code: def get_concat_tarred_dataset(
      config: dict,
      shuffle_n: int,
      global_rank: int,
      world_size: int,
      tokenizer: Optional["TokenizerSpec"] = None,
      augmentor: Optional["AudioAugmentor"] = None,
  ) -> ConcatDataset:
      """
      Instantiates a ConcatDataset containing multiple Word Piece/BPE Encoding based TarredAudioToBPEDataset or a char based TarredAudioToCharDataset.
  
      Args:
          config: Config of the TarredAudioToBPEDataset or TarredAudioToCharDataset.
          shuffle_n: How many samples to look ahead and load to be shuffled.
              See WebDataset documentation for more details.
          tokenizer: An instance of a TokenizerSpec object if BPE dataset is needed.
          global_rank: Global rank of this device.
          world_size: Global world size in the training method.
              Passsing None would return a char-based dataset.
          augmentor: Optional AudioAugmentor object for augmentations on audio data.
  
      Returns:
          An instance of ConcatDataset containing one or more TarredAudioToBPEDatasets or TarredAudioToCharDatasets.
      """
  
      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_dataset(
              config=conf,
              tokenizer=tokenizer,
              shuffle_n=shuffle_n,
              global_rank=global_rank,
              world_size=world_size,
              augmentor=augmentor,
          )
          datasets.append(dataset)
  
      dataset = ConcatDataset(
          datasets,
          sampling_technique=config.get(
              "concat_sampling_technique", "temperature"
          ),
          sampling_temperature=config.get(
              "concat_sampling_temperature", 5
          ),
          sampling_scale=config.get("concat_sampling_scale", 1),
          sampling_probabilities=config.get(
              "concat_sampling_probabilities", None
          ),
          shuffle=config.get("concat_shuffle", True),
          seed=config.get("concat_sampling_seed", None),
          global_rank=global_rank,
          world_size=world_size,
      )
      return dataset
  
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 6 for calling a global function with example usage: get_concat_tarred_dataset(config, shuffle_n, global_rank, world_size, tokenizer, augmentor) and returns: dataset