In some cases, it would be advantageous to use domain knowledge in the form of a Gaussian process prior for Bayesian optimization. If I know that some parameter values are likely to result in better outcomes, it would help to encode this knowledge in a prior distribution over functions. How can I do this in Ax or BoTorch?