code: def __init__(self, in_channels, out_channels, k=50):
      super().__init__()
      self.k = k
      self.binarize = nn.Sequential(
          nn.Conv2D(
              in_channels,
              in_channels // 4,
              3,
              padding=1,
              weight_attr=ParamAttr(
                  initializer=nn.initializer.KaimingNormal()
              ),
          ),
          nn.BatchNorm2D(
              in_channels // 4,
              weight_attr=ParamAttr(
                  initializer=nn.initializer.Constant(1)
              ),
              bias_attr=ParamAttr(
                  initializer=nn.initializer.Constant(1e-4)
              ),
          ),
          nn.ReLU(),
          nn.Conv2DTranspose(
              in_channels // 4,
              in_channels // 4,
              2,
              2,
              weight_attr=ParamAttr(
                  initializer=nn.initializer.KaimingNormal()
              ),
          ),
          nn.BatchNorm2D(
              in_channels // 4,
              weight_attr=ParamAttr(
                  initializer=nn.initializer.Constant(1)
              ),
              bias_attr=ParamAttr(
                  initializer=nn.initializer.Constant(1e-4)
              ),
          ),
          nn.ReLU(),
          nn.Conv2DTranspose(
              in_channels // 4,
              1,
              2,
              2,
              weight_attr=nn.initializer.KaimingNormal(),
          ),
          nn.Sigmoid(),
      )
  
      self.thresh = self._init_thresh(in_channels)
  
file_path: PaddleOCR/benchmark/PaddleOCR_DBNet/models/head/DBHead.py
imports: import paddle
  from paddle import nn, ParamAttr
label_desc: Use image node_id 1 to create a new DBHead object from inherited base classes: nn with example: obj = DBHead(in_channels, out_channels, k)