code: def test_recognition_results_multi_outputs(
      etype, dtype, num_spkrs, spa, m_str, text_idx1
  ):
      const = 1e-4
      numpy.random.seed(1)
  
      # ctc_weight: 0.5 (hybrid CTC/attention), cannot be 0.0 (attention) or 1.0 (CTC)
      for text_idx2, ctc_weight in enumerate([0.5]):
          args = make_arg(
              etype=etype,
              ctc_weight=ctc_weight,
              num_spkrs=num_spkrs,
              spa=spa,
          )
          m = importlib.import_module(m_str)
          model = m.E2E(40, 5, args)
  
          if "pytorch" in m_str:
              init_torch_weight_const(model, const)
          else:
              init_chainer_weight_const(model, const)
  
          data = [
              (
                  "aaa",
                  dict(
                      feat=numpy.random.randn(100, 40).astype(
                          numpy.float32
                      ),
                      token=["", ""],
                  ),
              )
          ]
  
          in_data = data[0][1]["feat"]
          nbest_hyps = model.recognize(in_data, args, args.char_list)
  
          for i in range(num_spkrs):
              y_hat = nbest_hyps[i][0]["yseq"][1:]
              seq_hat = [args.char_list[int(idx)] for idx in y_hat]
              seq_hat_text = "".join(seq_hat).replace("<space>", " ")
  
              assert re.match(r"[aiueo]+", seq_hat_text)
  
file_path: espnet/test/test_multi_spkrs.py
imports: import argparse
  import importlib
  import re
  import numpy
  import pytest
  import torch
label_desc: Use image node_id 4 for calling a global function with example usage: test_recognition_results_multi_outputs(etype, dtype, num_spkrs, spa, m_str, text_idx1) without return types