code: def test_classification_mnist_new_labels(
      mnist_visiondata_train, mnist_visiondata_test, device
  ):
      # Arrange
      def modified_labels_collate(data):
          images, labels = mnist_collate_without_model(data)
          modified_labels = [x if x > 3 else -x for x in labels]
          return {"images": images, "labels": modified_labels}
  
      def collate_fn(data):
          images, labels = mnist_collate_without_model(data)
          return {"images": images, "labels": labels}
  
      modified_test = replace_collate_fn_visiondata(
          mnist_visiondata_test, modified_labels_collate
      )
      train = replace_collate_fn_visiondata(
          mnist_visiondata_train, collate_fn
      )
      train.label_map.clear()
      modified_test.label_map.clear()
      check = NewLabels().add_condition_new_label_ratio_less_or_equal(0)
      # Act
      result = check.run(train, modified_test)
      # Assert
      assert_that(
          result.value,
          equal_to(
              {
                  "new_labels": {"-1": 28, "-2": 16, "-3": 16},
                  "all_labels_count": 200,
              }
          ),
      )
  
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 7 for calling a global function with example usage: test_classification_mnist_new_labels(mnist_visiondata_train, mnist_visiondata_test, device) and returns: dict, dict