code: 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.
  
      Returns
      -------
      viz : ExplainedVariance
          Returns the fitted, finalized visualizer
  
      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')
  
      """
  
      # Instantiate the visualizer
      visualizer = ExplainedVariance(X=X)
  
      # Fit and transform the visualizer (calls draw)
      visualizer.fit(X, y, **kwargs)
      visualizer.transform(X)
      visualizer.finalize()
  
      # Return the visualizer object
      return visualizer
  
file_path: yellowbrick/yellowbrick/features/decomposition.py
imports: from yellowbrick.style import palettes
  from yellowbrick.features.base import FeatureVisualizer
  from sklearn.pipeline import Pipeline
  from sklearn.decomposition import PCA
  from sklearn.preprocessing import StandardScaler
label_desc: Use image node_id 1 for calling a global function with example usage: explained_variance_visualizer(X, y, ax, scale, center, colormap) and returns: visualizer