code: def find_models(paths):
      all_models_path = []
      for path in paths:
          all_models_path.extend(glob.glob(os.path.join(path, "*")))
      print(all_models_path)
      all_models = []
  
      for model in all_models_path:
          model = ModelStatus(model)
          all_models.append(model)
          model.print()
  
      for i, m_i in enumerate(all_models):
          for j, m_j in enumerate(all_models[:i]):
              print(
                  m_i.path,
                  m_j.path,
                  get_corr(m_i.test_pred, m_j.test_pred),
              )
  
      em = EnsembleModels(all_models)
      for i in range(10):
          em.step()
      count = Counter()
      for path in em.best_model_paths:
          count[path] += 1
  
      for model in all_models:
          print(model.path, model.val_acc)
  
      for key, value in count.items():
          print(key, value)
      print("Best-Cross Full", em.best_val_acc)
  
imports: import glob
  import os
  import pandas
  import numpy
  import json
  import tqdm
  from collections import Counter
  from ogb.lsc import MAG240MDataset, MAG240MEvaluator
  import torch
label_desc: Use image node_id 2 for calling a global function with example usage: find_models(paths) without return types