code: def archive_ckpt(ckpt_eval, ckpt_objective, ckpt_path):
      """Archive a checkpoint if the metric is better."""
      ckpt_dir, ckpt_name = os.path.split(ckpt_path)
  
      saved_objective_path = os.path.join(
          ckpt_dir, "best_objective.txt"
      )
      saved_objective = float("-inf")
      if tf.gfile.Exists(saved_objective_path):
          with tf.gfile.GFile(saved_objective_path, "r") as f:
              saved_objective = float(f.read())
      if saved_objective > ckpt_objective:
          logging.info(
              "Ckpt %s is worse than %s",
              ckpt_objective,
              saved_objective,
          )
          return False
  
      filenames = tf.gfile.Glob(ckpt_path + ".*")
      if filenames is None:
          logging.info("No files to copy for checkpoint %s", ckpt_path)
          return False
  
      # Clear the old folder.
      dst_dir = os.path.join(ckpt_dir, "archive")
      if tf.gfile.Exists(dst_dir):
          tf.gfile.DeleteRecursively(dst_dir)
      tf.gfile.MakeDirs(dst_dir)
  
      # Write checkpoints.
      for f in filenames:
          dest = os.path.join(dst_dir, os.path.basename(f))
          tf.gfile.Copy(f, dest, overwrite=True)
      ckpt_state = tf.train.generate_checkpoint_state_proto(
          dst_dir,
          model_checkpoint_path=ckpt_name,
          all_model_checkpoint_paths=[ckpt_name],
      )
      with tf.gfile.GFile(
          os.path.join(dst_dir, "checkpoint"), "w"
      ) as f:
          f.write(str(ckpt_state))
      with tf.gfile.GFile(
          os.path.join(dst_dir, "best_eval.txt"), "w"
      ) as f:
          f.write("%s" % ckpt_eval)
  
      # Update the best objective.
      with tf.gfile.GFile(saved_objective_path, "w") as f:
          f.write("%f" % ckpt_objective)
  
      logging.info("Copying checkpoint %s to %s", ckpt_path, dst_dir)
      return True
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import json
  import os
  import sys
  from absl import logging
  import numpy
  import tensorflow.compat.v1
  from tensorflow.python.tpu import tpu_function
label_desc: Use image node_id 4 for calling a global function with example usage: archive_ckpt(ckpt_eval, ckpt_objective, ckpt_path) and returns: True, False, False