code: def test_correctness(self, implementation):
      sequence = np.arange(72).reshape((3, 6, 4)).astype("float32")
      layer = layers.LSTM(
          3,
          kernel_initializer=initializers.Constant(0.01),
          recurrent_initializer=initializers.Constant(0.02),
          bias_initializer=initializers.Constant(0.03),
          implementation=implementation,
      )
      output = layer(sequence)
      self.assertAllClose(
          np.array(
              [
                  [0.6288687, 0.6288687, 0.6288687],
                  [0.86899155, 0.86899155, 0.86899155],
                  [0.9460773, 0.9460773, 0.9460773],
              ]
          ),
          output,
      )
  
      layer = layers.LSTM(
          3,
          kernel_initializer=initializers.Constant(0.01),
          recurrent_initializer=initializers.Constant(0.02),
          bias_initializer=initializers.Constant(0.03),
          go_backwards=True,
          implementation=implementation,
      )
      output = layer(sequence)
      self.assertAllClose(
          np.array(
              [
                  [0.35622165, 0.35622165, 0.35622165],
                  [0.74789524, 0.74789524, 0.74789524],
                  [0.8872726, 0.8872726, 0.8872726],
              ]
          ),
          output,
      )
  
      layer = layers.LSTM(
          3,
          kernel_initializer=initializers.Constant(0.01),
          recurrent_initializer=initializers.Constant(0.02),
          bias_initializer=initializers.Constant(0.03),
          unroll=True,
          implementation=implementation,
      )
      output = layer(sequence)
      self.assertAllClose(
          np.array(
              [
                  [0.6288687, 0.6288687, 0.6288687],
                  [0.86899155, 0.86899155, 0.86899155],
                  [0.9460773, 0.9460773, 0.9460773],
              ]
          ),
          output,
      )
  
      layer = layers.LSTM(
          3,
          kernel_initializer=initializers.Constant(0.01),
          recurrent_initializer=initializers.Constant(0.02),
          bias_initializer=initializers.Constant(0.03),
          unit_forget_bias=False,
          implementation=implementation,
      )
      output = layer(sequence)
      self.assertAllClose(
          np.array(
              [
                  [0.57019705, 0.57019705, 0.57019705],
                  [0.8661914, 0.8661914, 0.8661914],
                  [0.9459622, 0.9459622, 0.9459622],
              ]
          ),
          output,
      )
  
      layer = layers.LSTM(
          3,
          kernel_initializer=initializers.Constant(0.01),
          recurrent_initializer=initializers.Constant(0.02),
          bias_initializer=initializers.Constant(0.03),
          use_bias=False,
          implementation=implementation,
      )
      output = layer(sequence)
      self.assertAllClose(
          np.array(
              [
                  [0.54986924, 0.54986924, 0.54986924],
                  [0.86226785, 0.86226785, 0.86226785],
                  [0.9443936, 0.9443936, 0.9443936],
              ]
          ),
          output,
      )
  
file_path: keras/keras/layers/rnn/lstm_test.py
imports: import numpy
  import pytest
  from absl.testing import parameterized
  from keras import initializers
  from keras import layers
  from keras import testing
label_desc: Use image node_id 2 for calling the LSTMTest obj's underlying member method code with example usage: obj.test_correctness(implementation) without return types