code: def sync_model_state_across_replicas(
      train_state: train_utils.TrainState,
  ) -> train_utils.TrainState:
      """Sync the model_state (like batch statistics) across replicas.
  
      Args:
        train_state: TrainState; Current state of training.
  
      Returns:
        Updated state of training in which model_state is synced across replicas.
      """
      #  We simply do "mean" here and this doesn't work with
      #  statistics like variance. (check the discussion in Flax for fixing this).
      if jax.tree_util.tree_leaves(train_state.model_state):
          # If the model_state is not empty.
          new_model_state = train_state.model_state.copy(
              {
                  "batch_stats": train_utils.pmap_mean(
                      train_state.model_state["batch_stats"]
                  )
              }
          )
          return train_state.replace(  # pytype: disable=attribute-error
              model_state=new_model_state
          )
      else:
          return train_state
  
file_path: scenic/scenic/projects/ncr/utils.py
imports: import os
  from typing import Any, Dict, Optional, Tuple
  from flax import jax_utils
  from flax.training import checkpoints
  import jax
  import jax.numpy
  import numpy
  from scenic.train_lib import train_utils
  from tensorflow.io import gfile
label_desc: Use image node_id 2 for calling a global function with example usage: sync_model_state_across_replicas(train_state) and returns: train_state, train_state