code: def test_p(self, p, same_on_batch, device, dtype):
      torch.manual_seed(76)
      f = RandomJigsaw(
          p=p, data_keys=["input"], same_on_batch=same_on_batch
      )
  
      input = torch.randn((12, 3, 256, 256), device=device, dtype=dtype)
  
      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 3 for calling the TestRandomJigsaw obj's underlying member method code with example usage: obj.test_p(p, same_on_batch, device, dtype) without return types