Given a probability tensor, how can I use torch.bernoulli to generate multiple samplings while ensuring they are all distinct? Is there any better way than the naive way, i.e., just sampling and re-trying if colliding? Any method using other libraries (e.g., numpy, scipy) are also fine!
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