code: def testReturnsConfig_KerasRNNLayerWithKerasRNNCells(self):
      lstm_cell = l.LSTMCell(3)
      gru_cell = l.GRUCell(2)
      model = keras.Sequential(
          [l.RNN([lstm_cell, gru_cell], input_shape=(3, 2))]
      )
      layer = model.layers[0]
  
      quantize_config = self.quantize_registry.get_quantize_config(
          layer
      )
  
      (weights, weight_quantizers) = self._convert_list(
          quantize_config.get_weights_and_quantizers(layer)
      )
      (activations, activation_quantizers) = self._convert_list(
          quantize_config.get_activations_and_quantizers(layer)
      )
  
      self._assert_weight_quantizers(weight_quantizers)
      self.assertEqual(
          [
              lstm_cell.kernel,
              lstm_cell.recurrent_kernel,
              gru_cell.kernel,
              gru_cell.recurrent_kernel,
          ],
          weights,
      )
  
      self._assert_activation_quantizers(activation_quantizers)
      self.assertEqual(
          [
              lstm_cell.activation,
              lstm_cell.recurrent_activation,
              gru_cell.activation,
              gru_cell.recurrent_activation,
          ],
          activations,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import unittest
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tensorflow_model_optimization.python.core.quantization.keras import quantizers
  from tensorflow_model_optimization.python.core.quantization.keras import utils
  from tensorflow_model_optimization.python.core.quantization.keras.experimental.default_n_bit import default_n_bit_quantize_registry
label_desc: Use image node_id 12 for calling the QuantizeRegistryTest obj's underlying member method code with example usage: obj.testReturnsConfig_KerasRNNLayerWithKerasRNNCells() without return types