code: def covariance_3d_plot_test():
      import matplotlib.pyplot as plt
      from filterpy.stats import plot_3d_covariance
  
      mu = [13456.3, 2320, 672.5]
  
      C = np.array(
          [[1.0, 0.03, 0.2], [0.03, 4.0, 0.0], [0.2, 0.0, 16.1]]
      )
  
      sample = np.random.multivariate_normal(mu, C, size=1000)
  
      fig = plt.gcf()
      ax = fig.add_subplot(111, projection="3d")
      ax.scatter(xs=sample[:, 0], ys=sample[:, 1], zs=sample[:, 2], s=1)
      plot_3d_covariance(mu, C, alpha=0.4, std=3, limit_xyz=True, ax=ax)
  
imports: from __future__ import division
  from math import exp
  import warnings
  import matplotlib.pyplot
  from mpl_toolkits.mplot3d import axes3d
  import numpy
  from numpy.linalg import inv
  import scipy
  from scipy.spatial.distance import mahalanobis
  from filterpy.stats import norm_cdf, multivariate_gaussian, logpdf, mahalanobis
  from scipy import linalg
label_desc: Use image node_id 10 for calling a global function with example usage: covariance_3d_plot_test() without return types