code: def __init__(self, backbone):
      super().__init__()
      # use the pretrained ResNet backbone
      self.backbone = backbone
  
      # freeze the backbone
      deactivate_requires_grad(backbone)
  
      # create a linear layer for our downstream classification model
      self.fc = nn.Linear(512, 10)
  
      self.criterion = nn.CrossEntropyLoss()
      self.validation_step_outputs = []
  
imports: import copy
  import pytorch_lightning
  import torch
  import torch.nn
  import torchvision
  from lightly.data import LightlyDataset
  from lightly.loss import NTXentLoss
  from lightly.models import ResNetGenerator
  from lightly.models.modules.heads import MoCoProjectionHead
  from lightly.models.utils import batch_shuffle, batch_unshuffle, deactivate_requires_grad, update_momentum
  from lightly.transforms import MoCoV2Transform, utils
label_desc: Use image node_id 1 to create a new Classifier object from inherited base classes: pl with example: obj = Classifier(backbone)