import tensorflow as tf

            ch_emb = tf.reshape(tf.nn.embedding_lookup(
                self.char_mat, self.ch), [N * PL * self.max_p_num, CL, dc])
            qh_emb = tf.reshape(tf.nn.embedding_lookup(
                self.char_mat, self.qh), [N * QL * self.max_p_num, CL, dc])
            ch_emb = tf.nn.dropout(ch_emb, 1.0 - 0.5 * self.dropout)
            qh_emb = tf.nn.dropout(qh_emb, 1.0 - 0.5 * self.dropout)

            ch_emb = conv(ch_emb, d,
                          bias=True, activation=tf.nn.relu, kernel_size=5, name="char_conv", reuse=None)
            qh_emb = conv(qh_emb, d,
