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 = MoCoProjectionHead()
      self.query_backbone = copy.deepcopy(self.backbone)
      self.query_projection_head = MoCoProjectionHead()
      self.criterion = NTXentLoss(
          temperature=0.2,
          memory_bank_size=(65536, 128),
          gather_distributed=True,
      )
  
      self.online_classifier = OnlineLinearClassifier(
          num_classes=num_classes
      )
  
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 torch.optim import SGD
  from torchvision.models import resnet50
  from lightly.loss import NTXentLoss
  from lightly.models.modules import MoCoProjectionHead
  from lightly.models.utils import batch_shuffle, batch_unshuffle, get_weight_decay_parameters, update_momentum
  from lightly.transforms import MoCoV2Transform
  from lightly.utils.benchmarking import OnlineLinearClassifier
  from lightly.utils.scheduler import CosineWarmupScheduler
label_desc: Use image node_id 1 to create a new MoCoV2 object from inherited base classes: LightningModule with example: obj = MoCoV2(batch_size_per_device, num_classes)