code: def simulate(self):
      group_effect_var = self.dep_params[0]
  
      vcomp = self.dep_params[1:]
      vcomp.append(0)
  
      endog, exog, group, id_matrix = [], [], [], []
  
      for i in range(self.ngroups):
          iterators = [lrange(n) for n in self.nest_sizes]
  
          # The random effects
          variances = [
              np.sqrt(v) * np.random.normal(size=n)
              for v, n in zip(vcomp, self.nest_sizes)
          ]
  
          gpe = np.random.normal() * np.sqrt(group_effect_var)
  
          nest_all = []
          for j in self.nest_sizes:
              nest_all.append(set())
  
          for nest in product(*iterators):
              group.append(i)
  
              # The sum of all random effects that apply to this
              # unit
              ref = gpe + sum([v[j] for v, j in zip(variances, nest)])
  
              exog1 = np.random.normal(size=5)
              exog1[0] = 1
              exog.append(exog1)
  
              error = ref + self.error_sd * np.random.normal()
  
              endog1 = np.dot(exog1, self.params) + error
              endog.append(endog1)
  
              for j in range(len(nest)):
                  nest_all[j].add(tuple(nest[0 : j + 1]))
  
              nest1 = [len(x) - 1 for x in nest_all]
              id_matrix.append(nest1[0:-1])
  
      self.exog = np.array(exog)
      self.endog = np.array(endog)
      self.group = np.array(group)
      self.id_matrix = np.array(id_matrix)
      self.time = np.zeros_like(self.endog)
  
imports: from statsmodels.compat.python import lrange
  import scipy
  import numpy
  from itertools import product
  from statsmodels.genmod.families import Gaussian
  from statsmodels.genmod.generalized_estimating_equations import GEE
  from statsmodels.genmod.cov_struct import Autoregressive, Nested
label_desc: Use image node_id 2 for calling the Nested_simulator obj's underlying member method code with example usage: obj.simulate() without return types