def predict(self, X, break_ties="random", return_probs=False, **kwargs):
        """Predicts (int) labels for an input X on all tasks

        Args:
            X: The input for the predict_proba method
            break_ties: A tie-breaking policy (see Classifier._break_ties())
            return_probs: Return the predicted probabilities as well

        Returns:
            Y_p: An n-dim np.ndarray of predictions in {1,...k}
            [Optionally: Y_s: An [n, k] np.ndarray of predicted probabilities]
        """
        Y_s = self._to_numpy(self.predict_proba(X, **kwargs))
        Y_p = self._break_ties(Y_s, break_ties).astype(np.int)
        if return_probs:
            return Y_p, Y_s
        else:
            return Y_p
def predict ( self , X , break_ties = "random" , return_probs = False , ** kwargs ) : Y_s = self . _to_numpy ( self . predict_proba ( X , ** kwargs ) ) Y_p = self . _break_ties ( Y_s , break_ties ) . astype ( np . int ) if return_probs : return Y_p , Y_s else : return Y_p
Predicts (int) labels for an input X on all tasks

        Args:
            X: The input for the predict_proba method
            break_ties: A tie-breaking policy (see Classifier._break_ties())
            return_probs: Return the predicted probabilities as well

        Returns:
            Y_p: An n-dim np.ndarray of predictions in {1,...k}
            [Optionally: Y_s: An [n, k] np.ndarray of predicted probabilities]