code: def get_replicated_train_state(devices):
      # All variables will be replicated on all devices
      var_mesh = Mesh(devices, axis_names=("_"))
      # In NamedSharding, axes not mentioned are replicated (all axes here)
      var_replication = NamedSharding(var_mesh, P())
  
      # Apply the distribution settings to the model variables
      trainable_variables = jax.device_put(
          model.trainable_variables, var_replication
      )
      non_trainable_variables = jax.device_put(
          model.non_trainable_variables, var_replication
      )
      optimizer_variables = jax.device_put(
          optimizer.variables, var_replication
      )
  
      # Combine all state in a tuple
      return (
          trainable_variables,
          non_trainable_variables,
          optimizer_variables,
      )
  
file_path: keras/guides/distributed_training_with_jax.py
imports: import os
  import jax
  import numpy
  import tensorflow
  import keras
  from jax.experimental import mesh_utils
  from jax.sharding import Mesh
  from jax.sharding import NamedSharding
  from jax.sharding import PartitionSpec
label_desc: Use image node_id 5 for calling a global function with example usage: get_replicated_train_state(devices) and returns: trainable_variables,  non_trainable_variables,  optimizer_variables