code: def test_drift_max_drift_score_condition_fail(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()
  
      # Act
      result = check.run(train, test, model)
      condition_result, *_ = result.conditions_results
  
      # Assert
      assert_that(
          condition_result,
          equal_condition_result(
              is_pass=False,
              name="categorical drift score < 0.2 and numerical drift score < 0.2",
              details="Failed for 2 out of 4 columns.\n"
              "Found 1 categorical columns with PSI above threshold: {'categorical_with_drift': '0.22'}\n"
              "Found 1 numeric columns with Earth Mover's Distance above threshold: "
              "{'numeric_with_drift': '0.34'}",
          ),
      )
  
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 15 for calling a global function with example usage: test_drift_max_drift_score_condition_fail(drifted_data_and_model) without return types