SIFT_Xception_CIFAR10.ipynb

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  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install einops > /dev/null\n",
        "import torch\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "import pickle\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import preprocessing\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.svm import SVC\n",
        "from tqdm.notebook import tqdm\n",
        "from sklearn.metrics import f1_score\n",
        "from PIL import Image\n",
        "import matplotlib.pyplot as plt\n",
        "import cv2\n",
        "from sklearn.cluster import KMeans\n",
        "import scipy.ndimage as nd\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "import pandas as pd\n",
        "from sklearn.metrics import accuracy_score\n",
        "from skimage.feature import local_binary_pattern\n",
        "import tensorflow as tf\n",
        "#from tensorflow.keras import layers, models\n",
        "from sklearn.preprocessing import Normalizer\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.decomposition import IncrementalPCA\n",
        "\n",
        "from torchvision.utils import save_image\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import os\n",
        "import glob\n",
        "import PIL\n",
        "from torch.utils import data as D\n",
        "from torch.utils.data.sampler import SubsetRandomSampler\n",
        "import random\n",
        "import torchvision.models as models\n",
        "from torch.optim.lr_scheduler import ReduceLROnPlateau\n",
        "import einops\n",
        "import torch.nn.functional as fonctional\n",
        "import torch.nn.functional as F"
      ],
      "metadata": {
        "id": "IlEQ3OGaVl-M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import DataLoader, random_split\n",
        "from torchvision import transforms\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import f1_score\n",
        "from torch.utils.data import Dataset, DataLoader"
      ],
      "metadata": {
        "id": "ovIU658ZV6Vf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Install appropriate open cv libraries\n",
        "!pip install opencv-python==3.4.2.17  \n",
        "!pip install opencv-contrib-python==3.4.2.17\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HJt2wD4kXWZc",
        "outputId": "e2e1e4cc-3e40-4a08-a516-d051df0ae47a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: opencv-python==3.4.2.17 in /usr/local/lib/python3.7/dist-packages (3.4.2.17)\n",
            "Requirement already satisfied: numpy>=1.14.5 in /usr/local/lib/python3.7/dist-packages (from opencv-python==3.4.2.17) (1.21.6)\n",
            "Requirement already satisfied: opencv-contrib-python==3.4.2.17 in /usr/local/lib/python3.7/dist-packages (3.4.2.17)\n",
            "Requirement already satisfied: numpy>=1.14.5 in /usr/local/lib/python3.7/dist-packages (from opencv-contrib-python==3.4.2.17) (1.21.6)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Reading the required Data"
      ],
      "metadata": {
        "id": "CewRYSQNOC3n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "def get_cifar10_dataloaders(val_percentage=0.1, batch_size=20):\n",
        "  input_size = 32\n",
        "\n",
        "  transform_test = transforms.Compose(\n",
        "    [transforms.Resize(input_size),\n",
        "     transforms.ToTensor(),\n",
        "    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
        "     ])\n",
        "  \n",
        "  transform_train = transforms.Compose([transforms.RandomHorizontalFlip(),\n",
        "                        #transforms.RandomVerticalFlip(),\n",
        "                        transforms.RandomRotation(5),\n",
        "                        transforms.RandomRotation(7),\n",
        "                        transforms.Resize(input_size),\n",
        "                        transforms.ToTensor(),\n",
        "                        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
        "                        ])\n",
        "\n",
        "  dataset = torchvision.datasets.CIFAR10(root='./data', train=True,download=True, transform=transform_train) #len=50000\n",
        "\n",
        "  dataset_test = torchvision.datasets.CIFAR10(root='./data', train=False,download=True, transform=transform_test) #len=10000\n",
        "\n",
        "  len_train = int(len(dataset) * (1.-val_percentage))\n",
        "  len_val = len(dataset) - len_train\n",
        "\n",
        "  dataset_train, dataset_val = random_split(dataset, [len_train, len_val])\n",
        "\n",
        "  data_loader_train = torch.utils.data.DataLoader(dataset_train, batch_size=batch_size,shuffle=True, num_workers=2)\n",
        "\n",
        "  data_loader_val   = DataLoader(dataset_val, batch_size=batch_size,shuffle=True,num_workers=2)\n",
        "\n",
        "  data_loader_test  = DataLoader(dataset_test, batch_size=batch_size,shuffle=True,num_workers=2)\n",
        "\n",
        "  return data_loader_train, data_loader_val, data_loader_test\n",
        "\n",
        "classes = ('plane', 'car', 'bird', 'cat',\n",
        "           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')\n",
        "\n",
        "# _ = get_cifar10_dataloaders()"
      ],
      "metadata": {
        "id": "MZiEE5II-IXw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def im_convert(tensor):\n",
        "  image = tensor.cpu().clone().detach().numpy()\n",
        "  image = image /2 + 0.5     # unnormalize\n",
        "  image = image.transpose(1, 2, 0)\n",
        "  return image\n",
        "\n",
        "a,_,_=get_cifar10_dataloaders( batch_size=40)\n",
        "dataiter = iter(a)\n",
        "images, labels = dataiter.next()\n",
        "fig = plt.figure(figsize=(25, 4))\n",
        "\n",
        "for idx in np.arange(20):\n",
        "  ax = fig.add_subplot(2, 10, idx+1, xticks=[], yticks=[])\n",
        "\n",
        "  plt.imshow(im_convert(images[idx]))\n",
        "  ax.set_title(classes[labels[idx].item()])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355,
          "referenced_widgets": [
            "0c449bac6fa946529c7ea7ae516da6af",
            "6423be452d1d4efe874dba5d81a358a3",
            "b17bca38111e486fb6f3ce7c5fa9314d",
            "52d85b28c8b642ac83781e21ef21aed8",
            "ae947bad503047e6bd8eccc8300d720f",
            "abd6c0a09f5a4cf7941d97f9d22e298a",
            "0ce7b7e92fa446a7a8c755efe4b09445",
            "fa61141d770942e18581d76d45de91a3",
            "5d2596b749d949569fb014e511cd77ed",
            "1e095118ac8544e4a0f9bcae445f65d2",
            "ea1b532f2c1246689ebbbf36dad6900f"
          ]
        },
        "id": "6lxu6n9Eb_gv",
        "outputId": "d29e70e7-9cad-4e74-f531-6a8062a137f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/170498071 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0c449bac6fa946529c7ea7ae516da6af"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/cifar-10-python.tar.gz to ./data\n",
            "Files already downloaded and verified\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x288 with 20 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "radius = 3\n",
        "n_points = 8*radius\n",
        "METHOD = 'uniform'\n",
        "item_name='car'\n",
        "item = classes.index(item_name)\n",
        "idx=list(range(images[labels==item].shape[0]))\n",
        "fig, ax = plt.subplots(6,len(idx), figsize=(35,15))\n",
        "fig.tight_layout(pad=3.0)\n",
        "fig.subplots_adjust(wspace=0.001)\n",
        "\n",
        "t=0\n",
        "  #\n",
        "for i in idx:\n",
        "  \n",
        "  contrast = images[labels==item][i].std()\n",
        "  img=cv2.normalize(np.array(images[labels==item][i]), None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U).transpose(1, 2, 0)\n",
        "  image_repr = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n",
        "\n",
        "  sift = cv2.xfeatures2d.SIFT_create(nfeatures=150, nOctaveLayers=20, sigma=0.8, contrastThreshold=0.001*contrast)\n",
        "  keypoints_1, descriptors_1 = sift.detectAndCompute(image_repr,None)\n",
        "\n",
        "  imgtemp = cv2.drawKeypoints(image_repr,keypoints_1,image_repr)\n",
        "  kp = np.array([k.pt for k in keypoints_1]).astype(int)\n",
        "  temp = np.zeros_like(image_repr)\n",
        "  for j in kp:\n",
        "    temp[j[1],j[0]] = 255\n",
        "  closing = nd.binary_closing(temp,iterations=1).astype(int)\n",
        "  sl = np.where(closing == 1)[0].min()\n",
        "  el = np.where(closing == 1)[0].max()\n",
        "  sc = np.where(closing == 1)[1].min()\n",
        "  ec = np.where(closing == 1)[1].max()\n",
        "  cropped = cv2.resize(image_repr[sl:el,sc:ec],dsize=(32,32))\n",
        "  lbp = local_binary_pattern(cropped, n_points, radius, METHOD)\n",
        "  \n",
        "  ax[0][t].imshow(image_repr)\n",
        "  ax[0][t].set_title(\"original image = \" + item_name + '#'+str(i))\n",
        "  ax[1][t].imshow(imgtemp)\n",
        "  ax[1][t].set_title(\"SIFT_img\")\n",
        "  ax[2][t].imshow(closing)\n",
        "  ax[2][t].set_title(\"SIFT + morphology = masked\")\n",
        "  ax[3][t].imshow(cropped)\n",
        "  ax[3][t].set_title(\"Cropped\")\n",
        "  \n",
        "  ax[4][t].imshow(lbp)\n",
        "  ax[4][t].set_title(\"Local binary pattern = texture\")\n",
        "  n_bins = int(lbp.max())\n",
        "  ax[5][t].hist(lbp.ravel(), density=True, bins=n_bins, range=(0, n_bins),\n",
        "                   facecolor='0.5')\n",
        "  t = t+1\n",
        "    "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 690
        },
        "id": "adqHQxXBN2u4",
        "outputId": "7b513140-26ca-45af-d5a4-74b2e9fbd1fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2520x1080 with 30 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def feature_xtr(image):\n",
        "  img=cv2.normalize(np.array(image), None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U).transpose(0,2, 3, 1)\n",
        "  x_mask = np.zeros_like(img[:,:,:,-1])\n",
        "  x_masked = np.zeros_like(img[:,:,:,-1])\n",
        "  x_cropped = []\n",
        "  X = []\n",
        "  Xi = []\n",
        "  xi_masked = []\n",
        "  xi_cropped = []\n",
        "  # for i in tqdm(range(int(len(img)))):\n",
        "  for i in range(int(len(img))):\n",
        "    image_repr = cv2.cvtColor(img[i], cv2.COLOR_RGB2GRAY)\n",
        "    X.append(image_repr)\n",
        "    contrast = image_repr.std()\n",
        "    sift = cv2.xfeatures2d.SIFT_create(nfeatures=150, nOctaveLayers=20, sigma=0.8, contrastThreshold=0.001*contrast) #\n",
        "    keypoints_1, descriptors_1 = sift.detectAndCompute(image_repr,None)\n",
        "    kp = np.array([i.pt for i in keypoints_1]).astype(int)\n",
        "    for j in kp:\n",
        "      x_mask[i, j[1],j[0]] = 255\n",
        "    x_mask[i] = nd.binary_closing(x_mask[i]).astype(int)\n",
        "    x_masked[i] = (x_mask[i])*image_repr\n",
        "    if (x_mask[i].max(axis=1).max()==1 and x_mask[i].max(axis=0).max()==1):\n",
        "      sl = np.where(x_mask[i] == 1)[0].min()\n",
        "      #print(sl)\n",
        "      el = np.where(x_mask[i] == 1)[0].max()\n",
        "      #print(el)\n",
        "      sc = np.where(x_mask[i] == 1)[1].min()\n",
        "      #print(sc)\n",
        "      ec = np.where(x_mask[i] == 1)[1].max()\n",
        "      #print(ec)\n",
        "\n",
        "      if (sl!=el and sc!=ec):\n",
        "        x_cropped.append(cv2.resize(image_repr[sl:el,sc:ec],dsize=(32,32)))\n",
        "      else:\n",
        "        x_cropped.append(image_repr)\n",
        "    else:\n",
        "      x_cropped.append(image_repr)\n",
        "\n",
        "  # for i in tqdm(range(len(image))):\n",
        "  for i in range(len(image)):\n",
        "    Xi.append(cv2.resize(X[i], dsize=(299,299)))\n",
        "    xi_masked.append(cv2.resize(x_masked[i], dsize=(299,299)))\n",
        "    xi_cropped.append(cv2.resize(x_cropped[i], dsize=(299,299)))\n",
        "  # X=np.array(X)\n",
        "  # x_masked=np.array(x_masked)\n",
        "  # x_cropped=np.array(x_cropped)\n",
        "  Xtr = np.stack([Xi, xi_masked, xi_cropped], axis=-1)\n",
        "\n",
        "  return Xtr\n",
        "\n"
      ],
      "metadata": {
        "id": "ON4xN78VxxZT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Experiment:\n",
        "\n",
        "    def __init__(self,model=None):\n",
        "        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "        self.use_cuda = torch.cuda.is_available()\n",
        "        self.model = model.cuda()\n",
        "        self.optimizer = None\n",
        "        self.scheduler = None\n",
        "        \n",
        "\n",
        "        self.criterion = torch.nn.CrossEntropyLoss().cuda() if self.use_cuda else torch.nn.MSELoss()\n",
        "        self.train_losses = []\n",
        "        self.train_accs = []\n",
        "        self.train_f1_log = []\n",
        "        self.val_losses = []\n",
        "        self.val_accs = []\n",
        "        self.val_f1_log = []\n",
        "        self.start_epoch = 1\n",
        "\n",
        "    def train(self,train_data_loader,\n",
        "              val_data_loader,\n",
        "              save_dir,\n",
        "              model_name,\n",
        "              num_epochs=75, \n",
        "              learning_rate=0.001,\n",
        "              validate_each_epoch=False,\n",
        "              verbose=False):\n",
        "       \n",
        "\n",
        "        self.model.train()\n",
        "\n",
        "        self.optimizer = torch.optim.Adam(self.model.parameters(), lr=learning_rate, weight_decay=1e-5)\n",
        "        self.scheduler = ReduceLROnPlateau(self.optimizer, mode='min', factor=0.1, patience=1, verbose=True)\n",
        "        \n",
        "        best_val_f1 = 0.4\n",
        "\n",
        "        for epoch in range(self.start_epoch, num_epochs + 1):\n",
        "\n",
        "            print('Epoch {}/{}'.format(epoch, num_epochs))\n",
        "\n",
        "            epoch_losses = []\n",
        "            epoch_accs = []\n",
        "            epoch_f1 = []\n",
        "            i = 0\n",
        "            if verbose :\n",
        "              pbar = tqdm(train_data_loader)\n",
        "            else :\n",
        "              pbar = train_data_loader\n",
        "            for data in pbar:\n",
        "\n",
        "                images,labels  = data\n",
        "                images=torch.from_numpy(feature_xtr(images).transpose(0,3,1,2))             #################################### added\n",
        "                images = images.float()\n",
        "                labels = labels.to(self.device)\n",
        "                images = images.to(self.device)\n",
        "                \n",
        "\n",
        "                self.optimizer.zero_grad()\n",
        "\n",
        "                outputs = self.model.forward(images)\n",
        "\n",
        "                loss = self.criterion(outputs, labels)\n",
        "                loss.backward()\n",
        "\n",
        "                self.optimizer.step()\n",
        "\n",
        "                epoch_losses.append(loss.data.cpu())\n",
        "                \n",
        "                batch_accuracy = accuracy(outputs, labels)\n",
        "\n",
        "                epoch_accs.append(batch_accuracy) \n",
        "                batch_f1 = f1_score_(outputs, labels)\n",
        "                epoch_f1.append(batch_f1) \n",
        "\n",
        "                if verbose:\n",
        "                  pbar.set_postfix({\"Batch loss \":loss.data.cpu().numpy(),' Epoch loss ': np.mean(epoch_losses),\" Batch Acc \":batch_accuracy.numpy(),\" Epoch Acc \":np.mean(epoch_accs),\n",
        "                                    \" Batch f1 \":batch_f1,\"  Avg Epoch f1 \":np.mean(epoch_f1)})   \n",
        "\n",
        "            self.train_losses.append(np.mean(epoch_losses))\n",
        "            self.train_accs.append(np.mean(epoch_accs))\n",
        "            self.train_f1_log.append(np.mean(epoch_f1))\n",
        "\n",
        "\n",
        "\n",
        "            if validate_each_epoch:\n",
        "                val_loss,val_acc,val_f1= self.validate(val_data_loader)\n",
        "                self.val_losses.append(val_loss)\n",
        "                self.val_accs.append(val_acc) \n",
        "                self.val_f1_log.append(val_f1) \n",
        "                if verbose:  \n",
        "                  print(\"Val loss : \",val_loss,' Val Acc : ',val_acc,\" Val f1 : \",val_f1)   \n",
        "            \n",
        "            #Lr scheduler\n",
        "            self.scheduler.step(np.mean(epoch_losses))\n",
        "\n",
        "\n",
        "            if val_f1 > best_val_f1 :\n",
        "              self.save_parameters(model_name+\" \"+str(val_f1.round(2)), save_dir,epoch)\n",
        "              best_val_f1 = val_f1\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "    def validate(self,data_loader):\n",
        "        \n",
        "        self.model.eval()\n",
        "        losses = []\n",
        "        predictions = []\n",
        "        y_val = []\n",
        "        with torch.no_grad():\n",
        "            for data in tqdm(data_loader):                \n",
        "                images,labels  = data\n",
        "                images=torch.from_numpy(feature_xtr(images).transpose(0,3,1,2))             #################################### added\n",
        "                images = images.float()\n",
        "                labels = labels.to(self.device)\n",
        "                images = images.to(self.device)\n",
        "                \n",
        "                outputs = self.model.forward(images)\n",
        "                loss = self.criterion(outputs, labels)\n",
        "                losses.append(loss.data.cpu())\n",
        "                _, outputs = torch.max(outputs, dim=1)\n",
        "                predictions.extend(list(outputs.view(-1).cpu().numpy()))\n",
        "                y_val.extend(list(labels.view(-1).cpu().numpy()))\n",
        "\n",
        "        acc = accuracy_score(y_val, predictions)\n",
        "        f1 = f1_score(y_val, predictions,average='micro')\n",
        "\n",
        "        self.model.train()\n",
        "        \n",
        "        return np.mean(losses),acc,f1\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "    def predict_loader(self,data_loader):\n",
        "        self.model.eval()\n",
        "        predictions = []\n",
        "        y_true=[]\n",
        "\n",
        "        with torch.no_grad():\n",
        "            for data in tqdm(data_loader):                \n",
        "                images,labels  = data\n",
        "                images=torch.from_numpy(feature_xtr(images).transpose(0,3,1,2))             #################################### added\n",
        "                images = images.float()\n",
        "                images = images.to(self.device)\n",
        "                \n",
        "                outputs = self.model.forward(images)\n",
        "                _, outputs = torch.max(outputs, dim=1)\n",
        "                predictions.extend(list(outputs.view(-1).cpu().numpy()))\n",
        "                y_true.extend(list(labels.view(-1).cpu().numpy()))\n",
        "\n",
        "        self.model.train()\n",
        "        return predictions,y_true\n",
        "    \n",
        "    def save_parameters(self,model_name, directory,epoch):\n",
        "        \"\"\"Saves the parameters of the network to the specified directory.\n",
        "\n",
        "        Parameters\n",
        "        ----------\n",
        "        epoch : int\n",
        "            The current epoch\n",
        "        directory : str\n",
        "            The directory to which the parameters will be saved\n",
        "        \"\"\"\n",
        "        if not os.path.exists(directory):\n",
        "            os.makedirs(directory)\n",
        "        torch.save({\n",
        "            'epoch': epoch,\n",
        "            'model_state_dict': self.model.state_dict(),\n",
        "            'optimizer_state_dict': self.optimizer.state_dict(),\n",
        "            'train_losses': self.train_losses,\n",
        "            'val_losses': self.val_losses\n",
        "        }, os.path.join(directory, model_name +'.pth'))\n",
        "\n",
        "    def load_parameters(self, path):\n",
        "        \"\"\"Loads the given set of parameters.\n",
        "\n",
        "        Parameters\n",
        "        ----------\n",
        "        path : str\n",
        "            The file path pointing to the file containing the parameters\n",
        "        \"\"\"\n",
        "        self.optimizer = torch.optim.Adam(self.model.parameters())\n",
        "        checkpoint = torch.load(path, map_location=self.device)\n",
        "        self.model.load_state_dict(checkpoint['model_state_dict'])\n",
        "        self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n",
        "        self.train_losses = checkpoint['train_losses']\n",
        "        self.val_losses = checkpoint['val_losses']\n",
        "        self.start_epoch = checkpoint['epoch']\n",
        "\n",
        "    def plot(self):\n",
        "\n",
        "      fig, axes = plt.subplots(3, 1, figsize = (10,10))\n",
        "\n",
        "      train_losses = np.array(self.train_losses)\n",
        "      val_losses  = np.array(self.val_losses)\n",
        "      axes[0].plot(train_losses, label = \"training loss\")\n",
        "      axes[0].plot(val_losses, label = \"val loss\")\n",
        "      axes[0].set_title('loss variation during training', fontsize = 8)\n",
        "      axes[0].legend()\n",
        "\n",
        "\n",
        "      train__accs = np.array(self.train_accs)\n",
        "      val_accs  = np.array(self.val_accs)\n",
        "      axes[1].plot(train__accs, label = \"training accuracy\")\n",
        "      axes[1].plot(val_accs, label = \"val accuracy\")\n",
        "      axes[1].set_title('accuracy variation during training', fontsize = 8)\n",
        "      axes[1].legend()\n",
        "\n",
        "\n",
        "\n",
        "      train_f1_log = np.array(self.train_f1_log)\n",
        "      val_f1_log  = np.array(self.val_f1_log)\n",
        "      axes[2].plot(train_f1_log, label = \"training f1 score\")\n",
        "      axes[2].plot(val_f1_log, label = \"val f1 score\")\n",
        "      axes[2].set_title('f1 score variation during training', fontsize = 8)\n",
        "      axes[2].legend()\n",
        "\n",
        "      plt.show()\n",
        "\n",
        "\n",
        "\n",
        "      \n",
        "def accuracy(outputs, labels):\n",
        "    _, preds = torch.max(outputs, dim=1)\n",
        "    return torch.tensor(torch.sum(preds == labels).item() / len(preds))\n",
        "\n",
        "\n",
        "from sklearn.metrics import f1_score,accuracy_score\n",
        "\n",
        "def f1_score_(outputs, labels):\n",
        "    _, preds = torch.max(outputs, dim=1)\n",
        "    return f1_score(preds.detach().cpu().numpy(), labels.cpu().numpy(), average='micro')"
      ],
      "metadata": {
        "id": "HsCnYkjn1yvm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#https://towardsdatascience.com/review-xception-with-depthwise-separable-convolution-better-than-inception-v3-image-dc967dd42568\n",
        "\n",
        "class SeparableConv(nn.Module):\n",
        "  \"\"\"\n",
        "  Modified and Xception-adapted version of the depthwise seperable convolution.\n",
        "  It's basically a depthwise spacial convolution followed by a pointwise convolution.\n",
        "  For a resulting outpout that we will call the SeperableConv.\n",
        "  \"\"\"\n",
        "  def __init__(self, inputs, outputs,**kwargs):\n",
        "        super().__init__()\n",
        "        #majuscule utiliser pour le ouput de notre architecture a la fin qui est plus regulier\n",
        "        #first depthwise spacial convolution\n",
        "        self.DepthwiseConv = nn.Conv2d(\n",
        "            inputs, \n",
        "            inputs, \n",
        "            3, \n",
        "            padding=1, \n",
        "            groups=inputs, \n",
        "            bias=False,\n",
        "            **kwargs)\n",
        "        \n",
        "        #pointwise convolution\n",
        "        self.PointwiseConv = nn.Conv2d(\n",
        "            inputs, \n",
        "            outputs, \n",
        "            1, \n",
        "            bias=False,\n",
        "            **kwargs)\n",
        "\n",
        "  def forward(self, x):\n",
        "        depthwise = self.DepthwiseConv(x)\n",
        "        outpout = self.PointwiseConv(depthwise)\n",
        "        return outpout"
      ],
      "metadata": {
        "id": "0ioJOHhRZila"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#some of the variables names will have a  UpperCase on the variables first letters to have a final architecture that will follow the same etiquettes as in torch.nn\n",
        "\n",
        "class Entryflow(nn.Module):\n",
        "    \"\"\"\n",
        "    This first flow will containt 4 blocs with a total of 2 convolutional layers,\n",
        "    6 seperable convolutional layers and 3 skip-connections (shortcuts). ALl the activations will be Relu.\n",
        "    Other than bloc1, all other 3 will have a MaxPooling Layer.\n",
        "    \"\"\"\n",
        "    def __init__(self, input):\n",
        "\n",
        "        super().__init__()\n",
        "        #first bloc with the conv32 &conv 64 (see picture above of architecture)\n",
        "        self.bloc1 = nn.Sequential(\n",
        "            nn.Conv2d(input, 32, 3, 2, 1, bias=False),\n",
        "            nn.BatchNorm2d(32),\n",
        "            nn.ReLU(inplace=True),\n",
        "            \n",
        "            nn.Conv2d(32, 64, 3, 1, 1),\n",
        "            nn.BatchNorm2d(64),\n",
        "            nn.ReLU(inplace=True)\n",
        "        ) \n",
        "\n",
        "        #seperableconv 128 followed by another seperableconv 138\n",
        "        self.bloc2 = nn.Sequential(\n",
        "\n",
        "            SeparableConv(64, 128),\n",
        "            nn.BatchNorm2d(128),\n",
        "            nn.ReLU(inplace=True),\n",
        "            \n",
        "\n",
        "            SeparableConv(128, 128),\n",
        "            nn.BatchNorm2d(128),\n",
        "            nn.MaxPool2d(3, 2, 1)\n",
        "        )\n",
        "        #skip connection conv 1x1\n",
        "        self.SkipConnection1 = nn.Conv2d(64, 128, 1, 2, 0)\n",
        "\n",
        "        #seperable conv 256 (x2)\n",
        "        self.bloc3 = nn.Sequential(\n",
        "            nn.ReLU(inplace=True),\n",
        "            \n",
        "            SeparableConv(128, 256),\n",
        "            nn.BatchNorm2d(256),\n",
        "            \n",
        "            nn.ReLU(inplace=True),\n",
        "\n",
        "            SeparableConv(256, 256),\n",
        "            nn.BatchNorm2d(256),\n",
        "            \n",
        "            nn.MaxPool2d(3, 2, 1)\n",
        "        )\n",
        "        #skip connection conv 1x1\n",
        "        self.SkipConnection2 = nn.Conv2d(128, 256, 1, 2, 0)\n",
        "        \n",
        "        #separableconv 728 (x2))\n",
        "        self.bloc4 = nn.Sequential(\n",
        "            nn.ReLU(inplace=True),\n",
        "\n",
        "            SeparableConv(256, 728),            \n",
        "            nn.BatchNorm2d(728),\n",
        "            \n",
        "            nn.ReLU(inplace=True),\n",
        "\n",
        "            SeparableConv(728, 728),\n",
        "            nn.BatchNorm2d(728),\n",
        "            \n",
        "            nn.MaxPool2d(3, 2, 1)\n",
        "        )\n",
        "        #skip connection conv 1x1\n",
        "        self.SkipConnection3 = nn.Conv2d(256, 728, 1, 2, 0)\n",
        "\n",
        "    def forward(self, x):\n",
        "      #do the 4 blocs +residuals :\n",
        "\n",
        "        bloc1 = self.bloc1(x)\n",
        "        bloc2_res = self.bloc2(bloc1) + self.SkipConnection1(bloc1)\n",
        "\n",
        "        bloc3_res= self.bloc3(bloc2_res) + self.SkipConnection2(bloc2_res)\n",
        "        out = self.bloc4(bloc3_res) + self.SkipConnection3(bloc3_res)\n",
        "        return out\n",
        "    "
      ],
      "metadata": {
        "id": "eKQ6Pzq8ZpR3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Middleflow(nn.Module):\n",
        "    \"\"\"\n",
        "    This 2nd flow (the easiest one to understand) is a 3 times repetition of a separable conv with a relu activation\n",
        "    \"\"\"\n",
        "    def __init__(self):\n",
        "\n",
        "        super().__init__()\n",
        "        # 3 times a separable conv with relu activation\n",
        "        self.bloc8 = nn.Sequential(\n",
        "            \n",
        "            nn.ReLU(inplace=True),\n",
        "\n",
        "            SeparableConv(728, 728),\n",
        "            nn.BatchNorm2d(728),\n",
        "            \n",
        "            nn.ReLU(inplace=True),\n",
        "\n",
        "            SeparableConv(728, 728),\n",
        "            nn.BatchNorm2d(728),            \n",
        "                        \n",
        "            nn.ReLU(inplace=True),\n",
        "\n",
        "            SeparableConv(728, 728),\n",
        "            nn.BatchNorm2d(728)\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "\n",
        "        #we repeat the bloc 8 times like it is displayed in the picture of the architecture\n",
        "        for i in range(8):\n",
        "          x = self.bloc8(x) + x\n",
        "        out=x\n",
        "        return out\n"
      ],
      "metadata": {
        "id": "_EDglEchZrLo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Exitflow(nn.Module):\n",
        "    \"\"\"\n",
        "    This is the 3rd and final flow of the Xception architecture. It will have 2 blocs, one with 2 seperable conv with an addition of a skip-connection.\n",
        "    Anothr bloc will contain 2 seperable conv and a global average pool.\n",
        "    Finally we will have a fully connected  layer form a 2048-dimensional vectors + logicstic regression.\n",
        "    \"\"\"\n",
        "    def __init__(self, classes):\n",
        "        super().__init__()\n",
        "\n",
        "        # BLoc 1 :two seperableconv : 728 followed by a 1024\n",
        "        self.bloc1 = nn.Sequential(\n",
        "            nn.ReLU(inplace=True),\n",
        "\n",
        "            SeparableConv(728, 728),\n",
        "            nn.BatchNorm2d(728),\n",
        "            \n",
        "            nn.ReLU(inplace=True),\n",
        "\n",
        "            SeparableConv(728, 1024),\n",
        "            nn.BatchNorm2d(1024),\n",
        "            \n",
        "            nn.MaxPool2d(3, 2, 1)\n",
        "        )\n",
        "\n",
        "        #skip-connection of conv from bloc 1\n",
        "        self.SkipConnection = nn.Conv2d(728, 1024, 1, 2, 0)\n",
        "        \n",
        "        #2nd bloc with only 2 seperableconv of 1536 and 2048.\n",
        "        self.bloc2 = nn.Sequential(\n",
        "\n",
        "            SeparableConv(1024, 1536),\n",
        "            nn.BatchNorm2d(1536),\n",
        "            nn.ReLU(inplace=True),\n",
        "            \n",
        "\n",
        "            SeparableConv(1536, 2048),\n",
        "            nn.BatchNorm2d(2048),\n",
        "            nn.ReLU(inplace=True)\n",
        "        )\n",
        "\n",
        "        #majuscule pour larchitecture finale du fullyconnected layer\n",
        "\n",
        "        #fully connected last layer\n",
        "        self.FullyConnectedLayer = nn.Linear(2048, classes)\n",
        "    def forward(self, x):\n",
        "        x = self.bloc1(x) + self.SkipConnection(x)\n",
        "        x = self.bloc2(x)\n",
        "        #avg pool\n",
        "        avg_pool = fonctional.adaptive_avg_pool2d(x, output_size=(1, 1))                \n",
        "        global_avg_pool = avg_pool.view(avg_pool.size(0), -1)\n",
        "\n",
        "        out = self.FullyConnectedLayer(global_avg_pool)\n",
        "        \n",
        "        return out  "
      ],
      "metadata": {
        "id": "mddmAoqdZt2R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Xception(nn.Module):\n",
        "    def __init__(self, input, classes):\n",
        "      super().__init__()\n",
        "      #merging all 3 of the flows into creating the overall architecture of the Xception model\n",
        "      self.entry=Entryflow(input)\n",
        "      self.middle=Middleflow()\n",
        "      self.exit=Exitflow(classes)\n",
        "      \n",
        "      # add logistic regression ???? \n",
        "\n",
        "    def forward(self,x):\n",
        "\n",
        "      entry_flow= self.entry(x)\n",
        "      middle_flow=self.middle(entry_flow)\n",
        "      out=self.exit(middle_flow)\n",
        "\n",
        "      return out"
      ],
      "metadata": {
        "id": "gWPC18nFZzbZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "net = Xception(3, 10)\n",
        "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
        "net.to(device)\n",
        "\n",
        "\n",
        "exp_2 = Experiment(net)\n",
        "\n",
        "for param in exp_2.model.parameters():\n",
        "    param.requires_grad = True"
      ],
      "metadata": {
        "id": "69KRCqizZIAu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_loader, validation_loader, test_loader=get_cifar10_dataloaders()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CwDxCDoKZw6z",
        "outputId": "7c0cfb6a-7ee6-425b-c33e-1000cd0665c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# %%timeit\n",
        "exp_2.train(train_data_loader=train_loader,\n",
        "                        val_data_loader=validation_loader,\n",
        "                        save_dir=\"/content/\",\n",
        "                        model_name = 'model_xception_',\n",
        "                        num_epochs=10,\n",
        "                        learning_rate=0.001, \n",
        "                        validate_each_epoch=True,\n",
        "                        verbose=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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          "text": [
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            "Val loss :  1.2733103  Val Acc :  0.5534  Val f1 :  0.5534\n",
            "Epoch 2/10\n"
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            "Val loss :  1.0486985  Val Acc :  0.632  Val f1 :  0.632\n",
            "Epoch 3/10\n"
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            "Epoch 4/10\n"
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            "Val loss :  0.7179418  Val Acc :  0.7462  Val f1 :  0.7461999999999999\n",
            "Epoch 5/10\n"
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            "Val loss :  0.72697425  Val Acc :  0.7584  Val f1 :  0.7584000000000001\n",
            "Epoch 6/10\n"
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            "Val loss :  0.62663734  Val Acc :  0.7874  Val f1 :  0.7874\n",
            "Epoch 7/10\n"
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            "Val loss :  0.5803231  Val Acc :  0.7992  Val f1 :  0.7992\n",
            "Epoch 8/10\n"
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            "Val loss :  0.5835175  Val Acc :  0.7986  Val f1 :  0.7986\n",
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            "Val loss :  0.5799857  Val Acc :  0.8056  Val f1 :  0.8056000000000001\n",
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9ZkCE5bnVdR7",
        "outputId": "a1318f6f-7e8d-498a-cd00-59300377a1fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 3 Axes>"
            ],
            "image/png": 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/fnr27HnYXS5evJiZM2fi5+dHjx49OOWUU6oX47z66qspKyvjggsuYNSoUQwYMIDU1FRuuukmpk2bxllnndWan75NtHjOlDEmAbgQeLLl5XQ8cyYPYOa4Pjz+5VbeTNnl63JERETa3MUXX8zbb7/NG2+8wYwZMwB45ZVXSE9PZ8WKFaxevZoePXpQXNyyS7FNnjyZhQsXkpCQwKxZs3jppZeIiYlhzZo1TJkyhaeeeoprr722NT5Sm2qNpREeBm6z1lYaYxptaIyZA8wB6Nu3c6zjZIzhvvOHsSuriN+9u47EmBBOGhjv67JERORo0MgIUluaMWMGs2fPJiMjg6+++gqA3NxcunfvTkBAAF9++SU7duxocn+TJk3i6aef5sorryQrK4uFCxfy4IMPsmPHDhITE5k9ezYlJSWsXLmSc845h8DAQC666CKOO+44Lr/88rb6mK2mNcJUMvC6G6TigXOMMeXW2vfrNrTWzgXmAiQnJ3eaSUgBfh4ev+wEpj/5Ddf/ayXv3nASA7sdHad7iojI0Wfo0KEcPHiQhIQEevXqBcBll13Geeedx/Dhw0lOTub445s+9eXCCy9kyZIljBw5EmMMf/3rX+nZsycvvvgiDz74IAEBAYSHh/PSSy+xe/durrrqKiorKwG4//772+QztibTlMlj7pyp/1hrhx2m3Qtuu7cP12dycrJNSUlpWpUdxK6sQi54/GvCg/1574aJxIYF+rokERHpYjZt2sTgwYN9XcZRrb7fwBizwlqbXF/7piyN8BqwBDjOGJNmjLnGGPNzY8zPW6XiTqRPbCjPXJnM3txi5mjJBBEREaFpZ/PNbGpn1tpZLaqmEzihbwx/v2QkN766itveWcvDM0ZxuLliIiIi0nXp2nzNcO6I3uzILOTBT74nKS6MX515rK9LEhERER9RmGqmG6YMZFtGAY98sYWk+FAuHJ3o65JERKSLsNbqqIePNHUhUm+6Nl8zGWP4vwuHM35ALLe9vY5vt2X5uiQREekCgoODyczMbNYfdWkZay2ZmZkEBwcf0fuadDZfW+iMZ/PVJ6ewlJ888Q3ZhaW8d8NEkuLDfF2SiIh0YmVlZaSlpbV4QUxpnuDgYBITEwkICKj1emNn8ylMtYLtGQVc+MTXxIQG8u4NJxEdqiUTREREupIWLY0gh5cUH8bcnyWTll3EdS+voLS80tcliYiISDtRmGolY5Ni+ev0ESzblsUd767TsW4REZGjhM7ma0UXjE5ge2YBD3++hf7xodx42iBflyQiIiJtTGGqld18+iB2ZBby0Keb6RcXxnkje/u6JBEREWlDOszXyowxPHDRcMYmxXDLW2tYsSPb1yWJiIhIG+q6YaqyEpY8AcW57b7rIH8/nr4imV5Rwcx5KYWdmYXtXoOIiIi0j64bpnYthU/ugEdGwtePQFlRu+4+NiyQ52aNpbzSctUL35JbVNau+xcREZH20XXDVL+TYM5XkDAGPrsLHh0NKc9BRfuFmoHdwnnq8jHszCrkhldWUFahJRNERES6mq4bpgB6j4LL34FZH0F0X/jPr+DxcbDubecwYDuYMDCO+38ygq9/yOQP76/XkgkiIiJdTNcOU1WSJsLVn8DMN8A/BN65Bp6eDJs/gXYIN9PHJHLjqcfw+vJdPL0wtc33JyIiIu3n6AhTAMbAcVPh54vhJ89A6UF49RJ4/mzY8U2b7/7XZx7LuSN68cDH3/Hxur1tvj8RERFpH0dPmKri8cCIS+DGFJj2d8ja5gSqVy6GvWvbcLeGhy4eyei+0fy/N1azeldOm+1LRERE2s9hw5Qx5jljzAFjzPoGtl9mjFlrjFlnjPnGGDOy9ctsA34BMPYa+OUqOOMe2PUtPD0J3r4aMre2yS6DA/x45mfJdIsI4toXU0jL1pIJIiIinV1TRqZeAKY2sn0bcIq1djjwR2BuK9TVfgJD4eRfwc1rYNIt8P3H8NhY+PBmyNvT6ruLDw/i+VljKSmv4JoXUsgr1pIJIiIindlhw5S1diGQ1cj2b6y1Vct8LwUSW6m29hUSDaffBb9c7YxYrXrFWU7h0zuhsMGP3yyDekTw5GVj2Jqez42vrqJcSyaIiIh0Wq09Z+oa4ONW7rN9RfSAcx6Em1Jg6IXwzWPOwp9f/RVK8lttNycPiuePFwxj4eZ07vlwg5ZMEBER6aRaLUwZY07FCVO3NdJmjjEmxRiTkp6e3lq7bhsxSXDhU3DDEug/Gb78sxOqlj4J5SWtsouZ4/py3eQB/GvpTuYt3tYqfYqIiEj7apUwZYwZATwLnG+tzWyonbV2rrU22Vqb3K1bt9bYddvrPhh++gpc+wX0GALzb4d/joFV/4KK8hZ3f9vU45k6tCd//mgTn27Y1woFi4iISHtqcZgyxvQF3gWusNZubnlJHVRiMlz5IVzxPoTFwwe/gCcnwMYPWrTwp8dj+MeMUYxIiOLm11ezfnf7X5hZREREmq8pSyO8BiwBjjPGpBljrjHG/NwY83O3yV1AHPCEMWa1MSalDev1vYGnwuwv4ZKXAQNv/gyeORW2/q/ZoSok0I9nrkwmNiyQa15czt7c9r0os4iIiDSf8dXE5+TkZJuS0slzV2UFrHkdFtwPubsgaZKzZlVicrO6+25fHtOfXEKf2FDe+vkEwoP8W7VcERERaR5jzAprbb1/4I++FdBbk8cPRl8GN62AqX+BA5vg2dPhtUudx0fo+J6RPHbpaDbvP8gvX1tFRaXO8BMREenoFKZag38QjP+5s/DnqXfC9kXwxAR49zrI3n5EXU05rjv3/Hgo//vuAH/8z8a2qVdERERajcJUawoKh1NudULVSTfBxvfhn8nw39/Awf1N7uaK8f24emJ/XvhmOy9+s73t6hUREZEWU5hqC6GxcNYfnev+jb4cUp6DR0fBF/dBUdMucPz7aYM5Y3AP7v1wA19+d6CNCxYREZHmUphqS5G94byH4cblcNw5sOhvzsKfi/8BpY1f5NjPY3jkp6MY3CuSG19dycY9ee1UtIiIiBwJhan2EDcQps+Dny+GPifC5/c4I1XLn4Xy0gbfFhbkz7wrxxIRHMA1Ly5nf15x+9UsIiIiTaIw1Z56DofL3oSr5kPsAPjvLfD4WFj7JlTWf7HjnlHBzJuVTG5RGde8uJzC0pavui4iIiKtR2HKF/pNgKs+hkvfgsAIeHc2PHUyfP9xvQt/Du0dxT9njmbjnjxufn21lkwQERHpQBSmfMUYOPYsuG4hXDQPyovgtZ/Ccz+C7YsPaX764B7cOW0In23czwMfH/kaViIiItI2FKZ8zeOB4dPhF9/CuQ9Dzk54YRq8/BPYs7pW06smJvGzCf14ZtE2Xlm2w0cFi4iIiDeFqY7CLwCSr3KWUzjzj7BnJcw9Bd68EjK2AGCM4a5zhzDluG7c9cEGFm5O93HRIiIiojDV0QSEwMRfOgt/Tv4tbPkMHj8RPrgRctPw9/Pw2KUnMKh7OL94ZSXrd+f6umIREZGjmi503NHlpzvrU6XMAwyMvRYm/Zo9ZWFc8PjXZOSXcPbwXlw3eQAjEqN9Xa2IiEiX1NiFjhWmOoucnbDgL7DmVQgIhQk3kj78Wp5LyeRfS3dwsLickwbGcd0pA5k8KB5jjK8rFhER6TIUprqS9O/hf3+CTf+G0DiYdAsHh/+M11emM2/xNvblFXN8zwh+fspApo3oRYCfjuSKiIi0lMJUV7R7pXOtv9QvIaoPTLmD0qGX8O91+3n6q61sOZBPQnQI15zcnxlj+xAW5O/rikVERDothamuLHWBc3maPaug22A4/S4qB03ly83pPP1VKt9uzyIqJICfTejHlSclER8e5OuKRUREOh2Fqa7OWtj4PnzxR8jaCn3Gwxn3QL8JrNiRzdyFW/l0434C/TxMH5PI7EkDSIoP83XVIiIinUaLwpQx5jngXOCAtXZYPdsN8AhwDlAIzLLWrjxcUQpTbaCiDFa97ExUz98Hx06F0++CHkPZmp7Ps4tSeWfFbsoqKzl7WE+umzyQkX10BqCIiMjhtDRMTQbygZcaCFPnADfhhKkTgUestSceriiFqTZUWgjLnoLFD0NJHoz8KZz6O4juy4GDxbzw9XZeds8AHD8glutOGciUY7vpDEAREZEGtPgwnzEmCfhPA2HqaWCBtfY19/n3wBRr7d7G+lSYageFWbD4H/DtXLCVkHwNTP4NhMWTX1LO69/uZN7ibezNdc4AnDN5AOeN7K0zAEVEROpoLEy1xl/NBGCX1/M09zXxtdBYOOuPcNNKZ3Tq26fhkVGw4C+EU8y1kwbw1a2n8reLR1JpLb9+cw2n/PVLnl2USn5Jua+rFxER6RTadQjCGDPHGJNijElJT9d15dpNVAL8+J9wwzIYOAUW/B88OgqWzSWQci4ak8gn/28yz88aS5/YUP70302cdP8XPPjJd6QfLPF19SIiIh2aDvMdjdJSnOUUti+CmCQ49U4YdhF4nGy9amc2cxemMn/DPgL8PFx0QiKzJ/VnQLdwn5YtIiLiK209Z2oacCM1E9AftdaOO1yfClM+Zi388IUTqvavgx7D4Yy74ZgzwJ2Ivi2jgGcWpfL2ijTKKir50ZCeXHfKAEb3jfFt7SIiIu2spWfzvQZMAeKB/cDdQACAtfYpd2mEx4CpOEsjXGWtPWxKUpjqICorYf078OWfIHs79DvZWaOqz9jqJukHS3jxm+28tGQ7ecXljOsfy89PGcCUY7vj8egMQBER6fq0aKccXnkprHwRvvoLFKTD8ec6a1R1O666SX5JOW8s38W8RansyS3m2B7hzJk8kB+P7E2gv84AFBGRrkthSpquJB+WPgFfPwplBTDqMphyO0QlVjcpq6jkP2v38PRXqXy37yA9I4O55uT+/HRcHyKCA3xYvIiISNtQmJIjV5ABi/4Gy58FDJw4B07+tbPcgstay1fuNQCXpGYSEezP5eP7cdVJSXSPDPZd7SIiIq1MYUqaL2cnfHk/rHkNgiLh5JvhxOshMLRWszW7cpi7MJWP1+/F3+PhJyckMHvyAAbqDEAREekCFKak5fZvhC/ug80fQ3hPmHIbjL4C/Gof1tueUcCzi1N5KyWN0opKzhzcg+tOGciYfjoDUEREOi+FKWk9O5Y4yynsWgqxA+H0P8CQC6qXU6iSkV/CS99s58UlO8gtKmNsUgzXTR7IacfrDEAREel8FKakdVkLm+fD5/dC+iboNcpZTmHgqYc0LSgp582UXTy7aBu7c4oY1D2cOZMHcP6oBJ0BKCIinYbClLSNygpY+wZ8+X+QuwsGTHFCVe/RhzQtq6jko3V7eeqrVDbtzaNHZBBXT+zPzBP7EqkzAEVEpINTmJK2VV4Cy+fBwgehKAuGXgin/QHiBh7S1FrLoi0ZPL1wK1//kElEkD+Xju/L1RP700NnAIqISAelMCXtozgPvvknLHkcyothzJVwym0Q0bPe5uvScnl64VY+WuecAXjhaOcMwGO66wxAERHpWBSmpH3lH3BGqVKed872G389nPRLCImut/nOzEKeWZTKmym7KCmv5MwhPbhu8gCSk2LrbS8iItLeFKbEN7K2wZd/hnVvQUiMs+jnuDkQUP/hvMz8El5asoMXl2wnp7CMMf1iuHpifyYdG695VSIi4lMKU+Jbe9fCF/fCD59DZAJMuQNGzgQ//3qbF5aW81ZKGs8sSiUtuwiPgWEJUUwYGMeEAXGMTYolLKj+94qIiLQFhSnpGLYtgs/vht0rIP44Z42q4889ZI2qKuUVlXy7PYulWzNZkprJ6l05lFVY/D2GEYlV4SqeMf1iCAn0a+cPIyIiRxOFKek4rIVNHzqrqWdugcSxznIKSScf9q2FpeWs2JHNEjdcrU3LpaLSEujnYVSfaMa7I1ej+0YTHKBwJSIirUdhSjqeinJY86pz3b+De+CYM+GMu6Hn8CZ3kV9SzvLtWU642prJhj25VFoI8vdwQt8YJgyM46SBcYxIjNYCoSIi0iIKU9JxlRXBt3Nh0d+hOBeGXwyn/R5iko64q9yiMr7dllU9crVpbx4AIQF+JCfFVM+5Gp4Qhb+fwpWIiDSdwpR0fEU58PXDsPQpqCyH5Kth8q0Q3q3ZXWYXlLJsW2Z1uNq8P8PD8xEAACAASURBVB+A8CB/xlaHq3iG9I7ET9cLFBGRRrQ4TBljpgKPAH7As9baB+ps7wu8CES7bW631n7UWJ8KU1KvvL3w1QOw8mXwD4Zxs2HQWZAwpsElFZoqI7+Epak14So1vQCAyGB/xvWPqx65Or5nhC7GLCIitbQoTBlj/IDNwJlAGrAcmGmt3ejVZi6wylr7pDFmCPCRtTapsX4VpqRRGT/A//4IGz8ALPgFQkIy9DvJufUZB0ERLdrF/rxilqZm8s0PTrjamVUIQExoACf2j+OkY5xwdUz3cEwDZxyKiMjRobEw1ZTFesYBP1hrU93OXgfOBzZ6tbFApPs4CtjT/HJFgPhj4JIXoTALdi2DHV/Djm9g8T9g0UNg/KDXSDdcTYS+4yH0yFZM7xEZzPmjEjh/VAIAu3OKqiezL03NZP6GfU4p4UGMHxBbPXLVPz5M4UpERKo1ZWRqOjDVWnut+/wK4ERr7Y1ebXoBnwIxQBhwhrV2RWP9amRKmqUkH9K+dYLVjm8gLQUqSpxt3YfWjFz1O6nBawI2hbWWXVlFLEnNqD4suD/P2U+PyCAmDIirnnPVJzZE4UpEpItr6WG+poSpX7t9/c0YMwGYBwyz1lbW6WsOMAegb9++Y3bs2NGCjyUClBXDnpU1I1c7l0GZMxeK2AE1I1f9ToLofg0uEHo41lq2ZRSwJDWTb7Zmsiw1k4z8UgASokMYXxWuBsaREB3SWp9OREQ6iJaGqQnAPdbaH7nP7wCw1t7v1WYDTuDa5T5PBcZbaw801K9GpqRNVJTDvjU1I1c7voHiHGdbZILXyNVEiD+2ReFqy4H8msOC2zLJKSwDoF9cqNfIVRzdI1s2cV5ERHyvpWHKH2cC+unAbpwJ6Jdaazd4tfkYeMNa+4IxZjDwBZBgG+lcYUraRWUlpG9yg5U7epW/39kWGg/9JtSMXPUYBp7mrZxeWWn5bt9BlrhnCy7blsnB4nIABnQLqw5X4wfEER8e1FqfTkRE2klrLI1wDvAwzrIHz1lr/2yMuQ9Isdb+2z2D7xkgHGcy+m+ttZ821qfClPiEtZCVWhOsdnwNOTudbUGRzkT2qpGrXqPAP7BZu6motGzck1c95+rbbVkUlFYAcGyP8OpwdWL/OGLCmrcPERFpP1q0U6QxObtg55Kaw4IZ3zuv+4dAn7E1I1cJyRAY2qxdlFdUsm53bvXIVcr2bIrKnHA1ID6MYQlRjEiMYlhCFEN7RxIRHNBan05ERFqBwpTIkchP9wpXX8O+dYAFTwAknFAzctVnHARHNWsXpeWVrEnLYVlqJmvSclm/O5e9ucWAM42rf3wYwxOiqm9DE6IID2rKSiYiItIWFKZEWqIoB3Z9W3NocM9K55I3xuNcmLlq5KrvBAiLb/Zu0g+WsH53Luuqbmm57MurHbBGJDijVwpYIiLtS2FKpDWVFkLa8pqRq7TlUO6EHrod74SqqoAVldCiXVUFrLVpTsBav7t2wBpQNYKVGO0ErN6RhClgiYi0OoUpkbZUXgp7VnmtdbUUSg8626L71QSrfic5a1+1cIHPAweLnRGstDx3FCunekFRY2Bgt/CaQ4SJUQzp5YOAVVkBFaVQUQYBoeCngCcinZvClEh7qqxw5llVjVztXAKFmc628J6117rqdjx4PIf2YW1NIKksc0JJRZn7vNzrsfN6dn4BO/bnsD09l7SMPPZk5lJQVEygKSeACnpG+NM30p+ESH96RfjRI8yPANx+Kstrgk9FmdtnqbNml/fj6v2VH74uvP5dCQiFxGToe5JztmTiWAgKb5efQkSktShMifiStZCxuWbkavvXcNC9fGVwFASE1R9a2lgZ/lQaf/ALwOMfiF9AIB6/AOei0p4AZzSp+nHVLRA8/l6P69sW6Dyvald1tuT+9WAr3esqjnAOh/Yd79yHd2/zzysi0hIKUyIdibWQs8MJVru+dYKTJ6BOCAmsCSnV2/ybEHQC6ryvdtA5UFjJ+n2FrNldwPo9eazdnUv6QecQocfAMd3Dqye4j0iMYkivKEICm7eQ6SGK85zrKu5c6ty855rFDnTnmk1w7lvhcKiISGtSmBKRBu3PK2ZdmtdZhPUErOEJ0QxPiHTnYLVSwCovhb1rnFGrqltRtrMtrJs7auUeGuw5QvOuRMSnFKZE5IhUBay17hmEa9NyycivCViDukfUWmh0SK/IlgesykrI3OKu8eWGqxz3YugBYc4CqlWHBhPHQmBYCz+liEjTKUyJSItYa9mfV+K1BlYO63bnVQcsP49hkNchwqqzCIMDWhiw8va4o1ZLnft96wHrzrsaWXNosM94CO/W8g8qItIAhSkRaXVVAWttWk6txUYz8kuBmoB1XM8I+sWG0jcujH5xofSNDaV7RBCmOXOiinNh1/Kaw4K7V9TMu4o7xh25ckevNO9KRFqRwpSItAtrLfvcQ4TrdzuHCX84kM+enCIqvf6pCQ7w0Dc21L3VhKy+caEkxoQQ5N/EEa3yEmfeVdX6XjuXQHGOsy28R83Zgn0nQI9hmnclIs2mMCUiPlVaXsmenCJ2ZBWyM7OAnVmF7MgsZGeWcyssrahuawz0igymb1wo/WLD6OsGrarAFR0a2PCOKiudC1V7HxrM2elsCwx35lr1cye1t+DC1SJy9FGYEpEOy1pLRn4pO7O8QpYbtHZkFVafWVglMtiffnFh1SNZ/WJrRrV6RYXg56lzaC93d+1wtX8DzoWr/aHXqNqjV2Fx7ffBRaRTUZgSkU6rsLTcGcGqClheI1pp2YWUVdT8GxbgZ0iMqT2S5TwOo09sCKGB/s6Fq6uurbhzqTPvqsINbPHH1l6SISZJ865EBFCYEpEuqqLSsje3iJ2ZzihWVejakVXAjsxCDhaX12rfLSKo1khWv7hQ+kX50b90C9HpKzC73AVFq+dd9XRCVdWhwR7DwNNKi5iKSKeiMCUiR6WcwtJaI1k73PlaOzML2ZtXjPc/f6GBfs4oVkwwY0L3M8Juon/hOuIyV+B/cLfTKDAC+ozzWu8qGQJCfPPhRKRdtThMGWOmAo8AfsCz1toH6mlzCXAPzhVO11hrL22sT4UpEfGl4rIK0rKL2OWGrB1Zhe5jJ3iVlFdWt000GZwRvp2JgZsZVrGRnsXbMFgqPQHY7kPxi+0HkQkQ2du5RVTd9wL/RibMi0in0aIwZYzxAzYDZwJpwHJgprV2o1ebQcCbwGnW2mxjTHdr7YHG+lWYEpGOqrLSkp5fwo5MJ2jtcifD78h0AldZQRZjPFsY6/me4SaVBE82vUwmIRQf0ld5SDc8UQl4oqrCVi+v4JXgBC6dVSjS4TUWppqy6Mo44Adrbarb2evA+cBGrzazgcettdkAhwtSIiIdmcdj6BEZTI/IYMb1jz1k+8HiMna6I1mbsgr5IqeYvTmF5OZkUZm7m6Ci/fQ0WfQii57lmfTKzyJh3zp6ma+IsPmH9FcRHIMnshfGO2RVjXJFJjgBLChSk+FFOqimhKkEYJfX8zTgxDptjgUwxnyNcyjwHmvt/FapUESkg4kIDmBo7yiG9o6qd3tJeQX7covZk1PM3twiNuYW83lOEXtzi8nMzqYidw8RpQfoSRa9TBY9y7PoVZBFYnoqPUkhxmYf0mdlQBgmKgHjHbYi6oxyhcYqcIn4QGstB+wPDAKmAInAQmPMcGttjncjY8wcYA5A3759W2nXIiIdS5C/H/3iwugX1/DFmAtKytmbW1QduNbnFPNZrhO4DmTnUZG3l+iydCdsmSx6lmfTuySLPpl76GnWE1uZhYfKWn1avyCI7O0Grt71j3KFddMZiSKtrClhajfQx+t5ovuatzRgmbW2DNhmjNmME66Wezey1s4F5oIzZ6q5RYuIdHZhQf4c0z2CY7pH1LvdWktuUVl12NqTW8y6nCI+zS1mT04RB3IKKM/bT1xlBj1MzQhXQmYWfXOz6WW2EluZib8tq92v8cNE9KonbPWqPeLlF9AeX0PbshZsZSM3dzs4I3rG41xE23jquRmN+kmDmhKmlgODjDH9cULUT4G6Z+q9D8wEnjfGxOMc9kttzUJFRI4mxhiiQwOJDg1kSO/IettUVloyCkrYWxW4copZm1vE/Nxi9uYUsTeniLKD6fQgi54ms3qUq09uNn0LcuixbwXxFfMJtLUnzlsMJrx7zZmJ/oGHBpBGg0oD27CHb1Nr+xHugzqvtf6v4gQrT32By9R5XrdN3e3e/dSzrVY/jW33gKeRbcbjBOPovs7Fv6tuQfWHeGmew4Ypa225MeZG4BOc+VDPWWs3GGPuA1Kstf92t51ljNkIVAC3Wmsz27JwEZGjncdj6B4RTPeIYEb2ia63TXlFJfsPlrA3xxnd2ptTxKrcYv7rzuHam1NIaUGOM2G+6pAiWfQryKFvSQ490zcS6KnE4/Hg5+dXffP38+Dv54fH44ep9w+8H5iAxsNEYyGhKrgcLkiYprSrZxterwFgobLiMKHtcNsr6+mjvkBYcZjt3v1U1VXWQD+NBM5K9768GAozav8PI6x7TbCKG1A7aAXXPxdQGqZFO0VEjnLFZRXszytmd05RzShX1ehWbjHpB0vIKiylvj8XwQEe4sODqm/dIgJrPY8PD6RbRBDxEUFEBPljdKjMN0ryIXsbZG6FrFT3tg2ytsLBvbXbhsZ7Ba2B7uP+zn1IjG/q7wC0ArqIiLRIeUUlWYWlpB8sISO/lIyDJWTkV91Kycgvqd6WVVBCZT1/WgL9PXRzA1Z12PIKX90i3PvwICJDFLzaTWkBZG+vE7TcW16dKdIhsbVHsarD1gDnbNIurKXrTImIyFHO389TfUjxcCoqLVkFpV5hq4SMg27gcsPXntxi1u7OJauglIp6klegn4e46tDl3kd4h65AN5gFER0aoODVEoFh0GOoc6urrMgJWlmptcPWziWw7i2ci564gqMbCVpxXXoCv8KUiIi0Kj+PoVuEE3oOp7LSkl1YWj265T3Cle6Ofh04WMLGvXlk5pdSXk/w8vcYr+BVJ3BF1D7kGBMaiMfTdf+ot7qAEOg+2LnVVVYMOTsODVpp38KGd2ufBBAUVXOosG7YCuvW6YOWwpSIiPiMx2OICw8iLjyI42j8DLPKSme5CO8RrqrAVXPYsZTN+w+SkV9CWcWhwcvPY4gNC6w9nys8iJjQQOLCAokJCyQ2LIDYsCBiQwOJCPZX+GpIQDB0O8651VVeAjk7Dw1ae1bBxg+cyfNVAiMaDlrhPTpF0FKYEhGRTsHjMcS4gWdQj8aDl7WWvKJy0qtHug495JiRX0JqegHp+SWUel3Y2pufxxAT6gSsmNBA4tzRrdiwwEOeV92CA7QoKv5BED/IudVVXgq5uw4NWvvWwqYPawetgNDaIcs7bIX3dJaF6AA0AV1ERI5q1lqKyirIzC8lu7CUrALnvuZ5GVkFJWQXlJHltb2hP58hAX7VwSomzB3xCvUa8aoTzKJDA/HT6JejoswraHlPht8K2Tug0msRWv+QmhGt48+FUTPbtDRNQBcREWmAMYbQQH9CY/3pExvapPdUVFryisrILKgJYFW37ILSmtBVUMq2jHyy8kspKK2oty9jICokwAlgoV4BzH1+SDALCyQs0K9rTrr3C6gZfTqmzraKcshL8xrR2uY8ztjihC0fUpgSERE5Qn5ehxybqrisgpzCMjK9R7nyS8gqLHMCmHvblVXI6l05ZBfUP+EenGUmqoJXzRyvAPd5YK1gFhsWSHRoAEH+nfzwo58/xCQ5t4Gn+bqaWhSmRERE2kFwgB89o/zoGXX45SXAOfx4sKSc7IJSZwTMK3BlFdZ+vj4nl8z8EvKKyxvsLzTQzz2sGFDrPiY0wL10Ud3XNQG/qRSmREREOiBjDJHBAUQGB9AvLqxJ7ymrqCSnsKzWnK/MglJyC0vJdl+v2r47p4jswlJyi8oanP/lMdQOWiFO8IpxR8HqC2DRoQFH3SR8hSkREZEuIsDPU7PGV4+mvadq/ld2YSk5RWXkFJaSXVA7eFXd78ktZtPePLILyygqq38OGDiXGaqaXB9TJ3B533tvjwwJ6LQT8RWmREREjmLNmf8FNXPAsgtLDwleOe5IWNX9pn151a83MA2seiJ+TAMjYdFhhwazmNBAQgJ9PwqmMCUiIiJH7EjngIGz8OrB4vLqUTAnfDkjYTl1DkXuzyvm+30HySls+ExIgCB/D1eM78ed5w5pjY/VLApTIiIi0i48HkNUaABRoQFH9L6S8gpyC8u8wlbt4DUyMbqNKm4ahSkRERHp0IL8/ege6Uf3yKaPgrWnjrEOu4iIiEgnpTAlIiIi0gIKUyIiIiItoDAlIiIi0gIKUyIiIiItYGxDa8i39Y6NSQd2tMOu4oGMdtiPtA39fp2ffsPOT79h56bfr3X0s9Z2q2+Dz8JUezHGpFhrk31dhzSPfr/OT79h56ffsHPT79f2dJhPREREpAUUpkRERERa4GgIU3N9XYC0iH6/zk+/Yeen37Bz0+/Xxrr8nCkRERGRtnQ0jEyJiIiItJkuG6aMMVONMd8bY34wxtzu63rkyBhj+hhjvjTGbDTGbDDG3OzrmuTIGWP8jDGrjDH/8XUtcuSMMdHGmLeNMd8ZYzYZYyb4uiY5MsaYX7n/hq43xrxmjOmYVwru5LpkmDLG+AGPA2cDQ4CZxpghvq1KjlA5cIu1dggwHviFfsNO6WZgk6+LkGZ7BJhvrT0eGIl+y07FGJMA/BJIttYOA/yAn/q2qq6pS4YpYBzwg7U21VpbCrwOnO/jmuQIWGv3WmtXuo8P4vwjnuDbquRIGGMSgWnAs76uRY6cMSYKmAzMA7DWllprc3xblTSDPxBijPEHQoE9Pq6nS+qqYSoB2OX1PA39Ie60jDFJwGhgmW8rkSP0MPBboNLXhUiz9AfSgefdQ7XPGmPCfF2UNJ21djfwELAT2AvkWms/9W1VXVNXDVPSRRhjwoF3gP9nrc3zdT3SNMaYc4ED1toVvq5Fms0fOAF40lo7GigANP+0EzHGxOAclekP9AbCjDGX+7aqrqmrhqndQB+v54nua9KJGGMCcILUK9bad31djxyRicCPjTHbcQ6zn2aM+ZdvS5IjlAakWWurRoTfxglX0nmcAWyz1qZba8uAd4GTfFxTl9RVw9RyYJAxpr8xJhBnwt2/fVyTHAFjjMGZq7HJWvt3X9cjR8Zae4e1NtFam4Tz/7//WWv1X8SdiLV2H7DLGHOc+9LpwEYfliRHbicw3hgT6v6bejo6iaBN+Pu6gLZgrS03xtwIfIJz9sJz1toNPi5LjsxE4ApgnTFmtfva76y1H/mwJpGjzU3AK+5/lKYCV/m4HjkC1tplxpi3gZU4Z0ivQquhtwmtgC4iIiLSAl31MJ+IiIhIu1CYEhEREWkBhSkRERGRFlCYEpEOxxjzz0a2zTLGeNzHt7uXzGjp/hY0sd1UY8y0Zu5jijFmwBG0b+w7eNi9bJaIdACagC4i9TLGeKy17b56+eH26wafM6y15a24zwXW2iktqasJ+7gHWGyt/bw1+xUR31OYEunkjDG9gVeAAGCttfYGd+RmLjAIKLTWnm2MmQj8FSgDngRCAH9r7bPuH/oFbpe/du+fBKYCo3BGsS+z1u40xlyAsxJ2EXCP236WtTbbGPMw8HLVyufGmN8Bq6y1HxtjzgMGA5/iXEA3GPjAWvt/xphZOBcmDwd+DzxmrT3ZGHOHW0Mw8HP3M34KrMG55t/pwJ9wLnvyChAJrLbW/tLt8zz3cwJMs17/4Blj5gBXA0uA0dbaKcaYxdbak93tC9zXFgDf4qwg/TnOkjKfAy8DB4Ak4HxrbZox5nmcBYN3Ajuttfe4fQUCm4Fc973r6nze3wE9gBJgurU2r6oWd/8rcK6T95S1dl5VoATuBAa4tW231l5rjBkIvArsc/u/xlq7HRFpMzrMJ9L5ZQBnuiEg0hgzCOcSEgestafgXGwY4H6cP/pTgLca6S/QWvtja+3HwB1uH/cC17kh7ffAqdbaU4FFbl8XudtG1rmEzNvARe7ji9zn3wNTrLUnAmcaY6rCTo61dpq1drXX+x9x938Z8Btr7bfAauB0a+3LXu3mAG9YaycDocaYE93X06y15+BcAWFEVWP3oq/XACcf5ruo8l49i46GAxcDf3c//4lAibX2DPczVnMvuP4CcIu19pZ6Pu8s93O+CcyoZ///cmu9sp5tq9x99jXGRAO/AW4GfgJ0b8JnE5EW6pKLdoocZeKAJ90/pEk4oxTHAt8AeB1CMtbajKrXjDHew9LG6/FKr8e/NcacjjMitAnoBuyw1hZ59fM+zqjQFmChd2HW2s3GmAFuYEq01qYaY4YAfzPGhALHUfMHv77r+F1hjLkM52LJjQ2jDwSqFnRNAY5xH69373cD0V7t493PUW6MOWS/7mrR3uqrbaP7+Xe7++sPrHW3rQYmNFJvdZ/u3KcHjTHDcUbW3qun7XprbZkxpr7DgVWfcQ8QVVWHtbbCGLO+nvYi0so0MiXS+V0KvO+OOH2NE4y+B8aDMyfHbWeNMXFer+UCvdxtw736q3TbxOGMIE0C/uD2m44zAhJc1Y+1Nh/IwxkNea2e+hYA9wH/c59fD/zFHYn5gZogV19QuAGYAsz2aleGc2UDb1uBMe7jZPc51A5g3gEpA+jnBpnR3m2MMUHU/j4aqq1u39u83jfi0OaH1F3V5yggzB1Ve7xOnfXtq0l1uJ9taCPvE5FWojAl0vn9D7jFHSEKc1/7N9DLGLMQ+I/72h3Ah8aYL3EOT30BTDXGNHTdymwg3xjzP9xDhe4o1/3AV+7rk9y2rwP9rbXf1dPP28Cv3HuA/wKPGWPeBEoP89m+xRnt8r6MyX+B940xF3m99gzwU2PMIpxDbUsb69SdvP48zujdT7w2vQAsBqYfpq76+lwGBBtjvsAJU2V1miwAfm+MuavO698Dxxhj5gPjjnS/9XgIZ07aezi/Yd06RKSVaQK6iLSYMeYcYKi19kFf1+JLxhh/99DhbTgT0OsbqWuvGvxwRipPbs0zH0XkUJozJSIt4o4Q/Qpn0vvRbp4xpj/OIdSLfVTDAGPMszijlPMUpETankamRERERFpAc6ZEREREWkBhSkRERKQFFKZEREREWsBnE9Dj4+NtUlKSr3YvIiIi0mQrVqzIsNZ2q2+bz8JUUlISKSkpvtq9iIiISJMZY3Y0tE2H+URERERaQGFKREREpAUUpkRERERaoEOtgF5WVkZaWhrFxcW+LkXaUHBwMImJiQQEBPi6FBERkRbrUGEqLS2NiIgIkpKSMKa+C6dLZ2etJTMzk7S0NPr37+/rckREpJOoqLTkFZWRW89tYLdwJgyM81ltHSpMFRcXK0h1ccYY4uLiSE9P93UpIiLSzsorKskrLq83EFUHpcL6tx0safgykzPH9VWY8qYg1fXpNxYR6bzKKyobD0OH3Mqrt+U3EogAggM8RIUEVN96RwdzfK+IWq/Vewv17bSRDhemfCknJ4dXX32VG2644Yjfe8455/Dqq68SHR3dYJu77rqLyZMnc8YZZ7SkTBERkRYpq6isDjg5TRgZ8t5eUFrRaN91A1FCdDCDmxCIIkMCCA7wa6dvoHUpTHnJycnhiSeeqDdMlZeX4+/f8Nf10UcfHbb/++67r0X1+cLhPreIiPhGSXlFrZCTV3To4bO6o0VNDUQhAX61gk5iTChRvb3Djz9RoYeGoaiQAIL8O2cgagn9lfRy++23s3XrVkaNGsWZZ57JtGnT+MMf/kBMTAzfffcdmzdv5oILLmDXrl0UFxdz8803M2fOHKBmRff8/HzOPvtsTj75ZL755hsSEhL44IMPCAkJYdasWZx77rlMnz6dpKQkrrzySj788EPKysp46623OP7440lPT+fSSy9lz549TJgwgc8++4wVK1YQHx9fq9brr7+e5cuXU1RUxPTp07n33nsBWL58OTfffDMFBQUEBQXxxRdfEBoaym233cb8+fPxeDzMnj2bm266qbrm+Ph4UlJS+M1vfsOCBQu455572Lp1K6mpqfTt25f777+fK664goKCAgAee+wxTjrpJAD+8pe/8K9//QuPx8PZZ5/N7Nmzufjii1m5ciUAW7ZsYcaMGdXPRUTEYa2lqKyiwSBUFX4OCUTFzn1xWWWj/YcG+tUKOn1iQxsdFap57H9UBqKWUJjy8sADD7B+/XpWr14NwIIFC1i5ciXr16+vPvPsueeeIzY2lqKiIsaOHctFF11EXFztSW9btmzhtdde45lnnuGSSy7hnXfe4fLLLz9kf/Hx8axcuZInnniChx56iGeffZZ7772X0047jTvuuIP58+czb968emv985//TGxsLBUVFZx++umsXbuW448/nhkzZvDGG28wduxY8vLyCAkJYe7cuWzfvp3Vq1fj7+9PVlbWYb+LjRs3snjxYkJCQigsLOSzzz4jODiYLVu2MHPmTFJSUvj444/54IMPWLZsGaGhoWRlZREbG0tUVBSrV69m1KhRPP/881x11VVH+lOIiHQK1loOlpTXCjyNjRRVhaGqNmUVttH+I4L9nYAT7ASdgd3Cq+cIOa/71wpCVcEoMjiAQP8utpRkRRkUpEP+fsg/4N7cx33GwfDpPiutw4apez/cwMY9ea3a55Dekdx93tAjes+4ceNqncL/6KOP8t577wGwa9cutmzZckiY6t+/P6NGjQJgzJgxbN++vd6+/z979x0fVZX/f/x1piWZTMqk0UISUKQjJTSxYEHBAgoWsKyogKi46n6/u6t+1+7uz921siqKXaxYcF3XVWQtiKKSACpNQEhCCCU9mdSZzPn9cW+SISQhQMKkfJ6Pxzzmzr137v1MRpM355x77vTp0+v2ef/99wFYtWpV3fEnT56M2+1u9L1Lly5l8eLF+Hw+9uzZw6ZNm1BK0aNHD0aPHg1AZGQkACtWrGD+/Pl18w7qFAAAIABJREFU3XUxMTGH/NxTp04lLCwMMOb/WrBgAevXr8dqtbJ169a6415zzTU4nc4DjjtnzhxeeuklHn30Ud5++21++OGHQ55PCCGCpcavKa1srFXId3ArUeXBLUf+ZvKQRXFQ2OnlDjsgIEU1aBWqXY4ItWO1dPILdvw1UJ5/YDAqaxCUapcrmmgICIkEe6iEqfYsPDy8bvnLL79kxYoVrF69GqfTycSJExudYDQkJKRu2Wq1UlFR0eixa/ezWq34fM1f4RBo586dPPzww6xZswa3283s2bOPaKJTm82G3280Ezd8f+Dnfuyxx+jWrRs//vgjfr+f0NDQZo87Y8aMuha2UaNGHRQ2hRCirVX5asjzVJNXWkWep/ZRTW6D13meKorKvc0ey25VB3SFxYQ7SIkNbzQINQxOrhBb17uCWWuoKKwPQXWtSfvAk3tgaCrLBd1Id6UtDFwJ4OoGscdB8nhj2ZUA4Qn1y64EsIcd+8/YsNxgF9CUw21Bag0RERGUlpY2ub24uBi3243T6WTLli189913rV7DhAkTWLp0KX/84x9Zvnw5hYWFB+1TUlJCeHg4UVFR7Nu3j//85z9MnDiR/v37s2fPHtasWcPo0aMpLS0lLCyMSZMm8eyzz3L66afXdfPFxMSQkpJCeno6U6ZM4b333mv2cycmJmKxWHjllVeoqTEGLk6aNIn777+fK6644oBuvtDQUM455xxuuOGGJrsphRDicFVU15DnqSLXU2WGpOqAYFRFXml13fbSysb/gRoRYiMuIoQ4l4N+CS7G943FHe5oZiyRjTC7tesFooa0hqrSBsFofxMtSvvB30hAtdjrQ1BUL+g1wngdnlAfnGoDksMFHehn3qIwpZSaDDwBWIHntdYPNdieBLwCRJv73K61PvTlbe1MbGwsEyZMYMiQIUyZMoXzzjvvgO2TJ0/mmWeeYeDAgfTv359x48a1eg333HMPs2bNYsmSJYwfP57u3bsTERFxwD4nnngiI0aMYMCAAfTu3ZsJEyYA4HA4ePvtt7n55pupqKggLCyMFStWMGfOHLZu3cqwYcOw2+3MnTuXBQsWcM8993Dddddx1113MXHixCZruvHGG5kxYwavvvoqkydPrmu1mjx5MuvXryc1NRWHw8G5557LX/7yFwCuuOIKli1bxtlnn93qPyMhROdRVuU7oLUot5HWpDwzPDV1BVpkaG1ACmFgj0hOcTmIc4XUrYtzOYg3lzvqpfdtprr8wBDUXFebr5FeFmWF8HhwxRthKGGQ+TogGNUuh0Z3qIB0OJTWzQ9+U0pZga3AJCAbWAPM0lpvCthnMbBOa71IKTUI+FhrndLccVNTU3VaWtoB6zZv3szAgQOP5HN0GlVVVVitVmw2G6tXr+aGG26oGxDfkTz88MMUFxfzwAMPNLpdvmshOqfaAdkHtRyVmkGpQUtShbfxgOR22s0gFFLXkhTnCiHeFUJchKNuW6zL0bWvPNMafFXgLYdqjxGOvGVQXVa/XNeitP/grrbqJnpjnHEHhqG6gNStPji5ukFYDFg62UD3Jiil0rXWqY1ta0nL1Bhgu9Z6h3mwt4BpwKaAfTQQaS5HATlHXm7XlpWVxaWXXorf78fhcPDcc88Fu6TDdtFFF/Hrr7/y+eefB7sUIUQr0FpTXOE1Wo5KG+9aqxuT5Kmi2nfwGBilIDa8PgQlJzkPaj2Kc4UQHxFCTLgDu7WT/YH21xgBx1tuBp2yBgGoieXqMjMcNbZsPuvm54yqExpV363W48SDg1HteKTwOLDKjegPR0vCVC9gV8DrbGBsg33uBZYrpW4GwgGZ4vsI9evXj3Xr1gW7jKNSezWiEKJ9qm09KvBUk19WTb6nioKy2uVqCsqqyC+rprC0Am9ZISXlFXhrjH81a1Tds8ViIdrpICY8hG6uEAbFhBAbEUlsuIMYVwixrlDzYexjs1oBZXb1NHhuD90/WoOvsvHWnQMCUBOvD1o2Q5G33Dju4bCFgcMJjnCwh9cvh7mNZ4fTXH+IZYfLWHbGGVe8iTbRWgPQZwEva60fUUqNB5YopYZofeAQfaXUPGAeQFJSUiudWgghuraWhKOS0lK8nnx0WT6WykJc/hJiVCnReHArD9GqlIF4iLV4iLGU4aYUl/YYJ7Cbj8b4gGLz0SqaCFstej6c9wfs6/fVh6bGrixrslSrGVZqQ4/57IwBe2L9Nrvz0MuBocnuBEsX7rrsgFoSpnYDvQNeJ5rrAl0HTAbQWq9WSoUCccD+wJ201ouBxWCMmTrCmoUQolNrNhyVVlFRmk+1J58aTz6qogBbZSERupRoVYrbDEeJlDJMGctuVUoo1fUnaPCbv8Yejg51YwmPxeLsbYQBZ6wxHibMDVab0WpjFAfog5+b23bQMy3cr7nnwzlfczVijPlxuOrDUGAwam7Z6mgfLWoi6FoSptYA/ZRSfTBC1Ezg8gb7ZAFnAi8rpQYCoUBuaxYqhBAdVVPhqLC0jPLifLyl+/F58tHlBVgrC7FXFxGhjWAUo0pxq1L64iFaeYjGg001aD0xf5NrLHgdUegwNzhjsbl6Yw2PNQKRM8YIR3XPseayG6st5OCihRAtdsgwpbX2KaUWAJ9iTHvwotZ6o1LqfiBNa/0h8D/Ac0qp2zBy/mx9qMsEhRCiA9Nak+upYldBBfmllZSUFFNenEtlSR4+Tx66PB9VXoituojQ6iIiKcFtBqIBlBKtPESqxif0xQo+i4Nqhxt/qBvCYrG6TsDuisPmiq0PRc6A5TA3KjQaRxe5skqI9qRFY6bMOaM+brDu7oDlTcCE1i2tY3C5XHg8nmCXIYRoA36/Zk9JJZn5ZWTml5OVW4R3z2ZCCzYRV7aNfv4M+lr2MIRSQlTTs2hXOlxU26PxhUajwxKxOGOwRMThjYzH7optpNUoBpvdiU26kIToENrtDOiiZXw+X90994QQh89X42d3UQUZ+eVk5peRkVdOVkEZRbk5uIq30M+fwUBLFsNVJherHOzKuAzda3FQHNUPb8xpFEd2IyQyDmdUPHZXXINw5CbUakeuoxKi85K/wgFuv/12evfuzU033QTAvffei8vlYv78+UybNo3CwkK8Xi8PPvgg06ZNa/ZYF154Ibt27aKyspJbbrmFefPmAfDJJ59w5513UlNTQ1xcHP/973/xeDzcfPPNpKWloZTinnvuYcaMGQe0er377rt89NFHvPzyy8yePZvQ0FDWrVvHhAkTmDlzJrfccguVlZWEhYXx0ksv0b9/f2pqavjjH//IJ598gsViYe7cuQwePJiFCxfywQcfAPDZZ5/x9NNPy3QGolOr9NaQXVhORl45mQVmaDLD095CD0k6h4Eqk0GWLE63ZjHYkkWMLjQGNlihKqwb/oTBWBMvhu5DoPtQ7DHHEWeVX6FCCAlTB7jsssu49dZb68LU0qVL+fTTTwkNDWXZsmVERkaSl5fHuHHjmDp1arP3anrxxReJiYmhoqKC0aNHM2PGDPx+P3PnzmXlypX06dOHggLjDtgPPPAAUVFR/PzzzwCN3o+voezsbL799lusVislJSV8/fXX2Gw2VqxYwZ133sl7773H4sWLycjIYP369XX35HO73dx4443k5uYSHx/PSy+9xLXXXtsKPz0hgqu82kdm/oFByXhdTk5xBVpDJB4Gql0Md+ziytDdDCCTniEZ2LRxpZu22CG+P6r7ZOg2xAhO3YYSEi436xZCNK39hqn/3A57f27dY3YfClMeanLziBEj2L9/Pzk5OeTm5uJ2u+nduzder5c777yTlStXYrFY2L17N/v27aN79+5NHmvhwoV1rT27du1i27Zt5Obmcuqpp9KnTx8AYmJiAFixYgVvvfVW3XvdbvchP8oll1yC1WrMQ1JcXMzVV1/Ntm3bUErh9Xrrjjt//vy6bsDa81111VW89tprXHPNNaxevZpXX331kOcToj0orvDWhaWsgNCUkV9ObmlV3X4KPyc6CznJtYfZ4dkc59xJj4rtOCv21B/MGmeGpbON3w3dhqDiTgCbIwifTAjRkbXfMBUkl1xyCe+++y579+7lsssuA+D1118nNzeX9PR07HY7KSkpVFY2PZvtl19+yYoVK1i9ejVOp5OJEyc2u39TAlu+Gr6/9mbDAHfddRenn346y5YtIyMjo9mbFgNcc801XHDBBYSGhnLJJZfImCvRbmityS+rPqiFqTY8FZYfOMi7W2QI/d2K2b33Mti6iz6+ncSXbSOs8BeUtwxKAGWB2H5w3IS6lia6DzFunyEDvIUQraD9/hVtpgWpLV122WXMnTuXvLw8vvrqK8Bo+UlISMBut/PFF1+QmZnZ7DGKi4txu904nU62bNnCd999B8C4ceO48cYb2blzZ103X0xMDJMmTeKpp57i8ccfB4xuPrfbTbdu3di8eTP9+/dn2bJlRERENHm+Xr16AfDyyy/XrZ80aRLPPvssp59+el03X0xMDD179qRnz548+OCDrFix4mh/ZEIcFr9fs7+0ioz8soCgVG6+LsdT5avb16KgZ3QYybFOpgzpzpDwYgaoTJKqd+Au/QXr/o2wb2f9wUOijKCUcqUZnIZAwkCwhwXhkwohuor2G6aCZPDgwZSWltKrVy969OgBwBVXXMEFF1zA0KFDSU1NZcCAAc0eY/LkyTzzzDMMHDiQ/v37M27cOADi4+NZvHgx06dPx+/3k5CQwGeffcaf/vQnbrrpJoYMGYLVauWee+5h+vTpPPTQQ5x//vnEx8eTmpra5BQMf/jDH7j66qt58MEHOe+88+rWz5kzh61btzJs2DDsdjtz585lwYIFdZ8pNzeXgQMHtsaPTYiDFFd42bynhO37PQeNY6oKuBGuzaLoHeMkOdbJ6JQYkmOd9ImycDy76FaxDXvuJti7AX7ZCFW19yxRENPH6J4bfnn9+Kao3tLaJIQ45lSw5tZMTU3VaWlpB6zbvHmz/HE/RhYsWMCIESO47rrrgnJ++a47D601+0qq2JhTzMacEjbllLBxTzG7CuonpAyxWUiOdZIcG05yjJPkuHBSYp2kxDjpYSnElrsJ9v1shKZ9GyB/e/090uzh0G1wfUtT96GQMAhCXEH6xEKIrkgpla61Tm1sm7RMdUGjRo0iPDycRx55JNiliA7G79fszC9jY04JG3OK2WSGp/yy+vu+pcQ6GdYrmpmjkxjcM5ITukXQPTIUi98Leb/A3h+NwPTLz8ZzeX79CaKSjNA06ML68OTuY9w7TQgh2ikJU11Qenp6sEsQHUCVr4atez31LU57Sti8p4TyamPSSrtV0S8hgjMGJDCsWwhDY/2cEFGN01cE5TuhvAD2FMDGHUZoyv0F/OYAcluoMZap/7l1V9LRbTCERQfxEwshxJGRMCWEoKTSa3TP5ZSwJTuX3Tm7Kc7fS6QuJYZSutvLuSSiipRulfSwlxNrKcVZU4KlvAC25sPGsqYP7uputDIdf1Z9cIo9HmTCSyFEJ9HufptprZudDFN0fHIP7GPIVw0VBUZXWnk+uiyf0sL95O3PoSR/H1UluejyAsJ8RfSilCGqFJcyp+GwNziWB/BGgdNt3GDXlWC0LjljIcxcV/eIqV9vbXggIYToXNpVmAoNDSU/P5/Y2FgJVJ2U1pr8/HxCQ+VOZYetxmt0nZnBKDAkGesDX+ejywtQ1aUHHEIBkeajVIdRaomk2hGNiu6BPXIYlpgEiE6oD0VhMQHLbpnQUgghGtGuwlRiYiLZ2dnk5uYGuxTRhkJDQ0lMTAx2GcGnNRTsgNK9B4QgygsaBKV8KC8MmBagkUM5XHgdbjzWSPL9EeR4+5JZNYj9PheFRFCiIgiP7kZ8tx706pXIcUlJDEiMpWeotBoJIcTRaldhym63191qRYhOx++H3M2Q8Q1kroLMb6GskX842MMDuspiIKbvAa1DFfZoMspD2VLi4KcCG2n7YXNuFT6/0X0a7rAyqGckgwdFMahHJJN7RtKvm4sQm/UYf2AhhOga2lWYEqJT8fuNq9gyv4EMMzxVGDe3JjIRjjsTkk8Cd3JAl1rMAbN17y+prJuGYOMu44q6zPxyc6uPOJeVwT0jmTewlxGgekaRHOPEYpFuciGEOFYkTAnRWvw1sPcns+XpGyM8VRYZ26KToP8USJ4AKRMgOvmAmbr9fk1mQTkbNxeyMSezbvLLPE/9zXuTY50M7hnJpam9GdQjksE9I0mIlLFnQggRbBKmhDhSNT7Y86PRZZfxDWR9Vz+uyd0HBl4AKScbASq6d93btNbkFFeSnlnI2sxCNuwuZvOeEsrM+ZtsFkW/bhFM7B/P4J6RDOoRycCekUTK+CYhhGiXJEwJ0VI1XshZDxlfGy1PWd9D7dVysf1gyEWQfLLR8hTZs+5t3ho/m7OLSMsoJD2rkPSMQvaWGNMPhNmNbrqLRyXWddPJ+CYhhOhYJEwJ0RRfFexeW9/ytOsH8JqTU8YPgGGXGsEpeQJEdK97W1F5NWu37CM9s5C0jEJ+zC6i0mvcZ65XdBij+8SQmuxmVLKbAd0jsFnlVilCCNGRSZgSopa3Enan1V9tt2sN+Myb9SYMhhFXGMEpeQK44gGjy25HXhnpW3aRnmm0PG3f7wHAalEM7hnJrDFJjDLDU4+osKbOLoQQooOSMCW6Lm+F0dqU+Y0RoLLXQE0VoIzbn4yabbQ8JZ0E4bEAVHpr+HFXEelp21mbWUh6ZiGF5cb95qLC7IxKdnPRiF6MSnYzLDEKp0P+FxNCiM5OftOLrqO6DHZ9X3+13e50qKkGZYHuw2DMXLPlabwx2zewr6SS9B2FpGduIi2zkI27i+vmc+obH85ZA7uRmmK0OvWNc8mUBEII0QVJmBKdV1WpMUi8dsxTzlrw+0BZoedwGDvfuNouaRyERuGr8fPLvlLS1xeSnplBemYh2YVGN1+IzcKJvaOZe2pfRiW5GZnsJiZcbq0ihBBCwpToTCqLjekJMlYZLU8560HXgMUGPUfCSTcbV9sljYWQCEoqvazLKiL9632kZ25mfVZR3fQECREhpKa4mX1SCqkpMQzqEYnDJgPFhRBCHEzClOi4Kgohc3X9DON7fwLtB4sdElPh5NuMMU+9x6LtTrIKyo0r7D7OYG1mIb/sK0VrsCgY0D2S6SMTSU1xMzLJTaI7TG62LYQQokUkTImOo7ygfrB45irYuwHQYA2BxNFw6u+NMU+Jo6myhLBhdwnpmQWkf7uZ9MyiutnEI0JsjEh2M2VID0YluxmeFI0rRP5XEEIIcWRa9BdEKTUZeAKwAs9rrR9qsP0x4HTzpRNI0FpHt2ahoguqLoftnxmtThnfwP6NxnpbGPQeDRPvMFqeeqWSV6WMGcW3FJL+6Xp+2l1Mtc+Y2yk51smp/eIYmewmNcVNv4QIrDJQXAghRCs5ZJhSSlmBp4BJQDawRin1odZ6U+0+WuvbAva/GRjRBrWKrqKiCNY8B98tgvJ8sDuh91hjhvGUU/D3GMG2/Gqjy+6HAtZmribDvPmvw2phSK9IZp+UwsgkNyOTo0mIkPvXCSGEaDstaZkaA2zXWu8AUEq9BUwDNjWx/yzgntYpT3Qpnv3w3dOw5gWoKoF+Z8P4BZR1H8OPOWWkZRaSvqKQtVlfUlrpAyA23MGoZDezxiSRmuJmcM8oQu1yKxYhhBDHTkvCVC9gV8DrbGBsYzsqpZKBPsDnR1+a6DKKsuDbf8DaV41buAy+iIqxv+X1zCiWfbSbzXs+x69BKTghIYLzh/Wsux1LcqxTBooLIYQIqtYedTsTeFdrXdPYRqXUPGAeQFJSUiufWnQ4uVvhm8fhp7cBBSfOpHjkTbywxcorL2VQXJHDyKRoFpx+PCOT3YxIchMVZg921UIIIcQBWhKmdgO9A14nmusaMxO4qakDaa0XA4sBUlNTdQtrFJ1NznpY9Shs+hBsoTB6LnuHzGHx+mreXJxJhbeGSYO6ccPE4xiZ5A52tUIIIUSzWhKm1gD9lFJ9MELUTODyhjsppQYAbmB1q1YoOo/Mb+HrR2D7CgiJglP+hx3H/4ZFPxTxwTNb8WuYdmJP5k88jhO6RQS7WiGEEKJFDhmmtNY+pdQC4FOMqRFe1FpvVErdD6RprT80d50JvKW1lhYnUU9rIzx9/QhkrQZnHJx5Dxt6XcyT3+Ty6YoNOKwWLh+TxNxT+5Lodga7YiGEEOKwqGBln9TUVJ2WlhaUc4tjwF8Dmz80QtTenyEyET3ht3wXdS5PrtrNN9vziQi1cfX4FGZPSCHOFRLsioUQQogmKaXStdapjW2TaZ9F6/JVGwPKv3kc8rdDbD/8U5/iM9upPL0yix+zfyI+IoQ7pgzg8rFJRITKgHIhhBAdm4Qp0Tqqy42pDb5dCCW7ofswvDNe4YPK4TzzRQa/5v5MUoyTP180hBkjE2UuKCGEEJ2GhClxdCqKYM3z5mzleZB0EpVTHuONvH48/9FOcoo3MrBHJAtnjeDcId2xWS3BrlgIIYRoVRKmxJHx5JqzlT9fN1t56eibeTGrBy+/s5PC8s2MSYnhz9OHMvGEeJlYUwghRKclYUocnqJd5mzlr5izlV9I3vCbeHZrOG+8lkVZ9VbOHJDADROPIzUlJtjVCiGEEG1OwpRombxtsOpx+Okt4/WJM9k1cC5P/Wzh/Zd3U6NzuWBYD+ZPPI4B3SODW6sQQghxDEmYEs3b8yN8/Shs+qc5W/kcful7NQvTKvn4pd3YrRYuHZ3IvFOOIylW5ogSQgjR9UiYEo07YLbySPTJvyOt+2Us/L6Ir7/aSUSIjfmnHce1E/oQHyFzRAkhhOi6JEyJeo3MVu4/426+jJzKP77dz7oV24lzOfjD5P5cOS6ZSJkjSgghhJAwJQiYrfxR2PsTRCZSc85f+ch6Jk99k8PWfVtJdIfxwIVDuGSUzBElhBBCBJIw1ZX5quHnpbDqMXO28uOpPv9J3q4cxzNf7WJ30Vb6d4vg8cuGc/6wHjJHlBBCCNEICVNdUXU5rFsC3yyEkmzoPoyyaS/ycsFgXvzPLvLLtjIq2c390wZzev8ELBaZI0oIIYRoioSprqSy2Jhkc/XTdbOVF535dxZlp/D6B7vwVP3KxP7x3DjxeMb0kTmihBBCiJaQMNUVeHLh+0Xww3PGbOXHT2LvsBtZ+Gs8776Tja9mJ+cN68n80/oyuGdUsKsVQgghOhQJU51ZcbYxW3n6K+CrhEHT2DHgeh7bGMa/38zBZslmxqhErj+1Lylx4cGuVgghhOiQJEx1Rnnb4ZvH4EdztvJhM/kpZTaPrdN88UYu4Y4S5p7Sl+tO7kNCZGhwaxVCCCE6OAlTnUmD2cp16rV8k3A5j6+pIO27/cSEO/jfs0/gqnEpRDlljighhBCiNUiY6gwyV5uzlX8GIZH4J9zGp5EX8cTqIraszKFXdBj3TR3Mpam9CXPIHFFCCCFEa5Iw1VFpDb/+F1Y+AlnfgjMW78Q/8Z51Mk+tzmVXwS76Jbh45JITmTq8J3aZI0oIIYRoExKmOhqtYft/4cv/B7vTIDKRyrP+wqtVp7J41T7yPFkM7x3NXecN4qyB3WSOKCGEEKKNSZjqKLSGbZ/BVw/B7nSISqJ00sM8WzyOVz7LobQqi1P6xXHjxBGM6xuDUhKihBBCiGNBwlR7pzVsWw5fPgQ5ayE6CS5YSO5x05m26Af2lGRx7pAe3DDxOIb0kjmihBBCiGNNwlR7pTVs/dRoicpZZ4Soqf+AE2dRra3c+Px3FJRXs+zGCQzvHR3saoUQQoguS8JUe6M1bP3EaInasx6ik2Hqk3DiTLAa0xk88MEG1mQUsnDWCAlSQgghRJBJmGovtIZf/mO0RO35EdwpMO0pGHZZXYgCeHtNFku+y+T6U/sy9cSewatXCCGEEICEqeDTGn752GiJ2vsTuPvAtKdh2KUHhCiAtVmF3PXBRk7pF8cfJg8IUsFCCCGECCRhKli0hi3/Nlqi9v5shKgLF8HQS8F68Neyv6SS+UvS6R4Vyj9mjcAqUx4IIYQQ7UKLwpRSajLwBGAFntdaP9TIPpcC9wIa+FFrfXkr1tl5+P3wy7/hy7/Cvp8hpi9c+AwMvaTREAVQ5ath/mvpeKp8vHrdGKKdjmNctBBCCCGacsgwpZSyAk8Bk4BsYI1S6kOt9aaAffoBdwATtNaFSqmEtiq4w/L7YctH8NVfYd8GiDkOLnoWhlzcZIgC0Fpzzz83sjariEVXjGRA98hjWLQQQgghDqUlLVNjgO1a6x0ASqm3gGnApoB95gJPaa0LAbTW+1u70A7L74ct/zJaovZvhNjj4aLFMGRGsyGq1uvfZ/HWml3cdPpxTBna4xgULIQQQojD0ZIw1QvYFfA6GxjbYJ8TAJRS32B0Bd6rtf6kVSrsqPx+2Pyh0RK1fxPE9oPpzxkhytKymw3/sLOAez/cyOn94/ndpP5tXLAQQgghjkRrDUC3Af2AiUAisFIpNVRrXRS4k1JqHjAPICkpqZVO3c74/bDpA1j5dyNExZ0A05+HIdNbHKIA9hRXcOPr6fSOcfL4TBlwLoQQQrRXLQlTu4HeAa8TzXWBsoHvtdZeYKdSaitGuFoTuJPWejGwGCA1NVUfadHtkr/GCFFf/R1yN0Ncf5jxAgy+6LBCFEClt4b5S9Kp9Pp5a94oosLsh36TEEIIIYKiJWFqDdBPKdUHI0TNBBpeqfcBMAt4SSkVh9Htt6M1C223/DWwcZnREpW7BeIHHHGIAmPA+f8t28CP2cUsvmoUxydEtEHRQgghhGgthwxTWmufUmoB8CnGeKgXtdYblVL3A2la6w/NbWcrpTYBNcDvtdb5bVl40NWGqK/+Bnm/GCHq4pdg0IVgsRzxYV/5NoP31mZz61n9OHtw91YsWAghhBBtQWkdnN621NRUnZaWFpRzHxV/DWx4H1b+DfK2QvxAmPhHGDjtqEIUwOpf87nyhe85Y0ACz17IixsrAAAgAElEQVQ5CouMkxJCCCHaBaVUutY6tbFtMgN6S/lrYMN7RktU/jZIGASXvAIDpx51iALILiznpjfWkhLr5NFLT5QgJYQQQnQQEqYOpcZnhKiVf4P87ZAwGC59FQZc0CohCqCiuobrl6Tj9fl57jepRITKgHMhhBCio5Aw1ZQaH2x412iJKvgVug2BS5fAgPNbLUSBMeD89vd/YtOeEl64OpW+8a5WO7YQQggh2p6EqYZqfPDzO0ZLVMEO6DYULnsN+p/XqiGq1gurdvLP9Tn8/pz+nDGgW6sfXwghhBBtS8JUrRof/LzUmOKgYAd0HwqXvQ79z22TEAWwalsef/l4M+cO7c6NE49rk3MIIYQQom1JmKrxwU9vGyGqcCd0HwYz3zBClGq7QeBZ+eUseHMt/RIi+PvFJ6La8FxCCCGEaDtdN0zVeOHHt+Drh6EwA3qcCDPfhP5T2jREAZRX+5i3JA2tYfFvRhEe0nW/BiGEEKKj63p/xWu88OObsPJhKMqEHsNh1ltwwuQ2D1FgDDj//Ts/sXVfKS9dM4bk2PA2P6cQQggh2k7XCVM1Xlj/htESVZQFPUfAlL/BCecckxBVa9FXv/Lvn/dwx5QBnHZC/DE7rxBCCCHaRucPU75q+PEN+PoRM0SNhHMfhn5nH9MQBfDFL/v5+6e/cMGJPZl3at9jem4hhBBCtI3OG6Z81bD+dfj6USjOgl6j4NxHoN+kYx6iAHbmlXHLm+sY2D2Sv80YJgPOhRBCiE6i84apnHXw0a3QKxXOfxSOPysoIQrAU+Vj3qtpWC2KZ68aRZjDGpQ6hBBCCNH6Om+YShoL162AxNSghSgAv1/zu7fXsyOvjCXXjqF3jDNotQghhBCi9bXNbJTtRe/RQQ1SAE9+sZ3lm/Zx57kDOen4uKDWIoQQQojW17nDVJB9tmkfj362lekjenHthJRglyOEEEKINiBhqo1s3+/htrfXM7RXFH+ZPlQGnAshhBCdlISpNlBS6WXeq2mE2Cw8e9UoQu0y4FwIIYTorDrvAPQg8fs1t721nqyCcl6fM5ae0WHBLkkIIYQQbUhaplrZ4yu28t8t+7nngkGM7Rsb7HKEEEII0cYkTLWiTzbsYeHn27k0NZErxyUHuxwhhBBCHAMSplrJL3tL+d3SHxneO5r7pw2RAedCCCFEFyFhqhUUl3uZtySN8BAbz1wpA86FEEKIrkTC1FGq8WtufmsdOUUVPHPlSLpHhQa7JCGEEEIcQ3I131H6+6e/sHJrLv9v+lBGJccEuxwhhBBCHGPSMnUUPvoph2e++pUrxiYxa0xSsMsRQgghRBBImDpCm3JK+P07P5Ga7OaeCwYHuxwhhBBCBEmLwpRSarJS6hel1Hal1O2NbJ+tlMpVSq03H3Nav9T2o7CsmnlL0ogKs/P0lSNx2CSTCiGEEF3VIcdMKaWswFPAJCAbWKOU+lBrvanBrm9rrRe0QY3tiq/Gz4I317K/pIql88eTECEDzoUQQoiurCVNKmOA7VrrHVrrauAtYFrbltV+PfSfLXyzPZ8HLxrC8N7RwS5HCCGEEEHWkjDVC9gV8DrbXNfQDKXUT0qpd5VSvVulunZm2bpsnl+1k9knpXBpaqf8iEIIIYQ4TK012OdfQIrWehjwGfBKYzsppeYppdKUUmm5ubmtdOpjY8PuYm5/72fG9onh/84bGOxyhBBCCNFOtCRM7QYCm2ESzXV1tNb5Wusq8+XzwKjGDqS1Xqy1TtVap8bHxx9JvUGR56li3qtpxLlCePqKkditMuBcCCGEEIaWpII1QD+lVB+llAOYCXwYuINSqkfAy6nA5tYrMbi8NX5uen0t+WXVPHvVKGJdIcEuSQghhBDtyCGv5tNa+5RSC4BPASvwotZ6o1LqfiBNa/0h8Ful1FTABxQAs9uw5mPqz//ezPc7C3jsshMZ0isq2OUIIYQQop1p0e1ktNYfAx83WHd3wPIdwB2tW1rwLU3bxcvfZjDn5D5cNCIx2OUIIYQQoh2SwT9NWJdVyJ+WbWDC8bHcPmVAsMsRQgghRDslYaoR+0srmf9aOgmRITw5ayQ2GXAuhBBCiCa0qJuvK6n2+bnxtbWUVPh474aTcIc7gl2SEEIIIdoxCVMN3PevjaRlFvLk5SMY1DMy2OUIIYQQop2T/qsAb3yfxevfZzH/tOM4f1jPYJcjhBBCiA5AwpQpPbOAez7cwKknxPP7c/oHuxwhhBBCdBASpoC9xZXMf20tPaPD+MfMEVgtKtglCSGEEKKD6PJjpiq9NVz/WjplVT5enzOWKKc92CUJIYQQogPp0mFKa83d/9zAj7uKeObKkZzQLSLYJQkhhBCig+nS3XxLvstkaVo2vz3jeCYP6XHoNwghhBBCNNBlw9T3O/K5/1+bOHNAAreedUKwyxFCCCFEB9Ulw1ROUQU3vr6WpFgnj80cjkUGnAshhBDiCHW5MFXpreH6JelU+fwsviqVyFAZcC6EEEKII9elBqBrrbnj/Z/5eXcxz/8mleMTXMEuSQghhBAdXJdqmXrxmwyWrdvN7yadwFmDugW7HCGEEEJ0Al0mTH2zPY+/fLyZcwZ3Y8Hpxwe7HCGEEEJ0El0iTO0qKGfBG2vpGxfOI5fKgHMhhBBCtJ5OH6YqqmuYtyQdn1+z+DepuEK61DAxIYQQQrSxTp0stNb84b2f2LK3hBdnj6ZPXHiwSxJCCCFEJ9OpW6YWr9zBv37M4ffn9Of0/gnBLkcIIYQQnVCnDVNpGQX89ZMtnDe0BzecdlywyxFCCCFEJ9Vpu/mGJUbzu0kncO3JfVBKBpwLIYQQom102jDlsFlYcEa/YJchhBBCiE6u03bzCSGEEEIcCxKmhBBCCCGOgoQpIYQQQoijIGFKCCGEEOIoSJgSQgghhDgKSmsdnBMrlQtkHoNTxQF5x+A8om3I99fxyXfY8cl32LHJ99c6krXW8Y1tCFqYOlaUUmla69Rg1yGOjHx/HZ98hx2ffIcdm3x/bU+6+YQQQgghjoKEKSGEEEKIo9AVwtTiYBcgjop8fx2ffIcdn3yHHZt8f22s04+ZEkIIIYRoS12hZUoIIYQQos102jCllJqslPpFKbVdKXV7sOsRh0cp1Vsp9YVSapNSaqNS6pZg1yQOn1LKqpRap5T6KNi1iMOnlIpWSr2rlNqilNqslBof7JrE4VFK3Wb+Dt2glHpTKRUa7Jo6o04ZppRSVuApYAowCJillBoU3KrEYfIB/6O1HgSMA26S77BDugXYHOwixBF7AvhEaz0AOBH5LjsUpVQv4LdAqtZ6CGAFZga3qs6pU4YpYAywXWu9Q2tdDbwFTAtyTeIwaK33aK3XmsulGL/EewW3KnE4lFKJwHnA88GuRRw+pVQUcCrwAoDWulprXRTcqsQRsAFhSikb4ARyglxPp9RZw1QvYFfA62zkD3GHpZRKAUYA3we3EnGYHgf+APiDXYg4In2AXOAls6v2eaVUeLCLEi2ntd4NPAxkAXuAYq318uBW1Tl11jAlOgmllAt4D7hVa10S7HpEyyilzgf2a63Tg12LOGI2YCSwSGs9AigDZPxpB6KUcmP0yvQBegLhSqkrg1tV59RZw9RuoHfA60RznehAlFJ2jCD1utb6/WDXIw7LBGCqUioDo5v9DKXUa8EtSRymbCBba13bIvwuRrgSHcdZwE6tda7W2gu8D5wU5Jo6pc4aptYA/ZRSfZRSDowBdx8GuSZxGJRSCmOsxmat9aPBrkccHq31HVrrRK11Csb/f59rreVfxB2I1novsEsp1d9cdSawKYglicOXBYxTSjnN36lnIhcRtAlbsAtoC1prn1JqAfApxtULL2qtNwa5LHF4JgBXAT8rpdab6+7UWn8cxJqE6GpuBl43/1G6A7gmyPWIw6C1/l4p9S6wFuMK6XXIbOhtQmZAF0IIIYQ4Cp21m08IIYQQ4piQMCWEEEIIcRQkTAkhhBBCHAUJU0J0Esrwb6XUSqXUKKXUz+bUBB2CeT/N85rYFq2Umh7w+h+tcL7ZSqnZLdz3cfM2VUdynmsPY9/mfgbdlVL/dyQ1CCHalgxAF6KTUEr1BB7VWs9USkVgzDz+qdb65DY4l0Vr3Wozmx/qeOYs+A+25vQKtUFKa/3y0dTWgvOsavgdtPbPTwgRXNIyJUTn8TfgdKXU81rrUq11WWM7KaUcZgvWl0qppea6CUqpb8x1lymlbOYd5leazzal1ESl1IdKqQ+Bc5RSc5RSX5uPkQ3O8ZVSymIuv2O2qtxhrv9eKTXC3PalUupvwKtmS9EcpZRdKfVf89zvmS1C84BJ5v7xSqlV5vvPUkp9Zz7OCjjmI0qpNUqp6xr57P9USn0CTDXXTVRKPWguzzYfKUqpL8zLymebx7Qppe5VSr2qlFqhlHrefM9x5mf6p1l3SsD5pgJDzffX1l/7eYcH/DzuDDj/HPP8X5ufP10plWiue83c7zul1HNKqfVKqcnmuvnm+r8qpb48kv+AhBBHRsKUEJ3Hn4DPtNZzDrFfbyBXaz0RuMxc9/+Aaea6d4CLgE1a61OBjcAMcz+H1noqxsS4UzFuhDsNuLvBOb4BJiilnIDLnADyCa31acAVwP8G7LusQYuTDzjfPPdm4AyMuXE+01pP1FrnBux7L3C2+bg/YP1rwMnA1Q3quhD4QWs9Gchr8idkSAAu01q/2GD9Oq31WUCSUira/Cy3ANPN99TRWn8I/GzW/VmDz/sLMFFrPRYjKIY1OI8LuAR4lPqff60Y4P8wbiR9vTJuYjsbY342uVuAEMdYp5y0UwjRNK31r8oYT/U6kI7xx1pprfPM7X6l1HEYE/0BpAGjgH0B6/oCJwJfNHGadzGCTALwH3PdVUqpKzC6HwPHFzS8f184sFgp1QvoBmwzH018HOOejUqpmoD1G7TWXqVUw660vhgTFwaeN7AWFfD6R6114DHrjm0+5wBRGPc9+0lrXaOU2tDI/g3VnrcP8IgZOPvTIIhhhFm/Umo3cHyDbbla6/1gjCcD4oAss4b1CCGOKWmZEqKLUUqFAI9pra8AJiulugFaKRVrbrcAv2IEKIBU8zUYQQhgJ7DGbHGZCEwKPIfWei0wHKNF5T1z9Y3ARGAuRmip1TDwnANsNVux3jP39WLczaAhi1IqUikV2WB7U4NBd2KEQIAR5nMx0MNcHtpMXY0dW5nHHGp2Rw4+xP6Bx70B+Kv5Obdz4M+ksfM0ty0P6G1+d8OaqFsI0UYkTAnRCSmleiulVgBDzPE9KQGbk4GVSqnVQC6wH7gD+JdS6guMrqUPgMFKqZUYAeO9wOObXW21Vw5+AdzeSBnfA4la69qbjP8ArOTQtyT5HuMmyR8BtXXvBWKUUu8qpWIC9r0P+Mx83HeI42J+rpOUUp8C0ea6n4CeSqmPgfgWHKOhh4EngGVAIUbwC/SDUuoDpdQpDdb/G3hSGePWqo/gvHW01j7gFeBb4PJGahBCtCG5mk8IIY6CUspm3g/UijFW7GQz3ASrjrHAtVrr6491DUJ0VTJmSgghjk5f88q+cOCFYAQp081KqQsBBwcPvBdCtCFpmRJCCCGEOAoyZkoIIYQQ4ihImBJCCCGEOAoSpoQQQgghjkLQBqDHxcXplJSUYJ1eCCGEEKLF0tPT87TWjU6fErQwlZKSQlpaWrBOL4QQQgjRYkqpzKa2STefEEIIIcRRkDAlhBBCCHEUJEwJIYQQQhyFdjUDutfrJTs7m8rKymCXIlpJaGgoiYmJ2O32YJcihBBCtIl2Faays7OJiIggJSUFpRreJF10NFpr8vPzyc7Opk+fPsEuRwghRAfkrfFTUuGluMEjcN3w3m7OG9YjaDW2qzBVWVkpQaoTUUoRGxtLbm5usEsRQggRRN4af9NhqPzgoBS4vay6ptljh9mtABKmAkmQ6lzk+xRCiM6h2udvsmWouTBUXOGlvAWBKCrMbjycdnrHOOtfN3hEHrBsI8RmPUY/gaa1uzAVTEVFRbzxxhvceOONh/3ec889lzfeeIPo6Ogm97n77rs59dRTOeuss46mTADeeecd7r77brp37867777LxRdfzJo1a5g9ezZPPvnkUR9fCCFE51Plq2k8CJV7Ka7wNRuWKrzNByKnw3pA4Okd42RICwJRVJgdh61jXw8nYSpAUVERTz/9dKNhyufzYbM1/eP6+OOPD3n8+++//6jqC/TCCy/w3HPPcfLJJ1NWVsYDDzzAhg0b2LBhQ6udozlaa7TWWCwd+38AIYToSLTWVHqb6TJr8Lqk8sD1lV5/s8cPNwNRbdhJjm3QQuRsPAxFhnb8QHQ0JEwFuP322/n1118ZPnw4kyZN4rzzzuOuu+7C7XazZcsWtm7dyoUXXsiuXbuorKzklltuYd68eUD9jO4ej4cpU6Zw8skn8+2339KrVy/++c9/EhYWxuzZszn//PO5+OKLSUlJ4eqrr+Zf//oXXq+Xd955hwEDBpCbm8vll19OTk4O48eP57PPPiM9PZ24uLi6Ou+//35WrVrFddddx9SpU/n73//OySefzPbt2w/5+T788ENsNhtnn302Dz/8MPv27WP+/Pns2LEDgEWLFnHSSSfx6KOP8uKLLwIwZ84cbr31VjIyMjjnnHMYO3Ys6enpfPzxxyxdupSlS5dSVVXFRRddxH333ddG344QQnQOWms8VT4z9PgaDUQNQ1Dt9pIKH9U1zQciV4gtIPDY6BMXfsiWodp1dmvXDURHo92Gqfv+tZFNOSWtesxBPSO554LBTW5/6KGH2LBhA+vXrwfgyy+/ZO3atWzYsKHuarQXX3yRmJgYKioqGD16NDNmzCA2NvaA42zbto0333yT5557jksvvZT33nuPK6+88qDzxcXFsXbtWp5++mkefvhhnn/+ee677z7OOOMM7rjjDj755BNeeOGFg95399138/nnn/Pwww+Tmpraos+en5/PsmXL2LJlC0opioqKAPjtb3/LaaedxrJly6ipqcHj8ZCens5LL73E999/j9aasWPHctppp+F2u9m2bRuvvPIK48aNY/ny5Wzbto0ffvgBrTVTp05l5cqVnHrqqS2qSQghOiq/X1Na6Ts48DQZggL38VHj100eWymIDD0w6PSICj14vFDowYEoItSGrbMFIq2hshg8+6FsP3j2Gct1j31w/Fkwbn7QSmy3Yaq9GDNmzAGX9S9cuJBly5YBsGvXLrZt23ZQmOrTpw/Dhw8HYNSoUWRkZDR67OnTp9ft8/777wOwatWquuNPnjwZt9vdKp8jKiqK0NBQrrvuOs4//3zOP/98AD7//HNeffVVAKxWK1FRUaxatYqLLrqI8PDwujq//vprpk6dSnJyMuPGjQNg+fLlLF++nBEjRgDg8XjYtm2bhCkhRIegtaakwkdBeXWT3WZNDbL2VPnQTechbBZVH3DC7EQ5HSTFhhMVZms0DEUGPEeE2LBYusDFO9VlDYLRvoDA1CA01VQd/H6LDcLjwZUANdXHvv4A7TZMNdeCdCzVBgowWqpWrFjB6tWrcTqdTJw4sdEJRkNCQuqWrVYrFRUVjR67dj+r1YrP52vlyg9ks9n44Ycf+O9//8u7777Lk08+yeeff37Yxwn8eWitueOOO7j++utbs1QhhDhifr+mqMJLnqeKvNIqcj1V5Hmq617nBbzO91Q322UWYrMc0BLULTKUE7pFmEHIdvC4oYBlp8PaNa9m9laaYSjXDEP7oCxguW79fvCWNXIABeFx4OpmBKXYfkZYciUY61wJEG4uh7mhnYzbbbdhKhgiIiIoLS1tcntxcTFutxun08mWLVv47rvvWr2GCRMmsHTpUv74xz+yfPlyCgsLW+W4Ho+H8vJyzj33XCZMmEDfvn0BOPPMM1m0aBG33nprXTffKaecwuzZs7n99tvRWrNs2TKWLFly0DHPOecc7rrrLq644gpcLhe7d+/GbreTkJDQKjULIQRAjV9TUGYGotpHqfG6LiyZQamgrBpfI11oNosizhVCXISDOFcI/btHGK9dDmLCHY2OHwq1B/+S+3ahxgtleY0EowZdbWX7je64xoS5zRCUAL1GHRiQwgOWnbFg7XjRpEUVK6UmA08AVuB5rfVDDbYnAa8A0eY+t2utD315WzsTGxvLhAkTGDJkCFOmTOG88847YPvkyZN55plnGDhwIP3796/r7mpN99xzD7NmzWLJkiWMHz+e7t27ExERccj3paSkUFJSQnV1NR988AHLly9n0KBBddtLS0uZNm0alZWVaK159NFHAXjiiSeYN28eL7zwAlarlUWLFjF+/Hhmz57NmDFjAGMA+ogRIw7qrjz77LPZvHkz48ePB8DlcvHaa69JmBJCHJKvxk9BWTW5nipyS5tuPaoNSI0NMXJYLcS5HMRFhNA9KpQhvSLNgBRCXIQRlOLN11Fh9q7RddZSfj+U5zcyBqmRrrbyAqCxLyCiPgR1GwSu0w9sOaoNTOHxYAs5+P2diNLNdfoCSikrsBWYBGQDa4BZWutNAfssBtZprRcppQYBH2utU5o7bmpqqk5LSztg3ebNmxk4cOCRfI5Oo6qqCqvVis1mY/Xq1dxwww11A+I7Kvlehegaqn1+8ssathrVv65/VFNYXt3omKNQu6U+ELlCiDdbkuofDjMohRAZauuaXWlag7cCvOVQ7YHq8maWy6CqtL5lqbarrSwXdCPzRtnCmuhWaxiQEsDhPPafPYiUUula60av+mpJy9QYYLvWeod5sLeAacCmgH00EGkuRwE5R15u15aVlcWll16K3+/H4XDw3HPPBbskIUQXVumtqW8pKj0wEOU2aEkqrvA2eoxwh7UuAPWJC2d0Skxd61G8y3FAa1J4ZxprVOM1BllXl5kBpyXLhwpH5v6NtRQ1xeqoH6gd2Qt6DDeDUTdwxdcvh8dDSIRxOaE4LC0JU72AXQGvs4GxDfa5F1iulLoZCAeOforvLqpfv36sW7cu2GUIITqpSm8NBWXV5HuqyS+rCliupqDMGJRd4KmkuqwQXZZHiLcEKzVoFBrjj6xGEeawEeV00DfMzogoB1E9HEQ7Q4hy2ol2hhDtdOB2hhDtchBmtwHK/COt6v9Yq2rAC6rceF2mjEfD/Q5676Geafl+KPD76kNKYLg5rBDUyLK/8XDZJHs4OMKNFp/A5fD4xtc3texwgd1Zv97mONz/TMRhaq1RXrOAl7XWjyilxgNLlFJDtNYHXCahlJoHzANISkpqpVMLIUTXdahwVFzqobo0H12eD+UFhPpKcKtS3HiMZ+WhD6WMUh5iLWVEKw8R2oMFP1iA5oa6VJiPgmPzWdsFq8MILfba4GIuuxIaX9/kcoMQZA+TFqEOrCVhajfQO+B1orku0HXAZACt9WqlVCgQB+wP3ElrvRhYDMaYqSOsWQghOq0mw5GnirLSQqpL8vCV5UNFIdaKApw1xbiVh2iMYBSHh35mSIpRHpwETN9iAQIaKWpsYehQN8oZiyW8J8oZA2Ex4IwxrqoKizGuwrLajHE6aLN3yfz1XbfuSJ4P9X5a8ViNPAe+R1nNcBNutui4mg5BHfBKM9H2WvJfxRqgn1KqD0aImglc3mCfLOBM4GWl1EAgFMhtzUKFEKIjaiwcFZSUU1aST3VpLj6z1chSWYitspBwfwnReHAro+VoiBmSovDgUA0GDFvMB+C1R1ET6gZnDNbw47FFxKHCYsHprg9IDZ6t9rBj/vMQojM6ZJjSWvuUUguATzGmPXhRa71RKXU/kKa1/hD4H+A5pdRtGJF/tj7UZYJCCNFBaa3J9VSRmV/O7twCygtzqSzJxefJo6YsH1VRiK2qEEd1EeH+EmLMQJSChxGqlKjaMUINWaDGaqPaEY0vxAhGFmdf7BGx2F1xZotRY61H0dgtVuzH9scghDC1qL3SnDPq4wbr7g5Y3gRMaN3SOgaXy4XH4zlo/cKFC1m0aBEjR47krrvu4pprrmHt2rX8+c9/5n//93+DUKkQ4nD4/Zo9JZVk5pWRkV9OZn4pFXu248jfRGzZVo73ZzDQksVoldfkMaqsTqrCovE6otFhPbCEx0B4LJWRcYRExKHC44xutMDWIoeLMBk7I0SHIp2/beTpp59mxYoVJCYmsn//fhYuXMgHH3xwzM7v8/mw2eTrFaI53ho/uwsryCwoJzO/jIw843l/fh7Owl/opzMYqLIYaMlkmtpFuDLuD+ZXFkoiU6iKHUt+j0GERfcgLCoOVdtS5DTGGoXYQpodvy2E6Bzkr22A22+/nd69e3PTTTcBcO+99+JyuZg/fz7Tpk2jsLAQr9fLgw8+yLRp05o8zvz589mxYwdTpkzh2muv5bbbbiMhIYF///vfTb6npqaG6667jrS0NJRSde/bvn078+fPJzc3F6vVyjvvvEPfvn35wx/+8P/bu/Pwqqp7/+PvlZmEJCQh8wxGhjAICYig4FBxqgNFC1SttbVoq9bqvW2xt7V9eu+v7a23LQ4o4tBWSouKY1st2taJQSRBQEiYhCRkgswJmYf1+2MfICrUIEl2cvJ5PU+e5Jyz2fsbj4RP1lr7u3jttdcwxvCjH/2IBQsW8NZbb/HjH/+YiIgIdu3aRX5+PkuWLOGtt96itbWV22+/XfvoyZDT0t5JcU0TBZVNFFQ1UlTd5BlpaqS4pom4rgrG+xQyzhQxw6+IW3wPktBVduynY4d/KJ0xmfgnXghxEyF2Aj4x4xih9UYi4jFww9RrS6D8w949Z9xEuOyXJ315wYIFfPe73z0Wpp599lnWrl1LUFAQL774ImFhYVRWVjJjxgyuuuqqkzaWW758OX//+9958803GTlyZI9K27p1KyUlJezYsQOA2tpaAK6//nqWLFnCvHnzaGlpoaurixdeeIGtW7eybds2KisrmTZtGrNnzwZgy5Yt7Nixg/T0dFasWEF4eDibN2+mtbWVWbNmMXfuXNLT03v8n0xkMGhs7aCu+PgAAB+nSURBVKCwqomi6sZjQamgsomi6iZK65qxFgJp40xTzJTAYq4LKmGsKSR52AGCOp39OC0GItMxsdOOhSbiJuAXnoyfpt1E5N8YuGHKBVOmTOHw4cOUlpZSUVFBREQEycnJtLe388Mf/pB33nkHHx8fSkpKOHToEHFxcb127VGjRrF//37uvPNOrrjiCubOnUtDQwMlJSXMmzcPgKCgIADWrVvHokWL8PX1JTY2ljlz5rB582bCwsKYPn36sbD0+uuvs337dtasWQM4GzXv3btXYUoGpbqmdgqPhqXKxuNTc1VNVDS0djvSMia4kXNDy7g+tISM4QXEt+wjtLEAc7T1XWcIxGZC3HWe0DQREzMeAoe78r2JyOA2cMPUvxlB6kvXXXcda9asoby8nAULFgCwatUqKioqyM3Nxd/fn7S0NFpaWj7jTKcmIiKCbdu2sXbtWpYvX86zzz7LAw88cMrnCQkJOfa1tZaHHnqISy65pDdLFekT1lqqGtsorGqksOr4VNzRz7VNH+8mHRsWyKjIAL6SUsdEv4Okdx4gtmkvwTW78GmugqOb14enQOIEiL0W4iY44SkiHXx8+v+bFBGvNHDDlEsWLFjAN7/5TSorK3n77bcBZ0QnJiYGf39/3nzzTQoLC3v9upWVlQQEBDB//nzGjBnDDTfcQGhoKElJSbz00ktcc801tLa20tnZyXnnncdjjz3GTTfdRHV1Ne+88w73338/u3bt+tg5L7nkEh599FEuvPBC/P392bNnD4mJiR8LXCL9qavLcrihlYKqxo8FpcKqJgqrmjjS2nHsWB8DCSOGkRYVwuUT4xkb2so4nyJS2/cT2bAHv4qdcHj38S07/IIgZhyMvfz4NF1sJgwb4dJ3KyJDhcLUJ2RmZtLQ0EBiYiLx8fGAs27pyiuvZOLEiWRnZzN27NhTOmd5eTnZ2dnU19fj4+PD0qVLycvLIyws7NgxJSUl3HzzzXR1OdMQv/jFLwBYuXIlt956K/fddx/+/v4899xzzJs3j40bNzJ58mSMMfzqV78iLi7uU2HqlltuoaCggKlTp2KtJTo6ul/vKJShq7Wjkz3lR8grq2Pf4SMfC02tHcd3mfLzMaREBpMSFcy0tEhSo4JJjwhklG858c378K94Hw7tgI92wJHy4xcIjXfCUsbFx6bpiByt7tQi4grjVm/N7Oxsm5OT87Hn8vPzGTdunCv1SN/R++rd6lvaySutZ2dpPTtL68grrWff4SN0dDk/WwL9fEiNCiY1KoS0qGBSPJ/TokKID2x1RpjKd8ChD53PFbugwzON7uMP0WOPT88d/RzSsxs7RER6izEm11qbfaLX9GuciPSItc4U3c7SOnaWOOEpr6yeourj3byjQwMZHx/GhWNjyEwIJzMhjJTIYHywUHPAGWUq3wEHPJ/rio5fIDjKGWGadsvxabqRZ2rHexEZ8BSmRORTurosBVWNntEmJzTlldZReaTt2DFpUcFMSgjm5snDyBzRQUZoGxE0QFMRNFVBaQ3sq4Lq/XA4D9o8OwUYH4jKgORpkH3z8eAUGgdqQSAig5DClMgQ19rRyd5DR8grruTAwRLKykqorihjWEcdkaaBkT5HuCa4le+ENBM7opEIGgjuqMOnuQb21cG+k5w4YLjTCTw8Gc66/vgUXcw4UMNLEfEiAy5MWWtP2gxTBh/td+2Szg5oroHmameUyPPRWl9BdUU5R2oO0d5QiU9zNUEddSRTz4Tum+/6AN1n17pCgCjw92yuG3ym53NUt33loj7+nJ82UhGRoWFAhamgoCCqqqqIiopSoPIC1lqqqqqONRuVz6mrE5prPxaKaKrqFpS6BybP1y21JzxVIBBuA+kklE4TSkdQBE0jUrEjYuiKiiM8KhafkG6h6Og+cxpJEhE5qQEVppKSkiguLqaiosLtUqSXBAUFkZSU5HYZA1NzLRTnQEPZp8NQ9xGl5lrgJCN8fkHY4CjaAkZQ7xNGhRlFid8EDphAituGUWNDqSaUYWHRxMYlkJKUxJlJsWQmhDE2TCFXRKQ3DKgw5e/vr61OxHs1VUPhBihYB4XrnLvZuock38CPT5PFTXI+d5s+awsIp6hlGLvqAtha5cPW8jbyy+ppbOsEnL5NGbGhZI4PIzMhjCviwxiXEEZYkL8737OIyBAwoMKUiFdprITC9U54KlgPh3c6z/sFQdI0OP9eSJ0JEalOWPIP/tjdbA0t7eSXNTitCIqcu+r2HW6gvdO5oy4kwJdx8WFcm5VEZkI44xPCyIgdTqCfrxvfrYjIkKUwJdJbGg45I04F650QVeHpSO8fDMlnw4R5kHouJE791OLsw/Ut7DxQ4Wl+WcfO0noKq44vCB85PIDxCeGcPyaazIQwxseHkRYVgo+P1haKiLhNYUrk86ov9QQnT4Cq2us8HzAcUmbApAWQdi7En/WxxpONrR1sK6gkt7CGLUU1fFhST+WR1mOvp0QGk5kQxnXdRpxiQgN1U4aIyAClMCXSU3XFnim7dc7IU/V+5/nAMEg5B6be6Iw8xU8+tkectZbSuhZyCkrYUlhDblEN+WUNdHq2WsmIGc6cM53RpswErW8SERmMFKZETqam8HhwKlgHtYXO80HhkDoLsr8BabOcheI+zjql9s4u8krryfUEp9yCGsrrnX3mggN8OSt5BN8+fzRZqRFMSY4gPFjBSURksFOYEgGwnr3jCrqteao76Lw2LMIJTzO+5XyOzTwWnmqb2tiyp5KcghpyC2vYVlxLS3sXAIkjhjEtPZLs1AiyUiMYGxeKn6+PW9+hiIj0EYUpGZqshaqPoOBdz8jTemgodV4LHumMOM38jvM5ehz4+GCtZX9lI7m5pcdGnvYddvab8/UxZCaEsWh6Clme8BQfrkaXIiJDgcKUDA3WQuUeJzwdHXk6csh5bXisM+KUNstZ8xQ9Boyhpb2TbQdryc3f76x3KqyhpqkdgPBh/mSlRjBvSiJZqRFMSgonOEB/nUREhiL99Bfv1NXltCY42iCzcAM0ejrrhyZA+mxPgDoXos4AYzhU30JuYQ05m/LJLaphZ0kdHZ6F4qOiQ7h4fOyxUadRI4erLYGIiAAKU+IturqcppjH7rbb4GzJAhCWBKMv8ow8zYLIUXR0WXYfaiB3bw25/9hKbmENxTXNAAT6+TA5eQTfnD2KrJQIpqZGEBkS8G8uLiIiQ5nClAxOXZ1Qvv34lF3hhuOb+45IhTGXHZ+6G5FKfWsHHxTVkrulhtzCTWwtqj22BUtMaCDZaRF8bWYa2WmRjI8PI8BPC8VFRKRnFKZkcOjsgLJtxxtkFr0HrXXOa5GjYNyVzpRd6ixseBJF1U3OlN1bNWwpfJfdhxqwFnwMjI0LY35WElmpEUxNiSApYpgaYoqIyOfWozBljLkUeADwBZ6w1v7yE6//FrjA8zAYiLHWjujNQmUIqj0IO9Y403ZFm6CtwXk+KuP41ixps2gNjmVHST25hdXk/uUQuYW7j3UUDw30Y0pqBJdNiCc7LYLJySMYHqjfIUREpPd85r8qxhhfYBlwMVAMbDbGvGKtzTt6jLX27m7H3wlM6YNaZaio2APrl8L2Z6CrA6LHwqQvH1vzVGkinK1YCmvIWV/Ah8XbaOt0ejulRgUzO2MkWWnOQvGMmFB8tVBcRET6UE9+RZ8O7LPW7gcwxqwGrgbyTnL8IuAnvVOeDCmlW2HdbyDvFfALgmm30HX2t9nbFulM2eVVs+W1fAo8GwAH+PowITGMr81KY2qKE56iQwM/4yIiIiK9qydhKhE42O1xMXD2iQ40xqQC6cC/Tr80GTIKN8C7v4Z9/4DAcDjvPygZcxOP5tTx8oO7aGjpACAqJICs1AgWTU8hOy2CzIRwgvx9XS5eRESGut5ePLIQWGOt7TzRi8aYxcBigJSUlF6+tAwq1jrh6d1fQ9FGp+v4RT9hT8qXWbaxgr/+cwc+Bq6clMCsM0aSlRpBalSwFoqLiMiA05MwVQIkd3uc5HnuRBYCt5/sRNbaFcAKgOzsbNvDGsWbdHVC/itOiCr/0OkBddn9bIm6gofXlfKvv20jOMCXr89K4xvnjiIuPMjtikVERP6tnoSpzUCGMSYdJ0QtBL7yyYOMMWOBCGBjr1Yo3qGjDT58Ftb9Fqr2QVQG9uplvB14Po+8c5D3C7YSEezPPRefyVfPSWVEsJpkiojI4PCZYcpa22GMuQNYi9Ma4Slr7U5jzM+AHGvtK55DFwKrrbUacZLj2ppgy9Ow4SGoL4a4SXTO/z1/68zm0bcLyC/bRnx4EPd9cTwLpydrfzsRERl0jFvZJzs72+bk5LhybekHzbWw+Ql471FoqoSUmbTN/C7P1Y5hxbsHKKxqYnR0CLfNGc3VZyWq47iIiAxoxphca232iV7TMID0riMV8N4jTpBqrYczLqbp7O/wdGkiTz5/gIqGnUxOCufeG7KYOz5WmwWLiMigpzAlvaP2oDOVt+Vp6GiBzGuomXoHT+4L5elVBdS37OLcM0aydMFZzBwdpbvyRETEayhMyemp3AvrlsL21c7jyQspn3grj37owzO/P0hrxyEuzYzjtjmjmZysHYZERMT7KEzJ51O2Dd79DeS9fKxb+f6Mm3k4t4WXnyjGAPOmJHLrnNGcETPc7WpFRET6jMKUnJrCjZ5u5W9AYBicdw/bkxbx0KY63nj7I4b5+3LTOWnccl46CSOGuV2tiIhIn1OYks9mLez7p6db+QYIHom98D7WR87j4Q2HeO+NPYQP8+euizK4aWYakSHqESUiIkOHwpScXFcn5P/F0618O4Ql0XXp/7I2YC7L1pewoySfuLAgfnTFOBZNTyEkUP87iYjI0KN//eTTPtWt/Azav/gQL3bM4tF1BzlQmU/6yBD+d/5ErpmSSKCfNhsWEZGhS2FKjmtrgg9WwvoHPd3KJ9J8zVOsqpvE468Xcqh+FxMSw3jk+qlckhmHr3pEiYiIKEwJ0FLnNNnc+IinW/k5NMz9Px4vSecPLxdR17yHc0ZFcf+1kzkvY6R6RImIiHSjMDWUHamATY/C+48f61ZeOeV2lu2PYfUzB2lu/4i542O57fzRTE2JcLtaERGRAUlhaiiqK3a6lef+welWPv5qijK/xYN5w3hpVQlQyFVnJfCtOaPJiA11u1oREZEBTWFqKKncB+t/C9ueASxMWsiu0V9n6VZY+8dyAv18uGFGKrecl05SRLDb1YqIiAwKClNDQdl2WPcb2PkS+AVis28mJ+FGluY0sf69csKC/LjjgjP42sw0ooYHul2tiIjIoKIw5c0KNzohau/rEBiGnXU3b0bMZ+l7tWx/5yAxoYH88PKxLJqeQmiQv9vVioiIDEoKU97mU93Ko+g4/0f8JfByHtpQwf6KQlKjgvn5vIl8aWoiQf7qESUiInI6FKa8RVcX7PJ0Ky/bBmGJtF38c/7UcQGPbSijrK6AcfFhPLRoCpdPjFePKBERkV6iMDXYdbbD9qPdyvdC1Bk0XbqUJ+vP5ql/FlPTdIDp6ZH84ksTmXNmtHpEiYiI9DKFqcGqvcXTrfwBqDsIcROpueJxHikfx6pXS2hqO8BFY2P49gWjyUqNdLtaERERr6UwNdi0t8CWPzgjUQ1lkDyDsvN+ztIDqbzwUgld9iBXTorntvNHMzYuzO1qRUREvJ7C1GDR3uw02Vy/1AlRqbM4eMFSfpE3kteeP0SAbykLp6WwePYokiPVI0pERKS/KEwNdO3NkPt7WLcUjpRD6rnwpcf5aPgUrnl4PVDFt+aM5uZZ6USHqkeUiIhIf1OYGqjamyHnd85I1JFDkHYezH8C0s+joaWdxcvW4+/nwyt3zFK3chERERcpTA00bU2Q+ztnYfnREHXtU5B2LgBdXZa7n9lGQVUTf/zG2QpSIiIiLlOYGijamiDnKSdENR6G9Nlw7e8gbdbHDnvgn3v5R/4hfnrleM4ZHeVSsSIiInKUwpTb2hq7hagKSJ8D5/8BUmd+6tDXd5bzwD/3cm1WEjfNTOv/WkVERORTFKbc0tYIm590QlRTJYw6H+YsgdRzTnj43kMN3P3MViYnhfM/10xQ800REZEBokdhyhhzKfAA4As8Ya395QmO+TLwU8AC26y1X+nFOr1HWyNsfgLWP+gJURfA+UsgZcZJ/0hdczuLV+YyLMCP5TdmaT89ERGRAeQzw5QxxhdYBlwMFAObjTGvWGvzuh2TAdwLzLLW1hhjYvqq4EGr9YgTojY8CE1VMPpCZyQq5ex/+8c6uyx3rf6A4pom/vzNGcSHD+ungkVERKQnejIyNR3YZ63dD2CMWQ1cDeR1O+abwDJrbQ2AtfZwbxc6aLU2wPuPw8aHPSHqImckKnl6j/74b97YzVu7K/ifayaQnaZtYURERAaanoSpROBgt8fFwCeHU84EMMasx5kK/Km19u+9UuFg1doA76+ADQ9DczWc8QVnJCp5Wo9P8bftZSx78yMWTU/m+rNT+rBYERER+bx6awG6H5ABnA8kAe8YYyZaa2u7H2SMWQwsBkhJ8dJw0FLvhKiND0NzDWTMhTk/gKTsUzrNrvJ6/vO5bUxNGcFPr8rUgnMREZEBqidhqgRI7vY4yfNcd8XAJmttO3DAGLMHJ1xt7n6QtXYFsAIgOzvbft6iB6SWenj/Mdi4zBOiLvGEqKxTPlVtUxuLn84lNMiP5TdkEeinBeciIiIDVU/C1GYgwxiTjhOiFgKfvFPvJWAR8DtjzEicab/9vVnogNVSD5sec0aiWmrhzEthzvch8dRDFEBHZxd3/vkDyutaWH3rDGLCgnq5YBEREelNnxmmrLUdxpg7gLU466GestbuNMb8DMix1r7ieW2uMSYP6AS+Z62t6svCXddS5wlRyzwh6jJPiJp6Wqe9f+1u3t1bya/mT2JqSkQvFSsiIiJ9pUdrpqy1rwKvfuK5+7p9bYF7PB/erbnWCVHvLXMC1ZjLnRCVMOW0T/3y1hIee2c/Xz0nlS9PS/7sPyAiIiKuUwf0nmquhU3LYeMj0FoHY67whKizeuX0O0rq+MHz25meFsmPvzi+V84pIiIifU9h6rM018J7jzofrXUw9otOiIqf3GuXqDrSyq0rc4kIDmDZ9VPx9/XptXOLiIhI31KYOpnmmm4hqt4Ton4A8ZN69TIdnV3c8acPqDjSyprbziE6NLBXzy8iIiJ9S2Hqk5qqnQC1abkTosZd6YSouIl9crmfv7qLjfur+PV1k5mUNKJPriEiIiJ9R2HqqKZq5868TY9BWwOMu8oToib02SWfzy3mqfUH+PqsdOZnJfXZdURERKTvKEw1VTs9ojatcELU+KudEBWb2aeX3V5cy70vfsjM0VH88PKxfXotERER6TtDN0w1Vjkh6v0V0NboCVHf7/MQBVDR4Cw4jx4eyMNfmYqfFpyLiIgMWkMvTDVWwcaH4P3HnRCVeQ3M/j7E9k87graOLr69KpeapjbW3DaTyJCAfrmuiIiI9I2hE6YaK2GDJ0S1N0HmPGckKmZcv5bx33/NY3NBDQ8sPIsJieH9em0RERHpfd4fphorYcOD8P4TToiaMB9mfw9i+n+d0jObi1j5XiG3zh7F1Wcl9vv1RUREpPd5b5hqrIL1S2HzE9DeDBOvdUJU9BhXytlSVMOPX9rJeRkj+f6lWnAuIiLiLbw3TB055LQ6ODoSFX2ma6Ucrm/htpW5xIUH8dCiKfj6GNdqERERkd7lvWEqdjzckwehca6W0drRyW1/zOVIawdPf2M6I4K14FxERMSbeG+YAteDlLWWn7y8ky1FtTxy/VTGxoW5Wo+IiIj0PjU46kOrNhWxevNBbr9gNJdPjHe7HBEREekDClN95P0D1fz0lZ1cMCaaey52Z9G7iIiI9D2FqT5QVtfMt1flkhwZzNKFWnAuIiLizbx7zZQLWto7uW1lLi3tXaxenEX4MH+3SxIREZE+pDDVi6y1/NeLO9hWXMeKG7M4IybU7ZJERESkj2marxf9YUMBz28p5q6LMpib6e6dhCIiItI/FKZ6ycaPqvjvv+XzhXGx3HVRhtvliIiISD9RmOoFxTVN3P6nLaRFBfPbBZPx0YJzERGRIUNh6jQ1t3Vy68pc2ju6ePyr2YQGacG5iIjIUKIF6KfBWsuSF7aTV1bPkzdlMyp6uNsliYiISD/TyNRpeHLdAV7eWsp/zh3DhWNj3S5HREREXKAw9Tmt21vJz1/N5/KJcXz7/NFulyMiIiIu6VGYMsZcaozZbYzZZ4xZcoLXv2aMqTDGbPV83NL7pQ4cRVVN3PHnLWTEhHL/tZMxRgvORUREhqrPXDNljPEFlgEXA8XAZmPMK9bavE8c+oy19o4+qHFAaWrrYPHKHLq6LCu+mkVIoJadiYiIDGU9GZmaDuyz1u631rYBq4Gr+7asgclay/ee286eQw089JWppEaFuF2SiIiIuKwnYSoRONjtcbHnuU+ab4zZboxZY4xJ7pXqBphH3/6Iv31Yxg8uHcucM6PdLkdEREQGgN5agP4XIM1aOwl4A/jDiQ4yxiw2xuQYY3IqKip66dL9483dh7l/7W6unJzA4tmj3C5HREREBoiehKkSoPtIU5LnuWOstVXW2lbPwyeArBOdyFq7wlqbba3Njo4ePCM7ByobuevPHzAuLoxfzZ+kBeciIiJyTE/C1GYgwxiTbowJABYCr3Q/wBgT3+3hVUB+75XoriOtHSx+OgdfH8NjN2YxLMDX7ZJERERkAPnMW9GstR3GmDuAtYAv8JS1dqcx5mdAjrX2FeA7xpirgA6gGvhaH9bcb7q6LPc8s5X9lY08/fXpJEcGu12SiIiIDDA9uq/fWvsq8Oonnruv29f3Avf2bmnue/jNfbyed4gff3E8s84Y6XY5IiIiMgCpA/pJvJF3iN+8sYcvTUnk67PS3C5HREREBiiFqRPYd/gIdz+zlYmJ4fz8SxO14FxEREROSmHqE+pb2ln8dA6Bfj48dmMWQf5acC4iIiInp71Quunqsty9eitF1U2suuVsEkYMc7skERERGeA0MtXN0n/s4Z+7DnPfleM5e1SU2+WIiIjIIKAw5fH3HWU8+K99fDk7iRtnpLpdjoiIiAwSClPA7vIG7nl2G2clj+BnV0/QgnMRERHpsSEfpuqa2lm8MoeQQD+W36AF5yIiInJqhnSY6uyy3Ln6A0prm1l+w1TiwoPcLklEREQGmSF9N9/9a3fzzp4KfvGliWSlRrpdjoiIiAxCQ3Zk6q/bS1n+9kd85ewUFk1PcbscERERGaSGZJjKK63ne89tJzs1gp9emel2OSIiIjKIDbkwVdPYxuKVOYQN8+ORG6YS4Dfk/hOIiIhILxpSa6Y6Oru4489bOFzfyrO3nUNMqBaci4iIyOkZUmHql6/tYv2+Kn517STOSh7hdjkiIiLiBYbMHNeLHxTzxLoDfG1mGl/OTna7HBEREfESQyJM7SipY8nzH3J2eiT/dcU4t8sRERERL+L1YarySCuLn84hKiSAZddPxd/X679lERER6UdevWaqvbOL21dtoaqxjTW3zWTk8EC3SxIREREv49Vh6v/9LZ9NB6r57YLJTEwKd7scERER8UJeO+e18aMqfr+hgFvOTWfelCS3yxEREREv5bUjUzNGRfLQoilcNiHO7VJERETEi3ltmDLGcOXkBLfLEBERES/ntdN8IiIiIv1BYUpERETkNChMiYiIiJwGhSkRERGR06AwJSIiInIajLXWnQsbUwEU9sOlRgKV/XAd6Rt6/wY/vYeDn97DwU3vX+9ItdZGn+gF18JUfzHG5Fhrs92uQz4fvX+Dn97DwU/v4eCm96/vaZpPRERE5DQoTImIiIichqEQpla4XYCcFr1/g5/ew8FP7+Hgpvevj3n9mikRERGRvjQURqZERERE+ozXhiljzKXGmN3GmH3GmCVu1yOnxhiTbIx50xiTZ4zZaYy5y+2a5NQZY3yNMR8YY/7qdi1y6owxI4wxa4wxu4wx+caYc9yuSU6NMeZuz8/QHcaYPxtjgtyuyRt5ZZgyxvgCy4DLgPHAImPMeHerklPUAfyHtXY8MAO4Xe/hoHQXkO92EfK5PQD83Vo7FpiM3stBxRiTCHwHyLbWTgB8gYXuVuWdvDJMAdOBfdba/dbaNmA1cLXLNckpsNaWWWu3eL5uwPkhnuhuVXIqjDFJwBXAE27XIqfOGBMOzAaeBLDWtllra92tSj4HP2CYMcYPCAZKXa7HK3lrmEoEDnZ7XIz+IR60jDFpwBRgk7uVyClaCnwf6HK7EPlc0oEK4HeeqdonjDEhbhclPWetLQH+DygCyoA6a+3r7lblnbw1TImXMMYMB54HvmutrXe7HukZY8wXgcPW2ly3a5HPzQ+YCjxqrZ0CNAJafzqIGGMicGZl0oEEIMQYc4O7VXknbw1TJUByt8dJnudkEDHG+OMEqVXW2hfcrkdOySzgKmNMAc40+4XGmD+6W5KcomKg2Fp7dER4DU64ksHjC8ABa22FtbYdeAGY6XJNXslbw9RmIMMYk26MCcBZcPeKyzXJKTDGGJy1GvnW2t+4XY+cGmvtvdbaJGttGs7fv39Za/Ub8SBirS0HDhpjxnieugjIc7EkOXVFwAxjTLDnZ+pF6CaCPuHndgF9wVrbYYy5A1iLc/fCU9banS6XJadmFnAj8KExZqvnuR9aa191sSaRoeZOYJXnl9L9wM0u1yOnwFq7yRizBtiCc4f0B6gbep9QB3QRERGR0+Ct03wiIiIi/UJhSkREROQ0KEyJiIiInAaFKREREZHToDAlIiIichoUpkREREROg8KUiIiIyGlQmBIRERE5Df8fwmOBBWQVs+8AAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#With SIFT\n",
        "exp_2.validate(test_loader)"
      ],
      "metadata": {
        "id": "iKS26yDBxgIw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68,
          "referenced_widgets": [
            "9f89949ea90e49c1ba4f667f73b0b6bd",
            "7506f9a6c52c4fbf85e598403c55cfea",
            "d4b443dcb3904668afb1869982f76c10",
            "107ef30155d64ffebe08db76de681677",
            "0d29c96c382642a6b326abde0b62501b",
            "0d874e6368bb4fc69fa799879bd65487",
            "5e20c23c4aa7447c9ea5448b6bb0bcf1",
            "7acee375af414453b5ec6da6f654be71",
            "82ca0fab75a64293a33bc098f53aefac",
            "1d31007a52694c0aa218a40eba146795",
            "2f4cefe5960b43d889facbee34af36d6"
          ]
        },
        "outputId": "eae70f08-dee1-4738-8d10-6d43200fc90c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/500 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9f89949ea90e49c1ba4f667f73b0b6bd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.46759894, 0.8404, 0.8404)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sn\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "\n",
        "#test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size_val)\n",
        "y_hat,y_true = exp_2.predict_loader(test_loader)\n",
        "#y_true = dataset.labels[test_dataset.indices]\n",
        "print(\"f1 score : \",f1_score(y_hat,y_true, average='micro'))\n",
        "print(classification_report(y_true, y_hat,target_names=list(classes)))\n",
        "\n",
        "M = confusion_matrix(y_true, y_hat)\n",
        "\n",
        "df_cm = pd.DataFrame(M, index = [i for i in list(classes)],\n",
        "                      columns = [i for i in list(classes)])\n",
        "plt.figure(figsize = (15,12))\n",
        "sn.heatmap(df_cm, annot=True)"
      ],
      "metadata": {
        "id": "7XPeFOlS6-rb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "5188e0a857a147ea839d898047b9a6d7",
            "34e48c14a9fd4f059d74ca97ce02d1c8",
            "5e950f32787a41ecbf5ccca1deae20fa",
            "e5262ada076b4ddfb3a26ea933adac2a",
            "630a65205806437e939d84bfaa813068",
            "e485c71102734b4881ce9dfe6db957ca",
            "3bfd43e96de048d79fadd52acb7d7e77",
            "d3e0f146d8794bb0be0bdc049d3d1922",
            "615d0007a089434c82fce2cdad829f24",
            "7a39ac111d8545908ea3bfe2b8483b63",
            "54f84fd53a684fbda693e3826700a49b"
          ]
        },
        "outputId": "d07738e7-68b5-49f8-f6ba-22a91b8044b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/500 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5188e0a857a147ea839d898047b9a6d7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "f1 score :  0.8404999999999999\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       plane       0.83      0.88      0.86      1000\n",
            "         car       0.93      0.91      0.92      1000\n",
            "        bird       0.78      0.75      0.76      1000\n",
            "         cat       0.68      0.76      0.72      1000\n",
            "        deer       0.81      0.81      0.81      1000\n",
            "         dog       0.82      0.77      0.79      1000\n",
            "        frog       0.94      0.80      0.86      1000\n",
            "       horse       0.89      0.90      0.89      1000\n",
            "        ship       0.96      0.86      0.91      1000\n",
            "       truck       0.82      0.97      0.89      1000\n",
            "\n",
            "    accuracy                           0.84     10000\n",
            "   macro avg       0.85      0.84      0.84     10000\n",
            "weighted avg       0.85      0.84      0.84     10000\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9cb1ae9610>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x864 with 2 Axes>"
            ],
            "image/png": 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Kw7FERERERJykienqhIiIiIiIiLPUCRERERERcZJVJ0SdEBERERERcZSKEBERERERcZSGY4mIiIiIOEkT09UJERERERERZ6kIERERERERR2k4loiIiIiIk3R0LHVCRERERETEWeqEiIiIiIg4SRPT1QkRERERERFnqQgRERERERFHaTiWiIiIiIiTNDFdnRAREREREXGWOiEiIiIiIk7SxHR1QkRERERExFkqQkRERERExFE3HI5ljPEDhllrn3Ugj4iIiIhI6qbhWDfuhFhrY4E7jDHpHMiTLDweDytXzGbSxKFuR7kpI6bN5/E3uvP4690YPm3+Vbf/eSCcFh17UerpN/hp8tzbss2L0dF0+HIIj7b9mObv9eRgxFEAlq7fytMdetD0ze483aEHyzduvy3bc1rdOtXZvOl3tm1ZzDsd2rodx3Up/TVyO3w/6EvCDqxn3drf3I7iE/QauUz7Il7+/HmZO2ccG9bPZ/26ebR79QW3I7kmMDCQpUumsXrVr6xfN4+PPnzL7UiO++/LLZi9ZAKzFv/CN4M+J11gOvIXzMfEOSOYv3IqfX/4goAATT2Wf+btcKw9wBJjzAfGmPaXLskZ7HZ6rd3/2Lptp9sxbsrOfWH8MvcPRvbowLjeHfl91Sb2HTqSZJ1MIRl474WnaNX4kX/9+w9GHOW/H3591fIJvy0lU8Zgpvf7mJYNa/D18MkAZA7JSN+OLzHhq050a9eSTn2G3dwDc5HH46HPN91p2KgFxR6uwdNPP8Z9993tdixXpeTXyO0ybNhYHm2ohi/oNXIl7YvLYmJi6PBOFx56uAaVKjeiTZvn0+y+uHDhArXqNKNU6dqUKl2HunWqU65sSbdjOSZXnpw837o5jWv+H/UqP4Gfn4dGTevx3kevM7j/CGqUacTJE6do1uJxt6P6Lhvn7sUHeFuE7AamJawfcsXF5+XLl4f69WsyZMgot6PclD8PhPPQ3YUIDkyHv58fpR8owtzl65Ksky00hAeL3IG/v99V95+2cAXN3+3JU299RtcBo4iN9e6Jt2DFBhpXLwdA7QolWL5xO9Za7ruzADmzZgagSIE8nL8YzcXo6Ft8lM4qW6YEu3f/xZ9/7iM6OpqxYyfTuFFdt2O5JqW/Rm6XRYuXc+z4Cbdj+AS9Ri7TvrgsPDyCtes2ARAVdYZt23aSL29ul1O558yZswAEBPjjHxCAtdblRM7y8/cjKCgQPz8/goKDORIeSYUqZZk55VcAfhk9hToN/v2Xo5J2eFWEWGu7XOuS3OFuhy+/7ELHjt2IS6Fj74oUzMuarbs4cTqKcxcusmjNZg5HHvfqvnsOhDNryRqGdm/PuC874vF4mL5opVf3PXzsJLmyZwHA38+PjOmDOXH6TJJ1fl22jvsKFyBdQMC/e1Auy5svN/sPhCVeP3DwEHnT8BtpSn+NyO2n18hl2hfXdscd+Sn+8IMsX7HW7Siu8Xg8rFo5h0MHN/Dbb7+zYmXa2ReHD0Xw/bdDWbJ+Nsu3zOX0qdNsXL+FUydPExsbC0B42GFy5cnpclLxZV4N1jPG5ADeAR4Agi4tt9Zes8Q1xrQGWgN4/ELxeDLcetKb0KBBLY5ERLJm7UaqVq3gSoZbdWf+3Pznsdq81LUfwYHpuLdQfjwe7xpYyzdsZ+uefTR/9wsAzl+MJmtoRgDe6DGIgxFHiY6J5VDkMZ566zMAnn20Oo89cuN9tWvfIb4ePpmBH6bd8dGpQWp4jYiIszJkSM/YMd/T/u2POH06yu04romLi6N0mTqEhmbil3GDeeCBe9m8OWXOk/y3MoWGULtBDaqWbMCpk6fp92NPqtWs5HaslEVf/Hl9ssKfgTFAQ+BloBVw5J9WttYOAgYBBKTL51p/smLF0jRsWId69R4hKCiQTJlCGPpTH1o9/5pbkW5K01oVaVqrIgDf/DyFXNkye3U/i6Vx9XK83qLJVbd9/W5rIH5OyAffDmdI1zeS3J4rayiHI4+TO1sWYmJjiTp7jswh8cVk+NHjvPnFILq/1pICuXPcykNzRdjBcArkz5t4PX++PISFhbuYyD2p5TUit5deI5dpXyTl7+/PuDHfM2rURCZNmul2HJ9w8uQpFixcEn8AgzRShFSuVp79ew9y7Gj8yIzZ036jVLniZAoNwc/Pj9jYWHLnzcXhQxEuJxVf5u2ckGzW2sFAtLV2obX2v4DPD/Tr3PlzCt9ZmrvvKc+zLV5h/vwlKfLD1dGTpwE4dOQYvy1bT4Mqpb26X7li9/Lr0nWJ9z95+gxhEce8um/1MsWYsmA5AL8uXUvZB+/BGMOpM2d5tfsAXm/RhBJF77qJR+O+lavWUaRIYQoVKkBAQADNmjVh6rQ5bsdyRWp5jcjtpdfIZdoXSX0/6Eu2btvF198McjuKq7Jnz0poaCYAgoKCqFWzKtu373Y5lXPCDoZTovRDBAXHD46pWLUcu7bvYdnildRvXBuAJ55pzK8zrz6ipyTQxHSvOyGXZh4fMsY8CoQBWZMnkvxd+54/cPL0Gfz9/Hj/xWZkypCesbMXAdCsbhUij5/imXe+4My583iMYcS0BUz6phN3FcjDq80b8nLXb4mLs/j7x98/b84b/9c9XrMi7/cZxqNtPyY0Ywa+ePM/AIye+Tv7wo8wcNxMBo6L/xZswIevki00RRynAIDY2Fhef6MzM6aPxM/j4aehY9iyZYfbscRlI4b3o1rVCmTPnpW/9qyiS9de/PjTaLdjuUKvkcu0Ly6rVLEMLVs8yYaNW1i1Mr4Q++CDz5k5a57LyZyXJ08uhgz+Gj8/Dx6Ph/HjpzJ9xu05RH5KsG71RmZO+ZVp80cTExPLlo3bGDV0PPPm/E7fH77grffbsmXjNsaOmOh2VPFhxpujORhjGgKLgAJAXyAT8LG1duqN7uvmcCxfE7XmJ7cj+IwMJZ93O4LPMG4H8CH6YyEi8u8UCMnudgSf8ufR9SnibfXc5C9cfcsLbvKO6/vJ207IU8Bia+0moIYxJivQC7hhESIiIiIiIlfQxHSv54Q8ZK1NPIC+tfYYUCJ5IomIiIiISGrmbSfEY4zJYq09DpDQCfH2viIiIiIicomPTA53k7eFxJfAUmPMuITrTwHdkyeSiIiIiIikZl4VIdbaYcaYVVw+LG9Ta+2W5IslIiIiIiKplddDqhKKDhUeIiIiIiK3QhPTvZ6YLiIiIiIicluoCBERERERkSSMMW8aYzYbYzYZY0YZY4KMMYWNMcuNMbuMMWOMMekS1g1MuL4r4fZCN/r9KkJERERERJwUF+fu5QaMMfmA14DS1toHAT/gGaAH8JW1tghwHHgh4S4vAMcTln+VsN51qQgREREREZG/8weCjTH+QHrgEPEHqRqfcPtQ4LGEn5skXCfh9prGmOuelV1FiIiIiIiIk6x193LDePYg0AvYR3zxcRJYDZyw1sYkrHYAyJfwcz5gf8J9YxLWz3a9bagIERERERFJQ4wxrY0xq664tP7b7VmI724UBvICGYB6tzODznouIiIiIpKGWGsHAYOus0ot4E9r7REAY8wEoBKQ2Rjjn9DtyA8cTFj/IFAAOJAwfCsUOHq9DOqEiIiIiIg4yccnphM/DKu8MSZ9wtyOmsSfL3A+8GTCOq2AyQk/T0m4TsLt86y9/rgvFSEiIiIiIpLIWruc+Anma4CNxNcMg4B3gfbGmF3Ez/kYnHCXwUC2hOXtgfdutA0NxxIRERERcVIKOGO6tfYj4KO/Ld4DlL3GuueBp/7N71cnREREREREHKUiREREREREHKXhWCIiIiIiTrK+PxwruakTIiIiIiIijlInRERERETESSlgYnpyUydEREREREQcpSJEREREREQcpeFYIiIiIiJOuv7JxNMEdUJERERERMRR6oSIiIiIiDhJE9PVCREREREREWcleycka3BIcm8ixchQ8nm3I/iMU182cTuCzyjYaa7bEXxGev9AtyP4jGPno9yO4DNi4mLdjuATLBpDfkmcvkVOdDDqqNsRRG6KhmOJiIjPUgEiIqmSCmkNxxIREREREWepEyIiIiIi4iSrTog6ISIiIiIi4igVISIiIiIi4igNxxIRERERcZCN09Hu1AkRERERERFHqQgRERERERFHaTiWiIiIiIiTdJ4QdUJERERERMRZ6oSIiIiIiDhJ5wlRJ0RERERERJylIkRERERERByl4VgiIiIiIk7SeULUCREREREREWepEyIiIiIi4iQdoledEBERERERcZaKEBERERERcZSGY4mIiIiIOEnDsdQJERERERERZ6kTIiIiIiLiJKtD9KoTIiIiIiIijlIRIiIiIiIijtJwLBERERERJ2liujohIiIiIiLiLHVCREREREScFKeJ6dctQowxfYF/3EvW2tdueyIREREREUnVbjQcaxWwGggCSgI7Ey7FgXTJG01ERERERFKj63ZCrLVDAYwxbYDK1tqYhOsDgEXJH+/mrNzwG2eizhAbG0tMbCx1qz/J2++9SotWT3E08hgAn3b9it9+/d3lpM6qW6c6vXt3xc/jYciPo/iiZz+3I/0rfx0/w7uzNyVeP3jyHG3K3cmzxQsmLlt14DhvzlhP3kzBADxyZw5eKnvnLW33YmwcH/y6ma1HThMaFECPug+SN1Mwy/Ydpc/S3UTHxhHg5+GNSkUomz/rLW3LSR6Ph7kLJxB+6DDNm73E1FkjyZgxAwA5cmRlzeqNPNf8FZdTJq87ixSi3+CeidcLFspP78/6MXjACABebPscH3zSgYeLVOH4sRNuxXREvnx5+P6HL8mZMzvWWn4cMorvvvuJocP6cvc98a+h0NBMnDx5iorlH3U5bfIaOLAn9evX5MiRo5QqVRuA4cP7cU/CfsicORMnTpyiXLn6bsZ0xMCBvWiQsC9KlqoFQOfOb/Lf/zQnMvIoAB9+2INZs+e7GdMVO3csIyoqitjYOGJiYihfoYHbkRwzaGAvGjSoxZEjkZQoGf+8yJIlMz///B133FGAvXv307x5G06cOOlyUh9mNTHd2zkhWYBMwLGE6xkTlvmspg2f49jfPjQM/G4o/fsOcSmRuzweD32+6U69Bv/HgQOHWLZ0BlOnzWHr1p1uR/NaoSwZGPNMOQBi4yx1f1pMjTtzXLVeiTyZ6dOo+L/+/WGnzvHh3C380LRUkuWTtoQREhjAlJYVmbUjnG/+2EWPesXIHJyOrx99mJwZA9l1NIpXpqxjzn8q39yDc8FLbVqxc8duQkIyAtCoXvPE234c3peZM35zK5pj9uz6i/rVngLiXyMrNv/GrGnxjztPvlxUrVGRA/vD3IzomJjYGDp27M76dZvJmDEDi5ZMZd68xbR6rl3iOp9+1olTp065mNIZw4ePo3//oQwe/FXispYt2yb+/PnnnTl16rQb0RwXvy9+Ysjgr5Ms79v3B776eqBLqXxHrdpPcfTocbdjOG7Y8HF81/8nfhxy+XnxToe2zJ+3hJ69+tHh7ba806Et73f61MWU4uu8PTrW58BaY8xPxpihwBpAz6wUpGyZEuze/Rd//rmP6Ohoxo6dTONGdd2OddNWHDhG/kzBiR0Pb0zffogWY1fy9OjldJu/lVgvJ4Ut2HOERkXzAFCrSE5WHDiOtZaiOULImTEQgLuyZuBCTCwXY1PGNxt58uaidt3qjBg67qrbMoZkoErV8syY9qsLydxTqVo59v21n4MHDgHwUfd3+PSj3tg0clbbw+FHWL9uMwBRUWfYvn0XefLmTrJO0ycaMG7sVDfiOWrx4hUcP/7Pna8nn2zImDGTHUzknsWLl193X0jadK3nRaNGdRg+Iv49ZfiIcTRunHI/Y4gzbliEGGM8wHagHDARmABUuDRUyzdZxkwazJyFv9Dy+WaJS//74rPMXzKZr7/tTmjmTC7mc17efLnZf+DyN7oHDh4i798+YKQks3cept49ua5524bwkzQbtZy2U9ax+2gUAHuOnWHOzgh+fKIUY54ph8cYZuwI92pbEWcukDskvtjw93jImM6fE+ejk6wzd3cERXOEkM4vZRz1uvvnnejy4RfEXeM45Q0a1ub3hUuJOn3GhWTuady0PpN/mQlA7fo1CD8UwdbNO1xO5Y6CBfPx8MP3s2rlusRllSqVJXJAwFgAACAASURBVCIikt27/3IvmA+oXLkshw9rP7zcphWrVs5h4MBeZM4c6nYcV1hrmTljFMuXzeR/LzzrdhzX5cyZnfDwCADCwyPImTO7y4l8XJx19+IDbjgcy1obZ4zpZ60tAaSIr34a1W1O+KEIsmfPythJQ9i5Yw9DB4+i9xffYa3lvc6v06Xbu7zxaie3o8pNiI6NY+GfkbSrcNdVtxXNGcKMVpVIn86fRX9F8uaMDUxpWZEVB46xJeIULcatBOBCTBxZg+OPrdB+xgYOnjpHdGwc4VEXeHr0cgCaP1SAJvfnvWGe3Uej6PPHbr5r8u+HgLmhTr3qREYeZf26zVSqXPaq25s+2ZARQ8e6kMw9AQH+1K5XnR5dvyEoOIhX2/+PFk1fcjuWKzJkSM/Po/rz7jufcPp0VOLyp5o1ShNdkBtp1qwJY8emiLfCZDNo0HA+/fQbrLV8/HEHevT4gJdeetvtWI6rXuNxwsLCyZEjG7Nmjmbb9l0sXrzc7Vg+I610keXmeTsn5DdjzBPABOvFs8oY0xpoDRASlIvgdJlvIeK/F34ovhKPjDzGjGlzKVHqIZb9sSrx9hFDxzFiTH9HM7kt7GA4BfJf/kCdP18ewsK86wT4msV7j1I0RwjZ0gdedVvGdJef0lUKZeezhds5fu4i1kKjonl4rWKRq+7Tu8FDwD/PCcmZIZDw0xfIlTGImLg4oi7GkDkoAIDDUedpP2MDn9S+nwKh6W/nw0w2ZcuVol79mtSqXY3AoEBCQjLS//uetHmxA1mzZqFkqWK0SuUT0v+ueq0qbNqwlcgjR7n3vrspUDAfsxaNB+KHrs1YMJbGtf6PIxFHXU6avPz9/fl5ZH/GjJ7MlMmzE5f7+fnRuHE9Kldu5GI69/n5+dGkST0qVkzdE/NvJCIiMvHnIUNGMnHCT+6FcdGl99AjR44yafJMypQpnqaLkIiISHLnzkl4eAS5c+fkyJHU/ffyVlmdMd3rOSEvAeOAC8aYU8aY08aYf5ydaK0dZK0tba0t7XQBkj59MBkSjvCTPn0w1R+pxLYtO8iZ6/IE5gYNa7EtBU3Ivh1WrlpHkSKFKVSoAAEBATRr1oSp0+a4HeumzNoZTr27rz0UK/LMhcRvXzYdPom1lsxBAZQtkIW5uyM4dvYiACfPRxN26pxX26tWODtTt8XPE5i7K4Iy+bNgjOH0hWjaTV3PaxWLUDyPs8/zW9Gty5c8dF9VShZ7hNb/eZPFvy+jzYsdAGj8WF3mzFrAhQsXXU7prCZPXB6KtX3rTkreW51KxetRqXg9DoUdpkH1Zqm+AAH4rn8Ptm/fxbd9BydZXuORSuzYsZuwgynzi4vb5ZFHKrNjx24OpvH9kDt3zsSfmzSux+bN211M44706YMTjyaYPn0wtWtVS5P74UpTp/1KyxbxB/po2eIppk5NmZ8xxDledUKstSHJHeR2yZEzGz+O+BYAP38/Jo6fxvzfFvPtwB48WOw+rLXs33eQt9/4yOWkzoqNjeX1NzozY/pI/Dwefho6hi1bUt5493PRsSzfd4zO1e9LXDZu0wEAnnowP3N3RzBu00H8jCHI38NndR/EGMNdWTPStvxdtJmyFmvB32N4r9q9Xk1sf+z+vHT+dQuNh/9BpsAAPq/7IACjNxxg/8mzDFr5J4NW/glA/8YlyJo+5Z5C5/EnHuWbrwa5HcNRwemDqVK9Ah3f7Op2FFdVqFCa5s82ZdPGbfyxbDoAH3/UkzmzF/Dkk40YN26KywmdM2xYX6pUqUD27FnYtWs53br15qefxtCsWWPGjEk7+wFg2LBvqVqlPNmzZ2X3rhV80u1LqlatwMMPPYC1lr17D9D21ffcjum4XLlyMH5cfLHu5+/H6NGTmDNngbuhHDR82LdUrVqB7Nmzsmf3Srp+8iU9e37LyJEDeP4/z7Bv3wGaN2/jdkzxceZ6o6uMMUWttduMMSWvdbu1ds2NNpArtKgGBSY4ei5tHNLRG6e+bOJ2BJ9RsNNctyP4jPT+Vw+xS6uOnY+68UppQExcrNsRfIZFb6eXXOugGmmVMcbtCD7l4oUDKWKHnOn+nKsv6Aydhrm+n27UCWlP/NyOLyHJXz+TcP2RZMolIiIiIiKp1I3OmN464ccGwCtAZeKLj0VA2prZLSIiIiJyO+iM6V4fHWsocArok3C9OTAMaPaP9xAREREREbkGb4uQB621919xfb4xZktyBBIRERERkdTN2yJkjTGmvLV2GYAxphyw6gb3ERERERGRv/ORs5a76bpFiDFmI/FzQAKAP4wx+xKu3wFsS/54IiIiIiKS2tyoE9LQkRQiIiIiImmFDjN9w6Nj7XUqiIiIiIiIpA0etwOIiIiIiEja4u3EdBERERERuR00MV2dEBERERERcZY6ISIiIiIiTtIZ09UJERERERERZ6kIERERERERR2k4loiIiIiIkzQxXZ0QERERERFxljohIiIiIiIOsjpjujohIiIiIiLiLBUhIiIiIiLiKA3HEhERERFxkiamqxMiIiIiIiLOUhEiIiIiIiKO0nAsEREREREnaTiWOiEiIiIiIuIsdUJERERERJxkdZ4QdUJERERERMRRKkJERERERMRRGo4lIiIiIuIkTUxP/iLk1MVzyb0JSYEKdprrdgSfsa/Xo25H8Bn52k91O4LPyBqU0e0IPuHw2RNuR/AZBuN2BJ+hj2+X+Xv83I4gclPUCRERERERcZBVJ0RzQkRERERExFkqQkRERERExFEajiUiIiIi4iQNx1InREREREREnKVOiIiIiIiIk+J0xnR1QkRERERExFEqQkRERERExFEajiUiIiIi4iRNTFcnREREREREnKVOiIiIiIiIk9QJUSdEREREREScpSJEREREREQcpeFYIiIiIiIOslbDsdQJERERERERR6kTIiIiIiLiJE1MVydEREREREScpSJEREREREQcpeFYIiIiIiJO0nAsdUJERERERMRZKkJERERERMRRGo4lIiIiIuIgq+FY6oSIiIiIiIiz1AkREREREXGSOiHqhIiIiIiIiLNUhIiIiIiIiKO8KkKMMZW8WSYiIiIiIjcQ5/LFB3jbCenr5TIREREREZHruu7EdGNMBaAikMMY0/6KmzIBfskZ7Gblz5+HH374ipw5s2OtZciQkfTr9yNNmzagU6c3KVq0CFWqNGbNmo1uR3VUYGAgC+b9QrrAQPz9/ZgwYTpdun7pdizHeTwe5i6cQPihwzRv9hJTZ40kY8YMAOTIkZU1qzfyXPNXXE55Y38di+LdaWsTrx88eY42Fe/m2VKFk6y3av9Res7fQkycJXNwOgY/Xf6WtnsxJpYPZm5ga8RJQoMC6NGwBHlD07PsryP0WbSd6Lg4Ajwe3qhWlLIFs9/StpwQGJiO6bNHERiYDj9/f6ZMmsXn3b+hSrXyfNK9I+nSBbBu7SbavdKR2NhYt+MmqzuLFKLf4J6J1wsWyk/vz/qROWtm6tSvQVxcHEcjj/FW284cDj/iYtLkN3BgLxrUr8mRI0cpWaoWAJ992olHH63FxYvR7Nmzlxdbv8XJk6dcTpr8Bg7sSf2EfVGqVG0AHnrofvr2/ZSgoEBiYmJ5/fVOrFq13uWkzkrr76n/9FkrS5ZQhg/vxx135Gfv3gO0aPEKJ06k/tfJzdAhesFY+887wRhTDagOvAwMuOKm08BUa+3OG20gOPgOR/dy7tw5yZ07J+vWbSJjxgz88cc0mjVrjbWWuLg4vv32Uzp27O5KERIdG+P4Nq+UIUN6zpw5i7+/P78vmMib7T9i+Yo1rmTJHJTBle22afsfipd8kJCQjDRv9lKS234c3peZM35j7KhJjmba1+vRW7p/bJyl7sDfGPZsJfJmCk5cfvp8NK1GLaXfE2XIkymYY2cvkDV9oFe/M+zkWT6ctYEf/la0jF23lx1HTtG5djFmbQtj/s7D9GhUgm2HT5I1QyA5MwaxK/I0r/yygjkv1fzXjyVf+6n/+j636srXxcxfR9Ppve4MGdqHJg1bsnvXX3Ts/Dr794UxYtg4R3OFpAu+8UrJxOPxsGLzbzSp3ZyTJ08RdfoMAP9p3Zy7772L99/6xLEsh8+ecGxbl1SuXI6oqDMMGfx1YhFSq1ZV5s9fQmxsLN27dQSgU+fPHM1lMI5uD6By5bJERZ1l8OCvEouQadNG0KfPD8yZs4C6dWvw1lsvU6fO047miolz/0sBX3lPDfBz/kCn//RZq2XLJzl+/AS9evXn7bfbkDlzKJ07f+5otnPn9jr/QrkJJ559xNUqJPPP81zfT9cdjmWtXWit7QKUt9Z2ueLS25sCxA3h4RGsW7cJgKioM2zbtou8eXOxffsudu7c43I6d505cxaAgAB//AMCuF4BmhrlyZuL2nWrM2Lo1R8mM4ZkoErV8syY9qsLyW7Nin2R5M+cIUkBAjBzWxg1785FnoTlVxYg07ccpMXPS3h62CK6/bqRWC+/kVmw6zCNHsgPQK17crNiXyTWWormCiVnxiAA7sqWkQsxcVyMcf9DgjeufF0EBAQQGxvHxYvR7N71FwAL5i2hcZO6LiZ0XqVq5dj3134OHjiUWIAApE8fnCb+bixevJzjx5MWP3Pn/p7YDVu+Yi358udxI5rjFi9ecdW+sNaSKVMIAKGhIRw6dNiNaK5Ly++p//RZq2HD2owY8QsAI0b8QqNGddyMKT7O2/L5rDGmJ/AAEHRpobX2kWRJdZsULJif4sUfYOXKdW5H8Qkej4cVy2dR5K5C9B/wEytWrr3xnVKR7p93osuHXyQOv7pSg4a1+X3h0iQfuFKK2dsOUa/o1R+I9h4/Q0xsHP8bs4yzF2P4v5KFaPRAfvYcjWLO9kP8+EwFAvw8fDp3EzO2HkwsLq4nIuo8uUPi/wT4ezxkDAzgxLlosqRPl7jO3J3hFM2ZiXT+Pjli8yoej4cFiydR+M47GDxoBKtXrcff34/iJR5k3dpNNH6sXpr5wHlJ46b1mfzLzMTrHTq144lnGnP61GmebvyCi8l8w/OtmjFuvPNdO1/x9ttdmDZtOJ9/3gljPNSo8bjbkVyR1t9TL7nys1bOnNkJD48A4guVnDl9f1iuazQcy+uJ6T8D24DCQBfgL2DlP61sjGltjFlljFkVExN1yyFvRoYM6Rk1agAdOnTl9Gl3MviauLg4Spepwx2FS1OmdAkeeOBetyM5pk696kRGHmX9us3XvL3pkw2ZMH6aw6luXXRsHAt3H6b2PVd/SI6Ns2yNOEXfpqXp90RZvl+2i73HolixL5Ith08mdkJW7DvKwZPx3+i1n7yap4ct4tUJq9hy+CRPD1vE08MWMXnTfq/y7I48TZ/ft9O59oO39XEmp7i4OKpWbMwD91amZOmHue/+u3nh+Tf4tEcn5i74haioM6l+PsiVAgL8qV2vOtMnz0lc1rN7X8oXq82kcdN5/sX/czGd+959tx0xMbGMGjXR7Siuad26JR06dKVIkfK8805XBgzoeeM7pUJp+T31kht91kpDzSG5Cd52QrJZawcbY1631i4EFhpj/rEIsdYOAgaB83NCAPz9/Rk1agBjxkxi8uRZTm/e5508eYoFC5dQt051Nm/e7nYcR5QtV4p69WtSq3Y1AoMCCQnJSP/ve9LmxQ5kzZqFkqWK0SoFTEj/u8V/HqForlCyZbh6rkfOjEGEBgUQHOBPcACUzJ+VHUdOYy00eiAfr1UpetV9ejcpBfzznJCcGYMIP32eXCHBxMTFEXUhmszBAQAcPn2O9lNW80n9hyiQ2Z05P7fi1MnTLPp9GTVrVeXbPoNpUCf+w3aNRypzV5HCN7h36lG9VhU2bdhK5JGjV902cdx0ho79jt6ff+dCMve1bPkUDerXpF79Z9yO4qoWLZ7grbc+AuCXX6bRv38PlxO5Ky2+p8K1P2tFRESSO3dOwsMjyJ07J0eORLqc0of5yGFy3eRtJyQ64d9DxphHjTElgKzJlOmWDRjwBdu376JPnx/cjuIzsmfPSmhoJgCCgoKoVbMq27fvdjmVc7p1+ZKH7qtKyWKP0Po/b7L492W0ebEDAI0fq8ucWQu4cOGiyyn/vVnbwq45FAugepFcrAs7TkxcHOeiY9l06ASFs2Wk7B3ZmLsjnGNnLwBw8txFwk6d82p71e7KydTNBwCYuyOcMgWzYYzh9Plo2k1cxWtVilI8n8/+abhKtuxZyRQaP7Y9KCiQGo9UYueOPWTPEf8Y0qVLx+vtW/Pj4JFuxnRUkyeSDsUqdGfBxJ/rNHiE3Tv/dCOW6+rUrs5b7V/miSf/y7lz592O46pDhw5TtWr8FxQ1alRiV8L8qbQkrb+nwrU/a02fPpcWLZ4A4ovVaSlwnqU4x9tOSDdjTCjwFvHnB8kEvJFsqW5BxYqlefbZJ9i4cSvLls0A4KOPehIYmI7evbuQPXtWJkz4kQ0bttC48XMup3VOnjy5GDL4a/z8PHg8HsaPn8r0GXPdjuUTHn/iUb75apDbMf61c9ExLN8bmWTo07j1ewF46uE7uDNbRioWykGzoYvxGHi8WAGKZI//wN220j20Gb8Ca8HfY3iv5gNXTWy/lseKFaDzzPU0HryATEEBfP5oCQBGr9vL/uNnGbR0J4OWxh+zov+TZb0+GpdbcufKwXeDeia+LiZOmMHsWfPp2u1d6tSvgcd4GPLDSBYtXOZ2VEcEpw+mSvUKdHyza+Ky9z56g7uKFCIuznJwfxgdHTwylluGDfuWqlXKkz17VnbvWsEn3b7knQ6vki4wHTOmxxekK1as4dV277ucNPkNG9aXKlUqkD17FnbtWk63br155ZX36NXrY/z9/Th//gJt277ndkzHpfX31H/6rNWr13eMGPEdrVo9zb59B2nRIuWNMBDnXPcQvYkrGTMUeN1aeyLhelagl7X2vze6rxvDsXyV24fo9SVuHaLXF93qIXpTEzcO0eur3DxEry9x4xC9vsqNQ/T6Kl84RK+vcOMQvb4spRyi9/hT1V39fJxl3ALX95O3w7EeulSAAFhrjwElkieSiIiIiIikZt6Wzx5jTBZr7XFI7ISo9BYRERER+bc0Md3rQuJLYKkx5tJZ3p4CuidPJBERERERSc28KkKstcOMMauASycnbGqt3ZJ8sUREREREJLXyekhVQtGhwkNERERE5BZYnTHd64npIiIiIiIit4WKEBERERERcZSOcCUiIiIi4iQdHUudEBERERERcZY6ISIiIiIiDrLqhKgTIiIiIiIizlIRIiIiIiIiSRhjMhtjxhtjthljthpjKhhjshpjfjXG7Ez4N0vCusYY08cYs8sYs8EYU/JGv19FiIiIiIiIk+JcvnjnG2CWtbYo8DCwFXgP+M1aezfwW8J1gPrA3QmX1kD/G/1yFSEiIiIiIpLIGBMKVAUGA1hrL1prTwBNgKEJqw0FHkv4uQkwzMZbBmQ2xuS53jZUhIiIiIiIOMjGuXsxxrQ2xqy64tL6bxELA0eAH40xa40xPxhjMgC5rLWHEtYJB3Il/JwP2H/F/Q8kLPtHOjqWiIiIiEgaYq0dBAy6zir+QEmgnbV2uTHmGy4Pvbr0O6wxxt5sBnVCRERERETkSgeAA9ba5QnXxxNflBy+NMwq4d+IhNsPAgWuuH/+hGX/SEWIiIiIiIiTfHxiurU2HNhvjLk3YVFNYAswBWiVsKwVMDnh5ynAcwlHySoPnLxi2NY1aTiWiIiIiIj8XTvgZ2NMOmAP8B/iGxhjjTEvAHuBZgnrzgAaALuAswnrXpeKEBERERERB6WEM6Zba9cBpa9xU81rrGuBtv/m92s4loiIiIiIOEpFiIiIiIiIOErDsUREREREHJQShmMlN3VCRERERETEUeqEiIiIiIg4SJ0QdUJERERERMRhKkJERERERMRRyT4cK/6wwSLyT/K/Nc3tCD4jbPj/3I7gM7I27+92BPExgf4BbkfwGbEXY92O4DOiY2PcjiA3wxq3E7hOnRAREREREXGUJqaLiIiIiDhIE9PVCREREREREYepCBEREREREUdpOJaIiIiIiINsnCamqxMiIiIiIiKOUhEiIiIiIiKO0nAsEREREREH6ehY6oSIiIiIiIjD1AkREREREXGQ1RnT1QkRERERERFnqQgRERERERFHaTiWiIiIiIiDNDFdnRAREREREXGYOiEiIiIiIg7SGdPVCREREREREYepCBEREREREUdpOJaIiIiIiIOsdTuB+9QJERERERERR6kTIiIiIiLiIE1MVydEREREREQcpiJEREREREQcpeFYIiIiIiIO0nAsLzohxhg/Y8zPToQREREREZHU74adEGttrDHmDmNMOmvtRSdCiYiIiIikVjpEr/fDsfYAS4wxU4AzlxZaa3snSyoREREREUm1vC1CdidcPEBI8sUREREREZHUzqsixFrbBcAYk95aezZ5I4mIiIiIpF6amO7lIXqNMRWMMVuAbQnXHzbGfJesyW7SwIE92bdvDatX/5pkeZs2z7N+/TzWrJlL9+7vu5TOPfnz52XunHFsWD+f9evm0e7VF9yO5AqPx8O8RZMYOXYgAFWqVWDe7xOZv3gy02aPovCdBV1O6ByPx8OCxZMZNW5QkuWfffEB+w6tcynVzRm+aDNNe0/kia8m8t6oBVyIjvnb7Zto2nsCT309idbfzyLseNQtb/Pk2Qu89MNsGvUcz0s/zObU2QsATF+7m6e+nsSTX03kue+msT3s2C1vywkDBvRk797VrFo1J3FZsWL3sWDBRFaunM348YMJCcnoYkLnDBzYi/371rJm9dzEZR999DarVs5hxfJZTJ/2M3ny5HIxoXMCA9Mxb8EEFi+dxrKVM+nY6XUAvhvwBRs2LWDRH1NZ9MdUihW7z+WkzgsNzcTo0YPYuHEhGzYsoHy5Um5HcoU+X8jN8vY8IV8DdYGjANba9UDV5Ap1K4YPH0fjxs8lWVatWgUaNapDmTL1KFmyFl9/PdCldO6JiYmhwztdeOjhGlSq3Ig2bZ7nvvvudjuW415q04qdO3YnXu/11ce89L+3qVG5Cb+Mm0r7Dq+4mM5ZL7/Sih3bdydZVrzEg2TOnMmlRDfn8MkzjPpjCyPbNeKXNx8nNs4ya/2fSdYpmjcbP7/amHFvPEatYoX4euZKr3//yt2H+GDsoquWD1mwgXJF8jC1w5OUK5KHIQs3AJAva0YGt67P+Dcfp3XN4nwyccmtPUCHDB8+jiZNWiVZ1r9/Dzp3/pwyZeoyZcps3nzzJZfSOWv48HE0atwyybLevQdQukwdyparx4wZc+n0/usupXPWhQsXafRoCypXaEjlCo2oVasqpcsUB+CDzp9TpWIjqlRsxMaNW11O6ryvendlzuz5FCtWjVKlarN12063I7lCny9ujrXG1Ysv8Ppkhdba/X9bFHubs9wWixev4PjxE0mWvfhiS3r1+o6LF+MP7nXkyFE3orkqPDyCtes2ARAVdYZt23aSL29ul1M5K0/eXNSuW50RQ8clLrPWEpIpAwCZMoUQfijCrXiOyps3N7XrVmf40LGJyzweD126vcvHH3zhYrKbExsXx4XoWGJi4zgfHUOOTOmT3F7mrjwEp4sfffpQgRwcPnl5VOlPCzfS/NupPPX1JL77da3X21ywZR+NShYBoFHJIszfvA+A4nfkIlP6wGtuy5ctWbKCY8eS/u0sUqQwixcvB2DevEU89lh9N6I5bvHi5Ve9j5w+fbl7lj5D+jR1ZJszZ+KfwwEB/gQE+GPT0oP/B5kyhVC5cjmG/DgKgOjoaE6ePOVyKnfo84XcLG+LkP3GmIqANcYEGGPeBlLM1x53312YSpXK8vvvk/n117GUKvWQ25Fcdccd+Sn+8IMsX+H9B67UoPvnnejy4RfExcUlLnvj1c6MHv89G7b+TrNnmvDNV2mjS/Zpj058/EHSffHiSy2ZNeM3Dh8+4mKyfy9XaAaeq/Ig9T4fS+1PR5MxKB0V78n3j+tPXLWDygm3/7HjIPuOnuLntg0Z81oTth6MZPWecK+2ezTqfGKxkz0kmKNR56+7rZRo69adNGpUB4CmTR8lf/48LidyV5cu77Br13L+75nH6dK1l9txHOPxeFj0x1R2/bmC+fOWsHrVegA++PAtliybzqefdyJdunQup3RW4cIFiYw8yuAfvmLlitkMHNCT9OmD3Y7lurT6+UJujrdFyMtAWyAfcBAonnD9mowxrY0xq4wxq2Jjb33s9a3y9/cnS5ZQqlZtQseO3fn5Z5+czuKIDBnSM3bM97R/+6Mk3+yldnXqVScy8ijr121Osvzlts/zzJMv8tB9VRk14he6fZr65wvVqVeDI0eS7ovcuXPS5PF6DBow3MVkN+fU2Qss2LKP6e88xZz3n+HcxRimr919zXWnr93NlgNHaVWtGADLdh5k6Y4wnu4zhWf6TuGvIyfZdzT+28wW/abS7JvJdJ2whIVb99Hsm8k0+2Yyf+w4eNXvNcbw9+b2yt2HmLRy5/+zd9/xUZRrG8d/TwqEllBCR6SJIKL03nu3YwEEjggKihVe9KhIsRwV5YhHIQjSBATpHWlSpIRehNAUpIROqBKSfd4/EgJIyVIys0mu7/nsx93Z2d1r54Tdufd+nhlea1zurr5fJ3Xq1I2OHduwbNl0MmbMQHT0Rbcjuapnz88oUqQiY8ZO4uWX27kdxzEej4fqVZrzwP1VKVPuYYo/UJRePT+nXJn61K7xGFmyZOb1Nzu6HdNRAf7+lC5dkkGDRlC+QkPOnj1H9+6vuB3LVal1/+J2WY+7F1/g7dGxjgKtvH1Sa20YEAYQFJTf9b7t/v0HmTJlNgCrV2/A47GEhmbl6NHkMWH0bgkICGD8T4MZM2YSkyfPcjuOoypULEujxnWpV78maYPSkilTRsaMD6NI0UKsXR03ln/SxJmMmzjE5aRJr2KlMjRuUpf6DS5vi99WzeRCdDRrNsRNY8wdSwAAIABJREFUxE2fPh2r18+jXKl6LqdN3IqdB8ibNRNZMwYBULfEvazfc5impQtfvd6OA3y/YANDOjUmTYA/EHeyqBdql+TJisWued5RXZoDccXE1DU76dOy+lX3Z8sYxJFT58genJ4jp84lvD7A9oPH6TVhGf9rX5/MGYJIrrZv30Xz5nFzI4oUKUjjxnVcTuQbxo6dxJTJI+jTJ3WdKisq6jRLFi+nXr0aDPj6ewCio6P5cdTPvNq1g8vpnLVv/0H27TvIqvC4X/wnTJxB926ptwhJzfsXcvu8PTpWUWPMfGPM5vjbDxlj3kvaaHfP1KlzqVmzMhD3RZomTWCqK0AABof1Y+u2nfT/b1jiK6cwfXv146HiNShTsg4d27/B0sUraP3MywQHZ6JwkQIA1Kpd9ZqJ2ilRnw/78WCx6pR6sDYd2r3OksUrKJS/HMWLVKHUg7Up9WBtzp07nywKEIDcmTOyce8RzkfHYK1l5a4DFMoectU62/Yfo++k3+jfti5ZM14eMlG5aF4mr97BuQtxv/AfijrL8TPnvXrdmg/kZ9ranQBMW7uTWg/EHVnt4MkzvDVqAX2frs69/8iR3GTPng2I6/T06PEqgwf/6HIi9xQpXCDhevNmDYiI2OleGAdlC81KSEjc6cGCgtJSu041tm/fRc6c2RPWadqsPlt/3+5WRFccOnSEffsOULRo3I8ddepUY+vW1LUNrpSa9y/k9nl7ssLBQDdgEIC1dqMxZjTQN6mC3a4RIwZQvXplQkOzsHPnSvr2/ZLhw38iLOxz1qz5hejoaDp0eNPtmI6rWqU8bVo/ycZNv7M6PO4QnO+//ymzZi9wOZl7YmNjeePV9/hh5AA8HkvUySi6dkn5w7FSmpL5s1OvZAGeHTAVfz9DsTzZeKLi/Xw7dy0P5Aul1gP5+WpWOOeiL9Ltx0UA5M6cgf+2rUeVonn54/BJnv92OgDp0wby0dM1ripUbuRfNUvSffQiJoVvJ0+WjHz2XG0Awuat5+TZC3w8eQUAAX6G0a+2SJL3fjcNH/71FZ+dK+jT5ysyZkxPp05xRxucMmU2I0aMS+RZUoYRI76hRvVKhIZmZdfOVfTp249GDetQtGhhPB4Pe/fu45VXU8dnRa6c2RkY9jl+/v74+fkxaeIM5sxeyLQZo8gWmhVjDJs2/s4br73vdlTHvf7G+4wYPoA0aQLZ/cfeVLlvAdq/uF0eHzlClZuMN0e5MMaEW2vLG2PWWWtLxy9bb60tldhjfWE4lq+I8fjkAcVckTkog9sRfIZHR5pJsH+Eji9/SdbnvnM7gk/w+MrgZR8QFJC6Jn/fzLnoaw8EkVrpG+RqMdH7k8Xe/fbijVz9v67o1tmubydvOyFHjTGFif9bN8Y8CRxMslQiIiIiIimUr5yrw03eFiFdiJtoXswYsx/4g1uYqC4iIiIiInLJTYsQY8yVAxxnAguJm8x+FngCSF2HBhERERERkTuWWCckU/x/7wfKA1MAA7QBViVhLhERERGRFMl6NBzrpkWItbYXgDFmMVDGWns6/vaHwIwkTyciIiIiIimOt3NCcgLRV9yOjl8mIiIiIiK3QAfG9L4IGQGsMsZMir/9KDAsSRKJiIiIiEiK5lURYq39yBgzC6gev6i9tXZd0sUSEREREZGUyttOCNbatcDaJMwiIiIiIpLiaWJ63OF2RUREREREHON1J0RERERERO6cR2dMVydEREREREScpSJEREREREQcpeFYIiIiIiIOshqOpU6IiIiIiIg4S50QEREREREH6Yzp6oSIiIiIiIjDVISIiIiIiIijNBxLRERERMRBOk+IOiEiIiIiIuIwdUJERERERBykQ/SqEyIiIiIiIg5TESIiIiIiIo7ScCwREREREQfpPCHqhIiIiIiIiMNUhIiIiIiIiKM0HEtERERExEE6T4gDRUisJzapX0KSoejYGLcj+IyiwXndjuAzsj73ndsRfMbxAU+4HcEnZOr8k9sRfEasx+N2BJ+h4fSX+RntzErypE6IiIiIiIiDdJ4QzQkRERERERGHqQgRERERERFHaTiWiIiIiIiDNDFdnRAREREREXGYOiEiIiIiIg7SEd7UCREREREREYepCBEREREREUdpOJaIiIiIiIM0MV2dEBERERERcZg6ISIiIiIiDtIZ09UJERERERERh6kIERERERERR2k4loiIiIiIgzxuB/AB6oSIiIiIiIij1AkREREREXGQRRPT1QkRERERERFHqQgRERERERFHaTiWiIiIiIiDPNbtBO5TJ0RERERERBylIkRERERERByl4VgiIiIiIg7y6OhY6oSIiIiIiIiz1AkREREREXGQzhOiToiIiIiIiDhMRYiIiIiIiDjKq+FYxphNwD+PaBwFrAb6WmuP3e1gIiIiIiIpkcftAD7A2zkhs4BYYHT87WeA9EAkMAxofteTiYiIiIhIiuRtEVLPWlvmitubjDFrrbVljDGtkyLY3RISEsygQV9QosT9WGvp+OJbrFi5xu1Yrhgc1o+mTepx+MhRSpWu63YcR6VNm4bZc38iTdo0BPj7M2XybD7+qD8dO7Whc5f2FCpcgAL5y3L82Am3o3rlgy97UK1+FU4cPcHTtdtec3/NhtV4qXsHPB4PsbGx9Pvgazas2nRHrxmcOROfDOxF7ntycfCvSHp0+oDTUWdo9Hh92nZphTFw9sw5Pu3Rjx2/77qj13JKvny5+f77r8iRIxRrLUOHjuZ///uBjz9+lyZN6hIdfZE//thDx47diIo65XbcRP15/Azdp65LuL0/6hwvVy1K63IFE5YNW7WLmb8fACDWevjj2BkWdqlPSLo0t/260TGxvDdzA1sPRRGSLg3/aV6avCHpWf7nEb5evI2LsZZAf8MbNYtT4d7Q23+DLkibNi2LFkwgTdq0BAT4M3HiDHr17ud2LMekTZuGOb+MI22aNAQE+DN58iw+6tufe+/Nx7ARA8iaNTPr122mwwtvcvHiRbfjOiY1f59eT9euHfhX+2ex1rJ58zY6vPgWFy5ccDuWT9PEdO/nhPgbYypcumGMKQ/4x9+Mueup7qKvvuzN3DkLKVmyJmXL1mfrth1uR3LNiBHjaNqsldsxXHHhQjTNmrSiaqWmVK3cjHr1a1C+fClWrFhDi2Zt2LNnn9sRb8m0cbN49bm3b3j/qiVreLZuO1rV/xe93/iU9/v9n9fPXbZyKXr2f/ea5e1eac2qpWt4vOpzrFq6hnavxP3+cGDvQTo+/grP1GnHkP7D+ffn3W/5/bglJiaWHj36UqZMPWrWfJROnZ6nWLH7mD9/CWXLNqBChUbs2PEH3bp1djuqVwpkzci4dtUZ1646Y56vRlCAP3Xuy3nVOu0qFE5Yp2v1YpS9J5vXBcj+qHO8MHb5NcsnbfqL4KBApr1Ym9ZlC/LfX7cBkCVdGv77eHl+bl+DPo1L8e+Z6+/8TTrswoUL1GvQkrLl6lO2XAMaNqhFxQplEn9gCnHhQjRNGz9H5UpNqFypKfXq16R8+VL06duD/w0YwsMla3PyZBRt27V0O6qjUvP36T/lyZOLLl3+RaXKTSldph7+/v60bNnC7ViSDHhbhHQAhhhj/jDG/AkMAToYYzIAnyRVuDsVHJyJatUqMvSHMQBcvHgxWfyamVSWLF3J8RMn3Y7hmrNnzwEQGBhAQGAA1lo2bvidvXv3u5zs1q1bsYFTJ278t3z+3PmE6+nSB2Ht5SldbV5+luGzwhgzfxgd3/6X169Zs2E1po+bDcD0cbOp1ag6ABtXb+Z01BkANq3ZQo7c2W/pvbgpMvIw69dvBuDMmbNs27aTPHlyMn/+EmJjYwFYtWodefPmdjPmbVm55yj5MqcnT0j6G64za9sBGhXLk3B7xpZ9tBq5lJbDltBnziZiPf+cCnh9i3YeonmJfADUuz8Xq/YexVpLsZwh5MgYBEDh0IxciPEQHRN7B+/KHVd/dgRe9e8pNbjy/QcGBmCBmjUrM2nSLAB+HDWBZs0auJjQean9+/SfAvwDSJcuCH9/f9KlT8fBg4fcjiTJgFdFiLU23FpbEigFPGytfSh+2Vlr7bikjXj7ChbMz9Gjxxjy/VeEr5rDoIGfkz59OrdjiUv8/PxYunw6u/4MZ+GCZaxevcHtSEmqVuPq/LxkFP1HfkbvNz4FoGLN8txTKB9tG3fkuXrtKf7Q/ZSu9LBXz5c1exaOHY47BsWxw8fImj3LNes88mwzfluw8u69CQflz5+PUqVKEB5+9a/1zz/fkjlzFrkT6g7M2XaAxsXz3PD+8xdj+e2PI9QrmguA3cdOMyfiIMOeq8K4dtXx84OZv3tXoB8+8ze5guOKjQA/PzKmCeTk+auH5szbHknxHMGkCfC/3lP4ND8/P1aHz+Xg/o3Mn7+YVeHrEn9QCuLn58dvK2bwx57VLJi/lD927+Fk1KmEQn3//kjy5MmZyLNISnXgQCRf9R/Erp0r2btnLaeiTjNv3mK3Y/k8j8sXX+Dt0bFCgJ5AjfjbvwK9rbVRN1i/I9ARwM8/BD+/DHcn7S0K8PendOmSvP76+6wKX8eX/XrRvfsrfPjh567kEXd5PB6qVW5GSEgmfhwzkOIPFGXr79vdjpVkFs1awqJZSyhd6WFe6t6BLk+/QaWa5alUszw//jIUgPQZ0pG/YD7WrdjAsBmDCEwTSPoM6QjOHJywzoCPBrJi0aprnv+fPwaXrVKaR55rSodHuiT5e7vbMmRIz5gxA+nWrTenT59JWN69+yvExsYwduwkF9PduouxHn7ddYiuNYrdcJ3Fuw5RKm+WhKFYq/YcY2tkFK1GLgPgQkwsWdOnBeCNSavZH3WeGI+Hg6fO03LYEgCeK1uAR0vek2ienUdP899ft/HdUxUSXdcXeTweypVvQEhIMBPGD6FEifvZsiXC7ViO8Xg8VKnUlJCQTIwZO4iiRQu7HUl8SObMITRv1oCi91fm5MlTjB0zkOeefZzRYya6HU18nLcT04cCm4FLgz7bAD8Aj19vZWttGBAGEJgmr2t96337D7Jv38GEX60mTJxB926vuBVHfERU1GmWLF5Bvfo1UnQRcsm6FRvIe28eQrKGYIxh2IBRTBw59Zr12jXtBMTNCWn2dBN6vf7xVfcfP3KCbDmycezwMbLlyMaJo5cn8RcpXpj3+/0fXVt1I+omw8R8UUBAAGPGDOSnnyYzZcrshOWtWz9JkyZ1adz4WRfT3Z6luw9TLEcI2TKkveE6s7dePRTLYmn+YL7rFi5fPVYOiJsT8sGsDQx5pvJV9+fIGETkqb/JmSkdMR4PZ6IvkjldIACHTp/nzclr6NPkYe7J4s4PUndLVNQpFv26jIYNaqWqIuSSqKjTLF68nAoVy5A5JBh/f39iY2PJmzcXBw5o+E1qVbdONf788y+OHj0OwOTJs6hUuayKkET4SjfCTd7OCSlsre1prd0df+kFFErKYHfDoUNH2LfvQMKvNnXqVGPr1pS/0ynXyhaalZCQTAAEBaWldp1q7IjY7XKqpJOvQN6E6/eXLEqaNIFEHY9i+aJVtHimKenihyVmzxVKlmyZvXrOX+cuo1nLRgA0a9mIX+csBSBn3hx8PqQvH7zal727/7rL7yTpDRz4GRERO/n66+8TltWvX5M333yJJ598gfPn/3Yx3e2Zve0AjW4yFOv0hYus2Xec2kUuD6GpkD+UXyIOcvxs3BFtos5HcyDqnFevV7NwTqZtiTu4w7yISMrnD8UYw6m/L/LqhHBeq3E/pfNlvYN35J7Q0KyEhAQDEBQURL26NYiISB5Hf7sbQv/x2VmnTnUiInayePEKHnusMQCtWj/BjBm/uBlTXLT3rwNUrFiadOnihmTWrl2Nbdt2upxKkgNvOyHnjTHVrLVLAYwxVYHziTzGJ7z+xvuMGD6ANGkC2f3HXjp0eNPtSK4ZNfJ/1KxRmdDQrPy5ezW9en/BD8PGuh3LEbly5WBg2Of4+/vj52eYNGEms2cv4KWX2/LaGx3JmTM7y1fOZO6cRbza5R234ybqo297UrZKaTJnDWHGmgmEfTGUgMC4f84TRkyhbtOaNHmqETEXY7jw9wXeeaknACt/Dafgfffyw/TvADh39jzvv9KHE8cSn2A5/JtRfDKoN48825SD+w7xTqcPAHjxjfaEZAnh/z6J+7cVGxvL841eTIq3fddVqVKOVq2eYNOmraxYMROAnj0/p1+/D0mbNg3Tp48C4iand+36bzejeu18dAwr/jzKew1KJiwbv34PAE+VuheABTsiqVwglHRpLn8FFA7NxCvV7+el8auw1hLgb3in3oM3ndh+yWMP3cO/Z6yn+eCFBAcF8p/mcUeP+mndn+w9eY5Bv+1k0G9xOyUDn6pA1pt0aHxN7tw5GTqkP/7+fvj5+fHzz9OYMXOe27EckzNXDsIGf4G/X9xn58SJM5g9awHbtu5g2IgBvN/zLTZu+J3hw3x2emiSSM3fp/8UHr6OiRNnsmrlbGJiYli/fgvff/+j27EkGTDeHOXDGPMwMAIIiV90Amhrrd2Y2GPdHI7la7QhLksfmHx2QpJa0eC8ia+USmw5udftCD7j+IAn3I7gEzJ1/sntCD4jKOD2z+WS0vwdE+12BJ/hZ3S+iStFX9iXLDbIjJzPurpb2PTQGNe30007IcaYK9sGI4BLA3rPAvWARIsQERERERGRKyU2HCtT/H/vB8oDUwADtAauPVyOiIiIiIjclMf1PoT7blqExE9AxxizGChjrT0df/tDYEaSpxMRERERkRTH26Nj5QSuHIAZHb9MRERERETklnh7dKwRwCpjzKUzdj0KDEuSRCIiIiIiKZgHjcfyqgix1n5kjJkFVI9f1N5auy7pYomIiIiISErlbScEa+1aYG0SZhERERERSfF02gbv54SIiIiIiIjcFSpCRERERETEUV4PxxIRERERkTvncTuAD1AnREREREREHKUiREREREREHKXhWCIiIiIiDvIYnSdEnRAREREREXGUOiEiIiIiIg7SeULUCREREREREYepCBERERERkWsYY/yNMeuMMdPjbxc0xqw0xuw0xvxkjEkTvzxt/O2d8fcXSOy5VYSIiIiIiDjI4/LlFrwGbL3i9n+Ar6y1RYATwAvxy18ATsQv/yp+vZtSESIiIiIiIlcxxuQDmgLfx982QB3g5/hVhgOPxl9/JP428ffXjV//hjQxXURERETEQR6Xj9BrjOkIdLxiUZi1Nuwfq/UHugOZ4m9nA05aa2Pib+8D8sZfzwv8BWCtjTHGRMWvf/RGGVSEiIiIiIikIvEFxz+LjgTGmGbAYWvtGmNMraTIoCJERERERESuVBVoYYxpAgQBwcB/gczGmID4bkg+YH/8+vuBe4B9xpgAIAQ4drMX0JwQEREREREHeTCuXhJjrX3HWpvPWlsAeAZYYK1tBSwEnoxfrS0wJf761PjbxN+/wFp709OhqAgRERERERFv/B/wpjFmJ3FzPobELx8CZItf/ibQI7En0nAsEREREREHJaczpltrFwGL4q/vBipcZ52/gadu5XnVCREREREREUepCBEREREREUcl+XCsRM5TkqokMj8nVQnw83c7gs84cfGs2xHEB4V2neh2BJ9wemTHxFdKJYLb3PBompKKad8ieXL7PCG+QJ0QERERERFxlCami4iIiIg4yON2AB+gToiIiIiIiDhKRYiIiIiIiDhKw7FERERERBykwwmoEyIiIiIiIg5TJ0RERERExEE6RK86ISIiIiIi4jAVISIiIiIi4igNxxIRERERcZDOE6JOiIiIiIiIOExFiIiIiIiIOErDsUREREREHKThWOqEiIiIiIiIw9QJERERERFxkNV5QtQJERERERERZ6kIERERERERR2k4loiIiIiIgzQxXZ0QERERERFxmDohIiIiIiIOUidEnRAREREREXGYihAREREREXGUhmOJiIiIiDjIuh3AB6gTIiIiIiIijlInRERERETEQR6dMV2dEBERERERcVainRBjzGmuHboWBawG3rLW7k6KYCIiIiIikjJ5MxyrP7APGA0Y4BmgMLAWGArUSqpwIiIiIiIpjc4T4t1wrBbW2kHW2tPW2lPW2jCgobX2JyBLEue7ZWGDvmDfX+tZt3ZewrIsWTIzc+ZotmxZwsyZo8mcOcTFhO5ImzYty5dNZ83qX9iwfgE9P3jL7Uiu8PPzY9HSKYwZHwbAoO/7sXLtHJatnMGAbz8hICB1TJNq/1IrZi0dz6wl4+gf9jFp0qahSvUKTFnwI9MWjuGn6UO4t+A9bsdMcvny5Wb27LGsXTuPNWt+oUuX9gBkyRLC9Omj2LRpEdOnjyJz5mCXkya9vHlzM3PWGFav+YXw1XPp3DluW7z779fZsXMFy1fMZPmKmTRsWMvdoHdg5G/beHzADJ74ZgY9xi/jwsXYO3q+IYu30Lz/VB757zR+23EAgMios3QYOo/HB0zn8QEz+HH5trsR3af4+fkRvmoOkycNdzuKa/SderWQkGDGjg1j06Zf2bhxEZUqlnU7kiQD3hQh54wxLY0xfvGXlsDf8ff53BHGRowcT7Pmra9a1r1bFxYuWEaJEtVZuGAZ3bt1cSmdey5cuEC9Bi0pW64+Zcs1oGGDWlSsUMbtWI57qXNbtkfsSrg9ftxUKpZpSNWKTQkKCqJN25YupnNGzlzZafviMzxarzWNq7fEz8+P5o81pPcX7/Bmp/doXvtZpk6YTZc3X3A7apKLiYmlR4++lClTj5o1H6VTp+cpVuw+3n67M4sWLaNkyVosWrSMt9/u7HbUJBcbG8O77/SlXNn61K71GB07taFYsSIAfDNgCJUrNaFypSbMmbPI3aC36dCpc4xZEcHolxoy4ZWmxHosszfv8eqxjb+ccs2yXYejmLNpDxNeacq3z9fm4+mrifV48Pfz461GZZj4ajNGdmzAT6t2sOtw1N1+O67q+moHtm7b4XYMV+k79WpffdmbuXMWUrJkTcqWrZ/q/z684XH54gu8KUJaAW2Aw/GXNkBrY0w64JUkzHZbli5dyYkTJ69a1rx5A0aOGg/AyFHjadGioRvRXHf27DkAAgMDCAgMxFqfqyGTVJ48uajfsBYjh49LWDZv7q8J19eu2UCevDndiOa4gAB/goLS4u/vT7r06TgUeQRrLRkzZQAgU3BGDkUedTll0ouMPMz69ZsBOHPmLNu27SRPnpw0a1afUaMmADBq1ASaN2/gZkxHREYeYf36LUDctoiI2EWePLlcTnV3xXosFy7GEhPr4e+LMWTPlI7fDxznhSHzePa7Wbw8fAFHTp/36rkWbdtHw5L3kibAn7xZMnJP1oxs3neM7JnSUTxPVgAypA2kUPZgDp86l5Rvy1F58+amceO6DB06xu0orkvt36mXBAdnolq1igz9Ie5v4uLFi0RFnXI5lSQHiRYh1trd1trm1trQ+Etza+1Oa+15a+1SJ0LeqRw5QomMPAzE7XTkyBHqciJ3+Pn5sTp8Lgf3b2T+/MWsCl/ndiRHffyff/Ph+5/h8Vz7G0BAQAAtn3mU+fOWuJDMWYcij/D9/0ayZP1Mlm+Zy+lTp1m6aAXvvN6HIWO/ZunGWTzasimD/vuD21EdlT9/PkqVKkF4+PpU/5mRP38+Hn74AcLD1wPQ6aW2rFw5i+8GfpZsh6blDE7P81WL0ejLKdT/fBIZgwIpXzAnn85YzefPVGPMy415tExhvpm3wavnO3zqHLlC0l9+/pD0HP5HAbP/xBm2HTxByXwp5++nX79evPNO3+t+jqY2qf079ZKCBfNz9Ogxhnz/FeGr5jBo4OekT5/O7ViSDCRahBhj8hljJhljDsdfJhhj8iXymI7GmNXGmNWe2LN3L+1dklp/rfB4PJQr34B7C5ajfLnSlChxv9uRHNOgUW2OHDnGhvhfev/pi68+ZPmycFb8ttrhZM4LDslEvca1qFW2GVUebEj69Ol45Kkm/OulVrzwTFeqPdSYCWOm8m7fN92O6pgMGdIzZsxAunXrzenTZ665PzV9ZGTIkJ7RY76je/e4bfH94FE8WKIGlSo1ITLyMJ98+p7bEW/LqfPRLNq2nxlvtGBut8c4Hx3LsCW/s+vwSV4avoCW385k8K+bORTftRj862ZafjuTlt/O5Mjp8wnXP54e7tXrnbtwkbfHLqFb47JkDApMyrfmmCZN6nHk8FHWrtvkdhSfkJq/U68U4O9P6dIlGTRoBOUrNOTs2XN07+5zA2V8jnX54gu8mYX7A3FHxnoq/nbr+GX1b/SA+MnrYQBp0uZz/b0ePnyUXLlyEBl5mFy5cnDkyDG3I7kqKuoUi35dRsMGtdiyJcLtOI6oWKkMjZvUpX6DmqQNSkumTBkZOPgLXnrxbbr3eIVsoVl5o2vqmCtUtWZF/tqzn+PH4oYtzpm+gLIVHqZYifvYsDZuaNL0SXP5Ydw3bsZ0TEBAAGPGDOSnnyYzZcps4HqfGSl/aBrEbYvRowfy09jJTJ0yB4jbFpf8MHQsEyYMcSveHVmxK5K8WTKQNUMQAHUfyMeUtbspnD2EER2vHaL7Ys0HebHmg0DcnJBxnZtcdX+O4PRERl0eZnUo6hw5MsX9+nsx1sNbY5fQ5KEC1H0g5RzgoUqVcjRr1oBGjeoQFJSW4OBMDB/2NW3bdXU7mqtS43fqlfbtP8i+fQcTOkETJs6gezcVIZI4b+aEZLfW/mCtjYm/DAOyJ3Guu2ra9F9o0zquhmrT+immTZvrciLnhYZmJSQkbhhFUFAQ9erWIOKKCdopXZ8P+/FgseqUerA2Hdq9zpLFK3jpxbdp0/Yp6tSrzovt30g1HbID+yIpVa4kQenidsaq1KjAzojdZArOSIHC+QGoVqsiu7b/4WZMxwwc+BkRETv5+uvvE5bNmDGP1q2fAKB16yeYPv0Xt+I56rvv/kNExE4GDLhcaOTKdfnjvkW2VFrUAAAgAElEQVSLhmz5fbsb0e5Y7pD0bPzrGOejY7DWsnL3IWoXz8eJcxfYsPcIEFc87Dx8MpFnilOzWF7mbNpDdEws+0+cYe/x0zyYLxvWWnpNXkHB7CG0qVo8Kd+S495771MKFirHfUUr0ap1ZxYuXJZqC5DU/p16pUOHjrBv3wGKFi0MQJ061di6NXl+ToizvOmEHDPGtAYuzUJ7FvDZVsLIEd9Qo0ZlQkOzsntXOL379OPzz79h9OiBtGv/DHv37uO55152O6bjcufOydAh/fH398PPz4+ff57GjJnzEn9gCtevf2/+2nuAOfPjDlwwfepcPv9Pyu4AbFi7mdnT5jN1wY/ExsSyZVMEY0dMJPLAYb794XM8HktU1Cl6dO3ldtQkV6VKOVq1eoJNm7ayYsVMAHr2/JwvvviWUaO+pW3bp9m7dz+tW6f8o2NVrlyO51o9weZNW1kevy0+7PkZTz3VgoceegBrLXv27qPrq++6nPT2lLwnlHol7uHZgbPx9zMUy52Fp8rfR9kCOfls5mrO/H2RGI+lVeX7KZIjc6LPVyRHZuo/mJ/HB8zA38/wTtPy+Pv5sW7PYaZv+JP7cmam5bdx2/HVeg9TvWjepH6L4iB9p17t9TfeZ8TwAaRJE8juP/bSoUPqGc57uzzG7QTuM4n9+muMuRcYAFQmbhjZb0BXa+1eb17AF4Zj+QpPKvml3RvBadMnvlIqkSVtJrcj+IwDZ3329w3H+Rl9QwEc/aG92xF8RnCbMLcj+Ax9m16mT4qrXYzenyw2yWf3tnb1z7j7nlGub6ebdkKMMf7Ax9baFg7lERERERFJ0XR8uUTmhFhrY4F7jTFpHMojIiIiIiIpnDdzQnYDy4wxU4GE4+1aa79MslQiIiIiIpJi3bATYowZGX+1BTA9ft1MV1xEREREROQW6TwhN++ElDXG5AH2EjcxXURERERE5I7drAgZCMwHCgJXnkbaEFdEFUrCXCIiIiIiKZLHZ/oR7rnhcCxr7dfW2uLAD9baQldcClprVYCIiIiIiMhtSfSM6dba1HdmPxERERERSTLeHB1LRERERETuEp0nxItOiIiIiIiIyN2kToiIiIiIiIM0LV2dEBERERERcZiKEBERERERcZSGY4mIiIiIOEgT09UJERERERERh6kTIiIiIiLiII9xO4H71AkRERERERFHqQgRERERERFHaTiWiIiIiIiDPDpTiDohIiIiIiLiLHVCREREREQcpD6IOiEiIiIiIuIwFSEiIiIiIuIoDccSEREREXGQzpiuToiIiIiIiDhMRYiIiIiIiDhKw7FERERERByk84SoEyIiIiIiIg5TJ0RccT4m2u0IPuN8zDG3I/gMa/XLUAJj3E7gEzK1CXM7gs84s2qQ2xF8RsYKndyO4DOMPiuSJX3bqRMiIiIiIiIOUxEiIiIiIiKO0nAsEREREREH6Twh6oSIiIiIiIjD1AkREREREXGQDtGrToiIiIiIiDhMRYiIiIiIiDhKw7FERERERBykwVjqhIiIiIiIiMPUCRERERERcZAO0atOiIiIiIiIOExFiIiIiIiIOErDsUREREREHGQ1NV2dEBERERERcZY6ISIiIiIiDtLEdHVCRERERETEYSpCRERERETEURqOJSIiIiLiII8mpqsTIiIiIiIizlInRERERETEQeqD3EInxBhzrzGmXvz1dMaYTEkXS0REREREUiqvihBjzIvAz8Cg+EX5gMlJFUpERERERFIub4djdQEqACsBrLU7jDE5kiyViIiIiEgKpYnp3g/HumCtjb50wxgTgIaziYiIiIjIbfC2CPnVGPMukM4YUx8YD0xLulgiIiIiIpJSeTscqwfwArAJ6ATMBL5PqlAiIiIiIimVx+0APsCrIsRa6wEGA4ONMVmBfNZaDccSEREREZFb5u3RsRYZY4LjC5A1xBUjXyVttNsTNugL9v21nnVr5yUse+LxpqxfN5+/z++lTJmHXEznroYNarFl82K2/b6U7t26uB3HUfny5Wb27LGsXTuPNWt+oUuX9gBkyRLC9Omj2LRpEdOnjyJz5mCXkyY9bYvLBg36nL1717JmzS9XLX/55XZs2LCAtWvn8dFH77qUzll58+Zm5qwxrF7zC+Gr59K5c9zfxfsfvMnKlbNYvmImU6eOIFfu1HVMkrRp07J82XTWrP6FDesX0PODt9yOdFf8OHMxj7/1OY+99RmjZiy+4+eb+ms4zV/7hOavfcLUX8MBOH8hmlc+/Z5H3viUx976jP6jp9/x6/iKwWH9OLBvA+vXzXc7iiuut6+VJUtmZs4czZYtS5g5czSZM4e4mND3WZf/5wu8nRMSYq09BTwOjLDWVgTqJl2s2zdi5HiaNW991bItv0fQ8ukXWbJkpUup3Ofn58fX//2IZs1bU/Lh2jz99KMUL36f27EcExMTS48efSlTph41az5Kp07PU6zYfbz9dmcWLVpGyZK1WLRoGW+/3dntqElO2+KykSPH06LF81ctq1mzMs2bN6B8+UaUKVOP/v0H3eDRKUtsbAzvvtOXcmXrU7vWY3Ts1IZixYrQ/6swKlZsTOVKTZg1awHvvPOa21EddeHCBeo1aEnZcvUpW64BDRvUomKFMm7HuiM79h5kwvyV/Pjxa4z/7C0Wr/2dvZFHvXrsC72+Zf/h41ctizpzjoE/z2XUR6/x40evMfDnuZw6cw6A55vVYspXPRj3nzdZH/EnS9dtvevvxw0jRoyjabNWbsdwzfX2tbp368LCBcsoUaI6CxcsS3U/dsqt87YICTDG5AZaAj79U8bSpSs5ceLkVcu2bdvJ9u27XUrkGyqUL82uXX/yxx97uXjxIuPGTaFF84Zux3JMZORh1q/fDMCZM2fZtm0nefLkpFmz+owaNQGAUaMm0Lx5AzdjOkLb4rKlS1dd83nx4ott+OKLb4mOjjsg4JEjx9yI5rjIyCOsX78FiPu7iIjYRZ48uTh9+kzCOhkypCc1jsQ9ezZuhzowMICAwMBkvw3+2H+YkvflJ13aNAT4+1P2gcLMX7mRvyKP8vLHYTzT4yva9fyGP/Yf8ur5ftuwjUolixKSMT3BGdNTqWRRlm3YRrq0aajwYBEAAgMCKF4wH4eORyXlW3PMkqUrOf6Pz47U5Hr7Ws2bN2DkqPEAjBw1nhYtUs8+htweb4uQXsAcYKe1NtwYUwjYkXSx5G7LkzcXf+07kHB73/6D5MmTy8VE7smfPx+lSpUgPHw9OXKEEhl5GIjbOc+RI9TldM7StrjWffcVpGrVCixePIVffhlH2bKpbwhn/vz5ePjhBwgPXw9Azw/fJmL7bzz99CP07fOly+mc5+fnx+rwuRzcv5H58xezKnyd25HuSJF7crF2225Onj7L+QvRLF23lchjJ+k9eDw92j/G2E/f4M3WzfloyESvnu/w8ShyZcuccDtntswc/kexcerseX5ds4WKD6aeDnxqo++QW+Nx+eILEp2YbozxB+6x1iZ8E1trdwNPJGUwkaSQIUN6xowZSLduva/6hfeSZP4D5y3Rtri+gIAAsmQJoUaNRyhX7mF+/PFbihWr5nYsx2TIkJ7RY76je/fLfxe9PvyCXh9+wdtvd6bTS235qK9PTglMMh6Ph3LlGxASEsyE8UMoUeJ+tmyJcDvWbSuULyftW9ThpY/CSJc2DfcXyMOF6ItsiPiTbl+NSFgv+mIMAJMXrmL0rCUA7I08yiuffk9ggD95cmSl/9vtE329mNhYenw9iucaVSdfzmxJ86bE5yT3jqEkvUSLEGttrDHmWcDrbx1jTEegI4C/f2b8/DPcfkK5Kw7sj+SefHkSbufLm5sDByJdTOS8gIAAxowZyE8/TWbKlNkAHD58lFy5chAZeZhcuXJw5Ih346KTO22LG9u//2DCNlm9egMejyU0NCtHjx5P5JHJX0BAAKNHD+SnsZOZOmXONfePHTuZSZN+SHVFyCVRUadY9OuyuIN8JOMiBODxOhV5vE5FAL4eM5NsmTORad1Wxn127cT7R2tX4NHaFYC4OSG9X36GvDmyJtyfI2sI4Vt2Jdw+dOwk5UsUTrjdO2w8+XOF0rppjaR6O+IDrv0OSR1DWW+Xr0wOd5O3w7GWGWO+McZUN8aUuXS50crW2jBrbTlrbTkVIL4hfPV6ihQpSIEC9xAYGEjLlo8wbfpct2M5auDAz4iI2MnXX18+xc2MGfNo3Tquqde69RNMn/7LjR6eomhb3NjUqXOpWbMyAEWKFCRNmsBUUYAAfPfdf4iI2MmAAUMSlhUuXCDherNm9YnYvus6j0y5QkOzEhISd6S4oKAg6tWtQURE8t8Gx6JOA3Dw6Anmr9pI8xrlyJsjK3OXbwDifsWO+PPAzZ4iQZWHi7F843ZOnTnHqTPnWL5xO1UeLgbAN2Nncebc33Rv+0jSvBHxGdOm/0Kb1k8B0Kb1U0yblrr2MeTWGW/aZcaYhddZbK21dRJ7bJq0+Rwt9UaO+IYaNSoTGpqVQ4eO0rtPP04cP8lXX/Uhe/asnDx5ig0bt9CsWevEn+wu87jcmmzcqA79+vXC38+PYcN/4pNPv3YtS6C/t+fJvDuqVCnH/PkT2LRpKx5P3GjInj0/Jzx8HaNGfcs99+Rh7979tG7dmRMnUsbEyRvx5W3hdPt+xIgBVK9emdDQLBw6dJS+fb/kxx8nEhb2OQ89VILo6GjeeecjFi36zdFcAP5+3v5GdHdUrlyOefN/ZvOmrQmfVR/2/Izn2z5N0fsK4fF42PvXfrp2/TcHD3g3YfluuBBz0bHXup6SJYszdEh//P398PPz4+efp9H3o/6uZDmz6u4dqa1dz2+IOn2OAH8/3n6+BRVLFmXf4WN89P0Ejp44RUysh4ZVSvHSk1cfoOJ6nRCASQtXMmRS3OFqOzxWj0drV+DQsZM06NyHgnlykCYw7jP/mYZVebxupTvOn7FCpzt+jjsxauT/qHnFvkav3l/ww7CxrmTxM8bx17zevtbUqbMZPXog99yTl7179/Hccy9fM3ndCdEX9jm/QW5D+wJPuLpT+MOfE1zfTl4VIXfC6SLEl7ldhPgSp4sQSR40hvgyp4sQX+V2EeJL7mYRkty5XYT4EjeKEF+WXIqQti4XIcN9oAjx9mSFIcaYL40xq+Mv/YwxOguNiIiIiIjcMm9/jh4KbCbuPCEAbYAfiDt5oYiIiIiIeEmjY7wvQgpba688JG8vY8z6pAgkIiIiIiIpm7eDjs8bYxIOlG+MqQqcT5pIIiIiIiKSknnbCXkZGH7FPJATQNukiSQiIiIiknJpMJb3RchW4DOgMJAZiAIeBTYmUS4REREREUmhvC1CpgAngbXA/qSLIyIiIiKSsnnUC/G6CMlnrW2UpElERERERCRV8HZi+m/GmJJJmkRERERERFKFm3ZCjDGbiJs7EwC0N8bsBi4ABrDW2oeSPqKIiIiISMphNRwr0eFYzRxJISIiIiIiqcZNixBr7R6ngoiIiIiISOrg7cR0ERERERG5CzxuB/AB3k5MFxERERERuSvUCRERERERcZDOE6JOiIiIiIiIOExFiIiIiIiIOErDsUREREREHKTzhKgTIiIiIiIiDlMnRERERETEQTpErzohIiIiIiLiMBUhIiIiIiKSwBhzjzFmoTHmd2PMFmPMa/HLsxpjfjHG7Ij/b5b45cYY87UxZqcxZqMxpkxir6EiRERERETEQdZaVy9eiAHestY+AFQCuhhjHgB6APOttfcB8+NvAzQG7ou/dAS+S+wFVISIiIiIiEgCa+1Ba+3a+Ounga1AXuARYHj8asOBR+OvPwKMsHFWAJmNMblv9hqamC4iIiIi4iC3z5hujOlIXMfikjBrbdgN1i0AlAZWAjmttQfj74oEcsZfzwv8dcXD9sUvO8gNqAgREREREUlF4guO6xYdVzLGZAQmAK9ba08ZY658DmuMue1qSsOxRERERETkKsaYQOIKkB+ttRPjFx+6NMwq/r+H45fvB+654uH54pfdkIoQEREREREHeVy+JMbEtTyGAFuttV9ecddUoG389bbAlCuWPx9/lKxKQNQVw7auK8mHYwWnTZ/UL5FsnI+JdjuCz7gQc9HtCD7D74rWZmrn8e6IHalCjCfW7Qg+Qf86LstYoZPbEXzG6XkfuR3BZ+Rtqm0hSaIq0AbYZIxZH7/sXeBTYJwx5gVgD9Ay/r6ZQBNgJ3AOaJ/YC2hOiIiIiIiIg6zLE9MTY61dyo1/B6p7nfUt0OVWXkPDsURERERExFEqQkRERERExFEajiUiIiIi4iC3zxPiC9QJERERERERR6kTIiIiIiLiIKujQaoTIiIiIiIizlIRIiIiIiIijtJwLBERERERB3lz1vKUTp0QERERERFxlIoQERERERFxlIZjiYiIiIg4yOo8IeqEiIiIiIiIs9QJERERERFxkM6Yrk6IiIiIiIg4TEWIiIiIiIg4SsOxREREREQcZK2GY6kTIiIiIiIijlInRERERETEQZqYrk6IiIiIiIg4TEWIiIiIiIg4SsOxREREREQcpDOmqxMiIiIiIiIOUydERERERMRBHh2i1/sixBiTBigGWCDCWhudZKlERERERCTF8qoIMcY0BQYCuwADFDTGdLLWzkrKcCIiIiIikvJ42wnpB9S21u4EMMYUBmYAKkJERERERG6BBmN5PzH99KUCJN5u4HQS5BERERERkRTO2yJktTFmpjGmnTGmLTANCDfGPG6MeTwJ8902Pz8/FiyZzOhxgwCoXrMyCxZPYuHSKUyfM4aChfK7nDDp5c2bm5mzxrB6zS+Er55L587tr7q/a9cOnD33J9myZXEpoTvSpk3L8mXTWbP6FzasX0DPD95yO5KjwgZ9wb6/1rNu7byEZU883pT16+bz9/m9lCnzkIvp3JMvXx7mzR3Pxg0L2bB+Aa++8oLbkVyjbXEtPz8/wlfNYfKk4W5HcU1K+Owc+csqHv9gME/0HEyPsMlcuBhzzTpzwrfy+AdhPP7BYHoMnnLHrxl19jydvhxD838PpNOXYzh19jwAM1Zs5qkPv+fJD7/n+U9HEPHXoTt+Laf5+fmxaOkUxowPA+Dr/33M4t+msmT5NIaNHECGDOldTui7PFhXL77A2yIkCDgE1ARqAUeAdEBzoFmSJLtDnV5uy47tuxJuf/HVh3Tq8Da1qz3ChPHTeLNbZxfTOSM2NoZ33+lLubL1qV3rMTp2akOxYkWAuAKlbt0a7N27z+WUzrtw4QL1GrSkbLn6lC3XgIYNalGxQhm3YzlmxMjxNGve+qplW36PoOXTL7JkyUqXUrkvJiaGbt178dDDtalarTkvv9yO4sXvczuWK7QtrtX11Q5s3bbD7RiuSu6fnYdOnGbM/NWMfq8dE3q9SKzHMnvV71ets+fQcYbOWs6w/2vDxN4v0v3pel4/f3jEHt4fOv2a5UNnLadi8QJM++glKhYvwNBZKwDIG5qZId1a8fOHHejYtCp9Ria/Ee4vdW7L9ojL+1r/7vExNaq0oHrl5uzbd4AOnVrf5NGS2nlVhFhr29/k8q+kDnmrcufJSf2GtRg1fHzCMmstmYIzABAcnInIg4fdiueYyMgjrF+/BYAzZ84SEbGLPHlyAfCfz97nvfc+IbUeIe7s2XMABAYGEBAYiE1FG2Lp0pWcOHHyqmXbtu1k+/bdLiXyDZGRh1m3fjMQ9+9l27Yd5I3/95LaaFtcLW/e3DRuXJehQ8e4HcV1yf2zM9bj4cLFGGJiPfwdfZHsmTNedf/EJet5unYZgjOkAyBr/H4DwLA5K3iu7w889eH3fDtlsdevuWj9DppXLglA88olWbh+OwCliuRLeJ2HCuXh0InkNco9T55c1G9Yi5HDxyUsO336TML1oKCgVLuPId656cR0Y0x3a+1nxpgBXGcOjbW2a5IluwMfffpven3wGRkzXv7weP2V9xj782D+Pn+B06fP0LDuUy4mdF7+/Pl4+OEHCA9fT9Nm9Tl44BCbNm11O5Zr/Pz8WLVyNkUKF+C7gcNYFb7O7UjiQ+69Nx+lHn6Qlav0d6FtAf369eKdd/qSMVPGxFdO4ZLzZ2fOLJl4vkFFGv3f/wgKDKDSAwWpUqLQVevsOXQcgLafjsDjsbzUohpVHyzMb1t2s/fQCX78dzushde+Gc+a7XspWzTxod3HTp1NKHZCQzJw7NTZa9aZtHQj1R4sfBfepXM+/s+/+fD9q/e1AL757lPqNahJxLadvP/uJy6l832+MiTKTYl1Qi7tpa4G1lzncl3GmI7GmNXGmNV/R0fdlaDeatCoFkePHmNDfAfgkpe6tOOZJ1/koeI1GDNqAn0/ftfRXG7KkCE9o8d8R/fuveOGWXTrQp8+X7ody1Uej4dy5Rtwb8FylC9XmhIl7nc7kviIDBnSM+6nwbz5ds+rftVLjbQtoEmTehw5fJS16za5HcUnJOfPzlNnz7No/Q5mfNKZuZ+/yvnoi8xYsfmqdWJjPew9dILv327Fpy8+Qu8Rszh17m9W/P4Hy3//g6d7D+WZPkP5M/IYew+dAKD1x8No2WsIvYfP5NcNO2jZawgtew3ht83XdpeNMRhjrloWvm0Pk5du4LUnaiXZe7/bGjSqzZEj1+5rAbzycg8euK8q2yN28dgTTV1IJ8nFTTsh1tpp8f+9pZl41towIAwgNLioo6VehYpladS4LvXq1yRtUFoyZcrImPFhFClaiLWrNwIwaeJMxk0c4mQs1wQEBDB69EB+GjuZqVPmUKLE/RS4Nx8rVsaNPc2bNxfLfptOzRqPcujQEZfTOi8q6hSLfl1Gwwa12LIlwu044rKAgADG/zSYMWMmMXly8huffTdpW8SpUqUczZo1oFGjOgQFpSU4OBPDh31N23Y+ORDAMcnxs3PF1j/JGxpC1kxxk6Xrlr6f9bv20bTSgwnr5MwSzIOF8hAY4E/e7Jm5N2dW9h46jrXwQuPKPFmz9DXPO+rddkDcnJCpyzbR519XT5XNFpyBIyfPkD1zRo6cPJPw+gDb9x2m14iZ/K9rSzJnTD6TuCtWKkPjJnWp3+DyvtbAwV/w0otvA3HF6sQJM+j6+ouMHjXB5bS+KbkNZUwKXs0JMcYUNcaEGWPmGmMWXLokdbjb0bdXPx4qXoMyJevQsf0bLF28gtbPvExwcCYKFykAQK3aVa+aSJWSfffdf4iI2MmAAXFF15YtERQoUI4HilfjgeLV2L8/kqpVmqWqAiQ0NCshIcFA3JjVenVrEJFK/h7k5gaH9WPrtp30/2+Y21Fcp20R5733PqVgoXLcV7QSrVp3ZuHCZam2AEnun525swazcfcBzl+4iLWWldv+pFCu0KvWqV36PlZH7AHgxOlz7Dl0nHzZM1O5REEmL9vAub+jgbhJ7sevM6zqemo+fB/Tlsd10qYt30StUnEHeTh4LIq3vp1A3381595c2e7W23REnw/78WCx6pR6sDYd2r3OksUreOnFt6868mjjJnWuOkCQyD95e7LC8cSdMf17IDbp4iSN2NhY3nj1PX4YOQCPxxJ1MoquXVL+cKzKlcvxXKsn2LxpK8tXzATgw56fMWfOIneDuSx37pwMHdIff38//Pz8+PnnacyYOS/xB6YQI0d8Q40alQkNzcruXeH07tOPE8dP8tVXfciePStTJg9nw8YtNGuWuo5qUrVKedq0fpKNm35ndfhcAN5//1NmzfbJ31uSlLaFXE9y/+wsWSgv9crez7N9h+Lv50ex/Dl5okYpvp2ymAfuzU2tUvdRpUQhlm/5g8c/CMPPz483nqxD5ozpqVKiEH8cPMbzn44AIH3aQD56ocVVE9dv5F+NK9F90GQmLd1AnmwhfNbpUQDCpi/j5Nm/+fjHOQAE+Psx+r32N3sqn2aM4dtBn5EpU0aMMWzetI233+jpdizxYcabdpAxZo21tuztvIDTw7F82fmYaLcj+IwLMRfdjuAz/P4xPjg186g9Lf+gfx2X6V/HZafnfeR2BJ+Rt6m2xZWOn96RLD42KuSp6eo/6VUHfnV9OyV2dKys8VenGWM6A5OAC5fut9YeT8JsIiIiIiKSAiU2HGsNcT++XKqWunH1jzGFrnmEiIiIiIjITSR2dKyCAMaYlsBsa+0pY8z7QBmgjwP5RERERERSFKsBlt4dHQt4L74AqQbUIW6C+nf/396dh0tR3Qkf//4uIKiAGxoX4ooxxnGJYtwTklEMKC5xRPOqwZhEUYNbNA+OxmjUiRlfM46vRgMJI8OoryLRoEZBJRBBw6Ygm/sOiBrDahCBM390QTfIcsV7q273/X6e5z63tu7+1a+rquvUOaeq8cKSJEmSVKvqWwhZcUesY4D+KaVHgI0aJyRJkiSpdqWUCv1rCupbCJkZEb8FTgH+FBGtP8NrJUmSJGml+hYkegLDgKNTSnOBLSl1UpckSZKkz6ReDytMKX0E/KFifDYwu7GCkiRJkmrVcjum26RKkiRJUr7qVRMiSZIkqWE0lc7hRbImRJIkSVKuLIRIkiRJypXNsSRJkqQc2THdmhBJkiRJObMmRJIkScpRsibEmhBJkiRJ+bIQIkmSJClXNseSJEmScrTc54RYEyJJkiQpX9aESJIkSTmyY7o1IZIkSZJyZiFEkiRJUq5sjiVJkiTlyI7p1oRIkiRJypk1IZIkSVKO7JhuTYgkSZKknFkIkSRJkpSrRm+ONXfxosb+iKrRqoWt3/Rpyc5p0lq5d2hN2h95RdEhNBkfzXqq6BC0AeyYbk2IJEmSpJxZCJEkSZKUK9sHSZIkSTny7ljWhEiSJEnKmTUhkiRJUo7smG5NiCRJkqScWQiRJEmSlCubY0mSJEk5smO6NSGSJEmScmZNiCRJkpSjlJYXHULhrAmRJEmSlCsLIZIkSZJyZXMsSZIkKUfL7ZhuTYgkSZKkfFkTIkmSJOUo+cR0a0IkSZIk5ctCiCRJkqRc2RxLknkgkG8AABNWSURBVCRJypEd060JkSRJkpQza0IkSZKkHNkx3ZoQSZIkSTmzECJJkiQpV+ttjhURu6SUXl9t2oEppfGNF5YkSZJUm5bbHKteNSFDImKHFSMR8Q1gQOOFJEmSJKmW1acQcg7wYERsGxHdgVuA7o0bliRJkqRatd7mWCml8RFxATAcWAwcmVJ6v9EjkyRJkmpQ8jkhay+ERMRDsEqGNgHmAb+PCFJKxzV2cJIkSZJqz7pqQv5vblFIkiRJzYTPCVlHn5CU0qiU0ijgLWBsxfg44M28Avw8WrduzTNjHmbihMeZPGkEP7/qJ0WHlKs77riRN9+cyIQJw1dO23vvPRk58gHGjx/G/ff/nnbt2hYYYTE6dtyeJ4YP5vnJf2bypBH0+fEPig6pcHV1dYwfN4wHHxhYdCiFOrprF6ZN/QsvTB/NTy87v+hwCmUuysxFmblYVTUfOwfd9yAnnN6b4087h0H3PvCp+QPuup+Tep3PSb3O54TTe7PPEccwb/6Cz/WZS5Ys4Sc/+yXdep7Fd390ETNnzwHg6XHP0vOsPpx4xrn0PKsPYydO+lyfo+pQn47pg4HlFePLsmlN3scff8yRXXtyQOejOKBzV47u2oWDvrZ/0WHlZtCgwRx/fK9Vpt1++6+48sobOPDAoxk6dBgXX3xOQdEVZ+nSpVz202vYZ99vctjhPTj33DPZc8/diw6rUBf0+SEzXni56DAKVVdXxy3/eT3H9jidvff9JqecckKz3S7MRZm5KDMXn1atx86XX3uDIUMf457f3cyQgb9h1NPjeOudWassc9Zp/8KQgbcxZOBtXNT7TDrvtzebtW9Xr/efOXsOZ/74p5+a/oeHh9O+XVsevW8AZ5xyAr/+Telmq1ts3p5bf3U1Dwy6neuv/AmX/8LGOM1BfQohLVNKS1aMZMMbNV5IDWvRoo8AaNWqJS1btWpW1V9jxozjww/nrjKtU6ddGD16LAAjRjzFCSd0KyK0Qr377ns8N2kqAAsXLuKFF15mh+23LTiq4uyww3Z06/bPDBhwT9GhFOprB36VV199g9dff4tPPvmE++77I8f1OLrosAphLsrMRZm5WFU1Hztfe+Nt9t5rDzZu04aWLVvQeb+9eWLUmLUu/6cnRtH9qG+sHH9o2AhO/eGFnNTrfK7591tYtmxZvT53xFPPcHz3IwHo2uUIxk6cREqJPb/UiW223gqATrvsxOKPP2bJkiXrequqt5xU6F9TUJ9CyPsRsbITekQcD3zQeCE1rLq6OiaMH87smc/z5JN/Ydz454oOqVAzZrxMjx5dAfjOd46hY8ftCo6oWDvt1JH99v0nxo5rvtvFTTddw+WXX8fy5cvXv3AN236HbXm74krgOzNns30zLZyaizJzUWYuVlXNx85Ou+7Es5OnMXfefP6xeDFPPTOed+es+can/1i8mNF/ncBRXQ4H4NU33uKxJ0cx6I6bGDLwNurq6nh4+J/r9bnvvf83tt2mAwAtW7ag7aabMHfe/FWWeXzkaL6yRyc22qhqrndrA633Fr1Ab+CuiLgVCOBt4HvrekFEnA2cDRAtNqOubtPPG+cGW758OZ0P7Mpmm7VnyODfs9deezBt2ouFxVO0c865jJtuupq+fS/gkUceZ8mST4oOqTCbbroJ993bn0su/TkLFiwsOpxCdO9+JO+/9wHPPjeFr3/9kKLDkaSqUO3Hzt123pGzTjuZsy++go3btGGP3Xelrm7N16VHjh7LV/f5ysqmWGMnTGL6C69w6g8uBEpN37fcYnMALrj8F8ycNYdPln7C7Dnvc1KvUr+h03sez4nHdF1vXK+89ia//s0A+v3H9Q2xmk1ac2qZszb1eU7Iq8DBEdE2G1/v2VpKqR/QD6DlRjs0iSzPmzefkaPGlDrVNeNCyEsvvUqPHmcApaZZ3bp9q+CIitGyZUsG39ufe+55gAcffLTocApz6KGdOfbYrnz729+iTZvWtG/fjoF33kKvMy8oOrTczZr5Ll/suP3K8Y47bMesWe8WGFFxzEWZuSgzF2W1cOw8qcfRnJQ1p7v5jjtX1lCs7tEnR9H9yC4rx1NKHNftSC4+9/ufWvaWX14FlPqEXHH9Tdx567+vMn+brbfi3fc+YNtttmbp0mUsXPQRm2/WHoB333ufC//1Wv7tZ5eyY8V2ptq13uZYEXFVRFwFXAJcUjHe5HXosCWbZRt3mzZtOPKfv86LL75acFTF2jprcxkR9O3bh/797yo4omL073cTM154hZv/s1/RoRTqyitvYJddO7P7lw7mtNPP489/HlNVP6INafyESXTqtAs77/xFWrVqRc+ex/PQw8PX/8IaZC7KzEWZuSirhWPn3/5e6jM6+933eHLUGLof1eVTyyxYuIgJz03hm0eUa3sO7rwfj48cvfL18+YvYNa7c+r1md88/GD++KcnABg+8ikOOmBfIoL5CxZy3mU/56Le32f/ffb6nGumalGf5liLKobbAMcCMxonnIa13XZfYMDvb6ZFizrq6uq4//6HeCTb+JuDgQNv4YgjDqFDhy145ZW/cu21/0Hbtptwzjml1nR//ONj/Pd/31dwlPk77NADOeP0f+H5KdOZML70A/qzn93Ao4+NKDgyFWnZsmVceNGV/OmRu2lRV8edA+9l+vSXig6rEOaizFyUmYvacvG/Xsfc+fNp2bIlV/zkPNq3a8u9DzwCwCknHgPAk6Oe5tCv7c8mG7dZ+brddtmJPj/6HmdfdAXL03JatWzJFZecx/bbfmG9n/mdY4/m8mtvpFvPs9isfTtuvKYvAPcMeYi335nFHf91N3f8190A9Lv5erbKmnnVouU2xyI+a5u0iGgNDEspdanP8k2lOVZT0KpFfcp8zcMny5YWHUKTEUUH0IR4sJBUHx43yz6a9VTRITQprTrsWhWbx5btdi/0J+/DBS8XnqcNOSveBOjY0IFIkiRJzYEd0+tRCImIKZQvULYAtgZ+0ZhBSZIkSapd9akJObZieCkwJ6VkWxpJkiRJG2SdhZCIaEGp/8eXc4pHkiRJqmlN5anlRVrnLXpTSsuAFyNix5zikSRJklTj6tMcawtgWkSMo+J2vSml4xotKkmSJKlG2TG9foWQFc8GWSGAXzVOOJIkSZJqXX0KIS1TSqMqJ0TExo0UjyRJkqQat9ZCSEScC5wH7BoRz1fMageMaezAJEmSpFrkE9PXXRNyN/Ao8Eugb8X0BSmlDxs1KkmSJEk1a62FkJTSPGAe8N38wpEkSZJqW/IWveu+Ra8kSZIkNTQLIZIkSZJyVZ+7Y0mSJElqIHZMtyZEkiRJUs4shEiSJEnKlc2xJEmSpBwlm2NZEyJJkiQpX9aESJIkSTnyOSHWhEiSJEnKmYUQSZIkSbmyOZYkSZKUIzumWxMiSZIkKWfWhEiSJEk5sibEmhBJkiRJObMQIkmSJGkVEfHtiHgxIl6JiL4N/f42x5IkSZJy1NQbY0VEC+A24CjgHWB8RAxNKU1vqM+wJkSSJElSpa8Br6SUXkspLQH+P3B8Q35Ao9eELF0yMxr7M+ojIs5OKfUrOo6mwFyUmYsyc1FmLkrMQ5m5KDMXZeaizFx8NkWfH0fE2cDZFZP6rfb97QC8XTH+DnBQQ8bQnGpCzl7/Is2GuSgzF2XmosxclJiHMnNRZi7KzEWZuagiKaV+KaXOFX+5FyCbUyFEkiRJ0vrNBL5YMd4xm9ZgLIRIkiRJqjQe2D0idomIjYBTgaEN+QHN6e5YtlMsMxdl5qLMXJSZixLzUGYuysxFmbkoMxc1JKW0NCJ+DAwDWgADUkrTGvIzwic2SpIkScqTzbEkSZIk5cpCiCRJkqRc1VQhJCJGRkTnouNQ0xARO0fE1DVM/11EfKUerz8zIm5tnOiatojoEhGHFh1HY4qIqyPi0qLjaEqac04i4oKImBERdxUdS57WdpzUmkXEGxHRYQ3Tj4uIvkXE1FgiYvOIOK+B3qtLRDzcEO+l2lFThRB9PhHRLG5UkFL6YUpp+urTI6JFEfE0UV2Ami6ENITmss80E+cBR6WUTlsxwe933cxPWUppaErphqLjaGCbU9ovVuH3roZSlYWQ7MrNCxFxV3bl6v6I2GS1ZW6PiAkRMS0irqmY/kZEXBMRz0bElIj4cjZ904gYEBHjIuK5iGjQR9PnLSK+FxHPR8TkiBgUET0iYmy2bk9ExBey5a7O5o8BBhUcdmNoufp2UlljFhELI+KmiJgMHBIR34+IlyJiHHBYsaE3vPpsFxGxM9AbuDgiJkXEEcVG3XAi4ors+x0N7JFN2y0iHouIiRHxVMUxYeuIGBIR47O/w7LpNbXPrCUn+0XEX7Nt5YGI2CKbfmA2bVJE3FgrV9Aj4g5gV+DRiJhX+f1mvzcjsvV+MiJ2zF6zW5ajKRFxXUQsLHQlPp8WEdE/+70cHhEbr2MbGBkRN0fEBODCiDg5IqZmx5S/ZMu0yLaP8dnrzyl07TZQdl7wSLZuUyPilGxWnzWcQ6ysOY+IOyPijuwc5KWIOLawlfh8bgB2y/b38dnxcSgwPVarQYuISyPi6my4U/Z7MjnL026Vb5odR55bfbqaoZRS1f0BOwMJOCwbHwBcCowEOmfTtsz+t8im75ONvwH0yYbPA36XDf8bcHo2vDnwErBp0eu6gfnZK4u/w4pcAFtQvhvaD4GbsuGrgYnAxkXHXdB2koCe2fB2wFvA1sBGwBjg1qLXo8Dt4tKiY27g9T8AmAJsArQHXsm2hyeB3bNlDgJGZMN3A4dnwzsCMypyUxP7zDpy8jzwjWyZXwA3Z8NTgUOy4RuAqUWvQwPm4g2gw+rfL/AQ0CsbPgt4MBt+GPhuNtwbWFj0Omzgeu8MLAX2y8bvA05fxzYwEvhNxeunADtkw5tn/88GrsyGWwMTgF2KXtcNyM1JQP+K8c1Y+znEmSt+L4A7gccoXejdHXgHaFP0+mzgtjE1G+4CLFrxPVbOy8YvBa7OhscCJ2bDbbLjS5dsnzk02792LHr9/Cv+ryprQjJvp5TGZMP/Axy+2vyeEfEs8Bylk6/KPgB/yP5PpLQjAXQF+kbEJEoH2TaUTjyq0beAwSmlDwBSSh9SetLlsIiYAlxGKScrDE0p/SP/MHOxvu1kGTAkGz4IGJlSej+ltAS4N6cY8/JZt4tacwTwQErpo5TSfEoPXWpD6UdxcLbv/5ZSYRTgSODWbPpQoH1EtM3m1co+s6acbErpZHJUtsxA4OsRsTnQLqX0TDb97vzDzU3l93sI5XUdRPkYcggwOBuu9ly8nlKalA1PBHZjDdtAxfKVx8YxwJ0R8SNKF/2g9Hv6vWzfGQtsRelkvNpMAY6KiF9FxBEppXnZ9DWdQ6zuvpTS8pTSy8BrwJcbN9RcjEspvb6uBSKiHaVC6QMAKaXFKaWPstl7UnqWSI+U0luNG6qqQTW361v9AScrxyNiF0ql8gNTSn+PiDspnWys8HH2fxnlHARwUkrpxcYJt3D/D/h1SmloRHShdLVvhUWFRJSPtW4nmcUppWV5BdMErWu7aA7qgLkppf3WMu/glNLiyokRAbW9z6j5fb8fVwwvo9QaYF1W5iel1DsiDgKOASZGxAGUfk/7pJSGNXikOUopvRQR+wPdgesi4sls1prOIT718vWMV6PK/WIpqzbpb8P6zc6W+yowqwHjUpWq5pqQHSPikGz4/wCjK+a1p7SzzItS34du9Xi/YZTaeQZARHy1IYPN2Qjg5IjYCiAitqRUjTwzm9+rqMAKsK7tZHVjgW9ExFYR0Qo4udGjy9dn2S4WAO3yDa/R/QU4IWvv3g7oAXwEvB4RJwNEyb7Z8sOBPiteHBFrKqhUuzXlZBHw9yj3BToDGJVSmgssyE44AU7NP9xCPE15XU8DnsqG/0qpuQ7UXi7msYZtYE0LRsRuKaWxKaWrgPeBL1L6PT03O44SEV+KiE1ziLtBRcT2wEcppf8BbgT2/wwvPzki6rJ+D7sC1XiBc12/A3OAbbLfy9bAsQAppQXAOxFxAkBEtI5yn925lAqrv8wueqmZq+ZCyIvA+RExg1K79ttXzEgpTabUDOsFStXkY9b4Dqu6FmgFPB8R07LxqpRSmgZcD4yKUofrX1O6wj04IiYCHxQYXt7Wup2sLqU0m1KenqG0zczII8C8fMbt4iHgxKihjukppWcpNSOZDDwKjM9mnQb8IMvJNGDFTSkuADpnHWunU2r3X1PWkZNewI0R8TywH6U+AQA/APpnzWw2pXSyWuv6AN/PcnEGcGE2/SLgkmx6J2ovF2vbBlZ3Y9ZBeyqlAttk4HfAdODZbPpvqc6WF3sD47Lt/efAdZ/htW8B4yjtV71Xr1GtBimlvwFjsu/wxtXmfUJpmxgHPE7pfGuFM4ALsm3naWDbitfNoVRgua3igoaaqRUdUqtKlO7e83BK6Z8KDkWSmo2IaJtSWpgN9wW2SylduJ6X1aTs6u4/UkopIk6l1Em9qu+qqIaRNQF/OKV0f9GxSE1ZNV6ZkCQV45iIuJzSb8eblO4I1FwdQOnGBUGpmclZBccjSVWlKmtCJEmSJFWvau4TIkmSJKkKWQiRJEmSlCsLIZIkSZJyZSFEkiRJUq4shEiSJEnK1f8CpcaqG5OYZP0AAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "t3oafNH1egHu"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}