code: def distort_image_with_randaugment(
      frames,
      num_layers,
      magnitude,
      cutout_const=40,
      translate_const=100,
  ):
      """Applies the RandAugment policy to `image`.
  
      The original rand_augment implementation is for images. To be temporally
      consistent in video, we
        -- Reshape the video clip [t, h, w, c] to [t * h, w, c]
        -- Only apply functions that do not depend on spatial extent (ie rotate,
            shear, translate)
        -- We do, however, use a modified cutout.
  
      Args:
        frames: `Tensor` of shape [t, h, w, 3] representing an image.
        num_layers: Integer, the number of augmentation transformations to apply
          sequentially to an image. Represented as (N) in the paper. Usually best
          values will be in the range [1, 3].
        magnitude: Integer, shared magnitude across all augmentation operations.
          Represented as (M) in the paper. Usually best values are in the range [5,
          10].
        cutout_const: multiplier for applying cutout.
        translate_const: multiplier for applying translation.
  
      Returns:
        The augmented version of `frames`.
      """
      available_ops = [
          "AutoContrast",
          "Equalize",
          "Invert",
          "Posterize",
          "Solarize",
          "Color",
          "Contrast",
          "Brightness",
          "Sharpness",
          "Cutout",
          "SolarizeAdd",
          # 'Rotate', 'ShearX', 'ShearY', 'TranslateX', 'TranslateY',
      ]
  
      input_shape = _get_shape(frames)
      num_frames = input_shape[0]
      image = tf.reshape(frames, [-1, frames.shape[2], frames.shape[3]])
      input_image_type = image.dtype
  
      if input_image_type != tf.uint8:
          image = tf.clip_by_value(image, 0.0, 255.0)
          image = tf.cast(image, dtype=tf.uint8)
  
      replace_value = [128] * 3
      min_prob, max_prob = 0.2, 0.8
  
      for _ in range(num_layers):
          op_to_select = tf.random.uniform(
              [], maxval=len(available_ops) + 1, dtype=tf.int32
          )
  
          branch_fns = []
          for i, op_name in enumerate(available_ops):
              prob = tf.random.uniform(
                  [], minval=min_prob, maxval=max_prob, dtype=tf.float32
              )
              func, _, args = _parse_policy_info(
                  op_name,
                  prob,
                  magnitude,
                  replace_value,
                  cutout_const,
                  translate_const,
              )
  
              if op_name == "Cutout":
                  args = (args[0], num_frames)
  
              branch_fns.append(
                  (
                      i,
                      # pylint:disable=g-long-lambda
                      lambda selected_func=func, selected_args=args: selected_func(
                          image, *selected_args
                      ),
                  )
              )
              # pylint:enable=g-long-lambda
  
          image = tf.switch_case(
              branch_index=op_to_select,
              branch_fns=branch_fns,
              default=lambda: tf.identity(image),
          )
  
      image = tf.cast(image, dtype=input_image_type)
      return tf.reshape(image, input_shape)
  
imports: import functools
  import math
  from typing import Optional
  from absl import logging
  from dmvr import builders
  from dmvr import processors
  import simclr.tf2.data_util
  import tensorflow
  from official.vision.image_classification import augment
label_desc: Use image node_id 16 for calling a global function with example usage: distort_image_with_randaugment(frames, num_layers, magnitude, cutout_const, translate_const) and returns: tf