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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 feature_importances(model, X, y=None, ax=None, labels=None, relative=True, absolute=False, xlabel=None, stack=False, **kwargs):
 """ Displays the most informative features in a model by showing a bar chart of features ranked by their importances. Although primarily a feature engineering mechanism, this visualizer requires a model that has either a ``coef_`` or ``feature_importances_`` parameter after fit. Parameters model : Estimator A Scikit-Learn estimator that learns feature importances. Must support either ``coef_`` or ``feature_importances_`` parameters. X : ndarray or DataFrame of shape n x m A matrix of n instances with m features y : ndarray or Series of length n, optional An array or series of target or class values ax : matplotlib Axes, default: None The axis to plot the figure on. If None is passed in the current axes will be used (or generated if required). labels : list, default: None A list of feature names to use. If a DataFrame is passed to fit and features is None, feature names are selected as the column names. relative : bool, default: True If true, the features are described by their relative importance as a percentage of the strongest feature component; otherwise the raw numeric description of the feature importance is shown. absolute : bool, default: False Make all coeficients absolute to more easily compare negative coeficients with positive ones. xlabel : str, default: None The label for the X-axis. If None is automatically determined by the underlying model and options provided. stack : bool, default: False If true and the classifier returns multi-class feature importance, then a stacked bar plot is plotted; otherwise the mean of the feature importance across classes are plotted. 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 Returns the axes that the parallel coordinates were drawn on. """

    # Instantiate the visualizer
    visualizer = FeatureImportances(
        model, ax, labels, relative, absolute, xlabel, stack, **kwargs)

    # Fit and transform the visualizer (calls draw)
    visualizer.fit(X, y)
    visualizer.finalize()

    # Return the axes object on the visualizer
    return visualizer.ax