code: def forward(
      self, left_image: Tensor, right_image: Tensor
  ) -> Tuple[Tensor, Tensor]:
      def _process_image(img: PIL.Image.Image) -> Tensor:
          if not isinstance(img, Tensor):
              img = F.pil_to_tensor(img)
          if self.resize_size is not None:
              # We hard-code antialias=False to preserve results after we changed
              # its default from None to True (see
              # https://github.com/pytorch/vision/pull/7160)
              # TODO: we could re-train the stereo models with antialias=True?
              img = F.resize(
                  img,
                  self.resize_size,
                  interpolation=self.interpolation,
                  antialias=False,
              )
          if self.use_gray_scale is True:
              img = F.rgb_to_grayscale(img)
          img = F.convert_image_dtype(img, torch.float)
          img = F.normalize(img, mean=self.mean, std=self.std)
          img = img.contiguous()
          return img
  
      left_image = _process_image(left_image)
      right_image = _process_image(right_image)
      return left_image, right_image
  
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 2 for calling the StereoMatching obj's underlying member method code with example usage: obj.forward(left_image, right_image) and returns: left_image,  right_image, img