import tensorflow as tf

            # rep_map_dp = dropout(rep_map, keep_prob, is_train)
            bn = block_num
            bl = block_len

        with tf.variable_scope('self_attention'):
            # @2.self-attention in block
            # mask generation
            sl_indices = tf.range(block_len, dtype=tf.int32)
            sl_col, sl_row = tf.meshgrid(sl_indices, sl_indices)
            if direction == 'forward':
                direct_mask = tf.greater(sl_row, sl_col)  # bl,bl
            else:
                direct_mask = tf.greater(sl_col, sl_row)  # bl,bl
            direct_mask_tile = tf.tile(
                tf.expand_dims(tf.expand_dims(direct_mask, 0), 0), [bs, bn, 1, 1])  # bs,bn,bl,bl
            rep_mask_tile_1 = tf.tile(tf.expand_dims(rep_mask_split, 2), [1, 1, bl, 1])  # bs,bn,bl,bl
