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 extra_repr(self):
      if self.scale_factor is not None:
          info = f"scale_factor={self.scale_factor}"
      else:
          info = f"size={self.size}"
      info += f", mode={self.mode}"
      return info
  
imports: from collections import OrderedDict
  import math
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 3 for calling the Upsample obj's underlying member method code with example usage: obj.extra_repr() and returns: info