code: def backward(ctx, *grad_outputs):
      if dist.get_backend(group=ctx.group) is dist.Backend.NCCL:
          rank = dist.get_rank()
          gx = torch.empty_like(grad_outputs[rank])
          _Reduce_Scatter.apply(
              ReduceOp.SUM, ctx.group, gx, *grad_outputs
          )
      else:
          # As many backends doesn't support ReduceScatter, we use AlltoAll with .sum()
          # to emulate the ReduceScatter behavior
          tensor_list = [
              torch.empty_like(tensor) for tensor in grad_outputs
          ]
          gxs = _AlltoAll.apply(ctx.group, tensor_list, *grad_outputs)
          gx = torch.sum(torch.stack(gxs), dim=0)
      return (None, gx)
  
imports: import torch
  import torch.distributed
  from torch.autograd import Function
  from torch.distributed import group, ReduceOp
label_desc: Use image node_id 2 for calling the _AllGather obj's underlying member method code with example usage: obj.backward(ctx) and returns: None,  gx