from tensorflow.python.feature_column import feature_column_lib as core_feature_column

        loss_reduction=losses.Reduction.SUM_OVER_NONZERO_WEIGHTS)

    model = estimator.GradientBoostedDecisionTreeRanker(
        head=head_fn,
        learner_config=learner_config,
        num_trees=1,
        examples_per_layer=3,
        model_dir=model_dir,
        config=config,
        use_core_libs=True,
        feature_columns=[
            core_feature_column.numeric_column("f1"),
            core_feature_column.numeric_column("f2")
        ],
        ranking_model_pair_keys=("a", "b"))

    model.fit(input_fn=_ranking_train_input_fn, steps=1000)
    model.evaluate(input_fn=_ranking_train_input_fn, steps=1)
    model.predict(input_fn=_infer_ranking_train_input_fn)


class CoreGradientBoostedDecisionTreeEstimator(test_util.TensorFlowTestCase):
