def METHOD_NAME(self, x, y):
    y_pred_mean = 0.0
    # Make a prediction and update the weights accordingly for each model
    total = 0
    for i, regressor in enumerate(self):
        y_pred = regressor.predict_one(x=x)
        y_pred_mean += self.weights[i] * (y_pred - y_pred_mean) / len(self)
        loss = self.loss(y_true=y, y_pred=y_pred)
        self.weights[i] *= math.exp(-self.learning_rate * loss)
        total += self.weights[i]
        regressor.learn_one(x, y)
    # Normalize the weights so that they sum up to 1
    if total:
        for i, _ in enumerate(self.weights):
            self.weights[i] /= total
    return y_pred_mean