code: def get_laf_descriptors(
      img: Tensor,
      lafs: Tensor,
      patch_descriptor: Module,
      patch_size: int = 32,
      grayscale_descriptor: bool = True,
  ) -> Tensor:
      r"""Function to get local descriptors, corresponding to LAFs (keypoints).
  
      Args:
          img: image features with shape :math:`(B,C,H,W)`.
          lafs: local affine frames :math:`(B,N,2,3)`.
          patch_descriptor: patch descriptor module, e.g. :class:`~kornia.feature.SIFTDescriptor`
              or :class:`~kornia.feature.HardNet`.
          patch_size: patch size in pixels, which descriptor expects.
          grayscale_descriptor: True if ``patch_descriptor`` expects single-channel image.
  
      Returns:
          Local descriptors of shape :math:`(B,N,D)` where :math:`D` is descriptor size.
      """
      KORNIA_CHECK_LAF(lafs)
      patch_descriptor = patch_descriptor.to(img)
      patch_descriptor.eval()
  
      timg: Tensor = img
      if lafs.shape[1] == 0:
          warnings.warn(
              f"LAF contains no keypoints {lafs.shape}, returning empty tensor"
          )
          return torch.empty(lafs.shape[0], lafs.shape[1], 128)
      if grayscale_descriptor and img.size(1) == 3:
          timg = rgb_to_grayscale(img)
  
      patches: Tensor = extract_patches_from_pyramid(
          timg, lafs, patch_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 :)
      B, N, CH, H, W = patches.size()
      return patch_descriptor(patches.view(B * N, CH, H, W)).view(
          B, N, -1
      )
  
imports: import warnings
  from typing import ClassVar, Dict, List, Optional, Tuple
  import torch
  from kornia.color import rgb_to_grayscale
  from kornia.constants import pi
  from kornia.core import Device, Module, Tensor, concatenate, deg2rad
  from kornia.core.check import KORNIA_CHECK_LAF
  from kornia.geometry.subpix import ConvQuadInterp3d
  from kornia.geometry.transform import ScalePyramid
  from .affine_shape import LAFAffNetShapeEstimator
  from .hardnet import HardNet
  from .keynet import KeyNetDetector
  from .laf import extract_patches_from_pyramid, get_laf_center, get_laf_orientation, get_laf_scale, scale_laf
  from .lightglue import LightGlue
  from .matching import GeometryAwareDescriptorMatcher, _no_match
  from .orientation import LAFOrienter, OriNet, PassLAF
  from .responses import BlobDoG, BlobDoGSingle, BlobHessian, CornerGFTT
  from .scale_space_detector import Detector_config, MultiResolutionDetector, ScaleSpaceDetector, get_default_detector_config
  from .siftdesc import SIFTDescriptor
label_desc: Use image node_id 1 for calling a global function with example usage: get_laf_descriptors(img, lafs, patch_descriptor, patch_size, grayscale_descriptor) and returns: patch_descriptor, torch