torch.nn.functional.upsample
torch.nn.functional.upsample(input, size=None, scale_factor=None, mode='nearest', align_corners=None)
Upsamples the input to either the given "size" or the given
   "scale_factor"
Warning:
 This function is deprecated in favor of
 "torch.nn.functional.interpolate()". This is equivalent with
 "nn.functional.interpolate(...)".

Note:
 This operation may produce nondeterministic gradients when given
 tensors on a CUDA device. See Reproducibility for more
 information.

The algorithm used for upsampling is determined by "mode".
Currently temporal, spatial and volumetric upsampling 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 upsampling are: nearest, linear (3D-
   only), bilinear, bicubic (4D-only), trilinear (5D-only)