code: def create_prediction_estimator(hyper_params, model, checkpoint_path=None):
      
      if checkpoint_path is None:
          chkpts = sorted([name for name in os.listdir(hyper_params.train.checkpoint_path)])
          checkpoint_path = hyper_params.train.checkpoint_path + "/" + chkpts[-1]
          print("Latest found checkpoint: {}".format(checkpoint_path))
  
      estimator_spec = create_tf_estimator_spec(checkpoint_path, model, create_loss=None)
  
      # Create the estimator.
      estimator = tf.estimator.Estimator(estimator_spec,
                                         model_dir=checkpoint_path,
                                         params=hyper_params)
  
      return estimator
docs: Create an estimator for prediction purpose only.
      :param hyper_params: The hyper params file.
      :param model: The keras model.
      :param checkpoint_path: (Optional) Path to the specific checkpoint to use.
      :return: