code: def test_real_keynet(self, device, dtype, data):
      torch.random.manual_seed(0)
      # This is not unit test, but that is quite good integration test
      matcher = LocalFeatureMatcher(
          KeyNetHardNet(500), DescriptorMatcher("snn", 0.9)
      ).to(device, dtype)
      ransac = RANSAC("homography", 1.0, 1024, 5).to(device, dtype)
      data_dev = utils.dict_to(data, device, dtype)
      pts_src = data_dev["pts0"]
      pts_dst = data_dev["pts1"]
      with torch.no_grad():
          out = matcher(data_dev)
      homography, inliers = ransac(out["keypoints0"], out["keypoints1"])
      assert inliers.sum().item() > 50  # we have enough inliers
      # Reprojection error of 5px is OK
      assert_close(
          transform_points(homography[None], pts_src[None]),
          pts_dst[None],
          rtol=5e-2,
          atol=5,
      )
  
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 7 for calling the TestLocalFeatureMatcher obj's underlying member method code with example usage: obj.test_real_keynet(device, dtype, data) without return types