import tensorflow as tf



def _fake_model_preprocessor_fn(image):
  return (image, tf.expand_dims(tf.shape(image)[1:], axis=0))


def _fake_image_resizer_fn(image, mask):
  return (image, mask, tf.shape(image))


class DataTransformationFnTest(test_case.TestCase):

  def test_combine_additional_channels_if_present(self):
    image = np.random.rand(4, 4, 3).astype(np.float32)
