code: def get_sample_size(population_size):
      """calculates the sample size for a 99% confidence and 1% margin of error"""
      margin_of_error = 0.01
      fraction_picking = 0.50
      z_score = 2.58  # Corresponds to confidence level 99%
      numerator = (
          z_score**2 * fraction_picking * (1 - fraction_picking)
      ) / (margin_of_error**2)
      sample_size = 0
      for train_size in range(population_size, 0, -1):
          denominator = 1 + (
              z_score**2 * fraction_picking * (1 - fraction_picking)
          ) / (margin_of_error**2 * train_size)
          sample_size = int(numerator / denominator)
          if 2 * sample_size + train_size <= population_size:
              break
      return sample_size
  
imports: import codecs
  import fnmatch
  import os
  import random
  import subprocess
  import sys
  import tarfile
  import unicodedata
  import wave
  import librosa
  import pandas
  import requests
  import soundfile
  from deepspeech_training.util.importers import validate_label_eng
label_desc: Use image node_id 11 for calling a global function with example usage: get_sample_size(population_size) and returns: sample_size