code: def test_Speech2Text_quantization(
      asr_config_file, lm_config_file, quantize_params
  ):
      if (
          not is_torch_1_5_plus
          and quantize_params.get("quantize_dtype") == "float16"
      ):
          with pytest.raises(ValueError):
              speech2text = Speech2Text(
                  asr_train_config=asr_config_file,
                  lm_train_config=None,
                  beam_size=1,
                  token_type="char",
                  quantize_asr_model=True,
                  **quantize_params,
              )
      else:
          speech2text = Speech2Text(
              asr_train_config=asr_config_file,
              lm_train_config=None,
              beam_size=1,
              token_type="char",
              quantize_asr_model=True,
              **quantize_params,
          )
  
          speech = np.random.randn(100000)
          _ = speech2text(speech)
  
imports: import string
  from argparse import ArgumentParser
  from distutils.version import LooseVersion
  from pathlib import Path
  from typing import List
  import numpy
  import pytest
  import torch
  from espnet2.asr_transducer.beam_search_transducer import Hypothesis
  from espnet2.bin.asr_transducer_inference import Speech2Text, get_parser, main
  from espnet2.tasks.asr_transducer import ASRTransducerTask
  from espnet2.tasks.lm import LMTask
label_desc: Use image node_id 11 for calling a global function with example usage: test_Speech2Text_quantization(asr_config_file, lm_config_file, quantize_params) without return types