Training.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"restartCT.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","metadata":{"id":"3-xgwCGJpN_7","colab_type":"text"},"source":["## Installing keras efficientnets package"]},{"cell_type":"code","metadata":{"id":"frFNh-hkpboD","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":252},"executionInfo":{"status":"ok","timestamp":1594580413362,"user_tz":-330,"elapsed":42527,"user":{"displayName":"saimanoj akondi","photoUrl":"","userId":"10529021043337064225"}},"outputId":"700bc009-b7fb-4aae-9c36-49c076526ef7"},"source":["!pip install keras-efficientnets"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Collecting keras-efficientnets\n","  Downloading https://files.pythonhosted.org/packages/3a/41/4dce4e88042b4934003b2b51cfb6a99fc446d375d5fc1ffb2fdf8e069d36/keras_efficientnets-0.1.7-py2.py3-none-any.whl\n","Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from keras-efficientnets) (1.4.1)\n","Requirement already satisfied: scikit-learn>=0.21.2 in /usr/local/lib/python3.6/dist-packages (from keras-efficientnets) (0.22.2.post1)\n","Requirement already satisfied: keras>=2.2.4 in /usr/local/lib/python3.6/dist-packages (from keras-efficientnets) (2.3.1)\n","Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.6/dist-packages (from scipy>=1.1.0->keras-efficientnets) (1.18.5)\n","Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn>=0.21.2->keras-efficientnets) (0.15.1)\n","Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.6/dist-packages (from keras>=2.2.4->keras-efficientnets) (1.12.0)\n","Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from keras>=2.2.4->keras-efficientnets) (1.1.2)\n","Requirement already satisfied: pyyaml in /usr/local/lib/python3.6/dist-packages (from keras>=2.2.4->keras-efficientnets) (3.13)\n","Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from keras>=2.2.4->keras-efficientnets) (1.0.8)\n","Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras>=2.2.4->keras-efficientnets) (2.10.0)\n","Installing collected packages: keras-efficientnets\n","Successfully installed keras-efficientnets-0.1.7\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"gMiT55Euprgh","colab_type":"text"},"source":["## Importing all dependencies"]},{"cell_type":"code","metadata":{"id":"oya2fBIFTXiI","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1594580413363,"user_tz":-330,"elapsed":42442,"user":{"displayName":"saimanoj akondi","photoUrl":"","userId":"10529021043337064225"}},"outputId":"176a18dd-4c15-4a32-f1b0-26ab73228029"},"source":["import keras\n","import keras.backend as K\n","import cv2\n","import random\n","import tensorflow as tf\n","import numpy as np\n","from google.colab.patches import cv2_imshow\n","from google.colab import drive\n","import matplotlib.pyplot as plt\n","from keras_efficientnets import EfficientNetB0\n","from sklearn.metrics import accuracy_score\n","from sklearn.metrics import precision_score\n","from sklearn.metrics import recall_score\n","from sklearn.metrics import f1_score\n","from sklearn.metrics import roc_auc_score\n","from sklearn.metrics import confusion_matrix\n","from keras.callbacks import ModelCheckpoint"],"execution_count":2,"outputs":[{"output_type":"stream","text":["Using TensorFlow backend.\n"],"name":"stderr"}]},{"cell_type":"markdown","metadata":{"id":"HnjgNAhGpwBi","colab_type":"text"},"source":["## Mounting google Drive"]},{"cell_type":"code","metadata":{"id":"cDhiX8F1S-EJ","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":279},"executionInfo":{"status":"error","timestamp":1594580415273,"user_tz":-330,"elapsed":44321,"user":{"displayName":"saimanoj akondi","photoUrl":"","userId":"10529021043337064225"}},"outputId":"3e112c9a-2caa-4c82-b0bd-a695941ca75f"},"source":["drive.mount('/content/gdrive')\n"],"execution_count":3,"outputs":[{"output_type":"error","ename":"TypeError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-3-837f90cc644e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdrive\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/gdrive'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/google/colab/drive.py\u001b[0m in \u001b[0;36mmount\u001b[0;34m(mountpoint, force_remount, timeout_ms, use_metadata_server)\u001b[0m\n\u001b[1;32m    240\u001b[0m       \u001b[0mauth_prompt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'\\nEnter your authorization code:\\n'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    241\u001b[0m       \u001b[0;32mwith\u001b[0m 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\u001b[0mwrote_to_fifo\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'dict' and 'str'"]}]},{"cell_type":"code","metadata":{"id":"P5mk2DX2TWhP","colab_type":"code","colab":{},"executionInfo":{"status":"aborted","timestamp":1594580415268,"user_tz":-330,"elapsed":44294,"user":{"displayName":"saimanoj akondi","photoUrl":"","userId":"10529021043337064225"}}},"source":["model = EfficientNetB0(\n","    include_top=False,\n","    input_shape=(512 ,512 ,3) ,\n","    weights = 'imagenet' ,\n","    dropout_rate = 0.4\n",")"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"_5VbQD8A1YzG","colab_type":"code","colab":{},"executionInfo":{"status":"aborted","timestamp":1594580415270,"user_tz":-330,"elapsed":44280,"user":{"displayName":"saimanoj akondi","photoUrl":"","userId":"10529021043337064225"}}},"source":["model.summary()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"3TNxH2GIp4uS","colab_type":"text"},"source":["## Building model"]},{"cell_type":"code","metadata":{"id":"a7qP3fjCTGot","colab_type":"code","colab":{},"executionInfo":{"status":"aborted","timestamp":1594580415271,"user_tz":-330,"elapsed":44263,"user":{"displayName":"saimanoj akondi","photoUrl":"","userId":"10529021043337064225"}}},"source":["def predict(y):\n","    return model.predict(y)\n","def preprocess(y):\n","    return ((y/255)-0.5)*2\n","x = keras.layers.Input((512 ,512 ,3))\n","x_ = keras.layers.Input((512 ,512 ,1))\n","multiply = keras.layers.Multiply()([x ,x_])\n","#pre = keras.layers.Lambda(preprocess)(multiply)\n","final = model(multiply)\n","conv1 = keras.layers.Conv2D(512 ,kernel_size = (2 ,2) ,strides = 2 ,kernel_initializer='glorot_uniform' ,activation = 'relu')(final)\n","drop1 = keras.layers.Dropout(0.4)(conv1)\n","conv2 = keras.layers.Conv2D(512 ,kernel_size = (3 ,3) ,kernel_initializer='glorot_uniform' ,activation = 'relu' ,name = 'pen_ultimate')(drop1)\n","pool1 = keras.layers.MaxPooling2D((6 ,6))(conv2)\n","flatten1 = keras.layers.Flatten()(pool1)\n","drop2 = keras.layers.Dropout(0.4)(flatten1)\n","dense1 = keras.layers.Dense(512 ,activation = 'relu')(drop2)\n","dense1 = keras.layers.Dense(1 ,activation = 'sigmoid' ,name = 'final')(dense1)\n","classifier = keras.models.Model(inputs = [x ,x_],outputs = dense1)\n","classifier.summary()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"nIN1uwzBp9fz","colab_type":"text"},"source":["## Importing the preprocess image arrays and respective masks"]},{"cell_type":"code","metadata":{"id":"cShtMcYLTTZQ","colab_type":"code","colab":{},"executionInfo":{"status":"aborted","timestamp":1594580415271,"user_tz":-330,"elapsed":44261,"user":{"displayName":"saimanoj akondi","photoUrl":"","userId":"10529021043337064225"}}},"source":["X = np.load('/content/gdrive/My Drive/JPGCT/small_data/.NPY/X_preprocess.npy')\n","masks = 1 -np.load('/content/gdrive/My Drive/JPGCT/small_data/.NPY/masks.npy')\n","#X = np.load('/content/gdrive/My Drive/JPGCT/X.npy')\n","Y = np.load('/content/gdrive/My Drive/JPGCT/small_data/.NPY/Y.npy')\n","val_X = np.load('/content/gdrive/My Drive/JPGCT/small_data/.NPY/val_X_preprocess.npy')\n","val_masks = 1 - np.load('/content/gdrive/My Drive/JPGCT/small_data/.NPY/val_masks.npy')\n","val_Y = np.load('/content/gdrive/My Drive/JPGCT/small_data/.NPY/val_Y.npy')"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"kcLlhiPPqHHd","colab_type":"text"},"source":["## Conveting the masks to binary masks"]},{"cell_type":"code","metadata":{"id":"kmmnjbdHnEQG","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":50},"executionInfo":{"status":"ok","timestamp":1594119718247,"user_tz":-330,"elapsed":3130,"user":{"displayName":"saimanoj akondi","photoUrl":"","userId":"10529021043337064225"}},"outputId":"63b4bf50-1f13-488f-f035-2908f032d16d"},"source":["print('max : {} min : {}'.format(masks.max() ,masks.min()))\n","masks[masks<0.5] = 0\n","masks[masks>=0.5] = 1\n","print('max:{} min:{}'.format(masks.max() ,masks.min()))\n","val_masks[val_masks<0.5] = 0\n","val_masks[val_masks>=0.5] = 1\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["max : 1.0 min : 1.3113021850585938e-06\n","max:1.0 min:0.0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"eKpIPgK4qsaS","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1594119718249,"user_tz":-330,"elapsed":2854,"user":{"displayName":"saimanoj akondi","photoUrl":"","userId":"10529021043337064225"}},"outputId":"e50969a4-d555-4735-cf3a-22517c68462a"},"source":["masks.shape"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(684, 512, 512, 1)"]},"metadata":{"tags":[]},"execution_count":9}]},{"cell_type":"markdown","metadata":{"id":"yAhThE3eqPip","colab_type":"text"},"source":["## Visualizing the images and their respective masks"]},{"cell_type":"code","metadata":{"id":"T1Kho1tjUc1m","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":387},"executionInfo":{"status":"error","timestamp":1594120909836,"user_tz":-330,"elapsed":2471,"user":{"displayName":"saimanoj akondi","photoUrl":"","userId":"10529021043337064225"}},"outputId":"7da6fa1b-1721-4752-d33e-c8d152ece1a8"},"source":["i = random.choice([k for k in range(X.shape[0])])\n","print('prediction is {}'.format(Y[i]))\n","img = (((X[i ,: ,: ,:]/2)+0.5)*255).astype('uint8')\n","fig ,(ax1 ,ax2 ,ax3) = plt.subplots(1 ,3,fig_size = (10,10))\n","ax1.imshow(img.astype('uint8'))\n","ax2.imshow(masks[i].reshape((512 ,512)))\n","img_ = X[i ,: ,: ,:]*(masks[i])\n","img_ = ((img_/2)+0.5)*255\n","ax3.imshow(img_.astype('uint8'))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["prediction is [0.]\n"],"name":"stdout"},{"output_type":"error","ename":"TypeError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-11-8e8cb01e4039>\u001b[0m in 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\u001b[0;34m,\u001b[0m\u001b[0;36m512\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36msubplots\u001b[0;34m(nrows, ncols, sharex, sharey, squeeze, subplot_kw, gridspec_kw, **fig_kw)\u001b[0m\n\u001b[1;32m   1175\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1176\u001b[0m     \"\"\"\n\u001b[0;32m-> 1177\u001b[0;31m     \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mfig_kw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1178\u001b[0m     axs = fig.subplots(nrows=nrows, ncols=ncols, sharex=sharex, sharey=sharey,\n\u001b[1;32m   1179\u001b[0m                        \u001b[0msqueeze\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubplot_kw\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubplot_kw\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mfigure\u001b[0;34m(num, figsize, dpi, facecolor, edgecolor, frameon, FigureClass, clear, **kwargs)\u001b[0m\n\u001b[1;32m    544\u001b[0m                                         \u001b[0mframeon\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mframeon\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    545\u001b[0m                                         \u001b[0mFigureClass\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFigureClass\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 546\u001b[0;31m                                         **kwargs)\n\u001b[0m\u001b[1;32m    547\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    548\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mfigLabel\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mnew_figure_manager\u001b[0;34m(cls, num, *args, **kwargs)\u001b[0m\n\u001b[1;32m   3355\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mFigure\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3356\u001b[0m         \u001b[0mfig_cls\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'FigureClass'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mFigure\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3357\u001b[0;31m         \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig_cls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3358\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnew_figure_manager_given_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3359\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mTypeError\u001b[0m: __init__() got an unexpected keyword argument 'fig_size'"]}]},{"cell_type":"code","metadata":{"id":"T3ggmER8p0f-","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":52},"executionInfo":{"status":"ok","timestamp":1593983223005,"user_tz":-330,"elapsed":130401,"user":{"displayName":"saimanoj akondi","photoUrl":"","userId":"10529021043337064225"}},"outputId":"cb5ee3b9-7f11-49d5-b79d-1c673421fccc"},"source":["'''i = random.choice([k for k in range(X.shape[0])])\n","plt.imshow(X[i].reshape(512 ,512 ,3))\n","X_masked = X*(masks)\n","val_X_masked = val_X*val_masks\n","X = ((X_masked/255)-0.5)*2\n","val_X = ((val_X_masked/255)-0.5)*2'''"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"application/vnd.google.colaboratory.intrinsic":{"type":"string"},"text/plain":["'i = random.choice([k for k in range(X.shape[0])])\\nplt.imshow(X[i].reshape(512 ,512 ,3))\\nX_masked = X*(masks)\\nval_X_masked = val_X*val_masks\\nX = ((X_masked/255)-0.5)*2\\nval_X = ((val_X_masked/255)-0.5)*2'"]},"metadata":{"tags":[]},"execution_count":12}]},{"cell_type":"code","metadata":{"id":"SobAXoBLvOQm","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":119},"executionInfo":{"status":"ok","timestamp":1593983223006,"user_tz":-330,"elapsed":129403,"user":{"displayName":"saimanoj akondi","photoUrl":"","userId":"10529021043337064225"}},"outputId":"1979b6b4-dc21-4c8b-9d42-873215cb3a4e"},"source":["'''\n","def preprocess(arr):\n","    return ((arr/255)-0.5)*2\n","train_dir = '/content/gdrive/My Drive/JPGCT/small_data/train'\n","valid_dir = '/content/gdrive/My Drive/JPGCT/small_data/valid'\n","datagen = tf.keras.preprocessing.image.ImageDataGenerator(preprocessing_function = preprocess )\n","valid_datagen = tf.keras.preprocessing.image.ImageDataGenerator(preprocessing_function = preprocess)\n","train_gen = datagen.flow_from_directory(train_dir ,\n","                                        target_size = (512 ,512) ,\n","                                        class_mode = 'binary' ,\n","                                        batch_size = 8\n","                                        )\n","valid_gen = valid_datagen.flow_from_directory(valid_dir ,\n","                                        target_size = (512 ,512) ,\n","                                        class_mode = 'binary' ,\n","                                        batch_size = 8\n","                                        )'''"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"application/vnd.google.colaboratory.intrinsic":{"type":"string"},"text/plain":["\"\\ndef preprocess(arr):\\n    return ((arr/255)-0.5)*2\\ntrain_dir = '/content/gdrive/My Drive/JPGCT/small_data/train'\\nvalid_dir = '/content/gdrive/My Drive/JPGCT/small_data/valid'\\ndatagen = tf.keras.preprocessing.image.ImageDataGenerator(preprocessing_function = preprocess )\\nvalid_datagen = tf.keras.preprocessing.image.ImageDataGenerator(preprocessing_function = preprocess)\\ntrain_gen = datagen.flow_from_directory(train_dir ,\\n                                        target_size = (512 ,512) ,\\n                                        class_mode = 'binary' ,\\n                                        batch_size = 8\\n                                        )\\nvalid_gen = valid_datagen.flow_from_directory(valid_dir ,\\n                                        target_size = (512 ,512) ,\\n                                        class_mode = 'binary' ,\\n                                        batch_size = 8\\n                                        )\""]},"metadata":{"tags":[]},"execution_count":13}]},{"cell_type":"markdown","metadata":{"id":"l3ILJjhAqknl","colab_type":"text"},"source":["## Defining focal loss and training"]},{"cell_type":"code","metadata":{"id":"P4ww19iLUJDv","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1593989184903,"user_tz":-330,"elapsed":560311,"user":{"displayName":"saimanoj akondi","photoUrl":"","userId":"10529021043337064225"}},"outputId":"a14b8db5-96f3-47da-e1d2-27769e756946"},"source":["def myloss(gamma):\n","    def focal_loss(y_true ,y_pred):\n","        y_pred = tf.keras.backend.clip(y_pred ,1e-7 ,1-1e-7)\n","        loglik = ((1-y_pred)**gamma)*y_true*tf.keras.backend.log(y_pred) + (y_pred**gamma)*(1-y_true)*tf.keras.backend.log(1-y_pred)\n","        return -tf.keras.backend.sum(loglik)\n","    return focal_loss\n","filepath = '/content/gdrive/My Drive/finel.hdf5'\n","checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')\n","callbacks_list = [checkpoint]\n","classifier.compile(optimizer = tf.keras.optimizers.Adam(lr = 0.00001) ,loss = myloss(4.0) ,metrics = ['acc'])\n","history = classifier.fit([X ,masks] ,Y ,batch_size = 2,epochs = 30 ,verbose = 1,validation_data = ([val_X ,val_masks] ,val_Y) ,callbacks = callbacks_list)\n","#classifier.save('/content/gdrive/My Drive/cache_images.h5')\n","#classifier.save_weights('/content/gdrive/My Drive/cachew_images.hdf5')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Train on 684 samples, validate on 62 samples\n","Epoch 1/30\n","684/684 [==============================] - 87s 127ms/step - loss: 0.5955 - acc: 0.5351 - val_loss: 0.1889 - val_acc: 0.4677\n","\n","Epoch 00001: val_acc improved from -inf to 0.46774, saving model to /content/gdrive/My Drive/finel.hdf5\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/keras/engine/saving.py:165: UserWarning: TensorFlow optimizers do not make it possible to access optimizer attributes or optimizer state after instantiation. As a result, we cannot save the optimizer as part of the model save file.You will have to compile your model again after loading it. Prefer using a Keras optimizer instead (see keras.io/optimizers).\n","  'TensorFlow optimizers do not '\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 2/30\n","684/684 [==============================] - 77s 113ms/step - loss: 0.1926 - acc: 0.5687 - val_loss: 0.0889 - val_acc: 0.5645\n","\n","Epoch 00002: val_acc improved from 0.46774 to 0.56452, saving model to /content/gdrive/My Drive/finel.hdf5\n","Epoch 3/30\n","684/684 [==============================] - 77s 113ms/step - loss: 0.1439 - acc: 0.5980 - val_loss: 0.0749 - val_acc: 0.7903\n","\n","Epoch 00003: val_acc improved from 0.56452 to 0.79032, saving model to /content/gdrive/My Drive/finel.hdf5\n","Epoch 4/30\n","684/684 [==============================] - 77s 113ms/step - loss: 0.1029 - acc: 0.6520 - val_loss: 0.1329 - val_acc: 0.4677\n","\n","Epoch 00004: val_acc did not improve from 0.79032\n","Epoch 5/30\n","684/684 [==============================] - 77s 113ms/step - loss: 0.0849 - acc: 0.6959 - val_loss: 0.0755 - val_acc: 0.7903\n","\n","Epoch 00005: val_acc did not improve from 0.79032\n","Epoch 6/30\n","684/684 [==============================] - 77s 113ms/step - loss: 0.0746 - acc: 0.7456 - val_loss: 0.0589 - val_acc: 0.7419\n","\n","Epoch 00006: val_acc did not improve from 0.79032\n","Epoch 7/30\n","684/684 [==============================] - 77s 113ms/step - loss: 0.0782 - acc: 0.7515 - val_loss: 0.0601 - val_acc: 0.7903\n","\n","Epoch 00007: val_acc did not improve from 0.79032\n","Epoch 8/30\n","684/684 [==============================] - 77s 113ms/step - loss: 0.0648 - acc: 0.7880 - val_loss: 0.0878 - val_acc: 0.6452\n","\n","Epoch 00008: val_acc did not improve from 0.79032\n","Epoch 9/30\n","684/684 [==============================] - 77s 113ms/step - loss: 0.0486 - acc: 0.8406 - val_loss: 0.1072 - val_acc: 0.6290\n","\n","Epoch 00009: val_acc did not improve from 0.79032\n","Epoch 10/30\n","684/684 [==============================] - 77s 112ms/step - loss: 0.0478 - acc: 0.8567 - val_loss: 0.0920 - val_acc: 0.7258\n","\n","Epoch 00010: val_acc did not improve from 0.79032\n","Epoch 11/30\n","684/684 [==============================] - 77s 112ms/step - loss: 0.0583 - acc: 0.8333 - val_loss: 0.0923 - val_acc: 0.7097\n","\n","Epoch 00011: val_acc did not improve from 0.79032\n","Epoch 12/30\n","684/684 [==============================] - 77s 113ms/step - loss: 0.0361 - acc: 0.9035 - val_loss: 0.1903 - val_acc: 0.5484\n","\n","Epoch 00012: val_acc did not improve from 0.79032\n","Epoch 13/30\n","684/684 [==============================] - 77s 113ms/step - loss: 0.0409 - acc: 0.9225 - val_loss: 0.5033 - val_acc: 0.5161\n","\n","Epoch 00013: val_acc did not improve from 0.79032\n","Epoch 14/30\n","684/684 [==============================] - 78s 114ms/step - loss: 0.0552 - acc: 0.8699 - val_loss: 0.0702 - val_acc: 0.6290\n","\n","Epoch 00014: val_acc did not improve from 0.79032\n","Epoch 15/30\n","684/684 [==============================] - 78s 113ms/step - loss: 0.0462 - acc: 0.8860 - val_loss: 0.1295 - val_acc: 0.7258\n","\n","Epoch 00015: val_acc did not improve from 0.79032\n","Epoch 16/30\n","684/684 [==============================] - 78s 114ms/step - loss: 0.0423 - acc: 0.9094 - val_loss: 0.0553 - val_acc: 0.7903\n","\n","Epoch 00016: val_acc did not improve from 0.79032\n","Epoch 17/30\n","684/684 [==============================] - 77s 113ms/step - loss: 0.0257 - acc: 0.9386 - val_loss: 0.1489 - val_acc: 0.7903\n","\n","Epoch 00017: val_acc did not improve from 0.79032\n","Epoch 18/30\n","684/684 [==============================] - 77s 113ms/step - loss: 0.0491 - acc: 0.8977 - val_loss: 0.2022 - val_acc: 0.5968\n","\n","Epoch 00018: val_acc did not improve from 0.79032\n","Epoch 19/30\n","684/684 [==============================] - 77s 113ms/step - loss: 0.0182 - acc: 0.9401 - val_loss: 0.0748 - val_acc: 0.7742\n","\n","Epoch 00019: val_acc did not improve from 0.79032\n","Epoch 20/30\n","684/684 [==============================] - 77s 113ms/step - loss: 0.0277 - acc: 0.9401 - val_loss: 0.5315 - val_acc: 0.5968\n","\n","Epoch 00020: val_acc did not improve from 0.79032\n","Epoch 21/30\n","684/684 [==============================] - 77s 112ms/step - loss: 0.0482 - acc: 0.9137 - val_loss: 0.3620 - val_acc: 0.7742\n","\n","Epoch 00021: val_acc did not improve from 0.79032\n","Epoch 22/30\n","684/684 [==============================] - 77s 112ms/step - loss: 0.0445 - acc: 0.9298 - val_loss: 0.0677 - val_acc: 0.7581\n","\n","Epoch 00022: val_acc did not improve from 0.79032\n","Epoch 23/30\n","684/684 [==============================] - 77s 113ms/step - loss: 0.0327 - acc: 0.9254 - val_loss: 0.0515 - val_acc: 0.7581\n","\n","Epoch 00023: val_acc did not improve from 0.79032\n","Epoch 24/30\n","684/684 [==============================] - 77s 112ms/step - loss: 0.0295 - acc: 0.9313 - val_loss: 0.7352 - val_acc: 0.7419\n","\n","Epoch 00024: val_acc did not improve from 0.79032\n","Epoch 25/30\n","684/684 [==============================] - 77s 112ms/step - loss: 0.0156 - acc: 0.9693 - val_loss: 0.1067 - val_acc: 0.7258\n","\n","Epoch 00025: val_acc did not improve from 0.79032\n","Epoch 26/30\n","684/684 [==============================] - 77s 112ms/step - loss: 0.0077 - acc: 0.9766 - val_loss: 0.3725 - val_acc: 0.7903\n","\n","Epoch 00026: val_acc did not improve from 0.79032\n","Epoch 27/30\n","684/684 [==============================] - 77s 112ms/step - loss: 0.0084 - acc: 0.9737 - val_loss: 0.5540 - val_acc: 0.7419\n","\n","Epoch 00027: val_acc did not improve from 0.79032\n","Epoch 28/30\n","684/684 [==============================] - 77s 112ms/step - loss: 0.0252 - acc: 0.9795 - val_loss: 0.3444 - val_acc: 0.5968\n","\n","Epoch 00028: val_acc did not improve from 0.79032\n","Epoch 29/30\n","684/684 [==============================] - 77s 112ms/step - loss: 0.0603 - acc: 0.8713 - val_loss: 0.1363 - val_acc: 0.7258\n","\n","Epoch 00029: val_acc did not improve from 0.79032\n","Epoch 30/30\n","684/684 [==============================] - 77s 112ms/step - loss: 0.0191 - acc: 0.9547 - val_loss: 0.3485 - val_acc: 0.6935\n","\n","Epoch 00030: val_acc did not improve from 0.79032\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"AJFSkyrFLCGk","colab_type":"code","colab":{}},"source":["model.save('/content/gdrive/My Drive/myhope.h5')"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"LWHdHaesqvKT","colab_type":"text"},"source":["## Plotting learning curves"]},{"cell_type":"code","metadata":{"id":"lRUlCKulkD7A","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":573},"executionInfo":{"status":"ok","timestamp":1593989786057,"user_tz":-330,"elapsed":1680,"user":{"displayName":"saimanoj akondi","photoUrl":"","userId":"10529021043337064225"}},"outputId":"ffb6a776-edca-48a7-c34b-b311f1871c1a"},"source":["# summarize history for accuracy\n","plt.plot(history.history['acc'])\n","#plt.plot(history.history['val_acc'])\n","plt.title('model acc')\n","plt.ylabel('acc')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'val'], loc='upper left')\n","plt.savefig('/content/gdrive/My Drive/acc1.jpeg')\n","plt.show()\n","# summarize history for loss\n","plt.plot(history.history['loss'])\n","#plt.plot(history.history['val_loss'])\n","plt.title('model loss')\n","plt.ylabel('loss')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'val'], loc='upper left')\n","plt.savefig('/content/gdrive/My Drive/loss1.jpeg')\n","plt.show()\n"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"YCe8mI7tq0ad","colab_type":"text"},"source":["## Defining gradcams"]},{"cell_type":"code","metadata":{"id":"VHjMODdK6Dsp","colab_type":"code","colab":{}},"source":["#defining grad cam function\n","def grad_cam(image, mask ,cls ,model):\n","    \"\"\"GradCAM method for visualizing input saliency.\"\"\"\n","    epsilon = 1e-10\n","    y_c = model.output[0]\n","    conv_output = model.get_layer('pen_ultimate').output\n","    grads = K.gradients(y_c, conv_output)[0]\n","    # Normalize if necessary\n","    # grads = normalize(grads)\n","    gradient_function = K.function([model.inputs], [conv_output, grads])\n","\n","    output, grads_val = gradient_function([image ,mask])\n","    output, grads_val = output[0], grads_val[0]\n","\n","    weights = np.mean(grads_val, axis=(0, 1))\n","    print('grad_val shape {} output {}'.format(grads_val.shape ,output.shape))\n","    cam = np.dot(output, weights)\n","    print(cam.shape)\n","    # Process CAM\n","    cam = cv2.resize(cam, (512 ,512), cv2.INTER_LINEAR)\n","    cam = np.maximum(cam, 0)\n","    print(cam.max())\n","    cam = cam / (cam.max()+epsilon)\n","    return cam"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"C2_-uEuXq5WR","colab_type":"text"},"source":["## Plotting gradcams"]},{"cell_type":"code","metadata":{"id":"flzkPPCEjNS7","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":622},"executionInfo":{"status":"ok","timestamp":1593989663671,"user_tz":-330,"elapsed":7017,"user":{"displayName":"saimanoj akondi","photoUrl":"","userId":"10529021043337064225"}},"outputId":"6f03f7cd-3e7e-4f6f-d5a6-2d40cac3f329"},"source":["i = random.choice([k for k in range(0 ,60)])\n","k = val_Y[i]\n","print(k)\n","mask = val_masks[i ,: ,: ,:].reshape((1 ,512 ,512 ,1))\n","cam = grad_cam(val_X[i ,: ,: ,:].reshape((1 ,512 ,512 ,3)) ,mask ,int(k) ,classifier)\n","print('prediction:{}'.format(classifier.predict([val_X[i ,: ,: ,:].reshape((1 ,512 ,512 ,3)) ,mask])))\n","print(val_X[i ,: ,: ,:].shape)\n","img = (((val_X[i ,: ,: ,:]/2)+0.5)*255).astype('uint8')\n","plt.imshow(img)\n","plt.imshow(cam.reshape(512 ,512) ,cmap = 'jet' ,alpha = 0.5)\n","plt.show()\n","ret, cam = cv2.threshold(cam, np.mean(cam), 1, cv2.THRESH_BINARY) \n","plt.imshow(img.astype('uint8'))\n","plt.imshow(cam.reshape(512 ,512) ,cmap = 'jet' ,alpha = 0.5)\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["[1.]\n","grad_val shape (6, 6, 512) output (6, 6, 512)\n","(6, 6)\n","0.0005770991\n","prediction:[[0.04219145]]\n","(512, 512, 3)\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAQYAAAD8CAYAAACVSwr3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOy9PawtS5bn9YuI/Nh7n4977quqru7qaTQY440BDuMiISRASO21GBxAoHYYn/Zwx0VihNTGCMahwQOjJZCQEBbSSHgggVqj6a5Xr169+3HO2d+ZGRELI3Jlrsx7XtW9RT/mtnRDOu+8u0/uzMiIWCv+678+wokIX9qX9qV9abb5f9Ed+NK+tC/t82tfFMOX9qV9aR+0L4rhS/vSvrQP2hfF8KV9aV/aB+2LYvjSvrQv7YP2RTF8aV/al/ZB+0EUg3Pu33LO/d/Oub9wzv3JD/GML+1L+9J+uOb+uuMYnHMB+H+AfxP4GvinwN8Xkf/rr/VBX9qX9qX9YO2HQAz/GvAXIvLPRKQH/gz4wx/gOV/al/al/UCt+gHu+fvAz82/vwb+3q/7wusf3cjP/uDV9G8HiPl/nANA0Y1zDkTIIuUzARAEB84jziPMv7Mwf4YnO48IZBxZHCIexCMZXAbEQRZyEiQJZIEEIkAGJw4nILk837kMZETK7/LvJRIbX0HfqLzD+E76Uz5yjK83XTd/7pgRniAy/11kHh/vrb6X6Zn6nblPdlztdebb5m96vXtpPrDPd2V8czbvYPvO9E4v3cf2c37m/I5lrNyL93jpnXOWqQ8vXStS+ux9mP4uksfv+PGz/L3jbdtL76DzMd9D+y2reXbTeM+3niVhvvf8He8d4BDJ5CzTmOvn9l5v3vyztyLykw86/UL7IRTDRzXn3B8Dfwzwsz94xZ/9z/8JMUaqqqKua3LOiAghBLz3pJQYhqF0uqqIMXK+nEHKv0WEJDC4DUO4IVc3DNUNg9twzTUXWga3IfoNV7ZccsU5N/Q09F1Duuygc/irx3eO4Rg5vz2TjhE/BDhDPiXkDK5zyCWTLpkQEnWdEDnQ98/AiaoaCKEHIKVEznmcRE9VVXjvCSEgIvR9T9/3OOeo65qqqqYFIiJUVTWNAUCMERHBez/9vWkaoAhiCIG6rheCqAsq5zx9X1tVlSWgY22frf27XC7TfADTdboIm6bBe88wDKSUpucPwzD1yfZBn6vzmlKa+qnXpZQW8++9xzlH13X0fY/3nqZppnFUZaH91HG7Xq+cz2eapuHm5gYRmdaRPmcYBkIIbLdbvPfTM+q6ZrPZTO8WYySEQNu25Jy5Xq/T++mc6njGGBdrOIRA13UMwzBd65yb3r9tW5qmmdaL/XHOTfPsnCPGSEoJ59x0bx0H7bv2RfuQc+Yf/aM/+suPlc8fQjH8AvgD8++/NX62aCLyp8CfAvzdf+X3Zf2SwDQAIkJKaTGgIoJkmQQhxsgwJMSXRSF1TcKXgUEIPiChIuGRLOScSDGSXFhoducceXweQFXXBPFEorkGxAd8HQjB431mGHQS17tfaSoQet9xDIAiWHVdTwKVc14IRl3X03urQKkCseNhd2h9Zoxzv7WFEKZn6MJbzc0kaKqkdQHrgtQffYaOnc6d9lXfUZWZNr1eFea80/mpPzr/+rn2S8dG+66fqyDNO/Q8JjHGaePRfnnvp3GfUYibFC0wCXJKabqHrkP9XJWDiEzjrWOh76Bzpn2y46FjaMdujc50DlTh6X2GYZgUgSqypmmm6+18fkr7IRTDPwX+jnPuX6YohH8P+Pd/7Tcc08ToItEB1UFQQcg5l112KLusD54smZQTIXhC05Kqih4hpkgWjwvlOhFhiAOJgIifFi9SEYPHDZBTIvZxmmCAlDN5nEjnHEkykjNOtK8DOadxsVSEIDgXp0Wjk2IXre4oqtn1c110uhtapaBNF7sujJQSXddNQqs7rApKXde0bTuNoX7XCvcwDNNis6hkaZYwfWYXJ3yoCPXaJfSf0YD+3X7XIhwLrXWeFIXo9fY9bd+1byo0eg9dT957Yox0Xbd4nh2Tuq4X/dS+KoKySEWVlX0XqzyskK7fbX3/ddPx1TVjFac+N6VE3/cMw8But5tQjn5+vV4XG9LHtL92xSAi0Tn3D4D/CQjAPxaR//M3fe+lhaMCo4OhGrkfymRXdVWQQC6IIFQNMQTiOFneeYIPuKomO0+KiZQEFxxNU5OkpRNPzok4RIiONCRyLM8PIeA8iBPEOTKjIIjgnF8IQ5lYP/U/5zhNmoXedhF679lsNrRtO+0oVnh1savAQ9nBrtfrwiSwu4vuVNYs0UU3DMOkQPRnLeR2l7boTc0CRTGquHTh6iLVd1T0owpw3R8rQOvvW4Wjf1NFZRWpjqu+sypaRWBqItR1zc3NDZvNZjEX+q4FbQ6TIrHrzb6vVUI63vZa+x6qFOxc2ne1m6B+X+fDjstaoag8rJGnfv96vSIibLfbCV1Y2frY9oNwDCLy58Cf/xbfA2a7Vz+zg6Aw0jmHw00CootFJz27DL4McJTMEAdyrqhCTfaemAui6GKm7zMpVbg0s34ihXTMGSSVxeAAHwK+cZDdaJLEFRklI/Ezw2ndOXUBt23LZrOZYJ/dLSbl1/d0XbcwBXTxq5CqANr72AWkAna9XrlcLlyvV/q+nxa3tcUVdQxDNNCzEFcpRfq+oCIl4JqmXtiw4Mg5MQxqhzeIQN935Cw0TUPbttT1PLcqtHVdAzOKUYWk//+SQOn1Vnj1GmsGqZBut9vpOTqell+xSljHv+/7aX1ZE8/2YT3eVrlZdLZWFLo2QgjTfOpmsVb0+v/WfLKKZc2xXC4XUkrsdjuqqpoU4qe0f2Hk46KtICTMNpUdMF0wMY3cA0LKhoSpKqLMAxl8YFDzQQI+NHjnGeLANQnZeZwLOFe0sA+e5DPCSPykjEuUvwePeEFyJicHGXLK5BwpDHYRjlk5LYk6EaFtW+7u7iaop++pCsAuTN3R7S6tiEInXBe3fYZ+v+s6LpcLx+OR/f7A5XKZnpHzTPjVdUNVVeNuc5mIObtALRFmCUhrKlhUEkJgs9lM77aeP20zSioCm1LEOUfbbri5ueHmZsd2u53QibY1lNbnW+HV91MUsNlsFmhs3XQNVVVF27bsdrsJVSjxqD/WvFFC8CUlZT+zHIhVLlbgrTK0XIQlhq3yVNPK8j7WhNA1p8Tmp7TPQjFYXsQSORbS2kFMMRFTLPA0VPhQSCRhaTvPuwAQyuSnUeBD8NRVQ8oVg/fIdO9x8lNGsuCcCsC4U/cJHwM++YIgRhNCZIanzg14PyOEpmkmhGAXjdqr1iywcNxC8rqu2e121HW9HIuUuFwunE4n9vs9+/2e0+k0oQPd9exitPxN2cWXBKM+z9rRStRZhabzsyYO17uiNYXs/8/j1S3sZX1e27ajgriZTAFViGqarBGlIi79zCKAzWbzgWKIMU5oCpiQjSpcFSxdS33fcz6fp13ZkptWYO24rck/O9bKhdhrrTllv6d/V+Wr72u5EIteFB0WZdt+ikh+HooBx0KbW5ttzbqrl0Gva+pmGoiYIqggZ2GQgYwnhBoJFVnv791CsER0QoSkHIL3+AAujrtQjCBCqCqCBFwCiQVN5Kz9VehbAWXydrsdNzc30/vFGLler9MuZJljnVR12W42m4UwAh8ogqenJ56fnzmdTh/c00JwmE00a6sqKWfNnrUdrYu27JAt3he3oQqhXmd3dbuLW4Gxba10AK7XK13XTa69vu85Ho80TcN2u53G8+7ublrsduwsOaq/Y4ycz+cJyVjzRM2Zvu/Z7/eTF8I+Tzkg7z3b7ZbNZrMQbOV8LB+gCkPnTzc8nZemaWiaZkIkVnHqfKznwW6YisJ0rdhNVN35uh7UHf4p7bNQDGsbTQdchckOliXLnAn66Yce72uquiLiybrjh2JSRGfsT8bgD/kw8MU5V3iEyuNqh0QhyQgfx8UhOZNixmWHBjOVSWoIIZNSRKQsotvb20kglRw6n88L0k6honIPulsBk814OBx4fn7m8fGR5+dnLpfLBCOtTbu20e3fLNehO651MyqaUAWsc6O8hvZ3Gv/VPS2UtYJgF+3aPLFKxzm/gOvaFFlcLhe6rmO/P3A6nXh4eJg4FIXLum70Z7vdTjv0+XyehETHWpWFmnmKtKw5puOl3h39ns5r0zTsdruJ3FXTUOdW0ZdyOXYD0PUNSy+OVQrWRLTjqe+oiM+aGXZ8FTl8SvssFAMjPNIXViLN2tlZMuSZhIMla64LNjlHiomcV66wLGTJOFcGMzuPpJmJ7/ue3AteRlIoF05hkFjMj9E9KTmPEY9jXJmorVxTutKNO8KsFCxjfD6fP2D9dXHaQKXj8cjj4yNv377j3bu37Pf7acGud2crjFZY7Y+OkTZrbtlmWWxrYliBtkSq3tcqJZ23l7wHay5AP89Z8F4Wdrs1KyxJOgwXhqHnfL7Q9x2Hw4Gmabi/f8WrV/e8evVqIRj6nvos5V5UIejuXdf1pFx0zlQ5dF3H+XxGRF40bfQe2+12+t7pdJpMxe12O5kBygGs+QWrmO24al8Utdj5/D6Xsh0vYOF+/5j2WSgGYakFrcazCz1LGbDgw2Ki57/LOLCFVPTOk3VgcyJTkID3nhwLKRmTEOM4cNmBeFzOSBIkZrIKcVXhIkUbeIfzOgEO70sEJERinL0F1kbXyb+7u1tAXhWAvu958+YNT09PHA4HHh8feXp6WsBMXTRW8HUMLOqxO4d+BrNpMSnCnBfjDTO0LgJdMQyz+84iH72fRXkWwlvS1MLhNQ+h76HPsddZNFN+Zthcxqyb+IFhGGjbFudefYBUtF+KkopyKQJ6OBymdacMvnIMihJubm6mMdNnPT09TbyP9k/HTb+33W4nBAFMSGLNJSwV5IzU1tyEfq7XWRSwNI3ntaHj+DcTMfBhxJtdiH3fF+/DaDqkPNtY9vuSZ8KryhXDSAhmyYXHCBVMk5JISXfA0ZdMCXDKQ8YPDkmGWHMOGS2QEDxVG0ocRdC8iEhKGp8gxJimQBiFnRphZ6Hx8Xjk7du3vHv3jv1+P9nueg3MCMru1Hay14tHx0Of1/clPHutPNbmgF2g5fp5kel3FJ2o0AMfCL99hhXS9c6t72BJPP3umifwvkSZ6v87V7xBKsRtu+HVq1eTx8ZuKjoO2kfL2+i7pJTY7/c8Pz9PJsj9/f2obGZTQr0VGveQc+Z8Pk+BVUo0t207cUxqziha1B9d52vC1CIJmDdLu6EoYWmbVQgzsftSfshvbp+VYtBFotoXipa9Xq/goG1GZnXMj2jqZqVZ593TOUdOmSQJQlEK4j15IjGLp8G6hEoySolfEPHkXNyTnnFBNgEieByegI8ekTRyCj2gwUyenOddWRd+SonT6cTj4xOHw37afdR7kHOeFqAqxZeEf4GSDAy1XIx+d40s7PeBBXKwi0h3VLtA18Jvx1r/to45sa5O63pW6G4VvD5L0cdmsxk9MQ2amFWEOE6LvxCEN9ze3ixIRb2f7ZdFWzZQTNeb5iqklDgej6SUFoFCliuwLllVEnbs1+ZbCIHb21s2mw3X63XKs1iPi5pd1uWq97FmtCVuraK3G6yd97+xisEG8qhS0NBnKIuwH/piu4ZqigVY2LxVRRqFO0tBCiIlAlIXQ8SRJJAz5Dy6L8csySlbcgziqesKv3GFdMTjMiWWgVHoUh4VQ8I5oao8KY397p/p+57D4cjhsJ9YdlUEL+UwWHvRTrJ1X1kTy7oJ7T3WMN0KnBJoNl5CmzU/rCJ5yY0Gy3BdbUr+2bDcddKSyBzwJKJEZJp2TmDkDO55/fr1xL1YgdEfhfpt205hwSrEa2GxCk29O4fDYcyhqNlsZp+/IhHlC7qucEe3t7cLL5O6VdWladn/NTKzpO92u10Q0d+ndHWcVXnrtWuXpa4Je70l6j+1fR6KQeaAERsTX6IV50UoWXB+jpt/ya0WU6RPjuQcMO6CxibLGrw0KobialS7rvwnBE/laqoc8GM6dpaE4AhVwDeeNCRSP6dXD0PPfv+e5+dv2O9/Rdc9TUjA+ql1V1NSUt9jvRAs0bR2QdrFo39f25v626IDXZBVVS1i6BV2WnPFNgthbd9gGXehO+nt7S0PDw+0bcswDB/EVYCaR2FUlv2026rZdXNzy/39PXd3d9N7WpLV9lXdiS8vLVkoB/1M3cHH44nz+TxxOZtNMUlev349vY8mJV2vV56eyrxut9uFiWjnz86DjeLVZysiurm5mVCIxlFYdGYVsr2nKnV9/5diQ3TeRErG5bpvv6l9HorBDKC+sPpgJZuoyDDapTJrUOu5ECfICPsrV1FRkakYNDWdMUkqR1Iqz7O7SVXXhE1Z9FWscNkjURCU9Bzt1Shch47T4cj5vOdyec/x+C37/XdcLm8QOeN9t9gxVCmstbxllq2dr+9sTZHvQwP6Y12cVoCVo1D4rgKoUNiaIDYd2hJcqrTtorQ/Ft0MQ1EySrS2bcvj49M0BuqS1Dle8x3Fw+MnmL4W+vX4aIq0jou139fuVEvQFbSxQySPId+ZpqkX6dzqXdEffQflh4ApmnUtfJbYVcWr86NzoVxE0zSTiaHXKkJQRTKPT7Xonz7Lriu9Tsd4raB+U/s8FIMsIx51ICcCyXmtOYGj/HutIZ0v8QchBCpf0afRLPAOxqIqZcEIWSrAMuUBX1XUvsYnB8khESQlGEOshaK0ukPH8e2Rx2+fePruiePxkb5/JqVncj4Dkbr2k+vJCv6ajVc+xaIkSwBal6QqlTVTbe18FXp7DztONlfC7mYWqlqEsSYElWxTwdJrrIKAgp5Op9MUIFTe30aipsVzl0RjmZvj8UTX9ZxOZ3a77SLGY23iqPBa4df3UZSj72WVStu2PDw8LDxF0yaxyqNQQVMFq8KrJsk6sU2FXdGZmiLq1rTzXhDSzaQ0r9frB4rBmpLOuakPVnmLzESzVZTrtfQx7fNQDE4IDvyY+xCHjhx7PMU16X0RTOUfyoLI5JjxZKoxacm7EtAUEcgRpAKKaTHESIyBITfgIz601N4h4pCqAjyuK6gExgH1BWFczlcObw8c3x45vTtx/O7I9elCfxxI6YpzQzE/qgqNn7Ck3xoR2F3AZiHa4BcrnFa5rAmpqa/jgl9naFp7HJbh5frdJQH7YWafVQprdGAFuxB47RQdaN+zKMo0mS9rJafvHYInpcz1euF0OtJ1V7rujtvbG1JKE/qxBPVaAVoG35qb+l52jFWRWERkd2g7vmsvig0wUgG3imTtDVHlrGOprk7tm+UedK50jC1XpJvE+m/6HDUlFE39Nu2zUAwOCNJDApciLvZUJJzzRWE4nRiofAISWUbXjveIy6SUcXgyiSr3VK6iloF+AOKAk5bKORov1HWgCqHUU0iO3GSGnPG1I+QALcQ+cR069o973v3iHe+/fs/5/Yl0SMgFfHSj+6zBe8H7TM79yF1kQpgnFpbCqxNo4/31s3XNhLULS3+vySq7aDRsuEDeK/v9M+fzGWBhTuj31u49hfDWZNBdy+7W9rcKe9M0vH79wM3NzbTYFc2ALJKR9PsW3ShZqenh+veuu6L2YFWVd1Q0YqG/RTxWIbzENejn2tQE0TnTQCv1jJ1OJ7qum4LRdNdXJWNRhr6Pej02m830jJzzRGjq/G632w/GTAnNl9aP5al0rdjPNbjJ8jGf0j4TxSDUFBidhgGfBtxoM9ce6nqsmiQJN8YcaBlb50Z46go5GFOHE6EBrklgGPBsaKqRoJLA4IVMwhGovSDBlxiJLhCvifPpzOHNgcdvHnn+5TOnd0e6fQcDhFARKj+mXjMRl9b1WfjOOQrQLsqXhFq/Z3dBtcH1cytA63us4etms+Xm5pbtdsP9feL29ob9fs/lcln0Az70YsAqtd3YzWv+w1acEpEx6WnH7e0t2+12YSsrmri5uVkEGFnmXIXSohMN/lISV4OFlEQtXMY9P/7xj6aSa1aYNNTcKjpL9q6Rz5on0PiGN2/ecjjsJ0Hf7cp7amKbejIsH7JGjVZpqdLo+34yhdbl4BSRWO5I60XY7FqdRzs/+pmSm99Hzn5f+zwUgwghdaS+R7qewCgkwdNWDg200QCnnDMplrRrnCsmh3OI9yQZ8Glc9FJRuYo2OEqdz0yQyJAi3g80vgYfyJK5Xq48vz/z/ptHHn/xyOnNievThXTMuMER6lCSp8RDJdCrLTsqLGfzAcb3cm4xYXYRru1hYCKjdMFY+9AuDm1WOVj2/Pn5iePxwGazYbvdsdtt+clPfsL12o1JSjPJteY97CKydq19jg24Wcyj2fGBya5W4X/9+oGvvnpNznnKAtV763f0Gaogv48jyDlPtSgLUvLsdrtpTDQBrWkabm9vF6aL5XDWaMiOh8Y67PcH9vvnSSlofYvD4TDOWfFkfPXV68lbYYlkjU1Yk6wWuVjTUdGX/k2Rg3II+l0bfq5jp80GoenzP6V9NorBXwZc1xPGXaZxnkYCVYI8zDHzMIYGx4h3Nlw20MeBS06kVOO90LotuarG+godDkfjG/Ae7xxZMl3sOByPfPOrI998fWL/7ZnheYBeBcVTNVWpv9ALSMJlRxhrNEAhNEUy3is8XQqVLgadxDX8UxNAfejOuUWsg23rXciSWLqj6HNPpxNNcxh38LKz3d/f4f0rrtduitgrBVaGyQNgk3J0Z9cd0S6yNcLQd+m6kgdwvV5xjim1W3evrusnItSaKtY80dgFRRJqw6tdXlXVVPg0pTQ+q3BPfT9wPJ6o6xIRmXPm5uZmGjMLy9cCY5X55XJhvz9MsQ5Wmdvw5qLkjgxDz49+9KPJjLPP+D6Pkv7dbhC6NqynCOYqWvp3qyQtobx+N3vfj22fhWJAwA0ZH4WQHE2oaVxNJR4XKaXaBVwuqdE+Qz3GFwSgcr6UeB96qDK13+F8SyJTpYEgHu8828bT1EXIz5crh9ORN4eeb95f+Pb9hadHSLGiqgLVpkJy8WaQ3JjqLYgDN1ZxK4LZUFWbMfpxIGfdKZZZj5bkW0NWu3AsQrD2vhUcO/GKFNYCZGH48XjkfD7jnOPmpsQYvHp1z1dfvZ4IMs3eDKHi7u4WgP1+z/v377lerxM3MYcnL0u+6UI+nU4Tn6GxARosZOMxLOS3SMSaOXYH1/eyIczWddr3/ZShCbYGY/GQKJNvEZyF93auci5JbO/fP/L8/DTVXtBxtdcWRdfx9PQ09VnNKhVSq/gsaWjnSk1HHQdY1njQMbPP0HsqyoSZo7Ib0KeaEfDZKAYhXzMSweOpcoAB0pAIoRCMWEiLI8exdkIAV6WSCp0TDYLfCr1LJBkYcDRBqJqKVFfs+4790zPfvj/z3b7n/Tnx/pTphoqqafGpwuFx4sAXL4cbU7BznUjXEQXEiEShjPls075kv6rwql1pF5Y2ZaxtiK5ev55kqyBsfUO1k+dhXZJUc1WnAw8Pr3n9+mFR+EWbLljL7qvwQbHvt9vNtEOnNHMEllS0zL/2R+EtLNOxFS15HxYCoGOradC2/oMtT1+eOddbKP0q46wp6rYehaZK2+pQ+tzr9crz8zPH42F6Z836tHEo9t2GYWC/3+Ncyd7cbnfc399NQUwWEViloorKIsG56vmwUC5rM29NWOs12icbBPU30l0pGRgoVZEcuFjCj3GuHO7iHZJLUpPICB8yeHHkITO4Ae8dVQh4BCHiJVL7ho33eB+4SmL//J5fvDnwV9898+YQOaeang1eGpoQQBx9JeCEKKON5gM+FDdm1dSEjRD7iPSZTCL2mZwHoMc5ZdEDWtFJWf/dbocy3LbuooV5ugPqIrRweq0YVOB1J7Z1IHXRaqiu2rwhhKkgSVn8T2O/POfzicPhQM55Yt2teVHcjXPJOe+1jmIz7V7rha/PrqqapqnJuWRRKpGo7yMik2kxBxJpTMscmWljAax9roRriZvYTjzA9XqalMkwzPUbNRlKIyu14Isq7fP5PKZ09wuBLehiRnOW9FUkoO7Iw6GU0/vxj3/Eq1evJuVgI1h1rKx3wXItFlVZBbh2Wa/H3v7Nxqh8SvtMFIPAALWvcDhCLrkJIpT0Z9JIakUYickqVIhAjAMiGVfVBF8qQVfdQN4MhJAJHq7dlfePT/zVmwO/eHfk6ZyJuUX8Fpwn4HFSAwm84CuPBIHK4eLoARndj652SBAiS/Y754RzpfajSLHbX2L2103hJcy2ugoCfGgfWihrv6P3sgKzTjNeoxHrLrOFT23yl95rbRZ13XWK3QhhjgzU/q53t7ITzrDZBiRZs8DuksBiZ51rJrTM3qBlzQJVZqfTmdPpuOAg7EEsGnR0PB6pqnrqV3kmC9NN+1GeMQfe6fNhrmgOM7opzy7XPzw8LEwRi1KsZ8aaShYlWH5E7/GSR8muE3vt+u+/qX0WigGBKlfUYQwIGUrA4Rp+efEE7/ESYJCSIh0LgnAyxjRQBDRsEt5ljtcjb7995ue/2vP20DMMFRUbPBFJEecTwQuVSwwhEarMeGJdCXZyQsoRl3w5ni47fOVpNi0+l5yJ6/VESnrE3Jw/oZBcF401L6ydCzNasIvEwkC7S9jv6z1sbIIlLa3QqGLQHVLdodpPyyNoWjgsw211cQ7DwPPz84RcrOdA5037WEyMgaZpcW4O5lkHXllFa9/fcjPl/ZffKc8YOB6PXC6FS9G6jNacUvNITR4VXou4dHwU7bwUeqymiOUn9P2VALZVvlMqf3/9+vWLpKclk+37v6T0LTrQ++gY67+1T/Zef0O9EpTS7d4VElEEYRmHH3wo0clZSEMkpnKYpKPYlkRIlFLutdS4IfLUPfHd2wO//OY9z08dkS11fceQE2SPk4IqmsoRJTBkiD4RKXyFlxJyKUnwOZOGWGIZYkXtaqqmovIVzm2o6x2lUnQpUw9xsRuoLamLxtraihCsKbDeFfRaXRzwYWqvuvjWZOUcbhwWu7G1R1V4mjG1XbNBrbtLF6ouPBtVt34XbaoAdOe2Ss4qgGmew/IgG3sfS8YWL8cc2KShxDoOVvHaXdfeW+G/zRWZFZqnbRuqqp6Qh0Jym+X4Ej+j99C+Ho8Hyollfs+ijswAACAASURBVArKslzUB/Iw8gzrQC1FRKrArNdBx9S6Lteu3k9pn4ViEIHc5SkXAvVCaHXG7KY6ajGmAtspwUZFs1fjgAihDvTS8/zuHd8c3vGrxwvnQ4ZUPAiQCCRqH8hA9ELnhCARlz1IJLsBfDnBCi9kJ+RRKUkUUsykayINiYpqFJ6WUslJd452wRorWWZdTsBCMNeMtIWBigrs9yyysLu5hY2WudfFZgVcF7kufOsNgfm0KxXwwh1sxu9eFwk6NubB3seSYHqd2tZqe1u0pEpEuY1iEs2Rk3YXLTvrsIifKGRvya1QdKP8iDL9Nox5baOrC7eU1hcul/MKTWxGRDAXgbWciZ0fVajFdVw4I835UBeuKnLrdta+2X5ZhLVGAapwX+KaPpV4hM9EMTgcldSEpFl9qhkcfmSmM+NgZKhDs6jT54BBBhKJ6/HKd6c3/OLxa97HPYNUBBpqX5NcxrtME4RGIEkmSsLnAScVLg8IFS4Ivg646BAvMJ5GVTUVPntiF0mSkD6TJOJ9D/R431PXkaZxCyjqnKfrrou6ltZFZwVFg1lUgHXRWlvafh/mfAhbsFV3MhUKJQO99xPEtXBezQwVAP3RYCt9TvkdcW5ONdZ7WCSkc6NwXeGu/lbBqKr6xTGxY6PPhGUR2TUEt+x+GQemWo0qaNZTYpWPRWpKHur9tfJSWXMlelNdxF13/cCO1/e3rtlhGDifL1NilZ0jNWe0L1a52jnXz9acgSWyl7zXsgjOp7TPQjF4HD46hn5Z3sr7UiRFRHAI3gWcK1mQm2pDcAFimcjz6czhfODd9R3fHb7jKT4Tm4i72bAJ1VifIXGViKMmEJHscQxUNdSS8c5T14LkANEj3aicQvGMtJuW2pdS4/21J/uMxDn6McYLw3DmcjnjfdkB1H1mlcJ6Qdtw3ZdcfJabsM2GwCqctKhDmwr5OmNQm+UZSp9mzmGNSEoAUze5VnWulgIpk6KxHIlVCMX1V1HcisuCMVZQ12hHd04VgvX5EhallfeZz6wsHpR5N7WEqx0HYAq91r7r+A9Dj8gM5W3ymx0n7/2CwykKoCipqir1IC1HZIPf7I6visCiKMut2HHS8bWo8KX5/pj2WSgGBIbT6Ie3IZxJECcE7wmG9a1dQRcSM9euYz+WVX/z/Ib35/ecw4VUR9yto2oHUtNSeSFIxktBB14cta/JnnKStdQERiKygugpRV0kl3MoKk8eMjGPJ0ZJiZ0gB9R800VVhKBfQPaXSCM7oTBnMcISllvFoEKjEN0KkS4ce2+FzdbXDbNffn3vl1CNNVNgPoVav2O5DG0ppYVwqQB8WB2pfzEGw5ZLsynEMNdsHIaBy+UyEaY6bmpWarbiHHswE7PqLbDuQUsQW45COYsYI8/PzxO6K8fvzQcT2/MbrOK3BOHxeMQ5x+3tzaQcbGyEHWtVaDPpuqzYZbko7cfNzc0UGq5uYat0P7Z9Nooh9XkiGV0cPxwHrQ0NznnioHaoRwbhcr3y/t073rx9w/t373k8PHL1Hf62eA6IjhCFqkk0PpczLCVTk9lUDkIJZjrlDKmD3OJcRiSSxBMlgYPgHZkxgWXwhRw1ULaqAlCRcykZB3mlJJYVlqxbaz3ZdgfTBWUX25qwsv9WZWEZ/u8jD+0Op9faHc4qAstdrHkPu3D1WhsVaZ+pGYRF0OYydRqfoKHT9kg4RRiKVtTdqmOl97dmh312zkJVlT4W4reblJrdVbXv2n+rbKyyOJ1OpJQmnuD+/hXb7RZgRFEz32HNFUUeWuzXFq61dReARbr2GjnawDdrUm02m0kpaL+1lP2a1/qY9lkoBskljdl7hySZKjpVVYUTR7qW06O9d1RNQHo4X868ffuWb7/9Je/evuN0PtHnAX/nqSkxDjknXILKZ1qfSQJRhD4LvWQCEU+EPKZ8U+El4lxPHnMhXOVgdFvKSIDqWZfR2fMcy4+aQCJuQgBW+HSnhqUgAh8gCruT2M90h18jCl1YNg3Z2q2WhLMwX4VG+2N98vaZ9lnWt25tYd3p9FoLkzU7UqslicxnQ85FS+qpD8BCQEEVqFu4LK1ysmy8CkSMheeJcViYP/qMtcdBFaH3fuHKnNarsd83m5JRqmajlrGzXgKLylQxHA6HMYJ0uygNZxWBNSv0nXQ+12eMaFEcO1bOyNHfSFPCAT4FZMi4wGjLV0gS4rjj1uPApy7xdHjkzZs3/PLbX/L+3Xsuo5vKN57WNVS+IpOJAiRHGzOxjpB6JHsklVOri9Mh4xHqABscmVIwxgeQ4BAnkMdj78f4Bh9KjINzSixFQlCY7ykVqOfSaNaGL3kKD+ScpgxDS1JZ21HbGmK+BFN14TZNw6tXD4QQOJ/nyD9rg1v2XlOSbbCTVVRKpFkPg1V2ljiF2QRQs8Hu7utzNJfkYpqyJde7o+UUrMDrO9hIRCVB1czSZ9uxsu+oz1ZlqddaF68mlen3dPfXnBB9V61fqTUsFfWs51KjT62Zo8+xpoGd97WiV2IZmO7z61yStg8f0z4LxYCAdGPJqtDQupbUJ/q+vHhbtzShQfrM47tHvvnmG7777juen59HJnyGsG5w5C4jmueQHT6B8wNEILtSDp6SS+FSoKKi8uAFXM44l4ubkjyndutp1jAiBj2LQiZCq5gUjmFYxiLAsmoQQIxL15JesxZ8qygsUrD31d+6yJQpPx6PizBju7OrXVxCfPMi38KSpNaksGjFChvMkYKWHFShbduW29tbvA9cLucp3kDtd6vY7HkJOn7WFHopd2EteDBHFNoxVVSiwqruWh1bS9xZNKQeBp1TG0Wqh+AUdJEWgU12fCzqE5EFgatzY2M47HtYlGbXkq4DVaJ2DKy596kxDPCZKAbJ4IZSobf1LemauI4Zbdvtlta35D7z/v0jX//8a375y19yOBzJKU31H504iBC7CFXZ2QlSkp8awbkISfAEKtcQJSJDV8wYXxO8EIA6OEIGiIirSzi0p0RheYeMpCTe4UKJtCw5nhQ3Kx9WYtKWc+b5+Xlx3JxdnFYJWNZ7zSPYaEFLuq1zJ9Z2qF388/3ngBlN3rEKTQVdF9kHc2d4CLsIbSTnZrPhRz/6MbvdlsPhQAgll8H68u1iVsViSTVrktR1zatXD9zf3wGlupPNa1AEs45RsN4ZrX2h5KdWUtZ30OhQfXdVsKok1HQoBG4J39fpVlSmSMKSr3Z+TqfTFOOi86vKzPJENgBLx1z7WVUVd3d3i2P9tAK4Ki91r35K+ywUAwK7Zsem2ZBiou96chTaZsOm3kAUnt8/8fVf/ZxvfvEN++f9zGJ7V2bEQbomXABfB6pNICch9xnpEy5kqhConZCcMEgqtRUkg0QqL9zUFd7V9H0gVBCDgwA+FJ5Ba9LCjFDEqWYHiIhEIFNVxe5Tl5893symH5fvLnd/sDEDS4LspUWkY6H2uBUGywfogrIH2mh/4ENl9pJ/XJ+/tn9tQJP2XZXM7e0td3e3vH79eqqo/Py8H3fWYXEfi6D0He3f9ei3kgB1+8EZkapwddxtUFDOeQp8KjUqNmPR2etCIVrTRF2F1gNkFesw9BwOJalsTRbC7EHRv9loy9PpNHEmwOTatgjBerQsemuaZlIISn6qgtNxUC+JJWw/tn0WisE7xyZsYIDr6UrfDyVjLmxgcOyfn/nm59/w7c+/5fB8LEVgQyiHwMi48IGcSshyLSXvIhIZNEGr9TQNJIQuDaTc4VxF5RuCJGJOVB6a4Nm5iiG3uL4mR0GGkhbuvCfUFa52JZnKl7yIos0zkMk5ktJAzkvibe2Z0GYFDJb2vd0BX2Ko9bey+bro7GJYw2y7E6udqva1Hq8GTCnaa0GxwmEhvzU/LIrQCkelmtR2stedc2MRlGEyH9bnfVpor/1o25bd7oaqCqNSKHEj6hpVDsLGNJQqTnfc3d2x3W4ml6lzJZFMUYuaSDpXNotTxyeEihDmWph6bTmq8EPlq89RE0jHT/ta3uFIVc2l3FQZrpWxHV9VCHYzOJ/PY8GaJRF8uVz++rMrnXP/GPh3ge9E5O+On30F/HfA3wb+OfBHIvLoSu//C+DfAc7Afygi/8dveobH45On7wfiORKcZxNaQvKc9me++atv+OavvuH5cU/SCDFR11L5/zj6rv22cAb0lFoNoRSMZhBCEwk+UXthGwJ109JWG4jNqGUvDKHG+ZrtLiB9S4xCvEYGevDgqiIUMcUxpsFWY0oLoVVb2hJIa2LRjPNil7CQWv9uIb4Ka9u2I/k0M88hFGhbKi2fJu7Apv4qzwCzGTCfUpU/sPVtf6wpo0pmnfuvO9y67LsmLxV3HlM5Oo2QtARgKc/WAP1iHPq+5+3bd6Q0F5a1ppLdVXe7HXd3d7x69aqQ1yPULkI0V7FaR4Hq9zWGATDRmmEc0+IKLcKpZ27M52BYEtFGpdofRQ7gpvFfow59b72nmhD2TFPbd0tQ6t/saWAf0z5GjfzXwH8J/BPz2Z8A/4uI/EPn3J+M//7PgH8b+Dvjz98D/qvx969tDke6ZvpLj0ueTdtSS01/Gvju61/xy7/8JY9vn4j9ULwJhFJqTTIuODJjOG6V8b0vwVJuzuXPXiAIvhWqNtPWgardINsbruEO6Tz9JdANA0N/pPOOrgp00ZGiI/YDMSeCBPwYtj30Aznl0UfeIBLI2S12V8vyrzW+RQjrv2mzNra1OW2LMS7Cd9u25e7unu12w2bTjt6J8yKNW3/U/oeZE7Cmi9rTNqJu3ScVTA0ZtvyIjsP1ep36eDqd+O677yYYrYlbenSfKj/97jD0k9Kw9nnXddOurgKifVRvh6INra8wDP10ZqTOj+V4dO50PlRQddxKXYkG0NT4jpQcIQgiA8MwJ87ZNaDIwpphSjjmnKcsVq3ubdGgXSNrzkWfoaaDIjGdP0UgWlH7U9pvVAwi8r855/726uM/BP718f//G+B/pSiGPwT+iZSe/e/OuQfn3O+JyC9//UNgOPXEa8maa32L9MLTmye++eff8P5X74ndMB4lG6hcQLQWw+gd8NGBr2BwDNcBCeAqYCyzQHKECK7uyf2FRIuEG2g62vqOe98Qu4rrxXMaBvrhTD8EcgyknEpJN1dqM6SUyChkr6gqR6litLTBx/FbaHuY7U67s6yJwrWS0GYJwJzz5DPXaxVKbrcbXr9+zcPDA+fzhcNhP3kp9NkFUm8QmatH6c6i8NouUusNsYtwzWPAjHDO5/OUcLTZbLleLzw/P0/EstrH1iSy6ER3W0UwVjHAHLK87pO9h57wpOdE6rtZz48NBFP0UASrxvthHLd+fF6clG35WzcpR5vrocrAekgs+vDeTwoRmFLFrXL9vnXwfZyPNfcU8eiYfkr7bTmGnxph/xb46fj/vw/83Fz39fjZB4rBOffHwB8D/EsP9wzXSEXFpt7is+d4PPKrX3zLu+/e0Z37yftQchPGxRkFXCZUFZWr8SOJk4ZSsNXnUs/JpVzyMZwne7ikRB6udOcDfe9h17Lb3DGEinOGkBw+jW7LUJWEqhp878gpkXI5gt35OVdfBVvldob1y8hD+5k2O8lW8HVHsChjjTjMeE59KZWge370o3IwbEqJ5+db3r59N5VA19b3c00CuxjXbjLbF2tSqO2t/blcLhPppfdRU6Zpzgt+Q8fEppyrkFiuRJl9HRu7U2o/LKfg3Jy2bO9tx1znYc2ZqMmnJkchjrvJy5BzIqVloJENDluXjJs5iJl0tPNuTZfDYT/1u7h3lzkP2qyJou9lE8RsxGYIYfKAfEr7/0w+iog45z5NHZXv/SnwpwD/6u/9rnSnge1ui0uB86Xj21+84dtfvOG4v5CioBInZCQ4gveQPFnA5blmQx7GMyyvAq1A5wibQFPVVHWF85GLgy5lUnfm2jlwW0K4IbhbNm3NrXj6ruI6CMlTvBHeT1mWuLGcfBXw3pGzooGXGXvdNXSCbZCQGY8FjLXKwv77pfgF63HQRX06lQKwt7e3Y3Xo+8l00AV6uZynQqfWd2+F9yUXpf5Noar2w8Jc2zeYTRUbMLS2m+376Tvrbq730p1ws9lM5oR+zwZOWbJNx0ZP+bZjaIXKvr+aQLPCn+ex77sF+2+JZVUolu9Q4dbfds50rShJ+Pz8NIU323Vj4zVUKSqq0TEBJsWqZK6aXy8R37+u/baK4VdqIjjnfg/4bvz8F8AfmOv+1vjZr20pC90ATao4XyLfffeev/yrb3n77sD1mhFxRKd+Yo+IR0IgJSgHqGaapqKuPQwluCn3Gbl6fOuRIROvkXiI9MMAbUNwNcEJLg1cT3uGWBFbaNoH7m62DHVNnwPx7EiuEItkP8YvmIAjSrmvMnnq4lqeTG21vk6u3YG12YW5jh1YKwK7SPTfNp//dDrx5s0bvPfTydN3d3eTUBaI/2ENRWvPruMHYN6t1olLti/6ua3TYCMJ7f3X/IruzGtzzCqv+/t7fvrT36Vp6rEK9on9/nkiLxVCr9GWCqoqFFUWVuGoYNudfY63aCay8HK5LEwyK3yKktZKVZ+zjj+xhG1BXN1iTHW9aFOy1gathRCmg3UV6ei1L6GO39R+W8XwPwL/AfAPx9//g/n8Hzjn/oxCOj7/Rn4ByCL4ZkMOFafrle+ennm7P3DqB5ILMNZpcc6RRRiS4PJIlsVIVdcgDncVagRfBVz2eHGQHTJkhmtEvCBBaG4gh0AST+c8XeoZLkeufcBJS9jdcrPdkKtdSbMm0qee1CfyOZf+jEohp2WWocjyjMi17QcfKoz1zvoSx2DtYFgK8JrM0sVwPB4RKbzD/f39xLBfr1fev3/k8fGRnPN4gtQNMB+yovew/IdVUvq+lsxcQ1y91oYt293cEo2232oS2M/tONV1w+3tzYQaxio+k3sVlsjDxhFYtKA/Nq5EPRaqmIZhGONRWqoq0PdLBanEp0UaqsDWngjdwXUc1ieN6XeVU7Hh0uu5tehSmxKtXdct3L5WUXxs+xh35X9LIRp/7Jz7GvjPKQrhv3fO/cfAXwJ/NF7+5xRX5V9Q3JX/0cd0wnkPbUsHPJ3PvHl+5vlyoQd8VS20vvceRgURcyZXFS4EOiCnTOgz0shIOJaISqJH+gwN+Oyps9C7AVKHREdOjkRPzB398UAcavKu5ub2NXc3P4G7imNz5DmXMyAH3xdzJSvfoYKiyVMfFv/QBWkXkH2vl4gmO/EqYHrfNelkoyfV3++9H/MliiKo6zLdWrzEZjU2TTv2bRmOrP9vn2f//n2I5yW+ZM2mWzLTvpMqJHsPFTY9A/Px8Ynr9TLt3HVd/PrWbl+7MtdNr4U5ZdzmTVhBL67R0j89fs8KnLp79V1Uqdg50R1d30Xn1I5F4QTiApG8tBZeMvEWMuXm0HWbsfux7WO8En//e/70b7xwrQD/6Sf1AIqgh+IufLff8/b5mVPf4105di7nTJZSl8GNrqghpVJ+rarAe9I40EN0+GtCqkyqHL72uLaQlkQp+RJdRJorkkth2UBF7TLRl5OprukEbHD1KwIdVSqnVpWFkkebpgQ3hVDRbhq220AIhZzKeSYC14tnrRhUYKxSUARgBXFtb67JSyvE61Jrugu+hEAUch+PhwWhZ3d8vd4Sqnr/l6CxtdfXdr+F8laA1maIIieLRLS+QlVVnE5HHh8fiTGO0ZRb2rZBRm9VycBc9kXHQsk+WxvTHiBreQf9bB39aHd8FWjrpl0rOEUQlguxCssqwpRKnQqbZGXRpSqFGOPCpAshTOXwLfL6/9Mr8dfaxDl64NB1vN3veb5euY52rHOOKFIUQ864YSCNmtA7R+Ucblw4LeBzZucysgEXQfqMGzw++3KiVYZKHE1wVEAQqEK5zxj5TBugk8Tp+R3nS4JDS/+2pz905CjFQxI8oXHUu4ZNFSju7gsxljMlyuR2ix3L7vCwhNnrxWJ/7ELQa6zgrPkGWKb7rll8u/DVNafmwzrlWgXKFlOxYb76XKsYrP/flnvT99RdbE1UWpNjDfvrumaz2bDZbHFuThPX58+2tJ4onSbeoBSN9aO3pltA9+v1ukAl1m38fccFWuHW8bcFaMqhOWkiL20xHsurTOtf5riD2UWcp8/sc+yc2ohQO04hhAUaVHPiU9rnoRiAiwiPpxPvjkdOw0B0jiSCy5ko5YA4r1pvRBhISWESEYYYSc6ViMaqpakrfOtJIZF9KTSbc4Yk5dwKl/F5oHINm+BIviK6wE3VUlUbRAKnPnK5HomnDmLJlfBhCelSShyvHfv9W+AJeCaEyySIKhRrV5UVeLsrwtLcsIFF+j2YDyaxiMMuVus5sArJkn5WWdhdT+9r/fHW1tfnwHysuyolDYay5JdCfU1NtqnICneVYV8faa99spF+fd9N8RtzZex+FNB6jJZ0iHTj/QdiZPIWKKS36GiN2PS9bLUonQOrPOx4KiloA8W073qdLfVn72N5G+8d6uFaezbWnMRL3oY1p6HRpp/SPhvFcBoG3h2PPF0uXEbToTe7nvOebKA1GhugixvYNA1hs6GuPJsaosQSnNQJblOyL2UQUkz4IVP5itY7ovcEiQSJeAQnmZwGcip1Il1oqNsa13hylchhPHtCBMmZoe/Jucf7iPeF3Fjv9vChnagTvt5FbOCNhcK2es8aVuq1lttQodOd2qIMK9wqvGvfuy0LpkVM1wfXWMLMKhy9ruu6ceddVqnScHEroFYwVXmoCaKFY7VfwCIgSnf++/v78eBePx7Hd+R8vkwl4FVQdZztAb46hnaX1s91zK2SVgG1B/goQtLx1JDvl5Tpel1Yolc/W9dzWJt2L5kI6pa2COP29vbXC+GqfRaKIQNvDwe+fvOG96cT51VhDe/ncmraQgg4IKbEMC7UwXt64NwNpPcdcRuRe0qQU+vwjUPaYl6E6NlsPdE5fB6QVJNlIHMlywakKmdPuFQyK52Wk9fEasr/T30qVYLsga/WzlQNbt2RMMPHNUm5hut6HbCoEGTHyP62HIbexzLo1rRRAdRFbD0SNsDIui1f4kh0h18L1SxYVy6X84I51x3R7rj6PT34ZbPZjkpFxkIoc9KVHTvvPbvdltevX9M0DZfLZfTvP3K9Xj4w0bS/Gley9hoBk+tyLYiWbFxnUFpzS00e5SfUZJvWsHOLuZwV1FIx2FPF1p6fddN3m5apn08P/9j2WSiGlBJff/st3757x/50KhrTl6PqHSCpHC6jHgkRwcVYUETOJOcQ4JwS574n5Awp4ivBRV88FD14CVQ+kEjklHGSkNThaKhcLinYOZLTmD6dPc5lnIfs5pOGsiSCG3dHr7v6MiUXltr++4RWdx0LabXpDqJCvd1uJ0HZ7XYTvLXoQO1dha9rF6A9y1KfofBUyVLdkafyem6OUAQWlZVgWUPAmkeKhoCxpNl1EnxVRDYtfV0jUqH33d0dzsHxeJyyPrUf9tpSm6Gboi9Pp3IeZzlOL0x5IcoDqIq3c2RJXRvRaM0aHT99ro6TvovOox6LZ0lA64Ww3IYdR83KVPeyKkHrKbFK/6WmY6pzsI76/E3ts1AMQ0q8eXpifz5zGXf/KoTJdMgKqY29lXPGKfR0pSRbI0LP6C+QQPAjZHcZASoXqEMNriCNFHtEKnwlpcxbzuU3Qk4DIg1145E+lLJuIuXoOudK+rV3ZIZxYS1tvpkMW9qt6zh9fRc7wRZR6ELQisgiwmaz5fb2doqzt2G5dve2XIXuWNo/uwvqcyxC0c8UnaxNGgu3rTtsfa01LdSNqgKtCV+bTcv1emW/30+5FbpLFtKxHRVXno6010IpaoqoUss5cT5fRp/+aXrvqqqpazdldtb1XD/Szps1d9ah35opCky1NXSsLXqJsbg3rZmi31dzTIlJVTgWiVgic10XdG1WvERkljMszlONCksof2z7LBQDKZFPJ6phYCNCBbRm18WNJ1mPyGCQUgtyItfGBbgB2pSoRGh3IA1ILbjGQSOlTLwDCUKuYciO6Cqyr3FhQx22xGpHRQO5wVEVkyJBjrlUmhKHr8rRdOLH3QEoMQxLosjazbDMrNQd1/5br7E7iDU9dDFeLpdp94CZvLRCq5/DMjfDcgL6DG02ks4qK8t1WJefXm89C8rAq0LQd9DFDozZjnOJt5ub3+NnP/sZP/vZz7hcLuz3B4ahH1HSbnz3jr7vForMvo+iGVUs1vOh/VRPkSIWS3aqSaPzZtOU9fr5Xd3i86LoZ4/L2oOj/bCeGf2+FWyrZOz8rt3D1vyy8ydSXMFa8tAqZutV+Zj2WSgGSQl/PrPLmew9HqhzLhyCummUd/Ce1jnSaGpU3lONp1G1znGbEre14LcetuBuHP7Ok28yeZtxjcPvPLKBFFsIW3y9xVU3BH9L5XdIbMBXZKmJgy8nbmfw4vBVwIcCIbPPOKcTozv07Lt/ydZ7SVnAsrafhZrWLWV98dp0N7ALHGbXmz7fPlt5A4tarIljd1DdmdUWv1wuk9CoXb5WSNamvr29Hasl7QDh+fl5ynQUKV6X63U+Lt45x/FYYhSOxxPH42Ha/dQksuhGyUJbu1Gb7roWDdj+rrmGDzgsg3qstwTmYiylvFuLZlza2AibJq7ftb8t2tLrlVex5qVuIvr/+r21y/hyufD09DQRvtbkeYmk/HXts1AMLmc2KREqc0y4CoLZjSaE4FbBPuNkb5zjvvK0u4x/5fEPHrkR8jbDFsJtoLqrkB24ZsPG3VG1X3EJ9/RpQ4qBaxS6CIM4xPmCGMRBypAdwQWquiql5cNYgjwIUOFcRakQXSpIW8LO+ue12UnVRWC9AnYy7SJ9aaHrfSz7DbOpoj8ztJ7H0XoTtL9rV6suNFUSqkDWJokSct6XcyR+8pOf8NVXXxFCmKpiaxJUCGGsmv1qIsesQD0+vp+qaOvc2wQsq8jW47o2iYo7tAhy4QYqtNCLKqjdroSFa0Uoa45Zz4tWckppJL2HUQGRdQAAIABJREFUfkJA2leL0HR8tRK3chPWRQ1lvSiPZBW6voO+p86P9RJp5enr9fqBqWfv87Hts1AMHrgTIUIhF0eOoeRAjJPsSkZlCAGBkqOgpJDCVee43TnyjcPtHG7jyG0mhohrHNVNhd94+taRXEv2G6JvuUbHsc9cU2ZwjkRAfINIA7nG48kUM4IMkoShG0omp8hIZJVwYt1R1Ha1kM9Gsa0XCCxj+rWtvQ8WPuqCs/BdbV19ht3VgUkx2L7ovSxS0D6op0ADZtZKRL/rvZ+ETCH2zc0tr1694u7uboKyiiJKzcY7vvrqKx4eHhauT0VAes+U0kRUasKSmlWKqJRYbRqNYLxOY6icTtPUbLebcRwSInOy093dPT/96e8A8P79I6fTcYLg6nYtzyhkaEEIF2KcT8xao0Qdaw2U0hoO65J72mzKNvCB4lt7RZQD0shWTayypers+vmU9lkohso7Xm0q+j7PVZ+dwwfwLiCUaMMwnijkmO15u+ibxuFvIW0S6SaR20yqEqnJhNYjlZDrzMU1RRlI5tx3HJJwyo7Y3CG+Juca8TUkj5MxbNqXwq8uG8JtyDjx6PmLZSIE58C52ca0XICdYGv3WVsdWOwOqgwszLcejXUsvN35raBbxKL30nh+C7HXAThrBGHNlTU/Ync6FVr1Elwu1zEbNrDb7bi9vZ1gsyomu4OXMOeZi9BxWptfFo2Vx8/ZitZLU4hPzaAsgr7ZbCckpAI+DBr+HGjbDSFUDEPPZrPh4eEVbdtyOp2IcZgIPsvD2DlYN6uU7bVt2/Lq1QN3d3eLJDN7n7VHQhV/Mc2Kp2aOvlyG3n9q+ywUgw+O2/uay2UssokiAQhBd0lHVfmFRrREUYwRXzvYApvy47eealdR3VZwB27nGBpPdA1S70C2pBjocyD5huwqkqvIrsKFGhnqkmqt5GOUclJWN55yxUgOunKwbYHlDU2z5fZ2zljc70v1JEtiqdDbH0voKRJYT6o1O9YKQEkp6zu31ygMhvn8Br1Xznli3Z1zk5fAQnlLZK1dfNpn7T8UIvBXv0qTorlczhMc7/vA8Xia7qE/XddxOBx4//49p9M8ZrrQ14ScrYSku6f2U5WYumirqma3206migrc5XLleDzw9PQ4EZQzSrnn4eEVpQisJ6U88h9HjsfjFJew9sTYmhu6AaxjQHS8vPe8fv0VP/7xj9jtdgviVt95veNbhV0U+NJc1bldm6Qf2z4LxYAv9n8RFtXy9TSh1l9soXROmaEfGGQg+4IOxBdUQA2ucYSbgLt1pDbRh8TV1UTXEDavuG1fQ24Zuoo41AyppsuBITv6DF1XztR0Y6VoEsiQSTHjoiOgPIEW/Niy3Tp2uy3bbbFjdZFZ8nANCa0tqO0lb4VtNvpxhu43hBAWQUI2PNeaBzbASsd0t9vx+vVr6roeXaNMDLut8GxPmyqFVh+mxJ1ydmMey+WXiEN9pt1ZVQHs9xv2+wNNUxNjGoW7m8qxWe+IhfRWMVnlZ4OximDI6NYtxVvv7m6nKEDtU98Pk7mkJ3ntdtupiK3ev+t6TqfjxPrbubTz9RLHoWOswmxjFu7u7nh4eGC73U7X6Lvp7zUaWceoqILW59ux+W1Qw2ehGJx31Pc1Td1Q5UwVChPeblqqMFbX8aM/vB+49mOdQ4RUJbIbhaoC2QiyEXJb0qzdeIR9dJFjzpwuDX0l5fyJtsbVO3wKpF44R7jEzCUnLjnT9xk3JEIqxWeVXyAvaypMJeydJyXheDxzOJynCbQJNDrx6wmbw35nAlYXmgqUNSXWisWy5GWB2WPa54Kt1hWncH/mLsJ0bkNVVWNQUll8BfKXnV+zHG9ubvid3/kdvvrqK6qqYr/f8+bNW67XAzEO08K3O5fd/UqswXmKLVDPgxYvfXh4WLgAFRUoKrFuPFgWQtFx77rrdDJXSjcMwz0iMpGkIQRub2/ougf6vqNtW+7v77m9vSXnEjehyECfbwu9WF7IxgusTQb9tyWWdc7KAbfbqc9r01Gbnf81GrARt+vvWXP7Y9tnoRh85bn76R3hFBjGBRWqUAQ9CIm5NFbve3KTCb4UcCFS6kBKLgdCbYEWpBFylUvade3oqsQ5B84JeoF0HhjikQuZfax57muuEhhwRAnkHMiDw0dwyU/1JEWPp2N0Y0omJ9Dj6S6XHpELzl0WUXGWZ4BloRVr41vBtUlM64jAtUdD3VVVVY/k3t10srRC7fP5zPF4WhSGVbu6EHqFC1A79eHhAYDHx0een5+nBKEQwohOqonjUARwOp2m+oIqfJZRV4Fee1Isj6EkX9s2lFL2/bQbKoJR4Z+C3cyuauG0dcX2fcfT0/OoDG4nUrBpGm5uShXlGCP7/WFSAKfTear5aPkde7SeFWbbB30/G91q3c/Kbdzf30+l7XOes4rXEZEvIQVdJ7r5rBGmRYmf0j4LxRDqwKvffYU/+GnXcM6RXSZJWux2ySWqtiLURWB66Yky7sibgN96wm1ANlI8EztHrCNnH+hSQ+9aIjVd8lyuiTOJU6roM0TnSS6QXUVOdSn/5iuausE3nuQTg7NVeXJBEOhC0LDT5XHqajvbyVubEzaM2h5vpq69tf/e8giWJyi72mWqXPzw8DAJQTl45ZavvnrN8/Mz79695/n5abq3FpG9vZWJsBMplZ7fvXvH4XBAozeL+/F5OthVi8rqu+mCtH79NYFquQklHOe+XOi66wcCoMpG76PeC71uPmdjTkzT/3fO/b/UvU2obdu2HvT13scY83/O9bPX3ufccw9ESEArYhQUsSIRC1p5FU2CEBIJvIoWRAsJVrRgIVaUgBJ58ArvifAMUUkQLYhGJKAWXhQFU4khz3vfu+/cc/b6mf9zjP5jofWv9zbHXvecveEV9hmHxdpnrjHHb++tt/a1r30Nu90Wl4soWm02m9ym7oLz+ZLvv6YctTo1PyNFu2kanE6nwkYdbzSC/BvfkzYY7CbF69XPSadHXyOn6dBlTIgbLxqvYRQ/tH0WhsE0Bm4tLe9hgTjEq1Vk8IoW2gJoxZPoUw/feqQmSYawA+zcwiwM2lkr2MI0YG8j+tAiNTMkzHBBh5M3OBuRdusDcEkO3jkEZCakaeBSI/0wew+cgdB7RLEgsJDCKmsNbGNhbUBKDsY0kFSlvXp5enB9X2oQqF2PJPa8RdM4bLc7PD6+LzH2dQhQXUWShx4fH3PPyHv85Cc/wWazUZoG00Kk6boOz89POJ1OhZG4Xq+K10CVaTIKKU+vMw4MBQDpvKyNFAflWLWZfAWdNqUrTg9Hpz91KlYbh7bt0LYNDrnGpm3bXFthMjHqDDaBMcYWzkHMHczXaxR5+2vvor9qNS8ZjGkR1x2HRszwtG171eWbjFU+N22wNpsN7u7uSnpZ80u4GI6xBc3o1N6k9jjHHuWY2/Ix22dhGGCA0Hn0tscZZ1ziBT5KyfQQRauxa6nynI0HItAALuZ4MgSYSUTocll0k2CswclGHLzBObUY3AR96jCkBr2ZINgJku0QMUEKDTxaRNPAe4f+DITTAHMB4jnAXIxIxSURoDRJwh3TGaQhISXWGzik1EK6J/lCTaWR4IvWoKpe9TRhhStYjF1plnK5XDCbzUr2QK/C+ocDG0CJ2XXzlK7rcHt7e6WqxAlCb4Ru9ps3bwAA0+kMT0+P5bg8h3bnuenJT+EWa21J7xWvMFahEY2qj1NzvE9OAnoy9GC0sRJyknTj0iu0tZUrMgw9ttuXfGyDGJkWrOnNruvKPfK6SGTShkEXmtX3et0HlO+f9yAhxAaLxaIsgDQoutxdZ2x0+lMbMeC6wlNfG9/Fj9MwWCBOIk7mhK3f4uIvgDEwDkgOaJyD65yUPQMIKTPhnFIhjg4mpytDFxDaAO+AU+pwQYseEwyYAN0CXbtCNHOE0CKEBhET+NRiiAaxadCYORaTOcIsIgwB/uhFwyEYGBhEnzUbohgL3w9I6QJj6AE4pGSvXDztbmrevOYijONHDqbb29uyWr1mDHgObvysZibqscbAF8MCHbpocZeUJMf+xRdfYLValRDk6emxpGBp3HhtunJytVoVd9kY6Vf5/Pxc8AxqJIwNAME8DZTqlVnvoz2nEAK2WwE/+azlnVzzR9iufjqd4eZmkztni0EVzGEBay222y3ev39/VfikC5P4DjXQTMPxmqQan9d6vcFmsy6rvr5WviMdAtED4+c6hBx7UbxH7TV86vaZGAaDNE3wrUffSHu5xikJcQsEFzAkjxQjkgG6tkU3766opdGKx4AJENqEc9NhcDNEO4H3HS7oENDCpxanYHAYEk7RwBtbOAzNZIFF+4BuscLQDtiet9jGrWAJycLCATYrGQ0BCAkioJqByJgb1UCITtw0Eq9XAb5gAAWMatu2rMry4tlsVo5Bl58DSpcv68FAvGK/32G/3xdWHT2T4/GIX/7yW/zyl99gv99nPYbrNJs2JGynzqwFcQm9ynNAzmYzbDY3ePv2oShUWytS9nd3t7mHwjYzDKvx7PtLqaPQBpUTgN5QjBGLxQKr1So/E2lBp42rdvXFc+kwnVZBXBHFPWC9XuXMwLw8V/EOTthut6XMW3MnxiQleoFAxRMo2kIPQIc67969LWlTjSNofoXGqMbKV9rz5LsaYwsa7/nRegxu6eCWDvZigOhgndTLpyh/h5Wu2BK7o6Qmm1kWOokJZ3/G0A5IncXFNLikCaKbIWKKZFrENME5WBz6AcdocIZDbxx610p4kRr0h4TBnNFGIOwCLucL4hCkgEqQBaFIE2mHyRmJCbw/I0YOEqmdSOnDoim6nPz3GIjjVjkDPRaLOe7ubjGbTbHb7UtmQbMTx0ATY/Xn5+cSSnRdh8PhgOfnF7y8POPl5aW45JKe2+N0OpWagrFr2jQNNptNjp1PV/oIOh4mp2C5XBRDAjA9uMRiscBisSjnl2chYruMuYEqdqO7RdMrEW5Fh8vljPNZiru0GA03Gi3RgRQDMZ/Py7W/vLwU72axWOB8PuNwOJTGMtpI8flqrIRGbwy8cpLrfWezGd6+fYubm5sPPCLNL6EXQAr4GLzU3hnPpb1ChoL0qD51+ywMg3HA7H6O1bDCERLDpZRyxUFeXV2Nywvw5gzszGIyz0UnA5CsxaWxiOcIHzoMZoYLZohNhxA7nHuLo7cYTIPoJhjQIiSLYBoEOJwPAei3sJcD2qGFGQwa1yKZCBNNzkLkFc6KzoOItOhUVQNrGxjj8t8+JJhod18DRnSvU0qYTqfYbG5wf39/hQd4L41s//APv8G33/6yuLhjspROkz0/P6PvxciwYYoGrnh+rqTMEowHIoDiceguTNoYcRJtt1uwPdqbN2+u6L6agPXy8nLFsGSa1zlXsga66a244mssFgvM53NMp4JfMCvC1Vo/E666LI7i8/HeY7/fF9k4GlOSum5v73A6VeBVH1Onj3Vqmc9LZ1QYqt3c3OL+/v6KKq/DQmImJJQRvNSehH7PGqB8jTFL4/Cj5DEkC3S3HVZmjaM9Ib6kKwksWAvjEowVrcUI0YRMKeHcn3HAAU3bIhrg1La4pBah7RCNxck3OESDHgbHYHAcgD6JUQh2Ah879BA2ZAwOwyUBF4+2B1JoYKKBCdKGDjEBSRiQNjnAWIQ4IIWIpgEmkynadomUgJQCUhoAVCku4HqyAtfxHweGXi05SCaTSRm4zCjIRD5/AFZx04g/QTM9wV7LDByPRzw9PZXzjdOBHHgvL9VjoTHgRNbh3cvLC/b7Pd6/f4/lcoX5vDIovQ8lrcqsCAFPFhSRUanVnebzOR4eHvDmzZvSxZklxy8v25JCpKdBLokGLwHdGs/kZ9PnbEUNF6bTSWZzhpJO1SAyDQB/U3RV4wv0FG5ubvHw8KY08tXehZ70uus1QxmdwaIHxlCRxkjzH3Q2jF7Np2yfhWGIBjjPW1i3wdQGbF3EsBWWI5BXIacalQTF+HIJzniYEOCaDv3gcE4NvO3Qu0mmNzc4BQkVgpsAZoJgZ+ItGIdgulw81aGByH61VryF2EcgC7QgJCAayUg4J+QrEzLhp8VyuQKwxOn0HQ4Hj74/IUauCIDwG9yVB8FJCVzHiVwp9nshDRF70O66BqY4yHVqSyP/9AC46cyIBvWOxyO+/fZb9H2fW9qxmlE0E5dLARLJteCqxoHLf2swMYSA3W5XulyP03+TibA16RlwctGAagKT/pwuNlf5w+GI3W571b9hzDTkykzAVAvInM+XYgyA2n2a96qJRARY9WqseQPaA5hOp3jz5gFffPGuMCr5Dvg9Vl2uVivM5/NSX3I4HHC5XEqqmbgKwxAS1fgc9MbrGYdWH7N9HoYBBofQIDkHtzBolhHxDJz7A2ISF97BwVKJ1V5r8Huy5rzDORlcYJC6Dna6wmQ2RT80OJ8jYnCwZgKDGayboXMzhNBgCA1iP4HFBBPXwAYLk4zoQkLOnaLEwPAJjkSmjCdYY2FtgxgTYhxwufQ4n7mKp5ylCLC2hbWk7HKCVMBIu39cmbmC0xXlRDscjnh6esTLy0uJJ/ldZjk08KQNAH80G05P7mEYrmJ/gl5NI3wBrj6LxQJ3d3e4XC5FC0BXa7527nGtwGw2w3y+wHq9KW69Tu2Rgj2dTgvDcr/f4w/+4A/w+Ph4pY3ASUSjQO9jfA0aG5B6iKqyNJkIoE3gkefnO3ots9M0LUxWwaZB10zVxWKB+/s7bDabYsT4rvguWDPBbEiMsXg+/JzhBSc5cRFtjDRIyWP/aMHHBItDEInx2HVoV4A7JYTe4nLpC+BnchrXNQ7JSTXkMAzoL0Iu8nAI1sHNlpis38LNbhC8hQ89vI2IaBHQAW6GbnYDN9vAxRb9toc/BsTBwgYhWMVLzPwFCwzyGUJCCkAYAlKfAC8NbC+DR98fYMwZxpwRwjFPMnFTjbHouhnatva3lJVsQAhV1CXGmrai+8vJyMHGQcCQgBORP7pNWmGLhuvya41D6FVUT2SggmdMWaaUsNvtSvx9f3+P6XSGlKqh1iGS1mDgubXB4LV1ndC4p9Mpnp+fMgGp4hZE5zVnQHsWGntidkaLqmjPQjeRYYHbfL7AdDrJKUsRnX15ecZ+LwVgzMDolCSfi9ap4H0DOi25xsPDAzabTQmxuB+PQaPAIqqUUuGsbDabD5ic3Gj49HmJTfHZ83q1VN3HbJ+FYYiwOKcJkNNWZmbRrQHXW/iwQwxS4owkgi02WlwGB+OB/pLgfdYm6KZoZht0yzukboWno8e32zMO3sJMVrDtBP0FOA8GMAFIA87JYLcLOO0ijPdofIN4jugPPdIxwg4OzeAEa7AWAdLAxAwGNkiGhJ4C4GGMh3MWznU5IxFgLeAcLT3DigjRlCBhx8CYDkLMGcCCJu1SElzTbuGYRcnYWA9iHWboFJeOQ3lcnZfXf9feDEOAlID3778rE1m7/jq+Lu85Vn0Ebfym0ynu7qTs+OZmg6enZ+x22wpCp8pd0DUK49CpbVvM5ws0jfR/jDHkWgvdim4KKZ8ORYpNDF9XuBZUo+Yz4HOnURnfm+5Uxb/TKHz55Zd4eHjAZDK5Sk3z/TB8IGENqN7bfD4vxkI/Qxodnf3QRphYD/8+DAN2u90nzcnPwzAk4BjFTXbGwbsGmAJuAaSLxSUPEABAAlwWT0khQhZyh852mM1uYGZr7HyD5+0WL4cBp+iAyQpNanH2Doch4QIHf/a4nA7oB4/TAQi9Q+MhpdV9lB+P7CUk2JQ5/UiIiYMR2YU0MKZFSh6Sk2/gXAcRb6GUt4f3kqEwRkhQcgy6txYxeoSQEAIHPjKQ6eH9h12k9caBxklAl13H9EzxaaRbews0CjptxmPrgcdY1xiD8/l0lTYDrtvS6eulNgJdbl4vXebZbFbOHUJVfubxdFzP++PGySN9J+gthUJmopE8nY7F8GnGIhmc0+kU8/miNMjlMxwzVXXWRrvw9EqEq/AO7969K5iM5iGkJLUS6/W6GBygpqiZqnwtBNBhkX4+GusgfsLnTm2Qj90+D8MAi4sVy2iNhTcNMHXo1g0673DGFpfzGcHndJZ1sCmvTqZBcgmwLayd4ewbPJ8GvBwGHINDchMgdTieo3TEdhMkM4EPDpfeYeiBNLRwwcAGg9Rn7QUPmAiYwEBGOAwxSWoyRa6yBnAywWNsEKNUWUpBlZO/I+UshYGkMLnS9wDoMYTyXZNViLxH9igcrAWEFyFEKkCMi3PX2g2aVj0G7QAU48Cyau6v0106zACuVyQAxYvpupqBYBqQXsAYDGM4wO9ysgLA+/fv0bYt7u7u8uQ9X4UCOl7XLji/r5l/WiGaG70JnSrlD4CrylNJKW5grcE333yDx8fHMgl5j9pNp5fEd9B1HR4eHvDu3btSNcl9dehAmrpuBEMDQxXwscHlu+R+WphG10eM8QV+9inbZ2EYknG4uJXw+hIQTEDq5mjcCgu7xNC+4Pz0hPPhICtRssIpQBK9BSNt5rxvEKLFObYYXIdoHLxp4UMDb1sEJzqPZD+maAVX8BYODgZCcY7RwtkGBkDXTtCZrky6YELRY3DGihx96U2VEKNBCB5ESWXCAkAD5wxAXoYBnJsgRvZEbGAtEKOHlDPzpRI4ctkgBEgPCwCIxZhYK2xL8Sxq30QdDlAbcFwEpLMaGmjT3yd6L+lTGdRSolwLqWh0xmXVGuxkSk+nDU+nEx4fn4phkV6T1bBpjQpOSo0jSFpzCmNQ/q6zEbqcW9+TnkAkbJ1OU9ze3uLt27cYBl/CJHoeQMUPdKgGSAHZmzcP+OqrnxTeyZjqbq0tBK/XMgUEWDmpuZ8O5/geeT3jsm4aYP38NdD8MdtnYRhCMtj7RjwBa0V/AQm2tZjcLLGebOCbJS7mO9EPjJJChEH5jjEGfXAIsUFvGoTGYggN+uRE+NVNpdx6SBiiRQwtYm8RzwHOAy442GARU4SNFsEHpJB1/psWERH90Oe6iAgTmTpNiJ4cAoemcbC2gzF9BhYtmkYrPHuIaGwlQ0nunKtOC2trUYz3F3gf8neQvQfm4Zl5cACiWlnFk3DOom0bWGswDB4h9MUF12GETq9x9dGZiOl0ioeHt3h4eIPlcln+Tomz+fxype7E2JkDWp5NJfy85skI8eqYwcJwxXbUhC29aY9mPreYzxdwzuJ0OuNw2BcAj0akGp4KxjG8YcHU8/MznHO4u7vDw8MbeO/xy19+g5eXl6uaBhpaGs3VaoU3b97g4eEhp3mv+3py0pKY9ZpR0NWqmjtC74Dn1lkX/Sw02Qq49iR/nOnKZHAMHZwR/kBMWeYcFm3XonELLM0cvlkCz88FHIohCtnIWTjrYFyDYKV68pwcLslhQAOfWjEY0eF0MfBDEuzgEhGOHiY2SCYh9hHDsUd/GBCOHqnPrEZrEE4Bw2lAGET7EVEMAusl6EJbO8tGISIlkyey7k4sQKVMwAmcE6JPXSEjpACLtQcGIRikhAxqMi7us7cACImqVhNay74SQNs2aJoOxgR43yGlULAPttVzrnbM0m46V2Qh52xwe3tbCEcActVkV0hN9AYYlzNTwYkJXCtO6Xi7akLWrlpc5V/jIdBgSRjRIRUZOu5fkXpOCs1doNYCUI3WbrdD3/d4fHzEF198kUvWv8RiMcfPfvazwu/Qqcrlcon7+3u8eSNGUwvlaqynbVus1+urzIMOLQBkXYhzNnTzQktn2TY9Jw0W08gyVKOB0O+TbNZP2T4Lw5CMxdAsMADog0zy1CZEYwArk9Yuplh3a9jZHezzkwiV0p2KEEISHC6DwzlaeEhRFGsgfHIYhgZDb+DPFi4kWG9gg0PqE3wagD5rPAaDBpKiCjHgHM5g77vWNUBjkFKETxnxR+3vEKPPuIJF00wxnXZ5MrEnZADQICWLGE0GKhvIosg28ynzHCj+YpQX4fLKf70aN41TE00mvrRr8+j7lAFS7d7XDtQyScUFE3d8QMxq2F0XSzmwnsh0eZn2I39AZz3oarM4jMQdCpwA1Qsg+EdAj7G3DnX0yicSbBvc3t7AGIOnJ6n7SIlNgK5b0BPXaNsWi8US8zmu2KC8H31P2+0W7969w2KxwJdffgkA2O12RY5NVJ2XRd+C+AONHg3kbDbDarUq+zAUkFTpvFwjvTS9vzYgOsWrwzV6QmNZ+r7vCwHqU7cfNAzGmK8B/DaAd5DA+TdSSn/VGHMH4L8E8McA/EMAfzql9GTkyv4qgH8ZwBHAX0gp/d3vO0eChW+WCD4g+oimrZ2FBmTLbyewc4vV7BbT2y+wOZ1w2O+x2+1KhR+sg/EWTTIiytLMEWBx9g6+b5Fii3hp4E9BxFyjg/VWAMY+11r0CWKhgPP+jHAKaNBIU1xv4ZKDP3rsH/fYn/YQmXjxDFhVKZNcSE+smUipyS84g5fJCJbhbCbIuAycUdxEVIkZM6UEhEA03+SQg3l+6ZkQAmNwUzIfKRl4L4ahAlkGKRmEIKFJ103zRKQ6UI+m4bHnMKbD5RLw9LRDSiKKut1u8fz8/kqbgDG0XrFYNLVer2GMweFwLAOdE5ceQwiiwCQ0Z5O1KGpRlab+inEasN8fEII0p2GzFSFMrTIH5Ijt9qUYF6ZACUbO5/NybM2XuFwu+Oabb7DdbnF/L+rN7959ga+++qpMXAKjDE20K0+PhPvymknd9t6XjASAUtDVNE3u3CWeBb0ajRnoDBIxk3GoQA9ot9uV+/qU7WM8Bg/g30kp/V1jzArA7xpj/gcAfwHA/5hS+ivGmL8M4C8D+EsA/iUAfyL//DMA/lr+/as365C6DYIdMPQDLiGhMQ2sybX5NqfbrFjFqXOYrCOmNxfMDkdcLrn01zVZ5dkiNTPYdor+4nA5A6FvYGML9EDsI2IfYAaD1nbiKZw90EsWQryIiNP2jNhHtLGBjRYmG5Hz9oxH+4iJmWCYDvBnKg0N8F5KsI1JACLO5x7ei1r8EP3HAAAgAElEQVSxAIMu/81k4+DgPeB9QgjIfydtWjwPARNRjiO4BTsNCUnqfCYVGfk70gTHGFeATBola7Ub2iEllzMgFt4bAA2aZoEQBgwD8PR0xHZ7yXgHKwb7khYzps2GyGQXHnnVNoUvwBQpU5NjNJ9ewny+yKlQlPJz1oPoUnRrbdGXZIETQCBOvCNNRtIcDXoH/H9NztKAbOU4SG3K7e1tCRcIXDJc0C4+z0n6MsMs6cu5hfe+VJcyS7PdbtH3fVGL5nF081y9aUOs2ZBApdOPK1I/ZftBw5BS+gWAX+R/74wxfw/AVwB+DcA/n3f7LQD/M8Qw/BqA305yJ/+bMebGGPNlPs6rm3EtML/HcDzi5E+SCgyilGSNRWMEmByMxSUKXRkAPByGrkGyC0TGrWhgk0PwDr6fArFBCwObhK0oYYKIvaaYZMIHqYNICZl47RARMW0nUlZtWhgv2Qef3bjVaoVFu0AaEnZPYpmPx76EBGLhY0k5ykSRSVnVfQJCIHlHjAInzXXKyZUVPMYhT/4G4p24nAXhpBD6NTthCftSrkUMhfTXlP0l2+G9g/dMUQoekZJHCBExAiFUAE+uJ+VUqYWQtFAMkgZOxbAMOBw8LhdknobNng4bzAhfg/wBKd5ilaWEBDyvbjNPbGEYKmcCEKMhlO5npYJ1XdikGaTEdjh5eQwApcT84eEtbm425fzcT39fpyPJ86A3wB4dlHcjQUxrah4OB8xms+IpkBLNKtgxEU3/v85CeC/UcF3foUlZH7t9EsZgjPljAP4kgP8dwDs12f8QEmoAYjR+pr728/zZrzQMERZnTHFKARcAiUVGKRf7BCfpxHjd9FVc6qkoQRsjnIbQIPQGl3NE7C1sBNIQM4UZwAA5jreIFw/vB7jkYKJF7D1SFMaCRYO2a2GdFFL1fsDlfEE4BTg43N/eobMThEvAarbEy8sCLy9TbLcGfW+Q0gBj6Ppd1w00jbh13ps8sKPKNrgS+8v+QNO0aBqq9pzL/tzqO2fYYvKqTa4De0fQcwh5X2YlDJxrsyeDDOAZdR2SKhVDwOMKLhECcQdJmYoXw1AJiFFCndlsia5rsgfQ5xWchWN9vk65JwKYdOkZTvD/AVYjVpCSLrX8tsXD0FWL8syvWYIMBWhQNPHo5ua2NNulp0FAlvtrVuVsNivUboZTx+Mxl7z3VzJ3WkfhdBKS2GKxuMoo6PBJpyN1uEYWKY/1/PyM0+n0QX+NT90+2jAYY5YA/isA/1ZKaatBjpRSMhxVH3+8Xwfw6wCw+uorHPYtgl/BD1LQgiQrdEm5ZPUiYw2QAB8SAIeu7WQCGwtbRFwB218QTj384IFgYDxEaj4ASAbWG4TBIF5yR6loYL1FYxxa10lGpGnhooW3HlOX0CwbNHCw0WVPQ8KS1WyZq+JmmEwcnp5+icPhJbvllUiTksmrtAxWScvJfVxrN4QMTFrUDEXKYUoD54RdKSuUYBDO1QnMVRwArG3RNJNspJCNQ3WXaSz4I9/x2XiRphuzl0B3GxCgFAiBg87AGGZM5BzWTjL2YmHtDG07gTEeMZ7yewXEa4oYBovDYcDpdAFQlah1KbEu/eZqTUPB5jCkhI/l6TQPQLv9/DcBUpZHa4Ogy5j1dXBVpkHgas/rE4WoY/ESmOHRXgFTnlSQ4jXRgGmgkV4bPRxmZXi+/X5fzqfrR7Q+w8duH2UYjDEtxCj8Fyml/zp//A1DBGPMlwB+mT//fQBfq6//NH92taWUfgPAbwDAF//4P5mmuEVqANMfsTvvy6CXwUuJ8ZiBO3GhjTEIbYN0lpWscUDrZLLHQ0Q4SuGTzR4BMqMRJtOezxHhEgETJGRAg9Z2sNFK5aQ1pX+Fmzg0aKWQy0NqxU0qsWyMCfP5AindZxf6gtMpZLeYcZ4QlmLkgGQ7Ok5OC2O4YseyaksKk+Xdq1IYJDJkA0SZ2hVMghiChCUTODfJz0zAT2uFPdk0BrOZTP6+v0CwEZ47QAwCe18IuSqloRgjeiHigVyXEYthaEoq9HxO6Ptzfo+tmmxV4KbvI4bhAucMlssOTVOb/hIwZB3AdDq9av6icQGurPw3UX8dQgDIz3NRwhiqTUnF57xoUtCT4ETVq76ueNRhCv+tPQDd5Zs8iBhjyUBogFF7PHwHPCafASnPNAoiOFNp45q38UduGHKW4TcB/L2U0n+k/vS3APx5AH8l//6b6vN/0xjzOxDQ8eX78AUAEtVf5KFM4xTnQUC/FIxUM0JZTQAmYwHGWoRLxBAEuY02INiA1EeES5Cwwecu2ckhDqx7sFIHMRiYIa+SxsA1DYwHwuCRckm1Tx7n/RnwwGa1QWc7tp5BCsD5fMJ3332Hvu9LbOn9HYbhjBhTJtj4PGnJa6BQC9NQFkwlAsjZDAJa4ro712E+n+Uy24TLhR6JyNUDDkKCCnklbtC2Ddp2AslMeIQgoYlkShiOtBCFZFb+1ViZDWVCGArvgSXK0kowZWNXsx0y8Tyk+3cDa7u8ugvGIIQrOSZAtF1IXs5Jf9KUAk6nmBvPyrPmZBf5exE60fwGAFehAF1pXdhFt5wiMDc3N2Wl5v465crUKdOG7Fmh28xzIzOSxoDEKY1d0JhI2byAplT8HtdcMLzRwCK9E+IXPD9TqzHG4jnR+NE46NqXj9k+xmP45wD8OQD/tzHm/8yf/bsQg/DXjTF/EcDvAfjT+W//HSRV+fch6cp//QfPEAGcxNdNA5AOSdpFJYjXnRLQADbn6g0AYy3gs9X1udrM5bzuYOC8BXpItyhA8IpogF6yHCZY2MEgZTCybVu0sUGKEPDTA9FE+NOA0/6I4TSgP/aYT+eYNlNM2kkBQReLJSaTAcZI+pEDX9SZtxgG3ZbOoG2FmyDt32KeFFnIzkjfzoqeCwbRtgKYHo8+d0i65PieZbcO5C8A9FAmANrMnKzhinNMB0f0vWQ8nJuibU3eVypFhWsBiBw+43QDY87ZcwBI+9ZsTGupPF0JWCEMMGaSwxymd00OcahpiBIOysrrYW11/duW6c1YADudNaCh4LPjalvb7gkhS/CDm1LRqAldnNjEExiaGGMKJbzv+3JuGh9OaGY6dNhDLwGoKlmHw+GKlEQPgl28OKEBFA9Fh0g0CtTOOJ/PVyXYut6lLkQfv31MVuLvgPLEH27/wiv7JwD/xiddRQRwkAnsTx5hH2V1T0nUkqxDC1FXMjlYLumiPiL2EcaJypOJQAwABgMXLGJIWclZiEgm5tTNkOCCQwpA4xo0qZXCrJCkmCpkoGoIcMbBTRogAofdARd7xmKyQNdIpeByuRwBRcDNzV0my3iEcMxYgc+gnMnuP6snCf4x80D+gRRSiXvvcLlEXC7CDpQ4nmXcLOVuskdAWXohUom3kjJnosmKReyOBUwmTXZnpzifT4UUI5MzZdyDGZaEEBpItgMAbMY3TA4/HJxjKCP3J8ZFrkvCpARrW0g605dziEJ1zJmbShIyxmIyWWK9nmM67XC5nMr1MQzpukkJC6w1V6s0BW4AXE1wbQwAXLno1tpCwtLhApF+varr+gROaA02a1CVKtQEGzWRi308tdye8DLmH5CXaEienp6w2+0KZsK/0fN5jePwMdtnwXxEBNIl88oPPcIpIA6spLRo2hZNbNDE3A491Jy9zeFA6IPE1saJl5CbzxovhVFJVFvRGMEbUkxVxs00sMkgDlFUqaNB9AHJk9UoL8UZh+gTfD/gdDjDuyA1l0nH9bGQVPr+thQMkUoLVO6/uMEVTddub2W8xWxMovqpQJ/gMD5PNuIUDk1TW8yJLY0AHJpmgtlsWVZpaw2m01kWkhHvQgzKpQCL4gW5PPGZGhTPQiYKVyqd+UDGHqqsHVOUIcRswJA9HHoQgoOIF+VhjKT+bm4WuLu7wWo1hbXA4bAFEIoLPpvNsF6vP6hm5IQV/KIvYje6c1YFXG3RRdDUa4rUMm6nAVgsFuV96gnLTacIOYl3u13pqL1er4ukXPWIao+PlFIx4q8ZBVH6fr4yJMQudD0Kn8O4zuSHts/CMKSUasYgGGBIiENCTndn/oBHcgmNcwgxwud4MqYkeEECkkswNh9DsLOc8pQUZIMGzjSIPopsWzJojPAYgo+IQwCSgYlA8mIkbMwVjhCtCGsTQgqZkCihQUw+swglqyCl1MBiMcXNzQreX7DfS0wuK2RNQVWasmAAtPJiEHy+CUCqNglSEkAzOc6X59i2rNcwaBrJgEg2I+RJHRCCcBqco+cgg1diXuFGdF2LlKaQmg9eK0FTXwxOCLUIi/dOMlZF8WMxWjI2hc0pk0Y+Yzo3pb4Al8YIMPrmzQbv3j1gvV5CMhoezgHTaVPQed1nU2cbXqMnE5MgDkHvQK/0QPUsOMmYhWAnsPFk1WnPsavP2g8219F1ENrzILFJb+PVnp7Fy8vLVck1jYNedPhbX8/Hbp+FYZAYQuTTUq5VgAdSJjilIWEIPQYALgMrkWitGpiAkVAgGTS2yTgEpEw6qyqkkBB6CRHgAZtxAiQjHgYMTL4eZJahs068mgDpQhXkbwkJyC5wDQM8vO8hhVIJi8UMIWzQtg0ul3MhrUi1X43/ZAIatG0D51pwwjKKo+cgBUMWMfZ5YlQSy3Q6QddNymS9HhwE+qSpC3P9ImbSIyVgsZjnBrjCZGR2QjySWH7rwSYGTYRmasOdSuIKgZoTjTIA9D5iDpMCYpT9JAuTsFgs8PbtHd6+vcdqtYC1AZeLPFtr01VqkIZAi6XwvgEU0ROGCdoYjFdivdHjYHZD11zQRecxaTzGYQRDDx2ujOXcabBoIHSGQm80ClRj4nl1KKMrLyvuEz+4tx/aPhPDII1lbBIFZgsr/zYikBJCRPC503Nm6BlIOICYYGEkSxFMVpAWGTabu1GbrL0YU0K4BPiLR/Qx6yqIATEJCDEBPn8//+eMg4WwC1NIMAnSjQr1QcsLSAhhQN+fEcK5rJ5t67DZLDGbTbLq8x5NY3E8Uva9NhNxjixCpv+Q04GAuOSVdCR8CKYF6wophmRcVSjhhUxWC+8DhJwkfSa8D2pgSVpxt3v5gIpbVYyQyU42p0tNuYbKutRq1lTHjmWfGENe5TTXwMOYWGoY1ut1KTLSpeICoL7ez5GGgZkDzRvQMfiY1yDXWQvANJeAz5KgIp9Lre5si3HSIQTPRxefP1orQgOeuvbhtY1FUZrOrY0br00fTwORn7J9FoYhJcB4wGUeQYMeHhLj+xgQBIGDa2wOD/ILDVE+txYIBjHFEj4MwyChgQda1yLZRkBIH4XglNF0ZxxsErDRhjxIAw1OVm5K4imkkOCSgzNOms0kqiklxOgxDFJPIAAaXeqIyYTS3/Oi3ENkWpq/RAyDuPpS3FQHsLj9uRUXpHCoAmli8OTfpoQpslK5PHlsXs1rv4S2ZZYk4zRWJvYwePS9MOd0eTEHutCdmxyytOi6IfevFGPAECAlqJU75usQg1uPZdE0xACE/Siv2WA+n2K9XmA2E7zAex3SVMOkJ4QOEUhUGguW6FQgUD0JbTS4ERci65KxO69hMpmUztfcdBiijQ5TpeRd0MDp4iaGReMQhRu9Fa2jwXtmqKRrQ2gYdXbkU7bPwjAAkNg+pTyxDWwyCMmUld0YIGbdQ65hKQGNs7DGIXgv2ghJvIcURESlMQ6Na+FgpRluMqIZaaXmQmTixWDQW0mIJe0nkbBgDX4ICBkwTKnW3Itgis9GglwA4SlUt1LSbcZM0LYWy+UM5/MC+/0Bx+MB5/MFonNYB4EGkCjKMgyXD1Y/qcR0ZUAyfk+ZAwEESAWmrNB9P+ByOWfPQfa7XE4YhsvVCiOToYqlyKCV5yJcgAmsRS6Squ3TQmDth71aAeW3YC2u9AkhCcxgPp9js5njiy8ecHd3i65zSIm0YxKXpPKUz4UYAFF+DdaxcOkauK20Yrrbr00avltet56IbHrLzIZuanN9r7Xgq2aDZNNaF5qn8H1Ggc9dh3K6MOz6vV1ToX+koQQkpk/CSIy9pCsRk/SrzLJrNl33UjSiqyaCKbkHRGMbOOPkWClJqjGJVxBzqFAGR4l5Y/YK5LdJItuWQsRw6YXlGJOItCQZnNeuM5HOACLr4haL2yuDnw1YgOm0w2IxQ4xLrNfLXIEZoCXNtN6BtJYbQEFY/ZKJZjOlJc+GKbQOTUNASkIMqXA8YhgIzMWcCaipP3oqEsbYjE+Ip5BSxPl8QkqxMAVDkDSqvMgaz9Zsi1yrzrjQSAiAKTyNu7sNfvKTL3B3d4Oua+D9KStPcaU/l2wGxXR1h27tHVBbQZctc4IyLNB6ilfDUXk13Fc/d01w4nik1iT3oUHkc6VhIFmKRh+ASg1fN+rRRoLkLrbs0y39dP2ErhnR7+FHaRgMMtgYJRthvIQWMaSip2icg21yqbBkwQAA0VRD0XYdJs0EzjgYJ95G9FFwCSMtayJl6KMUa8UgXAkTLNIwIEWDxgoQGfoA3wcBGgNAzkCMKQujJEjDGJZQoxgFYSDWAUatBpmwwkgEkMk3vgwiDiRJcfnselLxWG5cVKAiJA1pswrwvLiOQpjSak8pN2q9NgiCi9SBLFvIx4Ua+AltK9iHgG9DpiIHzGYTOIcP2HUsOycLEuAq3CvMQYrIgIDlco27uzsslys4Z8DKTHHnfTausbx4gn5aFEZ3oCIOoHUXq0dTjeB4haZR5rH4mTZqYy/IGFNUlph5GBd9zWazcr0aAOW9sCCLXae0SCwnPTcyG6lHqTkMOruiMyqfun02hsHkSjyGEpIFSAIiGnHx+VlkY1kjqURnHKyzaI0UPcmYlOyB8fKdgAw2BoPcAFPcX4jnACTELNkWTMjewpABR9YwEOEVL0G8A58nWe0hIdTgBK6gbC0vL60tnoYMMoPptKoF971H3w/wPqdjo0PXVZlyTjIOSA7eKmXO1BvBKcagA06nFZbLGV5eJqXTVE155TdhSEqKOWwgUQYjg+RxuZzQNLWJy+l0yM+GHops8oyYWhVcREC4HtYarNc3+Prrr3B/v5F0cBBDUJWL5JySWWixXi8wmdRwgRNSx9Z8Xq9lHrTnoDcN3o29Mj5njVtoT4GTUKdPGS7ofWlEOIHpBVAlq6Z+X8cEeF3sU6GNHr9D76mGulEZ/o/bPgvDwFAihSQFUAwlAkQF2op0e/JRhFdTpaIKQu0EgASyBwCG+kAySLm9us0sSkQg+FjAS5hKiEKUFCR7VKZcTCWTkOmwCgyKPkLIHkLMrD9mBGrIIVa7yenMWCaLuHyV2NT3Z6WKFLOrrydZlXoXYtC1C8kBx/Jtrl4y8Dp0XYOm0SXbnAzX/QpqjwwL8hEoJivfq6j9bDZB03QZJCS5JuZaCynskq3qUQ7DBW3b4e5ug5/+9Eu8ffsAYxKOxx2EOMV7EbUq5wwmkxazWZe9tVgKySjrrtNzGp/RKyrwITeA96Pjcu1R0IvQqUd+xsnO82tAkExK7dLX5xlKI19iFpT1/1WbDi+ZgeG98dxjbU19f5+yfR6GAUCKQBwCfO+LwGrp55AzEQkAoihDN1Yu3UBKpa2x0jouSNbBQMDF5IUAZTLRCYmsxoygRyCkkEHO7DUgFMNSueYhhwmCwLNfhDYO1qJMpspCFG+kTrgKWFprEYJD31+Q0k6lLhPadpInsK5ENBiGunoR1NQDQLIOHQDuFwvtWFiZLjdFbbFcznE4rLDdbrHfH8CuzsbUMILnYAMYDngCopfLCZNJq+J8k8vD1btN46rGBtPpHG/e3OLLL7/Azc0G0j2KzWzlnILLOEynAnQ2jYRAbPGnadF6suu0Y/U6tHGtIJ3+vN5XvDqW3k9CvOveFYznx+GJBki5nwYNAZQ+EsfjsUjgvZaZ4HH6vi+Aqj7XOITSFO3xPX3M9tkYBhNykZMXjQNWN5pMZWbb+yxEVB6Ma6UrlQlSpRlFyEFWfpiSeUAUBiWS4AUmMxyltb1wINKQEPqAUKTabU4Vysb0X2X4VeBRADEJH0hGYtepEJBXf3oPbGfmYG1toS73xNoDOZb8W4yaDD6CYBRb4UAzueS2RddNs7tfMRGRexNjMZm0mM9nuLlZFXWh9++/w9PTYxl0Ek6IS0/UW55BrYUYBlOatQj/QLQnZdIMSMnDOSnkYvpzOu1we3uL+/v7LGM2QUq+ZB4oSktjKiXRMzSNURjHBezHwZXw2vA45VHWCTbO+euJzFVX04o1aKfLpbWQDON94hnaa9FsS31NOsThT0qphHY67QqgSN+zEpNy8jze+P51OvNHjTGUUIIdoIKBzb0iTTKAMUgxIeWUmPSxzG501lkohiMIWcqkTLqJQAwmhwhyLmectJoLIRsJ+W3hIFQJIQ21bQcpyKkdnKU6sLIbmX4jgULYf5ombLLLF/NKXmNWWe0l9SmVmAkiwmJBRiExAglB2DhFdA2Z8uNiSeqt9CWo1Y0cuBysXFGEs7/BcrnAZrPCt9+u8Pj4HrvdHofDtjDxuBFTgVLFlvBiAMBy34Su66E5D6IhACyXKzw8PODh4Q3m8wUAzdKrNQsyIBLatsNsNs1kKq6We4TQf5Ax4Hd5r3pScDXXXgR1DXTszTBMjF5NF+tUpOYRcEzo56s3nR3QbEzt1mtcgBgDqyVpNLpOjCm5GdTjYGijz911HZbLZen/wZDjR9nUFjlVeDn18L2XSc2UY0yEAcrqL3wDAR0LDpDTi2kg1yGXmmYjgZQxipDgJFOG2OcCo1w41TUt0HQF/ZbBkPLLZSjAUmSi1jFnJmTSxBhKSk32B0j8kc8orRbLpJaUHa06J6KFrqmQFZwZBPntfUDfH/NEPuB0OuUsg16NeE5AU5YnkymWyzk2m3UROZ3PF3h4uMX794+lPZtkH3xZ6Spxh/E4V2Gu8rEwLEV1WngXt7e3ePfuHe7vb8sEEB5F9WZqARavsUXTCLYj5LH+yksAqhcwnvya9adLp/k8X8tI8HjaBR+HKR/yPKr+o/Y8xuGE3mecAtXHI1tT8JNTeRZaMYr3oL/DHhf0NvgcKNzyqdtnYxjSAJnUXsIK5JcdM0AoYYUqZjKZ7Zi9CNdwdQwCHJrsWcQEl2S/SIDRAIhiWIgl0F2HnDpPJLIDA6wl9ThkK+5gjICJItQq7MUYUVxtIfqI8dErC8MEwSeEsUhqcx2wNSQByDqkSlDC8XjAdrvNZbeHomRUJ6/WO6DidD22MbUL082NCJ6+fXuH1WqNxWKJm5sN/vAPf4HvvvsO+/1epe8CpNqUqstCnIrR43A4Z9bkuUwCrnYPD/e4v78tugky6Wr9hgbojJFjSjEXsw7n/FyZGo5l0usCqtdANhoOovPamIxxBm5jgzA+HjcaD22QXgsTrnGh1zUSeO1aD1IDwlqBiuAvKz/ZGpDHIYdDhzmfsn0ehgFiFELOSIg6UC1uKg8nConJGiEgxVzdB6fEY6PJ2QyIUYnCU0ghAV4wi5QZgTazA7lI01PgSp0y2kmCEtOSzD7Ie60TmjqNUpJMncS6Qgl4WQ0D0f/aAVsMDwuqRMlJVI2EbSndpXe7F7x//x3ev38u0uyyYjbKAAUIQ1CKm8RDaeGcBYuhhFA14HTaY7t9xuPjLR4eHnB/f4/lcoGvvvoJ2rbFt99+i93uJWdLCLrWVKyAlh59fy7pPoCGp8VqVWXUJfxCTo9SSp9elzxHZk+AWFB4eR8fkqfGjM/q0Vy79pxQxErGgicfNUbTh3Lx3MYeiU4X6lBHr/TcT+s4VgxEeBmTSVfuU4OjvHZ6EjolSf4Es1vW2iKv/7Hb52EYEhAGISOZzFUAUklTVuCwZBdziJCzGSnCZlfAJAsDwSoQKu/BGJP7VQOltDoRmQaYw6+rO+XLOMHJ6mNq0uRJRjCR4UHtJCWDncBjk1dv5lF1qKBlwkSvIKXKe2jbLmslDHh+PuL9+/f47rv3OB4PefBzleKERfl/3hNrGQCuXLZ4QCHEohVwOh3Q92d88cU7dN0E6/Uaw3AGU6/SifpSzkGylHbnSXWm6KwM3jMuF5K7uhxW0YVmNkcYkMJRSBiGgL4fShaHhhn4cOXX+Xzx1q57ZNJQjDMOAIoh+z7+gN44IYk/0GCMS7+Bmi3Qx38NrNQYkPcek0mXsRiprRl3KNf703jS2JElyeeiZeA+dvtsDEMaIlLWYYhDhLMWhpV3qECPyyrNLKZKQUBFGVgShlAJ2kAMRwqR9ZfZ6AAoE4bb9WouE1142sxEyACLkB4MQk5KyeV6Av6/xPTiRSSwGS0xBDEsEnpQVFV0FwjoMTYWQyMkGKng2+1E7OP5+T2Oxz1CoOZigi5Qkmvm6sPJxAEpz0pATpMzJim7nye8vMQiJbZczpBSLOrF4t7vcTjYnBaLyvhxxa45fpkwAyaTBtNpl9/hFE3DZjQRrKikoarIvc/1EUN5dlUXs07yceXiGIln6k97FjQO9B7k2B9nEOiS6xVeA5xjfIFewWsGie+LXgNFXObzOe7v7wtWoI0MgcSxdzTGXbRGxad6RsDnYhiQS5kzl8FEwDoRV5HUoQiwGGNFLSmHAHnuiKQ8acvZKEgWQ4qjUkhSDYmKIZB9Jw9UYuca02tQUQYvwwQx8CFPCnvlAhsDyFgLeeLR9eNxeLd0BaWBrazqrD70+e8140Ctwxg9zudjUSKSe6EYbFT/j3y+OgDFCInMm0xE/eo5aCX9uN/v8fT0Ht6vMpPQYbNZYbmcY79fYL/f43w+FBqvrHLXisbEV1IK2O8PWCxeMJ9PcpZCDKsxbAbMZ0HDKeQt1jPQS+s66QTufa1L0JNETzi9OvOz2r3rOiQZ/3zvOB2lOPmZznS8xroc8yx08RcNiOA9N1d0ab1pL0FjFjrMoPeijYb+/bHb52EYEgqPIA4ZHTQGESnrK0TA2iwGS6KSUJ5Fw8GCfSgMKzN9lIxGEkzCQCPLACe/uKdyEVUiTcuhke7Mh6wkP7MAACAASURBVMv0JGNj+a4Ylbo/YPLK2OaVUSYqMwoAECPVfCXbwZQnZeSrrLwp56KxkOt25VzV0A1gaFFIH/WM5XjVzQZSsmoASSbhfD7lkuCItp1hNpvDuQbz+RSbzSLTtsVQHY/HomVIQlXfu7yqGkynwrqU1Q8IQTgNomVgs9GTOgqRKhNeRts2iLGF92y1ZnLqsqYYORn0aq1DBE0O0yQgnXngJB3XMLy2GWNKKrB25romNclzTGWSahCRP7r9HaXpfpVACzdmHHTHcRoGqYU5lJZ2/JsOpT5l+2wMA3xC7OVHx5+ldsJmfCHLn4lhkDQkIOxFMIWZi6Bin4VFGwem64Dq1pODL/RfhhH8WyoTnYQm72vDFskkkP5sMg/BQzAKypOREgxEplRT1UBsWxb16I7GzdW1CtYxlNVzuRSVpUqe4YokHaUEm9C4gxCpNAOQLrUYm1q2K96KyW3ljmgal0u1ReRFJuy00Hx12CACtcL36PshA18eTcNO1wtMpyKo6j3yhE3lHuVYHn0vWZ3ZbAIBeV3+TsoAWo3XOfm0+z6mQOvP+ZuxvUb49Xe/bxNwtC3FWbpHpLzL+pz1dfEzvZpTvZoZhe/LktA7IlFsPOnpRTLEGWdBPnX7PAwDBBegQlIKYiustXAGSII+ZpFXIOWmsBYOYFelkOXbGlvqLhh75zOgcgUkFGBzWeopkFwjf6tMRv6uqxOPIfRegOIkyO6xrP4hSA0CQJYjMubAgiJiATymHuAyqSUVKroIxiRsNhu8eXPM6cFTERORVZReQDMa4HWQ1MEk119TnNfsOGtT7r2wwXI5z6s3qwtNvu+2hA3Tqag+WyvNa87nKjsm++tMAZmiJlOtmQmSkO5ySWgaU/QjZrNZluCXBsEaYNQhwZgXQKPCCaPlzzi5NPZQDeYPb8QbdC2ETifyGnWzGwAlBCIJSas/afKZNg6kf48VrfU7Ba5DKF7Pp3oK3D4Pw5CZj0XrMUXEIJNcALKMAjdWecaSpZCHngtfmkx/Nnzpkgkg+s/sAlN4kj6s7n/NPOhUYnXljdEt5lNezau8eg0vTHaZuXpocEtz3C2ohSBGIWRvwuUUZVVnlnoHKXN++/YBTdPg5eUFu92haEgSSBTAjvgHJ3Qs6cwKitb7RQZ427bB7e0GX3/9U3z55U8wn0+U8SAeY4sxEXfdg4CmhEQEAHvUfpryPGvlIyDEJY3pkPgTMlmrQdeJ4lTbzpFSl4lO13UbvypdqVdWPXleowgztAK+n8OgN3oPOo2pQUZdbBVCyJ6T6GbonhYMN2i8xtdF5SdeHw2eBmB5b7oB7thofsr2eRgGEBDkqkwwTlxiacwS1A1XlWE+ADZcrbEXuzkDwyCuqrxsegPEDmSlEpc/lr9XbIEAYSo/cn3UYgj5XBrk4z1V/EDCFUq01Xb1NBr8XfEPo/aVLEWMosS0Wi0wnXa4u7vFfn/Afi8x/n6/x+l0Qd/XNnJSU0C3OhWvhWQldq0Wt7bDmzcP+Prrn+Ldu3doW1fqAqohrKXQYmREWVpwAJdJSRL+nM8XTKdQ2RrJRhC/qd6DLcdIyaFtxTjUlnUCwAq/QVK3WthEr7avAZEyBkgVv471NSeB3hRX/I/ZeHy69KxrYBUmPQBhls4/EHjRrejYu2IcStDr0PcIVMPDe9FsT028YnbiU7bPyDDI6ij/rk1T6PrJw+IEqXx6gBaUUumS3qo8+HRlBCRsoHtbtRbrKqEND7UXSHGOJd6XmB6obnrdX9iPNXQAKp+gNpSVEEYGAp9BVWcGKkWYHAm+dGnAMslNVpa4v/cYBo/D4ZgzBtJRWqr2zvBejIMmygheMMNsNsVqtcJqtcB6vcbd3T02m2UBtKQNe39Frmkag7ZlY5aE08kUurLgLwG73Q7H4x7GbDCfsxMTAVSKxfpiZEOI8L7HMETEKLoL1iIrOEntRddN0DSmUIB1X0jBIcRwabYgt3FKj59x8moFKK66P6RhQIOku2rzc6BiCLqgK6VUKiRlQXNXRmh8jcxWOOew2+0KkKk9Iw1Edl1XPBB9jB9pujKBlF8CUs4hT16TBzXAuF3Kb01xoypwE7K7r917Ic7IhEfOEAQAbfYA6srDkEOYi9f/FoJSKtcEIMfHHOyVBEWDVbMWY4+IsaBoS8qEctl7AeiNxmhhrUwe71M2amQeGhjT5IkyxXxusVyucH9/n58NcD6fsdsdcDye8mB3eVI5tG2jdACmeZLJ/XFgnc/nvIpzVSSrr8nApKQVYzxgv99mF1mwhcvliPP5hBjXpd+FcDMINsbyjMiATIkNZMUQOmdK6CBG4lIWibHgKz0JDSTyPrRXwDHDyfhakZU2GD+UyqSXQOIRvQU2xn2NiqwzISldN7t9zVPRIRP/fxwaaUyDRk5nQ36cQi1ghoCurri5XFVJ9qFbListVwQNtPg8AUN+YFatupLKq+FAj9oSjR4JPYualuQEFq+ikpyoMqQnO11sVljK4G7LtbLuIkYakkadB2D8TtKQnEPCj0qnFgKVAKTEPCKMmcDauvJx8qzXa6QsxQ+gHEOeuc1YhilcBGmPl7uCFX0E6iAgT95LXsF5bxI/bzYbrFZzrNcLrNcLvLy8YBj6XCYtxWUy+MV7qG5wzRgIPtLl915Xb7lOlHd9Op1K6TErDknq0ROEYQcl1zToyOfBlXuc1uT1USJ+vPLqTAHdfD0x6/Ou3zHGXPWiGBuq79t4nxo8HfMk9GLJ8/E6PmX7TAxDAjDA2oCmSRgGn3UO7Wgf2QRsZH07VZopMBLBrs+iduTy4PV5sPkSR3LVknjZqElP95+1E5Ry0+rQA4gvaGxCp0RJkGJaUQOQ8qKqZJy4yy1YrFWNTQthYDJcYWiBPFGlDyQ7atOA1FScyQagtkyv5B8P720+n7/6XPAMDthQQqKUAi6XHufzCU1jsFgscHt7i9vbW1ROCLBei9BtCBF9fyrpNFassl5D2J1y36I90RU1au/FoEioURmMfH8a3DsejypMqu3pAZk08/k8P7PqORDwG9c+sNZAZzLGE4v7SKhVRWBpVMkn0JNeh3K6kErXVPCcY0+D99C2bdGXJAuS2AhDDI3HjbMYH7t9NoZBVvMA55KKj2gFNboas4vdgorMYyab9HRg3BVgrcs8A+abOYB1Lj3mCSqpS6LjglkwzGEWobZSS5mtx+PoZjGCm0j8XAEtm/dx5bcuy5Y2cmTs2ewVMKZkdWcl51ThGPFIiJPItVA4VsIr9pgE2MhG9iUnQ55Dja+lEKhHCEzPWQCiZbjbvWC5nGOxWECnc7fbLYCI9XqT3XMRQp1MJjge9zl7EjPdmd2h5X3Ke2kznpOQEinHQxaNrX0X9PvWYB69A04OSq9RoZk0Y8buUoNCnQsxjFr3URsEjREcj8cy8dfrdfkbcQcaLTJDtbISW9lrL0Qbll+FB+hrnkwm2O125Vo5vsYpyx93uhKSyhIaLSnBnCjydwCoqUeh9rKX4/UgYSxeSUbAAPFIoCYRBULk3+KlXLIrRh6BAVOWZBxWNL9qLojRoddR8QV5WSzKIldAYxGVSs0MhBiDKuzBMIjeSA15WDsgk1KeHQuamnyvTY4/r/sdkHbsnIUWoSGNuqY8KzFISp4ldep9X2JqXos0qzmjaSyaZoKakdHcBQ5YeQ7kODCcEICT3uK1l6QLtegN6dhbu+LEHLhPjLGkdCeTydUE4jVRt4DHYohQx1Vtg3c4CB2c6cfx6s7r4xZCKNWO4++zm9WnkJBYJMXQ4vn5+Ur/kfvw2bwmfPtD22diGADAQ7oRnbMb7vLgljiYbpG4oDaDiGSXkW9OinCEMQHCV5D8vkwAUT8SryDmNJ0wDcXll5Am5O7YzCzIpCRZRI6vXzxyXQXz8wBK6CDfCfk7Ru3HGFGyGykNJZQwxhZ8gZiEDPImGyMCkCEbMCm5Fm8KMGaAc5VgJIOieiZyvJDDq6iMAifdgBhpjHjvEqYMgxiEzWaDxWIGAHh+fsHv/d4/wN3dHb766qtiMI/HI56e3iOEgPV6jcViCms7nM8R1vbZCzO5ehOgZLwAzMK2rAVprkxYHUczdAAqWUu7zXrScl96FXqVttYWifemabBara6MB4ACyMYYy6pvjCkpXW40JJycxCgWi0WRwtM4CbGQcb8KHuNXeRDOUb+zKdWxY/KWxjo+ZftMDEPKYFaTmX4AJ6pYO6dWLxaKCAW4FuHI5CpsKRgQnKuS5nq1HlCbq0r4IRRlxuEGTcNBVjkNvDbSgenCi3sfs8GqJbV69ZA4WqPeJhsvvjiSgyrpiZ4RgGwgxXOQY0AZR7rftY8kMQZAV/rZbFj7cq00trw3McYo9wjQOMg5pK+kqBpfLmf8/Of/Hx4fn3B7e4eUAo7HC37xi1/g22+/Qdd1uL+/Rwg9+p4AKEM1wVYkTtYTQSTh5FpFqUoa3rT5++mqhTw3fp/PXiP/nKws/BpTi4HrNOW47T11FlNKRfKdYQW9J40VaIMg717CAK70p9OplEuzzkQo70tMp9MCYl7Nknw+lljT+1ksFui6Drvd7iqlqftL/GgNgzEeTYMs5cUGHyk3XaWraCDswISKtBNsIfeBoq10Y0n2oSvK+DpBOkZzJWVaMwLwJZtwjWPI1YrbXhmDDAuoLKQ3zV+vHoR4LpUsRTZhADMO+dsQqrNgEeKZUOOvMjAJEDIsIVim6wkAjxibfE9a1MMXo8vyZ3I5KpOvsgKdM0WKvu/PeHp6RAgeX3/9E7x9K6nS/X6L9++/RYwRy+Ucs1mHlIC+P0NwGmIMDAlUWb2TEIjiNDqboQ2D1kHgBOe9jtOM3Bj7j8VkdPl0TV1XA0+CkbU2dwOvQK7OQPDYNDAMEXi8vu8LxrFYLK6yJMQmTqfTlXHT189j7Pf7YqCXy2UxYJvNBs45vLy8XBkCDUJ+7PbZGAbAwxifgcM68bhKVTTal4mWkr26YXlJXV6VkV1iX0CuKp3GFddnl7niAvQuTC6GqhOfjEhztT8g9RIcABrB1jGfzgYQJ6jYCI8lk18GakQIVUqupvdCNg4AIFkXYRYyhVkNiGRoKnDJkKg+L4sYswz/FQhLgJCZHT63lF18m1d6eRc3N+tSLiwdv/ssNzbBZELAk5WeSYVlLCmfKOwjZUNQvQuNsNOYaKFajciPDbPGITSNmvsyBuextIYj/5+gps4kjFOFPK6uneA1a7l5UqOZOXHOZfp3KPu/1o2KmwZI2eWKFGvnHJbLJVJKpYXd9eLw8dsPGgZjzBTA/wJgkvf/Gymlf88Y848A+B0A9wB+F8CfSyn1xpgJgN8G8E8BeA/gz6SU/uH3n4XUYg4KsuM4UDmYyCdgNaHPGQtxn6Uvg5CXxCvwCOGCypK0IIAnGIRgCrLaxzzwJeanrDsl4sWbEMyC6U2ZyCirh0aE9cAbAz/X4YWskJVxyRULqCEK6dEVOM1vp6zsJldRsg7C2toaTs4joKY8H1xNBjE+VReitpmPGAaGVimv9imTjixYIcmBLn0mxK1u2yq7Jqu+K+dhylIbnJopIocj5vd33eaexleDqXIP6epZ68mgU5TXi8z1O+PE12k/zVFh1kXXROjVXYcL9Az4w3oHysNrQ6IrK3VIoqnZum0evSVrbeljOZvNCt6wWq3QdV05n06nfuz2MR7DBcCfSintjTEtgL9jjPnvAfzbAP7jlNLvGGP+MwB/EcBfy7+fUkp/3BjzZwH8hwD+zPedQB4KJ5ykLGuemmirzfEzyRxsG19BP1kda81BjNIXsRKVtGssassml11z0pHdqGXExhjDa4NPp0yrm369eo3vuaLiNRUpgKNQmKuoigNZj2LwWMmYIKCiJkYxA1I9EQkP6JFwJRvK/5PTQfslq3VECPR+Yv6MxWQmG7BaZBajxzBI12wg5vhXOBkkQbFmg6GBrPwXpITsiYixZVhD8hrPwdhZGwb+XdOCKy5Ss1X8rbM9DBH0++Pn+j3psGQcamjjAZBtuivhAl194gAMNY7H4xU7cr1elzCAnoBzDvP5vBgx3YVKX0OMsYCYDFFqilj6W/6Ry8cnueN9/t82/yQAfwrAv5Y//y0A/z7EMPxa/jcA/A0A/4kxxqTvMVlE7o0R9zYEU2r2x7yAiuSzYWpCjCQK2QJKUluBXkitZaBQKtl8TPXV8IDpQzanrSv1tZ6BXnH0vWhUeAz8jD0IvWqTtl1DKP6NxCniLNqTIMVWir3k0AwtCFRaUBeiPgN6Wgy7YuYr1CxFVaL6ULJMJoSUmNOQaPqyiLkyHGNhHFdwGl5egxie+rwENL5+PumDHzm+vXrm3Hfs7uuV/7XvAtWLGu/DTWco9Gf6fLwHegiHwwGLxaLwFsjEDCHgeDwipVSAThqRp6enUksRQsB8Pr+6v0peux5X1H/U3spisQAAnE4nfMr2URiDkcD1dwH8cQD/KYD/F8BzIrkf+DmAr/K/vwLwMwBIKXljzAsk3PhudMxfB/DrALBafQGSdeogJLuPwJ4WNr12u6kVCADeS2aibatAKlvRVze8Zi60FiPBPOIQ5Bnke3l1gn9Irvqw+9F45dKffzig2XtQUnTM+YvyE0FVYicxg5ja6+Bq6WAMuQhM9wEySXtQtUnulyK31KRgmCFpXTEYtUM196fRbRo5hpDB+gLu8tnXprZ8npVrwnSwYCrhA0M69sj089L78KeGL7W6UId09Cz0+xzjBfw73y/JSdq7GIOb4/eusYD3799jv9/j7u6ueAibzQan0wmHg7QBYAUmU6b7/R7b7RbPz8+IMWK1WhVvgODj+N5TSgVYZeqTeBy7Wn3s9lGGIcns+SeMMTcA/hsA/+gnneX1Y/4GgN8AgHfv/rEkL9VkYgp78AFcObhRBQkgiMSXICu89yHrD7ZF04BuLBWbAA6umn7Un9M1z9d5NSDHg0lPar0//60NSCULVQERAmJ6QPL/WZosxrIBqd/IUm5VBZpy9jVWr16REIqMmeSVvaZuhWVZJ3HFbxhyUZkZAJpsiCtJiuCn8Ec8JF18raTNMLAaOtHBqD03KhjIjZNKhwea+6/fgX43Gt/R72C81ed73TFMg5EpiSrSy8sLHh+fcuu+cPUOmcoUoHCS2wN2xSAxJGGq89tvv831JIIFUGRXeofucblccHNzg8Vigc1mg67r8Pj4iO12WzIi0+kUIQQcDocypjiGtFENIZR05jg8+pjtk/ZOKT0bY/42gH8WwI0xpslew08B/H7e7fcBfA3g50ZQtQ0EhPye40ZcLidYOwf7KHAlc66m+GS19LkUt5ZW116SZEdSsIIDjqAXSVBasKU2fJWHWNHoaxygotqveRFjAzH2JoC60vDf45WOxmJsjOQekbMoVaVJrpMS9CLaagzDggGAdImWY9biMMk0UPUJ+fq1pB2voYZYwvewKvSggSGpCqj8jh6C4bTK4PJZsaDtugFMvY6aHdBgowbhgGviEo3BeLKP38HYgOgYne+EBqjvezw9PWU+xrdFKEV7IzyvLgMnbjCfL7BcLq6YjTFGPD8/YxgG3N5KRy56EE9PT9hut2XFX61WmM1meHh4wMvLSwkvqA3J1KXWnazzqd6T7q35KdvHZCUeAAzZKMwA/IsQQPFvA/hXIJmJPw/gb+av/K38//9r/vv/9H34Am9kvz+gadrcYKNB07RZYIVNWRk+AJpDwHhcr+Bc0UJgylKIReIiDyC9uYaLdQVlnl0r6nBgjl3csfEArif8ryrd1QOZ3xmDZzUle93CrA7slD0KIs4RznWo1Z0UV2FqlKxM4Yzkuyiufa0UrYQp8QiI7RDEZAqVIC1ZpQakngs20eTaCqb8Xm9TP04ljsOEcXimnwGf2dgj4/e4/xgEHoOTGmMAcEWHnk5FqFV34xofW6ciX15eCkGKPIPFYoH5fF5i/77v8fLygvV6XcRdb29vS9bj8fERKSWsVitMJhPc399jNptht9thu91iPp8XOvXlcimMRz4Lvfjwvv7IDQOALwH8VsYZLIC/nlL6b40x/w+A3zHG/AcA/g8Av5n3/00A/7kx5u8DeATwZz/mQihfllKX0z4dRG2YLjzAVJbQpStLEWDWgQrKDnwO/C3g3CW7VTaj/gEsJya9WL9sua5rgPH7wMTXXNzxKvWrMAf+v+b+ayOhzzMGwORvJEql7NJTJo4hFAlBso+AfVQxIgmMg92AuAVxBOIzenzxeDpTML5ubTTHXpUewOPQTN8v93kt7aa/o71I7T3o4/OdvOZR1HsSI8XJvFwu8d133+H9++9wOp2u3pv2HPT9sLX9+/fvS+GUMEY32GzWZRxp4/D27VvMZjMcDgccDgd474sRoAKU5mJYa0toofkdZEeymvWHlK9f2z4mK/F/AfiTr3z+DwD80698fgbwr37SVUBWJalW6wtPvm27/AABdkziCxYgDMWrqMfhaiKag/L/lSBDMlAt+w1lgtT+jHXwMs3zGj31Nes8nvx6hdHf1b/HA2sc++pj6vOMXcjxMYVFCIix6OG9XI/k3fk5AVd6ILXZLsMyveJqY6efuTYG42vhv8ff0eHT+NmNn8H3Tejx5B4bcQ1IXodn13gEj8V3xiyCUKJ7HI+Hq7Tf+J7G2SYem7RpwSsesVqtcHt7i7u7O4QQikfRNA1ubm4wn8/x8vKC/X5fOAoMITTpSRs+AFfy9ySAveaFfcz2mTAfUVI88tIWubhJSBo6faVdc2Ym/v/2vjXGti0r65v7/a6qU1Wnzj3n3vS9BBLDD6NIEAIxLYlGWwJ/EFqJoiHpRGOCIQa6Y2LUaAL+UDAaoBNMwIgNqIQOURF5/FMezbMRWxrThHtvn1PP/d61n9Mfc31zfWvsWfdU0ffes0+smVRq77XXmms+xuMbY445Js+CoBORy5nLZUjoElYoPMJxb+XMa+7AzVSEsrTXlBh1b7vuUtNEHkBRcylhq8+BxMliIW5KYGh9VgAq8antq4yj5kjKFtX69LOaTnbpzzIo77GxAEqY1qdihZ3WZeE/v5NZbfShmmzqD2I7NXxa67RjynuIPDgG4TSwBtrtPG0/TU0rFC2kVzpiLAKXFefzBabTGRqNOrrdkH2L28MfPHiQnT42isuaXPJkW9X3Qt8Ez6lgNisqtJc0JDqflOBYXKFcrmUwqJoRxloG3oNmRYCOQNGppZ79kPClVitnGYdDHofgnCtlYcf5xLEtSjA0K1Jmg7bfFutXSNVt7wew9Q6LDlL13oQuUlpNNai+1wq7fAl4tcV4Wgf9IWRC1qGCygqo1CqM1qeMm3pGx0WXJikUNKOSjrkV5mpy3DRGQVhkZ5SUy4X+6jiqANP22fnmuI5GQ4zHI5RKYWdnvz/AkyeP0ev1InqoVqsx2pJHBbAfbAeFifZP2z+bzTAYDJI0d1PZCcFAuA8ELT6bXWcMX4nrrwHmcydjuJe+BYCQt5gmvlIpgwfThsQoPpoXIXx6sSXpLfMpQQE585DouI1XbT9FGCR8RQP6Lrv+zevUeDc5jqzmVcceUBQuymj6XgpE9QFYc4Z9swFOyrR8LwWDFbJ8htdU0ykis1Bf35Eyp1hSQkG1vt5nfURkdJoIugqlCiNs0a/F/qmwoxBKmX8quK1/QH0Cw+Ew2x05xAc+8AEcHx8XIian0ylmsxlms1n0WdRqtbipql6vxyxVDI4CEM2Yl/O06+gdZyhzkNDlctggEpg+7GPQcNCi4zGPQiOa0FTuIRuTx2IRTqbOfQx54fPKcMD20qJlVGVwJSgWEpfC65ugJoAtgrYmA9uk5kLo47apYGF2qh+6RGrhv4XGlgE0HkPbqgJH+6F/yqQpB6S+iwJSzT3WmZ9VsT0vqfG7yZxKmX0qqMJmpeKpURowZVGHbnu246ibtSgg5vM5ptMphsMh3njjDbz66qtx3wXrCnk0l2i1WjFOgUJls9lEXwUdkHn6/7v5GXZCMHjPzURcQgwx9CHugEtlYSNPmCzdw8A6OKkAw5k1XiEQEpOQMHJvewnRbpxhofbkZ06uCgCLDNQjr8ypTB3aXNoSBHzGwuni9u1ts0OLoggVRqzT2qfq0dc235TsQ7U+261IRJnQCiYdC/ZL95jo+FtG5TUdY52fPL5lW8gouqLWt0KNwoh+p/AX6Ck491w8J1IRhgY1qfJS9KdbtFVw82+xWODs7CyLipzijTdeR6/XQ6lUisuXzPeo+yOY7JYCCUAUCnfdJwHsiGAAtid/uVxuQXUlTEtouW3FPIshtDfkaOCJQStwKzafJwHQXksJCyDXvkBRMABIMqtlMnsgikJsGwl5k2ffmh03aT4Lh/m7vssKPudcIfGptkPHwgoYLVagcbnMtlORiG2HzrMKJT3hOSUM1eThM7wnhb60T4q0VGgqKmq1mqjXaxiPJ7i6CqHMi8WikIil2WxGbU3mtWNP2lYBZcdvvQ7ncvzBH3wOm80aH/jAB7C/vx9Dm6vVasw5yQhKRlGyDqW/u6IFYEcEQ6kUzif03sedZal1V+tMS0F7Ior8hGQ6l+ijKNrZVqtxkwuJQxkgZQun4K/eyzZY6GyfU21omT+1emGZmnXY+5QgbzIzdLx5XYOHLBOpZtexKAroorC07bbMedMYWqK2Dj91Hio92L6qIrE+C/VzWKSiqCKsIkzkZO8iumFglCaCYT0WDangIULhe3nvdDrF22+/jeVyhSdPHuP4+DgGSXU6nYJJZ+e9VAonYxPVvJSmRKkUAjW894W8dyRW3fXG+wFsTWJu5+X5Gckry2X4DuR73DlJ1I6EXJxUZuRNeaBv0ngKHwEUILLui1BCVBjPOrVPKWGiCEbhtK4I8F0aFsvrlomtycJ67W+27cqEWh/7YVGLtjfVN63TzrcSeMohymIFpY6bChSOmbY1haiWyyUGgwH6/X5MqkIkq2PJVQOde76nVCoVsjvR7FBBbK+vVisMBoOIjyka2QAAIABJREFUQBaLBR49eoRms1kY51SxSjSlaN+p7IRg2Gw2GI1GABDNB510BppQSqt0LUI/gNuNcxgYHI9ctVgur2Ne/vl8kaWOWxWgMwdUs/jWanU0m40oLPQUY0UXhLxqbqhzTLWFhdT6P6UFdPkPwFYkoBL6TWaHIhdFA2xLyqvOftkxT6EvZeybEJH9ryaX7bdFbHzO+k9S7dA6tF4VTCqQdNw43tfX1zg/v0C/fxVzG2h77fzp3JDZtf/OuehroNORNKPIgvUxd4NzF3HuTk5O4k7LVOE7GPD00voYvEfBc8pAJ4Vx1KqqMYBtQuI1hkevVgvMZmOMxyPMZuO45MNMQ9aeV+Kj9GZb6vU6er0eer1e9ApbvwQny0JcEoFts4XQlshTmlkFjN6rEFn7ZFFNyhSxDKjMoba2bYPCdGpF1dQKnS3asWaXFZQpwWYRyk0mlWXUm0w17S/nmwJhOBzi7Owcg0E/joMyvM6R3dylv/MeChT1Ndh9DrqUqQhnsVig37+K/Tk6Ooq7NFPFORczUzMG4i5lJwQDgLhDjVJRYbclLjvBOWMC+YYoZOnLL3F1dY7xeIjFYlaoW+silE+FytLM4PLPaDTKIuHacTmJqEZNoJRziXWrA4qMYh1e9lmrUaxJwZJiGH2P1ayKIFi3LdYkuAkt6LxYLczPVqhYQab3qemjzKUCPVWH9ll9IdbsUy1eqVSwWq0wGo1wdnaG8/NzTCaTaK/zvdZMTPk57DioYtD3q6OaGp70pjQPIDuTYozRKOzh8N7HYKhUYbtbrdaW/+l5ZUcEQ75ZB5J8FZKgJY9JcPF+HngbiMeLJF5hMhni/PwU5+fnmM0mYDYmhfKWOHWSVNtZrcgdbaPRKBJUOJi0iVarGXMgAmnvsPoGdHnUChULf1mUGelD0HV+nr7EoqseTI92k9ACira51e4qLJVRLQNqOy2Tq0Z8HpzXP0UdkXK830J+KmBTSInKgYxKgT4ajXB+foHT02fxEBfSQsjHkI61YB+sw1WFyE1jTzrjHwWC9R+piTKdTiTJLtDtduPqiC3OubhN+y5lRwTD9i5GC69ok6lE1gHTZa0wwae4uDjD9fUETPQKSRdm61A4a5e72C46IuloojOpXq9HuBYyJM8yYRbyJ4YNYZXoUVYhZB1d1mutJpNqXRUMbDfbp4LOOhv5vPZNGS3F8DovWiiUiPKs8LDCzQpZtoloKWU+qLDhmKnAsg5Gfdb2T300GltQKpWiL+Hzn38bFxfhkBwKeBvApXNHQUxfEhnUmmMpWmMdelCM+qe467JUKkW0Op/PI92xXev1OiZ2sYUC6z3J4PRel5vsYv7Gayk70UKtwSCcaXB+fo7r61lGGMVnOFEWCuohp1bL894QibmJwS9KmPkJyxWUSuEMh/V6FffyM4NwYDIXnwk5KPL6VTtYgUkm0j0AHJN6vR4J2fpjeJ8G2Vg4znHmuOr3m+bMmgW6imHnkb+xTXbZ0AoMbVfKx2Cfs7SjbbUmo6Ku+XyOi4uAFEajUcHcIgqziEWFr9KvHUc7vpwLRXE6PooY1RdBYQAgnj0R2oeolLgzU3lJ67pL2QnBAKDgZFSC4QCkltsUbtF7OxwOMBgM41Fi1NzObXu4ORl2Sc4yo/f51lkyv3OuIIUJ2QJ62GC1us4CT4KjcrHgyccuSz+3zOLkg/CgQODxZRQa9F+oE1TbaP0W6glnUdhM55pdhWAfLNGmvP4ccy3KENYHoPcQcSlS0N9Vm7PwHosIyLh6zQoqtonjqX4sHhd3eXmJs7MzjMfjrZUEi9xYHz39lp5uMqfYD5vsxW5Q0zaS7qxpt9mEXZr9fj+7tok5Ih8/fhwP2dXyUgoGSxA6mFY6W4cRByuEfs4xn8+wWMwQNlStETZnpT3cGufO/9Z+5GTzXp5wzANNOYnO5WcfhjXvK4xGI7TbbdTrjcwHUZd+1qOpwTrCSUTX2YYXV/BVWHiuY6QmlnqzSXQaFWjvu8nXwOfpJCWaUW3OdlmfgG0ni42CtNpXr6UQi9XY7LvOKduqqeG1zzwvcjgcYjgcYTwe4fr6Guv1Os4DfTRM1Var1eKhuNpP9Q+pWWuFlY6TnglB85SOTYuOKZh0pUN9EMxJSWEe0M4Gb7zxeswO/UctOyEY1EywWgzYTpetA6tmQRhADeig5kC8pjviVHMqwamXmJGQDDAhrGNRZhuPqzHajAS4XC6jt3k65U7RcG5iMC2aKJVCTMVqtUQ47KWMZrMRA1k0vJbmghIZtVBYEyexhgzS1WolaiEeraZJPOz4KnEqQ6l2pgC3yCq1PZ3jyXYrsknBcyu4lA6siWKFgrZP7fDNZoPxeILBYIDJZFw4n8H6srT92jarNEhzfI7nWfKYOUbw6jillFur1YpLipp7Qd/BOuyJ1kBY2g/00sJ6vcL5+RlarSZee+21Gx2Styk7IRiAIiRTBxMJOeVkIgHw+VBHLpnDGRM82bqYdFSddyntQ82uGlUnlNqH93vvo+2n9ixhK4Oq1JzhllvaucvlMradpgbgsFwGOEnnZW53rmKATH7CFk+UCid5K6JUbzwJn20ITjSig5AqPgjGStSeitCsQ1DrrVQqUYDS38Edgnxnar8ABTYPnOUzhNQ8zFXPTViv1zE2ZTKZYDa7juZAvV6LgXHD4RCDwSBuP9bTo0hHFrESsqecj9yj0G530O12UK/XMZ8vcHl5GQ+/LdJlcZVHzQntt/o3+Ju2yx6Gw/6TL7gJ68GDB9jb2yvM113KjgiGkBE61+w5k4dO6dHn+QErq1UgZtYRdpPl5kGYzA0qle2j0i1008GjdNYTgRTaWQhvHZWKPABE7aUrGyR2nnLM96iQIfPQfGk2WzEP4Ww2w2o1LiAm+jjYjuUy9GE4HMaddmxD0CYO8/l1RCTsC/vGPnHsw9HvHbRaeYYgCjOOT7VajZ56CkMytfchCxGdZFwNGI1GBajN/xS+s9kM/X4fk8kErVYbDx8eo9vtYrVa4fz8As+ePcVwOIxBPBqTwr4EuipFBKhokUxnGZjzpmghR2arDCk0sV6vMR5PMBoF08T6eVTYqL+Bjmz6wygEVWjwef0PYCuidr1eR0Q4mUxwenoaI3d1DG5bdkQwhGStOSEW4wu8LzqCqNWcy1cI8jFT+2+FcPR8zozWOcUJ46CraaFQUWPziQLUEapOTIXaQC4YCO34DhYyDZCbRHwHoamimHq9FtsRgqzq0exRZlA0wDayDdTa83kzmj6z2TSaJWw/EVk48KSDk5OHkampSdWPQeHGPIXqq+BhrQznHQ6HePr0GS4vL2Li04ODB+j1egA8ptNp3EUIAPV6A81m7tuZTqcYjcKhLEQCamJyeY/oQ5OivpMpon4riyrVXOG+CbZf/TYMVFLEwPbRrLJp6ijU7TF0fK81Z6zZHVacgkC9uurHg4ZVIN227IRgKNpttNk5eZysYkSg93la7GCf51l0QtZj2tHFIBgyGxlFpS4Zltc1sSYFg2V+tbHVe63MwjrJlKlkI0p4aiop4/X7fYzH46hRw1JnPdqp1GL6W6PRKMBfmi3X19doNpvRU97vD3B5CVxdXUWkwvEKmZK72N/fi0zJ/qoNrKaFQli109lHHu5ar4d8h8wnsFwuMJmMM6FcRqfTjcIF8Li+Dt74SqWCy8ur2F6dS75DGVJXQezKhEJ1Fay6OkQEoXSjJpRG0wK5qaLoQ30arF8d29asCvywnaNCP3vvCwF2AYWEvUHj8Rh7e3sxFuIuZUcEQylLw7bGek0hofDeZwiBZyM4VKth/b9ebxTg6GpFIYHMVg612SUwFQT8zpITYvhMyE9bWf0VWo91qvF3Mik3tFgISyJTLc12VSrhTEMlQNa/XocTicbjcWwrTYpqtRYDZBhc1Wq1ojCg0FMveRgDh8UiBNyEzTsBlTAuX+MoVEjy/XZcVAurb4Ybk0JAWB17e/uZ+RCIOhwR4LJTnIL/gMvF5+dBszJFO4UKhS+FH5nYMrOGlavzWAWaCjj1c1mTwKI/pR/nXGwPr7EOCiWlFUWlFCwqxKwpAQDNZhP7+wfodjuoVmuZEApJZvv9cHaFxsbctuyIYAgHewQ7mEtCIcVbCkJVKmXU6zWErM9AtVoBD1PNNROf2Y5fUA0PFJcl+Z1MyGKz7Vq7T5eu+F0RBTdgOVfKCHoSnVQpp5QSMolFEYgSNQNcUtF3fL7RaODo6AgPHjxAs9mM1ylgggCcY7GYF5YoySCj0QjX1zPUajW02+2YiZjjRmHGmAs9tIVIhUw0Go3w1ltv4fLyEgDQ6XRwcvIIjUYd8/kK4/EoPqvz32g0omChhma0nzUN2W6OkQazacSg0gHb6L2PfhgqBnU+WuFXpOUcIegKhEUwFOBUNNPpNJ4loUFYqnh0btXEOzjYR7PZBBDM0tlshdlsisVijsGgF8+uuEvZCcFABBBgeyWaETnUWsWBVIJmKZXKaDTqMU4AQKZ1eAJTcRuw9RVoUcckJ1KdcMqUJDo1hVLEQNSwv7+PbreL5XKJ8/MLnJ+fRQenbZv6U9Ru1gg4PmfbqQIkjMUmeucXi0U8A7HRaKBcLmM2m+H09AxPnz6N6EMdolxhYFvOz/PziZX5aCcTWai/gho0ZDN2kdCVuXnA63Q63Vri5BgTHbTb7XjWwnodkqgEZLeMaEHnBUBBILAvKsytOamKQhEeiz7L++y9anZac0oFEf+rY5Q+GxWOGrUZTMbgRFbHN9tGp+j19fWdly53RDCEEvYVhDTYYfyCj2G9XmUaQJffcpPDOURbOw84Kk6YDhgZ1SYRVYip/7UeoLjUaQlBnUHUTnlU5jC2IZwMnSf40PrVvicxKPRk0fdTQwJ5Dketi8eiLRYLNJvNLHCnDiBsOpvN8lwDFsqGZcMqqtVKXG+nJ51Ci/2ezWZRs1Uq4TzNxWIel/0Y16FOyHa7jc1mjclkFnMe6NKs+o8AoNVq4fj4IQ4PH6Ber0eGms1mODs7x7NnT6NwUNSm82pXkhRBVKvVuMJjhYaakYoelEaUFlRYEAlwbrnioghHaUfbrMvFnJdAXyvM59eo1arxWpirKhaLBSaTEL/xUu6VyHdUAvlGp3C9XAbKZTZzAxUI+nw4WKaaDVoJpVI11qnmA6U3iY0TYL3EhI9qEpAQNHyb3xUlkKlU+k8mEzx9+hSnp6dRk9qVDACFtgAoeKjpG1CTQx1kRFRqbqhfQj3pDL0mJCUjcjuvbiNXE4Uh30Bx2zCZTA9ZJQok73DZksxPE4fjTVRCpylRDvvB8ODA2JdYr9cFlHh9fY3JZFxY6lVkqRqc2lgdtSELdCM66wISmRfm1q5qqGBQrZ6vlhV9LZxj3p96VhGr+po4N0C+tB2W1Reo1fKAqkqljFqtHueXiWvvUnZCMISJCrn7i8Ul7y+W3KafTmcxYKhUyhnkJilP4uRnDTclwynTclKsSWGhMydTGdo5V9iRmWviSmROJWS1XS2ktaYKBQZjHNiXEOuQbx0mI6xWK0wmkwICWa1WMb+EEiiZGUBEbIzcpObKHZE5lA3HwteyOQx/HEsbwcfxaLfbUQhwW7tmjuI40B5/9uxpFFga+MU5j1Qk/Un5CiqVkGm50wlZmJfLcNK1HmRrHY+WpigIdGWE7aWy0TlVv4YiPy1sN8+R4NxpewBE5ucKUq5AViI85jezUKLshGCgxgjruIBLyoMiUggDGcyM6+trXF5e4c03/xCDwRV40jOwzWw6+Na5Q6IlYeVmyfa+fv63Kc84YdYMSb1fDx8lESgTWEeXfraOLCIFOjT5HjIIiYU2O69xHIgWqJV0KS9oq3q2lDiJjrVyuVxIS8ZrXD4tl8sxeKpcLqPb7aLb7WGzWWep9eZxrFutNtrtcGBKv9/PDl8ZFZAUj18rlUox5JwIw6bcsxCfyEfby3Fk1Ol0Oo0BWuPxONavcSWcq5tOQbdmnSJNS3f2N+sHoyCnr0VPtmawGv0zZP6wwleJ/gfvGQxYTBLzvLITgsF7j36/H6FwpRIGNTAng1IYCZnnwVss5ri+DoeFnp2d4fLyLHNOOZRKuaamxNZYfk4q30/BwPfyGdtOa1LwujqGWNTnoFofKC5pUfuqMFIoqs4ruwTIwmg8XatnnWQ+NW+4QUsJsFQqFUKZORZcRiQzWnOHQqRcLmeOzWZM7lsuV9BuV9BqhdOe2Z7FYoHRaBy18mIRfAL0f3Q6nRgYpeYSGV/R2GazKUSpKoOp8Fc/AueDY0rBQKerBjelgteUFtTkTL1XUYGlJbZF0YXSnpoi9NPoEnOO2EK8zfX1NVqtJiqVcrZatwYPK75L2QnBAADeb6K2C3kMwlmBAUKFoA0yNsOhQwz8COfnZ7i6uopBM0QVqmHVLrf2G++xzj+rdXkfr6mzjw4tEvFNRbUziVw3QakAUCK2KyUa96BmD4WA3dugKzk851ChsWpQvkv7SLtdx9MKVx6XtlwuMRzWUa/X4hHuzDLEOeAGsdFoiPPzM0ynU9Trdezv76PZbKFeb8TAJ25GIyJSs41MpELTrvmrELAmhiI19bnYmBWlhxTzK0qx86+rRaQTVSK6F4jt0Pqt6avKZj6fRwGxXC4zE7WaLV+6zAzcXn17XtkJwcAVh1zqllGpuOg3KJXm2GzWMXS4XA4SkjnwhsMR5vM8JBYo5vxXLaIMwkHXyQW29/3b5+yE6qYkJTy7xOWcQ6fTQa/Xy84F8BgOBxgOh4WMPKqR8j4V61PvNduiBAQgwt3UEh2/p6Cv+k6IIlRDknh1NSVfWl5nkDZA/MPDIxwcHMQENdpOhntPp9MC8zeb03iv+gT4n8iKioKMoQycMuUUwdnrGvzUarXQ7fZQq1VxfX2Nq6srye+R38s2WoGh86QKgGPKVGtEZ4oWdA44l5b+aJqpP4wIMcyvj8Ft02kposm7lJ0QDFqK0DqggzBQiD4FANhsSphMpphMptFbHUwIh80mX8dVFGCFAoACo6jXmb/lULoYkso6bnIS2j9qyQcPDvHKK4+y0437GI2GsX0pJKOftY1qvqhfhHVUKhXs7e2h3e5kgS5DzGbTuOUYQCEPAAUchS83SRHC6hZmayOz/SRORTJhA9Qg1qnXuXyq+xhUsPBeMlW9XsfDhw/R6XQwnc5wcXEe4y5U4FjHnvqQrKmlUJ0O0IBamlgsljF8XAOPUj4rFTgqnJxzcUw1Atbm5VQaIA3Z0H0V8opMid74rslkjFarmR2IW8V6ffdj6nZEMHh4n0cuhrBYajifOVCW2GyWCGMRovQmkyFmszE2myVCotcyeCI2UHTWAfmSj3P5OQnKGKplVIDYOHcSHO9TWG+1tsLBer2OdrsV7e/pNA/1ZfuUmBUdqJZnUWel1TL55qlN5pcpBikp/E1pJ/abAkNtfQ3+0fG1wU7r9Rr9/hX6/autsWdbABQCmXjKE9ulYcTeezQaTRwfH8ddm3Riat+sD0jn1qJD63/YbPLdjqrR1SxTh6iaeHZs9d3U2kSXqqh0HjmOWj/9CABiXy2NUnDTCRliSPJDlN8zU8I5VwbwqwDe8t5/nXPuDQCfAHAI4FMA/pr3fuGcqwP4EQB/CsAFgG/23n/uner2vph8JaU1VTIHLbbAbDbbSrgRBETYbm2JxAatkOjscqQiC62bGkMzLlkPuDKzEguvzeeLLB/lHOfnZxiNRoVNS6qNVCNoXUrcKpBUYAU7fxgJnlqYRKZCRldW1KRQBySJTn/T72y/+jdoA1PwacoyIgDCce5HoZ9I8yVQOIU9FmcImZXCLstOpxPnQueA/5WOSAfWPFQmVoSgY279BJZOrXlHptRt3mpy6XtVEahfhzRLM0yPs7fv03kLjt1hdsRBq7CEettyF8Tw7QB+F0Av+/49AP6F9/4TzrkfAPBtAL4/+3/lvf9i59yHs/u++TYv0AmzDh0dbP7GePyb7CfagyzqcVctokxGracDzXYwwKdSqWwtt6VgocJULrFdXJyjVCplMe2zAgGq1teJJ4NqTgnVAJbQ+Z12O59Rz7hCX2obG3mncSC6ZVwRkgpCXVlhKLU1z7S9qqX1NxaL0NbrNYbDIVqtFnq9Lk5OHqJWq+Ly8jI6R22CXq2L86jOPzK6RsGyDvX3qNCzaMD2i+On42lNWj6vSkTnRQPT1LlKPrBKTGl6swkb4Pr9K3jv0W7nO2JvW24lGJxzrwL4SwD+KYDvcKG3Xwvgr2a3/DCAf4ggGL4h+wwA/wHAv3LOOZ/yBsVSXNbhoOoylXOu4BUP5kYRSVQqIcrOewcgJ3Teo8t/VtPrZyUWYHvfO+MPVCDZZbK8nfmzCsfZR0b+6fBY1MH2sV7bbmV4ZQp62cmcuuFI4agyHglPozktLOd8qBAAtr3rvMeaO3Y1RwWadYLm/qZyVAaE+jwefr0u7m7VMVcBrX4C9kU3fxFNNRqNCMk1AlZ9ThaNqEmhPg3OjZqeKhjYV6UXjqnNp6G8oPVzbJSOGNkazK/6e7ZX4nsBfCeAbvb9EEDfe0/OeBPAk+zzEwB/mHV05ZwbZPfnO28AOOc+AuAjANDpHEWGJUOmGMxOOCErByjcTs1bzLiU0uj8z88qcXUy2TZNqMJ2puSdvkthsWp6MoE6NlVLWSco26dtVq2haIOEr85XS3wWDajpQ9TE/rE9aoKEPAo97O/vRWdaCAoKK0gaV2ERgTI9i2p0hfgqGHnfbDbDcDiM0YCM9iSKA7DVJx1L66RlG2nSha3M+3CuhOvrWWQyHSv6N2whzdGZStpRZlaUaVehGA/CudJ5K5VKhdwTugKm/WO9edRqETnfpjz3bufc1wE49d5/yjn3wTvV/g7Fe/9xAB8HgOPjL/YM97ROJEtEueRNw/cw8SVsNrr8WUztrZpQ/RosKXgGFCG8dS5q22w9lnGtttFnVfgpU/B5/ayCzz6rfhF+1nBa6zdRYmU/dZlLITDHJdj6zRgtGWz060JoOd9l604JfhVs2l/b5vl8jqurq+jlzw8pztPXCZ0Vxp4oyppf3uerAKHeBvb2epEZWRdXUyaTSVQUllZ1rHSPh9KpBtHZJVn1/6j5w7HQbGBWIVi6ocK0vPK8chsx8tUAvt459yEADQQfw/cB2HfOVTLU8CqAt7L73wLwGoA3nXMVAHsITsh3LCoMUjasmhckEquVSqVKoT4Lw1X78ZpCSxsERaagtFWmU1SRMiEUIvOatTf1XqBos1rGVqFgx4UagoJB32HND9XKyixqN1OgqulD5mbJBXDQ4KPRGJPJJDpTrXlgBYPa31YAaz+1/RqVeHUV7Of9/f04R4TfKZTB70QGbJ/6eIgEyuUyrq9n8bjBVqsV/UrD4Sj6buwcc9x0Q5guLdrgKkurnBeLXC2q5bssDamAco4nWTWSPrDnlecKBu/9xwB8LOvABwH8Pe/9tzjnfgLANyKsTHwrgJ/KHvlk9v1/ZL//vE/h7eJbAIQ4BO/DykJgPloqIT6BEVxhKTMcOxf2RGyw2XisVsBmA3gf1ul1EDloCqmUsaw9rcRF6c4JUohupTSLmi+WMPmcZeKb7FYSi06uPmN9JmoiqD2sTK/Cle1QnwrrsFGBtMHb7Q7K5TIGg0F2RsOwwDDaPxWcbKNqaku01sSwwpsKoVwOW7bDwT7tuGvTmoMWnek8a3/ZVobcc0MZ4y7G4wnG41HB5lcbn/3iO3Q+rW9C/Qvqf+D7+TtNJcaAUABy6ZeCTk9vdy44yjudDvb29mISl7uULySO4bsAfMI5908A/DqAH8qu/xCAf+uc+yyASwAfvk1lJCYAW5paJ0EZkgNGSBd+y7UmixKGdY6pYNCdiBbiqXbQtlgbXu9RCKwMod+1DtZrtQF/SxXWpczUaDSwt7ePXq8bN9+Mx+OYWFV3IHKbMRlAEZjCXbWdQ9ThMgYocdmYzKaoSxmUTMG5o3blPYqKdIytdue4LJdLXF5eot/vx+hJ6wS0iEPnSQWVjj/pkGd8UDDq/NkVAyCPNOUzFCBst5pQ2i47/xTq3IzGHbjcMq/0pfOpdZfLIas4VySscHxeuZNg8N7/IoBfzD7/XwBfkbjnGsBfvlMrkAsDdbykGJCOvNVqFTddqalAdGFtdcuAqhXpUGI7FL4pAysT6kSrdgSKcFyhphKBrk4oM6h9bRGE3s92eu/jMio1+cHBAQ4O9mOa+fPz860VA21bpcIowHlkdABRYxKWKtwej8cF80LnyTp71WQgYatJqPfxzybRsdCav+tBQKpcWI815xTt0aRJ2eZWSISw44CWJpNJgYZItxZ5qqan/4xa3yJQpWH2g6HZ6/U6bjXXFPmkA1Wk7GutVkOz2SwI/buUHYl83PbkKyTU64AGfgQbkNFgIQLSwfscsitc1QnXOlV6a8yAXZbSyfQ+D3Gl41S1nyV4y5TKVFaL6fPWxuR3hvS2220cHh7i4OAgLkeyjvl8HjP4ULsr8dJrPRgMIqExXwS99YrQVOunEJUKczK/RVQqgIsCvTjHKSc0iw0yY10ps45jZYWp1gUUz/ekJtYsWyH8foL1ehXn3Z5SzWcpHMvlMg4ODtDpdOH9JppbNAds0ho6OhmQdnV1hdlsFu+1JpI1RzkvVBSdTjvSxHuKGN7LYiX7TV5cEly1WkWv18V6HSZ7MBhgPl9gvd4OTVVmABBj89WutETNyVOYxraR2CiheeAttaGt14Yys7+qwZTp+Lu+j0KL94Z9Fw9wcnKCXq+H1WqFwWCI4XCA6XQKAFFgjUYjTCaTAorS+oBikltqUc1doIgB2E6Dp/2yPgEVyir4LFri2KvpwnFUWuCWbxVOmm1K26H3cuzUVEr1YbPZZMjpGrVaODKO51dowhO2hXWktDJPBlsswlgyoU6lUsVkMo4h4KqEWNd0Oi2kymN/1IxhX1W5NZtNHBwcxCSwmsz2tmVnBAMnKgXhLWznZLTb7Ywack7kAAAgAElEQVQYNpltvMByucBq5eNAWqcc61R7Xr8D295su/JAIUWnj3qoldBVe6UYXQWXhaEKo/W3arWKvb09PHgQDmYpl8s4PT3F+XnYUKSnPjF5idW+LGq+ADkTqxOSbScctpBbGdFCd4XiypDqhwFyCGx9MipYdRw5/pp/QhPMEMZbelLhq6aU0pa2IZx0lidGYdAT20fkYpGU0tJsFg7FGY1GmM/nme3fjFvfOQcca/ZV54T9Tjk2rVBjNvDDw0M0m82Ifl5KxBAmdIFSycO5KkolooaQm4Ebgbx3cI7wyQEoodms4/BwD2HVdI3BoI/1ungGIO12JRBOthKhtkf3L3CidMLppdf9++qHSGlDLTrJKniUQLRdzrloNhweHqFSCSsCl5eXkehYrxKOeqktcuL9tu3AdjCVdaLyHjUh2F/N8ERNqHNB4aN+ntT4qRa20JkBRlzTt+aDCh9rv6eEJOtV04BzrPVyzlImYcrW5wG3RJQ0ORkDYlPwWz+H0oedO+0TEITCgwcP4g5UPqPC/7ZlJwSDc2Ej1Wq1Bg9j9b4EXa5kqjadFOeAWq2KcrknjLjEcJg7uZSROYne+4K2Yb2lUr6vX+8B8kAg1XyqdYDccWmhtBIk36Wa0/pAVLOSuZrNJvb29tFut3B9PcPV1VW0QRV2K+FQw3OJkSinGPuRB/fQMRbmJCe6VM4Bi3CA4rIfBYDCdWVGFSpAUSPyHjWdVKjr6gnv1aAljn9qtyrnR4WUc0Unns7narVGOLgoncyGfeFzXHGhGWr3oWg7tB67bVzpQs1aII/Z4Bg556JQODk5QbfbLSAqSxe3KTshGHQlId+WGv42GzJ4Gc5RKueHygRmrmUOHg8gaKvhcFgI0AGKvgtd8+VAW22o2lRRhrXHLeEB26G/6o0nEeQpwNdbxMZ3My0+16LPzs4wGAyibarviqMp/Qi+mF4M0LFtpC9BtwPzNxKyLhMq0apwVAiuJx9R2PJ5vU8FifYh1caURucYaro3iz4AFM63UMGhqJErJmwzBeJqtUSpVAdTsrOv2i62lUIBQFwp4XUKXDVdFZlqf9lP0m4K6Wj/Go0GDg4O8PDhw2hesj9WgN627IRgCINcgferCN8CElA7OzhxQoc3GcHltnC1WsmOUQOazRZOT5/h4uKiYP/rYObvLcftvzzuDMhXJXQ3ndWcChnJWClIq0LDalllBhVGZEASOdtGaKooQ98HFJdCuYypiVFYdEnQmgwWCWj9bK8yMO9LCUagGB6udZIBU89YYXlTHIAyFh3LGvqsgoR+AYsItU+KJmychTVB7BipGaLfU1De+lxKpXw/ivo91KQiouT3er2Og4MDnJw8wv7+Xnxe5yNlyj6v7IRgAPTwTg4oiSlENXq/QTjPsBx/A7gEtcnW4hG1a7PZQLVaw/n52dbmJ51gMi4JidqcUMza6qpxlLnVbubkKfzVYu16AFsEz0mls4vtpCBTcyZlqrBOMgB3g6pgVE1pBRfHR+G29tXa/+pD0IQsuk+A7VcND+QMQo2cyoXAovOoz3pfPESIc6kmoHr8VftaU6DRaEThMp1OI8PyHSkHqY61jqG2U+dbkY0KD0Vf6idRhGXNh6OjY+zt9aLAs7SntHHbsjOCISXRQudCDsfNhofPUGKTYUkYALBBpcJcg4eRSK+uLqPGVQLmIFonmmo1C0EZogrkx9urVrQOKqtltVgC4/M3mTlqY2u7LBEosXEHoK71c2zVEUhobZGI1ZI32aokbDrUCKOZM0PHnf9pBvB3PV+Rppf6dezYantUIFmEps7BzWYTA49sfRofoQ5DtpPBQqVSaSs1m5pK6tRW6J8yCZTWrWDWQCgVDBQKXH3g6pyl17sKAy07IxiC2bABVyCIHMKqBJlY8xoGp2WONDYANlivc0fU/v4+Wq0W9vZ6ODs7x9XVZYzqU9tfmV4HWAWIogJqJGYsDu0sLsFZ5rLIQH0LyjQqGHi/OsXshNOGVx8In6MNy+cY12BtZAtP1SGm7bDaW/f4q/DM57QcI/54P+vTPQBqClj/AufWmi38rVQqFTY6qQ8HyFOhWc1OQcB1fm5n5jiq0FekyESuyuh6j+2HXk8pBqtAOPeqENQ88T6k2D88PMTR0XE8i0NNDZ0TFZB3KTsjGIB8U1ToSHAuVipllMvAcrnCculRKvHQlIAaAGC1ChuuAlMjLt3peZY8BPX8/KwQqKIET4YBiiYHv/O/wm/Woffws33WOo70/ps0C1DMq8BCSKsrICzqrANQIHqF1hQcSni8R/uhfVQoSyGpdjqTqAB5WC7NA61TNT3Tn6kHn+1PQXCdG5oPFG4UROpQDadM8Zj44ro+36nOV6t5adJxrNSpqDTDZxURWDSmqISmh/ZRxyk15kTDJycnBaSgQkmVj/UX3bbsjGBwroRSCSiXg+YHcgHBVYrNZpHZjflgB4ESdl4GgilGHHLCe70e2u029vZ6ePvtz+Ps7DRGA1qJrh511UyWsVWDWqHA/0o0qpGsNrIEpnWraWFt781mE1GQtluXs3TJ1RJNymGoaIn9sOaXIi62TW14Cp52ux1P+La+Hr5T1/K1net1fo6ENZfYT9r+ekITmW29XqPVauHBgwd4/PgxWq0Wrq+vcXZ2juFwEIWQ5pPQvgFFprQrI3ZuKay5AqEh83yOfafwUsSmyFDHnuPLiMaHD08ke9U6Kcito/clFQx5XELY0cbB32CzCeZEEAQVhO3Vq/gcpbJzyFBGrvUIhzmJtVoNR0dH2UlHbTx79mxrWVM1mWpdJQwLE3kNSCdwsc+oBuE9qoV1F6CF8vocl6WUuUmgKSeWCjZbrIOPwkCRlDUdtN22rQBiavhWK6C1xYLr+qu4RZtt5HOqbZVJtQ86D1YJ2HR7bAfzKMxmM1xeXmA8HkfTgP2iELLmDOvn2PJMC7v6wHcye5MiEBUG/FP/khXSFAi8p9vtZmd0BPNYncNW2Nql9z9K2RHBAISU76EQPVhtV63SI7zGculj4ElwUJYBLLFe534Dtb2pTXjaUaPRQK/Xw8XFJfr9q7jBRX0OanurEOBntR8VWVjkoQRsGV0heconYTWHFShKwAqtScz2XbbotZTQUI1lNyGpsOM46/gA3Jy0RKnksFjMt7Qi7fmUOcb+276pgLTzoX4Y+l9msxmePv081ut1zPLE38nwTOmvAov1KB1WKuEA3Pl8HpEakG+8YqIX+ld07jWozqJPHX9FYpVKBb1eDw8fPowb5ay5aU0GS3dKk7ctOyQYAGTnU4bOlBGWI5dxRYIEv1wGrVMqMU4dMbZBJ7JUyvMHADn0rNXC0WndbhcnJycYDAY4PT3F6elpthlrXoB9/J/SvsD26oJ19Cij6K5C9c6zHrbdCijradcVCPbNet/5XzWI9kt9Fyl0Y4lWbWC7AmLHiW3hmZDcZ8BITetH0ASzGrFI/wOwfaaomlQpCM12qSNRhbi2tVarb5l4Ord8tlqtxuVMmkaqODiHaoKoT4H1aZCVzrWWWq0WTQemmVOEonNmTV7WzbF6aZ2PoZPsqIsMn+d2VG0dzIbQcZog+XMWJXAy9fRqIPfot1otHB0d4cmTJzg9PcXTp09xeXlZSACjWkmjyRTSsaimtto170NOSGQWNR+sFtZnGcSjeTJ5j03Zbm1nEiXHhI5BLs1ZBuc72S6OmyaxVbgNFM/61Ph/zs1sNotMpuG9mgRYkYyF2FYYqWDQttoQdcJ8e1AuAMzn15hOpwWntDUnNptN3AjFXZJ6boaaYywWvVBx6YqDjrX34Xg5noh1dHSEXq8H51xhlyTr4bhpDkjOQalUKjhN71J2RjCEiaIZwYAZJ3ELZLggJALT+AymMoR62za2y4nqCLIHmB4fH+Pg4ACPHj3Cm2++hWfPnmI0GkVPu7U/WY+V9EqolmD52RKwPkdBo6dJ8zlC2W63G4UKn9PVEhIjmUPjLZxz0ZRar8NZDRoRyXv0fo3SU6JO9c1mxSZEp6bVXYqKaPgMl1j1BCYGaCnhc8w4zrrawjFTm55okVpcz7/gYS6qze3eCaIZKhvukLRIwyITFaC2r4pGnMszVB8cHKDb7cYTwoHi5iylaQb10VRKmXt3LTsjGDabnElDCYweNk9Z50qpsEau0tgSDuE0d+IxqzGZk1lxdGnz8PAQ3W4XT548xrNnzyKCoPQlgbBNqZUNdZ6poLL2odqEaqJYU4X3087WPAtKcDpOWnc+zkEz5enerzEejwtMkPJ3WKGg2orMaOEq+0+CBYpLmKyT79HlRt7XbnewXoe+UkhbpMCxIQ1ostdSKT+UlwiN85ebpsuCc1EdejommkNT6UyjIe18WZOL80Jhoiir0+ng4OABHjx4gP39PbTbbTSbzYLZlVI4KhDpYLX33LXshGBwLp21iZ3WQed1/uczmuQ1JVU3m02MfuSAM77Bex+ZrNPpxN+OjsJJzU+ePMHbb38eb731Ji4uLgoeZWuzWiJgO8lsauvyTwUDiYyhxLoqoJpsPp8Xsv+QsVMErc+rTc6TsGz7laDVT6FLoM65mIiVyIX3q9miEJmCUGMXUsKSzBqyHVUB+KjVGXHIOVfHJFDMoGSFK8dEc3UoElGfhfqM7HV1mOp71ERVPwqfUfNT/Uj1ej06GB89eoS9vT00Go3Yv9sWNUvsuN+17IRgQHYgRsqxohOjTjf+RqbSiWTR+8hw1PoUELVaDXt7e6jX65hMJjFpKhFErVbD/v4+ut0uHj9+BU+fPsWbb76Jfr9f8EGwHSn7mNffCdIpwRD6UvCx36yDkXrWF2FXMlSDKZGzDoW36kDVObB2NjVrs9kspFW3UZOsj22nJraRfWRUrvczGxZTzk2n02hfEybrWOv46ftUObDd1oQjc1KYqBlG85KC2MaScPVCFRL7rxGjqtCs1q9UKmi1Wjg+PsaTJ09wfHwclyJ5n20n67E7gknbKuxVgd5VOOyIYNhedlHtocUGu1DzKGECxYNUOKGcxNVqFU8XarVaqNfraDZD/kjC3nCGwDASSKPRwP7+Pnq9Hp48eYKzszM8ffo05kRQAlOBxb6o/Z9yllmBZomIzxAWW0RC+KuftS1K0JYBFKrSXFDmsxCZwovmmWpyDStn4TwpsglnHtQFyZQRTjXP0QadbfS1qBNQmUTHVJ9X5KNjZZnECnL2k05eIj31J63XeTp9G2Ckc27pgG1uNBo4PDzE48eP8ejRI3Q6nYJA505aXd4kDVGIst2r1QqTySQ6sVUJkBZeylWJlCK1drgKCmUw2n3qZCIxqb1IwaGZezebTUQI1Wo1mhD02POEo+FwiPF4HIUHQ2xfffVVTCYTXF5e4uzsLGZTsge1qBZWZKCwXvtLu121j7XtrQAAts80UGgLFG3flE+E79ZIRApr1YIUnto3Fep8F6+z3RQo1h8SoHx+kCzba4OAFM7bFQfVrvzMACZ16qZoTMeEY6GoikJA67BohfVZ00h9AqVSnqvz8ePHMamKmkGz2Sym+QcQ6VFzWmgOCh4NQFPObqW3NHLbshOCgUW93Sk4Tk2mQoD2KgeXEFuz3SjRKsNxsDjx9JzTvmu322i32zGbLx07hIBMkLG/v4/XXnsN/X4fz549w7Nnz9Dv96OXm4yRIiwVWgqBVTvqOCgjW22gz6j/RRGMrnQopLfmCp2AGlegRdfeiTL4brXtyXDWM886LGNqH/ib+mVSyW1Sfihr+2sf1CnKezkXFq0StagQ1L4qXXGOdJ5YF7dIP378GI8fP44JVXjParXCeDzG1dUVAGSH6LSiOWPNo+VyeSNKUPSkbb5L2QnBYLWWSm8LAzk4nFzdTqxLW9am5POsk3/qVV6v8/z9TIfGVQzmQKAAUV8Eod3Dhw9xdHSE119/HRcXF3j69BkuLy8wmUwKqcZZ1NlK4mab1aZUjatjRg+5evftuGrd7K+aEJrKTRlHGRPINwylIvdSTG0ZktpfNZrWy7qZzFZ9BVq3pQlLP/qdCoIrEBoJSwWiPhG2V5UJhZKucuifIhmOG+eNy6OdTgePHj3Cq6++iv39/UJAG8/cpPnKYC8iWCuUaQbzXA+2166AaazNXU+6BnZIMFD7W1hn7+M9ygg6qSQgjXajncg6yBj6Dmoewt31eh2PX+O2XmZDqtfrMQkJk5I656IvotPpoNPp4PHjx5hMJri6usLp6SkuLy+jc8jGHNh+qLOVzMN7UsVqakUKllmVqNlfK5DsEpku4bFNwHawlgozJVqNRi2VStF+Vx+FOtlse1KxEdYJqehJFQiAAsPbBK+KKLnxyXu/Feyk/VOBwfFnu2iK7u/v4/j4GCcnJ1EgcJx4shSXFpV+wzJtu0DPNN8mk0lcstXlUzUxtV+6YnOXshOCAcgz7NpVBqDocVbCSQW0qF1J55gODgNd1L63Zgbj0UulkJCDk0EEQSIj3GO26MVigdFoFAVEtVqNpsbjx48xHA7j4S+Xl5cYDofR055iBo3pp51p7W7rL1DNyrEjw+lZGMD2Lj+OuxIU61UEkwrT5typoFN7n+NZqYTEtt1uD5vNJq4EcbVDkQjbyDo034IyMtudihuxK13WaflORf0/dts4/+s8VSoVdDod9Hp7Wb6E/CAg51xEm4wyLZfLMXaG9ZH+KIiJECaTSRRoKXTG6xw/FdzWJL1N2RnBYDup2g8oesf1mmorDXMlc/MIM2o9C7nVrFBtx3srlUp0JuoSIeujQ5IhsmRCCgvCQvoujo6OYnDS+fk5+v0+xuMxxuNxNDfsrkO7dGmhtRUMtn8qWOjE0rp0TAg9uWJAbc66KGhUMKRWCHR1iM9wPPnsfH5dMLH4rJ1nFS6271Y4sQ+WRoDiGQ3KjGqKphy11lejCIX96na7ODgIZ33s7fXiNn8y6Gw2i34nCsd6vV5IVqP1ElFQIGg77YqVPqv+HusHuUvZCcHgXG5ObDZ57j8SlCU2qxn1Ou+nX4CQcLFYRIJQrWSJTQlOB1kL61wulxiPx9FJRIFUrVZjmjI7QVz+5IExtC1HoxH6/QGGw0H0MusJxppcRKMureml2oK/cRswx0eDkHi/amAGAGl6M9atY6PvBIqh2/S7UDgDxaPlrq9nhdOx9c8SuhVI1rbnfeq5VzNKx0WhOeeE92nCFyoD3ksNzz5yLpvNJo6Pj+OSI4WfHnhDJDmfz1EqleLKFpGt0i43nXFlgu1mXbYfLIpuUgIjZZq/U9kJwQAUgzmAYhwCO2oJXhlaiZZ10azwPvcuc+A055/VEnyHet51IpQBuMTEpC/qh9DTj1k/tT81DWMowh6NRYTW/f4A/f5VRBIKZVVLWqFFxKTmFe/XQB0dZ32WfWJEoIWl7L8KB2VeKyR0+ZhmGM0uu39D51KX5FifDcrSCEYAhRUL9lsFq66GKMpRxGlXKHTMeH+lEjKS7+2FTU5HR4cxBkZNV6UvRtSq4GV/GYdA01cFEQXMTUUZX00eFSa6OnHbshOCwfucMDi4mt1GzQwLe/M6cvSgZgcZRU0A+7w+m4KxvFelNoUM7yHzUdNwDwMJgYJCVwHW63V0XFJAcBPN4eEhJpMJJpMJRqNRNDe4bGptS9WEQHFzFk0a/Z3tJjOoJtaTo2nXqwBQQW2PaGPhZ/pbWBc1In0GimbYJh1zvWZRgGp4Xk8dCU+mp41uhQIZisLDChj2tdVqYX9/P25y2tvbi+1POZP5mT4pHXcKq+l0Gjex0SFLpErFBqAgRDk2KbOHf7o6cVf/ArAjgkELJ1IFg/WsA+kAKKC4Z18Jmh5wQkbr8SWhEi5SYgPYEio2XNbmWND20Yxh/AOfZTCV9ayzcBk02K4H0ea8uLjA2dlZDKRSorDCUZfQ2B8SKAUwizWveE2FiUJuEi2Dy7jESEag1iQys0KExWZb5ninVimURqwQVwHAouiCQkOfs6ajFfbOuRivwsCk4+NjNJvNAt3ptma2neOlvgv1HZBhqUho+toU8NZcUOFskY76SFJ8cpeyM4JBtQQHDECBefQ+y4BaLFPo8hqFgzJ7ylazKxXWJ6EedHXkaRuAYjAMVyDomeZEKiTmRJP56K9Yr9fodrvodrvY399Hv9+PSIIOKhKTCjUdN4X/VrOwqK+BfeBnRSisX8+QZF0UxLr/JYxrPjbU4HpuRmpO1XwE8rTynF8iG2uW6Bxr9KsKE17jc0R2FAYUzHQi8nciDDXb7DIp58MKG13S5LxbmtZVHx0bFYhKo+qH4Hso9C1d3rbslGCwpoJ65hXK6zU+q84wHUyFd1q/EqzWo85OC7VTGlUzDKnDVOEs36k7ILkpiPY04T4FAwWCMhHr7Ha76HQ68D6cUHV1dYXBYFDIHWE90SmvvrXv2TYlOv6u46E+C/6uXn4+r6nMQh8mcZVI512fs74K64HXuVbNr+hQGUeZQs1RRTBcJeh0uuj1utExqP3XvBZ8XpUIfVq6Bdv6cxgjQYGiPhXrz1LaZxstP2jMiR0bpRfllduWnREMykhA3hFFDqrFUgRlS8pTa7USJzZla6eeSzn+OHFqRyqKUPSgdi/rYB913Z+BP9pP1Xbq3T44OIj5FYbDcOQ6fRHq+VdiUkesjoe2X2G3MqFtjzq5dDyVAWjD26hGvsMKJn2nHUuOnc6ZzomiIwpv0g+doK1WOzutrBrvoWmkWp+CQNGjXUlQU47X1EFNbU7zU/tlfQUqVKxCU8Fpx9+a3Vqswr1NuZVgcM59DsAIwBrAynv/5c65BwB+DMDrAD4H4Ju891cuzPD3AfgQgCmAv+G9/7XbvCdlT+kgq29AGY7PKupIaZ/UNV5PeZRVu2oQkLZV4biudKjG4H32T4sG7SgRWoLXwBf6RUqlfHXj8PAwLjNOp1MMBkOMx6O4sqHZk1hsEJD2Qb9b4WidWur9Zp2MJ6GvRYlf91noOGvdakfbcVMbnkJZ0UCIHQnHFTLgjL8R4WiyFCIgDZqyZqM1KZVJ9Zr2Sa+TXrjCYmnG0rHOC4siQhVWKqh0jNmOu5S7IIY/670/l+8fBfBz3vvvds59NPv+XQD+IoAvyf7+NIDvz/6/Y7GMyGvqSNTMPEAxCYUdTF22ScEpvkeXdcg8fL/dgKXv4zv1HSQe3ahkHVNWMKkWJDNQE5JoVAto9J8SAbUzTZBarYZer4fj4+O4NMhVEPomSJyqydWLrfOg/Vd4bIlO0YMKDuso5Xjoe5QB1EdCf4sKkXK5HBlfd9JWKvkxgoowLaOxLZyf1H4KZXLOj5pPOhasQwUI51LHSlGYjoO+R5UN26vOSPU9sA26FKs0ruN8l/KFmBLfAOCD2ecfBvCLCILhGwD8iA8z/D+dc/vOuVe8959/p8qsVrcdVK1Nh4/+bidDIVfqHqvJNaafhG0FScqMsVAwZTYoIknBPBaFwZxwqxH4jMb+q1mgGpfatNFoRMLjPRrXERyIc6zXK9nME9L0p6C5MniplOdYCKilhWqVGZ8r8XOKOEM9eYAbUEQBOgYa08J3q3mpKCPlbNb501gC9aWoAFcBwDo09kOhu6IVO7eKAFUBqFOa88l3pWjNFq2PzmoVVkpXuux523JbweAB/DcXEjD+oPf+4wBOhNmfAjjJPj8B8Ify7JvZtYJgcM59BMBHAKDdPix0yjI1y2ZT3BWXsvlIHLosB6CwgUXrVQmsQTWqCayHX5lUiU4hHv90whUOW1NCn1cBp3s7UtpE+0piUQGk2lGfbzQahb6oQzEF57WdLOoJpwDS8N4UUrupqNB73j2KLFToKjOn0AHbrMuCegiM7kWwPg2OFZGcCkhtGz9blGWhvjK+daaqMqHQ4DI636nOT1UEOm9Ej/V6/R2DpFLltoLha7z3bznnHgL4Wefc/9Yfvfc+Exq3Lplw+TgAHB297i2svKmoJ9ZqZZXOKW3AidUNW2qrKUwHiolCNDCJ9emkalFUQmmuPhPeo+22zysKoQnF3/heXSJkfbxPn+dzlii1HeyTc24ra5GN7+B3ZTK7VKbp6BWtKNHyvSqcbJv4XbU036OCTrM82TkgOiISYV+m02lhdyNXg9hWvlfjWSxSVbpjUbNQBTk/22X0lFJQZKHfrQBSpKgmBiNw2VfuFL1tuZVg8N6/lf0/dc79JICvAPCMJoJz7hUAp9ntbwF4TR5/Nbv2vHcUJsNCbHuf1QgkJitJ1Wud0oDq2LR1kBFIVFar2Xv5TpXO7BPhq06u9Tlom1OrJ/oM255KL2bt3BTzWw2l//ludUrqGGp7VBjxGUZpUgNrf6gF6ZTk2KqDUdvPsSOhc6wZAwHksS66AnGTiUbkpqnTNFaGgkCXrVWgqyZnSfmOlJZVqFuatgLTCnR+toKfgkwVG6MztT9MMqTBbLcpzxUMzrk2gJL3fpR9/vMA/jGATwL4VgDfnf3/qeyRTwL4O865TyA4HQf+Of4F8n1qAJVp9DtQPAXaPmuv6zUlcqud+Yz3vrA2r5OTElT2PhKYEgw3Vql0B7AldDR6M9Uni1BSqwpWU6WgJJmOQkKFjz7DPuvYE8nwuzKnRvGxbvVDcKMZd6AqilMBrZvSVOCTCbgJSX0S2heaClyyZf2cB31W6UWhP+dUnYgUDrzHmg32j0VNOp1HvdeaPuov0fgYpR9NXMyxZE5TNV/uUm6DGE4A/GTWiQqAH/Xe/1fn3K8A+HHn3LcB+AMA35Td/58Rlio/i7Bc+Tef9wLnsEV0LBZCW5uOv6sTCtjOGWDr0PushtR36449fa+2lSiBmkbzKLBemiq8bk8H0v4oysnHqJj30mpjhajsl2phXldv9k3oTP00aoKQ6ZWAuZrhvY+EychBnSsNArNzo+1TcyFlpukYqG2uB9hosJCF+cqAWr+uOmi7bRi1tl/pJEVnSlMWGaTapfSkwl2Fq7aDW/kVIczn87iTk0JV33vb4u4qSd6L4pwbAfjMi27HLcsRgPPn3vXiy8vSTmVvLDAAAAQjSURBVODlaevL0k4g3dYPeO+Pb/PwrkQ+fsZ7/+UvuhG3Kc65X30Z2vqytBN4edr6srQT+MLberc4yftyX+7L/xflXjDcl/tyX7bKrgiGj7/oBtyhvCxtfVnaCbw8bX1Z2gl8gW3dCefjfbkv92W3yq4ghvtyX+7LDpUXLhicc3/BOfcZ59xnXdil+SLb8m+cc6fOuU/LtQfOuZ91zv1e9v8gu+6cc/8ya/dvOee+7H1u62vOuV9wzv0v59zvOOe+fRfb65xrOOd+2Tn3m1k7/1F2/Q3n3C9l7fkx51wtu17Pvn82+/3196Od0t6yc+7XnXM/vePt/Jxz7redc7/hnPvV7Nq7N/ca8PN+/wEoA/h9AF8EoAbgNwF86Qtsz58B8GUAPi3X/hmAj2afPwrge7LPHwLwXwA4AF8J4Jfe57a+AuDLss9dAP8HwJfuWnuz93Wyz1UAv5S9/8cBfDi7/gMA/lb2+W8D+IHs84cB/Nj7PK7fAeBHAfx09n1X2/k5AEfm2rs29+9bR27o3FcB+Bn5/jEAH3vBbXrdCIbPAHgl+/wKQswFAPwggL+Suu8FtfunAPy5XW4vgBaAX0MIlT8HULF0AOBnAHxV9rmS3efep/a9CuDnAHwtgJ/OGGnn2pm9MyUY3rW5f9GmxE1btHep3HV7+fteMhj7JxG08c61N4Pnv4Gw0e5nEVBi33vPnT3altjO7PcBgMP3o50AvhfAdwJg/PDhjrYTyFMhfMqFFAbAuzj3uxL5+FIU7+++vfy9Ls65DoD/CODveu+HZt/DTrTXe78G8Cecc/sAfhLAH3vBTdoqzrmvA3Dqvf+Uc+6DL7o9tyjveioELS8aMfyRtmi/z+WZC9vK4d6F7eXvZnHOVRGEwr/z3v+n7PLOttd73wfwCwiQfN85R8WkbYntzH7fA3DxPjTvqwF8vQv5TT+BYE583w62E0AxFQKCsI2pELI2fUFz/6IFw68A+JLM81tDcOJ88gW3yRZuLwe2t5f/9czj+5W4xfbyd7O4AA1+CMDveu//+a621zl3nCEFOOeaCH6Q30UQEN94QzvZ/m8E8PM+M4zfy+K9/5j3/lXv/esIdPjz3vtv2bV2AiEVgnOuy88IqRA+jXdz7t8vZ8k7OFE+hOBR/30Af/8Ft+XfI6SgWyLYYd+GYDf+HIDfA/DfATzI7nUA/nXW7t8G8OXvc1u/BsHO/C0Av5H9fWjX2gvgjwP49aydnwbwD7LrXwTglxG25/8EgHp2vZF9/2z2+xe9ADr4IPJViZ1rZ9am38z+fod8827O/X3k4325L/dlq7xoU+K+3Jf7soPlXjDcl/tyX7bKvWC4L/flvmyVe8FwX+7Lfdkq94LhvtyX+7JV7gXDfbkv92Wr3AuG+3Jf7stWuRcM9+W+3Jet8v8Ak4IoB4Y30jwAAAAASUVORK5CYII=\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"iCISyYLajpLA","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1593989668615,"user_tz":-330,"elapsed":9333,"user":{"displayName":"saimanoj akondi","photoUrl":"","userId":"10529021043337064225"}},"outputId":"1357cb3c-9d9c-4d78-a8f6-0fe88283c192"},"source":["pred = classifier.predict([val_X ,val_masks] ,verbose = 1).reshape(-1)\n","y_true = val_Y.reshape(-1)\n","y_pred = np.zeros_like(val_Y)\n","for i in range(pred.shape[0]):\n","    if pred[i] >= 0.5:\n","        y_pred[i] = 1"],"execution_count":null,"outputs":[{"output_type":"stream","text":["62/62 [==============================] - 5s 79ms/step\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"irS1MFJ2Q8FD","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":403},"executionInfo":{"status":"ok","timestamp":1593989668619,"user_tz":-330,"elapsed":8878,"user":{"displayName":"saimanoj akondi","photoUrl":"","userId":"10529021043337064225"}},"outputId":"e6f34295-99be-4efe-d070-6a581bea2071"},"source":["accuracy = accuracy_score(y_true, y_pred)\n","print('Accuracy: %f' % accuracy)\n","precision = precision_score(y_true ,y_pred)\n","print('Precision: %f' % precision)\n","recall = recall_score(y_true ,y_pred)\n","print('Recall: %f' % recall)\n","f1 = f1_score(y_true ,y_pred)\n","print('F1 score: %f' % f1)\n","auc = roc_auc_score(y_true ,y_pred)\n","print('ROC AUC: %f' % auc)\n","matrix = confusion_matrix(y_true ,y_pred)\n","print('condusion matrix:{}'.format(matrix))\n","plt.imshow(matrix ,cmap = 'gray')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Accuracy: 0.693548\n","Precision: 0.937500\n","Recall: 0.454545\n","F1 score: 0.612245\n","ROC AUC: 0.710031\n","condusion matrix:[[28  1]\n"," [18 15]]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7f0dc8e26fd0>"]},"metadata":{"tags":[]},"execution_count":34},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"N4OXR7_cn5Uh","colab_type":"code","colab":{}},"source":[""],"execution_count":null,"outputs":[]}]}