code: def test_stack_birnn_simplernncell(self):
      inputs = tl.layers.Input(
          [self.batch_size, self.num_steps, self.embedding_size]
      )
      rnnlayer = tl.layers.BiRNN(
          fw_cell=tf.keras.layers.SimpleRNNCell(
              units=self.hidden_size, dropout=0.1
          ),
          bw_cell=tf.keras.layers.SimpleRNNCell(
              units=self.hidden_size + 1, dropout=0.1
          ),
          return_seq_2d=False,
          return_last_state=False,
      )
      rnn = rnnlayer(inputs)
      rnnlayer2 = tl.layers.BiRNN(
          fw_cell=tf.keras.layers.SimpleRNNCell(
              units=self.hidden_size, dropout=0.1
          ),
          bw_cell=tf.keras.layers.SimpleRNNCell(
              units=self.hidden_size + 1, dropout=0.1
          ),
          return_seq_2d=True,
          return_last_state=False,
      )
      rnn2 = rnnlayer2(rnn)
      dense = tl.layers.Dense(n_units=1)(rnn2)
      outputs = tl.layers.Reshape([-1, self.num_steps])(dense)
      rnn_model = tl.models.Model(inputs=inputs, outputs=outputs)
      print(rnn_model)
  
      optimizer = tf.optimizers.Adam(learning_rate=0.01)
  
      rnn_model.train()
      assert rnnlayer.is_train
      assert rnnlayer2.is_train
  
      for epoch in range(50):
          with tf.GradientTape() as tape:
              pred_y = rnn_model(self.data_x)
              loss = tl.cost.mean_squared_error(pred_y, self.data_y2)
  
          gradients = tape.gradient(loss, rnn_model.trainable_weights)
          optimizer.apply_gradients(
              zip(gradients, rnn_model.trainable_weights)
          )
  
          if (epoch + 1) % 10 == 0:
              print("epoch %d, loss %f" % (epoch, loss))
  
imports: import os
  import unittest
  import numpy
  import tensorflow
  import tensorlayer
  from tests.utils import CustomTestCase
label_desc: Use image node_id 19 for calling the Layer_RNN_Test obj's underlying member method code with example usage: obj.test_stack_birnn_simplernncell() without return types