code: def cal_gcmvn_stats(features_list):
      features = np.concatenate(features_list)
      square_sums = (features**2).sum(axis=0)
      mean = features.mean(axis=0)
      features = np.subtract(features, mean)
      var = square_sums / features.shape[0] - mean**2
      std = np.sqrt(np.maximum(var, 1e-8))
      return {
          "mean": mean.astype("float32"),
          "std": std.astype("float32"),
      }
  
imports: import csv
  from pathlib import Path
  import zipfile
  from functools import reduce
  from multiprocessing import cpu_count
  from typing import Any, Dict, List, Optional, Union
  import io
  import numpy
  import pandas
  import sentencepiece
  from fairseq.data.audio.audio_utils import convert_waveform, _get_kaldi_fbank, _get_torchaudio_fbank, is_npy_data, is_sf_audio_data
  import torch
  import soundfile
  from tqdm import tqdm
label_desc: Use image node_id 10 for calling a global function with example usage: cal_gcmvn_stats(features_list) and returns: dict