code: def _read_sample_list(self):
      json_path = self.json_path
      file_list = self.file_list
      with open(json_path, "r") as f:
          json_info = json.load(f)
      ann_dict = {
          item["image_id"]: item for item in json_info["annotations"]
      }
      assert len(ann_dict) == len(json_info["annotations"])
      img_dict = {item["id"]: item for item in json_info["images"]}
      assert len(img_dict) == len(json_info["images"])
  
      sample_list = []
      with open(file_list, "r") as f:
          for line in f:
              items = line.strip().split(self.sep)
              if len(items) != 2:
                  if self.mode == "train" or self.mode == "val":
                      raise ValueError(
                          "File list format incorrect! In training or evaluation task it should be"
                          " image_name{}label_name\\n".format(self.sep)
                      )
                  image_path = os.path.join(self.dataset_root, items[0])
                  label_path = None
              else:
                  image_path = os.path.join(self.dataset_root, items[0])
                  label_path = os.path.join(self.dataset_root, items[1])
              image_id = self._get_image_id(image_path)
              ann_info = ann_dict[image_id]
              img_info = img_dict[image_id]
              sample = build_info_dict(
                  _type_="sample",
                  img=image_path,
                  label=label_path,
                  img_path=image_path,
                  lab_path=label_path,
              )
              sample["img_h"] = img_info["height"]
              sample["img_w"] = img_info["width"]
              sample["image_id"] = image_id
              seg_info = ann_info["segments_info"]
              for item in seg_info:
                  item["category_id"] = self.convert_id_for_train(
                      item["category_id"]
                  )
              seg_info = [
                  item
                  for item in seg_info
                  if item["category_id"] != self.ignore_index
              ]
              sample["ann"] = seg_info
              sample_list.append(sample)
      return sample_list
  
imports: import json
  import copy
  import os
  import numpy
  import paddle
  from paddleseg.transforms import Compose
  from paddlepanseg.cvlibs import build_info_dict
label_desc: Use image node_id 2 for calling the COCOStylePanopticDataset obj's underlying member method code with example usage: obj._read_sample_list() and returns: sample_list