import tensorflow as tf

        train_input_fn = input_fn_builder(
            input_files=input_files,
            max_seq_length=FLAGS.max_seq_length,
            max_predictions_per_seq=FLAGS.max_predictions_per_seq,
            is_training=True,
        )
        eval_input_fn = input_fn_builder(
            input_files=input_files,
            max_seq_length=FLAGS.max_seq_length,
            max_predictions_per_seq=FLAGS.max_predictions_per_seq,
            is_training=False,
        )
        tf.estimator.train_and_evaluate(
            estimator,
            train_spec=tf.estimator.TrainSpec(input_fn=train_input_fn, max_steps=100),
            eval_spec=tf.estimator.EvalSpec(input_fn=eval_input_fn, steps=10),
        )
    # if FLAGS.do_train:
    #   tf.logging.info("***** Running training *****")
    #   tf.logging.info("  Batch size = %d", FLAGS.train_batch_size)
    #   train_input_fn = input_fn_builder(
    #       input_files=input_files,
    #       max_seq_length=FLAGS.max_seq_length,
    #       max_predictions_per_seq=FLAGS.max_predictions_per_seq,
    #       is_training=True)
    #   estimator.train(input_fn=train_input_fn, max_steps=FLAGS.num_train_steps)

    # if FLAGS.do_eval:
    #   tf.logging.info("***** Running evaluation *****")
    #   tf.logging.info("  Batch size = %d", FLAGS.eval_batch_size)
