code: def save_training(file_path, meta_data):
      rows = []
      for char_cnt in meta_data:
          data = meta_data[char_cnt]["data"]
          for d in data:
              rows.append(d["row"] + "\n")
  
      random.shuffle(rows)
      with open(file_path, "w+") as f:
          for row in rows:
              f.write(row)
  
file_path: TTS/notebooks/dataset_analysis/analyze.py
imports: import matplotlib.pyplot
  from statistics import stdev, mode, mean, median
  from statistics import StatisticsError
  import argparse
  import os
  import csv
  import seaborn
  import random
  from text.cmudict import CMUDict
label_desc: Use image node_id 5 for calling a global function with example usage: save_training(file_path, meta_data) without return types