class_docstr: PriorBox
  
  Anchor boxes utility for decoding the prior box.
  
  Args:
      cfg (`Dict`):
          retinaface configs.
      image_size (`Linst[int]`):
          input image size.
code: def forward(self):
      anchors = []
      for k, f in enumerate(self.feature_maps):
          min_sizes = self.min_sizes[k]
          for i, j in product(range(f[0]), range(f[1])):
              for min_size in min_sizes:
                  s_kx = min_size / self.image_size[1]
                  s_ky = min_size / self.image_size[0]
                  dense_cx = [
                      x * self.steps[k] / self.image_size[1]
                      for x in [j + 0.5]
                  ]
                  dense_cy = [
                      y * self.steps[k] / self.image_size[0]
                      for y in [i + 0.5]
                  ]
                  for cy, cx in product(dense_cy, dense_cx):
                      anchors += [cx, cy, s_kx, s_ky]
  
      # back to torch land
      output = torch.Tensor(anchors).view(-1, 4)
      if self.clip:
          output.clamp_(max=1, min=0)
      return output
  
imports: from itertools import product
  from typing import Dict, List
  from math import ceil
  import torch
label_desc: Use image node_id 2 for calling the PriorBox obj's underlying member method code with example usage: obj.forward() and returns: output