code: def test_max_drift_score_condition_pass(drifted_data):
      # Arrange
      train_ds, test_ds = drifted_data
      train_ds = Dataset(
          train_ds.data.drop(
              columns=["numeric_with_drift", "categorical_with_drift"]
          ),
          label=train_ds.label_name,
      )
      test_ds = Dataset(
          test_ds.data.drop(
              columns=["numeric_with_drift", "categorical_with_drift"]
          ),
          label=test_ds.label_name,
      )
      check = (
          MultivariateDrift().add_condition_overall_drift_value_less_than()
      )
  
      # Act
      result = check.run(train_ds, test_ds)
      condition_result, *_ = check.conditions_decision(result)
  
      # Assert
      assert_that(
          condition_result,
          equal_condition_result(
              is_pass=True,
              details="Found drift value of: 0, corresponding to a domain classifier AUC of: 0.5",
              name="Drift value is less than 0.25",
          ),
      )
  
file_path: deepchecks/tests/tabular/checks/train_test_validation/multivariate_drift_test.py
imports: import random
  import string
  import numpy
  import pandas
  from hamcrest import assert_that, close_to, greater_than, has_entries, has_length
  from deepchecks.tabular.checks import MultivariateDrift
  from deepchecks.tabular.dataset import Dataset
  from tests.base.utils import equal_condition_result
label_desc: Use image node_id 4 for calling a global function with example usage: test_max_drift_score_condition_pass(drifted_data) without return types