code: def test_score_test(self):
      res1 = self.res1
      modr = self.klass(endog, exog.values[:, :-1])
      resr = modr.fit(method="newton", maxiter=300)
      params_restr = np.concatenate(
          [resr.params[:-1], [0], resr.params[-1:]]
      )
      r_matrix = np.zeros((1, len(params_restr)))
      r_matrix[0, -2] = 1
      exog_extra = res1.model.exog[:, -1:]
  
      from statsmodels.base._parameter_inference import score_test
  
      sc1 = score_test(
          res1, params_constrained=params_restr, k_constraints=1
      )
      sc2 = score_test(resr, exog_extra=(exog_extra, None))
      assert_allclose(sc2[:2], sc1[:2])
  
      sc1_hc = score_test(
          res1,
          params_constrained=params_restr,
          k_constraints=1,
          r_matrix=r_matrix,
          cov_type="HC0",
      )
      sc2_hc = score_test(
          resr, exog_extra=(exog_extra, None), cov_type="HC0"
      )
      assert_allclose(sc2_hc[:2], sc1_hc[:2])
  
imports: import warnings
  import numpy
  from numpy.testing import assert_allclose, assert_equal
  import pytest
  from statsmodels.tools.tools import add_constant
  from statsmodels.base._prediction_inference import PredictionResultsMonotonic
  from statsmodels.discrete.discrete_model import BinaryModel, Logit, Probit, Poisson, NegativeBinomial, NegativeBinomialP, GeneralizedPoisson
  from statsmodels.discrete.count_model import ZeroInflatedPoisson, ZeroInflatedNegativeBinomialP, ZeroInflatedGeneralizedPoisson
  from statsmodels.sandbox.regression.tests.test_gmm_poisson import DATA
  from .results import results_predict
label_desc: Use image node_id 2 for calling the CheckExtras obj's underlying member method code with example usage: obj.test_score_test() without return types