code: def test_batch_filter():
      f = KalmanFilter(dim_x=2, dim_z=1)
  
      f.x = np.array([2.0, 0])  # initial state (location and velocity)
  
      f.F = np.array(
          [[1.0, 1.0], [0.0, 1.0]]
      )  # state transition matrix
  
      f.H = np.array([[1.0, 0.0]])  # Measurement function
      f.P *= 1000.0  # covariance matrix
      f.R = 5  # state uncertainty
      f.Q = 0.0001  # process uncertainty
  
      zs = [None, 1.0, 2.0]
      m, c, _, _ = f.batch_filter(zs, update_first=False)
      m, c, _, _ = f.batch_filter(zs, update_first=True)
  
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 7 for calling a global function with example usage: test_batch_filter() without return types