code: def shared_step(
      self, batch: Tuple[Tensor, ...], batch_idx: int
  ) -> Tuple[Tensor, Dict[int, Tensor]]:
      images, targets = batch[0], batch[1]
      predictions = self.forward(images)
      loss = self.criterion(predictions, targets)
      _, predicted_labels = predictions.topk(max(self.topk))
      topk = mean_topk_accuracy(predicted_labels, targets, k=self.topk)
      return loss, topk
  
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 3 for calling the LinearClassifier obj's underlying member method code with example usage: obj.shared_step(batch, batch_idx) and returns: loss,  topk