code: def run(func):
      """Decorator used to run the elastic training process.
  
      The purpose of this decorator is to allow for uninterrupted execution of the wrapped function
      across multiple workers in parallel, as workers come and go from the system. When a new worker is added,
      its state needs to be brought to the same point as the other workers, which is done by synchronizing
      the state object before executing `func`.
  
      When a worker is added or removed, other workers will raise an exception to bring them back to such a sync
      point before executing `func` again. This ensures that workers do not diverge when such reset events occur.
  
      It's important to note that collective operations (e.g., broadcast, allreduce) cannot be the call to
      the wrapped function. Otherwise, new workers could execute these operations during their initialization
      while other workers are attempting to sync state, resulting in deadlock.
  
      Args:
          func: a wrapped function taking any number of args or kwargs. The first argument
                must be a `horovod.common.elastic.State` object used to synchronize state across
                workers.
      """
      return run_fn(func, _reset)
  
imports: from horovod.common.elastic import run_fn
  from horovod.torch.mpi_ops import init, shutdown
  from horovod.torch.elastic.sampler import ElasticSampler
  from horovod.torch.elastic.state import TorchState
label_desc: Use image node_id 1 for calling a global function with example usage: run(func) and returns: run_fn