code: def test_grade():
      df = None
  
      file = "h2oGPT.cleaned.chopped.human_bot.shorter.parquet"
      output_file = "h2oGPT.cleaned.graded1.human_bot.shorter.parquet"
      if not os.path.exists(output_file):
          if df is None:
              df = pd.read_parquet(file).reset_index(drop=True)
          df = add_textstat_grade(df)
          min_grade = 10
          max_grade = 25
          df = df[df["flesch_grade"] >= min_grade]
          df = df[df["flesch_grade"] <= max_grade]
          print("After Flesch grade")
          print(df.describe())
          df.to_parquet(output_file, index=False)
  
      file = output_file
      output_file = "h2oGPT.cleaned.graded2.human_bot.shorter.parquet"
      if not os.path.exists(output_file):
          # slower than alt-profanity, do last, but do before deberta grading, since that's slower
          if df is None:
              df = pd.read_parquet(file).reset_index(drop=True)
          df = add_better_profanity_flag(df)
          before_rows = df.shape[0]
          df = df[df["better_profanity"] == 0]
          df = df.drop(["better_profanity"], axis=1)
          after_rows = df.shape[0]
          print(
              "Dropped %d rows out of %d due to better_profanity"
              % (before_rows - after_rows, before_rows)
          )
          print(df.describe())
          df.to_parquet(output_file, index=False)
  
      file = output_file
      output_file = "h2oGPT.cleaned.graded3.human_bot.shorter.parquet"
      if not os.path.exists(output_file):
          if df is None:
              df = pd.read_parquet(file).reset_index(drop=True)
          df = add_deberta_grade(df)
          min_grade = 0.3
          max_grade = np.inf
          before_rows = df.shape[0]
          df = df[df["grade_deberta"] >= min_grade]
          df = df[df["grade_deberta"] <= max_grade]
          after_rows = df.shape[0]
          print(
              "Dropped %d rows out of %d due to deberta grade"
              % (before_rows - after_rows, before_rows)
          )
          print("After DeBERTa grade")
          print(df.describe())
          df.to_parquet(output_file, index=False)
  
      file = output_file
      output_file = "h2oGPT.cleaned.graded.human_bot.shorter.parquet"
      if df is None:
          df = pd.read_parquet(file).reset_index(drop=True)
      df.to_parquet(output_file, index=False)
  
imports: import ast
  import concurrent.futures
  import contextlib
  import hashlib
  import json
  import os
  import shutil
  import signal
  import sys
  import traceback
  from concurrent.futures import ProcessPoolExecutor
  import psutil
  import pytest
  import pandas
  import numpy
  from tqdm import tqdm
  from utils import flatten_list, remove
  from joblib import Parallel, delayed, effective_n_jobs
  from sklearn.utils import gen_even_slices
  from sklearn.utils.validation import _num_samples
label_desc: Use image node_id 31 for calling a global function with example usage: test_grade() without return types