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, self.prototypes]
      )
      optimizer = LARS(
          [
              {"name": "swav", "params": params},
              {
                  "name": "swav_no_weight_decay",
                  "params": params_no_weight_decay,
                  "weight_decay": 0.0,
              },
              {
                  "name": "online_classifier",
                  "params": self.online_classifier.parameters(),
                  "weight_decay": 0.0,
              },
          ],
          # Smaller learning rate for smaller batches: lr=0.6 for batch_size=256
          # scaled linearly by batch size to lr=4.8 for batch_size=2048.
          # See Appendix A.1. and A.6. in SwAV paper https://arxiv.org/pdf/2006.09882.pdf
          lr=0.6
          * (self.batch_size_per_device * self.trainer.world_size)
          / 256,
          momentum=0.9,
          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),
              end_value=0.0006
              * (self.batch_size_per_device * self.trainer.world_size)
              / 256,
          ),
          "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, ModuleList
  from torch.nn import functional
  from torchvision.models import resnet50
  from lightly.loss.swav_loss import SwaVLoss
  from lightly.models.modules import SwaVProjectionHead, SwaVPrototypes
  from lightly.models.modules.memory_bank import MemoryBankModule
  from lightly.models.utils import get_weight_decay_parameters
  from lightly.transforms import SwaVTransform
  from lightly.utils.benchmarking import OnlineLinearClassifier
  from lightly.utils.lars import LARS
  from lightly.utils.scheduler import CosineWarmupScheduler
label_desc: Use image node_id 6 for calling the SwAV obj's underlying member method code with example usage: obj.configure_optimizers() and returns: list,  list