code: def forward(self, input):
      recurrent, _ = self.rnn(input)
      T, b, h = recurrent.size()
      t_rec = recurrent.view(T * b, h)
  
      output = self.embedding(t_rec)  # [T * b, nOut]
      output = output.view(T, b, -1)
  
      return output
  
imports: import torch.nn
label_desc: Use image node_id 2 for calling the BidirectionalLSTM obj's underlying member method code with example usage: obj.forward(input) and returns: output