between gradients and "allreduce" communication buckets. When
        gradients are views, "detach_()" cannot be called on the
        gradients. If hitting such errors, please fix it by referring
        to the "zero_grad()" function in "torch/optim/optimizer.py" as
        a solution. Note that gradients will be views after first
        iteration, so the peak memory saving should be checked after
        first iteration.
  * **static_graph** (*bool*) --

    When set to "True", DDP knows the trained graph is static.
    Static graph means 1) The set of used and unused parameters
    will not change during the whole training loop; in this case,
    it does not matter whether users set "find_unused_parameters =
    True" or not. 2) How the graph is trained will not change
    during the whole training loop (meaning there is no control
    flow depending on iterations). When static_graph is set to be
    "True", DDP will support cases that can not be supported in
