code: def test_gradcheck(self, device):
      batch_size, channels, height, width = 2, 1, 32, 32
      patches = torch.rand(
          batch_size, channels, height, width, device=device
      )
      patches = utils.tensor_to_gradcheck_var(patches)  # to var
      ori = OriNet().to(device=device, dtype=patches.dtype)
      assert gradcheck(ori, (patches,), raise_exception=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 6 for calling the TestOriNet obj's underlying member method code with example usage: obj.test_gradcheck(device) without return types