code: def d2_labels_deaugment(
      logits: Tensor, reduction: MaybeStrOrCallable = "mean"
  ) -> Tensor:
      """
      Deaugment input tensor (output of the model) assuming the is 2D tensor (See d2_augment).
      Args:
          logits: Tensor of [B * 4, C] shape
          reduction: Reduction model for aggregating outputs. Default is taking mean.
  
      Returns:
          Tensor of [B, C] shape if reduction is not None or "none", otherwise returns de-augmented tensor of
          [4, B, C] shape
      """
      b1, b2, b3, b4 = split_into_chunks(logits, 4)
      logits: Tensor = torch.stack([b1, b2, b3, b4])
  
      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 17 for calling a global function with example usage: d2_labels_deaugment(logits, reduction) and returns: _deaugment_averaging