code: def on_algorithm_init(self, *, algorithm, **kwargs):
      # Add a custom policy to algorithm.
      algorithm.add_policy(
          policy_id="test_policy",
          policy_cls=(
              PGTorchPolicy
              if algorithm.config.framework_str == "torch"
              else PGTF2Policy
          ),
          observation_space=gym.spaces.Box(low=0, high=1, shape=(8,)),
          action_space=gym.spaces.Discrete(2),
          config={},
          policy_state=None,
          evaluation_workers=True,
      )
  
imports: from collections import defaultdict
  import gymnasium
  import numpy
  import time
  import unittest
  import ray
  from ray.util.state import list_actors
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig
  from ray.rllib.algorithms.callbacks import DefaultCallbacks
  from ray.rllib.algorithms.dqn.dqn import DQNConfig
  from ray.rllib.algorithms.impala import ImpalaConfig
  from ray.rllib.algorithms.pg import PGConfig
  from ray.rllib.algorithms.pg.pg_tf_policy import PGTF2Policy
  from ray.rllib.algorithms.pg.pg_torch_policy import PGTorchPolicy
  from ray.rllib.algorithms.ppo import PPOConfig
  from ray.rllib.env.multi_agent_env import make_multi_agent
  from ray.rllib.evaluation.rollout_worker import RolloutWorker
  from ray.rllib.examples.env.random_env import RandomEnv
  from ray.rllib.utils.test_utils import framework_iterator
  from ray.tune.registry import register_env
label_desc: Use image node_id 2 for calling the AddPolicyCallback obj's underlying member method code with example usage: obj.on_algorithm_init() without return types