code: def test_correctness(self):
      sequence = np.arange(24).reshape((2, 3, 4)).astype("float32")
      layer = layers.SimpleRNN(
          4,
          kernel_initializer=initializers.Constant(0.01),
          recurrent_initializer=initializers.Constant(0.02),
          bias_initializer=initializers.Constant(0.03),
      )
      output = layer(sequence)
      self.assertAllClose(
          np.array(
              [
                  [0.405432, 0.405432, 0.405432, 0.405432],
                  [0.73605347, 0.73605347, 0.73605347, 0.73605347],
              ]
          ),
          output,
      )
      layer = layers.SimpleRNN(
          4,
          kernel_initializer=initializers.Constant(0.01),
          recurrent_initializer=initializers.Constant(0.02),
          bias_initializer=initializers.Constant(0.03),
          unroll=True,
      )
      output = layer(sequence)
      self.assertAllClose(
          np.array(
              [
                  [0.405432, 0.405432, 0.405432, 0.405432],
                  [0.73605347, 0.73605347, 0.73605347, 0.73605347],
              ]
          ),
          output,
      )
  
      layer = layers.SimpleRNN(
          4,
          kernel_initializer=initializers.Constant(0.01),
          recurrent_initializer=initializers.Constant(0.02),
          bias_initializer=initializers.Constant(0.03),
          go_backwards=True,
      )
      output = layer(sequence)
      self.assertAllClose(
          np.array(
              [
                  [0.11144729, 0.11144729, 0.11144729, 0.11144729],
                  [0.5528889, 0.5528889, 0.5528889, 0.5528889],
              ]
          ),
          output,
      )
      layer = layers.SimpleRNN(
          4,
          kernel_initializer=initializers.Constant(0.01),
          recurrent_initializer=initializers.Constant(0.02),
          bias_initializer=initializers.Constant(0.03),
          go_backwards=True,
          unroll=True,
      )
      output = layer(sequence)
      self.assertAllClose(
          np.array(
              [
                  [0.11144729, 0.11144729, 0.11144729, 0.11144729],
                  [0.5528889, 0.5528889, 0.5528889, 0.5528889],
              ]
          ),
          output,
      )
  
file_path: keras/keras/layers/rnn/simple_rnn_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