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,
      export_onnx: bool = False,
      num_classes: Optional[int] = None,
  ):
      from packaging.version import Version
  
      if Version(torchmetrics.__version__) < Version("0.11.0"):
          # Older version accepts num_classes = None
          metrics = {"acc": _AccuracyWithLogits()}  # type: ignore # pylint: disable=no-value-for-parameter
      else:
          if num_classes is None:
              raise ValueError(
                  "num_classes must be specified for torchmetrics >= 0.11. "
                  "Please either specify it or use an older version of torchmetrics."
              )
          metrics = {
              "acc": torchmetrics.Accuracy(
                  "multiclass", num_classes=num_classes
              )
          }
  
      super().__init__(
          criterion,
          metrics,  # type: ignore
          learning_rate=learning_rate,
          weight_decay=weight_decay,
          optimizer=optimizer,
          export_onnx=export_onnx,
      )
  
imports: import logging
  import os
  import warnings
  from pathlib import Path
  from typing import Any, Dict, Union, Optional, List, Type
  import pytorch_lightning
  import torch.nn
  import torch.nn.functional
  import torch.optim
  import torchmetrics
  import torchmetrics.classification
  import torch.utils.data
  import nni
  from nni.common.serializer import is_traceable
  from nni.nas.evaluator import MutableEvaluator
label_desc: Use image node_id 1 to create a new ClassificationModule object from inherited base classes: SupervisedLearningModule with example: obj = ClassificationModule(criterion, learning_rate, weight_decay, optimizer, export_onnx, num_classes)