docs: 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