ENH: Add config / params dataclasses for high-level functions

[NickleDave]

ENH: Add config / params dataclasses for high-level functions

[NickleDave]

I have thought about a couple of ways to do this:

1. Use introspection to literally make a dataclass directly from the training function and its type annotations -- this is the Params approach, since we make a dataclass whose attributes are literally the parameters of the function
2. Write a high-level TrainConfig class and then subclass it. This potentially has all the usual issues with subclassing, with the caveat that these are just attributes and not methods proper so we seem less likely to bang up against those issues. Worst case we might just end up adding/removing attributes a lot.

Either way we should end up with the following:
high-level functions that just take a TrainConfig/TrainParams,
something like this

def train(
    config: TrainFrameClassificationConfig | TrainParametricUMAPConfig | TrainAvaConfig
)
    # validate config is an instance of one of those configs we type hint with, then
    train_kwargs = asdict(config)
    if isinstance(config, TrainFrameClassificationConfig):
        train_frame_classification_model(**train_kwargs)
    elseif ...