import tensorflow as tf

        self.c_len = tf.reduce_sum(tf.cast(self.c_mask, tf.int32), axis=1)
        self.q_len = tf.reduce_sum(tf.cast(self.q_mask, tf.int32), axis=1)
        self.dropout = tf.placeholder(tf.float32, name="dropout")

        self.global_step = tf.Variable(0, name="global_step", trainable=False)

    """
    :descrition: The embedding layer, question and passage share embeddings
