code: def fit(self, start_params=None, maxiter=10000, maxfun=5000, **kwds):
      # we have one additional parameter and we need to add it for summary
      self.exog_names.append("alpha")
      if start_params is None:
          # Reasonable starting values
          start_params = np.append(np.zeros(self.exog.shape[1]), 0.5)
          # intercept
          start_params[-2] = np.log(self.endog.mean())
      return super().fit(
          start_params=start_params,
          maxiter=maxiter,
          maxfun=maxfun,
          **kwds,
      )
  
file_path: statsmodels/examples/python/generic_mle.py
imports: import numpy
  from scipy import stats
  import statsmodels.api
  from statsmodels.base.model import GenericLikelihoodModel
  import numpy
  from scipy.stats import nbinom
  from statsmodels.base.model import GenericLikelihoodModel
  import statsmodels.api
label_desc: Use image node_id 3 for calling the NBin obj's underlying member method code with example usage: obj.fit(start_params, maxiter, maxfun) and returns: super