"forward" method of the module returned by the "module_cls".
  For example, if "module_cls" returns an instance of "nn.Linear",
  that has "forward" method signature: "def forward(input: Tensor)
  -> Tensor:", the generated "RemoteModule" will have 2 methods
  with the signatures:

     "def forward(input: Tensor) -> Tensor:"
     "def forward_async(input: Tensor) -> Future[Tensor]:"

Parameters:
      * remote_device (str) -- Device on the destination worker
        where we'd like to place this module. The format should be
        "/", where the device field can be parsed
        as torch.device type. E.g., "trainer0/cpu", "trainer0",
        "ps0/cuda:0". In addition, the device field can be optional
        and the default value is "cpu".
  * **module_cls** (*nn.Module*) --

    Class for the module to be created remotely. For example,

    >>> class MyModule(nn.Module):
    >>>     def forward(input):
