code: def aicc_sigma(sigma2, nobs, df_modelwc, islog=False):
      """
      Akaike information criterion (AIC) with small sample correction
  
      Parameters
      ----------
      sigma2 : float
          estimate of the residual variance or determinant of Sigma_hat in the
          multivariate case. If islog is true, then it is assumed that sigma
          is already log-ed, for example logdetSigma.
      nobs : int
          number of observations
      df_modelwc : int
          number of parameters including constant
  
      Returns
      -------
      aicc : float
          information criterion
  
      Notes
      -----
      A constant has been dropped in comparison to the loglikelihood base
      information criteria. These should be used to compare for comparable
      models.
  
      References
      ----------
      https://en.wikipedia.org/wiki/Akaike_information_criterion#AICc
      """
      if not islog:
          sigma2 = np.log(sigma2)
      return sigma2 + aicc(0, nobs, df_modelwc) / nobs
  
imports: import numpy
  from statsmodels.tools.validation import array_like
label_desc: Use image node_id 17 for calling a global function with example usage: aicc_sigma(sigma2, nobs, df_modelwc, islog) and returns: unknown