class_docstr: Create a AutoAugment transform. This autoaugment differs from the
  torchvision implementation by allowing variability in the augmentation
  intensity.
  ":param policy_name: String. One of 'v0', 'v0r', 'original', 'originalr'.
  One of a set of learned augmentation sequences.
  :param magnitude_std: standard deviation of magnitude. If > 0, introduces
  random variability in the augmentation magnitude.
  :kwargs: Other params for the AutoAugmentation scheme. See RandAugment
  class above, or AugmentOp class in ClassyVision. Probability and
  intensity are overwritten because they're determined by the learned
  AutoAugment policy.
code: def __call__(self, img):
      sub_policy = random.choice(self.policy)
      for op in sub_policy:
          img = op(img)
      return img
  
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 2 for calling the AutoAugment obj's underlying member method code with example usage: obj.__call__(img) and returns: img