code: def train_step(train_state, x, y):
      (
          trainable_variables,
          non_trainable_variables,
          optimizer_variables,
      ) = train_state
      (loss_value, non_trainable_variables), grads = compute_gradients(
          trainable_variables, non_trainable_variables, x, y
      )
  
      (
          trainable_variables,
          optimizer_variables,
      ) = optimizer.stateless_apply(
          optimizer_variables, grads, trainable_variables
      )
  
      return loss_value, (
          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 4 for calling a global function with example usage: train_step(train_state, x, y) and returns: loss_value,  trainable_variables,  non_trainable_variables,  optimizer_variables