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)
      img = utils.tensor_to_gradcheck_var(img)  # to var
      local_feature = LocalFeature(
          ScaleSpaceDetector(2), LAFDescriptor(SIFTDescriptor(PS), PS)
      ).to(device, img.dtype)
      assert gradcheck(
          local_feature,
          img,
          eps=1e-4,
          atol=1e-4,
          nondet_tol=1e-8,
          raise_exception=True,
          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 4 for calling the TestLocalFeature obj's underlying member method code with example usage: obj.test_gradcheck(device) without return types