A consistent interface to make predictions from this model.

        Parameters
        ----------
        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.

def predict(self, X, y=None, output="margin", tree_limit=None):
        """ A consistent interface to make predictions from this model.

        Parameters
        ----------
        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.
        """

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

        # 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.dtype:
            X = X.astype(self.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.values.shape[0]:
            tree_limit = self.values.shape[0]

        if output == "logloss":
            assert y is not None, "Both samples and labels must be provided when explaining the loss (i.e. `explainer.shap_values(X, y)`)!"
            assert X.shape[0] == len(y), "The number of labels (%d) does not match the number of samples to explain (%d)!" % (len(y), X.shape[0])
        transform = self.get_transform(output)
        
        if True or self.model_type == "internal":
            output = np.zeros((X.shape[0], self.n_outputs))
            assert_import("cext")
            _cext.dense_tree_predict(
                self.children_left, self.children_right, self.children_default,
                self.features, self.thresholds, self.values,
                self.max_depth, tree_limit, self.base_offset, output_transform_codes[transform], 
                X, X_missing, y, output
            )

        elif self.model_type == "xgboost":
            assert_import("xgboost")
            output = self.original_model.predict(X, output_margin=True, tree_limit=tree_limit)

        # drop dimensions we don't need
        if flat_output:
            if self.n_outputs == 1:
                return output.flatten()[0]
            else:
                return output.reshape(-1, self.n_outputs)
        else:
            if self.n_outputs == 1:
                return output.flatten()
            else:
                return output