code: def setUp(self):
      data = dx.datasets.load_titanic()
      self.X = data.drop(columns=["survived", "class", "embarked"])
      self.y = data.survived
      self.X.gender = LabelEncoder().fit_transform(self.X.gender)
  
      # this checks for no feature_importances_ attribute
      model = MLPClassifier(
          hidden_layer_sizes=(50, 50), max_iter=400, random_state=0
      )
      model.fit(self.X, self.y)
      self.exp = dx.Explainer(model, self.X, self.y, verbose=False)
  
      data2 = dx.datasets.load_fifa()
      self.X2 = data2.drop(
          [
              "nationality",
              "overall",
              "potential",
              "value_eur",
              "wage_eur",
          ],
          axis=1,
      ).iloc[0:2000, 0:10]
      self.y2 = data2["value_eur"].iloc[0:2000]
  
      # this checks for feature_importances_ attribute
      model2 = RandomForestRegressor(random_state=0)
      model2.fit(self.X2, self.y2)
      self.exp2 = dx.Explainer(model2, self.X2, self.y2, verbose=False)
  
file_path: DALEX/python/dalex/test/test_model_surrogate.py
imports: import unittest
  from sklearn.neural_network import MLPClassifier
  from sklearn.ensemble import RandomForestRegressor
  from sklearn.preprocessing import LabelEncoder
  from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
  from sklearn.linear_model import LogisticRegression, LinearRegression
  import dalex
  import pandas
label_desc: Use image node_id 1 for calling the ModelSurrogateTest obj's underlying member method code with example usage: obj.setUp() without return types