code: def main():
      """
      main func
      """
      predictor, rerun_flag = load_predictor(
          FLAGS.model_path,
          device=FLAGS.device,
          use_trt=FLAGS.use_trt,
          use_mkldnn=FLAGS.use_mkldnn,
          precision=FLAGS.precision,
          use_dynamic_shape=FLAGS.use_dynamic_shape,
          cpu_threads=FLAGS.cpu_threads,
      )
  
      if FLAGS.image_file:
          warmup, repeats = 1, 1
          if FLAGS.benchmark:
              warmup, repeats = 50, 100
          predict_image(
              predictor,
              FLAGS.image_file,
              image_shape=[FLAGS.img_shape, FLAGS.img_shape],
              warmup=warmup,
              repeats=repeats,
          )
      else:
          reader_cfg = load_config(FLAGS.reader_config)
  
          dataset = reader_cfg["EvalDataset"]
          global val_loader
          val_loader = create("EvalReader")(
              reader_cfg["EvalDataset"],
              reader_cfg["worker_num"],
              return_list=True,
          )
          clsid2catid = {v: k for k, v in dataset.catid2clsid.items()}
          anno_file = dataset.get_anno()
          metric = COCOMetric(
              anno_file=anno_file,
              clsid2catid=clsid2catid,
              IouType="bbox",
          )
          eval(predictor, val_loader, metric, rerun_flag=rerun_flag)
  
      if rerun_flag:
          print(
              "***** Collect dynamic shape done, Please rerun the program to get correct results. *****"
          )
  
imports: import os
  import argparse
  import time
  import sys
  import cv2
  import numpy
  import paddle
  from paddle.inference import Config
  from paddle.inference import create_predictor
  from ppdet.core.workspace import load_config, create
  from ppdet.metrics import COCOMetric
  from post_process import PPYOLOEPostProcess
label_desc: Use image node_id 9 for calling a global function with example usage: main() without return types