Expose `MixedGradient`

It'd be nice to have a function that exactly gets the diagonal mixed gradient of a PFB, i.e. for a given real-valued function $T(\mathbf{x},y;\mathbf{c})$ for given parameters $\mathbf{c}$, it'd be nice to get the value of $\nabla_\mathbf{c}\partial_y T(\mathbf{x},y;\mathbf{c})$.

In practice, this is possible currently, but it's somewhat annoying (and I'm not really sure how stable it is). In Julia code, you have to do something like this

T = # ...
samples = #...
logdet_eval = LogDeterminant(T, samples)
logdet_grad = LogDeterminantCoeffGrad(T, samples)
mixed_grad = logdet_grad
for j in 1:size(samples,2)
    mixed_grad[:,j] *= exp(logdet_eval[j])
end

This comes from the fact that this mixed gradient is given by
$$\nabla_\mathbf{c}\partial_yT(\mathbf{x},y;\mathbf{c}) = \exp(\log\partial_y T(\mathbf{x},y;\mathbf{c})))\nabla_\mathbf{c}\log\partial_y T(\mathbf{x},y;\mathbf{c})$$
The exponential worries me and it seems like a waste to evaluate two functions when you only need one.

Start by locating the LogDeterminant and LogDeterminantCoeffGrad entry points used in the Julia example, then trace how their underlying operations are exposed. Define the MixedGradient entry point so callers can obtain the diagonal mixed gradient directly without separately evaluating both functions, and verify the public API and relevant behavior.