class_docstr: The semantic branch of BiSeNet, which has narrow channels but deep layers.
class_docstr_tok: ["The","semantic","branch","of","BiSeNet",",","which","has","narrow","channels","but","deep","layers","."]
code: def __init__(self, in_channels: int):
      super().__init__()
      C1, C3, C4, C5 = in_channels
  
      self.stem = StemBlock(3, C1)
  
      self.stage3 = nn.Sequential(
          GatherAndExpansionLayer2(C1, C3, 6),
          GatherAndExpansionLayer1(C3, C3, 6),
      )
  
      self.stage4 = nn.Sequential(
          GatherAndExpansionLayer2(C3, C4, 6),
          GatherAndExpansionLayer1(C4, C4, 6),
      )
  
      self.stage5_4 = nn.Sequential(
          GatherAndExpansionLayer2(C4, C5, 6),
          GatherAndExpansionLayer1(C5, C5, 6),
          GatherAndExpansionLayer1(C5, C5, 6),
          GatherAndExpansionLayer1(C5, C5, 6),
      )
  
      self.ce = ContextEmbeddingBlock(C5, C5)
  
imports: import os
  from typing import Union, List, Tuple
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddlehub.module.module import moduleinfo
  import paddlehub.vision.segmentation_transforms
  from paddlehub.module.cv_module import ImageSegmentationModule
  import bisenet_cityscapes.layers
label_desc: Use image node_id 1 to create a new SemanticBranch object from inherited base classes: nn with example: obj = SemanticBranch(in_channels)