code: def forward(self, x):
      feat_p = self.conv_p1(x)
      feat_p = self.pam(feat_p)
      feat_p = self.conv_p2(feat_p)
  
      feat_c = self.conv_c1(x)
      feat_c = self.cam(feat_c)
      feat_c = self.conv_c2(feat_c)
  
      feat_fusion = feat_p + feat_c
  
      outputs = []
      fusion_out = self.out(feat_fusion)
      outputs.append(fusion_out)
      if self.aux:
          p_out = self.conv_p3(feat_p)
          c_out = self.conv_c3(feat_c)
          outputs.append(p_out)
          outputs.append(c_out)
  
      return tuple(outputs)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from pywick.models.segmentation.da_basenets.segbase import SegBaseModel
label_desc: Use image node_id 2 for calling the _DAHead obj's underlying member method code with example usage: obj.forward(x) and returns: tuple