docstr: 1. interpolate
  2. log transform (keep unvoiced frame 0)
docstr_tok: ["1.","interpolate","2.","log","transform","(","keep","unvoiced","frame","0",")"]
code: def preprocess_f0(self, f0, stats):
      """
      1. interpolate
      2. log transform (keep unvoiced frame 0)
      """
      # TODO: change this to be dependent on config for naive quantizer
      f0 = f0.clone()
      if self.interpolate_f0:
          f0 = interpolate_f0(f0)
  
      mask = f0 != 0  # only process voiced frames
      if self.log_f0:
          f0[mask] = f0[mask].log()
      if self.normalize_f0_mean:
          mean = (
              stats["logf0_mean"] if self.log_f0 else stats["f0_mean"]
          )
          f0[mask] = f0[mask] - mean
      if self.normalize_f0_std:
          std = stats["logf0_std"] if self.log_f0 else stats["f0_std"]
          f0[mask] = f0[mask] / std
      return f0
  
imports: import json
  import logging
  import os
  import random
  from pathlib import Path
  import numpy
  import torch
  import torch.utils.data
  from .None import data_utils
  from fairseq.data.fairseq_dataset import FairseqDataset
label_desc: Use image node_id 3 for calling the CodeDataset obj's underlying member method code with example usage: obj.preprocess_f0(f0, stats) and returns: f0