//  [python]
def directional_attention_with_dense(rep_tensor, rep_time, rep_length, direction=None, scope=None,
                                     keep_prob=1., is_train=None, wd=0., activation='elu',
                                     tensor_dict=None, name=None):    
    def scaled_tanh(x, scale=5.):
        return scale * tf.nn.tanh(1./scale * x)

    bs, sl, vec = tf.shape(rep_tensor)[0], tf.shape(rep_tensor)[1], tf.shape(rep_tensor)[2]
    rep_mask = tf.sequence_mask(rep_length, sl)
    ivec = rep_tensor.get_shape()[2]
    with tf.variable_scope(scope or 'directional_attention_%s' % direction or 'diag'):
        # mask generation
        sl_indices = tf.range(sl, dtype=tf.int32)
        sl_col, sl_row = tf.meshgrid(sl_indices, sl_indices)
        if direction is None:
            direct_mask = tf.to_int32(tf.diag(0 - tf.ones([sl], tf.int32)) + 1) > 0
        else:
            if direction == 'forward':
                direct_mask = tf.greater(sl_row, sl_col)
            else:
                direct_mask = tf.greater(sl_col, sl_row)
        direct_mask_tile = tf.tile(tf.expand_dims(tf.cast(direct_mask,tf.bool), 0), [bs, 1, 1])  # bs,sl,sl
        rep_mask_tile = tf.tile(tf.expand_dims(rep_mask, 1), [1, sl, 1])  # bs,sl,sl
        attn_mask = tf.logical_and(direct_mask_tile, rep_mask_tile)  # bs,sl,sl

        #position
        length_col = tf.tile(tf.expand_dims(rep_time, -1), [1, 1, sl])
        length_row = tf.transpose(length_col, [0, 2, 1])
        position = - tf.abs(tf.subtract(length_col, length_row))


        # non-linear
        rep_map = bn_dense_layer(rep_tensor, ivec, True, 0., 'bn_dense_map', activation,
                                False, wd, keep_prob, is_train)
        rep_map_tile = tf.tile(tf.expand_dims(rep_map, 1), [1, sl, 1, 1])  # bs,sl,sl,vec
        rep_map_dp = dropout(rep_map, keep_prob, is_train)

        # attention
        with tf.variable_scope('disan_attention'):  # bs,sl,sl,vec
            f_bias = tf.get_variable('f_bias',[ivec], tf.float32, tf.constant_initializer(0.))
            dependent = linear(rep_map_dp, ivec, False, scope='linear_dependent')  # bs,sl,vec
            dependent_etd = tf.expand_dims(dependent, 1)  # bs,1,sl,vec
            head = linear(rep_map_dp, ivec, False, scope='linear_head') # bs,sl,vec
            head_etd = tf.expand_dims(head, 2)  # bs,sl,1,vec

            logits = scaled_tanh(dependent_etd + head_etd + f_bias, 5.0)  # bs,sl,sl,vec

            logits_masked = exp_mask_for_high_rank_position(logits, attn_mask, position)
            attn_score = tf.nn.softmax(logits_masked, 2)  # bs,sl,sl,vec
            attn_score = mask_for_high_rank(attn_score, attn_mask)

            attn_result = tf.reduce_sum(attn_score * rep_map_tile, 2)  # bs,sl,vec

        with tf.variable_scope('disan_output'):
            o_bias = tf.get_variable('o_bias',[ivec], tf.float32, tf.constant_initializer(0.))
            # input gate
            fusion_gate = tf.nn.sigmoid(
                linear(rep_map, ivec, True, 0., 'linear_fusion_i', False, wd, keep_prob, is_train) +
                linear(attn_result, ivec, True, 0., 'linear_fusion_a', False, wd, keep_prob, is_train) +
                o_bias)
            output = fusion_gate * rep_map + (1-fusion_gate) * attn_result
            output = mask_for_high_rank(output, rep_mask)

        # save attn
        if tensor_dict is not None and name is not None:
            tensor_dict[name + '_dependent'] = dependent
            tensor_dict[name + '_head'] = head
            tensor_dict[name + '_attn_score'] = attn_score
            tensor_dict[name + '_gate'] = fusion_gate
        return output