code: def test_traj_view_attention_functionality(self):
      action_space = Box(float("-inf"), float("inf"), shape=(3,))
      obs_space = Box(float("-inf"), float("inf"), (4,))
      max_seq_len = 50
      rollout_fragment_length = 201
      policies = {
          "pol0": (
              EpisodeEnvAwareAttentionPolicy,
              obs_space,
              action_space,
              None,
          ),
      }
  
      def policy_fn(agent_id, episode, worker, **kwargs):
          return "pol0"
  
      config = (
          ppo.PPOConfig()
          # The Policy used to be passed in, now we have to pass in the RLModuleSpecs
          .experimental(_enable_new_api_stack=False)
          .framework("torch")
          .multi_agent(policies=policies, policy_mapping_fn=policy_fn)
          .training(
              model={"max_seq_len": max_seq_len},
              train_batch_size=2010,
          )
          .rollouts(
              num_rollout_workers=0,
              rollout_fragment_length=rollout_fragment_length,
          )
          .environment(normalize_actions=False)
      )
  
      rollout_worker_w_api = RolloutWorker(
          env_creator=lambda _: MultiAgentDebugCounterEnv(
              {"num_agents": 4}
          ),
          config=config,
      )
      batch = rollout_worker_w_api.sample()
  
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 8 for calling the TestTrajectoryViewAPI obj's underlying member method code with example usage: obj.test_traj_view_attention_functionality() and returns: str