code: def test_gradcheck(self, device):
      matcher = LocalFeatureMatcher(
          SIFTFeature(5), DescriptorMatcher("nn", 1.0)
      ).to(device)
      patches = torch.rand(1, 1, 32, 32, device=device)
      patches05 = resize(patches, (48, 48))
      patches = utils.tensor_to_gradcheck_var(patches)  # to var
      patches05 = utils.tensor_to_gradcheck_var(patches05)  # to var
  
      def proxy_forward(x, y):
          return matcher({"image0": x, "image1": y})["keypoints0"]
  
      assert gradcheck(
          proxy_forward,
          (patches, patches05),
          eps=1e-4,
          atol=1e-4,
          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 3 for calling the TestLocalFeatureMatcher obj's underlying member method code with example usage: obj.test_gradcheck(device) and returns: matcher