code: def _apply_loss_reduction(
      loss: TensorLikeType, reduction: str
  ) -> TensorLikeType:
      if reduction == "sum":
          return torch.sum(loss)
      elif reduction == "mean":
          return torch.mean(loss)
      else:  # reduction == "none"
          return loss
  
imports: import math
  from functools import wraps
  from typing import Callable, Optional, Union
  import torch
  import torch._prims
  import torch._prims_common
  import torch._refs
  from torch._decomp import register_decomposition
  from torch._prims_common import ELEMENTWISE_TYPE_PROMOTION_KIND, NumberType, ShapeType, TensorLike, TensorLikeType
  from torch._prims_common.wrappers import elementwise_type_promotion_wrapper, elementwise_unary_scalar_wrapper, out_wrapper
  from torch._refs import _make_inplace
label_desc: Use image node_id 19 for calling a global function with example usage: _apply_loss_reduction(loss, reduction) and returns: torch, torch, loss