def _log_multi_stack(self, agent_mode, iter_nr, score_total, upper_conf95=None, lower_conf95=None):
    '\n        Additional logging for multistack evaluations\n        '
    graph_name = ('Evaluation/' + self._eval_env_bldr.env_cls.WIN_METRIC)
    self._ray.remote(self._chief_handle.add_scalar, self._exp_name_multi_stack[agent_mode], graph_name, iter_nr, score_total)
    if self._log_conf_interval:
        assert (upper_conf95 is not None)
        assert (lower_conf95 is not None)
        self._ray.remote(self._chief_handle.add_scalar, self._exp_names_multi_stack_conf[agent_mode][0], graph_name, iter_nr, lower_conf95)
        self._ray.remote(self._chief_handle.add_scalar, self._exp_names_multi_stack_conf[agent_mode][1], graph_name, iter_nr, upper_conf95)
Additional logging for multistack evaluations
def _log_multi_stack(self, agent_mode, iter_nr, score_total, upper_conf95=None, lower_conf95=None):
    '\n        \n        '
    graph_name = ('Evaluation/' + self._eval_env_bldr.env_cls.WIN_METRIC)
    self._ray.remote(self._chief_handle.add_scalar, self._exp_name_multi_stack[agent_mode], graph_name, iter_nr, score_total)
    if self._log_conf_interval:
        assert (upper_conf95 is not None)
        assert (lower_conf95 is not None)
        self._ray.remote(self._chief_handle.add_scalar, self._exp_names_multi_stack_conf[agent_mode][0], graph_name, iter_nr, lower_conf95)
        self._ray.remote(self._chief_handle.add_scalar, self._exp_names_multi_stack_conf[agent_mode][1], graph_name, iter_nr, upper_conf95)