class_docstr: This dataset is based on BPE datasets from audio_to_text.py.
  Unlike native NeMo datasets, Lhotse dataset defines only the mapping from
  a CutSet (meta-data) to a mini-batch with PyTorch tensors.
  Specifically, it performs tokenization, I/O, augmentation, and feature extraction (if any).
  Managing data, sampling, de-duplication across workers/nodes etc. is all handled
  by Lhotse samplers instead.
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code: def __init__(self, tokenizer):
      super().__init__()
      self.tokenizer = TokenizerWrapper(tokenizer)
      self.load_audio = AudioSamples(fault_tolerant=True)
  
file_path: NeMo/nemo/collections/asr/data/audio_to_text_lhotse.py
imports: from typing import Dict, Optional, Tuple
  import torch.utils.data
  from lhotse.dataset import AudioSamples
  from lhotse.dataset.collation import collate_vectors
  from nemo.collections.common.tokenizers.aggregate_tokenizer import AggregateTokenizer
  from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec
  from nemo.core.neural_types import AudioSignal, LabelsType, LengthsType, NeuralType
label_desc: Use image node_id 2 to create a new LhotseSpeechToTextBpeDataset object from inherited base classes: torch.utils.data with example: obj = LhotseSpeechToTextBpeDataset(tokenizer)