code: def test_midas_bootstrap_smoke(self):
      # Note strictly needed if simulation passes since just a special case
      vol = MIDASHyperbolic()
      resids = self.resid
      backcast = vol.backcast(resids)
      var_bounds = vol.variance_bounds(resids)
      params = np.array([0.1, 0.9, 0.4])
      vol.forecast(
          params,
          resids,
          backcast,
          var_bounds,
          horizon=10,
          start=100,
          method="bootstrap",
      )
  
imports: import numpy
  from numpy.random import RandomState
  from numpy.testing import assert_allclose
  import pandas
  import pytest
  from arch.univariate import arch_model
  from arch.univariate.distribution import Normal, StudentsT
  from arch.univariate.mean import ConstantMean
  from arch.univariate.volatility import APARCH, EGARCH, FIGARCH, GARCH, HARCH, BootstrapRng, ConstantVariance, EWMAVariance, FixedVariance, MIDASHyperbolic, RiskMetrics2006
label_desc: Use image node_id 41 for calling the TestVarianceForecasts obj's underlying member method code with example usage: obj.test_midas_bootstrap_smoke() without return types