code: def __init__(
      self,
      config,
      max_num_policies,
      num_gpus_per_policy,
      replay_actor_class,
      replay_actor_args,
  ):
      # For now, remain in config dict-land (b/c we are dealing with Policy classes
      # here which do NOT use AlgorithmConfig yet).
      if isinstance(config, AlgorithmConfig):
          config = config.to_dict()
      self.config = config
      self.has_replay_buffer = False
      self.max_num_policies = max_num_policies
      self.num_gpus_per_policy = num_gpus_per_policy
      self.replay_actor_class = replay_actor_class
      self.replay_actor_args = replay_actor_args
  
      self.replay_actor: Optional[ActorHandle] = None
      self.policy_actors: Dict[str, ActorHandle] = {}
  
imports: import math
  from typing import Any, Dict, List, Optional, Type
  import ray
  from ray.actor import ActorHandle
  from ray.rllib.algorithms.algorithm import Algorithm
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig
  from ray.rllib.policy.policy import PolicySpec
  from ray.rllib.utils.actors import create_colocated_actors
  from ray.rllib.utils.tf_utils import get_tf_eager_cls_if_necessary
  from ray.rllib.utils.typing import AlgorithmConfigDict, PolicyID
label_desc: Use image node_id 1 to create a new _Shard object  with example: obj = _Shard(config, max_num_policies, num_gpus_per_policy, replay_actor_class, replay_actor_args)