code: def maybe_cut_eos(
      produced_tokens, produced_duration, produced_f0, eos_idx
  ):
      if eos_idx in produced_tokens:
          eos_index = produced_tokens.index(eos_idx)
          produced_tokens = produced_tokens[:eos_index]
          produced_duration = produced_duration[:eos_index]
          produced_f0 = produced_f0[:eos_index]
      return produced_tokens, produced_duration, produced_f0
  
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 4 for calling a global function with example usage: maybe_cut_eos(produced_tokens, produced_duration, produced_f0, eos_idx) and returns: produced_tokens,  produced_duration,  produced_f0