code: def save_checkpoint(
      workdir: str,
      train_state: TrainState,
      max_to_keep: int = 3,
      overwrite: bool = False,
      keep_every_n_steps: int = 50000,
  ):
      """Saves a checkpoint.
  
      First syncs the model state across replicas, then it unreplicates it by taking
      the train state of the first replica and saves it as 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).
        keep_every_n_steps: Keep every checkpoints every n steps.
      """
      if jax.process_index() == 0:
          checkpoint_state = jax.device_get(train_state)
          checkpoints.save_checkpoint(
              workdir,
              checkpoint_state,
              int(checkpoint_state.global_step),
              overwrite=overwrite,
              keep=max_to_keep,
              keep_every_n_steps=keep_every_n_steps,
          )
  
imports: import os
  from typing import Any, Dict, Tuple, Optional
  import flax
  from flax import jax_utils
  from flax import struct
  from flax.training import checkpoints
  import jax
  import jax.numpy
  import jax.profiler
  import ml_collections
  import optax
  from scenic.dataset_lib import dataset_utils
  from tensorflow.io import gfile
label_desc: Use image node_id 1 for calling a global function with example usage: save_checkpoint(workdir, train_state, max_to_keep, overwrite, keep_every_n_steps) without return types