code: def main(_):
      x_data = np.array([0.0] * 50, dtype=np.float32)
  
      mu = Normal(loc=0.0, scale=1.0)
      x = Normal(loc=mu, scale=1.0, sample_shape=50)
  
      with tf.variable_scope("posterior"):
          qmu = PointMass(params=tf.Variable(1.0))
  
      inference = ed.MAP({mu: qmu}, data={x: x_data})
      inference.run(n_iter=10)
  
      sess = ed.get_session()
      saver = tf.train.Saver()
      saver.save(sess, "test_saver")
  
file_path: edward/tests/data/generate_test_saver.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import edward
  import numpy
  import tensorflow
  from edward.models import Normal, PointMass
label_desc: Use image node_id 1 for calling a global function with example usage: main(_) without return types