import tensorflow as tf

        # instead of transfering a word id to one-hot-format vector and then
        # multiply by the embedding matrix.
        # embed is the outputs of the hidden layer (embedding layer), it is a
        # row vector with 'embedding_size' values.
        with tf.variable_scope(name):
            embeddings = tf.get_variable(
                name='embeddings', shape=(vocabulary_size, embedding_size), initializer=E_init, dtype=LayersConfig.tf_dtype, **E_init_args)
            embed = tf.nn.embedding_lookup(embeddings, self.inputs)
