code: def fit(
      self,
      x=None,
      y=None,
      batch_size=None,
      epochs=1,
      verbose="auto",
      callbacks=None,
      validation_split=0.0,
      validation_data=None,
      shuffle=True,
      class_weight=None,
      sample_weight=None,
      initial_epoch=0,
      steps_per_epoch=None,
      validation_steps=None,
      validation_batch_size=None,
      validation_freq=1,
  ):
      self._assert_compile_called("fit")
      # TODO: respect compiled trainable state
      self._eval_epoch_iterator = None
      if validation_split and validation_data is None:
          # Create the validation data using the training data. Only supported
          # for TF/numpy/jax arrays.
          (
              x,
              y,
              sample_weight,
          ), validation_data = data_adapter_utils.train_validation_split(
              (x, y, sample_weight), validation_split=validation_split
          )
  
      if validation_data:
          (
              val_x,
              val_y,
              val_sample_weight,
          ) = data_adapter_utils.unpack_x_y_sample_weight(
              validation_data
          )
  
      # Create an iterator that yields batches for one epoch.
      epoch_iterator = JAXEpochIterator(
          x=x,
          y=y,
          sample_weight=sample_weight,
          batch_size=batch_size,
          steps_per_epoch=steps_per_epoch,
          shuffle=shuffle,
          class_weight=class_weight,
          steps_per_execution=self.steps_per_execution,
      )
  
      self._symbolic_build(iterator=epoch_iterator)
  
      # Container that configures and calls callbacks.
      if not isinstance(callbacks, callbacks_module.CallbackList):
          callbacks = callbacks_module.CallbackList(
              callbacks,
              add_history=True,
              add_progbar=verbose != 0,
              verbose=verbose,
              epochs=epochs,
              steps=epoch_iterator.num_batches,
              model=self,
          )
      self._record_training_state_sharding_spec()
  
      self.make_train_function()
      self.stop_training = False
      callbacks.on_train_begin()
  
      for epoch in range(initial_epoch, epochs):
          self.reset_metrics()
          callbacks.on_epoch_begin(epoch)
  
          trainable_variables = [
              v.value for v in self.trainable_variables
          ]
          non_trainable_variables = [
              v.value for v in self.non_trainable_variables
          ]
          optimizer_variables = [
              v.value for v in self.optimizer.variables
          ]
          metrics_variables = [v.value for v in self.metrics_variables]
  
          self._purge_model_variables()
          for step, data in epoch_iterator.enumerate_epoch():
              # Callbacks
              callbacks.on_train_batch_begin(step)
  
              # Train step
              state = (
                  trainable_variables,
                  non_trainable_variables,
                  optimizer_variables,
                  metrics_variables,
              )
              logs, state = self.train_function(state, data)
              (
                  trainable_variables,
                  non_trainable_variables,
                  optimizer_variables,
                  metrics_variables,
              ) = state
  
              # Setting _jax_state enables callbacks to force a state sync
              # if they need to.
              self._jax_state = {
                  "trainable_variables": trainable_variables,
                  "non_trainable_variables": non_trainable_variables,
                  "optimizer_variables": optimizer_variables,
                  "metrics_variables": metrics_variables,
              }
  
              # Callbacks
              callbacks.on_train_batch_end(
                  step, self._pythonify_logs(logs)
              )
              if self.stop_training:
                  break
  
          # Reattach state to model variables.
          # NOTE: doing this after each step would be a big performance
          # bottleneck.
          self.jax_state_sync()
  
          # Override with model metrics instead of last step logs
          # The jax spmd_mode is need for multi-process context, since the
          # metrics values are replicated, and we don't want to do a all
          # gather, and only need the local copy of the value.
          with jax.spmd_mode("allow_all"):
              epoch_logs = self.get_metrics_result()
  
          # Run validation.
          if validation_data and self._should_eval(
              epoch, validation_freq
          ):
              # Create JAXEpochIterator for evaluation and cache it.
              if getattr(self, "_eval_epoch_iterator", None) is None:
                  self._eval_epoch_iterator = JAXEpochIterator(
                      x=val_x,
                      y=val_y,
                      sample_weight=val_sample_weight,
                      batch_size=validation_batch_size or batch_size,
                      steps_per_execution=self.steps_per_execution,
                      steps_per_epoch=validation_steps,
                      shuffle=False,
                  )
              val_logs = self.evaluate(
                  x=val_x,
                  y=val_y,
                  sample_weight=val_sample_weight,
                  batch_size=validation_batch_size or batch_size,
                  steps=validation_steps,
                  callbacks=callbacks,
                  return_dict=True,
                  _use_cached_eval_dataset=True,
              )
              val_logs = {
                  "val_" + name: val for name, val in val_logs.items()
              }
              epoch_logs.update(val_logs)
  
          callbacks.on_epoch_end(epoch, epoch_logs)
          training_logs = epoch_logs
          if self.stop_training:
              break
  
      if (
          isinstance(self.optimizer, optimizers_module.Optimizer)
          and epochs > 0
      ):
          self.optimizer.finalize_variable_values(
              self.trainable_weights
          )
  
      # If _eval_epoch_iterator exists, delete it after all epochs are done.
      if getattr(self, "_eval_epoch_iterator", None) is not None:
          del self._eval_epoch_iterator
      callbacks.on_train_end(logs=training_logs)
      self._jax_state = None
      return self.history
  
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 9 for calling the JAXTrainer obj's underlying member method code with example usage: obj.fit(x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq) and returns: self