6CL-3HL-l2Norm.ipynb

{"nbformat":4,"nbformat_minor":5,"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.10"},"papermill":{"default_parameters":{},"duration":10471.284473,"end_time":"2021-11-27T19:56:44.493599","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2021-11-27T17:02:13.209126","version":"2.3.3"},"colab":{"name":"6CL-3HL-l2Norm.ipynb","provenance":[]}},"cells":[{"cell_type":"code","metadata":{"_cell_guid":"c16de852-f104-4f04-b483-9092378e7688","_uuid":"e4cd0c5c-5372-4042-ab57-4c3fb0dc0b4f","execution":{"iopub.execute_input":"2021-11-27T17:02:20.422718Z","iopub.status.busy":"2021-11-27T17:02:20.421189Z","iopub.status.idle":"2021-11-27T17:02:25.520440Z","shell.execute_reply":"2021-11-27T17:02:25.520998Z","shell.execute_reply.started":"2021-11-19T22:15:08.463336Z"},"id":"d6b3673c","jupyter":{"outputs_hidden":false},"papermill":{"duration":5.115331,"end_time":"2021-11-27T17:02:25.521266","exception":false,"start_time":"2021-11-27T17:02:20.405935","status":"completed"},"tags":[],"executionInfo":{"elapsed":5871,"status":"ok","timestamp":1637231012124,"user":{"displayName":"Davide Mantegazza","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXw0xaICGwHW9LHKWW7gvtwEXpicWVaKdEREVR4f8=s64","userId":"01264624922693821428"},"user_tz":-60},"outputId":"4b3266f4-050c-4f19-cba3-24fe34dcdbea"},"source":["import tensorflow as tf\n","import numpy as np\n","import os\n","import random\n","import matplotlib as mpl\n","import matplotlib.pyplot as plt\n","\n","tfk = tf.keras\n","tfkl = tf.keras.layers\n","print(tf.__version__)"],"id":"d6b3673c","execution_count":null,"outputs":[{"name":"stdout","output_type":"stream","text":["2.6.0\n"]}]},{"cell_type":"code","metadata":{"execution":{"iopub.execute_input":"2021-11-27T17:02:25.546057Z","iopub.status.busy":"2021-11-27T17:02:25.543284Z","iopub.status.idle":"2021-11-27T17:02:25.547936Z","shell.execute_reply":"2021-11-27T17:02:25.548387Z","shell.execute_reply.started":"2021-11-19T22:15:13.953719Z"},"id":"4df1e7ae","papermill":{"duration":0.016105,"end_time":"2021-11-27T17:02:25.548523","exception":false,"start_time":"2021-11-27T17:02:25.532418","status":"completed"},"tags":[]},"source":["seed = 42\n","\n","random.seed(seed)\n","os.environ['PYTHONHASHSEED'] = str(seed)\n","np.random.seed(seed)\n","tf.random.set_seed(seed)\n","tf.compat.v1.set_random_seed(seed)"],"id":"4df1e7ae","execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"execution":{"iopub.execute_input":"2021-11-27T17:02:25.572738Z","iopub.status.busy":"2021-11-27T17:02:25.571913Z","iopub.status.idle":"2021-11-27T17:02:34.550331Z","shell.execute_reply":"2021-11-27T17:02:34.550927Z","shell.execute_reply.started":"2021-11-19T22:15:13.963685Z"},"id":"f68bbf6e","papermill":{"duration":8.994337,"end_time":"2021-11-27T17:02:34.551138","exception":false,"start_time":"2021-11-27T17:02:25.556801","status":"completed"},"tags":[],"executionInfo":{"elapsed":470,"status":"error","timestamp":1637231018683,"user":{"displayName":"Davide Mantegazza","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgXw0xaICGwHW9LHKWW7gvtwEXpicWVaKdEREVR4f8=s64","userId":"01264624922693821428"},"user_tz":-60},"outputId":"15aa5b27-ea0f-4a43-dd0f-21529e8fd18b"},"source":["from tensorflow.keras.preprocessing.image import ImageDataGenerator\n","\n","data_dir = '../input/leafdataset/training'\n","\n","train_val_data_gen = ImageDataGenerator(rotation_range=30,\n","                                        height_shift_range=50,\n","                                        width_shift_range=50,\n","                                        zoom_range=0.3,\n","                                        horizontal_flip=True,\n","                                        vertical_flip=True, \n","                                        fill_mode='reflect',\n","                                        rescale=1/255.,\n","                                        validation_split=0.2)\n","\n","train_gen = train_val_data_gen.flow_from_directory(directory=data_dir,\n","                                                   target_size=(256,256),\n","                                                   color_mode='rgb',\n","                                                   classes=None, # can be set to labels\n","                                                   class_mode='categorical',\n","                                                   batch_size=32,\n","                                                   shuffle=True,\n","                                                   seed=seed,\n","                                                   subset='training')\n","val_gen = train_val_data_gen.flow_from_directory(directory=data_dir,\n","                                                 target_size=(256,256),\n","                                                 color_mode='rgb',\n","                                                 classes=None, # can be set to labels\n","                                                 class_mode='categorical',\n","                                                 batch_size=32,\n","                                                 shuffle=True,\n","                                                 seed=seed,\n","                                                 subset='validation')"],"id":"f68bbf6e","execution_count":null,"outputs":[{"name":"stdout","output_type":"stream","text":["Found 14189 images belonging to 14 classes.\n","Found 3539 images belonging to 14 classes.\n"]}]},{"cell_type":"code","metadata":{"execution":{"iopub.execute_input":"2021-11-27T17:02:34.595651Z","iopub.status.busy":"2021-11-27T17:02:34.594622Z","iopub.status.idle":"2021-11-27T17:02:34.602578Z","shell.execute_reply":"2021-11-27T17:02:34.601752Z","shell.execute_reply.started":"2021-11-19T22:15:28.343119Z"},"id":"f4c835c6","papermill":{"duration":0.03223,"end_time":"2021-11-27T17:02:34.602742","exception":false,"start_time":"2021-11-27T17:02:34.570512","status":"completed"},"tags":[]},"source":["input_shape = (256, 256, 3)\n","epochs = 60"],"id":"f4c835c6","execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"execution":{"iopub.execute_input":"2021-11-27T17:02:34.653413Z","iopub.status.busy":"2021-11-27T17:02:34.650469Z","iopub.status.idle":"2021-11-27T17:02:34.677956Z","shell.execute_reply":"2021-11-27T17:02:34.678973Z","shell.execute_reply.started":"2021-11-19T22:15:28.350172Z"},"id":"6c7c275f","papermill":{"duration":0.054172,"end_time":"2021-11-27T17:02:34.679184","exception":false,"start_time":"2021-11-27T17:02:34.625012","status":"completed"},"tags":[]},"source":["def build_model(input_shape):\n","\n","    input_layer = tfkl.Input(shape=input_shape, name='Input')\n","\n","    conv1 = tfkl.Conv2D(\n","        filters=16,\n","        kernel_size=(3, 3),\n","        strides = (1, 1),\n","        padding = 'same',\n","        activation = 'relu',\n","        kernel_initializer = tfk.initializers.GlorotUniform(seed),\n","        kernel_regularizer=tfk.regularizers.l2(1e-5),\n","    )(input_layer)\n","    pool1 = tfkl.MaxPooling2D(\n","        pool_size = (2, 2)\n","    )(conv1)\n","\n","    conv2 = tfkl.Conv2D(\n","        filters=32,\n","        kernel_size=(3, 3),\n","        strides = (1, 1),\n","        padding = 'same',\n","        activation = 'relu',\n","        kernel_initializer = tfk.initializers.GlorotUniform(seed),\n","        kernel_regularizer=tfk.regularizers.l2(1e-5),\n","    )(pool1)\n","    pool2 = tfkl.MaxPooling2D(\n","        pool_size = (2, 2)\n","    )(conv2)\n","\n","    conv3 = tfkl.Conv2D(\n","        filters=64,\n","        kernel_size=(3, 3),\n","        strides = (1, 1),\n","        padding = 'same',\n","        activation = 'relu',\n","        kernel_initializer = tfk.initializers.GlorotUniform(seed),\n","        kernel_regularizer=tfk.regularizers.l2(1e-5),\n","    )(pool2)\n","    pool3 = tfkl.MaxPooling2D(\n","        pool_size = (2, 2)\n","    )(conv3)\n","\n","    conv4 = tfkl.Conv2D(\n","        filters=128,\n","        kernel_size=(3, 3),\n","        strides = (1, 1),\n","        padding = 'same',\n","        activation = 'relu',\n","        kernel_initializer = tfk.initializers.GlorotUniform(seed),\n","        kernel_regularizer=tfk.regularizers.l2(1e-5),\n","    )(pool3)\n","    pool4 = tfkl.MaxPooling2D(\n","        pool_size = (2, 2)\n","    )(conv4)\n","\n","    conv5 = tfkl.Conv2D(\n","        filters=256,\n","        kernel_size=(3, 3),\n","        strides = (1, 1),\n","        padding = 'same',\n","        activation = 'relu',\n","        kernel_initializer = tfk.initializers.GlorotUniform(seed),\n","        kernel_regularizer=tfk.regularizers.l2(1e-5),\n","    )(pool4)\n","    pool5 = tfkl.MaxPooling2D(\n","        pool_size = (2, 2)\n","    )(conv5)\n","\n","    flattening_layer = tfkl.Flatten(name='Flatten')(pool5)\n","    flattening_layer = tfkl.Dropout(0.3, seed=seed)(flattening_layer)\n","\n","    classifier_layer1 = tfkl.Dense(units=512, name='Classifier1', kernel_initializer=tfk.initializers.GlorotUniform(seed), kernel_regularizer=tfk.regularizers.l2(1e-5), activation='relu')(flattening_layer)\n","    classifier_layer1 = tfkl.Dropout(0.3, seed=seed)(classifier_layer1)\n","    \n","    classifier_layer2 = tfkl.Dense(units=256, name='Classifier2', kernel_initializer=tfk.initializers.GlorotUniform(seed), kernel_regularizer=tfk.regularizers.l2(1e-5), activation='relu')(classifier_layer1)\n","    classifier_layer2 = tfkl.Dropout(0.3, seed=seed)(classifier_layer2)\n","    \n","    classifier_layer3 = tfkl.Dense(units=128, name='Classifier3', kernel_initializer=tfk.initializers.GlorotUniform(seed), kernel_regularizer=tfk.regularizers.l2(1e-5), activation='relu')(classifier_layer2)\n","    classifier_layer3 = tfkl.Dropout(0.3, seed=seed)(classifier_layer3)\n","    \n","    output_layer = tfkl.Dense(units=14, activation='softmax', kernel_initializer=tfk.initializers.GlorotUniform(seed), name='Output')(classifier_layer3)\n","\n","    model = tfk.Model(inputs=input_layer, outputs=output_layer, name='Model-6CL-3HL-l2norm')\n","\n","    model.compile(loss=tfk.losses.CategoricalCrossentropy(), optimizer=tfk.optimizers.Adam(), metrics='accuracy')\n","\n","    return model"],"id":"6c7c275f","execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"execution":{"iopub.execute_input":"2021-11-27T17:02:34.716768Z","iopub.status.busy":"2021-11-27T17:02:34.715417Z","iopub.status.idle":"2021-11-27T17:02:38.356320Z","shell.execute_reply":"2021-11-27T17:02:38.356709Z","shell.execute_reply.started":"2021-11-19T22:15:28.37341Z"},"id":"bdfacd02","papermill":{"duration":3.66242,"end_time":"2021-11-27T17:02:38.356869","exception":false,"start_time":"2021-11-27T17:02:34.694449","status":"completed"},"tags":[],"outputId":"dd751e77-09a5-42e4-bdc2-6c66fa9a6043"},"source":["model = build_model(input_shape)\n","model.summary()\n","tfk.utils.plot_model(model)"],"id":"bdfacd02","execution_count":null,"outputs":[{"name":"stderr","output_type":"stream","text":["2021-11-27 17:02:34.823188: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-11-27 17:02:34.958397: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-11-27 17:02:34.959399: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-11-27 17:02:34.961758: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\n","To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n","2021-11-27 17:02:34.963040: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-11-27 17:02:34.964253: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-11-27 17:02:34.965453: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-11-27 17:02:37.012157: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-11-27 17:02:37.012916: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-11-27 17:02:37.013732: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-11-27 17:02:37.014373: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 15403 MB memory:  -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0\n"]},{"name":"stdout","output_type":"stream","text":["Model: \"Model-6CL-3HL-l2norm\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","Input (InputLayer)           [(None, 256, 256, 3)]     0         \n","_________________________________________________________________\n","conv2d (Conv2D)              (None, 256, 256, 16)      448       \n","_________________________________________________________________\n","max_pooling2d (MaxPooling2D) (None, 128, 128, 16)      0         \n","_________________________________________________________________\n","conv2d_1 (Conv2D)            (None, 128, 128, 32)      4640      \n","_________________________________________________________________\n","max_pooling2d_1 (MaxPooling2 (None, 64, 64, 32)        0         \n","_________________________________________________________________\n","conv2d_2 (Conv2D)            (None, 64, 64, 64)        18496     \n","_________________________________________________________________\n","max_pooling2d_2 (MaxPooling2 (None, 32, 32, 64)        0         \n","_________________________________________________________________\n","conv2d_3 (Conv2D)            (None, 32, 32, 128)       73856     \n","_________________________________________________________________\n","max_pooling2d_3 (MaxPooling2 (None, 16, 16, 128)       0         \n","_________________________________________________________________\n","conv2d_4 (Conv2D)            (None, 16, 16, 256)       295168    \n","_________________________________________________________________\n","max_pooling2d_4 (MaxPooling2 (None, 8, 8, 256)         0         \n","_________________________________________________________________\n","Flatten (Flatten)            (None, 16384)             0         \n","_________________________________________________________________\n","dropout (Dropout)            (None, 16384)             0         \n","_________________________________________________________________\n","Classifier1 (Dense)          (None, 512)               8389120   \n","_________________________________________________________________\n","dropout_1 (Dropout)          (None, 512)               0         \n","_________________________________________________________________\n","Classifier2 (Dense)          (None, 256)               131328    \n","_________________________________________________________________\n","dropout_2 (Dropout)          (None, 256)               0         \n","_________________________________________________________________\n","Classifier3 (Dense)          (None, 128)               32896     \n","_________________________________________________________________\n","dropout_3 (Dropout)          (None, 128)               0         \n","_________________________________________________________________\n","Output (Dense)               (None, 14)                1806      \n","=================================================================\n","Total params: 8,947,758\n","Trainable params: 8,947,758\n","Non-trainable params: 0\n","_________________________________________________________________\n"]},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAVsAAAd1CAYAAAAluXyRAAAABmJLR0QA/wD/AP+gvaeTAAAgAElEQVR4nOzdeXTU1d0/8PdsGSYLk+WBJASEFFAPEoKmFEOJIQQJlCWACWEJEAXkiFaBCijYynmwtVYFV0T0VOk5RRJoiQapD4ggxYSyPAgWIUBa/LENJCzZE7J8fn/YmYdhJsnMJLmTgffrnDlH7vfOnc/3O3feTu73OzMaEREQEVF72qT1dgVERHcChi0RkQIMWyIiBRi2REQK6L1dgC9atWoVCgoKvF0GkVcsWrQI8fHx3i7D5/CdrQcKCgqwb98+b5dBpNzmzZtx9uxZb5fhk/jO1kMPPvggNm3a5O0yiJTSaDTeLsFn8Z0tEZECDFsiIgUYtkRECjBsiYgUYNgSESnAsCUiUoBhS0SkAMOWiEgBhi0RkQIMWyIiBRi2REQKMGyJiBRg2BIRKcCwVSQwMBAajcbu9tprr3m7LCVup32/nfaF1GLYKlJRUYHDhw8DAFJTUyEiePbZZ71clRq3077fTvtCajFsyanAwEAMHTrU22V4xZ2879R+GLZERAowbImIFGDYellubq7dyZYzZ84gIyMDwcHBCAsLw9ixY1FUVGTr/9prr9n6du/eHQcOHEBycjKCgoLg7++PpKQkfPPNN7b+L730kq3/zX8af/HFF7b2//qv/3IYv7KyEt98842tj17f9r+gdCfse319PbKzs/Hwww8jIiICJpMJMTExePPNN9HY2AgAuH79usNJt5deesl2/5vb09LSbGMXFxfj6aefRq9eveDn54cuXbpg0qRJ+Pbbb5s8xoWFhZg8eTLCwsJsbSUlJR7vH7lByG1paWmSlpbm9v0OHz4sACQ1NdVhW2pqqm1bfn6+VFRUyI4dO8RkMsmgQYMc+sfGxkpAQIDEx8fb+h84cEAGDBggfn5+snv3brv+AQEB8vOf/9xhnLi4OAkLC3Nob6q/VVJSkoSGhkpBQYEru35b7Xtz+3KrvLw8ASC/+93v5OrVq1JcXCxvvfWWaLVaefbZZ+36pqSkiFarldOnTzuMEx8fL3/+859t/75w4YL07NlTwsPD5fPPP5fy8nL55z//KYmJidKpUyfJz8+3u7/1GCcmJsquXbuksrJS9u3bJzqdToqLi1vcDysAkp2d7XJ/sslh2HqgPcM2Ly/P4bEAOLwgYmNjBYAcPnzYrv3o0aMCQGJjY+3a2zpwEhMTJSQkxOFF3ZTbad/dDdthw4Y5tGdmZorBYJDS0lJb2//8z/8IAJk/f75d371790pUVJTcuHHD1jZr1iwBYBfAIiIXL14Uo9EocXFxdu3WY7xt27YWa24Ow9ZjOVxG6GAGDRpk9+8ePXoAAC5cuODQNyAgAAMHDrRri4mJQbdu3XDkyBFcvHix3ercvXs3rl69ivj4+DYb01f23R1jx47Frl27HNpjY2NRV1eHY8eO2dpGjhyJmJgYfPzxx7hy5Yqt/dVXX8Uvf/lLGAwGW1tubi60Wi3Gjh1rN25ERATuu+8+HDp0COfOnXN43J/97GdtsVvkAYZtB2M2m+3+7efnBwC29b2bBQcHOx2ja9euAIDLly+3cXXt63bc99LSUvzmN79BTEwMQkJCbOukixcvBgBUVVXZ9V+wYAGqqqqwZs0aAMDJkyfx1Vdf4fHHH7f1qa2tRWlpKRobG2E2mx3We//3f/8XAHDq1CmHegICAtprV6kFDFsfduXKFYiIQ7s1aKzBAwBarRY3btxw6Hv9+nWnY2s0mjaqsn34yr6PGzcOK1euxNy5c3Hy5Ek0NjZCRLB69WoAcNiH6dOnIzw8HO+88w5qa2vx+uuvY9asWQgJCbH1MRqNCA4Ohl6vR11dHUTE6S0pKanN9oNaj2Hrw2pqanDgwAG7tu+++w4XLlxAbGwsIiMjbe2RkZE4f/68XV+LxYL/9//+n9Ox/f397QLqnnvuwbp169qw+tbp6Puu1+tx7NgxfPPNN4iIiMDTTz+NLl262IK8urra6f2MRiPmz5+Py5cv4/XXX8ef//xnPPPMMw79Jk2ahPr6erurL6xeeeUV3HXXXaivr3erZmpfDFsfZjabsWzZMhQUFKCyshIHDx5EZmYm/Pz88Oabb9r1HTlyJC5cuIB33nkHFRUVKCoqwjPPPGP3DvBmDzzwAE6ePImzZ8+ioKAA//rXv5CQkGDbPnz4cISFhWHfvn3tuo9N8ea+u0qn02HYsGGwWCx49dVXUVJSgurqauzatQtr165t8n7z58+HyWTCCy+8gBEjRqBPnz4OfV5++WX07t0bjz32GP72t7+htLQUV69exfvvv4///u//xmuvvdYul+tRK3jr1Jwv8+RqhICAAAFgd3v11VeloKDAoX358uUiIg7tY8aMsY0XGxsrUVFR8v3330tKSooEBQWJyWSSxMRE2bt3r8PjX79+XebMmSORkZFiMplk6NChcuDAAYmLi7ONv3TpUlv/EydOSEJCggQEBEiPHj3k3XfftRsvISHB5asRbqd9d7YvTd2OHz8uxcXFMm/ePOnRo4cYDAYJDw+XrKwsee6552z9br1yQERk7ty5AkC+/vrrJo/rlStXZNGiRfKTn/xEDAaDdOnSRUaOHCk7duyw9XF2jFvzsgevRvBUjkbEycIXNSs9PR0AsGnTJq/VMHDgQJSUlDg943y7uxP2/aOPPsK7776LgwcPersUOxqNBtnZ2Zg8ebK3S/E1m7iMQNQBrV27FosWLfJ2GdSGGLZEHcCHH36IiRMnoqKiAmvXrsW1a9f47vE2w7D1MdbP7x85cgTnz5+HRqPBCy+84O2ylLjd9z03NxchISF47733sHHjRp7gus1wzdYDHWHNlsgbuGbrMa7ZEhGpwLAlIlKAYUtEpADDlohIAYYtEZECDFsiIgUYtkRECjBsiYgUYNgSESnAsCUiUoBhS0SkAMOWiEgBhi0RkQL8DjcP7du3z/btX0RELWHYeiA+Pt7bJdyxiouLcfz4cTz00EPeLuWOlJaWhh49eni7DJ/E77Mln5KTk4OMjAxw2pKP4ffZEhGpwLAlIlKAYUtEpADDlohIAYYtEZECDFsiIgUYtkRECjBsiYgUYNgSESnAsCUiUoBhS0SkAMOWiEgBhi0RkQIMWyIiBRi2REQKMGyJiBRg2BIRKcCwJSJSgGFLRKQAw5aISAGGLRGRAgxbIiIFGLZERAowbImIFGDYEhEpwLAlIlKAYUtEpADDlohIAYYtEZECDFsiIgUYtkRECjBsiYgUYNgSESmg93YBRE05d+4cZs2ahYaGBltbSUkJ9Ho9hg0bZtf3nnvuwfvvv6+4QiLXMWypw+revTvOnDmDf/3rXw7bvv76a7t/JyQkqCqLyCNcRqAObebMmTAYDC32mzJlioJqiDzHsKUObfr06airq2u2T79+/XDfffcpqojIMwxb6tD69OmDAQMGQKPRON1uMBgwa9YsxVURuY9hSx3ezJkzodPpnG6rr6/H5MmTFVdE5D6GLXV4U6dORWNjo0O7RqPB4MGD0atXL/VFEbmJYUsdXrdu3TBkyBBotfbTVafTYebMmV6qisg9DFvyCTNmzHBoExE88sgjXqiGyH0MW/IJ6enpdu9sdTodRowYga5du3qxKiLXMWzJJ4SEhGDkyJG2E2UigszMTC9XReQ6hi35jMzMTNuJMr1ej/Hjx3u5IiLXMWzJZ4wfPx5Go9H23507d/ZyRUSu43cjtLGcnBxvl3Bbe+CBB5Cfn4/o6Gge63bUo0cPxMfHe7uM24pGRMTbRdxOmvqkE5EvSUtLw6ZNm7xdxu1kE5cR2kF2djZEhLd2uN24cQNLlizxeh238y0tLc3bL6HbEsOWfIrBYMCKFSu8XQaR2xi25HNMJpO3SyByG8OWiEgBhi0RkQIMWyIiBRi2REQKMGyJiBRg2BIRKcCwJSJSgGFLRKQAw5aISAGGLRGRAgxbIiIFGLbklmvXrmHt2rUYPnw4QkNDYTKZ0LdvX0yfPh1HjhxxaYyNGzdCo9FAo9GgU6dObVbbgQMHkJWVhejoaJhMJoSGhqJ///545JFH8N5776GoqKjNHqutuHs8AwMDbcfOetNqtQgJCUFsbCzmz5+PQ4cOeWFPqEVCbQqAZGdne7uMdjN79mzR6/XyxhtvyMWLF6WyslL27Nkj/fr1E51OJ1u2bHF5rOTkZDEaja2uqaGhQZ599lnR6/WyePFiOX78uNTU1IjFYpHt27fLiBEjBIAAkLq6ulY/Xlvy5HgePnxYAEhqaqqIiNTX14vFYpHc3FxJSkoSAJKVlSWVlZUe1ZSWliZpaWmt2i9ykMOwbWN3Qtg+/vjjDu3ffvutAJC+ffu6PFZbhe2yZcsEgKxbt87p9vr6ehk9enSHDVt3j+etYXurJUuWCAAZP368NDY2ul0Tw7ZdMGzb2u0ets0xmUyi1WpdfoG3RdgeP35ctFqtxMXFNdsvPz+/Q4Ztc5o6ni2FbWNjowwePFgAyIYNG9x+XIZtu8jhmi21icrKSlRXV6N///5Kfxpo3bp1aGxsRHp6erP94uPjISLQ633jZ/daczw1Gg2eeuopAMCaNWvaozzyAMO2A7hy5QoWLVqE3r17w2g0onv37hgxYgQ+/vhjVFdXO+3n5+eHkJAQjB49Grt27bL1yc3NtTt5cubMGWRkZCA4OBhhYWEYO3as7UTR9evXHU62vPTSSwCA+vp6u/aWfirF+ntVy5cvd9h24sQJTJgwAWazGQEBAUhISMDevXtbfdwAYM+ePQCAAQMGuH1fXz2erhg6dCgAYN++fairq/NoDGpj3n5vfbuBm8sIFy9elOjoaImIiJC8vDwpKysTi8UiK1euFACyevVqu37h4eGSl5cnpaWlUlhYKJMmTRKNRiMffPCB3bipqam2PzXz8/OloqJCduzYISaTSQYNGmTXd9SoUaLVauX06dMO9cXHx7f4p6jFYpHw8HCZM2eOw7ZTp05JcHCwREVFyfbt26W8vFyOHj0qI0eOlF69ejldRkhKSpLQ0FApKCho8fhFRkYKAPnHP/7RYt+b+erxFGl5GUFEpLq62nZS8MKFC80+3q24jNAuuGbb1twN26ysrCbvM2rUKFvYWvt98skndn1qamqkW7duYjKZxGKx2Nqt4ZCXl2fXPy0tTQBIcXGxre3LL78UADJ//ny7vnv37pW77rqr2XXOkpISGThwoGRkZEh9fb3D9vT0dAEgmzdvtms/f/68GI1Gp2GbmJgoISEhkp+f3+TjWlnDdv/+/S32vZmvHk8R18K2qqqKYduxMGzbmrthazabBYCUlZV53G/GjBkCQNavX29rs4bDzYEhIrJw4UIBIEeOHLFrv//++8Xf319KSkrsxli1alWTNVVUVEhcXJxMmzatyWAICgoSAFJeXu6wLSYmptUnyOLi4gSAbNu2za37+erxFHEtbIuKigSAGAwGuXHjRpP9nGHYtgueIPOm2tpalJaWolOnTggKCvK4X3h4OADAYrE4bDObzXb/9vPzAwA0Njbatf/qV79CVVWV7YTKyZMnsWfPHsyZM8dpTfX19UhPT0dUVBTWr18PnU7ntO7y8nJ06tQJgYGBDtu7du3qdGx3JCYmAgCOHj3q8n189Xi6w7omHh8fD4PB0KqxqG0wbL3IaDTCbDajpqYG5eXlHve7dOkSACAiIsLjWjIyMtCjRw+88847qK2txeuvv465c+c2+T+BefPmoba2Fjk5OXZn+Pv06YN9+/bZ6g4KCkJNTQ0qKiocxrh69arH9d5ch16vx+bNm5vtt2TJEmi1Wpw4ccJnj6erGhsb8e677wIAnnzySY/3gdoWw9bLJk6cCADYtm2bw7b7778fCxcutOv3+eef2/Wpra3Fzp07YTKZkJKS4nEder0ezzzzDC5fvozXX38dGzduxNNPP+2074oVK3Ds2DF8+umnMBqNzY47evRoAMAXX3xh115SUoLCwkKP67W6++678eKLL+LgwYP44x//6LRPYWEh3n//fUyePBn33nsvAN89nq54/vnnsX//fkycOLHFS+JIIW8vZNxu4OHVCJGRkbJ161YpKyuTs2fPyhNPPCHh4eHyww8/2PWznj0vKyuzO3t+66enrGuM1dXVdu1Lly4VAHL48GGHWsrKysRsNotGo5GZM2c6rfejjz6ynXhp6nbzVQSnT5+W0NBQu6sRjh07JikpKdK1a9dWX41g9dxzz4nBYJClS5dKYWGh1NbWyrlz5+TDDz+UyMhIGTp0qFRUVDgcd187niKOa7YNDQ1y6dIlyc3NleHDhwsAeeyxx6Sqqsrl43czrtm2C54ga2vuhq3Ij2egFyxYINHR0WIwGCQyMlKmTJkiJ0+ebLaf2WyWlJQU2blzp61PQUGBw4t1+fLlttpuvo0ZM8ahlsWLFzs94WM1ZswYt8OhsLBQJkyYIJ07d7ZdKrV161ZJTk623Wf27Nm2/gkJCS5fjXCz/fv3y4wZM6RHjx5iMBgkKChIHnzwQXnzzTeltrbWob8vHs+AgACH7RqNRsxms8TExMgTTzwhhw4dcuu43Yph2y5yNCIirX9/TFYajQbZ2dmYPHmyt0sh8oh16cH6wQpqE5u4ZktEpADDlohIAYYtEZECDFsiIgUYtkRECjBsiYgUYNgSESnAsCUiUoBhS0SkAMOWiEgBhi0RkQIMWyIiBRi2REQKMGyJiBRg2BIRKcCwJSJSgGFLRKSAvuUu5K6CggJvl0DksXPnzqF79+7eLuO2w5/FaWMajcbbJRC1WlpaGn8Wp21t4jvbNsb/d7WvnJwcZGRk8DiTz+GaLRGRAgxbIiIFGLZERAowbImIFGDYEhEpwLAlIlKAYUtEpADDlohIAYYtEZECDFsiIgUYtkRECjBsiYgUYNgSESnAsCUiUoBhS0SkAMOWiEgBhi0RkQIMWyIiBRi2REQKMGyJiBRg2BIRKcCwJSJSgGFLRKQAw5aISAGGLRGRAgxbIiIFGLZERAowbImIFGDYEhEpwLAlIlKAYUtEpADDlohIAYYtEZECem8XQNSU4uJibNmyxa7t4MGDAIB169bZtQcGBmLatGnKaiNyl0ZExNtFEDlTW1uLLl26oLKyEjqdDgAgIhARaLX/90dZXV0dZs6cifXr13urVKKWbOIyAnVYRqMR6enp0Ov1qKurQ11dHerr69HQ0GD7d11dHQDwXS11eAxb6tCmTZuGGzduNNsnODgYycnJiioi8gzDljq0pKQkdOnSpcntBoMBmZmZ0Ot5+oE6NoYtdWharRbTpk2Dn5+f0+11dXWYOnWq4qqI3MewpQ5v6tSpTS4lREZGIj4+XnFFRO5j2FKHN3jwYPTs2dOh3WAwYNasWdBoNF6oisg9DFvyCTNmzIDBYLBr4xIC+RKGLfmE6dOn2y7zsurTpw8GDBjgpYqI3MOwJZ9w7733ol+/frYlA4PBgEcffdTLVRG5jmFLPmPmzJm2T5LV1dVh8uTJXq6IyHUMW/IZU6ZMQUNDAwAgLi4Offr08XJFRK5j2JLP6NmzJwYNGgTgx3e5RD5FWiEtLU0A8MYbb7zd9rfs7OzWxGVOqz/j+OCDD2LhwoWtHYbIJWVlZVizZg2ee+45b5dCd5CMjIxWj9HqsO3evTtPVJBSiYmJ6Nu3r7fLoDtIW4Qt12zJ5zBoyRcxbImIFGDYEhEpwLAlIlKAYUtEpADDlohIAYYtEZECDFsiIgUYtkRECjBsiYgUYNgSESnAsCUiUoBhS0SkAMPWR7322mvQaDTQaDTo3r27t8sBAFy7dg1r167F8OHDERoaCpPJhL59+2L69Ok4cuSIS2Ns3LjRtl+dOnXyuJaKigrbONZbQUFBi/dbvHix3X1eeuklj2twRWBgoEOdGo0GWq0WXbp0wYQJE3DgwIF2reF2mEvOjqNWq0VISAhiY2Mxf/58HDp0yAt7cpPWfnl4Wlpaa4agVoqNjZWoqCi7tvLycunTp4+MGTNGaS2zZ88WvV4vb7zxhly8eFEqKytlz5490q9fP9HpdLJlyxaXx0pOThaj0djqmg4fPmz78ufRo0c327ekpEQCAwMFgEyfPr3Vj+0qa42pqam2tuvXr8tf//pX6dq1qxgMBtmxY0e71+Hrc+nW41hfXy8Wi0Vyc3MlKSlJAEhWVpZUVla6XQ/a4MvD+c72NiQiaGxsRGNjo/LHfuyxx/DMM88gIiIC/v7+SEhIwIYNG9DQ0IAlS5YorwcATCYTevbsib/97W84ePBgk/1Wr16NHj16KKysaWazGRMnTsSqVatQV1eHBQsWeKUOX55LOp0O4eHhSE1NxVdffYUlS5bg448/xtSpU/FjfqrFsL0NBQUFoaioCNu2bVP6uB9++CHef/99h/bY2FiYTCYUFRV5Z5JrtbZfdmhqWeD69et47733sHTpUpWltSgpKQkAcOzYMVy/fl35499Oc+n3v/89Bg8ejM8++wwbN25sq1JdxrCldldZWYnq6mr0798fGo3GKzU8+uijiIqKwmeffYajR486bH/rrbfwi1/8Ar179/ZCdU27OVC8dew6ktbMJY1Gg6eeegoAsGbNmvYor1lKwzY3N9duAfuHH35ARkYGgoKCEBYWhhkzZuDatWs4c+YMxo0bh6CgIERGRmLu3LkoLy+3G6u+vh7Z2dl4+OGHERERAZPJhJiYGLz55pt2f/IMHTrU7jEzMzMBACNGjLBrd/Vdw60nEw4cOIDk5GQEBQXB398fSUlJ+Oabbxzud+XKFSxatAi9e/eGn58fQkJCMHr0aOzatatVfVs6xjU1NU7bz5w5g4yMDAQHByMsLAxjx45FUVGRw3gnTpzAhAkTYDab4e/vj5/97GfYunWr3fGbM2dOszVt2rQJALB8+fJmxw8ICEBCQgL27t3b4n66y2g0YvHixRAR/Pa3v7XbVlFRgbfffhvLli1r8v7emm+7d+8GANx3330wm80AOJcA53PJFUOHDgUA7Nu3D3V1dR6N4bHWrPh6eoIsNTVVAMikSZPk4MGDUlFRIX/6059sJzFSU1Pl8OHDUl5eLmvXrhUAsnDhQrsx8vLyBID87ne/k6tXr0pxcbG89dZbotVq5dlnn7Xr++2330pAQIDExsZKRUWFiIjU1NTI4MGD5ZNPPvFo32NjYyUgIEDi4+MlPz9fKioq5MCBAzJgwADx8/OT3bt32/pevHhRoqOjJTw8XPLy8qS0tFQKCwtl0qRJotFo5IMPPvCor7WOW09q3HyMq6urnbanpqba6t6xY4eYTCYZNGiQXd9Tp05JcHCwREVFyfbt26W8vFz++c9/yogRI6RLly4uncCyWCwSHh4uc+bMcdjmbPyjR4/KyJEjpVevXk7HT0pKktDQUCkoKGjxsUV+PGkSEBAgIiJVVVUSHh4uWq1Wvv/+e1uf3//+9zJ58mQREfn73//u9ARZe843ZyfISktLnZ4g41xyPpeaOo63qq6utp0wvXDhQouPaYU2OEHm1bD9/PPP7drvu+8+ASBff/21XXt0dLTcc889dm15eXkybNgwh7EzMzPFYDBIaWmpXXtOTo4t4BsbG2XWrFmybNkyt2u3io2NFQBy+PBhu/ajR48KAImNjbW1ZWVlCQCHF1pNTY1069ZNTCaTWCwWt/ta6/DkBZKXl2fXbv1Z+uLiYltbenq6AJDNmzfb9b18+bL4+/u3+AIpKSmRgQMHSkZGhtTX1ztsb2r88+fPi9FodDp+YmKihISESH5+frOPbXVz2IqIvPLKKwJAMjMzRUSksrJSwsPD5ciRIyLSfNi213y7+YoJ602j0UhYWJiMHz9e9u/fb+vLueR8Lom4FrZVVVV3ZtheunTJrv3hhx8WAA6XZgwdOlSCgoJcGvvVV18VAE5fjMuXLxcAMmTIEBk7dqw0NDS4XbuV9Z2tM926dbN7Ms1mswCQsrIyh74zZswQALJ+/Xq3+1rr8OQFcvOLTERk4cKFAsAWOiIiQUFBAkDKy8sdxn/ggQeafYFUVFRIXFycTJs2rckXR3Pjx8TEtNmlXzc/T+Xl5RIWFiY6nU5OnTolq1atsntxNhW2TWmL+eZKSFhxLjmfSyKuHceioiIBIAaDQW7cuNFkv1u1Rdh69QRZ586d7f6t1Wqh0+ng7+9v167T6RwuPSktLcVvfvMbxMTEICQkxLbms3jxYgBAVVWVw+OtXLkSgwcPRn5+PtLT06HVtm73g4ODnbZ37doVAHD58mXU1taitLQUnTp1QlBQkEPf8PBwAIDFYnGrb2tZ1/+s/Pz8AMB2nGtra1FeXo5OnTohMDDQ4f4hISFNjl1fX4/09HRERUVh/fr10Ol0Dn1aGt96DNtaYGAgFixYgIaGBrz44ot47bXX8MILL7R4v44w3ziXnM8ld1jPB8THx8NgMLRqLHf57NUI48aNw8qVKzF37lycPHkSjY2NEBGsXr0aAJxeFrJ7926UlpYiJiYG8+fPd/lTTU25cuWK08e5fPkygB8Dw2g0wmw2o6amxuEkHwBcunQJABAREeFW3/ZmNBoRFBSEmpoaVFRUOGy37qMz8+bNQ21tLXJycqDX623tffr0wb59+1wa/+rVq22wF8798pe/hNlsxoYNGxAbG4uf/vSnLd6nI8w3ziXnc8lVjY2NePfddwEATz75pFv3bQs+GbYNDQ345ptvEBERgaeffhpdunSxXQZSXV3t9D7//ve/MXv2bPzlL3/BZ599BpPJhNTUVBQXF3tcR01NjcNHKb/77jtcuHABsbGxiIyMBABMnDgRAPD555/b9a2trcXOnTthMpmQkpLidt/2Nnr0aADAF198YddusVhw8uRJp/dZsWIFjh07hk8//RRGo9Gj8UtKSlBYWOhp2S0ym81YtGgRzGazS+9qO8p8AziXWuP555/H/v37MXHiRKSnp7d6PLe1ZhGitWu2t64BpaSkiE6nc+ifmJjosD46fPhwASB/+MMfpLi4WKqqquSrr76Su+66SwDYfbyxvLmrMQ4AACAASURBVLxcBgwYIJ9++qmtbffu3WIwGOShhx5ya+3GKjY2VsxmsyQnJ7t9NUJZWZndWeF169Z51NdahyfrbLe2L1261OGE3+nTpyU0NNTuDPJ3330no0aNkp49ezqss3300UcOJ3puvd18FYGz8Y8dOyYpKSnStWvXNr8awRVNrdm253xzZ82Wc8n5XHJ2HBsaGuTSpUuSm5tre/4ee+wxqaqqavE43wq+doKsoKDA4YAtX75cDhw44ND+8ssv2yb+zbcXX3xRRESKi4tl3rx50qNHDzEYDBIeHi5ZWVny3HPP2frGxcXJk08+aXf/7777ToqLix3GXblypVv7bp2Y33//vaSkpEhQUJCYTCZJTEyUvXv3OvQvKSmRBQsWSHR0tBgMBjGbzZKSkiI7d+70qK/1xMytx3LLli0O7dOnT2/y2IuIQ/vNn4MvLCyUCRMmSOfOncXf31+GDBkiX3/9tQwbNkz8/f3t6h4zZozbL5Cbx7deMrR161ZJTk623Wf27Nm2/gkJCS5fjRAQEGD32CkpKc32d1bv22+/LSLtN99urRGAw5U3t+JccpxLzo6jRqMRs9ksMTEx8sQTT8ihQ4eaPa7NaYuw1fxnII9Y34pbLzS+kwwcOBAlJSU4d+6ct0vxinvvvRfV1dX44YcfvF0K+ThfmEsajQbZ2dmYPHmyp0Ns8sk1W1LDYrEgNDTU4ZM2Z86cQVFREYYPH+6lysjXcC756AkyUufatWuYN28ezp49i6qqKuzfvx8ZGRno3Lkzfv3rX3u7PPIhd/pcYtjexNmXON96s35J8ZEjR3D+/HloNBqXzmj7ooiICHz55Ze4fv06HnroIYSEhGD8+PHo27cv9u/fj5/85CfeLpF8BOcSoG+5y52jFcvXt63k5GQkJyd7uwy6Ddzpc4nvbImIFGDYEhEpwLAlIlKAYUtEpADDlohIAYYtEZECDFsiIgUYtkRECjBsiYgUYNgSESnAsCUiUoBhS0SkAMOWiEiBVn/r1+bNm20/fkdERM616mdxCgoKcPbs2bash6hZBQUFeOONN5Cdne3tUugOM2TIEHTv3t3Tu29qVdgSqZaTk4OMjAx+9zD5Gv4GGRGRCgxbIiIFGLZERAowbImIFGDYEhEpwLAlIlKAYUtEpADDlohIAYYtEZECDFsiIgUYtkRECjBsiYgUYNgSESnAsCUiUoBhS0SkAMOWiEgBhi0RkQIMWyIiBRi2REQKMGyJiBRg2BIRKcCwJSJSgGFLRKQAw5aISAGGLRGRAgxbIiIFGLZERAowbImIFGDYEhEpwLAlIlKAYUtEpADDlohIAb23CyBqSl1dHSoqKuzaKisrAQDXrl2za9doNAgODlZWG5G7GLbUYV25cgXdu3dHQ0ODw7bQ0FC7fw8bNgy7du1SVRqR27iMQB1WREQEHnroIWi1zU9TjUaDqVOnKqqKyDMMW+rQZsyYAY1G02wfrVaLRx55RFFFRJ5h2FKH9sgjj0Cn0zW5XafTYdSoUQgLC1NYFZH7GLbUoXXu3BmjRo2CXu/89IKIIDMzU3FVRO5j2FKHl5mZ6fQkGQD4+flh7Nixiisich/Dljq8cePGwd/f36Fdr9dj4sSJCAwM9EJVRO5h2FKH16lTJ0yaNAkGg8Guvb6+HtOnT/dSVUTuYdiST5g2bRrq6urs2jp37oyHH37YSxURuYdhSz5hxIgRdh9kMBgMmDJlCvz8/LxYFZHrGLbkE/R6PaZMmWJbSqirq8O0adO8XBWR6xi25DOmTp1qW0oIDw9HQkKClysich3DlnzGz3/+c3Tr1g3Aj58sa+ljvEQdCb+IxgWrVq1CQUGBt8sgAEFBQQCAw4cPIz093cvVEAAsWrQI8fHx3i6jw+NbAxcUFBRg37593i6DANx1110ICgpCSEiIt0shAJs3b8bZs2e9XYZP4DtbFz344IPYtGmTt8sgADk5OZg8ebK3yyCgxS8Jov/Dd7bkcxi05IsYtkRECjBsiYgUYNgSESnAsCUiUoBhS0SkAMOWiEgBhi0RkQIMWyIiBRi2REQKMGyJiBRg2BIRKcCwJSJSgGFLAIBr165h7dq1GD58OEJDQ2EymdC3b19Mnz4dR44ccWmMjRs3QqPRQKPRoFOnTq2qZ9u2bbj77ruh17fPF9MdOHAAWVlZiI6OhslkQmhoKPr3749HHnkE7733HoqKitrlcVvD3ecoMDDQ9nxYb1qtFiEhIYiNjcX8+fNx6NAhL+zJHUqoRWlpaZKWlubtMtrV7NmzRa/XyxtvvCEXL16UyspK2bNnj/Tr1090Op1s2bLF5bGSk5PFaDR6VMfp06dl3LhxMmDAAOncubPodDqPxmlKQ0ODPPvss6LX62Xx4sVy/PhxqampEYvFItu3b5cRI0YIAAEgdXV1bfrYreXJc3T48GEBIKmpqSIiUl9fLxaLRXJzcyUpKUkASFZWllRWVnpUEwDJzs5u1X7dIXIYti64U8L28ccfd2j/9ttvBYD07dvX5bFaE7ZTp06Vl19+Werq6iQqKqrNw3bZsmUCQNatW+d0e319vYwePbrDhq27z9GtYXurJUuWCAAZP368NDY2ul0Tw9ZlDFtX3Alh2xyTySRardblF2Nrwraqqsr2320dtsePHxetVitxcXHN9svPz++QYducpp6jlsK2sbFRBg8eLABkw4YNbj8uw9ZlOVyzpWZVVlaiuroa/fv3V/Kt/CaTqd3GXrduHRobG1v87bL4+HiISLutF7e11jxHGo0GTz31FABgzZo17VEe/QfDth1duXIFixYtQu/evWE0GtG9e3eMGDECH3/8Maqrq5328/PzQ0hICEaPHo1du3bZ+uTm5tqd6Dhz5gwyMjIQHByMsLAwjB071nZS5/r16w4nRl566SUAQH19vV17Wlpas/tg/Smg5cuXO2w7ceIEJkyYALPZjICAACQkJGDv3r2tPm7tZc+ePQCAAQMGuH1fX32OXDF06FAAwL59+2w/FU/twNvvrX2BJ8sIFy9elOjoaImIiJC8vDwpKysTi8UiK1euFACyevVqu37h4eGSl5cnpaWlUlhYKJMmTRKNRiMffPCB3bipqam2Pwvz8/OloqJCduzYISaTSQYNGmTXd9SoUaLVauX06dMO9cXHx7f4Z6PFYpHw8HCZM2eOw7ZTp05JcHCwREVFyfbt26W8vFyOHj0qI0eOlF69enm8jHAzV5YRkpKSJDQ0VAoKClocLzIyUgDIP/7xD7fq8NXnSKTlZQQRkerqattJwQsXLjT7eLcClxFcxTVbV3gStllZWU1OxFGjRtnC1trvk08+setTU1Mj3bp1E5PJJBaLxdZufSHn5eU51AhAiouLbW1ffvmlAJD58+fb9d27d6/cddddza5JlpSUyMCBAyUjI0Pq6+sdtqenpwsA2bx5s137+fPnxWg0KgvbxMRECQkJkfz8/BbHs4bt/v373arDV58jEdfCtqqqimHb/hi2rvAkbM1mswCQsrIyj/vNmDFDAMj69ettbdYX8s0vbhGRhQsXCgA5cuSIXfv9998v/v7+UlJSYjfGqlWrmqypoqJC4uLiZNq0aU2+iIOCggSAlJeXO2yLiYlRFrbuiIuLEwCybds2t+7nq8+RiGthW1RUJADEYDDIjRs3muznDMPWZTxB1h5qa2tRWlqKTp06ISgoyON+4eHhAACLxeKwzWw22/3bz88PANDY2GjX/qtf/QpVVVW2kx8nT57Enj17MGfOHKc11dfXIz09HVFRUVi/fj10Op3TusvLy9GpUycEBgY6bO/atavTsb0tMTERAHD06FGX7+Orz5E7rOvs8fHxMBgMrRqLmsawbQdGoxFmsxk1NTUoLy/3uN+lS5cAABERER7XkpGRgR49euCdd95BbW0tXn/9dcydO7fJ/wnMmzcPtbW1yMnJsTsb36dPH+zbt89Wd1BQEGpqalBRUeEwxtWrVz2utz3NmzcPer0emzdvbrbfkiVLoNVqceLECZ99jlzV2NiId999FwDw5JNPerwP1DKGbTuZOHEigB8/dnqr+++/HwsXLrTr9/nnn9v1qa2txc6dO2EymZCSkuJxHXq9Hs888wwuX76M119/HRs3bsTTTz/ttO+KFStw7NgxfPrppzAajc2OO3r0aADAF198YddeUlKCwsJCj+ttT3fffTdefPFFHDx4EH/84x+d9iksLMT777+PyZMn49577wXgu8+RK55//nns378fEydObPGSOGolby9k+ILWXI0QGRkpW7dulbKyMjl79qw88cQTEh4eLj/88INdP+uZ7rKyMrsz3bd+0sm6HlhdXW3XvnTpUgEghw8fdqilrKxMzGazaDQamTlzptN6P/roI9tJkqZuN5/xP336tISGhtpdjXDs2DFJSUmRrl27dsirEayee+45MRgMsnTpUiksLJTa2lo5d+6cfPjhhxIZGSlDhw6ViooKW39ffY5EHNdsGxoa5NKlS5KbmyvDhw8XAPLYY4/ZfZjEHeCarat4gswVnn6CrKSkRBYsWCDR0dFiMBgkMjJSpkyZIidPnmy2n9lslpSUFNm5c6etT0FBgcMLa/ny5SIiDu1jxoxxqGXx4sVOT85YjRkzxu0XcmFhoUyYMEE6d+5su6xp69atkpycbLvP7Nmz3TpmeXl5TT7+rZdYiYgkJCS4fDXCzfbv3y8zZsyQHj16iMFgkKCgIHnwwQflzTfflNraWof+vvgcBQQEOGzXaDRiNpslJiZGnnjiCTl06JBbx+1WDFuX5WhERFrzzvhOYP3zynrxOBH9SKPRIDs7G5MnT/Z2KR3dJq7ZEhEpwLAlIlKAYUvt7tbvAHB2W7FihbfLJGpXvvG1RuTTeFqAiO9siYiUYNgSESnAsCUiUoBhS0SkAMOWiEgBhi0RkQIMWyIiBRi2REQKMGyJiBRg2BIRKcCwJSJSgGFLRKQAw5aISAF+65eL9u3bxx/EIyKPMWxdEB8f7+0S6D+Ki4tx/PhxPPTQQ94uhQCkpaWhR48e3i7DJ/A3yMin5OTkICMjg9+RS76Gv0FGRKQCw5aISAGGLRGRAgxbIiIFGLZERAowbImIFGDYEhEpwLAlIlKAYUtEpADDlohIAYYtEZECDFsiIgUYtkRECjBsiYgUYNgSESnAsCUiUoBhS0SkAMOWiEgBhi0RkQIMWyIiBRi2REQKMGyJiBRg2BIRKcCwJSJSgGFLRKQAw5aISAGGLRGRAgxbIiIFGLZERAowbImIFGDYEhEpwLAlIlKAYUtEpIDe2wUQNeXcuXOYNWsWGhoabG0lJSXQ6/UYNmyYXd977rkH77//vuIKiVzHsKUOq3v37jhz5gz+9a9/OWz7+uuv7f6dkJCgqiwij3AZgTq0mTNnwmAwtNhvypQpCqoh8hzDljq06dOno66urtk+/fr1w3333aeoIiLPMGypQ+vTpw8GDBgAjUbjdLvBYMCsWbMUV0XkPoYtdXgzZ86ETqdzuq2+vh6TJ09WXBGR+xi21OFNnToVjY2NDu0ajQaDBw9Gr1691BdF5CaGLXV43bp1w5AhQ6DV2k9XnU6HmTNneqkqIvcwbMknzJgxw6FNRPDII494oRoi9zFsySekp6fbvbPV6XQYMWIEunbt6sWqiFzHsCWfEBISgpEjR9pOlIkIMjMzvVwVkesYtuQzMjMzbSfK9Ho9xo8f7+WKiFzHsCWfMX78eBiNRtt/d+7c2csVEbnO4bsRzp07h/z8fG/UQtSiBx54APn5+YiOjkZOTo63yyFyytm13xoRkZsbcnJykJGRoawoIqLbzS2xCgCbmvzWLyedibyurq4OL7zwAl555RVvl0LkoLk3q1yzJZ9iMBiwYsUKb5dB5DaGLfkck8nk7RKI3MawJSJSgGFLRKQAw5aISAGGLRGRAgxbIiIFGLZERAowbImIFGDYEhEpwLAlIlKAYUtEpADDlohIAYZtB/Laa69Bo9FAo9Gge/fu3i4HAHDt2jWsXbsWw4cPR2hoKEwmE/r27Yvp06fjyJEjLo2xceNG23516tSpVfVs27YNd999N/T6Jr+wzi0VFRW22qy3goKCFu+3ePFiu/u89NJLbVJPUwIDAx3q1Gg00Gq16NKlCyZMmIADBw60aw23w/x0dhy1Wi1CQkIQGxuL+fPn49ChQ+1TrNwiOztbnDSTQrGxsRIVFWXXVl5eLn369JExY8YorWX27Nmi1+vljTfekIsXL0plZaXs2bNH+vXrJzqdTrZs2eLyWMnJyWI0Gj2q4/Tp0zJu3DgZMGCAdO7cWXQ6nUfjNOXw4cMCQADI6NGjm+1bUlIigYGBAkCmT5/epnU0x1pjamqqre369evy17/+Vbp27SoGg0F27NjR7nX4+vy89TjW19eLxWKR3NxcSUpKEgCSlZUllZWVbtfTTH7m8J2tjxARNDY22n6DS6XHHnsMzzzzDCIiIuDv74+EhARs2LABDQ0NWLJkiZIafv3rX2PIkCE4dOgQgoKC2uUxTCYTevbsib/97W84ePBgk/1Wr16NHj16tEsN7jKbzZg4cSJWrVqFuro6LFiwwCt1+PL81Ol0CA8PR2pqKr766issWbIEH3/8MaZOndq23+vtRjKTIs7eOXREJpNJtFqtNDY2utS/Ne9sq6qqbP8dFRXVLu9sAwIC5L333nN493iza9euSWhoqHz88ccd4p2t1fnz523vzK9du9audfj6/GzuOIqINDY2yuDBgwWAbNiwwa3H5DtbanOVlZWorq5G//79odFo2v3xVH2H7aOPPoqoqCh89tlnOHr0qMP2t956C7/4xS/Qu3dvJfW4Sm56B6bi+ejoWjM/NRoNnnrqKQDAmjVr2qymVodtbm6u3WLzDz/8gIyMDAQFBSEsLAwzZszAtWvXcObMGYwbNw5BQUGIjIzE3LlzUV5ebjdWfX09srOz8fDDDyMiIgImkwkxMTF488037f48GTp0qN1jZmZmAgBGjBhh1379+nWX9uHWhf8DBw4gOTkZQUFB8Pf3R1JSEr755huH+125cgWLFi1C79694efnh5CQEIwePRq7du1qVd+WjnFNTY3T9jNnziAjIwPBwcEICwvD2LFjUVRU5DDeiRMnMGHCBJjNZvj7++NnP/sZtm7danf85syZ02xNmzZtAgAsX7682fEDAgKQkJCAvXv3trifHYHRaMTixYshIvjtb39rt62iogJvv/02li1b1uT9vTWHd+/eDQC47777YDabAXB+As7npyuGDh0KANi3bx/q6uo8GsOBG2+Dm5WamioAZNKkSXLw4EGpqKiQP/3pT7YTDqmpqXL48GEpLy+XtWvXCgBZuHCh3Rh5eXkCQH73u9/J1atXpbi4WN566y3RarXy7LPP2vX99ttvJSAgQGJjY6WiokJERGpqamTw4MHyySefuF2/yI9/HgUEBEh8fLzk5+dLRUWFHDhwQAYMGCB+fn6ye/duW9+LFy9KdHS0hIeHS15enpSWlkphYaFMmjRJNBqNfPDBBx71tdbh7M806zGurq522p6ammqre8eOHWIymWTQoEF2fU+dOiXBwcESFRUl27dvl/LycvnnP/8pI0aMkC5durj0Z77FYpHw8HCZM2eOwzZn4x89elRGjhwpvXr18ngZ4WauLCMkJSVJaGioFBQUuDSmdRlB5Mcli/DwcNFqtfL999/b+vz+97+XyZMni4jI3//+d6fLCO05h539+VtaWur0BBnnp/P52dRxvFV1dbVtWebChQstPqZVc8sIbR62n3/+uV37fffdJwDk66+/tmuPjo6We+65x64tLy9Phg0b5jB2ZmamGAwGKS0tta8+J8cW8I2NjTJr1ixZtmyZ27VbxcbGCgA5fPiwXfvRo0cFgMTGxtrasrKyBIDDi6Kmpka6desmJpNJLBaL232tdXgymfPy8uza09LSBIAUFxfb2tLT0wWAbN682a7v5cuXxd/fv8XJXFJSIgMHDpSMjAypr6932N7U+OfPnxej0agsbBMTEyUkJETy8/NdGvPmsBUReeWVVwSAZGZmiohIZWWlhIeHy5EjR0Sk+bBtrzl88xUT1ptGo5GwsDAZP3687N+/39aX89P5/BRxLWyrqqo6ftheunTJrv3hhx8WAA6XUQwdOlSCgoJcGvvVV18VAE5fOMuXLxcAMmTIEBk7dqw0NDS4XbuV9Z2tM926dbM78GazWQBIWVmZQ98ZM2YIAFm/fr3bfa11eDKZb35BiIgsXLhQANgCQkQkKChIAEh5ebnD+A888ECzk7miokLi4uJk2rRpTU7k5saPiYlRFrbuujVsy8vLJSwsTHQ6nZw6dUpWrVpl9+JsKmyb0hZz2JWQsOL8dD4/RVw7jkVFRQJADAaD3Lhxo8l+t1J6gqxz5852/9ZqtdDpdPD397dr1+l0DpeJlJaW4je/+Q1iYmIQEhJiW59ZvHgxAKCqqsrh8VauXInBgwcjPz8f6enp0Gpbt0vBwcFO27t27QoAuHz5Mmpra1FaWopOnTo5vQwpPDwcAGCxWNzq21rWtTorPz8/ALAd59raWpSXl6NTp04IDAx0uH9ISEiTY9fX1yM9PR1RUVFYv349dDqdQ5+WxrceQ18QGBiIBQsWoKGhAS+++CJee+01vPDCCy3eryPMYc5P5/PTHdZzDPHx8TAYDK0ay6pDXY0wbtw4rFy5EnPnzsXJkyfR2NgIEcHq1asBwOk1b7t370ZpaSliYmIwf/58lz/V1JQrV644fZzLly8D+DEwjEYjzGYzampqHE7yAcClS5cAABEREW71bW9GoxFBQUGoqalBRUWFw3brPjozb9481NbWIicnx+7TW3369MG+fftcGv/q1attsBfq/PKXv4TZbMaGDRsQGxuLn/70py3epyPMYc5P5/PTVY2NjXj33XcBAE8++aRb921OhwnbhoYGfPPNN4iIiMDTTz+NLl262C7ZqK6udnqff//735g9ezb+8pe/4LPPPoPJZEJqaiqKi4s9rqOmpsbhY4/fffcdLly4gNjYWERGRgIAJk6cCAD4/PPP7frW1tZi586dMJlMSElJcbtvexs9ejQA4IsvvrBrt1gsOHnypNP7rFixAseOHcOnn34Ko9Ho0fglJSUoLCz0tGyvMJvNWLRoEcxms0vvajvKHAY4P1vj+eefx/79+zFx4kSkp6e3ejwbN9YcmtXUek1KSorT9bXExESH9dHhw4cLAPnDH/4gxcXFUlVVJV999ZXcddddAsDuo4jl5eUyYMAA+fTTT21tu3fvFoPBIA899JBb6yxWsbGxYjabJTk52e2rEcrKyuzO4K5bt86jvtY6PFkTu7V96dKlDif8Tp8+LaGhoXZne7/77jsZNWqU9OzZ02FN7KOPPnI4KXPr7eYz/s7GP3bsmKSkpEjXrl194moEVzS1Ztuec9idNVvOT+fz09lxbGhokEuXLklubq7t+XvsscfsPkjjqnY9QVZQUOCwc8uXL5cDBw44tL/88su2SXrz7cUXXxQRkeLiYpk3b5706NFDDAaDhIeHS1ZWljz33HO2vnFxcfLkk0/a3f+7776T4uJih3FXrlzp1oGyTqLvv/9eUlJSJCgoSEwmkyQmJsrevXsd+peUlMiCBQskOjpaDAaDmM1mSUlJkZ07d3rU13oS5dZjuWXLFof26dOnN3nsRcSh/ebPrBcWFsqECROkc+fO4u/vL0OGDJGvv/5ahg0bJv7+/nZ1jxkzxu3JfPP41st7tm7dKsnJybb7zJ49263nxnpJlbPbrZcmiYgkJCS4fDVCQECA3XgpKSnN9ndWw9tvvy0i7TeHb60RgMPVPLfi/HScn86Oo0ajEbPZLDExMfLEE0/IoUOHmj2uzWkubDX/2XGbnJwcZGRktO1ngn3EwIEDUVJSgnPnznm7FK+49957UV1djR9++MHbpRA58IX52Ux+buowa7akhsViQWhoqMOnYs6cOYOioiIMHz7cS5UR3d7zk2F7B7p27RrmzZuHs2fPoqqqCvv370dGRgY6d+6MX//6194uj+5wt+v8vO3D1tkXLt96s36h8JEjR3D+/HloNBqXzj77ooiICHz55Ze4fv06HnroIYSEhGD8+PHo27cv9u/fj5/85CfKanHluVmxYoWyesj7OtL8bGtt83X3HdiduPbckuTkZCQnJ3u7DD435FRHmZ9t7bZ/Z0tE1BEwbImIFGDYEhEpwLAlIlKAYUtEpADDlohIAYYtEZECDFsiIgUYtkRECjBsiYgUYNgSESnAsCUiUoBhS0SkQJPf+pWTk6OyDiIin1dQUNDktibDNiMjo12KISK6Ezn8BhlRR3Yn/0Ye+TT+BhkRkQoMWyIiBRi2REQKMGyJiBRg2BIRKcCwJSJSgGFLRKQAw5aISAGGLRGRAgxbIiIFGLZERAowbImIFGDYEhEpwLAlIlKAYUtEpADDlohIAYYtEZECDFsiIgUYtkRECjBsiYgUYNgSESnAsCUiUoBhS0SkAMOWiEgBhi0RkQIMWyIiBRi2REQKMGyJiBRg2BIRKcCwJSJSgGFLRKQAw5aISAGGLRGRAnpvF0DUlOLiYmzZssWu7eDBgwCAdevW2bUHBgZi2rRpymojcpdGRMTbRRA5U1tbiy5duqCyshI6nQ4AICIQEWi1//dHWV1dHWbOnIn169d7q1SilmziMgJ1WEajEenp6dDr9airq0NdXR3q6+vR0NBg+3ddXR0A8F0tdXgMW+rQpk2bhhs3bjTbJzg4GMnJyYoqIvIMw5Y6tKSkJHTp0qXJ7QaDAZmZmdDrefqBOjaGLXVoWq0W06ZNg5+fn9PtdXV1mDp1quKqiNzHsKUOb+rUqU0uJURGRiI+Pl5xRUTuY9hShzd48GD07NnTod1gMGDWrFnQaDReqIrIPQxb8gkzZsyAwWCwa+MSAvkShi35hOnTp9su87Lq06cPBgwY4KWKiNzDsCWfotqbxQAAIABJREFUcO+996Jfv362JQODwYBHH33Uy1URuY5hSz5j5syZtk+S1dXVYfLkyV6uiMh1DFvyGVOmTEFDQwMAIC4uDn369PFyRUSuY9iSz+jZsycGDRoE4Md3uUS+hF9E44L09HRs3rzZ22UQdUjZ2dlc0mnZJn7G0UUPPvggFi5c6O0y7nhlZWVYs2YNnnvuOW+XQgAyMjK8XYLPYNi6qHv37vy/dweRmJiIvn37ersMAsPWHVyzJZ/DoCVfxLAlIlKAYUtEpADDlohIAYYtEZECDFsiIgUYtkRECjBsiYgUYNgSESnAsCUiUoBhS0SkAMOWiEgBhi0RkQIMWwIAXLt2DWvXrsXw4cMRGhoKk8mEvn37Yvr06Thy5IhLY2zcuBEajQYajQadOnXySg2uOnDgALKyshAdHQ2TyYTQ0FD0798fjzzyCN577z0UFRW16eO1BXePT2BgoO35sN60Wi1CQkIQGxuL+fPn49ChQ17YkzuUUIvS0tIkLS3N22W0q9mzZ4ter5c33nhDLl68KJWVlbJnzx7p16+f6HQ62bJli8tjJScni9Fo9GoNTWloaJBnn31W9Hq9LF68WI4fPy41NTVisVhk+/btMmLECAEgAKSurq7Vj9eWPDk+hw8fFgCSmpoqIiL19fVisVgkNzdXkpKSBIBkZWVJZWWlRzUBkOzs7Fbt1x0ih2HrgjslbB9//HGH9m+//VYASN++fV0eqzVh21Y1NGXZsmUCQNatW+d0e319vYwePbrDhq27x+fWsL3VkiVLBICMHz9eGhsb3a6JYesyhq0r7oSwbY7JZBKtVuvyi9HTsG3LGpw5fvy4aLVaiYuLa7Zffn5+hwzb5jR1fFoK28bGRhk8eLAAkA0bNrj9uAxbl+VwzZaaVVlZierqavTv3x8ajcana1i3bh0aGxuRnp7ebL/4+HiICPR63/ghk9YcH41Gg6eeegoAsGbNmvYoj/6DYduOrly5gkWLFqF3794wGo3o3r07RowYgY8//hjV1dVO+/n5+SEkJASjR4/Grl27bH1yc3PtTnScOXMGGRkZCA4ORlhYGMaOHWs7qXP9+nWHEyMvvfQSAKC+vt6uPS0trdl92LRpEwBg+fLlDttOnDiBCRMmwGw2IyAgAAkJCdi7d2+rj5s7Nbhjz549AIABAwa4fV9ffY5cMXToUADAvn37UFdX59EY5AJvv7f2BZ4sI1y8eFGio6MlIiJC8vLypKysTCwWi6xcuVIAyOrVq+36hYeHS15enpSWlkphYaFMmjRJNBqNfPDBB3bjpqam2v4szM/Pl4qKCtmxY4eYTCYZNGiQXd9Ro0aJVquV06dPO9QXHx/f4p+NFotFwsPDZc6cOQ7bTp06JcHBwRIVFSXbt2+X8vJyOXr0qIwcOVJ69erVZssIzdUgIpKUlCShoaFSUFDQ4liRkZECQP7xj3+4VYOvPkciLS8jiIhUV1fbTgpeuHCh2ce7FbiM4Cqu2brCk7DNyspqciKOGjXKFrbWfp988oldn5qaGunWrZuYTCaxWCy2dusLOS8vz6FGAFJcXGxr+/LLLwWAzJ8/367v3r175a677mp2TbKkpEQGDhwoGRkZUl9f77A9PT1dAMjmzZvt2s+fPy9Go7FNwralGkREEhMTJSQkRPLz81sczxq2+/fvd6sOX32ORFwL26qqKoZt+2PYusKTsDWbzQJAysrKPO43Y8YMASDr16+3tVlfyDe/uEVEFi5cKADkyJEjdu3333+/+Pv7S0lJid0Yq1atarKmiooKiYuLk2nTpjX5Ig4KChIAUl5e7rAtJiam1WHrSg3uiouLEwCybds2t+7nq8+RiGthW1RUJADEYDD8f/buPbrp+v4f+POTNE3TNoS2h14oKBVQj1iKdgxhVC5FCuNSCpRyaQG5jAFOuQgobJMNN92m4F1ED4LbQShsogX1iCgyoKzAqdQhFOiEw63QQm/pJfby+v3hN/mRJm2TtP2k0efjnJwj788777zy+bzzNH1/Pknk+++/b7KfMwxbl/EEWXuwWCwoKytDQEAAjEajx/0iIiIAAIWFhQ7bTCaT3b/9/f0BAA0NDXbty5cvR1VVle3kx9mzZ3Hw4EHMmzfPaU11dXVITU1FdHQ0tm7dCq1W67TuiooKBAQEIDg42GF7eHi407Fd5UoNnhgyZAgAIC8vz+X7+Ooxcod1nX3gwIHQ6XStGouaxrBtB3q9HiaTCTU1NaioqPC43/Xr1wEAkZGRHteSlpaG7t2747XXXoPFYsGLL76I+fPnN/k/gQULFsBisSAzM9PubHyvXr1w9OhRW91GoxE1NTUwm80OY9y6dcvjel2twdNx/fz8sGvXrmb7rVy5EhqNBmfOnPHZY+SqhoYGvP766wCAxYsXe/wcqGUM23aSkpICAPj4448dtj3wwANYunSpXb+9e/fa9bFYLNi/fz8MBgOSkpI8rsPPzw9PPPEEbty4gRdffBHbt2/H448/7rTv2rVrcerUKXz44YfQ6/XNjjt69GgAwKeffmrXXlxcjPz8fI/rdacGd91999145plncPz4cWzevNlpn/z8fLz11luYMmUK7r33XgC+e4xc8fTTTyMnJwcpKSktXhJHreTthQxf0JqrEaKiomTPnj1SXl4uly5dkoULF0pERIRcvHjRrp/1THd5ebndme7Gn3SyrgdWV1fbta9atUoASG5urkMt5eXlYjKZRFEUmTlzptN63333XdtJkqZut5/xP3/+vISGhtpdjXDq1ClJSkqS8PBwj9Zs3a1BxL2rEayeeuop0el0smrVKsnPzxeLxSKXL1+Wd955R6KiomTw4MFiNptt/X31GIk4rtnW19fL9evXZffu3TJ8+HABIHPmzJGqqiqX99/twDVbV/EEmSs8/QRZcXGxLFmyRGJiYkSn00lUVJRMnTpVzp4922w/k8kkSUlJsn//fluf7OxshxfWmjVrREQc2seMGeNQy4oVK5yenLEaM2aM2y/k/Px8mTBhgnTq1Ml2WdOePXskMTHRdp+5c+e6vL88qSEhIcHlqxFul5OTIxkZGdK9e3fR6XRiNBrloYcekpdfflksFotDf188RkFBQQ7bFUURk8kksbGxsnDhQjlx4oRb+60xhq3LMhUREfffD/+0WP+8sl48TkQ/UBQFO3bswJQpU7xdSke3k2u2REQqYNgSEamAYUvtrvF3ADi7rV271ttlErUr3/haI/JpPC1AxHe2RESqYNgSEamAYUtEpAKGLRGRChi2REQqYNgSEamAYUtEpAKGLRGRChi2REQqYNgSEamAYUtEpAKGLRGRChi2REQq4Ld+uWjXrl1QFMXbZRCRj+LP4rggOzsbly5d8nYZhB+OxUsvvYQdO3Z4uxT6P4MGDUK3bt28XUZHt5NhSz4lMzMTaWlp/I5c8jX8DTIiIjUwbImIVMCwJSJSAcOWiEgFDFsiIhUwbImIVMCwJSJSAcOWiEgFDFsiIhUwbImIVMCwJSJSAcOWiEgFDFsiIhUwbImIVMCwJSJSAcOWiEgFDFsiIhUwbImIVMCwJSJSAcOWiEgFDFsiIhUwbImIVMCwJSJSAcOWiEgFDFsiIhUwbImIVMCwJSJSAcOWiEgFDFsiIhUwbImIVMCwJSJSAcOWiEgFft4ugKgptbW1MJvNdm2VlZUAgJKSErt2RVHQuXNn1WojchfDljqsmzdvolu3bqivr3fYFhoaavfvoUOH4ssvv1SrNCK3cRmBOqzIyEg8/PDD0Gian6aKomDatGkqVUXkGYYtdWgZGRlQFKXZPhqNBpMmTVKpIiLPMGypQ5s0aRK0Wm2T27VaLUaNGoWwsDAVqyJyH8OWOrROnTph1KhR8PNzfnpBRJCenq5yVUTuY9hSh5eenu70JBkA+Pv7Y+zYsSpXROQ+hi11eOPGjUNgYKBDu5+fH1JSUhAcHOyFqojcw7ClDi8gIAATJ06ETqeza6+rq8OMGTO8VBWRexi25BOmT5+O2tpau7ZOnTrhkUce8VJFRO5h2JJPGDFihN0HGXQ6HaZOnQp/f38vVkXkOoYt+QQ/Pz9MnTrVtpRQW1uL6dOne7kqItcxbMlnTJs2zbaUEBERgYSEBC9XROQ6hi35jF/84hfo2rUrgB8+WdbSx3iJOhKHK8Wzs7Oxfv16b9RC1CKj0QgAyM3NRWpqqperIXJu586dDm0Obw0uXbqEXbt2qVIQkbvuuOMOGI1GhISEeLsUIgeXL19uMj+b/IpFZ8lM1BFkZmZiypQp3i6DyEFmZibS0tKcbuOiF/kcBi35IoYtEZEKGLZERCpg2BIRqYBhS0SkAoYtEZEKGLZERCpg2BIRqYBhS0SkAoYtEZEKGLZERCpg2BIRqYBhS0SkAoZtB/LCCy9AURQoioJu3bp5uxwAQElJCTZu3Ijhw4cjNDQUBoMBvXv3xowZM3Dy5EmXxti+fbvteQUEBHilhqaYzWZbbdZbdnZ2i/dbsWKF3X2effbZVtXRkuDgYIc6FUWBRqNBly5dMGHCBBw7dqxda/gxzE9n+1Gj0SAkJARxcXFYtGgRTpw40T7FSiM7duwQJ82kori4OImOjrZrq6iokF69esmYMWNUrWXu3Lni5+cnL730kly7dk0qKyvl4MGDct9994lWq5UPPvjA5bESExNFr9d7tYam5ObmCgABIKNHj262b3FxsQQHBwsAmTFjRqsf21XWGpOTk21tpaWl8q9//UvCw8NFp9PJvn372r0OX5+fjfdjXV2dFBYWyu7du2XYsGECQGbPni2VlZVu19NMfmbyna2PEBE0NDSgoaFB9ceeM2cOnnjiCURGRiIwMBAJCQnYtm0b6uvrsXLlyh9NDQaDAXfeeSc++eQTHD9+vMl+GzZsQPfu3dvkMVvLZDIhJSUF69evR21tLZYsWeKVOnx5fmq1WkRERCA5ORlffPEFVq5ciS1btmDatGkQkbYr1I1kJpU4e+fQERkMBtFoNNLQ0OBSf0/f2bZlDU3Jzc2VoKAgefPNNx3ePd6upKREQkNDZcuWLR3ina3VlStXbO/MS0pK2rUOX5+fze1HEZGGhgYZMGCAAJBt27a59Zh8Z0ttrrKyEtXV1bj//vuhKMqPpoZHH30U0dHR+Oijj5CXl+ew/ZVXXsEvf/lL9OzZs00er63Ibe/AvHU8OpLWzA1FUfDYY48BAN544402q6nVYbt79267xeaLFy8iLS0NRqMRYWFhyMjIQElJCS5cuIBx48bBaDQiKioK8+fPR0VFhd1YdXV12LFjBx555BFERkbCYDAgNjYWL7/8st2fJ4MHD7Z7zPT0dADAiBEj7NpLS0tdeg6NF/6PHTuGxMREGI1GBAYGYtiwYTh8+LDD/W7evIlly5ahZ8+e8Pf3R0hICEaPHo0vv/yyVX1b2sc1NTVO2y9cuIC0tDR07twZYWFhGDt2LAoKChzGO3PmDCZMmACTyYTAwED8/Oc/x549e+z237x585qtyfqzSWvWrGl2/KCgICQkJODQoUMtPk93NVeDp/R6PVasWAERwZ/+9Ce7bWazGa+++ipWr17d5P29NYcPHDgAAOjTpw9MJhMAzk/A87kxePBgAMDRo0dRW1vr0RgO3Hgb3Kzk5GQBIBMnTpTjx4+L2WyW9957z3bCITk5WXJzc6WiokI2btwoAGTp0qV2Y2RlZQkA+fOf/yy3bt2SoqIieeWVV0Sj0ciTTz5p1/frr7+WoKAgiYuLE7PZLCIiNTU1MmDAAHn//ffdrl/khz+PgoKCZODAgXLkyBExm81y7Ngx6du3r/j7+8uBAwdsfa9duyYxMTESEREhWVlZUlZWJvn5+TJx4kRRFEXefvttj/pa63D2Z5p1H1dXVzttT05OttW9b98+MRgM0r9/f7u+586dk86dO0t0dLR89tlnUlFRIf/9739lxIgR0qVLF5f+zC8sLJSIiAiZN2+ewzZn4+fl5cnIkSOlR48ebbaM0FwNIiLDhg2T0NBQyc7Odmk86zKCiEhVVZVERESIRqORb7/91tbn+eeflylTpoiIyL///W+nywjtOYed/flbVlbm9AQZ52fTc6OlZQQRkerqatuyzNWrV1t8TKvmlhHaPGz37t1r196nTx8BIF999ZVde0xMjNxzzz12bVlZWTJ06FCHsdPT00Wn00lZWZl99ZmZtoBvaGiQWbNmyerVq92u3SouLk4ASG5url17Xl6eAJC4uDhb2+zZswWAw4uipqZGunbtKgaDQQoLC93ua63Dk8mclZVl1z558mQBIEVFRba21NRUASC7du2y63vjxg0JDAxscTIXFxdLv379JC0tTerq6hy2NzX+lStXRK/Xt0nYtlSDiMiQIUMkJCREjhw54tKYt4etiMhf/vIXASDp6ekiIlJZWSkRERFy8uRJEWk+bNtrDt9+xYT1piiKhIWFyfjx4yUnJ8fWl/Oz6bnhSthWVVV1/LC9fv26XfsjjzwiABwuoxg8eLAYjUaXxv7b3/4mAJy+cNasWSMAZNCgQTJ27Fipr693u3Yr6ztbZ7p27Wq3400mkwCQ8vJyh74ZGRkCQLZu3ep2X2sdnkzm218QIiJLly4VALaAEBExGo0CQCoqKhzGf/DBB5udzGazWeLj42X69OlNTuTmxo+NjW112LpSgycah21FRYWEhYWJVquVc+fOyfr16+1enE2FbVPaYg67EhJWnJ9Nzw1X9mNBQYEAEJ1OJ99//32T/RpT9QRZp06d7P6t0Wig1WoRGBho167Vah0uEykrK8Pvf/97xMbGIiQkxLY+s2LFCgBAVVWVw+OtW7cOAwYMwJEjR5CamgqNpnVPqXPnzk7bw8PDAQA3btyAxWJBWVkZAgICYDQaHfpGREQAAAoLC93q21rWtTorf39/ALDtZ4vFgoqKCgQEBCA4ONjh/iEhIU2OXVdXh9TUVERHR2Pr1q3QarUOfVoa37oPPeVKDW0lODgYS5YsQX19PZ555hm88MIL+O1vf9vi/TrCHOb8bP3csJ5jGDhwIHQ6XavGsupQVyOMGzcO69atw/z583H27Fk0NDRARLBhwwYAcHrN24EDB1BWVobY2FgsWrSo1Z8ounnzptPHuXHjBoAfAkOv18NkMqGmpsbhJB8AXL9+HQAQGRnpVt/2ptfrYTQaUVNTA7PZ7LDd+hydWbBgASwWCzIzM+Hn52dr79WrF44ePerS+Ldu3WpV/a7U0JZ+85vfwGQyYdu2bYiLi8PPfvazFu/TEeYw52fr5kZDQwNef/11AMDixYvdum9zOkzY1tfX4/Dhw4iMjMTjjz+OLl262C7ZqK6udnqf7777DnPnzsU///lPfPTRRzAYDEhOTkZRUZHHddTU1Dh87PGbb77B1atXERcXh6ioKABASkoKAGDv3r12fS0WC/bv3w+DwYCkpCS3+7a30aNHAwA+/fRTu/bCwkKcPXvW6X3Wrl2LU6dO4cMPP4Rer/do/OLiYuTn53tatls1tBWTyYRly5bBZDK59K62o8xhgPOzNZ5++mnk5OQgJSUFqamprR7Pxo01h2Y1tV6TlJQkWq3Wof+QIUMc1keHDx8uAOSvf/2rFBUVSVVVlXzxxRdyxx13CAC7jyJWVFRI37595cMPP7S1HThwQHQ6nTz88MNurbNYxcXFiclkksTERLevRigvL7c7g7tp0yaP+lrr8GRNrHH7qlWrHE74nT9/XkJDQ+3O9n7zzTcyatQoufPOOx3WxN59912HkzKNb7ef8Xc2/qlTpyQpKUnCw8M9WrN1twaR1l2N4Iqm1mzbcw67s2bL+dn03Gi8H+vr6+X69euye/du2/GbM2eOVFVVtbifG2vXE2TZ2dkOT27NmjVy7Ngxh/bnnnvONklvvz3zzDMiIlJUVCQLFiyQ7t27i06nk4iICJk9e7Y89dRTtr7x8fGyePFiu/t/8803UlRU5DDuunXr3NpR1kn07bffSlJSkhiNRjEYDDJkyBA5dOiQQ//i4mJZsmSJxMTEiE6nE5PJJElJSbJ//36P+lpPojTelx988IFD+4wZM5rc9yLi0H77Z9bz8/NlwoQJ0qlTJwkMDJRBgwbJV199JUOHDpXAwEC7useMGeP2ZL59fOvlPXv27JHExETbfebOnevycfGkhoSEBJevRggKCrIbKykpqdn+zh7/1VdfFZH2m8ONawTgcDVPY5yfjnPD2X5UFEVMJpPExsbKwoUL5cSJE83u1+Y0F7bK/z1xm8zMTKSlpbXtZ4J9RL9+/VBcXIzLly97uxSvuPfee1FdXY2LFy96uxQiB74wP5vJz50dZs2W1FFYWIjQ0FCHT8VcuHABBQUFGD58uJcqI/pxz0+G7U9QSUkJFixYgEuXLqGqqgo5OTlIS0tDp06d8Lvf/c7b5dFP3I91fv7ow9bZFy43vlm/UPjkyZO4cuUKFEVx6eyzL4qMjMTnn3+O0tJSPPzwwwgJCcH48ePRu3dv5OTk4K677lKtFleOzdq1a1Wrh7yvI83PtubXchff9lNce25JYmIiEhMTvV0Gjw051VHmZ1v70b+zJSLqCBi2REQqYNgSEamAYUtEpAKGLRGRChi2REQqYNgSEamAYUtEpAKGLRGRChi2REQqYNgSEamAYUtEpAKGLRGRCpr81q82/aEzIqKfgOZ+5cXhnW337t0xefLkdi2IyFNFRUU4ePCgt8sgcqpbt25N5qfDb5ARdWQ/5d/II5/G3yAjIlIDw5aISAUMWyIiFTBsiYhUwLAlIlIBw5aISAUMWyIiFTBsiYhUwLAlIlIBw5aISAUMWyIiFTBsiYhUwLAlIlIBw5aISAUMWyIiFTBsiYhUwLAlIlIBw5aISAUMWyIiFTBsiYhUwLAlIlIBw5aISAUMWyIiFTBsiYhUwLAlIlIBw5aISAUMWyIiFTBsiYhUwLAlIlIBw5aISAUMWyIiFTBsiYhUwLAlIlKBn7cLIGrK5cuXMWvWLNTX19vaiouL4efnh6FDh9r1veeee/DWW2+pXCGR6xi21GF169YNFy5cwP/+9z+HbV999ZXdvxMSEtQqi8gjXEagDm3mzJnQ6XQt9ps6daoK1RB5jmFLHdqMGTNQW1vbbJ/77rsPffr0UakiIs8wbKlD69WrF/r27QtFUZxu1+l0mDVrlspVEbmPYUsd3syZM6HVap1uq6urw5QpU1SuiMh9DFvq8KZNm4aGhgaHdkVRMGDAAPTo0UP9oojcxLClDq9r164YNGgQNBr76arVajFz5kwvVUXkHoYt+YSMjAyHNhHBpEmTvFANkfsYtuQTUlNT7d7ZarVajBgxAuHh4V6sish1DFvyCSEhIRg5cqTtRJmIID093ctVEbmOYUs+Iz093XaizM/PD+PHj/dyRUSuY9iSzxg/fjz0er3tvzt16uTliohcx+9GcEF2djYuXbrk7TIIwIMPPogjR44gJiYGmZmZ3i6HAAwaNAjdunXzdhkdniIi4u0iOrrU1FTs2rXL22UQdUg7duzgB0tatpPLCC6aPHkyRIQ3L9++//57rFy50ut18PbDjVzHsCWfotPpsHbtWm+XQeQ2hi35HIPB4O0SiNzGsCUiUgHDlohIBQxbIiIVMGyJiFTAsCUiUgHDlohIBQxbIiIVMGyJiFTAsCUiUgHDlohIBQxbIiIVMGwJAFBSUoKNGzdi+PDhCA0NhcFgQO/evTFjxgycPHnSpTG2b98ORVGgKAoCAgLcrkFEcPjwYSxevBh333039Ho9wsPDMXjwYPzjH/9o02+ZOnbsGGbPno2YmBgYDAaEhobi/vvvx6RJk/Dmm2+ioKCgzR6rrbh7jIKDg23Hw3rTaDQICQlBXFwcFi1ahBMnTnjhmfxECbVo8uTJMnnyZG+X0a7mzp0rfn5+8tJLL8m1a9eksrJSDh48KPfdd59otVr54IMPXB4rMTFR9Hq92zWcPn1aAMiIESPk5MmTUl1dLQUFBTJt2jQBIMuXL3d7zMbq6+vlySefFD8/P1mxYoWcPn1aampqpLCwUD777DMZMWKEABAAUltb2+rHa0ueHKPc3FwBIMnJySIiUldXJ4WFhbJ7924ZNmyYAJDZs2dLZWWlRzUBkB07drTqef1EZDJsXfBTCdtf/epXDu1ff/21AJDevXu7PFZrwtbPz09u3bpl126xWCQsLEz0er3U1NS4Pe7tVq9eLQBk06ZNTrfX1dXJ6NGjO2zYunuMGodtYytXrhQAMn78eGloaHC7Joatyxi2rvgphG1zDAaDaDQal1+MnoZtc/r16ycApLS01OMxTp8+LRqNRuLj45vtd+TIkQ4Zts1p6hi1FLYNDQ0yYMAAASDbtm1z+3EZti7L5JotNauyshLV1dW4//77oSiKV2ooLS3FuXPn8MADD8BkMnk8zqZNm9DQ0IDU1NRm+w0cOBAiAj8/3/iJvtYcI0VR8NhjjwEA3njjjfYoj/4Pw7Yd3bx5E8uWLUPPnj2h1+vRrVs3jBgxAlu2bEF1dbXTfv7+/ggJCcHo0aPx5Zdf2vrs3r3b7kTHhQsXkJaWhs6dOyMsLAxjx461ndQpLS11ODHy7LPPAgDq6urs2idPntzsc9i5cycAYM2aNQ7bzpw5gwkTJsBkMiEoKAgJCQk4dOhQq/ebVXl5OQ4fPozx48cjMjIS7733XqvGO3jwIACgb9++bt/XV4+RKwYPHgwAOHr0KGpraz0ag1zg7ffWvsCTZYRr165JTEyMREZGSlZWlpSXl0thYaGsW7dOAMiGDRvs+kVEREhWVpaUlZVJfn6+TJw4URRFkbfffttu3OTkZNufhUeOHBGz2Sz79u0Tg8Eg/fv3t+s7atQo0Wg0cv78eYf6Bg4c2OKfjYWFhRIRESHz5s1z2Hbu3Dnp3LmzREdHy2effSYVFRWSl5cnI0eOlB49erR6GcG6nwDI0KFDJS8vz2m/YcOGSWhoqGRnZ7c4ZlRUlACQ//zZf4R4AAAgAElEQVTnP27V4qvHSKTlZQQRkerqatu+vnr1arOP1xi4jOAqrtm6wpOwnT17dpMTcdSoUbawtfZ7//337frU1NRI165dxWAwSGFhoa3d+kLOyspyqBGAFBUV2do+//xzASCLFi2y63vo0CG54447ml2TLC4uln79+klaWprU1dU5bE9NTRUAsmvXLrv2K1euiF6vb5M1W4vFIqdPn5Zf//rXotVq5Y9//KNDnyFDhkhISIgcOXKkxfGsYZuTk+NWHb56jERcC9uqqiqGbftj2LrCk7A1mUwCQMrLyz3ul5GRIQBk69attjbrC/n2F7eIyNKlSwWAnDx50q79gQcekMDAQCkuLrYbY/369U3WZDabJT4+XqZPn97ki9hoNAoAqaiocNgWGxvb5ifIUlJSBIDs27fP4zHi4+MFgHz88cdu3c9Xj5GIa2FbUFAgAESn08n333/fZD9nGLYu4wmy9mCxWFBWVoaAgAAYjUaP+0VERAAACgsLHbY1PlHk7+8PAGhoaLBrX758OaqqqmwnP86ePYuDBw9i3rx5Tmuqq6tDamoqoqOjsXXrVmi1Wqd1V1RUICAgAMHBwQ7bw8PDnY7dGuPGjQMA7Nmzx+MxhgwZAgDIy8tz+T6+eozcYV1nHzhwIHQ6XavGoqYxbNuBXq+HyWRCTU0NKioqPO53/fp1AEBkZKTHtaSlpaF79+547bXXYLFY8OKLL2L+/PlN/k9gwYIFsFgsyMzMtDsb36tXLxw9etRWt9FoRE1NDcxms8MYt27d8rjepuj1+laPvWDBAvj5+WHXrl3N9lu5ciU0Gg3OnDnjs8fIVQ0NDXj99dcBAIsXL/b4OVDLGLbtJCUlBQDw8ccfO2x74IEHsHTpUrt+e/futetjsViwf/9+GAwGJCUleVyHn58fnnjiCdy4cQMvvvgitm/fjscff9xp37Vr1+LUqVP48MMPbeHWlNGjRwMAPv30U7v24uJi5Ofne1Trk08+ifT0dKfbPvnkEwBA//79PRobAO6++24888wzOH78ODZv3uy0T35+Pt566y1MmTIF9957LwDfPUauePrpp5GTk4OUlJQWL4mjVvL2QoYvaM3VCFFRUbJnzx4pLy+XS5cuycKFCyUiIkIuXrxo1896pru8vNzuTHfjTzpZ1wOrq6vt2letWiUAJDc316GW8vJyMZlMoiiKzJw502m97777ru0kSVO328/4nz9/XkJDQ+2uRjh16pQkJSVJeHi4R2u2y5cvF0VR5A9/+IN89913UlNTI999953tU07x8fFSVVVldx93rkaweuqpp0Sn08mqVaskPz9fLBaLXL58Wd555x2JioqSwYMHi9lstvX31WMk4rhmW19fL9evX5fdu3fL8OHDBYDMmTPHYb+6ClyzdRVPkLnC00+QFRcXy5IlSyQmJkZ0Op1ERUXJ1KlT5ezZs832M5lMkpSUJPv377f1yc7OdnhhrVmzRkTEoX3MmDEOtaxYscLpyRmrMWPGuP1Czs/PlwkTJkinTp1slzXt2bNHEhMTbfeZO3euy/urrKxM3nnnHUlKSpIePXqIv7+/BAcHS3x8vDz33HNOAyEhIcHlqxFul5OTIxkZGdK9e3fR6XRiNBrloYcekpdfflksFotDf188RkFBQQ7bFUURk8kksbGxsnDhQjlx4oRb+60xhq3LMhWRNvwqpR8p659X1ovHiegHiqJgx44dmDJlirdL6eh2cs2WiEgFDFsiIhUwbKndNf4OAGe3tWvXertMonblG19rRD6NpwWI+M6WiEgVDFsiIhUwbImIVMCwJSJSAcOWiEgFDFsiIhUwbImIVMCwJSJSAcOWiEgFDFsiIhUwbImIVMCwJSJSAcOWiEgF/NYvF12+fBmZmZneLoOIfBTD1kVHjx5FWlqat8sgIh/F3yAjn5KZmYm0tDR+Ry75Gv4GGRGRGhi2REQqYNgSEamAYUtEpAKGLRGRChi2REQqYNgSEamAYUtEpAKGLRGRChi2REQqYNgSEamAYUtEpAKGLRGRChi2REQqYNgSEamAYUtEpAKGLRGRChi2REQqYNgSEamAYUtEpAKGLRGRChi2REQqYNgSEamAYUtEpAKGLRGRChi2REQqYNgSEamAYUtEpAKGLRGRChi2REQqYNgSEamAYUtEpAKGLRGRCvy8XQBRU4qKivDBBx/YtR0/fhwAsGnTJrv24OBgTJ8+XbXaiNyliIh4uwgiZywWC7p06YLKykpotVoAgIhARKDR/P8/ymprazFz5kxs3brVW6UStWQnlxGow9Lr9UhNTYWfnx9qa2tRW1uLuro61NfX2/5dW1sLAHxXSx0ew5Y6tOnTp+P7779vtk/nzp2RmJioUkVEnmHYUoc2bNgwdOnSpcntOp0O6enp8PPj6Qfq2Bi21KFpNBpMnz4d/v7+TrfX1tZi2rRpKldF5D6GLXV406ZNa3IpISoqCgMHDlS5IiL3MWypwxswYADuvPNOh3adTodZs2ZBURQvVEXkHoYt+YSMjAzodDq7Ni4hkC9h2JJPmDFjhu0yL6tevXqhb9++XqqIyD0MW/IJ9957L+677z7bkoFOp8Ojjz7q5aqIXMewJZ8xc+ZM2yfJamtrMWXKFC9XROQ6hi35jKlTp6K+vh4AEB8fj169enm5IiLXMWzJZ9x5553o378/gB/e5RL5EocvosnMzERaWpq36iEi8nlOvt9rZ5OfcdyxY0f7VkPkgfLycrzxxht46qmnvF0KkYPs7Gy89NJLTrc1GbY8+UAd1ZAhQ9C7d29vl0HkVFNhyzVb8jkMWvJFDFsiIhUwbImIVMCwJSJSAcOWiEgFDFsiIhUwbImIVMCwJSJSAcOWiEgFDFsiIhUwbImIVMCwJSJSAcOWiEgFDNsO5IUXXoCiKFAUBd26dfN2OQCAkpISbNy4EcOHD0doaCgMBgN69+6NGTNm4OTJky6NsX37dtvzCggIcLsGEcHhw4exePFi3H333dDr9QgPD8fgwYPxj3/8w9l3h7rMbDbbarPesrOzW7zfihUr7O7z7LPPelyDK4KDgx3qVBQFGo0GXbp0wYQJE3Ds2LF2reHHMD+d7UeNRoOQkBDExcVh0aJFOHHiRPsUK43s2LFDnDSTiuLi4iQ6OtquraKiQnr16iVjxoxRtZa5c+eKn5+fvPTSS3Lt2jWprKyUgwcPyn333SdarVY++OADl8dKTEwUvV7vdg2nT58WADJixAg5efKkVFdXS0FBgUybNk0AyPLly90es7Hc3FwBIABk9OjRzfYtLi6W4OBgASAzZsxo9WO7ylpjcnKyra20tFT+9a9/SXh4uOh0Otm3b1+71+Hr87Pxfqyrq5PCwkLZvXu3DBs2TADI7NmzpbKy0u16msnPTL6z9REigoaGBjQ0NKj+2HPmzMETTzyByMhIBAYGIiEhAdu2bUN9fT1WrlypSg1+fn7IzMxE3759ERAQgLvuugtbtmxBWFgYXnvtNVgsllY/hsFgwJ133olPPvkEx48fb7Lfhg0b0L1791Y/XlswmUxISUnB+vXrUVtbiyVLlnilDl+en1qtFhEREUhOTsYXX3yBlStXYsuWLZg2bVqr/mpy4EYyk0qcvXPoiAwGg2g0GmloaHCpv6fvbJvTr18/ASClpaWtGic3N1eCgoLkzTffdHj3eLuSkhIJDQ2VLVu2dIh3tlZXrlyxvTMvKSlp1zp8fX42tx9FRBoaGmTAgAECQLZt2+bWY/KdLbW5yspKVFdX4/7774eiKF6pobS0FOfOncMDDzwAk8nUJmM++uijiI6OxkcffYS8vDyH7a+88gp++ctfomfPnm3yeG1FbnsH5q3j0ZG0Zn4qioLHHnsMAPDGG2+0WU2tDtvdu3fbLTZfvHgRaWlpMBqNCAsLQ0ZGBkpKSnDhwgWMGzcORqMRUVFRmD9/PioqKuzGqqurw44dO/DII48gMjISBoMBsbGxePnll+3+PBk8eLDdY6anpwMARowYYddeWlrq0nNovPB/7NgxJCYmwmg0IjAwEMOGDcPhw4cd7nfz5k0sW7YMPXv2hL+/P0JCQjB69Gh8+eWXrerb0j6uqalx2n7hwgWkpaWhc+fOCAsLw9ixY1FQUOAw3pkzZzBhwgSYTCYEBgbi5z//Ofbs2WO3/+bNm9dsTTt37gQArFmzptnxg4KCkJCQgEOHDrX4PF1VXl6Ow4cPY/z48YiMjMR7773XZmPr9XqsWLECIoI//elPdtvMZjNeffVVrF69usn7e2sOHzhwAADQp08f2/94OD+dz09XDB48GABw9OhR1NbWejSGAzfeBjcrOTlZAMjEiRPl+PHjYjab5b333rOdcEhOTpbc3FypqKiQjRs3CgBZunSp3RhZWVkCQP785z/LrVu3pKioSF555RXRaDTy5JNP2vX9+uuvJSgoSOLi4sRsNouISE1NjQwYMEDef/99t+sX+eHPo6CgIBk4cKAcOXJEzGazHDt2TPr27Sv+/v5y4MABW99r165JTEyMRERESFZWlpSVlUl+fr5MnDhRFEWRt99+26O+1jqc/Zlm3cfV1dVO25OTk21179u3TwwGg/Tv39+u77lz56Rz584SHR0tn332mVRUVMh///tfGTFihHTp0sWlP/MLCwslIiJC5s2b57DN2fh5eXkycuRI6dGjR6uXEdatW2f7c3no0KGSl5fntN+wYcMkNDRUsrOzXRrXuowgIlJVVSURERGi0Wjk22+/tfV5/vnnZcqUKSIi8u9//9vpMkJ7zmFnf/6WlZU5PUHG+el8fja1Hxurrq62zbOrV6+2+JhWzS0jtHnY7t271669T58+AkC++uoru/aYmBi555577NqysrJk6NChDmOnp6eLTqeTsrIy++ozM20B39DQILNmzZLVq1e7XbtVXFycAJDc3Fy79ry8PAEgcXFxtrbZs2cLAIcXRU1NjXTt2lUMBoMUFha63ddahyeTOSsry6598uTJAkCKiopsbampqQJAdu3aZdf3xo0bEhgY2OJkLi4uln79+klaWprU1dU5bG9q/CtXroher2+TNVuLxSKnT5+WX//616LVauWPf/yjQ58hQ4ZISEiIHDlyxKUxbw9bEZG//OUvAkDS09NFRKSyslIiIiLk5MmTItJ82LbXHL79ignrTVEUCQsLk/Hjx0tOTo6tL+en8/kp4lrYVlVVdfywvX79ul37I488IgAcLqMYPHiwGI1Gl8b+29/+JgCcvnDWrFkjAGTQoEEyduxYqa+vd7t2K+s7W2e6du1qt+NNJpMAkPLycoe+GRkZAkC2bt3qdl9rHZ5M5ttfECIiS5cuFQC2gBARMRqNAkAqKiocxn/wwQebncxms1ni4+Nl+vTpTU7k5saPjY1t8xNkKSkpAqDVlzw1DtuKigoJCwsTrVYr586dk/Xr19u9OJsK26a0xRx2JSSsOD+dz08R1/ZjQUGBABCdTifff/99k/0aU/UEWadOnez+rdFooNVqERgYaNeu1WodLhMpKyvD73//e8TGxiIkJMS2PrNixQoAQFVVlcPjrVu3DgMGDMCRI0eQmpoKjaZ1T6lz585O28PDwwEAN27cgMViQVlZGQICAmA0Gh36RkREAAAKCwvd6ttajU8S+fv7A4BtP1ssFlRUVCAgIADBwcEO9w8JCWly7Lq6OqSmpiI6Ohpbt26FVqt16NPS+NZ92JbGjRsHANizZ0+bjhscHIwlS5agvr4ezzzzDF544QX89re/bfF+HWEOc346n5/usJ5jGDhwIHQ6XavGsupQVyOMGzcO69atw/z583H27Fk0NDRARLBhwwYAcHrN24EDB1BWVobY2FgsWrTI5U81NeXmzZtOH+fGjRsAfggMvV4Pk8mEmpoah5N8AHD9+nUAQGRkpFt925ter4fRaERNTQ3MZrPDdutzdGbBggWwWCzIzMyEn5+frb1Xr144evSoS+PfunWrDZ6FPb1e325j/+Y3v4HJZMK2bdsQFxeHn/3sZy3epyPMYc5P5/PTVQ0NDXj99dcBAIsXL3brvs3pMGFbX1+Pw4cPIzIyEo8//ji6dOliu2Sjurra6X2+++47zJ07F//85z/x0UcfwWAwIDk5GUVFRR7XUVNT4/Cxx2+++QZXr15FXFwcoqKiAAApKSkAgL1799r1tVgs2L9/PwwGA5KSktzu295Gjx4NAPj000/t2gsLC3H27Fmn91m7di1OnTqFDz/80BZu7o5fXFyM/Px8j2p+8sknbWfrG/vkk08AAP379/do7OaYTCYsW7YMJpPJpXe1HWUOA5yfrfH0008jJycHKSkpSE1NbfV4Nm6sOTSrqfWapKQk0Wq1Dv2HDBnisD46fPhwASB//etfpaioSKqqquSLL76QO+64w2FdrqKiQvr27Ssffvihre3AgQOi0+nk4YcfdmudxSouLk5MJpMkJia6fTVCeXm53RncTZs2edTXWocna2KN21etWuVwwu/8+fMSGhpqd7b3m2++kVGjRsmdd97psCb27rvvOpyUaXy7/Yy/s/FPnTolSUlJEh4e7tGa7fLly0VRFPnDH/4g3333ndTU1Mh3330nK1euFAASHx8vVVVVdvdpzdUIrmhqzbY957A7a7acn87np7P9WF9fL9evX5fdu3fbjt+cOXMc5pQr2vUEWXZ2tsOTW7NmjRw7dsyh/bnnnrNN0ttvzzzzjIiIFBUVyYIFC6R79+6i0+kkIiJCZs+eLU899ZStb3x8vCxevNju/t98840UFRU5jLtu3Tq3dpR1En377beSlJQkRqNRDAaDDBkyRA4dOuTQv7i4WJYsWSIxMTGi0+nEZDJJUlKS7N+/36O+1pMojfflBx984NA+Y8aMJve9iDi03/6Z9fz8fJkwYYJ06tRJAgMDZdCgQfLVV1/J0KFDJTAw0K7uMWPGuD2Zbx/fennPnj17JDEx0XafuXPnunxcysrK5J133pGkpCTp0aOH+Pv7S3BwsMTHx8tzzz3n9EWRkJDg8tUIQUFBds8nKSmp2f7O9sGrr74qIu03hxvXCMDhap7GOD8d56ez/agoiphMJomNjZWFCxfKiRMnmt2vzWkubJX/e+I2mZmZSEtLa9vPBPuIfv36obi4GJcvX/Z2KV5x7733orq6GhcvXvR2KUQOfGF+NpOfOzvMmi2po7CwEKGhoQ6firlw4QIKCgowfPhwL1VG9OOenwzbn6CSkhIsWLAAly5dQlVVFXJycpCWloZOnTrhd7/7nbfLo5+4H+v8/NGHrbMvXG58s36h8MmTJ3HlyhUoiuLS2WdfFBkZic8//xylpaV4+OGHERISgvHjx6N3797IycnBXXfdpVotrhybtWvXqlYPeV9Hmp9tza/lLr7tp7j23JLExEQkJiZ6uwweG3Kqo8zPtvajf2dLRNQRMGyJiFTAsCUiUgHDlohIBQxbIiIVMGyJiFTAsCUiUgHDlohIBQxbIiIVMGyJiFTAsCUiUgHDlohIBQxbIiIVNPmtX9YfqiMiotZzCNtBgwZhx44d3qiFqEXZ2dl46aWXOEfJ5zj8BhlRR/ZT/o088mn8DTIiIjUwbImIVMCwJSJSAcOWiEgFDFsiIhUwbImIVMCwJSJSAcOWiEgFDFsiIhUwbImIVMCwJSJSAcOWiEgFDFsiIhUwbImIVMCwJSJSAcOWiEgFDFsiIhUwbImIVMCwJSJSAcOWiEgFDFsiIhUwbImIVMCwJSJSAcOWiEgFDFsiIhUwbImIVMCwJSJSAcOWiEgFDFsiIhUwbImIVMCwJSJSAcOWiEgFft4ugKgptbW1MJvNdm2VlZUAgJKSErt2RVHQuXNn1WojchfDljqsmzdvolu3bqivr3fYFhoaavfvoUOH4ssvv1SrNCK3cRmBOqzIyEg8/PDD0Gian6aKomDatGkqVUXkGYYtdWgZGRlQFKXZPhqNBpMmTVKpIiLPMGypQ5s0aRK0Wm2T27VaLUaNGoWwsDAVqyJyH8OWOrROnTph1KhR8PNzfnpBRJCenq5yVUTuY9hSh5eenu70JBkA+Pv7Y+zYsSpXROQ+hi11eOPGjUNgYKBDu5+fH1JSUhAcHOyFqojcw7ClDi8gIAATJ06ETqeza6+rq8OMGTO8VBWRexi25BOmT5+O2tpau7ZOnTrhkUce8VJFRO5h2JJPGDFihN0HGXQ6HaZOnQp/f38vVkXkOoYt+QQ/Pz9MnTrVtpRQW1uL6dOne7kqItcxbMlnTJs2zbaUEBERgYSEBC9XROQ6hi35jF/84hfo2rUrgB8+WdbSx3iJOhJ+EY0L1q9fj+zsbG+XQQCMRiMAIDc3F6mpqV6uhgBg2bJlGDhwoLfL6PD41sAF2dnZOHr0qLfLIAB33HEHjEYjQkJCvF0KAdi1axcuXbrk7TJ8At/Zuuihhx7Czp07vV0GAcjMzMSUKVO8XQYBLX5JEP1/fGdLPodBS76IYUtEpAKGLRGRChi2REQqYNgSEamAYUtEpAKGLRGRChi2REQqYNgSEamAYUtEpAKGLRGRChi2REQqYNgSEamAYUsAgJKSEmzcuBHDhw9HaGgoDAYDevfujRkzZuDkyZMujbF9+3YoigJFURAQENAmdY0fPx6KouDZZ59tk/Gsjh07htmzZyMmJgYGgwGhoaG4//77MWnSJLz55psoKCho08drC+4eo+DgYNvxsN40Gg1CQkIQFxeHRYsW4cSJE154Jj9NDFsCAKxYsQK/+c1vkJycjG+//RY3b97E5s2b8fXXXyM+Ph67d+9ucYypU6dCRJCYmNgmNb333nvIyspqk7GsGhoasGLFCgwaNAjh4eH45JNPUFpaitOnT2PDhg0oLy/HokWL0KtXL9TV1bXpY7eWu8fIbDYjNzcXAJCcnAwRQW1tLc6cOYM//vGPOHPmDH72s5/h0UcfRVVVlTee0k+LUIsmT54skydP9nYZ7Wru3Lnyq1/9yqH966+/FgDSu3dvl8dKTEwUvV7fqnquXLkiISEhkpGRIQBk3bp1rRrPavXq1QJANm3a5HR7XV2djB49WgBIbW1tmzxmW/HkGOXm5goASU5OdjrmypUrBYCMHz9eGhoa3K4JgOzYscPt+/0EZfKdLQEA3nnnHbz11lsO7XFxcTAYDCgoKICIqFbP/PnzkZqaipEjR7bZmGfOnMHzzz+P+Ph4zJ8/32kfrVaL3/3ud232mG2pPY7R888/jwEDBuCjjz7C9u3b26pUcoJhS82qrKxEdXU17r//ftW+lX/z5s04deoUXnjhhTYdd9OmTWhoaGjxt8sGDhwIEYGfn2/8kElrjpGiKHjssccAAG+88UZ7lEf/h2Hbjm7evIlly5ahZ8+e0Ov16NatG0aMGIEtW7agurraaT9/f3+EhIRg9OjR+PLLL219du/ebXei48KFC0hLS0Pnzp0RFhaGsWPH2k7qlJaWOpwYsZ5gqqurs2ufPHlys8/B+lNAa9ascdh25swZTJgwASaTCUFBQUhISMChQ4datc8uX76M5cuXY/PmzbYfd2wrBw8eBAD07dvX7fv66jFyxeDBgwEAR48etf1UPLUDL69j+ARP1myvXbsmMTExEhkZKVlZWVJeXi6FhYWybt06ASAbNmyw6xcRESFZWVlSVlYm+fn5MnHiRFEURd5++227cZOTk21rcEeOHBGz2Sz79u0Tg8Eg/fv3t+s7atQo0Wg0cv78eYf6Bg4cKNu2bWv2ORQWFkpERITMmzfPYdu5c+ekc+fOEh0dLZ999plUVFRIXl6ejBw5Unr06OHxmm1SUpIsWrTI9u+///3vza7ZDhs2TEJDQyU7O7vFsaOiogSA/Oc//3GrJl89RiItr9mKiFRXVwsAASBXr15t9vEaA9dsXZXJsHWBJ2E7e/bsJifiqFGjbGFr7ff+++/b9ampqZGuXbuKwWCQwsJCW7v1hZyVleVQIwApKiqytX3++ecCwC68REQOHTokd9xxR7MngIqLi6Vfv36SlpYmdXV1DttTU1MFgOzatcuu/cqVK6LX6z0K202bNsldd90lZrPZ1tZS2A4ZMkRCQkLkyJEjLY5vDducnBy36vLVYyTiWthWVVUxbNsfw9YVnoStyWQSAFJeXu5xP+uZ+K1bt9rarC/k21/cIiJLly4VAHLy5Em79gceeEACAwOluLjYboz169c3WZPZbJb4+HiZPn16ky9io9EoAKSiosJhW2xsrNthe/HiRTGZTHLgwAG79pbC1h3x8fECQD7++GO37uerx0jEtbAtKCgQAKLT6eT7779vsp8zDFuX8WqE9mCxWFBWVoaAgIBm1x1b6hcREQEAKCwsdNhmMpns/u3v7w/gh+tIb7d8+XJUVVXZTn6cPXsWBw8exLx585zWVFdXh9TUVERHR2Pr1q3QarVO666oqEBAQACCg4MdtoeHhzsduzlZWVkoKyvD0KFD7dYrMzIyAAC/+93vbG3nz593e3wAGDJkCAAgLy/P5fv46jFyh3WdfeDAgdDpdK0ai5rGsG0Her0eJpMJNTU1qKio8Ljf9evXAQCRkZEe15KWlobu3bvjtddeg8ViwYsvvoj58+c3+T+BBQsWwGKxIDMz0+5sfK9evXD06FFb3UajETU1NTCbzQ5j3Lp1y+06Fy9eDBFxuP39738HAKxbt87W1qtXL7fHtz43Pz8/7Nq1q9l+K1euhEajwZkzZ3z2GLmqoaEBr7/+OoAfjgG1H4ZtO0lJSQEAfPzxxw7bHnjgASxdutSu3969e+36WCwW7N+/HwaDAUlJSR7X4efnhyeeeAI3btzAiy++iO3bt+Pxxx932nft2rU4deoUPvzwQ+j1+mbHHT16NADg008/tWsvLi5Gfn6+x/W2p7vvvhvPPPMMjh8/js2bNzvtk5+fj7feegtTpkzBvffeC8B3j5Ernn76aeTk5CAlJaXFS+Kolby4huEzWnM1QlRUlOzZs0fKy8vl0qVLstiTN6gAACAASURBVHDhQomIiJCLFy/a9bOe6S4vL7c70934k07W9cDq6mq79lWrVgkAyc3NdailvLxcTCaTKIoiM2fOdFrvu+++aztJ0tTt9jP+58+fl9DQULurEU6dOiVJSUkSHh7e6k+QWbXl1QhWTz31lOh0Olm1apXk5+eLxWKRy5cvyzvvvCNRUVEyePBgu5N0vnqMRBzXbOvr6+X69euye/duGT58uACQOXPmSFVVlcv773bgmq2reILMFZ5+XLe4uFiWLFkiMTExotPpJCoqSqZOnSpnz55ttp/JZJKkpCTZv3+/rU92drbDC2vNmjUiIg7tY8aMcahlxYoVTk/OWI0ZM8btF3J+fr5MmDBBOnXqZLusac+ePZKYmGi7z9y5c93ebyIiCxYscFpDUlKSXb+EhASXr0a4XU5OjmRkZEj37t1Fp9OJ0WiUhx56SF5++WWxWCwO/X3xGAUFBTlsVxRFTCaTxMbGysKFC+XEiRNu7bfGGLYuy1REVPwMpo+y/nllvXiciH6gKAp27NiBKVOmeLuUjm4n12yJiFTAsCUiUgHDltpd4+8AcHZbu3att8skale+8bVG5NN4WoCI72yJiFTBsCUiUgHDlohIBQxbIiIVMGyJiFTAsCUiUgHDlohIBQxbIiIVMGyJiFTAsCUiUgHDlohIBQxbIiIVMGyJiFTAb/1y0dGjR/mDeETkMYatCwYOHOjtEuj/FBUV4fTp03j44Ye9XQoBmDx5Mrp37+7tMnwCf4OMfEpmZibS0tL4Hbnka/gbZEREamDYEhGpgGFLRKQChi0RkQoYtkREKmDYEhGpgGFLRKQChi0RkQoYtkREKmDYEhGpgGFLRKQChi0RkQoYtkREKmDYEhGpgGFLRKQChi0RkQoYtkREKmDYEhGpgGFLRKQChi0RkQoYtkREKmDYEhGpgGFLRKQChi0RkQoYtkREKmDYEhGpgGFLRKQChi0RkQoYtkREKmDYEhGpgGFLRKQChi0RkQoYtkREKvDzdgFETbl8+TJmzZqF+vp6W1txcTH8/PwwdOhQu7733HMP3nrrLZUrJHIdw5Y6rG7duuHChQv43//+57Dtq6++svt3QkKCWmUReYTLCNShzZw5EzqdrsV+U6dOVaEaIs8xbKlDmzFjBmpra5vtc99996FPnz4qVUTkGYYtdWi9evVC3759oSiK0+06nQ6zZs1SuSoi9zFsqcObOXMmtFqt0211dXWYMmWKyhURuY9hSx3etGnT0NDQ4NCuKAoGDBiAHj16qF8UkZsYttThde3aFYMGDYJGYz9dtVotZs6c6aWqiNzDsCWfkJGR4dAmIpg0aZIXqiFyH8OWfEJqaqrdO1utVosRI0YgPDzci1URuY5hSz4hJCQEI0eOtJ0oExGkp6d7uSoi1zFsyWekp6fbTpT5+flh/PjxXq6IyHUMW/IZ48ePh16vt/13p06dvFwRkescvhvh8uXLOHLkiDdqIWrRgw8+iCNHjiAmJgaZmZneLofIKWfXfisiIrc3ZGZmIi0tTbWiiIh+bBrFKgDsbPJbv5x0JvK62tpa/Pa3v8Vf/vIXb5dC5KC5N6tcsyWfotPpsHbtWm+XQeQ2hi35HIPB4O0SiNzGsCUiUgHDlohIBQxbIiIVMGyJiFTAsCUiUgHDlohIBQxbIiIVMGyJiFTAsCUiUgHDlohIBQxbIiIVMGw7kBdeeAGKokBRFHTr1s3b5QAASkpKsHHjRgwfPhyhoaEwGAzo3bs3ZsyYgZMnT7o0xvbt223PKyAgoE3qGj9+PBRFwbPPPtuqccxms6026y07O7vF+61YscLuPq2toyXBwcEOdSqKAo1Ggy5dumDChAk4duxYu9bwY5ifzvajRqNBSEgI4uLisGjRIpw4caJ9ipVGduzYIU6aSUVxcXESHR1t11ZRUSG9evWSMWPGqFrL3Llzxc/PT1566SW5du2aVFZWysGDB+W+++4TrVYrH3zwgctjJSYmil6vb3VNW7duFQACQNatW9fq8UREcnNzbWOOHj262b7FxcUSHBwsAGTGjBlt8viusNaYnJxsaystLZV//etfEh4eLjqdTvbt29fudfj6/Gy8H+vq6qSwsFB2794tw4YNEwAye/ZsqaysdLueZvIzk+9sfYSIoKGhwfYbXGqaM2cOnnjiCURGRiIwMBAJCQnYtm0b6uvrsXLlSlVruXr1KpYsWeL0p81by2Aw4M4778Qnn3yC48ePN9lvw4YN6N69e5s/vidMJhNSUlKwfv161NbWYsmSJV6pw5fnp1arRUREBJKTk/HFF19g5cqV2LJlC6ZNm9am3+vNsPURRqMRBQUF+Pjjj1V93HfeeQdvvfWWQ3tcXBwMBgMKCgpU/aL5+fPnIzU1FSNHjmzzsTUaDZ566ikAaHJZoLS0FG+++SZWrVrV5o/fGsOGDQMAnDp1CqWlpao//o9pfj7//PMYMGAAPvroI2zfvr2tSmXYkmcqKytRXV2N+++/H4qiqPKYmzdvxqlTp/DCCy+022M8+uijiI6OxkcffYS8vDyH7a+88gp++ctfomfPnu1WgyduDxS1jkdH1pr5qSgKHnvsMQDAG2+80WY1tTpsd+/ebbfYfPHiRaSlpcFoNCIsLAwZGRkoKSnBhQsXMG7cOBiNRkRFRWH+/PmoqKiwG6uurg47duzAI488gsjISBgMBsTGxuLll1+2+/Nk8ODBdo+Znp4OABgxYoRdu6v/h2+88H/s2DEkJibCaDQiMDAQw4YNw+HDhx3ud/PmTSxbtgw9e/aEv78/QkJCMHr0aHz55Zet6tvSPq6pqXHafuHCBaSlpaFz584ICwvD2LFjUVBQ4DDemTNnMGHCBJhMJgQGBuLnP/859uzZY7f/5s2b12xNO3fuBACsWbOm2fGDgoKQkJCAQ4cOtfg8m3P58mUsX74cmzdvhtFobNVYzdHr9VixYgVEBH/605/stpnNZrz66qtYvXp1k/f31hw+cOAAAKBPnz4wmUwAOD8B5/PTFYMHDwYAHD16FLW1tR6N4cCNBd5mJScnCwCZOHGiHD9+XMxms7z33nu2Ew7JycmSm5srFRUVsnHjRgEgS5cutRsjKytLAMif//xnuXXrlhQVFckrr7wiGo1GnnzySbu+X3/9tQQFBUlcXJyYzWYREampqZEBAwbI+++/73b9Ij8s/AcFBcnAgQPlyJEjYjab5dixY9K3b1/x9/eXAwcO2Ppeu3ZNYmJiJCIiQrKysqSsrEzy8/Nl4sSJoiiKvP322x71tdbR+ATE7fu4urraaXtycrKt7n379onBYJD+/fvb9T137px07txZoqOj5bPPPpOKigr573//KyNGjJAuXbq4dAKrsLBQIiIiZN68eQ7bnI2fl5cnI0eOlB49enh8giwpKUkWLVpk+/ff//73Zk+QDRs2TEJDQyU7O9ul8XNzcyUoKEhERKqqqiQiIkI0Go18++23tj7PP/+8TJkyRURE/v3vfzs9Qdaec9jZCbKysjKnJ8g4P53Pz6b2Y2PV1dW2E6ZXr15t8TGtmjtB1uZhu3fvXrv2Pn36CAD56quv7NpjYmLknnvusWvLysqSoUOHOoydnp4uOp1OysrK7KvPzLQFfENDg8yaNUtWr17tdu1WcXFxAkByc3Pt2vPy8gSAxMXF2dpmz54twP9j786jmy7z/YG/0yRN04V0EbqBgAIyagnHilqkllKhcFgqS1vKLuowAuN4OYhw3TjqmXHhuowXRusyOvc4Qwt3qCDI6GBxBtoOtVPAAVoFL6NQCl2kdCdtP78//DUSkrZJlycJfb/OyTn0+T7fJ5/v0jfp8/0mgd0vRVNTk0RFRYnRaJTy8nKX+7bX0Z2TedeuXTbt8+bNEwBSUVFhbUtNTRUAsn37dpu+Fy5cEH9//y5P5srKShk7dqykp6dLS0uL3fKOxj979qwYDIZuhW1mZqbccMMN1kAS6TpsExISJCQkRPLy8px6jivDVkTkxRdfFACyaNEiERGpr6+X8PBwOXLkiIh0HrZ9dQ5fecdE+0Oj0UhYWJjMmjVLDh06ZO3L89Px+SniXNg2NDR4ftieP3/epn3y5MkCwO42igkTJkhQUJBTY7/88ssCwOEvzhNPPCEAZPz48TJjxgxpbW11ufZ27a9sHYmKirLZ8SaTSQDIpUuX7PouXrxYAMgHH3zgct/2OrpzMl/5CyEi8h//8R8CwBoQIiJBQUECQGpra+3Gv+222zo9mevq6iQ2NlYWLFjQ4Ync2fgxMTEuh+2///1vMZlMNn9ViHQdtq66Omxra2slLCxMtFqtfPPNN/LKK6/Y/HJ2FLYd6Y1z2JmQaMfz0/H5KeLcfjx16pQAEL1eL5cvX+6w39WU3vo1YMAAm599fHyg1Wrh7+9v067Vau1uE6mpqcHTTz+NmJgYhISEWOdnHnvsMQBAQ0OD3fM999xzuPPOO5GXl4fU1FT4+PRsk4KDgx22Dxo0CABw4cIFNDc3o6amBn5+fg7nD8PDwwEA5eXlLvXtqfa5una+vr4AYN3Pzc3NqK2thZ+fHwIDA+3WDwkJ6XDslpYWpKamIjo6Gh988AG0Wq1dn67Gb9+Hrti1axdqamowceJEm/m/9lu/nnrqKWvbyZMnXR6/I4GBgXj00UfR2tqKZ555Bps2bcKTTz7Z5XqecA7z/HR8frqi/RpDXFwc9Hp9j8Zq51F3I8ycORPPPfccHnroIXz99ddoa2uDiODVV18FAIe3cOzfvx81NTWIiYnBypUrnX5XU0eqqqocPs+FCxcA/BgYBoMBJpMJTU1Ndhf5AOD8+fMAgIiICJf69jWDwYCgoCA0NTWhrq7Obnn7NjqyYsUKNDc3Izs7Gzqdzto+YsQIFBQUODV+dXW1yzWvWrUKImL3+J//+R8APwZVe9uIESNcHr8zv/zlL2EymfDHP/4RZrMZt99+e5freMI5zPPT8fnprLa2NmzevBnAj+dfb/GYsG1tbcXBgwcRERGBRx55BAMHDrTestHY2Ohwnf/7v//DAw88gP/93//Fzp07YTQakZKSgoqKim7X0dTUZPe2x6+++gplZWUwm82IjIwEAMyePRsAsHv3bpu+zc3N2LdvH4xGI5KTk13u29emTZsGANi7d69Ne3l5Ob7++muH62zcuBHHjh3DRx99BIPB0K3xKysrUVpa2t2y3cJkMmHNmjUwmUxOvar1lHMY4PnZExs2bMChQ4cwe/ZspKam9ng8KxfmHDrV0XxNcnKyaLVau/4JCQl286OTJk0SAPLSSy9JRUWFNDQ0yOeffy7XX3+9ALB5K2Jtba2MGTNGPvroI2vb/v37Ra/Xyz333OPSPEs7s9ksJpNJkpKSXL4b4dKlSzZXcDMzM7vVt72O7syJXd3++OOP213wO3nypISGhtpc7f3qq69k6tSpMnToULs5sd///vd2F2Wuflx5xd/R+MeOHZPk5GQZNGhQr7xdV6Rv70ZwRkdztn15DrsyZ8vz0/H56Wg/tra2yvnz5yUnJ8d6/JYvXy4NDQ1d7uer9ekFsvz8fLuNe+KJJ6SwsNCu/Te/+Y31JL3y8cwzz4iISEVFhaxYsUKGDBkier1ewsPDZdmyZbJ+/Xpr39jYWFm1apXN+l999ZVUVFTYjevqxZP2k+j48eOSnJwsQUFBYjQaJSEhQQ4cOGDXv7KyUh599FEZPny46PV6MZlMkpycLPv27etW3/aLKFfvyx07dti1L1y4sMN9LyJ27Ve+Z720tFTuu+8+GTBggPj7+8v48ePliy++kIkTJ4q/v79N3dOnT3f5ZL5y/Pbbez7++GNJSkqyrvPAAw+4dGzarVixwmENycnJNv3i4+OdvhshICCg07Gu5uj533jjDRHpu3P46hoB2N3NczWen/bnp6P9qNFoxGQySUxMjDz88MNSVFTU6X7tTGdhq/n/G26VnZ2N9PR0pW/B9BRjx45FZWUlzpw54+5S3GL06NFobGzEv//9b3eXQmTHG87PTvJzm8fM2ZIa5eXlCA0NtXtXzOnTp3Hq1ClMmjTJTZURXdvnJ8O2H/rhhx+wYsUKfP/992hoaMChQ4eQnp6OAQMG4KmnnnJ3edTPXavn5zUfto4+cPnqR/sHCh85cgRnz56FRqNx6uqzN4qIiMBf//pXXLx4Effccw9CQkIwa9YsjBw5EocOHcINN9ygrBZnjs3GjRuV1UPu50nnZ2/Tdd3Fu/XHueeuJCUlISkpyd1l8NiQQ55yfva2a/6VLRGRJ2DYEhEpwLAlIlKAYUtEpADDlohIAYYtEZECDFsiIgUYtkRECjBsiYgUYNgSESnAsCUiUoBhS0SkAMOWiEiBDj/1Kzs7W2UdREReLz8/v8NlHYZtenp6nxRDRNQf2X0HGZEn68/fkUdejd9BRkSkAsOWiEgBhi0RkQIMWyIiBRi2REQKMGyJiBRg2BIRKcCwJSJSgGFLRKQAw5aISAGGLRGRAgxbIiIFGLZERAowbImIFGDYEhEpwLAlIlKAYUtEpADDlohIAYYtEZECDFsiIgUYtkRECjBsiYgUYNgSESnAsCUiUoBhS0SkAMOWiEgBhi0RkQIMWyIiBRi2REQKMGyJiBRg2BIRKcCwJSJSgGFLRKSAzt0FEHWkoqICO3bssGn78ssvAQCZmZk27YGBgViwYIGy2ohcpRERcXcRRI40Nzdj4MCBqK+vh1arBQCICEQEPj4//VFmsViwZMkSfPDBB+4qlagr2ziNQB7LYDAgNTUVOp0OFosFFosFLS0taG1ttf5ssVgAgK9qyeMxbMmjLViwAJcvX+60T3BwMJKSkhRVRNQ9DFvyaImJiRg4cGCHy/V6PRYtWgSdjpcfyLMxbMmj+fj4YMGCBfD19XW43GKxICMjQ3FVRK5j2JLHy8jI6HAqITIyEnFxcYorInIdw5Y83p133omhQ4fatev1eixduhQajcYNVRG5hmFLXmHx4sXQ6/U2bZxCIG/CsCWvsHDhQuttXu1GjBiBMWPGuKkiItcwbMkrjB49GjfffLN1ykCv1+P+++93c1VEzmPYktdYsmSJ9Z1kFosFaWlpbq6IyHkMW/Ia8+fPR2trKwAgNjYWI0aMcHNFRM5j2JLXGDp0KMaNGwfgx1e5RN6EH0TTR7Kzs5Genu7uMohcwjjoM9v4Hsc+lpWV5e4SrimXLl3Cli1bsH79eneXck3Jz8/Ha6+95u4yrmkM2z7Gizi9LyEhASNHjnR3Gdcchm3f4pwteR0GLXkjhi0RkQIMWyIiBRi2REQKMGyJiBRg2BIRKcCwJSJSgGFLRKQAw5aISAGGLRGRAgxbIiIFGLZERAowbImIFGDYepDAwEBoNBqnHu+88w42bdpk/Xnw4MHuLt8jcB+Sp2LYepC6ujoUFxcDAFJSUiAiDh8JCQkAgLVr10JEYDab3Vm2R+E+JE/FsCUAP74inDBhQreXE/chdY4fHu6F9u/f7+4SvB73IanGV7ZeZPXq1Xj00UfdXYZX4z4kd2HYXqNaWlqQlZWFyZMnIyIiAkajETExMXj99dfR1tZm7dd+gai+vh4HDx60XizS6XROLW9XUVGBRx55BMOGDYOvry8GDhyIOXPm4PDhw9Y+OTk5NheoTp8+jfT0dAQHByMsLAwzZszAqVOn1OwgJ3AfUq8S6hNZWVnSnd1bXFwsADp8/OpXv7Jbx2w2S3R0tE3brl27BID8+te/lurqaqmoqJDf/va34uPjI2vXrrUbIyAgQO6+++4O6+pseVlZmQwdOlTCw8Nl9+7dUltbK//6178kISFB/Pz8JC8vz6Z/SkqKAJCUlBTJy8uTuro6+eyzz8RoNMq4cePsxk9MTJTQ0FDJz8/vsL4rcR/a78OudPd8Jadlc+/2kZ6GbUpKit2yVatWuRQUEydOtOu7aNEi0ev1UlNTY9Pek6BYunSpAJAPP/zQpv3cuXNiMBgkNjbWpr09KHbt2mXTPm/ePAEgFRUVNu0JCQkSEhJiFzgd4T6034ddYdj2uWxOI1yjZsyYgdzcXLt2s9kMi8WCY8eO9dpz5eTkwMfHBzNmzLBpj4iIwC233IKioiKcOXPGbr1x48bZ/DxkyBAAQFlZmU37/v37UV1djbi4uF6r2RnX0j4k9+PdCF7kv//7v53uW1NTg//6r//Cjh07cObMGVy8eNFmeUNDQ6/U1NzcjJqaGgCAyWTqsN8333xj96aBq/v7+voCgM18aG/jPiR34Svba9TMmTPx3HPP4aGHHsLXX3+NtrY2iAheffVVAICI2PTXaDSdjtfRcoPBgODgYOh0Olgslg7fRJCYmNg7G6YQ9yH1JobtNai1tRUHDx5EREQEHnnkEQwcOND6i97Y2OhwHX9/f1y+fNn680033YTMzEynls+ZMwctLS04ePCg3bgvvvgirr/+erS0tPTKtqnCfUi9jWF7DdJqtZg4cSLKy8vx8ssvo7KyEo2NjcjNzcWbb77pcJ3bbrsNX3/9Nb7//nvk5+fj22+/RXx8vFPLf/Ob3+DGG2/E8uXL8cknn6CmpgbV1dV466238Oyzz2LTpk12tzm5YtKkSQgLC0NBQUG3x3DVtbYPyQOovyjXP3Tn6m5AQIDdbUrh4eEd9n/55Zft+j/xxBMiIlJRUSErVqyQIUOGiF6vl/DwcFm2bJmsX7/e2vfKK9wlJSUSHx8vAQEBMmTIENm8ebPNc3W1vKqqStasWSM33HCD6PV6GThwoEyZMkU+++wza5/8/PwO6726ffr06db14uPjnb4bgfvQ8T7sCu9G6HPZGpGrJp6oV2RnZyM9Pd1uXo/IE/F87XPbOI1ARKQAw5aISAGGLRGRAgxbIiIFGLZERAowbImIFGDYEhEpwLAlIlKAYUtEpADDlohIAYYtEZECDFsiIgUYtkRECjBsiYgUYNgSESnAsCUiUoBhS0SkAL/UqI919Y2rRNQ/MGz7yPjx45GVleXuMq45+fn5eO2117hvyevwO8jIq/C7sshL8TvIiIhUYNgSESnAsCUiUoBhS0SkAMOWiEgBhi0RkQIMWyIiBRi2REQKMGyJiBRg2BIRKcCwJSJSgGFLRKQAw5aISAGGLRGRAgxbIiIFGLZERAowbImIFGDYEhEpwLAlIlKAYUtEpADDlohIAYYtEZECDFsiIgUYtkRECjBsiYgUYNgSESnAsCUiUoBhS0SkAMOWiEgBhi0RkQIMWyIiBRi2REQK6NxdAFFHLBYL6urqbNrq6+sBAD/88INNu0ajQXBwsLLaiFzFsCWPVVVVhcGDB6O1tdVuWWhoqM3PEydORG5urqrSiFzGaQTyWBEREbjnnnvg49P5aarRaJCRkaGoKqLuYdiSR1u8eDE0Gk2nfXx8fDB37lxFFRF1D8OWPNrcuXOh1Wo7XK7VajF16lSEhYUprIrIdQxb8mgDBgzA1KlTodM5vrwgIli0aJHiqohcx7Alj7do0SKHF8kAwNfXFzNmzFBcEZHrGLbk8WbOnAl/f3+7dp1Oh9mzZyMwMNANVRG5hmFLHs/Pzw9z5syBXq+3aW9pacHChQvdVBWRaxi25BUWLFgAi8Vi0zZgwABMnjzZTRURuYZhS17h3nvvtXkjg16vx/z58+Hr6+vGqoicx7Alr6DT6TB//nzrVILFYsGCBQvcXBWR8xi25DUyMjKsUwnh4eGIj493c0VEzmPYkte4++67ERUVBeDHd5Z19TZeIk/CD6LpgVdeeQX5+fnuLqNfCQoKAgAUFxcjNTXVzdX0L2vWrEFcXJy7y/BafGnQA/n5+SgoKHB3Gf3K9ddfj6CgIISEhLi7lH5l+/bt+P77791dhlfjK9seuuuuu7Bt2zZ3l9GvZGdnIy0tzd1l9CtdfRgQdY2vbMnrMGjJGzFsiYgUYNgSESnAsCUiUoBhS0SkAMOWiEgBhi0RkQIMWyIiBRi2REQKMGyJiBRg2BIRKcCwJSJSgGFLRKQAw9YDbN26FRqNBhqNBn5+fu4ux2sFBgZa92P7w8fHByEhITCbzVi5ciWKiorcXSb1UwxbDzB//nyICJKSktxdilerq6tDcXExACAlJQUiAovFgpKSEjz77LMoKSnB7bffjvvvvx8NDQ1urpb6G4YtuVVgYCAmTJjQZ+NrtVqEh4cjJSUFn3/+OdatW4f3338fGRkZEJE+e15P09f7mbrGsKV+5YUXXsCdd96JnTt3YuvWre4uh/oRhi31KxqNBqtXrwYAbNmyxc3VUH/CsHWDkpIS3HfffTCZTAgICEB8fDwOHDhg1y8nJ8fmYk9paSnS0tIQFhZmbausrAQAVFVVYc2aNbjxxhvh6+uLkJAQTJs2Dbm5udbxNm3aZF1v8ODBKCwsRFJSEoKCguDv74/ExEQcPHjQrg5nxn7++eetY1/55+revXut7dddd51dLfX19Th48KC1j07X99/U1F5fQUEBLBYL9zOpIdRt8+bNk3nz5rm0zjfffCPBwcESHR0tn376qdTW1srRo0dlypQpMmzYMDEYDHbrpKSkCABJSEiQ3Nxcqa+vl4KCAtFqtVJRUSHnzp2T4cOHS3h4uOzatUtqamqktLRU5syZIxqNRt5++22b8cxmswQEBEhcXJzk5eVJXV2dFBYWypgxY8TX11f2799v7evq2AEBAXL33XfbbUNsbKyEhYXZtXfUv11iYqKEhoZKfn5+l/tWRKS4uFgASEpKSod9GhsbBYAAkLKyMmt7f97PXQEgWVlZ3V6fJJth2wPdCdvU1FQBINu3b7dpP3v2rBgMhk7Dds+ePQ7HXLZsmQCQP/3pTzbtTU1NEhUVJUajUcrLy63tZrNZAEhxcbFN/6NHjwoAMZvN3R67t0MgISFBQkJCJC8vr8M+V3ImbBsaGjoN2/64n7vCsO2xbE4jKLZ3714AQHJysk17VFQURo0a1em6d9xxh8P2HTt2AACmT59u024wGJCUlITGxkb85S9/tL0CrwAAIABJREFUsVkWEBCAsWPH2rTFxMQgKioKR44cwblz57o9dm/av38/qqurERcX12tjtm+bXq+3+ZO7XX/cz9T3GLYKNTc3o7a2Fn5+fggMDLRbPmjQoE7XDwgIcDhmTU0N/Pz8EBQUZLc8PDwcAFBeXm7THhwc7PA52mu4cOFCt8f2dO3z43FxcdDr9XbLuZ+pLzBsFTIYDAgKCkJTUxPq6urslldXV3drTJPJhKamJtTW1totP3/+PAAgIiLCpr2qqsrhfaYXLlwA8GMYdGdsHx8fXL582a7vxYsXHdav0Wg62rQ+0dbWhs2bNwMAVq1a5fR63M/UUwxbxaZNmwbgp+mEdpWVlSgtLe3WmLNnzwYA7N6926a9ubkZ+/btg9FotJu2aGpqQmFhoU3bV199hbKyMpjNZkRGRnZr7MjISJw9e9amb3l5Ob777juHtfv7+9uExk033YTMzMwut7m7NmzYgEOHDmH27NlITU11aV3uZ+oRd88ae7PuXCA7efKkhIaG2tyNcOzYMUlOTpZBgwZ1eoGssbHR4ZhXX8m+dOmSzZXszMxMm/5ms1lMJpMkJSW5fJW8q7FXr14tAOSNN96Q2tpaOXnypKSlpUl0dLTDCzdTp04Vk8kk3333neTl5YlOp5Pjx49bl/f0boTW1lY5f/685OTkyKRJkwSALF++XBoaGrifr9jPXQEvkPUU70boie6ErYhIaWmp3HfffTJgwAAxGo0ybtw4+fjjjyUpKcl6lfyBBx6Q/Px8689XPhyprKyURx99VIYPHy56vV5MJpMkJyfLvn377PqazWaJjo6W48ePS3JysgQFBYnRaJSEhAQ5cOBAj8a+ePGiPPjggxIZGSlGo1EmTJgghYWFEhsba63/8ccft/YvKSmR+Ph4CQgIkCFDhsjmzZttxouPj3f6boSAgAC7faXRaMRkMklMTIw8/PDDUlRUZLce93PXGLY9lq0R6UdvEO9l7X+Gbtu2zc2VuGbs2LGorKzEmTNn3F3KNe1a2s8ajQZZWVlIS0tzdyneahvnbImIFGDYEhEpwLDtR9rfJ3/kyBGcPXsWGo0GTz75pLvLuuZwP5MjnLPtAW+dsyVyFedse4xztkREKjBsiYgUYNgSESnAsCUiUoBhS0SkAMOWiEgBhi0RkQIMWyIiBRi2REQKMGyJiBRg2BIRKcCwJSJSgGFLRKSAzt0FeLuCggKXvziQiPofhm0PxMXFubuEfqeiogInTpzAPffc4+5S+pV58+ZhyJAh7i7Dq/HzbMmrZGdnIz09HTxtycvw82yJiFRg2BIRKcCwJSJSgGFLRKQAw5aISAGGLRGRAgxbIiIFGLZERAowbImIFGDYEhEpwLAlIlKAYUtEpADDlohIAYYtEZECDFsiIgUYtkRECjBsiYgUYNgSESnAsCUiUoBhS0SkAMOWiEgBhi0RkQIMWyIiBRi2REQKMGyJiBRg2BIRKcCwJSJSgGFLRKQAw5aISAGGLRGRAgxbIiIFGLZERAowbImIFNC5uwCijpw5cwZLly5Fa2urta2yshI6nQ4TJ0606XvTTTfhrbfeUlwhkfMYtuSxBg8ejNOnT+Pbb7+1W/bFF1/Y/BwfH6+qLKJu4TQCebQlS5ZAr9d32W/+/PkKqiHqPoYtebSFCxfCYrF02ufmm2/GLbfcoqgiou5h2JJHGzFiBMaMGQONRuNwuV6vx9KlSxVXReQ6hi15vCVLlkCr1Tpc1tLSgrS0NMUVEbmOYUseLyMjA21tbXbtGo0Gd955J4YNG6a+KCIXMWzJ40VFRWH8+PHw8bE9XbVaLZYsWeKmqohcw7Alr7B48WK7NhHB3Llz3VANkesYtuQVUlNTbV7ZarVa3HvvvRg0aJAbqyJyHsOWvEJISAimTJlivVAmIli0aJGbqyJyHsOWvMaiRYusF8p0Oh1mzZrl5oqInMewJa8xa9YsGAwG678HDBjg5oqInMfPRnBBfn4+vv/+e3eX0a/ddtttyMvLw/Dhw5Gdne3ucvq18ePHY/Dgwe4uw2toRETcXYS3SE1Nxfbt291dBpFHyMrK4htKnLeN0wgumjdvHkSEDzc9Ll++jHXr1rm9jv7+INcxbMmr6PV6bNy40d1lELmMYUtex2g0ursEIpcxbImIFGDYEhEpwLAlIlKAYUtEpADDlohIAYYtEZECDFsiIgUYtkRECjBsiYgUYNgSESnAsCUiUoBhq1BhYSGWLVuG4cOHw2g0IjQ0FLfeeivmzp2L3/3udzh16hQAYNOmTdBoNNBoNF71eaEnTpxAeno6IiIioNPprNsQHBysrIY9e/Zg1KhR0Ol676OaAwMDrdvS/vDx8UFISAjMZjNWrlyJoqKiXns+ujYxbBVoa2vDY489hvHjx2PQoEH45JNPcPHiRZw4cQKvvvoqLl26hJUrV2LEiBFoaWnB2rVrISIwm83uLt1GXV0dRo4ciRkzZtgtO336NOLi4nDixAn8+c9/xqVLl3Dp0iVkZ2fbfQV5Xzh16hRmzZqFDRs24Pz58706dl1dHYqLiwEAKSkpEBFYLBaUlJTg2WefRUlJCW6//Xbcf//9aGho6NXnpmsHw1aBp556Cps2bcKWLVvw0ksvYfTo0TAYDAgPD8fkyZOxd+9eTJs2zd1ldklE0NbWZv0esCtlZmaipqYGmzdvxvjx4+Hv74+goCCkpqaiurq6z2t76qmnMH78eBQVFSEoKKjPn0+r1SI8PBwpKSn4/PPPsW7dOrz//vvIyMjg572SQ/xanD5WUlKCF154AbGxsXjooYcc9tFqtXjqqafwySefKK7ONUFBQdapjqt98803AIAxY8aoLMnq3XffdetHL77wwgv44osvsHPnTmzduhUZGRluq4U8E1/Z9rHMzEy0tbUhNTW1035xcXEQkV6da1TJYrEAgPULGVVz92fcajQarF69GgCwZcsWt9ZCnolh28f+9re/Aei9V3wtLS3IysrC5MmTERERAaPRiJiYGLz++ut2f943Nzfj6aefxujRo+Hv74/Q0FDMnDkTO3fuRGtrq0v9cnJybC4QNTU12bR/9NFHAH4MvasvJmk0GixbtsymtoqKCjzyyCMYNmwYfH19MXDgQMyZMweHDx+29rn6OUtLS5GWloawsDBrW2VlZa/s194wYcIEAEBBQYH1Px+ge9t6+vRppKenIzg4GGFhYZgxY4bdXxXOHl9na6A+JuS0efPmybx581xaJzIyUgDIP/7xD5efz2w2S3R0tE3brl27BID8+te/lurqaqmoqJDf/va34uPjI2vXrrXp++CDD4rJZJJPP/1UGhoapLy8XNauXSsAJDc31+V+IiIpKSkCQBobG51qr6ioEACydOlSa1tZWZkMHTpUwsPDZffu3VJbWyv/+te/JCEhQfz8/CQvL8/h2AkJCZKbmyv19fVSUFAgWq1WKioq7PZbdHS0aLXaTvdtYmKihIaGSn5+fqf92hUXFwsASUlJ6bBPY2OjABAAUlZW1qNtTUlJkby8PKmrq5PPPvtMjEajjBs3zqavs8fN1RqcAUCysrJcXq8fy2bYuqAnYXvo0CGXn6+jsJ04caJd30WLFoler5eamhpr2/Dhw2X8+PF2fUeNGmXzy+hsP5HeCdulS5cKAPnwww9t+p47d04MBoPExsY6HHvPnj12NTriTNgmJCRISEiI00HjTNg2NDTYhW13t3XXrl027fPmzRMANv+5OHvcXK3BGQxbl2VzGqGPRUVFAUCv/bk7Y8YM5Obm2rWbzWZYLBYcO3bM2jZ16lTk5eXh5z//OQoKCqx/WpaWlmLixIku9+stOTk58PHxsbuFLCIiArfccguKiopw5swZu/XuuOOOXqth//79qK6uRlxcXK+Nee7cOQA/finlddddB6D72zpu3Dibn4cMGQIAKCsrs7Y5e9y6WwP1LoZtH0tISAAAHD16tFfGq6mpwdNPP42YmBiEhIRY5/gee+wxALC5z3Pz5s34wx/+gG+//RZJSUkYMGAApk6dih07dtiM6Wy/3tDc3Iyamhq0tbXBZDLZze3+85//BPDT3Q1XCggI6PV6etOBAwcA/HixU6/X92hbTSaTzc++vr4AYDMv78xx60kN1LsYtn1sxYoV0Ol02L59e6f91q1bBx8fH5SUlHTab+bMmXjuuefw0EMP4euvv0ZbWxtEBK+++ioA2NzjqdFosHjxYvz1r3/FxYsXkZOTAxHBnDlz8Morr7jcrzcYDAYEBwdDp9PBYrFARBw+EhMTe/V5+1pbWxs2b94MAFi1ahWAvt9WZ47btbq/vRHDto+NGjUKzzzzDL788ku89957DvuUlpbirbfeQlpaGkaPHt3hWK2trTh48CAiIiLwyCOPYODAgdBoNACAxsZGu/7BwcHW8Nbr9Zg8ebL1qvfu3btd7tdb5syZg5aWFhw8eNBu2Ysvvojrr78eLS0tvf68fWnDhg04dOgQZs+ebXObX19uq7PH7Vrc396IYavAk08+ifXr1+MXv/gF1q9fj6+//hqXL1/G2bNn8e677yIxMRFjxozBu+++2+k4Wq0WEydORHl5OV5++WVUVlaisbERubm5ePPNNx2u84tf/AJHjx5Fc3MzLly4gJdeegkigkmTJnWrX2/4zW9+gxtvvBHLly/HJ598gpqaGlRXV+Ott97Cs88+i02bNvX5/caTJk1CWFgYCgoKurV+W1sbLly4gI8++ghJSUl46aWXsHz5cnz44YfW/wCBvt9WZ46bJ+xvAm/9ckV37ka40qFDh2Tx4sUyZMgQ0ev1EhQUJHfddZe8/vrr0tzcbO338ssvW69qtz+eeOIJEfnx6v6KFSusY4SHh8uyZctk/fr11r7tV5cPHz4sK1askJ/97Gfi7+8voaGhctddd8nbb78tbW1t1udzpt+OHTvsalq4cGGH7SIiycnJdsv+/ve/i4hIVVWVrFmzRm644QbR6/UycOBAmTJlinz22WfWuvLz8+3W7+iUbb8lztHj7bfftusfHx/v9N0IAQEBdmNqNBoxmUwSExMjDz/8sBQVFXW4fne3tf2YX90+ffp0l46vszW4ArwbwVXZGhG+kdtZ7X8ebtu2zc2VELmXRqNBVlYW0tLS3F2Kt9jGaQQiIgUYtkRECjBsiYgUYNgSESnAsCUiUoBhS0SkAMOWiEgBhi0RkQIMWyIiBRi2REQKMGyJiBRg2BIRKcCwJSJSgGFLRKQAw5aISAGGLRGRAgxbIiIF+MVDLjpz5gyys7PdXQYReRmGrYsKCgqQnp7u7jKIyMvwO8jIq2RnZyM9PR08bcnL8DvIiIhUYNgSESnAsCUiUoBhS0SkAMOWiEgBhi0RkQIMWyIiBRi2REQKMGyJiBRg2BIRKcCwJSJSgGFLRKQAw5aISAGGLRGRAgxbIiIFGLZERAowbImIFGDYEhEpwLAlIlKAYUtEpADDlohIAYYtEZECDFsiIgUYtkRECjBsiYgUYNgSESnAsCUiUoBhS0SkAMOWiEgBhi0RkQIMWyIiBRi2REQKMGyJiBTQubsAoo5UVFRgx44dNm1ffvklACAzM9OmPTAwEAsWLFBWG5GrNCIi7i6CyJHm5mYMHDgQ9fX10Gq1AAARgYjAx+enP8osFguWLFmCDz74wF2lEnVlG6cRyGMZDAakpqZCp9PBYrHAYrGgpaUFra2t1p8tFgsA8FUteTyGLXm0BQsW4PLly532CQ4ORlJSkqKKiLqHYUseLTExEQMHDuxwuV6vx6JFi6DT8fIDeTaGLXk0Hx8fLFiwAL6+vg6XWywWZGRkKK6KyHUMW/J4GRkZHU4lREZGIi4uTnFFRK5j2JLHu/POOzF06FC7dr1ej6VLl0Kj0bihKiLXMGzJKyxevBh6vd6mjVMI5E0YtuQVFi5caL3Nq92IESMwZswYN1VE5BqGLXmF0aNH4+abb7ZOGej1etx///1urorIeQxb8hpLliyxvpPMYrEgLS3NzRUROY9hS15j/vz5aG1tBQDExsZixIgRbq6IyHkMW/IaQ4cOxbhx4wD8+CqXyJvwg2iukp2djfT0dHeXQeTVGCt2tvE9jh3IyspydwnkwKVLl7BlyxasX7/e3aWQA/n5+XjttdfcXYZHYth2gBdfPFdCQgJGjhzp7jKoAwxbxzhnS16HQUveiGFLRKQAw5aISAGGLRGRAgxbIiIFGLZERAowbImIFGDYEhEpwLAlIlKAYUtEpADDlohIAYYtEZECDFsiIgUYtn1k69at0Gg00Gg08PPzc3c5XmvPnj0YNWoUdLre+4C6wMBA67Fpf/j4+CAkJARmsxkrV65EUVFRrz0fEcCw7TPz58+HiCApKcndpXilU6dOYdasWdiwYQPOnz/fq2PX1dWhuLgYAJCSkgIRgcViQUlJCZ599lmUlJTg9ttvx/3334+GhoZefW7qvxi21OsCAwMxYcKEHo3x1FNPYfz48SgqKkJQUFAvVdYxrVaL8PBwpKSk4PPPP8e6devw/vvvIyMjo19960BvHDtyjB8eTh7p3XffhdFodNvzv/DCC/jiiy+wc+dObN26FRkZGW6rha4NfGVLHsmdQQsAGo0Gq1evBgBs2bLFrbXQtYFh20tKSkpw3333wWQyISAgAPHx8Thw4IBdv5ycHJsLM6WlpUhLS0NYWJi1rbKyEgBQVVWFNWvW4MYbb4Svry9CQkIwbdo05ObmWsfbtGmTdb3BgwejsLAQSUlJCAoKgr+/PxITE3Hw4EG7OpwZ+/nnn7eOfeWflnv37rW2X3fddXa11NfX4+DBg9Y+vXlxS6X2bS4oKIDFYuGxo54RspGVlSWu7pZvvvlGgoODJTo6Wj799FOpra2Vo0ePypQpU2TYsGFiMBjs1klJSREAkpCQILm5uVJfXy8FBQWi1WqloqJCzp07J8OHD5fw8HDZtWuX1NTUSGlpqcyZM0c0Go28/fbbNuOZzWYJCAiQuLg4ycvLk7q6OiksLJQxY8aIr6+v7N+/39rX1bEDAgLk7rvvttuG2NhYCQsLs2vvqH93RUdHi1ar7bRPYmKihIaGSn5+vlNjFhcXCwBJSUnpsE9jY6MAEABSVlZmbeex61h3fn/6iWzulat052RJTU0VALJ9+3ab9rNnz4rBYOg0bPfs2eNwzGXLlgkA+dOf/mTT3tTUJFFRUWI0GqW8vNzabjabBYAUFxfb9D969KgAELPZ3O2xvSFsExISJCQkRPLy8pwa05mwbWho6DRseezsMWw7lM1phF6wd+9eAEBycrJNe1RUFEaNGtXpunfccYfD9h07dgAApk+fbtNuMBiQlJSExsZG/OUvf7FZFhAQgLFjx9q0xcTEICoqCkeOHMG5c+e6Pban279/P6qrqxEXF9drY7bvL71eb/MndzseO3IFw7aHmpubUVtbCz8/PwQGBtotHzRoUKfrBwQEOByzpqYGfn5+Dm97Cg8PBwCUl5fbtAcHBzt8jvYaLly40O2x+6P2Ofe4uDjo9Xq75Tx25AqGbQ8ZDAYEBQWhqakJdXV1dsurq6u7NabJZEJTUxNqa2vtlrff5B8REWHTXlVV5fCe0AsXLgD48Re3O2P7+Pjg8uXLdn0vXrzosH6NRtPRpnmNtrY2bN68GQCwatUqp9fjsaOOMGx7wbRp0wD8NJ3QrrKyEqWlpd0ac/bs2QCA3bt327Q3Nzdj3759MBqNdtMWTU1NKCwstGn76quvUFZWBrPZjMjIyG6NHRkZibNnz9r0LS8vx3fffeewdn9/f5tf8JtuugmZmZldbrMn2bBhAw4dOoTZs2cjNTXVpXV57Mghd88ae5ruTPCfPHlSQkNDbe5GOHbsmCQnJ8ugQYM6vUDW2NjocMyrrzpfunTJ5qpzZmamTX+z2Swmk0mSkpJcvqLd1dirV68WAPLGG29IbW2tnDx5UtLS0iQ6OtrhRZapU6eKyWSS7777TvLy8kSn08nx48dd2qdXUnE3Qmtrq5w/f15ycnJk0qRJAkCWL18uDQ0Nduvy2HWMF8g6xLsRrtbdk6W0tFTuu+8+GTBggBiNRhk3bpx8/PHHkpSUZL2i/cADD0h+fr715ysfjlRWVsqjjz4qw4cPF71eLyaTSZKTk2Xfvn12fc1ms0RHR8vx48clOTlZgoKCxGg0SkJCghw4cKBHY1+8eFEefPBBiYyMFKPRKBMmTJDCwkKJjY211v/4449b+5eUlEh8fLwEBATIkCFDZPPmzS7vz127djncTwDsbm8SEYmPj3f6boSAgAC7MTUajZhMJomJiZGHH35YioqK7Nbjsesaw7ZD2RqRfvTGbydkZ2cjPT3d694PP3bsWFRWVuLMmTPuLoVcdC0dO2/9/VFgG+dsiYgUYNgSESnAsPVy7e9pP3LkCM6ePQuNRoMnn3zS3WU5dPUHdjt6bNy40d1lKuNNx456jnO2V+GcE1H38fenQ5yzJSJSgWFLRKQAw5aISAGGLRGRAgxbIiIFGLZERAowbImIFGDYEhEpwLAlIlKAYUtEpADDlohIAYYtEZECDFsiIgV07i7AU/FbRomoNzFsrzJ+/HhkZWW5uwzqQH5+Pl577TUeI/I6/Dxb8ir8vFTyUvw8WyIiFRi2REQKMGyJiBRg2BIRKcCwJSJSgGFLRKQAw5aISAGGLRGRAgxbIiIFGLZERAowbImIFGDYEhEpwLAlIlKAYUtEpADDlohIAYYtEZECDFsiIgUYtkRECjBsiYgUYNgSESnAsCUiUoBhS0SkAMOWiEgBhi0RkQIMWyIiBRi2REQKMGyJiBRg2BIRKcCwJSJSgGFLRKQAw5aISAGGLRGRAjp3F0DUEYvFgrq6Opu2+vp6AMAPP/xg067RaBAcHKysNiJXMWzJY1VVVWHw4MFobW21WxYaGmrz88SJE5Gbm6uqNCKXcRqBPFZERATuuece+Ph0fppqNBpkZGQoqoqoexi25NEWL14MjUbTaR8fHx/MnTtXUUVE3cOwJY82d+5caLXaDpdrtVpMnToVYWFhCqsich3DljzagAEDMHXqVOh0ji8viAgWLVqkuCoi1zFsyeMtWrTI4UUyAPD19cWMGTMUV0TkOoYtebyZM2fC39/frl2n02H27NkIDAx0Q1VErmHYksfz8/PDnDlzoNfrbdpbWlqwcOFCN1VF5BqGLXmFBQsWwGKx2LQNGDAAkydPdlNFRK5h2JJXuPfee23eyKDX6zF//nz4+vq6sSoi5zFsySvodDrMnz/fOpVgsViwYMECN1dF5DyGLXmNjIwM61RCeHg44uPj3VwRkfMYtuQ17r77bkRFRQH48Z1lXb2Nl8iT8INoXPDKK68gPz/f3WX0a0FBQQCA4uJipKamurma/m3NmjWIi4tzdxlegy8NXJCfn4+CggJ3l9GvXX/99QgKCkJISIi7S+nXtm/fju+//97dZXgVvrJ10V133YVt27a5u4x+LTs7G2lpae4uo1/r6sOByB5f2ZLXYdCSN2LYEhEpwLAlIlKAYUtEpADDlohIAYYtEZECDFsiIgUYtkRECjBsiYgUYNgSESnAsCUiUoBhS0SkAMOWiEgBhq1ChYWFWLZsGYYPHw6j0YjQ0FDceuutmDt3Ln73u9/h1KlTAIBNmzZBo9FAo9Fg8ODBbq7aeSdOnEB6ejoiIiKg0+ms2xAcHNynz/vDDz/gzTffxKRJkxAaGgqj0YiRI0di4cKFOHLkSI/HDwwMtG5L+8PHxwchISEwm81YuXIlioqKemFL6FrGsFWgra0Njz32GMaPH49Bgwbhk08+wcWLF3HixAm8+uqruHTpElauXIkRI0agpaUFa9euhYjAbDa7u3QbdXV1GDlyJGbMmGG37PTp04iLi8OJEyfw5z//GZcuXcKlS5eQnZ3d59+o8Nhjj+GXv/wlUlJScPz4cVRVVeG9997D4cOHERsbi5ycnB6NX1dXh+LiYgBASkoKRAQWiwUlJSV49tlnUVJSgttvvx33338/GhoaemOT6Fok5LR58+bJvHnzXF7vP//zPwWAZGZmOlze0tIi06ZNEwBisVis7WazWaKjo7tdb2+7dOmS3HDDDTJt2jS7ZRs2bBAA8re//U15XQ888ID8/Oc/t2s/fPiwAJCRI0f2+DmKi4sFgKSkpDhcvm7dOgEgs2bNkra2th4/n6cDIFlZWe4uw5tk88PD+1hJSQleeOEFxMbG4qGHHnLYR6vV4qmnnsInn3yiuDrXBAUFWac6rvbNN98AAMaMGaOyJADAO++847DdbDbDaDTi1KlTEJE+/cDrF154AV988QV27tyJrVu3IiMjo8+ei7wTpxH6WGZmJtra2rr8vqy4uDiICHQ67/z/r/1bbw0Gg5sr+Ul9fT0aGxtx66239vk3C2g0GqxevRoAsGXLlj59LvJODNs+9re//Q1A773ia2lpQVZWFiZPnoyIiAgYjUbExMTg9ddfR1tbm03f5uZmPP300xg9ejT8/f0RGhqKmTNnYufOnWhtbXWpX05Ojs0FoqamJpv2jz76CABgNBrtLiZpNBosW7bMpraKigo88sgjGDZsGHx9fTFw4EDMmTMHhw8ftva5+jlLS0uRlpaGsLAwa1tlZWWH+6r964ueeOKJ7u9wF0yYMAEAUFBQYP3PB+jetp4+fRrp6ekIDg5GWFgYZsyYYfdXhbPH19kaqI+5eyLDm3RnzjYyMlIAyD/+8Q+Xn8/RnO2uXbsEgPz617+W6upqqaiokN/+9rfi4+Mja9euten74IMPislkkk8//VQaGhqkvLxc1q5dKwAkNzfX5X4iIikpKQJAGhsbnWqvqKgQALJ06VJrW1lZmQwdOlTCw8Nl9+7dUltbK//6178kISFB/Pz8JC8vz+HYCQkJkpubK/X19VJQUCBarVYqKioc7rvy8nIJDw+XBx980OHyxMRECQ0Nlfz8fIfLr9bVnK2ISGNjowAQAFJWVtajbU1JSZG8vDypq6sFAX+DAAAgAElEQVSTzz77TIxGo4wbN86mr7PHzdUanAHO2boqm2Hrgp6E7aFDh1x+vo7CduLEiXZ9Fy1aJHq9Xmpqaqxtw4cPl/Hjx9v1HTVqlM0vo7P9RHonbJcuXSoA5MMPP7Tpe+7cOTEYDBIbG+tw7D179tjV6EhlZaWMHTtW0tPTpaWlxWGfhIQECQkJcTponAnbhoYGu7Dt7rbu2rXLpn3evHkCwOY/F2ePm6s1OINh67JsTiP0saioKADo9M9dV8yYMQO5ubl27WazGRaLBceOHbO2TZ06FXl5efj5z3+OgoIC65+WpaWlmDhxosv9ektOTg58fHzsbiGLiIjALbfcgqKiIpw5c8ZuvTvuuKPLsevr65GcnIybb74ZH374IbRarcN++/fvR3V1NeLi4rq3EQ6cO3cOAKDX63HdddcB6P62jhs3zubnIUOGAADKysqsbc4et+7WQL2LYdvHEhISAABHjx7tlfFqamrw9NNPIyYmBiEhIdY5vsceewwAbO7z3Lx5M/7whz/g22+/RVJSEgYMGICpU6dix44dNmM62683NDc3o6amBm1tbTCZTHZzu//85z8B/HR3w5UCAgI6HbulpQWpqamIjo7GBx980GHQ9pUDBw4A+PFip16v79G2mkwmm599fX0BwGZe3pnj1pMaqHcxbPvYihUroNPpsH379k77rVu3Dj4+PigpKem038yZM/Hcc8/hoYcewtdff422tjaICF599VUAgIhY+2o0GixevBh//etfcfHiReTk5EBEMGfOHLzyyisu9+sNBoMBwcHB0Ol0sFgsEBGHj8TERJfHXrFiBZqbm5GdnW1zV8eIESNQUFDQm5thp62tDZs3bwYArFq1CkDfbivg3HHr6xrIeQzbPjZq1Cg888wz+PLLL/Hee+857FNaWoq33noLaWlpGD16dIdjtba24uDBg4iIiMAjjzyCgQMHWm9pamxstOsfHBxsDW+9Xo/Jkydbr3rv3r3b5X69Zc6cOWhpacHBgwftlr344ou4/vrr0dLS4tKYGzduxLFjx/DRRx+55fazDRs24NChQ5g9e7bNbX59sa3tnD1ufVkDuUDhBLHX6+47yERE1q9fL3q9Xh5//HEpLS2V5uZmOXPmjLzzzjsSGRkpEyZMkLq6Opt1HF0gmzRpkgCQl156SSoqKqShoUE+//xzuf766wWAfPbZZ9a+JpNJEhIS5MiRI9LU1CTnz5+XjRs3CgB5/vnnXe4n0jsXyM6fPy833nij3HDDDbJnzx65ePGiVFVVyZtvvin+/v52F146Grvd73//e+uFqY4eV9910NO7EVpbW+X8+fOSk5NjPSbLly+XhoYGm/V6a1sff/xxASDFxcXWNmePm6s1OAO8QOYq3o3gip6ErYjIoUOHZPHixTJkyBDR6/USFBQkd911l7z++uvS3Nxs7ffyyy/bhcUTTzwhIj+G14oVK6xjhIeHy7Jly2T9+vXWvu1Xlw8fPiwrVqyQn/3sZ+Lv7y+hoaFy1113ydtvv23zllJn+u3YscOupoULF3bYLiKSnJxst+zvf/+7iIhUVVXJmjVr5IYbbhC9Xi8DBw6UKVOm2PxnkZ+f7zA4rzZ9+nSXwzY+Pt7puxECAgLsxtNoNGIymSQmJkYefvhhKSoq6nD97m5r+zG/un369OkuHV9na3AFw9Zl2RqRKyb5qFPtfx623yxP1F9pNBpkZWUhLS3N3aV4i22csyUiUoBhS0SkAMOWiEgBhi0RkQIMWyIiBRi2REQKMGyJiBRg2BIRKcCwJSJSgGFLRKQAw5aISAGGLRGRAgxbIiIFGLZERAowbImIFGDYEhEpwLAlIlJA13UXulJBQYHNF/oRETmDYeuCuLg4d5fQ71VUVODEiRO455573F1KvzZv3jwMGTLE3WV4FX4HGXmV7OxspKeng6cteRl+BxkRkQoMWyIiBRi2REQKMGyJiBRg2BIRKcCwJSJSgGFLRKQAw5aISAGGLRGRAgxbIiIFGLZERAowbImIFGDYEhEpwLAlIlKAYUtEpADDlohIAYYtEZECDFsiIgUYtkRECjBsiYgUYNgSESnAsCUiUoBhS0SkAMOWiEgBhi0RkQIMWyIiBRi2REQKMGyJiBRg2BIRKcCwJSJSgGFLRKQAw5aISAGGLRGRAjp3F0DUkTNnzmDp0qVobW21tlVWVkKn02HixIk2fW+66Sa89dZbiiskch7DljzW4MGDcfr0aXz77bd2y7744gubn+Pj41WVRdQtnEYgj7ZkyRLo9fou+82fP19BNUTdx7Alj7Zw4UJYLJZO+9x888245ZZbFFVE1D0MW/JoI0aMwJgxY6DRaBwu1+v1WLp0qeKqiFzHsCWPt2TJEmi1WofLWlpakJaWprgiItcxbMnjZWRkoK2tza5do9HgzjvvxLBhw9QXReQihi15vKioKIwfPx4+Pranq1arxZIlS9xUFZFrGLbkFRYvXmzXJiKYO3euG6ohch3DlrxCamqqzStbrVaLe++9F4MGDXJjVUTOY9iSVwgJCcGUKVOsF8pEBIsWLXJzVUTOY9iS11i0aJH1QplOp8OsWbPcXBGR8xi25DVmzZoFg8Fg/feAAQPcXBGR8/jZCFc5c+YM8vLy3F0GdeC2225DXl4ehg8fjuzsbHeXQx3gvc/2NCIi7i7Ck2RnZyM9Pd3dZRB5NcaKnW2cRuiAiPDhgY/Lly9j3bp1bq+DD8ePrKwsd//qeiyGLXkVvV6PjRs3ursMIpcxbMnrGI1Gd5dA5DKGLRGRAgxbIiIFGLZERAowbImIFGDYEhEpwLAlIlKAYUtEpADDlohIAYYtEZECDFsiIgUYtkRECjBs+8jWrVuh0Wig0Wjg5+fn7nK8yg8//IA333wTkyZNQmhoKIxGI0aOHImFCxfiyJEjPR4/MDDQemzaHz4+PggJCYHZbMbKlStRVFTUC1tC9BOGbR+ZP38+RARJSUnuLsXrPPbYY/jlL3+JlJQUHD9+HFVVVXjvvfdw+PBhxMbGIicnp0fj19XVobi4GACQkpICEYHFYkFJSQmeffZZlJSU4Pbbb8f999+PhoaG3tgkIoYt9b7AwEBMmDChR2MsX74cv/rVrxAREQF/f3/Ex8fjj3/8I1pbW7Fu3bpeqvQnWq0W4eHhSElJweeff45169bh/fffR0ZGBkT6zwdh98axI8f4tTjkcd555x2H7WazGUajEadOnYKIQKPR9FkNL7zwAr744gvs3LkTW7duRUZGRp89F/UPfGVLXqO+vh6NjY249dZb+zRoAUCj0WD16tUAgC1btvTpc1H/wLDtJSUlJbjvvvtgMpkQEBCA+Ph4HDhwwK5fTk6OzYWZ0tJSpKWlISwszNpWWVkJAKiqqsKaNWtw4403wtfXFyEhIZg2bRpyc3Ot423atMm63uDBg1FYWIikpCQEBQXB398fiYmJOHjwoF0dzoz9/PPPW8e+8k/LvXv3Wtuvu+46u1rq6+tx8OBBax+drnf+gNq2bRsA4IknnuiV8brSvs0FBQWwWCw8dtQzQjaysrLE1d3yzTffSHBwsERHR8unn34qtbW1cvToUZkyZYoMGzZMDAaD3TopKSkCQBISEiQ3N1fq6+uloKBAtFqtVFRUyLlz52T48OESHh4uu3btkpqaGiktLZU5c+aIRqORt99+22Y8s9ksAQEBEhcXJ3l5eVJXVyeFhYUyZswY8fX1lf3791v7ujp2QECA3H333XbbEBsbK2FhYXbtHfXvifLycgkPD5cHH3zQ4fLExEQJDQ2V/Px8p8YrLi4WAJKSktJhn8bGRgEgAKSsrMzazmPXse78/vQT2dwrV+nOyZKamioAZPv27TbtZ8+eFYPB0GnY7tmzx+GYy5YtEwDypz/9yaa9qalJoqKixGg0Snl5ubXdbDYLACkuLrbpf/ToUQEgZrO522O7O2wrKytl7Nixkp6eLi0tLQ77JCQkSEhIiOTl5Tk1pjNh29DQ0GnY8tjZY9h2KJvTCL1g7969AIDk5GSb9qioKIwaNarTde+44w6H7Tt27AAATJ8+3abdYDAgKSkJjY2N+Mtf/mKzLCAgAGPHjrVpi4mJQVRUFI4cOYJz5851e2x3qa+vR3JyMm6++WZ8+OGH0Gq1Dvvt378f1dXViIuL67Xnbt9fer3e5k/udjx25AqGbQ81NzejtrYWfn5+CAwMtFs+aNCgTtcPCAhwOGZNTQ38/PwQFBRktzw8PBwAUF5ebtMeHBzs8Dnaa7hw4UK3x3aHlpYWpKamIjo6Gh988EGHQdtX2ufc4+LioNfr7Zbz2JErGLY9ZDAYEBQUhKamJtTV1dktr66u7taYJpMJTU1NqK2ttVt+/vx5AEBERIRNe1VVlcN7Qi9cuADgx1/c7ozt4+ODy5cv2/W9ePGiw/p7606BFStWoLm5GdnZ2TYXakaMGIGCgoJeeY6OtLW1YfPmzQCAVatWOb0ejx11hGHbC6ZNmwbgp+mEdpWVlSgtLe3WmLNnzwYA7N6926a9ubkZ+/btg9FotJu2aGpqQmFhoU3bV199hbKyMpjNZkRGRnZr7MjISJw9e9amb3l5Ob777juHtfv7+9v8gt90003IzMzscpuvtHHjRhw7dgwfffQRDAaDS+v2hg0bNuDQoUOYPXs2UlNTXVq3vx876oC7Z409TXcm+E+ePCmhoaE2dyMcO3ZMkpOTZdCgQZ1eIGtsbHQ45tVXnS9dumRz1TkzM9Omv9lsFpPJJElJSS5f0e5q7NWrVwsAeeONN6S2tlZOnjwpaWlpEh0d7fAiy9SpU8VkMsl3330neXl5otPp5Pjx407vz9///vfWC1MdPa6+66CndyO0trbK+fPnJScnRyZNmiQAZPny5dLQ0GC3Lo9dx3iBrEO8G+Fq3T1ZSktL5b777pMBAwaI0WiUcePGyccffyxJSUnWgHjggQckPz/fYXg4UllZKY8++qgMHz5c9Hq9mEwmSU5Oln379tn1NZvNEh0dLcePH5fk5GQJCgoSo9EoCQkJcuDAgR6NffHiRXnwwQclMjJSjEajTJgwQQoLCyU2NtZa/+OPP27tX1JSIvHx8RIQECBDhgyRzZs3u7Qvp0+f7nLYxsfHO303QkBAgN14Go1GTCaTxMTEyMMPPyxFRUV26/HYdY1h26FsjUg/euO3E7Kzs5Genu5174cfO3YsKisrcebMGXeXQi66lo6dt/7+KLCNc7ZERAowbImIFGDYern297QfOXIEZ8+ehUajwZNPPunushy6+gO7HT02btzo7jKV8aZjRz3HOdurcM6JqPv4+9MhztkSEanAsCUiUoBhS0SkAMP2/7V359FNlfkbwJ/bJulOughd2BGxihC0IFTbX8HKNiyVAi1Ly+KgKDCMSwUZZxCRozh6QMcBFT1zHI5zhhbO0GHTERRUukipBVRoBYFBKNS22NJSWtL2+/vD04whKSRd3jTyfM7JOfS9733f781NHpJ7bxIiIgUYtkRECjBsiYgUYNgSESnAsCUiUoBhS0SkAMOWiEgBhi0RkQIMWyIiBRi2REQK6G7c5eaUkZHh6hKI3E5OTo6rS+iwGLbNSE5OdnUJRPQrwu+zJbfC70slN8XvsyUiUoFhS0SkAMOWiEgBhi0RkQIMWyIiBRi2REQKMGyJiBRg2BIRKcCwJSJSgGFLRKQAw5aISAGGLRGRAgxbIiIFGLZERAowbImIFGDYEhEpwLAlIlKAYUtEpADDlohIAYYtEZECDFsiIgUYtkRECjBsiYgUYNgSESnAsCUiUoBhS0SkAMOWiEgBhi0RkQIMWyIiBRi2REQKMGyJiBRg2BIRKcCwJSJSQOfqAoiaU1paiq1bt1q1HTx4EACwYcMGq3Z/f3/MmDFDWW1EztJERFxdBJE9dXV16Ny5My5fvgxPT08AgIhARODh8b83ZWazGbNmzcLf//53V5VKdCObeRiBOiwvLy9MnToVOp0OZrMZZrMZ9fX1aGhosPxtNpsBgK9qqcNj2FKHNmPGDFy9evW6fQIDAxEfH6+oIqKWYdhShzZixAh07ty52eV6vR4pKSnQ6Xj6gTo2hi11aB4eHpgxYwYMBoPd5WazGdOnT1dcFZHzGLbU4U2fPr3ZQwnh4eGIjo5WXBGR8xi21OENHToUPXv2tGnX6/WYPXs2NE1zQVVEzmHYkltITU2FXq+3auMhBHInDFtyCzNnzrRc5tWkb9++GDhwoIsqInIOw5bcQmRkJO68807LIQO9Xo+5c+e6uCoixzFsyW3MmjXL8kkys9mMpKQkF1dE5DiGLbmNadOmoaGhAQAQFRWFvn37urgiIscxbMlt9OzZE0OGDAHw86tcInfCL6JxwtSpU7FlyxZXl0HUIaSnp/NQjuM28zOOTho2bBiefPJJV5dx07p06RLWr1+PZ5991tWl3NSSk5NdXYLbYdg6qVu3bvzf3MXi4uJw2223ubqMmxrD1nk8Zktuh0FL7ohhS0SkAMOWiEgBhi0RkQIMWyIiBRi2REQKMGyJiBRg2BIRKcCwJSJSgGFLRKQAw5aISAGGLRGRAgxbIiIFGLYK5eXlYc6cOejduzd8fHwQHByMu+66C5MnT8Zbb72F77//HgDw2muvQdM0aJqGbt26ubhqxx07dgzJyckICwuDTqezbENgYGC7zisiyMrKwsKFC9GvXz94eXmhS5cuiImJwQcffIDWfmWzv7+/ZVuabh4eHggKCoLJZMKCBQuQn5/fRltDv1YMWwUaGxvxzDPP4L777kOXLl3w4YcfoqKiAseOHcPatWtx6dIlLFiwAH379kV9fT3S0tIgIjCZTK4u3Up1dTVuu+02jB8/3mbZ6dOnER0djWPHjuFf//oXLl26hEuXLiEjIwMeHu37MCsqKkJMTAy+++47bNmyBZWVlcjNzUWPHj2QmpqKZ555plXjV1dXo6CgAACQkJAAEYHZbEZhYSFWrlyJwsJCDB48GHPnzkVNTU1bbBL9Ggk5bMqUKTJlyhSn1/vDH/4gAGTDhg12l9fX18vYsWMFgJjNZku7yWSSrl27trjetnbp0iXp06ePjB071mbZsmXLBIB8/vnnyus6duyY6HQ6uXjxolV7XV2dhISEiJeXl9TW1rZqjoKCAgEgCQkJdpcvWbJEAMjEiROlsbGxVXO5AwCSnp7u6jLcSQZf2bazwsJCrF69GlFRUXjkkUfs9vH09MSf/vQnxZU5LyAgAN9//z127dpls+z48eMAgIEDB6ouC5GRkTCbzQgKCrJqNxgM6N69O+rq6lBbW9uuNaxevRpDhw7Ftm3bsGnTpnadi9wTw7adbdiwAY2NjZg6dep1+0VHR0NEoNO5549nmM1mAICXl5eLK/mfiooKHD9+HHfffTeMRmO7zqVpGhYtWgQAWL9+fbvORe6JYdvOPv/8cwBt94qvvr4e6enpGDlyJMLCwuDj44MBAwbgjTfeQGNjo1Xfuro6LF++HJGRkfD19UVwcDAmTJiAbdu2WX4S3NF+mZmZVieIml4pNrX/+9//BgD4+PjYnEzSNA1z5syxqq20tBSLFy9Gr169YDAY0LlzZyQmJuLQoUOWPtfOWVRUhKSkJISEhFjaysrKbO6jS5cuISsrCxMnTkRYWBg2btzYJvf9jcTExAAAcnNzLf/5AC3b1tOnTyM5ORmBgYEICQnB+PHjLSdQmzi6fx2tgdqZqw9kuJOWHLMNDw8XAPLll186PZ+9Y7bbt28XAPLSSy/JxYsXpbS0VP7yl7+Ih4eHpKWlWfWdN2+eGI1G+fjjj6WmpkYuXLggaWlpAkD27t3rdD8RkYSEBAEgV65ccai9tLRUAMjs2bMtbcXFxdKzZ08JDQ2VnTt3SlVVlXzzzTcSFxcn3t7ekp2dbXfsuLg42bt3r1y+fFlyc3PF09NTSktLrfq++OKLAkAAyPDhw+XIkSN279sRI0ZIcHCw5OTk2F1+rRsdsxURuXLlimXu4uLiVm1rQkKCZGdnS3V1tezevVt8fHxkyJAhVn0d3W/O1uAI8JitszIYtk5oTdgeOHDA6fmaC9vhw4fb9E1JSRG9Xi+VlZWWtt69e8t9991n07dfv35WT0ZH+4m0TdjOnj1bAMg//vEPq77nz58XLy8viYqKsjv2rl27bGq0p66uTo4dOyaPPfaYeHp6ysqVK236xMXFSVBQkMNB40jY1tTU2IRtS7d1+/btVu1TpkwRAFb/uTi635ytwREMW6fxBFl7i4iIAAC7b3dbYvz48di7d69Nu8lkgtlsxrfffmtpGzNmDLKzs/Hoo48iNzfX8tayqKgIw4cPd7pfW8nMzISHh4fNJWRhYWHo378/8vPzcfbsWZv17r33XofGNxgMiIyMxFtvvYWJEydi+fLl2LNnj1Wfffv24eLFi4iOjm75hlzj/PnzAAC9Xo9bbrkFQMu3dciQIVZ/d+/eHQBQXFxsaXN0v7W0BmpbDNt2FhcXBwA4cuRIm4xXWVmJ5cuXY8CAAQgKCrIc42u6lvSX13muW7cOGzduxMmTJxEfH49OnTphzJgx2Lp1q9WYjvZrC3V1daisrERjYyOMRqPNsd2vvvoKwP+ubvglPz8/p+ebMGECAGDHjh2tK9wB+/fvB/DzyU69Xt+qbb32hJ7BYAAAq+Pyjuy31tRAbYth287mz58PnU6HLVu2XLffkiVL4OHhgcLCwuv2mzBhAl588UU88sgj+O6779DY2AgRwdq1awHA6tNSmqYhNTUVe/bsQUVFBTIzMyEiSExMxJo1a5zu1xa8vLwQGBgInU4Hs9kMEbF7GzFiRJvNBwAXL15sk/Ga09jYiHXr1gEAFi5caJm7PbfVkf2m+v6m5jFs21m/fv3w/PPP4+DBg/jb3/5mt09RURHeeecdJCUlITIystmxGhoakJWVhbCwMCxevBidO3eGpmkAgCtXrtj0DwwMtIS3Xq/HyJEjLWe9d+7c6XS/tpKYmIj6+npkZWXZLHvllVfQo0cP1NfXOzxeWloaUlJS7C778MMPAdi+LW9ry5Ytw4EDBzBp0iSry/zaelt/ydH91p41kBMUHiB2ey39BJmIyLPPPit6vV6WLl0qRUVFUldXJ2fPnpX33ntPwsPDJSYmRqqrq63WsXeC7IEHHhAA8uc//1lKS0ulpqZGPv30U+nRo4cAkN27d1v6Go1GiYuLk8OHD0ttba2UlJTIihUrBICsWrXK6X4ibXOCrKSkRG699Vbp06eP7Nq1SyoqKqS8vFzefvtt8fX1tTnx0tzYTZ5++mnRNE1eeOEFOXXqlNTW1sqpU6csn+qKioqSmpoaq3VaezVCQ0ODlJSUSGZmpmWfPPzwwzbztNW2Ll26VABIQUGBpc3R/eZsDY4AT5A5i1cjOKM1YSsicuDAAUlNTZXu3buLXq+XgIAAGTZsmLzxxhtSV1dn6ffqq69azmo33Z577jkR+Tm85s+fbxkjNDRU5syZI88++6ylb9PZ5UOHDsn8+fPljjvuEF9fXwkODpZhw4bJu+++a/WRUkf6bd261aammTNnNtsuIjJ69GibZV988YWIiJSXl8tTTz0lffr0Eb1eL507d5ZRo0ZZ/WeRk5Njs7691weVlZXy3nvvyejRo6VXr15iMBjE399foqKi5OWXX7YJQBGR2NhYh69G8PPzs6lB0zQxGo0yYMAAefzxxyU/P7/Z9Vu6rU37/Nr2cePGObV/Ha3BGQxbp2VoIq38SqSbSNPbw82bN7u4EiLX0jQN6enpSEpKcnUp7mIzj9kSESnAsCUiUoBhS0SkAMOWiEgBhi0RkQIMWyIiBRi2REQKMGyJiBRg2BIRKcCwJSJSgGFLRKQAw5aISAGGLRGRAgxbIiIFGLZERAowbImIFGDYEhEpoHN1Ae5my5Ytlh9ZJCJyFH8Wxwk5OTn44YcfXF3GTS0nJwevv/460tPTXV3KTe++++5Dt27dXF2Gu9jMsCW3kpGRgeTkZPBhS26Gv0FGRKQCw5aISAGGLRGRAgxbIiIFGLZERAowbImIFGDYEhEpwLAlIlKAYUtEpADDlohIAYYtEZECDFsiIgUYtkRECjBsiYgUYNgSESnAsCUiUoBhS0SkAMOWiEgBhi0RkQIMWyIiBRi2REQKMGyJiBRg2BIRKcCwJSJSgGFLRKQAw5aISAGGLRGRAgxbIiIFGLZERAowbImIFGDYEhEpwLAlIlJA5+oCiJpjNptRXV1t1Xb58mUAwE8//WTVrmkaAgMDldVG5CyGLXVY5eXl6NatGxoaGmyWBQcHW/09fPhw7N27V1VpRE7jYQTqsMLCwvB///d/8PC4/sNU0zRMnz5dUVVELcOwpQ4tNTUVmqZdt4+HhwcmT56sqCKilmHYUoc2efJkeHp6Nrvc09MTY8aMQUhIiMKqiJzHsKUOrVOnThgzZgx0OvunF0QEKSkpiqsich7Dljq8lJQUuyfJAMBgMGD8+PGKKyJyHsOWOrwJEybA19fXpl2n02HSpEnw9/d3QVVEzmHYUofn7e2NxMRE6PV6q/b6+nrMnDnTRVUROYdhS25hxowZMJvNVm2dOnXCyJEjXVQRkXMYtuQWHnzwQasPMuj1ekybNg0Gg8GFVRE5jmFLbkGn02HatGmWQwlmsxkzZsxwcVVEjmPYktuYPn265VBCaGgoYmNjXVwRkeMYtuQ27r//fkRERAD4+ZNlN/oYL1FHwi+iuUZOTg7WrFnj6jKoGQEBAQCAgoICTJ061cXVUHM2b97s6hI6HL40uMYPP/yALVu2uLoMakaPHj0QEBCAoKAgV5dCdpw9e5bPn2bwlW0z+D9zx5WRkYGkpCRXl0F2ZGRkIDk52dVldEh8ZUtuh0FL7ohhS0SkAMOWiEgBhi0RkQIMWyIiBRi2REQKMGyJiBRg2BIRKcCwJSJSgGFLRKQAw5aISAGGLRGRAgxbIiIFGLbtZNOmTdA0DZqmwdvb29XluDlxt/MAABMDSURBVBURQVZWFhYuXIh+/frBy8sLXbp0QUxMDD744AOISKvG9/f3t+ybppuHhweCgoJgMpmwYMEC5Ofnt9HWEP2MYdtOpk2bBhFBfHy8q0txO0VFRYiJicF3332HLVu2oLKyErm5uejRowdSU1PxzDPPtGr86upqFBQUAAASEhIgIjCbzSgsLMTKlStRWFiIwYMHY+7cuaipqWmLTSJi2FLb8/f3R0xMTKvG0Ol0yMjIwMCBA+Ht7Y0+ffrg/fffR0hICP7617+irq6ujar9maenJ0JDQ5GQkIBPP/0US5Yswfvvv4/p06e3+pW0O2mLfUf2MWypw4mMjITZbLb5NQaDwYDu3bujrq4OtbW17VrD6tWrMXToUGzbtg2bNm1q17no5sCwJbdRUVGB48eP4+6774bRaGzXuTRNw6JFiwAA69evb9e56ObAsG0jhYWFeOihh2A0GuHn54fY2Fjs37/fpl9mZqbViZmioiIkJSUhJCTE0lZWVgYAKC8vx1NPPYVbb70VBoMBQUFBGDt2LPbu3WsZ77XXXrOs161bN+Tl5SE+Ph4BAQHw9fXFiBEjkJWVZVOHI2OvWrXKMvYv31p+9NFHlvZbbrnFppbLly8jKyvL0kena92vL126dAlZWVmYOHEiwsLCsHHjxlaN56imbc7NzYXZbOa+o9YRspKeni7O3i3Hjx+XwMBA6dq1q3z88cdSVVUlR44ckVGjRkmvXr3Ey8vLZp2EhAQBIHFxcbJ37165fPmy5Obmiqenp5SWlsr58+eld+/eEhoaKtu3b5fKykopKiqSxMRE0TRN3n33XavxTCaT+Pn5SXR0tGRnZ0t1dbXk5eXJwIEDxWAwyL59+yx9nR3bz89P7r//fpttiIqKkpCQEJv25vq3xIsvvigABIAMHz5cjhw5YrffiBEjJDg4WHJychwat6CgQABIQkJCs32uXLlimbu4uNjSzn3XvJY8f24SGbxXrtGSB8vUqVMFgGzZssWq/dy5c+Ll5XXdsN21a5fdMefMmSMA5J///KdVe21trURERIiPj49cuHDB0m4ymQSAFBQUWPU/cuSIABCTydTisV0ZtiIidXV1cuzYMXnsscfE09NTVq5cadMnLi5OgoKCJDs726ExHQnbmpqa64Yt950thm2zMngYoQ189NFHAIDRo0dbtUdERKBfv37XXffee++1275161YAwLhx46zavby8EB8fjytXruA///mP1TI/Pz8MGjTIqm3AgAGIiIjA4cOHcf78+RaP7UoGgwGRkZF46623MHHiRCxfvhx79uyx6rNv3z5cvHgR0dHRbTZv0/2l1+ut3nI34b4jZzBsW6murg5VVVXw9vaGv7+/zfIuXbpcd30/Pz+7Y1ZWVsLb2xsBAQE2y0NDQwEAFy5csGoPDAy0O0dTDT/++GOLx+4oJkyYAADYsWNHu8/VdMw9Ojoaer3eZjn3HTmDYdtKXl5eCAgIQG1tLaqrq22WX7x4sUVjGo1G1NbWoqqqymZ5SUkJACAsLMyqvby83O41oT/++COAn5+4LRnbw8MDV69etelbUVFht35N05rbtFbz8vIC0LL71RmNjY1Yt24dAGDhwoUOr8d9R81h2LaBsWPHAvjf4YQmZWVlKCoqatGYkyZNAgDs3LnTqr2urg6ffPIJfHx8bA5b1NbWIi8vz6rt66+/RnFxMUwmE8LDw1s0dnh4OM6dO2fV98KFCzhz5ozd2n19fa2e4Lfffjs2bNhww21ukpaWhpSUFLvLPvzwQwDAkCFDHB6vJZYtW4YDBw5g0qRJmDp1qlPr3sz7jq7D1UeNO5qWHOA/ceKEBAcHW12N8O2338ro0aOlS5cu1z1BduXKFbtjXnvW+dKlS1ZnnTds2GDV32QyidFolPj4eKfPaN9o7EWLFgkAefPNN6WqqkpOnDghSUlJ0rVrV7snWcaMGSNGo1HOnDkj2dnZotPp5OjRow7fn08//bRomiYvvPCCnDp1Smpra+XUqVOyZMkSASBRUVFSU1NjtU5rr0ZoaGiQkpISyczMlAceeEAAyMMPP2wzjwj33fXwBFmzeDXCtVr6YCkqKpKHHnpIOnXqJD4+PjJkyBDZsWOHxMfHW85o//a3v5WcnBzL37+82VNWViZPPPGE9O7dW/R6vRiNRhk9erR88sknNn1NJpN07dpVjh49KqNHj5aAgADx8fGRuLg42b9/f6vGrqiokHnz5kl4eLj4+PhITEyM5OXlSVRUlKX+pUuXWvoXFhZKbGys+Pn5Sffu3WXdunVO3ZeVlZXy3nvvyejRo6VXr15iMBjE399foqKi5OWXX7YbgLGxsQ5fjeDn52dz/2uaJkajUQYMGCCPP/645Ofn26zHfXdjDNtmZWgiN9EHvx2QkZGB5ORkt/s8/KBBg1BWVoazZ8+6uhRy0q9p37nr80eBzTxmS0SkAMOWiEgBhq2ba/pM++HDh3Hu3DlomoY//vGPri7Lrmu/sNvebcWKFa4uUxl32nfUejxmew0ecyJqOT5/msVjtkREKjBsiYgUYNgSESnAsCUiUoBhS0SkAMOWiEgBhi0RkQIMWyIiBRi2REQKMGyJiBRg2BIRKcCwJSJSgGFLRKSAztUFdFTO/sgfEeFX8WsT7YWvbK/RvXt3TJkyxdVlUDNKS0vx+eefu7oMaka3bt34/GkGv8+W3Aq/L5XcFL/PlohIBYYtEZECDFsiIgUYtkRECjBsiYgUYNgSESnAsCUiUoBhS0SkAMOWiEgBhi0RkQIMWyIiBRi2REQKMGyJiBRg2BIRKcCwJSJSgGFLRKQAw5aISAGGLRGRAgxbIiIFGLZERAowbImIFGDYEhEpwLAlIlKAYUtEpADDlohIAYYtEZECDFsiIgUYtkRECjBsiYgUYNgSESnAsCUiUoBhS0SkAMOWiEgBnasLIGrO2bNnMXv2bDQ0NFjaysrKoNPpMHz4cKu+t99+O9555x3FFRI5jmFLHVa3bt1w+vRpnDx50mbZZ599ZvV3bGysqrKIWoSHEahDmzVrFvR6/Q37TZs2TUE1RC3HsKUObebMmTCbzdftc+edd6J///6KKiJqGYYtdWh9+/bFwIEDoWma3eV6vR6zZ89WXBWR8xi21OHNmjULnp6edpfV19cjKSlJcUVEzmPYUoc3ffp0NDY22rRrmoahQ4eiV69e6osichLDljq8iIgI3HffffDwsH64enp6YtasWS6qisg5DFtyC6mpqTZtIoLJkye7oBoi5zFsyS1MnTrV6pWtp6cnHnzwQXTp0sWFVRE5jmFLbiEoKAijRo2ynCgTEaSkpLi4KiLHMWzJbaSkpFhOlOl0OkycONHFFRE5jmFLbmPixInw8vKy/LtTp04urojIcfxuBMXOnj2L7OxsV5fhtu655x5kZ2ejd+/eyMjIcHU5bovXJquniYi4uoibSUZGBpKTk11dBt3k+LRXbjMPI7iIiPDWgtvVq1exZMkSl9fhrrf09HRXP/RvWgxbcit6vR4rVqxwdRlETmPYktvx8fFxdQlETmPYEhEpwLAlIlKAYUtEpADDlohIAYYtEZECDFsiIgUYtkRECjBsiYgUYNgSESnAsCUiUoBhS0SkAMPWzRw8eBBz5sxBr1694O3tjcDAQAwZMgQrV65ERUWFq8vrcPz9/aFpmtXNw8MDQUFBMJlMWLBgAfLz811dJt0EGLZuZNmyZRg2bBiCgoKwY8cOVFRU4NSpU3j++eexdetW9OvXD1lZWa4us0Oprq5GQUEBACAhIQEiArPZjMLCQqxcuRKFhYUYPHgw5s6di5qaGhdXS79mDFs3sWrVKqxevRrr1q3D2rVrcdddd8Hb2xtBQUEYP348srKy0KNHD4wdOxaFhYWtmsvf3x8xMTFtVHnHm9/T0xOhoaFISEjAp59+iiVLluD999/H9OnTIcIv1ab2wbB1AydOnMALL7yAe+65B/Pnz7fbx9fXF2vXrkVVVRUWL16suEL3tnr1agwdOhTbtm3Dpk2bXF0O/UoxbN3A22+/jfr6ekydOvW6/WJjYxEREYHdu3fj5MmTiqpzf5qmYdGiRQCA9evXu7ga+rVi2LqBzz77DABgMplu2LepzxdffIFVq1ZZTgr98m35Rx99ZGm/5ZZbLO2vvfYaNE3D5cuXkZWVZemj0+mslmuahm7duiEvLw/x8fEICAiAr68vRowYYXXMuK3nb09N9eXm5sJsNlvaS0tLsXjxYvTq1QsGgwGdO3dGYmIiDh06ZOmTmZlpdQLu9OnTSE5ORmBgIEJCQjB+/Hh8//33VvPV1dVh+fLliIyMhK+vL4KDgzFhwgRs27YNDQ0NVn0dqYHcgJBS6enp4uzdHh4eLgDkyy+/vGHflJQUASAvvfSSpc3Pz0/uv/9+m75RUVESEhJi095c/yYmk0n8/PwkOjpasrOzpbq6WvLy8mTgwIFiMBhk3759Do3X0vlHjBghwcHBkpOT02yfXyooKBAAkpCQ0GyfK1euCAABIMXFxSIiUlxcLD179pTQ0FDZuXOnVFVVyTfffCNxcXHi7e0t2dnZVmMkJCRY5mm6X3bv3i0+Pj4yZMgQq77z5s0To9EoH3/8sdTU1MiFCxckLS1NAMjevXst/Zyt4UZa8vijNpHBV7ZuRNO0dunbEpcvX8b69esRHR0NPz8/DB48GB988AGuXr2K3//+9+06d2Njo+UHDNuKvbGWLVuG//73v1izZg1+85vfwN/fH/3798emTZsgIvjd735nd6x58+ZZ7pcHH3wQ48aNQ15eHsrKyix9PvnkE/Tv3x8jR46Ej48PQkND8eqrr6Jfv35tUgN1PAxbNxAREQEAKC8vv2Hfpj5N67QXPz8/DBo0yKptwIABiIiIwOHDh3H+/Pl2m3vfvn24ePEioqOj22zMpnr1er3l0EZmZiY8PDwwfvx4q75hYWHo378/8vPzcfbsWZuxhgwZYvV39+7dAQDFxcWWtjFjxiA7OxuPPvoocnNzLYcOioqKMHz4cEu/ltZAHQ/D1g3ExcUBgEPH6A4fPgwAVk/Y9hAYGGi3vUuXLgCAH3/8sV3nb2v79+8HAERHR0Ov16Ourg6VlZVobGyE0Wi0+WDEV199BQA4fvy4zVhGo9Hqb4PBAODnV+RN1q1bh40bN+LkyZOIj49Hp06dMGbMGGzdutXSpzU1UMfDsHUD8+fPh06nw+bNm6/bb//+/SguLsaECRPQo0cPS7uHhweuXr1q07+5T5w5cgiivLzc7lvvppBtCt32mr8tNTY2Yt26dQCAhQsXAgC8vLwQGBgInU4Hs9lsOWxx7W3EiBEtmlPTNKSmpmLPnj2oqKhAZmYmRASJiYlYs2aNkhpILYatG+jXrx+ef/55fPXVV3jnnXfs9qmpqcETTzyBkJAQvP7661bLwsPDce7cOau2Cxcu4MyZM3bH8vX1tQrH22+/HRs2bLDqU1tbi7y8PKu2r7/+GsXFxTCZTAgPD2/X+dvSsmXLcODAAUyaNMnq8rrExETU19fb/VTeK6+8gh49eqC+vr5FcwYGBlo+fKLX6zFy5EjLVQ07d+5UUgMppviM3E2vNWeDly1bJp6envLkk0/KN998I7W1tfLTTz/J9u3b5e6775auXbvKwYMHbdZbtGiRAJA333xTqqqq5MSJE5KUlCRdu3a1ezXAmDFjxGg0ypkzZyQ7O1t0Op0cPXrUstxkMonRaJT4+HiHrkZo6/lbezVCQ0ODlJSUSGZmpjzwwAMCQB5++GGpqamxWq+kpERuvfVW6dOnj+zatUsqKiqkvLxc3n77bfH19ZX09HSr/k1XI1y5csWqfenSpQJACgoKLG1Go1Hi4uLk8OHDUltbKyUlJbJixQoBIKtWrWpxDTfCqxFcJoP3umKtfbDn5eXJ7NmzpWfPnmIwGCQgIEAGDx4sq1atkoqKCrvrVFRUyLx58yQ8PFx8fHwkJiZG8vLyJCoqynK509KlSy39CwsLJTY2Vvz8/KR79+6ybt06q/FMJpN07dpVjh49KqNHj5aAgADx8fGRuLg42b9/f7vPHxsbK0FBQQ5d9uTn52eZo+mmaZoYjUYZMGCAPP7445Kfn9/s+uXl5fLUU09Jnz59RK/XS+fOnWXUqFGye/duS5+cnBybOZ577jkREZv2cePGiYjIoUOHZP78+XLHHXeIr6+vBAcHy7Bhw+Tdd9+VxsZGp2twFMPWZTI0EX4YXKWMjAwkJye79WfwBw0ahLKyMp4Fd0O/hsefm9rMY7ZERAowbImIFGDYksOavrvg8OHDOHfuHDRNwx//+EdXl0XkFtr/Gz7oVyMtLQ1paWmuLoPILfGVLRGRAgxbIiIFGLZERAowbImIFGDYEhEpwLAlIlKAYUtEpADDlohIAYYtEZECDFsiIgUYtkRECjBsiYgUYNgSESnAb/1ykYyMDFeXQDehnJwcV5dw02LYukhycrKrSyAihfgbZERE7Y+/QUZEpALDlohIAYYtEZECDFsiIgX+H213VN1G0yfRAAAAAElFTkSuQmCC\n","text/plain":["<IPython.core.display.Image object>"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}]},{"cell_type":"code","metadata":{"execution":{"iopub.execute_input":"2021-11-27T17:02:38.387034Z","iopub.status.busy":"2021-11-27T17:02:38.385388Z","iopub.status.idle":"2021-11-27T17:02:38.387683Z","shell.execute_reply":"2021-11-27T17:02:38.388155Z","shell.execute_reply.started":"2021-11-19T22:15:30.838662Z"},"id":"215b28e4","papermill":{"duration":0.019836,"end_time":"2021-11-27T17:02:38.388291","exception":false,"start_time":"2021-11-27T17:02:38.368455","status":"completed"},"tags":[]},"source":["patience = 10\n","early_stopping = tfk.callbacks.EarlyStopping(monitor='val_accuracy', mode='max', patience=patience, restore_best_weights=True)"],"id":"215b28e4","execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"execution":{"iopub.execute_input":"2021-11-27T17:02:38.418672Z","iopub.status.busy":"2021-11-27T17:02:38.417815Z","iopub.status.idle":"2021-11-27T19:56:16.788907Z","shell.execute_reply":"2021-11-27T19:56:16.787992Z","shell.execute_reply.started":"2021-11-19T22:15:30.845312Z"},"id":"917e99ee","papermill":{"duration":10418.388529,"end_time":"2021-11-27T19:56:16.789055","exception":false,"start_time":"2021-11-27T17:02:38.400526","status":"completed"},"tags":[],"outputId":"39e8baca-e904-47b5-abf4-572574945908"},"source":["history = model.fit(\n","    x = train_gen,\n","    epochs = epochs,\n","    validation_data = val_gen,\n","    callbacks = [early_stopping],\n",").history"],"id":"917e99ee","execution_count":null,"outputs":[{"name":"stderr","output_type":"stream","text":["2021-11-27 17:02:39.122307: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n"]},{"name":"stdout","output_type":"stream","text":["Epoch 1/60\n"]},{"name":"stderr","output_type":"stream","text":["2021-11-27 17:02:41.647368: I tensorflow/stream_executor/cuda/cuda_dnn.cc:369] Loaded cuDNN version 8005\n"]},{"name":"stdout","output_type":"stream","text":["444/444 [==============================] - 342s 753ms/step - loss: 2.0072 - accuracy: 0.3911 - val_loss: 1.4574 - val_accuracy: 0.5276\n","Epoch 2/60\n","444/444 [==============================] - 253s 570ms/step - loss: 1.4851 - accuracy: 0.5399 - val_loss: 1.3626 - val_accuracy: 0.6030\n","Epoch 3/60\n","444/444 [==============================] - 260s 584ms/step - loss: 1.1973 - accuracy: 0.6375 - val_loss: 1.1065 - val_accuracy: 0.6507\n","Epoch 4/60\n","444/444 [==============================] - 252s 568ms/step - loss: 1.0172 - accuracy: 0.6893 - val_loss: 1.0194 - val_accuracy: 0.6889\n","Epoch 5/60\n","444/444 [==============================] - 254s 570ms/step - loss: 0.9293 - accuracy: 0.7178 - val_loss: 0.8634 - val_accuracy: 0.7480\n","Epoch 6/60\n","444/444 [==============================] - 254s 571ms/step - loss: 0.8486 - accuracy: 0.7482 - val_loss: 0.7863 - val_accuracy: 0.7528\n","Epoch 7/60\n","444/444 [==============================] - 254s 572ms/step - loss: 0.7616 - accuracy: 0.7799 - val_loss: 1.0543 - val_accuracy: 0.7282\n","Epoch 8/60\n","444/444 [==============================] - 256s 575ms/step - loss: 0.6885 - accuracy: 0.8059 - val_loss: 0.7607 - val_accuracy: 0.7754\n","Epoch 9/60\n","444/444 [==============================] - 256s 575ms/step - loss: 0.6423 - accuracy: 0.8236 - val_loss: 0.8717 - val_accuracy: 0.7819\n","Epoch 10/60\n","444/444 [==============================] - 256s 575ms/step - loss: 0.5903 - accuracy: 0.8412 - val_loss: 0.6858 - val_accuracy: 0.8124\n","Epoch 11/60\n","444/444 [==============================] - 256s 576ms/step - loss: 0.5518 - accuracy: 0.8578 - val_loss: 0.7471 - val_accuracy: 0.8160\n","Epoch 12/60\n","444/444 [==============================] - 257s 579ms/step - loss: 0.5414 - accuracy: 0.8639 - val_loss: 0.7128 - val_accuracy: 0.8330\n","Epoch 13/60\n","444/444 [==============================] - 257s 580ms/step - loss: 0.4981 - accuracy: 0.8810 - val_loss: 0.6485 - val_accuracy: 0.8452\n","Epoch 14/60\n","444/444 [==============================] - 258s 582ms/step - loss: 0.4728 - accuracy: 0.8874 - val_loss: 0.5571 - val_accuracy: 0.8627\n","Epoch 15/60\n","444/444 [==============================] - 260s 586ms/step - loss: 0.4583 - accuracy: 0.8939 - val_loss: 0.6405 - val_accuracy: 0.8415\n","Epoch 16/60\n","444/444 [==============================] - 260s 584ms/step - loss: 0.4628 - accuracy: 0.8975 - val_loss: 0.5285 - val_accuracy: 0.8836\n","Epoch 17/60\n","444/444 [==============================] - 261s 588ms/step - loss: 0.4380 - accuracy: 0.9028 - val_loss: 0.5358 - val_accuracy: 0.8672\n","Epoch 18/60\n","444/444 [==============================] - 262s 590ms/step - loss: 0.4242 - accuracy: 0.9101 - val_loss: 0.6309 - val_accuracy: 0.8494\n","Epoch 19/60\n","444/444 [==============================] - 268s 602ms/step - loss: 0.4243 - accuracy: 0.9102 - val_loss: 0.8141 - val_accuracy: 0.8214\n","Epoch 20/60\n","444/444 [==============================] - 263s 593ms/step - loss: 0.4088 - accuracy: 0.9156 - val_loss: 0.7693 - val_accuracy: 0.8593\n","Epoch 21/60\n","444/444 [==============================] - 265s 596ms/step - loss: 0.4013 - accuracy: 0.9185 - val_loss: 0.7945 - val_accuracy: 0.8211\n","Epoch 22/60\n","444/444 [==============================] - 264s 595ms/step - loss: 0.4011 - accuracy: 0.9236 - val_loss: 0.5672 - val_accuracy: 0.8805\n","Epoch 23/60\n","444/444 [==============================] - 266s 598ms/step - loss: 0.3853 - accuracy: 0.9218 - val_loss: 0.5336 - val_accuracy: 0.9011\n","Epoch 24/60\n","444/444 [==============================] - 269s 604ms/step - loss: 0.3595 - accuracy: 0.9347 - val_loss: 0.6094 - val_accuracy: 0.8618\n","Epoch 25/60\n","444/444 [==============================] - 266s 599ms/step - loss: 0.3823 - accuracy: 0.9251 - val_loss: 0.6022 - val_accuracy: 0.8669\n","Epoch 26/60\n","444/444 [==============================] - 272s 612ms/step - loss: 0.3752 - accuracy: 0.9292 - val_loss: 0.7057 - val_accuracy: 0.8471\n","Epoch 27/60\n","444/444 [==============================] - 272s 613ms/step - loss: 0.3661 - accuracy: 0.9333 - val_loss: 0.4497 - val_accuracy: 0.9183\n","Epoch 28/60\n","444/444 [==============================] - 271s 608ms/step - loss: 0.3579 - accuracy: 0.9343 - val_loss: 0.5975 - val_accuracy: 0.8813\n","Epoch 29/60\n","444/444 [==============================] - 270s 609ms/step - loss: 0.3636 - accuracy: 0.9351 - val_loss: 0.6036 - val_accuracy: 0.8802\n","Epoch 30/60\n","444/444 [==============================] - 269s 606ms/step - loss: 0.3523 - accuracy: 0.9355 - val_loss: 0.6813 - val_accuracy: 0.8785\n","Epoch 31/60\n","444/444 [==============================] - 269s 606ms/step - loss: 0.3675 - accuracy: 0.9359 - val_loss: 0.7571 - val_accuracy: 0.8262\n","Epoch 32/60\n","444/444 [==============================] - 275s 620ms/step - loss: 0.3564 - accuracy: 0.9392 - val_loss: 0.4581 - val_accuracy: 0.9084\n","Epoch 33/60\n","444/444 [==============================] - 270s 607ms/step - loss: 0.3398 - accuracy: 0.9450 - val_loss: 0.5096 - val_accuracy: 0.9113\n","Epoch 34/60\n","444/444 [==============================] - 269s 605ms/step - loss: 0.3354 - accuracy: 0.9451 - val_loss: 0.4772 - val_accuracy: 0.9022\n","Epoch 35/60\n","444/444 [==============================] - 270s 608ms/step - loss: 0.3410 - accuracy: 0.9439 - val_loss: 0.5201 - val_accuracy: 0.9166\n","Epoch 36/60\n","444/444 [==============================] - 269s 605ms/step - loss: 0.3233 - accuracy: 0.9468 - val_loss: 0.4465 - val_accuracy: 0.9130\n","Epoch 37/60\n","444/444 [==============================] - 265s 597ms/step - loss: 0.3514 - accuracy: 0.9422 - val_loss: 0.6981 - val_accuracy: 0.8771\n"]}]},{"cell_type":"code","metadata":{"execution":{"iopub.execute_input":"2021-11-27T19:56:25.593281Z","iopub.status.busy":"2021-11-27T19:56:25.592490Z","iopub.status.idle":"2021-11-27T19:56:28.229627Z","shell.execute_reply":"2021-11-27T19:56:28.230377Z","shell.execute_reply.started":"2021-11-19T23:21:47.857642Z"},"id":"127f43c5","papermill":{"duration":7.196136,"end_time":"2021-11-27T19:56:28.230588","exception":false,"start_time":"2021-11-27T19:56:21.034452","status":"completed"},"tags":[],"outputId":"bbca12e4-1817-413e-cc18-a3c2efea35d9"},"source":["model.save('Model-6CL-3HL-l2norm')"],"id":"127f43c5","execution_count":null,"outputs":[{"name":"stderr","output_type":"stream","text":["2021-11-27 19:56:26.355334: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.\n"]}]},{"cell_type":"code","metadata":{"execution":{"iopub.execute_input":"2021-11-27T19:56:36.977442Z","iopub.status.busy":"2021-11-27T19:56:36.976565Z","iopub.status.idle":"2021-11-27T19:56:37.265831Z","shell.execute_reply":"2021-11-27T19:56:37.266247Z"},"papermill":{"duration":4.833661,"end_time":"2021-11-27T19:56:37.266408","exception":false,"start_time":"2021-11-27T19:56:32.432747","status":"completed"},"tags":[],"id":"384984af","outputId":"174772fd-6dfa-4e77-fbed-ff1d8c50aeb4"},"source":["plt.figure(figsize=(15,5))\n","plt.plot(history['accuracy'], label='Training accuracy', alpha=.8, color='#F7D426')\n","plt.plot(history['val_accuracy'], label='Validation accuracy', alpha=.8, color='#0E8729')\n","plt.title('Accuracy')\n","plt.legend(loc='lower right')\n","plt.grid(alpha=.3)\n","plt.savefig('Model-6CL-3HL-l2norm-graph.png')\n","plt.show()"],"id":"384984af","execution_count":null,"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 1080x360 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}]}]}