code: def append(
      self, endog, exog=None, refit=False, fit_kwargs=None, **kwargs
  ):
      # MLEResults.append will concatenate the given `exog` here with
      # `data.orig_exog`. However, `data.orig_exog` already has had any
      # trend variables prepended to it, while the `exog` given here should
      # not. Instead, we need to temporarily replace `orig_exog` and
      # `exog_names` with the ones that correspond to those that were input
      # by the user.
      if exog is not None:
          orig_exog = self.model.data.orig_exog
          exog_names = self.model.exog_names
          self.model.data.orig_exog = self.model._input_exog
          self.model.exog_names = self.model._input_exog_names
  
      # Perform the appending procedure
      out = super().append(
          endog, exog=exog, refit=refit, fit_kwargs=fit_kwargs, **kwargs
      )
  
      # Now we reverse the temporary change made above
      if exog is not None:
          self.model.data.orig_exog = orig_exog
          self.model.exog_names = exog_names
      return out
  
imports: from statsmodels.compat.pandas import Appender
  import warnings
  import numpy
  from statsmodels.tools.data import _is_using_pandas
  from statsmodels.tsa.statespace import sarimax
  from statsmodels.tsa.statespace.kalman_filter import MEMORY_CONSERVE
  from statsmodels.tsa.statespace.tools import diff
  import statsmodels.base.wrapper
  from statsmodels.tsa.arima.estimators.yule_walker import yule_walker
  from statsmodels.tsa.arima.estimators.burg import burg
  from statsmodels.tsa.arima.estimators.hannan_rissanen import hannan_rissanen
  from statsmodels.tsa.arima.estimators.innovations import innovations, innovations_mle
  from statsmodels.tsa.arima.estimators.gls import gls
  from statsmodels.tsa.arima.specification import SARIMAXSpecification
label_desc: Use image node_id 1 for calling the ARIMAResults obj's underlying member method code with example usage: obj.append(endog, exog, refit, fit_kwargs) and returns: out