code: def maybe_aggregate_correlations(results, world_size):
      if world_size > 1:
          output = [None for _ in range(world_size)]
          torch.distributed.all_gather_object(output, results)
          (
              mean_dur_prefix,
              mean_dur_cont,
              mean_f0_prefix,
              mean_f0_cont,
          ) = [torch.cat([x[i] for x in output]) for i in range(4)]
      else:
          (
              mean_dur_prefix,
              mean_dur_cont,
              mean_f0_prefix,
              mean_f0_cont,
          ) = results
  
      corr_dur = scipy.stats.pearsonr(
          mean_dur_prefix.numpy(), mean_dur_cont.numpy()
      )[0]
      corr_f0 = scipy.stats.pearsonr(
          mean_f0_prefix.numpy(), mean_f0_cont.numpy()
      )[0]
      values = torch.tensor([corr_dur, corr_f0])
  
      return values
  
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 8 for calling a global function with example usage: maybe_aggregate_correlations(results, world_size) and returns: values