class_docstr: AutoAugment data augmentation method based on
  `"AutoAugment: Learning Augmentation Strategies from Data" <https://arxiv.org/pdf/1805.09501.pdf>`_.
  
  This transformation works on images and videos only.
  
  If the input is :class:`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, optional): Desired policy enum defined by
          :class:`torchvision.transforms.autoaugment.AutoAugmentPolicy`. Default is ``AutoAugmentPolicy.IMAGENET``.
      interpolation (InterpolationMode, optional): 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 __init__(
      self,
      policy: AutoAugmentPolicy = AutoAugmentPolicy.IMAGENET,
      interpolation: Union[
          InterpolationMode, int
      ] = InterpolationMode.NEAREST,
      fill: Union[_FillType, Dict[Union[Type, str], _FillType]] = None,
  ) -> None:
      super().__init__(interpolation=interpolation, fill=fill)
      self.policy = policy
      self._policies = self._get_policies(policy)
  
imports: import math
  from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
  import PIL.Image
  import torch
  from torch.utils._pytree import tree_flatten, tree_unflatten, TreeSpec
  from torchvision import transforms, tv_tensors
  from torchvision.transforms import _functional_tensor
  from torchvision.transforms.v2 import AutoAugmentPolicy, functional, InterpolationMode, Transform
  from torchvision.transforms.v2.functional._geometry import _check_interpolation
  from torchvision.transforms.v2.functional._meta import get_size
  from torchvision.transforms.v2.functional._utils import _FillType, _FillTypeJIT
  from ._utils import _get_fill, _setup_fill_arg, check_type, is_pure_tensor
label_desc: Use image node_id 1 to create a new AutoAugment object from inherited base classes: _AutoAugmentBase with example: obj = AutoAugment(policy, interpolation, fill)