code: def test_classification_mnist_with_condition(
      mnist_visiondata_train, mnist_visiondata_test
  ):
      # Arrange
      check = NewLabels().add_condition_new_label_ratio_less_or_equal(0)
      # Act
      result = check.run(mnist_visiondata_train, mnist_visiondata_test)
      # Assert
      assert_that(
          check.conditions_decision(result),
          has_items(
              equal_condition_result(
                  is_pass=True,
                  name="Percentage of new labels in the test set is less or equal to 0%",
                  details="No new labels were found in test set.",
              )
          ),
      )
      assert_that(result.value["new_labels"], has_length(0))
  
file_path: deepchecks/tests/vision/checks/train_test_validation/new_labels_test.py
imports: from hamcrest import assert_that, equal_to, greater_than, has_entries, has_items, has_length
  from deepchecks.vision.checks import NewLabels
  from deepchecks.vision.datasets.classification.mnist_torch import collate_without_model
  from deepchecks.vision.datasets.detection.coco_torch import collate_without_model
  from deepchecks.vision.utils.test_utils import replace_collate_fn_visiondata
  from tests.base.utils import equal_condition_result
label_desc: Use image node_id 5 for calling a global function with example usage: test_classification_mnist_with_condition(mnist_visiondata_train, mnist_visiondata_test) without return types