code: def write_most_recent_gs_result_to_file(
      trained_gs, most_recent_filename, timestamp
  ):
      timestamp_time = timestamp
      grid_scores = trained_gs.grid_scores_
      scorer = trained_gs.scorer_
      best_score = trained_gs.best_score_
  
      file_name = most_recent_filename
  
      write_header = False
      make_header = False
      if not os.path.isfile(most_recent_filename):
          header_row = [
              "timestamp",
              "scorer",
              "best_score",
              "cv_mean",
              "cv_all",
          ]
          write_header = True
          make_header = True
  
      rows_to_write = []
  
      for score in grid_scores:
          row = [timestamp_time, scorer, best_score, score[1], score[2]]
  
          for k, v in score[0].items():
              if make_header:
                  header_row.append(k)
              row.append(v)
          rows_to_write.append(row)
          make_header = False
  
      with open(file_name, "a") as results_file:
          writer = csv.writer(results_file, dialect="excel")
          if write_header:
              writer.writerow(header_row)
          for row in rows_to_write:
              writer.writerow(row)
  
imports: import csv
  import datetime
  import numbers
  import os
  import pkg_resources
  import numpy
  import pandas
  import scipy
  from sklearn.datasets import load_boston
  from sklearn.model_selection import train_test_split
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import LabelEncoder
  from sklearn.utils.metaestimators import if_delegate_has_method
  from sklearn.utils import column_or_1d
  from auto_ml._version import __version__
label_desc: Use image node_id 3 for calling a global function with example usage: write_most_recent_gs_result_to_file(trained_gs, most_recent_filename, timestamp) without return types