code: def test_ppo_attention_net_learning(self):
      ModelCatalog.register_custom_model("attention_net", GTrXLNet)
      config = dict(
          self.config,
          **{
              "num_workers": 0,
              "entropy_coeff": 0.001,
              "vf_loss_coeff": 1e-5,
              "num_sgd_iter": 5,
              "model": {
                  "custom_model": "attention_net",
                  "max_seq_len": 10,
                  "custom_model_config": {
                      "num_transformer_units": 1,
                      "attention_dim": 32,
                      "num_heads": 1,
                      "memory_inference": 5,
                      "memory_training": 5,
                      "head_dim": 32,
                      "position_wise_mlp_dim": 32,
                  },
              },
          },
      )
      tune.Tuner(
          "PPO",
          param_space=config,
          run_config=air.RunConfig(stop=self.stop, verbose=1),
      ).fit()
  
file_path: ray/python/ray/rllib/models/tests/test_attention_nets.py
imports: from gymnasium.spaces import Box, Dict, Discrete, MultiDiscrete, Tuple
  import unittest
  import ray
  from ray import air
  from ray import tune
  from ray.rllib.examples.env.random_env import RandomEnv
  from ray.rllib.examples.env.stateless_cartpole import StatelessCartPole
  from ray.rllib.models.catalog import ModelCatalog
  from ray.rllib.models.tf.attention_net import GTrXLNet
  from ray.rllib.utils.test_utils import framework_iterator
label_desc: Use image node_id 4 for calling the TestAttentionNets obj's underlying member method code with example usage: obj.test_ppo_attention_net_learning() without return types