code: def write_gs_param_results_to_file(trained_gs, most_recent_filename):
      timestamp_time = datetime.datetime.now()
      write_most_recent_gs_result_to_file(
          trained_gs, most_recent_filename, timestamp_time
      )
  
      grid_scores = trained_gs.grid_scores_
      scorer = trained_gs.scorer_
      best_score = trained_gs.best_score_
  
      file_name = "pipeline_grid_search_results.csv"
      write_header = False
      if not os.path.isfile(file_name):
          write_header = True
  
      with open(file_name, "a") as results_file:
          writer = csv.writer(results_file, dialect="excel")
          if write_header:
              writer.writerow(
                  [
                      "timestamp",
                      "scorer",
                      "best_score",
                      "all_grid_scores",
                  ]
              )
          writer.writerow(
              [timestamp_time, scorer, best_score, grid_scores]
          )
  
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 2 for calling a global function with example usage: write_gs_param_results_to_file(trained_gs, most_recent_filename) without return types