code: def test_quantized_librispeech():
      url = "dev-clean"
      root = "./data"
  
      pathlib.Path(root).mkdir(exist_ok=True)
  
      dense_model_name = "hubert-base-ls960"
      quantizer_name = "kmeans"
      vocab_size = 100
  
      encoder = SpeechEncoder.by_name(
          dense_model_name=dense_model_name,
          quantizer_model_name=quantizer_name,
          vocab_size=vocab_size,
          need_f0=True,
          deduplicate=True,
          f0_normalizer=None,
          f0_quantizer=None,
      )
  
      quantized_dataset = QuantizedLibriSpeech(
          root=root, speech_encoder=encoder, url=url, download=True
      )
      item = quantized_dataset[0]
  
      # checking a few invariants
      assert (
          item["units"].size(0)
          == item["durations"].size(0)
          == item["f0"].size(0)
      )
      assert item["durations"].sum().item() == item["dense"].size(0)
  
imports: import pathlib
  from textless.data.quantized_datasets import QuantizedLibriSpeech
  from textless.data.speech_encoder import SpeechEncoder
label_desc: Use image node_id 1 for calling a global function with example usage: test_quantized_librispeech() without return types