code: def main(_):
      ed.set_seed(42)
  
      # Prior on scalar hyperparameter to Dirichlet.
      alpha = Gamma(1.0, 1.0)
  
      # Prior on size of Dirichlet.
      n = 1 + tf.cast(Exponential(0.5), tf.int32)
  
      # Build a vector of ones whose size is n; multiply it by alpha.
      p = Dirichlet(tf.ones([n]) * alpha)
  
      sess = ed.get_session()
      print(sess.run(p))
      # [ 0.01012419  0.02939712  0.05036638  0.51287931  0.31020424  0.0485355
      #   0.0384932 ]
      print(sess.run(p))
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import edward
  import tensorflow
  from edward.models import Exponential, Dirichlet, Gamma
label_desc: Use image node_id 1 for calling a global function with example usage: main(_) without return types