


inputs = [torch.tensor([1]).float() for _ in range(10 + rank)]
     >>>     with model.join():
     >>>         for _ in range(5):
     >>>             for inp in inputs:
     >>>                 loss = model(inp).sum()
     >>>                 loss.backward()
     >>>     # Without the join() API, the below synchronization will hang
     >>>     # blocking for rank 1's allreduce to complete.
     >>>     torch.cuda.synchronize(device=rank)




join_hook(**kwargs)
  Returns the DDP join hook, which enables training on uneven
  inputs by shadowing the collective communications in the forward
  and backward passes.

  Parameters:
     **kwargs** (*dict*) -- a "dict" containing any keyword
     arguments to modify the behavior of the join hook at run
     time; all "Joinable" instances sharing the same join context
     manager are forwarded the same value for "kwargs".

  The hook supports the following keyword arguments:
