code: def backward(ctx, grad_output):
      if dist.get_backend(group=ctx.group) is dist.Backend.NCCL:
          world_size = dist.get_world_size(group=ctx.group)
          out_size = list(grad_output.size())
          if out_size[0] % world_size != 0:
              raise RuntimeError(
                  f"Tensor with dimensions: {out_size} does "
                  f"not have first dimension divisible by world_size: {world_size}"
              )
          out_size[0] = out_size[0] // dist.get_world_size(
              group=ctx.group
          )
          gx = torch.empty(
              out_size,
              device=grad_output.device,
              dtype=grad_output.dtype,
          )
          dist._reduce_scatter_base(
              gx, grad_output, ReduceOp.SUM, ctx.group
          )
      else:
          raise RuntimeError("Backend not supported!")
      return (None, gx, None)
  
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 _AllGatherBase obj's underlying member method code with example usage: obj.backward(ctx, grad_output) and returns: None,  gx,  None