code: def forward(self, x):
      size = x.size()[2:]
      x = self.conv1(x)
      x = self.bn1(x)
      x = self.relu(x)
      x = self.maxpool(x)
      x = self.layer1(x)
      x = self.layer2(x)
      x = self.layer3(x)
      if self.training and self.aux:
          x_dsn = self.dsn(x)
      x = self.layer4(x)
      x = self.head(x)
  
      if not self.training or not self.aux:
          return F.interpolate(
              x, size, mode="bilinear", align_corners=True
          )
      else:
          return [
              F.interpolate(
                  x_dsn, size, mode="bilinear", align_corners=True
              ),
              F.interpolate(
                  x, size, mode="bilinear", align_corners=True
              ),
          ]
  
imports: import torch.nn
  import torch
  import torch.nn.functional
  from torch.nn import BatchNorm2d
label_desc: Use image node_id 3 for calling the GALDNet obj's underlying member method code with example usage: obj.forward(x) and returns: F, list