code: def x13_arima_select_order(
      endog,
      maxorder=(2, 1),
      maxdiff=(2, 1),
      diff=None,
      exog=None,
      log=None,
      outlier=True,
      trading=False,
      forecast_periods=None,
      start=None,
      freq=None,
      print_stdout=False,
      x12path=None,
      prefer_x13=True,
      tempdir=None,
  ):
      """
      Perform automatic seasonal ARIMA order identification using x12/x13 ARIMA.
  
      Parameters
      ----------
      endog : array_like, pandas.Series
          The series to model. It is best to use a pandas object with a
          DatetimeIndex or PeriodIndex. However, you can pass an array-like
          object. If your object does not have a dates index then ``start`` and
          ``freq`` are not optional.
      maxorder : tuple
          The maximum order of the regular and seasonal ARMA polynomials to
          examine during the model identification. The order for the regular
          polynomial must be greater than zero and no larger than 4. The
          order for the seasonal polynomial may be 1 or 2.
      maxdiff : tuple
          The maximum orders for regular and seasonal differencing in the
          automatic differencing procedure. Acceptable inputs for regular
          differencing are 1 and 2. The maximum order for seasonal differencing
          is 1. If ``diff`` is specified then ``maxdiff`` should be None.
          Otherwise, ``diff`` will be ignored. See also ``diff``.
      diff : tuple
          Fixes the orders of differencing for the regular and seasonal
          differencing. Regular differencing may be 0, 1, or 2. Seasonal
          differencing may be 0 or 1. ``maxdiff`` must be None, otherwise
          ``diff`` is ignored.
      exog : array_like
          Exogenous variables.
      log : bool or None
          If None, it is automatically determined whether to log the series or
          not. If False, logs are not taken. If True, logs are taken.
      outlier : bool
          Whether or not outliers are tested for and corrected, if detected.
      trading : bool
          Whether or not trading day effects are tested for.
      forecast_periods : int
          Number of forecasts produced. The default is None.
      start : str, datetime
          Must be given if ``endog`` does not have date information in its index.
          Anything accepted by pandas.DatetimeIndex for the start value.
      freq : str
          Must be givein if ``endog`` does not have date information in its
          index. Anything accepted by pandas.DatetimeIndex for the freq value.
      print_stdout : bool
          The stdout from X12/X13 is suppressed. To print it out, set this
          to True. Default is False.
      x12path : str or None
          The path to x12 or x13 binary. If None, the program will attempt
          to find x13as or x12a on the PATH or by looking at X13PATH or X12PATH
          depending on the value of prefer_x13.
      prefer_x13 : bool
          If True, will look for x13as first and will fallback to the X13PATH
          environmental variable. If False, will look for x12a first and will
          fallback to the X12PATH environmental variable. If x12path points
          to the path for the X12/X13 binary, it does nothing.
      tempdir : str
          The path to where temporary files are created by the function.
          If None, files are created in the default temporary file location.
  
      Returns
      -------
      Bunch
          A bunch object containing the listed attributes.
  
          - order : tuple
            The regular order.
          - sorder : tuple
            The seasonal order.
          - include_mean : bool
            Whether to include a mean or not.
          - results : str
            The full results from the X12/X13 analysis.
          - stdout : str
            The captured stdout from the X12/X13 analysis.
  
      Notes
      -----
      This works by creating a specification file, writing it to a temporary
      directory, invoking X12/X13 in a subprocess, and reading the output back
      in.
      """
      results = x13_arima_analysis(
          endog,
          x12path=x12path,
          exog=exog,
          log=log,
          outlier=outlier,
          trading=trading,
          forecast_periods=forecast_periods,
          maxorder=maxorder,
          maxdiff=maxdiff,
          diff=diff,
          start=start,
          freq=freq,
          prefer_x13=prefer_x13,
          tempdir=tempdir,
      )
      model = re.search(
          "(?<=Final automatic model choice : ).*", results.results
      )
      order = model.group()
      if re.search("Mean is not significant", results.results):
          include_mean = False
      elif re.search("Constant", results.results):
          include_mean = True
      else:
          include_mean = False
      order, sorder = _clean_order(order)
      res = Bunch(
          order=order,
          sorder=sorder,
          include_mean=include_mean,
          results=results.results,
          stdout=results.stdout,
      )
      return res
  
imports: from statsmodels.compat.pandas import deprecate_kwarg
  import os
  import subprocess
  import tempfile
  import re
  from warnings import warn
  import pandas
  from statsmodels.tools.tools import Bunch
  from statsmodels.tools.sm_exceptions import X13NotFoundError, IOWarning, X13Error, X13Warning
label_desc: Use image node_id 14 for calling a global function with example usage: x13_arima_select_order(endog, maxorder, maxdiff, diff, exog, log, outlier, trading, forecast_periods, start, freq, print_stdout, x12path, prefer_x13, tempdir) and returns: res