That being said, its usually pretty difficult to get code from papers to build/run successfully due to dependencies (e.g. depending on a specific version of Python and OpenCV 2 and requiring CUDA support)
TensorFlow is a crapshoot as far as I understand, constantly changing making newer versions incompatible; but why do other libraries break backwards compatibility without a major version bump?
You might be relying on a function that only exists in 3.7 and not in 3.6, code written in 3.6 would work but new code using 3.7 features won’t be backwards compatible. With compiled code the errors are usually very hard for “more used to scripts“ people to decode. You get stuff like missing symbols in the linker phase.
ML projects usually have a lot of libraries so you also get in the transient dependencies breaking quite often...
Some language-specific package managers can do similar things, but you really only get reproducibility for the whole system with a general-purpose package manager. Poetry gets you pretty far within the Python ecosystem, but if you need a specific version of Python/specific native libraries/etc... it doesn't get you all the way there.