This is a side question

I have read in a different thread that the Fisher information matrix I(theta) = X^TVX where X is the design matrix and V is the variance matrix for the response, y and that the diagonals of its inverse equal Var(theta).

https://stats.stackexchange.com/questions/89484/how-to-compute-the-standard-errors-of-a-logistic-regressions-coefficients

I worked through an example, assuming (X^TVX)^-1 = I(theta)^-1 = Var(theta) and did not obtain a diagonal matrix for I(theta) as is shown in your linked slide. I'm trying to relate these two different representations of the Fisher matrix, basically