code: def fit(self, X, y, **args):
          
          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
docs: 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.