code: def create_lists_from_splitted_dataset(
      base_folder_splitted,
      data_json="dataset.json",
      train_images_dir="imagesTr",
      train_labels_dir="labelsTr",
  ):
      lists = []
      json_file = join_paths(base_folder_splitted, data_json)
      with open(json_file) as jsn:
          d = json.load(jsn)
          training_files = d["training"]
      num_modalities = len(d["modality"].keys())
      for tr in training_files:
          cur_pat = []
          for mod in range(num_modalities):
              cur_pat.append(
                  join_paths(
                      base_folder_splitted,
                      train_images_dir,
                      tr["image"].split("/")[-1].split(".")[0]
                      + "_%04.0d.nii.gz" % mod,
                  )
              )
          cur_pat.append(
              join_paths(
                  base_folder_splitted,
                  train_labels_dir,
                  tr["label"].split("/")[-1],
              )
          )
          lists.append(cur_pat)
      return lists, {
          int(i): d["modality"][str(i)] for i in d["modality"].keys()
      }
  
imports: import os
  import json
  import shutil
  import pickle
  import numpy
  import SimpleITK
  from scipy.ndimage import binary_fill_holes
  from multiprocessing import Pool
  from collections import OrderedDict
  from .path_utils import join_paths
label_desc: Use image node_id 2 for calling a global function with example usage: create_lists_from_splitted_dataset(base_folder_splitted, data_json, train_images_dir, train_labels_dir) and returns: lists,  unknown