code: def _loglike(self, params, wy, wx, weights, nobs):
      nobs2 = nobs / 2.0
      wresid = wy - wx @ params
      ssr = np.sum(wresid**2, axis=0)
      llf = -np.log(ssr) * nobs2  # concentrated likelihood
      llf -= (1 + np.log(np.pi / nobs2)) * nobs2  # with constant
      llf += 0.5 * np.sum(np.log(weights))
      return wresid, ssr, llf
  
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 6 for calling the RollingWLS obj's underlying member method code with example usage: obj._loglike(params, wy, wx, weights, nobs) and returns: wresid,  ssr,  llf