Is it the standardization formula, which is basically (xi - x_mean) / std_dev ?
If it is, then how come it is not deterministic, for example
>>> x = t.Tensor([1,-1,0,2])
>>> x.normal_()
tensor([-0.3429, -0.7214,  0.0883, -0.2900])
>>> x = t.Tensor([1,-1,0,2])
>>> x.normal_()
tensor([0.7380, 1.9640, 0.3068, 0.2396])

whereas if I was using this standardization then it should be
>>> x = t.Tensor([1,-1,0,2])
>>> x = x.numpy()
>>> (x - x.mean()) / x.std()
array([ 0.4472136, -1.3416407, -0.4472136,  1.3416407], dtype=float32)

So how can we see what’s really going on here?
Here it says “standardization transforms data to have a mean of zero and a standard deviation of 1.” - https://www.statisticshowto.datasciencecentral.com/normalized/ 8
In the torch docs it says
normal_(mean=0, std=1, *, generator=None) → Tensor

-https://pytorch.org/docs/stable/tensors.html#torch.Tensor.normal_ 86
so I assumed it was doing standardization but I guess not.