class_docstr: RandAugment data augmentation method based on
  `"RandAugment: Practical automated data augmentation with a reduced search space"
  <https://arxiv.org/abs/1909.13719>`_.
  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:
      num_ops (int): Number of augmentation transformations to apply sequentially.
      magnitude (int): Magnitude for all the transformations.
      num_magnitude_bins (int): The number of different magnitude values.
      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 _augmentation_space(
      self, num_bins: int, image_size: Tuple[int, int]
  ) -> Dict[str, Tuple[Tensor, bool]]:
      return {
          # op_name: (magnitudes, signed)
          "Identity": (torch.tensor(0.0), False),
          "ShearX": (torch.linspace(0.0, 0.3, num_bins), True),
          "ShearY": (torch.linspace(0.0, 0.3, num_bins), True),
          "TranslateX": (
              torch.linspace(
                  0.0, 150.0 / 331.0 * image_size[1], num_bins
              ),
              True,
          ),
          "TranslateY": (
              torch.linspace(
                  0.0, 150.0 / 331.0 * image_size[0], num_bins
              ),
              True,
          ),
          "Rotate": (torch.linspace(0.0, 30.0, num_bins), True),
          "Brightness": (torch.linspace(0.0, 0.9, num_bins), True),
          "Color": (torch.linspace(0.0, 0.9, num_bins), True),
          "Contrast": (torch.linspace(0.0, 0.9, num_bins), True),
          "Sharpness": (torch.linspace(0.0, 0.9, num_bins), True),
          "Posterize": (
              8
              - (torch.arange(num_bins) / ((num_bins - 1) / 4))
              .round()
              .int(),
              False,
          ),
          "Solarize": (torch.linspace(255.0, 0.0, num_bins), False),
          "AutoContrast": (torch.tensor(0.0), False),
          "Equalize": (torch.tensor(0.0), False),
      }
  
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 2 for calling the RandAugment obj's underlying member method code with example usage: obj._augmentation_space(num_bins, image_size) and returns: dict