code: def __init__(self, world_size):
      self.ob_rrefs = []
      self.agent_rref = RRef(self)
      self.rewards = {}
      self.saved_log_probs = {}
      self.policy = Policy()
      self.optimizer = optim.Adam(self.policy.parameters(), lr=1e-2)
      self.eps = np.finfo(np.float32).eps.item()
      self.running_reward = 0
      self.reward_threshold = DummyEnv().reward_threshold
      for ob_rank in range(1, world_size):
          ob_info = rpc.get_worker_info(worker_name(ob_rank))
          self.ob_rrefs.append(remote(ob_info, Observer))
          self.rewards[ob_info.id] = []
          self.saved_log_probs[ob_info.id] = []
  
imports: import numpy
  from itertools import count
  import torch
  import torch.distributed.rpc
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from torch.distributed.rpc import RRef, rpc_sync, rpc_async, remote
  from torch.distributions import Categorical
  from torch.testing._internal.dist_utils import dist_init, worker_name
  from torch.testing._internal.distributed.rpc.rpc_agent_test_fixture import RpcAgentTestFixture
label_desc: Use image node_id 1 to create a new Agent object  with example: obj = Agent(world_size)