code: def aicc(llf, nobs, df_modelwc):
      """
      Akaike information criterion (AIC) with small sample correction
  
      Parameters
      ----------
      llf : {float, array_like}
          value of the loglikelihood
      nobs : int
          number of observations
      df_modelwc : int
          number of parameters including constant
  
      Returns
      -------
      aicc : float
          information criterion
  
      References
      ----------
      https://en.wikipedia.org/wiki/Akaike_information_criterion#AICc
  
      Notes
      -----
      Returns +inf if the effective degrees of freedom, defined as
      ``nobs - df_modelwc - 1.0``, is <= 0.
      """
      dof_eff = nobs - df_modelwc - 1.0
      if dof_eff > 0:
          return -2.0 * llf + 2.0 * df_modelwc * nobs / dof_eff
      else:
          return np.inf
  
imports: import numpy
  from statsmodels.tools.validation import array_like
label_desc: Use image node_id 13 for calling a global function with example usage: aicc(llf, nobs, df_modelwc) and returns: unknown, np