import sklearn  # version 1.1.2
import shap  # version 0.41.0
import xgboost  # version 1.5.1

X, y = shap.datasets.iris()
X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, test_size=0.2, random_state=0)
model = xgboost.XGBClassifier(use_label_encoder=False).fit(X_train, y_train)
explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X_test)
shap.dependence_plot(2, 
                     shap_values[0], 
                     X_test.values, 
                     feature_names=X_test.columns)