code: def train_on_batch(
      self,
      x,
      y=None,
      sample_weight=None,
      class_weight=None,
      return_dict=False,
  ):
      self._assert_compile_called("train_on_batch")
      if class_weight is not None:
          if sample_weight is not None:
              raise ValueError(
                  "Arguments `sample_weight` and `class_weight` "
                  "cannot be specified at the same time. "
                  f"Received: sample_weight={sample_weight}, "
                  f"class_weight={class_weight}"
              )
          sample_weight = (
              data_adapter_utils.class_weight_to_sample_weights(
                  y, class_weight
              )
          )
      data = (x, y, sample_weight)
      data = _distribute_data(data)
  
      # Maybe build model
      self._symbolic_build(data_batch=data)
      self._record_training_state_sharding_spec()
      self.make_train_function()
  
      # Train step
      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]
      state = (
          trainable_variables,
          non_trainable_variables,
          optimizer_variables,
          metrics_variables,
      )
      logs, state = self.train_function(state, [data])
  
      # State sync
      (
          trainable_variables,
          non_trainable_variables,
          optimizer_variables,
          metrics_variables,
      ) = state
      self._jax_state = {
          "trainable_variables": trainable_variables,
          "non_trainable_variables": non_trainable_variables,
          "optimizer_variables": optimizer_variables,
          "metrics_variables": metrics_variables,
      }
      self.jax_state_sync()
  
      # Format return values
      logs = tree.map_structure(lambda x: np.array(x), logs)
      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 12 for calling the JAXTrainer obj's underlying member method code with example usage: obj.train_on_batch(x, y, sample_weight, class_weight, return_dict) and returns: self, logs