class_docstr: A dummy environment that implements the required subset of the OpenAI gym
  interface. It exists only to avoid a dependency on gym for running the
  tests in this file. It is designed to run for a set max number of iterations,
  returning random states and rewards at each step.
class_docstr_tok: ["A","dummy","environment","that","implements","the","required","subset","of","the","OpenAI","gym","interface",".","It","exists","only","to","avoid","a","dependency","on","gym","for","running","the","tests","in","this","file",".","It","is","designed","to","run","for","a","set","max","number","of","iterations",",","returning","random","states","and","rewards","at","each","step","."]
code: def reset(self):
      self.iter = 0
      return torch.randn(self.state_dim)
  
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 3 for calling the DummyEnv obj's underlying member method code with example usage: obj.reset() and returns: torch