Kernels for derivatives of Gaussian processes

It is sometimes useful (at least in my own work) to make posterior predictions of the derivative of a process or to make predictions of a process based on observations of the derivatives (see e.g. Rasmussen and Williams 9.4 or this paper). For these purposes, it would be nice to have some interface for associating partial derivatives with respect to kernel arguments (not parameters) with a kernel and applying transformations to a kernel and its derivatives. I'm imagining some sort of DifferentiableKernel type, but no idea if this is the best way to go about it. Any thoughts on how to approach this and do people think it is of wide enough interest to be implemented here?

Thanks in advance! I've enjoyed KernelFunctions so far, and look forward to watching it develop.

The issue names no files, tests, or entry points. Start by surveying the existing kernel interface and the cited derivative Gaussian-process references, then clarify the proposed design with maintainers; done would require an agreed interface for kernel argument derivatives and transformations, with corresponding implementation coverage and tests.