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

def fit(self, X, y=None, **kwargs):
        """
        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
        """
        # 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