code: def load_data():
      dataset = datasets.load_boston()
      data = dataset.data
      target = dataset.target.reshape((-1, 1))
  
      data_scaler = preprocessing.MinMaxScaler((-3, 3))
      target_scaler = preprocessing.MinMaxScaler()
  
      data = data_scaler.fit_transform(data)
      target = target_scaler.fit_transform(target)
  
      return target_scaler, train_test_split(
          data, target, test_size=0.15
      )
  
imports: import numpy
  import matplotlib.pyplot
  from sklearn import datasets, preprocessing
  from sklearn.model_selection import train_test_split
  from neupy import algorithms, layers
label_desc: Use image node_id 2 for calling a global function with example usage: load_data() and returns: target_scaler,  train_test_split