet(Module):
-    """
-    UNet architecture with ResNet encoder.
-    
-    Default ResNet is a ResNet18 trained on Sentinel-2 3 channel RGB satellite imagery.
-    """
-    def __init__(self, ResNet: Optional[ResNet] = None, input_image_size=256):
-        super(ResNet_UNet, self).__init__()
-        if ResNet is None:
-            ResNet = resnet18(
-                weights=get_weight("ResNet18_Weights.SENTINEL2_RGB_SECO")
-            )
-        self.input_image_size = input_image_size
-        for param in ResNet.parameters():
-            param.requires_grad = False
-        
-        self.layer1 = nn.Sequential(
-            ResNet.conv1,
-            ResNet.bn1,
-            nn.ReLU(),
-            ResNet.maxpool,
-            ResNet.layer1,
-        )
-        self.layer2 = ResNet.layer2
-        self.layer3 = ResNet.layer3
-        self.layer4 = ResNet.layer4
-
-        dummy_input = torch.randn(1, 3, input_image_size, input_image_size)
-        x = self.layer1(dummy_input)
-        x = self.layer2(x)
-        x = self.layer3(x)
-        x = self.layer4(x)
-        
-        # Define feature dimensions
-        feature_dim = x.shape[1]
-        half_dim = feature_dim // 2
-        quarter_dim = feature_dim // 4
-        eighth_dim = feature_dim // 8
-        sixteenth_dim = feature_dim // 16
-        
-        # Center
-        self.center = Decoder(feature_dim, int(feature_dim // 1.5), half_dim)
-
-        # Skip connections
-        self.skip_conv1 = Conv2d(half_dim, half_dim, kernel_size=1)
-        self.skip_conv2 = Conv2d(quarter_dim, quarter_dim, kernel_size=1)
-        self.skip_conv3 = Conv2d(eighth_dim, eighth_dim, kernel_size=1)
-
-        #decoder
-        self.decoder1 = Decoder(feature_dim, half_dim, quarter_dim)
-        self.decoder2 = Decoder(half_dim, quarter_dim, eighth_dim)
-        
-        self.classification_head = nn.Sequential(
-            Upsample(quarter_dim, eighth_dim, sixteenth_dim),
-            Conv2d(sixteenth_dim, 1, kernel_size=2, padding=1),
-            nn.Upsample(
-              size=(input_image_size, input_image_size),
-              mode="bilinear",
-              align_corners=False,
-            ),
-            Conv2d(1, 1, kernel_size=3, padding=1) # smooth output
-        )
-
-    def forward(self, image, timestep=None):
-        if len(image.shape) == 3:
-            image = image.unsqueeze(0)
-        image = image[:, :3, :, :]
-
-        if timestep:
-            # Encode with timestep embeddings
-            x1 = self.add_timestep_embedding(self.layer1(image), timestep)
-            x2 = self.add_timestep_embedding(self.layer2(x1), timestep)
-            x3 = self.add_timestep_embedding(self.layer3(x2), timestep)
-            x4 = self.add_timestep_embedding(self.layer4(x3), timestep)
-        else:
-            # Encode
-            x1 = self.layer1(image)
-            x2 = self.layer2(x1)
-            x3 = self.layer3(x2)
-            x4 = self.layer4(x3)
-      
-        # Center
-        x = self.center(x4)
-
-        # Decode
-        x = torch.cat((x, self.skip_conv1(x3)), dim=1)
-        x = self.decoder1(x)
-        x = torch.cat((x, self.skip_conv2(x2)), dim=1)
-        x = self.decoder2(x)
-        x = torch.cat((x, self.skip_conv3(x1)), dim=1)
-        x = self.classification_head(x)
-
-        return x
-
-class ResNet_FC(Module):
-    """
-    ResNet with Fully Connected output layer.
-    """
-    def __init__(self, ResNet : Optional[ResNet] = None, num_classes=1, input_image_size=128):
-        super(ResNet_FC, self).__init__()
-        self.num_classes = num_classes
-        self.input_image_size = input_image_size
-        if ResNet is None:
-            ResNet = resnet18(
-                weights=get_weight("ResNet18_Weights.SENTINEL2_RGB_SECO")
-            )
-        ResNet.fc = nn.Identity()
-        self.resnet = ResNet
-
-        dummy_input = torch.randn(1, 3, input_image_size, input_image_size)
-        features = ResNet(dummy_input)
-        feature_dim = features.shape[1]
-        # Add a fully connected layer for binary segmentation
-        self.classification_head = nn.Linear(feature_dim, input_image_size*input_image_size * num_classes)
-
-    def forward(self, image):
-        image = image[:, :3, :, :]
-        x = self.resnet(image)
-        x = x.flatten(start_dim=1)
-        x = self.classification_head(x)
-        x = x.view(-1, self.num_classes, self.input_image_size, self.input_image_size)
-        return x
-    
+"""       
 class ResNet_UNet_Diffusion(Module):
-    def __init__(self, unet: Optional[ResNet_UNet] = None, num_timesteps=1000, opt=None):
+    def __init__(self, unet = None, num_timesteps=1000, opt=None):
         super(ResNet_UNet_Diffusion, self).__init__()
         if unet is None:
             unet = ResNet_UNet()
@@ -228,6 +92,7 @@ def forward(self, image: torch.tensor, diffuse: bool = False, return_encoding_on
             x = self.classification_head(x)
 
         return x
+
 """
   _    _          _                             
  | |  | |        | |                            
@@ -239,83 +104,6 @@ def forward(self, image: torch.tensor, diffuse: bool = False, return_encoding_on
                      |_|                        
 """
 
-class SegmentModelWrapper(Module):
-    def __init__(self, model: nn.Module, threshold=0.5):
-        super(SegmentModelWrapper, self).__init__()
-        self.model = model
-        self.model.eval()
-        self.threshold = threshold
-        
-        # Standard mean and std values for ResNet
-        mean = [0.485, 0.456, 0.406]
-        std = [0.229, 0.224, 0.225]
-
-        # Convert mean and std to tensors with shape [C, 1, 1]
-        self.mean_tensor = torch.tensor(mean, dtype=torch.float32).view(1, 3, 1, 1)
-        self.std_tensor = torch.tensor(std, dtype=torch.float32).view(1, 3, 1, 1)
-
-    
-    def forward(self, image: np.ndarray):
-        """
-        Expects a numpy array of dimensions CxHxW.
-         
-        It can also accept batched images of size BxCxHxW.
-        """
-        image = torch.tensor(image, dtype=torch.float32)
-        image.div_(255.0)
-        if len(image.shape) == 3:
-            image = image.unsqueeze(0)
-        
-        image = (image - self.mean_tensor) / self.std_tensor
-        
-        out = self.model.forward(image)
-
-        out = torch.sigmoid(out)
-
-        return (out > self.threshold).to(torch.uint8)
-
-class Upsample(Module):
-    """
-    Helper class for the UNet architecture.
-    Uses convolutional layers to upsample the input.
-    """
-    def __init__(self, in_channels, mid_channels, out_channels):
-        super(Upsample, self).__init__()
-        self.upsampler = nn.Sequential(
-            nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1),
-            nn.BatchNorm2d(mid_channels),
-            nn.ReLU(inplace=True),
-            nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1),
-            nn.BatchNorm2d(out_channels),
-            nn.ReLU(inplace=True),
-            nn.ConvTranspose2d(out_channels, out_channels, kernel_size=2, stride=2)
-        )
-    
-    def forward(self, x) -> torch.Tensor:
-        return self.upsampler(x)
-
-class Decoder(Module):
-    """
-    Helper class for the UNet architecture.
-    Uses convolutional layers to upsample the input.
-    Includes dropout layer to prevent overfitting.
-    """
-    def __init__(self, in_channels: int, mid_channels: int, out_channels: int):
-        super(Decoder, self).__init__()
-        self.decoder = nn.Sequential(
-            nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1),
-            nn.BatchNorm2d(mid_channels),
-            nn.ReLU(inplace=True),
-            nn.Dropout(0.5),
-            nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1),
-            nn.BatchNorm2d(out_channels),
-            nn.ReLU(inplace=True),
-            nn.ConvTranspose2d(out_channels, out_channels, kernel_size=2, stride=2)
-        )
-
-    def forward(self, x) -> torch.Tensor:
-        return self.decoder(x)
-    
 class DiffusionLayer(nn.Module):
     def __init__(self, latent_dim, time_embedding_dim):
         super(DiffusionLayer, self).__init__()