Bootstrap wrapper for metrics? about torchmetrics

[maximsch2]

Yes, we need to decide how we want to represent the bootstrapped result (e.g. mean + std, or maybe 5%, 50%, 95% percentiles, possibly even configurable), but yes, your compute function is approximately what I have in mind.

I don't have too much bandwidth to do it right now, so throwing it out there if someone wants to take it on. I might get back to it in the future though.

[SkafteNicki]

Hi @maximsch2, great idea. Would you be up for doing a PR?
Just to be clear, would the compute method look something like:

def compute(self):
    computed_vals = torch.stack([m.compute() for m in self.metrics], dim=0)
    return computed_vals.mean(dim=0), computed_vals.std(dim=0)

or would you just return the computed values?