code: def simulate(self):
      endog, exog, group, time = [], [], [], []
  
      for i in range(self.ngroups):
          gsize = np.random.randint(
              self.group_size_range[0], self.group_size_range[1]
          )
  
          group.append(
              [
                  i,
              ]
              * gsize
          )
  
          time1 = np.random.normal(size=(gsize, 2))
          time.append(time1)
  
          exog1 = np.random.normal(size=(gsize, 5))
          exog1[:, 0] = 1
          exog.append(exog1)
  
          # Pairwise distances within the cluster
          distances = scipy.spatial.distance.cdist(
              time1, time1, self.distfun[0]
          )
  
          # Pairwise correlations within the cluster
          correlations = self.dep_params[0] ** distances
          correlations_sr = np.linalg.cholesky(correlations)
  
          errors = np.dot(correlations_sr, np.random.normal(size=gsize))
  
          endog1 = np.dot(exog1, self.params) + errors * self.error_sd
          endog.append(endog1)
  
      self.exog = np.concatenate(exog, axis=0)
      self.endog = np.concatenate(endog)
      self.time = np.concatenate(time, axis=0)
      self.group = np.concatenate(group)
  
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 AR_simulator obj's underlying member method code with example usage: obj.simulate() without return types