code: def correlation(
      args, dataset, model, criterion, tgt_dict, rank, world_size
  ):
      is_discrete_duration = dataset.discrete_dur
      is_discrete_f0 = dataset.discrete_f0
  
      f0_decoder = None
      if is_discrete_f0:
          assert dataset.discrete_f0
          f0_decoder = Naive_F0_Decoder(
              args.f0_discretization_bounds,
              dataset.config.f0_vq_n_units,
          ).cuda()
  
      if is_discrete_f0:
          assert f0_decoder  # correlation on tokens is meaningless
  
      dataset = InferenceDataset(
          dataset,
          args.prefix_length,
          filter_short=True,
          presort_by_length=True,
          min_length=args.min_length,
      )
      sampler = (
          None
          if world_size == 1
          else DistributedSampler(
              dataset, num_replicas=world_size, rank=rank, shuffle=False
          )
      )
      dataloader = DataLoader(
          dataset,
          batch_size=1,
          shuffle=False,
          collate_fn=dataset.collater,
          sampler=sampler,
      )
  
      Ts = args.T_token, args.T_duration, args.T_f0
      decoder = TemperatureDecoder(
          Ts,
          discrete_dur=is_discrete_duration,
          discrete_f0=is_discrete_f0,
      )
  
      mean_dur_prefix, mean_dur_cont = [], []
      mean_f0_prefix, mean_f0_cont = [], []
  
      for batch in dataloader:
          batch = explode_batch(batch, args.batch_explosion_rate)
          batch = move_to_cuda(batch)
  
          assert len(batch["prefix"]) == 1
  
          if args.teacher_force_tokens:
              autoregressive_steps = (
                  batch["target"].size(1) - args.prefix_length - 1
              )
          else:
              autoregressive_steps = (
                  args.max_length - args.prefix_length
              )  # + max_shift?
  
          if args.copy_target:
              produced_durations, produced_f0 = (
                  batch["dur_target"],
                  batch["f0_target"],
              )
          else:
              _, produced_durations, produced_f0, outputs = do_sampling(
                  model,
                  batch,
                  tgt_dict.eos(),
                  decoder,
                  autoregressive_steps=autoregressive_steps,
                  teacher_force_tokens=args.teacher_force_tokens,
                  teacher_force_duration=args.teacher_force_duration,
                  teacher_force_f0=args.teacher_force_f0,
              )
  
              # first tokens actually correspond to BOS
              produced_durations = produced_durations[:, 1:]
              produced_f0 = produced_f0[:, 1:]
  
          dur_target = batch["dur_target"]
          if is_discrete_duration:
              produced_durations = produced_durations.float()
              dur_target = dur_target.float()
  
          if is_discrete_f0:
              produced_f0 = f0_decoder(produced_f0).squeeze(-1)
              f0_target = batch["raw_f0"]
          else:
              f0_target = batch["f0_target"]
  
          # prefix values
          prefix = batch["prefix"][0]
          dur_prefix_mean = dur_target[:, :prefix].sum(dim=-1) / (
              (~batch["dur_mask"][:, :prefix]).sum(dim=-1)
          )
  
          non_voiced = f0_target[:, :prefix] == 0.0
          f0_mask = batch["f0_mask"][:, :prefix].logical_or(non_voiced)
          f0_prefix_mean = f0_target[:, :prefix].sum(dim=-1) / (
              (~f0_mask).sum(dim=-1)
          )
  
          # continuation values
          dur_cont_mean = produced_durations[:, prefix:].sum(dim=-1) / (
              (~batch["dur_mask"][:, prefix:]).sum(dim=-1)
          )
  
          non_voiced = produced_f0[:, prefix:] == 0.0
          f0_mask = non_voiced
          f0_cont_mean = produced_f0[:, prefix:].sum(dim=-1) / (
              (~f0_mask).sum(dim=-1)
          )
  
          assert not f0_cont_mean.isnan().any()
  
          mean_dur_prefix.append(dur_prefix_mean.cpu())
          mean_dur_cont.append(dur_cont_mean.cpu())
  
          mean_f0_prefix.append(f0_prefix_mean.cpu())
          mean_f0_cont.append(f0_cont_mean.cpu())
  
          if args.debug and len(mean_dur_prefix) > 10:
              break
  
      mean_dur_prefix, mean_dur_cont = torch.cat(
          mean_dur_prefix
      ), torch.cat(mean_dur_cont)
      mean_f0_prefix, mean_f0_cont = torch.cat(
          mean_f0_prefix
      ), torch.cat(mean_f0_cont)
  
      return (
          mean_dur_prefix,
          mean_dur_cont,
          mean_f0_prefix,
          mean_f0_cont,
      )
  
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 4 for calling a global function with example usage: correlation(args, dataset, model, criterion, tgt_dict, rank, world_size) and returns: mean_dur_prefix,  mean_dur_cont,  mean_f0_prefix,  mean_f0_cont