code: def __init__(self, models, beam_size=16):
      self.based_models = {m.path: m for m in models}
      self.beam_size = beam_size
  
      self.start_models = sorted(models, key=lambda x: -x.val_acc)
      self.start_models = [EnsembleModelStatus(self.start_models)]
      self.best_model_paths = self.start_models[0].paths
  
      self.best_val_acc = self.start_models[0].val_acc
      self.best_test_pred = self.start_models[0].test_pred
      print("Before Ensemble Best", "Cross Full", self.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 1 to create a new EnsembleModels object from inherited base classes: object with example: obj = EnsembleModels(models, beam_size)