code: def setup(self, config):
      # Call super's `setup` to create rollout workers.
      super().setup(config)
      # Create local replay buffer.
      self.local_replay_buffer = MultiAgentReplayBuffer(
          num_shards=1, capacity=50000
      )
  
imports: import argparse
  import os
  import ray
  from ray import air, tune
  from ray.rllib.algorithms.algorithm import Algorithm
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig
  from ray.rllib.algorithms.dqn.dqn import DQNConfig
  from ray.rllib.algorithms.dqn.dqn_tf_policy import DQNTFPolicy
  from ray.rllib.algorithms.dqn.dqn_torch_policy import DQNTorchPolicy
  from ray.rllib.algorithms.ppo.ppo import PPOConfig
  from ray.rllib.algorithms.ppo.ppo_tf_policy import PPOTF1Policy
  from ray.rllib.algorithms.ppo.ppo_torch_policy import PPOTorchPolicy
  from ray.rllib.evaluation.postprocessing import Postprocessing
  from ray.rllib.execution.rollout_ops import synchronous_parallel_sample
  from ray.rllib.execution.train_ops import train_one_step
  from ray.rllib.utils.replay_buffers.multi_agent_replay_buffer import MultiAgentReplayBuffer
  from ray.rllib.examples.env.multi_agent import MultiAgentCartPole
  from ray.rllib.policy.sample_batch import MultiAgentBatch, concat_samples
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.metrics import NUM_AGENT_STEPS_SAMPLED, NUM_ENV_STEPS_SAMPLED, NUM_TARGET_UPDATES, LAST_TARGET_UPDATE_TS
  from ray.rllib.utils.sgd import standardized
  from ray.rllib.utils.test_utils import check_learning_achieved
  from ray.rllib.utils.typing import ResultDict
  from ray.tune.registry import register_env
label_desc: Use image node_id 1 for calling the MyAlgo obj's underlying member method code with example usage: obj.setup(config) without return types