import tensorflow as tf

    if FLAGS.num_gpus > 1:
      soft_placement = True
      util.auto_parallel(metagraph, m)

  with tf.Graph().as_default():
    tf.train.import_meta_graph(metagraph)
    for model in models.values():
      model.import_ops()
    sv = tf.train.Supervisor(logdir=FLAGS.save_path)
    config_proto = tf.ConfigProto(allow_soft_placement=soft_placement)
    with sv.managed_session(config=config_proto) as session:
      for i in range(config.max_max_epoch):
        lr_decay = config.lr_decay ** max(i + 1 - config.max_epoch, 0.0)
        m.assign_lr(session, config.learning_rate * lr_decay)

        print("Epoch: %d Learning rate: %.3f" % (i + 1, session.run(m.lr)))
