code: def ms_labels_deaugment(
      logits: List[Tensor],
      size_offsets: List[Union[int, Tuple[int, int]]],
      reduction: MaybeStrOrCallable = "mean",
  ):
      """
      Deaugment multi-scale label predictions.
      This function does not apply interpolation, since the outputs are expected to be scalar or 1d vector.
  
      Args:
          logits: List of tensors of shape [B, C]
          size_offsets: Not used
          reduction:
      """
      if len(logits) != len(size_offsets):
          raise ValueError(
              "Number of images must be equal to number of size offsets"
          )
  
      logits = torch.stack(logits)
      return _deaugment_averaging(logits, reduction=reduction)
  
imports: from collections import defaultdict
  from functools import partial
  from typing import Tuple, List, Optional, Union, Callable, Dict, Mapping
  import torch
  from torch import Tensor, nn
  from .None import functional
  from ..utils.support import pytorch_toolbelt_deprecated
label_desc: Use image node_id 32 for calling a global function with example usage: ms_labels_deaugment(logits, size_offsets, reduction) and returns: _deaugment_averaging