def METHOD_NAME(self, **kwargs: np.ndarray) -> float:
    self.agent.load_state_dict(kwargs)
    try:  # safeguard against nans
        with torch.no_grad():
            reward = self.runner.run(self.agent)
    except RuntimeError as e:
        warnings.warn(f"Returning 0 after error: {e}")
        reward = 0.0
    assert isinstance(reward, (int, float))
    return self.reward_postprocessing(reward)