code: def create_enqueue_mask(batch_size, world_size):
      # enqueue every other embedding
      local_enqueue_mask = [
          (torch.randint(0, 2, size=(batch_size,))).bool()
          for _ in range(world_size)
      ]
      global_enqueue_mask = torch.cat(local_enqueue_mask, dim=0)
      return local_enqueue_mask, global_enqueue_mask
  
imports: import logging
  import os
  import unittest
  import torch
  import torch.distributed
  import torch.multiprocessing
  import torch.optim
  from torch.nn.parallel import DistributedDataParallel
  from pytorch_metric_learning.losses import ContrastiveLoss, CrossBatchMemory
  from pytorch_metric_learning.miners import PairMarginMiner
  from pytorch_metric_learning.utils import distributed
  from ..None import TEST_DEVICE, TEST_DTYPES
label_desc: Use image node_id 8 for calling a global function with example usage: create_enqueue_mask(batch_size, world_size) and returns: local_enqueue_mask,  global_enqueue_mask