code: def test_fit_with_invalid_multi_label_data(self):
      train_set = random_sklearn_dataset(
          10,
          num_classes=3 if self._is_multi_label() else 2,
          multi_label=self._is_multi_label(),
      )
  
      # possibility of error: more than one unique value in y.data
      train_set.y.data = np.random.choice(
          [0, 1], train_set.y.indices.shape[0]
      )
      train_set.y.data[0:3] = [0, 1, 2]
      clf = self._get_clf()
  
      expected_str = "Invalid input: Given labeling must be recognized as multi-label"
      with self.assertRaisesRegex(ValueError, expected_str):
          clf.fit(train_set)
  
imports: import unittest
  import numpy
  from scipy.sparse import csr_matrix
  from small_text.base import LABEL_UNLABELED
  from small_text.classifiers.classification import ConfidenceEnhancedLinearSVC, SklearnClassifier
  from small_text.data.datasets import SklearnDataset
  from tests.utils.datasets import random_sklearn_dataset, random_matrix_data
label_desc: Use image node_id 3 for calling the SklearnClassifierMultiLabelTest obj's underlying member method code with example usage: obj.test_fit_with_invalid_multi_label_data() without return types