import tensorflow as tf

                    options.gen_hidden_size,
                    initializer=tf.random_uniform_initializer(-0.1, 0.1, seed=113),
                    state_is_tuple=True)
        self.placeholders = placeholders

        with tf.variable_scope("embedding"), tf.device('/cpu:0'):
            self.embedding = tf.get_variable('word_embedding', trainable=(options.fix_word_vec==False),
                                    initializer=tf.constant(self.vocab.word_vecs), dtype=tf.float32)

        if options.with_phrase_projection:
            self.max_phrase_size = placeholders.max_phrase_size
            if options.add_first_word_prob_for_phrase:
                self.in_passage_words = placeholders.in_passage_words
                self.phrase_starts = placeholders.phrase_starts
