code: def __init__(self, filters=12, kernel_size=3, **_):
      super(UNet960, self).__init__()
  
      # 960
      self.down1 = UNetDownStack(3, filters)
      # 480
      self.down2 = UNetDownStack(filters, filters * 2)
      # 240
      self.down3 = UNetDownStack(filters * 2, filters * 4)
      # 120
      self.down4 = UNetDownStack(filters * 4, filters * 8)
      # 60
      self.down5 = UNetDownStack(filters * 8, filters * 16)
      # 30
      self.down6 = UNetDownStack(filters * 16, filters * 32)
      # 15
      self.center = UNetDownStack(
          filters * 32, filters * 64, pool=False
      )
      # 15
      self.up6 = UNetUpStack(filters * 96, filters * 32, kernel_size)
      # 30
      self.up5 = UNetUpStack(filters * 48, filters * 16, kernel_size)
      # 60
      self.up4 = UNetUpStack(filters * 24, filters * 8, kernel_size)
      # 120
      self.up3 = UNetUpStack(filters * 12, filters * 4, kernel_size)
      # 240
      self.up2 = UNetUpStack(filters * 6, filters * 2, kernel_size)
      # 480
      self.up1 = UNetUpStack(filters * 3, filters, kernel_size)
      # 960
      self.classify = nn.Conv2d(
          filters, 1, kernel_size, stride=1, padding=1
      )
  
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new UNet960 object from inherited base classes: nn with example: obj = UNet960(filters, kernel_size)