code: def generate_list(data_name, data_params_local, domain):
      """
  
      Args:
          data_name (string): name of dataset
          data_params_local (dict): hyperparameters from configure file
          domain (string): domain type (source or target)
  
      Returns:
          data_path (string): image directory of dataset
          train_listpath (string): training list file directory of dataset
          test_listpath (string): test list file directory of dataset
      """
  
      if data_name == "EPIC":
          dataset_path = os.path.join(
              data_params_local["dataset_root"],
              data_name,
              "EPIC_KITCHENS_2018",
          )
          data_path = os.path.join(dataset_path, "frames_rgb_flow")
      elif data_name in ["ADL", "GTEA", "KITCHEN"]:
          dataset_path = os.path.join(
              data_params_local["dataset_root"], data_name
          )
          data_path = os.path.join(dataset_path, "frames_rgb_flow")
      else:
          raise ValueError(
              "Wrong dataset name. Select from [EPIC, ADL, GTEA, KITCHEN]"
          )
  
      train_listpath = os.path.join(
          dataset_path,
          "annotations",
          "labels_train_test",
          data_params_local["dataset_{}_trainlist".format(domain)],
      )
      test_listpath = os.path.join(
          dataset_path,
          "annotations",
          "labels_train_test",
          data_params_local["dataset_{}_testlist".format(domain)],
      )
  
      return data_path, train_listpath, test_listpath
  
imports: import os
  from copy import deepcopy
  from enum import Enum
  import torch
  import kale.prepdata.video_transform
  from kale.loaddata.dataset_access import DatasetAccess
  from kale.loaddata.video_datasets import BasicVideoDataset, EPIC
label_desc: Use image node_id 3 for calling a global function with example usage: generate_list(data_name, data_params_local, domain) and returns: data_path,  train_listpath,  test_listpath