from tensorflow.python.framework import sparse_tensor

        linear_feature_columns=(bucketized_feature,),
        dnn_feature_columns=(cont_feature,),
        dnn_hidden_units=(3, 3))

    input_fn = test_data.iris_input_multiclass_fn
    metrics = classifier.fit(input_fn=input_fn, steps=_ITERS).evaluate(
        input_fn=input_fn, steps=100)
    self._assertCommonMetrics(metrics)

  def benchmarkPartitionedVariables(self):

    def _input_fn():
      features = {
          'language':
              sparse_tensor.SparseTensor(
                  values=('en', 'fr', 'zh'),
                  indices=((0, 0), (0, 1), (2, 0)),
                  dense_shape=(3, 2))
      }
      labels = constant_op.constant(((1,), (0,), (0,)))
      return features, labels

    # The given hash_bucket_size results in variables larger than the
    # default min_slice_size attribute, so the variables are partitioned.
    sparse_feature = feature_column.sparse_column_with_hash_bucket(
        'language', hash_bucket_size=2e7)
    embedding_feature = feature_column.embedding_column(
        sparse_feature, dimension=1)
