class MultiAgentEnv(object):
    def step(self, actions):
        """Returns obss, reward, terminated, truncated, info"""
        raise NotImplementedError

    def get_obs(self):
        """Returns all agent observations in a list"""
        raise NotImplementedError

    def get_obs_agent(self, agent_id):
        """Returns observation for agent_id"""
        raise NotImplementedError

    def get_obs_size(self):
        """Returns the shape of the observation"""
        raise NotImplementedError

    def get_state(self):
        raise NotImplementedError

    def get_state_size(self):
        """Returns the shape of the state"""
        raise NotImplementedError

    def get_avail_actions(self):
        raise NotImplementedError

    def get_avail_agent_actions(self, agent_id):
        """Returns the available actions for agent_id"""
        raise NotImplementedError

    def get_total_actions(self):
        """Returns the total number of actions an agent could ever take"""
        # TODO: This is only suitable for a discrete 1 dimensional action space for each agent
        raise NotImplementedError

    def reset(self, seed=None, options=None):
        """Returns initial observations and info"""
        raise NotImplementedError

    def render(self):
        raise NotImplementedError

    def close(self):
        raise NotImplementedError

    def seed(self, seed=None):
        raise NotImplementedError

    def save_replay(self):
        raise NotImplementedError

    def get_env_info(self):
        env_info = {
            "state_shape": self.get_state_size(),
            "obs_shape": self.get_obs_size(),
            "n_actions": self.get_total_actions(),
            "n_agents": self.n_agents,
            "episode_limit": self.episode_limit,
        }
        
        if hasattr(self, "unit_dim"):
            env_info["unit_dim"] = self.unit_dim

        return env_info


    def get_stats(self):
        return {}
