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(
      self,
      output,
      input_list,
      op=ReduceOp.SUM,
      group=None,
      async_op=False,
  ):
      if DS_COMM_REDUCE_SCATTER_OFF:
          if int(os.getenv("RANK", "0")) == 0:
              utils.logger.warning("REDUCE SCATTER  is OFF")
          return Noop()
      else:
          return torch.distributed.reduce_scatter(
              output=output,
              input_list=input_list,
              op=self._reduce_op(op),
              group=group,
              async_op=async_op,
          )
  
imports: from deepspeed import utils
  from .utils import *
  from .backend import *
  from .comm import *
  import os
label_desc: Use image node_id 11 for calling the TorchBackend obj's underlying member method code with example usage: obj.reduce_scatter(output, input_list, op, group, async_op) and returns: Noop, torch