code: def test_calculate_variable_importance(self):
      variables = {}
      for col in self.X.columns:
          variables[col] = col
      vi = utils.calculate_variable_importance(
          self.exp,
          "ratio",
          rmse,
          variables,
          100,
          2,
          "aaa",
          1,
          True,
          None,
      )
  
      self.assertIsInstance(vi, tuple)
      self.assertIsInstance(vi[0], pd.DataFrame)
      self.assertIsInstance(vi[1], pd.DataFrame)
      self.assertTrue(
          np.isin(
              np.array(["dropout_loss", "variable", "label"]),
              vi[0].columns,
          ).all()
      )
  
      vi = utils.calculate_variable_importance(
          self.exp,
          "difference",
          mae,
          variables,
          100,
          5,
          "aaa",
          2,
          False,
          None,
      )
  
      self.assertIsInstance(vi, tuple)
      self.assertIsInstance(vi[0], pd.DataFrame)
      self.assertIsNone(vi[1])
      self.assertTrue(
          np.isin(
              np.array(["dropout_loss", "variable", "label"]),
              vi[0].columns,
          ).all()
      )
  
imports: import unittest
  import numpy
  from plotly.graph_objs import Figure
  from sklearn.compose import ColumnTransformer
  from sklearn.impute import SimpleImputer
  from sklearn.neural_network import MLPClassifier
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import StandardScaler, LabelEncoder, OneHotEncoder
  import dalex
  from dalex.model_explanations._model_performance.utils import *
  from dalex.model_explanations._variable_importance import utils
label_desc: Use image node_id 3 for calling the FeatureImportanceTestTitanic obj's underlying member method code with example usage: obj.test_calculate_variable_importance() without return types