I am working on the first assignment. I am wondering what is an elegant way of computing a matrix of dimension N x M whose entries are coming from a Multivariate Gaussian. Each row of A is a vector of size M. To generate this vector, I have figured out I need to instantiate a `MultivariateNormal`

and sample from it

`dist = MultivariateNormal(torch.zeros(M), torch.eye(M))`

`vec1 = dist.sample()`

How can I do this `N`

times in a pythonic way without using a for loop?

OK. I actually figured it out myself from the documentation. There is a `sample_shape`

parameter where we specify a tuple with the number of vectors we want `sample_shape=(N,)`

.

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