docstr: This method is stateless and is intended for use with jax.grad.
docstr_tok: ["This","method","is","stateless","and","is","intended","for","use","with","jax.grad","."]
code: def compute_loss_and_updates(
      self,
      trainable_variables,
      non_trainable_variables,
      x,
      y,
      sample_weight,
      training=False,
      optimizer_variables=None,
  ):
      """This method is stateless and is intended for use with jax.grad."""
      kwargs = {}
      if self._call_has_training_arg:
          kwargs["training"] = training
      y_pred, non_trainable_variables, losses = self.stateless_call(
          trainable_variables,
          non_trainable_variables,
          x,
          return_losses=True,
          **kwargs,
      )
  
      trainable_mapping = zip(
          self.trainable_variables, trainable_variables
      )
      with backend.StatelessScope(state_mapping=trainable_mapping):
          # Note that this is needed for the regularization loss, which need
          # the latest value of train/non-trainable variables.
          loss = self.compute_loss(
              x, y, y_pred, sample_weight, allow_empty=True
          )
      if losses:
          loss += ops.sum(losses)
      unscaled_loss = loss
      if training and self.optimizer is not None:
          # Scale loss with a StatelessScope, to use an update scale variable.
          mapping = list(
              zip(self.optimizer.variables, optimizer_variables)
          )
          with backend.StatelessScope(state_mapping=mapping):
              loss = self.optimizer.scale_loss(loss)
      return loss, (unscaled_loss, y_pred, non_trainable_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 2 for calling the JAXTrainer obj's underlying member method code with example usage: obj.compute_loss_and_updates(trainable_variables, non_trainable_variables, x, y, sample_weight, training, optimizer_variables) and returns: loss,  unscaled_loss,  y_pred,  non_trainable_variables