code: import pytest
  import numpy as np
  from numpy.testing import assert_allclose
  
  import keras
  from keras.utils.test_utils import layer_test
  from keras.utils.test_utils import keras_test
  from keras.layers import recurrent
  from keras.layers import embeddings
  from keras.models import Sequential
  from keras.models import Model
  from keras.engine.topology import Input
  from keras.layers.core import Masking
  from keras import regularizers
  from keras import backend as K
  
  num_samples, timesteps, embedding_dim, units = 2, 5, 4, 3
  embedding_num = 12
  
  
  @keras_test
  def rnn_test(f):
      """
      All the recurrent layers share the same interface,
      so we can run through them with a single function.
      """
      f = keras_test(f)
      return pytest.mark.parametrize('layer_class', [
          recurrent.SimpleRNN,
          recurrent.GRU,
          recurrent.LSTM
      ])(f)
  
  
  @rnn_test
  def test_return_sequences(layer_class):
      layer_test(layer_class,
                 kwargs={'units': units,
                         'return_sequences': True},
                 input_shape=(num_samples, timesteps, embedding_dim))
  
  
  @rnn_test
  def test_dynamic_behavior(layer_class):
      layer = layer_class(units, input_shape=(None, embedding_dim))
      model = Sequential()
      model.add(layer)
      model.compile('sgd', 'mse')
      x = np.random.random((num_samples, timesteps, embedding_dim))
      y = np.random.random((num_samples, units))
      model.train_on_batch(x, y)
  
  
  @rnn_test
  def test_stateful_invalid_use(layer_class):
      layer = layer_class(units,
                          stateful=True,
                          batch_input_shape=(num_samples,
                                             timesteps,
                                             embedding_dim))
      model = Sequential()
      model.add(layer)
      model.compile('sgd', 'mse')
      x = np.random.random((num_samples * 2, timesteps, embedding_dim))
      y = np.random.random((num_samples * 2, units))
      with pytest.raises(ValueError):
          model.fit(x, y)
      with pytest.raises(ValueError):
          model.predict(x, batch_size=num_samples + 1)
  
  
  @rnn_test
  @pytest.mark.skipif((K.backend() == 'cntk'),
                      reason='Not yet supported.')
  def test_dropout(layer_class):
      for unroll in [True, False]:
          layer_test(layer_class,
                     kwargs={'units': units,
                             'dropout': 0.1,
                             'recurrent_dropout': 0.1,
                             'unroll': unroll},
                     input_shape=(num_samples, timesteps, embedding_dim))
  
          # Test that dropout is applied during training
          x = K.ones((num_samples, timesteps, embedding_dim))
          layer = layer_class(units, dropout=0.5, recurrent_dropout=0.5,
                              input_shape=(timesteps, embedding_dim))
          y = layer(x)
          assert y._uses_learning_phase
  
          y = layer(x, training=True)
          assert not getattr(y, '_uses_learning_phase')
  
          # Test that dropout is not applied during testing
          x = np.random.random((num_samples, timesteps, embedding_dim))
          layer = layer_class(units, dropout=0.5, recurrent_dropout=0.5,
                              unroll=unroll,
                              input_shape=(timesteps, embedding_dim))
          model = Sequential([layer])
          assert model.uses_learning_phase
          y1 = model.predict(x)
          y2 = model.predict(x)
          assert_allclose(y1, y2)
  
  
  @rnn_test
  def test_statefulness(layer_class):
      model = Sequential()
      model.add(embeddings.Embedding(embedding_num, embedding_dim,
                                     mask_zero=True,
                                     input_length=timesteps,
                                     batch_input_shape=(num_samples, timesteps)))
      layer = layer_class(units, return_sequences=False,
                          stateful=True,
                          weights=None)
      model.add(layer)
      model.compile(optimizer='sgd', loss='mse')
      out1 = model.predict(np.ones((num_samples, timesteps)))
      assert(out1.shape == (num_samples, units))
  
      # train once so that the states change
      model.train_on_batch(np.ones((num_samples, timesteps)),
                           np.ones((num_samples, units)))
      out2 = model.predict(np.ones((num_samples, timesteps)))
  
      # if the state is not reset, output should be different
      assert(out1.max() != out2.max())
  
      # check that output changes after states are reset
      # (even though the model itself didn't change)
      layer.reset_states()
      out3 = model.predict(np.ones((num_samples, timesteps)))
      assert(out2.max() != out3.max())
  
      # check that container-level reset_states() works
      model.reset_states()
      out4 = model.predict(np.ones((num_samples, timesteps)))
      assert_allclose(out3, out4, atol=1e-5)
  
      # check that the call to `predict` updated the states
      out5 = model.predict(np.ones((num_samples, timesteps)))
      assert(out4.max() != out5.max())
  
  
  @rnn_test
  def test_masking_correctness(layer_class):
      # Check masking: output with left padding and right padding
      # should be the same.
      model = Sequential()
      model.add(embeddings.Embedding(embedding_num, embedding_dim,
                                     mask_zero=True,
                                     input_length=timesteps,
                                     batch_input_shape=(num_samples, timesteps)))
      layer = layer_class(units, return_sequences=False)
      model.add(layer)
      model.compile(optimizer='sgd', loss='mse')
  
      left_padded_input = np.ones((num_samples, timesteps))
      left_padded_input[0, :1] = 0
      left_padded_input[1, :2] = 0
      out6 = model.predict(left_padded_input)
  
      right_padded_input = np.ones((num_samples, timesteps))
      right_padded_input[0, -1:] = 0
      right_padded_input[1, -2:] = 0
      out7 = model.predict(right_padded_input)
  
      assert_allclose(out7, out6, atol=1e-5)
  
  
  @rnn_test
  def test_implementation_mode(layer_class):
      for mode in [1, 2]:
          # Without dropout
          layer_test(layer_class,
                     kwargs={'units': units,
                             'implementation': mode},
                     input_shape=(num_samples, timesteps, embedding_dim))
          # With dropout
          layer_test(layer_class,
                     kwargs={'units': units,
                             'implementation': mode,
                             'dropout': 0.1,
                             'recurrent_dropout': 0.1},
                     input_shape=(num_samples, timesteps, embedding_dim))
          # Without bias
          layer_test(layer_class,
                     kwargs={'units': units,
                             'implementation': mode,
                             'use_bias': False},
                     input_shape=(num_samples, timesteps, embedding_dim))
  
  
  @rnn_test
  def test_regularizer(layer_class):
      layer = layer_class(units, return_sequences=False, weights=None,
                          input_shape=(timesteps, embedding_dim),
                          kernel_regularizer=regularizers.l1(0.01),
                          recurrent_regularizer=regularizers.l1(0.01),
                          bias_regularizer='l2')
      layer.build((None, None, embedding_dim))
      assert len(layer.losses) == 3
      assert len(layer.cell.losses) == 3
  
      layer = layer_class(units, return_sequences=False, weights=None,
                          input_shape=(timesteps, embedding_dim),
                          activity_regularizer='l2')
      assert layer.activity_regularizer
      x = K.variable(np.ones((num_samples, timesteps, embedding_dim)))
      layer(x)
      assert len(layer.cell.get_losses_for(x)) == 0
      assert len(layer.get_losses_for(x)) == 1
  
  
  @rnn_test
  def test_trainability(layer_class):
      layer = layer_class(units)
      layer.build((None, None, embedding_dim))
      assert len(layer.weights) == 3
      assert len(layer.trainable_weights) == 3
      assert len(layer.non_trainable_weights) == 0
      layer.trainable = False
      assert len(layer.weights) == 3
      assert len(layer.trainable_weights) == 0
      assert len(layer.non_trainable_weights) == 3
      layer.trainable = True
      assert len(layer.weights) == 3
      assert len(layer.trainable_weights) == 3
      assert len(layer.non_trainable_weights) == 0
  
  
  @keras_test
  def test_masking_layer():
      ''' This test based on a previously failing issue here:
      https://github.com/fchollet/keras/issues/1567
      '''
      inputs = np.random.random((6, 3, 4))
      targets = np.abs(np.random.random((6, 3, 5)))
      targets /= targets.sum(axis=-1, keepdims=True)
  
      model = Sequential()
      model.add(Masking(input_shape=(3, 4)))
      model.add(recurrent.SimpleRNN(units=5, return_sequences=True, unroll=False))
      model.compile(loss='categorical_crossentropy', optimizer='adam')
      model.fit(inputs, targets, epochs=1, batch_size=100, verbose=1)
  
      model = Sequential()
      model.add(Masking(input_shape=(3, 4)))
      model.add(recurrent.SimpleRNN(units=5, return_sequences=True, unroll=True))
      model.compile(loss='categorical_crossentropy', optimizer='adam')
      model.fit(inputs, targets, epochs=1, batch_size=100, verbose=1)
  
  
  @rnn_test
  def test_from_config(layer_class):
      stateful_flags = (False, True)
      for stateful in stateful_flags:
          l1 = layer_class(units=1, stateful=stateful)
          l2 = layer_class.from_config(l1.get_config())
          assert l1.get_config() == l2.get_config()
  
  
  @rnn_test
  def test_specify_initial_state_keras_tensor(layer_class):
      num_states = 2 if layer_class is recurrent.LSTM else 1
  
      # Test with Keras tensor
      inputs = Input((timesteps, embedding_dim))
      initial_state = [Input((units,)) for _ in range(num_states)]
      layer = layer_class(units)
      if len(initial_state) == 1:
          output = layer(inputs, initial_state=initial_state[0])
      else:
          output = layer(inputs, initial_state=initial_state)
      assert initial_state[0] in layer.inbound_nodes[0].input_tensors
  
      model = Model([inputs] + initial_state, output)
      model.compile(loss='categorical_crossentropy', optimizer='adam')
  
      inputs = np.random.random((num_samples, timesteps, embedding_dim))
      initial_state = [np.random.random((num_samples, units))
                       for _ in range(num_states)]
      targets = np.random.random((num_samples, units))
      model.fit([inputs] + initial_state, targets)
  
  
  @rnn_test
  def test_specify_initial_state_non_keras_tensor(layer_class):
      num_states = 2 if layer_class is recurrent.LSTM else 1
  
      # Test with non-Keras tensor
      inputs = Input((timesteps, embedding_dim))
      initial_state = [K.random_normal_variable((num_samples, units), 0, 1)
                       for _ in range(num_states)]
      layer = layer_class(units)
      output = layer(inputs, initial_state=initial_state)
  
      model = Model(inputs, output)
      model.compile(loss='categorical_crossentropy', optimizer='adam')
  
      inputs = np.random.random((num_samples, timesteps, embedding_dim))
      targets = np.random.random((num_samples, units))
      model.fit(inputs, targets)
  
  
  @rnn_test
  def test_reset_states_with_values(layer_class):
      num_states = 2 if layer_class is recurrent.LSTM else 1
  
      layer = layer_class(units, stateful=True)
      layer.build((num_samples, timesteps, embedding_dim))
      layer.reset_states()
      assert len(layer.states) == num_states
      assert layer.states[0] is not None
      np.testing.assert_allclose(K.eval(layer.states[0]),
                                 np.zeros(K.int_shape(layer.states[0])),
                                 atol=1e-4)
      state_shapes = [K.int_shape(state) for state in layer.states]
      values = [np.ones(shape) for shape in state_shapes]
      if len(values) == 1:
          values = values[0]
      layer.reset_states(values)
      np.testing.assert_allclose(K.eval(layer.states[0]),
                                 np.ones(K.int_shape(layer.states[0])),
                                 atol=1e-4)
  
      # Test fit with invalid data
      with pytest.raises(ValueError):
          layer.reset_states([1] * (len(layer.states) + 1))
  
  
  @rnn_test
  def test_initial_states_as_other_inputs(layer_class):
      num_states = 2 if layer_class is recurrent.LSTM else 1
  
      # Test with Keras tensor
      main_inputs = Input((timesteps, embedding_dim))
      initial_state = [Input((units,)) for _ in range(num_states)]
      inputs = [main_inputs] + initial_state
  
      layer = layer_class(units)
      output = layer(inputs)
      assert initial_state[0] in layer.inbound_nodes[0].input_tensors
  
      model = Model(inputs, output)
      model.compile(loss='categorical_crossentropy', optimizer='adam')
  
      main_inputs = np.random.random((num_samples, timesteps, embedding_dim))
      initial_state = [np.random.random((num_samples, units))
                       for _ in range(num_states)]
      targets = np.random.random((num_samples, units))
      model.train_on_batch([main_inputs] + initial_state, targets)
  
  
  @rnn_test
  def test_specify_state_with_masking(layer_class):
      ''' This test based on a previously failing issue here:
      https://github.com/fchollet/keras/issues/1567
      '''
      num_states = 2 if layer_class is recurrent.LSTM else 1
  
      inputs = Input((timesteps, embedding_dim))
      _ = Masking()(inputs)
      initial_state = [Input((units,)) for _ in range(num_states)]
      output = layer_class(units)(inputs, initial_state=initial_state)
  
      model = Model([inputs] + initial_state, output)
      model.compile(loss='categorical_crossentropy', optimizer='adam')
  
      inputs = np.random.random((num_samples, timesteps, embedding_dim))
      initial_state = [np.random.random((num_samples, units))
                       for _ in range(num_states)]
      targets = np.random.random((num_samples, units))
      model.fit([inputs] + initial_state, targets)
  
  
  @rnn_test
  def test_return_state(layer_class):
      num_states = 2 if layer_class is recurrent.LSTM else 1
  
      inputs = Input(batch_shape=(num_samples, timesteps, embedding_dim))
      layer = layer_class(units, return_state=True, stateful=True)
      outputs = layer(inputs)
      output, state = outputs[0], outputs[1:]
      assert len(state) == num_states
      model = Model(inputs, state[0])
  
      inputs = np.random.random((num_samples, timesteps, embedding_dim))
      state = model.predict(inputs)
      np.testing.assert_allclose(K.eval(layer.states[0]), state, atol=1e-4)
  
  
  @rnn_test
  def test_state_reuse(layer_class):
      inputs = Input(batch_shape=(num_samples, timesteps, embedding_dim))
      layer = layer_class(units, return_state=True, return_sequences=True)
      outputs = layer(inputs)
      output, state = outputs[0], outputs[1:]
      output = layer_class(units)(output, initial_state=state)
      model = Model(inputs, output)
  
      inputs = np.random.random((num_samples, timesteps, embedding_dim))
      outputs = model.predict(inputs)
  
  
  @keras_test
  def test_minimal_rnn_cell_non_layer():
  
      class MinimalRNNCell(object):
  
          def __init__(self, units, input_dim):
              self.units = units
              self.state_size = units
              self.kernel = keras.backend.variable(
                  np.random.random((input_dim, units)))
  
          def call(self, inputs, states):
              prev_output = states[0]
              output = keras.backend.dot(inputs, self.kernel) + prev_output
              return output, [output]
  
      # Basic test case.
      cell = MinimalRNNCell(32, 5)
      x = keras.Input((None, 5))
      layer = recurrent.RNN(cell)
      y = layer(x)
      model = keras.models.Model(x, y)
      model.compile(optimizer='rmsprop', loss='mse')
      model.train_on_batch(np.zeros((6, 5, 5)), np.zeros((6, 32)))
  
      # Test stacking.
      cells = [MinimalRNNCell(8, 5),
               MinimalRNNCell(32, 8),
               MinimalRNNCell(32, 32)]
      layer = recurrent.RNN(cells)
      y = layer(x)
      model = keras.models.Model(x, y)
      model.compile(optimizer='rmsprop', loss='mse')
      model.train_on_batch(np.zeros((6, 5, 5)), np.zeros((6, 32)))
  
  
  @keras_test
  def test_minimal_rnn_cell_non_layer_multiple_states():
  
      class MinimalRNNCell(object):
  
          def __init__(self, units, input_dim):
              self.units = units
              self.state_size = (units, units)
              self.kernel = keras.backend.variable(
                  np.random.random((input_dim, units)))
  
          def call(self, inputs, states):
              prev_output_1 = states[0]
              prev_output_2 = states[1]
              output = keras.backend.dot(inputs, self.kernel)
              output += prev_output_1
              output -= prev_output_2
              return output, [output * 2, output * 3]
  
      # Basic test case.
      cell = MinimalRNNCell(32, 5)
      x = keras.Input((None, 5))
      layer = recurrent.RNN(cell)
      y = layer(x)
      model = keras.models.Model(x, y)
      model.compile(optimizer='rmsprop', loss='mse')
      model.train_on_batch(np.zeros((6, 5, 5)), np.zeros((6, 32)))
  
      # Test stacking.
      cells = [MinimalRNNCell(8, 5),
               MinimalRNNCell(16, 8),
               MinimalRNNCell(32, 16)]
      layer = recurrent.RNN(cells)
      assert layer.cell.state_size == (32, 32, 16, 16, 8, 8)
      y = layer(x)
      model = keras.models.Model(x, y)
      model.compile(optimizer='rmsprop', loss='mse')
      model.train_on_batch(np.zeros((6, 5, 5)), np.zeros((6, 32)))
  
  
  @keras_test
  def test_minimal_rnn_cell_layer():
  
      class MinimalRNNCell(keras.layers.Layer):
  
          def __init__(self, units, **kwargs):
              self.units = units
              self.state_size = units
              super(MinimalRNNCell, self).__init__(**kwargs)
  
          def build(self, input_shape):
              self.kernel = self.add_weight(shape=(input_shape[-1], self.units),
                                            initializer='uniform',
                                            name='kernel')
              self.recurrent_kernel = self.add_weight(
                  shape=(self.units, self.units),
                  initializer='uniform',
                  name='recurrent_kernel')
              self.built = True
  
          def call(self, inputs, states):
              prev_output = states[0]
              h = keras.backend.dot(inputs, self.kernel)
              output = h + keras.backend.dot(prev_output, self.recurrent_kernel)
              return output, [output]
  
          def get_config(self):
              config = {'units': self.units}
              base_config = super(MinimalRNNCell, self).get_config()
              return dict(list(base_config.items()) + list(config.items()))
  
      # Test basic case.
      x = keras.Input((None, 5))
      cell = MinimalRNNCell(32)
      layer = recurrent.RNN(cell)
      y = layer(x)
      model = keras.models.Model(x, y)
      model.compile(optimizer='rmsprop', loss='mse')
      model.train_on_batch(np.zeros((6, 5, 5)), np.zeros((6, 32)))
  
      # Test basic case serialization.
      x_np = np.random.random((6, 5, 5))
      y_np = model.predict(x_np)
      weights = model.get_weights()
      config = layer.get_config()
      with keras.utils.CustomObjectScope({'MinimalRNNCell': MinimalRNNCell}):
          layer = recurrent.RNN.from_config(config)
      y = layer(x)
      model = keras.models.Model(x, y)
      model.set_weights(weights)
      y_np_2 = model.predict(x_np)
      assert_allclose(y_np, y_np_2, atol=1e-4)
  
      # Test stacking.
      cells = [MinimalRNNCell(8),
               MinimalRNNCell(12),
               MinimalRNNCell(32)]
      layer = recurrent.RNN(cells)
      y = layer(x)
      model = keras.models.Model(x, y)
      model.compile(optimizer='rmsprop', loss='mse')
      model.train_on_batch(np.zeros((6, 5, 5)), np.zeros((6, 32)))
  
      # Test stacked RNN serialization.
      x_np = np.random.random((6, 5, 5))
      y_np = model.predict(x_np)
      weights = model.get_weights()
      config = layer.get_config()
      with keras.utils.CustomObjectScope({'MinimalRNNCell': MinimalRNNCell}):
          layer = recurrent.RNN.from_config(config)
      y = layer(x)
      model = keras.models.Model(x, y)
      model.set_weights(weights)
      y_np_2 = model.predict(x_np)
      assert_allclose(y_np, y_np_2, atol=1e-4)
  
  
  @keras_test
  def test_stacked_rnn_attributes():
      cells = [recurrent.LSTMCell(3),
               recurrent.LSTMCell(3, kernel_regularizer='l2')]
      layer = recurrent.RNN(cells)
      layer.build((None, None, 5))
  
      # Test regularization losses
      assert len(layer.losses) == 1
  
      # Test weights
      assert len(layer.trainable_weights) == 6
      cells[0].trainable = False
      assert len(layer.trainable_weights) == 3
      assert len(layer.non_trainable_weights) == 3
  
      # Test `get_losses_for`
      x = keras.Input((None, 5))
      y = K.sum(x)
      cells[0].add_loss(y, inputs=x)
      assert layer.get_losses_for(x) == [y]
  
  
  @rnn_test
  def test_batch_size_equal_one(layer_class):
      inputs = Input(batch_shape=(1, timesteps, embedding_dim))
      layer = layer_class(units)
      outputs = layer(inputs)
      model = Model(inputs, outputs)
      model.compile('sgd', 'mse')
      x = np.random.random((1, timesteps, embedding_dim))
      y = np.random.random((1, units))
      model.train_on_batch(x, y)
  
  
  def test_rnn_cell_with_constants_layer():
  
      class RNNCellWithConstants(keras.layers.Layer):
  
          def __init__(self, units, **kwargs):
              self.units = units
              self.state_size = units
              super(RNNCellWithConstants, self).__init__(**kwargs)
  
          def build(self, input_shape):
              if not isinstance(input_shape, list):
                  raise TypeError('expects constants shape')
              [input_shape, constant_shape] = input_shape
              # will (and should) raise if more than one constant passed
  
              self.input_kernel = self.add_weight(
                  shape=(input_shape[-1], self.units),
                  initializer='uniform',
                  name='kernel')
              self.recurrent_kernel = self.add_weight(
                  shape=(self.units, self.units),
                  initializer='uniform',
                  name='recurrent_kernel')
              self.constant_kernel = self.add_weight(
                  shape=(constant_shape[-1], self.units),
                  initializer='uniform',
                  name='constant_kernel')
              self.built = True
  
          def call(self, inputs, states, constants):
              [prev_output] = states
              [constant] = constants
              h_input = keras.backend.dot(inputs, self.input_kernel)
              h_state = keras.backend.dot(prev_output, self.recurrent_kernel)
              h_const = keras.backend.dot(constant, self.constant_kernel)
              output = h_input + h_state + h_const
              return output, [output]
  
          def get_config(self):
              config = {'units': self.units}
              base_config = super(RNNCellWithConstants, self).get_config()
              return dict(list(base_config.items()) + list(config.items()))
  
      # Test basic case.
      x = keras.Input((None, 5))
      c = keras.Input((3,))
      cell = RNNCellWithConstants(32)
      layer = recurrent.RNN(cell)
      y = layer(x, constants=c)
      model = keras.models.Model([x, c], y)
      model.compile(optimizer='rmsprop', loss='mse')
      model.train_on_batch(
          [np.zeros((6, 5, 5)), np.zeros((6, 3))],
          np.zeros((6, 32))
      )
  
      # Test basic case serialization.
      x_np = np.random.random((6, 5, 5))
      c_np = np.random.random((6, 3))
      y_np = model.predict([x_np, c_np])
      weights = model.get_weights()
      config = layer.get_config()
      custom_objects = {'RNNCellWithConstants': RNNCellWithConstants}
      with keras.utils.CustomObjectScope(custom_objects):
          layer = recurrent.RNN.from_config(config.copy())
      y = layer(x, constants=c)
      model = keras.models.Model([x, c], y)
      model.set_weights(weights)
      y_np_2 = model.predict([x_np, c_np])
      assert_allclose(y_np, y_np_2, atol=1e-4)
  
      # test flat list inputs
      with keras.utils.CustomObjectScope(custom_objects):
          layer = recurrent.RNN.from_config(config.copy())
      y = layer([x, c])
      model = keras.models.Model([x, c], y)
      model.set_weights(weights)
      y_np_3 = model.predict([x_np, c_np])
      assert_allclose(y_np, y_np_3, atol=1e-4)
  
  
  def test_rnn_cell_with_constants_layer_passing_initial_state():
  
      class RNNCellWithConstants(keras.layers.Layer):
  
          def __init__(self, units, **kwargs):
              self.units = units
              self.state_size = units
              super(RNNCellWithConstants, self).__init__(**kwargs)
  
          def build(self, input_shape):
              if not isinstance(input_shape, list):
                  raise TypeError('expects constants shape')
              [input_shape, constant_shape] = input_shape
              # will (and should) raise if more than one constant passed
  
              self.input_kernel = self.add_weight(
                  shape=(input_shape[-1], self.units),
                  initializer='uniform',
                  name='kernel')
              self.recurrent_kernel = self.add_weight(
                  shape=(self.units, self.units),
                  initializer='uniform',
                  name='recurrent_kernel')
              self.constant_kernel = self.add_weight(
                  shape=(constant_shape[-1], self.units),
                  initializer='uniform',
                  name='constant_kernel')
              self.built = True
  
          def call(self, inputs, states, constants):
              [prev_output] = states
              [constant] = constants
              h_input = keras.backend.dot(inputs, self.input_kernel)
              h_state = keras.backend.dot(prev_output, self.recurrent_kernel)
              h_const = keras.backend.dot(constant, self.constant_kernel)
              output = h_input + h_state + h_const
              return output, [output]
  
          def get_config(self):
              config = {'units': self.units}
              base_config = super(RNNCellWithConstants, self).get_config()
              return dict(list(base_config.items()) + list(config.items()))
  
      # Test basic case.
      x = keras.Input((None, 5))
      c = keras.Input((3,))
      s = keras.Input((32,))
      cell = RNNCellWithConstants(32)
      layer = recurrent.RNN(cell)
      y = layer(x, initial_state=s, constants=c)
      model = keras.models.Model([x, s, c], y)
      model.compile(optimizer='rmsprop', loss='mse')
      model.train_on_batch(
          [np.zeros((6, 5, 5)), np.zeros((6, 32)), np.zeros((6, 3))],
          np.zeros((6, 32))
      )
  
      # Test basic case serialization.
      x_np = np.random.random((6, 5, 5))
      s_np = np.random.random((6, 32))
      c_np = np.random.random((6, 3))
      y_np = model.predict([x_np, s_np, c_np])
      weights = model.get_weights()
      config = layer.get_config()
      custom_objects = {'RNNCellWithConstants': RNNCellWithConstants}
      with keras.utils.CustomObjectScope(custom_objects):
          layer = recurrent.RNN.from_config(config.copy())
      y = layer(x, initial_state=s, constants=c)
      model = keras.models.Model([x, s, c], y)
      model.set_weights(weights)
      y_np_2 = model.predict([x_np, s_np, c_np])
      assert_allclose(y_np, y_np_2, atol=1e-4)
  
      # verify that state is used
      y_np_2_different_s = model.predict([x_np, s_np + 10., c_np])
      with pytest.raises(AssertionError):
          assert_allclose(y_np, y_np_2_different_s, atol=1e-4)
  
      # test flat list inputs
      with keras.utils.CustomObjectScope(custom_objects):
          layer = recurrent.RNN.from_config(config.copy())
      y = layer([x, s, c])
      model = keras.models.Model([x, s, c], y)
      model.set_weights(weights)
      y_np_3 = model.predict([x_np, s_np, c_np])
      assert_allclose(y_np, y_np_3, atol=1e-4)
  
  
  if __name__ == '__main__':
      pytest.main([__file__])
  
apis: numpy.random.random numpy.ones numpy.zeros numpy.testing.assert_allclose