class_docstr: Borrowing the ``Policy`` class from the Reinforcement Learning example.
  Copying the code to make these two examples independent.
  See https://github.com/pytorch/examples/tree/master/reinforcement_learning
class_docstr_tok: ["Borrowing","the","``","Policy","``","class","from","the","Reinforcement","Learning","example",".","Copying","the","code","to","make","these","two","examples","independent",".","See","https",":","\/\/github.com\/pytorch\/examples\/tree\/master\/reinforcement_learning"]
code: def __init__(self):
      super().__init__()
      self.affine1 = nn.Linear(4, 128)
      self.dropout = nn.Dropout(p=0.6)
      self.affine2 = nn.Linear(128, 2)
  
      self.saved_log_probs = []
      self.rewards = []
  
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 Policy object from inherited base classes: nn with example: obj = Policy()