code: def build_toy_dataset(N):
      pi = np.array([0.4, 0.6])
      mus = [[1, 1], [-1, -1]]
      stds = [[0.1, 0.1], [0.1, 0.1]]
      x = np.zeros((N, 2))
      for n in range(N):
          k = np.argmax(np.random.multinomial(1, pi))
          x[n, :] = np.random.multivariate_normal(
              mus[k], np.diag(stds[k])
          )
  
      return x
  
file_path: edward/examples/mixture_gaussian_mh.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import edward
  import numpy
  import six
  import tensorflow
  from edward.models import Categorical, Dirichlet, Empirical, InverseGamma, Normal
  from scipy.stats import norm
label_desc: Use image node_id 1 for calling a global function with example usage: build_toy_dataset(N) and returns: x