code: def _aicc(model_results, nobs, add_constant):
      """Compute the corrected Akaike Information Criterion"""
      aic = model_results.aic
  
      # SARIMAX counts the constant in df_model if there was an intercept, so
      # only add one if no constant was included in the model
      df_model = model_results.df_model
      if add_constant:
          add_constant += 1  # add one for constant term
      return aic + 2.0 * df_model * (
          nobs / (nobs - df_model - 1.0) - 1.0
      )
  
imports: import pandas
  import numpy
  import numpy.polynomial.polynomial
  import numbers
  import warnings
  from scipy.stats import gaussian_kde, norm
  from sklearn.utils.validation import check_array
  from statsmodels import api
  from .None import _validation
  from ..base import BaseARIMA
  from ..compat.numpy import DTYPE
  from ..compat.sklearn import check_is_fitted, if_delegate_has_method, safe_indexing
  from ..compat import statsmodels
  from ..compat import matplotlib
  from ..utils import if_has_delegate, is_iterable, check_endog, check_exog
  from ..utils.visualization import _get_plt
  from ..utils.array import diff_inv, diff
  import pmdarima
label_desc: Use image node_id 2 for calling a global function with example usage: _aicc(model_results, nobs, add_constant) and returns: unknown