def METHOD_NAME(self, decision_requests: DecisionSteps) -> np.ndarray:
    mask = None
    if self.behavior_spec.action_spec.discrete_size > 0:
        num_discrete_flat = np.sum(self.behavior_spec.action_spec.discrete_branches)
        mask = torch.ones([len(decision_requests), num_discrete_flat])
        if decision_requests.action_mask is not None:
            mask = torch.as_tensor(
                1 - np.concatenate(decision_requests.action_mask, axis=1)
            )
    return mask