code: def forward(self, *inputs: Any) -> Any:
      flat_inputs, spec = tree_flatten(
          inputs if len(inputs) > 1 else inputs[0]
      )
  
      images, targets = self._extract_image_targets(flat_inputs)
  
      # 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, output_targets = [], []
  
      for image, target, paste_image, paste_target in zip(
          images, targets, images_rolled, targets_rolled
      ):
          # Random paste targets selection:
          num_masks = len(paste_target["masks"])
  
          if num_masks < 1:
              # Such degerante case with num_masks=0 can happen with LSJ
              # Let's just return (image, target)
              output_image, output_target = image, target
          else:
              random_selection = torch.randint(
                  0, num_masks, (num_masks,), device=paste_image.device
              )
              random_selection = torch.unique(random_selection)
  
              output_image, output_target = self._copy_paste(
                  image,
                  target,
                  paste_image,
                  paste_target,
                  random_selection=random_selection,
                  blending=self.blending,
                  resize_interpolation=self.resize_interpolation,
                  antialias=self.antialias,
              )
          output_images.append(output_image)
          output_targets.append(output_target)
  
      # Insert updated images and targets into input flat_sample
      self._insert_outputs(flat_inputs, output_images, output_targets)
  
      return tree_unflatten(flat_inputs, spec)
  
file_path: vision/torchvision/prototype/transforms/_augment.py
imports: from typing import Any, cast, Dict, List, Optional, Tuple, Union
  import PIL.Image
  import torch
  from torch.utils._pytree import tree_flatten, tree_unflatten
  from torchvision import tv_tensors
  from torchvision.ops import masks_to_boxes
  from torchvision.prototype import tv_tensors
  from torchvision.transforms.v2 import functional, InterpolationMode, Transform
  from torchvision.transforms.v2._utils import is_pure_tensor
  from torchvision.transforms.v2.functional._geometry import _check_interpolation
label_desc: Use image node_id 5 for calling the SimpleCopyPaste obj's underlying member method code with example usage: obj.forward() and returns: tree_unflatten