dummy_encoder_inputs = np.array([
            np.array([WordSequence.PAD]) for _ in range(batch_size)])
        dummy_encoder_inputs_length = np.array([1] * batch_size)

        for epoch in range(1, n_epoch+1):
            costs = []
            bar = tqdm(range(steps), total=steps,
                       desc='epoch {}, loss=0.000000'.format(epoch))
            for _ in bar:
                x, xl, y, yl = next(flow)
                x = np.flip(x, axis=1)

                add_loss = model.train(sess,
                                       dummy_encoder_inputs,
                                       dummy_encoder_inputs_length,
                                       y, yl, loss_only=True)
                add_loss *= -0.5

老师，这一步是干嘛的，训练上述只是batch_size个的PAD？