code: def forward(self, input_):
      output0 = self.init_conv(input_)
  
      down_1 = self.down_1(input_)
      down_2 = self.down_2(input_)
      down_3 = self.down_3(input_)
  
      output0_cat = self.bn_prelu_1(torch.cat([output0, down_1], 1))
  
      # DAB Block 1
      output1_0 = self.downsample_1(output0_cat)
      output1 = self.DAB_Block_1(output1_0)
      output1_cat = self.bn_prelu_2(
          torch.cat([output1, output1_0, down_2], 1)
      )
  
      # DAB Block 2
      output2_0 = self.downsample_2(output1_cat)
      output2 = self.DAB_Block_2(output2_0)
      output2_cat = self.bn_prelu_3(
          torch.cat([output2, output2_0, down_3], 1)
      )
  
      out = self.classifier(output2_cat)
      out = F.interpolate(
          out, input_.size()[2:], mode="bilinear", align_corners=False
      )
  
      return out
  
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the DABNet obj's underlying member method code with example usage: obj.forward(input_) and returns: out