code: def _fit_single(
      self, idx, wxpwx, wxpwy, nobs, store, params_only, method
  ):
      if nobs < self._min_nobs:
          return
      try:
          if (method == "inv") or not params_only:
              wxpwxi = np.linalg.inv(wxpwx)
          if method == "inv":
              params = wxpwxi @ wxpwy
          else:
              _, wy, wx, _, _ = self._get_data(idx)
              if method == "lstsq":
                  params = lstsq(wx, wy)[0]
              else:  # 'pinv'
                  wxpwxiwxp = np.linalg.pinv(wx)
                  params = wxpwxiwxp @ wy
  
      except np.linalg.LinAlgError:
          return
      store.params[idx - 1] = params
      if params_only:
          return
      y, wy, wx, weights, _ = self._get_data(idx)
  
      wresid, ssr, llf = self._loglike(params, wy, wx, weights, nobs)
      wxwresid = wx * wresid[:, None]
      wxepwxe = wxwresid.T @ wxwresid
      tot_params = wx.shape[1]
      s2 = ssr / (nobs - tot_params)
  
      centered_tss, uncentered_tss = self._sum_of_squares(
          y, wy, weights
      )
  
      store.ssr[idx - 1] = ssr
      store.llf[idx - 1] = llf
      store.nobs[idx - 1] = nobs
      store.s2[idx - 1] = s2
      store.xpxi[idx - 1] = wxpwxi
      store.xeex[idx - 1] = wxepwxe
      store.centered_tss[idx - 1] = centered_tss
      store.uncentered_tss[idx - 1] = uncentered_tss
  
imports: from statsmodels.compat.numpy import lstsq
  from statsmodels.compat.pandas import Appender, Substitution, cache_readonly, call_cached_func, get_cached_doc
  from collections import namedtuple
  import numpy
  from pandas import DataFrame, MultiIndex, Series
  from scipy import stats
  from statsmodels.base import model
  from statsmodels.base.model import LikelihoodModelResults, Model
  from statsmodels.regression.linear_model import RegressionModel, RegressionResults
  from statsmodels.tools.validation import array_like, int_like, string_like
label_desc: Use image node_id 5 for calling the RollingWLS obj's underlying member method code with example usage: obj._fit_single(idx, wxpwx, wxpwy, nobs, store, params_only, method) without return types