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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 plot_ks_statistic_with_cv(clf, X, y, title='KS Statistic Plot', do_cv=True, cv=None, shuffle=True, random_state=None, ax=None, figsize=None, title_fontsize="large", text_fontsize="medium"):
 """Generates the KS Statistic plot for a given classifier and dataset. Args: clf: Classifier instance that implements "fit" and "predict_proba" methods. 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)):
 Target relative to X for classification. title (string, optional):
 Title of the generated plot. Defaults to "KS Statistic Plot". do_cv (bool, optional):
 If True, the classifier is cross-validated on the dataset using the cross-validation strategy in `cv` to generate the confusion matrix. If False, the confusion matrix is generated without training or cross-validating the classifier. This assumes that the classifier has already been called with its `fit` method beforehand. cv (int, cross-validation generator, iterable, optional):
 Determines the cross-validation strategy to be used for splitting. 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. For integer/None inputs, if ``y`` is binary or multiclass, :class:`StratifiedKFold` used. If the estimator is not a classifier or if ``y`` is neither binary nor multiclass, :class:`KFold` is used. shuffle (bool, optional):
 Used when do_cv is set to True. Determines whether to shuffle the training data before splitting using cross-validation. Default set to True. random_state (int :class:`RandomState`):
 Pseudo-random number generator state used for random sampling. ax (:class:`matplotlib.axes.Axes`, optional):
 The axes upon which to plot the learning curve. If None, the plot is drawn on a new set of axes. figsize (2-tuple, optional):
 Tuple denoting figure size of the plot e.g. (6, 6). Defaults to ``None``. title_fontsize (string or int, optional):
 Matplotlib-style fontsizes. Use e.g. "small", "medium", "large" or integer-values. Defaults to "large". text_fontsize (string or int, optional):
 Matplotlib-style fontsizes. Use e.g. "small", "medium", "large" or integer-values. Defaults to "medium". Returns: ax (:class:`matplotlib.axes.Axes`):
 The axes on which the plot was drawn. Example: <matplotlib.axes._subplots.AxesSubplot object at 0x7fe967d64490> .. image:: _static/examples/plot_ks_statistic.png :align: center :alt: KS Statistic """

    y = np.array(y)

    if not hasattr(clf, 'predict_proba'):
        raise TypeError('"predict_proba" method not in classifier. '
                        'Cannot calculate ROC Curve.')

    if not do_cv:
        probas = clf.predict_proba(X)
        y_true = y

    else:
        if cv is None:
            cv = StratifiedKFold(shuffle=shuffle, random_state=random_state)
        elif isinstance(cv, int):
            cv = StratifiedKFold(n_splits=cv, shuffle=shuffle,
                                 random_state=random_state)
        else:
            pass

        clf_clone = clone(clf)

        preds_list = []
        trues_list = []
        for train_index, test_index in cv.split(X, y):
            X_train, X_test = X[train_index], X[test_index]
            y_train, y_test = y[train_index], y[test_index]
            clf_clone.fit(X_train, y_train)
            preds = clf_clone.predict_proba(X_test)
            preds_list.append(preds)
            trues_list.append(y_test)
        probas = np.concatenate(preds_list, axis=0)
        y_true = np.concatenate(trues_list)

    ax = plotters.plot_ks_statistic(y_true, probas, title=title,
                                    ax=ax, figsize=figsize,
                                    title_fontsize=title_fontsize,
                                    text_fontsize=text_fontsize)

    return ax