code: def validation_step(self, batch, batch_idx, dataloader_idx=0):
      input_ids, input_type_ids, input_mask, labels = batch
      model_output = self(
          input_ids=input_ids,
          token_type_ids=input_type_ids,
          attention_mask=input_mask,
      )
  
      if self.task_name == "sts-b":
          val_loss = self.loss(preds=model_output, labels=labels)
      else:
          val_loss = self.loss(logits=model_output, labels=labels)
  
      if self.task_name != "sts-b":
          model_output = torch.argmax(model_output, 1)
  
      eval_tensors = {"preds": model_output, "labels": labels}
      output = {"val_loss": val_loss, "eval_tensors": eval_tensors}
      self.validation_step_outputs.append(output)
      return output
  
imports: import os
  from typing import Dict, Optional, Union
  import numpy
  import torch
  from omegaconf import DictConfig
  from pytorch_lightning import Trainer
  from nemo.collections.common.losses import CrossEntropyLoss, MSELoss
  from nemo.collections.nlp.data.glue_benchmark.glue_benchmark_dataset import GLUE_TASKS_NUM_LABELS, GLUEDataset
  from nemo.collections.nlp.models.glue_benchmark.metrics_for_glue import compute_metrics
  from nemo.collections.nlp.models.nlp_model import NLPModel
  from nemo.collections.nlp.modules.common import SequenceClassifier, SequenceRegression
  from nemo.collections.nlp.parts.utils_funcs import list2str, tensor2list
  from nemo.core.classes import typecheck
  from nemo.core.neural_types import NeuralType
  from nemo.utils import logging
label_desc: Use image node_id 8 for calling the GLUEModel obj's underlying member method code with example usage: obj.validation_step(batch, batch_idx, dataloader_idx) and returns: output