Retain calibration data

add_calibration_data() would solve the problems of

• Having the calibration data being peeled off of the training/analysis set
• Users are unable to use a static calibration set.
• More reproducible workflows (see tidymodels/tune#1038) and easier debugging.
• Enable more advanced postprocessors for different cases (forecasting, applicability domains, etc.)

When using add_calibration_data(), a tibble that conforms to the training set mold is required.

The calibration set would be stored in worflow$pre$calibration (similar to case weights).

When fit.workflow() or .fit_post() are called, they first preprocess the calibration set (if any), make predictions, and pass those predictions^* to the calibrator.

^*I believe that we should also have a way to pass in the molded calibration set for the trailer adjustments. We have 2-3 cases where those are required.

Start by reading the proposed add_calibration_data() entry point and the fit.workflow() and .fit_post() call paths. Trace how workflow$pre currently stores case weights and how a training-set mold is represented. Done should include a decided design for retaining, preprocessing, and passing calibration data to calibrators, including molded data needed for trailer adjustments.