def get_dfa_conjecture(self):
        """
        Utilize the observation table to construct a Mealy Machine.
        The library used for representing the Mealy Machine is the python
        bindings of the openFST library (pyFST).
        Args:
            None
        Returns:
            MealyMachine: A mealy machine build based on a closed and consistent
        observation table.
        """
        dfa = DFA(self.alphabet)
        for s in self.observation_table.sm_vector:
            for i in self.alphabet:
                dst = self.observation_table.equiv_classes[s + i]
                # If dst == None then the table is not closed.
                if dst == None:
                    logging.debug('Conjecture attempt on non closed table.')
                    return None
                obsrv = self.observation_table[s, i]
                src_id = self.observation_table.sm_vector.index(s)
                dst_id = self.observation_table.sm_vector.index(dst)
                dfa.add_arc(src_id, dst_id, i, obsrv)

        # Mark the final states in the hypothesis automaton.
        i = 0
        for s in self.observation_table.sm_vector:
            dfa[i].final = self.observation_table[s, self.epsilon]
            i += 1
        return dfa
Utilize the observation table to construct a Mealy Machine.
        The library used for representing the Mealy Machine is the python
        bindings of the openFST library (pyFST).
        Args:
            None
        Returns:
            MealyMachine: A mealy machine build based on a closed and consistent
        observation table.