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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 learning_curve(model, X, y, ax=None, groups=None, train_sizes=DEFAULT_TRAIN_SIZES, cv=None, scoring=None, exploit_incremental_learning=False, n_jobs=1, pre_dispatch="all", shuffle=False, random_state=None, **kwargs):
 """ Displays a learning curve based on number of samples vs training and cross validation scores. The learning curve aims to show how a model learns and improves with experience. This helper function is a quick wrapper to utilize the LearningCurve 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. groups : array-like, with shape (n_samples,) Optional group labels for the samples used while splitting the dataset into train/test sets. train_sizes : array-like, shape (n_ticks,) default: ``np.linspace(0.1,1.0,5)`` Relative or absolute numbers of training examples that will be used to generate the learning curve. If the dtype is float, it is regarded as a fraction of the maximum size of the training set, otherwise it is interpreted as absolute sizes of the training sets. 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 <http://scikit-learn.org/stable/modules/cross_validation.html>`_ 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 model evaluation documentation for names of possible metrics. exploit_incremental_learning : boolean, default: False If the estimator supports incremental learning, this will be used to speed up fitting for different training set sizes. n_jobs : integer, optional Number of jobs to run in parallel (default 1). pre_dispatch : integer or string, optional Number of predispatched jobs for parallel execution (default is all). The option can reduce the allocated memory. The string can be an expression like '2*n_jobs'. shuffle : boolean, optional Whether to shuffle training data before taking prefixes of it based on``train_sizes``. random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. Used when ``shuffle`` is True. kwargs : dict Keyword arguments that are passed to the base class and may influence the visualization as defined in other Visualizers. These arguments are also passed to the `poof()` method, e.g. can pass a path to save the figure to. Returns ------- ax : matplotlib axes Returns the axes that the learning curve were drawn on. """

    # Initialize the visualizer
    oz = LearningCurve(
        model, ax=ax, groups=groups, train_sizes=train_sizes, cv=cv,
        scoring=scoring, n_jobs=n_jobs, pre_dispatch=pre_dispatch,
        shuffle=shuffle, random_state=random_state,
        exploit_incremental_learning=exploit_incremental_learning,
    )

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