code: def __init__(
      self,
      config,
      in_channels=3,
      num_classes=2,
      stage2_blocks=2,
      stage3_blocks=8,
  ):
      super().__init__()
      assert stage2_blocks == 2
      dim1 = 16
      dim2 = 48
      dim3 = 96
  
      self.level1 = CBR(in_channels, 12, 3, 2)
  
      self.level2_0 = SESeparableCBR(12, dim1, 3, 2, divide=1)
  
      self.level2 = nn.LayerList()
      for i in range(0, stage2_blocks):
          if i == 0:
              self.level2.append(
                  S2module(dim1, dim2, config=config[i], add=False)
              )
          else:
              self.level2.append(S2module(dim2, dim2, config=config[i]))
      self.BR2 = BR(dim2 + dim1)
  
      self.level3_0 = SESeparableCBR(dim2 + dim1, dim2, 3, 2, divide=2)
      self.level3 = nn.LayerList()
      for i in range(0, stage3_blocks):
          if i == 0:
              self.level3.append(
                  S2module(dim2, dim3, config=config[2 + i], add=False)
              )
          else:
              self.level3.append(
                  S2module(dim3, dim3, config=config[2 + i])
              )
      self.BR3 = BR(dim3 + dim2)
  
      self.classifier = C(dim3 + dim2, num_classes, 1, 1)
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.models import layers
  from paddleseg.cvlibs import manager
  from paddleseg.utils import utils
label_desc: Use image node_id 1 to create a new SINetEncoder object from inherited base classes: nn with example: obj = SINetEncoder(config, in_channels, num_classes, stage2_blocks, stage3_blocks)