code: def test_fit_where_y_train_contains_unlabeled(self):
      train_set = random_sklearn_dataset(
          10,
          num_classes=3 if self._is_multi_label() else 2,
          multi_label=self._is_multi_label(),
      )
  
      if self._is_multi_label():
          train_set.y = csr_matrix(
              np.array([[LABEL_UNLABELED] * 3] * 10)
          )
      else:
          train_set.y = np.array([LABEL_UNLABELED] * 10)
  
      classifier = self._get_clf()
  
      with self.assertRaisesRegex(
          ValueError, "Training set labels must be labeled"
      ):
          classifier.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 SklearnClassifierSingleLabelTest obj's underlying member method code with example usage: obj.test_fit_where_y_train_contains_unlabeled() without return types