code: def __init__(self, in_size, out_size, is_deconv):
      super(Upold, self).__init__()
      self.conv = unetConv2(in_size, out_size, False)
      if is_deconv:
          self.up = nn.ConvTranspose2d(
              in_size, out_size, kernel_size=4, stride=2, padding=1
          )
      else:
          self.up = nn.UpsamplingBilinear2d(scale_factor=2)
  
      # initialise the blocks
      for m in self.children():
          if m.__class__.__name__.find("unetConv2") != -1:
              continue
          init_weights(m, init_type="kaiming")
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn import init
label_desc: Use image node_id 1 to create a new Upold object from inherited base classes: nn with example: obj = Upold(in_size, out_size, is_deconv)