object detection: object belonging to two classes

Say an object belongs to two classes (a human might place it in either of the two). Can that be handled by labeling it with two overlapping bounding boxes, one for each class?

Does the ssd model as implemented in the object detection api handle the two boxes independently during training? From my understanding of ssd, it makes an independent membership-prediction for each of the N+1 classes (including a “no object” prediction) so I think it should work out, but I might have misunderstood.