code: def testGetTrainStep(self, train_step):
      path = os.path.join(self.get_temp_dir(), "saved_policy")
      if train_step is None:
          # Use the default argument, which should set the train step to be -1.
          saver = policy_saver.PolicySaver(self.tf_policy)
          expected_train_step = -1
      else:
          saver = policy_saver.PolicySaver(
              self.tf_policy,
              train_step=common.create_variable(
                  "train_step", initial_value=train_step
              ),
          )
          expected_train_step = train_step
      saver.save(path)
  
      eager_py_policy = py_tf_eager_policy.SavedModelPyTFEagerPolicy(
          path, self.time_step_spec, self.action_spec
      )
  
      self.assertEqual(
          expected_train_step, eager_py_policy.get_train_step()
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tf_agents.agents.ddpg import actor_network
  from tf_agents.environments import batched_py_environment
  from tf_agents.environments import random_py_environment
  from tf_agents.policies import actor_policy
  from tf_agents.policies import policy_saver
  from tf_agents.policies import py_tf_eager_policy
  from tf_agents.policies import random_tf_policy
  from tf_agents.specs import array_spec
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import time_step
  from tf_agents.utils import common
  from tf_agents.utils import nest_utils
  from tf_agents.utils import test_utils
label_desc: Use image node_id 4 for calling the SavedModelPYTFEagerPolicyTest obj's underlying member method code with example usage: obj.testGetTrainStep(train_step) without return types