code: def make_predict_function(self, force=False):
      if self.predict_function is not None and not force:
          return self.predict_function
  
      def one_predict_step(state, data):
          data = data[0]
          return self.predict_step(state, data)
  
      def multi_predict_steps(state, data):
          outputs, state = one_predict_step(state, data[:1])
  
          for single_step_data in data[1:]:
              step_outputs, state = one_predict_step(
                  state,
                  [single_step_data],
              )
              outputs = tree.map_structure(
                  lambda t1, t2: jax.numpy.concatenate([t1, t2]),
                  outputs,
                  step_outputs,
              )
          return outputs, state
  
      if self.steps_per_execution > 1:
          predict_step = multi_predict_steps
      else:
          predict_step = one_predict_step
  
      if not self.run_eagerly and self.jit_compile:
  
          @jax.jit
          def compiled_predict_step(state, data):
              return predict_step(state, data)
  
          self.predict_function = compiled_predict_step
  
      else:
          self.predict_function = predict_step
  
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 8 for calling the JAXTrainer obj's underlying member method code with example usage: obj.make_predict_function(force) and returns: self, self, outputs,  state, predict_step