code: def _sync_module_params_and_buffers(
      module: nn.Module,
      params: List[nn.Parameter],
      process_group: dist.ProcessGroup,
  ) -> None:
      """
      Synchronize module states (i.e. parameters ``params`` and all not-yet-synced buffers) by broadcasting from rank 0 to all ranks.
  
      Precondition: ``sync_module_states == True`` and ``self.process_group`` has
      been set.
      """
      module_states: List[torch.Tensor] = []
      for buffer in module.buffers():
          # Avoid re-synchronizing buffers in case of nested wrapping
          if not getattr(buffer, FSDP_SYNCED, False):
              setattr(buffer, FSDP_SYNCED, True)
              detached_buffer = buffer.detach()
              if is_traceable_wrapper_subclass(detached_buffer):
                  # NOTE: Here we assume no nested subclasses, at most one level of subclass
                  # in both model's buffers and params
                  attrs, _ = detached_buffer.__tensor_flatten__()  # type: ignore[attr-defined]
                  inner_buffers = [
                      getattr(detached_buffer, attr) for attr in attrs
                  ]
                  module_states.extend(inner_buffers)
              else:
                  module_states.append(detached_buffer)
  
      for param in params:
          detached_param = param.detach()
          if is_traceable_wrapper_subclass(detached_param):
              attrs, _ = detached_param.__tensor_flatten__()  # type: ignore[attr-defined]
              inner_params = [
                  getattr(detached_param, attr) for attr in attrs
              ]
              module_states.extend(inner_params)
          else:
              module_states.append(detached_param)
  
      _check_module_states_for_sync_module_states(module_states)
      _sync_params_and_buffers(
          process_group,
          module_states,
          PARAM_BROADCAST_BUCKET_SIZE,
          src=0,
      )
  
imports: import collections
  import itertools
  import os
  import warnings
  from typing import Any, Callable, Deque, Dict, Generator, Iterable, Iterator, List, no_type_check, Optional, Set, Tuple, Union
  import torch
  import torch.distributed
  import torch.distributed.fsdp._exec_order_utils
  import torch.distributed.fsdp._traversal_utils
  import torch.distributed.fsdp.fully_sharded_data_parallel
  import torch.nn
  from torch.distributed.algorithms._comm_hooks import default_hooks
  from torch.distributed.device_mesh import _mesh_resources, DeviceMesh
  from torch.distributed.distributed_c10d import _get_default_group
  from torch.distributed.fsdp._common_utils import _FSDPDeviceHandle, _FSDPState, _get_module_fsdp_state, _is_fsdp_flattened, _named_parameters_with_duplicates, clean_tensor_name, TrainingState
  from torch.distributed.fsdp._flat_param import _FSDP_USE_FULL_PREC_IN_EVAL, FlatParameter, FlatParamHandle, HandleShardingStrategy
  from torch.distributed.fsdp._limiter_utils import _FreeEventQueue
  from torch.distributed.fsdp.api import BackwardPrefetch, CPUOffload, FullOptimStateDictConfig, FullStateDictConfig, MixedPrecision, ShardingStrategy, StateDictConfig, StateDictType
  from torch.distributed.fsdp.wrap import _Policy
  from torch.distributed.tensor.parallel.fsdp import DTensorExtensions
  from torch.distributed.utils import _sync_params_and_buffers
  from torch.utils._python_dispatch import is_traceable_wrapper_subclass
  from torch.utils.hooks import RemovableHandle
label_desc: Use image node_id 33 for calling a global function with example usage: _sync_module_params_and_buffers(module, params, process_group) without return types