code: def test_same(self, device, dtype):
      B, C, H, W = 1, 1, 64, 64
      PS = 16
      img = torch.rand(B, C, H, W, device=device, dtype=dtype)
      det = ScaleSpaceDetector(10)
      desc = SIFTDescriptor(PS)
      local_feature = LocalFeature(det, LAFDescriptor(desc, PS)).to(
          device, dtype
      )
      lafs, responses, descs = local_feature(img)
      lafs1, responses1 = det(img)
      assert_close(lafs, lafs1)
      assert_close(responses, responses1)
      patches = extract_patches_from_pyramid(img, lafs1, PS)
      B1, N1, CH1, H1, W1 = patches.size()
      # Descriptor accepts standard tensor [B, CH, H, W], while patches are [B, N, CH, H, W] shape
      # So we need to reshape a bit :)
      descs1 = desc(patches.view(B1 * N1, CH1, H1, W1)).view(B1, N1, -1)
      assert_close(descs, descs1)
  
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 2 for calling the TestLocalFeature obj's underlying member method code with example usage: obj.test_same(device, dtype) without return types