code: def prepare_epsilon(dataset_folder, nrows):
      url_train = (
          "https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary"
          "/epsilon_normalized.bz2"
      )
      url_test = (
          "https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary"
          "/epsilon_normalized.t.bz2"
      )
      pickle_url = os.path.join(
          dataset_folder,
          "epsilon"
          + ("" if nrows is None else "-" + str(nrows))
          + "-pickle.dat",
      )
      local_url_train = os.path.join(
          dataset_folder, os.path.basename(url_train)
      )
      local_url_test = os.path.join(
          dataset_folder, os.path.basename(url_test)
      )
  
      if os.path.exists(pickle_url):
          return pickle.load(open(pickle_url, "rb"))
      print("Preparing dataset ...")
  
      if not os.path.isfile(local_url_train):
          urlretrieve(url_train, local_url_train)
      if not os.path.isfile(local_url_test):
          urlretrieve(url_test, local_url_test)
  
      X_train, y_train = load_svmlight_file(
          local_url_train, dtype=np.float32
      )
      X_test, y_test = load_svmlight_file(
          local_url_test, dtype=np.float32
      )
      X_train = X_train.toarray()
      X_test = X_test.toarray()
      y_train[y_train <= 0] = 0
      y_test[y_test <= 0] = 0
  
      if nrows is not None:
          print(
              "Warning: nrows is specified, not using predefined test/train split for epsilon."
          )
  
          X_train = np.vstack((X_train, X_test))
          y_train = np.append(y_train, y_test)
          X_train = X_train[:nrows]
          y_train = y_train[:nrows]
          X_train, X_test, y_train, y_test = train_test_split(
              X_train,
              y_train,
              random_state=77,
              test_size=0.2,
          )
  
      data = Data(
          X_train.astype("|f4"),
          X_test.astype("|f4"),
          y_train,
          y_test,
          LearningTask.CLASSIFICATION,
      )
      pickle.dump(data, open(pickle_url, "wb"), protocol=4)
      return data
  
imports: import os
  from enum import Enum
  import pandas
  import pickle
  import numpy
  from sklearn.model_selection import train_test_split
  from sklearn.datasets import load_svmlight_file
  from urllib.request import urlretrieve
label_desc: Use image node_id 6 for calling a global function with example usage: prepare_epsilon(dataset_folder, nrows) and returns: data, pickle