code: def configure_optimizers(self):
      # Don't use weight decay for batch norm, bias parameters, and classification
      # head to improve performance.
      params, params_no_weight_decay = get_weight_decay_parameters(
          [self.backbone, self.projection_head]
      )
      global_batch_size = (
          self.batch_size_per_device * self.trainer.world_size
      )
      base_lr = _get_base_learning_rate(
          global_batch_size=global_batch_size
      )
      optimizer = LARS(
          [
              {"name": "vicreg", "params": params},
              {
                  "name": "vicreg_no_weight_decay",
                  "params": params_no_weight_decay,
                  "weight_decay": 0.0,
              },
              {
                  "name": "online_classifier",
                  "params": self.online_classifier.parameters(),
                  "weight_decay": 0.0,
              },
          ],
          # Linear learning rate scaling with a base learning rate of 0.2.
          # See https://arxiv.org/pdf/2105.04906.pdf for details.
          lr=base_lr * global_batch_size / 256,
          momentum=0.9,
          weight_decay=1e-6,
      )
      scheduler = {
          "scheduler": CosineWarmupScheduler(
              optimizer=optimizer,
              warmup_epochs=(
                  self.trainer.estimated_stepping_batches
                  / self.trainer.max_epochs
                  * 10
              ),
              max_epochs=self.trainer.estimated_stepping_batches,
              end_value=0.01,  # Scale base learning rate from 0.2 to 0.002.
          ),
          "interval": "step",
      }
      return [optimizer], [scheduler]
  
imports: from typing import List, Tuple
  import torch
  from pytorch_lightning import LightningModule
  from torch import Tensor
  from torch.nn import Identity
  from torchvision.models import resnet50
  from lightly.loss.vicreg_loss import VICRegLoss
  from lightly.models.modules.heads import VICRegProjectionHead
  from lightly.models.utils import get_weight_decay_parameters
  from lightly.transforms.vicreg_transform import VICRegTransform
  from lightly.utils.benchmarking import OnlineLinearClassifier
  from lightly.utils.lars import LARS
  from lightly.utils.scheduler import CosineWarmupScheduler
label_desc: Use image node_id 5 for calling the VICReg obj's underlying member method code with example usage: obj.configure_optimizers() and returns: list,  list