Upsample
class torch.nn.Upsample(size=None, scale_factor=None, mode='nearest', align_corners=None, recompute_scale_factor=None)
Upsamples a given multi-channel 1D (temporal), 2D (spatial) or 3D
   (volumetric) data.
The input data is assumed to be of the form minibatch x channels x
   [optional depth] x [optional height] x width. Hence, for spatial
   inputs, we expect a 4D Tensor and for volumetric inputs, we expect
   a 5D Tensor.
The algorithms available for upsampling are nearest neighbor and
   linear, bilinear, bicubic and trilinear for 3D, 4D and 5D input
   Tensor, respectively.
One can either give a "scale_factor" or the target output "size" to
   calculate the output size. (You cannot give both, as it is
   ambiguous)
Parameters:
      * size (int or Tuple[int] or Tuple[int,
        int] or Tuple[int, int, int],
        optional) -- output spatial sizes
  * **scale_factor** (*float** or **Tuple**[**float**] or
