import tensorflow as tf

                                                        [2, self.pretrained_char_mat.get_shape()[1]],
                                                        dtype=tf.float32,
                                                        initializer=tf.constant_initializer(
                                                            self.vocab.char_embeddings[:2],
                                                            dtype=tf.float32),
                                                        trainable=True)

                self.char_mat = tf.concat([self.char_pad_unk_mat, self.pretrained_char_mat], axis=0)

            else:
                self.word_mat = tf.get_variable(
                    'word_embeddings',
                    shape=[self.vocab.word_size(), self.vocab.word_embed_dim],
                    initializer=tf.constant_initializer(self.vocab.word_embeddings),
                    trainable=True
                )

                self.char_mat = tf.get_variable(
                    'char_embeddings',
                    shape=[self.vocab.char_size(), self.vocab.char_embed_dim],
                    initializer=tf.constant_initializer(self.vocab.char_embeddings),
                    trainable=True
                )

            self.ch_len = tf.reshape(tf.reduce_sum(
                tf.cast(tf.cast(self.ch, tf.bool), tf.int32), axis=2), [-1])
