code: def forward(
      self, fsp: paddle.Tensor, fcp: paddle.Tensor
  ) -> paddle.Tensor:
      fcat = paddle.concat([fsp, fcp], axis=1)
      feat = self.convblk(fcat)
      atten = F.adaptive_avg_pool2d(feat, 1)
      atten = self.conv1(atten)
      atten = self.relu(atten)
      atten = self.conv2(atten)
      atten = self.sigmoid(atten)
      feat_atten = paddle.multiply(feat, atten)
      feat_out = feat_atten + feat
      return feat_out
  
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 FeatureFusionModule obj's underlying member method code with example usage: obj.forward(fsp, fcp) and returns: feat_out