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(self._default_loss, dtype=tf.float32),
                  tf.cast(loss, dtype=tf.float32),
                  name="assert_loss",
              )
              return tf.constant(expected_train_result)
  
          head = ranking_head.create_ranking_head(
              loss_fn=_make_loss_fn(), train_op_fn=_train_op_fn
          )
          # Create estimator spec.
          spec = head.create_estimator_spec(
              features={},
              mode=tf_estimator.ModeKeys.TRAIN,
              logits=self._default_logits,
              labels=self._default_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(self._default_loss, loss)
              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 7 for calling the RankingHeadTest obj's underlying member method code with example usage: obj.test_train() and returns: tf