code: def test_jit(self, device, dtype):
      B, C, H, W = 1, 1, 32, 32
      patches = torch.rand(B, C, H, W, device=device, dtype=dtype)
      patches2x = resize(patches, (48, 48))
      inputs = {"image0": patches, "image1": patches2x}
      model = (
          LocalFeatureMatcher(
              SIFTDescriptor(32), DescriptorMatcher("snn", 0.8)
          )
          .to(device)
          .eval()
      )
      model_jit = torch.jit.script(model)
  
      out = model(inputs)
      out_jit = model_jit(inputs)
      for k, v in out.items():
          assert_close(v, out_jit[k])
  
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 8 for calling the TestLocalFeatureMatcher obj's underlying member method code with example usage: obj.test_jit(device, dtype) without return types