code: def test_gradcheck(self, device):
      batch_size, channels, height, width = 1, 1, 13, 13
      ori = PatchDominantGradientOrientation(width).to(device)
      patches = torch.rand(
          batch_size, channels, height, width, device=device
      )
      patches = utils.tensor_to_gradcheck_var(patches)  # to var
      assert gradcheck(
          ori, (patches,), raise_exception=True, fast_mode=True
      )
  
imports: import pytest
  import torch
  from torch.autograd import gradcheck
  import kornia.testing
  from kornia.feature.orientation import LAFOrienter, OriNet, PassLAF, PatchDominantGradientOrientation
  from kornia.geometry.conversions import rad2deg
  from kornia.testing import assert_close
label_desc: Use image node_id 5 for calling the TestPatchDominantGradientOrientation obj's underlying member method code with example usage: obj.test_gradcheck(device) without return types