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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def factorize( self, na_sentinel: int = -1, ) -> Tuple[np.ndarray, ABCExtensionArray]: """ Encode the extension array as an enumerated type. Parameters na_sentinel : int, default -1 Value to use in the `labels` array to indicate missing values. Returns ------- labels : ndarray An integer NumPy array that's an indexer into the original ExtensionArray. uniques : ExtensionArray An ExtensionArray containing the unique values of `self`. .. note:: uniques will *not* contain an entry for the NA value of the ExtensionArray if there are any missing values present in `self`. See Also -------- pandas.factorize : Top-level factorize method that dispatches here. Notes ----- :meth:`pandas.factorize` offers a `sort` keyword as well. """

        # Impelmentor note: There are two ways to override the behavior of
        # pandas.factorize
        # 1. _values_for_factorize and _from_factorize.
        #    Specify the values passed to pandas' internal factorization
        #    routines, and how to convert from those values back to the
        #    original ExtensionArray.
        # 2. ExtensionArray.factorize.
        #    Complete control over factorization.
        from pandas.core.algorithms import _factorize_array

        arr, na_value = self._values_for_factorize()

        labels, uniques = _factorize_array(arr, na_sentinel=na_sentinel,
                                           na_value=na_value)

        uniques = self._from_factorized(uniques, self)
        return labels, uniques