class_docstr: T5 Agent.
  
  Relies on the T5 model implemented in huggingface
class_docstr_tok: ["T5","Agent",".","Relies","on","the","T5","model","implemented","in","huggingface"]
docstr: Override vectorize for T5.
  
  T5 dict already adds the end token.
docstr_tok: ["Override","vectorize","for","T5",".","T5","dict","already","adds","the","end","token","."]
code: def vectorize(self, *args, **kwargs):
      """
      Override vectorize for T5.
  
      T5 dict already adds the end token.
      """
      kwargs["add_start"] = False  # model does this in module code
      kwargs["add_end"] = False  # T5 tokenizer takes care of this
      return TorchAgent.vectorize(self, *args, **kwargs)
  
imports: import torch
  from typing import Optional, Dict, Any, Tuple
  from transformers import T5ForConditionalGeneration
  from parlai.agents.hugging_face.hugging_face import HF_VERSION
  from parlai.agents.hugging_face.dict import T5DictionaryAgent
  from parlai.core.opt import Opt
  from parlai.core.params import ParlaiParser
  from parlai.core.torch_agent import Batch, TorchAgent
  from parlai.core.torch_generator_agent import TorchGeneratorAgent, TorchGeneratorModel
  from parlai.utils.fsdp import is_fsdp, delay_halving
label_desc: Use image node_id 4 for calling the T5Agent obj's underlying member method code with example usage: obj.vectorize() and returns: TorchAgent