code: def add_int(self, name, min, max, warp=None):
          
          min, max = map(int, (min, max))
          if max < min:
              raise ValueError('variable %s: max < min error' % name)
          if warp not in (None, 'log'):
              raise ValueError('variable %s: warp=%s is not supported. use '
                               'None or "log",' % (name, warp))
          if min <= 0 and warp == 'log':
              raise ValueError('variable %s: log-warping requires min > 0')
  
          self.variables[name] = IntVariable(name, min, max, warp)
docs: An integer-valued dimension bounded between `min` <= x <= `max`.
          Note that the right endpoint of the interval includes `max`.
  
          When `warp` is None, the base measure associated with this dimension
          is a categorical distribution with each weight on each of the integers
          in [min, max]. With `warp == 'log'`, the base measure is a uniform
          distribution on the log of the variable, with bounds at `log(min)` and
          `log(max)`. This is appropriate for variables that are "naturally" in
          log-space. Other `warp` functions are not supported (yet), but may be
          at a later time. Please note that this functionality is not supported
          for `hyperopt_tpe`.