code: def testBatchedEnvironment(
      self, max_steps, max_episodes, expected_length
  ):
      expected_trajectories = [
          trajectory.Trajectory(
              step_type=np.array([0, 0]),
              observation=np.array([0, 0]),
              action=np.array([2, 1]),
              policy_info=np.array([4, 2]),
              next_step_type=np.array([1, 1]),
              reward=np.array([1.0, 1.0]),
              discount=np.array([1.0, 1.0]),
          ),
          trajectory.Trajectory(
              step_type=np.array([1, 1]),
              observation=np.array([2, 1]),
              action=np.array([1, 2]),
              policy_info=np.array([2, 4]),
              next_step_type=np.array([2, 1]),
              reward=np.array([1.0, 1.0]),
              discount=np.array([0.0, 1.0]),
          ),
          trajectory.Trajectory(
              step_type=np.array([2, 1]),
              observation=np.array([3, 3]),
              action=np.array([2, 1]),
              policy_info=np.array([4, 2]),
              next_step_type=np.array([0, 2]),
              reward=np.array([0.0, 1.0]),
              discount=np.array([1.0, 0.0]),
          ),
          trajectory.Trajectory(
              step_type=np.array([0, 2]),
              observation=np.array([0, 4]),
              action=np.array([2, 2]),
              policy_info=np.array([4, 4]),
              next_step_type=np.array([1, 0]),
              reward=np.array([1.0, 0.0]),
              discount=np.array([1.0, 1.0]),
          ),
      ]
  
      env1 = driver_test_utils.PyEnvironmentMock(final_state=3)
      env2 = driver_test_utils.PyEnvironmentMock(final_state=4)
      env = batched_py_environment.BatchedPyEnvironment([env1, env2])
  
      policy = driver_test_utils.PyPolicyMock(
          env.time_step_spec(),
          env.action_spec(),
          initial_policy_state=np.array([1, 2]),
      )
      replay_buffer_observer = MockReplayBufferObserver()
  
      driver = py_driver.PyDriver(
          env,
          policy,
          observers=[replay_buffer_observer],
          max_steps=max_steps,
          max_episodes=max_episodes,
      )
      initial_time_step = env.reset()
      initial_policy_state = policy.get_initial_state()
      driver.run(initial_time_step, initial_policy_state)
      trajectories = replay_buffer_observer.gather_all()
  
      self.assertEqual(
          len(trajectories),
          len(expected_trajectories[:expected_length]),
      )
  
      for t1, t2 in zip(
          trajectories, expected_trajectories[:expected_length]
      ):
          for t1_field, t2_field in zip(t1, t2):
              self.assertAllEqual(t1_field, t2_field)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tf_agents.drivers import py_driver
  from tf_agents.drivers import test_utils
  from tf_agents.environments import batched_py_environment
  from tf_agents.trajectories import trajectory
label_desc: Use image node_id 9 for calling the PyDriverTest obj's underlying member method code with example usage: obj.testBatchedEnvironment(max_steps, max_episodes, expected_length) without return types