code: def input_reshard(
      module: torch.nn.Module,
      tp_device_mesh: DeviceMesh,
      input_reshard_dim: Optional[int] = None,
  ) -> torch.nn.Module:
      """
      Register hooks to an nn.Module for input resharding, enabling sharding and restoration during backward computation.
  
      Register hooks to an nn.Module with input resharding so that we can shard
      per the given `tp_device_mesh` and `input_reshard_dim` and restore the
      input back when recomputing the activations in the backward. The reason
      why we can do this is that for Tensor Parallel(TP), the input are same
      across all TP ranks.
  
      Args:
          module (:class:`nn.Module`):
              Module to be registered with input resharding.
          tp_device_mesh (:class:`DeviceMesh`):
              Object which describes the mesh topology
              of devices for Tensor Parallel.
          input_reshard_dim (Optional[int]):
              The dimension of where we perform the sharding
              of input. If set None, there is no sharding of input.
              Default: None
  
      Return:
          A :class:`nn.Module` object registered with TP input resharding.
      """
      cx: Optional[torch.autograd.graph.saved_tensors_hooks] = None
  
      def input_reshard_forward_pre_hook(
          _: torch.nn.Module, _i: Tuple[Any, ...]
      ) -> None:
          saved_tensor_hooks = torch.autograd.graph.saved_tensors_hooks(
              partial(_pack_hook_tp, tp_device_mesh, input_reshard_dim),
              partial(
                  _unpack_hook_tp, tp_device_mesh, input_reshard_dim
              ),
          )
          saved_tensor_hooks.__enter__()
          nonlocal cx
          cx = saved_tensor_hooks  # type: ignore[name-defined]
  
      def input_reshard_backward_hook(
          _: torch.nn.Module, _i: Tuple[Any, ...], _o: Any
      ) -> Any:
          nonlocal cx
          cx.__exit__()  # type: ignore[name-defined, union-attr]
  
      if input_reshard_dim is None:
          return module
      module.register_forward_pre_hook(input_reshard_forward_pre_hook)
      module.register_forward_hook(input_reshard_backward_hook)
      return module
  
imports: from functools import partial
  from typing import Any, Optional, Tuple
  import torch
  from torch.distributed._tensor import DeviceMesh, DTensor, Replicate, Shard
label_desc: Use image node_id 1 for calling a global function with example usage: input_reshard(module, tp_device_mesh, input_reshard_dim) and returns: module, module