class_docstr: Randomly choose one of the best 24 Sub-policies on ImageNet.
  
  Example:
  >>> policy = ImageNetPolicy()
  >>> transformed = policy(image)
  
  Example as a PyTorch Transform:
  >>> transform=transforms.Compose([
  >>>     transforms.Resize(256),
  >>>     ImageNetPolicy(),
  >>>     transforms.ToTensor()])
class_docstr_tok: ["Randomly","choose","one","of","the","best","24","Sub-policies","on","ImageNet",".","Example",":",">",">",">","policy","=","ImageNetPolicy","(",")",">",">",">","transformed","=","policy","(","image",")","Example","as","a","PyTorch","Transform",":",">",">",">","transform=transforms.Compose","(","[",">",">",">","transforms.Resize","(","256",")",",",">",">",">","ImageNetPolicy","(",")",",",">",">",">","transforms.ToTensor","(",")","]",")"]
code: def __call__(self, img: Any) -> Any:
      policy_idx = random.randint(0, len(self.policies) - 1)
      return self.policies[policy_idx](img)
  
imports: import random
  from enum import auto, Enum
  from functools import partial
  from typing import Any, Callable, NamedTuple, Sequence, Tuple
  import numpy
  from classy_vision.dataset.transforms import ClassyTransform, register_transform
  from PIL import Image, ImageEnhance, ImageOps
label_desc: Use image node_id 2 for calling the ImagenetAutoAugment obj's underlying member method code with example usage: obj.__call__(img) and returns: self