class_docstr: A 2d-conv upsampling block with a variety of options for upsampling, and following best practices / with
  reasonable defaults. (LeakyReLU, kernel size multiple of stride)
class_docstr_tok: ["A","2d-conv","upsampling","block","with","a","variety","of","options","for","upsampling",",","and","following","best","practices","\/","with","reasonable","defaults",".","(","LeakyReLU",",","kernel","size","multiple","of","stride",")"]
code: def forward(self, x, skipped=None):
      if skipped is not None:
          input = torch.cat([skipped, x], dim=1)
      else:
          input = x
      return self.net(input)
  
imports: import torch
  import torch.nn
  import torchvision.utils
  from torch.nn import functional
label_desc: Use image node_id 2 for calling the UpBlock obj's underlying member method code with example usage: obj.forward(x, skipped) and returns: self