I need to generate a large random vector, whose entries are independent but not identically distributed.  Each entry has its own mean and variance.  We could loop over the entries and sample a scalar Gaussian distribution, but that would need many function calls, slowing down the speed.
Alternatively, I could use the ``torch.distributions.multivariate_normal()`’, but the co-variance matrix was large enough to cause an out-of-memory exception.
Thanks.