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 forward(self, x: paddle.Tensor) -> paddle.Tensor:
      stage2 = self.stem(x)
      stage3 = self.stage3(stage2)
      stage4 = self.stage4(stage3)
      stage5_4 = self.stage5_4(stage4)
      fm = self.ce(stage5_4)
      return stage2, stage3, stage4, stage5_4, fm
  
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 2 for calling the SemanticBranch obj's underlying member method code with example usage: obj.forward(x) and returns: stage2,  stage3,  stage4,  stage5_4,  fm