code: def forward(self, input_src):
      # batch_size x src_length x emb_size
      src_emb = self.dropout(self.embedding(input_src))
      # output: [batch size, src len, hid dim * num directions],
      # hidden: a tuple of length "n layers * num directions",
      # each element in tuple is batch_size * enc_hidden_size
      output, (hn, cn) = self.rnn(src_emb)
      # this can be concatennate or add operation.
      hn_output = torch.cat((hn[-2, :, :], hn[-1, :, :]), dim=1)
      cn_output = torch.cat((cn[-2, :, :], cn[-1, :, :]), dim=1)
  
      # hidden = torch.tanh(self.fc(
      #     torch.cat((hn[-2, :, :], hn[-1, :, :], cn[-2, :, :], cn[-1, :, :]), dim=1)))
      return output, (hn_output, cn_output)
  
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 SequenceEncoder obj's underlying member method code with example usage: obj.forward(input_src) and returns: output,  hn_output,  cn_output