code: def validation_step(self, batch, batch_idx):
      prefix = "test" if self.trainer.testing else "val"
  
      input_ids, input_attn_mask, unique_ids, labels = batch
      loss, per_sample_perplexity = self.forward(
          input_ids, input_attn_mask, labels
      )
      generated_answers = self._generate_candidates(
          input_ids, input_attn_mask
      )
  
      labels[labels == -100] = self.tokenizer.tokenizer.pad_token_id
  
      loss = {
          "unique_ids": unique_ids,
          f"{prefix}_loss": loss,
          "per_sample_perplexity": per_sample_perplexity,
          "input": self.tokenizer.tokenizer.batch_decode(
              input_ids, skip_special_tokens=True
          ),
          "ground_truth_answers": self.tokenizer.tokenizer.batch_decode(
              labels, skip_special_tokens=True
          ),
          "generated_answers": generated_answers,
      }
      if prefix == "val":
          self.validation_step_outputs.append(loss)
      else:
          self.test_step_outputs.append(loss)
  
      return loss
  
imports: import collections
  from typing import List, Optional
  import torch
  from omegaconf import DictConfig, OmegaConf, open_dict
  from pytorch_lightning import Trainer
  from torch.cuda.amp import autocast
  from transformers import AutoModelForSeq2SeqLM
  from nemo.collections.nlp.data.question_answering.data_processor.qa_processing import QAProcessor
  from nemo.collections.nlp.data.question_answering.dataset.qa_s2s_dataset import S2SQADataset
  from nemo.collections.nlp.metrics.qa_metrics import QAMetrics
  from nemo.collections.nlp.models.language_modeling.megatron_t5_model import MegatronT5Model
  from nemo.collections.nlp.models.question_answering.qa_base_model import BaseQAModel
  from nemo.core.classes.common import PretrainedModelInfo, typecheck
  from nemo.utils import logging
label_desc: Use image node_id 3 for calling the S2SQAModel obj's underlying member method code with example usage: obj.validation_step(batch, batch_idx) and returns: loss