code: def test_predict_parts(self):
      pai = self.aspect.predict_parts(self.X.iloc[12])
      groups = {
          "personal_info": ["gender", "age"],
          "ticket_info": ["class", "embarked", "fare"],
          "other": ["sibsp", "parch"],
      }
      pai2 = self.aspect2.predict_parts(
          self.X.iloc[12], variable_groups=groups
      )
      pai3 = self.aspect.predict_parts(self.X.iloc[19], type="shap")
      n_aspects = 4
      pai4 = self.aspect2.predict_parts(
          self.X.iloc[22], sample_method="binom", n_aspects=n_aspects
      )
      self.assertEqual(set(groups.keys()), set(pai2.result.aspect_name))
  
      for pai_x in [pai, pai2, pai3, pai4]:
          self.assertIsInstance(
              pai_x, dx.aspect.PredictAspectImportance
          )
          self.assertIsInstance(pai_x.result, pd.DataFrame)
          self.assertEqual(
              set(pai_x.result.columns),
              set(
                  [
                      "aspect_name",
                      "variable_names",
                      "variable_values",
                      "importance",
                      "min_depend",
                      "vars_min_depend",
                      "label",
                  ]
              ),
          )
          self.assertGreaterEqual(1, pai_x.result.min_depend.max())
          self.assertGreaterEqual(pai_x.result.min_depend.min(), 0)
          fig = pai_x.plot(show=False)
          self.assertIsInstance(fig, Figure)
          fig2 = pai_x.plot(
              show=False,
              max_aspects=3,
              digits=4,
              bar_width=23,
              title="Test plot",
              vertical_spacing=0.2,
          )
          self.assertIsInstance(fig2, Figure)
          fig3 = pai_x.plot(show=False, bar_width=23)
          self.assertIsInstance(fig3, Figure)
          fig4 = pai_x.plot(show=False, vertical_spacing=0.2)
          self.assertIsInstance(fig4, Figure)
          fig5 = pai_x.plot(show=False, max_aspects=3)
          self.assertIsInstance(fig5, Figure)
          fig6 = pai_x.plot(
              show=False,
              max_aspects=3,
              digits=4,
              bar_width=23,
              title="Test plot",
              vertical_spacing=0.2,
          )
          self.assertIsInstance(fig6, Figure)
          for index, row in pai_x.result.iterrows():
              self.assertTrue(
                  len(row["variable_values"])
                  == len(row["variable_names"])
              )
              self.assertTrue(
                  set(row["vars_min_depend"]).issubset(
                      set(row["variable_names"])
                  )
              )
  
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 3 for calling the AspectTestTitanic obj's underlying member method code with example usage: obj.test_predict_parts() without return types