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.student_backbone,
              self.student_projection_head,
              self.student_prediction_head,
          ]
      )
      optimizer = LARS(
          [
              {"name": "byol", "params": params},
              {
                  "name": "byol_no_weight_decay",
                  "params": params_no_weight_decay,
                  "weight_decay": 0.0,
              },
              {
                  "name": "online_classifier",
                  "params": self.online_classifier.parameters(),
                  "weight_decay": 0.0,
              },
          ],
          # Settings follow original code for 100 epochs which are slightly different
          # from the paper, see:
          # https://github.com/deepmind/deepmind-research/blob/f5de0ede8430809180254ee957abf36ed62579ef/byol/configs/byol.py#L21-L23
          lr=0.45
          * 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),
          ),
          "interval": "step",
      }
      return [optimizer], [scheduler]
  
imports: import copy
  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 import NegativeCosineSimilarity
  from lightly.models.modules import BYOLPredictionHead, BYOLProjectionHead
  from lightly.models.utils import get_weight_decay_parameters, update_momentum
  from lightly.transforms import BYOLTransform
  from lightly.utils.benchmarking import OnlineLinearClassifier
  from lightly.utils.lars import LARS
  from lightly.utils.scheduler import CosineWarmupScheduler, cosine_schedule
label_desc: Use image node_id 7 for calling the BYOL obj's underlying member method code with example usage: obj.configure_optimizers() and returns: list,  list