[D] Is there a way to determine if the representations a model learns are spherical or hyperbolic?

Title. Is there a way to determine the degree of sphericity or hyperbolicity of the embeddings a feature extractor learns for a set of examples it has been trained on / will be tested on?

I am new to geometry in deep learning. It would be amazing if anyone could also point me to a paper or a book to get started on this. Thanks in advance.

deep learning embeddings examples feature geometry machinelearning set will