class_docstr: RandAugment data augmentation method based on
  `"RandAugment: Practical automated data augmentation with a reduced search space"
  <https://arxiv.org/abs/1909.13719>`_.
  
  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:
      num_ops (int, optional): Number of augmentation transformations to apply sequentially.
      magnitude (int, optional): Magnitude for all the transformations.
      num_magnitude_bins (int, optional): The number of different magnitude values.
      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,
      num_ops: int = 2,
      magnitude: int = 9,
      num_magnitude_bins: int = 31,
      interpolation: Union[
          InterpolationMode, int
      ] = InterpolationMode.NEAREST,
      fill: Union[_FillType, Dict[Union[Type, str], _FillType]] = None,
  ) -> None:
      super().__init__(interpolation=interpolation, fill=fill)
      self.num_ops = num_ops
      self.magnitude = magnitude
      self.num_magnitude_bins = num_magnitude_bins
  
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 RandAugment object from inherited base classes: _AutoAugmentBase with example: obj = RandAugment(num_ops, magnitude, num_magnitude_bins, interpolation, fill)