code: def _compute_multivariate_sample_pacf(endog, maxlag):
      """
      Computer multivariate sample partial autocorrelations
  
      Parameters
      ----------
      endog : array_like
          Sample data on which to compute sample autocovariances. Shaped
          `nobs` x `k_endog`.
      maxlag : int
          Maximum lag for which to calculate sample partial autocorrelations.
  
      Returns
      -------
      sample_pacf : list
          A list of the first `maxlag` sample partial autocorrelation matrices.
          Each matrix is shaped `k_endog` x `k_endog`.
      """
      sample_autocovariances = _compute_multivariate_sample_acovf(
          endog, maxlag
      )
  
      return _compute_multivariate_pacf_from_autocovariances(
          sample_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 16 for calling a global function with example usage: _compute_multivariate_sample_pacf(endog, maxlag) and returns: _compute_multivariate_pacf_from_autocovariances