code: def init_gmm_params(num_components, D, scale, rs=npr.RandomState(0)):
      return {
          "log proportions": rs.randn(num_components) * scale,
          "means": rs.randn(num_components, D) * scale,
          "lower triangles": np.zeros((num_components, D, D))
          + np.eye(D),
      }
  
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 1 for calling a global function with example usage: init_gmm_params(num_components, D, scale, rs) and returns: dict