code: def test_numerical(self, device, dtype):
      torch.manual_seed(22)
      f = RandomJigsaw(grid=(2, 2), p=1.0, data_keys=["input"])
  
      input = torch.arange(32, device=device, dtype=dtype).reshape(
          2, 1, 4, 4
      )
  
      out_image = f(input)
  
      expected = torch.tensor(
          [
              [
                  [
                      [2.0, 3.0, 0.0, 1.0],
                      [6.0, 7.0, 4.0, 5.0],
                      [8.0, 9.0, 10.0, 11.0],
                      [12.0, 13.0, 14.0, 15.0],
                  ]
              ],
              [
                  [
                      [16.0, 17.0, 18.0, 19.0],
                      [20.0, 21.0, 22.0, 23.0],
                      [24.0, 25.0, 26.0, 27.0],
                      [28.0, 29.0, 30.0, 31.0],
                  ]
              ],
          ],
          device=device,
          dtype=dtype,
      )
  
      assert_close(out_image, expected, rtol=1e-4, atol=1e-4)
  
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 TestRandomJigsaw obj's underlying member method code with example usage: obj.test_numerical(device, dtype) without return types