code: def _est_unregularized_naive(mod, pnum, partitions, fit_kwds=None):
      """estimates the unregularized fitted parameters.
  
      Parameters
      ----------
      mod : statsmodels model class instance
          The model for the current partition.
      pnum : scalar
          Index of current partition
      partitions : scalar
          Total number of partitions
      fit_kwds : dict-like or None
          Keyword arguments to be given to fit
  
      Returns
      -------
      An array of the parameters for the fit
      """
  
      if fit_kwds is None:
          raise ValueError(
              "_est_unregularized_naive currently "
              + "requires that fit_kwds not be None."
          )
  
      return mod.fit(**fit_kwds).params
  
imports: from statsmodels.base.elastic_net import RegularizedResults
  from statsmodels.stats.regularized_covariance import _calc_nodewise_row, _calc_nodewise_weight, _calc_approx_inv_cov
  from statsmodels.base.model import LikelihoodModelResults
  from statsmodels.regression.linear_model import OLS
  import numpy
label_desc: Use image node_id 2 for calling a global function with example usage: _est_unregularized_naive(mod, pnum, partitions, fit_kwds) and returns: mod