[QUESTION] How exactly does auxiliary output in a NN help regularize

Hi
In chapter 10, under "use cases for multiple outputs", the last point says that auxiliary outputs can help regularize.

The exact bullet point is -
Another use case is as a regularization technique (i.e., a training constrain whose objective is to reduce overfitting and thus improve the model's ability to generalize). For example, you may want to add some auxiliary outputs in a NN architecture to ensure that the underlying part of the network learns something useful on its own, without relying on the rest of the network.

I just don't understand this. How exactly does it regularize? I detailed explanation would be appreciated!
Thanks