code: def test_step(self, state, data):
      (
          trainable_variables,
          non_trainable_variables,
          metrics_variables,
      ) = state
      x, y, sample_weight = data_adapter_utils.unpack_x_y_sample_weight(
          data
      )
      loss, aux = self.compute_loss_and_updates(
          trainable_variables,
          non_trainable_variables,
          x,
          y,
          sample_weight,
          training=False,
      )
      (unscaled_loss, y_pred, non_trainable_variables) = aux
  
      with backend.StatelessScope(
          state_mapping=[
              (ref_v, v)
              for ref_v, v in zip(
                  self.metrics_variables, metrics_variables
              )
          ]
      ) as scope:
          self._loss_tracker.update_state(unscaled_loss)
          logs = self.compute_metrics(x, y, y_pred, sample_weight)
  
      new_metrics_variables = []
      for ref_v in self.metrics_variables:
          new_v = scope.get_current_value(ref_v)
          if new_v is None:
              new_v = ref_v.value
          new_metrics_variables.append(new_v)
      metrics_variables = new_metrics_variables
  
      (
          trainable_variables,
          non_trainable_variables,
          _,
          metrics_variables,
      ) = self._enforce_jax_state_sharding(
          trainable_variables=trainable_variables,
          non_trainable_variables=non_trainable_variables,
          optimizer_variables=None,
          metrics_variables=metrics_variables,
      )
      state = (
          trainable_variables,
          non_trainable_variables,
          metrics_variables,
      )
      return logs, state
  
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 4 for calling the JAXTrainer obj's underlying member method code with example usage: obj.test_step(state, data) and returns: logs,  state