How to use the hmmlearn.__version__.split function in hmmlearn

group.hmm = hmm_data.get_default_hmm_atac_histone()
                    else:
                        group.hmm = hmm_data.get_default_hmm_dnase_histone()

    # Creating scikit HMM list
    for group in group_list:

        if (group.flag_multiple_hmms):

            hmm_list = []
            for hmm_file_name in group.hmm:

                try:
                    hmm_scaffold = HMM()
                    hmm_scaffold.load_hmm(hmm_file_name)
                    if (int(hmm_ver.split(".")[0]) <= 0 and int(hmm_ver.split(".")[1]) <= 1):
                        scikit_hmm = GaussianHMM(n_components=hmm_scaffold.states, covariance_type="full",
                                                 transmat=array(hmm_scaffold.A), startprob=array(hmm_scaffold.pi))
                        scikit_hmm.means_ = array(hmm_scaffold.means)
                        scikit_hmm.covars_ = array(hmm_scaffold.covs)
                    else:
                        scikit_hmm = GaussianHMM(n_components=hmm_scaffold.states, covariance_type="full")
                        scikit_hmm.startprob_ = array(hmm_scaffold.pi)
                        scikit_hmm.transmat_ = array(hmm_scaffold.A)
                        scikit_hmm.means_ = array(hmm_scaffold.means)
                        scikit_hmm.covars_ = array(hmm_scaffold.covs)

                except Exception:
                    error_handler.throw_error("FP_HMM_FILES")
                hmm_list.append(scikit_hmm)

            group.hmm = hmm_list

scikit_hmm.means_ = array(hmm_scaffold.means)
                        scikit_hmm.covars_ = array(hmm_scaffold.covs)

                except Exception:
                    error_handler.throw_error("FP_HMM_FILES")
                hmm_list.append(scikit_hmm)

            group.hmm = hmm_list

        else:

            scikit_hmm = None
            try:
                hmm_scaffold = HMM()
                hmm_scaffold.load_hmm(group.hmm)
                if (int(hmm_ver.split(".")[0]) <= 0 and int(hmm_ver.split(".")[1]) <= 1):
                    scikit_hmm = GaussianHMM(n_components=hmm_scaffold.states, covariance_type="full",
                                             transmat=array(hmm_scaffold.A), startprob=array(hmm_scaffold.pi))
                    scikit_hmm.means_ = array(hmm_scaffold.means)
                    scikit_hmm.covars_ = array(hmm_scaffold.covs)
                else:
                    scikit_hmm = GaussianHMM(n_components=hmm_scaffold.states, covariance_type="full")
                    scikit_hmm.startprob_ = array(hmm_scaffold.pi)
                    scikit_hmm.transmat_ = array(hmm_scaffold.A)
                    scikit_hmm.means_ = array(hmm_scaffold.means)
                    scikit_hmm.covars_ = array(hmm_scaffold.covs)


            except Exception:
                error_handler.throw_error("FP_HMM_FILES")
            group.hmm = scikit_hmm