


def forward(input):
    >>>         return input + 1
    >>>
    >>> module_cls = MyModule




  * **args** (*Sequence**, **optional*) -- args to be passed to
    "module_cls".

  * **kwargs** (*Dict**, **optional*) -- kwargs to be passed to
    "module_cls".

Returns:
      A remote module instance which wraps the "Module" created by the
      user-provided "module_cls", it has a blocking "forward" method
      and an asynchronous "forward_async" method that returns a future
      of the "forward" call on the user-provided module on the remote
      side.
Example::
      Run the following code in two different processes:
  >>> # On worker 0:
  >>> import torch
  >>> import torch.distributed.rpc as rpc
  >>> from torch import nn, Tensor
  >>> from torch.distributed.nn.api.remote_module import RemoteModule
  >>>
  >>> rpc.init_rpc("worker0", rank=0, world_size=2)
  >>> remote_linear_module = RemoteModule(
