code: def test_fit_with_multi_label_data_on_single_label_classifier(self):
      train_set = SklearnDataset(
          *random_matrix_data(
              "dense", "sparse", num_samples=10, num_labels=3
          )
      )
      clf = self._get_clf()
  
      expected_str = "Given labeling is recognized as multi-label labeling but the classifier "
      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 4 for calling the SklearnClassifierSingleLabelTest obj's underlying member method code with example usage: obj.test_fit_with_multi_label_data_on_single_label_classifier() without return types