code: def __init__(
      self, max_epochs=108, learning_rate=0.1, weight_decay=1e-4
  ):
      super().__init__()
      self.save_hyperparameters(
          "learning_rate", "weight_decay", "max_epochs"
      )
      self.criterion = nn.CrossEntropyLoss()
      self.accuracy = AccuracyWithLogits()
  
imports: import click
  import nni
  import nni.retiarii.evaluator.pytorch.lightning
  import torch.nn
  import torchmetrics
  from nni.retiarii import model_wrapper, serialize
  from nni.retiarii.experiment.pytorch import RetiariiExperiment, RetiariiExeConfig
  from nni.retiarii.nn.pytorch import NasBench101Cell
  from nni.retiarii.strategy import Random
  from pytorch_lightning.callbacks import LearningRateMonitor
  from timm.optim import RMSpropTF
  from torch.optim.lr_scheduler import CosineAnnealingLR
  from torchvision import transforms
  from torchvision.datasets import CIFAR10
  from base_ops import Conv3x3BnRelu, Conv1x1BnRelu, Projection
label_desc: Use image node_id 1 to create a new NasBench101TrainingModule object from inherited base classes: pl with example: obj = NasBench101TrainingModule(max_epochs, learning_rate, weight_decay)