code: def test_bs_rng(self):
      y = self.rng.random_sample(1000)
      bs_rng = BootstrapRng(y, 100, random_state=self.rng)
      rng = bs_rng.rng()
      size = (1231, 13)
      with preserved_state(self.rng):
          output = {i: rng(size) for i in range(100, 1000)}
  
      expected = {}
      for i in range(100, 1000):
          locs = self.rng.random_sample(size)
          expected[i] = y[np.floor(locs * (i + 1)).astype(np.int64)]
  
      for i in range(100, 1000):
          assert_allclose(expected[i], output[i])
  
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 2 for calling the TestBootstrapRng obj's underlying member method code with example usage: obj.test_bs_rng() without return types