class_docstr: BERT encoder with QA head training.
class_docstr_tok: ["BERT","encoder","with","QA","head","training","."]
code: def validation_step(self, batch, batch_idx):
      if self.trainer.testing:
          prefix = "test"
      else:
          prefix = "val"
  
      (
          input_ids,
          input_type_ids,
          input_mask,
          unique_ids,
          start_positions,
          end_positions,
      ) = batch
      logits = self.forward(
          input_ids=input_ids,
          token_type_ids=input_type_ids,
          attention_mask=input_mask,
      )
      loss, start_logits, end_logits = self.loss(
          logits=logits,
          start_positions=start_positions,
          end_positions=end_positions,
      )
  
      tensors = {
          "unique_ids": unique_ids,
          "start_logits": start_logits,
          "end_logits": end_logits,
      }
      loss = {f"{prefix}_loss": loss, f"{prefix}_tensors": tensors}
      self.validation_step_outputs.append(
          loss
      ) if prefix == "val" else self.test_step_outputs.append(loss)
      return loss
  
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 4 for calling the QAModel obj's underlying member method code with example usage: obj.validation_step(batch, batch_idx) and returns: loss