code: def __init__(
      self,
      *,
      use_gray_scale: bool = False,
      resize_size: Optional[Tuple[int, ...]],
      mean: Tuple[float, ...] = (0.5, 0.5, 0.5),
      std: Tuple[float, ...] = (0.5, 0.5, 0.5),
      interpolation: Union[
          InterpolationMode, int
      ] = InterpolationMode.BILINEAR,
  ) -> None:
      super().__init__()
  
      # pacify mypy
      self.resize_size: Union[None, List]
  
      if resize_size is not None:
          self.resize_size = list(resize_size)
      else:
          self.resize_size = None
  
      self.mean = list(mean)
      self.std = list(std)
      self.interpolation = _check_interpolation(interpolation)
      self.use_gray_scale = use_gray_scale
  
file_path: vision/torchvision/prototype/transforms/_presets.py
imports: from typing import List, Optional, Tuple, Union
  import PIL.Image
  import torch
  from torch import Tensor
  from torchvision.transforms.v2 import functional, InterpolationMode
  from torchvision.transforms.v2.functional._geometry import _check_interpolation
label_desc: Use image node_id 1 to create a new StereoMatching object from inherited base classes: torch.nn with example: obj = StereoMatching()