code: def testGradVtm1(self):
      with self.test_session() as sess:
          # Take gradients of the negative loss, so that the tests here check the
          # values propagated during gradient _descent_, rather than _ascent_.
          gradients = tf.gradients(
              [-self.tdlearning.loss], [self.logits_v_tm1]
          )
          grad_v_tm1 = sess.run(gradients[0])
          # Correct gradient directions.
          expected = np.array(
              [
                  [-1, -1, 1],
                  [-1, 1, -1],
                  [-1, -1, 1],
                  [-1, 1, -1],
                  [-1, 1, -1],
              ]
          )
          self.assertAllClose(np.sign(grad_v_tm1), expected)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  import tensorflow.compat.v1
  from trfl import dist_value_ops
label_desc: Use image node_id 6 for calling the CategoricalDistTDLearningTest obj's underlying member method code with example usage: obj.testGradVtm1() without return types