class_docstr: BERT model with a QA (token classification) head 
class_docstr_tok: ["BERT","model","with","a","QA","(","token","classification",")","head"]
code: def _setup_dataloader_from_config(self, cfg: DictConfig, mode: str):
      processor = QAProcessor(cfg.file, mode)
  
      dataset = BERTQADataset(
          data_file=cfg.file,
          processor=processor,
          tokenizer=self.tokenizer,
          keep_doc_spans=self._cfg.dataset.keep_doc_spans,
          doc_stride=self._cfg.dataset.doc_stride,
          max_query_length=self._cfg.dataset.max_query_length,
          max_seq_length=self._cfg.dataset.max_seq_length,
          version_2_with_negative=self._cfg.dataset.version_2_with_negative,
          num_samples=cfg.num_samples,
          mode=mode,
          use_cache=self._cfg.dataset.use_cache,
      )
  
      data_loader = torch.utils.data.DataLoader(
          dataset=dataset,
          batch_size=cfg.batch_size,
          collate_fn=dataset.collate_fn,
          drop_last=cfg.drop_last,
          shuffle=cfg.shuffle,
          num_workers=cfg.num_workers,
          pin_memory=cfg.pin_memory,
      )
  
      return data_loader
  
imports: import collections
  from typing import List, Optional
  import numpy
  import torch
  from omegaconf import DictConfig
  from pytorch_lightning import Trainer
  from transformers.models.bert.tokenization_bert import BasicTokenizer
  from nemo.collections.common.losses import SpanningLoss
  from nemo.collections.common.parts.utils import _compute_softmax
  from nemo.collections.nlp.data.question_answering.data_processor.qa_processing import QAProcessor
  from nemo.collections.nlp.data.question_answering.dataset.qa_bert_dataset import BERTQADataset
  from nemo.collections.nlp.metrics.qa_metrics import QAMetrics
  from nemo.collections.nlp.models.question_answering.qa_base_model import BaseQAModel
  from nemo.collections.nlp.modules.common import TokenClassifier
  from nemo.collections.nlp.parts.utils_funcs import tensor2list
  from nemo.core.classes.common import PretrainedModelInfo, typecheck
  from nemo.utils import logging
label_desc: Use image node_id 11 for calling the BERTQAModel obj's underlying member method code with example usage: obj._setup_dataloader_from_config(cfg, mode) and returns: data_loader