torch.nn.functional.interpolate
torch.nn.functional.interpolate(input, size=None, scale_factor=None, mode='nearest', align_corners=None, recompute_scale_factor=None, antialias=False)
Down/up samples the input to either the given "size" or the given
   "scale_factor"
The algorithm used for interpolation is determined by "mode".
Currently temporal, spatial and volumetric sampling are supported,
   i.e. expected inputs are 3-D, 4-D or 5-D in shape.
The input dimensions are interpreted in the form: mini-batch x
   channels x [optional depth] x [optional height] x width.
The modes available for resizing are: nearest, linear (3D-
   only), bilinear, bicubic (4D-only), trilinear (5D-only),
   area, nearest-exact
Parameters:
      * input (Tensor) -- the input tensor
  * **size** (*int** or **Tuple**[**int**] or **Tuple**[**int**,
    **int**] or **Tuple**[**int**, **int**, **int**]*) -- output
    spatial size.
