code: def auto_augment_transform(config_str, hparams):
      """
      Create a AutoAugment transform
      :param config_str: String defining configuration of auto augmentation.
      Consists of multiple sections separated by dashes ('-'). The first
      section defines the AutoAugment policy (one of 'v0', 'v0r', 'original',
      'originalr').
      The remaining sections, not order sepecific determine
          'mstd' -  float std deviation of magnitude noise applied
      Ex 'original-mstd0.5' results in AutoAugment with original policy, magnitude_std 0.5
      :param hparams: Other hparams (kwargs) for the AutoAugmentation scheme
      :return: A PyTorch compatible Transform
      """
      config = config_str.split("-")
      policy_name = config[0]
      config = config[1:]
      for c in config:
          cs = re.split(r"(\d.*)", c)
          if len(cs) < 2:
              continue
          key, val = cs[:2]
          if key == "mstd":
              # noise param injected via hparams for now
              hparams.setdefault("magnitude_std", float(val))
          else:
              assert (
                  AssertionError
              ), "Unknown AutoAugment config section"
      aa_policy = auto_augment_policy(policy_name, hparams=hparams)
      return AutoAugment(aa_policy)
  
imports: import math
  import random
  import re
  import numpy
  import PIL
  from classy_vision.dataset.transforms import register_transform
  from classy_vision.dataset.transforms.classy_transform import ClassyTransform
  from PIL import Image, ImageEnhance, ImageOps
label_desc: Use image node_id 38 for calling a global function with example usage: auto_augment_transform(config_str, hparams) and returns: AutoAugment