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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 validation_curve(model, X, y, param_name, param_range, ax=None, logx=False, groups=None, cv=None, scoring=None, n_jobs=1, pre_dispatch="all", **kwargs):
 """ Displays a validation curve for the specified param and values, plotting both the train and cross-validated test scores. The validation curve is a visual, single-parameter grid search used to tune a model to find the best balance between error due to bias and error due to variance. This helper function is a wrapper to use the ValidationCurve in a fast, visual 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. param_name : string Name of the parameter that will be varied. param_range : array-like, shape (n_values,) The values of the parameter that will be evaluated. ax : matplotlib.Axes object, optional The axes object to plot the figure on. logx : boolean, optional If True, plots the x-axis with a logarithmic scale. groups : array-like, with shape (n_samples,) Optional group labels for the samples used while splitting the dataset into train/test 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. 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'. 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 The axes object that the validation curves were drawn on. """


    # Initialize the visualizer
    oz = ValidationCurve(
        model, param_name, param_range, ax=ax, logx=logx, groups=groups,
        cv=cv, scoring=scoring, n_jobs=n_jobs, pre_dispatch=pre_dispatch
    )

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