code: def maybe_aggregate_normalize(values, normalizers, world_size):
      if world_size > 1:
          torch.distributed.barrier()
  
          torch.distributed.all_reduce_multigpu([values])
          torch.distributed.all_reduce_multigpu([normalizers])
  
      return values / normalizers
  
imports: import os
  import numpy
  import scipy
  import torch
  import torch.multiprocessing
  from fairseq import checkpoint_utils, options
  from fairseq.data.codedataset import CodeDataset, ExpressiveCodeDataConfig
  from fairseq.dataclass.utils import convert_namespace_to_omegaconf
  from torch.utils.data import DataLoader, DistributedSampler
  from fairseq.utils import move_to_cuda
  from fairseq import utils
  from fairseq.criterions.speech_ulm_criterion import nll_loss, mae_loss
  import time
  from types import SimpleNamespace
  import sys, pathlib
  from naive_decoder import Naive_F0_Decoder
  from inference_dataset import InferenceDataset, explode_batch
  from sample.sample import do_sampling, TemperatureDecoder, FilterNamesDataset
label_desc: Use image node_id 7 for calling a global function with example usage: maybe_aggregate_normalize(values, normalizers, world_size) and returns: unknown