code: def test_set_labels(self):
      ds = self._random_data(
          num_samples=self.NUM_SAMPLES, num_classes=self.NUM_LABELS
      )
      ds_new = self._random_data(
          num_samples=self.NUM_SAMPLES, num_classes=self.NUM_LABELS + 1
      )
  
      if self.multi_label:
          assert_csr_matrix_not_equal(ds.y, ds_new.y)
      else:
          assert_array_not_equal(ds.y, ds_new.y)
  
      if self.target_labels == "explicit":
          with self.assertRaisesRegex(
              ValueError, "Error while assigning new labels"
          ):
              ds.y = ds_new.y
      else:
          ds.y = ds_new.y
  
          if self.multi_label:
              assert_csr_matrix_equal(ds.y, ds_new.y)
          else:
              assert_array_equal(ds.y, ds_new.y)
              self.assertTrue(isinstance(ds.y, np.ndarray))
              self.assertEqual(self.NUM_SAMPLES, ds.y.shape[0])
  
          if self.target_labels == "explicit":
              assert_array_equal(
                  np.arange(self.NUM_LABELS), ds.target_labels
              )
          else:
              assert_array_equal(
                  np.arange(self.NUM_LABELS + 1), ds.target_labels
              )
  
imports: import unittest
  import pytest
  import numpy
  from scipy.sparse import csr_matrix
  from numpy.testing import assert_array_equal
  from parameterized import parameterized_class
  from small_text.base import LABEL_UNLABELED
  from small_text.integrations.pytorch.datasets import PytorchDatasetView
  from small_text.integrations.pytorch.exceptions import PytorchNotFoundError
  from tests.utils.misc import increase_dense_labels_safe
  from tests.utils.testing import assert_array_not_equal, assert_csr_matrix_equal, assert_csr_matrix_not_equal, assert_list_of_tensors_equal, assert_list_of_tensors_not_equal
label_desc: Use image node_id 9 for calling the TransformersDatasetTest obj's underlying member method code with example usage: obj.test_set_labels() without return types