code: def dboxes300_coco():
      figsize = 300  # 输入网络的图像大小
      feat_size = [38, 19, 10, 5, 3, 1]  # 每个预测层的feature map尺寸
      steps = [8, 16, 32, 64, 100, 300]  # 每个特征层上的一个cell在原图上的跨度
      # use the scales here: https://github.com/amdegroot/ssd.pytorch/blob/master/data/config.py
      scales = [
          21,
          45,
          99,
          153,
          207,
          261,
          315,
      ]  # 每个特征层上预测的default box的scale
      aspect_ratios = [
          [2],
          [2, 3],
          [2, 3],
          [2, 3],
          [2],
          [2],
      ]  # 每个预测特征层上预测的default box的ratios
      dboxes = DefaultBoxes(
          figsize, feat_size, steps, scales, aspect_ratios
      )
      return dboxes
  
imports: from math import sqrt
  import itertools
  import torch
  import torch.nn.functional
  from torch.jit.annotations import Tuple, List
  from torch import nn, Tensor
  import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: dboxes300_coco() and returns: dboxes