class_docstr: Support distributed all_gather for any arbitrary tensor while
  preserving its gradient.
code: def forward(ctx: Any, input: torch.Tensor) -> torch.Tensor:
      world_size = dist.get_world_size()
      x_gather = [torch.ones_like(input) for _ in range(world_size)]
      dist.all_gather(x_gather, input, async_op=False)
      x_gather = torch.cat(x_gather, dim=0)
      return x_gather
  
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 1 for calling the AllGatherWithGradient obj's underlying member method code with example usage: obj.forward(ctx, input) and returns: x_gather