I'm pretty sure that if your notebook is big enough to debug what's in it you'd be doing everyone a disservice keeping it contained like that.
This can convert a Jupyter notebook into a Python script but unless the notebook has been written with some care (with the intention of converting it into a script later), a lot of manual twiddling will be required.
What I generally do is use Jupyter notebooks when I'm exploring a problem or dataset. When I'm done with the exploration phase, I immediately mark the notebook as deprecated (trying to keep it in sync with code is a nightmare) and link the the relevant source code elsewhere in the project that it was deprecated for.
Databricks does a decent job productionizing their notebooks, and have seen people be quite productive on that platform.
Papermill and some light container tooling can take you a long way. It gets bonus points because data science folks are quicker to jump into a notebook and debug their own equations than they are a Python module.