class_docstr: Support distributed all_gather for any arbitrary tensor while
  preserving its gradient.
code: def backward(ctx: Any, grad_output: torch.Tensor) -> torch.Tensor:
      reduction = dist.all_reduce(grad_output, async_op=True)
      reduction.wait()
  
      world_size = dist.get_world_size()
      N = grad_output.size(0)
      mini_batchsize = N // world_size
      cur_gpu = dist.get_rank()
      grad_output = grad_output[
          cur_gpu * mini_batchsize : (cur_gpu + 1) * mini_batchsize
      ]
      return grad_output
  
imports: import functools
  import logging
  import pickle
  from typing import Any, List, Optional, Tuple, TypeVar
  import torch
  import torch.distributed
label_desc: Use image node_id 2 for calling the AllGatherWithGradient obj's underlying member method code with example usage: obj.backward(ctx, grad_output) and returns: grad_output