How to use the hmmlearn.base.ConvergenceMonitor function in hmmlearn

----------
        X : array-like, shape (n_samples, n_features)
            Feature matrix of individual samples.
        lengths : array-like of integers, shape (n_sequences, )
            Lengths of the individual sequences in ``X``. The sum of
            these should be ``n_samples``.
        Returns
        -------
        self : object
            Returns self.
        """
        X = check_array(X)
        self._init(X, lengths=lengths)
        self._check()

        self.monitor_ = ConvergenceMonitor(self.tol, self.n_iter, self.verbose)
        for iter in range(self.n_iter):
            stats = self._initialize_sufficient_statistics()
            curr_logprob = 0
            for i, j in iter_from_X_lengths(X, lengths):
                framelogprob = self._compute_log_likelihood(X[i:j])
                logprob, fwdlattice = self._do_forward_pass(framelogprob)
                curr_logprob += logprob
                bwdlattice = self._do_backward_pass(framelogprob)
                posteriors = self._compute_posteriors(fwdlattice, bwdlattice)

                # fix posteriors
                if self.states_prior is not None and self.fp_state is not None:
                    for k in range(len(self.states_prior)):
                        if self.states_prior[k] == 0:
                            # non footprint states
                            posteriors[k][self.fp_state] = 0.0