code: def simulate(self, x0, x1, nobs, nrepl=1, ddt=1.0, sigma=1.0):
      nobs = nobs + 1
      dt = ddt * 1.0 / nobs
      t = np.linspace(dt, ddt - dt, nobs)
      t = np.linspace(dt, ddt, nobs)
      wm = [t / ddt, 1 - t / ddt]
      # wmi = wm[1]
      # wm1 = x1*wm[0]
      wmi = 1 - dt / (ddt - t)
      wm1 = x1 * (dt / (ddt - t))
      su = sigma * np.sqrt(t * (1 - t) / ddt)
      s = sigma * np.sqrt(dt * (ddt - t - dt) / (ddt - t))
      x = np.zeros((nrepl, nobs))
      x[:, 0] = x0
      rvs = s * np.random.normal(size=(nrepl, nobs))
      for i in range(1, nobs):
          x[:, i] = x[:, i - 1] * wmi[i] + wm1[i] + rvs[:, i]
      return x, t, su
  
imports: import numpy
  from scipy import stats, signal
  import matplotlib.pyplot
label_desc: Use image node_id 2 for calling the BrownianBridge obj's underlying member method code with example usage: obj.simulate(x0, x1, nobs, nrepl, ddt, sigma) and returns: x,  t,  su