From 384d135307c35b7a9c02335fb4c92de377341bd0 Mon Sep 17 00:00:00 2001 From: Aydin <108932477+Aydinhamedi@users.noreply.github.com> Date: Fri, 2 Feb 2024 19:19:31 +0330 Subject: [PATCH] modified: Model_T&T.ipynb --- Model_T&T.ipynb | 7337 ++++++++++++++++++++++++++++++++--------------- 1 file changed, 5050 insertions(+), 2287 deletions(-) diff --git a/Model_T&T.ipynb b/Model_T&T.ipynb index 2399064..e5bbdbc 100644 --- a/Model_T&T.ipynb +++ b/Model_T&T.ipynb @@ -267,7 +267,7 @@ "\u001b[0m\u001b[0m\u001b[0mOriginal num_samples: \u001b[0m\u001b[0;32m23681\u001b[0m\n", "\u001b[0;33mshuffling data...\u001b[0m\n", "\u001b[0;33mSaving TS...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0mSample dir: \u001b[0m\u001b[0;32mSamples/TSR400_y2024_m01_d23-h12_m17_s17\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0mSample dir: \u001b[0m\u001b[0;32mSamples/TSR400_y2024_m01_d26-h14_m43_s44\u001b[0m\n", "\u001b[0;32mDone.\u001b[0m\n" ] } @@ -678,7 +678,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T02:31:27.380088800Z", @@ -14718,2428 +14718,3964 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from efficientnet.keras import EfficientNetB4 as KENB4\n", - "# FUNC\n", - "def Eff_B4_NS(freeze_layers):\n", - " base_model = KENB4(input_shape=(\n", - " img_res[0], img_res[1], img_res[2]), weights='noisy-student', include_top=False)\n", - " print('Total layers in the base model: ', len(base_model.layers))\n", - " print(f'Freezing {freeze_layers} layers in the base model...')\n", - " # Freeze the specified number of layers\n", - " for layer in base_model.layers[:freeze_layers]:\n", - " layer.trainable = False\n", - "\n", - " # Unfreeze the rest\n", - " for layer in base_model.layers[freeze_layers:]:\n", - " layer.trainable = True\n", - "\n", - " # Calculate the percentage of the model that is frozen\n", - " frozen_percentage = ((freeze_layers + 1e-10) /\n", - " len(base_model.layers)) * 100\n", - " print(\n", - " f'Percentage of the base model that is frozen: {frozen_percentage:.2f}%')\n", - " # adding CDL>>>\n", - " #GlobalAveragePooling2D\n", - " base_model_FT = GlobalAveragePooling2D(name='FC_INPUT_Avg-Pooling')(base_model.output)\n", - " #Dense\n", - " Dense_L1 = Dense(512, activation='relu',\n", - " kernel_regularizer=l2(0.02),\n", - " name='FC_C_Dense-L1-512'\n", - " )(base_model_FT)\n", - " #Dropout\n", - " Dropout_L1 = Dropout(0.1,\n", - " name='FC_C_Dropout-L1-0.1'\n", - " )(Dense_L1)\n", - " #BatchNormalization\n", - " BatchNorm_L2 = BatchNormalization(name='FC_C_Avg-BatchNormalization-L1'\n", - " )(Dropout_L1)\n", - " #Dense\n", - " Dense_L2 = Dense(512, activation='relu',\n", - " kernel_regularizer=l2(0.01),\n", - " name='FC_C_Dense-L2-512'\n", - " )(BatchNorm_L2)\n", - " #BatchNormalization\n", - " BatchNorm_L3 = BatchNormalization(name='FC_C_Avg-BatchNormalization-L2'\n", - " )(Dense_L2)\n", - " #Dense\n", - " Dense_L3 = Dense(128, activation='relu',\n", - " name='FC_C_Dense-L3-128'\n", - " )(BatchNorm_L3)\n", - " #Dense\n", - " # predictions = Dense(2, activation='softmax')(Dense_L3) / predictions = Dense(1, activation='sigmoid')(Dense_L3)\n", - " predictions = Dense(2, activation='softmax',\n", - " name='FC_OUTPUT_Dense-2')(Dense_L3)\n", - " # CDL<<<\n", - " model_EfficientNetB7_NS = Model(\n", - " inputs=base_model.input, outputs=predictions)\n", - " print('Total model layers: ', len(model_EfficientNetB7_NS.layers))\n", - " # OPT/compile\n", - " opt = SGD(momentum=0.9, nesterov=False)\n", - " # opt = Nadam()\n", - " # opt = Adamax()\n", - " # opt = RMSprop(momentum=0.9)\n", - " # opt = Adagrad()\n", - " # opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=5e-4, print_change_log=False, total_steps=0, amsgrad=False)\n", - " # opt = Yogi()\n", - " model_EfficientNetB7_NS.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) # categorical_crossentropy / binary_crossentropy\n", - "\n", - " return model_EfficientNetB7_NS\n", - "\n", - "print('Creating the model...')\n", - "# Main\n", - "freeze_layers = 0\n", - "model = Eff_B4_NS(freeze_layers)\n", - "model.summary(show_trainable=True, expand_nested=True)\n", - "print('done.')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### LR FINDER" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import gc\n", - "# Garbage Collection (memory)\n", - "gc.collect()\n", - "tf.keras.backend.clear_session()\n", - "#CONF/Other\n", - "LRF_OPT = SGD(momentum=0.9)\n", - "LFR_batch_size = 1 # or any other batch size that fits in your memory\n", - "LRF_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(LFR_batch_size)\n", - "# Instantiate LrFinder\n", - "lr_find = LrFinder(model, LRF_OPT, tf.keras.losses.categorical_crossentropy)\n", - "\n", - "# Start range_test\n", - "lr_find.range_test(LRF_dataset)\n", - "lr_find.plot_lrs(skip_end=0, suggestion=True, show_grid=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Model vis" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dot_img_file = 'model_1.png'\n", - "keras.utils.plot_model(model, to_file=dot_img_file, show_shapes=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Model Save (Beta)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Copyright (c) 2024 Aydin Hamedi\n", - "# \n", - "# This software is released under the MIT License.\n", - "# https://opensource.org/licenses/MIT\n", - "import json\n", - "import numpy as np\n", - "from keras.models import model_from_json\n", - "from keras.optimizers import get as get_optimizer\n", - "\n", - "def save_model(model, optimizer, filename):\n", - " \"\"\"\n", - " Save a Keras model's architecture and weights into a single gzipped file.\n", - "\n", - " Args:\n", - " model (tf.keras.Model): The Keras model to save.\n", - " optimizer (str): The name of the Keras optimizer to use.\n", - " filename (str): The filename to use for the saved file.\n", - " \"\"\"\n", - " # Save the architecture, weights and optimizer into a dictionary\n", - " model_dict = {\n", - " 'architecture': model.to_json(),\n", - " 'weights': [w.tolist() for w in model.get_weights()],\n", - " 'optimizer': optimizer.get_config()['name']\n", - " }\n", - "\n", - " # Write the dictionary to a gzipped file\n", - " with gzip.GzipFile(f'{filename}.gz', 'w') as f:\n", - " f.write(json.dumps(model_dict).encode('utf-8'))\n", - "\n", - "def load_model(filename):\n", - " \"\"\"\n", - " Load a Keras model's architecture and weights from a gzipped file.\n", - "\n", - " Args:\n", - " filename (str): The filename of the saved file.\n", - "\n", - " Returns:\n", - " tf.keras.Model: The loaded Keras model.\n", - " \"\"\"\n", - " # Read the dictionary from the gzipped file\n", - " with gzip.GzipFile(f'{filename}.gz', 'r') as f:\n", - " model_dict = json.loads(f.read().decode('utf-8'))\n", - "\n", - " # Create a model from the architecture\n", - " model = model_from_json(model_dict['architecture'])\n", - "\n", - " # Set the model's weights\n", - " model.set_weights([np.array(w) for w in model_dict['weights']])\n", - "\n", - " # Get the optimizer\n", - " optimizer = get_optimizer(model_dict['optimizer'])\n", - "\n", - " # Compile the model with the loaded optimizer\n", - " model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])\n", - "\n", - " return model\n", - "\n", - "save_model(model, SGD(), 'PAI_model_REV2')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Loading the model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Loading the full model" - ] - }, - { - "cell_type": "code", - "execution_count": 5, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[92mLoading model done.\n", - "Compiling the AI model...\u001b[0m\n", - "Model: \"model\"\n", + "Creating the model...\n", + "Total layers in the base model: 569\n", + "Freezing 0 layers in the base model...\n", + "Percentage of the base model that is frozen: 0.00%\n", + "Total model layers: 577\n", + "Model: \"model_1\"\n", "_____________________________________________________________________________________________________________\n", " Layer (type) Output Shape Param # Connected to Trainable \n", "=============================================================================================================\n", - " input_1 (InputLayer) [(None, 224, 224, 3 0 [] Y \n", + " input_2 (InputLayer) [(None, 224, 224, 3 0 [] Y \n", " )] \n", " \n", - " stem_conv (Conv2D) (None, 112, 112, 64 1728 ['input_1[0][0]'] Y \n", + " stem_conv (Conv2D) (None, 112, 112, 48 1296 ['input_2[0][0]'] Y \n", " ) \n", " \n", - " stem_bn (BatchNormalization) (None, 112, 112, 64 256 ['stem_conv[0][0]'] Y \n", + " stem_bn (BatchNormalization) (None, 112, 112, 48 192 ['stem_conv[0][0]'] Y \n", " ) \n", " \n", - " stem_activation (Activation) (None, 112, 112, 64 0 ['stem_bn[0][0]'] Y \n", + " stem_activation (Activation) (None, 112, 112, 48 0 ['stem_bn[0][0]'] Y \n", " ) \n", " \n", - " block1a_dwconv (DepthwiseConv2 (None, 112, 112, 64 576 ['stem_activation[0][0]'] Y \n", + " block1a_dwconv (DepthwiseConv2 (None, 112, 112, 48 432 ['stem_activation[0][0]'] Y \n", " D) ) \n", " \n", - " block1a_bn (BatchNormalization (None, 112, 112, 64 256 ['block1a_dwconv[0][0]'] Y \n", + " block1a_bn (BatchNormalization (None, 112, 112, 48 192 ['block1a_dwconv[0][0]'] Y \n", " ) ) \n", " \n", - " block1a_activation (Activation (None, 112, 112, 64 0 ['block1a_bn[0][0]'] Y \n", + " block1a_activation (Activation (None, 112, 112, 48 0 ['block1a_bn[0][0]'] Y \n", " ) ) \n", " \n", - " block1a_se_squeeze (GlobalAver (None, 64) 0 ['block1a_activation[0][0]'] Y \n", + " block1a_se_squeeze (GlobalAver (None, 48) 0 ['block1a_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block1a_se_reshape (Reshape) (None, 1, 1, 64) 0 ['block1a_se_squeeze[0][0]'] Y \n", + " block1a_se_reshape (Reshape) (None, 1, 1, 48) 0 ['block1a_se_squeeze[0][0]'] Y \n", " \n", - " block1a_se_reduce (Conv2D) (None, 1, 1, 16) 1040 ['block1a_se_reshape[0][0]'] Y \n", + " block1a_se_reduce (Conv2D) (None, 1, 1, 12) 588 ['block1a_se_reshape[0][0]'] Y \n", " \n", - " block1a_se_expand (Conv2D) (None, 1, 1, 64) 1088 ['block1a_se_reduce[0][0]'] Y \n", + " block1a_se_expand (Conv2D) (None, 1, 1, 48) 624 ['block1a_se_reduce[0][0]'] Y \n", " \n", - " block1a_se_excite (Multiply) (None, 112, 112, 64 0 ['block1a_activation[0][0]', Y \n", + " block1a_se_excite (Multiply) (None, 112, 112, 48 0 ['block1a_activation[0][0]', Y \n", " ) 'block1a_se_expand[0][0]'] \n", " \n", - " block1a_project_conv (Conv2D) (None, 112, 112, 32 2048 ['block1a_se_excite[0][0]'] Y \n", + " block1a_project_conv (Conv2D) (None, 112, 112, 24 1152 ['block1a_se_excite[0][0]'] Y \n", " ) \n", " \n", - " block1a_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1a_project_conv[0][0]'] Y \n", + " block1a_project_bn (BatchNorma (None, 112, 112, 24 96 ['block1a_project_conv[0][0]'] Y \n", " lization) ) \n", " \n", - " block1b_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1a_project_bn[0][0]'] Y \n", + " block1b_dwconv (DepthwiseConv2 (None, 112, 112, 24 216 ['block1a_project_bn[0][0]'] Y \n", " D) ) \n", " \n", - " block1b_bn (BatchNormalization (None, 112, 112, 32 128 ['block1b_dwconv[0][0]'] Y \n", + " block1b_bn (BatchNormalization (None, 112, 112, 24 96 ['block1b_dwconv[0][0]'] Y \n", " ) ) \n", " \n", - " block1b_activation (Activation (None, 112, 112, 32 0 ['block1b_bn[0][0]'] Y \n", + " block1b_activation (Activation (None, 112, 112, 24 0 ['block1b_bn[0][0]'] Y \n", " ) ) \n", " \n", - " block1b_se_squeeze (GlobalAver (None, 32) 0 ['block1b_activation[0][0]'] Y \n", + " block1b_se_squeeze (GlobalAver (None, 24) 0 ['block1b_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block1b_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1b_se_squeeze[0][0]'] Y \n", + " block1b_se_reshape (Reshape) (None, 1, 1, 24) 0 ['block1b_se_squeeze[0][0]'] Y \n", " \n", - " block1b_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1b_se_reshape[0][0]'] Y \n", + " block1b_se_reduce (Conv2D) (None, 1, 1, 6) 150 ['block1b_se_reshape[0][0]'] Y \n", " \n", - " block1b_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1b_se_reduce[0][0]'] Y \n", + " block1b_se_expand (Conv2D) (None, 1, 1, 24) 168 ['block1b_se_reduce[0][0]'] Y \n", " \n", - " block1b_se_excite (Multiply) (None, 112, 112, 32 0 ['block1b_activation[0][0]', Y \n", + " block1b_se_excite (Multiply) (None, 112, 112, 24 0 ['block1b_activation[0][0]', Y \n", " ) 'block1b_se_expand[0][0]'] \n", " \n", - " block1b_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1b_se_excite[0][0]'] Y \n", + " block1b_project_conv (Conv2D) (None, 112, 112, 24 576 ['block1b_se_excite[0][0]'] Y \n", " ) \n", " \n", - " block1b_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1b_project_conv[0][0]'] Y \n", + " block1b_project_bn (BatchNorma (None, 112, 112, 24 96 ['block1b_project_conv[0][0]'] Y \n", " lization) ) \n", " \n", - " block1b_drop (FixedDropout) (None, 112, 112, 32 0 ['block1b_project_bn[0][0]'] Y \n", + " block1b_drop (FixedDropout) (None, 112, 112, 24 0 ['block1b_project_bn[0][0]'] Y \n", " ) \n", " \n", - " block1b_add (Add) (None, 112, 112, 32 0 ['block1b_drop[0][0]', Y \n", + " block1b_add (Add) (None, 112, 112, 24 0 ['block1b_drop[0][0]', Y \n", " ) 'block1a_project_bn[0][0]'] \n", " \n", - " block1c_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1b_add[0][0]'] Y \n", + " block1c_dwconv (DepthwiseConv2 (None, 112, 112, 24 216 ['block1b_add[0][0]'] Y \n", " D) ) \n", " \n", - " block1c_bn (BatchNormalization (None, 112, 112, 32 128 ['block1c_dwconv[0][0]'] Y \n", + " block1c_bn (BatchNormalization (None, 112, 112, 24 96 ['block1c_dwconv[0][0]'] Y \n", " ) ) \n", " \n", - " block1c_activation (Activation (None, 112, 112, 32 0 ['block1c_bn[0][0]'] Y \n", + " block1c_activation (Activation (None, 112, 112, 24 0 ['block1c_bn[0][0]'] Y \n", " ) ) \n", " \n", - " block1c_se_squeeze (GlobalAver (None, 32) 0 ['block1c_activation[0][0]'] Y \n", + " block1c_se_squeeze (GlobalAver (None, 24) 0 ['block1c_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block1c_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1c_se_squeeze[0][0]'] Y \n", + " block1c_se_reshape (Reshape) (None, 1, 1, 24) 0 ['block1c_se_squeeze[0][0]'] Y \n", " \n", - " block1c_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1c_se_reshape[0][0]'] Y \n", + " block1c_se_reduce (Conv2D) (None, 1, 1, 6) 150 ['block1c_se_reshape[0][0]'] Y \n", " \n", - " block1c_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1c_se_reduce[0][0]'] Y \n", + " block1c_se_expand (Conv2D) (None, 1, 1, 24) 168 ['block1c_se_reduce[0][0]'] Y \n", " \n", - " block1c_se_excite (Multiply) (None, 112, 112, 32 0 ['block1c_activation[0][0]', Y \n", + " block1c_se_excite (Multiply) (None, 112, 112, 24 0 ['block1c_activation[0][0]', Y \n", " ) 'block1c_se_expand[0][0]'] \n", " \n", - " block1c_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1c_se_excite[0][0]'] Y \n", + " block1c_project_conv (Conv2D) (None, 112, 112, 24 576 ['block1c_se_excite[0][0]'] Y \n", " ) \n", " \n", - " block1c_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1c_project_conv[0][0]'] Y \n", + " block1c_project_bn (BatchNorma (None, 112, 112, 24 96 ['block1c_project_conv[0][0]'] Y \n", " lization) ) \n", " \n", - " block1c_drop (FixedDropout) (None, 112, 112, 32 0 ['block1c_project_bn[0][0]'] Y \n", + " block1c_drop (FixedDropout) (None, 112, 112, 24 0 ['block1c_project_bn[0][0]'] Y \n", " ) \n", " \n", - " block1c_add (Add) (None, 112, 112, 32 0 ['block1c_drop[0][0]', Y \n", + " block1c_add (Add) (None, 112, 112, 24 0 ['block1c_drop[0][0]', Y \n", " ) 'block1b_add[0][0]'] \n", " \n", - " block1d_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1c_add[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1d_bn (BatchNormalization (None, 112, 112, 32 128 ['block1d_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1d_activation (Activation (None, 112, 112, 32 0 ['block1d_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1d_se_squeeze (GlobalAver (None, 32) 0 ['block1d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1d_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1d_se_squeeze[0][0]'] Y \n", - " \n", - " block1d_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1d_se_reshape[0][0]'] Y \n", - " \n", - " block1d_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1d_se_reduce[0][0]'] Y \n", - " \n", - " block1d_se_excite (Multiply) (None, 112, 112, 32 0 ['block1d_activation[0][0]', Y \n", - " ) 'block1d_se_expand[0][0]'] \n", - " \n", - " block1d_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1d_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1d_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1d_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1d_drop (FixedDropout) (None, 112, 112, 32 0 ['block1d_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1d_add (Add) (None, 112, 112, 32 0 ['block1d_drop[0][0]', Y \n", - " ) 'block1c_add[0][0]'] \n", - " \n", - " block2a_expand_conv (Conv2D) (None, 112, 112, 19 6144 ['block1d_add[0][0]'] Y \n", - " 2) \n", + " block2a_expand_conv (Conv2D) (None, 112, 112, 14 3456 ['block1c_add[0][0]'] Y \n", + " 4) \n", " \n", - " block2a_expand_bn (BatchNormal (None, 112, 112, 19 768 ['block2a_expand_conv[0][0]'] Y \n", - " ization) 2) \n", + " block2a_expand_bn (BatchNormal (None, 112, 112, 14 576 ['block2a_expand_conv[0][0]'] Y \n", + " ization) 4) \n", " \n", - " block2a_expand_activation (Act (None, 112, 112, 19 0 ['block2a_expand_bn[0][0]'] Y \n", - " ivation) 2) \n", + " block2a_expand_activation (Act (None, 112, 112, 14 0 ['block2a_expand_bn[0][0]'] Y \n", + " ivation) 4) \n", " \n", - " block2a_dwconv (DepthwiseConv2 (None, 56, 56, 192) 1728 ['block2a_expand_activation[0][ Y \n", + " block2a_dwconv (DepthwiseConv2 (None, 56, 56, 144) 1296 ['block2a_expand_activation[0][ Y \n", " D) 0]'] \n", " \n", - " block2a_bn (BatchNormalization (None, 56, 56, 192) 768 ['block2a_dwconv[0][0]'] Y \n", + " block2a_bn (BatchNormalization (None, 56, 56, 144) 576 ['block2a_dwconv[0][0]'] Y \n", " ) \n", " \n", - " block2a_activation (Activation (None, 56, 56, 192) 0 ['block2a_bn[0][0]'] Y \n", + " block2a_activation (Activation (None, 56, 56, 144) 0 ['block2a_bn[0][0]'] Y \n", " ) \n", " \n", - " block2a_se_squeeze (GlobalAver (None, 192) 0 ['block2a_activation[0][0]'] Y \n", + " block2a_se_squeeze (GlobalAver (None, 144) 0 ['block2a_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block2a_se_reshape (Reshape) (None, 1, 1, 192) 0 ['block2a_se_squeeze[0][0]'] Y \n", + " block2a_se_reshape (Reshape) (None, 1, 1, 144) 0 ['block2a_se_squeeze[0][0]'] Y \n", " \n", - " block2a_se_reduce (Conv2D) (None, 1, 1, 8) 1544 ['block2a_se_reshape[0][0]'] Y \n", + " block2a_se_reduce (Conv2D) (None, 1, 1, 6) 870 ['block2a_se_reshape[0][0]'] Y \n", " \n", - " block2a_se_expand (Conv2D) (None, 1, 1, 192) 1728 ['block2a_se_reduce[0][0]'] Y \n", + " block2a_se_expand (Conv2D) (None, 1, 1, 144) 1008 ['block2a_se_reduce[0][0]'] Y \n", " \n", - " block2a_se_excite (Multiply) (None, 56, 56, 192) 0 ['block2a_activation[0][0]', Y \n", + " block2a_se_excite (Multiply) (None, 56, 56, 144) 0 ['block2a_activation[0][0]', Y \n", " 'block2a_se_expand[0][0]'] \n", " \n", - " block2a_project_conv (Conv2D) (None, 56, 56, 48) 9216 ['block2a_se_excite[0][0]'] Y \n", + " block2a_project_conv (Conv2D) (None, 56, 56, 40) 5760 ['block2a_se_excite[0][0]'] Y \n", " \n", - " block2a_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2a_project_conv[0][0]'] Y \n", + " block2a_project_bn (BatchNorma (None, 56, 56, 40) 160 ['block2a_project_conv[0][0]'] Y \n", " lization) \n", " \n", - " block2b_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2a_project_bn[0][0]'] Y \n", + " block2b_expand_conv (Conv2D) (None, 56, 56, 240) 9600 ['block2a_project_bn[0][0]'] Y \n", " \n", - " block2b_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2b_expand_conv[0][0]'] Y \n", + " block2b_expand_bn (BatchNormal (None, 56, 56, 240) 960 ['block2b_expand_conv[0][0]'] Y \n", " ization) \n", " \n", - " block2b_expand_activation (Act (None, 56, 56, 288) 0 ['block2b_expand_bn[0][0]'] Y \n", + " block2b_expand_activation (Act (None, 56, 56, 240) 0 ['block2b_expand_bn[0][0]'] Y \n", " ivation) \n", " \n", - " block2b_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2b_expand_activation[0][ Y \n", + " block2b_dwconv (DepthwiseConv2 (None, 56, 56, 240) 2160 ['block2b_expand_activation[0][ Y \n", " D) 0]'] \n", " \n", - " block2b_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2b_dwconv[0][0]'] Y \n", + " block2b_bn (BatchNormalization (None, 56, 56, 240) 960 ['block2b_dwconv[0][0]'] Y \n", " ) \n", " \n", - " block2b_activation (Activation (None, 56, 56, 288) 0 ['block2b_bn[0][0]'] Y \n", + " block2b_activation (Activation (None, 56, 56, 240) 0 ['block2b_bn[0][0]'] Y \n", " ) \n", " \n", - " block2b_se_squeeze (GlobalAver (None, 288) 0 ['block2b_activation[0][0]'] Y \n", + " block2b_se_squeeze (GlobalAver (None, 240) 0 ['block2b_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block2b_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2b_se_squeeze[0][0]'] Y \n", + " block2b_se_reshape (Reshape) (None, 1, 1, 240) 0 ['block2b_se_squeeze[0][0]'] Y \n", " \n", - " block2b_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2b_se_reshape[0][0]'] Y \n", + " block2b_se_reduce (Conv2D) (None, 1, 1, 10) 2410 ['block2b_se_reshape[0][0]'] Y \n", " \n", - " block2b_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2b_se_reduce[0][0]'] Y \n", + " block2b_se_expand (Conv2D) (None, 1, 1, 240) 2640 ['block2b_se_reduce[0][0]'] Y \n", " \n", - " block2b_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2b_activation[0][0]', Y \n", + " block2b_se_excite (Multiply) (None, 56, 56, 240) 0 ['block2b_activation[0][0]', Y \n", " 'block2b_se_expand[0][0]'] \n", " \n", - " block2b_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2b_se_excite[0][0]'] Y \n", + " block2b_project_conv (Conv2D) (None, 56, 56, 40) 9600 ['block2b_se_excite[0][0]'] Y \n", " \n", - " block2b_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2b_project_conv[0][0]'] Y \n", + " block2b_project_bn (BatchNorma (None, 56, 56, 40) 160 ['block2b_project_conv[0][0]'] Y \n", " lization) \n", " \n", - " block2b_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2b_project_bn[0][0]'] Y \n", + " block2b_drop (FixedDropout) (None, 56, 56, 40) 0 ['block2b_project_bn[0][0]'] Y \n", " \n", - " block2b_add (Add) (None, 56, 56, 48) 0 ['block2b_drop[0][0]', Y \n", + " block2b_add (Add) (None, 56, 56, 40) 0 ['block2b_drop[0][0]', Y \n", " 'block2a_project_bn[0][0]'] \n", " \n", - " block2c_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2b_add[0][0]'] Y \n", + " block2c_expand_conv (Conv2D) (None, 56, 56, 240) 9600 ['block2b_add[0][0]'] Y \n", " \n", - " block2c_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2c_expand_conv[0][0]'] Y \n", + " block2c_expand_bn (BatchNormal (None, 56, 56, 240) 960 ['block2c_expand_conv[0][0]'] Y \n", " ization) \n", " \n", - " block2c_expand_activation (Act (None, 56, 56, 288) 0 ['block2c_expand_bn[0][0]'] Y \n", + " block2c_expand_activation (Act (None, 56, 56, 240) 0 ['block2c_expand_bn[0][0]'] Y \n", " ivation) \n", " \n", - " block2c_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2c_expand_activation[0][ Y \n", + " block2c_dwconv (DepthwiseConv2 (None, 56, 56, 240) 2160 ['block2c_expand_activation[0][ Y \n", " D) 0]'] \n", " \n", - " block2c_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2c_dwconv[0][0]'] Y \n", + " block2c_bn (BatchNormalization (None, 56, 56, 240) 960 ['block2c_dwconv[0][0]'] Y \n", " ) \n", " \n", - " block2c_activation (Activation (None, 56, 56, 288) 0 ['block2c_bn[0][0]'] Y \n", + " block2c_activation (Activation (None, 56, 56, 240) 0 ['block2c_bn[0][0]'] Y \n", " ) \n", " \n", - " block2c_se_squeeze (GlobalAver (None, 288) 0 ['block2c_activation[0][0]'] Y \n", + " block2c_se_squeeze (GlobalAver (None, 240) 0 ['block2c_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block2c_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2c_se_squeeze[0][0]'] Y \n", + " block2c_se_reshape (Reshape) (None, 1, 1, 240) 0 ['block2c_se_squeeze[0][0]'] Y \n", " \n", - " block2c_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2c_se_reshape[0][0]'] Y \n", + " block2c_se_reduce (Conv2D) (None, 1, 1, 10) 2410 ['block2c_se_reshape[0][0]'] Y \n", " \n", - " block2c_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2c_se_reduce[0][0]'] Y \n", + " block2c_se_expand (Conv2D) (None, 1, 1, 240) 2640 ['block2c_se_reduce[0][0]'] Y \n", " \n", - " block2c_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2c_activation[0][0]', Y \n", + " block2c_se_excite (Multiply) (None, 56, 56, 240) 0 ['block2c_activation[0][0]', Y \n", " 'block2c_se_expand[0][0]'] \n", " \n", - " block2c_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2c_se_excite[0][0]'] Y \n", + " block2c_project_conv (Conv2D) (None, 56, 56, 40) 9600 ['block2c_se_excite[0][0]'] Y \n", " \n", - " block2c_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2c_project_conv[0][0]'] Y \n", + " block2c_project_bn (BatchNorma (None, 56, 56, 40) 160 ['block2c_project_conv[0][0]'] Y \n", " lization) \n", " \n", - " block2c_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2c_project_bn[0][0]'] Y \n", + " block2c_drop (FixedDropout) (None, 56, 56, 40) 0 ['block2c_project_bn[0][0]'] Y \n", " \n", - " block2c_add (Add) (None, 56, 56, 48) 0 ['block2c_drop[0][0]', Y \n", + " block2c_add (Add) (None, 56, 56, 40) 0 ['block2c_drop[0][0]', Y \n", " 'block2b_add[0][0]'] \n", " \n", - " block2d_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2c_add[0][0]'] Y \n", + " block2d_expand_conv (Conv2D) (None, 56, 56, 240) 9600 ['block2c_add[0][0]'] Y \n", " \n", - " block2d_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2d_expand_conv[0][0]'] Y \n", + " block2d_expand_bn (BatchNormal (None, 56, 56, 240) 960 ['block2d_expand_conv[0][0]'] Y \n", " ization) \n", " \n", - " block2d_expand_activation (Act (None, 56, 56, 288) 0 ['block2d_expand_bn[0][0]'] Y \n", + " block2d_expand_activation (Act (None, 56, 56, 240) 0 ['block2d_expand_bn[0][0]'] Y \n", " ivation) \n", " \n", - " block2d_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2d_expand_activation[0][ Y \n", + " block2d_dwconv (DepthwiseConv2 (None, 56, 56, 240) 2160 ['block2d_expand_activation[0][ Y \n", " D) 0]'] \n", " \n", - " block2d_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2d_dwconv[0][0]'] Y \n", + " block2d_bn (BatchNormalization (None, 56, 56, 240) 960 ['block2d_dwconv[0][0]'] Y \n", " ) \n", " \n", - " block2d_activation (Activation (None, 56, 56, 288) 0 ['block2d_bn[0][0]'] Y \n", + " block2d_activation (Activation (None, 56, 56, 240) 0 ['block2d_bn[0][0]'] Y \n", " ) \n", " \n", - " block2d_se_squeeze (GlobalAver (None, 288) 0 ['block2d_activation[0][0]'] Y \n", + " block2d_se_squeeze (GlobalAver (None, 240) 0 ['block2d_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block2d_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2d_se_squeeze[0][0]'] Y \n", + " block2d_se_reshape (Reshape) (None, 1, 1, 240) 0 ['block2d_se_squeeze[0][0]'] Y \n", " \n", - " block2d_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2d_se_reshape[0][0]'] Y \n", + " block2d_se_reduce (Conv2D) (None, 1, 1, 10) 2410 ['block2d_se_reshape[0][0]'] Y \n", " \n", - " block2d_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2d_se_reduce[0][0]'] Y \n", + " block2d_se_expand (Conv2D) (None, 1, 1, 240) 2640 ['block2d_se_reduce[0][0]'] Y \n", " \n", - " block2d_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2d_activation[0][0]', Y \n", + " block2d_se_excite (Multiply) (None, 56, 56, 240) 0 ['block2d_activation[0][0]', Y \n", " 'block2d_se_expand[0][0]'] \n", " \n", - " block2d_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2d_se_excite[0][0]'] Y \n", + " block2d_project_conv (Conv2D) (None, 56, 56, 40) 9600 ['block2d_se_excite[0][0]'] Y \n", " \n", - " block2d_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2d_project_conv[0][0]'] Y \n", + " block2d_project_bn (BatchNorma (None, 56, 56, 40) 160 ['block2d_project_conv[0][0]'] Y \n", " lization) \n", " \n", - " block2d_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2d_project_bn[0][0]'] Y \n", + " block2d_drop (FixedDropout) (None, 56, 56, 40) 0 ['block2d_project_bn[0][0]'] Y \n", " \n", - " block2d_add (Add) (None, 56, 56, 48) 0 ['block2d_drop[0][0]', Y \n", + " block2d_add (Add) (None, 56, 56, 40) 0 ['block2d_drop[0][0]', Y \n", " 'block2c_add[0][0]'] \n", " \n", - " block2e_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2d_add[0][0]'] Y \n", + " block2e_expand_conv (Conv2D) (None, 56, 56, 240) 9600 ['block2d_add[0][0]'] Y \n", " \n", - " block2e_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2e_expand_conv[0][0]'] Y \n", + " block2e_expand_bn (BatchNormal (None, 56, 56, 240) 960 ['block2e_expand_conv[0][0]'] Y \n", " ization) \n", " \n", - " block2e_expand_activation (Act (None, 56, 56, 288) 0 ['block2e_expand_bn[0][0]'] Y \n", + " block2e_expand_activation (Act (None, 56, 56, 240) 0 ['block2e_expand_bn[0][0]'] Y \n", " ivation) \n", " \n", - " block2e_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2e_expand_activation[0][ Y \n", + " block2e_dwconv (DepthwiseConv2 (None, 56, 56, 240) 2160 ['block2e_expand_activation[0][ Y \n", " D) 0]'] \n", " \n", - " block2e_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2e_dwconv[0][0]'] Y \n", + " block2e_bn (BatchNormalization (None, 56, 56, 240) 960 ['block2e_dwconv[0][0]'] Y \n", " ) \n", " \n", - " block2e_activation (Activation (None, 56, 56, 288) 0 ['block2e_bn[0][0]'] Y \n", + " block2e_activation (Activation (None, 56, 56, 240) 0 ['block2e_bn[0][0]'] Y \n", " ) \n", " \n", - " block2e_se_squeeze (GlobalAver (None, 288) 0 ['block2e_activation[0][0]'] Y \n", + " block2e_se_squeeze (GlobalAver (None, 240) 0 ['block2e_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block2e_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2e_se_squeeze[0][0]'] Y \n", + " block2e_se_reshape (Reshape) (None, 1, 1, 240) 0 ['block2e_se_squeeze[0][0]'] Y \n", " \n", - " block2e_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2e_se_reshape[0][0]'] Y \n", + " block2e_se_reduce (Conv2D) (None, 1, 1, 10) 2410 ['block2e_se_reshape[0][0]'] Y \n", " \n", - " block2e_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2e_se_reduce[0][0]'] Y \n", + " block2e_se_expand (Conv2D) (None, 1, 1, 240) 2640 ['block2e_se_reduce[0][0]'] Y \n", " \n", - " block2e_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2e_activation[0][0]', Y \n", + " block2e_se_excite (Multiply) (None, 56, 56, 240) 0 ['block2e_activation[0][0]', Y \n", " 'block2e_se_expand[0][0]'] \n", " \n", - " block2e_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2e_se_excite[0][0]'] Y \n", + " block2e_project_conv (Conv2D) (None, 56, 56, 40) 9600 ['block2e_se_excite[0][0]'] Y \n", " \n", - " block2e_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2e_project_conv[0][0]'] Y \n", + " block2e_project_bn (BatchNorma (None, 56, 56, 40) 160 ['block2e_project_conv[0][0]'] Y \n", " lization) \n", " \n", - " block2e_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2e_project_bn[0][0]'] Y \n", + " block2e_drop (FixedDropout) (None, 56, 56, 40) 0 ['block2e_project_bn[0][0]'] Y \n", " \n", - " block2e_add (Add) (None, 56, 56, 48) 0 ['block2e_drop[0][0]', Y \n", + " block2e_add (Add) (None, 56, 56, 40) 0 ['block2e_drop[0][0]', Y \n", " 'block2d_add[0][0]'] \n", " \n", - " block2f_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2e_add[0][0]'] Y \n", + " block3a_expand_conv (Conv2D) (None, 56, 56, 240) 9600 ['block2e_add[0][0]'] Y \n", " \n", - " block2f_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2f_expand_conv[0][0]'] Y \n", + " block3a_expand_bn (BatchNormal (None, 56, 56, 240) 960 ['block3a_expand_conv[0][0]'] Y \n", " ization) \n", " \n", - " block2f_expand_activation (Act (None, 56, 56, 288) 0 ['block2f_expand_bn[0][0]'] Y \n", + " block3a_expand_activation (Act (None, 56, 56, 240) 0 ['block3a_expand_bn[0][0]'] Y \n", " ivation) \n", " \n", - " block2f_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2f_expand_activation[0][ Y \n", + " block3a_dwconv (DepthwiseConv2 (None, 28, 28, 240) 6000 ['block3a_expand_activation[0][ Y \n", " D) 0]'] \n", " \n", - " block2f_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2f_dwconv[0][0]'] Y \n", + " block3a_bn (BatchNormalization (None, 28, 28, 240) 960 ['block3a_dwconv[0][0]'] Y \n", " ) \n", " \n", - " block2f_activation (Activation (None, 56, 56, 288) 0 ['block2f_bn[0][0]'] Y \n", + " block3a_activation (Activation (None, 28, 28, 240) 0 ['block3a_bn[0][0]'] Y \n", " ) \n", " \n", - " block2f_se_squeeze (GlobalAver (None, 288) 0 ['block2f_activation[0][0]'] Y \n", + " block3a_se_squeeze (GlobalAver (None, 240) 0 ['block3a_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block2f_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2f_se_squeeze[0][0]'] Y \n", + " block3a_se_reshape (Reshape) (None, 1, 1, 240) 0 ['block3a_se_squeeze[0][0]'] Y \n", " \n", - " block2f_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2f_se_reshape[0][0]'] Y \n", + " block3a_se_reduce (Conv2D) (None, 1, 1, 10) 2410 ['block3a_se_reshape[0][0]'] Y \n", " \n", - " block2f_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2f_se_reduce[0][0]'] Y \n", + " block3a_se_expand (Conv2D) (None, 1, 1, 240) 2640 ['block3a_se_reduce[0][0]'] Y \n", " \n", - " block2f_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2f_activation[0][0]', Y \n", - " 'block2f_se_expand[0][0]'] \n", + " block3a_se_excite (Multiply) (None, 28, 28, 240) 0 ['block3a_activation[0][0]', Y \n", + " 'block3a_se_expand[0][0]'] \n", " \n", - " block2f_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2f_se_excite[0][0]'] Y \n", + " block3a_project_conv (Conv2D) (None, 28, 28, 64) 15360 ['block3a_se_excite[0][0]'] Y \n", " \n", - " block2f_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2f_project_conv[0][0]'] Y \n", + " block3a_project_bn (BatchNorma (None, 28, 28, 64) 256 ['block3a_project_conv[0][0]'] Y \n", " lization) \n", " \n", - " block2f_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2f_project_bn[0][0]'] Y \n", - " \n", - " block2f_add (Add) (None, 56, 56, 48) 0 ['block2f_drop[0][0]', Y \n", - " 'block2e_add[0][0]'] \n", + " block3b_expand_conv (Conv2D) (None, 28, 28, 384) 24576 ['block3a_project_bn[0][0]'] Y \n", " \n", - " block2g_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2f_add[0][0]'] Y \n", - " \n", - " block2g_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2g_expand_conv[0][0]'] Y \n", + " block3b_expand_bn (BatchNormal (None, 28, 28, 384) 1536 ['block3b_expand_conv[0][0]'] Y \n", " ization) \n", " \n", - " block2g_expand_activation (Act (None, 56, 56, 288) 0 ['block2g_expand_bn[0][0]'] Y \n", + " block3b_expand_activation (Act (None, 28, 28, 384) 0 ['block3b_expand_bn[0][0]'] Y \n", " ivation) \n", " \n", - " block2g_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2g_expand_activation[0][ Y \n", + " block3b_dwconv (DepthwiseConv2 (None, 28, 28, 384) 9600 ['block3b_expand_activation[0][ Y \n", " D) 0]'] \n", " \n", - " block2g_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2g_dwconv[0][0]'] Y \n", + " block3b_bn (BatchNormalization (None, 28, 28, 384) 1536 ['block3b_dwconv[0][0]'] Y \n", " ) \n", " \n", - " block2g_activation (Activation (None, 56, 56, 288) 0 ['block2g_bn[0][0]'] Y \n", + " block3b_activation (Activation (None, 28, 28, 384) 0 ['block3b_bn[0][0]'] Y \n", " ) \n", " \n", - " block2g_se_squeeze (GlobalAver (None, 288) 0 ['block2g_activation[0][0]'] Y \n", + " block3b_se_squeeze (GlobalAver (None, 384) 0 ['block3b_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block2g_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2g_se_squeeze[0][0]'] Y \n", + " block3b_se_reshape (Reshape) (None, 1, 1, 384) 0 ['block3b_se_squeeze[0][0]'] Y \n", " \n", - " block2g_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2g_se_reshape[0][0]'] Y \n", + " block3b_se_reduce (Conv2D) (None, 1, 1, 16) 6160 ['block3b_se_reshape[0][0]'] Y \n", " \n", - " block2g_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2g_se_reduce[0][0]'] Y \n", + " block3b_se_expand (Conv2D) (None, 1, 1, 384) 6528 ['block3b_se_reduce[0][0]'] Y \n", " \n", - " block2g_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2g_activation[0][0]', Y \n", - " 'block2g_se_expand[0][0]'] \n", + " block3b_se_excite (Multiply) (None, 28, 28, 384) 0 ['block3b_activation[0][0]', Y \n", + " 'block3b_se_expand[0][0]'] \n", " \n", - " block2g_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2g_se_excite[0][0]'] Y \n", + " block3b_project_conv (Conv2D) (None, 28, 28, 64) 24576 ['block3b_se_excite[0][0]'] Y \n", " \n", - " block2g_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2g_project_conv[0][0]'] Y \n", + " block3b_project_bn (BatchNorma (None, 28, 28, 64) 256 ['block3b_project_conv[0][0]'] Y \n", " lization) \n", " \n", - " block2g_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2g_project_bn[0][0]'] Y \n", + " block3b_drop (FixedDropout) (None, 28, 28, 64) 0 ['block3b_project_bn[0][0]'] Y \n", " \n", - " block2g_add (Add) (None, 56, 56, 48) 0 ['block2g_drop[0][0]', Y \n", - " 'block2f_add[0][0]'] \n", + " block3b_add (Add) (None, 28, 28, 64) 0 ['block3b_drop[0][0]', Y \n", + " 'block3a_project_bn[0][0]'] \n", " \n", - " block3a_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2g_add[0][0]'] Y \n", + " block3c_expand_conv (Conv2D) (None, 28, 28, 384) 24576 ['block3b_add[0][0]'] Y \n", " \n", - " block3a_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block3a_expand_conv[0][0]'] Y \n", + " block3c_expand_bn (BatchNormal (None, 28, 28, 384) 1536 ['block3c_expand_conv[0][0]'] Y \n", " ization) \n", " \n", - " block3a_expand_activation (Act (None, 56, 56, 288) 0 ['block3a_expand_bn[0][0]'] Y \n", + " block3c_expand_activation (Act (None, 28, 28, 384) 0 ['block3c_expand_bn[0][0]'] Y \n", " ivation) \n", " \n", - " block3a_dwconv (DepthwiseConv2 (None, 28, 28, 288) 7200 ['block3a_expand_activation[0][ Y \n", + " block3c_dwconv (DepthwiseConv2 (None, 28, 28, 384) 9600 ['block3c_expand_activation[0][ Y \n", " D) 0]'] \n", " \n", - " block3a_bn (BatchNormalization (None, 28, 28, 288) 1152 ['block3a_dwconv[0][0]'] Y \n", + " block3c_bn (BatchNormalization (None, 28, 28, 384) 1536 ['block3c_dwconv[0][0]'] Y \n", " ) \n", " \n", - " block3a_activation (Activation (None, 28, 28, 288) 0 ['block3a_bn[0][0]'] Y \n", + " block3c_activation (Activation (None, 28, 28, 384) 0 ['block3c_bn[0][0]'] Y \n", " ) \n", " \n", - " block3a_se_squeeze (GlobalAver (None, 288) 0 ['block3a_activation[0][0]'] Y \n", + " block3c_se_squeeze (GlobalAver (None, 384) 0 ['block3c_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block3a_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block3a_se_squeeze[0][0]'] Y \n", + " block3c_se_reshape (Reshape) (None, 1, 1, 384) 0 ['block3c_se_squeeze[0][0]'] Y \n", " \n", - " block3a_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block3a_se_reshape[0][0]'] Y \n", + " block3c_se_reduce (Conv2D) (None, 1, 1, 16) 6160 ['block3c_se_reshape[0][0]'] Y \n", " \n", - " block3a_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block3a_se_reduce[0][0]'] Y \n", + " block3c_se_expand (Conv2D) (None, 1, 1, 384) 6528 ['block3c_se_reduce[0][0]'] Y \n", " \n", - " block3a_se_excite (Multiply) (None, 28, 28, 288) 0 ['block3a_activation[0][0]', Y \n", - " 'block3a_se_expand[0][0]'] \n", + " block3c_se_excite (Multiply) (None, 28, 28, 384) 0 ['block3c_activation[0][0]', Y \n", + " 'block3c_se_expand[0][0]'] \n", " \n", - " block3a_project_conv (Conv2D) (None, 28, 28, 80) 23040 ['block3a_se_excite[0][0]'] Y \n", + " block3c_project_conv (Conv2D) (None, 28, 28, 64) 24576 ['block3c_se_excite[0][0]'] Y \n", " \n", - " block3a_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3a_project_conv[0][0]'] Y \n", + " block3c_project_bn (BatchNorma (None, 28, 28, 64) 256 ['block3c_project_conv[0][0]'] Y \n", " lization) \n", " \n", - " block3b_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3a_project_bn[0][0]'] Y \n", + " block3c_drop (FixedDropout) (None, 28, 28, 64) 0 ['block3c_project_bn[0][0]'] Y \n", " \n", - " block3b_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3b_expand_conv[0][0]'] Y \n", + " block3c_add (Add) (None, 28, 28, 64) 0 ['block3c_drop[0][0]', Y \n", + " 'block3b_add[0][0]'] \n", + " \n", + " block3d_expand_conv (Conv2D) (None, 28, 28, 384) 24576 ['block3c_add[0][0]'] Y \n", + " \n", + " block3d_expand_bn (BatchNormal (None, 28, 28, 384) 1536 ['block3d_expand_conv[0][0]'] Y \n", " ization) \n", " \n", - " block3b_expand_activation (Act (None, 28, 28, 480) 0 ['block3b_expand_bn[0][0]'] Y \n", + " block3d_expand_activation (Act (None, 28, 28, 384) 0 ['block3d_expand_bn[0][0]'] Y \n", " ivation) \n", " \n", - " block3b_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3b_expand_activation[0][ Y \n", + " block3d_dwconv (DepthwiseConv2 (None, 28, 28, 384) 9600 ['block3d_expand_activation[0][ Y \n", " D) 0]'] \n", " \n", - " block3b_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3b_dwconv[0][0]'] Y \n", + " block3d_bn (BatchNormalization (None, 28, 28, 384) 1536 ['block3d_dwconv[0][0]'] Y \n", " ) \n", " \n", - " block3b_activation (Activation (None, 28, 28, 480) 0 ['block3b_bn[0][0]'] Y \n", + " block3d_activation (Activation (None, 28, 28, 384) 0 ['block3d_bn[0][0]'] Y \n", " ) \n", " \n", - " block3b_se_squeeze (GlobalAver (None, 480) 0 ['block3b_activation[0][0]'] Y \n", + " block3d_se_squeeze (GlobalAver (None, 384) 0 ['block3d_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block3b_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3b_se_squeeze[0][0]'] Y \n", + " block3d_se_reshape (Reshape) (None, 1, 1, 384) 0 ['block3d_se_squeeze[0][0]'] Y \n", " \n", - " block3b_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3b_se_reshape[0][0]'] Y \n", + " block3d_se_reduce (Conv2D) (None, 1, 1, 16) 6160 ['block3d_se_reshape[0][0]'] Y \n", " \n", - " block3b_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3b_se_reduce[0][0]'] Y \n", + " block3d_se_expand (Conv2D) (None, 1, 1, 384) 6528 ['block3d_se_reduce[0][0]'] Y \n", " \n", - " block3b_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3b_activation[0][0]', Y \n", - " 'block3b_se_expand[0][0]'] \n", + " block3d_se_excite (Multiply) (None, 28, 28, 384) 0 ['block3d_activation[0][0]', Y \n", + " 'block3d_se_expand[0][0]'] \n", " \n", - " block3b_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3b_se_excite[0][0]'] Y \n", + " block3d_project_conv (Conv2D) (None, 28, 28, 64) 24576 ['block3d_se_excite[0][0]'] Y \n", " \n", - " block3b_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3b_project_conv[0][0]'] Y \n", + " block3d_project_bn (BatchNorma (None, 28, 28, 64) 256 ['block3d_project_conv[0][0]'] Y \n", " lization) \n", " \n", - " block3b_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3b_project_bn[0][0]'] Y \n", + " block3d_drop (FixedDropout) (None, 28, 28, 64) 0 ['block3d_project_bn[0][0]'] Y \n", " \n", - " block3b_add (Add) (None, 28, 28, 80) 0 ['block3b_drop[0][0]', Y \n", - " 'block3a_project_bn[0][0]'] \n", + " block3d_add (Add) (None, 28, 28, 64) 0 ['block3d_drop[0][0]', Y \n", + " 'block3c_add[0][0]'] \n", " \n", - " block3c_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3b_add[0][0]'] Y \n", + " block3e_expand_conv (Conv2D) (None, 28, 28, 384) 24576 ['block3d_add[0][0]'] Y \n", " \n", - " block3c_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3c_expand_conv[0][0]'] Y \n", + " block3e_expand_bn (BatchNormal (None, 28, 28, 384) 1536 ['block3e_expand_conv[0][0]'] Y \n", " ization) \n", " \n", - " block3c_expand_activation (Act (None, 28, 28, 480) 0 ['block3c_expand_bn[0][0]'] Y \n", + " block3e_expand_activation (Act (None, 28, 28, 384) 0 ['block3e_expand_bn[0][0]'] Y \n", " ivation) \n", " \n", - " block3c_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3c_expand_activation[0][ Y \n", + " block3e_dwconv (DepthwiseConv2 (None, 28, 28, 384) 9600 ['block3e_expand_activation[0][ Y \n", " D) 0]'] \n", " \n", - " block3c_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3c_dwconv[0][0]'] Y \n", + " block3e_bn (BatchNormalization (None, 28, 28, 384) 1536 ['block3e_dwconv[0][0]'] Y \n", " ) \n", " \n", - " block3c_activation (Activation (None, 28, 28, 480) 0 ['block3c_bn[0][0]'] Y \n", + " block3e_activation (Activation (None, 28, 28, 384) 0 ['block3e_bn[0][0]'] Y \n", " ) \n", " \n", - " block3c_se_squeeze (GlobalAver (None, 480) 0 ['block3c_activation[0][0]'] Y \n", + " block3e_se_squeeze (GlobalAver (None, 384) 0 ['block3e_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block3c_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3c_se_squeeze[0][0]'] Y \n", + " block3e_se_reshape (Reshape) (None, 1, 1, 384) 0 ['block3e_se_squeeze[0][0]'] Y \n", " \n", - " block3c_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3c_se_reshape[0][0]'] Y \n", + " block3e_se_reduce (Conv2D) (None, 1, 1, 16) 6160 ['block3e_se_reshape[0][0]'] Y \n", " \n", - " block3c_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3c_se_reduce[0][0]'] Y \n", + " block3e_se_expand (Conv2D) (None, 1, 1, 384) 6528 ['block3e_se_reduce[0][0]'] Y \n", " \n", - " block3c_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3c_activation[0][0]', Y \n", - " 'block3c_se_expand[0][0]'] \n", + " block3e_se_excite (Multiply) (None, 28, 28, 384) 0 ['block3e_activation[0][0]', Y \n", + " 'block3e_se_expand[0][0]'] \n", " \n", - " block3c_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3c_se_excite[0][0]'] Y \n", + " block3e_project_conv (Conv2D) (None, 28, 28, 64) 24576 ['block3e_se_excite[0][0]'] Y \n", " \n", - " block3c_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3c_project_conv[0][0]'] Y \n", + " block3e_project_bn (BatchNorma (None, 28, 28, 64) 256 ['block3e_project_conv[0][0]'] Y \n", " lization) \n", " \n", - " block3c_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3c_project_bn[0][0]'] Y \n", + " block3e_drop (FixedDropout) (None, 28, 28, 64) 0 ['block3e_project_bn[0][0]'] Y \n", " \n", - " block3c_add (Add) (None, 28, 28, 80) 0 ['block3c_drop[0][0]', Y \n", - " 'block3b_add[0][0]'] \n", + " block3e_add (Add) (None, 28, 28, 64) 0 ['block3e_drop[0][0]', Y \n", + " 'block3d_add[0][0]'] \n", " \n", - " block3d_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3c_add[0][0]'] Y \n", + " block4a_expand_conv (Conv2D) (None, 28, 28, 384) 24576 ['block3e_add[0][0]'] Y \n", " \n", - " block3d_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3d_expand_conv[0][0]'] Y \n", + " block4a_expand_bn (BatchNormal (None, 28, 28, 384) 1536 ['block4a_expand_conv[0][0]'] Y \n", " ization) \n", " \n", - " block3d_expand_activation (Act (None, 28, 28, 480) 0 ['block3d_expand_bn[0][0]'] Y \n", + " block4a_expand_activation (Act (None, 28, 28, 384) 0 ['block4a_expand_bn[0][0]'] Y \n", " ivation) \n", " \n", - " block3d_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3d_expand_activation[0][ Y \n", + " block4a_dwconv (DepthwiseConv2 (None, 14, 14, 384) 3456 ['block4a_expand_activation[0][ Y \n", " D) 0]'] \n", " \n", - " block3d_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3d_dwconv[0][0]'] Y \n", + " block4a_bn (BatchNormalization (None, 14, 14, 384) 1536 ['block4a_dwconv[0][0]'] Y \n", " ) \n", " \n", - " block3d_activation (Activation (None, 28, 28, 480) 0 ['block3d_bn[0][0]'] Y \n", + " block4a_activation (Activation (None, 14, 14, 384) 0 ['block4a_bn[0][0]'] Y \n", " ) \n", " \n", - " block3d_se_squeeze (GlobalAver (None, 480) 0 ['block3d_activation[0][0]'] Y \n", + " block4a_se_squeeze (GlobalAver (None, 384) 0 ['block4a_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block3d_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3d_se_squeeze[0][0]'] Y \n", + " block4a_se_reshape (Reshape) (None, 1, 1, 384) 0 ['block4a_se_squeeze[0][0]'] Y \n", " \n", - " block3d_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3d_se_reshape[0][0]'] Y \n", + " block4a_se_reduce (Conv2D) (None, 1, 1, 16) 6160 ['block4a_se_reshape[0][0]'] Y \n", " \n", - " block3d_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3d_se_reduce[0][0]'] Y \n", + " block4a_se_expand (Conv2D) (None, 1, 1, 384) 6528 ['block4a_se_reduce[0][0]'] Y \n", " \n", - " block3d_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3d_activation[0][0]', Y \n", - " 'block3d_se_expand[0][0]'] \n", + " block4a_se_excite (Multiply) (None, 14, 14, 384) 0 ['block4a_activation[0][0]', Y \n", + " 'block4a_se_expand[0][0]'] \n", " \n", - " block3d_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3d_se_excite[0][0]'] Y \n", + " block4a_project_conv (Conv2D) (None, 14, 14, 128) 49152 ['block4a_se_excite[0][0]'] Y \n", " \n", - " block3d_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3d_project_conv[0][0]'] Y \n", + " block4a_project_bn (BatchNorma (None, 14, 14, 128) 512 ['block4a_project_conv[0][0]'] Y \n", " lization) \n", " \n", - " block3d_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3d_project_bn[0][0]'] Y \n", - " \n", - " block3d_add (Add) (None, 28, 28, 80) 0 ['block3d_drop[0][0]', Y \n", - " 'block3c_add[0][0]'] \n", - " \n", - " block3e_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3d_add[0][0]'] Y \n", + " block4b_expand_conv (Conv2D) (None, 14, 14, 768) 98304 ['block4a_project_bn[0][0]'] Y \n", " \n", - " block3e_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3e_expand_conv[0][0]'] Y \n", + " block4b_expand_bn (BatchNormal (None, 14, 14, 768) 3072 ['block4b_expand_conv[0][0]'] Y \n", " ization) \n", " \n", - " block3e_expand_activation (Act (None, 28, 28, 480) 0 ['block3e_expand_bn[0][0]'] Y \n", + " block4b_expand_activation (Act (None, 14, 14, 768) 0 ['block4b_expand_bn[0][0]'] Y \n", " ivation) \n", " \n", - " block3e_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3e_expand_activation[0][ Y \n", + " block4b_dwconv (DepthwiseConv2 (None, 14, 14, 768) 6912 ['block4b_expand_activation[0][ Y \n", " D) 0]'] \n", " \n", - " block3e_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3e_dwconv[0][0]'] Y \n", + " block4b_bn (BatchNormalization (None, 14, 14, 768) 3072 ['block4b_dwconv[0][0]'] Y \n", " ) \n", " \n", - " block3e_activation (Activation (None, 28, 28, 480) 0 ['block3e_bn[0][0]'] Y \n", + " block4b_activation (Activation (None, 14, 14, 768) 0 ['block4b_bn[0][0]'] Y \n", " ) \n", " \n", - " block3e_se_squeeze (GlobalAver (None, 480) 0 ['block3e_activation[0][0]'] Y \n", + " block4b_se_squeeze (GlobalAver (None, 768) 0 ['block4b_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block3e_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3e_se_squeeze[0][0]'] Y \n", + " block4b_se_reshape (Reshape) (None, 1, 1, 768) 0 ['block4b_se_squeeze[0][0]'] Y \n", " \n", - " block3e_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3e_se_reshape[0][0]'] Y \n", + " block4b_se_reduce (Conv2D) (None, 1, 1, 32) 24608 ['block4b_se_reshape[0][0]'] Y \n", " \n", - " block3e_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3e_se_reduce[0][0]'] Y \n", + " block4b_se_expand (Conv2D) (None, 1, 1, 768) 25344 ['block4b_se_reduce[0][0]'] Y \n", " \n", - " block3e_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3e_activation[0][0]', Y \n", - " 'block3e_se_expand[0][0]'] \n", + " block4b_se_excite (Multiply) (None, 14, 14, 768) 0 ['block4b_activation[0][0]', Y \n", + " 'block4b_se_expand[0][0]'] \n", " \n", - " block3e_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3e_se_excite[0][0]'] Y \n", + " block4b_project_conv (Conv2D) (None, 14, 14, 128) 98304 ['block4b_se_excite[0][0]'] Y \n", " \n", - " block3e_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3e_project_conv[0][0]'] Y \n", + " block4b_project_bn (BatchNorma (None, 14, 14, 128) 512 ['block4b_project_conv[0][0]'] Y \n", " lization) \n", " \n", - " block3e_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3e_project_bn[0][0]'] Y \n", + " block4b_drop (FixedDropout) (None, 14, 14, 128) 0 ['block4b_project_bn[0][0]'] Y \n", " \n", - " block3e_add (Add) (None, 28, 28, 80) 0 ['block3e_drop[0][0]', Y \n", - " 'block3d_add[0][0]'] \n", + " block4b_add (Add) (None, 14, 14, 128) 0 ['block4b_drop[0][0]', Y \n", + " 'block4a_project_bn[0][0]'] \n", " \n", - " block3f_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3e_add[0][0]'] Y \n", + " block4c_expand_conv (Conv2D) (None, 14, 14, 768) 98304 ['block4b_add[0][0]'] Y \n", " \n", - " block3f_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3f_expand_conv[0][0]'] Y \n", + " block4c_expand_bn (BatchNormal (None, 14, 14, 768) 3072 ['block4c_expand_conv[0][0]'] Y \n", " ization) \n", " \n", - " block3f_expand_activation (Act (None, 28, 28, 480) 0 ['block3f_expand_bn[0][0]'] Y \n", + " block4c_expand_activation (Act (None, 14, 14, 768) 0 ['block4c_expand_bn[0][0]'] Y \n", " ivation) \n", " \n", - " block3f_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3f_expand_activation[0][ Y \n", + " block4c_dwconv (DepthwiseConv2 (None, 14, 14, 768) 6912 ['block4c_expand_activation[0][ Y \n", " D) 0]'] \n", " \n", - " block3f_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3f_dwconv[0][0]'] Y \n", + " block4c_bn (BatchNormalization (None, 14, 14, 768) 3072 ['block4c_dwconv[0][0]'] Y \n", " ) \n", " \n", - " block3f_activation (Activation (None, 28, 28, 480) 0 ['block3f_bn[0][0]'] Y \n", + " block4c_activation (Activation (None, 14, 14, 768) 0 ['block4c_bn[0][0]'] Y \n", " ) \n", " \n", - " block3f_se_squeeze (GlobalAver (None, 480) 0 ['block3f_activation[0][0]'] Y \n", + " block4c_se_squeeze (GlobalAver (None, 768) 0 ['block4c_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block3f_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3f_se_squeeze[0][0]'] Y \n", + " block4c_se_reshape (Reshape) (None, 1, 1, 768) 0 ['block4c_se_squeeze[0][0]'] Y \n", " \n", - " block3f_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3f_se_reshape[0][0]'] Y \n", + " block4c_se_reduce (Conv2D) (None, 1, 1, 32) 24608 ['block4c_se_reshape[0][0]'] Y \n", " \n", - " block3f_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3f_se_reduce[0][0]'] Y \n", + " block4c_se_expand (Conv2D) (None, 1, 1, 768) 25344 ['block4c_se_reduce[0][0]'] Y \n", " \n", - " block3f_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3f_activation[0][0]', Y \n", - " 'block3f_se_expand[0][0]'] \n", + " block4c_se_excite (Multiply) (None, 14, 14, 768) 0 ['block4c_activation[0][0]', Y \n", + " 'block4c_se_expand[0][0]'] \n", " \n", - " block3f_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3f_se_excite[0][0]'] Y \n", + " block4c_project_conv (Conv2D) (None, 14, 14, 128) 98304 ['block4c_se_excite[0][0]'] Y \n", " \n", - " block3f_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3f_project_conv[0][0]'] Y \n", + " block4c_project_bn (BatchNorma (None, 14, 14, 128) 512 ['block4c_project_conv[0][0]'] Y \n", " lization) \n", " \n", - " block3f_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3f_project_bn[0][0]'] Y \n", + " block4c_drop (FixedDropout) (None, 14, 14, 128) 0 ['block4c_project_bn[0][0]'] Y \n", " \n", - " block3f_add (Add) (None, 28, 28, 80) 0 ['block3f_drop[0][0]', Y \n", - " 'block3e_add[0][0]'] \n", + " block4c_add (Add) (None, 14, 14, 128) 0 ['block4c_drop[0][0]', Y \n", + " 'block4b_add[0][0]'] \n", " \n", - " block3g_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3f_add[0][0]'] Y \n", + " block4d_expand_conv (Conv2D) (None, 14, 14, 768) 98304 ['block4c_add[0][0]'] Y \n", " \n", - " block3g_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3g_expand_conv[0][0]'] Y \n", + " block4d_expand_bn (BatchNormal (None, 14, 14, 768) 3072 ['block4d_expand_conv[0][0]'] Y \n", " ization) \n", " \n", - " block3g_expand_activation (Act (None, 28, 28, 480) 0 ['block3g_expand_bn[0][0]'] Y \n", + " block4d_expand_activation (Act (None, 14, 14, 768) 0 ['block4d_expand_bn[0][0]'] Y \n", " ivation) \n", " \n", - " block3g_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3g_expand_activation[0][ Y \n", + " block4d_dwconv (DepthwiseConv2 (None, 14, 14, 768) 6912 ['block4d_expand_activation[0][ Y \n", " D) 0]'] \n", " \n", - " block3g_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3g_dwconv[0][0]'] Y \n", + " block4d_bn (BatchNormalization (None, 14, 14, 768) 3072 ['block4d_dwconv[0][0]'] Y \n", " ) \n", " \n", - " block3g_activation (Activation (None, 28, 28, 480) 0 ['block3g_bn[0][0]'] Y \n", + " block4d_activation (Activation (None, 14, 14, 768) 0 ['block4d_bn[0][0]'] Y \n", " ) \n", " \n", - " block3g_se_squeeze (GlobalAver (None, 480) 0 ['block3g_activation[0][0]'] Y \n", + " block4d_se_squeeze (GlobalAver (None, 768) 0 ['block4d_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block3g_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3g_se_squeeze[0][0]'] Y \n", + " block4d_se_reshape (Reshape) (None, 1, 1, 768) 0 ['block4d_se_squeeze[0][0]'] Y \n", " \n", - " block3g_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3g_se_reshape[0][0]'] Y \n", + " block4d_se_reduce (Conv2D) (None, 1, 1, 32) 24608 ['block4d_se_reshape[0][0]'] Y \n", " \n", - " block3g_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3g_se_reduce[0][0]'] Y \n", + " block4d_se_expand (Conv2D) (None, 1, 1, 768) 25344 ['block4d_se_reduce[0][0]'] Y \n", " \n", - " block3g_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3g_activation[0][0]', Y \n", - " 'block3g_se_expand[0][0]'] \n", + " block4d_se_excite (Multiply) (None, 14, 14, 768) 0 ['block4d_activation[0][0]', Y \n", + " 'block4d_se_expand[0][0]'] \n", " \n", - " block3g_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3g_se_excite[0][0]'] Y \n", + " block4d_project_conv (Conv2D) (None, 14, 14, 128) 98304 ['block4d_se_excite[0][0]'] Y \n", " \n", - " block3g_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3g_project_conv[0][0]'] Y \n", + " block4d_project_bn (BatchNorma (None, 14, 14, 128) 512 ['block4d_project_conv[0][0]'] Y \n", " lization) \n", " \n", - " block3g_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3g_project_bn[0][0]'] Y \n", + " block4d_drop (FixedDropout) (None, 14, 14, 128) 0 ['block4d_project_bn[0][0]'] Y \n", " \n", - " block3g_add (Add) (None, 28, 28, 80) 0 ['block3g_drop[0][0]', Y \n", - " 'block3f_add[0][0]'] \n", + " block4d_add (Add) (None, 14, 14, 128) 0 ['block4d_drop[0][0]', Y \n", + " 'block4c_add[0][0]'] \n", " \n", - " block4a_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3g_add[0][0]'] Y \n", + " block4e_expand_conv (Conv2D) (None, 14, 14, 768) 98304 ['block4d_add[0][0]'] Y \n", " \n", - " block4a_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block4a_expand_conv[0][0]'] Y \n", + " block4e_expand_bn (BatchNormal (None, 14, 14, 768) 3072 ['block4e_expand_conv[0][0]'] Y \n", " ization) \n", " \n", - " block4a_expand_activation (Act (None, 28, 28, 480) 0 ['block4a_expand_bn[0][0]'] Y \n", + " block4e_expand_activation (Act (None, 14, 14, 768) 0 ['block4e_expand_bn[0][0]'] Y \n", " ivation) \n", " \n", - " block4a_dwconv (DepthwiseConv2 (None, 14, 14, 480) 4320 ['block4a_expand_activation[0][ Y \n", + " block4e_dwconv (DepthwiseConv2 (None, 14, 14, 768) 6912 ['block4e_expand_activation[0][ Y \n", " D) 0]'] \n", " \n", - " block4a_bn (BatchNormalization (None, 14, 14, 480) 1920 ['block4a_dwconv[0][0]'] Y \n", + " block4e_bn (BatchNormalization (None, 14, 14, 768) 3072 ['block4e_dwconv[0][0]'] Y \n", " ) \n", " \n", - " block4a_activation (Activation (None, 14, 14, 480) 0 ['block4a_bn[0][0]'] Y \n", + " block4e_activation (Activation (None, 14, 14, 768) 0 ['block4e_bn[0][0]'] Y \n", " ) \n", " \n", - " block4a_se_squeeze (GlobalAver (None, 480) 0 ['block4a_activation[0][0]'] Y \n", + " block4e_se_squeeze (GlobalAver (None, 768) 0 ['block4e_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block4a_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block4a_se_squeeze[0][0]'] Y \n", + " block4e_se_reshape (Reshape) (None, 1, 1, 768) 0 ['block4e_se_squeeze[0][0]'] Y \n", " \n", - " block4a_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block4a_se_reshape[0][0]'] Y \n", + " block4e_se_reduce (Conv2D) (None, 1, 1, 32) 24608 ['block4e_se_reshape[0][0]'] Y \n", " \n", - " block4a_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block4a_se_reduce[0][0]'] Y \n", + " block4e_se_expand (Conv2D) (None, 1, 1, 768) 25344 ['block4e_se_reduce[0][0]'] Y \n", " \n", - " block4a_se_excite (Multiply) (None, 14, 14, 480) 0 ['block4a_activation[0][0]', Y \n", - " 'block4a_se_expand[0][0]'] \n", + " block4e_se_excite (Multiply) (None, 14, 14, 768) 0 ['block4e_activation[0][0]', Y \n", + " 'block4e_se_expand[0][0]'] \n", " \n", - " block4a_project_conv (Conv2D) (None, 14, 14, 160) 76800 ['block4a_se_excite[0][0]'] Y \n", + " block4e_project_conv (Conv2D) (None, 14, 14, 128) 98304 ['block4e_se_excite[0][0]'] Y \n", " \n", - " block4a_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4a_project_conv[0][0]'] Y \n", + " block4e_project_bn (BatchNorma (None, 14, 14, 128) 512 ['block4e_project_conv[0][0]'] Y \n", " lization) \n", " \n", - " block4b_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4a_project_bn[0][0]'] Y \n", + " block4e_drop (FixedDropout) (None, 14, 14, 128) 0 ['block4e_project_bn[0][0]'] Y \n", " \n", - " block4b_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4b_expand_conv[0][0]'] Y \n", + " block4e_add (Add) (None, 14, 14, 128) 0 ['block4e_drop[0][0]', Y \n", + " 'block4d_add[0][0]'] \n", + " \n", + " block4f_expand_conv (Conv2D) (None, 14, 14, 768) 98304 ['block4e_add[0][0]'] Y \n", + " \n", + " block4f_expand_bn (BatchNormal (None, 14, 14, 768) 3072 ['block4f_expand_conv[0][0]'] Y \n", " ization) \n", " \n", - " block4b_expand_activation (Act (None, 14, 14, 960) 0 ['block4b_expand_bn[0][0]'] Y \n", + " block4f_expand_activation (Act (None, 14, 14, 768) 0 ['block4f_expand_bn[0][0]'] Y \n", " ivation) \n", " \n", - " block4b_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4b_expand_activation[0][ Y \n", + " block4f_dwconv (DepthwiseConv2 (None, 14, 14, 768) 6912 ['block4f_expand_activation[0][ Y \n", " D) 0]'] \n", " \n", - " block4b_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4b_dwconv[0][0]'] Y \n", + " block4f_bn (BatchNormalization (None, 14, 14, 768) 3072 ['block4f_dwconv[0][0]'] Y \n", " ) \n", " \n", - " block4b_activation (Activation (None, 14, 14, 960) 0 ['block4b_bn[0][0]'] Y \n", + " block4f_activation (Activation (None, 14, 14, 768) 0 ['block4f_bn[0][0]'] Y \n", " ) \n", " \n", - " block4b_se_squeeze (GlobalAver (None, 960) 0 ['block4b_activation[0][0]'] Y \n", + " block4f_se_squeeze (GlobalAver (None, 768) 0 ['block4f_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block4b_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4b_se_squeeze[0][0]'] Y \n", + " block4f_se_reshape (Reshape) (None, 1, 1, 768) 0 ['block4f_se_squeeze[0][0]'] Y \n", " \n", - " block4b_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4b_se_reshape[0][0]'] Y \n", + " block4f_se_reduce (Conv2D) (None, 1, 1, 32) 24608 ['block4f_se_reshape[0][0]'] Y \n", " \n", - " block4b_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4b_se_reduce[0][0]'] Y \n", + " block4f_se_expand (Conv2D) (None, 1, 1, 768) 25344 ['block4f_se_reduce[0][0]'] Y \n", " \n", - " block4b_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4b_activation[0][0]', Y \n", - " 'block4b_se_expand[0][0]'] \n", + " block4f_se_excite (Multiply) (None, 14, 14, 768) 0 ['block4f_activation[0][0]', Y \n", + " 'block4f_se_expand[0][0]'] \n", " \n", - " block4b_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4b_se_excite[0][0]'] Y \n", + " block4f_project_conv (Conv2D) (None, 14, 14, 128) 98304 ['block4f_se_excite[0][0]'] Y \n", " \n", - " block4b_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4b_project_conv[0][0]'] Y \n", + " block4f_project_bn (BatchNorma (None, 14, 14, 128) 512 ['block4f_project_conv[0][0]'] Y \n", " lization) \n", " \n", - " block4b_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4b_project_bn[0][0]'] Y \n", + " block4f_drop (FixedDropout) (None, 14, 14, 128) 0 ['block4f_project_bn[0][0]'] Y \n", " \n", - " block4b_add (Add) (None, 14, 14, 160) 0 ['block4b_drop[0][0]', Y \n", - " 'block4a_project_bn[0][0]'] \n", + " block4f_add (Add) (None, 14, 14, 128) 0 ['block4f_drop[0][0]', Y \n", + " 'block4e_add[0][0]'] \n", " \n", - " block4c_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4b_add[0][0]'] Y \n", + " block4g_expand_conv (Conv2D) (None, 14, 14, 768) 98304 ['block4f_add[0][0]'] Y \n", " \n", - " block4c_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4c_expand_conv[0][0]'] Y \n", + " block4g_expand_bn (BatchNormal (None, 14, 14, 768) 3072 ['block4g_expand_conv[0][0]'] Y \n", " ization) \n", " \n", - " block4c_expand_activation (Act (None, 14, 14, 960) 0 ['block4c_expand_bn[0][0]'] Y \n", + " block4g_expand_activation (Act (None, 14, 14, 768) 0 ['block4g_expand_bn[0][0]'] Y \n", " ivation) \n", " \n", - " block4c_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4c_expand_activation[0][ Y \n", + " block4g_dwconv (DepthwiseConv2 (None, 14, 14, 768) 6912 ['block4g_expand_activation[0][ Y \n", " D) 0]'] \n", " \n", - " block4c_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4c_dwconv[0][0]'] Y \n", + " block4g_bn (BatchNormalization (None, 14, 14, 768) 3072 ['block4g_dwconv[0][0]'] Y \n", " ) \n", " \n", - " block4c_activation (Activation (None, 14, 14, 960) 0 ['block4c_bn[0][0]'] Y \n", + " block4g_activation (Activation (None, 14, 14, 768) 0 ['block4g_bn[0][0]'] Y \n", " ) \n", " \n", - " block4c_se_squeeze (GlobalAver (None, 960) 0 ['block4c_activation[0][0]'] Y \n", + " block4g_se_squeeze (GlobalAver (None, 768) 0 ['block4g_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block4c_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4c_se_squeeze[0][0]'] Y \n", + " block4g_se_reshape (Reshape) (None, 1, 1, 768) 0 ['block4g_se_squeeze[0][0]'] Y \n", " \n", - " block4c_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4c_se_reshape[0][0]'] Y \n", + " block4g_se_reduce (Conv2D) (None, 1, 1, 32) 24608 ['block4g_se_reshape[0][0]'] Y \n", " \n", - " block4c_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4c_se_reduce[0][0]'] Y \n", + " block4g_se_expand (Conv2D) (None, 1, 1, 768) 25344 ['block4g_se_reduce[0][0]'] Y \n", " \n", - " block4c_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4c_activation[0][0]', Y \n", - " 'block4c_se_expand[0][0]'] \n", + " block4g_se_excite (Multiply) (None, 14, 14, 768) 0 ['block4g_activation[0][0]', Y \n", + " 'block4g_se_expand[0][0]'] \n", " \n", - " block4c_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4c_se_excite[0][0]'] Y \n", + " block4g_project_conv (Conv2D) (None, 14, 14, 128) 98304 ['block4g_se_excite[0][0]'] Y \n", " \n", - " block4c_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4c_project_conv[0][0]'] Y \n", + " block4g_project_bn (BatchNorma (None, 14, 14, 128) 512 ['block4g_project_conv[0][0]'] Y \n", " lization) \n", " \n", - " block4c_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4c_project_bn[0][0]'] Y \n", + " block4g_drop (FixedDropout) (None, 14, 14, 128) 0 ['block4g_project_bn[0][0]'] Y \n", " \n", - " block4c_add (Add) (None, 14, 14, 160) 0 ['block4c_drop[0][0]', Y \n", - " 'block4b_add[0][0]'] \n", + " block4g_add (Add) (None, 14, 14, 128) 0 ['block4g_drop[0][0]', Y \n", + " 'block4f_add[0][0]'] \n", " \n", - " block4d_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4c_add[0][0]'] Y \n", + " block5a_expand_conv (Conv2D) (None, 14, 14, 768) 98304 ['block4g_add[0][0]'] Y \n", " \n", - " block4d_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4d_expand_conv[0][0]'] Y \n", + " block5a_expand_bn (BatchNormal (None, 14, 14, 768) 3072 ['block5a_expand_conv[0][0]'] Y \n", " ization) \n", " \n", - " block4d_expand_activation (Act (None, 14, 14, 960) 0 ['block4d_expand_bn[0][0]'] Y \n", + " block5a_expand_activation (Act (None, 14, 14, 768) 0 ['block5a_expand_bn[0][0]'] Y \n", " ivation) \n", " \n", - " block4d_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4d_expand_activation[0][ Y \n", + " block5a_dwconv (DepthwiseConv2 (None, 14, 14, 768) 19200 ['block5a_expand_activation[0][ Y \n", " D) 0]'] \n", " \n", - " block4d_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4d_dwconv[0][0]'] Y \n", + " block5a_bn (BatchNormalization (None, 14, 14, 768) 3072 ['block5a_dwconv[0][0]'] Y \n", " ) \n", " \n", - " block4d_activation (Activation (None, 14, 14, 960) 0 ['block4d_bn[0][0]'] Y \n", + " block5a_activation (Activation (None, 14, 14, 768) 0 ['block5a_bn[0][0]'] Y \n", " ) \n", " \n", - " block4d_se_squeeze (GlobalAver (None, 960) 0 ['block4d_activation[0][0]'] Y \n", + " block5a_se_squeeze (GlobalAver (None, 768) 0 ['block5a_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block4d_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4d_se_squeeze[0][0]'] Y \n", + " block5a_se_reshape (Reshape) (None, 1, 1, 768) 0 ['block5a_se_squeeze[0][0]'] Y \n", " \n", - " block4d_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4d_se_reshape[0][0]'] Y \n", + " block5a_se_reduce (Conv2D) (None, 1, 1, 32) 24608 ['block5a_se_reshape[0][0]'] Y \n", " \n", - " block4d_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4d_se_reduce[0][0]'] Y \n", + " block5a_se_expand (Conv2D) (None, 1, 1, 768) 25344 ['block5a_se_reduce[0][0]'] Y \n", " \n", - " block4d_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4d_activation[0][0]', Y \n", - " 'block4d_se_expand[0][0]'] \n", + " block5a_se_excite (Multiply) (None, 14, 14, 768) 0 ['block5a_activation[0][0]', Y \n", + " 'block5a_se_expand[0][0]'] \n", " \n", - " block4d_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4d_se_excite[0][0]'] Y \n", + " block5a_project_conv (Conv2D) (None, 14, 14, 176) 135168 ['block5a_se_excite[0][0]'] Y \n", " \n", - " block4d_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4d_project_conv[0][0]'] Y \n", + " block5a_project_bn (BatchNorma (None, 14, 14, 176) 704 ['block5a_project_conv[0][0]'] Y \n", " lization) \n", " \n", - " block4d_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4d_project_bn[0][0]'] Y \n", - " \n", - " block4d_add (Add) (None, 14, 14, 160) 0 ['block4d_drop[0][0]', Y \n", - " 'block4c_add[0][0]'] \n", - " \n", - " block4e_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4d_add[0][0]'] Y \n", + " block5b_expand_conv (Conv2D) (None, 14, 14, 1056 185856 ['block5a_project_bn[0][0]'] Y \n", + " ) \n", " \n", - " block4e_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4e_expand_conv[0][0]'] Y \n", - " ization) \n", + " block5b_expand_bn (BatchNormal (None, 14, 14, 1056 4224 ['block5b_expand_conv[0][0]'] Y \n", + " ization) ) \n", " \n", - " block4e_expand_activation (Act (None, 14, 14, 960) 0 ['block4e_expand_bn[0][0]'] Y \n", - " ivation) \n", + " block5b_expand_activation (Act (None, 14, 14, 1056 0 ['block5b_expand_bn[0][0]'] Y \n", + " ivation) ) \n", " \n", - " block4e_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4e_expand_activation[0][ Y \n", - " D) 0]'] \n", + " block5b_dwconv (DepthwiseConv2 (None, 14, 14, 1056 26400 ['block5b_expand_activation[0][ Y \n", + " D) ) 0]'] \n", " \n", - " block4e_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4e_dwconv[0][0]'] Y \n", - " ) \n", + " block5b_bn (BatchNormalization (None, 14, 14, 1056 4224 ['block5b_dwconv[0][0]'] Y \n", + " ) ) \n", " \n", - " block4e_activation (Activation (None, 14, 14, 960) 0 ['block4e_bn[0][0]'] Y \n", - " ) \n", + " block5b_activation (Activation (None, 14, 14, 1056 0 ['block5b_bn[0][0]'] Y \n", + " ) ) \n", " \n", - " block4e_se_squeeze (GlobalAver (None, 960) 0 ['block4e_activation[0][0]'] Y \n", + " block5b_se_squeeze (GlobalAver (None, 1056) 0 ['block5b_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block4e_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4e_se_squeeze[0][0]'] Y \n", + " block5b_se_reshape (Reshape) (None, 1, 1, 1056) 0 ['block5b_se_squeeze[0][0]'] Y \n", " \n", - " block4e_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4e_se_reshape[0][0]'] Y \n", + " block5b_se_reduce (Conv2D) (None, 1, 1, 44) 46508 ['block5b_se_reshape[0][0]'] Y \n", " \n", - " block4e_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4e_se_reduce[0][0]'] Y \n", + " block5b_se_expand (Conv2D) (None, 1, 1, 1056) 47520 ['block5b_se_reduce[0][0]'] Y \n", " \n", - " block4e_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4e_activation[0][0]', Y \n", - " 'block4e_se_expand[0][0]'] \n", + " block5b_se_excite (Multiply) (None, 14, 14, 1056 0 ['block5b_activation[0][0]', Y \n", + " ) 'block5b_se_expand[0][0]'] \n", " \n", - " block4e_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4e_se_excite[0][0]'] Y \n", + " block5b_project_conv (Conv2D) (None, 14, 14, 176) 185856 ['block5b_se_excite[0][0]'] Y \n", " \n", - " block4e_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4e_project_conv[0][0]'] Y \n", + " block5b_project_bn (BatchNorma (None, 14, 14, 176) 704 ['block5b_project_conv[0][0]'] Y \n", " lization) \n", " \n", - " block4e_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4e_project_bn[0][0]'] Y \n", + " block5b_drop (FixedDropout) (None, 14, 14, 176) 0 ['block5b_project_bn[0][0]'] Y \n", " \n", - " block4e_add (Add) (None, 14, 14, 160) 0 ['block4e_drop[0][0]', Y \n", - " 'block4d_add[0][0]'] \n", + " block5b_add (Add) (None, 14, 14, 176) 0 ['block5b_drop[0][0]', Y \n", + " 'block5a_project_bn[0][0]'] \n", " \n", - " block4f_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4e_add[0][0]'] Y \n", + " block5c_expand_conv (Conv2D) (None, 14, 14, 1056 185856 ['block5b_add[0][0]'] Y \n", + " ) \n", " \n", - " block4f_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4f_expand_conv[0][0]'] Y \n", - " ization) \n", + " block5c_expand_bn (BatchNormal (None, 14, 14, 1056 4224 ['block5c_expand_conv[0][0]'] Y \n", + " ization) ) \n", " \n", - " block4f_expand_activation (Act (None, 14, 14, 960) 0 ['block4f_expand_bn[0][0]'] Y \n", - " ivation) \n", + " block5c_expand_activation (Act (None, 14, 14, 1056 0 ['block5c_expand_bn[0][0]'] Y \n", + " ivation) ) \n", " \n", - " block4f_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4f_expand_activation[0][ Y \n", - " D) 0]'] \n", + " block5c_dwconv (DepthwiseConv2 (None, 14, 14, 1056 26400 ['block5c_expand_activation[0][ Y \n", + " D) ) 0]'] \n", " \n", - " block4f_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4f_dwconv[0][0]'] Y \n", - " ) \n", + " block5c_bn (BatchNormalization (None, 14, 14, 1056 4224 ['block5c_dwconv[0][0]'] Y \n", + " ) ) \n", " \n", - " block4f_activation (Activation (None, 14, 14, 960) 0 ['block4f_bn[0][0]'] Y \n", - " ) \n", + " block5c_activation (Activation (None, 14, 14, 1056 0 ['block5c_bn[0][0]'] Y \n", + " ) ) \n", " \n", - " block4f_se_squeeze (GlobalAver (None, 960) 0 ['block4f_activation[0][0]'] Y \n", + " block5c_se_squeeze (GlobalAver (None, 1056) 0 ['block5c_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block4f_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4f_se_squeeze[0][0]'] Y \n", + " block5c_se_reshape (Reshape) (None, 1, 1, 1056) 0 ['block5c_se_squeeze[0][0]'] Y \n", " \n", - " block4f_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4f_se_reshape[0][0]'] Y \n", + " block5c_se_reduce (Conv2D) (None, 1, 1, 44) 46508 ['block5c_se_reshape[0][0]'] Y \n", " \n", - " block4f_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4f_se_reduce[0][0]'] Y \n", + " block5c_se_expand (Conv2D) (None, 1, 1, 1056) 47520 ['block5c_se_reduce[0][0]'] Y \n", " \n", - " block4f_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4f_activation[0][0]', Y \n", - " 'block4f_se_expand[0][0]'] \n", + " block5c_se_excite (Multiply) (None, 14, 14, 1056 0 ['block5c_activation[0][0]', Y \n", + " ) 'block5c_se_expand[0][0]'] \n", " \n", - " block4f_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4f_se_excite[0][0]'] Y \n", + " block5c_project_conv (Conv2D) (None, 14, 14, 176) 185856 ['block5c_se_excite[0][0]'] Y \n", " \n", - " block4f_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4f_project_conv[0][0]'] Y \n", + " block5c_project_bn (BatchNorma (None, 14, 14, 176) 704 ['block5c_project_conv[0][0]'] Y \n", " lization) \n", " \n", - " block4f_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4f_project_bn[0][0]'] Y \n", + " block5c_drop (FixedDropout) (None, 14, 14, 176) 0 ['block5c_project_bn[0][0]'] Y \n", " \n", - " block4f_add (Add) (None, 14, 14, 160) 0 ['block4f_drop[0][0]', Y \n", - " 'block4e_add[0][0]'] \n", + " block5c_add (Add) (None, 14, 14, 176) 0 ['block5c_drop[0][0]', Y \n", + " 'block5b_add[0][0]'] \n", " \n", - " block4g_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4f_add[0][0]'] Y \n", + " block5d_expand_conv (Conv2D) (None, 14, 14, 1056 185856 ['block5c_add[0][0]'] Y \n", + " ) \n", " \n", - " block4g_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4g_expand_conv[0][0]'] Y \n", - " ization) \n", + " block5d_expand_bn (BatchNormal (None, 14, 14, 1056 4224 ['block5d_expand_conv[0][0]'] Y \n", + " ization) ) \n", " \n", - " block4g_expand_activation (Act (None, 14, 14, 960) 0 ['block4g_expand_bn[0][0]'] Y \n", - " ivation) \n", + " block5d_expand_activation (Act (None, 14, 14, 1056 0 ['block5d_expand_bn[0][0]'] Y \n", + " ivation) ) \n", " \n", - " block4g_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4g_expand_activation[0][ Y \n", - " D) 0]'] \n", + " block5d_dwconv (DepthwiseConv2 (None, 14, 14, 1056 26400 ['block5d_expand_activation[0][ Y \n", + " D) ) 0]'] \n", " \n", - " block4g_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4g_dwconv[0][0]'] Y \n", - " ) \n", + " block5d_bn (BatchNormalization (None, 14, 14, 1056 4224 ['block5d_dwconv[0][0]'] Y \n", + " ) ) \n", " \n", - " block4g_activation (Activation (None, 14, 14, 960) 0 ['block4g_bn[0][0]'] Y \n", - " ) \n", + " block5d_activation (Activation (None, 14, 14, 1056 0 ['block5d_bn[0][0]'] Y \n", + " ) ) \n", " \n", - " block4g_se_squeeze (GlobalAver (None, 960) 0 ['block4g_activation[0][0]'] Y \n", + " block5d_se_squeeze (GlobalAver (None, 1056) 0 ['block5d_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block4g_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4g_se_squeeze[0][0]'] Y \n", + " block5d_se_reshape (Reshape) (None, 1, 1, 1056) 0 ['block5d_se_squeeze[0][0]'] Y \n", " \n", - " block4g_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4g_se_reshape[0][0]'] Y \n", + " block5d_se_reduce (Conv2D) (None, 1, 1, 44) 46508 ['block5d_se_reshape[0][0]'] Y \n", " \n", - " block4g_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4g_se_reduce[0][0]'] Y \n", + " block5d_se_expand (Conv2D) (None, 1, 1, 1056) 47520 ['block5d_se_reduce[0][0]'] Y \n", " \n", - " block4g_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4g_activation[0][0]', Y \n", - " 'block4g_se_expand[0][0]'] \n", + " block5d_se_excite (Multiply) (None, 14, 14, 1056 0 ['block5d_activation[0][0]', Y \n", + " ) 'block5d_se_expand[0][0]'] \n", " \n", - " block4g_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4g_se_excite[0][0]'] Y \n", + " block5d_project_conv (Conv2D) (None, 14, 14, 176) 185856 ['block5d_se_excite[0][0]'] Y \n", " \n", - " block4g_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4g_project_conv[0][0]'] Y \n", + " block5d_project_bn (BatchNorma (None, 14, 14, 176) 704 ['block5d_project_conv[0][0]'] Y \n", " lization) \n", " \n", - " block4g_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4g_project_bn[0][0]'] Y \n", + " block5d_drop (FixedDropout) (None, 14, 14, 176) 0 ['block5d_project_bn[0][0]'] Y \n", " \n", - " block4g_add (Add) (None, 14, 14, 160) 0 ['block4g_drop[0][0]', Y \n", - " 'block4f_add[0][0]'] \n", + " block5d_add (Add) (None, 14, 14, 176) 0 ['block5d_drop[0][0]', Y \n", + " 'block5c_add[0][0]'] \n", " \n", - " block4h_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4g_add[0][0]'] Y \n", + " block5e_expand_conv (Conv2D) (None, 14, 14, 1056 185856 ['block5d_add[0][0]'] Y \n", + " ) \n", " \n", - " block4h_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4h_expand_conv[0][0]'] Y \n", - " ization) \n", + " block5e_expand_bn (BatchNormal (None, 14, 14, 1056 4224 ['block5e_expand_conv[0][0]'] Y \n", + " ization) ) \n", " \n", - " block4h_expand_activation (Act (None, 14, 14, 960) 0 ['block4h_expand_bn[0][0]'] Y \n", - " ivation) \n", + " block5e_expand_activation (Act (None, 14, 14, 1056 0 ['block5e_expand_bn[0][0]'] Y \n", + " ivation) ) \n", " \n", - " block4h_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4h_expand_activation[0][ Y \n", - " D) 0]'] \n", + " block5e_dwconv (DepthwiseConv2 (None, 14, 14, 1056 26400 ['block5e_expand_activation[0][ Y \n", + " D) ) 0]'] \n", " \n", - " block4h_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4h_dwconv[0][0]'] Y \n", - " ) \n", + " block5e_bn (BatchNormalization (None, 14, 14, 1056 4224 ['block5e_dwconv[0][0]'] Y \n", + " ) ) \n", " \n", - " block4h_activation (Activation (None, 14, 14, 960) 0 ['block4h_bn[0][0]'] Y \n", - " ) \n", + " block5e_activation (Activation (None, 14, 14, 1056 0 ['block5e_bn[0][0]'] Y \n", + " ) ) \n", " \n", - " block4h_se_squeeze (GlobalAver (None, 960) 0 ['block4h_activation[0][0]'] Y \n", + " block5e_se_squeeze (GlobalAver (None, 1056) 0 ['block5e_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block4h_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4h_se_squeeze[0][0]'] Y \n", + " block5e_se_reshape (Reshape) (None, 1, 1, 1056) 0 ['block5e_se_squeeze[0][0]'] Y \n", " \n", - " block4h_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4h_se_reshape[0][0]'] Y \n", + " block5e_se_reduce (Conv2D) (None, 1, 1, 44) 46508 ['block5e_se_reshape[0][0]'] Y \n", " \n", - " block4h_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4h_se_reduce[0][0]'] Y \n", + " block5e_se_expand (Conv2D) (None, 1, 1, 1056) 47520 ['block5e_se_reduce[0][0]'] Y \n", " \n", - " block4h_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4h_activation[0][0]', Y \n", - " 'block4h_se_expand[0][0]'] \n", + " block5e_se_excite (Multiply) (None, 14, 14, 1056 0 ['block5e_activation[0][0]', Y \n", + " ) 'block5e_se_expand[0][0]'] \n", " \n", - " block4h_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4h_se_excite[0][0]'] Y \n", + " block5e_project_conv (Conv2D) (None, 14, 14, 176) 185856 ['block5e_se_excite[0][0]'] Y \n", " \n", - " block4h_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4h_project_conv[0][0]'] Y \n", + " block5e_project_bn (BatchNorma (None, 14, 14, 176) 704 ['block5e_project_conv[0][0]'] Y \n", " lization) \n", " \n", - " block4h_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4h_project_bn[0][0]'] Y \n", + " block5e_drop (FixedDropout) (None, 14, 14, 176) 0 ['block5e_project_bn[0][0]'] Y \n", " \n", - " block4h_add (Add) (None, 14, 14, 160) 0 ['block4h_drop[0][0]', Y \n", - " 'block4g_add[0][0]'] \n", + " block5e_add (Add) (None, 14, 14, 176) 0 ['block5e_drop[0][0]', Y \n", + " 'block5d_add[0][0]'] \n", " \n", - " block4i_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4h_add[0][0]'] Y \n", + " block5f_expand_conv (Conv2D) (None, 14, 14, 1056 185856 ['block5e_add[0][0]'] Y \n", + " ) \n", " \n", - " block4i_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4i_expand_conv[0][0]'] Y \n", - " ization) \n", + " block5f_expand_bn (BatchNormal (None, 14, 14, 1056 4224 ['block5f_expand_conv[0][0]'] Y \n", + " ization) ) \n", " \n", - " block4i_expand_activation (Act (None, 14, 14, 960) 0 ['block4i_expand_bn[0][0]'] Y \n", - " ivation) \n", + " block5f_expand_activation (Act (None, 14, 14, 1056 0 ['block5f_expand_bn[0][0]'] Y \n", + " ivation) ) \n", " \n", - " block4i_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4i_expand_activation[0][ Y \n", - " D) 0]'] \n", + " block5f_dwconv (DepthwiseConv2 (None, 14, 14, 1056 26400 ['block5f_expand_activation[0][ Y \n", + " D) ) 0]'] \n", " \n", - " block4i_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4i_dwconv[0][0]'] Y \n", - " ) \n", + " block5f_bn (BatchNormalization (None, 14, 14, 1056 4224 ['block5f_dwconv[0][0]'] Y \n", + " ) ) \n", " \n", - " block4i_activation (Activation (None, 14, 14, 960) 0 ['block4i_bn[0][0]'] Y \n", - " ) \n", + " block5f_activation (Activation (None, 14, 14, 1056 0 ['block5f_bn[0][0]'] Y \n", + " ) ) \n", " \n", - " block4i_se_squeeze (GlobalAver (None, 960) 0 ['block4i_activation[0][0]'] Y \n", + " block5f_se_squeeze (GlobalAver (None, 1056) 0 ['block5f_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block4i_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4i_se_squeeze[0][0]'] Y \n", + " block5f_se_reshape (Reshape) (None, 1, 1, 1056) 0 ['block5f_se_squeeze[0][0]'] Y \n", " \n", - " block4i_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4i_se_reshape[0][0]'] Y \n", + " block5f_se_reduce (Conv2D) (None, 1, 1, 44) 46508 ['block5f_se_reshape[0][0]'] Y \n", " \n", - " block4i_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4i_se_reduce[0][0]'] Y \n", + " block5f_se_expand (Conv2D) (None, 1, 1, 1056) 47520 ['block5f_se_reduce[0][0]'] Y \n", " \n", - " block4i_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4i_activation[0][0]', Y \n", - " 'block4i_se_expand[0][0]'] \n", + " block5f_se_excite (Multiply) (None, 14, 14, 1056 0 ['block5f_activation[0][0]', Y \n", + " ) 'block5f_se_expand[0][0]'] \n", " \n", - " block4i_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4i_se_excite[0][0]'] Y \n", + " block5f_project_conv (Conv2D) (None, 14, 14, 176) 185856 ['block5f_se_excite[0][0]'] Y \n", " \n", - " block4i_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4i_project_conv[0][0]'] Y \n", + " block5f_project_bn (BatchNorma (None, 14, 14, 176) 704 ['block5f_project_conv[0][0]'] Y \n", " lization) \n", " \n", - " block4i_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4i_project_bn[0][0]'] Y \n", + " block5f_drop (FixedDropout) (None, 14, 14, 176) 0 ['block5f_project_bn[0][0]'] Y \n", " \n", - " block4i_add (Add) (None, 14, 14, 160) 0 ['block4i_drop[0][0]', Y \n", - " 'block4h_add[0][0]'] \n", + " block5f_add (Add) (None, 14, 14, 176) 0 ['block5f_drop[0][0]', Y \n", + " 'block5e_add[0][0]'] \n", " \n", - " block4j_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4i_add[0][0]'] Y \n", + " block5g_expand_conv (Conv2D) (None, 14, 14, 1056 185856 ['block5f_add[0][0]'] Y \n", + " ) \n", " \n", - " block4j_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4j_expand_conv[0][0]'] Y \n", - " ization) \n", + " block5g_expand_bn (BatchNormal (None, 14, 14, 1056 4224 ['block5g_expand_conv[0][0]'] Y \n", + " ization) ) \n", " \n", - " block4j_expand_activation (Act (None, 14, 14, 960) 0 ['block4j_expand_bn[0][0]'] Y \n", - " ivation) \n", + " block5g_expand_activation (Act (None, 14, 14, 1056 0 ['block5g_expand_bn[0][0]'] Y \n", + " ivation) ) \n", " \n", - " block4j_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4j_expand_activation[0][ Y \n", - " D) 0]'] \n", + " block5g_dwconv (DepthwiseConv2 (None, 14, 14, 1056 26400 ['block5g_expand_activation[0][ Y \n", + " D) ) 0]'] \n", " \n", - " block4j_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4j_dwconv[0][0]'] Y \n", - " ) \n", + " block5g_bn (BatchNormalization (None, 14, 14, 1056 4224 ['block5g_dwconv[0][0]'] Y \n", + " ) ) \n", " \n", - " block4j_activation (Activation (None, 14, 14, 960) 0 ['block4j_bn[0][0]'] Y \n", - " ) \n", + " block5g_activation (Activation (None, 14, 14, 1056 0 ['block5g_bn[0][0]'] Y \n", + " ) ) \n", " \n", - " block4j_se_squeeze (GlobalAver (None, 960) 0 ['block4j_activation[0][0]'] Y \n", + " block5g_se_squeeze (GlobalAver (None, 1056) 0 ['block5g_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block4j_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4j_se_squeeze[0][0]'] Y \n", + " block5g_se_reshape (Reshape) (None, 1, 1, 1056) 0 ['block5g_se_squeeze[0][0]'] Y \n", " \n", - " block4j_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4j_se_reshape[0][0]'] Y \n", + " block5g_se_reduce (Conv2D) (None, 1, 1, 44) 46508 ['block5g_se_reshape[0][0]'] Y \n", " \n", - " block4j_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4j_se_reduce[0][0]'] Y \n", + " block5g_se_expand (Conv2D) (None, 1, 1, 1056) 47520 ['block5g_se_reduce[0][0]'] Y \n", " \n", - " block4j_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4j_activation[0][0]', Y \n", - " 'block4j_se_expand[0][0]'] \n", + " block5g_se_excite (Multiply) (None, 14, 14, 1056 0 ['block5g_activation[0][0]', Y \n", + " ) 'block5g_se_expand[0][0]'] \n", " \n", - " block4j_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4j_se_excite[0][0]'] Y \n", + " block5g_project_conv (Conv2D) (None, 14, 14, 176) 185856 ['block5g_se_excite[0][0]'] Y \n", " \n", - " block4j_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4j_project_conv[0][0]'] Y \n", + " block5g_project_bn (BatchNorma (None, 14, 14, 176) 704 ['block5g_project_conv[0][0]'] Y \n", " lization) \n", " \n", - " block4j_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4j_project_bn[0][0]'] Y \n", + " block5g_drop (FixedDropout) (None, 14, 14, 176) 0 ['block5g_project_bn[0][0]'] Y \n", " \n", - " block4j_add (Add) (None, 14, 14, 160) 0 ['block4j_drop[0][0]', Y \n", - " 'block4i_add[0][0]'] \n", + " block5g_add (Add) (None, 14, 14, 176) 0 ['block5g_drop[0][0]', Y \n", + " 'block5f_add[0][0]'] \n", " \n", - " block5a_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4j_add[0][0]'] Y \n", + " block6a_expand_conv (Conv2D) (None, 14, 14, 1056 185856 ['block5g_add[0][0]'] Y \n", + " ) \n", " \n", - " block5a_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block5a_expand_conv[0][0]'] Y \n", - " ization) \n", + " block6a_expand_bn (BatchNormal (None, 14, 14, 1056 4224 ['block6a_expand_conv[0][0]'] Y \n", + " ization) ) \n", " \n", - " block5a_expand_activation (Act (None, 14, 14, 960) 0 ['block5a_expand_bn[0][0]'] Y \n", - " ivation) \n", + " block6a_expand_activation (Act (None, 14, 14, 1056 0 ['block6a_expand_bn[0][0]'] Y \n", + " ivation) ) \n", " \n", - " block5a_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 ['block5a_expand_activation[0][ Y \n", + " block6a_dwconv (DepthwiseConv2 (None, 7, 7, 1056) 26400 ['block6a_expand_activation[0][ Y \n", " D) 0]'] \n", " \n", - " block5a_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block5a_dwconv[0][0]'] Y \n", + " block6a_bn (BatchNormalization (None, 7, 7, 1056) 4224 ['block6a_dwconv[0][0]'] Y \n", " ) \n", " \n", - " block5a_activation (Activation (None, 14, 14, 960) 0 ['block5a_bn[0][0]'] Y \n", + " block6a_activation (Activation (None, 7, 7, 1056) 0 ['block6a_bn[0][0]'] Y \n", " ) \n", " \n", - " block5a_se_squeeze (GlobalAver (None, 960) 0 ['block5a_activation[0][0]'] Y \n", + " block6a_se_squeeze (GlobalAver (None, 1056) 0 ['block6a_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block5a_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block5a_se_squeeze[0][0]'] Y \n", + " block6a_se_reshape (Reshape) (None, 1, 1, 1056) 0 ['block6a_se_squeeze[0][0]'] Y \n", " \n", - " block5a_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block5a_se_reshape[0][0]'] Y \n", + " block6a_se_reduce (Conv2D) (None, 1, 1, 44) 46508 ['block6a_se_reshape[0][0]'] Y \n", " \n", - " block5a_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block5a_se_reduce[0][0]'] Y \n", + " block6a_se_expand (Conv2D) (None, 1, 1, 1056) 47520 ['block6a_se_reduce[0][0]'] Y \n", " \n", - " block5a_se_excite (Multiply) (None, 14, 14, 960) 0 ['block5a_activation[0][0]', Y \n", - " 'block5a_se_expand[0][0]'] \n", + " block6a_se_excite (Multiply) (None, 7, 7, 1056) 0 ['block6a_activation[0][0]', Y \n", + " 'block6a_se_expand[0][0]'] \n", " \n", - " block5a_project_conv (Conv2D) (None, 14, 14, 224) 215040 ['block5a_se_excite[0][0]'] Y \n", + " block6a_project_conv (Conv2D) (None, 7, 7, 304) 321024 ['block6a_se_excite[0][0]'] Y \n", " \n", - " block5a_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5a_project_conv[0][0]'] Y \n", + " block6a_project_bn (BatchNorma (None, 7, 7, 304) 1216 ['block6a_project_conv[0][0]'] Y \n", " lization) \n", " \n", - " block5b_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5a_project_bn[0][0]'] Y \n", - " ) \n", + " block6b_expand_conv (Conv2D) (None, 7, 7, 1824) 554496 ['block6a_project_bn[0][0]'] Y \n", " \n", - " block5b_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5b_expand_conv[0][0]'] Y \n", - " ization) ) \n", + " block6b_expand_bn (BatchNormal (None, 7, 7, 1824) 7296 ['block6b_expand_conv[0][0]'] Y \n", + " ization) \n", " \n", - " block5b_expand_activation (Act (None, 14, 14, 1344 0 ['block5b_expand_bn[0][0]'] Y \n", - " ivation) ) \n", + " block6b_expand_activation (Act (None, 7, 7, 1824) 0 ['block6b_expand_bn[0][0]'] Y \n", + " ivation) \n", " \n", - " block5b_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5b_expand_activation[0][ Y \n", - " D) ) 0]'] \n", + " block6b_dwconv (DepthwiseConv2 (None, 7, 7, 1824) 45600 ['block6b_expand_activation[0][ Y \n", + " D) 0]'] \n", " \n", - " block5b_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5b_dwconv[0][0]'] Y \n", - " ) ) \n", + " block6b_bn (BatchNormalization (None, 7, 7, 1824) 7296 ['block6b_dwconv[0][0]'] Y \n", + " ) \n", " \n", - " block5b_activation (Activation (None, 14, 14, 1344 0 ['block5b_bn[0][0]'] Y \n", - " ) ) \n", + " block6b_activation (Activation (None, 7, 7, 1824) 0 ['block6b_bn[0][0]'] Y \n", + " ) \n", " \n", - " block5b_se_squeeze (GlobalAver (None, 1344) 0 ['block5b_activation[0][0]'] Y \n", + " block6b_se_squeeze (GlobalAver (None, 1824) 0 ['block6b_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block5b_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5b_se_squeeze[0][0]'] Y \n", + " block6b_se_reshape (Reshape) (None, 1, 1, 1824) 0 ['block6b_se_squeeze[0][0]'] Y \n", " \n", - " block5b_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5b_se_reshape[0][0]'] Y \n", + " block6b_se_reduce (Conv2D) (None, 1, 1, 76) 138700 ['block6b_se_reshape[0][0]'] Y \n", " \n", - " block5b_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5b_se_reduce[0][0]'] Y \n", + " block6b_se_expand (Conv2D) (None, 1, 1, 1824) 140448 ['block6b_se_reduce[0][0]'] Y \n", " \n", - " block5b_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5b_activation[0][0]', Y \n", - " ) 'block5b_se_expand[0][0]'] \n", + " block6b_se_excite (Multiply) (None, 7, 7, 1824) 0 ['block6b_activation[0][0]', Y \n", + " 'block6b_se_expand[0][0]'] \n", " \n", - " block5b_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5b_se_excite[0][0]'] Y \n", + " block6b_project_conv (Conv2D) (None, 7, 7, 304) 554496 ['block6b_se_excite[0][0]'] Y \n", " \n", - " block5b_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5b_project_conv[0][0]'] Y \n", + " block6b_project_bn (BatchNorma (None, 7, 7, 304) 1216 ['block6b_project_conv[0][0]'] Y \n", " lization) \n", " \n", - " block5b_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5b_project_bn[0][0]'] Y \n", + " block6b_drop (FixedDropout) (None, 7, 7, 304) 0 ['block6b_project_bn[0][0]'] Y \n", " \n", - " block5b_add (Add) (None, 14, 14, 224) 0 ['block5b_drop[0][0]', Y \n", - " 'block5a_project_bn[0][0]'] \n", + " block6b_add (Add) (None, 7, 7, 304) 0 ['block6b_drop[0][0]', Y \n", + " 'block6a_project_bn[0][0]'] \n", " \n", - " block5c_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5b_add[0][0]'] Y \n", - " ) \n", + " block6c_expand_conv (Conv2D) (None, 7, 7, 1824) 554496 ['block6b_add[0][0]'] Y \n", " \n", - " block5c_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5c_expand_conv[0][0]'] Y \n", - " ization) ) \n", + " block6c_expand_bn (BatchNormal (None, 7, 7, 1824) 7296 ['block6c_expand_conv[0][0]'] Y \n", + " ization) \n", " \n", - " block5c_expand_activation (Act (None, 14, 14, 1344 0 ['block5c_expand_bn[0][0]'] Y \n", - " ivation) ) \n", + " block6c_expand_activation (Act (None, 7, 7, 1824) 0 ['block6c_expand_bn[0][0]'] Y \n", + " ivation) \n", " \n", - " block5c_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5c_expand_activation[0][ Y \n", - " D) ) 0]'] \n", + " block6c_dwconv (DepthwiseConv2 (None, 7, 7, 1824) 45600 ['block6c_expand_activation[0][ Y \n", + " D) 0]'] \n", " \n", - " block5c_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5c_dwconv[0][0]'] Y \n", - " ) ) \n", + " block6c_bn (BatchNormalization (None, 7, 7, 1824) 7296 ['block6c_dwconv[0][0]'] Y \n", + " ) \n", " \n", - " block5c_activation (Activation (None, 14, 14, 1344 0 ['block5c_bn[0][0]'] Y \n", - " ) ) \n", + " block6c_activation (Activation (None, 7, 7, 1824) 0 ['block6c_bn[0][0]'] Y \n", + " ) \n", " \n", - " block5c_se_squeeze (GlobalAver (None, 1344) 0 ['block5c_activation[0][0]'] Y \n", + " block6c_se_squeeze (GlobalAver (None, 1824) 0 ['block6c_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block5c_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5c_se_squeeze[0][0]'] Y \n", + " block6c_se_reshape (Reshape) (None, 1, 1, 1824) 0 ['block6c_se_squeeze[0][0]'] Y \n", " \n", - " block5c_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5c_se_reshape[0][0]'] Y \n", + " block6c_se_reduce (Conv2D) (None, 1, 1, 76) 138700 ['block6c_se_reshape[0][0]'] Y \n", " \n", - " block5c_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5c_se_reduce[0][0]'] Y \n", + " block6c_se_expand (Conv2D) (None, 1, 1, 1824) 140448 ['block6c_se_reduce[0][0]'] Y \n", " \n", - " block5c_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5c_activation[0][0]', Y \n", - " ) 'block5c_se_expand[0][0]'] \n", + " block6c_se_excite (Multiply) (None, 7, 7, 1824) 0 ['block6c_activation[0][0]', Y \n", + " 'block6c_se_expand[0][0]'] \n", " \n", - " block5c_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5c_se_excite[0][0]'] Y \n", + " block6c_project_conv (Conv2D) (None, 7, 7, 304) 554496 ['block6c_se_excite[0][0]'] Y \n", " \n", - " block5c_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5c_project_conv[0][0]'] Y \n", + " block6c_project_bn (BatchNorma (None, 7, 7, 304) 1216 ['block6c_project_conv[0][0]'] Y \n", " lization) \n", " \n", - " block5c_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5c_project_bn[0][0]'] Y \n", + " block6c_drop (FixedDropout) (None, 7, 7, 304) 0 ['block6c_project_bn[0][0]'] Y \n", " \n", - " block5c_add (Add) (None, 14, 14, 224) 0 ['block5c_drop[0][0]', Y \n", - " 'block5b_add[0][0]'] \n", + " block6c_add (Add) (None, 7, 7, 304) 0 ['block6c_drop[0][0]', Y \n", + " 'block6b_add[0][0]'] \n", " \n", - " block5d_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5c_add[0][0]'] Y \n", - " ) \n", + " block6d_expand_conv (Conv2D) (None, 7, 7, 1824) 554496 ['block6c_add[0][0]'] Y \n", " \n", - " block5d_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5d_expand_conv[0][0]'] Y \n", - " ization) ) \n", + " block6d_expand_bn (BatchNormal (None, 7, 7, 1824) 7296 ['block6d_expand_conv[0][0]'] Y \n", + " ization) \n", " \n", - " block5d_expand_activation (Act (None, 14, 14, 1344 0 ['block5d_expand_bn[0][0]'] Y \n", - " ivation) ) \n", + " block6d_expand_activation (Act (None, 7, 7, 1824) 0 ['block6d_expand_bn[0][0]'] Y \n", + " ivation) \n", " \n", - " block5d_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5d_expand_activation[0][ Y \n", - " D) ) 0]'] \n", + " block6d_dwconv (DepthwiseConv2 (None, 7, 7, 1824) 45600 ['block6d_expand_activation[0][ Y \n", + " D) 0]'] \n", " \n", - " block5d_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5d_dwconv[0][0]'] Y \n", - " ) ) \n", + " block6d_bn (BatchNormalization (None, 7, 7, 1824) 7296 ['block6d_dwconv[0][0]'] Y \n", + " ) \n", " \n", - " block5d_activation (Activation (None, 14, 14, 1344 0 ['block5d_bn[0][0]'] Y \n", - " ) ) \n", + " block6d_activation (Activation (None, 7, 7, 1824) 0 ['block6d_bn[0][0]'] Y \n", + " ) \n", " \n", - " block5d_se_squeeze (GlobalAver (None, 1344) 0 ['block5d_activation[0][0]'] Y \n", + " block6d_se_squeeze (GlobalAver (None, 1824) 0 ['block6d_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block5d_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5d_se_squeeze[0][0]'] Y \n", + " block6d_se_reshape (Reshape) (None, 1, 1, 1824) 0 ['block6d_se_squeeze[0][0]'] Y \n", " \n", - " block5d_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5d_se_reshape[0][0]'] Y \n", + " block6d_se_reduce (Conv2D) (None, 1, 1, 76) 138700 ['block6d_se_reshape[0][0]'] Y \n", " \n", - " block5d_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5d_se_reduce[0][0]'] Y \n", + " block6d_se_expand (Conv2D) (None, 1, 1, 1824) 140448 ['block6d_se_reduce[0][0]'] Y \n", " \n", - " block5d_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5d_activation[0][0]', Y \n", - " ) 'block5d_se_expand[0][0]'] \n", + " block6d_se_excite (Multiply) (None, 7, 7, 1824) 0 ['block6d_activation[0][0]', Y \n", + " 'block6d_se_expand[0][0]'] \n", " \n", - " block5d_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5d_se_excite[0][0]'] Y \n", + " block6d_project_conv (Conv2D) (None, 7, 7, 304) 554496 ['block6d_se_excite[0][0]'] Y \n", " \n", - " block5d_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5d_project_conv[0][0]'] Y \n", + " block6d_project_bn (BatchNorma (None, 7, 7, 304) 1216 ['block6d_project_conv[0][0]'] Y \n", " lization) \n", " \n", - " block5d_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5d_project_bn[0][0]'] Y \n", + " block6d_drop (FixedDropout) (None, 7, 7, 304) 0 ['block6d_project_bn[0][0]'] Y \n", " \n", - " block5d_add (Add) (None, 14, 14, 224) 0 ['block5d_drop[0][0]', Y \n", - " 'block5c_add[0][0]'] \n", + " block6d_add (Add) (None, 7, 7, 304) 0 ['block6d_drop[0][0]', Y \n", + " 'block6c_add[0][0]'] \n", " \n", - " block5e_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5d_add[0][0]'] Y \n", - " ) \n", + " block6e_expand_conv (Conv2D) (None, 7, 7, 1824) 554496 ['block6d_add[0][0]'] Y \n", " \n", - " block5e_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5e_expand_conv[0][0]'] Y \n", - " ization) ) \n", + " block6e_expand_bn (BatchNormal (None, 7, 7, 1824) 7296 ['block6e_expand_conv[0][0]'] Y \n", + " ization) \n", " \n", - " block5e_expand_activation (Act (None, 14, 14, 1344 0 ['block5e_expand_bn[0][0]'] Y \n", - " ivation) ) \n", + " block6e_expand_activation (Act (None, 7, 7, 1824) 0 ['block6e_expand_bn[0][0]'] Y \n", + " ivation) \n", " \n", - " block5e_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5e_expand_activation[0][ Y \n", - " D) ) 0]'] \n", + " block6e_dwconv (DepthwiseConv2 (None, 7, 7, 1824) 45600 ['block6e_expand_activation[0][ Y \n", + " D) 0]'] \n", " \n", - " block5e_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5e_dwconv[0][0]'] Y \n", - " ) ) \n", + " block6e_bn (BatchNormalization (None, 7, 7, 1824) 7296 ['block6e_dwconv[0][0]'] Y \n", + " ) \n", " \n", - " block5e_activation (Activation (None, 14, 14, 1344 0 ['block5e_bn[0][0]'] Y \n", - " ) ) \n", + " block6e_activation (Activation (None, 7, 7, 1824) 0 ['block6e_bn[0][0]'] Y \n", + " ) \n", " \n", - " block5e_se_squeeze (GlobalAver (None, 1344) 0 ['block5e_activation[0][0]'] Y \n", + " block6e_se_squeeze (GlobalAver (None, 1824) 0 ['block6e_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block5e_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5e_se_squeeze[0][0]'] Y \n", + " block6e_se_reshape (Reshape) (None, 1, 1, 1824) 0 ['block6e_se_squeeze[0][0]'] Y \n", " \n", - " block5e_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5e_se_reshape[0][0]'] Y \n", + " block6e_se_reduce (Conv2D) (None, 1, 1, 76) 138700 ['block6e_se_reshape[0][0]'] Y \n", " \n", - " block5e_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5e_se_reduce[0][0]'] Y \n", + " block6e_se_expand (Conv2D) (None, 1, 1, 1824) 140448 ['block6e_se_reduce[0][0]'] Y \n", " \n", - " block5e_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5e_activation[0][0]', Y \n", - " ) 'block5e_se_expand[0][0]'] \n", + " block6e_se_excite (Multiply) (None, 7, 7, 1824) 0 ['block6e_activation[0][0]', Y \n", + " 'block6e_se_expand[0][0]'] \n", " \n", - " block5e_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5e_se_excite[0][0]'] Y \n", + " block6e_project_conv (Conv2D) (None, 7, 7, 304) 554496 ['block6e_se_excite[0][0]'] Y \n", " \n", - " block5e_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5e_project_conv[0][0]'] Y \n", + " block6e_project_bn (BatchNorma (None, 7, 7, 304) 1216 ['block6e_project_conv[0][0]'] Y \n", " lization) \n", " \n", - " block5e_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5e_project_bn[0][0]'] Y \n", + " block6e_drop (FixedDropout) (None, 7, 7, 304) 0 ['block6e_project_bn[0][0]'] Y \n", " \n", - " block5e_add (Add) (None, 14, 14, 224) 0 ['block5e_drop[0][0]', Y \n", - " 'block5d_add[0][0]'] \n", + " block6e_add (Add) (None, 7, 7, 304) 0 ['block6e_drop[0][0]', Y \n", + " 'block6d_add[0][0]'] \n", " \n", - " block5f_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5e_add[0][0]'] Y \n", - " ) \n", + " block6f_expand_conv (Conv2D) (None, 7, 7, 1824) 554496 ['block6e_add[0][0]'] Y \n", " \n", - " block5f_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5f_expand_conv[0][0]'] Y \n", - " ization) ) \n", + " block6f_expand_bn (BatchNormal (None, 7, 7, 1824) 7296 ['block6f_expand_conv[0][0]'] Y \n", + " ization) \n", " \n", - " block5f_expand_activation (Act (None, 14, 14, 1344 0 ['block5f_expand_bn[0][0]'] Y \n", - " ivation) ) \n", + " block6f_expand_activation (Act (None, 7, 7, 1824) 0 ['block6f_expand_bn[0][0]'] Y \n", + " ivation) \n", " \n", - " block5f_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5f_expand_activation[0][ Y \n", - " D) ) 0]'] \n", + " block6f_dwconv (DepthwiseConv2 (None, 7, 7, 1824) 45600 ['block6f_expand_activation[0][ Y \n", + " D) 0]'] \n", " \n", - " block5f_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5f_dwconv[0][0]'] Y \n", - " ) ) \n", + " block6f_bn (BatchNormalization (None, 7, 7, 1824) 7296 ['block6f_dwconv[0][0]'] Y \n", + " ) \n", " \n", - " block5f_activation (Activation (None, 14, 14, 1344 0 ['block5f_bn[0][0]'] Y \n", - " ) ) \n", + " block6f_activation (Activation (None, 7, 7, 1824) 0 ['block6f_bn[0][0]'] Y \n", + " ) \n", " \n", - " block5f_se_squeeze (GlobalAver (None, 1344) 0 ['block5f_activation[0][0]'] Y \n", + " block6f_se_squeeze (GlobalAver (None, 1824) 0 ['block6f_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block5f_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5f_se_squeeze[0][0]'] Y \n", + " block6f_se_reshape (Reshape) (None, 1, 1, 1824) 0 ['block6f_se_squeeze[0][0]'] Y \n", " \n", - " block5f_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5f_se_reshape[0][0]'] Y \n", + " block6f_se_reduce (Conv2D) (None, 1, 1, 76) 138700 ['block6f_se_reshape[0][0]'] Y \n", " \n", - " block5f_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5f_se_reduce[0][0]'] Y \n", + " block6f_se_expand (Conv2D) (None, 1, 1, 1824) 140448 ['block6f_se_reduce[0][0]'] Y \n", " \n", - " block5f_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5f_activation[0][0]', Y \n", - " ) 'block5f_se_expand[0][0]'] \n", + " block6f_se_excite (Multiply) (None, 7, 7, 1824) 0 ['block6f_activation[0][0]', Y \n", + " 'block6f_se_expand[0][0]'] \n", " \n", - " block5f_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5f_se_excite[0][0]'] Y \n", + " block6f_project_conv (Conv2D) (None, 7, 7, 304) 554496 ['block6f_se_excite[0][0]'] Y \n", " \n", - " block5f_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5f_project_conv[0][0]'] Y \n", + " block6f_project_bn (BatchNorma (None, 7, 7, 304) 1216 ['block6f_project_conv[0][0]'] Y \n", " lization) \n", " \n", - " block5f_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5f_project_bn[0][0]'] Y \n", + " block6f_drop (FixedDropout) (None, 7, 7, 304) 0 ['block6f_project_bn[0][0]'] Y \n", " \n", - " block5f_add (Add) (None, 14, 14, 224) 0 ['block5f_drop[0][0]', Y \n", - " 'block5e_add[0][0]'] \n", + " block6f_add (Add) (None, 7, 7, 304) 0 ['block6f_drop[0][0]', Y \n", + " 'block6e_add[0][0]'] \n", " \n", - " block5g_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5f_add[0][0]'] Y \n", - " ) \n", + " block6g_expand_conv (Conv2D) (None, 7, 7, 1824) 554496 ['block6f_add[0][0]'] Y \n", " \n", - " block5g_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5g_expand_conv[0][0]'] Y \n", - " ization) ) \n", + " block6g_expand_bn (BatchNormal (None, 7, 7, 1824) 7296 ['block6g_expand_conv[0][0]'] Y \n", + " ization) \n", " \n", - " block5g_expand_activation (Act (None, 14, 14, 1344 0 ['block5g_expand_bn[0][0]'] Y \n", - " ivation) ) \n", + " block6g_expand_activation (Act (None, 7, 7, 1824) 0 ['block6g_expand_bn[0][0]'] Y \n", + " ivation) \n", " \n", - " block5g_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5g_expand_activation[0][ Y \n", - " D) ) 0]'] \n", + " block6g_dwconv (DepthwiseConv2 (None, 7, 7, 1824) 45600 ['block6g_expand_activation[0][ Y \n", + " D) 0]'] \n", " \n", - " block5g_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5g_dwconv[0][0]'] Y \n", - " ) ) \n", + " block6g_bn (BatchNormalization (None, 7, 7, 1824) 7296 ['block6g_dwconv[0][0]'] Y \n", + " ) \n", " \n", - " block5g_activation (Activation (None, 14, 14, 1344 0 ['block5g_bn[0][0]'] Y \n", - " ) ) \n", + " block6g_activation (Activation (None, 7, 7, 1824) 0 ['block6g_bn[0][0]'] Y \n", + " ) \n", " \n", - " block5g_se_squeeze (GlobalAver (None, 1344) 0 ['block5g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5g_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5g_se_squeeze[0][0]'] Y \n", - " \n", - " block5g_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5g_se_reshape[0][0]'] Y \n", - " \n", - " block5g_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5g_se_reduce[0][0]'] Y \n", - " \n", - " block5g_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5g_activation[0][0]', Y \n", - " ) 'block5g_se_expand[0][0]'] \n", - " \n", - " block5g_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5g_se_excite[0][0]'] Y \n", - " \n", - " block5g_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5g_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5g_project_bn[0][0]'] Y \n", - " \n", - " block5g_add (Add) (None, 14, 14, 224) 0 ['block5g_drop[0][0]', Y \n", - " 'block5f_add[0][0]'] \n", - " \n", - " block5h_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5g_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5h_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5h_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5h_expand_activation (Act (None, 14, 14, 1344 0 ['block5h_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5h_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5h_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5h_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5h_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5h_activation (Activation (None, 14, 14, 1344 0 ['block5h_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5h_se_squeeze (GlobalAver (None, 1344) 0 ['block5h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5h_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5h_se_squeeze[0][0]'] Y \n", - " \n", - " block5h_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5h_se_reshape[0][0]'] Y \n", - " \n", - " block5h_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5h_se_reduce[0][0]'] Y \n", - " \n", - " block5h_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5h_activation[0][0]', Y \n", - " ) 'block5h_se_expand[0][0]'] \n", - " \n", - " block5h_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5h_se_excite[0][0]'] Y \n", - " \n", - " block5h_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5h_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5h_project_bn[0][0]'] Y \n", - " \n", - " block5h_add (Add) (None, 14, 14, 224) 0 ['block5h_drop[0][0]', Y \n", - " 'block5g_add[0][0]'] \n", - " \n", - " block5i_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5h_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5i_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5i_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5i_expand_activation (Act (None, 14, 14, 1344 0 ['block5i_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5i_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5i_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5i_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5i_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5i_activation (Activation (None, 14, 14, 1344 0 ['block5i_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5i_se_squeeze (GlobalAver (None, 1344) 0 ['block5i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5i_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5i_se_squeeze[0][0]'] Y \n", - " \n", - " block5i_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5i_se_reshape[0][0]'] Y \n", - " \n", - " block5i_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5i_se_reduce[0][0]'] Y \n", - " \n", - " block5i_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5i_activation[0][0]', Y \n", - " ) 'block5i_se_expand[0][0]'] \n", - " \n", - " block5i_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5i_se_excite[0][0]'] Y \n", - " \n", - " block5i_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5i_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5i_project_bn[0][0]'] Y \n", - " \n", - " block5i_add (Add) (None, 14, 14, 224) 0 ['block5i_drop[0][0]', Y \n", - " 'block5h_add[0][0]'] \n", - " \n", - " block5j_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5i_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5j_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5j_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5j_expand_activation (Act (None, 14, 14, 1344 0 ['block5j_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5j_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5j_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5j_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5j_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5j_activation (Activation (None, 14, 14, 1344 0 ['block5j_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5j_se_squeeze (GlobalAver (None, 1344) 0 ['block5j_activation[0][0]'] Y \n", + " block6g_se_squeeze (GlobalAver (None, 1824) 0 ['block6g_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block5j_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5j_se_squeeze[0][0]'] Y \n", + " block6g_se_reshape (Reshape) (None, 1, 1, 1824) 0 ['block6g_se_squeeze[0][0]'] Y \n", " \n", - " block5j_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5j_se_reshape[0][0]'] Y \n", + " block6g_se_reduce (Conv2D) (None, 1, 1, 76) 138700 ['block6g_se_reshape[0][0]'] Y \n", " \n", - " block5j_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5j_se_reduce[0][0]'] Y \n", + " block6g_se_expand (Conv2D) (None, 1, 1, 1824) 140448 ['block6g_se_reduce[0][0]'] Y \n", " \n", - " block5j_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5j_activation[0][0]', Y \n", - " ) 'block5j_se_expand[0][0]'] \n", + " block6g_se_excite (Multiply) (None, 7, 7, 1824) 0 ['block6g_activation[0][0]', Y \n", + " 'block6g_se_expand[0][0]'] \n", " \n", - " block5j_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5j_se_excite[0][0]'] Y \n", + " block6g_project_conv (Conv2D) (None, 7, 7, 304) 554496 ['block6g_se_excite[0][0]'] Y \n", " \n", - " block5j_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5j_project_conv[0][0]'] Y \n", + " block6g_project_bn (BatchNorma (None, 7, 7, 304) 1216 ['block6g_project_conv[0][0]'] Y \n", " lization) \n", " \n", - " block5j_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5j_project_bn[0][0]'] Y \n", + " block6g_drop (FixedDropout) (None, 7, 7, 304) 0 ['block6g_project_bn[0][0]'] Y \n", " \n", - " block5j_add (Add) (None, 14, 14, 224) 0 ['block5j_drop[0][0]', Y \n", - " 'block5i_add[0][0]'] \n", + " block6g_add (Add) (None, 7, 7, 304) 0 ['block6g_drop[0][0]', Y \n", + " 'block6f_add[0][0]'] \n", " \n", - " block6a_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5j_add[0][0]'] Y \n", - " ) \n", + " block6h_expand_conv (Conv2D) (None, 7, 7, 1824) 554496 ['block6g_add[0][0]'] Y \n", " \n", - " block6a_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block6a_expand_conv[0][0]'] Y \n", - " ization) ) \n", + " block6h_expand_bn (BatchNormal (None, 7, 7, 1824) 7296 ['block6h_expand_conv[0][0]'] Y \n", + " ization) \n", " \n", - " block6a_expand_activation (Act (None, 14, 14, 1344 0 ['block6a_expand_bn[0][0]'] Y \n", - " ivation) ) \n", + " block6h_expand_activation (Act (None, 7, 7, 1824) 0 ['block6h_expand_bn[0][0]'] Y \n", + " ivation) \n", " \n", - " block6a_dwconv (DepthwiseConv2 (None, 7, 7, 1344) 33600 ['block6a_expand_activation[0][ Y \n", + " block6h_dwconv (DepthwiseConv2 (None, 7, 7, 1824) 45600 ['block6h_expand_activation[0][ Y \n", " D) 0]'] \n", " \n", - " block6a_bn (BatchNormalization (None, 7, 7, 1344) 5376 ['block6a_dwconv[0][0]'] Y \n", + " block6h_bn (BatchNormalization (None, 7, 7, 1824) 7296 ['block6h_dwconv[0][0]'] Y \n", " ) \n", " \n", - " block6a_activation (Activation (None, 7, 7, 1344) 0 ['block6a_bn[0][0]'] Y \n", + " block6h_activation (Activation (None, 7, 7, 1824) 0 ['block6h_bn[0][0]'] Y \n", " ) \n", " \n", - " block6a_se_squeeze (GlobalAver (None, 1344) 0 ['block6a_activation[0][0]'] Y \n", + " block6h_se_squeeze (GlobalAver (None, 1824) 0 ['block6h_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block6a_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block6a_se_squeeze[0][0]'] Y \n", + " block6h_se_reshape (Reshape) (None, 1, 1, 1824) 0 ['block6h_se_squeeze[0][0]'] Y \n", " \n", - " block6a_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block6a_se_reshape[0][0]'] Y \n", + " block6h_se_reduce (Conv2D) (None, 1, 1, 76) 138700 ['block6h_se_reshape[0][0]'] Y \n", " \n", - " block6a_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block6a_se_reduce[0][0]'] Y \n", + " block6h_se_expand (Conv2D) (None, 1, 1, 1824) 140448 ['block6h_se_reduce[0][0]'] Y \n", " \n", - " block6a_se_excite (Multiply) (None, 7, 7, 1344) 0 ['block6a_activation[0][0]', Y \n", - " 'block6a_se_expand[0][0]'] \n", + " block6h_se_excite (Multiply) (None, 7, 7, 1824) 0 ['block6h_activation[0][0]', Y \n", + " 'block6h_se_expand[0][0]'] \n", " \n", - " block6a_project_conv (Conv2D) (None, 7, 7, 384) 516096 ['block6a_se_excite[0][0]'] Y \n", + " block6h_project_conv (Conv2D) (None, 7, 7, 304) 554496 ['block6h_se_excite[0][0]'] Y \n", " \n", - " block6a_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6a_project_conv[0][0]'] Y \n", + " block6h_project_bn (BatchNorma (None, 7, 7, 304) 1216 ['block6h_project_conv[0][0]'] Y \n", " lization) \n", " \n", - " block6b_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6a_project_bn[0][0]'] Y \n", + " block6h_drop (FixedDropout) (None, 7, 7, 304) 0 ['block6h_project_bn[0][0]'] Y \n", " \n", - " block6b_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6b_expand_conv[0][0]'] Y \n", + " block6h_add (Add) (None, 7, 7, 304) 0 ['block6h_drop[0][0]', Y \n", + " 'block6g_add[0][0]'] \n", + " \n", + " block6i_expand_conv (Conv2D) (None, 7, 7, 1824) 554496 ['block6h_add[0][0]'] Y \n", + " \n", + " block6i_expand_bn (BatchNormal (None, 7, 7, 1824) 7296 ['block6i_expand_conv[0][0]'] Y \n", " ization) \n", " \n", - " block6b_expand_activation (Act (None, 7, 7, 2304) 0 ['block6b_expand_bn[0][0]'] Y \n", + " block6i_expand_activation (Act (None, 7, 7, 1824) 0 ['block6i_expand_bn[0][0]'] Y \n", " ivation) \n", " \n", - " block6b_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6b_expand_activation[0][ Y \n", + " block6i_dwconv (DepthwiseConv2 (None, 7, 7, 1824) 45600 ['block6i_expand_activation[0][ Y \n", " D) 0]'] \n", " \n", - " block6b_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6b_dwconv[0][0]'] Y \n", + " block6i_bn (BatchNormalization (None, 7, 7, 1824) 7296 ['block6i_dwconv[0][0]'] Y \n", " ) \n", " \n", - " block6b_activation (Activation (None, 7, 7, 2304) 0 ['block6b_bn[0][0]'] Y \n", + " block6i_activation (Activation (None, 7, 7, 1824) 0 ['block6i_bn[0][0]'] Y \n", " ) \n", " \n", - " block6b_se_squeeze (GlobalAver (None, 2304) 0 ['block6b_activation[0][0]'] Y \n", + " block6i_se_squeeze (GlobalAver (None, 1824) 0 ['block6i_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block6b_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6b_se_squeeze[0][0]'] Y \n", + " block6i_se_reshape (Reshape) (None, 1, 1, 1824) 0 ['block6i_se_squeeze[0][0]'] Y \n", " \n", - " block6b_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6b_se_reshape[0][0]'] Y \n", + " block6i_se_reduce (Conv2D) (None, 1, 1, 76) 138700 ['block6i_se_reshape[0][0]'] Y \n", " \n", - " block6b_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6b_se_reduce[0][0]'] Y \n", + " block6i_se_expand (Conv2D) (None, 1, 1, 1824) 140448 ['block6i_se_reduce[0][0]'] Y \n", " \n", - " block6b_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6b_activation[0][0]', Y \n", - " 'block6b_se_expand[0][0]'] \n", + " block6i_se_excite (Multiply) (None, 7, 7, 1824) 0 ['block6i_activation[0][0]', Y \n", + " 'block6i_se_expand[0][0]'] \n", " \n", - " block6b_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6b_se_excite[0][0]'] Y \n", + " block6i_project_conv (Conv2D) (None, 7, 7, 304) 554496 ['block6i_se_excite[0][0]'] Y \n", " \n", - " block6b_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6b_project_conv[0][0]'] Y \n", + " block6i_project_bn (BatchNorma (None, 7, 7, 304) 1216 ['block6i_project_conv[0][0]'] Y \n", " lization) \n", " \n", - " block6b_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6b_project_bn[0][0]'] Y \n", + " block6i_drop (FixedDropout) (None, 7, 7, 304) 0 ['block6i_project_bn[0][0]'] Y \n", " \n", - " block6b_add (Add) (None, 7, 7, 384) 0 ['block6b_drop[0][0]', Y \n", - " 'block6a_project_bn[0][0]'] \n", + " block6i_add (Add) (None, 7, 7, 304) 0 ['block6i_drop[0][0]', Y \n", + " 'block6h_add[0][0]'] \n", " \n", - " block6c_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6b_add[0][0]'] Y \n", + " block7a_expand_conv (Conv2D) (None, 7, 7, 1824) 554496 ['block6i_add[0][0]'] Y \n", " \n", - " block6c_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6c_expand_conv[0][0]'] Y \n", + " block7a_expand_bn (BatchNormal (None, 7, 7, 1824) 7296 ['block7a_expand_conv[0][0]'] Y \n", " ization) \n", " \n", - " block6c_expand_activation (Act (None, 7, 7, 2304) 0 ['block6c_expand_bn[0][0]'] Y \n", + " block7a_expand_activation (Act (None, 7, 7, 1824) 0 ['block7a_expand_bn[0][0]'] Y \n", " ivation) \n", " \n", - " block6c_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6c_expand_activation[0][ Y \n", + " block7a_dwconv (DepthwiseConv2 (None, 7, 7, 1824) 16416 ['block7a_expand_activation[0][ Y \n", " D) 0]'] \n", " \n", - " block6c_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6c_dwconv[0][0]'] Y \n", + " block7a_bn (BatchNormalization (None, 7, 7, 1824) 7296 ['block7a_dwconv[0][0]'] Y \n", " ) \n", " \n", - " block6c_activation (Activation (None, 7, 7, 2304) 0 ['block6c_bn[0][0]'] Y \n", + " block7a_activation (Activation (None, 7, 7, 1824) 0 ['block7a_bn[0][0]'] Y \n", " ) \n", " \n", - " block6c_se_squeeze (GlobalAver (None, 2304) 0 ['block6c_activation[0][0]'] Y \n", + " block7a_se_squeeze (GlobalAver (None, 1824) 0 ['block7a_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block6c_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6c_se_squeeze[0][0]'] Y \n", + " block7a_se_reshape (Reshape) (None, 1, 1, 1824) 0 ['block7a_se_squeeze[0][0]'] Y \n", " \n", - " block6c_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6c_se_reshape[0][0]'] Y \n", + " block7a_se_reduce (Conv2D) (None, 1, 1, 76) 138700 ['block7a_se_reshape[0][0]'] Y \n", " \n", - " block6c_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6c_se_reduce[0][0]'] Y \n", + " block7a_se_expand (Conv2D) (None, 1, 1, 1824) 140448 ['block7a_se_reduce[0][0]'] Y \n", " \n", - " block6c_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6c_activation[0][0]', Y \n", - " 'block6c_se_expand[0][0]'] \n", + " block7a_se_excite (Multiply) (None, 7, 7, 1824) 0 ['block7a_activation[0][0]', Y \n", + " 'block7a_se_expand[0][0]'] \n", " \n", - " block6c_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6c_se_excite[0][0]'] Y \n", + " block7a_project_conv (Conv2D) (None, 7, 7, 512) 933888 ['block7a_se_excite[0][0]'] Y \n", " \n", - " block6c_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6c_project_conv[0][0]'] Y \n", + " block7a_project_bn (BatchNorma (None, 7, 7, 512) 2048 ['block7a_project_conv[0][0]'] Y \n", " lization) \n", " \n", - " block6c_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6c_project_bn[0][0]'] Y \n", - " \n", - " block6c_add (Add) (None, 7, 7, 384) 0 ['block6c_drop[0][0]', Y \n", - " 'block6b_add[0][0]'] \n", - " \n", - " block6d_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6c_add[0][0]'] Y \n", + " block7b_expand_conv (Conv2D) (None, 7, 7, 3072) 1572864 ['block7a_project_bn[0][0]'] Y \n", " \n", - " block6d_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6d_expand_conv[0][0]'] Y \n", + " block7b_expand_bn (BatchNormal (None, 7, 7, 3072) 12288 ['block7b_expand_conv[0][0]'] Y \n", " ization) \n", " \n", - " block6d_expand_activation (Act (None, 7, 7, 2304) 0 ['block6d_expand_bn[0][0]'] Y \n", + " block7b_expand_activation (Act (None, 7, 7, 3072) 0 ['block7b_expand_bn[0][0]'] Y \n", " ivation) \n", " \n", - " block6d_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6d_expand_activation[0][ Y \n", + " block7b_dwconv (DepthwiseConv2 (None, 7, 7, 3072) 27648 ['block7b_expand_activation[0][ Y \n", " D) 0]'] \n", " \n", - " block6d_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6d_dwconv[0][0]'] Y \n", + " block7b_bn (BatchNormalization (None, 7, 7, 3072) 12288 ['block7b_dwconv[0][0]'] Y \n", " ) \n", " \n", - " block6d_activation (Activation (None, 7, 7, 2304) 0 ['block6d_bn[0][0]'] Y \n", + " block7b_activation (Activation (None, 7, 7, 3072) 0 ['block7b_bn[0][0]'] Y \n", " ) \n", " \n", - " block6d_se_squeeze (GlobalAver (None, 2304) 0 ['block6d_activation[0][0]'] Y \n", + " block7b_se_squeeze (GlobalAver (None, 3072) 0 ['block7b_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block6d_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6d_se_squeeze[0][0]'] Y \n", + " block7b_se_reshape (Reshape) (None, 1, 1, 3072) 0 ['block7b_se_squeeze[0][0]'] Y \n", " \n", - " block6d_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6d_se_reshape[0][0]'] Y \n", + " block7b_se_reduce (Conv2D) (None, 1, 1, 128) 393344 ['block7b_se_reshape[0][0]'] Y \n", " \n", - " block6d_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6d_se_reduce[0][0]'] Y \n", + " block7b_se_expand (Conv2D) (None, 1, 1, 3072) 396288 ['block7b_se_reduce[0][0]'] Y \n", " \n", - " block6d_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6d_activation[0][0]', Y \n", - " 'block6d_se_expand[0][0]'] \n", + " block7b_se_excite (Multiply) (None, 7, 7, 3072) 0 ['block7b_activation[0][0]', Y \n", + " 'block7b_se_expand[0][0]'] \n", " \n", - " block6d_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6d_se_excite[0][0]'] Y \n", + " block7b_project_conv (Conv2D) (None, 7, 7, 512) 1572864 ['block7b_se_excite[0][0]'] Y \n", " \n", - " block6d_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6d_project_conv[0][0]'] Y \n", + " block7b_project_bn (BatchNorma (None, 7, 7, 512) 2048 ['block7b_project_conv[0][0]'] Y \n", " lization) \n", " \n", - " block6d_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6d_project_bn[0][0]'] Y \n", + " block7b_drop (FixedDropout) (None, 7, 7, 512) 0 ['block7b_project_bn[0][0]'] Y \n", " \n", - " block6d_add (Add) (None, 7, 7, 384) 0 ['block6d_drop[0][0]', Y \n", - " 'block6c_add[0][0]'] \n", + " block7b_add (Add) (None, 7, 7, 512) 0 ['block7b_drop[0][0]', Y \n", + " 'block7a_project_bn[0][0]'] \n", " \n", - " block6e_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6d_add[0][0]'] Y \n", + " block7c_expand_conv (Conv2D) (None, 7, 7, 3072) 1572864 ['block7b_add[0][0]'] Y \n", " \n", - " block6e_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6e_expand_conv[0][0]'] Y \n", + " block7c_expand_bn (BatchNormal (None, 7, 7, 3072) 12288 ['block7c_expand_conv[0][0]'] Y \n", " ization) \n", " \n", - " block6e_expand_activation (Act (None, 7, 7, 2304) 0 ['block6e_expand_bn[0][0]'] Y \n", + " block7c_expand_activation (Act (None, 7, 7, 3072) 0 ['block7c_expand_bn[0][0]'] Y \n", " ivation) \n", " \n", - " block6e_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6e_expand_activation[0][ Y \n", + " block7c_dwconv (DepthwiseConv2 (None, 7, 7, 3072) 27648 ['block7c_expand_activation[0][ Y \n", " D) 0]'] \n", " \n", - " block6e_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6e_dwconv[0][0]'] Y \n", + " block7c_bn (BatchNormalization (None, 7, 7, 3072) 12288 ['block7c_dwconv[0][0]'] Y \n", " ) \n", " \n", - " block6e_activation (Activation (None, 7, 7, 2304) 0 ['block6e_bn[0][0]'] Y \n", + " block7c_activation (Activation (None, 7, 7, 3072) 0 ['block7c_bn[0][0]'] Y \n", " ) \n", " \n", - " block6e_se_squeeze (GlobalAver (None, 2304) 0 ['block6e_activation[0][0]'] Y \n", + " block7c_se_squeeze (GlobalAver (None, 3072) 0 ['block7c_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block6e_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6e_se_squeeze[0][0]'] Y \n", + " block7c_se_reshape (Reshape) (None, 1, 1, 3072) 0 ['block7c_se_squeeze[0][0]'] Y \n", " \n", - " block6e_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6e_se_reshape[0][0]'] Y \n", + " block7c_se_reduce (Conv2D) (None, 1, 1, 128) 393344 ['block7c_se_reshape[0][0]'] Y \n", " \n", - " block6e_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6e_se_reduce[0][0]'] Y \n", + " block7c_se_expand (Conv2D) (None, 1, 1, 3072) 396288 ['block7c_se_reduce[0][0]'] Y \n", " \n", - " block6e_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6e_activation[0][0]', Y \n", - " 'block6e_se_expand[0][0]'] \n", + " block7c_se_excite (Multiply) (None, 7, 7, 3072) 0 ['block7c_activation[0][0]', Y \n", + " 'block7c_se_expand[0][0]'] \n", " \n", - " block6e_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6e_se_excite[0][0]'] Y \n", + " block7c_project_conv (Conv2D) (None, 7, 7, 512) 1572864 ['block7c_se_excite[0][0]'] Y \n", " \n", - " block6e_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6e_project_conv[0][0]'] Y \n", + " block7c_project_bn (BatchNorma (None, 7, 7, 512) 2048 ['block7c_project_conv[0][0]'] Y \n", " lization) \n", " \n", - " block6e_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6e_project_bn[0][0]'] Y \n", + " block7c_drop (FixedDropout) (None, 7, 7, 512) 0 ['block7c_project_bn[0][0]'] Y \n", " \n", - " block6e_add (Add) (None, 7, 7, 384) 0 ['block6e_drop[0][0]', Y \n", - " 'block6d_add[0][0]'] \n", + " block7c_add (Add) (None, 7, 7, 512) 0 ['block7c_drop[0][0]', Y \n", + " 'block7b_add[0][0]'] \n", " \n", - " block6f_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6e_add[0][0]'] Y \n", + " top_conv (Conv2D) (None, 7, 7, 2048) 1048576 ['block7c_add[0][0]'] Y \n", " \n", - " block6f_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6f_expand_conv[0][0]'] Y \n", - " ization) \n", + " top_bn (BatchNormalization) (None, 7, 7, 2048) 8192 ['top_conv[0][0]'] Y \n", " \n", - " block6f_expand_activation (Act (None, 7, 7, 2304) 0 ['block6f_expand_bn[0][0]'] Y \n", - " ivation) \n", + " top_activation (Activation) (None, 7, 7, 2048) 0 ['top_bn[0][0]'] Y \n", " \n", - " block6f_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6f_expand_activation[0][ Y \n", - " D) 0]'] \n", + " FC_INPUT_Avg-Pooling (GlobalAv (None, 2048) 0 ['top_activation[0][0]'] Y \n", + " eragePooling2D) \n", " \n", - " block6f_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6f_dwconv[0][0]'] Y \n", - " ) \n", + " FC_C_Dense-L1-512 (Dense) (None, 512) 1049088 ['FC_INPUT_Avg-Pooling[0][0]'] Y \n", " \n", - " block6f_activation (Activation (None, 7, 7, 2304) 0 ['block6f_bn[0][0]'] Y \n", - " ) \n", + " FC_C_Dropout-L1-0.1 (Dropout) (None, 512) 0 ['FC_C_Dense-L1-512[0][0]'] Y \n", " \n", - " block6f_se_squeeze (GlobalAver (None, 2304) 0 ['block6f_activation[0][0]'] Y \n", - " agePooling2D) \n", + " FC_C_Avg-BatchNormalization-L1 (None, 512) 2048 ['FC_C_Dropout-L1-0.1[0][0]'] Y \n", + " (BatchNormalization) \n", " \n", - " block6f_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6f_se_squeeze[0][0]'] Y \n", + " FC_C_Dense-L2-512 (Dense) (None, 512) 262656 ['FC_C_Avg-BatchNormalization-L Y \n", + " 1[0][0]'] \n", " \n", - " block6f_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6f_se_reshape[0][0]'] Y \n", + " FC_C_Avg-BatchNormalization-L2 (None, 512) 2048 ['FC_C_Dense-L2-512[0][0]'] Y \n", + " (BatchNormalization) \n", " \n", - " block6f_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6f_se_reduce[0][0]'] Y \n", - " \n", - " block6f_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6f_activation[0][0]', Y \n", - " 'block6f_se_expand[0][0]'] \n", + " FC_C_Dense-L3-128 (Dense) (None, 128) 65664 ['FC_C_Avg-BatchNormalization-L Y \n", + " 2[0][0]'] \n", " \n", - " block6f_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6f_se_excite[0][0]'] Y \n", + " FC_OUTPUT_Dense-2 (Dense) (None, 2) 258 ['FC_C_Dense-L3-128[0][0]'] Y \n", " \n", - " block6f_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6f_project_conv[0][0]'] Y \n", - " lization) \n", + "=============================================================================================================\n", + "Total params: 29,895,282\n", + "Trainable params: 29,720,498\n", + "Non-trainable params: 174,784\n", + "_____________________________________________________________________________________________________________\n", + "done.\n" + ] + } + ], + "source": [ + "from efficientnet.keras import EfficientNetB5 as KENB5\n", + "# FUNC\n", + "def Eff_B5_NS(freeze_layers):\n", + " base_model = KENB5(input_shape=(\n", + " img_res[0], img_res[1], img_res[2]), weights='noisy-student', include_top=False)\n", + " print('Total layers in the base model: ', len(base_model.layers))\n", + " print(f'Freezing {freeze_layers} layers in the base model...')\n", + " # Freeze the specified number of layers\n", + " for layer in base_model.layers[:freeze_layers]:\n", + " layer.trainable = False\n", + "\n", + " # Unfreeze the rest\n", + " for layer in base_model.layers[freeze_layers:]:\n", + " layer.trainable = True\n", + "\n", + " # Calculate the percentage of the model that is frozen\n", + " frozen_percentage = ((freeze_layers + 1e-10) /\n", + " len(base_model.layers)) * 100\n", + " print(\n", + " f'Percentage of the base model that is frozen: {frozen_percentage:.2f}%')\n", + " # adding CDL>>>\n", + " #GlobalAveragePooling2D\n", + " base_model_FT = GlobalAveragePooling2D(name='FC_INPUT_Avg-Pooling')(base_model.output)\n", + " #Dense\n", + " Dense_L1 = Dense(512, activation='relu',\n", + " kernel_regularizer=l2(0.008),\n", + " name='FC_C_Dense-L1-512'\n", + " )(base_model_FT)\n", + " #Dropout\n", + " Dropout_L1 = Dropout(0.125,\n", + " name='FC_C_Dropout-L1-0.1'\n", + " )(Dense_L1)\n", + " #BatchNormalization\n", + " BatchNorm_L2 = BatchNormalization(name='FC_C_Avg-BatchNormalization-L1'\n", + " )(Dropout_L1)\n", + " #Dense\n", + " Dense_L2 = Dense(512, activation='swish',\n", + " kernel_regularizer=l2(0.004),\n", + " name='FC_C_Dense-L2-512'\n", + " )(BatchNorm_L2)\n", + " #BatchNormalization\n", + " BatchNorm_L3 = BatchNormalization(name='FC_C_Avg-BatchNormalization-L2'\n", + " )(Dense_L2)\n", + " #Dense\n", + " Dense_L3 = Dense(128, activation='relu',\n", + " name='FC_C_Dense-L3-128'\n", + " )(BatchNorm_L3)\n", + " #Dense\n", + " # predictions = Dense(2, activation='softmax')(Dense_L3) / predictions = Dense(1, activation='sigmoid')(Dense_L3)\n", + " predictions = Dense(2, activation='softmax',\n", + " name='FC_OUTPUT_Dense-2')(Dense_L3)\n", + " # CDL<<<\n", + " model_EfficientNetB7_NS = Model(\n", + " inputs=base_model.input, outputs=predictions)\n", + " print('Total model layers: ', len(model_EfficientNetB7_NS.layers))\n", + " # OPT/compile\n", + " opt = SGD(momentum=0.9, nesterov=False)\n", + " # opt = Nadam()\n", + " # opt = Adamax()\n", + " # opt = RMSprop(momentum=0.9)\n", + " # opt = Adagrad()\n", + " # opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=5e-4, print_change_log=False, total_steps=0, amsgrad=False)\n", + " # opt = Yogi()\n", + " model_EfficientNetB7_NS.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) # categorical_crossentropy / binary_crossentropy\n", + "\n", + " return model_EfficientNetB7_NS\n", + "\n", + "print('Creating the model...')\n", + "# Main\n", + "freeze_layers = 0\n", + "model = Eff_B5_NS(freeze_layers)\n", + "model.summary(show_trainable=True, expand_nested=True)\n", + "print('done.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### LR FINDER" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import gc\n", + "# Garbage Collection (memory)\n", + "gc.collect()\n", + "tf.keras.backend.clear_session()\n", + "#CONF/Other\n", + "LRF_OPT = SGD(momentum=0.9)\n", + "LFR_batch_size = 1 # or any other batch size that fits in your memory\n", + "LRF_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(LFR_batch_size)\n", + "# Instantiate LrFinder\n", + "lr_find = LrFinder(model, LRF_OPT, tf.keras.losses.categorical_crossentropy)\n", + "\n", + "# Start range_test\n", + "lr_find.range_test(LRF_dataset)\n", + "lr_find.plot_lrs(skip_end=0, suggestion=True, show_grid=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Model vis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dot_img_file = 'model_1.png'\n", + "keras.utils.plot_model(model, to_file=dot_img_file, show_shapes=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Model Save (Beta)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# Copyright (c) 2024 Aydin Hamedi\n", + "# \n", + "# This software is released under the MIT License.\n", + "# https://opensource.org/licenses/MIT\n", + "import json\n", + "import numpy as np\n", + "from keras.models import model_from_json\n", + "from keras.optimizers import get as get_optimizer\n", + "\n", + "def save_model(model, optimizer, filename):\n", + " \"\"\"\n", + " Save a Keras model's architecture and weights into a single gzipped file.\n", + "\n", + " Args:\n", + " model (tf.keras.Model): The Keras model to save.\n", + " optimizer (str): The name of the Keras optimizer to use.\n", + " filename (str): The filename to use for the saved file.\n", + " \"\"\"\n", + " # Save the architecture, weights and optimizer into a dictionary\n", + " model_dict = {\n", + " 'architecture': model.to_json(),\n", + " 'weights': [w.tolist() for w in model.get_weights()],\n", + " 'optimizer': optimizer.get_config()['name']\n", + " }\n", + "\n", + " # Write the dictionary to a gzipped file\n", + " with gzip.GzipFile(f'{filename}.gz', 'w') as f:\n", + " f.write(json.dumps(model_dict).encode('utf-8'))\n", + "\n", + "def load_model(filename):\n", + " \"\"\"\n", + " Load a Keras model's architecture and weights from a gzipped file.\n", + "\n", + " Args:\n", + " filename (str): The filename of the saved file.\n", + "\n", + " Returns:\n", + " tf.keras.Model: The loaded Keras model.\n", + " \"\"\"\n", + " # Read the dictionary from the gzipped file\n", + " with gzip.GzipFile(f'{filename}.gz', 'r') as f:\n", + " model_dict = json.loads(f.read().decode('utf-8'))\n", + "\n", + " # Create a model from the architecture\n", + " model = model_from_json(model_dict['architecture'])\n", + "\n", + " # Set the model's weights\n", + " model.set_weights([np.array(w) for w in model_dict['weights']])\n", + "\n", + " # Get the optimizer\n", + " optimizer = get_optimizer(model_dict['optimizer'])\n", + "\n", + " # Compile the model with the loaded optimizer\n", + " model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + " return model\n", + "\n", + "save_model(model, SGD(), 'PAI_model_REV2')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading the model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loading the full model" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[92mLoading model done.\n", + "Compiling the AI model...\u001b[0m\n", + "Model: \"model\"\n", + "_____________________________________________________________________________________________________________\n", + " Layer (type) Output Shape Param # Connected to Trainable \n", + "=============================================================================================================\n", + " input_1 (InputLayer) [(None, 224, 224, 3 0 [] Y \n", + " )] \n", " \n", - " block6f_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6f_project_bn[0][0]'] Y \n", + " stem_conv (Conv2D) (None, 112, 112, 64 1728 ['input_1[0][0]'] Y \n", + " ) \n", " \n", - " block6f_add (Add) (None, 7, 7, 384) 0 ['block6f_drop[0][0]', Y \n", - " 'block6e_add[0][0]'] \n", + " stem_bn (BatchNormalization) (None, 112, 112, 64 256 ['stem_conv[0][0]'] Y \n", + " ) \n", " \n", - " block6g_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6f_add[0][0]'] Y \n", + " stem_activation (Activation) (None, 112, 112, 64 0 ['stem_bn[0][0]'] Y \n", + " ) \n", " \n", - " block6g_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6g_expand_conv[0][0]'] Y \n", - " ization) \n", + " block1a_dwconv (DepthwiseConv2 (None, 112, 112, 64 576 ['stem_activation[0][0]'] Y \n", + " D) ) \n", " \n", - " block6g_expand_activation (Act (None, 7, 7, 2304) 0 ['block6g_expand_bn[0][0]'] Y \n", - " ivation) \n", + " block1a_bn (BatchNormalization (None, 112, 112, 64 256 ['block1a_dwconv[0][0]'] Y \n", + " ) ) \n", " \n", - " block6g_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6g_expand_activation[0][ Y \n", - " D) 0]'] \n", + " block1a_activation (Activation (None, 112, 112, 64 0 ['block1a_bn[0][0]'] Y \n", + " ) ) \n", " \n", - " block6g_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6g_dwconv[0][0]'] Y \n", - " ) \n", + " block1a_se_squeeze (GlobalAver (None, 64) 0 ['block1a_activation[0][0]'] Y \n", + " agePooling2D) \n", " \n", - " block6g_activation (Activation (None, 7, 7, 2304) 0 ['block6g_bn[0][0]'] Y \n", - " ) \n", + " block1a_se_reshape (Reshape) (None, 1, 1, 64) 0 ['block1a_se_squeeze[0][0]'] Y \n", " \n", - " block6g_se_squeeze (GlobalAver (None, 2304) 0 ['block6g_activation[0][0]'] Y \n", - " agePooling2D) \n", + " block1a_se_reduce (Conv2D) (None, 1, 1, 16) 1040 ['block1a_se_reshape[0][0]'] Y \n", " \n", - " block6g_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6g_se_squeeze[0][0]'] Y \n", + " block1a_se_expand (Conv2D) (None, 1, 1, 64) 1088 ['block1a_se_reduce[0][0]'] Y \n", " \n", - " block6g_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6g_se_reshape[0][0]'] Y \n", + " block1a_se_excite (Multiply) (None, 112, 112, 64 0 ['block1a_activation[0][0]', Y \n", + " ) 'block1a_se_expand[0][0]'] \n", " \n", - " block6g_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6g_se_reduce[0][0]'] Y \n", + " block1a_project_conv (Conv2D) (None, 112, 112, 32 2048 ['block1a_se_excite[0][0]'] Y \n", + " ) \n", " \n", - " block6g_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6g_activation[0][0]', Y \n", - " 'block6g_se_expand[0][0]'] \n", + " block1a_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1a_project_conv[0][0]'] Y \n", + " lization) ) \n", " \n", - " block6g_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6g_se_excite[0][0]'] Y \n", + " block1b_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1a_project_bn[0][0]'] Y \n", + " D) ) \n", " \n", - " block6g_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6g_project_conv[0][0]'] Y \n", - " lization) \n", + " block1b_bn (BatchNormalization (None, 112, 112, 32 128 ['block1b_dwconv[0][0]'] Y \n", + " ) ) \n", " \n", - " block6g_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6g_project_bn[0][0]'] Y \n", + " block1b_activation (Activation (None, 112, 112, 32 0 ['block1b_bn[0][0]'] Y \n", + " ) ) \n", " \n", - " block6g_add (Add) (None, 7, 7, 384) 0 ['block6g_drop[0][0]', Y \n", - " 'block6f_add[0][0]'] \n", + " block1b_se_squeeze (GlobalAver (None, 32) 0 ['block1b_activation[0][0]'] Y \n", + " agePooling2D) \n", " \n", - " block6h_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6g_add[0][0]'] Y \n", + " block1b_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1b_se_squeeze[0][0]'] Y \n", " \n", - " block6h_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6h_expand_conv[0][0]'] Y \n", - " ization) \n", + " block1b_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1b_se_reshape[0][0]'] Y \n", " \n", - " block6h_expand_activation (Act (None, 7, 7, 2304) 0 ['block6h_expand_bn[0][0]'] Y \n", - " ivation) \n", + " block1b_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1b_se_reduce[0][0]'] Y \n", " \n", - " block6h_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6h_expand_activation[0][ Y \n", - " D) 0]'] \n", + " block1b_se_excite (Multiply) (None, 112, 112, 32 0 ['block1b_activation[0][0]', Y \n", + " ) 'block1b_se_expand[0][0]'] \n", " \n", - " block6h_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6h_dwconv[0][0]'] Y \n", - " ) \n", + " block1b_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1b_se_excite[0][0]'] Y \n", + " ) \n", " \n", - " block6h_activation (Activation (None, 7, 7, 2304) 0 ['block6h_bn[0][0]'] Y \n", - " ) \n", + " block1b_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1b_project_conv[0][0]'] Y \n", + " lization) ) \n", " \n", - " block6h_se_squeeze (GlobalAver (None, 2304) 0 ['block6h_activation[0][0]'] Y \n", - " agePooling2D) \n", + " block1b_drop (FixedDropout) (None, 112, 112, 32 0 ['block1b_project_bn[0][0]'] Y \n", + " ) \n", " \n", - " block6h_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6h_se_squeeze[0][0]'] Y \n", + " block1b_add (Add) (None, 112, 112, 32 0 ['block1b_drop[0][0]', Y \n", + " ) 'block1a_project_bn[0][0]'] \n", " \n", - " block6h_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6h_se_reshape[0][0]'] Y \n", + " block1c_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1b_add[0][0]'] Y \n", + " D) ) \n", " \n", - " block6h_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6h_se_reduce[0][0]'] Y \n", + " block1c_bn (BatchNormalization (None, 112, 112, 32 128 ['block1c_dwconv[0][0]'] Y \n", + " ) ) \n", " \n", - " block6h_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6h_activation[0][0]', Y \n", - " 'block6h_se_expand[0][0]'] \n", + " block1c_activation (Activation (None, 112, 112, 32 0 ['block1c_bn[0][0]'] Y \n", + " ) ) \n", " \n", - " block6h_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6h_se_excite[0][0]'] Y \n", + " block1c_se_squeeze (GlobalAver (None, 32) 0 ['block1c_activation[0][0]'] Y \n", + " agePooling2D) \n", " \n", - " block6h_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6h_project_conv[0][0]'] Y \n", - " lization) \n", + " block1c_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1c_se_squeeze[0][0]'] Y \n", " \n", - " block6h_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6h_project_bn[0][0]'] Y \n", + " block1c_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1c_se_reshape[0][0]'] Y \n", " \n", - " block6h_add (Add) (None, 7, 7, 384) 0 ['block6h_drop[0][0]', Y \n", - " 'block6g_add[0][0]'] \n", + " block1c_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1c_se_reduce[0][0]'] Y \n", " \n", - " block6i_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6h_add[0][0]'] Y \n", + " block1c_se_excite (Multiply) (None, 112, 112, 32 0 ['block1c_activation[0][0]', Y \n", + " ) 'block1c_se_expand[0][0]'] \n", " \n", - " block6i_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6i_expand_conv[0][0]'] Y \n", - " ization) \n", + " block1c_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1c_se_excite[0][0]'] Y \n", + " ) \n", " \n", - " block6i_expand_activation (Act (None, 7, 7, 2304) 0 ['block6i_expand_bn[0][0]'] Y \n", - " ivation) \n", + " block1c_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1c_project_conv[0][0]'] Y \n", + " lization) ) \n", " \n", - " block6i_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6i_expand_activation[0][ Y \n", - " D) 0]'] \n", + " block1c_drop (FixedDropout) (None, 112, 112, 32 0 ['block1c_project_bn[0][0]'] Y \n", + " ) \n", " \n", - " block6i_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6i_dwconv[0][0]'] Y \n", - " ) \n", + " block1c_add (Add) (None, 112, 112, 32 0 ['block1c_drop[0][0]', Y \n", + " ) 'block1b_add[0][0]'] \n", " \n", - " block6i_activation (Activation (None, 7, 7, 2304) 0 ['block6i_bn[0][0]'] Y \n", - " ) \n", + " block1d_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1c_add[0][0]'] Y \n", + " D) ) \n", " \n", - " block6i_se_squeeze (GlobalAver (None, 2304) 0 ['block6i_activation[0][0]'] Y \n", + " block1d_bn (BatchNormalization (None, 112, 112, 32 128 ['block1d_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1d_activation (Activation (None, 112, 112, 32 0 ['block1d_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1d_se_squeeze (GlobalAver (None, 32) 0 ['block1d_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block6i_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6i_se_squeeze[0][0]'] Y \n", + " block1d_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1d_se_squeeze[0][0]'] Y \n", " \n", - " block6i_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6i_se_reshape[0][0]'] Y \n", + " block1d_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1d_se_reshape[0][0]'] Y \n", " \n", - " block6i_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6i_se_reduce[0][0]'] Y \n", + " block1d_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1d_se_reduce[0][0]'] Y \n", " \n", - " block6i_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6i_activation[0][0]', Y \n", - " 'block6i_se_expand[0][0]'] \n", + " block1d_se_excite (Multiply) (None, 112, 112, 32 0 ['block1d_activation[0][0]', Y \n", + " ) 'block1d_se_expand[0][0]'] \n", " \n", - " block6i_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6i_se_excite[0][0]'] Y \n", + " block1d_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1d_se_excite[0][0]'] Y \n", + " ) \n", " \n", - " block6i_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6i_project_conv[0][0]'] Y \n", - " lization) \n", + " block1d_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1d_project_conv[0][0]'] Y \n", + " lization) ) \n", " \n", - " block6i_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6i_project_bn[0][0]'] Y \n", + " block1d_drop (FixedDropout) (None, 112, 112, 32 0 ['block1d_project_bn[0][0]'] Y \n", + " ) \n", " \n", - " block6i_add (Add) (None, 7, 7, 384) 0 ['block6i_drop[0][0]', Y \n", - " 'block6h_add[0][0]'] \n", + " block1d_add (Add) (None, 112, 112, 32 0 ['block1d_drop[0][0]', Y \n", + " ) 'block1c_add[0][0]'] \n", " \n", - " block6j_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6i_add[0][0]'] Y \n", + " block2a_expand_conv (Conv2D) (None, 112, 112, 19 6144 ['block1d_add[0][0]'] Y \n", + " 2) \n", " \n", - " block6j_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6j_expand_conv[0][0]'] Y \n", - " ization) \n", + " block2a_expand_bn (BatchNormal (None, 112, 112, 19 768 ['block2a_expand_conv[0][0]'] Y \n", + " ization) 2) \n", " \n", - " block6j_expand_activation (Act (None, 7, 7, 2304) 0 ['block6j_expand_bn[0][0]'] Y \n", - " ivation) \n", + " block2a_expand_activation (Act (None, 112, 112, 19 0 ['block2a_expand_bn[0][0]'] Y \n", + " ivation) 2) \n", " \n", - " block6j_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6j_expand_activation[0][ Y \n", + " block2a_dwconv (DepthwiseConv2 (None, 56, 56, 192) 1728 ['block2a_expand_activation[0][ Y \n", " D) 0]'] \n", " \n", - " block6j_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6j_dwconv[0][0]'] Y \n", + " block2a_bn (BatchNormalization (None, 56, 56, 192) 768 ['block2a_dwconv[0][0]'] Y \n", " ) \n", " \n", - " block6j_activation (Activation (None, 7, 7, 2304) 0 ['block6j_bn[0][0]'] Y \n", + " block2a_activation (Activation (None, 56, 56, 192) 0 ['block2a_bn[0][0]'] Y \n", " ) \n", " \n", - " block6j_se_squeeze (GlobalAver (None, 2304) 0 ['block6j_activation[0][0]'] Y \n", + " block2a_se_squeeze (GlobalAver (None, 192) 0 ['block2a_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block6j_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6j_se_squeeze[0][0]'] Y \n", + " block2a_se_reshape (Reshape) (None, 1, 1, 192) 0 ['block2a_se_squeeze[0][0]'] Y \n", " \n", - " block6j_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6j_se_reshape[0][0]'] Y \n", + " block2a_se_reduce (Conv2D) (None, 1, 1, 8) 1544 ['block2a_se_reshape[0][0]'] Y \n", " \n", - " block6j_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6j_se_reduce[0][0]'] Y \n", + " block2a_se_expand (Conv2D) (None, 1, 1, 192) 1728 ['block2a_se_reduce[0][0]'] Y \n", " \n", - " block6j_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6j_activation[0][0]', Y \n", - " 'block6j_se_expand[0][0]'] \n", + " block2a_se_excite (Multiply) (None, 56, 56, 192) 0 ['block2a_activation[0][0]', Y \n", + " 'block2a_se_expand[0][0]'] \n", " \n", - " block6j_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6j_se_excite[0][0]'] Y \n", + " block2a_project_conv (Conv2D) (None, 56, 56, 48) 9216 ['block2a_se_excite[0][0]'] Y \n", " \n", - " block6j_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6j_project_conv[0][0]'] Y \n", + " block2a_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2a_project_conv[0][0]'] Y \n", " lization) \n", " \n", - " block6j_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6j_project_bn[0][0]'] Y \n", - " \n", - " block6j_add (Add) (None, 7, 7, 384) 0 ['block6j_drop[0][0]', Y \n", - " 'block6i_add[0][0]'] \n", - " \n", - " block6k_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6j_add[0][0]'] Y \n", + " block2b_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2a_project_bn[0][0]'] Y \n", " \n", - " block6k_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6k_expand_conv[0][0]'] Y \n", + " block2b_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2b_expand_conv[0][0]'] Y \n", " ization) \n", " \n", - " block6k_expand_activation (Act (None, 7, 7, 2304) 0 ['block6k_expand_bn[0][0]'] Y \n", + " block2b_expand_activation (Act (None, 56, 56, 288) 0 ['block2b_expand_bn[0][0]'] Y \n", " ivation) \n", " \n", - " block6k_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6k_expand_activation[0][ Y \n", + " block2b_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2b_expand_activation[0][ Y \n", " D) 0]'] \n", " \n", - " block6k_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6k_dwconv[0][0]'] Y \n", + " block2b_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2b_dwconv[0][0]'] Y \n", " ) \n", " \n", - " block6k_activation (Activation (None, 7, 7, 2304) 0 ['block6k_bn[0][0]'] Y \n", + " block2b_activation (Activation (None, 56, 56, 288) 0 ['block2b_bn[0][0]'] Y \n", " ) \n", " \n", - " block6k_se_squeeze (GlobalAver (None, 2304) 0 ['block6k_activation[0][0]'] Y \n", + " block2b_se_squeeze (GlobalAver (None, 288) 0 ['block2b_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block6k_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6k_se_squeeze[0][0]'] Y \n", + " block2b_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2b_se_squeeze[0][0]'] Y \n", " \n", - " block6k_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6k_se_reshape[0][0]'] Y \n", + " block2b_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2b_se_reshape[0][0]'] Y \n", " \n", - " block6k_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6k_se_reduce[0][0]'] Y \n", + " block2b_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2b_se_reduce[0][0]'] Y \n", " \n", - " block6k_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6k_activation[0][0]', Y \n", - " 'block6k_se_expand[0][0]'] \n", + " block2b_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2b_activation[0][0]', Y \n", + " 'block2b_se_expand[0][0]'] \n", " \n", - " block6k_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6k_se_excite[0][0]'] Y \n", + " block2b_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2b_se_excite[0][0]'] Y \n", " \n", - " block6k_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6k_project_conv[0][0]'] Y \n", + " block2b_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2b_project_conv[0][0]'] Y \n", " lization) \n", " \n", - " block6k_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6k_project_bn[0][0]'] Y \n", + " block2b_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2b_project_bn[0][0]'] Y \n", " \n", - " block6k_add (Add) (None, 7, 7, 384) 0 ['block6k_drop[0][0]', Y \n", - " 'block6j_add[0][0]'] \n", + " block2b_add (Add) (None, 56, 56, 48) 0 ['block2b_drop[0][0]', Y \n", + " 'block2a_project_bn[0][0]'] \n", " \n", - " block6l_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6k_add[0][0]'] Y \n", + " block2c_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2b_add[0][0]'] Y \n", " \n", - " block6l_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6l_expand_conv[0][0]'] Y \n", + " block2c_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2c_expand_conv[0][0]'] Y \n", " ization) \n", " \n", - " block6l_expand_activation (Act (None, 7, 7, 2304) 0 ['block6l_expand_bn[0][0]'] Y \n", + " block2c_expand_activation (Act (None, 56, 56, 288) 0 ['block2c_expand_bn[0][0]'] Y \n", " ivation) \n", " \n", - " block6l_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6l_expand_activation[0][ Y \n", + " block2c_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2c_expand_activation[0][ Y \n", " D) 0]'] \n", " \n", - " block6l_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6l_dwconv[0][0]'] Y \n", + " block2c_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2c_dwconv[0][0]'] Y \n", " ) \n", " \n", - " block6l_activation (Activation (None, 7, 7, 2304) 0 ['block6l_bn[0][0]'] Y \n", + " block2c_activation (Activation (None, 56, 56, 288) 0 ['block2c_bn[0][0]'] Y \n", " ) \n", " \n", - " block6l_se_squeeze (GlobalAver (None, 2304) 0 ['block6l_activation[0][0]'] Y \n", + " block2c_se_squeeze (GlobalAver (None, 288) 0 ['block2c_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block6l_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6l_se_squeeze[0][0]'] Y \n", + " block2c_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2c_se_squeeze[0][0]'] Y \n", " \n", - " block6l_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6l_se_reshape[0][0]'] Y \n", + " block2c_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2c_se_reshape[0][0]'] Y \n", " \n", - " block6l_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6l_se_reduce[0][0]'] Y \n", + " block2c_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2c_se_reduce[0][0]'] Y \n", " \n", - " block6l_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6l_activation[0][0]', Y \n", - " 'block6l_se_expand[0][0]'] \n", + " block2c_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2c_activation[0][0]', Y \n", + " 'block2c_se_expand[0][0]'] \n", " \n", - " block6l_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6l_se_excite[0][0]'] Y \n", + " block2c_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2c_se_excite[0][0]'] Y \n", " \n", - " block6l_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6l_project_conv[0][0]'] Y \n", + " block2c_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2c_project_conv[0][0]'] Y \n", " lization) \n", " \n", - " block6l_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6l_project_bn[0][0]'] Y \n", + " block2c_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2c_project_bn[0][0]'] Y \n", " \n", - " block6l_add (Add) (None, 7, 7, 384) 0 ['block6l_drop[0][0]', Y \n", - " 'block6k_add[0][0]'] \n", + " block2c_add (Add) (None, 56, 56, 48) 0 ['block2c_drop[0][0]', Y \n", + " 'block2b_add[0][0]'] \n", " \n", - " block6m_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6l_add[0][0]'] Y \n", + " block2d_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2c_add[0][0]'] Y \n", " \n", - " block6m_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6m_expand_conv[0][0]'] Y \n", + " block2d_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2d_expand_conv[0][0]'] Y \n", " ization) \n", " \n", - " block6m_expand_activation (Act (None, 7, 7, 2304) 0 ['block6m_expand_bn[0][0]'] Y \n", + " block2d_expand_activation (Act (None, 56, 56, 288) 0 ['block2d_expand_bn[0][0]'] Y \n", " ivation) \n", " \n", - " block6m_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6m_expand_activation[0][ Y \n", + " block2d_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2d_expand_activation[0][ Y \n", " D) 0]'] \n", " \n", - " block6m_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6m_dwconv[0][0]'] Y \n", + " block2d_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2d_dwconv[0][0]'] Y \n", " ) \n", " \n", - " block6m_activation (Activation (None, 7, 7, 2304) 0 ['block6m_bn[0][0]'] Y \n", + " block2d_activation (Activation (None, 56, 56, 288) 0 ['block2d_bn[0][0]'] Y \n", " ) \n", " \n", - " block6m_se_squeeze (GlobalAver (None, 2304) 0 ['block6m_activation[0][0]'] Y \n", + " block2d_se_squeeze (GlobalAver (None, 288) 0 ['block2d_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block6m_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6m_se_squeeze[0][0]'] Y \n", + " block2d_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2d_se_squeeze[0][0]'] Y \n", " \n", - " block6m_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6m_se_reshape[0][0]'] Y \n", + " block2d_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2d_se_reshape[0][0]'] Y \n", " \n", - " block6m_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6m_se_reduce[0][0]'] Y \n", + " block2d_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2d_se_reduce[0][0]'] Y \n", " \n", - " block6m_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6m_activation[0][0]', Y \n", - " 'block6m_se_expand[0][0]'] \n", + " block2d_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2d_activation[0][0]', Y \n", + " 'block2d_se_expand[0][0]'] \n", " \n", - " block6m_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6m_se_excite[0][0]'] Y \n", + " block2d_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2d_se_excite[0][0]'] Y \n", " \n", - " block6m_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6m_project_conv[0][0]'] Y \n", + " block2d_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2d_project_conv[0][0]'] Y \n", " lization) \n", " \n", - " block6m_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6m_project_bn[0][0]'] Y \n", + " block2d_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2d_project_bn[0][0]'] Y \n", " \n", - " block6m_add (Add) (None, 7, 7, 384) 0 ['block6m_drop[0][0]', Y \n", - " 'block6l_add[0][0]'] \n", + " block2d_add (Add) (None, 56, 56, 48) 0 ['block2d_drop[0][0]', Y \n", + " 'block2c_add[0][0]'] \n", " \n", - " block7a_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6m_add[0][0]'] Y \n", + " block2e_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2d_add[0][0]'] Y \n", " \n", - " block7a_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block7a_expand_conv[0][0]'] Y \n", + " block2e_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2e_expand_conv[0][0]'] Y \n", " ization) \n", " \n", - " block7a_expand_activation (Act (None, 7, 7, 2304) 0 ['block7a_expand_bn[0][0]'] Y \n", + " block2e_expand_activation (Act (None, 56, 56, 288) 0 ['block2e_expand_bn[0][0]'] Y \n", " ivation) \n", " \n", - " block7a_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 20736 ['block7a_expand_activation[0][ Y \n", + " block2e_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2e_expand_activation[0][ Y \n", " D) 0]'] \n", " \n", - " block7a_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block7a_dwconv[0][0]'] Y \n", + " block2e_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2e_dwconv[0][0]'] Y \n", " ) \n", " \n", - " block7a_activation (Activation (None, 7, 7, 2304) 0 ['block7a_bn[0][0]'] Y \n", + " block2e_activation (Activation (None, 56, 56, 288) 0 ['block2e_bn[0][0]'] Y \n", " ) \n", " \n", - " block7a_se_squeeze (GlobalAver (None, 2304) 0 ['block7a_activation[0][0]'] Y \n", + " block2e_se_squeeze (GlobalAver (None, 288) 0 ['block2e_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block7a_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block7a_se_squeeze[0][0]'] Y \n", + " block2e_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2e_se_squeeze[0][0]'] Y \n", " \n", - " block7a_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block7a_se_reshape[0][0]'] Y \n", + " block2e_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2e_se_reshape[0][0]'] Y \n", " \n", - " block7a_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block7a_se_reduce[0][0]'] Y \n", + " block2e_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2e_se_reduce[0][0]'] Y \n", " \n", - " block7a_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block7a_activation[0][0]', Y \n", - " 'block7a_se_expand[0][0]'] \n", + " block2e_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2e_activation[0][0]', Y \n", + " 'block2e_se_expand[0][0]'] \n", " \n", - " block7a_project_conv (Conv2D) (None, 7, 7, 640) 1474560 ['block7a_se_excite[0][0]'] Y \n", + " block2e_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2e_se_excite[0][0]'] Y \n", " \n", - " block7a_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7a_project_conv[0][0]'] Y \n", + " block2e_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2e_project_conv[0][0]'] Y \n", " lization) \n", " \n", - " block7b_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7a_project_bn[0][0]'] Y \n", + " block2e_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2e_project_bn[0][0]'] Y \n", " \n", - " block7b_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7b_expand_conv[0][0]'] Y \n", + " block2e_add (Add) (None, 56, 56, 48) 0 ['block2e_drop[0][0]', Y \n", + " 'block2d_add[0][0]'] \n", + " \n", + " block2f_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2e_add[0][0]'] Y \n", + " \n", + " block2f_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2f_expand_conv[0][0]'] Y \n", " ization) \n", " \n", - " block7b_expand_activation (Act (None, 7, 7, 3840) 0 ['block7b_expand_bn[0][0]'] Y \n", + " block2f_expand_activation (Act (None, 56, 56, 288) 0 ['block2f_expand_bn[0][0]'] Y \n", " ivation) \n", " \n", - " block7b_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7b_expand_activation[0][ Y \n", + " block2f_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2f_expand_activation[0][ Y \n", " D) 0]'] \n", " \n", - " block7b_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7b_dwconv[0][0]'] Y \n", + " block2f_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2f_dwconv[0][0]'] Y \n", " ) \n", " \n", - " block7b_activation (Activation (None, 7, 7, 3840) 0 ['block7b_bn[0][0]'] Y \n", + " block2f_activation (Activation (None, 56, 56, 288) 0 ['block2f_bn[0][0]'] Y \n", " ) \n", " \n", - " block7b_se_squeeze (GlobalAver (None, 3840) 0 ['block7b_activation[0][0]'] Y \n", + " block2f_se_squeeze (GlobalAver (None, 288) 0 ['block2f_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block7b_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7b_se_squeeze[0][0]'] Y \n", + " block2f_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2f_se_squeeze[0][0]'] Y \n", " \n", - " block7b_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7b_se_reshape[0][0]'] Y \n", + " block2f_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2f_se_reshape[0][0]'] Y \n", " \n", - " block7b_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7b_se_reduce[0][0]'] Y \n", + " block2f_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2f_se_reduce[0][0]'] Y \n", " \n", - " block7b_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7b_activation[0][0]', Y \n", - " 'block7b_se_expand[0][0]'] \n", + " block2f_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2f_activation[0][0]', Y \n", + " 'block2f_se_expand[0][0]'] \n", " \n", - " block7b_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7b_se_excite[0][0]'] Y \n", + " block2f_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2f_se_excite[0][0]'] Y \n", " \n", - " block7b_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7b_project_conv[0][0]'] Y \n", + " block2f_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2f_project_conv[0][0]'] Y \n", " lization) \n", " \n", - " block7b_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7b_project_bn[0][0]'] Y \n", + " block2f_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2f_project_bn[0][0]'] Y \n", " \n", - " block7b_add (Add) (None, 7, 7, 640) 0 ['block7b_drop[0][0]', Y \n", - " 'block7a_project_bn[0][0]'] \n", + " block2f_add (Add) (None, 56, 56, 48) 0 ['block2f_drop[0][0]', Y \n", + " 'block2e_add[0][0]'] \n", " \n", - " block7c_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7b_add[0][0]'] Y \n", + " block2g_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2f_add[0][0]'] Y \n", " \n", - " block7c_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7c_expand_conv[0][0]'] Y \n", + " block2g_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2g_expand_conv[0][0]'] Y \n", " ization) \n", " \n", - " block7c_expand_activation (Act (None, 7, 7, 3840) 0 ['block7c_expand_bn[0][0]'] Y \n", + " block2g_expand_activation (Act (None, 56, 56, 288) 0 ['block2g_expand_bn[0][0]'] Y \n", " ivation) \n", " \n", - " block7c_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7c_expand_activation[0][ Y \n", + " block2g_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2g_expand_activation[0][ Y \n", " D) 0]'] \n", " \n", - " block7c_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7c_dwconv[0][0]'] Y \n", + " block2g_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2g_dwconv[0][0]'] Y \n", " ) \n", " \n", - " block7c_activation (Activation (None, 7, 7, 3840) 0 ['block7c_bn[0][0]'] Y \n", + " block2g_activation (Activation (None, 56, 56, 288) 0 ['block2g_bn[0][0]'] Y \n", " ) \n", " \n", - " block7c_se_squeeze (GlobalAver (None, 3840) 0 ['block7c_activation[0][0]'] Y \n", + " block2g_se_squeeze (GlobalAver (None, 288) 0 ['block2g_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block7c_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7c_se_squeeze[0][0]'] Y \n", + " block2g_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2g_se_squeeze[0][0]'] Y \n", " \n", - " block7c_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7c_se_reshape[0][0]'] Y \n", + " block2g_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2g_se_reshape[0][0]'] Y \n", " \n", - " block7c_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7c_se_reduce[0][0]'] Y \n", + " block2g_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2g_se_reduce[0][0]'] Y \n", " \n", - " block7c_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7c_activation[0][0]', Y \n", - " 'block7c_se_expand[0][0]'] \n", + " block2g_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2g_activation[0][0]', Y \n", + " 'block2g_se_expand[0][0]'] \n", " \n", - " block7c_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7c_se_excite[0][0]'] Y \n", + " block2g_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2g_se_excite[0][0]'] Y \n", " \n", - " block7c_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7c_project_conv[0][0]'] Y \n", + " block2g_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2g_project_conv[0][0]'] Y \n", " lization) \n", " \n", - " block7c_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7c_project_bn[0][0]'] Y \n", + " block2g_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2g_project_bn[0][0]'] Y \n", " \n", - " block7c_add (Add) (None, 7, 7, 640) 0 ['block7c_drop[0][0]', Y \n", - " 'block7b_add[0][0]'] \n", + " block2g_add (Add) (None, 56, 56, 48) 0 ['block2g_drop[0][0]', Y \n", + " 'block2f_add[0][0]'] \n", " \n", - " block7d_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7c_add[0][0]'] Y \n", + " block3a_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2g_add[0][0]'] Y \n", " \n", - " block7d_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7d_expand_conv[0][0]'] Y \n", + " block3a_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block3a_expand_conv[0][0]'] Y \n", " ization) \n", " \n", - " block7d_expand_activation (Act (None, 7, 7, 3840) 0 ['block7d_expand_bn[0][0]'] Y \n", + " block3a_expand_activation (Act (None, 56, 56, 288) 0 ['block3a_expand_bn[0][0]'] Y \n", " ivation) \n", " \n", - " block7d_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7d_expand_activation[0][ Y \n", + " block3a_dwconv (DepthwiseConv2 (None, 28, 28, 288) 7200 ['block3a_expand_activation[0][ Y \n", " D) 0]'] \n", " \n", - " block7d_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7d_dwconv[0][0]'] Y \n", + " block3a_bn (BatchNormalization (None, 28, 28, 288) 1152 ['block3a_dwconv[0][0]'] Y \n", " ) \n", " \n", - " block7d_activation (Activation (None, 7, 7, 3840) 0 ['block7d_bn[0][0]'] Y \n", + " block3a_activation (Activation (None, 28, 28, 288) 0 ['block3a_bn[0][0]'] Y \n", " ) \n", " \n", - " block7d_se_squeeze (GlobalAver (None, 3840) 0 ['block7d_activation[0][0]'] Y \n", + " block3a_se_squeeze (GlobalAver (None, 288) 0 ['block3a_activation[0][0]'] Y \n", " agePooling2D) \n", " \n", - " block7d_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7d_se_squeeze[0][0]'] Y \n", + " block3a_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block3a_se_squeeze[0][0]'] Y \n", " \n", - " block7d_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7d_se_reshape[0][0]'] Y \n", + " block3a_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block3a_se_reshape[0][0]'] Y \n", " \n", - " block7d_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7d_se_reduce[0][0]'] Y \n", + " block3a_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block3a_se_reduce[0][0]'] Y \n", " \n", - " block7d_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7d_activation[0][0]', Y \n", - " 'block7d_se_expand[0][0]'] \n", + " block3a_se_excite (Multiply) (None, 28, 28, 288) 0 ['block3a_activation[0][0]', Y \n", + " 'block3a_se_expand[0][0]'] \n", " \n", - " block7d_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7d_se_excite[0][0]'] Y \n", + " block3a_project_conv (Conv2D) (None, 28, 28, 80) 23040 ['block3a_se_excite[0][0]'] Y \n", " \n", - " block7d_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7d_project_conv[0][0]'] Y \n", + " block3a_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3a_project_conv[0][0]'] Y \n", " lization) \n", " \n", - " block7d_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7d_project_bn[0][0]'] Y \n", + " block3b_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3a_project_bn[0][0]'] Y \n", " \n", - " block7d_add (Add) (None, 7, 7, 640) 0 ['block7d_drop[0][0]', Y \n", - " 'block7c_add[0][0]'] \n", + " block3b_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3b_expand_conv[0][0]'] Y \n", + " ization) \n", " \n", - " top_conv (Conv2D) (None, 7, 7, 2560) 1638400 ['block7d_add[0][0]'] Y \n", + " block3b_expand_activation (Act (None, 28, 28, 480) 0 ['block3b_expand_bn[0][0]'] Y \n", + " ivation) \n", " \n", - " top_bn (BatchNormalization) (None, 7, 7, 2560) 10240 ['top_conv[0][0]'] Y \n", + " block3b_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3b_expand_activation[0][ Y \n", + " D) 0]'] \n", " \n", - " top_activation (Activation) (None, 7, 7, 2560) 0 ['top_bn[0][0]'] Y \n", + " block3b_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3b_dwconv[0][0]'] Y \n", + " ) \n", " \n", - " global_average_pooling2d (Glob (None, 2560) 0 ['top_activation[0][0]'] Y \n", - " alAveragePooling2D) \n", + " block3b_activation (Activation (None, 28, 28, 480) 0 ['block3b_bn[0][0]'] Y \n", + " ) \n", " \n", - " dense (Dense) (None, 512) 1311232 ['global_average_pooling2d[0][0 Y \n", - " ]'] \n", + " block3b_se_squeeze (GlobalAver (None, 480) 0 ['block3b_activation[0][0]'] Y \n", + " agePooling2D) \n", " \n", - " dropout (Dropout) (None, 512) 0 ['dense[0][0]'] Y \n", + " block3b_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3b_se_squeeze[0][0]'] Y \n", " \n", - " batch_normalization (BatchNorm (None, 512) 2048 ['dropout[0][0]'] Y \n", - " alization) \n", + " block3b_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3b_se_reshape[0][0]'] Y \n", " \n", - " dense_1 (Dense) (None, 512) 262656 ['batch_normalization[0][0]'] Y \n", + " block3b_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3b_se_reduce[0][0]'] Y \n", " \n", - " batch_normalization_1 (BatchNo (None, 512) 2048 ['dense_1[0][0]'] Y \n", - " rmalization) \n", + " block3b_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3b_activation[0][0]', Y \n", + " 'block3b_se_expand[0][0]'] \n", " \n", - " dense_2 (Dense) (None, 128) 65664 ['batch_normalization_1[0][0]'] Y \n", + " block3b_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3b_se_excite[0][0]'] Y \n", " \n", - " dense_3 (Dense) (None, 2) 258 ['dense_2[0][0]'] Y \n", + " block3b_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3b_project_conv[0][0]'] Y \n", + " lization) \n", " \n", - "=============================================================================================================\n", - "Total params: 65,741,586\n", - "Trainable params: 65,428,818\n", - "Non-trainable params: 312,768\n", - "_____________________________________________________________________________________________________________\n", - "done.\n" - ] - } - ], - "source": [ - "import efficientnet.tfkeras\n", - "# Configuration\n", - "PRMC = False\n", - "freeze_from_opposite = False\n", - "Extra_EXT = '_T'\n", - "freeze_layers = 0 \n", - "randomly_frozen_layers = 0 \n", - "freeze_last_seven = False \n", - "# CEC_opt = Adagrad()\n", - "# CEC_opt = Yogi()\n", - "# CEC_opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-3)\n", - "CEC_opt = SGD(momentum=0.9, nesterov=False)\n", - "# CEC_opt = Adam()\n", - "# Main\n", - "try:\n", - " if SAVE_TYPE == 'TF':\n", - " model = load_model(f'PAI_model{Extra_EXT}', compile=PRMC)\n", - " else:\n", - " model = load_model(f'PAI_model{Extra_EXT}.h5', compile=PRMC)\n", - "except (ImportError, IOError) as e:\n", - " print(f'\\033[91mfailed to load the model ERROR:\\n{e}')\n", - "else:\n", - " print('\\033[92mLoading model done.')\n", - " if not PRMC:\n", - " print('Compiling the AI model...\\033[0m')\n", - " \n", - " for layer in model.layers:\n", - " layer.trainable = True\n", - " \n", - " # Select random layers to freeze\n", - " frozen_layer_indices = random.sample(range(len(model.layers)), randomly_frozen_layers)\n", - " \n", - " for i, layer in enumerate(model.layers):\n", - " if i in frozen_layer_indices:\n", - " layer.trainable = False\n", - " else:\n", - " if freeze_from_opposite and (i > len(model.layers) - freeze_layers):\n", - " layer.trainable = False\n", - " elif (not freeze_from_opposite) and i < freeze_layers:\n", - " layer.trainable = False\n", - " else:\n", - " layer.trainable = True\n", - " \n", - " for layer in model.layers[-7:]:\n", - " layer.trainable = not freeze_last_seven\n", - " \n", - " model.compile(optimizer=CEC_opt, loss='categorical_crossentropy', metrics=['accuracy'])\n", - " model.summary(show_trainable=True, expand_nested=True)\n", - " print('done.')" - ] - }, + " block3b_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3b_project_bn[0][0]'] Y \n", + " \n", + " block3b_add (Add) (None, 28, 28, 80) 0 ['block3b_drop[0][0]', Y \n", + " 'block3a_project_bn[0][0]'] \n", + " \n", + " block3c_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3b_add[0][0]'] Y \n", + " \n", + " block3c_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3c_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3c_expand_activation (Act (None, 28, 28, 480) 0 ['block3c_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3c_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3c_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3c_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3c_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3c_activation (Activation (None, 28, 28, 480) 0 ['block3c_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3c_se_squeeze (GlobalAver (None, 480) 0 ['block3c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3c_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3c_se_squeeze[0][0]'] Y \n", + " \n", + " block3c_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3c_se_reshape[0][0]'] Y \n", + " \n", + " block3c_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3c_se_reduce[0][0]'] Y \n", + " \n", + " block3c_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3c_activation[0][0]', Y \n", + " 'block3c_se_expand[0][0]'] \n", + " \n", + " block3c_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3c_se_excite[0][0]'] Y \n", + " \n", + " block3c_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3c_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3c_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3c_project_bn[0][0]'] Y \n", + " \n", + " block3c_add (Add) (None, 28, 28, 80) 0 ['block3c_drop[0][0]', Y \n", + " 'block3b_add[0][0]'] \n", + " \n", + " block3d_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3c_add[0][0]'] Y \n", + " \n", + " block3d_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3d_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3d_expand_activation (Act (None, 28, 28, 480) 0 ['block3d_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3d_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3d_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3d_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3d_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3d_activation (Activation (None, 28, 28, 480) 0 ['block3d_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3d_se_squeeze (GlobalAver (None, 480) 0 ['block3d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3d_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3d_se_squeeze[0][0]'] Y \n", + " \n", + " block3d_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3d_se_reshape[0][0]'] Y \n", + " \n", + " block3d_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3d_se_reduce[0][0]'] Y \n", + " \n", + " block3d_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3d_activation[0][0]', Y \n", + " 'block3d_se_expand[0][0]'] \n", + " \n", + " block3d_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3d_se_excite[0][0]'] Y \n", + " \n", + " block3d_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3d_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3d_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3d_project_bn[0][0]'] Y \n", + " \n", + " block3d_add (Add) (None, 28, 28, 80) 0 ['block3d_drop[0][0]', Y \n", + " 'block3c_add[0][0]'] \n", + " \n", + " block3e_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3d_add[0][0]'] Y \n", + " \n", + " block3e_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3e_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3e_expand_activation (Act (None, 28, 28, 480) 0 ['block3e_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3e_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3e_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3e_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3e_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3e_activation (Activation (None, 28, 28, 480) 0 ['block3e_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3e_se_squeeze (GlobalAver (None, 480) 0 ['block3e_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3e_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3e_se_squeeze[0][0]'] Y \n", + " \n", + " block3e_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3e_se_reshape[0][0]'] Y \n", + " \n", + " block3e_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3e_se_reduce[0][0]'] Y \n", + " \n", + " block3e_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3e_activation[0][0]', Y \n", + " 'block3e_se_expand[0][0]'] \n", + " \n", + " block3e_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3e_se_excite[0][0]'] Y \n", + " \n", + " block3e_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3e_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3e_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3e_project_bn[0][0]'] Y \n", + " \n", + " block3e_add (Add) (None, 28, 28, 80) 0 ['block3e_drop[0][0]', Y \n", + " 'block3d_add[0][0]'] \n", + " \n", + " block3f_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3e_add[0][0]'] Y \n", + " \n", + " block3f_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3f_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3f_expand_activation (Act (None, 28, 28, 480) 0 ['block3f_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3f_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3f_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3f_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3f_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3f_activation (Activation (None, 28, 28, 480) 0 ['block3f_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3f_se_squeeze (GlobalAver (None, 480) 0 ['block3f_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3f_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3f_se_squeeze[0][0]'] Y \n", + " \n", + " block3f_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3f_se_reshape[0][0]'] Y \n", + " \n", + " block3f_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3f_se_reduce[0][0]'] Y \n", + " \n", + " block3f_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3f_activation[0][0]', Y \n", + " 'block3f_se_expand[0][0]'] \n", + " \n", + " block3f_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3f_se_excite[0][0]'] Y \n", + " \n", + " block3f_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3f_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3f_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3f_project_bn[0][0]'] Y \n", + " \n", + " block3f_add (Add) (None, 28, 28, 80) 0 ['block3f_drop[0][0]', Y \n", + " 'block3e_add[0][0]'] \n", + " \n", + " block3g_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3f_add[0][0]'] Y \n", + " \n", + " block3g_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3g_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3g_expand_activation (Act (None, 28, 28, 480) 0 ['block3g_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3g_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3g_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3g_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3g_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3g_activation (Activation (None, 28, 28, 480) 0 ['block3g_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3g_se_squeeze (GlobalAver (None, 480) 0 ['block3g_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3g_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3g_se_squeeze[0][0]'] Y \n", + " \n", + " block3g_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3g_se_reshape[0][0]'] Y \n", + " \n", + " block3g_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3g_se_reduce[0][0]'] Y \n", + " \n", + " block3g_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3g_activation[0][0]', Y \n", + " 'block3g_se_expand[0][0]'] \n", + " \n", + " block3g_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3g_se_excite[0][0]'] Y \n", + " \n", + " block3g_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3g_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3g_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3g_project_bn[0][0]'] Y \n", + " \n", + " block3g_add (Add) (None, 28, 28, 80) 0 ['block3g_drop[0][0]', Y \n", + " 'block3f_add[0][0]'] \n", + " \n", + " block4a_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3g_add[0][0]'] Y \n", + " \n", + " block4a_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block4a_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4a_expand_activation (Act (None, 28, 28, 480) 0 ['block4a_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4a_dwconv (DepthwiseConv2 (None, 14, 14, 480) 4320 ['block4a_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4a_bn (BatchNormalization (None, 14, 14, 480) 1920 ['block4a_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4a_activation (Activation (None, 14, 14, 480) 0 ['block4a_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4a_se_squeeze (GlobalAver (None, 480) 0 ['block4a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4a_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block4a_se_squeeze[0][0]'] Y \n", + " \n", + " block4a_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block4a_se_reshape[0][0]'] Y \n", + " \n", + " block4a_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block4a_se_reduce[0][0]'] Y \n", + " \n", + " block4a_se_excite (Multiply) (None, 14, 14, 480) 0 ['block4a_activation[0][0]', Y \n", + " 'block4a_se_expand[0][0]'] \n", + " \n", + " block4a_project_conv (Conv2D) (None, 14, 14, 160) 76800 ['block4a_se_excite[0][0]'] Y \n", + " \n", + " block4a_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4a_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4b_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4a_project_bn[0][0]'] Y \n", + " \n", + " block4b_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4b_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4b_expand_activation (Act (None, 14, 14, 960) 0 ['block4b_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4b_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4b_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4b_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4b_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4b_activation (Activation (None, 14, 14, 960) 0 ['block4b_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4b_se_squeeze (GlobalAver (None, 960) 0 ['block4b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4b_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4b_se_squeeze[0][0]'] Y \n", + " \n", + " block4b_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4b_se_reshape[0][0]'] Y \n", + " \n", + " block4b_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4b_se_reduce[0][0]'] Y \n", + " \n", + " block4b_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4b_activation[0][0]', Y \n", + " 'block4b_se_expand[0][0]'] \n", + " \n", + " block4b_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4b_se_excite[0][0]'] Y \n", + " \n", + " block4b_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4b_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4b_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4b_project_bn[0][0]'] Y \n", + " \n", + " block4b_add (Add) (None, 14, 14, 160) 0 ['block4b_drop[0][0]', Y \n", + " 'block4a_project_bn[0][0]'] \n", + " \n", + " block4c_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4b_add[0][0]'] Y \n", + " \n", + " block4c_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4c_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4c_expand_activation (Act (None, 14, 14, 960) 0 ['block4c_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4c_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4c_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4c_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4c_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4c_activation (Activation (None, 14, 14, 960) 0 ['block4c_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4c_se_squeeze (GlobalAver (None, 960) 0 ['block4c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4c_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4c_se_squeeze[0][0]'] Y \n", + " \n", + " block4c_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4c_se_reshape[0][0]'] Y \n", + " \n", + " block4c_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4c_se_reduce[0][0]'] Y \n", + " \n", + " block4c_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4c_activation[0][0]', Y \n", + " 'block4c_se_expand[0][0]'] \n", + " \n", + " block4c_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4c_se_excite[0][0]'] Y \n", + " \n", + " block4c_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4c_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4c_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4c_project_bn[0][0]'] Y \n", + " \n", + " block4c_add (Add) (None, 14, 14, 160) 0 ['block4c_drop[0][0]', Y \n", + " 'block4b_add[0][0]'] \n", + " \n", + " block4d_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4c_add[0][0]'] Y \n", + " \n", + " block4d_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4d_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4d_expand_activation (Act (None, 14, 14, 960) 0 ['block4d_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4d_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4d_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4d_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4d_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4d_activation (Activation (None, 14, 14, 960) 0 ['block4d_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4d_se_squeeze (GlobalAver (None, 960) 0 ['block4d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4d_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4d_se_squeeze[0][0]'] Y \n", + " \n", + " block4d_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4d_se_reshape[0][0]'] Y \n", + " \n", + " block4d_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4d_se_reduce[0][0]'] Y \n", + " \n", + " block4d_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4d_activation[0][0]', Y \n", + " 'block4d_se_expand[0][0]'] \n", + " \n", + " block4d_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4d_se_excite[0][0]'] Y \n", + " \n", + " block4d_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4d_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4d_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4d_project_bn[0][0]'] Y \n", + " \n", + " block4d_add (Add) (None, 14, 14, 160) 0 ['block4d_drop[0][0]', Y \n", + " 'block4c_add[0][0]'] \n", + " \n", + " block4e_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4d_add[0][0]'] Y \n", + " \n", + " block4e_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4e_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4e_expand_activation (Act (None, 14, 14, 960) 0 ['block4e_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4e_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4e_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4e_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4e_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4e_activation (Activation (None, 14, 14, 960) 0 ['block4e_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4e_se_squeeze (GlobalAver (None, 960) 0 ['block4e_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4e_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4e_se_squeeze[0][0]'] Y \n", + " \n", + " block4e_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4e_se_reshape[0][0]'] Y \n", + " \n", + " block4e_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4e_se_reduce[0][0]'] Y \n", + " \n", + " block4e_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4e_activation[0][0]', Y \n", + " 'block4e_se_expand[0][0]'] \n", + " \n", + " block4e_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4e_se_excite[0][0]'] Y \n", + " \n", + " block4e_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4e_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4e_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4e_project_bn[0][0]'] Y \n", + " \n", + " block4e_add (Add) (None, 14, 14, 160) 0 ['block4e_drop[0][0]', Y \n", + " 'block4d_add[0][0]'] \n", + " \n", + " block4f_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4e_add[0][0]'] Y \n", + " \n", + " block4f_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4f_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4f_expand_activation (Act (None, 14, 14, 960) 0 ['block4f_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4f_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4f_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4f_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4f_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4f_activation (Activation (None, 14, 14, 960) 0 ['block4f_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4f_se_squeeze (GlobalAver (None, 960) 0 ['block4f_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4f_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4f_se_squeeze[0][0]'] Y \n", + " \n", + " block4f_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4f_se_reshape[0][0]'] Y \n", + " \n", + " block4f_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4f_se_reduce[0][0]'] Y \n", + " \n", + " block4f_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4f_activation[0][0]', Y \n", + " 'block4f_se_expand[0][0]'] \n", + " \n", + " block4f_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4f_se_excite[0][0]'] Y \n", + " \n", + " block4f_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4f_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4f_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4f_project_bn[0][0]'] Y \n", + " \n", + " block4f_add (Add) (None, 14, 14, 160) 0 ['block4f_drop[0][0]', Y \n", + " 'block4e_add[0][0]'] \n", + " \n", + " block4g_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4f_add[0][0]'] Y \n", + " \n", + " block4g_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4g_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4g_expand_activation (Act (None, 14, 14, 960) 0 ['block4g_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4g_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4g_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4g_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4g_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4g_activation (Activation (None, 14, 14, 960) 0 ['block4g_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4g_se_squeeze (GlobalAver (None, 960) 0 ['block4g_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4g_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4g_se_squeeze[0][0]'] Y \n", + " \n", + " block4g_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4g_se_reshape[0][0]'] Y \n", + " \n", + " block4g_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4g_se_reduce[0][0]'] Y \n", + " \n", + " block4g_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4g_activation[0][0]', Y \n", + " 'block4g_se_expand[0][0]'] \n", + " \n", + " block4g_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4g_se_excite[0][0]'] Y \n", + " \n", + " block4g_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4g_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4g_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4g_project_bn[0][0]'] Y \n", + " \n", + " block4g_add (Add) (None, 14, 14, 160) 0 ['block4g_drop[0][0]', Y \n", + " 'block4f_add[0][0]'] \n", + " \n", + " block4h_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4g_add[0][0]'] Y \n", + " \n", + " block4h_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4h_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4h_expand_activation (Act (None, 14, 14, 960) 0 ['block4h_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4h_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4h_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4h_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4h_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4h_activation (Activation (None, 14, 14, 960) 0 ['block4h_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4h_se_squeeze (GlobalAver (None, 960) 0 ['block4h_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4h_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4h_se_squeeze[0][0]'] Y \n", + " \n", + " block4h_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4h_se_reshape[0][0]'] Y \n", + " \n", + " block4h_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4h_se_reduce[0][0]'] Y \n", + " \n", + " block4h_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4h_activation[0][0]', Y \n", + " 'block4h_se_expand[0][0]'] \n", + " \n", + " block4h_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4h_se_excite[0][0]'] Y \n", + " \n", + " block4h_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4h_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4h_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4h_project_bn[0][0]'] Y \n", + " \n", + " block4h_add (Add) (None, 14, 14, 160) 0 ['block4h_drop[0][0]', Y \n", + " 'block4g_add[0][0]'] \n", + " \n", + " block4i_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4h_add[0][0]'] Y \n", + " \n", + " block4i_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4i_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4i_expand_activation (Act (None, 14, 14, 960) 0 ['block4i_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4i_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4i_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4i_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4i_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4i_activation (Activation (None, 14, 14, 960) 0 ['block4i_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4i_se_squeeze (GlobalAver (None, 960) 0 ['block4i_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4i_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4i_se_squeeze[0][0]'] Y \n", + " \n", + " block4i_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4i_se_reshape[0][0]'] Y \n", + " \n", + " block4i_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4i_se_reduce[0][0]'] Y \n", + " \n", + " block4i_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4i_activation[0][0]', Y \n", + " 'block4i_se_expand[0][0]'] \n", + " \n", + " block4i_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4i_se_excite[0][0]'] Y \n", + " \n", + " block4i_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4i_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4i_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4i_project_bn[0][0]'] Y \n", + " \n", + " block4i_add (Add) (None, 14, 14, 160) 0 ['block4i_drop[0][0]', Y \n", + " 'block4h_add[0][0]'] \n", + " \n", + " block4j_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4i_add[0][0]'] Y \n", + " \n", + " block4j_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4j_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4j_expand_activation (Act (None, 14, 14, 960) 0 ['block4j_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4j_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4j_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4j_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4j_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4j_activation (Activation (None, 14, 14, 960) 0 ['block4j_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4j_se_squeeze (GlobalAver (None, 960) 0 ['block4j_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4j_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4j_se_squeeze[0][0]'] Y \n", + " \n", + " block4j_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4j_se_reshape[0][0]'] Y \n", + " \n", + " block4j_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4j_se_reduce[0][0]'] Y \n", + " \n", + " block4j_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4j_activation[0][0]', Y \n", + " 'block4j_se_expand[0][0]'] \n", + " \n", + " block4j_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4j_se_excite[0][0]'] Y \n", + " \n", + " block4j_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4j_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4j_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4j_project_bn[0][0]'] Y \n", + " \n", + " block4j_add (Add) (None, 14, 14, 160) 0 ['block4j_drop[0][0]', Y \n", + " 'block4i_add[0][0]'] \n", + " \n", + " block5a_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4j_add[0][0]'] Y \n", + " \n", + " block5a_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block5a_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block5a_expand_activation (Act (None, 14, 14, 960) 0 ['block5a_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block5a_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 ['block5a_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block5a_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block5a_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block5a_activation (Activation (None, 14, 14, 960) 0 ['block5a_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block5a_se_squeeze (GlobalAver (None, 960) 0 ['block5a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5a_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block5a_se_squeeze[0][0]'] Y \n", + " \n", + " block5a_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block5a_se_reshape[0][0]'] Y \n", + " \n", + " block5a_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block5a_se_reduce[0][0]'] Y \n", + " \n", + " block5a_se_excite (Multiply) (None, 14, 14, 960) 0 ['block5a_activation[0][0]', Y \n", + " 'block5a_se_expand[0][0]'] \n", + " \n", + " block5a_project_conv (Conv2D) (None, 14, 14, 224) 215040 ['block5a_se_excite[0][0]'] Y \n", + " \n", + " block5a_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5a_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5b_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5a_project_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block5b_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5b_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5b_expand_activation (Act (None, 14, 14, 1344 0 ['block5b_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5b_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5b_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5b_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5b_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5b_activation (Activation (None, 14, 14, 1344 0 ['block5b_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5b_se_squeeze (GlobalAver (None, 1344) 0 ['block5b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5b_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5b_se_squeeze[0][0]'] Y \n", + " \n", + " block5b_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5b_se_reshape[0][0]'] Y \n", + " \n", + " block5b_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5b_se_reduce[0][0]'] Y \n", + " \n", + " block5b_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5b_activation[0][0]', Y \n", + " ) 'block5b_se_expand[0][0]'] \n", + " \n", + " block5b_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5b_se_excite[0][0]'] Y \n", + " \n", + " block5b_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5b_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5b_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5b_project_bn[0][0]'] Y \n", + " \n", + " block5b_add (Add) (None, 14, 14, 224) 0 ['block5b_drop[0][0]', Y \n", + " 'block5a_project_bn[0][0]'] \n", + " \n", + " block5c_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5b_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5c_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5c_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5c_expand_activation (Act (None, 14, 14, 1344 0 ['block5c_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5c_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5c_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5c_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5c_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5c_activation (Activation (None, 14, 14, 1344 0 ['block5c_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5c_se_squeeze (GlobalAver (None, 1344) 0 ['block5c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5c_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5c_se_squeeze[0][0]'] Y \n", + " \n", + " block5c_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5c_se_reshape[0][0]'] Y \n", + " \n", + " block5c_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5c_se_reduce[0][0]'] Y \n", + " \n", + " block5c_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5c_activation[0][0]', Y \n", + " ) 'block5c_se_expand[0][0]'] \n", + " \n", + " block5c_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5c_se_excite[0][0]'] Y \n", + " \n", + " block5c_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5c_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5c_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5c_project_bn[0][0]'] Y \n", + " \n", + " block5c_add (Add) (None, 14, 14, 224) 0 ['block5c_drop[0][0]', Y \n", + " 'block5b_add[0][0]'] \n", + " \n", + " block5d_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5c_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5d_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5d_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5d_expand_activation (Act (None, 14, 14, 1344 0 ['block5d_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5d_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5d_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5d_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5d_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5d_activation (Activation (None, 14, 14, 1344 0 ['block5d_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5d_se_squeeze (GlobalAver (None, 1344) 0 ['block5d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5d_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5d_se_squeeze[0][0]'] Y \n", + " \n", + " block5d_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5d_se_reshape[0][0]'] Y \n", + " \n", + " block5d_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5d_se_reduce[0][0]'] Y \n", + " \n", + " block5d_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5d_activation[0][0]', Y \n", + " ) 'block5d_se_expand[0][0]'] \n", + " \n", + " block5d_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5d_se_excite[0][0]'] Y \n", + " \n", + " block5d_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5d_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5d_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5d_project_bn[0][0]'] Y \n", + " \n", + " block5d_add (Add) (None, 14, 14, 224) 0 ['block5d_drop[0][0]', Y \n", + " 'block5c_add[0][0]'] \n", + " \n", + " block5e_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5d_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5e_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5e_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5e_expand_activation (Act (None, 14, 14, 1344 0 ['block5e_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5e_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5e_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5e_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5e_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5e_activation (Activation (None, 14, 14, 1344 0 ['block5e_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5e_se_squeeze (GlobalAver (None, 1344) 0 ['block5e_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5e_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5e_se_squeeze[0][0]'] Y \n", + " \n", + " block5e_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5e_se_reshape[0][0]'] Y \n", + " \n", + " block5e_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5e_se_reduce[0][0]'] Y \n", + " \n", + " block5e_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5e_activation[0][0]', Y \n", + " ) 'block5e_se_expand[0][0]'] \n", + " \n", + " block5e_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5e_se_excite[0][0]'] Y \n", + " \n", + " block5e_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5e_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5e_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5e_project_bn[0][0]'] Y \n", + " \n", + " block5e_add (Add) (None, 14, 14, 224) 0 ['block5e_drop[0][0]', Y \n", + " 'block5d_add[0][0]'] \n", + " \n", + " block5f_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5e_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5f_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5f_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5f_expand_activation (Act (None, 14, 14, 1344 0 ['block5f_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5f_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5f_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5f_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5f_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5f_activation (Activation (None, 14, 14, 1344 0 ['block5f_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5f_se_squeeze (GlobalAver (None, 1344) 0 ['block5f_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5f_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5f_se_squeeze[0][0]'] Y \n", + " \n", + " block5f_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5f_se_reshape[0][0]'] Y \n", + " \n", + " block5f_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5f_se_reduce[0][0]'] Y \n", + " \n", + " block5f_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5f_activation[0][0]', Y \n", + " ) 'block5f_se_expand[0][0]'] \n", + " \n", + " block5f_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5f_se_excite[0][0]'] Y \n", + " \n", + " block5f_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5f_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5f_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5f_project_bn[0][0]'] Y \n", + " \n", + " block5f_add (Add) (None, 14, 14, 224) 0 ['block5f_drop[0][0]', Y \n", + " 'block5e_add[0][0]'] \n", + " \n", + " block5g_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5f_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5g_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5g_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5g_expand_activation (Act (None, 14, 14, 1344 0 ['block5g_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5g_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5g_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5g_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5g_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5g_activation (Activation (None, 14, 14, 1344 0 ['block5g_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5g_se_squeeze (GlobalAver (None, 1344) 0 ['block5g_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5g_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5g_se_squeeze[0][0]'] Y \n", + " \n", + " block5g_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5g_se_reshape[0][0]'] Y \n", + " \n", + " block5g_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5g_se_reduce[0][0]'] Y \n", + " \n", + " block5g_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5g_activation[0][0]', Y \n", + " ) 'block5g_se_expand[0][0]'] \n", + " \n", + " block5g_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5g_se_excite[0][0]'] Y \n", + " \n", + " block5g_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5g_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5g_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5g_project_bn[0][0]'] Y \n", + " \n", + " block5g_add (Add) (None, 14, 14, 224) 0 ['block5g_drop[0][0]', Y \n", + " 'block5f_add[0][0]'] \n", + " \n", + " block5h_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5g_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5h_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5h_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5h_expand_activation (Act (None, 14, 14, 1344 0 ['block5h_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5h_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5h_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5h_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5h_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5h_activation (Activation (None, 14, 14, 1344 0 ['block5h_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5h_se_squeeze (GlobalAver (None, 1344) 0 ['block5h_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5h_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5h_se_squeeze[0][0]'] Y \n", + " \n", + " block5h_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5h_se_reshape[0][0]'] Y \n", + " \n", + " block5h_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5h_se_reduce[0][0]'] Y \n", + " \n", + " block5h_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5h_activation[0][0]', Y \n", + " ) 'block5h_se_expand[0][0]'] \n", + " \n", + " block5h_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5h_se_excite[0][0]'] Y \n", + " \n", + " block5h_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5h_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5h_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5h_project_bn[0][0]'] Y \n", + " \n", + " block5h_add (Add) (None, 14, 14, 224) 0 ['block5h_drop[0][0]', Y \n", + " 'block5g_add[0][0]'] \n", + " \n", + " block5i_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5h_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5i_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5i_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5i_expand_activation (Act (None, 14, 14, 1344 0 ['block5i_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5i_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5i_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5i_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5i_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5i_activation (Activation (None, 14, 14, 1344 0 ['block5i_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5i_se_squeeze (GlobalAver (None, 1344) 0 ['block5i_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5i_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5i_se_squeeze[0][0]'] Y \n", + " \n", + " block5i_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5i_se_reshape[0][0]'] Y \n", + " \n", + " block5i_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5i_se_reduce[0][0]'] Y \n", + " \n", + " block5i_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5i_activation[0][0]', Y \n", + " ) 'block5i_se_expand[0][0]'] \n", + " \n", + " block5i_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5i_se_excite[0][0]'] Y \n", + " \n", + " block5i_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5i_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5i_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5i_project_bn[0][0]'] Y \n", + " \n", + " block5i_add (Add) (None, 14, 14, 224) 0 ['block5i_drop[0][0]', Y \n", + " 'block5h_add[0][0]'] \n", + " \n", + " block5j_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5i_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5j_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5j_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5j_expand_activation (Act (None, 14, 14, 1344 0 ['block5j_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5j_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5j_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5j_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5j_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5j_activation (Activation (None, 14, 14, 1344 0 ['block5j_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5j_se_squeeze (GlobalAver (None, 1344) 0 ['block5j_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5j_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5j_se_squeeze[0][0]'] Y \n", + " \n", + " block5j_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5j_se_reshape[0][0]'] Y \n", + " \n", + " block5j_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5j_se_reduce[0][0]'] Y \n", + " \n", + " block5j_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5j_activation[0][0]', Y \n", + " ) 'block5j_se_expand[0][0]'] \n", + " \n", + " block5j_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5j_se_excite[0][0]'] Y \n", + " \n", + " block5j_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5j_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5j_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5j_project_bn[0][0]'] Y \n", + " \n", + " block5j_add (Add) (None, 14, 14, 224) 0 ['block5j_drop[0][0]', Y \n", + " 'block5i_add[0][0]'] \n", + " \n", + " block6a_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5j_add[0][0]'] Y \n", + " ) \n", + " \n", + " block6a_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block6a_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block6a_expand_activation (Act (None, 14, 14, 1344 0 ['block6a_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block6a_dwconv (DepthwiseConv2 (None, 7, 7, 1344) 33600 ['block6a_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6a_bn (BatchNormalization (None, 7, 7, 1344) 5376 ['block6a_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6a_activation (Activation (None, 7, 7, 1344) 0 ['block6a_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6a_se_squeeze (GlobalAver (None, 1344) 0 ['block6a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6a_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block6a_se_squeeze[0][0]'] Y \n", + " \n", + " block6a_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block6a_se_reshape[0][0]'] Y \n", + " \n", + " block6a_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block6a_se_reduce[0][0]'] Y \n", + " \n", + " block6a_se_excite (Multiply) (None, 7, 7, 1344) 0 ['block6a_activation[0][0]', Y \n", + " 'block6a_se_expand[0][0]'] \n", + " \n", + " block6a_project_conv (Conv2D) (None, 7, 7, 384) 516096 ['block6a_se_excite[0][0]'] Y \n", + " \n", + " block6a_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6a_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6b_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6a_project_bn[0][0]'] Y \n", + " \n", + " block6b_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6b_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6b_expand_activation (Act (None, 7, 7, 2304) 0 ['block6b_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6b_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6b_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6b_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6b_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6b_activation (Activation (None, 7, 7, 2304) 0 ['block6b_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6b_se_squeeze (GlobalAver (None, 2304) 0 ['block6b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6b_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6b_se_squeeze[0][0]'] Y \n", + " \n", + " block6b_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6b_se_reshape[0][0]'] Y \n", + " \n", + " block6b_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6b_se_reduce[0][0]'] Y \n", + " \n", + " block6b_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6b_activation[0][0]', Y \n", + " 'block6b_se_expand[0][0]'] \n", + " \n", + " block6b_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6b_se_excite[0][0]'] Y \n", + " \n", + " block6b_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6b_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6b_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6b_project_bn[0][0]'] Y \n", + " \n", + " block6b_add (Add) (None, 7, 7, 384) 0 ['block6b_drop[0][0]', Y \n", + " 'block6a_project_bn[0][0]'] \n", + " \n", + " block6c_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6b_add[0][0]'] Y \n", + " \n", + " block6c_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6c_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6c_expand_activation (Act (None, 7, 7, 2304) 0 ['block6c_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6c_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6c_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6c_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6c_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6c_activation (Activation (None, 7, 7, 2304) 0 ['block6c_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6c_se_squeeze (GlobalAver (None, 2304) 0 ['block6c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6c_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6c_se_squeeze[0][0]'] Y \n", + " \n", + " block6c_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6c_se_reshape[0][0]'] Y \n", + " \n", + " block6c_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6c_se_reduce[0][0]'] Y \n", + " \n", + " block6c_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6c_activation[0][0]', Y \n", + " 'block6c_se_expand[0][0]'] \n", + " \n", + " block6c_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6c_se_excite[0][0]'] Y \n", + " \n", + " block6c_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6c_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6c_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6c_project_bn[0][0]'] Y \n", + " \n", + " block6c_add (Add) (None, 7, 7, 384) 0 ['block6c_drop[0][0]', Y \n", + " 'block6b_add[0][0]'] \n", + " \n", + " block6d_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6c_add[0][0]'] Y \n", + " \n", + " block6d_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6d_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6d_expand_activation (Act (None, 7, 7, 2304) 0 ['block6d_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6d_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6d_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6d_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6d_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6d_activation (Activation (None, 7, 7, 2304) 0 ['block6d_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6d_se_squeeze (GlobalAver (None, 2304) 0 ['block6d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6d_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6d_se_squeeze[0][0]'] Y \n", + " \n", + " block6d_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6d_se_reshape[0][0]'] Y \n", + " \n", + " block6d_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6d_se_reduce[0][0]'] Y \n", + " \n", + " block6d_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6d_activation[0][0]', Y \n", + " 'block6d_se_expand[0][0]'] \n", + " \n", + " block6d_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6d_se_excite[0][0]'] Y \n", + " \n", + " block6d_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6d_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6d_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6d_project_bn[0][0]'] Y \n", + " \n", + " block6d_add (Add) (None, 7, 7, 384) 0 ['block6d_drop[0][0]', Y \n", + " 'block6c_add[0][0]'] \n", + " \n", + " block6e_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6d_add[0][0]'] Y \n", + " \n", + " block6e_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6e_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6e_expand_activation (Act (None, 7, 7, 2304) 0 ['block6e_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6e_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6e_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6e_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6e_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6e_activation (Activation (None, 7, 7, 2304) 0 ['block6e_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6e_se_squeeze (GlobalAver (None, 2304) 0 ['block6e_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6e_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6e_se_squeeze[0][0]'] Y \n", + " \n", + " block6e_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6e_se_reshape[0][0]'] Y \n", + " \n", + " block6e_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6e_se_reduce[0][0]'] Y \n", + " \n", + " block6e_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6e_activation[0][0]', Y \n", + " 'block6e_se_expand[0][0]'] \n", + " \n", + " block6e_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6e_se_excite[0][0]'] Y \n", + " \n", + " block6e_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6e_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6e_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6e_project_bn[0][0]'] Y \n", + " \n", + " block6e_add (Add) (None, 7, 7, 384) 0 ['block6e_drop[0][0]', Y \n", + " 'block6d_add[0][0]'] \n", + " \n", + " block6f_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6e_add[0][0]'] Y \n", + " \n", + " block6f_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6f_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6f_expand_activation (Act (None, 7, 7, 2304) 0 ['block6f_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6f_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6f_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6f_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6f_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6f_activation (Activation (None, 7, 7, 2304) 0 ['block6f_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6f_se_squeeze (GlobalAver (None, 2304) 0 ['block6f_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6f_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6f_se_squeeze[0][0]'] Y \n", + " \n", + " block6f_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6f_se_reshape[0][0]'] Y \n", + " \n", + " block6f_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6f_se_reduce[0][0]'] Y \n", + " \n", + " block6f_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6f_activation[0][0]', Y \n", + " 'block6f_se_expand[0][0]'] \n", + " \n", + " block6f_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6f_se_excite[0][0]'] Y \n", + " \n", + " block6f_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6f_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6f_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6f_project_bn[0][0]'] Y \n", + " \n", + " block6f_add (Add) (None, 7, 7, 384) 0 ['block6f_drop[0][0]', Y \n", + " 'block6e_add[0][0]'] \n", + " \n", + " block6g_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6f_add[0][0]'] Y \n", + " \n", + " block6g_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6g_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6g_expand_activation (Act (None, 7, 7, 2304) 0 ['block6g_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6g_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6g_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6g_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6g_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6g_activation (Activation (None, 7, 7, 2304) 0 ['block6g_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6g_se_squeeze (GlobalAver (None, 2304) 0 ['block6g_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6g_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6g_se_squeeze[0][0]'] Y \n", + " \n", + " block6g_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6g_se_reshape[0][0]'] Y \n", + " \n", + " block6g_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6g_se_reduce[0][0]'] Y \n", + " \n", + " block6g_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6g_activation[0][0]', Y \n", + " 'block6g_se_expand[0][0]'] \n", + " \n", + " block6g_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6g_se_excite[0][0]'] Y \n", + " \n", + " block6g_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6g_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6g_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6g_project_bn[0][0]'] Y \n", + " \n", + " block6g_add (Add) (None, 7, 7, 384) 0 ['block6g_drop[0][0]', Y \n", + " 'block6f_add[0][0]'] \n", + " \n", + " block6h_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6g_add[0][0]'] Y \n", + " \n", + " block6h_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6h_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6h_expand_activation (Act (None, 7, 7, 2304) 0 ['block6h_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6h_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6h_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6h_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6h_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6h_activation (Activation (None, 7, 7, 2304) 0 ['block6h_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6h_se_squeeze (GlobalAver (None, 2304) 0 ['block6h_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6h_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6h_se_squeeze[0][0]'] Y \n", + " \n", + " block6h_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6h_se_reshape[0][0]'] Y \n", + " \n", + " block6h_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6h_se_reduce[0][0]'] Y \n", + " \n", + " block6h_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6h_activation[0][0]', Y \n", + " 'block6h_se_expand[0][0]'] \n", + " \n", + " block6h_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6h_se_excite[0][0]'] Y \n", + " \n", + " block6h_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6h_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6h_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6h_project_bn[0][0]'] Y \n", + " \n", + " block6h_add (Add) (None, 7, 7, 384) 0 ['block6h_drop[0][0]', Y \n", + " 'block6g_add[0][0]'] \n", + " \n", + " block6i_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6h_add[0][0]'] Y \n", + " \n", + " block6i_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6i_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6i_expand_activation (Act (None, 7, 7, 2304) 0 ['block6i_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6i_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6i_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6i_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6i_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6i_activation (Activation (None, 7, 7, 2304) 0 ['block6i_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6i_se_squeeze (GlobalAver (None, 2304) 0 ['block6i_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6i_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6i_se_squeeze[0][0]'] Y \n", + " \n", + " block6i_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6i_se_reshape[0][0]'] Y \n", + " \n", + " block6i_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6i_se_reduce[0][0]'] Y \n", + " \n", + " block6i_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6i_activation[0][0]', Y \n", + " 'block6i_se_expand[0][0]'] \n", + " \n", + " block6i_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6i_se_excite[0][0]'] Y \n", + " \n", + " block6i_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6i_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6i_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6i_project_bn[0][0]'] Y \n", + " \n", + " block6i_add (Add) (None, 7, 7, 384) 0 ['block6i_drop[0][0]', Y \n", + " 'block6h_add[0][0]'] \n", + " \n", + " block6j_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6i_add[0][0]'] Y \n", + " \n", + " block6j_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6j_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6j_expand_activation (Act (None, 7, 7, 2304) 0 ['block6j_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6j_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6j_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6j_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6j_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6j_activation (Activation (None, 7, 7, 2304) 0 ['block6j_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6j_se_squeeze (GlobalAver (None, 2304) 0 ['block6j_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6j_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6j_se_squeeze[0][0]'] Y \n", + " \n", + " block6j_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6j_se_reshape[0][0]'] Y \n", + " \n", + " block6j_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6j_se_reduce[0][0]'] Y \n", + " \n", + " block6j_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6j_activation[0][0]', Y \n", + " 'block6j_se_expand[0][0]'] \n", + " \n", + " block6j_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6j_se_excite[0][0]'] Y \n", + " \n", + " block6j_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6j_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6j_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6j_project_bn[0][0]'] Y \n", + " \n", + " block6j_add (Add) (None, 7, 7, 384) 0 ['block6j_drop[0][0]', Y \n", + " 'block6i_add[0][0]'] \n", + " \n", + " block6k_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6j_add[0][0]'] Y \n", + " \n", + " block6k_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6k_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6k_expand_activation (Act (None, 7, 7, 2304) 0 ['block6k_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6k_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6k_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6k_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6k_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6k_activation (Activation (None, 7, 7, 2304) 0 ['block6k_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6k_se_squeeze (GlobalAver (None, 2304) 0 ['block6k_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6k_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6k_se_squeeze[0][0]'] Y \n", + " \n", + " block6k_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6k_se_reshape[0][0]'] Y \n", + " \n", + " block6k_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6k_se_reduce[0][0]'] Y \n", + " \n", + " block6k_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6k_activation[0][0]', Y \n", + " 'block6k_se_expand[0][0]'] \n", + " \n", + " block6k_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6k_se_excite[0][0]'] Y \n", + " \n", + " block6k_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6k_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6k_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6k_project_bn[0][0]'] Y \n", + " \n", + " block6k_add (Add) (None, 7, 7, 384) 0 ['block6k_drop[0][0]', Y \n", + " 'block6j_add[0][0]'] \n", + " \n", + " block6l_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6k_add[0][0]'] Y \n", + " \n", + " block6l_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6l_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6l_expand_activation (Act (None, 7, 7, 2304) 0 ['block6l_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6l_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6l_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6l_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6l_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6l_activation (Activation (None, 7, 7, 2304) 0 ['block6l_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6l_se_squeeze (GlobalAver (None, 2304) 0 ['block6l_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6l_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6l_se_squeeze[0][0]'] Y \n", + " \n", + " block6l_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6l_se_reshape[0][0]'] Y \n", + " \n", + " block6l_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6l_se_reduce[0][0]'] Y \n", + " \n", + " block6l_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6l_activation[0][0]', Y \n", + " 'block6l_se_expand[0][0]'] \n", + " \n", + " block6l_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6l_se_excite[0][0]'] Y \n", + " \n", + " block6l_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6l_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6l_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6l_project_bn[0][0]'] Y \n", + " \n", + " block6l_add (Add) (None, 7, 7, 384) 0 ['block6l_drop[0][0]', Y \n", + " 'block6k_add[0][0]'] \n", + " \n", + " block6m_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6l_add[0][0]'] Y \n", + " \n", + " block6m_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6m_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6m_expand_activation (Act (None, 7, 7, 2304) 0 ['block6m_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6m_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6m_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6m_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6m_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6m_activation (Activation (None, 7, 7, 2304) 0 ['block6m_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6m_se_squeeze (GlobalAver (None, 2304) 0 ['block6m_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6m_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6m_se_squeeze[0][0]'] Y \n", + " \n", + " block6m_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6m_se_reshape[0][0]'] Y \n", + " \n", + " block6m_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6m_se_reduce[0][0]'] Y \n", + " \n", + " block6m_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6m_activation[0][0]', Y \n", + " 'block6m_se_expand[0][0]'] \n", + " \n", + " block6m_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6m_se_excite[0][0]'] Y \n", + " \n", + " block6m_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6m_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6m_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6m_project_bn[0][0]'] Y \n", + " \n", + " block6m_add (Add) (None, 7, 7, 384) 0 ['block6m_drop[0][0]', Y \n", + " 'block6l_add[0][0]'] \n", + " \n", + " block7a_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6m_add[0][0]'] Y \n", + " \n", + " block7a_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block7a_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block7a_expand_activation (Act (None, 7, 7, 2304) 0 ['block7a_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block7a_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 20736 ['block7a_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block7a_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block7a_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block7a_activation (Activation (None, 7, 7, 2304) 0 ['block7a_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block7a_se_squeeze (GlobalAver (None, 2304) 0 ['block7a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block7a_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block7a_se_squeeze[0][0]'] Y \n", + " \n", + " block7a_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block7a_se_reshape[0][0]'] Y \n", + " \n", + " block7a_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block7a_se_reduce[0][0]'] Y \n", + " \n", + " block7a_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block7a_activation[0][0]', Y \n", + " 'block7a_se_expand[0][0]'] \n", + " \n", + " block7a_project_conv (Conv2D) (None, 7, 7, 640) 1474560 ['block7a_se_excite[0][0]'] Y \n", + " \n", + " block7a_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7a_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block7b_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7a_project_bn[0][0]'] Y \n", + " \n", + " block7b_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7b_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block7b_expand_activation (Act (None, 7, 7, 3840) 0 ['block7b_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block7b_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7b_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block7b_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7b_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block7b_activation (Activation (None, 7, 7, 3840) 0 ['block7b_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block7b_se_squeeze (GlobalAver (None, 3840) 0 ['block7b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block7b_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7b_se_squeeze[0][0]'] Y \n", + " \n", + " block7b_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7b_se_reshape[0][0]'] Y \n", + " \n", + " block7b_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7b_se_reduce[0][0]'] Y \n", + " \n", + " block7b_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7b_activation[0][0]', Y \n", + " 'block7b_se_expand[0][0]'] \n", + " \n", + " block7b_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7b_se_excite[0][0]'] Y \n", + " \n", + " block7b_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7b_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block7b_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7b_project_bn[0][0]'] Y \n", + " \n", + " block7b_add (Add) (None, 7, 7, 640) 0 ['block7b_drop[0][0]', Y \n", + " 'block7a_project_bn[0][0]'] \n", + " \n", + " block7c_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7b_add[0][0]'] Y \n", + " \n", + " block7c_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7c_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block7c_expand_activation (Act (None, 7, 7, 3840) 0 ['block7c_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block7c_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7c_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block7c_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7c_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block7c_activation (Activation (None, 7, 7, 3840) 0 ['block7c_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block7c_se_squeeze (GlobalAver (None, 3840) 0 ['block7c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block7c_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7c_se_squeeze[0][0]'] Y \n", + " \n", + " block7c_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7c_se_reshape[0][0]'] Y \n", + " \n", + " block7c_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7c_se_reduce[0][0]'] Y \n", + " \n", + " block7c_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7c_activation[0][0]', Y \n", + " 'block7c_se_expand[0][0]'] \n", + " \n", + " block7c_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7c_se_excite[0][0]'] Y \n", + " \n", + " block7c_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7c_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block7c_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7c_project_bn[0][0]'] Y \n", + " \n", + " block7c_add (Add) (None, 7, 7, 640) 0 ['block7c_drop[0][0]', Y \n", + " 'block7b_add[0][0]'] \n", + " \n", + " block7d_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7c_add[0][0]'] Y \n", + " \n", + " block7d_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7d_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block7d_expand_activation (Act (None, 7, 7, 3840) 0 ['block7d_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block7d_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7d_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block7d_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7d_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block7d_activation (Activation (None, 7, 7, 3840) 0 ['block7d_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block7d_se_squeeze (GlobalAver (None, 3840) 0 ['block7d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block7d_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7d_se_squeeze[0][0]'] Y \n", + " \n", + " block7d_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7d_se_reshape[0][0]'] Y \n", + " \n", + " block7d_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7d_se_reduce[0][0]'] Y \n", + " \n", + " block7d_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7d_activation[0][0]', Y \n", + " 'block7d_se_expand[0][0]'] \n", + " \n", + " block7d_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7d_se_excite[0][0]'] Y \n", + " \n", + " block7d_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7d_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block7d_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7d_project_bn[0][0]'] Y \n", + " \n", + " block7d_add (Add) (None, 7, 7, 640) 0 ['block7d_drop[0][0]', Y \n", + " 'block7c_add[0][0]'] \n", + " \n", + " top_conv (Conv2D) (None, 7, 7, 2560) 1638400 ['block7d_add[0][0]'] Y \n", + " \n", + " top_bn (BatchNormalization) (None, 7, 7, 2560) 10240 ['top_conv[0][0]'] Y \n", + " \n", + " top_activation (Activation) (None, 7, 7, 2560) 0 ['top_bn[0][0]'] Y \n", + " \n", + " global_average_pooling2d (Glob (None, 2560) 0 ['top_activation[0][0]'] Y \n", + " alAveragePooling2D) \n", + " \n", + " dense (Dense) (None, 512) 1311232 ['global_average_pooling2d[0][0 Y \n", + " ]'] \n", + " \n", + " dropout (Dropout) (None, 512) 0 ['dense[0][0]'] Y \n", + " \n", + " batch_normalization (BatchNorm (None, 512) 2048 ['dropout[0][0]'] Y \n", + " alization) \n", + " \n", + " dense_1 (Dense) (None, 512) 262656 ['batch_normalization[0][0]'] Y \n", + " \n", + " batch_normalization_1 (BatchNo (None, 512) 2048 ['dense_1[0][0]'] Y \n", + " rmalization) \n", + " \n", + " dense_2 (Dense) (None, 128) 65664 ['batch_normalization_1[0][0]'] Y \n", + " \n", + " dense_3 (Dense) (None, 2) 258 ['dense_2[0][0]'] Y \n", + " \n", + "=============================================================================================================\n", + "Total params: 65,741,586\n", + "Trainable params: 65,428,818\n", + "Non-trainable params: 312,768\n", + "_____________________________________________________________________________________________________________\n", + "done.\n" + ] + } + ], + "source": [ + "import efficientnet.tfkeras\n", + "# Configuration\n", + "PRMC = False\n", + "freeze_from_opposite = False\n", + "Extra_EXT = '_T'\n", + "freeze_layers = 0 \n", + "randomly_frozen_layers = 0 \n", + "freeze_last_seven = False \n", + "# CEC_opt = Adagrad()\n", + "# CEC_opt = Yogi()\n", + "# CEC_opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-3)\n", + "CEC_opt = SGD(momentum=0.9, nesterov=False)\n", + "# CEC_opt = Adam()\n", + "# Main\n", + "try:\n", + " if SAVE_TYPE == 'TF':\n", + " model = load_model(f'PAI_model{Extra_EXT}', compile=PRMC)\n", + " else:\n", + " model = load_model(f'PAI_model{Extra_EXT}.h5', compile=PRMC)\n", + "except (ImportError, IOError) as e:\n", + " print(f'\\033[91mfailed to load the model ERROR:\\n{e}')\n", + "else:\n", + " print('\\033[92mLoading model done.')\n", + " if not PRMC:\n", + " print('Compiling the AI model...\\033[0m')\n", + " \n", + " for layer in model.layers:\n", + " layer.trainable = True\n", + " \n", + " # Select random layers to freeze\n", + " frozen_layer_indices = random.sample(range(len(model.layers)), randomly_frozen_layers)\n", + " \n", + " for i, layer in enumerate(model.layers):\n", + " if i in frozen_layer_indices:\n", + " layer.trainable = False\n", + " else:\n", + " if freeze_from_opposite and (i > len(model.layers) - freeze_layers):\n", + " layer.trainable = False\n", + " elif (not freeze_from_opposite) and i < freeze_layers:\n", + " layer.trainable = False\n", + " else:\n", + " layer.trainable = True\n", + " \n", + " for layer in model.layers[-7:]:\n", + " layer.trainable = not freeze_last_seven\n", + " \n", + " model.compile(optimizer=CEC_opt, loss='categorical_crossentropy', metrics=['accuracy'])\n", + " model.summary(show_trainable=True, expand_nested=True)\n", + " print('done.')" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -17220,7 +18756,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T07:04:23.573633300Z", @@ -17235,1087 +18771,2371 @@ "Training the model...\n", "\u001b[0;33m\n", "Setup Verbose:\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSetting TensorBoard Log dir to \u001b[0m\u001b[0;32m[logs/fit/y2024_m01_d23-h15_m21_s03]\u001b[0m\u001b[0;36m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSetting TensorBoard Log dir to \u001b[0m\u001b[0;32m[logs/fit/y2024_m01_d26-h14_m53_s22]\u001b[0m\u001b[0;36m...\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;36mUse_extended_tensorboard \u001b[0m\u001b[0;32m[False]\u001b[0m\u001b[0;36m.\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;36mDebug_OUTPUT_DPS \u001b[0m\u001b[0;32m[True]\u001b[0m\u001b[0;36m.\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;36mOneCycleLr_UFTS \u001b[0m\u001b[0;32m[False]\u001b[0m\u001b[0;36m.\u001b[0m\n", "\u001b[0;33mSetup Verbose END.\u001b[0m\n", "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m1\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 0)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m1\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 0)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Fitting ImageDataGenerator...\u001b[0m\n", + "\u001b[0;33m- ImageDataGenerator fit done.\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;31m- Debug DP Sample dir: \u001b[0m\u001b[0;32mSamples/TSR_SUB_400_y2024_m01_d26-h14_m59_s09\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 1/6\n", + "256/256 [==============================] - 64s 189ms/step - loss: 8.6530 - accuracy: 0.7173 - val_loss: 7.5201 - val_accuracy: 0.7356\n", + "Epoch 2/6\n", + "256/256 [==============================] - 45s 176ms/step - loss: 5.9175 - accuracy: 0.8672 - val_loss: 4.5553 - val_accuracy: 0.9119\n", + "Epoch 3/6\n", + "256/256 [==============================] - 46s 178ms/step - loss: 3.6912 - accuracy: 0.8940 - val_loss: 2.8975 - val_accuracy: 0.9279\n", + "Epoch 4/6\n", + "256/256 [==============================] - 46s 177ms/step - loss: 2.4085 - accuracy: 0.9192 - val_loss: 2.0007 - val_accuracy: 0.9263\n", + "Epoch 5/6\n", + "256/256 [==============================] - 46s 179ms/step - loss: 1.7321 - accuracy: 0.9355 - val_loss: 1.5934 - val_accuracy: 0.9343\n", + "Epoch 6/6\n", + "256/256 [==============================] - 45s 173ms/step - loss: 1.4544 - accuracy: 0.9568 - val_loss: 1.4990 - val_accuracy: 0.9295\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-005-0.9343.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9343\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m1.5934\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m 0.000000 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m 0.934295\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32minf \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m1.5933588743\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m655.30 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m292.33 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m362.97 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [1] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m2\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 6)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 7/12\n", + "256/256 [==============================] - 50s 182ms/step - loss: 1.5912 - accuracy: 0.8970 - val_loss: 1.4238 - val_accuracy: 0.8702\n", + "Epoch 8/12\n", + "256/256 [==============================] - 46s 179ms/step - loss: 1.2142 - accuracy: 0.9092 - val_loss: 0.9844 - val_accuracy: 0.9327\n", + "Epoch 9/12\n", + "256/256 [==============================] - 45s 176ms/step - loss: 0.8487 - accuracy: 0.9282 - val_loss: 0.8125 - val_accuracy: 0.8654\n", + "Epoch 10/12\n", + "256/256 [==============================] - 45s 175ms/step - loss: 0.6126 - accuracy: 0.9473 - val_loss: 0.6094 - val_accuracy: 0.9183\n", + "Epoch 11/12\n", + "256/256 [==============================] - 45s 177ms/step - loss: 0.4659 - accuracy: 0.9631 - val_loss: 0.5463 - val_accuracy: 0.9327\n", + "Epoch 12/12\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.3893 - accuracy: 0.9695 - val_loss: 0.5088 - val_accuracy: 0.9343\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-012-0.9343.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9343\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5088\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9342948794. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m1.5933588743 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.5087834001\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m346.00 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m278.16 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m67.85 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [2] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m3\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 12)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 13/18\n", + "256/256 [==============================] - 49s 178ms/step - loss: 0.5400 - accuracy: 0.9124 - val_loss: 0.5205 - val_accuracy: 0.8734\n", + "Epoch 14/18\n", + "256/256 [==============================] - 44s 170ms/step - loss: 0.4499 - accuracy: 0.9204 - val_loss: 0.6405 - val_accuracy: 0.8061\n", + "Epoch 15/18\n", + "256/256 [==============================] - 44s 172ms/step - loss: 0.3581 - accuracy: 0.9326 - val_loss: 0.3466 - val_accuracy: 0.9263\n", + "Epoch 16/18\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.2619 - accuracy: 0.9526 - val_loss: 0.3205 - val_accuracy: 0.9343\n", + "Epoch 17/18\n", + "256/256 [==============================] - 45s 175ms/step - loss: 0.2040 - accuracy: 0.9636 - val_loss: 0.2980 - val_accuracy: 0.9231\n", + "Epoch 18/18\n", + "256/256 [==============================] - 44s 172ms/step - loss: 0.1561 - accuracy: 0.9810 - val_loss: 0.2959 - val_accuracy: 0.9327\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-016-0.9343.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9343\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3205\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9342948794. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.5087834001 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.3204655647\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m340.34 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m272.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m67.75 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [3] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m4\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 18)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 19/24\n", + "256/256 [==============================] - 47s 173ms/step - loss: 0.3401 - accuracy: 0.9219 - val_loss: 0.3586 - val_accuracy: 0.9231\n", + "Epoch 20/24\n", + "256/256 [==============================] - 45s 174ms/step - loss: 0.2926 - accuracy: 0.9314 - val_loss: 0.2720 - val_accuracy: 0.9391\n", + "Epoch 21/24\n", + "256/256 [==============================] - 45s 175ms/step - loss: 0.2480 - accuracy: 0.9417 - val_loss: 0.2342 - val_accuracy: 0.9407\n", + "Epoch 22/24\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.1886 - accuracy: 0.9578 - val_loss: 0.2387 - val_accuracy: 0.9391\n", + "Epoch 23/24\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.1363 - accuracy: 0.9719 - val_loss: 0.2509 - val_accuracy: 0.9439\n", + "Epoch 24/24\n", + "256/256 [==============================] - 45s 176ms/step - loss: 0.0934 - accuracy: 0.9846 - val_loss: 0.3320 - val_accuracy: 0.9311\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-023-0.9439.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2509\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m 0.934295 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m 0.943910\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.3204655647 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.2508632839\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m343.70 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m274.36 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m69.34 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [4] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m5\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 24)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 25/30\n", + "256/256 [==============================] - 50s 182ms/step - loss: 0.2724 - accuracy: 0.9258 - val_loss: 0.2060 - val_accuracy: 0.9359\n", + "Epoch 26/30\n", + "256/256 [==============================] - 46s 180ms/step - loss: 0.2342 - accuracy: 0.9324 - val_loss: 0.2237 - val_accuracy: 0.9247\n", + "Epoch 27/30\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.1993 - accuracy: 0.9460 - val_loss: 0.1988 - val_accuracy: 0.9439\n", + "Epoch 28/30\n", + "256/256 [==============================] - 45s 176ms/step - loss: 0.1530 - accuracy: 0.9565 - val_loss: 0.1867 - val_accuracy: 0.9343\n", + "Epoch 29/30\n", + "256/256 [==============================] - 45s 175ms/step - loss: 0.1135 - accuracy: 0.9714 - val_loss: 0.2152 - val_accuracy: 0.9375\n", + "Epoch 30/30\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.0714 - accuracy: 0.9858 - val_loss: 0.2431 - val_accuracy: 0.9423\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-027-0.9439.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1988\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9439102411. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.2508632839 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1988100111\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m353.15 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m279.03 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m74.12 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [5] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m6\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 30)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 31/36\n", + "256/256 [==============================] - 50s 182ms/step - loss: 0.2368 - accuracy: 0.9280 - val_loss: 0.2502 - val_accuracy: 0.9071\n", + "Epoch 32/36\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.2017 - accuracy: 0.9353 - val_loss: 0.1626 - val_accuracy: 0.9487\n", + "Epoch 33/36\n", + "256/256 [==============================] - 45s 176ms/step - loss: 0.1691 - accuracy: 0.9482 - val_loss: 0.1943 - val_accuracy: 0.9375\n", + "Epoch 34/36\n", + "256/256 [==============================] - 44s 172ms/step - loss: 0.1253 - accuracy: 0.9617 - val_loss: 0.2888 - val_accuracy: 0.9247\n", + "Epoch 35/36\n", + "256/256 [==============================] - 44s 172ms/step - loss: 0.0962 - accuracy: 0.9771 - val_loss: 0.2163 - val_accuracy: 0.9247\n", + "Epoch 36/36\n", + "256/256 [==============================] - 45s 174ms/step - loss: 0.0675 - accuracy: 0.9858 - val_loss: 0.2298 - val_accuracy: 0.9327\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-032-0.9487.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1626\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m 0.943910 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m 0.948718\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1988100111 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1625901014\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m349.49 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m274.86 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m74.64 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [6] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m7\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 36)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 37/42\n", + "256/256 [==============================] - 48s 179ms/step - loss: 0.2136 - accuracy: 0.9304 - val_loss: 0.1726 - val_accuracy: 0.9455\n", + "Epoch 38/42\n", + "256/256 [==============================] - 45s 174ms/step - loss: 0.2011 - accuracy: 0.9375 - val_loss: 0.2487 - val_accuracy: 0.9327\n", + "Epoch 39/42\n", + "256/256 [==============================] - 45s 175ms/step - loss: 0.1592 - accuracy: 0.9548 - val_loss: 0.1889 - val_accuracy: 0.9359\n", + "Epoch 40/42\n", + "256/256 [==============================] - 45s 174ms/step - loss: 0.1222 - accuracy: 0.9641 - val_loss: 0.1968 - val_accuracy: 0.9375\n", + "Epoch 41/42\n", + "256/256 [==============================] - 45s 175ms/step - loss: 0.0787 - accuracy: 0.9817 - val_loss: 0.2208 - val_accuracy: 0.9343\n", + "Epoch 42/42\n", + "256/256 [==============================] - 45s 174ms/step - loss: 0.0509 - accuracy: 0.9905 - val_loss: 0.2115 - val_accuracy: 0.9455\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-037-0.9455.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1727\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9487179518. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1625901014. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m339.99 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m273.62 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m66.36 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [7] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m8\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 42)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 43/48\n", + "256/256 [==============================] - 49s 178ms/step - loss: 0.2009 - accuracy: 0.9336 - val_loss: 0.2026 - val_accuracy: 0.9327\n", + "Epoch 44/48\n", + "256/256 [==============================] - 45s 174ms/step - loss: 0.2026 - accuracy: 0.9370 - val_loss: 0.2794 - val_accuracy: 0.8958\n", + "Epoch 45/48\n", + "256/256 [==============================] - 45s 174ms/step - loss: 0.1615 - accuracy: 0.9556 - val_loss: 0.2324 - val_accuracy: 0.9151\n", + "Epoch 46/48\n", + "256/256 [==============================] - 45s 174ms/step - loss: 0.1263 - accuracy: 0.9661 - val_loss: 0.4028 - val_accuracy: 0.8670\n", + "Epoch 47/48\n", + "256/256 [==============================] - 45s 174ms/step - loss: 0.0831 - accuracy: 0.9758 - val_loss: 0.2567 - val_accuracy: 0.9199\n", + "Epoch 48/48\n", + "256/256 [==============================] - 45s 175ms/step - loss: 0.0602 - accuracy: 0.9839 - val_loss: 0.2553 - val_accuracy: 0.9279\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-043-0.9327.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9327\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2026\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9487179518. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1625901014. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m337.97 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m273.68 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m64.29 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [8] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m9\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 48)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 49/54\n", + "256/256 [==============================] - 49s 180ms/step - loss: 0.2027 - accuracy: 0.9404 - val_loss: 0.1770 - val_accuracy: 0.9487\n", + "Epoch 50/54\n", + "256/256 [==============================] - 45s 176ms/step - loss: 0.1915 - accuracy: 0.9414 - val_loss: 0.2318 - val_accuracy: 0.9375\n", + "Epoch 51/54\n", + "256/256 [==============================] - 45s 176ms/step - loss: 0.1626 - accuracy: 0.9514 - val_loss: 0.1680 - val_accuracy: 0.9423\n", + "Epoch 52/54\n", + "256/256 [==============================] - 45s 176ms/step - loss: 0.1098 - accuracy: 0.9683 - val_loss: 0.1973 - val_accuracy: 0.9375\n", + "Epoch 53/54\n", + "256/256 [==============================] - 45s 176ms/step - loss: 0.0854 - accuracy: 0.9729 - val_loss: 0.1925 - val_accuracy: 0.9407\n", + "Epoch 54/54\n", + "256/256 [==============================] - 45s 176ms/step - loss: 0.0523 - accuracy: 0.9890 - val_loss: 0.2014 - val_accuracy: 0.9375\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-049-0.9487.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1770\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9487179518. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1625901014. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m341.17 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m276.21 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m64.97 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [9] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m10\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 54)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 55/60\n", + "256/256 [==============================] - 49s 179ms/step - loss: 0.2028 - accuracy: 0.9387 - val_loss: 0.1677 - val_accuracy: 0.9407\n", + "Epoch 56/60\n", + "256/256 [==============================] - 45s 175ms/step - loss: 0.1863 - accuracy: 0.9414 - val_loss: 0.3440 - val_accuracy: 0.8606\n", + "Epoch 57/60\n", + "256/256 [==============================] - 45s 176ms/step - loss: 0.1613 - accuracy: 0.9507 - val_loss: 0.1691 - val_accuracy: 0.9471\n", + "Epoch 58/60\n", + "256/256 [==============================] - 45s 175ms/step - loss: 0.1220 - accuracy: 0.9639 - val_loss: 0.2088 - val_accuracy: 0.9247\n", + "Epoch 59/60\n", + "256/256 [==============================] - 45s 175ms/step - loss: 0.0851 - accuracy: 0.9797 - val_loss: 0.2744 - val_accuracy: 0.9167\n", + "Epoch 60/60\n", + "256/256 [==============================] - 45s 175ms/step - loss: 0.0552 - accuracy: 0.9875 - val_loss: 0.2893 - val_accuracy: 0.9183\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-057-0.9471.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1692\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9487179518. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1625901014. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m337.68 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m274.72 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m62.96 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [10] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m11\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 60)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 61/66\n", + "256/256 [==============================] - 49s 181ms/step - loss: 0.2094 - accuracy: 0.9370 - val_loss: 0.1859 - val_accuracy: 0.9359\n", + "Epoch 62/66\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.1882 - accuracy: 0.9414 - val_loss: 0.1760 - val_accuracy: 0.9407\n", + "Epoch 63/66\n", + "256/256 [==============================] - 45s 176ms/step - loss: 0.1549 - accuracy: 0.9558 - val_loss: 0.1782 - val_accuracy: 0.9375\n", + "Epoch 64/66\n", + "256/256 [==============================] - 45s 175ms/step - loss: 0.1094 - accuracy: 0.9680 - val_loss: 0.1783 - val_accuracy: 0.9391\n", + "Epoch 65/66\n", + "256/256 [==============================] - 46s 181ms/step - loss: 0.0806 - accuracy: 0.9797 - val_loss: 0.1968 - val_accuracy: 0.9423\n", + "Epoch 66/66\n", + "256/256 [==============================] - 45s 177ms/step - loss: 0.0524 - accuracy: 0.9897 - val_loss: 0.2237 - val_accuracy: 0.9391\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-065-0.9423.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1968\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9487179518. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1625901014. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m340.91 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m277.76 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m63.15 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [11] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m12\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 66)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 67/72\n", + "256/256 [==============================] - 49s 180ms/step - loss: 0.2125 - accuracy: 0.9351 - val_loss: 0.2021 - val_accuracy: 0.9215\n", + "Epoch 68/72\n", + "256/256 [==============================] - 45s 177ms/step - loss: 0.1656 - accuracy: 0.9487 - val_loss: 0.1583 - val_accuracy: 0.9503\n", + "Epoch 69/72\n", + "256/256 [==============================] - 45s 175ms/step - loss: 0.1388 - accuracy: 0.9568 - val_loss: 0.3528 - val_accuracy: 0.8910\n", + "Epoch 70/72\n", + "256/256 [==============================] - 45s 175ms/step - loss: 0.1090 - accuracy: 0.9709 - val_loss: 0.1715 - val_accuracy: 0.9391\n", + "Epoch 71/72\n", + "256/256 [==============================] - 45s 175ms/step - loss: 0.0702 - accuracy: 0.9834 - val_loss: 0.1996 - val_accuracy: 0.9455\n", + "Epoch 72/72\n", + "256/256 [==============================] - 45s 176ms/step - loss: 0.0405 - accuracy: 0.9907 - val_loss: 0.2232 - val_accuracy: 0.9455\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-068-0.9503.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1583\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m 0.948718 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m 0.950321\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1625901014 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1583294570\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m350.48 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m276.20 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m74.28 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [12] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m13\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 72)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 73/78\n", + "256/256 [==============================] - 49s 180ms/step - loss: 0.1781 - accuracy: 0.9424 - val_loss: 0.1552 - val_accuracy: 0.9391\n", + "Epoch 74/78\n", + "256/256 [==============================] - 45s 177ms/step - loss: 0.1650 - accuracy: 0.9460 - val_loss: 0.1671 - val_accuracy: 0.9375\n", + "Epoch 75/78\n", + "256/256 [==============================] - 44s 173ms/step - loss: 0.1449 - accuracy: 0.9573 - val_loss: 0.2712 - val_accuracy: 0.8910\n", + "Epoch 76/78\n", + "256/256 [==============================] - 44s 173ms/step - loss: 0.0998 - accuracy: 0.9731 - val_loss: 0.2384 - val_accuracy: 0.9215\n", + "Epoch 77/78\n", + "256/256 [==============================] - 45s 173ms/step - loss: 0.0739 - accuracy: 0.9834 - val_loss: 0.2009 - val_accuracy: 0.9327\n", + "Epoch 78/78\n", + "256/256 [==============================] - 45s 175ms/step - loss: 0.0348 - accuracy: 0.9951 - val_loss: 0.2481 - val_accuracy: 0.9199\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-073-0.9391.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1552\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9503205419. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1583294570 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1552029550\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m340.80 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m274.05 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m66.76 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [13] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m14\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 78)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 79/84\n", + "256/256 [==============================] - 50s 183ms/step - loss: 0.1764 - accuracy: 0.9387 - val_loss: 0.1677 - val_accuracy: 0.9407\n", + "Epoch 80/84\n", + "256/256 [==============================] - 45s 177ms/step - loss: 0.1616 - accuracy: 0.9446 - val_loss: 0.3176 - val_accuracy: 0.9247\n", + "Epoch 81/84\n", + "256/256 [==============================] - 45s 174ms/step - loss: 0.1349 - accuracy: 0.9580 - val_loss: 0.1958 - val_accuracy: 0.9263\n", + "Epoch 82/84\n", + "256/256 [==============================] - 45s 177ms/step - loss: 0.0955 - accuracy: 0.9707 - val_loss: 0.2602 - val_accuracy: 0.9327\n", + "Epoch 83/84\n", + "256/256 [==============================] - 45s 176ms/step - loss: 0.0651 - accuracy: 0.9829 - val_loss: 0.2379 - val_accuracy: 0.9359\n", + "Epoch 84/84\n", + "256/256 [==============================] - 45s 177ms/step - loss: 0.0410 - accuracy: 0.9915 - val_loss: 0.3032 - val_accuracy: 0.9343\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-079-0.9407.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1676\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9503205419. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1552029550. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m349.64 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m277.37 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m72.27 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [14] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m15\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 84)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 85/90\n", + "256/256 [==============================] - 49s 180ms/step - loss: 0.1780 - accuracy: 0.9399 - val_loss: 0.1802 - val_accuracy: 0.9375\n", + "Epoch 86/90\n", + "256/256 [==============================] - 46s 177ms/step - loss: 0.1567 - accuracy: 0.9495 - val_loss: 0.1706 - val_accuracy: 0.9487\n", + "Epoch 87/90\n", + "256/256 [==============================] - 44s 173ms/step - loss: 0.1379 - accuracy: 0.9583 - val_loss: 0.1877 - val_accuracy: 0.9343\n", + "Epoch 88/90\n", + "256/256 [==============================] - 45s 174ms/step - loss: 0.1008 - accuracy: 0.9724 - val_loss: 0.1901 - val_accuracy: 0.9439\n", + "Epoch 89/90\n", + "256/256 [==============================] - 45s 176ms/step - loss: 0.0644 - accuracy: 0.9836 - val_loss: 0.2305 - val_accuracy: 0.9311\n", + "Epoch 90/90\n", + "256/256 [==============================] - 45s 177ms/step - loss: 0.0454 - accuracy: 0.9895 - val_loss: 0.2917 - val_accuracy: 0.9295\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-086-0.9487.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1707\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9503205419. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1552029550. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m343.28 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m274.73 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m68.55 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [15] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m16\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 90)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 91/96\n", + "256/256 [==============================] - 50s 182ms/step - loss: 0.1800 - accuracy: 0.9375 - val_loss: 0.1534 - val_accuracy: 0.9455\n", + "Epoch 92/96\n", + "256/256 [==============================] - 45s 173ms/step - loss: 0.1601 - accuracy: 0.9536 - val_loss: 0.1803 - val_accuracy: 0.9279\n", + "Epoch 93/96\n", + "256/256 [==============================] - 45s 174ms/step - loss: 0.1180 - accuracy: 0.9658 - val_loss: 0.2998 - val_accuracy: 0.8958\n", + "Epoch 94/96\n", + "256/256 [==============================] - 44s 172ms/step - loss: 0.0983 - accuracy: 0.9709 - val_loss: 0.1664 - val_accuracy: 0.9407\n", + "Epoch 95/96\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.0749 - accuracy: 0.9795 - val_loss: 0.2747 - val_accuracy: 0.9247\n", + "Epoch 96/96\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.0451 - accuracy: 0.9912 - val_loss: 0.2425 - val_accuracy: 0.9295\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-091-0.9455.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1533\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9503205419. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1552029550 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1533422768\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m349.55 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m276.03 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m73.52 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [16] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m17\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 96)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 97/102\n", + "256/256 [==============================] - 49s 181ms/step - loss: 0.1688 - accuracy: 0.9441 - val_loss: 0.1706 - val_accuracy: 0.9407\n", + "Epoch 98/102\n", + "256/256 [==============================] - 45s 173ms/step - loss: 0.1649 - accuracy: 0.9490 - val_loss: 0.2913 - val_accuracy: 0.9022\n", + "Epoch 99/102\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.1309 - accuracy: 0.9587 - val_loss: 0.3671 - val_accuracy: 0.8878\n", + "Epoch 100/102\n", + "256/256 [==============================] - 44s 173ms/step - loss: 0.0870 - accuracy: 0.9768 - val_loss: 0.2044 - val_accuracy: 0.9311\n", + "Epoch 101/102\n", + "256/256 [==============================] - 46s 177ms/step - loss: 0.0652 - accuracy: 0.9841 - val_loss: 0.1967 - val_accuracy: 0.9423\n", + "Epoch 102/102\n", + "256/256 [==============================] - 45s 176ms/step - loss: 0.0432 - accuracy: 0.9905 - val_loss: 0.2328 - val_accuracy: 0.9407\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-101-0.9423.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1968\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9503205419. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1533422768. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m346.56 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m276.24 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m70.31 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [17] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m18\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 102)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 103/108\n", + "256/256 [==============================] - 49s 178ms/step - loss: 0.1996 - accuracy: 0.9341 - val_loss: 0.1652 - val_accuracy: 0.9391\n", + "Epoch 104/108\n", + "256/256 [==============================] - 45s 174ms/step - loss: 0.1715 - accuracy: 0.9470 - val_loss: 0.3157 - val_accuracy: 0.9054\n", + "Epoch 105/108\n", + "256/256 [==============================] - 45s 177ms/step - loss: 0.1315 - accuracy: 0.9558 - val_loss: 0.1886 - val_accuracy: 0.9407\n", + "Epoch 106/108\n", + "256/256 [==============================] - 45s 175ms/step - loss: 0.1008 - accuracy: 0.9695 - val_loss: 0.3061 - val_accuracy: 0.9183\n", + "Epoch 107/108\n", + "256/256 [==============================] - 45s 173ms/step - loss: 0.0711 - accuracy: 0.9812 - val_loss: 0.2253 - val_accuracy: 0.9375\n", + "Epoch 108/108\n", + "256/256 [==============================] - 44s 172ms/step - loss: 0.0413 - accuracy: 0.9919 - val_loss: 0.2657 - val_accuracy: 0.9359\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-105-0.9407.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1886\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9503205419. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1533422768. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m340.35 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m273.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m66.76 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [18] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m19\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 108)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 109/114\n", + "256/256 [==============================] - 49s 179ms/step - loss: 0.2013 - accuracy: 0.9360 - val_loss: 0.1608 - val_accuracy: 0.9407\n", + "Epoch 110/114\n", + "256/256 [==============================] - 45s 175ms/step - loss: 0.1561 - accuracy: 0.9431 - val_loss: 0.1782 - val_accuracy: 0.9343\n", + "Epoch 111/114\n", + "256/256 [==============================] - 45s 175ms/step - loss: 0.1202 - accuracy: 0.9666 - val_loss: 0.3958 - val_accuracy: 0.8894\n", + "Epoch 112/114\n", + "256/256 [==============================] - 45s 176ms/step - loss: 0.0780 - accuracy: 0.9810 - val_loss: 0.2509 - val_accuracy: 0.9231\n", + "Epoch 113/114\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.0485 - accuracy: 0.9888 - val_loss: 0.3369 - val_accuracy: 0.9247\n", + "Epoch 114/114\n", + "256/256 [==============================] - 44s 173ms/step - loss: 0.0294 - accuracy: 0.9954 - val_loss: 0.3194 - val_accuracy: 0.9247\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-109-0.9407.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1608\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9503205419. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1533422768. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m341.27 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m274.92 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m66.35 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [19] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m20\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 114)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 115/120\n", + "256/256 [==============================] - 48s 178ms/step - loss: 0.1663 - accuracy: 0.9468 - val_loss: 0.1575 - val_accuracy: 0.9439\n", + "Epoch 116/120\n", + "256/256 [==============================] - 45s 174ms/step - loss: 0.1488 - accuracy: 0.9468 - val_loss: 0.1760 - val_accuracy: 0.9583\n", + "Epoch 117/120\n", + "256/256 [==============================] - 44s 172ms/step - loss: 0.1150 - accuracy: 0.9619 - val_loss: 0.2182 - val_accuracy: 0.9295\n", + "Epoch 118/120\n", + "256/256 [==============================] - 44s 172ms/step - loss: 0.0815 - accuracy: 0.9775 - val_loss: 0.1539 - val_accuracy: 0.9503\n", + "Epoch 119/120\n", + "256/256 [==============================] - 44s 172ms/step - loss: 0.0501 - accuracy: 0.9885 - val_loss: 0.1603 - val_accuracy: 0.9503\n", + "Epoch 120/120\n", + "256/256 [==============================] - 45s 174ms/step - loss: 0.0322 - accuracy: 0.9922 - val_loss: 0.1937 - val_accuracy: 0.9487\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-116-0.9583.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9583\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1760\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m 0.950321 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m 0.958333\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1533422768. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m344.40 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m271.57 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m72.83 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [20] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m21\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 120)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 121/126\n", + "256/256 [==============================] - 49s 180ms/step - loss: 0.1935 - accuracy: 0.9341 - val_loss: 0.2449 - val_accuracy: 0.9247\n", + "Epoch 122/126\n", + "256/256 [==============================] - 45s 177ms/step - loss: 0.1680 - accuracy: 0.9456 - val_loss: 0.1690 - val_accuracy: 0.9519\n", + "Epoch 123/126\n", + "256/256 [==============================] - 45s 177ms/step - loss: 0.1332 - accuracy: 0.9595 - val_loss: 0.1742 - val_accuracy: 0.9391\n", + "Epoch 124/126\n", + "256/256 [==============================] - 45s 174ms/step - loss: 0.0813 - accuracy: 0.9763 - val_loss: 0.2512 - val_accuracy: 0.9199\n", + "Epoch 125/126\n", + "256/256 [==============================] - 45s 176ms/step - loss: 0.0581 - accuracy: 0.9873 - val_loss: 0.2040 - val_accuracy: 0.9439\n", + "Epoch 126/126\n", + "256/256 [==============================] - 45s 177ms/step - loss: 0.0363 - accuracy: 0.9915 - val_loss: 0.2348 - val_accuracy: 0.9423\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-122-0.9519.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9519\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1690\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9583333135. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1533422768. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m347.26 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m276.84 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m70.42 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [21] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m22\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 126)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 127/132\n", + "256/256 [==============================] - 50s 182ms/step - loss: 0.1753 - accuracy: 0.9434 - val_loss: 0.1615 - val_accuracy: 0.9503\n", + "Epoch 128/132\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.1602 - accuracy: 0.9497 - val_loss: 0.1568 - val_accuracy: 0.9519\n", + "Epoch 129/132\n", + "256/256 [==============================] - 45s 174ms/step - loss: 0.1141 - accuracy: 0.9651 - val_loss: 0.2989 - val_accuracy: 0.8926\n", + "Epoch 130/132\n", + "256/256 [==============================] - 45s 176ms/step - loss: 0.0841 - accuracy: 0.9775 - val_loss: 0.2142 - val_accuracy: 0.9231\n", + "Epoch 131/132\n", + "256/256 [==============================] - 45s 175ms/step - loss: 0.0540 - accuracy: 0.9873 - val_loss: 0.1712 - val_accuracy: 0.9471\n", + "Epoch 132/132\n", + "256/256 [==============================] - 45s 175ms/step - loss: 0.0378 - accuracy: 0.9915 - val_loss: 0.1735 - val_accuracy: 0.9407\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-128-0.9519.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9519\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1568\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9583333135. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1533422768. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m347.80 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m276.36 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m71.44 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [22] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m23\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 132)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 133/138\n", + "256/256 [==============================] - 49s 179ms/step - loss: 0.1620 - accuracy: 0.9475 - val_loss: 0.1623 - val_accuracy: 0.9487\n", + "Epoch 134/138\n", + "256/256 [==============================] - 44s 173ms/step - loss: 0.1447 - accuracy: 0.9507 - val_loss: 0.1766 - val_accuracy: 0.9375\n", + "Epoch 135/138\n", + "256/256 [==============================] - 45s 177ms/step - loss: 0.1114 - accuracy: 0.9666 - val_loss: 0.1674 - val_accuracy: 0.9375\n", + "Epoch 136/138\n", + "256/256 [==============================] - 45s 176ms/step - loss: 0.0812 - accuracy: 0.9773 - val_loss: 0.1690 - val_accuracy: 0.9423\n", + "Epoch 137/138\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.0480 - accuracy: 0.9893 - val_loss: 0.1477 - val_accuracy: 0.9567\n", + "Epoch 138/138\n", + "256/256 [==============================] - 45s 174ms/step - loss: 0.0368 - accuracy: 0.9919 - val_loss: 0.1655 - val_accuracy: 0.9423\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-137-0.9567.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9567\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1477\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9583333135. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1533422768 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1477165371\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m345.90 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m274.90 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m71.00 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [23] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m24\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 138)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 139/144\n", + "256/256 [==============================] - 49s 179ms/step - loss: 0.1769 - accuracy: 0.9451 - val_loss: 0.1534 - val_accuracy: 0.9535\n", + "Epoch 140/144\n", + "256/256 [==============================] - 45s 173ms/step - loss: 0.1530 - accuracy: 0.9519 - val_loss: 0.2885 - val_accuracy: 0.8990\n", + "Epoch 141/144\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.1160 - accuracy: 0.9634 - val_loss: 0.1553 - val_accuracy: 0.9503\n", + "Epoch 142/144\n", + "256/256 [==============================] - 45s 174ms/step - loss: 0.0779 - accuracy: 0.9766 - val_loss: 0.1784 - val_accuracy: 0.9391\n", + "Epoch 143/144\n", + "256/256 [==============================] - 45s 174ms/step - loss: 0.0565 - accuracy: 0.9863 - val_loss: 0.2021 - val_accuracy: 0.9295\n", + "Epoch 144/144\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.0349 - accuracy: 0.9932 - val_loss: 0.1458 - val_accuracy: 0.9583\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-144-0.9583.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9583\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1458\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9583333135. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1477165371 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1457828581\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m344.62 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m275.40 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m69.22 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [24] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m25\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 144)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 145/150\n", + "256/256 [==============================] - 49s 182ms/step - loss: 0.1718 - accuracy: 0.9451 - val_loss: 0.1678 - val_accuracy: 0.9583\n", + "Epoch 146/150\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.1439 - accuracy: 0.9546 - val_loss: 0.1693 - val_accuracy: 0.9391\n", + "Epoch 147/150\n", + "256/256 [==============================] - 46s 180ms/step - loss: 0.0999 - accuracy: 0.9678 - val_loss: 0.2323 - val_accuracy: 0.9279\n", + "Epoch 148/150\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.0725 - accuracy: 0.9800 - val_loss: 0.1792 - val_accuracy: 0.9487\n", + "Epoch 149/150\n", + "256/256 [==============================] - 45s 176ms/step - loss: 0.0514 - accuracy: 0.9861 - val_loss: 0.2630 - val_accuracy: 0.9167\n", + "Epoch 150/150\n", + "256/256 [==============================] - 46s 180ms/step - loss: 0.0287 - accuracy: 0.9939 - val_loss: 0.2022 - val_accuracy: 0.9359\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-145-0.9583.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9583\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1679\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9583333135. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1457828581. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m350.95 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m279.51 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m71.45 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [25] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m26\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 150)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01094\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 151/156\n", + "256/256 [==============================] - 50s 184ms/step - loss: 0.1642 - accuracy: 0.9468 - val_loss: 0.1581 - val_accuracy: 0.9567\n", + "Epoch 152/156\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.1524 - accuracy: 0.9512 - val_loss: 0.2170 - val_accuracy: 0.9167\n", + "Epoch 153/156\n", + "256/256 [==============================] - 45s 175ms/step - loss: 0.1175 - accuracy: 0.9629 - val_loss: 0.1746 - val_accuracy: 0.9375\n", + "Epoch 154/156\n", + "256/256 [==============================] - 45s 175ms/step - loss: 0.0827 - accuracy: 0.9810 - val_loss: 0.1589 - val_accuracy: 0.9535\n", + "Epoch 155/156\n", + "256/256 [==============================] - 45s 176ms/step - loss: 0.0487 - accuracy: 0.9880 - val_loss: 0.1704 - val_accuracy: 0.9615\n", + "Epoch 156/156\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.0386 - accuracy: 0.9912 - val_loss: 0.1708 - val_accuracy: 0.9615\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-155-0.9615.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9615\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1704\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m 0.958333 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m 0.961538\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1457828581. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m353.91 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m278.01 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m75.89 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [26] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m27\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 156)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01088\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 157/162\n", + "256/256 [==============================] - 49s 180ms/step - loss: 0.1748 - accuracy: 0.9468 - val_loss: 0.1345 - val_accuracy: 0.9583\n", + "Epoch 158/162\n", + "256/256 [==============================] - 45s 177ms/step - loss: 0.1506 - accuracy: 0.9556 - val_loss: 0.1497 - val_accuracy: 0.9503\n", + "Epoch 159/162\n", + "256/256 [==============================] - 45s 175ms/step - loss: 0.1231 - accuracy: 0.9646 - val_loss: 0.1463 - val_accuracy: 0.9503\n", + "Epoch 160/162\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.0756 - accuracy: 0.9807 - val_loss: 0.1345 - val_accuracy: 0.9599\n", + "Epoch 161/162\n", + "256/256 [==============================] - 45s 176ms/step - loss: 0.0556 - accuracy: 0.9861 - val_loss: 0.1793 - val_accuracy: 0.9535\n", + "Epoch 162/162\n", + "256/256 [==============================] - 45s 176ms/step - loss: 0.0403 - accuracy: 0.9912 - val_loss: 0.1488 - val_accuracy: 0.9583\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-160-0.9599.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9599\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1345\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1457828581 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1344851404\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m356.31 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m277.12 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m79.19 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [27] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m28\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 162)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01082\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 163/168\n", + "256/256 [==============================] - 50s 183ms/step - loss: 0.1722 - accuracy: 0.9465 - val_loss: 0.1380 - val_accuracy: 0.9567\n", + "Epoch 164/168\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.1446 - accuracy: 0.9563 - val_loss: 0.1513 - val_accuracy: 0.9551\n", + "Epoch 165/168\n", + "256/256 [==============================] - 45s 175ms/step - loss: 0.0937 - accuracy: 0.9717 - val_loss: 0.2236 - val_accuracy: 0.9375\n", + "Epoch 166/168\n", + "256/256 [==============================] - 45s 177ms/step - loss: 0.0685 - accuracy: 0.9775 - val_loss: 0.2023 - val_accuracy: 0.9407\n", + "Epoch 167/168\n", + "256/256 [==============================] - 45s 175ms/step - loss: 0.0507 - accuracy: 0.9902 - val_loss: 0.2218 - val_accuracy: 0.9439\n", + "Epoch 168/168\n", + "256/256 [==============================] - 45s 177ms/step - loss: 0.0256 - accuracy: 0.9958 - val_loss: 0.1981 - val_accuracy: 0.9439\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9567}, \u001b[0m\u001b[0;33mloss{0.1380}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1345}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1980\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1344851404. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m354.68 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m277.33 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m77.35 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [28] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m29\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 168)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01076\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 169/174\n", + "256/256 [==============================] - 50s 185ms/step - loss: 0.1736 - accuracy: 0.9446 - val_loss: 0.1688 - val_accuracy: 0.9311\n", + "Epoch 170/174\n", + "256/256 [==============================] - 47s 182ms/step - loss: 0.1313 - accuracy: 0.9626 - val_loss: 0.1326 - val_accuracy: 0.9503\n", + "Epoch 171/174\n", + "256/256 [==============================] - 47s 182ms/step - loss: 0.1220 - accuracy: 0.9636 - val_loss: 0.1720 - val_accuracy: 0.9359\n", + "Epoch 172/174\n", + "256/256 [==============================] - 45s 175ms/step - loss: 0.0843 - accuracy: 0.9753 - val_loss: 0.1356 - val_accuracy: 0.9599\n", + "Epoch 173/174\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.0540 - accuracy: 0.9863 - val_loss: 0.1855 - val_accuracy: 0.9471\n", + "Epoch 174/174\n", + "256/256 [==============================] - 46s 180ms/step - loss: 0.0307 - accuracy: 0.9932 - val_loss: 0.2005 - val_accuracy: 0.9407\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-172-0.9599.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9599\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1357\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1344851404. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m358.33 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m281.32 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m77.01 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [29] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m30\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 174)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0107\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 175/180\n", + "256/256 [==============================] - 50s 183ms/step - loss: 0.1858 - accuracy: 0.9370 - val_loss: 0.1737 - val_accuracy: 0.9391\n", + "Epoch 176/180\n", + "256/256 [==============================] - 46s 180ms/step - loss: 0.1612 - accuracy: 0.9509 - val_loss: 0.1555 - val_accuracy: 0.9519\n", + "Epoch 177/180\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.1049 - accuracy: 0.9700 - val_loss: 0.1565 - val_accuracy: 0.9423\n", + "Epoch 178/180\n", + "256/256 [==============================] - 47s 185ms/step - loss: 0.0882 - accuracy: 0.9766 - val_loss: 0.1467 - val_accuracy: 0.9551\n", + "Epoch 179/180\n", + "256/256 [==============================] - 45s 177ms/step - loss: 0.0484 - accuracy: 0.9880 - val_loss: 0.2215 - val_accuracy: 0.9295\n", + "Epoch 180/180\n", + "256/256 [==============================] - 45s 174ms/step - loss: 0.0314 - accuracy: 0.9946 - val_loss: 0.1765 - val_accuracy: 0.9487\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9551}, \u001b[0m\u001b[0;33mloss{0.1467}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1345}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1765\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1344851404. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m357.65 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m280.88 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m76.77 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [30] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m31\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 180)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01064\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 181/186\n", + "256/256 [==============================] - 51s 186ms/step - loss: 0.1757 - accuracy: 0.9451 - val_loss: 0.2124 - val_accuracy: 0.9295\n", + "Epoch 182/186\n", + "256/256 [==============================] - 47s 182ms/step - loss: 0.1258 - accuracy: 0.9612 - val_loss: 0.1451 - val_accuracy: 0.9471\n", + "Epoch 183/186\n", + "256/256 [==============================] - 46s 180ms/step - loss: 0.0908 - accuracy: 0.9714 - val_loss: 0.1544 - val_accuracy: 0.9407\n", + "Epoch 184/186\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.0597 - accuracy: 0.9814 - val_loss: 0.1675 - val_accuracy: 0.9407\n", + "Epoch 185/186\n", + "256/256 [==============================] - 47s 183ms/step - loss: 0.0437 - accuracy: 0.9885 - val_loss: 0.1770 - val_accuracy: 0.9423\n", + "Epoch 186/186\n", + "256/256 [==============================] - 46s 181ms/step - loss: 0.0309 - accuracy: 0.9934 - val_loss: 0.1718 - val_accuracy: 0.9455\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9471}, \u001b[0m\u001b[0;33mloss{0.1451}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1345}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1718\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1344851404. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m356.89 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m284.16 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m72.73 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [31] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m32\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 186)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33m└───Shuffling data...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01058\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 187/192\n", + "256/256 [==============================] - 52s 190ms/step - loss: 0.1776 - accuracy: 0.9463 - val_loss: 0.1711 - val_accuracy: 0.9391\n", + "Epoch 188/192\n", + "256/256 [==============================] - 46s 180ms/step - loss: 0.1528 - accuracy: 0.9534 - val_loss: 0.1292 - val_accuracy: 0.9471\n", + "Epoch 189/192\n", + "256/256 [==============================] - 45s 175ms/step - loss: 0.0933 - accuracy: 0.9744 - val_loss: 0.2000 - val_accuracy: 0.9327\n", + "Epoch 190/192\n", + "256/256 [==============================] - 45s 176ms/step - loss: 0.0623 - accuracy: 0.9827 - val_loss: 0.2264 - val_accuracy: 0.9375\n", + "Epoch 191/192\n", + "256/256 [==============================] - 45s 175ms/step - loss: 0.0344 - accuracy: 0.9927 - val_loss: 0.1794 - val_accuracy: 0.9423\n", + "Epoch 192/192\n", + "256/256 [==============================] - 45s 175ms/step - loss: 0.0360 - accuracy: 0.9907 - val_loss: 0.1729 - val_accuracy: 0.9423\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-188-0.9471.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1292\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1344851404 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1292192638\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m371.26 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m279.52 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m91.75 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [32] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m33\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 192)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Fitting ImageDataGenerator...\u001b[0m\n", - "\u001b[0;33m- ImageDataGenerator fit done.\u001b[0m\n", "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;31m- Debug DP Sample dir: \u001b[0m\u001b[0;32mSamples/TSR_SUB_400_y2024_m01_d23-h15_m27_s55\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01052\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 1/6\n", - "256/256 [==============================] - 94s 302ms/step - loss: 8.9813 - accuracy: 0.6421 - val_loss: 7.5518 - val_accuracy: 0.8782\n", - "Epoch 2/6\n", - "256/256 [==============================] - 73s 285ms/step - loss: 6.1605 - accuracy: 0.8184 - val_loss: 4.7986 - val_accuracy: 0.8141\n", - "Epoch 3/6\n", - "256/256 [==============================] - 74s 287ms/step - loss: 3.8019 - accuracy: 0.8838 - val_loss: 2.9593 - val_accuracy: 0.9071\n", - "Epoch 4/6\n", - "256/256 [==============================] - 73s 285ms/step - loss: 2.4911 - accuracy: 0.9016 - val_loss: 2.1548 - val_accuracy: 0.8942\n", - "Epoch 5/6\n", - "256/256 [==============================] - 73s 285ms/step - loss: 1.8127 - accuracy: 0.9175 - val_loss: 1.6893 - val_accuracy: 0.8846\n", - "Epoch 6/6\n", - "256/256 [==============================] - 73s 284ms/step - loss: 1.5209 - accuracy: 0.9370 - val_loss: 1.5838 - val_accuracy: 0.8974\n", + "Epoch 193/198\n", + "256/256 [==============================] - 49s 179ms/step - loss: 0.1605 - accuracy: 0.9482 - val_loss: 0.1399 - val_accuracy: 0.9503\n", + "Epoch 194/198\n", + "256/256 [==============================] - 45s 175ms/step - loss: 0.1440 - accuracy: 0.9563 - val_loss: 0.1420 - val_accuracy: 0.9503\n", + "Epoch 195/198\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.0963 - accuracy: 0.9709 - val_loss: 0.2075 - val_accuracy: 0.9247\n", + "Epoch 196/198\n", + "256/256 [==============================] - 47s 183ms/step - loss: 0.0608 - accuracy: 0.9817 - val_loss: 0.1606 - val_accuracy: 0.9519\n", + "Epoch 197/198\n", + "256/256 [==============================] - 46s 181ms/step - loss: 0.0335 - accuracy: 0.9917 - val_loss: 0.1792 - val_accuracy: 0.9455\n", + "Epoch 198/198\n", + "256/256 [==============================] - 46s 180ms/step - loss: 0.0258 - accuracy: 0.9927 - val_loss: 0.1566 - val_accuracy: 0.9535\n", "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-003-0.9071.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9071\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m2.9593\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m 0.000000 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m 0.907051\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32minf \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m2.9592649937\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m897.03 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m462.10 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m434.94 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [1] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9535}, \u001b[0m\u001b[0;33mloss{0.1399}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1292}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1566\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1292192638. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m359.96 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m280.23 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m79.73 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [33] END|--------------------------------------->\u001b[0m\n", "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m2\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 6)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m34\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 198)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;33mPreparing train data...\u001b[0m\n", "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01046\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 7/12\n", - "256/256 [==============================] - 80s 295ms/step - loss: 2.8681 - accuracy: 0.8806 - val_loss: 2.4747 - val_accuracy: 0.9135\n", - "Epoch 8/12\n", - "256/256 [==============================] - 74s 287ms/step - loss: 2.1103 - accuracy: 0.8884 - val_loss: 1.6685 - val_accuracy: 0.9006\n", - "Epoch 9/12\n", - "256/256 [==============================] - 75s 293ms/step - loss: 1.4251 - accuracy: 0.9038 - val_loss: 1.1568 - val_accuracy: 0.9199\n", - "Epoch 10/12\n", - "256/256 [==============================] - 73s 284ms/step - loss: 1.0017 - accuracy: 0.9233 - val_loss: 0.9066 - val_accuracy: 0.9215\n", - "Epoch 11/12\n", - "256/256 [==============================] - 73s 284ms/step - loss: 0.7535 - accuracy: 0.9421 - val_loss: 0.7657 - val_accuracy: 0.9087\n", - "Epoch 12/12\n", - "256/256 [==============================] - 74s 287ms/step - loss: 0.6424 - accuracy: 0.9558 - val_loss: 0.7111 - val_accuracy: 0.9247\n", + "Epoch 199/204\n", + "256/256 [==============================] - 49s 181ms/step - loss: 0.1732 - accuracy: 0.9475 - val_loss: 0.1640 - val_accuracy: 0.9407\n", + "Epoch 200/204\n", + "256/256 [==============================] - 45s 176ms/step - loss: 0.1279 - accuracy: 0.9626 - val_loss: 0.1706 - val_accuracy: 0.9391\n", + "Epoch 201/204\n", + "256/256 [==============================] - 45s 176ms/step - loss: 0.0953 - accuracy: 0.9734 - val_loss: 0.2047 - val_accuracy: 0.9263\n", + "Epoch 202/204\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.0660 - accuracy: 0.9824 - val_loss: 0.1426 - val_accuracy: 0.9567\n", + "Epoch 203/204\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.0415 - accuracy: 0.9910 - val_loss: 0.1959 - val_accuracy: 0.9311\n", + "Epoch 204/204\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.0274 - accuracy: 0.9934 - val_loss: 0.2281 - val_accuracy: 0.9327\n", "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-012-0.9247.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9247\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.7111\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m 0.907051 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m 0.924679\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m2.9592649937 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.7111035585\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m543.81 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m449.15 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m94.66 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [2] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9567}, \u001b[0m\u001b[0;33mloss{0.1426}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1292}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9327\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2282\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1292192638. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m354.28 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m277.99 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m76.29 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [34] END|--------------------------------------->\u001b[0m\n", "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m3\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 12)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m35\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 204)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;33mPreparing train data...\u001b[0m\n", "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0104\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 13/18\n", - "256/256 [==============================] - 78s 290ms/step - loss: 0.7519 - accuracy: 0.8962 - val_loss: 0.6257 - val_accuracy: 0.9375\n", - "Epoch 14/18\n", - "256/256 [==============================] - 72s 280ms/step - loss: 0.6196 - accuracy: 0.9102 - val_loss: 0.5282 - val_accuracy: 0.9167\n", - "Epoch 15/18\n", - "256/256 [==============================] - 73s 284ms/step - loss: 0.4699 - accuracy: 0.9268 - val_loss: 0.4071 - val_accuracy: 0.9407\n", - "Epoch 16/18\n", - "256/256 [==============================] - 72s 280ms/step - loss: 0.3683 - accuracy: 0.9404 - val_loss: 0.3678 - val_accuracy: 0.9391\n", - "Epoch 17/18\n", - "256/256 [==============================] - 73s 285ms/step - loss: 0.2945 - accuracy: 0.9490 - val_loss: 0.4586 - val_accuracy: 0.9087\n", - "Epoch 18/18\n", - "256/256 [==============================] - 75s 290ms/step - loss: 0.2461 - accuracy: 0.9580 - val_loss: 0.3428 - val_accuracy: 0.9311\n", + "Epoch 205/210\n", + "256/256 [==============================] - 49s 182ms/step - loss: 0.1669 - accuracy: 0.9514 - val_loss: 0.1420 - val_accuracy: 0.9503\n", + "Epoch 206/210\n", + "256/256 [==============================] - 45s 177ms/step - loss: 0.1423 - accuracy: 0.9543 - val_loss: 0.1907 - val_accuracy: 0.9343\n", + "Epoch 207/210\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.1001 - accuracy: 0.9695 - val_loss: 0.1412 - val_accuracy: 0.9535\n", + "Epoch 208/210\n", + "256/256 [==============================] - 45s 176ms/step - loss: 0.0686 - accuracy: 0.9790 - val_loss: 0.1942 - val_accuracy: 0.9471\n", + "Epoch 209/210\n", + "256/256 [==============================] - 45s 176ms/step - loss: 0.0297 - accuracy: 0.9949 - val_loss: 0.2981 - val_accuracy: 0.9359\n", + "Epoch 210/210\n", + "256/256 [==============================] - 45s 176ms/step - loss: 0.0242 - accuracy: 0.9944 - val_loss: 0.2094 - val_accuracy: 0.9519\n", "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-015-0.9407.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4071\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m 0.924679 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m 0.940705\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.7111035585 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.4071271718\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m538.01 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m444.11 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m93.90 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [3] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9535}, \u001b[0m\u001b[0;33mloss{0.1412}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1292}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9519\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2093\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1292192638. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m354.04 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m277.38 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m76.66 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [35] END|--------------------------------------->\u001b[0m\n", "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m4\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 18)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m36\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 210)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;33mPreparing train data...\u001b[0m\n", "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01034\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 19/24\n", - "256/256 [==============================] - 81s 297ms/step - loss: 0.4613 - accuracy: 0.9072 - val_loss: 0.3596 - val_accuracy: 0.9375\n", - "Epoch 20/24\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.4214 - accuracy: 0.9070 - val_loss: 0.5467 - val_accuracy: 0.9054\n", - "Epoch 21/24\n", - "256/256 [==============================] - 75s 292ms/step - loss: 0.3277 - accuracy: 0.9272 - val_loss: 0.2573 - val_accuracy: 0.9423\n", - "Epoch 22/24\n", - "256/256 [==============================] - 75s 290ms/step - loss: 0.2878 - accuracy: 0.9353 - val_loss: 0.2760 - val_accuracy: 0.9439\n", - "Epoch 23/24\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.1992 - accuracy: 0.9561 - val_loss: 0.2420 - val_accuracy: 0.9439\n", - "Epoch 24/24\n", - "256/256 [==============================] - 74s 287ms/step - loss: 0.1700 - accuracy: 0.9692 - val_loss: 0.2573 - val_accuracy: 0.9423\n", + "Epoch 211/216\n", + "256/256 [==============================] - 50s 182ms/step - loss: 0.1801 - accuracy: 0.9473 - val_loss: 0.1868 - val_accuracy: 0.9375\n", + "Epoch 212/216\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.1259 - accuracy: 0.9617 - val_loss: 0.1831 - val_accuracy: 0.9391\n", + "Epoch 213/216\n", + "256/256 [==============================] - 45s 176ms/step - loss: 0.0919 - accuracy: 0.9712 - val_loss: 0.2217 - val_accuracy: 0.9311\n", + "Epoch 214/216\n", + "256/256 [==============================] - 45s 177ms/step - loss: 0.0478 - accuracy: 0.9871 - val_loss: 0.2754 - val_accuracy: 0.9087\n", + "Epoch 215/216\n", + "256/256 [==============================] - 45s 177ms/step - loss: 0.0359 - accuracy: 0.9907 - val_loss: 0.2529 - val_accuracy: 0.9343\n", + "Epoch 216/216\n", + "256/256 [==============================] - 46s 177ms/step - loss: 0.0211 - accuracy: 0.9961 - val_loss: 0.2805 - val_accuracy: 0.9327\n", "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-022-0.9439.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2760\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m 0.940705 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m 0.943910\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.4071271718 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.2759856582\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m555.99 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m453.42 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m102.57 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [4] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9391}, \u001b[0m\u001b[0;33mloss{0.1831}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1292}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9327\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2804\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1292192638. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m353.95 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m278.26 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m75.69 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [36] END|--------------------------------------->\u001b[0m\n", "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m5\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 24)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m37\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 216)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;33mPreparing train data...\u001b[0m\n", "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01028\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 25/30\n", - "256/256 [==============================] - 81s 296ms/step - loss: 0.3227 - accuracy: 0.9119 - val_loss: 0.2258 - val_accuracy: 0.9487\n", - "Epoch 26/30\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.3038 - accuracy: 0.9199 - val_loss: 0.2747 - val_accuracy: 0.8990\n", - "Epoch 27/30\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.2345 - accuracy: 0.9358 - val_loss: 0.2601 - val_accuracy: 0.9247\n", - "Epoch 28/30\n", - "256/256 [==============================] - 75s 292ms/step - loss: 0.1907 - accuracy: 0.9468 - val_loss: 0.1984 - val_accuracy: 0.9551\n", - "Epoch 29/30\n", - "256/256 [==============================] - 74s 287ms/step - loss: 0.1505 - accuracy: 0.9604 - val_loss: 0.1831 - val_accuracy: 0.9519\n", - "Epoch 30/30\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.1158 - accuracy: 0.9722 - val_loss: 0.1972 - val_accuracy: 0.9407\n", + "Epoch 217/222\n", + "256/256 [==============================] - 50s 183ms/step - loss: 0.1722 - accuracy: 0.9502 - val_loss: 0.1632 - val_accuracy: 0.9295\n", + "Epoch 218/222\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.1240 - accuracy: 0.9602 - val_loss: 0.1678 - val_accuracy: 0.9327\n", + "Epoch 219/222\n", + "256/256 [==============================] - 45s 177ms/step - loss: 0.0765 - accuracy: 0.9766 - val_loss: 0.2456 - val_accuracy: 0.9279\n", + "Epoch 220/222\n", + "256/256 [==============================] - 45s 177ms/step - loss: 0.0513 - accuracy: 0.9858 - val_loss: 0.2473 - val_accuracy: 0.9327\n", + "Epoch 221/222\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.0336 - accuracy: 0.9910 - val_loss: 0.2045 - val_accuracy: 0.9391\n", + "Epoch 222/222\n", + "256/256 [==============================] - 45s 177ms/step - loss: 0.0229 - accuracy: 0.9951 - val_loss: 0.2415 - val_accuracy: 0.9327\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9391}, \u001b[0m\u001b[0;33mloss{0.1632}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1292}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9327\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2415\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1292192638. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m355.66 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m278.94 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m76.72 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [37] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m38\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 222)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01022\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 223/228\n", + "256/256 [==============================] - 50s 182ms/step - loss: 0.1697 - accuracy: 0.9495 - val_loss: 0.1663 - val_accuracy: 0.9407\n", + "Epoch 224/228\n", + "256/256 [==============================] - 46s 177ms/step - loss: 0.1207 - accuracy: 0.9602 - val_loss: 0.2250 - val_accuracy: 0.9375\n", + "Epoch 225/228\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.0851 - accuracy: 0.9734 - val_loss: 0.2260 - val_accuracy: 0.9183\n", + "Epoch 226/228\n", + "256/256 [==============================] - 45s 176ms/step - loss: 0.0534 - accuracy: 0.9844 - val_loss: 0.2689 - val_accuracy: 0.9183\n", + "Epoch 227/228\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.0336 - accuracy: 0.9912 - val_loss: 0.1695 - val_accuracy: 0.9487\n", + "Epoch 228/228\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.0270 - accuracy: 0.9944 - val_loss: 0.2000 - val_accuracy: 0.9391\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9487}, \u001b[0m\u001b[0;33mloss{0.1663}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1292}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2000\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1292192638. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m356.09 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m278.79 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m77.30 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [38] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m39\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 228)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01016\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 229/234\n", + "256/256 [==============================] - 49s 181ms/step - loss: 0.1689 - accuracy: 0.9468 - val_loss: 0.1285 - val_accuracy: 0.9631\n", + "Epoch 230/234\n", + "256/256 [==============================] - 45s 176ms/step - loss: 0.1272 - accuracy: 0.9563 - val_loss: 0.1452 - val_accuracy: 0.9599\n", + "Epoch 231/234\n", + "256/256 [==============================] - 46s 181ms/step - loss: 0.0854 - accuracy: 0.9717 - val_loss: 0.1544 - val_accuracy: 0.9455\n", + "Epoch 232/234\n", + "256/256 [==============================] - 47s 185ms/step - loss: 0.0443 - accuracy: 0.9890 - val_loss: 0.2259 - val_accuracy: 0.9471\n", + "Epoch 233/234\n", + "256/256 [==============================] - 47s 183ms/step - loss: 0.0327 - accuracy: 0.9912 - val_loss: 0.1893 - val_accuracy: 0.9423\n", + "Epoch 234/234\n", + "256/256 [==============================] - 46s 180ms/step - loss: 0.0184 - accuracy: 0.9973 - val_loss: 0.2051 - val_accuracy: 0.9455\n", "\u001b[0;32mSubset training done.\u001b[0m\n", "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-028-0.9551.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9551\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1984\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m 0.943910 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m 0.955128\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-229-0.9631.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9631\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1285\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m 0.961538 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m 0.963141\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.2759856582 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1984292269\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1292192638 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1284706891\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m555.50 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m452.69 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m102.82 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [5] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m364.50 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m283.24 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m81.26 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [39] END|--------------------------------------->\u001b[0m\n", "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m6\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 30)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m40\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 234)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;33mPreparing train data...\u001b[0m\n", "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0101\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 31/36\n", - "256/256 [==============================] - 81s 298ms/step - loss: 0.2811 - accuracy: 0.9155 - val_loss: 0.2328 - val_accuracy: 0.9215\n", - "Epoch 32/36\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.2584 - accuracy: 0.9199 - val_loss: 0.2153 - val_accuracy: 0.9167\n", - "Epoch 33/36\n", - "256/256 [==============================] - 75s 292ms/step - loss: 0.1972 - accuracy: 0.9470 - val_loss: 0.2884 - val_accuracy: 0.9599\n", - "Epoch 34/36\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.1848 - accuracy: 0.9500 - val_loss: 0.1846 - val_accuracy: 0.9471\n", - "Epoch 35/36\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.1391 - accuracy: 0.9648 - val_loss: 0.1701 - val_accuracy: 0.9519\n", - "Epoch 36/36\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.1067 - accuracy: 0.9727 - val_loss: 0.1741 - val_accuracy: 0.9455\n", + "Epoch 235/240\n", + "256/256 [==============================] - 50s 184ms/step - loss: 0.1315 - accuracy: 0.9578 - val_loss: 0.1410 - val_accuracy: 0.9455\n", + "Epoch 236/240\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.1090 - accuracy: 0.9663 - val_loss: 0.2091 - val_accuracy: 0.9199\n", + "Epoch 237/240\n", + "256/256 [==============================] - 47s 181ms/step - loss: 0.0828 - accuracy: 0.9773 - val_loss: 0.2883 - val_accuracy: 0.9103\n", + "Epoch 238/240\n", + "256/256 [==============================] - 47s 182ms/step - loss: 0.0460 - accuracy: 0.9880 - val_loss: 0.1810 - val_accuracy: 0.9487\n", + "Epoch 239/240\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.0343 - accuracy: 0.9912 - val_loss: 0.1887 - val_accuracy: 0.9471\n", + "Epoch 240/240\n", + "256/256 [==============================] - 45s 176ms/step - loss: 0.0202 - accuracy: 0.9961 - val_loss: 0.2339 - val_accuracy: 0.9343\n", "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-033-0.9599.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9599\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2884\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m 0.955128 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m 0.959936\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1984292269. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m553.63 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m453.41 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m100.22 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [6] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9487}, \u001b[0m\u001b[0;33mloss{0.1410}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9343\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2340\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m368.47 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m282.06 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m86.41 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [40] END|--------------------------------------->\u001b[0m\n", "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m7\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 36)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m41\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 240)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;33mPreparing train data...\u001b[0m\n", "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01004\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 37/42\n", - "256/256 [==============================] - 81s 297ms/step - loss: 0.2490 - accuracy: 0.9263 - val_loss: 0.1767 - val_accuracy: 0.9567\n", - "Epoch 38/42\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.2297 - accuracy: 0.9290 - val_loss: 0.2202 - val_accuracy: 0.9359\n", - "Epoch 39/42\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.2021 - accuracy: 0.9382 - val_loss: 0.1495 - val_accuracy: 0.9567\n", - "Epoch 40/42\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.1596 - accuracy: 0.9570 - val_loss: 0.1926 - val_accuracy: 0.9567\n", - "Epoch 41/42\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.1204 - accuracy: 0.9685 - val_loss: 0.1584 - val_accuracy: 0.9487\n", - "Epoch 42/42\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.0946 - accuracy: 0.9758 - val_loss: 0.1562 - val_accuracy: 0.9535\n", + "Epoch 241/246\n", + "256/256 [==============================] - 51s 186ms/step - loss: 0.1753 - accuracy: 0.9495 - val_loss: 0.1715 - val_accuracy: 0.9503\n", + "Epoch 242/246\n", + "256/256 [==============================] - 45s 176ms/step - loss: 0.1165 - accuracy: 0.9619 - val_loss: 0.2048 - val_accuracy: 0.9423\n", + "Epoch 243/246\n", + "256/256 [==============================] - 45s 176ms/step - loss: 0.0879 - accuracy: 0.9734 - val_loss: 0.2442 - val_accuracy: 0.9247\n", + "Epoch 244/246\n", + "256/256 [==============================] - 47s 181ms/step - loss: 0.0521 - accuracy: 0.9836 - val_loss: 0.1963 - val_accuracy: 0.9519\n", + "Epoch 245/246\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.0258 - accuracy: 0.9946 - val_loss: 0.2522 - val_accuracy: 0.9439\n", + "Epoch 246/246\n", + "256/256 [==============================] - 45s 176ms/step - loss: 0.0192 - accuracy: 0.9954 - val_loss: 0.2851 - val_accuracy: 0.9407\n", "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-037-0.9567.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9519}, \u001b[0m\u001b[0;33mloss{0.1715}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2852\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m359.00 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m280.02 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m78.98 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [41] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m42\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 246)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;31m- Debug DP Sample dir: \u001b[0m\u001b[0;32mSamples/TSR_SUB_400_y2024_m01_d26-h18_m59_s01\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00998\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 247/252\n", + "256/256 [==============================] - 50s 180ms/step - loss: 0.1802 - accuracy: 0.9492 - val_loss: 0.1543 - val_accuracy: 0.9503\n", + "Epoch 248/252\n", + "256/256 [==============================] - 45s 176ms/step - loss: 0.1269 - accuracy: 0.9604 - val_loss: 0.1767 - val_accuracy: 0.9455\n", + "Epoch 249/252\n", + "256/256 [==============================] - 45s 176ms/step - loss: 0.0867 - accuracy: 0.9758 - val_loss: 0.1717 - val_accuracy: 0.9423\n", + "Epoch 250/252\n", + "256/256 [==============================] - 46s 180ms/step - loss: 0.0567 - accuracy: 0.9841 - val_loss: 0.1735 - val_accuracy: 0.9519\n", + "Epoch 251/252\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.0339 - accuracy: 0.9922 - val_loss: 0.2147 - val_accuracy: 0.9439\n", + "Epoch 252/252\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.0228 - accuracy: 0.9958 - val_loss: 0.2881 - val_accuracy: 0.9359\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9519}, \u001b[0m\u001b[0;33mloss{0.1543}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9359\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2880\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m368.36 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m279.80 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m88.57 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [42] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m43\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 252)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00992\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 253/258\n", + "256/256 [==============================] - 50s 182ms/step - loss: 0.1547 - accuracy: 0.9504 - val_loss: 0.1559 - val_accuracy: 0.9487\n", + "Epoch 254/258\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.1207 - accuracy: 0.9563 - val_loss: 0.1928 - val_accuracy: 0.9423\n", + "Epoch 255/258\n", + "256/256 [==============================] - 46s 180ms/step - loss: 0.0783 - accuracy: 0.9785 - val_loss: 0.1629 - val_accuracy: 0.9439\n", + "Epoch 256/258\n", + "256/256 [==============================] - 46s 180ms/step - loss: 0.0500 - accuracy: 0.9873 - val_loss: 0.1587 - val_accuracy: 0.9471\n", + "Epoch 257/258\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.0477 - accuracy: 0.9883 - val_loss: 0.1575 - val_accuracy: 0.9407\n", + "Epoch 258/258\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.0250 - accuracy: 0.9949 - val_loss: 0.1581 - val_accuracy: 0.9503\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9503}, \u001b[0m\u001b[0;33mloss{0.1559}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1581\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m370.91 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m280.74 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m90.16 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [43] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m44\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 258)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00986\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 259/264\n", + "256/256 [==============================] - 49s 180ms/step - loss: 0.1612 - accuracy: 0.9504 - val_loss: 0.1524 - val_accuracy: 0.9471\n", + "Epoch 260/264\n", + "256/256 [==============================] - 45s 175ms/step - loss: 0.1203 - accuracy: 0.9631 - val_loss: 0.1503 - val_accuracy: 0.9455\n", + "Epoch 261/264\n", + "256/256 [==============================] - 45s 175ms/step - loss: 0.0883 - accuracy: 0.9719 - val_loss: 0.1366 - val_accuracy: 0.9471\n", + "Epoch 262/264\n", + "256/256 [==============================] - 45s 174ms/step - loss: 0.0584 - accuracy: 0.9846 - val_loss: 0.1622 - val_accuracy: 0.9439\n", + "Epoch 263/264\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.0304 - accuracy: 0.9934 - val_loss: 0.1895 - val_accuracy: 0.9567\n", + "Epoch 264/264\n", + "256/256 [==============================] - 45s 175ms/step - loss: 0.0203 - accuracy: 0.9951 - val_loss: 0.1946 - val_accuracy: 0.9567\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9567}, \u001b[0m\u001b[0;33mloss{0.1366}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9567\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1767\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9599359035. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1984292269 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1766962409\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m556.27 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m452.14 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m104.12 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [7] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1947\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m358.87 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m275.41 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m83.46 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [44] END|--------------------------------------->\u001b[0m\n", "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m8\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 42)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m45\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 264)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;33mPreparing train data...\u001b[0m\n", "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0098\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 43/48\n", - "256/256 [==============================] - 81s 295ms/step - loss: 0.2312 - accuracy: 0.9297 - val_loss: 0.1708 - val_accuracy: 0.9647\n", - "Epoch 44/48\n", - "256/256 [==============================] - 74s 287ms/step - loss: 0.2093 - accuracy: 0.9390 - val_loss: 0.1545 - val_accuracy: 0.9535\n", - "Epoch 45/48\n", - "256/256 [==============================] - 73s 286ms/step - loss: 0.1908 - accuracy: 0.9436 - val_loss: 0.2027 - val_accuracy: 0.9247\n", - "Epoch 46/48\n", - "256/256 [==============================] - 73s 286ms/step - loss: 0.1480 - accuracy: 0.9600 - val_loss: 0.1526 - val_accuracy: 0.9599\n", - "Epoch 47/48\n", - "256/256 [==============================] - 73s 287ms/step - loss: 0.1102 - accuracy: 0.9729 - val_loss: 0.1835 - val_accuracy: 0.9551\n", - "Epoch 48/48\n", - "256/256 [==============================] - 73s 286ms/step - loss: 0.0904 - accuracy: 0.9758 - val_loss: 0.1743 - val_accuracy: 0.9583\n", + "Epoch 265/270\n", + "256/256 [==============================] - 49s 180ms/step - loss: 0.1588 - accuracy: 0.9521 - val_loss: 0.1405 - val_accuracy: 0.9519\n", + "Epoch 266/270\n", + "256/256 [==============================] - 45s 174ms/step - loss: 0.1229 - accuracy: 0.9602 - val_loss: 0.1648 - val_accuracy: 0.9407\n", + "Epoch 267/270\n", + "256/256 [==============================] - 45s 174ms/step - loss: 0.0756 - accuracy: 0.9807 - val_loss: 0.2319 - val_accuracy: 0.9343\n", + "Epoch 268/270\n", + "256/256 [==============================] - 45s 174ms/step - loss: 0.0609 - accuracy: 0.9846 - val_loss: 0.1934 - val_accuracy: 0.9407\n", + "Epoch 269/270\n", + "256/256 [==============================] - 45s 175ms/step - loss: 0.0374 - accuracy: 0.9905 - val_loss: 0.2329 - val_accuracy: 0.9471\n", + "Epoch 270/270\n", + "256/256 [==============================] - 45s 175ms/step - loss: 0.0181 - accuracy: 0.9973 - val_loss: 0.2512 - val_accuracy: 0.9503\n", "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-043-0.9647.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9647\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1708\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m 0.959936 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m 0.964744\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1766962409 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1707910001\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m553.50 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m448.99 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m104.51 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [8] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9519}, \u001b[0m\u001b[0;33mloss{0.1405}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2513\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m353.94 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m274.32 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m79.62 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [45] END|--------------------------------------->\u001b[0m\n", "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m9\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 48)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m46\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 270)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;33mPreparing train data...\u001b[0m\n", "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00974\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 49/54\n", - "256/256 [==============================] - 81s 299ms/step - loss: 0.2180 - accuracy: 0.9312 - val_loss: 0.1975 - val_accuracy: 0.9615\n", - "Epoch 50/54\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.2168 - accuracy: 0.9275 - val_loss: 0.1622 - val_accuracy: 0.9599\n", - "Epoch 51/54\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.1757 - accuracy: 0.9480 - val_loss: 0.1945 - val_accuracy: 0.9471\n", - "Epoch 52/54\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.1616 - accuracy: 0.9519 - val_loss: 0.1737 - val_accuracy: 0.9439\n", - "Epoch 53/54\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.1107 - accuracy: 0.9707 - val_loss: 0.2138 - val_accuracy: 0.9551\n", - "Epoch 54/54\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.0950 - accuracy: 0.9761 - val_loss: 0.2240 - val_accuracy: 0.9423\n", + "Epoch 271/276\n", + "256/256 [==============================] - 51s 184ms/step - loss: 0.1579 - accuracy: 0.9509 - val_loss: 0.1906 - val_accuracy: 0.9343\n", + "Epoch 272/276\n", + "256/256 [==============================] - 47s 183ms/step - loss: 0.1273 - accuracy: 0.9624 - val_loss: 0.2282 - val_accuracy: 0.9327\n", + "Epoch 273/276\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.0970 - accuracy: 0.9739 - val_loss: 0.1680 - val_accuracy: 0.9519\n", + "Epoch 274/276\n", + "256/256 [==============================] - 45s 177ms/step - loss: 0.0563 - accuracy: 0.9863 - val_loss: 0.1776 - val_accuracy: 0.9455\n", + "Epoch 275/276\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.0373 - accuracy: 0.9912 - val_loss: 0.1876 - val_accuracy: 0.9455\n", + "Epoch 276/276\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.0260 - accuracy: 0.9939 - val_loss: 0.1915 - val_accuracy: 0.9455\n", "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-049-0.9615.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9615\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1975\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1707910001. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m556.17 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m453.72 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m102.44 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [9] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9519}, \u001b[0m\u001b[0;33mloss{0.1680}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1916\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m363.82 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m281.51 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m82.30 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [46] END|--------------------------------------->\u001b[0m\n", "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m10\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 54)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m47\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 276)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;33mPreparing train data...\u001b[0m\n", "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00968\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 55/60\n", - "256/256 [==============================] - 81s 299ms/step - loss: 0.2157 - accuracy: 0.9365 - val_loss: 0.1587 - val_accuracy: 0.9583\n", - "Epoch 56/60\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.2221 - accuracy: 0.9285 - val_loss: 0.2882 - val_accuracy: 0.9375\n", - "Epoch 57/60\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.1853 - accuracy: 0.9402 - val_loss: 0.2852 - val_accuracy: 0.9343\n", - "Epoch 58/60\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.1597 - accuracy: 0.9526 - val_loss: 0.1912 - val_accuracy: 0.9471\n", - "Epoch 59/60\n", - "256/256 [==============================] - 73s 285ms/step - loss: 0.1129 - accuracy: 0.9702 - val_loss: 0.1927 - val_accuracy: 0.9487\n", - "Epoch 60/60\n", - "256/256 [==============================] - 73s 285ms/step - loss: 0.0713 - accuracy: 0.9817 - val_loss: 0.2114 - val_accuracy: 0.9583\n", + "Epoch 277/282\n", + "256/256 [==============================] - 50s 184ms/step - loss: 0.1668 - accuracy: 0.9451 - val_loss: 0.1678 - val_accuracy: 0.9359\n", + "Epoch 278/282\n", + "256/256 [==============================] - 46s 180ms/step - loss: 0.1198 - accuracy: 0.9580 - val_loss: 0.1721 - val_accuracy: 0.9487\n", + "Epoch 279/282\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.0848 - accuracy: 0.9751 - val_loss: 0.1921 - val_accuracy: 0.9327\n", + "Epoch 280/282\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.0504 - accuracy: 0.9858 - val_loss: 0.2122 - val_accuracy: 0.9375\n", + "Epoch 281/282\n", + "256/256 [==============================] - 45s 177ms/step - loss: 0.0362 - accuracy: 0.9902 - val_loss: 0.2112 - val_accuracy: 0.9327\n", + "Epoch 282/282\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.0209 - accuracy: 0.9956 - val_loss: 0.2481 - val_accuracy: 0.9343\n", "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-055-0.9583.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9583\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1587\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1707910001 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1586984992\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m558.73 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m451.32 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m107.41 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [10] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9487}, \u001b[0m\u001b[0;33mloss{0.1678}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9359\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2367\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m369.68 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m280.23 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m89.44 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [47] END|--------------------------------------->\u001b[0m\n", "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m11\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 60)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m48\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 282)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;33mPreparing train data...\u001b[0m\n", "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00962\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 61/66\n", - "256/256 [==============================] - 80s 293ms/step - loss: 0.2160 - accuracy: 0.9370 - val_loss: 0.2318 - val_accuracy: 0.9183\n", - "Epoch 62/66\n", - "256/256 [==============================] - 73s 285ms/step - loss: 0.2069 - accuracy: 0.9297 - val_loss: 0.2511 - val_accuracy: 0.8766\n", - "Epoch 63/66\n", - "256/256 [==============================] - 74s 287ms/step - loss: 0.1770 - accuracy: 0.9417 - val_loss: 0.1905 - val_accuracy: 0.9503\n", - "Epoch 64/66\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.1392 - accuracy: 0.9619 - val_loss: 0.1880 - val_accuracy: 0.9503\n", - "Epoch 65/66\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.1214 - accuracy: 0.9673 - val_loss: 0.2390 - val_accuracy: 0.9439\n", - "Epoch 66/66\n", - "256/256 [==============================] - 75s 292ms/step - loss: 0.0832 - accuracy: 0.9807 - val_loss: 0.1751 - val_accuracy: 0.9551\n", + "Epoch 283/288\n", + "256/256 [==============================] - 50s 184ms/step - loss: 0.1480 - accuracy: 0.9580 - val_loss: 0.1542 - val_accuracy: 0.9391\n", + "Epoch 284/288\n", + "256/256 [==============================] - 47s 181ms/step - loss: 0.1069 - accuracy: 0.9658 - val_loss: 0.1577 - val_accuracy: 0.9439\n", + "Epoch 285/288\n", + "256/256 [==============================] - 46s 177ms/step - loss: 0.0650 - accuracy: 0.9844 - val_loss: 0.1959 - val_accuracy: 0.9327\n", + "Epoch 286/288\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.0519 - accuracy: 0.9875 - val_loss: 0.1977 - val_accuracy: 0.9343\n", + "Epoch 287/288\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.0305 - accuracy: 0.9905 - val_loss: 0.2265 - val_accuracy: 0.9151\n", + "Epoch 288/288\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.0187 - accuracy: 0.9968 - val_loss: 0.2272 - val_accuracy: 0.9263\n", "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-066-0.9551.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9551\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1752\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1586984992. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m550.43 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m450.90 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m99.53 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [11] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9439}, \u001b[0m\u001b[0;33mloss{0.1542}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9279\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2199\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m371.78 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m281.41 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m90.36 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [48] END|--------------------------------------->\u001b[0m\n", "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m12\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 66)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m49\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 288)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;33mPreparing train data...\u001b[0m\n", "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00956\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 67/72\n", - "256/256 [==============================] - 81s 299ms/step - loss: 0.2145 - accuracy: 0.9341 - val_loss: 0.2676 - val_accuracy: 0.9471\n", - "Epoch 68/72\n", - "256/256 [==============================] - 74s 290ms/step - loss: 0.1984 - accuracy: 0.9331 - val_loss: 0.1683 - val_accuracy: 0.9215\n", - "Epoch 69/72\n", - "256/256 [==============================] - 75s 292ms/step - loss: 0.1689 - accuracy: 0.9465 - val_loss: 0.1710 - val_accuracy: 0.9583\n", - "Epoch 70/72\n", - "256/256 [==============================] - 74s 290ms/step - loss: 0.1257 - accuracy: 0.9648 - val_loss: 0.1663 - val_accuracy: 0.9519\n", - "Epoch 71/72\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.0932 - accuracy: 0.9739 - val_loss: 0.1488 - val_accuracy: 0.9535\n", - "Epoch 72/72\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.0588 - accuracy: 0.9856 - val_loss: 0.1736 - val_accuracy: 0.9583\n", + "Epoch 289/294\n", + "256/256 [==============================] - 50s 184ms/step - loss: 0.1762 - accuracy: 0.9473 - val_loss: 0.2162 - val_accuracy: 0.9455\n", + "Epoch 290/294\n", + "256/256 [==============================] - 46s 180ms/step - loss: 0.1135 - accuracy: 0.9678 - val_loss: 0.3055 - val_accuracy: 0.8974\n", + "Epoch 291/294\n", + "256/256 [==============================] - 46s 180ms/step - loss: 0.0840 - accuracy: 0.9761 - val_loss: 0.2265 - val_accuracy: 0.9279\n", + "Epoch 292/294\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.0474 - accuracy: 0.9893 - val_loss: 0.2201 - val_accuracy: 0.9327\n", + "Epoch 293/294\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.0344 - accuracy: 0.9907 - val_loss: 0.2938 - val_accuracy: 0.9295\n", + "Epoch 294/294\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.0204 - accuracy: 0.9963 - val_loss: 0.2755 - val_accuracy: 0.9359\n", "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-069-0.9583.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9583\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1709\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1586984992. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m559.33 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m454.70 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m104.63 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [12] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9455}, \u001b[0m\u001b[0;33mloss{0.2162}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9343\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2545\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m373.06 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m281.72 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m91.34 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [49] END|--------------------------------------->\u001b[0m\n", "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m13\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 72)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m50\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 294)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;33mPreparing train data...\u001b[0m\n", "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0095\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 73/78\n", - "256/256 [==============================] - 81s 299ms/step - loss: 0.2089 - accuracy: 0.9321 - val_loss: 0.1373 - val_accuracy: 0.9471\n", - "Epoch 74/78\n", - "256/256 [==============================] - 76s 294ms/step - loss: 0.2021 - accuracy: 0.9385 - val_loss: 0.1914 - val_accuracy: 0.9535\n", - "Epoch 75/78\n", - "256/256 [==============================] - 74s 290ms/step - loss: 0.1662 - accuracy: 0.9519 - val_loss: 0.3171 - val_accuracy: 0.9487\n", - "Epoch 76/78\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.1388 - accuracy: 0.9597 - val_loss: 0.1874 - val_accuracy: 0.9359\n", - "Epoch 77/78\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.0989 - accuracy: 0.9734 - val_loss: 0.2316 - val_accuracy: 0.9423\n", - "Epoch 78/78\n", - "256/256 [==============================] - 75s 290ms/step - loss: 0.0733 - accuracy: 0.9824 - val_loss: 0.2070 - val_accuracy: 0.9535\n", + "Epoch 295/300\n", + "256/256 [==============================] - 51s 185ms/step - loss: 0.1516 - accuracy: 0.9563 - val_loss: 0.2346 - val_accuracy: 0.9471\n", + "Epoch 296/300\n", + "256/256 [==============================] - 46s 180ms/step - loss: 0.1040 - accuracy: 0.9695 - val_loss: 0.1616 - val_accuracy: 0.9359\n", + "Epoch 297/300\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.0782 - accuracy: 0.9771 - val_loss: 0.2492 - val_accuracy: 0.9295\n", + "Epoch 298/300\n", + "256/256 [==============================] - 46s 180ms/step - loss: 0.0430 - accuracy: 0.9878 - val_loss: 0.5520 - val_accuracy: 0.9391\n", + "Epoch 299/300\n", + "256/256 [==============================] - 46s 180ms/step - loss: 0.0277 - accuracy: 0.9927 - val_loss: 0.2504 - val_accuracy: 0.9439\n", + "Epoch 300/300\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.0185 - accuracy: 0.9963 - val_loss: 0.2979 - val_accuracy: 0.9391\n", "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-074-0.9535.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1914\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1586984992. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m564.02 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m455.80 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m108.22 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [13] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9471}, \u001b[0m\u001b[0;33mloss{0.1616}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2976\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m374.47 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m282.33 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m92.14 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [50] END|--------------------------------------->\u001b[0m\n", "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m14\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 78)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m51\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 300)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;33mPreparing train data...\u001b[0m\n", "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00944\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 79/84\n", - "256/256 [==============================] - 82s 300ms/step - loss: 0.2117 - accuracy: 0.9314 - val_loss: 0.1623 - val_accuracy: 0.9551\n", - "Epoch 80/84\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.1814 - accuracy: 0.9426 - val_loss: 0.1552 - val_accuracy: 0.9487\n", - "Epoch 81/84\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.1780 - accuracy: 0.9436 - val_loss: 0.1850 - val_accuracy: 0.9471\n", - "Epoch 82/84\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.1360 - accuracy: 0.9580 - val_loss: 0.2193 - val_accuracy: 0.9503\n", - "Epoch 83/84\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.1011 - accuracy: 0.9717 - val_loss: 0.1582 - val_accuracy: 0.9535\n", - "Epoch 84/84\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.0647 - accuracy: 0.9827 - val_loss: 0.1968 - val_accuracy: 0.9551\n", + "Epoch 301/306\n", + "256/256 [==============================] - 51s 185ms/step - loss: 0.1597 - accuracy: 0.9553 - val_loss: 0.1904 - val_accuracy: 0.9343\n", + "Epoch 302/306\n", + "256/256 [==============================] - 46s 180ms/step - loss: 0.1218 - accuracy: 0.9609 - val_loss: 0.1980 - val_accuracy: 0.9327\n", + "Epoch 303/306\n", + "256/256 [==============================] - 46s 180ms/step - loss: 0.0741 - accuracy: 0.9775 - val_loss: 0.2119 - val_accuracy: 0.9311\n", + "Epoch 304/306\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.0419 - accuracy: 0.9897 - val_loss: 0.2723 - val_accuracy: 0.9311\n", + "Epoch 305/306\n", + "256/256 [==============================] - 46s 181ms/step - loss: 0.0236 - accuracy: 0.9941 - val_loss: 0.2654 - val_accuracy: 0.9327\n", + "Epoch 306/306\n", + "256/256 [==============================] - 47s 181ms/step - loss: 0.0173 - accuracy: 0.9966 - val_loss: 0.2744 - val_accuracy: 0.9359\n", "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-079-0.9551.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9551\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1623\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1586984992. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m560.73 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m453.11 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m107.62 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [14] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9359}, \u001b[0m\u001b[0;33mloss{0.1904}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9359\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2743\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m375.52 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m283.03 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m92.49 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [51] END|--------------------------------------->\u001b[0m\n", "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m15\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 84)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m52\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 306)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;33mPreparing train data...\u001b[0m\n", "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00938\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 85/90\n", - "256/256 [==============================] - 82s 300ms/step - loss: 0.2043 - accuracy: 0.9319 - val_loss: 0.2282 - val_accuracy: 0.9311\n", - "Epoch 86/90\n", - "256/256 [==============================] - 75s 294ms/step - loss: 0.1907 - accuracy: 0.9375 - val_loss: 0.2106 - val_accuracy: 0.9471\n", - "Epoch 87/90\n", - "256/256 [==============================] - 75s 291ms/step - loss: 0.1522 - accuracy: 0.9514 - val_loss: 0.2437 - val_accuracy: 0.9327\n", - "Epoch 88/90\n", - "256/256 [==============================] - 74s 290ms/step - loss: 0.1215 - accuracy: 0.9653 - val_loss: 0.3168 - val_accuracy: 0.9183\n", - "Epoch 89/90\n", - "256/256 [==============================] - 76s 294ms/step - loss: 0.0905 - accuracy: 0.9753 - val_loss: 0.2246 - val_accuracy: 0.9503\n", - "Epoch 90/90\n", - "256/256 [==============================] - 76s 294ms/step - loss: 0.0612 - accuracy: 0.9868 - val_loss: 0.1948 - val_accuracy: 0.9551\n", + "Epoch 307/312\n", + "256/256 [==============================] - 50s 185ms/step - loss: 0.1610 - accuracy: 0.9500 - val_loss: 0.2043 - val_accuracy: 0.9343\n", + "Epoch 308/312\n", + "256/256 [==============================] - 46s 180ms/step - loss: 0.1183 - accuracy: 0.9592 - val_loss: 0.1794 - val_accuracy: 0.9295\n", + "Epoch 309/312\n", + "256/256 [==============================] - 46s 181ms/step - loss: 0.0720 - accuracy: 0.9788 - val_loss: 0.1945 - val_accuracy: 0.9295\n", + "Epoch 310/312\n", + "256/256 [==============================] - 46s 180ms/step - loss: 0.0340 - accuracy: 0.9929 - val_loss: 0.2620 - val_accuracy: 0.9263\n", + "Epoch 311/312\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.0259 - accuracy: 0.9939 - val_loss: 0.2835 - val_accuracy: 0.9263\n", + "Epoch 312/312\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.0240 - accuracy: 0.9944 - val_loss: 0.2667 - val_accuracy: 0.9311\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9343}, \u001b[0m\u001b[0;33mloss{0.1794}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9311\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2667\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m374.00 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m281.37 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m92.63 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [52] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m53\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 312)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00932\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 313/318\n", + "256/256 [==============================] - 50s 182ms/step - loss: 0.1599 - accuracy: 0.9497 - val_loss: 0.1730 - val_accuracy: 0.9359\n", + "Epoch 314/318\n", + "256/256 [==============================] - 46s 181ms/step - loss: 0.1092 - accuracy: 0.9656 - val_loss: 0.2860 - val_accuracy: 0.9054\n", + "Epoch 315/318\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.0641 - accuracy: 0.9812 - val_loss: 0.3437 - val_accuracy: 0.9279\n", + "Epoch 316/318\n", + "256/256 [==============================] - 46s 177ms/step - loss: 0.0368 - accuracy: 0.9897 - val_loss: 0.3023 - val_accuracy: 0.9295\n", + "Epoch 317/318\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.0274 - accuracy: 0.9927 - val_loss: 0.3100 - val_accuracy: 0.9439\n", + "Epoch 318/318\n", + "256/256 [==============================] - 46s 180ms/step - loss: 0.0178 - accuracy: 0.9966 - val_loss: 0.3571 - val_accuracy: 0.9455\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9455}, \u001b[0m\u001b[0;33mloss{0.1730}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2571\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m370.10 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m280.50 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m89.60 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [53] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m54\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 318)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00926\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 319/324\n", + "256/256 [==============================] - 51s 186ms/step - loss: 0.1551 - accuracy: 0.9514 - val_loss: 0.1751 - val_accuracy: 0.9375\n", + "Epoch 320/324\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.1101 - accuracy: 0.9644 - val_loss: 0.3133 - val_accuracy: 0.9167\n", + "Epoch 321/324\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.0659 - accuracy: 0.9805 - val_loss: 0.4211 - val_accuracy: 0.9006\n", + "Epoch 322/324\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.0390 - accuracy: 0.9895 - val_loss: 0.2079 - val_accuracy: 0.9343\n", + "Epoch 323/324\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.0272 - accuracy: 0.9929 - val_loss: 0.4473 - val_accuracy: 0.9006\n", + "Epoch 324/324\n", + "256/256 [==============================] - 46s 177ms/step - loss: 0.0224 - accuracy: 0.9941 - val_loss: 0.4203 - val_accuracy: 0.9022\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9375}, \u001b[0m\u001b[0;33mloss{0.1751}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9022\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4201\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m375.47 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m281.53 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m93.95 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [54] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m55\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 324)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0092\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 325/330\n", + "256/256 [==============================] - 50s 183ms/step - loss: 0.1420 - accuracy: 0.9592 - val_loss: 0.2373 - val_accuracy: 0.9038\n", + "Epoch 326/330\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.1116 - accuracy: 0.9670 - val_loss: 0.2164 - val_accuracy: 0.9215\n", + "Epoch 327/330\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.0674 - accuracy: 0.9800 - val_loss: 0.1920 - val_accuracy: 0.9311\n", + "Epoch 328/330\n", + "256/256 [==============================] - 45s 177ms/step - loss: 0.0435 - accuracy: 0.9873 - val_loss: 0.2784 - val_accuracy: 0.9231\n", + "Epoch 329/330\n", + "256/256 [==============================] - 47s 184ms/step - loss: 0.0299 - accuracy: 0.9944 - val_loss: 0.2236 - val_accuracy: 0.9391\n", + "Epoch 330/330\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.0192 - accuracy: 0.9958 - val_loss: 0.2388 - val_accuracy: 0.9359\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9391}, \u001b[0m\u001b[0;33mloss{0.1920}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9359\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2387\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m367.53 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m281.00 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m86.54 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [55] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m56\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 330)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00914\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 331/336\n", + "256/256 [==============================] - 50s 184ms/step - loss: 0.1605 - accuracy: 0.9539 - val_loss: 0.2191 - val_accuracy: 0.9119\n", + "Epoch 332/336\n", + "256/256 [==============================] - 46s 181ms/step - loss: 0.1122 - accuracy: 0.9666 - val_loss: 0.2464 - val_accuracy: 0.9167\n", + "Epoch 333/336\n", + "256/256 [==============================] - 46s 181ms/step - loss: 0.0759 - accuracy: 0.9780 - val_loss: 0.2621 - val_accuracy: 0.9231\n", + "Epoch 334/336\n", + "256/256 [==============================] - 46s 181ms/step - loss: 0.0429 - accuracy: 0.9873 - val_loss: 0.2545 - val_accuracy: 0.9311\n", + "Epoch 335/336\n", + "256/256 [==============================] - 47s 182ms/step - loss: 0.0257 - accuracy: 0.9934 - val_loss: 0.3023 - val_accuracy: 0.9343\n", + "Epoch 336/336\n", + "256/256 [==============================] - 47s 183ms/step - loss: 0.0208 - accuracy: 0.9944 - val_loss: 0.3138 - val_accuracy: 0.9375\n", "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-090-0.9551.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9551\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1948\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1586984992. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m569.16 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m458.22 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m110.94 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [15] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9375}, \u001b[0m\u001b[0;33mloss{0.2191}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9375\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3130\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m380.36 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m284.58 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m95.78 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [56] END|--------------------------------------->\u001b[0m\n", "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m16\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 90)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m57\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 336)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;33mPreparing train data...\u001b[0m\n", "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00908\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 91/96\n", - "256/256 [==============================] - 83s 306ms/step - loss: 0.1922 - accuracy: 0.9399 - val_loss: 0.1989 - val_accuracy: 0.9487\n", - "Epoch 92/96\n", - "256/256 [==============================] - 75s 294ms/step - loss: 0.1595 - accuracy: 0.9524 - val_loss: 0.3425 - val_accuracy: 0.9295\n", - "Epoch 93/96\n", - "256/256 [==============================] - 74s 290ms/step - loss: 0.1470 - accuracy: 0.9570 - val_loss: 0.2482 - val_accuracy: 0.9215\n", - "Epoch 94/96\n", - "256/256 [==============================] - 75s 291ms/step - loss: 0.1097 - accuracy: 0.9692 - val_loss: 0.2973 - val_accuracy: 0.9327\n", - "Epoch 95/96\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.0764 - accuracy: 0.9773 - val_loss: 0.3116 - val_accuracy: 0.9279\n", - "Epoch 96/96\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.0454 - accuracy: 0.9890 - val_loss: 0.2784 - val_accuracy: 0.9391\n", + "Epoch 337/342\n", + "256/256 [==============================] - 50s 183ms/step - loss: 0.1435 - accuracy: 0.9561 - val_loss: 0.2077 - val_accuracy: 0.9359\n", + "Epoch 338/342\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.0970 - accuracy: 0.9692 - val_loss: 0.2622 - val_accuracy: 0.9311\n", + "Epoch 339/342\n", + "256/256 [==============================] - 47s 182ms/step - loss: 0.0615 - accuracy: 0.9824 - val_loss: 0.2184 - val_accuracy: 0.9391\n", + "Epoch 340/342\n", + "256/256 [==============================] - 46s 181ms/step - loss: 0.0291 - accuracy: 0.9922 - val_loss: 0.2240 - val_accuracy: 0.9359\n", + "Epoch 341/342\n", + "256/256 [==============================] - 48s 185ms/step - loss: 0.0200 - accuracy: 0.9956 - val_loss: 0.2535 - val_accuracy: 0.9455\n", + "Epoch 342/342\n", + "256/256 [==============================] - 46s 180ms/step - loss: 0.0157 - accuracy: 0.9963 - val_loss: 0.2630 - val_accuracy: 0.9439\n", "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-091-0.9487.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1989\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1586984992. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m570.80 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m457.47 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m113.32 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [16] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9455}, \u001b[0m\u001b[0;33mloss{0.2077}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2717\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m388.31 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m284.30 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m104.01 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [57] END|--------------------------------------->\u001b[0m\n", "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m17\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 96)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m58\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 342)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;33mPreparing train data...\u001b[0m\n", "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00902\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 97/102\n", - "256/256 [==============================] - 81s 296ms/step - loss: 0.1904 - accuracy: 0.9355 - val_loss: 0.1833 - val_accuracy: 0.9519\n", - "Epoch 98/102\n", - "256/256 [==============================] - 73s 286ms/step - loss: 0.1852 - accuracy: 0.9448 - val_loss: 0.2454 - val_accuracy: 0.9391\n", - "Epoch 99/102\n", - "256/256 [==============================] - 74s 287ms/step - loss: 0.1449 - accuracy: 0.9514 - val_loss: 0.4665 - val_accuracy: 0.9231\n", - "Epoch 100/102\n", - "256/256 [==============================] - 75s 293ms/step - loss: 0.1387 - accuracy: 0.9629 - val_loss: 0.1466 - val_accuracy: 0.9551\n", - "Epoch 101/102\n", - "256/256 [==============================] - 74s 290ms/step - loss: 0.0675 - accuracy: 0.9819 - val_loss: 0.3165 - val_accuracy: 0.9391\n", - "Epoch 102/102\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.0555 - accuracy: 0.9871 - val_loss: 0.3152 - val_accuracy: 0.9375\n", + "Epoch 343/348\n", + "256/256 [==============================] - 51s 187ms/step - loss: 0.1574 - accuracy: 0.9529 - val_loss: 0.1972 - val_accuracy: 0.9263\n", + "Epoch 344/348\n", + "256/256 [==============================] - 47s 183ms/step - loss: 0.1014 - accuracy: 0.9658 - val_loss: 0.1675 - val_accuracy: 0.9359\n", + "Epoch 345/348\n", + "256/256 [==============================] - 47s 183ms/step - loss: 0.0612 - accuracy: 0.9783 - val_loss: 0.1686 - val_accuracy: 0.9343\n", + "Epoch 346/348\n", + "256/256 [==============================] - 48s 186ms/step - loss: 0.0375 - accuracy: 0.9905 - val_loss: 0.1797 - val_accuracy: 0.9487\n", + "Epoch 347/348\n", + "256/256 [==============================] - 47s 182ms/step - loss: 0.0228 - accuracy: 0.9961 - val_loss: 0.2343 - val_accuracy: 0.9391\n", + "Epoch 348/348\n", + "256/256 [==============================] - 47s 184ms/step - loss: 0.0148 - accuracy: 0.9973 - val_loss: 0.1911 - val_accuracy: 0.9423\n", "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-100-0.9551.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9551\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1467\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1586984992 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1466508806\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m568.54 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m452.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m115.96 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [17] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9487}, \u001b[0m\u001b[0;33mloss{0.1675}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1911\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m392.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m288.05 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m104.53 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [58] END|--------------------------------------->\u001b[0m\n", "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m18\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 102)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m59\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 348)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;33mPreparing train data...\u001b[0m\n", "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00896\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 103/108\n", - "256/256 [==============================] - 80s 296ms/step - loss: 0.1963 - accuracy: 0.9402 - val_loss: 0.3182 - val_accuracy: 0.9103\n", - "Epoch 104/108\n", - "256/256 [==============================] - 75s 292ms/step - loss: 0.1846 - accuracy: 0.9417 - val_loss: 0.2220 - val_accuracy: 0.9327\n", - "Epoch 105/108\n", - "256/256 [==============================] - 74s 287ms/step - loss: 0.1474 - accuracy: 0.9563 - val_loss: 0.2642 - val_accuracy: 0.9327\n", - "Epoch 106/108\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.1230 - accuracy: 0.9666 - val_loss: 0.5477 - val_accuracy: 0.8926\n", - "Epoch 107/108\n", - "256/256 [==============================] - 75s 294ms/step - loss: 0.0984 - accuracy: 0.9749 - val_loss: 0.1391 - val_accuracy: 0.9631\n", - "Epoch 108/108\n", - "256/256 [==============================] - 74s 290ms/step - loss: 0.0597 - accuracy: 0.9863 - val_loss: 0.3226 - val_accuracy: 0.9215\n", + "Epoch 349/354\n", + "256/256 [==============================] - 51s 186ms/step - loss: 0.1658 - accuracy: 0.9541 - val_loss: 0.1718 - val_accuracy: 0.9343\n", + "Epoch 350/354\n", + "256/256 [==============================] - 46s 180ms/step - loss: 0.1242 - accuracy: 0.9634 - val_loss: 0.2321 - val_accuracy: 0.9183\n", + "Epoch 351/354\n", + "256/256 [==============================] - 47s 183ms/step - loss: 0.0737 - accuracy: 0.9797 - val_loss: 0.1836 - val_accuracy: 0.9375\n", + "Epoch 352/354\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.0465 - accuracy: 0.9890 - val_loss: 0.2034 - val_accuracy: 0.9391\n", + "Epoch 353/354\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.0335 - accuracy: 0.9910 - val_loss: 0.1833 - val_accuracy: 0.9487\n", + "Epoch 354/354\n", + "256/256 [==============================] - 46s 177ms/step - loss: 0.0207 - accuracy: 0.9966 - val_loss: 0.1793 - val_accuracy: 0.9471\n", "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-107-0.9631.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9631\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1391\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1466508806 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1390501112\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m556.04 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m453.86 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m102.18 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [18] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9487}, \u001b[0m\u001b[0;33mloss{0.1718}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1793\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m390.46 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m282.99 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m107.46 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [59] END|--------------------------------------->\u001b[0m\n", "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m19\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 108)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m60\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 354)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;33mPreparing train data...\u001b[0m\n", "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0089\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 109/114\n", - "256/256 [==============================] - 82s 300ms/step - loss: 0.1888 - accuracy: 0.9426 - val_loss: 0.1798 - val_accuracy: 0.9423\n", - "Epoch 110/114\n", - "256/256 [==============================] - 74s 290ms/step - loss: 0.1749 - accuracy: 0.9507 - val_loss: 0.1890 - val_accuracy: 0.9551\n", - "Epoch 111/114\n", - "256/256 [==============================] - 73s 286ms/step - loss: 0.1338 - accuracy: 0.9575 - val_loss: 0.1898 - val_accuracy: 0.9551\n", - "Epoch 112/114\n", - "256/256 [==============================] - 74s 290ms/step - loss: 0.1019 - accuracy: 0.9714 - val_loss: 0.1749 - val_accuracy: 0.9567\n", - "Epoch 113/114\n", - "256/256 [==============================] - 73s 286ms/step - loss: 0.0842 - accuracy: 0.9778 - val_loss: 0.2070 - val_accuracy: 0.9551\n", - "Epoch 114/114\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.0572 - accuracy: 0.9851 - val_loss: 0.2040 - val_accuracy: 0.9551\n", + "Epoch 355/360\n", + "256/256 [==============================] - 50s 182ms/step - loss: 0.1412 - accuracy: 0.9607 - val_loss: 0.1491 - val_accuracy: 0.9487\n", + "Epoch 356/360\n", + "256/256 [==============================] - 46s 177ms/step - loss: 0.0949 - accuracy: 0.9705 - val_loss: 0.1979 - val_accuracy: 0.9343\n", + "Epoch 357/360\n", + "256/256 [==============================] - 45s 177ms/step - loss: 0.0541 - accuracy: 0.9875 - val_loss: 0.1727 - val_accuracy: 0.9455\n", + "Epoch 358/360\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.0342 - accuracy: 0.9919 - val_loss: 0.2038 - val_accuracy: 0.9375\n", + "Epoch 359/360\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.0283 - accuracy: 0.9929 - val_loss: 0.2312 - val_accuracy: 0.9375\n", + "Epoch 360/360\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.0118 - accuracy: 0.9990 - val_loss: 0.2565 - val_accuracy: 0.9423\n", "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-112-0.9567.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9567\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1748\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1390501112. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m557.52 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m452.44 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m105.09 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [19] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9487}, \u001b[0m\u001b[0;33mloss{0.1491}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2566\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m374.52 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m279.27 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m95.26 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [60] END|--------------------------------------->\u001b[0m\n", "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m20\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 114)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m61\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 360)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;33mPreparing train data...\u001b[0m\n", "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00884\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 115/120\n", - "256/256 [==============================] - 82s 303ms/step - loss: 0.1987 - accuracy: 0.9419 - val_loss: 0.2110 - val_accuracy: 0.9519\n", - "Epoch 116/120\n", - "256/256 [==============================] - 75s 294ms/step - loss: 0.1816 - accuracy: 0.9480 - val_loss: 0.1721 - val_accuracy: 0.9551\n", - "Epoch 117/120\n", - "256/256 [==============================] - 74s 290ms/step - loss: 0.1515 - accuracy: 0.9561 - val_loss: 0.1748 - val_accuracy: 0.9535\n", - "Epoch 118/120\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.1192 - accuracy: 0.9675 - val_loss: 0.1692 - val_accuracy: 0.9519\n", - "Epoch 119/120\n", - "256/256 [==============================] - 74s 290ms/step - loss: 0.0736 - accuracy: 0.9834 - val_loss: 0.1637 - val_accuracy: 0.9487\n", - "Epoch 120/120\n", - "256/256 [==============================] - 74s 290ms/step - loss: 0.0594 - accuracy: 0.9878 - val_loss: 0.1796 - val_accuracy: 0.9487\n", + "Epoch 361/366\n", + "256/256 [==============================] - 50s 182ms/step - loss: 0.1452 - accuracy: 0.9561 - val_loss: 0.1841 - val_accuracy: 0.9327\n", + "Epoch 362/366\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.0998 - accuracy: 0.9673 - val_loss: 0.1665 - val_accuracy: 0.9375\n", + "Epoch 363/366\n", + "256/256 [==============================] - 45s 176ms/step - loss: 0.0691 - accuracy: 0.9802 - val_loss: 0.3034 - val_accuracy: 0.9183\n", + "Epoch 364/366\n", + "256/256 [==============================] - 46s 177ms/step - loss: 0.0478 - accuracy: 0.9883 - val_loss: 0.2609 - val_accuracy: 0.9343\n", + "Epoch 365/366\n", + "256/256 [==============================] - 46s 180ms/step - loss: 0.0278 - accuracy: 0.9958 - val_loss: 0.2328 - val_accuracy: 0.9423\n", + "Epoch 366/366\n", + "256/256 [==============================] - 46s 177ms/step - loss: 0.0173 - accuracy: 0.9980 - val_loss: 0.2608 - val_accuracy: 0.9439\n", "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-116-0.9551.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9551\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1721\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1390501112. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m562.81 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m456.48 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m106.33 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [20] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9439}, \u001b[0m\u001b[0;33mloss{0.1665}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2652\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m373.67 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m279.16 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m94.52 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [61] END|--------------------------------------->\u001b[0m\n", "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m21\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 120)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m62\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 366)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;33mPreparing train data...\u001b[0m\n", "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00878\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 121/126\n", - "256/256 [==============================] - 81s 297ms/step - loss: 0.1849 - accuracy: 0.9373 - val_loss: 0.2483 - val_accuracy: 0.9151\n", - "Epoch 122/126\n", - "256/256 [==============================] - 75s 291ms/step - loss: 0.1633 - accuracy: 0.9548 - val_loss: 0.2154 - val_accuracy: 0.9327\n", - "Epoch 123/126\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.1182 - accuracy: 0.9653 - val_loss: 0.4623 - val_accuracy: 0.9151\n", - "Epoch 124/126\n", - "256/256 [==============================] - 74s 286ms/step - loss: 0.1025 - accuracy: 0.9717 - val_loss: 0.6142 - val_accuracy: 0.8349\n", - "Epoch 125/126\n", - "256/256 [==============================] - 73s 284ms/step - loss: 0.0777 - accuracy: 0.9785 - val_loss: 0.1497 - val_accuracy: 0.9519\n", - "Epoch 126/126\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.0460 - accuracy: 0.9895 - val_loss: 0.2228 - val_accuracy: 0.9391\n", + "Epoch 367/372\n", + "256/256 [==============================] - 50s 182ms/step - loss: 0.1499 - accuracy: 0.9548 - val_loss: 0.1774 - val_accuracy: 0.9359\n", + "Epoch 368/372\n", + "256/256 [==============================] - 45s 175ms/step - loss: 0.1333 - accuracy: 0.9617 - val_loss: 0.1801 - val_accuracy: 0.9359\n", + "Epoch 369/372\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.0664 - accuracy: 0.9832 - val_loss: 0.2012 - val_accuracy: 0.9375\n", + "Epoch 370/372\n", + "256/256 [==============================] - 45s 176ms/step - loss: 0.0472 - accuracy: 0.9880 - val_loss: 0.2177 - val_accuracy: 0.9375\n", + "Epoch 371/372\n", + "256/256 [==============================] - 45s 176ms/step - loss: 0.0232 - accuracy: 0.9941 - val_loss: 0.3455 - val_accuracy: 0.9375\n", + "Epoch 372/372\n", + "256/256 [==============================] - 46s 177ms/step - loss: 0.0182 - accuracy: 0.9963 - val_loss: 0.2887 - val_accuracy: 0.9359\n", "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-125-0.9519.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9519\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1497\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1390501112. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m556.72 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m451.57 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m105.15 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [21] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9375}, \u001b[0m\u001b[0;33mloss{0.1774}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9359\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2886\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m371.03 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m277.69 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m93.34 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [62] END|--------------------------------------->\u001b[0m\n", "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m22\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 126)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m63\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 372)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;33mPreparing train data...\u001b[0m\n", "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00872\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 127/132\n", - "256/256 [==============================] - 80s 297ms/step - loss: 0.1778 - accuracy: 0.9446 - val_loss: 0.1662 - val_accuracy: 0.9599\n", - "Epoch 128/132\n", - "256/256 [==============================] - 75s 291ms/step - loss: 0.1505 - accuracy: 0.9541 - val_loss: 0.1411 - val_accuracy: 0.9615\n", - "Epoch 129/132\n", - "256/256 [==============================] - 73s 284ms/step - loss: 0.1151 - accuracy: 0.9666 - val_loss: 0.1438 - val_accuracy: 0.9599\n", - "Epoch 130/132\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.0878 - accuracy: 0.9753 - val_loss: 0.1397 - val_accuracy: 0.9599\n", - "Epoch 131/132\n", - "256/256 [==============================] - 74s 287ms/step - loss: 0.0624 - accuracy: 0.9814 - val_loss: 0.1991 - val_accuracy: 0.9583\n", - "Epoch 132/132\n", - "256/256 [==============================] - 73s 283ms/step - loss: 0.0397 - accuracy: 0.9905 - val_loss: 0.1805 - val_accuracy: 0.9599\n", + "Epoch 373/378\n", + "256/256 [==============================] - 50s 182ms/step - loss: 0.1479 - accuracy: 0.9595 - val_loss: 0.1663 - val_accuracy: 0.9359\n", + "Epoch 374/378\n", + "256/256 [==============================] - 45s 176ms/step - loss: 0.0961 - accuracy: 0.9695 - val_loss: 0.3375 - val_accuracy: 0.9167\n", + "Epoch 375/378\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.0637 - accuracy: 0.9822 - val_loss: 0.1676 - val_accuracy: 0.9423\n", + "Epoch 376/378\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.0350 - accuracy: 0.9902 - val_loss: 0.4006 - val_accuracy: 0.9167\n", + "Epoch 377/378\n", + "256/256 [==============================] - 45s 177ms/step - loss: 0.0212 - accuracy: 0.9951 - val_loss: 0.2728 - val_accuracy: 0.9375\n", + "Epoch 378/378\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.0156 - accuracy: 0.9968 - val_loss: 0.2517 - val_accuracy: 0.9343\n", "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-128-0.9615.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9615\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1411\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1390501112. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m566.70 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m449.32 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m117.38 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [22] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9423}, \u001b[0m\u001b[0;33mloss{0.1663}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9343\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2518\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m369.92 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m278.86 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m91.07 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [63] END|--------------------------------------->\u001b[0m\n", "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m23\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 132)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m64\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 378)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33m└───Shuffling data...\u001b[0m\n", "\u001b[0;33mPreparing train data...\u001b[0m\n", "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00866\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 133/138\n", - "256/256 [==============================] - 79s 291ms/step - loss: 0.1854 - accuracy: 0.9392 - val_loss: 0.1833 - val_accuracy: 0.9375\n", - "Epoch 134/138\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.1595 - accuracy: 0.9458 - val_loss: 0.2810 - val_accuracy: 0.9359\n", - "Epoch 135/138\n", - "256/256 [==============================] - 73s 285ms/step - loss: 0.1267 - accuracy: 0.9612 - val_loss: 0.2568 - val_accuracy: 0.9327\n", - "Epoch 136/138\n", - "256/256 [==============================] - 74s 287ms/step - loss: 0.1031 - accuracy: 0.9712 - val_loss: 0.2219 - val_accuracy: 0.9471\n", - "Epoch 137/138\n", - "256/256 [==============================] - 73s 286ms/step - loss: 0.0668 - accuracy: 0.9841 - val_loss: 0.2431 - val_accuracy: 0.9519\n", - "Epoch 138/138\n", - "256/256 [==============================] - 75s 291ms/step - loss: 0.0502 - accuracy: 0.9856 - val_loss: 0.2001 - val_accuracy: 0.9567\n", + "Epoch 379/384\n", + "256/256 [==============================] - 51s 186ms/step - loss: 0.1409 - accuracy: 0.9590 - val_loss: 0.1699 - val_accuracy: 0.9407\n", + "Epoch 380/384\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.0980 - accuracy: 0.9697 - val_loss: 0.2313 - val_accuracy: 0.9311\n", + "Epoch 381/384\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.0569 - accuracy: 0.9829 - val_loss: 0.2113 - val_accuracy: 0.9391\n", + "Epoch 382/384\n", + "256/256 [==============================] - 47s 182ms/step - loss: 0.0316 - accuracy: 0.9907 - val_loss: 0.2091 - val_accuracy: 0.9455\n", + "Epoch 383/384\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.0214 - accuracy: 0.9949 - val_loss: 0.2218 - val_accuracy: 0.9439\n", + "Epoch 384/384\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.0166 - accuracy: 0.9958 - val_loss: 0.2408 - val_accuracy: 0.9423\n", "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-138-0.9567.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9567\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2001\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1390501112. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m559.01 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m448.92 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m110.08 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [23] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9455}, \u001b[0m\u001b[0;33mloss{0.1699}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2411\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m381.90 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m282.21 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m99.69 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [64] END|--------------------------------------->\u001b[0m\n", "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m24\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 138)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m65\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 384)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;33mPreparing train data...\u001b[0m\n", "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0086\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 139/144\n", - "256/256 [==============================] - 79s 292ms/step - loss: 0.1864 - accuracy: 0.9465 - val_loss: 0.1530 - val_accuracy: 0.9535\n", - "Epoch 140/144\n", - "256/256 [==============================] - 72s 281ms/step - loss: 0.1584 - accuracy: 0.9531 - val_loss: 0.2170 - val_accuracy: 0.9519\n", - "Epoch 141/144\n", - "256/256 [==============================] - 72s 281ms/step - loss: 0.1250 - accuracy: 0.9653 - val_loss: 0.3537 - val_accuracy: 0.9279\n", - "Epoch 142/144\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.0960 - accuracy: 0.9714 - val_loss: 0.3708 - val_accuracy: 0.9263\n", - "Epoch 143/144\n", - "256/256 [==============================] - 73s 284ms/step - loss: 0.0655 - accuracy: 0.9849 - val_loss: 0.1498 - val_accuracy: 0.9535\n", - "Epoch 144/144\n", - "256/256 [==============================] - 73s 284ms/step - loss: 0.0452 - accuracy: 0.9915 - val_loss: 0.1787 - val_accuracy: 0.9535\n", + "Epoch 385/390\n", + "256/256 [==============================] - 51s 186ms/step - loss: 0.1582 - accuracy: 0.9563 - val_loss: 0.1527 - val_accuracy: 0.9359\n", + "Epoch 386/390\n", + "256/256 [==============================] - 45s 177ms/step - loss: 0.0932 - accuracy: 0.9702 - val_loss: 0.1701 - val_accuracy: 0.9327\n", + "Epoch 387/390\n", + "256/256 [==============================] - 47s 181ms/step - loss: 0.0601 - accuracy: 0.9846 - val_loss: 0.2358 - val_accuracy: 0.9295\n", + "Epoch 388/390\n", + "256/256 [==============================] - 46s 180ms/step - loss: 0.0384 - accuracy: 0.9907 - val_loss: 0.2870 - val_accuracy: 0.9327\n", + "Epoch 389/390\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.0196 - accuracy: 0.9939 - val_loss: 0.3137 - val_accuracy: 0.9343\n", + "Epoch 390/390\n", + "256/256 [==============================] - 46s 180ms/step - loss: 0.0127 - accuracy: 0.9976 - val_loss: 0.2885 - val_accuracy: 0.9391\n", "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-139-0.9535.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1529\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1390501112. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m548.76 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m443.60 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m105.16 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [24] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9391}, \u001b[0m\u001b[0;33mloss{0.1527}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2886\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m376.96 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m282.35 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m94.61 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [65] END|--------------------------------------->\u001b[0m\n", "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m25\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 144)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m66\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 390)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;33mPreparing train data...\u001b[0m\n", "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00854\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 145/150\n", - "256/256 [==============================] - 80s 292ms/step - loss: 0.1591 - accuracy: 0.9497 - val_loss: 0.2199 - val_accuracy: 0.9519\n", - "Epoch 146/150\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.1562 - accuracy: 0.9509 - val_loss: 0.1797 - val_accuracy: 0.9551\n", - "Epoch 147/150\n", - "256/256 [==============================] - 73s 286ms/step - loss: 0.1233 - accuracy: 0.9641 - val_loss: 0.1523 - val_accuracy: 0.9583\n", - "Epoch 148/150\n", - "256/256 [==============================] - 72s 281ms/step - loss: 0.0972 - accuracy: 0.9739 - val_loss: 0.2174 - val_accuracy: 0.9455\n", - "Epoch 149/150\n", - "256/256 [==============================] - 73s 285ms/step - loss: 0.0707 - accuracy: 0.9814 - val_loss: 0.2697 - val_accuracy: 0.9471\n", - "Epoch 150/150\n", - "256/256 [==============================] - 75s 291ms/step - loss: 0.0596 - accuracy: 0.9866 - val_loss: 0.2078 - val_accuracy: 0.9551\n", + "Epoch 391/396\n", + "256/256 [==============================] - 51s 186ms/step - loss: 0.1452 - accuracy: 0.9553 - val_loss: 0.1814 - val_accuracy: 0.9359\n", + "Epoch 392/396\n", + "256/256 [==============================] - 47s 182ms/step - loss: 0.1002 - accuracy: 0.9690 - val_loss: 0.2043 - val_accuracy: 0.9407\n", + "Epoch 393/396\n", + "256/256 [==============================] - 46s 180ms/step - loss: 0.0487 - accuracy: 0.9871 - val_loss: 0.1993 - val_accuracy: 0.9423\n", + "Epoch 394/396\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.0382 - accuracy: 0.9890 - val_loss: 0.2489 - val_accuracy: 0.9359\n", + "Epoch 395/396\n", + "256/256 [==============================] - 46s 181ms/step - loss: 0.0230 - accuracy: 0.9941 - val_loss: 0.2892 - val_accuracy: 0.9263\n", + "Epoch 396/396\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.0138 - accuracy: 0.9961 - val_loss: 0.2592 - val_accuracy: 0.9375\n", "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-147-0.9583.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9583\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1523\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1390501112. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m563.24 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m448.66 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m114.58 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [25] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9423}, \u001b[0m\u001b[0;33mloss{0.1814}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9375\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2582\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m386.93 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m283.19 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m103.74 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [66] END|--------------------------------------->\u001b[0m\n", "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m26\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 150)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m67\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 396)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;33mPreparing train data...\u001b[0m\n", "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01094\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00848\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 151/156\n", - "256/256 [==============================] - 80s 293ms/step - loss: 0.1757 - accuracy: 0.9404 - val_loss: 0.1719 - val_accuracy: 0.9487\n", - "Epoch 152/156\n", - "256/256 [==============================] - 73s 286ms/step - loss: 0.1540 - accuracy: 0.9568 - val_loss: 0.3150 - val_accuracy: 0.9391\n", - "Epoch 153/156\n", - "256/256 [==============================] - 74s 290ms/step - loss: 0.1258 - accuracy: 0.9622 - val_loss: 0.2205 - val_accuracy: 0.9375\n", - "Epoch 154/156\n", - "256/256 [==============================] - 73s 285ms/step - loss: 0.0939 - accuracy: 0.9734 - val_loss: 0.3004 - val_accuracy: 0.9343\n", - "Epoch 155/156\n", - "256/256 [==============================] - 74s 291ms/step - loss: 0.0705 - accuracy: 0.9832 - val_loss: 0.3185 - val_accuracy: 0.9215\n", - "Epoch 156/156\n", - "256/256 [==============================] - 72s 283ms/step - loss: 0.0448 - accuracy: 0.9900 - val_loss: 0.3741 - val_accuracy: 0.9263\n", + "Epoch 397/402\n", + "256/256 [==============================] - 51s 186ms/step - loss: 0.1513 - accuracy: 0.9565 - val_loss: 0.1768 - val_accuracy: 0.9327\n", + "Epoch 398/402\n", + "256/256 [==============================] - 47s 181ms/step - loss: 0.0983 - accuracy: 0.9670 - val_loss: 0.1998 - val_accuracy: 0.9295\n", + "Epoch 399/402\n", + "256/256 [==============================] - 46s 181ms/step - loss: 0.0572 - accuracy: 0.9836 - val_loss: 0.1818 - val_accuracy: 0.9247\n", + "Epoch 400/402\n", + "256/256 [==============================] - 46s 181ms/step - loss: 0.0402 - accuracy: 0.9888 - val_loss: 0.2781 - val_accuracy: 0.9263\n", + "Epoch 401/402\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.0237 - accuracy: 0.9946 - val_loss: 0.3038 - val_accuracy: 0.9327\n", + "Epoch 402/402\n", + "256/256 [==============================] - 47s 183ms/step - loss: 0.0153 - accuracy: 0.9961 - val_loss: 0.3205 - val_accuracy: 0.9391\n", "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-151-0.9487.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1719\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1390501112. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m559.38 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m448.48 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m110.91 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [26] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9391}, \u001b[0m\u001b[0;33mloss{0.1768}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3206\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m383.02 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m284.55 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m98.47 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [67] END|--------------------------------------->\u001b[0m\n", "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m27\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 156)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m68\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 402)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;33mPreparing train data...\u001b[0m\n", "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01088\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00842\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 157/162\n", - "256/256 [==============================] - 81s 298ms/step - loss: 0.1679 - accuracy: 0.9468 - val_loss: 0.1615 - val_accuracy: 0.9599\n", - "Epoch 158/162\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.1576 - accuracy: 0.9490 - val_loss: 0.3046 - val_accuracy: 0.9535\n", - "Epoch 159/162\n", - "256/256 [==============================] - 75s 291ms/step - loss: 0.1247 - accuracy: 0.9626 - val_loss: 0.4555 - val_accuracy: 0.8958\n", - "Epoch 160/162\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.0983 - accuracy: 0.9705 - val_loss: 0.3122 - val_accuracy: 0.9487\n", - "Epoch 161/162\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.0695 - accuracy: 0.9824 - val_loss: 0.2654 - val_accuracy: 0.9423\n", - "Epoch 162/162\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.0435 - accuracy: 0.9900 - val_loss: 0.3325 - val_accuracy: 0.9407\n", + "Epoch 403/408\n", + "256/256 [==============================] - 51s 187ms/step - loss: 0.1445 - accuracy: 0.9587 - val_loss: 0.1564 - val_accuracy: 0.9375\n", + "Epoch 404/408\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.1045 - accuracy: 0.9670 - val_loss: 0.1896 - val_accuracy: 0.9343\n", + "Epoch 405/408\n", + "256/256 [==============================] - 45s 176ms/step - loss: 0.0665 - accuracy: 0.9814 - val_loss: 0.2436 - val_accuracy: 0.9215\n", + "Epoch 406/408\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.0350 - accuracy: 0.9900 - val_loss: 0.3153 - val_accuracy: 0.9327\n", + "Epoch 407/408\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.0263 - accuracy: 0.9932 - val_loss: 0.3088 - val_accuracy: 0.9295\n", + "Epoch 408/408\n", + "256/256 [==============================] - 46s 180ms/step - loss: 0.0158 - accuracy: 0.9973 - val_loss: 0.3129 - val_accuracy: 0.9327\n", "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9599}, \u001b[0m\u001b[0;33mloss{0.1615}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9647}, loss{0.1391}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3325\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1390501112. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m575.02 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m453.79 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m121.23 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [27] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9375}, \u001b[0m\u001b[0;33mloss{0.1564}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9327\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3130\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m385.61 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m281.81 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m103.80 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [68] END|--------------------------------------->\u001b[0m\n", "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m28\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 162)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m69\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 408)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;33mPreparing train data...\u001b[0m\n", "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01082\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00836\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 163/168\n", - "256/256 [==============================] - 81s 299ms/step - loss: 0.1753 - accuracy: 0.9492 - val_loss: 0.1844 - val_accuracy: 0.9327\n", - "Epoch 164/168\n", - "256/256 [==============================] - 76s 294ms/step - loss: 0.1455 - accuracy: 0.9521 - val_loss: 0.1986 - val_accuracy: 0.9471\n", - "Epoch 165/168\n", - "256/256 [==============================] - 75s 293ms/step - loss: 0.1178 - accuracy: 0.9663 - val_loss: 0.1527 - val_accuracy: 0.9535\n", - "Epoch 166/168\n", - "256/256 [==============================] - 75s 292ms/step - loss: 0.0860 - accuracy: 0.9773 - val_loss: 0.2426 - val_accuracy: 0.9247\n", - "Epoch 167/168\n", - "256/256 [==============================] - 76s 295ms/step - loss: 0.0597 - accuracy: 0.9846 - val_loss: 0.2100 - val_accuracy: 0.9599\n", - "Epoch 168/168\n", - "256/256 [==============================] - 74s 290ms/step - loss: 0.0460 - accuracy: 0.9900 - val_loss: 0.2788 - val_accuracy: 0.9487\n", + "Epoch 409/414\n", + "256/256 [==============================] - 51s 185ms/step - loss: 0.1507 - accuracy: 0.9609 - val_loss: 0.1957 - val_accuracy: 0.9231\n", + "Epoch 410/414\n", + "256/256 [==============================] - 47s 183ms/step - loss: 0.1024 - accuracy: 0.9675 - val_loss: 0.1784 - val_accuracy: 0.9391\n", + "Epoch 411/414\n", + "256/256 [==============================] - 47s 182ms/step - loss: 0.0554 - accuracy: 0.9819 - val_loss: 0.2212 - val_accuracy: 0.9359\n", + "Epoch 412/414\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.0261 - accuracy: 0.9946 - val_loss: 0.3009 - val_accuracy: 0.9343\n", + "Epoch 413/414\n", + "256/256 [==============================] - 47s 182ms/step - loss: 0.0181 - accuracy: 0.9949 - val_loss: 0.2524 - val_accuracy: 0.9423\n", + "Epoch 414/414\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.0161 - accuracy: 0.9966 - val_loss: 0.2830 - val_accuracy: 0.9391\n", "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9599}, \u001b[0m\u001b[0;33mloss{0.1527}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9647}, loss{0.1391}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2788\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1390501112. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m586.99 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m458.74 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m128.25 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [28] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9423}, \u001b[0m\u001b[0;33mloss{0.1784}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2830\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m386.47 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m284.14 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m102.33 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [69] END|--------------------------------------->\u001b[0m\n", "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m29\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 168)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m70\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 414)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;33mPreparing train data...\u001b[0m\n", "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01076\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0083\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 169/174\n", - "256/256 [==============================] - 81s 299ms/step - loss: 0.1764 - accuracy: 0.9473 - val_loss: 0.1456 - val_accuracy: 0.9535\n", - "Epoch 170/174\n", - "256/256 [==============================] - 73s 286ms/step - loss: 0.1427 - accuracy: 0.9553 - val_loss: 0.2146 - val_accuracy: 0.9487\n", - "Epoch 171/174\n", - "256/256 [==============================] - 74s 290ms/step - loss: 0.1340 - accuracy: 0.9644 - val_loss: 0.1950 - val_accuracy: 0.9439\n", - "Epoch 172/174\n", - "256/256 [==============================] - 74s 290ms/step - loss: 0.0793 - accuracy: 0.9788 - val_loss: 0.1637 - val_accuracy: 0.9455\n", - "Epoch 173/174\n", - "256/256 [==============================] - 75s 293ms/step - loss: 0.0587 - accuracy: 0.9856 - val_loss: 0.2221 - val_accuracy: 0.9503\n", - "Epoch 174/174\n", - "256/256 [==============================] - 75s 292ms/step - loss: 0.0444 - accuracy: 0.9897 - val_loss: 0.2128 - val_accuracy: 0.9567\n", + "Epoch 415/420\n", + "256/256 [==============================] - 50s 183ms/step - loss: 0.1625 - accuracy: 0.9526 - val_loss: 0.1950 - val_accuracy: 0.9359\n", + "Epoch 416/420\n", + "256/256 [==============================] - 47s 182ms/step - loss: 0.0943 - accuracy: 0.9695 - val_loss: 0.1502 - val_accuracy: 0.9519\n", + "Epoch 417/420\n", + "256/256 [==============================] - 47s 184ms/step - loss: 0.0576 - accuracy: 0.9827 - val_loss: 0.2426 - val_accuracy: 0.9375\n", + "Epoch 418/420\n", + "256/256 [==============================] - 46s 181ms/step - loss: 0.0388 - accuracy: 0.9924 - val_loss: 0.2247 - val_accuracy: 0.9407\n", + "Epoch 419/420\n", + "256/256 [==============================] - 46s 181ms/step - loss: 0.0186 - accuracy: 0.9963 - val_loss: 0.2401 - val_accuracy: 0.9471\n", + "Epoch 420/420\n", + "256/256 [==============================] - 46s 180ms/step - loss: 0.0181 - accuracy: 0.9966 - val_loss: 0.2419 - val_accuracy: 0.9455\n", "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9567}, \u001b[0m\u001b[0;33mloss{0.1456}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9647}, loss{0.1391}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9567\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2128\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1390501112. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m579.21 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m454.91 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m124.31 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [29] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9519}, \u001b[0m\u001b[0;33mloss{0.1502}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2419\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m385.35 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m284.52 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m100.83 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [70] END|--------------------------------------->\u001b[0m\n", "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m30\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 174)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m71\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 420)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;33mPreparing train data...\u001b[0m\n", "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0107\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00824\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 175/180\n", - "256/256 [==============================] - 82s 299ms/step - loss: 0.1776 - accuracy: 0.9434 - val_loss: 0.1638 - val_accuracy: 0.9551\n", - "Epoch 176/180\n", - "256/256 [==============================] - 75s 292ms/step - loss: 0.1480 - accuracy: 0.9539 - val_loss: 0.1788 - val_accuracy: 0.9471\n", - "Epoch 177/180\n", - "256/256 [==============================] - 75s 294ms/step - loss: 0.1091 - accuracy: 0.9668 - val_loss: 0.4104 - val_accuracy: 0.9311\n", - "Epoch 178/180\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.0808 - accuracy: 0.9758 - val_loss: 0.3871 - val_accuracy: 0.9215\n", - "Epoch 179/180\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.0559 - accuracy: 0.9854 - val_loss: 0.2503 - val_accuracy: 0.9423\n", - "Epoch 180/180\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.0421 - accuracy: 0.9890 - val_loss: 0.2214 - val_accuracy: 0.9503\n", + "Epoch 421/426\n", + "256/256 [==============================] - 51s 186ms/step - loss: 0.1338 - accuracy: 0.9619 - val_loss: 0.1441 - val_accuracy: 0.9471\n", + "Epoch 422/426\n", + "256/256 [==============================] - 47s 181ms/step - loss: 0.0927 - accuracy: 0.9712 - val_loss: 0.1686 - val_accuracy: 0.9439\n", + "Epoch 423/426\n", + "256/256 [==============================] - 46s 181ms/step - loss: 0.0593 - accuracy: 0.9846 - val_loss: 0.1884 - val_accuracy: 0.9439\n", + "Epoch 424/426\n", + "256/256 [==============================] - 46s 180ms/step - loss: 0.0420 - accuracy: 0.9883 - val_loss: 0.2641 - val_accuracy: 0.9471\n", + "Epoch 425/426\n", + "256/256 [==============================] - 47s 181ms/step - loss: 0.0265 - accuracy: 0.9929 - val_loss: 0.1887 - val_accuracy: 0.9455\n", + "Epoch 426/426\n", + "256/256 [==============================] - 47s 182ms/step - loss: 0.0181 - accuracy: 0.9968 - val_loss: 0.2185 - val_accuracy: 0.9439\n", "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9551}, \u001b[0m\u001b[0;33mloss{0.1638}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9647}, loss{0.1391}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2214\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1390501112. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m580.45 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m455.64 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m124.81 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [30] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9471}, \u001b[0m\u001b[0;33mloss{0.1441}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2184\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m387.29 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m284.25 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m103.05 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [71] END|--------------------------------------->\u001b[0m\n", "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m31\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 180)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m72\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 426)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;33mPreparing train data...\u001b[0m\n", "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01064\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00818\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 181/186\n", - "256/256 [==============================] - 82s 301ms/step - loss: 0.1767 - accuracy: 0.9487 - val_loss: 0.1722 - val_accuracy: 0.9503\n", - "Epoch 182/186\n", - "256/256 [==============================] - 75s 292ms/step - loss: 0.1454 - accuracy: 0.9568 - val_loss: 0.1515 - val_accuracy: 0.9519\n", - "Epoch 183/186\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.1120 - accuracy: 0.9683 - val_loss: 0.2423 - val_accuracy: 0.9391\n", - "Epoch 184/186\n", - "256/256 [==============================] - 74s 287ms/step - loss: 0.0786 - accuracy: 0.9795 - val_loss: 0.2353 - val_accuracy: 0.9487\n", - "Epoch 185/186\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.0570 - accuracy: 0.9854 - val_loss: 0.2281 - val_accuracy: 0.9503\n", - "Epoch 186/186\n", - "256/256 [==============================] - 75s 293ms/step - loss: 0.0359 - accuracy: 0.9910 - val_loss: 0.2425 - val_accuracy: 0.9535\n", + "Epoch 427/432\n", + "256/256 [==============================] - 51s 188ms/step - loss: 0.1452 - accuracy: 0.9609 - val_loss: 0.1648 - val_accuracy: 0.9407\n", + "Epoch 428/432\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.1003 - accuracy: 0.9702 - val_loss: 0.1981 - val_accuracy: 0.9407\n", + "Epoch 429/432\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.0583 - accuracy: 0.9832 - val_loss: 0.2143 - val_accuracy: 0.9247\n", + "Epoch 430/432\n", + "256/256 [==============================] - 45s 177ms/step - loss: 0.0400 - accuracy: 0.9893 - val_loss: 0.2011 - val_accuracy: 0.9407\n", + "Epoch 431/432\n", + "256/256 [==============================] - 46s 177ms/step - loss: 0.0192 - accuracy: 0.9958 - val_loss: 0.2325 - val_accuracy: 0.9407\n", + "Epoch 432/432\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.0115 - accuracy: 0.9978 - val_loss: 0.2928 - val_accuracy: 0.9391\n", "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9535}, \u001b[0m\u001b[0;33mloss{0.1515}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9647}, loss{0.1391}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2424\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1390501112. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m587.39 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m455.39 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m132.00 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [31] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9407}, \u001b[0m\u001b[0;33mloss{0.1648}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2920\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m387.78 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m281.21 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m106.58 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [72] END|--------------------------------------->\u001b[0m\n", "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m32\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 186)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m73\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 432)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33m└───Shuffling data...\u001b[0m\n", "\u001b[0;33mPreparing train data...\u001b[0m\n", "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01058\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00812\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 187/192\n", - "256/256 [==============================] - 82s 300ms/step - loss: 0.1724 - accuracy: 0.9485 - val_loss: 0.1654 - val_accuracy: 0.9551\n", - "Epoch 188/192\n", - "256/256 [==============================] - 75s 292ms/step - loss: 0.1345 - accuracy: 0.9614 - val_loss: 0.2014 - val_accuracy: 0.9503\n", - "Epoch 189/192\n", - "256/256 [==============================] - 75s 293ms/step - loss: 0.1049 - accuracy: 0.9692 - val_loss: 0.2510 - val_accuracy: 0.9567\n", - "Epoch 190/192\n", - "256/256 [==============================] - 75s 290ms/step - loss: 0.0767 - accuracy: 0.9780 - val_loss: 0.2297 - val_accuracy: 0.9455\n", - "Epoch 191/192\n", - "256/256 [==============================] - 74s 290ms/step - loss: 0.0492 - accuracy: 0.9863 - val_loss: 0.1745 - val_accuracy: 0.9519\n", - "Epoch 192/192\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.0297 - accuracy: 0.9937 - val_loss: 0.2283 - val_accuracy: 0.9535\n", + "Epoch 433/438\n", + "256/256 [==============================] - 51s 186ms/step - loss: 0.1506 - accuracy: 0.9561 - val_loss: 0.2703 - val_accuracy: 0.9439\n", + "Epoch 434/438\n", + "256/256 [==============================] - 47s 185ms/step - loss: 0.0979 - accuracy: 0.9675 - val_loss: 0.2695 - val_accuracy: 0.9167\n", + "Epoch 435/438\n", + "256/256 [==============================] - 47s 184ms/step - loss: 0.0595 - accuracy: 0.9827 - val_loss: 0.2406 - val_accuracy: 0.9343\n", + "Epoch 436/438\n", + "256/256 [==============================] - 47s 183ms/step - loss: 0.0400 - accuracy: 0.9922 - val_loss: 0.2288 - val_accuracy: 0.9407\n", + "Epoch 437/438\n", + "256/256 [==============================] - 47s 183ms/step - loss: 0.0267 - accuracy: 0.9929 - val_loss: 0.2331 - val_accuracy: 0.9391\n", + "Epoch 438/438\n", + "256/256 [==============================] - 46s 179ms/step - loss: 0.0196 - accuracy: 0.9958 - val_loss: 0.2661 - val_accuracy: 0.9391\n", "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9567}, \u001b[0m\u001b[0;33mloss{0.1654}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9647}, loss{0.1391}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2283\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1390501112. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m594.04 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m457.04 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m137.00 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [32] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9439}, \u001b[0m\u001b[0;33mloss{0.2288}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2667\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m400.23 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m286.87 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m113.36 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [73] END|--------------------------------------->\u001b[0m\n", "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m33\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 192)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m74\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 438)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;33mPreparing train data...\u001b[0m\n", "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01052\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00806\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 193/198\n", - "256/256 [==============================] - 83s 305ms/step - loss: 0.1587 - accuracy: 0.9551 - val_loss: 0.1971 - val_accuracy: 0.9583\n", - "Epoch 194/198\n", - "256/256 [==============================] - 74s 290ms/step - loss: 0.1452 - accuracy: 0.9531 - val_loss: 0.1756 - val_accuracy: 0.9375\n", - "Epoch 195/198\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.0976 - accuracy: 0.9741 - val_loss: 0.2353 - val_accuracy: 0.9231\n", - "Epoch 196/198\n", - "256/256 [==============================] - 75s 293ms/step - loss: 0.0591 - accuracy: 0.9832 - val_loss: 0.1904 - val_accuracy: 0.9471\n", - "Epoch 197/198\n", - "256/256 [==============================] - 76s 298ms/step - loss: 0.0411 - accuracy: 0.9900 - val_loss: 0.2127 - val_accuracy: 0.9503\n", - "Epoch 198/198\n", - "256/256 [==============================] - 75s 294ms/step - loss: 0.0302 - accuracy: 0.9932 - val_loss: 0.2056 - val_accuracy: 0.9487\n", + "Epoch 439/444\n", + "256/256 [==============================] - 51s 186ms/step - loss: 0.1462 - accuracy: 0.9575 - val_loss: 0.1617 - val_accuracy: 0.9455\n", + "Epoch 440/444\n", + "256/256 [==============================] - 46s 180ms/step - loss: 0.0956 - accuracy: 0.9688 - val_loss: 0.1821 - val_accuracy: 0.9423\n", + "Epoch 441/444\n", + "256/256 [==============================] - 47s 181ms/step - loss: 0.0683 - accuracy: 0.9785 - val_loss: 0.3554 - val_accuracy: 0.9391\n", + "Epoch 442/444\n", + "256/256 [==============================] - 47s 181ms/step - loss: 0.0348 - accuracy: 0.9897 - val_loss: 0.2999 - val_accuracy: 0.9407\n", + "Epoch 443/444\n", + "256/256 [==============================] - 46s 178ms/step - loss: 0.0242 - accuracy: 0.9946 - val_loss: 0.3233 - val_accuracy: 0.9407\n", + "Epoch 444/444\n", + "256/256 [==============================] - 46s 181ms/step - loss: 0.0161 - accuracy: 0.9973 - val_loss: 0.4936 - val_accuracy: 0.9391\n", "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9583}, \u001b[0m\u001b[0;33mloss{0.1756}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9647}, loss{0.1391}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2056\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1390501112. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m591.57 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m460.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m130.98 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [33] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9455}, \u001b[0m\u001b[0;33mloss{0.1617}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4279\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m395.39 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m283.55 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m111.84 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [74] END|--------------------------------------->\u001b[0m\n", "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m34\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 198)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m75\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 444)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", "\u001b[0;33mPreparing train data...\u001b[0m\n", "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.008\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", "\n", "KeyboardInterrupt.\n", "Training done.\n", @@ -19034,7 +21854,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T07:04:52.565658900Z", @@ -19043,64 +21863,12 @@ }, "outputs": [ { - "data": { - "image/png": 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uceDAAZfnadOmDYsXLyYyMpK77rqLgIAA+vXrR9++fVm6dCmPPPIIffr04dy5c8yaNYsmTZqQlpZW6P4EBwczYcKEfLebOHEiUVFRdO7cmZdeegkPDw++++47srOz+fTTTy3bvfXWW/zyyy888MADvPrqq/j7+/P9999Tp04dDh48aNkuKCiIb7/9lmeeeYY777yTJ554gpCQEGJiYli1ahV33303X3/9daH7o9PpmDhxosP6SpUq8dJLLzFlyhSGDx9Oly5dGDx4MPHx8XzxxReEh4dbUr9PnjzJ/fffz8CBA2nSpAkeHh78+eefxMfH88QTTwCmOgzffPMNjzzyCPXq1SM1NZXZs2cTFBTk8hdNIYQQorSQ7zu35/ed+fPno9FoLD945fbQQw/x3nvvsWjRIiIjI/nyyy/p3Lkzd955J88//zwRERGcP3+eVatWsX//fgA++eQT1q5dS5cuXXj++edp3LgxsbGx/P7772zevJkKFSrQs2dPateuzbPPPsubb76JRqNhzpw5lr4URM+ePfHy8qJfv3783//9H2lpacyePZvQ0FC7wGNQUBDTp0/nueee46677uLJJ5+kYsWKHDhwgIyMDLuAlaenJ0888QRff/01Go2GwYMHF/ixFMIt3DXNmxCi5JinIjXfvLy8lKpVqyo9evRQvvjiC8tUqbmdOXNGGTJkiFK1alXF09NTqVGjhtK3b19lyZIllm2cTV2blpamPPnkk0qFChUUwDKNrdFoVD755BOlTp06ire3t9K6dWtl5cqVytChQ51OdZub7dS1rjibulZRFGXv3r1Kr169lICAAMXPz0/p1q2bsnXrVof9Dx48qHTp0kXx8fFRatSooXz00UfKjz/+6DCVq/lcvXr1UoKDgxUfHx+lXr16yrBhw5Tdu3dbtino1LVDhw61e45sb/Xq1bNst3jxYqV169aKt7e3UqlSJeWpp55SLl26ZLk/ISFB+d///qc0atRI8ff3V4KDg5X27dsrv/32m91jMXjwYKV27dqKt7e3EhoaqvTt29eu3UIIIURpI993bt/vO1qtVqlcubJyzz335NmviIgIpXXr1pblw4cPK4888ohSoUIFxcfHR2nYsKEyduxYu30uXLigDBkyRAkJCVG8vb2VunXrKv/73/+U7OxsyzZ79uxR2rdvr3h5eSm1a9dWpk2bZnm92Pa3Tp06Sp8+fZy2bfny5UqLFi0UHx8fJTw8XJkyZYoyZ84cp4/Z8uXLlU6dOim+vr5KUFCQ0q5dO2XhwoUOx9y5c6cCKD179szzcRHidqBSlFz5c0IIIYQQQgghhChWBw4coFWrVvz8888888wz7m6OEHmSmkdCCCGEEEIIIUQJmz17NgEBAQwYMMDdTREiX1LzSAghhBBCCCGEKCErVqzg6NGjfP/994waNQp/f393N0mIfMmwNSGEEEIIIYQQooSEh4cTHx9Pr169+OWXXwgMDHR3k4TIV6katrZp0yb69etH9erVUalULFu2LM/tN2zYgEqlcrjFxcWVTIOFEEIIIYQQQggb58+fJzMzk2XLlkngSJQapSp4lJ6eTsuWLZk5c2ah9jtx4gSxsbGWW2hoaDG1UAghhBBCCCGEEKJsKVXBo969ezNx4kQeeeSRQu0XGhpK1apVLTe1ulR1WwghhBDCpZkzZxIeHo6Pjw/t27dn586dLredPXs299xzDxUrVqRixYp0797dYXtFURg3bhzVqlXD19eX7t27c+rUqeLuhhBCCCFuY+WiYHarVq3Izs6mWbNmTJgwgbvvvtvlttnZ2WRnZ1uWjUYj169fp3LlyqhUqpJorhBCCCFugqIopKamUr169XLzQ9HixYuJjIxk1qxZtG/fnhkzZtCrVy9OnDjhNNN6w4YNDB48mE6dOuHj48OUKVPo2bMnR44coUaNGgB8+umnfPnll/z0009EREQwduxYevXqxdGjR/Hx8SlQu4xGI1euXCEwMFC+PwkhhBC3sQJ/f1JKKUD5888/89zm+PHjyqxZs5Tdu3crW7ZsUYYPH654eHgoe/bscbnP+PHjFUBucpOb3OQmN7mV0tvFixeL+FvH7atdu3bK//73P8uywWBQqlevrkyaNKlA++v1eiUwMFD56aefFEVRFKPRqFStWlX57LPPLNskJSUp3t7eysKFCwvcrosXL7r9dSA3uclNbnKTm9wKfsvv+1OZzjxq2LAhDRs2tCx36tSJM2fOMH36dH755Ren+4wZM4bIyEjLcnJyMrVr1+bcuXNFWsxMp9Oxfv16unXrhqenZ5EdtzQor30vr/2G8tv38tpvKL99l367t9+pqalERESUm+KjWq2WPXv2MGbMGMs6tVpN9+7d2bZtW4GOkZGRgU6no1KlSgCcO3eOuLg4unfvbtkmODiY9u3bs23bNp544gmnx8mdua3kTOYr35+KTnntu/S7fPUbym/fy2u/ofz2/Xbpd0G/P5Xp4JEz7dq1Y/PmzS7v9/b2xtvb22F9pUqVCAoKKrJ26HQ6/Pz8qFy5crl6g0D57Xt57TeU376X135D+e279Nu9/Tafu7wMk0pISMBgMBAWFma3PiwsjOPHjxfoGG+//TbVq1e3BIvMM9I6O2Zes9VOmjSJDz74wGH9tm3b8PPzK1BbCsrPz48dO3YU6TFLi/Lad+l3+VNe+15e+w3lt++3Q78zMjKA/L8/lbvg0f79+6lWrZq7myGEEEII4VaTJ09m0aJFbNiwocC1jFzJnbmdkpJCrVq16NmzZ5H/+BYVFUWPHj3KVYAWym/fpd/lq99QfvteXvsN5bfvt0u/U1JSCrRdqQoepaWlcfr0acvyuXPn2L9/P5UqVaJ27dqMGTOGy5cv8/PPPwMwY8YMIiIiaNq0KVlZWfzwww+sW7eOtWvXuqsLQgghhBBFokqVKmg0GuLj4+3Wx8fHU7Vq1Tz3/fzzz5k8eTL//vsvLVq0sKw37xcfH2/3Y1t8fDytWrVyeTxXmduenp7F8oW4uI5bGpTXvku/y5/y2vfy2m8ov313d78Leu5SNRXJ7t27ad26Na1btwYgMjKS1q1bM27cOABiY2OJiYmxbK/Vann99ddp3rw5Xbp04cCBA/z777/cf//9bmm/EEIIIURR8fLyok2bNkRHR1vWGY1GoqOj6dixo8v9Pv30Uz766CNWr15N27Zt7e6LiIigatWqdsdMSUlhx44deR5TCCGEEGVbqco86tq1q6UAozPz5s2zW37rrbd46623irlVQgghhBDuERkZydChQ2nbti3t2rVjxowZpKenM3z4cACGDBlCjRo1mDRpEgBTpkxh3LhxLFiwgPDwcEsdo4CAAAICAlCpVIwePZqJEyfSoEEDIiIiGDt2LNWrV6d///7u6qYQQggh3KxUBY+EEEIIIYTVoEGDuHbtGuPGjSMuLo5WrVqxevVqS8HrmJgY1Gprovm3336LVqvlscceszvO+PHjmTBhAmD68S09PZ3nn3+epKQkOnfuzOrVq2+5LpIQQgghSi8JHgkhhBBClGKjRo1i1KhRTu/bsGGD3fL58+fzPZ5KpeLDDz/kww8/LILWCSGEEKIsKFU1j4QQQgghhBBCCCFEyZLgkRBCCCGEEEIIIYRwSYJHQgghhBCiVJiy5iSLz6iJS8lyd1OEEEKIckVqHgkhhBBCiFLhz31XSExXk5Sho1Zld7dGCCGEKD8k80gIIYQQQpQKapXpX0VxbzuEEEKI8kaCR0IIIYQQolRQqUzRI6NEj4QQQogSJcEjIYQQQghRKqjc3QAhhBCinJLgkRBCCCGEKB1k2JoQQgjhFhI8EkIIIYQQpYI6Z9iagkSPhBBCiJIkwSMhhBBCCFEqmIetGSV2JIQQQpQoCR4JIYQQQohSQWUZtibRIyGEEKIkSfBICCGEEEKUCubMIwkdCSGEECVLgkdCCCGEEKJUUFlSj9zbDiGEEKK8keCREEIIIYQoFcyxI6MMWxNCCCFKlASPhBBCCCFEqaDCPNuaEEIIIUqSBI+EEEIIIUSpYC2Y7d52CCGEEOWNBI+EEEIIIUSpoLaUPJLokRBCCFGSJHgkhBBCCCFKiZxhaxI7EkIIIUqUBI+EEEIIIUSpIMPWhBBCCPeQ4JEQQgghhCgVcmJHMmxNCCGEKGESPBJCCCGEEKWCWiXD1oQQQgh3kOCREEIIIYQoFSzD1tzbDCGEEKLckeCREEIIIYQoFczD1oySeiSEEEKUKAkeCSGEEEKI0kFSj4QQQgi3kOCREEIIIYQoFawFs4UQQghRkiR4JIQQQgghSgV1zjdXRYatCSGEECVKgkdCCCGEEKJUUOXkHhkldiSEEEKUKAkeCSGEEEKIUkFKHgkhhBDuIcEjIYQQQghRKlhqHsmwNSGEEKJESfBICCGEEEKUCipJPRJCCCHcQoJHQgghhBCiVJDYkRBCCOEeEjwSQgghhBClgnnYmlGGrQkhhBAlSoJHQgghhBCiVDAPW5PYkRBCCFGySlXwaNOmTfTr14/q1aujUqlYtmxZvvts2LCBO++8E29vb+rXr8+8efOKvZ1CCCGEEKLoqWXYmhBCCOEWpSp4lJ6eTsuWLZk5c2aBtj937hx9+vShW7du7N+/n9GjR/Pcc8+xZs2aYm6pEEIIIYQoLjLbmhBCCFGyPNzdgMLo3bs3vXv3LvD2s2bNIiIigqlTpwLQuHFjNm/ezPTp0+nVq1dxNVMIIYQQQhQDGbYmhBBCuEepCh4V1rZt2+jevbvdul69ejF69GiX+2RnZ5OdnW1ZTklJAUCn06HT6YqsbeZjFeUxS4vy2vfy2m8ov30vr/2G8tt36bd7++3u84viZy6YLbEjIYQQomSV6eBRXFwcYWFhduvCwsJISUkhMzMTX19fh30mTZrEBx984LB+7dq1+Pn5FXkbo6KiivyYpUV57Xt57TeU376X135D+e279Ns9MjIy3Hp+UfwsNY8k9UgIIYQoUWU6eHQzxowZQ2RkpGU5JSWFWrVq0bNnT4KCgorsPDqdjqioKHr06IGnp2eRHbc0KK99L6/9hvLb9/Labyi/fZd+u7ff5mzh8mbmzJl89tlnxMXF0bJlS7766ivatWvndNsjR44wbtw49uzZw4ULF5g+fbpDRvaECRMcfkhr2LAhx48fL64uFJhl2Jqb2yGEEEKUN2U6eFS1alXi4+Pt1sXHxxMUFOQ06wjA29sbb29vh/Wenp7F8oW4uI5bGpTXvpfXfkP57Xt57TeU375Lv913/vJm8eLFREZGMmvWLNq3b8+MGTPo1asXJ06cIDQ01GH7jIwM6taty+OPP85rr73m8rhNmzbl33//tSx7eNweXxnNw9aMEj0SQgghSlSpmm2tsDp27Eh0dLTduqioKDp27OimFgkhhBBCFJ1p06YxcuRIhg8fTpMmTZg1axZ+fn7MmTPH6fZ33XUXn332GU888YTTH8vMPDw8qFq1quVWpUqV4upC4ViKHkn0SAghhChJt8fPSAWUlpbG6dOnLcvnzp1j//79VKpUidq1azNmzBguX77Mzz//DMALL7zA119/zVtvvcWIESNYt24dv/32G6tWrXJXF4QQQgghioRWq2XPnj2MGTPGsk6tVtO9e3e2bdt2S8c+deoU1atXx8fHh44dOzJp0iRq167tcvuSmnBElRM00hsM5a5A+u1SmL6kSb/LV7+h/Pa9vPYbym/fb5d+F/T8pSp4tHv3brp162ZZNtcmGjp0KPPmzSM2NpaYmBjL/REREaxatYrXXnuNL774gpo1a/LDDz/Qq1evEm+7EEIIIURRSkhIwGAwOJ0c5FbqE7Vv35558+bRsGFDYmNj+eCDD7jnnns4fPgwgYGBTvcpqQlHEhPVgJpDhw/jG3+oyI5bmri7ML27SL/Ln/La9/Labyi/fXd3vws64UipCh517do1z9k15s2b53Sfffv2FWOrhBBCCCHKjt69e1v+btGiBe3bt6dOnTr89ttvPPvss073KakJR36/uhuSr9O0aVMebOs6E6osul0K05c06Xf56jeU376X135D+e377dLvgk44UqqCR0IIIYQQwqRKlSpoNBqnk4NUrVq1yM5ToUIF7rjjDrvSAbmV1IQjGrWpXKdKrSlXFxi23F2Y3l2k3+VPee17ee03lN++u7vfBT13mS6YLYQQQghRVnl5edGmTRu7yUGMRiPR0dFFOjlIWloaZ86coVq1akV2zJuWUzBb6mULIYQQJUsyj4QQQgghSqnIyEiGDh1K27ZtadeuHTNmzCA9PZ3hw4cDMGTIEGrUqMGkSZMAU5Hto0ePWv6+fPky+/fvJyAggPr16wPwxhtv0K9fP+rUqcOVK1cYP348Go2GwYMHu6eTNtTm2daQ6JEQQghRkiR4JIQQQghRSg0aNIhr164xbtw44uLiaNWqFatXr7YU0Y6JiUGttiaaX7lyhdatW1uWP//8cz7//HO6dOnChg0bALh06RKDBw8mMTGRkJAQOnfuzPbt2wkJCSnRvjmjykk9kswjIYQQomRJ8EgIIYQQohQbNWoUo0aNcnqfOSBkFh4enufkIwCLFi0qqqYVOVVO5pFRgkdCCCFEiZKaR0IIIYQQolQwj1pTZNiaEEIIUaIkeCSEEEIIIUoFlUqGrQkhhBDuIMEjIYQQQghRKpiHrUnsSAghhChZEjwSQgghhBClgmXYmqQeCSGEECVKgkdCCCGEEKJUkGFrQgghhHtI8EgIIYQQQpQKahm2JoQQQriFBI+EEEIIIUSpoMKceSThIyGEEKIkSfBICCGEEEKUDpJ5JIQQQriFBI+EEEIIIUSpYC2Y7dZmCCGEEOWOBI+EEEIIIUSpoFbJsDUhhBDCHSR4JIQQQgghSgWVDFsTQggh3EKCR0IIIYQQolSQYWtCCCGEe0jwSAghhBBClArmzCOjRI+EEEKIEiXBIyGEEEIIUSqoLDWP3NwQIYQQopyR4JEQQgghhCgVzJlHQgghhChZEjwSQgghhBClggqZbU0IIYRwBwkeCSGEEEKIUsFa88i97RBCCCHKGwkeCSGEEEKIUsEy25pbWyGEEEKUPxI8EkIIIYQQpYK1YLaEj4QQQoiSJMEjIYQQQghRKpiHrUnoSAghhChZEjwSQgghhBClgmWyNYkeCSGEECVKgkdCCCGEEKJUsBbMluiREEIIUZIkeCSEEEIIIUoFtbnmkZvbIYQQQpQ3EjwSQgghhBClgmW2NYkeCSGEECVKgkdCCCGEEKJ0sGQeSfRICCGEKEkSPBJCCCGEEKWCZB4JIYQQ7iHBIyGEEEIIUSqoc6JHEjwSQgghSpYEj4QQQgghRKmgkmFrQgghhFtI8EgIIYQQQpQKMmxNCCGEcA8JHgkhhBBCiFIhJ/EIo0SPhBBCiBIlwSMhhBBCCFEqmIetCSGEEKJklbrg0cyZMwkPD8fHx4f27duzc+dOl9vOmzcPlUpld/Px8SnB1gohhBCirBs/fjwXLlxwdzPKBRm2JoQQQrhHqQoeLV68mMjISMaPH8/evXtp2bIlvXr14urVqy73CQoKIjY21nKTL3dCCCGEKEp//fUX9erV4/7772fBggVkZ2e7u0llljnxSGJHQgghRMkqVcGjadOmMXLkSIYPH06TJk2YNWsWfn5+zJkzx+U+KpWKqlWrWm5hYWEl2GIhhBBClHX79+9n165dNG3alFdffZWqVavy4osvsmvXLnc3rcxR5eQeKZJ6JIQQQpQoD3c3oKC0Wi179uxhzJgxlnVqtZru3buzbds2l/ulpaVRp04djEYjd955J5988glNmzZ1uX12drbdL4YpKSkA6HQ6dDpdEfQEy/Fs/y1Pymvfy2u/ofz2vbz2G8pv36Xf7u23O8/funVrWrduzdSpU1mxYgVz587l7rvvplGjRjz77LMMGzaM4OBgt7WvrFBbCma7tx1CCCFEeVNqgkcJCQkYDAaHzKGwsDCOHz/udJ+GDRsyZ84cWrRoQXJyMp9//jmdOnXiyJEj1KxZ0+k+kyZN4oMPPnBYv3btWvz8/G69I7lERUUV+TFLi/La9/Labyi/fS+v/Yby23fpt3tkZGS49fxgyojR6XRotVoURaFixYp8/fXXjB07ltmzZzNo0CB3N7F0k2FrQgghhFuUmuDRzejYsSMdO3a0LHfq1InGjRvz3Xff8dFHHzndZ8yYMURGRlqWU1JSqFWrFj179iQoKKjI2qbT6YiKiqJHjx54enoW2XFLg/La9/Labyi/fS+v/Yby23fpt3v7bc4Wdoc9e/Ywd+5cFi5ciLe3N0OGDGHmzJnUr18fgK+++opXXnlFgke3SGWJHkn4SAghhChJpSZ4VKVKFTQaDfHx8Xbr4+PjqVq1aoGO4enpSevWrTl9+rTLbby9vfH29na6b3F8IS6u45YG5bXv5bXfUH77Xl77DeW379Jv953fHZo3b87x48fp2bMnP/74I/369UOj0dhtM3jwYF599VW3tK8skYLZQgghhHuUmoLZXl5etGnThujoaMs6o9FIdHS0XXZRXgwGA4cOHaJatWrF1UwhhBBClDMDBw7k/PnzrFq1iv79+zsEjsD0I5jRaCyW88+cOZPw8HB8fHxo3749O3fudLntkSNHePTRRwkPD0elUjFjxoxbPmZJyokdYZTMIyGEEKJElZrgEUBkZCSzZ8/mp59+4tixY7z44oukp6czfPhwAIYMGWJXUPvDDz9k7dq1nD17lr179/L0009z4cIFnnvuOXd1QQghhBBlzNixY6lRo4Zbzr148WIiIyMZP348e/fupWXLlvTq1YurV6863T4jI4O6desyefJkl5nbhT1mSVKrzLOtubkhQgghRDlTqoJHgwYN4vPPP2fcuHG0atWK/fv3s3r1aksR7ZiYGGJjYy3b37hxg5EjR9K4cWMefPBBUlJS2Lp1K02aNHFXF4QQQghRxjz66KNMmTLFYf2nn37K448/XqznnjZtGiNHjmT48OE0adKEWbNm4efnx5w5c5xuf9ddd/HZZ5/xxBNPOB2mfzPHLFEybE0IIYRwi1JT88hs1KhRjBo1yul9GzZssFuePn0606dPL4FWCSGEEKK82rRpExMmTHBY37t3b6ZOnVps59VqtezZs8cu61qtVtO9e3e2bdtWosfMzs4mOzvbsmwuXq7T6dDpdDfVFmeUnKF/BoOxSI9bGpj7K/0uH8prv6H89r289hvKb99vl34X9PylLngkhBBCCHE7SUtLw8vLy2G9p6dnsc4Al5CQgMFgsGRgm4WFhXH8+PESPeakSZP44IMPHNavXbsWPz+/m2qLM2cuqQANly5d4u+/Y4rsuKVJVFSUu5vgFtLv8qe89r289hvKb9/d3e+MjIwCbSfBIyGEEEKIW9C8eXMWL17MuHHj7NYvWrSo3AyVHzNmDJGRkZbllJQUatWqRc+ePQkKCiqy85xddwounqN6jRo8+GDzIjtuaaDT6YiKiqJHjx7lajZH6Xf56jeU376X135D+e377dLvgv7QJcEjIYQQQohbMHbsWAYMGMCZM2e47777AIiOjmbhwoX8/vvvxXbeKlWqoNFoiI+Pt1sfHx/vshh2cR3T29vbaQ0lT0/PIv1C7JEzk51KpS5XFxi2ivoxLS2k3+VPee17ee03lN++u7vfBT13qSqYLYQQQghxu+nXrx/Lli3j9OnTvPTSS7z++utcunSJf//9l/79+xfbeb28vGjTpg3R0dGWdUajkejoaDp27HjbHLMoqcyzrUnJbCGEEKJESeaREEIIIcQt6tOnD3369Cnx80ZGRjJ06FDatm1Lu3btmDFjBunp6QwfPhyAIUOGUKNGDSZNmgSYCmIfPXrU8vfly5fZv38/AQEB1K9fv0DHvB0oEjsSQgghStQtB48MBgOHDh2iTp06VKxYsSjaJIQQQgghCmDQoEFcu3aNcePGERcXR6tWrVi9erWl4HVMTAxqtTXR/MqVK7Ru3dqy/Pnnn/P555/TpUsXy6y1+R3TncxdUSR6JIQQQpSoQgePRo8eTfPmzXn22WcxGAx06dKFrVu34ufnx8qVK+natWsxNFMIIYQQ4vZkMBiYPn06v/32GzExMWi1Wrv7r1+/XqznHzVqFKNGjXJ6nzkgZBYeHl6gwEtex3QnFTnD1iR2JIQQQpSoQtc8WrJkCS1btgRgxYoVnDt3juPHj/Paa6/x3nvvFXkDhRBCCCFuZx988AHTpk1j0KBBJCcnExkZyYABA1Cr1UyYMMHdzStTckoeScUjIYQQooQVOniUkJBgmW3j77//5vHHH+eOO+5gxIgRHDp0qMgbKIQQQghxO5s/fz6zZ8/m9ddfx8PDg8GDB/PDDz8wbtw4tm/f7u7mlSk5sSPJPBJCCCFKWKGDR2FhYRw9ehSDwcDq1avp0aMHABkZGWhypk8VokgY9e5ugRBCCJGvuLg4mjdvDkBAQADJyckA9O3bl1WrVrmzaWWOebY1o0SPhBBCiBJV6ODR8OHDGThwIM2aNUOlUtG9e3cAduzYQaNGjYq8gaKcSr8ISyrBntfc3RIhhBAiTzVr1iQ2NhaAevXqsXbtWgB27dqFt7e3O5tW5siwNSGEEMI9Cl0we8KECTRr1oyLFy/y+OOPW74UaTQa3nnnnSJvoCinjk4BfSqcmAFtpru7NUIIIYRLjzzyCNHR0bRv356XX36Zp59+mh9//JGYmBhee01+BClK5mFrEj0SQgghSlahg0cAjz32mN1yUlISQ4cOLZIGCWFidHcDhBBCiAKZPHmy5e9BgwZRp04dtm7dSoMGDejXr58bW1b2mIetKRI9EkIIIUpUoYetTZkyhcWLF1uWBw4cSOXKlalZsyYHDx4s0saJQkg7X7ZqBCkGd7dACCGEyJdOp2PEiBGcO3fOsq5Dhw5ERkZK4KgYqHNSj4wSOxJCCCFKVKGDR7NmzaJWrVoAREVFERUVxT///MMDDzzAG2+8UeQNFAVweSUsj4BND7u7JUVHkcwjIYQQtz9PT0/++OMPdzej3LDOtibRIyGEEKIkFTp4FBcXZwkerVy5koEDB9KzZ0/eeustdu3aVeQNFAVwfJrp3yt/u7cdRUmCR0IIIUqJ/v37s2zZMnc3o3ywDFsTQgghREkqdM2jihUrcvHiRWrVqsXq1auZOHEiYPoFyGCQoUZuURaHeJXFPgkhhCiTGjRowIcffsiWLVto06YN/v7+dve/8sorbmpZ2WPNPHJrM4QQQohyp9DBowEDBvDkk0/SoEEDEhMT6d27NwD79u2jfv36Rd5AUQBlMUunLPZJCCFEmfTjjz9SoUIF9uzZw549e+zuU6lUEjwqQipV/tsIIYQQougVOng0ffp0wsPDuXjxIp9++ikBAQEAxMbG8tJLLxV5A0UBlMksHQkeCeE2F36DMz9Ap/ngE+Lu1ghx27Mtli2KlzonemSU1CMhhBCiRBU6eOTp6em0MPZrr71WJA0SN6EsBo/KYp+EKC22DDL9e+A9aP+9e9tS2iiKjKcRohjJsDUhhBDCPQodPAI4c+YMM2bM4NixYwA0adKE0aNHU7du3SJtnCigsjjEqyz2SYjC0qVB8mGo3N49YzW010v+nKWZYkSz7l46ZaWD8qC7WyNK0IgRI/K8f86cOSXUkrLP/FGoSMlsIYQQokQVOni0Zs0aHnroIVq1asXdd98NwJYtW2jSpAkrVqygR48eRd5IkY+ymKUjwaO86VJB7QkaH3e3RBSnf7vAjb3Q4SeoO8QNDZDiIoWSHoP6+g5CAJ0hHfByd4tECblx44bdsk6n4/DhwyQlJXHfffe5qVVlVc5saxI7EkIIIUpUoYNH77zzDq+99hqTJ092WP/2229L8MgdymKgpSwGxIqKPh1+DwKvSvBYortbI4rTjb2mf8/Nc0/wSKUu+XOWajZXs2Xxc1m49OeffzqsMxqNvPjii9SrV88NLSq71ObMIwkeCSGEECWq0FcGx44d49lnn3VYP2LECI4ePVokjRKFVCYDLdYLr70xN8jUlsU+3qSkw6Z/tdfl2/PtQDHC7lfh3C/ubknRU2mK/xxn5sCO58FYFt7jNu9Ho859zRC3BbVaTWRkJNOnT3d3U8oUGbYmhBBCuEehg0chISHs37/fYf3+/fsJDQ0tijaJwipg8ChDq+fL6FMcj0sp0PbX07VkaPW30rKbZ/Or/YBvtjLy5903d5giCq4YjQpXU7KK7Fg6wy1kJdg+32UycFjKXFoOJ7+Ebe4YVlbMSiLzaMezcGY2XF5e/OcqbrYBMEWCR8JUI1Kvd9P/o2WUSoatCSGEEG5R6GFrI0eO5Pnnn+fs2bN06tQJMNU8mjJlCpGRkUXeQAEpWTq8NGp8PDUYjAprj8TRpk5FQoN8+OdQLE0TU6ntadr21+0X2H42kemDWuGpsb/w+zL6NLM2nmFa1EnOT+7DngvXmb3pHO/1aUytSn522yZlaLnzoygCfTw4NKFXodusKAqqnJ8HM7UG5u+4QI8mYdSp7F/AA9gHRTafTijQblq9EU+NCpVKxaFLyQyft5M3ezVk0F21HbZNy9aTka0nNCj/ukFvLDnA0r2XmTOsLfc1CmP14VguJ2XxbOeIgvXHxlNzdnHpRhYb3uyKj6eGdcfjqVPZn3ohAQU7gO1jY9SB2vnbODY5k13nb9CneTU06qKtXXMyPpWqwT4E+XiSnq1n7dE47msYRrCfZ5GepzCMRoU0rZ4gnxJuQ1Zc8Z/DbVdJJThsTXsj/21ud7YBI8k8Kldyf/9RFIXY2FhWrVrF0KFD3dSqssmaeSSEEEKIklToK4OxY8cybtw4vvrqK7p06UKXLl34+uuvmTBhAmPHji2ONpY7BqNCcqbpwiM9W0+LCWvpNHkdAPN3XODF+Xt58Mv/AHhx/l6MNr92v7/sMCsPxrJ8/xUALl7PIDEpAdZ0pHbcF3bnefTbbaw+Esdri/c7tOHgpWQAUrNMv5hm6QzM2XyO8wnpAMQkZjBz/WlSskzt3HPhBr/vvmjZ9v5pG/nffFO9lun/nmTiqmM8MOM/y/EzbX6I3XPhBn/uu2RZ/nPfJZIysgv0WF1Lzeb01dScturoNDnakqX02m/7SUjT8vYfh5zu2+7jf2n3STQJaaZzZWoNGI2mr6PZegMrD17hRroWgKV7LwPw1brTALzw614+WnmUo1dSUBSFH/47y+ZTpgBXWraeOZvPEZucCcCOs4m8veQgyZk6FAV2X0giLiWLg5eS2XPhOiPm7eb+qRsBOJ+QzrPzdrH7/HXLY3kyPtWSQbXtTCI7z12z9OHr6GNsPGldTkyzPm7dp27klYX7WLAzxtKupAyt5X5zXwFupGv5dsMZ4pJN2VV/7b/M4O+3W453NSWLSzcyADhwMYme0zfRY5qpze/9eYjXFh9g1MK9Th/nMUsPEfn7wZwZzBUW7ozhWKwp+2372US6fLaeTTZ9KKjY5Ewm/3Ocy0mmx/ml+XtpMWEtp+JTC30svcHItdSCveYcGO2zCqZFneSbDabXiaIo6JJ3Y1z/IGRcKfgxdam5hnG56TLJSebR9rOJzNl8rsiy+vI6lys6g5HB32/ngxVHiuTUt5QJaMuotflbgkflyb59++xuBw8eBGDq1KnMmDHDvY0rY8w/TBkl9UgIIYQoUYUOHqlUKl577TUuXbpEcnIyycnJXLp0iZEjR7J169biaGO5YHshNui7bbT8YC2XbmRwPM50IXw9XYuiKPx77CoA2ozrcHwGIR7X0agcL3ySMnXcSNdyz6fr+eHHNyBxO096z3J67nM5ASGjUbEMU7OdFVxnMPJl9Ck+XHmUnjM2AdD/my18tuYEHyw31bl69NutvLnkILvPX2fbmUTOXktn1aFYALbkZA1l6kwXw9OiTvHOLg/Wn7hm2fe1xQc4cDGJw5eSGPPbDg5eLNgU4Xd9/C/dp23i0o0M1h2/SkKa1vIYZevts5eW7bvMkC8WkbGmJ8RFk5FTR+nAxSRupGtpPmENT/6wHYAZ/55i1IJ9DJ270+4YKuyfq8T0bP47lcDEVcd4+scdAHyw/AgfrjzKoO9Mxxr0/XYW777I51Gn0Nt811WrYF9Mkt3xX1m0j+jjV3ls1jYAhs/dRc/pm1hzJB6AwbO3MyPqmGX72ZtOMnSOqY1/7LlEm4n/8sW/pwBIz+nflhOXYONDfP71q7T6MIoMrZ7r6Vo6TIpm3F+m+klvLjnIlNXHeSanD68u2s+2s4lMizqJoii0+ySazlPWk5aTZQQQn2IKtizLCVT+lxM8+/doPK8u2kdqlo4snYGFO2NYcTCOG1r4+3A8Y5YeovcXpkDiUz/s4EJiBkNy+nAqPpVft1/AkBPYupqa5XK44MifdzNr4xme+8kULFx9xNSun7ddcLq9rQytnqij8ZZaWk/9sIO7Pv7XEtTKLVNrsDzvx+NS+N+CvZy5lma6U7EGj2KTM/ky+hSfrj5Btt7A34fjecxjIt4J/8KeV02bKwpc2wI7X3CebZMZD0sqoKzvabPS8SIpIS2bg5eSLMd8ddE+Plp51LJ84GIS6dnOh8ss3hXDKwv3odUbLds7DQbl1DwybwfwxPfb+XDlUbug5U2zO2fBs+M2n05g29lE5m457/R+g1GxBLrzs+fCDZqNX8MP/50t8PldScvIsC7kZCHZBmlNjdPCoQ8hcVeexzqXkM7+i0m33KYC06dD2vmSO18Zs379ertbdHQ0ixYt4vnnn8fDo9BJ3iIPlk8KiR0JIUSZolGKpkSIKD63NCYhMDCQwMBAAE6dOsU999xTJI0qb1YdjOWuj6PZec4UMNl9wXRBufJgLLbfjrL1RsssIx/UmAV7X+OXiLGocAweKYrCqaumi9tgTVqe5zefYcicnTSfsNaSiWOWqTOw9YxpVi/zReT1nIycrWfsh5OdTUi3K2LpcOEEfLvpHAAfrTput/5cQjrBB1/kePPHqOd9yeae/L8hmjOlLHsoisNIn9GL9/M/34n4JUbBuu6W9Wp0rD0ah96osP2s6Tn4MyfTKPdxVSoVWTrr461WqbiQaH+RGnXMFOiJuZ5ht/5cQjq2db9VKsfRSOdyXfBuO2t63OfvuGB57D2wHsRTZQ0OjFlqyrCa/u9J2PEcc8PHo8JIV/VSuLyCCdW+AeB8QgYLdlzgamq2JdDyb06bza8Zs6RMnV1/45IzMT+lD1dYD6dnk9tzP+/mr/1X+GbDGUuADkx9PXTZ/vE05Hp99Ji+ifeXHea33RfRG4y0+ziadp9Ek6UzkKUz8NaSA6zNCRIdvmwK9OQO+JjfI99sOG2XAfT3oVgu5jwn7/xxiJE/7+b9Zabg2Y6c996SPabX3UcrjjBnxZ9g0HL6aiqNx63m3T9Nj+/js7ax6mCsJWi174I1iFJhc1c8MGfrGYnKCWSaVsTy96FYWn0YBVGd4fR3nFo7ihHzdlmy3obO2cnvy2aAYkQVv86uX3qDkSNXki3vqbsnr+Ohr7dw4GISJ+JT+Wv/FX7cfA6jUeHvQ3E8PHMLT3xvCmAeupTMqAV7iUk09f/tPw6x/MAV/tp/GZ3ByINfbrZk7G04YdNmlZpft1+g2fg1/HfKPlhkfn3bBp1upGuJ/G0/23I+L44nqXjqx13WQFsuWVrrl4TENC39vtrM8gPWDC3bY+sNRkumY7bO4HQbszeXHKDr5xvsMhpdeXPJAbL1RiauOpbvtmAasmnOUkvO0PH5mhOW7McPl++3bmjU8eKve7h/2kaybNrLiS/g0HhY046rKVkuM7i6fb6B/jO3WDIYbaVn6/ll23nib9yAuHVFk+X0d0tYHgEpJ+xW7zibyJUkxzYU1o10Ld9sOO20P2XBuXPnOHXqlMP6U6dOcf78+ZJvUBkmw9aEEKLsUR+bQt+MJ1BdWurupog8yDzMt4H/LdhLQlo2I+Y5/hJte22dqTWgzvnWdH+QKVOjke8FNE6CR0ZFwVzyyDbAkJfNpxMwGBX+ORRrd94srSHPL2l2Fz+KtZglQJbeYPmi19TnDER3p4XvSfOmDsGlWkmLAKjhZb1Qte3fxesZ/PDfWTK0eofAg8omXUprMDotE1PX+7Ld8v+FLOHe/XdQMfOA3XpX6fAqcCginntLZwEz875am6dKZ1AKnHavUqks5/VSW8/vobK5iM5piQojnPmRbkF7aORznooqxwwRnaFg51WrVKTlyl5RFFBj4IvaU2Hn84R6JDrdNz45yy7zRa/Yv57zsi/mhmXIJMCNDC0/bT3Pb7sv8fwve/LcV6VSkZyp49PVJ/h09QmSM3WsOhTLx7+t5rsf3wB9piVA8cde++CCRq3iamoWWce+YUTqAPT/DeKbDWcAWLjTNCzT3C5zoG/tYetryjd5Jx0DTMNVsnWGXIEBNS/N32sZkgqQeOUg645fZcHOGM5cS2PjyWvsueg80DJhxRH6fLmZbzea2pOdE0zccibBLsCXpTfw+56LBGnSCEj6DxQj/b7ezMqDsby8aJ/dMZMzdRy8lMyx2BT+PXYVRVGYvem0zRYK7y87jNZgtAxDtX2ctXojvb/4jxdynpNJ/xxj6d7LDJ5tClp9e0zDzvM3iMwZGjth+RF6TNtIhlZPerae+z9dbTnekn1XOHQ5mVcWmtq4ZM8l2n0SzYGc7JsB326lRU5w25DrfZRb7mGmuaVl6y1D1Vy9X80+XX2cYXN3ojcYuZCYTs/pm7jr438B+GDlEb5ef5q+X20G4GKCTXDUqOOfw3GcS0jjxvphsPsV9AYjyg3rc9Duk2g+zglaJWfo2HTyGgajYtem3AFlgI9WHmXsX0c4tHQgrLuf2P/eYtxfhy3BtdjkTE5fdf46ik/J4t0/D1kmTvhr/2Ue+3YrpJleV1xeadl2b8wNBn2/3TJsOrfTV1OZs/mcJbCdpoPYnKGviqLw1/7LnMwZRvrG7wf4dPUJhs/NO+OqtBo2bJjT7OsdO3YwbNiwkm9QGWb+n77Ih84KIYRwG81hU/kbze7/c3NLRF4keHQb0Rsdg0C2w0Wy9AZLVkWm0Vrk2dmwNUUBfc5FVX7Bo9xfwO5I+YWql7+0LGfqDHkW7M22aWPuqXMztAZLMGl+3fcgPprF9cZYGmkayqbQN3gTflrnw0aq2wSS+s/cwsRVx/h8zUnLMLicQ9kNesnIdj4LmbfaeuH+frXZjKk2D42SyZ3XxlvWG42KyyCHSoVdNo1Wb7S70DMYHTOebNkGj7J09kG53MEwu/NiPa+3ylpXxZzlAtbAjJ/ams0RpEnHX2VfAyhbbyhw0Eqjsg+WZemMKIqCj9rahgCNi0wCFaTb7Ks1FLxGhUatsttXp1e4dKNgGQtqlcouaJWpNbD5VALL6kcysca3cGicy31VKsjSGnku5E8APK4sy/N5MZ3P/v1nfr9l6gy5RmU5Dssy5nwEp2TqLO8jveJkiIui8Ot2U+2q6VEn7e7SqFR2bczUGjAYFZbWe4OF9d6Fcz9b7judqx6UOte+WTojKtu6PWd+ZECFaAA8NGq7zyONSsXemBscj0tl9ZE4u2zH3MxDHOdtPc+pq2msOhjLzvPXyc62BkbSs+yzZ974/QDXUrN5/XdTYNecBbjhxDW711GW3sDlpEyenL2d6JwMOts25paUoaXZ+DX0n7kFAH2u5/fAxSTGLD1kqff1zYYzbDhxjf9OJ1iyQ812nDXXJjM9Lp4qax/0OtPjWNPzKtUSfoWTX9Ftyt/suWB/jB82mzIxn5i9nSFzdrJgZ4zda99DrSZLZ2Dw99tNQ1Iv/MYTSU9QzfMa3X1NQazQS1/y87YLTFtrem10nLSO7tM2kpiWbdl35npTIG30ov0s2BHDIzNNgY5XF+1nt02bVhy6yrC5OzEaFbY6majAdjhw92mb+HDlUeZtNdXAem+3B/d+vonULB0bTl7j1UX76TndNNQ5+rgpo808FLus2bdvH3fffbfD+g4dOjidoVbcPPOPaBI6EkKIMkh+GLitSfDoNqJyUvMjQ2tAjQEVRrJ0RsuXpkyjt2Wb3BevYAokmIMNthc0rugN5iFRetonjqNh7ETCPEwXDllZqTTxOIAa5wGZzFxDk2wvLmzvq+BhurD0VZsuypSc/vUO3sLXdT6l55l7nR7/v0bP8XGNr8GQTWK6loqaZFrGjUF71VqPSG802gWx0rV6p79Ketk8Fs+F/GX526iyztCV6ZAxYqVCZRe0ytIZ7AJNGVo9Blf7qlQYDFrMX3mzdEabi2CFLK3r58mTbIJ39OeFkCWMDltgWW/OPNIZjJY22waPFtcbQ2fVMrtjZWrtg0d5/XrbiO34nPrc0ubMnICXr8o6tFHl4iu8ChXpNkE8ndH6/0GAOiPP2bVUKvt90/N4XME+e0Stwi54lJatx6gohHgmmVZc+dtuX9v+a1SqnHNpLOvyCx555HpfmIO5mbrcGXuOH7cGRZ3TZhXZOj1Ta07j4xozHU9y7T/eq/YDGkzZh7b91ahVdu+zTJ0BLyWD+j45WVXnra8XtVpl9/7UqFWWoPXdAftRDk9Ek2uK+Wm1p1vaaPu4atT2wcBMncFlFk/u2f6Miim7xtvm/ajB+es/d4DWQ62yK3CdpTPw/p+H2HomkWdzhhICvBK6kGEBcwDTc2gOgpprcx25kmK5z9bDM7ewcGcME1cds3wugikYb7utoigO+9oG6jOzTe/DQI1N5lBWPE2NG5z20zz88s+9l+yy/QxGheUHrrDtbKJpSOqWQbTyPcbYatYho/qc19GZa2mWx6axz1my947jr92n2XY2kc/WmIaj7Y0xve9sP8cC1dYhttvPp7HhxDWOxqaQu474+uNXaTZ+DQtzivBb2n7hApp/mjCmmunxvnQj06GeW1mnUqlITXUMjCUnJ2MwOP+/U9wcc0y4oJmsQgghShP5cL+dFbiK4/Lly/O8/9y5c7fcGEGuixPIzs4guuELxOmqkKXb7DR4ZDusK0Cdwa913yczpQ/XK70JgJdd5pECqBwu8swX6v42WSTmLJ2qB4cyKWgtgSHD+f7ao3b7KYp9dompNo31wtv2AiU3RTEFW9r7H3a5jdlTlVfDiS+BJoyv/j0P+2+E/5YDpiEWmVqD3QVlhouhdl4uAmkGrMEj230frrAeEkKsG+bKPMrUGRyyPlxl1wSSzHBGEF6nBS9eeNeUAZSz74/hH+K99k08+AxnhYM7adbgn7iWd6rZrzcHjzJtglgB6rwydBTStQa7i0LboBvYvwZfML4EZ6Fr4Hj2pjfG8/o2jIZwSwAQsAsAoM8gQJ1BmtGXylwmI9vaYK1RhVFRUGNgb5MnUZaq8FT9hk4xPfa2QRy1CrsL6Ayt64Ceuf+WfdUqUm32Tc/WO/0Ro3fwZiK8rpCt62VZp1GbAiQBNsEj8/NZ0zMe0i/aHcNoVNCo7F/jaoyAkis4iNPMIyXnuVarQJ98nEcrOR8eBDAyZBnHsyJYmdqDDNv+quyztLzOfcePfjbThis22UJq+6CcSmUN8M6v+z4ch2E+vR3O/XLoQqr7ZpCe3dGyTpcrmJKWZQ3wNfU5A2nWTEK1Gru6Pz6G66izVXjbZLBVVl8l1CORq/rKkBlPI59zHM+KQGPzfAZrUlGrjKRm2wSPtEaHrDR/dQaRVefnbPA5T/98hsOXk9n8RicwWLc1GB0DQGbHYlPshk5q1PZZStl68/Or8E7VuXA+2T54pM0CVFTQWAMKM2tPtnvvOKNWqUjLsv9MtW2HWRVzMBQwYHq9eqhVlmGR/9zxClyCOwKSgIdyGp2IohjI/RlTwcPaRvNnilFRMOQEFr1VWpTDE/nsj4roDOGMWXqIwe1qA/BM5ZUMU29Fk36G/ws5w6TYETkZmI6P67DKy+kQcAgMPUDjlefjUNrce++9TJo0iYULF6LRmJ4Pg8HApEmT6Ny5s5tbVzbJsDUhhCiDFMekCHH7KHDwqH///vluo3JycSQKTqWyH6YGEJCyjQjvWCK8Y9mr1aLOSV7Isg0e2Vy8PlN5Fa38TkLaSX7PHg2Ap22NHAzo8SAr10xkaTkXn7W94izrzIGW4BtrARhSeaVD8Ajss4vStQa8Pa3LGVqDs2tmQKGe51kyslpaLqDzlXYGaEIz3zMOd2mzkshU/K3tcBEwUKucf9nUYr2QMQeA2vodMdX1WTsVc5BK7WQYV+5gku21qO2X2w6e0fio0ugdbBoukq0zkqkzoMHA/UG7IA2a+ZzksuJPA+8YFOVBy74+KuczR5kvVrNs2uDnahgZpkBPhlZvP+wnV4DPUlvJJihUz/sS46rPpu7xK5zwmc56tXXYpE/OxbAKI8rKxkQ3TOOnhL68pf+ZY5c/ANoApswjo6IQ6nnDVLfJCFU8kojVheScN1dAJNfQM9tRnbkvGmz3Bewuvk2ZR7b7mv79ts5kAJJinwGgo/8BKhnUpGVXxBf7zCNvVTabGz8Lf4GGvzCgQaUyvW88cgWPvgv/BIDTl79CUdpbz5tX5pFaRVam85nebNX0jHcILhgVxe6xCj0eab+TYpNpZPO4NvY5izo7lLTs6nabd/P6x+G8r+cEYs4n7resy9IZ7M6bmq3HYIRKmmSW1n8dz39epbHPlxzLqotGpUJ34nu6BcazMfVOHj7bGoCvNJ9Z9n/afx5PN5lHo0NL4K9arL5DR7fj36FRNyQ1S0e412U2NPo/4mLu56/g7wAYX/07QrZNwJsP7NprG6BRzi9g29l6VPe8hvc/9emhKGj4AQMaS1aaMxq1ylJDCEzvV32uALVRUegSsJcXQpfC1qV4qd+0Pj7Z2YAPlT2sdZBa+DnWYPJU6eDaFjQYMKBBrbIPfmZoDZYgji3bT019TrCzqiaW5IxGdttVy94BPMSdfsdQ/ujH+1UfZNzlF+22qaixvvb8c4LPGVqDJVj2QsgSVAcX8HcDCD9orYlU2yuWj2o4zuKZnq13yNICmFDje9OKPa9Aw1chuLHDvqXVlClTuPfee2nYsKFl8pD//vuPlJQU1q1zHRQWhSffNYUQoiyTHwZuZwUetmY0GvO9SWr2rVFhO+RLMdUPyrYWI9Zmp1mGtmUq1uBRkMY65KCKR5Ll735nWjGyylK7X8O9crKJ7GbBwvRlP8LrMisavGZZbzv8CSDEI4n7AnditLmAUlDsjpWerbcfQqN1/poYUGEdv1R/nqqHn3V6vzNKzkWw0eZl+1CFjbxfbTZDLrWkStomQCHE40ZO9lDBP3wCdBfR5AwP1Fz+nTB1HK38Tjhsp8J+iJA++wbtkicT4WUq0JuhtR+6oz+3gGermOrn6GwCVB7oydIbyNAaCLKZDU9BxbqGL/Bd+CforthecGhwxpJ5lJXO4rrvMK3WVPzVrqe59FZrydAa7PqQO/Bizkyp5mmtNRWoyaCut6nIdCPt6lyZR6bskVpe8agyYgjzvM5b1Ux1dhrHjqeKh2mYjNZoCsRUtzmu+TXWK2grXlFtaexjylbJHRDJPWwtS2fEU6WjT/B/vBq6gIxs24t8g8OwNbtgU8pxNDZDzfTJp6nheZWF9d5j+I0H0aVdoqmvNWtmMJO50+a1UDnnPWYO4jirOQZQ/+zLKDa/nlhfFvavy09qfEWD1AVos+xnonPGS63DV51tl5WVrTfa9Tc3xSZ4pMrJUqrtFcs/d7zC4Ct3k56Vja+qYFOjGmymcrfNiPFTZxK6qy8DfBZQz/si3jkB6ycqrSFInUYbn70EHnyJuREf2AVTatkEq81eDVtkmT2sXcBhPFQKKZl6nqhkCmJXTYsmXWuqlTa8ygp8U3bzgK91KKKiKHavT9Xe0bwR9gtbG4/AR38VH8M1anrF08r3BOmZmQ41j3KOQnPPA6SnWWsBpWXrc85rYg6QVLOpyWb7WZudM5NcJY+8n9dx1WZDVGdeDjNNFqBS2Qc/M3WGfAvc6xUP+ldYzySP/gQeed3uPo+caW8/rfkFKhSGVF5l7WXO+6KShzV4FKjJ4E6/Y1Q78z4qg+n/lrv8jzic02hUnGY5eqp0pGvtA7aZOgOPV1xrXXH6O1jbEfTOg+KlUZMmTTh48CADBw7k6tWrpKamMmTIEI4fP06zZs3c3bwyxTwKVhKPhBBCiJJV4MwjUfzM08B/WP1bugftYKXubzRa64WJITsVH8M1Ftd9h/YBzod6+doEDnyMN3iv+hyiU+6yrFtW/3UeOPmVXfBAg4HQfYNZ32iV3bGWN4jkhfNjLMveah1zIj4k64gv/up6ZOQU7c7ISsMDPXo8TIEJnQFfVRZdAvegzXL+y7K5jkrFxOUoykMFenwMBgPBmlTu8LHW2/iytjVzoUvqRJ6tch9jq//AsUsTMSrtLPe5CmKZVTac5tNaM/gnuTM1Dn/E8nBPZl17zNp3VTbZijeKYqBOzIc8XMGXv5K60fnam9TNjuKniFXce+JHSzYCmDJ3PLc/zdjqsC2tJUabAFBFjxSydKYgTkWbISO2ATttwh6gKQBGF9lZ5otVr8u/WV4T/6a0d7otgI8qmwytfXDFHDwKUqcRpEmzDIGq6WWdst32Ir+lcQ13+oVblv01WYBCA2/7Oihm6xr+Hy2O/IbOCJlao13wKCinHsxntWbgmZLBsvqRNDy8jEyt0S5AUjHhL8bp3iMxcBQbU+9Ef24Bh5o+ZyncrV8fxTe1G/C/mHfI1BnsMjfSsvSmoXk5D6EKI3/Uf8Nyvy4rldpe1vpLPU60sWt/V80fdK33h2U51PMGSYYgXg1bjP6qFz4q18OQHldbC89rrkYzo5aenxL7WdZ1C8qZOS5tDes9Jrs8jtn/Qn/niUpruZxhff9naPUONYVsmYNH9bwv8nudMWSeGUUDb9P2GnRUTlpDZY+CDSEyZsQCFQhUp3P/9dH4pAC8SJ/g/whM2sjIwI2cSnrFsn1ljxRWNBhNHW/r66epTeZgFSeBlRdDl1j+nlLzK87pVxGfuZVsxdrGjCwdwTZB13AP67DpbL3RIfA9Kuw3u+WJNb7hnsD9XD96AYPxQXKLDPuVV4IXw5aX6RX0LmtSOpGhNdhlIpmHrHrYDBueWmu65e8srdbyGLiiwsgzVUyBr9FhC5kR/xQalZ6KsT9xl5+BXRnNyNTqLdmBKptz2b7uKnmk8HbVeQCExv4A9LfcpzZmocFgrYGFabZEP3W25b1vO7SujtcVltZ/E65BQ89KhHg0pnOgdTbKRyqso53/YdIzOlmyDm0Fa9JIzdJbsrT6V1iPasfvfFbrJ/sNdcmQGQuB9V0+PqVN9erV+eSTT9zdjDLP/CNaQSdgEEIIUZrIZ/vtrNQVzJ45cybh4eH4+PjQvn17du7cmef2v//+O40aNcLHx4fmzZvz999/57m9O5kzj4ZUWUV1rwSGJtzHPSkfWe7XZ6fQizkuA0fgmC2Ue90dPjE8VGET6YnHLes6+u2hwvVVDvsBzAqf5LDO5/AY9jR5mum1puKvSuPOfW1N9VIw/RqfrTMwocZ3zAqfRMSFd/FAx9DKK1y22VmGjzMe5+dyoOlgl/erlWzGVv8BgMZX3ue9KjYXcke/Zladj/M8/qMV1xMZ9itgCpTZZt5U9UwEFOqyn/qJ3/BF7ak8XnEtdbOjAKjtHY+fOpN6hx5hR6Onqed9kTt8Llj2nxMxgcGe1kLID1XYxL0JkZB93e7CrYrGmumQYfCiU8B+ugTsoTZHnbbZnEHjnbDesu6bOq6DEM+HLMUr/aQlMKPBQGZWFqPD5nOw2RNsbvwcSsIuQKGGpzV4FOF1xe44H9b4zvL3D+Ef8XPEOLugnq0gTQYhHjfQGkyBDtvZ83oEbecO7/OW7DlvtZ4hlVfQTPsXfc60YVy17/FTZ9Lu8gsEkcgLIX/wbJW/CNwzxG7GNw/dNR6ssJUmvmfJ0hlJy9RS1dNUGDk9W4dvtv1Qx1Z+pyx/G7RJdtlf+antFUf/Cuv5X8giau/uQV3vyy637eWxwG65f8WN/GkTuLLVIO03p+tzq+yRjMfVDYAp6+vhpBfoF/843iotH1T/1mF7c0bo+OrfU0mTRI0LE2ngY63dVCt9NSNzZpfLjyrT1NdXwxbQWPs3ERl/08jnvF32Y/+KGyx/V/O8Zhc4ApgXYR1iZn6/5iXC4yxK0jGyjda6ZHptsl0Qsp6n9fk17vofa+4Ylecx7wncD0ClmC95NGiVJSiTka2jZ9A2XglbbNn2u/BPeKTCOu5InMmwpIfpHbwZDQa8Y34i3DOGAJu+2wpK3shdfoeppHGdedTExyboZfSgR9B2FgTcS/PLb/J7/XcAqJC2lcA0UyFw2/dYxVxBqWpe1izVRytGW/6uZLzA7iZP2237Ze3P2NZ4KF5Rd7G47tvUsAkUP1jBOt18bd0Ovq1jHwyZXnsagyuvxXPbIO4J2OfQp4qaFBpfeof26V/jpdLxac0v8L34k8N2QJ5F80ubuXPn8vvvvzus//333/npJxf9FzfFPGpNLi+EEKIMkppHt7VSlXm0ePFiIiMjmTVrFu3bt2fGjBn06tWLEydOEBoa6rD91q1bGTx4MJMmTaJv374sWLCA/v37s3fv3ts2jTzbpv6Mt2J/QavXpuKt5F0XJdjJRXDHgEN2yzNqT0W77ye8VLNRY2BK1cL/Uuqj1tK/4kb6VvgPD72R9gHxTK/1OVe0j5Kk7cqgSjlBlaTFLK20OM9j3elfsOBRfiop9hfx/QP/YZxmKNU9r1Hx6GgeCM7/GE18rRdzD1fYYPl7Y6PnmZfQl0TF+rr5rNaXtruyrP7rVEiJAU/oFbTNbuhMVU/76bnHVv8BsqC3UU1IgLVW03th1gBTwIXvWFD3OHnxUBnwU2cSnLgyz+3Mngv5CxL/YoAmgC9DHqNfhf9otvkMzcOs29Tfdx//NAjnv7TWlnWt/E46OZrVvYH7SDX4uby/a+AuBvvs4SL1uO5pvWB8KXQJL4UuwaioLPWoLIEpA4wIWc761LY2/dXzmrkQshOPVlhHdcMe7ru0lBGN0xkd8zqPxQxliE+iy3080k4wrvqmPPtn65s6k0nSB1iWzYGIW1VTv7fgG2ecp6lvIv0rbOAOw0YAOgUcYGgVxyCw540dvFvtR8JtAoDvVJtn+btJ9nKaVCnYab0zT3NfYBCdA/Zb1q2+42W2pTW3LHcKOGj5u5ZXfAE7lDd9+hXLkFuArtlfo/OraFlu7HWCWXU+ppZXPH7nzzo7hEsTqn7Fc5UWcyKrDj5/Ps734Y5DsabXngY5Md5v60xm1tVHqXPqD76qUZ3o5LYO2wPUT/qJ3/NJqll1x6uWv73VemaHT7S7f3n90bS4ZqqT1CiiNfcGWoM1NW2CsLnZZkABdtmNAH0rbDb9kbKf9gHQPsBxWBpAK6LB3+ld+FyL4rWqjus7BBzijpRfuQNo6VvHVN/MFW2S6/tKmUmTJvHdd985rA8NDeX5559n6NChbmhV2WTOs5TEIyGEKIvkw/12VqqCR9OmTWPkyJEMHz4cgFmzZrFq1SrmzJnDO++847D9F198wQMPPMCbb5qKmH700UdERUXx9ddfM2uWY5FPt1OBNsv1L7FhSSsJUe/P8xCWYTD58NIn8EQlU+0a/zxn58qbh029l0cqbgA2QP51f0vMwaZP3PS+3rkueoZVWcnPKRVcbm+bFfBy2OJ8Z1UC6KJeRhebCzDb58IvM+/AEZhm0tvX5Ek0RuezyLnir0pjjE0AIbfGvucJsQnyuCo0bqtPhS0u73u2yl808rrAnWwBJ4GKvI5vDkQCtPHP+zEZEZIzK2TO4WbUngr5/IBR9fofUMiJnyp4FDxTqTg0uTKWVQ3s1821yejJ7fkCZhblp1bqSuZEOAYqcweozUI9iyazJDjlP0aHLbQsd2UhXWvab/NA8LabPn5Nr6umYZoF/LHrhVDTMMY6Xlesr7liYFtg2zZwdDtr6WvN6jNnT7liyEp0Uc2t9ImJiSEiIsJhfZ06dYiJcZ6VKW6SJfNILjCEEKLMkV8GbmulJnik1WrZs2cPY8ZYa/Co1Wq6d+/Otm3OLxq2bdtGZKT9zEO9evVi2bJlLs+TnZ1Ndrb1oj8lxRQJ0el06HSFu0DPi/lYtsdUAdo017/UN0ua6apu8k2xHXokCmZI0K8F2q4ggaOi8FPd8cV2bHM9mrPqdtQ15j08ND+NfC/kv5ELfSv8d0vnFqVfi5TbMNhfTBINFfHw8CJYKZqsrZLWI2h7gbedHb2HoU8/goem6EbQF+X/04URGhrKwYMHCQ8Pt1t/4MABKleu7JY2lVXqnHFrcn0hhBBlkXy4385KTfAoISEBg8FAWFiY3fqwsDCOH3eejRAXF+d0+7g4xxl+zCZNmsQHHzj+gr927Vr8/FwPy7lZUVFRmJ8GvU7Hkb0bubMEnpVDGfVo7uc45f3NsB1y5G4705vQzt95faDikqCvYDfLna3nz7/H9+F511oqKpe0IXkOZSmoGfGD7bI8lsW3JjKkYMGj89nVCPeOvanzJuqD7Wbiulnns6vxa2Jv3q8+B4DF13vQ0OdCvkPvSqt1KW25oK3G8Cqu64rZitWFUs2mnpWwmhj7Iu9Xc6wb5cwHl0cyptrcvIdl3YREXSDpWb609r+9g0euPveDPQo+g1qIcpW1a1YXZbPIyHBeg6q4DR48mFdeeYXAwEDuvfdeADZu3Mirr77KE0/cfAascGSpeXR7fO0QQghRpOTD/XZW6DCFwWBg+vTp/Pbbb8TExKDVau3uv379uos9S4cxY8bYZSulpKRQq1YtevbsSVBQUJGdR6fTERUVRY8ePWCbqdixl5cXTeqFQj5JGrOuDuCF0KVO78swenNJG+ayeLHZ6ItvEOaZyIKcQtcHMhrQ0qaIcEFlGb146PR01t7xP5fb/JtyF92DdhX62M58e/Uxu9mYcjudVTvP4NGnsUMs08jfLL2itgzXmxQ7jDv9jtMr2PRr+/+df5fvwq01pI5lOQ5jKA570xvirdZRk8IHj76/9ojdsKbtac0hJ3h0Nrs6m5PrExlSsGO9GvMGfzV4Pf8NnVid3JGnKttfSC5M7Mngyms5nVUTBZVdoWdXLmrDSNRXsCyfza5BbSdTwpcVl7RhrErqXKDg0UVtGO9feqlYMtbevPgq71X70emQPoOiRqO6+QKI2UZPvNUFyyjpcGwe2xsPu6nz/JbYxRI8uqQNZWtaCwZW+tfptrG6KlzUhVEvj4LpNyPZEECi3lqg7Ux2DaJT2lneozpFg6fKWhuvx4mZLKj7HiGeSQDMufZQgYfSRca8Zpn5Mr/A+3V9EJVsinS3OrKQg81cB0WOZNalqa+p/tQ1XQVL+wB+uPYwz4X8Rb9WIahaOc52dyvM2cIl7aOPPuL8+fPcf//9eHiYvloZjUaGDBnCxx+XzA8I5YWl5pFcYAghRNkjvwzc1gqdK/7BBx8wbdo0Bg0aRHJyMpGRkQwYMAC1Ws2ECROKoYkmVapUQaPREB9v/2tsfHw8Vas6qdoJVK1atVDbA3h7exMUFGR3A/D09Czym/m4Firw0OcdfFuZ1JmTWXVc3m9Q1KQbfO3W2RazNUsx+LMnvbFl+c8b3fI8rysXtFU5lVXLsjzxyggyjN58FveMZd2W1FZEJdtPHx+nq2S3POfaQwU639ns6nnefzKrtuXvMZdG0fDQUjam3sm8hL5EHFzOz4l9C3QeV67pKvDI6amW5ev6IK7rrUFF24s+gARdhTyPd1VXka7H7YcPfpVW+ODLJV0YOqVgYxpXJd1ttzwj/km75R3p1qLgq5M7cSG7WoHbcTa7ptP1m1Jb0+DQnyxIfMDlvuedPLcTrrzAu5f+xzPnPuKKwfmxc0sx+HPd5nmI1VUhw+hToH1duag3vef+vNHVbv1/qa1u6bhmBzPqY1RU+W8IbEltwT3HrbOUJRkCSPVrxWWlHgYl7490L5WOREMBKsc7EZ/rPWv7vgeI0VbFoHaenXk0M4KN6R1v6rz/JHfi4Zh5Bd7+ur7g/Vt6oxvXPJtYllOM1kLoyYYAtqS3sSx/GT/I7nMtQV+BYJ9CFssqAKOi5orWGq399urjeAXUsCx3OjYPo9r6GR+vq4whqKllOc5YsPcJwJoU63OSrA/kvUsvudz28TNT7Ja9/Cq52BIWJD7A4pRHLMs9T860uz/FYHqcvZTUYvu/taR5eXmxePFiTpw4wfz581m6dClnzpxhzpw5eHt7u6VNZZV52JpBJuQRQogySIJHt7NCB4/mz5/P7Nmzef311/Hw8GDw4MH88MMPjBs3ju3bC17roLC8vLxo06YN0dHWKYiNRiPR0dF07Oj8oqRjx45224NpmJir7d3FR5VFR/8DeGAgIP1AntumGAJINbqY/gbThUeWYn9BE6tzrFCcYvAnW/HmiUvf0/fUDP64cX+h2230j+DzlPdRUDM67kMOh77LDwmP0OTwH8y8Osiy3XVDEMfU99jtuyHbemGxW+nJ315vcy2fQAuApmJzMjWhJOiDmZvQz+H+kf2s69IMvvRpFcHr1yYz4coLPHdPPV7o1sRhH7OrxmqcMLYkUamKorIm5U2Ne8quLzdsgkVJhiDSqGCzHMisqwMAiEpuj0Hjeqjj7vTGjEych9a3Hu/bXLT1u7Oh5e8Dtb4iyjjYZvlrp8e6qA2jYYj1ee987EeX592Q2oYPUz+1LGcYfcnwbwGYHjMFNSebLuWXxAeZeXUgbz98j6tDOXD22jxUZQxDzn2ETvHkrrqOsyKanfHsbLf8r+erRL3Ri6thw+jQrBXt77AGK0aef9/lcbSKJ9XCrNvG6apQMdB11uDeQNcXzGY/+89hwOnPeD/uDeIrWl+7Wz2HcbCJ62E3GZ61Xd5n64jhTqIb7uCGqla+23qq9VzUWgPgRtTMHNKJT1hM/UPLGHhmMmicB8u8VDpe6tXBsvzHjW5cCyzYez/L6MW8Kussy9Ep7fjJ2xoUuKgNw8M7wNmu+Ki1tKprfSzWZj2I3tP1a2HEuXGWv1UoPHp3O8vyBzfGca3FHJf71g1zHdTIrXafJYT4W4MNlfyt76GGYf589Lj1NZlm9KNbC2uQpkpIHar4WwN+H/ntdnmer+IHubwPYPAV2/erQtW61vdcgr4CnRpYn++PBnVFrbb+1z372W6EVLYGeId3d/3/28HKb9otd21W1/K3SmVkUMdGluVMxf6zK/dsin++ZB+EBris7sCWqt+Q0WomTw18nWuB3Zh27Vkiatj/4BFeLScYpi2aguq3kwYNGvD444/Tt29fKlasyLfffkvbts5n5BM3x0NjDh5J9EgIIcoalQSPbmuFHrYWFxdH8+amTJaAgACSk001Svr27cvYsWOLtnW5REZGMnToUNq2bUu7du2YMWMG6enpltnXhgwZQo0aNZg0aRIAr776Kl26dGHq1Kn06dOHRYsWsXv3br7//vtibWdhfVX7M3oE7+D7608RkZT3tPbJhgC7TJfcVBoP2gRYZ+ihQnMWxL7AANbbbZedE2Aa3qcfGpWKSgFeJCd9gfraJgLj/7Db1njHK6hPmqalz276Iarre/Bq8T7qym35MDmTHicTaBPehbpV/BllOMmF6xnc1yiExMw3OHVkLeraj/HKoDth2wWI+R2AOyqoIadUSNuIMD6/oyW+/3rnO9vR5CfvQ/E5z9nYFJ6uHgI7hsH5Xyz3h9VoATkznneoH8aTj7VEazCy7UwinepVIVunBxcTTwX2XEFoyJ2gGODPqpBtmt494453SQ+rjd+pT/kw/lVeeqC9ZWjhgLvq06NaIBz8DYBmEeFUazGNy5o+XEhowbKH74AN1nMY8CS+805m/vknEY168Fevzuw8d50Za704X7c+4U36Ep5gLQDfstNzXGw6gp/XNKN3yFFa3v081y7dScjmTnZtj2g5GN8068xGNWo2htB74arjFPSPdWhCiw7PYjjvx+QdAdzToApeXX7nRPSrRB42ZQbd0fIRYjw7McfHg/Z1K8Nph8PYidNVYvzlF+jXsrrDDwZ31Ajluc7hnD17ljpVgiCnrFF6lV74J6yxbDf75WGQchdc3w1V7qZ7YH1QqfhhaM6F13ZrYGL2ux/BPi0c+5TcEvQVaNigLiSYlj8f0pvaV47COedtb9nna1KuRzJmznzCA7N4o/ZyVMn2U5e/8mBnDrZsQfuISnjQAxabMgleub8BWVW6gYvRPt7h/dlysQp3Z42zW39JG2qa3SvH4I6NoMVdpFytD0l5D82r45/JvOF3ob/cB1XsP2TXGkL90ADeeqARNzJ0jO4+Eo7+CsmHHfb194TebZpDzmz27ds9TEiVSrAt2mHb3EIDPRjW7S4wvYXx9KlA55ZtIaccVqUqEQT4WZ9fY+3BqGNMwx/rBINXUGXISQTt2WUA1F0JN/bB6jbk9kjnTpAzGqxTTQjqUh+2DEa5+h/jHotEFW/9PMtW+eOtmOrsZNUeypJHO8EfDoe0avc9pJ6C4Ca0Da8ER6zD4Za80BFyJvfzUCkEBVkDXA+2bkizOtUh52mbPvQBOLYDTkxH8Y9g7MNtYIHzU97VvC2ENoLDzmfEW/jGCFjwLACtawej6TQAVpoCPfe1uIM7agOXTNs+0LwmHLMGrdrXC4WEipblapUqwYOHQZ8Kibthz8uW+1q0fgj+/Rzzm3Tmk9Y2N6sWSNU61SHn5efb5hO4tNTyGXJ343C7Nteq5BgYj/doT7t7nuNuc/ZPjXVEApFg99g82LYJ7KJMBo8A1q9fz5w5c1i6dCnBwcE88sgj+e8kCswjJ3hqMMoFhhBCCFGSCh08qlmzJrGxsdSuXZt69eqxdu1a7rzzTnbt2lXsqdmDBg3i2rVrjBs3jri4OFq1asXq1astRbFjYmLsfpHt1KkTCxYs4P333+fdd9+lQYMGLFu2jGbNmrk6hVv0CN4BwPCKi1GTd/HVZEMAd7XtAwFbwK82nPzK7v4gX2/wqw/X90BQQ3jwIGOaXodcs6gPbleHhmEB9GwShspcfbL2KyjKy5xddBd1lT2mdQ/HoNZ4Q07wyLtaV2hpDRJWC/Zl4F3WbIk3elmzZuAz1C0/ZrK3BjQa6PwbLDCdS622eel5BRNexR98fSC/WqteFVF5+NK0Vs6wDZXNUJ9aA/AIsLbl8Xb1UKlUeHto6NrQdBHo5eF6mImvX0XT8VQeENwcrm4AYGzfJsAn0Opd5nsGmMbi5gSPerduDInWIs8znrkPNN7ACJ7LdXxjyL2sTn+BntWa8vGrrSzr20VUYsH/3QPkZBvc2GvdSeNDrUowZPC7llUhtTtCaBe4utG0ovmHPNj8EVg22rLN7KFtweMf+M0xE6j9HXXB2wMa/o/3bJ6uGn1XUC1tHwMbmIbMdG9iU2zeOwSyXddTmugZxR5jImsfagoZW+DCQjhpypLy9vLm7V538Pffp1FXuQty6rT791wNB8fD4Q/hjpfRqFVQoZnp5kzd4XB2DlTMeexafmx6je94Fqr2hGo9MJ79mRatPqFl3dqQUxasds07wHsInMupddVhLhz7HJKPQKW70KhVBFWpx+RXxuChVqO6fh+s62F36kBfL+6ub87g8wKVGhQjvqF34uub19AlFRkqxwybqv4GsC3hozJ9bgXd+yPK3khUjd+EKMfMDoAw71TCGoZCg2WgS+Ztb9NMTnUq+7NgZE5W0Qmbz+KGoyGgLux5Fa97fgFP60V/zSA1eNoEo1t/BnUGw/W9sMl+KKmvhwo8rK+nZ++9A+q3A+0U8K3ByoiucGQgHNgPfjUxtJuHKmYxKox4hd5lfx7PYNN7rdKdps+FzQPtztWvfWvL8xekzqlhc/cCVIrR9FhVMQ2DNWr88XpgD8Qsghr98KnQAtS5/lvrfQC2Dobko9BtLVTtbv+50XISbHoYGrxE3ZAAUHuBUQuh94CXNSjTqm5ty/ME4OsXDC0+BL8aqGo9Sl46NKwLqQWboMBLDQRGQEA9yL7GkAf6gMYXLi+HEHMmVK4hjl4VrH97+EKFnAypyu0hKxaO5NRhq9AU1J6m/uVSNcjb7vnFMwiq9rAEj6Y/2RkW5tpJ4wcGa4FqxebxcdBsvCl4Vv95fGo+ABU2g4/rIeSlzeXLl5k3bx5z584lKSmJGzdusGDBAgYOHGj9P7YYzZw5k88++4y4uDhatmzJV199Rbt27Vxu//vvvzN27FjOnz9PgwYNmDJlCg8+aK0/NWzYMH766Se7fXr16sXq1UVb4PxmeKhNj6fOIMEjIYQQoiQVOnj0yCOPEB0dTfv27Xn55Zd5+umn+fHHH4mJieG1114rjjbaGTVqFKNGjXJ634YNGxzWPf744zz++OPF3KpbYLReQV7UVaeuV96Frvu0aUzjexqBZplpRa7gESoNdPgJTn0DzU1FcdvUqeQQPJo0wLEOEoBKpaJu0z5weI/pIsO/FuhtIjq2FykFUNHf+YW1ggZDi8lozs+Dpu/lnLwAL0eP3FlXNl/KO/9uuiisOwKu78Grpuv6Os6PHWj9u8Mc2P0yNH7Dus4zJ/NFpYK7voW0s1D5Lki0mYlM4ySAGlAf0k6j1HwM/ckCzNhX8xGoPRDCurrexuZ1Q/OcYJ4x27IqyMcT8ASvSqDNVUfL03nmWoC3Bz8MvctFmx6GM9Y6OzSfAIcmWBa/Gtwag1ExTbnt3wmqdLAEj2yfI6XWINAnQZWczKlmY6F6b6h4p8uuWoR2ht77wD+nCLnaA+qNgOoPgncVUHugbvwGlupa/XIKwGt8oOr98MBeCKxn6n9YN1P77njFcvhAn5xsCY3NBbRHIEQMcWzLI3GgSwHffC5+VWoyVSEogXeg8vA3ZdoAHt4VIaAW3Nhvv31ABKp7XaTGmdV4yNp/bxdTgKttXodtTAWRqTsMPAPtt/Oraf96qN4X/GpA0kHHYypG+6CLX05tnSZvWdc1fsMUaMwJ0Kz3nUaXGifRNB8H12yy4GzPqXUyw55XZdNr4sZe03vBzByc8K0G/U6j9gwGnyqWzzoHIXdDxRam516fDt5OhrTVfAj6XwTfnJpbvffDhUXQ+HUwWN9TePiZ+hXUCKrkDA3zDDBtlx+Vh/PPBlueQabXVNUepsf5wQOgz7Q+VnfbRG5yB2k8beo8aWxq3qlUEHa/NXjkVdH03ru6wRoo8o+A9HNQa4B98MjDz/48zgJDfY/C1f9gm6kWlFrJo6h5s7FQo48p+Kv2BN8w19uWIn/88Qc//vgjmzZtonfv3kydOpXevXvj7+9P8+bNSyRwtHjxYiIjI5k1axbt27dnxowZ9OrVixMnThAa6hi83rp1K4MHD2bSpEn07duXBQsW0L9/f/bu3Wv349oDDzzA3LlzLcu3S+0m87A1vWQeCSGEECWq0MGjyZMnW/4eNGgQderUYevWrTRo0IB+NvVmRAGlHrf8eaMAhWybR4SDJo9fd1Ua06/Ld810vU1+mo4xBR1q9DEta/ygUlvTMIigRnnvW0BZqgoYG0aiafa2daVtxkD3jfBvF8cd1bmKQtt+MTdf3HRwXe/HTlAjqP887M2ZXc/2wjogArqudL1vgxesfwfWz/s8PbdAwjaMob3g5Jq8twXQeEHnvIcv2gWPzAyZjusUJ+MAPfN/nTm4c5opG8WcFdXwVVNWw4beUPtxVCqV5Qs9kOtCM9f6htaADWoPU6CpoMxZR7ZcBXByPy+VWlv/9q9jyrJxxvYCuu9Ra5DElk+I6ZYflQpFpUHfcx+ent6wKOc1rkuCfidhYc7j5HRmCRV2YwDbmLL/iHjGyba5OAtU2L6+u/4DCdtMQcFEm1o95kwbp4HcnNdSh5/g2mb7oI6Z2hPq5+Tc6XSkqsMxtn4JjaenKchnZvsY6x1nZ0OtgW5rIO5fqOViyE9gPefrATotNGW53JVTjF7jnXfwxvY5Dm4MLXKGl2ls3meKYmp3n6P2nzsFofHBrsRgu9mw/y0IbABtvjCte2AvXPkb6o80LXv42z9OdvLIPLINHoEpUNp6qinLC6DTL6bArzlw2nMbJO6A6n0gab/NcfzItyyifx2IqGMNHuGY0WSh1piC7WXMoEGDePvtt1m8eDGBgYH571AMpk2bxsiRIy1D+GfNmsWqVauYM2cO77zzjsP2X3zxBQ888ABvvmkaGvnRRx8RFRXF119/zaxZsyzbeXt75znBiLto1OaaRxI8EkIIIUpSoYNHmzZtolOnTpapaDt06ECHDh3Q6/Vs2rSJe++9t8gbWZapbH51D1Bn5LFlDkNWPgcs2IxbedL4QKNXbY6pgp7bAcVxSEhhdfwZQ8Ierl52rHNil1UU6uR11GqK47rcF1GFUbUnNHrNdOGo9nJZZDhf1XpBy0+cBzYAfEJNF+m6gk01XiDOfuGv/Tic+REq2T62ToJHNkNxCswz0BRAiu5qWvbwh+oPwMMXwLdGnrs6zVi4ndlefKtv4pf2jj/DNnO2Us7rU+1pH/hUe+UKQDi5CPLws8/6C2oI1XoWrA0++cyQV/0B0y33uc2vDbWTGavMgci6Q0y3wvLwNwV1kvZbM8/AlNl1ZrZpWJktnyoQ7noq+DyFP3Hz+9qyfRzMwae8Ake+NSDzsnW54WhT7amw+yDVpnBYvRHWIJtZYD1o+DIF4pB5VMGmnbmCRyoVNI60LvvVhPY2WYS+YabsK8iVdecLdQbBgTHWTKuc4Zqu3FDfUbD2lyHPPvssM2fOZMOGDTzzzDMMGjSIihVv4jP2Jmm1Wvbs2cOYMWMs69RqNd27d2fbtm1O99m2bRuRkZF263r16sWyZcvs1m3YsIHQ0FAqVqzIfffdx8SJE6lc2UW2I5CdnU12tjVbLyXFNORUp9OhK8r//3IKZesMRstxVed/RpV0EGPLzwof3C1FzP0t0sezFJB+l69+Q/nte3ntN4DtN8/y1P/b5Tkv6PkLHQno1q0bsbGxDqnQycnJdOvWDYPBUNhDlm+KtcZRgDqPgj+V2pgyP8K65X08V8Gjzr/BlsGAAh1/cb5NXnJn/NysiGcw1nzC9At7bh3mwMa+ptoYztgOj7G4hS+JllpPtzisUaUyZWuVJGeZR3dOh0p32Wdq2F7o3b0YtIk3P1zENnBovqj2L8BsYpVd1924Ldn2M7+hRs5EPGMNHuW+yO+ywjQcsuNPuXZyEjzS+NoHj2wDBPlp/RmknYEG+c8kR3Bj079qL1PWGzjPPMojaFBg4U8AuYI63pWgzxGI7g7x+RfuLnFN3zcNN6zaI/9tH9gFf1a3LpuHDEL+Q8AKo1ovU10x81A7LxfD1grLNtNJ42fKwBxwzXr8Vp/CvjfgjlxDxx+OQZ98iuRdqTd/7lLqu+++Y8aMGfz222/MmTOH0aNH06tXLxRFwVgCs4ElJCRgMBgstR/NwsLCOH78uNN94uLinG4fFxdnWX7ggQcYMGAAERERnDlzhnfffZfevXuzbds2NBrn3wcmTZrEBx84FoVfu3Ytfn4FGLJdQFczATzIytby99+m7xIPp5uCsTtiKnLNo7XrncuIqKgodzfBLaTf5U957Xt57PfDNn+bP9vLE3c/5xkZBUhi4SaCR4qiOB3Dn5iYiL+/6ynkhQs2wSP/nODRjrRmnPAbQPSVinzUOZPaTR8xDQ/QJZlqfeTJxUVJ7cdvPUhS3Co0g4fPF26fW/qFsRT/OukseOQZCA3+z36d7QV/HSfDjAqjcgfT0KPABgXb/qGzkHEJKrYs2qyr4uZjc1GluckLnuoPwpW/MUYMh4snretr9DXdcnP2mOYOAngVYrihX3Xotb1g23oGwYB4+8w7pxmGxXwhfOfn8G9XaPZ+8Z6nsFp+VPBtfauhVGiJKumA431FkRVqdtdM07DbiKdNy7b12ooqeKTOCST6VLGuaxQJ1R5wHL7sXwvFqypQ/r7sAfj6+jJ06FCGDh3KqVOnmDt3Lrt37+buu++mT58+PPbYYwwYMMDdzSyUJ56wBnmbN29OixYtqFevHhs2bOD+++93us+YMWPsMppSUlKoVasWPXv2JCjI9SyxhXX2agof798Oag8efLCXaWXOLJDtWtVHqf2g651LOZ1OR1RUFD169MDT00mGaBkl/S5f/Yby2/fy2m/A8jkO2E3gUNbdLs+5OVs4PwUOHpm/+KhUKoYNG2ZXONFgMHDw4EE6derkanfhitEx8yjD6E39ji8zpF4V+209nFwUNHwNTtj8ul1UGUKlxi0EgPzDi6wVJc7JbEkuNiy6c6o10HVVwbcPiDDdShsPP3joDKC++WGaXVaYih+r/IGTrre7f72p4HD4U87bYaswmUeF5ZOrqG5xZR7lpWIreOx66RvmmJttkW1bLusX3QSvitB8nHXZNvB3s8NvIddrzkk2nEplnclNONWgQQM++eQTJk6cyKpVq/jxxx8ZPHiw3XCuolSlShU0Gg3x8fF26+Pj413WK6patWqhtgeoW7cuVapU4fTp0y6DR97e3k6Lant6ehbpF2Jfb1NgU280OhzXQ+MB5eCCq6gf09JC+l3+lNe+l9d+m5XHvrv7OS/ouQv8LT04OJjg4GAURSEwMNCyHBwcTNWqVXn++ef59ddfb7rB5ZZN7RoPleniLNPoTSUXs5Q5uPPznAvdHEX56/bt5L5/na8PyKNoritdVpmGXTR48dba5E7OMo+cKe4L/rIqoC4EhN/8/ip1wWYmDOtqmi3PWcDkVjKPbpVtRl+dwaZ/W04qgfOW8sARoHgEOL+j1qOmWm7m2SWLkm3Q51Yyj2xrfLmYlVEUjFqtpl+/fixbtoyLFy8W23m8vLxo06YN0dHWIZ9Go5Ho6Gg6duzodJ+OHTvabQ+mdHlX2wNcunSJxMREqlXLL/u5+JkLZjudba0M1zsSQpQSTidBEaJsKPDP6ubpWsPDw3njjTdkiFoRUWwyj8wylUIEj1Rq04WuZfkWC1rfjrwrm6Zad6bRaEi/YCpIXVA1HjTdSrO8psS21e572D4Mmk8oztaI4mAbBAi999YySgrLNujY7jtoOdH+c0a4ZGj3A6lR/fFvP8X+P1iNt2kWyeIQ3BSq9TbNAHgr2acqFdw1C7LiTQXaRZHIXSOyqEVGRjJ06FDatm1Lu3btmDFjBunp6ZbZ14YMGUKNGjWYNMkUAH711Vfp0qULU6dOpU+fPixatIjdu3fz/fffA5CWlsYHH3zAo48+StWqVTlz5gxvvfUW9evXp1evXsXal4LwyAkeKQoYjQpqtW3ASIJHQgg3yrgMa+6Ces9Biw/d3RohilyhIw3jx7soZixuimJwDAJkGb2p6FfA4FFuZTHzKK8+aXyg3bcl15bbRUEzj+oONU3B7VMl/23F7cW23tL9G0r23LbBI42PBI4KI6gJG3xn8GDNEgxQq9TQrYjqDeWumyZue4MGDeLatWuMGzeOuLg4WrVqxerVqy1FsWNiYlCrrVl9nTp1YsGCBbz//vu8++67NGjQgGXLltGsWTMANBoNBw8e5KeffiIpKYnq1avTs2dPPvroI6fD0kqap8YaINIZjXjf6nD9Gwch84rNDJRCCHGTjnwCmbFw+CMJHokyqUDBo9atWzstku3M3r17b6lB5Y3R4Fi7RtH44am5yeEbZTF4VPDRleVHYYb3SOCodLLNPCrpoRi2waOymM0oRBkzatQoRo0a5fS+DRs2OKx7/PHHefxx55No+Pr6smbNmqJsXpHS2GQaGXIPXbuZz8p/Wpr+7XMEgpvcQsuEEOWelIsQZVyBrgr69+9fzM0ovwwGx2FrHl63MCSwDNQMcVAW+3Sr7lkK/z0Kbb50d0tEcWk6Bq6stNYcKkm29ZqkhogQ4jbiYZNFpTMouS7WbuHzKvmYBI+EEEKIPBQoeCRD1YqP0UnNIy9vFwVXC6IsZh5J8MhR6L0w4Kpc2N8uOv8G24fD3YuL7pghnWDANfCuVHTHLKjgxtBqCvi4nn1JCCHcwcMm80hvMOaaGPBW/k+UIrdCiFsk38tFGXdT4xGSkpJYsmQJZ86c4c0336RSpUrs3buXsLAwatSoUdRtLNOMTmrX+PgE3vwBy2LwSIatOSf/Qd0+aj8ONQfcWrFiZ9w55LDJW+47txCljMFgYPr06fz222/ExMSg1doPSb9+/bqbWlb2qNUqVCgoqEzD1hRD0RxYhpsIIW6VzLQmyrhCX5UfPHiQO+64gylTpvD555+TlJQEwNKlSxkzZkxRt6/Mc1Yw289Pgkd2JPNIlAZFHTgSQpQaH3zwAdOmTWPQoEEkJycTGRnJgAEDUKvVTJgwwd3NK3PMNbN1uYNHt/KjigSPhBBCiDwV+qo8MjKSYcOGcerUKXx8rFNHP/jgg2zatKlIG1ceKE4yj/z8gm/+gBI8EkIIIUrU/PnzmT17Nq+//joeHh4MHjyYH374gXHjxrF9+3Z3N6/MMY9c0xuMYDf8X4atFStDFuhS3d2KW6MokHXN3a0QZZWMChBlXKGvynft2sX//Z/jVL41atQgLi6uSBpVnjgrmG0IanrzByyLwSMZtiaEEOI2FhcXR/PmzQEICAggOTkZgL59+7Jq1Sp3Nq1MMmce6Yty2JoEj/L3Ryj8HgT6DHe35ObteRWWhsLFpe5uiRBClDqFvir39vYmJSXFYf3JkycJCQkpkkaVJ7kzj9IMvijBEjyyI5lHQgghbmM1a9YkNjYWgHr16rF27VrA9IObt7e3O5tWJlmCRwYFFMcf4W6KDFvLnz4n6yj1pHvbcStOfmX6d/877m2HMLm+Bw59aMpqKxMk80iUbYW+Kn/ooYf48MMP0elMQQ+VSkVMTAxvv/02jz76aJE3sKzLHTw6klmXAJ9b+KJZloJHXhVN/1bt4d52CCGEEHl45JFHiI6OBuDll19m7NixNGjQgCFDhjBixAg3t67sMQ9b0xmM9plHtxIAkuBR3mwLAZeFosDyfN8eVreFQ+Ph6GfubokQogAKPdva1KlTeeyxxwgNDSUzM5MuXboQFxdHx44d+fjjj4ujjWWaYrT/xSzZEEhlr5uaBM+kLAWPHtgLl5dDvWfd3RIhhBDCpcmTJ1v+HjRoEHXq1GHr1q00aNCAfv36ubFlZZM588hhtrVbGsJWBgIixcku2FIWAi/yfN9Wkg64uwVCiAIodJQiODiYqKgoNm/ezMGDB0lLS+POO++ke/fuxdG+Mi938CjD6E2A900Ej+542ZSK23xC0TTsdhAQDg1fcXcrhBBCiDxt2rSJTp064eFh+v+7Q4cOdOjQAb1ez6ZNm7j33nvd3MKyxVIw25irYLZkHhUjm8dHUUy3i0sguDkEN3Jfs25WSWZPKUa4sAiqdICAuiV33lJFgnlClAY3neLSuXNnOnfuXJRtKZccg0c++HvfRPZQ2y+h1RTw8C2ilgkhhBCiILp160ZsbCyhoaF265OTk+nWrRsGQ1EVdRZgzTzSGW4x88gugCAXr3myDa4pRohdC5sHmpafLI2PXQm2+dzPsH246e9S+ViVgLIwFFKIcqBQNY+MRiNz5syhb9++NGvWjObNm/PQQw/x888/o8ib/uYYtHaLmUYf/G922JoEjoQQQogSpygKKidTNCcmJuLv7++GFpVt5uDRhhPX7AtmF3rYmm0dnzKUeXT0U4i6B/TpRXfM3I/t9d1Fd2y3KMHrlqv/ldy5Si25jhSiNChwlEJRFB566CH+/vtvWrZsSfPmzVEUhWPHjjFs2DCWLl3KsmXLirGpZZOSa5YQncoXtVoq9QshhBC3uwEDBgCmyUOGDRtmN7OawWDg4MGDdOrUyV3NK7MMOdeZszae4ZW2tfCz3FPIAFBZzTza/7bp39PfQ6PXiuaYdsE1pfTX2CzJH71L+2NVIsrQ+0+IMqzAwaN58+axadMmoqOj6datm91969ato3///vz8888MGTKkyBtZluUetqZT+bipJUIIIYQojODgYMD0A1tgYCC+vtYMYC8vLzp06MDIkSPd1bwyK8smCSZTq7UGjwqdeZRrKFZZo88owoPleqxUhZ6w+TZTksGj0v5YlYAyM4JFEgBE2Vbg4NHChQt59913HQJHAPfddx/vvPMO8+fPl+BRYeXKPDKo/FxsKIQQQojbydy5cwEIDw/njTfekCFqJUGfwUfVptLQ+xxrUjqi071sva/QNY9yZdPE/AGXlkG7791bCmD/GPAMhqbvuK8NuZW1zKMSDR6V9seqBB2ZDLpkaPqhu1sihHCiwKHwgwcP8sADD7i8v3fv3hw4INMsFppRZ7eoV0vwSAghhChNxo8fL4GjEqI+OpGHK2ygke8FXg1bhCH9ivXOwmYP5S4CvfkxOP8rHJ9aNI29GWnn4ehkODDGvn26lJuoYZRPgCQzruAZH7kfq9KeTSPD1m4ziul1dWCM6fWffs7dDRJCOFHgzKPr168TFhbm8v6wsDBu3LhRJI0qV3INWzOopei1EEIIcbtr3bq10yLZzuzdu7eYW1N+qOOj7Za1WpuhWUVVMDs9pvANKyqGTOvfisEUpNFnwu/BeKg8wG9JwY+VV4DkzI+w4zlo/Ba0nlKAY5W1zKMSHKZY6h+rEqDkmjmxSIdcCiGKSoGDRwaDAQ8P15trNBr0er3L+4VzuQtmB/pK5pEQQghxu+vfv7+7m1A+JR+2W9TpbDK4b3XYmplR67BpybEJSBr1oPaEtLOmexS98z6mnYXTs6HhaPC1/aE3j+DRnldN/x77tGDBI4f6UJJ5VGClPUursM4vAl0SNHihEDspuX5QLys1kIQoWwo121rumURsZWdnF1mjyhNVrsyjapWC3dQSIYQQQhTU+PHj3d2E8kcxolJyDffXZdrdXziugkdu/E5rm81m/oHRZp0aJ8GjqHsg8wok7oRua23uyOsCvJCFfe2GrelBbZNNoyj27S4VJHh0y4wG+9eB2dbBpn+rPQAB4QU8WK7Mo5spYO+qPSWp1L0PhCicAn+aDR06lNDQUIKDg53eQkNDpVj2zcj1Jah6pSA3NUQIIYQQNyspKYkffviBMWPGcP36dcA0XO3y5ctublkZohgwNIy0W2XQOQ5by9Yb2HwqgSxdPplIuev4mN1k5tH+i0lcScrMf8M8OQke2Xxddxo8ysyp+xS/Dv6obLN/EQZI7C7sDXZtyj35S+kgNY9uyY7nYVlNyL5uv972fZR9reDHU5Rcr6NCPj+nZ8OSCnB1c+H2E0IUSoEzj8wziogiliv9uEVt13WlhBBCCHH7OXjwIN27dyc4OJjz588zcuRIKlWqxNKlS4mJieHnn392dxPLBrUnxvAhaE5Ms6xyFjyasPwoC3fG8FwbL96/8yKEPwkaHycHdFHzyGDKPDp7LY3XfjvAqG716dEk7+9nJ+NT6T9zCwDnJ/cpXL9cMTpmHqnIJ1CjS7ZZKKbMI6PePiBi1JmG15UmJTo1/G2YeaRPhwuLoEY/8Akt/P5nZpv+PTsXGr9uXW97XWMsTFAxV+ZRYYNHO583/btlEDzizoC9bfC3iDPyko/D9T2mzzPJcBJucht+mpUvqly/1vj6SMFsIYQQojSJjIxk2LBhnDp1Ch8fa5DiwQcfZNOmTW5sWRmksg9SKHrH4NHCnaaC18PTn4Idz8LBcc6P5bLmkSl4FPnbAQ5cTGLkz7vzbdb2s4n5t70g7DJ8cr4j2gQ6nGYeuT6Y67sKffFpm6VlcAwelTpuyjwq0aBVHvaMNhVMX9f9Fg+U63Vk9/ot5OvCNth0M8PWcp/fLVwEpIvCqsaw7Wm4uLRojytEIUjwyN1yf7BWaOmedgghhBDipuzatYv/+7//c1hfo0YN4uLi3NCiMkztZbd4KjbBupDrYq2GV86wmcvLnR8rn2FrV1OyCtysTG0RXbTaXPwa9DqHdarCXBznGago5CWAYh882nvRJsNJgkd5K8JAm1JUwaeYnFn7kg7d2nFyByHtMo8K01f7zCPVzT5Ot9MQSnPtp6KWuN3p6jxfG4YsU220g1Krz442Gda0h2NT3d2SUkOCR25mzjz6iQ/h8RTwDHBzi4QQQghRGN7e3qSkpDisP3nyJCEhIW5oURmWK3h0PcUmiBG/3lQ0Nxcl6yo//vkri3Iykqx35MqmMcsZtqYzOl6MrTkSx7rj8fYrk4/jnXnGsqjV30LGgU07Pl2dM7OczQVxvsPWbGTpDLw0fw/Rx+Id7yxI5pE2Ca7+l1OPxr5g9oIdF2yWdRiMStEFNm7S1ZQsXvhlD1tOJ+S/cRFmhcQmZ/LCL3vY4Sr7zLZgtqIzvb7iN4ChcLW1UrN0dPlsA2OW3mLAJ3ebnNh8KoGVB68U4EC5jnOzwaPcNY9uNnhk+/5P2A5Zhai7VCRs3lcxvzvWhMrl09XHmR51snCnML92jQbT60ifzqUbGbT7JJqv151yvs+FRXBtMxz+sHDnKutOzDBNNLDvDXe3pNSQ4JGbmX9B0qv8wDPQza0RQgghRGE99NBDfPjhh5Zp41UqFTExMbz99ts8+uijbm5dGZOrto6P2uYCPD4a5fg0clNpb/Bs5jOsjjLV7zQaFbafTSRDZ3OBanfRawoeGXIFj1KzdPzfL3sYMW+3NdNIlwarGjPsei80OUPKMrS3kP1g0441hy7lrLMeL/ewtbwCNjvOJfD3oTie/cnZsLsCBI/WdoB/7zVdeOYKtKlshrFla7PoMW0jQ+fucnmofIuXF4GJq46x+kgcT/2wI/+N8wl0nb2WxtfrTpGWnf9z+daSg6w+Eseg751nhJxLtMlgM2pN9Xmiu8G+151u78rKg7HEXM+wDMu8JXkEjxRF4ekfdzBqwT4uXs9wuZ3pOMWTeVToIW+5zx+/EdZ2hOURN3ecopIrE2pfzA2+23gGg1EhIS2bbzac4YvoU6QX4HVmPWbOe+/Yp6bX0aZHmBZ1kmup2Xy+1kUg6iYnASjz9OnubkGpI8EjNzNnHikqr3y2FEIIIcTtaOrUqaSlpREaGkpmZiZdunShfv36BAYG8vHHH7u7eWVLrswju+ARcHnP1y537Rm0DYDZ/53lie+3M26ZTQaHzYWrkpMRojcY6Ru8iS9rf4qiy2D1YdMQRDUGDNtGwKlZkH3Vsp93TlvSb2UIm03dF43KJsMgR+7g0eu/HXB5KENWIjNrT6JboJOgTkEyj1JOmP69sAjbmkfKrpd4MXSJZfn4leucTUhn08lr6A2OGT1zt5yj6fg1bDhx1eG+W5Z8FDY9Ajf2O8x0dyo+lcn/HCc5w1kgQmH2prOsPWJ6TpMzdUxZfZwTcakAPPz1Fj5fe5JJfx/Ltwmn4tNc33njABFXJlmXjTo4l1NA/6Tr16pZhk1MQefksXXl4vUMpq49QWJattP79Ub75/96upaNJ69hNCpk22TOJaY7CTrkkbWlGKyPdXpWrmGfigK7X4ajnznuGBdlygAxu8nMI4P5/RO31vSvOThgeZ24fr8UlGI0oN36HJzI//nLXYPpkW+2Mumf4yw/cNnu+cwsTHBVMUDcv3DgXdNyXBR6Q35Zf+655P/hv7M88+OOEgke35zizZbcc+EGFxKLJkCVknV7DA8uNcGj69ev89RTTxEUFESFChV49tlnSUvL48Ma6Nq1KyqVyu72wgsvlFCLC8YcPFKpCzzxnRBCCCFuI8HBwURFRbFixQq+/PJLRo0axd9//83GjRvx9/d3d/PKltzBI5X9xXGmzvXFgKfKdAHzRbRpaMfmUzbBjHTrMKyEnCGIeqPC13U+5aEKmzj130e8ueQgAPcF7Sbg8s+w68Vcxzd9p8vI0kJGzoxPWVdN9UYUo3UdMHHlUV74ZY9DdpPtxaZHTnujj1n3yz3RytJ9rmeWuk+zlD4VtjA34gOX29hKztC5qN1kP2xNpb1OXW/rkCbF5kI/OdPxAueDFUcxGBX+75c9BWpHYRjX9YZLy2BtJ3w8NXb3DfxuG7M2nmHc8sMO++mNRr5Zs52Xf90KwOR/jvHthjP0/sJU4D41JxOkIEPg9MY8gilrO+VqcMEvABfvvsSYXR4s3m3KQLNNlnJ6MW40WF5jM/49xVfrTnPf1I1Oj52cZd/mx77dytA5O/lj7yVSs/LJgrHLYrG/lMyyyea7nvs6LemAKWC2/y3nx93yhM058nmcdKmmYZW5GPR60wOVbfO8GfWwuk3O66SDwz5HriQzYfkRrjsLlDnx3e9z8Dr/I+x5Of+NbfphmyV4ITGDbJ31OcjILkzwyAjretityjcWrNLks0HxmLjqGP+dSmBZHp9TZdX5hHQe/XYrXT7bcMvHWn/iKi0mrC38EMdiUGqCR0899RRHjhwhKiqKlStXsmnTJp5//vl89xs5ciSxsbGW26effloCrS04y5cACR4JIYQQpVrnzp156aWXeOutt+je/VZnMRJO5TVsDTDmMRzLS50T3MkJkKhtZxA7O8fyp1abTbbegN4msHM1/qzl7yC1zUWxTaaQl8p0oVjl0EhYVhNOfQdLwzCuaML+xf1M6y6tIC45ix82n2P1kTiOXslVK8s2eJSTZfTdBusFQ+FmW7N39EoKr/92gMtJmf/f3nnHR1Gtb/yZmS1ppJFGIJDQuzSBYEMJRSyIXCtXARUbqCgWVLAr9mu5itcGYhcLPwsiRUBEBESqdEgIJYX0vtlyfn9smzNtNxBYQt7v5xPdnTln5pxpzHn2ed8D+RDAKxpd8PIKDH15hXYonEGi7sPF/rxTpTX6A3Cvo6Wm3qFyCQXLnoJKn4OptLoeYq0nhMtZizCzv0+MMZR6HEdLd6hzPpmcldjUYzxWdZ0Mxhg25JQCAJRanl3H0VFYUecLNXIoKjmcLp/bQHAqwr4aED70n2X7AAAz/28HAD6MUtOF8OcEYGEb7N/8FXJL3Psvr7WjsFKd+F3pPDpQ5C7/8/Z8LlRPU0x0ygRbhWpRXevfl+BU7Ff+XSM3GYdR4mvmAhZEA1/H8dsEIApOYNerwL53/QtXjPSX8/w/p6ga3/59GC4Xw9i3/sC8P3LwoEccLq6yYfirq3TzB63bJxNCAuX5kh2rilp/n6LDzJzbKJjwSNlOA5Yoq6nHVxsO+bfLzfjHC4eVdXZsPFjaIGdbQ9ESOx1OF77eeNjzPGo8GGPILqpWC/PahRt133J25vmf7SeaD+5RT54z7w8foaRJiEc7d+7E4sWL8f7772PQoEE499xz8eabb+KLL77A0aPGydwiIiKQkpLi+4uOjj5FrQ4Och4RBEEQRNPF5XLhww8/xKWXXoqePXuiV69euPzyyzF//vyQJxA+I1H8gh4m8IPxLmG56BKWo1nVKvIDNLOkLTRZBDt251dyIVjnsq/QynwM18UvxpVxv/qWP/39Jv/2PeJR3LEF7gUb3G53sSYbfVyLAAAVG5/iwrcKK+tQZ3f6c8vIRBpRcCG/vM6XSwkA9pU7Mfj5lfgjmKTQMursTtz+yUb8uGk/Vi64E7D5Ewkv/XU+cktqUFZjR0GFDYdLNQZzBqFKc1bscrcXToTvehrIX4ZfdxXg7s83qUSOapsD499fhyHP/4rire8Be+cgt7gGd3++yScMLVOIPULOfLSz/wKH04UR//kNE+duwL7CKvyuOAYxphrMSJmLbmEHOAeUVyzcdrgcSlLMJaiud+req94B9V85Jfi/zW7RYMuhMpz34grc8IE7t5IyZOiBr7figpdW4pd/NGZabIDzqHcb/5ilpLoelXUOtDYX4sGUeaiuyFO3OedTAEDFX48jvaXf8VhQeBj4+36gfJe/GToiq9UkokrmPKqpd6C8xo6hL63AUz/s8FTm3X4u2SC9yua/H+317mv6l3/ycd+nv6Nu48O+dcUVZUZdx+48t5hnc7jwz9FyOJwuXP3OWtz9+SYwh+z6rDnM1TMJLnXy44JfoWTEa7/hvq+24LtNR1DvOcdel9kXGw5hb2GVbv6gOpfM/ajKmaM4rjKx8FCpX0h0uFyceKSVJ624yoZ5a7JReXQDsOkh/woNIVd5NifP/wsPfrMVLy12n3MmL6EQMMfN+QPj5vzhE88aC7l4YzapJYcP12Tj/gVbcMVbazTrz1q4HRe+vLLB4VoLNh7GhS+vDCrk9ETD1vYVVnHC34xvtmLkf35Dbb0Toug/5jV6ocz73oeQPTfgfrQmbwgVTUKxWLt2LWJjYzFgwADfsqysLIiiiHXr1mHs2LG6dT/99FN88sknSElJwWWXXYZZs2YhIiJCt7zNZoPNJlOJPdZlu93uS4TZGPiSanoeAAxSo27/dMbbz+bSXy/Ntd9A8+17c+030Hz7Tv0Obb9P9f4ZY7j88suxaNEinHXWWejVqxcYY9i5cycmTpyIb7/9FgsXLjylbWpuhInqnC6/dJ6K9K0/qpab4OAG3K2itfNNWkU7th4uV7lQvmw/A22tvLDx1/4jQCf3Z4to54QeLWps9diUW+b7vjOvAk//uAOHSmsxITMd58fkYai3vYITG3JK/LmPACw+xFBcU4/r31+HOeP7Ge5LTkWtHbklNXiz7Wu4LHI1t+5fFbdgs92b2J1h++FiHCyuwblcKX3xyOIJ17s89je0PvwKcPgl3LT1R0hwwqIYNOaW1GBrbjFMYGi53R1BMOlALPZXReNQaY3v2CyffgE6JEYBThtMG25BHwB/7pvi286hkhrVgPtS9iYuTPoGtyd9g31VE7l1ThfDZf/9HTm9NY5NTb3mEFKC0yce/esdd76sjIRIPPTNVtgcLvydWwaXi6kcG995QnTeWrEPI+P5bVbWVEM+PU5tvRObckvRr12cKuwuqYUVEpxwQkJhZR0q6ux4pNUHuCR2DerXrsHFh7/AweIaPHpJN4wf1NYnD1iEek48a/XPzUDVb8jb/gmm2xbiqTE9ES7zD8gH+VaTiMq6WrgH1QKqbA4s2HgIOcU1+HDNATx2SRfOTXOsohLDn1mKW87NwNShGaiu9Qskdnst9hdW4s6P1+ORVh8iTFjpW/f2sm2Y9a+hGkfdzf9W7cGwbkl45Lt/8P3WPEzL6oT1Oe6Zy+4YHI1u3oIBZo3Twzsj4so9fhHVK+aIMjdVbb0T4Rb+vLjk3gt7uWKmbMWVJBNq5KJsVZ2Dc+No5Umb9uVmrN5bhIm9L+WWl1TXIV5V2o/D6fI56X7eno8nx/TEgr+P4GpvAWcdIIXB7nTBLInY48nZxc3K6HLoRsXsP1YFq0lEmzj98TTgdjR5MUvq8/TTNre4eqzSfT0xCBA8x8/mcOLjP92hxL/tOYZLe6dydQ+X1sDmcLmfEXA/D37cmofxg9viNU9o1/u/Z2Pmpd0BwNdXFbJ/DzbnlqJP2zjDPsnZfqQcl775O9q1jMCqBy4E4BYeAWDpzgKYZOJRea0dkVbF8awvA9ZPhgmAFPG5avs7jlYgIcqCpOgwzVxyoaJJiEf5+flISkrilplMJsTHxyM/X0PV93D99dejXbt2SE1NxdatW/HQQw9h9+7d+Pbbb3XrzJ49G08+qY4NX7JkiaHodLy4HLWABOTlFWLRokWNvv3TmaVLl4a6CSGhufYbaL59b679Bppv36nfoaGmJsDMQI3MvHnz8Ntvv2H58uW48MILuXW//vorrrjiCsyfPx833njjKW1Xc0IZtmaEJDi4EAmTqP1rrllwYF22eoptpXAEAC0k/zU3NnYFbklcaNiGeocTh2XX6bu/HUCFx+nx4Zps7Inah6HtPe2FE3d9vglDW6jzIAHAHZ/+bbgvOWW1dsSEm3FZ7GrN9d6B3ottXsf5//yNc7b/F5t7eNcK+L9NhzBGZ9smuNvfzpLnW3Zf8seYlPA9JmW/BcD/Dn9s9zfY3vMOvJTvvycc9VUAonHgmN/F8f3mo7h3eGfO2bHloP+dv7zWjq55z3DtSGV+p8ixSv662JRbqtN6oKpW/dy4KeH/cH/KfEw+9DzsTn8I6pZDZVykS3F1PRe2JhcntQar+/JL0Vf2ffQbq5FdVI0B7eLw+a2DIQoCZi7cjphwM3o7fsZTPR/H1NyHUFmXiYpaOzqGuQenlvo8HCosQK0rAjMXbkenpCgM8mzTKtRz4YMJVe48Tq1MBfhjRzGmL9iC/0b523bgmD8MM1aqQP+/h+OlNn3xwOFpqKl3otqTj+fTjEfh+ul+iOd/5yu/4UAhymrs+GvtZ0D+E/BdMnA7j6zrJ2F996UocsRyx2HJthxMHl6HFNUR8hw7wYlP9onYcMx9Tf1vlTtsNNFUim5rO/oLHoe7U36ObBrhVPLTll1Uje6p0Xhz+V5sP1qON6/rx92DsFfgu00MdgfD1WenqR16LhtW7C7E0bJaTqSrtPHiUY3HvfLz9nz89x8R/c6tw+q92u7CwyXVhuJRhcw5lp4Qif3HqvD7vmJc3daz0GnDG8v34s1f9+Ixj7gir1f2z/uI2jIFtsyvEZlxGbftspp6DPPk0Trw3GiIooD12SXoktICMeF8SLFcwNQKiatSOIpcTIAkuI/RzrxK3f4xxjD27T9wrNKGF//VG1cPSMP17/+JQyW1OHCsCn3axuKoR5iqszvxn2V78NEfOXjmil74V/827n3bHIi0SJxj66GvNuCX+0fo7lfJKo/weLC4Bl9vPIwxffwCV53dyemI5bV2pMaG8xuw+/sowoHf9hbhaLkN/x7cDnPX5OCpH3ega0oLLJ52vm74bCgIqXg0Y8YMvPDCC4Zldu4MxnKmjTwnUq9evdCqVSsMGzYM+/fvR4cOHTTrPPzww7jvvvt83ysqKpCWloYRI0Y0asib3W7H0qVLYTEJAANap7XD6ItHN9r2T2e8fR8+fDjMZnPgCmcIzbXfQPPte3PtN9B8+079Dm2/vW7hU8Xnn3+ORx55RCUcAcBFF12EGTNm4NNPPyXx6CRiFYIXj0xwYvsRf+hSQXmtXNfwYRHs+HWnWijSIlryixt3JX8ZsHy9w8k5ECoUyYnlA1PvZ7mbqaXFARzH5D0FFXVIjrbqrvcmSb46fhnAgGvil/jWHauyYfXhQoxpoV3X2055aMzdnmNxe/S7WOTKxD91HbC7Lh3nH70JEIHHUt/zlQ33uMeiw02+Aefv+4pw7/DOyD12DN4xb3mt3/Hy2bpcfBXt3wYAVNlNgMckkltSDUHwaws/bs2DHlU1FdxAr87u9LXvmZSXUFLtn2zns/WH4JSJDwUVdXC6GDpac9EnYg9q67NwVdwSbKzpDrOkHuIXlvNJpLM9uYb+OliK1XuPoaLWgc/Xu/M4Heg1E6Lownvpz+DXuqmoqLPjn9oO6BLmXp9qPoa9tnYAgKPl/mvKKtoNkz/nFlfDKfs9fJvsnuhvXwArinFV/DI8cHgaHv52G8b1bY0rYlfgnBZbgUpgztcf4w7PODjG6j4W8zKeUO3Haa9Du4pvABPQ0sQ/m9vFCJizch/0UrmbBAc2HPOrOO0TI5Gbn4fFnadw5S59fTlKTQexpq1yC/rIw4zqnS6YJYEbnJfX2HBV3FJsqumC/ceq0Ck5Cq943CxLdxTALPhFj6LSQtz7pScfmgBcrXDolVRWYdJc92yH1w1M87ehzoE6WcLs6non8sprcfeXWwGI+L/N7uu1X4R6HKwlessFTPkzrmWkBSt2FfrypwFAflk5Xl3qzp2zYrffeQW4RZ7YLZMBAOIfY4AMvj/7Cv3Xb1G1DbvyKnHjh+vRu00Mvp96rqdMJersLk7Xq6134rtNh/HJn7l4dmxPdE2J9omSXuS92iwTe0tr7LA7Xbjvqy1oExeO6cM7+9xKry7Zg6sHpOFQifv6X76rEOP6tfbV3XXkGIR976IleuP+BS6M69ca7/52AC8s3oXbL+iA+xOZz0dWUuUWc77fchT/98d63NphBwZdNA0w+W8Wp4vh+Z934q+DpRhzll8sun/BFgxM99/vdqcLtfVOXBzzO/LqE1FeOxguF8PKPYUYkB6P6DAzl69LYA7cPN/9Y0CYWcJbK9z5znblV4IxtbsxlIRUPJo+fTomTpxoWKZ9+/ZISUlBYSE/vafD4UBJSQlSUvQ0azWDBrk1+X379umKR1arFVar+h9Xs9l8Ul6IRU/YmmiyNquBBnDyjunpTnPtN9B8+95c+w00375Tv0O3/1PJ1q1bDSfiuPjii/HGG2+cwhY1P7TC1vQwCQ7c/onfraMXCiAJLs0wEi2iJeOZf5XUO5yGyaLlSby94WpyQanGcXwJs2/4YD3SYrXD9AB3GIgcSdaOzYdKsbegHNARj7yzzGmRFf0nsqL/BADNUEIAiBRrPW3wH5edeRWwOZx46rv1eL+le1l2YQkAd1jJ+pwSQBGCVl4vAWHuzxsPlnKD172FldDLb1JbUw67bMa0uWty4J1HL1ysw0d/5HDtknPpm78DAJZ1uRMAcPD7DXgpbY2/v4o2HqvQd1RsPlTOhQ6VsTjEC8UAgI/XHkSVzeELEQSAGNm1V1LtFzTCBJumeFTq8J9Aec6jv2UDdcFRqRodFuz9EZ+0f8X3vaj0GOARj1qY9Qe1Toe+E7SiqoIT4ZSYFeGfdXYnXmjzukqEcjpsOFJVCzRAPMov9w/aS6rrER9pQUGF/zmSWrUED6S9DgB4t3wEpn252bcup7iaOwfbcg7Be03+sOUoru7FH4/FWw8CHn/Vfpmzrsrm4JKR19Q7OKeRzXOff9vxAVX76+3q+02+La+w4t2PzeHiniE7DvnH1MrZEYuqbGjl+Sx6XECvLt2Dj9fmYPygduiS4r+GjpTWYqEnD9jWw+Uora5HhFVC1qtup9ub1/k9ds/85BfBRr22Gn8+PIwT8VwuxonPXlEVcCfGX76zAD9scec5nnKh33mWX1GHoip/f12MQf5YD9v3Kma0fA13xkai9z9foqLWgdk/u/NAffznQUweZoM3UI053BMl3P35JqzqcgfaFeUD28qwNvohxEda0CWlBRZty8N7q7MBuMNK5ewv8t+PpdX1iK37B3PaPQ8A+KX2eny+IRePfrcdme1b4vNbBwNOfx9rZCH3X244hJhwM4o993B5rf20Eo9CmjA7MTERXbt2NfyzWCzIzMxEWVkZNm70T/H566+/wuVy+QShYNi8eTMAoFWrVsYFTyGC5+FICbMJgiAIomlRUlKC5ORk3fXJyckoLdUPlyFOHKsQfJ4rpcghCvov5Cu6TMbNCQsDbrOF2LBQSRdz+VwOLSPdYo5VsOGnrg/i/uT5XH4jE5x4rNW7+F/6c75lZsGBZ1v/F/MzZjV45jWl60XOjiNl3HeryB9X70BSixsTfsTPnaYi2VxsuP/307V9Jt90fBBPp77tE3skOPFBmwdxeMlNKCjxb/NQUZlmfS825hfH1ivCDosq6zlBTM7y7QdRXuPv7wuL/YmlwwUb3l65HwBweewqLOl8JzpYD+m2oZ1dO/mvl4IyXjya0PIH3+eP/sjBP0fL8V76U3gt7SXsr8/wrVu3Nxcbckp9M/oBwNcdH8IraW5RZ1+hf7tW0e6baU7uzMu3u1W4SKsJLuYfAm48WIar45ZgcacpSGb7VW3uGpbNfW8hc9sdLa3Udf+5HPrCrgk2mET9YahJcCDK5L4g/pP2Mh6LnoHRsX+oyhkJl3rskAmAR0prcXnc71jS+U50tuag3uFCi/p9vvWXHR4BKfdzrq58nzl5R9HBeghLOt+JjjU/qMLWVu/yz8zmnYEPcIs6dTIhuMrm4Fw9SkeiHJuGeFQty/8lzzVUZXPA5WKceLTnqF+kUs7EV7eJv0ePltXijeV7UVpjx39X7OOSfh8u5UPxft1ViM2yfG5bDpUhQqzFwo734p6kz2AR7Hg//Uk8kfoO3lm1nxOPXli8C4z5xaM8mZOupLoefx7w38+5xTVoa8nDL53vxFVxS7HxoP/fWMbcApKXmLKVAPwOUXnYcmWdAyWV/mNuEuy+0Nl2VnfYmy33B1z33p8Y+dpvYIxhV77/2jlYzD/7c2SCV1FVPVrU7fB9L6+x45M/3Y7BtQeK4XQxrNrhv6/K6hx4rNW7+LL9Q6isqeaSbRdV2VT590JJk5htrVu3bhg1ahQmT56M9evXY82aNZg6dSquvfZapKa6LWNHjhxB165dsX79egDA/v378fTTT2Pjxo3IycnB999/jxtvvBHnn38+evfWyJYXIiS4b3BRan6/UBMEQRBEU8bpdMJk0v/xR5IkOBwNH9wQwaOX86iNWWN6dsVAUzCYaSfDmodZqe8H3H/7mIYlaRfAYBVsuDxpG85u654R67LY1ehh2YGpyV8pnEdO3JT4PVffJDgxvuVinN9iEwZENiS1A8PAyO26a/flFaF72AHf93CRH1QaHaus6A3oFp6D8S0XG7YgK3qD7robEhbB6wzqE7EbmVHb0KFsPqJE/2AvDDXIjNyim5RcPgtWjmdg18F6CIMjt6KtYwMnzMn5c88hVNQ50FIq8xwjf1/lzrY32r6EzmG5eDf9GcNj6eWcqM2qZZsP8mFCT7b+n0+MKq+1I8VcjOHR63FF3CrI9YNWZveA36IQS8fFrUCYUIcdR/2DWqtgQ5hQh8GRW9HSVOZb7oKI86M2gjltXNLnPQWVeDHtDXQNP4izzWrxiymGi3LBNLeoDK3Mx5RV3PUc+g67cNGGo2X6wmufiN1IlPJwbtQmjI1biQsi12mWOx7x6G+Z2FBcXY9HY55E57BcvNr2P/jln3zk1/uTJrfCAbzR9mW0NhfirPDd2HGUF4/qa8vwTOu30TksF4+3fNbt0JMxIMzvdJS7myrqeOfR1kPl2FvgFwCLquqh55TrwtQiWq1MPJILT1V1DjhcjHv25RxzCzGJphKk2fm8aRlH+XQya/fzgvCefH8bD5fWcnnK1uwr8s1Y5y5bgjfbvog+EXtxb8pnuCb+F2RFb8DEhB+x82gZt93//XaA621RpfvaCRfqkFS1CpZjSxEtuoWenOJqPJn6DrqE5eKltNdRUOF/VrkYg8PlQivzMfSL2IlaJ5/sPL/Cf032DN8HVPifoVahHlsP8+2qtPufKUfKajl3ZG6JvnhUXF0Pp92/vrzWjk7WHHSyupOAf/LnQby/wv8MKa6z46bE7zEo6h90Fv7iRDll/rZQu5CajN3l008/xdSpUzFs2DCIoohx48ZxVnC73Y7du3f7kmVaLBYsW7YMr732Gqqrq5GWloZx48Zh5syZoeqCJiI5jwiCIAiiScIYw8SJEzXD3QFws7cSJwe9sLXfu92sWuYNN0mNCcPR8jpOqDleksPrAheSIcKFp1vPwdXxy7DMdS0W498Ikzk35AJHuEbf5MtaiMEnP+ofsRMft39Md31d+QEs63K373ukyA/8A80i1xhEiHWocYVzwkYbi18EnJjwAy6N/R0/lp2HqbkPqerLnUdWoR4MApZ3ucO37E7HDM39eq+hX7vchhhTNf594Gn/dkS1ONHBegRfdZiBGw48hdVV+jPefdpePebQEjuWd7kDPXf+iCq7P/k4AKSY/H33CjYWUS1WxpsqsDMvAt4sylbRgVsSF+L+lE+wrqqnr1yP8AOY3/5xvH3sGrhkLg/m0hZgRDjhgsRP8w4gVhY6ZhYcXPicnGPl5YDOXENhgg05RRXQy5h9RdwqXBG3SnulDMtxiEfrc7TdoPFSOV7aeBh9HC5AEeG5pttNAICLdr8Dc4T/HDhsZbCa/Pfv5twSdJKluro58f+wqrIffqvqz21vy6EyFMpEj8X/8BNAHThWreuqjBXV7bfXy/KoyULRiqpscLoYzDLn0d68YgDJ+K7j/WhjKcT1B57BH1V9IGg8D3/ezucK2ygLcdx4sIQTTNbsL0KExT+eHWZ7HcPi/IJxVvR63+cjxwqhOsiy66zI44h6o+1LGI51QDhweYcOuHTv69iVV4GRZn875AKW3emC0wUs7jQVMaZqHKuLBWT+jKNl7u22Nhfix07TuEkkLYIdmw+VcS0qtvn7s+NoBSce1ShCm7NlTqSiShtckTW+LlVVl+KN6BuBaKDLtm+xbGcBImQC/c5j5YBn0r7SGieyq/x9+nUX/0NITb0TMeGh8/80CecRAMTHx+Ozzz5DZWUlysvL8eGHHyIqyj81Ynp6OhhjGDp0KAAgLS0Nq1atQnFxMerq6rB37168+OKLjZr0ujHwikeiSM4jgiAIgmhKTJgwAUlJSYiJidH8S0pKomTZJ5mwhiTM9gygUmLciXGMQrGCJVJoWM4jAcydlBpAlvgFAN5f8GKb132f5eFBXl5Oe833WUtc0uMiA9cPAHRThCbJBzYSXI1yrALxYfqTkODk3DXp1qO+z5fG/u75/2p8kvGoqr7d5R/oRYk1iJN4F8iwFuuVVQC4j+OMlLmIMbmP9zlRW7j1r6a9oinU3ZQe2H2kRM8pc1GGvy1eWlv8uWm+7DADiaZSzfpxpgrUO/mB7G2J3wAABkWp23hn4pecQKd0M3nxihdyoQkAWkrBiUfOev17453053ClOEd3fbC8kvYqvmyvFhKNKC/ah/faPY3zonjXjUlwYn12CWw2fUfUgIgdnGA1JWYO+kXu9n3XEqRHxawFANyb/AnuTvKHwOWVawnPDDNbvYcP48bh/fSngu0Snol90Odg25DjD/EqralHy90P4MGUj3zLXk77DwCGNp7r67P2M9HOchRxkjofl3RkIff9UEkNnkydg7uTPseynYWwOo/ho4zHMD9jFlw1eVyuoglxX3N15c8UZ73/3mxvPYwP0p/kQmUvs3yC99OfxPAYv+OsZ7g7pPKNX/dxz/09HsfWfckf46HEOfh8fa7vXk40l/nKhQl1OPvwbbgydjm6hfPPO8AtzG4+VI5ImbOui/A3/tfuGdyV9Dnqfvs3co6V49KY3/BOu2cR5SmXYi7Ce+lPIaFypa/elsNlqKv1X//ZOf/4PsebypFbUsMdj2Sz/15/IvV/eDBlnu+7N8eSl5r60LqZye4SYkTmBARAIPGIIAiCIJoUc+fODXUTmj0NETW8oRutYsPQoeAQBmjMZNRQWgvqHDFGCBrt9YYkAUCU5P9lO1pDPJITJQWfbylQbqY2Fn5iGnm4mFWoh2gQttZYDI7ajqzodahn/nfidha/88HmMvmcQOe22KKqL3flJJtLuKTQAFDPtIc9yaZi3J70je97uTOKW39l3ApNgSQ63Iw25nxYNNxJenDTvMvoGV+F7xHFiUcRss9W0Y7HU/+nKfS0lMpVLpU/q3theLS2WAaAE4/0xJ8oqRadzLmIlHgXWq+Ivb7PRuJRC4Pr0yw4uWN+vKRaipBq0Z7SXo/nWr+FodEbMTxmHZfE3SQ4EcWKINaX6NYNF22GIZxa97dVqEcbcwHuSXaLxSsr+6OembGrLh2AAItgR4KpFEftSciwHMUtif8HgH8uBKJf2BYs73IH+v7zKY7ml6CV2Yk8eyLaWfIwKeEHrmyGNQ9DW2zklj3cai6+KFFPU/+/9OeQvvVHJJuKUO6MQoq5GBMSfgIAvHtsLG5O+D9c0MItwt2csBDP57sdWq3NhaptpVn87qoo2fPozbYvokf4Aa7sAykf6/Y10VTKTVSw9+BedLBW+2Z4fO/YWM1618X/gi71v+DVtr/gtpxHVOs7WA/jt5KOSDLxzq6RMX9iZIw76f+mqvZ4vLV7JsZsW2u8kD8R9yR9huHR6zEc67G4YAFqXOGoqXeitLLC57yrLtkHxLg/d7Qewl8lLRAR5xePzo7w50fqGHYYHcO+xjvH/oUK2bMozZKPo/WJqlnqTjVNxnl0RsIYJM8/IqJEOh5BEARBEA3nrbfeQnp6OsLCwjBo0CBf/kc9FixYgK5duyIsLAy9evXCokWLuPWMMTz22GNo1aoVwsPDkZWVhb179+psrengdQycH74Wy7vcgWfavH3C22yDPQ0qLyi+XxW3FFOTv9IsG0jwiZcqoJcXRbWtAELUQ60+4r7HmPgEzIJBcvHGJEKsQ5TkF00yZM4jrRAyOXKx7f863Yt4E+88sjPtH2pTFIm+K5yRqjLDNJxbKWwPfu92C37tcrthu+ToOY9i4RYKjNxk3cKzNcWjOFMFrIrcX9EBrp1YmXvoG40ZvQDghTav4/tO96kG8i1VYWva11Z0A8IqTyUdwg5rLo83VWB115vxYKv5unUjxVrDPEtaycNjrS53fh0P33e6D4s734Xr4905wt5s+wL+6HYTeoTt13TENIRNPcZjQ/cbsbbbJGRYjiBVJx/VvIwnuO+jYtZiXoZ2QvtUcyH+6HYTPm//KCcItrEUYmzcr77v4zyfL4zb4Qvzk5Ni9oty8u10sGqfDy3Ojc/Bmq6TOEfRhu43cuGp7Sz5GjWBJFmom/J+AYA32r6MmYn/MUz87xWO5O2OlPwi0Oft/aKUXNxPk4Xfftz+Mcxv/xjnPBoX7z+OXhJlIlZW9Dqs7noLHkt9F1mvrsKrS3aryp8qSLEIIYIsflwQ9adPJQiCIAiC0OLLL7/Efffdh3feeQeDBg3Ca6+9hpEjR2L37t1ISkpSlf/jjz9w3XXXYfbs2bj00kvx2Wef4YorrsDff/+Nnj3d+VFefPFFvPHGG/joo4+QkZGBWbNmYeTIkdixYwfCwsJOdRcbjTaWQtyS8C16NiDcq7ERwFDnsvgSfb+U9rpu2bs8v6TrEWeq0J1BTMnYuJVBtxHwClNuTpXzCACeb/MGJxKlW44alOa5OMafSNgsOLkQQAC4SMeJoxwsKhOr65FSr3Y/BUJPeBhd+wQeE97BM631Bc0osRZaHp9zYvZieDSfUFqeKFsL+cC0jUVbYDhPw92l5Mq4FTi/xd+a69Ks2oP4UGOU60wvAb+XlqZyFDtidNdfEqtOOD4sciWqXeq8eL3C9wK42OdqmZz4LfLsiYb7bwgrut7WKNv5wyME9Y3cjbc9U88DbseQXBBKNJdhRsqHGBB7RLUNJR+mP4nVVX2xuaYL7ExCsP+qfNJmasAy8lBXOZLM9ScXc+T8K345llUMDKot3vC2o/UJvmVnRezFWxmvod7h4J65T7R+l6t7duQOrK/uYbj9z9s/gmzpbKwuTMIlMe6Q3QkJP6GlqRyu2isAdAmqnY0NiUchRC4eSSYKWyMIgiAIomG8+uqrmDx5MiZNmgQAeOedd/DTTz/hww8/xIwZ6gTBr7/+OkaNGoUHHnC7DZ5++mksXboU//3vf/HOO++AMYbXXnsNM2fOxJgxYwAA8+fPR3JyMhYuXIhrr7321HXuJDAz9UMghIYIk+BAlSs84CA1GFqbC4MWOhpKnMxdYhXtjZJcPBiU7iL5r/oNRSmK6IkkSudRvKzvRkho+Dlsb9UeWEexIizqfDfa6wx8AXeoj10j9O7qmO9Vy5R9UhLIxQUAZp1cSEoSFA6vQMtDjdz1khJlUFCDJHMJ6ljDf/C/vMUvqmXJ5hIuSXWP8AO6IYCnC3LRpVt4jmr97UnfBrWdWFMVLotdjctiVzdW03zoiUdDWtXBm4++u0bbvbSz5umukxMl1UKAC+3j+UCuS1osC6p+J0+OKj2SzKVIwhIMSgHy7f4s7JfG/o661ucEtY+TAYlHIUSUiUcizbZGEARBEEQDqK+vx8aNG/Hwww/7lomiiKysLKxdu1azztq1a3Hfffdxy0aOHImFCxcCALKzs5Gfn4+srCzf+piYGAwaNAhr167VFY9sNhs3u1xFhXsAbrfbYbc3bDp7I+x2O5ryz20ZQQ5MgmF07B/ofoJhLnrIQ5OsQv0pSZgdLIet56CNTe3wOF6UzqNpyZ/rlDxx/hWnP7A0Eo4At5BW7QoPaj9RijxFhB95eOOf7S9tUN3GFDsuiv4Lb7R9yfe9U9ghdAozFhROR1xMgEMMh4UFn4PtZNLeoi3Qpoj+++uSGP3z+HCreUHt56yIvfiz20QUm7o2qH1euoTlBF1W7vACAHNkq0b9dxVA0NsjxSKEyJ1HotSUX4UIgiAIgjjVFBUVwel0Ijk5mVuenJyMXbt2adbJz8/XLJ+fn+9b712mV0aL2bNn48kn1TkzlixZgogInfm6j5Mxjbq1pk16I4pRerSz5mNCyx8CFzxFLCs9FxMjGk886hMROJ/XmsreOKfF1hPelzxXy/EQKIk6cfryY9l5yLG14nKcnQznzammxhUGF7PCEiDP1qlCPkObnITjCDMNRLK5BMlwh8v+U9telfjbiHYnENa5cUce8vYsClywAdTUBHf+SDwKIVzYGs22RhAEQRBEE+Xhhx/mHE0VFRVIS0vDiBEjEB0d3Wj7sdvtsC+MgBknNlBxMBGmU5QE+kzgwuiNgQudIpLS+wLqyZx81IpxCHeV6hc4DsqcLU54GxXOCEQ3YIY8LZThjvXMrJlEmzj5zK/4N86xrNRNwK3kq5Is/FbVH1LXKbij9AJu3c7adADaoWANxc4kmD35fW51rENs3qd4Me2N49rWq/njcV/Kp4Zl6pkZYRYzEKJJwOSzMIaSL0pG4OnW73DL5OeioZQ4olGYdAO6lrylWtfvnEvAWg4+ru3q4XULB4JmWwshInO/tNiZBEmiU0EQBEEQRPAkJCRAkiQUFPDJPwsKCpCSkqJZJyUlxbC89/8N2SYAWK1WREdHc38AYDabG/1vZfirqLWmN+hYKanUmFGLOP25J3c6XJY4wzLrY2bgubxJuGzvfxptv7Uu7ZS+2bZWQW/ji5JRmsv31nfA77FP4MuS4Q1u16+1w7DeOqHB9Xz7Nl903HVPJa8VXNfo29xtHsZ9P5Q4Gbm2ZJ3SalyCBWXOwEmTFthuxifis/itqj8AwGFWT2RQYG9pmIi7Iayt6o2Zh+/Ei453YTGZUKuRrFvOxOwnsLpaO0l0rcuKK/e9hE1Rt+rWF+CeCEDJkvLGFTe02JLwKN4uvDpgOTuTGrztv2rPwjuFV+qufzn/39z3Qns8rtr3PFYnvIKbc57ApOzHUeX0u27H7XsRb5fegoUJxhMhAG7h6Ip9r8ARnq653tSi7Un5tzUYSLEIIYIna5eTSTBLyslbCYIgCIIg9LFYLOjfvz+WL1/uW+ZyubB8+XJkZmZq1snMzOTKA8DSpUt95TMyMpCSksKVqaiowLp163S3eaqpEVNwuNXdx12fQcSMw3cFv78Ag6/jxQkJ7x9r3kF4G6u7wuYy4Uj0ZUGV/7n8XDhNsYZl6s1JePfYOGyr7YTDrJNh2b/azQtqv28VXg0X1APQKQcf1iitTa49Ff/UtlevEATsjr4RDx1u+DVtEezYEHF7g+t5+S3qIZQ7eCHVyfzDw+MZdAOAQ9APVV1dPwyrox4KeluHYsbhmF1fMLQpElhPyn5ct+yvYdN9n3+LfACr6/wOoEMZz+HVgn9rVdPEKZhRGoQjbaHzFmw1XeL7LkombLP3RbUzDBUecWFddU8un9XzeRODbocSAQyflIxGttgPZklEjY7w6WVl5QB8UTFWc10ts+Lvmm7YGPsgdtd31iwjCi4uj6+Xu3Pvb3jjAWzHBbrrSiIH+T5XuyJwIPF2CLJ8bA7Z9bon3i94vZDnF1jrXMElPF9cMxLP59+kuW5j9DT8t/BabrtVrghsqOmJ3Jhx+L1mIFZUns3N8LaxpjsW1I5HccTZWF3ZR3e/DibitpxHkFvfCvaIDO1CYfo/5JxsSDwKId6wNQeTIIkkHhEEQRAE0TDuu+8+vPfee/joo4+wc+dO3HHHHaiurvbNvnbjjTdyCbXvueceLF68GK+88gp27dqFJ554An/99RemTnVPgSwIAqZNm4ZnnnkG33//PbZt24Ybb7wRqampuOKKK0LRRW2Ehs945GXFWTnYEGCaZDl2V+NkeagzJWNukV8keSZiA57Nu/m4tlUOtXvhZLAr+WFcue+lwAV16LLtG9UyF/O/895/6F703fE5SqL1B4xy6pkZDpOx8wiSX+w7ImgPeL1Uxpzn+2wzGFRm17fGN+23YVVlf275rrp2xm2RwSChXNOpIsD9G7L2WGBNh8XYo7Mfs+BAvRTgeBghmlHPeMeBJAvnfPDQPce12XpTvO46AQK2Rd8S9LYEyYI6pi/gVrr8YbGbz/oDFQauwnzzWb7PdikOe53dfN8tJjHohOQAwAQzyh2BxSNJFCGJ/iG3KAh4uuY19NvxKc7e8Qm+z1iHOceu4oSPfHvLoNuhxDszoiQKkESBE48+rNAWQ2qYdj/qPMK5SRJxd8nb6LLtG3TfvoArI4Bxs8YBwJPW1ahj2qLVmj57UOnkj7PcwVcpJnLrqlyy8ymF+cSf7fU9YBJFHKn3l3/QtQS9//kCXbZ9iz1JfqHwYH2q7/POWh1BRoEd2tdCoaMltsW5hd4C2Sxo3uNsFkWYPRFFypA1kyjAIgkodGjfs790Wo8ROV9hQ01PAACL1BCbAe4Zd6oh8SiEeG9uB5NgEulUEARBEATRMK655hq8/PLLeOyxx9CnTx9s3rwZixcv9iW8zs3NRV6eP6nykCFD8Nlnn+Hdd9/FWWedha+//hoLFy5Ez549fWUefPBB3HXXXbj11ltx9tlno6qqCosXL0ZYmPEv2KeUE8gVaTKHwdmAV2BLI+XTYIKIQtlgg4kWMIi4OXsWci3n4LAzeCGiVowNumyl1VhAAfRdAlXhPRo0oFZi0xj0y50iNS4ralzhKEq8Bmsqewe3Ucm4PUwmLH4v3GdQEjBZojB6zxv4uGg0Pq7wh0bJB6S+7Zqi8ODR+/FFyQisqeyNvR3e1nQj6bdL0nGxCYbpK5zmOM3jCAAW0Q6TJOLBQ3cfV5iQKFlU4pGcQK4VXQT9bQqCexBd5QzuuhJEs6FbpFImfJjMEXAauKXqzCl48shkPJ83EQ5THH6yXY15RZdiYvbjsJokzTZNOPAkVjkvx1dRc7jlriCdRyZRgElmEpBE90RJNmZ13wsRbdzbkz2TqlzHP8mAV/wziQLMEi8eVaMFrtj7iqqOQ9C+vrx1TZIIUbLAxtz36/YOc31lBDAIjBePHFKsbvtM1jhctf9F/Fh2Ln6vGYzV7b7hrsFqMcH3uShsAF4om+HflxSGK/e9jM+LR+DF0gdglkR8V3oR5hZdhg/E/6BeikWFM8p9XE1ReLFoKl7Im8AJRovK1dPc/2B+FLswiFtmFbVzidUzq+9+LXT4n+fea8ckCTB5IopMAv9vh0kSYZJEXaHaYWmNCuYXQ4XINr7POY4MoNsDwBDjHFQnG1IsQojPeQRyHhEEQRAEcXxMnToVBw8ehM1mw7p16zBokP8leOXKlZg3bx5X/qqrrsLu3bths9mwfft2jB49mlsvCAKeeuop5Ofno66uDsuWLUPnzoEFiFNKgFlqy1z6bgyzJMLFGiAeCXbYDNxHC0qygtpOWcTZ+Ll8CACg0JXqe/dbXjkIPyV8hP/angi+TSz4qdh3pP0Hh+v1nUrzqybg+7KhcMiOSbHDPYCpiOzPOSIaA/l+aj3uBLMpDPcemq5ZvhR8Hhqtd2Z5+Igg+1W+VorHMlHf5WIxSdhR1x6zjt6JMuZ3e9x/6F4UIo0raxIFlDljMOPw3Rif/RwqUq7V3a4WDCJqNIQ4Bl5cUCGaYdJJumsR7JAkAV+VjsCtB2eiXGiYI00Qzahn+td23fGGbArqHDheRAEwiSJcOk4r1aYCiEe1zO/mMksmQ2FYEM2YWzwG7xz7FyRRABMteOLo7VhZeTZMCqHFy666dvjYNROHLEO45WbRGTCR+qKyIT4HkBdREDjTQJjJ/Vl+n1UbCGs1AYQlb/4hSRRhEvmwNRdM2FzbRZUHqcClHQblPe5mSeBSrFQn+h2UggDOeVSENj7xJN8jlpc5/YJImMWEXXUZmJo7AzOKn0VZ1GDueWwT/GX/aPcFNjjOxx9VbmG5pPVk7Khrj4eP3I0yJMIsCXDAhCeP3obdpgt5kU4Q8E3VGMw5dhUn8hyxJ+Gfev+PJQCw0XodPpee45Ztt58FLT6t+jfMnv3InUdekd0k6TuPzJL+vV7hjFCJyGEW/3liEIC+LwLp12vWP1WQeBRCBObPeUTiEUEQBEEQRJCI+oPaP8LvxD+OfrrrLSahQc4jSXDh7J2f6K7/omQEvinRTzz87rGxuG7/c9jR7g3k1LfG+bvew0zbfMhf/SQRcGn8+j9i9381t1klqp0xegiiiJF7tLfjxn0sDtb7Q0dG7H4blxz8FMwS1+jikShLrusVJ8ySwIV0rU//DL22f4khOz9EkcA7skRRwBZbL26ZPFRJEP1Cg0kUwAxcMBaTLL8PeEdUGfhk2JIoQBT8J807mHYGKUQKgoRqp1qcKGEJxuMAyQKzzoxqlSyGG4xWCg3LhaLlPPLmOaozp6LWIFxsVewz2FXrPzcvOt71fdZKoOxf53ZnMKbdZ6cUhZuzZ/nLi2bOlaPEAX/7TWazofNIkInObkeQf7uSKGg6fuzMDFEQIEphOHvHfN/yMKEeZQ7jmSTvzn0QJoV4pPwebnG3Vy5EVBm4/WwwdoMd8wglJtHtgJEne2cex6ZLcextiMSq7n/jncJx3HK7R1g0iSJ3nXnbDLgjaeqsfqH1dfMXvrLDdr+DRem/4aAj3bc+zCyrK7hFKSYTEutEv4grSGEQBAGTsp/AsN1zYE/yJ5WXRAEmmdjidfXI1wue7cqvcScT8Ug5n0zfJAqwmRJwzs4Pce7O97Gq65845nKL1iWec1zvMmH47rewzHax7/wVOWJ92/CHrQk+cUmJ2w0m4phG2Nq5Oz/0HDd/XfmxKnC11tzmqYbEoxAiD1sj8YggCIIgCCI4BANBoMTUCS5BfwBpEkVuwF8upemW9VKhkatmrXMkdltH4++arthVl65bt8oZgbXVvSGY3IOL3PpWsAktICrcCFqhI3ts/HZfzR+PHc7++CXisYBt9iKJ7uSyugjuY3FbzqMojz4PV+17HsXOWBQ4kyCKguFgXM6RDrOxpHwwNtfou9RuzXkUlbK2eF0vVrMEG7Pi3WNj8VPNZSiNHopKVySO2pNgAj89vSQIeK50JresUpYXRTD5j6MkCnAFKR45Bbl4FIYFpsexRxiEidlP+LYl04587gLtPEZu5KKWCyI3kK90hmNNZW+8W32fb7B9S84s5JgysZZd6isnilaV82hyzkysruyDj+umcvl0vg/n3ROlsVmYclA/ObUgWlDv8h+fn12TMP7As/ijqje2dvrccKauUksPznkjD6U0GtUIPueR9jDUFt6RE3EEyWIgRQEuwe+cEgUT52wD/O4XgBcWtRxB8hBNW3gHfFR0CUqcMe5QJFHwCTOAO6zJKGyt0B4HB0xukYMLW+NdPOEegeDtY1djdVU/LHbdg0P1+rO+2RS5hPbUtcUzR2/CrTkzURZ9AZ7x5FETPfutll1zgue5yDTOkDMsVTXjm1dkN0mC73oHeFFDALCn43tYU9kb1+yfDacp2ifiVLsiUB/eHvIbJ8zs3w4Dg6R4Hh8198U3pRfijYJrIEkiBLhDXffb0hBmUghPsuNq1jjO8vu1qvvzWFyeieUVA2GHFXbRf99Knv4dsSfhsD0F9rB037obDjwNW8sLcPX+F7DX1g4mSfIdi1JnDP6y3oD15mt97iqTJEIQtO8AQXALeu8fG4tVlfwPHBWuKEgS3+YwszuseU1lb8yrm6a5zVMNiUchhBJmEwRBEARBHAcGOY+YaIYL+qE4ZokfuBq5JIx43/kklsa9Bgb1QLhUlkjXO+OO3LWidLEIggBXgCTgP5adhzcKr8OL9rdQYu6C945dAQDICR9hWM8U6BXTM6DcZ2uLfb3+z5es1d12AY4g8/pUtrsTtx6cCZuLPzfrqtzbW22+CUsqMhUzZ7kbZ/EMxp7LuxlvVt3PCSI7JLfbYL/N/cu7JAJFLAn35d7rK8MlSVYIBEbXilUmHjngF0pqXOGoENvgQ8u7WFk5AABU4SZe4Wl+sXsmrTopAUrGH3jG99kFiRMnev2zAOOzn0OpkOwbByyrGIRvYueiRPAn+BU0nEdLKwbjhuxnUI4kyCdsLjO3x3fwJ7ne0/UL/FTuTwruMPNtFEy88+hHcSrWV/fE9Qeegy2ym2HOI5epBVeXyY670T0lgsEkCbphawKcPscL4BZ8mIG7i8muT1GS4JR9P9phNh49PEW2nnelmST+nqyROcOOdX4Bjx+9w7OOd7UAQJhg48QzpQPNK9AonUai4rv3OqpwRmFSztPYI1zIhXkVm7py261XOI9uznkM7xddiSUVg7Gv57fItyfI+seHrVk8OXiUx97FGEyiqDpr3nAysyTybilOPGJwRHXD+OznsK66l8p5Ixm4aRhzt1P+/BRFEdMPTcerBTeoBCB5XQAaziP+OMufsa6u9+H2g4/CARMYAJH57yl5kmvALSZ5+aeuA2rPW4LNtV1k+/Kv/63Fo1gaMUtznRYmUUSlKxITsp/CMYkX2s2KHMgWScTyykEYn/0cSgTeCRkqGmf6COK4kOc8Iu2IIAiCIAgiSEQDoUUwgRk5jySBG+gdr3gkCJLPPSQPA7klZxa21HTChu43AvAPIpUuB0kuJgkA00la62+nPxGuJAh4MX8C/qrujvPPuxrph5bo1wvwkumQhWvJxRSAQWqA88giaQ/wJ+fMxH1n5eGQdSiAI1yoh6+uQuGSD97Wm6/DzmNWfHrUPTOWKAgQBHeuEy9y8YgXCEQwQX+4Y5H8fZM7WGpcVrcLQN4mUeS+e/v734JrUB3RCwN79MHwXHf+sDVht+Nv11DUsyL/9iFqOnkkRQ4cURDcbfZelqKZcyzJEUVweVIkUYBdiPDV9V6f5+z8EBnWI3h7yHZE538pK2/mhBqlc8Mo55HTFM3VZYLV32ajsDXB7RDRyzsmMF48gmiG3UjAFOTCg4kLsxQVwqH82pAUgoikcB5JsAMeoUaZ9Bpw524qk4nElc4IxJqqfN+9zwRJEfKlPN/yzwyAUnswoRYuJkD05JGyC/y1IO+vMmzLLApcgnpv8n9lyKBXxFE6kvxha/oiTg0L5wQTZfiYUhzinzHudsr3K4p8XTly1xIAzsFlkgQ4Xfp15c8nxhhE5nc0SqIAs+wZZBZFTZeht65826LIu7KUApAcxhjXZuUPBkoziVKwPB04/VrUjBA94pGTSZwyShAEQRAEQegjGiXMFkyGs2CJAhROoYaLR9MP3etJ/OvJeyPb3rKKQTjmiMfRtAexv64N5hZd7tmvYsYl2aufKApwGgli8M+iJIkCRFGAnZnxS8UQOE0xunX+rumOuhb6+Z/sQiR+tV/u+25WDFZEQfDlwPH372zNbVlM2sOKClcUdkoX+UKGtPJ9yEUcxsAJa4Jowub6gSh1uvvpHWDJ21XpjODKy9uv5eg6VJ+MBw7dw7VZPkV9jSvMM8jnc9XIw1G8dR0wYYPjfDgs/lCjPHMf5EvduYG9iwmaM9cpnSjuffr7IIkSZuZP9+Veqc6YKlsnqISJv4VLsau2Hd47doXvmjtiT8LvVX1R2nGWLyRqrX0YTJLIuYd4QVNQiV2PHbnN95mZYnhhUZao3DDnkcBgkkTN0ClvXc55JJmxvqon/q52Oz9KLd2wpaaTvx2ca0Xi2iSK/LWrznkkFwHAuZZM8DtTJJlL6cW8G1FhSsdy3MCFYCrzJXmfMW7nkbyN/PmWiwmMMd9z4ebsWShDCv5sOZu7jpTOI4eO+Od1HsldP94ZxJROScagKlto7Y/NNV08bVS4rswibst5BIfrk/BAwROcYGKS+P4FdB4pxHxJ4I+VnmjFGFSijfJekOs4SjFJfo2aRIETl0ySP1+SVv9NCoHLpKjr5d8HngaLaIsbDjwFAHAp2rwy7nmUCq19LkqTQrDWE+RDyenXomaE4BOPRJXSSBAEQRAEQeggE1qqEY0lqV/51wkm5LAe+lUFfjAjnynICO/MQ/87diW+KR3mTqLrGdlo5W8pTH8Yw/a8g3JPaItcPBIFQZXzyCUYJ8KV4BePlA4RPf6d+wokSd95szh9BepluT+UApDSebRXOg9zi8Zobss/0FG3RxQFn5vo96o+6rqy/TIwVf+UQhvAuyc455HJL9CYJO2wtfN2fYAFpcO5/YrMn1fIxiyenDjKBLx+OOFBgC+nlbuNEkwiPyh2QcTOugxVW5QilSQJnFtKFAX8Y+uCfjs+Q/rWH1Hb62XZftUDdYcUiVF738KzebeoIxsi03Herg+QvvVHvGV/FiZJwFrPTFbe+r7+Sbx4VNprDpZXyKYzlxTXq0L8/KV8sKqvgCdhtmgQtqZwHomSBQ6YcOX+VzDdtBnLM5bgN0++GBdEznkkSSYuzFKQzJxIZTLIeaS8j1wR6bJ6/nP09rGr8WO7VaiRElAiyxHEhU5CZhxThscJgsqpI6/jXbW8chBejliCoqghnKhlBy9S2TnnEX8dKftk9oSt/eoRgL1CImNM5QBa1uZb336VYorVJOGXiiE4d9eH2Grrwbl2TIqcTmaFUKgUcZT5rySFMCPo1GWe8Ed53+WORVFRV34sGANKYtzhsFtqOvlyHvnbrDhuiu/K/XC5l2R5i36v6guMycHqqn6eNvPnqDKsG96JXoJvy4ap+gdF2eM0yDY6JB6FEMHzj5SdmUg8IgiCIAiCCBJBFn5ih9WXCBYAIEr4g13KuSTkKN3eSpfEK87/cfl0vIzY8xaOtH8O/8l3T5UsH3xqheBIiv0o854oxSS5Y0a73X7xSBluo8UVe19R7Ve9URNXXzl4kUQ+LEaEU3f2NeWv88rteLf9fdkF2NVmNqaVve9brwxlERWDenmzJME9JJQPSKtdEbgp+zE8VvIUmMXvbBIFAZAlzK6M7ItJBf/zfefEI05EFDi3ibcdcr3DpAhPEWTOG9HTB855BAnrq3vigUN3o+ycX7m6kkJcgMC7p+RnRXm+lUIb5x5ShVrx16BJFPF+0RV48shkLExdyueMEQTUyUKeJDhxxJ6Euw4+gLeFt2E2ify9I8qdRy48cHiazy0lJ1DYGlQ5j2TOKM919N/Cq/HU0clY3G6lKmxNLnZKCueRpAin4mZb8xybq/Y9jwcO3QO09DvsVDN7ecSkKlcEbsmZhbKzv+LyH3mPgresUqRSOoS8uF13kK1zu2nkScCNnUd8G1UiiMdN9fjR21HT4wVcvf95935hLJgor3UtAYhvMy+6Ml0Rxy1aya8FUeLPifxyVwpA8v2YJZF3QClcS8ok1tkd38LTR2/GLTmP+WZB0+uvsq5yP2YTX1d3v4zxuZUUzxhlXaVYdjpA4lEIEShsjSAIgiAIosHIw0/sCOMGmBDNgGDC/OLLkGPjk4zel3tvQPFov9gf35YNg83Fizm59a1Q0uZO1HlmO3LPGuV1Hmm5bfjvcm1FEpQzPfG5PgBgdt5E7rt3xi1JEFQiQCWLVe3fm+BVOdDj28T/gKlM9iqJIud6EAU+pIgra5Ao1u2Q8TuT8pJuwn6nPwEt5zxiUIljos4A0ouDifi1ciB+rztPJYYx2bWRl3o39tk7+r7LB8h/28+GXWyBPz0JvgM5U+Sni8E9rbh/v+59y4+dd4C8oHQEhKRzuW0pBSAmExJVIqTqHCnEI4UAxO2HE6nc+7UzM+YWj0FlWCdF+JzIh4R5xi0/lF+AfWIm5xAB+JnMBLgTQH+g4VLTSpidy/yJgwXmUjmP/P1zJ0W2MSs+LBqD2rAMXjyS+NnWRMnEu14UYiDn/vN83lDTEwtKh6uPq+I+8Z6XZRWD4Eodw81aB/C5zpQ5ciSF6CGHF4/cApdcIPqR3cKVl/fXzPXPL+L8WHYuGCSsdl0GwB1i5+hyL/LsiQDcCbMlUQCTPQqVThz5pSQ/bozx4VVmSVAITwLmV98KwO3alIspDGoXmkmUi3+8cKqsyzuc1InJ9cbXDAwIS8AHRWNxzBEHSRRV+ZP0hubK55OoDG9VuLT4/WqEFiquM3ldrr+nh3ZE4lEoESlhNkEQBEEQRMORDVQdsPAJsgWTT6jZVusXCrps+wbflg1TiTrKeADvgMPO1OFO3ABKFnqWb2+pKmvk+lDOIiSKAjdorG11Jf537F/8vj3tdIej+JebRAEbXRep9q/XDm6bksS7fBSDFUmR80iZj4bbj6eu1hhHlSBc5XDiB4XKgbv8lGkNCOWDa+UAErKcR4KZDy2St6mWRWJN72247sBzvu2och7J6qrcGCa5W4i581hxYWtGDi++v5WiX/RUiZAq5xHvZJCvVx4r5TlQ5lri6ir3C4fvs28QLPjPtsiFRxqMdD0Jo+XC1Iv41L+aObnzyYtHUA3UwYU8KZ1HAkpl7ielQ0bpaJOjPEdmAxeaJApc7ijAnzBb5TzSEBvkcG2S3M4UuYMtDx1w7k6/a49zHincUd42T819COsGZKMU/rxcynvdJIrIs/tn4+OcOIa5d5ThYyIfeiaJOODqii7bvsHsvJv4mp6cR/IJB7iE2QFmLlO6hbhzJOhJOHqCl76gx9UFU4ljcvHbsC5TH1ejcDl+v6cHNNtaCPHNtsYkClsjCIIgCIIIElEuHglWiKI8zMXvppl15A6c17Mb/r2iO2zMHVYTyHnkreseDNby+1UOvj3fV1YOwNaYO7GmuK1/vUHYmiBANcgXRQHeyClBI0+PBL/zSJmL5yvXNBwstWFCwk/qekbikWDmRCuliCOKioTEcOmGrRntRxTUIWB64RmA4tgow9Y8deWOEnmeG5NiYCd3HjEpUj8PCgDBFOHrr9Id5t6vgbjChUABJoHPF8XndZHvl6nCjfaahuKtQ1dhW01HTDlP/zpyf5d91myz37WgdEwoQ2gkhbgix/ujt7yd8ntHPvD1LtdKnC14nUecYCDJRsdO1MvFI0VomTokSu40krhrQRLcDrz/5F+P1LReiFVeg/L+GQh67u+K0CSFGGhj+s4j/pxwxTSufflnd3/lCfkFCDhsT8HTR2/BI5f1hWOr/1iZFefeL/oIgClCMXugwgEkCfilIhNzCv+F1A5DEa64j/TwCkDyslr3uvf5y9X1XPt694Y77FTHxcN40crtPFJsR889BLULTS+3knq/Gm4/hZNM17WkENo0Q2N193t6yEfkPAohcvGIwtYIgiAIgiCCQ57zyAELIM8XJJp8g/wyZzRKur2C7TIHkvIFXSUeCV7xSP0bq1IA8g84BWxLeBCbhWH+Zhg5j5ShZwK/XpAlb17lSQ48v/hSXxvkA0xREFCPcDx+9A5Ve5VtViJKEieIGDmC3PtyqRwW8nboIYkag0Kd/XjzoPjWCf5pyr3bUqPdB0kUuHAqmKN085Eop+BWO4+U+Uj4UDuzKKDeE+pYEnYWJEHghAy9/qrb7J7q/KX8CVhccY4n74vRseMHzMrrSk8wUH5XD/rd/z9S7w5tElqPlvWXqWaG4rbN3Cqo/N5aV+VOYr9WuNIjGGgP1lUJsxWXlaQUBxXCg9J5BACvF16PLaZLDGfPUx4bpXimFGa4NomCKmzN2z9BFaKq2I9B2JrXhfd6gTvPWlHC1b51HxRdAbEzf8/Lt6VMpq7OU6S+9hlEvJA/Ednhwzlh0WISVXl/5G2Uu3gEQR22ZjTKNTrubpFOG/c9p79f5b3A12UK15I6ybcRvOgqqvqrhzJPkzJsTfmM4eoatujUQeJRCBE9D1YnOY8IgiAIgiCCR+4mgYlLMCwIJsMQKfXLOVO4INyfDyryJQFQhbkoB6NGg0Sla0mZFFr+w7IoCxG6Ofsx5J23Bb9UDNHcj9FgJdB6UeFwUrkvlH1QOI+4KbY9dQs0QviUYWvKcBTlwFQVtqYQywDeeaTXZkkUeGFRitSood62ty4/xbqyrGK/ooB+Oz7DoB3zYLckQRJFTsjghBZleKDCmaIKvVPsR15XlTBb1k7DulC7L5QiFgBk7Z6Dc3d+ACm2O1dXEvmE2ZwA5Pl/udM/k9+/s5/B+bvew25xiCfnkbbbxBWWyochauTI4doon21N4EO8lDlj5OKKKneUwTWo/K7MASRpOY8894Zy9kDltW/kPPLu6/OSkbhw1/9wsPNbhkmg5dtWhlcZhooqjo06gbSRIML4GeMY76RzO4D0RByvACi/3hU5jwzEFHWONoVwrNdmqPNDyUPPjMLl1GG1yvxQAQQvRYif0TV5OkLiUQgRPC8GDojkPCIIgiAIgggSyeQPgWCCCEE24BAUSaCNRBzA7Y6Qh4V4q04/dB9qE7Iw/sAz/nWK7SoH25yTwWAwqgzFUg4C5c4jB0xgLTpxdXlRw/gdMpDzSB4OoRwkam1b7gqZV3c3VlT0x+Scmb7+PJN3C/6oHYjdnT9WtFn/nMhRCwRq8cgdMKUjHilcS/L8WKLFWDxShZQoXQGysloD9ypXBAocCZ5rgxfX5O4p5XFVCiKiYnAqR0u0kn9W5rBSCmL8fvXzJXk/17IwHLYnq8QGs2Jb/PFw9/XLkpH4pXwwHP3nwM7MyK1vBXgFA8a365r9s7Gqsh8q+3/M5dlSJmdWOjd44QHcvWxSHCujHFZKcVCd84g/Vkox0Cabmc5/BLzleUeQfOYspctF+/wKyK5vDZNk0hVTlG1WHitl2KVW4mpfGxQuNENBBOpcPfJtBaprkpSzsWmLiqq6CvcQoHZe6T5nlLmHRAEqx5PBgVYlJlcl29YRy6Der/w+MhsJXqeJ9YjEoxAieALbHcykkbyRIAiCIAiC0ELgnEeSarY1ZTJqOaqp7RnvPPIOWA7bk1E68Husqerj35Yi5438e8CEw3JHiKAhcsj7xxzcd1ViY0XCbN0xkszVsrKiPwCgxNpTtl0+NE/eH8aYpvNILh6VuBIwKedJLK0Y7BswHXPE4f7C51CZ4A9zEgW1MKMLg8qVpczLJAjA7rp2mtVVbhq5SGeKMgyhUTuPFE4Gw9A8flConKnOCFX4mNzFo7w2DBI7KwUgUTCe6lx9Xcm2FYT7Qu484pvlXm5jFtx2cCbETrdxa5R5bgRBwLrqXpiQ/RTQoiOfx0gmuilzxriFB955BE5MMj5WXNJr5bWucubwQqKyrM3Fi0e+/mm4w+RCgMoRpDjsStHKCFWonUGOJ66typBNRT4so9nHAD58zF1eKVpp1/MKXFrP30BtVgpeWjOZGbmW1Pm+ZP2RjEQcphC4+OeG2TDUTnH9NuAZc5poRyQehRLvr0oOJqlfZAiCIAiCIAhN5OFWTBCNw9aCcB7xg1V9kUPpgFGHDBnV5dvE57Hh+6cUj0SFIMCLWILhr9LedtyZOwM/xP4PW5Kekq1TDPoMQlsA44TZSowcMIHyoKinr5Zt17OdQ/UpGLvvZeCyvb51Wq4lk2CXbdgfRhVwv4KgGugFO5AVPYNR+TTqesM/rbrKa0UPBnV+KN7hxpdXbksuLqiuZ6P9auQ8UrZMDi/EuMOp5IIBJ3AZ3K8uLSEG6hxHfuTflQN3Pum1octFeZw1hIkSZzT3vdZl5cr7PgcY88lXqwQ+g2ngAf58KkUOpbAkR+lSUm4rUF2VSKe4b/SdhsxzDuXXrL5bSrlftZMueLGXS/AuAPJrxWh2Oc3QSXkbAs0Qp8jTxNU1cpOcJtYjmm0tpLhfDCjnEUEQBEEQRPDIBxguSFzYGiSzIkRKWZf/LoBp5u5x19UfyPpmSPOg/KU7UNiafL2gCGWRh60p6wqC/mDUwUSYBBdX1zsgqXGFIzf8IqRGmvFHVW/k1Seglcodpe++ANyuebsst4uk2JcXpmiXoBAmAoWt8eIJVM4jL5tqugItOgLYrbltSRRgFvxCnCSZA7iHjMVAw5AhlaNA4a4x8A4YOY8asl91wmyv2MBU+4FCEDFJ6jxcenjdQ4LMFSQXNYz66t22gyvvxyi3lDpsTQQTBJ9WpT63TLeu8rgaXReAMq+NumypgxeP7j80zdcCpTgYrAygNS28keqqzvEkP5+6qYdUjhhA4aYJ4MRRIhdIjERGbw4guXhklmQzJwYQmY1yPilDg5VtNnN5mvj1hnnkFCFvyue8Yb4kpl4vrx4o19LpAIlHIUTwJMx2QFT940wQBEEQBEFoI39tckGEIMqdR/wMYkaznrlh3MxPcozEFFHgBw6qkKEAziP5au9+nEx0CzJJ5/LtkAsx0E+Y7WSSSjxSh+OYcP2B5wAAXylG6qrE1RouLS4fDbTFI3mfvJ+NXFlylANZ5SAwwERIqpw4debWQe1Xq81mUVtU1N6v8loAuFngBP3hn1LUkF87gYITlGKZUWiWkdNBEkXVtnTRcH2oChisMUsiarmwNf961Xa5kb3aAWRvQCCN8r5pyOhLHU7F1y6Ri0cjN2Dv1gJ3i5WzBwZwCqr3K2p+DgQDFNevsXtImUxdjkkynm3NaFuBZhCTFGKa2nmkV1d9EJX3vtF+5c8YF2OKmRSNBVtlwmz5+TSLoq7A5xYD+eTiQbvfThP1iMLWQor7VyUnkyhhNkEQBEEQRJBwYTCQFDP0iEE7gAC3m4bpvBKr85HwooYqpMRgv5zwpBAIvB8v3P0/zDpyO4QeDyvq6rdLlA105PmIfGUV4RnKgY8RKhcIXNxMWKKe84hpiBrB5jwCf+xMovZsa7p1FcJTVXh33J17P/6174UGi0dqkU7XygBlol8p0MH11eXbrAw9k4RAIX76wpNoUFflTAlwn/BN9oat6YxoA4x0JZEPW1O6pXT3q5ytShS42daMUDtxjAUCJWaF40kdthbj/yLwrhZODBUENMRHoszjEyzqPE36168qfxCDWkzR3ZHyq3qWN6Mr2CSKioTZitnW9NqscQiV7ka9ui7GVHma5CUDue60nGe+/RocK+XsiAzq43y6Q+JRCPEnzKacRwRBEARBEMGich4ZuCtUOY5Ur1wubiArJ1DOI15sELkBjVGiblXeIk/oTW59K3xcfCkghem2QxDUjicvd+U+CAB47Ig/QbF6KvAgB+pQCwoCXNxsVoKO80iZI0YQjHNJqfbL1dV2aemhHGybJAHflw3FXzU9jBPwMmPxKGD4mGK/Wq6tYOpKkrGDLVCb+evK2LlkFB4XTHLmF/ImwsUE5LW+h1und11422ySBEVInx9v+5eWD0K1Mwxo+y+ursrFYzScld2QDPwU8oHyByk3E8g5xzmPFIJWsDOIqXesIXgFX1sVLqe7G4U7SolyVjNFExXb4q85syQYaK6e/cqFRKXIrK/XqhYIgvJe0G02dzxcirqCIODpK9wTC0wf3llVl3NpMfU9pvw36KFRXSEIwHNX9lLVVW63IU6rUEBha6HEE7bGINBsawRBEARBEEEiH1y7Z1uzylaGAaj3fVXnUFEM6hm4gazefgB+UCAK6jAYfj+K/cp2IYm86ydQ+gJV7iWdgeyKyrOBa+ow/9FlANwDDqO67l/9tdFKousWBWT7NhAJOMFH4KfClgRjVSOQaNeQvEUqkc7gUCsTrfOuAOMpx5W5spTXQ5ncmcLVVSe9Frj+6rcXMJ7FztC5AT4xsDLfijd0R8vh4XVQ/FPXAV23f4svzrsAwt4i1LksCBPrgfizjduscJso2wwAkw/OhFlwYK+1pazNTDULmKF4FN4K3kgPb5+U/QsWudvErDH7GJfzyOl//ihF2IYaBtR5moIUvMD482vQXxdT5EsCQ0KUdsLv4AhOxPE6nuRhw8rE+kHvUdAQP/VC7ZRuKaa+z/q1jcOeZy6GxSQqyipCz2D4SAEA3DG0A24+N0O1LSjuAmUfjNocKkiyCCFeBdHFRHIeEQRBEARBBIl8oOOCCJhb4NHDd2LmkTvAzLFcWaV4on7n4hNmc3WNwsUU7iGtgbveftXOI+NAFuUgynBWLMmqW1b5PdCPl1rJxbn1RuKR4lhJQe5XaxYlefGGhLx59+0l0GDUyF0S6Fgpp5D3jgqnHnwQNd2ewo76rvp1FS4trk2BwvQMhCejwahyv1rJ4fWqumROlXpm9oXWXbL3dcwtugwY8onuPr35ZvjZ1vh7wbOUS84OaOXT0RYIbst5BK8XXAe0Gskt54U2/Vw8Wm1WXxt85QpXpP9LfSm3TnntN0QIMJp50AhliJSh607j6ZMcHYZ3/t0fn9w8CEDg3FteElsonz+ivoDJvAnE5etlM18aOgXdbb4xsx3aJ0RibN/WqtAz/brq7+1aRqrKqcUe9bXAGBAbYeHKaO1Xa1vuwk1LAyDnUSjxXLkuReJDgiAIgiAIQh+tsLVPS0YDAC5UmEtUOY4Ur1zuec74wYCvrmpA7f+sTLpqEhWJXwPkkAl2OnZlXQH6ziMtVIm7GzAYVeeH4kddgkHOI8P8UEbJe6EOoQk2bI0xxuUPUmIYuqOYyl25H6PEv971XuRiyI/l5+PZLiOARat06xr1SQzgNlElyFZs12jwbSSWBRLauBmnPGX329Lw5NHbMCk8Rbce8+Sb0XP7GfVVec+5w8HU2/mlYgh+qRiCe+RuGtVMbWoByAjO8aR5/QqocoYjSqoF4vsC2ODfbwPyfam2KiveoJxHCP4Z49IQUwBgVE//eRzaJQnLdhYiympSlHUXfuv6fliXXYzLz0rFlsPl/v0ahWJ5x8I6QqJJFHDnhR2xLns9LundStU/AHhqTE934mmlmC8Yh63JcTGGjklReP/GASrxS0m3lGhF3iKGQRnxuO389uiQFBXcDr11NURE5R2bFh+OQyW1GNlD/546lZB4FFJcnv/qZ7AnCIIgCIIgePiZgfiJR5RTYQfKRwG4OOeR/Fd4tfvEQAAK4DwyqisIguaU13pt1st5FAzKZNRGJghViJ8sBAgIfrY1UVA7noIVrZQhJUaOGMB4gB1QLFMcVxfjrwWj2mqhzY9oOE06L0YwRQhRg5xHGm43ozGGPGmwspRPtNK4LpUOoIZO/CMZhCoZoTxWbsde8IE0J5Iw2yg5vpeBO+cjUqzFhuuTdOtKQvCzrSmLNaTNTDGDmJGoGkw6nesGtkXLSAv6to3TrHpJ71Y+gUcpeOnn8fH+X16Avy4u6JyI9Y8M48LoAP6y9F7jyrC1+0d0wfXvr8O/B7c17FtKjDvHXFb3ZN0yP919Lr7acAh3D+vEh/h57teHR3eTtcdwd+icHIU9BVXI7NASy3fkG5b97s5zsO5ACUb00G/bqYTEo5DiveopepAgCIIgCCJYlGFrysGZnMCpAVjQLghO8IEyhEaRJDdAO7icRw0ZewvGbpNABDvo10qiq0yErCceMWg4nOQDO8P9auSakn1uyIxpABq2X0WbXU7/CDVQjhwj0crIaeXdlxxOeBL0hTatNnPDcKO64AUFLf3AqK5RqJ13mWa+JLhzBjEmcmWDQelKYwxggmRQw7jNDblz1DOXqalxhaPGFa5qs/J+VYaJXdmvNb79+wiuH9gGQI5veUy4WTWVu3LPY/qk4v82H8XEIenc8ugwcwNEHL49WkK2JAq4uFcr1XJt94wf0Uh01RSt1Mc5KTpMVUor1E4pHg3pmIAtj49AdJi2W+rDiQOwM68S53ZM0G6fjB6pMXhyjDpvmVYXkqPDsKegSndbi+4+DzaHC5FWEyKtxtdvQpRV5boKJU1GtXj22WcxZMgQREREIDY2Nqg6jDE89thjaNWqFcLDw5GVlYW9e/ee3IY2BN/dRq4jgiAIgiCIhlDmcIcI7JKGqsKaGiI2CIxBL3mvEuWmjBxARk4jQOEuCeAAUrXjBMSjYGc9A7REHL6Ve5w9NespBSBB4YBpqJhi1KaGrHc7gIJ3D/F1g5+pTXkyDXM8aexX3sRAydQNnUcB6vLTs6vvAkO3lFx4CiAgqNYJAsqSPLOoRXdpUB4f5TTpB0xDAEB3xkQvoqA+lsG7eDSuZ0WZCZntAEAl4gDqc6QMjZp9ZS98PnkwHr3YnRfruSt6YFjXJEw6Jx1Ws9xpJSBZIaS8MK63u+4lbufL81f2Qla3JNyQ2U6dLF4HZdhaQ9AScXq2jkFGQiQy27uTnSuvw0s9YshtF7T3bEPmvBPU4ZCa+9W65jRyZ8WEm1X3fJu4CADARV2TMeXCjicUAaQltD0/rjfO65SAeZO0E8ebJBGRnvC/oZ0T0belCw+O7OTpw3E35ZTQZJxH9fX1uOqqq5CZmYkPPvggqDovvvgi3njjDXz00UfIyMjArFmzMHLkSOzYsQNhYWoF89TjmW1NaDIaHkEQBEEQxGnBsN3voHNYLjr0HIru3MCOLxd4YOAKOPD0YpTHSDlblaquwaxvDRm8CIpcmZLGQNaIQFOOc23UcR4N3jkPX/07FTkrowCU6NSV7VPRxkBhXMrjwYspxgMsoz6diGgVCGUelIbs10B3CiiWmRShZ9yxMrDXKBMqNwx+2nttF4iB9QjAyDGzgIJzgPj+wJK84PbK+NkDGYCD5kxcs302DtjaeLIM8dyb1RmfrDuIe7I6a4RhBtd/9+xqxsLTrEu748p+bdAjNZpbbhIFRaidgDuGdkROUY3PUWI1Scjs0BJ2ux0AcFX/1rh+cDoAIMJiwm3nt0eYWUKYWcLUizriUGkNLj8rFQAQZnbX9XLtwLa4dmBbT//4dugnrlY6jwwOhqqueplZErHsvgt81/Xjl3XH1f9bi9vO7wAAePXqPph8Xnv0au128vBhxsE9FwM55bTqLrg9E+/9dgCPXdZdd7uNQevYcHzsSTQeCEkUMLGzC6PPzQAAhJmCc9KFiiYjHj355JMAgHnz5gVVnjGG1157DTNnzsSYMWMAAPPnz0dycjIWLlyIa6+99mQ1tQEwz39JPCIIgiAIgmgIxc5YrK2ORQdBOKGpsAWDsDUlyoFrQ4QYo7A1SQw+D4qyHQ11LSn32xBE5haP8u0JqI/LBLBNfz8aLoDj2y9TOcmMTrFqkC/7HGi3ageQ8Xd/C9UhfnLcoWf6643aHOhyVoe88cfdqDqfO0yjXRA014SZJVX4mLKd0WEmlNbY9XcuiEBKlrudonHeF3kbLbILuFVMGAQA66p76da5J6sT7h7W0ZdXLKtbMmwOJ1Kiw1RtfmBkF7z0y27cPayT7/tXfx3C1Is6crNlWSQR4WZ+kG+SRJyVFuv7Pn14Z3zz92HcdkEHlMmOgyQKiLKa8Nb4fkH1GQCXT6dFmBlvj+8fVL2WsjxBhomrA3zXol/bWPydW4Yr+rTWXC+/PtonRmHDo1m+69xi4o+V/A4XgnwWB3IeaXF2ejzOTo8PavvBEqeYae1E+c81fTB5/l+YPqJzo263sWgy4lFDyc7ORn5+PrKysnzLYmJiMGjQIKxdu1ZXPLLZbLDZbL7vFRUVAAC73e5TgxsDu90OeP4BZhAbddunO96+Nqc+A82330Dz7Xtz7TfQfPtO/Q5tv0O9fyJ0NCQUS4sVlQMwyfoD7OZkQxFHHRbk/2wKIAAZCRMNbXKwOY+U7RGgnpFIt67mUn+OI1EwEjaYRpie//OJOHwampyZb4PRdOVBhDgGKVqpjnuA3FJcWajdF0a5akwGApAyXLAhbfY1RsbsK3vhoz9y8PDobirXUriFH1bOnTQQ9y/Ygkdlwoe3zQF2o4t3avdtT4yA08UQZpaCCvPxJ1QW8P6EAbr7vXNoB4zpk4rWse68RVMu7IgpF3b0rX/s0u5wMYaYCDPuGNoRv+8rxrh+2uLJXcM64S6PCFVV5/AtP5UzbMdHWvDZ5EGe46Te73mdErB6bxGuPZtPKN0mLlxVVsmHE8/Git2FQc8CZnQdyp2f3TzOrZaRgUSZ47+OGoPXr+2DXfmVOK9T4HxJDaF7ajTWzLioUbfZmJyx4lF+vlvBTk7mM5MnJyf71mkxe/Zsn8tJzpIlSxAREdGobWzvuehdLoZFixY16rabAkuXLg11E0JCc+030Hz73lz7DTTfvlO/Q0NNTU1I90+EDvm45HjyV7yQNwH769Jw7dg7gO36CpB83CegYaKVcqDOO3Ma1uaGOJ7kKB0yDZ423GAmOiVGuXqME1cb+x4MxTLVtgw3pd52ADHlRPKRBFvXYhIblosnQB6fF8b1xu2fbMQDI7tobqN9QiSOlNWie6tobDzIhyAqm3HdwLa4bqB65qqYcDOuH9gWS/7Jx0Vd3TON9UmLxbL7LtBss4og+xsTbgbgdt94Ud47r1x1FqYv2IKnxvQIuD0tZ5k3H44WN3lCiwC3MPPzPecF1e4TmZnuRBnSQV/cmPPv/vh9bxGGdkkEAMy/aSDWZ5dgjI6bSE5shAVj+7ZplDZ2axUD1Lk/R1lN2PbECM7pJcfrDnturNpt1r9dHMySgIyEyEZplxFj+rTGmJO+l9OPkIpHM2bMwAsvvGBYZufOnejatespahHw8MMP47777vN9r6ioQFpaGkaMGIHo6GiDmg3Dbrdj/8L3AQCCZMLo0aMbbdunO3a7HUuXLsXw4cNhNpsDVzhDaK79Bppv35trv4Hm23fqd2j77XULE80LrRmnGkodC8MnJaNxVVgbAId0y3ECEPh8M4Hy2ihRJrMNVucQBLVodbzDUcNwOc0kyHLnkbHjyTDptVHyaY1lXNiaYDB70wkSUADUzRmj/h6siOmtOi2rE/YVVmFgejz2FlQGVRdQJr1Wrx/VMwX/PDnSl6BXud8l954Ph8fF01DmjO+Hslo70uLdgsuXt2UGrKOdXNv4WL12TR98vfEw7huuEcqjqDqufxuM6pmi6m8oaSzX3Yly3/DOWLKjADedkw7ALdSM6ul3Dp3fORHnd0485e3qkhItn2SOEweVTLmwIyYOSdc8v5FWE7Y9MRJmiVLCnCxCeldNnz4dEydONCzTvn3749p2Sor7RigoKECrVv7p7QoKCtCnTx/delarFVarVbXcbDafhBdizz/AgtisBhleTs4xPf1prv0Gmm/fm2u/gebbd+p36PZPNE+CDcUKRMNnPVPWDb62vG5DdCdl7xrSX+UgPdBMXur6fvEokANIUght8n2fyOxGDW6zYPzdC2NM1a7h3ZLRNj4CfdvGNmifbeLDUXakYWG007KOL8dJMGKEkZBikkTo5egNdJq0pm4PhKSRWD7Qfq7o2xpX9NV2w1zcsxX+t+oAUmSzkAUrHJ0qE5DQSM+nE6VTcgvsfGqUrqsndDTsmBid3+MRQYngCal4lJiYiMTEk6NuZmRkICUlBcuXL/eJRRUVFVi3bh3uuOOOk7LPhuN9wTjdbmCCIAiCIIimAxdOZpiLJ9B2jn9g19CZq1TOowbEWCmnch/SoSWW7CgIWK9na/VMUA3qMuOdR0aOkQYasfy7OIGwJiVawooyN48R4RYJK+8f6hOsAh2rr2/PRF55HbqmRGP7kVPjhDxeIS6Y6+3BkV3x1I87cMPgdse1DzmPju6GD9dkY8YodUTJicgp3vC4VjENn0m7f7s4zF2TcwJ7Dw55/473vmgsTj/hCKf//PSEj9PHzxeA3NxclJSUIDc3F06nE5s3bwYAdOzYEVFRUQCArl27Yvbs2Rg7diwEQcC0adPwzDPPoFOnTsjIyMCsWbOQmpqKK664InQdkcOYJyveaXgTEwRBEARBNBFERdhaQ3PdNEpdDUdFoH15aYhopZwB3SQK+PfgdogON+vOJLT03vOxu6AS53VKxNbDZdx+9forX/xR0SWYkPATctNmAps8dTVeXyVRgNPF0CM1OmD+oGDplhKN7F38sq4pLbDtSLlunZvOycDGgyUY0SMZh0pquXUv/6s3bv14I+66yJ0I+bqBafh8/SFMH6GdE0h+bQU6SwMMZnLSDdPTFMuUpXXC5VTfT+BAA+iTFsd9n3ROOi7qmoS28See93Xy+e1xy3kZmmLXiWoHHZOijqveJb1awXkt800Zf7KQ9+/EztAZCo2FmwxNRjx67LHH8NFHH/m+9+3bFwCwYsUKDB06FACwe/dulJf7/yF58MEHUV1djVtvvRVlZWU499xzsXjxYoSFNVyZPjn4Z1sjCIIgCIIgjo8TnW3NX7dh5eUD/4a6luTNFIXjH1RKogCTJOLKfvrJazslt0Cn5BaadYPh8aO3w9rrIbRL7g7gT3ddjf7+fM95+GxdLu68sINq2+0T+SS28YrZlMySALuToY9nCu/l0y/A4dJa9GwdjZ8V+5l5SXdEWk24Umemq8cu6+773DEpCp/eMgiJLdxpKTolt8CK+4f61j83thfuHd4ZSS0Cjw8a4vJRTuXeEI73Cs5IiMTOvODzJSkZmBGPuRPPRron4bAgCL7PjYHe8TtZOawCIQhCUMmhTxRvom8ACNOLEWzWkPOoqdBkxKN58+Zh3rx5hmVUU10KAp566ik89dRTJ7FlJ4KnvaS2EgRBEARBHBcMSiGmgaFYMhoqAMkTszY0l4lS8OqZGoOftuYFrKccaDdULJO/LgffZgFVJn6QrXWsOie3wBOXa89ylRobju/uHOIbSD81picqajdjkid578/3nI9v/j6MW89z5zvtkBiFDolRsNvVuYNiIsy6+9HinI76M04JghCUcAQAydFWZBdVq5ZruYdG9EjGsK5JvnxJesJJMMajQKfp1+kXoLzWjlYxgadYD8SFntnSTiXK/iVHW1FQYTvl7ThZRFhMWHT3eRCE0zRsLOSQeNRUaDLi0ZmIwFwUtkYQBEEQBHGC8PmDGmc7wZASE4YJme0QZpYQbmmYo0CZ8+imc9PBwHCBzmxH1wxogx82H8L1A9ugpFaWuNpo1rNAbTiB5NOiKODagWlYe6AYZ3ncQoHo29YfFpUaG46vbvfPztUxKQoPaeTDAYA2kcH5sk4kPE5JWrxaiHnpX2fh0YXbMfk895TtYWYRdXYXMju0VJU1SyI+mHi277syzG9gRjzWZ5dg/MC2Advy3+v6YsLc9Zh1aXdu+UBPmFz7RH/YVrBntDGP1YmiFEA/vLEf7vt4DZ6+ZnCIWtT4dE9tvFm7zzgo51GTgcSjkMI8/6UbhiAIgiAI4ngQoM55dLwcT90nx/T0fwlyQM7AuH0JAmA1SbhzaEfdOs+M6Y7Bphy0jLJCNDl9yxsqACVF87MKD+nQEqv2HNMdv8VHWlBSXY+hXZJQVOV3g0iigMvPSkWHxKjjzjkTLDEWYOm0cxAXdeLOmkB8dVsmcktq0LtNrGpdWnwE5t800Pd9ybQLsHRnAa4PSgDqh0nzNmDGxW6BbP5NA7GvsAo9NEQFpbtsUPuW+OfJUb5rZsX9Q7FiVyGuH6Teb6Bx+JAOLfHH/mL8e3DgNp8qJmSm46sNh3BJb/fsbZ2TW+DO7i70DVKUJJo6NBZuKpB4FEIEmm2NIAiCIAjihFBOC38iM6a5hZjGs2Sc1ykBq/cW4bKzUlXr5M0MVrTyFktqEYa5E89GRAPdTgDQKiYcH0wYgGhP+NhN52YgLsKics9400H8/tCFKK6qR1p8BC8eCQIEQUDPIJINn2giZwBIbxkJs9kcuOAJMjAjHgMz9BNfy2nbMgI3n5sRVNmz0mKxcWaWL3wtzCzpHrsLurjdZ7EW/3GTXyMZCZHICHK/Sj66aSAOldRwbqVQExdpwZoZFx33zHFEE4eicJoMdKZCivsfBIFuGIIgCIIgGkhJSQnGjx+P6OhoxMbG4uabb0ZVVZVhnbq6OkyZMgUtW7ZEVFQUxo0bh4ICfop3wSMKyP+++OKLk9mVE0ZUhFMp8ToavP/XQzKYfex4+O/1/fDaNX3w/JW9uOUChOOebc3LhV2TMKi9OlwqGIZ1S/bNzGaWRFx9dhrSPDNqDfIIJ1cNSAPgztfiXSdvJY3zG06w4kjr2HCsfegCzOzrDFxYQSBB0SyJp5Vw5IWEo+YMnfumAjmPQopntjUSjwiCIAiCaCDjx49HXl4eli5dCrvdjkmTJuHWW2/FZ599plvn3nvvxU8//YQFCxYgJiYGU6dOxZVXXok1a9Zw5ebOnYtRo0b5vsfGxp6sbjQK8oGnVg6gl/7VG5eflYrzOuknTga0p58/EWLCzbiir/ZsTnLB6HQaN78/YQD+2F+MoV20cy95aUiI36nKr+OdUe1MICHKCvNxXI9X9G2N77ccxZAOxtc6QZw+nEYPQMIQEo9CiMBotjWCIAiCIBrOzp07sXjxYmzYsAEDBgwAALz55psYPXo0Xn75ZaSmqsOkysvL8cEHH+Czzz7DRRddBMAtEnXr1g1//vknBg/2J6eNjY1FSkrKqelMI8C7eNTrIywmjOwRuD+SIGjmoGlsGBjXzobO1HYyaRFmDvpYnS7Mv2kg5qzcj+fH9Qpc+AzHapLw6S1nTqJpohlAY+EmA4lHIcU7UwbdMARBEARBBM/atWsRGxvrE44AICsrC6IoYt26dRg7dqyqzsaNG2G325GVleVb1rVrV7Rt2xZr167lxKMpU6bglltuQfv27XH77bdj0qRJhmElNpsNNps/H05FRQUAwG63a061frx4tyXfpsvlgsPh/253OMBcTFVHyb66NugYdhj2KP+U706nA//q2wq19Q4MyohT1ZV/dzqdqvUul0uzrBKX0wWHwx+S5HQ6YLfrvw/Kj2NDjidjrFGPv9Ppb7PDYQ861EjrWAVLMP3OzIhFZkb/gOWaEsdzvs8Ummvfm2u/RReDN9iyufX9dDnnwe6fxKOQQs4jgiAIgiAaTn5+PpKSkrhlJpMJ8fHxyM/P161jsVhUIWjJyclcnaeeegoXXXQRIiIisGTJEtx5552oqqrC3Xffrdue2bNn48knn1QtX7JkCSIiIhrQs+BYunQpvK+xuQdzsWxpju/7r8uXI+ewCO+Pc4sWLdLYggk3Zj+FR9r+CJvpYt/SZUuXIdIMJAPI2eT+k78uu7fl/p59IBuLFu3ntnrkSOD9AsC+/fvxW8Ve3/fly5YhQvVWrtyvvO+BcNd1uVw67Tg+9lf4t/3zzz8H3Y7t27djUdG2E9p3cP0+82iu/Qaab9+bW7972Q6ivedzc+u7l1D3u6amJqhyJB6FEIESZhMEQRAEIWPGjBl44YUXDMvs3LnzpLZh1qxZvs99+/ZFdXU1XnrpJUPx6OGHH8Z9993nVCnfNQAAHzBJREFU+15RUYG0tDSMGDEC0dGNFwZmt9uxdOlSDB8+HFi7AgCQ1rYtsoZ3xCN/rQQADBs2DDtX7seagsMAgNGjR6u282neBqzPAdJGvouEKCvw9yoAwPDhwxEbwc/odc/aJQAAsyRg9OjRvu8Z7TMwelQXruyvX28DivJ09+ute37/njg7PQ7PbXbnmho5YgRahJk0y3q3Je97oFnHvHVFUcTo0SMNyzaE9TklwD9/+doUCG87evTsidED045rnw3p95lEc+030Hz73lz7LW5aCuxzf25ufT9dzrnXLRwIEo9CCiXMJgiCIAjCz/Tp0zFx4kTDMu3bt0dKSgoKCwu55Q6HAyUlJbq5ilJSUlBfX4+ysjLOfVRQUGCY32jQoEF4+umnYbPZYLVqJyS2Wq2a68xm80l5IZZvUxRFmE1mbp0kSpplvXx+aybKaurRMsqKYtn081arur1f3joYT/6wA09f0ZNbJ0mSqmxshMVwv/NvGog1+4tw3aB2yCn2/9JrtZhhNuu/lsu31ZBjKkBo1OPfMdk/tXxDthsVZjnhdpysa+l0p7n2G2i+fW92/VY8r5tV3z2Eut/B7pvEoxDidR5RziOCIAiCIAAgMTERiYnGs1wBQGZmJsrKyrBx40b07+/O9fLrr7/C5XJh0KBBmnX69+8Ps9mM5cuXY9y4cQCA3bt3Izc3F5mZmbr72rx5M+Li4nSFo9OBmHAz0uLD4XIB8ZEWZHVPxsd/HkRMuPYLsSQKaBnl7k/LKCuuG5gGURAQHaYuP6h9Syy657yg2jEtqzN2F1RiXL82muvP75yI8zurz29DZi4LJcnRYfi/KecgKiy4IcTDF3fF2gPFuPwsdQJ3giAIAJTCpQlB4lFIobA1giAIgiAaTrdu3TBq1ChMnjwZ77zzDux2O6ZOnYprr73WN9PakSNHMGzYMMyfPx8DBw5ETEwMbr75Ztx3332Ij49HdHQ07rrrLmRmZvqSZf/www8oKCjA4MGDERYWhqVLl+K5557D/fffH8ruapIWH45DJbW4pFcriKKAFdOHAnALMRd0TsS3dw5B+4TIoLY1+8rejdKmuEgLvrhVX4jTQyvv9FX922DBxsOYemHHRmhZ43FWWmzQZW+7oANuu6DDyWsMQRBnAE1DPCdIPAopgne2NRKPCIIgCIJoIJ9++immTp2KYcOGQRRFjBs3Dm+88YZvvd1ux+7du7lEmP/5z398ZW02G0aOHIm3337bt95sNuOtt97CvffeC8YYOnbsiFdffRWTJ08+pX0LhsX3nI9DpTXomuLOqWSS+Pepfm3jQtGs40Jr2vvZV/bCjZnp6JHa8JxRkijA6WLolBzVGM0jCII4eQQ5ayMRekg8CiXMO9uaZFyOIAiCIAhCQXx8PD777DPd9enp6WCMccvCwsLw1ltv4a233tKsM2rUKIwaNapR23myiLSafMJRUyTK6n8N15ry3iSJ6NUmRrU8GH6Yei7eX30A9w7vfNztIwiCODWQeNRUIPEohJDziCAIgiAIomlxTseWWLOvGFcP0M5rFCwpMWF4ZHRXRFhMjZ7zqHtqNF69pk+jbpMgCOKkQM6jJgOJRyGEEmYTBEEQBEE0LT6+aRAq6xyIiTjxmXFuPZ/yAREE0dwh8aipQKpFSPEkzBbpNBAEQRAEQTQFRFFoFOGIIAiCAEXhNCHoTIUQb9gazbZGEARBEARBEARBND/IedRUINUihPjC1kg8IgiCIAiCIAiCIJobNBZuMtCZCiGU84ggCIIgCIIgCIJovpDzqKlAqkVI8YStiVKI20EQBEEQBEEQBEEQpxoSj5oKJB6FEK/zSKDTQBAEQRAEQRAEQTQ3BBKPmgqkWoQQX8Jsmm2NIAiCIAiCIAiCaHaQeNRUINUihFDOI4IgCIIgCIIgCKL5QuJRU4FUixBCs60RBEEQBEEQBEEQzRYaCzcZ6EyFEG/YGqMbhiAIgiAIgiAIgmh2kPOoqUCqRQjxJcwm8YggCIIgCIIgCIJobtBYuMlAZyqEUNgaQRAEQRAEQRAE0WzpdAdYeBvsN10a6pYQASDVIoR4w9boNBAEQRAEQRAEQRDNDms8HJfsx3brLaFuCREAUi1CCDmPCIIgCIIgCIIgiGaNQHmPmgKkWoQQQfCIR3QaCIIgCIIgCIIgCII4TSHVIoT4wtbIeUQQBEEQBEEQBEEQxGkKqRYhhMLWCIIgCIIgCIIgCII43SHVIoR4nUcCiUcEQRAEQRAEQRAEQZymkGoRQrzOI0biEUEQBEEQBEEQBEEQpymkWoQQr3hEziOCIAiCIAiCIAiCIE5Xmoxq8eyzz2LIkCGIiIhAbGxsUHUmTpwIQRC4v1GjRp3chjYAClsjCIIgCIIgCIIgCOJ0xxTqBgRLfX09rrrqKmRmZuKDDz4Iut6oUaMwd+5c33er1XoymndciN6wNUghbglBEARBEARBEARBEIQ2TUY8evLJJwEA8+bNa1A9q9WKlJSUk9CiE8fvPBJC3BKCIAiCIAiCIAiCIAhtmox4dLysXLkSSUlJiIuLw0UXXYRnnnkGLVu21C1vs9lgs9l838vLywEAJSUlsNvtjdYuu90OR50LggBUm6pRXFzcaNs+3bHb7aipqUFxcTHMZnOom3PKaK79Bppv35trv4Hm23fqd2j7XVlZCQBgjIWsDYQb7zmoqKho1O16r7WKiopmdY8Bzbfv1O/m1W+g+fa9ufYbaL59P1367f23OtD70xktHo0aNQpXXnklMjIysH//fjzyyCO4+OKLsXbtWkiSdqjY7NmzfS4nORkZGSexpddhykncOkEQBEE0JyorKxETExPqZjRrvEJeWlpaiFtCEARBEEQwBHp/ElgIf56bMWMGXnjhBcMyO3fuRNeuXX3f582bh2nTpqGsrKzB+ztw4AA6dOiAZcuWYdiwYZpllM4jl8uFkpIStGzZslHDyyoqKpCWloZDhw4hOjq60bbbFGiufW+u/Qaab9+ba7+B5tt36ndo+80YQ2VlJVJTUyGKNBlFKHG5XDh69ChatGhB70+NRHPtO/W7efUbaL59b679Bppv30+Xfgf7/hRS59H06dMxceJEwzLt27dvtP21b98eCQkJ2Ldvn654ZLVaVUm1g53d7XiIjo5uVjeInOba9+bab6D59r259htovn2nfocOchydHoiiiDZt2py07Z8O11qoaK59p343P5pr35trv4Hm2/fTod/BvD+FVDxKTExEYmLiKdvf4cOHUVxcjFatWp2yfRIEQRAEQRAEQRAEQTRlmoynOzc3F5s3b0Zubi6cTic2b96MzZs3o6qqylema9eu+O677wAAVVVVeOCBB/Dnn38iJycHy5cvx5gxY9CxY0eMHDkyVN0gCIIgCIIgCIIgCIJoUjSZhNmPPfYYPvroI9/3vn37AgBWrFiBoUOHAgB2797tmx1NkiRs3boVH330EcrKypCamooRI0bg6aefVoWlhQKr1YrHH3/8tGjLqaa59r259htovn1vrv0Gmm/fqd/Nq9/Eqac5X2vNte/U7+bVb6D59r259htovn1vav0OacJsgiAIgiAIgiAIgiAI4vSmyYStEQRBEARBEARBEARBEKceEo8IgiAIgiAIgiAIgiAIXUg8IgiCIAiCIAiCIAiCIHQh8YggCIIgCIIgCIIgCILQhcSjEPHWW28hPT0dYWFhGDRoENavXx/qJp0Qv/32Gy677DKkpqZCEAQsXLiQW88Yw2OPPYZWrVohPDwcWVlZ2Lt3L1empKQE48ePR3R0NGJjY3HzzTejqqrqFPai4cyePRtnn302WrRogaSkJFxxxRXYvXs3V6aurg5TpkxBy5YtERUVhXHjxqGgoIArk5ubi0suuQQRERFISkrCAw88AIfDcSq70mDmzJmD3r17Izo6GtHR0cjMzMTPP//sW3+m9lvJ888/D0EQMG3aNN+yM7HvTzzxBARB4P66du3qW38m9lnOkSNH8O9//xstW7ZEeHg4evXqhb/++su3/kx8xqWnp6vOuSAImDJlCoAz/5wTpyf0/tT0ny1A831/oncnN83l3Qmg9yd6fzrD3p8Yccr54osvmMViYR9++CH7559/2OTJk1lsbCwrKCgIddOOm0WLFrFHH32UffvttwwA++6777j1zz//PIuJiWELFy5kW7ZsYZdffjnLyMhgtbW1vjKjRo1iZ511Fvvzzz/Z6tWrWceOHdl11113invSMEaOHMnmzp3Ltm/fzjZv3sxGjx7N2rZty6qqqnxlbr/9dpaWlsaWL1/O/vrrLzZ48GA2ZMgQ33qHw8F69uzJsrKy2KZNm9iiRYtYQkICe/jhh0PRpaD5/vvv2U8//cT27NnDdu/ezR555BFmNpvZ9u3bGWNnbr/lrF+/nqWnp7PevXuze+65x7f8TOz7448/znr06MHy8vJ8f8eOHfOtPxP77KWkpIS1a9eOTZw4ka1bt44dOHCA/fLLL2zfvn2+MmfiM66wsJA730uXLmUA2IoVKxhjZ/Y5J05P6P3pzHi2MNZ835/o3al5vTsxRu9P9P50Zr0/kXgUAgYOHMimTJni++50OllqaiqbPXt2CFvVeChfflwuF0tJSWEvvfSSb1lZWRmzWq3s888/Z4wxtmPHDgaAbdiwwVfm559/ZoIgsCNHjpyytp8ohYWFDABbtWoVY8zdT7PZzBYsWOArs3PnTgaArV27ljHmfnEURZHl5+f7ysyZM4dFR0czm812ajtwgsTFxbH333+/WfS7srKSderUiS1dupRdcMEFvhegM7Xvjz/+ODvrrLM0152pffby0EMPsXPPPVd3fXN5xt1zzz2sQ4cOzOVynfHnnDg9ofenM/PZwljzfn+id6cz+3zT+xO9P51J708UtnaKqa+vx8aNG5GVleVbJooisrKysHbt2hC27OSRnZ2N/Px8rs8xMTEYNGiQr89r165FbGwsBgwY4CuTlZUFURSxbt26U97m46W8vBwAEB8fDwDYuHEj7HY71/euXbuibdu2XN979eqF5ORkX5mRI0eioqIC//zzzyls/fHjdDrxxRdfoLq6GpmZmc2i31OmTMEll1zC9RE4s8/53r17kZqaivbt22P8+PHIzc0FcGb3GQC+//57DBgwAFdddRWSkpLQt29fvPfee771zeEZV19fj08++QQ33XQTBEE44885cfpB709uzrRni5fm+P5E705+zvS+0/sTvT+dKe9PJB6dYoqKiuB0OrmLAQCSk5ORn58foladXLz9Mupzfn4+kpKSuPUmkwnx8fFN5ri4XC5MmzYN55xzDnr27AnA3S+LxYLY2FiurLLvWsfGu+50Ztu2bYiKioLVasXtt9+O7777Dt27dz/j+/3FF1/g77//xuzZs1XrztS+Dxo0CPPmzcPixYsxZ84cZGdn47zzzkNlZeUZ22cvBw4cwJw5c9CpUyf88ssvuOOOO3D33Xfjo48+AtA8nnELFy5EWVkZJk6cCODMvc6J0xd6f/JzJj1bgOb3/kTvTs3n3Qmg9yd6fzqz3p9MId07QZxBTJkyBdu3b8fvv/8e6qacMrp06YLNmzejvLwcX3/9NSZMmIBVq1aFulknlUOHDuGee+7B0qVLERYWFurmnDIuvvhi3+fevXtj0KBBaNeuHb766iuEh4eHsGUnH5fLhQEDBuC5554DAPTt2xfbt2/HO++8gwkTJoS4daeGDz74ABdffDFSU1ND3RSCIM4wmtv7E707NZ93J4Den+j96cx6fyLn0SkmISEBkiSpMqoXFBQgJSUlRK06uXj7ZdTnlJQUFBYWcusdDgdKSkqaxHGZOnUqfvzxR6xYsQJt2rTxLU9JSUF9fT3Kysq48sq+ax0b77rTGYvFgo4dO6J///6YPXs2zjrrLLz++utndL83btyIwsJC9OvXDyaTCSaTCatWrcIbb7wBk8mE5OTkM7bvcmJjY9G5c2fs27fvjD7fANCqVSt0796dW9atWzef7fxMf8YdPHgQy5Ytwy233OJbdqafc+L0g96f/Jwpzxageb4/0btT8313Auj9id6fmvY5J/HoFGOxWNC/f38sX77ct8zlcmH58uXIzMwMYctOHhkZGUhJSeH6XFFRgXXr1vn6nJmZibKyMmzcuNFX5tdff4XL5cKgQYNOeZuDhTGGqVOn4rvvvsOvv/6KjIwMbn3//v1hNpu5vu/evRu5ublc37dt28Y9GJcuXYro6GjVA/d0x+VywWazndH9HjZsGLZt24bNmzf7/gYMGIDx48f7Pp+pfZdTVVWF/fv3o1WrVmf0+QaAc845RzWF9J49e9CuXTsAZ/YzDgDmzp2LpKQkXHLJJb5lZ/o5J04/6P3JzZnybKH3Jz/07tR83p0Aen+i96cmfs5Dmq67mfLFF18wq9XK5s2bx3bs2MFuvfVWFhsby2VUb2pUVlayTZs2sU2bNjEA7NVXX2WbNm1iBw8eZIy5p2GMjY1l//d//8e2bt3KxowZozkNY9++fdm6devY77//zjp16nRaT8PIGGN33HEHi4mJYStXruSmZKypqfGVuf3221nbtm3Zr7/+yv766y+WmZnJMjMzfeu90zGOGDGCbd68mS1evJglJiaeFtMxGjFjxgy2atUqlp2dzbZu3cpmzJjBBEFgS5YsYYyduf3WQj5jCGNnZt+nT5/OVq5cybKzs9maNWtYVlYWS0hIYIWFhYyxM7PPXtavX89MJhN79tln2d69e9mnn37KIiIi2CeffOIrc6Y+45xOJ2vbti176KGHVOvO5HNOnJ7Q+9OZ82xpru9P9O7kpzm8OzFG70/0/nRmvT+ReBQi3nzzTda2bVtmsVjYwIED2Z9//hnqJp0QK1asYABUfxMmTGCMuadinDVrFktOTmZWq5UNGzaM7d69m9tGcXExu+6661hUVBSLjo5mkyZNYpWVlSHoTfBo9RkAmzt3rq9MbW0tu/POO1lcXByLiIhgY8eOZXl5edx2cnJy2MUXX8zCw8NZQkICmz59OrPb7ae4Nw3jpptuYu3atWMWi4UlJiayYcOG+V5+GDtz+62F8gXoTOz7Nddcw1q1asUsFgtr3bo1u+aaa9i+fft868/EPsv54YcfWM+ePZnVamVdu3Zl7777Lrf+TH3G/fLLLwyAqi+MnfnnnDg9ofenM+PZ0lzfn+jdyU9zeHdijN6f6P3pzHp/Ehhj7FS5nAiCIAiCIAiCIAiCIIimBeU8IgiCIAiCIAiCIAiCIHQh8YggCIIgCIIgCIIgCILQhcQjgiAIgiAIgiAIgiAIQhcSjwiCIAiCIAiCIAiCIAhdSDwiCIIgCIIgCIIgCIIgdCHxiCAIgiAIgiAIgiAIgtCFxCOCIAiCIAiCIAiCIAhCFxKPCIIgCIIgCIIgCIIgCF1IPCIIokkiCAIWLlwYsNysWbNw6623Nuq+V65cCUEQUFZW1qjbDcSOHTvQpk0bVFdXn9L9EgRBEARxZkDvTwRBHC8kHhEE0WAmTpwIQRBUf6NGjQp10zjy8/Px+uuv49FHH/UtO3bsGO644w60bdsWVqsVKSkpGDlyJNasWRPClqoZOnQopk2bxi3r3r07Bg8ejFdffTU0jSIIgiAI4rih96eTD70/EcTJwxTqBhAE0TQZNWoU5s6dyy2zWq0hao0277//PoYMGYJ27dr5lo0bNw719fX46KOP0L59exQUFGD58uUoLi4OYUuDZ9KkSZg8eTIefvhhmEz0CCcIgiCIpgS9P4UGen8iiBOHnEcEQRwX3l+d5H9xcXEA3JboOXPm4OKLL0Z4eDjat2+Pr7/+mqu/bds2XHTRRQgPD0fLli1x6623oqqqiivz4YcfokePHrBarWjVqhWmTp3KrS8qKsLYsWMRERGBTp064fvvv+fWf/HFF7jssst838vKyrB69Wq88MILuPDCC9GuXTsMHDgQDz/8MC6//HIAQE5ODgRBwObNm7l6giBg5cqV3PbXrFmD3r17IywsDIMHD8b27dt96w4ePIjLLrsMcXFxiIyMRI8ePbBo0SLf+u3bt+Piiy9GVFQUkpOTccMNN6CoqAiA+5fJVatW4fXXX/f9KpmTkwMAGD58OEpKSrBq1apAp4ggCIIgiNMMen+i9yeCaKqQeEQQxElh1qxZGDduHLZs2YLx48fj2muvxc6dOwEA1dXVGDlyJOLi4rBhwwYsWLAAy5Yt415u5syZgylTpuDWW2/Ftm3b8P3336Njx47cPp588klcffXV2Lp1K0aPHo3x48ejpKQEAFBSUoIdO3ZgwIABvvJRUVGIiorCwoULYbPZTriPDzzwAF555RVs2LABiYmJuOyyy2C32wEAU6ZMgc1mw2+//YZt27bhhRdeQFRUFAD3y9RFF12Evn374q+//sLixYtRUFCAq6++GgDw+uuvIzMzE5MnT0ZeXh7y8vKQlpYGALBYLOjTpw9Wr159wu0nCIIgCOL0gt6f6P2JIE5bGEEQRAOZMGECkySJRUZGcn/PPvssY4wxAOz222/n6gwaNIjdcccdjDHG3n33XRYXF8eqqqp863/66ScmiiLLz89njDGWmprKHn30Ud02AGAzZ870fa+qqmIA2M8//8wYY2zTpk0MAMvNzeXqff311ywuLo6FhYWxIUOGsIcffpht2bLFtz47O5sBYJs2bfItKy0tZQDYihUrGGOMrVixggFgX3zxha9McXExCw8PZ19++SVjjLFevXqxJ554QrPtTz/9NBsxYgS37NChQwwA2717N2OMsQsuuIDdc889mvXHjh3LJk6cqHtsCIIgCII4/aD3J3p/IoimDAV8EgRxXFx44YWYM2cOtyw+Pt73OTMzk1uXmZnpszLv3LkTZ511FiIjI33rzznnHLhcLuzevRuCIODo0aMYNmyYYRt69+7t+xwZGYno6GgUFhYCAGprawEAYWFhXJ1x48bhkksuwerVq/Hnn3/i559/xosvvoj3338fEydODK7zGn2Mj49Hly5dfL8O3n333bjjjjuwZMkSZGVlYdy4cb72btmyBStWrPD9kiZn//796Ny5s+F+w8PDUVNT06C2EgRBEAQReuj9id6fCKKpQmFrBEEcF5GRkejYsSP3J3/5ORHCw8ODKmc2m7nvgiDA5XIBABISEgAApaWlqnphYWEYPnw4Zs2ahT/++AMTJ07E448/DgAQRfdjkTHmK++1UjeEW265BQcOHMANN9yAbdu2YcCAAXjzzTcBAFVVVbjsssuwefNm7m/v3r04//zzA267pKQEiYmJDW4TQRAEQRChhd6fjKH3J4I4fSHxiCCIk8Kff/6p+t6tWzcAQLdu3bBlyxZUV1f71q9ZswaiKKJLly5o0aIF0tPTsXz58uPef4cOHRAdHY0dO3YELNu9e3dfW7wvFXl5eb718uSPcuR9LC0txZ49e3x9BIC0tDTcfvvt+PbbbzF9+nS89957AIB+/frhn3/+QXp6uuoF0vtrosVigdPp1Nzv9u3b0bdv34D9IgiCIAiiaUHvT/T+RBCnKyQeEQRxXNhsNuTn53N/3tkuAGDBggX48MMPsWfPHjz++ONYv369L6Hj+PHjERYWhgkTJmD79u1YsWIF7rrrLtxwww1ITk4GADzxxBN45ZVX8MYbb2Dv3r34+++/fb88BYMoisjKysLvv//uW1ZcXIyLLroIn3zyCbZu3Yrs7GwsWLAAL774IsaMGQPA/avd4MGD8fzzz2Pnzp1YtWoVZs6cqbmPp556CsuXL8f27dsxceJEJCQk4IorrgAATJs2Db/88guys7Px999/Y8WKFb4XoylTpqCkpATXXXcdNmzYgP379+OXX37BpEmTfC886enpWLduHXJyclBUVOT7RTAnJwdHjhxBVlZW0MeCIAiCIIjTA3p/ovcngmiyhDrpEkEQTY8JEyYwAKq/Ll26MMbcyRjfeustNnz4cGa1Wll6erovEaKXrVu3sgsvvJCFhYWx+Ph4NnnyZFZZWcmVeeedd1iXLl2Y2WxmrVq1YnfddZdvHQD23XffceVjYmLY3Llzfd8XLVrEWrduzZxOJ2OMsbq6OjZjxgzWr18/FhMTwyIiIliXLl3YzJkzWU1Nja/ejh07WGZmJgsPD2d9+vRhS5Ys0Uz4+MMPP7AePXowi8XCBg4cyCWOnDp1KuvQoQOzWq0sMTGR3XDDDayoqMi3fs+ePWzs2LEsNjaWhYeHs65du7Jp06Yxl8vFGGNs9+7dbPDgwSw8PJwBYNnZ2Ywxxp577jk2cuTIBpwtgiAIgiBOB+j9id6fCKIpIzAmC0wlCIJoBARBwHfffef7FSlUMMYwaNAg3HvvvbjuuutC2pbGoL6+Hp06dcJnn32Gc845J9TNIQiCIAiiEaH3p5MDvT8RRONAYWsEQZyxCIKAd999Fw6HI9RNaRRyc3PxyCOP0IsPQRAEQRAnDXp/IghCC3IeEQTR6Jwuv5wRBEEQBEE0Fej9iSCI0xkSjwiCIAiCIAiCIAiCIAhdKGyNIAiCIAiCIAiCIAiC0IXEI4IgCIIgCIIgCIIgCEIXEo8IgiAIgiAIgiAIgiAIXUg8IgiCIAiCIAiCIAiCIHQh8YggCIIgCIIgCIIgCILQhcQjgiAIgiAIgiAIgiAIQhcSjwiCIAiCIAiCIAiCIAhdSDwiCIIgCIIgCIIgCIIgdPl/hEAb0/Q+ktMAAAAASUVORK5CYII=", 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", 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[91mFailed to load model history.\n", + "Error: name 'history' is not defined\n" + ] } ], "source": [ @@ -19317,7 +22085,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 7, "metadata": { "notebookRunGroups": { "groupValue": "" @@ -19328,15 +22096,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "1/1 [==============================] - 3s 3s/step\n", - "20/20 [==============================] - 2s 101ms/step\n", - "The accuracy of the model on validation data is 87.50%(87.50000%)\n", - "The accuracy of the model on test data is 96.96%(96.95513%)\n" + "1/1 [==============================] - 4s 4s/step\n", + "20/20 [==============================] - 2s 93ms/step\n", + "Val data acc:\n", + "+-----------+-----------+\n", + "| Metric | Value |\n", + "+-----------+-----------+\n", + "| Accuracy | 93.75 |\n", + "| Precision | 94.444444 |\n", + "| F1 Score | 93.72549 |\n", + "| Recall | 93.75 |\n", + "+-----------+-----------+\n", + "Test data acc:\n", + "+-----------+-----------+\n", + "| Metric | Value |\n", + "+-----------+-----------+\n", + "| Accuracy | 96.474359 |\n", + "| Precision | 96.533528 |\n", + "| F1 Score | 96.451081 |\n", + "| Recall | 96.474359 |\n", + "+-----------+-----------+\n" ] }, { "data": { - "image/png": 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H4p577om3vOUtsbS0VPub73Upulpj/PSnP31ZqPLOzk5MTU1d9Dn7jHd2dh7xHoUK5VR0tuhsoeuLis5ePZ0tVOhqUNHZ4mcLXV9UdLb42WuVyva9y6Q777wznvnMZ0a73Y4TJ07EZ3zGZ0SzWcf02u123HzzzbXP7rrrrtjY2Ijjx4+Pve/Zs2cjIuLee++NiIhnPOMZtb8fO3YslpeXH3ZstF4+97nPvfwJPcFjfDiamZmJvb29iz7f3d1Nfy9U6NFS0dmis4WuLyo6e/V0tlChq0FFZ4ufLXR9UdHZ4mevVSqg1GXSi1/84vS2gkvR1NTURYo9HA7j+PHj8f73v3/sd66Fk/mf7DGePHkyPvShD0VVVTXE+tSpUxERceONN17V5xf680lFZ68eFZ0tdDWo6GyhQtcXFZ29elT8bKGrQUVnC12rVECpq0xPe9rT4pd/+Zfjr/yVv/KwVY3bbrstIg5R3qc+9anp83Pnzl30xoBxz4iI+NjHPhaveMUrLnndpVofn4gxPhw9//nPj/e+973xJ3/yJ/Gc5zwnff67v/u76e+FCj1RVHT2kanobKFriYrOFip0fVHR2Uem4mcLXUtUdLbQ1aZyptRVpq/+6q+OwWAQ3/M933PR3/r9fqyvr0fE4R7fiYmJ+JEf+ZGoqipd8853vvMRn/GCF7wgbr/99njnO9+Z7gf5XrOzsxERF11ztcZ4ua/Q/PIv//KYmJiIH/3RH62N+z3veU/cdNNN8ZKXvOQR71Go0JWiorNFZwtdX1R09vJeVV2o0LVCRWeLny10fVHR2eJnrzaVTqmrTC972cviDW94Q3z/939//MEf/EG88pWvjImJibjrrrviZ37mZ+KHf/iH4yu/8ivj2LFj8W3f9m3x/d///fElX/Il8epXvzo+8pGPxP/8n/8zVlZWHvYZzWYz3v3ud8eXfumXxvOf//x4/etfHydPnoxPfOIT8cd//MfxwQ9+MCIiXvjCF0ZExDd/8zfHF33RF0Wr1YrXvOY1V22Ml/sKzZtvvjne8pa3xA/+4A/GwcFBfM7nfE783M/9XPzGb/xGvP/9749Wq/UYOF+o0GOjorNFZwtdX1R09vJeVf2+970v7r333uj1ehER8eEPfzje8Y53RETE3/7bfztVjwsVutpUdLb42ULXFxWdLX72qtMT+q6/65B4heb/+T//52Gve+1rX1vNzs5e8u8/9mM/Vr3whS+sZmZmqvn5+eozP/Mzq7e+9a3VQw89lK4ZDAbVd3/3d1cnT56sZmZmqpe//OXVxz72seq222572FdoQr/5m79ZfeEXfmE1Pz9fzc7OVs973vOqH/mRH0l/7/f71Td90zdVx44dqxqNxkWv07ySY6yqR/cKzcFgUH3f931fddttt1WTk5PVHXfcUf2n//SfLuu7hQqZis4WnS10fVHR2SdGZ1/2spdVETH2J59noUIPR0Vni58tdH1R0dniZ691alSV+tYKFSpUqFChQoUKFSpUqFChQoUKFXoCqJwpVahQoUKFChUqVKhQoUKFChUqVOgJpwJKFSpUqFChQoUKFSpUqFChQoUKFXrCqYBShQoVKlSoUKFChQoVKlSoUKFChZ5wKqBUoUKFChUqVKhQoUKFChUqVKhQoSecCihVqFChQoUKFSpUqFChQoUKFSpU6AmnAkoVKlSoUKFChQoVKlSoUKFChQoVesKpgFKFChUqVKhQoUKFChUqVKhQoUKFnnBqX+6FjUbjkn9rtVoxHA6j3W5Hq9VKnzUajZicnIxGoxGDwSBarVb6vN/vx+TkZDSbzdjZ2YlWqxXN5iFGNjExEfPz83HzzTfH/Px8NJvNmJqaimazGcPhMGZmZmJmZiYmJiZiamoqGo1GVFUVw+EwIiKazWY0Go30s7e3F41GI9rtdgwGg7jxxhvjMz/zM6PT6USr1YrJycno9Xqxu7sb/X4/ms1mTE5OxuzsbOzu7sbq6mrcfffd0e/3Y2VlJRqNRmxubka73Y7Z2dmYnp6OmZmZGA6HaV79fj8Gg0FMTk7G/Px8PPjgg9FoNGJ6ejra7XZMTU1FRMRgMIjt7e04ffp0HBwcRETE/Px8DIfDaDQa0Wq1YmJiInq9XiwsLERVVbGwsBCdTqfG//39/dpzd3Z20vfa7XY0Go04ODiIycnJODg4SPfe3d2NwWAQVVXF/v5+DIfDqKoqGo1GNJvN2jxYx8FgEPv7+9Hv96Oqqtjd3Y3p6eloNpuxuroajUYjtra2Es/39vai3+9Hv9+Pvb29GA6Hsb+/Hw8++GAcHBzExMREzMzMxOzsbEREuufe3l6SPfgxOTkZExMT0Ww2Y39/Pw4ODmJqaio6nU6SJ9YZWTw4OEhzioiYnp6O2dnZGAwGccMNN0RExP7+fvR6vaiqKprNZhwcHCTeI69TU1MxOTkZW1tbsbi4GHfddVf6jtdrf38/yeRgMEhz3tnZSf8eHBzE3t5e4s3u7m7s7OzE/v5+TbeqqrpcFb2Ims1mVFUVr33ta2NjYyPOnz8fZ8+ejW63m/6+v7+f5sv4kQH+3d3djampqdjd3U28r6oqyWtVVenHfECuIiLdix8IPj0SsXZex0t9b2pqKhYXF2N+fj5mZ2eTvZicnIypqamYn5+vyUxVVckOsN7cu91uR7PZTPLZbDZjcXEx5ubmIiKi3+8nnd7e3o7d3d1kR+bn59P94d/+/n7s7e0luT84OEj8Z3xLS0sxHA5jb28v2bf9/f3odDoxGAzi4OAgDg4Okr2pqir6/X7SE9ar1WrF+vp63HvvvUnet7a2kj1gLGtra9Hv9+Pg4CA2Nzdjc3Mz+v1+rK2txdbWVmxvb9fWDbuKvb0cQiZsl7Ex/N5ut5PuVlUV7XY7yRMy1Gq1otPpRLfbTXNHHqF2u53s+N7eXjSbzThy5Eh0Op244YYbYnJyMpaXl6PZbCbbOzMzE71eL9nofr8fEYc60mq1YmpqKvmlfr8fn/jEJ5JNZ3ytVisODg5ibW3tsvmSE/xAzuF7/hk8hV8veclL4gUveEH8m3/zb5IttgzDaz5/05veFM9+9rPj677u6+LEiRPxhje8Ib75m7857rzzzvie7/meOHLkSPT7/eTXl5aWkt7Mzc3F7OxsdDqdWFlZibm5uZicnIy5ubkk09PT07G8vBwzMzM128C6Mo9+vx/7+/vJZk5PT8fk5GQMh8OLbAYy32w2Y2JiItkY5JB7Ms/BYJB0t9lsxu7ubvq+741f29raSn6O8e7u7tbWd3d3N/kceIo9hTxefvCj+IKqqmJ7ezt6vV7s7e3FYDBIz8FXs37WvZwv6GE+F8bPGvrvxGn48ohI38HXEVsNBoOkZ9zH11t/PX+ek9voXBa4ttVqpevxgcgV8czOzk7Nt+Nfcz9kHjF+fK2J+K+qqtja2nrMOvv5n//5MT8/n+zD3Nxc7O/vx+LiYop5scf7+/sxPT0dOzs7SS6JN0+cOBE33nhjbG1t1XgNj7kW+e10OrG/vx8PPfRQnD17Nvkx4h50zTxBrriH14013dvbi4ceeijW1taSPHS73bjvvvvi9OnTsbOzk+TG1Gg0YmZmJk6ePBnHjh2L2267LV784hfHyspK8q3EvvzO/Pg+Nr7RaCQZxCdbvxm7ZYj7RMRFvsn2HJsxMTEREZH4xX0d8znm5R7oFDzjeY1GI3Z2duKTn/xk7O3tJf+6vb0d3W43ut1u0u2Dg4NYXV2N1dXVuOeee+L8+fOXLW+tViump6cTb8hVjhw5EkeOHKnZaWxy7nftY7FVyA6x+sTERAyHwxgMBnHixIk4ceJEksWZmZkaf5BV8h/WttVqxf333x/nzp1L9hSfCi8mJiZSjIGN3tjYSDnA7u5ubG1tRa/Xi263m9b7nnvuuWye5fQFX/AFaS4RkWyF5e/jH/94fOZnfmb85E/+ZHz+539+HD9+PCLqsSi0v78fL37xi+NNb3pT/M2/+Tfj2LFj8cIXvjC+/Mu/PF71qlfFX/krfyV2d3eTrPB9++TBYFDTV54FfxkX48xjhIhI8en8/HxaT3wldr/VasXMzEycOHEilpaWkh602+2YnJys2SvGxbPQs6qqkp+GyNMdl+KrPSd+z22VdZr4DT8SEclHEicOh8PkH/zM2dnZ6Ha76RqvM88zJkHey3z5DuN3PkdObHtDzIKN6Pf7tRyf5/JdYgDW3vkt+kaOYPtMzkgezP0YF9/J8yzHI/bn5JsHBwexvr4ep0+fruEXtosmx9zILGMxvkDsGDGyvz/xEz/xiLp52aBUTnligYDZ0fB5etj/P3j/3mw2Y3p6Oikdf4uINCnfy//H2OZJiYWQsSIUOF2CAgASno+weNEBeBzYssAE2CieAwcEudvtJv6g9MxvMBjUDHUeMHtuCApjxqjPzMwk52hnimITkHAPHAFOE0XnXxQU52tnDf/MZ5yXlcHgI2tK8EjCbSDTwS3PRy5sBLiW3wHHbMThqxMglJ4feABP4DV8tJw5wGZsubFzQsBYkSvLJeSgMw/4rxRVVRV33HFHCu53dnYS4NHr9RIQYrmBTwQpl5Jx+Mb18IL1tuxat5w4+LpHAjjsFB+JTxh+7EhVVTExMRGzs7MpYCJ5jjh0KhGRACLkwo7SoMre3l66D47MSeTU1FQCgQjuNjc30xxJfHnWYDCI6enplLA4cEYe4R+JWUQkh8L1OEPG1uv1Ym1tLWZmZuLg4CCBVQ68CGh3dnaSHYIXBinztXg0sponDNwXcjAAkMBz0Ls8gMbpYxv4PA+8md/8/HwsLy/H0tJSzM3NRbfbTckVcjs/P5/4isyQXCwuLsbRo0djbW0tVldXY3t7Ozlg/ARjfqIIPlZVFc973vPita99bfybf/NvanLGdf7XwDHUarXi4x//ePzkT/5ksg8EniTaExMTMTExkYKwRqMRvV6vJjNTU1O1IHJvby+NkTHAc6831zjItu7hKxyQ28/k1yIL6Bd+m+ebdnd3U/DuoM0+3HxiLMQA+BIXcZBLAzZO3ohB8OP4UK6dmJhI9tUAv+/p/5snEaOChPljcAJbzjwNBvN/xym5f3KcwjWsr5NO+wvIAThziIgaz0jc2+12TE9P164HyIQvuQ92XArxPcdJtt2PhwyM9vv92NzcjIWFhVRUQMbwM06GOp1OKkpsb2/X5NA/Lnbhj7vdbszOzsaJEydSUcRJO+vrpM0Jl+N19BBeHjlyJAaDQWxubsbU1FTMzMzE6upqAip3d3cvsnXY6JmZmdjZ2YkLFy7EH/7hH8Ydd9wRJ06cSHy3PLEGyAlyAy+RS0As5NZgkedK/Mo8x611nshHjJJV5xTOIXL5cqwLPzc2NuLMmTM1HeQe09PTaR7D4TC2t7fTeszMzFy2rJGbYH+Rh+np6RQ7TExMxOLiYrLTJPEUa9AzF6uxN46BWI/jx4/H4uJiyj/wu8iU7bdzEWRqdXU1xenkHRRw8K+sIzpjOeXH8eUTQfabuSxc6tqHI9u6/PPLpccSX1zqO482jrse6HLW4Ure89E872rx+s/bGj4qUIoFMPCCEwFZ5ToSnoi6MhpUwPgT3Pv7VVVFr9eLI0eOJGOLYcWZ0T1l45U7DAMT/Lu1tRXnzp2L5eXl5Nz5brPZjE6nk5z86upqnDt3LhnYra2tVH0ngEAocBYeD1VQAnqcBAnl6upqXLhwIQUnEKAQwRT3mpubS84jIlLy0Gg0UqDgABzekkiTiBFE2Qk5EM+DXlep7eRxXjs7O2ktnSga8CMo3tnZifX19ZicnEyBGnzLAT07J9YfAlg4ODiIXq93EajHv3yf9YWnKysryRmzNq5a+zskqABye3t7qZOO9YkYBZxOZlhvxptXqQmixlUfHw8tLS3Fb//2b8e3fdu3RavViqWlpaRLW1tbsbW1lZIskh/GY0cMSAOvDMoRQBAkuuMRvlj3nYh4nS6HLtf4DgaDVJV0NdFJDXKfywXyYjnkflVVxfT0dJp7p9OpdTxim9wV6uQ6YtS1AiDe6/WSzhDQcS+CSDpPHLzBD4N7EXUAaH19PQaDQczNzcXa2losLi7GYDCIXq8Xg8EgpqamanpLZyXjxAZdDmiYk+XHSaJ/+BuJBzY1ryJ5vozFsklnBWvoIJmu0hMnTsTi4mJ0Op10zcbGRq0w0mgcVrqZ//T0dLoeHdnZ2Yk/+7M/Sx2o2IWZmZmLuhwfCz1SAHQpHTg4OIgLFy4kPtCxN45ImNfX11Oitre3F/fcc0984hOfiE6nk7pX6Sql2or/sm7gW7DFdBQir9gEEsb9/f3EO2TMVVnIxQbbcXTYvh/7RdUT0BJ/aMCD7zE+yxvXAgzAT8bG93d2dpINY20svxGRdNkVYPz09vZ24o2B/YiR/aXLGP+Zg3HIguOePBn3vPkb/skxGP8yR8BFxyCsH9e48xEyqAV5PB6rddnxGjxwhwpFBa7v9XoptvDzGY/5g31D9gC7rNePh7BBVVXV4riqqi7yO8QSxAPYYXdN20eSvLvqzGfYazqD4RHd79zL98POM2cXHCMixSdzc3Opsxvw4ciRI7G2tpbA2HH2Li8a7+3txd133x2nTp2K22+/PU6ePHlRFyC+0gCVZQQQ0vrXaDQSgJTrD8/PO0+wEfgbeOmOPj63fObdmJYX1oI4no4fYil2dNDZ6+/TUdfpdB7WtptywIjiLbZ6YWEhpqenU25iu408uXPFMZjzpP39/djd3Y1jx47F0tJSzbdiV/NYDnvD3NmBAnA/NTWVQEGKFQCydEu5oEBsQmzgOPtKUs577An8MfieX2tZ9WfjnpHnVZd6/qXG6Ps82vmNo3HA6yPd43L5f6k5PdJcx/1tHJh3qXHn63A5432ka/z8/NqHA/zy3y0DkNfV1zwaGnf9OD5f6r55jH2p7+TxTt5Y4efmOMLDPT+nywalbLidpBqFtwEhqcERkHh5i4wDG7piPHG2lhDkeiwRkaoMTiwxcrkByyszGxsbsbW1FUePHr2oUt/tdhPQQWDPNifmjPHGsOKgDVJQefSWABz67u5ubGxsxOrqas0psA2PeeIYcUAGh1gTgpnp6ek4ODiInZ2dqKoq5ubmao6Dji+cBFuQcPhun+d3ACnGDIgEIEhw7goXfPHWMJzW3t5eapVfWlqqBQgRdcATAA0nSkLkyqa7PHg+11lxHPgeHBykdntkDxkhuXKQbJDN93RyZuCMIA6ZZ73cAk7Cgaw7iL5S9Nf/+l+PV7/61XHrrbfG3NxcnDx5Ms6cOZOCY/51Ik8iANhiGeD/zA9Aytc5sbHOwicnEo/W0V0uERz1er0EFtA55Y4i2y3GS0WT+eXgOTrIluOIEdDuJJZgF5CH4NCBNzx2skLgFjFKTr1FBVuDjQHo9HiHw2Gsrq6mbUXovbdtkuxSqUTOkc/BYJBa5h8NKGXnaseUAwJc524dy4TlMb+Xt2F5qwWf0T26tLRUS9ampqYSiO+tQPgeklWSPUCDqampOHLkSGxsbMSnPvWpFEQ7scFGPl5ZHheA8XnO5+HwcDsEttodfy996UvjH/2jfxTr6+tJ5prNZvzqr/5q/NRP/VQMh8N41ateFe9973uj0+nE/fffH3feeWeyh/Pz8wmcQk+mpqZqxSeDQsggCS68pvvO3S6sF/KLbc2TP8sJNgd+I5PIAN/PQU9AXSdMjhGsYwBa/r7Hgz8D2CDpbrfb0el0UkJFMkh3pG381tZW2h7LmvAc+yj4684Q22qDT3wHfvFM/HIeSMJT+E0xbTgcJrtAxyW84bvuVLhU0Gk9NjgFPdycmY/nRocQMQ9bore3t2vX22djOzwu20cAt8sFAx6O9vf3U7eLA3V3PNGtSpeJY1jGu7+/H2fPno3jx48nYCmivr3MIDBz293drcUa6Gyrdbh9m7gWHTavrCfoI9vIDQwy1gsXLkS3273obxBgkTv/uPb06dOxsbERT33qU2tHVORxv+2KY3bmjs/mOXlclidI6AExj3XHRTnHMq1WK+kCdt3dZNiBbrcbW1tb0e12E9DC/RkL/oXxw7fJyck4cuRInD17Nm3pfDhqtQ63XTF/7M7CwkLaskUnKzKQA8/E0/ALn4ccwS9vAcRuIlPOFfDTEVHTNwBgeMrRF8Qe7uCkGMy9tre3a6ArRRNyqytBxFS2YcgjgPHx48fj5MmTqZCbb9vKyQk68Rqdxpubm7U8ybLv4i2yxt/t/5Bv7DbxpIsmjq8cb+ZxGLrcaDRSjOSmENac2Aad8DVuJOEzd8nZtufFGcuJsQC+S7HKtogfF0W41nrmOBPesC75GhKHW66cyyKfPNNxLPowrmBE8Zq/u0hg4Dv3UcilO+/ztfNcuJ/B9nFAOvfm5+DgIOnn7u5unD9/PvkOd2jbhqOfPHtc/usCo/P03Mc/HD0qUAqhcHBgxB4h4G8wAgOJsWQCBD0Ro9ZtGEnQQMV3fn6+Jgz8OOhC0DCyVl5X6yYnJ6Pb7capU6dSVxTzooqJcBGII6yMnesJUAHOSPK41v86kbhw4UJsbW2lAJ7n0UGB4ed3ggUH0gg1wgVwhOMm2QRkInFFofPEgGfDM/gPr70tqNlspoSfIJ9qn4MA5gDIt7W1FYPBIHXt8BwnhCiiASLLG7zCaNjwUd2zU7aRtzJTvUJuCboJqJAZAEfA1aqq0vlAnAsRESkx5cdrbuNoBWd+l5v0Pxra3d2NxcXF2NzcjKo6rOLOz8/HjTfeGKurq9FsNtM2JmQZUAZ9wrg4iIuItE4GBeCdk2obUjseyEH8lSBkjrVGn7E/yAkJQkQkIBg5dtLs7XQkR6zv7u5uzM3NxcLCQu1sOwd8jIn5W37NV3iE7XDC0Ol0altP8mvgH0EvgFy73U6AreWUdQQsRm8si2tra9HtdlMwluvbI1EOJEFOuOEV8+AH8M8JtBNugghANSceJEWLi4vpPDGuZ922trbS1gbsFvbRNoCgf3FxMfb29uITn/hEAhndAer5Pl49tq6Yzw5G+P15z3tevOxlL4sf/dEfrQFiVVXF5uZm3H///ekMMdaes/6Q9VOnTsXi4mJ8+MMfjt///d+P48ePJ94Q6OBnSQIdmMFzZG9nZyed5+BzlyKipg/W0TxgsZ4YXPEZMFyPz2i327XOMMaUd2W44GC9ZjxeR/6PLcdG4F/cEUCnI5202HgXqPb391PByMUVgkkH65Z7xmvZMOBhntgG8f1cnpzcOJaxzhGnWXd9vzz55285MOWAfZyMO0ny3yJGoBmyaiCJoJ8zvhhTDmgwXnel0ZHKWubAyqMl1rbT6ST/0e120zmgc3NzNRA0YlTY9TibzWZcuHAhbrjhhmTnLNPYYdYdfuNHOO/OW0iRUYNM6J8BBWKXiJHszs3Nxe7ubmxubqbY4OjRo3HhwoUE/OfnS9H5Oz8/n85TctzYarXivvvui9nZ2Th69GgsLS2lsxJd+LackUs4NrMuMyeubzRGxy1wP8eZ6Lh1jnvyt7ywjK3ne1tbW+mcsxzQtr2hyEQHG7HW4uJitNvtBEgBaufnnkHNZjN1fbPu7fbhVsmjR4/G8ePHEy8dq9HxaH1EfuBnRKSiLj4PoBr5BkQl/uUzd+Ohm6xHq9WKtbW1aLfbsbGxUevWgqd5zAzf2KmCf+/1ejVg5vGSu/xyewEwhax/2Zd9Wfzpn/5pvPKVr4yzZ8+mNbJdxobv7e3FmTNnotPpxO7ubhw/fjyOHj2a+OUCfW4/PZaIkb4TE+GTXeDEhjF2zk51F7Bzd/vdfr8fq6urERHp/DvzgTXPAbM8DyZXYx62x/an+VjsLxgXQNTe3l7SddbKNs9xGnG6ASfyGOMV3vIN6AMvKAq4GQOfASBlHUCfiHlcJIAv8Ng2CDDdQJ154f/bjtgn2+4ZTAMjsM9Ar+AX1zMuOuzJGwDjKXZjSwxOIaf2nY71WQe2eht8vBx6VNv3SOrGMdgAEQEpgzV66wAX8t/dbs69t7a2Ym5urqZMZoKZZYNop+TEBQe1traWtrVw4ChBCwvGwiAoNmAEqj4XIiISsso4vMVve3s77f2HCHKZB9+x04Tf5iuHrWOsAPDMO3hGhRuFZZ3yBJh1RjBd1bMjxIhERDK0gGrb29vJKDKHzc3N6Ha7MRgcbifCeXpN8uTViaYTKwIpI/cRkQIDHybLOL12jcaoKoQR5+BI7utOIJyCtzLBs9nZ2VQxyA+szSt3jBdAgHV2QHglyUHI9vZ2LC4upgMOGT9JOQbPAR6GhACT77DWli9k0rLLvdyR5USFMVqnHu98898BVTudTgJqXFHgOnTXW4vytd7Z2amdVeXtbp1OJx2c604mOuKoYNqmmA/IOZ8DdtsmABxGjM7vQeZJiDiwf2trK209Q/cJmkh48goLwfXW1latW8sO0jaXv+cJp/UWucgDHQgeOUGKGFXakEUDGwCMThxYr4WFhQRK0ZU7MTERy8vL6cBUHD8JBbqL3UTekZutra245557UoCNbfC5eMh4Dro+WsoDVQMM5nFExOd+7ufG2972tnj3u98ds7Ozsby8nGTpD/7gD+LNb37zRfcneW232/Fbv/Vb8c/+2T9Lh5TffvvtNZnAr2CnmR/JTr7lut/vpy4KulF5IQe2kfERWDtwdaDrmMC+3skknxt8ykFPg4QOZvkeoAYvKzB/DYIgY/kZULOzs7WqJgcAY2siRmDx+vp6bduwfS4JyzgQivn4Wsbp4NkFvYhRdxn3dFXZdp5rKZCxPk4wx3UosEZ8n/Xw2mAHHIexLi785DYGogPcxwLwwg3iGH64P/+O4x8xGwU0x1iPlYhPOJsM20TssbW1lV7YAyDmsWHvsYWMN49hWEufCzgcDlNCROxqu+j4xQCh1z4HJvlup9OJI0eOpM7UpaWlOHHiRNx8883xe7/3e3H69OmYnp5OXV10Ndxwww2xsrKSOuJ5SQixY8ShX15bW4uNjY04duxY2lrtboc8tkeWsLGWFyc/li8n7tzHPsvxLXPPr+OZ/f7hWZXkCgAlGxsbETHaTYKfd5ydx45TU1OxubmZ1nN2djZmZ2djfX09Njc3a/JlQAobiI976lOfGjfccEM6N9HnSBk08PfIoSiGE0uQmFourMNsM+Q+2AzrwMHBQQKuIiJtyVteXq4l/qwXwCZ21S+L8doZhBnnEx8t2Wbb5rprjGLmM5/5zPiTP/mT+NIv/dL4S3/pL8W///f/PhXj0Zvf+Z3fiR/90R+N5zznOXH//fdHq9WK3/3d340v/dIvjZmZmVhfX4/FxcX0fMf7jqXgLS/ncc4MH5zTWEd4iY8Bb+JBA855HL6xsVHLufndNsRxukE19D636TzPdjBiVFDFTnIN8sJcvT7EojzToI/BcQNR/M2+iO+jj5Zx7u/uWfjIHJkLuT/3JpYxYETszjOYn+0vz/F1edNJ7ptcODJIy7/wDp6aXy66Eu9z7izfd3HAtsNxluM1iLHnwJRB/SsOSqEMZioL4mA1IlJHlB0DBhADi2NFuakouFPDycPGxkYsLy/XwJv9/f0UHDsZghEOzsxgFpotdD4Dw2grSRrjd3XcABSCQhWEeaB0BCwIAB0IBEhGgC2oeWAHkomhmpycTOfaoAiM1ckECSiC48oHgohSsn5GqBuNw8ofvJ6eno5jx46l5NXCmAc2Ozs7yZlXVZXe3JSDUA5OHYjwfBJXB7IOTvwWBp5Nwp4H2PCDbRRLS0u1bZHwh+cxHtbA1aG8RdGJuB2ADRH/8p0rAciMI4KnpaWl2N/fT/NdXl5O1RuCz9XV1RoKTiICgAbPHVCwNgQbzNMVWiofOS899xzYeKyUA1zoQK/Xi9XV1WRbsDkki3Y4dhZs8WKO6DTbdpAvgD1enoDe29YYpLNthKfoEUBwo9FIbxGBj9hgHAPzoDrCm0LR5/Pnz8fs7GwKNt2lyv+xWVNTUylJOHPmTC1Qd6DqxILKc362iHXA4AIBR0TdJqOjyBbP5TpfY+APm4IdXFhYiPn5+dob3yYmJtLh/j7Xg3ljNwksPB/4/+lPfzoefPDBWuCKvNvfXCkZHke+N7//2q/9WrzlLW+J4XAYH/7wh+P++++vyRwArLvkuD9A1Pz8fJw8eTLZTgf//g6few3RMfQGHe/1esnPkHi4wg+fsQ326XkQmQdnri5ijw0ys6YE4jwvtzMOzBxAOjm3D3Swh4y6sk8nNz8Uq9Cxzc3NVDBykm2AYJzvcwDNvXkm8Ri+lGfmspTb13HABHbE/3eR0UEtf89BKI8xBxJdhMiD1DyRsQxArVYrVaT5nUS91WpdJHOeG8/lM7awGSh9PARIhozhL4lpkTNATWwG3RTMdzg87Pw6ffp0LC4u1nidJwLIiQHHiNGB2mfOnKltieLv2NnhcLT9Dtkxz9GLVqsVKysrCcBvNptx4sSJePGLXxx/9Ed/FKdOnYpGoxHHjx+PycnJmJmZiRtuuCEBJOi9j7DItxACxLTb7VhZWYnl5eXUZclYsDX5+lonrFfWIceo/Jv74DzZ5t7W+4ODg7SFDL3j7ErLG3wjQSX+nZ+fj6qqEhBOIWlhYSEWFhai2+2mc7s4n4p4wYl9u92OI0eOxNOf/vR4+tOfnoB/zpZiPZ2vOfHHFyND2PNmc/QiJ2QFuaB4C7hmUNsy6K427ge/LfOOCZBrv2gF4DhPpj2Px0PObTyHRuOw++U5z3lOvP/970855ic/+cnY3NyMj3/84/HX/tpfu8hu7+zsxHOe85y4++6742/8jb8Rx48fT0c8MG/7qBxE73Q6CYhy40YOODDO3Ca4sO6/sQWTmId549tYe3IFzuAEmKCBgGIT+uFt6vaVnpvjP55pn0UMhRzYpzs2Y50cB/B8bCbygl5Zxm3LkE2DkeT+7vrhOsaXfx/eMVfH53zP8mXd4zkGGyMijd92LM8j85iGZznvzX01/4dv2PLz58/HAw88cFFcwXONf/gaxo5dzu3IOEDKvHgkelTb92xIMDQYLf6OoOboMxN1JZnrYZzvRWsawrq5uZmML0YaI2VHyzNIxFgMB1YseFVVceHChZieno6lpaV0PpH3GqNsdoZVVaVk1ovo5BanwP02NzfTq9fhgasNGC/G6So8hrKqRgdpggzDq+Xl5Tg4OEhniCCUrijSDYAiEpQZcNvd3Y2FhYUU4NqBGIzb2tpKLd2sL3yvqsMzu+Ab2yFJ2Ak4HBSRQHodDdjY6Rtww8D4UFu+w1pTRRiXaNEZwmG+vDWN7znxIpBD+ZGHdrudXiePgXOA5+/CJzsbK+2VBKdwalQTMHjI4MrKSgqcvFXAhzZjnJBNd4TYYbgb0IHfOKPsRMXy6EDysfABw8gac49W67CLcH19PSXI/f7orZoYXwcFzBcgFieNPtNVYNDE5/vY2SB33W433Qs5YiyMkySGLcY4BXhv8G9vby+mp6fTAdfYBM55w1FjQ9A562mjcVid3NzcjPX19cQjbI+7MADmmI+TSwd5XnPW2utiPcSJ4vRJqtAPfAS2hO6vwWCQwLHp6em46aabotPppHM5AEOWlpbiwoULsb+/H3Nzc+mMCmQUUAQ/ggOem5uLVqsV58+fj7NnzyabbVDEgAFrZxl+LJSD8eMIffnTP/3T+NM//dOYn5+Pj370o7GzsxN/7+/9vXjve9+bQGXk0+vmbkEHFnkF1vrkwMRBr5MKg1nY1u3t7Wg0Rgdns3bIFfxELgziG4jKPzcwhh4h38yZvztIYq3sz7A96Bn3BFzAB3Jdbrt8NhxzA/A+ODiItbW1GkCQdx2xVr6v1x9/wfNcFMg7nlgDA1n+O7wz+IXf85lkPI/CmX2x4yCDYpZb+znPJU/4beftC2w3vM0RoA9+O/mCtw6kc11yEXViYuKSLwN4NAQf+BkMBrGwsJB+ByQHmHI84U7aZnP0Fr4jR47UtpU6zmR9rQ/208TOw+Gw1gHorfbou9c5YlQsQLd5Yy0ADNtPp6am0javZz7zmXHHHXfE1NRUKm7ZzzFu4kQD28yZQuHZs2djdXU1nQe4sLBQ08k8cTQwatCK361r7l5Gjuy3fW9iMx+NYTAacMr+ycUK5BW74zP1fNYSIN9weNjtvLGxEXNzc3Hs2LGYmpqK7e3t2NjYSP56YmIijh07Fs961rPiWc96Vq17yXx2MY3xuEubsaC/2BHnNMgB9s0xA/xBpqpqtIWKe9lmopc0JNBF54InazUxMZFAEK+FQcTHS5YL20h8zf/7f/8vTp48GTfffHO85CUvie/4ju9I+RDnQ1kGsZl7e3spHkPWDGLiV9CRubm5FGugG4wpLyTmAKILQhTmDHTYZiCPEZHOerTeExOtra3F7u5uOtyedYuonwFo+WE8Bp/sc2ynDSZh+91hSNclRy7QOcmxLJ6bixhskSXn9nZCiDORkX/iVsbmwpzjT3TY/p+dDOPk3DLlmMaxjv0hMUre4GBQyePgMxdS8SWsBXLnRhnOU93f30/FZ3INN6Z4vRzn29ahK6yfc+29vb1YXFxMvoJ843LpskEpB0gAT7TrEtw7ETX6CWBgRwgz8oTdwmrB6ff7sb29XauqsngogIXJAQ/3zAUnItLre6ns8BmL68DIi8PYMdjMnYoCFVKqpAgcypZvz0PIvG3GwAlA1MzMTHIiAAFsIbjppptSWzTnpfT7/dQJxjOoIuOISVxxmLz612s3NzcX7XY78Z+ODD6LiHSmBgaX6jCOkUoRsmSkGsNtoKrVGr1+lnXlc6+5g+z9/f0UIGBo7Oxs4CMODSZvVmOuDowB8gAbkV8Dp3kwbZ0h4faaOljw9VcSkIqINL5Wq5V0B9kDHDxx4kTcf//96RXQVPGcPPGWNnQRAAv9z7cx4XQMgMCjvJLgdlDr9Th+5Nflf3cii1xwv3b78FyDwWAQN954YywuLtYCXXcHMhd3JVl/uJa3t3kfN46PrUsEqoyl0WikzirkED4BDLNOOD0Doawn8rO5uZn4z7ZbgLXp6el0nhj2w0AujoJtbWfPno2Ieou3HY5bqwlmnGQYpHQw7AQVh8k6Wd/4vtvPCQZwuBGRDmHt9/sxNzcXi4uLSZf4G/J/3333paCGs7XgO/PCzu/u7sbs7GzMzc1Fs9mM06dPx7333puCL+wBNtBbuQgYcsDh0RJ8y8GHnJrNZjqElvX4oi/6ovjar/3aBEqxjmypABjix3Nnfk4eDTrBX+YLCOD158c+GpAQu+HtHlzD7waUkA/bVAdOjhFYH78d1TxEby1X+AEDBMg0/rHfH70R0wcs4/fw/1RikV+6JinI0BmdP5v5uPrv5IhnGcBxoQ6fZTCEa/i/bRf3d+cJMQtdLV6Xqhqd55OvjX23E7Q8dsvX2n9H572e/I1/sYtTU1MxPT2d1oYuEwpeBMyu1jo5YpwGiNwZ9niJeAzfQeyFLSLRs34wzvn5+bS+JA3EItZvkgcXiPDD9p3InuNr1sCJVZ6Im3fwrNPppPgbOZ2ZmYljx46lbdG33HJLPOUpT0nFL+5JcRafg31wXO1YjoR1MBjExsZGkmFiTIrSeZLoWApZckcPvIFnBqwM+kWMOkjwf+5EwA5QLKKQZTvDfFgn7G5VVem8LXzPxMREnDhxIprNZnqrIXb1Mz7jM9KLinq9XnS73XRG6MmTJ+Po0aPpbZTMAx1w4ktuZr+C7mIPvaOAtcP2E78a9KWIR+zD8wD62u12bG5u1mw9vOB++B8XcsgV/AIgkn/W5ErFyGtra7Xzt6wb2J2jR4+mM6IiDgvxAIi+Ni/sOEZ1gwNxIEUA4kN0IQeIbbucr2DnvKYRFx/k7VzJABU+gxjTugrPecGYz1rNgXF3tzvPQ07QLdY9B3bgOzEKvKJJAlk0v+17+Tt5BUVf7zYwD+C9ddpbTSNGW6m5No/DbHMYvwFcA7zEmY7p7QtdUMJf2KfmMZB9J8/n3D9AcmJrHyEC0EeMdHBwEPfdd1/SYcdMjIeGkxxopEDX7XZr9sJrSh7iGIxml8uhRwVKNZvNVHkhOcPpIHhGDgFGLqUgVIkcGLKQOKeIUYVsc3Mz5ubmUiBosMLC7sVDGGzUEAgE2Khno9FIAY8rhrRicy+jvVSNFhYWEsDS7/fTdr28q8mGnutRZAsrSRC0sLCQgJqISHuU2SJHNwDzgQ8I6Pz8fJoXTnN+fj4F1XY2rJWBHLbMEESxfY9EG+PFYbc+PBHEO9+z7AqVnXpEHUXG+MB3riNw4O8Okm1UjKrzuQMZlAgZYpwGMg0mEnh43zKBkM8Zs8OwgiMHlvmrAUp5X7r5vrm5GUeOHEn70KlgPfjgg+kge3jNXPxiA+YJLwgu4LcDXHjI35042bYYnPRYsQEkJjgOWs1t6CPqbcNOmJvNw618p06diqqq4tixY0l++/1+qt7ROWlZNEBDcOkDtJk/8sN2EgAinAUH5DNeeG0Hap45cXNSjP3DEaO/OKaIqL0NyFVeZB37tL6+Hmtra7VgiM4OeM/6Mw6cdsRoq53fkIOcOEDClsBPJ81O6rBB1uFms5kOH+Xg4IWFhVheXo7JycnY2tqKtbW1FLC50or9JjDMx00S6K1P29vb8alPfSoFW4wVf+I1ZcxXgnIQapxNgHeA6az3fffdF2fPnk26iV/BfsGHnZ2dOHfuXNx66601UN339zoZrDJglYMOuc5GjA6BRu+Rn4iLDxc2kHKpBMSJdM6zXq9Xkx0HdQY/7fOdVPjNWATsrrjmgJCLG+gUSSvgPnrg+fJDEcqggAtozA0e5YAOn1tO8oo793O1ns/dsRIx6ui1X/W620973b0G9sHw3rqeAwBeZydfvif/YuuRZzo6OdOHsfo4A+YMv9FfA6iuUD8Wso32GBuNRgIkiQkYH3o3OzubbIeLt06cWCsDo8S+tk3WCewp8aLlxbEN30H2AAzcQdhsNtORC7w4ot0+3BbMdml4C5AyGAxqb0/23KyPBpUYL9dxODYFXmJh1s0FUeui9cf+A30k6SSeNzDKD7bd/tYxB/Es60xcaICZnIj1rKrDnQ4rKyu1zqrt7e1otVqxuLgYt912W23r4sTERNoK1m63Y2FhIckMxUMOwUa/8NcuShBbEK/hH1hfcgrGTlzjHINnGShl62qz2UxFaXIi1pHP/KZ0ujZs8ycnJ2vbRMkv/BbEx1v0MZHT5LFXRCTQsdPpxMc+9rF4+ctfnnjpoqpjT2w5+Ri6hM4jy/DbQCJ2xP7CvpfvuBMH4jvOr5HdPMb2ddZ57Az6SPzU7/fj6NGjtcPTAYac644Dm/hxUTPnf6Mxeis8a+C3K1u/HA+jWwY3JyYmEujCPH0WoQElYwe2ocRI3BNg30UAbAL5Tw4s8i9j5Md8Yf7E4+SR3IvvuKjmOTgmYN4Gl5A/YxYcOXL//fcnu0ycjx9EltFVxjkcDtM6eezwDp7mQL9zCfv2h6PLBqVIBn3AqRPdiFEnCs7FiYWTKiuyE0g7KIwiAsFCbGxspIrrcDisdWy4qsgzUCIrsQWnqg4rGBsbG6nlmvHwfaPd7XY7IezwgH23KO729nasrq6mjiucj4MXFvXg4CC1vbLACMi4hY84DM7oOqIqx7Yi8xIjikEjCZ6enk5vYfLZKrypqdvt1oJVggrWjMOCOezRZ+swFnebsV4oOXMxkIWBzJ14q9VKbZ0Qhs/AG8GVuyR4DooE75AHt1czBmSCz9jOxxqQQBjcI8AwoMI9MFZ8DyNjGWRcV4N8RsDy8nJap263m3SagCAi0sGmDmRyYA1+OBC2rjuQy7sTIupnPlhXDcw5ODQgxT0YO50YTpS5Jq88YL+qqoq1tbW0vYtKAlVe5oU9QlbtVOFZp9OJ7e3tJKMYbtqNZ2dnY2FhIXVXmQfIQKPRSHYA+YQPBAyuOu7v76dtaAbt6CAgYHVbriuc8HowOHyL0MbGRkqeCERY08FgkM4kgI9uEWdMjcbhljcCSwO0yIYr+tzHeuH1Zr4clMrWSsB//n7hwoXY3NxMfN7e3k5naaGTJJ2cjYA/IbhnbpOTkwm4P3PmTE0nHWwBAlrnzYvHQ5cbdD//+c+P7/iO74gPfehD8WM/9mPxvOc9L97xjnfEU57ylPjKr/zK+Omf/umLktz9/cO3TL70pS+N173udbGwsBBf/dVfHb/0S78Ui4uLaZ0i6mCYE7Z8zZ2Y8bkTYCfW3ooaMTrM3qBFnlzy/Zw/Bqfwwwa+HJz7vk7Sbb+cbNjPUtDxnJFRgnIS3WazmbqjAZ8NutjmGSwxn124ymMWZA9d4X7EID6k3Em3k6ncd/JDAO+jCuBvxOitW/g8xmT7anvmOeXyYD9iwMk6xryQSW9l89hmZ2fTK+ThSV5Jd1yY+xh3ljxWcmJi0B87RtUZ+8ZbW1mPfn+0TSXiUMZPnToVy8vLtSTJBwojq/bF7XY7vUiHRM6xLDKV2zJsWG5/czudF1bZYYCO4FMASYhN+ZwOYuYcUe8GceKJfnjLGJ2u3oZGJ6PlLS+GAMo6tsMv+GB1A4D5WPBp8AA/jOz6eSR7kItKLuwwHvSWw97Zdo9tGw6HNf3O4wgX3pDrXF4iRh3GxIDIJuvjHAPA18CK42jkBl46xnVBErvC2uQFaMsycQ5JOok09sO6+3hpMBikl9TY3vR6vfjsz/7s+B//43/EH/3RH8XLXvayePnLXx7/9b/+1/jIRz4Sf/Nv/s2YnZ29yBeyHrfffnv83M/9XHzDN3xDHD16tAZYWC6dlzjOhA+2fdbffBeC729QCV7nthCdd0zMd8lT0a1+v59AQsdpxHkAhhGRtqdyDb/nviCPH7Bvji8oHPj/3NN/d3OB9QLbAlACP/CJESO7Mzk5mc5ug+Ahtg7+Aaza5vrf3CfzHcuX8yVkPN9C6LgI2ff8+T/3IfYmzgM8w8b1+/3Uvf3JT36yVgzm/uZ3RNT0zv7CuQl+gOusS7YnrPHlbpW/bFCKQI6FxBC6is5CuALnyhXf9+K4gh0xSlLdaeIgYm9vL7a2ttLbHPx3/9+BkAEWmG5klzZwADcrNvdylZjtHY1GIx1wur+/H+vr63H+/PnUgUA3mZWa+7kihBJ5m5eVFOHx62dJMBgn++IJ/LnOCQEO0wrgDiEEamZmJlXh3TLO+g2Hw/S6Vhux9fX1pAzMc3p6OmZnZ2syxH3cbcYYcJbICGOOiFpygPwYQMDBN5ujAxZtFLgPABRdE/CD4IOKRH4NwQTjwFi6Im6gYFzA7Yq1E8DHm8yOIyqrBF6AJ1Q8V1dX48SJE+nVtxxyyPlLBFOu6OegD87VCY8rCcwPPsAvAmAo12GcAdsMcb5cy7YTACCCIdaBgJhxjausnjlzJvr9fhw7dqx2HgL8QsYIvHim7RvgjwFit7byhh7Pxw6VDp3cuOMoCBYMFjmhRf94NkECFTsSE7fo82aZXq+XxseceW23A2bmh+ygp4zN8t5ut2NxcTHZFtaUe9oRYlex/VVV1aox+A5s2OLiYurObbVaaawLCwtRVVV6uyfgE2u0sLAQq6urqfDAobqu8LVarVRsWF9fT9uTHfwD8ObdChB28vFQDkRcipApVwF//dd/PX7hF36hdtCnA0SA0r29vfi93/u9WFtbS0CoQV3G4cKSkzvbaHdOWX6RC77nrhW+ZzAKn5W3uBt8Yt72Q2ynJOHDD8AfvuvkyG9azEEOdC1i1DGIzGPv2eaJr3HXQ6/XSyBJxOjMFMcSPNOd2LaF8BG+Wu8texMTE6lThe7lcV3DXk/W0LLmQmAOPhp09Jqhk15jg05+ruXYibNjjksBV4ybuAYf6yLR7OxsbG1t1cA6g2PWUyfP3AfQ57GSK/PoBvLeaIwO+jeAuru7m2JHOj+5fn9/P7rdbjrzEX/q+AWb6QIxMU/EYazlrWWA7vDG5yQSTxqUihh1zjmhQ8as58imk+R+v592ENA5b5AM0MOJd67jjlENuliu6bjhby6WMD70jBjYRdDNzc2UUMNPwB94ure3F51OJ80RWbOMYyvcPY49Yy7cE72h249ii3XDcRCxQkR9q51jGr7rnIk4ia25rB1JIoVsg+P+voFS9B97hwyRBBsEwTbA91xmhsNhehvy9PR02g7pXAiQH1vD964UKOUOKeSJZ//RH/1RPPvZz46qOnyBxR/8wR/EHXfcEc3mqFuJQr3P4PrEJz4Rr371q6PRaMTS0lJNj3gGv7O2zjNZL9s++1X0E11kDrZfBnscp4/z5waC0QvytW63m8Bfv2GaeJ9zqZArv3TG4JRj1Rx8ZE3t5/Cj/Mu9bJciRr6aOUOMybmkr0HvuY7jLtx5mPssx3TwyjkjnzsHdBEFwr+5e4kiEHy1LOCncv+KXfFLBeAjPhH+AfCur6/HfffdV+uK81zdael8mvvjYyIiba/Fv9gfMy74Yp/MTpRHossGpVAUDCGDzBFDfkdYnXDxd28Rg0kIrBnsoMIAwPb2dszNzaWF48DfccGRqxh5QAtYgzGqqirt1UaAXYmxQSDoXFtbSzza3t6O9fX1dC0G3AtrgfMBguYj48GxR4zOqmEdEGoCVX4A7oymIyAHBwfpdecItBNaxobxweEwXipF6+vrqUOFtl5eQ4+BxmktLS0l3roy40qRE1MbTIgEIpcj5mr5c5WG+TuY4ft2dozBSbM7fgBO+XEljHEC2hitR0e4T47G2yk5kLpSNBwO09jRIVB0zlRCd1yZOXnyZExOTsaFCxdSxW44HNZaYSNGB9wZVLRB89zQcVcS0GkHURH19nwnvtyfa+2Q8y6MvCpF0ood4Fmbm5sxGAzilltuSWevMU/GgLwSMADGeWve7u5uAs9w6PCAZ/CGO5xwVR2e1ca2N5JcnBNBAvKDLLsTAlkjKIC3TlSmp6dTEOguzs3NzVpXkwFVt5Qzd+sNTtcgAPLGd+fm5mpnWnGdAUa+Q6DAelkveYa7p44dO1YLLgCP3cY8Pz8f6+vrte6o/f399LpyeMM8jx49Gvfee2/cfffd6TPuC6+9LS4iUiKIrD1eUOpygu6qquIP//AP42u/9msj4lDWP/KRj8Q3fdM3RUSkRDcPeqkO/+Iv/mL8yq/8SioaACLmQI1BdQg9NrBqYCUPirkH1xGwubJoEDQHNWw7mAuBl8ER7CsBct49YeAJf2tbYB/i5xJ88Xz038khsQD6lFcEHRvZ1hsQhf/MYxz/IiKBcAsLC7G4uBjLy8vJbhMcRozeLhUx6rZ0DOTqM9/HZzL/cQGxwRfGxN9zYCkP3P05vsB2wTLA9f6efT/fJX7hvCkXR7AfBlXyRNdA6mMl9IFY0T4C/9Dr9VIXJjxEhuyXkBfWBPDIcoleV1WVdgggNxMTE+ksGK4jxiWp5l98Kok2CUyuC/bt6BgJlWM9fmfurIPfmOqkBZsBD13gyP0A6721tRXN5uF5ehGjXQjoNGsB2IhM5MCki7E8zx097nLkzWkc9O6uPLoL6SzIu7O9Ro6boHa7XYu37UtbrVZtmyAFM/gGj80nYg62WToGc4JoUMLdIxTenEw3m81UsLEfRk4ofjiu4vvYQuQVG4eO4L/9meP4PEm+UqAU65UDOBGRdIHPASx4GZI73nL+Uygjf4D8jJwPxHPwyEUJx9H+P7pr2fA1uW22DcnvwRqbH5yNjL9kFxGxFIARcRXfdzGI+1r2I+pAdm734Eej0Ugdl+iVz8/jfo4jsfe26QBT5Ak8i/PYzCvnpOYbf3cXk4tXrIH1yCAj9sRd9b6XsQt+uA7ZyYEyyH7asXq73U6x8NraWnzqU59KsgV57uTzjuHy8Ri3gR/NZjOti8Fd5JtrNzY2En8eiS4blEJACOIxiO6EGg6HtS02EaNuGDNhHOPzKiWTMbgDU4fDw21oCFav14t2u117LTMVXRsMmIYDQHmqqkqvy+z1einZRJAwUhGR5kcnkZ9HAOJuDAdJdiwYYhyPW3wJ3h00s+gYCZwoFQ8qpVR2ePsg86VzCoOEoQeQIrhrNBq11xS7NRfDQJcW9+N1ovmB9vCR9XVy44AXhXaAPC7I9YF5ESPgiUCM529vbycZwnnbEWAMMQw44aqqEkBDIsRau202Ii56uyMyPS4w5zveGpEb0yuR0OYE4OH9wXt7ezE3Nxfb29spWB4MBrG0tBRVVaVAE10+e/ZsetsIOupuFvTLHS25XgPYYS+ckLgiyDNwIsi/t9ZE1PdMIyu8AAAi8DYAAn+9nZhkZWNjI8ks1a+8LZ0tYQThjBN95q0fEZEMNTKDXhOco3M+3BOwHUeKzeV3DjVEvggSsCGWQzr+pqamYn19PYGTOzs7cf78+TRfOmXs6JF/utR4kYLBHSfB6CbBFVVcnCMdOgaVWSM7L8YSMerCM6jC8xcWFpJcERBPTk4mniMr2E2SBwOc2AYCrunp6fj0pz8dd911V80WIq8Q252RTTtvbNgTQQ5A3QnBGjgg4Hr42mg0UoeNr/F9rWt5wMm/7rixnEdETeYtr4Al8Da3g8zHgWwOXuQdagQ7gO4GdhxvUKxxMmrgxIl93lbfaDRSMYckfjgcvSWXbXv9fj+9IdKFC9sSx0f2TYBc8C+i3mU6OTkZs7OzMT8/n95KRic2zxkMBmk7MuuTJ6185qQ2T1xZB3TGXYUGe7geXvonlyd3YtkHwhP7T3yF199Aj5MpZIot1I57Go3RW6KRK+xvrhuPlZAzXkSDjGEP6GKfn5+PiFGxkblubW2ls5icAHEP1gC+8ju6MDk5Gb1eL8kgB9fDbwolTjrzbYvtdjudG8URFADy6DExK4CJO47w/+6i4feISNurm83mRS8NwH8gTx4nMYmLkoBEzebhWVckfYwF3hAno9Ns7wZUAnhwxwHrCKjAmACknFRyrbtVXXwG9LcdwgbAV+QFXXVC7fyBPIt7ea50Q2H/kQ8DFjzDcRfxv2NXgFUXwT1++0TWb2pqKrrdbk2XsUkGR60vExMTsbGxkcbh3Ghtbe0icCPPFx8v2aczJ+uygRtyKxdJDajwXb6Tg6u+r+1W3kkTUe84tCzbr+egge2G4yWey/9zW28Qp9Fo1F4qxbjRO95AbP+6tLR00XpwT+bosUWM7Brrm++WQZ5cBMIGcD/LjPlIPOd8kbl0u93aji3m6SJcXvRAb2wvjSk4dsWX5p9xD45Ese3OC1Hwz/zgOeanbYQLZR4r8nPmzJm4//77a/aR71m/uN7ylefhyCdjAgegOOliI/NxTOy45+HoskEpVz6MFGMMncRDCCjgjgNcn00AIxESb+/BaNshIlRbW1vpjRzu+HAlwAz2whpptpDyClaSHh/uBmhBEspcbKy5r6tyKJa3peAox1UG3DXkMfPdiKglzoyB7XRbW1tJqX3ottuA7dyo2BlM8bMIrnu93kXGlTnQueaOGwf6jJXvE/BaKeFVjoY7OEZuCMZWVlaSYqDsPNsgkANz7o0CIV8kacybs7MIwKiQE4ChFwZVeHYOxjoZtzHAyF8pZ2vifCEMIgaCeSCvbv+mpbrX66W380VEAgTQAfhsGcBBs44GluGj7QM6g4xymCc6D3+RWxtEO2sH+A7wnJD6rZU4ZnSQ7/K2SKqXyC/t9ZzJgAwhK+g655tgH/JqHHLNVmHmAqCATBNkdjqdBL6RKPtNmVNTU7G0tJTsBW8s8mHwyOPu7m5sbGzEAw88kLqy0CWCStanqg47uAB74TWybkdj+xkx2jbCvfyaeeQC4B67ap6iS15r5AgbDYhncMnbGPNtVVRzWHsO8WTrJGdL3X///bUuUgoN/hf76QCfeaFbTwRhTyH71oj6dqhxCcXq6mp6HbUP1sX3eqs1a8k94bMBFQdTDs4gJyXwEN9tUDQHSA3YYIM9Z/M8B3hYH+INZMuvZ0c/LG/YZAfTBmogbA3bZg0acK+80AAfHKM4KXQy425L3grJWz2JT+imRN8pSOWxDTrFj/2fee/vcV3ExQfLO3Eap7MmB9cGH/13z99xIbyIGL0RCH9seSdZmJ6eTm9FZd2QX2wOBSXrzeMh7APzRy4YJ9t8dnd3E2BJTINNgveOLYjxnMjBPxfxDOCie/gxfN3CwkINEPa/Tjg4J9FdEradjm+QH98Df2EZMvDk2NBdOszLz2B8LnwQD7gjgu/Y3mMv0EfkwvmI7bz1HeAs4jB24VzDiEh+2HbVnUK53DoxJ84dDofp/ErGY3ttW0kMbzAQYBwfTWKegwAAc06urTPELo1GI8mlv49tcSGHv+Vrxxohpy5gj9Pv/f3DlzLRDIBvhe/Yevys7YJtx2Ol/F55QYnclljG+QO85LuOKYm1uN4+M5d/64jtsv2Z1wtZdnzN3xgDOpAD99g/r5NBCoNwBqyI6clnyNn6/X6sr69Hp9NJxUvHaciXdYLYA9752Av7Qm/lxL4BYMF/7Cc+HFmEP4yDvBDeeIs/ubZ9hX2xwVXbZP7vWIPxOx9nrIyN+N/NDDx3XEOC9cV2wX7AgJR/Jy7B5ht8tvwj8y76Oy4aB0xhLy1veec093Nx73LpskEpCyeG0N0MRiu5nkXxgbt2rkb9Go36GQskv/Pz8ymx6Xa76WBtOgaq6rCDgUO3O51OrR3UZ58wLv5GMs3CuSrkdjvmwZxzJNvJHERAikFHsIxIYvBJXv02L4wA1T/zjQ4xwBmSLowK/HJ1k/nRTeADU21gaVPFofT7h4fdEQgbyca5IoQ4NmTB3UR5gA+fndA4oYyIWmcDThdlpmoM8LK6upreRBYxOquMZIE1weh6TSJGyDGGknsQ7LBGvO7SyQ73Zs2dKNvx5ICtjdqVcLQ5HTt2LHV2TE1N1ba12IBFRDpbgXHOzc2lLqqbbropzp07F6urqyko9Llm/G6HCe8dGCFzOAbWlATDr8i10UaGCOr4cdDN7/n1DgDReQcHVVWlFvKIQ/lfXV1N58YAgLC+vEnT+u6DbW0DSUpcpYccDAACREQCR32uFTwmcMI+2XlTsSWJ6HQ66XDDvb292NjYiLNnz9YCQMAuQDvkmsCXAz2d4ESMzmNAP9y2HBE1PfI6+1XP9gfMAefnLd0GfbHPgOhU7ghwADlcpeXthvCcQIWAgw6FT3/602kLpINz5JB72l95HR3IPdHUarVSV1wOQkXUk0/sUcSoZbvRaNTejOSkygk01xo8tI93kOhA3kAZ+optBCBiHdEfyxK+LvdXzMVjiqj7+Bwwwx65g8GdMx6bk1vsDQBEs3lYfeUcMwfaACS2T8QkDujsBy1HzJ1OT7pYWAMOZsWfzczMxO7ubmxtbSUZdmBMfJMHpdhDx0r8znx91hH+It/aa3/KXJyM+MfxCITOw0OvJfNAdrAT1nHiOOIdbBn2jfGh85aZccnAoyXbMvytE0NkBr8D3/mO4wKf8QPoyLUutrClFL3wGHh7tLtv8Nuu1ue6HhHJpzNW7CNdvY5FmbsTG/ODRJc54ntJmCJG20XGAY38DgjDddhvxyHkDgYX3OEVEckfzs7OxtmzZ9P6u6PZiR/6Cl+RM+bkpJm4kgIL43eCOxweFko5R4rxst7OI9wlAoC2uLgYc3NzNWCOvwNS8zz+n4NltpUGRlg/xs78fCwAemSwg8/xy+ZdxAhUA9gkxt/f30+7TQC/BoNB2m7JvQwK2H49XsJucs4Nfgf5QW/dYQK/sSHuUgFw4Fqvt/UNvtvGOel3x42BIlPe3WbfZqAKHfE6+VmOWa3TuT/lM6+9ddkgHLrhgodtBOS5m7eMz80czJlxw1/4RIEyYmSPyNts88cBLdZxeGQf5RgU/+O5ERPwXeQVv4r/ZK55A4Ln5DFwvXWPuMT+2nESP91uNxVm2Ha5vr6ezo+F/7YxxC4G25h/no/lMuq42SCrY+lH42svO4IGkPIZHDzICmXE10bTk7Gz5D4sZL/fT62+y8vLqWvBiL8NFkkiQBWJCYvKD87YQpALlo0yQaWRVwwVXQZGz6mG+eAyO30HRX7bgKv9DmDsZB18k9xRNUQoSS4nJydjZmam5ph4NgrC58wbBeK53W43BXUHBwcJnPBcSDrhIYmROwjMWwumE02ei2GZmpqKhYWFdDaN93HDB+ba7/fj7Nmzcfr06SRTedXL3WF5IkNS4HO7LIfIKoEmcmTe0VrualTu3OG9x2F5ulrdFefPn4/l5eXU2k8wYEPf7/dje3s7rSfnHwBEwoMjR45EVVWxvr6ekiSqXAAU3rpm54eM2gn6QERkFSAEfcuBHOsuHVXe24/tQaewR/1+P3WAGXS03eF5zWYznRtBNQ/gk/m5vZ/tIjnwSDDtjsqI0flxBJboYrPZTLKeJ1kG621X6Fy0zaFbi8C/2+0mwBaH6sDZYN9wePhGJYPbtPG7kMC8nBjiH7BjHA7L2R+MFx1zMoYNckKQV+wY787OTgrMDfziEww2O8gBXOz1euklGdjDBx54IC5cuJBAEif2BJxcazljHq4EXamg+XKJdaIiaBr3/xxw8lkCnEXD+nM9OhsRtTW2T4XnOd9ta7HLrupzXd6FlieITg5tRy8V8HKNu665J5X6PGDiOd5KhE/0ixDwKWxbN1/gl/0sSb6LD4AHDsqRK+Td4By+JyLSGNg63Ov1ko4zf+bN/PgecwAw4/55wufkzF3f7lbk+4yXoN3r4A4CA1MOWL2uDoitU56Dxwjo4BgQsALZ8fw8BscJj4fgi4F4PkMuIkYxpc8PxLYwD4At/KDHh+zaLpJ84QPoMnaSBu+Ju6yX1lv4lPvcZvOwYMSYkGtemDEO6Mvv6+QpYrSFPgdBuRZeEQ97Kyzb5Dkf1QkdY2Ht4V/eJUEns4FO5wO9Xi8VcXNC/53QE2Ogpz6fiuexJvwNGbFPRtfsg1dWVlJcADkBtu7mSa4BDeuV7bHBP+7pDme+ZzDF62o/gIz2+6Pt/gYgsbvwjGdtbm6mnINxYWsce+SA9mMl7G/E4Y6IVquVzqB0jBoRtQYIeEVemst1noAjXwb/3QHl2NBrAr/ggbvV4J2PTzDQBKGDxGfW0UuBTowj5xXjQe64l20WTQnWe75vkM55q5/LHJxz+/mAzxSU3OWEDKLf2A/zwueB2jZbFvO1y0FY85kxGLTiWfYv5rd1051kkGOiHLzy/VgH29B+vx9ra2vp3FriGPCAiYmJWFtbq8W2BjV9f/6GPpt8DXNmTMQLzMX/Xi49qjOlEGwmaGPlAIQtECxCrqAIghWw0WjE4uJiLC4upkoCDmh7ezsh2wTR3BuklgW2kOBgjJ46SYbxMNRbQAyoWfEtXAiyUd48CGNuOCYUgaQIw+LAFyFmHgilFxcHBU9RBBS20WikbYgGYiYnJ1NnB8kma8LWNLqpIkYHSO/v78fRo0drYBHVWa5lnMhAXmVyoIJSYYSOHj0aKysrsbKyEjfddFN69S9zYywcJEuyjZHDoVnmUCgCszwYJhiAJxEjQ841Ozs7tQTaXRysvYm55b/niZevz43wlaJ77703nv3sZ6e5siUt39sMcEKwh2xxbhtjpJWZt8+QCDA3qtLeuuiqks/1IXBB3nnrhysP6Hqz2awBKayZ7VFE3fjlQDgBHk7GskdHEPzAmK+vrycAeHFxsZacN5uHFfkcSEKOMNquBJNwAhwjywCBrmrhRJmLk10CPmyMW2j5frfbjc3NzTh//nzqLMJuOxm1/eRwVEBFtlO6ujMcDpPd4Cw2QJk82GAOBs7p3KIjzBUx2ws7RM99d3c3yaVlDnlkPNhtV8nyTtipqam4cOFC3HfffSlgMaDuAoTtPrriYArZudKgVB4UOJhEp7zdOU++I+pVyTyowlYTrCwuLo6153wvB4sgnsu98wA7T2YiIgH+1hHfi3kbuPc8c5DQft7FiRw0GwxG5zKiW8zVBTNvm+G+XMPLDJBTrneBy99hfugDxRDPm2TfsYSrjnnVfG5uLra2tuLUqVPpzZB8F7/nJJV4w9cZuLL8mg9VNTp/h7/BV3wLfLDc5ACTk2f+9nB+0b7aPtj6YDsPf9FtdNTxBjwl5kI2Hg/BW7prnUBTZG21Wqm4hVy588a6YzsfMdpGha+liGaQg+9yjmdeRc/Xmw4sx7Wej/UdeSJeYt3n5uZieXk5Dg4OXz5EXIA8eP1ZB4M5ljvHCS7usE6AjcgJwDJd0NYR4hvuiZ8wQBURyd9iF7Cj7rDIOzCc2BqgJmZCtpwToIv55+aTY0bW+NixY6lQcClQJpcZ2xDzBXIMbJ2FB8RU7AhhdwrPpUMG/8h4baudnPb7/fQGRvxoozHaXsp62Lbl8k+seKUAqYhDnXrKU54SP/MzPxO33nprvOhFL4r/+B//Y3zO53xOnDhxIi5cuFADDeCX80c6VvmMQkEOTiP75r/J9s0yZwKwNEBlH2jbbH+TA1Emg9S2s77WsTVr7JyHa5vN0VlxPkoFnbefRSZtX5gT47KeAMDZl+cyHTEC4JvNZopJjQVY3r39lHvYP/DjuMX8Zk3Qb9/HfLZt5zv5v/lc7DN9rYsSzJdrut1unD17NsU1BvSxT2ArvPyHe0A5iObCuG25czxfj51DDm2THLc+Ej0qUCoPUN3uOA75dEDqSRpZpJrO3tS5ubno9/tx/vz52NzcjJmZmfQqcFft19fX0z0Bk2gjdIDI8w2MuXLE736Ll/dh8l0UCPDKSR6CRYBtY+2FPDg4SGCLKwIkc1S7HJAbYWfRSdDpTKIVmPNB6CAa5/hwmoPBIL0pwo6XtaGi4U4l7n/06NGYmZmJ1dXVGsqaI6X8S9Kd858E/ciRI3HzzTfHTTfdVHvbGGch0erb7XbTFgW3TObJpB1oxCG4Mc4x5w7aiW9etYN/eQANKAgP+Z3xGCgzGJInAVc6mY2IuP/+++PYsWOxsrIS58+fj7Nnz6Z50jmC8eCn2+3G0tJSTExMpDe0zc/Px+LiYmxsbMRNN90UFy5cSEAlXXVeB3hnJ0ZAy1yRO9aIs9qQJYMIdh6A0+a7D1n3GmNvJicnk1wxf/ifO1ivOWDVuXPnotvtxvLyciwsLFy0rW9hYSEZec+XtcZmME6fCxURCVQy0Olxkbxgi3Z3d5NMcx2B42Bw2L57/vz5ODg4SNVgB/3wBP6h4wsLC+m+/B35wAnZITF+eMu6INfMlUQGANDJgJNfkh07dQcsw+HoTVGNRiOdSUEFHz4PBoPUXesg0OckYW/PnDmTbBZJDsE0vIqob8+DJ65UX2lACv5YlwjWWFPeEovvNc9yEMCf+/7MbW9vLy5cuBD7+/uxvLwc8/PzNT1xImaw0ck04+RZ8CZPgGwDARBdCPK4DcpwHwdtBDz4ZNbFgSjzdaJtfuCL/UYyf8ct+HkHc0QkOwEgQBzg+Idxsg3ewbJ9EXbFMmXwMyJS8rOxsRGnTp1KADX89jzzLlzHZ8yHAJ7veb55MOoigIEpbAR/Rx8tH5aJPDnLZdj84dl8l3nwbOKpRqNxUdcrIDy+aWdnp5YIPdrzLsaRYzB0w/+SdDNPrw8xIddTBMu7ESkUYpvp4KmqKnVCcxwEemtdcgyMPruo4+QL/hmk4HvE2HxOMaPT6aTzDTc3N6PX69WSKSfH3Ase2MYhSzlw6rOq+CFG9XwBiuAtv1MQQa+Q1/ycrXHAhxPRiEjdbcgf48a/86/9mcEM89fxfbN5uF1yaWkpxSy5DjvRsz46UUeH4beLCPbNjtdy8IEucbYGI4fsWHFs5jVjvKwP9pTiSb/fTy/ZyW02McS5c+fS2NDPKwlIQchvRMQf/MEfxFvf+tb46Ec/GrfeemvccMMNKSG37Dp+zG0WczaARi7qszYBz+E3+Rx881q6EGpfjJ47fmKtAcHQU773cOAz488BNGQLf0b+iFyzbtbZXq9X2yUQMZJ3x3zMj7F4vPhl51aODW0bXCSx74evnhPzgodeK8d72Czrb8QoFud5XOPx5TEM43UeyPVeizwW8PdYf+fybEfe3t6O9fX1i2JV7CZ2kgYYCjd7e3sJdGaN7C/QW8ch2BByH8cnvkezWT/HdVzh6VL0qA46hzEQyYEX1crov6FMs7OzMTs7mzojOp1OLC4uptel33PPPSnoRkBJSqiut9vtlITBNLqpAKlc7UU5EEAni1ZMX4dxQRBwNA7E3PWEANnJWQgNyvkefC8HKAi0fW8baroCcLydTqf22lbWwEgl1WN45jEQmHNmF4AVlRoOVnRb8tTUVOog8YGFGJIczcfg8vn8/HycOHEiTp48mZIgB4k7OzuxtbWVXmXvKjJGjypdRP0NfwaVWFP+TrXH62CeRIzavV0RAxyxcbYTQUExAE5qvO78HQfuJOBK0nA4jIceeijm5uaSw3eA4eSSSm7EYTuztx2wz5/1X15eToHLxsZG3HvvvamjhCSgqqra26pIEFgLHAgOrdPppHOvfK35zO+AIDgOn7mBnhDw0ZFAa7HtCvLB+uGkCLypsGJgNzc30zh8NoY7cNCzqhodCO+EmkCY5Ahd4lncBzlxhwh66u40zrQZDAaxsbGRdGV1dTVtD+K8Ke6NE+I5rCm2Ap4AJmMnXZjo9/tpiwPb6XD+3tpMMArwxrqhy3SL8AwCHQOWrC3z5Fp4Bj/RXyc5BGcG+emAuu+++2qdtei+AYU8ARjXmpzb7sdL8OO5z31ufN7nfV685z3viUajEc961rPiJS95Sfy7f/fv4ujRo6nDM0/+x4FUOdlPwK+qqpI8HRwcxMLCQjpMN6IeqLlKx3cdzEDjwAUnqxEXHzqLn8vH7cQz57eDbgfxTrbs+52kR4y2qbg70XOjC5KOJHwC9m0wGKQ3Z9KRia1FLxqNw25CugW9jZ84x8Us2yLGwMsPtre3E/DsBAe+We75nHF47fwdg4x5UM2625bBB+I+AwyM33GGbb5tXL6mBlXHAaz5v+gl98G3z8/P154HoG9+28Y+VsIuuSoOoIS9GQ6H6awnbCkyAhDl5B4ZZM2QY+w59/d5jTMzM7Vt1S4E5naJGMR+y/qGnDBWdz+Mi+koMDYah8UCwCnO7PH4/Qx3QDvJ9LjxLeN4w7XIE/xFl6vq8O2GW1tbCZSyL3TMh0/ley7oGjzyfHi2fZntJH/rdDrR7XYTn/OCPsVegPFxcWVubyPq28XyZJ8xumvU68zccuAKPmxtbaVxASCZ1wAqxP3Ww4hI4Cmyyhh5huM65ut58DcD3J774yXGMDExEZ/1WZ8Vr3zlK1OybTDKeZ95By9tb5FhxwfEXOS1zWYzvTgHGwBIynqRjzBfYkqvle2NgaiIkf0FhGDcXmfmlNsJ20zW0dfl83e+wxjYSj03N5eeTW7gZzMW7PLS0tJF8prP2/EEf7c+MX7sL3OzDNm+mX/EjwZR7FMt+8wXvlimLaPIcM531oi52Sfio3OfzD12dnbiwoULya/nRXDu2+l0kuxxP/QWmz0/P5+6XDmOxIVgeG7+QcT7XouIUfHO/t3y83B02aAURt6LiCN28u1kkQCNCnmn04mFhYX0OuqJiYno9Xpx4cKFOH/+fGIayRwVQYMNGH4fAB4RCUjhehSSpAOFyB22Fypi1KXA9jQfDm0Ayr8jKE6iIXhVVaO3DThBdRBDIs29rLSM1UFrVVXpXB2qGGy/IfitqiptEer3+6mNFsfTaDRia2srms1m7RWtPpvLCSLnsgBaRRx2StCpxvzt7OE3c5yeno6FhYU4fvx4qvYDavFGscFgEFtbW9Htdmv35V4cik23D2BWrpgRdWDKhoBAA5AvYlSFdqJs9NeOFN54fSJGLbQQjhhwinva8F4NmpiYiBe84AXxjGc8I2ZmZuIXf/EX09+qqqodyso2tWbz8HA83uKEU+l0OrG+vh6zs7MJoG21WunvBMbcm3sRfMNXAxbwHOMIPwlUHQAbcMBpAXbl1RrumTuevFMmYnRIJ+vNWjgYtt3b399Pb8+jsr22thaLi4vpuYAlDixwctgkxuPtNGyN9Zl5ltWdnZ0EGpDYYqM2NzdjdXU1zQugCP2gkg+fsNErKysxGAxS9xbPdIDIGqNTdjjoBNutIfPaOsSB3FT3Hcxhnw06ex15TTnVn1br8ByIg4ODBMgDqvPmTGwmvCaJP3fuXGxsbNQORM4TFR+gaR/hRCBPlq9EwIxtf8YznhH/9J/+03jPe94TERHPfe5z41u/9Vvjfe97X6ysrKSOGr/unXE4oOGevrc/sz4gqxcuXEgytri4GPPz8xdt93HQ6IQnBxG4t22dbagDf38H3TbIlScJnqPnnQd02F3iirzyGzE6U9CAMPppveX/dEw2Go2YnZ1Nh097/E5O8e0GRNzlgY/le9hO5oyeb21tpdgn7+h1MO1g1wE1susEHv6Zd9gej8lrZFki8IyIms64G4PkxXZoXELtQBab5HXPr2Oe3p7orZRc4+Q34uIY7rESa8MYnJhRgGWtzZuqGhUu/AbSiEigwOTkZDqXj5jWemNAikIJdvVS6853nXDkSZCBagAFFxUMMvEM/DBvLeNFRaurq7Wk1eNvNpvJljs2gw9O+OA134uon+WWx5wRkc5DHQ5H3crIjbdl833LBp9TAGY8OTjCuo4DCRg76872fmzI5ORk7Q1m2CjHH5BBA/SCmBK9AryDJ/hynpn7K/t1jx9ZIaewnfELjCDfy7tAWGd0g5jIMYZlw0CsATLL6pUifMGtt94anU4n/tf/+l/x0pe+NNl120L47xgA8k4Agz8AJdgg9Io15G3JxDrElBTA3X2UdwOyFnRjEsPkvhmggRjcdtdzywsO9qPIpIETx8v2H7YDjUYjbaEGB3Cc4ufjX9yIAW9d0Ges2Kl83B4T96MwwDMdL/G5sQz7cMuvQRbP30XMPMZx/mL7gr31j+Nk21XLK7tTNjY2YmtrKxX0kbV8jfw7Omg+YT/ZHo6udbvd2otw8sIhvztOy//GnN0BeDl02aDUwcFB7TXmVgwYyAQ5bPH48eOxuLgYCwsLsbS0lJwrB3OyLQaBMJpJYpJvzeF53kLjVmdQO5BaEqGIqHU7+aBFLx7351yrg4ODFJxbAfndhpnqqZUFJ51XlnOB5n6u9O3u7tYAFYJOV94s0HROsIWFdcO5ELD0er2ak4mIlNSxRk444S8t5PzO/AE0jIbaSBAUdDqdmJmZiRMnTqRE10k3gNTc3FycOXMmzp8/n94C6K0BjBEldEKKcjgYxsijmMgSwAb8Y644d9YJmXCrKQaHFkUbLAfR44Jtnm+HdTVoZ2cn3vve99aSqV/5lV9Jc11cXIzNzc0UFLNGdEoh/7zdstlspvVgPj4sNCISQIgc06nE3HGi6DzgDQkd3R84JwIsJ4nMJTfwBqgcOBP84ZD5Dgef033EgdEOgvJqsbsC3Plo0A2QBHm0rvE2uKqq0hlejcbosHKSKhILgNONjY10f86zATjkYEODWvACneJ8Oew4wbYPWOWtZM1ms7aND/uYB7/z8/MxPz+fnoudou2fYGpc1wk2j85X3sTD2tHuzvyxOcgf68maVtXoEH5sOz4FPcO+7uzsxJkzZ2pdeba/jNH+AR44OM4DOj6/UrS3txfnzp1L/9/Y2IhbbrklvuZrviZ++Zd/OXWWOnjyGHJfxd88H/hk38Tf9/b24vz586mQsbS0lAIgeMb11jfbT/u9HODgWsabA0x5IuIkm89sn9FLJ4joqAEV1tzgE29sw58hN7TI+xwf/InvRdFnYmIinXEYUQfHI+pnfhDjUFCxDrn4xRbpXq+XAmqf2zcOnDO/82TZQTI6antqmXAADr+tv3lyQ5XVHVMuFBn04P7oeB4jUdTM5TUP1D1PZA4fRZGOz+2nXUR9PGS/zljgF+vkLUrEro6RDJgAzhsU5/7ElyShdFvYr8GfvKBLbOItji6g5XGw5dTr7M+t9zybfyla4VM3NzdrXZA8j/E7fjao4vOgbDsMYiA/dOTmwHXeacLao8vwxN25JNI5AGF7z/3MZ2TAnY/Mj10FzWYz+U7WPU/8xtlL8hz0kKNLfOQI3VbWSd87Tx7t/3JgCN+JjhIz8V1AK77HPPhBv3gzOgAJMY/HaUDRIAL250r62MXFxTh27Fjs7e3F/fffHy95yUvi277t2+LFL35xLC4u1uL9iNGZaLY1uW1y0s1ngM7osXdQYA+xxRQZyaGRe+TLuxCQK3wIsmG7bAAI++HiqMcTMdrynMub5814na/b5o27NmL0ciDnX4zL/LAu4TvQnYhRB2B+LAVyhi/NbZbzMn/ms5FYBwMtyIA7mngufLRvxdYQayO7eb5v/zvOjyJDbl7odruxtraWYg/bqkajUStsIC+Oh1gjfLS7pyKidnxBu92OxcXFmp0hBicPYJ2RKfPR8Z1zw8uhywalnOiR5NGmzu8oEsZ2bm4uocgHBwdx+vTp2NnZifPnz6dFg1FGegn8LPQIRMTIIDj4YJsIjLOgsAi0w1vw8vk1Go2U+MzPz8fKykoKVF2laTab6ZBF5ofTdbCFYUfhDIw4sLSh8HhsNLgOvvHax8XFxdje3q4F7Ainz8CIGBlLDhseDoexvLycBByDaDTZ5+EQyGAAADVYx6oadckQ8JD8Hj16NCU1OCQ6siYnJ+Po0aMxPT0d6+vrce7cudSmjuLjzDnDA+Vn+w7ngcFvB935Vgk7YOTDnQAGy1BAJ/s8n21YeUDhgM+G2M6e622kryRRKfi5n/u52N/fj2/5lm+J3/3d3421tbUUXB09ejRtjWRu8HRxcbEGlLjTjzddevsTAAaOFT1Glw3oYOzYtgeQ3Ww20xYVdBu9gP/uCHCFw2dMGDzB4RkAZkzoRKfTSc6Gv1t2XOEyeG77Zh3jbX95BQO5mp6eTm8uypNFJ9V0ajabzdjc3IyNjY0UePP9xcXFmr66suuKCHOPiFS99xYCrvOaOgHiM2QL8MlnaLCeDjKo4DNGKjPIAbbSiRfnL/h8EL5LEueCBOtgkMEB2NTUVOoIvffee5M9Rx9zYCfiMEHBcTvZczXficiVBKS4J0ERW7RnZmbiG77hG+Jnf/ZnU0ejAQCvcW7r8nszb/udPDACoDx79mz0+4dv4QS4RCYMGhsgypMc7me+Ofjl+Q7inCg78GEdnLS7Ymy7xXX2+chqRKSOFOwKFUN3nOzt7cXCwkIt+TCQhM2EN44DIBJyb4fIz2QxcE5BjK3DJIW2Q+YXYzJf7fOcpBCH5HKBTsB/eJ77sDzI99r6c5KI3M44SMbG5m+5yrtm8kQut3G2ORH1s8uckFnO8iT9sRD35ocOYHwpiTvgrjtZ3C1ve0VXiTvnWc88+KfwyN/dgevise0B6+hEzIkT98Kn8j13PjvpMQCSg8msL9cDsuLX3LmRyxh2ncIksgGRoDrZczGXH3ID7mcwi4SLopS35RL/GWQ28Tf46OKXec642SXghNZynuuqAcDhcJjOkGHNndhikwEpWNO8i4rvcW/Git7mBT3sVD53ZBv5J/4bDAbpeBX412q1EpBgMMrzIO6xDxrXkXUl6IUvfGEqKH/WZ31WfPu3f3t89md/djzzmc9ML/3IdQNfyVrmtpTfAely++ktU63W6LgMeGHAE7kwiEvcxA/Ag2WaQqeL7faf8M/rb9nBrqMrzlc9F/tzy5+fge7Bp8FgkI5acb5pWeN5ANkGpsxPvgvxXe9+AWSxrKEHjBf75bwWnSFutf55zbneoBk5gH2N7ad9tcfNdZZv9IquJb9cxSAjcZiPcoD3/X4/FhYWaoCpfaD9OuPgfhSwWQ8KaPjptbW1lKMZDMTvMEYXCy6HLhuUOn78eMzNzaWzoKanp9NrYql22xmSeG9sbMTm5mYy3k5sETYSWgdQXiQqXXTI+KBHkg8qlAcHB7G+vh5LS0sxOztbM25mfO64vN0GZ9nr9WJrayvm5ubSAccYlunp6XTgd7PZjK2trXjwwQfjzJkztX30rqA4KW82m4lfJNtWYAf4FsCIUffG9vZ2reWfAMYVR56VJ/kg+JOTk7GwsJCux7FQ7XXQ6s6Q4XAYa2trF1WyDK7Res7+ae5BBwcGlW6OVqsVDz30UJw+fTrOnz+fqsN2+hGHYBxriTEmUfVbFxiHwSHGiDGqqiqBBxGRgh+cAK/55X7IHb/nDn4cGm8iOHf16mrRYHDYrfT1X//10e1246u+6qtiaWkp1tfXE78wrBzAyyGFgLjw3CANAVpVHZ7XgMFxVxFy4TPIkE1Xnegy4LDX4XBYe+mA7QlrTUDLeAwSw387Opy2g3GcHHMzIOTgkKTTASdJBwkAgbYdF05wcXExOQkcH7zlvhGjZJBOHua/sbGRQBaCec7eYvsgjhD7TDLdaDTi3LlzMT8/H41Go8afubm59Cz0BmAQx073h4NHbDUHzzpharVa6dBx+Eowy7gMZkRErcuSNZ6dnU1VGmTG9+GHcWJz8AsENPgWuss6nU6cOXOmVmlqt9vJFzAH1pa5IQtOpPFNtntXGpTivhGHh2nfcMMNsbm5Gd/3fd+Xzm1xwgPlgSKfGSRxcJWDVw68nQzTYYssTE1NJQCUwA+dRPfhMevoRMpBf8QI7MuDOM8lr0YzNq9b7usNXpB0GpDNgRlkh2cazHbngJNpftjGfqkqKVVGeMz88a3IMXzCPvBmXsbn9WbeTobxRYzbAaFl1TzMCyjwNQeMGYOLMSQxuR/M9cfFISfs2GB8EjJrcNLrZLl3UO3nDYfDmp0cJ/MGbh4PMQYnRMg9SQ6+hLkRB1gu3dXHGnANMTTyyXW+V8ToDBj+b7tlsCMHNX0v/g7og89HdvkhXuRfA5YGkPH7AHL4dseYllPbbvIE+1b+TvKNLAL+MX8XUohZ7Z8ajUYqpLgLyOCyk0/ugx7nfsKgOz4dO+qzPQF0WF8DgT4Pl8/cQU0XM/qd5zF5AQCZc1zK/607zr1sF5gf34PXrD86lB934qTfdq/ZHL3UyTkN3dJ5npPr55Xwsx/96Efjcz/3c6Pdbsf/+T//J77gC74gnvOc58Tq6motpkTODIpA2DhyLOTXoAH5BfEPfGm327Xt3wYNbR95NvNuNBpptwuFqk6nU+uacUHdOm5irPbb7jry8+wP8o5Mg0qOB+3bsX22+Ry1gw4bNAYId7HM43ZuGzECTm23IkZxPXJo3czjt/w6ZNZ5kOMAg1HO+ZBzbBW8g0fWN9+Pv5nXNFuwXd84AvN3/MRYWRvHBOwIMRCV56cGL5kvz8MGka8PBoM4efJk9Hq9eOihh9IaIEfcD/2lweRy6LJBqRe96EW1AJQFB1HjrBUO2gUV9yIwOCbubRYsSC7kuXKB1jl4Q7lw3MPhMFZXV2N3dzcWFhZqKDaC4qQFoSG5c4IyHA5jY2MjdnZ20mHcMzMz6UwPo+DPec5zotPpxMc//vEkyA6qnOSTWIJ8+twaFtWBJNUgo+dc2+8fHiDvVmOUxYJhPviNWThC7oVz8xYFlJX70FrPK9o58JD5TE1NpeTSgA7BF6DU1NRUzM/Px/7+fpw6dSruuuuuOHfuXO2NAQQu3BPDZadm5UfJkTPPw23c5n0eKJPwGmFH0VyV4DPkyUYZ55B3cFl5bZSuNDGflZWVWgBv5+FqgCsVrh4CRFPlAMQD0DAwiM6wzjg55uuuGoNJdsoOCFxJwz64smcbY1DMlWg7BI/JnxlQslPDQeGALCMOwB2gMs7Jycm0hc+gGwk+SRNrBTiCzAPIOjgm4XeHqAEnv52O+Q+Hw3TmCMFMRKSKB/y0vfLhmLbR1oF2u50KBRQp7HgMyHOgIs90YsMa8K8LFRzAiL10ckx1FoeIT2CtpqamYnNzM3U8VVWVzppCfvKk3Z+5c8E21wlxHkBeiYA5J4oG7XY7PvGJT8Qv/MIvxNOe9rQ4d+5crSAQMfJd1i/G6r/nSQifOZHJbakDXN6uxRtZ8YG274BSHgeVNsAd85QxOKmzLPE3B1xOnqybDu4NkjioJUninvgT5MqdILbnTjThFfEOCSM2NO8CwXcaEPPWd+7vLnQHzg4m4Y/nCi8sDw7EbbcsB/YNDlqdmJmfxGHoCD7QSYGTLe7jTjTGyvXu8HRiyno6mfCa8yzLsH0R6+ptRrkuPx7K1xd5q6rRFm1X5LH/VLC9RdrxFjEYiQbApMEiv1XW/BkH4rGOXiPzwHwFVCYGIo4ySBMRKQZeWFiodchCObjKelNEpohBLIxtZ1z4NeuL44S86wH/AQhJQkwBEV67KMWZTjmwhpyjRxTEmA9+3sAEoAz3npqaiuXl5doaU0wwaJnbW/hGToLu8PyI+suniNmx7+N0x3qfg7esjRNV65FzieFw9AZy1oS4w/FvRP1A+kajEdvb2yneZzzEX+ipZdM7Cq6kfyU+g9+f+7mfG//23/7buP322+P222+vdaR4bew3LXuM0TloRCS5Gg6HySciz4yD+buD1mvLc5yH8LyIqG0xp2hMlwu+mevt4y1rtp++lh/Lau5HWRd3NTrG5pkU/A3cIefueseXQIwXG2ifYaAa287/xxW54Lm7S53burjOM/N40XNFtvncuyUg+wfbacuT5QcfeXBwEL1e76ItlnkuypzcMOIYyjxCnyMi+fA8/vK4+ZuPUfLaLi4uRqfTifvvv7/mq5BZN8LkYPul6LJBKZA2b2UikOJznIkrO3YYJBoYNCeT/M3VtjwpwhnZUMF8HA6OnMC52Wymqj73c/AQMQqAXA1xVRd0kMQR5cEQc5bJjTfeGDfccEN86lOfSjyxMDkAtAIRPKGkrkQwXgSRcdsQsR6Mn0SXZ3jbkwWfINLXOInEeboVEaV1NZm1JsifmZlJYJQDGQt7ozECDDY2NmJ1dTXuuuuuWF1drSVFBLMRo0Mt4Q9/43eCNnd18ByDJvDUDtZBG2P22rEeBJokH3mSZ0CA+WOwbHC5jvtfDWJ+g8EgXv/618fBwUHcfffdcfTo0dqaDAaD2NzcjEZjBFbmySUBH0aJAAnDaJsAiOwuqaoavTKdTgOeY6DJiQ3bYwGz2f5F8IsBhMe543Gi5WegZ3SHGaxiLRxMYFuGw9G5RoAhEVE7K299fT29aAB7QWcVjjkiYnNzM7WJM2Y6lfgxeGugjTl0u920lXVqaiq1+boa7UAbnaB7IE/Wsa22CYwbnvp8HebDVm1AL4MX6A5OHD1ykop+MFcScj/bawxAxXr6fClsH28cnJmZiUbjsDPs7NmzCSjAB8EjAiMOfIQfedXXgTn8u9KErT04OIinP/3p8cY3vjFOnDgRw+Ew3vOe98Ts7Gx6E2SehCJLBk7GgRQ8x9/LA9b8c9YV20VnGoCUZdXVNfxSq3XYBTc3Nxc7OzvpoHlsipNjJ+iMFZ57Pvwf2+rk1cCzbS2gU0SkzhPW1vxxYBcRSe7d9cLYLa++F/fBJzlBcOcT/zeoxljwX+MSSuZPFRNf6LVm/E4oWB8nXXkQmweyXgPsMPxwIGxyR8vExETaQuvxmd/YTccLTq69zdzrbz3I1xE/BS/xVfzdifxjIXea4w+Jw7Bx7uKEV5ZDbC4xg+1zxKgbgG07xGc802AWyR9rzhrYVuWAnPnIeFh77yJgHMzB4AQxPON1Msbz8kIccYbjSmI1x4HuDsiLiHxG/Oe43rG+bQP8x27hQ8xrfBTj9iHMPkCZs4Dc6dZqtdIxJnlc6PXCdrIm+CTO2nXcjWzkwIB5ZJvE9/OOkdy+5vLDWJADj3lnZyfFiE7ssRt8LweoGAfnSvFcfy/vLHROxDOuFCjl+DVi/HEG5qfzxdx+2dfm222tz9geirrWS98b++HdCcikdQO99bN4HgXNTqeTznUbNwe+k8sTlMc5OUCX2w2PF5lyTs38mLOL/8YNmBugD/ewj4SMCRhItC6yK8njYH6MP+cDxJyJY7kHc3CeYTDR+AbX5rEI12IjKNixdd/5o2WOZxlnce7Ds7jendbwD7kkTrd/hzfOl7Bh/pxc4tZbb43V1dU4c+ZMLbfku+j/5dBlg1KnT5++iLk2ZCyqk0A7CK5zEpILsrei2ZHZMRitc/cCwkESwrVbW1tRVVWqyNgQ5A7Y82Jx8yAeg0xyw2HQ/B1ntLGxkQwrThTDkI854uJtYzbGeULtYMYVOJJ2krPBYJAcAPPCwDqhHwwGKbHmnlSWEE6COBBTjDrPHw6H6Vwe3qhH0IRxpevGAXW3243z58/H6dOno9vtJiVBTvKAOSJqSp0bZRTcjoI50mHiDggbQysbz+I61gOAxLzi2Q6Y80TA/1qOrQdXmljjv/t3/2687W1vi/e9731x1113xdTUVJw4cSK63W6SJZIFDo0nwGQtfEZPxAgkIKnDkcJny6erQegDeuWgx4CW70EwMzMzk5xtxCiQJ5ix84oYdRF5HfO99owLcIM19CHbVBOazWbaCsf6TU5OxtLSUkRE2i587ty5qKpD0IhOnenp6eh2u7G1tRWNRiO9CryqDsE63paHfKBnBNsEuNgzuimQK86Xm52dTVsJ4TN6AdDnV8RimxxAmq+tVitdOxwOY35+vnZuFPOfmppK59pZF+GhkwzrJmuEXbKe4PBYK4P3/q7PCqDbwlsbB4PDSvzq6mqtQss48mTfAZXBAifc1ukrrcNOFpvNZhw9ejRe8YpXxNraWrz1rW+NG264IZ2DZD9iO+j/O8By8OIAyn/z9xygWpcajcPCyIULF2JzczOmpqZicXExbZsHRLXvihjZA+QdWSRByH2/k+aIOrBCDGBbyzNym819HdjjxxkTcm+7xL3wXQSAzA874ACY7sact/YVVTXqmPB6MG/AX2wSfMzjL64nuWB94IflmL97PMiJE1Pu4Q50yHbdiaSDUOsjfDHgjlxbnz0eAmi2zgMgWEbzZNVdIJZtz8Vvm42IWlL9eOgXfuEX4sKFCym2wRa+9a1vjaWlpbTWTmYdx+XyDlDupAVwCV7jc+G319Gy7uIMfMhlDv7xt7wrAp7ZPuc2kwIwRUF3RIx7vnMAx9v2F6xPs9lM/tD3hGfOG/jXXc4Gf3P/4c4Lyyy8wXcQyzsOYksNcQv3XFhYSIVsYmfGRlxju43sc5Yt55Ta3jo2te/Jf8bFygZX8uudF4yzA3S+oIOX2mIK34ixGUsOTDimgs/kT97ixZgch19Jmp6ejpMnT0a/34/bb7899vf34/Tp0/EZn/EZabzwyMVVA1HojIEkdAcZtT9wfEd81G63ay+scm4UEQlUwn4SNzKGvEAfMXrBAfoDaEtByMfa+FkGR8bxHvk26MT3xvls5IG1RE+d/zAPx9R0NuYxl2MRiqH2G+iMcxR2ghgIYwx5vJE/zzqUrw/+xX4P+5bHg6y7x+9nkKdh78kx3RmHfllXzTfH1o578hiKYgY20kVpA2CWLftnyzd+h5ia80ZPnz4da2trCXxlhw24wCPRZYNSgBt2ojhOJg6Dmbzf7GS00Y6Ne5KIYvwsBDaQOCYnz+7GsWHlet4Uh2LbOPjezIWxOECj88OJ0NraWtx4442xvLycDjmfmJiIW265JXVNIFwGn3iuO8swHHaKdt52HsPhMC04oACCRpKLgSJ5sNPhbzjVVquVDq0mMOAsG1dfHTTZUbHey8vLNQOFMXdywnwGg8PunJ2dnfQWRnfRGJizA4R4jtcbWQEg4vt5l5YTTZ6D7M7NzV0UbOM0AdmQHd/P6+Pn2VDjKFwNtBG80oQsP/OZz4zNzc34pm/6puh0OvGpT30qgRzoohOgZrOZKn95SzBnarH+6Azyh+zZGeTBD9UkznFA5uEbOo6cou8c6N/r9dK2UZ4L0BMRNTlHlwkS2f4J/33GjNuHrZs4v4hIh/YD6JGo8rZO5JDzn3hWt9uN7e3txFfAW/gAoIvsAnivrKzE5ORksl3on7s1er1ezM3NpTdvApwPh8P0sgnLqrvg3CUyGAwSbx10UIniO4BbExMTsbS0lM67ouKLjedZfOaEh+c1Go3EQ9YAX8G5UdgZ7sd2YXwRjpGA2aDE/v5+OhuQeRqQ5/7M1d1h6Lkds5MC9NvB+ZUktoK/9a1vjQceeCD+3t/7e7XXyzuYhGcO8sYFQfap+e8Obrg2T3i8PugX17PllPX2lpW8Msx2NwAs1i6iXmXn+wYZGJsTB9bL68A6uxCATYqIml7wPewEPo319TjgMQGewSa/8j0PhtFvEm4Hql5L7KkrjCS+llUnAsi7/aDX37JJHMYa+jt5ldoJLXNkDfm/wSV+XMDgPEnuHTEqGHAfxzbN5iG47niOmIt5Mw/f0wkK34Wf+Bj+78Tq8ertZ33WZ8UrX/nKWFpaiuPHj8d3f/d318BYYiyKBD6E1/GC4ySKjByKzbxt55BfgyoGCJApdxEgE15PeIKtxhY4YeGZeVecwWZiOic96LcTM+ub/YD11wUDJ2KWEWI9fCZ8tr9A1/muO9VIphgPfOE76KlBFroYHKPji+AT96OYFTHaxsb4PT9kgN0PdJdyf/scj5V5+TPLsuPS/Hv83cV5YiW/aZxYg+uJBfndOySw+ZabqhodSwA446643D4z5hyouNQcHivddttt8da3vjV2dnbizjvvjL/1t/5W/NAP/VDceeed8RVf8RVpC5+LnI5H+Zd19xsP3UAQEamAl+dSESNdIybDNiG7MzMzqdO+1Tp8m+Xy8nLiN2SbZn9un5bntNhUZJt/eZ71NfcxEfUzk53H+O95nOQX3ETUz+Vyhw8+D/lEJlgTYjuDfRGj87Rs53Ig1fqNrch1y7iD55zn5AbCmQ/r4u+bn/hHx8H8kNd4nH72OGLuyBfntJJbVFV10dEXBsohF0Ly+MTrxfjhaQ4+Hzt2LMXdR44ciao6LFoQGz4SXTYoBbrtxR+3aB5kq9VKCQ7CD3ONNuIoIqL2mZnBtjz/DcNm5+UtRTZsfE4lPQd+uA/JEQlRDnZQzeC8qoODgwToOGg6evRotNvtdMg7joyk33uW3TniRIngiiTdCQIHHoMoI2QIIXTDDTfEqVOn0rwMFlAp2tnZic3NzWTUWDNvcRkMBqlajAK7m4W3LwIAYiQRYgdKOCheZ8/rQm2wmS/zg5wAYtD4LgbLzi4HNj02+I9BRH74P+tJZcKGJ2JkBBxQ2Wi5HRWDYADgagJSUKvVite//vXx/d///fHGN74xfvqnfzp2d3fjz/7sz+L222+vvTDAxhs5AqTs9Xrpb8zLwB7krgj4THKHU4PXgByM086awIgtdoAtvHAAmQWU5X6NRiOdn8SZFQBrDhx4jivAANisi4NK5JFrO51OWmcnE4uLi7UOQnd1+eUCEZHOd0JWXA2enJyMY8eOxdLSUg10s11jXgT+x44dS9v3GD9vaAP08tZKAy+MAefB9qper5f02t+n6kanKPNyMoOdZu1JqPLOKZ+bw7h4mQb2iO14fpaDEV7MgOPzmw/Zl28Ay2tKgEYXWcTFh5nbOaPLBgigcUDQoyXuiU1tNBoxOzubQGE+43n+PZfvcbbPZFDHf8+BLgIO9IXnuFp8cDB6A+3BwUHaNp9/l4DGtpmzY0hsGIMJnhswIQYwqELwbR+Bz4wYvaRiYmIigejIJACZEyP8AmAvHY5O+pBviireKmBwKWLUocO4uIfBdIBab6cG/HUA7WTBCbVl0bGO192xmoE97oOd5l5OormH4zaDFfg9v0TECZefC09YT+Rlbm4u3YeAmXtbbx3/GVDxlhUDhAY9Xfx4rNTr9eJXfuVXks9pt9vxwz/8w/G93/u98fa3vz2OHTuW4icSfeIwy3MeY+SAGdvGuQb7xtr4fBDLhHXHcuD7WNa9zuPsgYEcxjcYDFIXL3rIOuCHDYDxLGIpbAJ+k2fmMmvAB33Ngd08gSTe5PnEHci5eWb7TiyJfLujgVjaBa2qqlKsT6LtM6zcuY0cE1/ngJnl22P0/Aza+57jyPptfjFXxmEQj3/dUYHvjIg0V5+dx33QOeTF4AOxhufQbrdrdhUbebVi5Pvvvz++4Au+ID3/2LFj8Yd/+Ifx5je/OW699da0HvwdOWW7J7LjDjlsJoAxcmH9znMWyzTyzP9pFiC/JObmuW6S8BpzL6+j8xIDKranxM8+KoF7WdYsb5ZT1pXCo22QwVpkinu528u5ccRom7vfgpwXrHmW/Si88Dl3gGh+rn0H9+NvjN+FGvPQ8/PfbOPs3+BtDtTxjP39/dob9PJrGMO42IF1dAdYRL3Ax5gj4qJ8u9/vpxjT/Pf9zDdkHX9K1y+yfvPNNydePvjgg7Ut7o9El+2NSXZsgGyg+MwHYjlRMbrpCWGQ7BQtsD6/xAufAw6uHoEQEiyT1JLY2DkxdgQgVxCCoa2trTh79mwCl7yNZX5+PiXrdP9gUDgAnPNeMFSuOA4Gg9RyyVwwdgBCGHY7RTpFSBIRLiq9ExMTsb29nZypjSwCYgCHYJ1qiP9uQ0SCT6LtoJNn+3qScdam0TjcnrC2tpZa9B2kOtBHOVAmH/DM/R3cOInkh3lwvZNxkhuewfNZZ+TTSLGDdXhuY4Qs8H8UF2M/br/81aLBYBA33XRT/ON//I/j9OnTsbKyEu985zvjgQceiFtvvTUWFhbS2/gsk5zn5FZyjKoDSZwx4JFB4tw5OWHirZWsWUS9AuXkhvUi4SQY5A1kbjudmpqKhYWFWFhYqL2VhG1CdPkRmPIcdB3b4zP0xgV/EZEcCQA28ra4uBiDwSA2NjYuOuC80TgEl7BrnjfXzM7Oxvz8fBw5cqS2vc7Vd2SIChg2ANnD1tqmGKg5ODhIgboT8ePHj0er1Yqtra04d+5cAv1IOAjK3HGGrWAOduzYX4KKmZmZ2pZP7AEVNCe/VFjb7XasrKxERMT58+fT1mJvy+V+VGMduPMabeSWNWec7p5BVuGNq4PogefqxI97Px5CjjyPqqpSB0J+PqKTRXcxWa/GBUm2V1AOUDhh5v/WR3TCcQBnInDuHofaAtjQuZcnpI4FnCzZ3yMfrnTm4KEDY65zYklgj1weHBxcVMjBZhNDUHji+05WkRNiDAfOfjY85S00yBXBId3OzAldd4zDmW2Qkz0nsE42WDd/nq+919pbobgeebGdJv4zuGydc/K0t7eXbJTXjmudMDgRoCDimM3yzO+5rPO7wUrrO76EOPTxglJ8f2FhIfb39+NXf/VXIyLiBS94QRrDYHDY+TQzM5O6wOCHQRjGSRLT6/Vifn4+rRtJhrfW+03J1k34Al8NNEC2H/6/fY7JiY/11Ik7tihi1DWLz/M4ct22PXWC54SKmIJEiDjDMVrEaCuY5brRaCRwmUIj8pHHjzlIZ95GRPKBEaNONG9btQ11go9u8B1kku+RB+TJrZPUXI/4u3MDjz/3T75XDopgOw2McC3/Z57EXgbdeU0880efyRsBn4hNeD5NA54HcmHZu1IEr1kjPrPP9xoydvyziyx5oYb7Wm7go4tprJGBQGxAnkcQH/F/g4SMz7pv3w05N3JRB92B7563gRvIOZlBTuuK5d4y7uaU3H+5YGBfbrllPru7u8mG0ghhsM42yb6GfC0Hc/LczTaSWN33cS4wLoayLDOPcXaPa4jH3eFq/ci/A0+4n+0Xf7PselcB/4LB2H46R6dwQuzm57mphnGBe8D3VqsVCwsL8Zmf+Zlx4cKFuHDhwmXp5mV7YwNHDpgcgDkQtLDmzPLhjnbEfA/l5vskTWY8htFt/9zfAgDzSKJIUPjMLY18H+WgK8qJCWPe2NiIU6dO1V7P7jfQoTQIBEaAM2VQDNozSQ7dWWEhYmwIpTssDOCwrZHk0R0Y8AMDBM/dJUO3AULMeszNzcXCwkItycbYwDO+A3hQVVXaboIhRAE5yNHrngMQzNlyYl7w4+DbiYqRc56dB8XcC96wVqxBHqzwf4y1/0aABDl4cuJlQ3wlHW1OjUYjdeqtrKzEu971rnjTm94U733ve6Pb7cb9998fz3jGM5LcEThEjF6XTPAG4OHtKoBX7FfHeNF95ZZkJ4b82BEgv4AnDsDhpY20t+8ayHUlywmIu5ecqLCOrA/ryfi8Fbbf7yd+ICf8y72pFtAJ2Gwevgq61+vF2tpa0tfhcJjm2ul0YnJyMubm5qLRaMT8/HwKeB1Ess0PMBBnjOxxCClJCkEU44mImi7Av9zhtFqtOHbsWCwvL8fBwUFsbGykgMW8oFONtc5BEZ8Jwhq6Kmyw3IGEO1ecrAL2nT9/vpbkcQYNsuKz7FjD7e3tlCAzz9zp53vquY5/8wDEf4ceL8iMPLkFO+LwFdYf+9jH4mu/9mvjJ37iJ9KbuwjGxiXXBpnyRD5P6g0GOlB30ppXLPOiFPxAT+i6QybZesCZK+afEyMnV77G9hY9AhjmewbFnVxjc/iez1vjudYlnm/f6aDUr3PHFzoJQ4YAP/ICS96l52Sg2Wym7hKvS+5zuRdjY86sFWO1D4UM8mA/7Tttm/O1ycGCccCVC2vmpe29E2jHbO6ia7fbqXCSB+x+Xp5sMzbbdPycY4V+v5/OvHisxHPN86qq4ty5c7VxsM3bILjtHF126HRVHYLjbAtvNuudgL6P423sK7FonpTxu/lmmcqvhdfjkjLLu5Np7g8YCTCRx9uMIY8lkQ/LFtfmia7tnAsH7mRotUZnI3EtftkgTH5/kjTHpsh2ntT77FjHdQbp6IYwz7Ax+Erk1DLt381z23I/z37J+pv7McsMa53HMozL+QdxnmM58iXHwwZqfMYRz6dRwGfowO/cruS+9vGQwRODfDyPNWTtAOvdDULMYxm3H3DSD89zwNLxh0Ee2yfsgm02z2F90RXLL3NBbp3PMV83bgAY8WzLBzLiMeYyxt/MR8ss1zUajZQrc1+eg+3A5li37TOxfRSU7P+JK2mesK4St9hWeR0cE3oOHr//NVZhvcrjGMu1c1MKeI7leC73ydeC+3rs3lKL7Nn2kcNhh+G5z/J0161jYT/f6+s5Ome33d3c3IzhcBi33HLLI6lkossGpebn59Mis9B2QAzI5zCweF4cKzUMQ+hIWgiSXP2wAUaYWRgU0JV/yIFKu91O57hsbGzE7u5uOu+A7yEorkQBOBGUY2zOnz9fO6TY7b3MncSx3W7H0tJStNuHB38xLwA3J3S8ypxqIQ4KY8L1KCjVaTohfKjdzs5OHDlyJAk/gFzuzLg/PMAQVlUVnU4nFhYWYmVlJTqdThJkvx6X7guMdLt9eA5QxChJ3NraSgCBZYXAyYmqDaOdYY6uIys2Rlxng4yiRowq894y4ADMz+Uz1jJiZCgIpOElOkCi4uo3n+eJloONK02NRiN+9Ed/NAXz73jHO5LeNRqNOH36dBw7diwWFxdrgNPMzEwyZCRvrBu8ZYtKv99PLcZ0xHW73cTbTqeTEHTvrc/XBtnzVlYHt06IIkYAovXGzrbRGAG/ecJn0MOVMb6XB2OsITaIZBMgFifqSjE8BZimAgqoy3h87gbJOjYAGTWf6Nz0m5dcjc9l18mG7bKdXp5oQvALwNGBD/bUVbaI0bZuwBV/DxtBYtJqtWpdZtzXAQn3ZRsv9pZ7EnDkW2Hm5+fTNsSI+kGRuaPlO8g5c2T9c/sA3+DzldRfj83027/923HjjTfGD/7gD8Z73vOeWFpaSoGFEwOvt3nKva1TEHNHxgz+8K8DP7d38yyfgwKP0BGKHBsbG3HhwoVYWVmJo0ePpgokttv3yoPb/DyMiEhvwjTQGzECmPLCDQkTsmm9dQzi4N9b7tgybx7n4JD1AVvCmBuNRtr2iz/h+cQVjUYjddOwTRM7wvYwZBP75uDacZL1wXzhhxjO/pJ7OeF3rIBfy/UTHjBvFyTgU26HmVezOTrDkC1qljvO8aOwwP1yW+bveG2ss04cncg8XnrKU56S+La4uBj9fj++5mu+JlZWVlKyx1ktrjL7BTLIqn1ev9+P8+fPp+0P6AEgCHbVhV78U54A5UCOeWSZto0n1jGf8gTMsZiTbGJbrzcxg/2HATAXIx2fMkbsDPPME+NxNg+Q34VtjzmiDtJaJwBoXLSam5urfQ8+MXbu4Y5M/iVmdseztxtX1Wh3iI8DcdLudcr1HfvlvzPOiNFLIHg+z3T3FsVF21t4iw7aJzYajdja2ko5gwv56DbjIZcxnwEY6GbP5e1qgFKWIY/HeuGjDvAD5DbEiLalBiew5QZbyXfsf/OYy80f/N26mPspfnLA0R3+rAX3dczn2AzZYN7esYQMkuPlumP5RM541riCmQET88KxC9chH3leT1wAAGWddwMAttKg27gcmLkwLhdUPUbHfvaB1ktkgXgdkNPA2LgOJPPBQCbP8Brm/LQ9ZQ1yAMu2mPg5366ZP8cyg4ziX5zbYvOQEfuie++9N1784hfH5dBlg1InTpyoBQjuUEIobKicuJPM+iwFJ4hGmd1Kj8PmfrST5QbKjPXfxqGerpTu7e2l11LnjsYdECg0SbcR/fPnz8fU1FTcfPPNaQHcoogiEzjQlYRDYPsAThNhbbVa0e12k4KxwGyrwkmCOJP0t9vt2tkDExMTCRiwIQEAhBcYWYwIb/+Zm5uLpaWl2vkxDuQnJiZStQOhjIiUbBoNR2DzgNhGJGIEkFnYcyOC0KPY4xQaGbChNQqPIfc2JztygxCcNcS65QbNVYJcWVFoknTL6ZV0tDk1Go14+9vfHsvLy9FsNmNlZSXe/v+fcYE+nTp1KhqNw+4cJ5bwiQDGFT7ON2EriR3xkSNHYmNjI22XarVGB3rjpOERPOb/rtLh1JEbHAy87/V6aQ5U4AGA+K7lwHYld/i5EUeGDSqifx4rh7+7WsP4qPAbnOx0Omn+fqmBqy3IEXpiPY2I6Ha7ae4QzyaI2N7ejvn5+Vq1DyDH23OwSegP+uhDqFkP9A9dYQzIA8EB46TTZzAYJNCcdcBXYBtY77zS5kDeQAfAN+OkI4CO04mJwzPQsJ/YCBypgVHLCfyxPWGNxgFF8N5J0OMlZMDdtWzfabVaceHChVhcXEy23Gvjn9xGOQnD3/KTB2b593Lba5mFRwbFmIcDbBISeLm3t1d7ZXo+nzzIRicYi+/n4NNgA0EsFX0nCxH1pM7JngN97H+e5DtoZszolgFTOho9P+Q5L1pwv+FwmL4DCIXtAaz2WiAb9jO5bzJ5jXJgB4DE8xgXNDNOy4tt3zhw2wkGMSMg3szMTK3zzkEzW67osPaasSa5P83jPnjlMwyxXY8XlJqeno4777wzdWwePXo0Pv3pT8cnP/nJOHLkSBoDHfT4AbbjRoy2PVveGddwOIz19fVk34i3GL9lFb3I15s1wEeYR5b9XDdy++Z1zvltkMjbm3i+bTHj4tB3V/jt/yyjfMcFWctSPmfWmfsACuCnOKoCvvnefGZ/gfzkHYDmidea7yP3+EFvY2RNc75TBM/XkN9tw/O1s+332kbU7XLEKL72/Wz/iPsoANpXNhojMMqNAk5m8zNhydWQbyfNfJ8ixdWMjw0+OVG3rW02m+kQe+sP68UaIuuskWXR9zIobF9tgMy20+CI7+1Y2XET40Nn8AURUfN7AMP5m7P5HvYDuea7uc8nb8x9hHmIH/Za5r7VfDLZT+Fb8fXwy6CwfQa8Q0991jF2k2usTzyD8TmPz+2dAanch+eUxwjO47vdbnpxUl5scmzjHIjnWG6MUdiXOjexz+ZvgEy2Y/DYfhyZYr6MM/cTjNF6vLe3F//9v//3i/gyjh7VQecOCryVhcnyQ4LIAKlmgoLjhCJGig9D/dpAt8nyTBbCnQJuxzPZ4DgJhuFsExoMBkko2FLT7XbTqzp5th0xQra/vx8PPvhgDIeH57G4HRKn0mg0UqWVA3xJOjjkO2J0sDKHC1dVlcAkQDSECJDEh0iToMNfzgpifhYq7stznVT2+/00zoWFhej3++mtXCSz5gnz9en9EfVKNq29rAcAAuNGMTCSdqquvtpgIz9WuLyFvt1u1w6td1W+3W7XDtilm8yKxViRDXdk2PgQVDqQZNzMi/G5o+tqUlVVaZsPRn95ebnm4NbX1+PYsWO1wAu55+0/rLmdnudrxxcRtVchNxqNxGPkh7F5ncwPO3J34PnsI2QJnQeYZYwYcjtU+O8AxAEoW4FYH1cHHLAAipBwDofD1BXoYAFbQCCBzHP+Fskp1xg4MECCbdvf309b6biH6eDgILa3txPQBK8JrKjywWOSdgeAdsKWX/4+GAxie3u7BggYtPPWS4JVA0IuIESM9Jh/cx3DpzAnxs5WUyf5gKreRuqiCHLn4NBBdN4BZHuZJ0b8e6V1mHW+9dZb46u/+qtjfn4+XvGKV8TP/dzPxXA4jNtuuy1e+cpXxgc/+MFYXFysAUJ58pGDS+P+7+fmOmGgyWBDHoDm5EDd9+Xfvb29uHDhQmxtbaVzF/PvOJBy8BkxKqhEjDqTvF6ukmIPrPdONvG95kEODACuOinLkxLOyPP5FpbtcUFtu92ObrcbEaMzEr2tkHnmxRQfku24JweEHLCPC5rzKm2j0Ui+OKIO0rvqaj+bgxNOFvK/4y9ymwpg4CQ+D7wjDrv1KQR4zbkuT9DHgWZUzK0zloHHSk996lMTIL68vBxf8RVfEU972tNq8V3ebcfaAKIDPiNr3lJJxyfJA+eYEtfYV+VyQLGXOAgew7e8+m6+8pl9jW2z9d92wuf1MA58mYEfgDr8KfrsrdbWy9xuW5/QOxcYndhtbW1FRP0cOPIAZD4HDpx7GPCOGMUntpuOjw2gc8aeeWh/jG4wZnSCOIc1zm2U75fLu9eSufrMV+dEuS5SjCG3wq5xLAFvs/VZZsRdjtGJm4nhDEhhQ7xVPf9hfFfaz2KnzC9k13EsuakL23yf+APeWf5z34j9dt7r6yk+2Ccxf+JG9CWibtvtky0X1mvbS5/n5Q4vxgXP4Q3/on/oKtd6S7R1AZ7luXcOlOH74Jt3KtFJaOCu1+vV4h5sBmPzWlmHPQYDUtjAiKj5Buuc1yP35fzNPjD3YzzbcmF/sbi4mM6e5i3aOThlnWDejteRLft020uDW45fsG3E0hQ9mJdzIWMEjIfY2zm4YzS/UfBy6LJBqT/+4z8eW3E2mIGDdBeME0FXBLxgJO0HBwfJcRDoUU21sYdZDoDcWYCSYVzdVhsR6dA4Mxg0j6CHBNUIN/fKq7NVVcUDDzwQg8Egjh07loAio9qNxugQeBxZuz3aTuhkjGDVwonScIYK7f7wEcPOswEMmYMR2NxZNxqN9FanvJPJnVEOylEoAq1Go1G7/97eXkrYASOtCK4+8Lzt7e3EX/PW96aja2dnJ30/VwYUE4CR79s526hNTEykgBfH2e12o9lsJiXlHpY9G2r+dRXQ/OB7TnicKFwNQjbgteXIBpR19gHWEaNWYsBSzmDjbWgEk9wP8JOzj6yT6DOGj+5AOvJyB8M6GYhCVrkG2WY97MgcDKPLyAXjtRNkfTCwfGaHhYy5WoAO2XkaIEFXGNv29naSF96+FzEC/ZFnJ7zIkYHWcVUVAkeCSNusiYmJtOXLwRL6jf0hGSJZwO4C7FDtzbdAsX6sEYcyc1YEcyRIRTcZ4zgwgcCYcTpp7fV6CQjEB+FD6DYdDA47urhfnszYBsJf5on8OPm23lwtQn5nZmbi/Pnz8R/+w3+IW265JSYmJuKTn/xkvP/974+VlZVa8JqDSdC4/0OurnIPkhMHzKxNfi/W3sGek2GDewYFIEBq9BjdMfDlsfkeJDTovjuTeSbygN1hC/E4IM0gihNofEYOgKDT7pzgTXF8j99zoIT/O7lFr7jGYIOLKTzTgTYdFfYrzMn6mMs883L1m3XL/++xYqdsjywbDtyZi+/Dd1g/4qKIEcCIjg8GgxTTYGMWFxdjdXW1FhvwvNyfmh9cm4Mxnv9jpeFwGK9//euj0+nEiRMn4p3vfGf82Z/9WTSbzdRtmq83vssJnUElxk4MByAQcWjvtra2UsxH9z1+ljkBQnieju3MC8eETkzxjwZ1mbP1H0J2nfDb1nj7Kb4HX2NwKPcP2CbHhvaVPMfjtq2niM53sBnWcQN5vl8eqyGn2Cv7MMsjn+FDDFbgn6qqStt0mTvj91Y4x7Umy7h9nP9vm5BvaXYMy/paXui8wF7zVl5sjju/rO/whzESRyDLjumZL/fz56xfXoB7vOTE3LyxzvCvgRu+k/s6N0/Ae9bcz8t3iExNTaW4OF+rvNjkdUZ20B/IYJnzNY8hImqxKv96rRwDOWZst9vpuJqIereOCxbwpNFo1Jo/iF0NonhNms1m7QD9fIdLzkvzw/4dP+nOd+dw8BM7Ymwhz5Eso753bmfMj4jRGYO+Ji+uWecA5WdnZxOQA0Dk4hA8IAa3jScG5vn2pfZBOSCLL9rd3U0FZ+Jn7CX3cXwTMSq2AFJHjM7OM28uN9+9bFDqgQceqDkbBN9O3RN0QtFu19+0h0DacPmtBE7w19fX02vdjXCCvAPo2IhxfweD+aIiLBGRvs9CoxgEigAh7Xa71vJopL/dbsd9990XW1tbMTc3F/Pz8zUhOTg4iPn5+RRcsMBTU1Oxvb0d29vb6c08gC4IBFtl3CXCvBy4cGBnv99PAk4SSOCHo8Pw4PQIApgrysJcDfqgXLu7u+l8LPiLgdjc3Kxt4XKLtg+EhAwgwSOSdGQiF2pkxwCjA0wDawQhbl1mTvC73W6nrTF0pvkV3MwtT2wdeDgxcaUJsjHLE7UrTflzXB2xoeIQ2RtuuKEWKPOWGgITB8vdbjedibKwsFDroPKB/ciGOwBYi9yJENz59dHoMc6XRNPtxoAODsKwDVQoSW4sh3TRoC+MD94hWxBjyfXfoJmTdWTWjhFgysEQcov9ccDtpA6n7gSM9cmDDb7rRA9wOCJStwWOyOOhS9MVfq7Jq32sN0A/AC5rj/1zIuoKMc8E7CQQYc0B8rEHDqhJjpEHklgnWRH1TgkXRmxfGAdrbtk0qOxE7moQ/PzEJz4Rb3rTm9L4l5eX46677opv/dZvjYmJiTh+/HhaAwM/TiidIDhocuLk7xmAzYNwyP7dfp/rHaiPA8ucVMFj7DPfNzDlxM8Jsn2C7TNjj4iaPqLzHg8/Lly40u9uw9zOu8vC98JXYmfdfcfY+v1+OjYAX+5Ygg4J7AbAPUUj9AX7aFDWfsg+MwebnLj6b7kMWAcAEfP4IF9X/u7rsB0eK3zkJQSAfAZssPf4qvn5+dT1kh8GnYNu/h15MwDuxOnx0O7ubvzO7/xONBqNWFlZiW63G//iX/yL+K7v+q609ZBnu2DACwBsS/AXzMdgDH+DZ2xpzBPYZnNUzGG+eYLl9cmTLtbe9s6+3+vtsec2CH3I5YskxmeS4j+JQS2bHhOyS9Ka/514Dh9BfGAQkuvt8ywrBmb8XecX5hX+hiQMnW42mymux+fZ9zFe7Aw5kOXZXZHmK2tpWfd65ICjwcQc8MltMjY3IlIsQHGHH8jxOXERawIvHeMxZ3jqjhA+d3H5avlZ4r0cjENmeXOz7XrE6O24lm3/nscSuQzzmeMz+zvLmNc854f1Kfc/fE6c/XA/OfCVxwQRoy3d5JTwxYUQb283iMgB9x6rx+yiifMqy6+3+HLvvPEFueGaXAYtf87bmLPl2jG8c6c8Z8v9KOtke2s++745GZuoqtELPubm5uLg4CB6vV5NVyIivRHYn7Xbo11UOUhv+WSMXOf/Y1/BHZxfM058GDG7813HBF6Py9XlywalEEgDQRH1Nx2RsBjdt+MwOoehzBWCfwm8AGFIPhAQmEQXgO/DM8yEcU533HdsRNwJxLw4xNkVBxwN27940xCGjVe8c9Al/w6Hh6fTnzt3LlUfoO3t7TS2brdbA87gL8oK73u9Xho3a8P3lpaWYmtrK4F7CBOBkVuu4ROG204HAeMtfSQU3s60t7eXgDaDASgYQTaOCSDMe495LjzBABhwQKHsAHLjbIduQ859kFMCYGRvZmamBrD48EYHMHmV2gbPwXfe/cUYrjbBNxwgib75vLm5GTMzM9HtdtMbnwxGwQc6UHAUzHtubq7Wcsw8CahckSBRdMBrI+aEjnWB9wRKNqwRkeTXsgoweuHChaSjeaLe6/Via2srhsNhOqzdjtjryz0tP0543aZs4J6qj9uCsaOAtXzHCaIBL+xcs3n4ds1utxvT09OxsbGREh5scg4WmG+sGetcVVWSe+STeU9OTsYNN9xQS4DRfxJH7k9iBcDlZ/sFEQQ3BqPRE1eRkU+2WSDH2EMn8tvb27G4uJhkhuc7kQVQszw6ycIOGZBg7U3jAv4rTea/CwQAp+iaAbZxfiwPLPN55MEp3+PfSwWnlwKj8iAd+4J9diCXB8MeIwEu37WvBBzl7x6vkzJ8GOvsc9PyeXFGZrM52ipsANlyy/fYAuYkEn1wJdjbzJhDxCHwtL6+Hr1e7yLgzhVNOnaJIWyr4CH3x544IYAPBmUcvNreGCQ2fxiTq82+Lzo0Tm4caw2Hw6TX+RZe6yQBOffBznrrDy+nMQCQB91OxD0Od9leKR/M/dly9t3f/d3x9re/PX7gB34gvuVbviWWl5fTM1kjur/gA8UIxg4vmQffZd6sQb/fT/4AnpFA+oUdeSw8DtAZF5fkcbOTLX+P/9s+GNwhlqNDyOCykzU6b/i+cwX7XecWzA8/DDhi8IT/061jH83bb929j7wxV+cT/j/2qNEYnZ/kN7xubGykrebeOjczM5OSSsePlin8JFt8L2XPvTa5b8I2jLvGz3Rsluse9g75ht/8GAzIcz7W3V0bHOXR7XZrnSC+lrFerRj5BS94Qfz+7//+Rdu8sDWsVUT9TXfwKv/cHXPj8shcByMu7lxrNuvbKw1eudCIXbBM+m+2g9ZF5z0Ro45f+x7HBox/cnIybRe2fXdHk8+LJI5EptBL+yHHNREjGSbOddGCeeZzupS9Is71uhrQx5e5wOo5I4eWYZ7rnNS5QW4fvW7mZbM5/iVu7kDyHOFXp9NJALHjVmxUnts6v4IH2EOe4RieGJ2XDcBDYiKeQ8OCj8hwp7qbBbwmrVYrXvnKV8bl0GWDUrlg2DCP6w5Asd2J02w2U/cP/8eB5IEZAs0zfOCwkzQcHQeHsWBGCp1QIihcg0C5gsI4HEw7AXOgHFFHzyMitd0tLCzE/Px8HDlyJAFUCAv7sjc2NmJ9fb12lg3V0TyY91sxqCaxFYpnDwaD9KZEvjc/Px9ra2sJyMoDDXevOSkCkXY1ByNkA0Q3xMTERHS73TQvJw8ks41GIwUGbFNxIICCch3JPMF+7ngdxHqtMCA5ks39q2p0iCzzZX4Ro1feNxqN1ILP9230jTAbvHBSZlCPezuJvFqEHDtAJyGwzGOIGDOHmKMr3W43FhcX03r6rVE++BO95u/WJztD7AB/swHHlqALJB58HyfP+RjopDuE2BZ45syZ6PV6MTc3V+smApDa2NhILf3INa/sJni2vWPtWEdALOwbY3aA1mw2U0UY0JUExPNibk42cCROPEk+c2fvbjF4TyLImtNhsLq6mmyT7Th2dXNzM63p0aNHo9Uavc3O+sk4fdgp+sN8fM4ctgD9wDnzXDtH5sj13gJoPvFKb4jrXLlj7uMKJe4EhWd8nvugcYH91SDLWp5Y54FdnqgYDBiXSOXX5dc76Myf58Aq3+rigJjvXSpJGpdYoUdOjokhkCVvnTdIYt+NzRgOhwmw5f7IIbrkLplOp5OCNPu2iKgl/O12u3Z4uW2KQU2DW8wdWfIZZx6TbZ8TAGyuX18Pb3iW9di21eMbt+asLc9GB/09F4YcFDt2cnLhgN8yyN8NqrDO7qLAnrZao+MS0HHWlhc30HHG+ByMezxO1nie+XIlCBDzP//n/xzz8/PxAz/wA3HkyJF0lqiLUo6d8uSSXQDc08muk0bGTpHh6NGjqSueeMZJWb7u9hvYYZ+fZn7n8uRYnf9HjN5OxnOdlDlOwjcQx7vI7KMZLCf5GtuXEB/7jZXwLz/0F/2x7/ZZajm47rlYZihQO8mF8PmWRXcEsRODIxHwpX6eC208J5+H19ZxPeOxbeR7zMHJvPM23wubBX8mJyej2+3WeOMuE9shZNw+m651AHeeSyf0zs5OLX5mbFea/vJf/svxP/7H/4inPvWp0WiMimUU0uCt+eZdFpZtf2bZc6dKRP2wccsU+u03+tnWogfkuOaN/Z7102O3zR/no/nMhTnWD0AEgJT420V32yTHrDQ/TE9Pp6MWiGHJGT1Gz8PxA7phntiP5OCS7YP9mX2aczODhybnTTzbhQRkNz8uJMdJ0HnbHuSNcduHWzdt/5xbOoa1LwBQMqhnwJe4GCDRcRXdqzSbDAaDpI/ELOYJ+QVYBPe13WPsm5ub8ZVf+ZWX0MY6XTYo5aSShzmIdeKxv78fJ06ciJe97GWxtbWVOk96vV588IMfvAh59jNIVri30UQWFoLhPHtiYiKBHN6yZTCLcbsDIe+8MRLNfk0cGx00LBBCZmXA0e3t7aVgF4MTEWlv9vr6ekqM+R5nsDjgzR0PgoWizs7O1oyOjQXdGoPBIHX9uGqMM5+dna11CvEMACSCRf7Gwek4FN5ksru7myogeVK+vr6etiHkiDXBBOvYaIwOyLZceAsSBp2xsY7wKE+I/Ls7QCxrzMnb9qhUU7HiPnlwHRE1/ufGiO9d7aTWY6RC4ATABo8kjPOIABkMVEZEba1WV1ej3++nNdvb20sO0/rD/0kAXRny2zCwLYCt3q5DQMQ2PAdCCwsLCejBiGJINzY20rlD6LCTWwwwtoP2dG/pstOEn66eGMhgPfPuGnjIvfIOQQOjOEV4kydtyDTgNnoPTy1vPANnQkC4ubkZ+/v76ey+5eXlmJiYSPfc3t6OTqcT29vbcfbs2djf30+H3RtsY66cLea3guUVJTrZSJbtYAnkSAw4DLaqRqCxgV+29qC7AHvIAMFtRH3bYZ5Y+aB85B57kgOREfVtD1eTHLQamIiIlBTkgFSeKEaMdNsym19n+5gHrA4y+Zd7IS/wJQ90eX6e2F0q0YSs8+N44O43fHyePOQBJ74Xu5ffz74e22VfhP3x3ByA4gOcXBnQdvzBPZBTB9p8n0SdezNuAsjBYJD0KCJSsM+z3LUIH+yn8kTLABBzYyuu7aq3IHuNIetYHtv5uawbxQv70YgRmGzgCNnHvvO7z/Gyv7dNdsLhGM8+xnHtY6G77rorzRPZ+MAHPhBvfvOb4xu/8RvjXe96Vzo3Blno9XoJ7CT2Qsds852QO7nNwQbWiHuaxoGG/h7rwt/za/w5lIPlzNsAZ0T9DZd0zQLI5AfqIuv4FN4g68Sdtbf+mRfwF13CJzEuznA1wI59MHBtueAeFJ24f344d0TUiss+n5B4wuAiOzC2t7ej3++n+eYxouNUj898z9c2By7yZNfravliDW0rrfdzc3Oxv79fe+N2RKRudOJNb1t27MsaoYeO6R0zwDfsw5WmH/mRH4lbb701IkZ+lQIIcSL8xudGjF7MZZ9h+2F/7Pnb79h3e40i6ucbQjnABA/N11znrX95HGGwzPITEalr2C8pctOFC9D4RcfJblqwnBO3omOMyaCymwscczj2omDJmN3VDE/yPDYvQuYAqv2zZS3XG9sS5874JcssepYDNBGR7B94BT4aHXOHtbuGuS//uqOcvMYvF2O+HiOAMM8COPT8kQnnfp1Op9Y0Q/7iDkjuZ5vPc5eWluJ1r3tdnDlzJh6JHhUo5SSd/1uI3Aly8803xxd/8RfHT/7kT0ZVVXHkyJH4qq/6qtje3o7f+73fS0wwQIVjItG0cESM9mDn1XgchBUF5jgZtcC68yZH//wdmG3ByANcyMriDg62PVFl2NzcjN3d3VhfX0/tcOZvo9FI5+pERA2oyt/GgZLizNzp4WDEyDZ8tgANBoPa+QY4FZ/3Af8RbhQYUIqkkjeE2YEDfKAwnB1QVVUCsHjjH8gtier29nbif949ZsPlhBiDZ1DTQTjy4nt6WyR7eJmzExgnySieFZHx+zooDyquJtnpOVgz6MLcAS85gLzT6dRAIbdz2pn5MFICDPTFDjd39Pyf+8JjAAy3z8I35B0ZPjg4SFUY1psuKXRhbm4una8BoMw5Dw4YIyJt/eI7TsK5FoDNSafBctsLO5a8m49teCTADnzpIMzl1+ArPMReIqcEE9g4b5fgRQIEuVtbWzE9PR1Hjx6NiKidfYGtOHfuXGxubqbtyNY/eO0gP2K0lduJDHNhm4QBNO6ZgyC2pQBsbhW238EHYIvghwMUgx4k2Q7K7Vwj6u3Z1terqbt5cAm5cu7AlvHkwbD9sgERX2uf5spvRL2dne8jUw5MxwXk1pU8afV8IAfo9sU8zwUE1jR/tm0/z0c28y5EB3jcn24bxuWKLmSwku/yOc9Ab3iWwbN2+/AFJiShDqbRF2TXfENXsGPNZrP25k7bzIhD+9VsNmvFLo+f3/OE3HIREcn3wzNkCD5ZXxz0E0NZjtjuyLo6UTCYxbwAGwxaOyln/HNzc+m4AICZiYmJdB4GY+WHz/wWvscLSv3Df/gP05jwSbu7u/G2t70tdVvjUw3GG2AlSUFO8qTTa2AAp91up5ffGHjKk1H7IwO3Tvyd3PJMx1W5bbDthI+O553gcT/O6WQ3gZ/rNUYf6CQCHDBogl92Z5YBHSfPyJDlj3vYnvGvE2fH2vhdA8d5UkaMa/8dMeqGwK7ZN/Z6vRowZd46OfVWmnFg1LjkNV9T22g+537jOsIiIsUOrdbhywacA3A/+Gow1TbBW//y88QiYmwcdbV8Lc0L+DMXLll3y4R9l/+PbBmkMvjj8dt3u+vKMaS7Z7wueczj8YzjkceQ62sOWDF+b2XEntDRYxkknjQYExG1z8YVZQBxDBZzLV2/njPyaLs3roPdOmrdt+8iriYXd+cX17qryrYrxwRyvGJ6ejrpgmNGruFfx8Tow+7ubu3YEHgAX/kOY7V+2zbnMgD/GLv56XwmIlLuz9zBJ4yTDIfDmJmZSTmSfZDBqlx/sdV03F0OPSpv7EAOhjjpzQXtz/7sz+K7v/u74+TJk7GyshLPfOYz44u/+IvjN3/zN1P7tZ2lAxsnJXzGoqDsPM/C2O12a2i3gzIcnx1oRNSqbFyXo5de5HzRvfeccU9PT8fy8nJC4Okw4XBwOgIYD3PgYDg7WgIo9m/SeUaQi1L5sHgHj1RmfYiyA3v4QMcDwY1R73GoOGtCV9HExERsbm7W2hsRWJwdAm6D1m63Y2NjIxqNwwN9AUZc7c7BEHhjBbDSQjybNbOR9NpGjBKRtbW12nYCZN5OymCB5Q+eOenGgPgcBQcrV4sMosGvcVUCjBRb3Zg319nYAnqwjZWXEEQcOnpAVmTZVbaqGm3DNSiKvvtMJGQDWUfGI0bnQ6yvr9dem+wAsdVqpbcAAm6SqG1ubtZez8x4aKv1+XkO3G3kkSnboLySgEwh6wTADhgcALnyQfCJrKC7jAV9pNWc8QK4wk/WECfjatVgcHhg7tTUVAqGWcO5ubnY3NxM24xxnraZdIWgP56bbaEBbAdIzAv7iIz5FeEuLgCOE9TyDGTSgLG74AweYrN4g6mD+Vwn8wDfnz0R5CASXpp/4wAsf54nHx67E1YnqPzNdtZzR86c8OdBee5LPa5xwXEedDtJBXRlK5638WNTvbWF9c2TTHwPMgEoybPRGQfU5jlzZf7457y45UTKMQSyy3kq2DIn/9Z/B7x5xwZgEgA78YbPKUKnfBi4ZYf7OEHOK+gONJFBdzJY5hwk54CF1xieR0QtjmRNASBYV+xZo9FIgLa3Y2G70HmKge12OzY3N2v21XLbaDRq3RyPhz70oQ8lnnD/Xq8XH/jAB6LRaMRtt92WEiHrE7z1S0GQV/MQ3jpm4X5zc3Nx5MiRWkzsbSgu2hGbeK0ZtxMm1j63e36+1x4/yb/mAzYZvXUsSJxnENf2hO2qjAVdIt6wHcmL2wbUnKPY7jnhM4BjX2/AAP/kfMTf9f2QRfMW3XEcbj4BIk9OTtYO2radZG3dgZ/7I39ODJFvTfL3+Jf4wXE0skYySm4yPz+fzo+1DGGrnJdFjABK+Oa4nvtzQLPHf7V8LfJKrGdAKmLkc20n/XMpuYKPtrn+HrwZ52sjIvGAmBfyNTl/cjA2t7/+f/5cPqNDCp9nwNbygBwYdEU3vL2ezyJGBQB4wEulGIf9pvnmLhzLBes3DoSH/74f3/V5o47r7beIjZFNxxM+6N2yAZ9oZrBuO59xPMDn2H/470JFrgueY0SkGDkiavw1uG+/7MKZ7ZUByaWlpdQ0kuetee7r+JnPsddeQ5qY8pjgUvSozpSCOQRwVk4jpgQVt956a/ydv/N34iUveUl0u934+Z//+VhfX08HXuMYcVIkx7nyWBg9nvw6GM71CBDBrasTCBWGlDGjTCwwoAnPsnFhcdxyS/C0sLCQDlCcnp6O9fX1OH/+fG1OdEMwNhSHMVJZ8St/OSCR4I0D6Hx4oxM6J8o8IyLSthajncPh4SGiAF3ckzX2esPfqqpSgI8DxAg1GqPD4u3AOXMIRUOxOIgd3pEcHxwcxPr6eu0eDh743eAmyhBRP0gb5Z2ZmalthcFpbm9vR7fbTUbEBnYcEIWhMMDi9kt0I09YnggC9DWNSxgdrLuKgJEhkQJcmJubi4WFhWRIuQaZdDeieeLg0YErQTLBJsbceu8WYoNWVCbRYa5jKwMOCFCDw/cxxg5McuCVdm5kw7pPwIe+wreZmZn0NkM7dDtZ5DBi9OZO5tvr9VJSiuMA3HP1iXPt3NWU88xOxZV4knuu2djYqL2VkPui35wNaJ5HjN5g464SnpknqHbO7uaamppKyQmyYKcJ3+l6gffoE2Aez8+7r1yttuygo7mzz4O8PMl+JDD5ch3vpcj+LwcQ8s/y4JZ/baPzcVmHnAjyO9caeDUv0FN3LOfPzMfKvVhP7pf7UicC6C8HkeNjp6eno9vtpkIISQ26zPpgZwB2mD+gE2Pq9XrJjlk/c/DY3ZvYF54JWeYNgEaMkjFkz2+yoyvTMu0t8+a/7Svt87wBlwO1kXsORnXyab/NWJmr/SfXWzfw864i+/5ebycDTqbhh7tNnODZVjsx2NjYiLm5ucTn2dnZ9GIXOmI5z7Lf76ft9o4ZDHpYBx6vznr8BoboYGLNHKCzhiTnyJe776zfLvgy5tnZ2VhaWkqyi11nS4h5ja54DfI1HGczxyXSJnyGZcb38Bp6G0luT22HHBsCTMEr7mWg1gWTvMOBNXau4Rgo/91JFv/HnznJNYDlQqvtoP0l4+c6isT8Tm5ioIa43wewwyPWOs+XvFY8G3vntRwXh5rP8Org4CCdL2oADZ/v7bbw0DE46wEgStwEP7HdfnGO18PreSWJfBP+Mvc87zTP+RwbB8+QhUv5bPtOz88+1vksMu2X2NhX5rqW2zj/3/f279jkHMiwz2ddc51xIQj5MhjlZg7bFq61LsArPrcdzQsguYzlAAhrwXjhP8+Cl74H5HiTHQCAWGAAvFm60+mkc5vd5IKceFuy/U0OkrEe6IF1zHaQ70SMmlgsi7bNFGX8kiAXghir/ZTtKfk630FvyQHhsfNZYnp8Ww4YMsc8F70UXTYoRdsdzDSCaPQ7n/wtt9wS/X4/Pu/zPi/e9ra3xQc/+MH48i//8oveKGHBd1BjA4hg22EQeObCPBweHobJW+/smLmWtk2j1Bh6J/RefCc4NjRW4vn5+bTdhVZuKkIcbkjLnhMjFIski7+5K4FnUkVEMQhEIAAshBNBaTZHreLNZjNVWgmgAGX8DFpUEXj+jjMh8Op2u6lFjwCE4Bl5gIeuOFdVlTpstra2Epjn9no7ZYwc5MB/XDIMPxwstdujt64gM4PBIM6dO1c7b8aOZdyP7513BTnwcteGt4RcbXJA4kqWwV47UQ4ZxVi5C2B/fz/m5uZieXk53a/f79cOqaRDJX8bEM+083WgB+EICBRsbGdmZmqH+CH/2ACunZg4PAOMc654HnqIzrZarXQuE3rNPGjnNm8YR3498kHCfnBwkA765wypiEPbxAsMuJfBUxv3qqpSN+Tk5GRq7x8MBrWtvawRv5twLn5jKOATZ7oAPPd6vVhYWEjrFBGJBzkAREUXncxBxlzebSddibNMYN+xUdh6gE4frg+v3XESEcnGGlC23tM9Atn+5jQuGXkkajQacfvttz/idY90D/514uwEms+tv5dKHvNx+x6+Bl7wuwHKPICCv77edtd+MwcsHKDnNhy702qNzlsDtBwMDre44J82NjZqAZB5gf1F9rmnK4d0//rNjIwZn+Q5osfuVnK84sSBbh4H6PYjDowdZzjgYy5O7KwX7saEf4uLi6lg5bMXrb8eO+vIGSkUDhz0jgMrGZ//9TVO0rwW3A/+O/l1/EQSwJiGw2GqJsNPgzqtViuOHz+etvD5vI48nnORgqTPfHwslNsP7JTBb363DYK3DvYdU5hfeWA/Pz8fS0tLKV4E0MBv8BILKAffDL543VgXJ76XAu+ciHmtWQPmTmKTnx3krnLHdP4/Ma/jLZ6dx8juBsoTcvwBcUueyOf203rCvdzRa355K5/11YcDu2jmeBFg2WsB/9i5MDc3V4vvG41GiqkBt/O8hN8Zv9eUv/n/8Nv8aTab0e12U5yA/nB9p9NJcX+ek8EHg1Twx+MFrMxjZhcwrga5+EEcaR1zvMzvjl3y6/N8g5hmXIwNuaDhGNJywPj8HK7HP1jWbePz2Ibf6TjlfF/ncpYZnsV6Rlz84gv8EH6P5+Cvh8NhklVsILzLAT/LLvN1Ack5uHUWH2A/nNsLr0nun2yjyGd8TIhtMAX4RqORcmbnx8TrefGb5/voI9vgS+WMzr88X3hkOeSavAhoIMvFY/jF3+Ex+bx9EM/g++zMGgwGCctwAwzjfLgY+1L0qEApCKFjwq4I4KgmJyfjnnvuiW/91m+NW265Jd74xjfGwcFB3HjjjbWAyoKYB8yeUKPRqBlLI4h+Nt9vtw/32nNOkZFLL4oNO4EKyo6zzx1bHoyy2BERs7OzMT8/nzp+IiIdLsx4qf7kymUFdecGiSx7VxEO7mEAgLH3+4f70xFuKxMGhECm2WwmcIkuIVfmEFyqGQTyjAVFYAuekw74ExHJQDE3Eg4rnB0EPOU6ZDAPrmz0ucav8M4NXVVVaeuZq9R0uDho955x7mH5Nxhl5cv/bz5cToJ7JQijbgPtxDJiJLcASxgWzl/CAbNuMzMzMTU1FZubmymZ2NnZqZ1HVlVVrfXXelxVVVpztpTh6AjO8wSTuZCo+g2eTnQxkgCOGFRAMeQ3Ii7aEuQ1xFYAWCPfDpiQJR8s2+8fHv6+tLQUVTU6N4F25bwd2t/j+awHvF5eXk7gCwESgSwgkceYg68Royrlzs5O6jZgm+Lq6mq6hjFyT64h6ARAI+kGNM4BXIL2PIDiGq5ztdJAFbLJmLe2tmovaEDG0EuScLYqob88y2cdGAS7VPDiRAn7Oe66XNeGw2G86lWvukztvDwiETB/IQcy9iMRF1dHxyVd3N+/47vzqq6DJQISJyjj7p0HWQ5ELRv5GPBtBEv9fj9tu/XWW4MZJPf4HftqfBvPI8inQoid8/fsA5Bpr0OeIOT2nSTUcZPBCCd//pz/5wAVsQC8tFy6i9QFHOyYtzzm9zCAnCdXrAP64PW1PXewjP00uGfdQ2ZI5B0/5b7SdgU7TVEBe4X/phCyvLwcW1tbKWkmhvN4G41GAhhyOb8SlHcKcJ4UHXLoGDEZsV3EKLY2qG67im6S3LjLBp+Knc913n7Ya21wzFV1f9fEeuTdb36+u+OxGb43cofPGgwG6SUb9g9ca/vhTpuIqO0KuJRNh7eWWY8PnfH38wSXMTkfQb8NwOQ2Gj/JmgI28HeDNHlMwH2Hw2EqojpH8BmsLrjktglb4OKdgUfsooECH5uxt7eXYjzm5JjYifc4kLrRGL0ZjK5n1iwv5trHXy3CtzBnyHKR84otdeZtDpY4l8tz1dxvcF/AqDxGgs/Oa+0v8VkR9cKDddu8xAZzrAXP8Fydz0A+0N+/u3uf2J05tVqtBFpG1Aum42yRu4objdFOghz0Nt9cfPGWXjAJx8COSfg/c6XzKyJq58E53vf33e3KvP2Dbm9ubtbi1Nze5nrjcRn4jBgdMUQXJb4eGXae5AYeivz5s3OZt85OTU0lPriYbFkiVzFYx99YL7CFh7PJ4+iyQaler5f27hvldvCeKwMK0+l04uMf/3h8z/d8T3zFV3xFzcjDlEsZIwuD24+NXhoYiRh1StCl5IQVYea7dhQ4Do/dQoMzMdjiwMnb9paXl+Po0aNx+vTp9EY6K+ju7m7MzMwk541SInx+pS9GAuFzsEgFt90+PF8KsAXDcezYsVhfX0/zJQhywg5f8uolTh6gyHvL/TnBEd/jOv6WO0tXQeH55ORkLC4uxokTJ2JhYSGNl+0VMzMztXO4LBsRo+okhmZcAmeAE3Bga2srFhcXY3Z2Ns6cOZMUnnUy6oxxciLowMZJjJMGG5o8gbmaxLra2ePI8mDWXTp0pTDvycnJVFGhaw+5BSBxgmQDjD7ZOSNzPvCQHxINeJYbUAcS+XZfnjHOSfAqYr5PFxKf22nyPKqv6AaJk7tzsDvmNWd95IE+zhvd9hkfDsQBmunwxPEBKAHmOZBxNc2JnhNc1hybMTc3F0ePHo1ut5scLFuK2V4wNzdXa99vNpupG9IJp5N3g2sRdUDC64ytxLaib7wJsd1ux+rqaqytrdWSGNbGYwCo5O8kC3mQ6IACuUHOrN95YvlIxDWf+tSnLkMzL01+vT0Fknyt7Tf5gRzcMC4HNzm4kAfILgo4WMkTRD6DV3zf9/TveSKYz4Hf8U3u0N3d3U3nmjnIy5NQJ5GODdBJVx7NCweBBlrRI85XyP1ZHszhP/OOXRNzs82Aj3kcFTHaImtQCrAYYKPT6dRezuCk3cG8g20H3CQ+kEH3XD7w7+M6GfJEdBzohf+x3wGg4xoHvKxbxCgwx95QFMNnHT16NN2bt9v1er2YmJhICYKf6a1RV5IMNDjxhnesP36WjnYKHO6UhyfuwqmqKr2Uwm8Gg/ck/vPz8+kzfCF20WsVUe9uze2DdRgbmncluYBAMuIOYBca4IfXwp1T7mZDnu03nGw6FrStttzn8ZZl0JTP1bE/PoqYOQdkrFcUnRlrzlPG0O8fvuk4B4uZN0UxvxmYrimDGSSidKnn68XY8wTY8/ZuEgBpiukRI9DCxTs6Ew0aUNCy/SGe4GB7v2beYEQOJFzNOJmO8Yj6eTjEB7bLw+Gw1rXO9+w/nFPZpzgezkEpPuP3XD58n/wcNce74/IK7pWDIa1Wq9YBj7zg91lP2y5y6HE+l/V1gYXfnd8ybh/bk4PS7rqKuDhmoGBK4cWxZp5z8Lu7uniWx0Iu45jPvs1+GR4wBxo1yBGYNwVlihA8H/4wNvvn3Hbla+wmFtYZXl8KaMaGsp2Q3BjKi7yMkbzMfKMgTd4GL8iJ/BIWx0nGSi5Xny8blCJ5w2kiWK4c2LhgtFqtVrzoRS+Kubm5+PVf//X4y3/5L8fa2lpta00O8LAgRgtZGASXZ9o446Qx2hhyEmELL8aTxWAxDSQAXOTBXI4ycu3KykosLS3F/Px8OnxyfX09tfKxYP1+P1V8qQLjDKxsOZqMws3NzcX09HT6Xr/fT10f8I/96LwB0MGXjT88wChERG3eGGK6zTAIPKvdbifnxN9QROQDoafjiYMeeSZJeKfTiU6nU+vSge/MIzdUecLoOdq5uOLkjpD19f+PuTPZkSxLzrO5e8w+xZSZ1V3NJpsUIQiQQEjQQk+gl9HLCHoWrbjSUgAX3FEUSKpZPVRlZmRE+ByDe7hrEfjMv2vpWZXdXZlZBwhEhA/3nsGG336zc+4o+zWZTBoOxIQB97LhqOy+Ayb3rxrsjwlyf6xGf2wcbORs2Kh4grywwaMUHVlDTplHxoesREQGDeiHDT/gDocAiDXwcXaRhqM3GLUDRuZdscn68z7nT5iMYo5sWxxYMW981utPYERQyP+QP4yTLEfE9vDHiC3gs31B3rk/5/hAWhhAOsCJ2FZGIbecn0ZGlTl9eHjIfuJs2e7M2vnpK4zNDpk1MMlJAE1/mC9KnbHP1mnG1G6301ZW0AOpZ102GGaNCHBq8GHg5jWtAIH3DeJ/iJzi+3/7t3/7UXr5ofbXf/3XGYjc3t4mGOUetF1Bo9+rwK76aMsa18U/VVDEPFfyhLWJiMY8Y3crqVH76u8Y8NOPiMhAF19pQtg2nusbeDlor3iBfu6y77bx9Ic++nB/E3T4Ya7nuTGWOTo6alQw+0EB2EfWgO8zBq7L/JCsOTk5iW632yC5mRt0hjHZLuySA5NsvF/lqlZxWTYN+Gl8lqSG7bXHbZmx/jnQc2CyWq2SfNpsNvHq1avodDpZIf8Xf/EX6Yt+/etfZ6VVRDSSc5/CH1uWkElwj0krAD/9ARdblhxw4Xd42jHXJDhwhrzf7zeSrCSQbMeRFfvhalcsA8Z+Xju21i4Wi50+HXkHR5C05HPICfPgANJrZF9kQvP7ZLo2vl+J4yr3xo2Od1ydYDKP/2vCDxxAMgk9nc1mGdA5UMXmIKtgd+61Xq8bFWWOX8AZ1b+ZSGB81i8TLfxdZYXPbTab1CXbKcuOyQmq5zxH1ZbTH+sM1/0UzfNW407WnvnodLbHPEDYoQMQrKyx59d+1X6h6pf9l/sX0axgqtXdvof9vJsrtvBn+JNK3ti3u4LIFXMVT9BX+gYxwfeIFxgn/oj+sKPFeJ/x0LD72Er7c9bDa0d/kGV02IQ2Nm+XD+c11tYcA79J4hqDugIb8g+dR5aqHnq9/fqH9BT76/8Zb8T7VV3MaT3awgledLrGQJYdHkzH0R1V5kw4cR/iHtbBhNj3tY8mpdbrdQatdqhMDhPIYH/729/G//yf/zMz7998803M5/P4+c9/Hn//938ff/M3f/NewOfFY2FrEMEEWinb7XY6aBhtC8zd3V0cHBxEt9ttHDBqh1MNCJPp0ngbS7OKVEa8ePEier1eGjDOsCF4I1uwXC5jPp/H4+NjbkvD+PR6vQYb6wNPKQPnAFNIACsr5BTCixA5e2uyy2Qb5zZEbA81JSjtdrsxm80yA+TvuoQRwUO4a7kh38GYMB6AJYeTMi4Cau4zn88ba2/lZVyVbUdumEvAGhlFQF5EZEDuZkBvUsXEGcbOAUFENIw5cvwpQPCHGgbB7LoBEGD56ekpK+0YD0EiZAaAGZlqtVoJQiMidQ3i0SW42AV0CwIyIjITaLBugGxwQpWVdRc7YXITsMocAA6RDQ5uR14ggPw6tg4Cje/WM1hwrM70oEcGrFQfRWwzRiaqHXzakRtAsS4RzzKNPaM6EedDNhLnyDo442Iimko4Auflctk4b45gwYAfIEIwYdsESWbSnmu7CrSCZ0jD9XodNzc372VSyQZzX+RuV4DL+GqFSw1E+L5f8/Wr0/1QM1D7Y9t/+2//LQ4PD+Mf/uEf4r//9/8eX3/9deOayKvvtYtw4rd9M3bXr/s9g9wKFP1928NdILv+zb0cPFXghP82cH98fIzZbJal5N7q4XtWsmyz2VYLQRSje/jKCqidZKnzix5hLyHjkSkHN/zYhrFm2EWuga2hP65Swwe6ag0750wtOkHDdjCvJnG95tjEKh+uIGRsthkeo69VgySTdCb3sa1eK4IZgLVl1PbZ2AzyDgIGWTk6Oorz8/P41a9+lRXj19fXMZvN4ttvv825mEwmeTC85+/HaB6bATx4kj77nLJagcx8OjHGHOIvSCpY5lgzcFe3221UFCBDVb532Ub+dwBYt+3iJxaLRW6lsX/2eVcR2208EKXYVeNk8DwNW24/WOMFX8s+ivtVeUM+0Su/X/0D9zNBzbWrPfRuAwdntr2sHQcn8x0Cb/puPMQ4XLXS7XYb9sH9NjFV19X4037cfXf8Y+Lc9tb43iQZn2MOnPhDb8F8xsxey09NStneVzLcto11J1GOnUYvSNA6sHfMapmtttMJuSp3YDHrWk3Q7iKnaJYJj8+21DbGcQp4zufhodvoDK3idvtixlDJC2JLbJG3jTKuiGjoG77Mtsi4z1jU36m+3oQSRBI4nut5LpFRk+Qm1/FBPJTr5OQkP7vLR9IPr59lpjaPlb+rbFWc58b6WNYsTybVuD54h79ZQ7gcJ7+JD5AV2wAnryKiMR/f1z6alFqtng8NxVm4mscCERFJSv2f//N/4q/+6q9isVjEwcFBvH79Ov7u7/4uzs7OchIMeGwUajaczxAksxAEaWwDwRF68nECHBBcD3Szs0apGA/3claNvrANqNvtRrfbbZyPQjUUWYLb29u4v7/Pw99YeD8OGIBkIOf3EP7JZBKr1fMTZljsu7u7xhPrAM2UEFqRbKD4vgNNhJvyYYCUg2cOQOQ7XJtg2GCRbVPz+Tz29vbyLKd+v984HA/QSlYFQ4WzRgkMYAyevZ6s365gCzA6m81yvRaLRZycnOT6IAsAC75rIFfBp8GRz1SwIn/OZpCGY8IZEKzTmMfRaJR6xTZKwDOyQHk6hi3imVjqdrs5l61WKyti7CxOTk7yWj5M3MSWs0M+EwQHxtiYX8uziR1IEAxnRGQQ6EAJ+zCbzeLh4SG3CQ2Hw5RxAkr0ynYLA04Fnh0sj06O2DpO9xubQZDC/wCfiEhgaAfl6jQOmKciE8Drx8HTT8r/OX+FOadiqtXaHnTI2CGpcFL0GycDoKF/EEesEZWIEdvKWttdO2Y+z7r4oGJn7JhLAlT7iaen7VNFHBjSGAN6YHvvfiBn9fvf1/6Qz+5qv/3tb+Pg4CDevHkTx8fHDVmpYNnEzC7Cx+9XkqjaxA8BGwch9MPVD7Uvta+7+ujPE4ByTdYB+25w/CF7WoE9awY5axmswZmBPf11tZHlh/va39h3Omvrg0Dx9RBkTpogp8ybM5hUnTJv+F/Pr8m6WhXKHOwK4g0gHfDzvufVgHiz2TR8bwXb/p+AwP1hPrBT+FnbaxOUvi6ywVkiYDCfm/Ff/st/ieVyGV9//XWMRqN4+fJlPD09xeXlZbx58yZtHU9v5P8fu9UxG+PyUBf8BuME+5j4QS4cDLkKx8FFXQceTBKxlUcO92eda6C0a90jIklVB8nEBIvFIslPJz+5r5N+YBCSYOg+NttPDkT2WXtjU1dUss3e9/WY6ho42c080DcH0A6uwL+slXUfWwp+Z26YD+uPibDBYJDvowtc18GnbR34F7t0eHiYFccm01utVuP8H9tW1tMkhXF0xJY8rNU5yAZyS/8dPznQ5bVWa0vGQYLU5K1l70/1oz/UnGzx3HJvx6eQf/aBFcOxXnwfXGEMzm++j91BZ2zX6Z/Xyd91v+s9do2HNUQXrcfGiPYnrE/1B94eDh4zjqJ/vEaS0T4Gn0PVa8UUfIZxc2+TPV4P6wt/4wut/yZJrecmsBwLm0irGBNs2ul0MmaIiMTLftBBXR/bpl14zONzsz32OvO/15If5AqdNdHkOfV8mqjmmszL09NTJux34SP67vFUMvP72keTUpAbTDIBAYuFANCJ/f39ePHiRRrHTue5BPI//af/lMDGSuggDCFhglgEG1iEA2dpg2zWne8ySTDeDlhYBJSARYNcqECSIJVqD5/xYCJjOp3GfD6Pd+/exbt373JM1bFbsKzgzIWF2qQHFSsoHFuwKJlEcQhoPS7KviEcMJA+bJoxr9frvBcgmj5wYPl0Oo3ZbJZZM5wn80drt7eHqgPCZrNZDAaD3NbAfZw1jog844ankZm8snGpSo4R2Ww2eY4NFWtshZxOp41MFWvI9whs2DJZgRv3Y7w2nv78p3a2bgbylnEHWzgGBxysPYYE9t/bdz3uKtf39/cxmUzi66+/zgwaYLPb7WYfcPoOCuzg0Q1XHTnrzw9zjx4SHB4fHzfOX2B8yDgE2u3tbcoqukRQhKwDpAiCILgBogBqOwWANbrifmDHyIYxT/zvOTARhQ3cbDYpwz6kfrPZ5KHfrBN68vj4GLe3t9Hv9/NhDMPhMNcA4heQ2+v1sozb1YOWbchNbAZVLQbHgCuDVh9KXXXIQUpNBtAc8Br8ujrD36nJButmdeQObvze52j/43/8j4h4thNnZ2dps90M7gxUI3Y/JriuRQVH9mset38cBNYz33b5rNoH+mFw6uvZbrs6mMDOgR6y7mvWceLn0HW2s9e1dfUVPhhZwrdGxHtbnxxsIWsmrZFf+uHqgpOTk5jNZrFerxsPbkCeeZBARDN4xB9GNLd1RGyDOgfOnieDbY+hkmrWsSpz1iFe8+9doLoSZNUXgsXwB/P5PLclQoJgJ5gjEgzGRT//+c+j2+3GxcVF/OM//mPin6+++ioD/t/85jeNoxLAL1W/foxmwG794ExBPuPxR2yrbRx88Dnrg4kE20EHJ268bxmiD/apfj1iG3T74QfoFYSUZcb6SF+dBOJvCNq9vb302xHbs36QGx+7Acm7SxcYs+0Z/Tc5uOtzyKMDYMu4SQMT9GBh5p+xsk7oP/iG9We98ZvML0lc7zYwUWQ7zG4LJ4R2PdyHxJTjF88Nn7X8OBDHT5tURaaZT9tKk+IO4MEuxC2Mi898boxsXMI9qx8EUw6Hwxyng/dKXiBrrFElQo1bndTzPNtPV4zrPrhVMs0kgWNG3gPve/x8nvWyreb7Ji+M5ezfvIuG5LRjOe6D3LJrAnxrAoT41Q9RQq7oq+fCOzOs83Xs9nuu4KzVnruIW8+1ZdexjysDva2zJtLsU6ue8xnzHVV+jVns13l/l73zfRij59D40L4FObUtY058nrT1x+NkPj6mfbQ3ZkHJ5FhY6bzZSgceDnj4fp1gO0cbLO7tfqCoBHr0xwaDyXEQRPXHZrNpZMpwDDgVXnMQjGDjSMjo8V2AFMHi7e1tvH79OiaTSW5Jw8F5niIig38/gcWOAgfHXPl8KgwI82EGG4BsI9LtdjPb5rJcOyiTQWbO6RPBMNddLBaxWCyyMoxtgMgBfTfRUQNvyDSq6qbTaZyfn2emD0cOwcE6sH3M96kOlMC60+nE+fl5kmE4B4Jun70BeYZRw4lgUH1tG230omal7KgdTHzKZuNrPXVAxRq7hDViC/QMCL39DGBsgBkRKf9U7uBwsAPcYxeQsTFkHg2EbSNwdhUkoI8mWngf8gdbNJlM4urqKkajUXQ6zS1EAAsIXcbPwYYEj1SOMQ8eI6QWW9LIXM5ms/fIAIME5sWOB72D7MWmkShABiGkTEjS34hIoEBQ3G63YzAYNKrWsNu9Xi/JLbbHcC2ui01w5hS5N4HhdbezNNjnNYIPtlyjZzyFFPkz6LBj5br0wTpNtQkBksGF5a7q5+fQ14jIw4kjmlm/iN3Z1ur8a1BiAsh6gi/j//odZ2ZZHwePvi/XBYy5Px/qL7rqYBfZQN4MgAGoBssmmDwOZM4+iuYqPACjr2McgW+NaD7J1IGDg0JIaZ+74HuTFaZSEV2kIjAi0pa6asREHvpmMMp63t/fp35WH1PXwnrh9yMi7fou2fD47ecsW/a/zIGzqP6874HvwD/v7z+fDwihiC8Cf3HdiIgXL17E8fFx/O///b8TM0wmk7T3p6en8Rd/8Rfx7bffxtu3b6PTea7oZOvBj93sD+27rFe2OTz4JiIaco9dw25hC03082O5ZF69DiZ0qn+pa2E5gRiiT/f391lVbHtfsQZj82HZ+PSjo6Os8jGBYTyFzfEcgBvdTxPJNMbIXDIe+wK/bjKX92sCzzLLPfFLDw8PMZ1Osx9+YA52hnWDKMJWMT88Xa/6x0pScd1qb50cQr5qZaVxcY3ZKv4yUcV1TFzwt7/P61RumuRw1TPj47vcl/8/NoD9Y5t9Dn1n3lygcHZ2lnNqzMa81F02XNsyyrhMIjjG4ruWyYj3HxzCa5Zjx6YR0cA0u2INzzny4RjXc28cusv+I8/G6sbrfGc6nSax5OIHZAoM4LOmHAM7aW65qJjMcmTZ93VsQx3fer7t103E8eAZ4yevkXEDfh55cfLLuNWYgj7Y1nh+WT/PEfLk9TGpWH1zlS3LApjMfeC7PiuMuUWG/bqbMRY69THtDzpT6v7+Pm5vb+Ply5eNTOVms0kiwoxoHZhZXy+2HZknoBovGkagLggL5T2/zn4QlPI+ZccoBo7EZZVeuM1mk+QT13RGn20zb968ibdv38ZkMslzWUx6Ebxxv1ZrW9JNQMj88nn6j2PiSYgGqT7jhiwggrZarRLk2kBgSGwYHMTuAswmcDgbi6eu+PwsA3Y7QCsy4+N76/U6ptNpjMfjmE6n8fLlyzxni+8yF2xTZCthlTnLFkE0RBb/s26czeN72Kk4COA+u0hLG0obG/ftczVk1A4LmYiIhg4ZnNqROevA/GHU0a+6xQqdoALu4uKiUR1YHQ+BIv8bdKMrNevvqjZew9lTvecqJYPc1WoV19fX8fbt25jNZjlfNTiFhMKgUkm12WwJ+tPT09xOwlYhxoa+2KYwT/TV5Gy1NYyvGnP6xueY/4eHh3zKJ8Gbgabv8fT0FOPxOF6/fp3BcgW1EVsij23DrVYry5MraWBAx5iZb+6N3NSnRtGQR2wjWziwLSaKcY4QggbIJlMM3g24HByiJxX0fA6A7GYywMDhQ+SA/672xcEIzXbR163gyvcz0es1c+BWt9Xuuo7tp/XVpKTPQHKSABvgwMWgGL135Qt+DBmgSoU1R175MQYxmPNhr7ZT2BbwgM/Rs++wftNndIeEDmfFUPnIfENqe9742xiK+XKCkMoJJyGY2wp+nbTwmtjGRmwDVloNemqzXvlelkHrGNfDJ9/d3cVkMol+v58PqMC2AP7pV6vVivF4nNvzwTnYnJ/97GexXC7jzZs3KbsQK5+imTzFZtVkDPPjKkDO/zD2YJy+5i4c6YSf8ZUr9oytjaG9BiSR/PABkobz+bzhf4yBKqFRExRU34JjWWvuj/+h0i1ii4E9RmTLY/HnGBsYntedXLWs25cx/w7mLJt8/vb2NiuWwP3MpY9voArfgRy6xev0h6rwvb3nbYnYE+QHG+iKcu7nbbVcwyQ1euYYzD7FVS4OaPmMX6vy6XiI9115zlqwRozJiSx/91PpJM3EfCUjkVmqpD7kk2vCqAb1ThqwBtY3yzK/vaZ837KPjPr/GmN/SG7ti7mOj1ow3kGvPCbGYbngt327fYeTh8S4rDn+De6g+gfrbMTW9/j/GqNZjiyv4HO+59ieZE7ElntAp+yrPZf8j47ZJvGDjXeFkecfe08ime/V+9k3V2zl+ef+PgvMJLbX0LrKOiCXJmVZD2MqksM1TsG/mMdxzPlJSCmcH4GaJ8tsmwds0G9jZANv5+KgyVVFVSBMIHjiALoskCfT24Ta7e2jzQF+3opo4MnEc4iyS/lwDlQSXF9fx3fffZdVDPUAuYjt48pdweMMikE2/cAwcG+fM+MMBsJMlpGtdHa8Fg6Ey0DUAMbbEQzkIRIWi0WjkopGQIERMfHAtfykFTtU5uD6+jru7+/j9PQ0zwShb6yh5cWll6yZZefw8DD766o2rlkBnwH3crnMSgGMCdetDtUgvDLhNXD8lK06LObJzhBZMIhF7kwEPT4+5hraoWMX9vaeD90muMKw3d/fZ1aU9TFQJevtA6wN/CKajzLn9ZrZJDMB2cH5TOggcj6bzeLt27fx7t27xnlVEc2nxFnHkS10A5n2lmMeMFBJD8ZLEI3dYRyQOzjqXbKBLh4cHDSqMLB3BMwEpQb82BX6gw0kgB+Px7mmZAWRcV7f39/Pc+AqSeNgyUEP16nyiMMkA27ghNxsNs9kt4ELTy1jmzDfqbK9KzCyTfFhmayvbXMFRp+72Ybues1BhIGg3zNosd3x+/a//o7Bjb/rxAU2xVUffM5BYO2v7baBK4Ha3d1dVv5hY6vP2GXT2u12Vi67z2AP5sn3xz6gQwTBPhORPvu8E8s+8oqf5PP2UU6K8b5BopNEJtrAE/TVQaQD8YjtmSRco4LJKj807C06YF+6S1ZqkOp1qEE8vsRz7mC2Bkz1WoyXZNHDw0PagFZr+4RPYwy24+OnOWfTZ+P1er3o9/sZ8C8Wi/gUrQZFrC19raQgvoMAzXjYNtf4jeuZyHI1EjoEEWuf4GMVCGocPLtqmL5y1IIDTeuln/BkDOAq3BpIkRChf+iTkwn002RMu93OymN8nklakx+eZweHHn/9HK8ZZ/M692bHwOPjY1bsY/+M+7FpbEUCx4JpqajwGjNGjrlgTh1o+nPIA/PqpJe/4zlx4z3G4GuxvujzLh9j++Dgl365As62xjjbPx8bwP6x7V//9V/jb/7mb2IymSQhYtu6t7cX5+fnDWLCcuF5s556TowHI5qJsDqHrtbhdyWkKt7hmrviuvo+5IllyDFbXYMqZ762dYRrVB1y/8EOyIETznyvbl/kGpY/bA36aJtKfzwX9Hk2m+VWV67hdSRu4XV8/mazLdyAJDam9Nzusj/2+U5AewcHmIMHunh9q7w4JrLPxt8zZuv4Lj9tOXbiybKAH3HcQIyCbWYM9IfxQ+zXrcO//vWv35PPXe0POugccDafz2M4HOaie8KcufbEfUipKkGFEUXYLUBmQ6tAeLKdLbSiUd2AE7HS4DzrgXuMbTgcxmAwyP957DwVBO12O8bjcVZfADCoGEKZajaMsUCK4cABzR4rykhm1gSSyYJ2u50BpI2Fsy7V2DHnruSK2BIHVnbWyyV79IXtcA6MaXzW2zhbrVbj0bK175xRtb+/H2dnZ3F+ft4AbRHNg+Wc1UB+WP9aAcZa8EQ3HDL3dgm4syeuynI/WE+P2yWbdlCfszk4MQOOU8EwudlgQrhAPtkIIyfOuNiQ06zbu7JGnKlgI2yiz46+AgTWlnNI7HytA7PZLL777rvMcNrIsz6eJ75r8skVT84EOcCtDsSGnfWgOSCoJA06zdo42OR/B9wOSOkbsm0isALAxWKRT7o7ODiI09PTRjCCTvb7/caWQQgrHHYF705U2I6Z8PFcMRdkz5AtrwOyVQPkSiSg35ZVJxhYF35Xv1R14XM1gyUHEfTDIIAqskrYVjDrYMcAx/bfusRvg2xnBG3fLNfMv4m+XfcwiYT83N3d5RZu+xdvRWWNCb6xS+5DJXYJyp2Qsv3zfED+mAjBDmLz0Td0D19kXTGYtOzZ97iKgWpCkjT4W64Nsez5BQdEvL/dxURfRPPJRF7nXbJmP1VJQAcBVV7cjGt2geFdMmH5RJbcJ4j3+/v7mM/ncXFxkfIPzkOuWOvhcBjj8TifGrTZPJ8penl5+V7Fz6dongPm30kfgh/6b7tlkG/cxXUrIVQDLSdDTGaaJNxlhyMiK6Tse0xIWQ99j0p8kMSyrJioBN/yXZNfXH86nTYwLWfCemurE0j013bKPoh7eDuJ4xL+Nt5wwIos8vQ4MDCHtjM3rA3fc6JkNpvF09NTHm3g40fwf07IQ2Bxb8aCb7PO2K5HNM9BqjGY5anKjG0Za1ftAtdg/fw6/WHMJPGwc7viiYrXP2VrtVqNWM/Be6vVyvOCwbzWLXAFc2HcxlrYJnE/fqoP5nuOXUza2F+5n1zb36v6hk20XfS5ZuwQ8tmk4EjiQMhfY7u63uAq26ZWa1v5x+ccQzAOk6DEn/ZFNXbiO9V3GKvbznjLGbqM/tvPIw9gT1eII7ueg6OjowbOBGeADZyQso9m3cC32DXPg22278n3LQuuNLScuWrJc2Q77wIcfwbMhyzQfxfvRMR7nzG+tl/Dfn9M+2hSqjKPvV6vIZB0zsaGv5mcahC9SFYwA+8KZmi7nJAX0wFxRLx3IDAHkkEuRUTuGUUJmfRut5sHAgMqIiJubm7i7u4uA7/xeNx4hLWdFqRUxDazySPUCaghY2qlBuNmO5KfUMLck/Ej8+pD3zAilc3092sJH0bJJAbEGE8RpMqIKhLPNUpSK1oQUrYo2Aj6N4KPkSKL/vj4GK9evcqDplmv4+PjPHzTQbgDeNYeYgl58BlRGCsH2CiXswLIeB2XjbeDwU9djryrVWdbjdIuEOuxMWc4EqqlIFSpfvKhvHZKBA2cl+EgzUaz0+nkEzoimgc38j46iq2wXNGXbrebZ4gg84xtPp/Hzc1NyoedBdfGKXibEMEz1zF4ZF1tvwwUsUeWwRpE2PkaCHislQDyGtIX3tvb28vzPkyWcg3bZ9Y1InLr4dXVVUQ8B3Q4XXTclWgEc9gN+sc8mXx3QFCrWQESFUzzN4DWfoAnGo7H45wD66KdckQzmcH8GeR4jaqDtg59jlbHX9/z2Pjt6gLbJwPnap8q2OF/A0ADbPtTy7GvYx01UK4gisypE0qWV+w3gJjvcA2CZuyHddj2ztUFyBD3tY/nM2z5JZgiW+qsJtjCgQo65a1BBsVcy8AbwM44T05OGoQcpBv6QzLNAe5ms8nq7V3zZDtb5Yt1pNWg3HPjIJH+cm3/cB3LWtUb+yDjECcQd8k8DQxwf38fl5eX+aAZCL1WqxXffPNN/OxnP4tXr15FRMRf/uVfxr/8y78kKXV6epryOplMPlm1VA3ajEvsg42XkR3jL/tvbOrT01PKhq9BoOn5xof6Pq7i8Pwid8i3MQDXN6atyRuCD1cqRTQPIXZA2ul0YrFYxGg0yopB+rtaPT/dr9vtJu7gfJrhcJjHOjiwQsft/03KcE9j4V3Ev7GDyWT8cz1DhS1AVFYZZxNngInwtY+Pj3F1dZVnSREH2I/ab1tvIuK9xHbFp7ZPtkWsOfPCZ13lVq9j3awEBK973SppusvnVFtWfz5l++u//uu0GR4T+kPRBXbUyX8TgeAjxlSxNp+tuM6JYJOjvG98Z/380NxYl71uR0dH+VRsmv00ONo7ZRgndr7qOWPZZds9Hssg33UFHnpGYUC7vX14Gv2z/NFP4zHmEd10DMP6MBb0ln5g05gTdLoWutivW15arVYmtx8fn58myxZz8IltjWWERqXo4eFhkoWOMSpGM7+Bzrui1MkG+siP54h1QuYtz45luA5yDslJ303KVhtxfHwcs9ks5/Lf/Jt/857c7mp/UKUUwsXBfjjHasg9OBxrNfyeRH+Oe1jRI5pbFRwMci0Hd7VKi+s8PT01DhlkYSzsfIYF6/f7MRgM8v4EaDc3NzGZTPJeo9EoptNpbhfygjFOtgt2u92IiHT+Ziwp20dJuT4OC8DqJ5JBpLAOvGcBRJjtLFEa7zuvRGDEVvgQvHoGk0kr7uUslbN/yAZK4iDFBj4icg8yyuDyy1evXuVWSvre7XZjvV7n04xYfxsZjAjVWii1jZ0BoA0RBs0gqG7PdADIunttPmejH8i1jZl1gDXy+rvPq9UqptNprknEs2wQSEHUIksYU4PDXWSoHd3e3vPZRd5WYFLbINJnW0FgukqKMaFP4/E4RqNR3N7eNjJ2zIOJRII/Z5YIAJARkxqV4KsgjnnkOw4cI7ZP0aqZH68P9hHQ7+08BuE44NFolAC4npniNbZNwGbd3t4mYfWzn/0snaUdJdtg5/N5Xod5MBhFzyKiQU6ZVDCpZjLRZ9zhFzjvjvGTca6BmefPgNi2sOor/1tvqpP9HK2CEMbhv/EZ9nc1mWAQxHvWc8uWZcKgyXNQ59HXtV9BL2vf6aureCB37u/v0z9gT4wl6KerN7ytnEA6YrvtH4wSEQ2csmtevX3IftEZauuLM4H0G1lnDCZmsaH4FOMC5otz8CAknE1mLV1lQ2KN8bvilXX2lg0TJLZV9ve2UyZQ6IMTk9Yty0DVKcuZ9clA1jLn902i1u+h+71eL7766qsYj8fx4sWLJBbBWpwlwnl06/X2iUiQIJ9Ktz3nFf+AcxyYOrNs7GQ87CDRiUkTJ6wPnzdBSsO2+rMRkX6OeUafWq1Wo1oAWa6Btwl+24jaL84hPT4+zrNdI7Yy6CpF8CG4HJ/gIJrrQy47aHYMgs7zuv/n/pZFy7KrGNxX5hn/ZDtjcg/589ms+N6IbWLIh7vbj0VE42Eq1da47dpBYpnk+9a/SqRX/faY/R0HwrYT/l6rtU32QwB43ukb9/jUDRvMHBn/9/v91DefL8bY7fesK3zf47JeW4782ocI/kriMTdeS96r9jjiWVaoxmOdjE2rf4dUJQbGn+BnTFZZN6uM8XrF+/a9dS5YC2QEnMx1kXXje2QY/2c/x71NjPI9vlu/w71MpHqevUPHZ6LyWSfTuBZ6zvuOzV3BDL7xLoRKSlXMgv2t5PSHfKrnsvpT+lflmea40A8f4r2IJqHFtYyXsHM/1P6gp+/x++HhIcbjcZyfn2fnfT5L3e6FMDmQ8yQ7QLCA2gF6YUyQcW07Ghtp7uXKBu7HpGEgvf1mf//5iVm9Xi8dQavVislkEq9fv46bm5uGILDNzPtOcfCMjUWhr94OsV6vG3tKHaiZRCLjSuUJ8+GAfrVaRa/Xa2S4zKYaXFM5hOIAkJ+enrcs+FwrAlcUjfVlXSCWrKz+zWcRXmd3+Rxz57OAIO8wWldXV7Fer+P09DTOz88zC8V1AAg2QD6Eluoetotxbx/ezGsOuEygWPENxA1afI369+do9A05MVkYEY1xQLw4g8qYqCywwTk4OIj5fN4glKmmcWBg+aaazdfnN+tssGsSKKJ5Fo3Xxf02+F8ulzEajeL6+jor7ZAp5oeGvfA2H1fwcMaGgwGCXsutg0g7Yd/D9giCxQ4eW0MlEwGvqwrZamxQzRlye3t7SaRPp9PsAySsq8wMuugj67q/vx8vXrzI8TNeCMTNZpNnXO0KHO3ovGY09NIJCMZjAsLgmblEXrCHBtXct5IPda12AbkalFQ5+aH2p+p3vVcFq/TXVRIGTxXQGiQ7q+hA1981CLIvJYNJVTD3qERNtTHuN7aEM1UgYhyQmogwcHNpvMlqtrKv1+skHwhcPU4CJfs520fsh88K5HvGFpBF9Xwt+m+gZpLGQYyzxQbc2AIn4LymnhPWdblcxnQ6TR01ucsPOu5zHr3u9L3qgeUKrGU/WwPh2vz9Kte1DxU4f+haNIIlxjQYDGI2m+VWPcvQ4+Nj3N7eJtnnTD6Jvk/VqkwbrPN/xHYrWw2AI5qJD9vSeh/7Z+s/8maiAjkztrG+RWzxtb8PHrSM2q6bMKvVlawXT510hZqDThNKq9V2Kzc2aLl8Plx8f3+/sWPDxJxjDebTes74waR1Duv6OGnEGKuN5ByVdvu5st/xDJ/HP/sAedaLLcJUS3l7MmOxDfAcI2dVJ6uPsA9xM9np+9iWgL8rmWSfwrx7nrmWk1W+vs+8ZCyfutEf9xM8xtM7mWPmhN/2bdblXb4aGWBe7Es999VP0sddc/Kh132NdrvdIFH8eXyh/TSv+x6OPfHdxrdUOLH2PMmUKnrjWlfy13gQOTY2rP3FxiA/1X9HbG2oP8/38e3uF+8hfxHNJ1xaVn0/fC2fY76ZK9bUiW/uyZxx7qGJqVar1Tjw3XrGmH19+/eKNZhf2wXb/ipPHr9ljDiAe67Xz6Q6Z92xVsgHa1ZjTOOoH2ofTUrZiD09PeUhfy5BJ6NBMxNZFcMTxWRgBDy5NTCoE2+H40neFXDQ92qULcAEXBgnO4Db29t48+ZNjMfjhuMjG+eAkT5xL7I8Fjpv9dkFFBBcZ259uDmCgeOAvLm7u4t+v99gblut7eMvnXWjjNEC60CEfjLPnLzv+aVSg8B9s9nknnsbJOaXKisACOtINvPu7i7nxJkhxrBer+Pm5iYD0pcvX+aBphB3ZshtrAEOACRA/K5sIt+roLmSpn6/ypsNmeX4czQbbP43GHN1VAVhJnkI7CzTgMga1LVazxVU/X6/4Wxq5t8ABZCLLHAtO1AMdkQ0qgIAcMgG91mtVjGbzWI0GuWh2awBxJifFome9/v9dDB2pABJz5mdtp+YFRGN6qWa1eX7DnZNTkW8fz4PJCpzyfU4M8dEGITN4+NjbhHudDoxn89jMpmk3BOs2rmaqJ9Op3FwcBBnZ2eph9jPdrsd3W73PT0ziDZBAYCzvrPWEdt9/K6YhEj2GXM+dwjbyBkVJgIMeOgL9zVY+pjA+mN1dlcw/WO0XX7QPoPP2C5HNAEGa2aA5coMPlevxf8QOpx5WNebe+Fjqu2l79Ylfk5OTvJ8H5Mutk0mcNwAm1QOOdNqGwJ5QRUkctvpPG8fIqPMeB1wODFzfHzcqECiD27Wp0qAc22wE0Feu91OQgU9iNhWeTF24x37RO7R6XQaT6njs9glP9GwypWDaJO4/NQgxuBzl8z6+nWO6vdrn+p79XPI63q9juvr61iv1/H69es4Pj6OwWCQ38HW9Xq9mEwmjQQm80WV3adqxgfL5TJxmskfxkO/fcCyA7nqmzw/zHlNDBp/uDngjGieP8T38F/2L8i+zyt0wGh9BV/y5G6wmc8m4drYfZOgFYfYp0yn0yT52GpussEBmDEO4+NeVYbBdLYHtrXGLvxPX/FX3sJnAoLx8zrYgXnHv5n0pu/EC/TJ+IG22TSfTlZ3nNhvmDBxAM3nbbd9P2O4+j+yYTKgyq777Pd/yKb8mM3xJXOxt7cXZ2dnaZuZS2NU5sXzaZmqPmGXjqLfrsRnXSOaZFf187aTNONy63L1/yZnaryDbnmtXVxhf2r8YIIsIvLsUfB13QnkIhVsnONLZKRiM+bEds7y6/E6+esYolYw8ds2iLXB/2LLGKttTJV321rrlBO/FHugL04UOLaoPniXr7ZefUjG6/vmT3bJkHEFtt5xHn1ZLpfR7XbzwWF83/ENnATnd3vHxve1P+jpe26AwOrsnFUxwcHi2jlHNDMS7Xb7vdcZrFud0Mr0WUjou0kQs40IBsAVpSKoZDKvrq7i6uoqtwDZaROw+SkOBFk+m4hxUHrHwZ2QfNPpNL9/dHQUg8Egt6IdHx83zpRCmB3oMn4LOD/ey+49/6yR//a2toeHhxgMBilQPmcDkI4Tfnp6yuzO27dvcz4xMIxtf38/KywAZxhv1sdVZa6oYtx1i96rV6+yEqfX6+X8YxC53nq9zvOkWP/vy0hUZhr5g9xy4LMLREdsiVA+97mag0z3h/+R3QoQIA8c9GCsuE7VV57Odnp62iht9XciokGEmDClGcz6MakRWzJqs9lkMEt1FfcCaE4mk5hOp40KI67Bo2GppoAU6vf7jWyLHQ19d9ksjgs9R7+oIgLQY/88TgfFJuz5/Hq9TtDv+WJ+AFFPT8/ltDUIns/n79nB09PT2N/fj+vr68aaExhD6nK+F1lt+oHuYQsI9trtdh5OayDFmgDA0EWDa2wzxLirU0yCsS5cj9cMjAAtDtK9hv5tQOJ+0Ow7PgYks37/9b/+1x/87Pc166ztDX2C1OF1ABxyYeBGAO7+V5BpEFQJgfoe1QrMuTP1DkZZwwqauLe3brbbz9vaB4NBBmj2ZayB54F+4KMMmrh3BUf4m36/n8ATfeG72BIH5xHbwzzr4c/2r98XXDmg24VveB8i2QEf887Y2VZhPbNNvr+/b1RyOaAyqf8hmWYcDkosfw5ePNa6RhXw+u96712f3fUd662Jg81mk08+Pj09zYrO5XKZj3T/D//hP8T/+l//KyIizzqsff5UrWIE2xbk8+npqfGUPPxcPXOT71XSwP6Zz9XEhv+vyQFfz983KUV/LRu7AnX0A3w7mUwaZEy1bYzX/ULuOaIBHG19p18+j9IBoNfVelrnh1Zljt/WVeavElLsSnBSvQbYNTDFnjN27udqq0oiVaxqkod1MX62XNX+V8xhufT6VDvFPSr57jiH97CzTgI5cGdO3L9PrY+0Gl/2+/3odrtpYx3UYy/4DRmBTFe9rjEfcsDrrnLfJauWiV195bVdc4ifdfEBMZSJZsuk7UO1wdVO+Z4QT1wP3GaZJdnCGMAlJAOMXTzvVR/rsTyuNDSxZdlF5jjM3TGHcb71wfplbO75NJ5kXLWSrNpTJ6pJ8Dk54Qrquu6WQx9x4qSg58s+wlVKvlYltkzk82N8hNy32+0cS6fTifF43MAM1hcwjWOTH2p/8PY9GqSUwag/6wCRw7x4z0K+S5EdaNpxGJQwkQ6uHLDVQNfOyItvsEiANxgM8n7L5TLevXsXV1dXMZ1O4+HhIbcg2GjRZ4IzBMeMMRU67Xa7wR4SEE4mk3h6esp99vf3940DEiHLEEzIFhYdQM/Bp/v7+9Hv9+P29jYiIssrvWYAVJST/00aodjO6FABBcCzcWP7FkQV5I2DU48P4wTpVgMN1sFVO51OJw+Xm06ncXJyEpeXl8nMs32RdedpfzzlycakOhYbRWTK2TE7GDsj5m9XtcvnJKNoHpMNTa2W89YBg62ISGIvYvvY1gr8mLNut9t4IlR9ytBwOEySscpMq9XKaiFvw6mZCGTy+Pg4zw2hj67S8yOakam6nZWMGIE068T3CO6YEwyxz3Pj2uiIt9AauOO0I5ql0na0nhfkHNmLaG77w+h7HbEB2ByXU6Mz2BGIZw5+Zzsg59mhXzy4odVqRa/Xy/PwuDfzZhKRPvo91gH9dBCFkyZ7xrU4Fwjd99NhCF4qSK8BEvLuflWf40qAChCr3/tQw+987GGO33cd/22SCrvjg0ArKcd79m8GRwaL+GWDWvtXzyXyxsMtut3uThDpANnf5f3Dw8N8GIGD7ohoEJL01RlW66a3CFQSh7nBTiEr6/U6bm9vE5dwvYhIHdlFqhiAuppj1zpFNM/rofG+CX/bHMA6JO9sNkvf7+3M3tJbA02TtlR/YavAR1QUQ9zQfxOdtk9e+w8Fi1VHKji2f6wk7y59qyQAr9f7OFiGABmPxzEej+Mf//Ef4z//nYSkGwABAABJREFU5/8c//E//sf4/e9/H7/85S9TlqiuHY/HDRv6Y7eKDegzcsQWEFe1VVtmvMz3jXMjmiSDyVgHJsZ4tRKx3jdi69uMm33fipP4jV2BYCaT3m63EzOTvPGRH8iniSsCa8ca+GD6jG+B9GDrleMB982+wRjQQX8NJm0XsSteX5LSBODr9Tr6/X6OAZwNNuCp3BDFfI7PcG1wDePfbDaZ9DW5vauvJo4qGWh5se/7Ppmt88DcOZBF5lypx3WMmT3H+A/ju8/RbOeJry4uLpIIbbWaFbLoT9VT5qXOoWNTjxM5NaatPt59rHjAPyZHPB6ua/K27h6p8hHRJKMZF7jUdof1tz7ZJuC3wdUegwksV3PaHxIbWzZ8f77j6/szVRe8LtYXCkKcBLBseC7wkZZz4xLHM+YVbF/RC5M2TtbWZrtfsa3tvMdf8dou2831/LsS44wZOxqxTYhhh1g37DzXcf/dl4/1s3+0N6a6x5lbC6rLHhHECni9iBY2lIkJqCwgn6sOxqye71MztzYwGKXVapUECcCfCime2gWgcXUSSmtyyQYflnY2m2UAaDIqIrLcHifsoN9n4BC0rlaruLm5iW6329jvyedMMkD4YGSrAeF6nU4nMzOsH8baSsR8Hh0d5XkegH6XmR8dHcVsNmuAZogDDiS3oPrgQwCMjVslzZBBMsOj0Si63W70er1YrZ7P1OJpfCiiDZxlxMbUzsXOpq6ZyQPLpOXCzrYas8/RPG6D1oj3jZAdjiuSIqIhg3zX16FKkCqEiGaGzZUVli3LOnq8v7+felJJZmwNe7IJDk1IzWazxtPncLwQGp57SFXAoKvvIrYya+KVw9QhfviuHYAdgzO8BJ7OGpr0c3DONTH+fo97ONvlylT0nM+x7382m+WYCK5ns1kGEFREuWrJhylT1gyBxZpGbKtXAM8VAHNNgm5sncfAnLIGXj8yXSYY6Jt9iUEWc8JcmVD2Ni90oAZmf4ieIqN/+7d/+9Hf+b72IQIgogm2rFfoLBV8jAm7bOLCpeImHAx0kIFafm+ShFbnnmv5uswTfeFabKO1HPswYwe7lgn8h8Er94D4dJJgvV7HbDbLp3TSsA91W17EdhuEbanXwRWlBqDYVgfHDmyxBZS381lfi0pjX5f3fGBqRNP3YsPBIj7Dy2SgK7AcDHl82Knvk8sKgr0ODur4sbzZJxoffp8MMcaq9+g0PuXFixfZ9/F4HBcXF3F7e5ukFHL8sRncP6Z5PJZlr5U/Y0xZK1wYJ3rpoNZzV20ZNrKSMvhmByrGpw5O65hYN2w3uszZja5iNCHCnNveR2yTHJA6xgYO2l1Zb/yJX62yZLtjmauknX13XTfLCDaPeUM/Li4ukjTDlvl+xj2z2SyPwYBE9jECYAx8LWsNDvOaezy77BZjcYBo27SLEHJs8CE59vdJHvpajpE8V7aNlu3PTUxVEncwGORaGPvaXvN5J9tYf/tfEygOyLG3lk+TMxHNZFK1vyYd3OxbkGN8pftPXG1sZSLUckrjM44bSVLST8dOyB3ywPW4JsS0dZp56nQ6WThgDGKMZ07A2GYXEUVfSJS7Oto7mCyTXMNHdTAGx6zMKzG5SUfHI06eMp9OalXSrhJDtlvGAI4rjdls2/x98BCve2zmX6y3zBlryefMT2w2m4y1iWdc7MLcMO8f0/5oUgrHyWHYFhJPLAGQA5NKDlQWzeDWTpNgq4IXGzIbCJqJAldJEGyhJGR/ud90Oo3xeNzINBDA2WhBWDnQdEZ3sVjkGVwOxrkuDYcGQcacsT0H5eZ6GEauZWeGMJgctPKbGIhonv+FIlEVQYbWZ/dgkJhTgzyUmqcJ7HKIbEGMiAQrrLsBF0QEVVQIP4bI1+73+/l43VarFcPhMAMe5tbkF7ISEY2zupgDP6LYymwC0IrOWBx8+L3P3UzQMk8eB+tUS4kjtttAed/kgw3s4eFhdLvd1B2+CxFwfHych58z955DB5DeY26njsxCRvnJeBj11WoV4/E4JpNJY2smh5s/PDzklrZ2u51b/zg01XrnIJ8xEgTSWq1WVgNigB1UAkJMhNkw10COsbiiiB9nHh24+xo86ODw8LARYLPGPJEPmSe7CzGFPeF/voscULnUbrfj8vKyUZHDQxc6nedz93xujQGqdcHrFhGNrZCQVsfHx5lVBrgbnFDthexUZx2xPa+hBoIGwTU48TX+EIC82Wzin//5nz/687ua72mwUskDO3z7EIMzj6v6W4PoXdf1PBtU1GC0glyvAe8Z9GJTIKWwtbaX9V74KoOoTqeTBKhBUg0u+f/p6SkPFt1sNrmlwHNtwuxDwa1/Gwxat2pAx7i5F2Rsp9NJH4uN4tpsQa/EnM+5o9W15Z5PT09xc3MTBwcH8erVqzznj/uxbsyTgaZ1oNPpvLeGlZyrhIADKZMeu1p9/UPYzmPd9Tlscb/fj6Ojo/j7v//76Ha78dVXX8Xvfve7mM1m2V8qbSHvPnVz4MbcOPAwlmJtkSfWinkF89V5cGBaiSiTFDUo5BrGxyakjJ2tM1zLxLKfpGcbTDCDHHG2G74eP4Dv9PlqJFbxT7vOtMJG0R8T81W21+t1VqnVIG6XLGJj6BvjcgUYFVrYLgf26DrjZScE92YHhW2L/T/Xwxa6WhK5MJZmrrChvm71LfaJDv6Nq/k88+X5tl3mmtg3B8Telm0yzBjcffnUjXlbr9dxdnYWFxcXiSF2+X3WquIC++iIZlBvMtb/e5wfupbl1dcxzuR+1k+vQ51nVyX7vYitLFd7bdxmG4aNQUdN5IAXIyK3qXENVxmizz6/yb93+V18jQtdvG7o9WazLWBg7rm3t805yektmSa7kGViIfrtmMrxihNAHquJNsaCTldyyvLnNbXseH6Ml/w9xxt8rup6jbWMfyLef0CT+RA+f3Z2FuPxOOfHc+RjOD6m/Ul1yzgAg1eDQCYOZcBAEVhRDVOBRwV1vqZfrxOFcph99mFr/I+wmmwYDoe5raDT6cRoNIrpdNooDwQkmtiwAScA5h5PT0+5jQgldcWEAS3Ox3NB9o8nxPDZ0WiUpA8HjnEtsivMMXNGH/i8FQjmGHbX5AA/ddz8zxwaONmQzefzfNoK5JWr0bx/m+Df82DDzGcYD6QD74/H4+j3+8mMY2Rge+mbAZfBGGOzfFjOragO6qrCVdBuUPS524fu6W0oBjae81odZUNm8pZKCsgU9Kjdft7WilNCrx042lhjvCB7ALDe7gUpZQO/Xq9jPB7H1dVVbjtFRtgCNp1OY7FY5LlR3jrjOQHsMmbkB8fp/dEVhHFNZMzy5IAAXTAocUAPEIdcdfaFLbQO8F0Vtlwu4/Xr17G/vx+Xl5epL34aJ9u29vb2snLJRDdB2uHhYQyHw7RD7XY7bm5uYr1+fvJlr9fLfh8cHOQ92GNuXcEGIyutVvPcMAMvgDdy5mDeQQbzyT1sdyqAA9j79V32perxH9p+DB3fFWyakDF4qSS+M6v4MusYts1zyZzsIqb4H11FjmuAaqKK/z0nDgjte7k/1+TeBtL4FoOmiO1ZT6yxA1vPnQE8us/cEEAbvzBeg0HPf10TN+t1RLMqg/+Zf9bSTzTzvKH39kXezuw1wsa4T5AEbMs9Pz/P/vf7/Qb54L7VoNRnUGIzK1A23quA2TK9K2CynDgo8md36aXv326381zOwWAQrVYr/vmf/zl+9rOfZfVst9uN8/Pz2Gyen9Y3nU7zyIRP1TyfDuqsr6wnth/SloCFz+NnIprbvvG7zBX3rX6W9/FNrrrx+lmGuT9+yGQ0SWkIKWPAiG3FL3KM3DjRiE9A18HGyD0+xokY8DC6zPhsywiGbI+s19jBGvxZnlxNQB9sf/k+/cVvEiSDsahYJLDl/E2C2b29vazStg2LaD40IWL7IAmwEeOnWt3zRHMAyXW8TpZNv+8qD9sEfpyIrnNVyfF6XxM8jNfj/NSN+52cnMSrV68SH4CXbP8sN8Z31RfQbCOZXzf7410+pvoUk1nVnkS8/0ATr1XE+2fD8b/HWBMn9tMmJZx8oCLStgvCnFjx4eEher1eo1ouImKxWGTS3z4O+1DxmfvJWHfFVZBPtpv87Zgb+2fbFtFM9rHDwK9xfh3Ygb6Ds/gePt1xCv23n2ZdSZox97vkwrGr5bHiHcudZdj9Yf4quVSxJw3+hDnmNdb88PAwHxJQeZr9/f08Mudj2p+8md6TRPMB0Agdh+h6Emp5XGXpvNAsCpMFoYIzZmF3gXY+X40zP5xz4eu59NhbYyroxpkSYKGoCCIK4TnabLZPscMpkcVFIb01qNvtNsganBxn7wBkAPBUr9A3g28Y3FpCiNGyAWbeeN0kjcfUarXy8Oinp6esbqB/bBtiPufzeQyHw7i/v8/HWaPoVNVwfpadGUpVzxpAtubzeW4pxZhCdFUA7rEBOPgOylyDHIw9gZ8DBH+P/tZArTqbz9UqADE4wVjTKhPuDJ2BHtex0UUOkS8/1YiMCWvogBS9Jfjx35ZJH95o50NF42w2a5RU393dJdkC6YrhR0YhgAaDQeO+bA3j/BU+t7+/n0GP54CA147GJBVz5EAEGWIdsEXMv6uRWC8HMev1uqFrgI3RaJRj5KBYgDDEnhtP6BuPxzne6XSaVY7e9stTlJAFtuKahLy/v89ts9W54rQ3m03jUGfma39/P8+Kg1Rkzvb29tK3GEgh49Xp2V7z2/pYicHPBYj/kGZ7YhCCjJuUqbabIIrmuXAF3C5Q43sZxJmYqqDRAYkBke0eMmtAxOteP1cb47vQB9tl98XjdKKnJgharWei+ejoKPXQc0Df6j1qNtJkoIEen0WmPf7NZpOA3UDcZB8kPnbGpBzrznZb7GIlw9hKy+vYQPQVcrgSZlx/l6800HTz/Hqs1d9ZXioZsOv1GmB5fPVzzBNJiE6nE4PBIP7pn/4p7u7ucrzMG8kNqqc+ZdsVkBvzsA4RW7nhfYI0Y1YHEdUGej4tx7vmjObrmdQyCYWeoJeQUD5AuZIMvg5j4IgMAlf33U+RBddafx1U2t9YPhx40QewCf1Ebxl3RFO+bcs8J9UuGrdQ1Vtxiqud+Z6rhLFR3mJEYOdjAZxkN2FAv8EtJLNsg22fHd+YiKwJgoitHXTy1X7G+IZ5tUx7fVkDE6HGSp+7gWu+/vrrrFwzRqPVNXclSLVHxjsmECK2D7io1/WPX98VwNtHWt92+V9fF5yAfLsww37F96yyYTKa7zgW55p8F3lfr7cJfSc7wdbgRQgqj8n67y1zfKYmYSKikbT1lsmIeA+32M+hcxHbBxrxObYJI//mA9AB96tuua88Bn2pBJWv68RP5R6qnd3lE6sf3iVPu/yJZQz8wnXhGNjOR9LB+n96ehq3t7cN2TcG/Zj2J5FSCI4n3wpSDb+3iEQ0t2rY0EZsWVkLvsE2i+gy9rpQ3NfsMK9bIXu9XlbvtNvtrHjiUOyIyIMLAT1UPj09PWUwDJlEZQXjN+FG5gTn3mq1EqTjUHzOC8YdIGnyx9VCvHZwcBA3Nze5Fuv1OsErvyO2GVcU104KAol+LxaLDEDJjFnBecoZSm4mnrWr1WmwqzaakHqdTief+sWWA7YbMW9+7eHhIbPC9/f3jUoXSvR5HLEdtdl0/q+ETQV6vO5AgjEaVH+Mk/kczQbM2RtXBSAnNQuI7EZEo6LNgAq9wTARdAFMbPj8mGRkwMSvs54OqDGOECsQFBHPGZfr6+uYTqcNkmu5XMZ0Os1DVYfDYfbT5Gqr1Yp+v5+PZ18ulzGZTDJYcTbw4eEhHU6/33+vhN6Oq4JmrmUw52wbzSQ19svGn/n09hv6x/dpd3d3MZvN4uDgIObzeW5ZPD8/z21+JrV7vV4cHBzEYrFIHSbTRXVaRCSxzKHRjIstd8gL2TCCQebFNpB5iNhu48VWIB+slcEuc2eAYVDtIMV+xNsq7KMqoVwD5C/d6AsgoSYVIpoZcVcRRDSf6ljJwgow/QPZajLUQS/yRl+4R+03fgV/ZcKGdcYmmIxCzt3nXZVT/Bhsuy/uM4QNFcYG0/THdtxE0y47X7Os1lvPqWWQ/uFHTC7SFweEyH5E5LZVB4KsO+sEkW7Sw/4NXOBtr5YTPuP5rEEUn6mtguQa6NqvWnaq3lVC0+voPpv4R2awb+v1OjP1rNPd3V1mdmezWZ7p9SnaLjvCGrOuXnNep4EL0QcIHstaDVYsW8wZ17L8Wt4qeer+s15gLAhOX9ekKq+78pijJWx3rJv4PJ9JyBY+1hYiwRge/Eer9sd+1rLj+3o9KiHtebW99PW5DolRbBR49eTkJLfrcV2fTWnfRpX4bDZrYGofcWCcYxngWui236v23XLyIX9nu1sDauIR99/xA+M0IbJLb/ncLtn71O34+DgrRumP/ZplxXJhUtl+LeL9ChO+4xiwzsGH7Co23t9B9ite2XXNuvbuk+MS+xLLUa32Ml41HnXs7kIMY2J+vEXd/Sf+NBZzMQpz6HtWEgX9MPmGf3Piyf6cMVt+0Z2IbRGHfa31grEYd9PnSprxWu1zrRzls34giWXsQ6366brWu/wx81CvUeXFCXTuQb+NyalsZUeFfdPH4uk/uVKqgiuCVc41sSIDknxIp42rFcUK4GoWgFdE89HGLK4VmHtWEE1fcaxkOtjWxtY9grYaEKxWqzzQMSKSqKGvBHrORvrJZSwywokiMTeci8M9nT1hu4uDN66FUjkrYSKB9WK+UDjmmn5BKjB3XMvgCDCwWCzyTC0Hyc6SGDjT97u7u1RCgJYV3k/eYkwuTeazbAfkXpSSs8YEBgA65oB1tON0H2uf+B7GkPmy0fT3DbgqCPvcrQZRfp2x1/JMB2hUkZGNcwVb1XGTPiaB2EbL3PAZZCsi8nw69oUjR+329gwoZLrVeiaI0VXALLJDlYCrDLmmt/AsFosYj8cREVmh4zJk5oLtaxERk8kkIiKrq5z9g6RhTGRZIORMNjFflVhnO4D/d0CP7tdSffTk7OwsSdj6Pc62Ozs7S1BGZRRgttPpxGQySSfPmVsmC7FHq9UqXr16lVVxBqhv3rzJfh4cHORWTusR8+KtKbUajvM2mK+I7RYJxs1c2fExdmfc0GcDCuvqrut8zlbPVzAYdmWLA1oDEWyeAamzXSY2I5qBP9fYBZ491w5+bANrYoPv7spoYgNsCyCLnHU1MWPQin2mbyal+I4BqgNQvk9fHZDbJjqQdWD89PSU8l4zygSbJOCMbdxv++RKStWgOKJ5Fgf2Af8N9rDf4VrHx8f5UBLsAPrpQIg+4icrcPX/lrn6vvXGvvVDJNSu/31ty9LH6CWJLbaQY2/4n3UgWQFh/7ka47AcmOQwhuDz1WbxuYimr/Z3fD8+710FbsZ+u9aVAGk+nzf8D9dFh7zOyBT64K0eVFubRHVVI/belTcRkTgTe0YcYWLAskQg5cSXP199p+2Zf2gO9j0H+JjFYhFv377NNWT83W43dXQ+n0ev18sHJjFXYB37SM8182aMX0kMYold8lL/pm/Mkz9jksr2wTgH3I3PMbFme1l9el33L4WJj46O4tWrVzlnzOMuTGXyw77UsRXzbsLImMh4zTaee9S/iZOMd+2X6W9d512+Azkz8cL17bsqKUTf+Y2sEDvVNcamcaSE+0aMwfhqvGFMQXPiDZ0zSc14jO/oW0Tk+Xbgd+IX+j6dThsxMhiUuMc+sY7lQzG1sZDtovGHx813nDziPT/lmzWtvtT4zXrL/xXD1c9Y7uxr/F3jrxrbsnb0++npKfr9fqzXz086Zi24xse0H4WUQhAqWeQFBViw1YTgzErkwA6A68PNmQAGagNSFzuiWTJJcOeFBbQdHx83HuvKocgswGKxyHNbHh8fM+hlywzBricdpeS6VAbYmaBUkE0QV5Q04oQA6Rwktl5vHz2LM6aSBeWzM/bjrlG4uo/XGW8IK4w1xnG5fH5CWafTyaCfrC1j9yPpTeC46qXVamW1GUG8BR4BxtEjU0dHR1nN4QzE3t5eOn++RzWGz2dg/A6EKviw7FbnXjMU3LeSkrval3TANcBg3TGYOFr0rTq2WkbvwM4Ob7lcZmUaekSVDCy6DaLlBlkiUEBGI6KxnRP98eO/sTFkRpDbo6OjPEgYopLtA5vNJs8gubm5yUNI7UwcNNZ+UwXk4Huz2TQOdccRU65MtRDjRdYrYW/nZLlFhtBHdJt1IVjudrsxHo8bQJb1hFBnDgeDQT4mPmL7tA2epDibzVJfq5M2kG+32zEYDNLu9Pv9rKZiPgk8sO1u2HTWuNPppCxi3w0GGb/nr4IJf68mKHgfu8NcV5D3uZufwlQPBbVcuDweotiABTnGp+0KNPnbpMSuYKQGbBW0UN1kQFODa/toB8HWWUCkKyhtm60LBDnc0yQucwY5jPy5Iht7QX9MQrlKyYFjJUsAxPQJXad/uwAizcCevjFvtsv4Mr5Df/E9yAr+1tXn9AcCi4DfB0fXubVMMCYHru4HWM065GYS2ESg14nx1O9XwO97f59+gs+4hrc4LpfLuL29jeFwGHd3d/HmzZuYTCZJBH+K5vVnHjjTL2I7R2zZtMwaK5m0Qb6s73y+BrzWGebE1zaRQEM3ITHBuj670DjKgQpBzP7+fj7h0qQpvy0XxlrYBDBzfWgJZy95XTmewbaKhi4zXsbmRBD3rYSUkyPGPpylhf4aQxETOJBbLpdxfHwcs9ksvzscDvN6EFRUJnM/zojCPpr0r2QQa43dJ0n39PSUmGKXHfVWP1eiICuOVZgTyH7ipl2JCMuGbaoDXcvlrkB5V/uxfPPLly/j9PQ04zwT8SYX7HtdCeQYwUeVgHMYjwsl0Ev7Tq5v/8LrjpmqrfZ81MSSk1XVJvg+JqLBR5UkN9Frf++Gn+LzYDcXAfC6cbX9Q0Q0EsHsNsKGY1NYH37XrfCMg34yB09PTzGZTNKeEtdbj3hgDziEiinzBuYh6I99mhPxYAPvjHDSzDrA3JoEbLe3x2HUOXfju17zeh0+Z+LPeu5WbSZzzXhM4NWYmEQATyO9ubnJh1p8bPLnTyalGJSFAkGr1TLtdjsziASBNmK7ggneM7NOAOcy5g8ZKxsDBx30AeOEE8EJUzHx8PAQk8kk5vN5CjPZuOVymeQT/Tdgq4GmhdekkcEBxt77cFFIkwnMr8k9gm3Pldnmh4eHxrkcBHHcF1bZAJ8fAAn9Wa1W71VtuPrFGR7IQeaY+y4Wi6xOM/uMYhE4MK9k6nq9XpJTEdvtXS5ZRh4gKOp1/eNAxRl65q82G3v2o7vaqhoHO4IvRUy5zyYzXX1B/5xJt4Os6wz56C18vIdemGRhywyG2k6LOQOQQux0Os9ng7C1leqm0WgU19fXeeYQfUEn0Bfkr9PZHlI4n89jNpvFZDLJg27RMT9RD3vGa9bt9XrdAKCcheTPMUebzSYdLGM0mLTjcuamyhH2h7HhrFyFRqUZQQ7BBffyNsjxeJzb81zhwfxhl9hOQGUjugWAHo/HGTSRJWm32zEcDjO7xfh99lUFVJYf/2/SH6BBQAzg5fMRWzvj/7kO9zRA9DybSPkS7fr6Otfx4uIiX8eGmbSP2PpVdBT/6O2vtk2+lm2gX99FTnEtPrcrsMTXmKiJiIZtsaxTCUs1Xg30nLnlvg4UuLb77nP+SDgZ0CE3R0dHDaIcmd21hSGiKUPoBHKMH0KHqPRwoMP/+G3On/E9fE7Per2tqnR1BMmWOhcQTZw3aT9rkgNChLHWQL76g+qzjM3qGtvH7QK6ztJX2atg2q/VgNXfqfpqwmA4HCYBRRUs7eHhIV6/fh3j8ThevnwZn7I58PbviMikgYlQY1avM80Yz/jXtg258tpXn7LLhnJ9kigciQDGqnoR0XxAwf7+fvR6vca5n7YRDup9JirEDLJ/dHSUidrJZJJYjDNWsWnL5TJevHjRCLSwMdzLuNxBO//XOfRnPfesEbiX10kkm6RptVqp5ySKwfQE7ff393Fzc5NnOtJnEkSQ6Jx/RkziLXy7yAzkrc69/QQYotVqNRIankf/b39i+cE+VfwesU2sEMdADLt9X+y2q/1Yvvni4qIRo1bdsO802RDRfMAGhK1jU8aEjnj7eyWQPAfVrtF2+dL6OvdxNY53mRjvew5r5Rf+GBlBLrBT9ne+jsljE5Bu2BX8NK8x98YJjM++xhWWvrcJMK5pOaNfs9ms0QfuxfU4nsMxAz63YkxsUSXxvCb03/Puz5vksRyYZKYPrGW1T27MoX1x1WnwOHK+q2q7YnDruvGC7Y8/x/d5qMj19XXGdR/TfhRSigmwc4pont/B55ggnBeVFA6aDEQqw+4FtOOt5BP3Y+JtvFutVnS73SQ3eH+1WuVWoIeHhxiNRjGbzfJsGjsixkYfMUieD/cBBhjiCBBp5eIxxQRxBL5ss0FIUU6fnUTZpIUYUEAA4G1XzB1O1qAVJUeBAAsVSNvZ1YDJIBTwQGWJDZsPSoNUQhZcXsr/nF+1Wq0ymDYDbwPLeBwkVQezywm3Wq1GxUIFJ1yXsduhM3au7WCwyufnbjUA4G/WiYDKTyDBCHnN7u/vk6ybTqdZxg3Q5DBd6xuVUJSMAxwtK8g1MnhychKt1vMTUgDQy+UyK6TIPCKPfjKfnRn/Pzw8xM3NTUwmk8aBofUAVa8vATPgzdckK+ZMDUA0IjLwjdgCNNsx5hD5jYiGXrEuEE/YMtYAkMD3kOvBYJAVhvymb/QJezeZTGI0GkW3243hcJhrCcnW6/XSPkREQ+7p18PDQ4zH46xow7axv3w0GmW1hvUzopkts7+odqquzS4d2uU3eN3fcUBXyZkfC/T+sa3f70fEdouUAZgz41TXOBg1oRLRPBOPSkHbJI/VADni/ac++fPYWtsHGnLu7/g63kLH2poE3rUF0LbZvysos4w5GPU2ICqImLt+v5/VNZyJBrGxXq+zehk9draT8fK3z1ix/0RXsHO0XXPL+LCXNXuNDDA/+FaSX9XGY0u8Jd/JCGyu59PkrW24QbbHUEkX37+CVtou4Gv9c4Dr1z8UvPl7BO/dbjf29/fzwQ+QVSTW2GZydXUVn7q5/55LzxH2rQJ/272Ipm3w+PENrqTlnu6DA4wajG82m9QHEggeQ9VBro/84YPBw44HsMsQ0BBezAfEy2azyXNVW61WnvfInDnhCeHT7XYbR1rYDtI83/TbGHcXJsQW8brPs7M/OTo6ipOTkxgMBvmQj3r+E58jKYRfBc/wns8DZI191AbXdaU547KtsC2B6CKxVKtw/Lh7k+nME3PkJ2kjt6yN54U+O2nN666utDxZtz9129vby6QIY6vEpDGq9QR7PpvNUn5dnQS2s67X+DPifULuQ39zTWOm+l0SLbUC09+nmfyuBAW40L7L93Yi2f08OTlJPeWaHjM2rJJa9p/cG1toXM14jN0YS0RkvGwyCszvCirer2SSx2I74CMCGIuTU/QDTGCuouJQ389cAfEHGJ8qUMYMIeyiFq9nnWv3lz54Xb3mxm8VYxgLGkdhI5ArH5PAGtFviCns1se0H4WUYmK8mA5GAUVMAtvjFotFPmHGgunvexuPldEs5t7eXmPfp0GgjSFCA8mDMADMyNzM5/N4+/ZtjEajzFziCOykcIwOqFyWB0jGWFABArOOogOiWGCD9FarlfPU7/fTaTEPPkOJgNQKauLETtQHVRq0sg7Mpx9XzXUgbBijiSoH0KwZzpdsCWvGEwcdMBh4cF9kAWcGQXh3dxcXFxeNrV0R28CZ641Go4ZDRFa9TYT1rE7RAB05RwlNsPrcG2TBhmUXcP9crToQv14BAQbEIM0sP+vM3JIp8rkJ3W63UfqOPDpoQz6ddWKtuQ7VUZ7X8Xgck8kkQZZLqQ3K7XgPDw/zvIfr6+vsv0urkUXuC1luILper/MpO8gmT7djfjebTW634Ls4JtaBKibmlf8dzBpcGPgA8ly2bIAb8Uxu93q9aLVajS2U6PB8Pm8EOmydWi6XcXp6mk8iZS739vbi7Ows7u7u0m5zVk1E5Fpgx6zXw+GwoWeWL29LwN4RZDCftlEOql094u0hVIYYCJqQsm+xDuzSiy/RSDR8CKDaVtl3MT77TYMmA4Nd4N/+1yDHFWYR7z/90fPse1Wgw9/oM2uCHQc8mpR1wOK5oA/+/+TkJLOcACiCMW/1qX6Ge+FTOajfW1lMijKnxhPYHmwUBKETFiaQwSuuFjTZGBENzIENgCjm+j6nx0RHRDS2raMX+Hiv/YeCJQeYXpddevGh1/xjPGhSscqQZemHfGbVU+uJ/QxE1GazicVikWCZBCH241M093GXzWEuwC7YT17DVxlfVj/OdaptoFVZNSnLD7rorUgmC+2jHbyj0/jTXbiTsVJ5xXEYrdbzYeZeQ9trY3eueXx8nDqN35nNZu/5y4hoVOHXgI3+VVLHa+XvMf8Omn0tSM7j4+O4vb1NH0gMwhN+IZo5MD4iGj647gRwP7FJJGLtN+m35cj2Ch12dYefqgte4nsRTQLC1ZXcyxVA/HbQauLKc8hY/F4lDD91A2u4oMEkAq/Zl6KP9/f3SUghA7bl1hHrXcT726l2kQL1837ftpIGIVx3uKATlbw0ybMrNmAdGZM/z9qadEUuamEE1ZaOLdFn5hQZpG/GxY5Z7Ud3kXmWH+R4l5+2j/M9K8GCzFf5NUHkdfYOG+MjXnNszX35jLEB98KmuqqL97km12V+vJ7+bQK6klmeQ9+/ypjXw+viGAo/Rbz08PAQR0dH8eLFi/fu/X3tRyOl3PGIaBg2gxvIkLu7u5hMJvlUO5wvRgIn5GvZUADKfHhaBSleQK5xeHgYw+GwUZGFMI7H43j9+nXc3NzEbDbL4LPd3h46BiFipxURjX6QNfEj6DebTVYhUInE1icLK8AUcsqP76RqygQLjpd+bjbbJ5Ig1GaHXdUFWCcwdiWYA975fN5gfH0/Z5kgrAjEI6IBvk1oRUQG9CgegYnBEobB1XSMBQBH4EsfvUUBA4nhdCbNcuKAwQaChuxVcN5qPVfyeL+sDd0PAevP2RwEWF7MbpsdB9xihP0dG+1Wq9XI0rMf24STHVYt9UXvzcgjM1wDmaayxyQ3OuY+sl5UOlEByWH9EdEoy3VAyPY3ALLJWIIaMm0Ei8hXxNaw1ycU1eoJA5JdAb7nwJ8zMe9qSb4POOZhAsvlsnGoKcEH32+3n7dVX19fx8PDQ5ydncXp6WkcHx/HdDrNewGu1+vteWH0jfv73CzAhc+swqZBaJmAj9iWNEc821pkk6w9WycMlDx3fB55NmCvDtdBV7UJP5VGP1lXZ6scQCBPDkgZL8QfAJHrVBKgBloR20eHY0+dEcNWI/91Pt0n+g9BjX748OQaBO8iHRxQQbyQnUUuwAX4WWyLyWGuA3CyLqLfy+X28ccGuZ5vBzUEjWxTAQvVCin7F+a9Bvv2xVSusGaHh4eNpwIxXkhKbC9r4qovxsz965mf9v+7qpkYt+0Q3/PfBqB1LZlDZ9S9vrUhL8ZJfL7qrJNCT09PmcCo/hk8VoOFT9F2ybDXHgzEfELM2/Y7sLLv4D1sAfavBkbc22SE++PEhO1qDUbokzGVE1jotysfsT+ucufg+Xa7nfLpQ/hZQ+RzPp/ntj18FuNhfalE4v5u9o+7fqwDNUAzlqsy7HVAL9FVE4hg1fV6+xAfY3uuzfmk2C3OozTx4+QZAXGtoqvBKPYAG4nvtt2z3bKMOgAnCYQNIB4x1kE+iQVMIoAJvDsD+/c5G/bVQbWTj65MsZ0jbvVYmD9XslQ9dexq3fNv67ntMO/Rqn81Icx33CfjJWTFOuKkEz6M142hjK3xvfh2Y2rkBJ1ATo+OjvKMUsZt+1v9Cn4TfG+M4fftd/xTiSlv8zNBZt+GTOBTWWfiVmIZZLbuwqm+0FWP9IP53dVX6513oPj6liXLhfHfLh+8y19W3+TvMRe+hpOGHp/jasg7ZO/k5CS+/vrr+Nj2o5JSDCSiqaAGsgidn+ZBpQAgqTpdgyOE3cEyk4XRrhlh7nl0dBRnZ2cZoCF04/E4rq+v89HyKIKf4kV/vCjtdju63W46HB88aueFgWaLgAMMB4aA4/X6+ZGKLDAZYBv9iMjtBmyxwTHw+FkMFdc5OTnJYJI5ImtrVt0KYCPVaj1ve6RvKLSztS5ZtqLx/3q9fawva8W4MJ4YBQMiDJgbn+HR9AYCBjaQhHYmZALdPkQM2OAzD1wbQsUG08QsfTG7/KUaBtXGrTpgZ9aQY8sAhqoGGtPpNB0kQARj5ZLdmhVANw1s6Qc6wrV4ChyEbbvdzvMr7Dz5vVwu89yoiEg9MclUy53tIKnm4XqUvptYQr8dDNrm2dlXgEoAjc5g4L0GrVYrH2/OvW3buA46xVZDzregioq59IH8XJt7R0Se+RcRcXp62iDnVqvnc1qOj4/z6Xw8SYh5hJRiHIwV4guin/GxlpAbDm4MvqmQrCX3tvHML+tZCW6DIf9t//Kl24cCH0gHdLIGWQS21in0nUoRJwccBHMfB52+Dvarzrez8vTNAVTEFmhDSvMZ7IAz/fSL7xkwMRYDwl3AnffcBz8MAJlkjE7OYFv4HHrMXHl8Bpp8luv6ASgeS/VLDkYYI3oIJjA5xb1Xq1VWWeAvITc4F4/r1q19m80mq1S8jg7G7e+ZA+SoZqr9GfppUL6rwqqSxQa7NK9/JQT8mueP12azWXz33XcxGAyi1+vlPZDFiEjb3+v1covzp2z00XIV0XwqmWXJQTx40WOo2BYMgk/hPZ/jgZ1grZCv+pQ3WiVjWHtXZZhYdJKC5MN8Pk/8gMzie20/kC18gNfWtubh4SEfpsI5VPhEki8R8Z7eMv7qIyq56ver76i4yYFtxLaym+3zg8Eg4wdIUfrDmG1X2+12PkBlvV6/F8Db9rnv6L1jE1dugKeZT/teyG1XAzE3rC/Xsf1Cho6OjnLnB/LqeMhn4laSwGtse8B4awD9YzbHi+Ap9MNVXw66IaSqLbOf5nvWcd5zq/Lk1+vf9TNcj7UlbrRtdsWjEz6su20JvqomT7DLJrVoPjbFsSav8T1/Bt8E5rTN4Br0yfGGcTlj8FEQ/jwy6nE4Vrcdi9g+hIEYiHllfCZVK49xd3eXvhO7yrWYC+u2cZ3XttrYWpBj4tT2wIRnjS39uRpXeZzGaRVzMc/GMR6/18h6tLe3l2uMjzk6OoqvvvoqPqb96KQUrSquXwM8TiaTuL29jV/+8peNck478Iit48NgmJRi8h3MGfShfJTP4rR4VDtkFAGvn4LHgeMYYgJyFpVrYgRQcBMz6/U6lYHXMRgRkQRSt9vNM3TY4geBBqFkcsRbm+jH1dVVVk45u+GgHYflLDBzhAJ67ycZdoMoC7SrxgguAcA4Ix82i9C6WoxtP3weGfGP2XfGz5lkVLl96HvtdjurqUajUa4f1/OWCI+bzzmYQw4NuAH9BH+Mk/4gF1+y2cnv+s37gHXWgsogyJPVapUkgbMUOKCILZA2OH56espgiPepOozYOncfVo++G7gzn3Urjo00htcl8jiKD5U0I7/1ccyMc7PZJOmE/NbDH90P9KVW3dFfV0cwLpNdvoZBDv0BLEVsAYpLjyO2W3gAhOi7z63DeUBW42x57eXLl3n2CnIxGAzS7mFHceKMw4EKoIH1p6/eBmlghxx6OwT6aELAY4dUcDOQqWvt4MC6/VNoNdhGlxwM7Qpg+aznE3+IHqOvuwAzr7N2rHfE+4fQ87rv76CI97imD88niMRfeJuOAz2uQxDkMwddWeTgln44aWDCvRJSVRf5Lq87aCSZ8fT01KhwrkS+56ESLp7jOt9e83quHz4dwOdrkFzCTtPvCiZZfwheGjrhdeM3fWCOTDJZPrlnJY7sW6yD3NPBbyWWeZ3vfChQrfdcrVbx+9//PitWeAocPv7p6bnikuMQZrPZe9f8sRvjWq/XmWRkbPgP66VJD+bd4wSHMeaqQ/ZD6Iwr9/n+dDrNalhjZ/rM+uBHTEjh8+17selsy4VYAQe64jUiGg8AoVL++Pg49dCBoUly+ua5A9uCffn8rsDtQ5jMMmZbZH2N2BLEdY663W6cnZ3FZDJJvASmgKhyhSfBHL681dqeXTmfz2O5XOZ5WSR/0Jtd8ZIPWneCDPvEd7DByIJ1ftcPc+r5g7SxrPI58BXrQD9cOedrV3vyqZttjdfPiWTby/F4nLFaJQDsS0zOWGZ8Pd9311grYcHfxGy74l5/D2zlJ9m6CozXWBt0xsR09fskdsBui8Ui+08C0YSwcQhjILZATyGVTbYyT3ye+Teu9RrabxvbdTqdJExrzOZEmJN9TqS6wsnJa9aBfnpN+THWd/94v66h+x3xfoUaa2R5q0SdbZbjXu5rP8r6eD7533oasU2m2z8bH1LcYRxed8UQ632sXv+opNQu0IAgGIRgyNiba9baimxQRBAVsWUPmYB6LwM5lGYwGOQjHwEp33zzTfzmN7+JiK2RJeNCn9jz7iofyBAMLwf6ETDiHHBYOOKrq6uGsSZLtbe3l5VjKDdEyXg8zmv2+/0EBTYGGMAXL17kPDJG38vCC/tvA9tqtWI8Huf1Kc2lUgNjwtzbmBPoIsTMNY7aCsjWir29vTzQ0gG318PryFj29vby+rTl8vkQ9NVqFYPBIE5PTxMQMLbLy8tYr9dxfX3duL6vUxW1gnW2ddBHAqaTk5OYTqc5Jx6T5wkZ/dytElGMuRpG/sYQMe+uzmHMjKM6G+acsnyvlQMYA2j6hHxwuDkNR8lZRnbQOEw7/Ol0GqPRKOWWdWDLBvbEW2IcWLFVD1vkqkYO83YWjUomiB6Pl6DBAN4G2pUWljleo7laypkinDfzjL04OTmJo6Oj+O677+LNmzdpPz2XlgEH99imh4eHOD8/T2fqrVER20oNzmdxdoxxQIoxpl3VO5YtAxHILOTPiQeDMXwM73s+DSTtVD+kJz8VcirifRtE3z1HrLlJWT67WCw++CjeCp7rnBi8GJCgDw6gd/l9B0voC7KFTzSYsb4zdnTHZ8ABOq1jZLIdvNueeMy1UsjnrTn5xG8CcifF6kNDmPdq2z0v2E36XMfNdw1esa307fHxMfr9fq6xz/BYrVZJsODDq583YVy3cRn4VxKoBo3+qbaOe5i8MhFlGa7v+x7GjtVHVJl1f9H30WiU2VkTh/g1/HYNMj9Fs6zwP3gJ28lrrkSuc4ouWf9sC20TLV9O4DCPrgy0Ha7HOJgA5vv4GGNxDkffbDaZeK3EWK1SmM1mmeDAD4LpIWIcWGH7u91uwz4xjx6H/SxywdzY5+2S8Sr/nrcafFoHTk9P87gA+gkROpvN0obUIyaIAfb29jJJi10jSAafEEPZrlknLB8mNfmbaxjfVazqggD6UZOE4BDmhc+baAYvcw9kqQbPu+z/p2z00SSS40t0DRxIZbjjVOw182lZZ0z2cbZ7tIoFbY8jmnJbbaNttu0reAmiyUeqoLN1/V14YR/G2tVER0QkIQE2gzzF1uzCwcgkY3FC12MhWYrPdCWfSWknZiO2lfqQULyP7eOYGleH8l2vGXaI/uLvqaqtcZ512TpU48m6vvx2PGQczHhZG6+z/dauuNzzvstfstZVd6ts1Ri4EoSOoW3jKWTA1n1s3Pujnym1qzFQKyAKMpvNYjwex+XlZS5ERFNAnMl0gI/QOuiM2AZ5GFbOSNlsNrmX/be//W38/ve/b2ztIRjlWgi9DxdngdjrTiBWz2zhOpxlA9FkoSV7TJBNVs+KCmkH8YWT4zHGgOeHh4es7GIfeq/Xi9lslv/DVuJkUCj+ZrwIHkqGo2N8df84a8yYqOSggolHz6Mg7DMdDoexv78fg8EgFZ7PGlwgW/TDa+/gxATg4+NjnJycxNnZWT5hcbVaxenpaT5mOKJ5VhQK6NcxWq42szx7u8cuubcDqSTDl2jVMH0oEGTcBn4GYzZYAH2A7t3dXR7wH/H+U28ITDGgBuoOtmx8mXMIZqpiaraPteUsERtLAreahfU8oHtsU6NP6Ke3q5ER8LlxNbiyY6UfnldndpgrBxnWU65XSRyvYbfbjYeHhzg8PEyi+PHxMSsC7u7uYrFY5FPekHMCYP5mfFTZdLvdDATQAc7QMOHgNcUOYxMYNxkWGplwn5lkQA6hhwy4IsXnkTEeEzmW0RpM18B6F7nyuZt1MyLe0z9kC7tfbaQBlp+i5dJ4xrqLIDH4sD002Weba712M3CpVR22705keGwO+mj4TwdFlgfGvNlszzrYFXBie46OjnKL0XA4zKfusZ2vJsqYM2MQPmfCg34BxHbhH+wKeu5qEMsCwRJV1vy2DcEOUaXCb38Om0tAW5/UVMmPSohUv+g19/eqXvl9k1W2+bvsZg22KqjeJXO+PzbbW31dneTkJX7qczTWmPNCbEuRIXwf2Nc4jIoj6xN/Y7NNInk+XY0D1uV77p/11n7YSVe+g/6u1+tYLBZZnVHPFsXPQrwQxIJvGT9BNclZn/2zWCwyIQw+JrFCX7ybgPmpOMeEFLrxoXXib8+xsa/tKHblq6++iv39/bi5ucnt86y5cat3OvhJm5B5rDNy4qp1/JlJQftXbG6tOqExrzXwreO3PjF+46fNZpMVcBHRCESNEcEqjs2cgPa6sG6fstnngHkgMrj/fD5P+fQam5DwNWoc4QCeNWQObRv5nP/33/YdxgImTUw0Mv+2+fTXulBxquMby7/j8ip3LijB7yCfi8UiE6OVeATPudrI2IU5t10gjrXf82d5zYkpYmIIRjAtWMH+xAQXfePeTvRR0Yn9YAeFZQdZYb2RD+N+y3v1+ybiKwbkHughGNl2yrLD2LAv7idzw5xVH0zCn0IDy5KrUlutVn4m4tnegMG49hchpb6vecCemPv7+7i5uYlXr14l6IQl5XsR2ywiAlLJEDOLvmev18uSWbJMv//97+Obb76J2WyWi4qgWomp2Gi1Wo0KKspvEbr9/f3cO75eP1eAjUaj3Ffvx01bCfmuDQ/sM4c6YuQRIISM4LvX6+W+fYJGhM/sJJUnJpVYi8VikUAVY+E9wyYTqNbAiHQ6nWSdvVfZTy5bLBYJMJi3u7u76Pf7DfKr2+1mVZYNKwQTh+I7kDdAxqkuFou4ubmJ09PTBpFwd3cXJycneYCzs4rImYkjKxGvVbLOIO7g4CCBnh0U/fspBb0RTZ2spFoN/nE2Hjvzxuuch7BYLBpbx5hTgisHTC6LpbmCiTXAAKJDyBqZCwDvzc1NHoROX00M8T9BC+9tNs/ZmYuLi+j1ekniYlec2YzYlk1bhiAusWMGezhqOyUHchFbR814eQ/7UmUdm8Z8UraNowDM+8l5vV4vdXA+n8dgMGjMATq12WyJaLZg7O3tJZllEOB7ofsAKM5CwT7S94jtmXAOhJE17JjJJoMj7s2hzbvIKMbha/Iec0QzKPiSbZd9wOkDPiK2wNaAie8zBzzlthJNBsLWf+u1wQ2//beDsV0EAp9zlRR65EfN7yInDHAIhCsgw/d6LR0IOKAmUYNM8Rr2Ghlmu4y3ODFWxok9ZCzoCW293m7P2mVTvRYmZ7gf7yHXrtRqt9tZiVLJH7ZT4POpQjw5OcmA0HrAmLFLYBv7M8ZTbRSg3RU1lpt6jSoTlkPmtfrGihONHd0qSeVrUH1iu2tym6e2YdM+VXNA53FbB5GbSjZREYyccyYL5AsY0QEIerzLrzMPxrn0hXvu2kpIn5jXVquV1f0+Qw1ZYvsQsuYtMFQzQEwdHh7m2V6sDcTwfD5PAvXg4CAuLi7y6ArWzgG1D/l3n5l/+1hjGzfm0UQstsBVNCZTfA0qRsAhft1P3iXoBONHROP4h5ocQl6pHgOnQ2x5K2S18bbtrCnXsC01PtxFHpF8Ml44PT3N83Wwe8ydfb4fsFB9SNWVT924D9V4zA9kqgsLKsmJ/Y/YkjTgPJO4NJOX9leWM9vNug6+nvGZ/RH9NBFlneS7rF1ENJIc9gH0CZmuCQn67ofscHA9uBqbBVGLHahzgf/yVmCwjjGb49VdNs1zyFzYP2IvGVe1Bcyz17Tau1arlcfoEIfwGrbauMb+sPop3jdRS/+qDjvpzW/HRh/ytdwP+TTO9ryZoK2YZL1eN45AsG8nPkcG3H8+y28IxY9pn42Uimjuk7fDMzsbsSWYLEQu8fP5FCaUaiYIp0UWsdvtxrt37+I3v/lNZnXIjkZEGieCPQeUFi6AtsEZGWnKlskGR2wXDHKqZiD8RCLvLz84OMjycjJre3vbrTLc20FLv99P54xwzGazhuFFyCGnut1ukimAWEqDz8/P4927d2lgIK8A9PP5PEkkxmuDwvhYNztinC0EQsT2seE+DM9PIYQoY8yWLe6NoX3z5k20Wq3o9XpZ6TGbzeLly5exWCxiOp02gFwNkJBJ5KuCWoNMqtwWi0UDIHK9n0rbFbRXIxXRPIjRmSIHrMheBccEuLVaypU4BE4Aq1r54Tnz02hsPCMinQPEbESkznA95BRnA6lFn3u9XvT7/dyWdnx8nAGqgb1BO86CABLHhNMEADggtFOOiLQrdi4GyciV18g2zoebes0sozxaGlKLNaIylAwndoHqEduup6enzBhif3w/bCOO30S/s8PYUq7L3zh0bAh2ape8IXNUXyELnkN/xxVS1mv/7/n+KTV8INlx5IHEAvNOlRvfwVaTLEDGLMfMAa95niK2vroSobZplld/l4bu8DAOP20PvfAaWD4N7qwTyFLEFqDTGNPx8XEmk3hCpis8jB2oyHCFigMOgOeuINTgzMCVRuYYW+EAISJS/o13SAyZlCFoJUClD/hX2wDmhzkbjUbps7H37itrbEBpgOmKTdtlB7z87yCWvlegavmr9+C9iGj0ibU2UK8+Yldrt9sxmUzi7OwsiUKP4/7+Pp8y+rkafa7bVnjNW+xMCDjL7WSIk2RV/3iv3hvciJyYrKRPvOf1sR3mvCjOBIUw43p+QhsyjD46eOz1ejEcDuPo6ChOT0+TTAUf9nq9rJw6PT3NpDX+yhVmJlptp3YR7MiB37NeO5Cvc2AbsYtQbbe3D1V68+ZNziXBmXUY+z4YDDJxZJvL9YknwOrWcdtscL5tO/dyQszXZX2Mr+xHjf+wQcQOxELMs7Gy7ZqvZfzJ3Nvnf59eVxLrj23Mmc8JNXEGNnWcavtv+8b/TpLX2MQxLP87IWs7ahvH3Pu61ZaaTEGvrb+2o8ZALrQAY5vwYk1MmtIPZI/YznjX80J/2A3kpD2FCiYEI7aFGuy4gOiKiPdkNGK7fdk+yGQ/8+ddEcThdV0tgyaN+N8xhHkGfrsyyOQ48+YiD8cy1k1jIlex0tAj9Mu2zvrGj+2T581ysetv4yzWxbpp/13tLa/xnY/x17TPSkpFvJ95A0CTzYxoVj05KEOAI7aA0QwzABblB3BGbAPAd+/exWg0ysffRmyNTLvdbmQWaa1Wq5Fp9daj+/v73HL28PAQi8UilRsgTqWDCTi+b0CwXC7j7Owsz3I5PDxMJ3N2dpaEBwZktVpllslnSACC5/N5Vh7xXe5jo4XALBaLGA6HqQzedrNer6PX62VWzFl7DDlrQ3BK9RN/o7TL5TIuLi4y24WxYOsFwszaUjWGUfOByTgTDBrOm/n47rvvYn9/P/r9fpyenqas8GQU7rkrG0/Q7znASNm5opze4mljZ/b+SzeDAQI1EyUR0Qj8dxk4xob+2Ti3Wq0Elsgacrleb/dm8330vILtiKaDxgbULF273U7d8xlFrmDCmUwmk4ZzPj8/z7PXOI8C+UJ/0UV0rdvtxunpaQOEYGcAmIBSssX0o5JbNMsSwQfOntddMWRgjm2pVQ/tdjvPhGu32wl63759m0RBt9vNa67X6wS7dSsRtnaz2VZNEcgB5LCNjB99hhDALjvrz7rYpgJuXClnvWT+0EU78Iit7bGDZb79OX/vY53l52gVnEY0A3LbXXQT8AbQW61Wea6J9cAZWsuWAVjdnmI5xYbX4NegyiCJILeSIfbVyDWfw+96HQ2yDYbpu4Mb2yv3m++bdKHvnOV4fn6en41oblUw2POcGSsYi/C/32OsrnJBl12Jib00MMXWUKnAePG99b6sOfP38PDQOGvGmKmW/u+SRfyt7Ss2x37bAZXnEvmrARK/d+lhrWLZpae79IXXW61W4rx+v9+oqrVdqJn3H7O5f8iRAzsHt3wOvFFtPvYTzOP58RpZDrxWEdHQJ3wN37MMQFLYNkdEVr7zZGpv0/Ih5mwZdjICAgNyhYQQeN1kihM3yL+Jc+t8Pd+N+ahzUQNV262ILQGFXFRCbxfZwGvIL/1k+zwJLj5nHcevcrQHDzai0gzyj34zTifhIfgdpNZqMa+xx/n0tD3Sw4RKxYP2PyZBmRfkixjHzaQd5EIlrGsg/n3tx9JV5onqfYoJ2DXiB1d5vF5/V0yB2fjb8lN9lP82tmHu7GNNYPIdmuNHfGMlpGxbuI7jUPR7VyxszOqqWO924DVXaxIHmZRkrpyoIaamWMNJbo+V8Zj8qHEZ88S8LxaL9wgl2wDknWva/37IzrhyH1/M9RzjVPvL37bDJs4tT9WfMXe+lj/nOajxK/23H/F75iEc11mu4AWIw3nffox7eE4tdyaxP6Z9dlIqYqtMdB7jC5sKKeHBma1HcMmgoAAGWJAobFfhMebX19eNskycAoLJ49MROgOs/f39fAoDnyWQhehhUeyEW63nMmQW2UYCh+vqL67Htgf25aLwDuo3m01Mp9P42c9+FsvlMiaTSfT7/TxLinNx9vb2siqC+TKB4gDPgocwmZQwwGE9VqvnpylgDCKa5zaw7QDB5SBIyEjGDxEQsQWPKD+H593f36cDRDl5zcoCQPj222/j5OQk/uZv/iYGg0Hc3NzE+fl5TKfTxvYWK7DnxgrsdcM4O8j2vPA68sp1vmSrhskNmTABZ8eEkYdsqkDGwS3VNVTf2Km4Mi6iWVbPmjqDwg86hn1AByeTSVbyRGyDMj+9wwElW2k5nw3ZMUlKNSAkT6/Xi9PT0/yMt1FB3lW5dyWEDwaP2O43Rzcd3PIdb6e1c3SlJGvKnJD1QvZZS0Ay97i7u8tD25nn6+vrBGhUj0AAU23lc/HQV8AmW5+8rXA8HjeqWVkDB7QmWgymatAMMHe1A46edTYBxdxYrmjVgf+UmoN1qlwYh+1NRDSA8Hq9Th9qf2PCwP40It4DGZXYiNh91oev68DM37EPdRm/yQsnHyzPyBSksAOWaket1/hdiBLmCBCLPpv8rOdIubKBZvvE/UwOOMtc7aGBrnXdPpx+07AdyD+YhIoU9NuYoxJmDmw855YZ9IXqx11Pj60BbV1n+mIfaXm17FT9dp/rehLA1kDJ/drlx7gG9+JIhHqPiO1jrz9VM1lS8Z/7v4vsjWhuMQO3kZCI2AatDoRNHlR/TibfZL9lB6KE/llfSW76CdXe5oSdR/dYP0gSHtRDEOox2vdh1zkqAvtv3EClQ8T28fTgZtt7H3/BPNSEFXNmOUSm7U92VWlYDlkD3qcKjLk6OjpKUo6+kBQzVuZhLjwRnHVm7FQ9eosU/zN+ZNoytku+nOQHz/E964x1cH9/P2OJSmrjn2twXu1DlfdKAHyoWU7+lIb8ITMPDw9ZXMC5wzTrreMC4zLLp/Es79kfOA5i7HWN7JNtpz1f1YebjOIz6LfntNoM94HrVfm2X3QSmYbM0W/IPsbO05T5vrG99cb3rglqxgZxy33RA/yq3zMhtl5vt5xzvA665ISByTT7LGydz06i6MM6gH12PO1qd8dW9d5cxxVS2GDmwLpkf+i5tBxUn2Mc40SRsaHjfa4xmUwy+c73OffOVfSee2PVj8XZX6RSKqL5lBDIlNPT04jYAmILh4UYJw7g4H8AHqwnT6ojYBqNRjGZTBpb1VxdULOTrVYrM88A/clkksyhF569/gQDzkivVqvo9Xq5oFQQWCi8lY3X2U/vw5UxoCcnJ8k0t9vtuL29jc1mE5eXl+kwCCoZQ6/Xi4iIyWQSg8EggcT+/n7Oy/39fWavIiLBBO8jWIA5jNFq9fz0o+l0mk/U4zUqWQ4ODpI8u7m5aZBvKCLBgSvT6gHMPEmxZuZQSGdx6Me3334bl5eX8W//7b9NhzQYDGI6nTYqQwxyDTbsNDFCGF8qFUxE2AEjvyjnl27V6DuorOAAPePzANHqMHHCNr4ALLL7ELAYKuuZq45MdEVEZrEMtpFtSGI7UQfDkK3tdju39HBtHMzJyUnqWr/fz74i1xCm/X4/twb6/DvPQ8T7B8c6KNwV9OPwbH+QdV/TFVV2Rs7wWIe4NnNxcHAQZ2dncXNzE5vNJs9qg8zATkAsMU/1rLTpdBqPj49pJwiSI7YH3zrDho3hfVdH2VkCGFi3iGiAG5w9oOPk5KQRqFhXK2lRCU6DxJ9Sq44bXWBO0UdeNzhGN2ezWcxms50EgsFGDVR2ZaHRbWcceX0XAVPlzltqkE8CSF5HfyB27OcczNfgEbvKNZ1JpYLI/sRgFRnEDvEasm4Cqc6T58/+hmCszh12MSIaWy2xZRVc8jnbKoho7KBBH9fw0QAV6EdE+kpsAcExyS8+7605DkprBtR+kmwo/9c+1sy9189ybrDsDK9tZ/VXtv1eI3wy+IP7mOTkHMRP1eq6WgeRWZMeNK+byQB0yrJB2xXccC1wENez72WdfG8+B9FkgpgtP/bFNSl3fHycZNHx8XESLegYlSj0GTtvXMU9IGT9tECCW8aJfakkkWXFWGCXXtdzbSyrtndVhjebTaM63HILPmBstt30G//PXHMPYgZwNNVnJvSMw0ggYbfQL/tmrs3cG2NYNmxzsY0RzYoP7Plms2nYV1elOkHG+tTdJn+ID/7YwPaHmkmW8Xic21Hxocwtc2AZd6WKcR/zYKyDjvh6vqZjFY9xlwxbLiOaZJ8JMN/f29T8OTAsONdnQtGQLQoH+HtXnIBvcCUvPo9rgmGNDcF0nmvLp22afa4TrsYFxAUeo+fbhQSr1Sp3GaBnXh+fvYxfxH6ZhLd/BjfXOXRitfosE8J8Hn/uBEE946yu1S7caztnv4NOW689dl6LaG7d5ygd7AzYnrU1abwrUfcx7YtUSkW8v53AgMaBMJNmkOtFxShWlv3k5CS3jiE8VOe49B9ltHFEkDudTp6BMZvNGmW4Blv87O3tZaaahbTgr9frfBKcs6woD46FiqP7+/s4OjqK8/PzJLxsIF0e3el08hA2nCvb8XyC/tPTU1YbIUj0jyDTikIAcXx8nMoOcGdLIIYAsBIRSSa5pJfy2MFgkO/7/pALAPLBYJBb9xzYtNvtPAPo3bt375VqOlOBQtzf38dvf/vb+LM/+7PodJ5LjS8vL+P6+jrm83mDNMJgOdhjfQzMTXowN/1+/72Dtu3kfyrNOlQrjGzIcGQYTQd+6Ii3i/EagBT9MzmD3JtA4TMRkU+ZsUE2GGA7CrqJDngM9JfrIi8E7YyRvkM8ueoGwhUgiJxz9hT6g3w7qxuxfbomJJeDAeapZtUimk/raLW253BUUoBxVvuB7QHc008cLTYGPeZsjouLizg+Po63b98mmGS+vZ0DfY2ImE6n8fT0lOTj09NTktLMB5kl5MngjvH68Osqp1SmAipqAOQKP16jmcCwU3RAAbjxvH6ptgvQRLy/3dBgjHGwnuPxeOfnHFhZv5EzExwmHBzgGBBvNtuKRpNW+HBXLdsPAJSRKXwHABW/YRvsvtMMen0+W6fTybnAztRAzSAeGZ/P59HtdtMuRETjqbO2kxUA2ncwZ9hG2zHsFLriPlSw6nM0IrYE29PTU86RCXgTk9Z/X2+9fj7bEl9fiW9sJOA1onnuJ8CTecT/fUh++YxlqsoyvoG54fU6d6yt54lWSQPWZLPZ5HmDBwcHuYXb9p9qlU/ZDPRtp6rdQV9M1lr+TO6iG8ijCZUawCKvBGV1G3WVPT7vbfG21/gUcDu6hb0nmcraci8Hku4TiQ0HYyZV8G/0qdvt5nZUJ0LsF5lDdKPKnW0e42Ku+I7xtm2qCVf0wQnlKttv375N+et2u7n9vdV6PqTcZ0HRL8h2Kqwspz5jFVny8SZgJHAQhKZtPfdnnCav7Ru5PutM38Bp6/Xzg5k4o5XXHA9Yn+kD8uB4yrryKRtyP5/PYzwe5xZuE/KuUjMRgPw6RmXtkAuuYf9sm8rcWw6Rd/te5sO+3iQN94+IXGPsNH11Vb5JD36js06mmrAkHjbJz/yZVEPWIppbEbmWCy/sq42XjR8Yu+Mw+uZ7I/eWWxMiJkDRTR9Ib78JdkDnrQfck/vXp/A5PmEtbcdtl7kec8dPja88h/AVzAtzYX1i7ivx7vvRdmHeXT4JGTK5GvEczzNnHAXC+8yFCat6/w+1z05KGWhXRWNC/KQOvmPQhTNk0SsQ2dvbno7PQkHMcJAvQZABtSeSLT2uwqmOP2IL5FhIH5IOGAcM7u/vx3Q6zetgROgzQS9zcnh4mBmI4XDYOAQSsBfRPFzMCkRllg0/hsGOCKOBovkpfhgRgxJAHsbO5YxHR0eNJ94ASAHH9/f3uTY1u824Ueo3b95ksAxTz/gIONiSxPpa+BkzfV0sFjEej+Pi4iIrtwA2nhvuQX+Q1drscCO2pcysA/PDtX4KrQJPB6cR758PQ+N9qsSQcfTFW9iQJ+uVKxYjtgGWjT2GHSfIHEKkUjbO9gHeg+DEuUKYmpByUBqxPRSSSkJINJzSev38ZD8qBCG5Z7NZ7O/vx8XFRY6Tz0dst7cwJmeCbMMimtvubMi5jh20txQ5OEc3mTNsm0Eq5Np6vc5zK6bTaZyfnze2OAJeqRbjPWxQt9vNOZpOp/mI7oeHh6y+xBbz48NorYvYQwBDv9+P+/v7xpl8zNEux0tAzn2YO2SIuXPWms85+LBO/BTarqAesIKdgtzwWSztdjvu7+/j9va28YRUj9HzGrGt4PQccl8DJK7hQMx+xkDUFZEeB/IIuGHbJzIGSGSLqLPt3MP+HF9lOUG3TPhUfbLNYltkRKTNsC8zkKOPBpyQGgbCNeG0i8hD/rAZ6Djza6IPf/T09JRJKcaHPjpjC9lQ7S9BPdd7fHzMc32QqYjtg05I1nn+dxFBbjWx4/vV4BdyBJ9pmxbRTEZ6ff2//VYFvA66J5NJbsGuBDT9+FTN/aAtl8tcQ3CiA1POAjQBR78hbzyOqocfCkKMtfm/BsDMCWtHZpx+OkjDVyJ7VM+CO7mWAzZwoOUAfXcFEFv8jo6O4vr6Ov05ASPzg43wWVvYRNt55Iy+GC/zGfyRg3WTTSalGI9l0HJOPw4PD+Ps7Cw2m+ftL1Q9mag4Pz+PdrudxwkQm5DgZt7QTZ/hSDOBx3voPHaBRFQl62jMiWXR9p7+kmhjLfl710NSjo6OYjqdNmIN39fVKdaXT93ATVdXVw1yrZJp9hmWAWTQyUbLTE2K2zbbpyI/1lX7Vn7qHJm44T41YW4Ckrnns8gs9t/+1fFPRNNvsf678K71g/8d4xLfMiYTpsZ4jmOxK5vNprFt2XNj0rDOo8eCreEePCkcfY7YEu703YSXiTJ+IwfoqXfR+D1wEWNkzMw96+PYwGPg/jzZ03NecZxf97p4zmj2I7ZrFVf7deYF3EGBAEeeGFPwHUi/j2mfnZSyoETEe0YwYptRNGHhxiJCYPF9AqAaZJuc4XtmSiO21QkPDw9xe3sbt7e3+cQunC99ptTPmR/6acKHbWeMyecz0QfujdJT4szTsebzeYPFR8EhvPx43F6vlwJ/dHTUINNwGmTRCeZxCmSs7YwithkZP9Ldj3alnZyc5Gcg6ggcmS/3lfvj8P1kCm+nA1Df3d3FxcVFIxPAml9cXMTj42NMJpPGwY/M/WbzzF5TfeYybQgyjJOzEozP5BLzyThxwPQd4OjqLV63AfhSDV3x74htNh5DG/H+9j3rpPXXMoZesY0VkGqw574A0PmfgJUSf9YRh0a1Hdtoac78ItNHR0fR7XZzi6WzxARk6Mp8Po/r6+sYjUYNYpJAGoDJekOKsU3B1Vo4HBPkDkAMEAgqDSAMbAmy7aRNrJmQwz6gy3yO0nSIuE7n+THO3W43Tk5O4ubmJq/Xbrfj1atX8fj4GFdXV5nFXq+fKyz89B9sE9t/TN5BimATfPYUoKBuWUT+dv1dAy0DCQPs6lT5rgkq5r1mq6wfP4WG7XTwbF9TwR02EHu3K7Po8e4KZg1adpEQyLRl1Wvzoe8DXgzurN/48lpe735bL9AjAq2IbWUi/sy2DLnD1tTKKYJbg3aXqXOdiOYTTE34OJh1dccuLOO/66HtPpvDpA0/EAV8hnvaP2EX6AdYxAQC88uBzB6rz5/0fSoZh4x5+wfrYzl2YIdsOKi33tl/mwTwvNUAzdeuDaw0GAzeSy6t1+uYzWY7v/djtQrySYDYv7riAFlkPo3fvFXaJES1BVzL84pNJjhD3izzbNPDX1WdNOGxv7+f5yahLyRO6SPHLDhZgI7ix0ejUT59D3LGtno0GiVu7Xa7sdlscs3QFcs/pEkNynf5A+TP/jhii3VMLvO6ty9W0tTBOPJ4eHgYg8Eg8Qpb5ZHLyWQSnU4n5wq7g44SuDNOjvNgHcEBtpPIkXHAbDaLXq8XJycnWXVlGWN+vP0fOfC8EQ8Rixi/sWbo7HK5TCIRjGDi3UTI5/S70+k0fvvb3zbsJTJiTGXbbyyP7XScadIpYnv+lW244wCvVbWLDuYroQAuwI+CPY0xWQ+Tr96Z4/UFmxsHOGbhOsb89BU94D3j8P39/dydg4y6GorrPD01z/VjLZz4Nk7x3NEf5or+4J9s3xyPsTvGVVTeloedwldGbM+uc7LM/YKgt++CbDfBay7A5D5zil03ZuAe/X4/np6eMnnrwhQnhZABzwP3sx3ku1WWHefaH2G3+Z59x8PDQ8xms4z/2RaPDDhR+X3tix10jrGK2DL0zjhYESO2ZWp2wCi7HQKv2VmvVqvcsnN8fNw4G8mZgLu7u3j37l06QDPLJiZ8JoGzpxhnDG4t49u1bx4gOBgMsoyX70CijEajRoBiAw7p5kAOJaDiwaQTAXhEvFcy72oWC6G3B1TSptPp5Hae4XAYt7e3abTv7u7S8VCRRMWLFQTQQP9RJB/o6mwnpYL+3t7eXpydneX2LIAQFVYRkaDJjpwzdiDw7CABUjYw9BFwx/o5W93tdmM8Hr/HQNsJfalmvbIs2UmYnGO8jMVGHsOJQcL4AFBNsjJ+iEmTW/4N4GLLlrP99/f3MZ1OYzweN4IgwJ3lFQBvecLRoI88Gefbb7+NyWQSEc/kapUdACTOCmAOKIyIJFvb7e02RRvziC0xvV6/nw2nugUCD8eAgXemgTlGVlkXssVv377Nvjozxdp1Os/bkkejUfziF79oBDfcY39/P4bDYb6G7Ft/sD0+0wt957BQ5sj6PJvNGo7fVY7IjR0u/QYsj0ajlCmDmV16ZYBl2UfGq2586WZAaT+GjTF5zHgAp1T14Vcc4DpQrcEqc1GDZ+5jOTVArAFQJZAimkFNxJYMcsayAuXq+7kHcofeR0SekYRPMDAH7HkbGBlS7IZ9MXrtAMLbRg3GHag7eIDcAYBDDNX1Wy6X6R8dtIAv6D+voUvGIjTmxFle5B19xfZ4/bjXeDzOBx6gk3t7e41sqGWu+kfbYeMpE4MVt+wil/jt6zhQquSCr+Nr2LfRb8bCGE36ueLkU7Sqa5U8A2MQ0Jkojdge1M/4KzHtpOkusp3P+JqMHSzrBAcybx1gDNgnEjok9cC/DvAJnhgPyRH0zE/vY0vb/v5+nJ2dJTk7mUyyQohEF3PFuhIMYQPsq9G9iGjok5OWNMsb/ohgyvgPm1txuHWVz2NbT05O4uuvv46rq6uGzYnYPjSHSn52E4Dhqcam31wXPO3qKw6j9w4H5mG1WuXREsQubBs0Acx1W61WBqD2N9XOO35j7o0ZuXeN0zxfzKu/9ynbb37zmwbx6GQ/Y0CeseHIiOM6+85KLDF2Xvc2uojmE9j8Hf6v5LIJCp8bxVw66WnZrkRVxJbIso03eYE/N9lbyS7bdEgdrm+Cxv7W84keMCb6BdbD9ngstvG2gb4O/Xd/LXPGN/7bVbPWB8dGxMcmZn3diK2NN7Hja7nSDJ3G5/O+SS/GYP9snaq41na6xpzgkBqbuvjAnIdjGMdxJuW5BnpDPMb60b+P9bNfhJTyhBswV2UwoPaCmZlEafi+P0cGkoPsMIA4gYhnhZ3NZjGdTnNLSj3LIKL5pAJnS5h8AiQf/Es5rq+DI4VJZow4V4OzTqcTg8EgBoNBnJ6eNrI/7uNsNmtsiXNGLGIbKAI4AMs2rHYQrANbGRBYQCwKC8hBQQigbai4Hk/Yo7IEgBERjaes+HvOBDw+PsZ8Po+rq6s4PT2NFy9eNMrEWftOp5MAGxCOUeBcLPbaVxmyEUA2uH+VWTsj1h85xWkwx9Vh/VQaY/6QMzTQZA4BRdaF8/PzRhkzc4IeEFBV0IwRRG4ODw/zHDjrwuPjY+rnfD5PkIwuuXKAJ04iG1zHhPZm8/zESp42hw6v1+u4vb1NHfLBopvNM2nd7/ezOsPO9vHxMbcDQdBFNA/7MxmLDXFWhLlx9jdi+2RJ5t6VhNU+QZJFbCvRmP/1ep2Hg6/X65hOp/HNN9/E/v5+/PKXv8zXXIXB3FGtGBG5vYC5sWPlPKn1ep32b7PZvJclnk6n2Ufr0WazaWw/o8oNGwJptVqtUlYqeKsEcM0eGUT+lPQxouknKrlpW83ZRwa8ZP9MCkTEe0SLbaXvuasPNQnkdbJ8OBA2EK9kg+fcugN5YuKp9qMS5R77dDrN4A3dZJ0BRk5gObsPUUXf0DtvN/TWSfpegSv2zT7RREoNeJlHz3ndkowNc7beuuC5YN3RSQf9DipMYEVEHlHA9lsqzsEPVT6QMScNHAw5SOV/A3TPV5UPA3het37X4IpmebEc8338hoMsvmfS9FM1Bxcmiu7u7uL09LSRlPWcObvcbm/PbeT71aeaWHCA6LPR8D/4EjCRs+o07IgDHw4tJwAhuIdkIbkwn88zYUEiYj6f5xObqPrhN0lU8D94AEzOma4EdVRE0U+2zjhY9tzZPzBnjNcyYNtmssVBu68bsa2GMQZijbBL2JzxeJx4wXpie8FWv3a7nVvkHfRDjtDHfr+fPhq9t83jc07yIkv0vyaLSTJ6Pq2vJKSQa55ExrywVq6O8twxdtaj6u+nbBAj2CITacwt+MmkRUQ08IXnljFVksDbRpmTXa3Gw5VciogGkWhihbU2Ro9okqvYBghQV/JCINm/bTbb5JH7A0ZF/vyefQFz22q18pgV9I8xMD/0m9dMbGPjwIWWQZp12XbTMZxjYB+b48pH65e5COSaecWXmMzxGnptmBfGT6sJPNsnksn4cid40DXsLK36QGTZiRjbS+MsbDmvIRP03/MHtnEi24Qo17m9vc2Eiqtff6h90YPOaV4Mk0wIOqXCzgBHNI2DM8g87r1uGdhsNumgyS6Mx+N0Eggz93cgHrHNWNWS5r295yfUHR0dxWAwyGoAFst9MxDjmoeHh3F+fh6z2SwfuRuxPfC53+8nCOTMl4jIMQAGOMCUx9sjgIBPHCvvWwAraF6tVvlkrqen57Obrq+vcy4JDhkP2az1eh3j8TharVZup8OIuepjOBxm/2kEJKwRYNlBVqfTyfU6OzuL09PTRkaauR0MBgmMGDdPmaCawGDDjLCdk0GGAwgHI8guGfXNZhNnZ2cJ9HAgnzoj+4e0SkSZaKNhrDDMJnyd9bejdXYRwgjyICIaTgfyB+CEw8HQAj7n83m8fv069aHV2lYccf9+v58B6dPTUxIZ3BPnc319nQAZOTBo4PPMCTqHjlGKD7kCuMZw1y26tmfOUjuIwGlAxDPPdoZ8n6DRxACv89re3l7ahV6v18h8AD7Zmz6ZTOLo6Chubm5iOBw2tj8zv8wTugeh7krXk5OTODg4yCpMxsZWARNkR0dHcXFxEbe3t40KRZw8djui+eQpsknYEW9tcLWVAw4DRmyVg1vrwk+BpHISw8G4wQ92ZjAYZH85sNV2GFtWM9WeE89FlclKNNEv20N/x/4Dn1z9p7PMEVvih4wt/TEw9XXRNxp2h+1pVEu4uoNkjYNE5AfAzOuDwSD29/ezPL7eE32rANFJCohwg07mCwIc/WbNDfwrUWVyjcAIQiEiGtXHXmMC+3Z7m0zyuuOTOFOPylGTjB63CRX01PdERk22mdQ0MeMgic/UDG6dB/eBVoG4A0Lb8Nlslg94qPJaA78fs+0K6JHtzWaTB167Dw6WkTO+b9tf+20yhL+pGiZ44P7gvvl8nljVPto4GJlBzipeYt3cL/B2RCSujYjcms9W74uLi2i1Wg1Sut/vp0yQYJpOp7n9kqDNQa6rXMCtxmZVhpgvywmvWZctb9X2+ZpOILiCgcZn69mYbHesthqZhTTkgUck2h10e959tIjHjc1iTVutVtzd3eVn/WRi+2HOlyUpxnqD6ek/azuZTBqJTNt16yMEov2H7d2nbsybiTrrHDESn8X+0jeTnzXZAfaoySBv0aprYrlkfSoW6XQ6mdy3TKKTYGInDR3fcc96JqvXxj74+7DULiLRNo55NUa23/Z1GRPyan0waez1YlzIqYkff8cYBbn0eEn6IM/MKTsvfP4a8Sn+3gkj44tq66uMmyhknSK2GAa8wjwTM3tuLB++p/XI/tkxgP0x81qTgdZVxsFnja1MNDlWZn5ms1luQ3Ys833ti5BSLJKZVw+WwMmOwRONQNtAIKCcj2BHhaNwRmI8HmeFlAUwIhpECBNMwMm9IiIrchycOSBgjFTsEDQ700OAyDh8ZsD19XUGXATkXGdvby8rpAySGSeOjy159/f3ja1BOHUMsskAO0fWBoMAIeXyyojIYBxg4y0/jAdHhIH2OVJPT0+Z6ec8LAgBO0Lm10/6oCKDPvL5k5OTaLfbSUIQ9Nftl4wLp2TywI7SSs5ryBifh0hgbWvm96cQ+NKqbjkgRabMfjMHGEpXBEAmorfeT062FD1BPll7wJTfB5jf3NzEzc1N6h4VcBGRWwCOj4/zPLXpdJrXtHzN5/O4vb3N8ngMPesCyULQCHGNo1mv13nmw+3tbZyfnzcyi8gWcuYA0HoFoHh6ej5nKWK7p977zJ+eto9vt5EHALH9h6otACCySx8IsCn/R/Yh7Ql0X79+HTc3N3F4eBgvX77MTAngxUEUfTJoi4gEbX46CzYZu4HNPjw8zLNIkD3sQk0G2AbZUXr7M3LItZBvdM3Bhgkp5M3g6ks2SCUTIZwfErH1f/yNTR+Pxw3QW4kA5s8EqbcP0Jh35gn5j2hWTrFGgCUDImTYIMrrCWHN+SkOaumD+2owg5wATll79IIqWIgWxhqxPXOKcfOUMMuZtxZYXhgD4zXoZ6w188tYnZ2sJJ6Df/sF5sjkg0ElvrfdbucB7egmJDL+kbmhWtxrvbe3lxXE3obrCshKTtofsyY0A9tKIjnRYxmqCQ6TU/YRTjrt8qF+zeCacdhv0Z+ILRH8KRvjMHHB2uMfwJLeEv74+JhV8AR4DjCwgcijdY/r4bf5Hr5lMpnkWYPcn6AWcoeKBubIa+nkDX7e9oCDr2lgu263m+QM1TVPT0/x+vXrODo6SlxL/w8PD3NbPfJDVRD412fLouOOM1gDy2YNfC1zFRdZB5nLigW5npPX/O9tSWdnZ40qyNXq+bzTdrudZz7t7e3lE7LBJLzO/Jv8IwHYaj1vZe52u0n00VewSSVAnAAyQc8c4X9cBMAP+rRarZL8NCauhIsTjthFX/dzNWTW/bWtA6OydvwQjyAvJjJMetinfYhkM04xJjGxELHVNY534TWuC8nnBLzjmBr7Ok50vGaCwbgBzPX0tD18f7lc5rnHNJNyjAFZAjvU5D8JIpN5+PYaM3kXjv2C147P+cBxx7OQ9OBg+ug18jZqxu41RVepZmJnBNfHl9lOYxeNqyo+c19d8GEfyRp+aJ2M0ZAl20bmFDlgLfELxnJ8jmv5LGlzNhFbHTZhxdgjIq6urjJh/UPti5BSu0CvAzw+U0G2lapun2m32xmcMmlmSzHK4/E4A10eD8w9/OhbK2zEdovRYDDIgA5BMehGYHz+E8aNwNbkENlZnAlCslgs4quvvsqxogAsfrvdjvPz8xwDignwdAUCBBXXdymgK75MvJj4s4H05w1IIAtYD6qZAB9cZz6f5yGLgCqqZQBYLk1lbQAo9A/A9rvf/S4Gg0F89dVXjfLmiEgizpVUMLYcWhexraRhPSvTzmsoqo29QYkBwHq9jl6vF4+Pj8nE+xpfutVAkn7ZONvxIefOEGGA0JmI7dMrHPxxDzt5+tDpdBL8uoR4s9nEmzdv4rvvvsvAlbVABumfASFr5a2rbP0jY2vSLKL5SNmIZxLAgRoOhbPTbDMMPiG17ZhcAWSiFtnBCdu42345M+IqBwfBdW385CwDfwjq1WqVQHO5XMbvfve7LMUnuD07O0vwTxCBbB8fH8doNIrFYpHbnpB9iDaf8cU5YZY77DVl5AbX2EYHqMiNq1wINGvywgG0CQVkmn7wHt//KbSrq6tYr9cxGAziV7/6VYzH44jYOn2Te6z71dVVbseI2IJY/jZQto5Y/+sWg4im3+V/5smgifvZVxgs1eovZJQHiXB/fAlrZMLbdhb7X5+UZ5CLbtJPE9SunCTxwb19wC2ElQMsfrs6EnnE3/J5/Cv/sxberlDnrAb7JmwiIvWQ63krOkEvldXoZUTkQw3wfwBVbJUPQ6cvtom2s6yjx8b6WQ5q4I/8OHAyzkIumcNKoBmP2X8Zg/m+lsPNZpPb1FyN+fDwEKPR6MMK+QkaeLdWGngc2F5IF1e3giPtJzxu5oHEH3MEPl0sFrlNm+9BcBq/4jtYa3xMRPOsOQgpV1xBCh8dHcXJyUleywnciN1nhb19+zY6nU58/fXXWcHT6/XycHNkbD6f57wgOyRSHMCaCLauuSKt+g+TE+gh/Yawts9wIpP+OdiF5OD/s7OzePv2be4kOD4+jpOTk+j1enF6eprnS9FYGzCzSQSPC90hViEmoHoGP2A9Xq/XSWBZdjw/rsBizSuRxHcgGbkPdg6ywLJa7eMuTPopmm00/akkhOMZE1jYdc7J3UXGRGy3Z9l/VoLKrzFeyy1zApnieAtCZD6fZ9VbRHOHQ60soiLGZI7vHdHcCucYljkiXjOpyD2Q+9vb2+wL13RyC7mwjhCn2haAqW1ruA4HaXMNsIZJNCdl+N79/X0+eGez2WSyGVnkwT32fcaPtpfVF/J9vldJI+sLfTcGqzgHH45N82Hr7CYyEeS5xWaYTLVPt00z6WbcjbxZb9BbcAx2w0kPJ0k+dAzAh9oX276HsLTb7WTnzQb6qUMR20CPRSQDhDD2+/04PT3NxYOYYYIAH2zfoQ9mCxEsB5QIwnA4jNPT04jY7ifFAQDm6AvKgRBERD7Nj4bBXi6XMRwOc/sfW99evXoVp6en8fS0fVoLZzExnuFwGD//+c/j6uqqsZ1oOBzG3d1dw5EcHh5mZoktcwAeSqshUCAKHh4eGo+ppfzdQrdcLhN0EPST7ScgJaBst9u5JY/1xHmSpTUxhvPF6Duzb4d2c3MTm80mzs/Pc65YR+77s5/9rBEMRGwPnsZZR2wfa22D5P+dZYVMcJUVQT99Jmvme/wUmp3nrn4hzzZyzpYi/zWgB/xDMrmCDZl3STPnU9Tg5ObmJt68edOoksBRIndUDEY8n9PAQal2GNPpNG5ubhIMQcZgG5CxTqeTT3EkaKUKi0CXMQO27WSYH9snyDHLUnUeBin0h/m2I4aAJyvGfLhigXtgB+x0/BnOo+F8guPj45jNZhlcv3nzJu7v7/Ogc/ed6kMABIFxRCRIQtatN1T1sD2Q+5+dncX9/X3jaUrYOYNi/AW2IyJyTZ2dZ42qzlWihfnjvep8/9j2ofMiPraRVNnf34/pdNogkACFEPmAh8Vi0QBvDjgcADhojdhWXSFzleyK2NqGagsitiST78PrlQRzxRRnj0EAGXg52GNtDXYJ3rg+AZO3gmCX+BzbuCMicQOYgzkzUe2AlLHX7Cn63ev1MrAxMVWrKaqNdGDLvBp3MF77TvpEcOr+YPPw5/hB7BtjRHZIlHjLHeNwvxxgYYtsp1nTmnm37Hg9P0Sg0D9AtPXVslvJBV+fv01ict9W6/k4Ac62mE6nOY8fC5b/lOZxOJiJaD5JlP9NPjow5Bqr1apR5c04ubYr8HmPcfPQHwco4KlOp5NPzGVd6nzati+Xy9wCCKm7Wq3yqbebzSb70ev1YjQaJQnih3scHx/n2YkRz1j09va2kdjs9/sp26vVKs+jQqepKMLWYN9MvliuTEjZz+A3eL3aOJMFXK8mj6zX4BxIAf4fDAaZvDk4OIh+v592iTnh3uiEcShbbcE1JycnqZ8m0vr9fgwGg8TwvF8D/s1mk2ev7e/vZyWzA+CaMGB+ncBlPtrtbaLWOJ7XPLf2xZ+jWY9MRjrRZYLE80k/d/layxLzxM6YiGa1sZuTPNVnm+ikD8y/t98aCzq5a7LAcsprlQAzLsWnuMLORA/+w8cn8Hn6gK/BT5Pcte56nFQIMq/0yclLY1rGQkyAzQArWvdNhDE37Lhg/sCz4HHsLOfyHRwcNJ5+x/19XAv9ND617qCn6B56Y0zlsUe8f46VY6/qL319y/uHYj5k3uSafZKLdRzXmZg0FrCPqLrzQ+2LHXROp+1ULWQMkkXCIUOCOLPLY2kBe63W81MjEITRaBQ3Nzcxn8+zbA9yxcGPBaDdbsdgMMgyYrbBuLQa4YYx50Bm7h0R+fQRxkCJq596QiCHsz08PMxsEUEgB9uenZ2lYmCU6CPzgoATZJKNoU8IN4DVwBRFRVk4hJbqCgSO9Tg9PW0ES/xwELkBZkRkpRkOC4OBgwNs4zCtmGbJTWBuNps8tPrFixeNg64fHx+zJJotWZBvVKxVRtyG2AbQmYiI5tkMNu4EFDjzzWaTBzv/FJoDDuuYQRq6iAEy0DKAI7Bh7SA6NptNnjPE+U7Mkbe3IbO8TuXOb3/728wCuFoC/b+8vGwYQJwPIO/4+Dgmk0nc3t42DCJ6y9ltJg+Hw2Hs7e3FaDSK2WyWFVxs+Ts5OYnBYJBybcdjkt3zYdAasbVxOAGTRXy22kG+zzoxT76nA/+a/UWWIXYZ9/39ffT7/firv/qr+N3vfhfX19fpIK+uruK7776LV69e5ZpjL6l0vLy8TGLKWTjsKf2aTCZxd3cXFxcX8fLlywbI29vbi5cvX+a1HfBDngNUIOQgMQzQnXGzjFfHzG8AtMfmz/yx7Q9xvruaz5hptVq5BYZ+YZeZA6peapWF/ZmrVxy0VztmcsDgyHPi7zB3lmN/1lt4IZ3u7+8T0KEnBjQ1eGe8PE0TcthkVAWwyAZ+1VXJEduDmJ3oMEmCXGLz8f+MHTKcZI4Ds5p95NqsIbbKlZ/MoQ+VtT/xeRsAZm879Lzh8wj46Rtjx6+6qhhdRI5YX95DP+wbTR4yXsuC8YZljf5aL+0XahWX1+VDumlgbh23vEZsz13j6Z1gFhNpn6K5LwB1AnX8JHqAHyMAo7GGu4IWZJS1trxCcD08PMRkMkkcUm0FBCakJcSBt96wjrYXVDw60OQJu1X32H5HX9lCOBwOc+1IGkHIkBR9enqKs7OzODo6in6/n4SwSQ1XS6EvVFPaPpggMPatQa6D9yrLtpdOlltOve5gBWwHa8XRGsg+a868mnB38Mp5uMa6BNWuOvU4wMEOqLFHXJf5YVuhZYH3mUvk7OTkpFEpjQ5jRyG0nCDH1li2PP+fujmOoJ/8b2ITgtBbnFhDy0lE88lvyJoTcw7iGbsr+nwNGrJDssSYDiLI5BJrxG9sDb+NiSpJy/0rRiDuNj5l/WnEkhHbnQfc10SUj1zADxoTR2wfrOXz8/D7NGJoyHNsJ0UV2BLGRmUX/ak2gD54x5Z9HNdgHTm72ZXZxv/Yd65ZE1Qmq7AjjNH9QQaYez+4Cbtq8si6WYl2x9LoOX6/xhpVZv2/Y0NeY47oG3E11yeJ/zHti5BSOFKqJAxo1+tt+ZsBNYaToBLQcnx8HOfn5zlpgK67u7uYTqdxe3ubASiKRgAXsd12xfUwst7aQ1943wDT+9wfHh7y6RdmjQGWlItTGWIDh/NBuBFQwAlB2XK5jLOzs+wTmSUy7D4scj6fx97eXj6Gl4oIjA6ZltVq1ahAQJlxNgQFZOcApswRVQ/dbjcJCJMJXIOnqLRarRgOhzEcDuPo6Cgmk0kGnE9PT3nmD8oe0cwCGiB7LXgk+osXL5Jg6Pf7edgzBBUGg8DCRhSj5vUz6UQATKCBweAHgAiJYMBjQPSlG/NXg0iDOz6DQccQQojiiGpFDjqL0YaYA3AzR6enp43Kpojnx9q/fv06CQoaOnR0dJTgarncHgbq6qvVapVECISTKwtcBs1DEU5PT/OAVa6BYWW8ZBBduVjJDZNeDvD5cfBmJ49ceCy2VZx3xRiwGzjZiMhMKXNMCbJtLDbE25nW63VmbdnGB5l3d3eX5BxBAFWYOEW2EU+n0+wz9wK4bjabuLm5if39/Xj58mUDmA+Hw3h6eorvvvsudb+CN+wnARbzx/9kyFwVUit6mBvGXMFJDbT/WL36U5rPAnKlq2WFOeVcFQMQxlvtjGXCQSXyVLO5JrS4ZiUXHBib2Ge+8e/Y9bu7uxiNRkksuh8Gx7ZN6C92iC1AyAX2udPp5BlVJnsimofattvtrLzodDpp7yHPN5tN4yBTtqM6CK3kE9fHH+JH3A9XO9mH8L9tA/MCuMbP0E+AK5UltkPYKjAWwSgVQvhmE5V1bfF7Ji0c2DiA9ed5rcoefTFZXt+vv7kO8+Tg+vvAbQ2yTCis1+t49+5dQ5f+VH39QxrjcHCEvuMDmSP7T3TA22awn/gK5iniOagj6cpr2GcT+T7LivNqSAZgH01wrFar3NLO97im9dQk9Gw2SzKVpC5EEzr98PAQvV4vD/Lm3BrwvSs6OTcNjOoqDvAAx2v4wHMnSyAAHXcgB3ze9tTfQQ9t8+p37GeIK3x/1ujw8DAuLy/zvNjFYpFxC3HEfD6PbrebRNxoNIrNZpOkEYl2CC3O30JuHAjjF/v9ftpjbI13mIANfBZORDT8Eba/YsFut5vbYYlfTF6Y1DIu9vv+/akatsy2xYQU6woOdSUq64i+RGwrSSOa27yxm04KVaLT461kRUQ0MCzXhczcNYZ6TdvuiO0RLMTVtaqX5moY+oXeQjSgF/WBGvRnPp/nNlL0H/1hy+hwOMx+mhyzLcEPVx9cCXia9dHXZZ2YQzCGq7ZZv5q4IUHopILfJ55lJ4arruuxN9hXbPr3JXJ2kZ687vXjNX/Xc1ATZSQN0HOKV0xYUziCj6LvHovjZdtK/p9MJjm3H9O+GCnF5JgVZJL9vo0TDoiJa7Va+ThdiJGDg4O4ubmJb7/9Nt69e9fI/NXMIiQMbCvGYL1e5yHDEc39sDyFArLn7u4uHYPPqIDlxZhAAB0eHsZkMomTk5MYDofpDCK2h+WR/ZjNZo1KMowSgYi/t16vMyhdrVap6BgUytUZ1/39fe5dtyJxXYIKjAz/uwqE+fSZVygHWw95ChhPDMSQLhaL7BuVU8ynjZGDD4gA1hMlMah+fHyM169fx3K5jPPz87i4uIjBYJBGgDUgQ+KnihAMIltcm4DXRJMBOWTUriDE9zOj/aWbQVPENqsSsWW+7Rishy5ZNQnlYI3gycy6A87hcJjrwrw/Pj7Gd999l+c4QQRFbJ96B8CDXIWE4L6r1Sqm02luc+U73BtwDwHFkwHpL9vSML7j8TgBSLfbzYoNP1XEJHnE7oM7CfoBAs6m8Lc/bzDpTAeOkHngml6TXq8Xy+Uygw5klnnwmRQEMBcXF7FYLOL6+jody3K5zCoxtvIBRBwsHx0dxatXr6Lb7cb19XVusWBuACvcc39/P375y1/G4eFhVs6wjZAsLmvO3DB+7/WPaJJWdoz2HTUIRa+x1diRn0KzTUe2GRNr6PNfbm9vG2dJGVThX00U2LZZz7Gv9oEmprmeQVX105Z5ZBTdZBy7qnjcT4BSq7WtckBGTU5hvwjcsO8++NkBDt8jw+wqWIPG5XKZ1V3oC3PDtfBp+Bvjikrcm6xzcOb1YC2xt3ze10J3DFyxQ+gjfoi5xrdCCqLD/hz+DSDpINKkFLiA9aH/XMNBu8k1xmKZY92x2R/SPcZrYpS26zvWd+u/X68JAa/tp2oOpCK2a2uCmNctSxAwzCs2jrFhv+2DfE/kcbFY5FmK1g1IHvTCcmOdB6/d3d1lsiVim7WnUr8GPegxPrnV2m7/xp+DmUgW0h/uPZ/PG1jdcgiBjH2gP5DO7r+3VVuuTDSZUGLuTKLwG5/MOrnag89XUstrbL2gSo4HtNgmkUSYTqcxGo3i5OQk55HPHBxsn3gLnvJ7rvDknqwPZ56ChbG5jje8rk5CeC6x9aylbZrtlknIWlFcscynbtgx5gesZCKHSiTkzeO3jvA9yJqIaMSp2Dyu5fcimmeQGqeZGDY+r9uj7KNNkvGZmlhwdRg+nOtgS7gGcmj5hwx1v1zJ73iAGNAVgHt722Nf7C8itrjHuklzRRt9ZZ6M8bA7NaZHv0wee56RVfCV/RjxiAkg2ykaHADJeNsfrmViiYZM2GbYplv/7BO8Jt56Z3xWx20cjLy7uIP3HZ8gXyapjLF4nSQZfaZPJAs+pn2xM6Uimqf1m4XF0KNYbL3yohBQArq63W7uWf/mm2/i17/+dTw9PeWj4hE2H+bH2TDcD/AWsX0SSg3IYRRHo1HuiwcUR2wNDMB7FzCK2CqnCR4C5PV6nWXWT09Pec4KbC5gl/cMZH0ODMZ9f38/Tk9PG9t2Wq1WXF9fJyCBJSWLdXFxkeOi7z683UA4YlvmR+YPwLFYLKLb7cbZ2VlcXFzE7373uyzp4zoEuwZf7H+30eDazCdEhh3xZvPMwH/77bcxGAwalSbMCQQeQY0NDfNox4xMMPdWeggn1soBGtlsG4qfQqvgK+J9kioi3jNcrJfJR3Sykm/OSKzX60ZWBV1lTvr9fjw8PMSvf/3ruLq6yiwrjo6tdfXMBAP1iMjKHshcwHLE9iwZdGO5XDa2A+HMeEwzegFwj4jMWNYyW5MvjB+SxWcq+OwsnA565DLcugWIObftwwkgYycnJ6nDACBXqRnwmtiClGXLxGAwiOvr6wxUydoC3C4vLxsVUzhDyDrO5cLW0mds63w+j9/+9rfRbrfjL/7iL1IW2RqJzSHYBxA5wOH6VKQxh4zdNqmSyLbDyJcD2C/dDLpMXNRgY29vL66vrxsZboCgwY3HaHCGjhq4ANCxlw5saSaRDE4syxAp/B/xDFoc0EICkUGzzTT5xNjQsfV6W8nB3652MnivZAk+p1aEAvKZexMDJnYBwcim7+e5gCR1ssOg1GDeVZH0yWOnz9gWy/nBwUES0NiYil/AVev1c5UFxLoBPf01GK14xllUg1z/mJTc5Tvcqk+sn+czrG3dAlH9lMlQ39Ovm1Q1efK5mu0TOoje8jp4xcEdSU7PTSWeaciaAxZ0m2QL2Lfb7TZkHF9jkgrSF0KKIAX7vre3l2eVMr/IF/50s9nE5eVlEsv0GR9kfOatrX64D+fDMTb7WwfAHB1ge8n7Dvz9Y6KB+Tdp7YDZhKDtowN34yPuZ7LGpBXy9/DwkFVnrP3FxUUmsTmcfjabZTWZbQOxxnw+zwPge71eYhx+sBPYIT+1ExlwHyHGbKMclNNfGhV3YDjOh+R7xFnIsIkf7v85msdjIgrbYJyPPNJnEhaV6Kk2yaRKJVeQdxNcVbYitmcv0zevC/pK9TCv2WeTZKC/xKUmZNw/n/npeMykB3pLwni1WiUBQ3+dsORz9sUQP3XOIrZb6Bi/sUK17fb3xt8ma2xbHes6jvPWQ3Ar/1esYCKWMTgxgP2z/2csNX7ydy03lQQ1QYTsIHtUzTn2MvFkDGjyHHzm5JlxIXPHGmPPsU98x3E6/zMGzoq1Dv1Q+2JnSgGa6vY9FpLJwHmZrQSMnZ6epqM9PT2Nd+/exd/93d81nkhHFoHrYmB9thMBMCSDqzRYSJwymVbOx4B0QGFcgWPHH7EFJdyL7BVk1P39fZyfnzecubdxMB47cJMCkHPOcBgAdzrbA8zr1o+zs7NYrVbR7/cbyoVTYk5d2mvWFQPBdi4blcfHxzxklDUjkGarJoE43+92u7ktk7WYz+fvMbUEaCgWBuzi4iJWq1VcXV3F5eVlPgUGJps+mqEGoDA25JD1Yky7DIbnmGuxDj+lSoyI98E6r0U0n77nMQF8mCODN2SCOSNTCbB1JSJ6TTUC63d7e5sGzAau2+3GcDjMajU/8h3Dj75wDRwOAbsr/HC4lmMIX/oFmcW9kC8TYbuyTABTAgh0OGL7ZCnkF31lLeyI0GvrlUlA/vYc4SAIhgFO7jfghgNj6QeEVETkfHQ6z+dKoQtsncMmmZhz1dXZ2VnOq7cw8j1s12QyyTOt2PbM04eoZAFERTzbOqo6/Wh7Ewy7AA5+wMEOcs/6IZM1e/UlmoMZAwMH751OJ7fG1AAp4v0zUfy/yStsuwOpmtFDNmtpu0Gy5TUiGjrHa5y1aHknY27QaLBo8tbZQL4bsd3WC5nG61U26XOtcIiIBrFXgy98qAE010ZGI7ZVzLafNZOM3bQ++zv2QRW4Mk7rPkk1tvY7o08QWnUCvISNw3Z5qy1rYACOHyP5gE/DxoN7wCnIKcDZ46jbDZDLOi80B+rGip676l8/RFztIgQ+tW+u12esBBn4xfv7+0Z2ndf39/czycf3IyLXOGJLwGOX8dsmEZBlP7UanUB/kGuSraPRqFE57DVClufzeUyn0zg6OkpfDb5/8eJFw+/7fBH0gmohEij4Cnww+MEEDkSKiWTky5USzImTy1UuHXw5oDdBY9vq+baeWMas0/4u/cR2oDMEy9iybrebRBRV2be3txkz0QcwP//bz3JsCYegYw8cU0VEIxHAuIxnPAfG/JWYw94Ri+Fnqi4Yr3ue3Dxvn6Ih98YEJqFc4bFcLvOIARcCOICnzyaQGOsurF3xMLpn/2Gf4/M6jeeQ5c1m+3AdkyHG4mBX9y9iG3O50p9ts8wLvpq+u0rI/tFjxFZUDMo12O7pvrTb2wIF5sYPG3LCyPNjLG7bYhxEY96Rb8Zu8hVOwDpCQhTZBF8wFxW7cS/rFfrjhHZNEKGbtSDG5BTyRxK9ftbjtT7bNuG7XSWFvbBvR4aRf/qAzzABxnvMB3EG8/0x7YtVSjk7R6ezUwKOgBs7aoico6Oj3HN9d3cX//zP/5zZFQSBbWKQLVRQsKAsJlUMLCzCwsGYVAVUUM9na8CD0FG6SBDONjqXyZqtZawnJyf5mG+UwU8pjNg+HTAiksEkk+JHr9rgeZvSYDDIbAZGgOvAqPd6vSTCHMRauCszulo9n+kzHo9jsVjEwcFBjEaj6PV6cX19nQdjdrvdePnyZZyeniYB5qB8f38/WefpdNpgs1FyztRywMD15vN5QxEw0j54kacnTSaTVDhnteiH2WQHLNzfwNpgBxl21vun1AyyDD5wdtWhmvxw1aFBdUQ0ruHAhQwgT9vhrCQef/z0tH0s+/n5eVa0QSZ6/lgbytuR8xrY4HTI+CwWi3zAAU+9tF5AfGBkJ5NJtFqtPNSfSp39/f3GIbUG68xnxFY3eQ/9AlTgLCDYeB8SyM7YoAQ7gyNutVpJNA8GgyS90FcAlR9AQPUZW2fRn7dv30ar9bxPHuDLVgEOO+/3+xnkAK5Zu729vRgMBjEej5MoxOYCrE16AqoYnysjsS2cH4UThzRst5/PyUCnXcZcQQGyzFq58mMXiP7crYJzg0ns9/7+foxGoyRX6zaLSkjZNzko4n/8kYF2DaoMquzn/D/3R9ewj+PxOHXASYaISDlHtxgLZDDfATjbRlGhhy3yuEwA1R/erw15idgCQGTHwJvr2g+aQNpstk8Kwv6ZIHZw5/HU/21DndwweVYbdgx9Bx+4/6vVKsbjcVaBMh7IR6+37+WAwoCdz+BrWT/GYftkMrO+ZvxVWyUaaL5W1SHe45qVWPA1PnVzoAGhyN/4N1dE2b6h88aNYCT6j7zzg8ziN9vt561irkJijVh3EojT6TTPKeL7y+XzOYQcl4EO2++aYGQcPJXP64GP4p5gMhrbemr1CWQ884X8Us3HeCPiPbk3SVB96q6gNaKZpEAHWS/Le/Ul3M8+39uD0Wu+s7e3l8kYkr/ItLEGRxG4MoIzjzgYHsKRatJWq5VJaAjB+sAI9LzVauU6MBZj3hpUmsBi/pDR4+PjxOxVFkh+cH4kuo2esCafsvkMQfrvMWK3SGqwVdoEO+tYSQnmtBIFxiARzW3NNfFhv+uqKK5tH7TLt7mf9tP0y7YWWWUcu+z3roonH17N/UmKomeuuKLf2Avus8t/Vh+GTvH56gN2+QETxxFb8tlVmuAdEzDYRxqJAReKVHKPQouI7XED2Gnsk/XEY2cd/LkqC/abzIOLYrzGlZyCeCOmdfEGfQEzU1ln3XblGPJI/5AzrmeMc3t724g9fvLb9whmIppnfTibuVptD0M34Oz1enF5eZn7Urvdbvy///f/YjweZ1BbhbTT6eQeajK2EdEIkAjM1ut1Zn+ozgFk46AjtuQE12EhrYQRzZI8BJFqDyqqyBQhgD7AfTabpfPmKQb9fj+dE4JPkA5IJ7glOAD0z+fzJGYwEgR9OFWDJLJm9K1m4JhfZ+5ZSz8qlKfekMEZj8cxnU7zIFaCL5eC393dZaDC1kSqYkxIYSxOT0/TCdJPSDUfknt/fx93d3dxeXkZ/X4/gxqyzlWxHeB4K6CV1cRYq9XKSpGq5D/F5nHULBBO0WMEbNg5sobIDZ/H6AEUIVsMTmazWcrj5eVlZlA4DJEKHsr40bO7u7uYTCaNqgvWv9vtNs6iwej6N+CEp+pBzFIViX1AP5Ej73tH1myYHeQ7mKUhQ5QLGxjajhBkGkD7uw4oIdzJntzf3793Lgmfw7G6MgawT+XSbDbL7CrrzRwfHh5Gr9dL58oa+fpk6dbrdZL6g8EgfvGLX+Qjqo+OjjKpQMUWZ0t1Op18WlDENlFg4IETdcWNgzUHCQafvO8kw09BN2sfmF/6bn2JaJ5X4nF5bHzGiRSaCQXm1gRBJQ+cEf1QcO/zomw/eN+Zcusr491sthXLflQzYAq/Yl1BzpDzCmQZO0kOfBw2wEDYAQVZWwfI6DdjZjz0hc8a5DKX3qbOerlCxEGiS+UZ02q1ajzFjOQVttV2zWX+JrMsLyYB0XPsDgEtpEZdNwf2ZJkNkmkObh1AuV98rsqUm4lHg3C3XUGtwbyDgA+RVJ+iOSABCxrYM/8kCZENsCdY2TjEOlrHjN5zHewxFf4OQpFFDtlG57AVh4eHWa1kfPD09PxEPA4v7/f7ecYq8onucZ8a/EZEkhWQSmDG0WiU6+ZKRB/KboIH0s1EqvWMeTK+2UWsMEaTuLuIpoqJfP16PdbAgdx8Po/ZbJbj7XSet6P3er3URcaILlPRzRmQfIYkG/jaNpRdHv1+v3GOl2Oq6hsdvGMLneh20gdbi7xgCxzvEPSD8fHp+Abu8Tl0MSLim2++yaf+sq7YcOwzevb0tH1KO4SDfSsygZ00IVpJ54j3q+e4lv+nsf6uiIQYcD8jmjsMPkRG2Sejw3zfVT+8hu9D73jPT761rSH+5nNPT09JHNvnss4kGfHfvrcxjG0P8lt9Zo2xHKcim8yvY1fLubGg9RmSnv93zaO3uLvvEJoQcSbk6KfHTX9qkoqxGNtUvfU6207zXesi19jbe96CjQ3eRT5bRqgs59583rocEbFYLHJdLy4u4h//8R8/pI6N9sVIKQIbhMuLxGBNIpgQYOsdRvXdu3fxzTffJAmAc0chCOrMFmI8mFSCm4eHh7i+vs5F4p4EXtyzCrlfRwBx7jQHAAjOavV8CCR77judTh4YzOLf39/nE/cQKA4TxlBQ0bDZbJLoQZh9OPjj42MaVbOaKF51VjhNO3KCQBwnzpBrYNxQHjI1ZAg5t4r7TSaTmEwmMRqNotPpxNnZWXz99dc5ryYVaskqZAQHx3tvOp+HEDMR2Wq1smIOkMNYnA1jzqsxwwCzntyT/139YuPxU2k2/BXgO3Cp2RlkiODKLD0kMk6Uz6Fr7XY7Tk9Ps+KFtWq32zEYDGI2m0Wv18vqD6rusA8OQGezWVbCQYJhL3CMZHMJbnmNe0Mc3d3d5bxAUALA+F6/38+n2rTb7XS09B85pTm4RX7YIucnXRIoo48+S4uANGILcLwfHhDPvENEGzDyuXZ7+5RRnKtBAOevcaj5aDSK6+vrzKjyXQjxVqsV4/E4bShEnUEaVZsXFxcNgL5cLmM6neYaDAaD7NfJyUmSZdiSk5OTtMdHR0d5KCxVXjy9qNvtxnw+b1RVVOBSAQ/24UtXSNFqdp7fJg5ms1mOOWK7FY21wBZvNs3zYgxcsF/4Qj5vUtFkqsG6wTb3tG1FNzgnxVlH3xdCijVA1pAfE0HYHcZDMGQQxthcBYb+mKipZLq37yAL1klkxyQWesZcVuzC3ybF8Dvuk2XUc2TQzFxwLZN4rVYrXr58mZW+2C2TTSYCGCv2mUQSSQACK+MaxuJkE+OsWe6I96vQrGfOSlc5Z36/r+HvkZU65w7CbN88z3WuPxcp5XVFHh4fH2MwGORnTAAaQ0FIgi9N+no8DqgqGcLa81RlbPX9/X0+rRafiZ45KN5sNukr0aHhcNg4V9NbgGyvGBN4NiJiPB5n/4fDYfz85z9v+BZvq0V3sB8Q1GBZ+0VI8aor6I/xGjJsIhl9rwSm7XGV+UpwVlIAnWB+IiKfdIvvm0wmucuA+0NYV/IHn//VV19lAt1VSU5GQOCBi7A/q9Wq8STESqYZa0dsK2SIBbx1noCW+eVJ39yTmMBnRXp+PmQLa/uxdJUnPvI0Tts2J7oYO1X9vV6vseW92nqwh7FgRPM8uYpFPCYn1PBlrDt2E1tsHbPMck1fn6Qp9sU+iPuBzdw/7BB/29dyLcbO+iN72Dj7PxK3vq+PkcEvMxfcj9exZcyD8S1yv+u4FJNg/r6Jecshn8fueacM2IvPICfuuxOAzI+PP2F8xjseJ33jPebe5JIryT1nrlh2Jd16vU3mVZ2zXHmenUxCN7gnfaba3fKJvhCjmN/5ofbFzpSyAfMeXRQe8Eeww+CofPBkA9IJ2lAAiAacrZUcA+IzKthqYmWkv1RfsZAoasSWPTw5OYmIbSAfsQW5lCST4SaIpD8EYwglW+JwsCw2joeAj4ASgmW9XjeeqsFBqBwIOxgMYjqdNoQf4QVcuBKDx2wzHp40aCFbrVb5uHruN5vNkuwhc+ayx/V6nQQGwQbjfvPmTUwmk3j16lW8fPkyxw4wPzg4yKqc6XSajtegJCIyAMHYQkz1er0Ednzn4uIiK8+sxBHNR0czbwYxvAfgciBlI/FDgPtLNgN3O0YHuM4E2bAyh5BCVEKZuIFM4vGwgGsq046Pj+MXv/hFLBaLrFg0McPPcrmM29vbBMdUB7HlDgPos5VYL7anISsEZt4ai94ReCNz3W43y+eRG5MdZHLJcqNH/I+cM79cAxny2UkOBJg35MoBvMlSAylIM2wU8oeNAOQTkNOH/f39ePHiRVxdXeXTBiOiYYvPzs7ykds3NzcZUEyn0xgMBgk8CTCPj4/j9PQ0ZYBgiPmpQMD9oTKLv7HvJvWp4PSZC3aaVbYrMeOkwfeB4c/VsL04dgJw3oMwrSAIH+mgwgCI9WB+HIQ4WeJt2iYcDH59bwf1yBVgieoKV1ryQ6W0q8AcAKKH6AjryW9sgUkodAW7VQGXz9dwtjBiCz4BY05QQFDVfjInJmmoOHNiw6QiiSOCTPsP+uJxe+s48w+5jY1Fj25vbxtVKNgkxoM9BntxL6qnHcCzftgtrlMJD9a8JuAc1DmBw3V2tV3BxK7G2rla17LpufT1HPQ6mPrUem88Yf1EVtjSzHusPz4Sm0D/PW6P1ddFfgh0WBt0Gtnm3CKOVKj6hX4ao3H2H4QJNh2bZbIaHE1icjabxWg0Sgw3HA7j+Pg4H6eOLI1Go0yGtlqt3CpuMo1Esbd52z+D7Zkb5tbYl/myrDAmdM+BpskefntNLOO2i/V/7CZxgXHEu3fvUrfBHK5i9W+S9ByzgT/ExkREoxIU3wi+clUPskL/KxlVEwrcw0Ewx42A9U0COAjGl2HXTFJ8XzOp96e0X/3qV5n0MkHDmOwbINba7XZcXFw0CGHjOGx7xcZguSoH9TeN69YqfyfPvJYuuLD99v2xl4yZuUdfbddNUpIgtA2zrDihw2vWMbb92ee6wgqdRK/Bs9WO+290HZyOTNp/VDzRbrcTYzMW2wS+4y131nNjVc8714toVrHa9zIu5gH7aJxfkwy2VcyJcZqrFnfJEPJs7BWxPcrE+Ic1QUeZJzCKk3vc37ocsSX7mJPpdJprEhHxu9/9Lv7sz/7sg/ro9kVIKTIANjAsvEGe2UCC3r29vdzSgaLf3d0ledJuP5831el0kowyQ8cWOTJubCUj24Bi44CdHXDGmMYCERC8ePEiDg4O4vr6OoUc4Wi32/nUsYhnY0eQRwDYarXi5uamQQ5RpYGwkslG0AlYCcadteK8l4itch4dHTWy11RrkCHmaSqAUQNjDi5D2HkKCACB7zBHrB2EHeehcLBxp9PJwB0SkWzd27dvY7FYxMXFRVZ+2fhyJtVm81wdBslnA4vRZZ2+++67ePHiRfzlX/5lAiue1tbr9TLjZIILgw6YswLiYOkX9zJAtzz/VCoy3DBoyDuGBsNjB2HDiS5ylpSrgtAvAxgHKQQ8EJ8ALAfXy+UyS18Jxplb2HnOggMI0W+DUtYF4tc6fn9/36jeOjk5yaAPgLxer+P09LRBJkGeoBvclwP8uT86DymG/tUAz3vU0WW2TVi27KRZs4hozDvzRNCADbBcR2zBpu3cwcFB9Pv9+PM///PM3qLfBB2AG57Mt1gs4vj4OEkrqjcBNAA/PgPhEBFJAnPWBUQDtt5yAxihqo4MW6fTyQoqO3ecdiW+LPe7gtQv2Wrpu6uH2u12+ilkPWIr77vIHVdOYLPwS3wW+YVEotkuRGxBofWA+1fCwXqKr8CfI9f0j+uhS/heA0+2LAKUbX8ZH2Szt/oYMB4eHubTM31vzzXy5884YKoEC7aAPuOzTR5jQyK2ySgHdbzH+OsWClejGXibVNnff37CLnPOmBmTbSQgl/tgB9FfE2r45vl83sha248xH87AmxjhdQf4uwIDv/9DjbWh6pQfZHCXrfR3+f0hguzHbsxbDc75/fT0lJW7DvL29vbykGXbKJP6lWCDIDE57d0C2JLHx8cYj8dxe3ubMulsvPWLreCDwaARJBM0Q5TzP3YZHZ5MJim7l5eX+V18RERkghEbCAlF8giZNZ7H5zEmCGSTrugZOxk8Z1zDRDCvoZe2rw5srcP42u8LFLEfDkYtD17Pm5ubaLVaicchsHhgD/fC7pi8BCtFbCssIThcYQhW4FiEbrfb0FnjQFfM2CZWoo0YgvMkvZb0jbn1GnmOvk8nud6f2pzEQiew/ayz9QfihCcPM3YS8vgzXgMfe12Zu11kpckYqr7BtuiQExMUUhgTc1/HiPgnfJR/GCf40gQPsfVischD8u3zWSvkH1lEvsDxbNWkX+zaIfZzfI8s2P9VnfQcW058dIQxoN83YeZ5x87wPvpwf3+fZHrtEwUtNHSDH9sM5t5b7PmM/bcxnfGPeRHkCX/uOajXNL7wa8g+Ddlz4s+2jXsyTgpe0F9jmL29vYwL6D/y9Otf//qjdPOLkFIHBweZPbdARmydd8Q220aAgvFAOQlwOI8GYMKZKiyiM4tMFHvnyRAxyRgf+kL1BQ4RpcAwuyqKbUR3d3eZMV6vnw9KZjwux+z3+/Hzn/88ut1uXF5eNpQCogSQibJSIbTZPD/1pNfrxd7eXszn8xgMBnF0dJRP6YjY7tfHOEVEBugEN5zfErHN3kHIEEwDTgEHCOJ0Ok3ltqF3Fpm5B9wC3vv9fhp1s/fM5+PjY1xfX8fbt2/j9PQ0Xrx40dg+ZeA/HA7znCjWnYaxxknyHbYOcSj0YDCIm5ubnDNne5k/nAYZOpe6A4zMoNuBW86/dHPAWQN4DK/BLmteM3ZkTdGPvb29PCuIbC56yn2RrYhnneAJbJPJJNrtdmYHIUEpa3eViLfo1IofB7gYe9aTcaDDXIfzrCIiwRmPVj47O8tMLWvqzEFENIJZ5snEMURcp9PJw//RKYgy5MzVMpxlAPFpp4Aco1tsYYAgBtDYqeAgsBmAfPqM83zx4kVEPJ+9wLZa1n02myWBT2YFoNDpdN4DHN42eXR01DiU3JVArNNwOIzN5rnyA2BGOTBb+VxFsFgs4vb2NgGGSWIHHsisSXXalyajaAYLzgwiz9jKCmA4s8J7/Xmv/kRsQVrNSpqoQo8MrlxlvIukcrbQ8ofvIIBDLiK25zFyMOb+/n5jC6CrL5bLZforbATzYwLOAQ+2niRERDT8vcdgAsifcZAHSHUCzQG6CTjIAwNrryuvGdhhi3mtkqwR72dBvZWYyhTG4G0ADoqZL8Zyf38f19fX8eLFixwbiQd/D313NQ2BF8GBg1sHFh6fAwfr6h/asK+sda2EqXPt+34OUop5cyUijfmB2GGdHQRBVkE2mZjdRbwZq9YqbZJqnP2ELpk0dEKOe5+dnaXuR2wfEe4+IA/4a2w1W9QIViMit9Bzv/F4HIeHh+mTiA9cycF9sAPYxPV6ndv+fDwFNqASz/gtY5uIZnWTv1t9vclEk4p8zzjKdrgS2k5GQdhxSDh2iipGH01gAgpfBnG32TzvnMDeYj/a7XbiD2+BxueS1OUcTubDFXb44hrk8j9j39vbS/LS9tWEKGtYiUbHDJ+yOWgHP9ag3bY2YrudsY4bHUJWTHzwu1Yv0kw20B8eKuM+sV5PT095XqjP8bQcO+7guhBRrpYiFmV9jO2x6yZSkD1wsxO8JniYG2TbZ0pxbe5prF4JFuTWyebqB5lXrgeGttzzecuy9bwmMOwTIfJZU7Aoto/4hr4gJ8g7cg1+8A4rr43X2LEX46txFzjF2+kqGUYsZDLTNp45AKcwTpKT1lXW1vNW7Rx69ObNmwZJyr0+Vq+/WKWUg1L+xkixlYztJi4PRbjb7XZuSXt6emqcicCEAn659nK5jJubmwYR5eoNGkIFSIjYlvSyMAiklWaz2cTV1VVm8wnMz87OkjRj60u73Y7z8/OG0LKdrdPpxFdffRVv375NJ8H2RAwO5wLgaMnIUo3BNkS2SOCsyXhGRAIOnM3x8XHc3d3l9jYqYJg7wCdjtWEwUUPwYSBs5Wb+/LQsZ9VNamG4yOb94he/iMvLy1REBzsc6gxLC2HEWvL3crmMf/3Xf43BYBAvX75MMHR8fBz9fj/P0bJxt1GyU0IuCVxYD1cPQPL9lJrXz0y5jaYNCvLvIIPAiOzXcrnMA8MNwpgTPmNdhWQCkDHvVBTu7T1vt5tMJnFzc5PzvNlssrrQWaNW6/lxyshEv99PUttnplVjD0ka8Wyfut1uVnHt7+8n8CCw8DYl+u5Mgx0zlR4mbnCcXJN1wAbgpKkoRQcZA2vj6zpbiR6YzCCbW8/0Qj6Rb4AaxBRZ7tVqled8sTbtdjszaRw6TyCLXFHSz7gMrHnd5CKJAbaTcC8CYrY787hmr+ne3l6SnHU7EHpag5GfCiFVm+UFMpNK1RpM1+BoFzBFZ5l/2zATTdh4/KKBHfLBa/QtIhq+lj6xrYQgFRnDllDFd3Nzk9trTf7a9mBLuLZ9L0RIxPvBAxWVrs5mXkwscV0Hb+gCoNLbKQhwDWhXq1UMh8OI2D5103bVVV4EGDQHdSYnua7vx9oxl+gM1+VeTuIZ0DtIIsABcE8mk7i8vGzMM/MGPqFPDsSQAfqGb3CAx3xXzPWH+EcH+jRXE1iGamLlQ4TBp27coxK8Hjc4BTy2t7fd4uoqDst8DTpMDDs4Zk04zBys4kQpZzzxP5WFFSc7+HTV63w+j8lkkhXGs9ksz0lF1iErqMyhnyQaWL+vvvoq/ZbJTs8ZZBe+uvoB+2fG5bWuhLwDYQfANdCrBK//j2geXMzcOyB2wOx7M4/4y8VikT57sVjkE6sjmmfDmSyEfOCJpvhUcBvzSRxFXyHGbm5uotfr5dPKWWsT1PP5vDF2k2zYG6pNptNpVntxT1fCOoawnnzqZszB38b69o2MlXjJhJRJOmO2SkIit/aZJleqr6I/JtGJdyGlTAzWmMQYnTUxHojY+umI7bZIk2T4eXABthz74IQTiU4nB9jhQBzqPnJ9dNSFEBHbKnt8P/piuxaxTeLxvquA+Dz+y/bOPoHfNdlscs52FTtFYpm+M/fMi301smFyh7FxfQ59Z058P2SmJpqJcavdsu2yrDphZNmE5Oda2BrWCfvA/4yTH3zU27dvG1uIGT9xy8e0L0ZKeQ89imPWnBJVFv3paftYSoIVgLWZvojtIxmZeM6pIbvvPbJ2RASc6/U6SQwWIqL5+GxXQwyHw4aRYfHZxsO+b8i2s7OzBKdkIV3GeH5+nk6FrXwox2KxyMdnE4AxTpwZgoczOjw8zEwURgYnD8vLvXHwvBYRKawAX7b/oTStViuri/wEAq6JkPLkI+YH4wRYWq/XGeCipM5A7+09H2o/m83iq6++yid1YTwgM3q9XiNgZbsg68N6/9//+3/j4uIi/v2///e5hfOrr76KyWSS48FI0l/+9npjNEzORGwNfKfTSSfyU2rVIUZsDSrvG0yzJi7lNIDloGnOc/P23M1mk4RVrSoCcEVsMwWtViu31lIOimF2KfFisUhnbUNNmbvHZEIZYoOx8jRK1vDh4SGurq6yssel0ciBs0cYZch0B/YEiLzvoIz30As7R5wG4IlqLjs7gkTrFTJYq0Io1fdTISHucOiMCxJvf38/fvWrX8U//dM/5eHkBAL0g8oW5mA2m8Xj42OeE2JnydrxA4kNScGcIXM4SCql+Dxz1el08nwx2yk7wOqsmTPmypmmL91McDC/9J95cBDigIDEg4Glg3DPPzIB6GEumaca3JqMrve2TpBBpaKJJInBm89DYAsRW/MIsrgm5KS3qPveyBBA1EQr/s66wrx6/Aar+CkIZe7FdSxbdTss2KWCf4JKcIrBeSWYwAX4dBM+fAZ7YAIKPxuxfaIwmIL5gVjDV3oc9vmTySQODw+zYspjs5zWDLH9K32vFWxVvpgPqp2+r1UidlcDB3J+WCVwLQeW5U/ZrC8OhnjNJCc/ECvGwk5oOLCIaJ4bRILE9wEHUxGDLoEZIyLOzs4Sc4NLIaNoriAAt41GoyRRsL9ch21/vV4v+/r4+JgP1uD76CGV6/ifbrebJBp42vpkHYd0rrK6K6A12U6rBKHnFhvJ35Ydy7/XycQWts5kPtiRz4CvIfCpAB6Pxzk2SGOqqfr9flZx7+/vZzzAEzrB0ySA0cFamUK8QHL59evXmVB3pUpEZGLdgarjN1eYnpycZOIRPG/yxInqz9mc3KQhH9ZXbC7Ve66a4XP4NZNVTuQwP/YH9vPIKqSffS4yMp1OM6axXppYxPaBg3nNhIRJSq7F3x67K4YjIv0ieu8ts058OUnjLXHYPWJ3nydpgrsmQegzmN1zxj1ddRexrQJ3MQDXcIUXa2E/wfjMDZh8weeZMGM+8anMt3dNIDf0LSIa9/FcmoSKiHxQAf3FX7ZarcbRJl4rZM3YB1mulfaWQZ6EjRzt7e3lQ9UYa02E+fq3t7fZT8ZoP/Ux7YuQUhYo/rfDZvLMLiI0Bm00yKTVapVn0AC0FotFVkd5D7X3WLNFD6UywMUg0ah2IvAGwLKNzULPljBXBfAIdJzs3t5eEib04eTkJJXMFUsY/YgtIYBTfnp6yqAxYisMEFq9Xi8Fl+w04AdgTx84v8mPnkbJqF5jjjudThI4sOiez4jIYJz+4nC9v9lPT7FCIfT9fj8NF9d+9epVPpUQA4A89Hq9uLm5iZubm3z8rMEAffuXf/mX+Mu//Ms4OjrK7WP+HME7xCjyYOMVsQ0gnV238/khwP0lmrN8bnYwDvrqnCBf/X4/P0MGFCfIPJHhI/jBKVGVxpyjTwSqzqA9Pm4fJmD7MBwOs6ppMBikrLP9LmJLdkVEksLYhKenp7i6uorJZBJHR0dxcXGRhCT97HQ6KcPoJvLqTFpEZCDOHDlIxVYB/nid6+JofPA5QNcggiDdNsoBF+uB7XBAj724vb1NnSaIo3SfalLs2atXr/L8OO7HewAgzgri+qvVqnFgPcAauSA4IpCAFOOMwG63mw90gCjs9XqNp7V4Xk1y2245c+VAGDlinX4KzQEOtgiyjfnyOtMMojw29NiBWUQ0QBh6wRwYYGNXsV/IkAkvy3DElsDnN9lfyCX8xHr9/Nj3yWSS/SfZYdBIkskHvPsQYO7FmpPld7Ur/QNQcW0yhCQ+PO/VPvKa54JkBJ/zXDE/Bpi8B7mKDTN55uws/sTzbrKK5Jm3ABjAU33MHGJHI7bncGBLmcO9vb0MfLFpldR3eb7lFdnikGPLFTYIGfec+rUfapY91rE2xo2tZV2qflgnPmWrRHElOOkP2MFyTQDgbaAEUxFbnErmnmvZHnIINrJgvQWnHBwcxHA4TOwH1nKAwucfHx/zCbhUpuNrsPveZo4N6Pf7aROoprq9vc0dAQ6+sAH1PXCCyQAnwtBf7JFJeNs41qWuPzJhn+x181o5ELUdrcSj++NrgS1NJK5WqxgMBnl8Bck+znqN2FZhXl9fx8XFReIexueAuN1u5/yRaGf8TvoiK2BzEsUcm0HfI7ZbfmwT+Zv32L5p4soEO/3w/O/Co5+igUOrDtpWGdt3Op2s3necSkWJ5ZY5sP0ndmPNHfNGPPsDnuznMwHv7+/zwT74TubQa1ZtKLbZlUP2k3zH/gVyCtnn2m7YJ2/Xti0zYWEfCs6o1/VnXCGMT/WYwA3E0lQWWVe9PdeN61tWGQ99pf/L5TKPh4HQBc/ip8Hovhbj4TrMqZMyrEP1XayJOQ8+9/j4GGdnZw1fzHfAO3XOTaZVW4dPwVb6KBFIqM3m+ZgOZJuYYzweZ3ECfAG+1gkPz4Pjm49pn52UOjw8jJcvX6ajwElCEBEEm7FFabyNjANDZ7NZBnIEkgjLu3fv4vr6umH8ut1uBkFMFtllX384HOZ2NAdOLnO0QYCsidhmFVutVhJMOM3z8/NcICp9Xr58mcL/+PiY++oBEZAqkFtUc+GEUEpXNxC4UdnCtZyFZhwRW8fpgI7zWiIihsNhjMfjBhOMwnF9xu01WK/X+XQQjCjbGsns7u3tNSq0UBgCZWf0cY53d3fx+9//PiIi/vzP/zyJDGfh7VQclDDm5XIZ3377bbx+/Tr+3b/7d3FzcxOj0SguLi5iPp/H7e3te8bEgZgDHjPpNvpU8/whTPHnal57B6cmOSK2gbLPPYBk8JY3yF6MMeATwwaoYg29JQbQ8vDwEKPRKKtt6qGAACUflkwF0ddffx17e3v5NE6cLFvv6N/j42NcXV3F69ev32P0TQYPBoM4Pz/PMXEfBwwmYyOiUeVHdoHXuQeO0JVMOGGqlFgTyBaAuINBiDhsn7Nizui4GsPzDVEAkDC4r+dAcdA7QIFtGujDyclJXF5e5tY5qt/QO2exATCsD2Dt6Ogorq+vcxsJdhWbiI2EQAZAmPymP71eL+UFHXbwYBtmXfixdOpP/T72gwoDtu4ZQEe8D2qYTyd5GH8FTyaaeY31qoAXH8z3DC65Tq2Gwp5zWD8yR8ZwOp3GdDp9j6QAKEVEbhVB9iK2gQtgiENhCdR5n0pIg0ATdOAOB4eQo54vMrHYcfrGZ6iUZB2QMWyqfSXrw9ygE+j+Lh1m/vHFvocDutVqFaPRKE5OTuLi4iJ1FV3zGnAuBtdDZiC/p9Np9Hq9HKvJHe7NXPG/A23Ps+WT9avz9EM6YRn+WBLp6ekp7aPPcjJwt3//VM1EMHYdHwuupQrIMkXlhElIJxgcfFimHbRSuUolMvc3mYj+sJ5ORrG+rVYr7TqEyHw+j9ls1khQMB78pc8+ooId0iUiEh/6wO2jo6PcmseDR5zAQAYJiIgd0JdKPjkhZX3kbxpzaBmzrFbyKWL7cAJXSpjEMq42sWz8jX3BNvIdbCO43ccQsPOCKjJiFSee6Iv764eumJhhvPP5vHH232w2i/V6HV999VWcnp6mLeUJ3yZ17A/W6+cdH6PRKG0L4yPwhWCz365B/qdqnn8nUfBFtsMR26RmJbEoLsBeV1vL9e3TkCP6gUxRtYj+rFarLKiIaFZuWfd95g/NfWdujQVolnXeqzbcCSIns8AEzIfJHY/T1TSeA2SMebDtQl8ocvDnjN3qjitXmEZEQ7e8prvIT68Z/UUeTEJz5hLrhY+N2BZSYK/QSVecmtwyUeTxMR6257Xb7RgOh41dKl4jxue40wQp74HPsAHgOBccgKFGo1HKyGKxyDMIWSd+sOmj0aiRXOO7Fb/9UPvspBQlphZqBIuAj0kiC8mE10obDOb+/n70er1k+GezWXz33XdJWEVsy0QddNqxY0jM7J+dnWVQSuAW8f6jtnGUzroyLpwmwID/qdoaDAZxeXmZ2/UQHhyNtyUR/B0cPD9OlwOIXbIMOcbfEFM+GyriWVgADVZUAj+AMp9fr9d5jhXBO+QRpBvfIQhZr58rZK6vr/N1glPGhmKg1MydHUC9Lsax3W7H1dVVPDw8NA5Bn81mWeo6HA5jb28vQTjZYQcW//AP/xB//ud/ns7y/Pw8lcqZb/rE/xgQqlmc/bFz/ikSUrVhHB1c8Zuni/C5GsibeYdIdPWiz/ICfPN6xHbLzXL5fKgnW9W8RQUyazqdZp8hni4vLxPwwPJD3JrQ2Ww28e2338ZkMklnjn5ERKPC4ujoKM7Pz2M4HCbYJ5uALakZZOQAp25Hi91BThyYODhEx9jGQT99z4jt+SO9Xi+dE9dotbZP5cGOmjyzo4BgBAxRuTYYDNKWYDv+P3f/sSTXlqRnwx4idYZKhQRwRInupug20mgke0AOOeGYN9ADmvFeyG/KEa+AF8AxRxzSaM02tlV1VdcRUKkzQ6aOiH+Q/+Px7IUEjqgCDqq3GQxAZsTea6/l4vXXffliHnZ3d5ME5h7j8TjJZp4znT5Ub6JrODvkx/phuwKZB1iez+fZV4ygHkBA8GPywQShAa9JZObpsZ//Ptfv+30DSWfcqIpxT5XHvssc8MefNSlqEAIQc1m/dZzvuFKGd3WQWAI+ZNefBWQOBoMYDAYREW9th5/NZmm/qczjIjCjB5wBMHoHmLT/YMwm1rze/Lsk0sAg+EZXS4JFTB6buGUMzAW2Ah0G1/D9clumfRDvYiKHPpTOXkMeDIfDaDQauc2WLfS8R0QkDmFNTdSgkw5+WJ+ymo9AGTnBBwLGrWe8G98tSZLHLv+uvMf3ucBSkFMOKj8mKQWGNAGIbCJPXuOVlZV4+vRpyhTzxfqbaCfhggzix87OznL7BWsP0dtoNLL5v+XQwSd2lp6OPmTCxA5jG41GMZs9VG2RkKXxuKu+7u/vY2dnJ/uY4qvoZQbJ7TVDZozDqJwmaYIdM9HuQM1ku+2cbRhrVcqt78l7sw4mtPg+PtD20j7ZmIsxsnsC3WEHAbbVlZFLS0vppy8vL+Po6KiyBRP7C3ZDz0g8gbMINpFN7DSx1d3dQ68Y1sP39XYw5oax8y7IAyemR0TGGcx9qScf+rLN43nIlHUT+0B/Hew8eJLxci++Y4LZRBL4NyIqsscaOZEHEWBMgG+xXHm7V+k3sMeusLJNtxw6TnGcCIaKWPhfLv7NvJmksp3is46f/DtknHEhZ+gsOjKfzysnrpqgNhlvghgZLMkSP5O1wHZwX2wPuJ9/m5xkNwVjpTUIXMD9/X0m2Y1RkBP7Aa+fMT3Yd2trK/G4cR59dI2FsfEUqdjX+g+HKRnDTafTTDyDOZn7Mjk+m80ypjLxyd+2id/n+uikFAro/dUIkLfmGSggnPSJMXtpA3B/fx+Hh4dxenqa95zP51m1Y+Nu5Y9YsPXtdjt7MhA4w0TamHiLoNlNyi3ZkkDgHRF5qtTa2lqcnZ0l2OTkvOPj4xSmwWCQWSkaxO7s7MRoNMoeVmxfYGtDrVaLnZ2dePPmTcxmD/s7CV4hgmj4ipPA8SCIgEwUEOPgknIYaissawBp1uv1stElSjcajTLbXIIbFMZzbkePPKAkrPf9/X28efMmzs7O4osvvohGo5HEBu/U6XRifX09zs/P8+dkIwg6BoNB7O/vV8oa7Qw8Bo/pMaCNPE2n08px4J/aZUbeARH/j1g0/jWbzuVKGQNBAoDr6+vK8cWtVqsiR3YcVARSpo4cRiyIQYhW1peqx0ajkd+NWPST8Xa309PTNJz1ej1P2PMWD2Q34oGgAvAyN2xFw9BHVJspmpCybTEY5v15Z7YIQlKjW9goHLmr/CDfIhYBF/aT9yNA4b5kJSMig/2IyC09y8vLSchTjba8vBztdjvOzs6yKg7Cd2trK1qtVlbwXF9fJ+j0li9stYlq9AE9u76+jtFoFGtra0kis5UW52sCEPBHZYmJKduFx4Ldx4gI1uhTII+3trZSTkiqINu2Sb5sg5y1dzAUUfWXkDXYKQd85X1ZT4NLPodeRkTqMDYFOTa4u729jX6/n0CfgzdYa1fzQpTyTuvr66mjfB6ZQE6o+IDQRSbwYdgDCBbe2ZXT3BsynLnEHjjgQsYMznkH5tC2syRJ+Ty+kDln7viuyS0ueneZbCi3BF9dXUWr1Yqtra3choCsY3siIm0n70CpPqQFiRywhokB+2n8OPrPOjghVF7fp1LqsX+/72dcBsjuRcrPPpbeW5fsF1yB70DIgQq6FLHwuRHVnpWu8kN2+v1+YkDsZK22aIGB/fXWDxNm6J9tBNiYPn48s2ycTgAV8ZA8ur+/j+3t7TwEgF5DyL0DPpI/4E/kq9frRa1Wy1M62c4PWRJRPcCAe5r4Yc1NIDkg8+ddhct6lbqLL3IygPXGZxkbsG6eb/SEdiDY0Pv7+3j69GmcnJzk88HuzBsBcURk1QJygXxZvk2YEzxTBcp8QGCAoZjXi4uLePbsWZIAa2tr2SsVP4++8V7r6+vZzyoiMhajt6Wrtd6nx3/oi+IA/KnX08E2ckg1FPJhEgqb6jiwJCmINUryDt/huUTXSbAyFnTRW7K5p2MU4h7kl/W0Hvu9+Zv3LsfH5aQidpN7gikYD9gzIiq+C7kjzuN3jMnxvHXRxQGOvUxwlWRLiZf4jPUjopr0cSKFdyJGwLY52YR98vy4j2pEVPCAY112MxnLWIYYm9ceHERxB+OLWCSZkNuSDDL2NvnqOB27x7r2+/1YWlqKbrebv+O+yNf5+XnyD1yWR+xdicXfdX1UUspOz8KCAGCcAZ0mq9bX12Nvby9JnIgFKLu+vo5+vx+vX7+O4+PjdOaPVQvYqSBQs9ksS5hR4LL8DiF3kGTlu79fnB6HwyRojlgAEEAgzpT9nASDk8kkGyu7Uqper8fFxUXs7+9XyDaCT8iuu7uHE9DcL6fb7Vb6ccCoRiyy2RgZhN2AJCIq1RKsWcSDIrrpJAaRTO7FxUWcn59HrVaL3d3dVEKOcKdSy9v5NjY2cg25FwbCYzV7fXNzE3/zN38T+/v7sbu7m2QaIH1lZSU6nU5cXFzkXuTZbJZbgyaTSW6HvL29jc3NzTg5OcnGwjYyOHackmWB3+Pobeg/1cvZzpIAYQ0wjvyMbXk7OzuVUyPo1cac8+5ra2tJULlRNcHCaDSKfr+fculSXioMAdCAKbYyQHAigyabrq+vs18U2WiAGsCaMaFDW1tbSVoB+AhWsSHMCTrBvBBwOAsVsSjZNZmCnHBZJpEtV1KZkKNa5P7+vnJSITIPuYW+llUr2Aps7v39fdomqk0Hg0F0u93Y3d3NDDg2qdlsZm89gl8DEsaA3by9vY3hcJgEPzYYp2iwwxaD0WhUAfNk3CnH3tzcjOFwmM102f5sO4Yd4r2tpyYTPzYoftf13/7bf4v19fX4X//rf8V//I//Mf71v/7XSTYwdgelEdXMp4kY3tNBl8n1iAVIQo4NXAw+eR6EMr6E53o8rloiAOI+FxcX2YPQ446IClkAEJvNHrLSVEr6974HwT334H3INAMUsUf4DWMNzxX2iW1FBnroOAQA8o0fAwf4WRGRPttEiUm9MvvNuAhcwB62Nc7YNpvNSjNaSG22unY6ncqWYogmkmzYYuwU96HPHz93Egs8Z2BPZtakuOeg/Bts4qsEuAbq/ll5PUZC+2/wI+B+Pp9XCLkPdbFmrkZB7/x7fga5il8z1mCOsV/gSs/pZDLJdgv0EeJzJDTBT8gg8gc+5KACSLFWq5U2lnVnW7WrmKnAur6+jt3d3Tx9uqzMgCi9vr7OnqERi55kjAmfiU6THAI/t1qtnA8Hnp5XMKR1jp+ZEItY7ISwv4co4J48A313AG8b+1gC0z83idNoPDR+926Bu7uHFhMk6mj5gZ80eUbQPJvNcusfcmMZIV4Aa62ursaTJ09iMBik3QL/uzcldg6iybGA/Tf6fH9/H8+ePYvBYJCJMyqewRvYQ9vgj3GRkMfnMYf2f/gfMEvEgnTDpm1ubla2h0FeMScRix5otpGWi4hIW4kdLgkbnovM8zv7vNLHGwdAwtm3Yqd5lvUTfbKfKf2CsQA+ykl9k2LMneN87zBxFTY/x++AI3g+/7ZeY+eYBydeygM+nHiyfSh9sQsbbFt4JnEOdgkZYZeHSUMSOtgZ7BnzzvNte4zhkCXrNfEPusN2W//MHEiJfx2rE3OU6zkYDCo9vJAVYgZaMLB+xiyWQVdTftf1UUmplZWVePLkSYXxxCkwORGLEz4Ag7XaQwUTi4HizucP1QvHx8dxfHycWRwm1aV2ZiD5HZlXhJNSfEgWbwHCcUVExegCsF0+jcIDMAiUGV+v14vZ7KHnlQN4qqMQxqurq9jb24vJZBLz+TwbB7bb7Tg6Oorl5eXsIUFwWKvVkkihHwkOBqdnJY6IDAq73W4Gm+PxOCIWR2DjwHyqIQ0uCf4IDldWVlJ5rKyXl5dJ1H322Wf5M+bYQSiVXfybeXelCaQZxOT9/X18/fXXERHx7NmzNJKMBaPAPHvdyPJvbW1lME7pskkA1vf29jYDe5ypSRQTL59CBcZ3XTiHMjA30GWtcdIYJQgimlA70MMgAVzRCZPC/X4/RqNR6iGBA/ru7Eev10swA2DHiJuUubu7i8FgkFnViEWGEuCFnvkeBE/OYGOf3DMH2eS7vKerqHAEzmoBzjD6AFuAg50/tssAhgwpZIuJAWet2N4MgDFBAZgoMyuM1VVmbKN6/vx5VltR5YnNJNAlmKEJZJl5clYUsOC5AVQ3Go3Y2trKbUgAI/QJu255Rf8dNPOO2D8HBiYF+H8ZGP8U17//9/8+Aca//Jf/MknI0slbJ0zSlyDQ78uaAQr9e9/PPhOg9ph+8HtsAv7R9/SYhsNhAm/7YT/f+kP1Q8Rim0D5f8sE/+fCdjtgjYjKu/A95ojeiOhcxMKumwCzTXfFFnqNLvE8Z6it08YkDmQN3rE/JrmZW96NyoPl5eUYDocJ+rF1g8GgchCEiVmq0SC1SpsQEfl7vmPS0TbadsbzjX4jvyaDf2wwal1wcMfvHruMN6nO3Nzc/FHP/76Xg12TMvhFZMLkKnjM23Vt05Ah3gkyCd3hRLuSBNzY2EgcyBy50uf29jYuLy/zBGLWCjzI/0kIU3UFsddut5P8ZP3H43HKA7bMFTbtdjsxIjJFchpf66odfAkJJlfyOThFty2L6AN+AV1mbcqACgzIfdBL9NzfI2BmTVhX3qVMaDkgx0a4egcs/otf/CL7pZoM4pRMAl4TBm4RQiUZmBW7iv4RVHOqN8lUqvzR0dlsFufn57G/v592d319PQ954n7M22w2S0zNScbgwxKvWFfed71Lr3/oBVHhky15Pmvk9ylJSQfzZbIoYnEya8TbVWDMJXoL2edELPcz0ehnmMxwIop/Q1b5XVkTMGhEpL7wXFdi2b4iwyY3mAv7SsZmQoYkrHWF8ZT3KvE0c29f4nWKWCRw8FFgfOugiUKwPHbC+sy6lwQy84zdYuyMkYpqzxu40+9mMht7wfwgg/aTxm+sHT0hIRqNDxw32L8a5xiLeM6YVxNIYJHxeFw5DCriobqaHQ3Mb8nt2JZ+3+ujklJUUhgAU4KGM4b9M+PJiXUEWQg21VEw/ARzkDhMKMCL/6+vr2c/CpeyNpvNbKxIFQWlxRGLvhh8x6SLwShKCZEzmUzSGfC5ZrMZ7Xa7QsRBHDm4Oz8/T6fDO9FLJ2Lh/Cj/JQNxf38fnU4nZrNZZrNsPCC5MHasDRUHriZiCyFA5O5u0Rvo5OQkCUSAMI4YUoyqC46ePzo6SoKS9WTrENk29x4wM84cAi4Ya8TixLEXL15EvV6PJ0+eZBaYQHZ5eTn7BAG+Ia/cxwawxXMwBowD8O6gCuXFcPH9T/my8bVsOHPlYITPNRqNSgNZ9IS5pAohImJ/fz9P3THwm81mcXx8nL3hMLoGlBjYra2tdCT9fj+m02kSpOgGATxkMceTYsTJjmC8Gcd0Oo1Wq5WkZbvdzu05BK61Wi3X2yX02AwcVsRii6mbxOL4CNCwD2R+ISIcEDoQpAcDz0V/LV8mSefzeVZceouAg3acGk57Ol00X0f+AV4vXryIdrsd29vbERFJ1CIXbL0bj8dJfHvbF/N8cXERp6en2bOLDKKDKlfltdvtBNfYSuwbTfYBDJDWvIezf3bUlvf3/f+nuHgn/ADBAbppv4gOlYG+/zxGfBDUlXrNfBHYOnNn0sgkosdlosP/vrm5yWrUkvyyPySpAzEKWDIB5Awia4qdwVe5NQDjRx95Fu9ZEm4G9waezAHfgVAm4EXXHZhiCx2IluNHNktAzO8MVLEVDtYd+LBlurRXBH739w/NSO/u7mJ3d7ey/dVbLelPERFJ8HutmS9sGfOEzWZeTKYQlDgA+S7Aan9Uft7ErPXWQUsZXLAOvjenWn2Mi3lhbrw9HPliPTY3NxOfkWilcT/vVRKbrF9JJs5ms2i1WtnLFTk2uUQyFvJyPp8nPgXPIJPIODq1tLQUz58/r2AGcB9b0gjK8Huu3qHSiUCae0BEsW74S9YOYiri7Ub6/Ix5x6aZuHmMwCKG4PuWNdu0kjzw+vqZjIEWGozNpITlwzIdEZk0B+PSIJ7qRwJmAlOqSqfTaVYo3d/fZwwC0dVqtdKG8V1kylv92ZKPbDJ3rmZhCzb2L2KB39kxcn5+nu9NPMbY2Kb2fYLXP5R/ns1m2c/2MaLDNtpBOxiN98NPObgHZ+Iny/v6HYjtykQS/gedxw/aJ/N5y719TMSi9Q16grwQDzueRhZ80iVjJs50sv3+/j7xv9tAMGfovOfW4+UzPKPEI/wb3wPuRZchV4yDrPMRC3IQX8q7uorLfob/u6qN919eXk57AeHEu+Kn7U9Lv+QYoYyvTCjxPiarbXPco5dxg12QT6+dE0yeM//usRYgJBvX19czhqd9Bz28fR/0ivk0djQe+K7ro5JSGHxAp7OGvAz/ZwJ3d3fjyZMnlS17jUYjjo6O4le/+lUaUhaMiiMWNSJysZaXl3NbDmzw+vp6dDqduL+/T2fMcwB2lDG7RJ/gFafu3yNMJsrYntZoPPQ86nQ60W6307GSTYK0ubu7i52dnUqWx4wx80eDQoJFnJ/3Mbfb7cyM4YypRCGYY78+DscKWK/Xs+SdNaIJWqvVyuA/IioEE1sJXSFhh8b2QwLhvb29PIUPp8XfCDiEHKQC1VooNM/56quvYjabxZMnT9IQeR7ZyudjjFkvE6UmB8zcY+ggrExy2Sl8CoHu97kMCOw4IiIzorwXFX8meyBRMdhkGDnOmcDGNuDly5dxdnaW6+cAi8+zvdVBkY8axqjSNJ+eVC5J9v06nU5W6xEoYzAnk0k2TWctDTQhpFhT3petaxC1GGpnY3gvE0MA10ajUdm2iMOE8J7NZpV+MAaCyCDbJZA1ACjzBlFEAI9NbDabFZthsHN399DXiHuOx+MMHrCtnP5Tqz30COr1enF5eZmHMNBomfJf1gd99ZZeV5gsLy/H/v5+bl3A7nBUOXrG37zru7JHDiJ4PwPgMoD9qS5nEi3btj8Rb4/XZArrWWZoTWQZnJogYV2weSbACBAJdJ3VNFERUW1izx9XSpYViawlOo+e4E9dpQg4XVtbi1arFbXaw0lVPAObMZsttgA6++dqW+SEcfCO+HWTZt6WgMyiv8xrmXHHtpSEFLpooMy98ZGsJ2PhPqw3pADJFggMb7931hqS6vLyMl6/fh29Xi9PRuX5PgKay4CTxCF2wdlfsBHry/v7MyUxbAKlvN6nj2Xg8ti/yyDXsmwb/DH1HtkjsIp4qPKkWgsdomq9DBhZX9svkqLgPbb7EsCBU5EFcBXbsEjs8T3mjcCd/oX1ej1/jxz0er28t0lQDgsBH5kI5r3u7++TfCNwNvkL9sMWjMfjrA4yOYz8l6SjcZjtVUmyc39jbWwB/3cyDXlx4pS1ZZ7eZ5+5FzYBuYxY7EzA5qOTbKekkrPb7UZEvJXEAueCE/jZbLY4nbrT6eSWM1ekeW6azWY2TWfumA8IF9sJ4h0H4cYbfI4qaq8fcvtYlcqHuhgX8sT4HyN2wSTezld+DjvClm7Wlne0HJhMBruYFMRve2u4fSxzhMxaLu1nPC7k2ImEiEWVkX0dVXb4GPyeyVxjA8Zqct1JG3y754tnorfoqROxnnu/H//mOyYBncRhayj34XN8zzGjZc42BByP3K+srGTszJqQDHX8w9yBEbBZ9j3lmpikgtBFv022YR/Bw6yB4zTrIjiM+zC+x0ht4wt+xljR/6urqyzaQX5NChoT8n/jie+6Piop5aoSHImJI36HMV5bW0uHcXNzExsbG7GxsRGvXr2Kb775JokEQBwZmM3Nzej3++nQWq1W9Hq9iIhKTwcMI4y3WVcCVpeYYhwAFs50sq+SIA/HbUDaaDQy6zGZTPLEkOvr6zg+Po6IyNOvarVaBvqrq6vZk2V1dTW3n1F5RNCKINlJunqCbNjy8qJJecTi6GzmByUC7JspR0nv7h5OSnOJL/17yIjjhFkf+gbQn8aCPp/Pk3ldW1uLXq8XNzc3cX5+niDHJZn0zGI8rIuBzzfffBN3d3ext7eXxpX7NBqN2N7ezmzNYDDIajBkqnQEDuIwbtyPvkYOTJDrTyHYfdfFOtmJRSwa7JmpZy7IpNVqtewhRT8ujDOnNGxtbaWRIri8urqKo6OjOD09TbLFQQykIc1QYfGduSKLzHqxj3s2m8Xa2lpsbGzkelD2DmjAgQHcACUQaGQbGUuj0UiSB8fnzBZ6Z7KmJPZs13gPb2l0YMFnXf7LGvDd2WzREBZylrkFELjaw/23mGd0DpDhraoELFRM3NzcxGg0SjIaEsrAg0pCCHeCCL6L3kF2cZDD06dPo91uV3wB42i322ljyNqsrKzE6elpOnz63S0tLWVQ7Yo4V0CW2RqDlk9BVy0/DnjKcZWyha3yVpMyWDeYjVj0WDBwdrWaq8wc4GEH0GmIJoMT2w4SJxFRAXOMD0DrCzDmIM0NmamertUeGh9Pp9O03656KMvTuZgnk+F+d/TG2Uo+b5vpLYIec61Wy/HyfJOKyCVz4qoF/CvzD/5wc14IKN6T8QFMOe0HG4O9iVg0nEU3jUMglvhDhasJMaqKrS/MBz6aOeUz2Bd+91gw8NhlUuld/37ssi97130/ts4bsEdEbqdypcTm5mZWDdJrycG/fQ1rSoDCz4ypNjc3KwSr5YCqKJKq6C1+Ax1huye6t76+Hpubm5VgDQLiMXtKdRT35HvHx8eVihKwYsSi6tHYu9/vx/LyclxcXESn04mnT5/me7niwzbIpAhzhKw+FigxZuMdB3ieI8bpQBR7yRozN81mM30t7+jgsEzK1Wq12NzcrFR3kozHj1P15EQB96vVatHtdjNe4Nj2+/v7OD09jYuLi9je3o7d3d3EcMYfvPvTp0+zBQY4yAmhiCpWBI+w7pADnU4nbm9v4/T0NOeaRD1z8106/Ye8nOihwgyi1ERmmazBRo7H48SYrowBs1GpHbEgVkqCjmcgX+w2sA5FLLZz2c9HLCqb7YcYrxPm7l9krG8CwqSjkzgkhCMWhCk6xBqbNPPP/Z5OuIAXmTuTMn6/MqFQ6rc5A/AEz2c+eA7jL/GA9cV+2iQY72+iGHl/rJITXYyICjEFzjEJSvzDHCFL2NN6fbGTwgk85oE4CxkjmeV3Y36Mh+0jHN9FRMowuuD+giapLIPmVOzbuaer/b/r+mik1NLSUlYkmWmdTqcZ0CLwZpufPHkSV1dXsb6+Hk+ePIk3b97E//k//yfBmcEm32c7DVvIXIJHIIeyAUBR7OFwWKm0YPGZfJwbztykS0Skcyd7hMPkFBuqEyIeForS1lqtls9GQTCSvV4ve+64/8H19XXs7OzEdDqN09PTPN2PDAnGBaVhLIzV2WgU0NvvarVaZs/a7XaWDKO0VKfM5/P8HYabC/Lo+Pg4Wq1Wbp2jlxNCa1AEUIFgxKiwJm5sDbGJkWBucTYvX76M6XQaz58/z2y3WfPPP/88f47RxuhwfwdygCcDCsAbFz8vA+BP+Sqz4xh0jGHEArBxQhryub29ncAGwLm+vp6n7VBJRrD05s2bODg4SPLS+64jIo02vQ0ajUal8oH1ILDiSGNkyBVukFUmF535iXg4FZOtrgAHE22Qac44mTygMT+Et7c/mUyLWJT3uoKEgNCZBcaPHi4tLSUwjFgQyYA+2yF0n+oSgllAAzpPrw5XVdgWogPMAd+HjGYc3W431wWAsrKyEq1WK66urjJAjoisnqKcfDwex8HBQTSbzeh0Oql/rgAbjUZZjQaRhX1dX19P4L68vDhB0D0g6vV6nhxoktVz8SkQUhFVAEqVAuOMWJBLdvDIl9/Dvgq5IigxAOSeDgixA7aXfhb/dwUUNh0Qgz2hdx/jcrDp06Pu7xe9FfDbjPn29rZypDF+imoQPoeeM3bsDr6A9/FVBpc8AyIcHWNtANEGck5EoTv8G4LJa8AYsZWMEZIf/8o9DFqxKcyL18yBgbGOt9/Z9xOwRERWvEBQuKFyGcDYZjgYNtkIBrJfQW78eQfwvh4joPy35+ddV6nPHpt//0OyuD/2QvYJWC1XyAvb0Tc3NzPodSWJP0vgh37aniFv3mbOuuEfTk5O4vDwsJLs80EXbNuKiPQJ9I0EOxFogJHcAoFnI0+NxsNWubOzs6xOb7fbsbW1lS0zZrPFllRsBvKNnKIv4D7Lme1eGSSXa86/+T/4z2S5k5GuorLMop8m6vH56A2kmv0v8Q9rw5YbyzTrAv7p9Xpxf3+fp5f6HdgKOhqNkgDk/pBJ+GzGTz9e8D0YB/8AziV245RdJ9Pu7++TVMXOcGGrlpeXKzEBmAX7XdqAj+GH7e/m84fWD/gvV2yxtpCps9kscantsom4Ug6ocIxYVErxb55vPXa84ftbfuihyRoZY5og5nNUqpdksclWvhsRGaMzH6xRWeWHnnIh98YkzCP+gqR2iSW4aJWCztlW+juPPdfEFX9DPnorvecXv2i/6ao5r4MTT2DmkhAHa7j3K4Q0Pf2cUC2TqMxHo/Gwy4kTb/kZtpY1dtxa+gJjBna2OI7Bj9MapEy6g01ubm5yO3B5WY/wT5ZxdMC24X3XRyOlMIyQGRgrFtLltwRB9DYYj8d5AtSvfvWrnESqJSKq3fKpjOK4cgQEp8d3eD4VOaenp5WSXZdZI7goqrO2AG1Atfsk4YxxunzX++P53vn5eY7Pzubo6Cg2NzdzKwzzicBR7YEyEHw5MKPkns9HLJhMv8dkMklgzYlnq6uruU0HVhbFZgse4AcwXzK3GxsbcXx8XDnNiG1UNGK0c2cOIKbItp2cnCRgQk7IShAYe/vTbDaLV69eRa1Wi2fPnlUA1MrKSmxubmaWm2o3Ki9M1qGMNlwEIs6mGQQ5G/6pXzZeEQv5ilgAL0gCDDVzidODaGDt2Io5m80ywKFvBXKIAazX63maG4CJ9acSzZkMKhpoZm5nzpyzlqwfTfWRN5wdIL7dbke32412u10JBj0n2AvbAtuLiMWpVs44Qaa6vNZlugSaJrj5HE7Sxh4bxrw4g8O4nCW6vb2tELysiQNFqiOoOHUgi2ywRvROo68YhD5bpGmQPps9bKHt9/vZLBfHSADO9t9G42F7JfJAMPb06dO00fV6PftsYKOwPdg3wApkJ3JBcFDOzffN4HyMy1tATHCUQL0E8ialeC9A52Pf9T0eC9ys+8hBGehFLIJE5AV9vb6+rpR5GwjjHwmSAbAANWw7GTr0yPfBtwJcuSe/p8TeQZ2zt3ze+mnilucZFDsxwX1ZMxPR2ATsI37AVQjlZ7FnvCO4xcGCqzDK6i/msFarRafTSX+2tLSUFee8P9u1IAapUGabF5dJNI/boB9/DZ6zncKHlnpm+SmBvy/LYymjj5EM/vm77uO5N4j/GJexDUEIeAXf2Wg0otVqZWIG++uEgUl73sPvvby8nIfpUC3He9NrkZP5lpeXYzAYVLA32NHVklTTOTOOLZ7NZrmdE3/qvia3t7cxGAxyvjmJl8QCclOSufgkMvsQesahXMZqDoRNsPidbAOtl5ZL1oN3sq9HFpl7bIZ1ljXHBmCf+L6JcP5Gj233WWP872w2S3xE9YZjKiqPaLkBGUD/NGQK29vv9+Py8jK63W5uG8WuW8+3t7dz7jxfpRyW9gnbSCW1T7s08Wj9/tAXz2ENnLhwxRTywDteX1/HxcVFVqh53JY32xr8YondLBcR1cpl6yP/9inUkB9UsxN/sK7EkfxxErSsxiKpD3Fh8gsdZOzIDLJffg/5sH3z80goRVSTAcwda8OzmQuTSCab+K7nlHvbX1m37TdZc+Mn/D3fcdLcuBt9s0wZC0UsDuBhHGy3A+vM5/Mk8E0omZDjQDbWjvdjjvDRfr4r5yIiYzS+i2x7XYjf7ffZgTYYDN4qvnjsMh/D3Ho+vs/1UUkpAKsdrhl3b+drtVqxv78fk8kkG+y+ePEihsNhJdNNwAmDTwM/SB+ALwpjAHV3dxdHR0dxfn5e6cfCeCMiM+8IBwrvEw1cmk4lVL1ezy1ABMBUZxmAMqajo6PMCLfb7TQuCAdKw7iZQ5odlwaQiqjhcBibm5sxmUzyu2Svl5cXDdQJgGwo3AiVsTxWDophRMit3LxHu92Ow8PDOD8/z6qx4XAYEQvHjfKxljZUOHx6X8FYY6htIEo2vNFoxOvXr6PRaMSzZ8/ylDgqyyIisz0oZ9mzh3GZPMBIGYzZQX0ssPv7XiUwt0Hn94AzO63RaJQ9mgCnGFkDaxNQbO+MiCRkms2HHgnOljq7g4HGmWBs+ZnJMGTC4C5iAfYZT632kJne2tqK/f39bBJMU22IUBw9xh4nDrFph23nAFnElgVnbCKqzt1O2nYH2cNhkcnkO8hlxGJbMgEBdhZbwxh4F+bPAIM1YQw4FG/r4Vl8lnL8ra2ttAVUTQKgVlZWYmtrq0J607cAOzQcDuPVq1extLRUOXUKIoux81nGyVh5JycF7LBZP4Pqkoj5lPQVEtNjNLAyScLPS2IKXxpRrYQsySju4e87sDPxhPzwpySr6K8wm82yqs1krU89wsbzHsibEzToBDrHe/iz6Cjva2K6JK4YL59xJSDgle/4Z9bTu7uHXnkGbyZi/F0CQgJwdBodsC6SWGFuyuQZPtiVxPhn5p37saas12g0SpvG3PA+8/k8SbBer1cJSPH9XiewDJiBNUG+HNRatvw7/GOZtfb1Pn18LHjl8+Xv/H9/piR2P+RlwiFicfLU/f19bkluNBp5Gtjy8nK2bUBWTAJGRMWfIEOsF0lTEzEXFxdxdHRUSYTgK1zNAv6kIbYTviZxsK8E8GzvoMLz/v7hMIqdnZ3Y3t7ObDun+u3u7ib5BNECCUUAx3cgpO7v72N/fz/nBjtFooL5sT00+WP77ws9KgNg9NefNzFlcgs5B7Mw7xELItv2x4kRLuN8AlqT5pyQ589ZlpABWp3c3NzEyclJpdE684R/RicHg0FcX19Hr9fLOAq8i++HFPP7lmQrOJj3AYtYxsFpJl28Zh/6ciUItgo5N9b0O11dXSWBG7FI2NhHIl9OiOALTIRFPFTKQjbwc2IqdB27fnd3l7GXq+0di7EW/By7EREZu1mPGT86jL8yDvUhXmBJZC0iUgadvCgTaZPJpHI4DfdBbkjqmJQybkcvsTMmoEyk+9/GCXzP93Syyckex7DGyLyLC1m8rsRI6AzJH/gJ8LvXhnknnmenCRc+lneyrURWebar3sr4Df1yxXQZIzv+9/pOpw+7jXxa6/e90B1kzwTe+66PRkoh1C41LJniMkNOULK3txeDwSB7BLlMeGNjIyIidnZ2otls5nYzyCEaOZq1v7+/j8PDw+j3+wn8S2Blw8BiuMGZS6Fx/IydTBOBKEG6q53YfuOyQnrZsG2I8aJcCCzBO6CWsm+zp5PJJLfJMGZYTyql7HDLoBfBJNDkcwbVrgJyJRrbaBjPyspK7O7uxtnZWZyensb9/cOJG3d3d7kVjB5eq6ur6bQISprNRQ+h2WyWx8WzbSticUqJg3hnK5aWlnKb0NOnT/PkPwx6q9XK9+T0RQwXVVGACFdlGWjaMOKYbJw/1cuyHlGtujAgjlic8Ib8d7vdfGdI5el0msCG0xTn83kcHBxkrzcMNsdBQ176qFMcCn0HqGiDWGXLnisa0T0a4pNZpYS2VqvF/v5+7O/vZ2UQgTKAnpP3CHLtGHDS6Ct6UVZNofcYd8sQRBXBcK1WSzk2kcB84yiwT1QbYgPtfJl/Z1mxQ2RQTfg6K4bzdPbI2xogm7wFuN/vx8nJSdze3sbOzk4lOGetJpNJtFqt2Nvbi7Ozs6y6ZM6Zz+FwGBcXF2m7LXf7+/vx4sWLbGjtfgHYG4NEZM7klMGL5duB6k+tqxAnVPb5KrOB5b+5vJ7erlYScHzXIKwEZhELYAiowFfaZqCf/I4xsH02orr1xuNysMZhBfy8JE0ZAyDMRLXxBPKLfrJlAF0pSXdIH+swgaD1ARmDqHVAxveowjX5h4/n+w7qqYIh8eFAFfn1XJfygi5DOkVEbpkFbM7n88oJpNge1mRpaSkPIdja2sp7OXvP50188z5gCeTM8mQ9K33L+6736eP30VPL+7t+5mDxQ188i2DAMoxdxb9g20jsRETiP3yGEwXWO8s/zxsOh3F0dJTJCzA0FRXD4bBSvUSQaBzn9cR3RiwIttXV1ej3+9Hv95N8Itk3HA7Tvy4vL8fe3l5WCPT7/Urghx5GRCYT3W+UqkdjbkgX427mqLTzEYvAtKySsi1ykOc1KWXHNsrz5NjB21dMTPF/xsH/0Uv+kAgHd7AdnTngucwjn4UEODo6SqzElmJvoVteXo719fVsIbK1tZVtGZgvxo+M8HPsAP/H5kAwMn8maLCtbuTsef3Ql2XDzzS5Z3uFHxuPx5mAc1KP7ztRADYxecm6k6jnc9ZdPgPGRUeRKQhbVz3xO+wyz+K+thWOS2w3kHHuy9jOz89jY2MjNjc3M5FonQGL4VOwWRDUxKfr6+uZ1DYOYO7cM9EEn0kj3sv6w894N2wCa2RsXOoYcm3587yVegVOIB51YoDvlusJNrL/LIsW7Bexb/hyf86JMu+2epdNKQk5iCKTyvhukhI+hG48HmfbkB/jJ0uC7/tcH4WUgh33ligE0xlfFt/ba1CGfr+fhgEhYKK3t7fzdw4ocZRuknpycpKBkYM3hCdicYwuAoQDRDhQNm/LczUW9+71elGv1zMYbrVaWcZ8d3eX21cganAAbMcjSCd7QbPl8XgcrVYrBcWCCSmEsqM0OLa1tbWYTCbR6/UqRCHG2IbJJYxkud1UzSx7eSKWWWMU48mTJ1mV5gozQG3EwxG0JycnsbW1lScrQVA4wIHkOz09jclkUjEcgDkDeozSmzdvYmVlJQNob7sg+02mjwDGWzWZb5dMW464ygD5U79scNBRgB3ziVw5QIRQAkThgKg8urt7OH68VqslQVWrPVQpbW9vp6NDfsjW44TIDF1fX6eB5LPO8EQssvAANweONDFfX1+vyD6VAThPKg9xEHbkdkg4FQhe3h85IHvmCg/IKMAKIJf3Rl7InqNLBAjc09tkTCYxtwS3yB7PMlljUPUYsVWr1XLOIx6A+fr6epLJPtZ7MpnEq1ev4uzsLD7//PNot9s5vlptcRhAp9OJ/f392NraiuPj4zg9Pa1UzDQajXjz5k1m6JG92WwWm5ubSRyzbZA+e4yN7C5ZetbN2b2ScLH8/9SEVESkr2D7Gz0HIqoBNO9gEsU/R1bwA6V++362a3wO/8xnIZjwrxGLAMS2HlKt3+/n9hHIEZojA/r57tLSUlxdXSVhDVA0yc/zHKjZX/l9fG98nk/wNHBmS0tEdbsjOgqAc7bW82hfUIJmKnLxc2VVxHQ6rTSLtv5HVHs+cjnhxbq4WgZd2N/fz0MC8JlUmxCUus8NJLC3wkMOlEQjuuqAzKDdwT3z42pNLr77Pr2z3L7r3++7ys89do+PRUoZa3oe/HvjEchTk0wmIEzsGqtFVGX5/Pw83rx5k3pDBZK32VFZs7W1lf4W/A3ZQ3CDPwL3XVxcZH/Rs7Oz1Pfr6+s4PDyMXq9X2dq9t7eXdh+Zsm0maDs5OclEENuA2VqPzPLO3AvcwrzZ9nnekU1X9EUsglqvSUS1bxCy7PtGLJIg/qx1x3bWxD73dzBeriG25/7+oXUBeuo2B67Kxgfgz/f29uL29uGwpNFolNiF3RyuLLm7u4uTk5OIeEj2k/i378GvgI2RV0gLsFu9Xk8bNxqN0g6X/qUkhz70ZTxl/S9jUeYFTAFW4d+uKGEesZFlryHWhLYhzJ/9LgkPVxuWiW4+T1GAfSF4MCKyyAC5KYsO+Dx2HHuCrPlvk0fMj5M2rtYCN1u38D3oJVVBJnadZEWfkUfrj2Nx6wdjKpMhEYuT9vhcqav2s/wMXeUqfb13QLB2jJV55Xv2k+6XBeHoAprNzc1ot9tv2Xpsru2dK/PsIyzLfi/+RCxsnWWdHRrI+GAwyHiA2M6Y44dc31e3Pwoptbz8cIoLQXzEoiphOp0msQP429jYiJ2dnQR0EZFVMUw6gSaCgHEtXxzFPzk5ydNmms1mbmmLqAqNt7DN54vqIQTeGY/ZbFGq65JV3hnj4Ow3gNh7RNvtdmxsbKSTqNUWp2TBmjqr5cCZjDogGEPIe+GwqDaZzWZ5ogsGq8zaRSwAEkEocwA4IEjh/Xh3gA7394kjBMwXFxeZxcexUsaIIaJX0P7+fu7fhogiyJ3NZvH8+fOYTCaVJmx+JwcpGLDhcBjn5+fx+eefV4IfMkQE4ry3s+Hcj3t6vgjqyyzLH9vlQAWjDlESEdmgGKIZGUUmaMZPtqxWq2WTzOn0oQlqt9uN+Xyep7Khc1Q9ldv2qHQyiDCIRa6Xlpbi/Py84nxpuk52gxP2INlotDqfLzKGOGSIJRwl9gMnXYLPiIXuEMgiQ3yGIMMg0+AV54dzR88NIhgrP4eMgbCKWGyH8nYCyH4CB1elOsNme3p2dlapimD+0bHxeBzT6cMpa6PRKPb39/PkHwAL1ZGsNRWS1n8qXH/zm99EvV6PL774Ip01ckBQzhYqbCJAnSawnNzIHCPXBvyey48VmH7XZVBTZvaQIS4DLP8sYqEbVO0YpJlMiIgK6EXeS+BKZQQAGIDkcnCApZsZU7lMLzJkHBAPec2JrD5R0nJMEoNgl+1mZcl7xAO2oC8ga2zy2kQsnyFw4vuuhCabaPIPOXTloUGzM5pgCj7nyhguV1a5WtKBm22F18fbtZATgD+BA72D3FOTebWNd3KA+fUWZeaWKj4H7TwTEoyAgnEx9pLMet9VkkiPEarlVQZe7yKifK8PfTlYtW5ubGxUSIy7u7vcMmccWlY8lriU+fUWlfl8Hq9fv47T09P0I96ijn7t7u7G7u5upWoDPYX0wN/UarU8IXo6nabuMoeuhiBxCxlxfX2dWBf9AF9GRG6bbzQaMR6PEys3Gg/9aDmhFTsAvvC2IttD/nY1lO3qY0GnSdXy5ySoiVPKZABrYzvNmPCxYHMuxmIfhbwg3/iEjY2NrKSkcury8jJPJSSG4PNgcRrrN5vN2NnZic3NzSQSWRuwG5VU9Xo9ezhC6ptcY3wkHXhXfme553cbGxsxGo2yR62xufH1x7h4nnXfCXjGjq0eDAYpa6wVcuV4MGLRr9AyaqKDHn88B9s5nT5sofTBNSas7K+xzWVVFJftDDgWLESyisQLvysTQKwPxRJ8zglM1sxbv0hCRET2lbVcIItO9OAP3QuLsSFz9ndcxgTMI3Nl32h5tRzbLpSkG2tjGQW3eD5dDYc8QDgxHmwHdtRb8pE51gws1Gq1Yn19vcJruNDisSo8bAM4xMUljlWxZ+4hFRG5i8XFJhxKxgmexgsf4voopJQDCWf9CSZQVgIQHFWtVstKJx8pjuOiEqLZXDS7diA4Go3i4uKickIXGX6MOJNvIOatAAY1GBcCcZTWzg5hpW9St9vNoJdteRik0WgUk8kk35UADeB3fn6ez8KxMJeeW4TSwe7l5WWST1S5MH9UUwFY2bNM1QuCSZCBoDtzzPf5PesH8KnX65mFA9Q3m83Y3d2N09PTGI/HqZzemudtgNfX1/HVV1/FaDSKnZ2d/IwJNJzdyspKVsABtp2NwcBzwte3334bjUYjfvnLX+a96vX6W1uLIhbkFIasNGoONMrPfRfo/pQuDKqNVkRUyuXdUB+y09+bzWaxu7ublUrIHWQOwIn7lwE1hu7m5iarIyljLisjykCR5trNZjO2t7eTzDRwevr0aTpkdKrf72cGa21tLT/r5o2My9mdUv8h0ameiqhuq4AgcKbIW2mp9LSjxpbwfQMD1gGyjAAT3cVesY0Q58R7AEQM2tFp5GF5eTlP/2QLRsQDGOj3+2mP+/1+6uXr16/j8PAwdnZ2MuPj94aApiEzVYno7P39fbx+/Tp6vV5la+fz58/T/tzf3+eBDvy/JL15H7YJ4KgBm8icyeWf+jLpb0LiXZdBvEkk/g8o9sXnIqoJGQccZYWwEzLlM/GngEICHMAgyRIDTPzneDxOsMN3fdIMiSeDRIJviHHPgxMHfj8q6Vh73suBHsEEW+ydfQS7OFikCsvglqpeqpkgliGG8a/4PLZhlIQ2PgWd9VZ5k9dUmeH3SXxRaUmfSmwC24F5LgGKE0tUymDvDbhNTjM/XlcTKU6OMZ8mit5FKpWXSaby8yYD3qUb/ozvVX7uY11lIGF7b30kILCt4rK+3N/fZzWLA6OLi4s4OztLP3lzc5Ny0Gw2o91ux97eXmI11tzBrYlEKlM5wGA2m+WWT8gM5Jog1Vt5vOUc2aH6nxNdr6+vo9vtpt1ATtvtdmxublbmyIEtiSMuZNRyxnchh4z7jBFZo4iFruFPyuQT72Gbih54nU38lzbUhJrJKZ6Ff5/PF0lyMP7R0VEMBoPY3d2NiMj5dDyDT2Udl5aWYnd3NwaDQYxGozx4Blu1vr6e8cv9/UOrk/39/Wi1WhX9XVlZyfWxv8ImIEs82/MDqcbW+zJR8qEv7JjJMI/TxOb9/X325HW1iW0c33FcwtqxvmBD1oe1r9freUo5MS3z6WocxgtWNA521SAygg2x7y6rBfE3XI5vmCfHBMSB+HfmxMkIyzr6TnUQPtaVS7Y5pR5GLCrBIhb2iLn1u4AxbCN4Z97HJJP1lLVwfMHnKAJxD9R6fXHIA2uOr2WtSp/Jd1kryD1iaOMSKlUpuCFmMvFrjMaz6BvGXDP/Jr2wM+DwiHjrZ8hivV6P3d3dytjgZVxp+4e8Ptr2PQSUwMnOFwHlxVl0MicYsaWlpaxo4AhPBIgFjnhQUow1QkAzSQCaM+8lIMCBR1RPyQB0o/QIbaPRqGw3WVtbi06nE6urqxkYA/wajUaSbowBJUcoMCbz+UPpda/Xy4ohKlY4aWoymSTzTqPIy8vLFGJAPu/J+7EVhvVh7szmj8fjfJb75zhIZw1dClir1fKZzE9EJOm0tbWVbDC9ApxJqNcXJwThxJvNZqXCi+8w5ysrK/HkyZNK5cZj5IHBzOHhYayvr2fFFIpoUgvWOWJxyoPLSTEuVMuZnPpQTPIf+jJYcGWQg4+NjY08GQ15g1igQT3ygYzjhGHjOUETEGKABvAmILq6ukoi2pkK9MK2w9/v9Xqxs7OTesAaYF/KflV2FBDVBnbD4TD6/X4sLS3l+yPfkFZ2asgMRArkkJ0JwSjfMalCX7WIBXHE53h3Z3a8JcYOGnknW81craysZNDt7UHYB5dpc8jD8vJyVppB4OHs3JOPOUGvIyI2NzczuGAcyBUnd/7yl7+MwWAQ/X4/35VsOqeDmVwj8PaJjgQHnU4n7fxwOKwkFuyYHbB+bED8vgs58FgMeD1uAy3WETl/jHTi9yZ4vB74Um/XcfDkwK+068jDeDzO6qj5fF7JCrvhZ6PRyL4zyJ9lGdtBAotxeKsqOg/RY1IkIlKfeUfsMyDcGWLGBPDyEehOLtjuO5g0OY1/AMtAGPO7iEgyH1+DbAJ8mVeAp32Pn+01tb1mfSMiSX1khN5B6JGrMpkXbFS9Xs/KnVptUcHNGJh3k3cG26xbKUvflax5H2Fl/XjXv/l/+T3mxTL1MS6Pi3VADufzeco6c4cP4sIvl+Qf98Of8bvJZJJtHk5PTzO5Q6IBm47sIQclweKE23A4TOzMmpPEAZcia6urq7G/v5/vSbDkpIsPzOCe9/f3cXZ2lhVXEZGEACQ0eJBgGL9qmQGnuuoTHWX9nVwskwDGfA6yTXLZF7MeDhAjFocJuNqhtOHWH/tjtljyfNal2WxmNTr3Ojg4iPPz82i1WtHpdLIiLeLBB9/d3WXDeOaFLb7n5+fZu4ikMHaJhORwOMytvybp2VppnbKvMu7gYCMSbe5pZf/1MS7Wkst21OvncZX2AjvLetmmOCYygQWxant9fX0d/X4/P2e5snziwxwnlkSY4yPHa/ho7LareSOqfc1cOGLfgz2ish17wfuYOOG7jt9IBqO7+E3jL/tgk0rMCXPk9i9eU++yAJ/g6/BN/AETurrUpJ3XwkSfd4BEROVERHQObEFizn2awCAm23gu64ctBJ9ERCUBxvqANUjy8W4uHjGXYcxBeyH6MxOHIEvT6TS5h6Ojo4rsQ5SVW/L/ENcHJ6WojnBwCLFjwsBZDyaKPi8oFMb15uamEgRHLLYKnZycxJs3b5JoIpDjHiZNIiJLMt1jIWKxt5fxAhZ6vV5+nnfgPRFcCBR+DhCo1+vpGAiC3bBub28v5vN5VlCRUTVbTaDK/an2cPCPMN/e3ka73Y6IByWhSoigPyIqmTMCdisOlUc2Qvf39xmMRiwMGobpMUIPgwVZcHZ2luSE+1cgI1Z4toOQ3aO5NsDCcrO3txdra2txcnKS2xohCDjymvmezWbxzTffxNraWjx58iRqtVq8efMmARvEGGsascgcuMwUGWYMGNo/lssBrp0q7w1gRm/phQa4hQgExPC9+XyxZ54SYB/z7qbhyD0ExPn5eZKsOCBObmN+nYHq9XppZFkLqrXINtBzCBAI4MIQ0x8DAgWbgR7hkAjasT/cw84Scgc7YQLAc4vMELQaLEBukcFmOxT20kEIlysqRqNRVkwQYBucsEWJoBknz1gg8ieTSRJFy8vLMRqN4vz8vLI2Nzc3MRwO0zZhp169ehW9Xi+63W46UuYDUFqv12NzczPtEsdSQ4bzu0ajkZVQ6CVEGvaQE4Rsy6nII2A36PSa/CEugNDve2E7Hcz4d9/nwv95ixvyYkDLZ223HDABdBwwRSx6IrGed3d3mfFFLiBkHaTd3t7mNgWDWAe72BsDURNu2F0ANIQKfqRWW/Tc8FZUZ2Rt6yIWW+jIFpY4xUQf7896l1sImWuPl2wyZJBJLe7r7cu8O/dCfxz8lBlOg1zGGFFtjg2QZV4Yh7erc1+2BIPhvJ7YC2Mry56r0MuA7vvoidfox1zWk5K48vx9jMuJA9sfZMgnoUHuY+Pdy8MyjLwYs0VEnliH/EAckVDa2dlJH47vMwHGc8fjcZycnMTFxUV+fjweJwZAnp10Zgz0jGI8vBtJqU6nk1jagbKxHI2gXSlmn+/+W9iCiCqJb/tu0tY+0zJmHFSSAyauXIVl+8R3S4LBBH5Jkj1mY3mW8Y5twHw+T2KK4PTi4iKTYmdnZ9Fut6PdblcqJ/CZxA29Xi+2t7fj8PAwLi4uYj6f52E2bttRq9Xi9PQ09vb28j62LQ64TaYQePs9iYW8fuU6fOjLsuExmNixP8SOmWzAXzB25pb7sE74EVfQ8LPb29s4Pz9/a1wlecq9bTtcGMBOBFduUcDhUxyR2zKhQgxYVuZa/lgvbBJrSrWyCVl8f8TisC7Wn91AvrfjbidWmC/Ic+YTgol7Ovno5JljBRdemLhDp4hxeSbjcbWaCZvShvBd7uldEBGReLccM2tgAtKxLXYXDsS2mjkwSWwyGzzEPYmhScIRZ7x8+TLHRZJhPl+0Pnnx4kXOB+/bbrcrO4v+UNdHI6XKLXFkVmzknckAxKHsS0tL0e/3M7NKw1WE4PDwMM7OzlIwS6PHvQmkzFJGRAbeBLH0miEwM7BlW1JEZN8lhM3Bl8c/m83i4uKiEtCyBaHRaES3260EUSz+5uZmGhc+S0k3gk0vnel0muXUBGsXFxdJZtkZ2IiyJi77jViU4NMfhEwbgDMi8thZyC4INwiHiGp2GdZ4b28vXr58mcYAQ8z6MH8GPnd3d0lQGfRgAJ2N29vbywzQdDrNY+ZNuOFc/t//+39Rr9ej1+ulEWC+6ZeAkXZA4Ooe5MwBhh3Ip3yVGSMDuEajkYGfS27ZekZvH2SU30VEOjl0xEHsdDrNSr2IyD4V9/cPjf9d0YAukF3FKVAZs7u7m8SFm0Mi671eLyuEIEy452AwSCKj0WhEp9PJYB7n22q1coso9ywzqAThkFsGpmQSAYZUjTBHkEbICoQKDgdZdaNQHAfPRHcAJwSQOFTuR4UpOoV9c+WDCZ2IyHUGFC0tLcXW1lasrKzEixcv8lkEWK6SazQetpFMp9M8ahziEpuJnYEI9pYttrAgB9hPsmH8TVWXExX2JwaMZeBhcvn3vbAJv8/3Da4cxJjQfBeR5p9hg+jVFbEgC9Bv3tkEiANcfJgBrf+wJtzDJA2yyb3q9Xpup3eDT4Msk3HIv20q1+bmZh7+EREZNBtMY7NcZh+xqMLyz0wKu8qY9XBfDf42vii3QGAb0LWISLKbObeOOvB2UOdgogTijNsBvbc6Q/ISwLO9YDabZQU6+Mw6Y7s2my22bfGOtVot5QLCge+CSfx3uXb2n+/zj+/Tx+/zO5OO5efLz3zsy3KNjSdRYALZmXOTNw6eSYCAO0nsENSiF5ubm7Gzs5PY12QrzwBDXlxcxHg8zm0kDproE+ptwVQIREQeGLK8vByTySQ6nU4GoyQG6SmJr7RO8v5gAvQInGtb5iC0JLBMQPN+6DgX2Jc5RR6tDyVpwrxy4atKcgtM6/njc6w1OmY9wZ44UEen/f16vZ5VzySZsRF3d3cxGo0q9hs/CFbB16+treX2PJJeYCywNrii3+/Hzs5O+gKSixDdjm3sGyAiGo2H/mA+bRhC3Pj5Q1/WH3wNa21Syp+PqJLY9lX2hbwzsQyyB0GIvt/d3cXh4WGlosr3NmnjZ/MMyEhiJfwo/sa+gsNhSEQSA9JfzlvvWAt0wicwsnslYrFV1Otv28TY7HeNZZyEwia5ApL59xzyc+5lAotnokO8D/Ll5zM/fMbxcElU2g5jZ5xodtUnRD86hhygd9zTRTDGOcw9OBl7N5vNslAF3AOn4viU3VnYCpJKrVYr55dCHwp/+DlrbOzw7NmzfJ4Pc4F3+BDx7QcnpcyW8+I27hYslJrP8W8WC4bfQgVRcXx8HBGRC+isgNlbxkL2iOoBrm63m1vxEDoTKjhI7jmfz7NPDoG6Bc1ZX8aOgFxeXuYWw/X19bi6ukrW3NsGXDnR6XRia2srTk9PY3Nzs5JZZXsLBpagE6MzGo0qCrC2tpbbK/w8O2kq0Mh+u++AtyfB1lJdhMEDZLB1kcw9+9Zp0sicwiYjJxh85GAymeSWRSowIhbbIezU9/b28jhLZ3e8PmQ8/vqv/zr+4i/+Ip48eZKljE+fPo2rq6vsXUNAYQCCXJUZEgfHfyyXgZznKGJRObi3t5fOARliXby1FgIiImI4HFYCNxh2njEcDmM4HFb6RmFQvYcckgMZbbVaeRqPe7YgNxEL546Br9VqcX5+Hv1+v5ItoRKPZwAKNzc3Y2Nj4y2C0VksA3tnU3F2rnaBOHVVhoENzRGxifTriIgkp9BF/h+xcMieC/TUv2OcbkZrh23H7fkBGPCOEB1/8id/kpWpGxsbudURIEQjX44j//zzzyt2hffEee7v7+cYmCueTRUWVaE4dapNIxYEGvdlflzJWAYVyMi7iJ6PednvmJAqP2OfVZLK/KwMrJhXkwKlL+Zvvo/cQDD5Hg6mHchwiADEE7J2fn4e5+fn6aPwaz79jnEha86y1mq1TGQZFDqwdHN9voM8zOfzSgPziGrZvIG4E0qeb8A/vT9arVZiCkgs7tlsNhP0sSbe4uDArlw3AmyvLQEfn3F2v7Q5yDeNqOlr6bWzfJlwYg3ASw56saGQYY8F+g5EIN3K7crMk59Ryvj79NE2qvz5Y7pQfu9d3/nQF2vksdObBfxDApR5xv45Yeskh4Njb78g0FxdXY2f//znmfxg3fkc97q8vMxTMOlnagIcLFer1bJHK7iXhBPb61jP7e3tSnsK/972qdvtVrZv9/v9uL9/OARoMpnE1tZWBsHYI+YzYmE7It5eY+sXcUZJdJu44h6sgfuuIJcmEBwL8F2Tfo4f0FO+VxIOfgfWhUCSIN9blur1h4oysPy3336b/R3Lwx4goKmmAEdgM7a3t2NnZyfOz88T87LWnU4nZXU0GiWp+JhsM25kkf5RnjNsYllh+TH9L7bLZJ8LDx4biytFvD4mB7DxrDVrBA4DJx8fH1fIfvta5Af8a3vIHLugAH1yQQDPJvFDQo81j1j0rqRyzr7x9vbhtEYqLPG7XGA4nkMCBn24urqq4FziJ35WYg30zfGrkz2sjX2di1xc0cTPwIcmHYnJ0Qt2JERUm3cbDxhPkriBfwAbRSwOFyoxHONnHWx/wPm8M7YZIjhikQy7vb2N4+PjjEuMY5hH5Gs8HuccUjyC/Sh1E3mBLCN2p23Lz372s/jVr36VdujZs2extrZWeW8u1ubHXh+NlDLLizN1yZ0/z6LwohhPCykky4sXL2I0GuUksLXPQYgDLUqMUQg32+YZJssiqiXwCDGC6FJ4xo3QXV9fx/n5ecxmszx6fjQaVSqVzs/PUynIJFNxtL6+nkF3vV6P7e3tLPlkf73BP0x8xCIra4fq/ad8xqSPK6ZgUbk/84ESRUQFXNArwFsUeLYrLAgMOp1O9h9BsSHPAB/OvDsAAugDYjiVpSQHVldXo9VqxeXlZfaLIhDGWMxms6yWiYj4t//238ba2lqSlRhB5hjDxjNQZAdlJYHxqV8lgDMZGhF58k232015uL6+jl6vl+SRM+PoMNk4nsG9ydJTFUVGln4VEZHZHztlsgaAduQ14qFyAqIUx8y2s16vF83mw/a/k5OTXHd0GeeKDTBZgkM1mISkqdfrleyB5QonHlElEZzlms/n6Xj4DLbB1SzOsqKfkFcAGMhhqhp90iH6yXvyvIjIzBO2AIdGAOLqI/Sbd4J83dnZidvb29RH7EbEQz8bQMjJyUnc3NzE06dPK01TG42Hnnxra2tZJcU7ExiRWcQmADoA3pCjvAe2rtySZLKtJALKwOCnuBhTGSQ9dpXjNgDiwmahTw7C+D168JgfjlhUqLlSis/4GcivARdB7OnpaSZcnE01seKMsYFjo9HIo+l5rn0zAJhnsubooG01PsYEpe/nSjwHLE5UIa/eCu7KKPAB20gdtHhdsSPMo9fewUYZKJvIhnCr1WqVnm2sOWObTCZ58tXu7m7KDvYDIg57wTyRrML+EFxBnuArPU6vqd/BgUGpe+/TBcvk+/7/2Hce+93HJKHK55sQilhs30NOx+NxynoZYDKXro7D/uFT+v1+BhX39/fZw7Neryf2REZsC05PT+PNmzd58nCj8VA17EDFW/3YRoLeEvhsb29nZQUY0rLSaDTy4AOqN0wsYS94f3Aw8+BA3JgiYrGFBuIJ+Xb1lIlm5h//YD0sSdGSAC/llud5PdEDE8BcJqx8H+wCCRfjXvtgglawc8Ti1Dcuqt2oFm02H3pDdbvdPIAJ/04l5dLSUp7QNxwOU7ZI2s1mszg9PU0shk2iGhUdJzaiX41JBPAR8xrx9vH1H/qyTzBByjy/i3Czr+V9iStMlLp4Yj6f52nv+JzBYJAVh2BkfsecuK8csk/s6kSmxwWeilhU4eOzjI+9PZOfEbtZHzqdTvoot+JwbOY4yFvVwLB8nvkymcN7G1dERMUmlvaa8eG3WEsnfxkf81ViPv8buS6fhc66sOWxxuFOPoNZmAf78YgFH+LqVhJgtD5wrA0GKS+SuKy/STbkjTXj2SsrK9mnFyLM7QT4rONeTgDkhFa2VP/Jn/xJ1Ov1+N//+3/nWpZJ1B9LTH2URudWaKoEIuItx8zL0ODcJ7Eh3DC5h4eH8e2332agQoBoBwVoQ2gw4hAjnAwFmMTYu5yexSEDi9BCHEVEAgL6RZH5RSGazWacnZ1lkIQB2djYiPX19ZjP50ms+b7u2cI4INFwwDhKA2kDaxwiFSARC+JhaWmpUlZL4IJyUn21srKS90boIKEwalRAsc0PQwYRR/NxhHdp6aFpfbfbjaOjo7xXGVBAwBEUOJCZTqdxfHwcw+Ewnj59mnvsIT5YT/pYmTzB8a+ursbFxUXc39/Hmzdv4s2bN/EXf/EXcXJyEtfX17G7u5tVPAaGGAqvJ4CJIO2P6bLeONhlHRxgAVYjFltQkE2MLvcpyQQ7KwAMQNoGDRDFXFMls7u7mw4Jo00vKOvwxcVFVgeQ7YFoISuMnAOUe71eBps4PIAyoMBEUEQk+YLjAlQ7E+SMEuDeoNRBMd915sNBBO9BcIr+RUSW6/N5CDAcG/qHvrJ2EJC2T+jHyspK9vLjD/PgUu6nT5/mvPi4Z8iFtbW1GI1GcXJykuvK+iwtPTSR39zcTIIMW2Lgy3szTwBhMkZkxEzEICvYZM/3HwN5XJJT/L8EWgaltpP83sCKz/lyUOYLP2OdRl5YWxNJBDfIX8RDpeRgMMiA2tl+vseYnFwg6OZZJlIAi7yHn1mCe291BQQa2GNf2AL8WEU2pI0DLoJ++wV8CtV8nnfWxH2ZbANMnKLnvAfPxdcDIE20QIhhO3gvKifpYXl+fp4VLMgN78szNjY2sqom4sG+DQaDmM/nCUx5JpgMH8C6YCtNgpsMiVhkeB+Te+becm1S4V3y+th9yuc/Fnh+6Au9Ae9GVBN7jMsHvDCP1ltvlSr9NeuNbSdYdp8qH5gxmUzixYsXcXJyUpGx+/v7ODo6ypOuITwhxLDf6Pze3l7FphL4RizIKXSd73HyJTJGr1Bk6fr6OrfPRywqSRgPssfFuJAp+wzm2HJgn+qfR7xNTpgMKIMuB7R85jECygk/8BCf5fflfYzLaIPiaiSCSt6bA4w4TAlMhA3ntFP6ZLZardRT5hiSamtrK/r9fmVrEgTKq1ev4he/+EUlBmEuiGHAASQIsQVgMM/xx04IkXAjmcEYjO0fu+xzTYZEROJlYlawJKdJYvun02klRuK7XCRz+L1jHuQBuS0JHcbOHLviyHgS2WN3Dd/lXujE+vp6JgrBUciePxexKB7x/Hm3E+8CGWdsYfKIxKrnPKJapeZYkniSeXTcjY5iQ/mu/QK2ymvqhNW7fA04wJgLXcG+8T6MwUUvJt3gRNzahPUox4PfHgwGlZ1ltmlUVxpPuDLq/v4+hsNhvq8PMICIvrq6yrh7eXk51tbWEuf3er08bI1xegzInuXAFZ7vuz44KWUnCih0eZuViZPq2M7VarViOBzmdwFdL1++jG+//bayZYqFoKLFxA0LQsaVQNILgHGlxA1CBEGdTCYJQiMegDYZoPl8nv9H2NkTaiGlabkrrixoLChBLL2svC0QIwpwYDsDQBgGlm12zI0NGEGlx8ncrqysZOmqj5LneYBOGzkHf8vLy9HpdBJs0LiY9wLck3WlKScZHRSHz/HOzBFGkOcS6B8dHcV0Oo3Nzc2sIDNbTpN31onfHRwcVLaJvXr1Kv78z/88yTVOl4PYMOPtzJAN/ace6D52lQCI+QEgOvi8u7vLPk0EPKxJp9NJPXYAa8M6GAxiMpnE2dlZLC0txZdffhk3NzcxHo+zqo3tWUtLS7G9vZ1OikDJxCTjJhhCFr/++uuo1+vx/Pnz2NnZSZ2j1BfgAPERschI3d3dZZNtfm7SxgC50WhUjDoEsCsEmQfmF3vjgNXrgH4CAiKiYit8ShekHw7X2VrIqIhIcEjg6tLrksCv1Wq5ncN2E/L58vIyienLy8uYTCaxvb0d/+gf/aN49epVnJ2dVcqFTe4dHR3FeDyOP/3TP80+IRGLyk3kxLbXpIQzhq5gxBFzQYLQv4LP/hQB6fe9HgM/P+R75XtZJgh0nAnz80ycINv+WQmmuD/BKn6I6sNarZb9aQA+yCJr7oSIM3qA8rIiqlZbHFLiqmt6HpSVSYwdX09FJfey/DswwQe6spb+TNg7ZzFJfjDHbi7rwJjPYkdcOo+M428trx6fQbLtCHrKll90zusOPjg5OYlutxtbW1sRsQhm8MHz+Tyrv6lkjVhkj/GhJMBcHV1mbktgX/7797m+rx4/Ruz+FBdr5S2lrvjl3/SypNUD26AtT65cxK866PTWOogJfMXl5WUcHR3FN998k2tNwIK9pt8gsjubzWJnZyexIKQGckLQUasttl7f399nNZ17yDoh7C3rkAJg2Xq9niQWzzWBw/OYS/SZywSRdchEqT/Dvex/jHvw5fbZ2BzWt3y2A27mCXK5JKQYJ3bIZERZGcPfS0tLSS5xnZ2dZVN54w12Drx+/ToPKyLwdC89CIvt7e08cZe1QX7H43FsbW3lvDQajay0i1hspbe/gbAyMeu46WNeECguQngfKWV5MFmE7EZEJVEREVlFxqFL4CwXUJSJNj5HrIbOlqTl+8h5ExHgXseGxHWOWWynnVBgnCZA8IuMkaQQ/57P55VqSnSBeTEOK3WW+TU+JmY03ude4EAXklgneR+/JxfP8e+t87YNfLfEK9iGsoK51F3blfL54CviCBJPJJZKnELBycbGRh5mZhtmTMa6wAmQlHZyGtkwrqYoBh+yvb0d9/cPp6N2u91KtZ3X913y+H2vD0pKmRTKBzYXeyTdKNFVShERnU4nS3/JpF9dXcWvf/3rODs7qzzHgYpLFlkMSochEJhIsjz8/P7+Pqtp3Iw3YlEJEfEQKJL5rdVqubfS/+fdUGQaQeLANzY28t6MjYbALnX29iVIMECglc3HpROMeWzu8wIo4fsQax4TTspZK7IL7CvFqUZEVi0AGlB0nD9scAmQIbBYFwyOnTTlwzCzJqxYO6rUnj9/nqQUTDEEWavViru7u2y6PRwOKxm8iIijo6MYDAZJrNCw1wbJxB6ginHZGP2xXawT7+byYfqXsXY4Hkha3pk5j1jojEtDR6NRVs7Z4ELmPHnyJKbTaTa4RlbZjgpINklNaTlyenx8HKPRKCIetnaSPY54ILXoe+QjsXl/B8ZkZCOqTRGRu4iF3aKEuwS3yC467ioxHAvVJSasXd0A6JvP51lyi66ZdHZFF87PlRoEA9gJng/RfH19XelfghO9u3s4VY0gh6AX/cSRjsfjWFtbi62trUoG3PYKWSKjC8mFY2MLJUHb7e1tZpyYI78b/qXZbCapRc8MyDMDOROa/P0pEVSPjeWHBO6PfR8wVxIb5f0NxviegRXzXmbuvF2GeyDT2GwAkElPAmV0ii0hJGCcVPG9TdiyvcekKu+EL+X9AFGALft03tFAE2IZf8Oc8N6NRiP9KQGYqycYN8G09bGcd75jsgsfarvM/ah4eWybgN+h2Vz0lrHtxGdeX1/H9vZ2JdCy33VCyraQLWGQUgS/vBfr7SCX+5pMMDnwvutd8vrY798FjN91v491lUEPeontdjIBu8uhESZEkH+q95ErTm3GBpanXiM/19fX8eLFi3j9+nUFxyDz3pqFHkHmEgSRPAQvu5qLShDjaOTIOJ/ngGHpU8U8IKfcCyw9HA4rSZeNjY3EKRFvnyZsohXZdIBskrmULWyCSX3e0QSg19dz6e9hDxxUu9LCOJNnO9hzhUcpR41GI1t90C+z0+lksOx1Zus8hwb94he/yNNymWc+u7q6Gjs7O3F4eJjrRjIcUoogmWpoJ8nAM8QSrVYrbm9vc4eIA3XHiR/6MlGG/EEAveuyPkQsCCxXrrE+To41m82Mn5hDvuNKJs+DZYjxRixwqHEd48C/lTIM/sWuuOoR7Mx78Y4kgVlbV/WRUHKi0NW76KJ3y3Bf5twkLsUMyDeYnHd3Qsd6bR1kTu1b+b1j0MfIJsfu1k/+b8KONTMxWK4D8eBkMsneyWBXMD0+nMscBNjCvZaJkUmOQWwvLS1VbL7tAXrn0/iIRYjTsEsRi1gGe8nuBRcSsaNrc3MzT2Xl8DnssVs4cP0Q3f4o2/cM5OwYyvK6ev2hIRynoJHhXl1djdPT0/j666/j9PS0Apr5G4NgYojyyVqtlqeIUH0UsTAy9BviWRGRP0Pg+RkED9kFtp9QKo1wkLWMWPQ8oiEwwR2gl73bNogw7mZmG42HbUYEXBZwxs94qQKxIEMQABam02mWZvq7VH4Mh8NsHN9oVLfrRDwIGs4FA9hsPvTuYX0JMA1EG41G7OzsxPLycpyfn+fvDg4OKuB7Pl9s48PBEciSQWPbDg79/Pw8Wq1WOl6+58Zts9lDfxvKF0sQcHx8HH/5l3+Zp8C5OSCf5b2sbCQErkQAAQAASURBVGXw+8d2IR8AUgI+iFDYc4ya52Y2e2gMT8UCZJUzMmTvXNVCfzCeAVECCQbAAdA6Q8S90A8aKeNo0XVIFdaLqjkcOYDKWQr0oAymSqdUry+2LfqPAaQrMwFi/j2ZYoNMZ1AICk0241AMdk0YcD+qMdFhk0WMk6yStyhDGpiUN+nD+xhYoacRkcdR39zcVA4bYMsy7/369evY3d2N/f39SpNq3tOHSjSbD6f/XF1dJci1bWYdhsNhRCx6Bbj3D+tYXp8KMYWDp1KBy2vGOvsyODKIKn/vNbLd9jMISMrfAdzxd+5tYzlot9txfX0dp6en6XcNhHkusoY/xDdQjWTSH7CGbwMI40MBbdgJv4PBYhkUcKEX+EVjlYioEMPoFkGcm79a5w2a8RMkgPBxEYvqJOTTRBQ2lrEwfuYI+Tdod7BNsuixAz+QlxcvXsTGxkaW5nMf7Ay2ATtzeXkZ3W43g122CRMQlQQSQVcZeGLvHiOISln1u/lvv28ps6X8P6YXEdWTAT/k5eAFIsrBGf+3PyFBY3mfz+eJLZEFtiJdXl5Gu92O9fX1Si9QvndxcRGvXr2Ker0eOzs72WsMW8t48Gus7+7ubmxtbSX5BRHLeqJbV1dXWYEDzubkYxKay8vLaaPv7u7yhOjT09OIeLDlW1tb6aNpco48gnlNApQxhnXIBE7pJ03EWi6c4Lau8rsySH4X0Yx9AgtTrcB9jAdKQuRdWJJ3cWLAhMQvfvGL2N7ejt/97neJe46OjqLX68Xnn3+e8QC26+DgIE5PT+Pzzz/Pg4NYUzDQzs5OfP3114m1b25uYjgcpnyAl0r8g01zSxOT9H7nj42ZTUx5TKUdiYi37Iaro8bjcfR6vUqFlX2Y4wYSimAxbCvPJ3laJmQYE6Sg7bdJKSeM+IwTIcaNxplUIhFv0SSbGNckOrEQfY0Yh9fPpAhrDE6IePsgJeuPcW1Z0WiSzlgIvbaus2beXmYC3qQU32PcPMt+kHHbrjLHvA/vCBFEJRIxEQkcYgK+Y5/GPHNPfAJbYo3pwULT6TR5BxIa6Kblt9Fo5POJ4cAxvV4vOY5GoxG7u7sV7DwajWI2myURTdJvMplU5smxDdcPwdcflJRyttH7mrkAPAQgOEKqZ1DY4+Pj+Nu//dt0niiZDQWLAZgHREECbW1tJePLs13SxgJx2hrVGVRAMMalpaVYX1+PTqeThA0sOIaEhr04X+7nALNer+fpc0tLSzEYDPJ9ms1mdLvd3MZgFpLKMSslDSzduNx9ngA2NCnlOWSLUcSIyH3+BNAYWgJQmhbyLoPBIEkMWFUCCYAo74QgU93SaDzsgV9ZWUly7Ouvv877O+DBODHf3jbmk4UiHqqdZrNZVsLwbhgdKndMmDhLdXx8HFdXV7G5uRmj0Si2t7fj4OAgj8u1fJtUxVm8q/z3U74Mypwx47q6usrjpKlA6XQ6Kc+9Xi/1FkfmZrlkQSF7HYQC1iIi9vb2IiLyZEqMGeSv9+Gvrq7GeDyOwWCQGTpkx048YnFEfbP50KSVLSnIeNmsMmIBCp2VwNG6d01EtWEnYM/ZpYioOAz0xw4PXcVh4nyQTYhhB68lKQBx6wpAnu+SXYMw2wEHvwYa6DLzhoONiKxm4t+cSDabzeLJkyfR7/crBAXzSLBFxoWgi3VqNBoJALwVwICNuWFO+CwVOvV6PWXLfqMkeT6VCxlGz5xs8N/lVQbj5edms+rJPwZdfP8xIspgyb4aMFYGbxGRGTn8N1W5DsABRfatDv4eCxgB3mUiweQxugBhg63HP1lfeQ/fg/njXfC/BHze4sJ7GIQBVNEx39fAvtzWYPKbOeR3BtXoKkSyyR6IBZ7lgBZbAqGMzSVgBkfs7u5WSDK2UaOb2HIqJwHMAFawBbYdghW89Rhp9Ye+SmLgsZ/zb8vlx7gIfiD0SIgY1NumO/nI/EJamVTmfuicM+DYydPT09wyD4lLMhVchdzT77PX60XEwn6gS5C4bPszweoglt0H6O5gMIiLi4u0cWzTffnyZZJZ0+nDaXwkHk3AMF7LvINRxsr97dMc6JY4rpQJk1u8S0lslj9713o76YuuvevZDvCdcDLBYcKBOUemsGsrKyvx2WefRavVirOzs+j3+3F9fR1nZ2fRarWi2+3mISQQYi9fvox6vZ4n7UF2gm13d3ez9xhjgKB2UgC/jr3ADvvkRx8kwVp8TD9cVtXM54tdMe8ah2UQYoYegnd3d3myIe/IDhlwD2SmSVNOquXnxjTGiJC+xn/YaD5rYhObywWm8q4OyExwOklh+8aIxe4e94vCL0Gi2b7j97iPq5LLpBgkFxd+w6SS7aL1wsQc78NnII94b3TDOAW9xKYaAzuuc1ED8Qp6Ufo2nuuY3XMOqU6rgYgH3I+dM1awbeD7PtCHOQSHX15eRqvVSr1lfbHv6CFrzMFpEE5LS0vx5s2b1OWtra1KRfb+/n4cHh7G1dVVXFxcxO3tbfR6vSwUQm+Ymx97fVBvjJKUPWYQutKZtNvteP78eZydncXGxkb0er04OTmJX/3qV0kutdvtODk5iYioTESz2cxgiK0pOIHLy8sEZWYQIa/G43FcXFxk+bNZfLPnNhg4UD5PBYD7qFxcXOSxmAa+DvoIcAn019bWMgPFFj4qPm5ubjKoh/Sy0HLqVuk0yZgaDNEPiGcRTCK49fpDqZ7JHwAJPZioaqF0HyBOgF+v1ytbJ6mKMWjFwHv/65s3b7J6zI34qH5hmyGyRd8fk0+Hh4dxf38fT548yXWbzWZxdnYW4/E42u12zgfvy5zNZrPsmTMcDrNPFSRJWTrrQAoH/ikFut/nsswAhAG1y8vLsbm5mTqII6aMtNPpxO7ubjLsyCpZeohZg1qMJsa/3W5Hp9OpVC8hy+vr61k1wtxDbBwdHcXZ2Vmub1lqTPYA4gS5JbPoTAdg++rqKrcuAgLQN5NRJlDIWuAgXOnowCFiQWB5O0ZZKYEu8VnuYfAKULUji1hspyuDcoh3bAYBLEEZdhFb5y05zDk2h8Mo0GMSBltbW5kNYgslFZv9fj/H5Qq76+vrePnyZUwmk3j27FluO/G7sUbMP4kOfhexaNKKHDoR4mawj2XWPpWLIDAiEvA5aDF5VAZDZXDjC93DD0RUm/my5hGL0/Z4joNCbB6yjfy7+ohGu8hTWe3G9uoyw8haGZSzhZ93Mjls0tUELeOPWOgcdt0n8jFXBPkGj3yHQMLbK/iDHGI7LLMEOWUgjP1yIGI/zTP9POOliKhk3ll3xoPdM3AG67i6hKAKnajVFr2jer1eVne7AgI7wVbqiEUQwXYey5WDGNbZgVkps74s5z+EOP4hJBfyyrt8jMsYo0yWRSwSEx4jmM0ZdS7uY98LEUAfqoiIk5OTOD4+rvRE4pk00IZY3N7ejr29vVhbW0t5xRcyJnSZ93GFXcSDbwM/cgLbdDqNVquVVdAQTxAmrsy2nSHBw9j5uYPCxwJT5gd9Anu6Mhn9NOFjTG15Qr+YU5Pz1sHHLn/HhJlJdmywSXCvb0moE88w3yWxjf/+7LPP4smTJ3F2dpYkO7s93PS+VqvFixcv4tmzZ7Gzs5PzeH9/nyT006dP4+zsLO3bZDJJn3V/vzgsImKRBENmIh4SiexmoHqzTIB9rAsZA+9HVA9WKG2OSVE3/I6IR2MZyAa3RUCfG42HimLk0kkIZHg6nWayFfxi20nchg8ymVnGrKwjGMpV48g4+Mk/X11djX6/n6RJRBW3UkEMHrCMEge5chlbge0A7zv+8xo8xhfwfJNF9sVcxglOfHG54pEkK7FHxNvkNBfYmbll/f1dnm9Zgsh3rIlNQY9d/e04B/seEVlw476VJP+Hw2Hs7u5WYtuNjY0Yj8e57vhiMAm+HdzG7ykyGY/H8S/+xb+IP/uzP4vRaBQvX77MHs47Ozvx29/+tuKzyrn7oddHqZQySxhRPRbR2R+yJufn50lkkUHhfq4agJnf3NzMz3s/s5lQO6/19fWYTqdxdHQU5+fnmYGHTMLhsj0hYnG8JsSXGWYUx2V6d3d3ecrF5eVl7OzspHEB8OEkmSOInIjFaTiAeDO+sJu8R0T19KGypL9ef6hwarVaFUDMmN0/h/9fXFzk3NnZm+BCeAlAIiImk0kCDAL1Xq8Xm5ub2RhzMBikUbHjpx8N643R5eL9WAuACrLlyifWt1arxZMnT3JvrZn4ra2tXH/uzZhRVmeSzdwbjJhUeKx08Y/hcqAasaiSoAqOjC7ZVZzv/f19nuREdZwZeche9AUSFOPXarWi3W4noQqIvbm5yV5CZFvRxXq9Hqenp3FwcBBXV1fZHJL1bbVaCcghpSF5MMSQmQBnnKQzTEtLS2mYHRhEVE/WRP5cyYXs2DAbuAKK+UPAiDNyxi1iUWVVlvVyIcONxuKYcG/H87O8BZBxWXfu7+8T0Fr/0TEcW7/fT9IQsMY7Ywcg6TmaHKdtQhB9Pj8/j2azGc+fP095cBKgXq9nTyzbOOwca8e8YOtub28z4OLnEVGZg0/lsu34PgE2awoY/T5AgM/bPwAaAUkEbuiF58rVA2VAGBEVsDSfzzMQghRyvwrWD39L1WJEtSF4+VwDd/y9E1QA4jJzjz5Yh7gnn7WdwU9aXwlymRswgxvcMsaSAHPigjli7iGjeRZYBvDKHPKuBrUlwW2ij+QAv+N72AlXTEZE+mdOPXPwi57f3t5mZTa9/uidwXj4N/fwmBl3KfO/71Xey3LxLvLrY14mxhkPpC0+w7aapKR1jJ8j566SghRwxcTJyUkSCdjiiEhSARn57LPPMvjktGISUWBI7H1JwmLPCc7Ozs7i/Pw8iQp0KOLBr0KEUQFNxR0+eH19Pfr9fiacwLw817gP3IzeRCzWHTyGLkUsthb5so/mb/wi/0eHuH9EtUl6abs9FhMFtrPlz23rTEqBOW2zTTpEVJNKjJ/Ee61Wyx5TnIQNEWCCeWVlJY6OjuL6+jo+++yz/Dn4pNFoxLNnz+Ls7CzXHNnDjjNWbAPxnclSWmO4CvRjXo4ZIDYesxVeU4in09PTrIri4l4UJ+BXSpKReUGny+opknTsJCEmY90tC5Ydxmw7axni+/4sz4bQIQbis+B8Hx5GMhri2BiR7/Jv8CA2n8Qyfty2A5/hhL6rnhx/MtdeHxN1tqv4Z2N63s9V6fhdk8TMj8cQscAXzK3vaeyAjbLMwQGQUGVNuYh9wdSWx+l0mtgIsp4efGBtbLbtyXw+r8RuxiCWGWJ/x9202Pk3/+bfxNdffx21Wi3+w3/4D/Ff/+t/jW63G6enpzk26/Hv41c/yul7dqI4RAAn2biNjY14/vx5DIfDWFlZiZ2dnXj9+nU2Y+SlUYzZbJZ9S1BCBIzqCO9XtiM7Pj6O4+PjNJgGeRGRCuUyZIxBaRT4m4qK6XSaARWBGcoGI0yVxt3dXW4T5DkIGwEWQJIggWCTf29sbOSWOgNuCz7kGoE9zc6418XFRfZ/8fvg5FFCG3FnfhzMo6AIPsfAQ3JByHU6ndyTzNjX1tZid3f3LSBgkgvSz1UbGCsbcAgrjs3kuHnKy3FEvV4vj89FhgiMUGBAkw27gaXBt5n4P8YLw+Kj2AG7t7e3sbm5WakGWl9fj62trQqxSnXO6elpjEajSoDIGkVEHjdrYhN5QmbZFgaRNZlMYjweZ08KTp6gZNb9rpCFlZWVlG9vL8A2oSusq3vRuaE7OgeAdrYWuaGKDJ0BnJgsd+DhrIlBC4SM5QzdxKFEVBvMUxKM/q2uruapkVSmRETaDBMO2CW2NHp+kIuyN8Dd3V1WP3a73ZzLu7u7bJ5MtWjEosH5+fl5EhQ4fOTs7Owsrq6u4tmzZ3nylMextPRwVPVkMkkdhozE9tVqtTy2vqxa/L7EzU91mQT8rut9AXj5c3/ea4rMe46dPXS2D9lzUOcMJ9Vw4/E47Sb+iy0AJtEiIm0s8kU2sQRzJmasN+iEDxdBRyMi7YrlzO8IIDfRZSIIYB8RlX9HLPrl2RfiC9Bp7mXQ5uC4JK6wJ/V6PX20CWsH5ryPqw3KbLgrRJw08RhNJoBRSAS4rxlrwTtbrsBWrI1Jblciczmj/UMuB1Xv+4wJnHd99rHA80NfzDX41zYf38m4TbaW1dwmSAlyqcpAT29vb+Ps7CzOzs7Sp0RE4iaSN71eL78LYUFQxAluYFPsbbPZzCp23uX6+jrtPdgW3EWw5tOk8R+DwSAiHmxzt9ut9Aqt1Wqxt7dX8Z8RUQmU8VmuZkCnHNjaxiGzJa53wsKxAeMz0YVs2VY43nmMcIKoYE6wD3wXvcBv4ZvRWQfKTsryzthssBmfqdVq2c7DCXLbXualXq/nidj7+/tZ2RYRGTNQ6eSkMViLueYda7Va9rGBcAErcX3s5BDjs70u/adlYXNzM7744ot4/fp1/szyVtob1tuVKSTk8IXIETINxgVDlaRVRLWK2f7PFTv2X9hi1sK+n++4whx/Q5/Her3+1o6c+XyesVu9vmiPgEy65Qxygy9wMuexRBi4mTl14YJjK8Zj/83lwgSSXfh7Y4+SoCt9y2NEnrGx7RHxApXFcAasQZlQK8kgk5fT6TTG43FsbGxkNWlJQGJj2u126iE42AdCgRnW19crB8eVB0/xud3d3Tg8PEyCi3iduO/6+jp2dnbi4uIiPvvss8qaWH+sDz/0+qCkFErFokRERTgMzjh+djgcRrfbjfPz8/j1r3+dn7HyPXnyJIM2CBI3abQTQhGvr6/j/Pw8JpPJW+XnLlHmPgaYs9ksM0gsHlVSBmmUyUPu8Bz2kBpwEOCjIOzNxmDhXAjkILvIUm5ubqZxMGvO1j4EkgDd2V4TYGylYT5xhoAYPgtRg6LjVAjQARFmu5lPG7PXr18nICCLA8HEszh68vz8POr1xUk/VLVwsgyONiIqfaZsCGezWfz93/99tNvtePLkSa6PK1K4N3JKg04MNFs0bVSQYTsh5OGP/cJQIkMATk6SAAzOZrN49uxZrK6uJsnBn6Ojo/j222+TRKQKYD6fR7fbzeo1gp+yIbdL9q+vr6Pf76fxZFtfRKRMkql3sIzMu3zYgJgtouhNp9OpNCL2NgKcqQkQ2zJXehiI4BAJDLEBBm5UPLpCBOCBgymrkCIiSS+q0CDtIVAB8ug9cxyxOC0TZ8Z4ONEQ3XIwxDh8GhLrcXx8nP0AcfxkZtH9wWCQpB/+gO2SBMP0IHrx4kVsbW1lFR5zzBx+8cUX8ebNm8zaMScmp2my7v52zjo6SPnUL4+1BPBl0O13Km3VY78v5wEg6iDLnzEZgx4A8NFxAxICXHwGa4lOsnXIW8fLMQL68O1UJV5dXaX+A5AMxHkXbBg+CbvgaiV01nqKjysJqpIow584YPR9jSdKQsFAHFk3CcjzsGcEtYzdZCw2lAs7sLa2lkkpYzLWDz01wQ6I7Xa7aWcsI/btEZFYgHkwdmMdeT5XSfQ9dr2LYPo+xO37rp+CnLYOWmdMnFpeOFYd+1jiD+SYigPwWbP50O/z9evX6Q+w68gd1R7YRmSRylInYtAdPkt/IILSi4uLuLy8zF5+JJaRBb5LXzdkia17Duio5kFefLouJHiZXPBz7F+dxDER6p0TXPh15tafQfbQb55ZBmEOILlnxCJQ5nfotckQ21Nffk/b5Nlslr7YlUz1+uIQIC50PCJy655JNmIBiCu2+wyHw/j8888Tg0P80/4D28+7Mxf2t/wMGwMG472wZR/zIq4r5cIXfmBzczOurq7i66+/jojFLhbm3e/tn/NuEYtKdh8sxXdqtYeWKPSytRyVRBff4ZngRMbL8xxr4j8tN6w58ST3uLu7q/hH2nVwXxd3TKfTmEwmaXvA3FdXV7mlcz5fHHiA3IP7sf34JhPOfoZJXnMG6ISLJng/E4PMBetu0ghdZp7QA/sc5MRrwPqa12Cs2NDr6+tMUDMO7Afz7CIVYi1ISYghOADss3HG6upq5TATDiBBT8tKLzAYNpsTeJEfSLBXr16lX+JUTwiq2WwWz58/z+361in/+8f65492+p6dnsEcLPRnn32WoGlrayt+85vfxMnJSWxsbOSCE7Ts7e3Fmzdvkq01MLKzbzQaMRgMcuJRKAeLEVVgZMAI+MP4Y8AAuQgWIJhyTgLtiCrrSrUGASInLTn7RLkzxIsBPex1ROQJd4B8tqfxLJfUWihZB1cgeD+6K5eWlpYqpBf3JdhlGyD3b7VaaaR4NgQRoIM5hRR0MEpJ98rKSp6ex/qsrKzEkydPYjAYJJkAUVBmwQkkarWHk7hoXt9sNuPLL7/MuQQg+8h5G3wDRBtG/ji4NVj5Y74AggQdrgYCKCPvm5ubeSLP3d1dNj4/PT2NFy9eJNA4OTlJINJut6Pb7UatVkv5tzPCaSEvp6encXx8nNlQwFdEtcdMxCILzRpCJGPwITIjIh2jHT9VjThXVyG4+s5AkjW3HkD24IwhjJD9iGqTchyeM+jYR2wK5BvO3KDZgZ5Ba5kF9LY9/vaWYW/l4V2c7WQunSHCOeO4ObGTyi3mGl2q1+vZn4vn48RZ84iFDcU/NBqNzLpTZmxZZT0hp8rAwGtVZp4+NWKqdObvG+P7bA7vbJtmsOb5YU3LLB6/Rx8iFqXpBrpOwjCv9GXj/waHfB5SyYDTAboJDfso2yLLJTaa93DiisDI2488h4B65qskk+z7eQf0ykS9ZQ3fRRDvihWe40wplwPTksQ23mFcJuRMClH1yZiwTawpdsTBPVtH6vWH7P7JyUl0Op30k9gt3o+qGCcfsUkO1k2AOtCCdHmXjH8XuH3f7x2wMefv+t3HvBxsecsT8olfcMCOXHuNSwISX3h1dRWHh4dxfn6e2BO539zczP6NkGLGNt7CwVZ61hncxjZBTrsFI89ms9jc3Mykqas0dnZ2Kj7ZSd319fU4OTnJ5CDywU4IsJ4/H7GoDOE7yHNpx0rSxDrKVQazXOipE+OW4fJiHokxbL/tc/i344+StOVCT7j4HGNlPq1nGxsbmZihLyi9PfkcJxv6gKd+vx8Ri2q0X//61/HP/tk/i+3t7Zzb2ezhFC5X5hm7MAd+b94X+0nsVBLpH+MimWbb54v1o7K7Vqtln1TbErCh5Q8shL1EztfX1yuxFjI5Ho/j6OgoCSknG8rYOWJBcEVUK06JgWwz8E9loob3x14Yx9m3IEv45FKmvQXOfsiHH5TEkH11SerxWf5vMukx4tP+usTz/qyTMXzG71zGfMZOPN9j5B0tu8w/uwf8LCepmDPu7Z1U19fXGb/aJ15dXaUvhyS0PXQvUhLjtg+2C2dnZ/merhzHrzebzdjf38/DEHj/3/zmN3FxcRG//OUvo9frxZs3byqHUTEHxlM/Blt/UFLKBteO1/1SYCfX1tbi4OAgew69fv06JpNJReloeGyCh6CU7BCOlj3tBpA8E0DtrYQE4QgNf/MMB58mcwzC+Bz9FmjK3el0YjabZcBEIGilA7yurq5Gt9uN1dXV6HQ6aVC8lWc2myU76gZupUNivpkXgzDANQbn7u4uGx8yH2TeyIYxlzghxo0R9u9YYzOmPJdG6TZsVDcsLT00wLRhdCkzpxJOJpP8jkkyb6MkKEbxXr58GY1GI7a3t/PIZDL03W43BoNBsvSsn7eCYJDKwIU5/GO/kBcHqK4gwgHbIEYsjDOk4ldffZXyBiEDicXask2AbA3rjT7f3T30paBBqjMRXJSVI2P+3MbGRmxtbaX8uKz9/v4+txmVp2a6UhLnD8GLHpoMQo6xF8yNQQT2z8223VMAWULGy6xWs/mw5ZBm4ibLHFhGLPSF3zM+5JjvRFQbrvNebNXgnbCn9KcyOLAcoOv39/eZQVleXo5utxv9fj/nkHmgCoDgCLvpLd1UUf3yl7+MbrebVadsI3n27Fnc3d1Fv9/PCimORYcgMSHlwCLi4/eT+T5XGeQYMHEZGFnu+d1j9zNYK8m4dwE5E5h8Dtlx5WDEgtAE2Lj6tQwCWR+ej81xRRI2n4qtiEib4qCnBLeeF2TCIJAMrIMh6yG6T5PnMsMI+OX90HkHG9Z9bBk6S7UAmWgDbVeZMS8Riz4Vnn+vu8EtgaF9X6PReOuUNANuzyf6gd3h38gBdoA1J4HgZADy4/VinWzr3iXzlvPHfu85Mrn1Pn1+TC/eRSp86Mv+ypUkJoWxlxHVXqqMvfQXkLvIAI3Nuf9gMIi1tbV48uRJ3NzcZCUMvgY9oQrEhyzwbHYZNBqNSpUxlSSQWdwDn9tsNnOrIFWt4/E4dQy/TN9RCOvLy8vcWojsYF+QIwd7zJ1/b+zppJFtKn7xfYQlc439sN6aRHJc44pp9K2UXdaX8RFIMi4nFfiMg0x8nHEy32W8kD+1Wq1y6BTjwqfO5w+n67Ldnt0M9Xo9vvrqq2i1Wrm2rEWr1aqMtwx0+TlyCR4h2H2sYu1jXG5274u5pA3E2dlZ2t2SAOJv/5x3cbwWEXloFTLRbDZzB0C/36+0S+FvbDH/x546UYidMIFl0ot3Msld2hMIaBKyyIYxsHef8N7NZjP9gSuG+Cyk1/X1dRIq/K5MxrhnnN+PeTBOj6jKt32adzJEVNsOWG95DvdyfG/bio22beZZZZLaSV+I4tIOoePIkyusGS/PBQNfXl5Gs9mM8XgcnU4nY2BsBbga2+jWPaV8U+DCe7fb7dwJNBwOMzbrdrvx+eef53iazWb8+Z//ecYg7ESgz6B9U4klfyjh/FG275XOtLx4ydlslqTUxcVF9kpyM2Sc4nw+z6a3LMrS0lKcnJzE69evM2sTsTD6Zf8LJpuKEDKDKKJBIBkkBImtdNyDah+yjGwlImvEe/KOBL/z+TwNPey6CSFIOwBrRFSO9SYLzHvxN6WDJk5MOqBA3Pvu7i5P2ODezqiQTcE42BAhgNwbhTO4xthS4QBAxpHiIAeDQVZKTacPPUpMLKJ47XY7M+1811tz3IfKhv7NmzcJzhgLSok8sc6z2SxBtzMIvDNy4Xn/Y76YJ97JzcZp1g+QXV5ezq1V6G6j0Yhvv/02/898LS0txe7ubrTb7QzQqB6AwGBr4HA4jDdv3mQmh/XEyDabzQqh7BMdGT8lrYAk7sO/Ly4uUlYMHkrCgnU3ScW/CSqR+4hq9tIVfJCb1lEcdgls0amIaoNRgxn02eDPhIDJhPLnpbNijCXAbzQaFdvircx2/tgYlx1DFBMMR0Qe+ADAiFj0zaB3FL3dJpNJZW/93d1d/NN/+k+zKS4EFoCIbb28N4kL9Jp5LYkZz8Mf6nI2+8dcduplJc9j12Mk07vuy8X9sIkOcvh5xAKguVoJG+yAeT5/aERPBRD+EPlDjgl2+ayre5xQKccDjjA4RtcZJ5/x9nX02RW9vD9BGAQoeoZvpNLPW9YMLPkOQb23HjJ2yHaDa3SmzCaiLyaImJuIRVNWJw0sA04koL/2rRAEln1sGvaDz5eZXHRnPB7nlg7mPSIqgQGf9zY+3gMfwd/IBu0DHqsQ+X2vxwgt/+5jB8MR1b5SDppIMpZJPuTGW7hdCRGxSIrWarW4uLiI09PTTEaSMKL/iH1XxOIIb7Z8kTyhCibiAU8NBoNs0cDz6GVFgIZtx7c5Wz8ejzOQAVfRL4qgnOpKkhYOctH5MghychjfUJJ4JvK8HRxdLoma0haZVGetTBSVtornliSB/Y91rLxM5vvivRwf+B2x6cwplRdgJmSCMRP41moPDeypYmeLj4nKV69exc9+9rOcb97TdtX2cmlpKXs/QnwwF04g/xSXyT5jAQiS9fX1JKQiqj3GjJHAZBAu5bZ3yDjvomHe+/1+Jl0hRnkW/sr40PLgamJsBD7MmMCEFTYe3WXtkVEIYmJZ3heZI/FfFkBERCaoeIar9GlJ42RH6YcYO+sQsbBpPMvfs+6hc/gmV356pw4X+lFWEPM5x5PmDSIi5buslmSsjA0ugGQB8uHErnGLf24y0z7x7u4uzs/PYz6fx5MnTyq4z3bAmAZszpzCX/h0dG//Y+3ATMzF1dVV/I//8T/i7u4u/uqv/ip3n+E7jFHfRVJ93+ujNDqPWJw6Y4IEcLi7u5sBUK1Wi6Ojozg5OUlSgX2SGDcIodlsloEHzcsjIoXA4J4JthGiMTAEiJvw4XAdqFHRhQN2hoYqAwwTit1qtWJrayuOj49zCyD3pGJgOBxmth/HzDtGLE7WM8vNXPB8jBJz7YwFoAcSql6v59ZHgshut5tzx7isiAgqDmw+n1cYWbPCGxsbmSEGVHgdIuItYwkwYB5psHl5eZlNx/2ujUYj9vb2skwdQ8m6konHWKDwt7e38fXXX8d8Po+tra08fh6S7PPPP6+QnxHx1ikbrDsgHoPyUwDcP/Rl48Y2K6roLG/1ej06nU5EPMjb9vZ2zOeLY0/JvHDqGn0KYPjtJHFab968iYODg9RfgCMACEIDwwoB5SrAtbW13EaIzaDJprPCy8vLsbGxUTmhA73ExkCoAJzQa4NNOyQMs/XQhJKzvOiKqxjt6EwA8Tz0Gt3nnZF7ghUcCmR9+dky4HTw4MbTEVHpJ8C683mPFedtIp8j5j1nJqL5Hpn0y8vLzNa4V+D19XW8fv06NjY2cs2QjS+//DK/w/ZvxgzQ5hTUj6Gjf0iQXQZIXAYj7wtsHgMH6JGr+BzQcv+yesnZYoNF1tP9E9FX/Ki3FPX7/QSmrrJDhrl3Gdj5WRFV/wEo4p7IIn8uLy9TXqz/1kPejX9HRG7xNghn7vic8Q3+BnyADBrA83kCCVcmEUwzfwBK1sHr6ZNkbSscbDNfThI5IIWciIjcqoXPdvAP9qHlAHYdu2lSyoGCbR5r4moO5rMkjt512Tchp9/n8vP8s/J+H+tywIHfYY4IFggA3WuT/owOjl3NtrKyEuPxOLfUUQFO9ZG3wDuZhJygdyQX7u4e+i5SFeUAs15/2BpKg/SIh8plB74cWkRPErb7gS0iFlVK3W43+03x7iSqqIjGtoDH8B32t6yliSITCuid44Dy99giy4gDUGTW+uj7oW98Dz1wrxqea9tme1badX5vOwSetj/lD7+7u7vLJBynYDvwjlhUT3AY0f7+flxcXKQccNr28fFxrKysxGeffVZJcpWktAkzyKeIxZY5KuHoWftDfOaH0FXfE5njyHsuy5ZtP+vsZILlbD5/2LYHmUHcyA4NfAXrRwWXcRpyAunjBAr2H78WsSBM7LvLvlDGYo4LeZ4rNUsyrJwXk5H4LOQdG8Z33XLG8al1GP9DDM98moBxjGDS2GQx9hQZ8/vxjhGLZHLpSxkP97adZl6Ye8e6xL74ztLHMZfIv7GQY6jSLkU8+OmDg4MYj8fx5Zdf5mFBJoVKXI5sWJ/fvHmTPtlkIM/e3NyMFy9exD/+x/84arVaHB8fx3A4jE6nE9fX13F6ehrD4bASe4EpePaPvT7a9r2IyADUgHZ1dTVPiVpbW8sXns/nGYSYIGBfLos1Go3it7/9bWWrBgJvobSC8TdB7nQ6zf4lGHICona7nd+noopMvJ0328nK/dIAMR/fCKAELGMw6vWHE3cosWWM/X4/q3icVbQxcCbYPTNMQtngLC0tZfUVhgXl4n0nk0kq/sbGRu5Nj1iQUDCyZLeYd7YLYCAwRlZo9sc6oEC52YrDdiJnDhw89Hq9JDJZazuH0vgw9hcvXkRExN7eXmxubuZWUSrcrq+vs5cNimpAwLvb+fxDIaVcrYMxvrq6ir29vQSCjUYj+0rc3NzkSVEmk5aXl6PX6+X2gohFVQ8VcRsbG3F7exuvXr2K4XBYCYaQSeyEWXtIVchb9NPZ4YgqqbK0tBTb29vRarUqFQI8DxKLnm5lNiSiuoUzorqHmp9TxePAFPDhoNoVRdyXsSNvtpUEgegRTSjttJ05455kOk3MuxqUcRpI82dpaXF8N1VJzCV6wrthO1ydxYmJ29vbsbm5GaPRKJ031VE4Q5y47RLzd3R0FJ1OJzqdTuzs7FQqWpE53t9ABZlmDQyAS7LnU7wsdwZl2KF3fcbAgM/zGYM2ZC5iUY2Izhssey4JEJEz1gsSm3t4zh3clkGbqyqQW9bS4JzflVlJ/m+yxL4OX0Ng3Gg08nRG3qEEvw5qywqmxwLXMrAE5DLXzmrzLr4Pc4tNwiYYB3BAA+MlgCkBKWtSEtvYTvytAxdsiSsivf3IPpxDL0rf6sNmShnAlnlOeGfWsKyUcoBuOfe8/xA98nyW9/vYF+8GWVRuEWFdOciBBCnryJp73hqNhx6q/X4/6vV6dLvd2NnZqVRm4XOoKERWSE7O5/MYjUaZDCQJib0mgMW+j8fjmEwmSaSRFKBBL7gQAqzZbOZnqd7j/bx9h21hyJkJ5+l0mkQJc2Ii57E/zLntYES1+T6fBdO+i7g0yVzipccILf/OAarJX79DxMK/mkDge8gOY7QNMDlt8oQqN4gpqozdosDVNs+fP4/19fU4ODiIwWCQa3lwcBCrq6vx/PnzXBe+b5m2f2JOfIgKf5NA+76Xkye/z+V7YIMoODg+Pn4rqGaNbOediGDeXQzRbDZzi+rd3aKBODqPzEHM4hOID8v+U8Qb3haP3jI+sHBElRzFL9fr9SSvsNnohaumSDagl/gqN8V2ghJMF7GQQe8w4LP4C9sTx+aucjSmZb085/4bv8dnmCvkG9kmjrAfMEmNHvi57l1p/oD5Lv0Uv/cuDJ5nkovx83ljDC7rOfN2e3sbg8Egvv322/jZz35WIaYosGDH1ft8/dHRUZ6SDU5DNugvt7W1lU3W/9W/+lfRaDTif/7P/xlHR0dvzRXv5Pfl3X7I9VEanXsRDLrcjwXBuLy8jMFgEKurq7G5uZl7j7vdbjrCev2hOdzr16/ztIiy0oFeUM4kIAQICguek/H/F1ozooBBHGnEwrmRIXaDOhaf4+5N4mAQcOyAAz7fbDYzgwR5FxEVxphMFEyxDQMKPxgM0olTygjpRhAAGGB+6/V6Vo5R+ktQ0mw2K6eq8DuMAIKPkYGZd4DoXk8oibPxMLY8A2NwdXUV5+fnOX56FEAkolCdTidOT08rAa2NlTMaEQ+Z5hcvXuSRyARVBOGTySSur69jZWUlRqPRo8CAd41YlNf+Q7icpYhYgLDxeJxZT4yXKwbRwYjIxqrT6bRySgNzRNP+g4OD+Oabb7Kipt1uZ3XMfD5P+fW21vn84QQ/9MvBEToLCUYJO+SMt81GRCWDAvmFI8XRQDxjp1xdYJvDPJgY8P9dUcfzcQbOzPDHvbbcN8TZMUA/Dhhilzl0c3cDSO6PTBuEm8xgLtE7qpTcz20+n+e2DfflA5QNBoP4+uuv49mzZ7G3t5d6HRFJ6PNe9NO7urrK7dv39w/bml+/fh2dTidt5Xg8jlarFc+fP89qKWckyQDjjHknbNXvk835WNe7iDMHXCbg3/VO5e8BMSVJwD1NRiDDnkODLE5PREf5TMSDXcSWOojxPfi8K3ws44y3VntIdlBujm1wZtkA2rLOWNz3wf6T72PPsCtukg655fvNZrPKtlKTgfztgNs4yLYEchh5NcjGLtlG2KeZJJ/P51klY//MNmeCQL5vYrHReDhkBtKfzzFv6Pn9/X0eHGK/TxXjY4F5KT/8zkHJd+nBD/k5l3XnMYLh+9zjQ1yWEa83gYETb75MHEQstuCSPAMTtVqtTL6gFyYp8BNUp1LZjKyQpKNCmiQQ+BS5IBnrCkXkBF8L9uW0ZaqqSSrZFlnn8b3G6a5aL+26/a7tIvPMv3kOeuTLdrJM0nAf9KYMNN9lp7GfJtUYv4mpUlZZYwe9xhkmoGxHLeckDyeTSfrZq6ur2N7ejna7HRGRRDWkDAnljY2N2N/fj9PT04qNOTg4SD/MRcW6q1+YR8bj6hETaT+kUsrbA3+fy8lE4rvDw8NMrpU2wX6mjCUcS0LkGGuZFCbmc/LRMaOrGZFPfAP66e8Qx9ke2HeboOKd8IFcyI77NtVqi9YM2HgSHcRqkFA+CMSJXJ7tNhCNxqLfsP0Pn7f8whMwRuu4iTxspr9brhHfQ9asg7Y/JJUdI5RFB05iQX6VOJ/1M3Fjm8E8EhvzLqwV72z9dgIO2Xj16lWeKo8dclKcd/M7sE53d3fx6tWrJJ143ueffx5nZ2dxf38fv/3tb2N/fz9ev34dv/vd7+L29ja2t7fj5OSkUvnIWrpyygS83+u7rg9OSnmAVtzHhGx1dTX6/X7M5/Po9XrZeBqSgox/v9+PFy9epGEjeBsOhxVAS+CEIFpIKA9ut9uVo98t2BERGxsbSYjR64Q+Rj6ZzuMjo0mPFCuK52BjYyOFbmVlJQOyjY2NPL2Ei+yQty0BEhCoer0ew+Gw0ucCI8vfzBUMO0D18vKysrXKp5NFLJqolspsAIDh4Z0hBvm9AyYHwwbtGBvmkxMAUAKTRxBYKAWVO4AgG2+ezTxFPDTc/eqrr+LP/uzPYm1tLba2thI0UPkzm83i4uKiQkoYOJXy8sd8lYGrs/nIysXFRXQ6nQS87i8AEfHLX/4yptNpDAaDBBEYS/T87u4ufvvb38bBwUE+l7WD4Ci36ZbVV8giTtbBrwlRb3mwrkII3d/fZzNusrfsB49YABKTMc5wIk/IOgcWOHNTkk4R1eNpsVuWVZyJQbC3wJgsQsdYE5w1R3sbALNm7uuGDgKOWH+cJjLgxpYEwJeXlxWCAFmA3CRbSAZya2urUl3Ku9v2QDgCmJGLr776Kra2tuLZs2dpj7Gb5+fn6RgZvys4kUXr76d8mSjib9tcB/1+nzJIccbRwMR2CzkyCcU6c7H+LsHneciXATQBUBl4O8DjgkRBXyOiorvYYm8FNQnMO9pHlVv0vN2lPErdc7O2tpYVyYyNyoyIeCeGYSz4TWwnB5x43pmjZrOZdsbbcFg7g2tXN/rZflat9vaWGUiF4XBYwSlODrqKwX3nuLf752GD2Abp4N3g3H/b7tkH827vCuitByWJWc6/dcGy/Sn6Z/QA24Y+ud9eRJUEKZMK7hGGjpBE5XCdRqNROWyEe41Go+j3+zEcDlMWSV5GRPYWgtjHp3U6nRgMBnF+fp5yzVZ35Hpvby/XFBu+tLQUz549S/0lMHeghyzd3d3lVkBkx+RSxEI2rSPlfNmfouPIcykz3DOiSgiRYImoVkhxmcCyHPMM4wR+Z0LJvzPZXeoLF2PgD+trmXLM5eSR/fnLly9zq5obUNdqtUxMz2azjEVev34dw+EwNjY2chtmq9WqjBP7yxhMivN7Pmti5qfwxb1eL05OTmJ9fT329vbit7/9bczn1Qb/XCYUWacSQ/AZbCQ7XiaTSfqTiIiLi4tKrzaeaVvLH/qBlRXL2GwwbClDxvCuJCptJva/JIsiIn0CcTRYDl0wAUn/JOaBv0sMwrxRSOLKSOYX+eC9nZiNqJ48WJJCxs32OxRnRMRbMmlMyDhtM3w/+2avifXZpJ4TZcYY3N/6iQ12H8t3VSGVmOzk5CT29/crh5vhmyEU8deOw7HNJycnsbGxkfwCp9yPx+P45ptv4ptvvont7e0s0iAONP7jsh0z9prNZvHFF1+8WyF1fRRSCsFgMSBC+AMDy2J2u90MMCjtRRBfvHiRHeARZgIxDGbEAsQxKfxhSx7l+648sgNcX1/PDGjEwohaOKwUGPOIxRal6XRRpokQs7+YTCOBe7PZjG63WzFYBIeuIlpbW8tM7t7eXvT7/QQFVJhRycQcAHxRCgijiOrR9Ajw6upqgnYCDRQFQ+StSCgJW6UAF86aQHwRnLiclPnjsjGjeuz8/DxOT0/j7u4uer1epXqN5zebzSSW2CqCMXRlHu+yvLwc/X4/jo6O4p//838enU4nK8JKx27DjsyVwd8/hMuBBSfjOeCkB9dkMnkrOFpaWsoTHM/OzpKcYD7pCXF+fh7ffvttyqRJDAgoHOLd3V10Op3shUBgC3nhSiMqbGjy774ZDpJNqtB49eDgIO1Tu93OjLDLob31ziXJOHiywwCKVqsVEW9X0VluDJAfyxTzB9tCxSDjsdNiPAYv2FtnUZB/3sEVRZBLDvDQWTvZiAfdZZsj8+4+KGyVwg7e3NzE+fl5bG5uxs7OTmxubiYgm88XpejYT07IjFgA3slkEqenp7G/v5/rQPZ8Y2Mj+5bRR8zbAktgZrDzx3hZPkpg73c0SCo/4387sCoJMOs4ekz21AGyK1lLn2nwjA7yPD8LP22Zs48y8eTADsCLLjhhhHw52+ttq64ggWjlQv95hrehR1SP5baslXJmctpkksmFstcP9/M2ZN7N/nw+n2e1qU9E5PMmAm5ubhL/YJ/x0awFa2B8xrixJ+PxuJIMgoB2gMuzGfNjROv7glLP4w+9HvteSSr8VJcDHFc/sfYEMuX7O2iyr8BmgoMjqmSf8djFxUW8fv26gut6vV76L2/jiIhMEiBjjJeDUDY2NhK/kmREZiFBW61WRef8bgSg+CtjUwi7iMhE4WNJHmyKCWrm2c9izpB1rwcy6u1EpZ5x35JYsh6iPw7QSjtsW4dv49/YuMfIBK6yehUZYEzYSO4HtudkrVqtFmdnZ/H111/Hl19+Gb1eL/Ex30XO2K7He9Tr9Tg+Po4nT55kuwsnGz33jMWE3nw+zzYAP9X1//1//1/81V/9Vezt7cXvfve799oEk5Qmf3gn/JKbg7thP2s0GAxSTyMWzcqJSZBtYkn8Y8QiQVNW/jIm9wJlfMhIiX0iFrguoupDjdNL/7+8vDhgiKSwCQ7jWONSxopcE5daR0oSCIKPhKIr3u3/mJuyusgEqeepJGusk6V+Od4rMbz1k3ki8VXiSttr/KcJeeuxd1aVJNtjMgfJfHh4GF988UWlTUG9Xs8Y2ePnnaieBhdh59H/u7u7GI1GcXJyUolXXr16lbreaDRyp1ipM+Vc/vf//t/fpWLV+fpen/qRF5PtbKaFFuEjeIF8okk1QS5Ey7fffpukAU7WRt/PW1payqCXQIkG4mQKy9MguKd7XOBEzV7iJDnSk2MRvTUIxwsTimDzN8pGc/alpaXMWiC4ZLjISKBINISk/40bsmPMAKg4e5o2Uy1FpRGCzr5U3qsEjSXBxPoY7JDxnc/nSSxEVE9XgiVHgQ2qAGYEvl4PttFB4m1vb2fGnoAFosw9cJyxKg0J33v9+nX8k3/yTzL7gJKtr69n9Z1BZAl6yqzJH/OFY2Ft0SeTjYCX8oQNZC0iUq+9j/7+/j6Ojo7i66+/rtyP59n5zecPlUDtdjv1F2fONjzLCveHHMK4opd3d3dxfHycBpf3KLe5QBjPZg8VPYwrYmFckUvu5eqAMgBF9iw72AjI+bJCygCYDBKXsz4uMXYAy3sToPJ99CUi8h4RkSQjVZd+NiQVYydA5TnYV+bT1R4msFmD2eyh8nA8Hsfz58+T5DTA4b0ajUbs7OzEZDLJCtqVlZU4Pj6Oq6urJKWooHOmcn19PQ/BwJ6WQcg/hOt9hNS7fud/mxwyMOVyZShr7eD5MZKBhtgORkxG2bZ7vOh1mYn2uEgGePze8uZTag3QrH/lvBm8U11IZpj3w8/gp9CN6XRxmqRJtIio+DhnKJlXA3JANDYVm2G/awLP4+L3Jqv5fwlomR/6DoFZXAkNPnAg4i3C2MuIqBzuwlyur6+nncBmg80sD5abd5FOZWDuq7xH+e/H7vnYZ38M4fWHuJAByHjm12QFpIzxbmnzIx7sOdXuTpqxDvgREkJUmWI7J5NJrmXEYl3RVfSS6lZs+u7ublZYIC+WG2NnZAFZ4t+8Cxe9ZSMitzuR5HDV72PEcCnz4AHe1dts7XORSd/HBKzv6cpkxo7OlkSUg3MTiyYLGEuJIfiuK3/9zmVwanKhlO1Wq5VVOoPBIOd+NBrFr371q/jyyy9jb2+vYnfwnbe3t7G+vh5PnjzJZONs9lBt1+v10i4Sd0C0M2aT+KyP5cHkxce6/st/+S+xv78fv/vd7yr27LHLOIw4w+SACWJkv9vtVqqcjo+Po16vV06L5t7gK0hl4q9arZYVLI+RCiagjDsZY8RCZvDLrtIxORqxIHCx28gTB8hQDRmxqPLHd6L3kE3oPDKMr+YzjIOYz8SRdcl+vaxO4h2wMchTRPUkbGM/7ut54d1LzEFcwhww78wN/oznMpe24VQyohNed57lnULsRMCGkJxgzkt/CKa+ubmJg4OD2N/frySX4DDsWxqNh3Yo/X4/5+vw8LCi/1z4+xcvXkSn04nz8/OIeOjFfHl5GWtra1mJ/VgcbFn9T//pP8Vf//Vfv1fXIj4CKRXxdraLBQXgUu7obQFkXAgu/vZv/zYBD4oMiLSxdpBH/wlK71lA+jhhzBEISAkW0JPM0aYw2nd3DyeTXF5eZnYYcI8SILwAWfZhlxlNFBEggGCShb69vc0tMFRP8R3eN2LRCwgnQnB+fX0d3W43G5cD4mezWW5hBGysrq7mqVkE9pzM4y0+AAXWs9vtpjGCfUVoMUplXwwMpTNoEAs2TO7zdHl5mcfEE6SWJFaz2Yxerxdra2t5XLyrungWckgpb6/Xy8Cc6jucbGm8fT0W/P0xXiYj0KvSOcxms9xSi0NwZQ3zheyhF5eXl3FwcBCvXr1KW+CtfdbBlZWV1BUHuBCw3NNggL42a2trsbm5GREPOntychKj0Sgd4MbGRvZMwFnQF67ZbOax585SRcRboIDttQRx6KMPVuCABpNC3MsZGFcRlMEyc85YcCo82xkeg1Jn1CIWmReT0VSOYUvpIwKRx3o4yCZB4CwVDpitj+ibSX9OQSTYmE6n8c0338TTp09jd3c3IiIbLRuo0VsQQEdPkuXl5Wyczrj4PkAAoFNW2ZTA41O93jW2x/yqicwycPHfDo74uQGZSSiTVXwWgGGyl6QSvoqqOAde2JOIReWOK/2QI0CtCSs+j747qGEO8BseS1lt4Orhm5ubCjHLNoEyOHWwZ0LI5BT3N2HAZ3g32yr8nAGg7QHzjQ4wD7YPXk/PRQlcwTLohpNq9IeMiEqFE2NmXCQMI6oZ4un04eADJ6UA1siJ78U74V8tHyb2fdnuWc49B5Zj/9sB+ruIp5/SBtjm0wrCMkQC1diINXKVOH5nOp1muwPWnfknKXN0dJRzj7w0m82sDMR3guE46AfCcjabpU1mu4hJq4iofB7CslarxenpaX4H38COBPA+9p8ttjc3N3kyNHJY+jyvrwlqvmP54rKuo5vWe96DZ5XVadgccCiBfClnfoZ9u8mZxy7bXNaqDN55j4jI+IIm86W94l3G43GMx+PcHk988M0338TJyUn8/Oc/z15T2PdarZaVx51OJ46Pj6PVamUjfNtO5sjbokzgc7CJq9p+Cv37v//3/yZp+l0Xdop5ZO7BHcQoJE+3t7eziqRWe9i1EREVu48ddjUPODYiotPpZOWVE+4817GOZYu5dELDcZVlFJnC7ziBx7PAik4wuHLeFUrgP+wEsQFj9mf528Qany+TuBGRsaO/4/dyVTDxo4lV4km/O7bWOmbb5Vje/ZmQCf4mpi6Tq+7PXBJexNKQPtgt+1rGAq6xTXN84p0ch4eH8ezZs9w9wJjX19cr7YZoWeQE+cuXL/MwofX19TyMajabZeHN+vp6FsnMZrNotVqxtraWO7XA906gIO9/8zd/8526FvGBSakyWDd4Q/hZLDcPrtVqWRbc7/fj7/7u7+L6+roykSiJDS9ZHxqusmgYAAArThIHRzNkl55CZiBwBLaATSqZqBLAgMBGR0Sl9JiGdGbcyWyVQQCEGkSO5wUFa7VaGTxzsh9zY2Z6MplEo9HI8c/n86yKouTUZaaMIyIq8+ptGQbgKLt7AVE1gUGzUhnEO/gsnSz3h6QwGcG7HRwcxO3tbTx58qRi6HGANOo7Pz9PgwGrzhjoLXZxcRGvXr2KP/3TP801gaBAXp0BKJn5j53p+VAX78gJLRGRxCtVesj35eVldDqdBJQEaqzzzs5O9lV78+ZNvHz5MoNVZ8xNgrDHH0KKMmFkLWIRrNl+lID45OQk3rx5kzqGzltGSgKU76PH7omGLuO8nJ10NUAJcpFhZ7NKJ1k66YhFPylXbZpogXSyTDpYdIDKs/x/PouDg8zm525ey+XtTuVc8Xw+t7y8HOPxOK6urvIAhXq9nqQ8tu7ly5dxfX0dz58/z+2YzO9kMomLi4tYX1+Pra2t6Pf7+f7j8Tg6nU4SZAAiyorp5cA4bT8d3H9q17uCZ//ewRGXZeixf/szBlQOvPwZLoBv+X2DTOZ/NptFv9/Pbdroju+JT0Cu/fMSVKMfvK/7VgDmTGzwXW8b4vP4MAcUfN7v72ohByMOXkw48X18IGNhvhx4IXsAUPdT83vw/jzLOMdzyIUvwsYw1pKA5XPoNb4Qwhhfy+cNfL3G+E8CLLbxISdk1dF9r0H5Ln7Wd13vI5ccrPyxXMyDiUOfZIv/IahAxknW8l0n7/g/wdja2lr0+/149epVHkNfq9WyihSSiOo2b6nH3kcsGox3Op08RAM9BiNZLl2lQGIDgi3igcAk+bm7u5vvcnl5GUdHR5VE0Wg0qgTkrDH41RgSufQW0tK28TNXcxiTWIZMqvPZiHjr/6V/5/1L/87clLr6WMVTOQZwTmn/rRdgKWMkxuFdG/xNnEXC/cWLF/HFF19Ep9OpVMJATG1sbES73a7EVASpyKCfV1akMHZ2Prhq7mNf+JPvczkxUhIjyCC6QFsCPn9+fh7tdjsTAyZOwFroW0QkjnEimPkjTo6Iil9mvbHN4EXWH/lx/GVCgp+ZXDE+xMdZ1uyDLPPG06U/I+406cO9uR9+1PjYz2VOTEoxBpPNJV6xLeD76JOr/P05J9JM8DrBxOceI60Y33w+z4Q9c8/neD//DuKIdYDY9RbP0h+SnDg4OIjPP/8841vWBbvtrdkRi0rxq6ur6Pf7sb29HbPZLO2CT5DEBuCDqG6fzWbx5MmTePHiRWIF5MiJ7u9zfZTtew4GvHgQKmba5vN5Vjednp7Gr3/96ww8UHQ3/OalMbBlA2OznmwFQpjMUgNyx+NxOueIqFT3RCwySGyDM3lUsrZUANXr9TTy9JKyAJpVr9cXp25BdPFZjAKBBME24G9tbS33hqI4VBA0m83sPxWxcGR3d3d5mhVgBFIMQ8u7AjaZF6qcGCMXSuZtVyZxMEBmx3l3y4fLJOkhRYYbJ0gDdNhhG2EMfavVitlsltU0Jiy3t7fz+WdnZ/Hs2bPodrsxn8/j9PQ0eyF4vLwDcvZTZXs+xGXARkUU4I95ZWvmwcFBtFqtrF4sT1xkK+RXX30VX3/9dQaLgBJ0EDmDhSfDAFAjoMSJQbBaPiMe1uTi4iIrgCIW+nt1dZVVhg7iqPrjsmP1dgGcAs4Mwswybf00qVQGYBHVk1tcsRmxyETZ+QL45vN5pVKJRtIQLwS7dszIJ2CAZ7qPF5VNPLvRaOQpeci+QRNAytkntpDwLLZhQOpRgm1b2Wg04uDgIKbTafzsZz+Lzc3NBM+12uJYZOw+DXexwREPNpkeOTzLmVyDiU89eDX4xdY4SDG4Q0beR0KVgN+A613klEley4tBIjYCXZ7P53F2dhbD4bBSJVOSaMgFgMtBif/m/U2AU2EM+HJW1fbAOkOA5mACEAwp5G2u9hs82/YdPcXHOsHBnD+WvGAMlsOIank+8s662ZexBo/dg3HV6/XKUeN+X28Noa8elaf4RUgM7C0X9oltF/h+HyEO0KWfUavVislkkv0/kQXkyXIYsTjF9bHKhceIqMdA+Y+5fkpyGltuG4qcOGD0dgxsPf7WFYqsGbgaHHt2dpb9bCIik06sZ6PRyGTB1dVV9nFlTZaWlio9yBqNRuUgD+5pe+3DdrzlEz9zcHCQh/LMZrPY3t5OPMs7IC9UwxI0Y3P47PX1deUgAicR7Y9LwsfBZRm0sj62kZYTxsY7lVXNtn0mdiz/XKXccw/smz/HuGzzS+zNmpFAHI1GcXl5mf1s8aVgefAN9vtv//Zv45e//GU8e/Ysq8qpqK7X6/Hll1/maYyQh51OJ+/lQNhVy9gRJwSxkx/7+iE6jyz5u07+m2SIqG5J5XAXJ8vLRufgrY2NjYo/dDxmm4kMuS9TWSmEnjFWY2jjPZO4kDwmErAFrFtZaY/NMTmODLL2jL18F/Ao5Lt7mqE7xNfMkRMtvK+JL5NKpV/2n5K08r0eI3v5P3NXYknLgeNh77iyPcSGEZOYxPM4uB/9Ual2cnzkseEXjo6OYn9/v4LpwWns4jBWwf7c3t7Gmzdv0rayFZUYifhrfX09sYoxOrjOfAX+zEUc77s+eKUUg/P/7TCazWYyy9PpNMHMcDiM3/zmN6nABEAoBoaeQIUJMsFElo7Pk9FhG4m3iuDADbYgayIWe9sxGigRDppg3caB94YIwiBYKBEaDAzEWmkceC+DRRw0AooBQCEABgiHq1xc+QQ4sbKZtfaWGzJkgAJAE1unxuNxxWhAjFG95uCX55OlJdPAHNv4usINhwBIoT/GF198kaXHlDBj2JaWluLo6CjndD6fR7fbTZYX+Tk4OIjt7e0cO+PnyHkHh4zxMSD9x3o9BrwgATA4EYsjgJEpjBP6BFAeDofR7/fTaVGFgO4jG650Y3tAmann9zgmwM/l5WUlm+ry34iFs2KtAE4Epu49MxgMYm1tLdrtdspqrVZLIDWfz7MvAD3gXD3oYN4EMzrm4KOcZwcjdo4EA8yD58Pyh0MiaCZQmU6nWYrvykh6rxkI2FabHAaAYOtYG9sK7ABZeGy2T/bgQp95xuHhYcxms3j27FlsbW2l02OuydDQPJ4xQm7iVwjkkQu/h4Hdp0pMoUNUlxp0/D6EmsET/4+oVqb63v6c5dAA/fLyMuebwIfv4pvsl6moLIkj9AO5AvTiF0lEID/0qzFxhy2BTMeeeJs36+9j6CMWQaWzorw7+g+gd+DpAJTvoU/Mm+9tkMt8O3Ndbv8pA2XexRjA4za+Qv9YY2weJDz4iHsBmL2+nU4nbaMz5awrtoFxQcDxXmRSx+NxrhHr6oRgGfT5KoMMy+6Pucr7sEY/1WWsenFxETs7OxXi0ckO5C1ikemHACJRgF2m7cVvf/vbOD4+TuwbUT0SnmATnEQQuL6+Ht1uNzqdTgW7cX/bVCdJbWdZ26urqxiNRpWt1jybilof6mO7T8adJCnYAdkBR7jiIqIa3JVJQ2TKgXTE2yeHWV+xRX4v5sGkcnmVz+VzzJHJahPQDjrts0q8aR1CLvjb9m53dzfjk36/n2Q0yToq4y8uLmI2m2WbhS+//DLJfhL/EQ/JXGzyZDJJ3B3xsAWUkxlNSEPMeN4+RR9cXk5OeN2tn9jX7e3tSqXe5eVlYmV2DZQyQYUq/zaRgS23zNqv2UegD7bV9jvovRPsll8IE/TGP4fAYrsm8oBftN/medgjt/vAfpgDsP9w4hSZZi7BFI7hyss/5/t8nvn23xHVpubMDfGLCTLub0xTYqOSXMa/YM88LhJt+EnbALf6wKdiXyGmuJeThTxzPB7H0dFRPHv2LN8LfuXs7CzXk3EyJ/V6PQ8TevbsWepsxEOiaTweZ8yNr+AecAO1Wi25CtbM/Tm/6/oop+/ZkUUsJjpiQd4MBoMMYO7u7uLVq1e5kA58IqLiQCl1jlgANL6DwsD6IRwXFxdZOkolRMQiq2njA9Akm4hAwl6yAAiwhZ/Pu1SfQM0kE5kInsux9hGLLBACS68nvueF5vmuQLHD5o8D31qtlr2mSiE1YHHZPdkrenYZAPjezAWEI2DJ1UVU0zBnGE/mq8w8ObAyEBoOh3FwcBBPnjxJcstr2mw+9JliDskO+prNFk2Y3dC33W7nCQSeXy6P6Y/9KoN2ghtO33FlAmuJI4uIdCzX19dxeHiY5fnX19fZG2o2myX5SgXh/f19dDqdBL+AdUifev3hlKfpdFHNiA7jxNkiO5vN0na43xPySmXEZDKpVO6QjTg/P88MEQSpS15xTAZc1i8DYztYg2kuk3nosqsnWBNnYPy3T0tC97C1nJzF+9nx40A9foADc8rPqICr1+tJ9LhKha2XNMRst9tZvckzqIilhB1b4a0fr169itPT0/jTP/3T2N/fj3q9nt+Zz+fRbrdje3s7bbLvxXi93dIAEp19DJR8ShcnI/Fulh2u7zt2gzNky7bKdhQbX5ZYm/yPqCYz+P319XUMh8P8jIM5ZNlbd/gcwRf3IkEBecVYS0LR+sW/XXWFznksBsyQMfadjAO8gPygTw6kSvyCT/BnjT+4L37YYNtAGwLZNhiS2e/JfRgPuoxeY0+95gaxJjfRQwc83NNbvByI8T1khaDi6OgoIiIDVPrWYBNNtJT20PNSyrDH7OtdIPexz/p35ecItH+KywkT7Dg2j75RfA4sxxojY8ZbxkxUSXF/5tm+g6Rjv9+Pra2tPNmYY+whEk2+3t/fV7ZlgoXBi1Q0opNHR0cxHo+TWCLxQ8/Ju7u7ODs7y6p0cIeDWgfg+LCbm5vEnw5ASxl3RVNpC0tCyLjVv/ffXH6OK/BLss72pyTKyu9wX35mkhsbUpIR/HEi+7H7LC0tRbfbrVTFkETiHvTKnM/n8fr166jVavH8+fNKr6D19fW0R1dXVzEcDmNvby/tB3gAmfPaOJnLGH/I9Zd/+ZfvJLA/xGXs9S4SBNwEEW9ZYM04qMUxsPGPkx/otnsRmZCGJKSIopR9Vyoz38TITqgQgyKfboXgXsW20dyH8VuGSXTil3gWtoR3c0zMOP0c9N84g/nnmfzM+ujfm3fwVn/0hDmz7hszWBetq6w572YcXdphYyASQayBfSA/K7dtltVF/IyEg+2inw2+OTk5iU6nU9maGBFpC2xby7iaMb9+/Trm83kSUPR2Bttvb29Hu92Oi4uLlHvH98gjcvt9rg9KStmhMkA7BaqkKA1lz+PLly/j8PDwre+gxGtra7G1tVUhdlBiFNuKieEdj8cxHA6z6oWmxy4x5pn0MYqIdLQus7ZSsYARiyPkEXyDUognDD9g8/b2NvfXk7lAqQgWMBiXl5e5Hc1G0oC/Xq9nA0OfnoPwuiKKLUrcj98zn97qFFENajCK7Ctlux5O2hlcZ7+ZR969DDgA8wZfZGmoxkK+MK612sPJMvP5PPb39ysN5XlPKt8Gg0EFABmQR0S8fPky/uzP/iz7NqyvrydpZ9LSwOcfysU6G4zakVK2Xa/X4+nTp1mNQ7DRaDwcEXpychInJyepn0+ePIla7aHpIz2CAJ7tdjvJYQOqMhC+vLyM4XAYx8fHcX19nZVK6IiDV5cnO3DkM2QAvMXWDowKDebCBtZVko8FdO5hhXNlbi3rEe8+DcSVS9gB7IgBIeOJqAbpVFd522MJXkyQ8z0TFCX49dYp7InLucnu4rxcmcq2LRwqJwDxLOzX9fV1/N3f/V3c39/H3t5eygLABhKM8XqbCPNogqF02MzTp3odHh4++vMSOJQ/f9+/TdY9dr+yiscBGhcy754WjcZDX8R+v5+6UgZcyKerHgmITM66sgpd5bIuOGtdgjveAxl0NR+/c6DJdw20IXNd8WOQzAVJwD1MYJXvhP9ibrELpb5yX+a7lFcTU7Z1jIOtEdzXlaNlNrUkaFn7srKMClQODnGWmPkHI/ggGObR743tcpBnH/8uUspBiH/u/z/mj7+vb3bD+x9z2Uf+mIu1ZNsNssE8uzLO713OgfEyDa273W42Wua+vic2fGVlJXq9XvR6vfz5fD7PZAxBCcGQSYyISAxPgpUerfSYM85y0omkig8Iiaj6fCogWU+2DoJRIxZbX01+lgGd/ZkJVq+hbR+64gpn/9x66O9wX/tqyyLPKmXbNqS0ow6YTYg/RrI5YVBiDfrgttvtrHQdjUZZieyE+Ww2y5OSP/vss+h0Orl2tVotq7EhPfgupBTywPuV8+gqkO/rk//zf/7PFTv8oS+P23jMhAx+kb5RvNtwOExZo3LMW53sP8AqzFnpk5gfiBr+UK3muWWNbGfBeNwLfEzsyne9mwbZ5v/cg3Gif/gd7uekjolJ/IUJF/vCkoTieaX9x+fwM8eX/N6+25VoToiVuubkiLeV2hY7juA+HqdjRObO+Jx5LCuZywISfh6xiMngC2q1Wh5OViYHuD9jffnyZfR6vRzPbDbLwzBOTk5SP83RwB9cXl7G2dlZzt/d3V0luQzR6Pe1bavX65Wigu97ffDte2Yc+T9KyVGXEAb1ej0Gg0HlqEKcCwxhq9VKZ2XlgLmNWAA6qiE4JQ9HyaTD6hoU1GqL42xxlmRsYI8JjAi4TKzg8AHXzAPjdMNl/6HCgwwZc+YtLsvLy7l9hvckSOT/CBW9CkwyOShgXjGSKAZHRpd/SiUsA2hKtDHQEDpk372PNaJaMumTFx2AcKFsMMkAGIA3ykoWbjqdxu7ubpJxJpQajUZ89tlnCZ7Ld4uI+Pu///vY2dmJjY2NGI1GuW6WXc/Lpxzk/pALuUQWTQazTnyOygeX/AJmLi4uot/vV7a+RUR89tlnMRwOEzxSqeQKHesEP7+7u4vRaBQXFxcxmUwqQBH9xDHgbL1vn6oI1gqWv1arJVFsXbm8vMzqPbYtov/Itwlq/gac4bQMniMWwU/ZcwFgYyIO+Wbc/MyZUxyZAYfXkXsQ7JtQZQ0iovIuzAWOH9JxPp/nqU4m8LCN9HxzoIBtubt7OKl0NBqlrOGAnb3Gbk+n0/jqq69iPp/HkydPsrmqCUiDgNlsFoPBICtf0dPpdJpNmPkT8WlXNhqAlcFuaXMeC+L9WcuDg/byOcgS8xZRPdHFvgvZ5R6TySS3GbqCCBKLHo+2nQZQlgHWCPl14IZusP4mRE3ulHNlcgp/jazhp/EnDv4NVk2UW6esl+XYH6sac2CJ/pswt4315eeX9zSQLH2nK7/9O2wKQY2TCthw5qNeX1TCmVj0s7EPt7e3MRgMsnm2ATD2lHko3/27ru/S18f0oSSpfuy9v+v6fQip+by6bQV/4eCHbdcEfNhyr7WTeMPhMI6OjipJX3QULA3p026388TaUo8goBnj1dVVTCaTTDBgr5F5Vy1PJpPE9rVarVJ9AdlJkINu0xN1Op3mlhYS0aPRKLa2tiqkkhM2VJW48bnnOGJB6iIn1pl3XaW827fwf69DaZv9t/Xf936X3LpSBZthPGEfbx2yfeU+BP2bm5sZEJPUOzw8zJ0g+HSP99tvv41WqxWtVivnl7XvdDpxdnYW19fX2QDdfoq1ddII/ObPft/r3/27f5f//j524/e98BURb596xu+xl91uN0m/iEgymG2SEYt2JsaUJjnAs45JwW8uKmBOKcRgLiBinGiMqJKAxI38nHck7o2IiqzxeTC5v1euNz1ImSfwA9ghYuEvkUtjUxPFzC/fwZbjpyFC7ddYJwg/V5I5nrbc2cczLyZUmQ/bC2xlic+YYyekIhaYH+LOOJ+YhfXHNq+trVWwGe/Az9yuwHNk3UVGbEtI6vI59JGx8je9J7ELnMA5n89Thm9ubmI8HldaIRnbPUasf9f1wUgpQIdf1MTN6upqnqjEEYSHh4dxcnISGxsbcXFxkUHm0tJDs2pIJCYZ4WRSWWAmcDgcJmg24cQ2HzPHvp+DVSa4rOLgns4YOsOKQJE9jFiUtRtkI8gmsajSQsCcfSA4uLq6yqbAZp0dmBHoujTUmQkH/lY2Ey+wnRGRRCDjLp07hmsymeS+V6oleDfmkWPDrSw2vDyb+zgYY+8qAN7Z3Vqtlpn7Z8+eJUOOMeJ0Rvod8V0Dupubm+j3+/H5559nRY17MvD+OI1PNcD9oZeNrbPsNog46OfPnyc5fHd3l6TyeDzO+aeacDZ7qDbr9XrRbrfj9PQ0Go1GdDqdfCaOHbklw3BxcRHn5+cJrKmURA4ZkwNIO0XsEODBWWOe52ONcazn5+exs7OTwA35Qp68ZdHz4mDVumY7Y7tj0tjOymS7SQWILmS5BLrYFX6HfvG+jNU6FbEACtZ/5pWfGQS4fwz2lm3Fzpyhe/yM9yCo4dhvLpMhb968ia2trTzEAJuArTaRPRwOM+mAXXHQ6/+/Lwj5qS+vtQGf/7yL4PF3H7tnxOMBePk9z5HHElHtZcOcG5zW6/XUE2eV+Q5kJn4a0BoRCYBM/pRb6rkH9za5A4AmACcBZMITuaO6F9/p+Ua3TFR7XhgD/t3bbwGlyGipU4Db8ufYhjKAZiysnbOPrkLodDoxHo/zHgT8EQtwyLhtLw0aTRAzr+jzzc1NnJ6eRq/Xy+oYZCFi0Sy53+/nFvr7+/usMja2sv45iHcGnKsMHkq5fFdA/0Oun9IeIF/Mgwlf3rvZbFaqXkmYQhxCyOC3+/1+Bc+SpAHLuGff06dPUxYt81wE0yZxkUGCKXQIXY14wGSc2su64bsIzCA57u8XBwe4cgO9xad5Sz5BZ8QiqYLcmixDTpAL+zvPNVdJJjmwNMFT2ln/nzEY2/s72CvjKi5XGvD80h7zGeuvL1d8IBe0yeBn4LNmsxlPnjzJBD5kIbKEHR0Oh9kEHzzEM9bX1yt9we7vH1peeMsSz7WN5x4/5Pp9COAfc9l3OOBGX7wrx3PCGqEXJHAbjUbGXNhpEwlgFfTEJKR94GNrb7zo6i7PnYk8xtdsLno9QsagX6W8Mm5kHPzHH28n5v3wX/gTbAdjASOA/U36Ms8RiyIMJ4bQL+MS421j3YhFtVfEooLc8awTSuYv+F3Eoke1iz2MHWzLIRmxlfi3Uk6oJnVl0Ww2q/AUzDdyAcFJDOZY2vbv5OQkT6ifzxenPXLqvGUCX8TuMC5sOe8J30Gs5ySc5Yu5I87+PtcHP32vDMoiFqeDLC0txWAwiP39/bi7u4uTk5Ps58Nn2fLhrRulAKBAbNPjuFn2SkcsypS91c/BYcTbW0n4Ds7IpAgEjcvzeF8As4M27oPQ+28DeIJgjP3S0lJlGxpC6WNIceaMgcwbggqgQKGZNzPp3ltsIgKBs7FxQIBSQ4bxO0g91oQx0JiesTojgIGnio3eQXbKBlvMh4NnFGc8HserV68yoAVIEdzyN5VVGCC2BpIZXl5ejrOzswy4J5NJBTD+QyGkuJAl1skZCsDv1tZWJbMbEakXo9EoP4ezW11dzYamjUYj9vb20ghTjYjjZ2452vTi4iIiqtte6vV69qOYzWbZwBN9Mwh25scgADnCmfnzBLWupnQAifxBdDxWpWQnEbGoorIjY64BILZVdpQO7NE56zhAnbWwQzIZbMdsAg+dN3jnfrYrOF/WiffmPugMBCU9um5ubmJlZSVarVYG8RDW6+vr6diYB/SYtXVA7yQA8skcet6QQd7X27N5nw+hux8ywDUwKi/7WJON/m4ZGJWBvQMZwBvyRw84LubcwVdEZOWjt2BHVEkWgmTrAPdDrux3LYfldjAuBw34GnAE8owe4Qd4V0AyF7pq0pd3NpltucTvMUb0iTkwKex78g4mc5kjbJeTPibgIGX5PYEPc+Gm0D7WGZ9t8pz5Bshbl7hubm7i7OwsIiL7yqCTJi3H43E2UsZvgs+ccMAWez3/UHr5XeTUjwmIP9RFdRF6xHqAIbGf9gd+P74fEXF6epqkFPKysrKSeKrb7cbW1la0Wq3ESa1Wq0LaIzPILBe2mXXHTkwmkySbCXg4+ZQKKLZ8OGHFZ9AX49PLy8tMPjEXo9Eo+0qyywJbYVl3ErQkMSOqzY4tc8YOfIf5KMktbE2pIw5kscOQ1J5bk/gO9h3X+B6l7+JCXkxsEPhaPz3H3o3BszqdTnS73Tg4OKhUXiwtLeWhLy9fvqwcVOFtfD5UhCSlx4jPZpzsyPgY1U6/78W4LQtOjpHcwBazdpDAVJPjl4g9IYRcVGDSwYlP5ovP8Ae7UCap+H+J4/BhJeHjJIl3FnCKZ0RU+kyVpCqklONexkD8Sl9adMJJEhMq3u5GzG4Cy3qILkYsKuc8D+iY3xmZR47B1o6BTTQyztLX2U7wfNsK5N48Be/tZ7p4g/fmeWB7J9pL/MFWu3I9XACC3eZ37Dqz7TL2MxZHlm2LJ5NJbG1tZZUl2MUVvNhWyOnvi40/SqPziAXgpUqq2+3GaDSKRqMRa2tr8eLFizg4OHgYlMqONzc3M+vG4hicE2gSwNze3ibbz7OdpfPFJCIUFiIrurcX4UidfXeAxpYFFhHyqN1uVz5L6bQJIzKQ5XwBKB0Am3nF+fA95pB3RDAjIokjKwjGy07CwYNJGObcJBJgk9/Z0VjQGS/vvby8nCwy4ya4ZKwcNcvckoXH0WI8bEiYz9FoFGdnZ/H8+fNs4ImTiIg8sp6tXpQwN5vNGA6H6UwgsZwh8vUPiZgyccoao39s2dvZ2UlSjzXh9KBXr16l86LEE6eyubmZxpa5x5iWIPf4+DiGw2HOu/tDsRUAp48OIzsOEA3eDYgiIvdHz+cPPZDQDbIENrJlsI2cA0xMdhm0cPHeBPGuwsD5RyxAJjqKI4hYbMfB3pl45rnOlBmoMEesBXPCnDtz4zGSuTYI4N0gfiwjd3d3WXnmeaOxLcGpD58g+ICYY544ifX169fx2Wef5XYO5sxZRU75MmjESbM+AEIDlj/09YcKqL/r9+X4Lefo0rsu7JcDhcdsvC8CPy5OxCsDQTc09TxzP1cDMk7u4+/ggx0IMGbkG/8bUQ0gCRLQTW9VRW9MHJUgmrHazqNDzAU66gSadcTzxvvbTz/2XAfEJrqZJxPLvC/3N8lk8oq/IeKwbQbXruwC9zC32EDm4u7uoSn1dDrNVgrYJfzucDis9L2kv5wDq3LdTIL+WB0q1+F9V0nO/lQXa8dYyoSrL8aJbXfwFhF5SIcTMKxzp9OJp0+fpk13hSJrR5C8vr6eSUXWiSpUPk+P1ZubmxgOhymL7idF1QU6OBgMss8OunN5eRkbGxspiySGCGZWV1ez6uby8jI2NzfTZuC7sT3okN+tJJwiqhl8B+OPYQavk22QSTSTLiXx74DZwXKJDcrnMC7riW2iyUlXl3J56xH39eE9rB/+nt0Az549i263G0dHR2/hv6urq3jx4kXFljj5wDvS8oB3AK84tnAVK3P4KV/2Q2Azxr++vh7b29sV2wzWGo1G+W9izPl8XsE6EQuyxfpTkhyORfkO8s6FfOBriPvsgxmPdxrhU60r2A6+y1rzPXyO22dMp4s+V+A/39NbQ5E97mVSBgzI/y3fJmX4vQsl8NUkm7Cx9mncOyIqa+GY3pf13aSRSWOvgf/YxjiJ57g4YtHSwu+KnYuI3AKJvfP8cOIlyT7HQIyVeW80GtHtdmMwGKQ8Q0QiD+BxzxGVkpubm9kO6eLiIg9fM7HqyqjvwqPl9dEqpbgQ7na7Hefn59HtdqNer8fZ2Vk6wuXl5djf349+v18p741YMPsYaEiG+XyeBAMMMwpkI2/hQUmsEI85mdJBIBBmLyGfOLWL59Bc24HXcDjMvj1UXFEyGREZZEUslJVnsuAEdy7Ft4GnMVlZVQRZBqDw53xalqu5Go1GGjfmrHTmJsG8zQdF537j8TifBxFg2bBBgJxEuXycrDNsKL8NUsQD6UDF3LNnzypl3ZAOAOvDw8Po9/sVJ+25N4go2fB/KJcdhU9Y4W+2hDjggCCi5xOlpPQgcMBhB4Xs0n+EdTo7O8tjiXEyJqQIfACpyJXJEfSJn0FkQRbzTmwptIOCsEJP2epgwOqsrAMFABhzaaNsAIaOeMugnZgJJc+ff+ZnIaN2guhFxGIvPTLNWtTr1d5zXIyRTA5NbLElzAP2gedBFEZEVhyaAIPE4v4uB8Zu4pgZ99LSUh5OwSlgJbkPyEN2mRfICObPVZefOpFs+bKMPEb0PPY9/87BEDabi/n0Z5kj/7/MZgL2nPFlvvm+A/4y0Ham0IEXF6DUpAq/J3D1WqI3gDETUdgd/CoyWAYF6AH/xl+UJ9SY4I2obquwfroiyJWOrCPvZ/0u36kcp4mCUmcBgtZndIzAtdlsZkKhPL3HJLyBseWJsZEdNUjmnbG9q6urMZlMMpnB2CwbPMfVWu+6Stm3Hr/re5+6nkcsMuwkGk2e2JaXyVJXENzc3MTJyUlELAgJ1nZzczM6nU6srKxkawVv7eSzrAUH71D9wNhIIOGDqbDCnzmYNmnSbDYziHGVMViN7Xqcgri+vl5pGA05RdUt/rfRWFTLmmA36cS/kWX8nwlg3t/zUF62hw72WAfbAAfHvjffNw5gLlx98Zgv9s+cRGI8rpjyuyJT4Ij19fVcR+wkLUawd2tra/H06dPEWCaMr66u4vDwMH72s5/lPDpecXWpSTSIcHAL3/1j0E8ur5vHTVKR+cfmY6dIzrJ9MiLeit3wKWyz9fdJ7CHz7ovEnDr+Ml63P+Md0GeTEN61E7E4ed0YCv+NP/F2Pe9WmUwmiQPq9YdeWxELOxcRSRgZyyErxsOuTLZvjViQUeC9kgQhxjNhXW5rtG806cQfZNm6xb89DubWc1xyDZCzjlFtJ/kd+sb7M24qHCHtmRd0m3YoZfNzqkrBaY5rSUCg38iKk23M02g0yu2EzWYzTk9PM8nrPmjG4J6f73t9cFLKWXwGTIOs+Xwe3W43jo+P4/DwMAmPtbW1ZJ5L5WWxIaOYDByWgyQDbFdSRCy2HyA4/A5BYOuRgVC3241ut1tpyE21EEDP2af5fJ6O1oEFJ+xBwOEIGTPCZ8eJwpqBZL7cYwqwiaNHae2sIMPsnE3soAgYHubQJZAEDawPz8LAMY/O+KJoOCgADoDUTfQwdID/VquVbC0BJsSCmWGcgrPh/X4/vv3229jb24v19fXMsKFQZPSYY4AZZeaMnQu5cXb6H8plgHd/f58EK4EpW0cjIqufbm9v4/z8PF69epUyD6uPjGOEMaJsPWF758XFRYxGowRQEYteGhA4OPd6/aHXGIQujmU6nWalRq32UFbONp1Go5EBKVvyKDE3ycuas+WT+6LnDl7RCVcN8p7MI/PnYI/vuSwX+2YHbKdIHw2IFWyoA1tnuubzeaX02vqK/hrAmIT3OEo7Cijhb55tkowx06eCU0VtIxgPIHk8Hsfm5mZsb29nPxIqX2ezWRwfH0er1cojbllz5tiJA2e7bNvKhManeJVBkQHS+0gorjJrx8U8lESD15bPGLRFLIgrZNil+gYvJi4dGJq8NGh/rErWFVBUxoEbGAe6aYImolodjB1gLlzpZR+FvNiO817Yi4iFjUCOnAzxfNo2GIS7uhH9x66ura3lZ00+oXt8F1tUPtuBt/EH783YXTFOQA/W4R3sq00IlqQIPrzUR+MrV7ibKDP5V5JT3+cq9eNd5FQZ4Je//xQuB5ZgpnKu+YyTAhEL3ZpOHw6aOTg4eIs8Xl5ejlarFZubmxWs5qDB1S6Xl5fZwzEiUkdp1kwF69raWm7Zury8TIKS5IdJdONB1hgcQRVuq9VKvIFctNvtit3C/7Xb7ay6ddK5JA3woyaMWX/HBSbFS//LHxO3jxGjfJexcxHQW7cYD/+3HtsO87uSjLW+l/bHSVTsnHtJoZfIDZ+DWMPu4GsHg0GMRqO0tc1mM05OTqLdbsfOzk5ur2csllHsOGPl3p5j5uNTvWxTLWNgWBJp/nxEVHweu2Rms0U/ZOw5OoqN9zyX+CticfIc8+cEDPEga2t8B2414WP/VCZITET6vSGf7ZMZq/WEucA2bW5uJint5ExEvKVXZZLKvhnbZSzJz4xlmXsT5Pb5fIZ7mx+w7SqJJt7XhBrPfAwzlQm9x2xLmVj1mLiIZweDQSUOYv4iIrflEZMQPzMuH1pExTQxgp/L+EvfeXl5GQcHB7G7uxs7Ozu5s40dEozZnMWPuT7a6XsswMrKSnS73bi4uIiNjY1YX1+Pb7/9NkvA2f8e8XA85dnZWb5ko9HIk2D6/X5mZ9lKQDWPMwgR8RZZZeVFKFA6gPHGxkaloTJCwedhCw1QDQYsuI1GIx0412g0ina7nc6abKKBGYGug0ADORSNTMd4PM7AnK0sHkezuTgBD2LBBtLKivEjGEaQvf2H+WKOIeYwsg5mHNQyDw5myRAAbAheDB7a7XbMZrPo9/t5uhsGGVAUUT3lCHk4ODiI6XQan3/+eZ4yUa/XkwxZX19PY4fB5t0M4P6hkVDlhfxfX19XKqIw0oBGqt44EYcyTvqgEXSS4UEPCKwiHnTg8PAwjo+PIyKyTNXED5k+fg4xdnl5mdsr/Tuq65AbdIRAimovnHmz2cweKJZ1E1OMn/9DRiPf1n1v73OgCvDD1tiBQ975WRGLbRoG0hHV6knGgi4RFDAXdv6eCweCJcjmD6R8xGL7kvWXoNOgotxiFPHgzPr9fupZr9fL7QPtdjsDC+w7oBiSsFZ7yNAeHR3lXnhvZZxOp9HtdjNQNqlOUO1S5E+VkOIqwdpjv3vfd8vvPAb6S/KrJAYMLL1tEkIYshOy1yQLeluOwWDUZCzjQP8gkMoAh7/RI96Ve7E9AoDqJIzv4y11JrgclFmHa7XFibzemuCx850SWPNe1huvIe8APjCGYDyufJnNZnmIioOLkki0LvO+BEHoallFXhLT2FT7RNaRZMJ0Oo2tra1K5Q2VMdjZq6urigyZeMSXoP/vk9WSgCrl633f88/K7/zUQbHnlT+Q8fzfMus1xS6fnZ3lsfNUKNdqtWi1WtHr9RLb0OvLR7kjc+PxOEajUZyfn6f8XlxcJLai3ym4j50JbNfudruZLCHps7S0lMkF9ACMwY4JV/zSe4pKa6ox8Kv2fWA4dNpETxkku0LRtglMyvM9x44XyqDT5AT/t0765yWJHBEVPcOGmUAzfrBul4QCv8e3+/AD9Ax9pSfcfL44aITvExAzr2W7AqqrwEaDwSA6nU7lHd41RyWxgr31O3yql30QeoddZH6wa5YB6xtYpfSvvDftZpjDspcg68t6mazw/FPVblxnopV3sJyXPgs94nnonLG7SRufqO4Cg4gqAeOqK35mObePdjVT6TP5LHMO1rBO8Fz0nPliXRgnmAXdse6Z8OIqCUrPK/pMAt4xN0ky8LTtOokCx72uoopYJAbMOdze3uYuojI+Wl9fj8FgUIlnGZNtEXwMBRk87/T0NLa3tx9NEk2n0zwsin6/PB+5xZ49lhz9PtcHI6WsdAh+xMPEsf8R8uL169cptD7ZhX4yCD4VFTTUJeNKgIoCo0yz2SxP7KjX67m4HiMZQ0Awz8T5IsjOrvo5zmQZBC8tLWU/HZ4PS87WPUChgQZEGErpDDFzWQbMGBRnolBcvzNbq3A8EQtnCjCwcTRQBPTb4CHgPBslc5WXA3NXGwGQme/Ly8sYDoepvBh91on/r66uZlNGwI3nG7Bkw8SYT05OYnV1Nfb396PRaFSOX221WhERFSNLOavHzX1tUP8hXMyRq4bm8wV7T3URgJfggjnmgAJ6+7jJuANaAPfZ2Vm8fPkygxevNTLHuuIIeZ4bJUPmmph0NtgOlN5GOF30Ht0t98wj89Y9b4ctM0muqDIA4f4GaOg18uM5MgFt0FBmUk0MO7Dme4+d3uJ/G+wi4+g172s7x9gdNBmMMeaIqFTCYLsPDw+j2+3G5uZmbG5u5jy5SpL17Xa70ev1smJxPp/HaDSKfr+fWXLe/ebmJra3t+P8/DwuLy8jIpKs9zYzr9WnfpnYK23Mu8DaY4QUn/f3LDuWpZLU4F7INDLlppplcIyMGZhZbjwOyCd8Gv6w3MblhFD5WT+DzzEm+ymDcuwK4MnzVG4zs15BTrkymD8kREy6YYPW1tZyKxtYgXuaLLJf4bkOrD13DlJdzQv57sv6XDZ1N2FGEIsvN1CPWFTh2T+en5/HbDarBKhgHfzz1dVVVl1gO5aWliqnrPFujwHhcg6+6/o++v0p2QATDVzMC+N0MItNZh1vb2+zPwgYj5OH6fUBZi2TI/hXDgeC3EI+eYYxNXJAFQ6+tFarJUmFvhOQRkSlohXfS7UePQc52Kj0PciVk6JgYFf6lQSObaX1i38b2xG0lTbB/rRcDzAwzzPB5ItxQRIxp8xlSYjxHctHaQOwScb8YB5sK4nlq6urCuFoApjxQO55LpaWlmJ/fz+Oj49jMBhkm5HJZBIHBwext7eX7+T5x54QfJvcZg0f0/VP6TLe85o6CRJR1UmvO7LtnnuuJmetyoQZ/3fik++gjy6uMLEIBkVXTdy4YqdM7JT40piW75MENtFRJhT8Ny1WrG/GiI6l7X/Qd5NwrvIi/ohYxJG+SpxT2gXHbtYBfJ//Rp9LAtr217Eu784OCc+f8Y/JIT6PvLjqmHlDhtA/3gdiyoUrNJT3VmdjCngTcDc4mvFdXV1V2h+UOrG6uhrHx8cVe2g9cLXdj7k+WqPziEWQhzNsNBoxHA7j8PAwbm9v49mzZxUFiojodDoxmUzi/Pw8jo6OkshgYTjhA0UCeM3n86yMQODdUNUZWYLV0lF5Ys1uogQsngFvuU2HzxNI03vK2/04ZhUHi4DheN0LZXl5OXZ3d1MhCKrN7KLo7u1k8gzwQgYKB+HPXV1dvUUmOqA24cj8uoSvVDjPi7/HvGH82epDtZyb20MsbGxsVEpih8PhWwaStbJczGazODg4iHq9HltbWxUDOZ1OY3t7O3ubWW69jv7ZYwHgH/PFmt3e3ub8mlhqNpuxu7sba2tr2StkdXU1zs/Po9VqpSGGDMCAloz96elpHB4epj5yId/eNooxjVhk2JaWlvJEzk6nUykfZYuniVtA5crKSh6c4N9TJRcRFXITx2QnTbYIQMl3cJTMlzNNDv5dQUj5rAN2g8WSNLCOQbhTpcLnqJQsmwqjEwb1JSBnLVxNZbngnXieHRn6aVtskMHPLy4uolarxbNnz2Jvby/tDfYX8M/PyPqwPfvw8DDX0SeqsQY+npatQ+PxOJ2xT+X7FK8SYJXE92OBlonGEgjY5pb/N5GFbJQEAQkaV3A4G4u8Ip/ov7OTBr74fUiriAX4t+zb36AvBpH+nt/HpFBENfCLWJwgVlZaMfYy4OD5fMdjMXAzDrDeMWfufVOuR/m3gzU/z3LgSkiew3uyZdmA1sSH+8iUBBl6zhphiw2E+TzvMhgM4v7+Prrd7luJNhMhzBHr4/d8jBgtdeAxPXjX9a7fW2++z30+xuX3c7BUyrx/xvciFr36XGm/srIS29vblee4MoB1J7kwGo0qdpbsPoHL2tpaVtvY32Gn8bEmoDiNt91ux+bmZp7SBoajP6UTBxGLhr4cPIPfpRr/+vo6dnd3U44cSHteTErzc2Nz5tTf4Sr10N+zDDnQLZNGfMb3Rlf9fRM2pd8ticpSVyMW+AN8UN4f3bu/v88Efq1Wi06nk98hEUx8hs/GB+zs7MR8/rBVs1arxXA4jKurq+h2u7llqNzi7HvZzvqkvk/5clxiMsaYziSm41r6lxKf+mCXiLf7GVuO0c2yctR+rPThjNHV/o6Bymo9k8xuI+H71Gq1HCPPMxYo56OsIMZH8W/eibGVpGRpi5EPf9bvzXxY50y+lzGqdd7kcYkLHys+QW95TqnTxmWe46WlpdQ5k8jEOT5My/0ESztlHQLjUHxBQsh+odfrxWQyqexC8LwzFnR/MplUxn95eZky4HdFvss+XsybY5cfe31wUipiwQrjhHzqHj1H6PVDBZUF8fr6Ok5OTpKlw7m5rxQBSsRCYGEXyR5CavCH4BSlZKIRSoIsfuaMocvm7OANdCMeStzm83mcn5/nVhRnI2hctrKyEicnJ/H8+fOKkyH4ImBnzASdzCcsvAkqG7oS2FgBeSfmzmWQZI5N8HAaoLM8bJEx6+6qEp7NH1c6+N4RD+w3p1bQ94dKKbJqm5ublWodjKeJNu7LO7BWv/vd7+Ly8jK63W5W95C12NjYSCDE/GCwHfxBsJRg5o/9wsGwPvf390mgknUrs+gYMOaQXixUBuLkbm5u4vj4OA4ODirlrY9V3HHh1Km8YlzIF9lagq3l5eW0JRA+ZGVXV1dzyzC6gQ4zZmc5kFPKoiMWGUQfZhCxIE/tGAyWywoDA2gDNMveu4gqg1WCS9YMe0jQbGDKHDmzg+5iw7CZdsjYEgJzV1QgFwS+AFNsA7bXvcKGw2GOZXd3N4Ex47Lzxt5AdpDp/eKLLyrVVTjik5OTSkNe23TG8ynrbBl4PxaQ21ZzAWbfF1yVgf9j/3aghN6S5EHvkRuDTtYAoFTeHwLJW1Jd/cT4fF9k2pVO5XwYEJJhNLg3SOZyJaPH6AorsATy4m3IBqgm3Mt3jqj2UQRY8jmvkcdpfSw/U8qJiST0FFtkIOvPQ8i7ogrsYEBOz79y+4lljbWbTCaJ78qEU61Wy1PWqIp+F9nid/S7/lidfUwf/LvHnvdTXSUpZXLKemS9oW2D9ZIG5OAjfAWYCOLftvn4+DguLi7yd/hMkjl8lgy7e3lyT7ZPExjf3d1Fr9eLbrdb2QIIjrP/No5zsOhebNj0Xq9X0Tlk0GNxMtakc+mXy+D+MbtQ+svS7tpf+fJ3eEZZXWESp9QD2zUnqExcsaaudMYumVgiieeEPcn9jY2NyhYiv59lhuB3MBjE2dlZLC0txcnJSfz85z+vEM7MtRN03lboefvUL8sIvogYo6yqY+3QGeMv3pXYjV5qJvF5BnMYsYhlHVeadLE84rNL0oVxs8aMtfTh+BEnUPB7EZE7f6yj2Bb03wkUY8UyyWQSyZ/hnZ38NN71fBp3Wlc8n9YlkzrgwjLJahti8tfbOPn5Y/pocto6RaLItpj3YVxeZzA9ds92Az/N94mNWVM+Q2sV5tu7TvgZdth2sVarxevXr9POWr7X19djOBw+6o9tp39slVTER+gpxSQj4BsbGwlQVldX4+XLl/myV1dXOblsv+j3+wl4mKDxeJyVPA40UOiSxWMBCEqtmNyTTAwXAQ3kj8EdGaHpdJrBKuNg4QHVKI0DNAJ4gN/GxkYq+mAwiIhIQaY5HPfjswATCBWABGADRfc2BdhgBBHjyRgBizZyzDEN6Pluo7E4zQrwABBiyw2GkLUhMEHp7FgN1lESylG9LWtpadH0nB5aHBNcZvB5Ju9DYDoej+N3v/td/PznP49ut5v9tebzeTbuximYeXfQ9/sA5U/xKgMj6w6Gm8oiVye6QTxzhHNBn2hy+OrVq/8fe+8WIvu23fWPqr5XVV/Wda+99tnn7HCSkJAgwduD4Ml5SBRFg6BIHgKJEBKJeRBEJRJIkChogogXjvgUDajBgBAkEkQ4hH9UJCFoQhRP4kmyz76se1+qq/pWVf+H5jPr8xs9a61ee6+9Vq99fgOa7q76/eZvzjHH5TvGHHP+4v79+wU0kXBAlgDRGFKezzlCnOlCH70FlGCMrWG8VZE2aIezFJyAyQ7GDu/s7KxRcQlYJ1Ec0TwkkufB04h5iTG2ITtR9APZJTlmZ5dXk91X2o2YH4TZ6XSKjZrNZo1VDwAigQUrp9YVJ3INBAiaGQM8Ojg4aOgfti+viHH/0tL5CwhWVs7PFcG2MC7kaDablS2XZ2dncXh4GA8ePIjNzc1yH/24efNmvPvuu7G7u1vaQAa9GnaVAtFF9Ly2ZREQyMkMB2EGIfl6ZM2LEsiO7SsEmAMEO5ihnbxai/81ePaLCvAX4IEaf2jPgSlADcpA3p8bOzB+ZNDVSHxOYMlzXNXtfmVb6sDW/cD3OYCtJe280OW5QtccULsKzHPNXDggsd6zsAPoBVdQPcOc2oa57UePHsXS0vnrpsEBvLn0+Pg4BoNBOSCbFeI8JlckZHqaTpjvmUf5umdd87LJONlbeSKalX7YcHzD0tJSWWxFLtfW1uLmzZsFm3JOI3PK565CcNVcxDz4Rh+NHR2oETy6CgSdZdHh+vXrJRHZ6ZyfcUX1P1s4IyL6/X6xNexA8EKP7Uiv12voV0Rz27k/c7Bvfvta9A2fY32yv7Bt9AJKTsLbNnl+M7bKbdk25zFNp/PKIz/Xdt/41BgG27G+vl6qwt0ffogHkCvreUSUA5Z/93d/t7T54MGDuHbtWly7dq1cS5VcTjRjxzOfrzI5RiQ2oYgAW+WtcMZYxnVOIoCfjU8joqFTzIl1EruN/tO+Fx+4zgu9YGrkCX8SMa+yi2jKjxee7ad9kDqxITEYb9ImznWCDH9GHsD+mH6az9lXO1YE49qXZjxMmxkToS88A3nMySzik3wP/8M/J+GYQ/rJfHHsDH3zkTrGNlm3eZbjdGPY6XRadgHcunWrFHAQd9tOGguAbTKucNwynU7j8PAwer1eOauQt6JyRMYifXFi7qPo9yeelILRuWKCQHd3d7ccdnh2dhZ37twpIPjRo0fx8OHDWF1djRs3bsSDBw+KUuAAPXivDkQ0twBQgsxzrbAIubf3RUTj4DgLPcYCBcHx04aNgg8powQaR0zfeO0q33ticS4kczBk0+m0rHAg/CgIqyIR8+w5yS36hXHyqiqrbjZyzKErWiBXKhC4d7vdUrmSwTKKvb6+3ghWnZ3Ojow5AOzwnZUUPrLCnBNiGNvpdFqU+vj4ON59993GGQbIFBVUEVFWiy1zrxrEfpIEv5xYtXFn7N4aZzlwZp62fu/3fq8kLQDTDpzscO34cWLIK29zW15eLsmIwWAQW1tbBSiwPRajTMLUeoGsc/6GE452+NgKB0+1A5jRHxK2ERfP4MDQ8x1BBLrF2A1EI5plsOZvvt42z/vt0Vk7Z95aSF+8Emv74qSZwasdm0FTDobpp1cDAUGz2XkS/N133y1v3ev1emUO6DMJsE6nUw7B3d3djXfffTdu374dm5ubMZudnzd17dq1IjfY4dXV1WJvDXQ+qsP8pMl9cjDzrOtyZQ20KOnk/2vBEzJBwh5gDdhkDvET2AzPPfKE74Dwu7TL/5wvCLEIgSzaPzgA4G/sP+3WeJUDTNqwHtt+GfQTLPgadLcW5KE/mecRTWDroNt9xi8aeMM/fjznOclkvwv2ms1mJcHkgCMHwqyQ40/H43Gpas9Jc2wJ+GFzc7MBrvPYc6Dk+fCqfU2WFyWfFtFlrr8KdiAnLS0fviaiGcgg7yws4PO4BjsI5vWWIXYaRETjUHr0jsA5B8QsOLjfLCqwAGU8S58nk0kcHh6WPoGpBoNB4wUp2J5cLcJnJFsioiEz5lct0WP9QacckOfraD/LELrl5C14IWJeTe1g27qS9SYHjA7Urcd85i1U9AE84diG/0kQc+C8q8NZ5IbAQ46BeFM3FXP9fr/41KOjo9jb24ubN28W/rgalkQGfPdB0NBV0L8aWX48Bw7q+Q2hN8yB7yHOAyPSvt9m7YUcJ+/AdTkZanzkqid4bP3NSSKuc3LNmNWLlV6EtU4gJ06y8Rk+Bh4ZN0fMcQvY3s8n/uB57rMTRNgRxyg5QeT7eSaxMPqGzKN7jMfFCbYbGSdl++GYwjENWzmNmVwNxTx562vE/DwxnuOFMN5UTVIQLITOUknrmJpYmrHa3jJXjx8/bhyxsba2Fnt7e0/VV2MyEnDPS59oUsrKg9HGWHoFiDeC8B3b9R4+fNgo4WWfrieKv32oMefCuISSYDIHiThgOw8bGSe2DOYIbumvDS1JEyY8l+phCCLOHQPBuvu2vLzcOKgYpSGBhuBY2BD+iChnRnmll+/5zf2ABFcxcY4Eist8+bOI5uuEyaganDOmvKru+UfIGXsGJxFRDtCkv6zYE6jSl4g5aGOlFyWhgos5Pjw8jHv37sU777xTtoROp9PY2dkpb/pjrgyuLdOfFjIY8wq9jaADMXjCllscsLf3LS8vx/vvvx9/8Ad/EGdnZ+UVz05+oCMR80P0qJaMOHd2PgeBCksSTABaqpciouGEnVBCBnimgR92gGeyUuvDHd2uZTWiuf8d+fIqJ87UwSD6xnUOMq0TDhwBJtYrg1B0yk4YnjtAjYhSQm5eof8O2OkPPLDd9fNJVPosBbaHoPMOtAksDg4O4vj4ON5+++1ythT8Yt6xhTzv/v37ce/evbh+/Xqsr6/H3t5eSSg/ePDgQkWHAd1VplpS6mnX8H+tSiqifnh0/sz/OyDigG7mm2pGv4USefNZXk5EApJrzyfxgi/Cl6Bvvs+J2KwjNb3hOfm6iGbVBNfVfiLmwWoeL9/nBJ39nnnqdh2Ycp35EREX/EsOjmjX9siykJNNUA6WzCMHDg5OsLe9Xi9Go1FZdHIVxmx2Xo15eHjYeDMX+guesV2p8Zu5X0Tus/nyUegq2QLrnZO7+CL66qTtbDYryQF87/b2duEnCQh0DAzFoqBfAsQce1EVHMCCMdiLfkVEeWHF5uZmeRujk8yz2ay8jIQ+469rCSljZ4J4fAo6Zt9NAEfCxDqU59d6B+W/M/6vfc9zkP+cKLBO8J2rTvJ8+3qeY5uOzFv2bcv8ve9Bjqhk5q1ZnEOJD0bn8Nmz2axUvnhbPsHs3bt34/333y928PHjx/HWW2/F5uZmI+B3sm5paakseDuop69XkYz57JcimhWdtpPYOcsn8QqxIEfQuBoH/AVP0b+I5m4NbGpElHacYOJe+zxkwQkivrPtNcakehLbwRgdG1AR6RjUxSfYCHYo5CMu3E9+u4qPGMJ64d85zvQYwI/ol5N1kPXO+uO4wLkL+mQdRI98rTFRzjN4HkgoOi7NuMG8yJVstnNHR0cxHA4bZ+bmeC5ivlvIvDw7Oys72OADfRgOhw37sAhnmqfZRnleLkOfWFLKwgIzSZAguBHzpAyO8uDgIB4+fBgPHjwoE4wjJFAEFHm1g0xkv9+PwWAQ29vbjTfleAUOx2Xg6cQG+9xJgHGtM5+sFrHy45VitqTQLs4/76vGgPM2msnk/HWL169fL9uabLyd3cUJ2ylGRDnrxm9AycrjA4IhlJDxA1ac9YyYr17zuY0RhhYlsELRrrP/VKY5eMwgFYCD4h4fH5ftm0tLS+VNXA7iqUrDMOAQvF2L8X/wwQexubkZb731Vtku2e12yyGhyBd9tRO6qs70o1AG++gdVXYcPM93VLEwl64iw5k8fPgw3n333WJoc6KUeUBmXCFFQgOdWlpail6vF1tbW7G5uVn0g3l0ctRVVugdcsd12AxWoLyKxOcEyj5ThbknULAO2nFZrg12zR+vKMJ7J3v8P7LnZIvPb0IuSfRAGQg7ye35Jsi0fQPgZGeY+0v72CXm1xUW9JXv6DPt7O/vx9e+9rW4c+dOSV4ig/CJoGcwGJQ3RY3H47h27VqjjPn3f//3Y39/v5HU99jhxVWmWjIhf56BxiIyOGDuc9AUEY1573Tmh7Yic/gvZJI2eDkFvsVA2c/ONhMd86oz7QImfV4a/V+UbMJWZ2BuYOkx2keht/Z5jAH5gzIotvx7rAa8OVlsIG2gji01OHRfM7iDVxFN3cwBU04+wR/7Mn6cPHbQxFmOVExxEDV4gefu7e2VF2XAr+Xl5Tg6OoqNjY04ODgofKMvrvR8VrIo8yD7Ln/u+brsd6+CXPkScfFFMPjA7e3tRrBFALy5uVmSPfjUiCiV+chfxNy3s0rPc6mcmc1mxY4zN66wMa5cXV0tOxB4gzFVV5ubmyVh5GMk+v3+hYCY9rP/ZjGZCmn8rCu1wILwLfPPtsP2yPKJjKIDtimZst3IvtvtZnvi68EJ4CD66oRwtjUZexr3mI/4c/t7eAcWQ4c7nc6Filjmgq2We3t7ZS57vV589rOfja997WsxnZ5XAA2Hw7h582YZFwUE5o2T5U/j71UhbyP3ogE2Cl4yZyxiOmZDR0ejUcGp+D3PLW35TE945YUdx2M+doTr+N9JM9sR7x5Bj7Et+Hg+oyraSXL64m29VP+YH5ubmyUG5Rw5MLcxM7Jp38wYSHgtWkTyuGr+wBgkYwHj+Fzt5BgVHaUtbCZt0M88HmP0HKfTNs9DFuxLPVY+ByMZQ9AO5zAjW046u5jHix3m0dbWVjx58uQCRhsOh7GxsRHdbrcsYj+NiPVrC+OX1fVPvFLKjsHOEmKSYf57770XBwcHhYHOXKIMOCX2l/d6vej3+7Gzs1POCPL+69rKq0EbSQuMBsEUgNgA0QbCSowAn52dlRVmVhUJkFjVstNASPhN8IYC81wUi5UveIMDQVi5t9/vl6QXyjedTstWQyd9GCNCxDkQdvCep+Xl8wPY4VGugiKBRf/yPPqZDqrhgwF6zTkfHR2VgxYjogAx5jNi/vphnKbPu6FNlP/+/ftx586dxiF+gG/z+XWptvio5KTJbHa+zYPtAPBqPB7HYDCITqdTZMzVeASmBwcH8f7775fElrdZAjBx0OgrRpwXF7DawhbL69evl62VGGkAsLeS2fH6oFRXUjH/AO8cyEXEBZ13oOykVHaaLqenHQd8yFS2STUjbt2nLeyN9dIJ+uwAPR76nEuGnXRywtBbZhmneeZEFqCI9lkhd2LQyTg/Zzqdlrd43bx5M27cuFGcKclowNry8nLs7OzE7u5uDIfDuHXrVgEMvBjCWzwiFm/numrkvuWApAbCmMOnjcm2PrfjBIllzAASQOND47mfz10hYR/pgAnZjYiG/qNbeetWrZ+0aT21zuQ2/Hz/NtA0cDfYRKa8WmzfVbs/ovn22WwfbEfgswPh2pgcyBqMQk6G+V6DZCfQbC8MbPM9TtQTHPitwWCe4+PjUtWCLvssN88FiwnmhXlp3FGjy+juomDl47T5SZL5A5h30GLZphLZ55J2u924efNm4SWYjGsfPXpUzmWl+gW/urS0VJKEBB/g7LzFylUCm5ubcf369dja2ir4G6xAosT+FpnDrtBPxknS08F+RJTF15wgYG5dweVjBGo6koNLjy3LQA5sLVOeGyeR/Wy+4/tMtOE5tt5l3Ovgms/4nnGyqJcXfOi/Zct4CXwLdjs7O4snT57EaDSKW7dulSQi/QOrbW1tlWqQg4ODMjZ8NIQNQRZIpsCHq0idTqcqc/YxtlkUA1iuJpNJ7O7uxunpaSMpw7zw1kInJ+2vvBhjf1xLbHQ6nVLBDE7KSVJkwLKWF1LAWfSTufMRCIyXaqrMq4go+M/+2N+hM97hRJUm43EizJRxQU5IWU+s7xk/OAntsfoZrlJzv5gH+EzcaKzL/Rxs7+15xFmuHkZGLEPgKyfuPA/MIwUy7EhxVRrz6WpTn3mGfDupadyR/dHTKMcgfu5l6RM/UypiXgbJlrdut1vADQ7w6OgoHj9+HO+++275jsHBLCZ2c3Mz+v1+bG9vx+3bt8vh5azaZLBbWzXlf6o06CNK68PQmUhPHECNibIj4UDA4XAYo9GolLWSVKKSyW8rIbu4sbERGxsb5Qwo9vh7G4tXbnHsOD54jHOhXwgfyTLmxZUgBIpUIOWAxM4OB5gDJgwWSkeiAVCUy0czGM/BN0roYAfjSfJye3u7rMrlTDBVFd62R/sYlr29vXj48GG89dZbBazRP/g0HA4bynUZBX0diflnnJah6XRaKiLYZkeChorHbrcbT548iffee6+xmonhQxZWV1fLgfXICwYeh8YzlpeXC6DG2JKcdhADKODlA8gMNsAJGpIt7h8rXU4MGyT7+uPj48be8OwscECW8YjmqjfXZTvpJJYDY4+JPrscFyfjBDDO0mCXINsg1QCGVXQSShHz1wSjNwYt2AP6zPOZN8blZFxtJWo6ncb+/n4cHh7GaDSKz3zmM7G8vFwqpAAz6Ch76eHByclJXLt2rTHvBudQDcxcFar1qQa8oGdVSS0KrOwbIy6+0Q/d8quMLQvIvQ/tpJI5L1IgX07E4vMMxr26a3m0/6WPDhZ8bQ6A+N4BmsdZ44/5wf3WXwM2Pw8yRrD/AoQjw151tY2w3NoWZ9vg77BTroh2gIz9JkFAe9gA+0c+w8aSvKdfzPHS0lIDSzjZNhwOY3t7uzHHfhGLbYDnxDJp8jifRZe5Js//q6asw7XEgoMMeMnfKysrje1uEecvGsGe7u3tFR3mDE2OPiCwnE7PK10Hg0F0u+cH1w+Hw5Lg39raiq2trdje3o7r16/H9evXy/a9iLnNYLHQAbXlw/Jtvc9BF7JmTGdfC0aHJ34Bh+U/J52yDaB96yuU9cifu93s430dPOA7621OsOfqJttqJxJ4hoPfiChVqsiCcTtts53KfcCfgzVGo1E8evQo9vb24u7du+WQY8c7169fj8ePH8fp6Wl5o6555kQOc4RteR59fhWU7ZMxJElXaDabNfAksgA+iZjb/5zM8rw7iQPG8u6imo8wr8E9EXMszLMj5lWFxpCODTkX2PqFj+A3lH0sskZSmnuM67me2CwnLhkb8u2kiSsJ+e17LMvZ1/M3tsf+O8+ZK8i4J/tzVwHZTpMcB9NiW52Qoi+Odbge/EQ/WfC1jbTMZTtHO8PhMK5fv96wEfgLEtAsGtEuL2Cr6UHm5bPIu0o+StL5E01KZQGAAevr6zEYDMrrvTud8+qJDz/8sDhATpKPaB6+t729Xd6uRXLKBsOAKQNuC5Ozpc5COyC1MMBkhJBrAeQYE79xhmotG6yc5MF4sC3R4JLyR/qCQGUHXyZT1Q8YtIgo2UpWUQxu4JedHWNEeOkjyjQajcp5PvDIh+IxpwYlbH/zPMAH5ggFtMLxuQ0bCnJychL7+/uFxyTEANk8c3t7OyLOtwjl1QPaf//99+ONN96Ifr8fm5ubxUngyEmAcR/g/9NEBsEAo4goBms2m5XznW7fvl0MYbfbLYdU7+3txXvvvRdPnjwp8+mVF4ITZJPkDrS8vBxbW1ul6hEjjSNFr3PAlYEn5G2uEfNkFADcztIrD+ihA11v83Mgjh3hftsP+BrRPNPCJcB2fBC2ISfV+R/Ztt45oHdgZ+BCux4zz2OcBu3ovHXQrwmOiLIiz1u66KPPnDHA4n/4Bm/9ZsXf+73fi6Ojo7h7924B0CTksVfXrl0rlVTYmG63W14AkavEXkYSqhZQPw9ZZhyA5aQU83zZ5HgOyvyDrNp24w+Pj48bZ5BEzPXNixmujDOQ8TPgD4kR65sTy35G1kfLaE5SuQKnNn7z0rJhjOI+2h9ZLxxU2w5xj5/vZLb10/11JarHxf3u/6Ig28/w6rn7k59rPeUzB5aZt06GRcwDYPtBklsREbu7uyXQyuA6JyHd3/y5x/k0yrpymeudEHiVlPFPTs4ZzxE42vYvLy8XrEbgNx6PY3d3txx30el0Ynd3NyaTSTnzy8Frt9ttVC1HnFdGr66uxhtvvBFvv/12vPHGG2WxyC/9gNeuorXMggMcNNqX00a217YVyBb8cQDO8QycgZfbsiw4+QrloA/Kul/DsFnPavfaDljmHFdArlb2wlYeA/bf9oB2WBBi4YBdDcgOC+cOfDmImfkdDofx5MmTOD09jc985jNlWz2JLQ5XPjg4aCTZ6RuLdrZZ+Hrz4iqSkyaWYcsjOMYH+jOe0WhUKhCRz4hmEQS+0zEPbdjWoydgJ8sL12Ef2JHDtciS40dXxETMZd8LhjkOcFzq5/tNeiSmnJSmXzzTsWynMz8/znLixS/iOsfrPJ+ijYhm1aATV/TdW48d4/KZY2B0AqolrZ3gYizGMLQzHo+L7nFEkbE+806/nYzM+QsneS1TOWn28OHDePPNNxu7HHzgODz2gjWVph8Xv0ZEWShhbK7Mvgy9lLfv2YBGRJmc09PT2NjYiP39/XjvvfdKwEuShvtXVlZiZ2cnBoNBXL9+Pfr9ftkil5XSARqUn59XBRD8iGiszKCIGAWcPmNzGaJXiygjphwaoeN7rzwC6FHQiOY5ETaIDuCzkwf4EShgGEweM8R2QK+485uSTeaDvlOSSBKN84bgq40t/CVTm8nbfwwm7OxxrDhV2oV/BwcHcXp6Gjs7O3H9+vUyLjL/y8vL5QBQtgiZv91uN4bDYTx8+DC+5Vu+pawK9fv9stLhV3FeVUf6cYlkhgNiJyMmk0njMHuv9FMZ+P/+3/+L+/fvN8BuRNPJo284WuS61+vFzs5OXLt2rWzDMui2s/RWSvrqJJJBXa74ccWGA2N02oGgHYETVP4cZ0LilzachKI9xt/pzLdZ+Hsb7xzMefWG50EOLPidK74Ag4ACAwm34SCC8eEEuYYtlgYkrrzKThU75zOEuB77Y2A+Go3iq1/9asxms3jrrbeK/fAz2d6HrhKk3bp1q7y22rJg+qT0+OO26coTg7GciMiLIoso+xH66P+Zb4NNAgrzFrvJvPkcRldIOQmK7CET+TOe7/us2/kn62NEPbh1EOf7DETdjitMcp98D7jE82KsAeCzP8NWEmDkecx9ykmVrCvMFc/OASky7/azrfP4vUDgIMl2knaxSeYxoNaAGVkYjUZl8YrPqdZm0c3zQDBLIsV8zbwx/2tJAdPTvs9HSrxssnzYBpsIhtFR+ytkkqQCeOnx48flRRPYTc4R4uwoB5tgNRZEV1ZW4nOf+1xcv369vOmUA4uz7Fjf7T+zrtBXB7sR88Aq38vYbZ8cH/haVy9br2r66v9ttxhLlinaoF3jG7fHtW4zJ43s55ygyomJiGjglowT7MO5l/n3Io9xB5UbLCTxJl4vNHkxJOI8MXnv3r1ynpT71Ov1yvNHo1HB3+Buxuw4reaPrxphL22bXLXjuGt7e7uxADcajcqiLMfBwHMvcjB38AQ8TXKA5/M3cuLqcsfIlm/ssp+VZcpVa4zHmM36GzGvTPTCIvGydcZVUuB6t539uCsk/Tz7d2ydx0WxhRd08k4c+1D+BovCGyd9zUPLsLe7GVP6b2xoRJRYFV3En5HE47xaHyHieIGiluXl5caZtuB1ZIGqPS9gR0Q8fvy48UKak5OTEqMhdz6L7/T0NFZXV0vM60QYPPsoOmQduCx9okkpiEkH1A4Gg1Jeura2Fvfu3YsHDx40yto5/Hx5eTmuX79eVmi2t7fLag6JFwTWiZ6Ii6WwEXPnbuOCsOM0LWgW3lw1hTGnDxZaJ7oMFnkeAs/q1HQ6PySYZJ2DUgNc7ltbWyuH0RmwGmRgkPjbK2k4ptlsXmpoZ2cQ6gQc8+lD+dbW1mJ9fb1hQOETMoBRRtD9JkUbBgJizxtt5bNi7IC5lio75gSF3draKtlrAyOe++DBg/iO7/iOWF5eLkZkdXU1Hj9+3AAVOUD8tJD5TdDJfCLLnDNlx0Bl44MHD+LDDz+8UNWGHPnVt3ZEnc55YvONN96Ia9euFb1HVpzgMOjy9xkQ07YTYF5NQYb4nPnELuSgy6DTQBwb5NVoAzq35cDU3/GTdZRnwK8Mtu3QHIT6GlZouHcymZTKl7x6ZxlAv7E/XnXjFdEG7Xam6KlX7DwewAw2x6tv3vI7mUzKmxvv3LnT2OrEIgEytbW1FQ8ePIiTk5NGhR3PNAj6JOmjOG8TPMdPRVw8WDdifjD908iJG89VTvT4eq4lQHXJN9/z4g5AJwDLupfBoec/98tjzNd6HE5mOYGUqwwts+ieg4GcUK4FoQBX+zF0gD45CZsDRsbN7+zL6JOTPMYWXjhyYon7c3KLzx0gOCihH/Q322fm2743Yh6AZL47+cRZQsgseGI6ncaTJ0/KAcnYMbAN11NBmRcNL0O1hFT+37JUoxexOvxxyQkG5sY4LCLKkQqM2ZgWjDadnp//cv/+/djd3Y2IKNv7qBQfDocR0XxLsee21+uVRaEbN26U+bKuO9iLqJ/jZH9ljM71JmNbfGV+BkGc9ReZMT4gqHewbV1xuxmnWjfRQ2NE4hiTsaFtqm02tsN6k+2xF9Lok32xEwc124Xdtl8gPlhbW4vNzc3odOZvyWZBh+NTkAtkhHk4OzsrO1k+85nPlGeQCNne3i7JGPxCTszh90mUORF3VclJEWTffmVtbS22t7cb20rPzs7i4cOHETHf1gcWmU7nL9FxdSo+j4Sv5zei6U/BVk6I0Ed4j9/mre5eVKRfWd6NXe030Flwrv8naUKcYD/rdlhQNAY2lsvJIyemfE0+1D8iGouUjAfyuI0BvUWRftiuZN8K1nRinHYimkUvGXdMJpNSHMEcObnps2ndDn3j5RO8LAis7P7axsJ7jqwhIch4bMNYnMA2nZ2dxc7OTjx8+PCCfDyvnjr/UCuQeRp94ged21GReFlamm/3Go1G8d5770XEvFQQJnNWENVRvKbU1UsIsZUWgMMPAm3BzJ9FzMG4HRHjQKEtPBkcR8z3U2JYUOC8hQll5SysbrdbwB2n3BNw+Q0m9Nv8dbDs4DQHHfAW/jImgAmBzmw2Kytl5g8OzGOmneXl5djb27uQ6c/BB4GMHXMNFDpINQhDecx/+Hl4eFjOleGtfLkqCsc8Go0aQINrXF57fHwcg8Gg8RY4B1GfRsrBlZ1CxMWqOb4/Pj6O3d3dhkxRFcMrn9FP5mRtbS1u3LgR165dKz84ZZ8ZlQPXiLgAEOy0ciBpAJjHmOWP652k4hoMNfLL365MBBRwvRM6tk8kaXOQ7CQclSgek/XRNgZd8xstCTrY9sa1zK1XZ5x0gDfZ6TkgYo79LHjHavzR0VEcHBwU0IId9dkFbBNDtnhTFH0Yj8fxta99LSaTSdy9e7cRoNuBAxIclMAjJzANvK4iDQaD8ndN7pkHJw4Wkf1d/jwTc2efhl9l3pAVVtLwxfzkRR6e78QszzcQc1CGXGYdzPPnZET2wQ5os4/0/fQ7y777DyEvxgEOTnNwnvXU19IuoBRwm+9z3+3r4F2ex5yQzkk0A1fmxIsQtqX4Wes9MpFxz2w2r4Bh/ofDYam0Pjw8bNhg/IEXCO1vavKZx+D+XIYWtbsIf7xMMo+n0/kh8Rlr5K1pBMb8j21/9OhRvPfee2UBAr3ER7HgdP/+/TIvm5ubce3atVIVBc7zYm9Oitg/5GAs21f0zbIPdjSOZ6x877mpLcxwnxMg2K2MHeyLrVc1mbLMP83WcD1t5qRtlk/6Dv719/TDtsWJbfiSq1J9L21TLecDqq9fvx5HR0exv78fo9GovAKexSFXybELgvk9Ozsrb+B78803YzqdH3g+mUxiZ2enXMdZo5PJ+TY/qvXMz6teLWV/6H67en91dTWuXbtWkj9gQfAguIhFQXAQuNhJ3l6vV3TVmBvdyjtrsM25UtGJff9vmeJ+x0Z+nv0l8SmxJfrPodw8G5vgpFq2D/7bRB+tn47NrFf2PSQDweO50tcVYeaR55Pvwcr8b13KY8FmOQ51gop+un2SifmZPpvZWwxtl1jA5WyqbJscW0TMK8eploKPR0dHsb6+XhJcjsXoE7uerJs1rPEsYgEKcv+eRS/loHML1Pb2dtmiMJvN4sMPPyxvbvBKwebmZty4caMko3jlLYkcFN+rRBhjv5XEWUnKDw18rcSubEKxLDwogcGznSzJGWedXWkCqEAAu91uOZg9A1UcLQFWPp/KmXdANQ6o5thxCnaIGSgTfDhg5a1pOckHaHVgy/P5nH6jYF4NRdk41B6lsfPKwJigk22Unk/mnf27N27ciMFgUIJeZBAHwFveDPx5jSl8Ozk5ie3t7eK0c3D4aaM8thwARkTjDT3I0Gw2i93d3eJsKQsl+ACQugR2fX093nzzzbh9+3Z5ZSyywipMxPw8OfcFXc3OIqJpa3LgkgFzDqCxIw7O/Fz6kYMYjK+Dy/w7B7mZ7+gH/XIAaqDiUmVX03gs1mGDbe6x8wI0sZXGJcgc+I+eGKTBH/TRgI3+TKfTAn4jogCw2WxWVmU3NzdLO67Mor9898EHH8RgMIgbN24UZ+pVsgymfH/EPFFx1fXXINH2xuRVz6eRZS7LoP/Pn9veO5CNiPKCDkBpTnpl+XA1s2Uo95Nr3I6Tk77fYNV9N4CtBY+u1Mi6DJ6wr8DWOKkJGagZbNn3Gpv4O+OT2Wy+DQHZN+7Adma55neW5wz+st1xAsh2zrbFbWTe0v/MS8YBz/j89PQ09vb2Gm/0pdLdlQDPoqwPz5OYelb7zwu4PwlyIOSkC3OM3PMmL3xN3g41m83i3r17xSZHRAMrssVteXk5RqNRrK+vx+3bt+POnTtx8+bN2NnZKavnEfPg3Ftn7Fds77OsZVvu6knrs3U6j7sWmHK/24+Ihm3he/u7LMuLfnNNjbK9cV+ZR55pnJ754oWYWhIde1PbjlTDPn4W9sL+mu2a4IzHjx/H/fv3y8uYqJTi7FSCU2SLSpEPPvggVlZW4vbt26VfVD/ylut+v1/8+dbWVuzu7hYe5fkB81w1yr4NfmK3bDuJ+brdbjx+/Lghy8g97XAGFYv3VMO4osX4DHnKWA4y7rFOOomFrbdv40BuJ0kZt9uDD543x84k43wOEtczDhZFbaNoJ9sR2xOuNQY2GZd7kcX6kbc62sca02cbAW/tz4n/7S+tb94u6xwAY8Efun2e70Scdd45Edu6jN1yziEiSvIJf+xzkvH1LBgtwpp8/lG2uGMHSVRfBrNGvKTtewjJyspKORiv0+mUc3ycSex2u7GzsxM3b96M9fX1cugxb+MiA+xEjbf9IYRcx4Ta8eFkrURsw8MIIATOXrr6wZliKzT/8xzeCuekGP0FKLBlj6wpyjCbzcoB0lY6Z0YRwJyZhe8G9pR3W2lRCkCx3+BCogcDZKUnEWYl4nlkrd1/b90zfzhfbDablYokGy94xd/j8bhRmjibzbcK8PzDw8Ni3AFYGA8cBCtJ8JpVf86csnEx+IhoGqtPG9lwz2azxhsWj4+P49q1a2WuCDSGw2EMh8NYXp7vr19ZWSkHxrOCQTBz/fr1uHv3bty5c6esDgGULZfMETJmJ5adZQ6OMwC2TvG9dTIHaE4Uc6+DKO7nu7zVh366fDg7QHSOcTspjrxjN3MQbLBj0II9IWlDH7GTBgTWX1bzfIguB4zDS76zs8VmYxc5MJE+OIHoZ7L1GF+AzGW+cv3x8XG89957sb29Haurq42VKRYD+Nz8chLc4N2BxFUi96kGFKhyuGzfM4hdBAINar1Aw7Yr20MDdoNY2svBZg44DbwNtJxgzQkgjyeDM3+ex2e/abm1nnoMXmjCz1NRsLS01Eg8I6e+JwfR5in88wriZDIp+uL7vGCWgaf9qIODDNx9P3y3z/dnOVnoQKAmk9hn5o3nMw7Op9jf32+c+wblxQ1v48vJBj878zl/X6On6cpVsQHIOHx1Et2LhBHz7SS+LyLKNqvj4+NGpYTftDSbnVefDgaD8ia9z372s+UNa7W32lofbcfhXfazNfsAWT/sF7jfv01Zp/xcB7zWxYxlajbKAav7mNvgb+uUfasTALSHXnqO7evoL5RxuZMfmRfuK+PgPusoScy8TXgwGMS1a9fK+UePHz8uuJrKHxIoyBWY4t69e7G1tVXkhDcwdjrzHQjdbre8EAl58Bz5LKJXQWtra08958bVgdaFTqdTqodIvhmjDYfDxkHfrkwnGU/sSHLKMW1Es9rPyRLPuxcXanpouYeclPKWQmTViYe8XRTZxG4zRv4GL/vHMZnHYP3Kcmk+W088HmNddIyxeZHUhSo+g856Qh9YeMI3Hx8fF/67qhH/nXlHmxx3AH43jmV+jaegGoZyTE3FEzsPPE/MLWPzdtLd3d3GEUN5gY0XWYDdl5bOt5Hav+BHnpfgC2T//zR6Kdv3CEK2t7fLwKfTabz33ntl8AgOCal+vx/9fj+uXbtWqom8t9WG1xVMCFAGpa6AQMCtoAhmXoXI2138bDthxogwY7Bd2eWDzJ1dtyPyFjz3n2cbKM5ms3LGkg+fRAn8Sk/G5zacROMMGBIIXlnLDh0eGDi7r1Q8TCaT8maOXq9XKmfgmQ3MbDYrFRs2NA5gptNpOQyfMuM8z1TWHB0dlVULViEi5oHP+vp67OzsxHA4jMlkEteuXYuIKK9QJjlI5ZTBcAYbnybyXDvwYT42NzcL6OWax48fl3O6tra2GgadtlgtuXnzZrz55ptx/fr1RhUgW1Xpg18dnhNQtT7XVpMy+MUZOPnlBAbgwwGewZ4BItc6aVmzL+gBDoF2XQHmpC/fZ6fnhBhjdb8gJ4M8B2wTsgM1yHdCyHyr8dBJfuYvV0Dy3Fu3bkWnM184MGgl6c+1GxsbZdzwx8HIcDiM999/P958883GQgc86PV6BQg7oGNurkoAehnKiQB+M4eXJesC/1un8rMizgE7VXK+nwpVtpLnFWO37aSw/RJtOZmbEzluB91G5tDJnDDJ43LgiZ4ivxFNeUAe3U8nrTNgzwk1B1YOZGjHNsl6jr/H13o8XunO/fWzjA3om23Oont9Ld9h091HrrMtx5Z5Ac/4ies5Z3I2m8X+/n4JXs/OzhrnT9quMgfYq6clKD6NxHg9j3xuPM15euA1Fj9OTk5iMBjE4eFhWfBjKzU89/mN29vbpULKL6GIaCZ4CDJzoin31zjL/aWNjNeyz+M6JzAsp7ZbyDXBKffZJuSkcQ5uvXXX9+fEVO6X7Q1xgrfn0I4xPnyi74wbPfNWrGwPc3LBvM643/iZvtnOOuHV6/XKG6eHw2Fsbm7G7u5uCX5JzDMWtuN9+OGH8bnPfa74Cvw3fp5583kytOGzgV6VHj8NC3S73cZipeeA8aytrcW1a9eK/BgDOUHvI1D4H99Cgifi4tZtZMKVPm6be+xLGZc/y+0Yu/E57fI3OMP+lFiSuIgx2yZA9MtnmfKMfB33ugIJGV5aWir4rmZnaBtflxdzyQc41nU8z/zihy3DPMtv/MRe8AzG4DNZfe6Tz1Tlx0meHBfUFmS98JWTQ17wZt7Qfdrc39+PGzdulPGcnp7G9vZ27O7uNhJtEedV8P1+P27evFnOI/w4+uk3Dk4mk4Ipn0WfWFLKwRMCurm5GYeHh7GyshKPHz+Oe/fuFaN5dnYWt27dip2dndjc3Ix+vx/b29tlz7zLJhGomkKSHMqgN/cnVwrUMtUR8wQOY7ISoRBObvE9gWbevkCfvKfXZ9IwJoJ0g3IcBIkonu9DvmkPI2nFRQlowxljVjMBPFZchNnggfvhFYAIgwY5wCSg8asxScL5nKHJZFLO1eI7DAQ0mZwfuEwZLPx2dpnE1GQyKW/f47m+bm1trTjeo6OjODw8jDfffDP29/djOBxGv98vzoX+fBopr/KZXxHnjrjf78d4PG7wg8q02WxWKtOePHlSrqON27dvx9tvvx3Xrl0rjp7VOBKgOQHDPGGw+TwDGstrxMUKDOQjA+ecoOE7f56BNrzJz/H1taDbdsU2Al1ibK4ktF5n0IszNT/8XPiWA3ZKeOmvS39xmq784j5XsrDyZOAdEeX1yNikfr9fSneHw2HZwry3t3ehkoP+srWWMWKjJ5NJ3L9/P7a3t+P69etxdnZ+MCOLAVTm0R9kD3Cdg4+rSLlflhvz5TL0LNDvQMaBDTppWfKiBv/bf9BeTh458eV+1wLVmg4anOVEku9xsOr7GZOTZxn4+3u3y2+fe+ZFI+u++5D74USNF2TgNbqYA4jMKyeJ8jzWwD4YxTYn98fyYRtgcJx5YwyWx+9AyIHJ3t5eDIfD2NnZKTLytAPNff5mJn9mXGVePeu7fM2rpuy/zGv7j4jmtg/m5/T0tGyh7/f78fjx48ZCLvjt5s2bcefOnbJtvtfrFf/rqtaIub+Dshxm+42e8rcrAn0f4zMOZ6zMRw527cOsjzkJDfZ08sX+2vgi89HjrAXQ7od5z+cklukX/fC46C/y7SMwcrIty2b289m2egzGqd56yTVOKPOGtIODg/Iyqb29vdjb24v79++XoJugPCLiww8/jFu3bsX169dLn1ZXV2M0GpW+2X7YbhM006dXQbU3gUPMZfZF/p8FCx+ZkuMi5piiByel+Nsy5N9gF+ywExq1/tIONr/WZh4H1zuOth+y/WQhKqJZqe/xui9gOT5DdryY6UVPJ4vMZ1OtytiFEb7HdpPYMfOuFj/AbxL+tONCBP62TSB2YSHFySmwM7/BzF50sU3FDoMDvDBBdR1vdDTmQKaRQ+aWohWeZXsQEY0XF3W75y8EM48+qn/MyfPL6vonWimFYC4vL5etVqenp9Hv9+PRo0dl4thTe+vWrfL39vZ2CXApPzUz7Wh4lkGRs69MOH/DaATcv2uONq+2AKgi5mV+TkBxrR0RiRPa4tA7tpy4corPcbKU4NKmAwQrjT/zm/HgO7yhwsiGyImIDHhIaEXMz/hxuSPfMybGQZ9IWrFi5619KAN89NyYj9zrVTNn15krlI9nsB0Rx2sjP5vN4saNG+WMKa82rq+vx97eXtl24KoTO/dPG2UDhHx5W8ZwOIxerxfdbrdUmuGE+/1+nJ6exuHhYYzH41IhdePGjbhz505sbGwUh+TD+71f2wDNQTNz5mswdAa86JAduauAfI+dgp1x/t79Ma982KcdPv1Ev31WE7pjp0z7Xi22veDZBneAF/rH995+QbLVADEHfE6wu428ymQbi0wA7GmLalCcJlWSEecJq62treJcHzx4EAcHBw2ni+3hnB0HHyRAHz58GJubm7G+vh6DwaA4cM42yEmDzOPXgRxoYN/Nj8tQbcwGGXnV0c+2rzTYZfuB/ah/rEeWsfwsyzH35cDPMpsTSbYJ1n3f688i6ufM8Tl99DMi4sL5bAaY3JsTweiEn+Mg1JT9Sb7XNqsWqHuM9M/gP68eYw+d8PY82W5hk/nciYQchOSERMQ8aGDR58mTJ2WBJyJKdYZ9fU58ZF7l+asB5vzZVUg6XYYYk1fhvShnOSEgjIiGv2WRbXV1tVQvc47UW2+9FXfu3CkvFeE4jIxXnTwxZR/IPcgE1+S2IqIh01kPsS+0STvomvUPv2Mb7+ATH0VbYO18nfthO1sLzO1buQefY3uW4xH44iR97qMXsj32jDFrATd89bXoOjg930t/0H9jNxZ4Dw8PY3t7u2xFe/jwYXnxERi80+nEkydP4u233y7PJfEJlqcvm5ubcXBwcAFPXWW9dKKT2NPn+iKzXnT0kSbYWFeEkohiuxRyULO/xqDGsRmjRjR9q2WN66yf1vUaBs26gpx6dwHP5HPrTvYByBa402NDx3Pc6WucmIPA264G9pitr/SFz7zgg690kjsn9PBVbMvzXNk+8h08BK/lsVIswQKzY1EXc2Qd73a7perYODfPLfG3Xy706NGjuHXrVmNeXcE5Ho+LnFJhm6s2PyqRu3AO4Vn0iSWlHDgSjAyHw7h27VpMp9O4f/9+Mai8dYstXpubm+UsJpc7wkwHL9l48GwrWk5K0ZYZnx2ujQLPWgS2GadL8AF2vs8ZTCuiK4cionFelbcEItQGqowd5WdrlZUcxfb5WCS5SHzBs+l0WvpBCSvX8B1JKYAR+3gt7BHRALTejsPqQkSUijAfaG6+Epyenp7GwcFBHBwclGdxHf1hvvKq+snJSTx69CiWls5LZuE928m63fPKHifQuJc38GXH8WmkDPqZLxJLzJX3TQNYKAuF/zdv3ixbH1lRQ6cxejik8XhcdAiZMwA1+X8ny3KJerYN1hcH25Z3B53Wa/+dgbvlwkF1TmJnQIq+1saSg3O3TWKAM9V8vwPGDCrgnQE7/aQ/2CXOZVpeXm7YDGwK/PKql21ztrM4YOa+0+kUO7+3txePHj26kJyirzh5xjSZTOLBgwcxGAziW7/1WwsPeBYAYzqd7+930PM6EjbpeSs0LT/5cygnUm3b+NsvhSCI9YJTXv20bixK4C/SHweIPMP6g2xk8MtY8tjRX/eFv62PNbCcz8lwW9ZV+9ockDtgcdWKeWz8EdE84NZ9reEOryw78DWP4ZnbMe8YD0FqthF+phejvEDkJEbNJqyvrze26s5m80Up8+pZsmu59N/52pwkyP9fRXKCISdJ+B4bT4Kg2z1fgHz06FHhWa/Xi9u3b8fXvva1ssD29ttvx+c+97nY3t4uOJsKDijj5VpSJM+3Fw2y3ED2wxEXdx/Q3iJMb/3NvtHXORDzc6xbxgH+zGScR3uuJrbvdPIp88o2zLrpPppH+X/blFqSynbGY8svUsj3wWN8Is+hCmN9fb0ki3lD8vvvv192HRCvnJyclPPJaJezIvlsOp3GYDBoLFxHzLHIVdRJ4054hc0jORURBQtZR31+DzaOcXJmMPNif5kXm/Lcck/2mf4u43f6b9/g6kff43H6es+R/ZT13voZ0TwbjWvcPgtcTmzZN/tZJMmM/3xQt3XEdsB9ywtFtGN7a3tmmWR+jTdcgYxc21+7cAM9ZIzHx8eNt13CD/tdEr8s2tew0tLS+YukrOd5kZg+PH78ON54441iqzhKaDQalefD3/39/eh2u/Hmm2+WLXxZNp+HeBYJwMvQJ5aUckZ+bW2tlIfeunUr9vf3Y39/v2TR7ty5U4IszpBiNTYrvYUJBbMTzY6C31YuO9Cs6LmM0D92pgA4b7ujbR9kjmCgtM4YWvEAbrXxUF1CEmhpaakEpiihhd/8sjBY2ZwxpxrLjhaecB3PZjz5TBwDbWdxM1i2EgOoeJ4PYqayC0fA6thwOGwoiTPgBOxUrwBSIs7fMvD48eOSMZ7NZnHt2rVSSRIxfwPTcDgsr8T1tsVsyD6N5OAHOSZDv7W1VWQWOWOFljlCJtfW1srWqlu3bkW/34/V1dWyZcAyZFthHUW20FUHqFxjx+ekVMRFJ+rkE79rASmyxvOZ97x6mlclqC7gu2xvfIA0/UEHfI9tht9yxjU4rOxEraf00/ww0HLiDbKN5HsHBR6LAyb4hH5DgDB/1u/3y0srNjY2SsUTbwJy+bmTWz7Q8fT0NN577734/Oc/3wA58MtnIwBWsA98flX1txZwe2v081AtyPdPLZEAeI6IItfYAVfguao1zxVtet4to07E1Prgv9FN+7Mc6NXad9LGFYA5iPOKoYG/zyXLgWgG/rXgxNf6cPQcmBqIZ33MASl2yaDTtozf6KLnAZ5BrjT3MzMozzrtZ3mObRNpzwkncAyLZmCE9fX1AoTpSw6G/Nz8WQ7QXmey77VvorLCuIfgaHl5ubwR02crkXRaXV2Nb/zGb4y33nqrHIvhV9HXkrGQZT/rj32r7826kRNS9qnWGQey1g3aqAXptOekQU5sWR8cuNFOTiZxj+Wx5v8cV7hK2nroRSDax4YaS9gvmU8Zy1j2a0mH2rw5GM/PyHymz/6cYgESVu+//37BbisrK3FwcBA3b968kMSAN05YkGDAZnhL1FUiy4/n3QtlXpx3HAX+sp+iWMFJhjzP2X/lZFHWw5xoxJflcdiHWma80JP9r30Lz8+LLlznOc92AXm3PkXMCz3syyKa1bjW+ZycdXLD8mT949lQXqgBD4IN7f8958RB8IB22LrpxK7nwMkwZCOiiYksO14ky3zMuQjmiap1+sj4bFuxTfgQrmNBmPFyrBK0vr4e77zzTtnN8HGIhZFXWimVBdRb17rdbjx48KBUy1y/fj36/X4sLZ2/qW5jY6NRIYVgACxdZeSf7EAxChEXHSafWQB9YF0GulwfUc9gI6hkcMmQ5nI8J2bc1mw2r3LCiGFMGIMFBiGDn5wR5eSNjYsTehzkhvPhPCCqj5aWzktOCYSoLHI5LrxB4FFuJ21QdhtD7ue6g4ODcqi4S7IpWXeSwSDNwm3Z4G9WEpE/2uNwt1u3bsXW1lYMBoPypobBYFACv4cPH5YknQ2jDemnlawT8PP4+LgcgAefvW2t0+kUuYJH3W437ty5U4Cxz47yirkrLwz2HPRmQ11zgNnBGmBbdjJIw3E5OLWjRY/9fXZ4WVc9Bn/GPdn5wAN4678JHKxXPNPbdNw+39vO8L/tnnlFu/SZBGMubTZ/rNfwnsQcbQ0Gg8bWWLYOr6ysNErbV1dXiz3wwfmz2fk5ZUdHRyVRjB2husoVnbmyxuN7nZLJ5i1+JQd8z6LsIzP4zQGT/Z5BuLdhLWoLf53BM997XLmN3I+s08hx3mpU63tE8+02tTnP8s9n2T5Yrp0sQfdqvPB4Mz5BDxyAO2hzG3mxi2v4LPtV/x3R1GnG6UruWrUK17o99JNksBeQcl/AH8ZEBGk8czwex9bWVnk+59ewagvVklJ5Dm2DLkNZTq4S2V4ZP+VrIubYC5u6t7dXElLGYJ/97GdjbW0t3nnnndja2irb+rJvyj51Edn/8n+uiuDzHFjncVj3re81P+h2+Z3b9eIIv30tbVmP6Kdl2gnVbMOs/xmX1vTUPh6fX4tFbNf9TM95vtbj9nO9C4B7M3YwrvVCmOWPs+CGw2F0u924du1aaffhw4fR6/XKuTtPnjyJnZ2d0o6TcWyrt83wWJ4XS9fs/ydBTj5Zxr0bxzgOoiJ0eXm5FFfAZ+yj27ef8rhypZbl29fnPtsPZBmrXe8EUu07yy/98xEJ1oEcl9OOcUG+BizHOEnouXDDvjn3xbLLZ5wpHRENX12Lj514Ioamn+aD2/PfNb/JPTybxT1sjcfuBVcXVkwmk8Zbf5Enz2+32y1vuM0xMbwjjvY2Rcdv9H1/f78cl/To0aO4fv16KZL5uOQ45TL0iZ4pFXGuXL1er2TLTk5O4vHjx7G0tFReS8qh5gBfEjMuR+ONaxlgYRgs+DzXWcXsgO00ud+KHxEXhDMrm/tmBbNj4NqI+cqAEzzZMHtFAcfAvRFREnxMNA4kIkpASJBGdpLzvEjWdLvzvbLw2udRObNPX1GKyWR+sDUVVhgeV0Eh/DmjTiLDhpWD2BxQzmbn2/xOTk7i8PCwZKU3NjbKPW7LST8OWXZSk/7t7e3FzZs3Y3t7O7rdblF8guf79+83zm6xgtupf9rJ4ycxAwiB36PRKE5PT0uQwRlCzNP29nZsbGyUN/+QkCL5SrLKoC0nWZysIqmSD6HMwdciZ2ywaxtgQ2/9hQ8kV53E4f9Op3nGQMQcUCCDUE4uof+2TRmQ25mTWHLf7ODo68nJSSkjd8CAjcwgNQMsAyYnim0PI5oA0cE7f5NsQvfG43Gcnp7GaDSKjY2NWF9fL6BtZWUltre34+DgILrdbjx8+LDI18rKSmxtbTW2sF2/fj1Go1FxpsgWDtcr4JaJWiB+lSgnGpjPiGYwYdBZG4cDyFrCIst99n1UAZNIhKfYXfuTvPqfx5OBsvvmvw1Efb39qOU9t1/7P4/JfMv88Wc1v+9kF995Xvg/L14YsKMb+EcnIwy8bcOcbPcYbQfyHJt/vsZJRvptvmBj/D9jzDiFfnE9SascxBmIe1Gr0+k0trN43rnXC3sel+my/nhRwHZVyIkBJ2oimvps3zAajeLw8LAsHnHd5uZm3L59u1Gd7KpzB6SQeZLtpANJ+92anXH/87XIobcR+flZ9yyLlj/PoXEe7eQKDevmogA688FyucjeLi0tNY4zyEm1iHky3bjF+ph5nTFnxinZPvK3E/hZpyxD5ocPWs/88Gvop9PzY1ZYTHL1yO7ubty4ceNCX7AJrvY2n9yf56FP2m9n/+RxMYcR86rdiPk5cFQgcjZytzt/c12Wx4zj/PysW+5XjqMW2bKaL1ikl0/Tc/pt/2SMy3jMM8t+LYm1qL9Z1r2om+XeCVn7YFcUg2v9EgL7QfMXmTYGsL1FV/KCMG3lY3BcnZ/tA7JjbILOOK8AD4nFLXP85txaY7ja2armnfMjnU6nxAxLS0sxGo1K9S1vy/645NzNs+gTP1OKSonRaBTb29txfHwco9Eotra2ymGL/X4/BoNBAb/OrjIggzy+y9nmiGgovlcmPJEOcB34RjSdMP/nZ/mQspzRteI7202ig344YO105q+VpG+AjM3NzaJQ7M9EaKfT+StXfVZFpzN/40PEXGhr50itr6/H4eFh+ZytWigBPPP9JCCOj4/Lax799rRud35wNfOJ4pKU4PmdzvmbC0h2GNyMx+MYj8fle1dFOFlg44JiYxAMTrrdbtnzDi/X1tYa+9w5FNRJEgcQVzGQfdFkUEGQT8YdQNntdkv5Z7c7f/NDxLkskGxGvznoPm+XwUAiV06cRMwTwzwXUGAgnIHlov8tX4zR20R5LsA1B38Gq/yfDT6g34A5ommUHfg50YQjybbBAR7J2VoQHBHFziD/Br0OatzvPFb+tyPPSTLzyqtHmU/ML8Ccw3h5kybbTGpg6N69e2WOut1uSYrz98nJSezv78fNmzdLtajH1+12G2/+fN3o9PS08AvKwd/TyHNcC3j8G1/S6/XKlkt8XUQTHFoO7EP9fda1PL8GRJDbYP7sd32fbVRuxzJPe/n5efw5YZF9dQ2cetXVPs4rutxvsJoXUuhz1q0cQHouPd5acOqkRm4z28BOZ7691dfn4DgvMOV+AcwduGFH8A9LS0sleQLO4YzBbLM5NynPpeWsRrXrFwVuV4UcjCBD4FbbWgjb9t577zUW4mazWezs7MSdO3fKGY75rdAmAs1a4tMBG2TZ8/c1u1LD0vb1iwJLy5mD2XyAP/qDDcoJXcs648xjyLi9Zkt8nxd0+N9HdeR+0Xf780X8qdkybEVNXrKc068cy3gunQxzfxir+YZuRsxtWL/fj8997nNxcHAQe3t7pb3xeFzevIsddFv8ULHhGOF5MPXL8ONZDpAvYhkIXTW/lpeXy/mpLLr5xTLGUbW41fLhZGFN19wW97jfXqzw9/7MFSwZE9rPZn2xDNGOF2DBjtZxV3nlWI/+2idm22O8m30Rn0c0345n/iFr3uZsrGtcnCviIqKxC4BKfvroakswG5WsXsyyLXB8he0gprW++DxnYnAwFwUdxNXeYebcBMUqYBb3nWv29/dLzPvo0aPy7I9Lnc688OQy9IkmpRyQHB4extLSUlkJ39raahysR2mZSz9ns1kjgeFVt+l02jgU2c/MGVsLOobBySoLVsS8KiA7Fh9SboOSy3QNOAGtkNvlb4THoJGJnE6n0ev1YjqdlqQdB6E58KIywkDPB751u/PqqLW1tXKOA8/wFkCMK69vp28oDwbX23Kc9cUgGbCj0LxFz1uiSBQ52ehEQVZm+ENijM/8HQaCfrsC7+joKO7duxd37twpiRYSVVkm+J65zuDw00Q5ILHsW36Yl729vej3+3F4eFjkcXl5uZwLxwqRqxoj5ltCPN8OSL3anoFsDl4c7BmQZaNr52fDz3deOQXget4N6vjfMm/bRYBvvXebBmX0B95622oOTD0+A2OvCpmXDlZIluHQkWvz14DPQCVv++Me260cMDmIpT0n1LEb4/G4YSNsr6fTaTx+/LhUSi4vn7/FkZVXKh+Hw2H0+/2GDej1erGyslJeUZ39wvMA4eehHKg/L8FH/M3Ttu1lGTflcRqs5qSC59ZbSTngnIop5jQn/2i/ljCqPd/2BX2pyUtNl3Mw5jZrAaT5Zh9tfa4lsfy35cY84z7PUe2ZEVHdDo5eoZPZ9mb+Gcijr5lHBuuLeAaZ9+Avn7Xhe/P43Wfziso6DjEHSywvL5eK8v39/Ubic2dnJ/b29hqLY7ZLT9PV2vfmkakW9F8lsg/CR7ly1/MNbx8+fNjYJbC1tRV37tyJra2tkmT2FuyIi8ErcmPK9jJ/lrFCxMXKQ8tkfo7b9G/f6z47sZRl0wvWOblmmwDfHNRm+cl2p8Yz2sRGmi9+Ls+znuTE7yJ/ZJ76s6zL/O+4w/zJGCGP2X6A+cmJNRaqqXgnXlheXo7hcBgnJyfx8OHDuHv3bnkGb9nlGbTlRTpk+CpRp9MpcSW8MV98nX2N48qtra1i8xyI55jCCVe+tyx5Lhfh2ix3/ty+wP3033nLfZYNL8742ZYlMH5E86zCrKf2mXlnkxdRzQu3lcdJX83LPC+1xR3ro/mRcwXcQ96BmHo2m5XzEdnBZXsC5gY/Gc86qe326SfFKM41MLZ8Vqx3nAwGgzIneesu2wEtF+YPce9oNCoVtTzvRWHl6XTaKFx4Gn0iSSlPpMv7u93z/Ypra2uxvb1dDkAng+dtZGxbYD9mTqxk58j3NnzZ8OfA1ZloX48B4noEyoftcr0DvVzexzUR86DUq6QIIUEiwSz8ItGEYBPUHR8fl4RSxHwlhuAWY0/gy+olwgivLfQkEXJJI+1mAEuigQytDZZXrZyVHgwGjb663JH9slRTMV7e8MGqA/w/PT2N4XBY3sZHv+0AI6KcZeTKsel0Gnt7e+VNNEtLS7G7u9vYisYcIgcZJH1aCSOfAw5WiUhcIruuSDs7OytvVfOqEnK9tbVVzhTyds2a07PeOmCzznEdv5/mbBxw5eQKcpeBNnwwUObvfGi5ecW46CdBqyu+zEPLZQ7UHbAzngz+acegArvi1TIS0JZpO24qKt0u4+dvO3/4bcBLmx6TwZdXiWw72ILX6ZxXa25tbRX56nQ6ZcvwjRs3otfrxf7+fsxmszg4OIiHDx/Gzs5OqeCczWZx8+bN+NrXvnYB1H3S9HFthGXFla+LaBFgyIDOn/s+flvH8LPHx8eN6gTLQkQUQOTtQNaRRWA7B378nwOl3O8MOLnW+ppBOLq0CNQ7oKB97kc3ud6AOyIu6JAT1k5OOYhdNCcZkON/nDDMAUUOwK1/+bNaksb8cPDq620PvQXR+u+xotdUCDAObAs6b3/i8x/Nv+yD8vzV5IFr/NvyU5OBq0I5yI2Y9xXe52p3qsgHg0FERLzxxhuxvb1dFuLQT+xwzZ/yHJ7Nb8sjv62/tXvcZsQcYzuAzfJlfPU0mwGPnDThc+yUKet2Hqf5XPvM48qBqn2obaL1wwstmbJO+3n+zhiltlCWeZftSx7LoqDffiD3cWlpqSQ3vU0UuTo4OIj9/f2SlALXd7vdci3zwI4OMH6Nv6+SrGMRzQQGsuwkL31fXl4usQTb9rxLY5Gtph3HrFln+Ny/+dt+raZb+V7724iLuKiGk2r2yPpf292QbbATJRHNSionUowRc3/db+tC9rXGz/ZrtUXijN3zXBn78AziGQo52HVjX8abpt0XfCPjyFVq2RYSr1BpfHZ2VqqKidstm/1+v+BpJ6mpdjRuy/bdMYL5sbxc30L/vMR4LkOf2EHnBGVkiikjPjw8jK2trfJ/v98vByATRO3t7ZVMocvqslL7f08uQbIzlA6AHWzZ6NNOdpj5ed7WYgW1AaIyyAeWZsBHIoWzGPLKEH0BxNGGwa/7ypgxmjZwOYkHWOx0OgX8bmxslHNfKBVEQX2wKXzknCHeBIjS2bB43jqdTmxubpZMMkEn8kFCirmfzWZF+f02pIj5AYpra2vloFQDcANdwEHEPEiezWbx4MGD+MxnPlPOrppMJrG5udmYL54Fz83/TxsZ+NmYkszIidD19fV4//33iwx0Op3Y3t5uHHqMMcJZoxfWP4NBJ2wyQPb/ThblYBcHRJs29E4CW17s7LAPXBcx30JswGCA6EQ2fYpoHrqcr8v66HEif9gtr3TgKJzEws74DTAGJvDFyQP4wriwG8y9x+uqGI/T8+F7HAy5hNnXuQ8+0w7HSz93d3fj3r17sbKyEoPBIFZWVmJzczMODg7K69Bv374dN2/eLGeskFw2cHAlwlUlQLyDr1qw8izK48yBeC2ApIKPoMIBRURzpZG59NY9VxgboPKZ+5ADLz6zrzJwzn65lljIfrtmN3LfMn/538niWh8z8OP/DKTzdr7MS2xeTop77j0W651xiCkDYds48z0nxLExtp30Mcsi8w0fc4IaoI7tAAtyD9jCbyXy3JKUylST4xx4P+v62py+arI8RTRxKPYfGw3O+vDDD6PTOV8xPzo6im/4hm+IwWBQ8I6PTqgFujlQzgkg+pED3Nr/3O/7+Cw/l88tm74+B6LWCcumfT735TjB/iyTA7ocnPKMHLjV+JD7RL9JSLldJ49sM2q8s1xaf7Os5ISU76vJvp+Xddxz5udGRMH5riTa3t6O8XgcZ2dn8e6778abb75ZdNm7XpxEwB9fRRxNv8F/LlzgzB1Xv8G/fLYiOJY2M7aLaCb9PDcRzQO1LYvoAdcs8itua1Ef+D/Li/Fqtq/WE+71dmO3577U4kE/BwzrYy6yPvH5+vp6eVFa5kv2xe4Pz8tJ+WxnbG/5nz7iw9wOc81xC+x2YKHVffWiMDjKL0AxlrcPd7KXZ2T/76IenuHzqFwgkvEXczocDssb/az7L4Iu62c/kaQUk0VFjc9gYjKoYPGKGkyimsLO1JUCFrYstK6igKkGzjzbwpvLKLPht8F2ljLi4iHmCAb3UbbuJIuf6copb3Mi60qZez7B3yXdJIjglcv6UKrZ7HzfN4JZA7wOWrMym2d28jbEvHWCw8NzQioHhSgffSSxBW+olHNml/sxXigczyWR6UPaa6BhMpnE/v5+qdzjVfIk55g3G06vjn1ayfPj5I2D5ZzwACTzRkMSghnAkXDg+uwYmCu/GcL9gLKxzLJR021sSA1E0ob10PNcc2ROovAbGfd5aTb6HoNXryPmlVcEaQ4su91uowyY79EnxubKQ/QyO58cWDoZlgNj2rWtMiipgQ8HBgYq5q+DUQNh7BY2mpJkkp74BVaMJpNJeSvfaDQqfR2PxwUQwGsDqasIhiEn/61DH5UWtVPTz5WVlbJFm0pln0dj++9Kv4ho+CQnMaBFiSXLYy0Bmvue57G20hkxB8HWYb7LCe0c/CMfBv/+7UCacXpF0W1avt1X65zxioOAiPlbQbmHYMWJwCzTWb4daBjPeB48B7SBL86J9WxnvajHywi80gsB6CeTSfG3PpzbgYdlZhG5P/7fY87352Dr4+rXi6SMRcF2x8fHjYAYuecA6tnsfAvkjRs3iv5wvas18Gu1xc+ajtnf+busx/nHem19NY7mO+uasSr3uD/0t4ZDM9a0X+52u40K1Oyv/DcL2NbfzAc+z/LHczlOw37HY13ET/MpY0w/P2OTy+DR3H9+2ybk9qzjnk+fD9fr9WJ7ezuePHkSDx48iLfeeqthFz3vLEY7IL9s/18WIYMRzSMTlpeXy/m5Z2dn5UxLdDIT9+KHWADwgpzjuYiL2135PuuMk6h5jjyOjPvst2o4qCbrGSNnGXKb6Fi25TWfBlEc4diTv/Et2DTLj7fSmVfwwXid7/OYM6bJ828cUtOJzGvmGrlgcXV/f7/xxnHbRHQFf2nsRNGGdy+QS/DOAtqEV/BpdXW1bCXlOu9E8nwzxrOzs3IURqczP+v6slVOJtuX5/GzLzwphQIjUAT3/X6/vNKb4JSElB0HjhaAbCFfpBQ81+A5Yr7yy+8MxMysXAFjp0SyIpcYIiw82w61FvDyfITPk21jxTY1v9HBzhieZt4QrEXMy+4MeCeTSQn44cvR0VExDPTDyo6CYIipTjo6OirKHzFf+aSvTmJYISKah827ryiFldEHUROskgDzHK+trcXjx4/jyZMnFzL3lk2fGzUajeIP/uAP4p133ik8j4jy1rjhcFh4YDB/lZzoJ0HIGQEEMuVVo06nU2QAeXFyD9mnMiobdfOb9khiZwfjgDZXKnF/Tr5g6J0giZgnQpExt8fY6Quy64pNyPoaEeXsneyM0S2PI2Ke3I6Yn3HF+JDfWnDuQNzJAdowr7wykh2QAU22qZ4/nB3yYLuZg5PM5wxUPP/oEXy0/RyNRkXfefX09vZ2PHz4MIbDYcxm50nNzc3NYq+omoqIkpzb3NyMDz/8sMyrE3lXlTLY4bPn7XP2c24/6wp2F7voM8/Q06wjGYjngCnLl+Us+3PmJl9rH7TIl9aAd40XxhiWhUXBXQ5csQMRza2OtlVOGDjg8txlYLxojnLAkQMW+6MM/nOVV8Yifo7JADw/k3E5ye+2XDXFdyQ1c5CF33Db+NtaMsNU49OzyDKS6Sr5cq+iWyZsw12JBlbq9/tx+/bt6Ha70ev1yjaiWnVULelhObOcZLnKn3me/FnNp7gd/2T5rrXn5zpAd3/zQrTtU02vMz8WfV9rI89P7jdn5GZdy/Y8f+bf+VrbwyyzeT5r1+S5832+37bV/Mk2zPpNUurw8DBOTk7K2755wRWJPuP+jBdeNvV6vRiNRtXvsvwaZzm5YZ8AXsN/Zjl2NY0TphB2PNu+RXph+TZGy/3P8p1xuPUp+93sf9xn5ITrHHNBXhSNiAbedZ/dVsbsGSM4qem5cezpMVvu6VNO8tX0Z5ENQwZchMLiwXR6ftYa503lBVef10ibyIPj7slkUmJP/LiPyHEyiqosxpwX3H3+Kv7CsUvmVUSUPA3tMN6P6it5BkndZ9Entn0PYaDSZ2lpqax0R0SpkiJ4iohyYDKf56DMW1R4hqugaNtGwaWR3oqTJ8OOMa94ejuRDSoJHmeuPdEEnhZwV4igYATANjQ2Yhg6gjEUlOfacOEA6IOVnXJuKqZow0LsRBa8QukPDw9L8Mu2GJ7Bm7HYW82ZJN6iiDIg5O6njZQNjyvtkAdKETmnYnV1tbw1iuQUFVbMEXPmSgTe7PfkyZO4c+dOqb7o9/tlDhzA5FXGTyPlAAu+w++jo6OyCossHB8fx/b2dqmCIrtuY57/pyKDecGh48gcqOZEYAaMODLLu3Ui6xttYE/y2JFZr2R1OvPqqIh6AOc+c40dsw09em2n6YCZPtKfPB/wnna4l98GJX62E8EGIW4bGwXfmQMH9gZhtaDaFRfeIuQ5pA9s2XXgwWpzr9crDnVlZSXu379fElMk4YfDYTkQfXNzM9599904OzuLN998s/xNX7FZVykYNWU5XwSaLkO1IJw5zAEV/sCVN7VAynPuuXeS0TLsYMdgLAe1BsUZhC/y01m3fG32izkIzEDX39cCQgM5wBUyiH7ybK8EG0ijM66GQSexj0762FYZdJsv2CUHD+aLfZflwnPs+ctygc910jsHKXyez9HkbCnOhjSW4Hn0bWtrK548edKYK9pfJNv2UxlYw5+cXMzX1HTkVVGWXezs+vr6hYoML96++eabsbGxUfTYC73wwPbYMmiq6Z+xfE0vfE2+13YmB0C5YiHbPeu5ZRnKOMwy4K0tliX6lfW91ten2QN46JeSeCycSVSz2dkG+Trjl5z0zff7J8cCvtb/Z39vHcuBao6tjAWMpViM3tzcjA8++CAePnwY77zzTiOug1/5Tdzo/6ugn/mZn4kf+ZEfuZAYy/iLCinHLOAZPiNezXqSk0xOSuTkrmNdJ6jMc/jOfRl3ek6znPE761pOhmVd4ZnZjlqPcz+JkRediejkZLfbbSST6Id3G9APFrht13IM4eNjkNFakirj2YjmS63A5rTvRBhysUhf+AxcQEw8mcwPMecaV88xBi/knpycxHA4bMw9xGK55xX7v7+/X0368bfPfYPQ9eFw2MAo2LnLni2VbR8y8fM///OXuv8TP+h8eXm5VM0cHByUTCLXwHhedY6CRjRX+ay0rpix8OQklp1xNvzZ4bD6lEFNBuYYIguCBZ8EDPe5ogmyIbBSUJESMT9YmtVqlDGfjYTyw2+uo8oFwfU9OVNNP9j7yhzCV9pme5wPYCPpSNILXrkShm05uZLBc8W5CE5G2RABihkzjgI5oqKCnwcPHhQDRelnxDzw5xyp2WwWjx49irt375akS6/XK9cy3q8HsrxbnwxkOp3zN/xEzFdwqZ5bWVmJg4ODRlIiJ2Uj5o6f9u2UvP026yK67oqdRWDWnxno8Z0rppxgyVVddqgGU3zP9bYL2RHwXFcTGKTb6eNQuQb+0TY6y3Oxm/DGztJBr9+uhQ3J4Mk2hf4g/zmZwX0R0QhGmcOIeSJ8NpvvmzcI81hpx7xENnzo9vb2duOV8qurq7GyshLj8TgODg7KWVKTySRu3rwZ6+vrsb+/HxHR4OtVJeyeK/lqwUmWzdr3OUjM/0fMwSv2k/LuvG3M97lPGdhkPUcmclv8jQ3wD/1y0FTTbb7L/LC/tn/LoM73+rfvI8A1AHYCztt4s5/IAD+vHGfAnO+xHY6YrzDDN297yPOex5rHlm2Uv/fc5sDVfMuVz54/eMUbfdy+t2djy7IccP0infX9mTyfeQ5q97+Ig1xfBDEn+CRjTOwqNvTg4CD6/X5Mp9PY2dlpLNQZB9eCSdqr0dPsRu0a9y/zvcb7WrBszOFnuC3LpNvK/ON3rha0rucqTz/TfjnbWP+2zzY/Fi32RFx8IUrmR+ZJphxcOrjGZ+D7bQc8F7X7zVcCefSOnzyXLBaTDOv3+3Ht2rV48uRJfO5znyvxAf2KmL9oxfjiVS3y/tzP/VxEXORzPiokormABm/Aj/y2/eIe8525MY7KOrTo/5pPpe2aL67hV2Qh89tHStgX1JJJJvxdTZedqMryZSwMbiUWqPlD2uFZxrSOHbjHOxAoWjBlzAsGNTav+cIaNnAimOcvLS2VJKUTa/ZxThbZ1kdE2SEE7iVB5B/kyeek0pecc+E5jmsyL8xnxlhb6Lqsn1yEr//JP/kn8ef//J9/5v0vPCllEOqzojqdTozH41LRYiadnJzE7u5uKTWLaJ5twP9MHiDRQmojgpBnIO2AtKagebXBzOVaJ38M7Pw3Riw7BQy1waXP30HBCJDt+JwJdsCPgcwrGj4PydURCPHR0VEpM8yvmvT2QQ7wOz09LWctEbzSLxJxJKUw1H4FfHZI/CZ7TLkiz19eXi4Bt8/xckKR65xk63bnW0M//PDDwi/6jMKOx+PC39FoFEdHR6VCCnAH7zzXGVR8Gok5s/EnAOOQafPm+vXr0ev1ytuAXJ02m80PnY+4uE+fBKT1xAayFtDSv1xJkFd9Ipp7ze2AGaf7g3FfBHbtUH2tV11wCtm5QR4L9+WyY3jh5IyfA0/z29F4fn4OOomzc8Wot1pTcoxe5vPvGIuBfc3Rra6uNhLCXGfn6OQj/bG8YXN4i0nE/Py97e3t2N3dLWOgn/fu3SvnmtlesQ3YYPiqEqvs3hYAZYD4NHtkgGtwm4M/rjWfOcuP139zH7JtHTV4zYA499t+OaKZnM66kvtvm2SMwT2ZF7kv3JuBpD+PiIZu1wBpRFwAyMg116A/tjfoMu27vdxX5t8+0sGhdTwHPjmwoc1sW/Ic0W/f56DC/HAw4ufnIAqcZB2NiNja2irXcBRBbd6dbF8k97UAzHKZ7/F1fM6b61414Vv8ltScyPfZSIPBoLwEBv21zHrM+Tm2C5bDbCtoy5SvqV1f47Plqma33I7nLieRbMe4zrbB3/n52f7lwNv65D5kPeGzXHGGfczX5nmwH/d4bc/4XRtHTed4PrKRrzPv8hzxfy3J5r550Q3chjxubGzE7du342tf+1pZAGas9IMxc/RGzRe9LPrv//2/R8RFbEYMSixBPx3Tej6cAOZeY8mI+WLbIh0xX91ubTHVvzPWXaTzNV+YfbZlns9rb0jMPt9zaB/P/4wvY2/HtdnPu2rbckys610BPCdXTdNf5itjWCd085jMJ1+f27fOMIckLcGe9BnfTyIJ7O7jTeyjeXPf0tL52VSbm5vx6NGjMie0MZvNyjEWjqk2NzdjOByWgh/wPwUljAUfY18fMX9jOp87Ds825Wlkmfov/+W/XOqeTyQphaIiLBb87e3txv7c6XRaVrI9YO71xEecM6uWNOE6EjoGNNznagOEsmaYs3Nw8OlxuuyPvlshCRgnk0kBDCiXn+v9wGRZHWR7hRanw7X0GXAHPxBYl9+jGFzL5zzfATxjnM1mje1zfN7pdEpFmKulZrPz7Q0oms/mwThQPUYyjDMmvLJCn7xqT3/pE8kjlAeFZOteRMT9+/cb/OMVyjZSnU4nHjx4UM5lwJA44ebV8a8HYq6ygx0MBqV6D1m9du1aTCaTODg4uBB0YpiZV+bZZ404CKmB0hx82YHRT+TXwNBJXZ6T283Jb/chIhrJyZpzcjVZXn1xCbH7xP3Yh5WVlcbhgx4z/cex0X9X8NkZuk22UfJc9ylXwtQAhecxg/4Mer0o0Ok0971nJw4P8sKCV5IioiSTmNd+vx9LS/OD8DlAkgOUHzx4EHfv3o1erxePHj0q92WZvMrk1agMLhcFGJl8Xw667OOcEMJfIWej0agsMqBHOXmEDHt+I5pA3XJhecHGZpCex4TNzf7HbdUCNFYNLZ+23zmQs20w+M42w/1xO/DBATR+zGX9DjawDbnKsja/DnAc5HjsHm8OPGzXuA87YP/qNnOiDt10IOXA3M8kgTKdTuPo6KiBcdbX14te+77MZ+RkkZwvkoU8l1kf/Dv//arJvOVsIgeVXlnf3NxsBNHYRycS7UMs1zkpZXmPqCei+F3TVfuLzHNTTha6D9lX89s+w21yLzrkzzOGt12gD1mGbRs8htxf+pN5nQPv3BbPz58t4mdefMv66XnL43Ggm/tiDGv9dbuu0LbfgMATfuZgMIjBYBCHh4cRESUGy4sJDnZfFZ6u6bxxC3+DU/FpPvfUPi4vZtomeeEwz3XGttxre53554KARRiNdphz49ecjM1Yz7FrpzNfIMl+K8tf1k0vBOZFFsfZng8wBTF6fjZjzokUcK3xTR4XlBeeHGf7upxcsw7nGN5yzji82ApGZXGfsYEb8gItfhn5423We3t7paCC3Qfb29vF/ngnFXG4j6yAv0dHRxd8ZMZqtpXkXcbj8QW9eRpl+3kZeuFJKQBYTfi73XkmmnIwDsezoeceC4nBoCscXJlgoF1LInAfmXo+y8IN2ehEzBlsB2ihtOLlwCui+cpGxo/wcuYCPPL5Cy4t5JnwiLN5ut1u440QjMNv7SOpQ9sOavf390tf6AfbYgBIBtU2luYhz+B+xp0dp52mHZ4TY8gKKy9OXE2n0xiPx2WFEMcxHo8b4+92u/HBBx+UOT86Ompss4CPT548idFoFDdu3Gg4zE6nUw5Rzs7j00iM0auynO8zm83KloGIiGvXrhWZ2d/fj8lkUs6Ki5i/Uc5JYgynS1gjLoLdbCCd9Myg2o7HK1wZHNccM/fZvthhcV9esa45IweOdkg4UdpChtEPAzU7NVdVcJ1X0f0iCHjJCofn08DdwNZOlzGg/670ZJz0y8/K8+YEhpPIfAZfxuPxhQpNxsOYsYsHBwexuroam5ub0emcb4nmVdSz2XkC8PDwMA4ODuLk5CRu374d9+7dK2NFJmyXryrVtgrXHHsOvvP1lpv8eQaPs9msUa3i5Cfz6R/7uPycHMjlgK4GXvltOTYAdL9zsJgBVa2ywNcbU2QAn/uGbLkalzkyKM1+LGOXRZUefo5/G7Aiux6nbaZl2kED7bhd6yjfe34ykLfe2h9im7DJfG+b4Wf4ldfwYm1treHXLQumpyWRs+zX7s/X1q65Ktv3IprHSMBf+pd5jz3kSAyuyTpRs9URzcUdnu1g0brpNt2XRddlG/C076AcoFsO3Y5tzyIblH272802yW1kjGedMP7I+N6L71m3a2N6lt1hzvKzFyXOHEBCT9uyk22n+8ffXlg237mu2+2WRWF4sLm5GQ8fPozPf/7zpZoPP81icK6Mv0oERrGvm06nJUGcFzW4jlgFXAUfj4+PG7KR5bWmqxHR8DnWYS9w+P4ce+Z+2p/yf9abvDiS/bXbQPbB5vgscD19sGzV+u6EMokPJ2scjxAjEzfzDB8NQwzgRJMTwfSjZhONWV3Jn/U6L+jy2/rJ9ySViIn5fmNjo3HIObg+F8wwNuOAbrdbzkOeTCaxu7sbd+7cKUeogOWcYM2Jd4pDHC/lBPHZ2fnLzWyLaP+j0GV1/YUmpey0IqJk7QB3bPsh0TIej2Nvb69kESOagNjZSwTXikNVkL+zYaC9HHzZmDNhXvXg2e6Pg8zaeEn28DkCxTPz4WaujuAz7kV4UHJnlxkPlVCU/9nIHB8fF+Vyhp6SwV6vd6G/KMHR0VFJQLl9r/pmZwfffMA1c2DQmo0ehthVFbXKDu7zeMj0zmbzw57ho43zrVu34uDgIHZ3d0vlRZajTuc88TIej+P69esNIB0RJSl1VR3piyR4h4xaV+AL23xYmX306FFEzN8S6RUI5ofzxzKYzaDKyaCIiwASOUEP7Mz53NdGNLcj+h6vOqOvvs7fe9uS++XnoyNUm1iv8//um7e20ldskp0zz/NWO2ysA5eccEavDCA8pwYZ9Ckn1WwjMoj23zkQtg23PSQB5RcrMGbPL8Err0XHHrFFd2trK8bjcYxGozg9PS2OFnkgcLMcXGV6UbalNk98Xpsj9AJbbVmwrBhUWsfth31d1ml0i3u53rrrpLH/zvLq9jweA2b6ju9AfrgOeXRSiXbps2XSQaaxTg46vJLOWGknb/9zIsA+xu2Zl55X6w68yaAZXarZ0kWJWvtn8xMdRW/NA8sK4+VvzkDCrvBdt9str/cGwGdsVqNFerJI5vP3lo2r5s+n02k5uoAjL8Bz+OCtra3o9/vFH1OJb7llXLZ52b9GXMS0tUA0t+3r8+f2x9YT5Ck/MwevGRPU9K3WTk2GbeOciKnd737nNhxou98EarVte7W+eZw1POHnWzb5OyfdF7Xf7V6sSMpjr1GWH+yJ7QHPwY+D61ZXV6Pf78fh4WHpr4NYMBU4PCfRXjXZN9kfeEEuormLJ9tJv4wi8yzfnxN8tvV5u1lE08fY3nteadOy7mf776xfPMNtuA/uoxcq/LfHThIP/2a/5Pa5xgsd9iG0PZ1OiywZ0zupbvvuhDE8z/Y/2xX6ZT9awxNc43tqWMRjcIWzC3e8CGE++DxLEk1sC8zJ8qWlpcb2bbcN9sZ3OBexyAZ5ziG2G3L/InJ72a5fhl54UgplBby4UofAlECDtyg5cWSQa8NAwMdzKK2MmK/GZYDnNzpZIAzKzTAMEEbY17rCiX6iFDWwTh/JunqLH/f5LYMZaFNRAq94DgGphc5npsB/nAHVKlYwhOrs7KwcQr61tRV7e3uNMbnqym8+YGuQFQoBdLBsQEn1hgPK0WjUOAAOoGvg5HHCY4Jm/j47O4vxeNwoy6S/q6urcffu3RiNRnFwcFCuIQFhefWWFUAy/EV+rnpQ+3Epg9LT09MYDAbR6XTKGyT4fmNjIw4ODkolC8nM7LRI1OTVRP+OuJjQ4O+8vaTmDBxA5r8d/JK0zInVHCRmQ5pBhJ+/yLFn+2InmlctnDSiTYAJFUO0zXhdfVYLIp2stwOzw+TZdrbwjfYzSM06QNveBuBkQh5bngsDKL43EFxfX4+zs7M4ODgoARoJqdPT03jy5EnMZudnw7jCpNPpxBtvvBFf/epXS0L+qtNlHPdlrjEY5p5a8Mg8+M1IEU1Qm5OF3GuZy3rtBIV11Hqc/TX3RMSF50Q0z4XM4DbbALeXfX7mUQ7UuQcf4bf95qAlYr49I/PFyS6ebYA+nc7fXuXErZM9TtRBxigeB8/OW4Xpi/83X7k2kys13b7lIieVadNJfPx3RDTe2uvAC76Q3MzPNRks5wC9phvZzvt3LcB/lYTuUF0Scc6bfr9f8NzR0VFsbm7G+vp6rK6ulhcH5eAZbJblo8bPHICY7zXbwX2L+J2xsD9jTFybA/eMvbk+3599r+/L81sL1nMS1X7bOlL7nz6AIXPfazy6jJzlwLDGz4i5bcuBJdf485xMr81jbs88MkY30U6v14vZbFb8tO8l7vHCMeeO7u3tXbBDr5Lsm9AnYgkvuMAH4rpc4e3xc33Nx9XwrNtwW9n2RkQDi0NP0zvrsZNPfGcZcFLGffYY8AE8F9+F7Sde9BjZ0h0xL1qJaG6jpz/GrcbNWc/4n4Sg30jtGC/Ldv7fMuu5zjbEspD9Jt/lYw2oYqJwg+dSCUWM6SN/8JHmDX0ZDAaxtLRUXk726NGjsmjb6cxzD71erxRwoMvwxdVg2U9AVPoxx5PJ+dsm/VK0Z9Hz6vgLT0pZMFkB29zcbJRE8spCVoGcfbVBsKA7659X/RE8BBEDYEHOjtlAt1Z2ng1HDuR4hoXWh4mxH9Rb0Bifz0nhWWREAXobGxtlqxTJAAJIkj4R8woJB7IkA3Gwh4eH0e12yzY3+kn/ucdvyiIjS9UHPEep/DYvgwAqjHygMQG9K+JwYqPRqCFDGLN8QB3z7i0NfsthxLzckPHzvJ2dnbhz504Mh8PyPECyVyeQQUD06upqSbJFNKv4Ps2EnhH8A2yokELmNzY24g/+4A/i+Pi4rII7API2PgdrkIEmfxvk1Rwy1xqU5q2A2VFjoK3DOZC1PchJJtsk2wgnjuyA0Gc/C/31uWmz2Xz1hj6YJ94+ybNyNYnBgYFPzR5GzM+/c9DMuDJ4t17YMZu/ObiEF/6sZlPRP2/txXbmVUAnBHmb18rKSmxtbcX9+/fL8zY2NmI4HDbePLK9vR3r6+tl9faqU82+5IDjWTYoy6J/8r3Y5fX19Tg+Pm6c80R/agDOumg/5ntrwaF1yX3wM7K85GdmXvj+GkD3inBEs0onB515hdp9Raetr/lcLOQ198d+zwkoj8V6mPWbcTi5lG1qtmd5zrA16KzHbYBtu5d57mSCF9NIsrlvtnEklXd2diIiGj4eW4CtytVRPvYgy0O2RTlZlXU+X3/VbAL4EtzBOPv9fpydnUW/349Op1OSyN4+FdHEq/ZT5sUivUQOoPyd5TwnsKDsE7NsPsu+5e/9f80uWbfts2vPIKD1PfZp+FfG4ISC/azPhCF2YOz2q3kOamPJ/XYCwHamJutPG6t/Y4+8sGodp3/Zz7vfXszFf4NzHPi7asx2zLbN+nyV9I9+2iZmGch4iHtsXx1D5GRU9lGmp9kqvs+ykeNaf28/mvWvlgir+Y78TMbnPjpJ5X6DQf2dY2e/nY4Y1Iu5+BQnyRbxq9YP+oCe5gri7Ff9uf24E9WONew/rTMRc53LPON+nsv99NNFJvCIHVV5oQd+0g9w79LSUqOQhbjfO4Oy/Vgkk36BGTx2MvEy9DTbX6MXfuorRodkDIrp1ZzJZBJPnjxpgCQ7uYhoBE5WKhQegbVBIANIsgRB9kRaiPJEIFQZaDppFjF3vAiKg2g+AzTwGYLhPuRD9ZaWlqLX60W/34+1tbVyPQk+J5v4nUv+ELy8GpkTKjzX1Vt+ax7lgOYH/XbiiVUEOxxWlw8PDxuGp9M5r66BNysrK9Hv98ubBlj181k7zAOrEjg9ZMzJyrOzsxiNRsUIra6uRq/Xi6Wlpdja2oo33nijVPMYRNsgMTaSMAcHB3F0dFRdSf60Ugb21jkDjIiIw8PD8r8NJ6CD5CbtICfoDXNpfTNgyqs0lkM7FYMByzlj4Td2Ieu+AYltERWZ+Zk81wkyPstVWNxrh4b8YR9JZDsBmqsQstMnYWhQbUBl22hnTBseB59lIGbbxv3ZwTh4zyA723b6BrhdWVkpK/7mPzrt6kdsJC9KWFo6P8OCSj5eioDcceZcxPx11Jd1jK+K7Jsyr7MePE97Ec0EvAMo5sQ+NaJ5wHX2w7SDrXTCh3lywFML8LwY5KALWXLAF3ExQDUQzd9nXhpgux/WXXTQ5fMGp7TjszGQxQyiuY9xIJdLS0vFb+UVWcs6/HciLAcgs9ms8aZTrrMNycFH5gH3GOhmO1ML+o19sl3Mz8nzg88lIY2vyFuk6XuWg9xWlolaYuOq633EfO6sV2AqVtRzwOwqWdqIuBhoZp8Q0aykW6SjWVf8v9vn2VlOuaY2fzW5ycGc5TKPxzYkorlYlfvkQJufbHdoN3+W+5fPasm8cn9rSQjrmZMd9sO1hETNt2d+14Ju9zXPZW1esz3B1mFP/D33gOGJVZDdk5OTsogELuT3s7YBvWxyLBcx5zn6lyv+I+a2zDzM9sa6nLGT7WvND2Z5qiV6vBjgdiLqSepsJ/2/24moJ7/42/bD8gCGoN/ZD3ENVTvgB/7udrsN7Os4fpF8W++zn8TPZlmjH8b+XiTOc4WPzjx2/3wf9sm41sUo/O8XdhEDEycjd9xPLLK0tBQbGxsxGAyi1+vF8vJyPHnypIybRFJExM7OzoXnI9fGGjXqdOZHH8BDFxzUcGjN/z4PvdBKqVz9gyOl4wCoBw8elIoXJxRgqINUQOfJyUkMBoMyQTwHBTAQc1CIArvaJ6JZuk8fAJD03waGTC7Jr4j5oehW5HyOFoa6dqhYbpf71tbWot/vR0SUMjmSfPzNgd6z2SyOjo4agp+rOexUut35Qe+dTqdRigcfva2m2+02thB6pYhryZ7ym0yvK9qYG34zZpJLPvjYyoLckIjDGNJ/5tNZ5ohonLGwuroaW1tbMZ1Oy8H6ebWZ5NV4PC4Hza+vr8fu7m4j8LhKTvRFU83AwOudnZ0i9yQrx+NxQ56ZX4ING0frq5+FHPEZ7VhXM0BCL+0c0XH0zWDhMluOrIOuRrJDz4GtbVQ+183AgvZzksXP4dluh7GyzZfPOKPLQIdksLfiebyeY/jq8678mXUK4lqqPA1Qsdde/bRc5P9roCUiLji+7NiZY7Z+A4a3t7fj8PAwRqNRw67s7e0Ve5eDtteBPFeWK+hpY8nX15JB1ssMHHPQyJxY5iGv+OU++ycHQlBOjtSeUeuPx8VnBtfIQg5KPQ77TECfgwgnStBfJ3t9LX32+NEt3oTDMzkjIgNjyAts7qfHyPMYjxfPbBtyv2rBvm1kDnAM/m2rAbYRzYpt664TK5yTRDtZhvJ8Wm4y2XdzreXClIOLHPBfJZvgxLG3usAzBxRU4nvhFcpBXMTFrWGZL5nsv3IQ6Ge5HetpxhM5GM/3OyCvJWUg64F9ODyq2Rg+py34Qxu54jnbs4wVfK2xRI1/TjbV5C7PcR5/DZe5bxlT1e7JmMc2qyYHngfPLX7CY0O/83EqbB1i90K3220k+q/Sdnpj1YiLW+uctABjcl/2X+iuZQM/ZDn1Z7SV9aimT5ZbKOtPJh8fw9z7WdmO1vQ3J00j5tWtGWPmZxv7erHBY7WPdbzKNV58ts2Dx37rurGjk1tZb/iedt0/j6fG+0W20zbC2CGiuUBnG20M7G3XTiJRMFEbN/kUYx7GQ0weMfcLLO56UbHmQy1XjJkXX/nlOItokTw+jV5YUsoTYQPOeUXeK3lwcNB4k4GDTQMzkhw2qFwDowGJDoicNDLIYeJIcvAM+pwDqewUnMzJRpV+MsFkOOEBCRMqINjnaUX1VjkHug7CuZ6zKLrdbjlwmpUH+HN0dBRHR0exsrJS9n3DHyuC34ZBuwgtPPEBfrlyBl7RN8ZgIJ3nzqCUPna73Tg6OmoEq4BeB84YFDsMf+7SRRzI8vJy9Hq9uH37dhweHpbX1sLDjY2NmEwmMRwO4/bt24UHOOEctH1aKYMwzia7fft2+R/Dx6u+mXdvvyAxEDFPQmZjHTHXtezwcjLDhs2rGRFPr+zKLx9w8jY7/dwGlJMmOUhCx/297YntT40HOAaegS566yvtoYckSgminfCGr04Wcy3Ppc3MR4N3+OwkP2PJK7WeO/+dk3cZ7DLHyIiB62w2rwTBduNHOI9ieXk57t69G9PpNO7fv98YC+2srq6WrbhXnWrA4OO0A3luIuqvdY5ovk0yV8FajiMubnfgua4I5BrkPgdSPDMDIQeeNb5kkJgXqbKu+r6sf/hbziVET3weo+XecuvxYB/5mzG6opgFIL6HJ6wa02fap3yevttm1cZuf+vr/D3YJQc8NQCa7XIOmuCnq2gzwHc/OReO77a3t+Po6KjopuXMY6kFzjXK8mFaBI6fFzR/UpT57aMKLFPme65iB385GHCyshZsZjsdcXF7uL9bpIN8Vguyc9uQ5dmUsXdN7hyYmTfGm8as+NRsa9xn89dt02/Lci14rcloLXFmfXPwXeNzLTB3u3y2aL7yM71teJFOGYNb97nex43ga3lTF5gQeXNiID/z4/q5F0VgK/rrAD+/oAd7Bw8i5nNjzOu2oSxvviZjsdq1NVlcdJ/9XN4pk3FbRFPXMi7nc+NieMVvL7KCS32/d1HwLGQ/J1atK8yNsbxxsv24x+N+Zl9Ys3eZh9yTMXGOB2oLLJl3theeI3+Wt/YhX+gZ56Lmz5wrePjwYdy4caORPIXHtg8k5Oib57VGJycnsbGx0ThofmNj48LxO57D/Pdl6YUnpXKHENDRaBTr6+vlTV1UJU0mk6L0TgrlM6gooYSxToggJD6ryQKzyME4K5szm/TfypuvpU0EhD4RCAIqySjOZrPyKkf6QYkez/ZZSlY2j9EH+XJuV7fbjV6vVw4uPzk5KdVVPqh1Njs/n2A2O39d6cnJSeO8K4NJDLMrM/if6iwbFSrj2J6AEtkZElgyB/CAJBjjsTOEz7kyjTb8JiD6Ph6PG+diRZwHBzs7O3Ht2rU4OTkpfOF1r5PJpJxFFRGNld383E8zMV8kUak063Q6pWyT5KH1lr/z1ko7A8tXDuwyCDOw9DV2Ep5z9933LlpJzH1HxxadUUV76GN++YED+OyE7MA9fu5Dh9Av25aaPcIe+nmuEPOLDnwN9taOFB1irBn42IY4QHJA7WCK5DjXOBjKVWzcY9/hBLv7lJMjT548iTfeeCNWV1fjM5/5TEwmk1LKPJvNysGP2ONa8HPV6EX0MQeG+XPrHd8DdvA99AUC+PB5LbDMgaCTswY9GaR7bixfBqs5uWn5gZAhPy/rMfcb0FvuMoD1a5vhl3Uh9z/PH+MwBsBn2se7/awbDlptNzx/DkpzAMy1eQ48z4vspOfA/9vfWp7w0/b7WY68aEAfnTCryUGmRTryNN1xP2qB3lUg5hebSrIy4ykSUvgjy7TP5bOs5ECsNm7biFrwvOj+fG1tzjxGX5MDcsu0dT5Xm9SCTfsc66377CA825ocqHts2Y64P+5r1lUnCRi7f9dsqalmqzJWynqWr/UYLDM1TAs/bB9s49xHjy9XazMHPNP2aTweX7lF3rxVCntEXFbblo599lh9v/mU58P85HParflv06L59me5+i/b41xN675GNOX3aQse2CnHd64CAlvzPMusn1/jlxM88NQymxe0coLXOut+uy9Zb2kT/cCeGPfm/mff6/agPKe2O8YBTt4hg+QVLHPe/n92dlbi1eXl5cZL1NDFjP8W+ehF5NiHOH91dbUkqjI9zXc8jV5oUiq/pYDgyBU0XinLys89BBGUhBJwWQm436tCGYhmJ4lwAzQNXv29BdfOfxFYQ4gJFskQk5BCaRkjxpsfK6YrHlDkwWDQOLw3Yu7UMDwOCqfTaTnct9/vl0okwA7/Mza22tkxZwGycXAQ7nJevxXQb/jzq56ZV1dfrK6ulsPYnRDw9flNivDcKxLZwJKg4/kkEt54443Y398vfez3+0WW/Pra4XDYAA+XVarXmZhfH0S7trZWKu2Yh9FoFMfHx+WNK+ghOuutuTkLb0NsQ828OzCpgV2DvuyouBbjbJ3Jziq3Y1uAbGUwZYBGP+0I8xh5Hg7be+V9rpJXjzD+yGROFhigGwQ6sPRqqKtKnWQ18PRWW5d345CzTYTHkPljh56BrO1UBgQGMNxnO0F/jo+Pi9158uRJ3LhxI3q9XmxsbJS38xkk4kg9rhr4v8qU/c+zqAYIcsCV9Qt7PZlMyhkhuYTebdfmDxlC5nOiZxHf7fNyUOfAptaPWjCcA0Luq211csWzkyX87RV/no1fQ8atg+YpPgzfY/2ogdvs3/mbfthuOXhwQsdBf+aV+ViTJ3/nPuT7bBMgBwi2FU6YePWcZ9SShK7mWpTgsDzUAmVfVwsIsny8akL/IubYz7jD8ouvhT+WLQcfHlvGdk8LnmqJkpq+1bD2or99D7/zXOV5rPmzWiDrcds32tfDuxzo2qc7aMvyZB9mu5b7n8fgMTMPNd7ltvIzuQ6dynq+yD5me2sZyEkp23r44+QF13thnOuxQa7IzvPlN/Tl6p1XSchN1hsnJPjfWLFGlrOcZI+4mIx1DNjpzF+Sk/GVE6h5vhcl7rNM8bljuZo+1HQ0207ut8/GZmU7RFxs3NfpdBrH0NC2q247nU6JZfEJHrP12/oOf11ZZL22Xrnv2Sb6hV6LbKDn1p/V+Gj+R0QDm3JfxufgKKqsnWz0uOkzSSn4SAzMIjUxrvWXPuY593cUjdiXU0SSKfuE59HzF3qmFMJhRmxtbUXE+cGz7733XkOwHVDlBA0VLq4iQoAAh5xdRFbQE+hgywrnYHk2u/gmgQwqMcBM/nQ6LRlCnslzIppvdfMK4OrqamM7Hte4FBCDzrUA3+Xl5bKyQNDPK4N5Ux4JFfpHnw8PDxtbFi2oKysrcXBw0FBUXtnMeE5OTkryiuoo+gtPXP0E7/0cqpLy4WguP7SBISA/Ojoqc2yDnLdDWuBdIcdqI5+trKzExsZGbG1txbVr14pBxGBGnB/cTfLU1QN+099VcaSfJLnCrNfrRcR8+6vfCLG5uRmPHz8uTt2VfsxVRHO7CnrhJEsOkGugGsrGMq8I+XMS2v4+J6Cd2DIoMyimvx4b+khSCXnhPuyYn21d8X2unsCJZ8fudtAx2zM+d3WU+ZVL8WtggPnht+1TBvm2qwYuHovH5yAzJwFJztFPAwkIe0ZSqtPpxOPHj4uN39nZKaB3MBjE/fv3S+WjbW6WoatENXlfFFxctq3a/bZlXpGLmIMhFoic7LANhmr6hP3wPObgKwNdfnv+LVt5AYexWSectAaU2g84eGKM2LJOp1P8HTgitwXRFgnfbFecTGacWYfyHDk44BkONtB3J3XQG+useex+5wDcspDH5f55HDkJzjUZXLtdz0lExN7eXmxubjYWMmwXMtX8gG01z7qMb3afM3C+CgRPjFkWnYsScXErSMav/P20pAWUbe4iH2we175f9L91NN9bC2KsU1nn+e3raosOtcTsIntaa7cWjOfF78wzy7111X3Nn9s/5XmqzYV5k8dhHa/ZXd9vfa/x3/qfMQU7KNBFzmHtdpvn1mIPwT7ZfrwKys/3vBqP4APtY6x7xmKeW1dURcznEl22XFqObMPdR89dbWHP85rl1ePk2U5qmnL1lPGw5SQnT9xP+znvbsnJGxa+vbibfR59Ym7sH/1sMIwp88R8c44g62Ueu3ltctzAc8wLPjNvaDvLRy6yIIYnzl1aOn/xGLjER9R4HGAvV8ryPzqbbellfafjeuSHOWSnRc1OZzv0LHqhlVL8RtBIkDCY4XBY/neZpJ0mgUl2IBYAlzaT7HBSy0KWVxd5tt/K5ACN+yKayo/RMnjvdDolA0nffPgXQStGmYlxpRBgzmfF8PrfwWBQxkX5NgZ/eXm5bLuj8oln+qBx2iYhhrMia9rv90u7BBEkHiKi8BYeWXhR7Fx5xrgi5ivKp6enpb9efYav/pw2l5aWSkLKAm3HbUOQAXS3e17RQ6IPJ7q6uhp3794tByHjUK20HKRs2bNj+LQSc2iZ+cxnPtOQ1wcPHsR0Oi2HyeNweGtjTvbYKVj+/EzI2Xvm3U7LlQ7ZIeeAlLHYINqh5eCP/73aY931PXwX0TyTzfaBtgkKSWQ70RkRReYc0GUni3PJxt0AFLuIk7Ddgl84MfSZsWO3nNR1tYL5nVf8+DuDTWwRSens8LHBPIsESHZi9Ik2lpaWykGLKysr8ejRo1hbW4vNzc3GGQfr6+txenpa3lxaA3tXjWr9ehH9zTqSq3Qi5vrW6/VKwp95R7asn7WAx37SvjQHhtl+18CmbW4GmFn2arY/VwbytwP8nAzxs7xVKgcEPNOJaMt2tiFZb7PdcoCRgw4DQaqE7QPNN+sOz/ZCkeWoFiS7P1xjOwlf4Ek+B8t9sq0xHmPxDP5tbGyUYx08908DsbVxPIuyDNbaugqUk8PwK2IuEyxE+qzP7Nsc4FkmasT3+SdTTmBkeaMt9yVjRj7Lz/f4fF/WmxwbPM025D4b69t32n5Yxz0+Y/gaH31vto2+L/vc3HZuPz8rf5dx6dPsbu0anp8XbBwv2K5CnGvpdlhQd58ODw/LmJw4fJV6x+IVGCxiXhjgHzAux1F4DiMuLnR6ES/i4hllvtdyYczoWDXLfpavPDfYZDCQ7W/GsDmmoq/2oznhYp+Wk2+Ov70IlP0YPHdFo8dof8szHR+DHSloiJifeeyEnzGsZTQiGslRxpKTX9YDPndFU17cMf8yRnZbi/7mh/7YDzrx47HxrF6vV3SY3Ao4IW9LrdlU+9taspLrjo+Py/E/4JFerxcHBwcNW2qazWYlWX0ZemFJKbJxMMuJl9XV1Xj8+HEBIAwwOxE7GyoQDGasdDCAcrQMhu2EYAy/XdLnvkQ0D+OlzdrKYEQ0KnVoE0PX6cxfk8qh5lbkHOjy3Mnk/MyXjY2N6Ha7jbfS1ZwWSu/AF0H0/51Op5TfUWmAgaSSgHtICB0fH5cAhYSOV4sJfDEaKysrpfrIZZ05wGY1msop+L3ooDr4Q5BLe8yLV8JRao/Fh6iurq7G+vp6TKfTuH37dimJ5DndbjeOj48bib5soD/thANBDzFCJFUODw+LbJOMiogS/PNWIL+kwADQBjMHxcxjXjmA8iqlP7NDzYdQ+nr+dlvWy5yIcVLWJcgOItExJ1boC87OiRk/0w6v5qidPMqBLnzj/tlsVt6KyBjQXfqcg30DDxyNX6TgADMn/g2McjDryjJ01M4Wu8FzqcTMNpdnu+IqbymgfY+NVUlXq/l8vqtItUDwo7ThgMg6F3HxTL48b4AYEsc5oWHfaT/shFdeCYSs7zkwcXIny7jllTk3WGTu+Zt+LwJc/G+94jvkZTKZNBKnXO/gwAA4B7fwPQd2fJ6DAuMn20I/w4A3jyVXoVgnM0j2dmHLi/tjDOU2jHf8vQMKMIsT4XkeZrNZOSfJGMiyYMrJs+elF6FbL5Osl9ZTFvpy5T3Jwoho8DknTSHLob/Pf/t6f5bby8FpDnysy9aLnPzhujz+bC/crj9HP2vJXl/rs8+sN+6b26LvPNNnsuQ+5ECbe23/XK3BM2txRm4n/28drD3T/a7xzGPONiX7e+5xIG/9hhwHjUaj6Pf7sbe31/DFr5KM+yOagTnYw/gWG+W40ONFL7mPmCPv4jEGyrKVcZVl2NUwlmljMsgJGu6xz8oY0zg6j8nP9Of20YwrV5FlrEsSnWf5O+JE+p6LFiic6Pf7JUlv+5GTSmASsC/P4hnZPmb55TPriHFUxp/mjflgPJB5lOfNMuc4g9ghx8hOqJKoY9xgaJ4HP9nxtAhPPY3gxXg8brxpczI5P8uVl4dl6nQ6F/TtafRCklLdbvPNBd1uN9bX16Pf7xdg5W0WNVBrwSAr7Td9UcoWMU+GAIQRYtpw1tlKj2FAmel7TUAjmoAV4bBCMtkIDIAe5aPf8IQtd64UQqmdgDs6Ooper1cSPN5Gdnx8XKp+CB7oC2co2VH4OXzmjKrBr89v4TWujJmEFIEk88Bc8h1JNb/pimfhyOmL3+7HnPlV2bUgysmKHCjYYNP+bDYrr4qnTfj3zjvvxFe/+tU4ODgoCs13Phw+4qJh/rQSxtOJRAJUy5GBJIdLU4Hot3ahE7TNvNlo1hxuzTlnAJ0/M5iMaK70QRlw22Egvxho9IixYN/426AXgHt8fNx4KQO2AJ5yv6u/nNSNaL6EwSAuA0PbTAeztGndpO/0hzm1HhHU2JbmRBKO3dWujN0OFRttW09C2WBjNpuVF13MZrPG6uv6+noBhBBt8Dzs5pMnT2Jtba2cocdWAuwkL3246lQLLJ6XHHTUApn84+8s095OaV9CP9HtWjIq98FBnQMff57Hne2vK/74PoNGB03mqUGX/TjX2oYYjOOj3E8HFE4C2Me6QhoAaaBuHXc/eAY65THVFm3cN8+Lddi21GMwMKUtB0DMfSYHsPa9Hours/ysHPiahGTiAAEAAElEQVSYj7YxNZlYlJCqybm/q12bg/SrQg4SvM3bvtC2NaL5EgKP6VmYxXpaq2Dy3zmhUrum5qNq12QfxudZLvK9vt8JypzEyRgCHnGNsW9eQMpB9dN4wrPyeE25DV/r+MK6s4jXmZ6FTfk+J4TzPfDTMmObkYNX62HGivgAjhshPvLb+ogDXGX5cZLNH4Uyv1hQZYzurzGsjzUB+2D77DPz4qkxpG1c3o6dMav9meeI76y7fJYTot5q7zZpyxia+bXN8eIO97nPxgfeKlZL+tI2dox7ptNp4ywykkeurLMcEeu6H94a6djRGDZiXnSBb8y85lkeX7a3eQ7MU+uZF4OyD7MMGk9l25fny881Fo6IUtnnOUUm4Jufa/zvZzzNb4Bv7Pf9hutFduuyfvaFoHQzAYEYDAaxubkZEfOsOecjsUeXCXAiot/vlyqLiLmA2ihGRCPoi7i4ysr13l/ppBgTRfvevkLQxk8G2zaiGFYUv9vtlion98OHi9lBMG4CQowMyScLFIGg927bmCCQ8Nd7ok9PT8u2QAAyik/bnj/+dmDc7XZL20tL56Wt8H8ymcR4PC6Kko0xyaiTk5Pyxr/Dw8Oi/MfHx+VvjLcDZMhVFP7f8megy/hwkszpYDAoByTbkMNzfnwmydcLOcHBNlKC++Fw2ABTERH9fr/h+OAnvGM1HANI0qK22kAy1/Ljn4i5vWEu7QiyDaglVSwj6IMBAzqfq3WwXTlRnat0CBrszNwv9IjP4ae3AXq8jCUDnhrwoU/WWSrZuBZdY9yuZLKz81v0vCpDwtb3OvBH53Do9CMHwdiA0WhU3kp6cnIS4/E4xuNxOT/KIGB1dTUGg0FZuOBg88PDwyKbvE2Tc2s8PicyryI5UFwEDC7T9wySbENr47ft9/xZj3LwFzFPWtjXZf+GLuSVR3+fgVBOBNneLwqichs5IOUz5CHzuwb6aiAt4xHrnQNBJ7h9rpnBbQagDgQcBNjGZbngWX6eA13sjYGk2/X/xj+Mi8/djxr//Ux8svkYMbcdJycnjRVbgsKcHIDwCZmepSeLdCnP9VUjsJf9C74DzOKAw4FzRFN+zUt/Vksim7Le8xntmOiL5XgRXxf1Lbedk638dr+yf0X2a2N+WlK7Jgu5XfPKlYAOjGtjtn7Wglh+48+zfc59zrz0eGv2NX/ne+wP/dtj97P4zljHi8u2CZbbwWDQSPTkRMqrJCeTPGb0jxgox1xcH3ExOWEZz4v/2Vf4eTmJmRfejV2ZD1+fn+3Yhu9oL1dMWae8UGPfbt9iH81vJ0isIxl3Gyc6hnMM7LON8yILFWiM3Ri40+k0qunzPOXkd7YpHmNE83zdnEDMPjrPh9vx3Dihnn2oP4cnljvjNJJBLLo6Z+DiHT7zXOT45nn0kN0YjPPk5KT04Wl2/zL0QiqlDEKcGWS7mF9N7tV4CMCEcBrAObhDyHzwHMLpTHJEs6LGgSZ9YHI9+RaopaWlsoIPgLWAeXXKyp0BJ0mkiCiZX1c0cO3S0lKMRqPY2NiIs7PzN+SZnyT16Pvx8XFsbGw0ti76OhznbDaLjY2NAgTph/ts44Eye14QXGe+cUguEfTKOudHWaG8/YZ5Go1G5Swi8xBDTsUWW+qclGSuUHQnLSw38JF7B4NBSZjcunUrnjx5UvbGkjSzUcmrQ18PBM9saFdWVuLhw4eNMtuIiK2trTg4OChGyatCEfOtIrYTEXMjlcEQ+lBztJbHfD9kZ2pHzj3ezmlAgKxgo9DXHJBTFuxVnJxgyhVR6C73nJycNOxHRJRVDCe80DGeAd+9fYa+2/Zi2/IKONdRTWSQgEOjbScNbT/Qa/MmByQ5sKAvdv7YOfyDA1nskit0SLBxeDnjZjsu+9pJVNnvkIjPCYarRu7boiDnWWOo6Zfb9PxYdg2OvIXPAJS5MaBxsq+W8KoFstZNz0sG47bt9NuVzIwPvfU1xgQ5MeWVVL43BuB/vs+BtMGXK4xpwyut+A/4aizDfR6vk/7mNzKcgXTmbeavA6Y89+5jjffmGTbBPDHIt9wB5KfTaVnVjogiT36TDzbVyXYHQNk31GSrRrn/+XPz5CqR58fndyGT2DvjLXjnBcFa0MTfNX01L2o2pmY7ana/pu9eaKqNteY7fK3lvJYEyn1xtWKee/hqG2bdcP+tExkTusqKz20XLKvc76ol66arN59m8zO/8t++3uTFodqcg82M0/M80YdadTZHcnCtMRI4HvySA9dX7Y/BPl6IZGzr6+vFTnkrlJMEefE82yv7WMuu7abn1fbV9jbiYsUUvMsJGPO0lsjKtjuiuZ00L5hg+3lWrQ+05xdiGR+7T47Lfa+/pz/mq/Gg/S7jxPdaPt1H+xae44Sk8al10r4pJ4Hto+zvHQtl3nsuPX7m3PNm/OPv2cbd6XTKMTXr6+ulPfOEWIPn0CfkxmPw4uEigse8GA2b2u/3YzgcVu+/rJ99IUkpFBImksHj78ePHzfOOjKjI+aGkWw6n0XMhdOBJAy0EvG5X9MMzWazYiCtTDgmV1BlwMr9NsQZzPgzZ4ENTB1EMS4DC5xCxPx8Lq9A2JjRb1ZfaNt7ZUkgDQaDC1uvIs635/F2O0Ckk3+U4fnVkg4Kl5eXy0onINPGmioqKtBms1mphGAco9GowS94zRzixBg/FW3cz3fe8sO8cL2d6+HhYTkj6eTkJIbDYWxtbTX2jDtZuMh4f5rJhnUymZQqG3hD1RPOmiQJb4aIuFhy7HNZDK7MW4I1HImdSQ1Q15IhkEGjA2lTBth85u9y0FYL7niWwUxOmPony2Yt2Od7HGNEM7HnfqA/OGzsSA56CWpxkt5KR1+xGVRQZUDlbYAGHTzbyVvacXUcc0dCLCKKPK2srDRkCD6hsz6vjPZJYrky7fT0NAaDQRwcHJSKyNpCyCLw/6rJdtDzc1nKMr3oc4Nrz3N+NiDF+mh9MrDOQNU6lnU498ltZ6DGtYsAXE6eIqNuk/v87Bx4eZw13sELJwEcaEAksyMugtisU9Zh98uJWOyBP7M+WZZz4M+Yc9I9J7My4M22pdZuBvs5GHeg5qQcti8D5zzPbrfmf2u23//bfmc/ktu5KlTTF2M0B1PGujkZnBPRkOcsf16zEVnPL8OrLFMRF6sVsy+t9dn9dF+yHDoIzJgg98HjqSWsMsb383Pw/azx5me5XdvdPG+2sfm7mi7WeEY/HWRnO2ib5HHQt1plq5NrzEmn0ykvnKKyzy9IcszHYtzy8nLjiI9XScQ52Bq/8ClXi0bMXwZkv8A1VFVhS4lR83W0Y1+afSQYFv7aX0c07ayTFnne/ayMha2HtUo+MGnN1mcZ9bO9tS77Eha7vBsKP0lixbgC/2d9oZ3xeNzA3sbTxi61ZLGTNU5geX6cjIuIBq62na7FGB4zc8p9jC8noOCH5wbecJ2v4bv19fUYDAYNGcsJVh+xAX8y3jZ/nkXs5uJ+fNL6+nqMx+ML11/Wz37spBRMgwnLy8vR7/djMBiU4IMzfehYdkhUJfCbiaSaBSCIMTDYIaCJmGdBfehqbRVzEZhhDNznckqXRtvY5moJTyzP45C2iGgcvubkGkZhOp3GYDAoykSAyFkp8HAymcTR0VGpbsAYnpycRK/XK1l/g2ifw3J4eBjj8bgkHuhTxHwLGwfKUb3mV1/TDoGgDyxmLgiEWSkl2OF7+GrQzfiYD2+XYs6c6DJwR/lwqE52OKHlYHY6ncbW1lZMJpPY39+Pu3fvFr4zvprB+bQSPEbPdnZ2YjgclgSiA3x0Fp3LyWDIxh8ZR4ftUP38vPJjg+2zCHyPDTXkpLXHxmfojnUYsG+w4FXpiPlbKbEvlK+SpLPO2eExZrfL/wA2ksyM0YEH4IQz3kgQkpi1bMN32yUSzhlYM14SSV5tyvxzwth23AFtDpbpC2MkKeaEshPbvV6vPHMyOd8azDZRwIhXdQCTo9Eoer1e4cn6+nocHh5eCDCuKo1Go/I3VbPP21/GaBCbKw8jmnaWBR1v9WR+kFnmtAbGc1CcAzkHMJY7A2EnMIwruD/3GTnz/RmMmxcQ/iH7GydWaN8JcgNDyzqf5eQt44N3xhO07epD+uZx+3/PlRcPuH+R/c3znMdZs7m14KRWPWEe1IKhXFWJLDmAsYzCy9xnBxUel2UpB9BZ/vJ81XDgqyYnWZxszfyIiIIBHZDmwDQnKdATB2o1LGx8lfW69nceQ+6Pr/cc+Hm+3z/5OYuST9YL/EbGDuiJMaVtk8ecE6HIW35+TVfMe88Z2IDPreeZJ04K5bYzL/13nne3a38OH3yP/bbtQcZDtOmqDY8p22VvK1pdXS1VzVeBcn/BGE7+53FNp9PGjhPI8uXg3/fyTI8/x8e2tcbX+CkvXDBfWTbsk7MuWF4995DH5AqpvLBnWbdOuHCCvtAG8aSrsiKinP3pBK4Xbt039w/e0L5tqOcj99+LnPbvXO+FYN9j+5iT7bYpnlPrkp9hjE172bdzHzu3wMnm4crKSnmJnPtELoAcBH3JGCYnwC9L4/E4er1e4dV4PC5vhsUmmD+XoReSlPKZPoAQhNVgJINGFMEHmiO0OGUCPw4C9gTOZvMsvLcPOhizMbEB4V6DSRIpBrtcTwBoxcuZ6hwkdTrnp84bmEZEI2jMBh6giiGiLyRiCLQ8JhJHbPmjvySb7HDX1tZiNBqVbWreFuDtPGStKcN3dQTt8v/GxkZj1cNKSQWXFdPGnuv539lqGxRvpXSgZAMGH/ltpzudTmNzczPOzs5iPB6X9iIirl+/HqPRqFEq74Qoz6gB/k8TORCD/7du3Yrd3d2ywj2dTst2UCdXSORFNN9+Be9sA5wsRH9dxZirADyPdu55hSiPI+JitVtOYGI73G8bT+toDeRDeVXRvKHv6EO+xoG5k98OhmvBf+aTx2F7BE2n08brxc0nEtpO+JI82tjYaJy1ZpDmVZyaXWQFMuI84XJ0dFQqJrHxJLuxN1Qydrvd4mhns1nZstzpdGJjY6P4Bj+LhZBerxdHR0fR7/er4P6q0q/+6q9Gr9eLX/3VX40f/dEfLYmpy9LTHL/9YURzgSgnC/Jqbw4O0WH8VF4s8nUZsBq4ZdAXcXGFMQdU/M1cIl8+z8K6iE0xIDUv3L8MEm3brLt8n4NI95XgxqDPY/D1tMf3LF4R/ABGjZncd9uEXKFkX+hxZ1uQA1T3x77ZdodrDOA9b97mbaBKYt0JdxagHOTbji8iz7X/z9fUZA07d1UIvoKB0UW+I/EeMcc47Epw0AHV/IeTnjW+5mRB5l3N3y4ai++BrDO0la9j7DnRRH98Ld9Zlnyt+5sxZn5+Ds6zfSDYXpTYMg+NNdyeY6K8dcjXgRfsvxbxOSfioez3fK3tu/noAoDsOy03xooeJ9/lrcrIb6fTiYcPH16ZpJSLGPxWMTCVd9PY7sIfHyeTz3WzPBHb1uKW7M8cHzFHfsGUFzGy/Pl/J68yBo6IxhzlxKkXZKwvGd97p5HlIMdy9NcJJ1fzoFs5iUNc4fHTjs9m9WLQdDotR/t4PPTXNt/9s0x7Pqz72WbWbI15j0y5ejr71pyURgdtAxlvxHyHAb5ieXk5NjY2imx4Z5aTek9L1LtPlyXv+KC909PT6PV6sb+//1xtQS/sTCkHNysrK2U1/vDwsAHmSHBYKVZWVhrbhAhUMMhWhlzJBENJXLkioWYU6If3k9tJc+aBk1PZgdtQ50oPAnfup+rC1RjOaDNWqiwcBHAmig0GwSF/A1QJ0hiDldPn0FB1gCD7fCCfOeVs6tnZWWxsbJR5coA4Go0aDmg2u3iw2tLSUrnO4NyBDPdgcGorxySnTk5OGm/HywlHJzqZT94QNx6PS8KOrPzt27fj93//9xtVY06omP+vI2XA87TrIpqrPTs7O3FychI3btyI4XAYBwcHsb6+3qiqwGCSkbds8znOmrniOSQCcxY/J5xyEJvvsSxEzLe72dk4Oevgh77kpDVkUGq5tDN3wOZrzH8nY+GZAaoPb8xAsNOZH666tLRUts52u93yN7YHfXUwOplM4vDw8EK5Mn/nVRPz1YGbk4zYouxU6ZcrJLAjTu4DMvjMgIYxdDqd6Pf75RnYSmwP88xW5dlsFsPhMHZ2dkp/B4NBkZdFAdRVoS996UuxvLwcH3zwwYVA57KUEzN53PY/Dux8pghbhrAFluEM4PI5cTmRYFBbC5xyEJWTGwbvObhyvxwQOMCqjdv6bJn3tQBeeGH/nQNfUwamrAyjm7YRBvLYK+MFJ31tD7PdpH957iMurnDntvjJga3BrPWeNrN95XOPKwe9liEHSD5PzrIDwe/Ma9tJ379IvvLfYLSrQsbFLJw5KHEA6Ap08ytXb5jvTsRkfFvTzxxw+W//7z5kG2sfHtGcS8aWxw9ZRswLPzdjeD8399f4uqa3XJ8/43fefZDHi464b1zjBIIDzhqP/dtJhJosc02urMu8zPbPn9eS+SxYe8Fw0f1UbhNXsKjkWAq+7+7uxtbWVuzu7jZeoPIqqNPpNLYgGV9yDE3E/EgVxuP7c1zqGNQ6x3zlKijLRsTFxVHiLfteJ2DzPRlHe06RC//YznvbPn3N+p5ja+aZ64yFc2LF/zM2nsPb2+A3caXjULCjfT920Mk+x7DWN8cqHle32y3xr3nvWNW+1zpJ/6zftjHgB/fZ/1sGFtlf6zY+gGccHR2V85DBK1TQEpPBV3Izlhvz5zJxYqbDw8PY3NxsbM3vdDqxubnZOF/qsm2/kKQUSYBO5zzpRHCwtrYWDx48KMKwtrbWWD3kWmdLI+aZUIBmdhZWhhx0GsDZUDtLnUE62cVOZ77nOQtiLfC1IUABfKA7fxssMW4LpyscDDhJvlBtxOvmOZQPHk+n03IIuM+y4rk4C6qkMJ75HB+ECh6QpIGvCL1BAHvDDbojomz3Y2yrq6txdHRUgh4qKAxG+Q5e2CGzqru0tBT9fr+R5MJQWY6s3DyLOfK+5U6nUyrPOLsmOx7ooyjsqyaP+1kBLrKaZRcDs7e3F4eHh3Hr1q1i/Eh+RJzzhy2hDmqYW3jqpK6Tk3aSEU1Aht1gvr2tk2fXgLiDtRoQt4PMYMs2wuDX/6Mztg15hdHPB9Tafnk7IoDXlZ+2CQ4eM6Cxw5rNZqXMl+AQB077gCPaxj57m67tJX2zs/eZbw5+cgDkRJftp0Guq1GwZdiXbLexLbRF4B8Rsb+/H9euXStv+bx+/Xq89957cXBwcCE4v2r07/7dvytzur6+XmQrBwNPs0WZ73k127Ljz7l3dXW1JPzQOSeJAUT2z9Y3+ocu5+Qy31uXHJi6b8w5/TbghZB1ywf9c4AY0fQr6J4TAZDtFrYQPXJQ6mAiJ5RqVQgOblh0ynOKTcmJmJyEd2LZ8lCTEQeufIcNyMkBLwZad63TtsWQ/YttCLqMHpvXxl3GApaJZ8k61yy67nXx22AR7KfPCD06OoqNjY1GkDYajWIwGFzwmRH1yjD0NtPT+JODpEXPyM+qjS3i4hv13Jb1ddH1tcSX5d36gn7kID9XCzzL79vH5v45PrBe2q46tkFOjTvsL/1cB/kO5m1z3Hfr66JxORjmsxxfuQ/etQJ5cZ/YiViCtpxkhzgXcmVlJW7duhVnZ2ext7f3TFz6SRLxgWMqxlPTFcsf1zpp6s/9jNrflg1jxJyodWLWCeqMcU15YdQykvtqPbHe2V9wH/GBn4EP8REsOXnmpFptrN4V5L65bb9YK9sF85Pv8LPZP5uXWfbpn5+1tLTUeFFO5ouLFoyPcgWusQz/2597XmuLSMYW3G+eOyaqyRC/V1dXGwvUbu9pNnwRdTqd4osg+rWxsRGHh4fP1e7HTkohUKXB5fNXZxL4+61x3ksKsHHpP8kHK4pL8JgEB7suAcwBGEFWRPO8AwxJxFxwUWzuISvLGLMgevwGeghjXmHIE2+FsXK4XNRg0OAQcLe6ulqqnrgOo09iySCefnLIOYeRwbelpaUYj8elP04gjkajIvQkuJhT3pA3Ho/LNksUdTablQy4g1/Pa54/eI9c5LJH/nb5In0myIDnvEHQc0SCi7cX7u/vx87OTsm4u68YEDvl14W8x9iJOidT4B8HD66vr5es97Vr12I8HpdE4HvvvRdLS0ulUsoJCJKozIuDUDsFyMENht8AzEEpNsY6G9F8g0feemodtzxENCsec2Bno55X8zkHKa9E0zdkt5aIi5i/LcPO2CDIiRtWHjMfOp1OSSzBR3Qe+Scxje6Ox+Nynd8CyJzDQ/jhbUP87/Pj6AdJZicP0BknxwAV2GRstF8EYTALHwEy8M1n3XEvJcs7OzsliYUPYY5v375dEjw876qS+WabZV5COejItAgMZ/Bh+2BfmIMFg9fsl3LyKCc0I5oHX/MsAyf6mReaDByZwwzkGBfksfg7v/3JQV8tiextUzXw6jkwKLUOZKxijOLVVOtRThjRLvrgZJX5g27STwN0zxHz5EUprsmA2LbQfXWfc3KhNoe5wt2YMdu3LBOXoVpwlpMUi/TlVQbFpk6nU7ZK+6yT5eXzoxm8qBgxPwDdAYdllTbxMbXqgNrc5UQU19fsZr7Xz8z+P/cpf2bbs6hftc88DsaKHbJ+RDTP7oHMQ/jodv2Tx2rbme2zr0f/sOlO+jrQ9H3W5Wzjcn98nXmU54U+m+fGOr4+xxPmD+04IQXWYBGYxDTPwg4QY21sbMTe3l68Sup0Og1bBB5CTuwHut1uqdS237F+WL5sa+0HmS9XPTq+i2hWr3FP/oxnZ9/C52AuPyPfG9GsmmWc9ME23rJq351tfuaL5buWMM1229cZc+Z40GNy37EByF/mXeaJsU6O8TNffY/7kfXXc/k0vGm8YBtGnJFxmJOi+Qc+k2Mxf3Jy0pg/j+FpmHIRsUjS6/VKVdbx8XH0+/2C5S9LHzsp5dVTg6WIudOMmB9oDbPW19fLq7stWACvLBQ2bgio37pEYIozQeFxdCQn+M6TZVBmB28DX8tSG9ByXS7PzwCQoJWEDrxZWloqySYCfCsYwSYVSCgrIJ2x0QYn4BOkUQGBYXUFFmMZDoeNsbC6ORgMShDIM0lYOcO9trZW5m86nZbqKL/anS1fBMfwx46Q71wKaKMIUOMMHO6DVw4msjFCEama2NjYiP39/bh9+3Z0u93Y3t4uiZi83eh1I17P2ev1Ym9vr2EAmTfvod/Y2Iherxe3bt2K8Xgc165di+n0/CwuZNbnmvBq6k5nvrUM+XRC0slG7wFHd2wDHJjYljgZ7SRRzSHw28Y6Ii7YEa/aWP78d8TcORt8efso1+akDrLuJDz9cIUCnzMeH2JOIOFEWk5IQXZs6BDz5ooQA2i/1QX9mkzmb3bieifbDMKp9jB481xiN7kHB5V9hoNRrtvf34/JZFKq8XLQwRZithWTRFtZWYn9/f3Y2tqKBw8elFc6e26fBhReJdUC5I/S1xpowTZmYDuZTBrnMXhBAX47uRIxD1gWAdEcSOVA1Uksfty33E4GTZY1+3TLLH3MVVWWYQNC65mrbg2G8WHuH7rsxAo+g6COv+mDdc5gPwNn+2MD1JpMOLjIINT3ZpDLj/njoMt20zbRgTH2z1Uonm/s4XQ6LWdfYj/yoomTl/DkeRJV5lcOeGp8uyq2AAzlSg0nUc/Ozgo2MS4kSIZHuUqhZmvtWzPZr/F/RNOn+m++41r/7R+3l+cl+wwHjYv6ZH3JyZgsz1km/LeDwdxOXoz0d1yPb/Q4vCAdEQ3s4Dmi705Sm2qBrXXTfTdfbONq8u2+ZNud5cYJzXwv3zGe2vYv95PneAfMqyTjQPu5TqfTKGqIOB8DurYIN+Zx1rAN3+WEaURzUSrbereT5zzrDX2iPzn+xl47RvLCQ57rnNTOyV36WEumZexlss7ltlk4dYLQW88gL5yAI/IxMB4v/QHD23+7zxHNaivsgWWkhiez/3UOw/mGbDs9FtqwL+Ra86gmK3zml4/x2dnZWePt18bo4D5f/yzyvMFzJ7tOTk7KwvFl2ot4QQedW7k6nflK/ng8boBKC723oRHI5fI8BuGqGQdPPrvIiR8YRQBtsJaBUl6N9P88z0FtTkgx0Vm4s8NDsB1wUlVEYgVDaLBsAY2IUp1AH7PgkazK5bYAHgsH+1Cp7PIbvAxIOcgvIhrBuKtiXLVC0OCAmwC90+mUV78D3h1kGoihMDkxRJt+O57lLKKZXGAsrvzgHJ61tbVYX18vvGWMvKHQh8G+bsRc5TN8kGlvvaL0Ej3r9/sRcW4EObvMCRG/rZE2kcGVlZUSeKCvyAP32dhnh0fQZwfvpDD9x6bwfcTFAMN6atBlMGGjn0Gpx1QL5mognesZu/vrUl94mVcSXPnHnOBEvbXYNgLdy8EoVX/sK0fvsLPwwwliA2jbNWQgB988m/HZzpIUo+/j8bhUVLKikxMJ3W63EawaEJAMx05gR9fW1hpB8XA4jLt378bBwUFsbm6WviIPBhpXiWqB2qLrDJ78ucFoBk/2XxCy5PMaDFBcyZoTuTlZlQG0gZ6vtU81wOU6j8uBkp/hMVo38/fWTxJwTooBGpEtV2xYHl3NaL7kt/9EzH2UK4T8rAwWM7j03DIeb/XLADevqrqvtbmnHXgH3zOfcyLaffM9OanmZzlYsp3GfntxjfadiHxeHa3JCf/X9OUq2IBut1vwsM9sdKBleWRRzTJkXXfSA/sccXFLZy0wqvm0RX/7GdxLeznBzN+Wd66tzUvtt/0iMpL1yGQZpZ+1pJeTKbnaOcuP8Saf2y7kBEXWF+uTP3P1ySI9zbzI/POY/KxsX7LcO7Cv8ZJA3227n+YXPhkfYpsL7vSRGc9TRfGiCbtvvUEG7Juyv1skZ1D2u8bAfI+sZPuPPGWb7oqdiMUYIF+zaK7ph2Uvx8ee29wG93vc7qP9PLEI/AbLuVq+pleWJyee+JzreF5ENDCq8bur/bwLgbFmHGEeep75nXXBem7K/OHajHP43gUsOW4xjywXnjP8ah7LbDY//gC8h34a8zwPWXY7nU4cHBzEYDC4EDPeunWrxNfPoo+VlMLZoTyrq6sxGAxKcuPJkycXwBMMo0KD6onJZFICWYCcHWl2gFmYXLHle1yO6axgxHwvrCsUMkCzgDvoysYrl+DaENNnv2oeoZlMzg/hpmSbLXkkizx2V4S5ygRld99Ho1G5xkCVQA6gw9tchsNhSc7wLJQZReA+Am6clN/+cnx8XBIPPIN7bOiogoKnXikimei3LFC9UUtecJ37jQx4pQYesGLLtkP+9yHqy8vzQ9GfV1GvCnW73bJVE31D1zD0fIaRWlo6r9i7detWCfQ7nU48fvw4ZrNZvPHGGyW5EXFxS95kMinnciEzEc3Kg4i5gbTO2SB7DLRdCx78OfpmZ74o2HIfbMCzU82JL8sR4+XaTmdeDeVA1Y6fZzvhik4Y+DkoRj8si7aBGSQxh7ZL8NHOCF7AKycJ+Y0DJKGETbBD98srDNzQNewkNo2/STx5jt1f+nh4eBinp6cxGAxKdR5jGY/HsbW1Va6nQnR/fz/u3r1bqq02NzfL/vYa8LgqlGWUz2o2aJE++FrLukGU/QaLBOg/84R94D745mC5BlbRKwPNrAM5UEXParqW5cHA0M/z5zk4ti9w0JgTzTwvt8F2dfctJ1Jq+kgli/nH5/TLK8N5rszHiGgkzdxvJ6AWJV3RNfcv8yUH8Zmfxk+eP8+Fx+r+LC0tRa/XK2MlIDGYt2wY7Ge59n2LdKWmS/5/0fcvmzqd84W6fr9fFiVXV1cbW5GZNy8Q5oSM59qLBFnfaM8+0vx2YsVz7L9ppzYn9C3LhwNxP9fzYB2utZv7Q7uuHvb1GU9YRvNWfMtwTqp6PNY/+J6Duow53GfarFVYZVmv2b08V3nuzZts//Pc+V5XPUO2CY6f6BPXmzcnJyflXOF8PIC3whF/vMqklN+c54QHu3i8GyTbJ2PfiLlNR/cyr51wcnLByQnuqVX+OvZ0P2rVqRHNM4wyFrUMOY70d7TtmMtFBD5rMSftav4NAssyrryzBTnh2ZZBJ5PyIqyxpOeK7zwOeJHxMb95jp9BH+2fPI/0ked77LmN3EfLgeeKe92ubVMuqiGmJSaGh4zTuIB4K6K54+2jkGVnNBpFv98vfWYu8f3PouffvJ8bUFaOg8sRSAL9LKjejgEIHo1GDYYgRBHN1dSczEAYSIbkwNFbAlZWVhpvpULRp9N5pZZBW3bc2aHl7YkYJCYH8O6EGAAXYcAokpAigcAbpgjEOIzPr5ClrwZ3zn7SNxtVyAK0t7dXxsfWGvo1nc7P+TJA6nQ6pQrDbxfo9XqN0kL3l+DVhpH5jIjyqnj+x+DZMTgQwGhAeXXQCQqveMBHthbSBxSJZCBJqRwEvE6EPGxsbDRW8fNrpG34vHLU7/cbZwPwRjQMp/XIzsay6vMx8upPDQA7cw9lgAc56WowFXFxlTWiWW6O7LgvtJcB8mw2a8gyvK05JDs9gxHLe67+MzgyCHT/kGfu8bwwz3a09NtzwmqRX4bgxJedtgEN9nE8HjdeRNHr9UpSw44tV0ISYHGA9tHRUezt7ZUFDMvQ0dFRGQP29PT0tFQuAnxZdSWx0uv1GmOnYnd/fz/u3LkT/X7/Am+vGtVAQQ7cnka287X7DKQimueq2ed6MYZ2ATgksbHn1mknJrmv9n9tq14GetYHJ04MdgneaTvzwH3KvsD9MFA00M3BG33MgYH5YD3qdDplC4IxiytfcoDgRJv7gc1Ad3lOTsx5RdjXWcdICuX5chBQC6pzAtz8yM/EtxofWA5yRbfHkRc7ngaYcx9zAJ6vyf+/at+OTBrvRszPa2Usfrs0/9t31nTdc+vnMR85wfi0v2s8yzKSg9qaD87BmK/L9+UkqvUUchvZdpgceBqD2DbkBYucyLWe+ccy6r7TvvtN39E188UVqp4fy7PneZE9si3M8+J7s3zkymU/N29l8/2eH7+5FZnN/fSi9auibrd7IUajght7HhHlKBW/IRxc4nszHzxmJ/TyS4R8PbJovYLX8NP22bFpxLzSJttpzyufG7fit9wXCGzJsS1gLMbgIzm8/XA2m12YX/rhZKtxsO2C+wof+I1/sa81DrBfo89OhIH1+T/7O3TcvtPHBfG5E4LMteXB7WabWdPxHPvkea7ZGT/fttAL7vYZftFaxhDZ/l2WjJOm02kMh8OS0+h2u41zXp9FHxuZG4SZKXkPtYOuwWDQ2DpEcoOJZHIdYACIOQjSZykxORm4ZuDMCj2TXAvSDZJcgeOqJSsQSQxnwTFgGDgHbfQXYSFgPzk5KUkQJpaqFldddDqdco6Kq62czCEhQMkixsEHfne73Xj//fdjb2+vnCVFQsorwvB1MpmUxBifcW1WIl7BHhEl8UMg49WIiCgBJc6Ktm1A2FqYwbYdCXNn55CdNME1z7937155dWa3242tra2GsQH8L0qIXHU6ODgoe7BZfchAiqSwncvS0lJ5lSdVE/1+vxyczrlRzAd65Uo+2sNBmofWGycJHDSSUPZ9tRUfgzjuhXJSGTmobRdxO8iUg0h0jP7bEeZKBz4z2MM5Gtz6M/qIzTCoMNDg/4im7SNotQ4wR04Yei85hP3yuUzw2wE884luGNBmkG7nzN+ulsTmYueZA/rGAgE6y/bD4XBYyoCxazVeYg9oH3nJY3+d6DI2KAOW2meeJ/73amktsLF++V4nV/3j5K5l18ArIhp6j25a/jx2A2uPw8kM95uxc20OoHLS2M/MAaCBYa1Syc/zoovBYbZxuZ8OGDPAdB9dlWjQbxxm/ngc/hwMkZMXBqgOSnMSAx3LAQ/2p7ZlzDjJz3BSnbnMgcqz6Gn6UbvfwdNVIHSQsRP8eiElolmdY7zmIIW54j7rRU7K1+yFn5PJelQLjLIOQjnZQV/8mduDFsmw281BeEQzuZcxCPcYb/tab1m2/TI2qPGnFmzmcXusfnYN5+Tra/zOSfVs3+l3bY7527bQ9tw8cYLF+p8TKB6TfQl6HRElnvioQfDHJeIYx4feibG8vFzehJl9Xs1Hem6ybUNWnCDJ/pX7wJqWI1MtwYyMWs/z3DsxFjFfnM02vTZ/jqOz73Jc5e+d+Mg+sJas9fmw1tUcv9mHWn7sN2jbxRqOL7GLtJXH74WbWqLKSaaan8afeYy2cxmLQXm87lOOYegPz+R+21AXfDjeyTLhfngMz0u5/+PxuLEL56VUSuXMN8EEAQeCAohaX18vCQgCoOPj47JtLwcPXuGHeQRXCLqdiCsgbDztzC1sBsbZodacAfe4UoBgzX9DWcEI6gBrk8n5ljSqt3IQi3E6Pj4ugZyBIEFXr9crFWk+4JgqBLa5eXvGw4cP48mTJ6XC7OjoqJHYyufamE8GQj7Xq9M5r55CWQ2mbJwi5oab5zjhxKvcawaSOfCKMwk42o2YH8ieA3KvNPOmQQyoq9x87+tKroKA13xumXCCM2K+5YSqFZKgOzs7sbx8/taUDEJsPL0fv9PplLn1G/vQBebTspJ1IeLiaibPd1LIYME6i2xaTt33DEjhEbakBpCdCOVegI2dPzYHAAwgySvjOQB08gi9o8IyO1g+Y45xyCRw2A4Mv6xHBs6Hh4fFHhgE2CZi/8y7nLwmkYktJylNVRWJIs66yi9dWF5eLg6MZ4xGozg+Po7xeFyqrabTadn6DB/oL1WXb775Zjk3LsvAZQLd15EsG5kWBX8Gxv7fYLq2VYMKutqzDN54JnrC/U9LNDkRlIMb/229zXYp9wXd9DhYLc/AjJcA1J7rcdh+IL9OqKM38NBtAJj57ZVM2jaIhhiz+5IDnQx+c1DrJJtXjQm88Qt+Zq1Nyx2AnL+xFfAlYn54LL4iA3rsgfX0Wfqa5flpsuDvr4Id8BxgEx3YTqfnlc68sAHKep75GHFx+6ef6TaeFhBF1BNZkPFzLTmU77HP9bPz31nvLH85gZvHExEN+XWcgL4YO3gM+R54bDk0zjc/a/OT//dv89yymJMF5k0euxNGtWfWEoh+fu6/dS/PrWOdnOSjLbBORDQwJ0UFbGcmZnkV1VLgE591C34Co9jn5Sp57HnWrYxTI6LBS2QuYxDLfc3nGReCId1vxzj+PienmTvsjf2IfYwLA/jbcRryzxiXl5dLAUJOXnrLJnGHeeWFilyQ4OotL2ra93c687eue+GVccITF7rY95mPtqHMf164cfXqIvvg3SaMhd/5p0a57Yxd4Kfjk2z/c/4i24b89utFfXke8rwcHR2Vt8YOBoNLnyn1QiqlEDYqTQgS6KDBSK/Xa4AOZ41xViiMk1EWWJJedkQOavkfJhkg+RwThBzFzArs15A7ucLBuhYqT7qD5Igo7TBmAjFen0hAxtjyloiI+fZAxrS8vByDwaCRjHNJbLfbjX6/X4I7BPjs7Kwko/r9fnQ65/unO51OOfybPd75PCWDpJwgIwEWEY0gE+OOUbDMRMz3oGMcmBsMpRUmJwUNULwt0DKFIc2r29zvYNhJ1KOjowsB2utGrv6j2gTdgO/wazabxdHRUYxGo7J9yknd2WwWm5ubZcWExIOTici39RYZcAIGvXESxz8R9a0cngc7pLxS4YQzcpET6N5SmEFFNub021uFGJvvM6BltdGJN66LiBLM5ySJqx0Yj7fkAoixZ9zD50tLS8WmsN3Otha+YgftvJzUJ7gkgbm+vt4oO88Bp+fMMkff+XECmXuwyz6vAP7gyDgrand3tzg75oO+RUTR9fX19fjd3/3d6PV6MR6PyxboRUHa60C1gGNRAFhLSuTvI6L4DcjgtcYjBx4G2fl7ZDiivnWAvw32aBeZt2+1fbDd51lOZjhwgAxe7QsAkDnAAGOsrKxcqKzMQbDlH9l3MjzbI651WwaY0+n82AM/wxjICTvbMK8A13hlHXXCAX55zrPdzP4zJ0M8npys5h4vSNA2ASLPzzJtoLuIclLAQNxyloO8pwUGL4vAXCcnJzEcDsvf2GB2EjiZZ37noCIHWDmpY19g2eBe8z8ne/Nc12yS//Y12c+7j/b9HpP7V7N1NRtnzEJ7XqjOQWZe1LKs1ZIH9Btcm4PkrENZd7I+5ooMYxB0yc/Of0fMq6nz4l5tXrg/41vzG1xfsweORTwe2yAf4YJNRH4J9n3Nyyb6lZOSjNk4LCeSSFrZR+WFCcbmGG4ymRRddrxifGSfVZOpRb+zztIu33s3j/XC+uDr+T4/w5Ve5ktEs3Lf+gWf8F1gTQhfN5vNGrEDvGae7PeIZcHX7p/xqfXWcSvX08e8ld3JVNoydvb88FPD2a46rPmhXK1keUAHs34xXuNv3+Pn0a53ZOU8iHUwLxI+D9lmw8v8ArvL0MdOUTMAwC2TEzE/BM9BFsw5PT2N0WhU3mozm80uCLUVz84DxsFMr5BnR2YB4R6Y5r+p3kIA/YawDMCcwIporgb4GhsfJgiBoerBwufsLGOvbach0OIZGPwa4CQJiDIeHx/HyspK9Pv9WFo636ble09OTmJ9fb1R6TAajWIwGJSDwOEZz3AlCPMVEeXVkzg5eMCcYwysWAaneW5tlOibExYEEM4mZ8DCHOEcyOS6dLfb7ZbkTAb9rxMx7xhRePY0Q7q2thaHh4extLQUm5ubZSUpYn4mkeV5aWmpusceR4KjMGCJmJfUunIw4iIIzHqXKa+0G1x4NYu+uCrAwNjBooGk551rDWJdUehDQi1/PtyXSsZOp1OcMfwjGYOdpKrRNo3DSEkOoidOYk8mkzg8PCxVgeYnzshv4qRf9B+9IynkQz83NjYu2BnrqhNLEfOXKwyHw8aZggRi2A7zjEQxSVXGgS3EhlEh6iTaYDCI4XAYnU4nHj58GG+//XZ5hgHo60SZ1897ryn7yQy4FwEoBz01kJz1wn7fwAv9ckWkQVlOyua+Q/ZBPNtyl4EvbaOnDoRZ3UWO4QPy7Coiry6iE/aV3Iu82hc7eM1+zPaCcWQ5ZWGLvlgectLbds1b4zJIz/KQ+2mQ6uflufFCkPUMXngs8NaHHLvtvMpsvuQAP/+9KJDLyQK3exVoMpm/rXpnZ6fYPHCKKxs8B8xVbR7hlYOgPH/+DD+S7TFk/uXgMz+3Rk6m5d/uJ9fWvs/41vc4EKtVo+Qx5LbcZg728j34O/Qr8zy37fazHBrb5D5kneD/nETI85ztTo0HmY9gB8uYg9aM4WwzHXTi2/1GZ5+/C/7Ixzu8bLJ8uwr49PS08UbunZ2dRqLCY64tmtqem7J854VaYh9fb7/l2DMn0ex/nSCjHT7PWAJ8ZSxL3Mo1jkfpB34Q3kVEieuQIeufYz1XBufESC7QmM1mBTPDc3TS25vzmBivddNyZt3CH8GLjDPwYZ5v427HFhFzX+yFb9vJvFV9kVzapxPjW4bMY66vxVku/LCdsK1/lu2+LGXbgt+i0vcy9LGTUh5gzog52FtbW4t+v1/2Fh4fH8f+/n5sbGwUI2Dw4KQQQZqz7hnQWiktbARJ2fnamXssFmwy2wielS0bgm63W87aAXQjxN7qRjIp4jwxsrm5WdqgtHE8HkfEfPUDgOKgb3l5OQ4PDxtCtr6+HoeHh6W6gX4S3PL2rPX19QKaAbqdTqccFGyDhOLNZrNy/pLL8xH0paXzN2G5Mgq+YNDJ0jrJ5oSTDd2ilRpnj8m8kxDLiUaP3xlkqjIizt/sxRsL7BRcvfW6EoYbw24nhgzyPRl9zjVbWlqKe/fuxTvvvBN7e3tx+/btiIhy8Pny8nIcHx8XvXQ5ew0I5yQFlUfMp1fXmD8HUdgF5sQOPetyDoDsbOABSZ18VpydqOXBzptnYo+QHYANyUwHxThUHDffUZHBlg2fl8dzSby4MsoVRdhYdAg7tbKyEltbWzEajUp/XBXDnHl1FX3PNsCBB89Hn31OXkS9MobvNzY2ylww5ycnJ6U6z9s6u91uedvecDgsthceHh8fF//B9fBqNBqVQ9kNgl71234+CuXgoQZkFpH9nhdTMoD2GxX5nN/wDb1EhpDHvCqfdReZcWIhB585YON+6yxtAV4j5sEa92TQbvn1wkMG58iQK4CNbewLDRAjmpXR6K71yaDRCwD2ofCPxOvR0VFZJQaHoK/un+ewJh8O9rIeOxDO/fA1jN2Bg5/tQMA209WiZ2dn5UxCdBI/zNEB+B2IayyHNcpAOicanqUzz6NPnySBZ/AH4GfOtsl4NSeo0I2IecWc5xmqJSjRHesm32VZyZg52wz3zbLhe7imlozwmGq/uddBln23Ezj5e8bvPrv9LNfMS01f4EWOO3KCexGfLNPuo/vkmCX7gfx/fk7WAwes/p/rGIvnxIt+7hexDuTkHXgSzNLtdoses6AGLzc2Nop9eJlEvENiEYy8vr7eeCs3R1cQw1gOfE6yz+uNaFYZZZ2y7Nbmb5Eeef7cRk5i5AWg2kIP15Dw5jqP0wu4HktOpnjRz9iZONC64pidBe58HzoMrkXOkE1XR2VfZ9vEW8CRPyftLOPoQ7ZT7vdgMGjgTDCA54b7fbSHcxo5uZifZ6rZG+aN+Xf1lrGPZQf9NV+NaVzg87T+PIuyfeD38fFxeXOxiw+eRh95yZgBwgyAnYO8fD1JF97CBIM5X8pO1SuJnU6nUZrf7/cv7OtksnlznZnkJBJ9AaBGNA885TkoI9s+IpoAPm9xsQD67Cgn1azgrq6IaDo+EgUknegziuXXmhvo872NoCtdAINra2sl+48Bpg8oMMFHp9Np8ICth/TVYJU+0H87ZwC9D0on8eOtZTnYd2DsebGiUsFB4pOD5W1YM0ihHZIwVK3UnvMqVnFeFMEj5DTvE+cHnpM0ReciosgVPOWcIm+7clWZnQvPqiUpclCXk8wOltxPxmXn5+os2jQ4ddsGInxP2xh8Vlloi/sBKDyXfkfMdcOrJxHz7azmPf9zgL/tJu07sW3dcHIBWaa/6BNl0CsrKwX4GUBzHX0lWejzxbAPHqf1yXYu98lnzfk+22LfR0IJQj4Yk1+FDo+oxLQDnE6nsb29XQL5TqcTW1tbDXnIoOp1oY/SZ/thB23Y1oiLB+mjE9YHg8ccqBrkWc/os5Mh7hP6mgPefD9yYwCWz3rg+5yEyW268slncHCfE0c1mUW2zaMcpFtH+cxtuT23b16xqsg1tWSag4E8Zvtdy00OSjzftaDXsuF7nwWofa1xlW2EA28H8Nx3GXnPgbX/rv1fC/w/Dhh/0YR+4ntdZZwTDARoEc2XsTiJkMnjh+dO4Po5bsv/57+5xgFXfuaiv2uBYG4v244cnNfkMs997Tn5eRC8rGGWzMPMk5qu5e+zvllWa3yNuLitJuu1g1bHQ1kXrVcZr+XFPXQ1J+Ucc5i3GWdTqOCtqPxYhp3celkE3iAeAgd1u93GUQWeY+Mix59gubw1LMuP41GwDd9bBnL1VZaBbNv8k88BNF5mTjzvjCtjTPyVbYP7D970eB13mU/GwrSHT2NcyAfE9cbFTkixmGu5dT9dbTubzRp4lGd6cddz5AWwTme+BS3LuefEvHbyy+3xmYtq+D4XYDjhzv/WG8fY9Ns6Cb53IsyL4WA7eJP970cl8xybsb+/XxbGL0MfuVJqkaF3KRvMIWEAwGElkEQAAkOAY4CZjZ+3o7CyZkNswNPtdsuquIXUQTCEQLnqwPtTYXheJTSwxChkQ4bC+WwAKiJY7Z9Op6XqxJUkJLZIXqGMtE1l09HRURwfH5fSbwtdp3OeLHL1FP2Dz1QeuHphMpmUSgWeAz/8KveIKI6HscJHOzAnfTAG+VBkzx9jZg5zZRRtwlMUz8aJZzh4yCs68Jqti4eHhyULncHA60rmLXNdAzUAYvh8eHhYzhxbX1+P4XBYymt5uyMr4A76mc9OZ/6WDK/EcK23jJG8MACC/zhO2nQptYPOPCYbR/5GbwzKbbsI9CwnfI7sco/bdR/QKZ6FfSLpC/l18dhLO3HLaMR89cNJMZJhVEqR2PY82GF6Jd46g974WWwXxC7QdqczT/ZY13IQz5jMoxw4OyECT23/8RFUU1Fxy7MJ3Kia2t7eLjYIW7a0tBTb29vVrUGfZrKfXhTEeN7hO4s2yJATU05M1trOQZEBcA7krGPYgCxTBl/uf5Ztt8/1uS+5DfcH/bZfZ7wOMpBHA0m3nX0ZehERDbtje0zf/Rk+iDPkaAO9IDCg33521jXGnxPZ9pXGUJkvTrKZx/aNObj2Z7PZrOAL+gsGcsKeZ3pO83NMWf5yoGwf4OtyH68K5cCJRQvmnwqVrD95Fd4VOPzOelJLYGTKegf5WvP3aXY164rtQ56nWns1X5512Dxx+/7fcu/vIQdoxiYeQ83+5Gfm/i3i4dOSXzWbbV7ktswjz6n7mJP+/m2faztoLIH+8n+OJ/j7+Pi4+HbwIjzZ39+PTqdTfPqrICeR8Hf4P+Mr4i9+Z56SHLEO2pc5YYW85AWVbL9oP+Mi+8TMe+Iy/uc+4+VOp9OoxjcWpq2zs7PGuVlZZ2r+xdXtOXkKT2gPrJHlh1gT4jn0lTEwF8gmMmY5Av8Sq2Q7QxuuEiM/wXM9v9mPev4zD/DJOQkM/41v7Vs9996pwPy5D56vjL2cN+E58Cr7DnzLZReBnkWLMObu7m70+/1LtfGRk1I2vhYkMuGelIhoVBsdHR1dCLpyuwgxzPTb/RAgV95kh2ugY4BKHy2kDgIjohHwOmjkHgTGCbPl5eWSePPZUfmQ7dlsnuUEbFKpgzB5u54NEMpDpRaH/QJUOPPJ4yFDmQEt/CWhMJvNSpk4b0PkbxtdryAAfFjpsLIxJq+Ec60dPYabcTsot6H3qnDeJwy/nPzwmC1j0+l81ZZrlpaWyhaiiIjhcFgSWZ+GhFTEHOB4Gxhz6JWBiHnFHAnTN954IyKaZ8TZwZBU9QoP2xCcDHFlZU4koRcRzTc/Igdch85jd5xoNFDzmUx51YF5R6fYQsvbGD3nXlmgP/AHnmWHZ3111SaJQJLKHi98dxKaeQEs0SbJrqOjo0ZFoBPRXvnje/fdlRjQ3t5ebG9vF/2D98wz/O73+9Hv9xvBdZYfzkfJAYR5wLX0eTQalb5gC6jegjcbGxsxGAxKv46OjhrnQaD/zD/3TafTxpt2akHY04LfTJ6TT5JqQRP0tGcvCu5qQSY2MWL+imyXWpu3tUAvJ5yQE/PZYMurqTnIy8FgDrwMyv2d73UbTmQbG0TMAXkG8Dk4dvLa7ecg2d9Zrm1T8LskY7PNgPDjOUD0+L1yj27nsRrU2h+an1lOPCb0MM93/t/b5w24vV2fRR/Lha/Nsp5lZ5HsGq/lQC7PVY0Wff4qaTY7P3eUc/MslwRV8CyPN+teTpJm35V1rEb5mhxwZfuQFya43xWauf2a3DKufE9NNmoBI9/ZJ+fqiHydbY+D8mxLnuYrshwbM/lZWZ98r/lXS2DweZ4fz4fHkvvrcWAj3I4D2lolEAvw5i9Y3Pwifjg7OyuV+A7gXwU56eQAHpvL0SY+a3MymZSYxjzgb//2uVRg21xB40pxf25biL9gDmiPfueFguw/+Yy5tMyZF5YrV2o67uIenxHovruQI+sc/xMTOOa1f8ROgXEdr9JeLUEE/vVROY5tuAYs6WRhXnTimA0/33yybtnveosg7fjZNV9kO2QbkH28iWc7jmGeXUSB7FgG/QwWsrNcfRSyreLZebx7e3uXautjnSllB5jL4fgekGvF4iBlDFVOLmUQZqF0ooPAIxtgK4MV0P3KZ0cY1KIUTKoDJKoSfD6FHYtLWL3KZcXmHhQGQ44xRFFIKKHITuY4a+/KCQtmfvsdDsGHtpE8oC0SUhHn+73Zjsf8jMfjRuUEz8qgkmtqh+RFzLc08XnEfAUaGfI4PDc2ngYs2bjxm3nyG8eQGQJukhfT6TTG43GjEuyTDjxfJuUETTbcdpbwbjAYNPg2HA5LxZ4TghmwYfRxJA5I6Mv6+nq88cYbce/evXImGdfniqHBYBBbW1uxv7/feL1oBsaDwSC+/OUvx8OHD+OLX/xiXL9+vRGQGTzv7OzEf/yP/zF2d3fjz/7ZP9vooxPb9McrMk5O0W/GbB4a3FmeDOxsOx2o2QkD5Khyms1mMRwOSz+y89rY2Ii33347fvM3fzN6vV45iwnCdkyn07h+/XrcvXs3/u///b8N3YmYgxTG/pWvfCXefffd+N7v/d7G2HPgh/0bj8elStQyQR8ZZ7/fj9FoVPahc6bDbDYrySd4u76+Hpubm8VO+nyH3d3d2NnZKf3GbtOeX+5geh49/6QSUjmQ+zjPyEFeTkYAwJ04duBTq2DLSQd/F9HUCb5zf7Juu48Gz1zvNnIAZz3mOj8PX4z/o10nWk0O/Pxcrncy3QFMnjN8lvnuJCbj8LZprskJMz5nvDzf2MW8ycDQ82Ab4QUfrs+A1n/nIAfb4fFY1hzg5+CXhTv33SB8kcznwPtplOfkee69CuRVe1cgGwf7BTYRF+2XE5/WlWfxIgette/8vfHbonv47cSYfYaDsyy3fJaDnHyf/U/ui/2pMXjmS636MWNJvuNFHdle+H/j1jymzB9/lxMT+ZperxcrKytxcHDQ4Jv76s/Ncyc/ciIxJ97MP9ttJ/Zs52iXyijeJMnftluHh4fl/NyXTXnRjviJ4wpyQYBlw2chzWbz7U/wnbYy1oZceWN/yXzga5zgqPlRruOziCYusd/w/8Tq3Ofnez7xnfTLCSo/C974iAX7Mf439vRLqTx27rF+0j8SxMSeyB/xB+2Q7Mpv37PPox2eZ34gu5bvnDD0OI0buL6WhHIeopYg9P2MxZjG/OP6vAUU/XRhDD/gYWQ4Y4uP4yNtqyD/z1nEl6GP/Rqi7CxglIWm1+uViRiNRmUinbjIbUEYDisfCRUUxquvLsnD8GSFdZLL19igo4R+204tUO50mm8UxEk562oQSX9JtLh9kiY2/CgY2VsbEFYgnEDhfC6qnkj6HR8fNz4jqM+ZfgfGCLIPcHYix1sda+X3yIATaX7VtY2QjWkO+LLTp30SZjYM8NdnhmEkasEGv+G3jYDPMPq0UE441L4zsMul1RjtnIh0gGqQwz05oPF9g8HgwgpLrW+rq6ulso/2akHsyspKvPfee/GVr3ylbBnJ+s31a2tr8dWvfjX+9//+3w0ZdgLZ8mhDm4FCdujun/XaSSoHZP7bTsKOG5tBmTaOPfMc28b5SozF+8oN3NfW1uLOnTsXxmsnTvuPHj2Kr3zlK9V2Mi98bg/zbWeLHSfR5EPT+c4Oz+DAh/e7fUAF8ov+Z19Tk7HL0vNc+6Loo4CGGiiGvwbaBlaeJwc2gLdcgexr8n215zMf/HBmg6t5DdiQF5+R4AAoB8i5HzlwdYDqNrKe22940YlglGTr6en5m4RZeeScQhKxfr59lfuC3EKAaiecn/WT+WDb7W1yBqOL/IH9pVfJPT85KUc/nJDnO4PiiCh2aHNzs+ixcYdttu3P0yj7mUW2mu+vKsE3zml0wGTM6aDZPq3bPa+EvXHjxgX5sM46weJgC9nJCRswteeqRvm+2k8Na9Rwdb6Pe/K5pcbsHo/bM28doGbcwbO8A8D4Hx5iB2jX4+l0mmfPGZ/SvpMOeXzMe63vERG/+qu/Gv/m3/ybC3bNc0s/HSvRh/xm0KwfbKtzvzJOiWieWWtZ4vqIeUDKMS7j8bjslvFRBi+T/OY/x3RgTB8Pko+a8D34JebGL5LJSQXrlvXQfPWZUI6n0V9jriz7xk3GrpCfwzU5nuW3fZSP+vBiTl54wGa48ijvfmHMmScZ4/JZjgfsd7KPj4jyEi901Tt7zB/8lOPZWiIbPcx2qjanmXc13JOxjXEAz/TzbQOMc42faQc5sc/NsbR9sV844PF+HMo2nrgtV2k9jT5WUsqMiWgeEmqD60wnW4JcRpeDNAyGnWF2ogSnVGrwBhcmiX4YXLk66OjoqAR3zthmB8L/vNWJ7SQWcK9oety0iXMlSKMqh+fg3EimUKmD0DjpQzIGIbRxo31nRCOiJKRIcA0Gg0bm2m8pIDikZNxGln54lYTEGUYnZ/hdXcJ8OzGV5cYGj981BbexBkDQN5ywg1YUzluRuNdJVB90byPw9UiLxr0I6Pu7y34ecXljmIHXonYxul65eVqbKysrRd79uft+2SAmX+d7LytHlwm+PP5F1zug4VqDpOzsc+XiomfD28uMJ89T5qufn/uTP6uNL7eVx1YDBq8jXVaGavLgwMsgzwsv2DmDSrYv0J7tpAHVojm1XudVaQcv7k9EfcU2B5kGkfbZecwAVtvyHLzmALsW0OJ3nWSNOPetVACAJfBHBssZY+B7fIZFBrr40pzQ5RonCMxvdNn/57nxGDKfPW7uI3HsYKyWTM+BbrbXYB3GCCYEP3kua7Z+kb2v6T39yXN51W2Ag1HjL+tEDqAimlhpeXk5er3eQhuaZcnPiGiefZZxeEQzwK3Z2Oyj+F3T7UW22jFAnjfrrwNzxkY//Fw/zzroMZmPtUoX98VJ5Kwvbieiaf8WjcnPcDBcm5/f+Z3fif/xP/7HBZsIOSllvaZf2cZl8iKBdRzKCWuPJSdLSfKweA8G9y6Kl03EPo4z/PIMV/JGNH0UnzvREjFfCHAMxHXYeWTKR8hwHe1nOc321PJbS0L4WtrOY7FfclKNz63/3j6cF+651n6VxQ/ziMUcj8W8c2Ux4zJ2pS+z2axR/IFsZ1klbsZW+riInDxz/iIvznkOHYvnuBsZYqz0xbpmWVukf7V5tXxxTeZ5LakH32pVf/jeHHd/XLLMogfWhcvQx0pK4fwMJvzwpaWlcngZikiHnVCYTCaNFWyDZCbA5WYASWcIefMSxsXVU1lwYFZtX35WSpJnPmAdIbUw0zbAlc+tABZErzhn8IoBR5hIhFESihL6LXg158yzADdsvZvN5lsC4RlGc21trRxyhxK6TNUJP1ZTSNaRSIM/0+m0vKVtNjtPFHie/MprA2/mwQYt/5ycnJSAgDYxeiQQmZMMgLx62Ov1ylgiomwf8rxcdRDbUp0uO2/WmU+SLmuUL0PZkX2UZ9bA9mWf/Ty8vWx/WnqxVAt8ciDn+TAIMyhyRazBeQ7q3E7WJ1ckG3RFzM/3MKDLANrgMYM9+uP2IqLhV/KqZw4oMg+eJ2jO17sd38dz/QwnHbxd0iDcZz5k8AmWqSXoHDDCj8zn3O9F/s5JNJ8J5jcuu+8OWumngw2vZPtMKK/wmwfPopwQ4O/X2Xc/K2mR6ZMea5bpRUmNmj3w39nu1K6r3Ve7vpYMqvWjFgwv8kWL9P1pfXmWX8vJhcvQ09rMSfinPS8/87L6lPn0UX23EyW5j68KDxBTkYjCt5CszwE1/YXs+xyER0QjyM/JIu6pVQBhU92meZ/tdK0dy0WuxKnpYb7OffSzSG44BiSOy4kUeGeeRETD52ae+JxgJ2NJzhMv8z/9JXdg3tO/Tme++4hxZn7x/Np8OJ9BQUWtQov7nBzyWPy/x57tacZN9CvjopxEck4CnrhQxsk9t5Hlhb69CII3q6urjWrzS907e529dksttdRSSy211FJLLbXUUksttdRSS68lfewzpVpqqaWWWmqppZZaaqmlllpqqaWWWmrpealNSrXUUksttdRSSy211FJLLbXUUksttfTSqU1KtdRSSy211FJLLbXUUksttdRSSy219NKpTUq11FJLLbXUUksttdRSSy211FJLLbX00qlNSrXUUksttdRSSy211FJLLbXUUksttfTSqU1KtdRSSy211FJLLbXUUksttdRSSy219NKpTUq11FJLLbXUUksttdRSSy211FJLLbX00qlNSrXUUksttdRSSy211FJLLbXUUksttfTSqU1KtdRSSy211FJLLbXUUksttdRSSy219NKpTUq11FJLLbXUUksttdRSSy211FJLLbX00qlNSrXUUksttdRSSy211FJLLbXUUksttfTSqU1KtdRSSy211FJLLbXUUksttdRSSy219NKpTUq11FJLLbXUUksttdRSSy211FJLLbX00qlNSrXUUksttdRSSy211FJLLbXUUksttfTSqU1KtdRSSy211FJLLbXUUksttdRSSy219NKpTUq9ZOp0Opf6+fKXv/yqu1qln//5n4/v+77vi2/6pm+KTqcTX/ziF191l1pq6ROn111vIyJ+8Rd/Mf7wH/7Dsb6+Hp/97GfjJ37iJ+Ls7OxVd6ullj4RanW2pZZeL2p1tqWWXi9qdbalF0nLr7oDX2/0cz/3c43///W//tfxn//zf77w+bd+67e+zG5dmr70pS/Fr//6r8cf+2N/LB49evSqu9NSSy+FXne9/U//6T/FX/gLfyG++MUvxj/9p/80fvM3fzN+6qd+Ku7fvx9f+tKXXnX3WmrphVOrsy219HpRq7MttfR6UauzLb1I6sxms9mr7sTXM/3oj/5o/PN//s/jWdMwGo2i1+u9pF4tpnfffTfeeuut6Ha78e3f/u1x8+bNK50Bb6mlT4JeN739tm/7tlhZWYlf+7Vfi+Xl87WIH//xH4+///f/fvz2b/92fMu3fMsr7mFLLX2y1OpsSy29XtTqbEstvV7U6mxLH4fa7XtXkL74xS/Gt3/7t8ev//qvxxe+8IXo9Xrxd/7O34mI81LJn/zJn7xwzzvvvBM/8AM/0Phsd3c3/vpf/+vx9ttvx9raWnzjN35j/IN/8A9iOp02rvvggw/i//yf/xOnp6fP7Nvbb78d3W4rNi21lOmq6u1v//Zvx2//9m/HD/3QDxWnGxHxIz/yIzGbzeIXfuEXPtqAW2rpNadWZ1tq6fWiVmdbaun1olZnW7ostdv3rig9evQo/syf+TPxvd/7vfF93/d98cYbbzzX/aPRKL7zO78z3nvvvfjhH/7h+OxnPxv/9b/+1/ixH/ux+OCDD+If/+N/XK79sR/7sfhX/+pfxVe/+tV45513XuxAWmrp64iuot7+xm/8RkRE/NE/+kcbn9+9ezc+85nPlO9baunrkVqdbaml14tanW2ppdeLWp1t6TLUJqWuKH344YfxL/7Fv4gf/uEf/kj3/6N/9I/id3/3d+M3fuM34pu+6ZsiIuKHf/iH4+7du/HTP/3T8Tf+xt+It99++0V2uaWWvu7pKurtBx98EBERb7755oXv3nzzzXj//fc/Ul9baunTQK3OttTS60WtzrbU0utFrc62dBlq92FdUVpbW4u/8lf+yke+/9//+38ff/JP/sm4du1aPHz4sPx813d9V0wmk/iVX/mVcu3P/uzPxmw2a6ukWmrpY9JV1NvxeFz6lml9fb1831JLX4/U6mxLLb1e1OpsSy29XtTqbEuXobZS6orSW2+9Faurqx/5/q985Svxv/7X/4pbt25Vv79///5Hbrulllqq01XU242NjYiIOD4+vvDd0dFR+b6llr4eqdXZllp6vajV2ZZaer2o1dmWLkNtUuqK0vMqw2Qyafw/nU7ju7/7u+Nv/a2/Vb3+m7/5mz9y31pqqaU6XUW9pTT5gw8+uFDe/MEHH8Qf/+N//LnbbKmlTwu1OttSS68XtTrbUkuvF7U629JlqE1KvWZ07dq12N3dbXx2cnJS9sZCn//852M4HMZ3fdd3vcTetdRSSzV6lXr7Hd/xHRER8Wu/9msNJ/v+++/H1772tfihH/qhF/asllr6tFCrsy219HpRq7MttfR6UauzLZnaM6VeM/r85z/f2DsbEfEv/+W/vJBV/st/+S/Hf/tv/y1++Zd/+UIbu7u7cXZ2Vv6/7OszW2qppY9Gr1Jvv+3bvi2+5Vu+5cLzvvSlL0Wn04m/9Jf+0kcZUkstfaqp1dmWWnq9qNXZllp6vajV2ZZMbaXUa0Y/+IM/GH/1r/7V+It/8S/Gd3/3d8f//J//M375l385bt682bjub/7Nvxm/+Iu/GH/uz/25+IEf+IH4I3/kj8Th4WH85m/+ZvzCL/xC/N7v/V6557Kvz4yI+JVf+ZViQB48eBCHh4fxUz/1UxER8YUvfCG+8IUvvPhBt9TSa06vWm9/+qd/Or7ne74n/tSf+lPxvd/7vfFbv/Vb8c/+2T+LH/zBH4xv/dZv/aSG3VJLry21OttSS68XtTrbUkuvF7U621KDZi29Uvprf+2vzfI0fOd3fufs277t26rXTyaT2d/+2397dvPmzVmv15v96T/9p2e/8zu/M/vc5z43+/7v//7GtQcHB7Mf+7Efm33jN37jbHV1dXbz5s3Zn/gTf2L2Mz/zM7OTk5Ny3fd///fPImL21a9+9Zn9/Ymf+IlZRFR/fuInfuJ5h99SS68lvW56O5vNZv/hP/yH2Xd8x3fM1tbWZp/5zGdmP/7jP95or6WWPs3U6mxLLb1e1OpsSy29XtTqbEsfhzqz2Wz28lNhLbXUUksttdRSSy211FJLLbXUUkstfT1Te6ZUSy211FJLLbXUUksttdRSSy211FJLL53apFRLLbXUUksttdRSSy211FJLLbXUUksvndqkVEsttdRSSy211FJLLbXUUksttdRSSy+d2qRUSy211FJLLbXUUksttdRSSy211FJLL53apFRLLbXUUksttdRSSy211FJLLbXUUksvndqkVEsttdRSSy211FJLLbXUUksttdRSSy+d2qTUa0DvvPNO/MAP/ED5/8tf/nJ0Op348pe//Mr6lCn3saWWvp6p1dmWWnq9qNXZllp6vajV2ZZaer2o1dmWnkZtUuoZ9LM/+7PR6XTKz/r6enzzN39z/OiP/mjcu3fvVXfvueiXfumX4id/8idfdTeq9Pf+3t+L7/me74k33ngjOp3Ole1nS1efWp19OTSdTuMf/sN/GN/wDd8Q6+vr8Yf+0B+Kf/tv/+2r7lZLryG1OvtyqPWzLb0oanX25VCrsy29KGp19uVQi40/Oi2/6g68LvR3/+7fjW/4hm+Io6Oj+P/+v/8vvvSlL8Uv/dIvxW/91m9Fr9d7qX35whe+EOPxOFZXV5/rvv+fvT8PkjVL6/vwJ5daM7PWu/Tt7tu3u2GYhaEHA0KIQSCLzYyQEGFrPEBYw1iICRkGENaCBglbwQSLBAiCRQqQQwqhkeUYhQwStkxopBFChhlhMcggyzOt6b3vVrfWrMxaM/P3R/0+Jz/vc/N23+57e6AdeSIqqiqX9z3nOc/yfb7nOef93//3/z1++qd/+vekIf/lv/yX44EHHoj/7D/7z+KXf/mXf7e7M23/H2hTm31t2/d+7/fGD/3QD8Wf/tN/On7f7/t98Yu/+Ivxjd/4jVGr1eJd73rX73b3pu112KY2+9q2aZydtvvdpjb72rapzU7b/W5Tm31t2xQbv/o2JaXusn3N13xNfMEXfEFERHzLt3xLrK+vx4/92I/FL/7iL8Y3fMM3TPxOr9eLVqt13/tSr9djfn7+vl/3d7M9/fTT8eijj8atW7fi/Pnzv9vdmbb/D7Spzb527cUXX4wf/dEfjW/7tm+Ln/qpn4qIMxl/2Zd9Wfz5P//n40/8iT8RjUbjd7mX0/Z6a1ObfW3bNM5O2/1uU5t9bdvUZqftfrepzb52bYqN761Nt++9yvaH//AfjoizgBER8c3f/M3RbrfjU5/6VLzjHe+ITqcT3/RN3xQRZ6V8P/7jPx6f/dmfHfPz83Hx4sV473vfG9vb25Vrjkaj+MAHPhAPP/xwLC4uxn/+n//n8R/+w3+47d532oP7sY99LN7xjnfE6upqtFqteOKJJ+InfuInSv9++qd/OiKiUr5Ju999jIj41Kc+FZ/61KfuSp6PPvroXX1u2qbt1bapzd4/m/3FX/zFODk5if/uv/vvymu1Wi3+zJ/5M/HCCy/Er//6r7/sNaZt2l6uTW12Gmen7fXVpjY7tdlpe321qc1OsfHvlTatlHqVDeVcX18vr52ensZXf/VXx5d8yZfEj/zIj5QyyPe+973xd//u3433vOc98R3f8R3x9NNPx0/91E/Fxz/+8fg//8//M2ZmZiIi4vu+7/viAx/4QLzjHe+Id7zjHfGbv/mb8VVf9VVxfHz8sv355//8n8fXfu3XxqVLl+I7v/M744EHHoj/+B//Y/zSL/1SfOd3fme8973vjatXr8Y//+f/PH7+53/+tu+/Fn388i//8oiIeOaZZ16ZcKdt2l6DNrXZ+2ezH//4x6PVasWb3/zmyutf+IVfWN7/ki/5kpeVwbRN20u1qc1O4+y0vb7a1GanNjttr682tdkpNv4900bT9pLt7/ydvzOKiNGHP/zh0cbGxuj5558f/cN/+A9H6+vro4WFhdELL7wwGo1Go3e/+92jiBh9z/d8T+X7v/qrvzqKiNEHP/jByuv/x//xf1Rev3nz5mh2dnb0R/7IHxkNh8Pyufe///2jiBi9+93vLq995CMfGUXE6CMf+choNBqNTk9PR4899tjoypUro+3t7cp9fK1v+7ZvG02a8teij6PRaHTlypXRlStXbrvfS7WNjY1RRIz+h//hf3hF35u2aaNNbfa1t9k/8kf+yOjxxx+/7fVerzdRptM2bS/VpjY7jbPT9vpqU5ud2uy0vb7a1Gan2Pj3eptu37vL9hVf8RVx/vz5uHz5crzrXe+Kdrsd/+v/+r/GQw89VPncn/kzf6by/4c+9KFYXl6Or/zKr4xbt26Vn8///M+PdrsdH/nIRyIi4sMf/nAcHx/H+973vkoZ4nd913e9bN8+/vGPx9NPPx3f9V3fFSsrK5X3fK07tdeqj88888x0JWjaftfa1GZfO5s9ODiIubm5217nbICDg4OXvca0TVtuU5udxtlpe321qc1ObXbaXl9tarNTbPx7tU23791l++mf/un4rM/6rGg2m3Hx4sV44xvfGPV6ldNrNpvx8MMPV1578sknY3d3Ny5cuDDxujdv3oyIiGeffTYiIt7whjdU3j9//nysrq6+ZN8ovXzrW9969wP6NPdx2qbt092mNvva2ezCwkIcHR3d9vrh4WF5f9qm7ZW2qc1O4+y0vb7a1GanNjttr682tdkpNv692qak1F22L/zCLyxPK7hTm5ubu82wh8NhXLhwIT74wQ9O/M7vhadpvB76OG3T9krb1GZfu3bp0qX4yEc+EqPRqLLKdO3atYiIePDBB1/T+0/b/zfb1GanbdpeX21qs9M2ba+vNrXZ165NsfG9tSkp9Rq3z/iMz4gPf/jD8fa3v/0lGdIrV65ExBnL+/jjj5fXNzY2bntiwKR7RET8zu/8TnzFV3zFHT93p9LHT0cfp23aXi9tarMv3z73cz83/vbf/tvxH//jf4y3vOUt5fWPfexj5f1pm7ZPV5va7LRN2+urTW122qbt9dWmNvvybYqN761Nz5R6jds73/nOGAwG8f3f//23vXd6eho7OzsRcbbHd2ZmJn7yJ38yRqNR+cyP//iPv+w9Pu/zPi8ee+yx+PEf//FyPZqv1Wq1IiJu+8xr1cdX8tjbaZu23yttarMvb7Nf93VfFzMzM/EzP/MzlX7/rb/1t+Khhx6KL/7iL37Za0zbtN2vNrXZaZydttdXm9rs1Gan7fXVpjY7xcavdZtWSr3G7cu+7Mvive99b/zgD/5g/NZv/VZ81Vd9VczMzMSTTz4ZH/rQh+InfuIn4r/6r/6rOH/+fPy5P/fn4gd/8Afja7/2a+Md73hHfPzjH49/9s/+WZw7d+4l71Gv1+Nv/s2/GX/0j/7R+NzP/dx4z3veE5cuXYr/9//9f+M//If/EL/8y78cERGf//mfHxER3/Ed3xFf/dVfHY1GI971rne9Zn18JY+9/fmf//l49tlno9/vR0TEv/7X/zo+8IEPRETEf/Pf/DeF1Z62aXut29RmX95mH3744fiu7/qu+Ot//a/HyclJ/L7f9/viF37hF+JXf/VX44Mf/GA0Go1XIflpm7ZX16Y2O42z0/b6alObndrstL2+2tRmp9j4NW+f5qf9ve4aj9D8jd/4jZf83Lvf/e5Rq9W64/s/+7M/O/r8z//80cLCwqjT6Yw+53M+Z/QX/sJfGF29erV8ZjAYjP7qX/2ro0uXLo0WFhZGf+gP/aHR7/zO74yuXLnyko/QpP2bf/NvRl/5lV856nQ6o1arNXriiSdGP/mTP1nePz09Hb3vfe8bnT9/flSr1W57nOb97ONo9Moee/tlX/Zlo4iY+JPHOW3T9lJtarOfHpsdDAajH/iBHxhduXJlNDs7O/rsz/7s0d//+3//rr47bdPmNrXZaZydttdXm9rs1Gan7fXVpjY7xca/11ttNFLd2rRN27RN27RN27RN27RN27RN27RN27RN27RN26ehTc+UmrZpm7Zpm7Zpm7Zpm7Zpm7Zpm7Zpm7Zpm7ZPe5uSUtM2bdM2bdM2bdM2bdM2bdM2bdM2bdM2bdP2aW9TUmrapm3apm3apm3apm3apm3apm3apm3apm3aPu1tSkpN27RN27RN27RN27RN27RN27RN27RN27RN26e9TUmpaZu2aZu2aZu2aZu2aZu2aZu2aZu2aZu2afu0tykpNW3TNm3TNm3TNm3TNm3TNm3TNm3TNm3TNm2f9jYlpaZt2qZt2qZt2qZt2qZt2qZt2qZt2qZt2qbt096ad/vBWq12V5+r1+vxtV/7tfFDP/RD8Za3vCVarVY0Go2o1Wrlp16vR6PRiHq9Xq77x/7YH4t3vvOd8bVf+7XxhV/4hTEzMxN/9I/+0fjiL/7i+L7v+75YW1uLer0ezWYzZmZmYn5+PhqNRszPz0e9Xo/5+floNptRq9ViZmYmFhcXy2dbrVb5fETEaDQq96fP9Ik+8jn+5rOj0Sjq9XqMRqOIiBgMBuXv4XAYo9EohsPhmXCbzajX6+UztVotBoNBnJ6elmudnJwUmRwfH5fPzMzMRL1ej36/X+kn/RsMBpV+NhqNGAwGcXJyEsPhMPb39+Pg4CBOTk6i0WjE6elpnJ6eRqPRKGMYDoflJyKi0WiU/xkHf49Go9L3er0ep6en5XV/5ujoKIbDYeUz3HswGMTR0VGcnJxERMTp6WmcnJyUa9dqtRgOh+WzJycnMRgMYjgcxsnJSbkeY6Yx/sXFxZidna28j87Mzs4WGR0eHsbu7m4cHByUe3m8nq88x7VarYw1N17nu75GtiPu5+v5O1z/+Pj45Y3uDu3Lv/zL4/DwMJ588sm4fv16fPEXf3HU6/WYm5srMnn7298e7XY7ut1udLvd2N/fj2azGScnJ7GwsFDkz+eHw2G0Wq04ODiIw8PDODg4iKWlpZidnY2bN2/G3t5ezMzMxGg0itXV1Wg2m8UuZ2dn4/Lly7G8vFxss9FoxL/9t/82fu3Xfi3+wl/4C7G4uFiRM3qBzWArs7OzxRb4HL8XFxfjx37sx+LRRx+Nr//6r4/Z2dmKjdKazWa02+2id56XRqMRzWaz6PLJyUk88sgjcfPmzTg4OCh2hP7Mzs4WOz08PIx2ux3Hx8flNa55fHwcR0dHMTc3F4PBID7jMz4jdnd3Y3d3N05PT8vcuB/oMXaP30Ae2Aq+BvnzQx97vV4MBoMynizn0WgU+/v70ev1ip6srKzE3NxcHBwcxGAwKD4SWzk9PY39/f24efNm9Pv92NjYiI997GPxhje8Ib7oi74ojo+Py/zjc5G17ZrftVot5ubmYnZ2NlqtVrRarfj4xz8ev/qrvxpf/dVfHZcvXy42fHh4WL6DjPDN+POdnZ148sknY2ZmJobDYczNzcX+/n68+c1vjj/xJ/5EfPd3f3fMzMzEYDCIhYWFGA6HcXx8HL1eL/r9fgwGgyKbw8PDInvkcXx8HMPhMLrdbnzxF39xfOhDH4pLly69aptdXV2t+MGFhYVYWlqKRqMRc3NzsbW1FefOnSv62WyOQzjyZY6QBT7bOoSuLywsxBvf+MZi08SPZrMZg8Egms1mLC0tRUTE3t5evPjii3F4eBjNZjMODw8rPgz/jAxnZ2fjypUr8cADD8Ty8nK0Wq2YmZkpvtjz1mw2Y25ursQ97GQ4HMb8/Hysr6/H/Px8mQ/iPDbGeBcWFooMRqNR9Pv9cq9erxcRUexhOByWa2A38/PzsbOzE0dHRxV/4XjH9fEh+AnsiHu32+0YDoexvLwcs7OzMTc3F6enp3Hu3Lm4detWzMzMlPsfHx9XbBb7xAfMzs4WG8FHHB0dxdbWVolrxA7b0/b2dmVOsQPi7v7+fvy7f/fv4jd+4zdibm4uFhYWotFoFP3CZ+FX8XP4NfQO3UPnhsNhXL58OR5//PE4OTmJbrcbzWaz9G1+fj5WV1djOBzGJz7xibh582YFS9g3IYeZmZmo1WpF7zJGwR6Pj48rcX1/fz+2traKjhhHcf2dnZ1XbbOdTqfYU7PZjIsXL5Zx1mq1WFpaisPDw5ibmyvyRE70Aew1GAzi4OAgDg4Oih9gLo2Jfq+2Wq1W5nZ1dbVgA8Y7MzMT6+vrsb6+XuLC3NxctFqtYlvGu81ms+jO4uJi5TOtVit6vV4Mh8M4ODiIfr8f/X4/dnZ2ot1ul/n4T//pP8VwOIx2u12wA5h4cXEx5ufnb8Pb+Hr7Co8FvMmYj46O4ujoKGq1WhwfH8fm5mb0+/1ic84DIs4wZb/fL3GR7x8dHcXh4WH0+/04OTkpfjBj35OTk4qPMK652zY7OxsrKyvRbDbj+Pg49vf3i5za7XaZA/wPfo7f4Jjl5eVYXFy8DVs5HszNzZXvLi4uFvnPz8+X+zhXIb7Oz8/HyclJ7O7uRrvdLr7s9PQ0PvKRj7xKLY0SPxYWFmJ2djZmZmZibm4uFhcXo91ux9zcXIxGozK+hYWFMkbiJrkn456bmyu6S86D3s/NzcWjjz4ay8vL0e12C55gzHNzc8UXEPPwxd1uN55//vniE/DLp6enJd8h7vA6OI95Oj4+jsPDw0rO3Ol0Ym9vL5555pnY3d2NjY2N2N/fj4ceeigeffTR4rvW19djaWkpVldX44EHHoilpaViE/gt+oV98nfOn+kXPtgxFX/gWEYsBUei644/zoX5vOMmeCRiHM+Zf+Szu7tbxsG1iHsRUXzw6elpJYdFN3d3d+Pw8DCuXr0aOzs7sb29Hc8991zs7e3FwcHBHXWw0WjEzMxMLC8vx9LSUtTr9VhZWYlLly4VP+VcB39CA/8a56FLp6enRQ7M0+XLl+Oxxx4r2Aj/Qv5Av5gz7tdut+OFF16IT33qU0Vu+HPmY3Z2NrrdbrF7+tDtdoscf+EXfuFlbfOuSamXagjlcz/3c+NbvuVb4kMf+lCsra2d3eD/75wQcFZigASdxlhQWgx3fn6+OH2UEwMkwQO4mDQwaDEQ4TWchskkJ6TZAOgXoJSAZPLBxFsmrbiGCRwm12AJx2v5YlgQcNyXvpBEELBs2HYCfDYiYmZmpvJZ5oT+8nk+g5GTyCFDAJMTajsKA0L6GDEmcmwIJi8Zd71ej5mZmeIYMnmDk8coI6LID1naSflayNikZNaHDJY933m8mbCyjk0iqia1uyWBX6p5fHdyjHcCM3mMuVkW+Tv5tfy9TOrhmO903VfaAKPYxd18/uXaJDIy68ArvSafe6nPvpy+3Ikcze/lz9l2XmmfX+q+9jfc55U0Pp8XBgAsL/W5V9JfgvjL9eWVyJ7k6F6aFzzq9XohR+jvuXPnip/H7yAbxxl8MtcBoAIKsZGIs4R8dXW1JPROejMJ1G634+joqBA8jo15HMPhMHZ2dgrwn5ubK77ZySXgxYDZMXgwGMT+/n4sLCxUCFLruckcL0zg30g0er1euReJh/HE8fFxIcu53tHRUSXWEZMAi/gaEtKjo6OSsM7Ozsbx8XH5v9lsxubmZvmsMYLJlYgoSV/EmEiLGANvEmf6fnR0VJK7k5OTEg8tFy+KRUQ888wz8X/9X/9XJVk3wVSr1WJxcbFiZ8wPemZ8QdyG8B8Oh7G3t1cIGuYMwEySmWMo805DXgBvz6/xm+M0nyG5JwF+Kb/4apoXNEwygXtZtOBvJ2xOatEhxy6wIDZo0u/3YhuNRnFwcFDRw1arVXzN3Nxc7OzsFEIAoo48AOIxy2YwGJTv7e/vF/1jYQKb5HpHR0cxMzMTBwcH0W63Y2dnJ3Z2dqLRaJRFavqKL8D/mZxnDtvtdoVkOH/+fMzPz0ev16v4AuZ+ZWWlYuPoYUSU+YVoPjo6Ktg6IkoSzXfRG5OXLHjTx1eqD+Bp6xd+j7liAcHJMDbWaDRiYWGh+FLnYbZF+os+2/9D7B0eHkatVotOpxP9fr/0kUICrs1cQbLcS3PehM/lx3kE/pAYFRHRarVKHuH8kDyFhRH0k/dv3boVKysrJeYgf/zayclJeQ1ijjlYWVmJbrdb7kn+RTw38ev4bn/bbDaj1+tFrVaL5eXlQjotLi7G7u5uPPjgg/Hcc8/FwcFBHB0dxblz54p+1Gq1shANIYfegT/4nHG3F3Doqxt6AL5hHLb7iLFv9PeYx4wF/RkWe/Ax+BXiIHOEX3YBB7EEu2U8EWe2wXwQ7w4PD4v/6PV6cXh4WPDbnXQQfel0OmVRDP4D2/Ni4+zsbPEX2Ae4BRlhy+gSZPsDDzwQFy9eLIR4t9st11pcXIzT09OyeAIO471utxsbGxvFB4AVIs5wysHBQezv75d+Hx4eFptgXu52MeW+kVIREQ8++GC8+93vju/8zu+MH/3RH40f+ZEfiT/35/5cLC4uxvb29h2/u7q6Wpg7nMH169ej2+3GpUuXYnFxsRAeZg29MofC8T6fhzWEVEGB8mqywY2NPWJsAHZOOJBMftFQ5kkgih+CmAMDJB59i6gaNs6YvqKUkHIAeIMaggxGasLNTpW/TWDRN8aPQdN/OyICEEbNWGh28A74JsEixhVmBugG5XzfzLadrxleVw54hZLrQIZkYJhJJr6PzE3AWE6TSAuTUe5rTrIz0Xav7WMf+1hEnDmNdrsdb3/72ysVRb1eL/7tv/238UVf9EUl4FCNwMolAYfqCFaFAW0mk5aWlgp5zDwuLi4WgDgcDuPFF18srzPWL/qiL4q3v/3tFaKAQJXJIPTTSSKkFja+t7cX3/qt31pWmjJBals/PDwsY8hz6HmYmZmJZ599tlzDFSruI/pBX/g817Z9zc7OxrPPPhuj0RkpzTW9asEKWPZPWecAIE54HNhJYCPGqysOZCSIrNwx79gowZB7cy1XtNTr9XjwwQfj67/+68v8MSb3x/6OufEKdKvVKnZ6eHgYn/M5nxNPPPFEAaVeeUZeJgL6/X65HsG11+uVxGN2djaefvrp+P7v//4Se6j6AxABOkl4mE9XbOLPSTp/8zd/Mz7rsz7rZcmul2v2D9yP/qAb+Ff+xt8DEn0dy8p+i89tb2/Hww8/XImJJBKOpdZ73suJOL9ZeKBystVqRafTKXEVnXYcRK5egMDPRJwR69g0q/uOAe4r+s5nIXhIlkk4kAMYw/Y7NzdXFn5WV1eLfQDkarVatFqtiBgnmlRDYdPLy8tF/6gat28Da2Cn8/PzZSGOBSInQOAbQKCBKLaNXaEPlhP6MBwOo9/vx9bWVkkUjRlyEmC/lX+bSOLvlZWVOHfuXPFjJhghH2dnZ+PatWuxvb1923XcT8vKsdRElgmp/GN9zTHZeODVNidF+E10CX9sMsGLif7NPHlOja2Qt/v/e6UZQ0ZEWX2HaKKikMqTo6OjWFtbK3bHmNvtdvR6vWI/zFG32y1k0f7+fpyensbKykqJBxDeEDv1ej329vYiIkqy1e/3Y3d3N5rNZiwvL5drr66uxrlz58p1+X69Xo+1tbUYDoelSrBWOyNpz507V0g1CHqI39PT0+j3+zEajYrP4XvMGdVPEWO7w26dp0SM8WfGD9zz1cQbL2xjV1QnWy9Jbk2mmhRF3523OLaYGCF+4edWV1fj4OCgzDX4nPvahuv1s8q4wWAQW1tblWqRV9OI5fYLLCqR8POZzc3NWF5eLhX+zs+wS3x0xBn+IAagM4wdAo5cjHgIFosYL0zZr3U6nbh06VLs7u5W8jjIAfQCOzw9PS25IvGXPm9ubsbh4WGcO3cu2u12yZVOTk4Kmdrr9Qpx7Oqczc3NEuuIY/g151Imp/JiixdiwHKTcp+MGWkep+OQbce+3tdHJy0j+1R8MLle9rn4Z4hSSL65ubk4PDyMvb29gon7/f4dyWIwDhh1cXExZmZmotPpxNraWiGmvFiHbzO2NmZyvCDmQL6tr6/H+fPnS6xnkQiMQRXe0tJS0VXkxMLS9vZ2JRdx5SS6Pj8/HwcHB5V5g9e5W+L8vpBS3OyjH/1ofP3Xf30Mh8P4rd/6rfi5n/u5+It/8S/Gl37pl8bf/tt/uyi0Heyv//qvx1/6S38pVlZWotVqxcLCQrRarfjjf/yPx+OPPx7vec974sqVK0XhMUCvnDMRBjFMhllOg54cRH2tiPEKsQGkKwD4jGWAUnB9s61810ZnhTfgMviMGCcXOO48udwDoInDQnFQJIgBrkWCQ79MCuBwAcWAeQwAWdlxmOzKqwt2UBik5W8WOxNS9CcbkleILEfLhwAXESWpcGJJ0jIcDkuAtkOloQMk4JmYMlGSdcltEunkJNTfv9d2cHAQ29vbsb6+XkrdWb126erh4WGsr69Ho9EoZahOcjqdTtRqZ2X5+/v7BWQia+ZsfX099vb2KnNBMohMB4NBbG9vl3JwAoFJTWzOssG2Pd9OnNAFXmelotPpFKLNSbXlzD1cBpznCyCVEyOvbKEL7j/yyYlLJqV5L9ujkw/0Hxk4qXLliMdCwPFKIN/xyiZtbm6u3Mdg2eOxn0LHRqPxdiUTBbZ7fCA+AT2iJJpxQIDa1g00sH3AHICf+6Dbh4eHZSuiyfvFxcXyPYiTdrtdtnkyf6x4ObE2QWXf5bm+l2aAEREvaQvcG0Ds+GLAna/NZ5lL7N0EgHUTsoVKHOszzWQxfWPFbHt7O9rtdllhBfC7X8y9YxK6GBGFiGGLH6ARG+Tejtf0i8ZnO51O9Hq9AvKwYcgsklDGcHp6Wkms6/Wz1c3j4+NCoJq8xPZMirOybD1BTx2fGb9jK9iCcaKzTkyRo8lw7B1bz69/4hOfiI9+9KOVlVFkYdmhC9gG18iytS9i1Re84nuj1yTv2DK2bZ3gmnyG79knWa6QmE4Eed/2Y99/r/HWCbLJM+Y32yDzbXKPmGDf6zjgLRPoV8YRk8bx6SKujIWwE2I85AfEEL+73W45ZoM4vbu7W7aRuXW73Tg+Po5Op1O23IFvqS6A7BqNRtHr9UplAfFkdnY2Ll26VL7HgigVQiy+IGe2s9KXRqMR7XY7Hn300ZJ8kbdAElPx5W3lth9v18JXgsNYGDSJ7FiJ/yKOOaF+JY3taugff0PuoW+eR5JjxpC3WWMD+AWSVXR1UpUYdu0Fyu3t7VhYWIjl5eXKcSbHx8fR7/djbm6ukFn30nLugl1hc46fjI37I3MTTv1+v+IHnGtRKWudReeZb/zApD4ix8XFxRgMBrG5uVk+g490wQDX8oI/C1vM7dbWVrEd/Cn4uNfrlarChYWF23Ap+u0czn7JebYxoLF0zr8zsWQZTLp2RDWn5HuOJcRU5OKclko0vuN7O6fx9jh8NLZCrAe/MpeHh4exs7NT4R7cjEfn5ubKYv7i4mLJw9gdBllFrISEBB871vA5584QTZcuXSqLgj7Wx9gSWYBBZmdnS9UfFX8mjD2P5FXkGnNzc2U7NdXin1ZSisFvbW3Fv/gX/yJqtVrcvHmzdOSTn/xkvP/97y+AgdUDOo7AIiI2Njbil37pl+LZZ5+NL/zCLyyKg4I5GXUy7+DPisVwOKwYoz+L8toR8LeBio3GwJ0++cdGYjBGgDNx4euy19dOhAk30IOJxDBc3uqknb57dY3rOCEmANFvVvCQD98zkRURZfULA4gYJ2w4K4AX8+HE2Y7K2+dcgYAzRmb0x6WV3rZi4zo+Pi4r3U6iMGLKFk1ukkTAnucEDlm4n1lXsj75/ZxYZtuZ9Nl7bU888USMRqN485vfHJcvXy7VInbSJycn8dGPfjT+0B/6Q7G0tBS7u7uV7RrMN4QVMkIXCGzY9vz8fMzNzcX29vbEPc4Axt3d3Qogt7wy6ZdXEG1/2I3ljt4BlvJWPuY127iTFuzQK3581sk+NsU8Zl9kOZqoccC2bnls1geTENnXOCg7KWeF2CSRCTSXK3MNgpUTUvede7Lqt729XYA9NumVMN8bsGfyBplDEOSzD9zwMYAC/sYnAO6o1uv3+6V/nGkxGo1ia2urJOHEB4gZQAwgvNVqldVNZOctR06irUuvthkocz0WGdAbqqSYZxO1BlXWK/t1V6LaB7oKyvHOSYe3Ifk+Ttzsw/AXGxsbsby8XM4wmUR+MIc+t4iECAKUcz+4V0RU+kLcXVhYqABOV+1SnQEQBzzSD+sK90Am6ARJETqBHS0sLJRVRpIJGvc3GW+9MblJnCaWGgjv7e1VfBx6wefZ0sSPYw/junnzZvz7f//vy7ZKV+MAenN1kfFIxhb+29tQ7AvxlRBWLBAZk+Dvs+/Pep79qheosv0wP/6e8eC9xlv7ANupCV7HDBNMyDUiit5TMeIFMvpJIgNuvJ/juJdGXyFyHPdrtfEOBpNVo9GonLsUcUY85flpt9vRaDQq59j0+/1YXV0tifpweHbeE1uRRqNRPPfcc2UrDVugmB/8N/4eu2ArGVtz2HbIuFZXV4t91+v1cvYXhBI+xdjUi9s05wuc5WSMY9LVVcjezmPCGfu7m9jDvOBXTbosLi6WCnnsksS4VquVBW/03TGbJNq+HLulf1TeEGM57w/Mw8IfFdH4L/rCdsxJi4f30ri/sbyxF1iHc6XwV/hw4mlElF0CXsjn9/7+fvT7/VhfX4/9/f1STejKNPw1BJYJmZw358UpbMbFA+TBi4uLsb+/H7VarWzBR8dXVlbi8PAwtra2ikyGw/HWWOYLfUDfsWPjWXQsL8xazvhyfy7n49blfB0vqNs3Gotzbdsg18C+wJnGpz6j0lXH9ufI2Bjw+Pi4HFOwtbV1x51hEeMFnIgz/8aZoRBL8/PzlXPN8J/Z12O79IEFGObr6OionANWq9Uq57sa41r/4RV8/e3t7bhx40aZH6rUySHBNWxHptLbuY4Xy16u3dczpd72trfFL/7iL8bVq1fj0qVLsbGxEaPRKJ5++ul46qmnKp/vdDqxtLQUKysrsba2VraHzM/Px1vf+tYYDofxzDPPxGd/9mdXStQYGCAqonoIJq+j+EwEKx4OENmx2dn5PRzTYDCokBhW1rzSFzFOfgkiORE1kHKll8vpzXyjnAaaDkh8DyPJ48hA0UqO0lCq63Fl+Tt5saPhPZww1zMYtlxQesZgMsDJlf+3/HxN9w8SC+PAeZ6cnJQDJHPwse7Qf8uJvmKQOGwnfVlH7cRsJy/V8nfupRl0sN+32+1WQBo6/dRTT8Xnfd7nlb3F6KYTRHQVGRwcHMTy8nJZiYyIShLFGRBOJniP7Sys1GQdsizQsYi4bU68go59oyushszMzJREFpnYdzgJQt9d0cm9LIesjzTbK0mECWgTSgYcNNu8x81rLj1GVgR1wBLjIkAge4+Dzzgg4Ru9km8bywkiB4FjXwYV9jWMOdu4+5HJL0AHc8v3nDB7+wHJjpNQZOkEGtkDBtmCMRyenX/kc01MtpIgEJR5PQOue00Mc+IMuPAB+MxFtkv7H/52PHLybz08OTkpiYBjo/0a+tBsNguotS3Znvxd5rnf78fzzz8frVarsqXF880YTDKRuNVqtVL5xoHF3u5kPSMOAzK5B8kNwNx2YhtwxRLv87/j3SRAxxk6KysrJQ4buOeEkgQWjIG/5FpUZ/IZyFMnIZYZ7zHvnL9CZQa+/cUXX4zt7e2KXZiUwjbt23KczPpugn1xcTEODg4KgeZFAwA4Sa5xQfZ79hnok8eHv3ZscfzmbJ5Mst/POGsbw0fTR/AH2M2xwI35tx8ntkGoemHEsTHiLCF47LHH4vz580UevV4vnnrqqeh2u/dlnC/VGB/YwlvpwAf8sBUFPQJjQCTjXx1jsUn06+DgoCzKnj9/vmCzVqtVqm+w916vV2TqhVHIaLbgcn3OHyPZarfbZftTxFk1wMbGRkREIYFZ/MEuyEmwXRaTrRfg0xw78gIa+uXXHd8co1+ucZA3eor8TRbiS9lK5O2BECcmTMGK2LIr5PgOpAq+PlciQeizDR99Mg43MY9uvdpmmTs/wUd6gYtxsCBAtR4LWmzlQp/QAS+scRD/7u5uXLhwoehg1gt8LH4bezLpgHwhdLmXq8qN48iFIKnIy3Z3d+PatWvRbrfLQ58gxAaDQayursZnfMZnlLOl8FHNZrM8EInt6c4rjROdbxMjHfudA9MyyYRsiL/5nE0vADnGTopRkIP4Gz9IALlGjHEM2/GNE9BLE2o8POvmzZtx/fr1O+od/pzFBYisdrsdq6ursby8XLAe2CDnQ5lzME4jN2s0GnH58uVYWlqq5PnO7awrFKR4QRiymgU48Lbl77k/ODgoeSH3w8+Rg95Nuy+kFIN+4YUX4gd+4Adid3c3vuALviC+9Eu/tAzGoGFhYSHOnz8fy8vLFSARcebk3/CGN5TyWysdgwc4EiS8qsekG3xQNeOKBt5DsPl/HBRCjxgDWI/FQCk7uIhq9ZXvbUXKwJ73LTcnigbLXgV0kI8YJ66u+nAFlJNf7uWDGTOZwDg8Bkgl38/JbkTVSedVQjukDPRz8uTEgf5nItFy5j6sKN6pysTJPkQMsrBDMwDMQcSfM9jNYCGD7tycBN4raHbJtxMuABfzMTs7G1evXo3HH388Hnroobh69WoJzicnJ+VcqNnZ2XLuDlttSbpcBkyiv7y8XM51WFhYKPLAOe7u7pbVs4hxAmKyj4aO2Sax1UxOWn98roVLzE24OaF1P9wIQjTrp+3E/XTAMBjOpLCTfj4bcfsBupMSKq5rf4jPQ38nJYVcP5MIljt+0KQjIIfVMvy59RXfiM3zPYAC8+BxOQmbRE4z7xFRAcnM4cHBQQFoJuUBjycn46dJmqRmLICN2dnZygGWkKzc01szTCzcr5aTTWwFmQAiMhhxjPLqoX+7nybPAUCQcNZ/E4rIO8cb+zN8qX0Yfe/1erG1tRXLy8vlYE/6YV23vmCrXHswGJTFq0lEI/Lhf/fHpIrHD1mArAH4yKndbhf/hh9AP9BxkihswjHJ/c2rmR4XfWb8jv/NZrNsJ7UvsM9BjiQ0JBoGscPh2bl+v/3bv136bMLAeMx6xr24T67isZ0vLi6WMzG8/RJ58nnGQzPR5pZfzziLecvbao0XM7Zyu9c4a913TMh26gUP2yzx2dt/qBhk/LZ1x42IM5meP38+vvu7vzv+6//6v46dnZ1oNpuxs7MT3/M93xP/9J/+0wp5fb+a/RRV/F4wyNWQxv/WA38Ocml2drZs42PuqFpFdpxdMjMzU4jq5557rhDf+Bh8NwlkXnAZDoflyW4R40VuMIyJAVf2D4dnT10l4Xd1I/HFC2bYJYsALBhhvyYNMjbFF9ieLVc+fydcyWICT75jDIwNQpHXlpeX4/Lly2VbUkQUPMeZX/hEdrOg48QEb3Hi2hkn48+Hw2HZKg+Zx1brfr9fnrII6W+/8WobfsyVwxHjMziJ+3lOfDA2BCZVO/hO27L1zWcNzczMlEU1+gPewF8bo/oQ/3r97LiM3d3din+kysVVsmAb8j9XpN24caPMvXXusz7rs+JNb3pTrK6uxuLiYlkEwicxlxCEzhmNN3MhhGOv80NkzWteFDK2cWy1Lk3aApnzKOKNF8JzPuiYjE47t0c/+NtbX3d3d+O55567bTHQzfk72/W4J0cb0J/cL3xqRFRI8PxwmlarFaurq8VnLSwsVIpyWGQD23uhhMUESOlerxfPPfdcwaAQsBHjJw8bc1MARCVfRJSnPOac6k7tvm3fazTOHl35sz/7s1Gr1eLatWvxDd/wDRERFeXlQEIHJJQU5cnb/FB6J3NWfhTVQC9XNCF8JjWvXGbyyaDTf7tqyADYwMEGmCun8n5MVo1QVO7jSqdMRqFcEVGSKoIyxo2yokwkfTgjgxwUi3vg9AwoGF92Osg9M+Jc308zyskk7+NMzIrTN3/HzRUQk4hAgjnj5jo4T5NZfMaMuxNX5sIg2HviXR1DszOcBBQmkVMYPaDgXkEkjp2qAJdlHh0dxdLSUikFbrVa8dGPfjS+4iu+omJLvV6v7KVHX3Fm7XY71tbWipyQG458bm4u1tbWYnNzs6yEYRcAjP39/cqjdNFF9BG5OIFGpoBEACSvWxdqtVrZfsX+bK7nsxFMyDBvPrPBZyigR9Yb5jzPr1+z/7LPc1/skxjPnUhK7NqAYDQaFcLIpFBOjrinSQUT/7xOYm95s+WA+cDf4Kdd6ZLHS7NsMvhw6T+A2GDNIBI5sXqFXbLyY1BP+TorY2wloOrRFQnIcmFhoZxn4lW9SSt82Z7vR7PcvU2VubHuGmC5GXBaV9BbCCP8AQDDiaTJIZIYj5XPmaTNBMfx8XE8//zzhbTGFzgemrQB/Jn8OTw8jI2Njbhw4UKpcOM71gnruYETGMDg1ws1JqZ5DXKXVW90k/nIgJqzGyLG8RHcsbe3V8EUXM82nrHOzMzZGVpeqPM4SWywTWJWruY9OTl7fPWTTz4Ze3t7ZX4gEfjbpKbxlW04/28ykQp4b0MzwOc1tuIYa9jv8dtx2j6Mz9uPGG9hj06GjU0Yx70221xe+KLfyAobIVZaB4xXrLMmVWz/jjEvvvhifOu3fmt867d+a0REvPWtb41v//Zvn+gP7mdjLti2D+lE8urt2pxR4j75LBTkh69ALtgQlZHN5tnZb2yl4vBntlft7OxErVarPLFzbW0t9vb2is+gcYD5/Px88X88/IJqPsZAsoa9jkajsn2Px7lD4rgqk4doEFtdGc4YXfkMmUM1hbevseWL+ztue7uR28zMTKysrJTD3bO/MxFfq509me3BBx8slQ/GXJwtiJ9B/8hj/FmTKpCV5GErKyvFt5IHYruuuGq1WqVilLzEC1T30uyz8KXgHfs/9JJxOt56QSeiemQERHmtVisLr8wT9/ACHf7b+S7kWMS44oz3BoNB2XKKLviphM4xjDGpDNzd3Y25ubl45plnKnnSm9/85vj8z//8WFlZqeQ8+cm4LMDwv2MndosuGttlcoo5sI/2QhjzwPWIJy4+4b4mkzKfQC6aD5XHx/K3K9KZv5zf833OuLx161a88MILty3SudnGms1mKZRhYZ55N5GXYzIEFDroPJgcBy6AGGNs5q11yIptgt5BxP263W7J5eBkmJ9arVZ0ALvHvqnkI8fDN91Nu2+klJOD4+Pj+OQnPxnf9E3fdBvwgZWNiArx5NUjVrdNPrkCCKDEdZm8XEVhUgDFM6BpNBoF2NOcnPJdDMtMciZP+PHqWE6ueY9JM7BEuTA6+sBrbpAXGJsNGMBNUKVZwU0y2TnA/GfiyUZm4snJTnYcJq1w9hmgurwXp2jgyWfQBROINAN8E1TZGSJjJ0y+hhNcr1SYxWfOec+Jk8edgTr3t05avzJodpC5l2agzw8AkpVBgy9+r66uxtbWVglE7ifgkSBEEAQoOvg1Go2yygIhAOnHfADqckLJPGZQ7Xl14GFsAB0Dea9smNwliNveHVQAy+ingx3zbb2x/uFH0CHrTt4qZR3g+/TfYNX6hfxojI/y2pzcWVa2D/7nmsjCTxgzoUtgczB00AOIGujRB6/80ycnlE6CafgN4obtHXt2ZQ4gjtWa7JNJDiBIDbqJR64wo8ql2+1WfItB1P1slpcBMYSb5YUtORHIesq4aNnfcM+9vb24ePFisV+vzlNB4DlyZWRORBw37M+R7YsvvhhLS0tx7ty528iZk5OTyqJIjsO8xjxbb3PS79U9x1HmHDzAiiK2bNt0vLDvNwHq+IOOsa2DfuWn+XAfyFTbk8lGEtRer1fxCVk29peMB8CMTI+Pj+Ppp5+O3/md3ynzm+0NfTChmPXR84H9uy88GAOA6nEzJvwhcnYFCp/1AuAk3cb+J8nBmMz+3XrK3/faHJPyQhm6Yz9u7OME136R8TvxjRiTqBngZz/faNx+Jt/9bmAgH8jr5NUxgYUjKnNss8RZFk3tV5jn/f39kkBFRGUBhBjuhcX8gIpGo1EOU6e6hfbwww+XQ9JJEjl3FP8wGp2dhcnWMggizoRB1/CX/X6/VCp6mzF5ELgJPfFiHHZBZS8VYcjStuEFFOs4rdFoxPLycqyurhY9RK6WP3Z88eLF+MzP/My4fv16bG1tFTLF59sw/pxn8KQ2x+PR6OwBKCS3ebubMb0Xt4nvPutxd3e3EAr3un2Plv0FrxE/qIDCT+/v7xdSxwSSyQL0mRgBWYnN9nq96HQ6lfjjLYnYDgtR3l3C9fmfKhhsxtsdI6LiQ72oNz8/H5cuXSqLbpy/+cQTT8QTTzxRFpvBT8yx/bgLMpg3x7aIap6DPmZ/bf/shRn7NN8bX0jfeM/N+HA4HJYtvBkzc238MX1wjmr8QFxzDsDC6cWLF2N/f78sBmWM5dwPEpz5XF5eLoRnLnIA/2Jro9Go0idwyfLycrRarco5dMwdPohrgTHQVbgZY20OKveiQb1eL0eiuBKPCm7kTg7oeTdh+lLtrkkpJic3AO7b3va2+JZv+ZZ43/veF7Ozs3Hjxo04d+5c/MRP/ER8+7d/e3kyE8FnOBwW8IYBZMVECCac8vklOTHKyXxmGgHd2ZFPAnk5Qc7gzImsCRWTFZMqFXAqgH1PnCcfB4KSmYBi4lE6ZFuv18s+WK9CIzPv5eYHY3ew4p70hTHhMEzgZLkZ9NnIDMQwLBN2njMn+u5rTsCQuR0YYMhz5tf9XYNeExjWJ2RqcG/n6coQ38/65YDn39mOcEw4vntpHFrJCjvnfHDGSEQUgHB6ehq9Xi8++tGPxpd+6ZeWYEXlhFfoWq1WDIfjp5zxNLOlpaXSbydYXkWMGANK5LC1tRUrKyvlfxww8+BgbydsWVrfmMeIcbDxk8PsLA2g+D7N98oJme8HqMR+c4DkXtYT/s9JDHbswI/eYwv4MDe+58qPTDbRsh3Y1gBDtmUAF4CSBJkgRjLiQ6i9V9+kQw7U2Lr7aduhr+gthEjEeKsHfpLH2RpIdjqd2NnZKXo+HI7PvjDh7ApWfKRJDeaKJMjAKP/cS8v6ZxIEmTBv9t983luB0AEnx8SQvPLHlgzHq0xmIYuIKFt6JxGanjfrEtft9XqxublZDvP0WQ0Rt9sJrzmBpdIHcM99/ORG5AQGYBwR44d6oAPcCx9PX5rNZtFp+zXfg88RJ7jv4eFh5fBfJ5H4JVd5MU7mjnlypQX6id35EePcAzKI+WDry8bGRnzyk58sibFtbdICgH2cY6FjmmMeq7+tVqtUak0iROk3Cab9uRcQ6EMmbKwf1r+MB0zeZKzjBPtemzEkulGv18vKPHPlxAdbM+HE69m+kS33cFyizc7OxsMPPxzr6+vR6/Xi3Llzsbq6es9jczOhSl/yo8qpVkKfIGmMcbEDV+gjJ8dtPyzIeIKtKP1+P+bn56PVapWn+kHGkTwROxcWFuLw8DD29/dLbOJpoJA/2Nbx8XG02+24fv16iXtgJsYB4YS94huJeWwVYneIYztjJkm2/vIeGAg/0+v1ykIR12BhxfmM/ezCwkKsr6/HyspKZWHVvgKCYnZ2Nt7whjfE448/Hk899VTs7+8X8pBKeOuit06CLdHZ4XBcOeWtd4wXXJmJd+wazNrpdG5bqAB73q/te/Y7jl9gKc588mIX9kdO5BjDuWgR44d8oPfoCPZp/+655ge8FRHlnCcwMH3kb3Yw4Hfpb14w4twufGCz2SxPkLx06VJcvny5EK/OQZlXEyXYo+Mr8XRSZVFe2PFY0VkvADvmRIyf5u1c1Z/L12OeIA8dfzKxlUkk2wmfhajEZ0NSbmxsRKfTiT/4B/9gvP3tb4/nnnsufvM3fzOefPLJ2xZvmROwDxjGfUIWzIHJfT7jCj12n5ngRXdtw4zJ1YnOXajyGw7PFme3trbi6tWrpU/mGJANPhG/5PzDlZv9fv+uY+1dk1IWmJsV6p3vfGe8733vK4nsww8/HF/zNV9THB/GR1CAjTs+Po4XXnghPvMzP7OUFx4eHpakJ4NBgLdJlFx5wGROIgVs1AZimbjgx8puAJsJnVzdlQ3OQDEnwQZVBGYM24G5VquVsncDSzPklOhxLRu1Cb887ogxAcV17CDcTzfeyxUVNiwnf2bWTXTmlW/Pg2Xq5IqA4s9a7nZak67n5NzvuU82QubAzDPEIe8jE8sqE6Z+jz67vz5n4dU29AhAjM6QsPd6vQI2AHeDwSC2trZibW2tPK57ODwrXQfIQUgcHByUfccu/d7f3y8rK4yBp+KYaLHucA2cIzrgg6dN1mQdxAYz2UhfR6NxhRKfddJq+U/yJQbL3N92E3H7o3HRYe5JQu0KH9sk+kxFjEkC64wrPq233q7LZ9y3iLFtInt/Jq/Ae9zYKhVDbMPAPlqtVqnQsF4zftug9cLjwFexmo2fMCnG/CMnZEZf8XvIZWZmJs6dO1fOXiB2eFsiSbJ9HPOF7tKPSb5jkj6+2pZ9BL8PDg7KYbvohJMAdMz+LMcfE2ueo3x/4gq6xNyhC8R3xz8+Z/mhQ7Z7EppnnnkmZmdn4+LFi+VzAHHsnrmdpJej0Sj29vYqlRdeNHF1Lb7D5GLEeDuzYwQHJEeMiSaTqVQDuOrAVQP4E2zCVV+Wpf0NvgjSFZ0kGaLKFHsAgDqW8x4yiBgfwkwi+KlPfSpeeOGFMgb645+sC/k9x0E3kp+VlZXyZDTivvWB7+3u7habNH5CFsYzlpfnAl1g3LaZiPHTHD0u25Z99b00tscjB2KmfVYeg8nfubm5Mk8mwfOCiRNgElbm6MKFC/H+978/vvEbv7Hc89/9u39Xkops56+2OVm17WQs5viJzVn26BQxmxhtH8NnkSXv7e7uFl/C9/DRJnjZGghuwcY46BsCjaMIILciIra2tqLZbJZzlHgKIKv9XthCDpDH5C72j8Ph+NB9bJnreKsX/ef7TgTtxywr/IXj4NLSUjzwwANl3LmCBp2an5+PCxcuxOOPPx6dTieeeeaZ2Nvbq1RlescFZDKy5xzC2dnZsiBgchIcgqyOjo7Kw3HW19dLPFhZWYmNjY3i18BKLEDhT8ltWMS815ZzPXJC/LtJl9PT07JtFFLCJIfnjLiBn0U3ITDBz9i64wE4xXMN3oEg9ENg8LEsmDcajbI9nvjUaDTKWUIRUWLvo48+WggMziE0seDcwfHAORz2yd/Ydc5xTKgg+7z4n3XUOR3yRJ+dRxiT+55gAF+DeIRs80IyvjdjJM8RxChbhFkcmp+fjwceeCDe+MY3RrPZjGeffTbW1tZifX095ubmij6vra3FhQsXip34zGv8FHbgI26ILXyPJ/cxFvoARuA3sdD5BT7FZ6qah8Bn0JAlRBT+Dnk5HmE7xth5If1O7Z5JqeFwGG94wxvin/2zf1aeSDEYDOILvuAL4ud+7udidXU1PvCBD8Rf/st/OVZWVkonzcr9gT/wB+J7vud7otFoxF/5K38lPvaxjxXghDKY9bUyT0pyzTgbLGaWlmtnBtggworoJNRMsH8yy4sRTFpZzSscfs+JuQEKLDhBKCvryclJhUzink5ckBHj4p422ogo+7j5TCbj3G8+4/vigFkpczLFtQBXdlgOuDQckSsGaCYgc7DO+msHYyKA4O9x5FVWCDUcSC7NxpEbsDN/1iO3rF8vlTC+2uYkGsfv1QMTbIuLi/E7v/M78SVf8iUF0PAZyAGezODKBlY+T05OChhBpwjGy8vLFWDmpKBWO9u/DIFtEsXJcSZS8jUyEDQ4iBg/ScKJmVczfE3Gbj0gibR+2M4nAT/mEWDC63bizL0JAHTO4Deieq6WAS9A0v4n2679jcfItegfv01yAIT8WOe5ubkC8n2o8SQfY//pe/KeE29sCHvApwHmspz5Hn+7inRhYaE8vrvZbJaDzKlKNHEZEeXcPRZNeNITAdhjcj/uh72iY5kMYa4MJpChgbNXfLPfdzzzKhl95xHdfM4rtbY9rkkFka+T59tEMX1fWFiI/f39+MQnPhH1+tmBrdZjwBs6BdD2YgRg6saNG3HhwoWy6AV5aCLb/eE6tmtXO+DLvNBxcnJSOffOW/f4n6ooxk/VhP0VOo1M6If7As45PT0t5fP0y7rAnGcCOvvC4XAY+/v78dxzz8Vzzz1XVmgh//Bn9NN/e84n+d4cu05PTws5TUUKusN9kIHPpLG/cxy2jhuPGdNlf+Z5Axh7gcQ2gE/zmF5Ne/TRR8v4t7e3KzjAMkKG+Awnv7xuH8qYnVSxKm6ccHJyErdu3Ypv+ZZvib/yV/5K/Pf//X8ff/gP/+H47u/+7viN3/iN4hfutTkHqNfH1U2Mg35CBOYVfOwbv0FyQ6wxJvS5OugPC39UvYIT0FkO8YbAGA7Hh0MTM9BNsDfz5aoXqp3OnTsXe3t75XxPKnyQOXpHXPAB1o7FOZfA9tF1k5gQFt5263joHQ8+rwu/MTMzE+vr6+UhUuiUcyDu0el04oknnohHH300jo+P45lnnonNzc0iN3DhYDCoVP14DLVareyCYT6Yx52dnaIrJNLIlr5BavFAHPINfCf+z8RlrVYrOO5+tJzHoFM5F0E/mGP88szMTCGq9vf3y5lk+GjkQt7AdW03LIrNzMxUsEq73a7EI6p4mR/8A/HBRBJ+28Qm/tkLIVQ8g999Biv3RgchJBl/xHhr1mg03ulh/2XCx3gvY0FkAg7k2tg843COlbElcYQYSZzDBhgL/tjzyjhcLGEMxt/Hx8exvb1dni4OMXR8fBx7e3uxubkZjUYjHn744XjTm94UnU6nkrM3m8148MEHSywejcaV7cQrE3+QjeRUjhsHBwcFj2M/5GiQTz6LLxOJju0m8w8ODmJ3d7fMgXN++/d89ij6g92DKfPC4ku1+3Km1N7eXvz4j/94/D//z/9TzpDp9XrxN//m34xr164Vh2RwxgBqtVrcuHEjPvjBDxbShdJMJspOlyoPgyU7fFoG8LzGtRwgbEAGAgZAnhQDPs7fiBivKnDviLitjwajJqn4vkkSg3qU06CF4OeyXhyVV2+dfHr8TlhNCvGb/johsixMNrFyYjLOfcsgM4NdJ5MYqQmTvLWDv/PcZqeXV9gnkV/I2v3wdQnkrDhYjoAEdMmB233xb5MF+bXs0O+lef8xyTiHF5K8kSSiZ8jgmWeeicceeyyuX79e2HoqC7y9CXJqaWmpsrK0vLxcZOI5JYjjGA0E2JZD3z2vzB/NgSPLiT3T2AvX4n44TZw5CYVtAz2OuPOh+sjMY6Cf6I3JY1cp2T84MckVKE7mTTIYzCN3J//0NyeSJsLcd8sa22bszCHbralmBfiyeunqCK6Vz0Dx+7yGPrr/+B98D4QeRJNXYbDDubm5SvKBrqIDvMeWMcgo/Cz9bzabZcsBoNIH1XorA3K7n437GKxBVOQD/ek738tAz2DDn8GGfY+IM7vyo6ath9Zh5sH3st3cKTGnMYcbGxtl2yfyJ6b6YRKZrDXBsbu7G2trazE/P1956ktEVB65jSyIKxBv2L+v7dVSfuObAYteKBoMBoXItI3SRwg+dBU/iQ5yH3wXFReOk+iEY6BX0jOeIJ5ev349nn322VIRQpUj8+uqKWOpPK9OBBwzsVGSFXyv8YuTDuRnvaK/JkGNzdwf+44sD+vZS4FgdNO49NW2paWlct+ZmZnY2tqqnAuGnp2enpbtP9m+0Xl00VXnlouraJkzEpu9vb3Y3t6On/mZn4nDw8P4jM/4jPj1X//1+0JI0ZAbukqfsUcnU8yJz5KyH0d/qE60PiErvn90dFRILh4W4KpXto0y9wcHB7G0tFTsFL/Z6/UK9oEAh7QCB5GUkwiS6OGDB4PxliuSWObUFRN83riXbc+udqYKHfmORqPKYjBkBPrgz9lul5aWYmlpqWxfNNmFfnU6neh0OvHYY4+VxHh7eztu3LhR4jtzsbKyUkmWjeX89FPwB/OAXLgnT1vkoTYR43Px7L+IJ+BD5rzRaJQqNuOu+9EcU7xonmORK6iIHfg7YgJ5rrejj0ajso3TlSfoOHHPT7hjjFzXeRhysR4wv2A2fDsYh+aHEUBWcE3GuLy8HP1+vyy+4zvAGI6t/h99Y3HGhSHGrsicMeYczTHH8YZrIG98tvtjkt5VabZlvm/5IAOTpcg252NHR0eVJ25iE2DEXq8XOzs7cXh4GOfOnYtHHnkkZmbGZ9RFROX8KOIvP85d0DG2D7tSHzlBUiIfYqxzCRNyjp/OYT0frVYrNjY2Ymtrq1zXcZwqPFd14icpKmAxEb2CQLubds+kVL1ejxs3bsQP//APR7PZjLW1tTg5OYnnnnsufuAHfqAIdm1trXw+A+Unn3wyPvnJT8bKykq02+04f/58RFS33BkcGahl5c8EVSYpDNrzeyYKcATucwZ9BqqZaJn0Pvf1KovLau0U6Qd/s5WK4M/1DAo9J4wPpTbbzechCSwDA2CCPitBljMMMXNAkoJRUwpKwCIQ80hUr2C7v36ssMsrnXw6iGCkBreWJTKnX/6MDdh72v0dO1Mn1r4GoGthYSH29vYqSav1wQRcTmQzML5XoBwRsbm5GRFRKp5arVbFAR0cHJQE/fDwMObm5qLX60Wr1Yrnn38+HnnkkXjggQdie3u7EpTsiJlzAI3Pi8LJwagT6JjTTPQQ1E5PT0u5qwGICRpkZNLUpJJBAEmiE2QCOzbiCgHPeUS1VD/Pvf2GHT73cfm7QcIkMstBkM84YaMvBpsAEl+DvvAbmRkIGwx6HHnVmtXNg4OD4ptZkQaEuzrHCRP99vskBDnYk8xyXUC5fZJBLL6L4AcIdCNBwB95q4Grnth64EUGiFgOjgfYTCIVM7Fzvxq2xm8AlIGVE3dXJ1q2nl/66O3BfB5Cx2QV/UDe9IH4sLq6Wp6ugr5h8zTHDq4BWH/22WejVqvFww8/XDmvg7FkGeMzuG7EmV1tbW2Vx1cTd4ghjg/4Jq/AEhPpY/6ubYa5cCLq/vlvnw1lOSA7b+Vhsc1n0jQajUrJO/e3nfF9bIHvkLxcu3Yt/u//+/+Ozc3Nck38tn0mc22yL/sQ7BSZe0En4myba6vVqixOmGRuNs/O5+p2u7G/v1/RSfQnV9bZJ5qscTLj/vl/z2n2rehVTrJeTQPbDofDcuj3iy++WPHtzBc7BNAnfLLn1H4Pu0f2+AMf9G3bazabsbe3F//4H//j+Et/6S/FwcFBfOhDH6pgyVfbiJUktujOYHC2PZT4D45zfEfOkAzuP/7FFXKWGQsC6Pba2lrlPDueALW9vV2uvbS0VOLi6elpbG1tFTlBjuDLwC4kcPh/9JinSPFQFj9B3HpqTJt9LnNIjCKGEl/wNySf9NuYx37KCwJUPXEWEAvYxv9zc3Nx+fLleOyxxyrnXG1sbMT29nZl8XdlZSWWlpbKWaEbGxuVI0EixtUT3M/xxjmKF4AioiS8+Akw3u7ubiEi81PSTk9Py0H3p6en5Xyl+9U8Z84t0E3mpdFolOoR+0nmwudkIR/IC3BHrhqhIsrkQc4hwZDOTUyMcS+2gXE/x3DIAnQaG4NQ4DVIRLA4sdzkBWPEJ1iGxJ6Icb7Dd4gHfM/5lvNOz0GOA/6s74HtoWvGTPX6uFrNZJz9IfHKY/HfYEMqgBuNRtn66x0nnM25trYWjzzySHQ6nTg5OSkVasPhsJwnhmwgcE0uGTdQPACBid9zDoUvcl6Mj+Q95obrgveMJx2PvMhkPXK8ZusiuoKvNq6y3t5NuydSCkfJY1eZXAZ+4cKF8jkGhAHyGZJaSg5x1JTjm/Qwu24GzxNpkJFBCIKZRADkxBJBehXR1wRomaiJGCeSgCaMnTHjXDKQMqNtUEZCiGKa+cVpGsgQbAG+EeMtAWbGXXrNuPifPrI65OTI1Wou4eQaEVE5KNYrDvywLx0AbjDvVVQSTj/ZwhVc3M8ElIOVibVMatrZMW4Ml76YlHJChp5YfpxfEHFWOegnDbivWTfpl537/UpunRiS4KFHAGhAFkHLh9O++OKL8cADD0StVitnKuB4YPqRA7IDGOGQIBEjxuQjJZ0QVK4o4ns8PcfgNycUWQ9cOcW1ccwQYp5b5howZj1Bb5k/rmt787XyPHtVIuuqnbT9hwOV/ZR/AwwJwA4YXNOkAvd2gsb38cEEchMOlgOkAeeCATT39/eLP6GZRPJ82qfSvwxo0FHvPzdJ5XOmHGgNfgiYXg3n/j4A2kSkV0FPTk7Kk0S8TYvv5sSDeb/fhJT1g3HhU+gvIAb9dlVelq99nV/zyqPjiMEnMdfXYKHDoMjxELnk+UZO2Gi/34+nn3465ubm4uGHHy7fAzyalMnydvw/OTmJzc3NWFlZKUDbfaGvPg/FlYYmnTKZhaxNxBD/iQuOy148AQfwHfQT+/UcUNnZ7XYrdmNbpsrKgNr2g0+DKHjxxRfj5OQkzp07V84Q3Nvbi1arVfx+TqSwhUkJEkm7CXzeO3fuXGWlHdkhfyoCOIvDMkROnjPbtPHYpESFe+Bz/Ohr6zS6R/Prr7ZR3dtsnj1sgfm8ceNGZdGPvvug2Xp9/AAQfDn+iX5hazleOHnxogXjv3HjRqmeofLzXprnAR+QySkIBSc4+FX6jM3wWWwNogSyAhuBRI2oPjk2Y20wLvGJ/4+OjqLVasXW1lZsbm5W5AlRiq+nT/Pz87G5uRm9Xi+2trYqi42dTifq9XqZa3BGrVarnHM4HI4rZ/A/jr/WDd7jdUgixxbsCZlhgxzYHjFeBGPb18WLF+ORRx6J5eXlyplEm5ubsbm5WXkq3Pz8fFy8eDFWV1fj+Pg4rl27Ft1ut0LSM0bygOPj4/JgA8aODuIDiJ3GXPh/CDnkiRwHg0FJ4knGIbL9ZMX72SxrE1KQCjQW2clVsW22RYNZ6vV6tNvtykOj7NNdgYu+oPsslOGLIVaMcSLGsdWfG41GZYGARUN0g8/WatUD5pE510DHvFBF5ZTPOzJGp2HPxEHs03gCOXtBhBgRUfX99ncR1eMrkBnfdczxfPJZ38vz7JbjLjqwvb0do9GoYvPgCWz98uXL0e12Y319vUK8DgZnW+pWV1fLAh/yxu95kRw5Oi+gwhmswNzyOeej4Cb7zUzkGaObVxkMzrYiU/nkqjN/3/iSeEW1JNczznnNSSkDexQrdzZinDw6CXCSmYUJ8EbY7D9H0DhwhOHgwmtO8iaRUhHVA6k9Jj7jyfJ1UG6DMvfHfYqo7sNnYhycuS9gBYDtBCz3hfvnic4AjCQBxeA9HBgGaWdhsEQij0M2MKQfGfB5PgxA/Dr7XVnxQKaDwaCwri4Lh+F3Yp9BGvfGuToJMmDz/DFvmczw+QIGQDgHJ+/WhUajUTmk2fLIepd1zw446+yrbfSd8mlW+9bX1wuIQAcgsAC08/PzcePGjbhy5Ur0+/2yV5758ApnxNnKH6tcTopd8TIajQqhhW6bpHUyB4gGTDE/Xg3IQY4+Aby4L9+1vjuJNFHB9TwGr0hkgtFkgX2Ekyzey8m+CTv0YRKZ4O87CLvyyYDCPseyyRVfJp9MJvleS0tL5cwIvnt4eFiZV/w/1S0+w8VAGP9G/00KRVQP8kUfTbqQvA2Hw1ISD7hi24VXAyOivE6VCAABWe3v75ft4rVarYD7fr9fOYDYoNw+xqDyfjf7Au7JPDL/yBD5OfGxvqFrTvzx4egih9DmmAMQM4FjoiJvKbKfNtHlahiuCyC7fv16WU20brH10j6ea3Ad7jUcDmNvby9WVlZKVZzHyrVssxHV6kPm2XIyuYFcIGhyXKK5gi+iumqMrNHzWq1WDpgF5LLFmPsBDE0OMk/IHJ0HMN+8eTNu3rxZVr/n5+djb2+v2K8PSiWRzMmA+24iyL4C4oAnIXorEvOFbLgHc2uC0TrlRM2x3J/nsybS+YGwz7iQftHulaiJGFdkHhwcRL/fj8uXL8e5c+diODw7s8i+Io8fGzNBkWXsRQx/BqLHOBHZXbt2LX72Z3823vve98atW7fil3/5lyvbm19tQ97I1cmw8RIJNckYPgn9xSf7PCLLw0Rdu90u9kosIeHvdrvF/2OzOzs7UavVSiUDxMv+/n40m83Y2dkp721sbESv14v5+fmyKMe1+v1+wUjEAc7Z8bk82CvjcHyOiIqdW4+Rp0neWq1WSF3HNnTEFevoNHh8cXExVldX49FHH43HHnusQpodHh5Gt9uN69evx87OTjn/intcuHChkG0bGxuxs7NTSCB8FfdpNpvlgTbIg3GRoDtedDqd8jS/iLOHIYAXiLER1dyPggRidKNxtoUPDHs/m7GjMb3zEny5iXwI0IjxdkT7dS9e8Hmfu8V8Mw/2r+iS/aexa44vk3xGvT4+Ww3yyRgLn+sH1JAD4UOpmGo0zrb1k0ug+/apmcjJ+aSxCd/DH2DbxusmnbO/54dYCvaxr8zf4W/IEubHpL5z8ogoZBTz4Qr7iLGf84LQ5cuXy/xDUlI5Sr8gvomL2DF9wL9AyvrAcnAflYzop30yMnBBDzHCY3SRBzm5z8Vifkzw+6ERYA0qgPHj9JGdL4PB4LXdvoeyYSj8nRNqs3w5GYwYkxs4bJNCTmBQGhwd4NKJH4pnZ+KkiWbDyYmYE1HGmX8mMX68ln8bTPG6V4z8GcYSMd7m48Tfn8PwvJpp4iWDmAyMLQcTfU4cfNaKwYbBu0G6WXMSAAPGSSsF3Jexmt210/RqQAapJnZMtHFPfz4njxmk8lmX1/s7mWCYdA87Ahyl72mHlpNOkxH3o5lxp3+cacB5DcwZToSgRBnwxsZGWansdrvl0M+caGJDDqSMx2Qrc+3qANusk63j4+PY398vBwJaPpmUdLBycKYM3AmqgxTzxGoWhAr3ccKfCSL0l2vyOb5jG3GCl0kWrmm7sh44KTQZ5aARcXtlisGOga37k7cIeNztdrtCOAA2CDT22bXa+MlpXCNX11gXs2ycWOXtKswL50rQHxIS/FhElC0WgAcCKXqJfqN7VDg6keae9HU0GpUtx/YH+ff9bNlXITtXqnBvxyeDMeuofVQmWInfyMDxEPtmzvLKJ8DKOmQ7s3zQQZOStVqtJEybm5vR6XRKFRP26/ht0pN72W8DhABcTnRN2nhlz1sgSB68YGGsYR/DPazbBuuQSCbVuadXRTnvwg8occwxCWYZO7nAZklANzY24umnn47t7e2y8h5xtkCB7zs+Pi4PlzA4dgJqME//TbZHnPmbc+fORbvdrlRvGm/gP3kfX8e8OAbTD+uOiRTbnf29m22A6+UYO+l7r6aRVNOuXr0aly5diitXrsTS0lLs7++Xigr0zQmCSQrmm2aC2P48L/6g+4y32WzGCy+8EO973/tiYWGhPBGx0WgUAvSVNieZjlP4b+u3D6XOfXZOkMk4cKdxAfaIz9jd3S3nR1ENMjMzUyovSQgjqvEWQo6dB+fOnStPsUJ3vW2Pqn78SbvdLvdhGztnxTQajULqGB8gF+84ILE0vvCCDsSW+0/lTESU8SHLVqsVn/VZnxWPP/542cLsvKfX68Xm5mbs7OyUii+OyVhcXCyE8v7+fty4caNSLc8CgbEtDzYx1jPWpQKHrU1OXlkUYnGTLU8k3YeHh+U8UrZOcu9+v1/OsvKiwv1oJimcQ9h/oIP4MpOhVNL6yBNyJ+tuznMdO6m24l7WD8dv+gfhYfyUsS//EwtN9jFmYoNzR+sXMZp+MlfYNTHdixronu0e+yZGTsKGjheTMLPxb47FEbcv+jsHReaM37tG6D+6i6yomufz3Jf59wNWGo1GLC0tRafTicFgUDn/09yF5W587Rx+NBqV8+Gcb2IL9m+Q5IxjZmamVBdiR8iGzzifB//RiA/4LPTWeXnGpsY4YGye1D0zM1MWbu6mvSpSikEQnLxiiXJk52zyAIeJUVNuy3W9QosxGDzbQGkObl7RNmvofnhSMxFF/5gUB1cMj2tiGA6ymfjKwMPkDUmYyQo7I2QEE2pSz/Ph/mfHZwcKaEAxed/bDiaRQ3yOv71Ka/BpRj47Sn/eQTmv+FpXCGrIjXLCDDYz+OW3t3piSNYvHDbOBiP1nHNf3p9ERNnZ4HwzWGU+Jv3NWPz7XhtJJlst6RuPlgc8Gpw7QPR6vWg0GvHEE08UeXItHp2MD4BsRYeQvZMNvuME0AkW/TMZgTMzyHLQdoJigtUg3o4/YrwFhPs6STApaWKU+9pmrNM5CXfSFVE9Twaw44TD3+ez9nXeXmdgYmBvEjLrlcv9bcvMefZPKysr0Wq1yrwjX4MrPutrMjbm3wAv21Qeq/2mgYJ9Ja3RaBRwTJ8Avt72C9B3EuYEERB4cnISy8vL0ev1Ktu5TDIwjmz7zPn9ar6W7+H3rYO5zDui+qAAdAxdRed9fcvciTuxg4oGdNYEEyAEWzVYpdE/dBMbI6E8OjqKF198MSIiLly4EKurq4WMpjLM/YqI2wglXoOIZAuL7TSiej4bSRKfQee4thP9PAbkOhgMygo4JBRVe5aD7Z3rcZYKOklstf/y9nVslftnbAEh++KLL8bNmzej3W5XfBQyoXIQApC5y/gr+2hfi34sLCxEu92Oen18poQxAZ/zln6P00Qnn81kTNZ9yyHjp4wPMzbKr91rYz5ZWDs+Po6nnnoqZmdn44EHHog3velNsb29HS+++GJsbGzctsIPSefqcsveGJbf+D0WdbmmY8RoNIpz584VHZqdnY0LFy7E6elp3Lx5s8Tju22OtyS9xAXPB+8PBoOy5cRVscxDr9crCwODwSBarVapiqEKqtPplFhqWwOHQSLht1utViwuLsb29nbZInp0dBSbm5uxtbVVyK7T09N4/vnnYzQ6O5OJBwA4YSQBByPNzs7GpUuX4uGHH45HH300bt68Gbu7u0VH2VGAf/Q5NmAOH1iOjJhL4uva2lo50J14av8OHl1aWorHHnssHnroobItaG9vL65fv17uAZbq9XpxeHhYCEq2d7VarVJVSYUUCzrIHYKRey8tLZVx0TqdTvR6vcp2PrbngSNbrVbMzMxEr9crftu6w7a8vb29ErOHw2FJ/CPGFUn7+/uv2E7vRreNY/ANbGMzVmTsYE5whoknbC5ivO3UCwL4VOcn6AJ/myyIqOIB+zPmGx3hPVetQ+BSiecY7rl0notuQx76CbMQTHyH140Hjf+Rpf18xBhD5r9NWkEUexeK8erMzEyxWS/Kel4jxvmh8bVJby9kspXX+XK9frYQx1Z4fFvEWRxcWVmpzEP22Szwe+GdMUBSsbU+Y4G8vZvFfXIwE4bIeVI/uJ8JJx9r4C1/rowH94APGo1GsXPyOx8rZNzDlte7aa+IlDKLy0A6nU7FOHjPyYedKkICbPIerC+DIRCYIfVTswwqMkFlo7VSosw5EWRCnYjmCfQ5S04+/Rnfk0kF6LofJnsYG9fIK0eZfCG41evjpxdijCReyBDlsNxhgfkcr3v12uAzIipJe+4bfYqI6Ha7ZVXE380Oj//RJRy3FRr5Tqo68hzlvvAe43Si5kCa5y0TIk6UfZ9JusVnTfTloOvx3ymR9bXvR2Ne0A3AHFvjcCYw+kdHR2W1bzQab61BZ9Epr9AgXwPqiHGlHQ4dO8D2kaeTL5MXlgMrZvlpeXa4zLlJsZxQImMnVt4+iB0Adq1bTvxyoDXZwuvZD/j+2IBfp4+TCBAnXVzbSSLXnUQE0F/k7CTW38X3DAaDWF9fL481pk8kzsjdxJTn3X3JAIO+oI/Wn9xHB0OuD/lBMuQYgf6io8jQZOpgMCj75JEzFXKNRqNUQ9G4rs/38HXvJxHlZt3xa/hv2w/vIfeI2/WIsdhXmcgE6AM8OSPS+kTpNYkOckUPDOANpk0c0Dd/3vF+MBjE888/X3k8NrHS37F+ObYgC2yarcKM21t8HENYiCHGOPnLPimi+gQ0YoSfmoRuoMMmYZwIcLYijT7lOeOaXmGmAdbxfxwe/+yzzxYiEVkZd/AaYzU4Rz62TSel1q3R6KxaBaDOU8msD/R3OByW7VQmNewbsu4gA/vonHD4u65kyEnbneLqvdox/fWiHotpm5ub0Wq14vz585UtEOgpT2yEAHeS7jHYjzIvBwcHFVmg47xn3XPlwuzsbFy8eDFu3rxZti7fbcM/k5wh8yxHPxzC88nf9NMxymeY2Nf78G+SJvzg8fFxqQyq18+qmVqtViwsLMQnPvGJkjivra3FxsZGkS/3RwfBJSYJ8ZNzc3OxtrYWDzzwQCwvL5czura3t0v/19fXy/ghcdBDJ2PkOj7rErkYN/DQBwgE/C5yv3jxYjz66KOxvr5enjLIgeXolqvOms2zJ+9xHcb6/PPPx6c+9am4detW6Tu6bNmAiVyxzvlwjcb4iXCLi4vR7/fLsSuj0ag8fALCimrNbHfLy8ulSoO5QTeccG9ubt43jOyGr2QujAXAJFRAQZQxZhqJOnaBDNFVqmKxobzwZv/nv/mf+GUyyKStZWq/TYPw6Pf7ZQEBm6P/EeMqI28XQx/tix3/jYON6YxVneuazDe+mZQHQRZl/M53yTPAJhFVXD4p97MsOYOQ7aGOdfYTxPoch+r1syeO02cXMRinubAEEgj9YMFoUg7pXSbID8KfefICq+MBcYfvkgeynTT7cMZucgl85Jjr+5h4Jw/zQjo54N20uyalzIrSYfaXInADOYNOJ65sC6IMk+/iQP15fryFzNt/+K4Ddk6GcmKUAQ2KamAB423nwMShZNwrg0iX5mYSKxus7+9+oWh2NgQsK4oTwQzyJiXk9MUEDv3weTNm6g0w3R904eDgoDx9yYex2/AAir4mzWQQcgAYVBRV5BbXcFJtB55lyudtVO6D+2XnhS76texwed8JivuTAQn387257v1uPoQRJwHZYjlQqejzAXw4440bN+LBBx8sq1wkSARfb781yWlCCtBCcIHdt60gIxNhdnTI1GSbnaiJMSdwDoA5wXIwtk9A10kY7EdoDngO/L4Pc+xkzsDewY/v4V9x+k7oM7mVfR/XcD+8jS77P8t7dnY2lpeXo91uFwBWq50dpEr1B/cnuaTRHz9Vyb7KPtKkr2Xg7+XP2k9xdhnVLN1ut/TBpNXGxka5j0kHZELgp1IHHUIvsQu+yz08v9mX3Gub5CM9l5wFlAlI3p/UHHcMDCOqB7oiC6+Mu0IK2WFfzC2yNKEfMZ4/9M/fx2Yco2q1WnS73bKdg0dYs0XQ48O+HWtsD9yHLR8kDiQB/qyrt/LKIOPLYN0gDB2gT/QPH8l3XC1CvLQ/Iob4TAZk6xjmZAm7oUJqc3OzUgHiMaNf+Hk/1cwrpibhjCOs88hmfn6+nOFlwpf55/rY5a1btypnuCHLSeRitgUnApPIdZN/yDbbRcY0L2U3d9ssH18fGV+7di1Go1G02+24fPly7OzsxM2bN8u8e1umkw6uafzGHIKZJmFLXjMhaLlFRKni2tjYKNss7rZ54cCxOGJc2e/kDHvCzxrHRkTZruVFsFqtVqqXNjc3C3kLOVKr1UryiM1SbfnAAw/ElStXYmVlJa5duxZHR0exu7sba2trcfPmzQrpZNvALxAD+L26uhoPP/xw2U7DdhQW6Or1eqkOIm4fHBwUIh9ZmKiCFDJ2JDlGrlQC80QvZPLGN76xPIDGD+9oNBpl69DCwkLB3pA89fp421u/34/nnnsurl69WvrDHFGNRhzFV/E6Y/JWsEajUZ7gvLCwEJ1OJ7rdbvEBnNnFw1EWFxdjeXk5bt68WbB/v98vhJxzTHCGFzwy3rrXluM4vsn2ZwKAflD95UP6TeYZI0acEQKQCeAXdC7jSf5nvM6h+HGVtIkAk7u8FzHeZrm0tFTIKS+cQ/zgJ4yLXenqHUzgJuRm2VmuOQ/PWIp+4x8se4/b8ds4Ls8bzUUh+Ex4BHILyGNIaF53EYgx88LCQoljfuqhdWRSDs6YINfn5uYKWTwajSrkGgtX/r4xE/bt1y0774Ih30KHeI//0Rf7CttGvT5+ivpwOCw6DIaAsMR/4MeQ22tCSj3xxBPxp/7Un4rj4+NYWVmJJ598Mn7sx36sPPLQyhIx+WBLSKlsKA4ATKyF6dVSJg4ldOKXk8mIagmgg7sNxk6Ycl2X/Xv1JINDJoT7uxLJzgijygCPScMZZLYc2XiCkXFOKIfDYQWMoSB8DudnosjJyiRH7/4gE1YDDg4OSgk2/YsYAxFXsnhuaE7qMHx+u5qBuc7ElgEMMs0JWJ4jg9ycVOKkTES5L+6/yTQcCfc1uCGYGCx6/AYsjOF+N5IN7IKyYsghgAorP9gKNrGxsREPPvhgRJw5YBwuFYu2N0rEI6LsywawIAvky30mEaUAmdnZ2fKId5LyiDH4Zc4yQeuk00mNE4eIqAQzPpN1i4CRSWpf2wSPP2Mbj4jKNWxzJgwAvAQN3uf71u2IKhlvW+d9k/rWefSS8ZEEOOnjmug4ZBDnc0xaobO8/WOSiZbPJ2FsJPQ+B4ix4OOckNRqtdIvdM8kCLZqH4sMFxYWygotRCDzRGyzj8x+436SUne6HjIjDhkokVhZB6x/li3kiJPnbBPYKbLDRojRTnCJC05U0Tv7YsZl8Iz+UCFCDBwOh3Hr1q1YW1uL5eXlMie5ugJbcIzwOJAZCRXEiD+HvSI/+gk4w9c5RuTkH30CsPmhEfxw3gir5RHj+BxRXfBhPrN+mYR39Sl/9/v9QsRypkTGDHyfM2W8QECfI8Znafk7xjPIq9PpVLb55iQCP+NtU+iH5c+17XfRKesXOmT/Zz3HD2D/th/L4U529moaW4mIWTwBme1kPGJ9Y2Oj2BA+x3ghY7tJ84+OQIiYpPQ1WBywDx0Mzh4ggh+cmZmJc+fORb1eL9vfXkomTj69aNJojKvdec8H+xqn8lnmHvlEjLc0nZ6eFjxLAgTZMRqNyhlds7OzsbS0VPAvZ2UeHx/Hzs5OPPTQQ7G5uRmnp6fFl1AR4YrfhYWFWFpainPnzhVcjK9ptVqxtLRUqrGwVxZHhsNhrKysRK1WixdeeKEcpu6tiRHVIwMYW61WK9dg/jKJbzJ9dXU1HnnkkVhZWSnYijmnegfZNhqNykM/8E9HR0dx/fr12NjYiP39/ZJfkQQPBoNi0/jXw8PD2N/fr5wlhI+jWo9EGz3t9/tlazZbxjh0mzyQbXxsF0TmJudMrNunIdf72ZynOB9hTsCg+B7OkRoOh5Wzrsix8H0QISZnnT8QU9BH5OndOcRa9MQ5D8Rozh0yRnV8Y5vY7OxsKSpgDtA/56q+lnMg5g7S0ffxd+2jM8EzyddxnYxRJuUNxjsmhhxP+I0vZD4ZA74P3+p7G6Mz15Ax+NHFxcVCNCFLrmO9RkbtdjuWlpaiXq+XBSV0gHN/fbabYwM6ZSIRHWIxFczhsdPsi0ajUbFlV/ghU3JFdNmEHQ0b4DOe/+Pj48oTIO+m3TUptba2Fu9973tjb28vfuZnfibe9KY3xZ/9s382fvInf7KyrcarJDZCDM5Jn5k8FILv810DO1cvAQABYJQluuKKCUQgVlyUhHtg2JMIKDskOyiUhaAQMa6W4n9eY6IMKP19XrdhOaEwAKdfOPnZ2dnKk88yuDCYzYmuE2MnbL4fnyNxiBhXSjjQcl8Tdlyb8dtAMHI7Aa5ngIWjdv+YWwd0flt21jc7vwx+Gb8JKAydz2fgzfjyirkTaMBSvo/7TLPjvh+AmWtgmxFReToIfQSU5nJt5rLValXGTHLHPDHXBGPLjiSBCowMPrhHRPWgevpAkCfQ0H8fxG8CJc/DnQgqJ6XWHVdKWO9z37iuky83O3Y3O2zG7uDBWNxH5GpSAP9jOecAlMkoxpoTR3woNkxQhWwcDM4eL7+zs3NbBaN9i8GKk1hkwVwZgPnzJjoiqolZJrNdnUPwJNYQmPf29orPwC8bEFk/DTgIpD5TACBAbHit2iQ9ZW7p48nJSfz+3//74+Mf/3gcHByUs0yYW3+eaznGZr/vxSATXTlRZq5zFbGJnnq9XkBujmX21+gmxAjAEJ3Y39+PF198MZaWlsqqHLZiHbZOeKy2eeKR7cKHf9I/iG6DLNtNtnWfDYF+eSsWY0UWjBNA6a1NJAPICL1kTowh8Be2/V6vF9evX48bN24U/fdnmB9jLFfSMu8mSHxeSkT1nNCIKKu8zWazYAGu7cQGfXLSZf02Mcdr1nfjtxw7bR95rrPvtZ74Gvfa0B+SDsD76elpOVOH8e/u7pbDs008YtfIzHEAfcgLYsbUyCiT6IwR3ULeHGA9NzcXDz74YBwcHJQnrt2JnKKPzAtzjN3gH50cIfOMLWq1MwJveXm5YgssWmMPe3t7xQZOTk5iZ2en4HVsZH5+Pi5cuBARUbaTGVOCO1ZXVyMiSgUPvmlmZiY6nU6srKxUniQ1MzNTqqNMhlPRMzc3F4uLi7G/v185K4zHqUPoeIHE2AhMZJwBYQNxNjc3V4goktXd3d0yx1TeQO7xc3p6Gtvb29Hr9WJnZye63W7p597eXkSMF+TwRzMzM7G2tlapwKvX63Hx4sUKjsLG7B+InRERe3t7JRfBHthiCBEJWUre4zyPpN9+GWwyOztbfNv9bvb3mdQwMYEO0BeqZJwfoAeuSGGuefCKnwTshd2IsV/E5jIhExGVBSP7xbxA436hh+gaVcTD4TD29/cLmYh9R4y3o7rwwH30E+l8tIfJPWMA+zL6Tg5p35fJLXyA8SVjIXZ5t5AXzOALIqqVnpxBStxyn02y2seiD+R3/hx+loYfYXuenyaez8tmPBBSzpORnXGOF2cZp/ODnH9kTIRtOp9g/HwWH25/SnzAdpGftwU7D38lhFTEKyCltre3IyLit3/7t+N7v/d7461vfWv8j//j/xjf9m3fFj/8wz8cjz32WGXlPWIM+FwOi+D8Gd43QETBDLQRPn/Pzs6WMtCVlZVCjhHEIqpnLhj05OsieE+6lcxkix02k5OTQ9/HAMoTm8E1n7N86GN2WP6uEz7u7bMqTL6QoNkBR4yBTmZmnZhyto9Xi504ZsO2TC1Xfuck2k4oV2pkpzoJtLrfJPEklJn8ysmfCQz3xcSAA5W/n+VgQoQkBaP3+9lh+Br3o5nAXVxcLPfE0dgBUwHF3DJewBEJDOcBEGQjqqSXGXrucXBwUNl3n+2FQMJ9rYfI3/NDxQ6fN1i3jP13Jp/s6AFlOdiZgLR9eFXYib/74H7nv02U8zpBM+u4kw/kjO+w7BxcMhhxUmeCCnCfCXknkI3G2dYgP0Eqy9T9NalnP+jAmeWGn7XcHfiRF/cg0LuiDD2s1+tl6wS6mpNUEgbmywsOVOIB7PxUPvyJffz9JqiyT8h63Gg0otfrxQc/+MH4nM/5nNja2oo/+Af/YDz99NO3+TC+46phX4/Pkhx4Dpn7/JAAEkeTDrVarZwbQnwwoEI/mEf79tFoVMrm0XGqha5evRqrq6tx7ty5ysqgq3i4hxMH65FjMH4Me6BaCBlxX1/TMZL3ka8TcHQVfWUrG/LF1wJksRGu50Uurk8ybTv2wg+/u91uXLt2LTY2NirbpywTx2D6OjMzfoQz450E+PGRzCO+otPpVEr6kQn+2PNsEtOkAe8hG+uHfaftwcmir4NOZps0BrON5bjwaptx33A4jHa7XfGlnDF1enpaKmh8DqjnynbixbWMeyKiVO/wxNHhcFgSdjAH9pr9CLqEn19dXS1PCtzZ2SlEmm2ZsXrh2fdy/9ERV3FkTLm4uFgIEBIkx1nHLg5AZ+w7OzuFENjc3IzRaBQPPfRQ1Ovjs1Zu3boVc3NzpQphfn4+lpaWCkYh5jMu7BQianZ2tiTtnNfG9Y+Pj2NhYaFUETGPEI6cTwgWyrYVEaVKK+/qQFZzc3Nx+fLlMi6qvvDNHOrO9qHh8OzMts3NzbItc29vrxBSJuKZB2wv4mwbJWNFB3lgBFXzzK91OOc74OVarVYqqfAz6CzHOdRqYzICPYLkMDnKoenYUl4cu1+NufKcOe8bjc7IQ3YJGEMTR7Br7M54wwUQ/Bhr5WIHx04n+paxY4b9pYmOTA7Qd2LpzMxMebjI3t5eqRx3vjopz0GfXY3McQ/2qxl3GoMbQxqXcQ9/l9eQh+M19mPZ+qEI+LOTk5PiL+9E3Bj/5zyTfkDiegz2dfi5paWlWF1dvQ0PeNy+vhe07RPs/7zQOhxWnwRJv/mu/QtxOOff9MvxiPtZ96zr2HVEFNukgpN7246dA79Ue0VnSkVEYeKff/75+Lmf+7n4n//n/zn++l//6wXcWXhMChPLpGCAJO3ejwnThoL4s/xut9vFUXLmhB2ElYPru1wvG6onxttJABURURJhPmdngJG6FDODfys2ACyTTwbwrBiwEuZ9v9wXpeQw13zujsdK47sAybyS6gQ6g3yCEPtcs+POBFIGkXzOK+ZuTkgtX4idDM6QW5Y3fYuo7ommf7nvvpdJpwym/D0HYsbue5rcsUwmtRwI85y92oaM7axYiSTRoNSTJ/HwNAVsEP08PDwsYJvvYL8GNsjAKxGunjKp7LnJegcAMmiMGNsJlTGWP8E+k66TAiljo19Z9pkY8vxRvcU1rFd24paJdQ3ZOMh5nDm4IifGbFtz5Y4BonU0BzD00nPnwGOS8fDwsIAULxo4mURmTj4ZW9Z5AyWDEI/JyZCBjCs6DEoBg/h4Kp0MOOwj2DLA6/V6vTw9ju+jq4CXxcXF8jQk7nm/CSm37NOyPzg6OopLly7FW97ylnj/+98ff/7P//m4du1aWalEpsRly2qSTLA5k0NOMHjfcZjEOSLKAa7Mha+bkz/At8lGrk0V5nA4jN3d3bh27VpcunSpUlGAHjJWkxxeKeV+tknHSQhHnweS5WRZZsCZgfpwOK7spGokYvy4d3wU3zV45JroPbrH61zH9z45OYm9vb24efNmbG1tVXyR7QZAiM35SATApDFJxnHIw3q4uLgY6+vr5YlO4Dvfa2FhoVRXW9eyb2TuuF8mJrgn488YzPOdCe6ccPie58+fj7e97W33/Hh5sDG6BTEHaYHuUiWFjHIsIq45GXfyhm7nyll0jafNZQxFrLLOWa9Nqq6trcXq6mqpyNnd3a3gXuwt+ydXK9jmnTw7YWq1WuX8I2z5+Pi4nGfIWXCQGlRrcF/GvLm5GQsLC3Hjxo2SP2B3i4uL0e12Y2VlJQ4ODspi3OLiYsGwEGKtVisuXboUCwsLsbu7W3wDcwrRBWnCWTxHR0flTC5vAfc2Ls8pfs/Vk5xLxOfZCrWyshIrKyvFH+Kn+AwPGKjVziolu91ubG1txQsvvBAvvvhipRLSpH4mAfDtkIRgPBOLrVYr9vb2KnaOvoILO51O3Lx5s+gLR7tAODabzVhcXIyNjY0KAXNyclKeCL2zsxPD4dm2YM6o2t/fL7LDx056svX9aBk7gpMjxtiYBN6kCmSZ4xPElP0xlXAQVz5bGdsAy2Qygms7Vk2K7die44sxKs0kvv3K+vp6IRcODw8L9nNBBs05OliJijafBem4kvMnx1XinzEKY240GpWn1Tk2uP/0kXjqhRyTOCwSGMdarpmMwpaNk43ds5ypBF1fX4/BYFCpbDJ5abIQP2pb5do0/DlyQLfYkYJf4bPEUuaCvuWqV3AB8vA8ZE4E3wCWcr7Ge3dLQuV216RUZvOXlpai3W5Hr9eLixcvFgFgQHaEKJ3JJQdVFJtVCRv7aDQ+vBanyecIrBG3V58wUQ4CVngnqhFV4zJo9nUczLMDsJEBHviuE0WUA6U2iM4lzzZYK5XH6nJ5gwGMjmsR/LmPD440kOV+rOgyJge3XIFhxp5rWB4Gh5OcyaRycRuFyQuuwZj8v52uSQLfOxNledXWFTyeB5xZrkjjM9YL6wkyczlmbtlR++97aWbe2YpkIgmg6GQAx0tlFe/xZD7r7CRSCfmcnp4WxpxycusF5fmZAIoYExc5oeV7kGaUybuqw5WSjJu5N0GFE7d+eBxuk8iBTDJmfbKOmGCJqB5AyxxkeZLE2n86STNIywka92T+ncQhJ5dkZ7nTSA54WqPHbULJyTIB03ZkIhr/aCLThLV9Ovdx0szcmTCIGO9r92oq12ac+PVabUywNxqNoqczMzOVR5RzPYCwgZX9+v1uL3Vd5NFoNKLb7cb/9r/9b/HlX/7lsbe3F51Op9I/J/8mOP2eQQeHn1KObQLU1RHWAeYOEOsqSTd/Ht/tpAm/SdwhCTs9PY3r16/H7OxsnDt3rlQPkXiSlGHXk2KQG0kWfUY3nPQiZ34mxVH02IsPJn6t804oHDvr9fFT0iBjra/4LHyYbYknJd68eTO2t7eLDfoMGtsMc4n/BH+ZBDYZzW/bHP5kZubsLCKSUSce1i3mPGMy7G4S6WSfgR/Mfs/Xoq/MHfLJGMS21WicHdD6lre8JX7kR34kdnZ27mhvd9Psh7BP6zPzhT/F/5lQpW/4Jv53Iupt5IuLi2Wr8fz8fDz88MOxubkZN27cKJ9jfiPGT4yclCyQbESMV9PZFnbhwoWig1R5eUEkxwHrmOMJVSPYd7vdvi2hYcvT/Px8ed9bTNAFZJqx9q1bt+LChQuFVMG2OHvJuBGy8OTkJDqdTlnY7fV6xdebCKICFNkNBoNSTYbfQ37YIpiPubdPQVd5sEOn0ymE4NraWqnowh/U6/Xin5FBrVYrVVDXrl2LZ555piwuWuYQXvSPa7IoyW/0wIsZi4uLUavVCkGYYzHXnZ+fj42Njdja2oparVYIJSoE8dnD4VklGnPP+61WK/b398sZYbdu3Soy2t3djX6/H8vLy9Hr9WJra+ue7PXlmuOI8wXmmjOHSeTpZ0QUog7djRiTGW4mYVzQ4VhBHOAafB75Z7Lafh7fZ+zsuIgNO55wXex3bm4ulpaW4vj4OG7dulUqDrkWvoJxe2EPMot8ncWYLFPLwvjCuaj/pvo9V7LbJvgfX2WZYdOQK7zmfM2FKu6X5YcP57Bz21ez2YylpaW4cOFCwTD02f3OuN92ZflkXXQhiQnNiCg5jcfhhWDGyesmv4hb9svGUJBe5hHoC2Nxf6jg9ZMN76bdNSkF2HGSxFkWHErKJDso2YAciCGhTGLxHYwZh+kVhDutxFvonnjAXURVcXKiSb8Mbg2G7DicGPH93CcmyaV63IeJZa80BuRr0Ed/l3uZnOGznnBWKryyGDHeyshWHBsycsd5kOAbBDKHPlQdp5nZ1ezY/WNiB6N0ou4+Mx/MXZa/58rJtZMgy8lO2sQV75ugYR49xxhrvpbnyUQaemfwScug2bK6H+3hhx+O4XAYGxsbZV5dym99JyGPiPLkFicV7InGEQHi0B9WLlnFsu4ZoLMiBnBGjp4LrzbwezQaFTIFWXNeFySatyNgz6xE1Wq1AhY8D9w727P7zhx63jLZ6RWHiCoRhQzRL34cYCAMTcYxVsZlgsGEnkl3J8LWKXxoTvocpBinK44A3u6XwZMrSbOs+JzBhwlbX8v9dpIOQeLAjTyczCJzEid0mkDLimuj0SgkKedGQTyYgDWIdlVc9jW/G82Jz7/8l/8ylpaWSkWBAZ51JgMa+2DiEO9BWjBmr0BCaiN7bMqHbWawk+fJ5A5+xLHAW5BqtbMtIN1utyRIJGb2vx4jzbHcgJj3eN+EI6Q7CXwG/SZJjGmYF/pEPHMsIs6huyZlTUL5PYC149zBwUHs7e3Fzs5OWWwwOMyxz3EX3EFjRRtwbTnZr+GjZmdnY3V1tTxJC1maXIgYb+nG79tuMtjPeuI5NP7IfjsnXOAtrxj7xzrRarXiX/2rfxVveMMbKrr5alpO1LAX4gQHTLPly/aRrwHuioiyjck6bN9LTCR2PProo6VqCHI+J6DoK/rt/405M9nINjO2TDuhcwyjf8RbdI4xQqgNh8OiT/Pz8zEcDks8pwIJ/99oNCoENmQHsYFkuVarxcbGRly8ePG2+V9cXCw+iooybIGqMPILfFCz2SyHcdMHE3vgeHzpzs5OIWScOLuCFDkzducei4uL8cADD5TqqG63W6kUs1/ED+zs7MTTTz8dGxsbUa/XiyztY4yn7AutE5wp6YXMiDEZAtmF3NET/CUyBBs2m+Nz+1g4bDab5UE4yBh5Li0txfLycnS73dKf/f39Mrf1+tn2RQ5Pz77/Xm3XLZMAludgMChHAxi7sFDKuJGnK0WZA+TnhwGYfHLugC+wTzfJ4NwlExkmKOxv7UOMdb3TxrGUs9Mg1kwScz1sHB9hWbLoY6IaH+38zqQP48x4K+cJ5M5gOC+yeI7pu49z8UPBnE+aDMsY2RgefJJlvbi4GOfPny+VwvTPZKbjGtf2XDofdoWrx42+YQ/O8xuNRnkAiWMf/3sRDEwHWcd1Hdecq/K6sQRz3mw2i38l9pGX5Pz3Tu0VV0rx98LCQjz11FPxAz/wAyWBxfFMSrD8Og6ZUkgGmFdXYF9tuAjcDt4Twj0d3G2sNITrBN0VPygRhoohAfpsPDQ7M6+Iul9mZF0pxvfsVBqNRiV5pxyUz+UxYJQkZSgG/eKUf1hsgh1AiuoqQDjlqtlwACM0nJSdtMfsMfG65WXgZFCEDvAdA1EnDvTN93HSwPzm/vFZgyschpM5BwAn5pA47qf7wHjsxHjNzQ6Xe98PYurnf/7nY3d3N77u676uzClz5UPHCYiujDNZxaoJh6Hj5JmDer1eymBdKusSZw7jNzhyYLMcsk7zWiYukDUrn5ks8HgAs8wbxJmJZ9uhk/qc2DCfBuDc2/pKALCPYZ4NeAh03kbh+c825OTewc39oA8R4ycVmkgyKLVeYwOcoWFSPwNVy9bzbh/uAGv/6nlm/MPhsGwFMqGCfXI4MHEEOdTr9ZKoc57UpMoVEwT2VybWWWG3H2KOMrjLLZMi96NlH29gubi4GN/0Td8U73znO2N/f78sRtAcj5yoo2vIrl6vlydcmTR1rMVWAd1ckznKBF6Oi76vSU6uTwJD3GackF6Hh4fR7XbL9yBiSBQz4HMinOONySD0gWQHn8U92DJrwpnrcj++YzBsP2cfwe+8WOV4go06CYFsOTo6in6/Hzs7O8WXM08R4wpFYzD7WV6zP2D1mb56NRR/QqU6OpITLWMt7pX9nmVlG7If85zleXOiYD/n+cwyzeSP70lMutfm8TreQ2T0+/1yzES73S59ZFHPsYOqilqtVrZY8AQkJ2QR46cwnZycxNbWVtTr9bhy5Uo8+OCDcevWrbh+/Xp52IOxEN/lSZDoWJ6LSclgXin3AgufhxjhPuh1vV4v1dYRUXY7mITb398vsZmHH/D+/Px82U4+NzcXrVar+Gp8Cw//wYd4SxHbkXiKH3ZYr9fj/PnzBQ+R6LLQ5XNWyAu4NpVv2Jy3OpKMg7WQMwtl/A0hw06QiPHZbF6gZ2Fxb2+vVJA+9dRT5Xwn5syLjdYrj435YJGZOeTn9PS0EJFgAPriBRsegrO1tVWS74xRyCFIUvGb6Mbx8XFcvXo1hsNh2Vq5trYWKysr0e/34/z58/Hiiy/G5uZm1Gq1crbXvbZM0KPr+EL02djBc8siFwut9ImcKqK6IA2ROBqNYmVlpcjJOQVx0gtExB/nO158MC7OcQqdcNWkx5rzWONJiOfR6Gwr5vHxcRlzRPWoCRq24IVCY3gTotZVk1DOt+3rTKhAkiIr/DkLis5BGZ+P3eC7yMCfR9b2mVyDSk/6whzlOElcYZ7A4Phdx3jmyHPLZ7JO2n+4eg49duzLepub9Yf+4Osg1Iw/LC+4EOSPTKhOhZxi/K+ERL5rUgomDAD3yCOPxO///b8/fvAHfzA+53M+p3QMB2fAhyIxoYB/E1cu4XWVhoEvAZFmpxsxLh8+PT0tTDWr4k5AbXg2AoMgK6m/y2v5+yiSV4YiomKEDuwEbgMSgw5vgzCLi6NwomIg5pV/GoGIygCvaqPoGCPfR9aAAkgrO1Lm0VUaliO/szM0WTYJEOUkzEDfnzXwR48yoWUAnB2P7+HEgde8bSIn2g4eyNDG5zEiP9uQdfBOOnWvCe6Xf/mXR8S4JJWtLvx2ZYPZ/JyotNvtSh9xQIAhrpUr80xAG/TYL7ghR9skOkKSxXVpTq5IbAHKJjdM+HBA6PHxcfT7/YmEkhOMiOrWkogo1RWuAsjNpbgeI3rmFRMaumhSxOO17vq7BiPoqv2Ev5OJcAd+gDrbGAjYBhAOevTRQIMAZn0w6UwfubbBCn6EJNxAmWubfBkMzsr7mZ9msxmdTqfIhHEDFLvdbpEp18xPSlpYWCjjJ/Dmc3Em2ea92mvWAwPFbC+/8iu/EoeHh9HpdOJP/ak/Fd///d8ft27dqnwegsC/DSKxK4gGHhzCYowrBoiBgBHigO2eftfr4yo/bNJz7XhP34gjTn5I6gzqeTrY4uJidDqdon9eNbSfN3j3HFmfeR29o0/eeoIemWB13OP7kFP4fPTYupO3yXF9ADPJOHaBvh8eHpYnpPF95gN5I1vjiRxPXLluXXNcR2b4Uho+1DJ1wpMXApkLkk/btPtnf+bfk1ZXs/+chOcm/e+Y8lLXf6UtPwyBcUGy8P/y8nJEnD25i2pkfA/Ek/Wd6xFj0YMsY+aHbU1ra2tx5cqVOH/+fNlSdXBwUCHpScjRcZMYGScbx0RE2ZaGPCEuwOp8j50O6FO9Xi/HcyAn9K3b7VYODUc/7J8iomBQCCDsfjQalS2N2CZnzkJ0EK/xbSSxvI7fYiy8FzEmiZANn3FVQbPZrDwICFkzhuFwWLZE4R9WVlbKtryIKFV1HBaN7I19BoNBXL16NV544YXKVmtaxqImpyPGR0p4brFVdKzRaJTDmdFtFj78tNdWqxU3btwoi0muvuCeLKbzOjgY32Lfz1ldt27dKrLb29uLmZmZeOCBB6Lb7cYLL7xwT5WNtDe+8Y3xxBNPlHlG/5999tn4+Mc/XqkONmbA9iADwDrolreYo6/GTeRZ2Lor+yOqD4g4PT0txOpoNH7iGr7ZizjZf6ITjkHcy99jzvAxxGPjhWbz7FB+/LjzuIgqgY0+YiuMCzyJToGhkS+4NBMrmSjyYhT3539XXzlOgyucT7ri2jiZPmdM7wPq/XnmsFarRbvdjgsXLtyGFcA0+GHnpMwBY0CWzss9nz7XKmNq5opKU+cgGfs7LzBxaJzJZ/O53M6HiMM8XZjXWPDw9+6m3TUpxQU5MPDd7353PPXUU/Hggw9WQKxJGSuZDc9klMGTySQUjh8rukmXTETgPHd3dysBzQ4hj8mlpgACgGFOTj2ZriKgL9mwIASsyIwJQI+CZYLKY7Jh+nX6xP1xaLzO3lkAAyDazoT741D5rPfzO/gbkDLnZnY9foPCiOoTDp0Q815+3deyo3AFg4EpRk7L18qG4aSA+UIXzSQ7MTKwQ4Z2JHw/991jzX2wnpkAu5dG8PfKHP118uGKCMZJ0IT19xaRfr9f+mmCgLk+PT07O6NerxdwlUkf+sGcOijTB3SOeXBlIbrs+bKTNNDOQZ/7kXCPRqPK3nTmcpKtZZIT3QDA2CYNDDPJwHVtHwY//O/PZEKdcfO6V+zROd+PIOVVFfSO9/F9+VwU25mTSq/g244MwtznSTLib8ASxJOrnCw7KkHta7xaaHIc/cK/+8wdDvCcm5sr5ORwOCzJHWNmrnIl0mvRLC8n0yZT6vV6vPOd7ywl4t/4jd8YKysrBXT5Wv6+ZYW9IyNW4h2/sTmDVubAD8rgzJa86sz3DUyslyStgCL/4J/wSQbkPgOMe/rpPwZWtiXbUfZB/I2sGJdJV/QKu7euc2/66aopryRm8G/ASbyOGFcHklBAcLASCTCnmtm4yqvBlgNytc0iK4gRr95OIsNJgt0YC3HGvpkYwRyaOM9jz7ZlMs3+J5PgNK+Qv1y7H/GVZnk4rppYIo5Bqm5ubpZzudiaar/sZMfJMfOTiSmSTKoJeRDQW97yljg9PY2tra1SSQq5EBHF3rLOeu4tdye4Jj+M6e1zrUurq6uVJBB7ofqp2+1GRBTSA1tCDpC9GQtjA1SWmTgy8YX+7e/vT9yq1ul0Sr/8NEDmmOtSpcE2vbylkRg1Ozsby8vLhShDF1ZWVipVh8iIBbPRaFSO0mCxEALs+Pg4tra2YmNjo+RczpOwpexruTc6xJygN+Q6EIfGXdyXhXR8drvdLhURzDn4i5zA1WvYMoQhJF5EdVsgFWj4/4iIXq9Xtm8vLS3dl3Olvvmbvzn+2//2v41f+7Vfi/n5+bhy5Up85md+Zvz9v//34z3vec9tJJTtDd1jXhzHIsbnebF1MW+xIt/C/5L0j0ajQkI7z3Q+nP0kss04Eh/i+O9FFO5lQo4+GivQFhcXb6tEcvPuJXJq+3D0iX4R74wzrc/O5ZwPYA/Or6z/yJHcm2YiDVIYe/axIPQXu4g4sx/nw8ZkyHxhYSHW19crhBwLDWxxtY81ceW4SMvjMxbNuYWJOsbGwod9sefV2MWLuuZauL71grlmLMwHfhZinTEhi7ttr/jpe4eHh3HlypX4L/6L/yIuXLgQb33rW4swvfqB8rhiwfu8s9D538riJNWkQCZU6vVxZZHJCSbP93fwtJAJJnwnIirXtJAxKBrXc2UC48oTghKwcsN4fD2Mh3FwzaOjo3IGh0G1yQAn1RHVQ5VPT08rhz5aMWlO6EheMAaSMozMBBMOycrvMWW2l/89h040LQtkbqdB/w0cuD66kueT6zlYe8yeWydU7pdXM+xs0QOTfNYDg49J4NtJhSvp7kezE8SxY19eJTo+Po7V1dXitOk/CShz3micnTHBWHn8McCRxIrvkuzShyzL7ACzXNk+aJudBL5ylaL116sMyJt712rjw2Vz1Z/nC7s0ue6VHH/G+sy9vdLq61qv3S++a5LCoD4TtPZhtiE+b/9n+3TC4XPisi+zXXkunJT5mui8fQp+0qXHLgXmO1QtobuM1avxyDqDPhMgyIs4wBxDxHIdJxSLi4uxvb1dIU35vOX+WjRfO8+RX19aWir/Y7MR1TPZGJtBH4mM/SrzYfmSDFhOzCMEEHIk7nHfSbbsxBW98YH79vvGAU50uT9zsr+/f9u9T0/HDwuZRAjbxuwHHK9p9lNZDyGPbXPWRwA3W4ByVQ/XYZWdlVLrwXB4VsXHliZX1ZC4eUs0/s/4AdlYl/BvrqgyFvNim0n54XC8BcKLLMwbK+HMF3EAmTgO87pjI74Fe+M9yw49dLLGuPBfeTxuOX7cj0b/8IX2GSQrEVHIIPT75OSkHCjO1jRiqPtvn25SxrbFFsFmsxndbjeOjo4qFTnnz5+Phx56KPr9fuzt7VVkaB07ODiobEEzMUFsB+M5+XXMiBgvSPNZPykOXR0Oh+WJcSSGHA3CFjswK7rRarVibm4ujo+PY3d3t2JH/X4/RqNReTIdek51AolqRJRtVO12u+AatvAi536/X+YUOXuBAlvw1uP5+fnysIz19fVYWloqPgl7w+dwDd7n4TL4JSq6WYQ5OTmJ7e3tUilp8gLZ4+s5HwZ/7zzMcTwnmtgXfsZ5HNj/8PCwzEOv1yv6bd/uRWyTs8w9FRWOqb4PfWFhdXZ2tmyVhEi519ZsNuMf/sN/GN/+7d8e9Xo93va2t8U//sf/OB5++OF485vfHJ/4xCfK+WfGMfhmKu15WqIJWedfjMk+k4UB5y/+bCZ1M5bKC5CT8tGI6gI0/sRbRCFOnF+h59mPQViSCztmOq76bz9gwX7X/eJ/5/zOW431THThQ13FiJ8Chzhu8D6xiJwn58z4Az7nPND9QSb42E6nU84W83jxEfhW/CvYd1Ks4vq8Rt9zrpvjJjLCf7DQa7nTMk4zSW9dZD74LH2hP8bQ3MMPRXD+eVd2eVefinFizyOA/+7f/bvx+OOPl5VJG2X+sVHYMRs84PxQTCvCcDisHJ6WQaonxYpgMGZw5cmnPyQ2ToQ5gynvvSdwOcDgpLKjNZCKGFeB8LcVJSu+jRZZ8j0+72bQOalczobPfd0/J3Z+nWBOBQxnAeBUneB4rFbCvCrsoGiwajBqg8wg2+DflWB2fDkpo9mY3Dc7Lu5vB0ffbcR2yJlcsAxzUu9E0TpNZdv9aNzX5b0QuNZR65bPd5mZOTu0kNJTnLBBKXKAfALIQZ76YQZeQcjJD3Nt+SAb5Gwgbh1zwMd3ZEBP8pyDlcknE8vWLQM3AwvrA806l/uQ32PcmbA04ZkJKF8TO7f8PE8ObIAAEywR42Dn1VNk4NVTfx755WQ3Bx3678DItWmcLeiEh7nHH9G8SOB+mMhyPGHM+HUnsq7gYZXHq2CQk9zTgfq1JKXsa/Jrfo8ECrmQkDKf1lH7TJOJyJnE3zHTq7Z5xQud9NaciOo5cSYq8Kfuk7e0eC5JtnyWCfdxssMq6N7eXgyHw0qVDsSE7Yr+8ZkM5CG0kBdVGXll1EDcNslY/Tl8qYE78vY5K+6zZQbxBAap1+slISCpMxHv+Od5BihjT45H+MuFhYXKdmaPO8e+7Ffwz4zXsY+Yh056TuzzfK8ci41rJvkaJyzosfvre/k7WQdebTMpz3xADLHFlHvhQzxnrVar4B76k58Q5cUKJzImW0iyuI6rTRqNRqlOuXjxYiE0LDf/b3wUMSbysQ3raLfbjZ2dnfKUVq6Hv6nVaqUCiPFGnPmwzc3N6PV6MTs7G2tra0VHGa9JqJmZmVheXi4yWVlZiatXrxbSiXtxaDnEBgQU9zRhSuVhrTYm0Nga6DMpj46OStUSW8wgbiLGpMHMzNmTKam6Go3GT+rifsgXeaLXjrfcf3l5uST23BffmXWG5DhjYvQDv4Dv9AKUdQzfx3WxSXzkaDQqpOHm5mapWMUnIUeuPRgMyqIi17NN85vvOv7Zx7ClcjAYlLm518aW2larFR//+Mfje7/3e+Pnf/7n4y/+xb8Yf/JP/skyP86ZyAeJuV7QjIhy3IW3yOEDkDUFBtiLcz1iHLJiXrL/sw/hdeel9qXGcXwmorr921WZp6enFfKP+Zmfn4/V1dXK+U2OQ64u4posFlgOHov7Y1xubG49gdThQRs5F4wY8xX8j7/H9zJG8AW2mw9pZ1xc3wQ3sZWHQLFQiK24UpinZDIuz63xMTyDcwV8ac5BjIOJB/bj/X6/VIXC03jRyL4ebMG5e56biGoVHD9UN7KYxnh5WAXXNL9xN+2uSSk6Nz8/H9evX49f+IVfiPe+973x9/7e34u1tbXSKU9GvV4vwc8K4STVRBbOn0Q2Jwsu9SOQGOxmootrImC/x/usUtlpn5yclFWjXq9XSYBRZm9VcJKIojrRNaAw8PKeXgeAvKKB0VvGGTTQAH8GpU4iATs0DNOJ66RyUZdFE5xZZULZMmjntUn9xQHn+UHGJjGYK+uhEyz0hETSBuWx85o/y315zXrD90lOcrJHYHCi6j5bT933TEYxHuaCVb97bU4+cgLhMeZEjznlwOjd3d0KEYnOuvLCZz2wYuiE1itbmUC0w7WMbFPuFz8mDSiTNrkGsQE4AMzRf77LfV3Rww8JHfNkctv6i47dKcnxOPMYTVJNet02wfcdvHJAzwkbcvZ7TghNgtpfGbQ6UXSARybun2WUSQvsxLZH/yAWqAiBDPW2uXxNrgdwyL4UXchgwDrnz5NoYS8m919LMoqWCRD7Rt/fxCXfAzzTV/t060FElUjhGsfHZ09b5PBa2wR+1v7VT/7i/h6HwalXMpvNZmXrLKCJMXrxxWRzjiPoAlV1JEvIJ2JMGHBN2yl9Ml5xkmlQ7JVD5OH/rccG1bThcFjZWkg5//HxcTmPxZUdYBDHJOaAOEGlmX0QuoLMvMASUX1gDbJot9vlcd4QG9iqt/Z4LCTHrqh1soEtm6QiTmSS1b7fPzkG2Bda7yeB8zvZk3109pWvtpmcpOKmVquVM02p4PHqed6qg89DZ/HxTjKNBZEn13FiYHuF8CFZ8Fmm6AkxknjnuSAGICuSEM859sfDCG7cuBE7OzvFX7BAdXp6WnSt3+9Hv9+P4+PjWF9fLwfA+2Dy4fCsksoEE9WBYIFHHnkknn/++Yq/Ozk5iRdeeCHOnTtXSe7pN2ebNRqN8vAWYwdIFRam2V7IU6tdRWKM3Gw2Y3V1NdbX10tfms1mxcfmHRnYJmdRMRd7e3tlLnn4BmcIkq/cuHGjYpuu0sM/ZExuHbPeoiPe0o7dHh0dlQUEttDNz89Ht9stCyL1er28t7e3V6m2g/SA7ONoB+v54uJiId3QmYjx7g7rO9jnfjR8NLZ2+fLl2Nrain/6T/9pJSewfzGhhz7wND7sgcPP8VXG4PZX9kdgavQrL+ba13ANfB3zbj/n+bWPdb5cq9WK7aI/3Bebs4/Ft6O/+Vw3+3X8BffKZJvjpvMlz23Gmvbd6KcXwfCPPjON+02SmfOjiDNykjmNqNpURJSzaO2/V1ZWyuID43Xex9ODwZX8mCOxTwcL0JBd7jNycP4ApzAJ71oO3p1gzG6SCiwEieqqeOevbGv03BHvsv7cTXvFpBTK8+Y3vzne/e53x//0P/1PZeIRsAGaAZKVz6uyNgaCl8GEV8g9cZ5YT4wdsQGJxwKgYhXDDthsoYkjru1EOwNBmo2HvvqzKAhyQCny4Ya5ssANJbCBe46yE8zJqomCnKhFjM+KIICxWsvfBwcH0W63i9ECFAyofP2cBDL3JCv+n/7y2cwOu++ucMvXvxMg9fetV1l/MC7myo4+okpSWL8zQGal0oaek16uw+t3mve7bSY47dyR33A4rJTUO8GEjMEhHhwcFFABAcA4nGjZ1nHQuTLHwBl5WY6Wp517xLiiAfvwXv4M+gHQBj+dTqcAMJNcvrd1gKBH33MCmPXE75s08j08H7lyzuO1XpgAcpmsEzX7Gfsa7pMTZ3/PAMz+3Gc7Wac93pe6j1f/vNqIDvo9dGh+fr6MkW0PgB9+01/m3f4lr0TRT/unbrdbxg7Q5TsGiSbs6Otr2RwnMjhzcz+8Ld664xjLawZyxD0fsjwana0GRowPxDUB7PuRpHibhkFPJjJMMDkJjBif9dXr9So2R0zw9+iPxxIRJbHzymDEOE7a9xtD2O/hS6w3Tkwcz/EhBtv4RxOEgFcAvTEE8nTlCWSC5Vev1wu5Ua/Xi12YlLLP5DtOmr14wBjb7XY5CLrZbMby8nLZFmUMwxzyw5gnJVuMP2+jAGvl5IAxGeNlvJTJZnSNv617tiPbxCQ74rP30kiAkD04iSfEbW5uVs4VonKEsUJ+GPR73pzAIluIL2zCn2PM+Mbj4+NytmO3243t7e1KZQP24G1zxl/MJT/gUirjqNo8d+5crK6uxsWLF+P4+Dhu3boV29vbBWfXauMnVXGd9fX1UmHN017xH+QTe3t7heT0YcMkUA8++GDs7u5Gr9erPF2v1+tVFs6oAjQx6icwUvVwcHBQ/Ov29nY5hB0SjXNv0WXkde7cubh8+XLRM8gZ+mo/VqvVylgajUbZuhcxrgbpdrsVWbdareJHsP+9vb1yfIITTPymfb/jLLEF7GXdQ9boEPoNXuTeW1tbt/kDntrFPILDdnd3Y3V1tfQTf2f512q1sj0MEhGCjKoYnq57P+IwsQMy7ZFHHomv+qqvin/0j/5RfOhDHyoVjN5JYUyKr+52u+U69vs+KD9ijINNENPAgcwBpL8XY4ztsHn7ShMPeUGauc4kC/6E+O6tcMRG6yXzROxwdbsXMO13c1wyOUq/uKZtyn7dYzcuR5cglnNFPn3HVzAfyBYC2v237TAGyEL8Oja5uLhYtgq7mIMGIYVc0Tv30VgI+0VOJjWd72fyz9f13Brf8Jk7VcV6wdH+gu9lbMrnHSvMnZhA9rVfrt01KWXmkf3hrGJQvmiHa+CX2TUUFGPn83aEFjSf4fsGhvm3kzt/1oQNCkUfGZufUMek5moKJtMTkpnnTAjxPQcEl3znvtoJOrDkCqacAHMPO2wrkkE54DkTSiYvcrJL6TONhH9hYaGyQjNJXpaJwaiTKTt93s8yvRNotWN28pWTCT5vx5GdOLLms9a/SY7CsrYDJsB7CxvXzqRdJiJ83VfbTIRkW0O3WZ2jemE0GpVzUJzwnZ6elu0AHBLK4ayApNnZ2coKWcT4AEsAIvfGWRKAI6pnI6FP1h10AvnwGQMyg3l/ZzgcFuA6Pz9f9n+bAM5O0+BtEuufnb91DR9C0pqDNGPwdZEzOgdos83xPfqMzBhnDqgEB9sWuuUVOcuL93wtxuNmO8yJpclsy8mytT8iGAMCO51O9Pv9cti09YWVLOuniVbLzwAMoEdVAr8ddwDL9JHvA75MFL3WzbLNrzN+xpr9EG2SL+T7fIczUCKi4g9YCYYUQB8NIt0/9xPdoZ8ZGBsL4EvZ1kNsyn52Erj1/fEl3JMqHwNzx9aIqLzneGSg5hjgWM97Jmcz+DIxm/0RfTZApELb8ms2m4WQgsAiprgv9vFZdyzviIiFhYWyDcMErVeZfWxBljtj9/jyvSPG/sxVwPbnfN56Yl0xAWUAnP2piY7sZyf59+zv7qWBEU22jkZnROTBwUHMz89Hu92unCFEImWyFd9DsoocIWgYM/EXW2U83oqFXthvesEAX4ut1Wq1svBETHCixPiI/yTF+Xw5+rS4uBiPPfZYvOlNb4rT09PY3t4uFSXezhMRZQufK6jRf8Y/Go3K9hhIOca5v78f8/PzpbJ7OBzGwsJCLC4uxsHBQfFvHI5NzuIqP4i1mZmZcmbT9vZ2vPjii2VMxJvl5eVYXV2NiPHTtBYXF2N9fb0iFxrEjOMPpCJEWMavtVqtxL9OpxMrKyvFL1PxxUHGi4uL5UwwYp59h+fJ+NU2ARHGnJjAJr+DuGS3Bf0gf4gY54dUeWb7sG1APKGPYKXsb7DrXq9Xtoia1H+17eDgIN785jfHf/lf/pfRbDbjXe96V1y6dCl+4id+IiKixD7jPpplaRvx+2A3Y26T9f1+P5aWlipYxXH6TuRERNV/OQ5mP42PzvHG/gS7NQkOgbu0tFSJkTl2QnZnAiLHUeyZuO6Y6Xze+QV6kzEZ4yFv4DVyGBM4kE7YA+NFr4jxzks93owNsC0XtOCLM9eAn8rb+p1XQ3BxD+7PfPB31j/H20m5Yp5jxs0YnGeQCyJDtjvjl3wOYs6LkZ9jtLmRXLRxN+0VnymFIyXZ5qBGr6AYfPjHREwWpokFky9MhMvOJw0yM8pcy/fnc4ABGwploi5d97XsnKyokwgfGwnByUbq3x4fY/frvq4Bl1nfl0qoHYBIBkw42Gm6Gsz9y2DB1+fMIYKlx57397pvdgae/0nza5KK5qBqWeRKkpwo5YTG7Ljvn2WanZT7Zr1zomCi5vT0tDzu+E4GmnXrXpvHCFAzkB0OxwfXRkRJdiCc+B7JEA7m9PQ01tfXix0xJuafagDugVwMNNEfdAX9wwFaDgbIDnJutvFMYDppxclubm5WiKl8aKjnYxL48GsGgln+ELd8l+saVDmg8T37iYixf/Aq1qQkzTbBvVztxBzw2ewHLa8c1JjrLAf+z6tagJQcD/isATH9RsdmZmai3W6X1xg3oIVkyuPimoBlEhRAU07Gsg/le4zDpN8rWem5l3a392CeckyIqB54Tctzz/eRAUCEhNjzahKUawOmbJd8PqJaZu64aR+Zff/MzEysrq6WUne/53s7pjvOez5ZTXe8z4Q1BLhXtLPNW7aOCQZdjnV54Ymx8zmfrQHo7na7MRqNKk/xMlGKjXOOB9WEBqz4DnTedkbcR/4zMzPR6XSi1WrF3t5exVd7ni2PiOqWyFxp7m38Bst+dHfe2jAJaKMftrmsx54D/nYVj9ukWJtxxr02V0pZLxqNRnlqmBMM6yo6wd/exkpDXo6l8/Pz0ev1Koc/e46dpNE/Y/TcV/4+PT0tuAVd9lPieI84av8DsQGu4PDu5eXlePTRR2N5eTmGw2H89m//domLnEXFghcHS5PwRoyPrPAiMqRIp9MpRBJbAyH2uP/m5maR+7Vr1ybqw+LiYjz00EMFI9VqZ0/xJjljsWR5eTkeeuihWF9fj263W84P4mgA+sV2O7arcU2260L4+5gD/BNJ3unpaVy8eLFU2JsM5Ay409Ozp9u12+3o9/tl2yRjxH/gA7huRHUxyNvm0B900nbJ+71er+gQ10CWLCrt7e1V4sfW1lYhOfC3XB/9oa/0B109ODiInZ2diLgdP73atr+/H29729viR3/0R2NmZiZ2dnbim7/5m+NXfuVXKraaczHfnzhxcnJStpnaj2K/4BFk3Ww2S+5EjDR+5LVJC64mdUxkGS861wE3ZmzouD5poQBd9tbEnJNxLewV8jVjQfpD/HEcJ0f2NU3gYR8mPYxTvPMBX8F7XhQ2rsXO/Dq6NolDoE8+DsS5pmOUSX4WUDNWsZ/O5KExs8lIxwQvKHsePP9+Pxf6cC8Tc479jgfopjFjJhVPT0/LwoJzzjvlui/V7pqUQgmazWa02+146qmn4jd+4zfiXe96V/yrf/WvyoQSABGIE1Irpt9zy8lWBpI2LhuK3/e1sqFlkIpR+4kCTK6Vw6/5fpME7smY9ONx0sw8GtTZoL1y5UCQE0kUxs7Q/UfBLIuIauJrAzJ5ZEdNH46Pj6PVahVAzPW9+mliIM8PjsCKTnDzd5CPx2MZoJ+Z+Z40N9kZux82bMtx0rxY51gJpDqI4HpwcHCb80LmJiGzTtyvdnh4WMrmIQsAWiahkC02huMzoM72YWdHablBqZMME6voh99nXj1/6KjJGPuTLE90IdumQQLfA1hRfeAEeJK+ZrLHc4Ut4E+QNfK2TC0/gCrfs0wYN9fmPX4Dfr31ytcfDAaV/fHYrs+K4ruUPnu++Q62iFwyMWX5ZABnn++5MlHBa7YR7KfT6ZS5Ynz201yf1bXRqHqwLHK2bF3x5MSXuYiIynxZ11/rZt3NsqRloOfXMlGfY5WBPvIxeLEOeG5MQDlhgRg0uc9nnEyjg3zG4Me2CtHR7XYrCzoGm67mMThjjrg+xFY+L8d+DtDP76yjXIv4bNCKfpjwgvSfFJ99APBgMCgVSr1er6xWMh8Q2jlZhZTNsct6TJ8mVaayfZlEPqKanCIvZO9VYOsICRh/2664Fj7dMRe9MekIzkIfs//EH3pF1j/0kwQGncm++0445F6b7Ywx2z68ZW00GpXtku4L5/ZAqjBf6Cr+0LrOGEgEhsPqdloqDLgu5Bj+m6SNlheMkBs+w+QWY2YrG0kIsYW4hO0vLS0VcuXy5cuxvr4eOzs7pY+tViuGw/HCGfaLrSA37Ajcyvay0Wi8RZYKolarFSsrK9FonJ07x/lRVMD4PKRarVY5xBh9xBeyfe7y5ctx4cKFMr/Iyrp6cnIS/X6/bNvhNeKxK4w4P44qOqrKSbyRa7/fj+Xl5VhaWoqFhYVSNWZbA4Pv7u5Wtgtl32h7Qwe9/alWqxX9BJPxlE9wyf7+/m3kOYk6RB0PakCXGUejcbb1C8IDwo8z9rgecuI8r0ajUanGudfWarXi2Wefjb/39/5erKysxNbWVplXL9BjK/yN/htXHB4elnFDiNono6v4ZXzZ3t5erKysVPJN7mf/zlz6zB/rqPGOcaqxF3LzdzP5wTX4js//su/hehl/eKEa2zXxyPVpJsmtkzmH9vjA6tg7mMX2iL8i5pi0Mb7nfsZ6zgNcuGFsZHk55jqWctZeztu5X+Y+JsnU13TMczxz7jwpdzHZZfLY34kYnynqs6+M+diu6thJ3OU3C2vWZ2zW8/5S7RWRUt73vbu7G7/0S78Uf+Nv/I3o9/vxsY99rKyomNk1AOY9J5UGghZ0Nhyakxx/h7/zZw1OTML4dUrmzZzmvhlMemL9+YjbiSs+Z5n487xvhxdRPd/HzscBMCfFAHeUASUAGJocIEgxHwbTyD5XHPE5NwdczkbgHjh3xpcdAfd2osuYsyFbTgbhfh0D9rzZuU1iq/mM++T+2miRCc0AFKdBUkXptZMkBxx0wsmOZXM/mq9rRz4cjh8bymodAI0+AZJ5VLOdO3qS+2un6sQMwGNdnmTDyMkreQZWdsI5qch6ku0s60BEVKoGrTu21xx8rCt+agqvQ44gfxKGrCcmimzTOSAS8HIwgAiwzSNXiGLunX0XK2AEWhKLk5OT2w6vtrwy6eb5s4xHo+pTpmxDnh+Tv3zH/+NHeJIRZ2wwHtsp+onN3cnPRVQPPsdf+5BVKqm4juX9u9Wsx37NhLF1J3/eRErE7aQLiav9JeSeKzBYcff82V9zr0xK+bf9d8YK+M/Z2dnY2dmpnJ1jsJZxQ/YJzD2Jn+2O+ecanAVimThOcD3e84q2yTL+py8kIfhevg9RvL+/X3yyt6QagObEEjmR2BjLMI/GIADpRqMRq6ursbS0VFZw6at9kW3ZCRRjwZejE9gb/n6SLWODxA7GhN1moJ1/DLQzjkDfvGCS7eRO9oR/frnPvlzLyUW9fraFCf/LOTn7+/tFx5lbY03kZKyQZUI8mJmZKQSOkz9jSZ6MR5ziKVGLi4vlwSXYoBMw5ENiRSLImU2QSGtra9FoNMr17I94f2VlpcSxvb292NnZKXFrfX09FhYW4ujoKPb29uKZZ56JbrcbW1tb0ev1SgUhROzu7m40m2fHRVy8eLFUcAwGg+h0OmUuTk5OKk/4e+Mb3xgvvPBCdLvdOHfuXDkgHD22X0KfvDjDos36+npcvHgxVlZWynZE+57BYBDb29uxs7NTCC7PGVVSPsoAW6Siyjii1WoVAoi5XFpainPnzsW5c+dKtZTJHA7Y3t3dLXYJ1su2mHWs3W5X/HZEVGKC8RQElokrrkW1DJVlnHmFbHmiKDKjWhAfgI7t7u4WvIJPMRF0r63dbseHP/zh+Kt/9a9GRMR3fMd3xC/8wi/EV37lV8bHPvax28gHy4qYC05g8RlCxltD7efwZV5Y54nmJtXtG/i8CZM75YDZV07CC5MWpSxb+xx8ALrolvMk39PklMkIz531z+SLr2NMk/GcC2EyUZZJLROEJnfA/8bH6DZ99CIQfaMZl/D/YDB+WEnGuf6cq5GZi5wHW06T5jnrBOPygjbjNonss8DoI4sA7g/3n1Qx5a39fD7Hcevp3ea1d01KXbt2Lf7JP/kn8Z/+03+K09Ozp2js7u7Ghz/84bh06dLZxZR42rHxf2ZE+YwBpoVuQOYkifdyMssEZ1Du7xjYRkRZuXAC5+tYkZ1MZcOfBJAN0HI5vL+TJw/nhDJkYzDAdQKAErIi7GBkGU1KYnCwrJSYMOH9nOQ7YWNbgecSR+pxWrYAWRuL588/EdV93Nyb+7may7J2wpQTf1qeW3SVe1qPGC/9JilhK+vx8XFZ1WF8JEbIxwHAc5sZ8HsFy5b7W9/61lLq3ul0KgTZc889Vwm0gN6IKE+6IYlDJtgyoDQHXq+OGGhbnwyiHYQI6MgDPcoAwToVUbULj8evmUi0X7FztX36da8w8zuTLVQAjEbjlVufc+PqPwK25UAiiQzYTozMIqKsnEeMH9UaEUW38GncB9kxtwBZ2yrzge/hsFPGx/xz7pj1ywHUtucEx9eyjA3U+J5tgKSRlfRut1vuyZgnJccEUY8fGTIfEVGSLn82E14ucX6tm32kXzNgtEztP3OSwd+ZHMq2ihyJCegK33PFEfJFlx1Pua4XNyKq28JsrxAJbAX09Tg8NJ9plH1FREz0I/merOKx3cWkZcSYzL0TmHWcdT8YO8mwwa79hoE4q8iuXrVPMVC038NXZAxjXRgOh2WVHtmsrKzEwsJCtNvtUrVreRlbOWbaB/K+7RtCBECfSSzrrEH/JP9hO814Llec8ZoTjYxNsu7zut+bRGa9moacGo1GqShBv3mqHISKq+GazfEZKLkiCF3gPfTOcZNqZ85SarVaZQtNrVYr2984NuD4+Di2trZid3e3LEZ0Op2SEPM99JfF2oODg9jf3y+PNEeus7Oz0el0Yn19PVZXV8u1FhcXY3V1tWAIdld0u93iq8FE3W43BoNBIXLm5+cLoYT/RzbENXSOJ91RUdRoNErlt4m6er0eKysr5XH2zWYzNjY2ih2YsLHucSwF2A78hJ4TC5HdyclJIZCYK3yYdbZWGz9tjwPNSWCdp3gRfTgcxv7+fvT7/dje3o79/f1YX1+vFALQwJk7OztFz+3vTKJBkjKv6A+HqrOICTnHToDDw8NyZpeTaZPTXsyEaGKMzBfVcl7sokKK+c9J7f1aHBoOh5Unyf3rf/2v4/nnn4/v+77vi6/7uq8r/UQX+Y59sn3f0dFRqYKEyATPOn7kuWCBwP7VObD9Vs4/7ft5P+emOd/M2AKdzPkXthsxXtR2bpblYp/Ka7lf/i5/g68yEee/aeQb3JvcwG1SLuUz6IzjXclEP/EjjvWMJfMOzmW5N36KubKPcVx07s3/zlksV881c+AFbWTjvhp/1WrVo1O4V0Tchon5juNYxm2Ow85PuGbGq/775dpdk1LXr1+PH/7hH45WqxVvfvObI+KM0Plbf+tvxezsbCwvL08ESRia/zcQzmA6r66bvPB1s2IbqOX3CaBmPyOiVBPkxNUJU37dQjdQduJjB8R1SPSZQCusP0czyMKJZedi4EgA86qCSwoNIB00vJXRSQsKRoDz3lgzvyTOHIJmQ2Z+M3i0s2U+MpHBZ5BFNmTfZ1KQsvO3HN2HSf207LmXV339PVey7O/vF1Kv0+lUzkbiGjz6m2uZqDDRxvv3ow2Hw+h0OvFP/sk/qWyHYZxHR0fxgQ98IDqdTnziE5+Y6ETRNfSAOXe5N+DZKwCMlaADAHAChK1kAiM7OO6X7SQiSkLrOc4Bkd/0L+tgxJj0BDDYiRq00zfbun87sctPx8p9sS2ZOMInTPIXOUE0cGI7RE4oDfiwU17zQbpcjwPheYS47RI/lucoB3f70AyE7E+xD/tQxmuSlOqDXq9Xru8tQwRlZONKNpIbl79jf1SOMRf4FMvPK0ivZbPMaJPum0GhY55JSif1th3bGT+cb5EXE7gP1+K7BqB8Jsdj+kG/HMOQK8m8bYXzpU5Ozp7clJOrDJ4zGHdlD59B1+wHScgyRrGMPE7rrQkt4i0VAn4Kn23S10YPAfyuSEJW3IO47SpGE9K8ZswESbC2tlZIgExw0Ies37YnfL+xGX6QvhEXvPDi6geIDioLcqI1SY9olod1kc852Z9kI/7fOuIE515aXnyiOm1ubq5UwGBbzAGLjugWOlur1coT5IyDHR9IMDhbjPNs2EZpMqDVasVoNCrbqCLOzlvb3d2NWq1WKqdYqPE8QUKBA8A0PsOl3+/H7u5uOZPpypUrsbq6WrYw8Xnr097eXmxubsbNmzfLVrV6vV7IkOXl5bKlCWIMUo82GAzK2XMPPfRQ6eP8/HycP3++xDW25kPCMT+Li4vR7XYrdtXpdMq2VHSD7X0myxjL6upqkROy8tZ2Q/uWMQABAABJREFU8ouIKMQT88aY+v1+ISVsWzMzM+VBMtlX7+/vx5NPPhkbGxvx0EMPFbIMPwMRXa/XY2trq/gF+8+I6hElxEaIJyoiZ2ZmSkKPrlDZCTHH+4wXX8i2zlarVSrCGB/xGX9E/PfvTDgY09yPBSLHj9nZ2fit3/qt+KEf+qH4oR/6ofiar/ma+Jf/8l9WfCT9MnbDbuknB+bnfCfH0/xAIPDHYDAoxLZjXM43vajqPND40vGWlnFbJlmcN3MP5sG7KdAdEyMmKYztvcjs2J9zbfplvaTP2f8xFzmHnIQHvL3Z5L/xgTE7WMS5rjGsY7exJ3kKi3hZxjmu0SdfHxk6d52UI7mB/xkP9zR+GI1GZReHMQY5A/6J63t+uAb5bs4H2HpsLOm45j7cTbtrUgpw44SRcmAbEIHDE2LyyaSSGX4m1aueGQSZwPJrNiT/zW+XmzO5lNGa0HCSmgG9lTIn0P6bsXI/s5P5OwbClomNOwP6rBQu4ycwRFSrWbivkwDf12NH5twDBeMzWd4ZGJqQMIllA89JtkkhHDxBmutaR+7k6N2fTDggSxsTr9Hnw8PDImv3zeWhXJc+IZ/T09PyFBjAS7vdju3t7YiIEpgpq3bSQQLDfe7XShCJArKp1+vx4Q9/OFZWVsrcz8/Px/d///fH4eFh/Ok//acLIJmUtFhP+v1+tNvtIgtIX4AHc+h5R2Zs0cnzzzy5GSgzDhNCnuusFw4+6JNLcU380OdJCShg04fUQ5IBNAHeWX/syJEj3+daeQuJx5RBCX8jJyd99NMgNqJaJu3HEOdrGdT42lyPPpDI5+86acxzg1+ZBEiyT/d9afmMqYODg8q2KKq8kEEmC514m3CCqGcuAcqAMMbz6SCk3NDLO/3v1yxD61pO5LPPp5Fw5gonbIi4RixCXvYPfM/EMffKPtl9rtXOkkm2CVM9hE958MEHo9FolKcuOSY5STUGsY4hB4/NYDZX2JHQR4yrx+y/3H8qknyYKvqDfTtOjEZnlQhsSfS1bBOOefQT/+VqZEhXNz+EZn19vRAlNNsJOu4KL2RpnbmT3lin0DsTbByJgJ0BgL1FgfftxzJeejn7s9+b1E/3l5axwb00xhMxjo8k+WBlCDqq2NHf0Wi8ik+MgxyAcHIMoO/Yog/25hH22CRypOrQ2/jAWt1ut5y1ZNlnnIUN5QVj7Bz8s7W1VQgl7kWOsLGxEVevXo1r166VJMhxrF6vlyqqiLMq7eXl5ZiZGT/0AnmxAHtychI3btyIpaWlaLVa0e/3Y25uLh555JHiV5rNZmxvbxe7GAwGJZ/BTtvtdrTb7Wg0GiXGoHvG6ZBQxPZ+vx+bm5uVh9mAgfIZa1RHQlRCihmr5hgK+WOiA/9w/fr16Pf78dhjj5UHz5gYoyLUmBMMZPIHApHthVS5sRDks8i2t7ej3W4XInQ4HJan/6Fz+Bdk0Gg0YmlpqciIa9l32B/ZN4L/GLPfv9cGWRwx9u3//t//+/jkJz8ZP/VTPxV/8k/+yfiVX/mV2zBcxpfoGaQAupnxnWM4ccgygKw2cZBzUuwev2IMZf+QX3NctD/Nv7kX93beTB+JD8Z//oxxp2O1sYH9C1jCfaeZLJqEN8GjHgPXcd+wQWMBxsDf5D3Yka/Fb+PJTITZ9njdmMdj9hxOwiwmPrPeeHzgM+ujZY1cMq4nPuGL2E5LZaT7m2MyOuX+YPM+U8o68kpi7F2TUkwkwCjidhKHiTDrbzaXQWSAzOdMSDExed+lwayF4mbFdkJE30ajUdmylwOxDY7f/uGaOJ9MblhZI8aHP1tBXanh3zY0A0MrNYElA/Dcb28/M6nkAOBHS9vZsMLFWLmvyTQrK7LIc2yG3dfz34zf42Des6PMxp3f497ZEdshuQ9OpMzs+ruMw+9ZzlzDoGFjY6M8khid5jD45eXlygoy4+GeedvgvTSDmIiz1bo/9sf+WHze531eqZI7f/58/C//y/9SAB7gjz4gO1cicW3K8f00NAAv+uUEM1fweJ7RazvgnKjZfk3a5kBmAsLBldW/nGgZ9BB4ba+AKQdR+lyr1Qr5Zz1GN6wn/pvx2cHbv9rPuSrDfigiShLs/niF0faeQYnJOuTEvZvNZlmp5TquZiKJcYVCTlKz783z5Tmw33R/nYAxPs7N8P2xQW9Ztn3hu3ifYIz+kiQBLJDbJJ/zWrcMyl7qM9nv57jgho3RADRZ5hGTz3/MsYkk2kkbffN8GnDl/ts/87Qm/GbEWWL60EMPxdWrV8uZPJOSOMdV/s9+xWA5x4hJQNo6RAxDvl4RJLFtNBoVUt6rvrZLJ/KeO+RuGZP44rsss4yl5ufnY3FxsZzjMxgMSn/43PHxcRwcHES/3y/XcVKA7Oxv6aOJZvqHD/HBvhBhjn2DwbhiLMt2Ujzn76xTjN+fNbFs+Uxq9k1ZF+9HYzyuvCQuMG629kRUHxyTCS0wREQUItOH7XJt/sdXQ0JaTt6iZTyeid6MQdEL5nLS6v/i4mIhL/r9fuzt7ZXqnaWlpRiNRrG9vR3PPPNMPP/882UxGILk6OioYAj7ll6vV85fqtfrpXoMIoGDhPf29uLw8DDOnz8fnU6nJFnohM8LhBQ8ODgoB6LTD/pcq50tutEGg/E2zIgo1U0LCwuxt7dXFoJ9T2NG8ANzsLe3V6o/kbFtjbmDuPEOB+cBg8HZtsenn346arVarK2tlfPLwGyj0aiyZde6Y/9Nf/lxZXKtNt4eT9IJocPDYjJxDTFPHzIZhg4hX37QC8ep7OtIgO+1ffjDHy5bXJHPb//2b8d73vOe+K7v+q44d+5cJU65P8xDPp/z5OSkHGfB91wgYJ/L+/aB+ANsNuPVTC4ZB2S8w2tZjvbv9IH3nD96DvjbeulY6/zJOatl4DHQB+dbjpssCPk999FY1Z+lP3zPGIcc1w88AcvS3H++6+IKmscSMdZhciAT7nyez0XcfqSB5ZDP7M1/m9RzQQc/7lvGvvgSfCfFOVzbZDWypW+MiUpPbByfAKfiXDvL9W7aKyKlzMA5kclJR1Y+BzMSB4NYE1gGUBkMG6D4x4HUk+ekiAkn4PgJGBZWrqaJqAKynMg52cPIIChIhp045QoIyxCHzHdpdjj+bDZogokTWMuFz7EahQLxXQIIyagTejsgOwjG6yeA5TnI82aHZmeEI+a6XkmwEfMev5H7JJ3IhhkxDjAmFinx5rqZeGA+MFyXTJJ8MC/N5tnjXkmihsOz8wDm5uZieXm5PJUEZ0AfAWf06X41O4jBYFAenwxY5DPdbjdWVlYiIsoB6CYocVaAQpwT54nV6/WyKsucAcZwdgbfBOZszwQQb63NpIptyXNvYO2AlAN8tm3ICJM+zDGr+/V6vWwBpt85AOf+OMjZpwFgHOxzwmCby0k/38EfcC37D0Cegww6YJl6fgna1kGv2BggAbgZu23F13X/nYyRhDim2K5yksQ4ABJUF+AHXM1Xr4+fMuVEkXtbNl6ccGUH1+b7v1ttkk5FvPwTJ/0/gAmb8Pu1Wq1si+QAZFomnJykmqhxP7Ku5IUpJ1bWkcFgULbptFqt8n1IqqtXr8bBwcFtMR1c4oUBXneMyzKLGCf16D4xhz6iKyae8vYzbzXBDtEhbDGTYNYp+wf6ZNlSkWACzfGIrT5smwJsZoB9eHhYEnTHVZp9ot9j3Nl/02dIFnw2Pt8JBn0w0TupmRScBGStizmhyzqf8YdlfT/bwcFB0TlXAA+Hw1JBu7CwEL1erywEmoRhvlilBqPV6/VCMNpHOuagr2zli4gKto2Ikij54Q0Zw2WC37Eqk9nYM5Uda2trZUyc77S3txetVqsQS5ubm6WiaFJSxaIVMuCsIu4BZkVutVqtbK2bm5uLvb29uHnzZun73t5e8QlgLWwTfHd4eFgWKKhO43w7qrEWFxeL3MDyxAuqzNBttu5gkzMzM4UgIu/Z2tqKvb29Cn6fpI/4E1cW0ZAfP5ubmxFx5ocuX74cc3Nz5RyxZrMZa2trUavVytM2sQOwPdXjYE/GPRqNyvZJ5oGqIOIp2/fs1/CPzAXy4lB798FkhseNXOxXwfqO9a+21Wq1+MhHPlL+dtx45pln4s/+2T9b/ndu5H7zXfwVfQM75xzB9sR8Op4i71arVeZmEimVCSGuk33lnfJjf87j4LPZ/n0/5tdkuPFSvr7jrcfP3JpMct7P/7nfmfTKeeIkf+/7gQt8fWTv/jum8L8J3Un94Nq2zRyXnLPwGV/TPjlijL29YGFbQ5685hhhXI5+OgZ4oWhS3owfc2W1F9JMyvK+x+z/J8XyO7W7JqVMNmEwdN5g0wmnJ8LKxvX4vllZA1qE5GtloUVUgYqVstFolNWQiDGQxskakOUkzasX/Pbq8aTAyv1Ho1EJcsgKMImB50myUuUxWrGHw/EWxwx0GfNoNCrABEMEiPKek0onKxA0ZqlnZmYKADBwxWlCXpgA4jfj8LzamXEPy4Xf6ITLgXMQyHPI9fy/G/2CRCPo0C/AtR0y1/I88Hkz074nOtLr9WJ/f78Y9N7eXiwvL8e5c+fi6aefLivWfNfln3drxHdqdtpuODfGG3HGgv+bf/Nv4pu+6ZtuC7jMFcHZJIj/r9fr5QlpVM45keIaEVG2/nGfbNMELOYCPb5T8u336D99YJ59L+wdQIp+IjM/AIEVWuYUMIscSYZzaTOfsS04weOz6LYDFvOXyW8SO963bwHoch8TK5ZVrTZecUfX7OPoE3LJMva9DTLcLwdN7pFXdXJSbl+R9TUnq/bVmfhgLgx4vTUX3+g+R4zPdaDPGTB/ulu+752SGOuz9cfJ5CQ9MrERUT3MFDLaD7/wii8+Gf9h+7Ieo8P8dpxH9lyPMfT7/Wi1WpUVzVarFQ8++GDcuHHjtrNJWAxhvI4rXp33Flu+GxHldScLJN0GunwGmVgHsWfbuSsF+E2fTMr57xyPRqNReQoq84cMqWZcWFiITqdT+sb84/dJlHx+zyQfyrhtj/wN8ZRjIJ/3PXx//ubR734QhG0L/ZoEYO1z3FfHFY8n9/2l2v2wbfSE1WIwEfPuLdW1Wq18ptVqxdHRUVlpdkKB/hweHsbS0lKlgtWVItmG/YAcGjiF60eMdz5EjH2iV/HtU7FtY/2IKEQo1SYRZ/O8tLQU9Xo9Njc34/nnn4+IszNpb926VakacvUxxMbMzEzlQHVwAHbNgh66iG4S6znfhLH6abjIBWLZ18X280MQmFu2N2ccik/jwSE+/Bx8hE85ODgo25Bp9i2M1+SYYyTX5H/6jM/c3NyMubm5OH/+fLG3wWBQ5mcwGBT7xMejL/jY1dXVWF1djbm5udjY2ChnpVrPfEYPf6MnrtbI26A4OB79z9VOxv5gKfIR7y4w0fFqm32DfYBjB3rh7/CTfRVVI+Am/FKukHKSnvGe8w0WBbNPwzbRDdsw/X45/2dfmbGayaYc+/g8sdt5b76+P+uFrYwh6bMXLPL38/e8yOGYy3f43/qF7/KZh1kfIqqHeuecwZ9Dviay8i4OL3bynUm7GJzTmlCy33WfTEIhH2SC7pjUzgQb+uLzHXnPMcL38Lw63rHI5QV9L+ZOiucv1+6alHJ1kA8Md9Dic54ABmpl8comf1tYNhInHTawPMjMNiJc+s09TLg4yfG9rQz8+P4GkTZixudJxBBwqg7qTtidMNgIMX6DfMvUMsEhmnDLZAD9zFsHDOx8fYDWYDAufTbJx+qYV/kwCK8Q0ezkkBsBKhOSdsgYR66gcSJrw8uOhXl1ubYTGK5Dfy0vG5UN10Zqh2m5R0RZTdre3o7t7e1YXFyMK1euxIMPPhjPPvtsKf+mEehfDlC/XLNeuwEsXLZar9fLAaEOtMjIQRD9woZNsPC3yRP3wY4csOzHlpu4oJ/ZLhhDTozsE7BZwJ1tmesyzwYY9LXb7VYqCjudTiUJd2C27pHg2payj5ikn4wpYrylw0DE+pVXhnIVh32t9YC+YAN3Sv7og+2PYO55NOlmMsjJfAYu9lH2MSYSmR/kY51jbNlXZEBj+TNXXMfbie1n0YNM/N2PxPWVtLtNpjNQcWNebCeeUwN/V0/x1Bi+T1LnbZwQoNyXJNP2mAGtddKxOYMxEs9er1fOt4PwPnfuXNTrZ2fTQPjkra62MeI09mfwhz/j82AaSKw8PusZuMUE26TFKt7LeIezHCAl6vVxJYTl4JVdFn1cGTMzMxPLy8uVrUJZ3mxTJcbYD9pvgoucGHq+8ae2MxPzJnszcLcuco1JcnKFRcYh9jtuk5IZ24D/zt+9X3btJ51mLNvtdotuQFTRbxIZFjCRkxNiHhc/Pz9ftlA5AYoYxzqu02q1ytlGfp/KHa++O0EmkSKeYBfG8ch0fn4+lpaWYmlpKSLGiQpVtv1+P/r9fpyenm3De+aZZ2J/f7/oLfLJC4EsBNlvIVdITSdcVDDxBEF0i/M92+12BUPbr9dqtbLVkT6ZhD88PKw8nAUdpFoLuXS73VLZhbw5INw4huol67rjuXXIeYSrhLyTgAW1ZrMZvV6vsvjGVkYW4BcWFmJlZaUsytdqtfKEPeZgdXW1nP3E0xB9IDf+xAQUPhDSLmJc9YMeIZPh8OxpdzxsBTmgP7b34+PjmJubK3K174YAutf2UvafK3hyUk8/iX3MCbjy+Pi4VB1n3eM66D/vsV0XmTnvIx7wOtewv89kRq6AyfmnsaXnyTHcC8vMkXNK76Lx7hLPJfcyke481jL3e9zLmIU+cU3rYsa7jAeZeBHePpZreFviJIzuXNIxjPhOcyVWzn19jaxbOYaZZzAB6VyA97yQYGwCfkC+4F+qc010Z/wcMX5y8CSClu+b4OJz6ID/vtt4e9ekFImJFRAHmgGeiSoLB0FzGKeTxQx8zAg7icqJjAGoEyArL/3gPIW8Sp+dBWMFqE4ibLyNh8+4f4CEbGTIgrGaxMgMLgGa79DPSYknTiInXVZYwC1bNcxgW1aecyfxyJnxs9rHGP0dz4UdM3LwfGUA4iTW13WpYDZQz4/7Yedi0OQE16De5eMG99YXJ7oO2NYV7sf4Se46nU7cunUrut1uPProo7G8vFwAnPXLMrjXllcFAKa1Wi3+wB/4A2Xcn/d5n1cBSARcQCwVQZRtO1g6QAIUs73iP9Bl7BqgarnxOW/9m5Q0WQf429/1weYG3jhUz2lElD3WfsIb2xP5vokSxmB7s2/03nKPDx+D38t6a/3OwcCVJx5vJlLpD4DWlSkmEvkOdoHMIC55nfmzP8x9xtbtn/ksc5hXSD0+95Hr49fcJiWv2X8CpLinfaPl5uTBtp0T509ny6AuN8/BpPmwn+I1X8/JcUQVABMj2TaGLLBFPs91kG9eEbXNZdCcAZr16vT07ADmdrtd/AJj4TBnznxxFbX9FiDYK4Em1l2BYYDs1c6I6vZsvoNNgE14opdjCos42MPCwkLMz8+XqgPLAJLB/azX68XHEreIu41Go8jG4+V/nx3howqMy7wYmAkh60e2ceTneUHWlqcTIMvFiRD3Qces+9Zj63wG8SZI3XIMzuO5n21S/DHZmgm1er1eCCpwBq8b/PM6DwHg6XFsparX65WnoLGAyBPUsu/CRzphsM4ZVzuee1cEcWJ5eblSgVOv12N5eTnm5ubixo0bZZ4gbPb29mJjY6OQZpAOxGjOL8LvuPIE7OetkeDrweDsOILl5eU4ODio6OXCwkL0+/2iWyajeW92djZ2dnbKk/YcQ0wi+5rIwpWWPtzaifrp6Wk5w41zXKwbzDvNC6b8TxIZEZWtt8Sz0ejs6YokpGCS8+fPR6PRKAeqr66uxmAwiBs3bpTdD/yGwIuI2NzcLNuonaO4cpwcZHZ2ttzbZ85AvkGaOU5BjoLFkJfjvMeHneMn8nk9n46WYxivGaOYSEae+YzLiLjN/5lsgpRC7x2ziEX2oTnOT+qb45txgP0MccgFHNZxCFBjC8ZtTG7fkXEJn3c/HVMYm0km38/XcTyi4AOMzr3zZ7lvLoBwnmoskrGSYx3Y0v32mHyUgGXkPMEYz/OT58zch/XR+mBdyucHZjmQR5NXTboOfeR7XrAFL+CLvMDvivdMTt2tzb4iUgoB4WQYiB0ovx1o+c3rPrQzg1iDJwvek+EVOvrmftIH9y87cQNkBM7YvPUos7BMFhOWq5Lorx+d6W0wBgJeabBSZufLhLofGVBn5buTA0UWdnwoJoDbgQi5ECyRIxUorhxxP3CoEVGeeJL7w/do1hvL2kHYyaP1y9e0vhpAQIAgP+ufDXZubq5idJ7z7My98g6AmQRCTUIuLi7Gzs5OPP/88/HQQw+VPpNo3a+Ai5wMsr7hG74hVldXC2h5//vfH8PhMH7t136tjJvzjiAhSEZNHmdixkHChITt22cWRJwBYYNn6wIyt03V69VtCYxxkszQV/pgkJi3Kjh4AJZICLkWc+3y21ptvL0pB2XrlfXaAAAZ0H/mi/exGT6X7ceEdSbq8B+c+ZXJHle/MFesdmDnAEzAKwmE/b/7hKzQEyeanifLIoMWmuVhvfBcWuYGHh47Y+K8D+YF2eZE14A3+9DfzZb7YkLARCiftU/PCzoGRiZwbMcR41UyAxFXJxmkQWYCxiKqT1nlf+6b475JUsbEGVeALOaPhNiHSTspwFdZNyyrRqNROTja5JV1k776xyui2YYz0WvCGqKWJGxhYaEk5oeHh2WxiPkyuYN/IX55S4hjD/ERUopEwpjKuMbA1/PmhAD55gWknHS4P/6eY7UfGMFnvbDDZ41zmK+8AGQcxGpvTsR870kY8X43j6vX65XKcnAFmIgfKlKII8y7MeJoNCoLJA899FAhpkzIc090jRjmKh9kT3+Mq5kvJ3YZM6Hbi4uL0el0YnFxseITlpaWotPpxM7OTtHrXq9XjidgKyLnmnU6neh0OrG8vFyZDzCXCYrRaHzkBBjdCbLPjcuksWMC1WZOCIlpXBeM4TjNvDEu2vz8fHS73QrW4fvYIedZ9Xq9ygNRkLvjMr4V/wrhyPY1ExQRUVlYq9Vq5YEIJg8feOCBqNfPtlLW6/VCTJ2ejp+6B4FyfHwcm5ublS24yDYfsE8FFT7YxyB4QTIXBkCoe5sxCTKfwXeYlDw8PKw8dj5jxde6OfZOWqwynowYLwxAcmJP/o63KObYiW2RRxoXZnLCMTuTHDR/ZhKeeSnMSg7hWIo/xg7puwmmiOoWOMdOXnMebzyZdY3PUCHHZ/h8Js6Rr2Vi+buPyBq7zfPquImvJI4hJy+OZVxg/J3nwvOR8Z0/k+VG35AbdmJij5iAnOgzdoQ8GJvn1xyHMRr5E4sjXvTFFzgmvRr8/IrPlMrMogmMnIRauAjSK++TgKmTLwvVk8C9M9NrIovA7/5moTvg0Df318riFSQ7JQcYGysK6hUoggr9MMGSq00YBw0ARx8nOR//tiLwecpKFxYW4uTkJHZ2dm5bObOxW9You7c3wLhm0pB7orAGWzmxnpSs+zMGFXbMHu9LOVkDrIhqlVkGuU7OkJEBj+eSPqK3vp/1xX2cmZmJTqdTKm8oT6ZM3Cti96M5wEWcPSL4537u5ypJ/XA4jH/xL/5F/ORP/mQBNBy6is7kwAjgyjbnZNBzZCCOLszPz08MpJYr98psvku9cxLC//ZFlkF+kp51ixJ0V865OojrWpdzQuYEKSeYOYHLYycAersQPpA+2Qda9g7weeUC/bVfcxCyfyToI2v0kbEBjg0wvFJkn2pfnv2mk0bLD5l4Tu0LTBB4Vcx+FVk6OSd5YpHA4zIocf/spz6dLeusm/uUyTrshM+gT/6ev4uu5hjA/8xhjoXIN2K8hZuFGANrj8MxLvc72wet1+vF/Px8XLhwIer1etnKwaHOHIBMMnR0dFTZpub+ZmLDic3c3FyJr15YyLpFkohfwt4ajUZlq5TtEjmQkPpcGttFTuq4N2cfcq6d44/vNxgMSuIGcHScM1j1/KIj9qv+rEla4ih+yD6Hg9VNBuKLIqLylB5XPVjvrKc5dlpfsh1k33one7L+2w9P+s4rae4TGI1tTsiH6h/sg2pdY1zsF4IRcgSZR5zFcMcWtq05Vnl7NPgH+8M+jLfsO+mLSR3kw4NaWq1WuUaj0YhOpxMRZxU2bCHkEHD0zL56MDh7apz1nTE6+aQv9Xq99NvJn7fK56dMNhqNUtXTbDYrjypHfoyL/vNd75Cw/nJN+0TrEt+zf3QyZ7+Bz7EcISbsnzIBy/XA3r4+WwSR061bt6LVasXS0lKcnp6WLY2XL18uh5VDsrFlGvKMe9ufQ3IiG8dS+waqg5wo5/gyOzsb+/v7RSbIFh2xDnMP4wHjoNe6GQvwv+XgBR3GMhgMotvtRqfTKdjDJAKknG2T3Ir8mCeYemsvn8VHGP9lvGJ8l+OhP5dzsJzHRIyxJrHF1bij0agUKWRfjF34TOKX87sujmC86IAf4pDJ81xIknNF7mm84UVqk55eXHXuERGVwgPwekRUchrew35MwjpHYSyT/MkkfXPzd5BDXhTMODtivFhGrPcCWP6sFzLQD3wNcT8TxdleXmoMk9orIqW8iuIAxvv+bWMzYHaCZeH7Wha6FcIOKSs4Azdxxr3y6qD3Pvr6VggnrE70mHw7Ce5tMgfgAQGRE1EHX0q57RwsQ5rPsqCfkxIXJ8z8dvAC6PJ9KiZMyiEns8muJIkYP3qY1eysjCSEnhNk4znO5IIDGI7ec27jmXQ9Gk4BPfGqVJZ3XqEkGDjJASC6Os59528nQDb+ev2sbP/g4CB2dnbKIZLIcmFhoQCw+7kSxLhPTk7iH/yDfxDLy8sxOzsbX/d1Xxd7e3vx1/7aXyv6gHw97/mR5HmuTBDUarUyDjtLvjMzMxPtdrs86poAYJ31/AGks+PmfQdeZOZVT/uUO8nVwcnJATbM9gjub103ocLr6AlA1gSAic1JSTL9nZ+fL3qEnGwTyJ05IrEFVOZ54v9MXjhIY98R48osk5HoEHIFaEMQuPKK+3qVZhJRa/vPc4ideUXMzUDBJBS+yj6CMVtugAUnK+6Xk+V7TVxfScv+LNtF9pcGaZZT/tyk3yYHnPQgDxMrLs0meWNbEaDTfoP/SZQdw/kxQPQ4AYRsJXRyTUP38FH21TmJMJHj2GmiyWPj8ybAXRZPUpkTR1peKebatk2IKJ8zw3VZvGN7vEEy+Il+n5ycRLfbrWwNyqA865RtLfuyHAdsG8RTJ1/0PxNMXJstTLm0/046Oelv+zpjhDvF/kn/c62M9e618RCFer0eCwsLlUSGJBPfimxdOYP+UN3pc2WazWaJBQcHB6VKqdvtVsZEfLY+GsMRk5xQZTmRQBKn/Hqz2SyEFOOCpBoOh3H16tWIOHuASb/fj729vYJtB4NBIayt/xySOxwO46GHHor5+fnSb5JcEnTec5VUs9ksTz70g32c2GN/bGtE3jn+uhKKqkWq0iB/8CMsWPpga/tEcBIV56504n6THrqwuLhYIcTBH/Y92HvGRDTIJyrArl27FnNzc7GyslI+u7S0FMvLywV/7u/vl+MjnJRzfTACzQvRrriAfImIyhzZ5vD3VKhCSkL+IU/kQP7FtUwmfjrbJF9lghP8iP5RJUeMzD4ZmRr7ODZ6IcRHW9iWTfy5cZ1J+Rbv2/ebcHAubLIaMgJ78GKn9cR2kklvdAYf4346BtEv+m3SxBVZ+CkWqcxN2LfnWEyDXMu7p/L8RlQrpsG9li0+whwJMs7b7I3jswwmYbaM9/OckiN4Qd2kGd/34oWrEPNnsUXugU3ix9Bv7mN7tawsy6yDL9VeESnl30yKDQsB2gFhfE5ouQZK7LJUg1VXFmGYnlgaRur/aSZETJDheHMilb/PZPz/qDuTHsnSLC0fM/PJZnP3iMjIzKpqdZd6A7uWUC9YsAEkFg0L/gASO4T4DbBHQmKDkJD4A6xpiU1LbBqWtNRAj1R25RgRPtjokw0sTM93n3vielZExlBZV3K5u9kdvuEM73nP+b6by0szcYKRgD12xsWsfp4c7kVfXV3h8xEmj71JGjtYzs8Czng7ALaBzFUdfr4Np/uCcWJ+vOad+zh7YjmwDGFgmV/6h9DbyObDz7IhMxmZg6HH5MTnWMkduGYDbAftNmUmHKdNEI9xd2bVBuN9HfR3uVzGv/gX/yJ+9rOflTfk/P2///fj7/7dvxv/43/8jxohA6Bkf4BMrHpMPf+WIwdt9I23ReWS5ixz2ehn52tjm50yuhgRNdtBO61r3NuVM57jrK/IfHZ8Tc9kDDJhgiw48LN+0j8DFC9b5P/8OZ9lp5PngvtiC7gHziW3jcwmoMUbqjowRa5zIGsbnJ1VJn085p4HL+XwPfKcZWKS6hrAPO1xZZQDONrpcf3Yh+2Z+8pYGNBk+ck/mYBkbGznGFP0wc+metBzgg2DXPbLBGhnBoj+O6JetfyY3wLgT6fTGAwGr8kxB3MNcWwAZhmkbfb1jAUVHrTLgN8VY9yDIJG2QNYQ3NomufIRWWZcW61qCTDjyv5AvV6vhiGYJypXIyp8QqWSfWcOKN1+Bxz+3GPF/bBlzvpCANIX7oOvpM0Qi05mOVDJZJNlw+eaHLSOWC+sP02fcTgQex+HsadtrmXQFVLIAG9xzeAfnIBcsY8RlSyj0ahUBfIsKl7wTa5ox0c7KWeSwdjHy79oF+ToZDKJTqdTbP3p6Wnsdrv4+uuv4+7uLrrdblmih11yxQvkgysmqfBbLpdxenoa5+fnhbRrtVpl3yrHEovFopBci8WiyD4EVdZ9B18OkD0mDm75jKWHjkU8pjzLY2nsA3lDH8DAjLntCnpPRYVjjE6nU0j3jF+xrdhb7r9areL6+jp2u130er34yU9+EpPJpIZNdrv9nl9+sx0EosfOdsp4gXHEX9p2m8BnnLgmB9cm9viba7gv19oOfsyjyV/yOb+NL9xOiLuM/ZBpk6TEeDwHTAaJ6TjcSfOIum/LNtC4z9/zd46THH8Q42Hr8/PACcy7MYp1gedROZr3Ycz43hjPPpbPvRLK8QDn24/lSkX8Js92lZ4TyY7hsGvg2BzveKmrsQg67PGKqL8gBf1w3NiEl5twkmOsiAorMA6tVqv4C9pi8rAJIzrmNVHM22JNULlyKmNz68ibHm9MSnnTzYgoTjYHURbwx9hQs69uNIA3A2wmi2s8sW6DD9+f5+Fc+Yw2mnRyAMSEuEzSk2Vl4Hz31Q4wor6HlLP8Vgi3PQdgeSwycOO+VhqDNwsuAJjMFe3LxtLjyT3cTsoe7Yhs0Ay6bOwcJPiwcclEm8+3oHu8DKQz+9xkmD2egAQbNjZy5PD1vjYTAP4ch+ugYjQalf0kuC9g9Pz8vOy19L6P9Xpd9gz4t//238Yf/MEfxL/8l/8y/uiP/qjoBoEV4NM6Z8PncbPzRWcc/Lbb7fI2raxP2YE06X0eb861I/X3GHvbI8gUB2TWUWwcesp9XUlh0Ol2cD6gotVqFZAZUe2rZrvF3xmk5CCRw+AMeYIEzg6LJQuWVztagIQJLuYV5+uqKUA7v9mLwu0BbPT7/RrZGFHPdtk+PAZKfNhRZqdnUA7YMIDiGv73GDvbw/h5fmy33taxvsvxJs+yb8sBBAfya7tof+nPOJ+5hCRhbMm89/v96Ha70e12a3uPWeeMAzjsL7EXbpf9hOV/t9vFdDqN9XpdiCm3FR/R7XZry/Dy3mCcl0lwCIX8bAPDiChVHoyFdT2i8rsE3raT3N/YBrvH94BY/DIA0nvn0A6ed3NzU8CiSTK+d4KnKbDLGC2Ph6uMvQk0vmq73daWoTlA4Vm0MeMY4yOel/Waz403+J3BeT48x03HY7jxhxwZDyHfyErOkGOP3D/G1PsTcj3tvL+/j/l8HuPxuLzZi/m0HUefaAvtcjLF8sC822c62OEte9zr4OCgVN78zd/8TUyn01Jd5AoKnmmZ5G+SF1ROm4wj8EHWuR9Y5ODgoBA9w+GwbH3gAJCXsUD8RUTZY9AyQZ/Ru3a7XSN4XcWD/eI+VG/RH+53dHRUKuHcf8bTiVxsKNgD/4o8sc/n4eFheS5zhgx7NQhjtV6vYzabRUTEq1evYjwex+effx79fr+8CbrV2pOXs9nsNbLICS4HsZkoMFbMRCb2i75n20N7Xa2SsUgmsJDzx/T6Qx32o/ZvGX9kzICN9IuB7LtzkQPXRtT3MvX/HFT+ZUIwxzzGvBknOlY3RqMNyJsJFFfSOK4BLzqB7XGgPV7KxjigtxGVHXXc6sPxbyavkH9fY/vIWPqtko7pPd+MOde5GtN20vPi5W4mq7kX+p9JTcfVzIvlwT7PnALtgeQzkcR97aP5jHvlBKXP8dY1jCMviyMRxtiY+M868LbY+a1IKU+KSQX+ZnLMYGYBYpK9LpuJZTAc9DGRHE2CmBWR+ziYhNE7ONi/Mc4DzZHZQRuQPHEG7xH14IC+uHQaYWwi7ADNvrbpeQa2nGOHb6PXJNSwzYwT+xw4W+yKDy/hwcnkqi8EFTDKnDvbZ2PoPjEOWRmsfNmJcb4rGTJp0ZTZtpza+Nhg00+PNcDGWcb8PBtdAJGBg8/nvrwOnPE2QfD7v//78Y//8T+ulei/y5GNAgZnOp3Gf/2v/zX+3t/7e/EP/+E/jD/+4z8uwSjtJ4tP2wBwAE7vN8WcGqTzQ4UUhsuywTm5OiwHq7kvHlvbBQM1A2zrgf826ELmLAfZ3hhQ2UHYDpr4sg1AZ+g7nznAt23NgWyWpYiK1ELmAQ0AfO6PXkRUm6TST+5N+yBxsh3iM0g27ss8MTZU2TH2jIuz5Nb7PO/2A03yZDvvMaHEnKyOdb9p6WRERcYbDGVZ/HUcOYj3Z/7OY2mZ830MDrIc+RwnNfAXJycncXJyEt1uN4bDYQlULNPMfU5C0EbbfoPtTDj4fA4A79HRUQkcPY+5LXmuPTbgD/oH6UMQn/WU8URHbFMMKLmGpVz2Ye67P4vY6+hgMChBtm2TMQhtJ0i2D/S427Y0yZLlAxkhcHLfbZezrYQUYLmws6bGMx7D/GOZNMbKY5cBvMeRv7PsZHni3Ca5eh/BrasHsK/YYn4gNNvtdm1jZ8bHWWzssrEa4B+7jR6y6bmXx9J/216Poe2fA1K3d7fbk5KTySQmk0lE7G1rt9uN8Xgcm80mfvnLX8b19XWtwisHiCaD+/1+bDabUhXUbrcLGXt3dxcvX76MiKj5XhNztB0dgNRpt9vx5MmTUjlCMogKIwjciKpaibkAv3Mv8B74FT1bLBaFgMnLZO7u7koyhnayNG65XJb5dDLOgS4Vocgz/hsZaLfbhXzjBQ0O7u0D+Q7dWa1WtWooVzbY51sPTPDtdtVG+x6r3a5a+u799JwMoD3oA/1qtaqNy90m+1/74KzvTf7rYx3ZJ2fb46pY40n2aTXu5TDeY/74beIOncFeY4sdJz3mR40NfJ7xL4dJchctgEddzWcbjEyYkLEfoV+0l3Ozf7bdwleZqHcbM3733Hj8Iyp7xDzyHOPwzEOYlGrC3pyb/fvt7W0hmHNM5Bg/Y1v7OJ6X9SkTbiSJwcsmp2lLnkfHQzlGYpy4D7JFFSwksvuQ8bLHP8efv+p4Y1LKpWx0AAEyAM5srjvdBGTpgAe76Z5NRJXvkckjK6QDKgTME2uQjHMwaeJspI2/z3sM9AA+cLQmqjw+vqcdk4FbFh4TKtwDAQJkG6S2WlVGhjHMwQuBq0kYGxOPtQE7jo5n8ExX2NkJ2rlkoaVvniP+9zhYcXP7LBeWvVzJZedg2eI6iCkTlXkMPI82stzLckggD7N9cnISBwcHsVgsiiO4vr6OFy9elE0r3/VoClDQh3//7/99PDw8xL/6V/8qjo+P44/+6I8K6MLJeImrdZj5t+HNwW27vX9jT6/Xe82B2XB6v4AMsjgcOGaDzXU4An+fDWQ2lK1WVTXjZSl2lpBGgIAsizboyIYBSkQVqBiAWUZsB03kIK8mCzOpZafOHEEeOMiJiNfsAVUgzDf3pU3+28Q948I+HIwJusLyI8+XiTMDWPfLYCPPVwYGtpeMPdeyRNbtdym6+2T7bRm1jH2Mw23g/6bn5+Cec/M9MimSSbx8P4LqTIyyN0ZE3SeZiHW2NSJq44ocm0jJoNZZO85ptVrl2ciL28jz+SyTnci/A1HbKPoIvjBg5Bn+22SOx8HEZvaf2dY4IZcJPnTcCSFnem9vb0uAaExkmW0Cu7ZRnl/GzGOY5T73m/Z6qZh1y4GQQSvnWG89jnnuuS6TyBk8N+FN6wOH8YI/e5+HZdRYjAodAj0HggSdzD121ZW9EAA3Nzcxn8+j3+8XAoUA1ePOvLChtfvJuLk6ysEeejAYDGIymZTl9t1uN54+fRoPDw/x5ZdfluVhYFXrGs+JiLJRP5XS9ksmWLHVP//5z2srMVwxxR5yxoPtdrsQQhFR9uTiWss3CcPBYFDGHRm6v78vCWtIH5bNQSpMJpMyNxCCjCOE+Ww2i9VqVca+0+mUfTYhlYzXrScmIkwugRWOj4/LkmkIMUidiKj5YfSZsWSpo20Ue0Fm8hgZgWR3xRbtQ39oG3PZarXKdSbRbS+pzkJ2GAv74xwfZiz3sY/8fP8QHyLLGZflZavouLFJ9s8eC65l7P3Cmcfi7WyH/X1EVbiR/YarzLHxJsps20zqYN9ynO8YM/sj+y/bIftN5jrLhDGK8Rufca3jDPtlHyyZt49gLo2V3XbH0x4n/nfs72vsP/O4mCyyTc/99WfenoQ28ENVk2U4Y8eMF22T6AuJJwo0sk5G1JcNNvn0Nz3emJRyAMXAYPSaBNDfZ4DN+RkARtTf8hdR3/U9op7d+r7qFQYX4Ab7n5UwG4BMxrjtVj4mzU6Y+zlwhsl0H2h7BuIWFgPT3EcHU152mAmW3P6s9JzjjUoJ5siA0CeTMRn8eFkMY+RMtbO4FmbukctPaR/PcxDd9OOxz+NlUorvPOZNzoDvYfJ7vV5x0FnWfNiYZGNtI+O5oI08Y71ex//8n/8z/viP/zgiIv7Nv/k3jc/6IQf9Z5nAbreLFy9exH/6T/8p/sk/+Sfxz/7ZP4s//MM/LMTObrer7SVkmULXAczou0ks+jUYDGrBRg5IcbCPZTyywbbeMpbInCukOLKxZJwBxavVqoB7AgZXNW6325KNyBnmVqtVcwp+04jBlc+3rGIjTZRap7PjsB2xE3IAYBKRJbrOYrttBpIm/3PghBN1wORz6aN1ycs9bBdt1yCn7F9sC91vz2EGNNjiLGd2tJYb21qPK/3NwPdtHesPPbIdy79t75p8A/9je3L7c4Caz7HNiohSAeANfP0c5JpKA3yHgTnPaQo0LM9NthV/5KQCe2zw7BzEcH8TxJ57k7uu6uYcZ+6tf7ZR2SfxHAI8rm2326XqgopTP8vj4/3u0C3mjHa5upY2QTBw4KeyvzPpyH0Zdy+5MMbie8sa+sueO7Q9L4e13NnuZnuGLnLeYzKfddZBk/XCbX7MV2cs9S6HQT+ygfw72GfTfvSr6U2wjC+ygC/i8/V6XZaGkdCi3yZrGSdvqcASK8uu5Z/7tFqt8ta2VmufQGBPqfV6Hb/85S/j4uKiyCNVSMa8yESn0yn2g4odKpFubm6KXEdEqRakEsz3cB+2221Jpjh5lnWJN+9BmFAt4Dd2LhaL8j3JwdVqFf1+v7wZmbGkSmwwGMTNzU2ZG/wbb/FarValWhdbgu+E2PFyWBPTEFu0x6RORLWUHj+M/kMiWJYhAyMiXr58GZvNJs7OzkoMgGz0er1C1jF+BJ/YhogKo/BiJuwU+5nRFpMmJriQr4eHh7IKICf/OMc2yDbj10FGcTTZiSaSw3gFe+2kiatx7Ceyj7QfR69sKzxfv+rIvsa6z3iDodgA3BjLz0FuTKq4UpMKde+PhSy4LYydcWNTnJ+vQxYygdcUF/Jdxn/+7RjCy/pIAhHfgOuxq46TTErZvzft45RjRMtV9keWEfri35DErvgGFxGbRVQYoCnx7/FxYskJIRNTOZ7KBUtu3w853piUcgWCJ92dY0L43xkTKx8KQeOtVAwYGQ7fzwGOQTbP5n4oM4KCU8AZ2ynntZEokEEwk22Qx3e0ywpDgBVRVUu4bwamVkQrjrP+7hf34bOmAD2PqxXU40JbM1nkMfcSHgwq9/X69gwQCCYAEFaKDEAdQLi/7jeKZsXymHNwnsk3yzBAMZeamzzKY8kG3bm6I4N1z0c2nA5emowpmShkLwcG7+Og0sHt3W63MZvN4r/8l/8Sf/AHfxD/9J/+0/jDP/zDYsxwQFkPc9UEBAg6wRyQeWwyqg5K8rhE1Pc4M2jJ+oTT53r/towBEl0V5TdHWPa9tI3vIfNMajIWTXuu0IYc4Lq/dkx2yHyXsyX85hpnrdBBngm44NXcEJG0n7XmWbchsWiPq7kIrnNQ3RRcbrfbsjeIiTrmjL46mMW+m4BkHrk2j6HHNeudg0Jnr5jj7IAtLx7zj3W8yfOa7I/HgP5HVEtJHajme/h8zx+BHdUNOSvLPBoIucLHfswJLJ6T7SOH8YQ3Xs7VHPYD3vfF42S9yoG/QT76kcchE5++r8cN0I3uM+YQ1ZDFBCr4TPyMs8lUQXAv20qCTCo3qMCwPHgvOPt22xKDU+arCUsxPpmgtq5wHf7ee/Jwf+5nHW2S64xtLA9czz1c8eP+f59MZf/8PvTbe05CcECc2mbxPfK2XC6LfNjmeXN7EzK5D7z50hvdoysO+HgzI2MWEbWgyv4VLHJ6elr8ebfbjfPz83h4eIhf/vKX8fLly9oSaSq1TKzaZtzf35fzwBPgym63WwJ33uyHz2BusdWtVitubm7KeOFX8OmdTiem02m0Wq2YTCZlz0O2DXj58mVZQjibzaLdrvaZ3Gw25Q2R9/f3ZT8mxvTk5CR6vV6Mx+M4Pj6O5XJZKnGNpdbr/YsZvvnmm5jP5+V+HnvwQrfbrck8xC72CT/N/66CiqhwAtVT+DfkBpuzXC5L5RJLhUmWu8LLto6ktHURPUMm8aXIPDKMLaY9xkOMh8kok1Y5nsSW2tZ8CGz8q46MC4zlncwDRzkxCAZxxY79hbEvGBMcx7OMV2yL7dezzfU45jjKRQSeA296z3cZp7r/TXbaMR6y6ypQY7yI6uUMjLPvzxhm2+6YJMdebhfn0A9jGLfHSWH6gJzSTvqF7fHeb36m95SEGMK+cR/alW1vxi8RUcM9fEZ/8BXgdeMISHLPjeMKy477FhE1TI4tyFXA3NvP4F5ZX97meGNSygpo0OlBNMDJZACD1VQdlZ/RRFz5WR7MDCwscDmw8nMxlAw+n9NHgwTui6LaYWRw7+CJA+Ntx+TxsUJZMNymJoDmMcnBOE7C1yPIOHwHoWaBIZVyMOG+MXaMh/vA5xF7J0gbmthyj5NJD4PKPDfOjnsMUXI77iw3dvRNRsGyYjnBCPGGF5MN2UE2BX5uC9fbcTGGyIDb8K7Her2Of/fv/l0BJjyTn7u7u/gP/+E/xGq1iul0WgtSyGg6aGMOmGfImNvb21LiGhG1TB79tlxi4LyZa3am/G0yAecO6Mp2yPPAD8DYgAiAhJ7TNpNQPJtgz+QbNop7kLnOgbgJGNrF7xxsuN9ZfjIYzHbSwRYAnsAHsOiqCQeUEdXrnW2rbVPcLutPk90G6PNjvUXXDL4dhAMcmoAn7WWcs24ZhGQCmfk2YPA42KY85lc+xpGfnQPpiNeJg6ZA38kR+mi/5vtmAjQiymuWPcaZJOLHe+Bg39AN/jfwy6DR/tPPog2ZCMa3Ww69TMSHl01kn4jeR1RZR1cTORHBGNmP21Z6U1/bB3zgbrcrb0Wjr/TN/grbQ8BLNTbgHp3iOXkM815u9NfBiQMOj4X9KDLEmBi8G6xblyGmsJUObmx3mnym58e62KSbOUjiyLjqMb19n/psucbGmGCwvtBub+wNnuV/B3C5ysQE52AwqFXm2bdCNMzn81IZ5HHjHg44wSHj8bgQP/1+P548eRKbzX4PqRcvXpS3AHINfe52u2XvOWRms9nUqpSwE+y5gt5uNptYLBZFL66ursr+UMgWBJT7A5HlZAx+zAHw6elpqcxqtVqFxLOuM1duE7rQ6/XKUkZeFGN/7C0rXr58Gb/85S9f85n2h7e3t4UYo/3gh1ZrTz5TLWXyDNuBHUEunCwyzifxFrEn5xaLRQlkvd1Gr9cr+1+ZCMoBNNXixnRUk+U3gCLjtBFbymcZd+c4iHPso/z/xz5sIx9rK/NjfUKPjWFt720DMu63bzAJn2ManmnMbJ/K/LngwrgHYtt7rnHYnzEOHNzXyzqtj8i1CUbaZX3N/sZ+KSe4aEOOeR1PGgfwuZNnuVAmz4v3PzNGjagS9fjgbN/BsZYbfHmv1ytzzfza57kvuR+0x/PuRLj7+vDwUAoLHHtxv4z/ea6TuPQR+81c8BzOsUzYLzdhsTc53piUsrHzgJj0cUDgjGk2/A7GELaIirzK+5A4EKEN7rABhpWSdjsg4lkEqZ5MKx0GxkbTjKkFOffNzojzbRysbAa4NrjZAGRhtTE0uOctKGYrc7aOscTpUf3B+HEe5BT/2zi4ZM+CbqV2QOLrbWi4tgmYezwcCOdx93dN5+dgB8fvCpm8vtcVBjwjV8NY5riPjZ+rZRhzz28O2ng2ZNX7Om5ubuJf/+t/XbJjXloAITmfz+M//sf/GK9evYqf//zntWxGDiYjqio5Lxmg/cynCSoHI9m5cJhItuP1GPM3y4VysOp7IZNUBAHQDBJwig48cUjWWwJDL+1Bx0ySMq7WT+9r4bn2ePG3D8uuiRQOZ+R82La22+0CmCMqgsx7nNixQxIyLh5zA6GmwNeBrudlt9uVAINABd3mXsx9Dvj5n/tmUptnMhbMXdNmjOgWsut5s5xaVu0ffqxHE2jhyOOWiYEMfvy95zyiWkqXyUeTGF6Kyfn2F26z/VZuN4SUlz4Q2Fq28OmQOBx+Jst4bF9yQJGXySAnVDfZn5icyhsGuyIbf+zkWkSdULRcmlTAzmSi0NUWnnPszm5X7T9i/+XA5jGfbTySAycSDPhJV+Y4KMIu5IQfY2M81SS/lr8m2czz4DFoOrJv9ZGx1g89nGj02PNs/x0RZRmE7eRmsylVidzHVcroF2/AHI1Gxb9SvXN9fV2ILTb7dgWKbbkz5REV9pxMJjEajcoSus8++yweHh7ir/7qr+Lrr78uS93JwnszbhMrDmiccGu1WiXrTrKL5Yj2BcYJOUBm6Rybj0NMLZfL1yo7TaROJpO4vb0tSxFdSUAfdrtd9Hq9ghcY78lkUioY8Y0sIYyo9hK7uroq+yWBMbFxjAf20mQ496UdNzc3MZ1OSyVTr9eLfr9fcI8xveW3CaMij3d3d7FYLGp7UnHtYDAoZCP4xkkHiAswI+3wmzZtT7geH+J4y9XZ1h/+Nj5FrzIB/evwx3k8s7+MqCd7jcFMOlu27RPsV/gbvco42vGsY4bsY90G+3NjJ7awYH7b7XapMoyoKoTsS9BLCGhjCq4xJ+DEkgkuk1CO1xznNcXAObYzBvF4ur3cw0cTqeVYIKJaMpursb31hecIec+x+mq1Kngkt894timeyTgUubBM0E8Tv04EZxIpJ4jto2kTeks/HPegq/xtn21deNvjrSql6IwrACw8VjiuAaCYfbWiOpDj9a2UqJdGitSCLMjCm5XOn+UBd5D6GOlh0GojA9lgYY6ImoMGUERUVTAOAC3wBnWMjX/nTKgn2aCW9tqpW7ARFpx0Zu0xmF5mwPkAZFcw5GVdVioElKVDw+GwJjMIcg4yGS//7TZng8Z1yFwOLD1HNs4OiCApLI8ZACNzzvL52Xmcs4GlzxjlJoOU+/NDlPmxY7PZxHw+L/PLXOI47u7uotvtxna7LWX6ebkOv6mG2u12tVciI3fc02XxTQ46y6Uz/+gp5eXZuQIG7dSti7n60csMuDcO3RVUBl/YKgdZOWNjIJmDKJPnBlf8bpp/6zNyZ6dsXcG2uTLL9wdYML8RVcUXQcJyuSyVf8yZS+/pv+U3t9dgwHYtOyg7U+tXRGUT0D+TapmE4l62Idgv93232xVyzcs73LYckHmOfh2g18//VQfjZFIyj3fuSwZ6JvQyEET3+N/nZLKA6zyHtr3oQgavBoqWD3ScDKuroDhckeJnOzhiXrfbbSGmTFRyOHil7WxkjBxxT8gj+1gvX/L5fLbZ7DdoZmNkY6mmQIG5pSqBecAW5iSf97RzAMy8Gcv4GU12KAcBYBm/8h4CmL99P34IWPPy/wyuOayLnG+yL9uBTCY/dmRg33T+u+o6eAACAfvk5EtOwnlukBXvgeS3Ont51fHxcSGk7u7uylI3/PpqtaotE42IUtlLpRAy5/Fot/dveHvy5ElE7O3+J598Eu12O7766qv47rvvYrFY1JIyLJ+jnfhSj4Er/JbLZcxms5jP5zGbzQoetoxwL9sQ/o6orzq4v7+Pi4uL4j/u7++j1+sV/3Z6ehp3d3flDXyMy8nJSXzyySdxf38f8/m8FlyTTKPNBwcH5UUtyCNvTh4Oh7HZbOLi4qLEEsvlstgDB4DG89lfg31oI8EgNma5XMZisSjkVLfbLS/JwQ/aPtuWuziAN2ZeXV3FcDgsG9i7agzdtc02nmcvKFfFMq74YVfkQEQ55nCCAj1hnDw+4KsmHf51+OfsX+3HkB3basefnltjFL63TY+or3zxNYyPsSrJIRcQ+KBdxlT8kMwBM5sUzIQG/eBe3sLD9pnVCMRXlkXHuo6NHQcbpxlDcP/8k/vs8eK52fe4TYwDP9gj/H8mpBhzZJ/rLSMm4CCmiYdbrfr+VNy3ySY/Jm/GG+gP40ecY73JvIFxh3GBz/e4RETB0W5bvjb78R9yvDEplY1dJlIeC8ppoJfoZKHx4CLQrpTIRsnPzgaZw0LOJLk6IjvADJp8DwfbXJMzAtlw+H+CR77zYSU3wUXfmOTMoDcFUihvU2DrslIAi1lyyAbAfS4RzWSf5yM/k/Fk6dfh4WEMh8OaozOQ9zUG05Y9j91jQDTLaP7cx2OGzEbB1VK0p9vtxmq1qpFg2VlB/kRUIMpOx+PqCjX+zjLyvg7mykEkRpZMuDMwyBp6iR67rD4vx2PuTJZwZKeYwTf3aCrztR5lsoxnbrf7bCBOFtlCzr15I4QMn5HR5Tw7XeuuZcRg7f7+/rVlfZbb3W5XwJ7n17pvu8FY5CxiRL2aMTs1A7qIiOl0Wl7bbdlst9slqOFczynnekkBY0E2mvmwbuVlFNl2PGbn+M6yakCCfbC94XleVmuH64DIMg2hbhn7PmLq1wGAaYef7f9zG7PtewyQ+JoM1Bg7gutMGuE/bZ+b5pO/LZvoC/KfMQJ/M58EhvZ7BKIG0SRRMuHiIBcg5WA5jw/66WDIoJp+5qqnrOuMG23HxzEO7fa+GoTKEMbBgayX0PhZ2AqCcOsPes0YMxZNeMPzneedv01W0WcTEh4Pk4EcVOqwYXJONjX520xoew79Oef6PlnmOR7DCu/z8GayJhOZU+xLJhcZQ2QPsoHxb7XqSzE7nf0ejdvtNq6urqLdbpdlcOwVhO11JRZ2EJLeVQv89Hq9ePr0abGNn3zySRweHsYXX3wRX3/9ddmknODay3jxF+D2iIp8IdnM1gDsb4V9yMS48ar32TRhARGGXs/n80IkI1NgtMFgUO41m81K8m00GsXDw0OpPnLVAVjo5OSktkUF/YqIOD8/L8Qg8QTVX7PZrLxsgGuwA8wFc2pyxvMeUbfTELyz2ayQZG57Thh5P8yIPW5luScb0/sZjK8xHDLihAA4CSKLz+ybcqyVCSnu1+l0yufZH/M/B7Ys+7KPfWS7ZGzq8Wd+ObDb2U5mfNtERHEeB/JIsgbc2BRfZoxmu7vZbIr8+kBuTHhyHwhIZJrVBzlu5lkmtSzbHJm88/e5zfTR+uEkjr+j7ZkTyLEysslb6sCRXurG37Z3EMS2Oeia5cJtxDazByFJY763XfBc+PA4OJ5jzIlFmuwIep1jbOyq70+i3jpOWzzfTf65yXe/zfFWy/csOAyChdZBSJMgeWL5m4GIqEAXWYdMPvmeubzZg2IWj6oonuP1kSZxXEFhAeE6G33aTxtMKERU7Ct9IhAioLchiqhXdNkxtFqt2pt0TNZlQGdjzpzk7CLKa4VjHLxkab1eF7D/8PBQe/uR58PBZg6YDYopC6VNOKMcEFk+fG8fOfiyc+DIBs5EXpMzye3Ph4kXNqlkX4Fs+JhzLwcxuPDYWQ8MArjH+z5MpubPLFuuKKISYbPZFHBIGwHjfOYg0p+77xx5Dq3HGFRnZNvt+jK0PI/b7b6sH2Nq52wHvFqtYr1eF5AdUdkd6zKBAvNNIEgfvckh2UZ0Cz0EyFonDAosezlIcdtpk/U0ExY8x8QbG7fyIoeIKHtVIF8EUFR8MQ/OtmM/+ZzrT05OyjIA2oidy5kZByAZWBgUYWvz9T7s6A2gaSP3sUz5edbnXIXzfc/9GAftzs/3+HFkYOJrTCpw36b+OLPFs01c2A45mPb/Bp3uAwEwlZUGhD6oumEuvdErMk3ixLrtZJGBlf0jfSY4ziXv/E0wiq3wW2bsa5Ff99XP81hyPjiEsYSUYoxN9BBMu5oAGeUZkMe0F7vEmNk+ewxMgJv0wv77HD43qUtWnXF2BZWxE+0EH3mJevaV9gNNiR6333qfCVXjIN/7+473pd/GTqvVKo6Pj0vVqdsLLnDb6P/x8XFZehVRr4qNiJpO7Ha7EtiAaZmTzWZfPdnr9Yoss5k4BJfvi9w/ffo0RqNRbLfb6Pf7MRwO4+LiomzYjfw6cOL+EVHTV8iIu7u7WC6XcX19XZaFGZ8ap+IvsQW2X5YbZNGvJr+8vCyVlZtNVZU4nU5jNBoVspaxYV7sr1gCSTIL3eN82sdz+/1+6RvjOJ/P4+XLl2WDc5O5kDn2V9gG2yRvmYFty7aMABpZ4w2po9GohnMdgxhrYlOXy2Wt2t26l4PljO9dHcizsCkExYwZ48f8ewNp6z7PYpzz97ZXP4Yj2yFjOz7zCgmTFvgR5gRszX2Mb4wdkQsTC9YVvneMmUkc5gofl4kO5igiCknu+JTrjVWQMfQKrJjjGNsd8wieU5O4ebwzNsJvZd+ScZzjLcsUCS54AbgBEu9OuHAPxt9vp3cRB8834c498OskpfJLBsxJZP9mX+kxsF9Ar3Iyz3LFT1OyiPMht+yzkNOcEOPeTbHwDznemJTiwZkwyANlosWAjQ648Y8RCQwuTiMDDQ9iJqRsjLfbbY0JxmCaELFSeFKYLO6ZM04Z8CKYEDq0iWfkTIuvJaAzseQgKwcUXOOAlT7QFgukwbINloPL5XJZxjcbT9rl4IF7ue8efysSSs/r6b3UKwNKG48cyDMe9NnZeLfX456VpOl5lpcsv3zuZX688cYEgeXTwWIGYbkd1qfc//d9ZFBv+ch/IxsEQ5AWjBXgjUwBY8Y5fg6G2GSMwRZts5HNdsBG347IlVzMSc7CQszM5/OymWpElIoMnu1qAzIm6/W6ZEQiooA4HIoDRdpi5+ef7CCayKosR00yxWeeV/+N09hsNjEcDuP6+rronW0bOh2xtynINfbEyzz58fU4cgIqV6J4nrmfgwHbO/63k8T5Z5vueXUfvFeabYPv53tlO8w1eTw/9mF5yJ/7sO/Mvi8nc3yeA0c+M0COqEBOJqT8uTP/nk/rs4mu7PPz3Bkv5ATIzc1NCfRss+0nkfnHQDDjY5liPOzPsB8G4rZbGew6qCQIQ6bx+8i1g812u13bPuDg4KCQ6n4mmMV7t/C2soeHh1L5kfEUhJFJao+p58sVUHnOvTSQMcMusBwIeSQZx3iaKPM9OCx3tv8ZgOc5sw140+N9AeamwzgAooCqp+wL6TNj3Wq14uTkJIbDYW15j+ffY8Fc2daCrwg0x+NxDIfD2ptvCYJcqRIR0ev14smTJzEej4sfG41GcX9/X5brRVTJizyG7tNut1/ywg+VQxCZYE7rnZdu244wngTFtNdVjia+OcCovBmP5xsDM2dOEpsU2W73CR1XQPr5BKT4RMY0bwNAH7yPoZcCuVoNO8HSINqUdce29/b2Nm5vb2O5XMZ8Po/tdhvj8biGEZAz/DwVdB53xxyOz6zL2D5XkLpClZUX2+22RkRjV/neMmxiw8+KeD25kj//dR22U8bNEfGarGQSgAQ/PiDb7BznRERtXmwXwOWOf7muKbbg/tgIJ10y/vScm6jKSQd8tasJmUvGxjaQCkdXfzbFOjnutu5ZDozdMink+aIfJn9s3/zjZLbjP88p/t4yzR6YzBVjYtIvzxfP9DYl9MeYt0k27At5puNxxxuOHZp8CvOY41U/xz7LL0qgwIS+5TjmhxxvTEphKDFEGbB5sBzQ0xELB+dmReAw6DQRElEH4DZmOE/Oc/YBAEgWMhMOTIIdiZ+H0LtMludzju+HoOWgs9PpFNDJ4Wz+brcrWVRnRi2czqpaebPg5XWlTaQbDobM2mKxqO3RAQHHdSxR6vV6EVG9XQNiwkaBwNTts3EFIHmcASb8nw8HBY+xy5YxO1MrGtcjJ1bkpiyDZfXgYL/HwMPDQ0yn09K27KAcrEHgGLRbVh3EOBj/ECA6A/sM9vNcmJhYrVbldcInJyfFwRpAsM+L5Y6xQ94ykWwix3ODfGMHPKeMGc8ke4hMuj1s4mjH6KDRy3vtuHe7XQG3Xo5GpYKdPw4pyzHz6So06yv94N4mRu0Is3MwUMhzy9Hp7PeRIHhE51zxgk2nnSaluIfBlfU8V221Wq1C8gFI/JYnO0TGxpl3lx2bLMhEnO2qP7dti4hCwqF3+RW/zIf1wOPY5Js+xpGfl21htg2WD+tQE0DxvXJ/Dd6c6HASx2PCuci2AwvPWRNIxpaagOZePBuSCACNXgHw7dPymBkbeHyQb+MWAin7EMbQpAp989ggX/TJe9A5yOIA19BGdBGbk9vrKkf7M8bl7u6ubMZMGxhbsrAOnrONNfDPwRX9ZPzdbgcptiXZrmU5s/w+hqGy3cuyb9tnefw+Pf2QOux+UR3UatXftJR1lPZ2Op2y9M4JAmMZ4+qI/R5WYC7Gz0HVYrGI3W6/JG84HBa5h4hwxeHh4WH0+/3yHGzm7e1t3NzclPs7YGH8ITuM9VgWz2bkrrCxfcbf8b33j2N86CP7u3nJrmWW5BJ9uL29LfsteU8jB1y2ifb3frsc7cBOeTkTc52Dt4jX90ikX9g2Ywn0MsdDJiitR45rrI/YDogPqvXoU7fbrdkwcBrjybxAKDFnJqPsN41VaRtj4apLbBUy7b2LMsFgu2//0URE/LqPJv8LVsYONlWvMpaZfPVPk9/hPMfG3MPyhqw53uL5Jk/tU4whI6qXaDD2JsBsu5BdqgddEUa7kHuqkI6Pj6Pf79degmRcy+HEUI6R7fNJauXxdCxqf8JcQVjTD/sT29wsc5vNplRQRUTNz/Od5ZhxgcxxO623Ju+a5jzH0cbSxlxNcSNj4f7RJuuZ54Pv/RZd7uXn5SRgjgd/yPHGpFRm0TPYxDlgNJvOZ2AsYP7OwsAkPzbwHDn7FhE1MoXAAyebFddBoh0o3+OkPZkWkLzhJwTYcrmMTmf/KlkLqQ8m2MSIQbkdIP/jmHIQEFEPzgxsDCSagjkC7fl8XnN0KAvnEtg6uPAeCMw7gT9OHCfoZTMYUtqLrDSxwzhmOzyTRvk699Fz2iS3zB1jbeDuIMhjxf4Pi8XiNfLQDpaqm7w5nMc/Zwc/9OFxMeAg84VxdMkm8gQzfnR0VF5KwOFgziCCINBAkIM5sNxaFz3v/p7PycA5aKKdXlLh55BhRi7oRyZOIDKcCbL+0l+DOPrhKpQctGIfvOTG15i8zG2PqN4Sxlig0wCETAYcHBzE6elpPDw8lLcVARxsfwhEAPSAWGTDZL7n0U6KgMwVNTkQs3zQTubTlXhNxKx9DXLJ+Xnp6OFh9UICxtfBh/XZ8pBt6a/jsD3lMEiJqC+zMomSbVy+X5P993NN/nJOJo4ymPYc2x8bjNtv2L/62U3+35Ud9v8mWrz8E703JrDNc1Inn9eEDTzO2Dv0xW0GIENAE+ChZ4yriR7GzESsxxF5NfCF9KU/2Lv5fB6DwaDMlX0L7TN54OcxDpYH+1SDfPsCL3924Ok2e66cRc96neU2z3e2Ia6u8Vy+qd7aj7zPAzuIPSNIMVZxksWZZ8bb/XJAwXhsNptSycNnBIYRUXzi3d1d9Pv9snzPNhyZI5mSq9p2uz3BlOXU1TXWXxKpWRaMRfGrrhRy4qvdrt76hY9yQnu32xWiCvIlLy/iyGSgl4OBzyD3IqoqEnTVuDbrDIG6NwiPqHy4baKvzyQP2JLrSOC4ehT75iQXnyEnyNrd3V1ZysgeUsy/4wqwE6QdlXpsSWG/wbn0w3JpjGI779+OOYzVsLUm7zLpjU0w8fVjODJR4f67v8aVnMdvJzZ8H+tfRB1fecw5P8fHJqO4HsxpMtryaRxFm+3HTYAhr7vdfgmxP7OfMiG92+1q+Jbr0D365bgWHMxhUpfn5SprdIzzkWWTzBFRxiKThfZBxKfZT2WuwL7HFWPospc1G6Na77mPCUxjoSxz5kAY75ygbvKDnGOM04QFSQ577P0/ZBrnZR/t/3/I8VbL9yBobJgggABlsO5WOjvDPAAIThNQzIy6f/teBjIR9TXUOBvvf+NMRQbrtMP9ykbSCsP3LqHGYVkgOZe3pGSgy5FZaSbZRFUmYDwmGegjtDZqnGvChWvpg8ePttNX3vgSEbWN3vL4WDgBqgSeed6yEFtZ7ODy9w6EcpDk6y1fmTxx8GxjxmGAjzySYTST3DT+GAAHz2bPs8P90I7XMsUY5CCIPls3OA9iMiLK0hEMsSukTGQA4gB63BPjbgLbhpJ2cPg7L1vIupftBEQifeO+VC0660wfqAjDrjFXbPxtB2h7x//0KyJqQNyyRwBnR5jH3ePJZyaf/Hd2lIwrS/j4HhtN32jHbrcrm8uyRAh58d+73a6MiXUr6zJZNAMOO15sEnOETfEcGnBl+8A4M4foG4G7K09MomUwYFubg3We9bGOpsA627SI+vK9bNsy0LXtt5/M/zPP2CyuzxsBW05ph4MLty/bYebJz/X5EIk8DznBPuUDcGcyxuCcoNfA277Pekw7GEt8dcTryTSf77HlHNrjZQWe2wx8sw8lsQXYdTtNHmB3WZ7lJJgDRMB7Xnbu9lhP6I/xF9dwvoNwA2PjNubBtpfrPS6P+f58+Dzjonw0Xdt0r3cBz00H8kjCxG9Js9zRdpMi+EP0zbqGfOCzkDk2lM8JiYiIq6urmM/ncX5+XvaLoooL/zwYDMob3UxwzOfzmM/nZVsHBx/215BfvE3Pb5UCR5pYaQq0PFeMC7KKDlqGI6pkE3rUVKWQbQAyxxsKGSv6wxji6/gcWWZOHPhDPBJoe/yN+7bbbakes67YL6GXbGDuYNo+wMuM8uoN7gEm8r44YBhjL2KVyWQSw+Gw4FnIdUhG5isiahgQ0h28Y5mlIsf4IJMoJi0zNnUc+DF98JscTf4Y7OhEd77GS9e4znY44xU+i2iOe63ztvfGMuA57IT9YRPuyXYRfYioyGfiQJNIxnSOkbO8Wcczbs4xK/3xWKGbuQqNMWI8sp6ZD8B3eTmjYzXuiR56fD0+nruICoNvt9Ubf/N82Z5nkt8JaY+pbWbuC//bPvN97g/jmLF7JoeNu+iX/7YM+fn++aHHW1VKAXjMRjbtM+FAA0eKQmYQ6AlykNIUTOAgmHQbqyzANuiZtLAxNHPJ97CcWRBN7iDYrqzxZAJOAAEsfTPQ3W6rrFq32y3XPlZhYCCfAXI2Jr4GwfQacOaPPuNc/VYglIYA3UaDefByCJw/5zqQsfJmY5ideZ4z9y8fTQYVI9KkGDhbV8BlufC5Hu9sgE5OTuLk5CTm83kxBrnMmYCBNvp5dtLoD+NgHXmfh8cxA0Lmu9frlWWZNzc3MRgMiv7ijFzdF1G97QWjCiAyWGq1WqVsN4+H9QY5sy3xHECimIjyZq9ch/Gkn14+yd8AWY8FeslmhxFRltGwd5LBKf3DHgJ46UeWXfqeg1kTCh4fz5t1ln5nueTA1hwdHcVkMimZb2S02+3G8fFxWfroTFREHYwgj5Qg+zPm/+TkpJZlA5ASVLCswGDEAJRz+J/7OGjgPDtmy5CrXGhLBnOc62s8jtnOvEmA+76O3LbvO7Kd9zW21Vn27CdsizkAR8y/ySfrpoFL9ul+NvOPLmVfiv5kn4CfyXbK7cBuEkzbjufKD+t+RFV636SfHA4ukBXjBp7nhB33yHr02E8mSgkUvUQxov5SCsgOvx0T24ydZS4tU/TJVVOPEQXGOfTNNtrjZHttINy0tOL7jqb5sMz8Kp3I92r62/+/C3h+7KCN+CbsKrKHvJNRh7DxKgOTF9Y3cC1z7url5XJZSAKewfHJJ5/EZDKJTqdTNrhmHDqdTnmZB5W/19fX5aUg9nXIH/PBshwwDsvCjMsdxDh2IDbgvicnJ4WIgjzKqx7AlfYBrqZy8qPX65Xx6vf7ZV86qsHxFVwDGciYgG/ALK4EQefZt412YodI/LjqgLfeealwROVTXVHmRJv3sAVf037Otf03Np/NZmWeHcSj39ttRVR2Op04OzuLxWJRXpJiG+Z9ooxdbTuwu8ghc20bRvtdsePrI6rkke3kr/toaocJExdrOH5qiiWRQ2OQHG/4fI+zY8QmvMyzuR79RHbyKoK8R3ImEmmD33zrNvPm9ozpSATTXp6NbTI2b/LPljPaYLzsZz02Pi4ocVxobgJsD+ltvBhR2Sp8tONzxtE+DtnGdrjS3OfSJiqPcoxpG8dzPS5+lnET4+Fx5R7GCiadTEoZd/k767Dvz9iYLH8XfX3rPaU6nU5t/bcVIAfdCK5frcrnCBUCZoDkTAwd9P8EnDZYnkjuB/hymZmFATDAZDuozOQbDtebRJINywGAAyPaivM1MOceGAnvx2JBNGhEaWm3BSMDeH9HO2inl++gpLwy1tUy+X4cDjhoPxs/G0gRoDM3yJANTRMQbQKhjwHTTER63KxIDgAMbhz8c67/p58mJDud/V4Q3qvMlUE8i+ABGcwKbgNlGTLh80OPXFbuw2NlwwJoJpB0sGkwhkx7M1Xrd3Yc6Jb76nmxLLnCKFdl2JBzPs9rIi/8uQ05YI4lvZ4Plro5c8leE6yPh3Qjq+m35iyXywJ2vdTOgJB20b+IOrCJqKow+Zu5yuNmW2rnz/+dTidOT0/j+vq6kHeAEWyBAbGrKvxsAHe73S52j70rer1eqbKifdYHg3nIwEwiMMfIl+fR9o+xsp9ptaoqVFe3ZNLPc8jYejxpT7Y7H+uwn8uf+bss025rBsDuk/+PqINf5tZZNPvHJuDj+c029DFc4DkxwWy/BnCFREWfOByI4k/8/Axa+Ywl1btdlbXmO19PFVOWPbfbY5THxkf2dSZ2PK88k30//D2y637ax2F/HUiCRSLq+1fRZlckMgYG1PygL07A+R60hWc6YHEA4D5zrsfdNi6fbxuRbUHGgE1z4HG2T/N17/Ogncwny8Z5PoQU/V+v968KzxlsfAT3gthH5iEKCPL4n7kisXR1dRWj0ai8oQ6SmPvZh7MnlCsQGStkE9lxdbUDQdtT+g1xBQ7Ods7y76Qy8+5KHCqj7DNM5hkn2L+jZxH1KghX8HO/+XxexojnOI6AfGKvHOa03+/HZlMtsTQup3rXbdntqqpeyB8T7Yyp/RXXup+5grLb7ZbEeLu932ur3+/HYDAo+oqNvb6+jslkEv1+vyTsvC0Cc4PfAWP4RTHYDiczHAAjlzl2Y2z8rIjXXwr1Yztsu9A3J7s5h9/2JS7i4PtMSkXUlzZH1PfNtI8Ht+VEjJO2tvWWe69qaUrEew8qV/b42dmuO2bCjuTqZ5NS9JXnWL7dZ48f5z3mc7N/NXbmt99AuNlsarruKie319VeTTExY+e4y8Q61/remSNAHjjHeue43f01HjBO8ZHnFtuDXTMRbOIK+fVWJtk/59jkhx5vTEpl8GNW2GDTBA8NdCDTdDwGFpoMF/eNqG8IiRCTdQDM+m17BjPeVBKwaiCL8+N/tylPOOf7Fc0GUbQDwguA6Dd/eINoCxhtxgFncOWspAEvbfy+bANjsFqtyhgwJlYoV0khC1kAUW7eVOZxwsn7uoioVa7Qp5x1Z9xxghiQHOxm42SQ5bGhn8yTDUsmYJy9yMsR+b7b7cZwOIzLy8vyXDsLO2kToSYDcFBeVpGDpB9yNBkGG06Pt4EPyw7yElayCRmMcD/LuANpnpv/9ni6yom2GaDbEecgxUFbRNR0Is/pzc1NrFar2O12ZQP0JiKI53sONpv9Zu/o9Gw2K3vHdTqdGI1GtaoCSBL+58CuZMDP8z1GORBuIjU9t3wGGGZMDw/3r72mKor5Y3md7VkO9vwZ8oqdBYxHVPs28cwMVCKiAJz1el0qPZqCfMBBrgSzTHuO2efNy78YA2TWMs99AMl28plc+BBB62OH7bv/z+fYV+WxITjkMMDlGoNaznHVs/WXv/15tpuZFOLag4ODUkGXz/frmDkXXbIsuaKhCZihY8hmRPUWLpPWmWRH1rh3DuyawJ+vxb/6PI9xzvialGaOXPVhHwB5Zp9Lptp4xZUUzlTyLAIC+s7n/M7Es+2uiUKPM+ejVzlTC2h1Ao9rsnxbbv139t98Zr/h+/rIfi/rc9apdwHQTUcG7fhV5mm73Uav1yuVPE6+MN/MvYkWtxcsDbZstVoxGAyKnJgMoGJms9kv1zs5OSkJlX6/X4iqiCgvDTk4OIh+v1+SNhDHkFImUaj4QB4sE5mU4e+7u7vodrsRUW1ZQd+QV4IlV1tQkdtut2tvc/ZG3k5mcG/wS7u9X+LKJu6uYkSPvQk42DgiakEq+kuw2ensV5KQmKUCg6pqrmdsIQMgfBnP7XZblkX1+/3iA70PpW20fabxNYQZMjKdTmOxWJQKzF6vV2QBEpJE0cHBQQyHw4JxXMmXfSRt4jNsDHuuYqMcCxlrZN03vn7M9v4Yjmxz0Pn8Pdgrn+94k7Fkbn0P+43sR+xvjBXzmGY8xz3wlfZhfI6+YA8c2/Ns6w5za7kwwch3TmwavxnjGuc/1g//dv8zDuEw2cVhf08s7MIQ7ksf/RZP7mWSlRjd+IR4Cn4gk03ICDJE4sE64qSEY1nsu9vj2CDHkNispu+5Dv9ve2k/xN6F9sFNOP9dfOobk1IYKxu7HLRgKJ3FtfFmYK1IHAZN3Jv7MAFmTj2IDuy9Wa4DUJ7HxPNMgm8PJIJoBtVsoYUfg+OMLvc3cGT5XgaPdiz8j4KYdadtVtwmRWbMs5F0/z1fEfVN6Qki2MTRgu5AE+UCSBv8t1r1t3AZePvZLgnOgaf7ncFpVgKTG/Qxl7E7iF+v12UpKn2h2sUOvikoAVwiRwAiZ/EcUDFPZsYNHDLB66DlXY88/xx2/nYWjMd6vV+mxhi6eslBC0GNl2h4Lpqe535GVGXh/I3+u0KDw7bB4+R+OgPIPXnTBqAMAtUVcxBIEGsnJycFkJI95D6e51evXhUAv9vtSuWUAzLbMmyKZc/jhrzQLut9RP2V2Hyex9qkFHN1e3sbo9EoFotFXF9fF4AJIGZ/MJcRZx1kbMnoeClfLsemDZZD2g3YZ2+6brdb2+CWvpv4pB/Iiu2d9+uyE2XekOOc8XYm1jbfbf11HFl/MjBvAqDZ/pk0ysDFn9umO6iwTcgg1sDG5JZJD19j24Lf8ItH0CeWzTM3GV9ExGvA336T5/vaTPza12SCBvLMb5/KuhURrwVhHlfaZzmjEiWT5k5QUGVof8HznADp9/vR6XQKkWCw6mUCkAibzaa8uCBn5plnJ64YGyeBGHfrjMk/AmnGxDYZO5v9e8ZYTcGU5d/XWx4e+/9NzrMuvM/DPhAZYFkleBFCAtJku62St5Z1Yz0wOL4lv8HKZKJlHLvOc7rdbpydncX5+Xm5B5VRt7e3hegZDAaFsODtd14twI+rjJqW7LiSis8gRriu3+/HaDSK3W5X4gt0A9xM4mc8Hhd5BzM4JkGfuC4iiq8Bo3tTYq65v7+PwWBQxv7w8DBWq1XZD9NbEDA3ufIYHHB8fBzD4bD0f7PZlAQm40CbwdpgafyxsTXJM/rIs3u93mvLr9joPJOBEfuY6Pb2tryRF2Kw1WqVt2+fnJyULRtYQoksZ4IJfTfeti2yT3AATz94Nng562eOf35MB/PQbldL0yPq20zkqjBjj6bkqf3SY/EOh0kLj1HG4cgaumuiGx9kOxNRbWbN0j8wrauF3H+ws3GW8SvX8nIC2m//aexr3+wYzKSOsUYeu3x+jrP4HD3hO/rK4b2m6JvnztXUjCF8geNqF3bg222zOY/KNsfM6K91wcvtPOd5HLL88GzPM3YFot4YwRjZ80UcnfXSY/9DjzcmpTLQZCAdvBCkMFEcJlA8ATZ07Xb7NbCXQQydxUhbybmee7Mvg9vgScKQuGKKzx2wuY+ABoOFXFXhyYaUYqIsjDCnHl8LsoMLP4cxMBBgzOyALMQR9Wy258Jz5QoXjBLPs1CboW1yIqvVKiLitVLxHBzYcHDkgKsJiOYgqimYdPACScSY4uhHo1EtCDKgbgKWGHAcMO0/Pj6OyWRSNpP2tQb6lmscN7LjubbMv+/D7eJZBgqcg2EEkJlogMSzjnJ+ROVsXNVGnx2EmjS0g454vXzZbeMZNsh+Dg4S8LtcLktwxByi55Ay9JG93QBnjA/PYFPE4XBY+s05HCbLI6JGzrjdXo5kh2wZ5m+ucz9t+zw+tNXAkKAfhwbZBtgmM9ztdgsghjhyX7i/N521XeN/2xHmyBls5gKiabfbv8KcvkZUpDwBnQEM9ss2kD6SwEBn+dvjZF9kG2kZs758rMPPekz/s13Pbcx/G2RknfFPRBU4kqxB55248TP5m3MjKlmFWLRPW6/XBegi3wcHByW4asr+Wbfsw8Ec1o3drnqphokTcIvHLQcF2CcIXeTLgB058l4+XrqLD2+yTREVqezxMvizLW61WiUQpjqV64xJCOhod67SgWjPY8Bc8zYi2sIc0r6mt+wYzPIZgNoBBu1y5aYP+3Tfq0mWm/7351mWv++wvHyIA9n2WDhJkO2Z7Ty+FLu12WxqS+xos4lD/BvVhxH1iklwzv39fVmOnitVV6tV3NzclLlcrValwsZv4cv4wTpkv805jAVVQyS3e71e2efq9PS0lnS+vb0te1seHBzEYDCIVmv/0o7xeFzkmjdO9nq9WCwWtdeuewnjarUqZBIbjjM2XmKPL7QNYG8tKpeIDZwMAR9RsXV4eBiXl5exXC6LXSOotR/ic5Iz+DuWwy8Wi1p8kQkhxowEqVdjQAZi/whkeT62hoq5iCj7hZJoe/bsWekHffUSS8dD6J63BnEVnG277WpE1OIQJxaMl34sR5ONIjFr/YiorwLKOI25M+7PuCT7EscU2RdG1O12jmHwH1TvuHoIwhDfhk54XzrbfuwN8mTZdlwbUeE45h/ymySU4wiPU44LM+ni8fAY2fc0xWBwFJxPQtoxPdfi2zN2x465WsxxZtPz6bdXF9CGPJ/WJ8tQxOt7PLn/OUZwWzw2xnDcy0kNJ9ccx7mYxHP1PvX0rSqlLDwYRA4DAQwjgsh5eSlGDggeUy4fDGAme0xKIXQYtkzo0D5n9LgOQ8pk5GVnFhgEkWtpB0Y7or4nFBOYyTcrlI2Sx9sMZwYFmU3OwX2urvDcdTqdkh2246RMz87NBi7LgQMPgnf2UyIYzuxxk+w0GZom5/R9AZvlJKJOGOSMr8lBz0cmHS1X2dB5iYqX4DG+NizZsJo4sOGzYf0Qh2XIZC66RTuRO2cGWPNMuwFEOUhhvAHU2Sn7ecxFRL3ihe+bxtHBK2PF8oL5fF6WOOBAI6JkT1nGRgDQRESyvMJzbn0C8CILnOtNHw3Isv7msbJzs23KZIoDV9sSxso2lXHFeU4mk7i5uYnZbBatVrWJqyuf+v1+sQOME84bAEaG1XLqQJi+OmHA/EREsTe5tJ0KRsYY0oFgyQE8B8EbJAgZH4MmKt9cOWWZYwwzmPh1Hk0BeQaltrn2GfafTXbTfieiIihyRR9kBra7SS6b2uRN7fmcMnaDbZ5hwtT2nn4ZCNOWvGzPMkdfDYYJwvMbqRzoYWsIMFme675lwoH7uerXeAD7mtuTfZ3768pdV594mT/zB6HufluWHYRQlUgbCIjz0lvGEx+Y/R4Biwk765R12tVnPmw3muQ9n+c5bZL97/OXGXM03ft9HVleTbbY39imWi8zEcV3zqLjwyL24wZx72rV3CaqnyaTScG5w+Gw2HmuXa/3+xCtVqvyg86bsOG3K6mxudbVdnu/nAtiKSJKtR/bPfR6vSKjuULv5uYmnjx5UghP9AG9o0rFhLqTHSTWWLpmwsnxCdWK2DjuZRm/u7sr5Bf9pK+tVqv28pvZbFbsp5dI2W45OWfMydI/qq2wh1RrMxaZeGJ8Ic34zGQY7YGMvrq6ik8//TQ+//zzgt05bzKZlDFk+ZLtDHNkYo4+uNKLIwfS9r/ck+uNx34MR45bbfOoAvL3xrERr7/tlhUbLPnkZ7vd1lYfZLxjwiLi9dUi/Pi6HE+alOBccFvTsmvHQuAw2mkM5wrA3W5XexMy/pDn5z5kDJOThI5pbd8c8/kzn++KSPvtPE5e+ZT9oJck5pjdcRw2MCfpjW263W5to3vHObnttBt7lfmMHC/m8cq+PPubnMgyLjNBZ+ziODHL3bvq7FvtKeXgIgfdHkQIKb6nE55wC18T+WQBNYD0d34mmV1KDheLxWuTRnucdeD5BCsOfjAIFnoTMLTFE4WSWsCYYM7nNb4O/m2EsrI6C5sFj344SOd5Zm39HDOfLoE2mefNj63MtJmKBA5XHDkY8hJG2uJlWj5ygN70mfudwVc2pE3t59hut+WtC91ut7acLwe/2XAC4i1/3W43JpNJyVbyvBzQ+G+egQxmdvpDHQYE6IBlKMuTnR5tA9zloBGDm4OxHLR47mxQLYuMvZ2QZZfvAdLoPmDL2X/6yNxyPs8ne8rcAgoJGjh6vV65t3UqO9HsNHLWhzZjjzLJZoIuyw+fc1/bGM7JSwhNDo1Go5IVz4Thdrst2V0AhsEWhH7epDyietsfz7QMezmrlzxYJtEDQFp21JkAwyd5fpwVdgUun/M/9ignHDw/nq8PEbw+dmQbH/F64J77nc/P12bQy3MsT5xrWXNSxeOS/4YwiYiyXMzVHgRJ3NM+P/vjDAJvb29L9S1V0mRZc3VpJuLsm+lnr9erBdL207QDf824WE8johDetNnBIdXXXvaCvmfdz+DQfot7eDkTwQ/yb/LQes9YZTDJ2ACIjQP43mPmyieDattly0L2HVk+s15Zfjz3PsefP3b/rJ9ZZw22s36/7yPb/4ioBfok+jab/VvhwMNZN5GR7GdNHnoJmElfB8T4NTa5breragh8ebu9r6SYTqcxnU5jNpsVOXJVjjEr7UQO8AkmQLG9JDxcweo37ZEkIRlKG30+emXswT0i9r754GC/bxPkGYknB1DL5TK2220MBoNYr9cxHA7L3yYRdrtq767dblf2k8xyhT5GRGkLvpWAHHnz27edBLeMe65JEoG7sKmQGcZm3NMygv12IMnYQzT5mT/96U9jMBjU7jGZTMoyRuIw70VnG+GqcKphTTI5MM440GPwroHthzrsRzM2ta46eeuKJGLEiCj6520KIuqYD1zNs42F7EN9vWNWxxrGicisl3nz46WYj2EfsDG6kokNjxG+k/uanMr4NCe2/Xz/7X5lkq7pesf/uZrJ9+Y6J2uZK/t1Vkwwd9gn77PID7qcSaVWq3pLrvvD/44ZuNaYK/uxJrLSP75PlunHxpzvkUV++5lNevouuvvGpFS/3y/ZGCsFvzGwGEWv36YTTDAlogy8wZUVPQNldzaDG4wh5BIG0QAB5xwRtYAFB8iyAhyNl8iZWY6osgN2dnyWBcHAsNVqlbexOeNqUshlgfTvMWLKwUIm7hgPHIHHNTtfrmMuXE7s/WZ4Xg6SWAaE0WVppOf4MVYVg2SB99z5aArS89z4sKHJcmNHSVm797ZhTBg72p9LWpEj1vKzGW0G6jYktNuOBWAHcGoi7t7HkceD/qGzyBvtdZaPduNoCe4AX7DrPMOEjQM0nu35yXpvh4ac0Fa/chndBZg7y4nM4mQMwHEWniMcjOUbR815kF3esDEvD4WYsw67n01ZLOtzJl3s5C0rnlM/w7qVHTl7W0yn03Ifg3y/Itfjv9tVJeeuesX+eu4ZEy8pycA7k5Z3d3exXC4L8c98kEnn9daMXybd6R8JCmT0+Pi4lIuzH1YOkBgf5thg2d996MNAIwOqDOANiH1ttnk+vJzCRIDtELbfyx75sQz6+fxN1ajPRwdzwGQZdXUypDByY/mxDd5u9xtGW1+a+m+7s9vtiv5iP9rtdrH7yAqBl6s9CAqRMdrrz7NPzpVnGTflax4eHkoAyKbIBH0sN0fvPZ7oMfc1CLWMGyxjo/D34A/8F+2170IWHWh6WS5tckDCwdzZrmVQm/FMEwH1trrYFFR9yMNyjWxnjMIeh+x7h8/JspF9AOPreTbhj8zxt5diDYfDgi+4FnIKfbq/v4/lclnuyd5DVBtF1ANQxteycXR0VN7kDI6lf5AZ3IOlcZ1Op1ZZCQ4HL9/c3JT91LjeFeosDfS+TsZbYBO29ej1eoXovru7K9VgXia5Xq9jPp/HdrstlZ+Qa8g/usg4sU8eVU7oTvY9yGQTgcC4IT/IB+Ql+1YythmfMA+2FYw3sgT5F7G3EavVKr766qvY7Xbxt//2347z8/Oyl9jBwUGcnZ3FcrksFS/en8h2GeyFXXKlFLaU9pgoa8KFxpk/hqMprouoF1DkOA3ZdpzMtZAz9NM6z5H1zDEbtpTDhB/3Brs7tuJ7lpoS92askV8ChM6Br3KFbkRVzelYG/LFeAL8Zb9hH+p+0W/LSo6jcmzla80/gFGQUeNSYiDGkBh9t9uV6kRsELaHcc2EUI6fjAHQH3wENsU6YGzl8bXvMyHneWtKGvA596XNEGj+HlthfO22Z+LMY/8Y7nyb441JKZeNY4AhUFA4N5DJtwM0KGGwvXTGztaDGVGRSHmCPKn53ggMbci739sgsAGyQREG1k4X5wpw9fIGhJx+QM7YwBq4GeyTWbBw8nwLjIPMnLk0uMX4McZNwUTOoHA+JIvBVafTKQEAAMZG0kEyIAFj7OUyeUyz8WFecwCfFa1J+JmjTEb63CaACpi4v7+PyWRS2GsHI4ybjQDjiEzwRhtKrM1uI+cG7NnJMKfb7bYsj/qQh+Ulol5mCsl4d3dXZNzVJtZVCAzLmI1ZRNSyQQBlxtjgm3b5XplYsdMF6FKazzw5AHRbrXu0GZ09OTmpZUR4OQFgEEDPnBPIARCtu3YE7idjw2FHmoGqx8RgPAcsmbTP92NMGMujo6M4OzsrpBHVULYd2JYMlBxsca4zvnaO/pzPXNrs8eCerLnntdV5ebSDe/plGcYm8zcyTWUny/uYw0wgNC1/eVcn+7ZHDtIjXl/W7LGzDc6f+VzLpXWIw6Aj6zLyke8XUckmVXjco9PpvPY66eyfALF8RxWiSWZsBvJuYI9P5fluo/GGsQFZbOT14OCg+F4+Yy81B4YG9nzGPe3fnd3m2YyJE3LWIbADQTM2AEKu09nv8UZw2263y6bJDoiMGbBZ6Hm2S+32vsLXe3nlpBBy58CI7213kBfPfSYtmvydbZVtpK/LupF/mvTHPvqx+31IvTb4j6iSgwRBLJG7u7sry71dceJ5RU663W7BUtg4cCvkqvvk6waDQQkm8X0RUSqiWPJO4ImuHR8fl/2arGvc3wmriH3ymr2g0AkvDYXYMukSUfkAgj3G6Pj4uJBV6A1j2O12a0scwW20HSKN4JnqJfaZdMDtvWMODg5iuVzWiBv8CvfynpUeD5O8g8GgkFr4PfqBDbOPIhDkM3wz/9NW4w5khSQCATS6SYwWUek0hDdtYb7v7u7i66+/jsPDw/j93//9GA6HpfL85OQknj59WmxSRGV/ia0cjxwdHcVsNqvFYca+2cdhmzJJ/WM8bHdsv2zPvTIGnOmqGuO+iArXuajA4+JnY19N0PB8V8c6IcKznAzF3xCr+zz6gO72er1CoONTLPvuP9c73jbBRRscM/iHfjr25RkR9W0D7J/y9R5j2yj6C0Fu/2gc6zgI+xcRsVgsSl/QQy9lRzaMV4yZ+UEPkY0cF3tMffB5U5zb5GON6S1HHMbS/OZ7bJP7yPjxN7r9vvT2jUkpr62OiJpBNwFiRpPBIkNBh7NQORNvJc8GzKDJnxsAtFqtYjQtpDg3H5zLhHJ/981CgdOgnQ526Q8Gh2vpP/e2wv8qYGWBQ7jpU86OQYJlI2ZA7jHKjD2/md/8Wsrdrtro3UpjhfE8GtximL3Ez6QAGZQc1FueaAPzlsmqpuA8K5sNA/fhs81mUzZzPD8/bwwcAC30z/MGsB+NRkVJ7bwNSHytiUna64D9Qx82aPywIWgeHwNhbyiaDaTXyCOXBrvMf9YxG3HOtU3gbwPdTFaaTHH1jzNzgDgceFOVE5lRMq9eXgu4gDi0w6Wqx/KZQUKuFMnBvuUi6xi2pCk445omQGIHElEtt0Xmyd7QfrLIBs+MkZcG2DZn8MB3OFwHzK7I8DJGKkVylpc5yxlfZM+BnG0NB/uFUAljAtZtzm3Pf3+Mg7HiaArMrT+cY9tmW24ygnMtb9h1L80EVOL3HZDm5/M/y2cI3khcOVHjDC6EMvbfb/vB1zq4oo3ZZjYtBzdYpY30w+3PG9SSmELGMlFj0Gi/4msYf76zjAHuGDsIp1zxGfH6Rq/s+2PdbrfbZZ8sZ8Ldf8Yc3WEulstl7Ha7snTd1YWMOX0BkNpf2O7TV8bbmIV70IZs53Jg5z773BwMNh3Zjvr/JnD/oQ7jIOwx82Viaj6f1wJN5s3Jms2memkOB99bTwgWXKHQ7/fj7OysVC8ybszpYrGI2WxWyE/8N74QYufk5KS2HAtMBI5zUIUcm+R3xR/YAR+L/to2swfU4eFh8VG8SXs8Hhffy15UJB0YE+wQ/fz000/jF7/4RRmbq6urmo6j+3wG2RcRpf/YN7/YgPE0ibxarQpRuF6v4/LysugupBsYI6JaGuQXGfBMVilQyWXsZDvGWIJlTk5OYr1ex2w2i/l8XuTi/v6+VKcZY9kufvPNN/Enf/In8Xf+zt+JXq8X19fXsd1u4+nTp7FcLmM+n5dYA1kFK1HRtdvtSuI+Il6zHcYs9g22Hx/b777pkckR/jbOsj7zve0pto0KXdu/bPcjXk/M2jY6Tja5Yv9lf4BuQzh6HnIM12q1YjAYlCID+xi/QMOYE91DV+i34x8O2/OmVRr0nfZhQ0ygOHb0eD/mS2yP5vN52bInk6aOV/kOGxER5e2VvGyBMWdMiZeYe8+HE4JZbuwHPY/fpxPMm20EB9dhCy2vxkiZ50AvHW+xtyV2NrfpfejsG5NS3oMBx4qhc9CVgx8rAY02sMvBvQffAuKMgg8HsByZsHHpoEEmINgGJQfAOFauBQSSMWGpS7tdvX2A4I5Ay443onozH2DdgM2kjAXH/bEie6xxxt67q4m48d9m5h2Y5+oXG0z6idJmxpy5RTa22+qVyGTcbTCYR55ho5oDbhtFjJyDFwdeBhwOInzfPJ5kyMbjcYzH46LsGFkApWWXPnc6nQLoXHnnpZh+Hv8bRBnsf8jDDiqPgcGas3YmEpFbzmEfB0qCAaeMlbMmfGdSgzljDA263Gb2lPHbgJAD7oEB5dkR9cDRjpTM8yeffBK9Xq82Z8gkGaLj4+NYLpcxmUxKMOySXirlCPKabJP1ycG15yOiTt7nIxPztg/ZmTMWeX8Q7n9/fx/z+bwExnd3d2XZLvtssZQZO8Y88UMb/GxIJ9pJnyw/GYDymzn2prpZf2lvdtbYbKrePEbutwNAruN3JjT8/cc4vs/JNxFUPjKoyUF6RLxme7E9LE/x/i+Z3OR8t4XlQWTrAT9+mQDnM7/GBbe3t+VcZ/NMqKLnyDRy+BgmcLDIfHsvJn5nIsRBO2PnPdUMfrF9nAf5zrkeN8bMpBfXoyfMg7EG/aZdAOEc9N3d3RU7lefdepntIfcYjUax3VabthuL+B4ZyEZUe1dxGC/YvzTZOwcKXJtBsu+Zf/w9z8i+w2OR28l4ve8DYJ+r1C2z6/W6vF3t5OSkJD8iqs3rjYts35kj/IvxEp8fHR3FaDQqL67A/h4eHsbNzU28ePGi2P7NZlOqA/2mZOTfZCbt5UUgHlvIF5Nq9JdnmzAhiOZNsLyk4vDwMKbTacF3kK7I+2KxiOVyWfY/AvfaLlhnkPHFYhHD4TDa7XaxOZwLkcb8Ma7j8TgGg0HZnuHFixfFpqG7EDJgouvr65Lwfnh4iOl0WqsqI5D1lhcsOwQv8QY+bKr3WsyBbbaHJBpIDjB3rnYw+eAY6+7uLn7xi1/E8+fP47d/+7fLSpL1eh3Pnj2LV69elTEyxrBfX61WtXmAdPBeVLYp1smMSX+shzFbtkeMB/KL/kG4mpTN9iETednO8bvJ/tGuxxKf4LKmOMnPxZ/5GSY0LDMQp8SNHMiffSZ/Zz2jrcYU/G9eIaK+aov43QUNeY7sD/x7Pp/X9oa138GmmJz2nDtWnk6ncXh4WN4g7G1/jD2tX61W9VIEF6jkOUOG0LOMVTw/OQ5gzMEkxAFe8eL555zdbldidxNyfuFKJs0sm++qt2+10Tkd9jINOolDoFE505gdq0GhDZFZPQ6UCQfsibFAR7y+ARiZUCaGQAphM8A002pygGsz4MGwIEAohrM0BwcHBazzm3ag2BYQk0QA3qw07iftYlx5ZkS9qsLz56wVn7layUDN88h4RtSDWwCUAUquosDpY+wM/jnHSpwFO/fD42CjxT0c+GfHYVlhrm28AEbtdruAB2egyFpBWFqRI6LsJ8BSFOTLgLsp+LAePEZIvM+jabzy/2RKTUgzz4Awf86mphEVSUq/cCZcY6KA51rHmScHoxAWDjZtR6iIZMkdcs4mpfzQztPT02i32zEajeLoaP/qaIhJsssEkhAzLE/gIIvE92SNHYR5vCMq4pxxc5Y3Aw6Pk8l+VzdkXWP8yYCbBGDcsFlkoFxuj2zjhCAObL+63W6xadg9gjHOMWngfpgUMrijYub+/r5UnKE7R0dH0e124/b2tkZEZnKf4BpZ8TgToGC7GRuCRECAZbIJPH+sIz/XgfdjIMz2LQf71nN8AWNB/w0euZYx456ZfPHSoIhqQ3InowhYIJ8gqACz+KCIKnHgzG8G/9gAL4cwSWQyyNVbXIsOcW902AAYGW46FxvI+AAasW9Ul/A87mnSBx1rsmW0kUCGubR/tt931SNzahmxn7NOsoSA18K7qgLd9BKsjLUyFsnP43O32VjL92wikOw3fY3/z9lhB2++t7+zfL3vw/KesWwOEpEBLwPnoF/YriwXERUmM45otVoxHA7L29Pa7aqScT6fxxdffBEvXryI3W5fedRut2sViuik5/bo6Kjsk7TbVRt842dItvJ5q9Uqy7F3uz0BulwuYzqdFvtAP3i5xsHBQUmGsMTO9gZCDV158eJFIbRICGZMzVs0rY8QPrPZrOge++Yax/d6vRiPx7WqCTAFOjafz0uierVaxWKxKPHFZrMpJDKJXkgBY9PNZhOz2az4081mU0i/dnu/51On04nT09PyObjDwTo66r01wTg3NzdxcXFR5MiBf7Zxq9Uq/u///b/x7Nmz+Oyzz+Jv/uZvSrL2/Pw8Xrx4UYvtTDTzN7bT84w8uXAgY0A+/zGTUo4v6C/9NwaOqKrSnRCzrjv2iXh9Ww1i35zEdTuyjXM85/uArcB9rkDGrhDPY0/RGe5lTLZerwsBy0HBipNaxH3+jCox2uD43zjYMbeLENBTlvcyHjl+Mo6LqPZQo6oR0t04cLvdlrebZjxrPoEx5+UJ6KR9tKsQGRdjLGMm5CXrkuXLvjPHzRwumGiKpZ0w9xhnjGU72kSIOVnp79/leGNSKmcfmRiCTO/PsF6vi4OgYwaXeUL5PDPEfO6SPzvzDPL4m+c6mPYgZ8aRiaYfFhSzxiaJHEDC2poA4x6QUQSsTDDZItpmgc1C534ZUAEWbdTtYDxGvsYCznxyjZ2JAwgyOZnBpv2uPrFhQRkBLowzssI1uRQwKxPPo3+POSx/bsPE/T2utMEgHaO83W7jxYsXcXt7G5PJpDafPANZwem22+3yWbfbjX6/X8rzHaBD5tihZbLqfThk9+mxsWoC7w508rz4bTfIBfKPfjCmuToKR5fnmPEha2AHbkDuwNVtwslb/vmfc1yhwzwahHu8DT49jq1Wq1RBWQ/5O8uzHU82+gbb2cFwZNKa5zG+vp42Z7kxgdFEECGjlOgTpKKnfrMQ8wfZylvRWJIwGo1qATV2wePqvz1vttu7XbWUgiUJ6FVe2+6MXQaGg8EgptNprS3IpG2uCcZ8/Cp7874PB5UGFQ7Ksw7Z/ud78dv+1OdRHcVYODtq/2sb6fYxzvg2bAZkKPdhqQB+KSdF/KyIaJRl2wp0CJBL/511tK9iDCFsPF4mz70UF92DcHKlBnJjn8QYIouAc+SRygnLucfStgRfyX0hnGgjFU3seWeZAORj6yzzHCaGmQ/8IfNq8G2bZQxG+7CXxnbc39Uoto/cN9tFV+VkecuynY+Md/JnPq8JI73PI2MOxswVMuACKgshNxhL5JKqT7/G3KQGMsZc9Hq9ePLkScn2U5Uxm83ir/7qr+Lrr78uRAdEEnLAElrIi+Pj45hMJmUO2JOJ5AB9OTo6KpXCLMM2+WxbbJLJPgZf4pcq7Xa70oeIak84cEEObo1H+v1+GVePL/rFMkATr5BcYHdkm3sPBoPyvO12G4vFooYFmWPmCFxDn5hPKiW8H6bt03a7jfl8Hu12O+bzeWlnu90uL+VBn/wWPftP67KJO5IB3ifT/rTVasX19XX85V/+Zfze7/1e9Pv9WCwWsVgs4ic/+UlcX1/HcrksNpHKd+wMxKHjlqYg2zbB/rspmP51H8bKEfW4kfmKqONu7CvywFh5XzA+dyKI67mfCWfOyz7dttjJnfV6XZKyEVGzscy3MbqTxvg0+zfsxM3NTamgI3lN1RS2y3JJm9DFh4eHUrXo+XaSk345iQpGMGFCe91uxgbZ5DzH3X4Du+NBklfYQMYVu0ACl7HabDblXvAOJHloC/bGdpp5tr/jGifomXfH6Tne5l4mlnIsnqvcjSeNdZAFz12TXubY8n3o7BuTUjgdB0gGEWZAydwwYAYZBtsGhQZg2ZB5EpqAt4XaQIwBx2HgHM1cZgKK/vh7vxreQTFj4KV6ERUj7TZwfwClNxY2oLaBM0jlmRg3g3ruz3WZZaZdObvrMaQvGDr+j6gyMJ1OpyzvccUU9wUosyQDIc8KZLCVlc3tcQDttub2WZasxHZ0Pi/fz58hI4wTxCsMuMutMbjMD/1iOQtAbDab1TalBADR3mwg3tfxmIHwmFg/mVOAL33zMgQ7MvrEZq3W64eHh0IoOJjhmda7iAqcYqwdYLnM3jJiObaht03wUsBsuL3s1I6r0+nUyqq9LKfTqe9tZNnJWSfLH7rjtphINlBABp1l4jpn1jJowWa4rwAKnCxBpx3dYDCI2WxW6yPnAlrJcqHTZNxMHM1ms9jtdsVe2p5lYPXY/w6ukc0meWb8bLMdoDUBXAcaEVEjCfIz/awPFbQ2HU12mc/tEzx2WTY4v4n0yDJr/WC+vblw1lNn+gggCbqYa29STvCD321KSD1GzhgcR8RrVYjO6NJG71No2cO+OZCIqC+FdYUKY2zgGVEH9BEVLnK1XsYtTkhwjeeA8637lt1MQuI/CNbBKAQ79IsxACg3JbwYC2wEy6+9jxC6xn0thwa8JiU9n8wVZIyDCMszbfIcZF/P31kn6Ve2iTzHz7RumDT/EIdtsfFPU6Cw2ezfRsdycObdVSVeEma7yfURex0Yj8elAqDb7cZ4PI75fB5/8Rd/EV9//XXZxPv+/j6ur69rS/y81QL+jAMihvYjbyb8kSWqhph3L7lnDvIyQHAW+Jkl5ZAq9BP98rYB4HBigIhqb1Sqp52cItGK7FAJhKzudrsSlFofnz59GpvNJl69elWW7ux2VTW238AMVifQxWblwM+JbL8kyEuHke/lcllIAewfwTrYC7nCFrLPVafTKckjngsOOjg4qFXs3d7exrfffhsXFxfx9OnTWCwWcXd3F+PxOPr9ftkkn4NxAiNn0tp205gXmcCm2X9/qMMk0tsc2T8bZ+UkCGPrefFSWGMYFz3YPvNMxhE5cAzusaRd/CbxSuUecuK5cNzie6FjTvSg21dXV+WlHdhRbMVgMCgJa3TU82wbfXh4GMPhsBZbRESNfLf/Yaw9H1k/LTc8K8eEYAlsD9idhCztabVaJd41ce198xgzMIDJK7a+yJVjJvjs57fbbdlLz8kwrrXs5DHI/IpxlImvpljIcuP5d/tYfRBR31P8fevqW5FSJhNMOLjCiQ5ZAF2ZlEE1HbSw8hwMtZ/ha7iPBZF79Hq9opQWMtrjYJM2IYSQSRFRqxACEGA8YYYjqnJJfnO9AS1kmSuM7PSt3H5ttQXRQM/j4YPxgiW30OQA3ffwvDqThyNH6chUodCZhHT7MGrMMWPVFJDzfSYZ3V7LjftnpXaw44CTcxwYGQwwbw4i7u/v48WLF3F2dlbW/NMmjAZ9hUggOBmNRnF6elrAGOPhwMEB4fskpDyvjx025vTLY4gx4jDRRFup+LP+OjADFNN3B2eANQx4DioAeqz7zg6IIxtVxpJ5Rr+87JiA2qAfvSR7A0C0beBa7g1BRZs5x8GedcJjC2HE/d1vy7SDjqYxYO5c8WUdwq7kAI/rIRW4N/NLBt3Vf4B35hbwAfCN2L/unD0xvKGrHTttM5HH87HNtNH+AlBlMEyfOAe7QJDDskMCDNtkiBjbdQNDy9XHOh57HvqBPLudBquWHS8vy4AT4pV5ZsPsDPr47QoiiEfmwzq2Wq1iOp2WjYnzj2279SqinoiAGOM8QKz7QtusP1564PF0oIY/sg76x8kNjwMEfdOzrVcZIGfbdHd3V6r+8LH39/elSgOsYr/I/1RIePyQcQcvjAdBKtUZ6Bt+nnblAMfPsH7QFvSRe+frbYt466WX05pct23yfTx+/vm+w9daTvzdxzrQP2MG99tkJnji9va2VpV+cHBQ/K77xVhgz0h4fvLJJ/H8+fNSxfrs2bNYrVbx//7f/4uvvvqq9nbHzWZTqrl5ERDESUQVaCFDyIR1Hn1qt9vlDX43Nzcxn8/j4eEhxuNx7Xn8TWLWW2BAXhMITqfTkvxgBcb9/X0sFova3jwkVXh7sskWEiZs7B9R35h6t9vvacXbLHl7nfvK2FxcXJS9v3h7IhiAtwCzr5YDOiqN8JfoDf6PH8aRxBhk82AwiN2u2iICws8JYd6waLyArmcM1ens36ZNEMz3Pof+/uVf/mWcn5/HaDSKV69exXq9js8++ywuLi5eW3UCZt7tdrUqE+8hmrFJtpEfQ0ffxadnjIB+40OzHyGezJjCtgxZtP+zL4+Imt2IqPZTxm4YazKvkIzIGm/TBufRZmQdeWS+8EXoP2QziSZwBNesVquCOzKGw5bZzi2Xy2JL0AHu7SSbY0Yno43nmuTGc+VYkb55mToHvoxiBLaRgIzy3LLNhDENvAK2Yjgc1t6O6eSvXypFPyOq7SYiKtzvwhfGNftDxtDxHLiAw/NMW63HJvbxO8w913mF1pv65Tc93mr5XkQUJ4KgOpvfRC5ZWeisDwbRwIRrzTzjyEyA+HqU1dk/C1dEvOYobLxxvg4CuZcJFBwAk+3JYvIN8JzJYDJxwrDBjK2NkDcotCFHQA08DUQBroyRgbVBrD/DELgixgGeSRRXg/GWLDtUSDmTlm6DwY37m4EkfW0KDOizA3SuM4GWs642TJY1/rZx8LywKeiTJ0/KHlMRUZsjdIH+YfDOz88LseWlSjgrlJyxasoMfKgjGxHmigw7841Dw4CZ5FgsFiW4xRaQAaBf3MeyacOO8bexa7X25AcbwdrGmIwxsIyIWiBrfXAwmwNT5h6wji5QlcHn3GexWES/36/Zsuw0DDQNOHheU9WpQSGH22ig4r5HvP72O763/jIWjAFBUatVz1DTD5ZxsMFrxF4Xrq6uSoC72WxqbwZiLMkkrVarQkyxhASQlu0wf0MYunyZ/tN2y5V12vOHjaWN3BsiCkDiefR8NenIxzqa7BMHviR/nwMuB5EZsHU6nUKAAG6ZG8YIO4fvxibyKnYDPGQJoOn9TaxjzFmec/s97Df4AdnJtsdkpoM75MpBELYoL0+ErEQ3CNSa/JL7m8fd9s823IAZWTUmMY5gLkajUUREqUb0kkLrOZlLzw864KyqiTfsOPvccD+WQaEzYAInHTiM7+i/x4z+en5dtWYfaMyWMWAO7nJQ16QzGau5LQ6CM/n/IQ7jDBP43nMJ3TE2RhYdyGdbaf12oHZ6ehrPnz8vMnV2dhbb7bYs2bu7uysY2Fl/bC4BrP0Dz7I+cx62FkLGy1roBxiBZA6yALHNHNFnqicWi8VrSUb7mTzOJEXG43HxTRBTkAa0hecwdsvlMtrtdnkTFwfV/9iX3W5XyFUqn6lcYiyaMCs+lL1nkHeW3to38iY7Yo2IqNnDzabaCoN5Yi9MkkGZ8ONcV7oRmBMw228yxjc3N/Htt9/GdDotmPb29jaePXsWR0dHZQ8u40bk2j7KOug5I1bwdz7/Qx3v4tc9t9n3Yu/RqYiKUAHjorP2oU6Wcm/Hzdzf42Lfia7x++bmppYUogIaItWkDNdDzjCfrqhCxvBHTkKBk9lDki0u7O8dD+DPWEbLXmpgDycuGVvGwpikyR9k+eLIiQ+KTabTafF5zAdv88Q+uX8RlS1B/00WspSYjf7R19VqVZ6BTDgW4TP7AqowsZEZy+f5NzZwnO+/PW7cG5vn+CHHGcQEWY99Xp6LH3q81UbnGLc8wXTaxAz/I8yegAxY8oD4/gZ8DuowCA7KCDBQNBSi1WoVZcoD72cZtDIJOCE+NzgiE4Aw2GlbmC10CCXn+G0jNk4Ip7OV7r+JM4+VhToz5w7UGQOuM8AxUKP9lHQDWCnrb7VaRYltJDxOgAPm3m8f4BwMoJUnB96AffrbRDpZMRm7x8CnZTETe3bM7Xa7vKnh+fPnr73NBrm3Aru65PT0NG5ubmKxWBTZZGxxYm5nBuAf4/BYWl4IBvPad35wFGQxkZVcYQTQNdOPc4AQwV7gHG5ubkp1ooO3TBh4LiKiBgx5Nk6ATcpvb2/LvghkkwwsyU5eX1+X/iP77KdB9SSbv1oGnW1ifLArWSbRZVeiOLDCtvCMHDBn2+nzGJ+cLXHwvN3ulxwgtwBV5gDdhoiGPECWISCcLYTk43OyQgBmfkx68psx43B1gXUkImqyud1WVYjYmU6nU6qlnPnzUkrbV+QqA853dbRvevjZ2ddZj5o+pw+ZhLI94b7MH6XlZOW5r8kb9IvXH9s3MN53d3clOMNP+DXz2e/kgMtLlGiPM3Pe68LEA7LBOU4M8azcfxIpBuy2xSZ4PCf8OEtIm2mLM50830R6Bok+xz6WKkPL9G63f2OQKxAIsI0xSHZ4vg2cWUoFueh22H7wSnp0J6Je3UvfM/iHTKbixBnqLOO20W6vZTcH+pahrDf+34fn732D6McOxj0/z6Sls9YR9eXVyJl9Mf4j94N5pFLo7u4unj17FoPBIP7iL/4ivvjii0LyGBsiQ9h+67P9Jt+DL11JBbbBl5qYQN4to1QEkngggQGBAh7wvlaMJziDNux21XKxbrdbq9gl6WDM4f2nvO+RE65cy9wRN7i6muXJzFf2c/Q5Jw/QT+MA5h3iajgcljcBY3vRWWIW7mM7Bl5aLBbRbrdjMpmUKg5sLHaONhijuxKaZcHb7f5NgvP5PM7Pz8v8RUScnp6WhJ3109gGXwPhgcybuHSs421JfmyHsSf/R7z+FkLIF/sKzoOAxP/mONYJD8eCJrOyr3N7qGRyrAfWwa9HVHrA904MMZf4X3QXuUCO0NeHh4fi+3lhkPGl5QN/S3tsA+kDbeQwMZexravGI+I1G+9n2AccHOxfYsCKqrw6yomYm5ubsuQ1Yp9QpioxzxX4DGLO9iGiWi3FXDqmwtdx2P65CtrxvbGwCWf7Ux/2pd7+gLiVv7HJ3i+Z8cUXOH54zLf+kOONSSmCNAycCSaMCdkQDxIbdNp4ImAogMkXD3IGMnmgHTy7LXwP2PRrHXmW2f3HqocMZm1cOAhmTCR46QAl+q7UsAM8PDwsm01yPRNOWxFSxszZjm63W4TDzGkGf5mQsnLS/pzlzQbRSsuYoSjO4DQFIJYHxh4DzqaY2XhkI8PfTYc/NymVQWwTOGSekdEmEO2g4bvvvounT5/Wllm4/xAblD0yT0+ePCn/M275LSgcH4uUMmjOwMxBo8kKzyXfmfwgy8+8kR3iPD8DsgQSKiJKFnk+n5eNUrOOtVrVkiMbdxtqQJfffoWxhZRijwQq/tBfgCygM6ICqcwfdhC7wvW8Fpb2osvcG13z2GUj7iDWtslVIZmY4RkcTQCR6z2ekMEnJydxfn7+2h5A6PJyuSz3JFimHQ5wGGOCnYgo6/TJpg8GgwIGIMM8Fi43NyGR7VJTFQHf44cAw2R1CbaoloKAA8gxP563HDh/yCPbvhw8M28m0Zuut7/lb/ePAAQ/RJCIL7JfRnaHw2Gtenm7rZYIODBDx1n6mf2iiRIDJO5BYIqdzH462xAIcAMnnmP7Sp8cCGBLDHbxT67sAKTzTIChbZttv6tcrIPOGvNcJ1HoO3LKsjyeTwUb5K+rqU04uAIFWc7zulgs4v7+PkajUW0Jq8cWstIYgx+ex+HvXfXDAQh29U3GdE5SNvnsJrvmuc7n5e8cLBtbfKiDviBPPsAN+CgID/tibKntvpMUTgw9ffo0nj17Vuz2aDSKr776Kv78z/88rq6uis9GR8ncc2/2cYK4JGEUUdlwKudms1kJWMm0o6+eU+yLt77I+D6/9ZUlPRF14htdo5IIvwAOi4jiT4wjGXv0ebvdRq/XK8E6xJgxLTrjmAV/yT49Ec37/lBFwXiBC4fDYekTcwCet8zTNwgDfDHz56ot5MAHzwZPOwGE7ff+r7SPSjnGnI3MWYr55MmT2lYmz58/j8ViUauOcyIB+bb/bCI/PM7W/x/7YX/qOADCAgzi/UmRa/BHjpmQCe7HufZvkB3G1PgfqpkiKtsDscDLl4hlPE8k8Fydbr+O3kVEqS7KVTyc6xckEN8hH9g4+6Rs5x/DdZYVX4vt4Psc//E7yxaFIyZs0WFwPFVO3JdxZNmei2C4D7aHdpu8ph0Q1/b99nnex5kEOPdAj3Os7CSZbSxtMkZ33Its4peRQc8J++UZv2DXebbjkHfBym+1yyPlx3TI4BngxsQxYHQgEyYMGOwgBt/CHRGvgVkOBjJXmmRSAeWiPQieA/Is5DYCCCYDbnaf9lISzYZuOBYyNwgR7XGwZ4Bu0GrQhsO3YTNp5pJbO8sMmgE6BnQmZBhHXwvANGEDWDGoMzkGkPVrQhFo/gaMWTZsZJhjz7uNC/fJ4DYbMRsij7X/5hlmnu0sHMRQfv306dPyWuHdrtrkkvECCDFfk8kk7u/v45tvvqnJsMGOdeVjOWUbcuu1SUrkF13wK8wdyJlAMAiJiAIA+T9v4Gn9Qpb4f7fblYwcskfVUp6jnGnC4XoJHnMCSHR1hR0yb8aBgPSGxuicq7NwSgB7jLsDuQzOGAOTT48RszmYarJhntOIaJRlPqcdAFSyb7arHNhkZ3JwmLwZyRlglstZnmjndDqN5XJZMsOj0SgGg0EtYcAYRDTvpWVnTvsAP7Z1yKSXSBFwUY3jpb6Mfw6W38XJvsuRA3fbNBNIzFmWGQeJvg59Ru9NmKJ7zMXR0VGcnp6WMeQ6iGOWnZgUPjw8LJl6v+XSWXK3z4kpSGNn/tAn5BASGJtgu2/izMCUgDXrVpPMYJ/8ZjsyqBGv749n0MeSV8bPfWi326UKkTZi8xyotdvVUiKDWgJCgkgy7oytlyLa/loX6StEF8t+7He47vj4OPr9fg1Tcd8cPABsbbt5xm63q1XPMVduo0G5CWbaY9uViY0mgjZf26TLH4qUaiLFjP0cRNFG+mwsi11G1i3b6DA68eTJk+j1ejGfz2MymcS3334bf/qnfxrffPNNSSowFr1er7yJDf3k/t5jBl03dl4sFnF5eVnTd5OsJq/cf8gaZNUYiXbQfuQRGSMoJtDFLvHb5M7t7W3ZH8pyHlFVVpjYjohSLWYZJEGVg2Uf7gPkDm1FF1hpMR6PC3HAdybx2T+KKiUSZ8iBn0F/0bVcHQ4u4Y2Hw+EwxuNx9Hq9gnNIKDA3yB9zybjd39/H5eVl/OQnPynnRkQMBoPo9/slsWdsYNvLHNnWOu6zXclY5sd2WGddWeJ9US33YEtICGIp7KF1nPtxbR5DbITPg1hkuV6O/5hPxpp5R46c4ER32BMNX5o3uO52u7VYGTISP+VVBZvNfkk8fXKSynG2P6OPERVZY5zga0xy2x8bD1i+HGu22+0Sl2FzsYX2w9yThBOYwPiRqisqjPBzxtPgZxM/jDtVxbZPtIHqT9t7+wv6m3XIMgTh6PHi4DnmThyH8nZQ/Ax2COKMNjTFIj/keGNSioDUJYMOrCwAZMPIeJhBo6PfF/hbMQ2krKQ818ELTiWXudo42JH6+SYvIFXskDPwQRFxMBgiGE3OYWwc/GeDwdg4O2MQ5f1nDHKbxjR/bqCdx5M2WrgB1QazGBPkwMqKwfLb0exomQPAM0aUZ3idrQFsBqWMHedl2WM++d4Be/7M//s6j72DvUzYsGHl06dPS0bf1TPWB4zRdrstSzLm83mtRLXdrlfUfMzD4Jex8PJXyyfZFBNUJq/ICuYgE+dlcgVZZu4jqiWc6BQH80NmBjKE+zprRF+oNMBpGIBhDyBFnJVg6QukaadTvTXv6OiorNM3KXt7exvdbreAyrzkIKJeFdUUQNk+NDlQf+drrAsOVuwgHGziCJ3BtC5gsxz0IMOU8wI8+J5gBGLKFW6Q85YpnDVzxXxNJpOyybztrh0rwMFtzoE0ywWYV3wSZABzxubLVAGip/Y/v+7D9t199t8RVd+5xrJiGdlsNqVSDTJqNBrVCMGIqqql0+nEeDwudp6S/aurq1IFHVGRNy59Rz+ZcwPSbF8i6q/QNsHoe2FDs44QgBLsISvtdn3vx4go2X6ex/Jd+9eIKIEG7TWOYGwhOLGDEa8TJf1+vyx7xR66mtbETES1AarHDDAJOERn8KG0DbvMs0wwomvYz4go+8kwNgQc2FXsZL/fj1arVcgN2xr67cSfA1TGCzudZdd6Zp/tz/2Zfbo/s63w954T2+CPcRjHIQ/Gmp4fn8N5TvR5SUn2zSROIPsHg0H85V/+ZXz33XclWKUq1JgOWUdfWNZJQpXKh5ubm7KXkvcdzJVe1iHOcYUhy0GRV87Ht0RELWFJH3m9OlW64EaqHfBdVD0Tq9C3drtd7m9ijHFFd7JfMgmDXcWesFKBvjK23BcCCrlHh/0mNAeaTuwS2DpesV2MiBrW5F4RUfPJ/LAp+m63i+FwWCMFkFGIQmx8RLWn1s3NTdze3pbzqKDDfhwcHMRisYiICsc5boF4QW6daGJs+c5y/mM7crtMMOBDmB/+xw7nvYFMfHiFUfZ7jE1E1Jb9mTSgQsoFDcRayKbtv/UzIkoiiLgdG4HMub/YePrECikvR4TYJAHBXo2ZTIYs8YtTGCPjVuNnxsi2NcsS35nUtr2MiIKFLi4uyphYJxg74pqsK1QxMy60iWXvxAJgVhNp4E8SA9ggfHy+L/MCPsZu4GdzvJGxInPmWDPjd+bPiTfOw34xb9g2J1De1/FWb9+jbNwlrBhUCyrEEAbZRiiTIFYKM/V2vgyKAYUFDKHz5DLxgK+IeokoQRnGwQYZgTQDiPGgv/P5vJZhoi0OEN13BBIhdT9sABhTfiOcDloN+HB4GByTfZ47Ky3jyvhb4TAcmcTyvAKaHcDSX1jd+/v7Evx4PwFnm3iOl0l4/bn7kI0Uf5sJ9xw39Zd59ucOaG3k6JvbwFi12/t9hdbrdXzyySfR6/ViNBqV5UkOwrbbbe11xZ988kkNyPl5Tf35kMdjjt8G1gQK8g/4cjY0IgqRiwPEmSIbDmIMLOkzegmRBChGLlxBxBiZSOB/V0bxPTrlKsXr6+si3xhngy2cM/0GZDC3nJtJOO/NYbm0zcoEU0SddM0/ea6yLlhffY/HyC2XiDNH1g3bMUhClmB42aoD8s1mU14TbaDhCrRMkDBfBwcHJUNMNtfjR5vwBb4f48u9I6LmlyDSMojhN4GJA4AfAyimHU1z77mNqLKETbbQhIT3EECuCbz421lZvwEKX/Pq1ati/9A7Ahn7IGcgAWrMOe1EV2krvgJddtAWUVX+AWZNctg/O7inTQ7muI8JbfBAJkVchZDJjYg6GcMYc72Jh06nU/OFtMO2jwBvu90W8oyEDyDZNjQn5agiwxdDCrB/jm0PZBNkBPchmHTi7PDwsLwBLGO1HOzgvwHNyJKDII+P5QZZNvby+Zb9Jh3NoDz7etufDOTf52FbjMyjX7bb9o9cxxjz2XpdvXQjE7k857PPPiuEz/n5eSyXy3jx4kUsFosSPPb7/UKWuKqfe1r2SCTM5/Oyh6I36caug3EyIeykFbYAe8IzkRdjXlcms0xvs9kUYhx/Q1upfqT9kC8QYIwzOnR9fV32BKXf+PRWq0qeuXIBuUEuIWqZUzb7JsEG9uDeJsUZOxN12I68JYGfiT2k6oox3e12xV/mxJ99+3a7jel0WuKW8/Pz2nJRiDD0BJnEnh0eHsbFxUUMh8OyCgIMyPNMbmd7TSLJ+N8+GAyFb3ifQe6HOGxfiCFtKyOqlUUm2k0oGMdkX85hjOPKGvRqsViUN17aZrrSyUvmwH1eugoGYz8y2m6sBWFFjEylJXjCLwXi+XyGzTcJgh02qcWz7LMz9rXtd0KE7+zfjAvMKeQ4bzwex9XVVU1/HGdjR6j0xC57D2vmHbvKvdjPlJgAW3BzcxPL5bK2pBIbQd+x1egDFdhUnjGeJOCRL2PBzKdwuPKaIiLLDHjfGAz8gawx/u/bj77V2/fswCLqS+sQPMATm+1FxGsNt9AYKHAfg48Mtq24BqIMGs+AIEBRTdRkwACwc6bDA48SkOEwywzgJNvKvd0P2osAo9gIPeAUJ8BeVLSVe9m4cV9XppjU8EEAyN8ojQ+TMwY/jMPh4f6tYzbG9AkHaUGPiFLNwv4x3NOsNe1vCrxcykjb+c3fLtvOAS/n0E4HnAaHVmj64HYZRNogYsA///zzoh8YUhsTSJbj4+OYTCYxn8/j66+/ro29dcJt+1CHZdOylCshcWK0ibEBVBpQeN79Jh8MJfYiP8NVYowFToH7Ije0k/sw1hhQ7ocTxTbQXwKEdrtdsr+AYEDTYDAo2U4v2YOU53+qBthHC8ILcJeXGFqX+dtBKTLDYbvI/7YnrlzLpFI+bF/524Qe4JIg1vNggs1Anfk0iQBx5QoTqss42LsIWwGoxslvt9sYDocls2jQBpm52+1iNpuVccRm4TjRO+zIdrt/M0peZgtZbBCUyQf6/rGJqhx8uy1NYCxfy2HbyEbIzPlwOCx7YCAb6PXBwUHZ3Ha32y83uLi4KEtKDJIzAWM/5CrliIo8QYYB1SaAkQPmCXtwcHBQe/W5A0X6nQlI2k9bcxUmASE6xN/oM37a/mu9rl5Pj9zk792uJjvgzzweJLtYWjebzWqvpmaM2u120VWAqm0t9yVA9/hT4cCY2SaQ0HMVCuew4etsNouIqOmsfSskm8kVdBBsZhKK8fW8NeEoy7ODDn9n+bcM5MAkf/++D9sR5iLjW+YLX9VqVfslWvYddFo3uBdL2iCe/vqv/zq+++67uL+/L8QyPsZLRXOyGDt6fX0d2+22LAtiE23Pbd4E15jbFbm0G1kgmcNvluTf39/HYrGIzz77LPr9fhwe7vc1Go1G0e/3C4YCf7sS5/j4uMiUZc/VKxBIBI/4OgeZBMfoClicH2xZ3ofGWIgxgTSbTqcFO2N3nTQj0CQx4+d4zxYvkbaf9djyXAhwfuzjwC6DwaCQU8zRwcFBWb5lfwg+MLERUa8mZa7Bztgp2kb/udaEjDHfbwIh5djBviminkAHRxJXMC6Moe2l9ZHngH9brVatsqbd3ifGIUEi6nscg638VmtIX1fHrNfr8hInkhTenoMKuYj9ck1INvQHvcU3oZ+tVqsUMCDv9gPcCx9D+x0X2l4jN/admWzJWO0xzsH3PDw8LG3HvpCocSIcEpEYBP13u8DvxhH0h8opb/+Bj99utzGZTIr+o6/eMoQ554UQYGvkivvy/KbYlXvQV4h0J+yRS2xnUyWWV5x8CP/5xqSUBcrKZAEAxLnTJiN8vllOjLoPG1GDl3wflNyGwg6Tcx30WLA8ORhdhMvLBPjcY4GCIpwuYQSYmqwxgMpvf2L8/MY+lA7BMEijHyZe6KtJJYNFj1VWVANYPrdzJpvNHOCYHTDlzLYF2GPpYAbQxPwBxJoqlbICZKWz4jDntNHMscckB/0OnmiLQTdjjcwuFot48eJFnJ+fFzkiCIcwQ1aYv/F4HLPZLObzeY0cdDvdrvd9ZIDPuNMvHxgqdABHBdFIaWtEvNZmk3sYb2QqOxaPvd/O6HagFybAqcj03kDZJnBPG9PlclnO4w1znU4nZrNZCfAi6ksHqex68eJF6WsOBjORbSLKukcf6JdL8T12njPrM+eaFHAgEBE14MPBXPueDnKwPwQhVFlwPz+PYNdBebfbjcViUd7I5OdB6nGYyHTAzvIO3kTkoJ3zeANKLiG2fzKhii4SmLTb7RgMBuWNmOx3QxVwtjsfi5Cy3Dz2fZ6/HGgjJwZ4JkEiqmXmfksmZBXgcjKZFB93dHQUs9msJBpygNbULj+fPlHSjr5iQ/Avlv+I+lt1sR3Zh5hozkQi1xhrGCtwYANcvUQlQpYn2mmdNcbhOeg1gRvEtpNnvpdJHM7jfiTELKcREZPJpIBo8AfYh8/oR7/fLwDctoX2OhkGyU/7bNshCAhQPQ62U2A4cKFxY5bpjHXyPHLfTMA2fZa/sw3ieR+DlLLNj6gSl/ZjxrG0i+DHc2hZR7c579mzZ+VFDoPBIO7u7kqVFPsvGvPRNubIGXy/MdVyZN10ULXbVdt1YDd3u2q5CjYZGTo9PY3RaFTTo1arFcPhsKysIGGbt8UwRjk9PS16xWFyiB8IAYJlJ1/AGNgOnuktJJwcZoywjyS/IqoXtDiWYQkkeIMqolZrX4k1nU5Lgpp2Mo7YXdsG8E3eSsPyj76hS8gLOsD9eP5qtYpPPvmkVINAaHnevNT7sYp165P9F1jCss4YcD4ylbHKj/lw+7Hr7DEbUd+z037Z9tTzQsUf9/Q1EdXbGfmO8Yqov3WNOQPPgnm8pxdYYLVa1ZIgJkTRf2JS9vzkWX5jpyufIqq31yEnnIteIWO2PRFR28oDzIf+GcOYhGuaD9v9Jl+XMezJyUmMRqNYLpelT8Rm7JkGZmQLiPl8Hg8PD3F+fl7ecMk+tJ4/fCRjw0qCrLdgIeNhiHTvTwd5iCzkBJ65DftVzw/yExE1/Oc97WiXOQB8DhVjtsdN/veHHm9VKWWDC/EAYMWwdzqduLi4eM1ImSRw5QL3NvFksoXrXQZpRpVzTCYB7jzxDBz3AXQB5DDwKA5G2hskoyQWfBNxfIbCGaTxmctTTdRwTg6qs8F/TBF5HoDfYIZ2EgBjLBzkmtDCKDGeGF323/E6epyNCUNXdmHcME4ogMcSxxzx+uvrTRJZHjwGOFo7NvrGObTHwMzBRA48zMpHRK0ay/O03W7j6uoqOp1OTCaTsqwLIMIcsAaXjPfTp08jYm+kMOoGitmQvs8jO347OsaF/xkbO8WI1zefBrDZSDpDBpHA/15iYj3h+dZ/2wdk3iXHZGghKJyxysSXswPcH9nF4U6n0zg7OyvOh4wFto49YmgHz/ISKJdnMy6PkQ0837bS8muw4ntgyxgX+moyJgfl+Rl87+DR+uuMLEFLRLW3ATaCjR4Z+06nU5Z8uHTZIMt7TWXgDJEIaGCcaDvzR3vwAQ5ec5CAQ3U2zwApB1+/LoCcA1UO22mPGTLH97ZvXL/dVtVn6LL3lsL+2qbjr7HbABGCLZ7PD/dwu6n2yQEuz7LvyMA6kxImkpApEybcO5Od9lW2CRH1DcqNTdBr2mn74UorHxBCEFs8l+wmfpOglPsRcBqUgqlY/g5pxnfMjTebB8ACjumjqw7RKewZhB9EGONHYHFxcRHdbjfG43GRgaOjo5hMJrFerwup67FmHp0kQJ6QCTCK59YY4jGcYz/uzzgn46Wmg/Np44c8kFlkgsN2jgDOgarJGO7DuBgbWa8hPahuMMFgm8EzsYfGK2TqnYwjqHX7wXUmEqy/6JqXb3e73Tg/P4/RaFRIVp6D7T48PCx+nGqDzWa/zyQb7vPSBWOGTPj0er3yGTafYMuEsW1Hu92ubdZsm4T+QmSReNxsNmV8COC32+qtpJA64/E4IqJUnSHnPI85pRIGgs7LiV1pYR+fCT7Oz/48orK5xAS8OY/9HI3RqNLihTDo/nA4LBtb287ZxpoMi3idsLYfQPZtc3/Mh32zdQs9ptIWfBVRJ5ciolThO9nqeYl43T8yhk62I2/sc+sYLhM0lnHwM4nmVqtVtrsxccS+uZCLzJVjWuQVn4OuOMmHTzEes81GLozfGGvOcT+yzwAv0O/sM3I8nWOebrcbX3zxRSFpeOkCW5GwJyRJ2tlsFp1OJ6bTadlbiuXtOcGC/YrYJ7FXq1Usl8vam02pKjs9Pa1ti2JehXZTxWwsk/UKOcNecC3zgG+OiJK4cJEJNoOEtOfFsZ/13bbnXY43JqVw3kwaR57ou7u7WC6XJTh1EO/yMECSyYmISmkj6pUWDlow4gY6BCcoHcJmkGQQwLrYfH/uR7befXdJLYrvMkgCQlhq+k27bMjon9vH82yAGFeDZd8XssSC4vmJiNeAAwJmw8l9s6ATPEKq+G0kMOAEK2a9bbAB3bCxERWwxviZnON6A04rg/thA4Os2VE3BXa5fR6HPB/bbfXaXIMus9P39/fx4sWLUgVlw+ySTp59dHQU5+fn5U0XOdj0OHyMw/KIsScrj2yxUSH67+wjZeDeRDXvO2RnElFVYPn5OFVKiAGNHpuIKJkLfjNHzAt6jgG2npHZMdFDNQFLUpB91msvFovSf98jIgpIJShsIlW9NMNjnEEH97OztLzTNhOkOGEf2dZkx+zDBAS6ScDBMkycMjYO8IG95HyPa6fTieFwGL1er8w18p5Ltg04DDAATlRgEUjwHWPPMlqqSDIJgX1muSbyyfMc4GTC2nPwMQ7LCP9nZ5/PsW3xdwRRJjoYG5YCuMLHv7HNJofIdqIHBqgRlf6y/Au7bztokt/BKzqIb7YccT90zDptX2aAhLzjuwFxlouIihDGnxvcRVR7L9BfPru7uyvBPIAyEwHIku0Bum0Zp+0mx9AZkhx8TwKj1WqVyrbtdlur7mTs/NYk+2hsOueY7DC4RF8JLkajUSGrTk5OYjAYlAA7Exa01z6beYE4s36ZYMiy7t8Rr+//+H2H9STfMyIKofahDmTHdsZ4APmBwMjLl0xecTjZYV/laiXk2XpKv9Ft7k2Qulqtyv6B6BYHco28evmsg2EIGvydk1C9Xq/gAuQIYhNyk+/YHysT3Sy1N4FpvIw/5/48GzzjJX5gNJMDTriyzM/B9MHBQRmnvAcUsk6Aav+I3E+n09pyeEgBljOxzNZ+zvYC2XDlCAe63mTf+NzYArkisF4ul3F2dlaW9kJ4DofDsupjsViUl7rQN8uIq015nmXPPgv5ed9B7Yc8cjxh32Of6yVW6KAJE/sBYz8/h9/InfFifonLaDQq/pbn5OIDzod4Al9F1F+usV6vo9/vx2AwKP6c9mG7qYL3CweICUjIurovIl6Ti2z/LRuMl7GcCfksV5xnm4gfN+mHb4QHoB3sC/zq1auanQb/Hh0d1TgBv9SJ2GGxWMRgMIjxePxarE4Cnme1Wq1CZuXKtNPT05L4o6/0DSyFD8beMd6WI+smegYuMSfjhGGO2dvtdiErkWPrrgs4sm780OOtKqUiohhRBIK/6SQ72Xu/k8wm28ByXwYTY8v/KGGuzOD5rrBZr9elrM6Zbxtz+mIGm+9Y2kF1VER9002W4kHG2EEToJtFzgGlAQn9iqiWviG8Hp9MwnisbOB8LwcSvrf7nAkwOy8LlslAl2xmkpC2ZADP/VAoMnNmuQEwmcR0X91n/21FyUY9k0tuoyvc3GcCuAxkTYJhRPgMWUD2WU7hdfaMG0Z8s9nE8+fPY7vdxqtXr8pzMMQfyzlneXF2FINOwAWDb7DW7/eLk7STwEDiYEyUAAgJwDwHjBmBXkRlewDvkFHsD8T5djpkgTPBy/URUSoMIqI4nc1mU/aUarfbJXiaz+cREWUPHgy3nYIJAO+3lANo2yc7Y8tc1v2IylY12QE+91gDyD0f2KbsRLDLDuKx7dZPL/Ug02ZSEPtloElFKPLC/Fjn7AecMbWtIagxUAckeTmYZY4+IXcE+gAA5gzy3W+Bso587MP2u+kwGdN0jkHbdrvPhOMr7cP4G3IC+WCPA/tcLxOIqL/4geByuVyWpBMgnfnMATLfR1TVDCaTmB90jDYbaAIYM8BFnlw1aZ/iKiQHT9h2NjJGjk3wZLtuP+AKEzABy7asq+iK9++w3Ha73ZJ9ZqyxvYyj97yzvcUmQtCykaqDlZwIMslAZZYTJpeXl9Fu75dfYT/H43Hc39/H1dVVbLdV9Tnjm32gsUZTxY+DexOdlvG3Ab/fR0g1ffe+D9tq+0XmwXOFzyV4yeSrcYmxM29SI1jJVWvII/7U2wgQmFFxzHIeBx3ch2DXAbXJG4gYE7C8xhwC3KsRxuNxIV+owmBOeCtuRD1pgY6hm5A5EAFcw/iReCYeYck3/eFFHGAC7o09w/d5Cw7aCYazDcQHEaDTH55FYoSAlvE/ODgom69jA/0mw9lsVjZL5p4m13McQN/xZcynxzOivh8rG2bPZrP49NNP48mTJ9HtduPp06e1Kor7+/tSSd7r9eL6+rpm00wMWMZdeRZRfyO5ZfrHftj+2Dey2f1gMCjz5vMZfyfikR/H0MQyTu6bZDXJ6dg4onoTojeVj9jPOdspuCiD+/JCH5J/LCVlTrfbbe2cTqdTlrGtVqt4+fJl3N3dFSJrNBrFdrtPZLKcmDGgv9gMx7GOq5AVzrF/MHHimBYc4Coxx3yMp0lZsES3240nT57Eq1evalt1sKQPf5u5ACcCIqpKyOFwWCo7qWyi7bvdruyLaTtgEm8ymZQtUly9adxMuyJefzmS+QCTWsgDsuT4iL+ZJ8dUXH9wcFAquyLqscn78qlvTEoxyRhrb9TNQG82m9rbLzj4O/92EOOgje+aAATC6YAZwwpjyT1NjPie3jCQcw2qAZ82OhlgENgAAjyhnmATYIyT+xgRNWefSRTuY0PYND45k+LsiAGcM7MmIxBwV14wRs6A42AiKoKSNvBctx1jQ7vIrPr17DnIdvVZ7qvl0QHSY4SUPzOYZaztBKzM7pOvN9vse5JBe/nyZdEPAgiqBna7ai8jAPuTJ09KdSHzk+X2Qx2WJ2e33XcAl+VjvV4X4gbGnuUcODzLuSsYMIYZrNuYWn+Rr7u7u7IBJyCS53leDQh5Hg7SZDN9ZB4wspATvDFxs9nUskmQIxHVGnjIEvSJLGIONnEqPNukldtjQjsHB5A7dnAOcgkY6GcOCjPZi7zhXBlTgtKXL18WB9xq7TfUhZTjGa4SxV7xd0SVxSIwcVKDefbbiKxnJkHv7u7iyZMnBXBji/yaccutM4Ueb48v4Ig9Tbi3jzxuH/qwPHie7dOy7fP82l8wbwBhAKHJFL7nfM8jMszSF9tEfO/V1VWRHeTo9vY2RqNRLZh2YoAjV3PQxxyM0kbmC9IZ/cInMU+5XzzXsmAwaT21D4T0QobRDQC0ZR17ZV9h4Mj96Ru/mQf7UIJl5ozlPOgrAbfxDCDSpHfEfok4QJtxy/Jh28wP/eaes9kshsNh8XUQhZYHLxegzw6OGCPrWbaBvjb7bI8X9qUJGzz2eQ6EPzQx5aAI3XRViAlf47fHyDTuybUme87OzqLT2W+fgW20jhNY9fv9gtWXy2WxpdhxnuXlHOiotxrAV0Io81Y2iNiIai9GdN/VRcg4S2QyThgMBhGx9+lUyiJPyBkvSSCwxJdDUo3H4zg8rF7Sg/4iz7T55uYmTk9PayTefD4v37vCH1LMZGGn04nRaFTD4uiq94WBRNxsNgULekuRiGoDZhIF4/E4bm5uYj6fl/0ajXusuxAK3If5hLymPdk+YttWq1W8ePEitttt/PznP6/tXYPvxG62Wq1CwngvSsaDvaSYN85BhjOO/k05bKsgRhhnbC9+7eDgoFYtHhG16j/7bJMltnHGLeAz21j8DPcZDAYFJ5N8A/9GRJE/khksdx0Oh2UueIb9MsTbYrEo8TbLWB1zgcVWq1XMZrOyrC0iavsWge3sMxkXx8PG0R5/xtNzEVHFMowfdi2iwgf2uTzryZMn8cknn8R3331X7Mbt7W0h6tynVqtVlq+ir+v1Oi4vL8u5EPn02f6m3W6XvfVYgWSycj6fF/2C/Fqv18X2QLIboxn7Iw/Gg4yH/TqfmUy2XUCHc1UkNsP6/L586VuRUgZN7mRERdTY0XoCDAg5HyfhUmKTFAYlOC0GLzsxlCyiIlNMOnAweRb6iL0DxuAbrLk/BnAODjEYBqD0O6JebeUgEwDsqgn6bSPFM7lfVmCfg4NiuVFEtfcMoMBzwd9WUj539QfnUR1hEod5QHFyFsQG1sbD1RF+trPktCMDTRMovtZjzXe+HjDB4SDPJEiWvaa+GGACAm9ubuLy8jKeP39eMpfMCWAHkBURZY3+YrEoRv9jBcDWC8teE1iwLph0cWY2IoqTMRFp8ornGAQTKBu4e/8qyoVxyKzPxnCSXej1esWR4PDI/jLukH9eKnB8fBzD4bBkUAGe6BwgnmV63n8HYpr9lJxRyHaKfpvoNUHH+S7D5+/smDly0MF4cC+ewzzYxmKzkWETaVT1HR7uXwuOc7q9vY3Ly8vSzsFgUIIKO1Gclp02uuisD7rDXib8TwUJWbrdblcy2+wpYgDsYMTZRMbbBCWA3vqLLGZSljH+mIflwQc2hrG1fbLNxa9wzWg0KnNkMoNqDNtZAhi+ox32C/gu3tpDaTfjHBFlvyNXNzmhhb+wXzeGoB+2K7ylivZBSiEzjF0Tqe+Mqoky+wfbdICb/QlAnCCbxBzPz34PoMmc+W3AbhPVos7wkrXmNxUP3g8NwMxzbbMsC2TKIV0nk0ntbVomb8FRBPVU0ex2+2qK6+vrmEwmRc8gArxsFsDuwInxY8x7vV4hA0xaZVzGdcwD3+ek02OHn5l1yrjnQx+ZdHXARX/AB8xpRBV4YduMqfE39/f30e/3o9vtxnK5LBXAVE1g1yEbCQr9Zj3sPm00yW2bDBl5fn4ep6enNWzJJr7GdSR1aKffbvX8+fNCirE8nKT24eFhnJ6elkoDfH+n0ykkK/3A78zn8xJYo9eMw9OnT+PVq1cFW7Idhfdp4m26V1dXRU4Gg0G5B8Gp9+J5eHgoxBxtcozCvOLjqBrudrulUpO5Yaywba1Wq7ycYLPZxNXVVXk++9LgG7EdBMeMfY5jst9ANviOOOjo6KhsW4BsYRsgx+nr2dlZvHjxojyP/pqAd6If+Y2o9oK0TvymHPaFyBpJE/Arcg8ZCRbCD3IO98PnZd+E/+R5+Dgq+yKqN6dGRM2fQUjZ1uDf7+/vS1U09hiMFFF/mRc2A1IUPOzkKgkbbNhsNqvZLmTbLxZj2bvjK2QTG+cD2+S/bcONAXIcY33MPqTT6cTPfvazop/oM9tDoMPtdru2pJ7qcBI5/X4/Wq1W+fzw8LBs65LjUOaf8WfuqUCOiFIxlTkDjwekc9Yn8w0ujgHz7nb7/WCJP/metkCouTIKom06nZZ7IbfvQ3/fipSKqBw5wCWiesvTdDqtgT9fm0mGiMpRA2YMfh3IGXi7LBxATSAKg+vqDpdBWnkMHm9vb8ubhfgsM4wEUV4P7iCG82mXy/XoixnuTBLl0lwLkQ+DBRNhXtJCG7m3MzAZoGG48jihtHZytI35Z3wYF/qRmWnGiucCbqg4QdAxArTVhoSD+XMQwvnOdjO+tPUxpWkCv/7N5w6U/J1BJn179epVdDr7JTCMJ7+pJKAflG6zJtzA/GMcuf8R9c3lc9WAnQeO9+zsrAAWyJ6IKIEL48QzrIMmLPwc7k2pKJkfnGa2M4A8QCoAn0AS8Nlq7d/0Q6BHxR7OdLerymUJgvkOZ0rGiqV92DZ0ib7zO4N9ywpjarDBZxFVFZKJJAd/jCk2Fmdnm0o7kNucKbUDY/zJPJ+fnxdSkGtub29Lhho97vf7xbFut/u9xwAtBMvIO/2ynTaoJ1DN+93wXIID9Aod4p58ZnsEycXn3Js3iUGEumqV42Ppoo8m544Niagvz2OMkCMH9oAi+ucqBa73EvhOZ7+5qfc0YJ4gEi4uLmrVFV4iQrvxE5SuOxmUgzXLG5lA2uPgfDQaFULcZC79Rg/zvpf2H8YWxgROcDF2TlAQeLD0hnbTRgddjKNJ5YioLbWzXfTyQwcEzIuJG8bWy4SQeWQ6Imp4yxlvAn/eMsq+FJl8Y9xt77bbbVxeXpZK0vV6v/fI2dlZTffAMvm5tjPIojfLdtDk9jDGTcSS/88B72OEGP9/rADYOAI5Mk5BFmmjK1+MR8ChyFdEtdQZooBNwk20IiPMI2QGuBDCgWeZoDKJj8012UMARaUkbYzYyzIkE8vmmHuu5y3EbB58eXlZiBhXexDsX11dFb9EMIbeMud8HlER2wSZyAZ7I2G7qHKAAEJOWYrDHmpUXdmPE+BBnrHvleeCJYmuZKD6Cp+Mn6T6DZn32DIHLIXHfvNSkYiK7DYpBdZAvrg3RB8yBgl+dHQUL168iNvb2/jss8+KrcCPQnCvVqsYDoexXC7LvY6Pj8vby0xiWZ4jKozP978JhJRxHrLOUkviOeJR4i/sOzaPJIVjYe6dMbP9uCv0HCdH1PdG9P50fgEA8tlqtWI0GtWSsRRjOBGFvdhsNmXfN/sNx6peOcUYQQYT7xFDO5FEfEYStAmvmidgXDxmjgVNSNn+26/Tbo8tn/d6vXj+/Hm8fPmy5hcZe2zsbreLJ0+eFLIeghZdNHZhr9rT09Ni9/DhrnB0jIqdYOUXy38d4ztm4nn2I/QRmWFccvxse+Gllo6DISdN4vv579OfvjEpdXJyUvaRMWPHALAEyQDXIMcgzhkvK14Oihi0DDoiqteTe8kASsE9DE68twpOAeM6nU5rG6VbGRACDoPZbJCsNAbCrs6BwEE5EU7aZEdnEOUxYvxwps4u01YHYATaFhqTODZyNiyMIYw+4zifz2uEWkSUIIVsgTfic795DoSAlxJk8srt9XjSPs+VDxMg2QFyHXLAebl6i2vNqJuUc3aC7yDxNptNIabYO4FnmiQkIDg5OYnz8/MSMFgXPvSBfBiUNBEl3hcI4+tNAE2AePwJipFlxg/SiLd/5cwFznw6ncZ0Oi3BDtdatgCmVG3gJHDK3Mtvl2m32yWwYm54wwZ9N7FMxsCvY2cdvasXcOaMpyvKLHeWXVdb0Fbb2Aw8MrnuihI7Cyo+vH9Sk+Pnb+yRbRwbTLuqDeBM2yFZZ7NZ7Ha7GA6HtWweTs37SuRgjM/9JpD8IgB0lFcvkzU22WW/QUYbecHGe48M7xNExiiT4Nn2fIwjAw5nu0z+8L3tteeXqjKDLpNR3Bt/MR6PazYOshnbxKa4JqJcRu8kDXPL3jLYfwJTg3PkA1nmPugKNsYymoMv6519P34VX2tSy9XV+Aba4WpHB4m29zwjE9J8j+wRHJj8sk2wffT2AlR88jYx60Gr1apt3G+yjDF1VSCBPe1Zr9dlCROVu3n8PPbY1cViUYISB1GZFHLw4SSjyQ/+z/6c//nbemGflfESfciElO9l3fqYR9PYMkbIn5fW0q9MqjI26MZ4PK4tjYKAcDZ/t9uVKiqWryHX9iF8zpzTXi9Rxybc3NzU3iBtP0X/wJ7I+O3tbVxdXcVkMilL5JbLZVxfX8d0Oo2Hh4e4vr6OwWBQloa12+0SaG82m1pV83g8jvPz87i7uyvL/SBu0HP+Ztx5AzL9xaYYA6C7vHUSPeZcCGYnnPmhwmixWJQqMTZHt82kYhUdi6hvicE8MUcR1cb87fa+gnsymcRgMIjlchnz+bxUUZFIc/APAdxqVftvOSCn6ss+gID66Ogonj9/XtvDkX7z3cPDQ6kyw8faFjCXTryaNAEv/NgP25qIvb6BhVlyDc5k/I01GQOTWCaAjfEcI4Lp8APZ1xsvQPKCAZE58DH3YtNz5N1YAV2lMhFiLKLal5l+mTjmPmz4TSIDHXSsSGzsgg/kIHMITXjxMZufP7Me2F87Nud/sAnkkquEwKf9fj9OT0/j9vY2ptNp0R9sI3wJeP/i4qIQuCRw0RN0fzQalTcVE1NATO12u0Jq0373i/YjM45LaTtz5hUmyJuTx8ZuJvgcG5oc5XvG/F2PNyalVqtVjTCgAe129bp7OkKQgPIZNBp4OEtPx/g8C5tZf4y4nQGlsc7gEgzZcVi4KV+mrN6TioHBcXuCAGH+PwfltN8BqBWJNtFeHw4wuR/tBpBaIBwQo2y0wWXPVIqYrDHZ4qyuS0UzocjSBr63IDKfrtJCITynZpi5LzJjw50BTtM45cNzyHluZ9N1tNvVZ77OgRJjboduIoBg/fLysrZU7+DgoObMN5tNAUfHx8fFyP2v//W/4pe//GUx0h/qyMCezzzWyD+G1gHaer0u+zCglw76LMfOrHhOXe4NsEV/cXTdbjem02mRZYMC2go5y71pA8CPecUecD7L+trtdgHBJrmOj4/LHkrYIb/1ir00HKxmOXdwaTBhsBlRBXG2E+6nHRB2AIBh/UKPAe6eY2xAztDRXsYOwNLpdKLX65XXhfPmnYgoQQn/Y2+oorLzZK5to/jc9o224CDZS4p52Wz2Fbn9fr88N2IP1p2xtTNlfPr9fik/d/B1c3NTG2eTJb+u4LXp+Vk3bZcySYK/jKjeikQljZdd8Tkl5iz7JoMHALm6uorLy8sCurg/9g6bGRFFr5BfKg4NOiEyucZkLxjA+MAZ1jwm+F7rkpNA9nnImH2O7YqfzfXWY7fJ9t5j7+wnQBLZZ68IwD1+BIIC28S9qAzDp/rV2664ARTTT9/LYwAxy5hvt9sSyA4Gg3KukxIOpKno4DzOBRPQJ6oimKOI+nI57mX9N96x70dmvs9/e35/lW4xtvmzD33Qv5xccBuMzXLgmccAYoKN7CGAIDzZFmA6ncb19XVcX1/XKmb8bGSIpAW+jeXYEVHLorvNzCVVQug1lRlsi9Fu7xNBk8kklstlIZ54+QX3ZVNl+0s2HWaD4Pl8XoLvm5ubWiXDcDgs40sFh/dGcgKCBCpVQOgEYzgej0vfR6NRjVyNqEgk8C6BrHEz5O/l5WUJdrmOSm3GE5vMUvX5fF6qqjKx1G63ywblrj7DprMkMicysBW0jwCYarDsnx8eHuLLL7+M3W4Xn376aVl+ySbn2K/JZBKXl5e17QNsO7OeeYxyHPFjPmgn4+qqYz53IgTyjWSa74NtB7OY4M+2AXLXRLZ9L+Po5Xr8H7HX09PT0xpZDdmKvDnZy/Jz9lVDJ7Hj+HHkp91ul2W92+22VEeBBw4ODsqSNrbEwHZtt9vX3l7bZAczuWSf7perGT8ZGzneyEkl5qPT6cTz589rS1LZ0479MiGWDg4OCu700mnuizzsdruyX3Wr1SpLJol5O51OuS9/o3tOwlPhZoIuy0kmkiIq28p9uI4KKMaELU+YLz7nXo4jvFrOz3/X441Jqcxy03kafn19XQYfUIbA5KyPQYsZdAQboXSHTXJ5PxLA3263qxlaDCWT4Yz4brffH4E15Zn8YbJyBQJt9fK8DHIIBGFAOcdvP3EAyTXeANLjwf1hJlFAxpkxMAhnTjw/Ea8HO1ZYgyEO7s0YOZuG0rCXjuUhLwni3l6WkctYmTO+5/keB98LZTH5ZhnLQR2fMY4eg3w/99/PpG2AfZMsDlRwxGSZPv/881Iq7kosjDrgEqLnd37nd2I4HNaIgw99uI/uA3121Z0zLyzBzMEeug1Jxbh6Lr2BsAlswLUryh4eHmI6ndaeZeDD3+12u2w46qyT36jGXKMrXt6EPHu5F78joka+40QhrSLq+wwgH9zDNg1nQN8tOyaOmpwOY2B59RjYVpgEz2OFvgFqHLwaeAAwIMDIiLlS0rIBeUTWh+eQVeU6iEcD/ew8Ccrd9vV6Xap2sU/MkQNhxo6+4wP6/X4BeN6HCjvspMjHPuxPDOr5jAPQauDF58wdVXwmFPBdzAF9HY/HZeNNz/FisYhXr14VWd9utzEYDOL6+roEj86cRUR5kw2ZWS+fdrBoPGCyFcIKGaOi1nJiX8NBNU+2Qx4vQCft9WuVTcZYJ02+QbA7uPfc8GxAnDENGVATR5vNpuz3ZYLcuu/k12AwKHsIgbPyHnKeC/tUgLPtBngmIspSgdFoVPye9QbbSiUFukjQDLlrMG5cBLjmXgTfrlJ2wIFPMBFiO+BAsEkXrCvWr+/7/kMexha25dlGI//4Acuyx3M4HBbSBMyKbUO+5vN5vHr1KqbTaXmDrMlAfC6+CGKKagCIbMiuiHrQQ8Bpgvfu7q7ga4K4u7u74lvRp263G8PhMNbrdW1fG5I8eV8eV9vir1utau8WiCZ0+vT0tFYZbUIAAgfyhpjAVd+cTxv4Yawiotg3qqjxgYyP44l2u/1aJTdjiw6z5xvyDQn85MmT2n6OzBsxDMkhJ4kg9fB1TRWtEdXbCs/OzoqN51zOu7u7iy+//DJarVb89m//dnQ6nXjx4kXZVLvV2m+0P5/P4+LiorTJsYf1AJuCDfx1+du3PWxnjLUcH+DXGBdXrEJg8ne+dyZijMcioqYHPo+KeIhgDvw4OBTZcTyMz4mIUp1+eXlZfCJkGvKL7NHfiCgkDboJseKX5NBW4uDMCxizZ0LJGNgVmcbbjtXsIzjfdi7Her4mIsrLm4hDwJwQN+il54xqTRLe3W43zs7OyrYTkLmbzX45JHrOW8TNE4ChsTkRe7IeLATm9aoK+sG8ZPybx5bxhpPABnl8GTf6j9w4psmx47seb0xKHRwc1PZW8UQ70wizysAgjHaqFjAHlhH11xzaUXqwMfAMoskllwOaLLGyo7wMuhX8scAQRcKpujKKtgJgWXuegVUmN8yK50AbQ2GQ3TQWjKOJHBNoBHYYM8bJ98MJOUg1sWPgCqONgt/c3JTAwQED92CuyEIBBAAOAPG8r4mD4jw3bpezMCge31vWOJrIDOTWhJVZao+vAwq/MpNnez5arVYsFot48eJF/NZv/VYxxBgR5JUxonT6008/LRmrD31kI0I/7EiQF1ceGeQ5OEH36J8rk+wMMnno+WHDRYgkwNpoNCrVjSaekSNeSWubxN4rZPcJlLfbbcna4mQIDMlokEV1tRH3W6/XZSmUyUbLl3XQRL0BH7LgygRXjGQZtj2NqFdyZIfsalHrg8lG64/JKDJ3BJyTySRevnxZnGu73S7Z7+FwWEr6yegy9u12uyztJsuz2exLyptknHmzLcfeej82fvt6ABHXmwQBJEGGMY9sLGu7Y+DJmH+sw8+lTw4g7AMyWcncQh5wvjfo99JxCL+jo6MiywCXiIjpdBqXl5claPKSn9FoVKoXyfjj6zzGBk7ogn2PCZeIKLqYAbvv02q1asv5kH8+xzYA7sAm2CPaYJDqfnMuNpC2ggmso94zxUv5DfDwk+iJyXICFtruecUHEdgw9gS0lhNArO9puQEjYdMgm8BqkI4E2SzXQobAEbbljBcVNd5CwfYMGWUOI+ovXbAM83cOWIwROcf+238/pk+P6cz7ANK/6vCcInPGg5xjXO2kAn4Dm8fyERI2zAX6zD3W63VZAm88yw9+GrnJAReVOvh9V0A6UMHusD/rzc1N2VcH+4K9ubm5KWQkFbi8rh5yhIQBMg9O9CbLrgKxjtJGbDxLThlj47pOpxPD4TAioiwHN2axbmLTqA6/uLgo80rw6qpBVyzvdrtiI5l/9pwiOUkijGoFlvx89tlnMR6Pi+04ONjvxQUWj6hXu0VEqQ6D2BuPx6VS2LiVii2IaHSNeMG+4v7+Pr788su4u7uLv/W3/lYhKmxLnz9/XgoU+BwyxMSYbfJv2uG4lcPzSgIIOcDOuZLfMUVEnZgxEcH1PI+4yYkC5AdCydiH+QRDMecRVQFDxJ7wQGfxn8ij94Q8ODgo+JulrdvtNubzeZyenhYCC8z+8PAQo9Go7LWED9psNrWKH9th9JuY1HGy/b1jtZy04LD/wRdzr8fiOifmIOfy9eB+7PLBwUFMJpOCM7G56/W6xHXGPSxTRkbgFmgLe516Kbb3oHNc4ASWiyTAAI4VkCcwiw/mKyLK6h7jj263W9v2wrG55fhd/ekbk1IIU94fZrfblVckImwR9axQRLymZBH1NfYIfBNoaCJi+G6z2dReB+625QqidrvaQ8pCZlBLGx0w+54mcmh73lPH4JfDhsVKFFGRYvSbSQdwmCzKoJVz/fw8Zs6GWYm5nmsNZt1ek2YRUUoZKQ/mWVmxfA8CBO6DA4e46fV6tX0t3F+XHGYQxw/98dyYZLICo7R2LiZg6JODc8uC5cPsPNdnwDudTuPly5dxdnZWGysTPrQNAsDVRR/yyESGATzPp6rBhpyNxR1EeryyQzSzzlxn5wLRRxCcCbxWq1UynDc3N8XoDwaD2uvnGXucI0CRORqPx4XgYPNGKiCWy2VZw82eDQ5IuReBpeUaebUNJENsciHbQOTAMtlEILlvJiYsw9adiKrCEnuYA8EcyNmZmURgzCCht9tt2byWvSx4LtlpQDSOGgKWcmiACcEuY2d9tywaxCAfOYgD0LvfBPQ4Yuy491bgvsgq51qfP8bBfBjwctiX8b+JOGfy0SfmwlVSEA7M+/n5eRweHhaSEQB5dXVV2xeB+9IOlgA60DU45Drawhi7Lzk54M+QWaqBrq6uyny6Oo42of/2006W8QzALkROREWKMab0y0sfPLYGrvh7MpkR9c1e2T/R48E59o8eP8B4k02ATIUo9LhyD8bNAbpBrv01mXP6jl08OTmJs7OzsrwTewpBYLDO+GEfIY05IDQYK87FD2bAb9LEZA3j1aSTmZD6Pv3y+W9y3bsexpcOQPk/Y1BstStYjWshGSyPNzc3JbjEP7JsiDdleuklS9V6vV6tignbZx+8XC6L3cbeXFxcxKtXr2p2EryW8TL+j3sfHlYbhm82+yXZ2B77OvYwAk9DyJL8hszpdDrl7XtsFD6dTuP09LRgCsYZ+YV8Qd+9BNAVfcwDm3v3er24vr6ukdQRVZBPfyOiNi6dTqcsz6ENxE/4IJZR3d3dxfX1dczn8xgMBnF5eRmXl5dxdXVVdC8iSoKYuaIaGSKPOUbXGQPsOptddzqdYv8duxweHtaW569Wq/jqq6+i0+nE7/7u7xaikGo1E/PYWlYD2O9a3yM+3hJaDsiHtz0cC1kvwW/MN/EyNs72nVgZGUG++N5xjAsgsNUmGlwxRxLXSY/drtq3CBvj6sHtdlswLy/UoO0RUWw2dp05Pjo6ivF4XGJBJwOn02lJWGPvvezbvszYHFvv5JD1ybbfPj3H6jluj4jaOYy55xI7hGygM77Xer3fJujw8DAmk0mZAypBmfeHh/1bqXlZg7e5AN8yb+wxZWwHdmUM8ecPDw/lBQKsUGHM4AkYF/rMONq/MMdZhjmfJcYuXoHwRPbsj5vix3c53piUYsBwklQmkIVwppPGIvQInJm6DGD5zAObAyZXlTCgKKkHJBsCJoOBhazyACOAfGbwGFERYM6qs3lbXkZHu23cmXzu5fsaUFoBcJg+N6K+j44Fy9kf/w0wdb8oszYI4DsLqIMJyK+IKFkonr3b7YrC2vm56gZCgXl2gIKy0j9XruQjB9X8baWzEUfWDMg5PF8YC8uQia0MiJF5HIADLRvXh4eH+Oabb2Kz2cT5+XmNgCEAJ6sFMLF+fOjD5Ib7iC6wbhzjx5IB+s04eb25yQ8TOC4P5zl8v9nsXzNv2bOxtSNZr9dlLDlM5HU6nTKeEFDtdrtUWrVarbIGnAA1IsoSB7IG33zzTVmyAmjebvdLJyDC0Asba5O19Nf6zWFHiry6ioDflifGnvGxrc3jmgPa7EyyjJkE5H8Ij/F4XOQBQEvVlIESsnB8fFycm7PHvM3v8HC/t8dwOCyBL33jucgSOgUhiH1k3jwuyCQAwWQAwB17hsxQ+Ya9sp5/TMCc/UH+Dnn2EhFnEyOi7GXkTDeyiy0GDPpNV5CPl5eXMZ1OawAYOeZZLPWjUsI22TbToMd2hqo1zzX2OWLvi/AnT548qRGGDt4zGOVgjm2PGC9XeuO/uT/nRETZwwnfkHUxJ4vYG8d+1pVQLn33GEEe8UwHu26/bYn9tAM8VzwjyxAT7LkJQWEfCz7iGfRju93G06dPSzDtqkXGLFfKYz+MFwhmvI8k+tvv98s+dBwmbJrsJedkm2o58HXIQvb9TeTvhzicCHSgZNImokr+OmA1WQexwcb3rtyN2AeRl5eX8dVXX8W3335bbC/7LBFYUcHEEkoT3RHNFfcRUZbkQFo7AcBcUJnszcSRaZbzgMUZC2/iz15Fu121FI6/7Z+c2IREY57xQRDFvEDG8oHMUuVLHyCwwNHYC+Tl8vIyrq+vy3ihk+ytQ/DGfEFsM5fEG/wPZmZvLAfI6MmrV6/i22+/jel0WluxEVFhdMaZMeJgThg3/ILH1nbFZAqYyX7/5OQk/vRP/zTa7Xb83u/9XkREIQf7/X58/vnnpTLTts54w/jjYxNSEVEbn7c5mBuvesDuIe/GEr7OxJRtl793PIVORlR7bBpbg5fZK67T2b/l0i8f4P7GYdhokomz2axgJt7KiazQJmKU8/PzgmVJyMxmsyKDrVarJByN+yPqK3AgO4mRc3+xibaRGUfYBxsT2E87vsi23tjXSVGu7Xa78fnnn8fl5WXxdSQDiCmwveg9+kPbIJxYQu0VG7vdfsnw/f19DIfDGpnk56F7tIs3YYO9ia2dvHfsm+W+Sb5o9/Hxccxms4IPIKRc+WcbmYnE93G8MSllUgLFwDlFRA2o5GytnT8T70ytJyNXiGTSod2u1pKbyLLDh61HoGnrcrksAm1WG3CHwTDAN6lEhhmnG1GBX86lnSgC93QlU1PfaEMm3HwewSFjiYHhWWSVMtHC32Zh6Xve08VC7TExUKU/sMNkO7kfxpiSPwfkZmn53IGMgw4b6KbDgaPlIYNWE1Y5O8G8AGpQ5mwEDX4ZOwdQtMX3s3GEvB2PxyVg9j0gCT1mBtUf8sgBo4FzRH0D7t1uv1nvkydPot1ulwoZgi9nzyOiLAWxHvi16iam2fPEJAfzwhg9POz3a/OmozgI2moymnJ1Xj3tOcbYEyQBxiAxAK3z+bwYZZMnzDlVjbTFAa71GNuW5z3LuUnOpgCMI9sP6xTg3/va0WcHtZZxvqetOCpXplhOAWIErAQoZICorDDwJFjlPpl87/V6MR6Pa+Bpu92WPewIoGhLlk0HpyYg6KODeBwubXZlFwF0E+HxIQ/m2YfBVAay+D6+gzQiY+7sPIETS2fa7f3mtJAvk8kkbm5u4uXLlyWos13A95nwc0LEyxPwPbSHNkPGz+fz2r5tEdVbw+y7WfYDQMJP0m/k0gEkY+Mss8fGFd0OKg0irbeudgBLIIO+Pvt8ZJR28T/jY8LGMnx8fFySZyam7K8AvgTm+GPmwG3EnmMTnjx5UuZrNpvV5pr2YTso2b+4uCgboePvaTvYg3FFz/zGX54fUe3racLUvtXBRa7C8Zz5uqyjOYDJ3yPPgP6PeVg2aStt8lzgk+wLTk5O4tmzZ9HtdmtL1/BxFxcX8d1338Xl5WXxXyQQCERZzksQgiw4mEI32Vh8uVwWMsYBEr6dzZXdbhNI9Au7RMUteJBxQXaRVYhLiACWF2HDcywBDkD+/XyewXm2HQ7e1+t1sZmOeSCObm5uYrFY1HQBu0T1LXtjoX/4ZcbO8QO2AT8XUS2nur+/L0uBqEaiqoVqdW8Xgp1kDkme+/ODg4Po9/vR7XbLG4tN+NMu4iwvEYS4uL29jT/5kz+Jn//853F2dlY2XF+v1yVhYV/sRLGJc2P434TDOIbD2IRz6Beyud1uX1vu6HjBGMz2AAwDzjTOgZBCtjebTSFLXamDnHpZ6mw2KwlfKvMWi0XRBydXR6NRTCaTmm2HFKaCEtsUUVVX0c7lcllIUOw2WPoxrMNYeExN6uU41ckzJ3JclICPRV8dG0fU3xYbUW0ZwJxFVNuLrNfrsmcqeoeN9m/sxJMnT4o+Y0vAXVQzbrfb2gtojIMYO8dV2A6TeiTJm2IH203HbpCYTgyyBzL2zESqY3x89vvU47eqlIqoAisCM0rqs7BEvP62EA9qRMVumpHl2lx95EwTA4ATcLDlbApgCcObswmZTUVBUWD6dHJyEv1+v7bHgrOBKKUBOW20Y47YE0kuY7ewozQeB5NADvYjomRVHBh4SUJuA8IN4GB8ORdwk0EcTh3FAUhi4LiGA4FFASArEGxXpZn1RwEcYPMMPqOtvr+NiAOCx7IhfjbyBDCiDQASGy4rt5WStmDkLZ9+xmKxiK+//jo+/fTTkhEHaLG8g3n1ppw/9GBunbFpGgsftNfg0kECYI378zfy4OybyRmMHoEO44TDpAQc58sc4NSRIRN2JiS8lwyEx3Q6jYuLi5ruA/yQmdVqVTIf6CNZXIz8YDAoJbqj0Si226oEOu9tw4G8ulrFZLXBBbYgX+9zclCFbFvvXHHhOWJcIirgz/1M8FiP2u12baNSdLDb7ZY33OGgsGds2EzGjvlz0oBncr+IKCA7IuLs7KwEQzzfhGNuu52wxw0Zsi/wPQCMAG73xzauiST6kEcGpO5vRF0/OR/9QAfxX4AbX8fhagb2eGm1WvHq1asS/ABWIIcYa+aDe5s03u2qzekhJTab/caeef8TbC39cB87nU5ZWoJue17cJxNbtlUmcrFF6LfBKefSZvoN+ep+EcBlAoVncmD/wQdgDBMqnGO7RVtJYhAwcE9XE0POe78O5B5s4H11GA/v38MbF9mLhHGkuoY94gDOrgiJqOzMeDyOzWa/z46TUthb/AiBFddvt9XGsc7i8p2DNT6zX6dPDi5M0vo8f79YLOIf/IN/EP/5P//nWC6XTar4Xg/bEZNltr2uXGMZKJvjg7d5IcF6vX/LHPK5XC7j66+/LqQRlQgskaPfo9GoEL0RUXQSAsUVEiyxZ285iC5wgZfjYFPRFTYPt3828QHB7SQq+oe8oyO8VcsbiWNjwAW26/xPJbNfysP+Vcj2arUqeN6rMIyNGReqQpHf9XpdfBRkFuODrQST4zMZA+NT8AfPIF4hfkL3/KZwcAefYctNcrjC6/b2Nnq9Xpyfn9eWKfb7/VKlAUkdUQXoruhEr2kPxNQ/+kf/qLyhlzcBnp+fx3fffVewHbbNgTbzZRv6m3CgT9hscGBE/SU8EHsmjSgciKjvZRxRvTHaNs8FEtiK3W5Xxhqb7wpycBh+GX+AnEJ4gsFddUl/NptNjEaj+Oyzz8pcQ+R6+w10fTQaxXw+L/je+J12ueIG0oWluU4wuUoPHXFyCVyNrbLfNTZy5WTG0y4eQA6tO2AvEheQffbP9In24uu9d5xxMcso2UgeDIZfJL6A6Kef2DSS8vhzYhbkER1mzm3LaIvHgj30nGRwHBBR7bPHPPkNrB6795nYeatKKRyKKwV4RXF2sCZPTNLkAcqAAsCWQSZtALBiECA5zOxTiUNARDmiB8/ABaNrA8D1ZCIQBBQMMOkyWgeZ9AMnhYEx4DKz688iKiWMqN6WkhWG5xjg+TyejyLRT8+NwZJJBCu3hZvlN86G8WwcsMfj/v6+ZHIyWZWZ3ExeMQ8ZXGa59Bj4aGJvHahaNvmN7NgR8zeGhnHmGZYZAm6ICI//er2OV69eRafTid/6rd+KiGqjXFc45MDzhx4mzt7kXsgN5JFJTUAhTgcAxBiQ2TTxAKHED/Nu8g4n/d1338Xt7W0JgizL6DlyxNyZcEDmvDxlvV7XshMGuQRWtjPdbrcQUBH1N9xZT3BWVHDQb3SA/rtSwbrLuR4bAgHaQv8cVLvix1kgj63HzgGcnbIdHjYTh0cAYYKepY+8Bejh4SFevXoVEVFKiVlyim0G3Jp4IDBg7Amksc/tdrtch4MejUYxGAxqS/G63W5pN/1ARvAPruBlnFzJke0+bSKY45XjH5OQYs4f+99/G1jxP+MLecwyvogK8JKdBHSy78jp6Wm0WtUSLg42ON7tdoU4NImTyR0CytVqFS9fviwl7SRwuHfOYNpGUA3JfhVZx1394OXnJooMHrnWfpB7YndNWCDvtucAPuSFz7DXPJ/fkDC00cuQsWnOcpq4xeeYfIuofIUJ7oeHh1itVjGfz8tYUwln24CssIwpImoZT97chc2/v7+vvW0IYuT+/j6ur6/LslvGkGDFiRwTiASy+BEna7g3n3O9dZTDftey4/G3rbUsc6/NZhODwSD+23/7b/H06dNotVol6PhQB3aJNjpj7fbRfvcbW0yA1Ons9//hxRHL5TK++OKLePHixWuyw30PDg7ik08+KYQcVQj28QTL7BmDX7Z95nNst5dPE4xmgtUVlvSNucdOZ9IDmQBvP3nyJJbLZW3PqFar2jcSUoZ9WCFrLSfIKG+gdBKN50C00Sa+ox85GQThElElia3bxh5OFHE9bYQY8JYafqEQOkZwaMLJpMfNzU0hmkgMYauPjvYvF2HMkAviOSpAvCzJQe7Dw0NpE3b5r//6r+Pi4iJ++tOfxmKxiMViEaenp/G7v/u78d1335U+uqIEfeC+xla/CQdjl2M5/BK+Dd8ZsZ/zk5OT2stduN7zhz8y8cuBH6NqLiJK0hNMyhvewLnoyN3dXVxeXr4Wd+92u/jmm2/K1ipUTJ+enhY/RR8hyB1fnpycxHA4LOQGlYVPnz59LYaMiJqNwZcax2U5cBz9WAyWiRQXezg2johaDIJOuj/Y6Bw/u1jBKy4ck3hVQqezr1BH/+kfY+bEBHviIiuQzPAF+Er8Lzp/fX0drVar7C+FjeJv5jrzGx43ZAobvdlsantFYxexC1xPjGvc9L6ONyalCNRdlUOWwUQMA8Jh0sAsskFoU3BmhTdxw2GGjoEFIJkQmM1mRTnJ6vtZBpRWVpY5WFFMnCF4ERUja8BLe3xvg6uI+lvqOM9gGsdr4OAAlO8YZ4M0kwkmwNxuz5nBUg5+eK6r1txvE120yaAKQGrS0OOTDTzKyLg9Fhz6HP5v+jE4NTGI4gI0eBZKF1EZPANeEwJ5rJBFjC/99fWU3T59+rQQUvf394XgZe7f1VH/xV/8RVxeXsbv//7vfy8x1RT4YrAASMg0QJH5NBkJyLNxJ4PA9wAlywDAzq+ORo7a7XZ5DWoTEWNdpuIGh73b7UrZLJmh29vbYhvQBRtd2simn9gMKilXq1V5W4+r4tbrdW3dtTONyBaALAdKrjjLNskklL+3rLlywHYGZ5nJKusTtgbgysGzrbMQzgTBVHLgmLE1yAkEB1k4snVsJO/XjNOu+/v7uLq6isViEdfX1zGZTOLZs2fllcMOdhgPj10TqcNnBAo411ZrT0Sy7ADSAIDHOW96fJ+Ovc09Hss6IVNO+vA8+wTAD8EAhAPyFxFFryL2pf673a5k6NnjgGW29icmjkx8OFh99epVbRkLckdg5QDNwRHEF2+rYQmCfR3yjdyYpLZ+uYIjoq43tJlxcJBF9V0TKYJNxk4wX4y/n0E76LN9MHOF3Wc+GbNMhDuw8TPta4+Pj8v+FATd6C/9pxLV8+C5QHYMTsFLJCEcpGy32zg7O4vtdluqc7xclh9sFGDagTIBAteYvMuyz1yZiPq+I/v+JmxEEPexjhyEY9uRK4hjDkjyVqsVw+EwhsNhkWmI2z//8z+Pr7/+upAakEJU/6BHHJ1OJ2azWVlaksfy5uYmrq+va5tXI2vMD/6OPjDP3ggdO8uSa2wGiShk3VUm+EKCeO87hp1BT7HnyK9XOHS73bi5uSkv42C5Y6u134sFUg0bSAUDzzZJjF/iDZPof7vdrmFg2hcRr9lwV0+wrIrldRFRCFmqpiKiVp3K2xNdkeGqK2yWkwqQS5PJpFQeEzizhBO5wzZYVqjaOj4+rlXDRlRLjtfrdfzZn/1Z/O7v/m6cnp6WbRLY6oSXYIGJ7Vdtx35TDtsSbCnzn+MsVkBYnqkGRM8d33Ge4x4TubvdrmxKjj2lHSYXfT8qtXghQUTUVsqwvxz7mf30pz8tFcEuNsEmobfoP+2jajciCsGB3HQ6nVgulyWBi8xDsDrOzUdTXJrjAPt7J2DAKk7O58QQckgMgB8HU2DHdrtdIfWoHIU4InHqPrNn6sHBQXkRG/08ODiITz/9tOyr6Thqva7ePOhkqauPXRRzcXFREoz032Qbf2cexVVkxo3YQAhq20DGw7G6deF9HW9FSjF5BCc4NTvWTJZwLd8b1PCZFdzlaDkThrI7k7bbVet2UVQUlA27IqqsoIEe5yJIDrwRIIAj7UBAuAeOFoNiMIBgO/DEiWS20cYJhec+9N2A2pVHzIsFn3s5y0zbLGRZSV3dQdsZf7LDBJmWC5TNVVLeD8FvdmIMXGljYs+G36DJ/bI8OBPGeb4+os6QN4FUB++USPtz2mm5dFs8np4T5oO2Mn5ff/11zZBDDriU9V2Pf/7P/3kt0/XYgd7a8Pu3l4RhRMmatdtVdQbGjIwpDswy7+ws+sVb9CKibDw+GAxeC5K8AaltCuvhCcpoL/KKjlPCvtvtCkD1konLy8vyRkGT3MgAG7yjx/zNfJtQysGTA2kO/28H4s/pp2Uo/2SnwHNpp0kYZ9IZc1/jZ/E3NghCicyO58FVLPSZQHa1WhVw7X1v3D+PATrotyN+9tln8fz587IPRh4/jx19cfCSK1yzT+GZJnec0X6T4304Ztt42yknYXIFHn1Cbt1uVxUwRviWTHQCliFdb25uajbcth6ZYNN6kj8mgZrk229Q5HsnCsgOE9RYXo0h6Dc6bsDpsaTttCnLuKtzmXODevtHiBcCQ9qU5clj66WHXJcDOw4CiMfuhX/O/ohjPB6XILDVapV9Xl6+fBntdjtOT0/LfRw8D4fDaLVaBXwjYyxVZp69T4//x2YCjkkwRFS20hUEPIv5Y/8sKrwiomYjOM94J9tW26zH9CrLhmXwQx+2DSYefRCYNNl35g8SfTQaxSeffBJfffVV/J//83+KDENuROx1qd/vF2IGf02VLxlxMO/x8XGpcLR/NHkCRib4Q3chWUx0GAdbVwmCIU3QqV6vV+Sdl2hQrQc5hhwZSyNfxr/r9br24oH1el0q+Uyy8D3XGiPzmUkB8BvJw1ar2isNshe5wp7kiiNjcuTARBj7zECuQSJCetleOUYiKN5ut+V8SDdsP8SUg1SIQcYWsgRbg41vWiVxf38fX331VXzxxRfx5MmT+PLLLwuh8fz581LNge5hq+m3SYPflCPHHcie+7Tdbgu5AIlIP8FKxiL2X+gWySI+m8/ncXl5Weyi/RekiP0jMfqLFy9itVoVcpbvwdSbzSaePn0aw+Gw6DfzTCIXAiUiynVskUGSEtk5Pz+Pk5OTshcVB/gKsssFLeAWJ4uNSWkL/fZ4gofynBhj+jeHY1NkEpuVYz9WiKxWqzg9PS22lpUZ3377bSFdf/azn5U3eZNUcAIKkv78/DweHh5KBToHcZOXEzPu9IM+2q9yIHOOszLeJdazrOATnbw1CWWZy9XM7/N4qz2lbMzW63V5bTCNtVF35hHFc8Bgh2vA7M5z8J1ZQCu0q01cfcHGbRFRA3MW3oioOTT2rImokxoEdgTjEVWW1gKQwUQWCJNCGOQcUDlIs+JxmNAzmDah0xRImDHlHDtc7u32er5cwcT4+HuqJTBOzkJDBOx2VVmrAwLOAQDRXwAQRjJXlDgos5Pm8N82bDn4cJmis+yZYWZOTEw5q805yJqV121arVbx5Zdfxu/8zu+USoZWq1WMf65Y+yHHf//v//2NzvOcYuScxSbQ4HuML0APB4ETjohamS/AxjqYyWDGmOwomcnM5FtWWB+/Wq1qr8SlLwafAN+I+muUF4tFuYbMx9nZWXEW2DRAAtlgb8BMf7IDsBxZB3PVhu1FRLwmT8yR++4gzPpLX7kXASbn+9wcpGeZIIAxqO71evH06dPYbDa1TTZN0kF4EVBwPckDstQR1csaeGZEtaE9YH21WsU333wTDw8P8ZOf/KQQll7mgVP1/lOMoSsH8ibVXl4B6EBGmyrbPvRhOYiIGkHtUnfsmPdQRH4JOrHBTQG5N791koHlLfzPhr2QkNhIqjLY04Q5w3ZQVQBRjB23DNp2RkQJTMEU3uuAtmY7bMIJ/bCe0J5MQvA5fzM+3jPKBBFtJpMIAe/lkLQNgL3b7WpzZ9LOuu/A3rbDJBy6yFi5Pwbu3h/ES+IiogQ0VIGzDDaiIvBNImVyljnDR3i5IMvI7u7uot/vx+npadlfivvZxzqowE5nss22E11w/31kTPmYPv2qzz/UkXUa+2PS07Lt69br/fK1p0+fljlns/r//b//d1xeXsZkMqklH9BBkjCz2azgmc2mqmz2Xkb2o8bUkBYZk7vSAF3GFhDcbDabsiF7q9UqVbUkp9rt/f6BLDfmrW2QJ4wPbQcjsMTQS2f4HjzR6XSKPhsbeDkiupexQ7ZP3D+iIloJaBkfzye2xXiSsWbPsMFgUHQVAoL9W7A3rjRlOxF0zRgzyxXze3Cw31MLovH+/r68SAS7Qp9I0JkY4G2sLOdFViMqPb26uorZbBbPnj2L09PTWK1W0W634/nz5/Fnf/ZnNbtiG+eA+mP62Hc9aC/zSYwDqTqbzeL09LQQi7wlDf9M9ZDJgYgq2W3CEv8wn89jOp1Gu92uVe6j561Wq6wQ4D4XFxfFBjP+YEOqmZ8+fVq2ZGB/6IgoLz1hvnq9Xtzd3ZUqSt4kx35ko9GokE6ZWN9ut0WuI6JGnoJXrG/ug+29Cy4c8/K/ixyQOfxOjskjooZZHNOaZEGX7u/vS5KGyif8J7JArPCLX/wi+v1+eWstmIHkj2NXCGCq9dHb9XodV1dXtTjIftDJFF5ewfJ7cwg8x3E6/coEv0lCzyHj6uQz9+B7b9Pxrscbk1I4BQSbrAtG0IwawpKJEXfSwb4FAeEyAHGQz7l8x0AyyBF7ppG3ynC9SSADEhwUIJ12mhiJqNZr4yRgUxF8ApyI+npp98UKwZh5bAgguUcOIiFw8rkcnAfAczknR67EcPDmtlkB+JusGeNpQGX5sIB7HDBONkQcKAOBho22GXQHmiiJg5NMUPk7y7JZ98woe05sQEwGPEZGud95LOkn10CqUFFCiXrT3P6Qw+P/fUcTmDfYtD44o4F+Mad+K5szaiZcIRk4AGXefJzyX+sItgagxoalLBki2+BAjedSRk2QBhFChp57Q3DQvru7u1L2TgUJALrf7xfZwrFwOJNp0seOL+tbngfbzixr2Jc8X65oshxxvisVLPces0z28zzuzTm8Ycc2l32fIBwhoY6OjuLly5dl2QOAmDkCYBGAr9frUi3D+KxWq3j16lVst9v49NNPS4DG95Yv94UDMMffeV4AFgbpTWTdhz6Yn5xAYZxzMgLAib00MUJWLiJqy6oiogS1VHOy7xFj5OpHdA55XywWZbkH5KPtJKTj4eFhDIfDUgJv32HglOcLX0Bw7P1ZrK/ZFtve0Q8Dxqbn2a65vD6Da1c1EMzTFvbscMWWA+NMDNsW4KP420kc7sWYUEnFfKBLEK08j0ol6yR4hcQbASgy70Si59JVMQT0PHuz2cSXX34ZERE/+9nPSiWKdefk5KSMDfjBtsx4xMk5+px9qcG0cZbtpq91UNNERH0scsp4wjKR22DZjqh8yfn5eUwmk/j2229jMBjEcDiMP/3TPy2bSV9dXZXnsKSayjNklTEBrzLvL1++jMvLy7LfGPYe/UZObGfxqyyrz1gY+aLCBlKFSop+v1+wgolVL/sm+QtmODg4KC8cOjw8jMlkUvNhrVarxCXIK7J7dHRU8Aljjg+i7WAQfKnbBo5B9rG99NUrG/wcV4tDSjhuMj7MCWjbBjAHZB7kH5UnrurAZmLL6CNvPF0ul2XPLctgr9crSYHdbr//HKQ2iWTbC3SLt/ERVJt4ozqUMWha+vybejCPEVFWgoxGo7L5PRjG+18iI47luJexnpet393dxfX1dU1ekEm2NeB56N3FxUXMZrOSbEPeWYJ2cnISP/3pTwvORpfRSxIM+Labm5uYzWa1l0I4HuDlTKvVKq6vr6Pd3ld2Yn/sz8D3+G4qDy2LTQQwfsxkncfO88I4YlMsr9hhk1Gcb37APtt7PJGsHo1GZYkr9na73cb19XUhjjebTZydnZWtK9rtduEc7u/vy3iwLBv/6orHiCjxhotaHHua68CWetzoJ741x8hOZEOc4qsolPC9POY5xn3X441JKSZrMplEq9Uq7J+XRBjYOQBwgI2AeML5HmedmUs7Hb53wMH9XGVBFiCXzQLenM2xo8qEV7u9L6fljQUYep5Le3KlhA2Wn+vAJwcgWeAYX9+LMXFwYoUzeM0OzxVgOfAxGWOljqhvKurSQDtezjOgdMbYwQjBBVndJiNgoGbD7cqMTAhlkovnIV8snXBwjqJlkIsDNmDiHswH82+A6cAqt8eEAM76m2++qW1YyvdUCbzL8avIqHwuP4yJs/04Scs8RtUg1jJMQGTww3i6AseAtt3eZ04jojbXEdUbDiGHIaa4jrY5GwXAyjLYarUKCeg3k6xWq1IR1Wq1YjQalUyX35DovXGQf1eDMBbcx7roINWfuTqhSR4ts02kpUkJ21WOpsot/udcL5HBFjrbhD6xIbwrzcgqQUqi/ycnJ/HJJ5/EcrmMV69elbewcG9AkYN/dIS5J+M7nU7LMiRAkJ0wwQPOE31ERqmEyokUZJHPPEYf+8h+wW2AsHMQS1vxV7whE3IGoBlRbQJqn2KASPUfYAlCCZBq+wCJZTIRu+9gEb3Aflu2yNAzf+5XRLW0m/OYEwNixiwvS8Em+F4c9tHOwDpgMhmI3XLggI9w8sdBYETUsuL2cTzDCRECFu6F788yGVFVouLLTCTRbttW9JD+Mq+0mWw7MsKRbQXyQRB6cHAQ3377bRwcHJSXd2w2m1JBa/00fjBIpu85UPNvk5AevyZ8aNxg3cmY0nL2oQ9jLOurfQFjgA1DLyASqUz82c9+Fg8PD/HFF18U8nG5XJaKRqoXTAJ73yZ8+mazKUs8IXMiqn00wWi73a68/tx6jEy64gZsyFK+wWBQguDBYBBPnz6N8XhcKqGMvZBVxsVBlYkd7Bm42htpI0/ez4r2ZgzNedvttthM+w7kBLIlotrTBQKr0+nUKlIjolYFyP48JmuNn6hKhdhnLywnlNrtdiEYwSjYUYgJJ/6oZLW9IQ5aLpexXC7j6uqqzAW209XhEXvdpBKWN5DzfJPLm80mvvvuu/it3/qtQo5cX1+XZB5yA6ZlaVOOa35TDtsb9AtMEVEtQedcsOZwOKz5A/CSK9Mi6nbK/t14iWpXt8UkM+MLYYUMmiwjoQu27Xa7MZvNYjqdlpfMzGaz+O6778qcdzrVWznPzs7i2bNn0W63a0s8e71e2ZaEqj/HnvQDHXMCyj4Xu5XtO33KPtl/e0ywBbbBEVWSA/vgQgXaSgwBmbrb7Svf7u7uYjabFfvjdp2dncXV1VVNF8fjcSGWwC3GEZC5Jso5lwo0j0VEFH2E/MfW2ad4LDK24nt+SDCwxBkfz2EuB7yX48b3cbwxKWUmbbPZxHw+j4h6FRIDwmGAaEHIINslYZzDhGVywt/lQcAAkpnje5ysB5CgxzvhZ8dCsLRarcpbvMiK4KQ416CRDAHG3cEP/fPfJoMyKYRwWZAwNq5WyUDfZBXXZJICB8tzcfKw0ZloySSNQaODbxNrGBqME+PrjBZZBhOGHgsyrLQpzyP953wHAR4Hn8t8ZYWlv96bKGdzLVMeHxtPPyODYBOv8/k8vvzyy/jZz35W5hTg/jEPG3YbKmf/bXjQjcViEf1+P7bb/Xp1HGVEtWQuB/iME3LqjbBdHYMOIpcPDw+ljDkvy3MWgYwRcwagQ29MPpydnZUlgGzAPRwOSwC22ew3CO52u3F9fV0yUyakIGitX7nSxjpsAGMiKRMgmdjKttBj6XnzfHreDFK53gDJbXEQyP8mIQAn6AgO0vt+0Q6yYJvNJk5PTwuAIesG0CLgZXkoPgd/Mx6P4+zsLCIiLi4uYjweF3lDRg4ODgqwpx0AA4hWl5EDrmlHJgCbyL8PfVgXs9+w/eNAFgliAYb877cwAXBMyvAc3qLjPdkWi0VcXFzU3mDrw8Gk2x9RVSl5Hz8AGfPK9VRicU/kGF0G2NNPKiYAUiaqsK0EWwBY7u1+e1y4f/al9MM+znpGxQJzZKxB9ZD9EH+bqDfQ9neQ+syHbUSuKkI+qGJ8eHgoQDXLEplfXgJxc3NT3qRkUiont0zy0d+I/dLAJ0+eFB/GcjLm37gC30rQwfdOIGbf7fG2HNpHWfby31mvfWSZ/hAH8mD5o1/GOsYojPNoNIrxeByr1Sq63W70+/34xS9+UQIfYyuW0BPosSQWGab/q9Uqrq6uYjqdRkSUN0XxbCfF8JcQRLQf8sgECnJ1cLBfBn9+fl6IDldkUkHJ/fGhnU6nLCHGXzvBzdvxCMqpCmN1hAM+ZAXcCg7g+k6nE1dXV6UqdL1el3gAIsdvBKaiADvDeQSOtoMEmBFRI3FNsvM3+s8WDtPptBaP0DbsMRUwXnrrN60yd5nUtQ48PDzExcVFPDw8xNnZWRkz9vREH5kP+uKKD5YxbbfbuLq6KlVV8/m8JE6I7Qhy0Xkns5rs04c+3uV5JlPQa8hkV8rZLnGO4yrwI23xfXgOseV4PC6bWoO9HEuwT9vl5WVNpmgH+vT8+fPSHvu09Xod0+m0vFX15cuXNUxI5d3Dw37T/Z/85CcxHo+LHJCIGI/HBcuBlf0sDs7JxJ7nxWQV44EeIoNZznNszYH8m0jxNfYNJm+wna1WVYjT6/Xi1atXsdlsypJHcJZtMb774eEhvvvuuzg4OIjT09NaRTFyYLwDHqG/8/m84F/2DrNNw8eC+7g3lYk59rUMu/IMe2ts5nHBd+f4JePRdz3eqlIKpo8SMwSOScmNNNg3uHPGE+ABmDZY5n55IC1IJkLW63Wt2oSBcxUT5+KUclv5jPW0BN84TZScrArXmUk1ccH9aUtTsOr7WDmbCKOI+oaYNnoez0y4EJx5uYeDVJ5lwsSZ791uV9ujBwbazzEYcABlQIk8bLfb8sYHrsvkBf0ymWAH2/Tj5+QAgbH0vLh/APyIqAWxOWC3scvPpQ+MZVZW5I8xjNgH2MfHx/H555+Xe5CN+FiH5ytn7AnuALvsgQAh4LeMZP23A8/OxZVmrrI0KYa+PTw8lDcGAYZ2u12NuLAN8PI/SCQ2bESWI6Jki7bbbdk3h7F49uxZLbuErFh/qXiz3iDTDo7db8bVsm7nbJmyDQUQR9Rf+mAi2EEkQNI6kNtEf13xGhG1a12Ngb3vdDqFxGCvOMu9r/MbaHiNPHsOPTw8xKtXr0oAzRIPywqZx8lkUltCvF7v91qhXN5Lz9br/Stzs52k7c5GIyO9Xq+AcuQpJ0w+9GH7z5HtJ39n8spvyVoul9Hv92MymdQCLkgafCB+F6AHqbFcLuObb74pywYYb65F5rgfY4wNhZzw0haCMEAsgRgVEN1uN4bDYanKJMjhPAIiZGu9XpeltgTJBKsew4whbJtoB8+0X2DsXdXIWOKz2bMG+bX+EXRERLE7yLN9UX4jFTYAwi0TVZyLTm232+KbCZBZbnNxcVGANLbKWWHas9nstyfgzVmnp6dl7pj7iMovRlRVIGBCiD3uDbGcx4T+QnLTN//kN3N6/uxXTJBl3ci2N+ODj+lffdjGOgCyzEGkHx4elg3OqW45Pj6Oy8vLsgfUdruNyWQSnU6nJFaY74goek5ilf1WF4tFmX98G0Ql7ZtOp4XkQB6YM46Tk5PyYgISC8yzK5SwGcgxJAUyAgYw4WhcaayIzhL4sq8Z+sT52Cr2OSLGQKfB8vbbjBtVfvgzrs/VEfhE43UH0LY3EVWC34QPOnlwsF96R3upBuatwuAf8Bi6eXt7G8PhsOwrw/gRl7ii1DEHiaH/T92Z7UaWJOnZIoJ77CRzq8runh71AIOBIEDQjS50pzfRS+peEKAbQYAgSBgt3V2VlRuTS+xcgxG6ID6P71geVmd1Z2ZVHYAgGXEWP+62/PabuTvLGhCjUenGZ4z/3d1dsdMscg2GctIagq3T6RSfylhgF7C3xtNf6/hbdN/4zHoQ8TCNj41C+B4fkmXP8ZntgMkKk6qsKcbMA+SFyh2vgcp4cy2Lc5M4NC6GDGu1WnFychKz2Sxubm6i2+2WRI19FGSzCzfQRXTfNh/9Bye773Os5/50HOB7OWarizWNoc0n5LjYiRDa4gQxY0GcQZ+tVg87aJ6ensZ6/bBWcr/fj9lsVogbiENsI2txnZ6exjfffBP9fr+0B9/pDQZ2dnZiMplUCiMuLy8rVfLEGOAJpsyaP+Fv97tjUMgv2y3jPLAH/p9p0Jbfz+1HfxLixrgxLxUHUsdUZhKGcxhcDHkmFUy8ZMIGgXU2ws/D6ZrkwGh7KpCNIkw1SonCemoRg8i1PN+DbwfMQNG+DJ7INmE4rAgm3FA8Z9UMxpxFQkCcDcuBmIUR5+E2Yuis2NwTBXUFmuMfv/gAAQAASURBVMk5l/lhKPje93MAwxg5u2fl8Hv5cN9athzIm4yzQhowMPZWLBTYBB6g2YG+x8RjYwfLOPFeDubMjq/X6zg9PY2tra148uRJOe9rHrQLA48xYoxY6I+2Qy57DQEyA/S/nYmrOKwjBLY8F4OObmxtPUwBef/+fVkTIYPJTIjQ34A/G087LAw4U9EgwLBrvEO32y2A1YGDp0TYCXhsOdcAwJkO3juT8MhutjW8hx2r7a6rQrJM0xa+Y9xMJvDO1ilXuzgo9ZQ3BwouC6dCIiJKhpSMznA4LAQnAdZisSjB0d3dXQHLx8fHlbWo6DsISAJrE1OABBOrVGGx+5Sz3vRVtptf88Au0/cR1SAaGbYdQX7oa++wQ/uxrwDQPK2Ffmq1WvHu3bs4OzuLq6urij+hLchMxGYhX3TbPsZEM+2xHWDDAZPGyBLtQ9aYkg8RTtLIOOL+/mHXTqourRv4MWwHvyM+1g/aYkDLO3n6YSarHJRhnyBQCZhJ5iDLrjbJz+B8Ajz6gDHNfpHMaKu12eGIqZfoqacHWKbs029vb+Pk5CT29vbi8PCwkiX3Isw7OzsxHA7LON7e3pblHUzeI5ce64wbHbibhM2+2z43Y6IMkLln1qUfu+ZLHrbftMU6THuMbSI25B6Bzmg0itPT0yJrrHVI0oX13trtdqzX60Lu3t7eFhuL/0ZvjMVdRTebzSq40BUh9tsmwQhqqMZZr9el2stVzNh4kt2QHawRiR+JiDL9j8oo7oGeNBoPVWIQbcggeINn0D+udEKmWf7D01uNL3mesSP+MZNO2E18JFgeYqHZbBbiybaDdkRESfZQWcwaT+iaiTdkHdvlJQUajUalSskLyEdEzOfzmEwmxX+zxql1hTVuSNzc3t6W8+hznoHtZa0gY7Ec92T9/DUctleuYl0ulxWSEZzR6XTK9OhGo1EqHiF4naSIqG50g/wQb7fb7YKVwNqMn0kfYtlGo1EqZ/GXJIdYmH1nZydGo1Esl8uYTqeFkLy+vo7RaBRHR0cF+ztB1Gw24+TkpKw39Zvf/KZU2tKG7e3tIh/g3hx7I/PYIickLdv0PX1k4ssxFrKNfhKvYKsYL7AQ0/u4Z8SGOHZMSp9QBfr27dvyXhcXF9Hv9+Po6Kiy6Dd9Rl+Px+P47rvvYjgcxjfffFN00QQy9sxkOXo9n8+j3+9XktOMuclGsBaYgbiIdpkP8TQ9qjo5zBf4HjmB9zn195NJKYwbU2joTByRG+rACUHLZWIRHwfCubzPwULEpnQbB22SCgaYLGlEfdAEqWSyCaePw+K+/OZ+VkaAuAFtBht25BEfG2EUxg4iE3FWLN8fp2cho695vgNbkzPu05yNzIEQ/cu9HbxioJhz64CJe2fSgDHM7cfA1j3TbXM/uYqJa9xGB/Luc//vDDSf0z5n+RxQZRLM9/b4W97tLNw+nn9zcxMnJydl7vHnWFPqpx7WXQdpdpCASr8HjoEg34Ew78jhvsFgd7vdAqSpsKB/mNPOTm9UQ0AcZkCIPvuZVJ2tVqsCBgwakNPJZFKm9LZarbJOB+9OUMy7RWycl0Eh7+kqHD5HD6n6cPbM5+VgjM/8rnXBpR25x9TBM0A6E58ea78DVWU4b5zT7e1tCZDW63UcHx+X9dG4xlP+1ut1ZS0RpkYcHh6WRSNns1mMRqMyBcC6Dej1zk2Upvf7/YiIQmqyeCTTUxhjMoqcw25POzs7ZVFJ3tvTzr7W8Sk6z7sYmFCNBtlBH7v92NjhcFixefTlcrmM2WwW4/G4rCGVK/FsGwlgGY/7+/uyM6UTA54KTQVIq9UqVTXIJKQw5zmAIvC1/EdEqZLz+5kMx+aCMfD1vIuJO661TtMW+p13hpzNBC4gH732Iu3IO/2FjzOZjl+zDbRvpd1gEEgCk4oE361Wq6zp42z79vZ2IRx5d1d58S5XV1fx6tWr2NnZiV6vV8gIxgsg7QoZ3xNZcNt4F2TPCUZjlzrSqg6/ZL3h+qxLtoP8799f+sj2OGKzSU/Ex1VEEFCdTidWq1WZqtdqteL9+/dxfn4e29vbZUcnqtbtl5HV9fphTajpdBrT6bSMkceR/semWhdZwwi939/fj8PDw2i32xERFRIJmXeVHGOeSVvkBtlBv9EdguK7u7t4/vx5mapye3tbqeLgnmAIL7J9f39fiBQwPnjAMkzyyzKKbJPMgIjDp9M/+/v7JYFCvxIrzOfzgiMhMLAxYA/vQsz7sN6Up8BCLBjjmjAm+M843XGY4x7OY41OqjHwxzke4D0gnKiaotL14uKiTK/HP3S73Yr9xd9gnzkcN/xajlzsgD2HPEXOIOogJx2jmpBCLzxm2DLiTpJq+JWLi4sYjUZlPBmLy8vL6Ha7lamnjKXH7Pr6OsbjcZycnMR8Pi9Ekqfpnp+fx4cPH6LT6cS3335byN6Tk5PKzKezs7Oix8QBELssC4CPNm+QExXWL5PltuvGDRGbQhVwhOXVZFSd7TOZ7ipGx6s5ljw8PIw//OEP8X/+z/+JZrMZv/nNb2K5XJZkAXE/sUqv14vhcBiNRiPG43GMx+O4u7uLJ0+elOpG3pVKciof2Tl1e3s7ZrNZNJsPG19Yh5i6a//tsUSGXLnpaZ4sdL+/v1+ewf2xf9ienHD/3D70J60pRadcX19Hr9crg+qAEINDY11mF7EhQbxugQ2fQaJBRER1IW2uw9i6DI/7kfUjsMqAis/X64edKbzNKm02cEBJAPsYEJTe7CxttSJlUs5Ej5XHQTt9hsGyczEANLuK83JG2AGtyR+MlkmTOkDofs2lmev1uhjJTNI5C+zg2MQdhssOn/u6ssrXOaioOzJh5HGhfSaLfD6feQwzIebxNLCkX/xdDiLdr/Q3IOz9+/fxhz/8oayt9DUP2mFCKpOby+WykDXMX3aww/RZnK+dhe+PHnMe4MsO6ebmJs7Pz0uFlCsXAWJkHmkb65PhnHDMjcampBznTgYSvYbYYLww0mxXjYyw/SrbuAKWeR/eIeu1g9CIDaGVp/+ZuKPvIqrAMGeQIqLWBmaSkcOBuPXZz+Fv3os+AcADXNDhs7OzAmwoKx8MBtHtdktgg9MFaDMuVHfgSJlH32q1YjgclrUKHIwjI8gSOwrl3b7cJzhpQDjAbLFYFJC5tbUV0+n0I1v5NQ7rH//bjtgWGiwxLo3GZjoOQLjRaBQylQVN2eHFCZrRaBSj0aj4j4jqAvwc6HxElClbx8fHJciicsbEodeqQo9dip6JXfSFe5oEsx65OtlkkgMvrkWuGVcHlvjzOpKZBAHPc/UY50dsCPFGo1Hst9dYYwFqdv10JRXvnANH4xDrOz4dGcaOREQhDsis7+7ull3yqDDjnd0vZNAZj/v7h/X0zs/PYzQaxfPnz+Pw8PCjjCtTFz2dCFKRzL7fERCep05wP/9vO0b/I1vGAllfuNb4q063vtZBv3i9GI4sh5YrKlcISiB1t7e3C+HI1FAOEh7YaJbbYH1U7GAeQ/sJdAr5QacJjiF4SCrhAyCC7u/vyxpl7BbIezoxcn//sNj64eFhNJvNUkl7dXUVZ2dnZcfVg4ODuLi4KGTW7e3DVunoP75/vd5MXYRU73Q6JdBqt9tlcxTLn4PCTAzTp6zrxfQ51pBB15xYZ1wYC/oZ4slkBDZge3u7TFEnATedTgu+IqiPiKLbBJSMH2OSfYZ3gOOAvCZGgpSib/r9frFjtI/vnKzrdrvFx3vKMbJk7E5fU6nM91+LHP5ch/E8/UCfLxaLkpRDT8Gqrn5hbIgDcmyK78XW+xlgWFfF2cagb5C4jlnBBefn52VKKESqqyJtH54+fVqmqCHDFxcXBR8z1dU7XRvnY/eZdWAyyvFwXexpebKO2r4bbxgnmgSlHTmWMxbmt2M4nkM8yjVUOzG24/G4JKT29vbKuqbr9Trev39f9MTLTbA8Avh2vV5XknzMEmAq3/b2dllTjqrk9Xpd8Jz7kr6l/3hf9xX/m4zDP9snQ8jja+gLnvU5j08mpQzIMNgevFxO5hc3yQRDB3iD0IioLoJuUGJAwTUGxgwwgaAVAQWoIzlwzCy666ocM6W8P+fbaHDwPry3jYCJChQF55UDT4MwvkcI6qbmuY95dwPAnIFgLHKmIo8ZbadNBncOit0+rjX5R59nIg85MEAmo2CZ87g7QLMjy4DTxi33F9fxrrw39+J9ABDuR/dtPuxgrAM8y1VDHrtMdJ2dnZWMhEHR1zoyGYoD4n3IAJLdxKE6mANQmZX3YtcRG4LXJAPZzP39/bKmDVtdY+zdVzwLcmh7e7tUmuXA0wEmFT/OIHjjAsaLRQkN8skKHx4elmCOd2QskXWTT9yX8+x8AYQ5SEKmuU/+O5P0EVWinr41wDD57MSAK6ewf9Ybkxf+ze49b968KQEF156dncWrV69iMBjEs2fPSiIDJ2qnZz3HcT958qRirxhDQDcgncAG2SQQYzeq09PT8o6QiARR2CRPS8JO/xz6x2F/YHmxLSRQxW4Cere3t0uGjrGAbGPdNAATU15OT09LwAqRg2wjL7SF6T0sNr5arSrBrn03MgVpjXy4yif75YiNPwKYsdW2+8DBFyCYe9NeiFMDYdtig2Pbfe5h3+mKKftv7J1ldLFYFICHrCLnJF/AUE5M0W/IKIEF75mzuX5XSDwnUwjonz59GpeXl8X+eQ3M1WpVyH3WHLq7uys7rtE/Z2dnsVgs4vDwsExHiNiQKrZ5BLr8TfupTrSO2QdShZYTPtYF29A6HeEc7ml9Mob4OQNhB0/GVMg13zP9B/nGZpEIGY/HJamX+2o6nZbqH9+bNRQhUSFw6H+qm9DR/f396PV6RdY9+wAchz8H+6ITrE3mHQGpxry7e1g0ebVaxcnJScn2dzqdWK/XJQHOelmeBse0VOwQuAr8AMFCQqrT6RQyChnEdkVUcbGrNfH/rJmDL3EVFWTX7u5uSYphK+hDE+jodESUcUWfaSN2G1KS6yGG8O2Q/cY6GYNCckGc8W4OsknYYU+m02kZY55vfeT56/W6EBOQEpy/Wm2maEVs1q/Db1Nl48TRr+3I8SA6xZjyN76FPjbxk5e8wGdxf5PxxE/o0fb2dvR6vco6b+12u1L5ythEPIw7i9JPJpOiD7SdNoKjmArfaDTi9PS0bCZAtRXEaKfTKYukR2yWqHE1WaPxkDihksxEiQs8TMjYTmcbl+2dx4DD2JfzuDZzC7bH2Te4DegRtvLVq1cxGo0qPo+xMUHPM4xdSAZi5+AwTJaBI5ATNnXAN4DXjA1ps+XTMTrfefopCT+ITvtX7IVjBH7nuOVvPT6ZlGo0GhVH4EGl4W4wrB7ODfBpQoMfADMOIZMNCI3L+VxV4C0fOQygTRAYyFG6yPoRkErO1DqIo+2AfgMLTyUzODMJgYMykOTd3DYH1CZPDJItFCZjcKieCkS7c9VARHUhYwAyzhOHkhlkjJgZaLeTNvAuKBdghTabaaW6waDWW8qa8bVcZNKJPnGA4fflO0+9sNG0Y7ARYaxNamVZ82e8N+CD961TYmcL7u7u4u3btx8tJvo1jhzwRlR3pyB4MfHhUnhkwwQL11GZ5HUoIjYBC2W/lB6/f/++7CSyXq/LQsgETR6T+/v7smYFFVzr9WanMXSNBVy5hvUOqLRC1ugDKjyYy41cUG1CRtZjjxxnUh7dNRFpMBqxWUugLttheeegjchvXWWQwbGfwfNNjOVAHRnA3kds1hAieORaT+0AfEAOnZ+fx9u3b+PZs2fx8uXLkg1m7jqEH2MC6MH5u9okB5lUSlHy/M033xT/s7W1Vbamt7/hvQl+AVD2R5nM/5rHjwEvxsj+waXuyCOBhkEawAZZJYhi2iL/M745sdJsPkz9Y5F6Ki8iNnKFTtE2EwzouTcNwD8zjgQuBqCAU+5FG7EBACv8B/LXbDbLunH2jegmtozx5j24187OTmWpAvs8T1twRhYg6sVo0XNPWWAcGTO/v3UcfwrA97bP4CZnyqmgYDoV96AtTBtifBhH6ze78dWRjFSKHB8fl+oWE/fIHf0cEQX7YRPsR+k/YyjGFUBv+cn2z7phXMD3mdjiaDabZXrY1zhsv/FH9vu8GzqB/Hi5Cb5nijT+DJlHt8BTs9mskMX4LOTThDZ2Dr9oLE6QScDFjAnwCbaXMb+6uorBYBC7u7uFTENutra2SqUF+tJqtUrFMrKE7BII4WeZzsjBNugmlNAPk3rgQL6bzWZFp6hAQg7xDzc3N6W/TNhiQyD2vPg6+sIYM+Zeq8rVhCbl8Ue8i+24kwnoCQufc39Pi0Xv6Qfb0rr1psBK6DG6MZvNSgKOKmTa7ynKT58+LdiPPjM2c1wVsVl/N8czPydJ/FMPkxSOZfjflay2XxGb9Uix/xmXOOHh7/DxLKHD/UkQR2zWjmS6L1NkV6uHxNFkMonZbFZ2zY6ImE6nlbg+Isq6V+Bi/NPTp08LCbm1tdlgAF2DZGWKuNvLGncmXXIcl2NevrMMu08zOcLzOS9iE2Pk8+2bM8a2LNoXoYPgC6bkvXr16qP+g2RmjS5Pn6O6MeIh5mNa9YsXL4rOgmuogiL5GvFgH/Bd/X6/EOR1vEzdO/EdfcBzsEv0MXaO/uK+YDFk/3Ni5Z9UKQWANcEE0DEpFbEJVDC8DigyKRVRvxuVf2yQbeRMNuDgECAEgfVLIJRwTMxX5/m0LRMO3N/r19jI2pk50Gw0NttFIzS8h4N+Dr87xgAHZAfFdVkA7eCysXf2hTZZKOsU0yST3zez+fSbsyduI++Ps3OG1X1GqakDGtrlfnKb+T8H2hABudwfecxEpoM32oFxJQCwXGYF99Sf3Cec6/d2EERf0/8XFxfx5s2bWjD9NQ4HX+g5fXl1dVWy5PxGv5FvZ2YtCywm3mw2K9vBRkTJnrBG0dnZWSGxkGlkqNlslmCK6QFMqYPEMLiHaOVZgMyIh4zGhw8fipxOJpPo9XrR6/UKabJarYrThgBz1jGiupYJdg8dZC0Z9B4wYXIqopp99Hd1AQxy43WD0C36n3PtlBxAu+LDmW/kEZ1337nqiwDIQSRToKmm49mXl5fx5s2bmM1m0e1248mTJ3F8fByNRqPswslznDnc3d0tYAb5I0ttGWU9kRcvXhSb7wwyQTaAfH9/vwADpqd4+9w8Nl/ryP3N4T7OeoWsA8oc+DoDPhgMImIz5S4i4v3794W8hTzmPnx2cHBQgs5Go1GIGsY3r69E8GXC1QEYBDUyx3sYmDvTyhh5sdiIatIDX8aYe/yQcbap5nv7EKq36Tt0H7lD1hzE8jm6TrCFnhO48R78ENwbDHr80UH7ZFe/8cy8BAJ2jj5hmibZ9N3d3bKbGmPdarUq08Ac2JuswOfjQ9++fRtXV1fx8uXLylg7cOdwVph3BN/wOf5/sVhUgLDHFvnjd52OZJ9ZF4ww9ru7u/HixYs6Nfzsh328sRjf0a8EBJCi+NUcFJGcwV5CNjabzZhMJiX4zOs5cc+IKm7ycyEnaJd3nzYON2mNzhM8UcnFAtq7u7vR6/XKc6jWAw84QEfWyNx3Op0YDoclEcIuX+gXO8CyyPJqtSqYAGLNVR88H1Kv1+uVKTfoBuQbSS76nPey7TMmdxIFGeVemRT3lH/0Dr3MFYSerhex2Q290+nE1tZWIR9Xq1VcXFyUvkU2sLtgBAJrB/zYLMcVEIbr9UPCgV3cGHfwHqQbhAVraUE2ZvLGtpa++7UdOfaMiFKhjawgQ+Al4zbHwdwHOxgRFb3nO8YDwimiurETyyRERKUS9+LiIs7Pzwu5BJ5vtVqFaGIDMwgR8PWLFy8qFc1gK6ZwOjlJG8GWzCyw/OP/bH/82/jTvIDJZz6zr+fw1EHu68KIjBuMy+lTpt6yrIgJQew45x8cHMTz58/LFGHsDNWJ4/E4Li8vyyLyPANbylhQhckOq05or9fr4v897ZUd+dAnV9OZ/HMyKMcW5ghMnJsYQybBCV/y+GRSinneGDsCKxsbO1462oPv4CgDhYgNuRNRJQtMSvkcwDDKjTKYPTbbjGDCGAPGPVAYTZSTTAzKRjv9bvQP12AwaC+AIwexVggUzJkN+gjw6YwnYIR3470jqqQfzoY+xagQtNrgZfbZGSAOBw6+L+AHJbLiA94dFNtgcA/aRcDDu/Db8mLgbrmyLHpcnV3OmQuPO9dzDwN9+iiPj/va7aCtzurTFpxFNgytViv+/b//9/Hv/t2/K8bnax7Wv7q+4vOIKGsrmKThHd0/djA5GPF0nlbroeR+PB6XfjJwsmHtdrvx9OnT4uAICM3eoysAcbLLZ2dncXJyUpwyY+yMKroM0QL4JIPprD4ADQNuMhX5czUowb7JX4MzO1m+yzaT/skZSd+7jvQyqeGg09mOTA5kW2VgSXAHiQdZ2Wo9TBEDLK/X65INIgt7cnISz58/Lwtvm0TKdgNAAzkCWCBYYE2Di4uLsghks9ksa4G4GjaTzgQn9BHtrQM8X/pA7pDjxw5sGWAFvVsuNwuEI9tMPzk4OCjkaqfTidevX1dsjGVte3s7jo+Pi97nNjFlCPIDm8k7kEWlkoIpQZ4SuVptdpyJiCIb6/XDzmHYFWTCyTB0ASIa+wAxRTBGoJ+BmhMqToTwLC+mT0LOwYWDBNsp7A6+GQyC7pGQwPaRhSbIN4EFKWvsQT8Z4Nv+0AcmndAtsITJXd4DUp9nX11dlURexIZUZBwjHgh9SHyCWfozYy/jQ0/ZN8HmqUucn32r7Y8P66nJnvy/9do28Gsclt2I6pT/iI3+uZoGEoUAJ2KzEyX9yDkEnxcXF2WRbO7JNHeIGvq92WyWKpzcNxEPet/tdovMco1JXHSfykmv49poNKLf75eqd/QT2cIHu2qHwPv6+jqePXtWIZtIMKBPJjgjorzz5eVlfPvtt8X+rNcPiQsW3kYu8DFM6aMPOp1OqbL0FOnJZFIhnzI+9ZptERGLxaIkXSCQms1mSYphr61n9BHJFewYOk1/o7+QQVyL30ZGSL44aQZWcF844OZ7cBXr5WxtPSxrQPv6/X6FKENmIbKM79rtdkn+0AbsRh3J/Es/7A+x7WBR7KkTbFzDEiVg05yM5G/GzL4K+caH4c+wn/hPL5dBJRQ6Z+LLvvvZs2elCpI1mPv9fjSbzTJtz1VZHO12u5zHDBcwtWOciKjIck6+oDdOiNin1sV7+AL+x3YaD/s8VwH5b3TaiTKvO5qLN7g/Y3RwcFCIWHCld6pkeSD8LgQ4ySLGZjQaxWq1it/97nfF10H+YXOxA1SBXl1dlU0f8GnG//Sp/XBOzEESIreTyaS8q30uvAO2MuLzr834yaQUwN/gw87VpA4D44wMTrWO5HDmCGHlsAA608BnzqoBAjEOACBXOKGggEnaAxg1YWLCikDUz8cQGZA7e5CDQAccPNMkm8kWAyY7GweM7sdMfnn9HZTZa4SY7GMceD6g16QS7+3n8X7cE2fD/HtnA634zvZlMtNgi3aZ3GMc8xxaOzUTcW6/CYs6Y2hiiiwDsmOjmMkGGysH+lZ+X+fsh+/JtbPZLL7//vtKBv1rHZYNDgd6tBEQ1Gg0Yj6fl12AXEa8tbWZDofxj9hkyQCk9LOr6Gwo6SMIVQJsMqfoFeMEQEa2ydbgEHAwtM8OExA2nU4LcDTJ7iwTYNYybLBnYjZnZ3hHbBb/+zfPtbyZ6ONAvvK0Wn5MKGRC2Yf1yKSqCVkHKw4UyCiz7hb64yon3oXMNoD97du38eTJk3j58mUhJrNPcAYP/0DgxbhCfA2Hw9jZ2SmfOeNo+0qwjm/yGPr31zxyQJgDcE81w94zxYYgAvkkANra2opvvvmm9Ofh4WHM5/N49epV7O3tVdZTI0gC8KA7Bjn0Ffpg8ocq1LOzszJe6O1yuSxkIKAXv2FfB4nG80xIMYb4CUrb7dewS9gQgCo2CMIHvbKN5qDNyIC3rAbbOIEEYcPmD4DIXDlb5y+ccMOm4DsiovKZq5ztf11dRx9GRJnWurW1WQfUeoEvhZhlilS73Y7FYlHWH4Hs4jnswNZsPuzOBHjHPrlSw3aFoIbncS7rWLlPba8iPiaVPGZ1ANkEkG1rtr+fG1Q/djgI8/jmd+Yc5BYc4EoazmH6zHq9jslkEqenp6WSgjGnnzzFGiLj4OCg2G6CJ/AQtna1WpWqaGNe7MR4PC7+lbWiGo1GWdum0+lEv9+vYMVGozrNE5K22WyWxc2xbdgodJjp9Ds7O/H8+fNS1QA2iIjK2pHECEz/x1fSDshc5NPxRMRmyvr9/X2pPsRnOJmDjtDXyDhtof+3t7eLjlH95L7GRjL1nTbYz6FvJCDwkcvlslQ+ssbj9fV1JU5yoh35wzZAwEVsqkvQ+a2trTg/P49Go1Gm4FPtjK/hPYfDYVxdXZVxBQPhl00Eui2/psNFBvgexo9ppowJBK2T8hHVahX7Afs49A3Ck9iJNvB/o9EofnN7ezvOzs7ihx9+KEtOeG2wxWJRqtoHg0EcHR0V4tCY2/5msVgU3A9BAgllLMx6Va72o0qZ6x3zoWv4ylztnEksx745Tvd52ffS7zm+8eE2We6Nn+1zeA+Sc2dnZ0Uu0P9ut1twAeQhNvbi4qLgL3zvcrmMP/7xj/HNN99Ev9+vzIZYr9fFtzupRYEAfel4yzKKbmPHbY/Bja1Wq1SE0gf2TeY7Hosl/pbjk0mp1WpVpqGYtCF4cHDvssEsSFzHPXPmyICbg2oInC8G2NMtPJ3GQWOz2SwdPJ1Oi0AYkHlnIJcxO2NFe2gLZc44JisGht2sqQ0/oNl9ZtLLIIX+MZBFKHMgngOaHGTRHxmsPUaK+XOU2eDYhAqg3Gz8zs5OmULJvVyaTFtQBBvsDH4IdlDQDFQNYPOP39HVPM6aGyjaGCPPjCFKaZngGdwL/eA7xshz/R8zio1GI/77f//v8V/+y3+JRqMR/+E//IdPU9DPdNDuiOpul85AYKzInGFULWcOGtAZfjLBARHBjmcHBwcFSOFscQ4sPg6hRJtd9ci4U3W1WCwqJdEEy+j93d1dWbic0lScJw6ZneCo4AMs8I4ec8uGCSEHHjkDxntwL2SNc6xrDs4ycWHd5ns+MyntzDT3dwWD287Y8iz62VkiFoElU4f+A0YgMNbrdZkGPp/PS9YInX/y5EkhONFDE9Xo5MHBQezs7MSHDx/KdDIAITJEQoL3pSKFNZEIHAjirf/4EOvD1zjy82gTMuNsGZ9TLWRghl4yToDKiAe79+rVqxI82oewFTjAkCwhfZHBIu11hQTT82hvfjdsABUM9Dfbw+M3qOoAFLJAtgOdiCp5SzCQbQxBnnUWu56rHn04SQb+8ZR/SBWv04Tvop1kn/FxbpPfj37IVQ32i/ym7bw3sk9guFqtSuBuO2Myg/Y6YRSx8dHNZrPsOEamF3zDVEpw4fn5ednemnf0rrpU5DEO+BHu7Y0PnNCpI99tn3xkf+zfj/39tY9MSIErwLd11UcRUcgLSEJ0BNmYz+fx/v37UpUD+YqMcE9+dzqdUomITcZOQia6/xkvxgzZYTF1AjDI7IgH2WWBdMs5P/Y3PPP09DQuLi7K1ErIDsaWxct5x4uLi7KIPxVfYOnxeFyIORN5YHdXCfHeERsCCBIqoor7m81mSbzc32+mrOfNHrzWDs9AT8/Pz+Py8jJms1nRR/ARNgwMenFxUab+oBtOHGAjSEigd0xV5odqYSf+wDjIoCtGOZdlF1arVUwmk7Jj2z/90z9VMJ0rSvAlEVGwlv2Nq16Rh1/TYX2ClMCnGIM41qXf6S+u4X/3DdfwA+lrn0ucyhTWiCiE49nZWZyenhZyYrFYRLPZLNP5qYICu6NTTD10xa39c8RmF8nz8/NKLEqlHQQNbYJcplCBRJZjr4iN/eb97St9Xl286viY8xkf9I7+N4Y2l8GBTJo8I77lXhGbmVom3g4ODspOexD9TAOEizg4OChkM5/5HRqNh2l5b9++jUajUYhC+IFGo1EWt7+6uiq7O1IxZZnMxSOMpTEUPpm+tS3wlMGMexxjf079/WRSihJQhCkTAzjRHHBlcoVO53AgaQAYUSVMTHwhNGRwEZ66YN8sM44TB0nbAQMQRghpBjC8x87OTtl5yIGhB8tKYuIsB6MIhYNJ3h0FsDBlJ+3qMwTDAUsG2yZuHCzzzHzPDAQzU2xjimIDqgEyJs94XwMVwA5jS9twjq6E8WHigzY4EPF5XOtxqWPMTW7xPUFxBve5TQ4YeF/u4wx5HRjGYDoD+XOAZg6TbwQ5fMZhgoUA2CQsf5tkidi8K0Dv8vIyLi4uKusF9fv9AiSXy4cFfj233c8lQ2OjPp1O4+TkpGRRI6I45OVyWXaiAjSy8x+BF4RzRBTCq9frlf4goxoRhVTJpC9rT9iZOSChD9EjDoNlj0fdubbDtst26HznyiPrCd/53pmUsUO3bcBxQVACirDXLic32PJ6NwSyVE3t7OzEcDiMo6OjCmEFUPI7dzqdAu4dYHU6ndKPe3t70e/3K9lq7kN1F2P0pRztpx6PjTnvy5oqyFez2fxoHR/b4ohNkgi5RacAIwSu3NdT81arh/VZ0E3L9/X1dVm/JCJKoIyMA9BNnFgmyR47sUXAhUxAQDPl0gv6ttvt6Ha7xZfgtzh4L+TH7eL98D2+liDMY7FarUo1hqss/Dn9xe86G17nbyxvDjqMlax/fMcYP4ZTOp1OXF9fl+oX3pmKEfcJcsJ7YM94P3ZE9S6XtJ+gk4qpfr9fqqGw0SQeAPIkHQjueQ/e0XIPFrDdyv1onFL3nbFqxi9f63Db8TONxkPFEPYR/aJdkAusUUIfQN7u7e3FZDKJV69eVTYAiIhKsgicy3gwld0yx7PRGxOpEDXT6bSSAEInCcRJHNHOo6OjypQgJ3oIYklwrVYPu/CxgDdT3ngWm6rkgJ4Fz12NYLw6m81KgIj+oyOLxaL0vRO79JHXfWo0HghoEiomIiDVeCfsMW2G9DNRjV5wX+IZAswPHz7Ed999VxZdpwrGdopjsViU6lPej5iDHYmvrq5iPp8Xwg1iGR9On9RVSGAnI6JUks/n85jP5zEYDIpNYOoufdbtdkuykfjKyXcwYE6u/dIP++SITfIAsj/Hu46DMwnjBKf9DteOx+M4Pz8v5CZEBToKMQIJdnFxEZPJJCI2xA42ej6flzUi7+/vo9/vl2n94Cj8W56aDv7CHtze3ka3260QU+i9p4tHbIo9kHliAw7H43Vxj3GHfZ2vN6FiHchY1+Ni+8/YgOPRbfAAeIHxxf8yzW9nZye63W7M5/MYjUZxdHRUWeKCDZj6/X48f/68jCvV3ugVpPlqtYqzs7MyFdDxPL+dkPRMDvejZx6Za2Ac6Df6heQgSUzky7Kcx+qxuPavOX7SmlIYJwYF42gg5MAwA2yXjBkYGGjVXRuxAWM4Du9WE7HZVj1iU6nA4LCbhwXfLLSzp3XMPQON4UXBfA/fs45E4T0BfA7qrAAOBnDYBgtkfrgngsj75oqpbOBcNsrnmUTLyk//2ij5HAO+TPaQdUXRGAMHvBjX3G/ODDvQ9XP539fmNriNHttMStGfGGHOg9i0LJrk8vUEJbTfjj23JR+5nT/XYcNHO25ubsr42bHimCAaAFyWaQJGgiEv4rdcLssCgYwbAVWz2SybEbAWBHJOOyCc0ZUPHz6UcaS6DlAKsPKufBGbBcIbjYeydIJJKitwnnZwHsPt7c1W95Zv5MYkEw4zokrOWU+tY87kWF4tU7apmcSwHtuZcK4zeTzbckv7fA7n4RPW63V0Op2yxpffDwLQwQ4JBZeiIy+AsNFoFKPRKL755psyTYgdZ7gGXSUIns/nMR6Pyzx9E5UmHcj+5ADGoJGA63M52k89coLCwWlOemD/Hht3dOro6Kjcs9PpFNDI+YAefAlViK7+Y8rPfD4vi1xbFwCxVCXm6SAGcQ6q7+/vK7uNEVgxFgTiyJp3ymTs0GVk17aJoMdyy9/Ig8lS60nGNAT26Dr3N8DGP9MfrjxA373Ac0RUfDx2g/fm/pDmrrI2OCfggzRHBqi2uLt72AEJnWFs3PZckZX9+3A4jGfPnpWpdnxHmxeLRUwmk7LrGnoKmY+ddSUG/cN9aHfGYI8lkLCTHNadv3TU4cyvcfBc+shVjQTt/E+VuAmcVqsV3W43rq6u4vT0NF6/fh2z2azgIN6J8SXRhf8m6MQOEFQha+w0S5C9Wq3K2DnJ6ypUvxN2AVKp3+8XAhy5zhVVERGTyaQExn/4wx/KWmUkOrAh/DY5dXd3F+PxOPr9fkkg8V5UgVDB6eopE2reGIUgMSIqwbmJc56bdQX5prIp41Js6/X1dek72uJA/f7+vlI51u/3y1RtpioyZjs7O4UkypXFvNvR0VEMBoO4uLgoCyTbRjvpwNhSCcU9+XswGMRoNIr7+/tCCEZEsY+NxkO1x7Nnz8puh058YbfRXfr158S9f+3BuC4Wi1KsANHpmApsaB9vrIxuWocXi0WMx+OCkyEiuAb7sL29HbPZLE5PT8sMAsaXZRLQBaZ6GQtAyoKRSf6wNhibBeCL0XWIFpbEuby8jPl8XojlVqtVZjdQSYgcQOAZ4yAbxPA5Nud7+/HsC/GnJm9yYtz3MQ7HPlmfsaHGxn4+sQX+u9vtxvX1dZycnESn04mXL19Gu90u/bZcLmMymcTu7m4cHh7G2dlZ6W9IciduX79+Hc+fP69sHHR3dxftdrtwIbTZU52xwdjrzG1goyI2U6g5B/tIf7gYyDNBIn48pv1rjp+0plQOVHNmkrI8BseOzgEAIIYXiqif0mQix8CWrKmZvlx6igBh7H1YCWg3CsD3Fl6Uj0A5OyA7HrcdcErFBGAfIJiVicMsb0Q1KMyZQhs4rnkMaPlzrjG54u/pR67zOPB+mfipA/MOKigvBBTkbKHLzbPRwcARDJj9NpHjINz94SDcGUfLnc91AM65OTNtucVwmYSiDw3268YiG2UHgz/XwXsATjCmtNPOCWeJA2EcswPxXGgcpjN3BHuA152dnfj2228LoMW+8ByAHRmG8XhcwDOLo7KrH/fneqaM+F7sDoRBhoReLBalXbx/s9ks1UEAc08FcuVNRFQcZR0xb4BtosEyaMLa8peDSP9tfY2oEv/WZ+uaA2jabjvnoJFMXUQUh4izs8ODaCPAgtQA5LL+BVUZW1sP24d/9913MZlM4unTp5XFuyEN6Wt2ZHJQ7vcwKCYw51pstDNNJCuY8vC19PExn2Db5GwY7WQMCZi4ZrV6WDPCGc7xeFymSx0eHpZKQQhjnkPZOKDm/Py8VDQ6AYMcYw9sO5EBLzxuMIisN5vNyhbs1p2I6pQHdM++2MkW7HROntkeG/T6sB44aAZ8A6qdKKGvs+9F5jgXPWGcqArlWR539AS9QzYs0w7svAAyz/QC/hB56A62kp1GTQr5PjwX3W40Hoh7kxnIZKPxQCg3Go14/vx5qYKlrdhgVyo6qCBAzsk5xt2BAf2YdSMftnuPHV/T17rN+DNIl9zWTOpaZgeDQZyensa7d+9KRhufaezFPcl+0898zznWFfqSaZk8l2dDHpsIifh4EeN+v1+mGKJnVHCgz9gcgvrValV2/AJvuyKEBJXxKTrAs5rNh40Jut1uLJfLUgkCnkHelstlCdrQDYJMxx6cS+C+tbVVpjBSGcoi/fSncS84A931hkz2rdYRyP3BYFDwCOvhsoC2K6OMnU0uer2+iAc7/OTJk2KLWGvKi8/n5D76R/vA8Fz77t27shMgbadihIpzgmLGx37McdOv7WDc0AvGH1lwgpfFqbHxyMNqtSr6FLGZPUNlmxOUTPOl+gwCbLFYxPv37yvn8zMYDOLg4KD4G5LDZ2dnFYIrx6TL5bJMwWy324VYev/+fdzc3BQ9xMbQB1tbW9FutwtnEPEgZ650xGfh6xzvRWzwgm2ziy+wh8a/ERvbmWM74ykwvolb+z9sGGPLePg+9qWTyaSM8+7ubhwfH0er1Sr9dHFxUXSec9brzXrYR0dHZSaXZxTQHmzmb3/723It74DcIEO8F98bXznuNkfDWPudSWKAIewbjMG/xPHJpBS7LzA4blAOkuxUeelMejgA436+ZyYVXPJJUMH3rqDhWkCg151yO31vDLnbapBE6Z23ZLbjsaKhJHUglUDIQJm/AVp2tjhug2oHhhaUrJwmzvIY+f0NtPPBc/kO5a3rS4N5gjwcMA4ZgErpqd+XQMMGOAdgGDxnoPxcKwqOIY+DjTX9Zxmkjz1OXr8EGcl9iTEwgcDzbCDyuJi4ydM1f64jk30EEoAr/3jM2Po1Iirgg3twEDhSRUMW3fK2tbVVCCVP48MZ0CbWjYLUAGxfXl5WdgVBV8kGYswht7e2tqLX6xXAxRiy8Lm3vwVsuRonYrODDZkrZBcb4yyDATV9kuU1k7MmmnBYmRjI7XGfW/YYG//NeYytwWwdSYDDYm47BERElL67u7srC2ECnFnMkcoN7x5nXWg2H6b/kUk6PDwsO8Ix3q1WqwTLZKxMnrqsu9frxdnZWSHMDIKQiel0Wuk725avcWBzsn2wrXKCxySN2+wMJKQs/cJvwBOVbNhtpmGuVg8VOh8+fCiVbeghemMCgzH0s5Elk2j2aa4YYmoc981A0ONQR4gBpnwd8pz9ghcIXq1W5W/uh3zQt4BIiHXwBWNiP2YdI9voCg8CY+uY2+LgGV/HFEbrNM+FHLKvYYxteyAc3ScXFxfx5s2b2NnZiaOjo1J1iP+2nbm+vi6EYbfbjf39/UJm2hefnZ3Fer2O58+fFxvADpheU9JkovWfv+kb+sV/1wWy2ZZlu5kPY8CvdVg20A3a6YTc9fV1WV8PDGvft16v4+TkJMbjcSWoBMeCNwgmwc/IGeOGThrrNpsP1RWnp6cxnU5LxRM654oPFk9mzRFsj8cQfbu/vy/TrU32L5fLWCwWRWaePHlSEkRUX7OW2/7+flxdXRVixlXP9CNkOp8x/Y2pMdgsqrUgs9brdamqpuIHO0CFCW1iWvrW1lbM5/PKOnr4Z6rJwAMkYVhjkeUEsB1MVcc3TiaTgrHQWRaRRndarc0MDiqW1ut1fPjwIebzeYUAAoMxFRBZZE0wZAG/aryPPB0cHMSTJ09isViUBML5+XkcHBzEYDAoMyIcgL98+TL+23/7b0XWcyxi2/FrOZBd2yP0GQxpuXSM6hgvIop8uMiDylbu7wQrdhTfNJvN4uLiosi+8TXjxLqfTBNll7Xb29uy9hqkckSU5CCkJ2v/sTvc06dP4/nz5zEYDCIiCqGMrjuGNt72OcYIEZuZA9nH2a9ynolSX2O99zg5ZnP8CM5wEoZ2e6z8PMeazWaz2CRwMOTh8+fPC2FlcphEBDaD8XVcjx0w6chu1Sb6wTzGQcRp7g8n4kyeRkR5PudnLJPjCFc0fwli6pNJqcViUREqwLFZRINJK52ZR4x0xMdTxzDyGVRg/JhDDRjEAfr+Fk6XmEdExVECOPltkoR2eEtPBoNrPJUNY29Cg+fh7LnOIMQkipXGCshhA2igRV/zbgbtXGdAnslEs8MOluvm5frdeIdMyiHw9D0HyuB1fFAqK6uJQYJYP8NTgBzYu10OXgxMeWcbKWTZRJsBsIkZ7uff6/VmYUv+d1+bjGJ8TDY4G5xJvp/zwLGZBHalINP2bLxMtmL0cJxMewXYRURxkAA/EzSUphMMe6cerh2PxzEej2M6nZaxREaQxfv7+8quUN79y8Y4TyFi7K+vr8v0A8A8NpDA0XqZHQsHjspgMhPOBoHYFeu6ZcIy4qxNRFTuYZLUtpbz6uTMTqmuEoQ20Vf8TVCDs51MJqVd2G7AGjLh/rOe2LdAXF5dXZW5+t7Vj3u0Wq0iNwYb2F1XATEO3nwAG+1ExpdyvI8d2W6jF5Y1FjPGZkMmIb/ehQUZ53re/+joKBqNhwWSc7JgZ2enVEbxHf4e3aQKBjBOH0PesqvS3t5e0QV+CFYioqKvvIerIX0uNphx5e+MF1qtVglWmboTUd09FluADyKLzTngCmwTJBxyaR1zUAIpytRhk4fIJCDR/eqDwN8VlZYFdMpA0/Li6YqudDXxQJA+n8/LQqzv37+Py8vL6Pf7MRwOS+Bu/cSeMB2k2+2WgAVMAzB//vx52QCBhW55ZwcOGbdlconPGXfbz+wr60B0tne5v76Wfme8ZrwVUd3R1PbR77K7uxvz+Txev35dFt5nGpjHietNMkZExU/QnxAz2ANsggkokkYkaLzgf6PRKLv4IR+dTieePHlSduDjWvuom5ubsoA+lckvXryIw8PDMt3fuBaCinsQyDvhc319HcfHx4WYxf+TjIJQz5WzEVGSTGDUHNBCtqxWq7ImT/b929vb5f4QWRFRyFzbCeIpAknaSr96jSYTRcZArAGET4SUYIdi6xg+z5UUEB3YOqqMXXFMYMxan/gBCLNmsxlv376N/f39+Lu/+7uC8+nz/f39Qohgt4x70Qd836/hyGSI7UwmZNAjbE6eJZTtUKPRKOsAMmZUU4KFIAjPzs7i4uKibCgAkYEdODo6iogosw/wp8jqzs5O2dzE/hEZnE6n0Wq1SgJxtVrFcDiMw8PD2NnZKWSUbTGzECI2SSfu7cocMCOyjb/CNoL7wbdgGcdwxtDc37jNyTDHeyakPHb0v6uo/AzaluNF/nb8yywO1t68v78vVclgE5LeLI+yvb0d5+fnlTXi+JlOp7GzsxO//e1vK1Vk9AX2nOIA7JnHxtjJPgPZiNjM6nB1p7FORBVLfe7jJ+2+58HHqNnh09Ds/H0tSpnBZA6ATFIBKh3Yo1S55JDsIkoNiMnklEGW2+HMM4YgK50FEEDL9bTPZJlLaw1KDFJwNg6wMPweA1cR8C4ZlNEWC62fwWePBbtZ4Q3i8nPcpwat/t7BPyDAWVfezf2fM+L0N8qSA4wMTpEFv0+WZ8t1frfMFhv858ycn2lygcOBTG6PP8uB+c912Lk6GDCwbLVaMZ/PP9pRw223jgGWCF4x1C4b5bmsSRLxAMy63W6F2PBUovv7h3Jy5Mm7g+JcyDxbv2yP9vb2YjgcFkdvUhUynl0k0WXsD8EzJK6Dadsz94Vl3vrDYbtUB1wsd/6fe9fJGM/KYCr/zeHqFwfTAPv1ukrG8t1oNCqB7nA4jP39/bIIudeUIaPqnb4gDAFOJjKQwdFoFOfn5/H8+fM4PDyM1WpVpq/Qp6624d68B6SBs0wAMN7Jmb6vfWRwamLNY4oME0zaLuVpGFRE4LPzVHfO5fyLi4uSEc1BG21A33kWbSIgo1TfOocdZKoHgSlAlB0VI6Kynpt9HlVBzuySIMLnci/Lbw4e6sDr7e1tqYqiL6gIpLrBY+B1cTwm+H1wS8RmUWJkjvPtMzn4jKlNjAE2MJf353fkoB18BwBer9eFVEJ+0AcWsmYKDplwT4mN2FSzrtfrUvEBoYH9PT09LdUTrhjIQRs2xf422y9+ckBi28q51qPcJ49hna91ZNI5YmM7eZdWq1V2x2McqEiiUubk5KRUO3iapqtnfA39jy1otR7WXYIQubm5idFoVCGaXTllG4R/xGYjZ9j9fr8fvV4vOp1OdDqdSkUdW9FDDtFmdMX+FewPAY/8s1AzcutpZj6H+ACCzdOpSHKTgKJvIjYJWnCKq78JDm27IL1IbDGu3n0yYjMNlkoxpsxRfb1eryuElDHX9fV1Ia7AM0wJIlG2WCxiMBhUiKnValUIRd6L611FzPR3SLHJZFIhy6nU4XwTK8jEd999F7u7uzEYDEqfjUajOD4+juPj43j79m3BTa7mAz99yUD3cx/ZJjkmjPh4B3U+4z0dm6CvyLH7n9gJ8plr9/b2Yjwexw8//FBk33rAAuSMNePu2HG9XpeqrsvLy7i8vIzz8/MKuQKu9bpgbHpCm7AzbIaGTWWqILiZZBqJqrq4NeNg5D9iE1/ZX2LXsHuZYHLRAffwOTwjcwOOV80h0D7jbHAH69Nxj5ubm0LGgntYNoHxcXt8HniEakeSa9PpNM7Ozsr0eN7JBBMkP9/TXnBSHUFlmeBcxwy2+TzTOvA5j08mpTCQDrrMAhP80eAMFHhBjFkmpKy8XGPAzP0ILFz6jLNBCVl0LaK68rw70OAGweZ+OB/Agg1NfncTXnUgCvCWSSgECCeWM56c68PAOpMiKM9jyub7Oivhd+OeVmArtcm/DNDdFoMX+spgjPE0kWAgYgLNANYgjqCqDnSaGMvBPW3gMyspfet5t3zmsWHMDfr9wzNoqw2Pp2z4cxMYP/eRiSXk4Pb2trLQsDN+1itnsukrlyZjcAnU6AdK2wGKJissd6PRKCaTSQFlyAxOn23MWXiR5zrLSyDEc9B91seiYoMAerValcofADXZW64FNNJWnE5EFDACkeNg3f1Mf0RUp/baGdCX1imPl/+2rtBXnJPtbSbRsFW0nesB2Rxkl6+vr8saYZB9x8fHJbhar6uLYTuwNTHp6bI4Q5w9mXUydIeHh7G/v1/WzzBhaABj+2695X0BUw5c/PO1DwOvDKqazWYJHgkinF10BUFEdSdFfOR4PC7vT4A3mUzi9PS0QsS7DNzEoUkmgh76jQpjSOr1el12ejKZa3ImYlPW7kWGvSC6cYKDVZMaHtOc5HIyzP7ZusV5EVECN4C4sYuv56CykTZ4Ok/2X676yliI+xoTQfjZX2O7TPZgW9FXYzTOx2YPBoNyvqdkQuyPx+OYTCbR7/fj8PCw9CUHPhZcyJoj9MN0Oo0XL15Es/lQDdbr9YptRvc5XA2IX8l2IT+ffvB3tmkZy9QRXl/7oB1ObPHO6DzTsCAwbm5u4smTJ8UHnZycFN3D3lEZSDIgT6FBX91HJoDn83lMJpPSJrAZOsxYUS2NzkJ0UvnAdf1+PyKikCUQOBA6trHeph4iZW9vL7rdbrk/1beuLsKOgMmoEonYELVUl2BDXEWK7JActY9j3UP6gO/AItZT/CTv5Z1labvxrQNE+gh/RD9i17CJrdbDrmrokgkArr++vo4//vGPsVwuS9UUpIMDVJ4TUd2cB+KJtbsiNlUTnU6nEse4io8+u7+/j3/+53+OXq8X//AP/xDb29ulkuubb76J9+/fV2IIExiunPylH44jM2nh97BtckxiQooDHcI/8x3yQtUNfU+1JDoPIQJha9ljbKn6JfHWaDTKjpofPnyoLH7PhgO8K5ju2bNnJV6D0IZQc5uRYzAbdsN6y2F/iiw4FjVhxGcZKxurgV2cJHfMnXkAjw++zDgwYjNl0DGluYtWq1WWA2HNTeKYZrNZyCTiBXSOPuEekEFs1DCdTsv3xC2z2Sz29/fj6OiovIdjhIgoU5Wxn7SPmNqy6DWaLZNUe0I++jqP3ec+PpmU4sCIkckE6GBUGVQabGGIqO6ykAkTnEhENYsI0OO5DkQ9jY65rwS7VhQMPMpPG/JOCWY+aVsmf7iWe+FsbOwNoE3S2RihPAgHzzSZlwNStwPBtmJZubMC+5z8t8FzJnXqSDJIIxuBfH5+D5ewAhogEPv9fmXXANpS12bLSD5sLH4suKStBjgeX5NPlkGTXlkeTH7QFp9Tlx2gjQ4+HMD8nEfWz9XqIRuKUzF4AmQ4G8f3vB+gcWtrqyzshz6THW61WpVqCAdv7O5EsIgTINAiMGo2m2V6F6SU57RbVrEhs9mssnOKg2aqJabTaZEBHKcDU08/5T3rZADdwtnYsWUg4/5FNiwrHivbVb5Dhh2kmiirC66dua+r2PTYuN3s6IL9BfgAdiFNqE5aLh/WPJjNZiWrTJBsXaLfCNAgob777ruIiLIGiR0ntj1X2ZocpY9d2Yif8TU/F1jGxtpeMv5k4ZBz9M/VvcgLYJQdkvBx1tFXr17F//2//zdWq1UJQDifPvEUQttEQAs+zEmivb29QuziY73WY87eYyeQPScmnNVDbiHOkGsHqXXBQUR10XTbXsgcTy3MwQZjwHPwHfQl7XdFEDrgNfcIEnw/dCBis84Dvo9sevav9qkRm+oMnsPaN9kHWabYscsLJzNO2J3FYlHIKWMLSGLeo9/vF9kaj8dxenpa1vVjjb+Tk5NK9Z5xk8fJINh6afDN4XGsA83GRfnzr3XwLE/lcnUN7d/e3i5T2CDbwUpv3rwpawpFRPT7/ZjNZmXtGHa6clLHSSP6nGqM6XQas9ksIqJMSbMO0VdO1HL/9XpdqiUvLi7KO7Cr1O7ublkQfHt7O87OzgoRwb0g0O/u7so6NZ1Op0wLxX83GtUkD/jD+kpAx3tOJpMYDAZlHSuP/3q9rqy1x1Q57By6zrP429Ujngbn5I+JK6b8Oebgc3CLk5y8E0k76wDnr1ar8j70iRfJhkTg3ky3Auvk6en0H3Z1Z2cnnj9/XshEKo8hQtkBEJ3n/bFv//k//+f4p3/6pzg8PIzZbBaz2ayQqk4mYxcZj18C5v0ph+07fhE7zTiQiHSs5nPpc+MrY2KTvpBOy+Uyzs/PKzEwRAZTpO0rTb4y1jc3N2WnvPF4XPSUZC4kCGRwu92ObrdbMLptCwvwM1307u6uVD07Po7YJADxUcZY4D/HXsZm/HbMZLtuGYKbsE/g/tYp7s/1jg/43LyEZZf+xzfxHX6UZOpwOCy6M51O4+bmpiRnmErP2MBxMAbgO2MWlldgMwf6BNKdMaR608SaCU/H8K60ypiKvnI8k2Prz3n8pOl7rKBvZcoEhgGPhcsBlQ0iL+fKHwf+/E31Sp7rSWfBILpM3lk2fhBCnKK3UIyISlu4j6uFeHcDQpTTW94aGJu8s5LxXiaEMhBw/3JkYoPxMZhwH+fAmUD/MaW2ocQZ+l70vwMf2pLHLmeDzD47cMahGcwT+HMPV7T53XkXZ/D4Phu0LGO0EedoxbQhY6zoZ5xB7j/Litvg7w38LAdfQsH/lgMdwpFARJsIJhNzdXVVnFKWOeSB0tWIDYAkG/P06dOKkbThv7u7i9FoVKYU4dCHw2Eslw8771EmfH9/X0B7s9ksi5JGbHSE4A7Z3d3dLYTUzs5OWSjVDj8iYjAYxLt378q7ZFvA+gqWKz6nPQYjftd82JbadvFZxMdrz2WZW6/XpVoDuXNlCONhoiyTWtaHHJCbNHAmmqkjZPBwpJSfIyfL5cOaFoeHhyVTT6l5o7GZTgFRyThiLyIi3r17F/f39/Htt98WEAWAJstM0MW7ABJNgDBufMc0JMbi5zh4Pm03IQQwhCBeLpe1a7AgM5yXify9vb344Ycf4vvvvy+ycHl5WdZwg0S0Pbe8AC4BTUwBoY224fhvyyH3ZBqM/RSg1f6HABeZZA2riI0+IHMR1WpUdJ2g0jJO+51gglRl6pqrHywTubKWv/G1OfgzUWziyXLJe+cKAgcbTO2zznIvr7tp/ITNgSj2ov/osvvKiT3WoGJreusXfbS/vx+DwSBms1lpAxWrJt85bNPcf7b9mZjKNtP/131nP1snE18rGHYg4yAMOYfYODo6iuFwWIgeqgf/+Z//uVSujUajYuvY4AEyJxNKVFOi/6wBxnbzEVGm23IwLrQPH8YYmOAwcX5wcFCmpjDOu7u75TkREd1ut6xjg24sl8t48uRJ9Pv9ikygp+g5JA99hn+3rSGo2tp6WIQcX4Ldot8jovgMpskY02MzWSONa/f39+PDhw8Fq0La4Nsh6gnQHZzzPq5ko7IFQow+jdgEm6xFc3JyUnYDRuexIUzDpB0RUXYthPBnjNbrdVmg3RVQEGL39/fR7/djMBiUig1XZdrmr9frUmE+Ho9jOBzG6elpPH/+PP785z/H1dVV9Hq9ysL0jndIUtWRzb+Gw4kVfiKikJ0RG0zmvnYsxW/HB978otVqxeHhYVxfXxdSHzuGLcdeG7PSNp7FtM/ZbFaSrCQWIjbLH4CZGGOmhVJ5Zzk5Pz8vfo5FuZnC64opsCyfoTN1CSRsJXYyolpwQFs5nGzxZ3V/20dgdz2jivs49sTemaBydXadDyFZBbYZDAZlJ77379/H4eFhtFqtsgsuY9doPJBR9hFOMqzX65IIIJ6hL+z3sQVO2DlBx/0iolKFTd/mMXJFFXLpZNHnOj6ZlGJOKM6BwTGYoEM5cpYxEysOvBwYmLQwmIrY7Khkhh7HQWbB9+IwOQARRXYB4OVpDzhE2so9TKrg8E2WAEgRRpy9WUbejX6rY3Mx2vRjJsu4zmPgYNKKkhUV4XO2i74xsEUwsyFwRs/O2uAyEz8GYq4mw/lHRFm7I5NzOXiHTEQ+skxxjt/9seAfw4PDtaJl48c4ZkNlwoFzeV9PYbQOOLDx+P+SDhsgB14OUFmglHe08ed8Ayju4/nxVNFQZk9wjayMx+OPHKcXR2Q9hK2trZJhdAmts3OARRzC9vZ2hdACsBOUR2zWGqKclfJ2E7foD/bJJLzJWwNq2846W5ODK48LRya3kF/rr3WJsTNJbvmjT5hugU0nU5dJsOVysyAqgXer1SpZVoAKGURAUafTKdN96EeIQesJuujpDrTz7u4uLi4uyvPZMheSBgLDAQMOnXFAFj0e2c5/7YP3fyy45vAUdcruyWryzvgd1gNh/S0ql/7X//pf8fbt2yKz9CW7xpBssV5jf4fDYSkph3xA7/ClzgA3m80CtvhhHPDjVAvQFognDic2AGCexgcuMRh3NtREXu7XuuAS8M+5/Ea+0Gt8GM93cJx9KoEp9tXtsB8GZzlQNmmWd9mxz0I/uC86wVhyPjtheiq1dYD7Oah9+/ZtdLvdePbsWWkX62uxTg/TgMBF2OoM4J29NWFGBaz9KZiqzi4+5ue5vo7EMl76GgfPNSGLjcIvWm+Q3Xa7HaPRKN6+fVshESEbhsNhPH/+vMwSMEmEnDB9bD6fx2w2K9uVI5+QCq6ApqLLuGa5XJZt5xkPsEy3262sQ4YuIPPb29vx7NmzUhmA7YW4oTITMnJ7ezsmk0mxbV4vkqoujyskFMkq+zpso8nfTqcTEZu1ovDBPAf8TkUJ519dXRVsYlzuTTNsA9EppjmReKGPaBc6ubu7WzZ4cYxDcoZ24estX8bkJiZcTY5MsVGNN5oxnoBQAY+xvpbtINgZ20Uf/PGPf4zf//73FXkaDodlpzfkl/53jPFrOGynCdbBwcaBvJOnqmdcgezhN7a2tiqL7iNHkBnz+bxgXaqQsb/IA2Qf+oe9G41GZd1AxoqYC5zFGqC0F0LKlbqtVquscYQ8Y3vwSdgxE2zYJWTA8XZdYUompHJclmMrY1z/bbnOhFPExwuaM8YRm/jMxRCO79A1x6O2wdj4/f396HQ68cMPP8T19XUhow4PD8uyKF66wLoTEaXyivdYLpdxcXFR1m2kT4gvnMTJMat9oGM0Dr43/4Kt/7HY5HMcn0xKOTDJQbQb6eDLgIyOdHaS6wzCOExiYDDpQBQcB4MimTygzTaggCoTUrSLueN2ol6EN7OMHnAUz2y/jY+Fm3fAkJnksBI6iMK5+32sgM72O7NpwTP4d9BlZfYYWmHNcvvHlRIIrNuP80cWTBTR524rBp4MvwMQDsbYGfgMRN0GA333sf8HUNA/jG8mSSOqa5Q5OHJQwY8VPb+nSQwDZ77PRuLnOnL/0U9UI+Q+y6CRc9AR3g9dOTo6qgRQACOc7vn5eQHBgLWIzaLLrqaBJNna2op+v18WVkSWWK+q1WqVxTgJvJk+gz1BX3Z3dwsY29raiuPj47i5uYlut1sBAdiiiM36L9wrT8PxNQ7GuNakcc4y2cm63znfwYjtre2GbQbfA2aROYJqV3Jiq2izq1e9ppTXo0BfKE/GjnEOB1VmEA3ICCSHz+G9Ib0ajUacnJzE/v5+2TLX0yuY7kfbkUNnkkw40l/039cMXLPD5/8MGLa2tgqZhoxBANJmxgYZImNHIPTHP/4xvvvuu3K+q9QIQtDlRuNh7ReqIJwhtP1H1h1s5SCGknXaxDhFREXeaD96gHyjr846+z1zksfjh8+xXc8+mHHHB5gQymvZ2J7zHGwT16JLjBtgnP7mWsbYOs57YFsgig0kcx9zj2Zzs+YY8sH/kBsA5oiPM/Z+J9uq5XIZZ2dnMZ1O4+nTp5UdubDJOzs7cXx8HN1ut4xf9v/2iZYPB/s8kzGmcs9+nH62zngssx+tw61f42DMLMe8J/gIgpWqF5InHz58iNFoVGTHck5gCkllm04/kLRlty6C1ojNuHOA6ehzZBibgJ676p61RwiQvbbNfD4v65dxPnK8Wq3KmjOMr6f3dLvdIqeuDIQkXa/X0e12C6nabrfL9ZZF5Ai5p0KBKibk1CRNRBSyjgCf/kGPnURBb7KPshx7CjVyTT9gh2g/63Ld3t7GeDyO+Xxe+oP3crKo0dhMcYTQiNgk8VjTi8SECQ1vgsCaYOv1ukwL5Xx2XFssFpXpyHd3d7FYLEr/sVA3U40uLy9jOBzG69evCybwYu6c92s5jPP5H//qDS5su22r8HdOnjBuyPp0Oi0Y9O7uLk5OTipV/2wkgL0wseKCi9VqVabpYvvtNyCHwdJURtJer5+aSW5kB/1fLpdxeHhYcD02DVyAjuUYzASeYyPHUpzn+NSxKDoVsYkH+N42zf7N/sF6a6yd4/Mc19uvtVqb5SdoM7iXcW00GvHu3bsy7Ro7BDll/E4fOt4mIY89nk6nMRgMKlgV3XJSD/vi+MN95TV64T7c757tYLzyuePUTyalULrLy8tK5hKhwZBmIsjgxuDBhEFEVYi4LwQSQsygw/yj/DhKgzk/MyJKpsLrFvEMK6GDId7JANxsvkEU7VqtNtOUDLQxEg4UcWC8YwYBfg6BJM+3opiYMcg1y2xQaDDnQNZKl42uz6fdmTDk3rwLhghhx5ES1JCF99o6yEKea26DYWWoA+QG+TY8zroa1DGGHlvkwAYRpXf2gb/zkckyZ+0YG75zQJXH7ec+TCIDemH10UEMpTMkZGHt+DwWkAXr9bqSYWHcrq+vYzweF0BDCb0DTwA6bQTUOkhstR52GQIkM5bIEEE8xp135V7YBvSHhbxfvXoV33zzTVlPhwUMPV0OG2C7ke1bxMdrpJiYslPNumkHbxnEaeTD15nstd3OlQtuA8G4q8S4Htt1dXUVk8mkyIZJqLy7UavVqtjkm5ubMs7IQ7vdjqurq7LlPO3CDhFoLxaLeP/+fRwfH5f39RhaJgFtOHeyWHa4HNbhr3FYPnJAzTi7PbSPwAaA48wYtjTiQTb29vbi4uIiXr9+XbFnBjLWEyogTJICtgiQnCE1sUewZRvtNVcc2DGeBnW8C+Niv2CfiFygp+iG/antOfc2CZV9p3fXg6TOYD5iU9lBXzNm2AOy3FwHAUw7nSjyONn38J6ZzMkJDOsItsEJHuwSwQT3IIhEL9EPT/XH/nOcnp7GeDyOv/u7vytb0rMehteMYkyd5UUeLE/0n6c85sC9LjmXiXzrhfvkx/7+Ggfjj80hsDPJQX8xtQay5uTkpJB1yLSnyoExCSLBxsjjfD6P09PTMrWdqh0q55zIc4Ub7XTiM+JhKqeDzUajEcPhMI6Ojsr0Tuz/er2O8/PzMs7cC53m89VqVYgJqnjW63VJJrE2FOuxoEuQrtg2/MnBwUHxG7xbRJSEFckjgnqvZYhfZ+ok1dytVqusg+qEkRM2TqDzOe2ircgfCTCen/vG+IF2UIWMTEEoMLXKFYbYKQgp9Oj+/r6sk8lOXlSRe8qZ16ThHfb392MymRRiyvFWRFSqvH7zm9/EH//4xyIf9Al+OCIqf/+aDtsO667JDBMgzDKy3XYsgi5sb2+X6XPo9tnZWSFiGQeWQfDzvITE/f3Dun5nZ2dlyic6yXPwi04oIc+tVqvskNzv94s+gdPOz89jNpuV6bgk9gaDQcHMLv5wxayxr3mBHNPlWDiiOrXMhBPj4ERSTq5wT8duGZvn2M72yff0+TyT5/s844FmsxnPnj2Lg4OD+NOf/lTWSX316lWs15tlTLBr+H2PHetJQVpeXV1Fo9GIXq9X0UW/t7E+fYC8OskFQenkGwvro9N+ry/hP39SpRRG3ICZQWi1WmVRvBy4WwhzkI/i8oxMrOQsrAFss9ksrD33xpD7frB8rrCK2FTbEFCRpTFxgHJxbwM3nIdJKBsdg1faa3KL53MdgI1zXcbHtZ7mY8PGezlg4T3zNArabzLEwa6dqd8jIkq7DHCzE8SBGiz/2LpdHnMCWxyijbyNV12wyDm57D/LMGDbymVSM5d++3o7mxzMmN02AZABst+DzyLqF67+uQ+PK7KAPONk1+t1hVjNsoTsRmycEM6O6VteGPzi4iI+fPjw0TjZgQHeIDgBeOiSM7VeR4axXS6XcXBwUEAchCl67F2G+H4+n5c1jBqNRhwfHxdiIyIqFR+2MfxtEriOWDDx5M+5loM+qHMM2YlyH49hbpfvwW/6CnnGYeGU7Hyxr55OhKMzOcwOZsgHa3kRtOMAsQHYfNYyYIcYwA194gWqybobjHu3GAfn9AnERx4T+jrbmS99ZNvgscmBtEnjOr1jwWQqKAAY79+/L4Qu/YGu0b/eip4sMH1h3+CsmW21waTJJOSF871jW0R8JEPIH3bEwQzywnkmGk2WI7+umkBGDISzn6JPkTUTs7yTSXL8B21lJy7O83Qov4//dlKGPsuZb4NkYwKew3tA5EEu4ZdpE3Z3Z2cnut1uNBqNslAqpBrEBc8mgGb8/sf/+B/x29/+Nv7+7/++0ieQLlm+bIf53L7FPtJ+1OD/MaxjW5f1J+t3nT59jSMnIRhfKmmePHkS4/G4rG94enoab968iYioTBViAXknASBcnHC7uroqlTbIA/2N347YVClSmcH/nGusjIwSjHa73Xjx4kW02+0yvuBp1hPLegxZFbEhcJ3IZbx3d3fj6dOnxecjR1Rc8h74cO7H7AeIKfAkNgKb5oWlIzaVztgDEleQ8Dkg5XPj2kajUdkxDXtBJQVVviwcfXFxUXwesp4JQ/zlcDiM8/PzoveMEQQ4Y3VwcFCIqvPz8xLEOj6KiLi4uIi7u7v43e9+V3CPK5xNWBLjMXWb6WCLxaLgrGazWZJ3bEpD4i5isxmD+ztj4l/Dgf3K8Y/fC58TsUlEmjjwdby/ZeX+/j4uLi4KSd1qtcpyCK6YMRYDa717966sfdZutyvrL0Iuck9wnQlz2s8aoY1Go5BO4OHDw8PKGnHoHMSjsa+5ABdI1MWvvs59ZW4gY1ZjX7BrjkmMqXMCIxNUPI9nGHMax+e4jf408UbxBQU9y+Uyjo+P4+LiomAVYgvW1nPMhX3Z3n5YP8zVTldXV/Hhw4doNpulYgqcZfxq3EsbeGf3rUlSFwsRH/HcL0VM/VW777lzARJ0QsTHO+y5wsXsf/4+Ey12rAYWdNzt7W1ZayYHz7QDthZBidhU4nAuCukqAQ+kgTYGGwXmPXBWVgYyUTYGtN1zzS3gBqf87/mi9K3LHfnO7TEhlw0ADtEK5uCG3zkg8mFh573dFrOqJv/oe+QIRbNiQwJ4KggH40gfmPAzKDcRYkNIv+QMbUQUxXe1DW2uczb+jn5wUIhuWMlNANI2nA/jZWP4cx+8Q8TGEFP1gh5TjeRrcKq5AgzixpWDBLsHBwdxdXUVf/7zn8vW9EzzoW/y2C8Wi1itVpV1pXCiADIHi+v1urLYJvaIqVsARgIldrMAYAGI9/f3S8n64eFhkV/LBM6EvjNhnoN2+iITEpl8zSRnPs/ya/uabUzWK2TWmT2ex//oNO23/WDxVCppIYoggQkMWCAZ2QDkRkSl3VtbWwXooEPD4TB6vV7JVHPwPQ56Pp9Hr9erkGgQjePxOJrNZqmKciKCfvA7f+2jDgxlmeCwDyXL7XGJiDg8PCxBJDYYkNvpdOL09LQEoPRjr9cra5YQuCEzVAosl8vKLkN+tolM5MaEoKuJ+A698pQi/IIrEuw/ODKRGxEFsFPtYUDMgf9xkoH7MW0XAop78z55uqF1CRuOLrm9rii3D3aVhpdIIMjzNGXexUS/k4C0i/5nmiD9aduE3WUagQMfgn2vVeQNB8jeHhwcFED9u9/9riIvyEne6hwbTj9Z9x14cC47tbq6jf6rI6Dy4aA3E1Jfy9faRmOTXL3baDxMkW232zEej8u6e69evYrRaFQWNN/e3o6jo6NoNptl2QrwQ6/Xi36/Xyp/qbCwn0UfkJmMeXN1oTer2NraKjtroV/gWa95AhGFPyUo3traKr6Ae0NkcQ+mC0M6r1YPFaBM0zNxTgWIK/D29vZiOp1Gr9eLo6OjIq/b29tlxyveDxl2Yor3JcZZrVZlBy2m8oMNkEFspWWNwBy8YZyN/mEz8YtUToDR2b2O9rP7GksbON5AR7w+DdMZs79H9lutVkni/PnPf47f/e53pXLKVcvZLyJDh4eHcXZ2VsjlRqNRqjjod6b3Grvn6UERm2TEr+HIMSK2kveHnHdyz1NAjeFcKcm9SKLiq5EVSENPUSUeZy208/PzePPmTUngrVargplsB8AW+DNkkXuB043XmcHAVN1GoxH9fr+Qq45pHW/nWD8ncuqIImTOWCJ/Z/vPYR9vDsE6kPFeJp8ca9u+RGymwWFDc2VvxCaWJH7HlrDxwN7eXjx58qTsMMranKenp0UviCsgt3lvV69686f3798Xf56Tzn5Hj30mlkx2sjOslyX5sWs/1/GTdt+LiFLeBViyY6MD+O3gPH8XUV1Dic+zsGbhQ0gpcTTIczsjNhlDs6UIeEQUMO7OzgIOiOZ+EZu1TEzG4JBMvDnjwPtiEOgzA2buY5CCY+MaP8OgNvern8l7mZDxeGVlJ0hw5Vdmsz2dgHsCzlFEg0wbRJhhgHbud4wHRtVGjuAL8IyBzu/IffJ4OjjJBg1y0TJhEGmD64DAY8JB+5AFA+b8TPd9JvF+CYf7wwf6RSYxIioglX5zaTCygbNer9dlHv5isYg//elPcX5+XsaCdaGYPoR8eeFEjDgl/66OsuO3wWVNqZubm0JUoBeAPcAiY20y3uAQ4pnqMbeLfsIW1Nm3HGBHVEnmiCoZz1GnN3zuTFKWVT6zfc5ZIsbOztjke56SxRiwICYgyCCr0WiU9cGojmo2Hxa/htTiPs4oESwQBDCt6uLioujXwcFBdDqdmM/npWKA8+l3Fvcm6GDKCzs0YW/5nUnDr3HYR/5YsO2xZloI+oWc0v+sCzIYDMqivUxZMVnJGi4Ev8ggFW55Ci0VwibwPTVztVqVKSPIPXqbp9wbuPlzADKkFYDfgBlZNRFk8G//j1+j3QRCttcQMXX+25VVJlPtv7m/EyvuT/TOQWzGMMYX1nGTSTmw4Z0JjrjWSS3bEwfxu7u7ZSdUyyDVpuPxOMbjcTQaDxtLAK6xp61WK87Pz2N3dzd+97vflSAbWaAiBZCPPEVsqjZ5rttJH5g4ZEzrMJv9Mb+zDf2aRJQPnosceDoL6++40sXrrl1dXZV+Zoe6yWRSMBRBpNf4ubi4KPaWSiyTMdgHy677x/gFGQHXmbgyOY2v90Lcw+GwQkS4oqfb7ZZkg6sKkaurq6tK1R5EsXUQ3AkZGrFZ3BtfQ9CHzb+7uyvTTY3ruCfkIP1AtRG2iT6kH/CfVJ/k/jHmx07yv9elwi+ayMKW0Le9Xi/u7u5iPB6X+ARdzEusUHUMoQshZ6KRsVmv1/HDDz/EZDKJb7/9tjKdOyLKulPL5Wax9ru7u7L2lZNPVEqhu+v1umwig3wR3Dux8Ws6jPeJdbB16DW+rtPpFOLSxDqFCyTqTIY4WQYmarVahexD3pG1xWIRb968iclkEs3mw2wE/CxVatZvCNCIzZpwrBuJXOCvIVfBbAcHB2W6GPoDuWUbVBdnZWLD8aVxjW04fVN3cJ6xs/2DrzOmMvbFJ7tt5i64d/YvjUaj4m9d7IDscx3fsbB8s9mM58+fx+vXryt6MBqNyrVHR0eVWUvo3Pb2dplSB0ZfLBYxn89L9aiTDTmWNQmFHTJpTZ85meiK5tz3n/P4ydQ0INcZHhrngTKgN3Bi0A0wEJBMbFiAOJwVj9is58B3EVHJkHKu74cRzoPgTIafkY2PlQgFWK1WlYV3yRpZaXm/iE02imflZ+cSW5yswWcOWq1oWcHyGODoHIwb+OF0yYD4eSa0PEYmazIxRDaAdjFv3UDIfdVobBYidtk1gQjXOqiq+8n9wJhxDUbJDsQl2ian/G4m5zgvGz+cuat7LMu0A6PPPUwe/NyHiQLGl+oG+hPyqNfrVcAp/YazjtjIkMmj3d3duLy8jB9++KFsL+u1mQhsh8NhYe8BViyqCuCij71gLLLn6iwW5L6+vi5z5J89exadTqcyBfnu7i4ODg6Ko51MJtHtdktQNZlMCliIqFYa8p6AeIMRzgFU2gGbgK8jTnFotl/ovQOKiOo0KwMAZNi2NgetdcGinZUDF4INAtmtrc123Nhb7gvRjpxfXV1Fv98vZAO6w31N9hmkf/PNN7FcLgs4j4iyffVkMomjo6MSdBB8P3nyJN68eVPRzYjqosCM7ZdwuJ9yOEB67MjkFSS/ZXx/f7+slQWZf3Z2VsmyUomwtbVVKRl3QqfZbJapP9hiptJjE7DJs9mskCa0EfkmOGLakQMjxhy74UDFgJUg3QEhvpHvXR1J31jvSAZxL/dBs9msBOv2Heiq10h0dtiELUGlQantgWXZlbm5SprDNpR2GnSaXGNxZt/D93JlOPcl+8r7YjdpE9VObGntqitswN3dXXz48CHa7XbxBQRdnhbpw0EvckI/0ne0NQcu+e9PPX6u4NeBqtc+itjs/GifSX+wm2y73Y6jo6NoNBoxmUwiYpMcYRxIGo9Go7JRAWOAz8auWu6MtSI2fowkA0Envj8iytRqkhFMK2FdO1dImIxG5iHMsOfYB551cHAQq9Wq2CUIFnB0s9ksa0tR8WM8xQ9TDJfLZfR6vRiPx0Xe9/b24vLysgTjrVYrLi8vi3xjI7wgsXewhHTyFD4n7F1pRDDPphsQDCT28DskXzudTvT7/ZjP56WiYrFYFJvVbrfLGmGMnQl4SF/stRPlPBOMY0yMjr98+TIODg7i4OCgEgd4N9aLi4uSBFqtVoXYjoiyNANVf9j6+Xxe8V+Mza/xMNEIprVtxTe60oUj+znLynK5jOl0WmwnRCmyQaINOaY6arlclso/KjGxI9lP4MNZn4pELraCZ1FIcXl5Gff399HtdivVryY6jCuxYRnnOm5Ej23bs792vJUPx7y+tzF2jn19vmNoE0P53BxDRlQrkIxVsDuQt05+mcxuNh82elqv1zGZTKLRaBTC8f7+Ps7OzkoSAhLK+Hd3dzd6vV5cXFyUcWctbI+zic4cW4ChrI8R1Wpv96n76ksdP2mh84j6eZhmI3MH+Nr8Pfcx6HMA5QA2AzQMKcbfGQw+8/MMUCmP5fm0sY64MPgzwMtVEA6gcABk/90e2pErYfjfc7jdrgzWuScAnf7KfeVnIKC0FQHP5JyDVo+Rxybi453ouK+vcyDuEtFms1nZxQkj6Eo2nkF/Ztn7MWPFQZ8BEPifayAwXXHn3U0YWz+Tca8jvuhzTw+1fGcDmf/nnb+k0v/UAwICYpCye2QoOyb0kL52kOpKCmeP3r17F2dnZxVZA0Qj+5PJpJQvQ1qxCDM6Yf25ubmJy8vLApAvLy9jPB4XcO3yeZ6BY8ZhU6lFEEq1BtP3mCqGwyXQpg/y+NvBITt1mZzsaH3wWSZcM1EREZWsMu/t5+R7+sCecK0BJOcjpwTi1n90C2BKxY7Xrbi+vi7ZUz63zrLgLM8wCAB0kQU0kTidTuPo6KisNXJ9fV3WpnIwnYNv/Equ/P3aR5395e98GPQ48fPs2bOIiEKeEPBT8bSzsxNPnz4t69cAHCM2GTQnX0xeGLyRscP+Y9eQBf5uNpslcOQ7gLXPwf6RIby6uipVJNadDJScpbTNzuQJ/ehpvHkqOJgCgGmCxL6cNjup47Ww3A78DHKMTaAPTBZy8K4ZLziBZWyC3aVdtg1cY/uBLSWYZgxZN8p+q9FolGrDy8vLspYR50VsSGFn529ubsr6P16XzPKMLNmG8U5Uyf1YsibrRQ5q3Jc/12G8QNIUAsNJBgdvVEmBDel7zoMgpCpiNpvFhw8fyqYdXI8sQ1ozvsgLz8T27ew87MCG/badp8IL/AZeQo5MTIE5kSkSQO/fv4/vv/8+er1ePHnyJIbDYXnG7e1tDAaDQoba56JX6KcDvNvbzc7NTBeEvHNVJ/qBHmNzqPBylWlOZoHvnRwHg69Wm7WoeGfGlWQamNhVDkxhNElJX2EvwCBObD99+jQajUbxYRHVXbeQdbafj9joFdUUTJfET4KBWq1WnJ6exsHBQQwGg0o1HLYKuxzxYBdZx/Pq6ioGg0GZUeNqmxzoGwP9nLr51x7EPcgk/YNO21+hX45zOM8+EcxMIq/T6UTEZs1S42gWHD8/Py9+wvdnTUjshNdU3N3dLdVRTiyuVqtSFYdPIjGMrGCTwR0spdPr9QoGB//Zf1gu6/xzRLUayf7NcmLfyed1fANjwOe+zjIYUcXKxtVO3ObxzHGfj0ajUfqLeIk28Xw2jqISlLgHrD4ej0s14nA4jOvr60LENxqNUnUF2c/MD4o3rFfuZyekMhaxHQLz5Tjjsdjkcxw/iZTyoFoRCVo5bHg88I+9jAMxD5gJHDIWEVGCB4Aenzk7wvOciSU4wUnbuZno4dmewuFMpoNvG1YrFQFVJjPyYVLD040MoHJ/WLExAPRDHfg08ZEzNwQxns9tgsHkSVZiVw65fDhis9Ak5xLsOEDGQTso5xwbRFdQmHjMhKTlyv1YJ2M5E+6xAFQxJm6Xz6mTaz+T59FuQEuezsf9LH+/pIPxcMDjagFkx9VAlJhGRKlGAlQy3vv7+zGfz2M0GsVoNCrOFgPt6hUIIAw3u4EAkMh6ctBWMqHs5Ldeb3ZB4f44jOl0WrE3jBGgc2dnpwTV7Xa7vBfl8hBfBrFZ/ywjdro5OLMz5UCOIj5e8816aWKXZ/K++fxsn3MVh6sl6zJYAGvGfr1el0wosnB/f18qJ0z4ep79eDyO2WxWMoJMGeEcADkEI6D47u6uTP3wun7I1fHxcQX8kzk/Pz8v70520HLjKrGvCZbrbEgGSf7eJJQJk+VyGZ1OpwBbCAL6Alvz9OnT2Nvbi/F4XCFtnJThXPSw2WyWdYYiNmQQBCMBK3KU3w9/5cDIQZ79ET4cAIdcIePYY2dkIzbVRFxv4tk+g8+pXiGQBJy5QgCQDV7Y3t6ubE1v2wfhCRFlPJIrf1zN6IxzRNU30AbOydgkJ/ycHPB59LcJA9rOovPYYmw8hMhqtSok09OnT2M+n5d1/bCLrdbDVL6Dg4NS5ZIXgqaNxguWbSfMPCaMN0fWzZy4sv7wvYMXt+VLH+ivk3MRmy3FwQUE88jp/f19PH36NHZ2dkqVD/diyvpqtYrxeBxv374tFSquOPO0t4jNjANwGqTEer0uVcZZNz0GyMJ0Oi3YfGtrq6xhNxqNyn3fvHlTqm/QJ6pbsffWIfsqpsf3er2YTqeF9MAe4CPwLb1er0wrevbsWbx586boIX3rSmiT8MgH2HO1WhWi5f7+PtrtdkRUF+pmHJhmSPU1fbxYLD7ardwJUNtuvjPOhMT1VGH8IITR+fl5TCaTsqPgzc1NSfSYLGc8wDt5IyHwFH0Bbrq4uIhvvvkmBoNBqYa7vLwstoh+uLu7K+ugPX36tMyoQeZtN40nf46p8n+r3tfFstgYV5LiX3jXbL8YF9si5A+C1CQCthCM/P79+4JbGcvlclnIivl8XrAWSYGIzVqF9hWr1ars5Ias2Hbj0+131+t1qY7GzlDRZQIV/YyI0oY8myJjnNxf2e9xrgkW3sXyZB9h+8//OQHphI/b4Xu5Xbk4w2OR9QvCCb4CPY6IgiUiolSu3t7exnQ6jfF4HM+fPy+b9njzhuFwWCopV6tV2RGRZD2YK8cI9pWexUN/5tle9A946Usdn0xKmY2kUgJWPmIDHk1mWHhsgHPAZRKF3zwPR2XiyIsd8nwU1Wu6OKN7c3MT8/m8smiYAaLblQMCB2muEnAA4+lXDgwioqIgGBUArzOqPCOvv+PgyAy7AY4dA2DO/VzHMHM4cK0jE7mWtliYLZyMWSZePM+YtvAs7uOqpFw5RUbb2WtXo9QZDYM+Pvf4ZhnkezsJV2hwjoMnnuGqM7+Dq5/cn7yXwQfX57H5JRw54HHm0+XKERujb1KEsedc+gRw8vbt2xiPx2XdBHSe+1mWPaXICzJGbMYQ0EXwPBqNSgUTa+awa2ez2Szbbk+n0xIsY2eazU0G1gutkmXe2tqKs7OzsrggAamzqfw4mODdbBuwKa5CyaRVdqh20hzZjpkIBJBbdi2Dbgd/07d+Jn8TtNBeB5mMJ+Rgs9ksVTo8k36D3OPavb29GA6HxYYCZHguDpk1NhgL7sO6SiwIHBGlHJ3di9xXyKzJeb/31zqyTc3fPUaU2WfyPs5oUsHiEvBerxer1So6nU6ZduGpFCRWsMXsuIN+Qg6x+LUJI7cVfUCuIXHxgRDNHgsSHKzHgmzx7iQNrK/IrINN7JUBLfaKc8ASbrMJLgNMB/YRVd1yoMvfYBT7M/tQT1d9jDhh7K37kPcEuLmSy0kaA1FXNUVEIQgmk0nFnwKUIaQsX+C//f39OD4+jq2trbIun6dNj8fjEggRrDPNmmwv93MfAop5fk56Zqzm37Z7dZ85EPvaR7Yvq9Vmyijy6yQqcr61tRXtdrskZXq9XqXyptlsxvn5ebx+/bpChNJHrVarssYTfWNfZHmKqC7HgI65AofvwZwmiQmsyeqfn58Xf2ncu7X1sGsfuoGut9vtso4dhBQBM3bEmBVCjMTVixcvYmdnJ87Ozio72DlxYnvPfaj2NbZerVYl0GaTDXYOZvyQT+sW48ZsifF4HOv1upA26AkxFMk1dNdkjYkGcAxJOKq5G42HBafZORMbZGxGZQzxEHae9TCz3UA2IyJ++OGHuLq6ik6nU6YkIXv0LePFToyOjSKitLPOfn7tZOznIKOzbWYM8YG24VlH6FtjL+yViyYc4xpr39zcxMnJSUyn01iv12XHTTAR/jFiQzgh+2A1MDbTU9Ev/KKrd8DlyM9qtYrRaBR3d3dl58jVahW9Xq9MOSTu866MtjHZPyFL2e8a32ZCj/MsRzl+qvOh9AFEGW2iv2gn93P8jL9FvjNHwPuY2Dc5hT+Eo/A6xvg7x0+tVquQj7/73e8qMT/nMXWPds3n84KtqQJ1u+hjV7PzPMtI5h14T8cBn/v4ZFKKTsSheN0dQGPEx2ubALAQ7BwAmVQw2cFvOp/7R3xMYhH0OOhDAba2tuLy8jJms1nJsNQRLDkQjNgMkKeuOFtkEExASl8hzGZx7YxpP8FzxKY8MxM9PIv+MHDm/iYF6BMTIhmgIVg4Jv6nX0zi1JEkBoIcVBzQj5RMR8RHawF4ygbvScAMS4zsYDwwlrTTVXHuq0xQ+bAy2xFYnjJwdv/Tzx5DA2MrrbPDtJvn5+DThsDM+y/lwNkavFAVxNQo5Ak5ZqxNcmRyCzY+6zTBrgOW/f39ePr0abTb7cp5jAsL4y+Xy5hMJiUgBxCT6WPc7QgAcZ5K5mwt74WT8Ra9PBsbZHtmx2E5c+Wfzyeo4DxXdObshu1f7g/6Fp3IBKnbYLtCe3zY5tl+e10xJy0AJF6nhLawnbAdHjbx9va2ZGC5jnejegDCCvDEopzIFuXqVFGxXojHzn0JYUC7IVe+NhnFkUnA/Pljx48F2/QhG4MQDJmUhKhl/PGHi8Uizs7Oii3jWYwZgbN3uHLQGLGpqDWZELGpZnI2DlvAjwkt7sGzIKMs4xzoeCZ40NmcSfV3JMGMDSz/GWCChcAADsZMsGR9j6iCYfsLJ12MoZzIcV8xLnXgmDHAljYam8rmq6urslvWbDYrfdDv90vVE/4WnW80NhXO19fX0ev1yuLnDqS5N2sDEbAOh8Oyo5gDFb8HYwhhgS2oSybZrj3m/xk3++qvedjGO4hF7kzK0RfWRSr88W+QG6vVKk5OTuK7774rQb+DHIh/9AaZIUhkyhp62Gg0Kjga8sS+39VF+JvBYBCNRiM+fPgQi8WiZO29Ux9rRkHCeA27iKhUhZD0oq/u7u7KLn30JzbMMcL9/X2ZVupNcZA/qnCtKwRuxgFUhWAj0YFer1ch9tEB/ArBPr6F6ZasAcP0aVc+gWMdCNKv2FaSOtkGNxqNODo6itPT0+I7nz17VnSQam76wD4asjLjdjAH65VRRfz999/Her2O4+PjePLkSSXWohKXsbi7uyuVXLPZrLQjY4y/hNe/1GHs/dce9m+OCxxbRHxMvNkveTod+IS4A/mlP6lWnc1mMRqNCsZFjyM2cTo6C6FsnwexBPaOqK6pCdmNXcb2U8E1m83KDry2yxBT2BAnKegL7sP3GZPmcTEp5LFz/Ml9HM9lbGy9yQl0xyRup8fJvsXt5jPGqc7/Wtf5YXx5Hr6YZAPtxBdsbW3FxcVFNBqNePnyZbmWc/b29qLf75fdWCEZ2SAqx13G3bap9keZx8kxxJc6PpmUurq6qmyR6t0gPHg4gTyQJl/sFDFqEdU1Qwx86aBms/nRWgecRylqnq9LoONgw47KTD4d76DNg4Oz4t5+HsaYzwB+nEdwZHBtJcRRurrC0woiqmtZ2cggtHxuhQJIeDx8byuygXd+bxNTOTjFgPEurj7DuZvwsyJbEXgGfenrCErJ4PIsDK8Z3ww6/Y6+J8/KZBYGnUUl7Syt2Pl53N+BQx0AzmSUM2T0nwm9X8LhTDwg0UHI3d1dZWdJ9AK9N8GAvDBPncVHAbyUwmPod3Z2otfrlQwj/YLxxbkD+C4vL8sin83mQyWU14pgkfPlclmAvqfBoocsGMmCk2R+LD+QJBhsZN/ZEHSTrDM2IgfpJiY5bPvsXOwU7QTtWA2CuCbrBc/lXbDVXGcnlp22bRXPMIHm59jOYvshDekbAH2j0SiVrXy+Wq3KjkjM02+324WwMCDg85ubmzg7O4vf/OY3RRYZw+Pj4/jw4UMB5hCaed2in+P4S4TUY9/7PAdryBTbB0M2rNfrEmwOBoPKOnG3t7dlO3nujT4i52R0KUf39DWeb6LBWUzkDD2MiMr074gqwOfHoA5/inzk98VHRVSzxZk45ntjDCdnsm22D+Bat8PVDiYLMzDmGn7brnEOvt3+xVUFxjP5nXgf9NY+Hx0nWGfreapX3717VwIaqmwY04jqDonNZrNMpzY+IxnI7lyLxaLYV9tGk9QGv3XYo07+6/7PRFU+/tL3X+rgWZZT/KcTPsgjU6rwU5bpiIiTk5P4/vvvy7Xc2z7YMk21DOvUMF70OdcRWFI9xFjhux0IYq/fvHlTCBcnoElYsVAyNmQ4HH7UH04E8nywBrJJonkwGBQZsUwzptgWbByzKCCLHKBGROVcEhTIH9OSl8tlnJ6eFiIJvwZxA97hHbBnBJvgOqbbmMSCELNdNklO+2wzsU8QQ1RHEKcwpRrizGRDRHWjIew44zabzaLVapW2Ytffvn0bW1tb8fTp0xJL0HZIMoJp/DS4DJLOOKbOf/0aD/xZjnvxi8ZEnAPGxCZOJpOyxpirpJCz5XJZpkzTfxBbkJDg6H6/X6oPkSPwL34M0pjqKjAA+rhaPUzRtQ9keij65Modvxv+Ahl3NSC+yX4O3wbGtq3LdtqYN2NT+9yIapV/vp+vi4iKP8q+2z7DU/JzvJdjaNtMbP56vdmFNmJTsez1P6lgu79/WK+t3W7H5eVlvHnzJn7zm9+UqbT0HQlZ4hsnjY0jnHBzHA/BZd/rWD/HD19KXz+ZlPL0CQdVGLKI6sr5vATKhvDlDKoHlKDNTgljF7HJIvE3A24mEgVstR5K1WHpc3VKFiQG0SRFVh4HW34PC6+BuDMm9FcGeFa2zM763ggebea3SRYEHEfBefQzbeAwEFmvN4tzcw/Gz4Cxru88PjlwNjDnPEAriolRRSFwZhgI2k1pI8rlANjBhw0VRAPKm5WMI5NTJlhNgpKdqxsLH/7e7cnykg1Ys9n8KIvwcx/0PXqObDEuBtg2voCVHOgDrNhNhrVH2HkPY0om5vj4OPr9fmXdCzJ8GFgcJbpmWwSAXK1WJZA2GOc7gjCIL8jQ6+vrUma/u7tb1vFw9dhyuSzA1YtpG0zaQXB4jOkfBxh87gCQzwx+soxxb64zYHAmnHOss64itawDygHFXGcZ9tRJggeACCAeApCpEl4IlX5FB6hco99wnC9evCjjRD/TRtscHDQ2gICk1+vF27dvI6Ka6QRA0QdfWw/zGP6lAz0xYFqv1zEcDivkKrpABaDBD9cNh8O4uLiI8/PzCiHIPbBN+Ev3G4QhATYAk3FAB5En5AQCHr/OYqzoDYEh5GOWf+uF/QGfOdHF74iPiSF0gWlE6An2P8sV8mTihz52v+akBriEdzCOyNXb1iuTgfb16KSDe+5t3eYc23LWd2NcaY918/7+YffTdrsd/X6/QnzQPvQau0xFyGq1KjuHMc16vV6XRc9ZSgEZMinF3w6+LeP5fw7LB9f9Uo6M2+hrqnfQZRId1pmIqKytSRBzfn7+UaKCc42lWAh9f38/+v1+hfRHL6wrVGmgf/Qna0Bhh12BChaAANnb2yvTzfDlkDe2rV7PDF1C1wl8jZGpwD07O4vhcFhZI4pnRWx0zViO5FnEJtHONGFIMcYI/efdwQxOKKEjxusOFDmH78E+EE/oJQlQE0/WLfR1uVyWXQIhkU3OUz1HX2xvb5dp8GAk7wxKFRezA+gr22eebSLyu+++i4iIwWBQEsVUerTb7TJd9+7uYZHm6+vr+P777ysxVrajTkj8Wg7GiMozx0jGEhHVmAD5tj2/ubmJ0WhUpmVm34bMz+fzgn/pY/xVt9stRQz4M0+Vd8Kb5N1qtSo+CbllSj5ywNS+m5ubmEwmlUIM/CjPQWad+HIsjT2pIz5og/1LPpxoyf3KPU06Zd8fsVk3ieust26jn8lhwsz2M8fJ2DFfB951bImcDAaDGI1GZUomsQ4VpPTj7e1tnJycRKPRKHYrYrOOaESUJUv83vSH3x0fy29sG+/iKZhOvPE+X4KY+mRSCuNB0GCwRePYot1OAsDhrIQHLqIauPvgf5MSBnQMFELnjCIZAmdnLMwESVm4EUpnhfyOBFwWXJwdAkBfWFgtfDmINIACrOSKKpSU5/PbgmLnyPvwDBwMisFhQ+Hzc8YO55lJGN/TwetfAoXuNwJFZ61sjPmcShiyTpzHWOUgPT/f5A/9wzOyIpoc5f39TtaHLOce19wmf+6+sJFrNBql8u+XcjBO2WCbjQd40pcQCywgTH8DkCAsIqKsa4NBhgA8OjqqTD+gDRCUlN2zkLmrQAiAsFWQTJDbgDrAG9vfIs/YDxwBlVr84DQAq6PRKHq9XmVhSGwlxt2ErvuJd8ahc2B//H8mgzL5ZEIsAyKe7TG0ncs6yD0JckwyOpuGjgB+TCIQJAB++D7i46waz6RCYzwelzaxu9Th4WH5mx37cPY8x5W5s9kser1exW+57ZnssC2JqAKSr3VY97OfyH/TPgJYAhfIHvoF4Ovyed6z0+nEZDKJs7OzOD8/L/bOU/24jyuq+NyEpWUyYjNFC99Hu9FnyAxkhqxqJh/4m0wuAbZttf06/oXvaYttsfvaZI/llICJ9+DeVFPQtwa0yFVEdR02bCS2w+taZaDLNRlvoGvIMO9nf23fVpeQsb9z8EvwCbkIIXd9fR3T6TT6/X6pPMUWk9WnT70Y62QyifF4XKoqbm9v4/DwMEajUYzH4zKujINtmslNj1edf38sgKnT5zrd+tKH7ajtKbLl4BWiimnq2DLb1MlkEt9//30sl8vo9/vlPOM8670TKQSyPDNXBSJj2GCmWprw9VRrpv8xZmAF7L3XLMKW2IaRhKCtJiwcNA8Gg9KfJCQg2rAvEPCQer1eLyIeSDoCfq9xSfUOdpT2ok8mc9GdRmOTCGdsbVcI4nMiiZjAwaUP9JbKbEimfr9fcBT9CzHF1Ort7e2y02BOgDGme3t7cXBwEFdXV7FYLCprPhrvg5VoC7aGZBv24fXr1zGdTuNf/It/Ufqe7etJ1IHZ+/1+2fbeBCr9BUb4NR62H9hfr7caUbUzyJqnb2EH6F9sq3EiU/YYK8aENZwYA9tt9IdqR+sheoyfxHczu4XKHXB2p9OpbBbjIobValOps1gsKmtKwQc4vqzrQ+QfPbJPtc2o890ZKzjxz3X2oZm7qMMsHHVt9rkcYC/IvxwLgp+J1bkPNr3b7cazZ8+K/KCDLsTBtqO3JGe5Pwk8fDLThj2TiX6O2JBz/g4s4v7h+TmJ9CV85yeTUgY8vBTOxMGVX9CVFAA+Cwid6IDEQMzZHreDASdA4n4IHQsseuFBt91ZVZTR7UChDDC5hvcywMMIkBXimTg8g+KITXbbU4VspBl8B5RZkOgjrskZUp9PmzLxYWKFcfLzcCx1ipsDI4+1QU4dWWPyDAWM2BCfVmQHWpB7VF7QZ8hBJo5MFnEY9PI9/YJx4LsMZiE1AHz0K0Ce9+FdMvjl+0wQcD7jYOPztxzOPHyOw7IS8dB2FtTDkQF2TY6QvSMIQ14iogQ87CJxd3dXHN9gMIhut1ueHbHpVwLjxWIR0+k0FotFOY/MAoDbCzKTAeLZnU4nOp1OhQjxTjrObHS73Yq9A4xHbKYenZ2dVTJTtMmG3BURyJhlLwdptike2xxcc02WYc7387LTyY6I/omoZqL4jW0G5OAMIeR2dnai2+3G7u5uCYIAN+gz5AjjApAiCKFdfE6/sqvcbDYrBAyBhEvoAVsARPoAcuHw8LBMU7JdJKjKZNDXOn7Kc7NdAax6qj3ft9vtUvYfsVn7kIDt5OSkAEkCFqZfEGBk0pJxNmChf51gQR8hdE0uIUeWfWeZkRt8qRMSdf2GbbEt5n8nx+gHZ33pM9sp+wz8sgkvk6AOAtkMBlDpYI+2ogsZVHM+/Wa8wbtknw3GyplM7E/d/7xH7if7b7f37OysbPnOulPuH87D5p6fn8d0Oo2jo6OS2e90OqVaJdscBy/ocraLjx22a1km/Dtjsb90389xoBM5uGIsnBCDVNjd3Y35fB4RG0KIKppXr17FyclJIduRYfrKU+xyBaPXlYrYrCfD9CCIk4iokLERGwzEDn8mmre2tsp6S7wn7XUVzGq1qZQkEUQw7MpiKq2azWapAPDC4CSKuDc2AoxKVQjnIJPgchMkLA8RsSFKHKO4ehMdtP3A5uTkW8THwZsJa+IY+tnVSvjA0WhU+go/hix0Op1S3bVaPVQmsgGMp7aTCKRfIUyazc10H8YRUi0n2rNdbbVasVgs4vXr1/HNN9+UKUa+5vb2thAng8EgLi4uSnscSzoR92s7wCysrWoZgeTMMoDftX2DQGi1WmW6LjrNTogmge7vHyqYB4NBSXSg29zH8TQ/EQ96PJ1Oy33AvIzV1dVVXFxclPHpdrvF71Hpyvthb+bzeUwmk3jx4kV5T3xjxEY/XZhhPJZjSid5OIxBTTTZ9zxGIjn2Mp9hXTc565jaz/b9/Llxj5MDjHeWF/TYtmp3d7ckWu/u7soyJFtbW2VNQGO2VqsVL1++rMzYoGIu4iGRC1no+DzzOX4XcAHvDjZz8cbniinrjk8mpXLjGWD/5nODPgub2UHAKo6AzxFU2FwOghPOc+dGbMrJmbvsZ/FDZ5p5dTZja2urMoAE2QwmzsrBpkEVC/k5A0XAzTkoKM7WRBnfmZyhH024+NkGIg6sDGDdRp6ZFawOuHtagNldGzoHMO4bv5sJJsYrZ4m8RpmJD/qDvjcZavKL4DZnXNwXua8ctPNeNnqZ1OI7WGyXTrqKiP+5nz83EMrXue2fI3P0OYNpZNekDGTs9fV1ydwxTpCEBMjIj3dhox/pg4ODgzg8PIzpdFpZaBwnZkO/Wj1MxTs7O6sE2izAOBgMiiPgGi+gv16vC5mGLNAeQBnySoaRPoAcIUO5v79f+oEMEwsPslgods3l+NbRiKrj+rEgzEGa7QTjhI2z3nvdkJwF4pw83j/meOy86D9AMLrT7/cLWKPqDLtIthbdtYxFRMXecb/t7e04PDwsgTDTP9mhic8zcUqbCHpoc7/fj5OTkwpJXwdqvkbQ+qlHnU6bnLDd5X15dwANJB3A9ezsLN6+fVuC206nU1lbgIomngVRa/+ewSf2fmtrKzqdTsmsmlRhbTATL/gU3gngToDm6gmehQzZpjvRFFFd388JCJOPEGEmC3hm9pkmyU3Wcw195PFxVQK2w34Cu0SbPeXUhJenw2YyaL1eF1+YAyFjopwIvLy8LMkAvrP9dHXHdDotYPng4KDYSfoqIioVzWdnZ3F8fFwCtowLjB35jb9wAuvH9PAvEVL5sN37nH7ysYP3wQcw7uAYk/HoJu2k75GnP//5z3F+fl7WSGRqDfdHNrDH+ErIG/sGrsuJE6bukISDxEFO5/N5JdmMLQAvO1h1gseYGpI6oppodXXU3d1dHB4eRq/XK7oD0eUdAa+vr+Pw8DDW63UhZdCl7e3tmM/nhXQzMY0f7/V6xX4xDd/JZMhobCZToLCx7H6GHnhpACc50KlGo1HsLM8F51N9ZqLh/n6zJmBERLvdjm63G4PBoFIxfnJyUvqIe/E/4+7pg998801cX1/H+fl5zGazsl4RtoA2ZzsKObm7uxunp6dxfn4e//Jf/sv4V//qX5UxgXyEWDs4OIher1eIKeuBY8hf0+FYkr7xu0VUN5Hy//yNjNzd3ZVqJPoPIh9iEn3mXhBE1nvGizYRa0NM4ktdyUa7SeJ5DTHiYta3I1kQsalyxNZwneNa7JLxof0V/ZAJ+4xNMxFkjJoJKV+b418nicAO9ttwApyTiTH7J+7vWNI2yvGg42zbUq5htgY6y9RYzxBh057l8mE3TmaXPH36tEIYUQjAubQTTOaCnUxeeuzAMSbrsr/+3Mcnk1IenMeYNIMrCx/nZaHjvlmQ6hg8KpEiNo6L+2LoUSgCD2fd8uFnuCoKRXT7rQAE48485iAPY4ywOtua52YbzKLAdYPuz8x4W5FyJYRZcY5MlliRM4nirK8JKs7PBsb3wcDxns7mmeDKYx4RJcsPUOK9MGouT+Ww47Th+rEA3OOHDHAvlDevCcI1XgPF0yd4F8CPFR5j4/b4/d23n0PhP6fRsJ5zZFI6Ay/OQe55t7u7u7KGAg6TDM/R0VHJ1nA9C6o3m80CmHCc7DZxcHBQCezQU8AXVVW0lyoeyG+eM51OI+IB9O3t7VWqqnIFDYQLTpnDU4/QBcYU2XeWJBM89Dd/2yFyTh1xgrzXEecAEJOy2Ua4nVzDeFomLQP5bwj5RmOzLglkJCQ9AL/R2KxdgeMDYHM9/YQ+GgDQn/P5vARQbFEPmHdpM3YzJ02crbPj5t1sQ77GUQe8HutzA5Gs7+gQ5du8B8Bsa2srzs7OYjQafZTVYyeX+/v7mM/nJYgiQ8ozjQMIrtrtdrTb7QJkW62HnZwg8XmOgzy/D/Ke2+wxNMD/MSyR7RLBuvGIbS9VKpBcriji+nw4g+jxQReRMVeF8WyvsYLfcLDG4fG1/lq2MzGBD+ddCEh5X3TL+AA7zfcEOSaraP90Oo29vb0YDodFxhwEseMuAJkAiDVwMqnGuzmRmckjEyr5yEHLjx2fy8f+1MPgH3+VyX+I0fl8XggnCP63b9/G+fl5JYl4fn4eEVEqRrHBDpyQA/wUCRj3Az6VgIUKV69bFBHFV0dEqaYgw8840zZsAu+EXfbajfhoYzHkuNvtlmSD1y6C0HYVJYG9MRdT9CI2JC+6wW+qeZvNZlmCBP10BT4yzd/gfMgnbJFjEMgwB4LYNM/0AN86tjCBQKC7Xj8QyOz+S58S1Bpfm0CK2FRoIH/Yn7u7u4KfiLMcrNMek1z4TBIc6/U6Tk9P4+3bt/HixYuKjm9vb5cpoN9++22MRqOKrn6qzn7OI+Onv+ZwoO9+jqhuPmWZc3Lcuue+db/c3d3FaDQqGwBAEoCXfD/GLOJhlg4JVHT57OysfIbdp0KKe4KTPa7WsfV6Xaamch0yh90Bi0E447vq/GfGssbBxrQZ8/g7+4m/NMa+NvuXjPec1KrD2vm+OYZm7B3TOV5yvGhSbHd3N2azWTQajej1erG7u1um5vJczwh4//59bG9vV4j7nCzM7bPsmRPItsLJPb/Xl/Sdn0xK0UiDZACUX5ROQ4DdeAtaDnQcADlQx8lEfFzO7uspdcPZcD6/c1BIOwBTZCN4Nwe4gHgCVAy1CRwLutfVMMFiQ2Tl4FkWds7BeLhf6gQrn2+HZNDtZ1kp3GfZ2AJEGSMbkqxskAGZbKMPbHT5n7/5DRDKhshGzaWuyCdVW3VB3GOBSzYwls9MrOYxQma8IyOHgwj3l/vczslj9DUU/685IOI8jvztIJUxNNkbEZWgCWOH7ACoOp1ODIfDmEwmZYoAzwI0z+fzygKP9KHJRevz1tZWZQdOnC/P3tvbK4E0IDoTVhFVIhunbVkFeHtK2v39Zr00kzEOtjOhYBn9MVI9y7R/HIz4er8L1/p/H5Zfzqlrj8ljA0zeF5CPnWEtkp2dnbIRBQAGMAXg9VRq+nuxWJQ589hbtp9vtVoxHA4r/oXsncljviN7Tr+5ugWZMtj8GkfWe+S77rCdth12VaErCgw2Pnz4EBcXFx9lwnhmt9stFW1OAvEsr5O4tfWwbk2n0ym/vc6Fp9XQRqYqWdYNQJ0kMMDMtjb3DX2CXWKceb7tF/7DBImvoU8IBvxMV15wEKDSZpNn1nlkimoX+0nu77Gg7fYNxl++zr4r+23fK2MIxiavFUK2NSIKQQKhNZlM4vb2NobDYQyHw0pVMyQ4BKXfvdfrRbfbLdO9bFeMJYwp6wKBx76rOy+fU/fZlzwcsFjenJxoNpvFHjJWrA/3xz/+Md69e1fB38alrD+0tbUVi8WiYo9JnnmKGpt+NJsPlcv4VXxrRJS13tBXB5XZBhC8Wqcgtj3roY6M5n4kEUgK4ZeNLdB/fK1JNCqTIzbku+0VJAzPR6eo/CGRwvm0CUKLRCXVvvSTCQWILgJ+MAD+xMQb7fM6VqwFSF+CHyD7mLI5mUxiPp8XUmp/fz86nU5J+GGfbDe4X8TGJq1Wq7IAOtMp7+/vC2bi/thDMA/9d39/H+12OzqdTvzv//2/o9PpxMuXL0uVFLsh4/97vV4hvrA/Tnp/jePg4KDgy7/1QB7pa/rECQF/TkVMTjwgM1y3XC5jPB7HxcVFWTdtuVxWpsC7Ksm6YWL66uoqTk9PYzKZxHA4LPLEzAFPp4cIRd/ZURX5pTrdyQmwGfbB1UD4MDAg/cWBzbP+5Di3Dv/YpjsGNraO+HgJE3TQeNc4wNfnONltw8fT/+YKHN/nZ2MPjC9z7ILdm81mcXl5Gbu7u6WS0wU36OV0Oo23b9+WpRS4J2t5edkFY6wcv6OHTlzZ7iJjxs9f4vhJa0oxWAZvJnKcgcjECeDGABNh9EFgQIdGRCnPXa1WFadhQsMlbiaLqFRymSyDb9bfgoPhNfnE3yghz83EQiZPLKAO2HgvnCPC5gw9hsLKhzHgXO4TsdlxhTYjULSDNrsE02Pp4MbvaUIyV/uYnDMxkYNA7utpacgM7aMtgKy6aVuWMwAV70bGy3PfbWQMAtkJJpNBDohs3Gxg8vtjHAyWODeTTJZpxss64uf/0kgp64B1BiDl3WRoO6Ssp8FyDQeGGNDcbDbj8PAwdnZ2yha56Ph8Po/xeFzGgR+qJB3k0mYypZ7LfnNzU6YIMo0Po4vx99Qiy731HTAIQHUAOBqNylz8LGcmj+0kbSMy2c3z/L+nfvC5HUed3TEYspzlwDdn0hlHA0iAGGNqss+gGqDraRA4YkAPU0ZoN+eTxYXsJphiShjPvL29jdevX8d8Po/Dw8NK9jcv+EhA0+12i73x59hA2vG1jgzI+Dt/b5uC7eZvkwxUKpHcQZ7/9Kc/xZ///OeyHoXfn2oogI1JV7KrnNvtduPJkydlhy+ex3WeJphJL9tz5Nx23RUTjAN+BR+OnHEvfI4rF/Hn2H3LuUkd/087CQQIwvCfxjwmubAZOePYam2m7EVE8T8mzzIA9xQQ3g9fk+2Gz0EOkBf7MVeB0Kd1U7isA+ghVTju24iHCpuTk5O4vb2N58+fV6bSeyz29vbK9GoC6NxuE0V1Qaq/89/ZxtVdY53K132Nw7iA59p+etzsL5Ch8/PzmEwmxS4RQOI/d3Z2ymcRGwKQMcCHInPI4Gg0qmwTj3yxnhxJW/wipAkY2T4e3XKlOXK6XC5LJdbR0VEhfAiC8P0E3eApbyjS6XTKdavVqkwJZ82oTLBCHnFvbAW/6W/6Cn9Fe40THbwZO4NR8z1t15wkYqzB/ZzrSjb8JATGarUqaz854c/z8GHr9Tpms1khy9rtduzv71emQJu0w96ZvKJ/GbectDfBiLywLuf9/X38v//3/+Lg4CCOjo5iZ2enLIWAvDx58iTevXtXWbPKvuFrHP/m3/yb+E//6T/9zTYAnYbwNEFiu5aTEth8x4/IEPrOtEp2MGX9U28WwG9wk2377e1tSe6uVqvo9XpFjvAtlj/H2eiKE4KLxaKyw/Xu7m7x90w1hTxDtlqtVmUNM8dZ2e/Shtx3LrLwWGVsmvGSfQqf5+da5rnGiZv8jOyjsBXGXZnTcDzK9WASxgyMZUKYZOpqtarsmnpxcfERyXR5eRmj0aiyoyLJADC7bRy4hn6oSwAZYxgvfOnjJ60pxW8PTN2g+TfC7QxixMdzKn1vMqh0+N3dw05ZNpQczixwX57rANNTQszk2lGYITUpZeNhIsUKzVQJwGvuK2dIcJ4IAp+7VM5tMfnk0jz3cUQ12MTJmGQyGMqfoZgm4TCOPIOgjiMTFDYozlDhnD1H2cDdAIL/IRldKWGDAShz5t/VUg4YGCMUMwf8BDPOMJtcssFypZeNJv1l4sKBE++Vr6OvswH4mmD5Uw8TIyZ9rq6uyppLmflnyo6NHrqFrJON5Rmciy45I8hULfTAmU8/m3HE0XoKQgbcgHl0mDHhpw4srlarmM1mJUDnOdybzDHgvNfrVQglOycDiUyOG2jXOeCcxalLHPA/9zSJZBuRr3ObkEuDW+7hZxC02CYDrh0AcK/1el2ys5SCTyaTShaaZ/n/u7u7mM/nJTtEpv/29mEnxvl8Hn/4wx+KHhNUA56wXawpkvvTNsBB/Zc+cuDsw+NkH2gbb5IH+cH/QdKdn5/H+/fvC8mD/mbfTADrYNfBTrfbjV6vVwhk+hO7ZwIDfebvTLCi95lctU1ZrzckmSvhkFf7NwIhSEvezTpp22s/66ouE7AGtV5s3TLD/054IUOQetZ5J1xomxMfOcmBPzWBFFGtOnagbHDJeYBgnoPfcfUDY+MxcjKL97fun5+fR6vVisPDw0KE4JsjouLfsx20P8lEnIMAnpV15rHjsese07EvfZiosF1H/iBujo6OyliRbBuPxwVPe+o7VSqr1SpGo1HxbYyt/QvY8ebmJhaLRZka5GRBxIMOUOWILM9ms1JRgz2xLnqXLcvF1dVV+f/6+rqQ1pBP+H9sCLh6uVyWqXrobLvdLtP58c0kNUhekSiazWaVBKbxG2NAvxFkMxYEiFT8oXsEet4IAl1rNpsFQzg5BnHIc7BhrkpZrVaF2KEfjTvm83nlHfCX9N1isYirq6vK+kPD4TCePXtW+ha7wX3u7u5iOp1Gs9ksa+niE6jWe/LkSdzc3JQqOuIKkxXeIRCi8H/+z/8Z//bf/tsYDAZxdnYWOzs7pTKJKtq8lMnXPNrtdpHPvxZr2zbl+Ji/7YdtoyOqttPxEzHv+/fvYzKZlFiKamRiachJZG5/f79U3k6n07i4uPhIr9ndGhxnYgKcQHuJp+ynsOGujMYG3d/flyo9zrEvpU/s3xxzZXvo/rS/d9yb+zr/jS7Tz3zP/7aP9tUeF66pi9NcyIHdcxUs52d87fdk7NF5T6/kM+7T7XaLXWRdR8aTWQcHBwfFj4L/aDt9mbGXfbH7HdtmO5XH4VN15VPP/0lrSvFiZA7dSJ+Xg29XR2QhqjNI/I9C8ZmdK9dRbuxAyu1x6TkO1lljt5XzDZAdxDlQqVMggmGUGKdiggnDHxEVttzCynmZxTVQj9hsQ13HchrsWfggC1xlxDt4qiRtztmeDOrdzzg8z4134J2JL7eP53CYFDg4OPhoxyUHXhzIWt3nPMMy4exFNl4mNuoMYR2odRDl6gWDfJ7l+9nwuY9+aQftykRJJgtyls4Viq1WK8bjcQGn7Xa7gDD6mbEBXLIuBGtC8XxkDIdKCbGZ/1wGDyntMmNsCGPEO/LbujCZTGK9Xpct0skU+5n39/cla8R9e71e0QFnhiM24CQ7BdsaPs9ZWGcxfI/sZNw27h3xMalEn5i4weabqM9ly9g9zmH9mJxJR6+ws9gwbD2VZXd3d6Xsn6DD5AHXr1arsisiQQEZ5U6nE7///e/LewLGDZAZA4MLExFOEHyN4y857uzfnLhxm9E5dAW9mU6n8f333xdi1+tJOQlg8pVkBGTE/v5+WbSWaTaAWMhiZMkJIJPGTmb4fXIgbTvD/W2DkSFXS/HeBK7cj2uNYbjGY8y5XhfSyRv63cEo1xkLZFm3j3HmEpuzWq1KoIDvsF1BV53w8Xm8h7PtJqDdBvtCByEAUPtZ1qrIpKH7gXa8e/cuFotFHB8fl/HCHhBIX15eVtrrv21TfkwHsv/NxJXP9fe+708F1n/L8Viw4zYNBoN48uRJZTxWq1W8f/8+5vN5bVDWbrdjMBhExKZqnQCC4J/veGcHrI3GZokJdmajfff392VDHmTYmIgg22vceOoPiUXvbNfpdIpdof0mYiGe8NWsccRUInzM9fV1sUNgz3a7/VEFfMRmBzQwLnq7t7dXMK8DOK+VQ3Wj5YuxxMc3Go1C4ng6IcTW1tZWZfMlxsEE73q9rkwns75TXeYg1wGyk7HsIDyZTGI2mxXbCBaDoKCyhpgIEm2xWFTsN3j/6Ogobm5uSuCL3t/e3pZ1HLGX/H7z5k18++23lQpJ2tzv92M8HlcS+F/z+I//8T9W7PVfe9hOmlzgu7qYN6K6G3KjsVnrDzvLTnYcrMGGrJMgwiaDa+fzeUyn05hMJnF/f1/0jSp0VzOCTSM2G03hF/f29oosQSCiMxDlvV6vJK14B6/tRoWiMZb7wOQI/ZPjNvoxY1pjBJ+PjTLB5P43f5D/NgmTuYZ8LfYworomlGNbx5DYHuycsYDjJk95Pz4+LhXH6LwJZCoiXcQzn88LHjZGQOZyAjn3n+2L+wws4Tb/lOOnnP/JpFTEBoAgyCiEv+ccs6GPgQwGr45MMbmDw6DjrcisE5PBHedkh2elMNBESR2IIWxcbwHHcNNOA2buYYXjXH/mDC73zO+BcLkv6XOTUVaEHJBmIOMfgpfHlNSfuQ0ec9ruxSr5nUnF3O88m3dxAMAYAxwgHLLRd5vpYwA257kfMuGUHRP9WAdy/9JhIFeXFTWoz8+1AUAOfmmHybxMJDnoQSdNrvL31dVVzGazOD4+rqw35PeP2Kwv5kobjw3BG4s5IiM4be7d7/cLQDcgdpa30WhUiHbuu7e3F+fn52U9q8ViEU+ePCmECaQbJcqZ0KBvqOpxxsI/ObClTVkWrI8mTCxjli/uwbjlQJXzHbzzTPqXPoKQ4h6cA7nD2C4WixJMQFDmsmx0dHt7uyweGxElcF2v1zEYDMr0TQe3ucppNBoVu+sdFs/OzkqghzxFVCthbAfcp3WE4Nc4MojyeNYF3tmuRmyms798+bLIAbrx5s2bsjAy9ydbvr+/X7GvBCu0gc+ozMjT8+yH+M5trpPpbB9Navk82xEO65EJjdw31hfrpt+LagKCOPqMKjHah46bCOQ5yCXtcQVnRJTANYO8TM7hA5FXE1MOYDJZwH1NKHJeBt1OGKGH4BGqR7CDfGdsZFILmSNTPplMotVqxTfffBMRGzIaGXPW1f4bGcnki8fbR9YJ/s86mzGNx/9r67fxXybhkDfaxhiNx+MYj8eFuABb9Hq9sog88mLiAyIDmzqbzeL09LSQ9ow7i5Tjn1hTiilhnurjNqIvxizIAFWZ2AIqo3gGAa/1h3sOBoOCoSC2qaoy+ewqaII5+i1iMwUVP8Hn+AgIJxNs3BfZN8bnfE9/5BxsJ/rAO/k8iEXG32T6er3Z5dR2j+QO93P1CesSIUvECyYi2aQCwosp1lRvc2CvCJ5vbm7KrmzIZr/fj3a7XXbfXK/XpaqDwNrTo1+9ehXffvtt/Pa3vy1tYBHsb7/9Nv785z9/VLn2tQ7a+Lfew9jIGPbu7q5UHzM2LoLgXCfD0JXZbBYfPnwoY7u/v190PSIq/ofYuNFoxGw2i4uLi4KXILKsl+gJcok9ROdbrYeNKYbDYWV38VydTKWZ7el6vS56io0wrqK/jC9yPMfhZ2WbmccgtyMnrTI29rV8xzWOx3MMXRdnG6u7fdbxXPFvnJljYZamYNxdnAEOQZa63W6Mx+NiC8DS7HyLLnu6Lf1KOzJp6r9dIZX955c8fvL0PQMpFJEBArRAHkRs1pLhHrkTTHQhuBAZfO+F1iI2O2ExV9aOzeAMYfcibCYq6GiMMAx/RHW3nyzQPIdzUPQ8xcdVR/ygYM5GcG8TXwbtfJ+dGIKK8XDfcp2VxsScjQVt9EGmmHtxjUEI97Yx5jkuAyegdJVXlptcleT3oH8pezYwyUrifrcRzEEPB2DLhtNBKUcOnvLhc9frdWUdAp6Zs0I2xhADdQb6l3JgqPib8QQckvVkPHOwZX13sIjd4BwA4M3NTZydncVisSjA2fK/Xq8rwST3YicdgLYrFtFNAABBXEQU8Le3t1fe6f7+Pi4uLqLRaBQgRim7F3CO2EyhIeOKrpAB8bRSvovYOArLl3Ujj4EJf3/u60wSur+ynHts/B7ZnnNvZ1fI9NLu5fJh5xYvkMq97RgZE76n3JiDcfEmFIBz+pPsMSDYWXqc/eXlZbx79y4ODw8rZBtTRtjhhH5ClpCVn0MHPR5139UBg0wWQEiwlgi+b71ex9nZWUU/CcIgHSAQALQA3tVqVdZewxZHVHfCbTQatWsO2Rfb39je2+5mOeXeDhr5LNtd64zl22RRfp4DUFdSIO8mee7v74tMmhS2/tBf3I9zTHpbZ6xrvlcGsfblfIdNyWQcANZ+FXLLQQFYbWtrqwKCAbmc54DbuAEsdn9/X6YPRUS8e/cudnd34+joqNhhpkjt7e2V9TLq5Nr/uw+yPj4WyGa/zT19ZPzzpQ9sTF7HE2xE+5iuwxTIxWIR79+/L8R7RBRCodvtRsRmJz3rmP0hZMB4PC5rwiB7jD/Px39yTfZxBK1MoXP/ekrh3t5e2XGTcWchZ8tiTqqs1+tSicPaN5BKEF3ItYM0kta8C/JLjIAdhFgCUxDI8e7ZL9K/TK0Dm2QSK/tG8Dpyxpp71l8wiLGA7WHGgtgbJ6zpU1dMkKQDg0I0L5fLsi4ZGMf9BlanPYwxsRG+gMWyV6tV2dyA3fWazWapXru5uYn/+l//axwdHZUKaKZUejqhq66/1vE5gmv0mHaD8euqVNHFOvvrhPz9/X1MJpOYTqexWq0+2sWWcWFsIJbOz89jOp0WOeB8ZBWdto8h4YIsgZmfPXtWKgvBXcgavoEpxfZjjk+zj+b9ke+6aib7f/73WOXkCve3X7df4nsnZ+ybM/Y1NuFexoc5qeV3QS/dLn7zribh0Dffm4o2kt/39/fFPnijAPw7MwuMlcG9VDebp0FnaRc2y4QqYw4xD/5xjPw5dOfHjk8mpey8eZm7u7uyKKq/ozNQUErQGATfK4MRMgEmq+gMHMr9/X0pIwXs0KG5eggDi0CYcIAJJIDy8zAwGBKAON8Dvq1AFmo/x8Enht/nR1Sz4Q7QcFwAA5hX3ol+i9gsYm72mfaZjIrYECIO1ngW98rK6B/anh0092dNHUCYjbWNs+9Ju2x06AOqJDwuvs7j4OxDBraWU0hCZ45tTPL48RyPVQ6K/BmOGWNBvyAL9K/bmsf0l3Zk2eQwIYCBzplbzt/eflhgmhJkZMlO/OrqKqbTaWW9i8PDw1J1BLng6bFc22g0SoVMo9GokKsRm7Jh7z4EiGM3ssFgUEAw69O02+2y2DqyBokF8EXHceKWI96VsfbYW/6zw+Xv7EgtI9ZJiNaITWWbQV8GUwaD6HJEVHTX74Fc5ywKmVYnJRqNRikdZ6wAU5DqvC/yggN0lSz95l1JPFUEEtHyhKx8+PAh+v1+uR9TQpHVnZ2dsqMMh0meOjvwJY88rnxW93x0zuQRdo2AgvMo9zaI5L5U/RF0MiWo2awuPGyfSt/zLFdGIXecj18xgI+orqthn+j3tmwbyNI+ftuPOdDjfFdg8Xn2ufZVdX7DAUUGuPhmE0h15I2BcqvVKmt38T8klf0vz6a9bh/jmbO84BuucaWxgSj4BgAcEWW8GH9kyutYMNZMmTWmmEwm8d1338XW1la8fPmyBF98z3NzP9HfdTigzudmWcnjlY+MC752IMwzbf+Xy2V0Op04PDyMxWJRcNL+/n6MRqOSFGGh262trRgMBgWzovvGMASOk8kkPnz4UOQA4p3/wT9MryLgqcNtTOlZr9dlR05jYvDn7u5uPHv2rFRFQf4TBJMERg4jolTt4Cc58AfYsb29veh2uyUYb7VaMZ/PK9OE+v1+5Xpky7tn02f0IdUFeZ0egj1sGJummFDB12aCz0E2RALr/kAeeEo8/Wy/2mw2y/k5LvH/6K+XugB/8wwnDyaTSZycnESv14snT57E/v5+JeYCn+XqLp55fHwch4eHZZ0xgl70OiLKIvpv3ryJf/zHf4xOp1M2qbH9z9OIfi2HMZGxgkkox2boEocJQDAMC1ZD3PV6vUriDXtFQi4i4uLiIkajUWV5BJ5P7M0Ygb1ynA0eOzw8jL29vRIT7+7ulmQz/t3VV/gEnos+IA9gY/u8bK+NGxxz8z/nZtttYovxeEyOcjyJ3cu42vfLBFnG6jkOdDzLtZnHQD88oyLHJa5q3dvbK/oJVkZXr6+v4+jo6CMsT2KYyjp8Ro4n3FeOyx2bI7vIkQs6vtTxk9aUyhkVl6ryG2BlAcoDmjuD8xkUD5ZZZ4AlTCJKxXMwAgZfddkGGEBKVHMgBTCHfGOACGQM8sicWhh9DgfBqrfyzBnl3NeZDLLiR0TFmOVSSPokA3jau7OzU2G6c7CLcaFfab/HjfcyuPN78zyE2QQCz6BfeW+D1Ewgehc1t8HX2uHlNvme+f4ZyPoa903dWP3YGPKeNtYEeAaoGbD+GLD+OQ+MF84UcjpiM0XLmZdsrHFqLBZ6eXkZ3W63fOdMwXg8LuDNjghjSz953LEJkEoR1awFgGmxWJR3IpNEebOdB9fwjrTn+vo6rq6uyhopzv6t1+sCcL1QLO2EwPIiqdgfB2due/77MVLLem9C0AG7MyS+xvoOmIcM5h4EFOguMm5igt+sAcYzMuHf6/UKec37Ad49hYr2kQXG1mG7AWNk4T3Nm+zw73//+9jb2yvVHJCMgG6AWET9OlI/Bng+9+Hn1JFRdXLg79brdQyHw4iIEvBeXFzE+fl5BYhha0iUtFqtsgU7MuzqKJNRjL2nGyFDEdVq4vxOGRzZX2IzOPxdnY3O8uVnmeAyEcK75wpnDshlB/rud9qCDrhEHkyAf88EU66KzIDWQbT7gna4/01C5HY6A8+YeIMC9y3+liCkbtyazWZZn4QKFWyDF8A3FmKn1BcvXlTaXTe1I+LjymH/1Mk+7fsxX5m/t+wRrGVd/1IHfZYrY2wT6dPl8mEx28lkUkmasfA0a/8wflS64WfW63WMRqN49+5dwXn4GOMt1hmiosr3I3nkajoq1Q8PD8vaT/hMghfWoeEz6xcBz2KxKGseHR8fR6fTKYSnFzynyhj7RP9sb2+XxdxXq82GDvf3D9OU2KXP1X7j8TgiNtPmqJwCg3pHP5LO9BtVhKwTaVzopKN1Fn3nb+wBCXrWzAKL0A5Pu7QfpU2QWdyPSmMqWqhyRU/v7u5KZZireSAtp9NpIad6vV7FVkVECYY9dXkwGBRykCQi8Rp2cTqdRqPRiNevX8c//uM/VtYnghxxDPJrO7BNtvG23ciIY9FcHMB3VOyxw51xMgQscsUYLpfLePv2bZydnVWSCCY+0Bfux31YmiLiQSe9OPZ0Oq0kV8G1yBwJQBckIFs8l2QLcY+LRbgGX+G43ziWPuZ/+wfwbI4x8vhERAUXZOyR5a4u1jMGtc+kTdZ5j7cxlsed740zTV5BRp6ensbJyUksl8t48eJFdDqdgquw2+AzNjrApjGF2uvu+t24j7G94wNjcOQpz3r7Uscnk1J1BMBisYh+v18xzhFVcJoraPg+CwbGE0DkbW35TbBBMEi73DYLe7PZLErB/wQxBGRkfgwKcNxZyPnMABGn5cPZdZNJ/sxOG0BtIGjCxMLsPsnEEPe2I7QS8T51gQnfYWgwLr7eTLeVCKCRWVYHCj6Xd/P/7lO/o5/NvXG6BmCMja93UGLAbqPiz5xlqAPFjyniY8A43x/QBtAia2aAb6Lgl3jQVsbMwR3OCh2M+DiQi4iSeUQXAUwAQnb4sRNar9dlrrwXR2WR8oODgwKSMNo4YYwrGUi2yuV9IDOur69LhtnyH1HdVh4AHFGtGu10OoU0AVhDQqPvyCV991gAiFzzuWWU9uSsbCbgIzYVDwADAwRXh0RUF8OlbTgjt4nsrZMCODj6gyAGu829rHt8Z3tHP0+n0wLWHSjRl8PhMNrtdmX9Kvcz4+7kA0EVhCA+hP5cr9cV0isDmK91/BhIz7bIATbjz3QZJ1xOT09jOp3G/v5+WYAeuWWqjYnk/f396Ha7BbjmRBP+krFzdjMnU/L7uG8dbPod7Xf8Ge9sQPhY//j+ls9cZeS22NdYNyOq+Ib/va6Ugw7sOvpAn9FP2KKsc+gdY2hd5J3cV9abiOqahT6Me7gX02Cz7hDAEIhwLwIV2oeOencx2yhsuytE7+/vy6K8xhLYw5xU87hmcqkugKkLTOyfc3BHReDXOB4j1B6rcGs0GmVHUfu9k5OTSjDkAAddOj09jdPT00oCs9lsFqICgj9XN2IbWY8RzI1Pw0Z0u90ydYQdcSHc8OXG1BERl5eXZRc5H2yawHpj2CaIjsViUdrTaj1MReMcEgzYMWQTmet0OsVvU/2DfHe73UL4OBkOGcQGDvhQbwDQbDYriWtXYToABSuBAxqNzZpwvV4vBoNBqY7hviQxI6Js9AFmYpwhwbNtgDyqI9qxQbSXtaVub2/jw4cPcXl5GS9fviw7vNEXrvhxIoLd+A4PD8sGMOjyer2O2WwWOzs78f79+2g2m4U0BVOw2LnjoF/b4RgP+2Ub44IK+xr7Te7D+pgRD8Rsv9+vrEloPV8ulzEej+Pk5CSurq4q68shi8bQjjG9FhzygI1hDCOirPmGTfL6zfg0ZBH8MZ/PYzabVWYLILe+PzLkmDLHtzl2sK3zd/7tceF3Hg/HvX5f+ukvxXN1vy0Hvgfv4gSSCzxcFervDg4OotPpxGg0ivPz81iv1/HNN9/EYDAodhZfDEGPHkU8JN1J9lsu6QMn3TLGdSxvO+b3/JK6+pNrsQyevMuFAyQzc/mFUIwMMK2oGGYWD0R5IMJy9YQBqJ1sBjh0dMQmG8PBdy6HtfBHREWoODKREbEp4+UaKwLP9vUoWl2m14brMUBKYMezDboRIBtL7mPl9G/eMQe5OUjAEBlok4113/q9MrmZF5eN+HghdCsAFVONRqPMXbeRw1iSceI+NlL0m419znQ5uPpbjjwOzioul8syZxw5MPD5W4+/RKb9NQey4/nQliOm1LosPb9Pli2mggEm/R1OmfUPclbGBBS7TgCcmZqLjBCkQ34hG2TrCMTI5EVUt1D3+kmQLru7u6XkmekNOAvbGAfH3NtTJKyDyJ3Be13Ahc2wg/U5BrKAQUCxHSoBoe9rgI0Noropk9O0j2kGkEC2p5yzWm22Pub9sQG2mb1er0zR83QP5MpBwPX1dcm6A6D4Htmi4uDp06cVW08lAGRqJiIMlGx7v9Txlxx+BhC2qZ5KQgAKYGTK63A4LDvz8K6ssQXwHAwGMRgMKiSFgzb7KsgVExcG4IwDfWe76/fwNSatDIS4P2PMNdkHG2za/2XQayLKPtf39rtxoHPO1BL4ci6JH55BYImNoz/QB+5pEM0YMb7GL3n6pfvYGI0fbCA/VFYsFouiIwT+7k/bcH/mqSLY2zx1g/7ABuc1LWjHj/nabPeMYerOyfqR//Znmfj6koflMf9AgHiaFfo7nU4jIuLo6KisEcdUDggkEg3N5sOOTG/evCk7wxqvsnj5zc1NpdoYW+zkjXHZer0uvs34CXnDZ2a7G1Hd9dTBea/Xq/hq5IQ1h5i+71iCZzPtnrWt8OGdTqdMuXdiFqzQ7/dLcuL+/r5UWjWbzbKGCyQNa5+BJcfjcXlfcCW+3tUGYAfrjasHnewyCc14mIyCFHYFOjpDBQukEbv7QbLRr7YXJsGJp7jP3d1dXFxcxGQyiSdPnsTR0VFZzJrr7+8fpj0eHh5WgtnV6mFtqdXqYXOR9XpdpvPe3z+skWRikamS/X6/ggd+baRUXVzFGJsIcCLeWIsx4N2ZBp0XGrdPv7u7K+usQjA3Gg+LyDOWEVH03P5ja2urJF25F2Pq/mfcwInILD/oBzjPVfNXV1eV6nMqvbwhA/qQ+9H9FFHFBrSL8x3jZ8xhLOv72+bbl/KdY5VMajlGtK/h74yhc3t5pgtIiB9yQgadsP9Hr8DgLGME9tje3o5er1dmAlxeXsZkMol+v1/kzvLp9zI2Y4xpN+9mGfzFkFImM3JAkx2VK2AcxDmblgGnCSsOVyQ0Go0y3ScTNVxjQioTYRYKglsMtQk1B3IR1bW0DFx970y0ca7fzU7VfWowymcGxJ6KxufO5OfSRyugD97XztALRfp8jyFtyr8ziWbDZZItg32z/R677MhpcyZ1IL8AcxFRDCVBFPfL45DlORNTOcDJ5OmPkUUZLGdHle+B0TE5hXOoI3L+muNLGY5MaGadzRWTBmURH1fNXV9fx3g8LjvpUKJuvWLKqZ8J6WMHBcHUaDTKThQYfRymM48E7RDdnm/vMtjd3d2S+adfAQ/9fr8EiG6/F/fmnZ3t9FhnOaevHMjwv+2V7YXtqKs3+S5PvzXJVmeDuca2gPZ73YuIjU0ECOVpeThh7E7ERr/z7m2QzOwudH+/WaiT7B/Pp30QkJZH2tlqtQpoZ6zoR6oFvN5JJip4h1/Ske1RDvgiIubzebx8+bLstgUBhQ9gqke32y1+tt1ux3A4LBVSjBvjbuDl7F8mlLCjXGMZMOFioOiECO+SwRDv7ulp2bYgQ9iQ7L8yyIUw4d7GLX4mADjrZKPRqBDQ1k/7O4Ngk8TYDs6xjzB24b2zXcefe5oTbSbgs//0j3cWZFoStsNTQhqNzRpv4LDValWZkpMDMcaR4IX1R7mOoD+D+Tw+PvJY+vgxX8/3HtM8xl/6sL3kuSRbDg4OyvRhcADrxJC4osIawsTrLK1WD1UK3333XUyn06IfmVxF97CHYDKII4Ja1hDhWRA4+E/WG7K/53nssAbmg5T0VHZs83Q6jcPDwzg5OansAEZQbVvFmHmXOic88Der1apUOeET0I+IDZFO+y03rsJ3RQJtIXaIePBfEC2Mnf2yD0+XR5fdN+hio7FZD2y9Xpcpja4gww5g20jUUV0GyUngSnschOIPss2+u7uLd+/exeXlZQwGg+j3+5XKOZIVju+QmUajEZ1OJyaTSczn8+JvSCqR2OM6iFhj5l/j4TG3nkVUF9zmXMfEtgnIM+s45Wp8pvOOx+PKLsP4ZvwY8kXfgm1JJCN7VORBiJrIROaRQZLF+A0qrtmAwFN2WXu10WiUXastI17Pkz6pw1vGAnXkn8/zOLifjU9s//w842+3x7FyHlc+q0uwmRxzUivH2ugH92NdPyr/jUdJbJ2enlamVZvwcixzfX0dFxcXZZfTzF3k+MGYwP1G25CVOv/6uY+fVClVByAiqlNHsmHxC/olDbhyYGlygI4gMLEAIVxeo8dtze0FXFswPU3PQaM7vu6daJ8JIo5M5hiQWkEMmDMxZQfisuRmc7MmgKtqzBhnIO02GxCbRff59I0dD3+7nwE+HIwrRtCK6Xf09bQJMExfmXRz8M4PFVPMcYaUytd77HhfDCI/2fBZHuvk4DHQm4/HAK/vs1xuFiFkCk020L+kw4GODT19DUhlLJ1FsYPudrul1Hc0GsVoNIrZbBbNZrNkhQHbHjMIH7KEEZtx9VSa9XpdqmxwjtPpNGazWaxWq+h2u6W9yBZtRy+RY08l5pxerxedTif6/X6xG4BUV0U5WK+zGYBr7AH9w/NMOJksslxl2QX8MV6WQ5PcjJntMG0jS26ClnOzTuYg3nbb5ICTBwB+v7fvw71oL0Cd9buYdsTYsFNcRFSmKe3s7JSdh+7vH3ZSfPHiRVkTgcwT96Ud/v21gtaITwfm7jd0hL579uxZ8Ze7u7vx5s2bSqLh4OCgTIGnxJvpjMPh8CPba9nL/jn7mzowaZKEI5NFliOfa7LSshGxIcUykVpHTmSi1QCLZ0OgZx/n6avINjYO+XsM2NrXOiCjbeiCbeqPYQ+/fx2odnab7/DpJI0clKCHjC33ILC3Dbq7uyvTiXLyiMVX+VkuH9YyI0BZLBZxfHxcsQ2ZoPf75SDkscNykeUw92EdoP5aup39pfEV01wgHpCJs7OziHhYBBwyEL8YETEajUoffvjwoZAJYCDLYMao6C+7a5EI8LSQg4OD8ixjTXAL5BPVSRFRbDqVp41Go7yXsSVJqG63W9awIahmvUawr8cNEgl7TpUnuADcyRRR2rizs1NsnqeTkqCiT1yBSPANaWid4lyqQiC9nGxkzCAe6RfwDHJAIsxJW2wLJIXJBk+lQrZ4d84fDAYxmUw+Sh4xluivp9Lybuv1uiyjMJlM4vnz5/H3f//3Zb1B73qOLFDdNhgMCjHmpQIYe+SL9/Rzf62HfYj9ci7g4Fz/z4F9hkRwfIbtPT8/j/F4XJFdznO1akQUbEyS0AQQY09SClKJJCAkMIvYe20yk4guDMn6bT/HRgTG19j9bLtN9PB9JoT8d+7PnLStu86xcK6W8j3te4zJHdc6fn7Ml5kQ8n24lvFz9ZNxE8QhFf3j8bjsRp5jY/p5Pp/H5eVlWfvvMQxln+RYy4lf90W2JV/i+MmVUhZAGpmreRBqDxpADyfqjqGzbMAYIBw3INslxfkeER8HzREbBjgPhgMsT1WwUnEu72ChdqbCjCjXYLD9/hEbA0TQ5KDUAmyw6kqFXPZpoMM7ea5qFjwHHTzHpKIBU2aCcyCRBd1KBbilH5AVPsuBDW2EmPG4Mn5cv1o9TMHyjil8j/MDgPDOfie3PQc1DpCsgHVGKgPgPP6Pfe4xcebSO7Z9aoD6tQ/PJ4+oGiwCH7LgAMFM0uzu7hbDyTbY/Nzf38dgMCgL+9FXzWazZGwjPq6eAECSdSSb22w+lOdTUo6DBVibROJ+TBNmoVE+Z9cZAgmvIeXFUU0+4WBoN33m31n+uEd22llXbC/oB67N9pHvPPXRummbloNVE+t19hGZdTsA4ibbcMCZQPRhx+92s7gjC8ReX1+XrCwVQFw7mUxKwOQFVUejUSESqToYDAbx+vXryphBNHpMfq4jJ4Lsj+qSRExrwX+YvFuv13F0dBRXV1fR7XYLadLv94tcR2zGKoMw+xw/PxNYj8m65SMiPvo/y7y/zz7cBKrHKJ9rfUDWbWNN6ppAo284h75x9SD3zQDVfUOwzX2cHMH32t/kDKzBdPbl6ClYiXcz4KZfTERByub+MeYi8AGLEaBg/01YR0R0Op3Y3t6u+DL6lusIXvHzj+mV39N2zrLi8a471/2Z783xNf0scsLfEFD4DoJM+ms8HpclLMA6HO12O1arh+rfDx8+xJs3bwqxnvuL8XRyh8+pZIDM4Pf+/n50Op1YrVYfre8IBkdmaben6UGq8D2yxvtBsLVarZhOpyVwgoyyf7LvA19QOdButwupRkVXRBRCygka5A3MjV9wctU6RkzQaj2sYzWfz4vcRkRlShN9CYYhIeZdibMf43nYMO5L39mHOqYhhqLKCrviijXek9kmXgqAA3LL44OceLHyZrMZL1++jH6/X3ymSbiIDWlJgvX4+DguLi4qsRy6j71GXjjqfNkv+ci2g3dx1a39GP/b9jNWEJDHx8dFzrHJl5eXZWrler0uY+ZKKnyKl8HARtPfTPE6ODgomJWKOnCZ2w2+9dRex3joDM/f29sryUJ0G/ljergrDemzHI/xOX+bLOHINtt23jF+PiePl/1GHlfux1g91sZcyBFRjfdzW9w+nkF1I+9LhaTHDzvOmqvr9bqMpRMR3OPu7i5ms1kMh8Oig8QrxnIc2TfZDkPU1+H1z318MinlzFjOYmHIclYU5eJaD5oVlc4w2YWjQBkoB7ZCm0wws2wHCjvLoEJSmK11gJ2DM//vyh4DW44fA060yX3nQM5AmvZxntnvDGT9DO7PfV3F5MDez+Y7rjNjy73puxwwc43Bg40Yju0xYq7ZbJZsrNvgtqFoGYCidPf399HtditOnwAAVt5yZeOax8MBvwmz3Mf+vy7orzN0GSj7c55BFtqg5Zd4GFAzHi6dd7bw8vKybGvLtYzVZDKJ8/PzuLy8LOMSsdkZz4Fgq9WqTGOw3AF8ANURUTLATLfDyVP5gJFl9z+CAp4LEbWzsxP7+/vR7/djb28vBoNB2dmMgIJsrgM8xhg7Zgfmsc3A+7FAnCNnW/y3daAu+2nQZxm1reFvZ5Ks99Yd9z9tsD7VLSTOM3hHwBM2hnFyJslg3AvRs77KfD6PyWRStiYmmDg7O6sA4K2trZjNZnF+fh4vX74sAI71wFj8HoBA8Ewffq3D4Mk2xQeAExnwlFlPe3cZOP3ItFOuZVFV5NjA0fbWdpgj20f7tNzeOtmus/1+hkmfiGp1bV6bwvfNINN23bJfZ6/payoqIqpTlrkHssr3+BpwD/3B506K8E720bwffW4f5ba6b3iO9ZJ2896u2MBvssYY9+G5rrRiOhXTpRxgWwZpH3rE2JDJjYhSeTEYDEoiIFel0V+0ue7z/Pdjx6cA5zq9+pKHsU/Exn6SNIXo4W8wb0RU7HFElArQV69exdu3bwtmdAIEwsLvS1KT4Pbu7q6QUAQsHOAm2xbaYRzFO3j3P9YDxMfybCqaPNWU6T1OpHiqOXYDQiRj0IgogRpT8ng3yCHf3wF/o9Eo7eQ7LwMREWUDFKapMrUJQtgViPSbZZR20AZ8M6SM5ZrPsBlgXM5B3z0TgfP39/ej1+uVnbhI2EZU1021H3cCD0xnwh3Z+PDhQ1xcXMTR0VH8/d//fbknNoN4YzQaxXL5sN7XkydP4uzsrPj1Orzi+/zaDuxH9m3WC3AH728iH30CJ29tbZUqZt/36uqqVE2iBxGbxAdkq6t2kZlut1s2EWBaIFXlyAa2Fpny2nbMKDDeyHaFZ25vb1d2N0aWWJ7EU864xj63LqbOyZ58Hec85g9yfOZ7Gs8Yx9bF8Zzj8c3YzFgJX+/+Zfzq5MYVZOCxwWBQdll1PLFer0s/Hx8fV8YIfgOcNJ/PS9UqMmHcwHvTRmyCYwH6y+/0JY9PjnwRxGzYyEZEVEvyDc5QSJjAiM3gmCRxtiA/k4VsDSJRRmeCuI+VyKCatpt0cMBVR1bltSnywLhPTID4s4hqSX9+toEoQkW1mZXJWdY6QI5QWfjdzgzCs0NwMGhSwNVZribg3cjoMX8cJt/O1PelUsHBq9tVZ5isICY+qMbJIN/g2ddmo+h7um/rwO+nBKc5uMlBT76PDdZyuYzJZFIBh7+0AxlDPwBdOTPvICcTFuv1ukypwwlah5kTfXBwEO12u4Bmy4d1h/7CJrEzlNsJKNzb2yu78jDmyA4Gu9V6mL5wdHRUiCcIKsB3o9EopffImINBDgy8244N4MiErINS6yz/PyZP2KuIjf3MDjxXo/hetp0EsDkozg7V8hBR3UKXZ9I22loXfNu/+J38XPcnAJ+sPttbE1gcHx+XUnjkIuJhrSXaQyXd06dPYzQaFRnIVSBfWx9zUsEH/WLyLhMEq9WqVIq6soWdXYbDYfGp/X6/ZNxciZv7ws+2vNURBlnu6q73PUxO+J7Wa965zp4+Riw5EPV3Jqd4RiavsF3ZbgG68+fue7+r2+eEVu4v6xXf0Td+JzAF4wWIdMIq65HXHVksFmWalxNu+GR+IqKSyHOVsglnj3er1YrhcFiRo0ajUZIEnU6n3JvFs9FHv2NdMJJlqm7c644cOORrv3ZAnLENz6eizv6DgMJELHqMvbPuZ+zCfbgvBzLODpskByFxOp1ORFSn6FC14+ns3J+qYsihd+/exd3dXZkebFwPSYHsQnySuMKHgQkI6v1Oef1QfPPl5WXpy7u7h90lIUppL7IF5nPlsPE/fpS1nkh8sfaL74OdwQ/yPBOvrtK0LrttxiuufuTHOkg7TVJg2+/v72M6nRZCkHHjGuQuYxH30c7OTkkoUmVOpdO3334bOzs7xZZQlQHhzFS0o6OjYtdcqclaU9le/1qO7Nvcp4wb+Bc5Yp0w469Wq1WmmFJZxD3obzYmidhscEHfUQ1H1RxYmSl5/X6/VEfRVvtzvwtjYOLbmM24g9/IED5mZ2cn2u12SdiC81luAxm33EdUMeJj8Ve21bnddTjZfpcjx4E5Lva5OX7OMpCfXReDm8Sy3vodclywvb0dh4eHZS1V399rQZN4Zb1G2rO/v18q6q6ursr4172DfbyJwdxu7v9jvvZzHJ9MSgHSIqrOnCxDXqvEbFzEx1PfDMAQclc7mLnLC/JyPwNLzjVRlTuUZ9kY1wFqhAbBcXbW/VEXVFnJPMC00QDSv3NmMGdVud5r9Pj9fK88Rm4Pjo3zbAwyoPa1VjL3E8+iDVRaMYXEGTXuw/XIh9/bgZEDXkgo2sb4LZfLQkpRQWHl5lyceSYZ64gry0Jd8PPYkY2kx+AxA5r/JnCww/4lHnZGBtXWLS8+jwxARkAcWB8yucvaBRBCBqHONkdUyVSmBRosOnCkMsqZSJf9E6iTZQIsY/y9hp3l1Palbrxt63Di1h3Oz0GtZaiOjLYM+xmMSXbsmRDLbTRJQL/aQdnmRVTJcv7mc8a+zsY6qM7jSf+4T5wx5j0ATltbWyWrOxqN4ubmpmy5zQ5WZHQvLy/j8vKy7GJ1cHBQsk28t31D9mVf46iToXzY7/mziIe+393dLRVkfA5gHA6HcXl5Wd6dfnTW1VVm9AmyVgccLTf277YRyJSvz9fl964DxgZJ9ruPAVI/17bCJK2BssGjAWydz3ebXGWQ22LizXru5E7EpkLFWMS4yqCeAMV+POur++vu7i5Go1GFXGo2m2UhZ/wtNpppzBD6kLvr9bpkvjmfgLTb7Zad2qiyYrogYw1Q7nQ68f79+9r2ZgD9qUedXuTvecbX9LE52Ml2jf6n6mVnZydOTk5KPzNe29vb8eHDh3j9+nVl9zRXLmesY2IkYrMO2HA4rJCREGPYYc53BQ0kEfJ3e3sb4/G4+EMWYua5VH+Q5EBmcjKbGIMktpe2yHafc5F33gfsxJpLyCyy0G63Y73eLCBuG58r6yOi9AtTxtfrdcxms498IX1u/bEMehOBvBZsxEbnwRpO4kZsdlbFPpvgygkkyIjZbFaSxFSNO/lH/GYsgh2iKnlra6syfXO9Xsf3338fq9UqXrx4UTYO+fDhQ8X+4VePj48rU8eR962trTIGlrNf4+ExN0ahr+2HXD2EfFIZxUwY1pebz+dxfn4ei8WiVCFFVDeeACs3Gg9VUU+ePInBYBC9Xq/4emM+x6HIpitiIjbrjnKdZcxY2vbKvsobGvCcTHpyLTJsbGvMkLG7fShtzDFv/jyPFd/bLtTdi/ZmX+LncFg/8v39Lsh/TnJ6jLBPt7e30e/3C4ELyYRs8YzZbFbaQxEQBOH19XVlZ2njCvwBbXYCIr9P5iK+5PHJpBRAhB8CCWdN8loDBqMRm47MwMyZApyDdxcguDVQrAtyHCA+Bkos+M5AZcKJe1lJsvL6u0y48D3KyzkOCLMyZeCLc/Y6HxGbrWdxJjYIBr9uW1YMjhwE8LeJON8rV3cY3NvAAJ4s9Kxv4vbYGeUgx0bJQS8OPINnqmroFwdRlpM6YsoyhXLWEQA/dvytCsv7ut2/1MNBBH1rQGl5yOX/gKRWa7NrjB2kHRl2BRBrXTMByfOpkmIBU87F4XuXKL67u7uLfr8f7Xa7bLcK+UTWKSIq5fcmrCM2Rpv2OuuayVTbFTv3rP8/Rkpwr/yZr6dvIHVNEuWpT3UOnfMMuP03/cf7W395J2TBGWQO+jKDD+7H565KyUG27SmLQTYajVL2zFbEi8Wi2MXr6+s4Pz+P3/72txHxoO8sZkuFJ7JmsPQ1jjpfkP2odcWkxXK5jMFgUPzz1tZWnJ6exmw2i4hqdVOr9bB+hadE8h1+NuLj9eM4x0kby7X/txy63Tyfcz2GBvU+MkjFH/i98ju6vQbYGfRyjqdZWL+yzzbgjqhmWm0PLbfcm3uYwM3JmWwTMubIz+c53pqevnTw6bXFLD98jy91EsgVyfhu5AJiiXt48XiSUxAeVAM4UCMjbALPADnjqcdkwof73wGC7225+Bog222z/bRtAY9ERMl6U3lCdS6Ew3Q6jR9++CHm83khYvBjvBf2ANvGO9LfrdbDOkkEwN4cgoXDjYdYUgDShx25Wq2HSo/RaFQqiiOiYEBsLrJo/+g+QNY8HbXRaFTejwCexbY5kDVvTEIfUq3hDZPu7u5ib2+vskat126ind4ZzzqcsYrxKGPLfSAOHOSZMET3XC1OVZtnhtiW+Bn0S/bfjE2z2Sy45ubmpiQpnPhkdy4Tlvx4UXRs1XK5jLdv35aFzQmaabtxxmq1iqOjo9ja2orFYlFsP2tv/S3k8899ZPxk4te+wDES/tbxG5Uw6H+r9VDNf3Z2VpYU8PR75AF9h4RkaQn02j4rx2602TKDDzfxY1+G7CGTGetFbCoybYOZsptjMfefMUHdcziyz3fM6c8fi/+NbXku5/Pbn/kevtY49DG+we+Rycq6xFWOQ/HVnU6nkrggzlmvH9bRPT09jfv7++KLsf9Uu3pXatsSx0B1ffeYH+Xvuv79HMdPWujcRpe/MaYYNGdAHKhmgePgXgYq/s7C4sNMKwaUTJHn5mfAaMWrY39pt3/qWE0LZgYaBs51wVYmdhBEOzvalpXGQXtuk+/pz2w0uFcOtHIA4X7g70yWOLvrMaXPuY7AnWv8fN7bCkJAZaWm3zD4rq4AdLA9aQ6aaJNBocF9XcDF338NYM3GNAeY/ix/nvv9l3pkY2zSgQApyx1GGFIqIsruOxEbWbGzhWgA4EXER/qC/WHdEjKEEVEpc16v12UBduxSo9GIXq8X33zzTRwdHRW9o/KOefi2TwYP3uoWfW82mxVADkGWg1zsE58ZoGXHXScT7l8HWPk8TwHAjlvXaYv7NDulrPe+n21cJhXy3w5OcJDYU/uXTIiTXXcAb2LSYGo4HEa73S73I0BhnZO7u7uyZlij8VAV1G63yxS+nE1y/38pR+yjzlbXnWNgRn8cHR2Vfmo2mzGdTmM8HsdwOCwVUfjlZ8+eVWTaga3lKsvUY7LJONZ9lwFXvqcD1LrP7G8tR/lZdT7T+MIyz7tjB0xKQWAbQ/AMyyefMQ7YGu6HvKH/ln8HcNZL/J9Jd/TN48U1zmyzNT39RvULY75YLCrrPtJn1iva4ISRP2Mc5/N50R3v2MZUfvQbe0sFq8fG9jTLtMeyTv595KAk/52Pr6XL+TBBn3FQo9EoPgciD0x7f/+wSO3NzU28fv26rLu4XC7Lgt0sEp6DY2Sv2axWMbB2KFMrG41GSewxZpzXaDTKIsaLxSLG43GRa2IAfCYyz9hZdyM2AbwX2XYCBb1h4fzValWm99Fn3uCG/zNuBDeg19yfvmZqN3IOOYVuew014xtsBoEhgWL2savVqsRCJpnBQCZ81+vNGl91JDjv58S+A02ewbpXjCUE0M7OThweHsbe3l6MRqNSiZH1nGflahf8NO92d3cXr169infv3sXz589LpTmbi3jBbXbtG41GERFlPFwxhlz8Wg+TFDk25EDXWZMMOYIk9T0gHjxdFAJ4vV6XnXOPj4/j6dOn0el0yhIXEEPZtlhOjGezLbJtrvN79ul87+Qk7835Tj7U3dsxl3WsLn6yDuXY1GToY9e7L8wHmDQ2TjAG5Vkez4xL3D7fLyIq2MqfY7PdV/h/Kg5ZpP7y8rJURvE9uPbbb78t6yt7midrFdO3GR/luKPuc96pji/53MdPWk05DxxG0qCODjGYXC6XJYOSiQGTEllJIjZlr3YmPodBzEbfiuNn+XMPkoFzXUbVv61cfh8LJ99FRMUZ2iBwHUduawbxnPNj45OFimvcf5l48t/5vfzedQbObae9OavL/cj+5OsMRPP5Pg8niUMzIAdEuF11gBfD7mDWANhG14RVNoQ2PD7y5z9Fcbnux3Yl+iUcllvLmo2W+wE7ATFEMLNcPiyIySLjjUajAEHmpnc6nQIuWWvLmV+MM+Ca7GZEFLDMWlSe+krWf29vL168eBEvXrwo650A9NmdLO+Q6ec7aORdneHMTiA7eQfSdTaLc5Fjg9BMHDiIzw4ky3kmh7Ju2mEzvrSF6zNAzkcGaLxHXVv534R7tukOzv1cgyXGp9PpxNXVVSE8qQQiQ3t5eRnX19dlTZtmsxmDwaAyHq6Ce8zufe4jPyMTu/lcE4zNZjM6nU7Fdl1fX8dsNouXL1+WKVXoIuSBd89Cxrl/JpNycGkZrfOZlmkTkBGbqZr2U+hMBoOWA1cn2J8505+zmY3GZhqidwZGhh2Q1vk7t9FyYd/Oc0wSeboy7aRPuS5PnXFCyrbWdiEDdCpt8KHuY2ydtxB3QEQQ5GfT57TDz62rJul0OtHtdgtxYH+K/SSQj9gQhYyn/Qbfc85f0pcsi/mo+8425msdGSfyme0j62ydnJwU+WDR6t3d3Tg5OYnZbFZ8JnKDvoOLsAlMz0XmwctUR3otSGR7vd5U7zDm9/f3JekzmUzi8vKyVMJbthl3SCP0gufzvHa7XYg3FidHFlylb+ICuWUdHfoPefQzsWOQX/Qxbc34myCOdwCDeL0m8CVjCLlCBSDXYn+Ra67nPVwlBLbhWqa5OomV9cRJfyeIuAdjwjtS1Yad8IYxTKtstTbTF1nCgLiKdtr+rlarsqD6bDaL3//+9zEcDiuJQ/qc2QudTucjv8R7+B1+bUf2WY45Iqo2Bn+Czro6jvNvb29jOp1Go9GIwWBQIT25bjAYxMuXL2MwGBTSwrs/e0wzCYLs0jZjKMuv222fyOe8p5MnHnfG09OBrZsZ22Ts42fmuNQ2PRNhxh98n/2Lfbv7wec6PvS1OU6vk4fcHuNmPncSytX5+GBIay+R5PXxFotFSa6yCcT29na02+1iHyHesaPuPxeVuM2208Y12Xd9qeOTSSkbcZfa5rnAdaDaR2b2IzbOJq/dlLMRFlAEm9JdnBODb4dj4cmBkQkpVwzQvhx0WcjqBN0K7r7DIdgIuF/tgBx42QggSLl9PNttsLLkKXIR9espZEDuPsyywLvzvZ2W708/AXrrdgNh7H1kApS/83vYUQKQUWrWmSELmPvIhsd9nEnM/O6feuSxdd/6XjiliM3aAb90B+2KtogqAXF/v5ku5/FZr9clW4/t2NnZiSdPnsS7d+8KkGLhaq8lRWaRudJes4TneopIJhp5nkFAv9+P4XAYT548KYCJewPcTTqZAOedvBiywRWZYsafYNhZ6+y0IqICBnJQnx1qnQ7zfQ56sY3Yv4jq1DCcYCYWsj3gf+xUBhYO1O1Y69ro5xuoGMz7fK+nYeee2wVAo20QUAQzV1dXcXl5Gefn5/Hs2bNSWeexyWXotOFrHnV2z4eJyogqCeLyfbbwZopLs9ks2wQ3Gg8VL+5DZN335Tn271lOfa79rP0U32WZyX0LzrCeuH3Zx+Xn5CAny2DWIRNBlk3rmOXUIM/+whlbdJ9xyFPZuR9/mxgyGei25/cnqKWNEEDu19VqVXamZAFUTyWkzSQEkP28y5Oxh6uUmX4yGAwqi+Py7pD8VDPzWQ5eLEd1Y/xjR52fzgEN52W89nMclumIDVG5v79fqk6sS81mM8bjcZyenhbZg1BGJi1n9m+7u7slm44NZIypVMp6bT/Gb6bGk6n39uKMOSSHSVLPlEC+kFuqQbiGCmPWySIYgyQhUcQ7ugoQQrSO8OR6puyBCXZ2dor80h/ud3bvJhHt5Sfoa09rheix/Yiozn5wtSF6ZX12VRbVafZptId1MamQazQeqtkgobz7IOQ0B4k64qeIKPgJeaDv8OfIBm1Ddk5PT+Pu7i7+9b/+1zEcDmM0GlWI+Xa7HVdXV9Hv9yt2dzKZ1MYrv6bDOmwMbP+X7Yx9mXWD/1ncOuKhvzudTlxfX5d+ffbsWfz+97+PJ0+eFF+BfCOnPL8u3jQu5rnZDmafbCxkn2S/i+7aR6G/JnK47jHcQF9m/MWRsSjvZazMefmZtnF1MleHK37Mj4BXfb05DGNcxsJTlHkP9BWsjlzkDbxarVZJ1LOzHnHuxcVF9Hq9otvN5sPmck7g2e8YH2ViL+Nvf/aldfUnkVL8dvBpcOMsh68xqM0DaEGyU8Ho+XtnOFGqnJU0m8dzAeoR1XUUMph3EJ2zuBbKuoHFGGRAyvkZMOfPfU0e/Nz3dYpdZ/R4lv/P75TJGJN0roRw21H2zCi7nzPrjuLlPs9EoN/Lh0EsztQBMQQBU6U4h+DM72OD7L8dyDwGVvOY1H1vQ+Q+zmNto8P7sbDmL52Usp7ZETPOrsxDL8lael2niIcpfJPJpFSuMAUEJ428kdkn408ZNM+fz+cFeGHYXf5Phcje3l4cHh5Gv9+Pw8PDMkXQC69Cqtk2RGyAQzbotkmPOT8HqI/Jj/WP52V9yA68DtTZtnKu722bUmevbWcygWYAnT/nPl6LiP7CtrtdtiH0DfKDjAB0yBIa2Pp8gL8z6r1er7TfU2Du7+/jw4cP8eLFi4iIkp3u9/tlaml+r68NnB8D67bndbbfC1Pf39/H4eFhCVSazYfqCXaN9BQc7mdZcTuy/Gb/5R/OzcRWBuwOaE001iV8ODLArTsnX4sc5X61bcImW0+s53yHbCOT1gkDP4LhxwChfSiBv7PnrrJy4OI+sM7RRsbF1STowMHBQSwWi1I9sl6vyxozEEYEqgS99p+MFcSwbQAEhje34FpIr+vr6zJVxeNTZ9PyUWcDf+z7bJOyLmdc9LWOjKHJZHtdO2On7e3tmM/n8ebNm5jP58UHRWymUoOfea9utxv9fj9arVZlAWyPnxe/NhG6Xq9LYAyByRQQ2wnIFXTZ6wrhf3kH/DfEE5+zLhaECDqIrEEK8UzwUqPxkJDa3t4uC5GjQzzXa6k0m83iJzgfUh4iyCQs/QrxZoKG/jQ+oV85l75xlYixpm2AfSV4FX20nbm+vi565Y1asFG8v3/W680UWiqCwUQ8r91ux+7ubozH42g0HqY1srByo/EwDdDPYqkE7EZExHg8jj/96U/xD//wD2U8weDtdrsQgP1+PyaTSZH9HNP8mg7b84iP/VdEffwVsUnEX15elr6AOByPxyXZClamOqrf78e3334bh4eHRd9zDOMYN88wysm8iGoc6Bg422X/DyZGfzJOzvFLxp1139V9zuFrMzbN96jDuHUxJefWyWD2D/xvrOLY1tPfOXiuSSX7N3TJlY1gMcj6iCj+33YIvZrP58WurlarmM/n0e/3i41tNB5IYWyKx9d9RDv5jtiqTl6+tM/8qyql8uc5q+WBtmPyYUUiWPQ2wzhGhD1ikxlGODD4P8bAegDqwLOzuRwZzPg9c4DmZ/p5dYNuMO2gKmcKHdzWAS2e4/I6t9WB3mNg3MEB98rstIk57pWBtIPwbBjteN3XeZweG7NsaEyA8lzfP6K6+wAysrW1VciKOnlw32XDlf9/LFD8S0cOWCDVAKVe46ZOX35pRw6weDeXsa/X6wIi2R3IhAP3iYiyEGer1Spz4z3dxLLHGhjZhrArDuR2RBQQeX//sBDg8+fPo9vt/v/2/qVH1iW768dXZlXtqsqs+76cfbrdtsFgYQuBLDADS0geMAGEPMESAyZImDfAFAED3gECiRGvgZ+QpwwQCITlCSAuttvgPnSf7rNvdcmse+Z/UPpEfvK7I+vsc9n79Ol/hVTKyiefJ56IFevyXStWRNR4PG6bmxO8RBlXLW+ODIAwUMOAGACkcU++dlDFzjj19UDAKtonP9lQ5PVVzr3fw+92TCgZdDTQ9xhaL9oZt5zmxATtgR9wxtNI+h47QbQF0MxsMm3wGLFs9PHjx/XmzZuaTCYtcFBVLWuPpTP0saeLP0TxuFTVW2OS7XJmkZfwsLHq1dVVHR4ets387Qwn/zgIYn2ZQVfuTVrlRAP3UmxHrT8YcweHevY468ugQ1W9hQ1so3zNsmuepo0pqzke1k+0PXEStgie7dkuHHz0Y+771JM5ty1tOrLk/tze3tbe3l7L6KB+llKReUNQnn54WRR9sePOgQIsO2HzZHQeGRu02846dP08PZXl8/Rjguf8nrjvQxRjJ9pDdgSBQGfa4Fz88Ic/rP/7f/9v2zvGzkLVHe+woTVY2nzD5se8mwwbJm48IXB6elqTyaTtV0KAwUvOaLflxrbGgVv29FtbW2sBSSYqzs7O6vDwsO0zye9MKpA1BqbEWfOSeupDdhx4craw94jD58hJcgJqBFzBk4lXqMcyy3htbW3V6elpCyjapnu5I5gjM0K5NpstliSiv8l+4llvWeGlc64DWeVwGeR7NBq1pV/wnnlnMpnUdDpt+IclPewNZ/ldX1+vH//4x7W9vV2/8Au/0AJhe3t7TZddXFzU8+fP682bNzWdTt/yKb6NxTxom2f+MI5xwNUyxb3T6bTta2msvLm5WT//8z9fz549a6ci2vbyfttN+Db9Pwd53d6065TsV/pP/qMttjvYOGM/2zLXaxyY/l/q9GyX+2hbZQzpentJF2mHXAff6RP3e7uOquUsYkoPX+eKMyalzs/P22mmu7u7LeDO33Q6rfX19Xr8+HHd3NzUq1evmp9yfHxch4eHSxNSTLoTGE4sYV6hXV5amH153+ULB6VsUB0BrHrbGTIwtoFEmMwoOA+ZIm7nr6reAsB2fMxYVgQ9Ry/BNH20A9RzmNKBTMBsp9Qz/D0n1XXZYUuGsYLI91mh8X4DZIN92tVz9KCtx8vts1D1HMYcDwuyg1I9WrtvrtuFZ0ktNm2dgeUZX8ANSsNt8RjcFwDM4NAqINy7x/JgpU3/fYLNF33HN10MLOkbyowCCEXh8sxwuDhRj7r29vZqff3uhBanqXvfBt6HAzWbzdpSO2YXKDhOjMHu7m5997vfrSdPntT29natra0155x2OmhEgR/tRMLrGIIcWzuGfE9HJJ1284hlKI0k9ZmPEkhYL/T4PR3xVbKQhph7Uo+5/aY/3wlQYS9MS/ikalnPAqbcXhwG95Uxt751O2kHG5+z0fPV1VWdnp42gE/GELNKPJv0+RAl6drTlb7X/OW9Im5ubmp3d7cdCUwmIv1nU2wyA7281bof3Ws73ANlPZ7I/qTz6rbbhpg/Dfwz044AkHk17Y/pxP8O5pjPPKHjmXyyKehDbwy8nIjrtqcEZ9CDKX/GP3Z0HKiiXTkDXvX2EvpsG/0G5K6vr7dsEpYrE4yYTCZL+rpqeXbY2R87Ozs1Go2aTaPNLMvyEirLVOoUF+OSz5O7Hs+t4r/Uf/nM+yzGYZYt2uUl7wQGNjY2ajKZ1IsXL1qwqmqB1QaDwdKSPAIhs9ms3Q/NvWSU53gfNpoxPjk5qel02uw2mZXmQesI5CMDDTmRAG9U3Z0uyObeBJX29vaqqtpek9bNzmp0UMnL66qqXfd+VOAHAnPc6/2ocNpymW36AvQR2bAMX11dtWWGlhvuJ9iwvb3d2mkcNBgM3tq2xGNGvxiT+XxxaItP8ZvNZi3LjKCig4NMxM3n86VtL8hQJyB4fHy85KwSkEKeLU+3t7f14sWLOjo6qsePH9d8Pq+jo6M6Ozur6+vrdtjGxsZGm0CsWtj7b2tJjJ/+i3ENY279g9xcXFy0iTMCsAQvHz9+XB9//HE7dAddb5uZGVL4P8hG+iE9O0F/+N9Yl/so9ltsb21//U4nKjg4k75x+u1J68TE5iO+p3+a7b5v26Ee5mIMrb/9XnAIMuHtO5wllbT1J0Hzs7Ozev36dbMDo9GoBoPB0mTBzc3dPo6PHz9e2m/s/Py8Xrx40YLO6GbLmOMwvQyu3B4jJ/buw6NfR/nCQSlH0aqqEcRA00ESmM51WHgRJBicLBdSabO+qmrGlLY4+pmgJB0sC0hPGPLvPrBNfS4pGAnYDbK538KZYCnrT6CV4D5/M9jutcOBMIPg7L8FNpWUwXrSzIKYUXsHuOwk9MbRqeI2hlmnQTDAH4DHO3PWC1BjBca7esGDXvk8YOuZZYAEASkLPe//UED5qxTT0aDCM4g4K057dyYAwaOtra0aj8fNYQZ0zmazBprgA07tsdF19hXgEmV8cHBQz549q+fPny8tXWI5AzzloCw86f0fqmoJDDgg1Vs335OFqv4Slbwv+cGGpDcOqcNcDF7cBvO8g0BuYwKs+3ST9Xq+w32ysfbEg4N/vXbwLMWyjJyjGwgw2gHb3t5ujjLtmk6ntb29XdPp9K2lIp4cWEX7r7v05H7Ve+04ISueKLq+vq6dnZ02s+YZM4IRGSioqrf6bN4yrzkwm2213ee6v5tPqdsBEN6T9+X4u65Vk0dpx8zT1v3pHKZOc0p82lr3Mx0OZ3BULbI3E5MgF3bkLfeJETLo7CCanRHqcttub2/b0qSNjY22l9F0Om0bWTv4jw5m+RVtwrm2DScYcnh42PQ5WYzs0+cxW7Wk5F14/z6ZXPXbKj35vssqQA8OYJlU1d0+cOwlc3x83PbfYf83B29sxxgX9k8i8GyMzkROVbVsKNpAcIP3WA4daIIXnGF1eXnZxphN1AeDu43afSIjATJOgBsOF1leu7u7S/IFD6OjbQ+s59lXlgDTbHa3/cf29nbTdzzLpt9V1ZYomvbuo22YA3H0zcsJwXRct95BJrx36u3t7dJ+VM4Qhl84eIVi3ewJOj7JpAJDORvW21ow1mB/3oHeIBuHcQXjuL3wAEExAqiffvppG8udnZ2az+dtYmQ2m9Xu7m69ePFiKTjzbcC7vWLZ8Eb40MZ86aCU7ST8dnJyUq9fv67hcLGf3/b2dh0dHbWT1eBH6k3MZfuWGJX2GtvYR09cmjolJxN6Ez+mSdpl6ubP9jB9X5d8xyq/2P3JZ3ufaX9StziI08PUXMuJBvvH1J86tGfz8YnOzs7avn0c1MMBNvAYk6rj8biePHnSTtSsqnr16lUdHh7Wzs5Om6ygoD/go1U0dsLOh7aVXygolbMfSWh3gE5kaioOYHYSsEw9GEUIZOFxECI/e0AvBdEZDgmeewzt4EmP6S30GWAwnQzw7bjTNhwzv9t9gFF6kUue8wyqjbaVk8Gqg1JVtXSflSl9o1jxoWjTkUhlltkj6dz4eqZMYzS9oR9GNYWL+73W2dlS9Nv8kA67290bS383H+Q9NrreSwlZSMcsHamf5uLxtkPE2MMbfPfxpF5ulZt4vn79uk5OTlp2x3Q6bTOi5vXZbDEbTH3QzKf37e3t1eHhYY3H4wbImV12IMY85X1c7Pz6Wjq/VW8Hc6BR8p95yPrHTqgNGM+sAnE9QJG6KGXM77EOs65Kx783/u6T63H/LO92OByodWYtADbfZ1oT0PT37Lf1LHtavHr1qm5u7k5+PDs7q729vSWQZH6wnviQ8tgDQS4eN4+R9SftXV+/O7Xl4OCgqmppWbx1tcELpTfhY3uSwY6cdLB+47plOMG53+u++nreyz225alXKR5j5D1pWFUtaGSbSyF4hk7CqfP97pOdcfSg8VDaeiazeIYAkicDLbu8DyfUeMyZM7bRtIkg0+3tbdtbhgmUi4uLGo/H9ebNm3r9+nXDFthiToqDjrSdmVzqgcYs83NAzvLfc2R6WOs+58Xj3dNNPXD9IQG37T/jxT5No9GoLU1fW7vb0Pbly5f12WeftcByVbVlbpy8BR+Q/YJddLCEIIrrePnyZdtQGXlgCSf2j7FwMMIyCP4ieOGgCf3lXngaHIDtJ+DrQluN7xwcsr72UkFnVVVVe58xGPJK8MD9vbi4aHu5IH/e65b3397etsAAfJ/ON3gG+cg9ngg4zeeLzDgvHaTd1guWe3jHy/o4RIasprOzs6XAE/rJtEMHMF4Unnv06FE7KAQe9YmGtvlkS3300Uf17Nmztr+U99zZ29uryWTSvn9I+XsfBT7KlQJVy34Gf+xfCQ9U3cnjj370ozo9PW0n1e/s7NSTJ0/qyZMn7WTg9Ju8j7Jtg4MPlHy255fmPdTn78Zbfnfacj6t79Ad3kfN9ScGS52dflUPd38eJrX+sB7JOEBOlhhPuW3WP9lu4yB4xJNOjntwKvTJyUmzmWTIXl9fN38GOfe2JGRJoWP4nQOc0nfwUl9jgxxHy3Vv8uh9lC+90bmvmzEd4LBTtqpgDDniFgPkZVpOnYchzLwwdDpQdoaqliO/lGTEVLS+lmDHgDD7w++8o9cmwH1Go7NvrhNjnYHADAraaaFAu2xXRrx7oC/76XuraqkfVszuU9LUApxtSFDD/4AUO5o9JcOeAtDVx9tSf74/ARX9Szr2wO0qAAyI8f4dPYB8X30/jcX05ruVrhU9NOfkvTRkDioya+89LZgdcBCD8YO28M9gcDd7f3BwUIeHh23GgOPK2Y/C+w2l00r72WvBzl8a8jTMq4qNOt9Ny7zXzpmNxOfVZcCRjnZPzlMOem3huo1W795VNHCbXBcyZ2fZvOO+WUc48Olgn8fHRpYg9qNHj2pnZ6cdY82+HDxLHwDpmeX1IWTSY+3xTh6jn9ZbGZxnedb29nYdHh7W+vp6HR8ft31Eko/SbiW/8ck+fck/FPOZbVK2P4vbv4oemRWb/OF256fryVNg4Rd0Qga9cJ7ThmeQx7TLmVYwjvndmd4OJKHrvNTAQTTzrW0p7zLo9KffyYQBgSNs5Hg8bkv4Njc3mx19/fp1A8JktyIjpidBllevXrUMLAImXoJlcJ6TWhm09P89WexhFvNg3pP440MU8z90wimBptPptAUKNjc3azKZtH26nP1JoJL9hrj/0aNHS0tzmYSB3mtra63Os7Ozqqql/S3JlErca53sPuCQ4bQQmGL8vLyaJXmDwaAFf9BJbOBOW+mLcSCbwPu0QNoOXQiuWGc7GEPbBoPFfj5gyIuLixoOhy0oYPmAJ427GRPbLOSYQBZBYcsygWz7NJubm0vBscystD7ClnmZoR1faERGOThqOLxbast2B2TnQUf7AvAUe3sSzNvZ2WknHXKqNUssPRHy6aef1l/4C3+h7TVHFtnOzs7Sapj37eC+z2KbluNT1cdx8Eru9ccG52S8PX36tB4/ftz2gDTetP8Db6TvZL3f86ssH3l/Xst6MvhifFK1vKeSdYifc5vQIz68qvcOYz/6nbLoent6nTaxjxPt7dkFy30GG92vjIf0cIeLA9h+78XFRZ2enrbTNtnfj/Zhm7G1tI+lfFdXV/XixYsaDodt+fXjx4+XeI9PB++9b6XtMO/ISaP3Xd45KOWIZzpxnkFDYBwBhGGc5l21LADM0hDRR1hhPoIxVtB2jGiXDaILwa0E0QnmKAn+aa/fZQfVgD6DQ1ZOGCen96azm0EcR9npi9uBEUuwSp0OYvl3KxozndtAvaY542smzgwS8006P6a938/YJdCn3nRm6aNn0AyKPcsG7zAD5bExb3vszQurHHGPm+/170n7Hn9l/R8KKH+VQr/YqwFFPxgM2kyoeRLgB+BBwcIbKP7t7e3a3d1tp0RRJzOD5lEDqLW1tdrd3a2PP/649vb22kl+4/G4RqNRA/E50wHfeZ8rtzuDoVzrBSs8linb1GtwzDNuTxqAdKCs63r85t+yrWl0MkDtvqeMZFnFs9n+pEUacb8f+qfRNg/5xCHXa8DFePbAPBkhOD+eXFhbW2tBUdqVMv6+S9rYni1C71ctjyFABQePYAAbTxOkoh4DaAcJbU/ITu0B1x5femzsyPXsLsW87DoMOP0O828CXdfl8Xc2U8o+//OX2dz5bvMrdfi93ueQdhLQoa3D4bDtaeY2eJNSj3PS3GDa99uOwg8EIRnHtHtcgzdwUqsWTvN4PK7Xr1/X5eVlWwZNhhSON/aYjatPT0/r+fPnSxNvTDyYV03nVXZyFe/4Wk9Oe0D6m7Kvnm220+MlWs5ExLkA41VVCwaenp5WVS05uAQl0HVV1QKIJycnbR8njhKvqrf08bs4VXwaY1XV0l5IDu4fHBzU48ePW7ATWvA5mUxqNBrVyclJs9PIk7OZWULGO8ggIGjM/6bPYLDYwoG2O6PQWWGDwaAuLy/b3j2ZnTWfz5dOILTOIBvKwUKPKfQGO3Py4Xw+b8E73uWT96w7zDcEEZ3FYJvMcj4ClMgpS2rX19fbsiDrXGMEsuCgBePJEs3ZbNay3+HR4XDYAmI84/29Xr9+XePxuPHBt7n0bI75pert5V3wblW1JY+fffZZCyBir4+Ojmp3d3dpib0n/9Nfuk+Xpt3CXnkJru9BVyf+MpbEdhiXpB+dbTF/ph9PoOQ+P8j9vA//Zv97OCXbbnteVUtj6Pud6ex60zfxc6YLz3h/SYLIrApBBzDpbhs9Ho/r4OCg2Vjes7Oz0+4/PT1d2oPQQW5oTVY02bLW5zlO5tn3Xb5QUCqdjaq3T57xYDgAtQrUJ+hiJsd1MYvkEzfMDCZez1F0dDmZNIuFzaXnUHmQ8r3z+XxJwGBULzfxc/4OXdOZ439vKulrDoY56ARdEbLefTgjpqEVR9La7TRf9ITSSyBy7NwOnksHOAUc5eH3VC1n0c3n85YGD92Z0UH4Usm7f9mfzyufd1/SzcpwVX0/7YXxxPn3n1PTCVoClFgyxQyQI/YODDGziPx7ZjCV7XA4rP39/froo4/aqXqku7LvCRvJmhetn+zkObjqdyZQRragR8p71TKf0y9K6gzX42d797kOCn2zPnLwtld6OqinJ1fpKt7bM/SWKb4jbwYBpg/PeLyzL6syRpLGLJkF1JMtdXBwUJPJZGlPodls1jap/fTTT1u97uOHKElD+p+gxjbS/DybzVrQzadusbkskz8Go9CP66YjTh70T/7t6XbqtfOXOtb07AHNnh32GGffMyhJsRzzv5cCu+/mJ+qwjeM5ZwX0xo86nNqfe8Okjkh9k2Oettjt9ti5r9g6g23rLtdnEM6Yk21HgGFvb69lxHj22NlcOKosIesFGR49etQAeOqa5KW0w/fZ2t491p8pQ59nh7/u0sPQVW9nbm9sbNT5+Xm9evVqiUfZCxGcTPYvy7fgt93d3ebs3N7eLeV68+ZNc3oJ7nv5GPLqtrr07I+dFsuw9zBChwwGg7bUDNs+HA7bcjkyV+EhMCm8xybiBN7AddxPHwhUoftZJuh9nSyj/O8j2Ofzu8kuJjCgr/d9GgwGSwHk3OsUbGPZ9JLFqrtlW4wF92RAmXrBWWB5H/5CoA45c8aUxwpM7GWLbIBOf1iKSZtyz1WCYd403idGVlUdHR01fqPNBK5vb2/r6OiofvKTn3Qnu74NhX7n5BljCf+bzzyhjq8yHN6duPfixYsmR0dHR7W3t9cmUvHtuN9+jjNi07/wM4klbMsT1xifcX/aXOuwVZMLPb2bv+U1eMVYj/fRtrTRiS1W8ZQD+6ZLJrpgC6uWs5poAzLl68ZAxhIOdvM79GVygOV4Z2dnbdld4g2W5V5dXdXTp0/fClaib46Ojurly5ftfezzlz49+5D6MCjTxLqGvvZw6fsoXzpTygPijCgKSpx7eiCIAeKELu4hsm/HKh0/1+P68poBWLaBtvmZbOt9jqADVRYmmLIXtPIaYJ5fZdhTKE0HG+7smwXKQC8Bvw1fOgCeGXLdNrjp/K5SPBb6XtQ1wXjSzHQCMKTzaYOOsmZNPop8c3OzptPpW33yHgRJS49Btu2+8nkA2gL+eQD7p7WY7z0e19fXS3tdVC0bFfjGDhnfz8/PazqdtiV5AJmqagqc+phNICD1/PnzOjo6ahlS+/v77bQxgF3yLQE084Jl0Pqnp1fQYVyn2HFP59u0SOfJ/Ga5sQykbjAtzdumq+tn7FIO3YcEFHlfynqW1BkpcwY91j+p8z0rzLjZETF9ARPW6fxGMJpADbPdZM4NBouZLoKXbmvq8w9R7gMABkFVy8tOB4NBjUajltY9GAyWTvrE1noiwzSsWsgBWVL8Zp4zfblmQJjj6ucMQFeBXwPOpEuPX3p80Av62NbSTrcJZ9h6ICdrcJZpSwJ/xsRtdNCa+3v7eJmve/VSh7FE4hEXOzW23/AAtCCAQV1bW1tN3uCD/f39GgwGbfkXs/seYyYXCDocHx/XwcHBkr4zBqFNthU5tl+lJMbz9fcNsHttcXY5Y7y2ttaCDFXVvrN3D/eTwQMOOj8/b+NLoHBra6s5xbPZrI6Pj+uzzz5r+wJxP0EbxsWZfF+0T/AIQSb6BRY7Pz+vnZ2dlmHD8i0mi/b29trSMu9zRh1k7vGsJxvhbZ8gCgY5OztrS8iMFRNvWy5sW8CFOHFkEvBeT6bZZqETnD3EuKKfyBaHJxgDirMcLcPb29tLGWPuC04mQaccTzaxBxtz0IeX497c3DQneTAYLJ1S7D2yaM/+/n7d3t7W6elpDYeLJYKnp6d1cnLS6l9fX2/3sFF/b+Lh21KsZ3H0CRzs7OwsBSHsT8G3jN3Z2Vnjz6Ojo3r+/HkdHBy0TOceTuR9VcvBldT/trsOdKS97+Hxnt3h/RmIMlaipH5NXW58nb+v8rvyHYmd+d1BJmMKT4LQPtsaZL+XhW77lLSwT2PdwdjMZosTfMFp2DyyCslwms/nbTKRbV/QM2/evKnZbFanp6fNFvM7eGQ8Htf+/n4Nh8OaTCZNX1QtL61M/qX94IC870PZyy+0p5QVnJkfY2aAydpYoumux/+Tys3pDDiaBmvew4LBpQ2uK5kVxu4xf9XbM3Iudp79e6+OqkVQxExuR4Z+p5Ppeu3kplNohYZicSCvB0Z7DOR+0Z505PxsKrWkOTyRQR2PgZdEZUR/VTEN+e6ocAJ3ADbKGl5xSiUG206MHRE7LSgoBx969DSoWVU+7/ek77vQ56eh9BxLgjwAE4/3fD5vJ+wxNhhnFCF6wxt+bm1tLW2STuSfdN/Hjx/X8+fPW4bU7u5uy5JiQ/OeM+xAqfmjatnI9pzf5B94pWo5s4KS9aazy7tc76rArYMkNriW2R5I4Hnamvrc9/j/nm7oOXgJZnICgTrsABgIpe60vqO+7CO60MDfm6qif9ALnOC0tbVVe3t7baabmXjqMw28BOF9F8tKOk387rZl4BQa4UAhOyxl6c2M2tGgUF9PX/t7ApZsf88euA/mkQR7bovrSrucdSXvZwZDBplckF0vy/MBCoPBYimhN1Dm95yJdf+yjgws2w4nPVyfdQDvtA52cIp7zUOpB+F3A1HeTYCByYH5fOE4b21t1evXr9skYtXiNDO/l+UBYASe5bp5g3FMndMrq3hy1T35/V3s8vso0BfsBXbEBrIsCvk9Pj5uS3rseA2Hd7Pt19fXtb+/34IK8DubJ7MXGAEgMmFMD2co39dubAdBjOHwLitqd3e32dmtra2az+ctU2dt7W7vIk7/RF+TvX55eVk7OzstaIWeBQvM5/M6OTlp7yKwxWEozjoheHp7e9sypDMTfjAYtOtgDQcQZrNZ69/29nYdHx8vYVjaULXgaWcq4Q/5EACKg+yWXzCR/8dmETw03zgIMpvN2n51/F1cXKwcT/Ax2AteIEDGhC48sba21viP7S9sVzc2NhrWevXqVe3v71fV3STiy5cv67vf/W7jjVevXjWsBQ2+CRn8umTftgz86gl/+w/274bDYZNDTtx79OhRfec731latpeYE73uSZdVNsx95RN5wZb4eWPO+3x269r0PXuZNRRjVvenF2SyzbrPJzIOyXb5nuxb4pXsv9uUfTKGWjUJii7CLyWLE9ligpDg8mQyqTdv3rSsZO+xRdCJ5B1sbFXV3t5e05Oj0aiGw2EdHh62TNqTk5OmWy3PyUtJK/+PzgEjrPKFv67ypYNSVYtGe7NDb+J3c3PTDBR1eE+DqsXyAIJSdJg00pwNTiCZQIxrMEiuUfVzFiYHPNI54neDWvrv7wkWDcATQHOvAywo/F6kltRmt9vtsBDnO0w3A3g+ew6PBTkjzzkD7H7a2JlPDNBTKfCXTnnWkX9+Lv8wtpPJpPEggALnM3nI9fZ4jXuSj95H+SYM9ZcpHj8CUpapdBi3t7fb8oHZbNb+J5PDSwE5qpoZewogb2Njow4PD+s73/lOHR4eLu0fxUl7abST7zNwXfW2g+TfzWPIAkA9Hfg0jNAk+djvtBG2jPXqqloEmX1/Bp6Tl3qBIH/vvefznMRsXwbbevq3J/M8j47JpWKeYeqNV7Y3wRfLEvb391v6MgYfxwcgAC0dnP5QJQGZr7t/1tODwWAp+8Gz2dhoByBMl+Q7ZyBAB7+fkrbDvGp9T8lgpPkg+5dgvCcLqcc9QZEyx/u513aW8YVunhjiO3Uh872AmnES9cHHDkTBZ7lUKe1u2nuDfwPk+2yqf8+JK9t1ZyAafLJEjKU3OLHeV5INWtHZCfBZzsc1HzySe33eB3pz7FfxY88p6d3/IWWaYoDvbG7rvIuLi5pMJm18yfatWl5+yV4kBNm5/+TkpAWkGEP4iIxj3uVNp7Osra21TBeCPFxnM/LxeNzeTTAGXUvfCAA585JMkIODg9YWZIZNtJkQIBBCoGt3d/ct3UMffcIuGUM+Uc+Tk95XC78D3U/giiDsfD5vWNJLmquqTbBlIIq9paADGMd+En7TfD5vjiW6GieUZY/oLAIgvMOYF1nOpTnWgamvoBNLasfjcfPN4BmCpeZf2gEvYE9ZMvrZZ5/Vd77znbZX3WQyacHR9FO+bcU61TRBvkajUVXd+a3eTxk/j9PWzs7Oam1trfb39+vg4KD29vaavFMnm/47iHAfPuPT2VPGMlWrtyNYhdf4zHeswqb+PX0s/k8c3vPxbKMSZ/Adved7sx32RVZhlXwW3WV/kL/0U90mnmNCxzzCGGJLh8NhnZ2dtf0+J5NJnZ6etkl3AtDYCvTFdDptwXHklwl59M5kMqnpdFq7u7utDaa16eLDCsxn5iHHK95X+ULL9waDwVJ6qQXSCtbpXwmITAwLhZnJzJoGOxmaYpBr0McApDOaTNkDQsl8XKt6GxRm+82gbrMHF8eAuh3cs7NA+wyIE4yb1qZDD8T1Pk2DHlC7T/HkWGSGxKqsjFXA0/TMdqFQ3U4rtapaOoo69/5wFkQqNtdjPnFZ1eYsvfuSH+577psAy1+0INNE9aGXARffcVQAegSwAHOz2WLDTAw2QBmDuru7W4PBYnnv4eFh20NqNBrV7u7uEiDN/R8cLHOWoZeweElFXkMXWC9U1dImqpbBlOGe0cXoUFbJZ89w28BkHQ4mr6ojjXS+P+Wj18YeT9B3l5Qzv7unT1J3YRgdyM9ZKurKLLj8ZBkLM79MXnjTTwca6deHBM49/U4x8AGkUE5PT+vjjz9ectSOj48bXfhMYGfd6okV3pdBo1VjYT2cdrFHy+R18+WqQKOxAWOEXeRdyJXtRdKMd1GH7aodS2TabXYmlWXQYNT6gPZmMNA4yUs6bNtsP3v2L+uxzPP+HqagOFDoACzthL78jqxMp9O2THswGNSnn366tIeMJxmvrq6WMj7Y46d3Gut9mMw8mNc+z2b28FhmRb7vYlmh39AbmzSfz+v169d1cnLSth3wsnKyqezsOEhyfHxcP/zhD9vpelWLDD5vfG07YZljE3t48smTJ3V0dNROYmMDaxzn0WhUOzs7SxmrDlTQJ6+cIOBRVUvLg2ezWW1vb9fp6WkNBoO2Nx7BI7LwaC+BvY8++qgFZ7a3t2tvb6+Oj4+X9kUCW7BP2mCwCJzRRnAD2JEsMGcDzufzpYMMGCMypMCeDjg5aGEfgOdp16NHj1rbGOOqavTmGhlV8BT05RSu9fX1Nv4OaE8mk7b/E/fZSQZzsS/X/v5+q5vsDsbK+gX+/Oijj2p9fb3ZnBcvXrRA5cuXL1tgmoMOPi87732Vr0PmjWU8njc3N41Pq2pJj8KLjBljvbOzU0+ePGnLtrzEjneZXp6w6GHDqmUs5eVd9+nP/K3nR/oZCpjLur8X/MlP05H/cyIysWbvf3/2fCj7dD0c7bZkPXw376/qk/vh/iB/9pEIUM7n8/pTf+pP1dOnT+v169f18uXLev36dU2n06UlurzDq8o+/fTT+t73vteC7kwaVC2WDxJs8iRQz+5hW7jubElnk7p/76N8oUwpE8ffERZ+w6F8FwaczRbp8cxKYKBtsBNUVi1mLV08O8f/mdmzirCrGN/3JZjmXi9fM4A3oLNg9N7r6ygy06kH5DFyvahtr60uPcZKRed3WEGYhj360OcE5RYuhDyXLSQod5/SyXCbeQahgzd9Clfur0G9ppev3aeoV9GwR8uekrsPcH9IoPxVipd8uMDn7PlUtVgGxf+3t7d1cnLS9hdgholAFwAWYweQqaoajUb17Nmz2t/fr52dnRqPx22mjj01PHbmQ/OjjWDKjvnKQSnqywyadKjScCV/Wb9Zr/r3lDVkIOUudVsvKOUgfcpUOoaU1Js9HvYzGcDP+ymp23q0oi479zbwNqKmey+rKe85PT1ts8EY6p2dnZY+vb29/dbmrUnj91FW0Tlnp5I/CKCcnp62VG1kgU0y6S/6EV1om+oArXnWY+B33sc3KU9uM33qAUOXnp43qK9aLEns6c7e+DkAZSeR9PgMwCU90vY5OJd7RNmOpu7J9tqpN8A33/WCyn6H6ZnyU9XPXs22QlMHA9CRXlZt3vBeRaaL24VDvLOz0zJnkLEcQ7fJY7eKR8xbq37vXf/QTnGOHRlHTPaOx+MaDAZt5pwsIpx5L52qWgSR5vN5vXr1qubzeX322WdtSZADqg6IOQA7GCzvG+R9GA8ODtoeN9hrn6pGgIGleVXV9sZieR51wTvn5+dLy8tY4nd+fl57e3tL/EDbyS5CV7Mf1WBwt38etp9TIslYIYCHfoTnWe5mWtJGgj3IwP7+fguieok3tMT3ubq6avthsp+TA47GEPP5vGUfIa+z2awtJ/S9ZPUOh8O2RJMlO85euLi4qMePH7d9U8mkwMbf3t7Wmzdv6vT0tGVR+DCRqoW8Ufd8Pm/0YAKxpwNvb29bps/a2t3SITIq2czdmzVz+AH3vO/si/dZkKuqhe9hbAh9oDVBW4KAZPzv7u4uLdtL/vdkRtWyX+jrPSzlCba0ub6XenvX81oP6ybm5t6cNM3net+xf4mbKWnbs10eH9/vyTfjZUraf0+IGkOYfr2+WEayjb62vr5ee3t7LQj80Ucf1WQyqdevX9erV6/q+Pi4Tk5OWvYoWVH8/+rVq3r27FnLsqxaLAlG3ljeh+5C78Grzubz0kK+97Lz3ld556BU1WJQc2kTStkp7SgehAqwloCeQBQCZifRoAhmclq+wRj3WJnn4PcYxszo+xIAVr2dzcXzdmpTaSSQNaNDR99bVW3GLB23VWDW7zCQhfFSYaDk3FdHkRPE8o5kTJ5lv4I0bp6N7jk4FNqep22tcgQMkF2v68QYs27bmW0GwIArgifJL+aj5N2v2zlNfv02FGQC3sBAeybI0XuAI8qO2QDS5q0Qob037Nze3q6Dg4NmxLe3t9uyAtLCmV11+xyccUYD1+CPlPme3gB4uCSfZpA6Ay92pFzSwbITa0ez6u1T/Px8AmDX39OJ9N9BhqyX7/cZW78n++R32jHOYqc225ngxnVl5qll2M4xzt1kMqmjo6PmnGxtbbXNXX1Ckp+zE/M+iu1KgkYXOwNuH0t5WPZqvT6fL5blJZ1ms9nSEnHTL9vAs5kFBc/1Ami0Ofvo61mn7Z2zeVa9B9tXVUu2ld+QedeFDeDdvA+ARjuNF7wUKPGI+4WTa3kdDpe3FMjMXe6DB+mLbTr3+GQ/7LN1mO1s9sW0sqz5Ps94Z38tvwT0dnd325J53sFvjx49asCagwaghYE0f4l1eP/n2cW8x9+tk2wLPmSxbiOIid7Z2tqqp0+ftgDIbDZrm0gTlBoOh82eXl5etkDS8fFxy4zhBKfEzp48Xltbq729vZYtSpCAIBn7KZElxWmJTA5hM8ncyVMAnYkKDoAvHLSiP2RHV1Xt7Ow0/vOm6fQjcS1LAsGgnD7lccapzwwFlqWCP5L3wCM+nZDAMe1lCaX/CC4hL1dXV0t7XDmjqmqxrx9ywdYDvNsbvKPH6Qv0QGdtbW21I+E5Jh68tbm5Wfv7+3V+fl5nZ2c1nU5bACmxyPX1dR0fH9f5+XnDWdCd8SUANp/fnfhIH0ajUQs+8lm1yFRmgoTybQ1KpR12sBfeSSxqfV1Vbfnr3t5ew662Tz40grLK7/Fztpe9wE7Ptvcwac9HNfZKu9XzYVbhP9PGPirvyxU2iT9th3oY2HjW7fKklPG5x9XtSZq6jlWYx+1NH5jv6BB0GPaAvXGfPHlSp6en9eMf/7h+9KMftY3OwXnr6+v14sWLGo1GdXBw0HS+3/fmzZu2p58nM3zip/0i20W+O/ng6/Z7s7xzUMpCxHcDD4oj91X92SgYhdRfz1ZSEHA7AVZibocdDjOO76HOvK8HcnogpuekrRL2ntBQLAAJIHmG9jtbLAWQuhzQwUCnA+fvfq/ruS+93zQwuHdbTVsX18u7kl78b/D/eZF1KznX75llUrtvbxentwC2CFR5LL3fmQGPDY3HN/uQtPo8BZ/PA/g2Njbq+Pi4y+9fpNhBe1/FPAZ/ce3y8rJlbni8Dg8P6+TkpAFRlCrAiTH1aWHw0e7ubj179qxtUs2mowA36Ge9k4EmaOPZ2KpaCoQlj1lW4Qk79nmfxy4zsVJObLwyAGtdZjlO49bTT9SbQQRoYl2R7fi8LJZVjl+YvAvpAACRCUlEQVTvvrw3aZbgxrTs0R+nyroRcI/cZyCBWVnARNXiaF87OSyNgD7uXy8D6+suCZ56/yd/0LZ01ggE4KxAG2+gSd3efyQDAqvsmQGlgZ9nbw3aVgVCqpYzGW2HqCdBo3W+edRBOIrH3PR0H6GdlyG7/Q66I9/eLJoAg/vmAJg3FwaIOhCeINDBn+yn+dpy7Qwy6ObAVWKN1DV85qSAx4M/HG4vF2Kj5Kpqe9MwY0uglL6zPGh3d7edPrUKdyTvpwz6d487v/X+/6ZK4mECL2xe+0u/9Es1GAxaxuZoNKo3b968hVnRRWtra3VyclI/+clPlrJOLMeWwfl83gITZEB5cu/Ro0dtOd7Ozk7b3wYHhsA1POFsKWcnOyuKZfqcBMWyEC9NM8YlaMRYen87ywfL6+bzu0yyg4ODpYAwgTyyrpE9HEAygAgoWPfz3HQ6bYErAkHsvYScMJlG1h91MrHhJTDcW7XYJ9ZBCDIOuZZBiqqFPiOT3DaR+r0RPX0hMEZbx+Nxy168urqqk5OTFnik4ABDl52dncYfjM3V1VU9efKkhsNhoznZH2dnZ3V2drZ0SAgBQge8v61BKcp8Pm8BWbLoGHPsLzbWQU32ZEPePPbGRqmnbWO5LzFhL8vI9/je1JtpJ/N/l/t8O08AuV7bHr4n5ujhwV7wyTKQ97stLo4rmH7Wk4khEzO4zz08nu0znvZ7Ep8Yd25sbNR4PK7xeFwHBwf1k5/8pF68eNGWwrKXHxM+R0dHdXFx0fhtZ2enTk5O6s2bN3VwcNASAqoWE3duewaf4FfbrFV2+usq7xyUQpEa3EJUp7GacRyg6s1gM8MymUyW9vNgYLwEyyA0gXoC8wxSpZPFs64rBarH5OlcpdAnaO897+BTluxXboiYe+SsEsiMyPteL5Xz5yrl5fZ4DCieTcXQ9JSJgb6Nq5nf4027Uim73U7D9P2s0V9bW2zU6Pcw+8azqcCSNzD2vi/HzWOxSqH2+ux3DYfDmk6n9Tf/5t+sv/t3/+5XDiatau/XWdLB8b5o19fXdXJyUk+ePFk62nY8HreMFGbWz87OmlFmY2FSyBmjra2tevz48VKWFDLILEMuIUlny6DAuoI+WD/kb73lTh7nXpCK6z3naJWTZHlMvdNzyHr6yo6un3P7HOy3Ye7pl1V8m337vH7xe8pzT7fn91W6j9996h6fvgebhY4YDAYNnONksG8KDpjHO/X9+yw9IJrFurRqOWCC/iNrIYNCzqJIp8+Al5JZsikzBljmjdSFOJyp1w3EMgjLe7jWs2/5fjs7jLkzkOz8Vi1nCuLE9fBFBhVoBzaQUw8Nam07Wa6RfE32hGeb0y5zv/f1zH5bNswjiV2s/7z1AnqaMadtGYA0sAU4o2NxxC8uLhrGoz8s5THvYfNzLFfxffJU8t3n3fNNO8DOBiO4wJgOBoMWoNra2qrb27v9eMbjcb1586Y5vIPB3Ybeb968qU8//bTtTWO6mYeHw2Gbgd/f36/Dw8N6/PhxjUaj5sSMRqPa399vGU5s3O09mGxzrW8I0GxtbbXMmNxblmV+a2trdXZ2tnSiIwEqstbZX4nNvZEbZIrN1Ak4zWazpsvBa1XLtgC94+wE9L6D0tAX3UDWGvqUlR0UTxKxZxcbXRMkIkAxGNwtzby9vW3LbTY3N2s0Gr21mb33rfK4gkWQTXiKABFLdeAVsqxubm6WDhxgvGgjPMbSUffRWVNHR0ctmw4eefLkSQs8Mebc88knn9Sf//N/vukZBxtt07+thbEB89In/DffA5+hCzm1kPEynqHOXFFjXZYTNj2/4l1sMu+kpC75PPy6CgP2sF7is15AKq+5zh6/JO5OG+g4gu2ev4MH0kc2/rOPl/3tYeTErdaH3NubMPNqIGIjHGZxeHhY4/G4Pv3007Ytw2w2qx/96EctqI2cwUMvX76so6OjdjKmky08wWF/2HSgfR9CTr/QnlJmGsAe3z07bdDhAFUKQg4sirNqEexAofeEzEDTYNNBLDNQT+h8vQe40zHw7KGZKTML0mEykKMeZ1ukAujNNhN1T3DVc65z2Z6NF4KW7TQo7zGg+8OzFtReRhe0Mg2pI+me9LcC8FjmkhULOODBTgYgwo4DmRMI56pABu/oKaJ3LUlj97V37x/8wR/U7/7u79bt7W399m//9pd6Z9X7zZCqqiWeRh94/y47dxhn0syrqs2eAuIuLy/bcaaMMwCVPaQeP37c1l8zEwg/8w5vbu8xNZ9WLfNgBprSqHkj2eRJO1WUDIJT/GwvIMr70nDn/Smf/J7PZX1uZ87YZzvd1h7vpzynnK4K0t1XZ77Tfc/xcx+te6yP0XMADp+qNxzezeyygT712Olg41nT5H2WHthJG+S2ZF+rlp0kgxrrSjuU3gB1lS3o9d/Xe5MaBp1pPz02vq8XkPL7kjew9Q44QoPU5VWLTZUdAHKw2n30PbTdJ+Bk8e/WgdzvgCDvStvAhArfoQeOth0X46202QbTph3P5fhQcjliLwBoXV61WCrv/SkITGxtbTXnezabtawobDh4Jsc4+/J5ujR1YfJND298WXv+VQv999JOsIg3yEb3kPXiLOKqass6fKphOhFVd8EBTvVi/0X2YkQvPHr0qI6Ojlqwx7yPPvTSeOz8YDBoQY/r6+u2JAydy0TRYDBofayqFkxC/ghc+nAarmcWP3qLQEzVHQ86y8dZT/SR+qAXOsMZi5YTZJTsKsaGDBf6SF1kgFUtL6+FfjiL1MNySwKzBA7ZN8qyZzvInlrI3GCwyPCFfgSjqha+xnx+d7rfxsZGO9mRJd6Xl5dNVl+9elXT6bSOj49bfxgzxv36+rq+973v1c7OTh0cHLRJEGxq1SJz0xv2217Tx3R+vy0F/UgfPAFiPgHD9rAZe7l5uaaDKM5iNPaxPCSOtc1apTd7/cjJi/vqyPqweYlXsr70ET1p4ud6CQ49Pb7qXeanXtCtankFA/rE9tpYKWlsbOt3J671u/2Mky/gjZxwJQvVeoxls/v7+/XkyZN68+ZN03uTyaQ+++yzevr0aaOzt3NgpQB9xwf2eKQ/Yr1m/n6f5QudvgfxaZz/WPOKEiatFiVKVJy6Ejw6U4r7qxaGIfdwoRjgVi2flGOhTaeFexPQ56BULQtDzlwmcEtwsMqRSCFCKNyGdCAsrNDO70slBW3pZ9KNTwdwLCCmp+vNfiSQTCVmIbbgpxAmDd2G7KODdh6nzKqjP/CYl6ykwqQ+HFPenWP+RUoqqVXXKGtra/X973+//s//+T81n8/rX//rf/2l3/0hDL0NcoI5b+zoGXGALWndBJ8Ai0dHR01vMA57e3t1dHTUUlkBbh6zNEpuk8fQz1QtZCDBn2U+DT3vSJ3C/Wmw0sD3+C/1UuoI0zbfkYDa/TAtVhmU1GHuu+nYAzhJl/wt+fC+ukyvdJjdPtq8ivboU363A0JQlL4BGtlwF8cs+eNDzuiu0hHWgbZZ/D8ej+vi4qItryDDtmrZrppOpie0J3BiGie4yzau0qm9cbXd7T3n+nKcoQPtyxNWoY0BvWXEkxTQBbvhmVKwjTMpyOLE4XIwlzr9aWfUsuhC/3D20DdpgxN/ONAFvRx4Nb94vJLepjm6McG0s7ahGb/lmOAUA4LtUEEHDrSArh7rHi9lO/P7Kp2SfJd0+CZKYqOqanrI++3ApyzD2Nvba/x3fHzclnBUvZ2lz3s2NzfryZMn9fHHHze7y7JJMkH39/fbCbbJgxQmZeB961ccF/ZTwuHhfviJ983niwDX5uZm6zfBTU50dIDU2Z8EifKeXHKKzca5g84ZmJ7P5y0TyxPFVdWWRDqzje8Uy7oDajzrcWZcBoNBC9ihH9hEfTqd1vr6etuKwAENB6mdOYbMIUPn5+dLS8dYmke92Dj0Dvhsf3+/dnd3azqd1osXL1rmlOWcPcHW19frz/25P9f2DvYSecbk9evX9fjx4xoO7zLl2Gs4J91TJ34bSrbbAcGq5a1sej4JQUECUsZctj/maexYBmn5PbOt+N+2vecP0p/UlYmLU9/2Pu/DbDzvjCHrfd9nm9TTb66/91tiDcu26WEb7z4YY/dsxn04qKeLKch6r8/px9sOMHmwsbFRo9Go9vb26uTkpH74wx/Wq1evan19vU5OTmpnZ6eePn3a+HFzc7MuLy/bXnfoYd6TbfdvnJqb4/4+yxcOSlEcGfasydXV1VszejzPQFuocFxNHDYDZIY6I7hZYC4zscsqg23Hg2s90JZC7r+8P5md9/eUgQU4FYIFx/V6aZv3sHBfbMDSWLpNPYfTfbVCoviZngJx3R4HvzfpatpXLRShn3PdtKMXiMjZMeqGbsxC5jjk+Kbi+rLCeN9zq4AycvVtKNC8arGRL9ccSGYsnNp9fn7e0vOhAxF9gOlwOKy9vb169uxZW8bg0/UAvs6ugobwfk839Iy6eT8zEnoGKJeZJi+mzkrdV7V6L4XUadTZu4ffeoGnBA3ms16wlXockDGtuCfrW9WHXtuT7/P9vb6uur9qOTPUY2nblMF1Az/22phOp7WxsdFOg4Q+mYH7Psu70NPjTNCD648fP26OAE5Ojrf7lRMA0A49SenpbYM9l3SgDN5T1zrgkjbZoJz2ZHst98hyD3TybqfsuxiE9mw/deBEWlcbYHtzW0/y9AJIyU8eV4IPtIvvZIdwL9dsk227PbvZm5jy9dSHTODYcWD8PDbo4tlsVru7uy2wcXh4uJQ94nZntrPH7D4HJEtPz7ovq575aSjp/Mxmd/v37O/vNyyNfDvTZDqd1suXL2s6nS7VRRkMFhtRM6s+Ho/bUkoCUtvb20t72aRcEGyAd70ReNUiE5olmfACwRiCVz68xHqI72RD2ZZ7/z/znE/znc1mdXp6WoeHhw0Pz2Z3yxBPTk7aRDiT5NTnd1GPg3FVtbQPEPuPImeWf/qODvCkJ/WZF6EJkyOZgU0dzqQk2AANaT/twMYRFKQt3v7g8vKyTk5O6ubmpvb399uknjOlLPvsA7q1tVXHx8f16tWrpWXDLPmbTqf1+PHjNqlj+tAe9vNi0gfMZ4zxbcG7WXLSxpjC+pMxtD1GHr2PIfKWp+1ZB0M72830eXhvTw/aziQupiT+TD+Ve3xvD28nbkvf1PVnu/I9PX/TNiMDv3kv3x1wN2bovbvnG7gvPb/d/Uya8mm/KWnm4Bl8Y58VuWWSET569epVnZ+f1+npactedd9PT09rOp02fOt2r/I5sUGu533L6hc+fc/AxJ1BYDAaHswEHACsBFdV1YwvYMbp3T3nbzAYvCXsvtfGJJ/l+VWBEivpFEYr0l4k104Dz2QAyMWOU4L17Lvpn1FWg+Tso/8whIxVOvHpiJsGOQ6+P3kk25Bj5/us+AxG7gOXqQygswMVroNP9o7xWPT67+CX35G0WFVW8Vt+vmt9P43F42M+8jIPZNSGw2mhyOpwOKzT09PGx2tra/X48eM6Ojpqm2x6RjRnY7Md5nMbcNOaZxlrG3c7DT0nm+/wXU9uDaINym14MnCSJQ10/p/6KfW09VXPyCcP0h6/qycDfm/O/Jg37uPtXl1JZ89apb72OyzzBO1tn3gH+5X4d55nWQkzUr1ZrfdZPFa+loDJ99Ku8XjcNrpl3zbAC7w2HA7baVU+3MH2Iftq+817rReR95596NlCTygk4DU4z3Y5m9gBKZ6n8D+6JwO2ybfwF3gBjJKyhO4hI4RxAss4k8I60MUZpLn0Dt1D5rkzOAaDwVvBHI+9ecYOH5+pX4zL/Bv99X4o1J9ZaQ60MQ5HR0ftvsvLy5Zp4eVMXrrmkvjhPlvY4zNKDyv9tJXEl7ZhDhIQaCDThJPVegXdxhL30WjUTkVzIMRL+noZBD4lDh1StZiMpt2Xl5dtJh1HiaAKfOslYF5uxvJ72k29efIpfgXtdMaVZZjnCOKNRqN2QuFgsBzohdeRNZ+Cl5NH29vbjT7OoCfo4kAZhQyn3iQp7XOwiX7gF83ni8M7bm9vazwet0AZNPEG5BcXF3VyclInJyf14sWLFgxythZ04qTG4XC4FLC0sz6dTtt1fLBXr14tbVg+GAzaITVHR0eNNuiByWTSdOL29na9ePFiicdoz0+jbL5rsc9l+5XfZ7NZ22QePQ4PwLfQhczDDEhVLU/YVy2fkpr+o+1r6vbEEvf93yuu38/0fEPTyvcnHbm+KnbAffdhSPerZ0P4DUyQEyGpj4zXXVfalqwjcUvaqXxf4veq5exM9vJzfAX9aX33gx/8oF69elWPHj2q3d3d9vv+/n5dXl42/ku8RvCdlSu0Lyf9VgWvvs7yhfaUonHONuEPJXpzc9PScHsMmY4pxoSlOhgsNpuFeHZKKAamHly/08qBa2Yo6ukxcwq27/WzyfwpYD3HLJ0wg0wL4ap+W/klME0HzPWb7lZ6Pdrk8zCl35V0cfuT5k5DtoOc97nv1JkzdG6j6eNghGel3N7hcLi0+SrXTF9mfpJPVhnQVcqyN+5f9p6fxmJjZxAFra+vr+v09LSePXu2NIs4n98dIXx8fNzGABA8mUzq6uqqLdnb399fOmK6F/SoWvCUeZCxd4q9A00pH/ALfGjH1HK1yvimnNEey2y+N39LWc57M/hWtTzzZJlw26FX6imPYxpTSq+vjLvv6Y1L8rb7kXKT7fe7s1/56TGGjt7HAzoYgOOUsKwUsOgTp3DMso/vs/T4pAfgcdL4nU1/yTQmqOEgfFUt6T/eY8ABP2XQwuDX7VkV8KDkOKUNGQwGS/vRuW2r+u3iZXe2Z9jIBPNV1Sa9/AmfUQfHqOPQOlie9t0lsYSDRNk3O8CmUc/WJs2tI7CvGQhzfaZvPp96Ab6gbeA+fvem6PP58v5Qe3t77Vj44fDuAA/a4b1v6I9B7yrbvqqkLs6+9+6xjvgm7K37SdYJmUvQB6cBvnz8+HEdHx83m5llbW2t7Rm1u7tbOzs7bYkck707Ozt1dHTUsqVyiRtBDrePAFXVYq8mdMtgMGi6BOeIrTvgdfY2qlosmSN45CAY+tZLy/gNTMFyJ8sOy5Unk0mjn5eI0cbT09Ol5XJk71jefSIg7eEa/TWvslG4/SFsizNlrFfZ18l6BLoZ+zImHBpg7DyfL5Yh+5S/6XRab968aXuR7ezstI3z6R/vnkwmLUA8Go3aODNeBL6g4cbGRr1+/bplYHFi3w9+8IMaj8ctmQB55z6wG7SbzWbtJE7r/G9rgRfQj8a55gdsFNtVZCDJupHn7ZsYoxqf2rbx6cxxy0H6O4mHXTxZ40CR32M7Zxu2yh9Ku2gb5DYkhrZNyPosF85ipJ7Eb+l7EkDPdtlmrLKXxk2JY3vF8ZLP8+1pB3Whr3neOBYdsra2Vn/4h39YP/rRj+rq6qqOjo6qqmp7e7vxlnFd2nXHIObzRUzni9rkr1K+cKaUBc/AqqqaosEgVC1mDOg8RKRjt7e3SxthVi3vC2VQWbUMMhgwA6NsrwMbZpSeY7vK6ek5JAncaGcveGUmMzM7o4l7zCw9gUoAaebJlP1e8Ma0sCPhWd+ewuEe6sp7ew4t47xKYHtt818qCCtrj4Pvg49MD4IjgDtmClOJAkA8s+eZqVQ0PR7I33u8s6oYiH2bigM2VvqAj7Ozs0ZDb0C6v79ff/zHf9yMgjfeOz8/r62trZYh5X0hkHkbhaplcEBJZ8WyZTDEDKV/Tz5dFRhNw51BIMbUy6x4zu1Kw+4shHS23V/Lnf/Puv3dbUs6Zb+zmO6mg+U9393j7VU6oSf7HuNV7aekDmWGGdlnTymDQPaV4gjw0WhUr1+/bjS3k/KhSq9PFGjCH/bYAQmybaqWU/k5Ct1gNLNrTVs7gNmWnpwkD5qvcgIg5TD5x/0wIDPvcz2zMghKrpJLHD7LO3Qg48PLj8xXtCP33nLwhzbRd4M/70ll2bAesPxh//g0X1IPbYffDSZzqYD1hcfIY5b6xs42ejblAeeLPc0IBkDT+Xz+1mbxDiZnG7Ks0iXWj1Vv77fpT9fzTRTbA/Nh1ULPMMbw98bGRl1fX9fr16+XeJwyHA5rd3e3bVa+t7fXTkdjTJ4+fbo0wWO9WLWwq5YJB8YYI8aPewjuDAaLk43JLiU4zt5RtIV32NYRiHPAyu1nwoA6nVHlzEJ0+Pr6ep2fny9tJO7AG2OxsbHR8AY86bbSf4JAk8mkZS6gT8kKIrCETeF9Xrq4vb3dxsz+kXVFBrXwrRzYoN9M4M9ms5pOp3VyctJ47Orqqo6Pj1tQyw4xTu50Om3XyI5CPhmfq6urOjg4aM4tfMjJjX/wB39Qv/zLv9w2559MJkv8wERPOrsOlH8bi/UWeNeBJ/gAWg4Gy1naeRIrfGZ+6elHy59tuzFk2uOqt+31ff7JKqzjuijpk2absh3+nvX4Pba3ieXTTkIn7jc2tm3N9jue4euuO+lkLNSz1aZB9ifrNp4xLZypDLYYDBYHT9Af8OxsNqvDw8P6zne+U9///vebDjM9vOeZ+cr6Hp3lTC3qT9/ifZQvtKeUZwi45tMYMrXTwMYOVtXbmUceEIJQBqwpkAitGT3v8f8pdCm8Hpjsgw1nKiHXl+9JwGswa4Y1gztlmjb40+2ykeH5FKikkR1iz1h5bPNdqZzsCKTC8bj6d8YynU0/S9tybE0/BwYoOWbQ0GDAffGa/Nwc03zjWY6kY/JX8lzek2OZz/XG6ttSrPyrlnnLgUFo/ubNmwZax+Nxc+qcQs+4r62tLW1s7vT6lH3zb8pd8pFlFX2CjsrnrIPM48nLljEHcG2svdQ46UZZpceow3TPZ1xSjlN/JQ3u05Wup0e/1KFppLO+HsBwm7PfOc75zqyP/lIcDM3gymw2axsMn5yc1Gg0qoODg/rkk0/eWpL2vku+KwG725DZf3awcBINJJhVZTlf2jaeMX/bntim+P3ZPsuObXWOlYM31uGME/reY2abmUHQXtvMR8ljPIeDiV2wLXQwyviEenI5m2WaDIuqauMCPXA8evbQ/+f4z+fzt/AB9XmZNM4qmMw2PrNBLUfuQ+Iy05x73X/zBwGRqqr9/f1mj9kQ20dX0w/zak/n3MdnSaOerPawVO/7hyjpNHni9c2bN0tZe/DkZ599VhcXFy2oQ9nc3Ky9vb22STV7SZEdhT57/PhxOw3s5uamBUQIjhD8YFmIM3SgETxENg38hX02bncAluASsuNT/MjSOTk5eWtCFdrs7u7W1dVVC3IwiQWdwBD+rFpMPAyHw7b0//b2tgUGyCx1ljd8OxgM2hK4q6urOjs7axlHbiPOHHJYtXyYircpISOKvlsOkAEH27yPE20iUw3fi0DmD37wg/r+97/flt6xdPbVq1ftXfP5vAWxmLCpWuxjReYXOBk7en19XY8fP67b27uTNdfX1+vVq1d1cXFRZ2dnbRx+5Vd+pS0PhHdZMjoej1vgEVr/LBRsBfqN7LvZbLHslIKeRs54PjEJdLdu7flhq3RXYlLbFuvB1KHWpT2MbWzs+2yn/JztC8U63xNGrgu+Th/RfU/8l3rf8ul3Q9vclN71uj22xaswZgZsEq9Y33v/tapFpmzVYmlvvscYbja72wMOvWYsd3NzU48fP257S52enjYfC5yDDM7nby+lBzuwKTrthiYfIoD8zkEpzwbSQP4nbfT8/LxGo1FVLR97ayZLwUKh8z8GgpT5zI6xUKTQ9ATbIGeVw+Nn09nyvalEU1BWtafnUPn5dGYd8PHMTs9Z9H4ZXDOYSWWzyhHN5QIe8ywZzEtl0VMmGem+T2F6VsEKnWs+st3XzZfwn2ccUhllwMRKmnsNtHpK0e1fVXpjnmPl698ESP4qpWd0DPgAu1V3fZtOp22fkadPn9bZ2VkbU8Zte3u7Hj9+XNvb283BQWHzTvMTvGpe6Bl1P+NrnqW0Mes5zbx/FS1SD1GfZbpHv54zZXlJOc/SAwCuw45fr/Te3ZPvlG07sPTvPh7v6eFs46q2uO89oON6TffBYNBmJfk9AxBV1bJiWI/venuZIe+jrDL8bmfezzixDI1lU8gidtaBKejnAD52zmCRT8/+MR69mT0DsB4A9j0OKGXwN3kggauf85hyvQdcHVyjvYA5nxBsAO4gO/V4uwFsVs9WXV5etiACDuDZ2VmzKfAfOsiTLlzjPvrOJ86NxwK8dHl52faQcDDftMoxph12aEwv+ATau038EXin3Ts7OzWb3Z0GNplMmu4/ODhYwn7UaRBsvk9+yO89zJa/+dne9Q9dTN+1tbW2rAuHg3sePXpUr1+/rhcvXrzlnD569KgeP37c9pDa2dlpAZtHjx7VwcFBHR4ethO+eGY+n7cgj/d28uE5uRrCNtXt8FJD+Mk21Jmp8/m86WF4F3zAsjyCHuCH0WhUW1tb7XRAZ60iY2zM7cml09PT2t3draqq8/PzFmAyLQaDReDJuo72EoSh/9Dk6uqqxuNxk5vRaNSus48VY2oaIbebm5tLGQ45kZUY2+8n+wmabWxs1MuXL+uP//iP64c//GGjF7o+M+J4F/WDzc7Pz+vs7Ky1gWwz94XMqMPDw9rc3KxPPvmkptNpra2t1aefflrPnz9fynhjTJB1248MCHxbi8cIO2B/MXGXbbXHAlnwPmM8Yx/ENE191/OvuJ6YLHEa96HH7Zf1/C2X+zBtD7fZ5tC3tNe9d7uPiT/9vvS9LdfpK9ruJubNCS/bS7/DY5MBqsQ92FHiIwS9/Q42wLeepc2mHfWBb9HnT548qdPT05bVORgMmn71VgTQGJzoDF1iMT4w56cqKGUAmQ27ublpm6p6UC1sFqKqZaCBMiYNGEF1RD0ZsOrtZQHcyzWDyBRcM286VLQPsGpgZkF1PyirAJSZFsawonC7/B1hoQ4UVr7XAR33w+2GDk6HZpzMqFZadkjcD9pnR8bOe08ZAVwsDKZR9j/vQXgygNhTABai7De039raahs+ml6pJF16Y+Q+Zll13c+Zt5NmX6UgV+9zRioV1Xx+NxNHOjz8CgCezZZPiWEpAXLP+O7t7dWTJ08a8Kt6OxsPGtmRcyDK/1fVWwrezlRmchqAWm9ZLnp8YF3T01fWDT1nqccXPWOZ9a5yxFI3IQdpbH2P2+rv/J406bXB9Mn/KQYJvbYnbareBinZxuyPwR4gMAGiZ6YciIRW7DVl3nufpccD7ov7j4ND/2inl7yhz7yhqp0612vnxXbA7UJGM3jRA7jJhz05cr8zmJUp5rzfz9i22iGwrSDo5oAUTjH1WIdkfz3ZAY+gv+g/uMVOOY4n4+JTxRzAYbY0x5hlWxlQ8jgnLpvP523JiHUV+CHtzCqZNDA3kPYY8ZvH0rO9g8GgRqNR7ezstH2IvK+F9+Mx1unpi56u6unQbH9P/06n0/rN3/zN+kf/6B8tZfh/iGJ+hk8IWm5ubtZ0Om0BB7JdPvvss5pMJi3zhgwMMqSg8Xg8rvX19RqPxy1Q5cwf9AGTQKa1M2RSri1LXnLEcrX5fL60TxKBDAKl5r+q5eDxbDZruouT7pzJzp55YFV0HbJE23GgmNA2Np1MJq19VXcZXl7S5o3UjfkJkBHspe+z2axlix0fHzf6Qg8fhMC9ztZN3OTlfugRY5CqxcEw5+fnLeuSYO+LFy/q9evX7X3OlsU+OOvU/a5aLBF8/fp16+/W1lbbh+zg4KAePXrUDqHZ3t6u0WhU0+m0Tk9PW1Dvv/7X/1rf/e53lzKwqM/yen5+vpR19m0tOY7oSAfp7ehXLSaR2Iutqpoex2Ybn1Lw0eyrpJ7z2Nq29zBF+mt5j/vnd7kfzozM9/L8Kp/GtKOvGRyiX1kXJW30Knxm25x1eUVTD+OlLXT7e/gl20yxrk87yHX0zNXVVbMHmdXsfZ7gN2cws8n5s2fPlt4DlqWvtjnozaq7YD4nZDoZKSeL3lf5wntK5V/VYl3zzs5OIwyRcjNB1fJGh8z0menTOYBRzeyrlvBUvZ1+R3sSvCQg9jVfT4eUfhMoMyCu6i+vycAQkdl0/AwE+J7BLL+LNMAeDUxTz5K6T3bAeM7v5d1OUTRNTSO/z312v7wXht/HPT0lmU6SAwgGxj0FgTA6vRGgYueC77zLwpjtzO9J+88rPdC86p6vWj5UerSVlZ0XFJ1nX61IGZunT5+22Ugctf39/XZ0MeNx33ur3nZ4zYMZzHBAytf8v8G0yyqHh+dybC33PV5NQ+7n8p0ZkPW7fZ/1Xs9Yul33AYDUk3a4s61Zd29setfuu96T79Qrvd/4H31h+l9dXS3ZHQfo5/N529cM5wrw7eVY76sk6Mn+uFhvIUvoWNPE9sIZGKt4pOrt5d8Z3EhedckAL+/LYLJBLMVyBD1yjN2m1NOelPLyMHgAJwn7CxCzHPjTGbV8h1bUj64geJk2y5M/LA8ySHbgLR1q218cR8s4wSwvlYJuZEnY0UndmO01fkpdYsxF37jHpwKxRIn6t7a26tmzZ82hZfxx4NNRSD5fZX/zM3WYxxWa8P/m5mb9z//5P+uf/JN/UrPZrH73d3/3LZ59X8W8htMwHA5b9kyuMHj58mX94Ac/WAr4sDcUy/W2trbasrD9/f06ODho9fE+loo5aGSethN9eXm5lPECT5A542fgf2hsfqtaZHkMh8OljABvbs4z3ryXZSfmud3d3ZpOp0uBDk8YmHeYhGA/pul02jb+RhcQ4Lu4uGj9MV6xXbBuop0Euwms4eARIBoMBm3/LmiR+tFLaPh09hqBWwLN2K719fW6vLysyWRSZ2dnrS1V1eTRS3bQBbbttNt2nWuTyaTVQab69vZ2C1jd3Ny0jIyXL1+2ycbNzc16/vx5Vd2dBut6Dw4O6uTkZCmY/nXh3W+qWGextPLy8rLZA3Qh2ctgEQrjbFmwzrL9YRz87qqFjHHNdr3nv/bwY9pY+4z+PfXsqomMvGabnnjXdj6xu+nbswUZ2Or5/sbNpkEG73wf+jez1tyH5N2e/+pJI5Z35t5O9B+96Ykgyw91eEKHIL39/Y2NjTo8PKzT09N2D3iA/ptu1MWKN9pJsLlnl99X+UKZUlaaNlQAGzc+76HTLOth0LzhJwSHSCnAvWBN1SKTKoGlS09QzfS8x9FU35dAMwV71YD1wC4GxfsCrALFCEo6kFwDXFBvRlUtzE5tpgB0oTW05BmMVc6wpBGrenvPLdPHzpDpkLRMBzOVV4L9HA9/wnP0m08HP2wAcjlfOlE9XsrrvfH/PGHOtn9d5R/+w39Y5+fn9S//5b/82ursFQeichlc1cL5PD8/r/39/aXlk7PZ3b5ST548acsT2CPDaeiuh/r5TMOSJZ1oZ4+k8bXzSUlj/Xk8ayOc715leHvG1rLPNYN9t8nv781MehbIRttGmGtZt9u0ymFcRR+/o2p5GXjq4ftkpXef25z6OenJmGP4nanqjBkcDW+sX7W8JO19lgR6XOsFFc37BDssB2dnZ21mDBqYnxIs5aRMylSCxBwv32f9SUEnWzc4SGFZsR1y3Sn3ORlCm7zU0mDPDrPbkzO+vm4eGQwGS7yTY8F4+HQl/+asO9qG3UbP2engtwwc+DvPuH9kKhF085hYt6XNTj4zj3gix0tvrE9Y8uP2QxPa5SCGadGj47uW+3BYT8cOh8M6Pj6u3//933/nd3xdJbEP+oYTMm9ubur09LQFP46Pj1umz83NTe3u7tbP/dzPNT1PNtRoNKpnz57VeDxeCvCwybQ3/DZONE5iySf8Alb3fo9eRo8ceKm+g0zGlfTTDt3e3l7t7u4uZTFWVTs1lOWnthfePPvk5KS2trZaVjZL/Gir23x6etr6y/3O2K5ayJv3G4UWBLN49+XlZdsvyUEtcItPEEc2weHGzg4yEWTGjg8Gd9tVkBll7MBSuuvr65pMJks2ALni9DzjMp8m6CXda2trS6cWPn/+vC39IXOD56+vr+vRo0c1Go3qO9/5Tl1cXLSg84sXL+rJkyeNpz1eBwcH9ebNm6qqtkzVtu7bWCzH9nO5xnLaxGPwOM/Y1jpQkfbRuhneTCxFsa+VWNQY1JjKujRtOHW62DbQ7h4O5NkMHvMu2zNo4P773bZXrj+xOnanapEQ4yxRvz9pkX3FdrpvxuPm4x62T7uf453ByNvb25bx6AMfXNdgMGg2wxmxTFjwHV2Bv+8JNHQA/Ou2wVuO77zv8s5BqYxsQzRm6VySGdwRzxg8evSozZYgtCjKqoXQwax28uygVi0vu+CdGbWkpBK0sGf6INdpi99vkEi/3f+eYPKZWRiuP9tr5vCSpHRuobPTlhPMp8NsIAGDOjrre3jWgTvqcj/9nl5GWTqlvch50sCOJ3+99/JOOxekYJtGHh/6b3onvxlI9wBwttWfvv8+R65nVL5K+dVf/dV28tHXXbcLQec8VtW/p8KDz5D9vb29Oj09rfl8XkdHR7W/v99mgGm/+aVqOSMj+cLfnZFg5W+AaaWbcmtjYT7JgI77a6DAPb1gDPKVoCIBhN9j/dfTBVxLA+P7VhnfNMI8k/TvgYBsa9bvdycoyD6m7rcjlzLj/1O2fA/0Sp3p7CI+nWHAeFKndf77KEmz7E/eS5twINH9Z2dnzZbiGABKqAuHKPljlV7OIAvtSptngGq69wCObVrV26dTJr8xRs6QsF02L1CHQT+85CANAJP7wSXGNuADB33QYQa7xj0subDeQv85I4o+ebbck33mVy/Bsw4yrnDQwBsn98YwZdolsU/KpMfIdMy2bm1ttf2OGA9oRMDC8pZ9tl5Lue/pqcRc9MXy9U0V6yPavrW11bJQ1tbuTpcbjUY1mUzaKaDz+d0k5JMnT5rTT/bFaDSqJ0+e1P7+flVVC0QR2ILuXLcjDK+xnI22ObMPHuaEOmbQ4VVPslJn1YLX1tbW2lIxApcEkwigkQ3EZuxVtbSPFO1iGRmBNJYtbm5uNjxBMBbcNpvd7U11eXlZ+/v7rZ6zs7OWnXZ7e9vaZj8AeeV52wD6nriy6m6D/52dnZYdBj3RGRQ2/ycgSECMDCoye6Gjbdj19XVb4ulAOG3hZD02v2c5EEvn2GfGQfnb29va29uro6OjRlNsIrLNPlJspL+1tdWw5uXlZb18+bKePXtWGxsbLQBINil4yzrg216s/9CX6DGCoFXLG+BXLU6d9NI8ywx1cy/f7ZfY7lmn89112g4Zh/VwZ+JXP5PfKYmb77s/MbHtfvpQ/N/T9f7ddsN6lnp7mY9pV3p2huL3J273bzwLBvIqIcbfWI176a91EPeTJefTG9EX19fXdXx83PgI+7+1tdWWEjt5xRMQtJktVEwDB1pXYfavu3yh5XsJYCB21dvMA2AzIIUwFo7Nzc2ltd1Z7LT5eX5bBZD9f4ITt5P7es5mRmG5xjPuazpgq5yWBOJJv3SYXL+FzXv1OKKeguf2ZIDJ+474WffHysF0MO1T+VnJpIJIh6s3Lv7dNE6QagOQ9PV3UpfhIQTS4wgd8r3c02tjjn++P/v9ef10e76O8o//8T/u8vD7KOYDf7fyRfGxTwSn7VRVC0ytr6/X8+fP20k5acR7DgvFYC1lP2c/uR89lfzs7IQsdpLMIxQbALfDS4uox+1YVXrymM8l39pRyOcNblfJmNuZz6JHUse5D37WMpq6tseXSTfLeQKN1M0GB86W8XO+p2oRZM8ZPmi4CiR9iALfrNIdnrmrWj7lFMcOEMIz1vOUpK2zquwUQhfbmdRxXi7uMYDGtjE5yeKx7/FXjmfaupQp656qWlpyB6CjHtvRnm6oWpZtvwdbnPbddp664TOC+MYAdjqph/aZhjjCHkOc6gTbBM0yGO2AGOPZy1xKx8fF9KV+lg4RkCIwsLW11Q6vWKXTsNd2vLL0cNx92K53rff7hy7z+SK7AnzCsh/2k3r58mULAN3e3tbh4WEdHR21mfDd3d02wbuzs7O08gBHBts3ny9OhHR2DuOeQVPvtWc+tn5wyckf211m51mSRyYqzrr5eWNjY2krEO8FRWbS0dFRnZ6eLi1JnM3usrEfPXpU+/v7dXx83PjJcoluIoBF35B1+I5xYGnc2trdkkKyDuiL5Rr6epkVQRnr04uLizYGe3t7tba21rKh6Iud2O3t7XY/16CLs9S9l1vq752dnVpbW2uBNSYxCFozsUjgimAn160r4M/RaFSvX79uG9FPJpOaz+f18uXLOjo6qo2NjTo4OGiBTO+hZ7z4bS7GJw5E5eQ4Y1u1sEO2j9Rlm26sm9iWYluV9riHh43JrLsz4JN9dJ2817/52cSmidvS5q+iK2017uy9izrTdnlMeu3Nvvi5pAfXjOONozxx6X65ffYLepgt/Vp4hyxG+MaTDsg0WZ8E3EejUTvE4eDgoGVv8hx8gU41vVm6Bx+bl993+UJBKQYBxnCE104DgkX6sAejavlUJBQmipri+tL4JdBYxWRmqp4Tm4LItXSk3N5U+CkEtH1VcVaGmRUDlg6zaZ5RWv6H5ul4+Ttj4KUqjGHPCWAMbah7zqgBayocA0/qt1H1GPSczHR28npGpt32qkX6p8eZMfBR2YxnOm45K/VFypcR3q9b4D/99NP3Um+v5DsMoExDO7hVC/67vb2t0WhUjx8/bvtksP4ZsJV87f97Bjvly3ycwdPMkEyeS2PcM9zuq+XX7/L7ud8zGEnTVcY7QYbvs55yMC/7YJk3QMl+94x5DxT5np68UlLnZp/9jA20r9uQ57tztifrhS7r6+ttLzOPKXaNWSbvFYIRh7+/btla1ebee3wvzi1LSuxYOaUeZ/P8/Lw5J7PZrGVAOBBgnoWPbPMtx/mcQUwGOrjHz9iG5BgzXoD9nJgyYPa9lmX+yESqqpbCjm4AbNpGU/jfgV7Xa6cDIMi4GEd4XM2nvNPLGz2+znZKjNPTd6kbk6cYF08SOgDWwwSuy/LHWHsrgezn7e1tjcfjevr0acMhtLWnK3p6KMe7d38PG/g96Sys0uPvsyR+q6oWoHTA8uzsrD799NOGw7a3t+vw8LAtiXr06FELUJENzrP+P4NEjAefBMVYSkRWJfzVm2By1rG3ociALQEz9iWCj70EbDAYLOGx2exuI+2Li4t2iiCBLdp0cHBQo9Gorq6ulk6HQj8ljueaA1yeoET+ccjAH9Z3vJuMNmh8cXGxFMSjzxTsBxlgXhnCnlBexpX6grZWVdMrw+GwptNpTafTZquQZ97N0j32mIHGxhwO0jlrbTC4WzZ4enrant/Z2anT09NmC/f39+vly5c1n8/b8tHz8/OlTDqCp3znfdPp9IM6ullW2dQvWyzPtgXQ0wcCpG9mW5V1YidzMtWfxkjU0/Nt87p1pZ9LTGkf0OOVOMv4kjahr11H6mBKD89yPZ/xpEXStKfTuZd2OO5gfzH9AOs3j1FOmqTNy/ZY3pB198m6lfqRZzIrsZm9MRyPx23Z92w2a6tOdnd3a2Njo5046gA9manId1W1vaR4p3V7+iDvq3zh0/c82+kGW7iswJmRqKolAMSgXV9ft9kSAHXOWFb1nU6KDWHV25FZA17/nsJdVSsZZZWTw/+f5zgY5GVACVplcCaBt50EHx3MtV7b3VZH5bM+ZsAMVrnPoHMV3Qxo3fakvQ2tf/s8B3XV+KI4cnyraqk/HAVs3sq6TQ8KDlkGJHJ8e2O+SoB7YPh9AONcVvu+imlftVCoALB0dtEd0IF7Dw4O6nvf+15z7NIwVr2dPuvZXN5tp5VnLPM9Y+b22zmwoe3JVMr3Kvmwbkz+Q+7T6Ps9bluPd61Xec6OhdvQa7/r731PZ9B08vdsk+V+FRhJ/Wk6rKJH2oesz7Nr+R6Pp/cDMW+srd2dbMW+TGywyxHfOEN27r4Og+06ekFKF08OYQ/QV967h81uHfxELh1suk/HZdo7/JoBrwQu0DWXw6Rcm/49gAcfkNlh2c9sMcBfgkjuNfjEgaftZBX1eNmzkKtO60KXQW/be97rvuFwZPaYv/t6Bq7I9rJcJ6im/z2ngOs5Ph6TtJk5HtDG//ewGrO3uXwpJ7YyCJ1YoocBTdNVOi510TdVkpbYu6paGv/T09N69epVra/fHfzx9OnTFpQiQPX06dN69OhRW/7VkyHGwyfH2SED3wyHwyW762w/6qqq5sBkoNb9Iojh4JUzeeCd7e3tOj8/b9gMPvKSlqpqBwBQWNY2n8+XAj4sDxsOh7W7u7ukty4vL9vphGtra23CgX0u4S2fVle1wLY8Bw4m+Ew7mNTkXu5hmxICRsgtgSCW5PAbwST6TyDP9EjHkSwuZ9xR59bWVg0Gg5bF5MAV4+IsjPl83k7HOzw8bHtBPXr0qB4/flyDwV0Q8fj4uDm1OMXX19f15s2bms0W2WD4dATvxuNxvXz5comnfhYKfGZMu7m5WZPJpB0Exhien583mbe9oDig42uZANLTs+lj0TYHiO7DfX7e3xMrJ/Za5RP3cJjvSb2ePqjfY1vqtqe9sx9sG2a8kbYEmmcbexgydSLPJ60Ty9AmB6bcR3wjj3EmsgwGi30AoS11eq8oVlGRdWq5t2+IzUZXWmf6/w8lq+8clErA4KCUG29wno5D1QIQsIQnHRgTwIxjZ67HCKtASjqfq0qCulXXEnD33p2g3s/BHAalfobfzZR2DDF8Zuj5fJHix2+ul/r4nu1DCHxPRt7teGVkvWo5dTFn1jyOWTD+qVh7ApDXrBxy9trAFuPNhpqmg9+JgPdm33ttyfYmeM62Jl/dV/+3rVgvGPj6GsqU081wBgE2OC1bW1tLsmD+7hm3DDrlnwOLGfxNx79qdSov77PxWiXLPZ3gwLoNvJ2/fM51r9IpvfZbFty2nnHpgYt8Lu/PelJvpp7M7ymrltf8TLnqyZHH1MDGM3RViyyQNNLsm0FdPIfjxOy5Z9c5pSRTnL9K6dmZ3u9ZPLFAWwaDuyPDnzx50u6zg+WTsKqW99zymJneDi4zjj6VsAdQuW77bZmyjeE5O762f/CKQWEC77Q35jcAHn3BeXRgiWwqZJt9bmhvBsZx5HGoqd+OZE7imV8BiLTBe5yZpp6Bp37qtl1LufSsrvUNv2WdicGcXZN1w2veLJWgJ7RNevR4oTcxle3w9VU6dtXv9+nKb6JACzJuyF7Egbi6umr7Jq2trbWg1Hg8ru3t7RqNRjUajZo+2tnZaRmc1m22X2tra20fEfif4K4nRoyroGM6QPAzAZqqxcQxJwGOx+PWHhxwTxhxMi8O1dXVVU2n07q6umoZN/R/Z2enZehYxtHLTGzv7e01mSQjD97d29urqmpZVyxjIXhg+81eSPASOsEbtXPaHEFFZ3mZDtDOgS8Ht1hVQkbYmzdvlsYxHXSyGQgcoRfAT9xHBgQZFNgH2k0gcHNzs7a2tpYOc/CEB3qT3zjBEL4iYAhPnJ2d1fX1db1+/boF/ZE/lvkRQF0ll++7fN3vNY6B36reTkZILGlsSx1VyxvuO8nDJfGOdWtPN/LuVTjVOCCfcz+NpVxP8k6W9IHcJt5rHZ62n3t7k6u2E67Pz/TwddqiHt7vBZhW2Q3TNOlJO1hBZt1EyeAzOhmMgc5Fv6OX5vN5W2p7fn6+tI8gewgSsCL70xNkOS7YcAfIPpSsfqGglGeFDeKrqkvYZA7uRyEzOBhmByicVsrgeiaiR8QekEsGNqi2g9pzBsyoFjbPEPn5BEWrBhLa9JwrR4D5zbO8Bpf87oCdZ179LiuiHNcUjKSt/9w//npKqhdsSpBoA5106kWicyxy7HPz+HR87HjY8TSfQQ8v98q+J/2+ajG9vykj/VVLOhUYAaeke5aP1HyAEFF+r5UGlFYtz6ZXvW1Qe7MZgCkv0bROop4MbPecGvOcA5c2hD1DaR5yXfze0xE93eFr+R7LgGWtV29+d5/SOLncp8uyrDLeed1tNXDo1b9KD+RvCdTQbfBYry5moh1YPTs7q9lssTb/6uqqnjx5Uq9evaqtra06Pj5utMOhGg6HbXnF11E+bwzhZc+A+jOXi1TVW7bbtMeBSD6HrqZ5Ty/mPT3A52ezX2mrbVsM2G0bLPe+7gmRfAf0yHY4EGWbhGMInZn9z/Hg/Xb2ybza2tpqe8ZgnzOQxHJlgleMSQZtU16xbTgy3M89nrAyPSncC+aCh9w299XLbgy4zY+z2axNPtgO4MBOJpMl0Ov6ezzf05+94vaucs7yXffV974KdIFPNjY2WiAGrHd2dtbayqbgZG0ywYZ+IwBCxhSZF8aGBNHhb/gNDGaHDB7L7GAHRzhxr6paFggZN2tray2ITx+N4/mfSarJZNIySqqqLRODZ168eFGDwaDJHe22HLOB+fX1ddvrqKpaVh7ZVQTLbm9v6+TkpAXvWMZIkMmZLASk2GeFU+oYA5bPUSf4kfZBs6qq8/PzqqqlJdM4juvr67W3t9faRxYvS+/m83k7Uh7aWXczPjs7OzUajeri4qKOj49bEIgAEQFBcBbL9HB+wdOTyWQpc9PtffLkyRI95vO7DdD39vbq9evXdXp62vbdsl0kO5n+/CwVZI3xIQvK++whR146mva0ajl7OK9bJnMyiXuyXdg/7AS63pOsq9qQQRljsFX4q+rtydCs37+5nXmPl9A52MQ7eQ7blBg7/086pR+Q2WSJ9bOfq+7lfW6zYxy25ZmVhL5z3dhS5JxxhD5bW1s1Ho+XJu48kYB8067eMnowordg+ZDlCy3fM8Bw+jVC6MHnGQhi55Q6qqptgFj19vIyz1b3HEYDCq6lkFjYeg5XEj2VZA/EVS0D4IzAWiCzfs8UuU3cazqa2Xp987usXJxRYjCb7TEA4V47/72Is8eWOlCyOS4eQ9MRgbTDuMrB8ftWOTw2tgbKVurMEHn2yM6JHQXTj7Y6uGW65zj2xua+ksrsmwDIX7UwzuYt0pcdWLITNplMWlo5p+c8efKk7T1BJofXUafj2wsE+boDkNTRCyL3+uN6DRh68pcyQ/0pEz0nOevlu+UiZ97Miz2Hn/FY9W7rq5Sn1LP3jbmfv68YDPhdFDvYSd+87v97QCpBANcc0DBNCBi4bVWLzaHJ4GMmmyABpxahozMw/2VLj5/pjz/zGu9HxtB1u7u7zSbQb55jFr9qsWcR363fUy8bTPV0t8cwQRZtcNYA9diB7tEi/7fMISfO4kj+t80mEwK7wF4zduaxxwSXyGioqmYryLJIEMtSqaoFL+Gc2J7gmJpXfboO7wLMJv8mnWmjJ7KsR4wBMpDvCYBV2VHQnb7SF2+UatvvPkEz7yPI/7w3x87t69neLMkflN6kxTdta+fzxR5nGxsbLTOKyZo3b95UVTXdMxzeLUl7+vRpW8KX+0ehr5w5XrXYCw5esg1AV4J97DhzLwGs+Xy+tF8Q+yWxHxFBR+RpPr87+c3vQQ4vLi7q/Py8ptNp+45usL0jMEMmkQNx8Bl8Dr6jnbyfoAh9c5YBGQa0fzgctuxt3kfm1WCwCAATgHM/fHreYDBYWppF+2mfxwb6DAaDFhAjkLS1tVXT6bSdsGf6EPxj/L0cmaASG4yjKxmzquXlQtCZvkJbaMk7uba/v1/D4bD+5E/+pGWe7e7utmwNgnyXl5fteHrG//T09KdCBr+u4vGEPwkk4mtwD3ru+vq6ptNpsx/GrVl3TtKnbkssnBivNwHDvfm8JzRsKxKzYVN6eppPr67Je1x637nP/mf6r71sLePD7OcqfJN09eoi+xyJs3Oc8jMnk4x5KPZHXL9/o032KbzctmoRS1lbW2sTHKPRaGmyEQxL0Jw6qxarCKAzPIrt+FABqncOSnlpgNP+IYiX6FQtM4mBhgcSYOrOpoOSAuWBvc8xSkbINvk+g7ZkjnQy3J50bpPhLLA5s2kwDe28HJLPzIKiToTG7zIN3TaAOrTuOec9A5GBJzvfPaDPMw4CQKccXyu7BLKpXExbjwn94DkroQximW4GXHz6fVZ2HnfzU0/R5Gev7S49sJ/Xvi2FGRgUGUE+eJrA9GAwqL29vfrBD35Q0+m09vf3G+jm9CDqow42mKbYiOZsrmXLM4fmk57R5Vk+/b40FgbXNi7cR92py3ozU/l86hAb2Cw9XunpRPMsbTdPmpaedaO+7Ivr9Hv9nlXy8nnts7ya3j0dwnP+3RkyXOce7I2XnOAsDIfD7imwONNkIeBsASR51/r6ejsm+6uWpCs0gm+QKfM0+uri4qLG43HN5/PmKJrnMjuF8c7ZRetg20c7XB7TVZMgCSR7Nse2L21r8mnPlmf7vQeDs6t9yAd0zCxt34tN8R4vZEmZz/yZGVoEkmg3Og164TgbbKIreZ42Mtb5DvrK8+hL6yPzEP87kGZb7vrsnCbgJ8hk3jRP3dzctOVN3juHrAGOs5/P582JTltMsT7x+Fs/mXfz/175Jm2t7UTV3Qz3mzdvaj6ft6DSfD5vkzVkEHN6ITzoTBwHCDmNiYkhZJExdnCkaiHDPo0OOaK9FIJR2FeWjFEP+hQ+RH+yIfvZ2VkNBoOWLUMgy84RQRPklzZxKhSbZxOUoRweHrbMVfrn79AEGluX2bbDq8PhsMbj8ZJs0l72TPJSbtsk29zr6+uWKTmbzVpQPG2uZdWZbPP5vC3bq1oEOmgT+MlLiV+9etUyw7y0M301aMDG5tfX143HnKE1nU4bb6Evdnd36/nz5+1EQrKKb25u6uXLl0syje7a3t5uG6T/rBTT1XjEE0K+N22gg1JVy4kNtg2uuzdRn8Entw1e4Vr6ffb3jJGr6q32pW3mGu1I7JD3rKJhYm33O+0tbeT/nBii3da1zkaCtu5b4sykc95jv9c6wrjT2cv5nMfW40N/PCYOIHmiy/RxQMn6h4kO0wEciLyip8DKPZ/6Q5R3DkqtrS2vz04wVrUsFMyWmBEAf1ba3Eshug6B73OUYKhkADNhOkjJtL0giQ1KDxQ7KJLCZ7Bn4fT73A7e4QguNPBSCoC7gyimSc8Zc3sNmqEZdXs2NAM91OlxcF8ofkcCQ2hPsWAnqDT9cwxT8AyYbNQzem4e9ukrpqP5Bj41zcwnOX4pCznmvu7SMx5Ox7wPUP+0FQelLEtVi4A2Yzcej9vMMMsSiODTZxtRL7vidxvXNPI9WtsRrHo7EJLAoRdsynvNI6kLLNOpI1e10f3hXtqSAew01DbSaUh69+f/BsOW91V8m+9NMJXOjMepJ1PW9w4OWJf3Jg16Ot36z/0jYIqjQl04aCcnJ0uBLByZ8/PzGg6HLSPg7OystZ9Z58lk0ur5qqXXH65n8Mh8Zt67ublpzggO7HA4bFkOnjnvgSLzLPVaPlNn217we37PcaNvtjEulinbp6rlpXLm57RR+X6CIdbj1ImzSXYDGbUOfKOb+U4WlfdNcZaRA1I8a11kG+eJO7KwoJX30jH4tj5Mm+2JAOMslgRkMCszpSyrpi1tNiimLdb10JLArjNNbm/vlgxNJpO39OMq3Zhj2rOfPX2Qdef1b6rYgSDoRHAOWo1Go9rd3V3iVetCgiQ++RD5Rsc5eJG4lLG2zTYNM9jn5V3oFZ4ny4o2oi8IXhG48VIuywqOE8v4WUKCHPIOB5Tn83nbrHw0GjU+Jjh/dnbW9lMhexQnbHNzs7Wpqlr2lVdxbGxstABYVbVNugkQYSecdYVseW9M9A79BweNRqMmH8gjY4njWVVLkwHYHOwlS3bYY+z8/LwFl8bjcasLvcK+WOgsxps9pnwSIlnE2A7oQMBqNpvV3t5effTRR/XZZ5+1+6qqjo+P255X6C8mGXtJCt/2krbPY4k82B5ULWMVnvOEOfWlH1e1jNV6k2G+D7l2oCl9O9eJHvF96dv13pfY0M/Z1vQmwTLo5f+zP6t0PL/n//yOLvE4GSPa7zTtsg38ZtzqMXU7e7ye/bQ9zne7bgfv0X98X1u72zOQ0/bQx7wH39Z7CqK/vZXS+fl5y3R0gOtDlXcOSrGXBgrrvmKgB5hl/XkCwapFlNGng1QtTrugnqw76+uBlXReDGATBBv88t1BmqzXoJlrNkI9x8mBMwJQVcsbJucpd0knDG9GebMPydwYzuFw2IC325dC6YCY/0/Q3xPYdCIcREoaUX8CIrctFXn2zwKcCtHAyIDO+xFg9B1wMMjKcp9B7SnUVc+lsgeIsC7421LMB+nkAGagJ+C7apFyCqBKoE6qKaAqdUsGCauWgzg9Oc/A86ox8Xv82QPubrflwbrDziKgMA10z1jRzuzLfYYuZdf3JF/ZOPfkL8c39WxPL9jZ5v5VwZUMNiUISFqkToKmpn3SlP9zOTRtpaCHtre36+TkpNWNfsjMBGhXVW32/auU3vgkrSnWbdAER8T8Rb8IxhgkZt2eWYQ2fgf/p+wk8HT7VunJnixX1VL9yd+JA6wXaJsD414Oj87BmcYWUx/LZeg39pLfccTsdADq+MNZtg50sBicYx7mfusRB8eghwEp9xvwWucy5s4aY0ycIVK1mK3Nsc8AH/JFZl4GQvzsxcVFaws0IXOPgwPcJk/KpZ67zxnq6apV/NKzyb06P1Sx/EEDZ9/u7e3VkydPanNzs548eVJHR0fNCYGWdkYJ5rCMzBt5W554zhiNieAcE+tib/ZvnnNwiH6Zn+FlMoWQPQe8nBEFT5v/WCY7nU6X9nPjFNTZbNb2arq+vq69vb2az+et79RNXwmObG1tLW3nQP+n0+mSQ0fWF8EoZMiBIh/OQPaWg9tkDaIHbm5u2sQcgUXGA/mhj55YOT8/r9lsVuPxuO0dRebu+fn5UuZDD796EsOB6MymAZttbW3VxcVFWxpk28AS0o8//ri1Gf32p//0n66Tk5OaTqd1cHDQxhG9+lVt5U9bMS5iCbXtUwY+zJvoJ3h+1cRn731pd/2M61iFr5zNxrX77K7fn+3Ptrms6kvixtTTaUs9oZbJCUmfjB94DHyfcUr2q4dt/Rwlg2698Uk83qNBYrO0y73tIvi0H4v+tp3wuwnKE4hGZzBpZBv9IX3Rdw5KkQLLrEI6KKscMv63Aid11QqsahEFtHBl8CLf6/uq3o6M2vDageG3FEDXkeDa77PT5bYNh8Ml42RQa1rZEfO7LKw+DcN98KxWT5lk5DzfXbW8t4oVFo5N0sOGyAGkFKCe05zOuBWjaeC2ZOnd2xsrv8d0dhAkM6VSaZsuCHQGKO4rPUXW+33Vb5azb1NxyqhTWOkLRhqgRDCK9H4CUNRjXsLIU1Ku+fR4+a+nO9IQeIw9RiknlgWeW+XoJ4Dnz4B3lcGzkes5UvfJiduTBjjf2eNX6xjTumfM72tL1p9GeZXzmc9k37OdbqMNdi845SASDsXl5WUD3zjOnkk2Xw4Gg3YUuIESjsbXVUy/5H3TyHrf9EW2CLawbAaHgMAvdZpW/s20x3mB1oyBgyG5LMz63pMyPGNb5b4kGONe8zV0sGPpACvP0Eb3P98Nne14W37ItKCPOKtkX/SwijOTzf/wDMARx57fmZxIG2aaeVmhsY3liPdanqG9ZYbvfqexRdIeenl8CVbN5/Ol4BxZEh5/7redZozsaKQ89OzmKpxmXuo9TyCjN+n0vks6LfCV90w6PDysx48f13g8rsPDwyW7SWCGoAeBVuqrqrZUl2wZAiueWbe+5P1pDzIgnwFv29+qZfknM5NT8jj9jwxOZ/8zS0+ADExg2bDu3dvbq8Fg0PZPIdMR/kmdfnl52QJrDkhDL/aHstPLoSys/Hj16lXTHbSLyXr33zLPUhnTxcHo09PTdpodvxNgs1x46fVgcJfZROYavxEEwQewnfBkCm3nGvrLkzsXFxdt4/L9/f0mv9hL8BsBq+9+97t1dnZWOzs7dXt7W8+ePasf//jHdXJyUt/5znca7zFBmXbj215scxgL/FsHp6rueG0ymVRVvTU2DlymvuL+9FWcnHCfr+Rn4S3bDO5LzJzPr8KQ+X8Gaqr6+zHZNqZvmjjbz2SQnOu8xzJpOidOX+WPU/wbtjEnbTz+3u+Je2kf9O5lTedkmSe0GGePu/Gm7T960wH+qlo6bAWd7mXWLI/2UmRP7H2I8s5BKQSr53xQ0rjnoFRVS5tFmRoAOYjj2UR/9gQzSwot7beQ+o/f/WkG5ju/p8OY76bvabSrlh13P5PLnrK9vm76pqLhz/tKUBJkJw0M9jNoBNNaINw294N7XEcqj1ScLj3Farr7u9uQANlKAP7FqJLS7HfyrDOmXG/+/2VK8ltPOWeK77ehmHeSv6qWT5IaDAZ1dHS0NGPvQG5VLckLYNyOZU++Uep5ylo6sBgCG5Z07ih5LYPA1mHmeRfLHX1Iw8t9PV7LmS2XVbNn7jd1uz+rrvWc0bzXbXO7rfNWOZBZMmjmul0/fNFbvsgz1sluR9IBJ2EwGLQlFmtrd8eOHx8fV9Vi6QZpzJ7VZHb++Pi4ZSzwzFd1cG3fbDd7JXliNpvVZDKpo6OjJlM+KMD3OhhjJ9WyYZ6385p1Zcmgg4Ggx9K2xKAxdYB50v87QOYMOPSA25kz10we8T8TZGz2iw5ODHB+ft6CQ9CKvVbgI5x2y7z3dIFGvIdZShxz+ol+JJBfVS3AaKc3x8o6I/HScDhsS3JcP33pyT/fndHl6x5b6Oax9TIk+JKsC5xsj2/P4cn/P88G93RKtms4XCy3+iYKfbXzgKOyt7fXcMju7m4dHBy0DCn4meAUG3UTAOITWucyOzszGYTij/EiMARvO4s/9YQzLrBzyDYyybIzrlfd8dF4PF4K7MDjjONoNGobohPknE6n7XS32exuo26yw87OzpYyj2jX5eVly8rOoJQzuLy3Jfd40hx5ZrmM5dRL6xhTrrH3EjaINkyn07bszdjTKyLQIxyy4dMBeY5AGsV4yf0FV6VcMHbsgYX8HhwctDp8YvL6+nrLfHzy5Emdn5/Xp59+WsPhsJ4+fVrHx8eNLvTr5OTkreDAz0qxL+fTDa2Lc3moZaFqsULI4wVvOlHBuj4xj8ez5zdS4Jn0Bamn5weu0s3p+6aPk9iQtqXPCe0Sw6GnKOmP8rnKB3cfPemV7TROoV5nEbodiWdWPe8+W6/l8/Tbttb8QdvdbmMf6/3BYBEMRa85KYD+4L+wbC954UMHj985KOUU0gQNq5wKEx6CeT8DhNYMSek5QVXLqcFm/lVOWzpcGdToMWWvjqyv18eqtwNH7jt1J0Oa+cxAqwJfVirZfhi0F5Xu0Yx3ZzS053T26JrOvq+nwUlaWBn5nelYZ5DmPkfbxg5apKD3FAMgIpVOj8ZfBcT2eC7r5BSVd3Hq36UAat53YTyZ1UPe7HjAI8+fP2+ZJoCrqgXA6hlc84Hph2H18j+DY0pPDlPPUKjXbXE9vs/OZzpq5m3GII17yni2LfVSfppf3d80igYpyd+uE6c663e73W+3OQGMwYR/S/3QAzD5HGPhAH5PFnsBDuqm/wSWxuNxcywGg0VGlPcdQK/SXwIJOEG0FSf/qxTr6J4+TnrzDN9fvnxZz58/b/eyjOf09LRlDiCTHiN43ZlTli2uWb/27D4l7c8qnnWmmQNllJQRnoFOGcBCTzhgxXjxu3WAg0/gk9x3YTAYLGVLEERwJoL7yvIgnGJnN5hGtJEAVtpgMrAI3lQtMk+ol3qwaZbt1G0ENfI505cgxSr9OZ/Pu86ssYf3/8l+Ux/76zBjS3vIMOjZXOshX+/Z1LzXfVz1/UMX28aq5SWjT58+rZOTk6Ugx3g8ru3t7bZMi2VfxlPwKfawahHk2NzcbOOGjvRy5MwCJDizs7Pzlv43D60KOlcteMYYE4fbjhe20svpkDf4YWtrq2XZMKnNnk83Nzc1Go0a38FXLEFzJpXf50wDB05tu+gnMu92eYKjF8hHH+HYEWTimjOWhsNh2zTdWWxszk7bd3Z22t6oNzeLPXjRFQQv0I+MAUFFgl+0BdvHO+bzedtri71pLi4u2il/DpjAV+jw73znO3V1dVW7u7s1Go3ql37pl1oda2trLfjmk19/lgr0IRjs5VWTyaT29/cbj1pObb/47Nlb+Izia4mdKMmXxqq0zc9bt1sG/L/rhs8cRDN245p9K//mwIi/W387myv94sQAGXCFJuzXZ+zZw5WJq+x7pM/ocU96oZ/QG/SxRx+3hbb3AlFJm/l8vhQYp+9kJJous9ls6VRR6vCSvwxQ0uYPGTx+ZwSNM5ng2EDATpeNmpWijRLP9WbHVjllBie90nP4/BuD6tJz7rK+HkOm42MmtbBmtHkVWLLQWBCyne4LTGmhQdHZeTOtbZQxthboVHQ9xVf19ul8PZCY/YQ2fk/SvjfuSXcrDytYt4f7rfThO4Nf6GDBdDDEwOtdS8pKj7dybJGN8/Pzltr7dRTL7vsqOMA2cNCNIBV7XsBv4/G4ReadVQGA8xgYVJkvzJOeYXSwl3szi4/Ce1yfZSl/v8+gmM6rAu1+ZlXAy+32Z76vd60nSzbuaRz9bjuYSSvTJftvo5zy52upy12v68p+J5hynT1aug2pk3zfxcXF0kbgo9GoTk9P2x4oOIk485PJpNEDJw++wjn4qqXX/3T2qpYnWdBfBNtwBHDmfvSjH9VwuDhdC3p5RrJqeYkWE0imb04oQFdPMBgLWIYN2lJO0NM9wJbA2DRKOzsYDJrzx/1ess19LOPB3np5MMt9kAMDbgcs+c6nl+PxXoDpYLBYZkT7qxZbFnhPB+iE48K+Dx4H08bgEXry6SyVXoCQdmSmauoH2nXfMmra6wBujhEA2DbO7Z9Opw04m29Sd2ZJfZ/tMx8nzvwmivUjPDUajZqeOTo6aoHNg4ODNj7O7r6+vm5LONFBHic7Wcbe8AEBTvQAG2AjjxyUYEzp8aYkT1Mvbe7pGNpKPRsbGzWZTGo+XxweQEZfVbW9kjyRjUzt7+8v6XXzt/V7VbUsKbAep6pS187OzhK2XF9fb1lX0IXgEXRwANpZCNAAuhuHIiemn9tDNhF7MV1eXjZZ3t7erp2dnXr58mWbGCGDjuWa4KrBYBFs4nRj+k8Goyci2BfK+4Sx0fvr16/r8PCwtre3m70x1kP/PX36tLXn6OioLU28vr5uS8jZc+ubkr/3UegLG02TZWh5A38x1p6ANvZL38EBxp7+6mHRVQHjHrbgs+fr+jfrmKpa2tMQXJ/Yudfu1NN+N7bU/XeAxrbe/UkMn/Rw8LiXQMFz6Ir0bz3RQh/S5rifxkFZ0v/1+4wZMmDI79adyDIBavT0+vp6OymTYDTYkPd6MvD29rYt2SXDqof133d556CUB61X0gmoWnaEeNYpq8w4IJQ87yh8OokUO0oZwbXBpx4zXzouvT7dJ6D+32DIANzAzbMVPaDH/wb2PefKTgHPZATXY5XtR+BsuOmL73OAyrOdVhKmtWneu26+sOJ1NobH4j6F1aOflYoDbpkCCh9mO0yPBML8OQsn+9oraVR69Mk6tre36/Xr1/Wrv/qr9Xf+zt9pS4m+Stne3q6///f/fv3zf/7P3xr3r7sYEJNBAthl3wHo+ejRozo4OGhgF8cMR9iZbOZn87tlxcHEquUAtg2DdVLP8KbB8V/qHI9hyhDF/NjrD/Ul/yVd/Q73j/95V0+n9e51+Tyd/i7X3PachUrQkNdTrn1vyk7KK8+bRnx63B3UApxUVQP1LKtgBnw2m9Xm5uZShk3VYn+V0Wi0BMi9b9pXKe4z7806rR/R575GRsB8Pm+BMxxOLx1NnVxVS8EkBzdomwFzj+dtK9IhNrBMnve9yUcp04whzzuQxu/0tapaRibvch9wrJwF5f1ycFgTWFtn5cymAacdVvqMPqR/8BiOJ/fym3UI+9cQdLD9A4g6UEXf0LNpf62XLEdVCwcbnuEen9TDhIP5xu1JvoAPccqRtbT5q0ryrLFRD9fwmToy6/zQxY4FDsN8Pm/B8K2trTo6OmrZbQSZCag60Am/M1tOvZm5kAEdB6oYM5wa7zni+1gxAWZnkgmeh1/9TNXb2RBcc0DEwSrqnkwmjdcdvHYWvDFDZksTUOFZ6xD+CBDgtPmELnTE2triBDt4lqXb7I3F2FhXIGcEgLa3t5f2FyLoDJ3YT9T7R43H49Yn9jok2ObJB8rGxkaNx+O6uLhoeiZlwEvEkF0CzmdnZ63u8Xhc8/m81cM4sUyS8WQp48XFRT179qwuLy9bVhT0Go/H9Ud/9EeNTj9rBV703mtVy0F34xnLlmXSkytVC9lJDGcb7N+TH6qWMQPPpK23neu98/MC//xvfED9PR8ofTrekTShDmNu15c+ntvjoJgDPD1bke+rqreeTRxg3JlBLNtB406uWcd4/D2h43pMS48D98NP3MeEApME6Gjv0YeeIS7DZN19sZ73Xb7QnlJ89gaVYmavWgRbMBwM8CpwbENFfb3/VwEZt3OV8OTMjoFUMvnnCZIBEX3tvdd/yfhcT6PZaxuCZyDsNiHUzjzJccOA8htG3u1LRzqBDGNAu1NJJD2TVxz4Srq4rqRRD3S6PVz3hqFuh4ELxtnja4VvZUBboHe24V2LaeF+okB+9Vd/tX7nd36n/upf/attD4WvUtbX1+vXf/3Xv3I9n1fW1tbaPiFVy4YOujEegOfd3d0204tjz+8O2mSwKTMyfcJMBnssk5avXE/u+jIgRcngcuqB5PV0nNy+lPOU/57u6c04uST47PF1tte/3QccVtHKBpRineWS+iF1QrYj39VrY76nNzbWNaYTIGEymdTNzU2bIXI/GC+cRuqA59DD3hPmqxRO/VvlZPua7Zn19+3tbZvNZrNzQIf1N2318lnegw1OPukFXqBxBiKcEU1dBrbYE9riTILeu40dqhYb1TvoQrvoM3rHtoq2Mqb0n8wmn8JVtTjFLpcAMv69LHL0GPVx32w2a3rdWS52EKDRdDqt2WyRck/wgiCX7dza2lrLcrD+Ai8Mh8sbZOOU2k7zftvhDCTyjDdZR97Ze80A3s+ZZwm0HB8fv5V95vf1CtcTOPfwRuroz9OhH7LY1pAZxDKfwWBQh4eHSzqIpW3wvfeOQg4YVwdbPL5chycIwLBn3mAwaLpjMFg4zvCC92lykMnL+fg0LxKUsY31vrLM8ifm8/5JOF4nJye1vb1dFxcXzbFikuDi4qJGo1GTQZ4ZDoc1mUxacMmBIvq/vb1ds9ms2QDuJWMLnUHb2AOLoJnlCRp4A3HvHUYg6/LysgUAJ5NJy9IkSPbq1au6uLhoe4ihqwiW7+zstCDW7e1tk7+1tbs9EgeDuwMuaPPt7e3SnqrQHj0DD21sbLTMKuhTtdCRz549W9J/LDOtustGY3zQkxwowBK2qm8mGPy+i+UGvvMKmMziTX3lSSbbaevQlGX+N07mefuF1nvpy6W+hYexbcamLok10/e1PXF9FNphO25/2H1NPJDvTrzEfa472+17XHqB3MSZ7oOf6/2WAXf75+YB4zj7TTl26F4f7JKZj6sCS0wk0Y/JZNI98OM+G/w+yxfeAKM3gFnSGeUPkIYwQQQAqWcUidojjA4OcA8D57Yls/FMOoaOuprxzCBmQgd3qhZZRF6HX1VLsyrup5mLkgGsnqNsZWU6GNw7hdIABaUDzV0PguHAoEF9Kg6+O8sggzQ9Jz1BL/d5WYFnrdxnO7ergn1ZUrESCDFARyDJhHD/MyABjXsBUN73ZY0rSmh7e7sODw/r+9//fv3yL/9y/eZv/mbN5/Pa2dn5UvW6nJ6e1u/8zu90FfbXXRhPaEnAD0dsY2Oj9vb2GiCdzWZ1cHDQZMUOLxvQrpJHBxwcZEyHvertmXKu9cbNusszTuZxf0++zt/TuJlfXGfqBbdxlQHOd/cMF/R0393WrMdtzfa5HasAga+nY3ofbfx8r71Zsm7Tqveb32snejZbnMDFySPQjVlz78FhHjU4cODjqxSABbqJYC16HqciMxrQxdvb202nXFxctEyBHkD1fjJcoz7rPj+b4Bm5RZ+TQZF2Cronr2FvMpDJu01j3u8MH0+eMHbc6wBjLxjhYAiOoMfWy7tzDx3bI5+Ul0G2i4uLdg9/6CscVerCuTWvoRtxbkm9x5Y5QM+mz54U9JIfJgPs9Fct72GBg8mzFI/RYLDYRyYns5w5w3N2uqtqaYP51PvOgEuHKTGYees+feb7uWa98E0Ab9oBHplOp/X48eOaz+c1nU5blgq6KYPJ3mh7MBi0vdD4jf7m5OL6+vpSloqXffDn8bHttRNlJ7tnZwaDQcviQR4dROY7/WFsCXI4UwPfgb6ura21DDE25ObEN/bMQ2/e3NzU1tZWO3UKP8PLdh89elSnp6c1m91NnrOBOrQnqELABz5HZxinVFXLoEoHNLEp7x8MBm3ZNfLubF3kcmtrq22O7ckG8z5jR1Bpe3u7tre36/T09K3JGWjNGDAOTC4in8gzgejpdFqTyaTtNUaBNmTq0k8wNsv39vb22iTsNyV776NYB9oWYo+9vxD6u4e37JtaZ8HXlkNK7mWZ/lLa19SniaE8mYTs99rqT2d2GYslfRIPG9djb/2de2kDdhO9aDudmI//e/2BbsYYKSOmvQv9MM6xPTRtfL99TU6+y+BU1mM/28uDuYYsOX7An4PKw+FwyTbwLPdaP9Mu0+pDlS+1K+sqR9yBj6qF0fVGnhDFA2eiwKSeZeG+XsQwAUvVAjj6HVZ+qxxW/9675vcZlGc/vHmhn01B8bsd8aa+pNNstkibdrEDYQbOujwu6YR4ZjnpkjTyTJBp3Yume8zSeJvWdpbyPTmmfj919ATbwaQEBqnELdQYFLfXCnCV4vPnu5aNjY16+vRpDQaD+rVf+7X6e3/v79VkMmlp019HwfF7n4W+O/MNMMX/OGXj8bg2NzfbrCYK0JsQej8J6ucdjCv6xd+5l5L8xrVeMf/4/2xD1p3XsqwyTnzv8Y0BSmYZ9N7vdvv3dNz83vt4tfdM9r/3WwKrVXW6jvvsSW8MeuDJQYvUewZ5ftb79xF4opBhULVwmjy7ZEfbDjgTK1+2WIdiN+EB+stM9ng8bv189OhRbW1t1c7OTnOmTk9Pa2dnp6bTaes3QS3AmIMU5pekq8eKYr1ioESBzqsAnYE3to3C+NgeoZcdyHAA0fe6bbYN/O/gC7rdp1bhVK7a24L3eJ8ogkr8edNp7E9mjqU+INhwe3vbMkkcECO4b/6ENvAl7cSpZc8ZApnGBralrod7etjF7bcDYjALHgB8s7+F97Tx7HMPO/A+2/Ee4E8a9IJVqc/z80MX4x4CKzit7JPEBt4O0hCUuLq6qul0urT3H0u7HHRmjLhuuhs3MU6Jz4fDYXu2apEtxO84hGT+UKeDGubl3F8KbMCBE+g2B7rM2+gJ2uzJ7el02jKH0JfsbWT9hIyQfVVVLRBjZ4xnyED0Saamux39wWBx8jVOIONG35BNaGEbeHt7+9beczxDdpT3fyOoTZ/se5BJBpYisG2aVt1lXZGBCU09oY2ME+Q7Pz+v169f1+bmZj1+/LhtQD+bzVp233x+N6k6mUzaZA921kH8941LP2SBr515Bt84m86BRCYEnFW8ClciS97UumqhO11HYjHLvDFi2nnqcsKCJ2eNyTLbB362ru5hPQe4Uv/SDgfhU1fzfPp40LNq+STkpIN523LmuqFvL7OLd5t23NPLPkv8k/r54uJiafsHJ4pYt2BjjdPIjuR+Z3lar2NjwCOmv/e341n7Ex9aRr9UUGqVIc/GG1Aw0Jl9ANMnExpAYJhhOA9cz0m00HG/Hb+es9RzOLMvZj4LA/f3IqWrooy0085UAi+/O4Gis8qSnrm0D/oayJvxzKDOxvIYOrJsJyyFLvvc6xPXHMTsKTK3yw5O/u7xyj55RtogCeDj8ezV4TZnG3vG410LdBqNRnVwcFA/+clP6ud//ufrr/yVv1JbW1t1cnKy5MB8lfJVlxS9a4EugDgCnczoklbvICaO5tnZWXMKCbxm5N+8QVDAMwer2mQ5S5rms6k7eg65ZTV53wbDPGgjyr18Olsw9UrWaz3WK5Y/9816phegSfnM+2hfTzclfSg2qO9yfwJ0/286+Fkb2My0sGzzTmjt7MmqWgIqngVnk1aPjZc9UG/qiy9bDAQtK85ewVnlVC7PptvRtf3kunWteZXx8qbAXMN++GRMA7beOEIXPnmP3512ynX1xsWgjDoBYQ4W+RQpHFgcMWdsm2dogzMHWLJn2wuNydzghCpnNdnGYHsA8Jkd4uV/DhTYZiVgZmzX19dbZojpzhgxK+9x9p5D1od2sqFngn/Gz7TgN2jlzBmfsIcOJiPkxz/+cQ2HdxlcZ2dndXNz0/Yd3N/fb0sNk4+hjbPKoA2fjE863/CPnb9v0ilGrgeDuyXEtiVslsym14PBoC3pGgwGbf8eMuegH7qDAEhVLfGRHVSPnfebcnAPGXEg2HuQuC50JvsdQW/rR2cG0n9kCn1AMHU6nbZ6BoPB0tJQlrMRyIMW0+m06Wj2Pbq+vq6dnZ0WTCOT6eLiYilY6gxU9AJL0m5vb1vgxfu24fzSdr4zBtYf0JH+E8TFYfQyPNOcgF5VLb2HPZusV7AT7E9HMKtqea9BxhJdSXau9QHy7xUE2L2Tk5MWXHry5EkNh3enAnpLhqOjoybnyL0x0c9SQTdCN+zBzc1Ny2xDbhNzMsboSePBquWl7baDvLdqOchNMXYyfvF9GXTJSQLe4aCV7Y2zlv1uYz/7XcaT1OFkAAe06a/7atyXSRbYTN9rPM39uYm3dWLS0pMl6AXjGD6d9JAYNsfCGNbYgjbmO62LPbliP4P/p9NpjUajpQlN2obu8BhZF2dcBT5bFcN4X+UL7SllQq8C4ABFwJJnBfgNMJ0OmAGrhcmC43f3nJZe0It6uDeDQRmEWCXY3NObubNQ+p1Vy0dcmqkyK8kKwc6Ci40qtEnGhfm8iSR/ninyTA7AwDNKfmevfz1F6P6kUqGdKaiO7mf0f1WxgnbfLajJW263Z7QZK4+twR17elhZ9Pqbn/cV6MMRsb/2a79Wv/3bv11/8id/Un/yJ39Sn3zySW1sbNRf/st/+d56flrKbDZrR/1ijK+vr9ss2cXFRZ2dndVoNGoBqqrl5aQ4IIyjHQ3LTQa5U29Y1quW18VbFj2OqQOSV1P/eIyd1dm7z/fzZ8cYnqOfGZAwX1e9vRddjkM6p7TfgYTsu/+3HqIgM6aH25F61vV6TE0Htzd1VG8802D6Hp4zzfIegzzG2G3j/ul02vQk9otMKk688j4bvWDdVynoZGjDXieeWOC9u7u7tba2VuPxeGnpnWnh/VewvQm6HCA2eKXP3izY44PDm/YcmtIObJLrtjNqG0bpgUFnjHE/eoN+Exwy4PQkBI4CNCS13bLKO80zGUAi8xtnC/mij870slx6aYsdQZzmzPJwGz0mdhqtd+CV7e3ttleO25QYiDr83XqWeh0MM6/RHr+Ddm5tbS058exfwUlr6ELee3Bw0PgdDIkdwcFnPNmTxzaAfhEMI2DLe8wb9OHrlN13LR4nxtfZOM7iqaql4Mbl5WXbHwl+wfGfzWZtLyGCOvAm+zMxtvCyN8jN7AfjJNrjYLIzdAjgsGSEflLXo0ePWrDn6uqqjU06l1dXV23PMXAXASQCLNSF7nC24mBwl31ANhkyar2PDqd9DlZCf9qBLt7c3FziN95JUG8wuMucrLrjQ2wG9SIf3p+JMQczQWOCj/TPy6LhBcbAE8xra2stIAVv5UbwyDLvBuf2cPh8Pl+imx3Y6XRaL168qOfPn9fl5WULJjuQbGeZd5o3flbKfD5vkxRXV1ctkAy+yNUP+clYJJb1Nb8rfVLbAIpxXuI649fEaYnrej5bYsj0N30vesYZl+nfJb/YD/Tv2QbbrKzPOs74IJeX+8+ruow3eFePxsZqGWxLbJz4mKA7E/n2AYzTnLTj68g2gTboZd+J614qSF22HZ7U7GHtD1W+1kwpSkZOvZdUMpAHsbf0wQQx0d0Wg6gMFt3Xrp6wuz0pfCkgdoR678w60gmqWnZ+U7Dor4F6Bk2o2zOdMJfrtkPhiKjHJPvO+CRIT5pYYLP9pnvSzs+avhbkVN55Pd/Z+8198T12WkxXv8f0uM84vGsxrUejUR0eHtabN2/qe9/7Xv2lv/SXajAY1H/6T/+p/tW/+ld1eHhY/+yf/bMvVP+XKT0afpmC87q+vt426WV5HkB0Op3WwcFBG38A7ObmZjtpB5CI81H1Nk/0nOC8L2d00hB7PDGoHk8bIctn1tPLvjI9DfLzHa7TRj1lcxUQyPr8m/WV29SjlQGU34OMZJC66u0DIxxIdDG9ss0OUOQ16jQdrTfSYAIo4Le8j75k/+zgUw/gkntub++WVZBqjYHHSSLo8VVnlaA/TtLa2t3+KewV5RMqbUuur6+XnD47D9RpnY/sefwta702mW52aA22sDs9ueSaA2YJkpIP4T3ahm1LAJeAz/aKd2MPPQHjiRmyDbwxOe3wyXzmN3jH+3Ml+D49PV0CeqYP4zmbzVrWBs4j/EtWSE//4OB5eZad9IODg6XMGTuMPdlLjJLYI8e/arGMyIELxpLnetlw0JxgA5kqBIuePXtWNzc39eLFi5YBQ+AOEG8ecx/oBwFD+Mi8RLu/qQJ90VkEG6DD1dVV7ezsLAU4bm5u2sbV0NW4mWuWO/A3gWU2YSYY5X1V+J7ja73JuNkRshNk2+Xleg5Qw9PMxFtf7+3tNdkyHgAneHle2hUytQg8VVU7VY9n2DrAy3Wp4/b2tvb395usp06zTCCbOJbD4bBtRu9AF8stCSw7yMfY+xRKrnupDjxNBtn5+XnrM6fswR9kXs3n8xqNRu1ELdqDvL169arpSALX6VQzdtDPDvNweLec+PT0tHZ3d9szZKxMJpMajUZ1dnZWP/jBD+rjjz9uGWfo7J+lcnt72/aDY4xZOg9PWz8mhs2M+Z6/BT86+zSDJcY9iYWNB6renlz0Z/oF8GYvqJWTVbzLfmHPdzO/w5u00b5q+qaJH3hf9gdsbl6DP90HdJMxRAbG3IfeZIaDOhTjTtfl9zOBxGSoffj0R+y7z+eLzHEffsKkG7LKib29wBl4Fh2Vfkv250OUL7XR+bvcA/gCJAGY+AMkeIYSBjNzYaz8bupKB60XFEnHDiLn92w/yqLnDHtgvSG722hFko6VFYKBVWYxZH8MjgeDQZtlNbAy8M9oMI6v6cg4WSGm0LteviddUknlZ88h9D3UYcWQQQGPj/uV35Nf6KOXgVr52TikM+ex67X9qwgsG0V+5zvfqb/+1/96nZ6e1h/+4R/W//pf/2tpmeTXXZLmX6QfvfGg4Cxsb283UMQeKMyYn52dLQUCDb4w5lULBe89NRg3Ox0ZKDSPJf+Zxzw725tlSr2Q/ebT+qsHIqwDaKv3K0uDmv/3xspGxe10W7If9LNq4chmAIBrjIuNrGWFNjp7JdudJXVhT7fRXt/To3nvOffHmRep7wlCOPOEP3T5xsZGvX79eikzg6PaaZuP7LYucdDiy5abm5uWDYsjaefIYGE6nbZ7aROzrsgT9zt4an3vgC1Oq3UptomAl+/tBf4y0y1BdW/8DV6x+b2JE89y0k+eo6/OCOC9drBoh3kk5coZRTxncGwsAD/hrOHQksnD+wHugEb0mJcAeYkaNPE+QV5+B1/Sbxx595N3eD8p2uJMEeiNU5t6jTFCTsxLxkRpU01f6uKPe3HYjaO2t7fre9/7Xs1ms7ZMiX5lcNM6q2oxwYAuob3z+Xwp8wWb8qFBN22z80Nggc3BsYcEYRxomUwm7T6Pg3EWwap0WlkmhFPLc/np7EV4+/b2tgX8t7e3W2BqNpu1LKkenjeexPaY5yxHLD2BNz0+vG9ra+ut5a+MJfJDAHZ9fX3pZCnkh+VUBHOgB4dbGBMQxGKsCF47q9QBwu3t7VZn1WIpHfbEBx5cXV3V8fFxyxyrWgRLh8PFAQXUh05nf5jZbLEpOrqAALYDjmtrazUajZpO8pKwy8vLZkNok/V4+gXwGf3/5JNP6i/+xb/Y7JVx2sHBQf3oRz+qzz77rD766KMl/fWzVpA9dGgGMBmPqsWBA6alf/dS1ap667PnK/Zoartnu5DYx9fpC88YB6zysXyN96Zf5vrSfrhuZ+0lfY1N7bemH2h8wbg46Gtcm/22nrLdom0ZAzD9wSfpR7oPlLTpuU+k/XPuh78I6qOfPbbeLsBjgg43nqlaZGWusvkfOnj8hRD0Koejqj/rzvUclGRYjIuBHoxhILIK0PYCSNnuBMtVfSfKjloKtActnSG/OwUk71ulWLg3BQLGxIj3AHY6YJ4tdd29YIzHxO90kBAjapr3HPSew0y7rQw8hgZTSadULlWL7DI7zT3n1MKUTgszbzixOVbQ2MAtHefkg3Ry8tOKa319vZ4+fVonJye1t7dXv/Ebv1Hj8bj+zb/5N/Vf/st/qT/7Z//s0ulRX6YkX7mdLl9E6XwegDcovrm5aU4zxxrzW9ViVtebpQKIAFP3AeakMb/nrIoVe9ZlwJxOj3kcOvUMP8/6N8v5fXqmJ//5Xu63IU2n23Ws4kvTKWd//JudRuiZAMPvTsPb09lVyye2ZZ9Tp9uhSeNPG+345uQDvJZr4g0C7JzaScapoR1kITHDTJB1Op02RwKg8FUNOHbPetf6EL3ljKGbm7vNZ3d3d9tyrcFgsYHl5eVljcfjpWUY9Bu+ZUZtOBwu6Z3U13ZOoI/5kXv8u3Wo/08b5eUrtlGAJDIz+A6tPQHD+xyosVymXnHGCLR3BjJ1594ZtN1ZJfP5vGUBABahOTSFRxx085KB+XyxxNJj75NxPG6elTfvsfG9+276VS1vE4B9z7R99DJ0ZlycmQ028ZIz9pWxE4ruZ5kLzhvjxv/Q/eTkpNEUJxxZJ9hHdiBjYhrQJ2jF++EVgnUfGnRT7tPTVYvJC/o1mUxqOp22cXJABP6ER71/UlUtyQLXcJzJpOE+dJkxmQMT8IODMdbnDjIlrqTwHp5Fp/o3TpOsqjo8PKyzs7Ol7CXuG41GNR6Pm4yw9A459EQuvAV9fTomwRTkmnYT7GLLAZae+iQrAkHIKNlgtiObm5tt7yjr57R1BJ8Gg0GNRqOlrQ4c+CKYyfu2trYavcl8st4m05Yg2aNHj+rp06d1fHzcltZBCwp7KsI36EraDJ1OT0/r2bNnjQesO5wBarv1eVjy21iQV+jC3oNe6s84st1F+mFgVfSsfeOq5RNgjW3tkzDmPWxqPWnsm1jLJWXYdWdQqOcDJn7w+7PNtM/L7F0fxRgh8Wne4z/eCw+altDTyR4ZZDKG9fuxQaa93+n3mSb2hzxRZGzsAL6xu2MIvN986L4hs27vYDBoAWlwALqTetJ3ed/lC+0ptar0lEs6XenoZWdNKJ7HGbByTQfK70hm5Peew9Sry7/n+7K/do7SOUyh4z4rkKzbQDGDSZ5N8kaKSX+3w8KGoDhoZMa2QbTyMm0Nen3NjmbSKOvo/eY2u11+Z9KW5z2bZScTo59ts+JzMMQzYzYqOT4ZdFjFQ6uKldre3l7d3NzUL/7iL9Zv/dZv1SeffFKffPJJ/eEf/mFtb28vgYT3Wb5uYABdesqT75PJZGnMSV8HmBqkQfdUzEn3NEYp5zbqqewtA/xug+eSuif514ahajkbsWqZv3vGsvcet8MOcOoQy3gGnVYF1LIQ5EiAQV2WSQMpj1OvP6Z3lqRDb5wsO77HS9X8Hu8H5d+RcxwK5H84HDY+hGaZeeOTIsn8w7FmGQd7inzZwru9NMbLyi4uLmo8Hi9lVjBebHpLIIr92wicAZKZDQeQOJBj/oLeDv47mAJt4BEHpnoyYRtvUGtA50yKqlpyZHiOP/qdAUl/JzDhU8U49t76fjBY7I/lYBPXwCJuB+PvfbycUWLZt9PoyaWq5aV33o8HehhrOEuD4gwRaHV9fd2c2cQyDualvXXwzQEo+u0ZatrlwC/3MJ7r64sN0JFLQPDV1VU7+Quace/JyUnbl2UymbQ2eLN92xXbeDCPM/6gAY4O37+qvH6VYuxiTIVMExzf3t6u3d3dev36dVseZFkjOFC1yMCwTfP/Djrm5I7vIWBStWy/LTe9pSPZrqrFpsTIih02AlfO2iHAg36jboKsyDWBpNFo1DK3OJyC0+Rub29bII++QHP6Yx4iSGl+R454jqDoYDBowSPqpy7v1UoboUXVHeY5Oztrp7cib+j9DLASFGLijmV8s9lyhpR1KM8zZuBd7AFlfX29Tk9P26l/yJp1L+NN4JBxuLm5aQHDp0+fLu3bhk2lTvrtLK2ftZI+qDNlbbu8b6Tlt2r5gIgMOFnP8Wk8lr5y+tVV1XRoyn3i3dSdfi/3+rqTPlyP/ZjE7ehqP4eP621/UlfSBvc1fc6eTwpuQDazXUlvY5kM6CR+oVCH9y/MZeKJobi2trbW3RIAfOC+eNIn/a5cYQZGwfa6z+j66XRaZ2dnLYhqX/lDlncOSq1yXg0+87rBqIEVCg8io4wz0JLOlJmR75kBYYc2Szo7vX6Y2TzL4T75ecCWg04WhhT8++rKoFE6zDAXMxkZce05vNCHdllRwZQ+7SzpyDMGvp5F4v5UDulUZ9SeawYudoZ6Y9RzdtPJ4ZqdZICC08INoHIsMipthZ/vv8/ZNw3ML5ubm/X8+fO2j8av//qvV1XVf/gP/6H+23/7b7W/v18//OEPu3usfZGCfH3VjKsvWjLTBvoRCAR4ZYYAYM6OkOWFMUB5U9JI2Ih4bNOwOrjl0uM/32M5ymJ+y3sSNNjJs9FN40offc3yZ77N+9xu0yf1ikGT2+f/LcPZJvfFv2WbU4Z7360/c/wNZuhDBtqz75Z1jx36bj5fzKajJwEFtkNV1ZZAmJ5MnDDz+VWK+Rr5IBDFxsBe0pLZRgC6m5u7E5JY6oK8IVu9JTy2nQlQeRfyaj63A2Z597hZHqBv8mPqBAMmbFnaad/ryRd4xNc9ns5Q2NzcfEuP56lvOM9Vd6B+NBotTWwQMDo5OVmiLSfKOXhF4X+DTb/TvEdGB/3AsSAToaqa/OG0Js7yeEBPwKt1YWa3eNysByzrxkyeOGM8fPoWwSra7MkknyB3fHzc+m8dNxgMljAQz1ctMtHIKHHxkeXz+bzNCq/CsO+zJG0dvCMQU1Utg/H169d1enq6NEZeklm1WFJtOWbZ32g0aqfVGSdZx4N5sNEOPGQA0/wCnTNQyDssb87SrKqlzc4Hg0Hru08arKrWDxyr+fxuq4D9/f06ODhoWUPoYP4I+tA2AtKnp6ft3fAFGQLeHoNMF9o0Go2WMDG6zwE9Y2lPcuBok9HFMjqWhTszg/c52GisDG1ZxsPzYC/0MrIGZiKIRT+ZUNnZ2amqallTHnOCSpnVxdhzMMHp6Wk9ffq02U4H7um//ZB01H8WigNPyBUH/XiDe2TVdiH/0k+rWsa0OTnA2DtAWvW2ffH4oc8Z1wxqrfIr0cs9n9LYjDZDG9dhPz31vLGa6zB9uMfZuJY96uE+22oHA92mtIfYFU+q0ZdMMHGx/Uu5Me409jXd3A/jbGfh2U6nb0NQyYdPsBQcmsF/fi88tSrZ5EOUr7R8r+c8VS0ifoBjM4yjiID53KhxPp939/xwkMQg14zdcyYhbDo/vba7jnSs8n+3yYJnJnVqXjpFvbY4qEXbaWNet2NbVQ0Q8r4EczwPADLo8NikMFQtMhLoY0aNzdjpiJtuGUyw4+D7EiD3xiGdG7cZZeAURju8GTh0e6x4VwU4uf8+3ktlRZ+YbdrY2Kjnz5/Xzc3dZq7/43/8jxoMBnVyclIvXrz4yg7uN6FQqpYdDa9vthK0XGR72fciA9I2pjkmve/Q34ab+rjWC1Sngs+SQWLzceoby4l5hd8xbpb1bM+q9vUCB6ZlyuEqnvZ4Jc+mfGdJvjcgWBUUtB5xf9L5zXa7Lr8ns3DMJ1zP4DJA2TO32CFvOun6cSQ89vDX1tZWnZ+ffy2ZUraddsrQ1WT+2FEwb7FEY21t7a32AMSgCUDNcuaAAaB51XhRLJMeH2diJFA1QOQ5+k8bbZNytt063rbCNpR7vJwiAb7lLx0KL2NCVpnNhAdow/n5+dKSpc3NzZbZ8PHHH7fMhpQD/kcvevzJJuI6Y46TCth2H8ikYQmPf/M4O8Bp2e/JYI6z7bJlc2Njo02cOfhzcXHRsvKQLQNkBwbYl4aSs8HmQeqgHbPZYjmaZ6pzNr5qebnohy6mLTxIMGB/f78FEmazWQswnp2d1dbWVqM3wVL42Uur4PuqakvAyOohYIAcQS9n7PTaaQxL0IPAxmCwnIGDHvGyVYJu8AjtARNZjyF38CIOHnWTFXR0dNSCnlW1lG0IX+3v7zdep68skeX7bDZrNKI8evSotre364c//GFbSsgJfLTP/XKwyg469ON/gmqMzXw+rzdv3rSl19CCAJbbiXycn5+3zNiNjY12oiXtdsaHN3RfpQfW1u42UR+Px0ub7psPkSmCc8PhsHZ2dto9x8fHdXh42GhNoAo9aN1i3vpZK4lRWQHAxFfei71kTJAZJnny/qq+TqbYf7HtTSzsYqzmeu1z9/za+3zlxAkEQlb5TcbDVYvsINtk63qKT6OEV3NvRdtwY8GMJ2ScIZcLUxdy08Oy1p/Gm/Qzg0D+zZmmtl2Mne1+L9s6/XCPu2MttN/+GIEr6/lvonyh5Xt0MB2Xqv7JdOnMOGBhkJp7+sBUGC8zrQXe7+o5PwZPvSAQ70pncZWA9oSd+3tOV49OGV11W31/Brm45jT4dAoQ4qrFrKnHweDdfU/w5t9RJHbmVznM2e9etDVpasVrZ9I0sWHv8VTWy3f3eZWitFLKoEc6vxnJ7o2v6/Z3g7mdnZ26vb2tX/mVX6l/8A/+QQ0Gg/rd3/3d+v3f//16+vRp/eQnP2lOz1cpHzrtsmo5sIccn56e1vb2dtvXxgErxgj6ww8AXgd9/ce7Uha5bpnvyWvyA/dZhyT/mi9dZ+o0ty35BJ4yT5oGabjsTOU185SfTTq4JC3siFtXuL1pnHq0Ngh3GzPQlTrLtHJ77Dy6/9nvlFE/4zZgT2xT7HyjU9mg304VS37Oz89rbW2tJpNJPXr0qMbj8dImup5V/7IFZ53Z783NzeYIWT+zZxAz/1XVloyxFAodvLm52QLhDjLxO6nbGxsbbZmJsxfTsYKHTP+eTMHTOcbIOX+0xWCKgExmXHK/Z/igG+2jjwQLDYS5xxshUz8bKTtjIe2i2zAcLp+K5uAX7b24uKjRaNR4BgDtzCeAph1GLw2dz5f3cEJfzGazpX1JoCO86wCNHQuf0pgBPcu7ZdLf6atBMu/1Ur6bm5u6uLho7eQawYizs7N2ouV8Pq+Tk5P6yU9+0jAhoN0TaIkPGAfbb8YMPrdO43fa6GPav4liehOY8vjN54sli2T2eCmpg9L0gyVp/M8JbARUuQ9ZZ6sAO1rwu+nmzGt4JbOdzVMOFBFYgf+2trZaf6qWHWDbIgKudrC8KbT3BJvP5zUej1v2DnyEfjTd9vb2WoBsMpm0QNV8Pm+ZP+gOY0F0FKdmkoXm4I9pbBvkvhKoRdaht5e+WcdycMVwOGzZX+h5PtFnxkj8Dh0JjNgZdR+3trZaYOr29rbZHcaTCRECLfSdsft//+//1dHRUdsc/vj4uKru9gRDh6U/87NU0EnYEcbXAV/GGrti2cQ+pN9iXnBJLLTKv+Ea340r3XZ/N1ZNrNXzfdAfXmGU2Nd1GRs4OG8fNHG0sWbiAZ514C/xcuJu12G/Ou81rk8skJM+bqezFrG59Js6PBapc/mNOtwPcExVLckpOtInfkJ3t/P6+rotnZ/P522vR9r2Ux+UMnPwPUsybwqXB9sOvp/nWc9OrwINdl7sDPtdPSH2/zmTa8FZFfCoWj5dwEKfwmNFQn/ui4BbiDN7iDrc1xRu6OY0YvpiZs+2Uw8CynMIuMG2DRnFoDgVkemayqwXLLTSML/1xjPpY0ViIGNHivR0+uGouNOkXV8GRFaVXvvc962trfrud79bx8fHNZ1Oa39/v6qqnj171hTiZDJp+yp8G0s6ADZuKFqAmemaAVWnxjugUPV2AMhALscb2vtdPd1j56tXF9cdpLXD47psaKuWA9d5bw9spD7wczZ+Pb3hfuS7TLtVwab7+M513yeb/p5y6HfbGcnr+a4cQ/OXdblnxOwcAMhxkr2HEKAGIE5giI1kPeO/vb3djDiOyO3t3T44BBu+bHF2pB0S+N5OzmBwl7m1vb1dFxcX7TRPsnYGg0FbPkb76aN1PXTB2bKNhv4GRw7UpBPJO3o6dD5fZG/YQU77QAaHATH29vZ2sbmv6QHP0UbajBPgfVJShm2TbbPdb1/37+vr623MnT2RSy39G1ls8I5pYEDPu72Uz44d/fFeVUwkmW4eB655wimDGtY39DeDxbnswWPA5AMONpleg8GgLSfFIfbyzFW6pYcpzFeJJ42ReNZOgZdxfpNOsXXfYDBoG2HjSBCsm06nLSiBvFctgj22P3Z82MvIwVBn6TiA4cCtsXvVghehL/qPNnGfM98YE4LrnJqHzKGDc6wIcHnZm/c5I/BL0J1gJ3VcX1/XeDyuJ0+e1GQyaXrcAXv0wtbWVgvW0FbqddbX3t7e0nHptBF8wnPOKHVAzgHcyWRSZ2dnbQNx73/lLEPw6Ww2a6cweok5WWBVi9O2xuNxo4UzZBkX6IVtJJiFcwtd19fXa3d3t968ebN0MpcnHxgHB8irqk5PT+v58+dvBfefPHnyVsDtm3J632cxr0Pvs7OzFgBG50J3LwVHvpA1aGvMZByamCqLsbbvtQxW9U/MrFpeCeQ9bv3+nj/krXhSZ/u9tIl3cA27mc/av0+8uLa2VtPptK6urmpvb2+p39Zdro96jPf5vbeknt+c2ZR0RWYd1EksgQ4Cl7hd3OdJItrJc+6DV6Lx3WPM+FZV01dkItv/YtyMi3LcPlT5UplSlJ4TYofHxDFgtQAwcL7fwmiCU48FN52VdBDN8O5LOot53W3uOZPcDyO6T3YOsz1Zt4GVmbVHPwy+Hdzsm0Fmjp1pacPkez2j43FMWvEug5qkTdKba6t4qFfSke71q+cUu/1WON781GPAJ/R3UJRxWBWQsmJaVchYePbsWQ2Hwzo8PKxf+7Vfqx/84Af1B3/wB/W///f/roODgzo5OWmz39+EQviqBZo74IehcQYIoNYzvswGQEcM+GAwWNoPhPeYlz1O6WDx6T/zRM9wVr2tG7gneTCL25mAocf76dytCvDaYeC+nqFNQGB9xHsMEE2fVXKxqvTo43fRTtPBv3ksrEfdFoNe2xHP0NpJT11snqtanG4K2GIGmKynnN0mwDCfLx+/e3x83BwA6v6qQanBYLEhLQ49fcO55B4yt3BU6Bd71ZFJcHV1VYeHh61+2mongywB+kyAHpo6CIS+d1aFA1W2iwlc6Zf3uVslW4wBbfU9TJjgUFkXEDjEuU9Zx5k2/qCfyDxtv7m5aZsK7+7uNmfSwJwMF4CeMz/hh8Fg0DZhHo1GbfkN7WGsoDFjUFVv/WZZ2d7ebhkU3gcGp5pxAjfYqeVeg1HqdyaawbLlNpd+pg1OOsFD7LGSWVUOjNyncxIToAegF8AcO1612CPs0aNHbX+i29vbOjs7W2nX33ehbQ5qVt059k+ePFkKtG1sbNTu7m5VrT6JmowbaM6pdOZxByPNq8aqyKjHxxiIYARt8VJWHJ/EtN5g1/gWeUVPgRlYijYcDttG5c7Kq1q2Z0xipayzVBG+cmCSjb3JLEM26Au0BHucn5+3PqIzkSkyDqzXCNwgq96I/PLysk2MEAiiPvQuv/lQDfgk9S5BEAd6yVJMXwN+R6dabphoged4nqAVtPVm+4wLturNmzdtTzDG/uDgoM7OzpYwzX0y/tNabNt6Bfs5GAxa9hqTXIPBYEkPU4cDCh999FH93M/9XP3n//yfG5/xTsutbV5iInRGD3f69/TLqMuBiczE7NkDB8iYdEDufT/2zgdNWIcbg1Qtn3pnbJJ4YTab1c7OTv3bf/tv61/8i39Rv/d7v1effPLJ0sEk2EQCwxm8Qdan02nt7Oy8FTzFJtpWeP9O/05b7Ztbj9I3SgaCuA8a3d4u7x+Y9hb8YR1n7DUYDJqNMT09qWh94qzsb8IH/dKZUqvuSUFw5+yc+M+RVYwSDL1Kcdl45rvf1XHstTsdot7gmNnol420++qSRt9tcUAN5WXD7SASyikDShZS2uB6zfiZSWXa90AfY0If7Uz2gGg6zjnmOT6+lkrP/fTYpLLsjZ/bQdq3nVyUZYJiK0fXm8X3r/od4Dcajeqjjz6qV69e1c///M/Xb/3Wb9XR0VH9u3/37+rf//t/X3/mz/yZ+sEPfvCNKYOvqxj42QklEAK9q5aX2pnP+c64GSTm2FJ83bxp3jGPcc3fLfNOzfW9q3ihp3OS13rv77XFfOv+uT0pS6l7rQ8YFwcM3d80milf/JZgx8/YSXCbM2i4qt9JU2iX4C3fA9DHeHvpi9tgnqBNOBE+JcdZM7mMCIeaT48rM6BftRAQom5mAZGP2WzWjhbf3NysyWTSnJbT09Olk54Ae+x3NJ/P2zI1nGD6kTJGJoOdSe7b2tpqcmxbk9kV0DRlJ8ead3o/E9M1bcp8vtjEE4eVvtpO2rZxIh06H/vi0/gI9M1ms+YsAjbPzs6W9At1kjrP0jTv00TG63x+d2z6cDhccvzSZlN4nllNywBt5pP7uWbcgM6gfdZL8AGOAnKEbrYNRg57Tq71Lo4U/Hpzc1NnZ2cte2xtba1t2u3AX+65YT1BexNn0W/zj4MgHnsmPAhIwccEa7+JYvrhWMBT8Bj7bzGpNZ1O2wl8Xrbpwh4+BCoI+lgeEl+hV3gXutJBSnCpJ4oI7jmTET1cdUfjvb29JRtAvbYlZ2dnS6fIIffoWZzK3d3dur29Xdr4nDaTKUR7Ly8vW9Ye91mHXlxcLNEEfU+96JWzs7OljKeUAwLV8/m8ZagmnvFpnKv8A/ARgYDxeNwy25zpyvhWLU5SQ7Z80uXa2tqSriGw4bah+whiEUgBvx0eHtabN2/ews3oOXQdkyXz+bxevnxZP//zP1+np6dNzxDQZ9zx+XKPpZ+Fsgonviumt9/xRZ/9vPtX+dP33fN5WPeLlC/73LuU9fX1thy7hyl7PsPX2b5VmCb//7LlvnjIF23fT7OP+c5TRAAeG5ZeQMRBBDsNZhIbUoMqFL5nsh0kARx6FpbiyGQGQbJwrwEcytJtpI+AdX7zp2dh7ASlk7tEdM3e2lHK7ByMK4AUkJqbexooY0j5zc5gjp0Ngunp4FSOq4E0Bs6gxGOQwNH8k1HvLLQ5QVfyoN9luvr+BNWMJc+aRvQz2+ExX9Ve85z5DpBMmvezZ8/qV37lV+onP/lJ/d7v/V790R/9UX388cf1+vXrOj8/b6c1rZqN+WkvpgEgy8tOGAvf51mADEZ4hp7xh+9yb5mq6vIBoCr1D7yYM0h+njbxabnwu3r8aIfdjiI6xxsPutgZtA5JfnU/XEdmgDAG6QDyu9/lP/Ngz2nO79bDdrrchl7AyH2j/XaOe3LVC274/dwPrauqzVA7QOc6vScUM2EOAnmW8+bmZmk5BvV+lWLHbj6ftw129/f36+Liov7aX/trLcB7cnJS0+m0tra26uLiog4PD+vq6qo+/fTTJRsMH9I+68HhcNhm4lkmlPwNPXnm+9//fv3Tf/pP67//9/++tNFtLn22rOOUmZ/TZvqQDtdh3sI5RW5wqghee78Z7ODZ2VkdHh7Wb/zGb9TNzfKx5vw/GCyWE+PkspyF33nGe/YMBnezw7u7uy17AEzjjZOxnaenp/W3//bfbk791tbWUtYBdULrv/E3/sZbcgc9OGEs95HxjDqBVE/8VVXb0wY+ga8Tr9zc3NTTp0/rF3/xF1tGloMJvAcese7mHpY6kWkxnU7r9PS0ObNertnrJ/Ugu/Bi8hE0Y9wJAO7v79fHH39cNzc3zWnJAMU3UeClwWDQ+Nh7AM3nd5ubv379ugU4WY7nJYiMKwE+sm6qFsEnJoac/eRAUdXbp+YSAPHhALbJg8GgBSPIkCKwxjiMx+O35JnnqY8DIgiE0G7uZ5+jqmr8DB947zyWPnoPH2N47613dXXV9AZBNPSgM07AbCyxs++BDJHRVLXIdkNXWcdW3emiyWTSeN57EprnB4NBjcfj2t/fX1qWR2Dp0aNHjR/29vaabF5eXrZAJvxAwImgE3IPDzqIDW25fzQa1dOnT2t3d7fprN3d3dZmnnem5Ww2q5OTkyV5JjCaPsbPevkyjv/7DNx804GI9/n+xKRZ3iddvw3lmx77dy1fOFOqN7AGU72/nIX3cwAJ6siNxjJw5OBBAlve0SN+3t+7JwM4Nt7p6LjfveJg2iraJZ24j2edjp9AEXr0+uUZkQT+g8Fixij7kf3p3WMgw9h5BqXqbefS/c0xcZvNR+msphNrPkjQ7jq45jHNdrA+34ES81u2ucd3BvPmXQz11tZWPXv2rC4vL+uXfumX6m/9rb9Vs9ms/r//7/+r//gf/2P9wi/8Qn3yySdt1j1Pj/gyxe35OssqGXMBlMFDXi5mPgeEO7jpADB/mTWXeiUDUaQCQ8deEKdqOQDLteQd8755kd8p/Ob6+N20MH9lmm0GrvwO12feSH7zvQ4E2OlwvfTLY9PTCav0qv9wynGsrJcyuJR6r+f40BeWd7jtqWOcEYJzkc4zThlBJtONZXx2OhyAqqoWhLq8vGzHZxOE8N5T76v0Ajb87+vvs0Djd3Eqvg5Qbp75MvX0ZGbVu/z9y+rf+56Dr9619DJh3rUkb6yyZZ9XVunGL9qOVb+9C556l9/vK9YzX2e9X6Wg8y8vL2tnZ6ctKb24uKjT09M6ODioqgUOe/ToUe3t7TVb4qAHfdvZ2WkBEAKzGfS3Ds0M8cyk4Rr7SBkfpy1zgJflegQsWApWtdDrp6enDY8SoON+/AD64IwtMjUJirGskeA9AVBOsjs9PW1ZZWRIkvlEIIrnoQnBpdvb23r8+PHSqasElAjCOegPnWkv9OcaG/u/fPmyjo+P6+rqqsbjcTvNzkuS3X8yyEajUcvUdTD78vKyJpNJXVxcNKyFvWScnAUJDc1Lu7u7tb6+3jAoY8sS0NPT07Y02hgHHeVsObaj2NzcrLOzszYB15sYeygP5aE8FMpg/qAZHspDeSgP5aE8lIfyUB7KQ3koD+WhPJSH8lAeygcu38wOjw/loTyUh/JQHspDeSgP5aE8lIfyUB7KQ3koD+X/r8tDUOqhPJSH8lAeykN5KA/loTyUh/JQHspDeSgP5aF88PIQlHooD+WhPJSH8lAeykN5KA/loTyUh/JQHspDeSgfvDwEpR7KQ3koD+WhPJSH8lAeykN5KA/loTyUh/JQHsoHLw9BqYfyUB7KQ3koD+WhPJSH8lAeykN5KA/loTyUh/LBy0NQ6qE8lIfyUB7KQ3koD+WhPJSH8lAeykN5KA/loXzw8hCUeigP5aE8lIfyUB7KQ3koD+WhPJSH8lAeykN5KB+8PASlHspDeSgP5aE8lIfyUB7KQ3koD+WhPJSH8lAeygcvD0Gph/JQHspDeSgP5aE8lIfyUB7KQ3koD+WhPJSH8sHL/w/hzKhrO6XbuwAAAABJRU5ErkJggg==", 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", 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"\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\Utils\\Grad_cam.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(img_array, model, last_conv_layer_name, second_last_conv_layer_name, pred_index, sensitivity_map)\u001b[0m\n\u001b[0;32m 53\u001b[0m \u001b[0mheatmap\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mheatmap\u001b[0m \u001b[1;33m**\u001b[0m \u001b[0msensitivity_map\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 54\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 55\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0msecond_last_conv_layer_name\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 56\u001b[0m \u001b[1;31m# Compute heatmap for the second last convolutional layer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 57\u001b[1;33m \u001b[0mheatmap_second\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_compute_heatmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mimg_array\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msecond_last_conv_layer_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpred_index\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 58\u001b[0m \u001b[0mheatmap_second\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mheatmap_second\u001b[0m \u001b[1;33m**\u001b[0m \u001b[0msensitivity_map\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 59\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 60\u001b[0m \u001b[1;31m# Average the two heatmaps\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\Utils\\Grad_cam.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(model, img_array, conv_layer_name, pred_index)\u001b[0m\n\u001b[0;32m 22\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_layer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconv_layer_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutput\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 23\u001b[0m )\n\u001b[0;32m 24\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 25\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mGradientTape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mtape\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 26\u001b[1;33m \u001b[0mconv_layer_output\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpreds\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgrad_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimg_array\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 27\u001b[0m \u001b[0mclass_channel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpreds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpred_index\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 28\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 29\u001b[0m \u001b[0mgrads\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtape\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgradient\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mclass_channel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconv_layer_output\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\keras\\utils\\traceback_utils.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 68\u001b[0m \u001b[1;31m# To get the full stack trace, call:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 69\u001b[0m \u001b[1;31m# `tf.debugging.disable_traceback_filtering()`\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 70\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 71\u001b[0m \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m \u001b[1;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 553\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mcopied_args\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcopied_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 554\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 555\u001b[0m \u001b[0mlayout_map_lib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_map_subclass_model_variable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_layout_map\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 556\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 557\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\keras\\utils\\traceback_utils.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 68\u001b[0m \u001b[1;31m# To get the full stack trace, call:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 69\u001b[0m \u001b[1;31m# `tf.debugging.disable_traceback_filtering()`\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 70\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 71\u001b[0m \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m \u001b[1;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\keras\\engine\\base_layer.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1093\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1094\u001b[0m with autocast_variable.enable_auto_cast_variables(\n\u001b[0;32m 1095\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_compute_dtype_object\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1096\u001b[0m ):\n\u001b[1;32m-> 1097\u001b[1;33m \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcall_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1098\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1099\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_activity_regularizer\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1100\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_handle_activity_regularization\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\keras\\utils\\traceback_utils.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 154\u001b[0m \u001b[0mnew_e\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 155\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mnew_e\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 156\u001b[0m \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 157\u001b[0m \u001b[1;32mdel\u001b[0m \u001b[0msignature\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 158\u001b[1;33m \u001b[1;32mdel\u001b[0m \u001b[0mbound_signature\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\keras\\engine\\functional.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, inputs, training, mask)\u001b[0m\n\u001b[0;32m 506\u001b[0m \u001b[0mReturns\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 507\u001b[0m \u001b[0mA\u001b[0m \u001b[0mtensor\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mthere\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0ma\u001b[0m \u001b[0msingle\u001b[0m \u001b[0moutput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mor\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 508\u001b[0m \u001b[0ma\u001b[0m \u001b[0mlist\u001b[0m \u001b[0mof\u001b[0m \u001b[0mtensors\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mthere\u001b[0m \u001b[0mare\u001b[0m \u001b[0mmore\u001b[0m \u001b[0mthan\u001b[0m \u001b[0mone\u001b[0m \u001b[0moutputs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 509\u001b[0m \"\"\"\n\u001b[1;32m--> 510\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_run_internal_graph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtraining\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtraining\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\keras\\engine\\functional.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, inputs, training, mask)\u001b[0m\n\u001b[0;32m 663\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt_id\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtensor_dict\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mt_id\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mnode\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflat_input_ids\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 664\u001b[0m \u001b[1;32mcontinue\u001b[0m \u001b[1;31m# Node is not computable, try skipping.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 665\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 666\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnode\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap_arguments\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtensor_dict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 667\u001b[1;33m \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnode\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlayer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 668\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 669\u001b[0m \u001b[1;31m# Update tensor_dict.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 670\u001b[0m for x_id, y in zip(\n", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\keras\\utils\\traceback_utils.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 68\u001b[0m \u001b[1;31m# To get the full stack trace, call:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 69\u001b[0m \u001b[1;31m# `tf.debugging.disable_traceback_filtering()`\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 70\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 71\u001b[0m \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m \u001b[1;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\keras\\engine\\base_layer.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1093\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1094\u001b[0m with autocast_variable.enable_auto_cast_variables(\n\u001b[0;32m 1095\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_compute_dtype_object\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1096\u001b[0m ):\n\u001b[1;32m-> 1097\u001b[1;33m \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcall_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1098\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1099\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_activity_regularizer\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1100\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_handle_activity_regularization\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\keras\\utils\\traceback_utils.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 154\u001b[0m \u001b[0mnew_e\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 155\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mnew_e\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 156\u001b[0m \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 157\u001b[0m \u001b[1;32mdel\u001b[0m \u001b[0msignature\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 158\u001b[1;33m \u001b[1;32mdel\u001b[0m \u001b[0mbound_signature\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\keras\\layers\\reshaping\\reshape.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, inputs)\u001b[0m\n\u001b[0;32m 136\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mcall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 137\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtarget_shape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 138\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexecuting_eagerly\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 139\u001b[0m \u001b[1;31m# Set the static shape for the result since it might lost during\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 140\u001b[0m \u001b[1;31m# array_ops reshape, eg, some `None` dim in the result could be\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow\\python\\util\\traceback_utils.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 151\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 152\u001b[0m \u001b[0mfiltered_tb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 153\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 154\u001b[0m \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 155\u001b[1;33m \u001b[1;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow\\python\\util\\dispatch.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 1173\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1174\u001b[0m \u001b[1;31m# Fallback dispatch system (dispatch v1):\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1175\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1176\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mdispatch_target\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1177\u001b[1;33m \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1178\u001b[0m \u001b[1;31m# Note: convert_to_eager_tensor currently raises a ValueError, not a\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1179\u001b[0m \u001b[1;31m# TypeError, when given unexpected types. So we need to catch both.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1180\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop_dispatch_handler\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow\\python\\ops\\array_ops.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(input, out_type, name)\u001b[0m\n\u001b[0;32m 625\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 626\u001b[0m \u001b[0mReturns\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 627\u001b[0m \u001b[0mA\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0mof\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mout_type\u001b[0m\u001b[0;31m`\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 628\u001b[0m \"\"\"\n\u001b[1;32m--> 629\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout_type\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow\\python\\util\\traceback_utils.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 151\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 152\u001b[0m \u001b[0mfiltered_tb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 153\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 154\u001b[0m \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 155\u001b[1;33m \u001b[1;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow\\python\\util\\dispatch.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 1173\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1174\u001b[0m \u001b[1;31m# Fallback dispatch system (dispatch v1):\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1175\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1176\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mdispatch_target\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1177\u001b[1;33m \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1178\u001b[0m \u001b[1;31m# Note: convert_to_eager_tensor currently raises a ValueError, not a\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1179\u001b[0m \u001b[1;31m# TypeError, when given unexpected types. So we need to catch both.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1180\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop_dispatch_handler\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow\\python\\ops\\array_ops.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(input, name, out_type)\u001b[0m\n\u001b[0;32m 652\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 653\u001b[0m \u001b[0mReturns\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 654\u001b[0m \u001b[0mA\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0mof\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mout_type\u001b[0m\u001b[0;31m`\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 655\u001b[0m \"\"\"\n\u001b[1;32m--> 656\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mshape_internal\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moptimize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout_type\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mout_type\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow\\python\\ops\\array_ops.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(input, name, optimize, out_type)\u001b[0m\n\u001b[0;32m 693\u001b[0m input_shape)\n\u001b[0;32m 694\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mconstant\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_shape\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_list\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout_type\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 695\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mout_type\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 696\u001b[0m \u001b[0mout_type\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdtypes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mint32\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 697\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mgen_array_ops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout_type\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mout_type\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow\\python\\ops\\gen_array_ops.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(input, out_type, name)\u001b[0m\n\u001b[0;32m 9356\u001b[0m _ctx, \"Shape\", name, input, \"out_type\", out_type)\n\u001b[0;32m 9357\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0m_result\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9358\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0m_core\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9359\u001b[0m \u001b[0m_ops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mraise_from_not_ok_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 9360\u001b[1;33m \u001b[1;32mexcept\u001b[0m \u001b[0m_core\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_FallbackException\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 9361\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9362\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9363\u001b[0m return shape_eager_fallback(\n", - "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + "\u001b[1;31merror\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[7], line 55\u001b[0m\n\u001b[0;32m 53\u001b[0m img \u001b[38;5;241m=\u001b[39m x_val[i]\n\u001b[0;32m 54\u001b[0m heatmap \u001b[38;5;241m=\u001b[39m make_gradcam_heatmap(img[np\u001b[38;5;241m.\u001b[39mnewaxis, \u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m], model, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtop_activation\u001b[39m\u001b[38;5;124m'\u001b[39m, second_last_conv_layer_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtop_conv\u001b[39m\u001b[38;5;124m'\u001b[39m, sensitivity_map \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m2\u001b[39m) \n\u001b[1;32m---> 55\u001b[0m heatmap \u001b[38;5;241m=\u001b[39m \u001b[43mcv2\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresize\u001b[49m\u001b[43m(\u001b[49m\u001b[43mheatmap\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mimg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mimg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 56\u001b[0m heatmap \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39muint8(\u001b[38;5;241m255\u001b[39m \u001b[38;5;241m*\u001b[39m heatmap)\n\u001b[0;32m 57\u001b[0m \u001b[38;5;66;03m# Apply Adaptive Histogram Equalization\u001b[39;00m\n", + "\u001b[1;31merror\u001b[0m: OpenCV(4.8.0) :-1: error: (-5:Bad argument) in function 'resize'\n> Overload resolution failed:\n> - src is not a numpy array, neither a scalar\n> - Expected Ptr for argument 'src'\n" ] }, {