code: def _find_nans(self):
      nans = np.isnan(self._y)
      nans |= np.any(np.isnan(self._x), axis=1)
      nans |= np.isnan(self._weights)
      self._is_nan[:] = nans
      has_nan = np.cumsum(nans)
      w = self._window
      has_nan[w - 1 :] = has_nan[w - 1 :] - has_nan[: -(w - 1)]
      if self._expanding:
          has_nan[: self._min_nobs] = False
      else:
          has_nan[: w - 1] = False
  
      return has_nan.astype(bool)
  
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 3 for calling the RollingWLS obj's underlying member method code with example usage: obj._find_nans() and returns: has_nan