code: def test_drift_max_drift_score_condition_pass_threshold(
      drifted_data_and_model,
  ):
      # Arrange
      train, test, model = drifted_data_and_model
      check = FeatureDrift(
          categorical_drift_method="PSI",
          numerical_drift_method="EMD",
          max_num_categories=10,
          min_category_size_ratio=0,
      ).add_condition_drift_score_less_than(
          max_allowed_categorical_score=1, max_allowed_numeric_score=1
      )
  
      # Act
      result = check.run(train, test, model)
      condition_result, *_ = result.conditions_results
  
      # Assert
      assert_that(
          condition_result,
          equal_condition_result(
              is_pass=True,
              details="Passed for 4 columns out of 4 columns.\n"
              'Found column "categorical_with_drift" has the highest categorical drift score: 0.22\n'
              'Found column "numeric_with_drift" has the highest numerical drift score: 0.34',
              name="categorical drift score < 1 and numerical drift score < 1",
          ),
      )
  
imports: import numpy
  import pandas
  from hamcrest import assert_that, calling, close_to, equal_to, greater_than, has_entries, has_item, has_length, is_not, raises
  from deepchecks.core.errors import NotEnoughSamplesError
  from deepchecks.tabular.checks import FeatureDrift
  from tests.base.utils import equal_condition_result
label_desc: Use image node_id 17 for calling a global function with example usage: test_drift_max_drift_score_condition_pass_threshold(drifted_data_and_model) without return types