code: def configure_optimizers(
      self,
  ) -> Tuple[List[Optimizer], List[Dict[str, Union[Any, str]]]]:
      parameters = list(self.classification_head.parameters())
      if not self.freeze_model:
          parameters += self.model.parameters()
      optimizer = SGD(
          parameters,
          lr=0.1
          * self.batch_size_per_device
          * self.trainer.world_size
          / 256,
          momentum=0.9,
          weight_decay=0.0,
      )
      scheduler = {
          "scheduler": CosineWarmupScheduler(
              optimizer=optimizer,
              warmup_epochs=0,
              max_epochs=int(self.trainer.estimated_stepping_batches),
          ),
          "interval": "step",
      }
      return [optimizer], [scheduler]
  
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 6 for calling the LinearClassifier obj's underlying member method code with example usage: obj.configure_optimizers() and returns: list,  list