code: def _full_post_state_dict_hook(
      module: nn.Module,
      fsdp_state: _FSDPState,
      state_dict: Dict[str, Any],
      prefix: str,
  ) -> Dict[str, Any]:
      """
      Hook that runs after model.state_dict() is called before returning result to
      user. For FSDP, we may have to clone the tensors in state_dict as params go
      back to sharded version after _unshard_fsdp_state_params ends, and also remove
      the ``FSDP_WRAPPED_MODULE`` prefix.
      """
  
      def param_hook(
          state_dict: Dict[str, Any],
          prefix: str,
          fqn: str,
      ) -> None:
          clean_key = fqn
          clean_prefix = clean_tensor_name(prefix)
          # Strip prefix out of key if needed as buffer names and param names
          # do not have prefix considered as they are not computed in `state_dict`
          # call.
          if clean_key.startswith(clean_prefix):
              clean_key = clean_key[len(clean_prefix) :]
  
          # Clone parameters before exiting the `_unshard_fsdp_state_params()` context.
          if not getattr(state_dict[fqn], "_has_been_cloned", False):
              try:
                  state_dict[fqn] = state_dict[fqn].clone().detach()
                  state_dict[fqn]._has_been_cloned = True  # type: ignore[attr-defined]
              except BaseException as e:
                  warnings.warn(
                      f"Failed to clone() tensor with name {fqn} on rank {fsdp_state.rank}. "
                      "This may mean that this state_dict entry could point to invalid "
                      "memory regions after returning from state_dict() call if this "
                      "parameter is managed by FSDP. Please check clone "
                      f"implementation of {fqn}. Error: {str(e)}"
                  )
  
      return _common_unshard_post_state_dict_hook(
          module, fsdp_state, state_dict, prefix, param_hook
      )
  
imports: import contextlib
  import logging
  import math
  import warnings
  from typing import Any, Callable, cast, Dict, Generator, Iterator, no_type_check, Tuple
  import torch
  import torch.distributed
  import torch.distributed.algorithms._checkpoint.checkpoint_wrapper
  import torch.nn
  import torch.nn.functional
  from torch.distributed._shard.sharded_tensor import init_from_local_shards, Shard, ShardedTensor
  from torch.distributed._tensor import DTensor
  from torch.distributed.device_mesh import _mesh_resources
  from torch.distributed.fsdp._common_utils import _FSDPState, _get_module_fsdp_state_if_fully_sharded_module, _has_fsdp_params, _is_composable, _module_handle, clean_tensor_name, FSDP_PREFIX, FSDP_WRAPPED_MODULE
  from torch.distributed.fsdp._debug_utils import SimpleProfiler
  from torch.distributed.fsdp._runtime_utils import _cast_buffers_to_dtype_and_device, _get_orig_buffer_dtypes, _lazy_init, _reset_flat_param_grad_info_if_needed
  from torch.distributed.fsdp.api import FullStateDictConfig, ShardingStrategy, StateDictType
  from torch.distributed.utils import _replace_by_prefix
  from ._fsdp_extensions import _ext_all_gather_dtensor, _ext_chunk_dtensor, _ext_chunk_tensor, _ext_post_unflatten_transform, _ext_pre_load_state_dict_transform
  from ._unshard_param_utils import _unshard_fsdp_state_params, FLAT_PARAM
label_desc: Use image node_id 10 for calling a global function with example usage: _full_post_state_dict_hook(module, fsdp_state, state_dict, prefix) and returns: _common_unshard_post_state_dict_hook