torch.fmax
torch.fmax(input, other, *, out=None) -> Tensor
Computes the element-wise maximum of "input" and "other".
This is like "torch.maximum()" except it handles NaNs differently:
   if exactly one of the two elements being compared is a NaN then the
   non-NaN element is taken as the maximum. Only if both elements are
   NaN is NaN propagated.
This function is a wrapper around C++'s "std::fmax" and is similar
   to NumPy's "fmax" function.
Supports broadcasting to a common shape, type promotion, and
   integer and floating-point inputs.
Parameters:
      * input (Tensor) -- the input tensor.
  * **other** (*Tensor*) -- the second input tensor

Keyword Arguments:
      out (Tensor, optional) -- the output tensor.
Example:
  >>> a = torch.tensor([9.7, float('nan'), 3.1, float('nan')])
  >>> b = torch.tensor([-2.2, 0.5, float('nan'), float('nan')])
  >>> torch.fmax(a, b)
  tensor([9.7000, 0.5000, 3.1000,    nan])
