:type transformer: sklearn.tree.tree.BaseDecisionTree
        :type path: str
        :type model_name: str
        :type serialize_node: bool
        :param transformer:
        :param path:
        :param model_name:
        :return:

def serialize_to_bundle(self, transformer, path, model_name, serialize_node=True):
        """
        :type transformer: sklearn.tree.tree.BaseDecisionTree
        :type path: str
        :type model_name: str
        :type serialize_node: bool
        :param transformer:
        :param path:
        :param model_name:
        :return:
        """

        # Define attributes
        attributes = list()
        attributes.append(('num_features', transformer.n_features_))
        if isinstance(transformer, DecisionTreeClassifier):
            attributes.append(('num_classes', int(transformer.n_classes_)))

        inputs = []
        outputs = []
        if serialize_node:
            # define node inputs and outputs
            inputs = [{
                      "name": transformer.input_features,
                      "port": "features"
                    }]

            outputs = [{
                      "name": transformer.prediction_column,
                      "port": "prediction"
                    }]

        self.serialize(transformer, path, model_name, attributes, inputs, outputs, node=serialize_node)

        # Serialize tree.json
        tree_path = "{}/{}.node/tree.json".format(path, model_name)
        if not serialize_node:
            tree_path = "{}/{}/tree.json".format(path, model_name)
        with open(tree_path, 'w') as outfile:
            self.serialize_tree(transformer, transformer.feature_names, outfile)