def add_int(self, name, min, max, warp=None):
min, max = map(int, (min, max))<EOL>if max < min:<EOL><INDENT>raise ValueError('<STR_LIT>' % name)<EOL><DEDENT>if warp not in (None, '<STR_LIT>'):<EOL><INDENT>raise ValueError('<STR_LIT>'<EOL>'<STR_LIT>' % (name, warp))<EOL><DEDENT>if min <= <NUM_LIT:0> and warp == '<STR_LIT>':<EOL><INDENT>raise ValueError('<STR_LIT>')<EOL><DEDENT>self.variables[name] = IntVariable(name, min, max, warp)<EOL>
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`.