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: Override to add start end tokens.
  
  necessary for fixed cands.
docstr_tok: ["Override","to","add","start","end","tokens",".","necessary","for","fixed","cands","."]
code: def _vectorize_text(self, *args, **kwargs):
      """
      Override to add start end tokens.
  
      necessary for fixed cands.
      """
      if "add_start" in kwargs:
          kwargs["add_start"] = True
          kwargs["add_end"] = True
      return super()._vectorize_text(*args, **kwargs)
  
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 2 for calling the BiencoderAgent obj's underlying member method code with example usage: obj._vectorize_text() and returns: super