code: def forward(self, x: paddle.Tensor) -> paddle.Tensor:
      feat2, feat4, feat8, feat16, feat32 = self.backbone(x)
  
      feat8_hw = paddle.shape(feat8)[2:]
      feat16_hw = paddle.shape(feat16)[2:]
      feat32_hw = paddle.shape(feat32)[2:]
  
      avg = F.adaptive_avg_pool2d(feat32, 1)
      avg = self.conv_avg(avg)
      avg_up = F.interpolate(avg, feat32_hw, mode="nearest")
  
      feat32_arm = self.arm32(feat32)
      feat32_sum = feat32_arm + avg_up
      feat32_up = F.interpolate(feat32_sum, feat16_hw, 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, feat8_hw, mode="nearest")
      feat16_up = self.conv_head16(feat16_up)
  
      return feat2, feat4, feat8, feat16, feat16_up, feat32_up
  
imports: import os
  from typing import Union, List, Tuple
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddlehub.module.module import moduleinfo
  import paddlehub.vision.segmentation_transforms
  from paddlehub.module.cv_module import ImageSegmentationModule
  from stdc1_seg_voc.stdcnet import STDC1
  import stdc1_seg_voc.layers
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: feat2,  feat4,  feat8,  feat16,  feat16_up,  feat32_up