code: def test__copy_paste(self, label_type):
      image = 2 * torch.ones(3, 32, 32)
      masks = torch.zeros(2, 32, 32)
      masks[0, 3:9, 2:8] = 1
      masks[1, 20:30, 20:30] = 1
      labels = torch.tensor([1, 2])
      blending = True
      resize_interpolation = InterpolationMode.BILINEAR
      antialias = None
      if label_type == tv_tensors.OneHotLabel:
          labels = torch.nn.functional.one_hot(labels, num_classes=5)
      target = {
          "boxes": BoundingBoxes(
              torch.tensor(
                  [[2.0, 3.0, 8.0, 9.0], [20.0, 20.0, 30.0, 30.0]]
              ),
              format="XYXY",
              canvas_size=(32, 32),
          ),
          "masks": Mask(masks),
          "labels": label_type(labels),
      }
  
      paste_image = 10 * torch.ones(3, 32, 32)
      paste_masks = torch.zeros(2, 32, 32)
      paste_masks[0, 13:19, 12:18] = 1
      paste_masks[1, 15:19, 1:8] = 1
      paste_labels = torch.tensor([3, 4])
      if label_type == tv_tensors.OneHotLabel:
          paste_labels = torch.nn.functional.one_hot(
              paste_labels, num_classes=5
          )
      paste_target = {
          "boxes": BoundingBoxes(
              torch.tensor(
                  [[12.0, 13.0, 19.0, 18.0], [1.0, 15.0, 8.0, 19.0]]
              ),
              format="XYXY",
              canvas_size=(32, 32),
          ),
          "masks": Mask(paste_masks),
          "labels": label_type(paste_labels),
      }
  
      transform = transforms.SimpleCopyPaste()
      random_selection = torch.tensor([0, 1])
      output_image, output_target = transform._copy_paste(
          image,
          target,
          paste_image,
          paste_target,
          random_selection,
          blending,
          resize_interpolation,
          antialias,
      )
  
      assert output_image.unique().tolist() == [2, 10]
      assert output_target["boxes"].shape == (4, 4)
      torch.testing.assert_close(
          output_target["boxes"][:2, :], target["boxes"]
      )
      torch.testing.assert_close(
          output_target["boxes"][2:, :], paste_target["boxes"]
      )
  
      expected_labels = torch.tensor([1, 2, 3, 4])
      if label_type == tv_tensors.OneHotLabel:
          expected_labels = torch.nn.functional.one_hot(
              expected_labels, num_classes=5
          )
      torch.testing.assert_close(
          output_target["labels"], label_type(expected_labels)
      )
  
      assert output_target["masks"].shape == (4, 32, 32)
      torch.testing.assert_close(
          output_target["masks"][:2, :], target["masks"]
      )
      torch.testing.assert_close(
          output_target["masks"][2:, :], paste_target["masks"]
      )
  
imports: import collections.abc
  import re
  import PIL.Image
  import pytest
  import torch
  from common_utils import assert_equal, make_bounding_boxes, make_detection_masks, make_image, make_video
  from torchvision.prototype import transforms, tv_tensors
  from torchvision.transforms.v2._utils import check_type, is_pure_tensor
  from torchvision.transforms.v2.functional import clamp_bounding_boxes, InterpolationMode, pil_to_tensor, to_pil_image
  from torchvision.tv_tensors import BoundingBoxes, BoundingBoxFormat, Image, Mask, Video
  import importlib.machinery
  import importlib.util
  from pathlib import Path
label_desc: Use image node_id 4 for calling the TestSimpleCopyPaste obj's underlying member method code with example usage: obj.test__copy_paste(label_type) without return types