code: def batch_unshuffle(
      batch: torch.Tensor,
      shuffle: torch.Tensor,
      distributed: bool = False,
  ) -> torch.Tensor:
      """Unshuffles a batch.
  
      Args:
          batch:
              The batch to unshuffle.
          shuffle:
              Index to unshuffle the batch.
          distributed:
              If True then the batch is unshuffled across multiple gpus.
  
      Returns:
          The unshuffled batch.
  
      Examples:
          >>> # forward pass through the momentum model with batch shuffling
          >>> x1_shuffled, shuffle = batch_shuffle(x1)
          >>> f1 = moco_momentum(x1)
          >>> out0 = projection_head_momentum(f0)
          >>> out1 = batch_unshuffle(out1, shuffle)
      """
      if distributed:
          return batch_unshuffle_distributed(batch, shuffle)
      unshuffle = torch.argsort(shuffle)
      return batch[unshuffle]
  
imports: import math
  import warnings
  from typing import Iterable, List, Optional, Tuple, Union
  import numpy
  import torch
  import torch.distributed
  import torch.nn
  from numpy.typing import NDArray
  from torch.nn import Module
  from torch.nn.modules.batchnorm import _BatchNorm
  from torch.nn.parameter import Parameter
label_desc: Use image node_id 2 for calling a global function with example usage: batch_unshuffle(batch, shuffle, distributed) and returns: batch, batch_unshuffle_distributed