class_docstr: Left-to-right Transformer language model.
class_docstr_tok: ["Left-to-right","Transformer","language","model","."]
code: def setup_tokenizer(
      self,
      tokenizer_name=None,
      tokenizer_model=None,
      vocab_file=None,
      bpe_dropout=0.0,
      special_tokens=None,
  ):
      supported_tokenizers = [
          "yttm",
          "huggingface",
          "sentencepiece",
          "word",
      ]
      if tokenizer_name not in supported_tokenizers:
          raise NotImplementedError(
              f"Currently we only support tokenizers in {supported_tokenizers}."
          )
  
      self.tokenizer = get_tokenizer(
          tokenizer_name=tokenizer_name,
          tokenizer_model=self.register_artifact(
              "cfg.tokenizer.tokenizer_model", tokenizer_model
          ),
          vocab_file=vocab_file,
          bpe_dropout=bpe_dropout,
          special_tokens=special_tokens,
          use_fast=False,
      )
  
imports: import json
  import math
  from typing import Dict, Optional
  import numpy
  import torch
  import torch.utils.data
  from omegaconf import DictConfig, OmegaConf
  from pytorch_lightning import Trainer
  from nemo.collections.common.losses import SmoothedCrossEntropyLoss
  from nemo.collections.common.metrics import GlobalAverageLossMetric
  from nemo.collections.common.parts import transformer_weights_init
  from nemo.collections.nlp.data import SentenceDataset, TarredSentenceDataset
  from nemo.collections.nlp.metrics import SequencePerplexity
  from nemo.collections.nlp.modules.common import TokenClassifier
  from nemo.collections.nlp.modules.common.lm_utils import get_transformer
  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.utils import logging, model_utils
label_desc: Use image node_id 10 for calling the TransformerLMModel obj's underlying member method code with example usage: obj.setup_tokenizer(tokenizer_name, tokenizer_model, vocab_file, bpe_dropout, special_tokens) without return types