code: def evaluate(
      self,
      x=None,
      y=None,
      batch_size=None,
      verbose="auto",
      sample_weight=None,
      steps=None,
      callbacks=None,
      return_dict=False,
      **kwargs,
  ):
      self._assert_compile_called("evaluate")
      # TODO: respect compiled trainable state
      use_cached_eval_dataset = kwargs.pop(
          "_use_cached_eval_dataset", False
      )
      if kwargs:
          raise ValueError(f"Arguments not recognized: {kwargs}")
  
      if use_cached_eval_dataset:
          epoch_iterator = self._eval_epoch_iterator
      else:
          # Create an iterator that yields batches of input/target data.
          epoch_iterator = JAXEpochIterator(
              x=x,
              y=y,
              sample_weight=sample_weight,
              batch_size=batch_size,
              steps_per_epoch=steps,
              shuffle=False,
              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=1,
              steps=epoch_iterator.num_batches,
              model=self,
          )
      self._record_training_state_sharding_spec()
  
      self.make_test_function()
      self.stop_evaluating = False
      callbacks.on_test_begin()
      logs = None
      self.reset_metrics()
  
      trainable_variables = [v.value for v in self.trainable_variables]
      non_trainable_variables = [
          v.value for v in self.non_trainable_variables
      ]
      metrics_variables = [v.value for v in self.metrics_variables]
  
      self._purge_model_variables(optimizer_variables=False)
      for step, data in epoch_iterator.enumerate_epoch():
          callbacks.on_test_batch_begin(step)
  
          state = (
              trainable_variables,
              non_trainable_variables,
              metrics_variables,
          )
          logs, state = self.test_function(state, data)
          (
              trainable_variables,
              non_trainable_variables,
              metrics_variables,
          ) = state
  
          # Setting _jax_state enables callbacks to force a state sync
          # if they need to.
          self._jax_state = {
              # I wouldn't recommend modifying non-trainable model state
              # during evaluate(), but it's allowed.
              "trainable_variables": trainable_variables,
              "non_trainable_variables": non_trainable_variables,
              "metrics_variables": metrics_variables,
          }
          callbacks.on_test_batch_end(step, self._pythonify_logs(logs))
          if self.stop_evaluating:
              break
  
      # Reattach state back to model.
      self.jax_state_sync()
  
      # 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"):
          logs = self.get_metrics_result()
      callbacks.on_test_end(logs)
      self._jax_state = None
      if return_dict:
          return logs
      return self._flatten_metrics_in_order(logs)
  
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 10 for calling the JAXTrainer obj's underlying member method code with example usage: obj.evaluate(x, y, batch_size, verbose, sample_weight, steps, callbacks, return_dict) and returns: self, logs