code: def get_randaug(num_layers: int = 2, magnitude: int = 10):
      """Creates a function that applies RandAugment.
  
      RandAugment is from the paper https://arxiv.org/abs/1909.13719,
  
      Args:
        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,
          30].
  
      Returns:
        A function that applies RandAugment.
      """
  
      def _randaug(image):
          return autoaugment.distort_image_with_randaugment(
              image=image,
              num_layers=num_layers,
              magnitude=magnitude,
          )
  
      return _randaug
  
imports: from typing import Optional, Tuple
  import numpy
  from scenic.dataset_lib.big_transfer.preprocessing import autoaugment
  from scenic.dataset_lib.big_transfer.preprocessing import utils
  from scenic.dataset_lib.big_transfer.registry import Registry
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_addons import image
label_desc: Use image node_id 34 for calling a global function with example usage: get_randaug(num_layers, magnitude) and returns: _randaug, autoaugment