code: def test_plot(self):
      case1 = self.exp.predict_parts(self.X.iloc[0, :])
      case2 = self.exp2.predict_parts(self.X.iloc[1, :])
      case3 = self.exp.predict_parts(
          self.X.iloc[2, :], B=10, N=100, type="shap"
      )
  
      self.assertIsInstance(case1, dx.predict_explanations.BreakDown)
      self.assertIsInstance(case2, dx.predict_explanations.BreakDown)
      self.assertIsInstance(case3, dx.predict_explanations.Shap)
  
      fig1 = case1.plot(case2, min_max=[0, 1], show=False)
      fig2 = case2.plot((case2,), max_vars=3, baseline=0.5, show=False)
      fig3 = case3.plot(
          baseline=0.5,
          max_vars=3,
          digits=2,
          bar_width=12,
          min_max=[0, 1],
          show=False,
      )
      fig4 = case3.plot(
          title="title1",
          vertical_spacing=0.1,
          vcolors=("green", "red", "blue"),
          show=False,
      )
      fig5 = case1.plot(
          case2,
          rounding_function=np.ceil,
          digits=None,
          max_vars=1,
          min_max=[0.1, 0.9],
          show=False,
      )
  
      self.assertIsInstance(fig1, Figure)
      self.assertIsInstance(fig2, Figure)
      self.assertIsInstance(fig3, Figure)
      self.assertIsInstance(fig4, Figure)
      self.assertIsInstance(fig5, Figure)
  
file_path: DALEX/python/dalex/test/test_predict_parts.py
imports: import unittest
  import pandas
  import numpy
  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 plotly.graph_objs import Figure
label_desc: Use image node_id 5 for calling the PredictPartsTestTitanic obj's underlying member method code with example usage: obj.test_plot() without return types