"torch.distributed" "ProcessGroup" (default:
        "dist.group.WORLD" initialized by
        "torch.distributed.init_process_group()").
  * **parameters_as_bucket_view** (*bool**, **optional*) -- if
    "True", parameters are packed into buckets to speed up
    communication, and "param.data" fields point to bucket views
    at different offsets; if "False", each individual parameter is
    communicated separately, and each "params.data" stays intact
    (default: "False").

  * **overlap_with_ddp** (*bool**, **optional*) -- if "True",
    "step()" is overlapped with "DistributedDataParallel" 's
    gradient synchronization; this requires (1) either a
    functional optimizer for the "optimizer_class" argument or one
    with a functional equivalent and (2) registering a DDP
    communication hook constructed from one of the functions in
    "ddp_zero_hook.py"; parameters are packed into buckets
