docstr: Sequentially performs the distributed estimation using
  the corresponding DistributedModel
  
  Parameters
  ----------
  data_generator : generator
      A generator that produces a sequence of tuples where the first
      element in the tuple corresponds to an endog array and the
      element corresponds to an exog array.
  fit_kwds : dict-like
      Keywords needed for the model fitting.
  init_kwds_generator : generator or None
      Additional keyword generator that produces model init_kwds
      that may vary based on data partition.  The current usecase
      is for WLS and GLS
  
  Returns
  -------
  join_method result.  For the default, _join_debiased, it returns a
  p length array.
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code: def fit_sequential(
      self, data_generator, fit_kwds, init_kwds_generator=None
  ):
      """Sequentially performs the distributed estimation using
      the corresponding DistributedModel
  
      Parameters
      ----------
      data_generator : generator
          A generator that produces a sequence of tuples where the first
          element in the tuple corresponds to an endog array and the
          element corresponds to an exog array.
      fit_kwds : dict-like
          Keywords needed for the model fitting.
      init_kwds_generator : generator or None
          Additional keyword generator that produces model init_kwds
          that may vary based on data partition.  The current usecase
          is for WLS and GLS
  
      Returns
      -------
      join_method result.  For the default, _join_debiased, it returns a
      p length array.
      """
  
      results_l = []
  
      if init_kwds_generator is None:
          for pnum, (endog, exog) in enumerate(data_generator):
              results = _helper_fit_partition(
                  self, pnum, endog, exog, fit_kwds
              )
              results_l.append(results)
  
      else:
          tup_gen = enumerate(zip(data_generator, init_kwds_generator))
  
          for pnum, ((endog, exog), init_kwds_e) in tup_gen:
              results = _helper_fit_partition(
                  self, pnum, endog, exog, fit_kwds, init_kwds_e
              )
              results_l.append(results)
  
      return results_l
  
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 3 for calling the DistributedModel obj's underlying member method code with example usage: obj.fit_sequential(data_generator, fit_kwds, init_kwds_generator) and returns: results_l