code: def run(self, imgs):
      if not isinstance(imgs, (list, tuple)):
          imgs = [imgs]
  
      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_seg_handle = self.predictor.get_output_handle(
          output_names[0]
      )
  
      args = self.args
      if not os.path.exists(args.save_dir):
          os.makedirs(args.save_dir)
      cut_height = 160
      num_classes = 7
      postprocessor = tusimple_processor.TusimpleProcessor(
          num_classes=num_classes,
          cut_height=cut_height,
          save_dir=args.save_dir,
      )
  
      for i, im_path in enumerate(imgs):
          im = cv2.imread(im_path)
          im = im[cut_height:, :, :]
          im = im.astype("float32")
          im, _ = self.cfg.transforms(im)
          im = im[np.newaxis, ...]
  
          input_handle.reshape(im.shape)
          input_handle.copy_from_cpu(im)
  
          self.predictor.run()
  
          seg_results = output_seg_handle.copy_to_cpu()
  
          # get lane points
          seg_results = paddle.to_tensor([seg_results])
          postprocessor.predict(seg_results, im_path)
      logger.info("Finish")
  
imports: import argparse
  import codecs
  import os
  import sys
  import cv2
  import yaml
  import numpy
  import paddle
  from paddle.inference import create_predictor, PrecisionType
  from paddle.inference import Config
  from paddleseg.cvlibs import manager
  from paddleseg.utils import get_sys_env, logger, get_image_list
  from third_party import tusimple_processor
  import paddleseg.transforms.transforms
label_desc: Use image node_id 4 for calling the Predictor obj's underlying member method code with example usage: obj.run(imgs) without return types