code: def step(self, closure=None):
      if self._should_synchronize:
          if self._synchronized:
              warnings.warn(
                  "optimizer.step() called without "
                  "optimizer.skip_synchronize() context after "
                  "optimizer.synchronize(). This can cause training "
                  "slowdown. You may want to consider using "
                  "optimizer.skip_synchronize() context if you use "
                  "optimizer.synchronize() in your code."
              )
          self.synchronize()
      self._synchronized = False
      return super(self.__class__, self).step(closure)
  
imports: import os
  import warnings
  from contextlib import contextmanager
  import torch
  from horovod.common.util import split_list
  from horovod.torch.compression import Compression
  from horovod.torch.functions import broadcast_object
  from horovod.torch.mpi_ops import allreduce_async_, grouped_allreduce_async_, sparse_allreduce_async
  from horovod.torch.mpi_ops import synchronize
  from horovod.torch.mpi_ops import size
  from horovod.torch.mpi_ops import Average, Adasum, Sum
  from horovod.torch.mpi_ops import rocm_built
  from horovod.torch.mpi_ops import ProcessSet, global_process_set
label_desc: Use image node_id 12 for calling the _DistributedOptimizer obj's underlying member method code with example usage: obj.step(closure) and returns: super