class_docstr: Bi-encoder Transformer Agent.
  
  Equivalent of bert_ranker/biencoder but does not rely on an external library
  (hugging face).
class_docstr_tok: ["Bi-encoder","Transformer","Agent",".","Equivalent","of","bert_ranker\/biencoder","but","does","not","rely","on","an","external","library","(","hugging","face",")","."]
docstr: Add the start and end token to the text.
docstr_tok: ["Add","the","start","and","end","token","to","the","text","."]
code: def vectorize(self, *args, **kwargs):
      """
      Add the start and end token to the text.
      """
      kwargs["add_start"] = True
      kwargs["add_end"] = True
      obs = TorchRankerAgent.vectorize(self, *args, **kwargs)
      return obs
  
imports: from typing import Optional
  from parlai.core.params import ParlaiParser
  from parlai.core.opt import Opt
  import torch
  from .transformer import TransformerRankerAgent
  from parlai.core.torch_ranker_agent import TorchRankerAgent
label_desc: Use image node_id 1 for calling the BiencoderAgent obj's underlying member method code with example usage: obj.vectorize() and returns: obs