code: def test_plot(self):
      case1 = self.exp.model_parts()
      case2 = self.exp2.model_parts()
      case3 = self.exp3.model_parts()
  
      self.assertIsInstance(
          case1, dx.model_explanations.VariableImportance
      )
      self.assertIsInstance(
          case2, dx.model_explanations.VariableImportance
      )
  
      fig1 = case1.plot((case2, case3), show=False)
      fig2 = case2.plot(case3, split="variable", max_vars=2, show=False)
      fig3 = case1.plot(
          case2,
          max_vars=3,
          digits=2,
          rounding_function=np.round,
          show=False,
      )
      fig4 = case2.plot(split="variable", show=False)
      fig5 = case1.plot(
          case3,
          bar_width=12,
          vertical_spacing=0.2,
          title="title",
          show=False,
      )
  
      self.assertIsInstance(fig1, Figure)
      self.assertIsInstance(fig2, Figure)
      self.assertIsInstance(fig3, Figure)
      self.assertIsInstance(fig4, Figure)
      self.assertIsInstance(fig5, Figure)
  
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 10 for calling the FeatureImportanceTestTitanic obj's underlying member method code with example usage: obj.test_plot() without return types