code: def _constrain_sv_less_than_one_python(
      unconstrained, order=None, k_endog=None
  ):
      """
      Transform arbitrary matrices to matrices with singular values less than
      one.
  
      Parameters
      ----------
      unconstrained : list
          Arbitrary 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
      -------
      constrained : list
          Partial autocorrelation matrices. Should be a list of length
          `order`, where each element is an array sized `k_endog` x `k_endog`.
  
      See Also
      --------
      constrain_stationary_multivariate
  
      Notes
      -----
      Corresponds to Lemma 2.2 in Ansley and Kohn (1986). See
      `constrain_stationary_multivariate` for more details.
      """
  
      from scipy import linalg
  
      constrained = []  # P_s,  s = 1, ..., p
      if order is None:
          order = len(unconstrained)
      if k_endog is None:
          k_endog = unconstrained[0].shape[0]
  
      eye = np.eye(k_endog)
      for i in range(order):
          A = unconstrained[i]
          B, lower = linalg.cho_factor(eye + np.dot(A, A.T), lower=True)
          constrained.append(linalg.solve_triangular(B, A, lower=lower))
      return constrained
  
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 9 for calling a global function with example usage: _constrain_sv_less_than_one_python(unconstrained, order, k_endog) and returns: constrained