code: def missing_dispersion(X, y=None, ax=None, classes=None, alpha=0.5, marker="|", **kwargs):
      
      # Instantiate the visualizer
      visualizer = MissingValuesDispersion(
          ax=ax, classes=classes, alpha=alpha, marker=marker, **kwargs
      )
  
      # Fit and transform the visualizer (calls draw)
      visualizer.fit(X, y)
      visualizer.poof()
  
      # Return the axes object on the visualizer
      return visualizer.ax
docs: The Missing Values Dispersion visualizer shows the locations of missing (nan)
      values in the feature dataset by the order of the index.
  
      When y targets are supplied to fit, the output dispersion plot is color
      coded according to the target y that the element refers to.
  
      Parameters
      ----------
      alpha : float, default: 0.5
          A value for bending elments with the background.
  
      marker : matplotlib marker, default: |
          The marker used for each element coordinate in the plot
  
      classes : list, default: None
          A list of class names for the legend.
          If classes is None and a y value is passed to fit then the classes
          are selected from the target vector.
  
      kwargs : dict
          Keyword arguments that are passed to the base class and may influence
          the visualization as defined in other Visualizers.
  
      Attributes
      ----------
      features_ : np.array
          The feature labels ranked according to their importance
  
      classes_ : np.array
          The class labels for each of the target values
  
      Examples
      --------
  
      >>> from yellowbrick.contrib.missing import missing_dispersion
      >>> visualizer = missing_dispersion(X, y=y)