def METHOD_NAME(
    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)