code: def test_smoke(self, device, dtype):
      f = RandomJigsaw(data_keys=["input"])
      repr = "RandomJigsaw(grid=(4, 4), p=0.5, p_batch=1.0, same_on_batch=False, grid=(4, 4))"
      assert str(f) == repr
  
      # Test square and non-square images.
      f = RandomJigsaw(grid=(2, 2), p=1.0, data_keys=["input"])
      input = torch.arange(64, device=device, dtype=dtype).reshape(
          2, 1, 4, 8
      )
      f(input)
      input = torch.arange(64, device=device, dtype=dtype).reshape(
          2, 1, 8, 4
      )
      f(input)
      input = torch.arange(32, device=device, dtype=dtype).reshape(
          2, 1, 4, 4
      )
      f(input)
  
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 1 for calling the TestRandomJigsaw obj's underlying member method code with example usage: obj.test_smoke(device, dtype) without return types