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(data):
          data = data[0]
          return self.predict_step(data)
  
      def multi_predict_steps(data):
          outputs = one_predict_step(data[:1])
  
          for single_step_data in data[1:]:
              step_outputs = one_predict_step([single_step_data])
              outputs = tree.map_structure(
                  lambda t1, t2: np.concatenate([t1, t2]),
                  outputs,
                  step_outputs,
              )
          return outputs
  
      if self.steps_per_execution > 1:
          predict_step = multi_predict_steps
      else:
          predict_step = one_predict_step
  
      self.predict_function = predict_step
  
imports: import numpy
  import tree
  from keras import backend
  from keras import callbacks
  from keras.backend.common import standardize_dtype
  from keras.backend.common.keras_tensor import KerasTensor
  from keras.backend.numpy.core import is_tensor
  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 5 for calling the NumpyTrainer obj's underlying member method code with example usage: obj.make_predict_function(force) and returns: self, self, outputs