code: def load_data(dtype=np.float32, order="F"):
      """Load the data, then cache and memmap the train/test split"""
      ######################################################################
      # Load dataset
      safe_print("Loading dataset...")
      data = fetch_mldata("MNIST original")
      X = check_array(data["data"], dtype=dtype, order=order)
      y = data["target"]
  
      # Normalize features
      X = X / 255
  
      # Create train-test split (as [Joachims, 2006])
      safe_print("Creating train-test split...")
      n_train = 60000
      X_train = X[:n_train]
      y_train = y[:n_train]
      X_test = X[n_train:]
      y_test = y[n_train:]
  
      return X_train, X_test, y_train, y_test
  
imports: from __future__ import division, print_function
  import os
  from time import time
  import argparse
  import numpy
  from mlens.utils import safe_print
  from mlens.ensemble import Subsemble
  from sklearn.cluster import MiniBatchKMeans
  from sklearn.datasets import fetch_mldata
  from sklearn.datasets import get_data_home
  from sklearn.ensemble import ExtraTreesClassifier
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.dummy import DummyClassifier
  from sklearn.externals.joblib import Memory
  from sklearn.kernel_approximation import Nystroem
  from sklearn.kernel_approximation import RBFSampler
  from sklearn.metrics import zero_one_loss
  from sklearn.pipeline import make_pipeline
  from sklearn.svm import LinearSVC
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.utils import check_array
  from sklearn.base import clone
  from sklearn.linear_model import LogisticRegression
  from sklearn.neural_network import MLPClassifier
label_desc: Use image node_id 1 for calling a global function with example usage: load_data(dtype, order) and returns: X_train,  X_test,  y_train,  y_test