code: def get_concat_bpe_dataset(
      config: dict,
      tokenizer: "TokenizerSpec",
      global_rank: int,
      world_size: int,
      augmentor: Optional["AudioAugmentor"] = None,
  ) -> ConcatDataset:
      """
      Instantiates a ContactDataset based on several Byte Pair Encoding / Word Piece Encoding based AudioToBPEDatasets.
  
      Args:
          config: Config of the AudioToBPEDataset.
          tokenizer: An instance of a TokenizerSpec object.
          global_rank: Global rank of this device.
          world_size: Global world size in the training method.
          augmentor: Optional AudioAugmentor object for augmentations on audio data.
  
      Returns:
          An instance of ConcatDataset containing several instances of AudioToBPEDataset.
      """
      manifest_filepaths = config["manifest_filepath"]
      datasets = []
  
      # needed to support validation Concat Datasets that arrive here as
      # [[dataset1,dataset2]] otherwise ModelPT would interfere
      if len(manifest_filepaths) == 1 and not isinstance(
          manifest_filepaths[0], str
      ):
          logging.info(
              f"removing an extra nesting level from {manifest_filepaths}"
          )
          manifest_filepaths = config["manifest_filepath"][0]
  
      for manifest_filepath in manifest_filepaths:
          conf = copy.deepcopy(config)
          conf["manifest_filepath"] = manifest_filepath
          dataset = get_bpe_dataset(
              config=conf, tokenizer=tokenizer, 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 4 for calling a global function with example usage: get_concat_bpe_dataset(config, tokenizer, global_rank, world_size, augmentor) and returns: dataset