class_docstr: BERT language model pretraining.
class_docstr_tok: ["BERT","language","model","pretraining","."]
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,
      )
      self.tokenizer = tokenizer
  
imports: import os
  from typing import Dict, Optional
  import torch
  from omegaconf import DictConfig, OmegaConf
  from pytorch_lightning import Trainer
  from torch.utils.data import DataLoader
  from nemo.collections.common.losses import AggregatorLoss, CrossEntropyLoss, SmoothedCrossEntropyLoss
  from nemo.collections.common.metrics import Perplexity
  from nemo.collections.nlp.data.language_modeling.lm_bert_dataset import BertPretrainingDataset, BertPretrainingPreprocessedDataloader
  from nemo.collections.nlp.modules.common import BertPretrainingTokenClassifier, SequenceClassifier
  from nemo.collections.nlp.modules.common.lm_utils import get_lm_model
  from nemo.collections.nlp.modules.common.tokenizer_utils import get_tokenizer
  from nemo.core.classes.common import PretrainedModelInfo, typecheck
  from nemo.core.classes.modelPT import ModelPT
  from nemo.core.neural_types import NeuralType
  from nemo.utils import logging
label_desc: Use image node_id 14 for calling the BERTLMModel obj's underlying member method code with example usage: obj._setup_tokenizer(cfg) without return types