import tensorflow as tf

            for layer in range(self.num_layers):
                gru_fw, gru_bw = self.grus[layer]
                init_fw, init_bw = self.inits[layer]
                mask_fw, mask_bw = self.dropout_mask[layer]
                with tf.variable_scope("fw_{}".format(layer)):
                    out_fw, _ = tf.nn.dynamic_rnn(
                        gru_fw, outputs[-1] * mask_fw, seq_len, initial_state=init_fw, dtype=tf.float32)
                with tf.variable_scope("bw_{}".format(layer)):
                    inputs_bw = tf.reverse_sequence(
                        outputs[-1] * mask_bw, seq_lengths=seq_len, seq_dim=1, batch_dim=0)
                    out_bw, _ = tf.nn.dynamic_rnn(
                        gru_bw, inputs_bw, seq_len, initial_state=init_bw, dtype=tf.float32)
                    out_bw = tf.reverse_sequence(
                        out_bw, seq_lengths=seq_len, seq_dim=1, batch_dim=0)
                outputs.append(tf.concat([out_fw, out_bw], axis=2))
        if concat_layers:
