class_docstr: A light-weight wrapper class for torch.distributed API.
  Only a subset of functions are wrapped. Once the init_process_group
  is initialized, standard torch.distributed.* can be used directly
  so no need to wrap all the functions. We can keep adding wrappers as
  needed.
class_docstr_tok: ["A","light-weight","wrapper","class","for","torch.distributed","API",".","Only","a","subset","of","functions","are","wrapped",".","Once","the","init_process_group","is","initialized",",","standard","torch.distributed",".","*","can","be","used","directly","so","no","need","to","wrap","all","the","functions",".","We","can","keep","adding","wrappers","as","needed","."]
code: def reduce_scatter_base(
      self,
      output_tensor,
      input_tensor,
      op=ReduceOp.SUM,
      group=None,
      async_op=False,
  ):
      if self.has_reduce_scatter_base:
          return torch.distributed._reduce_scatter_base(
              output_tensor,
              input_tensor,
              op=self._reduce_op(op),
              group=group,
              async_op=async_op,
          )
      else:
          utils.logger.warning(
              "unable to find torch.distributed._reduce_scatter_base. will fall back to "
              "torch.distributed.reduce_scatter which will result in suboptimal performance. "
              "please consider upgrading your pytorch installation."
          )
          pass
  
imports: from deepspeed import utils
  from .utils import *
  from .backend import *
  from .comm import *
label_desc: Use image node_id 9 for calling the TorchBackend obj's underlying member method code with example usage: obj.reduce_scatter_base(output_tensor, input_tensor, op, group, async_op) and returns: torch