code: def test_from_arrays_with_lists(self):
      texts = [f"train data {i}" for i in range(10)]
      labels = np.array([random_labeling(3) for _ in range(10)])
  
      vectorizer = TfidfVectorizer()
  
      with patch.object(
          vectorizer, "fit_transform", wraps=vectorizer.fit_transform
      ) as fit_transform_spy:
          dataset = SklearnDataset.from_arrays(
              texts, labels, vectorizer
          )
  
          self.assertEqual(10, len(dataset))
          assert_array_equal(labels, dataset.y)
          is_fitted = [
              v
              for v in vars(vectorizer)
              if v.endswith("_") and not v.startswith("__")
          ]
          self.assertTrue(is_fitted)
  
          fit_transform_spy.assert_called_with(texts)
  
imports: import unittest
  import numpy
  from numpy.testing import assert_array_equal
  from sklearn.feature_extraction.text import TfidfVectorizer
  from unittest.mock import patch
  from small_text.data import SklearnDataset, TextDataset
  from tests.utils.datasets import random_labeling, random_labels
  from tests.utils.testing import assert_csr_matrix_equal
label_desc: Use image node_id 1 for calling the SklearnDatasetConstructionSingleLabelTest obj's underlying member method code with example usage: obj.test_from_arrays_with_lists() without return types