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 _set_text_vec(self, *args, **kwargs):
      """
      Add the start and end token to the text.
      """
      obs = super()._set_text_vec(*args, **kwargs)
  
      if "text_vec" in obs and "added_start_end" not in obs:
          obs.force_set(
              "text_vec",
              self._add_start_end_tokens(obs["text_vec"], True, True),
          )
          obs["added_start_end"] = True
  
      # check truncation after adding start end tokens
      if obs.get("text_vec") is not None:
          truncated_vec = self._check_truncate(
              obs["text_vec"], self.text_truncate, True
          )
          obs.force_set("text_vec", torch.LongTensor(truncated_vec))
  
      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 4 for calling the TransformerClassifierAgent obj's underlying member method code with example usage: obj._set_text_vec() and returns: obs