code: def run(self, imgs_path):
      if not isinstance(imgs_path, (list, tuple)):
          imgs_path = [imgs_path]
  
      input_names = self.predictor.get_input_names()
      input_handle = self.predictor.get_input_handle(input_names[0])
      output_names = self.predictor.get_output_names()
      output_handle = self.predictor.get_output_handle(output_names[0])
      results = []
      args = self.args
  
      if not osp.exists(args.save_dir):
          os.makedirs(args.save_dir)
  
      for i in range(0, len(imgs_path), args.batch_size):
          # Warm up
          if i == 0 and args.benchmark:
              for j in range(5):
                  data = np.array(
                      [
                          self._preprocess(img)
                          for img in imgs_path[0 : args.batch_size]
                      ]
                  )
                  input_handle.reshape(data.shape)
                  input_handle.copy_from_cpu(data)
                  self.predictor.run()
                  results = output_handle.copy_to_cpu()
                  results = self._postprocess(results)
  
          # Do inference
          if args.benchmark:
              self.autolog.times.start()
  
          data = np.array(
              [
                  self._preprocess(p)
                  for p in imgs_path[i : i + args.batch_size]
              ]
          )
          input_handle.reshape(data.shape)
          input_handle.copy_from_cpu(data)
  
          if args.benchmark:
              self.autolog.times.stamp()
  
          self.predictor.run()
  
          results = output_handle.copy_to_cpu()
          if args.benchmark:
              self.autolog.times.stamp()
  
          results = self._postprocess(results)
  
          if args.benchmark:
              self.autolog.times.end(stamp=True)
  
          self._save_imgs(results, imgs_path[i : i + args.batch_size])
      logger.info("Inference finished!")
  
imports: import os
  import os.path
  import argparse
  from ast import literal_eval
  import numpy
  from PIL import Image
  from paddle.inference import create_predictor, PrecisionType
  from paddle.inference import Config
  from paddleseg.utils import logger, get_image_list
  from paddleseg.deploy.infer import DeployConfig
  from paddlepanseg.cvlibs.info_dicts import build_info_dict
  from paddlepanseg.transforms.functional import id2rgb
label_desc: Use image node_id 5 for calling the Predictor obj's underlying member method code with example usage: obj.run(imgs_path) without return types