code: def test_against_wls_inference(data, use_t, cov_type):
      y, x, w = data
      mod = RollingWLS(y, x, window=100, weights=w)
      res = mod.fit(use_t=use_t, cov_type=cov_type)
      ci = res.conf_int()
  
      # This is a smoke test of cov_params to make sure it works
      res.cov_params()
  
      # Skip to improve performance
      for i in range(100, y.shape[0]):
          _y = get_sub(y, i, 100)
          _x = get_sub(x, i, 100)
          wls = WLS(_y, _x, missing="drop").fit(
              use_t=use_t, cov_type=cov_type
          )
          assert_allclose(get_single(res.tvalues, i - 1), wls.tvalues)
          assert_allclose(get_single(res.bse, i - 1), wls.bse)
          assert_allclose(
              get_single(res.pvalues, i - 1), wls.pvalues, atol=1e-8
          )
          assert_allclose(get_single(res.fvalue, i - 1), wls.fvalue)
          with np.errstate(invalid="ignore"):
              assert_allclose(
                  get_single(res.f_pvalue, i - 1),
                  wls.f_pvalue,
                  atol=1e-8,
              )
          assert res.cov_type == wls.cov_type
          assert res.use_t == wls.use_t
          wls_ci = wls.conf_int()
          if isinstance(ci, pd.DataFrame):
              ci_val = ci.iloc[i - 1]
              ci_val = np.asarray(ci_val).reshape((-1, 2))
          else:
              ci_val = ci[i - 1].T
          assert_allclose(ci_val, wls_ci)
  
imports: from io import BytesIO
  from itertools import product
  import warnings
  import numpy
  import pandas
  import pytest
  from numpy.testing import assert_allclose, assert_array_equal
  from statsmodels import tools
  from statsmodels.regression.linear_model import WLS
  from statsmodels.regression.rolling import RollingWLS, RollingOLS
label_desc: Use image node_id 9 for calling a global function with example usage: test_against_wls_inference(data, use_t, cov_type) without return types