How to use the mleap.bundle.serialize.Vector function in mleap

"name": transformer.prediction_column,
                  "port": "prediction"
                })

        outputs.append({
              "name": "raw_prediction",
              "port": "raw_prediction"
             })

        outputs.append({
              "name": "probability",
              "port": "probability"
            })

        # compile tuples of model attributes to serialize
        tree_weights = Vector([1.0 for x in range(0, len(transformer.estimators_))])
        attributes = list()
        attributes.append(('num_features', transformer.n_features_))
        attributes.append(('tree_weights', tree_weights))
        attributes.append(('trees', ["tree{}".format(x) for x in range(0, len(transformer.estimators_))]))
        if isinstance(transformer, RandomForestClassifier):
            attributes.append(('num_classes', transformer.n_classes_)) # TODO: get number of classes from the transformer

        self.serialize(transformer, path, model, attributes, inputs, outputs)

        rf_path = "{}/{}.node".format(path, model)

        estimators = transformer.estimators_

        i = 0
        for estimator in estimators:
            estimator.mlinit(input_features = transformer.input_features, prediction_column = transformer.prediction_column, feature_names=transformer.feature_names)

def serialize_to_bundle(self, path, model_name):
        # compile tuples of model attributes to serialize
        attributes = list()
        attributes.append(("labels", self.labels.keys()))
        attributes.append(("values", Vector(self.labels.values())))

        # define node inputs and outputs
        inputs = [{
            "name": self.input_features[0],
            "port": "input"
        }]

        outputs = [{
            "name": self.output_features,
            "port": "output"
        }]

        self.serialize(self, path, model_name, attributes, inputs, outputs)