code: def explained_variance_visualizer(X, y=None, ax=None, scale=True, 
                                    center=True, colormap=palettes.DEFAULT_SEQUENCE,
                                    **kwargs):
          
  
          # 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()
docs: 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
          --------
          >>> from sklearn import datasets
          >>> bc = datasets.load_breast_cancer()
          >>> X = bc = bc.data
          >>> explained_variance_visualizer(X, scale=True, center=True, colormap='RdBu_r')