docstr: Enforce the sharding spec constraint for all the training state.
  
  Since the output of the train/eval step will be used as inputs to next
  step, we need to ensure that they have the same sharding spec, so that
  jax.jit won't have to recompile the train/eval function.
  
  Note that this function will also rely on the recorded sharding spec
  for each of states.
  
  This function is expected to be called within the jitted train/eval
  function, especially around the end of the function.
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code: def _enforce_jax_state_sharding(
      self,
      trainable_variables=None,
      non_trainable_variables=None,
      optimizer_variables=None,
      metrics_variables=None,
  ):
      """Enforce the sharding spec constraint for all the training state.
  
      Since the output of the train/eval step will be used as inputs to next
      step, we need to ensure that they have the same sharding spec, so that
      jax.jit won't have to recompile the train/eval function.
  
      Note that this function will also rely on the recorded sharding spec
      for each of states.
  
      This function is expected to be called within the jitted train/eval
      function, especially around the end of the function.
      """
      trainable_variables = trainable_variables or []
      non_trainable_variables = non_trainable_variables or []
      optimizer_variables = optimizer_variables or []
      metrics_variables = metrics_variables or []
  
      for i in range(len(trainable_variables)):
          trainable_variables[i] = jax.lax.with_sharding_constraint(
              trainable_variables[i],
              self._trainable_variable_shardings[i],
          )
      for i in range(len(non_trainable_variables)):
          non_trainable_variables[i] = jax.lax.with_sharding_constraint(
              non_trainable_variables[i],
              self._non_trainable_variable_shardings[i],
          )
      for i in range(len(optimizer_variables)):
          optimizer_variables[i] = jax.lax.with_sharding_constraint(
              optimizer_variables[i],
              self._optimizer_variable_shardings[i],
          )
      for i in range(len(metrics_variables)):
          metrics_variables[i] = jax.lax.with_sharding_constraint(
              metrics_variables[i], self._metrics_variable_shardings[i]
          )
      return (
          trainable_variables,
          non_trainable_variables,
          optimizer_variables,
          metrics_variables,
      )
  
imports: import collections
  import itertools
  from functools import partial
  import jax
  import numpy
  import tree
  from keras import backend
  from keras import callbacks
  from keras import ops
  from keras import optimizers
  from keras.backend import distribution_lib
  from keras.distribution import distribution_lib
  from keras.trainers import trainer
  from keras.trainers.data_adapters import data_adapter_utils
  from keras.trainers.epoch_iterator import EpochIterator
  from keras.utils import traceback_utils
label_desc: Use image node_id 17 for calling the JAXTrainer obj's underlying member method code with example usage: obj._enforce_jax_state_sharding(trainable_variables, non_trainable_variables, optimizer_variables, metrics_variables) and returns: trainable_variables,  non_trainable_variables,  optimizer_variables,  metrics_variables