code: def test_stacked_gru_with_enabled_backwards_option(self):
      x_train, x_test, y_train, y_test = self.data
      x_train = x_train[:, ::-1]
      x_test = x_test[:, ::-1]
  
      network = algorithms.RMSProp(
          [
              layers.Input(self.n_time_steps),
              layers.Embedding(self.n_categories, 10),
              layers.GRU(10, only_return_final=False, backwards=True),
              layers.GRU(2, backwards=True),
              layers.Sigmoid(1),
          ],
          step=0.01,
          verbose=False,
          batch_size=10,
          loss="binary_crossentropy",
      )
      network.train(x_train, y_train, x_test, y_test, epochs=20)
  
      y_predicted = network.predict(x_test).round()
      accuracy = (y_predicted.T == y_test).mean()
  
      self.assertGreaterEqual(accuracy, 0.8)
  
imports: import numpy
  import tensorflow
  from sklearn.model_selection import train_test_split
  from neupy.datasets import reber
  from neupy.utils import asfloat, tensorflow_session
  from neupy.exceptions import LayerConnectionError
  from neupy.layers.recurrent import clip_gradient
  from neupy import layers, algorithms, init
  from base import BaseTestCase
label_desc: Use image node_id 10 for calling the GRUTestCase obj's underlying member method code with example usage: obj.test_stacked_gru_with_enabled_backwards_option() without return types