code: def main():
      deploy_file = os.path.join(FLAGS.model_dir, "infer_cfg.yml")
      with open(deploy_file) as f:
          yml_conf = yaml.safe_load(f)
      arch = yml_conf["arch"]
      detector_func = "Detector"
      detector = eval(detector_func)(
          FLAGS.model_dir,
          device=FLAGS.device,
          run_mode=FLAGS.run_mode,
          batch_size=FLAGS.batch_size,
          trt_min_shape=FLAGS.trt_min_shape,
          trt_max_shape=FLAGS.trt_max_shape,
          trt_opt_shape=FLAGS.trt_opt_shape,
          trt_calib_mode=FLAGS.trt_calib_mode,
          cpu_threads=FLAGS.cpu_threads,
          enable_mkldnn=FLAGS.enable_mkldnn,
          threshold=FLAGS.threshold,
          output_dir=FLAGS.output_dir,
      )
  
      # predict from video file or camera video stream
      if FLAGS.video_file is not None or FLAGS.camera_id != -1:
          detector.predict_video(FLAGS.video_file, FLAGS.camera_id)
      else:
          # predict from image
          if FLAGS.image_dir is None and FLAGS.image_file is not None:
              assert (
                  FLAGS.batch_size == 1
              ), "batch_size should be 1, when image_file is not None"
          img_list = get_test_images(FLAGS.image_dir, FLAGS.image_file)
          detector.predict_image(
              img_list, FLAGS.run_benchmark, repeats=10
          )
          if not FLAGS.run_benchmark:
              detector.det_times.info(average=True)
          else:
              mode = FLAGS.run_mode
              model_dir = FLAGS.model_dir
              model_info = {
                  "model_name": model_dir.strip("/").split("/")[-1],
                  "precision": mode.split("_")[-1],
              }
              bench_log(detector, img_list, model_info, name="DET")
  
imports: import os
  import yaml
  import glob
  from functools import reduce
  import cv2
  import numpy
  import math
  import paddle
  from paddle.inference import Config
  from paddle.inference import create_predictor
  import sys
  from benchmark_utils import PaddleInferBenchmark
  from picodet_postprocess import PicoDetPostProcess
  from preprocess import preprocess, Resize, NormalizeImage, Permute, PadStride, LetterBoxResize, Pad, decode_image
  from mot.visualize import visualize_box_mask
  from mot_utils import argsparser, Timer, get_current_memory_mb
label_desc: Use image node_id 7 for calling a global function with example usage: main() without return types