code: def test_sequential(self, n_spatial, device, dtype):
      torch.manual_seed(22)
      spatial_dimensions = [10] * n_spatial
      image = torch.rand(
          4, 3, *spatial_dimensions, device=device, dtype=dtype
      )
      mask = torch.randint(
          0, 2, (4, *spatial_dimensions), device=device, dtype=dtype
      )
  
      if n_spatial == 2:
          f = RandomTransplantation(p=1)
      elif n_spatial == 3:
          f = RandomTransplantation3D(p=1)
      else:
          raise ValueError(
              "n_spatial must be 2 or 3 since AugmentationSequential only supports 2D and 3D input"
          )
  
      torch.manual_seed(22)
      image_out, mask_out = f(image, mask)
  
      torch.manual_seed(22)
      image_out2, mask_out2 = AugmentationSequential(f)(
          image, mask, data_keys=["image", "mask"]
      )
  
      self.assert_close(image_out, image_out2)
      self.assert_close(mask_out, mask_out2)
  
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 7 for calling the TestRandomTransplantation obj's underlying member method code with example usage: obj.test_sequential(n_spatial, device, dtype) without return types