code: def _sync_ddp(
      result: Tensor,
      group: Optional[Any] = None,
      reduce_op: Optional[Union[ReduceOp, str]] = None,
  ) -> Tensor:
      """Reduces a tensor across several distributed processes.
  
      This operation is performed in-place, meaning the result will be placed back into the input tensor on all processes.
  
      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:
          The reduced value.
  
      """
      divide_by_world_size = False
      group = torch.distributed.group.WORLD if group is None else group
  
      op: Optional[ReduceOp]
      if isinstance(reduce_op, str):
          reduce_op = "avg" if reduce_op == "mean" else reduce_op
          if (
              reduce_op.lower() == "avg"
              and torch.distributed.get_backend(group) == "gloo"
          ):
              # The GLOO backend does not support the `ReduceOp.AVG` operation
              op = ReduceOp.SUM  # type: ignore[assignment]
              divide_by_world_size = True
          else:
              op = getattr(ReduceOp, reduce_op.upper())
      else:
          op = reduce_op
  
      # HPU doesn't support Long types, forcefully set it to float
      # TODO: move this to the `lightning_habana` package
      if (
          package_available("habana_frameworks")
          and os.environ.get("HCCL_DISTRIBUTED_BACKEND") == "1"
          and result.type()
          in (
              "torch.LongTensor",
              "torch.hpu.LongTensor",
          )
      ):
          rank_zero_info(
              "Long tensor unsupported on HPU, casting to float"
          )
          result = result.float()
  
      # Sync all processes before reduction
      torch.distributed.barrier(group=group)
      torch.distributed.all_reduce(
          result, op=op, group=group, async_op=False
      )
      world_size = torch.distributed.get_world_size(group)
  
      if not divide_by_world_size:
          return result
      # `torch.distributed.all_reduce` is in-place, so we should do the division in-place to leave the modified tensors
      # with the expected value
      if not torch.is_floating_point(result):
          return result.copy_(result / world_size)
      return result.div_(world_size)
  
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 5 for calling a global function with example usage: _sync_ddp(result, group, reduce_op) and returns: result, result, result