code: def setup_class(cls):
      cls.expected_params = [1, 0.5, 0.5]
      np.random.seed(1234)
      nobs = 200
      exog = np.ones((nobs, 2))
      exog[: nobs // 2, 1] = 2
      mu_true = np.exp(exog.dot(cls.expected_params[:-1]))
      cls.endog = truncatednegbin.rvs(
          mu_true, cls.expected_params[-1], 2, 0, size=mu_true.shape
      )
      model = TruncatedLFNegativeBinomialP(
          cls.endog, exog, truncation=0, p=2
      )
      cls.res = model.fit(method="nm", maxiter=5000, maxfun=5000)
  
imports: import warnings
  import numpy
  from numpy.testing import assert_allclose, assert_equal
  from statsmodels import datasets
  from statsmodels.tools.tools import add_constant
  from statsmodels.tools.testing import Holder
  from statsmodels.tools.sm_exceptions import ConvergenceWarning
  from statsmodels.distributions.discrete import truncatedpoisson, truncatednegbin
  from statsmodels.discrete.truncated_model import TruncatedLFPoisson, TruncatedLFNegativeBinomialP, HurdleCountModel
  from statsmodels.sandbox.regression.tests.test_gmm_poisson import DATA
  from .results.results_discrete import RandHIE
  from .results import results_truncated
  from .results import results_truncated_st
label_desc: Use image node_id 1 for calling the TestTruncatedNBP_predict obj's underlying member method code with example usage: obj.setup_class(cls) without return types