linear algebra – Why the multiplication of a covariance matrix with the inverse of its sum with the identity matrix is symmetric?

I have an empirical result (meaning it is always true by simple simulation e.g. in R) which I cannot prove to myself:

Let $A$ be a $n times n$ covariance matrix (i.e. it is symmetric PSD), let $I_n$ be the identity matrix, $theta_1$ and $theta_2$ some scalars (in my case they are always positive but it does not matter). Let:

$V = (theta_1 A + theta_2I_n)^{-1}A$

It seems that $V$ is always symmetric! Can we prove it?

E.g. in R:

A <- cov(rbind(c(1,2.1,3), c(3,4,5.3), c(3,4.2,0)))
isSymmetric(solve(2 * A + 3 * diag(3)) %*% A)
(1) TRUE

To anyone interested: it is important to me mainly because this means I have two symmetric matrices $A, B$ which multiply to a symmetric matrix $AB$, in which case its eigenvalues are in fact multiplications of the eigenvalues of $A$ and $B$ according to this, which also simplifies its trace.