code: def step(self, closure=None):
      loss = None
      if closure is not None:
          loss = closure()
  
      missing_p = self._requires_update - set(self._handles.keys())
      for p in missing_p:
          handle, ctx = self._allreduce_grad_async(p)
          self._handles[p] = (handle, ctx)
  
      for p, (handle, ctx) in self._handles.items():
          # This means step() is called before backward_passes_per_steps finished.
          # We do a synchoronous allreduce here.
          if not handle:
              handle, ctx = self._allreduce_grad_async(p)
              self._handles[p] = (handle, ctx)
          delta = synchronize(handle)
          delta = self._compression.decompress(delta, ctx)
          start = self._starting_models[p]
          start.data.add_(delta.data)
          p.data.copy_(start)
          self._allreduce_delay[p] = self.backward_passes_per_step
      self._handles.clear()
      return loss
  
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 8 for calling the _DistributedAdasumOptimizer obj's underlying member method code with example usage: obj.step(closure) and returns: loss