class_docstr: StringEncoder transform.
  The transform will cast everything to a
  string and the inverse transform will cast to the type defined in dtype.
class_docstr_tok: ["StringEncoder","transform",".","The","transform","will","cast","everything","to","a","string","and","the","inverse","transform","will","cast","to","the","type","defined","in","dtype","."]
docstr: Inverse transform string encoded categories back to their original
     representation.
  
  Parameters
  ----------
  Xt : array-like, shape=(n_samples,)
      String encoded categories.
  
  Returns
  -------
  X : array-like, shape=(n_samples,)
      The original categories.
docstr_tok: ["Inverse","transform","string","encoded","categories","back","to","their","original","representation",".","Parameters","--","--","--","--","--","Xt",":","array-like",",","shape=","(","n_samples",",",")","String","encoded","categories",".","Returns","--","--","--","-","X",":","array-like",",","shape=","(","n_samples",",",")","The","original","categories","."]
code: def inverse_transform(self, Xt):
      """Inverse transform string encoded categories back to their original
         representation.
  
      Parameters
      ----------
      Xt : array-like, shape=(n_samples,)
          String encoded categories.
  
      Returns
      -------
      X : array-like, shape=(n_samples,)
          The original categories.
      """
      return [self.dtype(x) for x in Xt]
  
imports: from __future__ import division
  import numpy
  from sklearn.preprocessing import LabelBinarizer
  from sklearn.utils import column_or_1d
label_desc: Use image node_id 4 for calling the StringEncoder obj's underlying member method code with example usage: obj.inverse_transform(Xt) and returns: unknown