class_docstr: ### Up-sample
  
  Each step in the expansive path up-samples the feature map with
  a $2        imes 2$ up-convolution.
class_docstr_tok: ["#","#","#","Up-sample","Each","step","in","the","expansive","path","up-samples","the","feature","map","with","a","$","2","imes","2","$","up-convolution","."]
code: def __init__(self, in_channels: int, out_channels: int):
      super().__init__()
  
      # Up-convolution
      self.up = nn.ConvTranspose2d(
          in_channels, out_channels, kernel_size=2, stride=2
      )
  
imports: import torch
  import torchvision.transforms.functional
  from torch import nn
label_desc: Use image node_id 1 to create a new UpSample object from inherited base classes: nn with example: obj = UpSample(in_channels, out_channels)