code: def setUp(self):
      super(PyTFEagerPolicyTest, self).setUp()
      self._observation_spec = array_spec.ArraySpec([2], np.float32)
      self._action_spec = array_spec.BoundedArraySpec(
          [1], np.float32, 2, 3
      )
      self._observation_tensor_spec = tensor_spec.from_spec(
          self._observation_spec
      )
      self._action_tensor_spec = tensor_spec.from_spec(
          self._action_spec
      )
      self._time_step_tensor_spec = ts.time_step_spec(
          self._observation_tensor_spec
      )
      info_spec = {
          "a": array_spec.BoundedArraySpec([1], np.float32, 0, 1),
          "b": array_spec.BoundedArraySpec([1], np.float32, 100, 101),
      }
      self._info_tensor_spec = tensor_spec.from_spec(info_spec)
      # Env will validate action types automaticall since we provided the
      # action_spec.
      self._env = random_py_environment.RandomPyEnvironment(
          self._observation_spec, self._action_spec
      )
  
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 PyTFEagerPolicyTest obj's underlying member method code with example usage: obj.setUp() without return types