code: def __init__(self, n_channels, n_classes, bilinear=True):
      super(UNet, self).__init__()
      self.n_channels = n_channels
      self.n_classes = n_classes
      self.bilinear = bilinear
  
      self.inc = DoubleConv(n_channels, 64)
      self.down1 = Down(64, 128)
      self.down2 = Down(128, 256)
      self.down3 = Down(256, 512)
      factor = 2 if bilinear else 1
      self.down4 = Down(512, 1024 // factor)
      self.up1 = Up(1024, 512 // factor, bilinear)
      self.up2 = Up(512, 256 // factor, bilinear)
      self.up3 = Up(256, 128 // factor, bilinear)
      self.up4 = Up(128, 64, bilinear)
      self.outc = OutConv(64, n_classes)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new UNet object from inherited base classes: nn with example: obj = UNet(n_channels, n_classes, bilinear)