🐛 Bug

Calling SingleTaskVariationalGP.posterior with an input_transform raises a warning, whereas the equivalent call with SingleTaskGP does not. I'm not sure if input_transform works correctly with SingleTaskVariationalGP or if I can safely interpret the resulting posterior. It also seems a little odd to me that this would be a warning and not an exception.

To reproduce

** Code snippet to reproduce **

import torch from botorch.models import SingleTaskGP, SingleTaskVariationalGP from botorch.models.transforms import Normalize X = torch.rand((20, 1)) y = torch.sin(X) model = SingleTaskGP(train_X=X, train_Y=y, input_transform=Normalize(1)) post = model.posterior(X) # No warning model = SingleTaskVariationalGP(train_X=X, train_Y=y, input_transform=Normalize(1)) post = model.posterior(X) # Warning

** Stack trace/error message **

RuntimeWarning: Could not update `train_inputs` with transformed inputs since SingleTaskVariationalGP does not have a `train_inputs` attribute. Make sure that the `input_transform` is applied to both the train inputs and test inputs.``` 

System information

botorch version = 0.8.5
gpytorch version = 1.10
torch version = 1.13.1