text: import torch
  import torchvision
  
  import cv2
  import numpy as np
  import time
  from torch.multiprocessing import Pool
  from utils.nms_wrapper import nms
  from utils.timer import Timer
  from configs.CC import Config
  import argparse
  from layers.functions import Detect, PriorBox
  from peleenet import build_net
  from data import BaseTransform, VOC_CLASSES
  from utils.core import *
  from utils.pycocotools.coco import COCO
  
  parser = argparse.ArgumentParser(description='Pelee Testing')
  parser.add_argument('-c', '--config', default='configs/Pelee_COCO.py')
  parser.add_argument('-d', '--dataset', default='COCO',
                      help='VOC or COCO dataset')
  parser.add_argument('-m', '--trained_model', default="./weights/Pelee_COCO_size304_epoch40.pth",
                      type=str, help='Trained state_dict file path to open')
  parser.add_argument('-t', '--thresh', default=0.2, type=float,
                      help='visidutation threshold')
  parser.add_argument('--show', action='store_true',
                      help='Whether to display the images')
  args = parser.parse_args()
  
  print_info(' ----------------------------------------------------------------------\n'
             '|                       Pelee Demo Program                              |\n'
             ' ----------------------------------------------------------------------', ['yellow', 'bold'])
  
  global cfg
  cfg = Config.fromfile(args.config)
  anchor_config = anchors(cfg.model)
  print_info('The Anchor info: \n{}'.format(anchor_config))
  priorbox = PriorBox(anchor_config)
  net = build_net('test', cfg.model.input_size, cfg.model)
  checkpoint = torch.load("./weights/Pelee_COCO_size304_epoch40.pth")
  # net.load_state_dict(checkpoint["state_dict"])
  net.cpu()
  net.eval()
  
  example = torch.rand(1,3,304,304).cpu()
  traced_module = torch.jit.trace(net, example)
  
  traced_module.save("torch_scrit.pt")
  