code: def main(*ars, **kwargs):
      # create data
      X, y = make_regression(
          n_samples=10000, n_features=10, random_state=42
      )
      X_train, X_test, y_train, _ = train_test_split(
          X, y, random_state=42
      )
  
      # train the model
      xgb_model = xgb.XGBRegressor(
          max_depth=6, n_estimators=100, random_state=42, base_score=0.5
      )
      xgb_model.fit(X_train, y_train)
  
      # Conversion to daal4py
      daal_model = d4p.mb.convert_model(xgb_model.get_booster())
  
      # SHAP contributions
      daal_contribs = daal_model.predict(X_test, pred_contribs=True)
  
      # SHAP interactions
      daal_interactions = daal_model.predict(
          X_test, pred_interactions=True
      )
  
      # XGBoost reference values
      xgb_contribs = xgb_model.get_booster().predict(
          xgb.DMatrix(X_test),
          pred_contribs=True,
          validate_features=False,
      )
      xgb_interactions = xgb_model.get_booster().predict(
          xgb.DMatrix(X_test),
          pred_interactions=True,
          validate_features=False,
      )
  
      return (
          daal_contribs,
          daal_interactions,
          xgb_contribs,
          xgb_interactions,
      )
  
imports: import numpy
  import xgboost
  from sklearn.datasets import make_regression
  from sklearn.model_selection import train_test_split
  import daal4py
label_desc: Use image node_id 1 for calling a global function with example usage: main() and returns: daal_contribs,  daal_interactions,  xgb_contribs,  xgb_interactions