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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 missing_bar(X, y=None, ax=None, classes=None, width=0.5, color='black', **kwargs):
 """The MissingValues Bar visualizer creates a bar graph that lists the total count of missing values for each selected feature column. When y targets are supplied to fit, the output is a stacked bar chart where each color corresponds to the total NaNs for the feature in that column. 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 color : string, default: black The color for drawing the bar chart when the y targets are not passed to fit. colors : list, default: None The color pallette for drawing a stack bar chart when the y targets are passed to fit. 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 -------- """

    # Instantiate the visualizer
    visualizer = MissingValuesBar(
        ax=ax, classes=classes, width=width, color=color, **kwargs
    )

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

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