class_docstr: AutoAugment data augmentation method based on
  `"AutoAugment: Learning Augmentation Strategies from Data" <https://arxiv.org/pdf/1805.09501.pdf>`_.
  If the image is torch Tensor, it should be of type torch.uint8, and it is expected
  to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions.
  If img is PIL Image, it is expected to be in mode "L" or "RGB".
  
  Args:
      policy (AutoAugmentPolicy): Desired policy enum defined by
          :class:`torchvision.transforms.autoaugment.AutoAugmentPolicy`. Default is ``AutoAugmentPolicy.IMAGENET``.
      interpolation (InterpolationMode): Desired interpolation enum defined by
          :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``.
          If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
      fill (sequence or number, optional): Pixel fill value for the area outside the transformed
          image. If given a number, the value is used for all bands respectively.
code: def __repr__(self) -> str:
      return f"{self.__class__.__name__}(policy={self.policy}, fill={self.fill})"
  
imports: import math
  from enum import Enum
  from typing import Dict, List, Optional, Tuple
  import torch
  from torch import Tensor
  from .None import functional, InterpolationMode
label_desc: Use image node_id 6 for calling the AutoAugment obj's underlying member method code with example usage: obj.__repr__() and returns: self, str, self, str, self, str