new_contents: import numpy as np
  
  def get_test_data(nb_train=1000, nb_test=500, input_shape=(10,), output_shape=(2,), 
      classification=True, nb_class=2):
      '''
          classification=True overrides output_shape 
          (i.e. output_shape is set to (1,)) and the output 
          consists in integers in [0, nb_class-1].
  
          Otherwise: float output with shape output_shape.
      '''
      nb_sample = nb_train + nb_test
      if classification:
          y = np.random.randint(0, nb_class, size=(nb_sample, 1))
          X = np.zeros((nb_sample,) + input_shape)
          for i in range(nb_sample):
              X[i] = np.random.normal(loc=y[i], scale=1.0, size=input_shape)
      else:
          y_loc = np.random.random((nb_sample,))
          X = np.zeros((nb_sample,) + input_shape)
          y = np.zeros((nb_sample,) + output_shape)
          for i in range(nb_sample):
              X[i] = np.random.normal(loc=y_loc[i], scale=1.0, size=input_shape)
              y[i] = np.random.normal(loc=y_loc[i], scale=1.0, size=output_shape)
  
      return (X[:nb_train], y[:nb_train]), (X[nb_train:], y[nb_train:])
  
subject: Add test utils