text: import torch
  from torch.nn import Conv2d, Sequential, ModuleList, ReLU, BatchNorm2d
  from ..nn.vgg import vgg
  
  from .ssd import SSD
  from .predictor import Predictor
  from .config import vgg_ssd_config as config
  
  
  def create_vgg_ssd(num_classes, is_test=False, convert_to_boxes=True):
      vgg_config = [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'C', 512, 512, 512, 'M',
                    512, 512, 512]
      base_net = ModuleList(vgg(vgg_config))
  
      source_layer_indexes = [
          (23, BatchNorm2d(512)),
          len(base_net),
      ]
      extras = ModuleList([
          Sequential(
              Conv2d(in_channels=1024, out_channels=256, kernel_size=1),
              ReLU(),
              Conv2d(in_channels=256, out_channels=512, kernel_size=3, stride=2, padding=1),
              ReLU()
          ),
          Sequential(
              Conv2d(in_channels=512, out_channels=128, kernel_size=1),
              ReLU(),
              Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=2, padding=1),
              ReLU()
          ),
          Sequential(
              Conv2d(in_channels=256, out_channels=128, kernel_size=1),
              ReLU(),
              Conv2d(in_channels=128, out_channels=256, kernel_size=3),
              ReLU()
          ),
          Sequential(
              Conv2d(in_channels=256, out_channels=128, kernel_size=1),
              ReLU(),
              Conv2d(in_channels=128, out_channels=256, kernel_size=3),
              ReLU()
          )
      ])
  
      regression_headers = ModuleList([
          Conv2d(in_channels=512, out_channels=4 * 4, kernel_size=3, padding=1),
          Conv2d(in_channels=1024, out_channels=6 * 4, kernel_size=3, padding=1),
          Conv2d(in_channels=512, out_channels=6 * 4, kernel_size=3, padding=1),
          Conv2d(in_channels=256, out_channels=6 * 4, kernel_size=3, padding=1),
          Conv2d(in_channels=256, out_channels=4 * 4, kernel_size=3, padding=1),
          Conv2d(in_channels=256, out_channels=4 * 4, kernel_size=3, padding=1), # TODO: change to kernel_size=1, padding=0?
      ])
  
      classification_headers = ModuleList([
          Conv2d(in_channels=512, out_channels=4 * num_classes, kernel_size=3, padding=1),
          Conv2d(in_channels=1024, out_channels=6 * num_classes, kernel_size=3, padding=1),
          Conv2d(in_channels=512, out_channels=6 * num_classes, kernel_size=3, padding=1),
          Conv2d(in_channels=256, out_channels=6 * num_classes, kernel_size=3, padding=1),
          Conv2d(in_channels=256, out_channels=4 * num_classes, kernel_size=3, padding=1),
          Conv2d(in_channels=256, out_channels=4 * num_classes, kernel_size=3, padding=1), # TODO: change to kernel_size=1, padding=0?
      ])
  
      return SSD(num_classes, base_net, source_layer_indexes,
                 extras, classification_headers, regression_headers, is_test=is_test, config=config, convert_to_boxes=convert_to_boxes)
  
  
  def create_vgg_ssd_predictor(net, candidate_size=200, nms_method=None, sigma=0.5, device=None):
      predictor = Predictor(net, config.image_size, config.image_mean,
                            nms_method=nms_method,
                            iou_threshold=config.iou_threshold,
                            candidate_size=candidate_size,
                            sigma=sigma,
                            device=device)
      return predictor
  