class_docstr: Build Seqence-to-Sequence.
  
  Parameters:
  
  * `encoder`- encoder of seq2seq model. e.g. roberta
  * `decoder`- decoder of seq2seq model. e.g. transformer
  * `config`- configuration of encoder model.
  * `beam_size`- beam size for beam search.
  * `max_length`- max length of target for beam search.
  * `sos_id`- start of symbol ids in target for beam search.
  * `eos_id`- end of symbol ids in target for beam search.
docstr: Make sure we are sharing the input and output embeddings.
  Export to TorchScript can't handle parameter sharing so we are cloning them instead.
docstr_tok: ["Make","sure","we","are","sharing","the","input","and","output","embeddings",".","Export","to","TorchScript","ca","n't","handle","parameter","sharing","so","we","are","cloning","them","instead","."]
code: def tie_weights(self):
      """Make sure we are sharing the input and output embeddings.
      Export to TorchScript can't handle parameter sharing so we are cloning them instead.
      """
      self._tie_or_clone_weights(
          self.lm_head, self.encoder.embeddings.word_embeddings
      )
  
imports: import torch
  import torch.nn
  import numpy
  from transformers import RobertaConfig, RobertaModel, RobertaTokenizer, BartConfig, BartForConditionalGeneration, BartTokenizer, T5Config, T5ForConditionalGeneration, T5Tokenizer
  import logging
label_desc: Use image node_id 3 for calling the Seq2Seq obj's underlying member method code with example usage: obj.tie_weights() without return types