class_docstr: AugMix data augmentation method based on
  `"AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty" <https://arxiv.org/abs/1912.02781>`_.
  
  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:
      severity (int, optional): The severity of base augmentation operators. Default is ``3``.
      mixture_width (int, optional): The number of augmentation chains. Default is ``3``.
      chain_depth (int, optional): The depth of augmentation chains. A negative value denotes stochastic depth sampled from the interval [1, 3].
          Default is ``-1``.
      alpha (float, optional): The hyperparameter for the probability distributions. Default is ``1.0``.
      all_ops (bool, optional): Use all operations (including brightness, contrast, color and sharpness). Default is ``True``.
      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 _sample_dirichlet(self, params: torch.Tensor) -> torch.Tensor:
      # Must be on a separate method so that we can overwrite it in tests.
      return torch._sample_dirichlet(params)
  
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 2 for calling the AugMix obj's underlying member method code with example usage: obj._sample_dirichlet(params) and returns: torch