:param transformer: Random Forest Regressor or Classifier
        :param path: Root path where to serialize the model
        :param model: Name of the model to be serialized
        :type transformer: sklearn.ensemble.forest.BaseForest
        :type path: str
        :type model: str
        :return: None

def serialize_to_bundle(self, transformer, path, model):
        """
        :param transformer: Random Forest Regressor or Classifier
        :param path: Root path where to serialize the model
        :param model: Name of the model to be serialized
        :type transformer: sklearn.ensemble.forest.BaseForest
        :type path: str
        :type model: str
        :return: None
        """

        # Define Node Inputs and Outputs
        inputs = [{
                  "name": transformer.input_features,
                  "port": "features"
                }]

        outputs = list()
        outputs.append({
                  "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)
            model_name = "tree{}".format(i)
            estimator.serialize_to_bundle(rf_path, model_name, serialize_node=False)

            i += 1