code: def _sigma_est_kpss(resids, nobs, lags):
      """
      Computes equation 10, p. 164 of Kwiatkowski et al. (1992). This is the
      consistent estimator for the variance.
      """
      s_hat = np.sum(resids**2)
      for i in range(1, lags + 1):
          resids_prod = np.dot(resids[i:], resids[: nobs - i])
          s_hat += 2 * resids_prod * (1.0 - (i / (lags + 1.0)))
      return s_hat / nobs
  
imports: from __future__ import annotations
  from statsmodels.compat.numpy import lstsq
  from statsmodels.compat.pandas import deprecate_kwarg
  from statsmodels.compat.python import Literal, lzip
  from statsmodels.compat.scipy import _next_regular
  from typing import Union
  import warnings
  import numpy
  from numpy.linalg import LinAlgError
  import pandas
  from scipy import stats
  from scipy.interpolate import interp1d
  from scipy.signal import correlate
  from statsmodels.regression.linear_model import OLS, yule_walker
  from statsmodels.tools.sm_exceptions import CollinearityWarning, InfeasibleTestError, InterpolationWarning, MissingDataError, ValueWarning
  from statsmodels.tools.tools import Bunch, add_constant
  from statsmodels.tools.validation import array_like, bool_like, dict_like, float_like, int_like, string_like
  from statsmodels.tsa._bds import bds
  from statsmodels.tsa._innovations import innovations_algo, innovations_filter
  from statsmodels.tsa.adfvalues import mackinnoncrit, mackinnonp
  from statsmodels.tsa.tsatools import add_trend, lagmat, lagmat2ds
label_desc: Use image node_id 21 for calling a global function with example usage: _sigma_est_kpss(resids, nobs, lags) and returns: unknown