code: def test_set_default_unbiased_with_fixed_params():
      # setting unbiased=None with fixed params should yield the exact same
      # results as setting unbiased=False
      endog = np.random.normal(size=1000)
      # unbiased=None
      p_1, other_results_2 = hannan_rissanen(
          endog,
          ar_order=1,
          ma_order=1,
          unbiased=None,
          fixed_params={"ar.L1": 0.69607715},
      )
      # unbiased=False
      p_2, other_results_1 = hannan_rissanen(
          endog,
          ar_order=1,
          ma_order=1,
          unbiased=False,
          fixed_params={"ar.L1": 0.69607715},
      )
  
      np.testing.assert_array_equal(p_1.ar_params, p_2.ar_params)
      np.testing.assert_array_equal(p_1.ma_params, p_2.ma_params)
      assert p_1.sigma2 == p_2.sigma2
      np.testing.assert_array_equal(
          other_results_1.resid, other_results_2.resid
      )
  
imports: import numpy
  import pytest
  from numpy.testing import assert_allclose
  from statsmodels.tsa.innovations.arma_innovations import arma_innovations
  from statsmodels.tsa.arima.datasets.brockwell_davis_2002 import lake
  from statsmodels.tsa.arima.estimators.hannan_rissanen import hannan_rissanen, _validate_fixed_params, _package_fixed_and_free_params_info, _stitch_fixed_and_free_params
  from statsmodels.tsa.arima.specification import SARIMAXSpecification
  from statsmodels.tools.tools import Bunch
label_desc: Use image node_id 13 for calling a global function with example usage: test_set_default_unbiased_with_fixed_params() without return types