from each device.
Note:
 There is a subtlety in using the "pack sequence -> recurrent
 network -> unpack sequence" pattern in a "Module" wrapped in
 "DataParallel". See My recurrent network doesn't work with data
 parallelism section in FAQ for details.

Parameters:
      * module (Module) -- module to be parallelized
  * **device_ids** (*list of python:int** or **torch.device*) --
    CUDA devices (default: all devices)

  * **output_device** (*int** or **torch.device*) -- device
    location of output (default: device_ids[0])

Variables:
      module (Module) -- the module to be parallelized
Example:
  >>> net = torch.nn.DataParallel(model, device_ids=[0, 1, 2])
  >>> output = net(input_var)  # input_var can be on any device, including CPU
