code: def test_gradcheck(self, device):
      torch.manual_seed(22)
      image = torch.rand(1, 3, 2, 2, device=device, dtype=torch.float64)
      mask = torch.randint(
          0, 2, (1, 2, 2), device=device, dtype=torch.float64
      )
  
      image = tensor_to_gradcheck_var(image)  # to var
      mask = tensor_to_gradcheck_var(mask)  # to var
  
      assert self.gradcheck(
          RandomTransplantation(p=1.0),
          (image, mask),
          raise_exception=True,
          fast_mode=True,
      )
  
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 9 for calling the TestRandomTransplantation obj's underlying member method code with example usage: obj.test_gradcheck(device) without return types