torch.fmin
torch.fmin(input, other, *, out=None) -> Tensor
Computes the element-wise minimum of "input" and "other".
This is like "torch.minimum()" 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 minimum. Only if both elements are
   NaN is NaN propagated.
This function is a wrapper around C++'s "std::fmin" and is similar
   to NumPy's "fmin" 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([2.2, float('nan'), 2.1, float('nan')])
  >>> b = torch.tensor([-9.3, 0.1, float('nan'), float('nan')])
  >>> torch.fmin(a, b)
  tensor([-9.3000, 0.1000, 2.1000,    nan])
