


remote_linear_module = RemoteModule(
      >>>     "worker1/cpu", nn.Linear, args=(20, 30),
      >>> )
      >>> input = torch.randn(128, 20)
      >>> ret_fut = remote_linear_module.forward_async(input)
      >>> ret = ret_fut.wait()
      >>> rpc.shutdown()



  >>> # On worker 1:
  >>> import torch
  >>> import torch.distributed.rpc as rpc
  >>>
  >>> rpc.init_rpc("worker1", rank=1, world_size=2)
  >>> rpc.shutdown()

  Furthermore, a more practical example that is combined with
  DistributedDataParallel (DDP) can be found in this tutorial.

get_module_rref()
  Returns an "RRef" ("RRef[nn.Module]") pointing to the remote
  module.

  Return type:
     *RRef*[*Module*]

remote_parameters(recurse=True)
  Returns a list of "RRef" pointing to the remote module's
  parameters. This can typically be used in conjuction with
  "DistributedOptimizer".

  Parameters:
     **recurse** (*bool*) -- if True, then returns parameters of
