import tensorflow as tf

  input_mask = tf.image.resize(datapoint['segmentation_mask'], (128, 128))

  if tf.random.uniform(()) > 0.5:
    input_image = tf.image.flip_left_right(input_image)
    input_mask = tf.image.flip_left_right(input_mask)

  input_image, input_mask = normalize(input_image, input_mask)

  return input_image, input_mask


def load_image_test(datapoint):
  input_image = tf.image.resize(datapoint['image'], (512, 512))
  input_mask = tf.image.resize(datapoint['segmentation_mask'], (128, 128))

  input_image, input_mask = normalize(input_image, input_mask)

  return input_image, input_mask


def main(_):
  train_examples = info.splits['train'].num_examples
  batch_size = 8
  steps_per_epoch = train_examples // batch_size
