code: def forward(self, concat_x, features_list):
      concat_x = self.conv(concat_x)
      score = self.enhanced_attention(concat_x)
      assert len(features_list) == self.out_features_num
      if self.type not in ["scale_channel_spatial", "scale_spatial"]:
          shape = features_list[0].shape[2:]
          score = F.interpolate(score, size=shape, mode="bilinear")
      x = []
      for i in range(self.out_features_num):
          x.append(score[:, i : i + 1] * features_list[i])
      return torch.cat(x, dim=1)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 3 for calling the ScaleFeatureSelection obj's underlying member method code with example usage: obj.forward(concat_x, features_list) and returns: torch