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 forward(self, input: Tensor) -> Tensor:
      KORNIA_CHECK_SHAPE(input, ["B", "1", "H", "W"])
  
      B, CH, W, H = input.size()
      self.bin_pooling_kernel = self.bin_pooling_kernel.to(
          input.dtype
      ).to(input.device)
      self.PoolingConv = self.PoolingConv.to(input.dtype).to(
          input.device
      )
      grads = spatial_gradient(input, "diff")
      # unpack the edges
      gx = grads[:, :, 0]
      gy = grads[:, :, 1]
      mag = torch.sqrt(gx * gx + gy * gy + self.eps)
      ori = torch.atan2(gy, gx + self.eps) + 2.0 * pi
      o_big = float(self.num_ang_bins) * ori / (2.0 * pi)
  
      bo0_big_ = torch.floor(o_big)
      wo1_big_ = o_big - bo0_big_
      bo0_big = bo0_big_ % self.num_ang_bins
      bo1_big = (bo0_big + 1) % self.num_ang_bins
      wo0_big = (1.0 - wo1_big_) * mag
      wo1_big = wo1_big_ * mag
      ang_bins = concatenate(
          [
              self.bin_pooling_kernel(
                  (bo0_big == i).to(input.dtype) * wo0_big
                  + (bo1_big == i).to(input.dtype) * wo1_big
              )
              for i in range(0, self.num_ang_bins)
          ],
          1,
      )
  
      out_no_norm = self.PoolingConv(ang_bins)
      out = normalize(out_no_norm, dim=1, p=2).clamp_(
          0, float(self.clipval)
      )
      out = normalize(out, dim=1, p=2)
      if self.rootsift:
          out = torch.sqrt(normalize(out, p=1) + self.eps)
      return out
  
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 DenseSIFTDescriptor obj's underlying member method code with example usage: obj.forward(input) and returns: out