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.
  * **out** (*Tensor**, **optional*) -- the output tensor.

See also:
 "torch.mean()" computes the mean value, propagating *NaN*.

Example:
  >>> x = torch.tensor([[torch.nan, 1, 2], [1, 2, 3]])
  >>> x.mean()
  tensor(nan)
  >>> x.nanmean()
  tensor(1.8000)
  >>> x.mean(dim=0)
  tensor([   nan, 1.5000, 2.5000])
  >>> x.nanmean(dim=0)
  tensor([1.0000, 1.5000, 2.5000])

  # If all elements in the reduced dimensions are NaN then the result is NaN
  >>> torch.tensor([torch.nan]).nanmean()
  tensor(nan)
