from tensorflow.contrib.learn.python.learn.estimators import test_data

    self.report_benchmark(
        iters=metrics['global_step'],
        extras={k: v
                for k, v in metrics.items() if k in _METRIC_KEYS})

  def benchmarkMatrixData(self):
    iris = test_data.prepare_iris_data_for_logistic_regression()
    cont_feature = feature_column.real_valued_column('feature', dimension=4)
    bucketized_feature = feature_column.bucketized_column(
        cont_feature, test_data.get_quantile_based_buckets(iris.data, 10))

    classifier = dnn_linear_combined.DNNLinearCombinedClassifier(
