code: def reduce(tensor, reduction="mean", scale=1.0):
      """
      Recursively reduce the tensors in a nested list/tuple/dictionary of lists of tensors across all processes by the
      mean of a given operation.
  
      Args:
          tensor (nested list/tuple/dictionary of `torch.Tensor`):
              The data to reduce.
          reduction (`str`, *optional*, defaults to `"mean"`):
              A reduction method. Can be of "mean", "sum", or "none"
          scale (`float`, *optional*):
              A default scaling value to be applied after the reduce, only valied on XLA.
  
      Returns:
          The same data structure as `data` with all the tensors reduced.
      """
  
      def _reduce_across_processes(tensor, reduction="mean", scale=1.0):
          state = PartialState()
          cloned_tensor = tensor.clone()
          if state.distributed_type == DistributedType.NO:
              return cloned_tensor
          if state.distributed_type == DistributedType.TPU:
              # Some processes may have different HLO graphs than other
              # processes, for example in the breakpoint API
              # accelerator.set_trigger(). Use mark_step to make HLOs
              # the same on all processes.
              xm.mark_step()
              xm.all_reduce(xm.REDUCE_SUM, [cloned_tensor], scale)
              xm.mark_step()
          elif (
              state.distributed_type.value
              in TORCH_DISTRIBUTED_OPERATION_TYPES
          ):
              torch.distributed.all_reduce(cloned_tensor, ReduceOp.SUM)
          if reduction == "mean":
              cloned_tensor /= state.num_processes
          return cloned_tensor
  
      return recursively_apply(
          _reduce_across_processes,
          tensor,
          error_on_other_type=True,
          reduction=reduction,
          scale=scale,
      )
  
imports: import pickle
  import warnings
  from functools import update_wrapper, wraps
  from typing import Any, Mapping
  import torch
  from ..state import PartialState
  from .constants import TORCH_DISTRIBUTED_OPERATION_TYPES
  from .dataclasses import DistributedType, TensorInformation
  from .imports import is_npu_available, is_torch_distributed_available, is_torch_version, is_tpu_available
label_desc: Use image node_id 27 for calling a global function with example usage: reduce(tensor, reduction, scale) and returns: recursively_apply, cloned_tensor, cloned_tensor