Use explicit configs instead of kwargs

[martinkim0]

Currently we use kwargs plumbing extensively to route different sets of arguments to model parameters, training, dataloaders, etc. For example, for SCVI:

model = SCVI(**kwargs) <- kwargs for VAE
model.train(
    datasplitter_kwargs=... <- kwargs for DataSplitter
    plan_kwargs=... <- kwargs for TrainingPlan
    **kwargs <- kwargs for Trainer
)

All these different sets of kwargs can be confusing since users need to refer to different parts of the documentation, and they might not know where a certain parameter should go (e.g. should learning rate go to the Trainer or TrainingPlan?)

One way to address this would be to use configs defined by dataclasses. For example:

@dataclass
class VAEConfig:
    n_hidden: int = 128
    n_latent: int = 10
    ...

So just initializing SCVI() would use the default config, but in order to change a parameter, a user would have to define a new dataclass VAEConfig(n_hidden=256) that can automatically check arguments. The advantage here would be even more clear for the train function, where the docstring would only contain descriptions for DataSplitterConfig, TrainingPlanConfig, and TrainerConfig. This means that all models using, e.g. TrainingPlan, will only have one config, leading to less redundancy.