code: def test_gradcheck(self, device):
      batch_size, channels, height, width = 1, 1, 21, 21
      patches = torch.rand(
          batch_size, channels, height, width, device=device
      ).float()
      patches = utils.tensor_to_gradcheck_var(patches)  # to var
      laf = torch.ones(batch_size, 2, 2, 3, device=device).float()
      laf[:, :, 0, 1] = 0
      laf[:, :, 1, 0] = 0
      laf = utils.tensor_to_gradcheck_var(laf)  # to var
      assert gradcheck(
          LAFOrienter(8).to(device),
          (laf, patches),
          raise_exception=True,
          rtol=1e-3,
          atol=1e-3,
          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 TestLAFOrienter obj's underlying member method code with example usage: obj.test_gradcheck(device) without return types