code: def test_loss_after_permutation(self):
      variables = {}
      for col in self.X.columns:
          variables[col] = col
      lap = utils.loss_after_permutation(
          self.X,
          self.y,
          self.exp.model,
          self.exp.predict_function,
          rmse,
          variables,
          100,
          np.random,
      )
      self.assertIsInstance(lap, pd.DataFrame)
      self.assertTrue(
          np.isin(
              np.array(["_full_model_", "_baseline_"]), lap.columns
          ).all(),
          np.random,
      )
  
      variables = {"age": "age", "embarked": "embarked"}
      lap = utils.loss_after_permutation(
          self.X,
          self.y,
          self.exp.model,
          self.exp.predict_function,
          mad,
          variables,
          10,
          np.random,
      )
      self.assertIsInstance(lap, pd.DataFrame)
      self.assertTrue(
          np.isin(
              np.array(["_full_model_", "_baseline_"]), lap.columns
          ).all()
      )
  
      variables = {"embarked": "embarked"}
      lap = utils.loss_after_permutation(
          self.X,
          self.y,
          self.exp.model,
          self.exp.predict_function,
          mae,
          variables,
          None,
          np.random,
      )
      self.assertIsInstance(lap, pd.DataFrame)
      self.assertTrue(
          np.isin(
              np.array(["_full_model_", "_baseline_"]), lap.columns
          ).all()
      )
  
      variables = {"age": "age"}
      lap = utils.loss_after_permutation(
          self.X,
          self.y,
          self.exp.model,
          self.exp.predict_function,
          rmse,
          variables,
          None,
          np.random,
      )
      self.assertIsInstance(lap, pd.DataFrame)
      self.assertTrue(
          np.isin(
              np.array(["_full_model_", "_baseline_"]), lap.columns
          ).all()
      )
  
      variables = {
          "personal": ["gender", "age", "sibsp", "parch"],
          "wealth": ["class", "fare"],
      }
      lap = utils.loss_after_permutation(
          self.X,
          self.y,
          self.exp.model,
          self.exp.predict_function,
          mae,
          variables,
          None,
          np.random,
      )
      self.assertIsInstance(lap, pd.DataFrame)
      self.assertTrue(
          np.isin(
              np.array(["_full_model_", "_baseline_"]), lap.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 2 for calling the FeatureImportanceTestTitanic obj's underlying member method code with example usage: obj.test_loss_after_permutation() without return types