class_docstr: The default image transform with data augmentation.
  
  It is often useful for training models on Imagenet. It sequentially resizes
  the image into a random scale, takes a random spatial cropping, randomly flips
  the image horizontally, transforms PIL image data into a torch.Tensor and
  normalizes the pixel values by mean subtraction and standard deviation division.
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docstr: Callable function which applies the tranform to the input image.
  
  Args:
      image: input image that will undergo the transform
docstr_tok: ["Callable","function","which","applies","the","tranform","to","the","input","image",".","Args",":","image",":","input","image","that","will","undergo","the","transform"]
code: def __call__(self, img):
      """Callable function which applies the tranform to the input image.
  
      Args:
          image: input image that will undergo the transform
  
      """
      return self.transform(img)
  
imports: import collections
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import torchvision.transforms
  from .None import build_transforms, ClassyTransform, register_transform
label_desc: Use image node_id 2 for calling the ImagenetAugmentTransform obj's underlying member method code with example usage: obj.__call__(img) and returns: self