code: def predict_image(
      predictor,
      image_file,
      image_shape=[640, 640],
      warmup=1,
      repeats=1,
      threshold=0.5,
  ):
      """
      predict image main func
      """
      img, scale_factor = image_preprocess(image_file, image_shape)
      inputs = {}
      inputs["image"] = img
      if FLAGS.include_nms:
          inputs["scale_factor"] = scale_factor
      input_names = predictor.get_input_names()
      for i, _ in enumerate(input_names):
          input_tensor = predictor.get_input_handle(input_names[i])
          input_tensor.copy_from_cpu(inputs[input_names[i]])
  
      for i in range(warmup):
          predictor.run()
  
      np_boxes, np_boxes_num = None, None
      cpu_mems, gpu_mems = 0, 0
      predict_time = 0.0
      time_min = float("inf")
      time_max = float("-inf")
      for i in range(repeats):
          start_time = time.time()
          predictor.run()
          output_names = predictor.get_output_names()
          boxes_tensor = predictor.get_output_handle(output_names[0])
          np_boxes = boxes_tensor.copy_to_cpu()
          if FLAGS.include_nms:
              boxes_num = predictor.get_output_handle(output_names[1])
              np_boxes_num = boxes_num.copy_to_cpu()
          end_time = time.time()
          timed = end_time - start_time
          time_min = min(time_min, timed)
          time_max = max(time_max, timed)
          predict_time += timed
      time_avg = predict_time / repeats
      print(
          "[Benchmark]Inference time(ms): min={}, max={}, avg={}".format(
              round(time_min * 1000, 2),
              round(time_max * 1000, 1),
              round(time_avg * 1000, 1),
          )
      )
      if not FLAGS.include_nms:
          postprocess = PPYOLOEPostProcess(
              score_threshold=0.3, nms_threshold=0.6
          )
          res = postprocess(np_boxes, scale_factor)
      else:
          res = {"bbox": np_boxes, "bbox_num": np_boxes_num}
      res_img = draw_box(
          image_file, res["bbox"], CLASS_LABEL, threshold=threshold
      )
      cv2.imwrite("result.jpg", res_img)
  
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 7 for calling a global function with example usage: predict_image(predictor, image_file, image_shape, warmup, repeats, threshold) without return types