[FEA] Build HNSW hierarchy from a base CAGRA graph on GPU

[cjnolet]

This feature succeeds #431 and improves the performance (and likely the quality) by building the HNSW hierarchy on a base CAGRA graph on the GPU.

There are two ways in which we can construct this hierarchy (both of which are used to construct the k-means tree in Google's SCaNN algorithm):

1. Top-down: nested (hierarchical) kmeans. I suspect we have a lot of this already in our balanced kmeans, but it hides the hierarchy away and extracts flattened clusters like HDBSCAN

2. Bottom-up: Using agglomerative clustering: Basically it's the single-linkage + condense hierarchy steps from HDBSCAN.

We suspect that building the hierarchy after the base graph is built will improve the insertion capability for CAGRA, but it will also have a positive impact on recall, since the hierarchy will be built by taking all vectors in the index into consideration, rather than being done in a greedy manner like HNSW where the quality of the hierarchy depends on the completely random ordering of the vectors during construction.