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.
docstr: Get parameters for autoaugment transformation
  
  Returns:
      params required by the autoaugment transformation
docstr_tok: ["Get","parameters","for","autoaugment","transformation","Returns",":","params","required","by","the","autoaugment","transformation"]
code: def get_params(transform_num: int) -> Tuple[int, Tensor, Tensor]:
      """Get parameters for autoaugment transformation
  
      Returns:
          params required by the autoaugment transformation
      """
      policy_id = int(torch.randint(transform_num, (1,)).item())
      probs = torch.rand((2,))
      signs = torch.randint(2, (2,))
  
      return policy_id, probs, signs
  
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 4 for calling the AutoAugment obj's underlying member method code with example usage: obj.get_params(transform_num) and returns: policy_id,  probs,  signs