code: def predict(
      self,
      x,
      batch_size=None,
      verbose="auto",
      steps=None,
      callbacks=None,
  ):
      # Create an iterator that yields batches of input data.
      epoch_iterator = TFEpochIterator(
          x=x,
          batch_size=batch_size,
          steps_per_epoch=steps,
          shuffle=False,
          distribute_strategy=self.distribute_strategy,
          steps_per_execution=self.steps_per_execution,
      )
  
      # Container that configures and calls callbacks.
      if not isinstance(callbacks, callbacks_module.CallbackList):
          callbacks = callbacks_module.CallbackList(
              callbacks,
              add_history=True,
              add_progbar=verbose != 0,
              verbose=verbose,
              epochs=1,
              steps=epoch_iterator.num_batches,
              model=self,
          )
  
      def append_to_outputs(batch_outputs, outputs):
          if outputs is None:
              outputs = tf.nest.map_structure(
                  lambda batch_output: [batch_output],
                  batch_outputs,
              )
          else:
              tree.map_structure_up_to(
                  batch_outputs,
                  lambda output, batch_output: output.append(
                      batch_output
                  ),
                  outputs,
                  batch_outputs,
              )
          return outputs
  
      def get_data(iterator):
          """Returns data for the next execution."""
          data = []
          for _ in range(self.steps_per_execution):
              try:
                  single_step_data = next(iterator)
              except (StopIteration, tf.errors.OutOfRangeError) as e:
                  if hasattr(data, "__len__") and len(data) > 0:
                      # Suppress the error when still have remaining data.
                      return data
                  else:
                      # Re-raise the error for
                      # TFEpochIterator.catch_stop_iteration() to catch when
                      # no data left.
                      raise e
              data.append(single_step_data)
          return data
  
      self.make_predict_function()
      self.stop_predicting = False
      callbacks.on_predict_begin()
      outputs = None
      with epoch_iterator.catch_stop_iteration():
          for step, iterator in epoch_iterator.enumerate_epoch():
              callbacks.on_predict_batch_begin(step)
              data = get_data(iterator)
              batch_outputs = self.predict_function(data)
              outputs = append_to_outputs(batch_outputs, outputs)
              callbacks.on_predict_batch_end(
                  step, {"outputs": batch_outputs}
              )
              if self.stop_predicting:
                  break
      callbacks.on_predict_end()
      outputs = tree.map_structure_up_to(
          batch_outputs, potentially_ragged_concat, outputs
      )
      return tf.nest.map_structure(convert_to_np_if_not_ragged, outputs)
  
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 13 for calling the TensorFlowTrainer obj's underlying member method code with example usage: obj.predict(x, batch_size, verbose, steps, callbacks) and returns: tf, outputs, data, data