code: def _sync_ddp_if_available(
      result: Tensor,
      group: Optional[Any] = None,
      reduce_op: Optional[Union[ReduceOp, str]] = None,
  ) -> Tensor:
      """Function to reduce a tensor across worker processes during distributed training.
  
      Args:
          result: The value to sync and reduce (typically tensor or number)
          group: The process group to gather results from. Defaults to all processes (world)
          reduce_op: The reduction operation. Defaults to sum.
              Can also be a string of 'avg', 'mean' to calculate the mean during reduction.
  
      Return:
          reduced value
  
      """
      if _distributed_is_initialized():
          return _sync_ddp(result, group=group, reduce_op=reduce_op)
      return result
  
imports: import contextlib
  import logging
  import os
  import time
  from contextlib import nullcontext
  from pathlib import Path
  from typing import TYPE_CHECKING, Any, Iterable, Iterator, List, Optional, Sized, Union
  import torch
  import torch.nn.functional
  from lightning_utilities.core.imports import package_available
  from torch import Tensor
  from torch.utils.data import Dataset, DistributedSampler, Sampler
  from typing_extensions import override
  from lightning.fabric.utilities.cloud_io import _is_local_file_protocol
  from lightning.fabric.utilities.data import _num_cpus_available
  from lightning.fabric.utilities.rank_zero import rank_zero_info
  from lightning.fabric.utilities.types import _PATH, ReduceOp
label_desc: Use image node_id 4 for calling a global function with example usage: _sync_ddp_if_available(result, group, reduce_op) and returns: result, _sync_ddp