Managing optional dependencies in Python projects

This is half a feature request and half a request for comment on my current solution.

In PySR I have four optional Julia dependencies: LoopVectorization, Bumper, Zygote, and ClusterManagers. Installing all of these takes a bit of time, especially Julia precompilation, so I chose to install them only when the user needs such features. They trigger extensions in the upstream Julia packages.

The way I currently have this set up is as follows:

```python
from typing import Optional
from .julia_import import Pkg, jl # Internal meta-loading file to set up the right ENV variables

def load_required_packages(
*,
turbo: bool = False,
bumper: bool = False,
enable_autodiff: bool = False,
cluster_manager: Optional[str] = None,
):
if turbo:
load_package("LoopVectorization", "bdcacae8-1622-11e9-2a5c-532679323890")
if bumper:
load_package("Bumper", "8ce10254-0962-460f-a3d8-1f77fea1446e")
if enable_autodiff:
load_package("Zygote", "e88e6eb3-aa80-5325-afca-941959d7151f")
if cluster_manager is not None:
load_package("ClusterManagers", "34f1f09b-3a8b-5176-ab39-66d58a4d544e")

def isinstalled(uuid_s: str):
return jl.haskey(Pkg.dependencies(), jl.Base.UUID(uuid_s))

def load_package(package_name: str, uuid_s: str) -> None:
if not isinstalled(uuid_s):
Pkg.add(name=package_name, uuid=uuid_s)

jl.seval(f"using {package_name}: {package_name}")
return None
```

Basically in my monolithic `PySRRegressor` object, I check the options the user selected and then install each of the packages they need. It seems to work reasonably well.

_Note I use the `jl.haskey(...)` check instead of a `try catch` because as I painfully discovered, the `try catch` fails to detect a package not being present in the active environment (since you can technically load packages from `@v1.10`). This is problematic because when doing distributed compute, the worker processes will not have access to `@v1.10`; only the current environment! So the haskey method is needed..._

I am wondering what the best way to do this is, and if we could have such a feature in juliacall or juliapkg at some point? I'm not really sure how it could work. I also don't want to increase complexity unnecessarily; maybe a `isinstalled` function is all we need here. The other thing I'm worried about is for multiple Julia-accelerated Python packages being loaded simultaneously (if not now, at some point) – how would this workflow interact between those packages?

Maybe the simplest things is to just require all potential extensions be installed at once... But it would get a bit much once I enable CUDA support for PySR, there would be so many installs required.

Start by reading the .julia_import setup and the load_required_packages, isinstalled, and load_package entry points shown in the issue, then compare how juliacall or juliapkg currently manage environments. Determine how optional dependencies should be detected and coordinated across Python packages and worker processes; done requires an agreed design and corresponding documented behavior.

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