code: def _insert_outputs(
      self,
      flat_sample: List[Any],
      output_images: List[torch.Tensor],
      output_targets: List[Dict[str, Any]],
  ) -> None:
      c0, c1, c2, c3 = 0, 0, 0, 0
      for i, obj in enumerate(flat_sample):
          if isinstance(obj, tv_tensors.Image):
              flat_sample[i] = tv_tensors.wrap(
                  output_images[c0], like=obj
              )
              c0 += 1
          elif isinstance(obj, PIL.Image.Image):
              flat_sample[i] = F.to_pil_image(output_images[c0])
              c0 += 1
          elif is_pure_tensor(obj):
              flat_sample[i] = output_images[c0]
              c0 += 1
          elif isinstance(obj, tv_tensors.BoundingBoxes):
              flat_sample[i] = tv_tensors.wrap(
                  output_targets[c1]["boxes"], like=obj
              )
              c1 += 1
          elif isinstance(obj, tv_tensors.Mask):
              flat_sample[i] = tv_tensors.wrap(
                  output_targets[c2]["masks"], like=obj
              )
              c2 += 1
          elif isinstance(
              obj,
              (proto_tv_tensors.Label, proto_tv_tensors.OneHotLabel),
          ):
              flat_sample[i] = tv_tensors.wrap(
                  output_targets[c3]["labels"], like=obj
              )
              c3 += 1
  
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 4 for calling the SimpleCopyPaste obj's underlying member method code with example usage: obj._insert_outputs(flat_sample, output_images, output_targets) without return types