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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 explained_variance_visualizer(X, y=None, ax=None, scale=True, center=True, colormap=palettes.DEFAULT_SEQUENCE, **kwargs):
 """Produce a plot of the explained variance produced by a dimensionality reduction algorithm using n=1 to n=n_components dimensions. This is a single plot to help identify the best trade off between number of dimensions and amount of information retained within the data. Parameters X : ndarray or DataFrame of shape n x m A matrix of n rows with m features y : ndarray or Series of length n An array or Series of target or class values ax : matplotlib Axes, default: None The aces to plot the figure on scale : bool, default: True Boolean that indicates if the values of X should be scaled. colormap : string or cmap, default: None optional string or matplotlib cmap to colorize lines Use either color to colorize the lines on a per class basis or colormap to color them on a continuous scale. kwargs : dict Keyword arguments that are passed to the base class and may influence the visualization as defined in other Visualizers. Examples -------- """


        # Instantiate the visualizer
        visualizer = ExplainedVariance(X=X)

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

        # Return the axes object on the visualizer
        return visualizer.poof()