My experience is that the world of Python dependency management is a mess which sometimes works, and sometimes forces you to spend hours-to-days searching for obscure error messages and trying maybe-fixes posted in Github issues for some other package, just in case it helps. This sometimes extends further - e.g. with hours-to-days spent trying to install just-the-right-version-of-CUDA on Linux...
Anyway, the (somewhat annoying but understandable) solution that some developers take is to make their utility/app/whatever as self-contained as possible with a fresh install of everything from Python downwards inside a venv - which results in (for example) multiple copies of PyTorch spread around your HDD. This is great for less technical users who just need a minimal-difficulty install (as IME it works maybe 80-90% of the time), good for people who don't want to spend their time debugging incompatibilities between different library versions, but frustrating for the more technically-inclined user.
This is just another approach to the same problem, which presumably also presents an even-lower level of work for the maintainers, since it avoids Python installs and packages altogether?
