def METHOD_NAME():
  """Eval CIFAR-10 for a number of steps."""
  with tf.Graph().as_default() as g:
    # Get images and labels for CIFAR-10.
    eval_data = FLAGS.eval_data == 'test'
    images, labels = cifar10.inputs(eval_data=eval_data)
    # Build a Graph that computes the logits predictions from the
    # inference model.
    logits = cifar10.inference(images)
    # Calculate predictions.
    top_k_op = tf.nn.in_top_k(logits, labels, 1)
    # Restore the moving average version of the learned variables for eval.
    variable_averages = tf.train.ExponentialMovingAverage(
        cifar10.MOVING_AVERAGE_DECAY)
    variables_to_restore = variable_averages.variables_to_restore()
    saver = tf.train.Saver(variables_to_restore)
    # Build the summary operation based on the TF collection of Summaries.
    summary_op = tf.summary.merge_all()
    summary_writer = tf.summary.FileWriter(FLAGS.eval_dir, g)
    while True:
      eval_once(saver, summary_writer, top_k_op, summary_op)
      if FLAGS.run_once:
        break
      time.sleep(FLAGS.eval_interval_secs)