code: def setup_tokenizer(self, cfg: DictConfig):
      tokenizer = get_tokenizer(
          tokenizer_name=cfg.tokenizer_name,
          tokenizer_model=cfg.tokenizer_model,
          special_tokens=OmegaConf.to_container(cfg.special_tokens)
          if cfg.special_tokens
          else None,
          vocab_file=cfg.vocab_file,
      )
      return tokenizer
  
imports: from typing import Dict, List, Optional, Tuple
  import torch
  from omegaconf import DictConfig, OmegaConf
  from pytorch_lightning import Trainer
  from transformers import AutoModel, BartForConditionalGeneration, EncoderDecoderModel
  from nemo.collections.common.metrics import Perplexity
  from nemo.collections.nlp.data.text2sparql import Text2SparqlDataset
  from nemo.collections.nlp.modules.common.tokenizer_utils import get_tokenizer
  from nemo.core.classes.common import typecheck
  from nemo.core.classes.modelPT import ModelPT
  from nemo.core.neural_types import ChannelType, MaskType, NeuralType
  from nemo.utils import logging
label_desc: Use image node_id 10 for calling the Text2SparqlModel obj's underlying member method code with example usage: obj.setup_tokenizer(cfg) and returns: tokenizer