code: def test_Speech2Text_interctc(
      asr_config_file, lm_config_file, encoder_class
  ):
      # Change the configuration file to enable InterCTC
      file = open(asr_config_file, "r", encoding="utf-8")
      asr_train_config = file.read()
      asr_train_config = yaml.full_load(asr_train_config)
      asr_train_config["encoder"] = encoder_class
      asr_train_config["encoder_conf"]["interctc_layer_idx"] = [1, 2]
      asr_train_config["model_conf"]["interctc_weight"] = 0.5
      with open(asr_config_file, "w", encoding="utf-8") as files:
          yaml.dump(asr_train_config, files)
  
      speech2text = Speech2Text(
          asr_train_config=asr_config_file,
          lm_train_config=lm_config_file,
          beam_size=1,
      )
      speech = np.random.randn(1000)
      results, interctc_res = speech2text(speech)
      for text, token, token_int, hyp in results:
          assert isinstance(text, str)
          assert isinstance(token[0], str)
          assert isinstance(token_int[0], int)
          assert isinstance(hyp, Hypothesis)
  
      assert isinstance(interctc_res, dict)
      for k, tokens in interctc_res.items():
          assert isinstance(k, int)
          assert isinstance(tokens, list)
          assert isinstance(tokens[0], str)
  
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 20 for calling a global function with example usage: test_Speech2Text_interctc(asr_config_file, lm_config_file, encoder_class) without return types