code: def fetch_HIGGS():
      url = (
          "https://archive.ics.uci.edu/ml/machine-learning-databases/"
          "00280/HIGGS.csv.gz"
      )
      if not os.path.exists("../../HIGGS.csv"):
          print("Downloading HIGGS dataset from {}".format(url))
  
          if not os.path.exists("../../HIGGS.gz"):
              filedata = urllib.request.urlopen(url)
              data2write = filedata.read()
  
              with open("../../HIGGS.gz", "wb") as f:
                  f.write(data2write)
  
          print("Finished downloading")
          print("Extracting HIGGS.gz")
  
          if not os.path.exists("../../HIGGS.csv"):
              with gzip.open("../../HIGGS.gz", "rb") as f:
                  with open("../../HIGGS.csv", "wb") as csv_out:
                      shutil.copyfileobj(f, csv_out)
  
          print("Extracted csv")
      print("Reading CSV file")
      df = pd.read_csv("../../HIGGS.csv", header=None)
      data = df.values
      X = data[:, 1:]
      y = data[:, 0]
  
      return X, y
  
file_path: DESlib/benchmarks/bench_speed_faiss.py
imports: import gzip
  import os
  import shutil
  import time
  import urllib.request
  import numpy
  import pandas
  from sklearn.ensemble import BaggingClassifier
  from sklearn.model_selection import train_test_split
  from deslib.des.knora_e import KNORAE
label_desc: Use image node_id 2 for calling a global function with example usage: fetch_HIGGS() and returns: X,  y