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