class_docstr: The root of the visual object hierarchy that defines how yellowbrick
  creates, stores, and renders visual artifacts using matplotlib.
  
  Inherits from Scikit-Learn's BaseEstimator class.
  
  The base class for feature visualization and model visualization
  primarily ensures that styling arguments are passed in.
  
  Parameters
  ----------
  ax : matplotlib Axes, default: None
      The axis to plot the figure on. If None is passed in the current axes
      will be used (or generated if required).
  
  fig : matplotlib Figure, default: None
      The figure to plot the Visualizer on. If None is passed in the current
      plot will be used (or generated if required).
  
  kwargs : dict
      Keyword arguments that are passed to the base class and may influence
      the visualization as defined in other Visualizers. Optional keyword
      arguments include:
  
      =============   =======================================================
      Property        Description
      -------------   -------------------------------------------------------
      size            specify a size for the figure
      color           specify a color, colormap, or palette for the figure
      title           specify the title of the figure
      =============   =======================================================
  
  Notes
  -----
  Visualizers are objects that learn from data (e.g. estimators), therefore
  they must be ``fit()`` before they can be drawn or used. Visualizers also
  maintain a reference to an ``ax`` object, a Matplotlib Axes where the
  figures are drawn and rendered, as well as to a ``fig`` object, a Matplotlib
  Figure on which the Visualizer will be plotted.
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docstr: The fitting or transformation process usually calls draw (not the
  user). This function is implemented for developers to hook into the
  matplotlib interface and to create an internal representation of the
  data the visualizer was trained on in the form of a figure or axes.
  
  Parameters
  ----------
  
  kwargs: dict
      generic keyword arguments.
code: def draw(self, **kwargs):
      """
      The fitting or transformation process usually calls draw (not the
      user). This function is implemented for developers to hook into the
      matplotlib interface and to create an internal representation of the
      data the visualizer was trained on in the form of a figure or axes.
  
      Parameters
      ----------
  
      kwargs: dict
          generic keyword arguments.
  
      """
      raise NotImplementedError(
          "Visualizers must implement a drawing interface."
      )
  
imports: import math
  import warnings
  import matplotlib.pyplot
  from sklearn.base import BaseEstimator
  from yellowbrick.utils import get_model_name
  from yellowbrick.utils.wrapper import Wrapper
  from yellowbrick.utils.helpers import check_fitted
  from yellowbrick.exceptions import YellowbrickWarning
  from yellowbrick.exceptions import YellowbrickValueError, YellowbrickTypeError
label_desc: Use image node_id 9 for calling the Visualizer obj's underlying member method code with example usage: obj.draw() without return types