old_contents: import os
  
  # Define paths
  CURRENT_PATH = os.path.dirname(__file__)
  IMG_PATH = os.path.join(CURRENT_PATH, r'../data/processed/train-tif-v2')
  LABEL_PATH = os.path.join(CURRENT_PATH, r'../data/processed/train_v2.csv')
  TRAIN_DATA_PATH = os.path.join(CURRENT_PATH, r'../data/processed/train_set.txt')
  VALIDATION_DATA_PATH = os.path.join(CURRENT_PATH, r'../data/processed/validation_set.txt')
  
  # Labels lookup table
  LABELS = {'primary': 0,
            'clear': 1,
            'agriculture': 2,
            'road': 3,
            'water': 4,
            'partly_cloudy': 5,
            'cultivation': 6,
            'habitation': 7,
            'haze': 8,
            'cloudy': 9,
            'bare_ground': 10,
            'selective_logging': 11,
            'artisinal_mine': 12,
            'blooming': 13,
            'slash_burn': 14,
            'blow_down': 15,
            'conventional_mine': 16}
  
  LABELS_SIZE = 17
  
  # Train-validation separation parameters
  TRAIN_VALIDATION_RATIO = 0.9
  SEED = 448
  
  # Raw image shape
  RAW_IMAGE_WIDTH = 256
  RAW_IMAGE_HEIGHT = 256
  RAW_IMAGE_DEPTH = 4
  
  # Training parameters
  NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = 5000
  
new_contents: import os
  
  # Define paths
  CURRENT_PATH = os.path.dirname(__file__)
  IMG_PATH = os.path.join(CURRENT_PATH, r'../data/processed/train-tif-v2')
  LABEL_PATH = os.path.join(CURRENT_PATH, r'../data/processed/train_v2.csv')
  TRAIN_DATA_PATH = os.path.join(CURRENT_PATH, r'../data/processed/train_set.txt')
  VALIDATION_DATA_PATH = os.path.join(CURRENT_PATH, r'../data/processed/validation_set.txt')
  
  # Labels lookup table
  LABELS = {'primary': 0,
            'clear': 1,
            'agriculture': 2,
            'road': 3,
            'water': 4,
            'partly_cloudy': 5,
            'cultivation': 6,
            'habitation': 7,
            'haze': 8,
            'cloudy': 9,
            'bare_ground': 10,
            'selective_logging': 11,
            'artisinal_mine': 12,
            'blooming': 13,
            'slash_burn': 14,
            'blow_down': 15,
            'conventional_mine': 16}
  
  LABELS_SIZE = 17
  
  # Train-validation separation parameters
  TRAIN_VALIDATION_RATIO = 0.9
  SEED = 448
  
  # Raw image shape
  RAW_IMAGE_WIDTH = 256
  RAW_IMAGE_HEIGHT = 256
  RAW_IMAGE_DEPTH = 4
  
  # Training parameters
  DATA_AUGMENTATION = True
  NORMALIZE_IMAGE = False
  
  IMAGE_WIDTH = 256
  IMAGE_HEIGHT = 256
  IMAGE_DEPTH = 4
  
  NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = 5000
  
  # Validation parameters
  NUM_EXAMPLES_PER_EPOCH_FOR_VALIDATION = 1000
  
subject: add data augmentation and normalization option