code: def get_data_simulated():
      np.random.seed(987456348)
      nobs = 500
      x = np.ones((nobs, 1))
      yn = np.random.randn(nobs)
      y = 1 * (1.5 + yn) ** 2
      y = np.trunc(y + 0.5)
      return y, x
  
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 a global function with example usage: get_data_simulated() and returns: y,  x