def METHOD_NAME(self, wave_inputs, get_hidden=False, no_pred=False):
    wave_len = [len(wave) for wave in wave_inputs]
    wave_inputs = pad_sequence(wave_inputs, batch_first=True)
    # (batch_size, audio_len)
    if self.no_grad:
        with torch.no_grad():
            x = self._forward(
                wave_inputs, wave_len, get_hidden=get_hidden, no_pred=no_pred
            )
    else:
        x = self._forward(
            wave_inputs, wave_len, get_hidden=get_hidden, no_pred=no_pred
        )
    return x