code: def _sum_of_squares(self, y, wy, weights):
      mean = np.average(y, weights=weights)
      centered_tss = np.sum(weights * (y - mean) ** 2)
      uncentered_tss = np.dot(wy, wy)
      return centered_tss, 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 7 for calling the RollingWLS obj's underlying member method code with example usage: obj._sum_of_squares(y, wy, weights) and returns: centered_tss,  uncentered_tss