code: def test_with_drift(self, dummy_multilabel_textdata_train_test):
      # Arrange
      train, test = dummy_multilabel_textdata_train_test
      default_properties = [
          "Text Length",
          "Average Word Length",
          "Max Word Length",
          "% Special Characters",
          "Language",
          "Sentiment",
          "Subjectivity",
      ]
      train.calculate_builtin_properties(
          include_properties=default_properties
      )
      test.calculate_builtin_properties(
          include_properties=default_properties
      )
      check = PropertyDrift(
          min_samples=20
      ).add_condition_drift_score_less_than(
          max_allowed_numeric_score=0.3,
          max_allowed_categorical_score=0.3,
      )
      # Act
      result = check.run(train_dataset=train, test_dataset=test)
      condition_results = check.conditions_decision(result)
  
      assert_that(
          condition_results,
          has_items(
              equal_condition_result(
                  is_pass=False,
                  details=(
                      "Failed for 1 out of 7 columns.\nFound 1 "
                      "numeric columns with Kolmogorov-Smirnov above threshold: "
                      "{'Text Length': '0.33'}"
                  ),
                  name=(
                      "categorical drift score < 0.3 and numerical drift score < 0.3"
                  ),
              )
          ),
      )
  
file_path: deepchecks/tests/nlp/checks/train_test_validation/property_drift_test.py
imports: import typing
  from hamcrest import *
  from deepchecks.nlp.checks import PropertyDrift
  from deepchecks.nlp.text_data import TextData
  from tests.base.utils import equal_condition_result
label_desc: Use image node_id 2 for calling the TestMultiLabelClassification obj's underlying member method code with example usage: obj.test_with_drift(dummy_multilabel_textdata_train_test) without return types