code: def test_predict_linear(self):
      res1 = self.res1
      ex = np.asarray(exog[:5])
      pred = res1.get_prediction(
          ex, which="linear", **self.pred_kwds_mean
      )
      k_extra = len(res1.params) - ex.shape[1]
      if k_extra > 0:
          # not zero-inflated models have params_infl first
          ex = np.column_stack((ex, np.zeros((ex.shape[0], k_extra))))
      tt = res1.t_test(ex)
      cip = pred.conf_int()  # assumes no offset
      cit = tt.conf_int()
      assert_allclose(cip, cit, rtol=1e-12)
  
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 CheckExtras obj's underlying member method code with example usage: obj.test_predict_linear() without return types