code: def predict(
      self,
      data_creator,
      batch_size,
      verbose,
      steps,
      callbacks,
      data_config,
      output_cols,
  ):
      config = copy.copy(self.config)
      if data_config is not None:
          config.update(data_config)
      config["batch_size"] = batch_size
      dataset_handler = DatasetHandler.get_handler(
          self.backend, 0, self.size  # no rank for predict
      )
      config, local_batch_size = dataset_handler._handle_batch_size(
          config
      )
  
      dataset = data_creator(config, local_batch_size)
      params = dict(
          batch_size=local_batch_size,
          verbose=verbose,
          steps=steps,
          callbacks=callbacks,
      )
  
      def predict_fn(shard):
          y = self.model.predict(shard["x"], **params)
          if output_cols is None:
              return {"prediction": y}
          else:
              if len(output_cols) == 1:
                  return {output_cols[0]: y}
              else:
                  return dict(zip(output_cols, y))
  
      for shard in dataset:
          yield predict_fn(shard)
      self._stop_log_monitor()
  
imports: import json
  import os
  import tempfile
  import shutil
  import copy
  import tensorflow
  from bigdl.orca.data.utils import partition_get_data_label
  from bigdl.orca.data.file import exists
  from bigdl.orca.learn.utils import save_pkl, duplicate_stdout_stderr_to_file, get_rank, process_tensorboard_in_callbacks, save_model, load_model, replace_specific_object_from_callbacks
  from bigdl.orca.learn.log_monitor import LogMonitor
  from bigdl.orca.learn.tf2.callbacks import ModelCheckpoint
  from bigdl.dllib.utils.log4Error import *
label_desc: Use image node_id 8 for calling the SparkRunner obj's underlying member method code with example usage: obj.predict(data_creator, batch_size, verbose, steps, callbacks, data_config, output_cols) and returns: dict, dict, dict