code: def forward(self, conv1_logit, x4):
      H = paddle.shape(x4)[2]
      W = paddle.shape(x4)[3]
      conv1_logit = F.interpolate(
          conv1_logit, size=[H, W], mode="bilinear", align_corners=True
      )
  
      conv1_logit = self.conv_x1(conv1_logit)
      x4 = self.conv_x4(x4)  # 1, 512, 81,161
  
      feats = paddle.concat([conv1_logit, x4], axis=1)
      y = self.conv0(feats)
      y = self.conv1(y)
  
      y = self.add(feats, y)
      y = self.relu(y)
  
      out = self.conv2d_list[0](y)
      for i in range(len(self.conv2d_list) - 1):
          out += self.conv2d_list[i + 1](y)
  
      return out
  
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 2 for calling the edge_branch obj's underlying member method code with example usage: obj.forward(conv1_logit, x4) and returns: out