def shap_interaction_values(self, X, y=None, tree_limit=None):
        """ Estimate the SHAP interaction values for a set of samples.

        Parameters
        ----------
        X : numpy.array, pandas.DataFrame or catboost.Pool (for catboost)
            A matrix of samples (# samples x # features) on which to explain the model's output.

        y : numpy.array
            An array of label values for each sample. Used when explaining loss functions (not yet supported).

        tree_limit : None (default) or int 
            Limit the number of trees used by the model. By default None means no use the limit of the
            original model, and -1 means no limit.

        Returns
        -------
        For models with a single output this returns a tensor of SHAP values
        (# samples x # features x # features). The matrix (# features x # features) for each sample sums
        to the difference between the model output for that sample and the expected value of the model output
        (which is stored in the expected_value attribute of the explainer). Each row of this matrix sums to the
        SHAP value for that feature for that sample. The diagonal entries of the matrix represent the
        "main effect" of that feature on the prediction and the symmetric off-diagonal entries represent the
        interaction effects between all pairs of features for that sample. For models with vector outputs
        this returns a list of tensors, one for each output.
        """

        assert self.model_output == "margin", "Only model_output = \"margin\" is supported for SHAP interaction values right now!"
        assert self.feature_dependence == "tree_path_dependent", "Only feature_dependence = \"tree_path_dependent\" is supported for SHAP interaction values right now!"
        transform = "identity"

        # see if we have a default tree_limit in place.
        if tree_limit is None:
            tree_limit = -1 if self.model.tree_limit is None else self.model.tree_limit

        # shortcut using the C++ version of Tree SHAP in XGBoost
        if self.model.model_type == "xgboost":
            assert_import("xgboost")
            if not str(type(X)).endswith("xgboost.core.DMatrix'>"):
                X = xgboost.DMatrix(X)
            if tree_limit == -1:
                tree_limit = 0
            phi = self.model.original_model.predict(X, ntree_limit=tree_limit, pred_interactions=True)

            # note we pull off the last column and keep it as our expected_value
            if len(phi.shape) == 4:
                self.expected_value = [phi[0, i, -1, -1] for i in range(phi.shape[1])]
                return [phi[:, i, :-1, :-1] for i in range(phi.shape[1])]
            else:
                self.expected_value = phi[0, -1, -1]
                return phi[:, :-1, :-1]

        # convert dataframes
        if str(type(X)).endswith("pandas.core.series.Series'>"):
            X = X.values
        elif str(type(X)).endswith("pandas.core.frame.DataFrame'>"):
            X = X.values
        flat_output = False
        if len(X.shape) == 1:
            flat_output = True
            X = X.reshape(1, X.shape[0])
        if X.dtype != self.model.dtype:
            X = X.astype(self.model.dtype)
        X_missing = np.isnan(X, dtype=np.bool)
        assert str(type(X)).endswith("'numpy.ndarray'>"), "Unknown instance type: " + str(type(X))
        assert len(X.shape) == 2, "Passed input data matrix X must have 1 or 2 dimensions!"

        if tree_limit < 0 or tree_limit > self.model.values.shape[0]:
            tree_limit = self.model.values.shape[0]

        # run the core algorithm using the C extension
        assert_import("cext")
        phi = np.zeros((X.shape[0], X.shape[1]+1, X.shape[1]+1, self.model.n_outputs))
        _cext.dense_tree_shap(
            self.model.children_left, self.model.children_right, self.model.children_default,
            self.model.features, self.model.thresholds, self.model.values, self.model.node_sample_weight,
            self.model.max_depth, X, X_missing, y, self.data, self.data_missing, tree_limit,
            self.model.base_offset, phi, feature_dependence_codes[self.feature_dependence],
            output_transform_codes[transform], True
        )

        # note we pull off the last column and keep it as our expected_value
        if self.model.n_outputs == 1:
            self.expected_value = phi[0, -1, -1, 0]
            if flat_output:
                return phi[0, :-1, :-1, 0]
            else:
                return phi[:, :-1, :-1, 0]
        else:
            self.expected_value = [phi[0, -1, -1, i] for i in range(phi.shape[3])]
            if flat_output:
                return [phi[0, :-1, :-1, i] for i in range(self.model.n_outputs)]
            else:
                return [phi[:, :-1, :-1, i] for i in range(self.model.n_outputs)]
Estimate the SHAP interaction values for a set of samples.

        Parameters
        ----------
        X : numpy.array, pandas.DataFrame or catboost.Pool (for catboost)
            A matrix of samples (# samples x # features) on which to explain the model's output.

        y : numpy.array
            An array of label values for each sample. Used when explaining loss functions (not yet supported).

        tree_limit : None (default) or int 
            Limit the number of trees used by the model. By default None means no use the limit of the
            original model, and -1 means no limit.

        Returns
        -------
        For models with a single output this returns a tensor of SHAP values
        (# samples x # features x # features). The matrix (# features x # features) for each sample sums
        to the difference between the model output for that sample and the expected value of the model output
        (which is stored in the expected_value attribute of the explainer). Each row of this matrix sums to the
        SHAP value for that feature for that sample. The diagonal entries of the matrix represent the
        "main effect" of that feature on the prediction and the symmetric off-diagonal entries represent the
        interaction effects between all pairs of features for that sample. For models with vector outputs
        this returns a list of tensors, one for each output.