code: def test_scan_gradients(self):
      with self.test_session() as sess:
          a = tf.Variable([1.0, 2.0, 3.0])
          op = tf.scan(lambda a, x: a + x, a)
          copy_op = ed.copy(op)
          gradient = tf.gradients(op, [a])[0]
          copy_gradient = tf.gradients(copy_op, [a])[0]
  
          tf.variables_initializer([a]).run()
          result_copy, result = sess.run([copy_gradient, gradient])
          self.assertAllClose(result, [3.0, 2.0, 1.0])
          self.assertAllClose(result_copy, [3.0, 2.0, 1.0])
  
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 Bernoulli, Categorical, Mixture, Normal
label_desc: Use image node_id 11 for calling the test_copy_class obj's underlying member method code with example usage: obj.test_scan_gradients() without return types