code: def predict_image(args):
      """
      predict image func
      """
      transforms = _transforms(args.dataset)
      transform = T.Compose(transforms)
  
      # Step1: Load image and preprocess
      data = transform({"img": args.image_file})
      data = data["img"][np.newaxis, :]
  
      # Step2: Prepare prdictor
      predictor, rerun_flag = load_predictor(args)
  
      # Step3: Inference
      input_names = predictor.get_input_names()
      input_handle = predictor.get_input_handle(input_names[0])
      output_names = predictor.get_output_names()
      output_handle = predictor.get_output_handle(output_names[0])
      input_handle.reshape(data.shape)
      input_handle.copy_from_cpu(data)
  
      warmup, repeats = 0, 1
      if args.benchmark:
          warmup, repeats = 20, 100
  
      for i in range(warmup):
          predictor.run()
  
      start_time = time.time()
      for i in range(repeats):
          predictor.run()
          results = output_handle.copy_to_cpu()
          if rerun_flag:
              print(
                  "***** Collect dynamic shape done, Please rerun the program to get correct results. *****"
              )
              return
      total_time = time.time() - start_time
      avg_time = float(total_time) / repeats
      print(
          f"[Benchmark]Average inference time: \033[91m{round(avg_time*1000, 2)}ms\033[0m"
      )
  
      # Step4: Post process
      if args.dataset == "human":
          results = reverse_transform(
              paddle.to_tensor(results),
              im.shape,
              transforms,
              mode="bilinear",
          )
          results = np.argmax(results, axis=1)
      result = get_pseudo_color_map(results[0])
  
      # Step5: Save result to file
      if args.save_file is not None:
          result.save(args.save_file)
          print(f"Saved result to \033[91m{args.save_file}\033[0m")
  
file_path: PaddleSeg/deploy/slim/act/test_seg.py
imports: import argparse
  import time
  import os
  import sys
  import cv2
  import numpy
  import paddle
  import paddleseg.transforms
  from paddleseg.cvlibs import Config
  from paddleseg.core.infer import reverse_transform
  from paddleseg.utils.visualize import get_pseudo_color_map
  from paddleseg.utils import metrics
  from paddleseg.cvlibs import SegBuilder
  from paddle.inference import create_predictor, PrecisionType
  from paddle.inference import Config
label_desc: Use image node_id 3 for calling a global function with example usage: predict_image(args) without return types