code: def _update_queue(
      projections: List[Tensor],
      queues: ModuleList,
  ):
      """Adds the high resolution projections to the queues and returns the queues."""
  
      if len(projections) != len(queues):
          raise ValueError(
              f"The number of queues ({len(queues)}) should be equal to the number of high "
              f"resolution inputs ({len(projections)})."
          )
  
      # Get the queue projections
      queue_projections = []
      for i in range(len(queues)):
          _, queue_proj = queues[i](projections[i], update=True)
          # Queue projections are in (num_ftrs X queue_length) shape, while the high res
          # projections are in (batch_size_per_device X num_ftrs). Swap the axes for interoperability.
          queue_proj = torch.permute(queue_proj, (1, 0))
          queue_projections.append(queue_proj)
  
      return queue_projections
  
imports: import math
  from typing import List, Tuple
  import torch
  from pytorch_lightning import LightningModule
  from torch import Tensor
  from torch.nn import Identity, ModuleList
  from torch.nn import functional
  from torchvision.models import resnet50
  from lightly.loss.swav_loss import SwaVLoss
  from lightly.models.modules import SwaVProjectionHead, SwaVPrototypes
  from lightly.models.modules.memory_bank import MemoryBankModule
  from lightly.models.utils import get_weight_decay_parameters
  from lightly.transforms import SwaVTransform
  from lightly.utils.benchmarking import OnlineLinearClassifier
  from lightly.utils.lars import LARS
  from lightly.utils.scheduler import CosineWarmupScheduler
label_desc: Use image node_id 1 for calling a global function with example usage: _update_queue(projections, queues) and returns: queue_projections