import tensorflow as tf

        # load pre-trained data from file
        # glove = np.load(params['glove'])['embeddings']  # np.array

        # training the embedding during training
        glove = np.zeros(
            (self.params["embedding_vocabulary_size"], self.params["embedding_dim"]),
            dtype=np.float32,
        )

        # Add OOV word embedding
        embedding_array = np.vstack([glove, [[0.0] * self.params["embedding_dim"]]])

        embedding_variable = tf.Variable(
            embedding_array, dtype=tf.float32, trainable=True
        )

        # embedding_variable = tf.get_variable(
        #     'embedding_variable',
        #     shape=(self.params["embedding_vocabulary_size"] + 1, self.params["embedding_dim"]),
        #     dtype=tf.float32,
        #     initializer=tf.contrib.layers.xavier_initializer(),
        #     regularizer=tf.contrib.layers.l2_regularizer(self.params["regularizer_rate"]),
        #     trainable=True
        # )
