code: def test_gradcheck(self, device):
      B, C, H, W = 1, 1, 32, 32
      PS = 16
      img = torch.rand(B, C, H, W, device=device)
      centers = torch.tensor(
          [[H / 2.0, W / 2.0], [2.0 * H / 3.0, W / 2.0]], device=device
      ).view(1, 2, 2)
      scales = torch.tensor(
          [(H + W) / 5.0, (H + W) / 6.0], device=device
      ).view(1, 2, 1, 1)
      ori = torch.tensor([0.0, 30.0], device=device).view(1, 2, 1)
      lafs = kornia.feature.laf_from_center_scale_ori(
          centers, scales, ori
      )
      img = utils.tensor_to_gradcheck_var(img)  # to var
      lafs = utils.tensor_to_gradcheck_var(lafs)  # to var
  
      class _MeanPatch(nn.Module):
          def forward(self, inputs):
              return inputs.mean(dim=(2, 3))
  
      lafdesc = LAFDescriptor(_MeanPatch(), PS)
      assert gradcheck(
          lafdesc,
          (img, lafs),
          eps=1e-3,
          atol=1e-3,
          raise_exception=True,
          nondet_tol=1e-3,
          fast_mode=True,
      )
  
imports: import sys
  import pytest
  import torch
  from torch import nn
  from torch.autograd import gradcheck
  import kornia
  import kornia.testing
  from kornia.feature import DescriptorMatcher, GFTTAffNetHardNet, KeyNetHardNet, LAFDescriptor, LocalFeature, ScaleSpaceDetector, SIFTDescriptor, SIFTFeature, extract_patches_from_pyramid, get_laf_center, get_laf_descriptors, get_laf_orientation, get_laf_scale
  from kornia.feature.integrated import LocalFeatureMatcher
  from kornia.geometry import RANSAC, resize, transform_points
  from kornia.testing import assert_close
  from kornia.utils._compat import torch_version_le
label_desc: Use image node_id 3 for calling the TestLAFDescriptor obj's underlying member method code with example usage: obj.test_gradcheck(device) and returns: inputs