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


    classifier = dnn_linear_combined.DNNLinearCombinedClassifier(
        model_dir=tempfile.mkdtemp(),
        linear_feature_columns=(bucketized_feature,),
        dnn_feature_columns=(cont_feature,),
        dnn_hidden_units=(3, 3))

    input_fn = test_data.iris_input_logistic_fn
    metrics = classifier.fit(input_fn=input_fn, steps=_ITERS).evaluate(
        input_fn=input_fn, steps=100)
    self._assertSingleClassMetrics(metrics)

  def benchmarkTensorData(self):

    def _input_fn():
      iris = test_data.prepare_iris_data_for_logistic_regression()
      features = {}
      for i in range(4):
        # The following shows how to provide the Tensor data for
        # RealValuedColumns.
        features.update({
            str(i):
                array_ops.reshape(
                    constant_op.constant(
                        iris.data[:, i], dtype=dtypes.float32), (-1, 1))
        })
      # The following shows how to provide the SparseTensor data for
      # a SparseColumn.
      features['dummy_sparse_column'] = sparse_tensor.SparseTensor(
          values=('en', 'fr', 'zh'),
