code: def get_f0_vq_naive_quantizer(self, log, norm_mean, norm_std):
      key = "log" if log else "linear"
      if norm_mean and norm_std:
          key += "_mean_std_norm"
      elif norm_mean:
          key += "_mean_norm"
      else:
          key += "_none_norm"
      return self.config["f0_vq_naive_quantizer"][key]
  
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 9 for calling the ExpressiveCodeDataConfig obj's underlying member method code with example usage: obj.get_f0_vq_naive_quantizer(log, norm_mean, norm_std) and returns: self