found in the dimension "dim".
This function is identical to "torch.median()" when there are no
   "NaN" values in a reduced row. When a reduced row has one or more
   "NaN" values, "torch.median()" will always reduce it to "NaN",
   while this function will reduce it to the median of the non-"NaN"
   elements. If all the elements in a reduced row are "NaN" then it
   will be reduced to "NaN", too.
Parameters:
      * input (Tensor) -- the input tensor.
  * **dim** (*int*) -- the dimension to reduce.

  * **keepdim** (*bool*) -- whether the output tensor has "dim"
    retained or not.

Keyword Arguments:
      out ((Tensor, Tensor), optional) -- The first
      tensor will be populated with the median values and the second
      tensor, which must have dtype long, with their indices in the
      dimension "dim" of "input".
Example:
  >>> a = torch.tensor([[2, 3, 1], [float('nan'), 1, float('nan')]])
  >>> a
  tensor([[2., 3., 1.],
