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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 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 return_probs: Return the predicted probabilities as well Returns: Y_p: A t-length list of n-dim np.ndarrays of predictions in [1, K_t] [Optionally: Y_s: A t-length list of [n, K_t] np.ndarrays of predicted probabilities] """

        Y_s = self.predict_proba(X, **kwargs)
        self._check(Y_s, typ=list)
        self._check(Y_s[0], typ=np.ndarray)

        Y_p = []
        for Y_ts in Y_s:
            Y_tp = self._break_ties(Y_ts, break_ties)
            Y_p.append(Y_tp.astype(np.int))

        if return_probs:
            return Y_p, Y_s
        else:
            return Y_p