class_docstr: Classifier based on Transformer.
class_docstr_tok: ["Classifier","based","on","Transformer","."]
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 = super().vectorize(*args, **kwargs)
      return obs
  
imports: from typing import Optional
  from parlai.core.params import ParlaiParser
  from parlai.core.opt import Opt
  from parlai.core.agents import Agent
  from parlai.utils.torch import padded_3d
  from parlai.core.torch_classifier_agent import TorchClassifierAgent
  from parlai.core.torch_ranker_agent import TorchRankerAgent
  from parlai.core.torch_generator_agent import TorchGeneratorAgent
  from parlai.utils.misc import recursive_getattr, warn_once
  from parlai.utils.logging import logging
  from parlai.utils.fsdp import should_use_fsdp
  from .modules import TransformerMemNetModel, TransformerGeneratorModel, TransformerLinearWrapper
  import torch
label_desc: Use image node_id 3 for calling the TransformerClassifierAgent obj's underlying member method code with example usage: obj.vectorize() and returns: obs