code: def load_dataset(filename, whiten=False):
      f = open(filename, "r")
      features = []
      labels = []
      tracer = 0
      for line in f:
          if tracer == 0:
              tracer += 1
              continue
          splits = line[:-1].split(",")
          features.append(splits[-1])
          labels.append(float(splits[-2]))
      features = np.array(features)
      labels = np.array(labels, dtype="float32").reshape(-1, 1)
  
      train_ind, val_ind, test_ins = split(features)
  
      train_features = np.take(features, train_ind)
      train_labels = np.take(labels, train_ind)
      val_features = np.take(features, val_ind)
      val_labels = np.take(labels, val_ind)
  
      return train_features, train_labels, val_features, val_labels
  
file_path: deepchem/contrib/mpnn/donkey.py
imports: import os
  import random
  from collections import OrderedDict
  import deepchem
  from deepchem.utils import ScaffoldGenerator
  from deepchem.utils.save import log
  import torch
  import torch.nn
  from torch.autograd import Variable
  import numpy
  from sklearn import preprocessing
  from sklearn.decomposition import TruncatedSVD
  from rdkit import Chem
  from rdkit import DataStructs
  from rdkit.Chem import AllChem
label_desc: Use image node_id 3 for calling a global function with example usage: load_dataset(filename, whiten) and returns: train_features,  train_labels,  val_features,  val_labels