code: def test_train(self):
      with tf.Graph().as_default():
          expected_train_result = b"my_train_op"
  
          def _train_op_fn(loss):
              tf.debugging.assert_near(
                  tf.cast(loss, dtype=tf.float32),
                  16.0,
                  name="assert_loss",
              )
              return tf.constant(expected_train_result)
  
          head1 = ranking_head.create_ranking_head(
              loss_fn=_make_loss_fn(),
              train_op_fn=_train_op_fn,
              name="head1",
          )
          head2 = ranking_head.create_ranking_head(
              loss_fn=_make_loss_fn(),
              train_op_fn=_train_op_fn,
              name="head2",
          )
          multi_head = ranking_head.create_multi_ranking_head(
              [head1, head2], [1.0, 2.0]
          )
  
          logits = {
              "head1": tf.convert_to_tensor(
                  value=[[1.0, 3.0], [1.0, 2.0]]
              ),
              "head2": tf.convert_to_tensor(
                  value=[[2.0, 3.0], [2.0, 2.0]]
              ),
          }
          labels = {
              "head1": tf.convert_to_tensor(
                  value=[[0.0, 1.0], [0.0, 2.0]]
              ),
              "head2": tf.convert_to_tensor(
                  value=[[0.0, 1.0], [0.0, 2.0]]
              ),
          }
          # Create estimator spec.
          spec = multi_head.create_estimator_spec(
              features={},
              mode=tf_estimator.ModeKeys.TRAIN,
              logits=logits,
              labels=labels,
          )
  
          # Assert spec contains expected tensors.
          self.assertIsNotNone(spec.loss)
          self.assertEqual(spec.eval_metric_ops, {})
          self.assertIsNotNone(spec.train_op)
          self.assertIsNone(spec.export_outputs)
  
          # Assert predictions, loss, and train_op.
          with self.cached_session() as sess:
              _initialize_variables(self, spec.scaffold)
              loss, train_result = sess.run((spec.loss, spec.train_op))
              self.assertAllClose(loss, 16.0)
              self.assertEqual(expected_train_result, train_result)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  from tensorflow import estimator
  from tensorflow_ranking.python import head
  from tensorflow_ranking.python import metrics
label_desc: Use image node_id 4 for calling the MultiRankingHeadTest obj's underlying member method code with example usage: obj.test_train() and returns: tf