class_docstr: Class to hold results from fitting an SARIMAX model.
  
  Parameters
  ----------
  model : SARIMAX instance
      The fitted model instance
  
  Attributes
  ----------
  specification : dictionary
      Dictionary including all attributes from the SARIMAX model instance.
  polynomial_ar : ndarray
      Array containing autoregressive lag polynomial coefficients,
      ordered from lowest degree to highest. Initialized with ones, unless
      a coefficient is constrained to be zero (in which case it is zero).
  polynomial_ma : ndarray
      Array containing moving average lag polynomial coefficients,
      ordered from lowest degree to highest. Initialized with ones, unless
      a coefficient is constrained to be zero (in which case it is zero).
  polynomial_seasonal_ar : ndarray
      Array containing seasonal autoregressive lag polynomial coefficients,
      ordered from lowest degree to highest. Initialized with ones, unless
      a coefficient is constrained to be zero (in which case it is zero).
  polynomial_seasonal_ma : ndarray
      Array containing seasonal moving average lag polynomial coefficients,
      ordered from lowest degree to highest. Initialized with ones, unless
      a coefficient is constrained to be zero (in which case it is zero).
  polynomial_trend : ndarray
      Array containing trend polynomial coefficients, ordered from lowest
      degree to highest. Initialized with ones, unless a coefficient is
      constrained to be zero (in which case it is zero).
  model_orders : list of int
      The orders of each of the polynomials in the model.
  param_terms : list of str
      List of parameters actually included in the model, in sorted order.
  
  See Also
  --------
  statsmodels.tsa.statespace.kalman_filter.FilterResults
  statsmodels.tsa.statespace.mlemodel.MLEResults
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code: def summary(self, alpha=0.05, start=None):
      # Create the model name
  
      # See if we have an ARIMA component
      order = ""
      if self.model.k_ar + self.model.k_diff + self.model.k_ma > 0:
          if self.model.k_ar == self.model.k_ar_params:
              order_ar = self.model.k_ar
          else:
              order_ar = list(self.model._spec.ar_lags)
          if self.model.k_ma == self.model.k_ma_params:
              order_ma = self.model.k_ma
          else:
              order_ma = list(self.model._spec.ma_lags)
          # If there is simple differencing, then that is reflected in the
          # dependent variable name
          k_diff = (
              0 if self.model.simple_differencing else self.model.k_diff
          )
          order = "(%s, %d, %s)" % (order_ar, k_diff, order_ma)
      # See if we have an SARIMA component
      seasonal_order = ""
      has_seasonal = (
          self.model.k_seasonal_ar
          + self.model.k_seasonal_diff
          + self.model.k_seasonal_ma
      ) > 0
      if has_seasonal:
          tmp = int(
              self.model.k_seasonal_ar / self.model.seasonal_periods
          )
          if tmp == self.model.k_seasonal_ar_params:
              order_seasonal_ar = int(
                  self.model.k_seasonal_ar / self.model.seasonal_periods
              )
          else:
              order_seasonal_ar = list(
                  self.model._spec.seasonal_ar_lags
              )
          tmp = int(
              self.model.k_seasonal_ma / self.model.seasonal_periods
          )
          if tmp == self.model.k_ma_params:
              order_seasonal_ma = tmp
          else:
              order_seasonal_ma = list(
                  self.model._spec.seasonal_ma_lags
              )
          # If there is simple differencing, then that is reflected in the
          # dependent variable name
          k_seasonal_diff = self.model.k_seasonal_diff
          if self.model.simple_differencing:
              k_seasonal_diff = 0
          seasonal_order = "(%s, %d, %s, %d)" % (
              str(order_seasonal_ar),
              k_seasonal_diff,
              str(order_seasonal_ma),
              self.model.seasonal_periods,
          )
          if not order == "":
              order += "x"
      model_name = (
          f"{self.model.__class__.__name__}{order}{seasonal_order}"
      )
  
      return super().summary(
          alpha=alpha,
          start=start,
          title="SARIMAX Results",
          model_name=model_name,
      )
  
imports: from warnings import warn
  import numpy
  import pandas
  from statsmodels.compat.pandas import Appender
  from statsmodels.tools.tools import Bunch
  from statsmodels.tools.data import _is_using_pandas
  from statsmodels.tools.decorators import cache_readonly
  import statsmodels.base.wrapper
  from statsmodels.tsa.arima.specification import SARIMAXSpecification
  from statsmodels.tsa.arima.params import SARIMAXParams
  from statsmodels.tsa.tsatools import lagmat
  from .initialization import Initialization
  from .mlemodel import MLEModel, MLEResults, MLEResultsWrapper
  from .tools import companion_matrix, diff, is_invertible, constrain_stationary_univariate, unconstrain_stationary_univariate, prepare_exog, prepare_trend_spec, prepare_trend_data
label_desc: Use image node_id 11 for calling the SARIMAXResults obj's underlying member method code with example usage: obj.summary(alpha, start) and returns: super