class_docstr: The Dual attention head.
  
  Args:
      num_classes (int): The unique number of target classes.
      in_channels (tuple): The number of input channels.
class_docstr_tok: ["The","Dual","attention","head",".","Args",":","num_classes","(","int",")",":","The","unique","number","of","target","classes",".","in_channels","(","tuple",")",":","The","number","of","input","channels","."]
code: def forward(
      self, feat_list: List[paddle.Tensor]
  ) -> List[paddle.Tensor]:
      feats = feat_list[-1]
      channel_feats = self.channel_conv(feats)
      channel_feats = self.cam(channel_feats)
      channel_feats = self.conv1(channel_feats)
  
      position_feats = self.position_conv(feats)
      position_feats = self.pam(position_feats)
      position_feats = self.conv2(position_feats)
  
      feats_sum = position_feats + channel_feats
      logit = self.cls_head(feats_sum)
  
      if not self.training:
          return [logit]
  
      cam_logit = self.aux_head_cam(channel_feats)
      pam_logit = self.aux_head_cam(position_feats)
      aux_logit = self.aux_head(feats)
      return [logit, cam_logit, pam_logit, aux_logit]
  
imports: import os
  from typing import Union, List, Tuple
  import paddle
  from paddle import nn
  import paddle.nn.functional
  import numpy
  from paddlehub.module.module import moduleinfo
  import paddlehub.vision.segmentation_transforms
  from paddlehub.module.cv_module import ImageSegmentationModule
  from danet_resnet50_voc.resnet import ResNet50_vd
  import danet_resnet50_voc.layers
label_desc: Use image node_id 2 for calling the DAHead obj's underlying member method code with example usage: obj.forward(feat_list) and returns: list, list