code: def random_forest(
      df_x_path: str, df_y_path: str, window_name: str, cores: int = 1
  ) -> Tuple[str, float, float]:
      # read in data
      df_x = pd.read_table(df_x_path, header=0, index_col=0)
      df_y = pd.read_table(df_y_path, header=0, index_col=0)
  
      # split training and testing data for the current window
      x_train = df_x[df_x.index != window_name]
      x_test = df_x[df_x.index == window_name]
  
      y_train = df_y[df_y.index != window_name]
      y_test = df_y[df_y.index == window_name]
  
      # run random forest
      max_features = 3 / 4
      rf = RandomForestRegressor(
          n_estimators=100,
          n_jobs=cores,
          max_features=max_features,
          oob_score=True,
          verbose=False,
      )
  
      rf.fit(x_train, y_train)
  
      # apply the trained random forest on testing data
      y_pred = rf.predict(x_test)
  
      # store obs and pred values for this window
      obs = y_test["oe"].to_list()[0]
      pred = y_pred[0]
  
      return (window_name, obs, pred)
  
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 1 for calling a global function with example usage: random_forest(df_x_path, df_y_path, window_name, cores) and returns: window_name,  obs,  pred