Otherwise, "dim" is squeezed (see "torch.squeeze()"), resulting in
   the output tensor having 1 (or "len(dim)") fewer dimension(s).
Parameters:
      * input (Tensor) -- the input tensor.
  * **dim** (*int** or **tuple of ints**, **optional*) -- the
    dimension or dimensions to reduce. If "None", all dimensions
    are reduced.

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

Keyword Arguments:
      dtype ("torch.dtype", optional) -- the desired data type of
      returned tensor. If specified, the input tensor is casted to
      "dtype" before the operation is performed. This is useful for
      preventing data type overflows. Default: None.
Example:
  >>> torch.nansum(torch.tensor([1., float("nan")]))
  1.0
  >>> a = torch.tensor([[1, 2], [3., float("nan")]])
  >>> torch.nansum(a)
  tensor(6.)
  >>> torch.nansum(a, dim=0)
  tensor([4., 2.])
  >>> torch.nansum(a, dim=1)
