code: def __init__(self, path):
      self.path = path.split("/")[-1]
      fold = 5
      self.val_pred = np.load(
          os.path.join(path, "all_eval_result.npy"), "r"
      )
      self.test_pred = 0
      self.all_test = []
      for i in range(fold):
          self.test_pred = self.test_pred + np.load(
              os.path.join(path, "test_%s.npy" % i), "r"
          )
          self.all_test.append(
              np.argmax(
                  np.load(os.path.join(path, "test_%s.npy" % i)), -1
              )
          )
      self.test_pred = self.test_pred / fold
  
      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 ModelStatus object from inherited base classes: object with example: obj = ModelStatus(path)