class_docstr: 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`.
class_docstr_tok: ["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","`","."]
code: def __init__(
      self,
      size=None,
      scale_factor=None,
      mode="nearest",
      align_corners=None,
  ):
      super(Upsample, self).__init__()
      if isinstance(scale_factor, tuple):
          self.scale_factor = tuple(
              float(factor) for factor in scale_factor
          )
      else:
          self.scale_factor = (
              float(scale_factor) if scale_factor else None
          )
      self.mode = mode
      self.size = size
      self.align_corners = align_corners
  
imports: from collections import OrderedDict
  import math
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new Upsample object from inherited base classes: nn with example: obj = Upsample(size, scale_factor, mode, align_corners)