code: def test_pack_unpack(self):
      d = DNCMemory(
          gym.spaces.Discrete(1), gym.spaces.Discrete(1), 1, {}, ""
      )
      # Add batch dim
      packed_state = [m.unsqueeze(0) for m in d.get_initial_state()]
      [m.random_() for m in packed_state]
      original_packed = [m.clone() for m in packed_state]
  
      B, T = packed_state[0].shape[:2]
      unpacked = d.unpack_state(packed_state)
      packed = d.pack_state(*unpacked)
  
      self.assertTrue(len(packed) > 0)
      self.assertEqual(len(packed), len(original_packed))
  
      for m_idx in range(len(packed)):
          self.assertTrue(
              torch.all(packed[m_idx] == original_packed[m_idx])
          )
  
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 3 for calling the TestDNC obj's underlying member method code with example usage: obj.test_pack_unpack() without return types