docstr: scan_discounted_sum should support static shape inference.
docstr_tok: ["scan_discounted_sum","should","support","static","shape","inference","."]
code: def testScanSumShapeInference(self):
      """scan_discounted_sum should support static shape inference."""
      # No session needed since we're not evaluating any ops.
      sequence_in = tf.placeholder(tf.float32, shape=[1647, 2001])
      decays_in = tf.placeholder(tf.float32, shape=[1647, 2001])
      bootstrap = tf.placeholder(tf.float32, shape=[2001])
      result = sequence_ops.scan_discounted_sum(
          sequence_in, decays_in, bootstrap, reverse=False
      )
      self.assertAllEqual(result.get_shape(), [1647, 2001])
  
      # Let's do it again with higher-dimensional inputs.
      sequence_in = tf.placeholder(
          tf.float32, shape=[4, 8, 15, 16, 23, 42]
      )
      decays_in = tf.placeholder(
          tf.float32, shape=[4, 8, 15, 16, 23, 42]
      )
      bootstrap = tf.placeholder(tf.float32, shape=[8, 15, 16, 23, 42])
      result = sequence_ops.scan_discounted_sum(
          sequence_in, decays_in, bootstrap, reverse=False
      )
      self.assertAllEqual(result.get_shape(), [4, 8, 15, 16, 23, 42])
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  from six.moves import xrange
  import tensorflow.compat.v1
  from trfl import sequence_ops
label_desc: Use image node_id 1 for calling the ScanDiscountedSumTest obj's underlying member method code with example usage: obj.testScanSumShapeInference() without return types