code: def forward(self, x):
      x = self.conv1(x)
      x = self.bn1(x)
      x = self.relu(x)
      x = self.maxpool(x)
      x = self.layer1(x)
  
      context_blocks = []
      context_blocks.append(x)
      x = self.layer2(x)
      context_blocks.append(x)
      c3 = self.layer3(x)
      context_blocks.append(c3)
      c4 = self.layer4(c3)
      context_blocks.append(c4)
      context_blocks.reverse()
  
      global_context = self.global_context(c4)
      last_feature = global_context
      context_outputs = []
      for i, (feature, arm, refine) in enumerate(
          zip(context_blocks[:2], self.arms, self.refines)
      ):
          feature = arm(feature)
          feature += last_feature
          last_feature = F.interpolate(
              feature,
              size=context_blocks[i + 1].size()[2:],
              mode="bilinear",
              align_corners=True,
          )
          last_feature = refine(last_feature)
          context_outputs.append(last_feature)
  
      return context_outputs
  
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 ContextPath obj's underlying member method code with example usage: obj.forward(x) and returns: context_outputs