code: def _compute_multivariate_pacf_from_coefficients(
      constrained, error_variance, order=None, k_endog=None
  ):
      r"""
      Transform matrices corresponding to a stationary (or invertible) process
      to matrices with singular values less than one.
  
      Parameters
      ----------
      constrained : array or list
          The coefficients matrices. If a list, should be a list of length
          `order`, where each element is an array sized `k_endog` x `k_endog`. If
          an array, should be the coefficient matrices horizontally concatenated
          and sized `k_endog` x `k_endog * order`.
      error_variance : ndarray
          The variance / covariance matrix of the error term. Should be 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
      -------
      pacf : list
          List of first `order` multivariate partial autocorrelations.
  
      See Also
      --------
      unconstrain_stationary_multivariate
  
      Notes
      -----
      Note that this computes multivariate partial autocorrelations.
  
      Corresponds to the inverse of Lemma 2.1 in Ansley and Kohn (1986). See
      `unconstrain_stationary_multivariate` for more details.
  
      Notes
      -----
      Coefficients are assumed to be provided from the VAR model:
  
      .. math::
          y_t = A_1 y_{t-1} + \dots + A_p y_{t-p} + \varepsilon_t
      """
  
      if type(constrained) is list:
          order = len(constrained)
          k_endog = constrained[0].shape[0]
      else:
          k_endog, order = constrained.shape
          order //= k_endog
  
      # Get autocovariances for the process; these are defined to be
      # E z_t z_{t-j}'
      # However, we want E z_t z_{t+j}' = (E z_t z_{t-j}')'
      _acovf = _compute_multivariate_acovf_from_coefficients
  
      autocovariances = [
          autocovariance.T
          for autocovariance in _acovf(
              constrained, error_variance, maxlag=order
          )
      ]
  
      return _compute_multivariate_pacf_from_autocovariances(
          autocovariances
      )
  
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 18 for calling a global function with example usage: _compute_multivariate_pacf_from_coefficients(constrained, error_variance, order, k_endog) and returns: _compute_multivariate_pacf_from_autocovariances