text: import torch.nn as nn
  import torch.nn.functional as F
  
  
  class Seq2Seq(nn.Module):
      def __init__(self, encoder, decoder, decode_function=F.log_softmax):
          super(Seq2Seq, self).__init__()
          self.encoder = encoder
          self.decoder = decoder
          self.decode_function = decode_function
  
      def flatten_parameters(self):
          pass
  
      def forward(self, input_variable, input_lengths=None, target_variable=None,
                  teacher_forcing_ratio=0):
  
          self.encoder.rnn.flatten_parameters()
          encoder_outputs, encoder_hidden = self.encoder(input_variable, input_lengths)
  
          self.decoder.rnn.flatten_parameters()
          decoder_output = self.decoder(inputs=target_variable,
                                        encoder_hidden=None,
                                        encoder_outputs=encoder_outputs,
                                        function=self.decode_function,
                                        teacher_forcing_ratio=teacher_forcing_ratio)
  
                                                                  
          return decoder_output
  
      @staticmethod
      def get_param_size(model):
          params = 0
          for p in model.parameters():
              tmp = 1
              for x in p.size():
                  tmp *= x
              params += tmp
          return params