code: def _unconstrain_sv_less_than_one(
      constrained, order=None, k_endog=None
  ):
      """
      Transform matrices with singular values less than one to arbitrary
      matrices.
  
      Parameters
      ----------
      constrained : list
          The partial autocorrelation matrices. Should be a list of length
          `order`, where each element is an array sized `k_endog` x `k_endog`.
      order : int, optional
          The order of the autoregression.
      k_endog : int, optional
          The dimension of the data vector.
  
      Returns
      -------
      unconstrained : list
          Unconstrained matrices. A list of length `order`, where each element is
          an array sized `k_endog` x `k_endog`.
  
      See Also
      --------
      unconstrain_stationary_multivariate
  
      Notes
      -----
      Corresponds to the inverse of Lemma 2.2 in Ansley and Kohn (1986). See
      `unconstrain_stationary_multivariate` for more details.
      """
      from scipy import linalg
  
      unconstrained = []  # A_s,  s = 1, ..., p
      if order is None:
          order = len(constrained)
      if k_endog is None:
          k_endog = constrained[0].shape[0]
  
      eye = np.eye(k_endog)
      for i in range(order):
          P = constrained[i]
          # B^{-1} B^{-1}' = I - P P'
          B_inv, lower = linalg.cho_factor(
              eye - np.dot(P, P.T), lower=True
          )
          # A = BP
          # B^{-1} A = P
          unconstrained.append(
              linalg.solve_triangular(B_inv, P, lower=lower)
          )
      return unconstrained
  
imports: import numpy
  from scipy.linalg import solve_sylvester
  import pandas
  from statsmodels.compat.pandas import Appender
  from statsmodels.tools.data import _is_using_pandas
  from scipy.linalg.blas import find_best_blas_type
  from .None import _initialization, _representation, _kalman_filter, _kalman_smoother, _simulation_smoother, _cfa_simulation_smoother, _tools
label_desc: Use image node_id 13 for calling a global function with example usage: _unconstrain_sv_less_than_one(constrained, order, k_endog) and returns: unconstrained