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: Primarily a pass-through to ensure that the text visualizer will
  work in a pipeline setting. This method can also call drawing methods
  in order to ensure that the visualization is constructed.
  
  Returns
  -------
  X : numpy array
      This method must return a numpy array with the same shape as X.
code: def transform(self, X):
      """
      Primarily a pass-through to ensure that the text visualizer will
      work in a pipeline setting. This method can also call drawing methods
      in order to ensure that the visualization is constructed.
  
      Returns
      -------
      X : numpy array
          This method must return a numpy array with the same shape as X.
      """
      return X
  
file_path: yellowbrick/yellowbrick/text/base.py
imports: from yellowbrick.base import Visualizer
  from sklearn.base import TransformerMixin
label_desc: Use image node_id 3 for calling the TextVisualizer obj's underlying member method code with example usage: obj.transform(X) and returns: X