error in notebook 07

[hima111997]

in notebook 07, this line :
hmm_4 = pyemma.msm.bayesian_hidden_markov_model(cluster.dtrajs, nstates=4, lag=1, dt_traj='1 ps', nsamples=50)
produces this error:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[5], line 1
----> 1 hmm_4 = pyemma.msm.bayesian_hidden_markov_model(cluster.dtrajs, nstates=4, lag=1, dt_traj='1 ps', nsamples=50)

File ~/miniconda3/envs/emma/lib/python3.11/site-packages/pyemma/msm/api.py:1375, in bayesian_hidden_markov_model(dtrajs, nstates, lag, nsamples, reversible, stationary, connectivity, mincount_connectivity, separate, observe_nonempty, stride, conf, dt_traj, store_hidden, show_progress)
   1217 r""" Bayesian Hidden Markov model estimate using Gibbs sampling of the posterior
   1218 
   1219 Returns a :class:`BayesianHMSM` that contains
   (...)
   1368 
   1369 """
   1370 bhmsm_estimator = _Bayes_HMSM(lag=lag, nstates=nstates, stride=stride, nsamples=nsamples, reversible=reversible,
   1371                               stationary=stationary,
   1372                               connectivity=connectivity, mincount_connectivity=mincount_connectivity,
   1373                               separate=separate, observe_nonempty=observe_nonempty,
   1374                               dt_traj=dt_traj, conf=conf, store_hidden=store_hidden, show_progress=show_progress)
-> 1375 return bhmsm_estimator.estimate(dtrajs)

File ~/miniconda3/envs/emma/lib/python3.11/site-packages/pyemma/_base/estimator.py:418, in Estimator.estimate(self, X, **params)
    416 if params:
    417     self.set_params(**params)
--> 418 self._model = self._estimate(X)
    419 # ensure _estimate returned something
    420 assert self._model is not None

File ~/miniconda3/envs/emma/lib/python3.11/site-packages/pyemma/msm/estimators/bayesian_hmsm.py:313, in BayesianHMSM._estimate(self, dtrajs)
    304 else:
    305     estimator = BayesianHMM.default(dtrajs, n_hidden_states=self.nstates, lagtime=self.lag,
    306                                     n_samples=self.nsamples, stride=self.stride,
    307                                     initial_distribution_prior=self.p0_prior,
   (...)
    310                                     stationary=self.stationary,
    311                                     prior_submodel=True, separate=self.separate)
--> 313 estimator.fit(dtrajs, n_burn_in=0, n_thin=1, progress=progress)
    314 model = estimator.fetch_model()
    315 if self.show_progress:

File ~/miniconda3/envs/emma/lib/python3.11/site-packages/deeptime/base.py:417, in _ImmutableInputData.__call__(self, *args, **kwargs)
    415 # here we invoke the immutable setting context manager.
    416 with self:
--> 417     return self.fit_method(*args, **kwargs)

File ~/miniconda3/envs/emma/lib/python3.11/site-packages/deeptime/markov/hmm/_bayesian_hmm.py:610, in BayesianHMM.fit(self, data, n_burn_in, n_thin, progress, **kwargs)
    608 # Collect data.
    609 models = []
--> 610 for _ in progress(range(self.n_samples), desc="Drawing samples", leave=False):
    611     # Run a number of Gibbs sampling updates to generate each sample.
    612     for _ in range(n_thin):
    613         self._update(sample_model, dtrajs_lagged_strided, temp_alpha, transition_matrix_prior,
    614                      initial_distribution_prior)

TypeError: ProgressCallback.__init__() got an unexpected keyword argument 'desc'

How to solve it?