code: def forward(self, x):
      size = x.size()[2:]
      spatial_out = self.spatial_path(x)
      context_out = self.context_path(x)
      fusion_out = self.ffm(spatial_out, context_out[-1])
      outputs = []
      x = self.head(fusion_out)
      x = F.interpolate(x, size, mode="bilinear", align_corners=True)
      outputs.append(x)
  
      if self.aux and self.training:
          auxout1 = self.auxlayer1(context_out[0])
          auxout1 = F.interpolate(
              auxout1, size, mode="bilinear", align_corners=True
          )
          outputs.append(auxout1)
          auxout2 = self.auxlayer2(context_out[1])
          auxout2 = F.interpolate(
              auxout2, size, mode="bilinear", align_corners=True
          )
          outputs.append(auxout2)
          return tuple(outputs)
  
      else:
          return outputs[0]
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from pywick.models.segmentation.da_basenets.resnet import resnet18
label_desc: Use image node_id 2 for calling the BiSeNet obj's underlying member method code with example usage: obj.forward(x) and returns: tuple, outputs