code: def burg(endog, ar_order=0, demean=True):
      """
      Estimate AR parameters using Burg technique.
  
      Parameters
      ----------
      endog : array_like or SARIMAXSpecification
          Input time series array, assumed to be stationary.
      ar_order : int, optional
          Autoregressive order. Default is 0.
      demean : bool, optional
          Whether to estimate and remove the mean from the process prior to
          fitting the autoregressive coefficients.
  
      Returns
      -------
      parameters : SARIMAXParams object
          Contains the parameter estimates from the final iteration.
      other_results : Bunch
          Includes one component, `spec`, which is the `SARIMAXSpecification`
          instance corresponding to the input arguments.
  
      Notes
      -----
      The primary reference is [1]_, section 5.1.2.
  
      This procedure assumes that the series is stationary.
  
      This function is a light wrapper around `statsmodels.linear_model.burg`.
  
      References
      ----------
      .. [1] Brockwell, Peter J., and Richard A. Davis. 2016.
         Introduction to Time Series and Forecasting. Springer.
      """
      spec = SARIMAXSpecification(endog, ar_order=ar_order)
      endog = spec.endog
  
      # Workaround for statsmodels.tsa.stattools.pacf_burg which does not work
      # on integer input
      # TODO: remove when possible
      if np.issubdtype(endog.dtype, np.dtype(int)):
          endog = endog * 1.0
  
      if not spec.is_ar_consecutive:
          raise ValueError(
              "Burg estimation unavailable for models with"
              " seasonal or otherwise non-consecutive AR orders."
          )
  
      p = SARIMAXParams(spec=spec)
  
      if ar_order == 0:
          p.sigma2 = np.var(endog)
      else:
          p.ar_params, p.sigma2 = linear_model.burg(
              endog, order=ar_order, demean=demean
          )
  
          # Construct other results
      other_results = Bunch(
          {
              "spec": spec,
          }
      )
  
      return p, other_results
  
imports: import numpy
  from statsmodels.tools.tools import Bunch
  from statsmodels.regression import linear_model
  from statsmodels.tsa.arima.specification import SARIMAXSpecification
  from statsmodels.tsa.arima.params import SARIMAXParams
label_desc: Use image node_id 1 for calling a global function with example usage: burg(endog, ar_order, demean) and returns: p,  other_results