code: def test_fit_and_predict_with_weights(self):
      ds = self.get_dataset()
      clf = self.get_clf()
  
      weights = np.random.randn(len(ds))
      weights = weights - weights.min() + 1e-8
  
      with self.assertRaisesRegex(
          ValueError, "Sample weights are not supported"
      ):
          clf.fit(ds, weights=weights)
  
imports: import unittest
  import pytest
  import numpy
  from scipy.sparse import csr_matrix
  from sklearn.utils.validation import check_is_fitted
  from small_text.classifiers.classification import ConfidenceEnhancedLinearSVC
  from small_text.classifiers.classification import SklearnClassifier
  from small_text.data.datasets import SklearnDataset
  from tests.utils.datasets import random_matrix_data
label_desc: Use image node_id 3 for calling the LinearSVCMultiLabelTest obj's underlying member method code with example usage: obj.test_fit_and_predict_with_weights() without return types