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 = SwaVProjectionHead()
      self.prototypes = SwaVPrototypes(n_steps_frozen_prototypes=1)
      self.criterion = SwaVLoss(sinkhorn_gather_distributed=True)
      self.online_classifier = OnlineLinearClassifier(
          num_classes=num_classes
      )
  
      # Use a queue for small batch sizes (<= 256).
      self.start_queue_at_epoch = 15
      self.n_batches_in_queue = 15
      self.queues = ModuleList(
          [
              MemoryBankModule(
                  size=(
                      self.n_batches_in_queue
                      * self.batch_size_per_device,
                      128,
                  )
              )
              for _ in range(CROP_COUNTS[0])
          ]
      )
  
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 1 to create a new SwAV object from inherited base classes: LightningModule with example: obj = SwAV(batch_size_per_device, num_classes)