def optimize(self, n_trials=100, save_params=True):
        if not TRAIN and os.path.isfile(self.params_path):
            with open(self.params_path, 'rb') as f:
                best_params = pickle.load(f)
            print("Loaded parameters from disk")
        elif not FINETUNE:
            best_params = {'lr': LEARNING_RATE, 'gamma': GAMMA}
            print(f"Using default parameters: {best_params}")
        else:
            print("Optimizing hyperparameters")
            study = optuna.create_study(direction='maximize')
            study.optimize(self.objective, n_trials=n_trials)
            best_params = study.best_params
            if save_params:
                with open(self.params_path, 'wb') as f:
                    pickle.dump(best_params, f)
                print("Saved parameters to disk")
        return best_params