class_docstr: Module which computes SIFT descriptors of given patches.
  
  Args:
      patch_size: Input patch size in pixels.
      num_ang_bins: Number of angular bins.
      num_spatial_bins: Number of spatial bins.
      clipval: clipping value to reduce single-bin dominance
      rootsift: if ``True``, RootSIFT (Arandjelović et. al, 2012) is computed.
  
  Returns:
      SIFT descriptor of the patches with shape.
  
  Shape:
      - Input: :math:`(B, 1, \text{num_spatial_bins}, \text{num_spatial_bins})`
      - Output: :math:`(B, \text{num_ang_bins * num_spatial_bins ** 2})`
  
  Example:
      >>> input = torch.rand(23, 1, 32, 32)
      >>> SIFT = SIFTDescriptor(32, 8, 4)
      >>> descs = SIFT(input) # 23x128
code: def get_weighting_kernel(self) -> Tensor:
      return self.gk.detach()
  
imports: import math
  from typing import Tuple
  import torch
  from torch import nn
  from kornia.core import Module, Tensor, concatenate, eye, normalize
  from kornia.core.check import KORNIA_CHECK_SHAPE
  from kornia.filters import get_gaussian_kernel2d, spatial_gradient
  from kornia.geometry.conversions import pi
label_desc: Use image node_id 4 for calling the SIFTDescriptor obj's underlying member method code with example usage: obj.get_weighting_kernel() and returns: self