code: def __init__(self, in_shape=(3, 1024, 1024), **kwargs):
      super(UNet1024, self).__init__()
      C, H, W = in_shape
      # assert(C==3)
  
      # 1024
      self.down1 = StackEncoder(C, 24, kernel_size=3)  # 512
      self.down2 = StackEncoder(24, 64, kernel_size=3)  # 256
      self.down3 = StackEncoder(64, 128, kernel_size=3)  # 128
      self.down4 = StackEncoder(128, 256, kernel_size=3)  # 64
      self.down5 = StackEncoder(256, 512, kernel_size=3)  # 32
      self.down6 = StackEncoder(512, 768, kernel_size=3)  # 16
  
      self.center = nn.Sequential(
          ConvBnRelu2d(768, 768, kernel_size=3, padding=1, stride=1),
      )
  
      # 8
      # x_big_channels, x_channels, y_channels
      self.up6 = StackDecoder(768, 768, 512, kernel_size=3)  # 16
      self.up5 = StackDecoder(512, 512, 256, kernel_size=3)  # 32
      self.up4 = StackDecoder(256, 256, 128, kernel_size=3)  # 64
      self.up3 = StackDecoder(128, 128, 64, kernel_size=3)  # 128
      self.up2 = StackDecoder(64, 64, 24, kernel_size=3)  # 256
      self.up1 = StackDecoder(24, 24, 24, kernel_size=3)  # 512
      self.classify = nn.Conv2d(
          24, 1, kernel_size=1, padding=0, stride=1, bias=True
      )
  
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new UNet1024 object from inherited base classes: nn with example: obj = UNet1024(in_shape)