code: def forward(self, input):
      """
      input : visual feature [batch_size x T x input_size]
      output : contextual feature [batch_size x T x output_size]
      """
      try:
          self.rnn.flatten_parameters()
      except:
          pass
      recurrent, _ = self.rnn(
          input
      )  # batch_size x T x input_size -> batch_size x T x (2*hidden_size)
      output = self.linear(recurrent)  # batch_size x T x output_size
      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