code: def test_balance_classes_with_drift_mode_auto(drifted_data):
      # Arrange
      train, test = drifted_data
      train = remove_label(train)
      test = remove_label(test)
  
      n_train = train.n_samples
      n_test = test.n_samples
  
      predictions_train = [0] * int(n_train * 0.95) + [1] * int(
          n_train * 0.05
      )
      predictions_test = [0] * int(n_test * 0.96) + [1] * int(
          n_test * 0.04
      )
      check = PredictionDrift(balance_classes=True)
  
      # Act
      result = check.run(
          train,
          test,
          y_pred_train=predictions_train,
          y_pred_test=predictions_test,
      )
  
      # Assert
      assert_that(
          result.value,
          has_entries(
              {
                  "Drift score": close_to(0.05, 0.01),
                  "Method": equal_to("Cramer's V"),
              }  # If cramer's V then proves it changed to prediction mode
          ),
      )
  
imports: import pandas
  from hamcrest import assert_that, calling, close_to, equal_to, greater_than, has_entries, has_length, raises
  from deepchecks.core.errors import DeepchecksValueError, NotEnoughSamplesError
  from deepchecks.tabular import Dataset
  from deepchecks.tabular.checks import PredictionDrift
  from tests.base.utils import equal_condition_result
label_desc: Use image node_id 13 for calling a global function with example usage: test_balance_classes_with_drift_mode_auto(drifted_data) without return types