code: def _local_pre_load_state_dict_hook(
      module: nn.Module,
      fsdp_state: _FSDPState,
      state_dict: Dict[str, Any],
      prefix: str,
  ) -> None:
      """
      This hook finds the local flat_param for this FSDP module from the
      state_dict. The flat_param should be a ShardedTensor. This hook converts
      the ShardedTensor to a tensor. No copy happen unless padding is required.
      """
      _lazy_init(fsdp_state, module)
      _replace_by_prefix(state_dict, prefix, f"{prefix}{FSDP_PREFIX}")
      fqn = f"{prefix}{FSDP_PREFIX}{FLAT_PARAM}"
      if fqn not in state_dict:
          assert not _has_fsdp_params(fsdp_state, module), (
              "No `FlatParameter` in `state_dict` for this FSDP instance "
              "but it has parameters"
          )
          return
      load_tensor = state_dict[fqn]
      assert isinstance(
          load_tensor, ShardedTensor
      ), "Tensors in local_state_dict should be ShardedTensor."
  
      # Convert the ShardedTensor to a Tensor.
      flat_param = _module_handle(fsdp_state, module).flat_param
      assert flat_param is not None
      valid_data_size = (
          flat_param.numel() - flat_param._shard_numel_padded
      )
      shards = load_tensor.local_shards()
      if valid_data_size > 0:
          assert len(
              shards
          ), "load_local_state_dict assume one shard per ShardedTensor."
          load_tensor = shards[0].tensor
  
          # Get the metadata of the flat_param to decide whether to pad the loaded
          # tensor.
          if flat_param._shard_numel_padded > 0:
              assert load_tensor.numel() < flat_param.numel(), (
                  f"Local shard size = {flat_param.numel()} and the tensor in "
                  f"the state_dict is {load_tensor.numel()}."
              )
              load_tensor = F.pad(
                  load_tensor, [0, flat_param._shard_numel_padded]
              )
      else:
          load_tensor = flat_param
      # TODO: Add DTensor state_dict support for LOCAL_STATE_DICT.
      state_dict[fqn] = load_tensor
  
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 16 for calling a global function with example usage: _local_pre_load_state_dict_hook(module, fsdp_state, state_dict, prefix) without return types