code: def handle_checkpointing(
      train_state: TrainState,
      chrono: Chrono,
      workdir: str,
      max_checkpoints_to_keep=3,
  ):
      """Handles all the bookkeeping around checkpointing.
  
      Syncs the training state and unreplicates it, stops & restarts Chrono
      (and handles its metadata) and writes the actual checkpoint.
  
      Args:
        train_state: A replicated TrainState.
        chrono: The Chrono object.
        workdir: the workdir of the process.
        max_checkpoints_to_keep: how many checkpoints to keep.
      """
      train_state = sync_model_state_across_replicas(train_state)
      if jax.process_index() == 0:
          unrep_train_state = jax_utils.unreplicate(train_state)
          metadata = unrep_train_state.metadata
          metadata["chrono"] = chrono.save()
          unrep_train_state = unrep_train_state.replace(
              metadata=metadata
          )
          save_checkpoint(
              workdir,
              unrep_train_state,
              max_to_keep=max_checkpoints_to_keep,
          )
          del unrep_train_state
  
imports: import collections.abc
  import copy
  import functools
  import os
  import re
  import time
  from typing import Any, Callable, Dict, Mapping, Optional, Sequence, Tuple, Union
  from absl import logging
  from clu import metric_writers
  import flax
  from flax import jax_utils
  from flax import struct
  import flax.linen
  from flax.training import checkpoints
  import jax
  import jax.numpy
  import ml_collections
  import numpy
  import optax
  from scenic.common_lib import debug_utils
  from scenic.dataset_lib import dataset_utils
  from scenic.dataset_lib import datasets
  from scenic.train_lib import optimizers
  from tensorflow.io import gfile
label_desc: Use image node_id 22 for calling a global function with example usage: handle_checkpointing(train_state, chrono, workdir, max_checkpoints_to_keep) without return types