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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 cv_scores(model, X, y, ax=None, cv=None, scoring=None, **kwargs):
 """ Displays cross validation scores as a bar chart and the average of the scores as a horizontal line This helper function is a quick wrapper to utilize the CVScores visualizer for one-off analysis. Parameters model : a scikit-learn estimator An object that implements ``fit`` and ``predict``, can be a classifier, regressor, or clusterer so long as there is also a valid associated scoring metric. Note that the object is cloned for each validation. X : array-like, shape (n_samples, n_features) Training vector, where n_samples is the number of samples and n_features is the number of features. y : array-like, shape (n_samples) or (n_samples, n_features), optional Target relative to X for classification or regression; None for unsupervised learning. ax : matplotlib.Axes object, optional The axes object to plot the figure on. cv : int, cross-validation generator or an iterable, optional Determines the cross-validation splitting strategy. Possible inputs for cv are: - None, to use the default 3-fold cross-validation, - integer, to specify the number of folds. - An object to be used as a cross-validation generator. - An iterable yielding train/test splits. see the scikit-learn `cross-validation guide <https://goo.gl/FS3VU6>`_ for more information on the possible strategies that can be used here. scoring : string, callable or None, optional, default: None A string or scorer callable object / function with signature ``scorer(estimator, X, y)``. See scikit-learn `cross-validation guide <https://goo.gl/FS3VU6>`_ for more information on the possible metrics that can be used. kwargs : dict Keyword arguments that are passed to the base class and may influence the visualization as defined in other Visualizers. Returns ------- ax : matplotlib.Axes The axes object that the validation curves were drawn on. """


    # Initialize the visualizer
    visualizer = CVScores(model, ax=ax, cv=cv, scoring=scoring)

    # Fit and poof the visualizer
    visualizer.fit(X, y)
    visualizer.poof(**kwargs)
    return visualizer.ax