code: def test_Speech2Text_pit(asr_config_file_pit, lm_config_file):
      file = open(asr_config_file_pit, "r", encoding="utf-8")
      asr_train_config = file.read()
      asr_train_config = yaml.full_load(asr_train_config)
      asr_train_config["frontend"] = "default"
      asr_train_config["encoder_conf"] = {"num_inf": 2}
      asr_train_config["ctc_conf"] = {"reduce": False}
      asr_train_config["model"] = "pit_espnet"
      asr_train_config["model_conf"] = {"num_inf": 2, "num_ref": 2}
      # Change the configuration file
      with open(asr_config_file_pit, "w", encoding="utf-8") as files:
          yaml.dump(asr_train_config, files)
      speech2text = Speech2Text(
          asr_train_config=asr_config_file_pit,
          lm_train_config=lm_config_file,
          beam_size=1,
          multi_asr=True,
      )
      speech = np.random.randn(10000)
      results = speech2text(speech)
      for ret in results:
          for text, token, token_int, hyp in ret:
              assert isinstance(text, str)
              assert isinstance(token[0], str)
              assert isinstance(token_int[0], int)
              assert isinstance(hyp, Hypothesis)
  
imports: import string
  from argparse import ArgumentParser
  from pathlib import Path
  import numpy
  import pytest
  import yaml
  from espnet2.bin.asr_inference import Speech2Text, get_parser, main
  from espnet2.bin.asr_inference_streaming import Speech2TextStreaming
  from espnet2.bin.whisper_export_vocabulary import export_vocabulary
  from espnet2.tasks.asr import ASRTask
  from espnet2.tasks.enh_s2t import EnhS2TTask
  from espnet2.tasks.lm import LMTask
  from espnet.nets.beam_search import Hypothesis
label_desc: Use image node_id 19 for calling a global function with example usage: test_Speech2Text_pit(asr_config_file_pit, lm_config_file) without return types