code: def load_data():
      X, y = datasets.fetch_openml(
          "mnist_784", version=1, return_X_y=True
      )
      X /= 255.0
      X -= X.mean(axis=0)
  
      target_scaler = OneHotEncoder(sparse=False, categories="auto")
      y = target_scaler.fit_transform(y.reshape(-1, 1))
  
      return model_selection.train_test_split(
          X.astype(np.float32),
          y.astype(np.float32),
          test_size=(1 / 7.0),
      )
  
imports: import numpy
  from sklearn.preprocessing import OneHotEncoder
  from sklearn import model_selection, metrics, datasets
  from neupy import algorithms, layers
label_desc: Use image node_id 1 for calling a global function with example usage: load_data() and returns: model_selection