code: def test_update_correlated():
      f = const_vel_filter(1.0, x0=2, R_std=1.0, Q_var=5.1)
      f.M = np.array([[1], [0]])
  
      for i in range(10):
          f.predict()
          f.update_correlated(3.0)
  
imports: from __future__ import absolute_import, division, print_function
  import numpy.random
  from numpy.random import randn
  import numpy
  import matplotlib.pyplot
  from pytest import approx
  from filterpy.kalman import KalmanFilter, update, predict, batch_filter
  from filterpy.common import Q_discrete_white_noise, kinematic_kf, Saver
  from filterpy.stats import mahalanobis
  from scipy.linalg import block_diag, norm
label_desc: Use image node_id 18 for calling a global function with example usage: test_update_correlated() without return types