code: def save_checkpoint(
      workdir: str,
      train_state: TrainState,
      max_to_keep: int = 3,
      overwrite: bool = False,
      **kwargs,
  ):
      """Saves a checkpoint.
  
      Args:
        workdir: Experiment directory for saving the checkpoint.
        train_state: An instance of TrainState that holds the state of training.
        max_to_keep: The number of checkpoints to keep.
        overwrite: Overwrite existing checkpoint  if a checkpoint at the current or
          a later step already exits (default: False).
        **kwargs: Passed on to flax.training.checkpoints.save_checkpoint.
      """
      if jax.process_index() == 0:
          # Get train state from the first replica.
          checkpoint_state = jax.device_get(train_state)
          checkpoints.save_checkpoint(
              workdir,
              checkpoint_state,
              int(checkpoint_state.global_step),
              overwrite=overwrite,
              keep=max_to_keep,
              **kwargs,
          )
  
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 8 for calling a global function with example usage: save_checkpoint(workdir, train_state, max_to_keep, overwrite) without return types