code: def setUp(self):
      super(SavedModelPYTFEagerPolicyTest, self).setUp()
      if not common.has_eager_been_enabled():
          self.skipTest("Only supported in eager.")
  
      observation_spec = array_spec.ArraySpec([2], np.float32)
      self.action_spec = array_spec.BoundedArraySpec(
          [1], np.float32, 2, 3
      )
      self.time_step_spec = ts.time_step_spec(observation_spec)
  
      observation_tensor_spec = tensor_spec.from_spec(observation_spec)
      action_tensor_spec = tensor_spec.from_spec(self.action_spec)
      time_step_tensor_spec = tensor_spec.from_spec(self.time_step_spec)
  
      self._actor_net = actor_network.ActorNetwork(
          observation_tensor_spec,
          action_tensor_spec,
          fc_layer_params=(10,),
      )
  
      self.tf_policy = actor_policy.ActorPolicy(
          time_step_tensor_spec,
          action_tensor_spec,
          actor_network=self._actor_net,
      )
  
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 1 for calling the SavedModelPYTFEagerPolicyTest obj's underlying member method code with example usage: obj.setUp() without return types