code: def test_qlb():
      ocrl = OnlineCressieRead(alpha=0.05, tau=0.999)
  
      ws = np.random.RandomState(seed=42).exponential(size=10)
      rs = np.random.RandomState(seed=2112).random_sample(size=10)
      qlbs = []
  
      ws_rs = [
          (0.4692680899768591, 0.08779271803562538),
          (3.010121430917521, 0.1488852932982503),
          (1.3167456935454493, 0.5579699034039329),
          (0.9129425537759532, 0.6202896863254631),
          (0.16962487046234628, 0.7284609393916186),
          (0.16959629191460518, 0.10028857734263497),
          (0.059838768608680676, 0.5165390922355259),
          (2.0112308644799395, 0.4596272470443479),
          (0.9190821536272645, 0.4352012556681023),
          (1.2312500617045903, 0.40365563207132593),
      ]
  
      np.testing.assert_almost_equal(list(zip(ws, rs)), ws_rs, 5)
  
      for w, r in zip(ws, rs):
          ocrl.update(1, w, r)
          qlbs.append(ocrl.qlb(w, r))
  
      exp_qlbs = [
          1,
          0.13620517641052662,
          -0.17768396518176874,
          0.03202698335276157,
          0.20163624787093867,
          0.19427440609482105,
          0.22750472815940542,
          0.01392757858090217,
          0.10533233309112934,
          0.08141788541188416,
      ]
  
      np.testing.assert_almost_equal(qlbs, exp_qlbs, 5)
  
imports: from distributionally_robust_data import OnlineCressieRead
  from math import exp, pi
  import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: test_qlb() without return types