code: def setUp(self):
      data = dx.datasets.load_titanic()
      data.loc[:, "survived"] = LabelEncoder().fit_transform(
          data.survived
      )
  
      self.X = data.drop(columns="survived")
      self.y = data.survived.values
  
      numeric_features = ["age", "fare", "sibsp", "parch"]
      numeric_transformer = Pipeline(
          steps=[
              ("imputer", SimpleImputer(strategy="median")),
              ("scaler", StandardScaler()),
          ]
      )
  
      categorical_features = ["gender", "class", "embarked"]
      categorical_transformer = Pipeline(
          steps=[
              (
                  "imputer",
                  SimpleImputer(
                      strategy="constant", fill_value="missing"
                  ),
              ),
              ("onehot", OneHotEncoder(handle_unknown="ignore")),
          ]
      )
  
      preprocessor = ColumnTransformer(
          transformers=[
              ("num", numeric_transformer, numeric_features),
              ("cat", categorical_transformer, categorical_features),
          ]
      )
  
      clf = Pipeline(
          steps=[
              ("preprocessor", preprocessor),
              (
                  "classifier",
                  MLPClassifier(
                      hidden_layer_sizes=(50, 100, 50),
                      max_iter=400,
                      random_state=0,
                  ),
              ),
          ]
      )
  
      clf.fit(self.X, self.y)
  
      self.exp = dx.Explainer(clf, self.X, self.y, verbose=False)
      self.exp2 = dx.Explainer(
          clf, self.X, self.y, label="model2", verbose=False
      )
      self.aspect = dx.Aspect(self.exp)
      self.aspect2 = dx.Aspect(self.exp2, depend_method="pps")
  
imports: import unittest
  import numpy
  import pandas
  from plotly.graph_objs import Figure
  from ipywidgets.widgets.widget_box import HBox
  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
  from sklearn.ensemble import RandomForestRegressor
  from traitlets.traitlets import Dict
  import dalex
  from dalex.model_explanations._variable_importance.loss_functions import *
  from dalex.aspect._predict_aspect_importance.object import PredictAspectImportance
  from dalex.aspect._model_triplot.object import ModelTriplot
  from dalex.aspect._predict_triplot.object import PredictTriplot
  from dalex.aspect._model_aspect_importance.object import ModelAspectImportance
label_desc: Use image node_id 1 for calling the AspectTestTitanic obj's underlying member method code with example usage: obj.setUp() without return types