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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 fit(self, X, y, **args):
 """ The fit method is the primary entry point for the manual alpha selection visualizer. It sets the alpha param for each alpha in the alphas list on the wrapped estimator, then scores the model using the passed in X and y data set. Those scores are then aggregated and drawn using matplotlib. """

        self.errors = []
        for alpha in self.alphas:
            self.estimator.set_params(alpha=alpha)
            scores = self.score_method(self.estimator, X, y)
            self.errors.append(scores.mean())

        # Convert errors to an ND array and draw
        self.errors = np.array(self.errors)
        self.draw()

        # Always make sure to return self from fit
        return self