code: def match_duration(
      produced_tokens, produced_duration, produced_f0, target_duration
  ):
      """
      >>> tokens = ['t'] * 4
      >>> F0s    = ['f0'] * 4
      >>> produced_duration = [1, 10, 10, 10]
      >>> match_duration(tokens, produced_duration, F0s, target_duration=100)
      (['t', 't', 't', 't'], [1, 10, 10, 10], ['f0', 'f0', 'f0', 'f0'])
      >>> match_duration(tokens, produced_duration, F0s, target_duration=5)
      (['t', 't'], [1, 4], ['f0', 'f0'])
      """
      if sum(produced_duration) <= target_duration:
          return produced_tokens, produced_duration, produced_f0
  
      running_duration = 0
      filtered_duration = []
  
      for next_tok_duration in produced_duration:
          if running_duration + next_tok_duration < target_duration:
              filtered_duration.append(next_tok_duration)
              running_duration += next_tok_duration
          else:
              to_add = target_duration - running_duration
              assert to_add <= next_tok_duration
              filtered_duration.append(to_add)
              break
  
      produced_duration = filtered_duration
      assert sum(produced_duration) == target_duration
  
      n_tok = len(filtered_duration)
  
      return (
          produced_tokens[:n_tok],
          produced_duration,
          produced_f0[:n_tok],
      )
  
imports: import os
  import torch.multiprocessing
  import numpy
  import json
  import torch
  from torch.distributions.categorical import Categorical
  from fairseq import checkpoint_utils, options, utils
  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
  import tqdm
  import random
  import pathlib
  import sys, pathlib
  from inference_dataset import InferenceDataset, explode_batch
  from naive_decoder import Naive_F0_Decoder
  from truncated_laplace import truncated_laplace
label_desc: Use image node_id 6 for calling a global function with example usage: match_duration(produced_tokens, produced_duration, produced_f0, target_duration) and returns: produced_tokens,  produced_duration,  produced_f0, produced_tokens,  produced_duration,  produced_f0