code: def test_new_label_reduce():
      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()
      # Act X
      result = check.run(
          train_dataset=train_dataset, test_dataset=test_dataset
      )
      reduce_value = check.reduce_output(result)
      # Assert
      assert_that(reduce_value["Samples with New Labels"], equal_to(1))
  
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 8 for calling a global function with example usage: test_new_label_reduce() without return types