code: def fit(self, X, y=None, **kwargs):
          
          # TODO: add support for other tagsets?
          if self.tagset == "penn_treebank":
              self.pos_tag_counts_ = self._penn_tag_map()
              self._handle_treebank(X)
  
          elif self.tagset == "universal":
              self.pos_tag_counts_ = self._uni_tag_map()
              self._handle_universal(X)
  
          self.draw()
          return self
docs: Fits the corpus to the appropriate tag map.
          Text documents must be tokenized & tagged before passing to fit.
  
          Parameters
          ----------
          X : list or generator
              Should be provided as a list of documents or a generator
              that yields a list of documents that contain a list of
              sentences that contain (token, tag) tuples.
  
          y : ndarray or Series of length n
              An optional array of target values that are ignored by the
              visualizer.
  
          kwargs : dict
              Pass generic arguments to the drawing method
  
          Returns
          -------
          self : instance
              Returns the instance of the transformer/visualizer