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=num_classes)
  
      if stage:
          self.log(f"{stage}_loss", loss, prog_bar=True)
          self.log(f"{stage}_acc", acc, prog_bar=True)
  
imports: import os
  import pytest
  import torch
  from unittest import TestCase
  from bigdl.nano.pytorch import Trainer
  from bigdl.nano.pytorch import TorchNano
  from bigdl.nano.utils.pytorch import TORCH_VERSION_LESS_1_12, TORCH_VERSION_LESS_2_0
  from torchvision.models.resnet import ResNet, BasicBlock
  from torchmetrics.functional import accuracy
  import pytorch_lightning
  import torch.nn.functional
  from test.pytorch.utils._train_torch_lightning import create_data_loader, data_transform
  from test.pytorch.utils._train_torch_lightning import create_test_data_loader
label_desc: Use image node_id 4 for calling the CustomResNet obj's underlying member method code with example usage: obj.evaluate(batch, stage) without return types