class_docstr: BERT encoder with QA head training.
class_docstr_tok: ["BERT","encoder","with","QA","head","training","."]
code: def setup_validation_data(
      self, val_data_config: Optional[DictConfig]
  ):
      if not val_data_config or not val_data_config.file:
          logging.info(
              f"Dataloader config or file_path for the validation is missing, so no data loader for test is created!"
          )
          self._test_dl = None
          return
      self._validation_dl = self._setup_dataloader_from_config(
          cfg=val_data_config, mode=EVALUATION_MODE
      )
  
imports: import json
  from typing import Optional
  import torch
  from omegaconf import DictConfig, OmegaConf
  from pytorch_lightning import Trainer
  from torch.cuda.amp import autocast
  from nemo.collections.common.losses import SpanningLoss
  from nemo.collections.nlp.data import SquadDataset
  from nemo.collections.nlp.data.question_answering_squad.qa_squad_processing import EVALUATION_MODE, INFERENCE_MODE, TRAINING_MODE
  from nemo.collections.nlp.models.nlp_model import NLPModel
  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 10 for calling the QAModel obj's underlying member method code with example usage: obj.setup_validation_data(val_data_config) without return types