code: def filter_model(pred, true):
      model = sklearn.linear_model.HuberRegressor(max_iter=10000)
      model.fit(pred, true)
      idx_trivial = np.abs(model.coef_ * len(model.coef_)) < 1.8
      print("remaining #models:", np.sum(~idx_trivial))
      return idx_trivial
  
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 3 for calling a global function with example usage: filter_model(pred, true) and returns: idx_trivial