text: # -*- coding: utf-8 -*-
  # file: td_lstm.py
  # author: songyouwei <youwei0314@gmail.com>
  # Copyright (C) 2018. All Rights Reserved.
  
  import torch
  import torch.nn as nn
  
  from ..layers.dynamic_rnn import DynamicLSTM
  
  
  class TD_LSTM(nn.Module):
      inputs = ['left_with_aspect_indices', 'right_with_aspect_indices']
  
      def __init__(self, embedding_matrix, opt):
          super(TD_LSTM, self).__init__()
          self.embed = nn.Embedding.from_pretrained(torch.tensor(embedding_matrix, dtype=torch.float))
          self.lstm_l = DynamicLSTM(opt.embed_dim, opt.hidden_dim, num_layers=1, batch_first=True)
          self.lstm_r = DynamicLSTM(opt.embed_dim, opt.hidden_dim, num_layers=1, batch_first=True)
          self.dense = nn.Linear(opt.hidden_dim * 2, opt.polarities_dim)
  
      def forward(self, inputs):
          x_l, x_r = inputs['left_with_aspect_indices'], inputs['right_with_aspect_indices']
          x_l_len, x_r_len = torch.sum(x_l != 0, dim=-1), torch.sum(x_r != 0, dim=-1)
          x_l, x_r = self.embed(x_l), self.embed(x_r)
          _, (h_n_l, _) = self.lstm_l(x_l, x_l_len)
          _, (h_n_r, _) = self.lstm_r(x_r, x_r_len)
          h_n = torch.cat((h_n_l[0], h_n_r[0]), dim=-1)
          out = self.dense(h_n)
          return {'logits': out}
  