code: def test_peephole_lstm_cell():
      def _run_cell(cell_fn, **kwargs):
          inputs = tf.one_hot([1, 2, 3, 4], 4)
          cell = cell_fn(5, **kwargs)
          cell.build(inputs.shape)
          initial_state = cell.get_initial_state(
              inputs=inputs, batch_size=4, dtype=tf.float32
          )
          output, _ = cell(inputs, initial_state)
          return output
  
      tf.random.set_seed(12345)
      first_implementation_output = _run_cell(
          PeepholeLSTMCell,
          kernel_initializer="ones",
          recurrent_activation="sigmoid",
          implementation=1,
      )
      second_implementation_output = _run_cell(
          PeepholeLSTMCell,
          kernel_initializer="ones",
          recurrent_activation="sigmoid",
          implementation=2,
      )
      expected_output = np.asarray(
          [
              [0.417551, 0.417551, 0.417551, 0.417551, 0.417551],
              [0.417551, 0.417551, 0.417551, 0.417551, 0.417551],
              [0.417551, 0.417551, 0.417551, 0.417551, 0.417551],
              [0.0, 0.0, 0.0, 0.0, 0.0],
          ],
          dtype=np.float32,
      )
      np.testing.assert_allclose(
          first_implementation_output, second_implementation_output
      )
      np.testing.assert_allclose(
          first_implementation_output,
          expected_output,
          rtol=1e-6,
          atol=1e-6,
      )
  
imports: import numpy
  import tensorflow
  from tensorflow_addons.rnn import PeepholeLSTMCell
label_desc: Use image node_id 1 for calling a global function with example usage: test_peephole_lstm_cell() and returns: output