code: def forward(
      self, images: List[torch.Tensor], targets: List[Dict[str, Tensor]]
  ) -> Tuple[List[torch.Tensor], List[Dict[str, Tensor]]]:
      torch._assert(
          isinstance(images, (list, tuple))
          and all([isinstance(v, torch.Tensor) for v in images]),
          "images should be a list of tensors",
      )
      torch._assert(
          isinstance(targets, (list, tuple))
          and len(images) == len(targets),
          "targets should be a list of the same size as images",
      )
      for target in targets:
          # Can not check for instance type dict with inside torch.jit.script
          # torch._assert(isinstance(target, dict), "targets item should be a dict")
          for k in ["masks", "boxes", "labels"]:
              torch._assert(
                  k in target, f"Key {k} should be present in targets"
              )
              torch._assert(
                  isinstance(target[k], torch.Tensor),
                  f"Value for the key {k} should be a tensor",
              )
  
      # images = [t1, t2, ..., tN]
      # Let's define paste_images as shifted list of input images
      # paste_images = [t2, t3, ..., tN, t1]
      # FYI: in TF they mix data on the dataset level
      images_rolled = images[-1:] + images[:-1]
      targets_rolled = targets[-1:] + targets[:-1]
  
      output_images: List[torch.Tensor] = []
      output_targets: List[Dict[str, Tensor]] = []
  
      for image, target, paste_image, paste_target in zip(
          images, targets, images_rolled, targets_rolled
      ):
          output_image, output_data = _copy_paste(
              image,
              target,
              paste_image,
              paste_target,
              blending=self.blending,
              resize_interpolation=self.resize_interpolation,
          )
          output_images.append(output_image)
          output_targets.append(output_data)
  
      return output_images, output_targets
  
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 SimpleCopyPaste obj's underlying member method code with example usage: obj.forward(images, targets) and returns: output_images,  output_targets