I think I’m a little skeptical.

#pragma is a real keyword in C but # is just a comment in python - why not use a pythonic @decorator?

Vendoring a library like this into an app seems like a lot more toil than just writing the native multiprocessing python code, or using something built for number crunching like arrayfire or numpy

Because (even if you could decorate for-loops, or instead decorated functions containing a loop and nothing else) the resulting behavior would be completely unlike the behavior of all other @decorators in Python. It would also be nearly impossible to implement this functionality as a decorator without introducing tons of fragility.

The preprocessor/import-hook hack that allows macro-like behavior in Python does arbitrary transforms at the text/code-load level, while decorators are evaluated at runtime and run regular Python code. Decorators can be composed/wrapped by other functions and decorators, whereas implementing Lucen-like functionality with a decorator would necessarily match on the name/symbol of the decorator and couldn't indirect "through" it.

Heck, even if you disallowed wrapping and matched on the decorator name during preprocessing (basically stripping it out and rewriting decorated code strings at load time), even identifying the correct decorator as a transformation target would require reimplementing a ton of Python's import/scope/name resolution behavior by hand at code-transformation time. If your decorator was called lucen.parallelize(), consider the difference between "import lucen; @lucen.parallelize", "from lucen import parallelize; @parallelize", "from lucen import parallelize as pl; parallelize = 123; @pl", and so on. You'd have to handle all of those cases, and many more (e.g. decorated functions defined inside other classes/functions) at the code-as-string or AST level. You couldn't run the decorator at runtime, because a) syntactic information is gone by then, and b) because then you'd need to unimport/reimport module code which had already been run with arbitrary global side effects.

More information on the methods available to get preprocessor-like functionality in Python:

Custom source encoding text transformers can be loaded via .pth files: https://pydong.org/posts/PythonsPreprocessor/

Import hooks can be configured at runtime, and run during subsequent import statements. They were originally designed to allow customized module discovery, but nothing stops you from using them like Lucen does: to intercept imports of local Python files and transform/replace the code imported at load time: https://peps.python.org/pep-0302/

You can @decorate a for-loop?

I wish there were easier ways to play with syntax in Python, but there we are.

Though I guess using @decorator semantics would make it look a lot like python-numba syntax