Consolidate model fitters into `ModelBuider`

[ColtAllen]

#1870 should be merged before opening a PR for this issue.

MMMs are the only models using ModelBuilder.fit directly. Other models have to override with their own fitters & samplers due to different data-loading requirements, which has led to code redundancies for idata extensions and prior/predictive checks. The CLVModels also provide fitters other than NUTS and a wrapper for arviz.fit_summary that could easily be moved into ModelBuilder for shared functionality.

Let's consolidate all shared boilerplate into ModelBuilder.fit, and pare down all the various model.fit(**specific_data_kwargs) methods into the bare minimum needed to call super().fit(**kwargs). We can start with this code created by @williambdean in #1467:

Model fit starter code

def create_fit_data(self) -> xr.Dataset:
	"""Create the fit_data group based on the input data."""
	if isinstance(self.data, pd.DataFrame):
		return self.data.to_xarray()
	return self.data

def fit(
	self,
	progressbar: bool | None = None,
	random_seed: RandomState | None = None,
	**kwargs: Any,
) -> az.InferenceData:
	"""Fit a model using the data provided at initialization.
	
	Sets attrs to inference data of the model.

	Parameters
	----------
	progressbar : bool, optional
		Specifies whether the fit progress bar should be displayed. Defaults to True.
	random_seed : Optional[RandomState]
		Provides sampler with initial random seed for obtaining reproducible samples.
	**kwargs : Any
		Custom sampler settings can be provided in form of keyword arguments.
	
	Returns
	-------
	self : az.InferenceData
		Returns inference data of the fitted model.
	
	Examples
	--------
>>> 	model = MyModel()
>>> 	idata = model.fit()
	Auto-assigning NUTS sampler...
	Initializing NUTS using jitter+adapt_diag...
	"""
	if not hasattr(self, "model"):
		self.build_model()
		
	sampler_kwargs = create_sample_kwargs(
		self.sampler_config,
		progressbar,
		random_seed,
		**kwargs,
	)
	with self.model:
		idata = pm.sample(**sampler_kwargs)
	
	if self.idata:
		self.idata = self.idata.copy()
		self.idata.extend(idata, join="right")
	else:
		self.idata = idata
	
	if "fit_data" in self.idata:
		del self.idata.fit_data
	
	fit_data = self.create_fit_data()

	with warnings.catch_warnings():
		warnings.filterwarnings(
			"ignore",
			category=UserWarning,
			message="The group fit_data is not defined in the InferenceData scheme",
		)
		self.idata.add_groups(fit_data=fit_data)
	self.set_idata_attrs(self.idata)
	return self.idata # type: ignore