import tensorflow as tf


        # finally project down if needed
        if use_proj:
            embedding = tf.matmul(embedding, W_proj_cnn) + b_proj_cnn

        # reshape back to (batch_size, tokens, dim)
        if use_highway or use_proj:
            shp = tf.concat([batch_size_n_tokens, [projection_dim]], axis=0)
            embedding = tf.reshape(embedding, shp)

        # at last assign attributes for remainder of the model
        self.embedding = embedding


    def _build_word_embeddings(self):
        projection_dim = self.options['lstm']['projection_dim']
