code: def test_predictions():
      s = 0.0
  
      for k in range(3, 22, 2):  # different kernel sizes
          for _ in range(1000):
              a = randn(100)
              kernel = normalize(randn(k))
              move = randint(1, 200)
              s += sum(
                  predict(a, move, kernel) - _predict(a, move, kernel)
              )
  
          assert s < 1.0e-8, "sum of difference = {}".format(s)
  
imports: from filterpy.discrete_bayes import predict, update, normalize
  from numpy.random import randn, randint
  import numpy
label_desc: Use image node_id 2 for calling a global function with example usage: test_predictions() without return types