def METHOD_NAME(output_dir, feature_cols):
  """Configures DNNBoostedTreeCombinedRegressor based on flags."""
  learner_config = learner_pb2.LearnerConfig()
  learner_config.learning_rate_tuner.fixed.learning_rate = (
      FLAGS.tree_learning_rate)
  learner_config.regularization.l1 = 0.0
  learner_config.regularization.l2 = FLAGS.tree_l2
  learner_config.constraints.max_tree_depth = FLAGS.tree_depth
  run_config = tf.contrib.learn.RunConfig(save_summary_steps=1)
  # Create a DNNBoostedTreeCombinedRegressor estimator.
  estimator = DNNBoostedTreeCombinedRegressor(
      dnn_hidden_units=[int(x) for x in FLAGS.dnn_hidden_units.split(",")],
      dnn_feature_columns=feature_cols,
      tree_learner_config=learner_config,
      num_trees=FLAGS.num_trees,
      # This should be the number of examples. For large datasets it can be
      # larger than the batch_size.
      tree_examples_per_layer=FLAGS.batch_size,
      model_dir=output_dir,
      config=run_config,
      dnn_input_layer_to_tree=True,
      dnn_steps_to_train=FLAGS.dnn_steps_to_train)
  return estimator