code: def maybe_synchronize(device: str):
      if device == "xpu" and torch.xpu.is_available():
          torch.xpu.synchronize()
      if device == "cuda" and torch.cuda.is_available():
          torch.cuda.synchronize()
  
file_path: pytorch_geometric/benchmark/multi_gpu/training/common.py
imports: import argparse
  import ast
  from time import perf_counter
  from typing import Any, Callable, Tuple, Union
  import torch
  import torch.distributed
  import torch.nn.functional
  from torch.nn.parallel import DistributedDataParallel
  from benchmark.utils import get_model, get_split_masks, test
  from torch_geometric.data import Data, HeteroData
  from torch_geometric.loader import NeighborLoader
  from torch_geometric.nn import PNAConv
label_desc: Use image node_id 3 for calling a global function with example usage: maybe_synchronize(device) without return types