code: def test_remote_training_loss(ray_start_2_cpus):
      net = ray.remote(TrainActor).remote()
      net_values = TrainActor().values
      loss, variables, _, sess, grads, train, placeholders = net_values
  
      before_acc = sess.run(
          loss,
          feed_dict=dict(zip(placeholders, [[2] * 100, [4] * 100])),
      )
  
      for _ in range(3):
          gradients_list = ray.get(
              [
                  net.training_step.remote(variables.get_weights())
                  for _ in range(2)
              ]
          )
          mean_grads = [
              sum(gradients[i] for gradients in gradients_list)
              / len(gradients_list)
              for i in range(len(gradients_list[0]))
          ]
          feed_dict = {
              grad[0]: mean_grad
              for (grad, mean_grad) in zip(grads, mean_grads)
          }
          sess.run(train, feed_dict=feed_dict)
      after_acc = sess.run(
          loss,
          feed_dict=dict(zip(placeholders, [[2] * 100, [4] * 100])),
      )
      assert before_acc < after_acc
  
imports: from numpy.testing import assert_almost_equal
  import ray
  import ray.experimental.tf_utils
  from ray.rllib.utils.framework import try_import_tf
label_desc: Use image node_id 10 for calling a global function with example usage: test_remote_training_loss(ray_start_2_cpus) without return types