code: def _post_state_dict_hook(
      module: nn.Module,
      state_dict: Dict[str, Any],
      prefix: str,
      *args: Any,
  ) -> Dict[str, Any]:
      """
      _post_state_dict_hook() is called after the state_dict() of this
      FSDP module is executed. ``fsdp_state._state_dict_type`` is used to decide
      what postprocessing will be done.
      """
      fsdp_state = _get_module_fsdp_state_if_fully_sharded_module(
          module
      )
      if fsdp_state.sharding_strategy == ShardingStrategy.NO_SHARD:
          context = _replace_with_full_state_dict_type(fsdp_state)
          warnings.warn(
              "When using ``NO_SHARD`` for ``ShardingStrategy``, full_state_dict will"
              "be returned."
          )
      else:
          context = contextlib.nullcontext()
  
      with context:
          _post_state_dict_hook_fn = {
              StateDictType.FULL_STATE_DICT: _full_post_state_dict_hook,
              StateDictType.LOCAL_STATE_DICT: _local_post_state_dict_hook,
              StateDictType.SHARDED_STATE_DICT: _sharded_post_state_dict_hook,
          }
          processed_state_dict = _post_state_dict_hook_fn[
              fsdp_state._state_dict_type
          ](module, fsdp_state, state_dict, prefix)
  
      if fsdp_state._is_root:
          logger.info(
              "FSDP finished processing state_dict(), prefix=%s", prefix
          )
          for key, tensor in sorted(processed_state_dict.items()):
              if key.startswith(prefix) and isinstance(
                  tensor, torch.Tensor
              ):
                  local_shape = tensor.shape
                  if isinstance(tensor, ShardedTensor):
                      local_shape = None
                      shards = tensor.local_shards()
                      if shards:
                          local_shape = shards[0].tensor.shape
                  elif isinstance(tensor, DTensor):
                      local_shape = tensor.to_local().shape
                  logger.info(
                      "FQN=%s: type=%s, shape=%s, local_shape=%s, dtype=%s, device=%s",
                      key,
                      type(tensor),
                      tensor.shape,
                      local_shape,
                      tensor.dtype,
                      tensor.device,
                  )
  
      return processed_state_dict
  
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 22 for calling a global function with example usage: _post_state_dict_hook(module, state_dict, prefix) and returns: processed_state_dict