code: def __init__(
      self,
      criterion: Type[nn.Module] = nn.CrossEntropyLoss,
      learning_rate: float = 0.001,
      weight_decay: float = 0.0,
      optimizer: Type[optim.Optimizer] = optim.Adam,
  ):
      super().__init__(
          criterion,
          {"acc": _AccuracyWithLogits},  # type: ignore
          learning_rate=learning_rate,
          weight_decay=weight_decay,
          optimizer=optimizer,
      )
  
imports: import warnings
  from typing import Dict, List, Optional, Union, Type
  import torch.nn
  import torch.optim
  import torchmetrics
  from torch.utils.data import DataLoader
  import nni
  from ..lightning import LightningModule, _AccuracyWithLogits, Lightning
  from .trainer import Trainer
label_desc: Use image node_id 1 to create a new _ClassificationModule object from inherited base classes: _MultiModelSupervisedLearningModule with example: obj = _ClassificationModule(criterion, learning_rate, weight_decay, optimizer)