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: Fit to data, transform it, then visualize it.
  
  Fits the text visualizer to X and y with optional parameters by
  passing in all of kwargs, then calls show with the same kwargs.
  This method must return the result of the transform method.
  
  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
      Pass generic arguments to the show method
  
  Returns
  -------
  X : numpy array
      This method must return a numpy array with the same shape as X.
code: def fit_transform_show(self, X, y=None, **kwargs):
      """
      Fit to data, transform it, then visualize it.
  
      Fits the text visualizer to X and y with optional parameters by
      passing in all of kwargs, then calls show with the same kwargs.
      This method must return the result of the transform method.
  
      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
          Pass generic arguments to the show method
  
      Returns
      -------
      X : numpy array
          This method must return a numpy array with the same shape as X.
      """
      Xp = self.fit(X, y, **kwargs).transform(X)
      self.show(**kwargs)
      return Xp
  
file_path: yellowbrick/yellowbrick/text/base.py
imports: from yellowbrick.base import Visualizer
  from sklearn.base import TransformerMixin
label_desc: Use image node_id 4 for calling the TextVisualizer obj's underlying member method code with example usage: obj.fit_transform_show(X, y) and returns: Xp