def METHOD_NAME(self, inputs):
    input_shape = tf.shape(inputs)
    batch_size, sequence_length = input_shape[0], input_shape[1]
    i = tf.range(sequence_length)[:, tf.newaxis]
    j = tf.range(sequence_length)
    mask = tf.cast(i >= j, dtype="int32")
    mask = tf.reshape(mask, (1, input_shape[1], input_shape[1]))
    mult = tf.concat(
        [
            tf.expand_dims(batch_size, -1),
            tf.constant([1, 1], dtype=tf.int32),
        ],
        axis=0,
    )
    return tf.tile(mask, mult)