code: def testRNNLayersSingleCell_ReachesTargetSparsity(self, layer_type):
      model = keras.Sequential()
      model.add(
          prune.prune_low_magnitude(
              layer_type(10), input_shape=(3, 4), **self.params
          )
      )
  
      model.compile(
          loss="categorical_crossentropy",
          optimizer="sgd",
          metrics=["accuracy"],
      )
      test_utils.assert_model_sparsity(self, 0.0, model)
      model.fit(
          np.random.randn(
              *self._batch(model.input.get_shape().as_list(), 32)
          ),
          np.random.randn(
              *self._batch(model.output.get_shape().as_list(), 32)
          ),
          callbacks=[pruning_callbacks.UpdatePruningStep()],
      )
  
      test_utils.assert_model_sparsity(self, 0.5, model)
  
      self._check_strip_pruning_matches_original(model, 0.5)
  
imports: import tempfile
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tensorflow_model_optimization.python.core.keras import test_utils
  from tensorflow_model_optimization.python.core.sparsity.keras import prune
  from tensorflow_model_optimization.python.core.sparsity.keras import prune_registry
  from tensorflow_model_optimization.python.core.sparsity.keras import pruning_callbacks
  from tensorflow_model_optimization.python.core.sparsity.keras import pruning_schedule
  from tensorflow_model_optimization.python.core.sparsity.keras import pruning_wrapper
  from tensorflow_model_optimization.python.core.sparsity.keras import test_utils
label_desc: Use image node_id 20 for calling the PruneIntegrationTest obj's underlying member method code with example usage: obj.testRNNLayersSingleCell_ReachesTargetSparsity(layer_type) without return types