code: def plot_gaussian_mixture(params, ax):
      for log_proportion, mean, cov_sqrt in zip(
          *unpack_gmm_params(params)
      ):
          alpha = np.minimum(1.0, np.exp(log_proportion) * 10)
          plot_ellipse(ax, mean, cov_sqrt, alpha)
  
file_path: autograd/examples/gmm.py
imports: from __future__ import absolute_import
  from __future__ import print_function
  import matplotlib.pyplot
  import autograd.numpy
  import autograd.numpy.random
  from autograd import grad, hessian_vector_product
  from scipy.optimize import minimize
  from autograd.scipy.special import logsumexp
  import autograd.scipy.stats.multivariate_normal
  from autograd.misc.flatten import flatten_func
  from data import make_pinwheel
label_desc: Use image node_id 6 for calling a global function with example usage: plot_gaussian_mixture(params, ax) without return types