code: def fit(
      self,
      start_params=None,
      method="BFGS",
      maxiter=100,
      full_output=True,
      disp=False,
      fargs=(),
      callback=None,
      retall=False,
      skip_hessian=False,
      **kwargs,
  ):
      rslt = super().fit(
          start_params=start_params,
          method=method,
          maxiter=maxiter,
          full_output=full_output,
          disp=disp,
          skip_hessian=skip_hessian,
      )
  
      crslt = ConditionalResults(
          self, rslt.params, rslt.cov_params(), 1
      )
      crslt.method = method
      crslt.nobs = self.nobs
      crslt.n_groups = self._n_groups
      crslt._group_stats = [
          "%d" % min(self._groupsize),
          "%d" % max(self._groupsize),
          "%.1f" % np.mean(self._groupsize),
      ]
      rslt = ConditionalResultsWrapper(crslt)
      return rslt
  
imports: import numpy
  import statsmodels.base.model
  import statsmodels.regression.linear_model
  import statsmodels.base.wrapper
  from statsmodels.discrete.discrete_model import MultinomialResults, MultinomialResultsWrapper
  import collections
  import warnings
  import itertools
label_desc: Use image node_id 3 for calling the _ConditionalModel obj's underlying member method code with example usage: obj.fit(start_params, method, maxiter, full_output, disp, fargs, callback, retall, skip_hessian) and returns: rslt