Hello Ryan,
Sparse tensors are supported for all the semantics. They have some specific logic though:

(Except for Categorical-set features), each indexed value of a sparse tensor is treated as a separate individual features e.g. value[:,0], value[:,1], etc.
Non specified sparse values are detected as missing feature values.
The maximum shape of a sparse tensor should be set if the sparse tensor contains multiple features. If the maximum shape is not set (i.e. value.shape[1] is None), it is assumed to be one i.e. there is only one feature. You can see the list of individual features at the start of the training logs.

If you have specific issues with them, don’t hesiate to share it (even if you solved the issue). This kind of feedback is great to improve the API, errors/warnings and documentation :).