class_docstr: An observer has exclusive access to its own environment. Each observer
  captures the state from its environment, and send the state to the agent to
  select an action. Then, the observer applies the action to its environment
  and reports the reward to the agent.
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code: def __init__(self):
      self.id = rpc.get_worker_info().id
      self.env = DummyEnv()
      self.env.seed(SEED)
  
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 Observer object  with example: obj = Observer()