code: def __init__(
      self, input_size=512, filters=12, kernel_size=3, max_stacks=6, **_
  ):
      super(UNet_stack, self).__init__()
      self.n_stacks = min(
          self.get_n_stacks((input_size, input_size)), max_stacks
      )
  
      # dynamically create stacks
      self.down1 = UNetDownStack(3, filters)
      prev_filters = filters
      for i in range(2, self.n_stacks + 1):
          n = i
          layer = UNetDownStack(prev_filters, prev_filters * 2)
          layer_name = "down" + str(n)
          setattr(self, layer_name, layer)
          prev_filters *= 2
  
      self.center = UNetDownStack(
          prev_filters, prev_filters * 2, pool=False
      )
  
      prev_filters = prev_filters * 3
      for i in range(self.n_stacks):
          n = self.n_stacks - i
          layer = UNetUpStack(
              prev_filters, prev_filters // 3, kernel_size
          )
          layer_name = "up" + str(n)
          setattr(self, layer_name, layer)
          prev_filters = prev_filters // 2
  
      self.classify = nn.Conv2d(
          prev_filters * 2 // 3, 1, kernel_size, stride=1, padding=1
      )
  
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 2 to create a new UNet_stack object from inherited base classes: nn with example: obj = UNet_stack(input_size, filters, kernel_size, max_stacks)