code: def _normalize(x, norm=None):
      """
      Apply one-hot encoding or z-score to a list of node features
      """
      if norm == "ohe":
          fnorm = OneHotEncoder(sparse=False, categories="auto")
      elif norm == "zscore":
          fnorm = StandardScaler()
      else:
          return x
      return fnorm.fit_transform(x)
  
file_path: spektral/spektral/datasets/tudataset.py
imports: import glob
  import os
  import shutil
  from os import path
  from urllib.error import URLError
  import numpy
  import pandas
  from sklearn.preprocessing import OneHotEncoder, StandardScaler
  from spektral.data import Dataset, Graph
  from spektral.datasets.utils import download_file
  from spektral.utils import io, sparse
label_desc: Use image node_id 1 for calling a global function with example usage: _normalize(x, norm) and returns: fnorm, x