code: def forward(self, x):
      H0, W0 = x.size()[2:]
      feat8, feat16, feat32 = self.resnet(x)
      H8, W8 = feat8.size()[2:]
      H16, W16 = feat16.size()[2:]
      H32, W32 = feat32.size()[2:]
  
      avg = F.avg_pool2d(feat32, feat32.size()[2:])
      avg = self.conv_avg(avg)
      avg_up = F.interpolate(avg, (H32, W32), mode="nearest")
  
      feat32_arm = self.arm32(feat32)
      feat32_sum = feat32_arm + avg_up
      feat32_up = F.interpolate(feat32_sum, (H16, W16), mode="nearest")
      feat32_up = self.conv_head32(feat32_up)
  
      feat16_arm = self.arm16(feat16)
      feat16_sum = feat16_arm + feat32_up
      feat16_up = F.interpolate(feat16_sum, (H8, W8), mode="nearest")
      feat16_up = self.conv_head16(feat16_up)
  
      return feat8, feat16_up, feat32_up
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from ParsingResnet 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: feat8,  feat16_up,  feat32_up