class_docstr: BERT language model pretraining.
class_docstr_tok: ["BERT","language","model","pretraining","."]
code: def _setup_dataloader(self, cfg: DictConfig):
      dataset = BertPretrainingDataset(
          tokenizer=self.tokenizer,
          data_file=cfg.data_file,
          max_seq_length=cfg.max_seq_length,
          mask_prob=cfg.mask_prob,
          short_seq_prob=cfg.short_seq_prob,
      )
      dl = torch.utils.data.DataLoader(
          dataset=dataset,
          batch_size=cfg.batch_size,
          collate_fn=dataset.collate_fn,
          drop_last=cfg.get("drop_last", False),
          shuffle=cfg.shuffle,
          num_workers=cfg.get("num_workers", 0),
      )
      return dl
  
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 15 for calling the BERTLMModel obj's underlying member method code with example usage: obj._setup_dataloader(cfg) and returns: dl