code: def test_Speech2Understand_lm(
      use_lm, token_type, slu_config_file, lm_config_file
  ):
      speech2understand = Speech2Understand(
          slu_train_config=slu_config_file,
          lm_train_config=lm_config_file if use_lm else None,
          beam_size=1,
          token_type=token_type,
      )
      speech = np.random.randn(1000)
      results = speech2understand(speech)
      for text, token, token_int, hyp in results:
          assert text is None or isinstance(text, str)
          assert isinstance(token[0], str)
          assert isinstance(token_int[0], int)
          assert isinstance(hyp, Hypothesis)
  
file_path: espnet/test/espnet2/bin/test_slu_inference.py
imports: import string
  from argparse import ArgumentParser
  from distutils.version import LooseVersion
  from pathlib import Path
  import numpy
  import pytest
  import torch
  from espnet2.bin.slu_inference import Speech2Understand, get_parser, main
  from espnet2.tasks.lm import LMTask
  from espnet2.tasks.slu import SLUTask
  from espnet.nets.beam_search import Hypothesis
label_desc: Use image node_id 8 for calling a global function with example usage: test_Speech2Understand_lm(use_lm, token_type, slu_config_file, lm_config_file) without return types