How to use the mleap.sklearn.preprocessing.data.MathUnary function in mleap

def math_unary_deserialize_exp_test(self):

        math_unary_tf = MathUnary(input_features=['a'], output_features=['log_a'], transform_type='exp')

        Xres = math_unary_tf.fit_transform(self.df.a)

        self.assertEqual(np.exp(self.df.a[0]), Xres[0])

        math_unary_tf.serialize_to_bundle(self.tmp_dir, math_unary_tf.name)

        node_name = "{}.node".format(math_unary_tf.name)
        math_unary_ds_tf = MathUnary()
        math_unary_ds_tf = math_unary_ds_tf.deserialize_from_bundle(self.tmp_dir, node_name)

        with open("{}/{}.node/model.json".format(self.tmp_dir, math_unary_tf.name)) as json_data:
            model = json.load(json_data)

        res_a = math_unary_tf.transform(self.df['a'])
        res_b = math_unary_ds_tf.transform(self.df['a'])

def math_unary_deserialize_exp_test(self):

        math_unary_tf = MathUnary(input_features=['a'], output_features=['log_a'], transform_type='exp')

        Xres = math_unary_tf.fit_transform(self.df.a)

        self.assertEqual(np.exp(self.df.a[0]), Xres[0])

        math_unary_tf.serialize_to_bundle(self.tmp_dir, math_unary_tf.name)

        node_name = "{}.node".format(math_unary_tf.name)
        math_unary_ds_tf = MathUnary()
        math_unary_ds_tf = math_unary_ds_tf.deserialize_from_bundle(self.tmp_dir, node_name)

        with open("{}/{}.node/model.json".format(self.tmp_dir, math_unary_tf.name)) as json_data:
            model = json.load(json_data)

        res_a = math_unary_tf.transform(self.df['a'])
        res_b = math_unary_ds_tf.transform(self.df['a'])

        self.assertEqual(res_a[0], res_b[0])

def math_unary_sin_test(self):

        math_unary_tf = MathUnary(input_features=['a'], output_features=['sin_a'], transform_type='sin')

        Xres = math_unary_tf.fit_transform(self.df.a)

        self.assertEqual(np.sin(self.df.a[0]), Xres[0])

        math_unary_tf.serialize_to_bundle(self.tmp_dir, math_unary_tf.name)

        expected_model = {
          "op": "math_unary",
          "attributes": {
            "operation": {
              "string": 'sin'
            }
          }
        }

def math_unary_exp_test(self):

        math_unary_tf = MathUnary(input_features=['a'], output_features=['log_a'], transform_type='exp')

        Xres = math_unary_tf.fit_transform(self.df.a)

        self.assertEqual(np.exp(self.df.a[0]), Xres[0])

        math_unary_tf.serialize_to_bundle(self.tmp_dir, math_unary_tf.name)

        expected_model = {
          "op": "math_unary",
          "attributes": {
            "operation": {
              "string": 'exp'
            }
          }
        }