code: def forward(self, input):
      x, low_level_features = self.xception_features(input)
      # print(x.size())
      x1 = self.aspp1(x)
      x2 = self.aspp2(x)
      x3 = self.aspp3(x)
      x4 = self.aspp4(x)
      x5 = self.global_avg_pool(x)
      x5 = F.upsample(
          x5, size=x4.size()[2:], mode="bilinear", align_corners=True
      )
  
      x = torch.cat((x1, x2, x3, x4, x5), dim=1)
  
      x = self.concat_projection_conv1(x)
      x = self.concat_projection_bn1(x)
      x = self.relu(x)
      # print(x.size())
  
      low_level_features = self.feature_projection_conv1(
          low_level_features
      )
      low_level_features = self.feature_projection_bn1(
          low_level_features
      )
      low_level_features = self.relu(low_level_features)
  
      x = F.upsample(
          x,
          size=low_level_features.size()[2:],
          mode="bilinear",
          align_corners=True,
      )
      # print(low_level_features.size())
      # print(x.size())
      x = torch.cat((x, low_level_features), dim=1)
      x = self.decoder(x)
      x = self.semantic(x)
      x = F.upsample(
          x, size=input.size()[2:], mode="bilinear", align_corners=True
      )
  
      return x
  
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
  from collections import OrderedDict
  import torch.utils.model_zoo
  from torch.nn.parameter import Parameter
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