class_docstr: TRA Wrapper for the DPR Models.
class_docstr_tok: ["TRA","Wrapper","for","the","DPR","Models","."]
docstr: Overrides BiencoderAgent to not add start/end.
docstr_tok: ["Overrides","BiencoderAgent","to","not","add","start\/end","."]
code: def vectorize(self, *args, **kwargs):
      """
      Overrides BiencoderAgent to not add start/end.
      """
      kwargs["add_start"] = False
      kwargs["add_end"] = False
      return TorchRankerAgent.vectorize(self, *args, **kwargs)
  
imports: from parlai.agents.transformer.transformer import TransformerRankerAgent
  from parlai.agents.transformer.modules import TransformerMemNetModel, TransformerEncoder
  from parlai.agents.hugging_face.dict import HuggingFaceDictionaryAgent
  from parlai.agents.rag.args import DPR_ZOO_MODEL
  from parlai.agents.rag.conversion_utils import BertConversionUtils
  from parlai.core.build_data import modelzoo_path
  from parlai.core.dict import DictionaryAgent
  from parlai.core.loader import register_agent
  from parlai.core.opt import Opt
  from parlai.core.torch_ranker_agent import TorchRankerAgent
  from parlai.utils.io import PathManager
  import parlai.utils.logging
  import os
  import torch
  import torch.cuda
  import torch.nn
  from transformers import BertConfig
  from typing import Dict, Optional
label_desc: Use image node_id 3 for calling the DPRAgent obj's underlying member method code with example usage: obj.vectorize() and returns: TorchRankerAgent