How to use the shap.dependence_plot function in shap

for model in models:
        model.fit(X, y)

        # explain the model's predictions using SHAP values
        explainer = shap.TreeExplainer(model)
        shap_values = explainer.shap_values(X)

        # visualize the first prediction's explaination
        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("RM", shap_values, X, show=False)

        # summarize the effects of all the features
        shap.summary_plot(shap_values, X, show=False)

import lightgbm
    except:
        print("Skipping test_lightgbm_multiclass!")
        return
    import shap

    # train lightgbm model
    X, Y = shap.datasets.iris()
    model = lightgbm.sklearn.LGBMClassifier()
    model.fit(X, Y)

    # explain the model's predictions using SHAP values
    shap_values = shap.TreeExplainer(model).shap_values(X)

    # ensure plot works for first class
    shap.dependence_plot(0, shap_values[0], X, show=False)

import xgboost
    except Exception as e:
        print("Skipping test_xgboost_multiclass!")
        return
    import shap

    # train XGBoost model
    X, Y = shap.datasets.iris()
    model = xgboost.XGBClassifier(objective="binary:logistic", max_depth=4)
    model.fit(X, Y)

    # explain the model's predictions using SHAP values (use pred_contrib in LightGBM)
    shap_values = shap.TreeExplainer(model).shap_values(X)

    # ensure plot works for first class
    shap.dependence_plot(0, shap_values[0], X, show=False)

# train model
    X, y = shap.datasets.boston()
    model = sklearn.ensemble.RandomForestRegressor(n_estimators=100)
    model.fit(X, y)

    # explain the model's predictions using SHAP values (use pred_contrib in LightGBM)
    shap_values = shap.TreeExplainer(model).shap_values(X)

    # visualize the first prediction's explaination
    shap.force_plot(shap_values[0, :], X.iloc[0, :])

    # visualize the training set predictions
    shap.force_plot(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("RM", shap_values, X, show=False)

    # summarize the effects of all the features
    shap.summary_plot(shap_values, X, show=False)

from sklearn.model_selection import train_test_split

    # train lightgbm model
    X_train,X_test,Y_train,Y_test = train_test_split(*shap.datasets.adult(), test_size=0.2, random_state=0)
    model = lightgbm.sklearn.LGBMClassifier()
    model.fit(X_train, Y_train)

    # explain the model's predictions using SHAP values
    shap_values = shap.TreeExplainer(model).shap_values(X_test)

    # validate structure of shap values, must be a list of ndarray for both classes
    assert isinstance(shap_values, list)
    assert len(shap_values) == 2

    # ensure plot works for first class
    shap.dependence_plot(0, shap_values[0], X_test, show=False)

]
    for model in models:
        model.fit(X, y)

        # explain the model's predictions using SHAP values
        explainer = shap.TreeExplainer(model)
        shap_values = explainer.shap_values(X)

        # visualize the first prediction's explaination
        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("RM", shap_values, X, show=False)

        # summarize the effects of all the features
        shap.summary_plot(shap_values, X, show=False)

]
    for model in models:
        model.fit(X, y)

        # explain the model's predictions using SHAP values
        explainer = shap.TreeExplainer(model)
        shap_values = explainer.shap_values(X)

        # visualize the first prediction's explaination
        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("RM", shap_values, X, show=False)

        # summarize the effects of all the features
        shap.summary_plot(shap_values, X, show=False)

X, y = shap.datasets.boston()
    model = xgboost.train({"learning_rate": 0.01, "silent": 1}, xgboost.DMatrix(X, label=y), 100)

    # explain the model's predictions using SHAP values
    explainer = shap.TreeExplainer(model)
    shap_values = explainer.shap_values(X)

    # visualize the first prediction's explaination
    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("RM", shap_values, X, show=False)

    # summarize the effects of all the features
    shap.summary_plot(shap_values, X, show=False)

def dependence_plot(
        self, feature, interaction=None, output_file="", **dependenceplot_kwargs
    ):
        """
        Plots a SHAP dependence plot.
        """

        import shap

        interaction = dependenceplot_kwargs.pop("interaction_index", interaction)

        shap.dependence_plot(
            feature,
            self.shap_values,
            self.x_test,
            interaction_index=interaction,
            show=False,
            **dependenceplot_kwargs,
        )

        if output_file:  # pragma: no cover
            pl.savefig(os.path.join(IMAGE_DIR, self.model_name, output_file))