code: def test_correctness(self):
      sequence = np.arange(24).reshape((2, 3, 4)).astype("float32")
      forward_layer = layers.SimpleRNN(
          2,
          kernel_initializer=initializers.Constant(0.01),
          recurrent_initializer=initializers.Constant(0.02),
          bias_initializer=initializers.Constant(0.03),
      )
      layer = layers.Bidirectional(
          layer=forward_layer,
      )
      output = layer(sequence)
      self.assertAllClose(
          np.array(
              [
                  [0.39687276, 0.39687276, 0.10004295, 0.10004295],
                  [0.7237238, 0.7237238, 0.53391594, 0.53391594],
              ]
          ),
          output,
      )
  
      layer = layers.Bidirectional(
          layer=forward_layer, merge_mode="ave"
      )
      output = layer(sequence)
      self.assertAllClose(
          np.array([[0.24845785, 0.24845785], [0.6288199, 0.6288199]]),
          output,
      )
  
      layer = layers.Bidirectional(layer=forward_layer, merge_mode=None)
      output1, output2 = layer(sequence)
      self.assertAllClose(
          np.array([[0.39687276, 0.39687276], [0.7237238, 0.7237238]]),
          output1,
      )
      self.assertAllClose(
          np.array(
              [[0.10004295, 0.10004295], [0.53391594, 0.53391594]]
          ),
          output2,
      )
  
      backward_layer = layers.SimpleRNN(
          2,
          kernel_initializer=initializers.Constant(0.03),
          recurrent_initializer=initializers.Constant(0.02),
          bias_initializer=initializers.Constant(0.01),
          go_backwards=True,
      )
      layer = layers.Bidirectional(
          layer=forward_layer,
          backward_layer=backward_layer,
          merge_mode="mul",
      )
      output = layer(sequence)
      self.assertAllClose(
          np.array([[0.08374989, 0.08374989], [0.6740834, 0.6740834]]),
          output,
      )
  
      forward_layer = layers.GRU(
          2,
          kernel_initializer=initializers.Constant(0.01),
          recurrent_initializer=initializers.Constant(0.02),
          bias_initializer=initializers.Constant(0.03),
          return_sequences=True,
      )
      layer = layers.Bidirectional(
          layer=forward_layer, merge_mode="sum"
      )
      output = layer(sequence)
      self.assertAllClose(
          np.array(
              [
                  [
                      [0.20937867, 0.20937867],
                      [0.34462988, 0.34462988],
                      [0.40290534, 0.40290534],
                  ],
                  [
                      [0.59829646, 0.59829646],
                      [0.6734641, 0.6734641],
                      [0.6479671, 0.6479671],
                  ],
              ]
          ),
          output,
      )
  
file_path: keras/keras/layers/rnn/bidirectional_test.py
imports: import numpy
  import pytest
  from keras import initializers
  from keras import layers
  from keras import testing
label_desc: Use image node_id 2 for calling the SimpleRNNTest obj's underlying member method code with example usage: obj.test_correctness() without return types