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 __getitem__(self, cuts) -> Tuple[torch.Tensor, ...]:
      audio, audio_lens, cuts = self.load_audio(cuts)
      tokens = [
          torch.as_tensor(
              self.tokenizer(
                  c.supervisions[0].text, c.supervisions[0].language
              )
          )
          for c in cuts
      ]
      token_lens = torch.tensor(
          [t.size(0) for t in tokens], dtype=torch.long
      )
      tokens = collate_vectors(tokens, padding_value=0)
      return audio, audio_lens, tokens, token_lens
  
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 3 for calling the LhotseSpeechToTextBpeDataset obj's underlying member method code with example usage: obj.__getitem__(cuts) and returns: audio,  audio_lens,  tokens,  token_lens