class_docstr: The detail branch of BiSeNet, which has wide channels but shallow layers.
class_docstr_tok: ["The","detail","branch","of","BiSeNet",",","which","has","wide","channels","but","shallow","layers","."]
code: def __init__(self, in_channels: int):
      super().__init__()
  
      C1, C2, C3 = in_channels
  
      self.convs = nn.Sequential(
          # stage 1
          layers.ConvBNReLU(3, C1, 3, stride=2),
          layers.ConvBNReLU(C1, C1, 3),
          # stage 2
          layers.ConvBNReLU(C1, C2, 3, stride=2),
          layers.ConvBNReLU(C2, C2, 3),
          layers.ConvBNReLU(C2, C2, 3),
          # stage 3
          layers.ConvBNReLU(C2, C3, 3, stride=2),
          layers.ConvBNReLU(C3, C3, 3),
          layers.ConvBNReLU(C3, C3, 3),
      )
  
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 DetailBranch object from inherited base classes: nn with example: obj = DetailBranch(in_channels)