code: def __init__(self, models):
      self.paths = []
      for model in models:
          if hasattr(model, "paths"):
              self.paths.extend(model.paths)
          if hasattr(model, "path"):
              self.paths.append(model.path)
  
      self.val_pred = models[0].val_pred
      for model in models[1:]:
          self.val_pred = self.val_pred + model.val_pred
      self.val_pred /= len(models)
  
      self.test_pred = models[0].test_pred
      for model in models[1:]:
          self.test_pred = self.test_pred + model.test_pred
      self.test_pred /= len(models)
  
      valid_label = labels[val_idx]
      valid_pred = np.argmax(self.val_pred, -1)
      self.val_acc = evaluator.eval(
          {"y_true": valid_label, "y_pred": valid_pred}
      )["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 1 to create a new EnsembleModelStatus object from inherited base classes: ModelStatus with example: obj = EnsembleModelStatus(models)