code: def forward(
      self, image: Tensor, target: Optional[Dict[str, Tensor]] = None
  ) -> Tuple[Tensor, Optional[Dict[str, Tensor]]]:
      _, orig_height, orig_width = F.get_dimensions(image)
  
      min_size = self.min_size[
          torch.randint(len(self.min_size), (1,)).item()
      ]
      r = min(
          min_size / min(orig_height, orig_width),
          self.max_size / max(orig_height, orig_width),
      )
  
      new_width = int(orig_width * r)
      new_height = int(orig_height * r)
  
      image = F.resize(
          image,
          [new_height, new_width],
          interpolation=self.interpolation,
      )
  
      if target is not None:
          target["boxes"][:, 0::2] *= new_width / orig_width
          target["boxes"][:, 1::2] *= new_height / orig_height
          if "masks" in target:
              target["masks"] = F.resize(
                  target["masks"],
                  [new_height, new_width],
                  interpolation=InterpolationMode.NEAREST,
              )
  
      return image, target
  
imports: from typing import Dict, List, Optional, Tuple, Union
  import torch
  import torchvision
  from torch import nn, Tensor
  from torchvision import ops
  from torchvision.transforms import functional, InterpolationMode, transforms
label_desc: Use image node_id 2 for calling the RandomShortestSize obj's underlying member method code with example usage: obj.forward(image, target) and returns: image,  target