code: def __init__(
      self,
      backbone="resnet101",
      hidden_dim=1024,
      learning_rate=1e-3,
      weights="DEFAULT",
  ):
      super().__init__()
      self.save_hyperparameters()
  
      self.learning_rate = learning_rate
      self.num_classes = 10
      self.backbone = get_torchvision_model(backbone, weights=weights)
  
      self.classifier = torch.nn.Sequential(
          torch.nn.Linear(1000, hidden_dim),
          torch.nn.Linear(hidden_dim, self.num_classes),
      )
      self.transform = transforms.Compose(
          [
              transforms.ToTensor(),
              transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
          ]
      )
      self._loss = []
  
file_path: pytorch-lightning/tests/parity_pytorch/models.py
imports: import torch
  import torch.nn.functional
  from lightning.pytorch.core.module import LightningModule
  from lightning.pytorch.utilities.imports import _TORCHVISION_AVAILABLE
  from lightning.pytorch.utilities.model_helpers import get_torchvision_model
  from tests_pytorch import _PATH_DATASETS
  from torch.utils.data import DataLoader
label_desc: Use image node_id 1 to create a new ParityModuleCIFAR object from inherited base classes: LightningModule with example: obj = ParityModuleCIFAR(backbone, hidden_dim, learning_rate, weights)