code: def forward(self, input_):
      x, low_level_features = self.xception_features(input_)
      x1 = self.aspp1(x)
      x2 = self.aspp2(x)
      x3 = self.aspp3(x)
      x4 = self.aspp4(x)
      x5 = self.global_avg_pool(x)
      x5 = F.interpolate(
          x5, size=x4.size()[2:], mode="bilinear", align_corners=True
      )
  
      x = torch.cat((x1, x2, x3, x4, x5), dim=1)
  
      x = self.conv1(x)
      x = self.bn1(x)
      x = self.relu(x)
      x = F.interpolate(
          x,
          size=(
              int(math.ceil(input_.size()[-2] / 4)),
              int(math.ceil(input_.size()[-1] / 4)),
          ),
          mode="bilinear",
          align_corners=True,
      )
  
      low_level_features = self.conv2(low_level_features)
      low_level_features = self.bn2(low_level_features)
      low_level_features = self.relu(low_level_features)
  
      x = torch.cat((x, low_level_features), dim=1)
      x = self.last_linear(x)
      x = F.interpolate(
          x, size=input_.size()[2:], mode="bilinear", align_corners=True
      )
  
      return x
  
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.model_zoo
label_desc: Use image node_id 2 for calling the DeepLabv3_plus obj's underlying member method code with example usage: obj.forward(input_) and returns: x