code: def unpack_gmm_params(params):
      normalized_log_proportions = log_normalize(
          params["log proportions"]
      )
      return (
          normalized_log_proportions,
          params["means"],
          params["lower triangles"],
      )
  
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 3 for calling a global function with example usage: unpack_gmm_params(params) and returns: normalized_log_proportions,  params,  params