code: def test_jit_script(self, device, dtype, align_corners, padding_mode):
      offset = 1.0
      h, w = 3, 4
      aff_ab = torch.eye(2, 3, device=device, dtype=dtype)[None]
      aff_ab[..., -1] += offset
  
      img_b = torch.arange(
          float(3 * h * w), device=device, dtype=dtype
      ).view(1, 3, h, w)
      net = DummyNNModule(3, 4, align_corners, padding_mode)
      script_net = torch.jit.script(net)
  
      assert isinstance(script_net, torch.jit.ScriptModule)
      assert_close(script_net(img_b, aff_ab), net(img_b, aff_ab))
  
imports: import sys
  import pytest
  import torch
  from torch.autograd import gradcheck
  import kornia
  import kornia.testing
  from kornia.testing import assert_close
  from kornia.utils._compat import torch_version, torch_version_lt
  from kornia.utils.helpers import _torch_inverse_cast
label_desc: Use image node_id 10 for calling the TestWarpAffine obj's underlying member method code with example usage: obj.test_jit_script(device, dtype, align_corners, padding_mode) without return types