How to use the nengo.base function in nengo

target = nengo_probe.target
        if isinstance(nengo_probe.target, nengo.base.ObjView):
            target = target.obj

        # if the target is a Node
        if isinstance(target, nengo.Node):
            target_object = nengo_probe.target

        # if the target is an ensemble, get the ensemble's app vert
        elif isinstance(target, nengo.Ensemble):
            target_object = target

        # if the target is a Neurons from an ensemble, backtrack to the
        # ensemble
        elif isinstance(target, nengo.ensemble.Neurons):
            if isinstance(nengo_probe.target, nengo.base.ObjView):
                target_object = nengo_probe.target.obj.ensemble
            else:
                target_object = nengo_probe.target.ensemble

        # if the target is a learning rule, locate the ensemble at the
        # destination.
        elif isinstance(target, nengo.connection.LearningRule):
            if isinstance(nengo_probe.target.connection.post_obj,
                          nengo.Ensemble):
                target_object = nengo_probe.target.connection.post_obj
            else:
                raise NotLocatedProbableClass(
                    "SpiNNaker does not currently support probing '{}' on "
                    "'{}'".format(
                        nengo_probe.attr, nengo_probe.target.
                        learning_rule_type.__class__.__name__))

def _get_reception_parameters(nengo_connection):
        """ returns the reception parameters of the nengo connection
        
        :param nengo_connection: the nengo connection in question
        :return: the reception parameters
        :rtype: ReceptionParameters
        """
        if (isinstance(nengo_connection.post_obj, nengo.base.NengoObject) or
                isinstance(nengo_connection.post_obj,
                           nengo.connection.LearningRule) or
                isinstance(nengo_connection.post_obj, nengo.ensemble.Neurons)):
            return ReceptionParameters(
                parameter_filter=nengo_connection.synapse,
                width=nengo_connection.post_obj.size_in,
                learning_rule=nengo_connection.learning_rule)

def _locate_correct_app_vertex_for_probe(
            nengo_probe, nengo_to_app_graph_map):
        """ locates the correct app vertex for a given probe
        
        :param nengo_probe: the nengo probe
        :param nengo_to_app_graph_map: the map between nengo objects and 
        nengo operator graph 
        :return: the nengo operator considered here
        """

        # ensure the target is of a nengo object
        target = nengo_probe.target
        if isinstance(nengo_probe.target, nengo.base.ObjView):
            target = target.obj

        # if the target is a Node
        if isinstance(target, nengo.Node):
            target_object = nengo_probe.target

        # if the target is an ensemble, get the ensemble's app vert
        elif isinstance(target, nengo.Ensemble):
            target_object = target

        # if the target is a Neurons from an ensemble, backtrack to the
        # ensemble
        elif isinstance(target, nengo.ensemble.Neurons):
            if isinstance(nengo_probe.target, nengo.base.ObjView):
                target_object = nengo_probe.target.obj.ensemble
            else: