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: Fits a visualizer to data and is the primary entry point for producing
  a visualization. Visualizers are Scikit-Learn Estimator objects, which
  learn from data in order to produce a visual analysis or diagnostic.
  They can do this either by fitting features related data or by fitting
  an underlying model (or models) and visualizing their results.
  
  Parameters
  ----------
  X : ndarray or DataFrame of shape n x m
      A matrix of n instances with m features
  
  y : ndarray or Series of length n
      An array or series of target or class values
  
  kwargs: dict
      Keyword arguments passed to the drawing functionality or to the
      Scikit-Learn API. See visualizer specific details for how to use
      the kwargs to modify the visualization or fitting process.
  
  Returns
  -------
  self : visualizer
      The fit method must always return self to support pipelines.
code: def fit(self, X, y=None, **kwargs):
      """
      Fits a visualizer to data and is the primary entry point for producing
      a visualization. Visualizers are Scikit-Learn Estimator objects, which
      learn from data in order to produce a visual analysis or diagnostic.
      They can do this either by fitting features related data or by fitting
      an underlying model (or models) and visualizing their results.
  
      Parameters
      ----------
      X : ndarray or DataFrame of shape n x m
          A matrix of n instances with m features
  
      y : ndarray or Series of length n
          An array or series of target or class values
  
      kwargs: dict
          Keyword arguments passed to the drawing functionality or to the
          Scikit-Learn API. See visualizer specific details for how to use
          the kwargs to modify the visualization or fitting process.
  
      Returns
      -------
      self : visualizer
          The fit method must always return self to support pipelines.
      """
      return self
  
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 8 for calling the Visualizer obj's underlying member method code with example usage: obj.fit(X, y) and returns: self