code: def init_gaussian_var_params(
      D, mean_mean=-1, log_std_mean=-5, scale=0.1, rs=npr.RandomState(0)
  ):
      init_mean = mean_mean * np.ones(D) + rs.randn(D) * scale
      init_log_std = log_std_mean * np.ones(D) + rs.randn(D) * scale
      return np.concatenate([init_mean, init_log_std])
  
file_path: autograd/examples/mixture_variational_inference.py
imports: from __future__ import absolute_import
  from __future__ import print_function
  import matplotlib.pyplot
  import autograd.numpy
  import autograd.numpy.random
  import autograd.scipy.stats.norm
  from autograd.scipy.special import logsumexp
  from autograd import grad
  from autograd.misc.optimizers import adam
label_desc: Use image node_id 6 for calling a global function with example usage: init_gaussian_var_params(D, mean_mean, log_std_mean, scale, rs) and returns: np