code: def replicate(self, T=10, num_reps=100, random_state=None):
          r
          random_state = check_random_state(random_state)
  
          x = np.empty((self.n, num_reps))
          for j in range(num_reps):
              x_T, _ = self.simulate(ts_length=T+1, random_state=random_state)
              x[:, j] = x_T[:, -1]
          if self.H is not None:
              v = random_state.randn(self.l, num_reps)
              y = self.G.dot(x) + self.H.dot(v)
          else:
              y = self.G.dot(x)
  
          return x, y
docs: r"""
          Simulate num_reps observations of :math:`x_T` and :math:`y_T` given
          :math:`x_0 \sim N(\mu_0, \Sigma_0)`.
  
          Parameters
          ----------
          T : scalar(int), optional(default=10)
              The period that we want to replicate values for
          num_reps : scalar(int), optional(default=100)
              The number of replications that we want
          random_state : int or np.random.RandomState, optional
              Random seed (integer) or np.random.RandomState instance to set
              the initial state of the random number generator for
              reproducibility. If None, a randomly initialized RandomState is
              used.
  
          Returns
          -------
          x : array_like(float)
              An n x num_reps array, where the j-th column is the j_th
              observation of :math:`x_T`
  
          y : array_like(float)
              A k x num_reps array, where the j-th column is the j_th
              observation of :math:`y_T`