code: def evaluate(self, batch, stage=None):
      x, y = batch
      logits = self(x)
      loss = F.nll_loss(logits, y)
      preds = torch.argmax(logits, dim=1)
      acc = accuracy(preds, y, "multiclass", num_classes=10)
  
      if stage:
          self.log(f"{stage}_loss", loss, prog_bar=True)
          self.log(f"{stage}_acc", acc, prog_bar=True)
  
imports: import os
  import torch
  import torch.nn
  import torch.nn.functional
  import torchvision
  from pytorch_lightning import LightningModule, Trainer, seed_everything
  from pytorch_lightning.callbacks import LearningRateMonitor
  from pytorch_lightning.loggers import TensorBoardLogger
  from torch.optim.lr_scheduler import OneCycleLR
  from torchmetrics.functional import accuracy
  from pl_bolts.datamodules import CIFAR10DataModule
  from pl_bolts.transforms.dataset_normalizations import cifar10_normalization
label_desc: Use image node_id 4 for calling the LitResnet obj's underlying member method code with example usage: obj.evaluate(batch, stage) without return types