class_docstr: Poly-encoder Agent.
  
  Equivalent of bert_ranker/polyencoder and biencoder_multiple_output but does not
  rely on an external library (hugging face).
class_docstr_tok: ["Poly-encoder","Agent",".","Equivalent","of","bert_ranker\/polyencoder","and","biencoder_multiple_output","but","does","not","rely","on","an","external","library","(","hugging","face",")","."]
docstr: Vectorize fixed candidates.
  
  Override to add start and end token when computing the candidate encodings in
  interactive mode.
docstr_tok: ["Vectorize","fixed","candidates",".","Override","to","add","start","and","end","token","when","computing","the","candidate","encodings","in","interactive","mode","."]
code: def vectorize_fixed_candidates(self, *args, **kwargs):
      """
      Vectorize fixed candidates.
  
      Override to add start and end token when computing the candidate encodings in
      interactive mode.
      """
      kwargs["add_start"] = True
      kwargs["add_end"] = True
      return super().vectorize_fixed_candidates(*args, **kwargs)
  
imports: from parlai.core.params import ParlaiParser
  from typing import Any, Dict, Optional, Tuple
  import torch
  from parlai.core.opt import Opt
  from parlai.core.torch_ranker_agent import TorchRankerAgent
  from parlai.core.torch_agent import Batch
  from parlai.utils.misc import recursive_getattr
  from parlai.utils.logging import logging
  from .biencoder import AddLabelFixedCandsTRA
  from .modules import BasicAttention, MultiHeadAttention, TransformerEncoder
  from .transformer import TransformerRankerAgent
label_desc: Use image node_id 7 for calling the PolyencoderAgent obj's underlying member method code with example usage: obj.vectorize_fixed_candidates() and returns: super