code: def validation_step(
      self, batch: Tuple[Tensor, ...], batch_idx: int
  ) -> Tensor:
      loss, topk = self.shared_step(batch=batch, batch_idx=batch_idx)
      batch_size = len(batch[1])
      log_dict = {f"val_top{k}": acc for k, acc in topk.items()}
      self.log(
          "val_loss",
          loss,
          prog_bar=True,
          sync_dist=True,
          batch_size=batch_size,
      )
      self.log_dict(
          log_dict, prog_bar=True, sync_dist=True, batch_size=batch_size
      )
      return loss
  
file_path: lightly/lightly/utils/benchmarking/linear_classifier.py
imports: from typing import Any, Dict, List, Tuple, Union
  import torch
  from pytorch_lightning import LightningModule
  from torch import Tensor
  from torch.nn import CrossEntropyLoss, Linear, Module
  from torch.optim import SGD, Optimizer
  from lightly.utils.benchmarking.topk import mean_topk_accuracy
  from lightly.utils.scheduler import CosineWarmupScheduler
label_desc: Use image node_id 5 for calling the LinearClassifier obj's underlying member method code with example usage: obj.validation_step(batch, batch_idx) and returns: loss