code: def forward(self, enc_s_top, dec_s_top, enc_2):
      dot = torch.bmm(enc_s_top, dec_s_top.unsqueeze(2))
      attention = self.softmax(dot.squeeze(2)).unsqueeze(2)
      enc_attention = torch.bmm(enc_s_top.permute(0, 2, 1), attention)
  
      if self.separate_attention == "separate_different_encoder_type":
          dot_2 = torch.bmm(enc_2, dec_s_top.unsqueeze(2))
          attention_2 = self.softmax(dot_2.squeeze(2)).unsqueeze(2)
          enc_attention_2 = torch.bmm(
              enc_2.permute(0, 2, 1), attention_2
          )
  
      if self.separate_attention == "separate_different_encoder_type":
          hid = F.tanh(
              self.linear_att(
                  torch.cat(
                      (
                          enc_attention.squeeze(2),
                          enc_attention_2.squeeze(2),
                          dec_s_top,
                      ),
                      1,
                  )
              )
          )
      else:
          hid = F.tanh(
              self.linear_att(
                  torch.cat((enc_attention.squeeze(2), dec_s_top), 1)
              )
          )
      h2y_in = hid
  
      h2y_in = self.dropout(h2y_in)
      h2y = self.linear_out(h2y_in)
      pred = self.logsoftmax(h2y)
  
      return pred
  
imports: import random
  import time
  import warnings
  import numpy
  import torch
  import torch.nn.functional
  import torch.nn.init
  from torch import nn, optim
  from ....modules.prediction.generation.TreeBasedDecoder import StdTreeDecoder, create_mask
  from ....modules.utils.tree_utils import DataLoader, Tree, to_cuda
label_desc: Use image node_id 2 for calling the AttnUnit obj's underlying member method code with example usage: obj.forward(enc_s_top, dec_s_top, enc_2) and returns: pred