implementation of KL divergence

[etzrisking]

I do understand that the comparison of the eigval from a uncorrelated matrix (i.e. identity matrix) would all be ones.
But, I'm not understanding how the KL divergence form is expressed as np.log(eigval) + 1/eigval.

I'm in the understanding that KL divergence = -sum[ f(x) log( g(x) / f(x) ]
maybe I"m just not understanding how f(x) and g(x) is expressed in the form of eigval.. hope to have some enlightment.. thanks