code: def process(cross=1):
      model_path = []
      crossvalid_pred = []
      leftvalid_pred = []
      test_pred = []
  
      for i, model_p in enumerate(model_cross_valid):
          model_name = osp.dirname(model_p)
          if cross == 1 and model_name.endswith("cross2"):
              continue
          elif cross == 2 and model_name.endswith("cross1"):
              continue
  
          model_path.append(osp.splitext(model_p)[0])
          crossvalid_pred.append(np.load(model_p)["arr_0"])
          leftvalid_pred.append(np.load(model_left_valid[i])["arr_0"])
          test_pred.append(np.load(model_test[i])["arr_0"])
  
      return (
          np.array(model_path).T,
          np.array(crossvalid_pred).T,
          np.array(leftvalid_pred).T,
          np.array(test_pred).T,
      )
  
file_path: PGL/examples/kddcup2021/PCQM4M/ensemble/ensemble.py
imports: import os
  import os.path
  import glob
  import pickle
  import numpy
  import pandas
  import sklearn
  import sklearn.linear_model
  import torch
label_desc: Use image node_id 2 for calling a global function with example usage: process(cross) and returns: np,  np,  np,  np