code: def forward(self, src, src_len=None):
      """
      Args:
          src (LongTensor):
             padded sequences of sparse indices ``(batch, src_len, nfeat)``
          src_len (LongTensor): length of each sequence ``(batch,)``
  
      Returns:
          (FloatTensor, FloatTensor, FloatTensor):
  
          * enc_out (encoder output used for attention),
            ``(batch, src_len, hidden_size)``
            for bidirectional rnn last dimension is 2x hidden_size
          * enc_final_hs: encoder final hidden state
            ``(num_layers x dir, batch, hidden_size)``
            In the case of LSTM this is a tuple.
          * src_len ``(batch)``
      """
  
      raise NotImplementedError
  
imports: import torch.nn
label_desc: Use image node_id 2 for calling the EncoderBase obj's underlying member method code with example usage: obj.forward(src, src_len) without return types