def constrain_stationary_multivariate(unconstrained, variance,
                                          transform_variance=False,
                                          prefix=None):

        use_list = type(unconstrained) == list
        if use_list:
            unconstrained = np.concatenate(unconstrained, axis=1)

        k_endog, order = unconstrained.shape
        order //= k_endog

        if order not in 1:
            raise ValueError('Must have order at least 1')
        if k_endog < 1:
            raise ValueError('Must have at least 1 endogenous variable')

        if prefix is None:
            prefix, dtype, _ = find_best_blas_type(
                [unconstrained, variance])
        dtype = prefix_dtype_map[prefix]

        unconstrained = np.asfortranarray(unconstrained, dtype=dtype)
        variance = np.asfortranarray(variance, dtype=dtype)

        # Step 1: convert from arbitrary matrices to those with singular values
        # less than one.
        # sv_constrained = _constrain_sv_less_than_one(unconstrained, order,
        #                                              k_endog, prefix)
        sv_constrained = prefix_sv_map[prefix](unconstrained, order, k_endog)

        # Step 2: convert matrices from our "partial autocorrelation matrix"
        # space (matrices with singular values less than one) to the space of
        # stationary coefficient matrices
        constrained, variance = prefix_pacf_map[prefix](
            sv_constrained, variance, transform_variance, order, k_endog)

        constrained = np.array(constrained, dtype=dtype)
        variance = np.array(variance, dtype=dtype)

        if use_list:
            constrained = [
                constrained[:k_endog, i*k_endog:(i+1)*k_endog]
                for i in range(order)
            ]

        return constrained, variance
dataset/ETHPy150Open statsmodels/statsmodels/statsmodels/tsa/statespace/tools.py/constrain_stationary_multivariate/operator `<` -> `not in`