code: def test_condition_number_of_new_labels_fail():
      train_data = {"col1": [1, 2, 3, 4], "col2": [1, 2, 3, 4]}
      test_data = {"col1": [1, 2, 3, 5], "col2": [1, 2, 3, 4]}
      train_dataset = Dataset(
          pd.DataFrame(data=train_data, columns=["col1", "col2"]),
          label="col1",
          label_type="multiclass",
      )
      test_dataset = Dataset(
          pd.DataFrame(data=test_data, columns=["col1", "col2"]),
          label="col1",
          label_type="multiclass",
      )
  
      # Arrange
      check = NewLabelTrainTest().add_condition_new_labels_number_less_or_equal(
          0
      )
  
      # Act
      result = check.conditions_decision(
          check.run(train_dataset, test_dataset)
      )
  
      assert_that(
          result,
          has_items(
              equal_condition_result(
                  is_pass=False,
                  details="Found 1 new labels in test data: [5]",
                  name="Number of new label values is less or equal to 0",
              )
          ),
      )
  
file_path: deepchecks/tests/tabular/checks/train_test_validation/new_label_test.py
imports: import pandas
  from hamcrest import assert_that, calling, equal_to, greater_than, has_items, has_length, raises
  from deepchecks.core.errors import DeepchecksValueError
  from deepchecks.tabular.checks.train_test_validation import NewLabelTrainTest
  from deepchecks.tabular.dataset import Dataset
  from tests.base.utils import equal_condition_result
label_desc: Use image node_id 11 for calling a global function with example usage: test_condition_number_of_new_labels_fail() without return types