text: import os
  import tensorflow as tf
  
  IMBALANCED_TRAIN_SIZE = 269695
  IMBALANCED_VAL__SIZE  = 123723
  IMBALANCED_TEST_SIZE = 125866
  
  BALANCED_TRAIN_SIZE = 13932
  BALANCED_VAL_SIZE = 6606
  # BALANCED_TEST_SIZE = 6604
  
  DOWNSAMPLED_TRAIN_SIZE = 69660
  DOWNSAMPLED_VAL_SIZE = 33030
  
  BALANCED_VY_TRAIN_SIZE = 9790
  BALANCED_VY_VAL_SIZE = 4758
  # BALANCED_VY_TEST_SIZE = 4500
  
  BALANCED_TRAINING_FILENAMES = 'balanced_train-part-*.tfrecord'
  BALANCED_VALIDATION_FILENAMES = 'balanced_val-part-*.tfrecord'
  BALANCED_TEST_FILENAMES = 'balanced_test-part-*.tfrecord'
  
  DOWNSAMPLED_TRAINING_FILENAMES = 'balanced_10_90_train-part-*.tfrecord'
  DOWNSAMPLED_VALIDATION_FILENAMES = 'balanced_10_90_val-part-*.tfrecord'
  DOWNSAMPLED_TEST_FILENAMES = 'balanced_10_90_test-part-*.tfrecord'
  
  IMBALANCED_TRAINING_FILENAMES = 'train-part-*.tfrecord'
  IMBALANCED_VALIDATION_FILENAMES = 'val-part-*.tfrecord'
  IMBALANCED_TEST_FILENAMES = 'test-part-*.tfrecord'
  IMBALANCED_CA_TRAINING_FILENAMES = 'train_ca_part*.tfrecord'
  
  BASE_PATH = '/workspace/app'
  # OUTPUT_PATH = os.path.join(BASE_PATH, 'models/supervised')
  TFR_PATH = os.path.join(BASE_PATH, 'data/processed')
  
  TEST_FILENAMES = os.path.join(TFR_PATH, "original", IMBALANCED_TEST_FILENAMES)
  TEST_SIZE = IMBALANCED_TEST_SIZE
  
  METRICS = [
      tf.keras.metrics.TruePositives(name='tp'),
      tf.keras.metrics.FalsePositives(name='fp'),
      tf.keras.metrics.TrueNegatives(name='tn'),
      tf.keras.metrics.FalseNegatives(name='fn'),
      tf.keras.metrics.BinaryAccuracy(name='accuracy'),
      tf.keras.metrics.Precision(name='precision'),
      tf.keras.metrics.Recall(name='recall'),
      tf.keras.metrics.AUC(name='auc'),
      # tfa.metrics.F1Score(name='tfa_f1', num_classes=43),
      # tfa.metrics.FBetaScore(name='tfa_f05', num_classes=1, beta=0.5),
      # tfa.metrics.FBetaScore(name='tfa_f2', num_classes=43),    # tfa.metrics.FBetaScore(name='tfa_f2', num_classes=1, beta=2.0), beta=2.0),
      # tfa.metrics.FBetaScore(name='tfa_f6', num_classes=1, beta=6.0)
  ]
  
  
  