Combining multiple UMAP for different embedding models

https://umap-learn.readthedocs.io/en/latest/composing_models.html
This tutorial shows how 2 views of same data can be combined. I was wondering if this would work on 2 different embeddings on the same data?
Usecase -- We often cluster using tsne or umap using representation embeddings from some ML model. And often times we have 2 different embeddings capturing different features of the same data. One way to combine them for similarity is
w1*sim1 + w2*sim2 and use that as a combined similarity score that capture both emb1 and emb2 feature similarity together.
Made me wonder if combining through umap would have an advantage to cluster data which will capture features from both embedding models.

Start with the composing models tutorial linked in the issue and assess whether its approach applies to two different embeddings of the same data. Compare that with the proposed weighted similarity combination, and define whether a reproducible example, documentation, or implementation is needed to establish the benefit for clustering.