code: def make_predict_function(self, force=False):
      if self.predict_function is not None and not force:
          return self.predict_function
  
      @tf.autograph.experimental.do_not_convert
      def one_step_on_data(data):
          """Runs a predict test step on a batch of data."""
          return self.predict_step(data)
  
      if not self.run_eagerly and self.jit_compile:
          kwargs = {"jit_compile": True}
          if self._supports_reduce_retracing:
              kwargs.update({"reduce_retracing": True})
          one_step_on_data = tf.function(one_step_on_data, **kwargs)
  
      @tf.autograph.experimental.do_not_convert
      def one_step_on_data_distributed(data):
          data = data[0]
          outputs = self.distribute_strategy.run(
              one_step_on_data, args=(data,)
          )
          outputs = reduce_per_replica(
              outputs,
              self.distribute_strategy,
              reduction=self.distribute_reduction_method,
          )
          return outputs
  
      @tf.autograph.experimental.do_not_convert
      def multi_step_on_data(data):
          outputs = one_step_on_data_distributed(data[:1])
          for single_step_data in data[1:]:
              step_outputs = one_step_on_data_distributed(
                  [single_step_data]
              )
              outputs = tf.nest.map_structure(
                  lambda t1, t2: concat([t1, t2]), outputs, step_outputs
              )
          return outputs
  
      if self.steps_per_execution > 1:
          predict_function = multi_step_on_data
      else:
          predict_function = one_step_on_data_distributed
  
      if not self.run_eagerly:
          kwargs = {}
          if self._supports_reduce_retracing:
              kwargs.update({"reduce_retracing": True})
  
          predict_function = tf.function(predict_function, **kwargs)
  
      self.predict_function = predict_function
  
imports: import contextlib
  import warnings
  import numpy
  import tensorflow
  import tree
  from packaging.version import Version
  from tensorflow.python.eager import context
  from keras import callbacks
  from keras import metrics
  from keras import optimizers
  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 TensorFlowTrainer obj's underlying member method code with example usage: obj.make_predict_function(force) and returns: self, self, outputs, outputs