code: def test_traj_view_attention_net(self):
      config = (
          ppo.PPOConfig()
          # Batch-norm models have not been migrated to the RL Module API yet.
          .experimental(_enable_new_api_stack=False)
          .environment(
              "ray.rllib.examples.env.debug_counter_env.DebugCounterEnv",
              env_config={
                  "config": {"start_at_t": 1}
              },  # first obs is [1.0]
          )
          .rollouts(num_rollout_workers=0)
          .callbacks(MyCallbacks)
          # Setup attention net.
          .training(
              model={
                  "custom_model": GTrXLNet,
                  "custom_model_config": {
                      "num_transformer_units": 1,
                      "attention_dim": 64,
                      "num_heads": 2,
                      "memory_inference": 50,
                      "memory_training": 50,
                      "head_dim": 32,
                      "ff_hidden_dim": 32,
                  },
                  "max_seq_len": 50,
              },
              # Test with odd batch numbers.
              train_batch_size=1031,
              sgd_minibatch_size=201,
              num_sgd_iter=5,
          )
      )
  
      for _ in framework_iterator(config, frameworks="tf2"):
          algo = config.build()
          rw = algo.workers.local_worker()
          sample = rw.sample()
          assert (
              sample.count == algo.config.get_rollout_fragment_length()
          )
          results = algo.train()
          assert (
              results["timesteps_total"] == config["train_batch_size"]
          )
          algo.stop()
  
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 5 for calling the TestTrajectoryViewAPI obj's underlying member method code with example usage: obj.test_traj_view_attention_net() without return types