a = np.random.randint(6,size=(4,5,3))
idx = np.nonzero(a)
a[idx] = 0

This is PyTorch

a = torch.randint(6,size=(4,5,3))
idx = torch.nonzero(a) # idx = a.nonzero()
# a[idx] = 0 # this throws an error because `nonzero` cannot be used as index
a[(idx[:, 0], idx[:, 1], idx[:, 2])] = 0 # this is a little counter-intuitive, can we instead accept multi-dim tensors as index?

I actually like torch.nonzero's single tensor better than the tuple that numpy returns, it is more elegant. But I do not know of a better way to use it as index

Maybe it is a bit unfortunate that nonzero has the dimensions in that order…
You can make the split explicit and have something that is close to NumPy’s nonzero indexing:

Thanks! .split makes it easier. As a suggestion if PyTorch tensors could also be indexed by another tensor (of coordinates) that could be even more intuitive than numpy.