code: def parallel_apply_transformations(x, transforms, black_border_size=0):
    
    transforms = tf.convert_to_tensor(transforms, dtype=tf.float32)
    x = _apply_black_border(x, black_border_size)
  
    num_transforms = transforms.get_shape().as_list()[0]
    im_shape = x.get_shape().as_list()[1:]
  
    # Pass a copy of x and a transformation to each iteration of the map_fn
    # callable
    tiled_x = tf.reshape(
        tf.tile(x, [num_transforms, 1, 1, 1]),
        [num_transforms, -1] + im_shape)
    elems = [tiled_x, transforms]
    transformed_ims = tf.map_fn(
        _apply_transformation,
        elems,
        dtype=tf.float32,
        parallel_iterations=1,  # Must be 1 to avoid keras race conditions
    )
    return transformed_ims
docs: Apply image transformations in parallel.
    :param transforms: TODO
    :param black_border_size: int, size of black border to apply
    Returns:
      Transformed images