code: def parallel_apply_transformations(
      x, transforms, black_border_size=0
  ):
      """
      Apply image transformations in parallel.
      :param transforms: TODO
      :param black_border_size: int, size of black border to apply
      Returns:
        Transformed images
      """
      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
  
imports: import warnings
  import numpy
  from six.moves import xrange
  import tensorflow
  import tensorflow_addons
  from cleverhans.attacks.attack import Attack
  from cleverhans.compat import reduce_mean, reduce_sum, reduce_max
  from cleverhans.model import Model
  from cleverhans import utils_tf
label_desc: Use image node_id 6 for calling a global function with example usage: parallel_apply_transformations(x, transforms, black_border_size) and returns: transformed_ims