code: def configure_optimizers(self):
      parameters = list(self.classification_head.parameters())
      parameters += self.model.parameters()
      optimizer = SGD(
          parameters,
          lr=0.05
          * 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=self.trainer.estimated_stepping_batches,
          ),
          "interval": "step",
      }
      return [optimizer], [scheduler]
  
file_path: lightly/benchmarks/imagenet/resnet50/finetune_eval.py
imports: from pathlib import Path
  from pytorch_lightning import Trainer
  from pytorch_lightning.callbacks import DeviceStatsMonitor, LearningRateMonitor
  from pytorch_lightning.loggers import TensorBoardLogger
  from torch.nn import Module
  from torch.optim import SGD
  from torch.utils.data import DataLoader
  from torchvision import transforms
  from lightly.data import LightlyDataset
  from lightly.transforms.utils import IMAGENET_NORMALIZE
  from lightly.utils.benchmarking import LinearClassifier, MetricCallback
  from lightly.utils.dist import print_rank_zero
  from lightly.utils.scheduler import CosineWarmupScheduler
label_desc: Use image node_id 1 for calling the FinetuneLinearClassifier obj's underlying member method code with example usage: obj.configure_optimizers() and returns: list,  list