def save_trained_model(self, model: BaseAlgorithm) -> None:
        """
        Save trained model optionally with its replay buffer
        and ``VecNormalize`` statistics

        :param model:
        """
        print(f"Saving to {self.save_path}")
        model.save(f"{self.save_path}/{self.env_name}")

        if hasattr(model, "save_replay_buffer") and self.save_replay_buffer:
            print("Saving replay buffer")
            model.save_replay_buffer(os.path.join(self.save_path, "replay_buffer.pkl"))

        if self.normalize:
            # Important: save the running average, for testing the agent we need that normalization
            vec_normalize = model.get_vec_normalize_env()
            assert vec_normalize is not None
            vec_normalize.save(os.path.join(self.params_path, "vecnormalize.pkl"))

        Save trained model optionally with its replay buffer
        and ``VecNormalize`` statistics

        :param model:
        
rl_zoo3/exp_manager.py