first elements of the input batches) even though mathematically it's
an identical computation.
Similarly, an operation applied to a tensor slice is not guaranteed to
produce results that are identical to the slice of the result of the
same operation applied to the full tensor. E.g. let "A" be a
2-dimensional tensor. "A.sum(-1)[0]" is not guaranteed to be bitwise
equal to "A[:,0].sum()".
Extremal values
When inputs contain large values such that intermediate results may
overflow the range of the used datatype, the end result may overflow
too, even though it is representable in the original datatype. E.g.:
import torch
   a=torch.tensor([1e20, 1e20]) # fp32 type by default
   a.norm() # produces tensor(inf)
   a.double().norm() # produces tensor(1.4142e+20, dtype=torch.float64), representable in fp32
Linear algebra ("torch.linalg")
Non-finite values
The external libraries (backends) that "torch.linalg" uses provide no