class_docstr: Module, which computes SIFT descriptor densely over the image.
  
  Args:
      num_ang_bins: Number of angular bins. (8 is default)
      num_spatial_bins: Number of spatial bins per descriptor (4 is default).
  You might want to set odd number and relevant padding to keep feature map size
      spatial_bin_size: Size of a spatial bin in pixels (4 is default)
      clipval: clipping value to reduce single-bin dominance
      rootsift: (bool) if True, RootSIFT (Arandjelović et. al, 2012) is computed
      stride: default 1
      padding: default 0
  
  Returns:
      Tensor: DenseSIFT descriptor of the image
  
  Shape:
      - Input: (B, 1, H, W)
      - Output: (B, num_ang_bins * num_spatial_bins ** 2, (H+padding)/stride, (W+padding)/stride)
  
  Examples::
      >>> input =  torch.rand(2, 1, 200, 300)
      >>> SIFT = DenseSIFTDescriptor()
      >>> descs = SIFT(input) # 2x128x194x294
code: def get_pooling_kernel(self) -> Tensor:
      return self.bin_pooling_kernel.weight.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 3 for calling the DenseSIFTDescriptor obj's underlying member method code with example usage: obj.get_pooling_kernel() and returns: self