class_docstr: Base class for text feature visualization to investigate documents
  individually or as a full corpus.
  
  TextVisualizers are used after a text corpus has been transformed
  in some way (e.g. normalized through stemming or lemmatization, via
  stopwords removal, or through vectorization). Thus a TextVisualizer
  is itself a transformer and can be used in a Scikit-Learn Pipeline
  to perform automatic visual analysis during build.
  
  Accepts as input a DataFrame or Numpy array.
docstr: These parameters can be influenced later on in the visualization
  process, but can and should be set as early as possible.
  
  Parameters
  ----------
  ax : axes
      the axis to plot the figure on
  
  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
      Pass generic arguments to the drawing method
code: def __init__(self, ax=None, fig=None, **kwargs):
      """
      These parameters can be influenced later on in the visualization
      process, but can and should be set as early as possible.
  
      Parameters
      ----------
      ax : axes
          the axis to plot the figure on
  
      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
          Pass generic arguments to the drawing method
  
      """
      super(TextVisualizer, self).__init__(ax=ax, fig=fig, **kwargs)
  
file_path: yellowbrick/yellowbrick/text/base.py
imports: from yellowbrick.base import Visualizer
  from sklearn.base import TransformerMixin
label_desc: Use image node_id 1 to create a new TextVisualizer object from inherited base classes: Visualizer, TransformerMixin with example: obj = TextVisualizer(ax, fig)