Sampling from gaussian distribution
To my knowledge there is no such library. The problem you are trying to solve is straightforward. Just check if the random number you get is 'far enough' from the mean. The complexity of that check is constant. The probability of a point not to be between one sigma from the mean is ~32%. It is not that unlikely.
My question is very specific. Given a k dimensional Gaussian distribution with mean and standard deviation, say I wish to sample 10 points from this distribution. But the 10 samples should be very different from each other. For example, I do not wish to sample 5 of those very close to the mean (By very close, we may assume for this example within 1 sigma) which may happen if I do random sampling. Let us also add an additional constraint that all the drawn samples should be at least 1 sigma away from each other. Is there a known way to sample in this fashion methodically? Is there any such module in PyTorch which can do so? 
Sorry if this thought is ill posed but I am trying to understand if such a thing is possible.