code: def test_traj_view_lstm_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 = 200
      assert rollout_fragment_length % max_seq_len == 0
      policies = {
          "pol0": (StatefulRandomPolicy, obs_space, action_space, None),
      }
  
      def policy_fn(agent_id, episode, worker, **kwargs):
          return "pol0"
  
      rw = RolloutWorker(
          env_creator=lambda _: MultiAgentDebugCounterEnv(
              {"num_agents": 4}
          ),
          config=ppo.PPOConfig()
          .experimental(_enable_new_api_stack=True)
          .framework("torch")
          .rollouts(
              rollout_fragment_length=rollout_fragment_length,
              num_rollout_workers=0,
          )
          .multi_agent(
              policies=policies,
              policy_mapping_fn=policy_fn,
          )
          .environment(normalize_actions=False)
          .training(
              model={
                  "use_lstm": True,
                  "max_seq_len": max_seq_len,
              }
          ),
      )
  
      for iteration in range(20):
          result = rw.sample()
          check(result.count, rollout_fragment_length)
          pol_batch_w = result.policy_batches["pol0"]
          assert pol_batch_w.count >= rollout_fragment_length
          analyze_rnn_batch_rlm(
              pol_batch_w,
              max_seq_len,
              view_requirements=rw.policy_map["pol0"].view_requirements,
          )
  
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 7 for calling the TestTrajectoryViewAPI obj's underlying member method code with example usage: obj.test_traj_view_lstm_functionality() and returns: str