code: def __init__(
      self,
      inplanes,
      out_channels,
      dilation_series,
      padding_series,
      num_classes,
  ):
      super(edge_branch, self).__init__()
      self.conv_x1 = nn.Conv2D(inplanes[0], 512, kernel_size=3)
      self.conv_x4 = nn.Conv2D(inplanes[1], 512, kernel_size=3)
  
      self.conv0 = resnet_vd.ConvBNLayer(
          in_channels=512 * 2,
          out_channels=out_channels,
          kernel_size=3,
          act="relu",
      )
      self.conv1 = resnet_vd.ConvBNLayer(
          in_channels=out_channels,
          out_channels=out_channels,
          kernel_size=3,
          act=None,
      )
  
      self.add = layers.Add()
      self.relu = layers.Activation(act="relu")
  
      self.conv2d_list = nn.LayerList()
      for dilation, padding in zip(dilation_series, padding_series):
          weight_attr = paddle.ParamAttr(
              initializer=nn.initializer.Normal(std=0.01),
              learning_rate=10.0,
          )
          bias_attr = paddle.ParamAttr(
              initializer=nn.initializer.Constant(value=0.0),
              learning_rate=10.0,
          )
          self.conv2d_list.append(
              nn.Conv2D(
                  out_channels,
                  num_classes,
                  kernel_size=3,
                  stride=1,
                  padding=padding,
                  dilation=dilation,
                  weight_attr=weight_attr,
                  bias_attr=bias_attr,
              )
          )
      self.classifier = nn.Conv2D(
          out_channels, num_classes, kernel_size=3, stride=1
      )
  
file_path: PaddleSeg/contrib/DomainAdaptation/models/deeplabv2.py
imports: import os
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager
  from paddleseg.models import layers
  from paddleseg.models.backbones import resnet_vd
  from paddleseg.utils import utils, logger
  from .gscnn import GSCNNHead
  from .backbones.resnet import ClassifierModule
label_desc: Use image node_id 1 to create a new edge_branch object from inherited base classes: nn with example: obj = edge_branch(inplanes, out_channels, dilation_series, padding_series, num_classes)