code: def main(df_x_path, df_y_path, output_path, python_image):
      backend = hb.ServiceBackend()
      b = hb.Batch(name="rf-loo", default_python_image=python_image)
  
      with hfs.open(df_y_path) as f:
          local_df_y = pd.read_table(f, header=0, index_col=0)
  
      df_x_input = b.read_input(df_x_path)
      df_y_input = b.read_input(df_y_path)
  
      results = []
  
      for window in local_df_y.index.to_list():
          checkpoint = checkpoint_path(window)
          if hfs.exists(checkpoint):
              result = b.read_input(checkpoint)
              results.append(result)
              continue
  
          j = b.new_python_job()
  
          result = j.call(random_forest, df_x_input, df_y_input, window)
          tsv_result = j.call(as_tsv, result)
          tsv_result = tsv_result.as_str()
  
          b.write_output(tsv_result, checkpoint)
          results.append(tsv_result)
  
      output = hb.concatenate(b, results)
      b.write_output(output, output_path)
  
      b.run(wait=False)
      backend.close()
  
file_path: hail/hail/python/hailtop/batch/docs/cookbook/files/run_rf_checkpoint.py
imports: import hailtop.batch
  import hailtop.fs
  import pandas
  from typing import Tuple
  from sklearn.ensemble import RandomForestRegressor
label_desc: Use image node_id 4 for calling a global function with example usage: main(df_x_path, df_y_path, output_path, python_image) without return types