import tensorflow as tf

        emb_values = list()
        for embedding_weight in tf_sparse_demo.embedding_weights:
            if args.save_params:
                filepath = r"./embedding_variables/"
                utils.try_make_dirs(filepath)
                emb_values.append(embedding_weight.read_value())
            else:
                emb_values = tf.constant(1.0)

    tf_results = list()
    with tf.Session(graph=graph) as sess:
        sess.run([init_op, iterator_init])
        sess.run(restore_op)
        sess.graph.finalize()

        for step in range(args.iter_num):
            loss_v, emb_vector_v = sess.run([*graph_results])
            print("*" * 80)
            print(f"step: {step}, loss: {loss_v}, embedding_vector:\n{emb_vector_v}")
            tf_results.append(emb_vector_v)
