code: def test_traj_view_next_action(self):
      action_space = Discrete(2)
      config = (
          ppo.PPOConfig()
          .experimental(_enable_new_api_stack=True)
          .framework("torch")
          .rollouts(rollout_fragment_length=200, num_rollout_workers=0)
      )
      config.validate()
      rollout_worker_w_api = RolloutWorker(
          env_creator=lambda _: gym.make("CartPole-v1"),
          default_policy_class=ppo.PPOTorchPolicy,
          config=config,
      )
      # Add the next action (a') and 2nd next action (a'') to the view
      # requirements of the policy.
      # This should be visible then in postprocessing and train batches.
      # Switch off for action computations (can't be there as we don't know
      # the next actions already at action computation time).
      rollout_worker_w_api.policy_map[
          DEFAULT_POLICY_ID
      ].view_requirements["next_actions"] = ViewRequirement(
          SampleBatch.ACTIONS,
          shift=1,
          space=action_space,
          used_for_compute_actions=False,
      )
      rollout_worker_w_api.policy_map[
          DEFAULT_POLICY_ID
      ].view_requirements["2nd_next_actions"] = ViewRequirement(
          SampleBatch.ACTIONS,
          shift=2,
          space=action_space,
          used_for_compute_actions=False,
      )
  
      # Make sure, we have TERMINATEDSs as well.
      rollout_worker_w_api.policy_map[
          DEFAULT_POLICY_ID
      ].view_requirements[SampleBatch.TERMINATEDS] = ViewRequirement()
      batch = convert_ma_batch_to_sample_batch(
          rollout_worker_w_api.sample()
      )
      self.assertTrue("next_actions" in batch)
      self.assertTrue("2nd_next_actions" in batch)
      expected_a_ = None  # expected next action
      expected_a__ = None  # expected 2nd next action
      for i in range(len(batch[SampleBatch.ACTIONS])):
          a, d, a_, a__ = (
              batch[SampleBatch.ACTIONS][i],
              batch[SampleBatch.TERMINATEDS][i],
              batch["next_actions"][i],
              batch["2nd_next_actions"][i],
          )
          # Episode done: next action and 2nd next action should be 0.
          if d:
              check(a_, 0)
              check(a__, 0)
              expected_a_ = None
              expected_a__ = None
              continue
          # Episode is not done and we have an expected next-a.
          if expected_a_ is not None:
              check(a, expected_a_)
          if expected_a__ is not None:
              check(a_, expected_a__)
          expected_a__ = a__
          expected_a_ = a_
  
imports: import gymnasium
  from gymnasium.spaces import Box, Discrete
  import numpy
  import unittest
  import ray
  from ray.rllib.algorithms.callbacks import DefaultCallbacks
  import ray.rllib.algorithms.dqn
  import ray.rllib.algorithms.ppo
  from ray.rllib.examples.env.debug_counter_env import MultiAgentDebugCounterEnv
  from ray.rllib.examples.env.multi_agent import MultiAgentPendulum
  from ray.rllib.evaluation.rollout_worker import RolloutWorker
  from ray.rllib.examples.policy.episode_env_aware_policy import EpisodeEnvAwareAttentionPolicy, StatefulRandomPolicy
  from ray.rllib.models.tf.attention_net import GTrXLNet
  from ray.rllib.policy.rnn_sequencing import pad_batch_to_sequences_of_same_size
  from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID, SampleBatch, convert_ma_batch_to_sample_batch
  from ray.rllib.policy.view_requirement import ViewRequirement
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.test_utils import framework_iterator, check
label_desc: Use image node_id 6 for calling the TestTrajectoryViewAPI obj's underlying member method code with example usage: obj.test_traj_view_next_action() without return types