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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 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)