code: def _allreduce_grad_async(self, p):
      if p.grad is None:
          # Gradient was not computed, but we still need to submit a tensor to allreduce
          # as one of the other ranks may have computed it (due to dynamic forward functions).
          #
          # NOTE: this will not work if the gradient is sparse and we perform an allgather.
          # Unfrotunately, there doesn't appear to be a good way to detect that the parameter will
          # produce sparse gradients before computing the gradient.
          p.grad = p.data.new(p.size()).zero_()
  
      name = self._parameter_names.get(p)
      tensor = p.grad
  
      if p.grad.is_sparse:
          if self.sparse_as_dense:
              tensor = tensor.to_dense()
          else:
              return self._sparse_allreduce_grad_async(p, name)
  
      tensor_compressed, ctx = self._compression.compress(tensor)
  
      if self.op == Average:
          # Split average operation across pre/postscale factors
          # C++ backend will apply additional 1 / size() factor to postscale_factor for op == Average.
          prescale_factor = 1.0 / self.gradient_predivide_factor
          postscale_factor = self.gradient_predivide_factor
      else:
          prescale_factor = 1.0
          postscale_factor = 1.0
  
      handle = allreduce_async_(
          tensor_compressed,
          name=name,
          op=self.op,
          prescale_factor=prescale_factor,
          postscale_factor=postscale_factor,
          process_set=self.process_set,
      )
      return handle, ctx
  
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 6 for calling the _DistributedOptimizer obj's underlying member method code with example usage: obj._allreduce_grad_async(p) and returns: handle,  ctx, self