class_docstr: Manages a TensorFlow model for training.
class_docstr_tok: ["Manages","a","TensorFlow","model","for","training","."]
code: def predict(
      self,
      data_creator,
      batch_size,
      verbose,
      steps,
      callbacks,
      data_config,
  ):
      config = self.config.copy()
      if data_config is not None:
          config.update(data_config)
  
      dataset = data_creator(config, batch_size)
      if not isinstance(dataset, ray.ObjectID):
          raise ValueError("Only xshards is supported for predict")
  
      partition = ray.get(dataset)
      params = dict(
          batch_size=batch_size,
          verbose=verbose,
          steps=steps,
          callbacks=callbacks,
      )
  
      if self.backend == "tf-distributed":
          local_model = self.model_creator(self.config)
          local_model.set_weights(self.model.get_weights())
      else:
          local_model = self.model
  
      def predict_fn(shard):
          y = local_model.predict(shard["x"], **params)
          return {"prediction": y}
  
      new_part = [predict_fn(shard) for shard in partition]
  
      return new_part
  
imports: import logging
  import json
  import os
  import numpy
  import ray
  from contextlib import closing
  import logging
  import socket
  from zoo.orca.data.utils import ray_partitions_get_data_label
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) and returns: new_part, dict