Finding the posterior covariance matrix from MATLAB GPR model

I am trying to find the posterior covariance matrix from a gaussian process using MatLab. The GP model is formed as follows:

    X = featurevector;
    Y = label;
    GPMdl = fitrgp(X,Y);

and predicting the mean and variance of test points is as follows:

    Xnew = newfeaturevectors;
    [PredU,PredS] = predict(Xnew,GPMdl)

PredS only gives me the variance vector of the test points. I want to find the covariance matrix of the test points. Any thoughts on how this can be computed?

[Sterling Baird]

Probably what you're looking for: https://www.mathworks.com/matlabcentral/answers/251782-calculating-the-matrix-k-at-test-inputs-after-training-a-gaussian-process-with-fitrgp