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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_select_best(X, y):
 """ Selects the best fit of the estimators already implemented by choosing the model with the smallest mean square error metric for the trained values. """

    models = [fit(X,y) for fit in [fit_linear, fit_quadratic]]
    errors = map(lambda model: mse(y, model.predict(X)), models)

    return min(zip(models, errors), key=itemgetter(1))[0]