code: def get_encoder_decoder_tokenizer():
      """Function to get a default pre-trained version of T5 in ONNX ready for use"""
      try:
          decoder_sess, encoder_sess = _handle_creation_of_sessions()
      except:
          filelist = glob.glob(os.path.join(_models_path, "*"))
          for f in filelist:
              os.remove(f)
          decoder_sess, encoder_sess = _handle_creation_of_sessions()
  
      # The tokenizer should be the one you trained in the case of fine-tuning
      tokenizer = T5Tokenizer.from_pretrained("t5-base")
  
      return decoder_sess, encoder_sess, tokenizer
  
file_path: onnxt5/onnxt5/api.py
imports: from pathlib import Path
  import tarfile
  import os
  import glob
  import requests
  from onnxruntime import InferenceSession
  from tqdm import tqdm
  from transformers import T5Tokenizer
label_desc: Use image node_id 2 for calling a global function with example usage: get_encoder_decoder_tokenizer() and returns: decoder_sess,  encoder_sess,  tokenizer