docs: def control(self, size=0, configure=None):
          """
          Finds all inclusion-minimal intervention strategies up to the given size.
          Intervention strategies found are saved in the attribute :attr:`strategies`
          as a :class:`caspo.core.clamping.ClampingList` object instance.
  
          Example::
  
              >>> from caspo import core, control
  
              >>> networks = core.LogicalNetworkList.from_csv('networks.csv')
              >>> scenarios = control.ScenarioList('scenarios.csv')
  
              >>> controller = control.Controller(networks, scenarios)
              >>> controller.control()
  
              >>> controller.strategies.to_csv('strategies.csv')
  
          Parameters
          ----------
          size : int
              Maximum number of intervention per intervention strategy
  
          configure : callable
              Callable object responsible of setting clingo configuration
          """
          self._strategies = []
  
          clingo = gringo.Control(['-c maxsize=%s' % size])
  
          clingo.conf.solve.models = '0'
          if configure:
              def overwrite(args, proxy):
                  for i in xrange(args.threads):
                      proxy.solver[i].no_lookback = 'false'
                      proxy.solver[i].heuristic = 'domain'
                      proxy.solver[i].dom_mod = '5,16'
  
              configure(clingo.conf, overwrite)
          else:
              clingo.conf.solver.no_lookback = 'false'
              clingo.conf.solver.heuristic = 'domain'
              clingo.conf.solver.dom_mod = '5,16'
  
          clingo.conf.solve.enum_mode = 'domRec'
  
          clingo.add("base", [], self.instance)
          clingo.load(self.encodings['control'])
  
          clingo.ground([("base", [])])
          clingo.solve(on_model=self.__save__)
  
          self.stats['time_optimum'] = clingo.stats['time_solve']
          self.stats['time_enumeration'] = clingo.stats['time_total']
  
          self._logger.info("%s optimal intervention strategies found in %.4fs", len(self._strategies), self.stats['time_enumeration'])
  
          self.strategies = core.ClampingList(self._strategies)