code: def test_scale(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), 1.0
      ).to(device, dtype)
      local_feature2 = LocalFeature(
          det, LAFDescriptor(desc, PS), 2.0
      ).to(device, dtype)
      lafs, responses, descs = local_feature(img)
      lafs2, responses2, descs2 = local_feature2(img)
      assert_close(get_laf_center(lafs), get_laf_center(lafs2))
      assert_close(
          get_laf_orientation(lafs), get_laf_orientation(lafs2)
      )
      assert_close(2.0 * get_laf_scale(lafs), get_laf_scale(lafs2))
  
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 TestLocalFeature obj's underlying member method code with example usage: obj.test_scale(device, dtype) without return types