code: def get_bpe_dataset(
      config: dict,
      tokenizer: "TokenizerSpec",
      augmentor: Optional["FeatureAugmentor"] = None,
  ) -> FeatureToBPEDataset:
      """
      Instantiates a Byte Pair Encoding / Word Piece Encoding based FeatureoToBPEDataset.
  
      Args:
          config: Config of the FeatureToBPEDataset.
          tokenizer: An instance of a TokenizerSpec object.
          augmentor: Optional FeatureAugmentor object for augmentations on audio features.
  
      Returns:
          An instance of FeatureToBPEDataset.
      """
      dataset = FeatureToBPEDataset(
          manifest_filepath=config["manifest_filepath"],
          tokenizer=tokenizer,
          normalize=config.get("normalize", "post_norm"),
          normalize_type=config.get("normalize_type", "per_feature"),
          use_rttm=config.get("use_rttm", False),
          rttm_mode=config.get("rttm_mode", "mask"),
          feat_min_len=config.get("feat_min_len", 4),
          feat_mask_val=config.get("feat_mask_val", None),
          frame_unit_time_secs=config.get("frame_unit_time_secs", 0.01),
          sample_rate=config.get("sample_rate", 16000),
          augmentor=augmentor,
          max_duration=config.get("max_duration", None),
          min_duration=config.get("min_duration", None),
          max_utts=config.get("max_utts", 0),
          trim=config.get("trim_silence", False),
          use_start_end_token=config.get("use_start_end_token", True),
          return_sample_id=config.get("return_sample_id", False),
          channel_selector=config.get("channel_selector", None),
      )
      return dataset
  
imports: from typing import Optional
  from nemo.collections.asr.data.feature_to_text import FeatureToBPEDataset, FeatureToCharDataset
  from nemo.utils import logging
label_desc: Use image node_id 2 for calling a global function with example usage: get_bpe_dataset(config, tokenizer, augmentor) and returns: dataset