code: def test_error_when_classes_could_not_be_inferred(
      self, net_cls, module_cls, data
  ):
      # Provide a better error message when net.classes_ does not exist,
      # though it is pretty difficult to know exactly the circumstanes that
      # led to this, so we have to make a guess.
      # See https://github.com/skorch-dev/skorch/discussions/1003
      class MyDataset(torch.utils.data.Dataset):
          """Dataset class that makes it impossible to access y"""
  
          def __len__(self):
              return len(data[0])
  
          def __getitem__(self, i):
              return data[0][i], data[1][i]
  
      net = net_cls(module_cls, max_epochs=0, train_split=False)
      ds = MyDataset()
      net.fit(ds, y=None)
  
      msg = (
          "NeuralNetClassifier 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 "
          "initializing NeuralNetClassifier"
      )
      with pytest.raises(AttributeError, match=msg):
          net.classes_
  
imports: from unittest.mock import Mock
  import numpy
  import pytest
  import torch
  from scipy.special import expit
  from sklearn.base import clone
  from torch import nn
  from skorch.tests.conftest import INFERENCE_METHODS
label_desc: Use image node_id 22 for calling the TestNeuralNet obj's underlying member method code with example usage: obj.test_error_when_classes_could_not_be_inferred(net_cls, module_cls, data) and returns: len, data,  data