code: def convert_to_config_list(initial_list):
      if type(initial_list) is str:
          initial_list = initial_list.split(",")
      if initial_list is None or initial_list == []:
          raise ValueError(
              "manifest_filepaths and tarred_audio_filepaths must not be empty."
          )
      if not isinstance(initial_list, ListConfig):
          initial_list = ListConfig([initial_list])
  
      for list_idx, list_val in enumerate(initial_list):
          if type(list_val) != type(initial_list[0]):
              raise ValueError(
                  "manifest_filepaths and tarred_audio_filepaths need to be a list of lists for bucketing or just a list of strings"
              )
      if type(initial_list[0]) is not ListConfig:
          initial_list = ListConfig([initial_list])
      return initial_list
  
imports: import copy
  import json
  import random
  from math import isclose
  from typing import Any, List, Optional, Union
  import torch
  from omegaconf import DictConfig, OmegaConf, open_dict
  from omegaconf.listconfig import ListConfig
  from pytorch_lightning.callbacks import BasePredictionWriter
  from torch.utils.data import ChainDataset
  from nemo.collections.asr.data import audio_to_text, audio_to_text_dali
  from nemo.collections.asr.data.huggingface.hf_audio_to_text_dataset import get_hf_audio_to_text_bpe_dataset, get_hf_audio_to_text_char_dataset
  from nemo.collections.asr.parts.preprocessing.perturb import process_augmentations
  from nemo.collections.common.data.dataset import CodeSwitchedDataset, ConcatDataset
  from nemo.utils import logging
label_desc: Use image node_id 13 for calling a global function with example usage: convert_to_config_list(initial_list) and returns: initial_list