code: def parse_dataset(self):
      logger.info("Loading CULane annotations...")
      if self.predict_dir is not None:
          logger.info("switch to predict mode")
          return
      # Waiting for the dataset to load is tedious, let's cache it
      os.makedirs("cache", exist_ok=True)
      cache_path = "cache/culane_paddle_{}.pkl".format(self.split)
      if os.path.exists(cache_path):
          with open(cache_path, "rb") as cache_file:
              self.data_infos = pkl.load(cache_file)
              self.max_lanes = max(
                  len(anno["lanes"]) for anno in self.data_infos
              )
              return
  
      with open(self.list_path) as list_file:
          for line in list_file:
              infos = self.load_annotation(line.split())
              self.data_infos.append(infos)
  
      # cache data infos to file
      with open(cache_path, "wb") as cache_file:
          pkl.dump(self.data_infos, cache_file)
  
imports: from ppdet.core.workspace import register, serializable
  import cv2
  import os
  import tarfile
  import numpy
  import os.path
  from ppdet.data.source.dataset import DetDataset
  from imgaug.augmentables.lines import LineStringsOnImage
  from imgaug.augmentables.segmaps import SegmentationMapsOnImage
  from ppdet.data.culane_utils import lane_to_linestrings
  import pickle
  from ppdet.utils.logger import setup_logger
  from .dataset import DetDataset, _make_dataset, _is_valid_file
  from ppdet.utils.download import download_dataset
label_desc: Use image node_id 4 for calling the CULaneDataSet obj's underlying member method code with example usage: obj.parse_dataset() without return types