code: def evaluate(self, batch, stage=None):
      x, y = batch
      logits = self(x)
      loss = self.criterion(logits, y)
      preds = torch.argmax(logits, dim=1)
      acc = accuracy(preds, y, "multiclass", num_classes=10)
  
      # If NNI need to evaluate the model, "default" metric will be used.
      if stage:
          self.log(f"default", loss, prog_bar=False)
          self.log(f"{stage}_loss", loss, prog_bar=True)
          self.log(f"{stage}_acc", acc, prog_bar=True)
  
file_path: nni/examples/compression/evaluator/lightning_evaluator.py
imports: import pytorch_lightning
  from pytorch_lightning.loggers import TensorBoardLogger
  import torch
  from torchmetrics.functional import accuracy
  import nni
  from examples.compression.models import build_resnet18
  from nni.compression import LightningEvaluator
label_desc: Use image node_id 3 for calling the MyModule obj's underlying member method code with example usage: obj.evaluate(batch, stage) without return types