code: def dosim(hyp, cov_struct=None, mcrep=500):
      # Storage for the simulation results
      scales = [[], []]
  
      # P-values from the score test
      pv = []
  
      # Monte Carlo loop
      for k in range(mcrep):
          # Generate random "probability points" u  that are uniformly
          # distributed, and correlated within clusters
          z = np.random.normal(size=n)
          u = np.random.normal(size=n // m)
          u = np.kron(u, np.ones(m))
          z = r * z + np.sqrt(1 - r**2) * u
          u = norm.cdf(z)
  
          # Generate the observed responses
          y = negbinom(u, mu=mu[hyp], scale=scale)
  
          # Fit the null model
          m0 = sm.GEE(
              y,
              x0,
              groups=grp,
              cov_struct=cov_struct,
              family=sm.families.Poisson(),
          )
          r0 = m0.fit(scale="X2")
          scales[0].append(r0.scale)
  
          # Fit the alternative model
          m1 = sm.GEE(
              y,
              x,
              groups=grp,
              cov_struct=cov_struct,
              family=sm.families.Poisson(),
          )
          r1 = m1.fit(scale="X2")
          scales[1].append(r1.scale)
  
          # Carry out the score test
          st = m1.compare_score_test(r0)
          pv.append(st["p-value"])
  
      pv = np.asarray(pv)
      rslt = [np.mean(pv), np.mean(pv < 0.1)]
  
      return rslt, scales
  
imports: import pandas
  import numpy
  from scipy.stats.distributions import norm, poisson
  import statsmodels.api
  import matplotlib.pyplot
label_desc: Use image node_id 2 for calling a global function with example usage: dosim(hyp, cov_struct, mcrep) and returns: rslt,  scales