code: def test_dnc_learning(self):
      ModelCatalog.register_custom_model("dnc", DNCMemory)
  
      config = (
          PPOConfig()
          .environment(StatelessCartPole)
          .framework("torch")
          .rollouts(num_envs_per_worker=5, num_rollout_workers=1)
          .training(
              gamma=0.99,
              lr=0.01,
              entropy_coeff=0.0005,
              vf_loss_coeff=1e-5,
              model={
                  "custom_model": "dnc",
                  "max_seq_len": 64,
                  "custom_model_config": {
                      "nr_cells": 10,
                      "cell_size": 8,
                  },
              },
          )
          .resources(num_cpus_per_worker=2.0)
      )
  
      tune.Tuner(
          "PPO",
          param_space=config,
          run_config=air.RunConfig(stop=self.stop, verbose=1),
      ).fit()
  
file_path: ray/python/ray/rllib/tests/test_dnc.py
imports: import gymnasium
  import unittest
  import ray
  from ray import air
  from ray import tune
  from ray.rllib.algorithms.ppo import PPOConfig
  from ray.rllib.examples.env.stateless_cartpole import StatelessCartPole
  from ray.rllib.models.catalog import ModelCatalog
  from ray.rllib.examples.models.neural_computer import DNCMemory
  from ray.rllib.utils.framework import try_import_torch
label_desc: Use image node_id 4 for calling the TestDNC obj's underlying member method code with example usage: obj.test_dnc_learning() without return types