code: def setup_class(cls):
      exog_infl = add_constant(DATA["aget"], prepend=False)
      mod_zinb = ZeroInflatedNegativeBinomialP(
          endog, exog, exog_infl=exog_infl, p=2
      )
  
      sp = np.array(
          [
              -6.58,
              -1.28,
              0.19,
              0.08,
              0.22,
              -0.05,
              0.03,
              0.17,
              0.27,
              0.68,
              0.62,
          ]
      )
      # using newton. bfgs has non-invertivle hessian at convergence
      # start_params not needed, but speed up
      res1 = mod_zinb.fit(start_params=sp, method="newton", maxiter=300)
      cls.res1 = res1
      cls.res2 = resp.results_zinb_docvis
      cls.pred_kwds_mean = {"exog_infl": exog_infl.mean(0)}
      cls.pred_kwds_6 = {"exog_infl": exog_infl[:6]}
      cls.k_infl = 2
      cls.rtol = 1e-4
  
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 1 for calling the TestZINegativeBinomialPPredict obj's underlying member method code with example usage: obj.setup_class(cls) without return types