code: def get_concat_tarred_speech_label_dataset(
      featurizer,
      config: dict,
      shuffle_n: int,
      global_rank: int,
      world_size: int,
  ):
      tarred_audio_filepaths = config["tarred_audio_filepaths"]
      manifest_filepaths = config["manifest_filepath"]
      datasets = []
      for dataset_idx, (
          tarred_audio_filepath,
          manifest_filepath,
      ) in enumerate(zip(tarred_audio_filepaths, manifest_filepaths)):
          conf = copy.deepcopy(config)
          conf["manifest_filepath"] = manifest_filepath
          conf["tarred_audio_filepaths"] = tarred_audio_filepath
          dataset = get_tarred_speech_label_dataset(
              config=conf,
              featurizer=featurizer,
              shuffle_n=shuffle_n,
              global_rank=global_rank,
              world_size=world_size,
          )
          datasets.append(dataset)
  
      dataset = ConcatDataset(
          datasets,
          sampling_technique=config.get(
              "concat_sampling_technique", "temperature"
          ),
          sampling_temperature=config.get(
              "concat_sampling_temperature", 5
          ),
          sampling_probabilities=config.get(
              "concat_sampling_probabilities", None
          ),
          global_rank=global_rank,
          world_size=world_size,
          shuffle=config["shuffle"],
      )
      return dataset
  
file_path: NeMo/nemo/collections/asr/data/audio_to_label_dataset.py
imports: import copy
  from omegaconf import DictConfig
  from nemo.collections.asr.data import audio_to_label
  from nemo.collections.asr.data.audio_to_text_dataset import convert_to_config_list, get_chain_dataset
  from nemo.collections.asr.parts.preprocessing.perturb import process_augmentations
  from nemo.collections.common.data.dataset import ConcatDataset
label_desc: Use image node_id 4 for calling a global function with example usage: get_concat_tarred_speech_label_dataset(featurizer, config, shuffle_n, global_rank, world_size) and returns: dataset