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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_real(self, X):
 r"""Predict the probability of being 1 for each label. Parameters X : array-like, shape=(n_samples, n_features) Feature vector. Returns ------- pred : numpy array, shape=(n_samples, n_labels) Predicted probability of each label. """

        X = np.asarray(X)
        if self.clfs_ is None:
            raise ValueError("Train before prediction")
        if X.shape[1] != self.n_features_:
            raise ValueError('given feature size does not match')

        pred = np.zeros((X.shape[0], self.n_labels_))
        for i in range(self.n_labels_):
            pred[:, i] = self.clfs_[i].predict_real(X)[:, 1]
        return pred