train_floodgtn_series.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3f47fa9d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-22 21:44:22.016626: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-07-22 21:44:23.619462: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "from math import sqrt\n",
    "from tensorflow import keras\n",
    "from sklearn.metrics import mean_squared_error as mse\n",
    "from sklearn.metrics import mean_absolute_error as mae\n",
    "from tensorflow.keras.models import load_model\n",
    "from tensorflow.keras.layers import *\n",
    "from tensorflow.keras.models import *\n",
    "from tensorflow.keras.optimizers import *\n",
    "from tensorflow.keras.callbacks import *\n",
    "\n",
    "\n",
    "import keras_tuner as kt\n",
    "from GraphTransformerPrerocess import graph_global_transformer_local_process\n",
    "from graph import graph_topology_5\n",
    "from models.graph_global_transformer_local import graph_global_transformer_local"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0092be29",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"7\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9b08072",
   "metadata": {},
   "source": [
    "### Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3ffce4e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ====== preprocessing parameters ======\n",
    "n_hours = 72\n",
    "K = 24 \n",
    "masked_value = 1e-10\n",
    "split_1 = 0.8\n",
    "split_2 = 0.9\n",
    "sigma2 = 0.1\n",
    "epsilon = 0.5\n",
    "\n",
    "\n",
    "# ===== model parameters ======\n",
    "embed_feat = 4\n",
    "head_size = 48*2\n",
    "num_heads = 3\n",
    "ff_dim = 48\n",
    "num_transformer_blocks = 1\n",
    "dropout = 0.5\n",
    "atte_reg = 1e-2\n",
    "l1_reg = 0\n",
    "l2_reg = 1e-4\n",
    "gcn_unit1 = 48\n",
    "gcn_unit2 = 32\n",
    "gcn_unit3 = 16\n",
    "lstm_units = 32\n",
    "learning_rate = 1e-3\n",
    "decay_steps = 10000\n",
    "decay_rate = 0.95\n",
    "PATIENCE = 500\n",
    "EPOCHS = 3000\n",
    "BATCH = 512"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb603da6",
   "metadata": {},
   "source": [
    "### Preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "791b75cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['WS_S1', 'WS_S4', 'FLOW_S25A', 'GATE_S25A', 'HWS_S25A', 'TWS_S25A',\n",
      "       'FLOW_S25B', 'GATE_S25B', 'GATE_S25B2', 'HWS_S25B', 'TWS_S25B',\n",
      "       'PUMP_S25B', 'FLOW_S26', 'GATE_S26_1', 'GATE_S26_2', 'HWS_S26',\n",
      "       'TWS_S26', 'PUMP_S26', 'MEAN_RAIN'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "train_26, val_26, test_26, \\\n",
    "train_25B, val_25B, test_25B, \\\n",
    "train_25A, val_25A, test_25A, \\\n",
    "train_1, val_1, test_1, \\\n",
    "train_4, val_4, test_4, \\\n",
    "train_ws_y, val_ws_y, test_ws_y, \\\n",
    "scaler, ws_scaler = graph_global_transformer_local_process(n_hours, K, masked_value, split_1, split_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "10d7d60b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(77069, 96, 6) (9634, 96, 6) (19268, 96, 6) (77069, 96, 6) (9634, 96, 6) (19268, 96, 6) (77069, 96, 4) (9634, 96, 4) (19268, 96, 4) (77069, 96, 2) (9634, 96, 2) (19268, 96, 2) (77069, 96, 2) (9634, 96, 2) (19268, 96, 2) (77069, 96) (9634, 96) (19268, 96)\n"
     ]
    }
   ],
   "source": [
    "print(train_26.shape, val_26.shape, test_26.shape, \n",
    "      train_25B.shape, val_25B.shape, test_25B.shape, \n",
    "      train_25A.shape, val_25A.shape, test_25A.shape, \n",
    "      train_1.shape, val_1.shape, test_1.shape, \n",
    "      train_4.shape, val_4.shape, test_4.shape, \n",
    "      train_ws_y.shape, val_ws_y.shape, test_ws_y.shape\n",
    "     )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72640137",
   "metadata": {},
   "source": [
    "### Graph topology"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b46493dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "node_indices: [0 0 0 0 1 1 2 2 3 3 4 4] \n",
      "neighbor_indices: [1 2 3 4 0 2 0 1 0 4 0 3]\n",
      "number of nodes: 5, number of edges: 12\n"
     ]
    }
   ],
   "source": [
    "train_adj_mat, val_adj_mat, test_adj_mat = graph_topology_5(n_hours, K, sigma2, epsilon, len(train_ws_y), len(val_ws_y), len(test_ws_y))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8003a2dc",
   "metadata": {},
   "source": [
    "### Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7c638aef",
   "metadata": {},
   "outputs": [],
   "source": [
    "S26_shape = (train_26.shape[1], train_26.shape[2])\n",
    "S25B_shape = (train_25B.shape[1], train_25B.shape[2])\n",
    "S25A_shape = (train_25A.shape[1], train_25A.shape[2])\n",
    "S1_shape = (train_1.shape[1], train_1.shape[2])\n",
    "S4_shape = (train_4.shape[1], train_4.shape[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a7d6a860",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-22 21:44:34.876550: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 10402 MB memory:  -> device: 0, name: NVIDIA GeForce GTX 1080 Ti, pci bus id: 0000:89:00.0, compute capability: 6.1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Layer LSTM will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                Output Shape                 Param #   Connected to                  \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)        [(None, 96, 6)]              0         []                            \n",
      "                                                                                                  \n",
      " input_2 (InputLayer)        [(None, 96, 6)]              0         []                            \n",
      "                                                                                                  \n",
      " input_3 (InputLayer)        [(None, 96, 4)]              0         []                            \n",
      "                                                                                                  \n",
      " input_4 (InputLayer)        [(None, 96, 2)]              0         []                            \n",
      "                                                                                                  \n",
      " input_5 (InputLayer)        [(None, 96, 2)]              0         []                            \n",
      "                                                                                                  \n",
      " masking (Masking)           (None, 96, 6)                0         ['input_1[0][0]']             \n",
      "                                                                                                  \n",
      " masking_1 (Masking)         (None, 96, 6)                0         ['input_2[0][0]']             \n",
      "                                                                                                  \n",
      " masking_2 (Masking)         (None, 96, 4)                0         ['input_3[0][0]']             \n",
      "                                                                                                  \n",
      " masking_3 (Masking)         (None, 96, 2)                0         ['input_4[0][0]']             \n",
      "                                                                                                  \n",
      " masking_4 (Masking)         (None, 96, 2)                0         ['input_5[0][0]']             \n",
      "                                                                                                  \n",
      " multi_head_attention (Mult  (None, 96, 6)                7782      ['masking[0][0]',             \n",
      " iHeadAttention)                                                     'masking[0][0]']             \n",
      "                                                                                                  \n",
      " multi_head_attention_1 (Mu  (None, 96, 6)                7782      ['masking_1[0][0]',           \n",
      " ltiHeadAttention)                                                   'masking_1[0][0]']           \n",
      "                                                                                                  \n",
      " multi_head_attention_2 (Mu  (None, 96, 4)                5476      ['masking_2[0][0]',           \n",
      " ltiHeadAttention)                                                   'masking_2[0][0]']           \n",
      "                                                                                                  \n",
      " multi_head_attention_3 (Mu  (None, 96, 2)                3170      ['masking_3[0][0]',           \n",
      " ltiHeadAttention)                                                   'masking_3[0][0]']           \n",
      "                                                                                                  \n",
      " multi_head_attention_4 (Mu  (None, 96, 2)                3170      ['masking_4[0][0]',           \n",
      " ltiHeadAttention)                                                   'masking_4[0][0]']           \n",
      "                                                                                                  \n",
      " dropout (Dropout)           (None, 96, 6)                0         ['multi_head_attention[0][0]']\n",
      "                                                                                                  \n",
      " dropout_2 (Dropout)         (None, 96, 6)                0         ['multi_head_attention_1[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " dropout_4 (Dropout)         (None, 96, 4)                0         ['multi_head_attention_2[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " dropout_6 (Dropout)         (None, 96, 2)                0         ['multi_head_attention_3[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " dropout_8 (Dropout)         (None, 96, 2)                0         ['multi_head_attention_4[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " tf.__operators__.add (TFOp  (None, 96, 6)                0         ['dropout[0][0]',             \n",
      " Lambda)                                                             'masking[0][0]']             \n",
      "                                                                                                  \n",
      " tf.__operators__.add_2 (TF  (None, 96, 6)                0         ['dropout_2[0][0]',           \n",
      " OpLambda)                                                           'masking_1[0][0]']           \n",
      "                                                                                                  \n",
      " tf.__operators__.add_4 (TF  (None, 96, 4)                0         ['dropout_4[0][0]',           \n",
      " OpLambda)                                                           'masking_2[0][0]']           \n",
      "                                                                                                  \n",
      " tf.__operators__.add_6 (TF  (None, 96, 2)                0         ['dropout_6[0][0]',           \n",
      " OpLambda)                                                           'masking_3[0][0]']           \n",
      "                                                                                                  \n",
      " tf.__operators__.add_8 (TF  (None, 96, 2)                0         ['dropout_8[0][0]',           \n",
      " OpLambda)                                                           'masking_4[0][0]']           \n",
      "                                                                                                  \n",
      " dense (Dense)               (None, 96, 48)               336       ['tf.__operators__.add[0][0]']\n",
      "                                                                                                  \n",
      " dense_2 (Dense)             (None, 96, 48)               336       ['tf.__operators__.add_2[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " dense_4 (Dense)             (None, 96, 48)               240       ['tf.__operators__.add_4[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " dense_6 (Dense)             (None, 96, 48)               144       ['tf.__operators__.add_6[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " dense_8 (Dense)             (None, 96, 48)               144       ['tf.__operators__.add_8[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " dropout_1 (Dropout)         (None, 96, 48)               0         ['dense[0][0]']               \n",
      "                                                                                                  \n",
      " dropout_3 (Dropout)         (None, 96, 48)               0         ['dense_2[0][0]']             \n",
      "                                                                                                  \n",
      " dropout_5 (Dropout)         (None, 96, 48)               0         ['dense_4[0][0]']             \n",
      "                                                                                                  \n",
      " dropout_7 (Dropout)         (None, 96, 48)               0         ['dense_6[0][0]']             \n",
      "                                                                                                  \n",
      " dropout_9 (Dropout)         (None, 96, 48)               0         ['dense_8[0][0]']             \n",
      "                                                                                                  \n",
      " dense_1 (Dense)             (None, 96, 6)                294       ['dropout_1[0][0]']           \n",
      "                                                                                                  \n",
      " dense_3 (Dense)             (None, 96, 6)                294       ['dropout_3[0][0]']           \n",
      "                                                                                                  \n",
      " dense_5 (Dense)             (None, 96, 4)                196       ['dropout_5[0][0]']           \n",
      "                                                                                                  \n",
      " dense_7 (Dense)             (None, 96, 2)                98        ['dropout_7[0][0]']           \n",
      "                                                                                                  \n",
      " dense_9 (Dense)             (None, 96, 2)                98        ['dropout_9[0][0]']           \n",
      "                                                                                                  \n",
      " layer_normalization_1 (Lay  (None, 96, 6)                12        ['dense_1[0][0]']             \n",
      " erNormalization)                                                                                 \n",
      "                                                                                                  \n",
      " layer_normalization_3 (Lay  (None, 96, 6)                12        ['dense_3[0][0]']             \n",
      " erNormalization)                                                                                 \n",
      "                                                                                                  \n",
      " layer_normalization_5 (Lay  (None, 96, 4)                8         ['dense_5[0][0]']             \n",
      " erNormalization)                                                                                 \n",
      "                                                                                                  \n",
      " layer_normalization_7 (Lay  (None, 96, 2)                4         ['dense_7[0][0]']             \n",
      " erNormalization)                                                                                 \n",
      "                                                                                                  \n",
      " layer_normalization_9 (Lay  (None, 96, 2)                4         ['dense_9[0][0]']             \n",
      " erNormalization)                                                                                 \n",
      "                                                                                                  \n",
      " tf.__operators__.add_1 (TF  (None, 96, 6)                0         ['layer_normalization_1[0][0]'\n",
      " OpLambda)                                                          , 'tf.__operators__.add[0][0]'\n",
      "                                                                    ]                             \n",
      "                                                                                                  \n",
      " tf.__operators__.add_3 (TF  (None, 96, 6)                0         ['layer_normalization_3[0][0]'\n",
      " OpLambda)                                                          , 'tf.__operators__.add_2[0][0\n",
      "                                                                    ]']                           \n",
      "                                                                                                  \n",
      " tf.__operators__.add_5 (TF  (None, 96, 4)                0         ['layer_normalization_5[0][0]'\n",
      " OpLambda)                                                          , 'tf.__operators__.add_4[0][0\n",
      "                                                                    ]']                           \n",
      "                                                                                                  \n",
      " tf.__operators__.add_7 (TF  (None, 96, 2)                0         ['layer_normalization_7[0][0]'\n",
      " OpLambda)                                                          , 'tf.__operators__.add_6[0][0\n",
      "                                                                    ]']                           \n",
      "                                                                                                  \n",
      " tf.__operators__.add_9 (TF  (None, 96, 2)                0         ['layer_normalization_9[0][0]'\n",
      " OpLambda)                                                          , 'tf.__operators__.add_8[0][0\n",
      "                                                                    ]']                           \n",
      "                                                                                                  \n",
      " conv1d (Conv1D)             (None, 96, 4)                52        ['tf.__operators__.add_1[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv1d_1 (Conv1D)           (None, 96, 4)                52        ['tf.__operators__.add_3[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv1d_2 (Conv1D)           (None, 96, 4)                36        ['tf.__operators__.add_5[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv1d_3 (Conv1D)           (None, 96, 4)                20        ['tf.__operators__.add_7[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv1d_4 (Conv1D)           (None, 96, 4)                20        ['tf.__operators__.add_9[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " concat_local (Concatenate)  (None, 96, 20)               0         ['conv1d[0][0]',              \n",
      "                                                                     'conv1d_1[0][0]',            \n",
      "                                                                     'conv1d_2[0][0]',            \n",
      "                                                                     'conv1d_3[0][0]',            \n",
      "                                                                     'conv1d_4[0][0]']            \n",
      "                                                                                                  \n",
      " reshape_global (Reshape)    (None, 5, 384)               0         ['concat_local[0][0]']        \n",
      "                                                                                                  \n",
      " input_lap (InputLayer)      [(None, 5, 5)]               0         []                            \n",
      "                                                                                                  \n",
      " GCNConv1 (GCNConv)          (None, 5, 48)                18480     ['reshape_global[0][0]',      \n",
      "                                                                     'input_lap[0][0]']           \n",
      "                                                                                                  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " GCNConv2 (GCNConv)          (None, 5, 32)                1568      ['GCNConv1[0][0]',            \n",
      "                                                                     'input_lap[0][0]']           \n",
      "                                                                                                  \n",
      " LSTM (LSTM)                 (None, 5, 32)                53376     ['reshape_global[0][0]']      \n",
      "                                                                                                  \n",
      " concat_global (Concatenate  (None, 5, 64)                0         ['GCNConv2[0][0]',            \n",
      " )                                                                   'LSTM[0][0]']                \n",
      "                                                                                                  \n",
      " dense_10 (Dense)            (None, 5, 32)                2080      ['concat_global[0][0]']       \n",
      "                                                                                                  \n",
      " attention (Attention)       (None, 5, 32)                0         ['dense_10[0][0]',            \n",
      "                                                                     'dense_10[0][0]']            \n",
      "                                                                                                  \n",
      " Flatten (Flatten)           (None, 160)                  0         ['attention[0][0]']           \n",
      "                                                                                                  \n",
      " final_dense (Dense)         (None, 96)                   15456     ['Flatten[0][0]']             \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 120740 (471.64 KB)\n",
      "Trainable params: 120740 (471.64 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model, GCNConv = graph_global_transformer_local(gcn1=gcn_unit1,\n",
    "                                                gcn2=gcn_unit2,\n",
    "                                                gcn3=gcn_unit3,\n",
    "                                                lstm_unit=lstm_units, \n",
    "                                                num_transformer_blocks=num_transformer_blocks,\n",
    "                                                head_size=head_size,\n",
    "                                                num_heads=num_heads,\n",
    "                                                ff_dim=ff_dim, \n",
    "                                                atte_reg=atte_reg, \n",
    "                                                l1_reg=l1_reg, \n",
    "                                                l2_reg=l2_reg, \n",
    "                                                dropout=dropout,\n",
    "                                                masked_value=masked_value,\n",
    "                                                embed_feat=embed_feat,\n",
    "                                                S26_shape=S26_shape, \n",
    "                                                S25B_shape=S25B_shape, \n",
    "                                                S25A_shape=S25A_shape, \n",
    "                                                S1_shape=S1_shape,\n",
    "                                                S4_shape=S4_shape\n",
    "                                               )\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5c1bdf2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-22 21:44:51.189230: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:432] Loaded cuDNN version 8600\n",
      "2023-07-22 21:44:52.292636: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fcc3c00b8e0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
      "2023-07-22 21:44:52.292699: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): NVIDIA GeForce GTX 1080 Ti, Compute Capability 6.1\n",
      "2023-07-22 21:44:52.303315: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:255] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
      "2023-07-22 21:44:52.751096: I ./tensorflow/compiler/jit/device_compiler.h:186] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 1: val_mae improved from inf to 0.31143, saving model to saved_models/graph_global_transformer_local_gate_pump_1.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/aul/homes/jshi008/miniconda3/envs/tf213/lib/python3.10/site-packages/keras/src/engine/training.py:3000: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "151/151 - 54s - loss: 0.6033 - mae: 0.1693 - val_loss: 0.2851 - val_mae: 0.3114 - 54s/epoch - 358ms/step\n",
      "Epoch 2/3000\n",
      "\n",
      "Epoch 2: val_mae improved from 0.31143 to 0.20491, saving model to saved_models/graph_global_transformer_local_gate_pump_1.h5\n",
      "151/151 - 22s - loss: 0.1010 - mae: 0.0923 - val_loss: 0.1005 - val_mae: 0.2049 - 22s/epoch - 147ms/step\n",
      "Epoch 3/3000\n",
      "\n",
      "Epoch 3: val_mae improved from 0.20491 to 0.11123, saving model to saved_models/graph_global_transformer_local_gate_pump_1.h5\n",
      "151/151 - 22s - loss: 0.0371 - mae: 0.0574 - val_loss: 0.0393 - val_mae: 0.1112 - 22s/epoch - 147ms/step\n",
      "Epoch 4/3000\n",
      "\n",
      "Epoch 4: val_mae improved from 0.11123 to 0.06268, saving model to saved_models/graph_global_transformer_local_gate_pump_1.h5\n",
      "151/151 - 22s - loss: 0.0204 - mae: 0.0409 - val_loss: 0.0210 - val_mae: 0.0627 - 22s/epoch - 147ms/step\n",
      "Epoch 5/3000\n",
      "\n",
      "Epoch 5: val_mae improved from 0.06268 to 0.04821, saving model to saved_models/graph_global_transformer_local_gate_pump_1.h5\n",
      "151/151 - 22s - loss: 0.0152 - mae: 0.0339 - val_loss: 0.0160 - val_mae: 0.0482 - 22s/epoch - 147ms/step\n",
      "Epoch 6/3000\n",
      "\n",
      "Epoch 6: val_mae improved from 0.04821 to 0.04522, saving model to saved_models/graph_global_transformer_local_gate_pump_1.h5\n",
      "151/151 - 22s - loss: 0.0131 - mae: 0.0292 - val_loss: 0.0144 - val_mae: 0.0452 - 22s/epoch - 147ms/step\n",
      "Epoch 7/3000\n",
      "\n",
      "Epoch 7: val_mae improved from 0.04522 to 0.04490, saving model to saved_models/graph_global_transformer_local_gate_pump_1.h5\n",
      "151/151 - 22s - loss: 0.0120 - mae: 0.0262 - val_loss: 0.0137 - val_mae: 0.0449 - 22s/epoch - 147ms/step\n",
      "Epoch 8/3000\n",
      "\n",
      "Epoch 8: val_mae improved from 0.04490 to 0.04233, saving model to saved_models/graph_global_transformer_local_gate_pump_1.h5\n",
      "151/151 - 22s - loss: 0.0113 - mae: 0.0244 - val_loss: 0.0128 - val_mae: 0.0423 - 22s/epoch - 147ms/step\n",
      "Epoch 9/3000\n",
      "\n",
      "Epoch 9: val_mae improved from 0.04233 to 0.04217, saving model to saved_models/graph_global_transformer_local_gate_pump_1.h5\n",
      "151/151 - 22s - loss: 0.0107 - mae: 0.0233 - val_loss: 0.0123 - val_mae: 0.0422 - 22s/epoch - 147ms/step\n",
      "Epoch 10/3000\n",
      "\n",
      "Epoch 10: val_mae improved from 0.04217 to 0.04089, saving model to saved_models/graph_global_transformer_local_gate_pump_1.h5\n",
      "151/151 - 22s - loss: 0.0101 - mae: 0.0225 - val_loss: 0.0117 - val_mae: 0.0409 - 22s/epoch - 147ms/step\n",
      "Epoch 11/3000\n",
      "\n",
      "Epoch 11: val_mae improved from 0.04089 to 0.04027, saving model to saved_models/graph_global_transformer_local_gate_pump_1.h5\n",
      "151/151 - 22s - loss: 0.0097 - mae: 0.0220 - val_loss: 0.0111 - val_mae: 0.0403 - 22s/epoch - 147ms/step\n",
      "Epoch 12/3000\n",
      "\n",
      "Epoch 12: val_mae did not improve from 0.04027\n",
      "151/151 - 22s - loss: 0.0092 - mae: 0.0216 - val_loss: 0.0107 - val_mae: 0.0406 - 22s/epoch - 145ms/step\n",
      "Epoch 13/3000\n",
      "\n",
      "Epoch 13: val_mae improved from 0.04027 to 0.03963, saving model to saved_models/graph_global_transformer_local_gate_pump_1.h5\n",
      "151/151 - 22s - loss: 0.0087 - mae: 0.0212 - val_loss: 0.0102 - val_mae: 0.0396 - 22s/epoch - 148ms/step\n",
      "Epoch 14/3000\n",
      "\n",
      "Epoch 14: val_mae improved from 0.03963 to 0.03851, saving model to saved_models/graph_global_transformer_local_gate_pump_1.h5\n",
      "151/151 - 22s - loss: 0.0083 - mae: 0.0209 - val_loss: 0.0097 - val_mae: 0.0385 - 22s/epoch - 147ms/step\n",
      "Epoch 15/3000\n",
      "\n",
      "Epoch 15: val_mae did not improve from 0.03851\n",
      "151/151 - 22s - loss: 0.0079 - mae: 0.0205 - val_loss: 0.0094 - val_mae: 0.0391 - 22s/epoch - 146ms/step\n",
      "Epoch 16/3000\n",
      "\n",
      "Epoch 16: val_mae improved from 0.03851 to 0.03844, saving model to saved_models/graph_global_transformer_local_gate_pump_1.h5\n",
      "151/151 - 22s - loss: 0.0075 - mae: 0.0202 - val_loss: 0.0090 - val_mae: 0.0384 - 22s/epoch - 147ms/step\n",
      "Epoch 17/3000\n",
      "\n",
      "Epoch 17: val_mae improved from 0.03844 to 0.03775, saving model to saved_models/graph_global_transformer_local_gate_pump_1.h5\n",
      "151/151 - 22s - loss: 0.0072 - mae: 0.0200 - val_loss: 0.0085 - val_mae: 0.0377 - 22s/epoch - 147ms/step\n",
      "Epoch 18/3000\n",
      "\n",
      "Epoch 18: val_mae did not improve from 0.03775\n",
      "151/151 - 22s - loss: 0.0068 - mae: 0.0196 - val_loss: 0.0082 - val_mae: 0.0379 - 22s/epoch - 146ms/step\n",
      "Epoch 19/3000\n",
      "\n",
      "Epoch 19: val_mae improved from 0.03775 to 0.03683, saving model to saved_models/graph_global_transformer_local_gate_pump_1.h5\n",
      "151/151 - 22s - loss: 0.0065 - mae: 0.0192 - val_loss: 0.0078 - val_mae: 0.0368 - 22s/epoch - 146ms/step\n",
      "Epoch 20/3000\n",
      "\n",
      "Epoch 20: val_mae improved from 0.03683 to 0.03618, saving model to saved_models/graph_global_transformer_local_gate_pump_1.h5\n",
      "151/151 - 22s - loss: 0.0061 - mae: 0.0187 - val_loss: 0.0074 - val_mae: 0.0362 - 22s/epoch - 147ms/step\n",
      "Epoch 21/3000\n",
      "\n",
      "Epoch 21: val_mae did not improve from 0.03618\n",
      "151/151 - 22s - loss: 0.0058 - mae: 0.0182 - val_loss: 0.0071 - val_mae: 0.0364 - 22s/epoch - 144ms/step\n",
      "Epoch 22/3000\n",
      "\n",
      "Epoch 22: val_mae improved from 0.03618 to 0.03514, saving model to saved_models/graph_global_transformer_local_gate_pump_1.h5\n",
      "151/151 - 22s - loss: 0.0055 - mae: 0.0176 - val_loss: 0.0067 - val_mae: 0.0351 - 22s/epoch - 147ms/step\n",
      "Epoch 23/3000\n",
      "\n",
      "Epoch 23: val_mae improved from 0.03514 to 0.03455, saving model to saved_models/graph_global_transformer_local_gate_pump_1.h5\n",
      "151/151 - 22s - loss: 0.0052 - mae: 0.0172 - val_loss: 0.0063 - val_mae: 0.0345 - 22s/epoch - 146ms/step\n",
      "Epoch 24/3000\n",
      "\n",
      "Epoch 24: val_mae improved from 0.03455 to 0.03373, saving model to saved_models/graph_global_transformer_local_gate_pump_1.h5\n",
      "151/151 - 22s - loss: 0.0049 - mae: 0.0166 - val_loss: 0.0060 - val_mae: 0.0337 - 22s/epoch - 146ms/step\n",
      "Epoch 25/3000\n",
      "\n",
      "Epoch 25: val_mae improved from 0.03373 to 0.03308, saving model to saved_models/graph_global_transformer_local_gate_pump_1.h5\n",
      "151/151 - 22s - loss: 0.0046 - mae: 0.0163 - val_loss: 0.0057 - val_mae: 0.0331 - 22s/epoch - 147ms/step\n",
      "Epoch 26/3000\n",
      "\n",
      "Epoch 26: val_mae improved from 0.03308 to 0.03289, saving model to saved_models/graph_global_transformer_local_gate_pump_1.h5\n",
      "151/151 - 22s - loss: 0.0043 - mae: 0.0159 - val_loss: 0.0054 - val_mae: 0.0329 - 22s/epoch - 146ms/step\n",
      "Epoch 27/3000\n",
      "\n",
      "Epoch 27: val_mae improved from 0.03289 to 0.03177, saving model to saved_models/graph_global_transformer_local_gate_pump_1.h5\n",
      "151/151 - 22s - loss: 0.0041 - mae: 0.0155 - val_loss: 0.0050 - val_mae: 0.0318 - 22s/epoch - 146ms/step\n",
      "Epoch 28/3000\n",
      "\n",
      "Epoch 28: val_mae improved from 0.03177 to 0.03171, saving model to saved_models/graph_global_transformer_local_gate_pump_1.h5\n",
      "151/151 - 22s - loss: 0.0038 - mae: 0.0152 - val_loss: 0.0048 - val_mae: 0.0317 - 22s/epoch - 146ms/step\n",
      "Epoch 29/3000\n",
      "\n",
      "Epoch 29: val_mae did not improve from 0.03171\n",
      "151/151 - 22s - loss: 0.0036 - mae: 0.0149 - val_loss: 0.0046 - val_mae: 0.0323 - 22s/epoch - 144ms/step\n",
      "Epoch 30/3000\n",
      "\n",
      "Epoch 30: val_mae did not improve from 0.03171\n",
      "151/151 - 22s - loss: 0.0034 - mae: 0.0149 - val_loss: 0.0044 - val_mae: 0.0323 - 22s/epoch - 145ms/step\n",
      "Epoch 31/3000\n",
      "\n",
      "Epoch 31: val_mae improved from 0.03171 to 0.03123, saving model to saved_models/graph_global_transformer_local_gate_pump_1.h5\n",
      "151/151 - 22s - loss: 0.0032 - mae: 0.0145 - val_loss: 0.0041 - val_mae: 0.0312 - 22s/epoch - 146ms/step\n",
      "Epoch 32/3000\n",
      "\n",
      "Epoch 32: val_mae improved from 0.03123 to 0.03037, saving model to saved_models/graph_global_transformer_local_gate_pump_1.h5\n",
      "151/151 - 22s - loss: 0.0030 - mae: 0.0143 - val_loss: 0.0039 - val_mae: 0.0304 - 22s/epoch - 146ms/step\n",
      "Epoch 33/3000\n",
      "\n",
      "Epoch 33: val_mae improved from 0.03037 to 0.03036, saving model to saved_models/graph_global_transformer_local_gate_pump_1.h5\n",
      "151/151 - 22s - loss: 0.0028 - mae: 0.0141 - val_loss: 0.0037 - val_mae: 0.0304 - 22s/epoch - 145ms/step\n",
      "Epoch 34/3000\n",
      "\n",
      "Epoch 34: val_mae did not improve from 0.03036\n",
      "151/151 - 22s - loss: 0.0026 - mae: 0.0140 - val_loss: 0.0036 - val_mae: 0.0312 - 22s/epoch - 143ms/step\n",
      "Epoch 35/3000\n",
      "\n",
      "Epoch 35: val_mae improved from 0.03036 to 0.03008, saving model to saved_models/graph_global_transformer_local_gate_pump_1.h5\n",
      "151/151 - 22s - loss: 0.0024 - mae: 0.0140 - val_loss: 0.0033 - val_mae: 0.0301 - 22s/epoch - 144ms/step\n",
      "Epoch 36/3000\n",
      "\n",
      "Epoch 36: val_mae did not improve from 0.03008\n",
      "151/151 - 21s - loss: 0.0023 - mae: 0.0139 - val_loss: 0.0035 - val_mae: 0.0330 - 21s/epoch - 142ms/step\n",
      "Epoch 37/3000\n",
      "\n",
      "Epoch 37: val_mae did not improve from 0.03008\n",
      "151/151 - 21s - loss: 0.0021 - mae: 0.0138 - val_loss: 0.0032 - val_mae: 0.0314 - 21s/epoch - 141ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 38/3000\n",
      "\n",
      "Epoch 38: val_mae improved from 0.03008 to 0.02995, saving model to saved_models/graph_global_transformer_local_gate_pump_1.h5\n",
      "151/151 - 22s - loss: 0.0020 - mae: 0.0138 - val_loss: 0.0029 - val_mae: 0.0300 - 22s/epoch - 143ms/step\n",
      "Epoch 39/3000\n",
      "\n",
      "Epoch 39: val_mae did not improve from 0.02995\n",
      "151/151 - 21s - loss: 0.0018 - mae: 0.0135 - val_loss: 0.0028 - val_mae: 0.0302 - 21s/epoch - 141ms/step\n",
      "Epoch 40/3000\n",
      "\n",
      "Epoch 40: val_mae improved from 0.02995 to 0.02946, saving model to saved_models/graph_global_transformer_local_gate_pump_1.h5\n",
      "151/151 - 22s - loss: 0.0017 - mae: 0.0134 - val_loss: 0.0026 - val_mae: 0.0295 - 22s/epoch - 143ms/step\n",
      "Epoch 41/3000\n",
      "\n",
      "Epoch 41: val_mae did not improve from 0.02946\n",
      "151/151 - 21s - loss: 0.0016 - mae: 0.0134 - val_loss: 0.0025 - val_mae: 0.0298 - 21s/epoch - 142ms/step\n",
      "Epoch 42/3000\n",
      "\n",
      "Epoch 42: val_mae did not improve from 0.02946\n",
      "151/151 - 21s - loss: 0.0015 - mae: 0.0133 - val_loss: 0.0026 - val_mae: 0.0314 - 21s/epoch - 141ms/step\n",
      "Epoch 43/3000\n",
      "\n",
      "Epoch 43: val_mae did not improve from 0.02946\n",
      "151/151 - 21s - loss: 0.0014 - mae: 0.0132 - val_loss: 0.0025 - val_mae: 0.0315 - 21s/epoch - 141ms/step\n",
      "Epoch 44/3000\n",
      "\n",
      "Epoch 44: val_mae did not improve from 0.02946\n",
      "151/151 - 21s - loss: 0.0013 - mae: 0.0132 - val_loss: 0.0024 - val_mae: 0.0310 - 21s/epoch - 141ms/step\n",
      "Epoch 45/3000\n",
      "\n",
      "Epoch 45: val_mae did not improve from 0.02946\n",
      "151/151 - 21s - loss: 0.0012 - mae: 0.0130 - val_loss: 0.0022 - val_mae: 0.0296 - 21s/epoch - 141ms/step\n",
      "Epoch 46/3000\n",
      "\n",
      "Epoch 46: val_mae did not improve from 0.02946\n",
      "151/151 - 21s - loss: 0.0011 - mae: 0.0128 - val_loss: 0.0022 - val_mae: 0.0308 - 21s/epoch - 141ms/step\n",
      "Epoch 47/3000\n",
      "\n",
      "Epoch 47: val_mae did not improve from 0.02946\n",
      "151/151 - 21s - loss: 0.0011 - mae: 0.0130 - val_loss: 0.0020 - val_mae: 0.0298 - 21s/epoch - 142ms/step\n",
      "Epoch 48/3000\n",
      "\n",
      "Epoch 48: val_mae improved from 0.02946 to 0.02927, saving model to saved_models/graph_global_transformer_local_gate_pump_1.h5\n",
      "151/151 - 22s - loss: 9.9517e-04 - mae: 0.0129 - val_loss: 0.0019 - val_mae: 0.0293 - 22s/epoch - 145ms/step\n",
      "Epoch 49/3000\n",
      "\n",
      "Epoch 49: val_mae improved from 0.02927 to 0.02885, saving model to saved_models/graph_global_transformer_local_gate_pump_1.h5\n",
      "151/151 - 22s - loss: 9.3076e-04 - mae: 0.0128 - val_loss: 0.0018 - val_mae: 0.0289 - 22s/epoch - 145ms/step\n",
      "Epoch 50/3000\n",
      "\n",
      "Epoch 50: val_mae did not improve from 0.02885\n",
      "151/151 - 22s - loss: 8.7517e-04 - mae: 0.0127 - val_loss: 0.0019 - val_mae: 0.0304 - 22s/epoch - 143ms/step\n",
      "Epoch 51/3000\n",
      "\n",
      "Epoch 51: val_mae did not improve from 0.02885\n",
      "151/151 - 22s - loss: 8.2587e-04 - mae: 0.0126 - val_loss: 0.0018 - val_mae: 0.0293 - 22s/epoch - 144ms/step\n",
      "Epoch 52/3000\n",
      "\n",
      "Epoch 52: val_mae did not improve from 0.02885\n",
      "151/151 - 22s - loss: 7.8084e-04 - mae: 0.0126 - val_loss: 0.0018 - val_mae: 0.0300 - 22s/epoch - 143ms/step\n",
      "Epoch 53/3000\n",
      "\n",
      "Epoch 53: val_mae did not improve from 0.02885\n",
      "151/151 - 22s - loss: 7.4370e-04 - mae: 0.0126 - val_loss: 0.0019 - val_mae: 0.0312 - 22s/epoch - 143ms/step\n",
      "Epoch 54/3000\n",
      "\n",
      "Epoch 54: val_mae did not improve from 0.02885\n",
      "151/151 - 22s - loss: 7.0671e-04 - mae: 0.0126 - val_loss: 0.0019 - val_mae: 0.0319 - 22s/epoch - 143ms/step\n",
      "Epoch 55/3000\n",
      "\n",
      "Epoch 55: val_mae did not improve from 0.02885\n",
      "151/151 - 22s - loss: 6.6975e-04 - mae: 0.0125 - val_loss: 0.0017 - val_mae: 0.0300 - 22s/epoch - 144ms/step\n",
      "Epoch 56/3000\n",
      "\n",
      "Epoch 56: val_mae did not improve from 0.02885\n",
      "151/151 - 22s - loss: 6.4094e-04 - mae: 0.0124 - val_loss: 0.0016 - val_mae: 0.0291 - 22s/epoch - 144ms/step\n",
      "Epoch 57/3000\n",
      "\n",
      "Epoch 57: val_mae did not improve from 0.02885\n",
      "151/151 - 22s - loss: 6.1463e-04 - mae: 0.0124 - val_loss: 0.0017 - val_mae: 0.0308 - 22s/epoch - 145ms/step\n",
      "Epoch 58/3000\n",
      "\n",
      "Epoch 58: val_mae did not improve from 0.02885\n",
      "151/151 - 22s - loss: 5.8772e-04 - mae: 0.0123 - val_loss: 0.0018 - val_mae: 0.0320 - 22s/epoch - 144ms/step\n",
      "Epoch 59/3000\n",
      "\n",
      "Epoch 59: val_mae did not improve from 0.02885\n",
      "151/151 - 22s - loss: 5.6727e-04 - mae: 0.0122 - val_loss: 0.0015 - val_mae: 0.0289 - 22s/epoch - 144ms/step\n",
      "Epoch 60/3000\n",
      "\n",
      "Epoch 60: val_mae did not improve from 0.02885\n",
      "151/151 - 22s - loss: 5.4949e-04 - mae: 0.0122 - val_loss: 0.0016 - val_mae: 0.0297 - 22s/epoch - 143ms/step\n",
      "Epoch 61/3000\n",
      "\n",
      "Epoch 61: val_mae did not improve from 0.02885\n",
      "151/151 - 22s - loss: 5.3273e-04 - mae: 0.0122 - val_loss: 0.0016 - val_mae: 0.0296 - 22s/epoch - 144ms/step\n",
      "Epoch 62/3000\n",
      "\n",
      "Epoch 62: val_mae did not improve from 0.02885\n",
      "151/151 - 22s - loss: 5.2941e-04 - mae: 0.0125 - val_loss: 0.0016 - val_mae: 0.0298 - 22s/epoch - 143ms/step\n",
      "Epoch 63/3000\n",
      "\n",
      "Epoch 63: val_mae did not improve from 0.02885\n",
      "151/151 - 22s - loss: 5.0114e-04 - mae: 0.0120 - val_loss: 0.0015 - val_mae: 0.0289 - 22s/epoch - 143ms/step\n",
      "Epoch 64/3000\n",
      "\n",
      "Epoch 64: val_mae improved from 0.02885 to 0.02868, saving model to saved_models/graph_global_transformer_local_gate_pump_1.h5\n",
      "151/151 - 22s - loss: 4.8609e-04 - mae: 0.0118 - val_loss: 0.0014 - val_mae: 0.0287 - 22s/epoch - 146ms/step\n",
      "Epoch 65/3000\n",
      "\n",
      "Epoch 65: val_mae did not improve from 0.02868\n",
      "151/151 - 22s - loss: 4.8163e-04 - mae: 0.0120 - val_loss: 0.0015 - val_mae: 0.0289 - 22s/epoch - 144ms/step\n",
      "Epoch 66/3000\n",
      "\n",
      "Epoch 66: val_mae did not improve from 0.02868\n",
      "151/151 - 22s - loss: 4.6964e-04 - mae: 0.0118 - val_loss: 0.0016 - val_mae: 0.0301 - 22s/epoch - 144ms/step\n",
      "Epoch 67/3000\n",
      "\n",
      "Epoch 67: val_mae improved from 0.02868 to 0.02824, saving model to saved_models/graph_global_transformer_local_gate_pump_1.h5\n",
      "151/151 - 22s - loss: 4.5989e-04 - mae: 0.0118 - val_loss: 0.0014 - val_mae: 0.0282 - 22s/epoch - 145ms/step\n",
      "Epoch 68/3000\n",
      "\n",
      "Epoch 68: val_mae did not improve from 0.02824\n",
      "151/151 - 22s - loss: 4.5561e-04 - mae: 0.0118 - val_loss: 0.0015 - val_mae: 0.0291 - 22s/epoch - 144ms/step\n",
      "Epoch 69/3000\n",
      "\n",
      "Epoch 69: val_mae did not improve from 0.02824\n",
      "151/151 - 22s - loss: 4.5299e-04 - mae: 0.0119 - val_loss: 0.0016 - val_mae: 0.0300 - 22s/epoch - 143ms/step\n",
      "Epoch 70/3000\n",
      "\n",
      "Epoch 70: val_mae did not improve from 0.02824\n",
      "151/151 - 22s - loss: 4.4737e-04 - mae: 0.0119 - val_loss: 0.0015 - val_mae: 0.0293 - 22s/epoch - 144ms/step\n",
      "Epoch 71/3000\n",
      "\n",
      "Epoch 71: val_mae did not improve from 0.02824\n",
      "151/151 - 22s - loss: 4.3482e-04 - mae: 0.0116 - val_loss: 0.0015 - val_mae: 0.0295 - 22s/epoch - 143ms/step\n",
      "Epoch 72/3000\n",
      "\n",
      "Epoch 72: val_mae improved from 0.02824 to 0.02810, saving model to saved_models/graph_global_transformer_local_gate_pump_1.h5\n",
      "151/151 - 22s - loss: 4.2738e-04 - mae: 0.0115 - val_loss: 0.0013 - val_mae: 0.0281 - 22s/epoch - 145ms/step\n",
      "Epoch 73/3000\n",
      "\n",
      "Epoch 73: val_mae did not improve from 0.02810\n",
      "151/151 - 22s - loss: 4.3154e-04 - mae: 0.0117 - val_loss: 0.0016 - val_mae: 0.0308 - 22s/epoch - 143ms/step\n",
      "Epoch 74/3000\n",
      "\n",
      "Epoch 74: val_mae did not improve from 0.02810\n",
      "151/151 - 22s - loss: 4.1865e-04 - mae: 0.0114 - val_loss: 0.0015 - val_mae: 0.0297 - 22s/epoch - 143ms/step\n",
      "Epoch 75/3000\n",
      "\n",
      "Epoch 75: val_mae did not improve from 0.02810\n",
      "151/151 - 22s - loss: 4.2002e-04 - mae: 0.0116 - val_loss: 0.0014 - val_mae: 0.0288 - 22s/epoch - 143ms/step\n",
      "Epoch 76/3000\n",
      "\n",
      "Epoch 76: val_mae did not improve from 0.02810\n",
      "151/151 - 22s - loss: 4.1431e-04 - mae: 0.0115 - val_loss: 0.0014 - val_mae: 0.0290 - 22s/epoch - 143ms/step\n",
      "Epoch 77/3000\n",
      "\n",
      "Epoch 77: val_mae improved from 0.02810 to 0.02798, saving model to saved_models/graph_global_transformer_local_gate_pump_1.h5\n",
      "151/151 - 22s - loss: 4.1177e-04 - mae: 0.0115 - val_loss: 0.0013 - val_mae: 0.0280 - 22s/epoch - 145ms/step\n",
      "Epoch 78/3000\n",
      "\n",
      "Epoch 78: val_mae did not improve from 0.02798\n",
      "151/151 - 22s - loss: 4.0932e-04 - mae: 0.0115 - val_loss: 0.0013 - val_mae: 0.0280 - 22s/epoch - 144ms/step\n",
      "Epoch 79/3000\n",
      "\n",
      "Epoch 79: val_mae did not improve from 0.02798\n",
      "151/151 - 22s - loss: 4.0040e-04 - mae: 0.0112 - val_loss: 0.0014 - val_mae: 0.0283 - 22s/epoch - 144ms/step\n",
      "Epoch 80/3000\n",
      "\n",
      "Epoch 80: val_mae did not improve from 0.02798\n",
      "151/151 - 22s - loss: 4.0032e-04 - mae: 0.0113 - val_loss: 0.0015 - val_mae: 0.0297 - 22s/epoch - 143ms/step\n",
      "Epoch 81/3000\n",
      "\n",
      "Epoch 81: val_mae did not improve from 0.02798\n",
      "151/151 - 22s - loss: 3.9960e-04 - mae: 0.0113 - val_loss: 0.0014 - val_mae: 0.0291 - 22s/epoch - 144ms/step\n",
      "Epoch 82/3000\n",
      "\n",
      "Epoch 82: val_mae did not improve from 0.02798\n",
      "151/151 - 22s - loss: 4.0039e-04 - mae: 0.0114 - val_loss: 0.0014 - val_mae: 0.0286 - 22s/epoch - 143ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 83/3000\n",
      "\n",
      "Epoch 83: val_mae did not improve from 0.02798\n",
      "151/151 - 22s - loss: 3.9401e-04 - mae: 0.0113 - val_loss: 0.0013 - val_mae: 0.0280 - 22s/epoch - 143ms/step\n",
      "Epoch 84/3000\n",
      "\n",
      "Epoch 84: val_mae did not improve from 0.02798\n",
      "151/151 - 22s - loss: 3.9061e-04 - mae: 0.0112 - val_loss: 0.0014 - val_mae: 0.0283 - 22s/epoch - 143ms/step\n",
      "Epoch 85/3000\n",
      "\n",
      "Epoch 85: val_mae did not improve from 0.02798\n",
      "151/151 - 22s - loss: 3.9066e-04 - mae: 0.0113 - val_loss: 0.0014 - val_mae: 0.0284 - 22s/epoch - 143ms/step\n",
      "Epoch 86/3000\n",
      "\n",
      "Epoch 86: val_mae did not improve from 0.02798\n",
      "151/151 - 22s - loss: 3.8255e-04 - mae: 0.0111 - val_loss: 0.0015 - val_mae: 0.0293 - 22s/epoch - 143ms/step\n",
      "Epoch 87/3000\n",
      "\n",
      "Epoch 87: val_mae did not improve from 0.02798\n",
      "151/151 - 22s - loss: 3.8250e-04 - mae: 0.0111 - val_loss: 0.0014 - val_mae: 0.0288 - 22s/epoch - 144ms/step\n",
      "Epoch 88/3000\n",
      "\n",
      "Epoch 88: val_mae improved from 0.02798 to 0.02774, saving model to saved_models/graph_global_transformer_local_gate_pump_1.h5\n",
      "151/151 - 22s - loss: 3.8204e-04 - mae: 0.0112 - val_loss: 0.0013 - val_mae: 0.0277 - 22s/epoch - 146ms/step\n",
      "Epoch 89/3000\n",
      "\n",
      "Epoch 89: val_mae improved from 0.02774 to 0.02774, saving model to saved_models/graph_global_transformer_local_gate_pump_1.h5\n",
      "151/151 - 22s - loss: 3.7794e-04 - mae: 0.0111 - val_loss: 0.0013 - val_mae: 0.0277 - 22s/epoch - 146ms/step\n",
      "Epoch 90/3000\n",
      "\n",
      "Epoch 90: val_mae improved from 0.02774 to 0.02744, saving model to saved_models/graph_global_transformer_local_gate_pump_1.h5\n",
      "151/151 - 22s - loss: 3.7461e-04 - mae: 0.0110 - val_loss: 0.0012 - val_mae: 0.0274 - 22s/epoch - 146ms/step\n",
      "Epoch 91/3000\n",
      "\n",
      "Epoch 91: val_mae did not improve from 0.02744\n",
      "151/151 - 22s - loss: 3.7708e-04 - mae: 0.0112 - val_loss: 0.0014 - val_mae: 0.0283 - 22s/epoch - 143ms/step\n",
      "Epoch 92/3000\n",
      "\n",
      "Epoch 92: val_mae improved from 0.02744 to 0.02731, saving model to saved_models/graph_global_transformer_local_gate_pump_1.h5\n",
      "151/151 - 22s - loss: 3.7151e-04 - mae: 0.0110 - val_loss: 0.0012 - val_mae: 0.0273 - 22s/epoch - 145ms/step\n",
      "Epoch 93/3000\n",
      "\n",
      "Epoch 93: val_mae did not improve from 0.02731\n",
      "151/151 - 22s - loss: 3.7179e-04 - mae: 0.0111 - val_loss: 0.0013 - val_mae: 0.0282 - 22s/epoch - 144ms/step\n",
      "Epoch 94/3000\n",
      "\n",
      "Epoch 94: val_mae did not improve from 0.02731\n",
      "151/151 - 22s - loss: 3.6775e-04 - mae: 0.0110 - val_loss: 0.0014 - val_mae: 0.0286 - 22s/epoch - 143ms/step\n",
      "Epoch 95/3000\n",
      "\n",
      "Epoch 95: val_mae did not improve from 0.02731\n",
      "151/151 - 22s - loss: 3.6337e-04 - mae: 0.0109 - val_loss: 0.0013 - val_mae: 0.0283 - 22s/epoch - 143ms/step\n",
      "Epoch 96/3000\n",
      "\n",
      "Epoch 96: val_mae did not improve from 0.02731\n",
      "151/151 - 22s - loss: 3.6432e-04 - mae: 0.0110 - val_loss: 0.0015 - val_mae: 0.0300 - 22s/epoch - 144ms/step\n",
      "Epoch 97/3000\n",
      "\n",
      "Epoch 97: val_mae did not improve from 0.02731\n",
      "151/151 - 22s - loss: 3.5595e-04 - mae: 0.0108 - val_loss: 0.0014 - val_mae: 0.0290 - 22s/epoch - 145ms/step\n",
      "Epoch 98/3000\n",
      "\n",
      "Epoch 98: val_mae did not improve from 0.02731\n",
      "151/151 - 22s - loss: 3.5753e-04 - mae: 0.0109 - val_loss: 0.0013 - val_mae: 0.0282 - 22s/epoch - 145ms/step\n",
      "Epoch 99/3000\n",
      "\n",
      "Epoch 99: val_mae did not improve from 0.02731\n",
      "151/151 - 22s - loss: 3.5764e-04 - mae: 0.0109 - val_loss: 0.0014 - val_mae: 0.0285 - 22s/epoch - 145ms/step\n",
      "Epoch 100/3000\n",
      "\n",
      "Epoch 100: val_mae did not improve from 0.02731\n",
      "151/151 - 22s - loss: 3.5368e-04 - mae: 0.0109 - val_loss: 0.0013 - val_mae: 0.0276 - 22s/epoch - 144ms/step\n",
      "Epoch 101/3000\n",
      "\n",
      "Epoch 101: val_mae did not improve from 0.02731\n",
      "151/151 - 22s - loss: 3.5472e-04 - mae: 0.0110 - val_loss: 0.0013 - val_mae: 0.0278 - 22s/epoch - 144ms/step\n",
      "Epoch 102/3000\n",
      "\n",
      "Epoch 102: val_mae did not improve from 0.02731\n",
      "151/151 - 22s - loss: 3.4545e-04 - mae: 0.0107 - val_loss: 0.0014 - val_mae: 0.0288 - 22s/epoch - 145ms/step\n",
      "Epoch 103/3000\n",
      "\n",
      "Epoch 103: val_mae did not improve from 0.02731\n",
      "151/151 - 22s - loss: 3.5009e-04 - mae: 0.0109 - val_loss: 0.0013 - val_mae: 0.0283 - 22s/epoch - 145ms/step\n",
      "Epoch 104/3000\n",
      "\n",
      "Epoch 104: val_mae did not improve from 0.02731\n",
      "151/151 - 22s - loss: 3.4482e-04 - mae: 0.0108 - val_loss: 0.0013 - val_mae: 0.0278 - 22s/epoch - 145ms/step\n",
      "Epoch 105/3000\n",
      "\n",
      "Epoch 105: val_mae did not improve from 0.02731\n",
      "151/151 - 22s - loss: 3.3668e-04 - mae: 0.0106 - val_loss: 0.0014 - val_mae: 0.0291 - 22s/epoch - 143ms/step\n",
      "Epoch 106/3000\n",
      "\n",
      "Epoch 106: val_mae did not improve from 0.02731\n",
      "151/151 - 22s - loss: 3.4047e-04 - mae: 0.0109 - val_loss: 0.0013 - val_mae: 0.0279 - 22s/epoch - 145ms/step\n",
      "Epoch 107/3000\n",
      "\n",
      "Epoch 107: val_mae did not improve from 0.02731\n",
      "151/151 - 22s - loss: 3.3201e-04 - mae: 0.0107 - val_loss: 0.0014 - val_mae: 0.0290 - 22s/epoch - 144ms/step\n",
      "Epoch 108/3000\n",
      "\n",
      "Epoch 108: val_mae did not improve from 0.02731\n",
      "151/151 - 22s - loss: 3.3675e-04 - mae: 0.0109 - val_loss: 0.0012 - val_mae: 0.0274 - 22s/epoch - 144ms/step\n",
      "Epoch 109/3000\n",
      "\n",
      "Epoch 109: val_mae did not improve from 0.02731\n",
      "151/151 - 22s - loss: 3.2785e-04 - mae: 0.0107 - val_loss: 0.0012 - val_mae: 0.0273 - 22s/epoch - 143ms/step\n",
      "Epoch 110/3000\n",
      "\n",
      "Epoch 110: val_mae did not improve from 0.02731\n",
      "151/151 - 22s - loss: 3.2765e-04 - mae: 0.0107 - val_loss: 0.0014 - val_mae: 0.0284 - 22s/epoch - 143ms/step\n",
      "Epoch 111/3000\n",
      "\n",
      "Epoch 111: val_mae did not improve from 0.02731\n",
      "151/151 - 21s - loss: 3.2363e-04 - mae: 0.0107 - val_loss: 0.0014 - val_mae: 0.0287 - 21s/epoch - 142ms/step\n",
      "Epoch 112/3000\n",
      "\n",
      "Epoch 112: val_mae did not improve from 0.02731\n",
      "151/151 - 22s - loss: 3.2534e-04 - mae: 0.0108 - val_loss: 0.0013 - val_mae: 0.0281 - 22s/epoch - 143ms/step\n",
      "Epoch 113/3000\n",
      "\n",
      "Epoch 113: val_mae did not improve from 0.02731\n",
      "151/151 - 22s - loss: 3.1988e-04 - mae: 0.0107 - val_loss: 0.0012 - val_mae: 0.0275 - 22s/epoch - 143ms/step\n",
      "Epoch 114/3000\n",
      "\n",
      "Epoch 114: val_mae did not improve from 0.02731\n",
      "151/151 - 22s - loss: 3.1649e-04 - mae: 0.0106 - val_loss: 0.0013 - val_mae: 0.0279 - 22s/epoch - 142ms/step\n",
      "Epoch 115/3000\n",
      "\n",
      "Epoch 115: val_mae did not improve from 0.02731\n",
      "151/151 - 22s - loss: 3.1373e-04 - mae: 0.0106 - val_loss: 0.0013 - val_mae: 0.0277 - 22s/epoch - 143ms/step\n",
      "Epoch 116/3000\n",
      "\n",
      "Epoch 116: val_mae did not improve from 0.02731\n",
      "151/151 - 21s - loss: 3.1039e-04 - mae: 0.0106 - val_loss: 0.0013 - val_mae: 0.0281 - 21s/epoch - 142ms/step\n",
      "Epoch 117/3000\n",
      "\n",
      "Epoch 117: val_mae improved from 0.02731 to 0.02712, saving model to saved_models/graph_global_transformer_local_gate_pump_1.h5\n",
      "151/151 - 22s - loss: 3.0537e-04 - mae: 0.0105 - val_loss: 0.0012 - val_mae: 0.0271 - 22s/epoch - 143ms/step\n",
      "Epoch 118/3000\n",
      "\n",
      "Epoch 118: val_mae did not improve from 0.02712\n",
      "151/151 - 22s - loss: 3.0605e-04 - mae: 0.0106 - val_loss: 0.0014 - val_mae: 0.0287 - 22s/epoch - 142ms/step\n",
      "Epoch 119/3000\n",
      "\n",
      "Epoch 119: val_mae did not improve from 0.02712\n",
      "151/151 - 22s - loss: 3.0863e-04 - mae: 0.0108 - val_loss: 0.0014 - val_mae: 0.0291 - 22s/epoch - 144ms/step\n",
      "Epoch 120/3000\n",
      "\n",
      "Epoch 120: val_mae did not improve from 0.02712\n",
      "151/151 - 22s - loss: 2.9951e-04 - mae: 0.0105 - val_loss: 0.0012 - val_mae: 0.0276 - 22s/epoch - 144ms/step\n",
      "Epoch 121/3000\n",
      "\n",
      "Epoch 121: val_mae did not improve from 0.02712\n",
      "151/151 - 21s - loss: 2.9533e-04 - mae: 0.0105 - val_loss: 0.0014 - val_mae: 0.0294 - 21s/epoch - 142ms/step\n",
      "Epoch 122/3000\n",
      "\n",
      "Epoch 122: val_mae improved from 0.02712 to 0.02694, saving model to saved_models/graph_global_transformer_local_gate_pump_1.h5\n",
      "151/151 - 22s - loss: 2.9573e-04 - mae: 0.0105 - val_loss: 0.0012 - val_mae: 0.0269 - 22s/epoch - 144ms/step\n",
      "Epoch 123/3000\n",
      "\n",
      "Epoch 123: val_mae did not improve from 0.02694\n",
      "151/151 - 21s - loss: 2.9200e-04 - mae: 0.0105 - val_loss: 0.0014 - val_mae: 0.0290 - 21s/epoch - 142ms/step\n",
      "Epoch 124/3000\n",
      "\n",
      "Epoch 124: val_mae did not improve from 0.02694\n",
      "151/151 - 22s - loss: 2.8700e-04 - mae: 0.0103 - val_loss: 0.0012 - val_mae: 0.0277 - 22s/epoch - 143ms/step\n",
      "Epoch 125/3000\n",
      "\n",
      "Epoch 125: val_mae did not improve from 0.02694\n",
      "151/151 - 21s - loss: 2.8813e-04 - mae: 0.0104 - val_loss: 0.0013 - val_mae: 0.0278 - 21s/epoch - 142ms/step\n",
      "Epoch 126/3000\n",
      "\n",
      "Epoch 126: val_mae did not improve from 0.02694\n",
      "151/151 - 22s - loss: 2.8684e-04 - mae: 0.0104 - val_loss: 0.0013 - val_mae: 0.0281 - 22s/epoch - 143ms/step\n",
      "Epoch 127/3000\n",
      "\n",
      "Epoch 127: val_mae did not improve from 0.02694\n",
      "151/151 - 22s - loss: 2.8586e-04 - mae: 0.0104 - val_loss: 0.0012 - val_mae: 0.0276 - 22s/epoch - 143ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 128/3000\n",
      "\n",
      "Epoch 128: val_mae did not improve from 0.02694\n",
      "151/151 - 22s - loss: 2.8326e-04 - mae: 0.0104 - val_loss: 0.0012 - val_mae: 0.0273 - 22s/epoch - 143ms/step\n",
      "Epoch 129/3000\n",
      "\n",
      "Epoch 129: val_mae did not improve from 0.02694\n",
      "151/151 - 22s - loss: 2.7915e-04 - mae: 0.0102 - val_loss: 0.0014 - val_mae: 0.0287 - 22s/epoch - 143ms/step\n",
      "Epoch 130/3000\n",
      "\n",
      "Epoch 130: val_mae did not improve from 0.02694\n",
      "151/151 - 21s - loss: 2.8086e-04 - mae: 0.0103 - val_loss: 0.0013 - val_mae: 0.0278 - 21s/epoch - 142ms/step\n",
      "Epoch 131/3000\n",
      "\n",
      "Epoch 131: val_mae did not improve from 0.02694\n",
      "151/151 - 21s - loss: 2.7520e-04 - mae: 0.0102 - val_loss: 0.0013 - val_mae: 0.0287 - 21s/epoch - 142ms/step\n",
      "Epoch 132/3000\n",
      "\n",
      "Epoch 132: val_mae did not improve from 0.02694\n",
      "151/151 - 21s - loss: 2.7542e-04 - mae: 0.0102 - val_loss: 0.0013 - val_mae: 0.0286 - 21s/epoch - 142ms/step\n",
      "Epoch 133/3000\n",
      "\n",
      "Epoch 133: val_mae did not improve from 0.02694\n",
      "151/151 - 21s - loss: 2.7221e-04 - mae: 0.0101 - val_loss: 0.0013 - val_mae: 0.0282 - 21s/epoch - 142ms/step\n",
      "Epoch 134/3000\n",
      "\n",
      "Epoch 134: val_mae did not improve from 0.02694\n",
      "151/151 - 22s - loss: 2.7357e-04 - mae: 0.0102 - val_loss: 0.0014 - val_mae: 0.0296 - 22s/epoch - 143ms/step\n",
      "Epoch 135/3000\n",
      "\n",
      "Epoch 135: val_mae did not improve from 0.02694\n",
      "151/151 - 22s - loss: 2.7674e-04 - mae: 0.0103 - val_loss: 0.0013 - val_mae: 0.0278 - 22s/epoch - 142ms/step\n",
      "Epoch 136/3000\n",
      "\n",
      "Epoch 136: val_mae did not improve from 0.02694\n",
      "151/151 - 21s - loss: 2.6794e-04 - mae: 0.0100 - val_loss: 0.0013 - val_mae: 0.0280 - 21s/epoch - 142ms/step\n",
      "Epoch 137/3000\n",
      "\n",
      "Epoch 137: val_mae did not improve from 0.02694\n",
      "151/151 - 22s - loss: 2.6974e-04 - mae: 0.0101 - val_loss: 0.0013 - val_mae: 0.0282 - 22s/epoch - 143ms/step\n",
      "Epoch 138/3000\n",
      "\n",
      "Epoch 138: val_mae did not improve from 0.02694\n",
      "151/151 - 21s - loss: 2.6336e-04 - mae: 0.0099 - val_loss: 0.0013 - val_mae: 0.0278 - 21s/epoch - 142ms/step\n",
      "Epoch 139/3000\n",
      "\n",
      "Epoch 139: val_mae did not improve from 0.02694\n",
      "151/151 - 21s - loss: 2.6601e-04 - mae: 0.0100 - val_loss: 0.0013 - val_mae: 0.0281 - 21s/epoch - 142ms/step\n",
      "Epoch 140/3000\n",
      "\n",
      "Epoch 140: val_mae did not improve from 0.02694\n",
      "151/151 - 21s - loss: 2.6321e-04 - mae: 0.0099 - val_loss: 0.0013 - val_mae: 0.0287 - 21s/epoch - 142ms/step\n",
      "Epoch 141/3000\n",
      "\n",
      "Epoch 141: val_mae did not improve from 0.02694\n",
      "151/151 - 22s - loss: 2.6653e-04 - mae: 0.0101 - val_loss: 0.0013 - val_mae: 0.0287 - 22s/epoch - 143ms/step\n",
      "Epoch 142/3000\n",
      "\n",
      "Epoch 142: val_mae did not improve from 0.02694\n",
      "151/151 - 22s - loss: 2.6255e-04 - mae: 0.0100 - val_loss: 0.0013 - val_mae: 0.0279 - 22s/epoch - 143ms/step\n",
      "Epoch 143/3000\n",
      "\n",
      "Epoch 143: val_mae did not improve from 0.02694\n",
      "151/151 - 22s - loss: 2.6087e-04 - mae: 0.0099 - val_loss: 0.0012 - val_mae: 0.0272 - 22s/epoch - 143ms/step\n",
      "Epoch 144/3000\n",
      "\n",
      "Epoch 144: val_mae did not improve from 0.02694\n",
      "151/151 - 22s - loss: 2.5930e-04 - mae: 0.0099 - val_loss: 0.0013 - val_mae: 0.0281 - 22s/epoch - 144ms/step\n",
      "Epoch 145/3000\n",
      "\n",
      "Epoch 145: val_mae did not improve from 0.02694\n",
      "151/151 - 22s - loss: 2.6431e-04 - mae: 0.0100 - val_loss: 0.0014 - val_mae: 0.0293 - 22s/epoch - 143ms/step\n",
      "Epoch 146/3000\n",
      "\n",
      "Epoch 146: val_mae did not improve from 0.02694\n",
      "151/151 - 21s - loss: 2.6104e-04 - mae: 0.0100 - val_loss: 0.0013 - val_mae: 0.0281 - 21s/epoch - 142ms/step\n",
      "Epoch 147/3000\n",
      "\n",
      "Epoch 147: val_mae did not improve from 0.02694\n",
      "151/151 - 21s - loss: 2.5470e-04 - mae: 0.0098 - val_loss: 0.0013 - val_mae: 0.0281 - 21s/epoch - 142ms/step\n",
      "Epoch 148/3000\n",
      "\n",
      "Epoch 148: val_mae did not improve from 0.02694\n",
      "151/151 - 22s - loss: 2.5458e-04 - mae: 0.0098 - val_loss: 0.0013 - val_mae: 0.0277 - 22s/epoch - 143ms/step\n",
      "Epoch 149/3000\n",
      "\n",
      "Epoch 149: val_mae did not improve from 0.02694\n",
      "151/151 - 21s - loss: 2.5738e-04 - mae: 0.0099 - val_loss: 0.0014 - val_mae: 0.0288 - 21s/epoch - 142ms/step\n",
      "Epoch 150/3000\n",
      "\n",
      "Epoch 150: val_mae did not improve from 0.02694\n",
      "151/151 - 22s - loss: 2.5556e-04 - mae: 0.0098 - val_loss: 0.0012 - val_mae: 0.0275 - 22s/epoch - 143ms/step\n",
      "Epoch 151/3000\n",
      "\n",
      "Epoch 151: val_mae did not improve from 0.02694\n",
      "151/151 - 22s - loss: 2.5188e-04 - mae: 0.0097 - val_loss: 0.0012 - val_mae: 0.0274 - 22s/epoch - 143ms/step\n",
      "Epoch 152/3000\n",
      "\n",
      "Epoch 152: val_mae did not improve from 0.02694\n",
      "151/151 - 22s - loss: 2.5060e-04 - mae: 0.0097 - val_loss: 0.0013 - val_mae: 0.0288 - 22s/epoch - 143ms/step\n",
      "Epoch 153/3000\n",
      "\n",
      "Epoch 153: val_mae did not improve from 0.02694\n",
      "151/151 - 22s - loss: 2.5245e-04 - mae: 0.0098 - val_loss: 0.0013 - val_mae: 0.0285 - 22s/epoch - 142ms/step\n",
      "Epoch 154/3000\n",
      "\n",
      "Epoch 154: val_mae did not improve from 0.02694\n",
      "151/151 - 22s - loss: 2.4867e-04 - mae: 0.0097 - val_loss: 0.0012 - val_mae: 0.0278 - 22s/epoch - 143ms/step\n",
      "Epoch 155/3000\n",
      "\n",
      "Epoch 155: val_mae did not improve from 0.02694\n",
      "151/151 - 22s - loss: 2.5241e-04 - mae: 0.0098 - val_loss: 0.0015 - val_mae: 0.0300 - 22s/epoch - 143ms/step\n",
      "Epoch 156/3000\n",
      "\n",
      "Epoch 156: val_mae did not improve from 0.02694\n",
      "151/151 - 22s - loss: 2.5098e-04 - mae: 0.0098 - val_loss: 0.0012 - val_mae: 0.0274 - 22s/epoch - 144ms/step\n",
      "Epoch 157/3000\n",
      "\n",
      "Epoch 157: val_mae did not improve from 0.02694\n",
      "151/151 - 22s - loss: 2.4740e-04 - mae: 0.0097 - val_loss: 0.0014 - val_mae: 0.0293 - 22s/epoch - 144ms/step\n",
      "Epoch 158/3000\n",
      "\n",
      "Epoch 158: val_mae did not improve from 0.02694\n",
      "151/151 - 22s - loss: 2.4615e-04 - mae: 0.0096 - val_loss: 0.0013 - val_mae: 0.0282 - 22s/epoch - 143ms/step\n",
      "Epoch 159/3000\n",
      "\n",
      "Epoch 159: val_mae did not improve from 0.02694\n",
      "151/151 - 22s - loss: 2.4922e-04 - mae: 0.0098 - val_loss: 0.0014 - val_mae: 0.0285 - 22s/epoch - 143ms/step\n",
      "Epoch 160/3000\n",
      "\n",
      "Epoch 160: val_mae did not improve from 0.02694\n",
      "151/151 - 22s - loss: 2.4783e-04 - mae: 0.0097 - val_loss: 0.0015 - val_mae: 0.0297 - 22s/epoch - 143ms/step\n",
      "Epoch 161/3000\n",
      "\n",
      "Epoch 161: val_mae did not improve from 0.02694\n",
      "151/151 - 22s - loss: 2.4452e-04 - mae: 0.0096 - val_loss: 0.0012 - val_mae: 0.0271 - 22s/epoch - 143ms/step\n",
      "Epoch 162/3000\n",
      "\n",
      "Epoch 162: val_mae did not improve from 0.02694\n",
      "151/151 - 22s - loss: 2.4397e-04 - mae: 0.0096 - val_loss: 0.0012 - val_mae: 0.0272 - 22s/epoch - 143ms/step\n",
      "Epoch 163/3000\n",
      "\n",
      "Epoch 163: val_mae did not improve from 0.02694\n",
      "151/151 - 22s - loss: 2.4494e-04 - mae: 0.0097 - val_loss: 0.0013 - val_mae: 0.0278 - 22s/epoch - 144ms/step\n",
      "Epoch 164/3000\n",
      "\n",
      "Epoch 164: val_mae did not improve from 0.02694\n",
      "151/151 - 22s - loss: 2.4255e-04 - mae: 0.0096 - val_loss: 0.0014 - val_mae: 0.0290 - 22s/epoch - 143ms/step\n",
      "Epoch 165/3000\n",
      "\n",
      "Epoch 165: val_mae did not improve from 0.02694\n",
      "151/151 - 22s - loss: 2.4394e-04 - mae: 0.0096 - val_loss: 0.0012 - val_mae: 0.0275 - 22s/epoch - 143ms/step\n",
      "Epoch 166/3000\n",
      "\n",
      "Epoch 166: val_mae did not improve from 0.02694\n",
      "151/151 - 22s - loss: 2.3834e-04 - mae: 0.0094 - val_loss: 0.0013 - val_mae: 0.0281 - 22s/epoch - 143ms/step\n",
      "Epoch 167/3000\n",
      "\n",
      "Epoch 167: val_mae improved from 0.02694 to 0.02664, saving model to saved_models/graph_global_transformer_local_gate_pump_1.h5\n",
      "151/151 - 22s - loss: 2.4262e-04 - mae: 0.0096 - val_loss: 0.0012 - val_mae: 0.0266 - 22s/epoch - 144ms/step\n",
      "Epoch 168/3000\n",
      "\n",
      "Epoch 168: val_mae did not improve from 0.02664\n",
      "151/151 - 21s - loss: 2.4610e-04 - mae: 0.0097 - val_loss: 0.0013 - val_mae: 0.0279 - 21s/epoch - 141ms/step\n",
      "Epoch 169/3000\n",
      "\n",
      "Epoch 169: val_mae did not improve from 0.02664\n",
      "151/151 - 22s - loss: 2.4023e-04 - mae: 0.0095 - val_loss: 0.0014 - val_mae: 0.0286 - 22s/epoch - 143ms/step\n",
      "Epoch 170/3000\n",
      "\n",
      "Epoch 170: val_mae did not improve from 0.02664\n",
      "151/151 - 22s - loss: 2.4102e-04 - mae: 0.0096 - val_loss: 0.0014 - val_mae: 0.0286 - 22s/epoch - 143ms/step\n",
      "Epoch 171/3000\n",
      "\n",
      "Epoch 171: val_mae did not improve from 0.02664\n",
      "151/151 - 22s - loss: 2.4140e-04 - mae: 0.0096 - val_loss: 0.0014 - val_mae: 0.0294 - 22s/epoch - 143ms/step\n",
      "Epoch 172/3000\n",
      "\n",
      "Epoch 172: val_mae did not improve from 0.02664\n",
      "151/151 - 21s - loss: 2.3560e-04 - mae: 0.0094 - val_loss: 0.0013 - val_mae: 0.0281 - 21s/epoch - 142ms/step\n",
      "Epoch 173/3000\n",
      "\n",
      "Epoch 173: val_mae did not improve from 0.02664\n",
      "151/151 - 21s - loss: 2.3491e-04 - mae: 0.0094 - val_loss: 0.0012 - val_mae: 0.0281 - 21s/epoch - 142ms/step\n",
      "Epoch 174/3000\n",
      "\n",
      "Epoch 174: val_mae did not improve from 0.02664\n",
      "151/151 - 21s - loss: 2.4033e-04 - mae: 0.0096 - val_loss: 0.0013 - val_mae: 0.0283 - 21s/epoch - 142ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 175/3000\n",
      "\n",
      "Epoch 175: val_mae did not improve from 0.02664\n",
      "151/151 - 22s - loss: 2.3524e-04 - mae: 0.0094 - val_loss: 0.0012 - val_mae: 0.0278 - 22s/epoch - 142ms/step\n",
      "Epoch 176/3000\n",
      "\n",
      "Epoch 176: val_mae did not improve from 0.02664\n",
      "151/151 - 21s - loss: 2.3517e-04 - mae: 0.0095 - val_loss: 0.0013 - val_mae: 0.0276 - 21s/epoch - 142ms/step\n",
      "Epoch 177/3000\n",
      "\n",
      "Epoch 177: val_mae did not improve from 0.02664\n",
      "151/151 - 22s - loss: 2.3459e-04 - mae: 0.0094 - val_loss: 0.0013 - val_mae: 0.0286 - 22s/epoch - 143ms/step\n",
      "Epoch 178/3000\n",
      "\n",
      "Epoch 178: val_mae did not improve from 0.02664\n",
      "151/151 - 22s - loss: 2.3463e-04 - mae: 0.0094 - val_loss: 0.0013 - val_mae: 0.0282 - 22s/epoch - 143ms/step\n",
      "Epoch 179/3000\n",
      "\n",
      "Epoch 179: val_mae did not improve from 0.02664\n",
      "151/151 - 22s - loss: 2.3884e-04 - mae: 0.0096 - val_loss: 0.0013 - val_mae: 0.0279 - 22s/epoch - 144ms/step\n",
      "Epoch 180/3000\n",
      "\n",
      "Epoch 180: val_mae did not improve from 0.02664\n",
      "151/151 - 22s - loss: 2.3327e-04 - mae: 0.0094 - val_loss: 0.0013 - val_mae: 0.0273 - 22s/epoch - 144ms/step\n",
      "Epoch 181/3000\n",
      "\n",
      "Epoch 181: val_mae did not improve from 0.02664\n",
      "151/151 - 22s - loss: 2.2893e-04 - mae: 0.0093 - val_loss: 0.0012 - val_mae: 0.0277 - 22s/epoch - 144ms/step\n",
      "Epoch 182/3000\n",
      "\n",
      "Epoch 182: val_mae did not improve from 0.02664\n",
      "151/151 - 22s - loss: 2.2960e-04 - mae: 0.0093 - val_loss: 0.0013 - val_mae: 0.0276 - 22s/epoch - 143ms/step\n",
      "Epoch 183/3000\n",
      "\n",
      "Epoch 183: val_mae did not improve from 0.02664\n",
      "151/151 - 21s - loss: 2.3238e-04 - mae: 0.0094 - val_loss: 0.0013 - val_mae: 0.0285 - 21s/epoch - 142ms/step\n",
      "Epoch 184/3000\n",
      "\n",
      "Epoch 184: val_mae did not improve from 0.02664\n",
      "151/151 - 22s - loss: 2.3070e-04 - mae: 0.0094 - val_loss: 0.0013 - val_mae: 0.0283 - 22s/epoch - 142ms/step\n",
      "Epoch 185/3000\n",
      "\n",
      "Epoch 185: val_mae did not improve from 0.02664\n",
      "151/151 - 21s - loss: 2.2847e-04 - mae: 0.0093 - val_loss: 0.0014 - val_mae: 0.0288 - 21s/epoch - 141ms/step\n",
      "Epoch 186/3000\n",
      "\n",
      "Epoch 186: val_mae did not improve from 0.02664\n",
      "151/151 - 21s - loss: 2.3060e-04 - mae: 0.0094 - val_loss: 0.0012 - val_mae: 0.0270 - 21s/epoch - 142ms/step\n",
      "Epoch 187/3000\n",
      "\n",
      "Epoch 187: val_mae did not improve from 0.02664\n",
      "151/151 - 22s - loss: 2.3063e-04 - mae: 0.0094 - val_loss: 0.0014 - val_mae: 0.0290 - 22s/epoch - 143ms/step\n",
      "Epoch 188/3000\n",
      "\n",
      "Epoch 188: val_mae did not improve from 0.02664\n",
      "151/151 - 22s - loss: 2.2878e-04 - mae: 0.0093 - val_loss: 0.0013 - val_mae: 0.0274 - 22s/epoch - 142ms/step\n",
      "Epoch 189/3000\n",
      "\n",
      "Epoch 189: val_mae did not improve from 0.02664\n",
      "151/151 - 22s - loss: 2.2700e-04 - mae: 0.0093 - val_loss: 0.0012 - val_mae: 0.0272 - 22s/epoch - 143ms/step\n",
      "Epoch 190/3000\n",
      "\n",
      "Epoch 190: val_mae improved from 0.02664 to 0.02619, saving model to saved_models/graph_global_transformer_local_gate_pump_1.h5\n",
      "151/151 - 22s - loss: 2.3009e-04 - mae: 0.0094 - val_loss: 0.0011 - val_mae: 0.0262 - 22s/epoch - 144ms/step\n",
      "Epoch 191/3000\n",
      "\n",
      "Epoch 191: val_mae did not improve from 0.02619\n",
      "151/151 - 21s - loss: 2.3006e-04 - mae: 0.0094 - val_loss: 0.0012 - val_mae: 0.0275 - 21s/epoch - 142ms/step\n",
      "Epoch 192/3000\n",
      "\n",
      "Epoch 192: val_mae did not improve from 0.02619\n",
      "151/151 - 21s - loss: 2.2365e-04 - mae: 0.0092 - val_loss: 0.0013 - val_mae: 0.0279 - 21s/epoch - 141ms/step\n",
      "Epoch 193/3000\n",
      "\n",
      "Epoch 193: val_mae did not improve from 0.02619\n",
      "151/151 - 21s - loss: 2.2555e-04 - mae: 0.0093 - val_loss: 0.0012 - val_mae: 0.0272 - 21s/epoch - 142ms/step\n",
      "Epoch 194/3000\n",
      "\n",
      "Epoch 194: val_mae did not improve from 0.02619\n",
      "151/151 - 21s - loss: 2.2583e-04 - mae: 0.0093 - val_loss: 0.0012 - val_mae: 0.0272 - 21s/epoch - 142ms/step\n",
      "Epoch 195/3000\n",
      "\n",
      "Epoch 195: val_mae did not improve from 0.02619\n",
      "151/151 - 21s - loss: 2.2513e-04 - mae: 0.0093 - val_loss: 0.0014 - val_mae: 0.0292 - 21s/epoch - 142ms/step\n",
      "Epoch 196/3000\n",
      "\n",
      "Epoch 196: val_mae did not improve from 0.02619\n",
      "151/151 - 21s - loss: 2.2997e-04 - mae: 0.0095 - val_loss: 0.0012 - val_mae: 0.0271 - 21s/epoch - 142ms/step\n",
      "Epoch 197/3000\n"
     ]
    }
   ],
   "source": [
    "lr_schedule = keras.optimizers.schedules.ExponentialDecay(initial_learning_rate=learning_rate, \n",
    "                                                          decay_steps=decay_steps,\n",
    "                                                          decay_rate=decay_rate)\n",
    "\n",
    "model.compile(optimizer=Adam(learning_rate=lr_schedule),\n",
    "              loss='mse',\n",
    "              metrics=['mae']\n",
    "             )\n",
    "\n",
    "\n",
    "es = EarlyStopping(monitor='val_mae', mode='min', verbose=2, patience=PATIENCE)\n",
    "mc = ModelCheckpoint('saved_models/graph_global_transformer_local_gate_pump_1.h5', \n",
    "                     monitor='val_mae', \n",
    "                     mode='min', \n",
    "                     verbose=2, \n",
    "                     custom_objects={'GCNConv': GCNConv},\n",
    "                     save_best_only=True\n",
    "                    )\n",
    "\n",
    "\n",
    "model.fit([train_26, train_25B, train_25A, train_1, train_4, train_adj_mat], train_ws_y,\n",
    "           validation_data=([test_26, test_25B, test_25A, test_1, test_4, test_adj_mat], test_ws_y),\n",
    "           epochs=EPOCHS,\n",
    "           batch_size=BATCH,\n",
    "           verbose=2,\n",
    "           shuffle=True,\n",
    "           callbacks=[es, mc]\n",
    "         )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45d04c0b",
   "metadata": {},
   "source": [
    "### Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3ce8b0a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c60c3dba",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Layer LSTM will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "603/603 [==============================] - 5s 7ms/step\n",
      "inv_y.shape, inv_yhat.shape (19268, 96) (19268, 96)\n",
      "MAE = 0.0738\n",
      "RMSE = 0.1001\n",
      "--- 5.85234808921814 seconds ---\n"
     ]
    }
   ],
   "source": [
    "saved_model = load_model('saved_models/graph_global_transformer_local_gate_pump_1.h5', custom_objects={'GCNConv': GCNConv})\n",
    "\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "\n",
    "yhat = saved_model.predict([test_26, test_25B, test_25A, test_1, test_4, test_adj_mat])\n",
    "\n",
    "inv_yhat = ws_scaler.inverse_transform(yhat)\n",
    "inv_y = ws_scaler.inverse_transform(test_ws_y)\n",
    "\n",
    "print(\"inv_y.shape, inv_yhat.shape\", inv_y.shape, inv_yhat.shape)\n",
    "print('MAE = {}'.format(float(\"{:.4f}\".format(mae(inv_y, inv_yhat)))))\n",
    "print('RMSE = {}'.format(float(\"{:.4f}\".format(sqrt(mse(inv_y, inv_yhat))))))\n",
    "\n",
    "\n",
    "end_time = time.time()\n",
    "\n",
    "print(\"--- %s seconds ---\" % (end_time - start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23f68c8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "inv_yhat = pd.DataFrame(inv_yhat)\n",
    "inv_yhat.to_csv('saved_results/gtn_s_inv_yhat.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "10108ca4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Yx8rFLEjTvn171KtXz6BtAED9+vUBAI8fPzZ4G4QQQgghhBBCCCGEWFupC0IGBQUBAJKSkgQXnjl//jwAoHnz5rK3r1QqsW7dOgD6F6TRJykpCUBR9iYhhBBCCCGEEEIIIcVRqQtCVqlSBcHBwQCADRs26Pw9KioKcXFxcHZ2FlxQRsyePXvw7NkzeHp6YvDgwQa388mTJ4iMjAQAtGrVyuDtEEIIIYQQQgghhBBibaUuCAkAs2bNAgAsWbIEFy9eZH+flJTErkY9ceJEeHt7s3/bvn07AgMD0bVrV9FtM1Oxhw0bJroCNwCEh4cjMTFR5/dXrlxB3759kZWVhVq1auGNN96Q9sIIIYQQQgghhBBCJDpyOwHZeUprN4OUEvzLNpdw/fv3x+TJk/H999+jTZs26Nq1K9zd3XHo0CEkJycjJCQECxcu1HhOSkoKbt++jezsbMHtJiQkYPfu3QCkTcWeN28ePvroIzRr1gw1atSAnZ0d7t+/j+joaBQUFKBatWqIiIiAs7OzcS+YEEIIIYQQQgghhCM5Mxej15zD222q4Yv+ja3dHFIKGBWEzMzMRGRkJI4dO4ZTp07h6dOnePHiBbKzs1GuXDmUL18e9evXR6dOndCpUyejFmkxtfDwcISEhODHH3/EyZMnkZeXh1q1aiEsLAzTpk2Dk5OT7G2uX78eeXl5aNiwIVq3bq338bNnz8aJEydw/fp1/Pfff8jIyICXlxfatWuHN954A+PGjaN6kIQQQgghJYhKpYJCobB2MwghhBDkKVUAgITUHCu3hJQWCpVKpZL7pIsXL+KXX37Bxo0bkZGRAUDdoRLcSWFHKygoCOPGjcOIESP0TlUmhktNTYW3tzdSUlLg5eVl7eYQQgghhJR6AWHq2TIPFvWGnR0FIQkhhFjfi7QcBH95ED0a+OHXkS2t3RxiRrYSJ5KVCXnp0iV8/PHHOHLkCBt0dHFxQfPmzREUFARfX1/4+PjA1dUVL1++xMuXLxETE4MzZ87g0aNHuHjxIj788EN8+umnmDVrFqZMmWJQxiEhhJDSKTtPCRdHe2s3gxBCDCZ79J8QQgghpISQHIR899138ddff6GgoAC+vr4YOnQoRowYgeDgYDg46N9MfHw8du3ahT///BNRUVEICwvDTz/9hD/++AMdOnQw6kUQQggp+XZeeoIpmy7hwmfdUM6DauUSQoon9UA+ZUISQgghpPSRvDr2+vXrERgYiI0bN+LZs2f44Ycf0LZtW0kBSADw8/PDBx98gGPHjuHBgwcYO3Ysnj59iiNHjhjceEIIIaXHiXuJAIDE9Fwrt4QQQgxHmZCEEEIIKa0kByE3btyIq1evYujQobC3N24qXPXq1fHTTz/h3r176Nq1q1HbIoQQUjooCjOHVHQLTwgpxgrkl2MnhBBCCCkRJE/HHjp0qMl3XqVKFVSpUsXk2yWEEFLy2BUOm9H9OyGkOKNzGCGEEEJKK8mZkIQQQoh1FWZC0g08IYQQQgghhBQ7RgUhQ0NDMXjwYMmPHz58OE2/JoQQYhSajk0IKc5oIIUQQgghpZXk6dh8jh49Cn9/f8mPP336NB49emTMLgkhhJRSisLFZOkGnhBSnFFNSEIIIYSUVhadjl1QUAAFcxdJCCGEyEBXD0JISUAhSEIIIbaGrk3EUiwWhFQqlUhISIC7u7uldkkIIaQEoUxIQkhJ8Dwl29pNIIQQQgixClnTsVNTU5GcnKzxO6VSibi4OKgE7gpVKhWSk5OxZs0a5OTkoEmTJgY3lhBCSOmlYBamobFaQkgx5uJI60ISQgixLTTjiFiKrCDk8uXLsWDBAo3fJSYmIiAgQNLzFQoF3nnnHTm7JIQQQgBQJiQhpGSg0kSEEEJsDXWviaXICkKqVCqNjEeFQiGYAamtcuXK+PDDDzFx4kR5LSSEEEJAI7SEEEIIIYQQUpzJCkJOmzYNo0ePBqAOSNasWRPly5fH2bNnBZ9jZ2cHLy8veHt7G9dSQgghBDRSSwgp3qQO4BNCCCGWQoP9xFIkByGnT58Ob29vzJs3j/1dp06dUK5cOVSvXt0sjSOEEEIYzBRGuoEnhBRndAojhBBia+jSRCxFcmXs7777Dr/88ovG744dO4aTJ0+avFGWsmXLFnTu3Blly5aFu7s7mjZtiqVLlyIvL0/WdtauXQuFQiH6s2/fPsHnx8fHY+LEiahRowacnZ3h5+eHwYMH4+LFi8a+REIIIYQQQgghhBBCrE5yJqRCoUBBQYE522JRU6dORXh4OBwcHBAaGgoPDw8cPnwYM2bMQEREBA4cOABXV1dZ26xVqxbat2/P+7fKlSvz/v7OnTvo0KEDEhISULNmTfTv3x8xMTHYunUrduzYgc2bN2PAgAGyXx8hhBBCCCGEEEKIPjQdm1iK5CCkt7c3Xr58iezsbLi4uJizTWa3Y8cOhIeHw8PDA8eOHUPz5s0BqFf6Dg0NRVRUFObMmYNvvvlG1nbbt2+PtWvXSn68SqXCsGHDkJCQgHfeeQdr1qyBvb09AODXX3/FuHHjMHLkSNy9exf+/v6y2kIIIYQQQgghhBCiD03HJpYiOQjZrFkzHDt2DO+++y7effddeHh4AAByc3MRGRkpq0ZXx44d5bfUhBYtWgQACAsLYwOQAODr64uVK1eiQ4cOWLFiBebMmWPWBXX27t2L6OholClTBitXrmQDkAAwduxYbN68GYcOHUJ4eDgWL15stnYQQkhxUFgSkjpJhJBijWpCEkIIIaS0khyEnDp1Ko4ePYqtW7di69at7O9fvXqFzp07S96hQqFAfn6+rEaa0pMnT3Du3DkAwIgRI3T+3r59e1StWhVxcXHYs2cPhg8fbra2bN++HQDQr18/NqjLNWLECBw6dAjbtm2jICQhhBSiG3hCCCGEEEJMh6ZjE0uRvDBNv379sGXLFrRq1Qqurq5QqVRQKBRQqVSyfqxdVzI6OhoA4OPjgxo1avA+pmXLlhqPlerevXv47LPPMHbsWEyfPh2///47EhMT9baF2Z9QO+7evYuMjAxZbSGEEEIIIbZHRfnchBBCbAxdmYilSM6EBIBBgwZh0KBB7L/t7Ozg7++Pp0+fmrxh5hITEwMAqFatmuBjqlatqvFYqU6cOIETJ05o/M7FxQXz58/HjBkzZLeFaYdKpUJsbCwaNmzI+7icnBzk5OSw/05NTZXVbkIIIYQQQgghhBBCzElyJmRJkZaWBgBwd3cXfAwzNVpqMM/f3x+zZ8/GmTNn8OLFC6SmpuLcuXMYOXIkcnJyEBYWxtahlNMW7hRtsbYsXrwY3t7e7A8TvCSEkJJEQRNFCCElAJWUIIQQYmuol00sxaggZExMDM6ePWuqthRbr732Gr744gu0atUKvr6+8PT0RMuWLfHHH3+wK2wvWLAA8fHxZtn/zJkzkZKSwv7ExcWZZT+EEEIIIcQ4FIMkhBBia+jaRCzFqCBk9erVUaVKFVO1xSI8PT0BQLTGYnp6OgDAy8vL6P1NmTIFvr6+yMnJwYEDB2S1hWmHvrY4OzvDy8tL44cQQkqqnDyltZtACCGEEEIIIUQmyUHIzMxMszTAXNsVEhAQAACi2YLM35jHGsPe3h516tQBADx+/Ji3LY8ePRJth0KhQPXq1Y1uCyGEFGeKwnkio9acs25DCCHECCqaj00IIcTG0HRsYimSg5A1atTAsmXLkJ2dbZIdnz9/Hq+//jq+/fZbk2xPqqCgIABAUlKS4MIz58+fBwA0b97cJPtMSkoCUJT5yGC2z+xPqB116tTRqA9JCCGlWa6ywNpNIIQQQgghpMSg4TFiKZKDkLm5ufjkk08QEBCAsLAwXL16VfbO0tPTsW7dOnTr1g2tW7fGnj174O3tLXs7xqhSpQqCg4MBABs2bND5e1RUFOLi4uDs7IzevXsbvb+LFy/izp07AIBWrVpp/G3AgAEAgF27dvFOyWbaN3DgQKPbQQghxR2N0BJCSgK60SOEEEJIaSU5CHnv3j2MHz8eL1++xNdff41mzZqhcePGGD9+PNauXYvo6Gg8fvwYmZmZUCqVSExMxJ07d7Bv3z4sWLAAffr0gb+/P0aPHo3Dhw+jfv36+PfffzF58mRzvj5es2bNAgAsWbIEFy9eZH+flJSE8ePHAwAmTpyoESDdvn07AgMD0bVrV41tZWZm4scff2RXuuY6fvw4Bg0aBABo3769ThCyV69eCAoKQnJyMsaPHw+lsqjO2a+//opDhw7Bw8MDU6ZMMfIVE0IIIYQQQgghhOiiwX5iKQ5SH1iuXDn88MMPmDx5Mr799lts2LAB169fx40bN/DLL7/ofT5T/6ZVq1aYMGEC3nrrLdjZGbUujsH69++PyZMn4/vvv0ebNm3QtWtXuLu749ChQ0hOTkZISAgWLlyo8ZyUlBTcvn1bZzp6bm4uJk6ciI8++ghBQUGoVq0a8vPzcefOHVy7dg0A0LhxY2zevFmnHQqFAhs3bkSHDh2wbt06REVFITg4mF113MHBAevWrYO/v7/53gxCCCGEEGIxVBKSEEKIraFLE7EU2VHAOnXq4Oeff8bTp0/x66+/YsCAAfD19YVKpeL9sbe3R3BwMD799FNER0fj9OnTeOedd6wWgGSEh4fj77//Rtu2bXHy5Ens2bMHVapUwZIlS3D48GG4urpK2o6bmxvmzJmD0NBQxMfHY+/evdi1axfi4+PRrVs3/PLLLzh//jwqVqzI+/x69erhypUrmDBhApRKJbZv346YmBgMHDgQZ86cYadsE0JIaaegIVpCCCGEEEIIKbYUKhMt0RcTE4OnT5/ixYsXyM7ORrly5VC+fHnUqVMH7u7uptgFkSg1NRXe3t5ISUmBl5eXtZtDCCkGVkfFYOG/NxCzuDcUNhrt+3L3DayKVC8oFrukj5VbQwgh8gSE7QYAHJzeEbUreOp5NCGEEGJ+L9JyEPzlQfRo4IdfR7a0dnOIGdlKnEjydGx9atSogRo1aphqc4QQQiwo4vJTAOppgjYagySEkBKBpmMTQoj5bTr7CME1fFCrvIe1m1Is0KWJWIp150QTQgixKbbcAbHVDE1CCCGEEGJbwrZdxZCfT1m7GTZPZdO9f1ISURCSEEIIy0QVOgghhAigsywhhFhGdp7S2k0oNmion1gKBSEJIYSwCujumBBCCCGEkFKFbgGIpVAQkhBCCMuWp2TQCC0hpCSghHNCCLEMOt1KQG8SsTAKQhJCCGH7H3RzTAghhBBCSgLq10pHg/3EUigISQghpHig3hEhpASw5YxzQggpSeh8Kx29U8RSKAhJCCGEVUBDxoQQQgghhJQK1PMnlkZBSEIIISyKQRJCiHnReZYQQiyDzrf6MQkINOGIWIpRQch169Zh3bp1iI+PN1V7CCGEWENhB8SW+2qKEtw9ep6SjaX7bkFFvWVCSjz6mhNCCLEVdE0ilmZUEHLUqFF4//334enpaar2EEIIsSKajm0dn0dcx8qj9xGfmmPtphArOHEvESFLDqOggL5/hBBCiKnQVVW/i49eAQAO3KDEMmIZRgUhfXx84OXlBTc3N1O1hxBCCCl17O3UWZ55ygIrt6T0yVMW4ElyllXbEH7oLp4kZyGvgD7/0oAWSiCEEAuh061e604+tHYTSCljVBAyMDAQKSkpSE9PN1V7CCGEWAHTR7PlREhFyZ2NDYfCIGQ+ZcJZ3Je7byJkyWFrN4MQQgghJkaDPoTYHqOnYyuVSvz222+mag8hhBArYIOP1FcjpQwzDcmq6HtXqtjyYA8hhJQkdL6VoAQP8hPbZFQQ8v3338egQYMwY8YMrFy5Evn5+aZqFyGEECugEWPrUFgpzbOgQIXsPKVV9s24l5COlMw8q+2fblCIpVHZBUIIsQy6xBNiexyMefKYMWPg4eEBZ2dnTJo0CXPnzkVwcDAqVKgAe3t73ucoFAqsXr3amN2azJYtW/Djjz/i8uXLyM3NRe3atfHWW29h2rRpcHR0lLyd6Oho7Nu3DwcPHsS1a9fw8uVLeHh4oFGjRhg2bBjGjh3Lu72jR4+iS5cuotv+6aef8OGHH8p+bYQQIgcTfKSAjHVZenXsBf/ewNqTsYhd0sei++XqtuwYalfwwMHpnazWBgDYfC4OVXxc0a6Wr9XaQN+/0mHAypNW/c4RQkhpYel+FSFEP6OCkGvXroVCoWC/3C9fvsT+/ft5H8s8zlaCkFOnTkV4eDgcHBwQGhoKDw8PHD58GDNmzEBERAQOHDgAV1dXvdvJz89H8+bNAQAeHh4IDg6Gn58fHj9+jFOnTiEqKgrr1q3D/v37UaZMGd5t+Pn54bXXXuP9W7169Qx+jYQQIpctd9Votojp/XvlmbWbAECdDWktTAD+03+uAAAFhwiR4UVaDsp7Olu7GYQQQgxly51/UiIZFYQcOXKk1aaQGWPHjh0IDw+Hh4cHjh07xgYRExMTERoaiqioKMyZMwfffPONpO21aNECM2bMQL9+/eDsXNQRu3r1Knr27ImzZ89i+vTp+P3333mfHxgYiLVr1xr9ugghxFDXnqQCoBFjQqyJvn6kODl+5wVG/n4WERPbo3EVb2s3hxBCdNBlVT8qxUQszehMyOJo0aJFAICwsDA2AAkAvr6+WLlyJTp06IAVK1Zgzpw58PYW71Q5ODjg/PnzvH9r3Lgxli5dinfeeQebNm3CL7/8ImuaNyGEWNrBm/EYGlzN2s3Qa+y68/h1ZEtrN8Nkit9wnmnEvcy0dhOgsKF3n24ESHFyJz4NAPDwZQYFIYlJPU/JRgVPZ9jZ2c75mZCSigZAiaUZtTBNcfTkyROcO3cOADBixAidv7dv3x5Vq1ZFTk4O9uzZY/T+goKCAABZWVlITEw0enuEEGJOM/65au0mCOIm3h+4EW+9hphRaesHdlh6xNpNsKnAH90IkOKEmQ1VQMctMaGMnHy0WXwIK4/es3ZTSAlA11X96C0ilmZUJmRxFB0dDQDw8fFBjRo1eB/TsmVLxMXFITo6GsOHDzdqf3fv3gUAODk5wcfHh/cx8fHxWLBgAZ48eQIXFxcEBgaiT58+qFbN9rORCCHEUmwpY83kSvBLE5KTb91VuYUkZ+aijJuTVfZNNwKkOGGS1KiMBzGl7Dz1tSH6UbJ1G0JIKUHncGJpJgtCxsfHY+vWrTh//jwSEhIAABUqVEBwcDAGDRoEPz8/U+3KKDExMQAgGuCrWrWqxmMNpVKpsHTpUgDA66+/rlEvkuvWrVuYN2+exu8cHBwwadIkLF26FA4O4h9TTk4OcnJy2H+npqYa1W5CCDFWWnYeZm2/hsUDG8PD2TzjXWnZefB0oRIXxZXSRtKntPveY9aew7bxIVZqi228J4RIwYydFNBxS0yIjibTyM5TIjtPabVBNSJfZm4+FFDA1cneovu1ke4YKUWMno6tVCoxa9YsVK9eHZMnT8a6deuwd+9e7N27F+vWrcOkSZNQvXp1zJ49G0ql9bMe0tLU9Wvc3d0FH+Ph4QHA+GDe559/jlOnTsHDwwNLlizR+bu3tzemTp2KY8eO4dmzZ8jIyMCVK1cwbdo0KBQKLF++HOPHj9e7n8WLF8Pb25v9YYKohBBiCsmZuUjOzJX8+DxlARrPP4CIy0+x79pzs7Vr/F8XzbZtU2m7+BDe+u20tZsh2W+RD/DdwTsW2Zetxi1iEjOstm8bfUsI4cVMx7bV7zIp3rTXPlWpVLj9PM06jSmGRq05i2YL/rN2M4gMjebtR4sv6DMjJZ/RQciRI0fiq6++Qm5uLpycnNCuXTsMHToUQ4cORbt27eDk5ITc3FwsWbIEo0aNMkGTi4d169ZhwYIFsLOzw++//446deroPCYoKAjLly9Hx44d4e/vDzc3NzRu3BjLli3Dpk2bAACrVq3CpUuXRPc1c+ZMpKSksD9xcXHmeEmEkFKqzeJDsjqyOfkFZmxNEWZRBFv2LCUbJ+4lSX685W/mNXf4xe6b+O7gXUs3ghSyxOffeP5+bDjzyPw7IiVe0XRs67aDlA7rTz9Ez++OI9aKA0XFyekHL63dBCJTgQrIzLV80hadwomlGRWE3LFjBzZu3AiVSoXp06fj2bNniIyMxMaNG7Fx40ZERkbi+fPn+Pjjj6FSqbBhwwbs2rXLVG03iKenJwAgI0P4Apaeng4A8PLyMmgfW7ZswZgxYwCog4iDBw+WvY2BAweiWbNmAICIiAjRxzo7O8PLy0vjhxBCTCU7zzJBRX20syJK0vSREl3vUoD252krXmXmWW/nFjim07Lzsew/y2S7kpKtaGGaEnQyJjaLyYJMz8m3cksIKVmoFAyxNKOCkKtXr4ZCocDs2bPxzTffoEyZMjqP8fb2xtKlSzF79myoVCqsWrXKmF0aLSAgAABEswWZvzGPlWPbtm0YMWIECgoK8Msvv7DBSEPUr18fAPD48WODt0EIIWKSM3ORp7SNIKNcJbPTVBJfk+WsOxWLgLDdePwqU/JzbOEwsvQK3SXzu0MsTUGZkMSsNEeqmDrC9nY2OoJFSDFF53BiaUYFIc+dOwc7Ozt8/PHHeh/78ccfw87ODufOnTNml0YLCgoCACQlJQkuPHP+/HkAQPPmzWVte8eOHRg2bBiUSiV++uknfPDBB0a1NSlJPYWPyd4kpKQqKEkpbcVMswX/YdrflyQ9dt+1Z+ZtjEwl6bCx1axAczJHp3fuzusAIPmYtjWWCkaWoK8OsSImg9vSQXRSWvAfVy/Scnh/Twjj9vM0BITttnYzig07CuwTCzMqCPnq1St2MRR9mMe9evXKmF0arUqVKggODgYAbNiwQefvUVFRiIuLg7OzM3r37i15uxERERgyZAjy8/Px008/Ydy4cUa188mTJ4iMjAQAtGrVyqhtEWLLkjNzUXPWHkRcfqr3sSmZeXiZIX2BFCLNoZsJkh734Z/yF4IxZcaVdheJpgAarzi+hSqVCo+SxDMd82VEqG3pLbDU50GZkMQUSuPgCbEe5ngb+ftZ6zaE2LzDt6T1a4nam80rW7sJpJQxKghZtmxZpKSkSFpFmlk0pWzZssbs0iRmzZoFAFiyZAkuXiy6qU5KSmJXo544caJGcHX79u0IDAxE165ddba3Z88evPnmm8jPz8fPP/8sOQAZHh6OxMREnd9fuXIFffv2RVZWFmrVqoU33nhD1usjpDhhRrSP3Xmh97EtvvgPzRfSqnG2ju5Li5/iEpTKzlOi1qw96Pj1EZx5kIToR/wDm8ZmVx+8EW/U8w1lqU+heHzaJVtJmgFQTE4fpJgQPp6od0EsJzM3H6ujYopN/8gYjvZFIaHS8HqJ9RkVhAwODkZBQQGWL1+u97HLly9HQUEBWrZsacwuTaJ///6YPHky0tPT0aZNG/Tq1QtvvvkmateujatXryIkJAQLFy7UeE5KSgpu376N+/fva/w+ISEBAwcORG5uLipXroyTJ09i1KhRvD/aAcd58+bB398fLVu2xODBgzF06FC0bNkSQUFBiI6ORrVq1RAREQFnZ2ezvyeEWJuUrqWc7CZS8pXEfhLzmtadisXT5CyL7dfUX63oR6+Qmi2+wIv2LmMkrHj675VnbFuH/noaA1ae5H2cUsbBwXfueX/decnPNyVTdv5Ts/Pwvz8v8H4OJSkAVlz9dbb4r1DOfHfoaCJiou4m4knh9WzCXxex9YLUWveaZ+eNJeA7QyzDFFnaPxy+h4X/3sC1J/qTrYo77jn8yuMUq7WDlB4Oxjx59OjR2L17NxYuXIj8/HzMmDEDHh4eGo9JS0vDV199hcWLF0OhUOC9994zqsGmEh4ejpCQEPz44484efIk8vLyUKtWLYSFhWHatGlwcnKStJ3MzEzk5KgzuR4/fow//vhD8LHz58+Hr68v++/Zs2fjxIkTuH79Ov777z9kZGTAy8sL7dq1wxtvvIFx48ZRPUhS4tHNC5FK+1gpSaO1TH/5QWIGXqTlYO7O65i78zruL+ptkSL86vfSdPsZsPIkOtTxxfr3WuvZZ5FFe25i1UjxgUq+FqpUKnaV3qLHSX8ttnQUmbIte648w95rz3EpLhnPUrIRPac7bhWuLmuK/dxLSMff5x5hdp8GJtha6fPkleUGGcyFpmMTKd5efQY+7k64OKc7dl99ht1Xn+HNFlWs3SxCROXmqxdtzCsonos3ysHtjtHq88QSjApCDhw4EEOGDMHmzZuxaNEiLFu2DMHBwahcWV1X4PHjxzh//jyys7OhUqkwdOhQDBgwwCQNN4UhQ4ZgyJAhkh7LZDNqCwgIMPhG+JNPPsEnn3xi0HMJKWnoZqZkMmeAx5aCR3wMWXV83PoL+Od/bdl/J2XkoIKni6R9PXqZiVrlPfQ+lo853sv7Cemif8/IUWq2QUIj+M4TVx6noGnVMjJaZrvUn387k2zLyUE92eVZSjYA4Kt9t7DpXJz6j5z3Ol9ZgF+OP8C77QLg4Sy9Wzh98yVceZxCQUgDlaTFXErQeFCpk5OvRG5+ATxdHM26Hzn1vEvSd4MUX2ymN+cEl5tfgL/PPcLbbarrDH4WZ9zvHBN8JcScjJqODQDr16/H9OnTYW9vj6ysLBw/fhybNm3Cpk2bEBkZiaysLNjb2+Ojjz7CunXrTNFmQkgJIvXm5eR93fqppHQpbje6n0dcl/xYbl929vZr7P/bSezkfrn7Jrp+ewxKA6fZWmKRn2tPUjD0l1NsG9ssPiR7G3xvxxs/nsDJ+4kYsPKEQe2ypYzaCw9Nt3if9rETn5rN/j/3FV98lIyv99/GHydjZW2f7waNlC5yMo5Naf2pWDxLKf6ZpLbgrVVn0Hj+AWs3g5etxniy85TYcOYRnftKOGbFaO7HvP70Q8zZeR3H75asexLuaxy99pz1GkJKDaMyIQHA0dER33zzDaZPn45//vkH58+fR0KCekWqChUqoGXLlhg0aBAqVapkdGMJISWX2M1MZm4+Rqw6Y8HWEFtU3LIjTt1PMuh5zJRZQPoE6etP1TV8DJ1WbYl7qW8P3MaZmJdIycqDjztfyRP9jRA6T9D5QZf2DTx3Wn+BSoU8ZQEc7e2QnafOSM3J08xMleqPk7EY3roanB3sDW5rqVS8TmeiLHluVqlUmLPzOnZdfootH6qzhjNz8/HfjXi80YxWeJXrvAkHPowRk5gBb1dHgWuDbfnp6H2EH7qLBpW80KyEZOGXNKaIXzPXUO7gblaueqpyScsW1D6DX32cgpvPUjEkuKpV2kNKPqOCkMePHwcANGnSBJUqVcKkSZNM0ihCSOkh5eaFFqQhvMxwWOQrC2BvpzDJNBs5tRzjXhqX1WNoRpIlv1n63lNDp2PzufokBWnZeWafYmjLtN9vbmZkZq4SdWbvReySPkbvZ37EDWTlFeB/nWsZva3S5EzMS2s3QceFh6+gLFChVQ0faU8oPKQsmRDGBAS4QYDFe25h/emHCA7wQaUyrpZrTAl18l4i3vvjPC7P68GWdTC3Lt8cRXlPZ5yb3Y3370npOXq3MX3zJVTzccPUbnVN3TwNGYU18wwpuUKKD+aayb0FKUlTsDVoncT7rogCAApCErMx6srSuXNndO3aldLRCbFhKpWK7TDZIub0Yc3r+oHrz3HzWclf/c5SzHVFsMSlpvbsvZi57apJtiV1KjUAnHrAnzUp9SUbm4lkjvdWe5PMu3HwZrykxxvrcQlY+MMY2kef0PHI/Fr2+8/ZXraBWZQAMHDlCYxbb53VyA2x79pzBITtRr6RAYhLccmmaZAJDfrpJIb8ckry462xOjYzKMkNBiRnqVeAz1fS/YgprI6KQVaeEmnZeWbdz/WnKbiXUJT5/yKtMNDI8zEqtS5SAWG7karVvm0Xn+C7g3dN3k5AvcL3+Vj1wIEl+qsjfz+LDWekrwa+7eJjBITtNjpDr6CEDPrzfUa5+QXYf/255G3YsddG3fekpMU+hF7NocL+2oYzj9Bx6RGkZJn3nEBKD6OCkN7e3vD29kbZsmVN1R5CiIn9eeYRGs7bb/OrnVkzCDl2/QX0Co+0XgNKMhP203RWxzbdpjVsPh9nku3ICUIKkdvPNfQ9MaQmZGJ6DlIypXdImbfj061XZO8LUAe65LynfC+poEBldPDI3IwJ6HFJea8KClQmqetnzKF+8VEy9l/nD0zbor/OPAQAZJnocyrOmEDgn6ceWmyfSjYIyWlH4X+LW8kOS8jIyWeDZ1JZ6l3s830Uui07Lrh/7mmF73y2I/qJwfu++OgVJvx1UfLj3159Bm/+rBmgN2cc6vidF5i1/SqO3Xkh6fE7Lz0FAGTnG35eOnU/CTVn7cHDpAyDt2HLfjh8F+PWX8CDF+KL5jGYY07u55yvLEBA2G6jjk9LE3qNH6xTDxDO2n4Vj15mYqiMQSpCxBgVhKxduzbS0tKQk6M/RZ4QYh0nCosn23I2pFxN5u8327ZfZeQiIS1b/wNFPEzKwNHbCSZqkbCXGbkmC1iYEneE2Jw3hbY+Ei1nOrYQqa+RCSQZ+pYY8rSWXxxE68UHDdrf7ivPdH4Xk5iBewIrat9LSEPgnH04K2MKK9+xF7btCmrP3iu9oVbwl4zsFzHah592JhEA5HICsjb+dbIZJXY6ngH+u6HOKrodn6bnkabDHKbc45XN5qVjWMes7Vfx5s+nDMpws9axznyOWXlKrI6KgUql4h0qMab/M/Ofq9h9Vfc6JIUl35f1p2Itti/m+vogsWQGIZlM2xyJ2aLMp8y34N/9FxnYfK5owJqb6MFka286Z5pruZAJf13Ej0fumWRbQn1N7ZfOrVlOiDGMCkIOGzYMeXl52Lx5s6naQwgxQFp2Hsb/dUF06szqqBiLtefq4xR2oQyp5Nw8pGabJ6CalatE60WH0OpL+av2cvX87jhGrTHv6nLKAhWaL/wPI38/a9b9GEvf55qnLJA8fai43WDacaJAAWG7BQNsYuTeNzKBt6fJWbJu0AwN6GbnCX92upssej8mbNDNQIlJzEC3Zcd4t3UvQX1TdOWJ9PMK30vafP6x5OcP//W05MeKuROfhlcZuaKP4bbVVNPhtO+T/7uhm23YYuF/Or87cP05fot8oHf7lznTia21SrI1FbPTkVlYI4OVOVdxz1ml4ehTqVT48cg9vNRzLtH2LFk9qMrNkPv2wG1M+/uS6L6sibmORd5NxMJ/b+D6U/5yOYv23ELLL/7jba+qcPEtAPjh0F2Tnc+Li7qfaQ62fXfwDo7ckjY4XlK/T9GPkgFIz9xXsDUhdY+vr/bdwqf/qGd1HL2dgEbz9uOG1nFqzvIQL9JysPvqM3y9/zYAdR/zj5Oxsrax89ITvFe4Erbclj5JzkJufgEiLj/lDdIWB8oCFe5acACNaDIqCDllyhS0bdsWEydOxJ49e0zVJkJsWmp2ns2dtP698gx7rj7HHp5R3X2F9U9+Pa7/ptJYTEew74oo9Pk+StZz5Q4sm6OT/O+Vp2xm0Je7bxi8HbHADCMlKw+rjj8w+HV8ufsmAMjKDDNEjgFTe1QC/88n9NujaDRPWmardmab1Hdu49lH+DziusRHm4691jHNDbAVFKgkvbeSp0lrZQK1W3IYUzddkvZcyA928rn5LBWJIosHmCJ5xNQ3Ry8zcgWn1QrV6ZSrx/LjGPTTSZNsS449V/XXvsrILXrtzPdr7PoL+KLw/CKV3M+29aKDWHbgtrwn2QhDjsE/Tz9E9CPbWIXYXGKNzJ7KzS9g64+JYU5VfOes4nkrLM3TlGx8vf82Fv4rr2/i7Ki+1cvMVUKlUqHh3H344fA9bLfyVFGxwUfty16BSnhORWJ6Lm/m3vKDd1GnMOv92//umOx8bmviXmZq9ANXHFZnxmm/v98dvIvRa8/hyK0EwX4n912e9vclzNpumvrYhiooUGHbxceS+8nZeUp0+voIbj5L1RkYy1cWyM7YZmazcPfOd627+lg9ONr7+0h8suUye/zmmTg4d+D6c5y8l4jHrzIR/GXRLBTm/dkm8Tv9e1QM3vzpJKZsuoRDhYFpsbc4huf7FbLkMEK/PYpJG6Mx0Ar9G66HSRm49TwV2XlKWTUrfzp6D92XHxfttxLzMSoIuWjRInTs2BEqlQp9+/ZFkyZN8L///Q/z5s3DggULBH8IsWUpWXk4yJMxwnjntzPovly3ho2lvMzI1Rht23npCe9CGrn5Bdh01rxTAbi2nI9DjZl7kJlrWJai7Np3Zrjb+IRTq25VpHkzR5fsvYUv99zEw6RMg56//nSsaRskwNgRzpnbrop2IONeZmlMCZUjM1cpaUGhmduuYs2JWPT/8YRFMzzEpmN/HnEd9T7bp3cbkoOQPA87E6P/povJqtl+UXqGoJBe4ZHo94Pw4IMpAohyZrhLeeuaL/zP4O+gHHKmt5mqhMGuy08lPY75Dsn5asS9NO49i0/NwfeHjZtG9iwly6plRqS8X3uuPsOq4w/w2Y5rGLDSujdq+hi7oIWxU7JXHLmH9/44rzeYybzvV5+ksAuTlIYp8sy1S+6KzA6FJ80v/r0BlUpz4MFUMnLyZdfajU8VLnvDFxQQ+75pZ5wlpGbj3yvq819A2G7e5zDBCrH+Pp/jnBqNQv2JfGUBeodH4srjZL3bu5eQLvpe6DvPdP32mMZiUr8efyD6WYxee47Nit91+Sn+9+cFnZXHM3OV2B79RNbCOOaw49ITTN98GTsuPcHSfbf0HvuRdxPxMCmTNyOQ+zauOh6DRxKu+0x/IytXic8jrgvOLuHOetly4TF7DTd1/emx6y9gxG9nkJSumQ296Zy8OuYL/r2B8w81B8XEDrMu3xzl/T2z+N9lKy+y1unro3jtu0iMWHUaTT8/IPl5t+PVs5Myc2yvrFVp4GDMk+fPnw+FQsGehK9du4br1/Vnm8ydO9eY3RJiVh9vuYz/bsQjdkkf3r9flTEd0Bze/OkkHiRmsO2bwsl24nZWVkfF4Kt9tyzWrgOFnZp0PVOlM3LyMXrNOYQPb4Yyrk7sCKJch24lYN2pWHi7OmLFiOYGbcMc+Grd8WGy4AxZEMSSDFlcRfsl5Reo4KidFmgInreqV3ik4HdV26W4ZCgLVHAwRVsk4Hvv0nPy4eHsgJ0SA0TGHB76bsy5N1DzI27g3XYBhu+s0NOUopsp7WCa1ENJpVJh07k49G9WGa5O9lrbkLEwjZXzoubvuo47nMBMQYFK42ZFiClOCXICFV/ukZf1CECnbq41QkBtFx9G0yre2DmxvUX3K+eUOF7GwheWpCxQ4dfjDzCmfQCcHdTfsY1GDloae9wy9dq0F9bIUxbA0Z6TM8HZT0JqDrxcHDltMLwRq44/QG0/D3SpV8HgbTDSc/LR/8cTWP1uS1Qv587+zsPZqNsugzDnzB2XnuLzNxpJeg7fwjBiGs7bj96N/bHyrRbyG6glIS0b/Vac0PidAgrRz/b3qBh89WYT9t9TRaaac/cDAPuvP0e3Bn6S2zfy97MY17EmAOHAzaI9t3DjWSp+OHwPq0a21Pgb8zqYz4WZIXHvy15wsJeXG9Tqy4O8g7hPkrNEn8esJj95YzQAYO+15xr9qMV75V8TzIEJmP989AFux6ehcllXDAyqotMvYDCLqOjzz8XHuBOfhohJmteOpftuITNXifn9GgIo+oy2XniMw7cSUN/fi3fgS3vAmTlU5Q7in7iXyPv77DwlAucUDVprX4P4ElG0KQtUOHU/Ce3r+PL+3dgB+oCw3dg+vh2Cqpl+seLc/AI42iv09v8uFk63l6rkD13ZNqMyITt27IiOHTuiU6dO7A/zO7EfQmwZ0xG2dk0cIdyMGrG0c0MzEo3GOavz1YU8//AVzsa+xMYzjzBn5zW2porY2z1/l+7gxgfrziPybiL+vfIM52NfYuVR+Vk1qdl5kkZD5eDWulPXAdTNDlm89ya2XVRPmzA00VDO4bnhzCMs2Wu5gLQ2W/oq5Vuwdg1fJuS49epOstT3xNDp2IA6y/HIrQQcuZWAZylZaL3oIHt+U29bcxM1ZuqWVcnOU5rsXCK1buD7f5zHzG1X8d3BOwDUWXeXCkfaL2iN3hu6v45Lj+CahAGlgLDdGu+ZHGtPxuLk/aJsVKkZv2KfeExiBqLu8t+ocP189L6kfUndrz6mSEQz5Di7LDCItSP6CQLCdpt34S4rnNcyc/MFb1TlOHo7AV/tu4U/TxcFHrnlIf42YEGF/ALTrzp/5XEy6szeq/Fd5Q4uZBUGKYpWxzbcl3tuYrTMWs5bzsdhe/Rjti1MhvCVx8m4l5COzefVGUpxLzPRaN5+7L+uv0SCFPuvPxct56EsULGZrdzLkFiWUBZPhqSc77VQ+YdLAllS3GsVN1sxOVO3X6tQiPeV/j6vmQmWnac07/ez8H3ZK7Cwze8nhGfS1Ji5BzVm7sFprenh3/53h/3/nZeKptXyvYx9157h4qNXSBC4NunrNmw5H6eTpZeTr2TLNvF9Boa6E5+GgLDdOnWgCwpU+C3ygehxzBx+zLll9vZreOs3/bU9pRy3fIOUK4/ex1pOFiWzHW4/7McjutdW7a4e83jmafnKAklZkUKraSfJrAPLZ82JGLy9+ozg7CGpq7ADEKw9aY4awdefpqDuZ3sRfuiuybfNsPaAdWllVBDy6NGjOHLkiOwfQiwhr3A6hJybVqDoYqJSiWeTxL3MxM5LT5CnLLBawFJsKpqlR3j43oI+30fp3AQy7fr+8D1svVA0BXT/jeeC7yMTsBPy5s+nsHSf9PpicS8zEX7wLob9chodvxY+J205L2+KA59DN3ULgf9yrKg+57pTsVCpVLKPITk3B7O2X8XPx+5L6ryZg6myPeVu5VJcsk4Rf2sHIU/cU994SH1PDF2YhjF67Tl2+lV8ag4bwMhTFmhk6QkJWXIYDeaqa3YGhO0WXIlRys211GOWqVGUVnh+67bsGH4+Jj+oJubRy0zJi3WFCkxFMhexqU1dvjmKt1efYf+tLFDhky2XdQZhnotM7xPyk4zApfbhK5ahwA2IiMmRUEuXIbYIGwB2KmaahEXMRq85iwOc4/fwrXg8TxF+/6yZPTFv53W89dsZhB+8i33XDA9oMefBx68ycfJ+Ir7Zf1vjM53xz1X8IvM7Z47FCZiZJ9wVWbntZAOfEj6UhLRsSUHp9l8dFv27skCFNSdikKcswCdbr2Da35cBqAcgOyw9wj4GAOwLvxdPCzPTLpqgLuijl5kYt/4C+nwfpdNvWHMiBvuuPcfEDRc5C5NIO2Lrz9UtDyK3lviDF7qLr3G/W9zAm9CNP1+fKU9ZgBVHxIMQ3EDmxUfJkktgqKDuSydnqvsKR24lYP/155LOWX+ceii+bZGvxKn7mkFI7tRtfX3eD/+8iIFa5R24p+DOeq5Z52Jf4S+tqdZ/nX6EzMJAtJTzphTzd13HZ9uvAdCdJRR1LxFf7L6JtSdiBZ/Pd1mRmu2m/VydGTpKFT7bcVVjVWttzGwW5rl8xyxfMkhR3Vr1/3VcegTNeRaB09dmdntajRcrF9Xqy4P46eh9nXMdM+X/XYHFLCMlDG4y5vEkhpjL9MLzq5zrndSa6wrO/T6xPKOCkOvWrcO6desQH2/51fFI6ZRfGPB7lJSptyZVUnoubjxLlb0gAHMzdalwBL7B3H2aqzAW/n34qtOYsukS6szeyxaCFjNv5zV2pOn+i3Rs49Rgy8lXSsrK0W2r/tdhqFXHH0ie2qCxX60OL5P9k5yZi1cZuexKbtqSM/PYKd3mNmljNJYfvIMbeuoJcmtEGkrfx3DzWSoW773Fm4UmlVBNo3xlAb7lLP5w4l4ScvKVbM0tKYFPgy7OPEXl+ehbMVi3LfIa0//HEzodP2Y0evP5ODxM0n+Tcut5quz9ZuUqkZtfIDiV/dfj9yV38g1ZUZuP9kv4au8t9AqPFHw804HVHoEXyrATWqxr6qZo7Lr8FPdfpMue2s+87zlG1qozVpqJ6g5yPwOVSoXFe2/yBrv2XnuOlKw8PHiRrpERw5Wek497CWn4ev9tbLnwWGcQxhwl8goKVGzwT/sbkSoSFHzrt9M6K7XyCVr4HwLCdmPW9qtYK5JJpCxQ4b21/Nemo7cTZGVU7rz0BEduv8DHWy6zvxuz9jxGrNI/YGOK7Am5NS23FA7cLT94Bx/+eUH2/vZde64RDFhzIhYjVp3BiiP3cF8rgLR47y10WCoekOOa8Y9h18uT9xPxLIV/+igT12TGc7LzlBq1zAasPKmRwSd2qm715SHBG3Cux6+yRIOV/914js8jbmgMogLAucLFQbLzlIhPLcxQUyiw/lQs8gprFmr3j87FvuQN3HHFvdTs7zLBsXsJ6TioFbD7POIGPvzzAvZybtgNORcw7+NKmRnVod8e0/kd9yMZxlmhWuiz4isjtO3iE42sXUMFhO3WyQSLT81Gw3n70WyBuq8weu05jFt/AWEyjucfj9yTPWjNdz08G/MSDebuM2hBwKO3hTPZ+GpsZmidJw2tzc0nNjED7RYfwtqTsTgby794IhOoF1vIUd/siRdpOQgI243bz/UPqGqfr289T8Ofpx9hs0g9Re1xZL5jtunnB3Q+S+acxDz8aUo2UiX0+bRf796rzxAQtlsnM1Wobu7luGQkpOXgq323MEGrFAjTdqHsWVPgvsfRj17pHSyUgimfxCQFPUzKQEDYbsEsZADo+d1x3prrL9JyNO/neZ6blJ5j0PePyGdUEHLUqFF477334Onpaar2ECKooECF2rP3YnVUDDp+fYQdcRYidIPwLCVLp54VF3NSYkYZM3OVvBdn7ugZdxqFkD9OPWQ7wEN/OY3pm4tuehZE3MDrP0TxZl5+s/82AsJ2IyBst05Ale8C/TQ5C/nKAmyL1l1oQs7qnF/uuckWr9a26vgDnkUP+N9v5nzfbMF/CFr4n2hNTWMXGeCb+sxoMn8/tl54jOhHrwSnBxni2pMUBITtRoJI5tGSvbcQELabt/7ludhXkhcQar7wPwz5+ZTO72895w+mRlx5ih+0AuTtvzrCjpIL3QQYu0CBNu0Emew8JY7deYGREm4ITY3JAPp06xWMWHVG9LEPXqTjte8idTIG9Kk/dx/6rYhiFwTQtmiP9KnxH6w7L2n6LUOlEg/WMrWiovV8B4SmY/Hd0N54msp7A6RSqeuQTd4Yja7fHsNukU4jn6T0XMHghCnICS6PWHXa6Km9B2/Gs8G1ZynZ+OXYA95SEwCwZO9N9P/xhEbNX64P/jiPbsuOC06bNaSWKyBckwoAlv13B43n80/n5GZ3azv9QH0TmptfgLiXmXoHHzaceYT5ETfQ9PMDeP2HSDRboLnPMWvPadzYMufzzNx8jFpzDhP+uijphg+A4PsrdqPGDPCJJf7l5hdoLCAnpOG8/SYvCSLmwz8vYMKGi/gtUvfz4ssSj3sp/fsnFkzguvjolUageMSqMxjwY1Hfhvu1ZG62meN50Z6bOoOjH225xOkLiX+nz8RoBkRUKhWW/3dHZ1A7cM4+jYFirkeFj80ROB9M3XSJDWqfuJeIOTuv449TsbyPHfzzKd7AHWPOjmvosFTd3+UL8uq7yY97mSnYj5MrIydfZwETKYROs3KmmPKV9jGU9vWUmwHG/c5G3UvEzWepeG/tORToyfL9ev9tfLL1CvKUBXhvLXdKv/p5iek56LbsmEa2pnb3QAEF1p6MQWauUmPhEe3rFN9Kxfq89p3ugKP25yK1zMOFh6+wpvA6lpKVp3PfkpCajc7fHNWoD83n38LBEKXIdZidmab1++TMXPQOj8TBm+pj+8D151oZjQqdgV6h3Yh9snbsuV79KH2JC4w+30dqPI/xKCmTzdLn3Z9WVOZw4awQ7YWLtAPIfM4JBH8tISBsNwasPInG8w8Izp5hvP3bGXQUuZ93dVTX/7z/IgMqlYrNpv6fSL3lu4WD92+siGLfu8zcfAR/eRDvrNa97+B+Si2+OIix64oG+F5m5IreWxLDGRWE9PHxgbe3N9zc3EzVHkJ4qVQqtubLF7uNK5jcdvFhtPryEDJy8nlvKrVXDBMidov3MCkDXb45itaLDvL+nbloB4TtRvdlx9gTJt90phWcE7j21HLdOiRAuyWH8dW+W7w3D9zVOfOUBag5czeO3tad+iKkoECFCRsu4ss9N9mC1rGJGcjJV7IX+O7LNTvU3+y/jcevpN1kcaevDvnlFD7hZKdI0W2ZetXyk/cSdTpuqdn5+O7gHdkrlCr1pPUznaCbAiOxCijw12n1lJ2+K/hXDpZ6w/wyIxdnY1/iUlwym1kBCN/88QUTufXthDpf3KwmQ7J9tJ+j3RH7YvcNvPv7WdmLPJliygR3FU19HTlm1eQHL6R1+jNy8jFqjbqDc+t5mqRs5OTMXL2ZME9FAnF/nXmIO/Fp7PmoQKXifZ+eFd4QMJnI+spUCH3uGblKnaL3156mSD5vynHgRjzaLpaeiSUF9yM5KqMG0sn7Sfhq3y2cF+nY30tIYzMy+AKckzZGY37EDQBF3wmhFdTzlCrRaWKnCjvirwSCxYYGId/6TTgwf4RzrTDkuzhqzVl0WHoEQZzsZL46dIyUrDxce5KqExDXrl3V/0f1IhbM5fPI7Rc4WxhsMjQjND0nXzCzScoml+y9hd7fC2cacz1OLro+xr3MZEsbHL2dgICw3UhKz0GHpYd5B8+uPUnBjK1XkJSeg1+P38eVx8nqIC5PcJt7PTgXq/t91TcF1FQGrjyJsH80F1J4npqtk91z+kESWzKBWdAplidge/VJisHT6vILVAg/dBfv/6GbWcs3+DPs11PsIFJKFv/38/zDonME03dgjnOmvyCEmz10Nz4N608XTfdlSnlwv9vXnqSyQX2+bDB9g/Vy9FsRhRZfFPVpn6Vk8S6A8s7qMxpBZqFryTQJi8cw5C44IUYs44/7nVVBnQ186FYC7mgFIYQGGJ4mZ7HlRLh+j4rRmdXAd27iq6t55PYLjSBo6LdHBdsvhC8TUjtIJTYdN4VzDp729yV8HnEDX/yrHiiaqFXrcflB/bX7cvKV+KcwyC82GKgQGFuIvJuIG89SMWfHNfZx4Qc1k0Gk1hBc+O8Nnd8xn5VCazq20PVKu6+XWBhEfvAiQ2O2w6CfT2LihmiR1vBfXd7TOj9J6ZNy37LdV57hN5HyM6aq+x2fkq0zoCM0+40RdS+RHdjh4+RQFKqSM2UcUNeM/ufiY9xLSGOvf1GcYLtQH/3YnRfs+fT17yPZe0tiWkYFIQMDA5GSkoL0dNNMF7O0LVu2oHPnzihbtizc3d3RtGlTLF26FHl5hqUPX7hwAYMHD4afnx9cXFxQo0YNTJo0CQkJ4kGe+Ph4TJw4ETVq1ICzszP8/PwwePBgXLxom6sqWsONZ6mSVv/i4na4YxMzdGqKNZy3H+2/OowNZx5BpVLhXOxLjVFKrqO3X7AjwEygUPvmj+kk5CsL0Onro4hJzCiakqOFe995NyHd4AzA97UyAsIPqS/CYvVCmMBrRk4+ClTAqDXnMHljNOJTs9mMS6GpdWk5+Tp1XTp/cxSztl1jO17aN4zrTz9E+6+kdYSZG/J8ZQHOxrzElguPBT8TISfuJWLEb2d4MjWhdzSbT61Ze1Dvs316swOFMpq+3HNTYzqn3EyCgLDdWKVVl4m56WasEBhp1DeVRajzxw0aaj9EexQ3MV09HUZsZdXjWkGDp8m6I+Sztl/VCfZm5Srx45F77Ocm5dO7+OgVIi4/FTxuuIsnFBSocPzOC53MggKVenrV6MKMBm6tJj7zd11HxOWnOHk/SSsjUH+LB648KZoJA0Ano/JRUiYCwnbj4qNXmL39GnosL+ogzdp+TSNYxOCe//Zdk5eRyKUsUCFkiWZgUOg4ssUyO9ymyi2+v+ZELN78+RRbO0xbt2XH0fM79Wehb7CDaceLtBzec4JKJb8eKLf2mqmmY99/kY6AsN2ISczg1MfSX8P2XkK6zvf5pFYNNKAoY8QkbTWidAHfzcjvArXKigJe6vdg/q7rWMfJdHtn9RnRhSn4FBSoUFCgwps/n8S49Rdw4l4i9hRmDl97moq4l1lYxZO9+PoPUfj7fBxafHEQi/bcwqCfTqqns/MsHPDOavHMbyFyMoZ/OXZfIxgW9zJT47hkxPKUwmAC6wqFejEL7tRdZtCTry1xL7N4y0FE3n3B9mkuPOQfPBCr9Xb5cTKbAXM25iVGrTnLZvUC6inxGgqPi0ROFturDKZ8gXr79xLSRWubDVh5EpM3RuNZShZ+EqjJyc2W+v1EDIYXlg7gm/2iz5kHut9JbU3m70dsYgbuFwY+vvj3BnLylWi7+DBClhzWKWsSeTdRUvaldr1mS/l6/218HnFd0vmVufRq12/kBkPWcL7rw3/VLOPALnTJs22FQqFRhumUyGfBDV6bqn6d2PRtrv9uxKPpggPsYCkTMGKCWtqLkYidL5iA+Tu/FWWj8WVg336ehpAlh/HkVZbgY7i/VygUGoPxfPcwWy6Ifz+4n0W3Zcfw/aG7bICSCVwJXVcPiQwutFl8iP1/5nhgEjMW/ntDo69qyjIqOflFi+FoLwqkjan7bawdl56adOBD28jfz2IGZwBLyqy2+JRsdFt2HL9p3Rc/eJGO7YULAfFllDN9OX0ZvcRwRk/HViqV+O2330zVHouZOnUqhgwZghMnTqBVq1Z47bXX8OjRI8yYMQOhoaHIypI3BWzr1q1o06YNtm7diurVq+ONN96AnZ0dVqxYgSZNmuDePf4gwZ07d9CkSRP8+OOPsLOzQ//+/VG9enVs3boVrVu3xvbt203xcos9Q2qCcWtkdf7mKO/qwInpuZi1/Sq2XXyCzyOEC+2OW38B//vrougJr8UX/0GlUuGIngv7yN/P6gQwrxeOqmpnjOkLvF7Rmt4rFPTk2nVJHUTi7mrX5afo+0NRlh5z4Qc0L8y69VHUG/lHYNqSXEyw5b7EzDM+TCYPk4L/4EU6as9S11s05mLCt/Ln0+QsNigbk5gh6WaNm0kg5O9zjzQ6Jkv23TJs8SORDs2W83GCnXCxXR3TOr5nFR6j3Lqo2s/XDtDy1WLccOYRdl7SDHCuinyAr/ffZqdfiC3YwRi48iQmbRQeaeZ+R1RQfx/5Mgu4o7cKhXANHkC9CvKkjdEG1VCVUjxfO6uNmX5/5oHujXXE5ac6o+baPvxT/wCXnMONb7VIAAavKm3rhFZk5tLXOWbe37OxL9Hii4O4rBXo5p5TpdZOHbu+aAqRoZmQ2pjv3rnYl2zmsJQFSLotOyZp4FDq4hFSaA/KGUtoFVHtE+vak7GYu/M6bjxNRdg/V2RnayigwICfTqLenL1s30BOlhgXN0Nem/ZUZKn+OBmLfGWBaN1PxuK9tzDs16KSIW/+fBJj119gF8FirmNXHqdgyd5bvDMxVCpo9EcYL9JyBN9b5jv5ojCgP2HDRY1pd4N+0i1j8iwli31Nd+J1A9j3X2SwGTBzdlyTHLThYjIFmSxGALy1PFUqFZuN9CAxA++tPS+Ymap7PVC/t/YGfOeHagXNXmXkYsXhuxpBs9TsfPzJycj8LSpGI4jU6eujOttdzCk5IrTglFimt7mtEVkMhaFSFZ3rxAahP48oyqTT7mNefpyCBy/SeRf9+nr/bbzOOc7FzqumrNcoF5ONyzd4rO1eQhqiRbJWW3zxH348ck+jnAZfcsCfpx/iSXIWvi/sV2pnymn38b7ef1sja/gSTxuYrEkhr2udc5bxlNnafJ7/XodvgE0M03fTXhyP+w1WqVR6A6dicvMLUHv2Xp3atdaSnadEnrIAG848wvt/nNP/hEJifVGxmRQM5pqqnYnM7XP1W3GCt4/Pl0FMTMeoIOT777+PQYMGYcaMGVi5ciXy8613QZFjx44dCA8Ph4eHB86cOYP9+/fjn3/+wd27d9G4cWNERUVhzpw5krf39OlTvPvuu8jPz8cvv/yCs2fP4u+//8adO3fw9ttvIz4+HiNGjNAJIqhUKgwbNgwJCQl45513cOfOHfz99984e/YsfvnlF+Tn52PkyJF4/tzwFRBLir/Pyiv4nKcskFV/7KMtl3HtiXitj8S0HNEVM19l5uGvM4/01g3TzgrjUmcmnkVA2G5sOPNINLvM0BoVzIi8dsCTWwOLu2DF6z9Eof1Xh/HZjqs6wWC+jCtj2NvZQVmgEsw0koPp1+y+8swkKyLzZRW+s/oMO5Weu09jrT2pueKiskAlOJXbUHuvPRecJqWxgIbW37ivcd+157yLCbVedEjj39zMkNVRMYJB5k+3XkHE5adssJHp+McVjhqbYsrvoJ9Ooss3R9X/YLJgVOqO8B882UOAeupi52+Oik7FtYaT94tuyA0NMEilvfo1t8OvfYPAt1qkNWjfkxuSCc0nU8/Ns5T9aJcCEAsezTVgJUpTr0vzW+QDdgrYvuvPec912n2ciyacos8MhuWL3Izzve8JqTk6U81+OnofAWG7NQZHUrLykJyZqzNYefHRKwSE7dbIlGCu8SpoXod7fx+JTSKLHAh5kpyFy4UlNrizD3VuvmQevrO3X0WLwqnvYu+bPucevsInW6+giUA9UG3MsZyYnsMO+vRYfhwn7yVqHDc/H7uPUWv4b0b5Bp6Dv9Q/gMcMkmnP2ODTdvFhtJQwKPhb5AOTLEKkjTtt8e9zcRrZSGLnUO3BZ4YpBh6CFv6Hbw7o1sjUnsoptNgI43lqNob8fApXHifzZuUWDyo26G1MH1LfTAcpmP6nqa5hcjD3LG/ryaSOfvQK3ZYdx22BReoAdf9Re3ou33tr7KEs9/ltFx/S/yAT2nn5KW9tT267pdbY1edjmWWtzCVwzj42U197QS2uggIV/r3yFOtOxeod8Lz6JFnvfpnyZ9qlbxztNUNgfCvKay/uQ0zLwZgnjxkzBh4eHnB2dsakSZMwd+5cBAcHo0KFCrC3t+d9jkKhwOrVq43ZrdEWLVoEAAgLC0Pz5s3Z3/v6+mLlypXo0KEDVqxYgTlz5sDb21vv9r777jtkZmaiW7duGDt2LPt7e3t7/PTTT4iIiMC5c+dw4MAB9OzZk/373r17ER0djTJlymDlypUa79nYsWOxefNmHDp0COHh4Vi8eLEpXnqxJVQT7fCteIQG+mHX5aeoUtYVzauVBQCkS6yxJ4sC0HcXcORWgk6duxYL/0Ovxv6SdqEsULGj7bO2i2eRGFqjomj6kXSPX2Xhz9OPdFYnnLND/s2xGHs7YOm+W/jluO60M7mYi5dY0Wtjad8o7LpsXE2tggIVas4qWiWbmw2pL0gu1+FbCfhOoHaP2A2XCipcfPQKh27Ga2TAPUnOwtPkLFQq48o7aq9SqXAnPp23/g4XM8Idu6QP244Z/1zF0OBqel+TVEznj/sqP1h3nreWE9ebP5/C5nFt0aJ6WcFaflxiHS1DJaXnsNP55GZcyaGC5vE3br1m9o5YVkELrRXJrWXoL6cwJLgqIi4/w86JIbyLOhnit6gY9GpcUfDvUs45YiuTa8vMyUd8arakldJfpOXA18OJraFnjFnbr6JhJS8AmpliEzdEo0cDP53HL/j3Bub1bcj+25T3y6//EIVt49vxZhQBwJ6rz3gXuuj9fSQ8nB1w7fOivtevx9Xb0A6OtFtyGJlamRXMAnXzdl3H/zrVQh0/T3ZqnkolbfaBPtybRCb49iIth82CYaaCyl3Yibug1pyd4llAYlQqFTsI8SgpEwdvxmNM+xqCj8/NL8DFR68wb6dm/+Dm8zQE1/DRuz853w1tCWk5egeCM3Ly9QZUuL7YfRO1yrsb3CY+DefuQwbnWONO339o4EJFppzKyTddXq6zsS/Rb8UJ/Q+0UdzB03yRQSJz0L6GMJ+tqQf+5RJaFCcrV8nWnZaL73Jp7KH8+JW82YyGtt1Qx++8wICVmt+N/2mtal8ScQdQvtl/GxW8nNl/xyRmwMFOgd8iH+CPU+pB7mO3X4jej/x87IHehR6vFS5qpX39dNBeBQjQWejP3IP7pZ1RQci1a9dCoVCwo98vX77E/v38dQWYx1k7CPnkyROcO6ceeR0xYoTO39u3b4+qVasiLi4Oe/bswfDhw/Vuk5kyzbc9Dw8P9OvXD+vXr8e2bds0gpDM8/r16wcPDw+d544YMQKHDh3Ctm3bSn0QUmiEd+PZOIxZWzQFK/LTLvB0cRBcddeY1OoHLzLwyVbd1Qm5+IIYSRm5OsE7IYv3GLfojhTM6dwUsTm+ouTGeJmRZ5IAJAA8T8nGs5Qsk90Ia2eOAnzTjsUDbPr8FmXca8/MzYeLgz0bgNDXkRO6oefSvqHLzS9gb8y1/R4Vg/FdavP+bc/V53rr0nDl5CsFp/maCnc6mL4AJGPIL6cwu3d9hNT2xebzcRbL8th8Lg6f8qyOaotMkXlsChm5Snba3cEb8SY7X6Vn5yNPWYAt5x9jeKuqOvUE9Y3eJ6TJu+E5dCtBJ7tYSPCXBzGzV6BJAhIbzjzC3Ncb8P6Nb7r5rktPMa9vQ7ZP+DQ5iy0dYAonRGrNLeeZOsdIz8nHzWepqF9RHVAVGkDQDkBybbv4BEdvv8DFOd3Z36mg0ihdYi7a9awNYcyUvD1Xn8PdST1IvuDfGzh4Mx7t6/iirp8n7+NzlfzXiPsv0nUy7Exte/QTts6XkF+OPxCdMspHSokYOV+5DK1jjW86uFymzH4249htsXRaQu1MU9JeeGTTuTj0blxRdAEPS+jCky0GAPXn7jN4m7+fiIGzox1mvBbI/k7Kon7FnXZN6pIcgOSr0b72ZCwc7Ys+Z75j69CtBLSpKTxwJaWuLN8ircmZubwz7mb8I2/tCWIco4KQI0eOLHYniuhodZaNj48PatTgH8lt2bIl4uLiEB0drTcImZaWxtZ7bNmypeD21q9fz+5buy1izwOAu3fvIiMjA+7uph2JLS5y8wt0VsNkaE+z0lcQV8p0HjFp5siw5DBkKpdcKpU6m+o3niL31mbKaQPrTz/UmDJqLL4gpPZotbHTUC/HyVstWluDufsxrlNNzOxVX9bzsrQCjaHfHMOKEUHoWt8P3ZdpTiX6V2Sam0IBTNnEX49RTgAS0B3J1lcTM09ZoLfmj6k8Sc7Cmz+fFA1amNKp+0n4W2ClXnPRXnymuPtit3EDBFy349Pw4foLOHQrAeU9nTFz2xUMblmV/bv46pdAqy/NO/Vr8d5b+LBTLZNsa4FA5nKCSL1PJgabX6DCa9+ZbuGZb0UCjXf1ZIk+T81mg5CG9ltfZuRq3EypVCgWAwNyF3fjwwwEMwtk9Fh+HLFL+sjaxoYzj9Csahmj22Ks7yWumksIQ2zRGEtgpkRz60+WJD8dvc8GIe/GpwmuPl5S8S1OV8zCK7JJrQnLU47faM0W2MZsndLO6EzI4iYmRj3loVo14al9VatW1XismNjYWPb/hbYptD19bWGep1KpEBsbi4YNG/I+LicnBzk5RSew1NSSdfIWmxbCLfYtBY3uqs3ZeQ17rpbcUTdz0D50EtNzZK+uq4/c6XZczGIv/12PZ4OQht5sZ+Up8fX+2+ha309nESUxqyJj4OvhZNA+tWm3XF9A+cLDVxYJ4gPA1ScpFgtAArB4ALIkEqu5aAgmc5ZZjIibVXxQZKVMSzFF9pxczPmGb8DG2vI4NQYNWcCDz10TZK8VF2kmWkTkUz2zSYqr9adikWrmQWoxAWG78b7IFHlS/JliMMGW5eYXoO5ne63dDKvgW7AycI7h2aUlib76s6T4MmphmuIoLU1dMFcsq5CZGi0lmMdsT2ybQtvT1xbuFG2xtixevBje3t7sDxO8LCmc7EvdYWpWKqgEVyskwlRabxl3NWhboL1wyI9H7hmVWXrreZpBK3Jz6ygZQzuAqr0qt7YtAqsWmsMFEy64QUhJwXxlbTAGyQ4ipWTm4Xmqaep/OTlQ34Sozdlp2vrYhtBePIaQ4iQn33IDu4QQ66MeVAkwc+ZMpKSksD9xcSUra8aROvomZYs3iMWBdnYPX4CurJujpZqjQ7s4s/YKhIZo8rm01VDNQbtsm1idwdz8ApOsqE4IMZ4tZkIyCytdeGS6rArtcjClzVMT14QmhJRe8SYaHCLEHAxJyiDiZEV3jh8/jtOnTxu8s2XLlmHBggUGP98UPD3VhbQzMoSn+Kanq6fYeHl5Sd6e2DaFtqevLczz9LXF2dkZXl5eGj8liQkW+SQcKsDqxa2Lo6CF/+EqZ2U3vpiYnKnLxYG5a6CKSc3S3LfYYh8rDt+Vtdo7IcT0XqTl4NT9JDSez79AobWtOxVr0gz2yRvFa3+WdO1KWN1YQoj1dFt23NpNIESQjay3WKLICkJ27twZgwYN4v1bq1atUKuWeCH0r7/+Gp9//rmcXZpcQEAAAIhmCzJ/Yx4rpnr16uz/P3rEvwKy0PaYf+t7nkKh0NhPaUODD6Z1PyFdbxF/wq/viij2/5MyDF9pnejHfa8B8SDk5ccpNpl9RUhpE7btisnrb5rK3J3XcVHmqshirFkDkBBCCDGlWuVL5wK4UthTRpTJyZ7nKpSOGhcXp7FIi60KCgoCACQlJQkuPHP+vLrQfPPmzfVuz8vLC7Vr19Z4ntTtMf/W97w6depo1IcsbWzzdqb4Wnsy1tpNKPZuPE212YV9UrLySuQ0QbHVKZ+lZIkGKQkhlvEwibLsCSGEkOLmzRYla00JYttKXbG9KlWqIDg4GACwYcMGnb9HRUUhLi4Ozs7O6N27t6RtDhgwQHB76enpiIiIAAAMHDiQ93m7du3inZLNbE/7eaXNM6o7RGxM7+8jrd0EQUkZuWgw1zanQ5qLnUKBBy+ES2wQQgghhBBC+DlQth+xoFIXhASAWbNmAQCWLFmCixcvsr9PSkrC+PHjAQATJ06Et7c3+7ft27cjMDAQXbt21dne1KlT4ebmhoMHD2LVqlXs75VKJcaPH4/k5GQEBwejR48eGs/r1asXgoKCkJycjPHjx0OpLFoZ7Ndff8WhQ4fg4eGBKVOmmOaFF1Mv0mjaKyFEWP2KXnhCgxXERlHHXldYr0BrN4EQQkgJ4+nsYO0mFFt21FchFlQqg5D9+/fH5MmTkZ6ejjZt2qBXr1548803Ubt2bVy9ehUhISFYuHChxnNSUlJw+/Zt3L9/X2d7lSpVwtq1a2Fvb4+xY8eiTZs2GDZsGOrWrYv169fDz88PGzZsgEKh+eVWKBTYuHEjypcvj3Xr1qFu3boYNmwYWrdujXHjxsHBwQHr1q2Dv7+/Wd8PW6f9vhFCDPN5v4bWboJZXIpLtnYTCBHk5epo7SbYnHxlgbWbQAghpIRxdDB/aMPJyH2sfEt/uTdrMNfddsNKJWvBXGIapTIICQDh4eH4+++/0bZtW5w8eRJ79uxBlSpVsGTJEhw+fBiurq6ytjd48GCcOXMGAwcOxIMHD7B9+3YolUpMmDABly9fZutGaqtXrx6uXLmCCRMmQKlUYvv27YiJicHAgQNx5swZdsp2aUYDM8SWFOd6i6818kfkp12s3QyTi0m0zFRsymgjhnC0p+NGG7N4zZrRwVZuCSGkuJrzegNrN4GUQl0DKxj1/Go+biZqiWHq+Xny/t5cOT/1/Pn3R0q3UhuEBIAhQ4bg2LFjSElJQWZmJq5evYoZM2bAyclJ57GjRo2CSqUSXXynRYsW+Oeff5CQkICcnBzExsZixYoV8PPzE22Hv78/VqxYgdjYWOTk5CAhIQH//POPpIVxSgNjMiGr+sgLJhOiT3Gtt1i/ohf8vFxQ1cqdn+KkZ0PxczchUjg72Fu7CTanhq96Fc6K3i4Gb2N851qmag7Lyd483eIdE0LMsl1zmNK1jrWbUKr0b1bJ2k0ollaMCMJ77WvgjzGtrN0UUspE3U006vkeVp4yvu49/u+MudqlMFuOJSnOSnUQkhQPxiQfDWpexXQNIaQY+6BDDfb/bSm4NtPA2nAL+zcycUs0TQ6tjSUDm6Bp1TLs7wIKAyeEyOFAmZA63mhWCYc+6oRA/6JpWiNaV5O1jclmCJbJbYNUzTjnEVs3rXtdXP+8p7WbUWp8NywIs3vXh6tj0WCFuY7DksLBToHXm6iDt8aeXZtU8db/IAAbPmht5J6sa2DzytZugkWoVCqz7yMtx7gZUQG+7vj57RYmao3puFM9TWJBsoOQubm5iIyMxPHjxzV+cnNzAYD3b9qPIUSONjXLGfzc8Z35p8ET6zFXpklxUsmI7B9T+G5okFX3zzWmfQ2Nf0vtmL3Tpro5msPq27QSyro7oUcDdcD26zebYNPYNvjJRmv5ENtlCyHIk2Gh2DUxBJvHtbV2U/D98CAoFArUKu+h8ftFAxpj+/h2krdjb4byCNyp802reOPsrK6oXKb0zagoSTejrzepiGOfdLZ2M3g1LQyAfdCxpkbgN6SWr7WaxMvYGnjmdCc+zajnv9GsKDjXo4EfutQrjy0f6p4n7Yp5ffplQ5oJ/u3P9+QFWBtU9EJQtTLGNciMwoc1E/ybrbT7tUb+VhvscXHkn53RsnpZs+xPWWB8Dejdk9tjwRsls659aSX7qvLq1St07twZXbp00fhJTk4GAN6/MT+vXr0ydftJKWBMZ9iWO062Zl5f09bWCSjHP+23UhndAFxpq7W3bbx1p+bZ2dDXQrtjL6eDaM5gbp3CmjnjOtbEujGtMLhlVfh6OKNVDR+z7ZOUbjXL62bazn29AWKX9DF625XKuKJJlTJmq/kkRwuRGx05OSz2ZngxjpxBsq3/a4cKXi44ERaKLwcYn3ltKze/YpYOamLtJmg4/FEno7fh5eqI6uVsM4t9JWfQzc5OgdglfRC7pA9CapdDGTfbWNCqVnl33FrwGta/1wrtahmeFGAu5T2dDX7uqHYB7EAjAEzoUhtrRrfSyEplFPcgpJj2dfQHvbnZuf9Oao/f37XNer4qaAaWtVli+v64jjU1/i00zdlcgz18xy+Xt6ujziriJ8NCzTZiWsZNt8ydHJ3qlkfDSt4Y2TYAY7XeW1J8yb4VValUBv8QYihby55rULHkrfQ1ql2ASbfXrT7/lN+NY9tgdu/6Gr+7NK+Hxr/fMFONJFvJaPH3dkFYr0C8374GfD2kd6B7N/Y3eJ9B1Ypu/B1sJAp5cHpHnT6PnD6QMfVixXTgdMgd7O3QsW559t/myL6yBT7uxnUSxZQz47Ztkb+XZnBcak1Ivuwn7UzhkkBs8EBOV9HODN9F5nswvFVVjYDkW62rY9/UDkYtpGOuAQyTvg02dHqr6+eB6uXcjQ581ZRYRiM4QF4W0JrRwYhd0kd2FhmjnLuTYJ+kjJsTLs3twfs3fUILF82oUtY0/Z0VI5rDzk6BDnXKY50N1F8MqV0OP3GCt3L6UIxFAxpj09g2mN+vIar6uLE1ahl8XQu537NAkcU4xAZibBX3+mRnp0BZdyeM6yQcEPrCzCVz9OnAE1jtVt8PXi7GB/c/7lFX9O/pWtO1/3rfslP5hcp/dKxbHhc+6wZAXSeey06hMFvtRmWByqj75gJOx2CW1v0jKb5k3YkeOXLE6J/Dhw+b67WQEuzg9E6Y2q0OvFzUIzf/01OQvmZ5dzZ7ZN/UDiZvz54pHXB5nmEdRK5//id96pm5mSOgw3ehrujtqnMzVqB151nGVXonwdFegUtzu+t9nIezA/ZM7oC321TDnS96mTXowvh3UnvBv33YqRY+e70B/L35O9B8QeG3Whs2BfmbwU01OtlMZ7qslTItWgX44OD0TqhdwVOnsy/nODRXYoJYIMQcgQ9DcKeFC70Pdf08+P/AQ1kgHv0RO5b1KS21NLs38EP9il74R2tKca9G/IMHSwc1wep3WwJQTxmdK5KNztw4GMvaR++tha/p+Y5Li0KuGWX6LJwJXWqhUWX19Ni+TXUHwgL9vdClnnGropoDUxvPFEx9fIhl5AwMEq9RFzGpPeztFNjwQRuj2jAmRB3I9/VQX/NrlnfHNa1pkDNeC8SKEdJLbYxsWx2d6qgHpwJ8pS32ZuyKulK5ONrh28FNDT5nc4OXgf6eGsEKWxiE+25oELpzshdDavvKKuPg5mSPEa2raZR62jS2Dfo2rcS+Vr4+gNw+stjK3UNaWrZe/a/vGF9/UFV4bn63bVE/VKyv9Hab6ohd0gdbeaa2m4N2yQW+GVbMQP6pmaFG7UvfLDvt1aAbVvLCToHFyfiCpcbifj8Y7k72WDemFcoJBO3tFIb3qfdOEb7Pbl6tDEaHBAhmdkuZ6cFcl0nJIisI2alTJ5P8ECJXtXJumNqtLk6EheLinO6Y8Zr4YhbcKUXcwvem5M0TKKsgc1qI1CBQlbKuaFzZGzsmhFg8m2/35Pb49LV6bEdeKrGYhva0Fu1MV6mdPX8vFywb0kxSqr8CgLebI77o31ijAyElW2DJwMaS2qOtUWVv9jgRGq1fM6oV7wqxdQunA385oBF7Q6w9Wi9Vd62sVIVCga0ftsUSzvfE1NPxxax4Kwi1K3iwbbm18DVO26Rvx1xBSO2gOJe+KaCWmkLXq3FFeBYOylTy5j+GtTvKYqPxYq/53bbV0aiyNy7O6Y7qWmUWdk0s6lgLZTyGD2sGPy9nNvh954tegvsS82GnWjgzq6tBz7WEVSNbYu+UDqhcxpUNsHw7uClC6+sGHzrWLY8hwVXRtb4ffhzRHN8Mbspm3nm5OOCnt5prBC2EbhzksvaMQqHSG0z2aFmJ07bMMRjg4eyINjXLIfLTLmhnhpp85poQxHymppgybig/L/7jU+zmVN8UaSkZxFJmcDDHyp4pHbB7cnsc/qizzvTI/3WuBT8v6eU9FrzRiN2u0PlXW/hwzVrM5hxEG9SiisFTILnHqfZgrblmH8jBFwjlzvTQZ9+Ujjq/8/NywQ/Dg9hrJn8QUnibfAN+rk7Cx6/UWveX5/ZgM3Q/68OfAebn5YzXGorPkhFri1RMv70a53tbUNjRnxyqWX+f29a6IhmhYupUkD6IChSdT5jPju84aVBJfT9YUeJ3Voijntl52vc0DvZ2Gosccq03MJNaTL4BNRiN+W4LXdfXv9cK28aHoGZ5D3yvdf6TKvLTLviou3jmKSmebGNOHiESebo4Sspgk3oybVhJeoDym8FNUdHbRTRoxZ22yYzM1uKp88XQdyEDgD2TOyBqRigiJrVHs6pldEa4pnWri3tfGnZTL0XDSt4Y37k2uvHcSItxdix6bcuGNMVXgxrjbmE7tT8eF0d7XJ3fg62ZJfVaGDGpPW/GCi+BbUrZ17BWhq9UyVyb90xWZyVoBxLLezojjGeF6GHBVbHhg9Z4q3V1/DA8CLFL+hicvWnPszpvywAfuDsV3Yg1NnCksT9n6rx2R1RIBU/Nmz1ukWxZ07ElPlpuTT3RIKRI8GPDB60RNUN3hL212aZhCrdl64dtdd6fkNrCgZW2Em6KfNyd8KNWtlCTKmXYldeFjk9PZ0ecmdUNF+d0R+SnXSR/v7Vr6Hk428sKFFgSN5AOFE11cnG0Rzl3dYBGaDpSnyYV2e+AvZ0C07rXRa/GFbHaiGw/yedFiYIDyurNXNNneKtqcOC55u2f2hH/Fp4fa5b3wJ7J+mcvcL+Gbpwb7JVvNZe1aNXgFroZSVV9pGW2cYlNGTbnwOGaUcHsTbcppngaeiMqlEltzizobwc3xfx+0hcqqODpgoaVdK9zVX2M+3zs7BSSzvFCdeHE8PUNDCFndXbu9Y9vSq2+qajGiF3SB5++Vk/0McaOP1QTqFfOxX0PBjVXnyMUEA70M4FNfy8X/DiiOTxdHNCcExjVDkqVcVVfKzvVLa8xM4j7/wend4K3myNWjWyJLR+2xeCWVXn3HVDOHT+MCIKLo+Y+bizoiYldahe23fjgcfcGfpjQpRbe4tSGZN6lTiIZ4obW0vygQ02j6vpz9/tmiyq4+2UvkyWlDGxeRXBG3v6pukFufRa+0RBhvQJFp/DLUY3nGlZD5F4UKMyENHB/fAMesUv6oEOdontiQ8omAOrrMV+/Qe5hxZQO4Avmm2qmCZGHgpCkRJLaSXmrdXVM6VpHUqabi6MdTswIxfFPurC/c9a6QDLFiHs29GNvPLXTyLlZmlV93ERXcQPU2Xti7BTQOUF/N1R8m1JxAy7a0wu0ad+gTg6tw/6/l4sjhgZXY4OuDjxBMU9OnRbtWiVC5GSdCR0S5qqBwujRQD1CXcHLBTsnhPCuuvhGs8q4saBoelirGj6ws1PoZOMY2pkTuvlpGVAW7Wv7yuo0bR/fDjM5N0bcUe7pPcRvHqSQ8xqFHtqnSUWd3zXXsyhEQDk3tnSDWBav9j65GcLtavnyZkqaOnmEuaFhtqtdc3nHhBC0DPCRtd/vhwdJOg74buBm9qqP21+8xvPoQoXtKOPmJCvAI/Q5vMepk8g37UiI9vnaVFwd7XVWm+zTpCKq+riiba1y8Pd2wfnPumH35PZsR1zoeLy/qDdGy8w65/PD8CC4Fwbn+FYGblTZCzsEpodpG9yiCrZ82A7LhjbDyLbSA3xS1fP31LhBqSOhjADzPTs7u6u6oH6h3o0rypoyOqJ1NUySOHgiRmymwIkw3YEJKfgy5LV14WTLmuJaZugW8vWUc+Ddl8jOzFUXms/bBpY54RIKlmjPmPmkJ/caqf/d/rBTLZ3sc334xtCYrHmm/AOf0SEBWP9eK/b5UTO6oGZ53e/ixNA6mNy1js7vDcHX1+MLFHNZIhuTezwzn6EK/CVxTs/syh7Lft4u6NOkIq7OV/fnhgWrA4fLtfrkzNRmOwWweVxRnzCIEyxmZouUcXNCcIAP7+wrQH2MONrbYXYfzdksbk4O6FqYPCBWmqVLvfKCf+NytLfDJz0DNa51TLBW7JRrzKd1xYCyV0x/iHufoYC0pA+pXB3tBWfk1fMvKjPUvFoZ3j6/tnfaBuDDTrUMGqjg07OhP07P7KrRV183RjzjUqFQGPTd8nR2QHlPZ8HFSKVgjs+gamXQtmY5dmCbb7E+htD3AeDPkJ/Zqz6uzO+B9zvU1OnrmmqmCZGHgpCkxOB21PSdSJnMBHs7YFr3upJH2+zsFBrTwLRPZN6ujvisT318OaAx28nQbkkXrbpAQqu4MSv26bsk8E1La8kpsK5vUYjdk9vj4HT9wQd9J+llQ5vhwaLe7L/Fpn/U8/PEgjd0MxiYzm/fJpUQNaOLzt+5JnapLatTUbks/wVS3zVXrPC2mH6FmUhfDmiEK/PVHammVcsIjga6OTng6vweuP3Fa/h7LH8NLFP3vV0c7fHn+631Bpi5VNAMEmjf8BhagJsZsXd2lP6ZCr0dA5pVxv1FvdmVRgH1ioiHOCutvq4VqDz6SRfU9FW/LrGF1LQX9RneSjM7gW/NH0OCA2I1Z8/O7lq4XTXmfonJtGOyXjpxMrOZANzSN/lXv3VxtBc8DlpzsiQdOS+QmUJrZ6cQnTppyHF7cHpHNK3CfzM62MB6Wv9NM085GL7XV6mMKyI/DWWzQ309nKFQKNi6dJNCTXMTL4Y5ivmmvbYKKCcrO4rxSc96+PS1erjOqaknPSAhLVDlaG+nt2YXc42v4Omik4Uh53jz9XA2OAvc3HZO5A8SCwUnDb0+XP+8JyZ00c3q4RvM0V50iVFgQBBSjPZLEbvhNNbA5kXnE26tXT4Hp3fSCBoxpvFMF2xdwwfz+2kGht5rXwOzeqsDA5UlLhxjiss+830RK2Mwr29DdKhTHlO61YGroz2qCPSZAGB697qiGaT6BtcZfDOF9C1k6sgziA3Iy/bUh3vtKRrs43+sv7cL+/5qvx6hfjCTOKCdsWZImYlKhfcz3O/gN4ObAlBPU49d0gcVRGYQ/PKOOjC98YM2sheBYt4T7cFj7j2YwauKK6AzuAeoP39uPX2hc5I5FzTV9zExfb5BLaogOKAou3Vy1zqiC1l9JdA/k2PlW82hUCjg7+2CcZ2Kzus6M1V4XoMhnxRzHts1qb3ecjncRem4C4btmtge1z/vie3jQ9SLl/ZpoE7a4DnXAuos+G0iaypoZ8gzQUlmUSK+vq6vhzN6N/bHZQMXBCPyURCSlBjcgJS+EynTifcunBLBXLu506m18QUStKcaqQC836EmfD2c2YuzdkC0vKczIia2x++jdEekuR23Kd3qoH5FL5TXqjMpd6V57iqCfBpW8kbtCvzBB7kXJO0O1KAWVdC4sjda1dScqqRQKDCybYDgdhQKiHaAAf2FirePb8fWlJkUWht/jOGf2piv5H8/xVZw1eeH4UHsZ+lgbyd5NT5PF0c4O9gLBtGFOnNixf9NQbujzP0u9GyoXW9SfFvcqTxc07qra766caaI65u+IfR+KBS606Y9XRxRq7wH+70L4AnMMM8Rq0tnb6dA5KddcO/LXjg1MxR1/DwxqHkVNqPDFJmQERPbC950B/p7skEX5jhh6v9ov+Zp3eriv2nqAQZmmlcPTuYg81noq6vWu3FRMMLVyZ5dCEq7I679Or1cHNC7sT88nDRH97XfDu0pbmdnd0XtCp6Y83oDHJzeie20C52nuMQyqJgsTqGbE+5gTBOBACifDzuJL5TG1bxwyqwlFnjQvnaoqferkhgQ1Obp4ojxnWvD3YCMDTmXLn01u/jeP+ZXzOrAfFPSuLaNb4eqPm5oXaMcavq6o5/M7Du5NRi5124pKzZX8HTBX++31qi7CkDjphbQDe22qekjud70wOaV4e7swN7Ic7/D2qUX1Pvi/xD5Ptsf9NQA45sRwdAerD32SWecnim/HqzQQAYX93vSq7Fu4JUxrlNN1K7gwbvKefNqZXH3y14aU5X/HtcWA4I0B0xcHO0xtmMt/PV+a/wukpXIJTc7SWz1WAWAqd3EBw2Gt6qGmwtFMtsLMVOKtcUu6YM3mlWGr4cTHO0VotldQrXkxAgNPv8hsHK3IavSO9jb4drnPdXlQ9jfah7km8e11Rgs2TUxBF/215xVJdRH8XB2wN9j22Cm1mel75ylLWpGFzagwp1C/iZPmQkhzEfQtlY53u+8GKZ2uU7tUO7/Gx6DZHFLs7St5atRekIoicTg4KcE+q7fzP1Jk8plNH4/vXtdtBdZiIb/mi1Pb61z2JpRwYKL4wFF5ShUKpVBnxUzoOrl4qi3XM4izqzDhf2LEjRcHO11+hRNq5YRTH4Z1KIKb5Y2F3NfdHB6R9GyHd0Ka+af/6wbVr7VAt5ujmzygjHZnUQ/CkKSYkusQ8pMPRHSo6Ef1owKZgMozM2B3ACf2H6r+bhhXKeavB3CxlW8ERqoO43wjWaV2fTzhpW8sXdKB50Ol3YLm1Ypo7MdhUKBeX0bYGKX2vBxF74Z0bdintDFUqzj2L2BHzvlqKK3KyImtZcchGNIuRDq6wQEVSvL1sDr3sBPpw4hIztPqfHv5UObInpOd7aIspTi+HxtM8eUIe0tMh0LKdP2jMGdrqhSAcGcTr12YEgscBi7pA++HMBf+sDeTsFmKAtl62kTeovFsglCA/3w1/utMYXnRszeToGf3mqOr99sKrpfpkYNEyj5dkhTdgEG5rgUGp3ns/GDNmx9oe4N/NBY5MaZe0PHvEpm6tjA5ppZ1XZ2Cp0sMe6/vxzQGLFL+gh20H56qzl+5hnEYLYh9D5HTFTX96vr54mVb7XQm93xVuvqvJ+5o70dalfwQPs6vjgRForXeDrS3FP2hC61ED5MPPCxbEhTtv6grqJ2itVf5dYYjV3SR9bUxPl9GyLyU/Esb2MtH6o+fv8e21ZnFWlDTkt6r4oSrps9G/phQhd5054jP+2C9e/xBxaaVtX8jqx/rxUOf9QZANChTnnELukjuFgKg6nZ5u3miMMfd5Zdu1FuAIVbNkVfFhzzPQ+p7YsmnGv8Sp5MvcaV1QMg3Ju4shLqB8cu6YNlQ5oBKPqM9R0fQgmPfNOx9QVUynOuFWM7as440F7xu4ybE/y1rnNMW7klcrj2TO6APw3MzOczs5dwcA9Qn68m8mQ4t6/ty06tZYTU9pU8BZD5SJjzqpDJXesgdkkfjZIZe6d0QAVPZ41FPqZ2M01Nxyk85z1uYP5EWCiuzu+Jo590FsxSlLOgDKD+XggFIYWycX99pwU7e0Duvqr6uAlmQpb3dNYYLGlSpYxO5iPf14k5L7WuWU4n06+rntrrTP+KGWTgDtb3KQw+MWVl+PCV4OD2pYXOG8EB/J/T8FZVcfTjzqJlVgztCjsXvjfq64D07zH7MenZr1hGopi2Ncvp7d83qOSFmMW9RftyfMwROO0SWEE0GaWGb9G5wZBZO9oZ82IZydz7g8plXGXfG2rjlubRVrGM+nohNHi98q3m2DEhBL9JHAwipkdBSFJsaXdI325dDXUqeODP91rrHSFRQIEugRXYCwlz0ZLbIQKAe1/2Yqd+crO47OwUmNmrvqSRrbWjgwVvtrRpd4T4AoWO9gqMDqmBj3vWE80e6iGyot6KEUE6AYh//teWvcEVsmpkS8k3m9O61cW3g3W3p+9CPLxVNTbbRQwzMix2YQ2sqPn+9Gzoj7LuTmhRvSw+61Mf/yvMcnITmV6uXZvSXOOv2m/L+M61zbtDdr+cXACVSjSzoa6fJ1tYXmghDn2GFBZgF7rJZ0YntTuC9fw8MaVrHXSsI17jKKS2Lxzt7fDbSN3OR6/GFfXWYRWjUKinCnGnfuvrV7atVQ6jC7MR5cQ1fnyrOQa3qMIuCjE6pIbOIjzGZNz1alyRN/AHqMtfCC2awHwf5AzpuGosTqTbZqEAUT1/3Q602CDUwOZVUNfPE3smd2CznTsUnkMViqL3n+9dmxxa2+ipOk4OdgYtfAIAa0YH6w2scfl7u+hklEmhHVQ1xSI3v7zTUvbrrurjhva1fXlvNLQHhzrUKa8zM4E5P/SQUTdUDu7CEw0r85/ruDddH3aUljHbsW55NtuY8c//2uJEWCh6N66o8716v31NRH7ahS29UsHTBbn56uxofddrBhMk48sQ5xKKNys5f5gcWhvDW1XVWfhv0YDGGNUuAD7uTlg1siW74AegO2gj5bzFHE/VyrnxTg1uUMlLo9a0NnNO1+T68/3WODjd8FIQTMBabkADUNdcPDu7G0a1C0Ct8u5sJrYpMKVTJnL6e9yaic4O6lq5vh7C9eL4pv+KBXjE6mMK9RvLuDkJDkJLoX2fwHB31j9Ard2kHg38cGaW8CIYTGBe6Ht7aHonwSmvFbxcELukj+jiK3x1kfUF1L7o30gwCKhQKHgXn+L2l/lmh+gzIKgyuxhmVR83eDg7sAkaepNFmFloejrGYhmJYjbylEti+qrcl2pIIoLQaxOqjajdL1ozWv5idkwdUmdHe5PcS2wa20ZwoZdGlb2xf2pHXPism0kWLeNm/9bz07yX03eY9G5c0aQlHIh8FIQkJUZ1X3f8N72T4IVFe+U4rs71yqNyGVe81bqa5GlMDAd7O6On1nWuV0FjFTEx3OlQfAsOrBrZ0uAOl2dhcKlLvfLoVt9PZ5S2RXUfDAiqwragj8jUJSmmdKuDQZxpI8x2xYKQbk72WDywsaT3nMnOEHvsyreKAq2hgRXYQLJCocD7HWqyI9t8o/6M1e8avoqtHNqdGoVIwESuOn7CwWoPCZ1trkaFheW7N/DjXV1TKqEOwlGBzBcXJ3tM615X8vexWwM/k61GyNWielmNrKQqZdwwuEUVHJimW3uVuTljkjjknEva1CyHrwc3FQ0cCG1OrOC3FMc+6aKTrcRgjsvyApk+fJ1z7vQqOVOFp3Wrizmvq+uuMa+Vu0I5XyYnoA5QnJzZFTGLe2u08+Kc7jj/WTedAZrIT7tgeo96bID6ywGN2LpbltKlXgWdqVZ8qvno/2yZDrqTgx3cnezZgYOFbzTEdE59u9glfTRqi/JxsLdDBRNMI+OjUCjYz1cu5ubX1Cs0Mzfydfw8sXlcW/w3rSMqervqDABcnttD4xrtxjmPit2g8i0216K6j2Ag3s5Ogao+bnBxtMdvI1ti0cDGmBxaBx7ODjrTgRldtYLT7Wr54tzsbjoDsboZyvzfTW4W2vQe9bB4YBOd1zCidTXM79cQF+d0R/cGfhoZ0gUyZ6FEftpFY1q6IRlEB6d3woYPhLOhbi4ompIsZcV2c1kxvLneQWonBzv05anhyajq44ZDH3XWyTxaMUI8c1wMU7akcllX/Dupvd7p93z4ptIaOiPJmMFDMdpH1oNFvXHoo06S+tlyA1EKhQKxS/oIfm+93fRPeTXWqpEtcfijTuyiJj7uTry1GbWFD2uGP8a0wsL+jTSmgzvY2+HcbHmrDy8f2kwjqQMAO9ukTU3dwDWX1KxufeQsjsUM4tz9opdR+xQa4A/09+T9fgX4FgXlY5f0QReRVcq1LRrQCMOCq2JK1zo4GRYKD2cHk9Sdd3G0F83yrufvabKFYLiniqBqZeDh7MDWaZd7TRESVK2M7FkSRBrTLMNEiA0oI1K43N3JHlO61cGiPbd4/17B04VdwZI5bcUu6YO78WnYf/05vjlwR3R1OVPSdw3gnle5UwZPzQyFAgqdDFEhzAIJXCdnhiJfqdI7lYvpJH4/PAi7rz6TtD85mPdg54QQPEnOwvi/LrJ/E+oMNa3ijcuPUzR+xyw4IJaNyq29Z2hmhHbmlQUWbzQ5b1dHXJrbHc0W/Kfx+1m9A9G9QVFAhjn8zszqyn5ONcu7C16k325THeU9nfUGMrQdnN4Jlcu4Yt/cfQCAbvUr4OT9JGTmFk2fLy5vc8UyLpjarS4yc/N1/sZMH+bL2lUopNXR+2NMK8QmZfD+jc3i4Gwn8tMuojdsbk72eMeIVZAVCgVWv9tSY9q+EKbAfKUyruhW3w8Hb8bL2peDvV3RubnwtTLf6QYVvQQzObltZW9aUDTVfFhwVTxLzsL3h+8BgE4WH98qqZbQvFpZrDkRq/E7TxcHpGWrj61TM0NF6ylqf2cuz+0BhQK4+PAVABh0g2tvp8CCNxriwz8v8v6dWenS0vgWiTI1oXpzXw5oJPodcxKph6ivhh1z/eW7znQrzPocElwVQ4Kr6j4AwH/TOvJmpfJdJ7V3ERzgg73Xnus8jsmE3PiBbpZQ9XJugtmoNXzdEZOYwWZzS6XdfibgHOjviVvP0yRto1o5N42py9pcnewxJqQGXm9aEQ0qGZbVbwrebo56B6nvyAx+dK5XHkdvv0BILcOywQB1VtO28e0QVLUMFAqFaJ1uoU/X0d4Otxa+hlvP03ivj9r09a061S2PY3de6N2OIdhFWOwUqKVnthWDaa6hNXgFt2vGTmZ39ruqvhZL3ZPQIpuAtFqHB6d3wui1ZxH3Mov3784O9rg4p7vOvV7fphXx45H7Oo839h3q2dAfOy89NXIr8gh9rgqFQvTa/BHPwlj61K7giSWD1INMzOJGxaVPzeAuBBXWK5B9PQDw6zstsfuK8Z/f9vH8C8QR41EQkhRrA4MqY9flp/hjTCvRqdTXC0e0mSCk1Ot3HT9P1PHzxIedaumM6stxYFpH9Fh+XNJj9XUuuF0Zbm0cfYX8tfGNuIlNXeJqUb0szsW+gr2dAq6O9pKmRkvRpLI3Lscls59P06plNKY6+3o4C9bv2PxhW+QrVUjPyUd6jrozOyy4Kro38NO7wAnD0H4dd8TN1dEeLarLL4QuVfiwZpi57Soyc5WSAlTvtJEeLOGr3TdWawohMx2K2yFiarEJ6Sky7V8IM7Jc3tMZyZm5WDWyJRrO2y/6HO3sHila1/CRfNNqKLHyBEwGD9/o/b+T2qPP91EA1DXv3ll9lvcY9XF3EqxhyGRWcm+CxKbFamdzyZGapf7eKQB0ra9/CmzDSl4aGZCNK3vj4M14vQst+WlloPB9D74c0AidJWYFMEEdbjaVQmGeuq7G6tu0EiZtjNb4HbeVcq8DTKZ3u9q+2DEhRNJCHtrUU9Fs771iPk+Tr9ys56VW83HTG6QO61UfB28m6Px+XKeakqdtb/1QeHVQPt3qV8BvMrP2me/AmJAaGNM+AL4ezth7bZ/O45jvYJuaute+YwKZ6+rnqZ8oNVu1UWUvXHuSytPOovb+OKI5KpUxTbbY3L6GZeHaOmbA1dgadM1lljB6vUlF/HulaODaTqGAi6O9xqwHc513D33UiXc6sl5sTUj55xFmQF9oER9G18AKsjK3ggPKYv91eQN2fM4LTJsFgLqFs0T0LRJpKrUreGBMSA18HnFD8DF8/ZyPe9TjrXNq7GHUu3FF+Ho4ITE9V/AxTvZ2GkkIpj527e0UUBaoYKdQ8L4eh8KRtp56BlulssU+j5i321THnJ3XAUCn/nk9f0/U869njWYRiUplEDItLQ2LFy/GP//8g0ePHsHd3R2tW7fGRx99hNDQUP0b4MjMzMThw4exb98+REZG4sGDB8jJyYGfnx9CQkIwadIkhITwR9FHjRqFP/74Q3T7WVlZcHExb+p9cbZsaDMsG9rMpNvkOwUbE4AEilaQE6srKFXbmuWw4cwjdbskTt2s4OmM4Bo+2M3p/BlzW7ZmdCu8SMsBAEmrKEo15/UGGNO+huCF8OMedQWn6Do72MPZQV2cnwl/KBQKwQDk98OCsPl8nMbvDL3+cvuOpnw/+LzRrDLOx77C+tMP2d/5e7vg/gv+TLjPXhcvps8ldkNS1s0Rg1tWlTXV3xS3/txpPPo+Hu4COlLNeb0BxstcMEOOmuXdBQvpA0UrNjOBEu6x37CSNxpU9MKNZ6nslGvZ07vkNtgIz1Oz1fvUs1OhP08MrY0BQZX1DoaUdXfCwekd8aDwmGeyv7lT6+VkKjIxKqHj/6tB/Isp2Qpm8GC1hALrRZmxut9O7rl1YpfaoplNXOpAt2kDfUJ2TQyRXLKAmXpqiZXIGVfm94CjQAom04qGlbx0FiphlPdw1ruQE0PqFPhjn3RGp6+PSlpdXkgNXzdJwQhDb2J7NvTHrokh6LfihOjjtn7YDnnKAp3fM99dBXQXSrC03ZPbIydft422ZNHAxmhVw8dsU5iFTO9eVyMIyXe4iAX7HPSkN4sdflKzF3W2Cf6akFK8174G6vl5sitYC1k9St7gQPiwILYPbgyxAfou9Srg9MyukmdX6fP32DYY+utpAOrVu7deeAxAPc36VYY60Dc6pAZGh8jLnFcoFHDkZJYbu8Aon1HtArD2ZKzO76/MV88keEPPeUsOR3sF8pTq11DADpDy95uYWrqmesnFKwRp/qCplAXeiOFKXRAyISEBHTp0wJ07d1CxYkX07dsX8fHx2Lt3L/bu3Yvw8HBMmjRJ8vY2bNiADz74AABQvXp1dO3aFQ4ODrh8+TL+/vtvbN68GQsXLsTs2bMFtxESEoLatflvgu3tjQ9akSL6RrXM6cr8HpJGnSeF1sbMbVcF/87NhJF6c3W2MJDzWZ8s3EtIxzurz0p6nhAPZwfRxUkM5eRghxoiGREtA0yXYdi+jq9O/VDtkTSpTFV7RC6FAlgzKhiNq3ij5RcHeR8jZ3VvscMp2sgFOcxB++tkSIfEwd7O7DWWGNqF0rlZh+xUL62XUM3HDTeepcLeToFPetZD/yDhKU9iLHmISl1hUfvjsrdTiE6P5KpdwZMNqtT188SJsFCD6/boqyElN7vQ0pjripTsU6nfkI976s8geK2hP/Zdfw4HO4XFji/uStH6fDmgETrW9cXrTSrhl+MPTNYGsePb2NU+zbE6avVy7tgzuQPqGFBSpmg6aZG5rzfAgn81s5Xq+Xnidrz8jPLQQD/8fiIGAESv/QwXR3vekizaGeXW1LCS/GxiS/P1cMb7HWrqf6CJCdW1lspRpIwBYJ4gyqDmlfHn6Yeob8BCe472dugSWIENGJrq+HRxtDd4gTM5TBWABNSrgQcHqGdRfdG/ERuENOR9lcKQlZ6FMAkk2gN92uciU+zxvfY18fMx9fRy5rrKt93QwApFK7eb6MgqZomQANTZ9+YY9Pn57RYaM3WI6UmOItSsaZqLlUKhwP37urUbLGXs2LG4c+cOunbtil27dsHNTX0S37NnD/r164epU6eiU6dOaNJEuxg3P0dHR4wZMwYTJ05EUFBR0ViVSoXly5fjo48+wmeffYb27dujUyf+1fHef/99jBo1yujXRvT7vF8jTNhwUTRYYa6RFak3J280qyxaW4VLblsreruyN9RWipsZRSh7xFRm95aeNcjFZAdONiATzxgqFdAlsAJvZoghzHEDXNpx39H8AuHPyd/bBc2rldGZus10Lu0UCsmrznMxn6nYitGmpm9sxByHmTGFw82ROWFO7k72yODURm1T0wd7rurW6eNToXB1bVOskvvNkKZQ/q1C/6DKeJaSbfT2TK2MmxOGBlfj/ZunGQbRTIFvoQ5TMLSmIXuTy/mKjGlfQycIubB/I5TjqTOtz+w+9TG1u3rRN2NeO9NOUywgcGlud5h4Bj8ppH3qlxso0p8JafqLSx0/T1z7vKdR26CuleWYamGawq1o/EvfAj2m2GdYr0DY20GjziXfdGw3J3ve87MxTBm4tZRNY9uaZbv66okT40nuhcXGxppkh9asN3Djxg3s3LkT9vb2WL16NRuABIDevXtj1KhRWL16NRYvXoyNGzdK2ua7776Ld999V+f3CoUC06dPx549e3Do0CGsX79eMAhJLKd7Az982KkWRrTivzEpLn56qzm+PnDbqG2YulC2uVgyMOYucmPKrSUUVK0MejTwx1f71DVGy3s64/YXr8nKOjQlU71D2u/1ujHiK3IKYaYOtzTxKKJOFkUx6DBx2yw2LdvJwQ7beApgsyPhBr5UVyd7fDu4KbrWN03dVkkkttVWPj/uggMav7dCW6Q4ERaqsYDUsiHNMLuPtAz/it6uuDK/h9EZe4A6I37VSHVmSBk3J1mLgsgVHFDWpBnLhz62fn9sft8G2H7pKS7HJbO/6yYhm9WSx2XRTa74Xp0c7Aya7mpvp2CPReb6Od2ARRaY8+zXOqt5y2fojAhLslMA+6d2RHeJtcZthe7sBb7HCF8XHPRkQnK5ONqZLUBhqGI23lWsSekzfdyjLh4mZWJLYVYmY3jhPWJRrVlTt07cJz0DeRbb0WzEF/0b4W5COjaejUPlsqaZrSH3dRpUY5WQQpKDkGvWrDFnOyxi+/btANTTn6tX160XNWLECKxevRoRERHIy8uDo6PxnfSgoCAcOnQIcXFx+h9MzM7JwQ5hvQKt3Qyj9WpcEb0aG1f3qLh0hqqY6OIqZlS7ALzMEL+JH9G6Oo7deYEjt1+gbc1y+F/nWmwQEpA37dncNo1tgzUnYjRWkpZCe3p/R5krWjOqlXPDnS96mS2rh1HOw4lZwLFYdIZcHO0Rs7g3svMKJA8CcFduNtSgFlWMeLZ8thJclIpbd4mrXGE9ILHBCWvw1lod1MXRXlYGmCkCkLzbdeXfrikGn7fIXIRFyJlZXY0OZhr6crTfh1EhNeDv7YIP/7yIWb0DdRYBE1K1sDajvqwcU2hWVT2Q1LpmObPvCzB8YSzmnfWwYMa3tTSrWgY7JqgHrNaODmbrxxUH2tcGvkFmsYD3mPbi9QK5W6vr5ylYQ9zSbOWKyK1LL3XBxuJL/7s+MVSdha0dhGSm4Q5vVQ3fH7or+X7JbIlWCt3rThk3JwQH+Bi1mKCxImd0sdq+SfEn+WrNl+1X3ERHq+votWzJX7yd+X1GRgbu3r2LBg2MXxXv7t27AICKFYUDRkeOHMHVq1eRlpaGcuXKoVWrVujduzecnUv6BYJYU3EJQloie3p+v4Z6H+PkYIcPO9XCkdsvrB5w1FcHpk3NcmhjwE2jKddvMHcAEgBWjGiO5gvVGWFCARBrq6lV50yhULArEkuhMkUU0sL0LkxjY3PTmCCk9g3y222qo6K3i83VBbK1948lcE2xhenu28a3Q1J6rsXqv4rhX4xD+vOnd6+LrvUroLzEhWmMUcPXXdJNrrU/Y1v9SpjanS96aVynO9ezYIa7Gcj92Lroeb22ehxY/wyoVr2cO34fpb7XredvnlqMUpjzczLFqYjpE0zvXhfTu9fFUk7CgSU52CmQzyxaaIH9yf1cPJ1ts99NioeSP2TIEROjLn5drRr/VFwvLy94eXkhNTUVMTExRgchr169it27dwMABg0aJPi4devW6fyuYsWK+P333/Haa/pX2s3JyUFOTtEqaampqQa0ljBstA9DbEBwgA9m966Pd9pKX3nXHIQ6WQ0reeGbwU0N3q7NBjcKabfOh7NynaMFV8CVavv4dqjjZ/iKtEDRzX1xqtcpPWvAvO2QqnIZdWaZduDc3k6BHg2pLpBUSs4HPzCoMka0roY3fz5lxRYVaV7NdIFk0x628rfm5GCHYBMu0mYK1g6y2Mq5xNzMMbjXtIo3Lj9OMfl2+Wh/TnzXNeP6IUXPtYGxD5ZDYf+kjIVXI+cTGqi/5ENxsGZ0MMrzZHP2aGiC16d17Og7JM1xrC19swn8vVww8vezhW0oakT4sGam3yGkzWIZ3KIKlr7ZBAUq6YujEsLH9uevmVBamrpWkbu78Ap8Hh7qmjbGBvLS09MxYsQI5Ofno2fPnujbt6/OY5o2bYrw8HBcu3YNqampiI+Px4EDB9CuXTs8e/YM/fr1w9GjR/Xua/HixfD29mZ/qlatalTbCSH87OwU+KBjTYtMg5OC6TA42Nvh91EtsXFsG5OtNHh2VleTbMeUxKYV28uoFWUpQdXKGr2KPBPYsS9Gd9lSO6a28opm9KqHjR+00QhqE/kKOHdilcq4ws1JfeyX/Gl/xrOheIkso0MCAFg/4FPcSkDYkj/fb43DH5m3Rqrg8WHij417mbSluudl3Jzw/fAgzOtr/Ay74qxotWfjP/gu9SqgUWXNlegvz+vBDsRzj4Vi1H1iDWlZFZUKy6wotN6xXo2MK8clRMr7FB2XDIVCQQFIYrRikwn56aefYteuXbKf99tvv6F9+/ZmaJGwvLw8DB48GNeuXUPNmjWxfv163sdNmzZN49+enp7o3r07unXrhgEDBmDnzp2YOnUqLl26JLq/mTNnYvr06ey/U1NTKRBJ9Bpp5Ww+YjxuJ9uUo9vVy7mhgg1MW9Q2r28DzHmdvxM/qbC2T0nDLGZji0FWIcUpYAqo67m2rWWZmnclWd8mlRD9KBl/vtcarWr4wMnBDqvfbYlOBtaWLWn4vhXF7Kuiw1aCf8X9fbQmTxdHeJqpTqw+/KUJTBM8tNezkral9WtaydpNsBnm+r5y6yUbswuhALblB1tUPP9n2tJJcvWmVaOJiZgkCHn58mX8+OOPiIqKwuPHj5GRkSH4WIVCgfz8fNn7ePr0KW7flr8acHp6Ovv/np7qaXFi7WMe7+VlWDZRfn4+hg0bhn379qF69eo4fPgwypeX1wFXKBT4/PPPsXPnTly+fBlxcXGiQUVnZ2eqH0lksWYhY2I8c99w2Uoxd20KhQJCsbghLUvmwMuiAY3RtmY5sy0mYg6ODlKXx6bIQUkypn0NnYUjukpY7ZlYP5PQUJNCa6NApULTKt76H2wBdEYpXkxdZoS7NVss0UIsx5hDy9bOxwqF5qJC5iqdJGWrdvS9IiZidBByxYoVmD59OpRKpVkLU//555/4888/jdpGQEAALl68iEePHvH+PTU1lZ2GHRAQIHv7SqUSb731FrZt24aqVaviyJEjvKtwS1G/fn32/x8/fkyZjRZE98WkNDv6cWf4e9teFmRpVd7TWe+KoLbCyd4OucoCdhqumGHBVfFuuwDzN6pQo8rWK8JPShZT3gCy0xOLab+jrLuTpIXdzM3W6xkTfvzZwcZ/lh7ODpjeva7R2yHmYelvq9zwhGD1AAs3nNvuupz64uaKA0r57jlQEJKYiFFByDNnzmDKlCkAgPHjx6NPnz7o3bs3fHx8sHnzZjx//hwHDx7Ehg0b4OXlhe+//150lWhza968ObZt24bz58/z/p35vbu7O+rWlXfxUiqVePvtt7F582Y2AFmjhuE3jklJSez/MxmchBBibgG+wjVzCRHj5KAOQkqxZFATM7emyNnZXY2uzUmIsfjv7yy38mlJRu9f8WTqTEhmewend6LBVBv31/utzfq9lVsqwsPZAek56pmaQkFLS2dIMrtjXknlMq54kpxl1UzI0SHFY1Cc2D6jCmZ8//33UKlUmDJlCn744Qd2JWcnJyeEhoZixIgR+P3333H69GkoFArMmTMHzZs3N0nDDdG/f38AwIkTJ3izITds2AAA6Nu3LxwdpU99KygowMiRI7Fp0yY2AFmrVi2j2rpp0yYA6mnh9erVM2pbhBDz+LBTLatksDC7tLUpI9ZCSTBETAVPF0nZmcXJpbndrd0EQmwGXQOKJ1N/bsz2bGlRGqJLoVAgpLYv2tX2NeM++P+fz8HpnXDk487svwu0OtfWrn1rqfOblP2404AuMRGjgpAnTpyAQqFgsyEZ2tOymzVrhh9++AH379/H119/bcwujdKwYUO88cYbUCqVeO+995CVlcX+be/evVi7di3s7Owwc+ZMneeOHDkSgYGBWLFihcbvCwoKMHr0aGzYsEFWAPLSpUvYtWuXTn3MgoICrF69GrNmzQIATJ48WVZAlBBiOWG9AhGz2PL1Nal7XeTfSe0RNSPU2s0gxKLKuNFK3sUV94a2OA8kVfVxtXYTdNC0bAIU7+9VSWbJj4V7JhjZNkD0sbUreKC8Z9HaCnLb2dBM5V60j2Nzn970nT/PzOpq3gaQUsWocHZ8fDycnZ016h7a2dkhOztb57EDBgyAo6Mjtm3bhgULFhizW6P8+uuvuHHjBg4ePIhatWqhQ4cOSEhIwLFjx6BSqRAeHo4mTXSniT169Ai3b99GYmKixu9XrFiBdevWAQBq1aqFhQsX8u43MDAQYWFh7L9jY2MxYMAAlC1bFs2bN4efnx+Sk5Nx7do1Nktz+PDhmDdvnqleOpGMOrGkeKD7LaBRZdtYEIHQmZOUDgObV8a6Uw8Nfj43S6t6OXX5iwaVilfN0gPTOsLXw3YWRKRzj22zVI096hMVD5b4mCaE1kZWnhKLBjSGg715V0pfNKAxxneubdZ9AMCnrwXi813Xzb4fIX5eVOKAmI5RQUg3NzedqLmnpydSU1ORk5OjsWKzo6Mj3Nzc8PCh4R03U6hQoQLOnz+PxYsX459//sHOnTvh7u6Onj174uOPP0bXrvKi/C9fvmT//+jRo4KP69Spk0YQsmnTppg6dSrOnz+PW7du4cSJE1CpVPDz88Obb76J0aNHo3fv3rJfHyGk9KDRfmJLOgdWQMTlp9ZuBiFmNb9vQ8x5vYHs5/FlmTSo5IVLc7sXu8xW7iIJtoB5b9UzsSgSVVyYa5ordY1smyWCxRU8XbD0zaaGPVlm59rF0R61K3gYti8Z+jWthH5NK5l9P4RYglFByMqVK+PWrVvIz8+Hg4N6U7Vq1UJ0dDTOnTuH9u3bs499+vQpUlJS4ObmZlyLTcDLywuLFy/G4sWLJT9HKMA4f/58zJ8/X3YbatSogeXLl8t+HjE/Gkm1PVO71cHpB0n6H1hK0CFKbNG3g5vicxtYKZcQc7KzU8DOiLOwduCluAUgbRFbJ9mqrSCmUNfP8GCOY2HGm3ZZMGIbqpR1xYWHr6BS2fa9lr83f6kJS9caVbELl1nnzVIoKNmBmI9R+cn169eHUqnE1atX2d917twZKpUKCxYsYKdl5+bmYvLkyQCAxo0bG7NLQkgpNLVbXWwa29bazbAZHeqUBwBULENTI4jtcHKwg487BVQIIZa14I1GeL1JRTjY2XBkg0ChAPZN7YCjH3fG7N714eSgexta0dsVsUt0a20vG6I/q23u6w0wvXtdVC5je/VKiXra8sYP2sDORr+nsUv6IGJie3Rv4GftpgAAKhUex/2aUfYjKXmMCkL26NEDKpUKERER7O8mTJgAZ2dnHDp0CFWqVEFISAgqV66M7du3Q6FQYOLEiUY3mhBCSrNuDfwQu6QPKnhSEJIQQkjp1qCSF1aMaE4L0xQDgf5eCPB1xwcda4o+zt3JXuPfTaror/9czsMZk7vWoePARrk7O6BtrXLWboaoxiLHmaUzEr1cHBG7pA9CzLiKuJiGxaxWMSlejApCDho0CPPmzUOlSkUR+ho1amDDhg3w9PTEy5cvcerUKSQlJUGhUODTTz/FW2+9ZXSjCSGEEEIIIYSUPNqzQCmwSKzN0tOxre2v99pYuwmkBDOqJmSZMmV4V28eMGAAOnXqhD179iAuLg7e3t7o0aMHatc2/8pRhBiLujmEEEIIIYTYBuqbE2spjfHv0MAK8HZzxImwUHRaegT5BaUrAEvMz6ggpBgfHx+8/fbb5to8IWZDp1lCCCGEmENpvKElpduQllUQcfkpyrk7S36O9oIYlAlJiOUMaVkFAFC5jCs8XByQnJln5RaRksao6diEEEIIIYQQaWi1UVLadKhTHrFL+sBVq86jHDa6lgkpBd5oVhnuTvZoXFl/XVJCiDRmy4QkpLiifg4hhBBbt2JEEGJeZFi7GYQQYnaWXhSEEEbtCh64vuA1azfDwuj7RszLqCBkaGio7OcoFAocOnTImN0SQgghhJRqrzeppP9BxObQrFJC9NNeBIS+N4RYx/y+DfHL8QfWbgYpYYwKQh49elTS45g6HiqVimp6EJtHhyghhBBCzKGcu5O1m0BIsUN9c0Ish/t96x9UGf2DKluvMaREMioIybcyNldKSgrOnDmDU6dOoVy5cvjf//4He3vD64EQYgkzXgvEtwfuWLsZhBBCCClBfnmnBVrX8LF2MwixebQwDSHWQ982Ym5mDUIyDh8+jIEDB+LGjRvYunWrMbskxOwGNq+Cgc2rWLsZhBBCCClBejb0t3YTCCmWCgpoRSdCzM3fywXPU7NRq4KHtZtCSjiLrI4dGhqK8PBwbN++Hb/99psldkkIIYQQQgghpJjRDjkqKQhJiNmdntUV9xf1Rq3yFIQk5mWRICQADB06FPb29hSEJIQQQgghhBAiSX5BgbWbQEipYG9Hk7GJ+VksCOni4gJ3d3fcvHnTUrskhBBCCCGEEFKM5SkpE5IQQkoKiwUhnzx5gpSUFKi0Kw0TQgghhBBCCCGAznzsen6e1mkHIYQQk7NIEDIrKwvjx48HADRu3NgSuxSVlpaGWbNmoV69enB1dYWvry/69OmDw4cPG7S9zp07Q6FQCP74+4sXIj948CB69+4NX19fuLq6IjAwELNnz0Z6erpB7SGEEEIIIYSQ4q6mrzvsaIooIYSUGEatjr1gwQLRv2dnZyMuLg779+9HUlISFAoFJkyYYMwujZaQkIAOHTrgzp07qFixIvr27Yv4+Hjs3bsXe/fuRXh4OCZNmmTQtnv27MkbcPT29hZ8zvLlyzF9+nQoFAp06NABfn5+iIyMxKJFi/DPP/8gKioKvr6+BrWHEEIIIYQQQooTlc7SNIQQQkoKo4KQ8+fPh0Khf2RKpVLBzs4On332GUaMGGHMLo02duxY3LlzB127dsWuXbvg5uYGANizZw/69euHqVOnolOnTmjSpInsbYeFhaFz586SHx8dHY2PPvoI9vb2iIiIQK9evQAAmZmZ6NevHw4dOoQPP/wQW7duld0WQgghhBBCCCGEEEJshVFByI4dO4oGIR0cHFC2bFk0bdoUQ4YMQZ06dYzZndFu3LiBnTt3wt7eHqtXr2YDkADQu3dvjBo1CqtXr8bixYuxceNGs7dn8eLFUKlUGD16NBuABAA3NzesXr0aNWvWxD///INbt24hMDDQ7O0hhBBCCCGEEGviLiFAOZGEEFKyGBWEPHr0qImaYRnbt28HAISEhKB69eo6fx8xYgRWr16NiIgI5OXlwdHR0Wxtyc3Nxe7du9n9aqtevTpCQkIQGRmJ7du3Y+bMmWZrCyGEEEIIIYQQQggh5mRUELK4iY6OBgC0bNmS9+/M7zMyMnD37l00aNBA1va3b9+OHTt2ICsrC35+fmjXrh169OgBOzvd9X/u3LmDzMxMve2JjIxk200IIYQQQgghhBBCSHFk9MI0Hh4emD59uqTHf//990hOTsbcuXON2a3BYmJiAADV/t/encdFVf3/A38NIAMMm4giKIgLoLmgJrmWuRMqLrinQKJmm6aWa6Wl5ae+SR81S801AXfNzCU3NPfCXVPDFBVBBBFmBASB8/vD39yP48wwM8DI4uv5eMwjvOece8/MPZ175z3n3OPlpTPd0dERjo6OUCqVuHHjhslByAULFmht8/X1RVRUFAICAnTWxdnZGQ4ODjr35+npqZFXn9zcXOTm5kr/ViqVJtWbiIiIiIioPOAUbCKiykt7iJ4JZs2ahW+//dbo/N999x0+//zzkhyyRFQqFQBAoVDozWNvbw/AtEDeq6++ip9++glXr15FVlYWEhMTsXXrVjRu3Bj//PMPunbtisuXL5utLnPnzoWTk5P0UgcviYiIiIiIiIiIyoMSBSGfp8mTJ6Nhw4Ymv44cOWL2us2ePRujRo2Cr68v7OzsUKtWLfTt2xenTp1CQEAAlEqlWZ/pOG3aNGRmZkqv27dvm+1YRERERMZYtWoVZDIZZDKZzvsxIQQ8PT0hk8nQq1cvnfvIyMiAjY0NZDKZ1g+6auHh4dJxnn3Z2NiU6nsyxrFjx9ChQwfY2dmhZs2aGDduHB4+fGh0+eXLl6NRo0awsbGBj48PFi5cqDfv+vXr0bZtWygUCjg7O6Ndu3Y4cOCAlJ6Tk4OIiAg0adIETk5OsLe3h7+/P+bPn4/Hjx+X6H0SmYsQHAtJRFRZPddnQqanpxf7ZjApKQlXr141udzTN33qac9ZWVkG8zs6Opp8rGfJ5XLMmDEDffv2xe7duzUWuynNusjlcsjl8hLXl4iIiKi02djYICYmBh06dNDYfujQISQmJhZ5D7Nx40bIZDLUrFkT0dHRmDNnjs58crkcy5Yt09puaWlZssqb6OzZs+jSpQsaNWqEyMhIJCYm4ttvv0V8fDx27dplsPySJUswduxYhISEYOLEiTh8+DDGjRuH7OxsTJkyRSPvrFmz8MUXX2DAgAEIDw/H48ePcfHiRdy5c0fKk5OTg0uXLiEoKAje3t6wsLDAsWPHMGHCBJw8eRIxMTGl/hkQlVStqra4nZ5T1tUgIiIzeG5ByI0bN0KlUsHPz69Y5aOiohAVFVWiOnh7e+P06dO4deuWznSlUilNffb29i7RsdQaNWoE4MlzG9PS0uDu7q6x/4yMDKhUKp3PhVSPaCytuhARERE9b0FBQdi4cSMWLFgAK6v/3XrGxMTg5ZdfRlpamt6yUVFRCAoKQp06dRATE6M3CGllZYXhw4eXet1NNX36dFStWhUHDx6UfkT29vbG6NGjsWfPHnTv3l1v2ZycHMyYMQM9e/bEpk2bAACjR49GYWEhZs+ejTFjxqBq1aoAgBMnTuCLL77AvHnzMGHCBL37dHFxwYkTJzS2jR07Fk5OTvj+++8RGRmJmjVrlvRtE5WqTWPb4dTNB3g3+nRZV4WIiEqZSdOx58+fj3r16kkvAEhNTdXY9uyrbt26qFq1KoYMGQKZTIaePXua5Y0Yo2XLlgCAuLg4nenq7QqFAr6+vqVyzPv370t/Px1o9PPzg52dnVH1UdebiIiIqKIZOnQo7t+/j71790rb8vLysGnTJgwbNkxvuVu3buHw4cMYMmQIhgwZghs3buDYsWPPo8rFolQqsXfvXgwfPlxjFktoaCjs7e2xYcOGIsvHxsbi/v37ePfddzW2v/fee8jKysKOHTukbf/9739Rs2ZNjB8/HkIIk6Z7A5o/hhOVN26ONmhf37Wsq0FERGZgUhAyIyMDCQkJ0gsACgoKNLY9+7p58yYyMzMhhEDnzp3LbGVsAOjbty8A4OjRozpHQ6qnpPTu3VuaNl1S69atA/BkRKR6oRkAsLa2lgKyuqbC3Lx5U7rR7tevX6nUhYiIiOh58/b2Rtu2bbF27Vpp265du5CZmYkhQ4boLbd27VooFAr06tULr7zyCurXr4/o6Gi9+dPS0rRexiw0+ODBA51ln31lZ2cXuZ8LFy4gPz8frVq10thubW2N5s2b48yZM0WWV6c/W/7ll1+GhYWFRvn9+/cjICAACxYsQPXq1eHg4AB3d3d8//33Ovedl5eHtLQ03L59G1u3bsW3336LOnXqoEGDBkXWiajMyMq6AkREZA4mTcfu27ev9MupEAIjR46Ek5MT/vvf/+otY2FhAUdHRzRp0gT169cvSV1LrHHjxujTpw+2bduGiIgI/Prrr7C1tQXw5GZ41apVsLCw0LmITGhoKP7880+8//77eP/996XtsbGxkMlk6NixI2Sy/10t8/LyEBkZKT1MfNKkSVr7nDp1KjZt2oSVK1ciJCQEgYGBAIDs7GxERESgoKAAISEhaNiwYal+DkRERETP07BhwzBt2jTk5OTA1tYW0dHR6NixIzw8PPSWiY6ORp8+faR7tcGDB2Pp0qWYP3++xrRu4MkztqtXr661jx49emD37t1F1q1Fixa4efOmwfcwc+ZMzJo1S296cnIyAEiP3nmau7s7Dh8+XOT+k5OTYWlpiRo1amhst7a2RrVq1ZCUlATgf0HTo0eP4sCBA5g5cya8vLywcuVKfPDBB6hSpQrefvttjX1s2bIFQ4cOlf7dqlUrrFixQutzJCIiIjInk+48/P394e/vL/175MiRsLW1RVhYWKlXzFyWLl2Kv//+G/v27UP9+vXx6quv4t69ezh06BCEEJg/fz6aNWumVe7WrVu4evWq1nOLzp07hwkTJsDNzQ3NmzdHtWrVkJqaivPnzyMlJQUA8NFHHyEiIkJrny1btsS8efMwceJEBAUFoWPHjqhRowYOHz6M5ORk+Pn5YfHixeb5IIiIiIiek0GDBuHDDz/Eb7/9hsDAQPz2229YsGCB3vznz5/HhQsXMHfuXGnb0KFD8dVXX+H333/XeryPjY0Ntm/frrUfV1fDUzqjo6ORk2N4EQz1o4j0Ue9D10I7NjY2Bo+Rk5MDa2trnWlPl1dPvb5//z7WrVuHwYMHAwAGDBiApk2bYs6cOVpByE6dOmHv3r3IyMjA/v37ce7cuSIXRyQqazKOhCQiqpRK9PNnYWFhadXjualRowbi4uIwd+5cbN68Gdu2bYNCoUCPHj3w0UcfoUuXLibtr2PHjnjnnXdw6tQpnD9/Hunp6bCwsICHhwd69OiBt99+G+3atdNbfsKECWjatCnmzZuHP//8E1lZWfDy8sK0adMwbdo0nQvWEBEREVUk1atXR9euXRETE4Ps7GwUFBRgwIABevNHRUVBoVCgXr16uHbtGoAngThvb29ER0drBSEtLS3RtWvXYtWtffv2xSr3LPWIzdzcXK20R48eSelFlc/Ly9OZ9nR59X+rVKmi8RlaWFhg8ODBmDlzJm7dugUvLy8pzc3NDW5ubgCeBCu/+uordOvWDfHx8VyYhsoldQxSCFGm9SAiotL1Qs7BcHR0xNy5czV+XTfk4MGDOre3aNECP/zwQ4nq07Vr12LfOBMRERFVBMOGDcPo0aNx9+5dvPHGG3B2dtaZTwiBtWvXIisrCy+99JJW+r179/Dw4UONZ22XRGpqKgoKCgzms7e3L/KY6mnY6mnZT0tOTi5y6rm6fEFBAe7du6cxJTsvLw/379+Xyru4uMDGxgbOzs6wtLTU2Ie63IMHDzSCkM8aMGAAZsyYgW3btmmNmiQiIiIyF5MWpnnWiRMn0LJlS7z33nsG844aNQotW7bUuxI0EREREVVe/fr1g4WFBU6cOFHkqtiHDh1CYmIivvjiC2zcuFHjtXTpUmRnZ+OXX34ptXoFBATA3d3d4Ovbb78tcj9NmjSBlZWV1r1uXl4ezp49i+bNmxdZXp3+bPm4uDgUFhZK6RYWFmjevDlSU1O1Rk6qnxup6/mYT1NP7c7MzCwyH1FZUT9rX8Z52URElUqJRkLGxMTg3LlzmDx5ssG8bdq0wYoVKxATE6O16h8RERERVW729vb48ccfkZCQgN69e+vNp56K/fHHH8PGxkYr/f/+7/8QHR2N4cOHl0q9SuuZkE5OTujatSuioqLw6aefSo/UWbNmDR4+fIiBAwdKebOzs3Hr1i24urpKz63s3LkzXFxc8OOPPyIoKEjK++OPP8LOzk5jCvrgwYNx4sQJrF69GqNHjwbwZMp2dHQ0XnrpJWnUZFpaGqpVq6YVyFm2bBkA7ZW4icoLTscmIqqcShSEPHToEACge/fuBvP269cPY8aMQWxsbEkOSUREREQVlKHFDHNzc7F582Z069ZNZwASAIKDgzF//nyNacv5+fmIiorSmb9fv35QKBR6j1laz4QEgC+//BLt2rVDx44dMWbMGCQmJmLevHno3r07AgMDpXx//vknOnXqpLHitq2tLWbPno333nsPAwcORI8ePXD48GFERUXhyy+/hIuLi1T+7bffxrJly/Dee+/hn3/+gZeXF9asWYObN29qLNATFRWFxYsXo2/fvqhXrx5UKhV+//137N27F71790bnzp1L7b0TERERGVKiIGRiYiKcnJw0bor0qVatGpycnHDnzp2SHJKIiIiIKqkdO3YgIyOjyJGSvXv3xrx587Bu3TqMGzcOwJPg5YgRI3Tmv3HjRpFByNLUsmVL7Nu3D1OmTMGECRPg4OCAiIgIo59D/u6776JKlSqYN28efv31V3h6euK7777D+PHjNfLZ2triwIEDmDx5MlasWIGsrCw0b94cO3bsQI8ePaR8HTp0wLFjx7B27VqkpKTAysoKfn5+iIyMxAcffFCq752oNHEWNhFR5SQTJRjjbmdnB2tra2RkZBiV39nZGbm5uUZNeaHiUyqVcHJyQmZmJhwdHcu6OkREREREREbLyStAo892w7uaHQ5+3Kmsq0NEVOGVlzhRiRamqVGjBlQqlfQQ7KLcuXMHSqVSeu4NERERERERERERvRhKFIRs06YNAGDRokUG86rztG7duiSHJCIiIiIiokqM07GJiCqnEgUhIyIiIITAN998g6VLl+rNt2TJEnzzzTeQyWSIiIgoySGJiIiIiIiIiIioginRwjTdunXDgAEDsGnTJrzzzjtYtGgRevXqhTp16gCAtELfpUuXIIRASEgI3njjjVKpOBEREREREREREVUMJQpCAsDq1ashk8mwceNGXLhwARcvXtRIV697M2TIECxfvrykhyMiIiIiIqJKjNOxiYgqpxJNxwYAW1tbrF+/Hvv27cOwYcNQp04dyOVy2NjYwNvbG2+++SYOHDiAmJgY2NralkadiYiIiIiIqJKSgVFIIqLKqMQjIdU6d+6Mzp07l9buiIiIiIiIiIiIqJIo8UhIYxUWFmL79u3o27fv8zokERERERERVTCcjk1EVDmV2khIfeLj47F8+XL8/PPPSElJMffhiIiIiIiIqAJTxyBFmdaCiIhKm1mCkNnZ2diwYQOWL1+OY8eOAfjfAjWNGjUyxyGJiIiIiIioEpBxKCQRUaVUqtOxT5w4gdGjR8Pd3R0RERE4duwYhBDw8/PDp59+ivPnz+PSpUulechiUalUmD59Ovz8/GBrawtXV1f07NkTBw4cMHlfBw8ehEwmM+p169YtjbLh4eEGyzx69Ki03jYREREREREREVGZKPFIyNTUVPz8889YsWIFrly5AuB/ox5lMhn++usvvPzyyyU9TKm5d+8eXn31Vfzzzz9wd3dH7969kZKSgl27dmHXrl2YP38+PvjgA6P3V7NmTYSFhelN//PPP3H58mXUr18fnp6eOvO0b98eDRo00JlmaWlpdF2IiIiIiIgqOo6DJCKqnIoVhBRCYOfOnVixYgV+++035OfnQwgBW1tb9O3bF2FhYQgMDARQ/qZfjxkzBv/88w+6dOmCX3/9FXZ2dgCAnTt3Ijg4GB9++CE6duyIZs2aGbW/hg0bYtWqVXrTX3rpJQDAyJEj9U4rGDVqFMLDw016H0RERERERJURZ2MTEVVOJgUh//33X6xYsQKrV69GcnIyhBCQyWTo0KEDQkNDMWjQIDg4OJirriX2999/Y9u2bbC0tMTy5culACQABAUFITw8HMuXL8fcuXOxdu3aEh/v+PHjuHz5MiwtLRlkJCIiIiIiIiKiF5ZJQUgfHx/IZDIIIVC3bl2EhoYiNDQUdevWNVf9StXWrVsBPJn+XKdOHa30YcOGYfny5di+fTseP36MKlWqlOh4K1asAAAEBgbCw8OjRPsiIiIiIiJ6EXBhGiKiyqlY07HHjRuHb775BtbW1qVdH7M6c+YMAKBVq1Y609Xbs7KyEB8fL02lLo7s7GysX78eABAREVFk3tjYWFy4cAEqlQrVqlXDK6+8gqCgIMjl8mIfn4iIiIiIiIiIqLwwKQgpl8uRm5uLhQsXIioqCoMHD8aIESPQpk0bc9WvVN24cQMA4OXlpTPd0dERjo6OUCqVuHHjRomCkBs3boRKpUKNGjXQq1evIvP+/PPPWtvc3d2xYsUK6dmaRcnNzUVubq70b6VSaXqFiYiIiIiIiIiIzMTClMzJyclYsGABmjVrhvT0dPz4449o3749/Pz88NVXX+HWrVvmqmepUKlUAACFQqE3j729PYCSB/LUU7FDQ0P1Tuv29/fH/PnzcfHiRSiVSqSkpGDPnj1o164dkpOTERwcjIMHDxo81ty5c+Hk5CS99K3CTUREREREREREVBZkQghRnIJnzpzBsmXLsHbtWmRkZEAmk0Emk+G1117DiBEjEBERAZlMBpVKpbEATHFNnjwZv/76q8nlli1bhg4dOgAAfH19ER8fj59++gmjRo3Smb9WrVpISkpCTEwMhg4dWqy6Xrt2DT4+PgCeLIZj6grhQgj069cP27Ztg7+/P86ePVtkfl0jIT09PZGZmQlHR0eT609ERERERFSWvKfuQJ1qdjj0caeyrgoRUYWnVCrh5ORU5nGiYj0TEgBatGiBRYsWITIyEps2bcLy5ctx6NAhHDx4EIcOHZLy7dmzB7169YKVVbEPBQBISkrC1atXTS738OFD6W/1yt1ZWVkG85fkpKhHQbZt29bkACTw5EHMn3/+ObZt24Zz587h9u3bRY5ulMvlfH4kERERERERERGVWyZNx9ZFLpfjzTffxIEDB3Dt2jXMmDEDtWrVAvBkRF9ISAhq1KiBt956Czt37kR+fn6xjhMVFQUhhMmvp5+p6O3tDQB6p40rlUppGrY6r6kKCgqkZzwaWpCmKE8HLxMTE4u9HyIiIiIiooqoeHP2iIiovCpxEPJpdevWxezZs3Hz5k3s3LkT/fv3h5WVFTIyMvDzzz+jd+/ecHNzK81DmqRly5YAgLi4OJ3p6u0KhQK+vr7FOsbvv/+OO3fuwN7eHoMHDy5eRQHcv39f+ls9gpOIiIiIiIiIiKgiKtUgpJpMJkNgYCA2bdqEO3fu4Ntvv0WjRo0ghEBGRoY5DmmUvn37AgCOHj2qczRkTEwMAKB37956F5MxZPny5QCAQYMGSYvcFMe6desAPJkW7ufnV+z9EBERERERVUQyWVnXgIiISpNZgpBPc3V1xcSJE3Hx4kUcO3asRFOUS6px48bo06cPCgoKEBERgZycHClt165dWLVqFSwsLDBt2jStsqGhoWjYsCG+//57vftPS0vD9u3bARiein327Fn8+uuvWtPTCwsLsXz5ckyfPh0AMG7cuGIHRImIiIiIiCoqTscmIqpcSrZajInatGmDNm3aPM9Dalm6dCn+/vtv7Nu3D/Xr18err76Ke/fu4dChQxBCYP78+WjWrJlWuVu3buHq1atIS0vTu+81a9bg8ePHaNiwIdq1a1dkPRISEtCvXz9UrVoVLVu2hJubGzIyMnDx4kVplObQoUMxc+bMkr1hIiIiIiIiIiKiMvZcg5DlQY0aNRAXF4e5c+di8+bN2LZtGxQKBXr06IGPPvoIXbp0Kfa+V65cCQAYOXKkwbz+/v748MMPERcXhytXruDo0aMQQsDNzQ0DBgzAW2+9haCgoGLXhYiIiIiIiIiIqLyQCcFB7pWNUqmEk5MTMjMz4ejoWNbVISIiIiIiMon31B3wcrHDH5M7lXVViIgqvPISJzL7MyGJiIiIiIiIiIjoxcYgJBEREREREREREZkVg5BERERERERERERkVgxCEhERERERERERkVkxCElERERERETlSk1HG0zq7lvW1SAiolJkVdYVICIiIiIiInraieldyroKRERUyjgSkoiIiIiIiIiIiMyKQUgiIiIiIiIiIiIyKwYhiYiIiIiIiIiIyKwYhCQiIiIiIiIiIiKzYhCSiIiIiIiIiIiIzIpBSCIiIiIiIiIiIjIrBiGJiIiIiIiIiIjIrBiEJCIiIiIiIiIiIrOyKusKUOkTQgAAlEplGdeEiIiIiIiIiIjKkjo+pI4XlRUGISshlUoFAPD09CzjmhARERERERERUXmgUqng5ORUZseXibIOg1KpKywsRFJSEhwcHCCTycq6OiWmVCrh6emJ27dvw9HRsayrQ+UU2wkZwjZChrCNkCFsI2QI2wgZwjZChrCNkDFMbSdCCKhUKnh4eMDCouyezMiRkJWQhYUFateuXdbVKHWOjo7shMkgthMyhG2EDGEbIUPYRsgQthEyhG2EDGEbIWOY0k7KcgSkGhemISIiIiIiIiIiIrNiEJKIiIiIiIiIiIjMikFIKvfkcjlmzpwJuVxe1lWhcozthAxhGyFD2EbIELYRMoRthAxhGyFD2EbIGBW1nXBhGiIiIiIiIiIiIjIrjoQkIiIiIiIiIiIis2IQkoiIiIiIiIiIiMyKQUgiIiIiIiIiIiIyKwYhqVzbuHEjXn/9dVStWhUKhQL+/v745ptv8Pjx47KuGhnp8ePH2L9/Pz7++GMEBATA2dkZVapUQc2aNREcHIwdO3boLDdr1izIZLIiX1euXNF73GvXriE8PBy1a9eGXC5H7dq1ER4ejuvXrxdZX5VKhenTp8PPzw+2trZwdXVFz549ceDAgRJ9DlS08PBwg+f70aNHOsueOnUKAwcOhJubG2xsbFC3bl188MEHuHfvXpHHTElJwfvvv4+6detCLpfDzc0NAwcOxOnTp4ssl5eXh6+//hr+/v5QKBSoWrUqXn/9dWzatKnY75+KlpCQYLB9qF9//PGHVI79SOVz9epVLFy4EOHh4WjatCmsrKwgk8kwZ84cg2X37duHoKAguLq6wtbWFg0bNsSMGTPw8OHDIss973ZQWFiIJUuWoHXr1nBwcICDgwNat26NpUuXgo9yN8zUNlJYWIhjx47hs88+Q4cOHVCtWjVUqVIFrq6u6NatG6Kjo/V+7qtWrTLYx+zevVtvXXkdKhvF6Ucq2vWE/UjJFKeNGHuf8vPPP2uUYz9SMRX3O67aC31PIojKqfHjxwsAwsrKSnTv3l30799fODs7CwCiQ4cOIjs7u6yrSEbYu3evACAAiJo1a4qePXuKQYMGiSZNmkjbx4wZIwoLCzXKzZw5UwAQ/v7+IiwsTOcrKSlJ5zGPHDki7OzsBADRuHFjMXjwYNG4cWMBQCgUCnH8+HGd5VJSUoSvr68AINzd3cXAgQPFa6+9JmQymZDJZGLBggWl/vnQE2FhYQKAaN++vd7znZeXp1Vu48aNwsrKSgAQAQEBYtCgQaJevXoCgHBzcxPx8fE6j3f16lVRo0YNAUDUq1dPDBo0SAQEBEh9zpYtW3SWy8rKEu3atRMAhLOzs+jfv7/o3r27VIdJkyaV6udCT6SmpuptF2FhYdK5c3BwEA8fPpTKsR+pfNT3Bs++Zs+eXWS5yMhIAUDIZDLx2muviYEDB4qaNWsKAMLPz0+kpqbqLPe820F+fr7o37+/ACDs7OxE7969Re/evYWtra0AIAYOHCgKCgpM+9BeMKa2kfj4eCmPi4uL6N69uxg8eLDUrwAQvXr1Erm5uVplV65cKQCI+vXr6+1jzp8/r/O4vA6VneL0IxXpesJ+pOSK00aKuk/p1KmTdA1KSEjQKMd+pGIq7ndcIXhPwiAklUtbt24VAIS9vb04deqUtD01NVU0bdqUnWMFsn//fhESEiL++OMPrbR169YJS0tLAUCsXr1aI019szdz5kyTjpeVlSU8PDwEADFt2jSNtGnTpgkAwtPTU2cQu0+fPgKA6NKli8jKypK279ixQ1haWgoLCwtx7tw5k+pDxlEHIVeuXGl0mTt37kgX4iVLlkjb8/PzxfDhw6XA5LMX/8LCQtGiRQsBQIwYMULk5+dLaUuWLJH6nuTkZK1jqm9KmzZtqnGDEBcXJ+zt7QUAsX37dhPeOZWGN954QwAQo0eP1tjOfqTy+emnn8RHH30koqOjxeXLl8WIESMMfjE8ffq0kMlkwtLSUuzcuVPanpWVJbp06SIAiJCQEK1yZdEOvvvuOwFA1KpVS1y/fl3afv36dakuCxcuLPpDesGZ2kauXbsmOnfuLHbt2qVxPRBCiIMHDwqFQiEAiM8//1yrrDp4EBYWZlIdeR0qW8XpRyrS9YT9SMkVp40U5Z133hEARLdu3bTS2I9UTMX9jst7EgYhqZxS/4IzZ84crbTDhw8LAEIul4uMjIwyqB2VpoiICKlTfFpxb/YWLVokAAhfX1+tX2YKCgqkX4EWL16skXbp0iUBQFhaWmr9Qvl0PYcMGWJSfcg4xQlCfvzxxwKA6Nq1q1aaSqUSTk5OAoDYvXu3RtqOHTukX35VKpVWWfUNwNSpUzW2p6enC2trawFAHDlyRKvc7NmzBQDRpk0bo98DlVxiYqKwsLAQAMSJEyc00tiPVH7qvqOoL4YDBw4UAMSoUaO00hISEqT2c/nyZY20590OCgoKpJEQUVFRWuXWrFkjAAgPDw+OYjKBMW2kKOq+vX79+lppxQ0e8DpUvhjTRirK9YT9iHmUpB/JycmRZvOtW7dOK539SOWk7zsu70mE4DMhqdy5c+cO/vrrLwDAsGHDtNI7dOgAT09P5ObmYufOnc+7elTKWrRoAQC4fft2qexv69atAIAhQ4bAwkKzi7OwsMDgwYMBAFu2bNFZrn379qhTp47WftVtcfv27XwmaTmhPme6+gl7e3sEBwcD0H+ug4ODYW9vr1VWvb9ny+3cuRN5eXnw8vJC+/bt9ZY7ceIEkpKSTH07VEyrVq1CYWEhGjdujNatW5fKPtmPVB55eXnSc5l09RV16tSR/n9Wnz+1590Ojh8/jrt370IulyMkJESrXEhICKytrZGUlISTJ08W8a6pNJX2fQrA69CLhP0Ibd68GRkZGXBxcUHfvn1Lbb/sR8o3XdcO3pP8//qalJvoOThz5gwAwMXFBXXr1tWZp1WrVhp5qeKKj48HALi7u+tMP336NKZOnYoxY8bg448/RkxMDFQqld79qduEuo08S1/bMbZcVlaWVGcqfbGxsZg0aRLGjBmDadOmYevWrcjNzdXKp1KpcO3aNQDmO9fx8fHIysoyuly9evXg4uICADh79qzOPFT6Vq1aBQCIiIjQm4f9yIvrn3/+QXZ2NgDznc/Sagfqco0bN4aNjY1WOVtbWzRu3FjnMcl8DN2nAE8WCvjkk08wZswYTJw4EStWrEBaWpre/LwOVVzl/XrCfqT8WbFiBQBg+PDhkMvlevOxH6lcdF07eE/yhJVJuYmegxs3bgAAvLy89Obx9PTUyEsV0927d6UAgq5fWIAnv8ps375dY5uTkxMWLFiA0NBQje0qlQr3798HoL/9qNtOamoqsrKyoFAoABhud46OjnB0dIRSqcSNGzfw0ksvGfEOyVTPrhgIPLl4r1ixAoGBgdK2hIQE6W9D5/rZfsLQuVaXE0IgISFBusAa0zfVrl0b6enp7Juek0OHDuHatWuwtrbGiBEj9OZjP/LiUp8TZ2dnODg46Myjq68oi3Zg7P3PmTNn2Mc8J9nZ2ViwYAEA/fcpAHD06FEcPXpUY5uNjQ1mzZqFKVOmaOXndajiKu/XE/Yj5UtCQgJiY2MBFP1jKcB+pDLR9x2X9yRPcCQklTvqXxPV/+Pooh5yrlQqn0udqPTl5+dj+PDhyMzMRNOmTfH2229rpNevXx9fffUVzpw5g/T0dKSnp+PIkSPo1asXMjMzERYWhujoaI0yT/8Sra/9PD1d4en2w3ZXtvz9/TF//nxcvHgRSqUSKSkp2LNnD9q1a4fk5GQEBwfj4MGDUn5TzvWz58vQuWYbqTjUowuCg4Ph6uqqlc5+hIp7TsqiHbD9lD/vvvsubty4AQ8PD0yfPl0rvWbNmpgxYwZOnjyJ1NRUKJVK/PXXXwgNDUVubi6mTp2Kr776Sqscr0MVT0W5nrCNlC8rV66EEAKtWrVCs2bNdOZhP1K5FPUdl/ckT3AkJBGVibFjx2L//v2oVq0aNm3aBGtra410XaOa2rdvj+3bt2PcuHFYuHAhJkyYgIEDB2qVpYpnwoQJGv92cHBAt27d0LVrV/Tr1w/btm3Dhx9+yGkhJFEqldi0aRMAYOTIkTrzsB8houKaPXs2Vq9eDRsbG2zYsAHVqlXTyhMYGKgxSh94MrVt9erV8Pf3x6RJk/DFF18gIiICbm5uz6vqZAa8npCpCgsLpdFw+u5TAPYjlY2h77jEkZBUDqmHJj/97IpnPXz4EMCT4cNU8YwfPx7Lly9H1apVsXfvXvj6+ppUftasWbC0tERqaqrGg3CfHtaur/2o2w6g2X7Y7sonmUyGzz//HABw7tw56eHOppzrZ8+XoXPNNlIxrFu3DtnZ2ahduzZ69Ohhcnn2Iy+G4p6TsmgHbD/lR2RkJD777DPI5XJs3bpV5+INhowfPx6urq7Izc3Fnj17NNJ4HapcytP1hG2k/Ni3bx9u3boFW1tbnYuQGIP9SMVi6Dsu70meYBCSyh1vb28ARa9CqE5T56WKY9KkSViwYAGcnZ2xZ88eaeUwU7i4uKBGjRoAgMTERGm7g4OD9CDlW7du6Syrbjuurq4aw8vVbUlfOaVSKQ01Z7t7vho1aiT9rT7fT6/uZuhcP3u+DJ1rdTmZTKZxHEPlnq4f24j5qadih4eHa60SaAz2Iy8G9eeckZGhd/EIXX1FWbQDY/oY3v+Y38KFCzFp0iRYW1tj8+bNWiOUjGVpaQkfHx8Amn0MwOtQZVOerifsR8oP9X1KSEgInJycirUP9iMVhzHfcXlP8gSDkFTuqP+HvX//vt6HnMbFxQEAWrZs+dzqRSU3efJkREZGwsnJCXv27NG7SpchBQUFyMzMBACth/qq24S6jTxLX9sxtpxCoTB55CaVjPpBzMD/zrejoyMaNGgAwHzn2sfHR+P5KobKXb9+Henp6QBQrOA6Ge/vv//GyZMnIZPJ8NZbbxVrH+xHXgx+fn6ws7MDYL7zWVrtQF3u0qVLePTokVa5nJwcXLp0SecxqXQsWrQI48aNkwKQPXv2LNH+1Nev4vYxvA5VDOXpesJ+pHxIT0/HL7/8AsDwgjSGsB8p/4z9jst7kv9PEJVDAQEBAoCYM2eOVtrhw4cFACGXy0VGRkYZ1I6KY8qUKQKAcHJyEn/++WeJ9rVlyxYBQMhkMpGcnKyRtmjRIgFA+Pr6ioKCAo20goIC4evrKwCIxYsXa6RdvHhRABCWlpbi5s2bWseMiIgQAMSQIUNKVHcyXWRkpAAgHB0dRV5enrT9448/FgBE165dtcqoVCrh7OwsAIjdu3drpO3YsUMAEM7OzuLhw4daZbt06SIAiKlTp2psT09PF9bW1gKAOHLkiFa52bNnCwCiTZs2xX2rZKSJEycKAKJz587F3gf7kcohLCxMABCzZ8/Wm2fgwIECgBg1apRWWkJCgrC0tBQAxOXLlzXSnnc7KCgoEDVr1hQARFRUlFa5NWvWCADCw8NDqz6knzFtRAghfvzxRwFAWFtbi+3bt5f4uKdOnRIABABx8uRJjTReh8oXY9uIPuXpesJ+xDxMbSMLFiwQAET9+vVFYWFhsY/LfqT8M/U7Lu9JhGAQksqlrVu3CgDC3t5enDp1StqelpYmmjZtKgCISZMmlWENyRQzZsyQLpLGdM43b94Ua9asETk5OVppW7duFS4uLgKAGD58uFZ6VlaW8PDwEADE9OnTNdKmT58uAIjatWuL7OxsrbJ9+vSRglpPp+/cuVNYWloKCwsLce7cOWPeMpngzJkzYtu2beLx48ca2wsKCsSyZcuEjY2NACA++eQTjfQ7d+4IOzs7AUAsXbpU2p6fny9GjBghAIiAgACtm7/CwkLRokULAUCEhoaK/Px8KW3JkiVS3/PsFwkhhBg/frwAIJo1aybS0tKk7adOnRL29vYCQKl8eSX98vLyRI0aNQQAER0drTcf+5EXgzFfDE+dOiVkMpmwtLQUu3btkrZnZWVJX9JCQkK0ypVFO/juu+8EAFGrVi1x/fp1afv169dFrVq1BACxcOHCoj8U0mBMG1m6dKmQyWQmBSCzsrLE999/L5RKpVbaoUOHhLe3twAgOnTooJXO61D5YqiNVLTrCfuR0mdqELJ58+YCgPjyyy+LzMd+pGIz9TuuELwnEYJBSCrHxo0bJwCIKlWqiMDAQBESEiKNbGrfvr3O/8Go/Nm2bZv0C16rVq1EWFiYztfTQeUzZ85IF85XX31VDBkyRPTp00f4+PhI++rUqZNQqVQ6j3nkyBEpONWkSRMxZMgQ0aRJEwFAKBQKcfz4cZ3lUlJSpGO4u7uLQYMGiddff13IZDIBQMyfP98sn9GLTv2jQ9WqVUWXLl3EsGHDRFBQkPDy8pLO99ChQ7WClEIIsWHDBukXw9atW4vBgweLevXqCQDCzc1NxMfH6zzmlStXRPXq1QUAUa9ePTF48GDxyiuvCADCyspKbNmyRWe5rKws0bZtW6m+ISEhIjAwUFSpUkUAEBMnTizVz4a0qUecODs76/xCqMZ+pHI6deqUaN26tfRydXWVbryf3p6UlKRRTj2iWiaTiddff10MGjRIuLu7CwDCz89PpKam6jze824H+fn5ol+/fgKAsLOzE8HBwSI4OFiqw4ABAzh6yQBT28iZM2ek89KwYUO99ylhYWEax3nw4IEAnszMadOmjRg0aJDo37+/1D4AiKZNm2q1RTVeh8pOcdpIRbqesB8pueJea4QQ4vTp09LIszt37hR5HPYjFVdxvuOqvej3JAxCUrm2fv168dprrwlHR0dha2srmjRpIv7zn/+I3Nzcsq4aGWnlypVSB13Uq06dOlKZtLQ0MWXKFNG5c2fh5eUlFAqFqFKlinB3dxe9evUSMTExBju8+Ph4ERoaKjw8PESVKlWEh4eHCA0NFdeuXSuyXGZmppg6darw8fERcrlcuLi4iMDAQLFv377S+DhIh+vXr4sPP/xQdOjQQdSqVUvY2NgIuVwuvLy8xIABA8SOHTuKLB8XFyf69+8vqlevLqytrUWdOnXEe++9J+7evVtkueTkZPHee++JOnXqCGtra1G9enXRv39/jdHXuuTm5oq5c+eKJk2aCFtbW+Hk5CRee+01sWHDBpPfO5muV69eAoB49913i8zHfqRyio2NNeqacuPGDa2ye/fuFYGBgcLFxUXI5XLh4+Mjpk2bpnMEytOedzsoKCgQixcvFq1atRIKhUIoFAoREBAgFi9eXKJpfS8KU9uIsfkBza9Nubm54tNPPxVvvPGGqFu3rnBwcBBWVlaievXqomvXrmLJkiUG71d5HSobpraRing9YT9SMiW51rz//vsCgAgKCjJ4HPYjFVdxvuM+7UW+J5EJIQSIiIiIiIiIiIiIzISrYxMREREREREREZFZMQhJREREREREREREZsUgJBEREREREREREZkVg5BERERERERERERkVgxCEhERERERERERkVkxCElERERERERERERmxSAkERERERERERERmRWDkERERERERERERGRWDEISERERERERERGRWTEISUREREQaZDIZZDIZDh48WNZVISIiIqJKgkFIIiIiokpEHUAszmvVqlVlXf1yJyEhAbNmzcKsWbPKuipEREREFZpVWVeAiIiIiEqPm5ubzu0PHz5EVlZWkXlsbW0BAH5+fgAAOzs7M9SwYklISMDnn38OAAxEEhEREZUAg5BERERElcjdu3d1bp81a5YUTNOXR+3KlSulXi8iIiIierFxOjYRERERERERERGZFYOQRERERKRB38I0CQkJUlpCQgJu3ryJ0aNHw8vLCzY2Nqhfvz4++eQTado3AFy8eBHDhw+Hp6cnbGxs4OPjgzlz5uDx48dF1iEhIQEffvghGjduDHt7e9jZ2aFhw4YYP348bt26Vez3duXKFYwZMwa+vr6ws7ODjY0NPD090aZNG0yfPl1jFKi3tzc6deqk9bmoX+Hh4Vr7V6lU+M9//oO2bdvCxcUFcrkcnp6eGDJkCI4fP673vT79ucbHxyM8PBy1a9eGXC6Hl5cXxo4di6SkJL3vKzExERMmTEDjxo2hUCggl8vh4eGBl19+GRMmTMBff/1V7M+MiIiIqDRwOjYRERERmez06dOIiIhARkYGHB0dkZ+fj+vXr+PLL7/EH3/8gf3792PPnj0YNGgQsrOz4eTkhLy8PFy7dg2ffvopLl68iHXr1uncd3R0NCIiIpCbmwsAkMvlsLCwwNWrV3H16lWsXLkSmzZtQvfu3U2q8969e9G7d29pv1WqVIFCoUBiYiISExNx8uRJWFtbS89+rF69OpRKJR48eABA+1maTk5OGv8+e/YsevfujcTERACApaUl7OzskJiYiPXr12PDhg348ssvMW3aNL11PHnyJEaPHg2VSgV7e3tYWlri9u3bWLJkCTZu3Ii9e/eiZcuWGmXOnTuHTp06SfW0tLSEo6Mj7t69i+TkZJw+fRoPHjzgwkNERERUpjgSkoiIiIhMFhERgZdffhmXLl1CZmYmVCoVFixYAEtLSxw+fBhffPEF3nzzTfTu3RsJCQnIyMiAUqnEjBkzAADr16/Hvn37tPa7d+9ehIaGoqCgAJMnT8aNGzeQk5ODrKwsXLlyBQMHDoRKpcLAgQNNHhH5zjvvIDc3F927d8eFCxeQl5eHBw8eICcnBxcvXsTnn38Ob29vKf9ff/2FLVu2SP++e/euxmv+/PlSWnJyMnr06IHExET0798fcXFxyMnJgVKpREpKCj799FNYWlpi+vTp+OWXX/TW8e2330bdunVx8uRJqFQqZGVl4ffff4eXlxfS09PRr18/qFQqjTKTJk3CgwcP0LJlSxw/fhyPHz9Geno6Hj16hH/++QfffvstGjdubNJnRURERFTqBBERERFVejNnzhQAhDG3f+p8sbGxGttv3LghpTVu3Fg8evRIq+yIESOkPN26dROFhYVaeV599VUBQERERGhsLygoED4+PgKAWLJkid76BQcHCwBi/PjxBt+LWkpKilSvpKQko8vFxsYa9bmNHDlSABDDhg3TmycyMlIAEP7+/hrbn/5cq1WrJlJSUrTK/v3338La2loAEN98841Gmq2trQAgjh07ZvT7IiIiInreOBKSiIiIiEw2YcIEyOVyre09evSQ/p46dSpkMpnePOfPn9fY/scffyA+Ph6urq4YNWqU3mOHhoYCAH7//Xej6+vg4AALiye3vsnJyUaXM8ajR48QExMDAJgyZYrefOp6nzt3DikpKTrzjB07FjVq1NDa3qhRIwwYMAAAtKaxOzs7Ayj990VERERUmvhMSCIiIiIy2SuvvKJz+9PPTQwICCgyj/oZhmpHjx4FAGRmZsLDw0PvsfPy8gAAN2/eNLq+tra26NKlC/bu3YvAwECMHTsWPXv2RIsWLWBtbW30fnQ5deoUHj16BABGP6fy5s2bWs+YBIDOnTvrLdO5c2fExMTg/PnzePz4MapUqQIA6NWrF3766SeEhYXh6NGjCA4ORkBAAOzs7IrxboiIiIjMg0FIIiIiIjKZg4ODzu1WVlZG53l2hWz16s+PHz/WO1LwaTk5OUbVVW3ZsmUIDg7GuXPnMHv2bMyePRvW1tYICAhAnz59EBERARcXF5P2+XS9ARhVbwDIzs7Wub1WrVp6y6jT8vPzkZ6eLgUxv/nmG1y7dg2xsbGIjIxEZGQkLC0t0bx5c/Ts2RNjxowpcr9EREREzwOnYxMRERFRuVBQUAAAaN26NYQQRr1M4eXlhdOnT2P37t0YN24cXn75ZRQWFuLo0aOYPHkyGjRogAMHDhS73sCTwKgx9X799ddNPo4+zs7OOHDgAA4fPozJkyejffv2sLKywqlTp/DFF1/Ax8cHa9euLbXjERERERUHg5BEREREVC7UrFkTgGnTrE1lYWGBHj16YP78+YiLi0N6ejqio6Ph5eWFBw8eYNiwYdJ0b2Op6w2UvO537twxmGZlZaVzxGaHDh3w9ddf48iRI8jIyMC2bdvQtGlT5OTkYOTIkUaP0iQiIiIyBwYhiYiIiKhcaN++PQDg7t27iIuLey7HdHBwwLBhw7B8+XIAT6ZTX7hwQUpXL2YDQO/Iy4CAAOm5ktu3by9RfWJjYw2mNWvWTHoepD42NjYIDg7Gli1bADxZPOfIkSMlqhsRERFRSTAISURERETlQqdOndCgQQMAT1bfNjQiMT093eh9G9qXra2t9PfTgUdHR0fp74yMDJ1lFQoFhg0bBgD4+uuvcevWrSKPVVS9Fy9ejLS0NK3tV69exaZNmwAAgwcPlrbn5+ejsLBQ7/70vS8iIiKi5413IkRERERULlhZWWHx4sWwsrLCkSNH8Nprr2H//v0aC9hcv34dixcvRkBAAH744Qej933s2DE0a9YM3333HS5fviwF7oQQOHbsGN555x0AQO3atdGsWTOpnK+vrzTKcdmyZXpHQ3711Vfw8PBAWloa2rZtizVr1kClUknpqamp2Lx5M/r164ehQ4fqrefjx4/RrVs3/PXXX1L99u3bhx49eiA3Nxeenp4YO3aslD8xMRE+Pj6YM2cOzpw5g/z8fCnt/PnzGD58OIAngdKOHTsa/XkRERERlTYGIYmIiIio3OjSpQs2btwIBwcHnDx5El27doVCoYCrqytsbGxQv359vPPOO4iLi4NMJjNp3xcuXMDEiRPx0ksvwcbGBq6urrC2tkb79u1x4cIFODo6IiYmBpaWllIZOzs7jBgxAgAwefJk2Nvbo06dOvD29sZHH30k5XN3d8e+ffvg6+uLpKQkhIaGwtnZGdWqVYO9vT1q1KiBAQMG4Jdffily5OKSJUvw77//4pVXXoGDgwPs7e3RrVs33Lx5E87OztiyZYvG6EzgSWD2008/RcuWLWFjY4Nq1apBLpfD398fBw8ehLW1NVatWlWslb+JiIiISotVWVeAiIiIiOhpffv2xbVr1/DDDz9g165diI+PR0ZGBhQKBRo2bIiAgAD07NkTQUFBRu8zICAAGzZsQGxsLP78808kJSUhLS0NNjY2aNCgAbp3747x48fDw8NDq+yiRYvg6emJzZs3499//5WmWz87bbpRo0Y4f/48Vq9ejc2bN+Ps2bNIT0+HtbU1GjRogBYtWqBbt24YMGCA3nq2bt0acXFx+PLLL7F//36kpqaiVq1aCAoKwmeffYbatWtr5K9VqxZ+/fVXxMbG4vjx40hMTMS9e/dgZWWFBg0aoFOnThg/fjx8fHyM/qyIiIiIzEEm9M0pISIiIiIis0tISEDdunUBADdu3IC3t3fZVoiIiIjIDDgdm4iIiIiIiIiIiMyKQUgiIiIiIiIiIiIyKwYhiYiIiIiIiIiIyKwYhCQiIiIiIiIiIiKz4sI0REREREREREREZFYcCUlERERERERERERmxSAkERERERERERERmRWDkERERERERERERGRWDEISERERERERERGRWTEISURERERERERERGbFICQRERERERERERGZFYOQREREREREREREZFYMQhIREREREREREZFZ/T+FneAkfPPS8gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wss = ['WS_S1', 'TWS_S25A', 'TWS_S25B', 'TWS_S26']\n",
    "inv_yhat_reshape = inv_yhat.reshape((-1, 24, 4))\n",
    "inv_y_reshape = inv_y.reshape((-1, 24, 4))\n",
    "\n",
    "for i in range(len(wss)):\n",
    "    plt.rcParams[\"figure.figsize\"] = (15, 3)\n",
    "    error = inv_yhat_reshape - inv_y_reshape\n",
    "    plt.plot(error[:, -1, i], linewidth=1)\n",
    "    plt.xticks(fontsize=16)\n",
    "    plt.yticks(fontsize=16)\n",
    "    plt.xlabel('Time steps', fontsize=18)\n",
    "    plt.ylabel('Actual Error (ft)', fontsize=18)\n",
    "    plt.title('{}'.format(wss[i]), fontsize=18)\n",
    "    plt.text(9000, -0.6, 'MAE = {}'.format(float(\"{:.3f}\".format(sum(abs(error[:, -1, i]))/len(error)))), fontsize=12)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "88da85fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(19268,)\n",
      "12\n",
      "(19268,)\n",
      "47\n",
      "(19268,)\n",
      "44\n",
      "(19268,)\n",
      "42\n"
     ]
    }
   ],
   "source": [
    "up_thre = 0.5\n",
    "low_thre = -0.5\n",
    "\n",
    "for i in range(len(wss)):\n",
    "    plt.rcParams[\"figure.figsize\"] = (15, 3)\n",
    "    error = inv_yhat_reshape - inv_y_reshape\n",
    "    error_24h = error[:, -1, i]\n",
    "    print(error_24h.shape)\n",
    "    print(np.sum(error[:, -1, i] > up_thre) + np.sum(error[:, -1, i] < low_thre))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "43865dcf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(19268,)\n",
      "8\n",
      "(19268,)\n",
      "27\n",
      "(19268,)\n",
      "11\n",
      "(19268,)\n",
      "40\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(wss)):\n",
    "    plt.rcParams[\"figure.figsize\"] = (15, 3)\n",
    "    error = inv_yhat_reshape - inv_y_reshape\n",
    "    error_24h = error[:, -1, i]\n",
    "    print(error_24h.shape)\n",
    "    print(np.sum(error[:, -1, i] > up_thre))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "166baa75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(19268,)\n",
      "4\n",
      "(19268,)\n",
      "20\n",
      "(19268,)\n",
      "33\n",
      "(19268,)\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(wss)):\n",
    "    plt.rcParams[\"figure.figsize\"] = (15, 3)\n",
    "    error = inv_yhat_reshape - inv_y_reshape\n",
    "    error_24h = error[:, -1, i]\n",
    "    print(error_24h.shape)\n",
    "    print(np.sum(error[:, -1, i] < low_thre))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95fea4d0",
   "metadata": {},
   "source": [
    "### Fine-tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9842c2ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "def transformer_encoder(inputs, head_size, num_heads, ff_dim, atte_reg, l1_reg, l2_reg, dropout=0):\n",
    "    # Normalization and Attention\n",
    "    x = layers.MultiHeadAttention(key_dim=head_size, \n",
    "                                  num_heads=num_heads, \n",
    "                                  dropout=dropout, \n",
    "                                  kernel_regularizer=l2(atte_reg)\n",
    "                                 )(inputs, inputs)\n",
    "    x = layers.Dropout(dropout)(x)\n",
    "    res = x + inputs\n",
    "    x1 = layers.LayerNormalization(epsilon=1e-6)(res)\n",
    "#     res = x + inputs\n",
    "\n",
    "    # Feed Forward Part\n",
    "    x = layers.Conv1D(filters=ff_dim, \n",
    "                      kernel_size=2, \n",
    "                      activation=\"relu\",\n",
    "                      padding='same', \n",
    "                      kernel_regularizer=keras.regularizers.L1L2(l1=l1_reg, l2=l2_reg)\n",
    "                     )(x1)\n",
    "    x = layers.Dropout(dropout)(x)\n",
    "    x = layers.Conv1D(filters=inputs.shape[-1], \n",
    "                      kernel_size=2,\n",
    "                      activation=\"relu\",\n",
    "                      padding='same', \n",
    "                      kernel_regularizer=keras.regularizers.L1L2(l1=l1_reg, l2=l2_reg)\n",
    "                     )(x)\n",
    "    x = layers.LayerNormalization(epsilon=1e-6)(x)\n",
    "    \n",
    "    return x + res\n",
    "\n",
    "\n",
    "\n",
    "def model_builder(hp):\n",
    "    #hp_embed_feat = hp.Int('embed_feat', min_value=2, max_value=5, step=1)\n",
    "    hp_ff_dim = hp.Int('ff_dim', min_value=48, max_value=128, step=16)\n",
    "    hp_num_heads = hp.Choice('num_heads', values=[1, 2, 3, 4, 5, 6])\n",
    "    hp_atte_reg = hp.Choice('attention_regular', values=[0.0, 1e-1, 1e-2, 1e-3, 1e-4, 1e-5, 1e-6])\n",
    "    hp_l1_reg = hp.Choice('l1_regular', values=[0.0, 1e-1, 1e-2, 1e-3, 1e-4, 1e-5, 1e-6])\n",
    "    hp_l2_reg = hp.Choice('l2_regular', values=[0.0, 1e-1, 1e-2, 1e-3, 1e-4, 1e-5, 1e-6])\n",
    "    hp_lr = hp.Choice('learning_rate', values=[5e-3, 1e-3, 5e-4, 1e-4, 5e-5])\n",
    "    hp_dropout = hp.Choice('dropout', values=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6])\n",
    "    hp_decay_steps = hp.Choice('decay_steps', values=[5000, 8000, 10000, 30000, 50000, 80000, 100000])\n",
    "    hp_decay_rate = hp.Choice('decay_rate', values=[0.6, 0.7, 0.8, 0.9, 0.95])\n",
    "    hp_gcn1_units = hp.Int('gcn1_units', min_value=16, max_value=64, step=16)\n",
    "    hp_gcn2_units = hp.Int('gcn2_units', min_value=16, max_value=64, step=16)\n",
    "    hp_rnn_units = hp.Int('rnn_units', min_value=32, max_value=128, step=16)\n",
    "    hp_num_transformer_blocks = hp.Int('num_transformer_blocks', min_value=1, max_value=3, step=1)\n",
    "    \n",
    "    # covariates\n",
    "    cov_inputs = keras.Input(shape=(96, 10), name='cov_inputs')\n",
    "    cov = layers.Masking(mask_value=mask_value)(cov_inputs)\n",
    "    for _ in range(hp_num_transformer_blocks):\n",
    "        cov = transformer_encoder(cov, hp_ff_dim*2, hp_num_heads, hp_ff_dim, hp_atte_reg, hp_l1_reg, hp_l2_reg, hp_dropout)\n",
    "    # cov = layers.MaxPooling1D(pool_size=2, padding='same', name='pooling')(cov)\n",
    "    cov = Dense(5)(cov)\n",
    "    cov_reshape = layers.Reshape((5, 96), name='cov_reshape')(cov)\n",
    "\n",
    "    inp_lap = Input((5, 5), name='inp_lap')\n",
    "    inp_seq = Input((5, 72), name='inp_seq')\n",
    "\n",
    "    # GCN\n",
    "    x = GCNConv(hp_gcn1_units, activation='relu')([inp_seq, inp_lap])\n",
    "    x = GCNConv(hp_gcn2_units, activation='relu')([x, inp_lap])\n",
    "\n",
    "    # RNN\n",
    "    xx = LSTM(hp_rnn_units, activation='relu', return_sequences=True)(inp_seq)\n",
    "\n",
    "    x = Concatenate(name='concate')([cov_reshape, x, xx])\n",
    "    x = layers.Attention()([x, x])\n",
    "    \n",
    "    x = Flatten()(x)\n",
    "    out = Dense(96)(x)\n",
    "\n",
    "    model = Model([cov_inputs, inp_seq, inp_lap], out)\n",
    "    # model.summary()\n",
    "    \n",
    "    # compile\n",
    "    lr_schedule = keras.optimizers.schedules.ExponentialDecay(initial_learning_rate=hp_lr, \n",
    "                                                          decay_steps=hp_decay_steps,\n",
    "                                                          decay_rate=hp_decay_rate)\n",
    "\n",
    "    model.compile(optimizer=Adam(learning_rate=lr_schedule),\n",
    "                  loss='mse',\n",
    "                  metrics=['mae']\n",
    "                 )\n",
    "    \n",
    "    return model\n",
    "\n",
    "\n",
    "tuner = kt.Hyperband(model_builder,\n",
    "                     objective='val_mae',\n",
    "                     max_epochs=400,\n",
    "                     factor=3,\n",
    "#                      overwrite=True,\n",
    "#                      directory='my_dir',\n",
    "                     project_name='gnn_transformer_parallel'\n",
    "                    )\n",
    "\n",
    "\n",
    "# stop_early = EarlyStopping(monitor='val_mae', mode='min', verbose=2, patience=100)\n",
    "\n",
    "# tuner1.search([train_cov, train_tws_reshape, train_adjacency_matrix], train_ws_y,\n",
    "#               validation_data=([test_cov, test_tws_reshape, test_adjacency_matrix], test_ws_y),\n",
    "#               batch_size=512,\n",
    "# #              verbose=2,\n",
    "#               shuffle=True,\n",
    "#               callbacks=[stop_early]\n",
    "#              )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3000eab6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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