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]
      )
      optimizer = LARS(
          [
              {"name": "dcl", "params": params},
              {
                  "name": "dcl_no_weight_decay",
                  "params": params_no_weight_decay,
                  "weight_decay": 0.0,
              },
              {
                  "name": "online_classifier",
                  "params": self.online_classifier.parameters(),
                  "weight_decay": 0.0,
              },
          ],
          # DCL uses SimCLR's learning rate scaling scheme.
          # Square root learning rate scaling improves performance for small
          # batch sizes (<=2048) and few training epochs (<=200). Alternatively,
          # linear scaling can be used for larger batches and longer training:
          #   lr=0.3 * self.batch_size_per_device * self.trainer.world_size / 256
          # See Appendix B.1. in the SimCLR paper https://arxiv.org/abs/2002.05709
          lr=0.075
          * math.sqrt(
              self.batch_size_per_device * self.trainer.world_size
          ),
          momentum=0.9,
          # Note: Paper uses weight decay of 1e-6 but reference code 1e-4. See:
          # https://github.com/google-research/simclr/blob/2fc637bdd6a723130db91b377ac15151e01e4fc2/README.md?plain=1#L103
          weight_decay=1e-6,
      )
      scheduler = {
          "scheduler": CosineWarmupScheduler(
              optimizer=optimizer,
              warmup_epochs=int(
                  self.trainer.estimated_stepping_batches
                  / self.trainer.max_epochs
                  * 10
              ),
              max_epochs=int(self.trainer.estimated_stepping_batches),
          ),
          "interval": "step",
      }
      return [optimizer], [scheduler]
  
imports: import math
  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.dcl_loss import DCLLoss
  from lightly.models.modules import SimCLRProjectionHead
  from lightly.models.utils import get_weight_decay_parameters
  from lightly.transforms import SimCLRTransform
  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 DCL obj's underlying member method code with example usage: obj.configure_optimizers() and returns: list,  list