code: def testActionWithSeed(self):
      if not common.has_eager_been_enabled():
          self.skipTest("Only supported in eager.")
  
      tf_policy = random_tf_policy.RandomTFPolicy(
          self._time_step_tensor_spec,
          self._action_tensor_spec,
          info_spec=self._info_tensor_spec,
      )
  
      py_policy = py_tf_eager_policy.PyTFEagerPolicy(tf_policy)
      time_step = self._env.reset()
      tf.random.set_seed(100)
      action_step_1 = py_policy.action(time_step, seed=100)
      time_step = self._env.reset()
      tf.random.set_seed(100)
      action_step_2 = py_policy.action(time_step, seed=100)
      time_step = self._env.reset()
      tf.random.set_seed(200)
      action_step_3 = py_policy.action(time_step, seed=200)
      self.assertEqual(action_step_1.action[0], action_step_2.action[0])
      self.assertNotEqual(
          action_step_1.action[0], action_step_3.action[0]
      )
  
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 PyTFEagerPolicyTest obj's underlying member method code with example usage: obj.testActionWithSeed() without return types