code: def test_repeating(self, wrapper, device, dtype):
      torch.manual_seed(22)
      image = torch.rand(4, 3, 10, 10, device=device, dtype=dtype)
      mask = torch.randint(
          0, 2, (4, 10, 10), device=device, dtype=dtype
      )
  
      f = wrapper(RandomTransplantation(p=0.5))
      image_out, mask_out = f(image, mask, data_keys=["input", "mask"])
      image_out_same, mask_out_same = f(
          image, mask, params=f._params, data_keys=["input", "mask"]
      )
      image_out_different, mask_out_different = f(
          image, mask, data_keys=["input", "mask"]
      )
  
      self.assert_close(image_out, image_out_same)
      self.assert_close(mask_out, mask_out_same)
      with pytest.raises(AssertionError):
          self.assert_close(image_out, image_out_different)
      with pytest.raises(AssertionError):
          self.assert_close(mask_out, mask_out_different)
  
imports: import copy
  import pytest
  import torch
  from kornia.augmentation import AugmentationSequential, RandomCutMixV2, RandomJigsaw, RandomMixUpV2, RandomMosaic, RandomTransplantation, RandomTransplantation3D
  from kornia.testing import BaseTester, assert_close, tensor_to_gradcheck_var
label_desc: Use image node_id 5 for calling the TestRandomTransplantation obj's underlying member method code with example usage: obj.test_repeating(wrapper, device, dtype) without return types