code: def test_waterfall_plot_for_decision_tree_explanation():
      # Regression tests for GH #3129
      X = pd.DataFrame({"A": [1, 2, 3], "B": [2, 1, 3]})
      y = pd.Series([1, 2, 3])
      model = DecisionTreeRegressor()
      model.fit(X, y)
      explainer = shap.TreeExplainer(model)
      explanation = explainer(X)
      shap.plots.waterfall(explanation[0], show=False)
  
file_path: shap/tests/plots/test_waterfall.py
imports: import matplotlib.pyplot
  import numpy
  import pandas
  import pytest
  from sklearn.tree import DecisionTreeRegressor
  import shap
label_desc: Use image node_id 5 for calling a global function with example usage: test_waterfall_plot_for_decision_tree_explanation() without return types