class_docstr: Manages a TensorFlow model for training.
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)
      if not isinstance(dataset, ray.ObjectID):
          invalidInputError(
              False, "Only xshards is supported for predict"
          )
  
      partition = ray.get(dataset)
      if len(partition) == 0:
          return []
      params = dict(
          batch_size=local_batch_size,
          verbose=verbose,
          steps=steps,
          callbacks=callbacks,
      )
  
      invalidInputError(
          hasattr(self, "model"),
          "The model has not yet been created. "
          "Please input a model_creator when creating estimator "
          "or use load function of the estimator to load a model.",
      )
  
      if self.backend == "tf-distributed":
          if self.model_creator is not None:
              local_model = self.model_creator(self.config)
          else:
              filepath = self.load_params["filepath"]
              file_name = os.path.basename(filepath)
              with tempfile.TemporaryDirectory() as tempdir:
                  temp_path = os.path.join(tempdir, file_name)
                  if is_file(filepath):
                      # h5 format
                      get_remote_file_to_local(filepath, temp_path)
                  else:
                      # savemodel format
                      os.makedirs(temp_path)
                      get_remote_dir_to_local(filepath, temp_path)
                  self.load_params["filepath"] = temp_path
                  local_model = self.process_model_load(
                      **self.load_params
                  )
                  self.load_params["filepath"] = filepath
          try:
              local_model.set_weights(self.model.get_weights())
          except Exception:
              logger.warning(
                  "Get a sample input from input data to init the model."
              )
              sample_shard = copy.deepcopy(partition[0])
              log4Error.invalidInputError(
                  isinstance(sample_shard, dict),
                  "Only dictionary is supported in tf_runner predict",
              )
              for k, v in sample_shard.items():
                  sample_shard[k] = v[0:1]
              local_model(sample_shard)
              local_model.set_weights(self.model.get_weights())
      else:
          local_model = self.model
  
      def predict_fn(shard):
          if "x" in shard:
              y = local_model.predict(shard["x"], **params)
          else:
              y = local_model.predict(shard, **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))
  
      new_part = [predict_fn(shard) for shard in partition]
  
      return new_part
  
imports: import json
  import os
  import socket
  import shutil
  import tempfile
  import copy
  import ray
  from contextlib import closing
  from bigdl.dllib.utils import log4Error
  from bigdl.orca.data.utils import partitions_get_data_label, partitions_get_tf_dataset
  from bigdl.orca.data.file import is_file, get_remote_file_to_local, get_remote_dir_to_local, get_remote_files_with_prefix_to_local, put_local_file_to_remote, put_local_dir_tree_to_remote, put_local_files_with_prefix_to_remote
  from bigdl.orca.learn.utils import process_tensorboard_in_callbacks
  from bigdl.dllib.utils.log4Error import *
label_desc: Use image node_id 8 for calling the TFRunner obj's underlying member method code with example usage: obj.predict(data_creator, batch_size, verbose, steps, callbacks, data_config, output_cols) and returns: new_part, list, dict, dict, dict