code: def __call__(self) -> torch.Tensor:
      anchors: List[float] = []
      for k, f in enumerate(self.feature_maps):
          min_sizes: List[int] = self.min_sizes[k]
          # NOTE: the nested loop it's to make torchscript happy
          for i in range(f[0]):
              for j in 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]
  
                      cx = (
                          (j + 0.5) * self.steps[k] / self.image_size[1]
                      )
                      cy = (
                          (i + 0.5) * self.steps[k] / self.image_size[0]
                      )
                      anchors += [cx, cy, s_kx, s_ky]
      # back to torch land
      output = torch.tensor(
          anchors, device=self.device, dtype=self.dtype
      ).view(-1, 4)
      if self.clip:
          output = output.clamp(max=1, min=0)
      return output
  
imports: import math
  from enum import Enum
  from typing import Dict, List, Optional, Tuple
  import torch
  import torch.nn.functional
  from torch import nn
  from kornia.geometry.bbox import nms
  from kornia.utils.helpers import map_location_to_cpu
label_desc: Use image node_id 3 for calling the _PriorBox obj's underlying member method code with example usage: obj.__call__() and returns: output