docs: def _eval_once(saver, summary_writer, top_1_op, top_5_op, summary_op):
    """Runs Eval once.
  
    Args:
      saver: Saver.
      summary_writer: Summary writer.
      top_1_op: Top 1 op.
      top_5_op: Top 5 op.
      summary_op: Summary op.
    """
    with tf.Session() as sess:
      ckpt = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir)
      if ckpt and ckpt.model_checkpoint_path:
        print("ckpt.model_checkpoint_path: {0}".format(ckpt.model_checkpoint_path))
        saver.restore(sess, ckpt.model_checkpoint_path)
  
        # Assuming model_checkpoint_path looks something like:
        #   /my-favorite-path/imagenet_train/model.ckpt-0,
        # extract global_step from it.
        global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
        print('Successfully loaded model from %s at step=%s.' %
              (ckpt.model_checkpoint_path, global_step))
      else:
        print('No checkpoint file found')
        return
  
      # Start the queue runners.
      coord = tf.train.Coordinator()
      try:
        threads = []
        for qr in tf.get_collection(tf.GraphKeys.QUEUE_RUNNERS):
          threads.extend(qr.create_threads(sess, coord=coord, daemon=True,
                                           start=True))
  
        num_iter = int(math.ceil(FLAGS.num_examples / FLAGS.batch_size))
        # Counts the number of correct predictions.
        count_top_1 = 0.0
        count_top_5 = 0.0
        total_sample_count = num_iter * FLAGS.batch_size
        step = 0
  
        print('%s: starting evaluation on (%s).' % (datetime.now(), FLAGS.subset))
        start_time = time.time()
        while step < num_iter and not coord.should_stop():
          top_1, top_5 = sess.run([top_1_op, top_5_op])
          count_top_1 += np.sum(top_1)
          count_top_5 += np.sum(top_5)
          step += 1
          if step % 20 == 0:
            duration = time.time() - start_time
            sec_per_batch = duration / 20.0
            examples_per_sec = FLAGS.batch_size / sec_per_batch
            print('%s: [%d batches out of %d] (%.1f examples/sec; %.3f'
                  'sec/batch)' % (datetime.now(), step, num_iter,
                                  examples_per_sec, sec_per_batch))
            start_time = time.time()
  
        # Compute precision @ 1.
        precision_at_1 = count_top_1 / total_sample_count
        recall_at_5 = count_top_5 / total_sample_count
        print('%s: precision @ 1 = %.4f recall @ 5 = %.4f [%d examples]' %
              (datetime.now(), precision_at_1, recall_at_5, total_sample_count))
  
        summary = tf.Summary()
        summary.ParseFromString(sess.run(summary_op))
        summary.value.add(tag='Precision @ 1', simple_value=precision_at_1)
        summary.value.add(tag='Recall @ 5', simple_value=recall_at_5)
        summary_writer.add_summary(summary, global_step)
  
      except Exception as e:  # pylint: disable=broad-except
        coord.request_stop(e)
  
      coord.request_stop()
      coord.join(threads, stop_grace_period_secs=10)