code: def validation_step(self, batch, batch_idx):
      x, y = batch
      y_hat = self(x)
  
      if self.export_onnx is not None:
          self.export_onnx.parent.mkdir(exist_ok=True)
          try:
              self.to_onnx(self.export_onnx, x, export_params=True)
          except RuntimeError as e:
              warnings.warn(
                  f"ONNX conversion failed. As a result, you might not be able to use visualization. Error message: {e}"
              )
          self.export_onnx = None
  
      self.log("val_loss", self.criterion(y_hat, y), prog_bar=True)
      for name, metric in self.metrics.items():
          self.log("val_" + name, metric(y_hat, y), prog_bar=True)
  
imports: import logging
  import os
  import warnings
  from pathlib import Path
  from typing import Any, Dict, Union, Optional, List, Type
  import pytorch_lightning
  import torch.nn
  import torch.nn.functional
  import torch.optim
  import torchmetrics
  import torchmetrics.classification
  import torch.utils.data
  import nni
  from nni.common.serializer import is_traceable
  from nni.nas.evaluator import MutableEvaluator
label_desc: Use image node_id 4 for calling the SupervisedLearningModule obj's underlying member method code with example usage: obj.validation_step(batch, batch_idx) without return types