Randomly sample from a Random Gaussian distribution

Question

I have been tasked with creating 1000 random samples that follow the distribution: $\begin{bmatrix}X_{1}\\X_{2}\end{bmatrix}\sim \mathcal{N} (\begin{bmatrix}3\\1\end{bmatrix}, \begin{bmatrix}7&3\\3&2\end{bmatrix}) $ using Python and Numpy. I have started by generating 1000 random samples 2x1 random vectors with mean 0 and whose covariance matrix is the 2x2 identity matrix using np.random.randn() but now I am not sure how to transform those samples so that they follow the given distribution.