code: def __init__(
      self, batch_size_per_device: int, num_classes: int
  ) -> None:
      super().__init__()
      self.save_hyperparameters()
      self.batch_size_per_device = batch_size_per_device
  
      resnet = resnet50()
      resnet.fc = Identity()  # Ignore classification head
      self.backbone = resnet
      self.projection_head = SimCLRProjectionHead()
      self.criterion = NTXentLoss(
          temperature=0.1, gather_distributed=True
      )
  
      self.online_classifier = OnlineLinearClassifier(
          num_classes=num_classes
      )
  
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.ntx_ent_loss import NTXentLoss
  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 1 to create a new SimCLR object from inherited base classes: LightningModule with example: obj = SimCLR(batch_size_per_device, num_classes)