code: def test_front_page_xgboost():
      xgboost = pytest.importorskip("xgboost")
  
      # load JS visualization code to notebook
      shap.initjs()
  
      # train XGBoost model
      X, y = shap.datasets.california(n_points=500)
      model = xgboost.train(
          {"learning_rate": 0.01}, xgboost.DMatrix(X, label=y), 100
      )
  
      # explain the model's predictions using SHAP values
      explainer = shap.GPUTreeExplainer(model)
      shap_values = explainer.shap_values(X)
  
      # visualize the first prediction's explanation
      shap.force_plot(
          explainer.expected_value, shap_values[0, :], X.iloc[0, :]
      )
  
      # visualize the training set predictions
      shap.force_plot(explainer.expected_value, shap_values, X)
  
      # create a SHAP dependence plot to show the effect of a single feature across the whole dataset
      shap.dependence_plot(5, shap_values, X, show=False)
      shap.dependence_plot("Longitude", shap_values, X, show=False)
  
      # summarize the effects of all the features
      shap.summary_plot(shap_values, X, show=False)
  
imports: import numpy
  import pytest
  import sklearn
  import shap
  from shap.utils import assert_import
label_desc: Use image node_id 1 for calling a global function with example usage: test_front_page_xgboost() without return types