code: def classes_(self):
      if self.classes is not None:
          if not len(self.classes):
              raise AttributeError(
                  "{} has no attribute 'classes_'".format(
                      self.__class__.__name__
                  )
              )
          return self.classes
  
      try:
          return self.classes_inferred_
      except AttributeError as exc:
          # It's not easily possible to track exactly what circumstances led
          # to this, so try to make an educated guess and provide a possible
          # solution.
          msg = (
              f"{self.__class__.__name__} could not infer the classes from y; "
              "this error probably occurred because the net was trained without y "
              "and some function tried to access the '.classes_' attribute; "
              "a possible solution is to provide the 'classes' argument when "
              f"initializing {self.__class__.__name__}"
          )
          raise AttributeError(msg) from exc
  
imports: import re
  import numpy
  from sklearn.base import ClassifierMixin
  import torch
  from torch.utils.data import DataLoader
  from skorch import NeuralNet
  from skorch.callbacks import EpochTimer
  from skorch.callbacks import PrintLog
  from skorch.callbacks import EpochScoring
  from skorch.callbacks import PassthroughScoring
  from skorch.dataset import ValidSplit
  from skorch.utils import data_from_dataset, is_dataset, get_dim, to_numpy
label_desc: Use image node_id 3 for calling the NeuralNetClassifier obj's underlying member method code with example usage: obj.classes_() and returns: self, self