code: def testUpdateFromCheckpoint(
      self, checkpoint_name, expected_train_step
  ):
      if not common.has_eager_been_enabled():
          self.skipTest("Only supported in TF2.x.")
  
      path = os.path.join(self.get_temp_dir(), "saved_policy")
      saver = policy_saver.PolicySaver(self.tf_policy)
      saver.save(path)
      self.evaluate(
          tf.nest.map_structure(
              lambda v: v.assign(v * 0 + -1), self.tf_policy.variables()
          )
      )
      checkpoint_path = os.path.join(
          self.get_temp_dir(), checkpoint_name
      )
      saver.save_checkpoint(checkpoint_path)
  
      eager_py_policy = py_tf_eager_policy.SavedModelPyTFEagerPolicy(
          path, self.time_step_spec, self.action_spec
      )
  
      # Use evaluate to force a copy.
      saved_model_variables = self.evaluate(eager_py_policy.variables())
  
      eager_py_policy.update_from_checkpoint(checkpoint_path)
      self.assertEqual(
          expected_train_step,
          eager_py_policy.get_train_step_from_last_restored_checkpoint_path(),
      )
  
      assert_np_not_equal = lambda a, b: self.assertFalse(
          np.equal(a, b).all()
      )
      tf.nest.map_structure(
          assert_np_not_equal,
          saved_model_variables,
          self.evaluate(eager_py_policy.variables()),
      )
  
      assert_np_all_equal = lambda a, b: self.assertTrue(
          np.equal(a, b).all()
      )
      tf.nest.map_structure(
          assert_np_all_equal,
          self.evaluate(self.tf_policy.variables()),
          self.evaluate(eager_py_policy.variables()),
          check_types=False,
      )
  
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 5 for calling the SavedModelPYTFEagerPolicyTest obj's underlying member method code with example usage: obj.testUpdateFromCheckpoint(checkpoint_name, expected_train_step) without return types