How to use the gym-unity.gym_unity.envs.__init__.UnityGymException function in gym-unity

def _check_agents(self, n_agents):
        if not self._multiagent and n_agents > 1:
            raise UnityGymException(
                "The environment was launched as a single-agent environment, however"
                "there is more than one agent in the scene."
            )
        elif self._multiagent and n_agents <= 1:
            raise UnityGymException(
                "The environment was launched as a mutli-agent environment, however"
                "there is only one agent in the scene."
            )
        if self._n_agents is None:
            self._n_agents = n_agents
            logger.info("{} agents within environment.".format(n_agents))
        elif self._n_agents != n_agents:
            raise UnityGymException(
                "The number of agents in the environment has changed since "
                "initialization. This is not supported."

def _check_agents(self, n_agents):
        if not self._multiagent and n_agents > 1:
            raise UnityGymException(
                "The environment was launched as a single-agent environment, however"
                "there is more than one agent in the scene."
            )
        elif self._multiagent and n_agents <= 1:
            raise UnityGymException(
                "The environment was launched as a mutli-agent environment, however"
                "there is only one agent in the scene."
            )
        if self._n_agents is None:
            self._n_agents = n_agents
            logger.info("{} agents within environment.".format(n_agents))
        elif self._n_agents != n_agents:
            raise UnityGymException(
                "The number of agents in the environment has changed since "
                "initialization. This is not supported."

def _check_agents(self, n_agents):
        if not self._multiagent and n_agents > 1:
            raise UnityGymException(
                "The environment was launched as a single-agent environment, however"
                "there is more than one agent in the scene."
            )
        elif self._multiagent and n_agents <= 1:
            raise UnityGymException(
                "The environment was launched as a mutli-agent environment, however"
                "there is only one agent in the scene."
            )
        if self._n_agents is None:
            self._n_agents = n_agents
            logger.info("{} agents within environment.".format(n_agents))
        elif self._n_agents != n_agents:
            raise UnityGymException(
                "The number of agents in the environment has changed since "
                "initialization. This is not supported."

self.game_over = False
        self._allow_multiple_visual_obs = allow_multiple_visual_obs

        # Check brain configuration
        if len(self._env.get_agent_groups()) != 1:
            raise UnityGymException(
                "There can only be one brain in a UnityEnvironment "
                "if it is wrapped in a gym."
            )

        self.brain_name = self._env.get_agent_groups()[0]
        self.name = self.brain_name
        self.group_spec = self._env.get_agent_group_spec(self.brain_name)

        if use_visual and self._get_n_vis_obs() == 0:
            raise UnityGymException(
                "`use_visual` was set to True, however there are no"
                " visual observations as part of this environment."
            )
        self.use_visual = self._get_n_vis_obs() >= 1 and use_visual

        if not use_visual and uint8_visual:
            logger.warning(
                "`uint8_visual was set to true, but visual observations are not in use. "
                "This setting will not have any effect."
            )
        else:
            self.uint8_visual = uint8_visual

        if self._get_n_vis_obs() > 1 and not self._allow_multiple_visual_obs:
            logger.warning(
                "The environment contains more than one visual observation. "

action (object/list): an action provided by the environment
        Returns:
            observation (object/list): agent's observation of the current environment
            reward (float/list) : amount of reward returned after previous action
            done (boolean/list): whether the episode has ended.
            info (dict): contains auxiliary diagnostic information, including BrainInfo.
        """

        # Use random actions for all other agents in environment.
        if self._multiagent:
            if not isinstance(action, list):
                raise UnityGymException(
                    "The environment was expecting `action` to be a list."
                )
            if len(action) != self._n_agents:
                raise UnityGymException(
                    "The environment was expecting a list of {} actions.".format(
                        self._n_agents
                    )
                )
            else:
                if self._flattener is not None:
                    # Action space is discrete and flattened - we expect a list of scalars
                    action = [self._flattener.lookup_action(_act) for _act in action]
                action = np.array(action)
        else:
            if self._flattener is not None:
                # Translate action into list
                action = self._flattener.lookup_action(action)

        spec = self.group_spec
        action = np.array(action).reshape((self._n_agents, spec.action_size))