code: def test_mask_only(self, device, dtype):
      torch.manual_seed(22)
  
      mask = torch.zeros(2, 3, 3, device=device, dtype=dtype)
      mask[0, 0:2, 0:2] = 1
      mask[1, 1:2, 1:2] = 2
  
      f = RandomTransplantation(
          p=1, excluded_labels=[0], data_keys=["mask"]
      )
      mask_out = f(mask)
  
      mask_out_expected = torch.tensor(
          [
              [[1, 1, 0], [1, 2, 0], [0, 0, 0]],
              [[1, 1, 0], [1, 1, 0], [0, 0, 0]],
          ],
          device=device,
          dtype=dtype,
      )
  
      self.assert_close(mask_out, mask_out_expected)
  
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 2 for calling the TestRandomTransplantation obj's underlying member method code with example usage: obj.test_mask_only(device, dtype) without return types