code: def dispersion_poisson(results):
      """Score/LM type tests for Poisson variance assumptions
  
      .. deprecated:: 0.14
  
         dispersion_poisson moved to discrete._diagnostic_count
  
      Null Hypothesis is
  
      H0: var(y) = E(y) and assuming E(y) is correctly specified
      H1: var(y) ~= E(y)
  
      The tests are based on the constrained model, i.e. the Poisson model.
      The tests differ in their assumed alternatives, and in their maintained
      assumptions.
  
      Parameters
      ----------
      results : Poisson results instance
          This can be a results instance for either a discrete Poisson or a GLM
          with family Poisson.
  
      Returns
      -------
      res : ndarray, shape (7, 2)
         each row contains the test statistic and p-value for one of the 7 tests
         computed here.
      description : 2-D list of strings
         Each test has two strings a descriptive name and a string for the
         alternative hypothesis.
      """
      msg = (
          "dispersion_poisson here is deprecated, use the version in "
          "discrete._diagnostic_count"
      )
      warnings.warn(msg, FutureWarning)
  
      from statsmodels.discrete._diagnostics_count import (
          test_poisson_dispersion,
      )
  
      return test_poisson_dispersion(results, _old=True)
  
imports: import warnings
  import numpy
  from scipy import stats
  from statsmodels.tools.decorators import cache_readonly
  from statsmodels.regression.linear_model import OLS
label_desc: Use image node_id 1 for calling a global function with example usage: dispersion_poisson(results) and returns: test_poisson_dispersion